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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# ***********************IMPORTANT NMAP LICENSE TERMS************************
# * *
# * The Nmap Security Scanner is (C) 1996-2013 Insecure.Com LLC. Nmap is *
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# * software; you may redistribute and/or modify it under the terms of the *
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# * ("Covered Software"): *
# * *
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# * or nmap-service-probes. *
# * *
# * o Is designed specifically to execute Covered Software and parse the *
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# * *
# * o Includes Covered Software in a proprietary executable installer. The *
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# * Software even if it actually retrieves a copy of Covered Software from *
# * another source during runtime (such as by downloading it from the *
# * Internet). *
# * *
# * o Links (statically or dynamically) to a library which does any of the *
# * above. *
# * *
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# * This list is not exclusive, but is meant to clarify our interpretation *
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# * right to know exactly what a program is going to do before they run it. *
# * This also allows you to audit the software for security holes (none *
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# * *
# ***************************************************************************/
|
"""
============
Array basics
============
Array types and conversions between types
=========================================
Numpy supports a much greater variety of numerical types than Python does.
This section shows which are available, and how to modify an array's data-type.
========== =========================================================
Data type Description
========== =========================================================
bool Boolean (True or False) stored as a byte
int Platform integer (normally either ``int32`` or ``int64``)
int8 Byte (-128 to 127)
int16 Integer (-32768 to 32767)
int32 Integer (-2147483648 to 2147483647)
int64 Integer (9223372036854775808 to 9223372036854775807)
uint8 Unsigned integer (0 to 255)
uint16 Unsigned integer (0 to 65535)
uint32 Unsigned integer (0 to 4294967295)
uint64 Unsigned integer (0 to 18446744073709551615)
float Shorthand for ``float64``.
float32 Single precision float: sign bit, 8 bits exponent,
23 bits mantissa
float64 Double precision float: sign bit, 11 bits exponent,
52 bits mantissa
complex Shorthand for ``complex128``.
complex64 Complex number, represented by two 32-bit floats (real
and imaginary components)
complex128 Complex number, represented by two 64-bit floats (real
and imaginary components)
========== =========================================================
Numpy numerical types are instances of ``dtype`` (data-type) objects, each
having unique characteristics. Once you have imported NumPy using
::
>>> import numpy as np
the dtypes are available as ``np.bool``, ``np.float32``, etc.
Advanced types, not listed in the table above, are explored in
section :ref:`structured_arrays`.
There are 5 basic numerical types representing booleans (bool), integers (int),
unsigned integers (uint) floating point (float) and complex. Those with numbers
in their name indicate the bitsize of the type (i.e. how many bits are needed
to represent a single value in memory). Some types, such as ``int`` and
``intp``, have differing bitsizes, dependent on the platforms (e.g. 32-bit
vs. 64-bit machines). This should be taken into account when interfacing
with low-level code (such as C or Fortran) where the raw memory is addressed.
Data-types can be used as functions to convert python numbers to array scalars
(see the array scalar section for an explanation), python sequences of numbers
to arrays of that type, or as arguments to the dtype keyword that many numpy
functions or methods accept. Some examples::
>>> import numpy as np
>>> x = np.float32(1.0)
>>> x
1.0
>>> y = np.int_([1,2,4])
>>> y
array([1, 2, 4])
>>> z = np.arange(3, dtype=np.uint8)
>>> z
array([0, 1, 2], dtype=uint8)
Array types can also be referred to by character codes, mostly to retain
backward compatibility with older packages such as Numeric. Some
documentation may still refer to these, for example::
>>> np.array([1, 2, 3], dtype='f')
array([ 1., 2., 3.], dtype=float32)
We recommend using dtype objects instead.
To convert the type of an array, use the .astype() method (preferred) or
the type itself as a function. For example: ::
>>> z.astype(float) #doctest: +NORMALIZE_WHITESPACE
array([ 0., 1., 2.])
>>> np.int8(z)
array([0, 1, 2], dtype=int8)
Note that, above, we use the *Python* float object as a dtype. NumPy knows
that ``int`` refers to ``np.int``, ``bool`` means ``np.bool`` and
that ``float`` is ``np.float``. The other data-types do not have Python
equivalents.
To determine the type of an array, look at the dtype attribute::
>>> z.dtype
dtype('uint8')
dtype objects also contain information about the type, such as its bit-width
and its byte-order. The data type can also be used indirectly to query
properties of the type, such as whether it is an integer::
>>> d = np.dtype(int)
>>> d
dtype('int32')
>>> np.issubdtype(d, int)
True
>>> np.issubdtype(d, float)
False
Array Scalars
=============
Numpy generally returns elements of arrays as array scalars (a scalar
with an associated dtype). Array scalars differ from Python scalars, but
for the most part they can be used interchangeably (the primary
exception is for versions of Python older than v2.x, where integer array
scalars cannot act as indices for lists and tuples). There are some
exceptions, such as when code requires very specific attributes of a scalar
or when it checks specifically whether a value is a Python scalar. Generally,
problems are easily fixed by explicitly converting array scalars
to Python scalars, using the corresponding Python type function
(e.g., ``int``, ``float``, ``complex``, ``str``, ``unicode``).
The primary advantage of using array scalars is that
they preserve the array type (Python may not have a matching scalar type
available, e.g. ``int16``). Therefore, the use of array scalars ensures
identical behaviour between arrays and scalars, irrespective of whether the
value is inside an array or not. NumPy scalars also have many of the same
methods arrays do.
""" |
"""
TestCmd.py: a testing framework for commands and scripts.
The TestCmd module provides a framework for portable automated testing
of executable commands and scripts (in any language, not just Python),
especially commands and scripts that require file system interaction.
In addition to running tests and evaluating conditions, the TestCmd
module manages and cleans up one or more temporary workspace
directories, and provides methods for creating files and directories in
those workspace directories from in-line data, here-documents), allowing
tests to be completely self-contained.
A TestCmd environment object is created via the usual invocation:
import TestCmd
test = TestCmd.TestCmd()
There are a bunch of keyword arguments available at instantiation:
test = TestCmd.TestCmd(description = 'string',
program = 'program_or_script_to_test',
interpreter = 'script_interpreter',
workdir = 'prefix',
subdir = 'subdir',
verbose = Boolean,
match = default_match_function,
diff = default_diff_function,
combine = Boolean)
There are a bunch of methods that let you do different things:
test.verbose_set(1)
test.description_set('string')
test.program_set('program_or_script_to_test')
test.interpreter_set('script_interpreter')
test.interpreter_set(['script_interpreter', 'arg'])
test.workdir_set('prefix')
test.workdir_set('')
test.workpath('file')
test.workpath('subdir', 'file')
test.subdir('subdir', ...)
test.rmdir('subdir', ...)
test.write('file', "contents\n")
test.write(['subdir', 'file'], "contents\n")
test.read('file')
test.read(['subdir', 'file'])
test.read('file', mode)
test.read(['subdir', 'file'], mode)
test.writable('dir', 1)
test.writable('dir', None)
test.preserve(condition, ...)
test.cleanup(condition)
test.command_args(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program')
test.run(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
chdir = 'directory_to_chdir_to',
stdin = 'input to feed to the program\n')
universal_newlines = True)
p = test.start(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
universal_newlines = None)
test.finish(self, p)
test.pass_test()
test.pass_test(condition)
test.pass_test(condition, function)
test.fail_test()
test.fail_test(condition)
test.fail_test(condition, function)
test.fail_test(condition, function, skip)
test.no_result()
test.no_result(condition)
test.no_result(condition, function)
test.no_result(condition, function, skip)
test.stdout()
test.stdout(run)
test.stderr()
test.stderr(run)
test.symlink(target, link)
test.banner(string)
test.banner(string, width)
test.diff(actual, expected)
test.match(actual, expected)
test.match_exact("actual 1\nactual 2\n", "expected 1\nexpected 2\n")
test.match_exact(["actual 1\n", "actual 2\n"],
["expected 1\n", "expected 2\n"])
test.match_re("actual 1\nactual 2\n", regex_string)
test.match_re(["actual 1\n", "actual 2\n"], list_of_regexes)
test.match_re_dotall("actual 1\nactual 2\n", regex_string)
test.match_re_dotall(["actual 1\n", "actual 2\n"], list_of_regexes)
test.tempdir()
test.tempdir('temporary-directory')
test.sleep()
test.sleep(seconds)
test.where_is('foo')
test.where_is('foo', 'PATH1:PATH2')
test.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
test.unlink('file')
test.unlink('subdir', 'file')
The TestCmd module provides pass_test(), fail_test(), and no_result()
unbound functions that report test results for use with the Aegis change
management system. These methods terminate the test immediately,
reporting PASSED, FAILED, or NO RESULT respectively, and exiting with
status 0 (success), 1 or 2 respectively. This allows for a distinction
between an actual failed test and a test that could not be properly
evaluated because of an external condition (such as a full file system
or incorrect permissions).
import TestCmd
TestCmd.pass_test()
TestCmd.pass_test(condition)
TestCmd.pass_test(condition, function)
TestCmd.fail_test()
TestCmd.fail_test(condition)
TestCmd.fail_test(condition, function)
TestCmd.fail_test(condition, function, skip)
TestCmd.no_result()
TestCmd.no_result(condition)
TestCmd.no_result(condition, function)
TestCmd.no_result(condition, function, skip)
The TestCmd module also provides unbound functions that handle matching
in the same way as the match_*() methods described above.
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_exact)
test = TestCmd.TestCmd(match = TestCmd.match_re)
test = TestCmd.TestCmd(match = TestCmd.match_re_dotall)
The TestCmd module provides unbound functions that can be used for the
"diff" argument to TestCmd.TestCmd instantiation:
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_re,
diff = TestCmd.diff_re)
test = TestCmd.TestCmd(diff = TestCmd.simple_diff)
The "diff" argument can also be used with standard difflib functions:
import difflib
test = TestCmd.TestCmd(diff = difflib.context_diff)
test = TestCmd.TestCmd(diff = difflib.unified_diff)
Lastly, the where_is() method also exists in an unbound function
version.
import TestCmd
TestCmd.where_is('foo')
TestCmd.where_is('foo', 'PATH1:PATH2')
TestCmd.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
""" |
"""
=============================
Byteswapping and byte order
=============================
Introduction to byte ordering and ndarrays
==========================================
The ``ndarray`` is an object that provide a python array interface to data
in memory.
It often happens that the memory that you want to view with an array is
not of the same byte ordering as the computer on which you are running
Python.
For example, I might be working on a computer with a little-endian CPU -
such as an Intel Pentium, but I have loaded some data from a file
written by a computer that is big-endian. Let's say I have loaded 4
bytes from a file written by a Sun (big-endian) computer. I know that
these 4 bytes represent two 16-bit integers. On a big-endian machine, a
two-byte integer is stored with the Most Significant Byte (MSB) first,
and then the Least Significant Byte (LSB). Thus the bytes are, in memory order:
#. MSB integer 1
#. LSB integer 1
#. MSB integer 2
#. LSB integer 2
Let's say the two integers were in fact 1 and 770. Because 770 = 256 *
3 + 2, the 4 bytes in memory would contain respectively: 0, 1, 3, 2.
The bytes I have loaded from the file would have these contents:
>>> big_end_str = chr(0) + chr(1) + chr(3) + chr(2)
>>> big_end_str
'\\x00\\x01\\x03\\x02'
We might want to use an ``ndarray`` to access these integers. In that
case, we can create an array around this memory, and tell numpy that
there are two integers, and that they are 16 bit and big-endian:
>>> import numpy as np
>>> big_end_arr = np.ndarray(shape=(2,),dtype='>i2', buffer=big_end_str)
>>> big_end_arr[0]
1
>>> big_end_arr[1]
770
Note the array ``dtype`` above of ``>i2``. The ``>`` means 'big-endian'
(``<`` is little-endian) and ``i2`` means 'signed 2-byte integer'. For
example, if our data represented a single unsigned 4-byte little-endian
integer, the dtype string would be ``<u4``.
In fact, why don't we try that?
>>> little_end_u4 = np.ndarray(shape=(1,),dtype='<u4', buffer=big_end_str)
>>> little_end_u4[0] == 1 * 256**1 + 3 * 256**2 + 2 * 256**3
True
Returning to our ``big_end_arr`` - in this case our underlying data is
big-endian (data endianness) and we've set the dtype to match (the dtype
is also big-endian). However, sometimes you need to flip these around.
Changing byte ordering
======================
As you can imagine from the introduction, there are two ways you can
affect the relationship between the byte ordering of the array and the
underlying memory it is looking at:
* Change the byte-ordering information in the array dtype so that it
interprets the undelying data as being in a different byte order.
This is the role of ``arr.newbyteorder()``
* Change the byte-ordering of the underlying data, leaving the dtype
interpretation as it was. This is what ``arr.byteswap()`` does.
The common situations in which you need to change byte ordering are:
#. Your data and dtype endianess don't match, and you want to change
the dtype so that it matches the data.
#. Your data and dtype endianess don't match, and you want to swap the
data so that they match the dtype
#. Your data and dtype endianess match, but you want the data swapped
and the dtype to reflect this
Data and dtype endianness don't match, change dtype to match data
-----------------------------------------------------------------
We make something where they don't match:
>>> wrong_end_dtype_arr = np.ndarray(shape=(2,),dtype='<i2', buffer=big_end_str)
>>> wrong_end_dtype_arr[0]
256
The obvious fix for this situation is to change the dtype so it gives
the correct endianness:
>>> fixed_end_dtype_arr = wrong_end_dtype_arr.newbyteorder()
>>> fixed_end_dtype_arr[0]
1
Note the the array has not changed in memory:
>>> fixed_end_dtype_arr.tobytes() == big_end_str
True
Data and type endianness don't match, change data to match dtype
----------------------------------------------------------------
You might want to do this if you need the data in memory to be a certain
ordering. For example you might be writing the memory out to a file
that needs a certain byte ordering.
>>> fixed_end_mem_arr = wrong_end_dtype_arr.byteswap()
>>> fixed_end_mem_arr[0]
1
Now the array *has* changed in memory:
>>> fixed_end_mem_arr.tobytes() == big_end_str
False
Data and dtype endianness match, swap data and dtype
----------------------------------------------------
You may have a correctly specified array dtype, but you need the array
to have the opposite byte order in memory, and you want the dtype to
match so the array values make sense. In this case you just do both of
the previous operations:
>>> swapped_end_arr = big_end_arr.byteswap().newbyteorder()
>>> swapped_end_arr[0]
1
>>> swapped_end_arr.tobytes() == big_end_str
False
An easier way of casting the data to a specific dtype and byte ordering
can be achieved with the ndarray astype method:
>>> swapped_end_arr = big_end_arr.astype('<i2')
>>> swapped_end_arr[0]
1
>>> swapped_end_arr.tobytes() == big_end_str
False
""" |
"""
Basic functions used by several sub-packages and
useful to have in the main name-space.
Type Handling
-------------
================ ===================
iscomplexobj Test for complex object, scalar result
isrealobj Test for real object, scalar result
iscomplex Test for complex elements, array result
isreal Test for real elements, array result
imag Imaginary part
real Real part
real_if_close Turns complex number with tiny imaginary part to real
isneginf Tests for negative infinity, array result
isposinf Tests for positive infinity, array result
isnan Tests for nans, array result
isinf Tests for infinity, array result
isfinite Tests for finite numbers, array result
isscalar True if argument is a scalar
nan_to_num Replaces NaN's with 0 and infinities with large numbers
cast Dictionary of functions to force cast to each type
common_type Determine the minimum common type code for a group
of arrays
mintypecode Return minimal allowed common typecode.
================ ===================
Index Tricks
------------
================ ===================
mgrid Method which allows easy construction of N-d
'mesh-grids'
``r_`` Append and construct arrays: turns slice objects into
ranges and concatenates them, for 2d arrays appends rows.
index_exp Konrad Hinsen's index_expression class instance which
can be useful for building complicated slicing syntax.
================ ===================
Useful Functions
----------------
================ ===================
select Extension of where to multiple conditions and choices
extract Extract 1d array from flattened array according to mask
insert Insert 1d array of values into Nd array according to mask
linspace Evenly spaced samples in linear space
logspace Evenly spaced samples in logarithmic space
fix Round x to nearest integer towards zero
mod Modulo mod(x,y) = x % y except keeps sign of y
amax Array maximum along axis
amin Array minimum along axis
ptp Array max-min along axis
cumsum Cumulative sum along axis
prod Product of elements along axis
cumprod Cumluative product along axis
diff Discrete differences along axis
angle Returns angle of complex argument
unwrap Unwrap phase along given axis (1-d algorithm)
sort_complex Sort a complex-array (based on real, then imaginary)
trim_zeros Trim the leading and trailing zeros from 1D array.
vectorize A class that wraps a Python function taking scalar
arguments into a generalized function which can handle
arrays of arguments using the broadcast rules of
numerix Python.
================ ===================
Shape Manipulation
------------------
================ ===================
squeeze Return a with length-one dimensions removed.
atleast_1d Force arrays to be > 1D
atleast_2d Force arrays to be > 2D
atleast_3d Force arrays to be > 3D
vstack Stack arrays vertically (row on row)
hstack Stack arrays horizontally (column on column)
column_stack Stack 1D arrays as columns into 2D array
dstack Stack arrays depthwise (along third dimension)
stack Stack arrays along a new axis
split Divide array into a list of sub-arrays
hsplit Split into columns
vsplit Split into rows
dsplit Split along third dimension
================ ===================
Matrix (2D Array) Manipulations
-------------------------------
================ ===================
fliplr 2D array with columns flipped
flipud 2D array with rows flipped
rot90 Rotate a 2D array a multiple of 90 degrees
eye Return a 2D array with ones down a given diagonal
diag Construct a 2D array from a vector, or return a given
diagonal from a 2D array.
mat Construct a Matrix
bmat Build a Matrix from blocks
================ ===================
Polynomials
-----------
================ ===================
poly1d A one-dimensional polynomial class
poly Return polynomial coefficients from roots
roots Find roots of polynomial given coefficients
polyint Integrate polynomial
polyder Differentiate polynomial
polyadd Add polynomials
polysub Substract polynomials
polymul Multiply polynomials
polydiv Divide polynomials
polyval Evaluate polynomial at given argument
================ ===================
Iterators
---------
================ ===================
Arrayterator A buffered iterator for big arrays.
================ ===================
Import Tricks
-------------
================ ===================
ppimport Postpone module import until trying to use it
ppimport_attr Postpone module import until trying to use its attribute
ppresolve Import postponed module and return it.
================ ===================
Machine Arithmetics
-------------------
================ ===================
machar_single Single precision floating point arithmetic parameters
machar_double Double precision floating point arithmetic parameters
================ ===================
Threading Tricks
----------------
================ ===================
ParallelExec Execute commands in parallel thread.
================ ===================
1D Array Set Operations
-----------------------
Set operations for 1D numeric arrays based on sort() function.
================ ===================
ediff1d Array difference (auxiliary function).
unique Unique elements of an array.
intersect1d Intersection of 1D arrays with unique elements.
setxor1d Set exclusive-or of 1D arrays with unique elements.
in1d Test whether elements in a 1D array are also present in
another array.
union1d Union of 1D arrays with unique elements.
setdiff1d Set difference of 1D arrays with unique elements.
================ ===================
""" |
#
# ElementTree
# $Id: ElementTree.py 2326 2005-03-17 07:45:21Z USERNAME $
#
# light-weight XML support for Python 1.5.2 and later.
#
# history:
# 2001-10-20 fl created (from various sources)
# 2001-11-01 fl return root from parse method
# 2002-02-16 fl sort attributes in lexical order
# 2002-04-06 fl TreeBuilder refactoring, added PythonDoc markup
# 2002-05-01 fl finished TreeBuilder refactoring
# 2002-07-14 fl added basic namespace support to ElementTree.write
# 2002-07-25 fl added QName attribute support
# 2002-10-20 fl fixed encoding in write
# 2002-11-24 fl changed default encoding to ascii; fixed attribute encoding
# 2002-11-27 fl accept file objects or file names for parse/write
# 2002-12-04 fl moved XMLTreeBuilder back to this module
# 2003-01-11 fl fixed entity encoding glitch for us-ascii
# 2003-02-13 fl added XML literal factory
# 2003-02-21 fl added ProcessingInstruction/PI factory
# 2003-05-11 fl added tostring/fromstring helpers
# 2003-05-26 fl added ElementPath support
# 2003-07-05 fl added makeelement factory method
# 2003-07-28 fl added more well-known namespace prefixes
# 2003-08-15 fl fixed typo in ElementTree.findtext (Thomas NAME 2003-09-04 fl fall back on emulator if ElementPath is not installed
# 2003-10-31 fl markup updates
# 2003-11-15 fl fixed nested namespace bug
# 2004-03-28 fl added XMLID helper
# 2004-06-02 fl added default support to findtext
# 2004-06-08 fl fixed encoding of non-ascii element/attribute names
# 2004-08-23 fl take advantage of post-2.1 expat features
# 2005-02-01 fl added iterparse implementation
# 2005-03-02 fl fixed iterparse support for pre-2.2 versions
#
# Copyright (c) 1999-2005 by NAME All rights reserved.
#
# EMAIL
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The ElementTree toolkit is
#
# Copyright (c) 1999-2005 by NAME By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
|
"""Configuration file parser.
A configuration file consists of sections, lead by a "[section]" header,
and followed by "name: value" entries, with continuations and such in
the style of RFC 822.
Intrinsic defaults can be specified by passing them into the
ConfigParser constructor as a dictionary.
class:
ConfigParser -- responsible for parsing a list of
configuration files, and managing the parsed database.
methods:
__init__(defaults=None, dict_type=_default_dict, allow_no_value=False,
delimiters=('=', ':'), comment_prefixes=('#', ';'),
inline_comment_prefixes=None, strict=True,
empty_lines_in_values=True):
Create the parser. When `defaults' is given, it is initialized into the
dictionary or intrinsic defaults. The keys must be strings, the values
must be appropriate for %()s string interpolation.
When `dict_type' is given, it will be used to create the dictionary
objects for the list of sections, for the options within a section, and
for the default values.
When `delimiters' is given, it will be used as the set of substrings
that divide keys from values.
When `comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in empty lines. Comments can be
indented.
When `inline_comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in non-empty lines.
When `strict` is True, the parser won't allow for any section or option
duplicates while reading from a single source (file, string or
dictionary). Default is True.
When `empty_lines_in_values' is False (default: True), each empty line
marks the end of an option. Otherwise, internal empty lines of
a multiline option are kept as part of the value.
When `allow_no_value' is True (default: False), options without
values are accepted; the value presented for these is None.
sections()
Return all the configuration section names, sans DEFAULT.
has_section(section)
Return whether the given section exists.
has_option(section, option)
Return whether the given option exists in the given section.
options(section)
Return list of configuration options for the named section.
read(filenames, encoding=None)
Read and parse the list of named configuration files, given by
name. A single filename is also allowed. Non-existing files
are ignored. Return list of successfully read files.
read_file(f, filename=None)
Read and parse one configuration file, given as a file object.
The filename defaults to f.name; it is only used in error
messages (if f has no `name' attribute, the string `<???>' is used).
read_string(string)
Read configuration from a given string.
read_dict(dictionary)
Read configuration from a dictionary. Keys are section names,
values are dictionaries with keys and values that should be present
in the section. If the used dictionary type preserves order, sections
and their keys will be added in order. Values are automatically
converted to strings.
get(section, option, raw=False, vars=None, fallback=_UNSET)
Return a string value for the named option. All % interpolations are
expanded in the return values, based on the defaults passed into the
constructor and the DEFAULT section. Additional substitutions may be
provided using the `vars' argument, which must be a dictionary whose
contents override any pre-existing defaults. If `option' is a key in
`vars', the value from `vars' is used.
getint(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to an integer.
getfloat(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a float.
getboolean(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a boolean (currently case
insensitively defined as 0, false, no, off for False, and 1, true,
yes, on for True). Returns False or True.
items(section=_UNSET, raw=False, vars=None)
If section is given, return a list of tuples with (name, value) for
each option in the section. Otherwise, return a list of tuples with
(section_name, section_proxy) for each section, including DEFAULTSECT.
remove_section(section)
Remove the given file section and all its options.
remove_option(section, option)
Remove the given option from the given section.
set(section, option, value)
Set the given option.
write(fp, space_around_delimiters=True)
Write the configuration state in .ini format. If
`space_around_delimiters' is True (the default), delimiters
between keys and values are surrounded by spaces.
""" |
"""
=====================================================
Optimization and root finding (:mod:`scipy.optimize`)
=====================================================
.. currentmodule:: scipy.optimize
Optimization
============
Local Optimization
------------------
.. autosummary::
:toctree: generated/
minimize - Unified interface for minimizers of multivariate functions
minimize_scalar - Unified interface for minimizers of univariate functions
OptimizeResult - The optimization result returned by some optimizers
OptimizeWarning - The optimization encountered problems
The `minimize` function supports the following methods:
.. toctree::
optimize.minimize-neldermead
optimize.minimize-powell
optimize.minimize-cg
optimize.minimize-bfgs
optimize.minimize-newtoncg
optimize.minimize-lbfgsb
optimize.minimize-tnc
optimize.minimize-cobyla
optimize.minimize-slsqp
optimize.minimize-dogleg
optimize.minimize-trustncg
The `minimize_scalar` function supports the following methods:
.. toctree::
optimize.minimize_scalar-brent
optimize.minimize_scalar-bounded
optimize.minimize_scalar-golden
The specific optimization method interfaces below in this subsection are
not recommended for use in new scripts; all of these methods are accessible
via a newer, more consistent interface provided by the functions above.
General-purpose multivariate methods:
.. autosummary::
:toctree: generated/
fmin - Nelder-Mead Simplex algorithm
fmin_powell - Powell's (modified) level set method
fmin_cg - Non-linear (Polak-Ribiere) conjugate gradient algorithm
fmin_bfgs - Quasi-Newton method (Broydon-Fletcher-Goldfarb-Shanno)
fmin_ncg - Line-search Newton Conjugate Gradient
Constrained multivariate methods:
.. autosummary::
:toctree: generated/
fmin_l_bfgs_b - Zhu, Byrd, and Nocedal's constrained optimizer
fmin_tnc - Truncated Newton code
fmin_cobyla - Constrained optimization by linear approximation
fmin_slsqp - Minimization using sequential least-squares programming
differential_evolution - stochastic minimization using differential evolution
Univariate (scalar) minimization methods:
.. autosummary::
:toctree: generated/
fminbound - Bounded minimization of a scalar function
brent - 1-D function minimization using Brent method
golden - 1-D function minimization using Golden Section method
Equation (Local) Minimizers
---------------------------
.. autosummary::
:toctree: generated/
leastsq - Minimize the sum of squares of M equations in N unknowns
least_squares - Feature-rich least-squares minimization.
nnls - Linear least-squares problem with non-negativity constraint
lsq_linear - Linear least-squares problem with bound constraints
Global Optimization
-------------------
.. autosummary::
:toctree: generated/
basinhopping - Basinhopping stochastic optimizer
brute - Brute force searching optimizer
differential_evolution - stochastic minimization using differential evolution
Rosenbrock function
-------------------
.. autosummary::
:toctree: generated/
rosen - The Rosenbrock function.
rosen_der - The derivative of the Rosenbrock function.
rosen_hess - The Hessian matrix of the Rosenbrock function.
rosen_hess_prod - Product of the Rosenbrock Hessian with a vector.
Fitting
=======
.. autosummary::
:toctree: generated/
curve_fit -- Fit curve to a set of points
Root finding
============
Scalar functions
----------------
.. autosummary::
:toctree: generated/
brentq - quadratic interpolation Brent method
brenth - Brent method, modified by Harris with hyperbolic extrapolation
ridder - Ridder's method
bisect - Bisection method
newton - Secant method or Newton's method
Fixed point finding:
.. autosummary::
:toctree: generated/
fixed_point - Single-variable fixed-point solver
Multidimensional
----------------
General nonlinear solvers:
.. autosummary::
:toctree: generated/
root - Unified interface for nonlinear solvers of multivariate functions
fsolve - Non-linear multi-variable equation solver
broyden1 - Broyden's first method
broyden2 - Broyden's second method
The `root` function supports the following methods:
.. toctree::
optimize.root-hybr
optimize.root-lm
optimize.root-broyden1
optimize.root-broyden2
optimize.root-anderson
optimize.root-linearmixing
optimize.root-diagbroyden
optimize.root-excitingmixing
optimize.root-krylov
optimize.root-dfsane
Large-scale nonlinear solvers:
.. autosummary::
:toctree: generated/
newton_krylov
anderson
Simple iterations:
.. autosummary::
:toctree: generated/
excitingmixing
linearmixing
diagbroyden
:mod:`Additional information on the nonlinear solvers <scipy.optimize.nonlin>`
Linear Programming
==================
Simplex Algorithm:
.. autosummary::
:toctree: generated/
linprog -- Linear programming using the simplex algorithm
linprog_verbose_callback -- Sample callback function for linprog
The `linprog` function supports the following methods:
.. toctree::
optimize.linprog-simplex
Assignment problems:
.. autosummary::
:toctree: generated/
linear_sum_assignment -- Solves the linear-sum assignment problem
Utilities
=========
.. autosummary::
:toctree: generated/
approx_fprime - Approximate the gradient of a scalar function
bracket - Bracket a minimum, given two starting points
check_grad - Check the supplied derivative using finite differences
line_search - Return a step that satisfies the strong Wolfe conditions
show_options - Show specific options optimization solvers
LbfgsInvHessProduct - Linear operator for L-BFGS approximate inverse Hessian
""" |
#
# XML-RPC CLIENT LIBRARY
# $Id$
#
# an XML-RPC client interface for Python.
#
# the marshalling and response parser code can also be used to
# implement XML-RPC servers.
#
# Notes:
# this version is designed to work with Python 2.1 or newer.
#
# History:
# 1999-01-14 fl Created
# 1999-01-15 fl Changed dateTime to use localtime
# 1999-01-16 fl Added Binary/base64 element, default to RPC2 service
# 1999-01-19 fl Fixed array data element (from Skip Montanaro)
# 1999-01-21 fl Fixed dateTime constructor, etc.
# 1999-02-02 fl Added fault handling, handle empty sequences, etc.
# 1999-02-10 fl Fixed problem with empty responses (from Skip Montanaro)
# 1999-06-20 fl Speed improvements, pluggable parsers/transports (0.9.8)
# 2000-11-28 fl Changed boolean to check the truth value of its argument
# 2001-02-24 fl Added encoding/Unicode/SafeTransport patches
# 2001-02-26 fl Added compare support to wrappers (0.9.9/1.0b1)
# 2001-03-28 fl Make sure response tuple is a singleton
# 2001-03-29 fl Don't require empty params element (from NAME 2001-06-10 fl Folded in _xmlrpclib accelerator support (1.0b2)
# 2001-08-20 fl Base xmlrpclib.Error on built-in Exception (from NAME 2001-09-03 fl Allow Transport subclass to override getparser
# 2001-09-10 fl Lazy import of urllib, cgi, xmllib (20x import speedup)
# 2001-10-01 fl Remove containers from memo cache when done with them
# 2001-10-01 fl Use faster escape method (80% dumps speedup)
# 2001-10-02 fl More dumps microtuning
# 2001-10-04 fl Make sure import expat gets a parser (from NAME 2001-10-10 sm Allow long ints to be passed as ints if they don't overflow
# 2001-10-17 sm Test for int and long overflow (allows use on 64-bit systems)
# 2001-11-12 fl Use repr() to marshal doubles (from NAME 2002-03-17 fl Avoid buffered read when possible (from NAME 2002-04-07 fl Added pythondoc comments
# 2002-04-16 fl Added __str__ methods to datetime/binary wrappers
# 2002-05-15 fl Added error constants (from NAME 2002-06-27 fl Merged with Python CVS version
# 2002-10-22 fl Added basic authentication (based on code from NAME 2003-01-22 sm Add support for the bool type
# 2003-02-27 gvr Remove apply calls
# 2003-04-24 sm Use cStringIO if available
# 2003-04-25 ak Add support for nil
# 2003-06-15 gn Add support for time.struct_time
# 2003-07-12 gp Correct marshalling of Faults
# 2003-10-31 mvl Add multicall support
# 2004-08-20 mvl Bump minimum supported Python version to 2.1
#
# Copyright (c) 1999-2002 by Secret Labs AB.
# Copyright (c) 1999-2002 by NAME EMAIL http://www.pythonware.com
#
# --------------------------------------------------------------------
# The XML-RPC client interface is
#
# Copyright (c) 1999-2002 by Secret Labs AB
# Copyright (c) 1999-2002 by NAME By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
|
# -*- coding: utf-8 -*-
############################ Copyrights and license ############################
# #
# Copyright 2012 NAME <EMAIL> #
# Copyright 2012 NAME <EMAIL> #
# Copyright 2012 NAME <EMAIL> #
# Copyright 2012 Zearin <EMAIL> #
# Copyright 2013 AKFish <EMAIL> #
# Copyright 2013 NAME <EMAIL> #
# Copyright 2013 NAME <EMAIL> #
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# Copyright 2013 NAME <EMAIL> #
# Copyright 2013 NAME <EMAIL> #
# Copyright 2013 martinqt <EMAIL> #
# Copyright 2014 NAME <EMAIL> #
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# Copyright 2015 edhollandAL <EMAIL> #
# Copyright 2016 USERNAME <EMAIL> #
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# Copyright 2016 Per Øyvind Karlsen <EMAIL> #
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# Copyright 2018 NAME <EMAIL> #
# Copyright 2018 USERNAME <EMAIL> #
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# Copyright 2018 NAME <EMAIL> #
# Copyright 2018 NAME <EMAIL> #
# Copyright 2018 NAME <EMAIL> #
# Copyright 2018 NAME <EMAIL> #
# Copyright 2018 NAME <EMAIL> #
# #
# This file is part of PyGithub. #
# http://pygithub.readthedocs.io/ #
# #
# PyGithub is free software: you can redistribute it and/or modify it under #
# the terms of the GNU Lesser General Public License as published by the Free #
# Software Foundation, either version 3 of the License, or (at your option) #
# any later version. #
# #
# PyGithub is distributed in the hope that it will be useful, but WITHOUT ANY #
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS #
# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more #
# details. #
# #
# You should have received a copy of the GNU Lesser General Public License #
# along with PyGithub. If not, see <http://www.gnu.org/licenses/>. #
# #
################################################################################
|
"""
Work out the first ten digits of the sum of the following one-hundred 50-digit numbers.
37107287533902102798797998220837590246510135740250
46376937677490009712648124896970078050417018260538
74324986199524741059474233309513058123726617309629
91942213363574161572522430563301811072406154908250
23067588207539346171171980310421047513778063246676
89261670696623633820136378418383684178734361726757
28112879812849979408065481931592621691275889832738
44274228917432520321923589422876796487670272189318
47451445736001306439091167216856844588711603153276
70386486105843025439939619828917593665686757934951
62176457141856560629502157223196586755079324193331
64906352462741904929101432445813822663347944758178
92575867718337217661963751590579239728245598838407
58203565325359399008402633568948830189458628227828
80181199384826282014278194139940567587151170094390
35398664372827112653829987240784473053190104293586
86515506006295864861532075273371959191420517255829
71693888707715466499115593487603532921714970056938
54370070576826684624621495650076471787294438377604
53282654108756828443191190634694037855217779295145
36123272525000296071075082563815656710885258350721
45876576172410976447339110607218265236877223636045
17423706905851860660448207621209813287860733969412
81142660418086830619328460811191061556940512689692
51934325451728388641918047049293215058642563049483
62467221648435076201727918039944693004732956340691
15732444386908125794514089057706229429197107928209
55037687525678773091862540744969844508330393682126
18336384825330154686196124348767681297534375946515
80386287592878490201521685554828717201219257766954
78182833757993103614740356856449095527097864797581
16726320100436897842553539920931837441497806860984
48403098129077791799088218795327364475675590848030
87086987551392711854517078544161852424320693150332
59959406895756536782107074926966537676326235447210
69793950679652694742597709739166693763042633987085
41052684708299085211399427365734116182760315001271
65378607361501080857009149939512557028198746004375
35829035317434717326932123578154982629742552737307
94953759765105305946966067683156574377167401875275
88902802571733229619176668713819931811048770190271
25267680276078003013678680992525463401061632866526
36270218540497705585629946580636237993140746255962
24074486908231174977792365466257246923322810917141
91430288197103288597806669760892938638285025333403
34413065578016127815921815005561868836468420090470
23053081172816430487623791969842487255036638784583
11487696932154902810424020138335124462181441773470
63783299490636259666498587618221225225512486764533
67720186971698544312419572409913959008952310058822
95548255300263520781532296796249481641953868218774
76085327132285723110424803456124867697064507995236
37774242535411291684276865538926205024910326572967
23701913275725675285653248258265463092207058596522
29798860272258331913126375147341994889534765745501
18495701454879288984856827726077713721403798879715
38298203783031473527721580348144513491373226651381
34829543829199918180278916522431027392251122869539
40957953066405232632538044100059654939159879593635
29746152185502371307642255121183693803580388584903
41698116222072977186158236678424689157993532961922
62467957194401269043877107275048102390895523597457
23189706772547915061505504953922979530901129967519
86188088225875314529584099251203829009407770775672
11306739708304724483816533873502340845647058077308
82959174767140363198008187129011875491310547126581
97623331044818386269515456334926366572897563400500
42846280183517070527831839425882145521227251250327
55121603546981200581762165212827652751691296897789
32238195734329339946437501907836945765883352399886
75506164965184775180738168837861091527357929701337
62177842752192623401942399639168044983993173312731
32924185707147349566916674687634660915035914677504
99518671430235219628894890102423325116913619626622
73267460800591547471830798392868535206946944540724
76841822524674417161514036427982273348055556214818
97142617910342598647204516893989422179826088076852
87783646182799346313767754307809363333018982642090
10848802521674670883215120185883543223812876952786
71329612474782464538636993009049310363619763878039
62184073572399794223406235393808339651327408011116
66627891981488087797941876876144230030984490851411
60661826293682836764744779239180335110989069790714
85786944089552990653640447425576083659976645795096
66024396409905389607120198219976047599490197230297
64913982680032973156037120041377903785566085089252
16730939319872750275468906903707539413042652315011
94809377245048795150954100921645863754710598436791
78639167021187492431995700641917969777599028300699
15368713711936614952811305876380278410754449733078
40789923115535562561142322423255033685442488917353
44889911501440648020369068063960672322193204149535
41503128880339536053299340368006977710650566631954
81234880673210146739058568557934581403627822703280
82616570773948327592232845941706525094512325230608
22918802058777319719839450180888072429661980811197
77158542502016545090413245809786882778948721859617
72107838435069186155435662884062257473692284509516
20849603980134001723930671666823555245252804609722
53503534226472524250874054075591789781264330331690
""" |
"""
Examples
========
::
# Single worker with explicit name and events enabled.
$ celeryd-multi start NAME -E
# Pidfiles and logfiles are stored in the current directory
# by default. Use --pidfile and --logfile argument to change
# this. The abbreviation %n will be expanded to the current
# node name.
$ celeryd-multi start NAME -E --pidfile=/var/run/celery/%n.pid
--logfile=/var/log/celery/%n.log
# You need to add the same arguments when you restart,
# as these are not persisted anywhere.
$ celeryd-multi restart NAME -E --pidfile=/var/run/celery/%n.pid
--logfile=/var/run/celery/%n.log
# To stop the node, you need to specify the same pidfile.
$ celeryd-multi stop NAME --pidfile=/var/run/celery/%n.pid
# 3 workers, with 3 processes each
$ celeryd-multi start 3 -c 3
celeryd -n celeryd1.myhost -c 3
celeryd -n celeryd2.myhost -c 3
celeryd- n celeryd3.myhost -c 3
# start 3 named workers
$ celeryd-multi start image video data -c 3
celeryd -n image.myhost -c 3
celeryd -n video.myhost -c 3
celeryd -n data.myhost -c 3
# specify custom hostname
$ celeryd-multi start 2 -n worker.example.com -c 3
celeryd -n celeryd1.worker.example.com -c 3
celeryd -n celeryd2.worker.example.com -c 3
# Advanced example starting 10 workers in the background:
# * Three of the workers processes the images and video queue
# * Two of the workers processes the data queue with loglevel DEBUG
# * the rest processes the default' queue.
$ celeryd-multi start 10 -l INFO -Q:1-3 images,video -Q:4,5:data
-Q default -L:4,5 DEBUG
# You can show the commands necessary to start the workers with
# the "show" command:
$ celeryd-multi show 10 -l INFO -Q:1-3 images,video -Q:4,5:data
-Q default -L:4,5 DEBUG
# Additional options are added to each celeryd',
# but you can also modify the options for ranges of, or specific workers
# 3 workers: Two with 3 processes, and one with 10 processes.
$ celeryd-multi start 3 -c 3 -c:1 10
celeryd -n celeryd1.myhost -c 10
celeryd -n celeryd2.myhost -c 3
celeryd -n celeryd3.myhost -c 3
# can also specify options for named workers
$ celeryd-multi start image video data -c 3 -c:image 10
celeryd -n image.myhost -c 10
celeryd -n video.myhost -c 3
celeryd -n data.myhost -c 3
# ranges and lists of workers in options is also allowed:
# (-c:1-3 can also be written as -c:1,2,3)
$ celeryd-multi start 5 -c 3 -c:1-3 10
celeryd -n celeryd1.myhost -c 10
celeryd -n celeryd2.myhost -c 10
celeryd -n celeryd3.myhost -c 10
celeryd -n celeryd4.myhost -c 3
celeryd -n celeryd5.myhost -c 3
# lists also works with named workers
$ celeryd-multi start foo bar baz xuzzy -c 3 -c:foo,bar,baz 10
celeryd -n foo.myhost -c 10
celeryd -n bar.myhost -c 10
celeryd -n baz.myhost -c 10
celeryd -n xuzzy.myhost -c 3
""" |
"""Higher-level Query wrapper.
There are perhaps too many query APIs in the world.
The fundamental API here overloads the 6 comparisons operators to
represent filters on property values, and supports AND and OR
operations (implemented as functions -- Python's 'and' and 'or'
operators cannot be overloaded, and the '&' and '|' operators have a
priority that conflicts with the priority of comparison operators).
For example:
class Employee(Model):
name = StringProperty()
age = IntegerProperty()
rank = IntegerProperty()
@classmethod
def demographic(cls, min_age, max_age):
return cls.query().filter(AND(cls.age >= min_age, cls.age <= max_age))
@classmethod
def ranked(cls, rank):
return cls.query(cls.rank == rank).order(cls.age)
for emp in Employee.seniors(42, 5):
print emp.name, emp.age, emp.rank
The 'in' operator cannot be overloaded, but is supported through the
IN() method. For example:
Employee.query().filter(Employee.rank.IN([4, 5, 6]))
Sort orders are supported through the order() method; unary minus is
overloaded on the Property class to represent a descending order:
Employee.query().order(Employee.name, -Employee.age)
Besides using AND() and OR(), filters can also be combined by
repeatedly calling .filter():
q1 = Employee.query() # A query that returns all employees
q2 = q1.filter(Employee.age >= 30) # Only those over 30
q3 = q2.filter(Employee.age < 40) # Only those in their 30s
A further shortcut is calling .filter() with multiple arguments; this
implies AND():
q1 = Employee.query() # A query that returns all employees
q3 = q1.filter(Employee.age >= 30,
Employee.age < 40) # Only those in their 30s
And finally you can also pass one or more filter expressions directly
to the .query() method:
q3 = Employee.query(Employee.age >= 30,
Employee.age < 40) # Only those in their 30s
Query objects are immutable, so these methods always return a new
Query object; the above calls to filter() do not affect q1. (On the
other hand, operations that are effectively no-ops may return the
original Query object.)
Sort orders can also be combined this way, and .filter() and .order()
calls may be intermixed:
q4 = q3.order(-Employee.age)
q5 = q4.order(Employee.name)
q6 = q5.filter(Employee.rank == 5)
Again, multiple .order() calls can be combined:
q5 = q3.order(-Employee.age, Employee.name)
The simplest way to retrieve Query results is a for-loop:
for emp in q3:
print emp.name, emp.age
Some other methods to run a query and access its results:
q.iter() # Return an iterator; same as iter(q) but more flexible
q.map(callback) # Call the callback function for each query result
q.fetch(N) # Return a list of the first N results
q.get() # Return the first result
q.count(N) # Return the number of results, with a maximum of N
q.fetch_page(N, start_cursor=cursor) # Return (results, cursor, has_more)
All of the above methods take a standard set of additional query
options, either in the form of keyword arguments such as
keys_only=True, or as QueryOptions object passed with
options=QueryOptions(...). The most important query options are:
keys_only: bool, if set the results are keys instead of entities
limit: int, limits the number of results returned
offset: int, skips this many results first
start_cursor: Cursor, start returning results after this position
end_cursor: Cursor, stop returning results after this position
batch_size: int, hint for the number of results returned per RPC
prefetch_size: int, hint for the number of results in the first RPC
produce_cursors: bool, return Cursor objects with the results
For additional (obscure) query options and more details on them,
including an explanation of Cursors, see datastore_query.py.
All of the above methods except for iter() have asynchronous variants
as well, which return a Future; to get the operation's ultimate
result, yield the Future (when inside a tasklet) or call the Future's
get_result() method (outside a tasklet):
q.map_async(callback) # Callback may be a task or a plain function
q.fetch_async(N)
q.get_async()
q.count_async(N)
q.fetch_page_async(N, start_cursor=cursor)
Finally, there's an idiom to efficiently loop over the Query results
in a tasklet, properly yielding when appropriate:
it = q.iter()
while (yield it.has_next_async()):
emp = it.next()
print emp.name, emp.age
""" |
"""Configuration file parser.
A configuration file consists of sections, lead by a "[section]" header,
and followed by "name: value" entries, with continuations and such in
the style of RFC 822.
Intrinsic defaults can be specified by passing them into the
ConfigParser constructor as a dictionary.
class:
ConfigParser -- responsible for parsing a list of
configuration files, and managing the parsed database.
methods:
__init__(defaults=None, dict_type=_default_dict, allow_no_value=False,
delimiters=('=', ':'), comment_prefixes=('#', ';'),
inline_comment_prefixes=None, strict=True,
empty_lines_in_values=True):
Create the parser. When `defaults' is given, it is initialized into the
dictionary or intrinsic defaults. The keys must be strings, the values
must be appropriate for %()s string interpolation.
When `dict_type' is given, it will be used to create the dictionary
objects for the list of sections, for the options within a section, and
for the default values.
When `delimiters' is given, it will be used as the set of substrings
that divide keys from values.
When `comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in empty lines. Comments can be
indented.
When `inline_comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in non-empty lines.
When `strict` is True, the parser won't allow for any section or option
duplicates while reading from a single source (file, string or
dictionary). Default is True.
When `empty_lines_in_values' is False (default: True), each empty line
marks the end of an option. Otherwise, internal empty lines of
a multiline option are kept as part of the value.
When `allow_no_value' is True (default: False), options without
values are accepted; the value presented for these is None.
sections()
Return all the configuration section names, sans DEFAULT.
has_section(section)
Return whether the given section exists.
has_option(section, option)
Return whether the given option exists in the given section.
options(section)
Return list of configuration options for the named section.
read(filenames, encoding=None)
Read and parse the list of named configuration files, given by
name. A single filename is also allowed. Non-existing files
are ignored. Return list of successfully read files.
read_file(f, filename=None)
Read and parse one configuration file, given as a file object.
The filename defaults to f.name; it is only used in error
messages (if f has no `name' attribute, the string `<???>' is used).
read_string(string)
Read configuration from a given string.
read_dict(dictionary)
Read configuration from a dictionary. Keys are section names,
values are dictionaries with keys and values that should be present
in the section. If the used dictionary type preserves order, sections
and their keys will be added in order. Values are automatically
converted to strings.
get(section, option, raw=False, vars=None, fallback=_UNSET)
Return a string value for the named option. All % interpolations are
expanded in the return values, based on the defaults passed into the
constructor and the DEFAULT section. Additional substitutions may be
provided using the `vars' argument, which must be a dictionary whose
contents override any pre-existing defaults. If `option' is a key in
`vars', the value from `vars' is used.
getint(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to an integer.
getfloat(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a float.
getboolean(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a boolean (currently case
insensitively defined as 0, false, no, off for False, and 1, true,
yes, on for True). Returns False or True.
items(section=_UNSET, raw=False, vars=None)
If section is given, return a list of tuples with (name, value) for
each option in the section. Otherwise, return a list of tuples with
(section_name, section_proxy) for each section, including DEFAULTSECT.
remove_section(section)
Remove the given file section and all its options.
remove_option(section, option)
Remove the given option from the given section.
set(section, option, value)
Set the given option.
write(fp, space_around_delimiters=True)
Write the configuration state in .ini format. If
`space_around_delimiters' is True (the default), delimiters
between keys and values are surrounded by spaces.
""" |
"""
============
Array basics
============
Array types and conversions between types
=========================================
NumPy supports a much greater variety of numerical types than Python does.
This section shows which are available, and how to modify an array's data-type.
========== ==========================================================
Data type Description
========== ==========================================================
bool_ Boolean (True or False) stored as a byte
int_ Default integer type (same as C ``long``; normally either
``int64`` or ``int32``)
intc Identical to C ``int`` (normally ``int32`` or ``int64``)
intp Integer used for indexing (same as C ``ssize_t``; normally
either ``int32`` or ``int64``)
int8 Byte (-128 to 127)
int16 Integer (-32768 to 32767)
int32 Integer (-2147483648 to 2147483647)
int64 Integer (-9223372036854775808 to 9223372036854775807)
uint8 Unsigned integer (0 to 255)
uint16 Unsigned integer (0 to 65535)
uint32 Unsigned integer (0 to 4294967295)
uint64 Unsigned integer (0 to 18446744073709551615)
float_ Shorthand for ``float64``.
float16 Half precision float: sign bit, 5 bits exponent,
10 bits mantissa
float32 Single precision float: sign bit, 8 bits exponent,
23 bits mantissa
float64 Double precision float: sign bit, 11 bits exponent,
52 bits mantissa
complex_ Shorthand for ``complex128``.
complex64 Complex number, represented by two 32-bit floats (real
and imaginary components)
complex128 Complex number, represented by two 64-bit floats (real
and imaginary components)
========== ==========================================================
Additionally to ``intc`` the platform dependent C integer types ``short``,
``long``, ``longlong`` and their unsigned versions are defined.
NumPy numerical types are instances of ``dtype`` (data-type) objects, each
having unique characteristics. Once you have imported NumPy using
::
>>> import numpy as np
the dtypes are available as ``np.bool_``, ``np.float32``, etc.
Advanced types, not listed in the table above, are explored in
section :ref:`structured_arrays`.
There are 5 basic numerical types representing booleans (bool), integers (int),
unsigned integers (uint) floating point (float) and complex. Those with numbers
in their name indicate the bitsize of the type (i.e. how many bits are needed
to represent a single value in memory). Some types, such as ``int`` and
``intp``, have differing bitsizes, dependent on the platforms (e.g. 32-bit
vs. 64-bit machines). This should be taken into account when interfacing
with low-level code (such as C or Fortran) where the raw memory is addressed.
Data-types can be used as functions to convert python numbers to array scalars
(see the array scalar section for an explanation), python sequences of numbers
to arrays of that type, or as arguments to the dtype keyword that many numpy
functions or methods accept. Some examples::
>>> import numpy as np
>>> x = np.float32(1.0)
>>> x
1.0
>>> y = np.int_([1,2,4])
>>> y
array([1, 2, 4])
>>> z = np.arange(3, dtype=np.uint8)
>>> z
array([0, 1, 2], dtype=uint8)
Array types can also be referred to by character codes, mostly to retain
backward compatibility with older packages such as Numeric. Some
documentation may still refer to these, for example::
>>> np.array([1, 2, 3], dtype='f')
array([ 1., 2., 3.], dtype=float32)
We recommend using dtype objects instead.
To convert the type of an array, use the .astype() method (preferred) or
the type itself as a function. For example: ::
>>> z.astype(float) #doctest: +NORMALIZE_WHITESPACE
array([ 0., 1., 2.])
>>> np.int8(z)
array([0, 1, 2], dtype=int8)
Note that, above, we use the *Python* float object as a dtype. NumPy knows
that ``int`` refers to ``np.int_``, ``bool`` means ``np.bool_``,
that ``float`` is ``np.float_`` and ``complex`` is ``np.complex_``.
The other data-types do not have Python equivalents.
To determine the type of an array, look at the dtype attribute::
>>> z.dtype
dtype('uint8')
dtype objects also contain information about the type, such as its bit-width
and its byte-order. The data type can also be used indirectly to query
properties of the type, such as whether it is an integer::
>>> d = np.dtype(int)
>>> d
dtype('int32')
>>> np.issubdtype(d, int)
True
>>> np.issubdtype(d, float)
False
Array Scalars
=============
NumPy generally returns elements of arrays as array scalars (a scalar
with an associated dtype). Array scalars differ from Python scalars, but
for the most part they can be used interchangeably (the primary
exception is for versions of Python older than v2.x, where integer array
scalars cannot act as indices for lists and tuples). There are some
exceptions, such as when code requires very specific attributes of a scalar
or when it checks specifically whether a value is a Python scalar. Generally,
problems are easily fixed by explicitly converting array scalars
to Python scalars, using the corresponding Python type function
(e.g., ``int``, ``float``, ``complex``, ``str``, ``unicode``).
The primary advantage of using array scalars is that
they preserve the array type (Python may not have a matching scalar type
available, e.g. ``int16``). Therefore, the use of array scalars ensures
identical behaviour between arrays and scalars, irrespective of whether the
value is inside an array or not. NumPy scalars also have many of the same
methods arrays do.
Extended Precision
==================
Python's floating-point numbers are usually 64-bit floating-point numbers,
nearly equivalent to ``np.float64``. In some unusual situations it may be
useful to use floating-point numbers with more precision. Whether this
is possible in numpy depends on the hardware and on the development
environment: specifically, x86 machines provide hardware floating-point
with 80-bit precision, and while most C compilers provide this as their
``long double`` type, MSVC (standard for Windows builds) makes
``long double`` identical to ``double`` (64 bits). NumPy makes the
compiler's ``long double`` available as ``np.longdouble`` (and
``np.clongdouble`` for the complex numbers). You can find out what your
numpy provides with``np.finfo(np.longdouble)``.
NumPy does not provide a dtype with more precision than C
``long double``s; in particular, the 128-bit IEEE quad precision
data type (FORTRAN's ``REAL*16``) is not available.
For efficient memory alignment, ``np.longdouble`` is usually stored
padded with zero bits, either to 96 or 128 bits. Which is more efficient
depends on hardware and development environment; typically on 32-bit
systems they are padded to 96 bits, while on 64-bit systems they are
typically padded to 128 bits. ``np.longdouble`` is padded to the system
default; ``np.float96`` and ``np.float128`` are provided for users who
want specific padding. In spite of the names, ``np.float96`` and
``np.float128`` provide only as much precision as ``np.longdouble``,
that is, 80 bits on most x86 machines and 64 bits in standard
Windows builds.
Be warned that even if ``np.longdouble`` offers more precision than
python ``float``, it is easy to lose that extra precision, since
python often forces values to pass through ``float``. For example,
the ``%`` formatting operator requires its arguments to be converted
to standard python types, and it is therefore impossible to preserve
extended precision even if many decimal places are requested. It can
be useful to test your code with the value
``1 + np.finfo(np.longdouble).eps``.
""" |
# vim: set fileencoding=utf-8 :
# ***********************IMPORTANT NMAP LICENSE TERMS************************
# * *
# * The Nmap Security Scanner is (C) 1996-2013 Insecure.Com LLC. Nmap is *
# * also a registered trademark of Insecure.Com LLC. This program is free *
# * software; you may redistribute and/or modify it under the terms of the *
# * GNU General Public License as published by the Free Software *
# * Foundation; Version 2 ("GPL"), BUT ONLY WITH ALL OF THE CLARIFICATIONS *
# * AND EXCEPTIONS DESCRIBED HEREIN. This guarantees your right to use, *
# * modify, and redistribute this software under certain conditions. If *
# * you wish to embed Nmap technology into proprietary software, we sell *
# * alternative licenses (contact EMAIL Dozens of software *
# * vendors already license Nmap technology such as host discovery, port *
# * scanning, OS detection, version detection, and the Nmap Scripting *
# * Engine. *
# * *
# * Note that the GPL places important restrictions on "derivative works", *
# * yet it does not provide a detailed definition of that term. To avoid *
# * misunderstandings, we interpret that term as broadly as copyright law *
# * allows. For example, we consider an application to constitute a *
# * derivative work for the purpose of this license if it does any of the *
# * following with any software or content covered by this license *
# * ("Covered Software"): *
# * *
# * o Integrates source code from Covered Software. *
# * *
# * o Reads or includes copyrighted data files, such as Nmap's nmap-os-db *
# * or nmap-service-probes. *
# * *
# * o Is designed specifically to execute Covered Software and parse the *
# * results (as opposed to typical shell or execution-menu apps, which will *
# * execute anything you tell them to). *
# * *
# * o Includes Covered Software in a proprietary executable installer. The *
# * installers produced by InstallShield are an example of this. Including *
# * Nmap with other software in compressed or archival form does not *
# * trigger this provision, provided appropriate open source decompression *
# * or de-archiving software is widely available for no charge. For the *
# * purposes of this license, an installer is considered to include Covered *
# * Software even if it actually retrieves a copy of Covered Software from *
# * another source during runtime (such as by downloading it from the *
# * Internet). *
# * *
# * o Links (statically or dynamically) to a library which does any of the *
# * above. *
# * *
# * o Executes a helper program, module, or script to do any of the above. *
# * *
# * This list is not exclusive, but is meant to clarify our interpretation *
# * of derived works with some common examples. Other people may interpret *
# * the plain GPL differently, so we consider this a special exception to *
# * the GPL that we apply to Covered Software. Works which meet any of *
# * these conditions must conform to all of the terms of this license, *
# * particularly including the GPL Section 3 requirements of providing *
# * source code and allowing free redistribution of the work as a whole. *
# * *
# * As another special exception to the GPL terms, Insecure.Com LLC grants *
# * permission to link the code of this program with any version of the *
# * OpenSSL library which is distributed under a license identical to that *
# * listed in the included docs/licenses/OpenSSL.txt file, and distribute *
# * linked combinations including the two. *
# * *
# * Any redistribution of Covered Software, including any derived works, *
# * must obey and carry forward all of the terms of this license, including *
# * obeying all GPL rules and restrictions. For example, source code of *
# * the whole work must be provided and free redistribution must be *
# * allowed. All GPL references to "this License", are to be treated as *
# * including the terms and conditions of this license text as well. *
# * *
# * Because this license imposes special exceptions to the GPL, Covered *
# * Work may not be combined (even as part of a larger work) with plain GPL *
# * software. The terms, conditions, and exceptions of this license must *
# * be included as well. This license is incompatible with some other open *
# * source licenses as well. In some cases we can relicense portions of *
# * Nmap or grant special permissions to use it in other open source *
# * software. Please contact EMAIL with any such requests. *
# * Similarly, we don't incorporate incompatible open source software into *
# * Covered Software without special permission from the copyright holders. *
# * *
# * If you have any questions about the licensing restrictions on using *
# * Nmap in other works, are happy to help. As mentioned above, we also *
# * offer alternative license to integrate Nmap into proprietary *
# * applications and appliances. These contracts have been sold to dozens *
# * of software vendors, and generally include a perpetual license as well *
# * as providing for priority support and updates. They also fund the *
# * continued development of Nmap. Please email EMAIL for further *
# * information. *
# * *
# * If you have received a written license agreement or contract for *
# * Covered Software stating terms other than these, you may choose to use *
# * and redistribute Covered Software under those terms instead of these. *
# * *
# * Source is provided to this software because we believe users have a *
# * right to know exactly what a program is going to do before they run it. *
# * This also allows you to audit the software for security holes (none *
# * have been found so far). *
# * *
# * Source code also allows you to port Nmap to new platforms, fix bugs, *
# * and add new features. You are highly encouraged to send your changes *
# * to the EMAIL mailing list for possible incorporation into the *
# * main distribution. By sending these changes to Fyodor or one of the *
# * Insecure.Org development mailing lists, or checking them into the Nmap *
# * source code repository, it is understood (unless you specify otherwise) *
# * that you are offering the Nmap Project (Insecure.Com LLC) the *
# * unlimited, non-exclusive right to reuse, modify, and relicense the *
# * code. Nmap will always be available Open Source, but this is important *
# * because the inability to relicense code has caused devastating problems *
# * for other Free Software projects (such as KDE and NASM). We also *
# * occasionally relicense the code to third parties as discussed above. *
# * If you wish to specify special license conditions of your *
# * contributions, just say so when you send them. *
# * *
# * This program is distributed in the hope that it will be useful, but *
# * WITHOUT ANY WARRANTY; without even the implied warranty of *
# * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Nmap *
# * license file for more details (it's in a COPYING file included with *
# * Nmap, and also available from https://svn.nmap.org/nmap/COPYING *
# * *
# ***************************************************************************/
|
"""
Purpose: Linear Algebra Parser
Based on: SimpleCalc.py example (author NAME in pyparsing-1.3.3
Author: NAME Ellis & Grant, Inc. 2005
License: You may freely use, modify, and distribute this software.
Warranty: THIS SOFTWARE HAS NO WARRANTY WHATSOEVER. USE AT YOUR OWN RISK.
Notes: Parses infix linear algebra (LA) notation for vectors, matrices, and scalars.
Output is C code function calls. The parser can be run as an interactive
interpreter or included as module to use for in-place substitution into C files
containing LA equations.
Supported operations are:
OPERATION: INPUT OUTPUT
Scalar addition: "a = b+c" "a=(b+c)"
Scalar subtraction: "a = b-c" "a=(b-c)"
Scalar multiplication: "a = b*c" "a=b*c"
Scalar division: "a = b/c" "a=b/c"
Scalar exponentiation: "a = b^c" "a=pow(b,c)"
Vector scaling: "V3_a = V3_b * c" "vCopy(a,vScale(b,c))"
Vector addition: "V3_a = V3_b + V3_c" "vCopy(a,vAdd(b,c))"
Vector subtraction: "V3_a = V3_b - V3_c" "vCopy(a,vSubtract(b,c))"
Vector dot product: "a = V3_b * V3_c" "a=vDot(b,c)"
Vector outer product: "M3_a = V3_b @ V3_c" "a=vOuterProduct(b,c)"
Vector magn. squared: "a = V3_b^Mag2" "a=vMagnitude2(b)"
Vector magnitude: "a = V3_b^Mag" "a=sqrt(vMagnitude2(b))"
Matrix scaling: "M3_a = M3_b * c" "mCopy(a,mScale(b,c))"
Matrix addition: "M3_a = M3_b + M3_c" "mCopy(a,mAdd(b,c))"
Matrix subtraction: "M3_a = M3_b - M3_c" "mCopy(a,mSubtract(b,c))"
Matrix multiplication: "M3_a = M3_b * M3_c" "mCopy(a,mMultiply(b,c))"
Matrix by vector mult.: "V3_a = M3_b * V3_c" "vCopy(a,mvMultiply(b,c))"
Matrix inversion: "M3_a = M3_b^-1" "mCopy(a,mInverse(b))"
Matrix transpose: "M3_a = M3_b^T" "mCopy(a,mTranspose(b))"
Matrix determinant: "a = M3_b^Det" "a=mDeterminant(b)"
The parser requires the expression to be an equation. Each non-scalar variable
must be prefixed with a type tag, 'M3_' for 3x3 matrices and 'V3_' for 3-vectors.
For proper compilation of the C code, the variables need to be declared without
the prefix as float[3] for vectors and float[3][3] for matrices. The operations do
not modify any variables on the right-hand side of the equation.
Equations may include nested expressions within parentheses. The allowed binary
operators are '+-*/^' for scalars, and '+-*^@' for vectors and matrices with the
meanings defined in the table above.
Specifying an improper combination of operands, e.g. adding a vector to a matrix,
is detected by the parser and results in a Python TypeError Exception. The usual cause
of this is omitting one or more tag prefixes. The parser knows nothing about a
a variable's C declaration and relies entirely on the type tags. Errors in C
declarations are not caught until compile time.
Usage: To process LA equations embedded in source files, import this module and
pass input and output file objects to the fprocess() function. You can
can also invoke the parser from the command line, e.g. 'python LAparser.py',
to run a small test suite and enter an interactive loop where you can enter
LA equations and see the resulting C code.
""" |
"""
=============================
Subclassing ndarray in python
=============================
Credits
-------
This page is based with thanks on the wiki page on subclassing by NAME - http://www.scipy.org/Subclasses.
Introduction
------------
Subclassing ndarray is relatively simple, but it has some complications
compared to other Python objects. On this page we explain the machinery
that allows you to subclass ndarray, and the implications for
implementing a subclass.
ndarrays and object creation
============================
Subclassing ndarray is complicated by the fact that new instances of
ndarray classes can come about in three different ways. These are:
#. Explicit constructor call - as in ``MySubClass(params)``. This is
the usual route to Python instance creation.
#. View casting - casting an existing ndarray as a given subclass
#. New from template - creating a new instance from a template
instance. Examples include returning slices from a subclassed array,
creating return types from ufuncs, and copying arrays. See
:ref:`new-from-template` for more details
The last two are characteristics of ndarrays - in order to support
things like array slicing. The complications of subclassing ndarray are
due to the mechanisms numpy has to support these latter two routes of
instance creation.
.. _view-casting:
View casting
------------
*View casting* is the standard ndarray mechanism by which you take an
ndarray of any subclass, and return a view of the array as another
(specified) subclass:
>>> import numpy as np
>>> # create a completely useless ndarray subclass
>>> class C(np.ndarray): pass
>>> # create a standard ndarray
>>> arr = np.zeros((3,))
>>> # take a view of it, as our useless subclass
>>> c_arr = arr.view(C)
>>> type(c_arr)
<class 'C'>
.. _new-from-template:
Creating new from template
--------------------------
New instances of an ndarray subclass can also come about by a very
similar mechanism to :ref:`view-casting`, when numpy finds it needs to
create a new instance from a template instance. The most obvious place
this has to happen is when you are taking slices of subclassed arrays.
For example:
>>> v = c_arr[1:]
>>> type(v) # the view is of type 'C'
<class 'C'>
>>> v is c_arr # but it's a new instance
False
The slice is a *view* onto the original ``c_arr`` data. So, when we
take a view from the ndarray, we return a new ndarray, of the same
class, that points to the data in the original.
There are other points in the use of ndarrays where we need such views,
such as copying arrays (``c_arr.copy()``), creating ufunc output arrays
(see also :ref:`array-wrap`), and reducing methods (like
``c_arr.mean()``.
Relationship of view casting and new-from-template
--------------------------------------------------
These paths both use the same machinery. We make the distinction here,
because they result in different input to your methods. Specifically,
:ref:`view-casting` means you have created a new instance of your array
type from any potential subclass of ndarray. :ref:`new-from-template`
means you have created a new instance of your class from a pre-existing
instance, allowing you - for example - to copy across attributes that
are particular to your subclass.
Implications for subclassing
----------------------------
If we subclass ndarray, we need to deal not only with explicit
construction of our array type, but also :ref:`view-casting` or
:ref:`new-from-template`. Numpy has the machinery to do this, and this
machinery that makes subclassing slightly non-standard.
There are two aspects to the machinery that ndarray uses to support
views and new-from-template in subclasses.
The first is the use of the ``ndarray.__new__`` method for the main work
of object initialization, rather then the more usual ``__init__``
method. The second is the use of the ``__array_finalize__`` method to
allow subclasses to clean up after the creation of views and new
instances from templates.
A brief Python primer on ``__new__`` and ``__init__``
=====================================================
``__new__`` is a standard Python method, and, if present, is called
before ``__init__`` when we create a class instance. See the `python
__new__ documentation
<http://docs.python.org/reference/datamodel.html#object.__new__>`_ for more detail.
For example, consider the following Python code:
.. testcode::
class C(object):
def __new__(cls, *args):
print 'Cls in __new__:', cls
print 'Args in __new__:', args
return object.__new__(cls, *args)
def __init__(self, *args):
print 'type(self) in __init__:', type(self)
print 'Args in __init__:', args
meaning that we get:
>>> c = C('hello')
Cls in __new__: <class 'C'>
Args in __new__: ('hello',)
type(self) in __init__: <class 'C'>
Args in __init__: ('hello',)
When we call ``C('hello')``, the ``__new__`` method gets its own class
as first argument, and the passed argument, which is the string
``'hello'``. After python calls ``__new__``, it usually (see below)
calls our ``__init__`` method, with the output of ``__new__`` as the
first argument (now a class instance), and the passed arguments
following.
As you can see, the object can be initialized in the ``__new__``
method or the ``__init__`` method, or both, and in fact ndarray does
not have an ``__init__`` method, because all the initialization is
done in the ``__new__`` method.
Why use ``__new__`` rather than just the usual ``__init__``? Because
in some cases, as for ndarray, we want to be able to return an object
of some other class. Consider the following:
.. testcode::
class D(C):
def __new__(cls, *args):
print 'D cls is:', cls
print 'D args in __new__:', args
return C.__new__(C, *args)
def __init__(self, *args):
# we never get here
print 'In D __init__'
meaning that:
>>> obj = D('hello')
D cls is: <class 'D'>
D args in __new__: ('hello',)
Cls in __new__: <class 'C'>
Args in __new__: ('hello',)
>>> type(obj)
<class 'C'>
The definition of ``C`` is the same as before, but for ``D``, the
``__new__`` method returns an instance of class ``C`` rather than
``D``. Note that the ``__init__`` method of ``D`` does not get
called. In general, when the ``__new__`` method returns an object of
class other than the class in which it is defined, the ``__init__``
method of that class is not called.
This is how subclasses of the ndarray class are able to return views
that preserve the class type. When taking a view, the standard
ndarray machinery creates the new ndarray object with something
like::
obj = ndarray.__new__(subtype, shape, ...
where ``subdtype`` is the subclass. Thus the returned view is of the
same class as the subclass, rather than being of class ``ndarray``.
That solves the problem of returning views of the same type, but now
we have a new problem. The machinery of ndarray can set the class
this way, in its standard methods for taking views, but the ndarray
``__new__`` method knows nothing of what we have done in our own
``__new__`` method in order to set attributes, and so on. (Aside -
why not call ``obj = subdtype.__new__(...`` then? Because we may not
have a ``__new__`` method with the same call signature).
The role of ``__array_finalize__``
==================================
``__array_finalize__`` is the mechanism that numpy provides to allow
subclasses to handle the various ways that new instances get created.
Remember that subclass instances can come about in these three ways:
#. explicit constructor call (``obj = MySubClass(params)``). This will
call the usual sequence of ``MySubClass.__new__`` then (if it exists)
``MySubClass.__init__``.
#. :ref:`view-casting`
#. :ref:`new-from-template`
Our ``MySubClass.__new__`` method only gets called in the case of the
explicit constructor call, so we can't rely on ``MySubClass.__new__`` or
``MySubClass.__init__`` to deal with the view casting and
new-from-template. It turns out that ``MySubClass.__array_finalize__``
*does* get called for all three methods of object creation, so this is
where our object creation housekeeping usually goes.
* For the explicit constructor call, our subclass will need to create a
new ndarray instance of its own class. In practice this means that
we, the authors of the code, will need to make a call to
``ndarray.__new__(MySubClass,...)``, or do view casting of an existing
array (see below)
* For view casting and new-from-template, the equivalent of
``ndarray.__new__(MySubClass,...`` is called, at the C level.
The arguments that ``__array_finalize__`` recieves differ for the three
methods of instance creation above.
The following code allows us to look at the call sequences and arguments:
.. testcode::
import numpy as np
class C(np.ndarray):
def __new__(cls, *args, **kwargs):
print 'In __new__ with class %s' % cls
return np.ndarray.__new__(cls, *args, **kwargs)
def __init__(self, *args, **kwargs):
# in practice you probably will not need or want an __init__
# method for your subclass
print 'In __init__ with class %s' % self.__class__
def __array_finalize__(self, obj):
print 'In array_finalize:'
print ' self type is %s' % type(self)
print ' obj type is %s' % type(obj)
Now:
>>> # Explicit constructor
>>> c = C((10,))
In __new__ with class <class 'C'>
In array_finalize:
self type is <class 'C'>
obj type is <type 'NoneType'>
In __init__ with class <class 'C'>
>>> # View casting
>>> a = np.arange(10)
>>> cast_a = a.view(C)
In array_finalize:
self type is <class 'C'>
obj type is <type 'numpy.ndarray'>
>>> # Slicing (example of new-from-template)
>>> cv = c[:1]
In array_finalize:
self type is <class 'C'>
obj type is <class 'C'>
The signature of ``__array_finalize__`` is::
def __array_finalize__(self, obj):
``ndarray.__new__`` passes ``__array_finalize__`` the new object, of our
own class (``self``) as well as the object from which the view has been
taken (``obj``). As you can see from the output above, the ``self`` is
always a newly created instance of our subclass, and the type of ``obj``
differs for the three instance creation methods:
* When called from the explicit constructor, ``obj`` is ``None``
* When called from view casting, ``obj`` can be an instance of any
subclass of ndarray, including our own.
* When called in new-from-template, ``obj`` is another instance of our
own subclass, that we might use to update the new ``self`` instance.
Because ``__array_finalize__`` is the only method that always sees new
instances being created, it is the sensible place to fill in instance
defaults for new object attributes, among other tasks.
This may be clearer with an example.
Simple example - adding an extra attribute to ndarray
-----------------------------------------------------
.. testcode::
import numpy as np
class InfoArray(np.ndarray):
def __new__(subtype, shape, dtype=float, buffer=None, offset=0,
strides=None, order=None, info=None):
# Create the ndarray instance of our type, given the usual
# ndarray input arguments. This will call the standard
# ndarray constructor, but return an object of our type.
# It also triggers a call to InfoArray.__array_finalize__
obj = np.ndarray.__new__(subtype, shape, dtype, buffer, offset, strides,
order)
# set the new 'info' attribute to the value passed
obj.info = info
# Finally, we must return the newly created object:
return obj
def __array_finalize__(self, obj):
# ``self`` is a new object resulting from
# ndarray.__new__(InfoArray, ...), therefore it only has
# attributes that the ndarray.__new__ constructor gave it -
# i.e. those of a standard ndarray.
#
# We could have got to the ndarray.__new__ call in 3 ways:
# From an explicit constructor - e.g. InfoArray():
# obj is None
# (we're in the middle of the InfoArray.__new__
# constructor, and self.info will be set when we return to
# InfoArray.__new__)
if obj is None: return
# From view casting - e.g arr.view(InfoArray):
# obj is arr
# (type(obj) can be InfoArray)
# From new-from-template - e.g infoarr[:3]
# type(obj) is InfoArray
#
# Note that it is here, rather than in the __new__ method,
# that we set the default value for 'info', because this
# method sees all creation of default objects - with the
# InfoArray.__new__ constructor, but also with
# arr.view(InfoArray).
self.info = getattr(obj, 'info', None)
# We do not need to return anything
Using the object looks like this:
>>> obj = InfoArray(shape=(3,)) # explicit constructor
>>> type(obj)
<class 'InfoArray'>
>>> obj.info is None
True
>>> obj = InfoArray(shape=(3,), info='information')
>>> obj.info
'information'
>>> v = obj[1:] # new-from-template - here - slicing
>>> type(v)
<class 'InfoArray'>
>>> v.info
'information'
>>> arr = np.arange(10)
>>> cast_arr = arr.view(InfoArray) # view casting
>>> type(cast_arr)
<class 'InfoArray'>
>>> cast_arr.info is None
True
This class isn't very useful, because it has the same constructor as the
bare ndarray object, including passing in buffers and shapes and so on.
We would probably prefer the constructor to be able to take an already
formed ndarray from the usual numpy calls to ``np.array`` and return an
object.
Slightly more realistic example - attribute added to existing array
-------------------------------------------------------------------
Here is a class that takes a standard ndarray that already exists, casts
as our type, and adds an extra attribute.
.. testcode::
import numpy as np
class RealisticInfoArray(np.ndarray):
def __new__(cls, input_array, info=None):
# Input array is an already formed ndarray instance
# We first cast to be our class type
obj = np.asarray(input_array).view(cls)
# add the new attribute to the created instance
obj.info = info
# Finally, we must return the newly created object:
return obj
def __array_finalize__(self, obj):
# see InfoArray.__array_finalize__ for comments
if obj is None: return
self.info = getattr(obj, 'info', None)
So:
>>> arr = np.arange(5)
>>> obj = RealisticInfoArray(arr, info='information')
>>> type(obj)
<class 'RealisticInfoArray'>
>>> obj.info
'information'
>>> v = obj[1:]
>>> type(v)
<class 'RealisticInfoArray'>
>>> v.info
'information'
.. _array-wrap:
``__array_wrap__`` for ufuncs
-------------------------------------------------------
``__array_wrap__`` gets called at the end of numpy ufuncs and other numpy
functions, to allow a subclass to set the type of the return value
and update attributes and metadata. Let's show how this works with an example.
First we make the same subclass as above, but with a different name and
some print statements:
.. testcode::
import numpy as np
class MySubClass(np.ndarray):
def __new__(cls, input_array, info=None):
obj = np.asarray(input_array).view(cls)
obj.info = info
return obj
def __array_finalize__(self, obj):
print 'In __array_finalize__:'
print ' self is %s' % repr(self)
print ' obj is %s' % repr(obj)
if obj is None: return
self.info = getattr(obj, 'info', None)
def __array_wrap__(self, out_arr, context=None):
print 'In __array_wrap__:'
print ' self is %s' % repr(self)
print ' arr is %s' % repr(out_arr)
# then just call the parent
return np.ndarray.__array_wrap__(self, out_arr, context)
We run a ufunc on an instance of our new array:
>>> obj = MySubClass(np.arange(5), info='spam')
In __array_finalize__:
self is MySubClass([0, 1, 2, 3, 4])
obj is array([0, 1, 2, 3, 4])
>>> arr2 = np.arange(5)+1
>>> ret = np.add(arr2, obj)
In __array_wrap__:
self is MySubClass([0, 1, 2, 3, 4])
arr is array([1, 3, 5, 7, 9])
In __array_finalize__:
self is MySubClass([1, 3, 5, 7, 9])
obj is MySubClass([0, 1, 2, 3, 4])
>>> ret
MySubClass([1, 3, 5, 7, 9])
>>> ret.info
'spam'
Note that the ufunc (``np.add``) has called the ``__array_wrap__`` method of the
input with the highest ``__array_priority__`` value, in this case
``MySubClass.__array_wrap__``, with arguments ``self`` as ``obj``, and
``out_arr`` as the (ndarray) result of the addition. In turn, the
default ``__array_wrap__`` (``ndarray.__array_wrap__``) has cast the
result to class ``MySubClass``, and called ``__array_finalize__`` -
hence the copying of the ``info`` attribute. This has all happened at the C level.
But, we could do anything we wanted:
.. testcode::
class SillySubClass(np.ndarray):
def __array_wrap__(self, arr, context=None):
return 'I lost your data'
>>> arr1 = np.arange(5)
>>> obj = arr1.view(SillySubClass)
>>> arr2 = np.arange(5)
>>> ret = np.multiply(obj, arr2)
>>> ret
'I lost your data'
So, by defining a specific ``__array_wrap__`` method for our subclass,
we can tweak the output from ufuncs. The ``__array_wrap__`` method
requires ``self``, then an argument - which is the result of the ufunc -
and an optional parameter *context*. This parameter is returned by some
ufuncs as a 3-element tuple: (name of the ufunc, argument of the ufunc,
domain of the ufunc). ``__array_wrap__`` should return an instance of
its containing class. See the masked array subclass for an
implementation.
In addition to ``__array_wrap__``, which is called on the way out of the
ufunc, there is also an ``__array_prepare__`` method which is called on
the way into the ufunc, after the output arrays are created but before any
computation has been performed. The default implementation does nothing
but pass through the array. ``__array_prepare__`` should not attempt to
access the array data or resize the array, it is intended for setting the
output array type, updating attributes and metadata, and performing any
checks based on the input that may be desired before computation begins.
Like ``__array_wrap__``, ``__array_prepare__`` must return an ndarray or
subclass thereof or raise an error.
Extra gotchas - custom ``__del__`` methods and ndarray.base
-----------------------------------------------------------
One of the problems that ndarray solves is keeping track of memory
ownership of ndarrays and their views. Consider the case where we have
created an ndarray, ``arr`` and have taken a slice with ``v = arr[1:]``.
The two objects are looking at the same memory. Numpy keeps track of
where the data came from for a particular array or view, with the
``base`` attribute:
>>> # A normal ndarray, that owns its own data
>>> arr = np.zeros((4,))
>>> # In this case, base is None
>>> arr.base is None
True
>>> # We take a view
>>> v1 = arr[1:]
>>> # base now points to the array that it derived from
>>> v1.base is arr
True
>>> # Take a view of a view
>>> v2 = v1[1:]
>>> # base points to the view it derived from
>>> v2.base is v1
True
In general, if the array owns its own memory, as for ``arr`` in this
case, then ``arr.base`` will be None - there are some exceptions to this
- see the numpy book for more details.
The ``base`` attribute is useful in being able to tell whether we have
a view or the original array. This in turn can be useful if we need
to know whether or not to do some specific cleanup when the subclassed
array is deleted. For example, we may only want to do the cleanup if
the original array is deleted, but not the views. For an example of
how this can work, have a look at the ``memmap`` class in
``numpy.core``.
""" |
"""This module tests SyntaxErrors.
Here's an example of the sort of thing that is tested.
>>> def f(x):
... global x
Traceback (most recent call last):
SyntaxError: name 'x' is parameter and global
The tests are all raise SyntaxErrors. They were created by checking
each C call that raises SyntaxError. There are several modules that
raise these exceptions-- ast.c, compile.c, future.c, pythonrun.c, and
symtable.c.
The parser itself outlaws a lot of invalid syntax. None of these
errors are tested here at the moment. We should add some tests; since
there are infinitely many programs with invalid syntax, we would need
to be judicious in selecting some.
The compiler generates a synthetic module name for code executed by
doctest. Since all the code comes from the same module, a suffix like
[1] is appended to the module name, As a consequence, changing the
order of tests in this module means renumbering all the errors after
it. (Maybe we should enable the ellipsis option for these tests.)
In ast.c, syntax errors are raised by calling ast_error().
Errors from set_context():
>>> obj.None = 1
Traceback (most recent call last):
SyntaxError: invalid syntax
>>> None = 1
Traceback (most recent call last):
SyntaxError: can't assign to keyword
It's a syntax error to assign to the empty tuple. Why isn't it an
error to assign to the empty list? It will always raise some error at
runtime.
>>> () = 1
Traceback (most recent call last):
SyntaxError: can't assign to ()
>>> f() = 1
Traceback (most recent call last):
SyntaxError: can't assign to function call
>>> del f()
Traceback (most recent call last):
SyntaxError: can't delete function call
>>> a + 1 = 2
Traceback (most recent call last):
SyntaxError: can't assign to operator
>>> (x for x in x) = 1
Traceback (most recent call last):
SyntaxError: can't assign to generator expression
>>> 1 = 1
Traceback (most recent call last):
SyntaxError: can't assign to literal
>>> "abc" = 1
Traceback (most recent call last):
SyntaxError: can't assign to literal
>>> b"" = 1
Traceback (most recent call last):
SyntaxError: can't assign to literal
>>> `1` = 1
Traceback (most recent call last):
SyntaxError: invalid syntax
If the left-hand side of an assignment is a list or tuple, an illegal
expression inside that contain should still cause a syntax error.
This test just checks a couple of cases rather than enumerating all of
them.
>>> (a, "b", c) = (1, 2, 3)
Traceback (most recent call last):
SyntaxError: can't assign to literal
>>> [a, b, c + 1] = [1, 2, 3]
Traceback (most recent call last):
SyntaxError: can't assign to operator
>>> a if 1 else b = 1
Traceback (most recent call last):
SyntaxError: can't assign to conditional expression
From compiler_complex_args():
>>> def f(None=1):
... pass
Traceback (most recent call last):
SyntaxError: invalid syntax
From ast_for_arguments():
>>> def f(x, y=1, z):
... pass
Traceback (most recent call last):
SyntaxError: non-default argument follows default argument
>>> def f(x, None):
... pass
Traceback (most recent call last):
SyntaxError: invalid syntax
>>> def f(*None):
... pass
Traceback (most recent call last):
SyntaxError: invalid syntax
>>> def f(**None):
... pass
Traceback (most recent call last):
SyntaxError: invalid syntax
From ast_for_funcdef():
>>> def None(x):
... pass
Traceback (most recent call last):
SyntaxError: invalid syntax
From ast_for_call():
>>> def f(it, *varargs):
... return list(it)
>>> L = range(10)
>>> f(x for x in L)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> f(x for x in L, 1)
Traceback (most recent call last):
SyntaxError: Generator expression must be parenthesized if not sole argument
>>> f((x for x in L), 1)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> f(i0, i1, i2, i3, i4, i5, i6, i7, i8, i9, i10, i11,
... i12, i13, i14, i15, i16, i17, i18, i19, i20, i21, i22,
... i23, i24, i25, i26, i27, i28, i29, i30, i31, i32, i33,
... i34, i35, i36, i37, i38, i39, i40, i41, i42, i43, i44,
... i45, i46, i47, i48, i49, i50, i51, i52, i53, i54, i55,
... i56, i57, i58, i59, i60, i61, i62, i63, i64, i65, i66,
... i67, i68, i69, i70, i71, i72, i73, i74, i75, i76, i77,
... i78, i79, i80, i81, i82, i83, i84, i85, i86, i87, i88,
... i89, i90, i91, i92, i93, i94, i95, i96, i97, i98, i99,
... i100, i101, i102, i103, i104, i105, i106, i107, i108,
... i109, i110, i111, i112, i113, i114, i115, i116, i117,
... i118, i119, i120, i121, i122, i123, i124, i125, i126,
... i127, i128, i129, i130, i131, i132, i133, i134, i135,
... i136, i137, i138, i139, i140, i141, i142, i143, i144,
... i145, i146, i147, i148, i149, i150, i151, i152, i153,
... i154, i155, i156, i157, i158, i159, i160, i161, i162,
... i163, i164, i165, i166, i167, i168, i169, i170, i171,
... i172, i173, i174, i175, i176, i177, i178, i179, i180,
... i181, i182, i183, i184, i185, i186, i187, i188, i189,
... i190, i191, i192, i193, i194, i195, i196, i197, i198,
... i199, i200, i201, i202, i203, i204, i205, i206, i207,
... i208, i209, i210, i211, i212, i213, i214, i215, i216,
... i217, i218, i219, i220, i221, i222, i223, i224, i225,
... i226, i227, i228, i229, i230, i231, i232, i233, i234,
... i235, i236, i237, i238, i239, i240, i241, i242, i243,
... i244, i245, i246, i247, i248, i249, i250, i251, i252,
... i253, i254, i255)
Traceback (most recent call last):
SyntaxError: more than 255 arguments
The actual error cases counts positional arguments, keyword arguments,
and generator expression arguments separately. This test combines the
three.
>>> f(i0, i1, i2, i3, i4, i5, i6, i7, i8, i9, i10, i11,
... i12, i13, i14, i15, i16, i17, i18, i19, i20, i21, i22,
... i23, i24, i25, i26, i27, i28, i29, i30, i31, i32, i33,
... i34, i35, i36, i37, i38, i39, i40, i41, i42, i43, i44,
... i45, i46, i47, i48, i49, i50, i51, i52, i53, i54, i55,
... i56, i57, i58, i59, i60, i61, i62, i63, i64, i65, i66,
... i67, i68, i69, i70, i71, i72, i73, i74, i75, i76, i77,
... i78, i79, i80, i81, i82, i83, i84, i85, i86, i87, i88,
... i89, i90, i91, i92, i93, i94, i95, i96, i97, i98, i99,
... i100, i101, i102, i103, i104, i105, i106, i107, i108,
... i109, i110, i111, i112, i113, i114, i115, i116, i117,
... i118, i119, i120, i121, i122, i123, i124, i125, i126,
... i127, i128, i129, i130, i131, i132, i133, i134, i135,
... i136, i137, i138, i139, i140, i141, i142, i143, i144,
... i145, i146, i147, i148, i149, i150, i151, i152, i153,
... i154, i155, i156, i157, i158, i159, i160, i161, i162,
... i163, i164, i165, i166, i167, i168, i169, i170, i171,
... i172, i173, i174, i175, i176, i177, i178, i179, i180,
... i181, i182, i183, i184, i185, i186, i187, i188, i189,
... i190, i191, i192, i193, i194, i195, i196, i197, i198,
... i199, i200, i201, i202, i203, i204, i205, i206, i207,
... i208, i209, i210, i211, i212, i213, i214, i215, i216,
... i217, i218, i219, i220, i221, i222, i223, i224, i225,
... i226, i227, i228, i229, i230, i231, i232, i233, i234,
... i235, i236, i237, i238, i239, i240, i241, i242, i243,
... (x for x in i244), i245, i246, i247, i248, i249, i250, i251,
... i252=1, i253=1, i254=1, i255=1)
Traceback (most recent call last):
SyntaxError: more than 255 arguments
>>> f(lambda x: x[0] = 3)
Traceback (most recent call last):
SyntaxError: lambda cannot contain assignment
The grammar accepts any test (basically, any expression) in the
keyword slot of a call site. Test a few different options.
>>> f(x()=2)
Traceback (most recent call last):
SyntaxError: keyword can't be an expression
>>> f(a or b=1)
Traceback (most recent call last):
SyntaxError: keyword can't be an expression
>>> f(x.y=1)
Traceback (most recent call last):
SyntaxError: keyword can't be an expression
More set_context():
>>> (x for x in x) += 1
Traceback (most recent call last):
SyntaxError: can't assign to generator expression
>>> None += 1
Traceback (most recent call last):
SyntaxError: can't assign to keyword
>>> f() += 1
Traceback (most recent call last):
SyntaxError: can't assign to function call
Test continue in finally in weird combinations.
continue in for loop under finally should be ok.
>>> def test():
... try:
... pass
... finally:
... for abc in range(10):
... continue
... print(abc)
>>> test()
9
Start simple, a continue in a finally should not be allowed.
>>> def test():
... for abc in range(10):
... try:
... pass
... finally:
... continue
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
This is essentially a continue in a finally which should not be allowed.
>>> def test():
... for abc in range(10):
... try:
... pass
... finally:
... try:
... continue
... except:
... pass
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
>>> def foo():
... try:
... pass
... finally:
... continue
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
>>> def foo():
... for a in ():
... try:
... pass
... finally:
... continue
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
>>> def foo():
... for a in ():
... try:
... pass
... finally:
... try:
... continue
... finally:
... pass
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
>>> def foo():
... for a in ():
... try: pass
... finally:
... try:
... pass
... except:
... continue
Traceback (most recent call last):
...
SyntaxError: 'continue' not supported inside 'finally' clause
There is one test for a break that is not in a loop. The compiler
uses a single data structure to keep track of try-finally and loops,
so we need to be sure that a break is actually inside a loop. If it
isn't, there should be a syntax error.
>>> try:
... print(1)
... break
... print(2)
... finally:
... print(3)
Traceback (most recent call last):
...
SyntaxError: 'break' outside loop
This should probably raise a better error than a SystemError (or none at all).
In 2.5 there was a missing exception and an assert was triggered in a debug
build. The number of blocks must be greater than CO_MAXBLOCKS. SF #1565514
>>> while 1:
... while 2:
... while 3:
... while 4:
... while 5:
... while 6:
... while 8:
... while 9:
... while 10:
... while 11:
... while 12:
... while 13:
... while 14:
... while 15:
... while 16:
... while 17:
... while 18:
... while 19:
... while 20:
... while 21:
... while 22:
... break
Traceback (most recent call last):
...
SystemError: too many statically nested blocks
Misuse of the nonlocal statement can lead to a few unique syntax errors.
>>> def f(x):
... nonlocal x
Traceback (most recent call last):
...
SyntaxError: name 'x' is parameter and nonlocal
>>> def f():
... global x
... nonlocal x
Traceback (most recent call last):
...
SyntaxError: name 'x' is nonlocal and global
>>> def f():
... nonlocal x
Traceback (most recent call last):
...
SyntaxError: no binding for nonlocal 'x' found
From SF bug #1705365
>>> nonlocal x
Traceback (most recent call last):
...
SyntaxError: nonlocal declaration not allowed at module level
TODO(jhylton): Figure out how to test SyntaxWarning with doctest.
## >>> def f(x):
## ... def f():
## ... print(x)
## ... nonlocal x
## Traceback (most recent call last):
## ...
## SyntaxWarning: name 'x' is assigned to before nonlocal declaration
## >>> def f():
## ... x = 1
## ... nonlocal x
## Traceback (most recent call last):
## ...
## SyntaxWarning: name 'x' is assigned to before nonlocal declaration
This tests assignment-context; there was a bug in Python 2.5 where compiling
a complex 'if' (one with 'elif') would fail to notice an invalid suite,
leading to spurious errors.
>>> if 1:
... x() = 1
... elif 1:
... pass
Traceback (most recent call last):
...
SyntaxError: can't assign to function call
>>> if 1:
... pass
... elif 1:
... x() = 1
Traceback (most recent call last):
...
SyntaxError: can't assign to function call
>>> if 1:
... x() = 1
... elif 1:
... pass
... else:
... pass
Traceback (most recent call last):
...
SyntaxError: can't assign to function call
>>> if 1:
... pass
... elif 1:
... x() = 1
... else:
... pass
Traceback (most recent call last):
...
SyntaxError: can't assign to function call
>>> if 1:
... pass
... elif 1:
... pass
... else:
... x() = 1
Traceback (most recent call last):
...
SyntaxError: can't assign to function call
Make sure that the old "raise X, Y[, Z]" form is gone:
>>> raise X, Y
Traceback (most recent call last):
...
SyntaxError: invalid syntax
>>> raise X, Y, Z
Traceback (most recent call last):
...
SyntaxError: invalid syntax
>>> f(a=23, a=234)
Traceback (most recent call last):
...
SyntaxError: keyword argument repeated
>>> del ()
Traceback (most recent call last):
SyntaxError: can't delete ()
>>> {1, 2, 3} = 42
Traceback (most recent call last):
SyntaxError: can't assign to literal
Corner-cases that used to fail to raise the correct error:
>>> def f(*, x=lambda __debug__:0): pass
Traceback (most recent call last):
SyntaxError: assignment to keyword
>>> def f(*args:(lambda __debug__:0)): pass
Traceback (most recent call last):
SyntaxError: assignment to keyword
>>> def f(**kwargs:(lambda __debug__:0)): pass
Traceback (most recent call last):
SyntaxError: assignment to keyword
>>> with (lambda *:0): pass
Traceback (most recent call last):
SyntaxError: named arguments must follow bare *
Corner-cases that used to crash:
>>> def f(**__debug__): pass
Traceback (most recent call last):
SyntaxError: assignment to keyword
>>> def f(*xx, __debug__): pass
Traceback (most recent call last):
SyntaxError: assignment to keyword
""" |
"""Generic socket server classes.
This module tries to capture the various aspects of defining a server:
For socket-based servers:
- address family:
- AF_INET{,6}: IP (Internet Protocol) sockets (default)
- AF_UNIX: Unix domain sockets
- others, e.g. AF_DECNET are conceivable (see <socket.h>
- socket type:
- SOCK_STREAM (reliable stream, e.g. TCP)
- SOCK_DGRAM (datagrams, e.g. UDP)
For request-based servers (including socket-based):
- client address verification before further looking at the request
(This is actually a hook for any processing that needs to look
at the request before anything else, e.g. logging)
- how to handle multiple requests:
- synchronous (one request is handled at a time)
- forking (each request is handled by a new process)
- threading (each request is handled by a new thread)
The classes in this module favor the server type that is simplest to
write: a synchronous TCP/IP server. This is bad class design, but
save some typing. (There's also the issue that a deep class hierarchy
slows down method lookups.)
There are five classes in an inheritance diagram, four of which represent
synchronous servers of four types:
+------------+
| BaseServer |
+------------+
|
v
+-----------+ +------------------+
| TCPServer |------->| UnixStreamServer |
+-----------+ +------------------+
|
v
+-----------+ +--------------------+
| UDPServer |------->| UnixDatagramServer |
+-----------+ +--------------------+
Note that UnixDatagramServer derives from UDPServer, not from
UnixStreamServer -- the only difference between an IP and a Unix
stream server is the address family, which is simply repeated in both
unix server classes.
Forking and threading versions of each type of server can be created
using the ForkingMixIn and ThreadingMixIn mix-in classes. For
instance, a threading UDP server class is created as follows:
class ThreadingUDPServer(ThreadingMixIn, UDPServer): pass
The Mix-in class must come first, since it overrides a method defined
in UDPServer! Setting the various member variables also changes
the behavior of the underlying server mechanism.
To implement a service, you must derive a class from
BaseRequestHandler and redefine its handle() method. You can then run
various versions of the service by combining one of the server classes
with your request handler class.
The request handler class must be different for datagram or stream
services. This can be hidden by using the request handler
subclasses StreamRequestHandler or DatagramRequestHandler.
Of course, you still have to use your head!
For instance, it makes no sense to use a forking server if the service
contains state in memory that can be modified by requests (since the
modifications in the child process would never reach the initial state
kept in the parent process and passed to each child). In this case,
you can use a threading server, but you will probably have to use
locks to avoid two requests that come in nearly simultaneous to apply
conflicting changes to the server state.
On the other hand, if you are building e.g. an HTTP server, where all
data is stored externally (e.g. in the file system), a synchronous
class will essentially render the service "deaf" while one request is
being handled -- which may be for a very long time if a client is slow
to read all the data it has requested. Here a threading or forking
server is appropriate.
In some cases, it may be appropriate to process part of a request
synchronously, but to finish processing in a forked child depending on
the request data. This can be implemented by using a synchronous
server and doing an explicit fork in the request handler class
handle() method.
Another approach to handling multiple simultaneous requests in an
environment that supports neither threads nor fork (or where these are
too expensive or inappropriate for the service) is to maintain an
explicit table of partially finished requests and to use select() to
decide which request to work on next (or whether to handle a new
incoming request). This is particularly important for stream services
where each client can potentially be connected for a long time (if
threads or subprocesses cannot be used).
Future work:
- Standard classes for Sun RPC (which uses either UDP or TCP)
- Standard mix-in classes to implement various authentication
and encryption schemes
- Standard framework for select-based multiplexing
XXX Open problems:
- What to do with out-of-band data?
BaseServer:
- split generic "request" functionality out into BaseServer class.
Copyright (C) 2000 NAME <EMAIL>
example: read entries from a SQL database (requires overriding
get_request() to return a table entry from the database).
entry is processed by a RequestHandlerClass.
""" |
# This code is part of Ansible, but is an independent component.
# This particular file snippet, and this file snippet only, is BSD licensed.
# Modules you write using this snippet, which is embedded dynamically by Ansible
# still belong to the author of the module, and may assign their own license
# to the complete work.
#
# Copyright (c), NAME <EMAIL>, 2012-2013
# Copyright (c), NAME <EMAIL>, 2015
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * 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.
#
# THIS 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.
#
# The match_hostname function and supporting code is under the terms and
# conditions of the Python Software Foundation License. They were taken from
# the Python3 standard library and adapted for use in Python2. See comments in the
# source for which code precisely is under this License. PSF License text
# follows:
#
# PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
# --------------------------------------------
#
# 1. This LICENSE AGREEMENT is between the Python Software Foundation
# ("PSF"), and the Individual or Organization ("Licensee") accessing and
# otherwise using this software ("Python") in source or binary form and
# its associated documentation.
#
# 2. Subject to the terms and conditions of this License Agreement, PSF hereby
# grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
# analyze, test, perform and/or display publicly, prepare derivative works,
# distribute, and otherwise use Python alone or in any derivative version,
# provided, however, that PSF's License Agreement and PSF's notice of copyright,
# i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
# 2011, 2012, 2013, 2014 Python Software Foundation; All Rights Reserved" are
# retained in Python alone or in any derivative version prepared by Licensee.
#
# 3. In the event Licensee prepares a derivative work that is based on
# or incorporates Python or any part thereof, and wants to make
# the derivative work available to others as provided herein, then
# Licensee hereby agrees to include in any such work a brief summary of
# the changes made to Python.
#
# 4. PSF is making Python available to Licensee on an "AS IS"
# basis. PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
# IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND
# DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
# FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT
# INFRINGE ANY THIRD PARTY RIGHTS.
#
# 5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
# FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
# A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON,
# OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
#
# 6. This License Agreement will automatically terminate upon a material
# breach of its terms and conditions.
#
# 7. Nothing in this License Agreement shall be deemed to create any
# relationship of agency, partnership, or joint venture between PSF and
# Licensee. This License Agreement does not grant permission to use PSF
# trademarks or trade name in a trademark sense to endorse or promote
# products or services of Licensee, or any third party.
#
# 8. By copying, installing or otherwise using Python, Licensee
# agrees to be bound by the terms and conditions of this License
# Agreement.
|
# # ===============================================================================
# # Copyright 2015 NAME #
# # Licensed under the Apache License, Version 2.0 (the "License");
# # you may not use this file except in compliance with the License.
# # You may obtain a copy of the License at
# #
# # http://www.apache.org/licenses/LICENSE-2.0
# #
# # Unless required by applicable law or agreed to in writing, software
# # distributed under the License is distributed on an "AS IS" BASIS,
# # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# # See the License for the specific language governing permissions and
# # limitations under the License.
# # ===============================================================================
#
# # ============= enthought library imports =======================
# from itertools import groupby
#
# from traits.api import HasTraits, Str, Bool, Float
# from traitsui.api import View, UItem, Item, VGroup, HGroup
# # ============= standard library imports ========================
# # ============= local library imports ==========================
#
# from pychron.pipeline.nodes.data import DVCNode
#
#
# class AnalysisMetadataOption(HasTraits):
# sample = Str(analysis=True)
# material = Str(analysis=True)
# project = Str(analysis=True)
# comment = Str(analysis=True)
# weight = Float(extraction=True)
# identifier = Float(identifier=True)
# aliquot = Float(identifier=True)
#
# sample_enabled = Bool(False)
# material_enabled = Bool(False)
# project_enabled = Bool(False)
# comment_enabled = Bool(False)
# weight_enabled = Bool(False)
# identifier_enabled = Bool(False)
# aliquot_enabled = Bool(False)
#
# def traits_view(self):
# v = View(VGroup(HGroup(Item('sample_enabled', label='Sample'),
# UItem('sample', enabled_when='sample_enabled')),
# HGroup(Item('material_enabled', label='Material'),
# UItem('material', enabled_when='material_enabled')),
# HGroup(Item('project_enabled', label='Project'),
# UItem('project', enabled_when='project_enabled')),
# HGroup(Item('comment_enabled', label='Comment'),
# UItem('comment', enabled_when='comment_enabled')),
# HGroup(Item('weight_enabled', label='Weight (mg)'),
# UItem('weight', enabled_when='weight_enabled'))),
# buttons=['OK', 'Cancel'])
# return v
#
# def get_edit_dict(self):
# am = {k: getattr(self, k) for k in self.traits(analysis=True) if getattr(self, '{}_enabled'.format(k))}
# ext = {k: getattr(self, k) for k in self.traits(extraction=True) if getattr(self, '{}_enabled'.format(k))}
# idn = {k: getattr(self, k) for k in self.traits(identifier=True) if getattr(self, '{}_enabled'.format(k))}
# return am, ext, idn
#
#
# class AnalysisMetadataNode(DVCNode):
# options_klass = AnalysisMetadataOption
# name = 'Analysis Metadata'
#
# def configure(self, *args, **kw):
# if self.unknowns:
# unk = self.unknowns[0]
# self.options.sample = unk.sample
# self.options.material = unk.material
# self.options.project = unk.project
# self.options.comment = unk.comment
# self.options.weight = unk.weight
# self.options.identifier = unk.identifier
# self.options.aliquot = unk.aliquot
#
# return super(AnalysisMetadataNode, self).configure(*args, **kw)
#
# def run(self, state):
# dvc = self.dvc
#
# am_dict, ext_dict, idn_dict = self.options.get_edit_dict()
# if am_dict or ext_dict or idn_dict:
# def key(xi):
# return xi.repository_identifier
#
# for repo_id, ans in groupby(sorted(self.unknowns, key=key), key=key):
#
# dvc.pull_repository(repo_id)
# for ai in self.unknowns:
# dvc.analysis_metadata_edit(ai.uuid, ai.record_id, ai.repository_identifier, am_dict, ext_dict,
# idn_dict)
#
# dvc.repository_commit(repo_id, 'Analysis Metadata edits')
#
# # ============= EOF =============================================
|
# #!/usr/bin/env python
#
# """
# @package mi.idk.dataset.test.test_package_driver
# @file mi.idk/dataset/test/test_package_driver.py
# @author NAME @brief test package driver object
# """
#
# __author__ = 'Emily NAME __license__ = 'Apache 2.0'
#
# from mi.core.unit_test import MiUnitTest
#
# from mi.core.log import get_logger ; log = get_logger()
# from nose.plugins.attrib import attr
# import unittest
#
# import sys
# import os
# import re
# import pkg_resources
# from os.path import exists
#
# from mi.idk.config import Config
#
# from mi.idk.dataset.metadata import Metadata
# from mi.idk.dataset.package_driver import PackageDriver
#
# MI_BASE_DIR = "mi/dataset/driver"
# DRIVER_DIR = "fake_platform/fake_driver"
# CONSTRUCTOR = "FakeDriver"
# METADATA_FILE = "metadata.yml"
#
# @attr('UNIT', group='mi')
# class TestPackageDriver(MiUnitTest):
#
# def setUp(self):
# """
# Setup the test case
# """
# # create the expected file and directories to go with the metadata
# self.createFakeDriver()
#
# def createMetadataFile(self, version="0.2.2"):
# """
# Copied from test_metadata
# """
#
# full_driver_path = "%s/%s/%s" % (Config().base_dir(), MI_BASE_DIR, DRIVER_DIR)
# if(not exists(full_driver_path)):
# os.makedirs(full_driver_path)
# md_file_path = "%s/%s" % (full_driver_path, METADATA_FILE)
# md_file = open(md_file_path, 'w')
#
# md_file.write("driver_metadata:\n")
# md_file.write(" author: NAME path_str = " driver_path: %s\n" % DRIVER_DIR
# md_file.write(path_str)
# md_file.write(" driver_name: fake_driver\n")
# md_file.write(" email: EMAIL md_file.write(" release_notes: some note\n")
# version_str = " version: %s\n" % version
# md_file.write(version_str)
# constr_str = " constructor: %s\n" % CONSTRUCTOR
# md_file.write(constr_str)
#
# md_file.close()
#
# current_dsa_path = Config().idk_config_dir() + "/current_dsa.yml"
# log.info("linking %s to %s", md_file_path, current_dsa_path)
# # exists doesn't catch when this link is broken but still there,
# # need to figure out how to find and delete
# if exists(current_dsa_path):
# os.remove(current_dsa_path)
#
# os.symlink(md_file_path, current_dsa_path)
#
# def createFakeDriver(self):
# """
# Write a fake driver
# """
# full_driver_path = "%s/%s/%s" % (Config().base_dir(), MI_BASE_DIR, DRIVER_DIR)
# if(not exists(full_driver_path)):
# os.makedirs(full_driver_path)
#
# fake_driver_path = "%s/%s/%s/driver.py" % (Config().base_dir(), MI_BASE_DIR, DRIVER_DIR)
#
# # create a fake driver file
# if not exists(fake_driver_path):
# # write a make driver which just has the class constructor in it
# fake_driver_file = open(fake_driver_path, 'w')
# log.info("creating fake driver %s", fake_driver_path)
# fake_driver_file.write("from mi.core.log import get_logger ; log = get_logger()\n\n")
# class_line = "class %s(object):\n\n" % CONSTRUCTOR
# fake_driver_file.write(class_line)
# fake_driver_file.write(" def sayHello(self):\n")
# fake_driver_file.write(" log.info('Hello from Dataset Driver')\n")
# fake_driver_file.close()
#
# # make sure we have __init__.py in both dirs
# init_path = "%s/%s/%s/__init__.py" % (Config().base_dir(), MI_BASE_DIR, "fake_platform")
# touch = open(init_path, "w")
# touch.close()
# init_path = "%s/%s/%s/__init__.py" % (Config().base_dir(), MI_BASE_DIR, DRIVER_DIR)
# touch = open(init_path, "w")
# touch.close()
#
# else:
# log.info("fake driver already initialized %s", fake_driver_path)
#
# # make sure there is a test directory
# test_dir_path = "%s/%s/%s/test" % (Config().base_dir(), MI_BASE_DIR, DRIVER_DIR)
# if not exists(test_dir_path):
# os.makedirs(test_dir_path)
#
# # make sure there is a resource directory
# resource_dir_path = "%s/%s/%s/resource" % (Config().base_dir(), MI_BASE_DIR, DRIVER_DIR)
# if not exists(resource_dir_path):
# os.makedirs(resource_dir_path)
#
# # the resource directory must contain strings.yml
# resource_path = "%s/strings.yml" % resource_dir_path
# if not exists(resource_path):
# touch = open(resource_path, "w")
# touch.close()
#
# test_driver_path = "%s/test_driver.py" % test_dir_path
# if not exists(test_driver_path):
# fake_driver_test = open(test_driver_path, 'w')
# log.info("creating fake test driver %s", test_driver_path)
# fake_driver_test.close()
# # make sure we have an __init__.py
# init_path = "%s/__init__.py" % test_dir_path
# touch = open(init_path, "w")
# touch.close()
# else:
# log.info("fake test driver test already initialized")
#
# #def test_package_driver(self):
# """
# # make a metadata file is initialized, otherwise it might not exist
# self.createMetadataFile()
#
# # create the metadata so we can use it for opening the egg
# metadata1 = Metadata()
#
# package_driver = PackageDriver()
# package_driver.run()
#
# # overwrite the original metadata file for the same driver and change the version
# self.createMetadataFile("0.2.5")
#
# # create the metadata so we can use it for opening the egg
# metadata2 = Metadata()
#
# # run package driver again to create the new driver version
# package_driver = PackageDriver()
# package_driver.run()
#
# log.info("Both driver eggs created")
#
# # load the first driver
# cotr1 = self.load_egg(metadata1)
# loaded_driver = cotr1()
# loaded_driver.sayHello()
# log.info("First driver done saying hello")
#
# # load the second driver
# cotr2 = self.load_egg(metadata2)
# loaded_driver2 = cotr2()
# loaded_driver2.sayHello()
# log.info("Second driver done saying hello")
#
# # just print out some entry point info
# for x in pkg_resources.iter_entry_points('drivers.dataset.fake_driver'):
# log.info("entry point:%s", x)
# """
#
# @unittest.skip("requires enter be pressed to run.")
# def test_package_driver_real(self):
# """
# Test with real hypm ctd driver code
# """
#
# # link current metadata dsa file to a real driver, the ctd
# current_dsa_path = Config().idk_config_dir() + "/current_dsa.yml"
# ctd_md_path = "%s/%s/hypm/ctd/metadata.yml" % (Config().base_dir(), MI_BASE_DIR)
# log.info("linking %s to %s", ctd_md_path, current_dsa_path)
# # exists doesn't catch when this link is broken but still there,
# # need to figure out how to find and delete
# if exists(current_dsa_path):
# os.remove(current_dsa_path)
# log.error(current_dsa_path)
# os.symlink(ctd_md_path, current_dsa_path)
#
# # create the metadata so we can use it for opening the egg
# metadata = Metadata()
#
# # create the egg with the package driver
# package_driver = PackageDriver()
# package_driver.run()
#
# startup_config = {
# 'harvester':
# {
# 'directory': '/tmp/dsatest',
# 'pattern': '*.txt',
# 'frequency': 1,
# },
# 'parser': {}
# }
#
# # load the driver
# cotr = self.load_egg(metadata)
# # need to load with the right number of arguments
# egg_driver = cotr(startup_config, None, None, None, None)
# log.info("driver loaded")
#
#
# def load_egg(self, metadata):
# # use metadata to initialize egg name and location, but only the egg name
# # should be used to figure out how to load the entry point
# egg_name = "%s-%s-py2.7.egg" % (metadata.driver_name_versioned, metadata.version)
# egg_cache_dir = "/tmp/%s/dist" % metadata.driver_name_versioned
# pkg_resources.working_set.add_entry(egg_cache_dir + '/' + egg_name)
# first_dash_idx = egg_name.find('-')
# second_dash_idx = egg_name[first_dash_idx+1:].find('-')
# first_under_idx = egg_name.find('_')
# driver_name = egg_name[first_under_idx+1:first_dash_idx]
# log.debug("found driver name %s", driver_name)
# digit_match = re.search("\d", driver_name)
# if digit_match:
# first_digit = digit_match.start()
# short_driver = driver_name[:first_digit-1]
# else:
# short_driver = driver_name
# log.debug("found short driver name %s", short_driver)
# version = egg_name[first_dash_idx+1:(second_dash_idx+first_dash_idx+1)]
# log.debug("found version %s", version)
# entry_point = 'driver-' + version
# group_name = 'drivers.dataset.' + short_driver
# log.info("entry point %s, group_name %s", entry_point, group_name)
# cotr = pkg_resources.load_entry_point('driver_' + driver_name, group_name, entry_point)
# log.info("loaded entry point")
# return cotr
#
#
#
#
#
#
#
|
#!/usr/bin/env python
# Try to determine how much RAM is currently being used per program.
# Note per _program_, not per process. So for example this script
# will report RAM used by all httpd process together. In detail it reports:
# sum(private RAM for program processes) + sum(Shared RAM for program processes)
# The shared RAM is problematic to calculate, and this script automatically
# selects the most accurate method available for your kernel.
# Licence: LGPLv2
# Author: EMAIL Source: http://www.pixelbeat.org/scripts/ps_mem.py
# V1.0 06 Jul 2005 Initial release
# V1.1 11 Aug 2006 root permission required for accuracy
# V1.2 08 Nov 2006 Add total to output
# Use KiB,MiB,... for units rather than K,M,...
# V1.3 22 Nov 2006 Ignore shared col from /proc/$pid/statm for
# 2.6 kernels up to and including 2.6.9.
# There it represented the total file backed extent
# V1.4 23 Nov 2006 Remove total from output as it's meaningless
# (the shared values overlap with other programs).
# Display the shared column. This extra info is
# useful, especially as it overlaps between programs.
# V1.5 26 Mar 2007 Remove redundant recursion from human()
# V1.6 05 Jun 2007 Also report number of processes with a given name.
# Patch from EMAIL V1.7 20 Sep 2007 Use PSS from /proc/$pid/smaps if available, which
# fixes some over-estimation and allows totalling.
# Enumerate the PIDs directly rather than using ps,
# which fixes the possible race between reading
# RSS with ps, and shared memory with this program.
# Also we can show non truncated command names.
# V1.8 28 Sep 2007 More accurate matching for stats in /proc/$pid/smaps
# as otherwise could match libraries causing a crash.
# Patch from EMAIL V1.9 20 Feb 2008 Fix invalid values reported when PSS is available.
# Reported by NAME <EMAIL>
# V3.3 24 Jun 2014
# http://github.com/pixelb/scripts/commits/master/scripts/ps_mem.py
# Notes:
#
# All interpreted programs where the interpreter is started
# by the shell or with env, will be merged to the interpreter
# (as that's what's given to exec). For e.g. all python programs
# starting with "#!/usr/bin/env python" will be grouped under python.
# You can change this by using the full command line but that will
# have the undesirable affect of splitting up programs started with
# differing parameters (for e.g. mingetty tty[1-6]).
#
# For 2.6 kernels up to and including 2.6.13 and later 2.4 redhat kernels
# (rmap vm without smaps) it can not be accurately determined how many pages
# are shared between processes in general or within a program in our case:
# http://lkml.org/lkml/2005/7/6/250
# A warning is printed if overestimation is possible.
# In addition for 2.6 kernels up to 2.6.9 inclusive, the shared
# value in /proc/$pid/statm is the total file-backed extent of a process.
# We ignore that, introducing more overestimation, again printing a warning.
# Since kernel 2.6.23-rc8-mm1 PSS is available in smaps, which allows
# us to calculate a more accurate value for the total RAM used by programs.
#
# Programs that use CLONE_VM without CLONE_THREAD are discounted by assuming
# they're the only programs that have the same /proc/$PID/smaps file for
# each instance. This will fail if there are multiple real instances of a
# program that then use CLONE_VM without CLONE_THREAD, or if a clone changes
# its memory map while we're checksumming each /proc/$PID/smaps.
#
# I don't take account of memory allocated for a program
# by other programs. For e.g. memory used in the X server for
# a program could be determined, but is not.
#
# FreeBSD is supported if linprocfs is mounted at /compat/linux/proc/
# FreeBSD 8.0 supports up to a level of Linux 2.6.16
|
""" Interface to :mod:`~pySPACE.environments.chains.node_chain` using the :class:`~pySPACE.environments.chains.node_chain.BenchmarkNodeChain`
.. image:: ../../graphics/node_chain.png
:width: 500
A process consists of applying
the given `node_chain` on an input for a certain parameters setting.
Factory methods are provided that create this operation based
on a specification in one or more YAML files.
.. note:: Complete lists of available nodes and namings can be found
at: :ref:`node_list`.
.. seealso::
- :mod:`~pySPACE.missions.nodes`
- :ref:`node_list`
- :mod:`~pySPACE.environments.chains.node_chain`
Specification file Parameters
+++++++++++++++++++++++++++++
type
----
Specifies which operation is used. For this operation
you have to use *node_chain*.
(*obligatory, node_chain*)
input_path
----------
Path name relative to the *storage* in your configuration file.
The input has to fulfill certain rules,
specified in the fitting: :mod:`~pySPACE.resources.dataset_defs` subpackage.
For each dataset in the input and each parameter combination
you get an own result.
(*obligatory*)
templates
---------
List of node chain file templates which shall be evaluated.
The final node chain is constructed, by substituting specified parameters
of the operation spec file. Therefore you should use the format
*__PARAMETER__*. Already fixed and usable keywords are
*__INPUT_DATASET__* and *__RESULT_DIRECTORY__*.
The spec file name may include a path and it is searched relative
the folder *node_chains* in the specs folder,
given in your configuration file you used, when starting :ref:`pySPACE`
(:mod:`pySPACE.run.launch`).
If no template is given, the software tries to use the
parameters *node_chain*, which simply includes the *node_chain* description.
.. todo:: Check that parameter fulfills format rule!
(*recommended, alternative: `node_chain`*)
node_chain
----------
Instead of using a template you can use your single node chain directly
in the operation spec.
(*optional, default: `templates`*)
runs
----
Number of repetitions for each running process.
The run number is given to the process, to use it for *choosing*
random components, especially when using cross-validation.
(*optional, default: 1*)
parameter_ranges
----------------
Dictionary with parameter names as keys and a list of values it
should be replaced with.
Finally a grid of all possible combinations is build and afterwards
only those remain fulfilling the constraints.
(*mandatory if parameter_setting is not used*)
parameter_setting
-----------------
If you do not use the *parameter_ranges*, this is a list of
dictionaries giving the parameter combinations, you want to test
the operation on.
(*mandatory if parameter_ranges is not used*)
constraints
-----------
List of strings, where the parameters values are replaced and
which is afterwards evaluated to check if is *True*.
If it is not, the parameter is rejected.
(*optional, default: []*)
old_parameter_constraints
-------------------------
Same as *constraints*, but here parameters of earlier
operation calls, e.g. windowing, can be used in the constraint def.
hide_parameters
---------------
Normally each parameter is added in the format
*{__PARAMETER__: value}* added to the *__RESULT_DIRECTORY__*.
Every parameter specified in the list *hide_parameters* is not
added to the folder name. Be careful not to have
the same name for different results.
(*optional, default: []*)
storage_format
--------------
Some datsets give the opportunity to choose between different
formats for storing the result. The choice can be specified here.
For details look at the :mod:`~pySPACE.resources.dataset_defs` documentation.
If the format is not specified, the default of the
dataset is used.
(*optional, default: default of dataset*)
store_node_chain
----------------
option to save as pickle file the total state of
the node chain after the processing;
separately for each split in cross validation if existing
(*optional, default: False*)
compression
-----------
If your result is a classification summary,
all the created sub-folders are zipped with the zipfile module,
since they are normally not needed anymore,
but you may have numerous folders, making coping difficult.
To switch this of, use the value *False* and if you want no
compression, use *1*. If all the sub-folders (except a single one)
should be deleted, set compression to ``delete``.
(*optional, default: 8*)
Exemplary Call
++++++++++++++
.. code-block:: yaml
type: node_chain
input_path: "my_data"
runs : 2
templates : ["example_node_chain1.yaml","example_node_chain2.yaml","example_node_chain3.yaml"]
.. code-block:: yaml
type: node_chain
input_path: "my_data"
runs : 2
parameter_ranges :
__prob__ : eval(range(1,4))
node_chain :
-
node: FeatureVectorSource
-
node: CrossValidationSplitter
parameters:
splits : 5
-
node: GaussianFeatureNormalization
-
node: LinearDiscriminantAnalysisClassifier
parameters:
class_labels: ["Standard","Target"]
prior_probability : [1,__prob__]
-
node: ThresholdOptimization
parameters:
metric : Balanced_accuracy
-
node: Classification_Performance_Sink
parameters:
ir_class: "Target"
.. code-block:: yaml
type: node_chain
input_path: "my_data"
runs : 2
templates : ["example_node_chain.yaml"]
parameter_setting :
-
__prob__ : 1
-
__prob__ : 2
-
__prob__ : 3
Here `example_node_chain.yaml` is defined in the node chain specification folder as
.. literalinclude:: ../../examples/specs/node_chains/example_node_chain.yaml
:language: yaml
""" |
"""
Limits
======
Implemented according to the PhD thesis
http://www.cybertester.com/data/gruntz.pdf, which contains very thorough
descriptions of the algorithm including many examples. We summarize here
the gist of it.
All functions are sorted according to how rapidly varying they are at
infinity using the following rules. Any two functions f and g can be
compared using the properties of L:
L=lim log|f(x)| / log|g(x)| (for x -> oo)
We define >, < ~ according to::
1. f > g .... L=+-oo
we say that:
- f is greater than any power of g
- f is more rapidly varying than g
- f goes to infinity/zero faster than g
2. f < g .... L=0
we say that:
- f is lower than any power of g
3. f ~ g .... L!=0, +-oo
we say that:
- both f and g are bounded from above and below by suitable integral
powers of the other
Examples
========
::
2 < x < exp(x) < exp(x**2) < exp(exp(x))
2 ~ 3 ~ -5
x ~ x**2 ~ x**3 ~ 1/x ~ x**m ~ -x
exp(x) ~ exp(-x) ~ exp(2x) ~ exp(x)**2 ~ exp(x+exp(-x))
f ~ 1/f
So we can divide all the functions into comparability classes (x and x^2
belong to one class, exp(x) and exp(-x) belong to some other class). In
principle, we could compare any two functions, but in our algorithm, we
don't compare anything below the class 2~3~-5 (for example log(x) is
below this), so we set 2~3~-5 as the lowest comparability class.
Given the function f, we find the list of most rapidly varying (mrv set)
subexpressions of it. This list belongs to the same comparability class.
Let's say it is {exp(x), exp(2x)}. Using the rule f ~ 1/f we find an
element "w" (either from the list or a new one) from the same
comparability class which goes to zero at infinity. In our example we
set w=exp(-x) (but we could also set w=exp(-2x) or w=exp(-3x) ...). We
rewrite the mrv set using w, in our case {1/w, 1/w^2}, and substitute it
into f. Then we expand f into a series in w::
f = c0*w^e0 + c1*w^e1 + ... + O(w^en), where e0<e1<...<en, c0!=0
but for x->oo, lim f = lim c0*w^e0, because all the other terms go to zero,
because w goes to zero faster than the ci and ei. So::
for e0>0, lim f = 0
for e0<0, lim f = +-oo (the sign depends on the sign of c0)
for e0=0, lim f = lim c0
We need to recursively compute limits at several places of the algorithm, but
as is shown in the PhD thesis, it always finishes.
Important functions from the implementation:
compare(a, b, x) compares "a" and "b" by computing the limit L.
mrv(e, x) returns list of most rapidly varying (mrv) subexpressions of "e"
rewrite(e, Omega, x, wsym) rewrites "e" in terms of w
leadterm(f, x) returns the lowest power term in the series of f
mrv_leadterm(e, x) returns the lead term (c0, e0) for e
limitinf(e, x) computes lim e (for x->oo)
limit(e, z, z0) computes any limit by converting it to the case x->oo
All the functions are really simple and straightforward except
rewrite(), which is the most difficult/complex part of the algorithm.
When the algorithm fails, the bugs are usually in the series expansion
(i.e. in SymPy) or in rewrite.
This code is almost exact rewrite of the Maple code inside the Gruntz
thesis.
Debugging
---------
Because the gruntz algorithm is highly recursive, it's difficult to
figure out what went wrong inside a debugger. Instead, turn on nice
debug prints by defining the environment variable SYMPY_DEBUG. For
example:
[user@localhost]: SYMPY_DEBUG=True ./bin/isympy
In [1]: limit(sin(x)/x, x, 0)
limitinf(_x*sin(1/_x), _x) = 1
+-mrv_leadterm(_x*sin(1/_x), _x) = (1, 0)
| +-mrv(_x*sin(1/_x), _x) = set([_x])
| | +-mrv(_x, _x) = set([_x])
| | +-mrv(sin(1/_x), _x) = set([_x])
| | +-mrv(1/_x, _x) = set([_x])
| | +-mrv(_x, _x) = set([_x])
| +-mrv_leadterm(exp(_x)*sin(exp(-_x)), _x, set([exp(_x)])) = (1, 0)
| +-rewrite(exp(_x)*sin(exp(-_x)), set([exp(_x)]), _x, _w) = (1/_w*sin(_w), -_x)
| +-sign(_x, _x) = 1
| +-mrv_leadterm(1, _x) = (1, 0)
+-sign(0, _x) = 0
+-limitinf(1, _x) = 1
And check manually which line is wrong. Then go to the source code and
debug this function to figure out the exact problem.
""" |
"""
=======================================
Signal processing (:mod:`scipy.signal`)
=======================================
Convolution
===========
.. autosummary::
:toctree: generated/
convolve -- N-dimensional convolution.
correlate -- N-dimensional correlation.
fftconvolve -- N-dimensional convolution using the FFT.
convolve2d -- 2-dimensional convolution (more options).
correlate2d -- 2-dimensional correlation (more options).
sepfir2d -- Convolve with a 2-D separable FIR filter.
B-splines
=========
.. autosummary::
:toctree: generated/
bspline -- B-spline basis function of order n.
cubic -- B-spline basis function of order 3.
quadratic -- B-spline basis function of order 2.
gauss_spline -- Gaussian approximation to the B-spline basis function.
cspline1d -- Coefficients for 1-D cubic (3rd order) B-spline.
qspline1d -- Coefficients for 1-D quadratic (2nd order) B-spline.
cspline2d -- Coefficients for 2-D cubic (3rd order) B-spline.
qspline2d -- Coefficients for 2-D quadratic (2nd order) B-spline.
cspline1d_eval -- Evaluate a cubic spline at the given points.
qspline1d_eval -- Evaluate a quadratic spline at the given points.
spline_filter -- Smoothing spline (cubic) filtering of a rank-2 array.
Filtering
=========
.. autosummary::
:toctree: generated/
order_filter -- N-dimensional order filter.
medfilt -- N-dimensional median filter.
medfilt2d -- 2-dimensional median filter (faster).
wiener -- N-dimensional wiener filter.
symiirorder1 -- 2nd-order IIR filter (cascade of first-order systems).
symiirorder2 -- 4th-order IIR filter (cascade of second-order systems).
lfilter -- 1-dimensional FIR and IIR digital linear filtering.
lfiltic -- Construct initial conditions for `lfilter`.
lfilter_zi -- Compute an initial state zi for the lfilter function that
-- corresponds to the steady state of the step response.
filtfilt -- A forward-backward filter.
savgol_filter -- Filter a signal using the Savitzky-Golay filter.
deconvolve -- 1-d deconvolution using lfilter.
sosfilt -- 1-dimensional IIR digital linear filtering using
-- a second-order sections filter representation.
sosfilt_zi -- Compute an initial state zi for the sosfilt function that
-- corresponds to the steady state of the step response.
sosfiltfilt -- A forward-backward filter for second-order sections.
hilbert -- Compute 1-D analytic signal, using the Hilbert transform.
hilbert2 -- Compute 2-D analytic signal, using the Hilbert transform.
decimate -- Downsample a signal.
detrend -- Remove linear and/or constant trends from data.
resample -- Resample using Fourier method.
resample_poly -- Resample using polyphase filtering method.
upfirdn -- Upsample, apply FIR filter, downsample.
Filter design
=============
.. autosummary::
:toctree: generated/
bilinear -- Digital filter from an analog filter using
-- the bilinear transform.
findfreqs -- Find array of frequencies for computing filter response.
firls -- FIR filter design using least-squares error minimization.
firwin -- Windowed FIR filter design, with frequency response
-- defined as pass and stop bands.
firwin2 -- Windowed FIR filter design, with arbitrary frequency
-- response.
freqs -- Analog filter frequency response.
freqz -- Digital filter frequency response.
group_delay -- Digital filter group delay.
iirdesign -- IIR filter design given bands and gains.
iirfilter -- IIR filter design given order and critical frequencies.
kaiser_atten -- Compute the attenuation of a Kaiser FIR filter, given
-- the number of taps and the transition width at
-- discontinuities in the frequency response.
kaiser_beta -- Compute the Kaiser parameter beta, given the desired
-- FIR filter attenuation.
kaiserord -- Design a Kaiser window to limit ripple and width of
-- transition region.
savgol_coeffs -- Compute the FIR filter coefficients for a Savitzky-Golay
-- filter.
remez -- Optimal FIR filter design.
unique_roots -- Unique roots and their multiplicities.
residue -- Partial fraction expansion of b(s) / a(s).
residuez -- Partial fraction expansion of b(z) / a(z).
invres -- Inverse partial fraction expansion for analog filter.
invresz -- Inverse partial fraction expansion for digital filter.
BadCoefficients -- Warning on badly conditioned filter coefficients
Lower-level filter design functions:
.. autosummary::
:toctree: generated/
abcd_normalize -- Check state-space matrices and ensure they are rank-2.
band_stop_obj -- Band Stop Objective Function for order minimization.
besselap -- Return (z,p,k) for analog prototype of Bessel filter.
buttap -- Return (z,p,k) for analog prototype of Butterworth filter.
cheb1ap -- Return (z,p,k) for type I Chebyshev filter.
cheb2ap -- Return (z,p,k) for type II Chebyshev filter.
cmplx_sort -- Sort roots based on magnitude.
ellipap -- Return (z,p,k) for analog prototype of elliptic filter.
lp2bp -- Transform a lowpass filter prototype to a bandpass filter.
lp2bs -- Transform a lowpass filter prototype to a bandstop filter.
lp2hp -- Transform a lowpass filter prototype to a highpass filter.
lp2lp -- Transform a lowpass filter prototype to a lowpass filter.
normalize -- Normalize polynomial representation of a transfer function.
Matlab-style IIR filter design
==============================
.. autosummary::
:toctree: generated/
butter -- Butterworth
buttord
cheby1 -- Chebyshev Type I
cheb1ord
cheby2 -- Chebyshev Type II
cheb2ord
ellip -- Elliptic (Cauer)
ellipord
bessel -- Bessel (no order selection available -- try butterod)
Continuous-Time Linear Systems
==============================
.. autosummary::
:toctree: generated/
lti -- Continuous-time linear time invariant system base class.
StateSpace -- Linear time invariant system in state space form.
TransferFunction -- Linear time invariant system in transfer function form.
ZerosPolesGain -- Linear time invariant system in zeros, poles, gain form.
lsim -- continuous-time simulation of output to linear system.
lsim2 -- like lsim, but `scipy.integrate.odeint` is used.
impulse -- impulse response of linear, time-invariant (LTI) system.
impulse2 -- like impulse, but `scipy.integrate.odeint` is used.
step -- step response of continous-time LTI system.
step2 -- like step, but `scipy.integrate.odeint` is used.
freqresp -- frequency response of a continuous-time LTI system.
bode -- Bode magnitude and phase data (continuous-time LTI).
Discrete-Time Linear Systems
============================
.. autosummary::
:toctree: generated/
dlti -- Discrete-time linear time invariant system base class.
StateSpace -- Linear time invariant system in state space form.
TransferFunction -- Linear time invariant system in transfer function form.
ZerosPolesGain -- Linear time invariant system in zeros, poles, gain form.
dlsim -- simulation of output to a discrete-time linear system.
dimpulse -- impulse response of a discrete-time LTI system.
dstep -- step response of a discrete-time LTI system.
dfreqresp -- frequency response of a discrete-time LTI system.
dbode -- Bode magnitude and phase data (discrete-time LTI).
LTI Representations
===================
.. autosummary::
:toctree: generated/
tf2zpk -- transfer function to zero-pole-gain.
tf2sos -- transfer function to second-order sections.
tf2ss -- transfer function to state-space.
zpk2tf -- zero-pole-gain to transfer function.
zpk2sos -- zero-pole-gain to second-order sections.
zpk2ss -- zero-pole-gain to state-space.
ss2tf -- state-pace to transfer function.
ss2zpk -- state-space to pole-zero-gain.
sos2zpk -- second-order sections to zero-pole-gain.
sos2tf -- second-order sections to transfer function.
cont2discrete -- continuous-time to discrete-time LTI conversion.
place_poles -- pole placement.
Waveforms
=========
.. autosummary::
:toctree: generated/
chirp -- Frequency swept cosine signal, with several freq functions.
gausspulse -- Gaussian modulated sinusoid
max_len_seq -- Maximum length sequence
sawtooth -- Periodic sawtooth
square -- Square wave
sweep_poly -- Frequency swept cosine signal; freq is arbitrary polynomial
Window functions
================
.. autosummary::
:toctree: generated/
get_window -- Return a window of a given length and type.
barthann -- Bartlett-Hann window
bartlett -- Bartlett window
blackman -- Blackman window
blackmanharris -- Minimum 4-term Blackman-Harris window
bohman -- Bohman window
boxcar -- Boxcar window
chebwin -- Dolph-Chebyshev window
cosine -- Cosine window
exponential -- Exponential window
flattop -- Flat top window
gaussian -- Gaussian window
general_gaussian -- Generalized Gaussian window
hamming -- Hamming window
hann -- Hann window
hanning -- Hann window
kaiser -- Kaiser window
nuttall -- Nuttall's minimum 4-term Blackman-Harris window
parzen -- Parzen window
slepian -- Slepian window
triang -- Triangular window
tukey -- Tukey window
Wavelets
========
.. autosummary::
:toctree: generated/
cascade -- compute scaling function and wavelet from coefficients
daub -- return low-pass
morlet -- Complex Morlet wavelet.
qmf -- return quadrature mirror filter from low-pass
ricker -- return ricker wavelet
cwt -- perform continuous wavelet transform
Peak finding
============
.. autosummary::
:toctree: generated/
find_peaks_cwt -- Attempt to find the peaks in the given 1-D array
argrelmin -- Calculate the relative minima of data
argrelmax -- Calculate the relative maxima of data
argrelextrema -- Calculate the relative extrema of data
Spectral Analysis
=================
.. autosummary::
:toctree: generated/
periodogram -- Compute a (modified) periodogram
welch -- Compute a periodogram using Welch's method
csd -- Compute the cross spectral density, using Welch's method
coherence -- Compute the magnitude squared coherence, using Welch's method
spectrogram -- Compute the spectrogram
lombscargle -- Computes the Lomb-Scargle periodogram
vectorstrength -- Computes the vector strength
""" |
# #!/usr/bin/env python
# # -*- coding: utf-8 -*-
# import os
# import math
# import mapnik
# import sys
# from utilities import execution_path, run_all
# from nose.tools import *
# def setup():
# # All of the paths used are relative, if we run the tests
# # from another directory we need to chdir()
# os.chdir(execution_path('.'))
# class PointDatasource(mapnik.PythonDatasource):
# def __init__(self):
# super(PointDatasource, self).__init__(
# geometry_type = mapnik.DataGeometryType.Point,
# envelope = mapnik.Box2d(0,-10,100,110),
# data_type = mapnik.DataType.Vector
# )
# def features(self, query):
# return mapnik.PythonDatasource.wkt_features(
# keys = ('label',),
# features = (
# ( 'POINT (5 6)', { 'label': 'foo-bar'} ),
# ( 'POINT (60 50)', { 'label': 'buzz-quux'} ),
# )
# )
# class ConcentricCircles(object):
# def __init__(self, centre, bounds, step=1):
# self.centre = centre
# self.bounds = bounds
# self.step = step
# class Iterator(object):
# def __init__(self, container):
# self.container = container
# centre = self.container.centre
# bounds = self.container.bounds
# step = self.container.step
# self.radius = step
# def next(self):
# points = []
# for alpha in xrange(0, 361, 5):
# x = math.sin(math.radians(alpha)) * self.radius + self.container.centre[0]
# y = math.cos(math.radians(alpha)) * self.radius + self.container.centre[1]
# points.append('%s %s' % (x,y))
# circle = 'POLYGON ((' + ','.join(points) + '))'
# # has the circle grown so large that the boundary is entirely within it?
# tl = (self.container.bounds.maxx, self.container.bounds.maxy)
# tr = (self.container.bounds.maxx, self.container.bounds.maxy)
# bl = (self.container.bounds.minx, self.container.bounds.miny)
# br = (self.container.bounds.minx, self.container.bounds.miny)
# def within_circle(p):
# delta_x = p[0] - self.container.centre[0]
# delta_y = p[0] - self.container.centre[0]
# return delta_x*delta_x + delta_y*delta_y < self.radius*self.radius
# if all(within_circle(p) for p in (tl,tr,bl,br)):
# raise StopIteration()
# self.radius += self.container.step
# return ( circle, { } )
# def __iter__(self):
# return ConcentricCircles.Iterator(self)
# class CirclesDatasource(mapnik.PythonDatasource):
# def __init__(self, centre_x=-20, centre_y=0, step=10):
# super(CirclesDatasource, self).__init__(
# geometry_type = mapnik.DataGeometryType.Polygon,
# envelope = mapnik.Box2d(-180, -90, 180, 90),
# data_type = mapnik.DataType.Vector
# )
# # note that the plugin loader will set all arguments to strings and will not try to parse them
# centre_x = int(centre_x)
# centre_y = int(centre_y)
# step = int(step)
# self.centre_x = centre_x
# self.centre_y = centre_y
# self.step = step
# def features(self, query):
# centre = (self.centre_x, self.centre_y)
# return mapnik.PythonDatasource.wkt_features(
# keys = (),
# features = ConcentricCircles(centre, query.bbox, self.step)
# )
# if 'python' in mapnik.DatasourceCache.plugin_names():
# # make sure we can load from ourself as a module
# sys.path.append(execution_path('.'))
# def test_python_point_init():
# ds = mapnik.Python(factory='python_plugin_test:PointDatasource')
# e = ds.envelope()
# assert_almost_equal(e.minx, 0, places=7)
# assert_almost_equal(e.miny, -10, places=7)
# assert_almost_equal(e.maxx, 100, places=7)
# assert_almost_equal(e.maxy, 110, places=7)
# def test_python_circle_init():
# ds = mapnik.Python(factory='python_plugin_test:CirclesDatasource')
# e = ds.envelope()
# assert_almost_equal(e.minx, -180, places=7)
# assert_almost_equal(e.miny, -90, places=7)
# assert_almost_equal(e.maxx, 180, places=7)
# assert_almost_equal(e.maxy, 90, places=7)
# def test_python_circle_init_with_args():
# ds = mapnik.Python(factory='python_plugin_test:CirclesDatasource', centre_x=40, centre_y=7)
# e = ds.envelope()
# assert_almost_equal(e.minx, -180, places=7)
# assert_almost_equal(e.miny, -90, places=7)
# assert_almost_equal(e.maxx, 180, places=7)
# assert_almost_equal(e.maxy, 90, places=7)
# def test_python_point_rendering():
# m = mapnik.Map(512,512)
# mapnik.load_map(m,'../data/python_plugin/python_point_datasource.xml')
# m.zoom_all()
# im = mapnik.Image(512,512)
# mapnik.render(m,im)
# actual = '/tmp/mapnik-python-point-render1.png'
# expected = 'images/support/mapnik-python-point-render1.png'
# im.save(actual)
# expected_im = mapnik.Image.open(expected)
# eq_(im.tostring('png32'),expected_im.tostring('png32'),
# 'failed comparing actual (%s) and expected (%s)' % (actual,'tests/python_tests/'+ expected))
# def test_python_circle_rendering():
# m = mapnik.Map(512,512)
# mapnik.load_map(m,'../data/python_plugin/python_circle_datasource.xml')
# m.zoom_all()
# im = mapnik.Image(512,512)
# mapnik.render(m,im)
# actual = '/tmp/mapnik-python-circle-render1.png'
# expected = 'images/support/mapnik-python-circle-render1.png'
# im.save(actual)
# expected_im = mapnik.Image.open(expected)
# eq_(im.tostring('png32'),expected_im.tostring('png32'),
# 'failed comparing actual (%s) and expected (%s)' % (actual,'tests/python_tests/'+ expected))
# if __name__ == "__main__":
# setup()
# run_all(eval(x) for x in dir() if x.startswith("test_"))
|
# The following CSI codes supported by xcode are not tested.
# Query ReGIS/Sixel attributes: CSI ? Pi ; Pa ; P vS
# Initiate highlight mouse tracking: CSI Ps ; Ps ; Ps ; Ps ; Ps T
# Media Copy (MC): CSI Pm i
# Media Copy (MC, DEC-specific): CSI ? Pm i
# Character Attributes (SGR): CSI Pm m
# Disable modifiers: CSI > Ps n
# Set pointer mode: CSI > Ps p
# Load LEDs (DECLL): CSI Ps q
# Set cursor style (DECSCUSR): CIS Ps SP q
# Select character protection attribute (DECSCA): CSI Ps " q [This is already tested by DECSED and DECSEL]
# Window manipulation: CSI Ps; Ps; Ps t
# Reverse Attributes in Rectangular Area (DECRARA): CSI Pt ; Pl ; Pb ; Pr ; Ps $ t
# Set warning bell volume (DECSWBV): CSI Ps SP t
# Set margin-bell volume (DECSMBV): CSI Ps SP u
# Enable Filter Rectangle (DECEFR): CSI Pt ; Pl ; Pb ; Pr ' w
# Request Terminal Parameters (DECREQTPARM): CSI Ps x
# Select Attribute Change Extent (DECSACE): CSI Ps * x
# Request Checksum of Rectangular Area (DECRQCRA): CSI Pi ; Pg ; Pt ; Pl ; Pb ; Pr * y
# Select Locator Events (DECSLE): CSI Pm ' {
# Request Locator Position (DECRQLP): CSI PS ' |
# ESC SP L Set ANSI conformance level 1 (dpANS X3.134.1).
# ESC SP M Set ANSI conformance level 2 (dpANS X3.134.1).
# ESC SP N Set ANSI conformance level 3 (dpANS X3.134.1).
# In xterm, all these do is fiddle with character sets, which are not testable.
# ESC # 3 DEC double-height line, top half (DECDHL).
# ESC # 4 DEC double-height line, bottom half (DECDHL).
# ESC # 5 DEC single-width line (DECSWL).
# ESC # 6 DEC double-width line (DECDWL).
# Double-width affects display only and is generally not introspectable. Wrap
# doesn't work so there's no way to tell where the cursor is visually.
# ESC % @ Select default character set. That is ISO 8859-1 (ISO 2022).
# ESC % G Select UTF-8 character set (ISO 2022).
# ESC ( C Designate G0 Character Set (ISO 2022, VT100).
# ESC ) C Designate G1 Character Set (ISO 2022, VT100).
# ESC * C Designate G2 Character Set (ISO 2022, VT220).
# ESC + C Designate G3 Character Set (ISO 2022, VT220).
# ESC - C Designate G1 Character Set (VT300).
# ESC . C Designate G2 Character Set (VT300).
# ESC / C Designate G3 Character Set (VT300).
# Character set stuff is not introspectable.
# Shift in (SI): ^O
# Shift out (SO): ^N
# Space (SP): 0x20
# Tab (TAB): 0x09 [tested in HTS]
# ESC = Application Keypad (DECKPAM).
# ESC > Normal Keypad (DECKPNM).
# ESC F Cursor to lower left corner of screen. This is enabled by the
# hpLowerleftBugCompat resource. (Not worth testing as it's off by
# default, and silly regardless)
# ESC l Memory Lock (per HP terminals). Locks memory above the cursor.
# ESC m Memory Unlock (per HP terminals).
# ESC n Invoke the G2 Character Set as GL (LS2).
# ESC o Invoke the G3 Character Set as GL (LS3).
# ESC | Invoke the G3 Character Set as GR (LS3R).
# ESC } Invoke the G2 Character Set as GR (LS2R).
# ESC ~ Invoke the G1 Character Set as GR (LS1R).
# DCS + p Pt ST Set Termcap/Terminfo Data
# DCS + q Pt ST Request Termcap/Terminfo String
# The following OSC commands are tested in xterm_winops and don't have their own test:
# Ps = 0 -> Change Icon Name and Window Title to Pt.
# Ps = 1 -> Change Icon Name to Pt.
# Ps = 2 -> Change Window Title to Pt.
# This test is too ill-defined and X-specific, and is not tested:
# Ps = 3 -> Set X property on top-level window. Pt should be
# in the form "prop=value", or just "prop" to delete the prop-
# erty
# No introspection for whether special color are enabled/disabled:
# Ps = 6 ; c; f -> Enable/disable Special Color Number c. The
# second parameter tells xterm to enable the corresponding color
# mode if nonzero, disable it if zero.
# Off by default, obvious security issues:
# Ps = 4 6 -> Change Log File to Pt. (This is normally dis-
# abled by a compile-time option).
# No introspection for fonts:
# Ps = 5 0 -> Set Font to Pt.
# No-op:
# Ps = 5 1 -> reserved for Emacs shell.
|
"""
=============================
Byteswapping and byte order
=============================
Introduction to byte ordering and ndarrays
==========================================
The ``ndarray`` is an object that provide a python array interface to data
in memory.
It often happens that the memory that you want to view with an array is
not of the same byte ordering as the computer on which you are running
Python.
For example, I might be working on a computer with a little-endian CPU -
such as an Intel Pentium, but I have loaded some data from a file
written by a computer that is big-endian. Let's say I have loaded 4
bytes from a file written by a Sun (big-endian) computer. I know that
these 4 bytes represent two 16-bit integers. On a big-endian machine, a
two-byte integer is stored with the Most Significant Byte (MSB) first,
and then the Least Significant Byte (LSB). Thus the bytes are, in memory order:
#. MSB integer 1
#. LSB integer 1
#. MSB integer 2
#. LSB integer 2
Let's say the two integers were in fact 1 and 770. Because 770 = 256 *
3 + 2, the 4 bytes in memory would contain respectively: 0, 1, 3, 2.
The bytes I have loaded from the file would have these contents:
>>> big_end_str = chr(0) + chr(1) + chr(3) + chr(2)
>>> big_end_str
'\\x00\\x01\\x03\\x02'
We might want to use an ``ndarray`` to access these integers. In that
case, we can create an array around this memory, and tell numpy that
there are two integers, and that they are 16 bit and big-endian:
>>> import numpy as np
>>> big_end_arr = np.ndarray(shape=(2,),dtype='>i2', buffer=big_end_str)
>>> big_end_arr[0]
1
>>> big_end_arr[1]
770
Note the array ``dtype`` above of ``>i2``. The ``>`` means 'big-endian'
(``<`` is little-endian) and ``i2`` means 'signed 2-byte integer'. For
example, if our data represented a single unsigned 4-byte little-endian
integer, the dtype string would be ``<u4``.
In fact, why don't we try that?
>>> little_end_u4 = np.ndarray(shape=(1,),dtype='<u4', buffer=big_end_str)
>>> little_end_u4[0] == 1 * 256**1 + 3 * 256**2 + 2 * 256**3
True
Returning to our ``big_end_arr`` - in this case our underlying data is
big-endian (data endianness) and we've set the dtype to match (the dtype
is also big-endian). However, sometimes you need to flip these around.
.. warning::
Scalars currently do not include byte order information, so extracting
a scalar from an array will return an integer in native byte order.
Hence:
>>> big_end_arr[0].dtype.byteorder == little_end_u4[0].dtype.byteorder
True
Changing byte ordering
======================
As you can imagine from the introduction, there are two ways you can
affect the relationship between the byte ordering of the array and the
underlying memory it is looking at:
* Change the byte-ordering information in the array dtype so that it
interprets the undelying data as being in a different byte order.
This is the role of ``arr.newbyteorder()``
* Change the byte-ordering of the underlying data, leaving the dtype
interpretation as it was. This is what ``arr.byteswap()`` does.
The common situations in which you need to change byte ordering are:
#. Your data and dtype endianess don't match, and you want to change
the dtype so that it matches the data.
#. Your data and dtype endianess don't match, and you want to swap the
data so that they match the dtype
#. Your data and dtype endianess match, but you want the data swapped
and the dtype to reflect this
Data and dtype endianness don't match, change dtype to match data
-----------------------------------------------------------------
We make something where they don't match:
>>> wrong_end_dtype_arr = np.ndarray(shape=(2,),dtype='<i2', buffer=big_end_str)
>>> wrong_end_dtype_arr[0]
256
The obvious fix for this situation is to change the dtype so it gives
the correct endianness:
>>> fixed_end_dtype_arr = wrong_end_dtype_arr.newbyteorder()
>>> fixed_end_dtype_arr[0]
1
Note the the array has not changed in memory:
>>> fixed_end_dtype_arr.tobytes() == big_end_str
True
Data and type endianness don't match, change data to match dtype
----------------------------------------------------------------
You might want to do this if you need the data in memory to be a certain
ordering. For example you might be writing the memory out to a file
that needs a certain byte ordering.
>>> fixed_end_mem_arr = wrong_end_dtype_arr.byteswap()
>>> fixed_end_mem_arr[0]
1
Now the array *has* changed in memory:
>>> fixed_end_mem_arr.tobytes() == big_end_str
False
Data and dtype endianness match, swap data and dtype
----------------------------------------------------
You may have a correctly specified array dtype, but you need the array
to have the opposite byte order in memory, and you want the dtype to
match so the array values make sense. In this case you just do both of
the previous operations:
>>> swapped_end_arr = big_end_arr.byteswap().newbyteorder()
>>> swapped_end_arr[0]
1
>>> swapped_end_arr.tobytes() == big_end_str
False
An easier way of casting the data to a specific dtype and byte ordering
can be achieved with the ndarray astype method:
>>> swapped_end_arr = big_end_arr.astype('<i2')
>>> swapped_end_arr[0]
1
>>> swapped_end_arr.tobytes() == big_end_str
False
""" |
#!/usr/bin/env python
# Try to determine how much RAM is currently being used per program.
# Note per _program_, not per process. So for example this script
# will report RAM used by all httpd process together. In detail it reports:
# sum(private RAM for program processes) + sum(Shared RAM for program processes)
# The shared RAM is problematic to calculate, and this script automatically
# selects the most accurate method available for your kernel.
# Licence: LGPLv2
# Author: EMAIL Source: http://www.pixelbeat.org/scripts/ps_mem.py
# V1.0 06 Jul 2005 Initial release
# V1.1 11 Aug 2006 root permission required for accuracy
# V1.2 08 Nov 2006 Add total to output
# Use KiB,MiB,... for units rather than K,M,...
# V1.3 22 Nov 2006 Ignore shared col from /proc/$pid/statm for
# 2.6 kernels up to and including 2.6.9.
# There it represented the total file backed extent
# V1.4 23 Nov 2006 Remove total from output as it's meaningless
# (the shared values overlap with other programs).
# Display the shared column. This extra info is
# useful, especially as it overlaps between programs.
# V1.5 26 Mar 2007 Remove redundant recursion from human()
# V1.6 05 Jun 2007 Also report number of processes with a given name.
# Patch from EMAIL V1.7 20 Sep 2007 Use PSS from /proc/$pid/smaps if available, which
# fixes some over-estimation and allows totalling.
# Enumerate the PIDs directly rather than using ps,
# which fixes the possible race between reading
# RSS with ps, and shared memory with this program.
# Also we can show non truncated command names.
# V1.8 28 Sep 2007 More accurate matching for stats in /proc/$pid/smaps
# as otherwise could match libraries causing a crash.
# Patch from EMAIL V1.9 20 Feb 2008 Fix invalid values reported when PSS is available.
# Reported by NAME <EMAIL>
# V3.5 29 Sep 2015
# http://github.com/pixelb/scripts/commits/master/scripts/ps_mem.py
# Notes:
#
# All interpreted programs where the interpreter is started
# by the shell or with env, will be merged to the interpreter
# (as that's what's given to exec). For e.g. all python programs
# starting with "#!/usr/bin/env python" will be grouped under python.
# You can change this by using the full command line but that will
# have the undesirable affect of splitting up programs started with
# differing parameters (for e.g. mingetty tty[1-6]).
#
# For 2.6 kernels up to and including 2.6.13 and later 2.4 redhat kernels
# (rmap vm without smaps) it can not be accurately determined how many pages
# are shared between processes in general or within a program in our case:
# http://lkml.org/lkml/2005/7/6/250
# A warning is printed if overestimation is possible.
# In addition for 2.6 kernels up to 2.6.9 inclusive, the shared
# value in /proc/$pid/statm is the total file-backed extent of a process.
# We ignore that, introducing more overestimation, again printing a warning.
# Since kernel 2.6.23-rc8-mm1 PSS is available in smaps, which allows
# us to calculate a more accurate value for the total RAM used by programs.
#
# Programs that use CLONE_VM without CLONE_THREAD are discounted by assuming
# they're the only programs that have the same /proc/$PID/smaps file for
# each instance. This will fail if there are multiple real instances of a
# program that then use CLONE_VM without CLONE_THREAD, or if a clone changes
# its memory map while we're checksumming each /proc/$PID/smaps.
#
# I don't take account of memory allocated for a program
# by other programs. For e.g. memory used in the X server for
# a program could be determined, but is not.
#
# FreeBSD is supported if linprocfs is mounted at /compat/linux/proc/
# FreeBSD 8.0 supports up to a level of Linux 2.6.16
|
"""
Tools for 3DCGA (g3c)
3DCGA Tools
==========================================================
Generation Methods
--------------------
.. autosummary::
:toctree: generated/
random_bivector
standard_point_pair_at_origin
random_point_pair_at_origin
random_point_pair
standard_line_at_origin
random_line_at_origin
random_line
random_circle_at_origin
random_circle
random_sphere_at_origin
random_sphere
random_plane_at_origin
random_plane
generate_n_clusters
generate_random_object_cluster
random_translation_rotor
random_rotation_translation_rotor
random_conformal_point
generate_dilation_rotor
generate_translation_rotor
Geometry Methods
--------------------
.. autosummary::
:toctree: generated/
intersect_line_and_plane_to_point
val_intersect_line_and_plane_to_point
quaternion_and_vector_to_rotor
get_center_from_sphere
get_radius_from_sphere
point_pair_to_end_points
val_point_pair_to_end_points
get_circle_in_euc
circle_to_sphere
line_to_point_and_direction
get_plane_origin_distance
get_plane_normal
get_nearest_plane_point
val_convert_2D_polar_line_to_conformal_line
convert_2D_polar_line_to_conformal_line
val_convert_2D_point_to_conformal
convert_2D_point_to_conformal
val_distance_point_to_line
distance_polar_line_to_euc_point_2d
midpoint_between_lines
val_midpoint_between_lines
midpoint_of_line_cluster
val_midpoint_of_line_cluster
val_midpoint_of_line_cluster_grad
get_line_intersection
val_get_line_intersection
project_points_to_plane
project_points_to_sphere
project_points_to_circle
project_points_to_line
iterative_closest_points_on_circles
closest_point_on_line_from_circle
closest_point_on_circle_from_line
iterative_closest_points_circle_line
iterative_furthest_points_on_circles
sphere_beyond_plane
sphere_behind_plane
Misc
--------------------
.. autosummary::
:toctree: generated/
meet_val
meet
normalise_n_minus_1
val_normalise_n_minus_1
val_apply_rotor
apply_rotor
val_apply_rotor_inv
apply_rotor_inv
euc_dist
mult_with_ninf
val_norm
norm
val_normalised
normalised
val_up
fast_up
val_normalInv
val_homo
val_down
fast_down
dual_func
fast_dual
disturb_object
project_val
get_line_reflection_matrix
val_get_line_reflection_matrix
val_truncated_get_line_reflection_matrix
interpret_multivector_as_object
normalise_TR_to_unit_T
scale_TR_translation
val_unsign_sphere
Root Finding
--------------------
.. autosummary::
:toctree: generated/
dorst_norm_val
check_sigma_for_positive_root_val
check_sigma_for_positive_root
check_sigma_for_negative_root_val
check_sigma_for_negative_root
check_infinite_roots_val
check_infinite_roots
positive_root_val
negative_root_val
positive_root
negative_root
general_root_val
general_root
val_annihilate_k
annihilate_k
pos_twiddle_root_val
neg_twiddle_root_val
pos_twiddle_root
neg_twiddle_root
square_roots_of_rotor
n_th_rotor_root
interp_objects_root
general_object_interpolation
average_objects
val_average_objects_with_weights
val_average_objects
rotor_between_objects
val_rotor_between_objects_root
val_rotor_between_objects_explicit
calculate_S_over_mu
val_rotor_between_lines
rotor_between_lines
rotor_between_planes
val_rotor_rotor_between_planes
Submodules
--------------------
.. autosummary::
:toctree: generated/
object_fitting
""" |
# This code is part of Ansible, but is an independent component.
# This particular file snippet, and this file snippet only, is BSD licensed.
# Modules you write using this snippet, which is embedded dynamically by Ansible
# still belong to the author of the module, and may assign their own license
# to the complete work.
#
# Copyright (c), NAME <EMAIL>, 2012-2013
# Copyright (c), NAME <EMAIL>, 2015
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * 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.
#
# THIS 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.
#
# The match_hostname function and supporting code is under the terms and
# conditions of the Python Software Foundation License. They were taken from
# the Python3 standard library and adapted for use in Python2. See comments in the
# source for which code precisely is under this License. PSF License text
# follows:
#
# PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
# --------------------------------------------
#
# 1. This LICENSE AGREEMENT is between the Python Software Foundation
# ("PSF"), and the Individual or Organization ("Licensee") accessing and
# otherwise using this software ("Python") in source or binary form and
# its associated documentation.
#
# 2. Subject to the terms and conditions of this License Agreement, PSF hereby
# grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
# analyze, test, perform and/or display publicly, prepare derivative works,
# distribute, and otherwise use Python alone or in any derivative version,
# provided, however, that PSF's License Agreement and PSF's notice of copyright,
# i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
# 2011, 2012, 2013, 2014 Python Software Foundation; All Rights Reserved" are
# retained in Python alone or in any derivative version prepared by Licensee.
#
# 3. In the event Licensee prepares a derivative work that is based on
# or incorporates Python or any part thereof, and wants to make
# the derivative work available to others as provided herein, then
# Licensee hereby agrees to include in any such work a brief summary of
# the changes made to Python.
#
# 4. PSF is making Python available to Licensee on an "AS IS"
# basis. PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
# IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND
# DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
# FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT
# INFRINGE ANY THIRD PARTY RIGHTS.
#
# 5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
# FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
# A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON,
# OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
#
# 6. This License Agreement will automatically terminate upon a material
# breach of its terms and conditions.
#
# 7. Nothing in this License Agreement shall be deemed to create any
# relationship of agency, partnership, or joint venture between PSF and
# Licensee. This License Agreement does not grant permission to use PSF
# trademarks or trade name in a trademark sense to endorse or promote
# products or services of Licensee, or any third party.
#
# 8. By copying, installing or otherwise using Python, Licensee
# agrees to be bound by the terms and conditions of this License
# Agreement.
|
{
'name': 'Web',
'category': 'Hidden',
'version': 'IP_ADDRESS',
'description':
"""
OpenERP Web core module.
========================
This module provides the core of the OpenERP Web Client.
""",
'depends': [],
'auto_install': True,
'post_load': 'wsgi_postload',
'js' : [
"static/src/fixbind.js",
"static/lib/datejs/globalization/en-US.js",
"static/lib/datejs/core.js",
"static/lib/datejs/parser.js",
"static/lib/datejs/sugarpak.js",
"static/lib/datejs/extras.js",
"static/lib/jquery/jquery-1.8.3.js",
"static/lib/jquery.MD5/jquery.md5.js",
"static/lib/jquery.form/jquery.form.js",
"static/lib/jquery.validate/jquery.validate.js",
"static/lib/jquery.ba-bbq/jquery.ba-bbq.js",
"static/lib/spinjs/spin.js",
"static/lib/jquery.autosize/jquery.autosize.js",
"static/lib/jquery.blockUI/jquery.blockUI.js",
"static/lib/jquery.placeholder/jquery.placeholder.js",
"static/lib/jquery.ui/js/jquery-ui-1.9.1.custom.js",
"static/lib/jquery.ui.timepicker/js/jquery-ui-timepicker-addon.js",
"static/lib/jquery.ui.notify/js/jquery.notify.js",
"static/lib/jquery.deferred-queue/jquery.deferred-queue.js",
"static/lib/jquery.scrollTo/jquery.scrollTo-min.js",
"static/lib/jquery.tipsy/jquery.tipsy.js",
"static/lib/jquery.textext/jquery.textext.js",
"static/lib/jquery.timeago/jquery.timeago.js",
"static/lib/qweb/qweb2.js",
"static/lib/underscore/underscore.js",
"static/lib/underscore/underscore.string.js",
"static/lib/backbone/backbone.js",
"static/lib/cleditor/jquery.cleditor.js",
"static/lib/py.js/lib/py.js",
"static/src/js/boot.js",
"static/src/js/testing.js",
"static/src/js/pyeval.js",
"static/src/js/corelib.js",
"static/src/js/coresetup.js",
"static/src/js/dates.js",
"static/src/js/formats.js",
"static/src/js/chrome.js",
"static/src/js/views.js",
"static/src/js/data.js",
"static/src/js/data_export.js",
"static/src/js/search.js",
"static/src/js/view_form.js",
"static/src/js/view_list.js",
"static/src/js/view_list_editable.js",
"static/src/js/view_tree.js",
],
'css' : [
"static/lib/jquery.ui.bootstrap/css/custom-theme/jquery-ui-1.9.0.custom.css",
"static/lib/jquery.ui.timepicker/css/jquery-ui-timepicker-addon.css",
"static/lib/jquery.ui.notify/css/ui.notify.css",
"static/lib/jquery.tipsy/tipsy.css",
"static/lib/jquery.textext/jquery.textext.css",
"static/src/css/base.css",
"static/src/css/data_export.css",
"static/lib/cleditor/jquery.cleditor.css",
],
'qweb' : [
"static/src/xml/*.xml",
],
'test': [
"static/test/testing.js",
"static/test/class.js",
"static/test/registry.js",
"static/test/form.js",
"static/test/data.js",
"static/test/list-utils.js",
"static/test/formats.js",
"static/test/rpc.js",
"static/test/evals.js",
"static/test/search.js",
"static/test/Widget.js",
"static/test/list.js",
"static/test/list-editable.js",
"static/test/mutex.js"
],
'bootstrap': True,
} |
"""Stuff to parse AIFF-C and AIFF files.
Unless explicitly stated otherwise, the description below is true
both for AIFF-C files and AIFF files.
An AIFF-C file has the following structure.
+-----------------+
| FORM |
+-----------------+
| <size> |
+----+------------+
| | AIFC |
| +------------+
| | <chunks> |
| | . |
| | . |
| | . |
+----+------------+
An AIFF file has the string "AIFF" instead of "AIFC".
A chunk consists of an identifier (4 bytes) followed by a size (4 bytes,
big endian order), followed by the data. The size field does not include
the size of the 8 byte header.
The following chunk types are recognized.
FVER
<version number of AIFF-C defining document> (AIFF-C only).
MARK
<# of markers> (2 bytes)
list of markers:
<marker ID> (2 bytes, must be > 0)
<position> (4 bytes)
<marker name> ("pstring")
COMM
<# of channels> (2 bytes)
<# of sound frames> (4 bytes)
<size of the samples> (2 bytes)
<sampling frequency> (10 bytes, IEEE 80-bit extended
floating point)
in AIFF-C files only:
<compression type> (4 bytes)
<human-readable version of compression type> ("pstring")
SSND
<offset> (4 bytes, not used by this program)
<blocksize> (4 bytes, not used by this program)
<sound data>
A pstring consists of 1 byte length, a string of characters, and 0 or 1
byte pad to make the total length even.
Usage.
Reading AIFF files:
f = aifc.open(file, 'r')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods read(), seek(), and close().
In some types of audio files, if the setpos() method is not used,
the seek() method is not necessary.
This returns an instance of a class with the following public methods:
getnchannels() -- returns number of audio channels (1 for
mono, 2 for stereo)
getsampwidth() -- returns sample width in bytes
getframerate() -- returns sampling frequency
getnframes() -- returns number of audio frames
getcomptype() -- returns compression type ('NONE' for AIFF files)
getcompname() -- returns human-readable version of
compression type ('not compressed' for AIFF files)
getparams() -- returns a tuple consisting of all of the
above in the above order
getmarkers() -- get the list of marks in the audio file or None
if there are no marks
getmark(id) -- get mark with the specified id (raises an error
if the mark does not exist)
readframes(n) -- returns at most n frames of audio
rewind() -- rewind to the beginning of the audio stream
setpos(pos) -- seek to the specified position
tell() -- return the current position
close() -- close the instance (make it unusable)
The position returned by tell(), the position given to setpos() and
the position of marks are all compatible and have nothing to do with
the actual position in the file.
The close() method is called automatically when the class instance
is destroyed.
Writing AIFF files:
f = aifc.open(file, 'w')
where file is either the name of a file or an open file pointer.
The open file pointer must have methods write(), tell(), seek(), and
close().
This returns an instance of a class with the following public methods:
aiff() -- create an AIFF file (AIFF-C default)
aifc() -- create an AIFF-C file
setnchannels(n) -- set the number of channels
setsampwidth(n) -- set the sample width
setframerate(n) -- set the frame rate
setnframes(n) -- set the number of frames
setcomptype(type, name)
-- set the compression type and the
human-readable compression type
setparams(tuple)
-- set all parameters at once
setmark(id, pos, name)
-- add specified mark to the list of marks
tell() -- return current position in output file (useful
in combination with setmark())
writeframesraw(data)
-- write audio frames without pathing up the
file header
writeframes(data)
-- write audio frames and patch up the file header
close() -- patch up the file header and close the
output file
You should set the parameters before the first writeframesraw or
writeframes. The total number of frames does not need to be set,
but when it is set to the correct value, the header does not have to
be patched up.
It is best to first set all parameters, perhaps possibly the
compression type, and then write audio frames using writeframesraw.
When all frames have been written, either call writeframes('') or
close() to patch up the sizes in the header.
Marks can be added anytime. If there are any marks, ypu must call
close() after all frames have been written.
The close() method is called automatically when the class instance
is destroyed.
When a file is opened with the extension '.aiff', an AIFF file is
written, otherwise an AIFF-C file is written. This default can be
changed by calling aiff() or aifc() before the first writeframes or
writeframesraw.
""" |
# This code is part of Ansible, but is an independent component.
# This particular file snippet, and this file snippet only, is BSD licensed.
# Modules you write using this snippet, which is embedded dynamically by Ansible
# still belong to the author of the module, and may assign their own license
# to the complete work.
#
# Copyright (c), NAME <EMAIL>, 2012-2013
# Copyright (c), NAME <EMAIL>, 2015
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * 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.
#
# THIS 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.
#
# The match_hostname function and supporting code is under the terms and
# conditions of the Python Software Foundation License. They were taken from
# the Python3 standard library and adapted for use in Python2. See comments in the
# source for which code precisely is under this License. PSF License text
# follows:
#
# PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
# --------------------------------------------
#
# 1. This LICENSE AGREEMENT is between the Python Software Foundation
# ("PSF"), and the Individual or Organization ("Licensee") accessing and
# otherwise using this software ("Python") in source or binary form and
# its associated documentation.
#
# 2. Subject to the terms and conditions of this License Agreement, PSF hereby
# grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
# analyze, test, perform and/or display publicly, prepare derivative works,
# distribute, and otherwise use Python alone or in any derivative version,
# provided, however, that PSF's License Agreement and PSF's notice of copyright,
# i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
# 2011, 2012, 2013, 2014 Python Software Foundation; All Rights Reserved" are
# retained in Python alone or in any derivative version prepared by Licensee.
#
# 3. In the event Licensee prepares a derivative work that is based on
# or incorporates Python or any part thereof, and wants to make
# the derivative work available to others as provided herein, then
# Licensee hereby agrees to include in any such work a brief summary of
# the changes made to Python.
#
# 4. PSF is making Python available to Licensee on an "AS IS"
# basis. PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
# IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND
# DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
# FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT
# INFRINGE ANY THIRD PARTY RIGHTS.
#
# 5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
# FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
# A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON,
# OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
#
# 6. This License Agreement will automatically terminate upon a material
# breach of its terms and conditions.
#
# 7. Nothing in this License Agreement shall be deemed to create any
# relationship of agency, partnership, or joint venture between PSF and
# Licensee. This License Agreement does not grant permission to use PSF
# trademarks or trade name in a trademark sense to endorse or promote
# products or services of Licensee, or any third party.
#
# 8. By copying, installing or otherwise using Python, Licensee
# agrees to be bound by the terms and conditions of this License
# Agreement.
|
#Problem 8:
#The four adjacent digits in the 1000-digit number that have the greatest product are 9 × 9 × 8 × 9 = 5832.
#73167176531330624919225119674426574742355349194934
#96983520312774506326239578318016984801869478851843
#85861560789112949495459501737958331952853208805511
#12540698747158523863050715693290963295227443043557
#66896648950445244523161731856403098711121722383113
#62229893423380308135336276614282806444486645238749
#30358907296290491560440772390713810515859307960866
#70172427121883998797908792274921901699720888093776
#65727333001053367881220235421809751254540594752243
#52584907711670556013604839586446706324415722155397
#53697817977846174064955149290862569321978468622482
#83972241375657056057490261407972968652414535100474
#82166370484403199890008895243450658541227588666881
#16427171479924442928230863465674813919123162824586
#17866458359124566529476545682848912883142607690042
#24219022671055626321111109370544217506941658960408
#07198403850962455444362981230987879927244284909188
#84580156166097919133875499200524063689912560717606
#05886116467109405077541002256983155200055935729725
#71636269561882670428252483600823257530420752963450
#Find the thirteen adjacent digits in the 1000-digit number that have the greatest product. What is the value of this #product?
|
"""Module for interactive demos using IPython.
This module implements a few classes for running Python scripts interactively
in IPython for demonstrations. With very simple markup (a few tags in
comments), you can control points where the script stops executing and returns
control to IPython.
Provided classes
----------------
The classes are (see their docstrings for further details):
- Demo: pure python demos
- IPythonDemo: demos with input to be processed by IPython as if it had been
typed interactively (so magics work, as well as any other special syntax you
may have added via input prefilters).
- LineDemo: single-line version of the Demo class. These demos are executed
one line at a time, and require no markup.
- IPythonLineDemo: IPython version of the LineDemo class (the demo is
executed a line at a time, but processed via IPython).
- ClearMixin: mixin to make Demo classes with less visual clutter. It
declares an empty marquee and a pre_cmd that clears the screen before each
block (see Subclassing below).
- ClearDemo, ClearIPDemo: mixin-enabled versions of the Demo and IPythonDemo
classes.
Inheritance diagram:
.. inheritance-diagram:: IPython.lib.demo
:parts: 3
Subclassing
-----------
The classes here all include a few methods meant to make customization by
subclassing more convenient. Their docstrings below have some more details:
- highlight(): format every block and optionally highlight comments and
docstring content.
- marquee(): generates a marquee to provide visible on-screen markers at each
block start and end.
- pre_cmd(): run right before the execution of each block.
- post_cmd(): run right after the execution of each block. If the block
raises an exception, this is NOT called.
Operation
---------
The file is run in its own empty namespace (though you can pass it a string of
arguments as if in a command line environment, and it will see those as
sys.argv). But at each stop, the global IPython namespace is updated with the
current internal demo namespace, so you can work interactively with the data
accumulated so far.
By default, each block of code is printed (with syntax highlighting) before
executing it and you have to confirm execution. This is intended to show the
code to an audience first so you can discuss it, and only proceed with
execution once you agree. There are a few tags which allow you to modify this
behavior.
The supported tags are:
# <demo> stop
Defines block boundaries, the points where IPython stops execution of the
file and returns to the interactive prompt.
You can optionally mark the stop tag with extra dashes before and after the
word 'stop', to help visually distinguish the blocks in a text editor:
# <demo> --- stop ---
# <demo> silent
Make a block execute silently (and hence automatically). Typically used in
cases where you have some boilerplate or initialization code which you need
executed but do not want to be seen in the demo.
# <demo> auto
Make a block execute automatically, but still being printed. Useful for
simple code which does not warrant discussion, since it avoids the extra
manual confirmation.
# <demo> auto_all
This tag can _only_ be in the first block, and if given it overrides the
individual auto tags to make the whole demo fully automatic (no block asks
for confirmation). It can also be given at creation time (or the attribute
set later) to override what's in the file.
While _any_ python file can be run as a Demo instance, if there are no stop
tags the whole file will run in a single block (no different that calling
first %pycat and then %run). The minimal markup to make this useful is to
place a set of stop tags; the other tags are only there to let you fine-tune
the execution.
This is probably best explained with the simple example file below. You can
copy this into a file named ex_demo.py, and try running it via::
from IPython.lib.demo import Demo
d = Demo('ex_demo.py')
d()
Each time you call the demo object, it runs the next block. The demo object
has a few useful methods for navigation, like again(), edit(), jump(), seek()
and back(). It can be reset for a new run via reset() or reloaded from disk
(in case you've edited the source) via reload(). See their docstrings below.
Note: To make this simpler to explore, a file called "demo-exercizer.py" has
been added to the "docs/examples/core" directory. Just cd to this directory in
an IPython session, and type::
%run demo-exercizer.py
and then follow the directions.
Example
-------
The following is a very simple example of a valid demo file.
::
#################### EXAMPLE DEMO <ex_demo.py> ###############################
'''A simple interactive demo to illustrate the use of IPython's Demo class.'''
print 'Hello, welcome to an interactive IPython demo.'
# The mark below defines a block boundary, which is a point where IPython will
# stop execution and return to the interactive prompt. The dashes are actually
# optional and used only as a visual aid to clearly separate blocks while
# editing the demo code.
# <demo> stop
x = 1
y = 2
# <demo> stop
# the mark below makes this block as silent
# <demo> silent
print 'This is a silent block, which gets executed but not printed.'
# <demo> stop
# <demo> auto
print 'This is an automatic block.'
print 'It is executed without asking for confirmation, but printed.'
z = x+y
print 'z=',x
# <demo> stop
# This is just another normal block.
print 'z is now:', z
print 'bye!'
################### END EXAMPLE DEMO <ex_demo.py> ############################
""" |
"""==============
Array indexing
==============
Array indexing refers to any use of the square brackets ([]) to index
array values. There are many options to indexing, which give numpy
indexing great power, but with power comes some complexity and the
potential for confusion. This section is just an overview of the
various options and issues related to indexing. Aside from single
element indexing, the details on most of these options are to be
found in related sections.
Assignment vs referencing
=========================
Most of the following examples show the use of indexing when
referencing data in an array. The examples work just as well
when assigning to an array. See the section at the end for
specific examples and explanations on how assignments work.
Single element indexing
=======================
Single element indexing for a 1-D array is what one expects. It work
exactly like that for other standard Python sequences. It is 0-based,
and accepts negative indices for indexing from the end of the array. ::
>>> x = np.arange(10)
>>> x[2]
2
>>> x[-2]
8
Unlike lists and tuples, numpy arrays support multidimensional indexing
for multidimensional arrays. That means that it is not necessary to
separate each dimension's index into its own set of square brackets. ::
>>> x.shape = (2,5) # now x is 2-dimensional
>>> x[1,3]
8
>>> x[1,-1]
9
Note that if one indexes a multidimensional array with fewer indices
than dimensions, one gets a subdimensional array. For example: ::
>>> x[0]
array([0, 1, 2, 3, 4])
That is, each index specified selects the array corresponding to the
rest of the dimensions selected. In the above example, choosing 0
means that the remaining dimension of length 5 is being left unspecified,
and that what is returned is an array of that dimensionality and size.
It must be noted that the returned array is not a copy of the original,
but points to the same values in memory as does the original array.
In this case, the 1-D array at the first position (0) is returned.
So using a single index on the returned array, results in a single
element being returned. That is: ::
>>> x[0][2]
2
So note that ``x[0,2] = x[0][2]`` though the second case is more
inefficient as a new temporary array is created after the first index
that is subsequently indexed by 2.
Note to those used to IDL or Fortran memory order as it relates to
indexing. Numpy uses C-order indexing. That means that the last
index usually represents the most rapidly changing memory location,
unlike Fortran or IDL, where the first index represents the most
rapidly changing location in memory. This difference represents a
great potential for confusion.
Other indexing options
======================
It is possible to slice and stride arrays to extract arrays of the
same number of dimensions, but of different sizes than the original.
The slicing and striding works exactly the same way it does for lists
and tuples except that they can be applied to multiple dimensions as
well. A few examples illustrates best: ::
>>> x = np.arange(10)
>>> x[2:5]
array([2, 3, 4])
>>> x[:-7]
array([0, 1, 2])
>>> x[1:7:2]
array([1, 3, 5])
>>> y = np.arange(35).reshape(5,7)
>>> y[1:5:2,::3]
array([[ 7, 10, 13],
[21, 24, 27]])
Note that slices of arrays do not copy the internal array data but
also produce new views of the original data.
It is possible to index arrays with other arrays for the purposes of
selecting lists of values out of arrays into new arrays. There are
two different ways of accomplishing this. One uses one or more arrays
of index values. The other involves giving a boolean array of the proper
shape to indicate the values to be selected. Index arrays are a very
powerful tool that allow one to avoid looping over individual elements in
arrays and thus greatly improve performance.
It is possible to use special features to effectively increase the
number of dimensions in an array through indexing so the resulting
array aquires the shape needed for use in an expression or with a
specific function.
Index arrays
============
Numpy arrays may be indexed with other arrays (or any other sequence-
like object that can be converted to an array, such as lists, with the
exception of tuples; see the end of this document for why this is). The
use of index arrays ranges from simple, straightforward cases to
complex, hard-to-understand cases. For all cases of index arrays, what
is returned is a copy of the original data, not a view as one gets for
slices.
Index arrays must be of integer type. Each value in the array indicates
which value in the array to use in place of the index. To illustrate: ::
>>> x = np.arange(10,1,-1)
>>> x
array([10, 9, 8, 7, 6, 5, 4, 3, 2])
>>> x[np.array([3, 3, 1, 8])]
array([7, 7, 9, 2])
The index array consisting of the values 3, 3, 1 and 8 correspondingly
create an array of length 4 (same as the index array) where each index
is replaced by the value the index array has in the array being indexed.
Negative values are permitted and work as they do with single indices
or slices: ::
>>> x[np.array([3,3,-3,8])]
array([7, 7, 4, 2])
It is an error to have index values out of bounds: ::
>>> x[np.array([3, 3, 20, 8])]
<type 'exceptions.IndexError'>: index 20 out of bounds 0<=index<9
Generally speaking, what is returned when index arrays are used is
an array with the same shape as the index array, but with the type
and values of the array being indexed. As an example, we can use a
multidimensional index array instead: ::
>>> x[np.array([[1,1],[2,3]])]
array([[9, 9],
[8, 7]])
Indexing Multi-dimensional arrays
=================================
Things become more complex when multidimensional arrays are indexed,
particularly with multidimensional index arrays. These tend to be
more unusal uses, but theyare permitted, and they are useful for some
problems. We'll start with thesimplest multidimensional case (using
the array y from the previous examples): ::
>>> y[np.array([0,2,4]), np.array([0,1,2])]
array([ 0, 15, 30])
In this case, if the index arrays have a matching shape, and there is
an index array for each dimension of the array being indexed, the
resultant array has the same shape as the index arrays, and the values
correspond to the index set for each position in the index arrays. In
this example, the first index value is 0 for both index arrays, and
thus the first value of the resultant array is y[0,0]. The next value
is y[2,1], and the last is y[4,2].
If the index arrays do not have the same shape, there is an attempt to
broadcast them to the same shape. If they cannot be broadcast to the
same shape, an exception is raised: ::
>>> y[np.array([0,2,4]), np.array([0,1])]
<type 'exceptions.ValueError'>: shape mismatch: objects cannot be
broadcast to a single shape
The broadcasting mechanism permits index arrays to be combined with
scalars for other indices. The effect is that the scalar value is used
for all the corresponding values of the index arrays: ::
>>> y[np.array([0,2,4]), 1]
array([ 1, 15, 29])
Jumping to the next level of complexity, it is possible to only
partially index an array with index arrays. It takes a bit of thought
to understand what happens in such cases. For example if we just use
one index array with y: ::
>>> y[np.array([0,2,4])]
array([[ 0, 1, 2, 3, 4, 5, 6],
[14, 15, 16, 17, 18, 19, 20],
[28, 29, 30, 31, 32, 33, 34]])
What results is the construction of a new array where each value of
the index array selects one row from the array being indexed and the
resultant array has the resulting shape (size of row, number index
elements).
An example of where this may be useful is for a color lookup table
where we want to map the values of an image into RGB triples for
display. The lookup table could have a shape (nlookup, 3). Indexing
such an array with an image with shape (ny, nx) with dtype=np.uint8
(or any integer type so long as values are with the bounds of the
lookup table) will result in an array of shape (ny, nx, 3) where a
triple of RGB values is associated with each pixel location.
In general, the shape of the resulant array will be the concatenation
of the shape of the index array (or the shape that all the index arrays
were broadcast to) with the shape of any unused dimensions (those not
indexed) in the array being indexed.
Boolean or "mask" index arrays
==============================
Boolean arrays used as indices are treated in a different manner
entirely than index arrays. Boolean arrays must be of the same shape
as the initial dimensions of the array being indexed. In the
most straightforward case, the boolean array has the same shape: ::
>>> b = y>20
>>> y[b]
array([21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34])
Unlike in the case of integer index arrays, in the boolean case, the
result is a 1-D array containing all the elements in the indexed array
corresponding to all the true elements in the boolean array. The
elements in the indexed array are always iterated and returned in
:term:`row-major` (C-style) order. The result is also identical to
``y[np.nonzero(b)]``. As with index arrays, what is returned is a copy
of the data, not a view as one gets with slices.
The result will be multidimensional if y has more dimensions than b.
For example: ::
>>> b[:,5] # use a 1-D boolean whose first dim agrees with the first dim of y
array([False, False, False, True, True], dtype=bool)
>>> y[b[:,5]]
array([[21, 22, 23, 24, 25, 26, 27],
[28, 29, 30, 31, 32, 33, 34]])
Here the 4th and 5th rows are selected from the indexed array and
combined to make a 2-D array.
In general, when the boolean array has fewer dimensions than the array
being indexed, this is equivalent to y[b, ...], which means
y is indexed by b followed by as many : as are needed to fill
out the rank of y.
Thus the shape of the result is one dimension containing the number
of True elements of the boolean array, followed by the remaining
dimensions of the array being indexed.
For example, using a 2-D boolean array of shape (2,3)
with four True elements to select rows from a 3-D array of shape
(2,3,5) results in a 2-D result of shape (4,5): ::
>>> x = np.arange(30).reshape(2,3,5)
>>> x
array([[[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]],
[[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29]]])
>>> b = np.array([[True, True, False], [False, True, True]])
>>> x[b]
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29]])
For further details, consult the numpy reference documentation on array indexing.
Combining index arrays with slices
==================================
Index arrays may be combined with slices. For example: ::
>>> y[np.array([0,2,4]),1:3]
array([[ 1, 2],
[15, 16],
[29, 30]])
In effect, the slice is converted to an index array
np.array([[1,2]]) (shape (1,2)) that is broadcast with the index array
to produce a resultant array of shape (3,2).
Likewise, slicing can be combined with broadcasted boolean indices: ::
>>> y[b[:,5],1:3]
array([[22, 23],
[29, 30]])
Structural indexing tools
=========================
To facilitate easy matching of array shapes with expressions and in
assignments, the np.newaxis object can be used within array indices
to add new dimensions with a size of 1. For example: ::
>>> y.shape
(5, 7)
>>> y[:,np.newaxis,:].shape
(5, 1, 7)
Note that there are no new elements in the array, just that the
dimensionality is increased. This can be handy to combine two
arrays in a way that otherwise would require explicitly reshaping
operations. For example: ::
>>> x = np.arange(5)
>>> x[:,np.newaxis] + x[np.newaxis,:]
array([[0, 1, 2, 3, 4],
[1, 2, 3, 4, 5],
[2, 3, 4, 5, 6],
[3, 4, 5, 6, 7],
[4, 5, 6, 7, 8]])
The ellipsis syntax maybe used to indicate selecting in full any
remaining unspecified dimensions. For example: ::
>>> z = np.arange(81).reshape(3,3,3,3)
>>> z[1,...,2]
array([[29, 32, 35],
[38, 41, 44],
[47, 50, 53]])
This is equivalent to: ::
>>> z[1,:,:,2]
array([[29, 32, 35],
[38, 41, 44],
[47, 50, 53]])
Assigning values to indexed arrays
==================================
As mentioned, one can select a subset of an array to assign to using
a single index, slices, and index and mask arrays. The value being
assigned to the indexed array must be shape consistent (the same shape
or broadcastable to the shape the index produces). For example, it is
permitted to assign a constant to a slice: ::
>>> x = np.arange(10)
>>> x[2:7] = 1
or an array of the right size: ::
>>> x[2:7] = np.arange(5)
Note that assignments may result in changes if assigning
higher types to lower types (like floats to ints) or even
exceptions (assigning complex to floats or ints): ::
>>> x[1] = 1.2
>>> x[1]
1
>>> x[1] = 1.2j
<type 'exceptions.TypeError'>: can't convert complex to long; use
long(abs(z))
Unlike some of the references (such as array and mask indices)
assignments are always made to the original data in the array
(indeed, nothing else would make sense!). Note though, that some
actions may not work as one may naively expect. This particular
example is often surprising to people: ::
>>> x = np.arange(0, 50, 10)
>>> x
array([ 0, 10, 20, 30, 40])
>>> x[np.array([1, 1, 3, 1])] += 1
>>> x
array([ 0, 11, 20, 31, 40])
Where people expect that the 1st location will be incremented by 3.
In fact, it will only be incremented by 1. The reason is because
a new array is extracted from the original (as a temporary) containing
the values at 1, 1, 3, 1, then the value 1 is added to the temporary,
and then the temporary is assigned back to the original array. Thus
the value of the array at x[1]+1 is assigned to x[1] three times,
rather than being incremented 3 times.
Dealing with variable numbers of indices within programs
========================================================
The index syntax is very powerful but limiting when dealing with
a variable number of indices. For example, if you want to write
a function that can handle arguments with various numbers of
dimensions without having to write special case code for each
number of possible dimensions, how can that be done? If one
supplies to the index a tuple, the tuple will be interpreted
as a list of indices. For example (using the previous definition
for the array z): ::
>>> indices = (1,1,1,1)
>>> z[indices]
40
So one can use code to construct tuples of any number of indices
and then use these within an index.
Slices can be specified within programs by using the slice() function
in Python. For example: ::
>>> indices = (1,1,1,slice(0,2)) # same as [1,1,1,0:2]
>>> z[indices]
array([39, 40])
Likewise, ellipsis can be specified by code by using the Ellipsis
object: ::
>>> indices = (1, Ellipsis, 1) # same as [1,...,1]
>>> z[indices]
array([[28, 31, 34],
[37, 40, 43],
[46, 49, 52]])
For this reason it is possible to use the output from the np.where()
function directly as an index since it always returns a tuple of index
arrays.
Because the special treatment of tuples, they are not automatically
converted to an array as a list would be. As an example: ::
>>> z[[1,1,1,1]] # produces a large array
array([[[[27, 28, 29],
[30, 31, 32], ...
>>> z[(1,1,1,1)] # returns a single value
40
""" |
# rgToolFactoryMultIn.py
# see https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home
#
# copyright NAME (ross stop lazarus at gmail stop com) May 2012
#
# all rights reserved
# Licensed under the LGPL
# suggestions for improvement and bug fixes welcome at https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home
#
# January 2015
# unified all setups by passing the script on the cl rather than via a PIPE - no need for treat_bash_special so removed
#
# in the process of building a complex tool
# added ability to choose one of the current toolshed package_r or package_perl or package_python dependencies and source that package
# add that package to tool_dependencies
# Note that once the generated tool is loaded, it will have that package's env.sh loaded automagically so there is no
# --envshpath in the parameters for the generated tool and it uses the system one which will be first on the adjusted path.
#
# sept 2014 added additional params from
# https://bitbucket.org/mvdbeek/dockertoolfactory/src/d4863bcf7b521532c7e8c61b6333840ba5393f73/DockerToolFactory.py?at=default
# passing them is complex
# and they are restricted to NOT contain commas or double quotes to ensure that they can be safely passed together on
# the toolfactory command line as a comma delimited double quoted string for parsing and passing to the script
# see examples on this tool form
# august 2014
# Allows arbitrary number of input files
# NOTE positional parameters are now passed to script
# and output (may be "None") is *before* arbitrary number of inputs
#
# march 2014
# had to remove dependencies because cross toolshed dependencies are not possible - can't pre-specify a toolshed url for graphicsmagick and ghostscript
# grrrrr - night before a demo
# added dependencies to a tool_dependencies.xml if html page generated so generated tool is properly portable
#
# added ghostscript and graphicsmagick as dependencies
# fixed a wierd problem where gs was trying to use the new_files_path from universe (database/tmp) as ./database/tmp
# errors ensued
#
# august 2013
# found a problem with GS if $TMP or $TEMP missing - now inject /tmp and warn
#
# july 2013
# added ability to combine images and individual log files into html output
# just make sure there's a log file foo.log and it will be output
# together with all images named like "foo_*.pdf
# otherwise old format for html
#
# January 2013
# problem pointed out by NAME added escaping for <>$ - thought I did that ages ago...
#
# August 11 2012
# changed to use shell=False and cl as a sequence
# This is a Galaxy tool factory for simple scripts in python, R or whatever ails ye.
# It also serves as the wrapper for the new tool.
#
# you paste and run your script
# Only works for simple scripts that read one input from the history.
# Optionally can write one new history dataset,
# and optionally collect any number of outputs into links on an autogenerated HTML page.
# DO NOT install on a public or important site - please.
# installed generated tools are fine if the script is safe.
# They just run normally and their user cannot do anything unusually insecure
# but please, practice safe toolshed.
# Read the fucking code before you install any tool
# especially this one
# After you get the script working on some test data, you can
# optionally generate a toolshed compatible gzip file
# containing your script safely wrapped as an ordinary Galaxy script in your local toolshed for
# safe and largely automated installation in a production Galaxy.
# If you opt for an HTML output, you get all the script outputs arranged
# as a single Html history item - all output files are linked, thumbnails for all the pdfs.
# Ugly but really inexpensive.
#
# Patches appreciated please.
#
#
# long route to June 2012 product
# Behold the awesome power of Galaxy and the toolshed with the tool factory to bind them
# derived from an integrated script model
# called rgBaseScriptWrapper.py
# Note to the unwary:
# This tool allows arbitrary scripting on your Galaxy as the Galaxy user
# There is nothing stopping a malicious user doing whatever they choose
# Extremely dangerous!!
# Totally insecure. So, trusted users only
#
# preferred model is a developer using their throw away workstation instance - ie a private site.
# no real risk. The universe_wsgi.ini admin_users string is checked - only admin users are permitted to run this tool.
#
|
#
# XML-RPC CLIENT LIBRARY
# $Id$
#
# an XML-RPC client interface for Python.
#
# the marshalling and response parser code can also be used to
# implement XML-RPC servers.
#
# Notes:
# this version is designed to work with Python 2.1 or newer.
#
# History:
# 1999-01-14 fl Created
# 1999-01-15 fl Changed dateTime to use localtime
# 1999-01-16 fl Added Binary/base64 element, default to RPC2 service
# 1999-01-19 fl Fixed array data element (from Skip Montanaro)
# 1999-01-21 fl Fixed dateTime constructor, etc.
# 1999-02-02 fl Added fault handling, handle empty sequences, etc.
# 1999-02-10 fl Fixed problem with empty responses (from Skip Montanaro)
# 1999-06-20 fl Speed improvements, pluggable parsers/transports (0.9.8)
# 2000-11-28 fl Changed boolean to check the truth value of its argument
# 2001-02-24 fl Added encoding/Unicode/SafeTransport patches
# 2001-02-26 fl Added compare support to wrappers (0.9.9/1.0b1)
# 2001-03-28 fl Make sure response tuple is a singleton
# 2001-03-29 fl Don't require empty params element (from NAME 2001-06-10 fl Folded in _xmlrpclib accelerator support (1.0b2)
# 2001-08-20 fl Base xmlrpclib.Error on built-in Exception (from NAME 2001-09-03 fl Allow Transport subclass to override getparser
# 2001-09-10 fl Lazy import of urllib, cgi, xmllib (20x import speedup)
# 2001-10-01 fl Remove containers from memo cache when done with them
# 2001-10-01 fl Use faster escape method (80% dumps speedup)
# 2001-10-02 fl More dumps microtuning
# 2001-10-04 fl Make sure import expat gets a parser (from NAME 2001-10-10 sm Allow long ints to be passed as ints if they don't overflow
# 2001-10-17 sm Test for int and long overflow (allows use on 64-bit systems)
# 2001-11-12 fl Use repr() to marshal doubles (from NAME 2002-03-17 fl Avoid buffered read when possible (from NAME 2002-04-07 fl Added pythondoc comments
# 2002-04-16 fl Added __str__ methods to datetime/binary wrappers
# 2002-05-15 fl Added error constants (from NAME 2002-06-27 fl Merged with Python CVS version
# 2002-10-22 fl Added basic authentication (based on code from NAME 2003-01-22 sm Add support for the bool type
# 2003-02-27 gvr Remove apply calls
# 2003-04-24 sm Use cStringIO if available
# 2003-04-25 ak Add support for nil
# 2003-06-15 gn Add support for time.struct_time
# 2003-07-12 gp Correct marshalling of Faults
# 2003-10-31 mvl Add multicall support
# 2004-08-20 mvl Bump minimum supported Python version to 2.1
#
# Copyright (c) 1999-2002 by Secret Labs AB.
# Copyright (c) 1999-2002 by NAME EMAIL http://www.pythonware.com
#
# --------------------------------------------------------------------
# The XML-RPC client interface is
#
# Copyright (c) 1999-2002 by Secret Labs AB
# Copyright (c) 1999-2002 by NAME By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
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# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
#
# things to look into some day:
# TODO: sort out True/False/boolean issues for Python 2.3
|
# -*- coding: utf-8 -*-
# Spearmint
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|
"""
==============
Array indexing
==============
Array indexing refers to any use of the square brackets ([]) to index
array values. There are many options to indexing, which give numpy
indexing great power, but with power comes some complexity and the
potential for confusion. This section is just an overview of the
various options and issues related to indexing. Aside from single
element indexing, the details on most of these options are to be
found in related sections.
Assignment vs referencing
=========================
Most of the following examples show the use of indexing when
referencing data in an array. The examples work just as well
when assigning to an array. See the section at the end for
specific examples and explanations on how assignments work.
Single element indexing
=======================
Single element indexing for a 1-D array is what one expects. It work
exactly like that for other standard Python sequences. It is 0-based,
and accepts negative indices for indexing from the end of the array. ::
>>> x = np.arange(10)
>>> x[2]
2
>>> x[-2]
8
Unlike lists and tuples, numpy arrays support multidimensional indexing
for multidimensional arrays. That means that it is not necessary to
separate each dimension's index into its own set of square brackets. ::
>>> x.shape = (2,5) # now x is 2-dimensional
>>> x[1,3]
8
>>> x[1,-1]
9
Note that if one indexes a multidimensional array with fewer indices
than dimensions, one gets a subdimensional array. For example: ::
>>> x[0]
array([0, 1, 2, 3, 4])
That is, each index specified selects the array corresponding to the
rest of the dimensions selected. In the above example, choosing 0
means that remaining dimension of lenth 5 is being left unspecified,
and that what is returned is an array of that dimensionality and size.
It must be noted that the returned array is not a copy of the original,
but points to the same values in memory as does the original array.
In this case, the 1-D array at the first position (0) is returned.
So using a single index on the returned array, results in a single
element being returned. That is: ::
>>> x[0][2]
2
So note that ``x[0,2] = x[0][2]`` though the second case is more
inefficient a new temporary array is created after the first index
that is subsequently indexed by 2.
Note to those used to IDL or Fortran memory order as it relates to
indexing. Numpy uses C-order indexing. That means that the last
index usually represents the most rapidly changing memory location,
unlike Fortran or IDL, where the first index represents the most
rapidly changing location in memory. This difference represents a
great potential for confusion.
Other indexing options
======================
It is possible to slice and stride arrays to extract arrays of the
same number of dimensions, but of different sizes than the original.
The slicing and striding works exactly the same way it does for lists
and tuples except that they can be applied to multiple dimensions as
well. A few examples illustrates best: ::
>>> x = np.arange(10)
>>> x[2:5]
array([2, 3, 4])
>>> x[:-7]
array([0, 1, 2])
>>> x[1:7:2]
array([1, 3, 5])
>>> y = np.arange(35).reshape(5,7)
>>> y[1:5:2,::3]
array([[ 7, 10, 13],
[21, 24, 27]])
Note that slices of arrays do not copy the internal array data but
also produce new views of the original data.
It is possible to index arrays with other arrays for the purposes of
selecting lists of values out of arrays into new arrays. There are
two different ways of accomplishing this. One uses one or more arrays
of index values. The other involves giving a boolean array of the proper
shape to indicate the values to be selected. Index arrays are a very
powerful tool that allow one to avoid looping over individual elements in
arrays and thus greatly improve performance.
It is possible to use special features to effectively increase the
number of dimensions in an array through indexing so the resulting
array aquires the shape needed for use in an expression or with a
specific function.
Index arrays
============
Numpy arrays may be indexed with other arrays (or any other sequence-
like object that can be converted to an array, such as lists, with the
exception of tuples; see the end of this document for why this is). The
use of index arrays ranges from simple, straightforward cases to
complex, hard-to-understand cases. For all cases of index arrays, what
is returned is a copy of the original data, not a view as one gets for
slices.
Index arrays must be of integer type. Each value in the array indicates
which value in the array to use in place of the index. To illustrate: ::
>>> x = np.arange(10,1,-1)
>>> x
array([10, 9, 8, 7, 6, 5, 4, 3, 2])
>>> x[np.array([3, 3, 1, 8])]
array([7, 7, 9, 2])
The index array consisting of the values 3, 3, 1 and 8 correspondingly
create an array of length 4 (same as the index array) where each index
is replaced by the value the index array has in the array being indexed.
Negative values are permitted and work as they do with single indices
or slices: ::
>>> x[np.array([3,3,-3,8])]
array([7, 7, 4, 2])
It is an error to have index values out of bounds: ::
>>> x[np.array([3, 3, 20, 8])]
<type 'exceptions.IndexError'>: index 20 out of bounds 0<=index<9
Generally speaking, what is returned when index arrays are used is
an array with the same shape as the index array, but with the type
and values of the array being indexed. As an example, we can use a
multidimensional index array instead: ::
>>> x[np.array([[1,1],[2,3]])]
array([[9, 9],
[8, 7]])
Indexing Multi-dimensional arrays
=================================
Things become more complex when multidimensional arrays are indexed,
particularly with multidimensional index arrays. These tend to be
more unusal uses, but theyare permitted, and they are useful for some
problems. We'll start with thesimplest multidimensional case (using
the array y from the previous examples): ::
>>> y[np.array([0,2,4]), np.array([0,1,2])]
array([ 0, 15, 30])
In this case, if the index arrays have a matching shape, and there is
an index array for each dimension of the array being indexed, the
resultant array has the same shape as the index arrays, and the values
correspond to the index set for each position in the index arrays. In
this example, the first index value is 0 for both index arrays, and
thus the first value of the resultant array is y[0,0]. The next value
is y[2,1], and the last is y[4,2].
If the index arrays do not have the same shape, there is an attempt to
broadcast them to the same shape. If they cannot be broadcast to the
same shape, an exception is raised: ::
>>> y[np.array([0,2,4]), np.array([0,1])]
<type 'exceptions.ValueError'>: shape mismatch: objects cannot be
broadcast to a single shape
The broadcasting mechanism permits index arrays to be combined with
scalars for other indices. The effect is that the scalar value is used
for all the corresponding values of the index arrays: ::
>>> y[np.array([0,2,4]), 1]
array([ 1, 15, 29])
Jumping to the next level of complexity, it is possible to only
partially index an array with index arrays. It takes a bit of thought
to understand what happens in such cases. For example if we just use
one index array with y: ::
>>> y[np.array([0,2,4])]
array([[ 0, 1, 2, 3, 4, 5, 6],
[14, 15, 16, 17, 18, 19, 20],
[28, 29, 30, 31, 32, 33, 34]])
What results is the construction of a new array where each value of
the index array selects one row from the array being indexed and the
resultant array has the resulting shape (size of row, number index
elements).
An example of where this may be useful is for a color lookup table
where we want to map the values of an image into RGB triples for
display. The lookup table could have a shape (nlookup, 3). Indexing
such an array with an image with shape (ny, nx) with dtype=np.uint8
(or any integer type so long as values are with the bounds of the
lookup table) will result in an array of shape (ny, nx, 3) where a
triple of RGB values is associated with each pixel location.
In general, the shape of the resulant array will be the concatenation
of the shape of the index array (or the shape that all the index arrays
were broadcast to) with the shape of any unused dimensions (those not
indexed) in the array being indexed.
Boolean or "mask" index arrays
==============================
Boolean arrays used as indices are treated in a different manner
entirely than index arrays. Boolean arrays must be of the same shape
as the initial dimensions of the array being indexed. In the
most straightforward case, the boolean array has the same shape: ::
>>> b = y>20
>>> y[b]
array([21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34])
The result is a 1-D array containing all the elements in the indexed
array corresponding to all the true elements in the boolean array. As
with index arrays, what is returned is a copy of the data, not a view
as one gets with slices.
The result will be multidimensional if y has more dimensions than b.
For example: ::
>>> b[:,5] # use a 1-D boolean whose first dim agrees with the first dim of y
array([False, False, False, True, True], dtype=bool)
>>> y[b[:,5]]
array([[21, 22, 23, 24, 25, 26, 27],
[28, 29, 30, 31, 32, 33, 34]])
Here the 4th and 5th rows are selected from the indexed array and
combined to make a 2-D array.
In general, when the boolean array has fewer dimensions than the array
being indexed, this is equivalent to y[b, ...], which means
y is indexed by b followed by as many : as are needed to fill
out the rank of y.
Thus the shape of the result is one dimension containing the number
of True elements of the boolean array, followed by the remaining
dimensions of the array being indexed.
For example, using a 2-D boolean array of shape (2,3)
with four True elements to select rows from a 3-D array of shape
(2,3,5) results in a 2-D result of shape (4,5): ::
>>> x = np.arange(30).reshape(2,3,5)
>>> x
array([[[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]],
[[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29]]])
>>> b = np.array([[True, True, False], [False, True, True]])
>>> x[b]
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29]])
For further details, consult the numpy reference documentation on array indexing.
Combining index arrays with slices
==================================
Index arrays may be combined with slices. For example: ::
>>> y[np.array([0,2,4]),1:3]
array([[ 1, 2],
[15, 16],
[29, 30]])
In effect, the slice is converted to an index array
np.array([[1,2]]) (shape (1,2)) that is broadcast with the index array
to produce a resultant array of shape (3,2).
Likewise, slicing can be combined with broadcasted boolean indices: ::
>>> y[b[:,5],1:3]
array([[22, 23],
[29, 30]])
Structural indexing tools
=========================
To facilitate easy matching of array shapes with expressions and in
assignments, the np.newaxis object can be used within array indices
to add new dimensions with a size of 1. For example: ::
>>> y.shape
(5, 7)
>>> y[:,np.newaxis,:].shape
(5, 1, 7)
Note that there are no new elements in the array, just that the
dimensionality is increased. This can be handy to combine two
arrays in a way that otherwise would require explicitly reshaping
operations. For example: ::
>>> x = np.arange(5)
>>> x[:,np.newaxis] + x[np.newaxis,:]
array([[0, 1, 2, 3, 4],
[1, 2, 3, 4, 5],
[2, 3, 4, 5, 6],
[3, 4, 5, 6, 7],
[4, 5, 6, 7, 8]])
The ellipsis syntax maybe used to indicate selecting in full any
remaining unspecified dimensions. For example: ::
>>> z = np.arange(81).reshape(3,3,3,3)
>>> z[1,...,2]
array([[29, 32, 35],
[38, 41, 44],
[47, 50, 53]])
This is equivalent to: ::
>>> z[1,:,:,2]
array([[29, 32, 35],
[38, 41, 44],
[47, 50, 53]])
Assigning values to indexed arrays
==================================
As mentioned, one can select a subset of an array to assign to using
a single index, slices, and index and mask arrays. The value being
assigned to the indexed array must be shape consistent (the same shape
or broadcastable to the shape the index produces). For example, it is
permitted to assign a constant to a slice: ::
>>> x = np.arange(10)
>>> x[2:7] = 1
or an array of the right size: ::
>>> x[2:7] = np.arange(5)
Note that assignments may result in changes if assigning
higher types to lower types (like floats to ints) or even
exceptions (assigning complex to floats or ints): ::
>>> x[1] = 1.2
>>> x[1]
1
>>> x[1] = 1.2j
<type 'exceptions.TypeError'>: can't convert complex to long; use
long(abs(z))
Unlike some of the references (such as array and mask indices)
assignments are always made to the original data in the array
(indeed, nothing else would make sense!). Note though, that some
actions may not work as one may naively expect. This particular
example is often surprising to people: ::
>>> x = np.arange(0, 50, 10)
>>> x
array([ 0, 10, 20, 30, 40])
>>> x[np.array([1, 1, 3, 1])] += 1
>>> x
array([ 0, 11, 20, 31, 40])
Where people expect that the 1st location will be incremented by 3.
In fact, it will only be incremented by 1. The reason is because
a new array is extracted from the original (as a temporary) containing
the values at 1, 1, 3, 1, then the value 1 is added to the temporary,
and then the temporary is assigned back to the original array. Thus
the value of the array at x[1]+1 is assigned to x[1] three times,
rather than being incremented 3 times.
Dealing with variable numbers of indices within programs
========================================================
The index syntax is very powerful but limiting when dealing with
a variable number of indices. For example, if you want to write
a function that can handle arguments with various numbers of
dimensions without having to write special case code for each
number of possible dimensions, how can that be done? If one
supplies to the index a tuple, the tuple will be interpreted
as a list of indices. For example (using the previous definition
for the array z): ::
>>> indices = (1,1,1,1)
>>> z[indices]
40
So one can use code to construct tuples of any number of indices
and then use these within an index.
Slices can be specified within programs by using the slice() function
in Python. For example: ::
>>> indices = (1,1,1,slice(0,2)) # same as [1,1,1,0:2]
>>> z[indices]
array([39, 40])
Likewise, ellipsis can be specified by code by using the Ellipsis
object: ::
>>> indices = (1, Ellipsis, 1) # same as [1,...,1]
>>> z[indices]
array([[28, 31, 34],
[37, 40, 43],
[46, 49, 52]])
For this reason it is possible to use the output from the np.where()
function directly as an index since it always returns a tuple of index
arrays.
Because the special treatment of tuples, they are not automatically
converted to an array as a list would be. As an example: ::
>>> z[[1,1,1,1]] # produces a large array
array([[[[27, 28, 29],
[30, 31, 32], ...
>>> z[(1,1,1,1)] # returns a single value
40
""" |
"""
WARNING: THIS EXTENSION IS EXPERIMENTAL
Experimental extension may (and probably will) change at any time. Please do
not rely on these features until they are more fully vetted.
The Reload Config Framework Extension enables applications Built on Cement
(tm) to easily reload configuration settings any time configuration files are
modified without stopping/restarting the process.
Requirements
------------
* Python 2.6+, Python 3+
* Python Modules: pyinotify
* Linux (Kernel 2.6.13+)
Features
--------
* Application configuration files (``CementApp.Meta.config_files``) are
reloaded if modified.
* Application plugin configuration files (Anything found in
(``CementApp.Meta.plugin_config_dirs``) are reloaded if modified.
* The framework calls ``CementApp.config.parse_file()`` on any watched files
once the kernel has signaled a modification.
* New configurations settings are accessible via ``CementApp.config`` nearly
immediately once the kernel (inotify) picks up the change.
* Provides a ``pre_reload_config`` and ``post_reload_config`` hook so that
applications can tie into the event and perform operations any time a
configuration file is modified.
* Asynchronously monitors configuration files for changes via inotify. Long
running processes are not blocked by the operations performed when files
are detected to be modified.
Limitations
-----------
* Currently this extension only re-parses configuration files into the
config handler. Some applications may need further work in-order to truly
honor those changes. For example, if a configuration settings toggles
something on or off, or triggers something else to happen (like making an
API connection, etc)... this extension does not currently handle that, and
it is left up to the application developer to tie into the events via the
provided hooks.
* Only available on Linux based systems.
Configuration
-------------
This extension does not currently honor any configuration settings.
Hooks
-----
This extension defines the following hooks:
pre_reload_config
^^^^^^^^^^^^^^^^^
Run right before any framework actions are performed once modifications to
any of the watched files are detected. Expects a single argument, which is
the ``app`` object, and does not expect anything in return.
.. code-block:: python
def my_pre_reload_config_hook(app):
# do something with app?
pass
post_reload_config
^^^^^^^^^^^^^^^^^^
Run right after any framework actions are performed once modifications to any
of the watched files are detected. Expects a single argument, which is the
``app`` object, and does not expect anything in return.
.. code-block:: python
def my_post_reload_config_hook(app):
# do something with app?
pass
Usage
-----
The following example shows how to add the reload_config extension, as well as
perform an arbitrary action any time configuration changes are detected.
.. code-block:: python
from time import sleep
from cement.core.exc import CaughtSignal
from cement.core.foundation import CementApp
from cement.core.controller import CementBaseController, expose
def print_foo(app):
print "Foo => %s" % app.config.get('myapp', 'foo')
class Base(CementBaseController):
class Meta:
label = 'base'
@expose(hide=True)
def default(self):
print('Inside Base.default()')
# simulate a long running process
while True:
sleep(30)
class MyApp(CementApp):
class Meta:
label = 'myapp'
base_controller = Base
extensions = ['reload_config']
with MyApp() as app:
# run this anytime the configuration has changed
app.hook.register('post_reload_config', print_foo)
try:
app.run()
except CaughtSignal as e:
# maybe do something... but catch it regardless so app.close() is
# called when exiting `with` cleanly.
print(e)
The following would output something like the following when ``~/.myapp.conf``
or any other configuration file is modified (spaces added for clarity):
.. code-block:: console
Inside Base.default()
2015-05-05 03:00:32,023 (DEBUG) cement.ext.ext_reload_config : config
path modified: mask=IN_CLOSE_WRITE,
path=/home/vagrant/.myapp.conf
2015-05-05 03:00:32,023 (DEBUG) cement.core.config : config file
'/home/vagrant/.myapp.conf' exists,
loading settings...
2015-05-05 03:00:32,023 (DEBUG) cement.core.hook : running hook
'post_reload_config' (<function print_foo
at 0x7f1b52a5ab70>) from __main__
Foo => bar
2015-05-05 03:00:32,023 (DEBUG) cement.ext.ext_reload_config : config
path modified: mask=IN_CLOSE_WRITE,
path=/home/vagrant/.myapp.conf
2015-05-05 03:00:32,023 (DEBUG) cement.core.config : config file
'/home/vagrant/.myapp.conf' exists,
loading settings...
2015-05-05 03:00:32,023 (DEBUG) cement.core.hook : running hook
'post_reload_config' (<function print_foo
at 0x7f1b52a5ab70>) from __main__
Foo => bar2
2015-05-05 03:00:32,023 (DEBUG) cement.ext.ext_reload_config : config
path modified: mask=IN_CLOSE_WRITE,
path=/home/vagrant/.myapp.conf
2015-05-05 03:00:32,023 (DEBUG) cement.core.config : config file
'/home/vagrant/.myapp.conf' exists,
loading settings...
2015-05-05 03:00:32,023 (DEBUG) cement.core.hook : running hook
'post_reload_config' (<function print_foo
at 0x7f1b52a5ab70>) from __main__
Foo => bar3
""" |
"""CPStats, a package for collecting and reporting on program statistics.
Overview
========
Statistics about program operation are an invaluable monitoring and debugging
tool. Unfortunately, the gathering and reporting of these critical values is
usually ad-hoc. This package aims to add a centralized place for gathering
statistical performance data, a structure for recording that data which
provides for extrapolation of that data into more useful information,
and a method of serving that data to both human investigators and
monitoring software. Let's examine each of those in more detail.
Data Gathering
--------------
Just as Python's `logging` module provides a common importable for gathering
and sending messages, performance statistics would benefit from a similar
common mechanism, and one that does *not* require each package which wishes
to collect stats to import a third-party module. Therefore, we choose to
re-use the `logging` module by adding a `statistics` object to it.
That `logging.statistics` object is a nested dict. It is not a custom class,
because that would:
1. require libraries and applications to import a third-party module in
order to participate
2. inhibit innovation in extrapolation approaches and in reporting tools, and
3. be slow.
There are, however, some specifications regarding the structure of the dict.::
{
+----"SQLAlchemy": {
| "Inserts": 4389745,
| "Inserts per Second":
| lambda s: s["Inserts"] / (time() - s["Start"]),
| C +---"Table Statistics": {
| o | "widgets": {-----------+
N | l | "Rows": 1.3M, | Record
a | l | "Inserts": 400, |
m | e | },---------------------+
e | c | "froobles": {
s | t | "Rows": 7845,
p | i | "Inserts": 0,
a | o | },
c | n +---},
e | "Slow Queries":
| [{"Query": "SELECT * FROM widgets;",
| "Processing Time": 47.840923343,
| },
| ],
+----},
}
The `logging.statistics` dict has four levels. The topmost level is nothing
more than a set of names to introduce modularity, usually along the lines of
package names. If the SQLAlchemy project wanted to participate, for example,
it might populate the item `logging.statistics['SQLAlchemy']`, whose value
would be a second-layer dict we call a "namespace". Namespaces help multiple
packages to avoid collisions over key names, and make reports easier to read,
to boot. The maintainers of SQLAlchemy should feel free to use more than one
namespace if needed (such as 'SQLAlchemy ORM'). Note that there are no case
or other syntax constraints on the namespace names; they should be chosen
to be maximally readable by humans (neither too short nor too long).
Each namespace, then, is a dict of named statistical values, such as
'Requests/sec' or 'Uptime'. You should choose names which will look
good on a report: spaces and capitalization are just fine.
In addition to scalars, values in a namespace MAY be a (third-layer)
dict, or a list, called a "collection". For example, the CherryPy
:class:`StatsTool` keeps track of what each request is doing (or has most
recently done) in a 'Requests' collection, where each key is a thread ID; each
value in the subdict MUST be a fourth dict (whew!) of statistical data about
each thread. We call each subdict in the collection a "record". Similarly,
the :class:`StatsTool` also keeps a list of slow queries, where each record
contains data about each slow query, in order.
Values in a namespace or record may also be functions, which brings us to:
Extrapolation
-------------
The collection of statistical data needs to be fast, as close to unnoticeable
as possible to the host program. That requires us to minimize I/O, for example,
but in Python it also means we need to minimize function calls. So when you
are designing your namespace and record values, try to insert the most basic
scalar values you already have on hand.
When it comes time to report on the gathered data, however, we usually have
much more freedom in what we can calculate. Therefore, whenever reporting
tools (like the provided :class:`StatsPage` CherryPy class) fetch the contents
of `logging.statistics` for reporting, they first call
`extrapolate_statistics` (passing the whole `statistics` dict as the only
argument). This makes a deep copy of the statistics dict so that the
reporting tool can both iterate over it and even change it without harming
the original. But it also expands any functions in the dict by calling them.
For example, you might have a 'Current Time' entry in the namespace with the
value "lambda scope: time.time()". The "scope" parameter is the current
namespace dict (or record, if we're currently expanding one of those
instead), allowing you access to existing static entries. If you're truly
evil, you can even modify more than one entry at a time.
However, don't try to calculate an entry and then use its value in further
extrapolations; the order in which the functions are called is not guaranteed.
This can lead to a certain amount of duplicated work (or a redesign of your
schema), but that's better than complicating the spec.
After the whole thing has been extrapolated, it's time for:
Reporting
---------
The :class:`StatsPage` class grabs the `logging.statistics` dict, extrapolates
it all, and then transforms it to HTML for easy viewing. Each namespace gets
its own header and attribute table, plus an extra table for each collection.
This is NOT part of the statistics specification; other tools can format how
they like.
You can control which columns are output and how they are formatted by updating
StatsPage.formatting, which is a dict that mirrors the keys and nesting of
`logging.statistics`. The difference is that, instead of data values, it has
formatting values. Use None for a given key to indicate to the StatsPage that a
given column should not be output. Use a string with formatting
(such as '%.3f') to interpolate the value(s), or use a callable (such as
lambda v: v.isoformat()) for more advanced formatting. Any entry which is not
mentioned in the formatting dict is output unchanged.
Monitoring
----------
Although the HTML output takes pains to assign unique id's to each <td> with
statistical data, you're probably better off fetching /cpstats/data, which
outputs the whole (extrapolated) `logging.statistics` dict in JSON format.
That is probably easier to parse, and doesn't have any formatting controls,
so you get the "original" data in a consistently-serialized format.
Note: there's no treatment yet for datetime objects. Try time.time() instead
for now if you can. Nagios will probably thank you.
Turning Collection Off
----------------------
It is recommended each namespace have an "Enabled" item which, if False,
stops collection (but not reporting) of statistical data. Applications
SHOULD provide controls to pause and resume collection by setting these
entries to False or True, if present.
Usage
=====
To collect statistics on CherryPy applications::
from cherrypy.lib import cpstats
appconfig['/']['tools.cpstats.on'] = True
To collect statistics on your own code::
import logging
# Initialize the repository
if not hasattr(logging, 'statistics'): logging.statistics = {}
# Initialize my namespace
mystats = logging.statistics.setdefault('My Stuff', {})
# Initialize my namespace's scalars and collections
mystats.update({
'Enabled': True,
'Start Time': time.time(),
'Important Events': 0,
'Events/Second': lambda s: (
(s['Important Events'] / (time.time() - s['Start Time']))),
})
...
for event in events:
...
# Collect stats
if mystats.get('Enabled', False):
mystats['Important Events'] += 1
To report statistics::
root.cpstats = cpstats.StatsPage()
To format statistics reports::
See 'Reporting', above.
""" |
# #!/usr/bin/env python3
#
# __author__ = "Ashwin NAME # GUI viewer to view JSON data as tree.
# # Ubuntu packages needed:
# # python3-pyqt5
#
# # Std
# import argparse
# import collections
# import json
# import sys
#
# # External
# from PyQt5 import QtCore
# from PyQt5 import QtGui
# from PyQt5 import QtWidgets
#
# class TextToTreeItem:
#
# def __init__(self):
# self.text_list = []
# self.titem_list = []
#
# def append(self, text_list, titem):
# for text in text_list:
# self.text_list.append(text)
# self.titem_list.append(titem)
#
# # Return model indices that match string
# def find(self, find_str):
#
# titem_list = []
# for i, s in enumerate(self.text_list):
# if find_str in s:
# titem_list.append(self.titem_list[i])
#
# return titem_list
#
#
# class JsonView(QtWidgets.QWidget):
#
# def __init__(self, D):
# super(JsonView, self).__init__()
#
# self.find_box = None
# self.tree_widget = None
# self.text_to_titem = TextToTreeItem()
# self.find_str = ""
# self.found_titem_list = []
# self.found_idx = 0
#
# # jfile = open(fpath)
# # jdata = json.load(jfile, object_pairs_hook=collections.OrderedDict)
#
# jdata = collections.OrderedDict(D)
#
# # Find UI
#
# find_layout = self.make_find_ui()
#
# # Tree
#
# self.tree_widget = QtWidgets.QTreeWidget()
# self.tree_widget.setHeaderLabels(["Key", "Value"])
# self.tree_widget.header().setSectionResizeMode(QtWidgets.QHeaderView.Stretch)
#
# root_item = QtWidgets.QTreeWidgetItem(["Root"])
# self.recurse_jdata(jdata, root_item)
# self.tree_widget.addTopLevelItem(root_item)
#
# # Add table to layout
#
# layout = QtWidgets.QHBoxLayout()
# layout.addWidget(self.tree_widget)
#
# # Group box
#
# gbox = QtWidgets.QGroupBox()
# gbox.setLayout(layout)
#
# layout2 = QtWidgets.QVBoxLayout()
# layout2.addLayout(find_layout)
# layout2.addWidget(gbox)
#
# self.setLayout(layout2)
#
# def make_find_ui(self):
#
# # Text box
# self.find_box = QtWidgets.QLineEdit()
# self.find_box.returnPressed.connect(self.find_button_clicked)
#
# # Find Button
# find_button = QtWidgets.QPushButton("Find")
# find_button.clicked.connect(self.find_button_clicked)
#
# layout = QtWidgets.QHBoxLayout()
# layout.addWidget(self.find_box)
# layout.addWidget(find_button)
#
# return layout
#
# def find_button_clicked(self):
#
# find_str = self.find_box.text()
#
# # Very common for use to click Find on empty string
# if find_str == "":
# return
#
# # New search string
# if find_str != self.find_str:
# self.find_str = find_str
# self.found_titem_list = self.text_to_titem.find(self.find_str)
# self.found_idx = 0
# else:
# item_num = len(self.found_titem_list)
# self.found_idx = (self.found_idx + 1) % item_num
#
# self.tree_widget.setCurrentItem(self.found_titem_list[self.found_idx])
#
#
# def recurse_jdata(self, jdata, tree_widget):
#
# if isinstance(jdata, dict):
# for key, val in jdata.items():
# self.tree_add_row(key, val, tree_widget)
# elif isinstance(jdata, list):
# for i, val in enumerate(jdata):
# key = str(i)
# self.tree_add_row(key, val, tree_widget)
# else:
# print("This should never be reached!")
#
# def tree_add_row(self, key, val, tree_widget):
#
# text_list = []
#
# if isinstance(val, dict) or isinstance(val, list):
# text_list.append(key)
# row_item = QtWidgets.QTreeWidgetItem([key])
# self.recurse_jdata(val, row_item)
# else:
# text_list.append(key)
# text_list.append(str(val))
# row_item = QtWidgets.QTreeWidgetItem([key, str(val)])
#
# tree_widget.addChild(row_item)
# self.text_to_titem.append(text_list, row_item)
#
#
# class JsonViewer(QtWidgets.QMainWindow):
#
# def __init__(self, D):
# super(JsonViewer, self).__init__()
#
# # fpath = sys.argv[1]
# json_view = JsonView(D)
#
# self.setCentralWidget(json_view)
# self.setWindowTitle("JSON Viewer")
# self.show()
#
# def keyPressEvent(self, e):
# if e.key() == QtCore.Qt.Key_Escape:
# self.close()
#
#
# def viewLipd(D):
# print("Opening the viewer. This may take a few moments...")
# qt_app = QtWidgets.QApplication(sys.argv)
# json_viewer = JsonViewer(D)
# qt_app.exec_()
#
# #
# # if "__main__" == __name__:
# # main()
|
# -*- coding: utf-8 -*-
# Copyright 2007-2011 The HyperSpy developers
#
# This file is part of HyperSpy.
#
# HyperSpy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# HyperSpy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with HyperSpy. If not, see <http://www.gnu.org/licenses/>.
#~ import traits.api as t
#~ import traitsui.api as tui
#~ from traitsui.menu import OKButton, CancelButton, Action, MenuBar, Menu
#~
#~ from hyperspy import io
#~ from egerton_quantification import EgertonPanel
#~
#~ import messages
#~ import tools
#~ from hyperspy.misc.interactive_ns import interactive_ns
#~ from hyperspy import Release
#~
#~ # File ###################################################
#~
#~
#~
#~ class LoadSpectrum(t.HasTraits):
#~ sp_file = t.File()
#~ traits_view = tui.View(
#~ tui.Item('sp_file', label = 'File'),
#~ buttons = [OKButton, CancelButton],
#~ kind = 'modal')
#~
#~ file_view = tui.View(
#~ tui.Group(
#~ tui.Group(
#~ tui.Item('f.hl_file'),
#~ ),
#~ tui.Group(
#~ tui.Item('micro.name', ),
#~ tui.Item('micro.alpha'),
#~ tui. Item('micro.beta'),
#~ tui. Item('micro.E0'),
#~ ),
#~ tui.Group(
#~ tui. Item('micro.name', ),
#~ tui. Item('micro.alpha'),
#~ tui. Item('micro.beta'),
#~ tui. Item('micro.E0'),))
#~ )
#~
#~
# ~ # Actions
#~
#~ menu_file_open = Action(name = "Open...",
#~ action = "open_file",
#~ toolip = "Open an SI file",)
#~
#~ menu_file_save = Action(name = "Save...",
#~ action = "save_file",
#~ toolip = "Save an SI file",)
#~
#~ menu_help_about = Action(name = "About",
#~ action = "notification_about",
#~ toolip = "",)
#~
#~ menu_tools_calibrate = Action(name = "Calibrate",
#~ action = "calibrate",
#~ toolip = "",)
#~
#~ menu_tools_egerton_quantification = Action(name = "Egerton Quantification",
#~ action = "egerton_quantification",
#~ toolip = "",)
#~
#~ menu_tools_savitzky_golay = Action(name = "Savitzky-Golay Smoothing",
#~ action = "savitzky_golay",
#~ toolip = "",)
#~
#~ menu_tools_lowess = Action(name = "Lowess Smoothing",
#~ action = "lowess",
#~ toolip = "",)
#~
#~ menu_edit_acquisition_parameters = Action(name = "Acquisition parameters",
#~ action = "edit_acquisition_parameters",
#~ toolip = "",)
#~
# ~ # Menu
#~ menubar = MenuBar()
#~
# ~ # File
#~ Menu(menu_file_open, menu_file_save, name = 'File')
#~
# ~ # Edit
#~
# ~ # Open
#~
#~ # Main Window ##################################################
#~
#~
#~ class MainWindowHandler(tui.Handler):
#~
#~ def open_file(self, *args, **kw):
#~ S = LoadSpectrum()
#~ S.edit_traits()
#~ if S.sp_file is not t.Undefined:
#~ s = io.load(S.sp_file)
#~ s.plot()
#~ interactive_ns['s'] = s
#~
#~ def save_file(self, *args, **kw):
#~ pass
#~
#~
#~ def egerton_quantification(self, *args, **kw):
#~ if interactive_ns.has_key('s'):
#~ ep = EgertonPanel(interactive_ns['s'])
#~ ep.edit_traits()
#~
#~ def savitzky_golay(self, *args, **kw):
#~ sg = tools.SavitzkyGolay()
#~ sg.edit_traits()
#~
#~ def lowess(self, *args, **kw):
#~ lw = tools.Lowess()
#~ lw.edit_traits()
#~
#~ def notification_about(self,*args, **kw):
#~ messages.information(Release.info)
#~
#~ def calibrate(self,*args, **kw):
#~ w = tools.Calibration()
#~ w.edit_traits()
#~
#~ class MainWindow(t.HasTraits):
#~
# ~ traits_view = tui.View( #view contents,
# ~ # ...,
#~ handler = MainWindowHandler(),
#~ title = 'HyperSpy',
#~ width = 500,
#~ menubar = MenuBar(
#~ Menu(
#~ menu_file_open,
#~ menu_file_save,
#~ name = 'File'),
#~ Menu(
#~ menu_edit_acquisition_parameters,
#~ name = 'Edit'),
#~ Menu(
#~ menu_tools_calibrate,
#~ menu_tools_egerton_quantification,
#~ menu_tools_savitzky_golay,
#~ menu_tools_lowess,
#~ name = 'Tools'),
#~ Menu(
#~ menu_help_about,
#~ name = 'Help'),
#~ ))
#~
#~
#~ if __name__ == '__main__':
#~ window = MainWindow()
#~ window.configure_traits()
|
"""
===============
Array Internals
===============
Internal organization of numpy arrays
=====================================
It helps to understand a bit about how numpy arrays are handled under the covers to help understand numpy better. This section will not go into great detail. Those wishing to understand the full details are referred to Travis Oliphant's book "Guide to Numpy".
Numpy arrays consist of two major components, the raw array data (from now on,
referred to as the data buffer), and the information about the raw array data.
The data buffer is typically what people think of as arrays in C or Fortran,
a contiguous (and fixed) block of memory containing fixed sized data items.
Numpy also contains a significant set of data that describes how to interpret
the data in the data buffer. This extra information contains (among other things):
1) The basic data element's size in bytes
2) The start of the data within the data buffer (an offset relative to the
beginning of the data buffer).
3) The number of dimensions and the size of each dimension
4) The separation between elements for each dimension (the 'stride'). This
does not have to be a multiple of the element size
5) The byte order of the data (which may not be the native byte order)
6) Whether the buffer is read-only
7) Information (via the dtype object) about the interpretation of the basic
data element. The basic data element may be as simple as a int or a float,
or it may be a compound object (e.g., struct-like), a fixed character field,
or Python object pointers.
8) Whether the array is to interpreted as C-order or Fortran-order.
This arrangement allow for very flexible use of arrays. One thing that it allows
is simple changes of the metadata to change the interpretation of the array buffer.
Changing the byteorder of the array is a simple change involving no rearrangement
of the data. The shape of the array can be changed very easily without changing
anything in the data buffer or any data copying at all
Among other things that are made possible is one can create a new array metadata
object that uses the same data buffer
to create a new view of that data buffer that has a different interpretation
of the buffer (e.g., different shape, offset, byte order, strides, etc) but
shares the same data bytes. Many operations in numpy do just this such as
slices. Other operations, such as transpose, don't move data elements
around in the array, but rather change the information about the shape and strides so that the indexing of the array changes, but the data in the doesn't move.
Typically these new versions of the array metadata but the same data buffer are
new 'views' into the data buffer. There is a different ndarray object, but it
uses the same data buffer. This is why it is necessary to force copies through
use of the .copy() method if one really wants to make a new and independent
copy of the data buffer.
New views into arrays mean the the object reference counts for the data buffer
increase. Simply doing away with the original array object will not remove the
data buffer if other views of it still exist.
Multidimensional Array Indexing Order Issues
============================================
What is the right way to index
multi-dimensional arrays? Before you jump to conclusions about the one and
true way to index multi-dimensional arrays, it pays to understand why this is
a confusing issue. This section will try to explain in detail how numpy
indexing works and why we adopt the convention we do for images, and when it
may be appropriate to adopt other conventions.
The first thing to understand is
that there are two conflicting conventions for indexing 2-dimensional arrays.
Matrix notation uses the first index to indicate which row is being selected and
the second index to indicate which column is selected. This is opposite the
geometrically oriented-convention for images where people generally think the
first index represents x position (i.e., column) and the second represents y
position (i.e., row). This alone is the source of much confusion;
matrix-oriented users and image-oriented users expect two different things with
regard to indexing.
The second issue to understand is how indices correspond
to the order the array is stored in memory. In Fortran the first index is the
most rapidly varying index when moving through the elements of a two
dimensional array as it is stored in memory. If you adopt the matrix
convention for indexing, then this means the matrix is stored one column at a
time (since the first index moves to the next row as it changes). Thus Fortran
is considered a Column-major language. C has just the opposite convention. In
C, the last index changes most rapidly as one moves through the array as
stored in memory. Thus C is a Row-major language. The matrix is stored by
rows. Note that in both cases it presumes that the matrix convention for
indexing is being used, i.e., for both Fortran and C, the first index is the
row. Note this convention implies that the indexing convention is invariant
and that the data order changes to keep that so.
But that's not the only way
to look at it. Suppose one has large two-dimensional arrays (images or
matrices) stored in data files. Suppose the data are stored by rows rather than
by columns. If we are to preserve our index convention (whether matrix or
image) that means that depending on the language we use, we may be forced to
reorder the data if it is read into memory to preserve our indexing
convention. For example if we read row-ordered data into memory without
reordering, it will match the matrix indexing convention for C, but not for
Fortran. Conversely, it will match the image indexing convention for Fortran,
but not for C. For C, if one is using data stored in row order, and one wants
to preserve the image index convention, the data must be reordered when
reading into memory.
In the end, which you do for Fortran or C depends on
which is more important, not reordering data or preserving the indexing
convention. For large images, reordering data is potentially expensive, and
often the indexing convention is inverted to avoid that.
The situation with
numpy makes this issue yet more complicated. The internal machinery of numpy
arrays is flexible enough to accept any ordering of indices. One can simply
reorder indices by manipulating the internal stride information for arrays
without reordering the data at all. Numpy will know how to map the new index
order to the data without moving the data.
So if this is true, why not choose
the index order that matches what you most expect? In particular, why not define
row-ordered images to use the image convention? (This is sometimes referred
to as the Fortran convention vs the C convention, thus the 'C' and 'FORTRAN'
order options for array ordering in numpy.) The drawback of doing this is
potential performance penalties. It's common to access the data sequentially,
either implicitly in array operations or explicitly by looping over rows of an
image. When that is done, then the data will be accessed in non-optimal order.
As the first index is incremented, what is actually happening is that elements
spaced far apart in memory are being sequentially accessed, with usually poor
memory access speeds. For example, for a two dimensional image 'im' defined so
that im[0, 10] represents the value at x=0, y=10. To be consistent with usual
Python behavior then im[0] would represent a column at x=0. Yet that data
would be spread over the whole array since the data are stored in row order.
Despite the flexibility of numpy's indexing, it can't really paper over the fact
basic operations are rendered inefficient because of data order or that getting
contiguous subarrays is still awkward (e.g., im[:,0] for the first row, vs
im[0]), thus one can't use an idiom such as for row in im; for col in im does
work, but doesn't yield contiguous column data.
As it turns out, numpy is
smart enough when dealing with ufuncs to determine which index is the most
rapidly varying one in memory and uses that for the innermost loop. Thus for
ufuncs there is no large intrinsic advantage to either approach in most cases.
On the other hand, use of .flat with an FORTRAN ordered array will lead to
non-optimal memory access as adjacent elements in the flattened array (iterator,
actually) are not contiguous in memory.
Indeed, the fact is that Python
indexing on lists and other sequences naturally leads to an outside-to inside
ordering (the first index gets the largest grouping, the next the next largest,
and the last gets the smallest element). Since image data are normally stored
by rows, this corresponds to position within rows being the last item indexed.
If you do want to use Fortran ordering realize that
there are two approaches to consider: 1) accept that the first index is just not
the most rapidly changing in memory and have all your I/O routines reorder
your data when going from memory to disk or visa versa, or use numpy's
mechanism for mapping the first index to the most rapidly varying data. We
recommend the former if possible. The disadvantage of the latter is that many
of numpy's functions will yield arrays without Fortran ordering unless you are
careful to use the 'order' keyword. Doing this would be highly inconvenient.
Otherwise we recommend simply learning to reverse the usual order of indices
when accessing elements of an array. Granted, it goes against the grain, but
it is more in line with Python semantics and the natural order of the data.
""" |
"""ctypes-based OpenGL wrapper for Python
This is the PyOpenGL 3.x tree, it attempts to provide
a largely compatible API for code written with the
PyOpenGL 2.x series using the ctypes foreign function
interface system.
Configuration Variables:
There are a few configuration variables in this top-level
module. Applications should be the only code that tweaks
these variables, mid-level libraries should not take it
upon themselves to disable/enable features at this level.
The implication there is that your library code should be
able to work with any of the valid configurations available
with these sets of flags.
Further, once any entry point has been loaded, the variables
can no longer be updated. The OpenGL._confligflags module
imports the variables from this location, and once that
import occurs the flags should no longer be changed.
ERROR_CHECKING -- if set to a False value before
importing any OpenGL.* libraries will completely
disable error-checking. This can dramatically
improve performance, but makes debugging far
harder.
This is intended to be turned off *only* in a
production environment where you *know* that
your code is entirely free of situations where you
use exception-handling to handle error conditions,
i.e. where you are explicitly checking for errors
everywhere they can occur in your code.
Default: True
ERROR_LOGGING -- If True, then wrap array-handler
functions with error-logging operations so that all exceptions
will be reported to log objects in OpenGL.logs, note that
this means you will get lots of error logging whenever you
have code that tests by trying something and catching an
error, this is intended to be turned on only during
development so that you can see why something is failing.
Errors are normally logged to the OpenGL.errors logger.
Only triggers if ERROR_CHECKING is True
Default: False
ERROR_ON_COPY -- if set to a True value before
importing the numpy/lists support modules, will
cause array operations to raise
OpenGL.error.CopyError if the operation
would cause a data-copy in order to make the
passed data-type match the target data-type.
This effectively disables all list/tuple array
support, as they are inherently copy-based.
This feature allows for optimisation of your
application. It should only be enabled during
testing stages to prevent raising errors on
recoverable conditions at run-time.
Default: False
CONTEXT_CHECKING -- if set to True, PyOpenGL will wrap
*every* GL and GLU call with a check to see if there
is a valid context. If there is no valid context
then will throw OpenGL.errors.NoContext. This is an
*extremely* slow check and is not enabled by default,
intended to be enabled in order to track down (wrong)
code that uses GL/GLU entry points before the context
has been initialized (something later Linux GLs are
very picky about).
Default: False
STORE_POINTERS -- if set to True, PyOpenGL array operations
will attempt to store references to pointers which are
being passed in order to prevent memory-access failures
if the pointed-to-object goes out of scope. This
behaviour is primarily intended to allow temporary arrays
to be created without causing memory errors, thus it is
trading off performance for safety.
To use this flag effectively, you will want to first set
ERROR_ON_COPY to True and eliminate all cases where you
are copying arrays. Copied arrays *will* segfault your
application deep within the GL if you disable this feature!
Once you have eliminated all copying of arrays in your
application, you will further need to be sure that all
arrays which are passed to the GL are stored for at least
the time period for which they are active in the GL. That
is, you must be sure that your array objects live at least
until they are no longer bound in the GL. This is something
you need to confirm by thinking about your application's
structure.
When you are sure your arrays won't cause seg-faults, you
can set STORE_POINTERS=False in your application and enjoy
a (slight) speed up.
Note: this flag is *only* observed when ERROR_ON_COPY == True,
as a safety measure to prevent pointless segfaults
Default: True
WARN_ON_FORMAT_UNAVAILABLE -- If True, generates
logging-module warn-level events when a FormatHandler
plugin is not loadable (with traceback).
Default: False
FULL_LOGGING -- If True, then wrap functions with
logging operations which reports each call along with its
arguments to the OpenGL.calltrace logger at the INFO
level. This is *extremely* slow. You should *not* enable
this in production code!
You will need to have a logging configuration (e.g.
logging.basicConfig()
) call in your top-level script to see the results of the
logging.
Default: False
ALLOW_NUMPY_SCALARS -- if True, we will wrap
all GLint/GLfloat calls conversions with wrappers
that allow for passing numpy scalar values.
Note that this is experimental, *not* reliable,
and very slow!
Note that byte/char types are not wrapped.
Default: False
UNSIGNED_BYTE_IMAGES_AS_STRING -- if True, we will return
GL_UNSIGNED_BYTE image-data as strings, instead of arrays
for glReadPixels and glGetTexImage
Default: True
FORWARD_COMPATIBLE_ONLY -- only include OpenGL 3.1 compatible
entry points. Note that this will generally break most
PyOpenGL code that hasn't been explicitly made "legacy free"
via a significant rewrite.
Default: False
SIZE_1_ARRAY_UNPACK -- if True, unpack size-1 arrays to be
scalar values, as done in PyOpenGL 1.5 -> 3.0.0, that is,
if a glGenList( 1 ) is done, return a uint rather than
an array of uints.
Default: True
USE_ACCELERATE -- if True, attempt to use the OpenGL_accelerate
package to provide Cython-coded accelerators for core wrapping
operations.
Default: True
MODULE_ANNOTATIONS -- if True, attempt to annotate alternates() and
constants to track in which module they are defined (only useful
for the documentation-generation passes, really).
Default: False
""" |
"""
Define a simple format for saving numpy arrays to disk with the full
information about them.
The ``.npy`` format is the standard binary file format in NumPy for
persisting a *single* arbitrary NumPy array on disk. The format stores all
of the shape and dtype information necessary to reconstruct the array
correctly even on another machine with a different architecture.
The format is designed to be as simple as possible while achieving
its limited goals.
The ``.npz`` format is the standard format for persisting *multiple* NumPy
arrays on disk. A ``.npz`` file is a zip file containing multiple ``.npy``
files, one for each array.
Capabilities
------------
- Can represent all NumPy arrays including nested record arrays and
object arrays.
- Represents the data in its native binary form.
- Supports Fortran-contiguous arrays directly.
- Stores all of the necessary information to reconstruct the array
including shape and dtype on a machine of a different
architecture. Both little-endian and big-endian arrays are
supported, and a file with little-endian numbers will yield
a little-endian array on any machine reading the file. The
types are described in terms of their actual sizes. For example,
if a machine with a 64-bit C "long int" writes out an array with
"long ints", a reading machine with 32-bit C "long ints" will yield
an array with 64-bit integers.
- Is straightforward to reverse engineer. Datasets often live longer than
the programs that created them. A competent developer should be
able create a solution in his preferred programming language to
read most ``.npy`` files that he has been given without much
documentation.
- Allows memory-mapping of the data. See `open_memmep`.
- Can be read from a filelike stream object instead of an actual file.
- Stores object arrays, i.e. arrays containing elements that are arbitrary
Python objects. Files with object arrays are not to be mmapable, but
can be read and written to disk.
Limitations
-----------
- Arbitrary subclasses of numpy.ndarray are not completely preserved.
Subclasses will be accepted for writing, but only the array data will
be written out. A regular numpy.ndarray object will be created
upon reading the file.
.. warning::
Due to limitations in the interpretation of structured dtypes, dtypes
with fields with empty names will have the names replaced by 'f0', 'f1',
etc. Such arrays will not round-trip through the format entirely
accurately. The data is intact; only the field names will differ. We are
working on a fix for this. This fix will not require a change in the
file format. The arrays with such structures can still be saved and
restored, and the correct dtype may be restored by using the
``loadedarray.view(correct_dtype)`` method.
File extensions
---------------
We recommend using the ``.npy`` and ``.npz`` extensions for files saved
in this format. This is by no means a requirement; applications may wish
to use these file formats but use an extension specific to the
application. In the absence of an obvious alternative, however,
we suggest using ``.npy`` and ``.npz``.
Version numbering
-----------------
The version numbering of these formats is independent of NumPy version
numbering. If the format is upgraded, the code in `numpy.io` will still
be able to read and write Version 1.0 files.
Format Version 1.0
------------------
The first 6 bytes are a magic string: exactly ``\\x93NUMPY``.
The next 1 byte is an unsigned byte: the major version number of the file
format, e.g. ``\\x01``.
The next 1 byte is an unsigned byte: the minor version number of the file
format, e.g. ``\\x00``. Note: the version of the file format is not tied
to the version of the numpy package.
The next 2 bytes form a little-endian unsigned short int: the length of
the header data HEADER_LEN.
The next HEADER_LEN bytes form the header data describing the array's
format. It is an ASCII string which contains a Python literal expression
of a dictionary. It is terminated by a newline (``\\n``) and padded with
spaces (``\\x20``) to make the total length of
``magic string + 4 + HEADER_LEN`` be evenly divisible by 16 for alignment
purposes.
The dictionary contains three keys:
"descr" : dtype.descr
An object that can be passed as an argument to the `numpy.dtype`
constructor to create the array's dtype.
"fortran_order" : bool
Whether the array data is Fortran-contiguous or not. Since
Fortran-contiguous arrays are a common form of non-C-contiguity,
we allow them to be written directly to disk for efficiency.
"shape" : tuple of int
The shape of the array.
For repeatability and readability, the dictionary keys are sorted in
alphabetic order. This is for convenience only. A writer SHOULD implement
this if possible. A reader MUST NOT depend on this.
Following the header comes the array data. If the dtype contains Python
objects (i.e. ``dtype.hasobject is True``), then the data is a Python
pickle of the array. Otherwise the data is the contiguous (either C-
or Fortran-, depending on ``fortran_order``) bytes of the array.
Consumers can figure out the number of bytes by multiplying the number
of elements given by the shape (noting that ``shape=()`` means there is
1 element) by ``dtype.itemsize``.
Notes
-----
The ``.npy`` format, including reasons for creating it and a comparison of
alternatives, is described fully in the "npy-format" NEP.
""" |
"""
Basic functions used by several sub-packages and
useful to have in the main name-space.
Type Handling
-------------
================ ===================
iscomplexobj Test for complex object, scalar result
isrealobj Test for real object, scalar result
iscomplex Test for complex elements, array result
isreal Test for real elements, array result
imag Imaginary part
real Real part
real_if_close Turns complex number with tiny imaginary part to real
isneginf Tests for negative infinity, array result
isposinf Tests for positive infinity, array result
isnan Tests for nans, array result
isinf Tests for infinity, array result
isfinite Tests for finite numbers, array result
isscalar True if argument is a scalar
nan_to_num Replaces NaN's with 0 and infinities with large numbers
cast Dictionary of functions to force cast to each type
common_type Determine the minimum common type code for a group
of arrays
mintypecode Return minimal allowed common typecode.
================ ===================
Index Tricks
------------
================ ===================
mgrid Method which allows easy construction of N-d
'mesh-grids'
``r_`` Append and construct arrays: turns slice objects into
ranges and concatenates them, for 2d arrays appends rows.
index_exp Konrad Hinsen's index_expression class instance which
can be useful for building complicated slicing syntax.
================ ===================
Useful Functions
----------------
================ ===================
select Extension of where to multiple conditions and choices
extract Extract 1d array from flattened array according to mask
insert Insert 1d array of values into Nd array according to mask
linspace Evenly spaced samples in linear space
logspace Evenly spaced samples in logarithmic space
fix Round x to nearest integer towards zero
mod Modulo mod(x,y) = x % y except keeps sign of y
amax Array maximum along axis
amin Array minimum along axis
ptp Array max-min along axis
cumsum Cumulative sum along axis
prod Product of elements along axis
cumprod Cumluative product along axis
diff Discrete differences along axis
angle Returns angle of complex argument
unwrap Unwrap phase along given axis (1-d algorithm)
sort_complex Sort a complex-array (based on real, then imaginary)
trim_zeros Trim the leading and trailing zeros from 1D array.
vectorize A class that wraps a Python function taking scalar
arguments into a generalized function which can handle
arrays of arguments using the broadcast rules of
numerix Python.
================ ===================
Shape Manipulation
------------------
================ ===================
squeeze Return a with length-one dimensions removed.
atleast_1d Force arrays to be > 1D
atleast_2d Force arrays to be > 2D
atleast_3d Force arrays to be > 3D
vstack Stack arrays vertically (row on row)
hstack Stack arrays horizontally (column on column)
column_stack Stack 1D arrays as columns into 2D array
dstack Stack arrays depthwise (along third dimension)
split Divide array into a list of sub-arrays
hsplit Split into columns
vsplit Split into rows
dsplit Split along third dimension
================ ===================
Matrix (2D Array) Manipulations
-------------------------------
================ ===================
fliplr 2D array with columns flipped
flipud 2D array with rows flipped
rot90 Rotate a 2D array a multiple of 90 degrees
eye Return a 2D array with ones down a given diagonal
diag Construct a 2D array from a vector, or return a given
diagonal from a 2D array.
mat Construct a Matrix
bmat Build a Matrix from blocks
================ ===================
Polynomials
-----------
================ ===================
poly1d A one-dimensional polynomial class
poly Return polynomial coefficients from roots
roots Find roots of polynomial given coefficients
polyint Integrate polynomial
polyder Differentiate polynomial
polyadd Add polynomials
polysub Substract polynomials
polymul Multiply polynomials
polydiv Divide polynomials
polyval Evaluate polynomial at given argument
================ ===================
Import Tricks
-------------
================ ===================
ppimport Postpone module import until trying to use it
ppimport_attr Postpone module import until trying to use its attribute
ppresolve Import postponed module and return it.
================ ===================
Machine Arithmetics
-------------------
================ ===================
machar_single Single precision floating point arithmetic parameters
machar_double Double precision floating point arithmetic parameters
================ ===================
Threading Tricks
----------------
================ ===================
ParallelExec Execute commands in parallel thread.
================ ===================
1D Array Set Operations
-----------------------
Set operations for 1D numeric arrays based on sort() function.
================ ===================
ediff1d Array difference (auxiliary function).
unique Unique elements of an array.
intersect1d Intersection of 1D arrays with unique elements.
setxor1d Set exclusive-or of 1D arrays with unique elements.
in1d Test whether elements in a 1D array are also present in
another array.
union1d Union of 1D arrays with unique elements.
setdiff1d Set difference of 1D arrays with unique elements.
================ ===================
""" |
#!/usr/bin/env python
# ***** BEGIN LICENSE BLOCK *****
# Version: MPL 1.1/GPL 2.0/LGPL 2.1
#
# The contents of this file are subject to the Mozilla Public License Version
# 1.1 (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
# http://www.mozilla.org/MPL/
#
# Software distributed under the License is distributed on an "AS IS" basis,
# WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License
# for the specific language governing rights and limitations under the
# License.
#
# The Original Code is font utility code.
#
# The Initial Developer of the Original Code is Mozilla Corporation.
# Portions created by the Initial Developer are Copyright (C) 2009
# the Initial Developer. All Rights Reserved.
#
# Contributor(s):
# NAME <EMAIL>
#
# Alternatively, the contents of this file may be used under the terms of
# either the GNU General Public License Version 2 or later (the "GPL"), or
# the GNU Lesser General Public License Version 2.1 or later (the "LGPL"),
# in which case the provisions of the GPL or the LGPL are applicable instead
# of those above. If you wish to allow use of your version of this file only
# under the terms of either the GPL or the LGPL, and not to allow others to
# use your version of this file under the terms of the MPL, indicate your
# decision by deleting the provisions above and replace them with the notice
# and other provisions required by the GPL or the LGPL. If you do not delete
# the provisions above, a recipient may use your version of this file under
# the terms of any one of the MPL, the GPL or the LGPL.
#
# ***** END LICENSE BLOCK ***** */
# eotlitetool.py - create EOT version of OpenType font for use with IE
#
# Usage: eotlitetool.py [-o output-filename] font1 [font2 ...]
#
# OpenType file structure
# http://www.microsoft.com/typography/otspec/otff.htm
#
# Types:
#
# BYTE 8-bit unsigned integer.
# CHAR 8-bit signed integer.
# USHORT 16-bit unsigned integer.
# SHORT 16-bit signed integer.
# ULONG 32-bit unsigned integer.
# Fixed 32-bit signed fixed-point number (16.16)
# LONGDATETIME Date represented in number of seconds since 12:00 midnight, January 1, 1904. The value is represented as a signed 64-bit integer.
#
# SFNT Header
#
# Fixed sfnt version // 0x00010000 for version 1.0.
# USHORT numTables // Number of tables.
# USHORT searchRange // (Maximum power of 2 <= numTables) x 16.
# USHORT entrySelector // Log2(maximum power of 2 <= numTables).
# USHORT rangeShift // NumTables x 16-searchRange.
#
# Table Directory
#
# ULONG tag // 4-byte identifier.
# ULONG checkSum // CheckSum for this table.
# ULONG offset // Offset from beginning of TrueType font file.
# ULONG length // Length of this table.
#
# OS/2 Table (Version 4)
#
# USHORT version // 0x0004
# SHORT xAvgCharWidth
# USHORT usWeightClass
# USHORT usWidthClass
# USHORT fsType
# SHORT ySubscriptXSize
# SHORT ySubscriptYSize
# SHORT ySubscriptXOffset
# SHORT ySubscriptYOffset
# SHORT ySuperscriptXSize
# SHORT ySuperscriptYSize
# SHORT ySuperscriptXOffset
# SHORT ySuperscriptYOffset
# SHORT yStrikeoutSize
# SHORT yStrikeoutPosition
# SHORT sFamilyClass
# BYTE panose[10]
# ULONG ulUnicodeRange1 // Bits 0-31
# ULONG ulUnicodeRange2 // Bits 32-63
# ULONG ulUnicodeRange3 // Bits 64-95
# ULONG ulUnicodeRange4 // Bits 96-127
# CHAR achVendID[4]
# USHORT fsSelection
# USHORT usFirstCharIndex
# USHORT usLastCharIndex
# SHORT sTypoAscender
# SHORT sTypoDescender
# SHORT sTypoLineGap
# USHORT usWinAscent
# USHORT usWinDescent
# ULONG ulCodePageRange1 // Bits 0-31
# ULONG ulCodePageRange2 // Bits 32-63
# SHORT sxHeight
# SHORT sCapHeight
# USHORT usDefaultChar
# USHORT usBreakChar
# USHORT usMaxContext
#
#
# The Naming Table is organized as follows:
#
# [name table header]
# [name records]
# [string data]
#
# Name Table Header
#
# USHORT format // Format selector (=0).
# USHORT count // Number of name records.
# USHORT stringOffset // Offset to start of string storage (from start of table).
#
# Name Record
#
# USHORT platformID // Platform ID.
# USHORT encodingID // Platform-specific encoding ID.
# USHORT languageID // Language ID.
# USHORT nameID // Name ID.
# USHORT length // String length (in bytes).
# USHORT offset // String offset from start of storage area (in bytes).
#
# head Table
#
# Fixed tableVersion // Table version number 0x00010000 for version 1.0.
# Fixed fontRevision // Set by font manufacturer.
# ULONG checkSumAdjustment // To compute: set it to 0, sum the entire font as ULONG, then store 0xB1B0AFBA - sum.
# ULONG magicNumber // Set to 0x5F0F3CF5.
# USHORT flags
# USHORT unitsPerEm // Valid range is from 16 to 16384. This value should be a power of 2 for fonts that have TrueType outlines.
# LONGDATETIME created // Number of seconds since 12:00 midnight, January 1, 1904. 64-bit integer
# LONGDATETIME modified // Number of seconds since 12:00 midnight, January 1, 1904. 64-bit integer
# SHORT xMin // For all glyph bounding boxes.
# SHORT yMin
# SHORT xMax
# SHORT yMax
# USHORT macStyle
# USHORT lowestRecPPEM // Smallest readable size in pixels.
# SHORT fontDirectionHint
# SHORT indexToLocFormat // 0 for short offsets, 1 for long.
# SHORT glyphDataFormat // 0 for current format.
#
#
#
# Embedded OpenType (EOT) file format
# http://www.w3.org/Submission/EOT/
#
# EOT version 0x00020001
#
# An EOT font consists of a header with the original OpenType font
# appended at the end. Most of the data in the EOT header is simply a
# copy of data from specific tables within the font data. The exceptions
# are the 'Flags' field and the root string name field. The root string
# is a set of names indicating domains for which the font data can be
# used. A null root string implies the font data can be used anywhere.
# The EOT header is in little-endian byte order but the font data remains
# in big-endian order as specified by the OpenType spec.
#
# Overall structure:
#
# [EOT header]
# [EOT name records]
# [font data]
#
# EOT header
#
# ULONG eotSize // Total structure length in bytes (including string and font data)
# ULONG fontDataSize // Length of the OpenType font (FontData) in bytes
# ULONG version // Version number of this format - 0x00020001
# ULONG flags // Processing Flags (0 == no special processing)
# BYTE fontPANOSE[10] // OS/2 Table panose
# BYTE charset // DEFAULT_CHARSET (0x01)
# BYTE italic // 0x01 if ITALIC in OS/2 Table fsSelection is set, 0 otherwise
# ULONG weight // OS/2 Table usWeightClass
# USHORT fsType // OS/2 Table fsType (specifies embedding permission flags)
# USHORT magicNumber // Magic number for EOT file - 0x504C.
# ULONG unicodeRange1 // OS/2 Table ulUnicodeRange1
# ULONG unicodeRange2 // OS/2 Table ulUnicodeRange2
# ULONG unicodeRange3 // OS/2 Table ulUnicodeRange3
# ULONG unicodeRange4 // OS/2 Table ulUnicodeRange4
# ULONG codePageRange1 // OS/2 Table ulCodePageRange1
# ULONG codePageRange2 // OS/2 Table ulCodePageRange2
# ULONG checkSumAdjustment // head Table CheckSumAdjustment
# ULONG reserved[4] // Reserved - must be 0
# USHORT padding1 // Padding - must be 0
#
# EOT name records
#
# USHORT FamilyNameSize // Font family name size in bytes
# BYTE FamilyName[FamilyNameSize] // Font family name (name ID = 1), little-endian UTF-16
# USHORT Padding2 // Padding - must be 0
#
# USHORT StyleNameSize // Style name size in bytes
# BYTE StyleName[StyleNameSize] // Style name (name ID = 2), little-endian UTF-16
# USHORT Padding3 // Padding - must be 0
#
# USHORT VersionNameSize // Version name size in bytes
# bytes VersionName[VersionNameSize] // Version name (name ID = 5), little-endian UTF-16
# USHORT Padding4 // Padding - must be 0
#
# USHORT FullNameSize // Full name size in bytes
# BYTE FullName[FullNameSize] // Full name (name ID = 4), little-endian UTF-16
# USHORT Padding5 // Padding - must be 0
#
# USHORT RootStringSize // Root string size in bytes
# BYTE RootString[RootStringSize] // Root string, little-endian UTF-16
|
"""
Module for running iterative sweeps of sensitivity analyses on surrogate models over a range of sample sizes
to test for confidence interval convergence.
OVERVIEW
==========
This module provides functions to run sample size convergence tests on surrogate model
sensitivity analysis. So what does all that BS actually mean? The module GenSAData.py is
used to run sensitivity analysis on surrogate models generated and trained by surrogate_modeler.py.
In order to test the dependence on certain input parameters or combinations of input parameters,
we have to generate an array of input parameter samples across ranges of each input parameter to
the model (see the text file DB10_ParamRanges.txt for an example of how parameter ranges are specified).
The outputs of GenSAData is a sensitivity of the model to each input parameter and,
(if second-order sensitivities are being calculated), the sensitivities of the model to every
combination of input parameters. With each sensitivity, there is an associated confidence interval,
or error margin. Generally, the lower the confidence interval, the higher the certainty of the
sensitivity value is, which is desirable for finding with parameter or combination of parameters
the model is most sensitive to.
One of the important hyperparameters we can tweak when running sensitivity analysis is the number
of samples generated. Theoretically, the more samples we generate to run SA on, the more certain
we will be of the sensitivities. The purpose of this model, then, is to run lots and lots of iterations
of GenSAData (well, as many as you specify with n_iter), collect the sensitivity confidences for
each iteration, and plot them as a function of n_iter. We should expect to see confidence intervals
approaching zero as n_iter -> infinity, which would indicate the error margin of our
sensitivities is decreasing with increasing sample size.
USAGE
==========
The main function of this module is run_convergence_tests(). As the name suggests, this function
does the core work of sweeping over a range of sample sizes and calls run_sensitivity_analysis()
from GenSAData for each sample size, and writes the first-order (S1) and total (ST) sensitivity
confidences for each parameter to a text csv.
The sample range tested on is specified by min_nsamples and max_nsamples. The number of samples
tested in that range is set by n_iter, and the sample list Nsample_list is generated using a simple
linear interpolation across the specified range.
The next most important function in this module is plot_convergence which, as the name once
again suggests, plots the results of the convergence test. This function plots an array of
confidence values across the sample space they were generated with. It expects an MxN array, where
the rows are the confidence values calculated with a certain number of samples.
M is the number of iterations run_convergence_tests() did, so basically n_iter. N is twice the
number of input parameters to the model, plus one for the first column which contains the number
of samples for that iteration. The rest of the columns contains the confidence intervals. If there
are P input parameters, column 0 is the sample number, columns 1:P are the first order sensitivity
confidences, and columns P+1:2*P+1 are the total confidences.
In the future, if second order sensitivity confidences are added, then there will be 2*P + P^2 +1 columns.
Columns 1:P will still be first order confidences, columns P+1 : P^2 will be second-order confidences,
and columns P^2+1:P^2+P+1 will be the total confidences.
In order to run the convergence tests and plot the results, you can setup a main() function to first
run the convergence test by calling run_convergence_test(), then reading the confidences csv that
is generated into an array that is then passed to plot_convergence().
TIPS AND TRICKS
==========
Some tricky aspects of using these functions:
- The surrogate model run_convergence_test() actuall tests is NOT set in this module. Whichever
model GenSAData is set to generate sensitivities for is what model run_convergence_tests() will
run on. You can specify which file to load the surrogate model from in the project root config file.
- The name of the file run_test_convergence() saves its results to is prefixed with the string set with
the variable "outfile" and postfixed with
""" |
# Configuration file for jupyter-notebook.
#------------------------------------------------------------------------------
# Configurable configuration
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
# SingletonConfigurable configuration
#------------------------------------------------------------------------------
# A configurable that only allows one instance.
#
# This class is for classes that should only have one instance of itself or
# *any* subclass. To create and retrieve such a class use the
# :meth:`SingletonConfigurable.instance` method.
#------------------------------------------------------------------------------
# Application configuration
#------------------------------------------------------------------------------
# This is an application.
# The date format used by logging formatters for %(asctime)s
# c.Application.log_datefmt = '%Y-%m-%d %H:%M:%S'
# The Logging format template
# c.Application.log_format = '[%(name)s]%(highlevel)s %(message)s'
# Set the log level by value or name.
# c.Application.log_level = 30
#------------------------------------------------------------------------------
# JupyterApp configuration
#------------------------------------------------------------------------------
# Base class for Jupyter applications
# Answer yes to any prompts.
# c.JupyterApp.answer_yes = False
# Full path of a config file.
# c.JupyterApp.config_file = u''
# Generate default config file.
# c.JupyterApp.generate_config = False
# Specify a config file to load.
# c.JupyterApp.config_file_name = u''
#------------------------------------------------------------------------------
# NotebookApp configuration
#------------------------------------------------------------------------------
# The number of additional ports to try if the specified port is not available.
# c.NotebookApp.port_retries = 50
# Extra variables to supply to jinja templates when rendering.
# c.NotebookApp.jinja_template_vars = traitlets.Undefined
# The url for MathJax.js.
# c.NotebookApp.mathjax_url = ''
# Supply extra arguments that will be passed to Jinja environment.
# c.NotebookApp.jinja_environment_options = traitlets.Undefined
# The IP address the notebook server will listen on.
# c.NotebookApp.ip = 'localhost'
# DEPRECATED use base_url
# c.NotebookApp.base_project_url = '/'
# Python modules to load as notebook server extensions. This is an experimental
# API, and may change in future releases.
# c.NotebookApp.server_extensions = traitlets.Undefined
# The random bytes used to secure cookies. By default this is a new random
# number every time you start the Notebook. Set it to a value in a config file
# to enable logins to persist across server sessions.
#
# Note: Cookie secrets should be kept private, do not share config files with
# cookie_secret stored in plaintext (you can read the value from a file).
# c.NotebookApp.cookie_secret = ''
# The default URL to redirect to from `/`
# c.NotebookApp.default_url = '/tree'
# The port the notebook server will listen on.
# c.NotebookApp.port = 8888
# The kernel spec manager class to use. Should be a subclass of
# `jupyter_client.kernelspec.KernelSpecManager`.
#
# The Api of KernelSpecManager is provisional and might change without warning
# between this version of IPython and the next stable one.
# c.NotebookApp.kernel_spec_manager_class = <class 'jupyter_client.kernelspec.KernelSpecManager'>
# Set the Access-Control-Allow-Origin header
#
# Use '*' to allow any origin to access your server.
#
# Takes precedence over allow_origin_pat.
# c.NotebookApp.allow_origin = ''
# The notebook manager class to use.
# c.NotebookApp.contents_manager_class = <class 'notebook.services.contents.filemanager.FileContentsManager'>
# Use a regular expression for the Access-Control-Allow-Origin header
#
# Requests from an origin matching the expression will get replies with:
#
# Access-Control-Allow-Origin: origin
#
# where `origin` is the origin of the request.
#
# Ignored if allow_origin is set.
# c.NotebookApp.allow_origin_pat = ''
# The full path to an SSL/TLS certificate file.
# c.NotebookApp.certfile = u''
# The logout handler class to use.
# c.NotebookApp.logout_handler_class = <class 'notebook.auth.logout.LogoutHandler'>
# The base URL for the notebook server.
#
# Leading and trailing slashes can be omitted, and will automatically be added.
# c.NotebookApp.base_url = '/'
# The session manager class to use.
# c.NotebookApp.session_manager_class = <class 'notebook.services.sessions.sessionmanager.SessionManager'>
# Supply overrides for the tornado.web.Application that the IPython notebook
# uses.
# c.NotebookApp.tornado_settings = traitlets.Undefined
# The directory to use for notebooks and kernels.
# c.NotebookApp.notebook_dir = u''
# The kernel manager class to use.
# c.NotebookApp.kernel_manager_class = <class 'notebook.services.kernels.kernelmanager.MappingKernelManager'>
# The file where the cookie secret is stored.
# c.NotebookApp.cookie_secret_file = u''
# Supply SSL options for the tornado HTTPServer. See the tornado docs for
# details.
# c.NotebookApp.ssl_options = traitlets.Undefined
#
# c.NotebookApp.file_to_run = ''
# DISABLED: use %pylab or %matplotlib in the notebook to enable matplotlib.
# c.NotebookApp.pylab = 'disabled'
# Whether to enable MathJax for typesetting math/TeX
#
# MathJax is the javascript library IPython uses to render math/LaTeX. It is
# very large, so you may want to disable it if you have a slow internet
# connection, or for offline use of the notebook.
#
# When disabled, equations etc. will appear as their untransformed TeX source.
# c.NotebookApp.enable_mathjax = True
# Reraise exceptions encountered loading server extensions?
# c.NotebookApp.reraise_server_extension_failures = False
# The base URL for websockets, if it differs from the HTTP server (hint: it
# almost certainly doesn't).
#
# Should be in the form of an HTTP origin: ws[s]://hostname[:port]
# c.NotebookApp.websocket_url = ''
# Whether to open in a browser after starting. The specific browser used is
# platform dependent and determined by the python standard library `webbrowser`
# module, unless it is overridden using the --browser (NotebookApp.browser)
# configuration option.
|
"""
=============================
Subclassing ndarray in python
=============================
Credits
-------
This page is based with thanks on the wiki page on subclassing by NAME - http://www.scipy.org/Subclasses.
Introduction
------------
Subclassing ndarray is relatively simple, but it has some complications
compared to other Python objects. On this page we explain the machinery
that allows you to subclass ndarray, and the implications for
implementing a subclass.
ndarrays and object creation
============================
Subclassing ndarray is complicated by the fact that new instances of
ndarray classes can come about in three different ways. These are:
#. Explicit constructor call - as in ``MySubClass(params)``. This is
the usual route to Python instance creation.
#. View casting - casting an existing ndarray as a given subclass
#. New from template - creating a new instance from a template
instance. Examples include returning slices from a subclassed array,
creating return types from ufuncs, and copying arrays. See
:ref:`new-from-template` for more details
The last two are characteristics of ndarrays - in order to support
things like array slicing. The complications of subclassing ndarray are
due to the mechanisms numpy has to support these latter two routes of
instance creation.
.. _view-casting:
View casting
------------
*View casting* is the standard ndarray mechanism by which you take an
ndarray of any subclass, and return a view of the array as another
(specified) subclass:
>>> import numpy as np
>>> # create a completely useless ndarray subclass
>>> class C(np.ndarray): pass
>>> # create a standard ndarray
>>> arr = np.zeros((3,))
>>> # take a view of it, as our useless subclass
>>> c_arr = arr.view(C)
>>> type(c_arr)
<class 'C'>
.. _new-from-template:
Creating new from template
--------------------------
New instances of an ndarray subclass can also come about by a very
similar mechanism to :ref:`view-casting`, when numpy finds it needs to
create a new instance from a template instance. The most obvious place
this has to happen is when you are taking slices of subclassed arrays.
For example:
>>> v = c_arr[1:]
>>> type(v) # the view is of type 'C'
<class 'C'>
>>> v is c_arr # but it's a new instance
False
The slice is a *view* onto the original ``c_arr`` data. So, when we
take a view from the ndarray, we return a new ndarray, of the same
class, that points to the data in the original.
There are other points in the use of ndarrays where we need such views,
such as copying arrays (``c_arr.copy()``), creating ufunc output arrays
(see also :ref:`array-wrap`), and reducing methods (like
``c_arr.mean()``.
Relationship of view casting and new-from-template
--------------------------------------------------
These paths both use the same machinery. We make the distinction here,
because they result in different input to your methods. Specifically,
:ref:`view-casting` means you have created a new instance of your array
type from any potential subclass of ndarray. :ref:`new-from-template`
means you have created a new instance of your class from a pre-existing
instance, allowing you - for example - to copy across attributes that
are particular to your subclass.
Implications for subclassing
----------------------------
If we subclass ndarray, we need to deal not only with explicit
construction of our array type, but also :ref:`view-casting` or
:ref:`new-from-template`. Numpy has the machinery to do this, and this
machinery that makes subclassing slightly non-standard.
There are two aspects to the machinery that ndarray uses to support
views and new-from-template in subclasses.
The first is the use of the ``ndarray.__new__`` method for the main work
of object initialization, rather then the more usual ``__init__``
method. The second is the use of the ``__array_finalize__`` method to
allow subclasses to clean up after the creation of views and new
instances from templates.
A brief Python primer on ``__new__`` and ``__init__``
=====================================================
``__new__`` is a standard Python method, and, if present, is called
before ``__init__`` when we create a class instance. See the `python
__new__ documentation
<http://docs.python.org/reference/datamodel.html#object.__new__>`_ for more detail.
For example, consider the following Python code:
.. testcode::
class C(object):
def __new__(cls, *args):
print 'Cls in __new__:', cls
print 'Args in __new__:', args
return object.__new__(cls, *args)
def __init__(self, *args):
print 'type(self) in __init__:', type(self)
print 'Args in __init__:', args
meaning that we get:
>>> c = C('hello')
Cls in __new__: <class 'C'>
Args in __new__: ('hello',)
type(self) in __init__: <class 'C'>
Args in __init__: ('hello',)
When we call ``C('hello')``, the ``__new__`` method gets its own class
as first argument, and the passed argument, which is the string
``'hello'``. After python calls ``__new__``, it usually (see below)
calls our ``__init__`` method, with the output of ``__new__`` as the
first argument (now a class instance), and the passed arguments
following.
As you can see, the object can be initialized in the ``__new__``
method or the ``__init__`` method, or both, and in fact ndarray does
not have an ``__init__`` method, because all the initialization is
done in the ``__new__`` method.
Why use ``__new__`` rather than just the usual ``__init__``? Because
in some cases, as for ndarray, we want to be able to return an object
of some other class. Consider the following:
.. testcode::
class D(C):
def __new__(cls, *args):
print 'D cls is:', cls
print 'D args in __new__:', args
return C.__new__(C, *args)
def __init__(self, *args):
# we never get here
print 'In D __init__'
meaning that:
>>> obj = D('hello')
D cls is: <class 'D'>
D args in __new__: ('hello',)
Cls in __new__: <class 'C'>
Args in __new__: ('hello',)
>>> type(obj)
<class 'C'>
The definition of ``C`` is the same as before, but for ``D``, the
``__new__`` method returns an instance of class ``C`` rather than
``D``. Note that the ``__init__`` method of ``D`` does not get
called. In general, when the ``__new__`` method returns an object of
class other than the class in which it is defined, the ``__init__``
method of that class is not called.
This is how subclasses of the ndarray class are able to return views
that preserve the class type. When taking a view, the standard
ndarray machinery creates the new ndarray object with something
like::
obj = ndarray.__new__(subtype, shape, ...
where ``subdtype`` is the subclass. Thus the returned view is of the
same class as the subclass, rather than being of class ``ndarray``.
That solves the problem of returning views of the same type, but now
we have a new problem. The machinery of ndarray can set the class
this way, in its standard methods for taking views, but the ndarray
``__new__`` method knows nothing of what we have done in our own
``__new__`` method in order to set attributes, and so on. (Aside -
why not call ``obj = subdtype.__new__(...`` then? Because we may not
have a ``__new__`` method with the same call signature).
The role of ``__array_finalize__``
==================================
``__array_finalize__`` is the mechanism that numpy provides to allow
subclasses to handle the various ways that new instances get created.
Remember that subclass instances can come about in these three ways:
#. explicit constructor call (``obj = MySubClass(params)``). This will
call the usual sequence of ``MySubClass.__new__`` then (if it exists)
``MySubClass.__init__``.
#. :ref:`view-casting`
#. :ref:`new-from-template`
Our ``MySubClass.__new__`` method only gets called in the case of the
explicit constructor call, so we can't rely on ``MySubClass.__new__`` or
``MySubClass.__init__`` to deal with the view casting and
new-from-template. It turns out that ``MySubClass.__array_finalize__``
*does* get called for all three methods of object creation, so this is
where our object creation housekeeping usually goes.
* For the explicit constructor call, our subclass will need to create a
new ndarray instance of its own class. In practice this means that
we, the authors of the code, will need to make a call to
``ndarray.__new__(MySubClass,...)``, or do view casting of an existing
array (see below)
* For view casting and new-from-template, the equivalent of
``ndarray.__new__(MySubClass,...`` is called, at the C level.
The arguments that ``__array_finalize__`` recieves differ for the three
methods of instance creation above.
The following code allows us to look at the call sequences and arguments:
.. testcode::
import numpy as np
class C(np.ndarray):
def __new__(cls, *args, **kwargs):
print 'In __new__ with class %s' % cls
return np.ndarray.__new__(cls, *args, **kwargs)
def __init__(self, *args, **kwargs):
# in practice you probably will not need or want an __init__
# method for your subclass
print 'In __init__ with class %s' % self.__class__
def __array_finalize__(self, obj):
print 'In array_finalize:'
print ' self type is %s' % type(self)
print ' obj type is %s' % type(obj)
Now:
>>> # Explicit constructor
>>> c = C((10,))
In __new__ with class <class 'C'>
In array_finalize:
self type is <class 'C'>
obj type is <type 'NoneType'>
In __init__ with class <class 'C'>
>>> # View casting
>>> a = np.arange(10)
>>> cast_a = a.view(C)
In array_finalize:
self type is <class 'C'>
obj type is <type 'numpy.ndarray'>
>>> # Slicing (example of new-from-template)
>>> cv = c[:1]
In array_finalize:
self type is <class 'C'>
obj type is <class 'C'>
The signature of ``__array_finalize__`` is::
def __array_finalize__(self, obj):
``ndarray.__new__`` passes ``__array_finalize__`` the new object, of our
own class (``self``) as well as the object from which the view has been
taken (``obj``). As you can see from the output above, the ``self`` is
always a newly created instance of our subclass, and the type of ``obj``
differs for the three instance creation methods:
* When called from the explicit constructor, ``obj`` is ``None``
* When called from view casting, ``obj`` can be an instance of any
subclass of ndarray, including our own.
* When called in new-from-template, ``obj`` is another instance of our
own subclass, that we might use to update the new ``self`` instance.
Because ``__array_finalize__`` is the only method that always sees new
instances being created, it is the sensible place to fill in instance
defaults for new object attributes, among other tasks.
This may be clearer with an example.
Simple example - adding an extra attribute to ndarray
-----------------------------------------------------
.. testcode::
import numpy as np
class InfoArray(np.ndarray):
def __new__(subtype, shape, dtype=float, buffer=None, offset=0,
strides=None, order=None, info=None):
# Create the ndarray instance of our type, given the usual
# ndarray input arguments. This will call the standard
# ndarray constructor, but return an object of our type.
# It also triggers a call to InfoArray.__array_finalize__
obj = np.ndarray.__new__(subtype, shape, dtype, buffer, offset, strides,
order)
# set the new 'info' attribute to the value passed
obj.info = info
# Finally, we must return the newly created object:
return obj
def __array_finalize__(self, obj):
# ``self`` is a new object resulting from
# ndarray.__new__(InfoArray, ...), therefore it only has
# attributes that the ndarray.__new__ constructor gave it -
# i.e. those of a standard ndarray.
#
# We could have got to the ndarray.__new__ call in 3 ways:
# From an explicit constructor - e.g. InfoArray():
# obj is None
# (we're in the middle of the InfoArray.__new__
# constructor, and self.info will be set when we return to
# InfoArray.__new__)
if obj is None: return
# From view casting - e.g arr.view(InfoArray):
# obj is arr
# (type(obj) can be InfoArray)
# From new-from-template - e.g infoarr[:3]
# type(obj) is InfoArray
#
# Note that it is here, rather than in the __new__ method,
# that we set the default value for 'info', because this
# method sees all creation of default objects - with the
# InfoArray.__new__ constructor, but also with
# arr.view(InfoArray).
self.info = getattr(obj, 'info', None)
# We do not need to return anything
Using the object looks like this:
>>> obj = InfoArray(shape=(3,)) # explicit constructor
>>> type(obj)
<class 'InfoArray'>
>>> obj.info is None
True
>>> obj = InfoArray(shape=(3,), info='information')
>>> obj.info
'information'
>>> v = obj[1:] # new-from-template - here - slicing
>>> type(v)
<class 'InfoArray'>
>>> v.info
'information'
>>> arr = np.arange(10)
>>> cast_arr = arr.view(InfoArray) # view casting
>>> type(cast_arr)
<class 'InfoArray'>
>>> cast_arr.info is None
True
This class isn't very useful, because it has the same constructor as the
bare ndarray object, including passing in buffers and shapes and so on.
We would probably prefer the constructor to be able to take an already
formed ndarray from the usual numpy calls to ``np.array`` and return an
object.
Slightly more realistic example - attribute added to existing array
-------------------------------------------------------------------
Here is a class that takes a standard ndarray that already exists, casts
as our type, and adds an extra attribute.
.. testcode::
import numpy as np
class RealisticInfoArray(np.ndarray):
def __new__(cls, input_array, info=None):
# Input array is an already formed ndarray instance
# We first cast to be our class type
obj = np.asarray(input_array).view(cls)
# add the new attribute to the created instance
obj.info = info
# Finally, we must return the newly created object:
return obj
def __array_finalize__(self, obj):
# see InfoArray.__array_finalize__ for comments
if obj is None: return
self.info = getattr(obj, 'info', None)
So:
>>> arr = np.arange(5)
>>> obj = RealisticInfoArray(arr, info='information')
>>> type(obj)
<class 'RealisticInfoArray'>
>>> obj.info
'information'
>>> v = obj[1:]
>>> type(v)
<class 'RealisticInfoArray'>
>>> v.info
'information'
.. _array-wrap:
``__array_wrap__`` for ufuncs
-------------------------------------------------------
``__array_wrap__`` gets called at the end of numpy ufuncs and other numpy
functions, to allow a subclass to set the type of the return value
and update attributes and metadata. Let's show how this works with an example.
First we make the same subclass as above, but with a different name and
some print statements:
.. testcode::
import numpy as np
class MySubClass(np.ndarray):
def __new__(cls, input_array, info=None):
obj = np.asarray(input_array).view(cls)
obj.info = info
return obj
def __array_finalize__(self, obj):
print 'In __array_finalize__:'
print ' self is %s' % repr(self)
print ' obj is %s' % repr(obj)
if obj is None: return
self.info = getattr(obj, 'info', None)
def __array_wrap__(self, out_arr, context=None):
print 'In __array_wrap__:'
print ' self is %s' % repr(self)
print ' arr is %s' % repr(out_arr)
# then just call the parent
return np.ndarray.__array_wrap__(self, out_arr, context)
We run a ufunc on an instance of our new array:
>>> obj = MySubClass(np.arange(5), info='spam')
In __array_finalize__:
self is MySubClass([0, 1, 2, 3, 4])
obj is array([0, 1, 2, 3, 4])
>>> arr2 = np.arange(5)+1
>>> ret = np.add(arr2, obj)
In __array_wrap__:
self is MySubClass([0, 1, 2, 3, 4])
arr is array([1, 3, 5, 7, 9])
In __array_finalize__:
self is MySubClass([1, 3, 5, 7, 9])
obj is MySubClass([0, 1, 2, 3, 4])
>>> ret
MySubClass([1, 3, 5, 7, 9])
>>> ret.info
'spam'
Note that the ufunc (``np.add``) has called the ``__array_wrap__`` method of the
input with the highest ``__array_priority__`` value, in this case
``MySubClass.__array_wrap__``, with arguments ``self`` as ``obj``, and
``out_arr`` as the (ndarray) result of the addition. In turn, the
default ``__array_wrap__`` (``ndarray.__array_wrap__``) has cast the
result to class ``MySubClass``, and called ``__array_finalize__`` -
hence the copying of the ``info`` attribute. This has all happened at the C level.
But, we could do anything we wanted:
.. testcode::
class SillySubClass(np.ndarray):
def __array_wrap__(self, arr, context=None):
return 'I lost your data'
>>> arr1 = np.arange(5)
>>> obj = arr1.view(SillySubClass)
>>> arr2 = np.arange(5)
>>> ret = np.multiply(obj, arr2)
>>> ret
'I lost your data'
So, by defining a specific ``__array_wrap__`` method for our subclass,
we can tweak the output from ufuncs. The ``__array_wrap__`` method
requires ``self``, then an argument - which is the result of the ufunc -
and an optional parameter *context*. This parameter is returned by some
ufuncs as a 3-element tuple: (name of the ufunc, argument of the ufunc,
domain of the ufunc). ``__array_wrap__`` should return an instance of
its containing class. See the masked array subclass for an
implementation.
In addition to ``__array_wrap__``, which is called on the way out of the
ufunc, there is also an ``__array_prepare__`` method which is called on
the way into the ufunc, after the output arrays are created but before any
computation has been performed. The default implementation does nothing
but pass through the array. ``__array_prepare__`` should not attempt to
access the array data or resize the array, it is intended for setting the
output array type, updating attributes and metadata, and performing any
checks based on the input that may be desired before computation begins.
Like ``__array_wrap__``, ``__array_prepare__`` must return an ndarray or
subclass thereof or raise an error.
Extra gotchas - custom ``__del__`` methods and ndarray.base
-----------------------------------------------------------
One of the problems that ndarray solves is keeping track of memory
ownership of ndarrays and their views. Consider the case where we have
created an ndarray, ``arr`` and have taken a slice with ``v = arr[1:]``.
The two objects are looking at the same memory. Numpy keeps track of
where the data came from for a particular array or view, with the
``base`` attribute:
>>> # A normal ndarray, that owns its own data
>>> arr = np.zeros((4,))
>>> # In this case, base is None
>>> arr.base is None
True
>>> # We take a view
>>> v1 = arr[1:]
>>> # base now points to the array that it derived from
>>> v1.base is arr
True
>>> # Take a view of a view
>>> v2 = v1[1:]
>>> # base points to the view it derived from
>>> v2.base is v1
True
In general, if the array owns its own memory, as for ``arr`` in this
case, then ``arr.base`` will be None - there are some exceptions to this
- see the numpy book for more details.
The ``base`` attribute is useful in being able to tell whether we have
a view or the original array. This in turn can be useful if we need
to know whether or not to do some specific cleanup when the subclassed
array is deleted. For example, we may only want to do the cleanup if
the original array is deleted, but not the views. For an example of
how this can work, have a look at the ``memmap`` class in
``numpy.core``.
""" |
#! /usr/bin/python3
#
#################################################################
## Copyright (c) 2013 NAME <EMAIL>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
#################################################################
# Ver:0.1.8 / Datum 07.02.2016 new modul based on:
# ht3_dispatch.py Ver:IP_ADDRESS/ Datum 04.03.2015
# and
# ht3_decode.py Ver:IP_ADDRESS/ Datum 04.03.2015
# additonal modifications are:
# 'new telegram 88 00 2a 00' added (TR-10F)
# 'new telegram 90 08 1a 00' added (FW/FR1x0)
# 'new telegram 88 10 16 00' added (heater-details)
# message:88001800 with length:=31 or 33
# message:9000ff00 with length:=29 added
# 'IPM_LastschaltmodulMsg' buscoding assigned to a0...af
# message:9000ff00 with length:=29 and 32 Byte added
# HeizkreisMsg_ID677_max33byte added for CWxyz handling
# Heating-circuit assignment corrected (6F...72).
# Ver:IP_ADDRESS/ Datum 10.02.2016 Methode: HeizkreisMsg_ID677_max33byte()
# 'Tsoll_HK' assigned to Byte12
# 'Vbetriebs_art' assigned to Byte27
# Ver:IP_ADDRESS/ Datum 22.02.2016 'IPM_LastschaltmodulWWModeMsg()'.
# fix for wrong msg-detection: 'a1 00 34 00'
# 'IPM_LastschaltmodulMsg()' fixed wrong HK-circuit assignment
# Ver:0.1.9 / Datum 27.04.2016 'HeizkreisMsg_ID677_max33byte()' corrected.
# 'msg_88002a00()' betriebsart assignment deleted
# 'HeizkreisMsg_FW100_200_xybyte()' entfaellt.
# Ver:0.1.10 / Datum 22.08.2016 Redesign of modul for MsgID-messageshandling.
# 'SolarMsg()' modified for msgID866 and
# for msgID910
# Display- and cause-code added for error-reports
# 'SolarMsg()' corrected
# message-ID handling added
# "Vbetriebs_art" replaced with "Voperation_status"
# Heater active time changed from minutes to hours.
# Ver:0.2 / Datum 29.08.2016 Fkt.doc added, minor debugtext-changes.
# Ver:0.2.2 / Datum 14.10.2016 CW100:=157 added on MsgID 2.
# no decoding of 'T_Soll' anymore at msgID:=53.
# 'msgID_259_Solar()' modified, 3-bytes for solarpump-runtime.
# 'msgID_51_DomesticHotWater()' added.
# 'msgID_296_ErrorMsg()' added.
# 'msgID_188_Hybrid()' corrected.
# Ver:0.2.3 / Datum 17.01.2017 'msgID_52_DomesticHotWater()' corrected.
# MsgID:24 for device:(10)hex added, see:
# https://www.mikrocontroller.net/topic/324673#4864801
# Ver:0.3 / Datum 19.06.2017 now debug-output in _search_4_transceiver_message().
# controller- and bus-type handling added.
# Ver:0.3.1 / Datum 18.01.2019 modified 'deviceadr_2msgid_blacklist[]'
# modified msgID_22_Heaterdevice()
# msgID_26_HeatingCircuit_HK1() updated and renamed
# msgID_30_T_HydraulischeWeiche() THydr.Device added
# msgID_35_HydraulischeWeiche() updated with hc_pump
# msgID_51_DomesticHotWater() saving data on target:= 0 only
# msgID_596_HeatingCircuit() added
# msgID_597_HeatingCircuit() added
# msgID_697_704_HeatingCircuit() Tsoll_HK
# msgID_727_734_HeatingCircuit() added
# msgID_737_744_HeatingCircuit() added
# msgID_747_754_HeatingCircuit() Frostdanger added
# _IsRequestCall() and _RequestCall() added
# msgID_569_HeatingCircuit() added
# msgID_52_DomesticHotWater() modified for tempsensor failure detection
# msgID_30_T_HydraulischeWeiche() modified for 'ch_Thdrylic_switch'
# using logitem HG:V_spare2.
# msgID_25_Heaterdevice() 'ch_Thdrylic_switch' (V_spare2) added.
# update of display errorcode-handling.
# modified 'msgID_260_Solar()', SO:V_spare_1 & SO:V_spare_2 used now.
# SO:'V_ertrag_sum_calc' used now for SO:'Ertrag Summe'
# msgID_52_DomesticHotWater() storing data only if valid.
# Ver:0.3.2 / Datum 08.09.2020 modified 'msgID_51_DomesticHotWater' for storing 'T-Soll max'
# no decoding if length is <= 8 for EMS2 heating-circuit messages.
# 'msgID_52_DomesticHotWater()' modified handling for not available sensor-values.
#################################################################
|
##
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#-------------------------------------------------------------------------------
# Name: mask.py
# Purpose: This file supports quality assurance for sensor frames.
#
# Author: NAME Created: 18.09.2014
# Copyright: (c) GrafR 2014
# Licence: Apache 2.0
#-------------------------------------------------------------------------------
#!/usr/bin/env python
|
"""==============
Array indexing
==============
Array indexing refers to any use of the square brackets ([]) to index
array values. There are many options to indexing, which give numpy
indexing great power, but with power comes some complexity and the
potential for confusion. This section is just an overview of the
various options and issues related to indexing. Aside from single
element indexing, the details on most of these options are to be
found in related sections.
Assignment vs referencing
=========================
Most of the following examples show the use of indexing when
referencing data in an array. The examples work just as well
when assigning to an array. See the section at the end for
specific examples and explanations on how assignments work.
Single element indexing
=======================
Single element indexing for a 1-D array is what one expects. It work
exactly like that for other standard Python sequences. It is 0-based,
and accepts negative indices for indexing from the end of the array. ::
>>> x = np.arange(10)
>>> x[2]
2
>>> x[-2]
8
Unlike lists and tuples, numpy arrays support multidimensional indexing
for multidimensional arrays. That means that it is not necessary to
separate each dimension's index into its own set of square brackets. ::
>>> x.shape = (2,5) # now x is 2-dimensional
>>> x[1,3]
8
>>> x[1,-1]
9
Note that if one indexes a multidimensional array with fewer indices
than dimensions, one gets a subdimensional array. For example: ::
>>> x[0]
array([0, 1, 2, 3, 4])
That is, each index specified selects the array corresponding to the
rest of the dimensions selected. In the above example, choosing 0
means that the remaining dimension of length 5 is being left unspecified,
and that what is returned is an array of that dimensionality and size.
It must be noted that the returned array is not a copy of the original,
but points to the same values in memory as does the original array.
In this case, the 1-D array at the first position (0) is returned.
So using a single index on the returned array, results in a single
element being returned. That is: ::
>>> x[0][2]
2
So note that ``x[0,2] = x[0][2]`` though the second case is more
inefficient as a new temporary array is created after the first index
that is subsequently indexed by 2.
Note to those used to IDL or Fortran memory order as it relates to
indexing. NumPy uses C-order indexing. That means that the last
index usually represents the most rapidly changing memory location,
unlike Fortran or IDL, where the first index represents the most
rapidly changing location in memory. This difference represents a
great potential for confusion.
Other indexing options
======================
It is possible to slice and stride arrays to extract arrays of the
same number of dimensions, but of different sizes than the original.
The slicing and striding works exactly the same way it does for lists
and tuples except that they can be applied to multiple dimensions as
well. A few examples illustrates best: ::
>>> x = np.arange(10)
>>> x[2:5]
array([2, 3, 4])
>>> x[:-7]
array([0, 1, 2])
>>> x[1:7:2]
array([1, 3, 5])
>>> y = np.arange(35).reshape(5,7)
>>> y[1:5:2,::3]
array([[ 7, 10, 13],
[21, 24, 27]])
Note that slices of arrays do not copy the internal array data but
also produce new views of the original data.
It is possible to index arrays with other arrays for the purposes of
selecting lists of values out of arrays into new arrays. There are
two different ways of accomplishing this. One uses one or more arrays
of index values. The other involves giving a boolean array of the proper
shape to indicate the values to be selected. Index arrays are a very
powerful tool that allow one to avoid looping over individual elements in
arrays and thus greatly improve performance.
It is possible to use special features to effectively increase the
number of dimensions in an array through indexing so the resulting
array aquires the shape needed for use in an expression or with a
specific function.
Index arrays
============
NumPy arrays may be indexed with other arrays (or any other sequence-
like object that can be converted to an array, such as lists, with the
exception of tuples; see the end of this document for why this is). The
use of index arrays ranges from simple, straightforward cases to
complex, hard-to-understand cases. For all cases of index arrays, what
is returned is a copy of the original data, not a view as one gets for
slices.
Index arrays must be of integer type. Each value in the array indicates
which value in the array to use in place of the index. To illustrate: ::
>>> x = np.arange(10,1,-1)
>>> x
array([10, 9, 8, 7, 6, 5, 4, 3, 2])
>>> x[np.array([3, 3, 1, 8])]
array([7, 7, 9, 2])
The index array consisting of the values 3, 3, 1 and 8 correspondingly
create an array of length 4 (same as the index array) where each index
is replaced by the value the index array has in the array being indexed.
Negative values are permitted and work as they do with single indices
or slices: ::
>>> x[np.array([3,3,-3,8])]
array([7, 7, 4, 2])
It is an error to have index values out of bounds: ::
>>> x[np.array([3, 3, 20, 8])]
<type 'exceptions.IndexError'>: index 20 out of bounds 0<=index<9
Generally speaking, what is returned when index arrays are used is
an array with the same shape as the index array, but with the type
and values of the array being indexed. As an example, we can use a
multidimensional index array instead: ::
>>> x[np.array([[1,1],[2,3]])]
array([[9, 9],
[8, 7]])
Indexing Multi-dimensional arrays
=================================
Things become more complex when multidimensional arrays are indexed,
particularly with multidimensional index arrays. These tend to be
more unusual uses, but they are permitted, and they are useful for some
problems. We'll start with the simplest multidimensional case (using
the array y from the previous examples): ::
>>> y[np.array([0,2,4]), np.array([0,1,2])]
array([ 0, 15, 30])
In this case, if the index arrays have a matching shape, and there is
an index array for each dimension of the array being indexed, the
resultant array has the same shape as the index arrays, and the values
correspond to the index set for each position in the index arrays. In
this example, the first index value is 0 for both index arrays, and
thus the first value of the resultant array is y[0,0]. The next value
is y[2,1], and the last is y[4,2].
If the index arrays do not have the same shape, there is an attempt to
broadcast them to the same shape. If they cannot be broadcast to the
same shape, an exception is raised: ::
>>> y[np.array([0,2,4]), np.array([0,1])]
<type 'exceptions.ValueError'>: shape mismatch: objects cannot be
broadcast to a single shape
The broadcasting mechanism permits index arrays to be combined with
scalars for other indices. The effect is that the scalar value is used
for all the corresponding values of the index arrays: ::
>>> y[np.array([0,2,4]), 1]
array([ 1, 15, 29])
Jumping to the next level of complexity, it is possible to only
partially index an array with index arrays. It takes a bit of thought
to understand what happens in such cases. For example if we just use
one index array with y: ::
>>> y[np.array([0,2,4])]
array([[ 0, 1, 2, 3, 4, 5, 6],
[14, 15, 16, 17, 18, 19, 20],
[28, 29, 30, 31, 32, 33, 34]])
What results is the construction of a new array where each value of
the index array selects one row from the array being indexed and the
resultant array has the resulting shape (number of index elements,
size of row).
An example of where this may be useful is for a color lookup table
where we want to map the values of an image into RGB triples for
display. The lookup table could have a shape (nlookup, 3). Indexing
such an array with an image with shape (ny, nx) with dtype=np.uint8
(or any integer type so long as values are with the bounds of the
lookup table) will result in an array of shape (ny, nx, 3) where a
triple of RGB values is associated with each pixel location.
In general, the shape of the resultant array will be the concatenation
of the shape of the index array (or the shape that all the index arrays
were broadcast to) with the shape of any unused dimensions (those not
indexed) in the array being indexed.
Boolean or "mask" index arrays
==============================
Boolean arrays used as indices are treated in a different manner
entirely than index arrays. Boolean arrays must be of the same shape
as the initial dimensions of the array being indexed. In the
most straightforward case, the boolean array has the same shape: ::
>>> b = y>20
>>> y[b]
array([21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34])
Unlike in the case of integer index arrays, in the boolean case, the
result is a 1-D array containing all the elements in the indexed array
corresponding to all the true elements in the boolean array. The
elements in the indexed array are always iterated and returned in
:term:`row-major` (C-style) order. The result is also identical to
``y[np.nonzero(b)]``. As with index arrays, what is returned is a copy
of the data, not a view as one gets with slices.
The result will be multidimensional if y has more dimensions than b.
For example: ::
>>> b[:,5] # use a 1-D boolean whose first dim agrees with the first dim of y
array([False, False, False, True, True])
>>> y[b[:,5]]
array([[21, 22, 23, 24, 25, 26, 27],
[28, 29, 30, 31, 32, 33, 34]])
Here the 4th and 5th rows are selected from the indexed array and
combined to make a 2-D array.
In general, when the boolean array has fewer dimensions than the array
being indexed, this is equivalent to y[b, ...], which means
y is indexed by b followed by as many : as are needed to fill
out the rank of y.
Thus the shape of the result is one dimension containing the number
of True elements of the boolean array, followed by the remaining
dimensions of the array being indexed.
For example, using a 2-D boolean array of shape (2,3)
with four True elements to select rows from a 3-D array of shape
(2,3,5) results in a 2-D result of shape (4,5): ::
>>> x = np.arange(30).reshape(2,3,5)
>>> x
array([[[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]],
[[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29]]])
>>> b = np.array([[True, True, False], [False, True, True]])
>>> x[b]
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29]])
For further details, consult the numpy reference documentation on array indexing.
Combining index arrays with slices
==================================
Index arrays may be combined with slices. For example: ::
>>> y[np.array([0,2,4]),1:3]
array([[ 1, 2],
[15, 16],
[29, 30]])
In effect, the slice is converted to an index array
np.array([[1,2]]) (shape (1,2)) that is broadcast with the index array
to produce a resultant array of shape (3,2).
Likewise, slicing can be combined with broadcasted boolean indices: ::
>>> y[b[:,5],1:3]
array([[22, 23],
[29, 30]])
Structural indexing tools
=========================
To facilitate easy matching of array shapes with expressions and in
assignments, the np.newaxis object can be used within array indices
to add new dimensions with a size of 1. For example: ::
>>> y.shape
(5, 7)
>>> y[:,np.newaxis,:].shape
(5, 1, 7)
Note that there are no new elements in the array, just that the
dimensionality is increased. This can be handy to combine two
arrays in a way that otherwise would require explicitly reshaping
operations. For example: ::
>>> x = np.arange(5)
>>> x[:,np.newaxis] + x[np.newaxis,:]
array([[0, 1, 2, 3, 4],
[1, 2, 3, 4, 5],
[2, 3, 4, 5, 6],
[3, 4, 5, 6, 7],
[4, 5, 6, 7, 8]])
The ellipsis syntax maybe used to indicate selecting in full any
remaining unspecified dimensions. For example: ::
>>> z = np.arange(81).reshape(3,3,3,3)
>>> z[1,...,2]
array([[29, 32, 35],
[38, 41, 44],
[47, 50, 53]])
This is equivalent to: ::
>>> z[1,:,:,2]
array([[29, 32, 35],
[38, 41, 44],
[47, 50, 53]])
Assigning values to indexed arrays
==================================
As mentioned, one can select a subset of an array to assign to using
a single index, slices, and index and mask arrays. The value being
assigned to the indexed array must be shape consistent (the same shape
or broadcastable to the shape the index produces). For example, it is
permitted to assign a constant to a slice: ::
>>> x = np.arange(10)
>>> x[2:7] = 1
or an array of the right size: ::
>>> x[2:7] = np.arange(5)
Note that assignments may result in changes if assigning
higher types to lower types (like floats to ints) or even
exceptions (assigning complex to floats or ints): ::
>>> x[1] = 1.2
>>> x[1]
1
>>> x[1] = 1.2j
<type 'exceptions.TypeError'>: can't convert complex to long; use
long(abs(z))
Unlike some of the references (such as array and mask indices)
assignments are always made to the original data in the array
(indeed, nothing else would make sense!). Note though, that some
actions may not work as one may naively expect. This particular
example is often surprising to people: ::
>>> x = np.arange(0, 50, 10)
>>> x
array([ 0, 10, 20, 30, 40])
>>> x[np.array([1, 1, 3, 1])] += 1
>>> x
array([ 0, 11, 20, 31, 40])
Where people expect that the 1st location will be incremented by 3.
In fact, it will only be incremented by 1. The reason is because
a new array is extracted from the original (as a temporary) containing
the values at 1, 1, 3, 1, then the value 1 is added to the temporary,
and then the temporary is assigned back to the original array. Thus
the value of the array at x[1]+1 is assigned to x[1] three times,
rather than being incremented 3 times.
Dealing with variable numbers of indices within programs
========================================================
The index syntax is very powerful but limiting when dealing with
a variable number of indices. For example, if you want to write
a function that can handle arguments with various numbers of
dimensions without having to write special case code for each
number of possible dimensions, how can that be done? If one
supplies to the index a tuple, the tuple will be interpreted
as a list of indices. For example (using the previous definition
for the array z): ::
>>> indices = (1,1,1,1)
>>> z[indices]
40
So one can use code to construct tuples of any number of indices
and then use these within an index.
Slices can be specified within programs by using the slice() function
in Python. For example: ::
>>> indices = (1,1,1,slice(0,2)) # same as [1,1,1,0:2]
>>> z[indices]
array([39, 40])
Likewise, ellipsis can be specified by code by using the Ellipsis
object: ::
>>> indices = (1, Ellipsis, 1) # same as [1,...,1]
>>> z[indices]
array([[28, 31, 34],
[37, 40, 43],
[46, 49, 52]])
For this reason it is possible to use the output from the np.nonzero()
function directly as an index since it always returns a tuple of index
arrays.
Because the special treatment of tuples, they are not automatically
converted to an array as a list would be. As an example: ::
>>> z[[1,1,1,1]] # produces a large array
array([[[[27, 28, 29],
[30, 31, 32], ...
>>> z[(1,1,1,1)] # returns a single value
40
""" |
"""==============
Array indexing
==============
Array indexing refers to any use of the square brackets ([]) to index
array values. There are many options to indexing, which give numpy
indexing great power, but with power comes some complexity and the
potential for confusion. This section is just an overview of the
various options and issues related to indexing. Aside from single
element indexing, the details on most of these options are to be
found in related sections.
Assignment vs referencing
=========================
Most of the following examples show the use of indexing when
referencing data in an array. The examples work just as well
when assigning to an array. See the section at the end for
specific examples and explanations on how assignments work.
Single element indexing
=======================
Single element indexing for a 1-D array is what one expects. It work
exactly like that for other standard Python sequences. It is 0-based,
and accepts negative indices for indexing from the end of the array. ::
>>> x = np.arange(10)
>>> x[2]
2
>>> x[-2]
8
Unlike lists and tuples, numpy arrays support multidimensional indexing
for multidimensional arrays. That means that it is not necessary to
separate each dimension's index into its own set of square brackets. ::
>>> x.shape = (2,5) # now x is 2-dimensional
>>> x[1,3]
8
>>> x[1,-1]
9
Note that if one indexes a multidimensional array with fewer indices
than dimensions, one gets a subdimensional array. For example: ::
>>> x[0]
array([0, 1, 2, 3, 4])
That is, each index specified selects the array corresponding to the
rest of the dimensions selected. In the above example, choosing 0
means that the remaining dimension of length 5 is being left unspecified,
and that what is returned is an array of that dimensionality and size.
It must be noted that the returned array is not a copy of the original,
but points to the same values in memory as does the original array.
In this case, the 1-D array at the first position (0) is returned.
So using a single index on the returned array, results in a single
element being returned. That is: ::
>>> x[0][2]
2
So note that ``x[0,2] = x[0][2]`` though the second case is more
inefficient as a new temporary array is created after the first index
that is subsequently indexed by 2.
Note to those used to IDL or Fortran memory order as it relates to
indexing. NumPy uses C-order indexing. That means that the last
index usually represents the most rapidly changing memory location,
unlike Fortran or IDL, where the first index represents the most
rapidly changing location in memory. This difference represents a
great potential for confusion.
Other indexing options
======================
It is possible to slice and stride arrays to extract arrays of the
same number of dimensions, but of different sizes than the original.
The slicing and striding works exactly the same way it does for lists
and tuples except that they can be applied to multiple dimensions as
well. A few examples illustrates best: ::
>>> x = np.arange(10)
>>> x[2:5]
array([2, 3, 4])
>>> x[:-7]
array([0, 1, 2])
>>> x[1:7:2]
array([1, 3, 5])
>>> y = np.arange(35).reshape(5,7)
>>> y[1:5:2,::3]
array([[ 7, 10, 13],
[21, 24, 27]])
Note that slices of arrays do not copy the internal array data but
also produce new views of the original data.
It is possible to index arrays with other arrays for the purposes of
selecting lists of values out of arrays into new arrays. There are
two different ways of accomplishing this. One uses one or more arrays
of index values. The other involves giving a boolean array of the proper
shape to indicate the values to be selected. Index arrays are a very
powerful tool that allow one to avoid looping over individual elements in
arrays and thus greatly improve performance.
It is possible to use special features to effectively increase the
number of dimensions in an array through indexing so the resulting
array aquires the shape needed for use in an expression or with a
specific function.
Index arrays
============
NumPy arrays may be indexed with other arrays (or any other sequence-
like object that can be converted to an array, such as lists, with the
exception of tuples; see the end of this document for why this is). The
use of index arrays ranges from simple, straightforward cases to
complex, hard-to-understand cases. For all cases of index arrays, what
is returned is a copy of the original data, not a view as one gets for
slices.
Index arrays must be of integer type. Each value in the array indicates
which value in the array to use in place of the index. To illustrate: ::
>>> x = np.arange(10,1,-1)
>>> x
array([10, 9, 8, 7, 6, 5, 4, 3, 2])
>>> x[np.array([3, 3, 1, 8])]
array([7, 7, 9, 2])
The index array consisting of the values 3, 3, 1 and 8 correspondingly
create an array of length 4 (same as the index array) where each index
is replaced by the value the index array has in the array being indexed.
Negative values are permitted and work as they do with single indices
or slices: ::
>>> x[np.array([3,3,-3,8])]
array([7, 7, 4, 2])
It is an error to have index values out of bounds: ::
>>> x[np.array([3, 3, 20, 8])]
<type 'exceptions.IndexError'>: index 20 out of bounds 0<=index<9
Generally speaking, what is returned when index arrays are used is
an array with the same shape as the index array, but with the type
and values of the array being indexed. As an example, we can use a
multidimensional index array instead: ::
>>> x[np.array([[1,1],[2,3]])]
array([[9, 9],
[8, 7]])
Indexing Multi-dimensional arrays
=================================
Things become more complex when multidimensional arrays are indexed,
particularly with multidimensional index arrays. These tend to be
more unusual uses, but they are permitted, and they are useful for some
problems. We'll start with the simplest multidimensional case (using
the array y from the previous examples): ::
>>> y[np.array([0,2,4]), np.array([0,1,2])]
array([ 0, 15, 30])
In this case, if the index arrays have a matching shape, and there is
an index array for each dimension of the array being indexed, the
resultant array has the same shape as the index arrays, and the values
correspond to the index set for each position in the index arrays. In
this example, the first index value is 0 for both index arrays, and
thus the first value of the resultant array is y[0,0]. The next value
is y[2,1], and the last is y[4,2].
If the index arrays do not have the same shape, there is an attempt to
broadcast them to the same shape. If they cannot be broadcast to the
same shape, an exception is raised: ::
>>> y[np.array([0,2,4]), np.array([0,1])]
<type 'exceptions.ValueError'>: shape mismatch: objects cannot be
broadcast to a single shape
The broadcasting mechanism permits index arrays to be combined with
scalars for other indices. The effect is that the scalar value is used
for all the corresponding values of the index arrays: ::
>>> y[np.array([0,2,4]), 1]
array([ 1, 15, 29])
Jumping to the next level of complexity, it is possible to only
partially index an array with index arrays. It takes a bit of thought
to understand what happens in such cases. For example if we just use
one index array with y: ::
>>> y[np.array([0,2,4])]
array([[ 0, 1, 2, 3, 4, 5, 6],
[14, 15, 16, 17, 18, 19, 20],
[28, 29, 30, 31, 32, 33, 34]])
What results is the construction of a new array where each value of
the index array selects one row from the array being indexed and the
resultant array has the resulting shape (size of row, number index
elements).
An example of where this may be useful is for a color lookup table
where we want to map the values of an image into RGB triples for
display. The lookup table could have a shape (nlookup, 3). Indexing
such an array with an image with shape (ny, nx) with dtype=np.uint8
(or any integer type so long as values are with the bounds of the
lookup table) will result in an array of shape (ny, nx, 3) where a
triple of RGB values is associated with each pixel location.
In general, the shape of the resultant array will be the concatenation
of the shape of the index array (or the shape that all the index arrays
were broadcast to) with the shape of any unused dimensions (those not
indexed) in the array being indexed.
Boolean or "mask" index arrays
==============================
Boolean arrays used as indices are treated in a different manner
entirely than index arrays. Boolean arrays must be of the same shape
as the initial dimensions of the array being indexed. In the
most straightforward case, the boolean array has the same shape: ::
>>> b = y>20
>>> y[b]
array([21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34])
Unlike in the case of integer index arrays, in the boolean case, the
result is a 1-D array containing all the elements in the indexed array
corresponding to all the true elements in the boolean array. The
elements in the indexed array are always iterated and returned in
:term:`row-major` (C-style) order. The result is also identical to
``y[np.nonzero(b)]``. As with index arrays, what is returned is a copy
of the data, not a view as one gets with slices.
The result will be multidimensional if y has more dimensions than b.
For example: ::
>>> b[:,5] # use a 1-D boolean whose first dim agrees with the first dim of y
array([False, False, False, True, True], dtype=bool)
>>> y[b[:,5]]
array([[21, 22, 23, 24, 25, 26, 27],
[28, 29, 30, 31, 32, 33, 34]])
Here the 4th and 5th rows are selected from the indexed array and
combined to make a 2-D array.
In general, when the boolean array has fewer dimensions than the array
being indexed, this is equivalent to y[b, ...], which means
y is indexed by b followed by as many : as are needed to fill
out the rank of y.
Thus the shape of the result is one dimension containing the number
of True elements of the boolean array, followed by the remaining
dimensions of the array being indexed.
For example, using a 2-D boolean array of shape (2,3)
with four True elements to select rows from a 3-D array of shape
(2,3,5) results in a 2-D result of shape (4,5): ::
>>> x = np.arange(30).reshape(2,3,5)
>>> x
array([[[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]],
[[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29]]])
>>> b = np.array([[True, True, False], [False, True, True]])
>>> x[b]
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29]])
For further details, consult the numpy reference documentation on array indexing.
Combining index arrays with slices
==================================
Index arrays may be combined with slices. For example: ::
>>> y[np.array([0,2,4]),1:3]
array([[ 1, 2],
[15, 16],
[29, 30]])
In effect, the slice is converted to an index array
np.array([[1,2]]) (shape (1,2)) that is broadcast with the index array
to produce a resultant array of shape (3,2).
Likewise, slicing can be combined with broadcasted boolean indices: ::
>>> y[b[:,5],1:3]
array([[22, 23],
[29, 30]])
Structural indexing tools
=========================
To facilitate easy matching of array shapes with expressions and in
assignments, the np.newaxis object can be used within array indices
to add new dimensions with a size of 1. For example: ::
>>> y.shape
(5, 7)
>>> y[:,np.newaxis,:].shape
(5, 1, 7)
Note that there are no new elements in the array, just that the
dimensionality is increased. This can be handy to combine two
arrays in a way that otherwise would require explicitly reshaping
operations. For example: ::
>>> x = np.arange(5)
>>> x[:,np.newaxis] + x[np.newaxis,:]
array([[0, 1, 2, 3, 4],
[1, 2, 3, 4, 5],
[2, 3, 4, 5, 6],
[3, 4, 5, 6, 7],
[4, 5, 6, 7, 8]])
The ellipsis syntax maybe used to indicate selecting in full any
remaining unspecified dimensions. For example: ::
>>> z = np.arange(81).reshape(3,3,3,3)
>>> z[1,...,2]
array([[29, 32, 35],
[38, 41, 44],
[47, 50, 53]])
This is equivalent to: ::
>>> z[1,:,:,2]
array([[29, 32, 35],
[38, 41, 44],
[47, 50, 53]])
Assigning values to indexed arrays
==================================
As mentioned, one can select a subset of an array to assign to using
a single index, slices, and index and mask arrays. The value being
assigned to the indexed array must be shape consistent (the same shape
or broadcastable to the shape the index produces). For example, it is
permitted to assign a constant to a slice: ::
>>> x = np.arange(10)
>>> x[2:7] = 1
or an array of the right size: ::
>>> x[2:7] = np.arange(5)
Note that assignments may result in changes if assigning
higher types to lower types (like floats to ints) or even
exceptions (assigning complex to floats or ints): ::
>>> x[1] = 1.2
>>> x[1]
1
>>> x[1] = 1.2j
<type 'exceptions.TypeError'>: can't convert complex to long; use
long(abs(z))
Unlike some of the references (such as array and mask indices)
assignments are always made to the original data in the array
(indeed, nothing else would make sense!). Note though, that some
actions may not work as one may naively expect. This particular
example is often surprising to people: ::
>>> x = np.arange(0, 50, 10)
>>> x
array([ 0, 10, 20, 30, 40])
>>> x[np.array([1, 1, 3, 1])] += 1
>>> x
array([ 0, 11, 20, 31, 40])
Where people expect that the 1st location will be incremented by 3.
In fact, it will only be incremented by 1. The reason is because
a new array is extracted from the original (as a temporary) containing
the values at 1, 1, 3, 1, then the value 1 is added to the temporary,
and then the temporary is assigned back to the original array. Thus
the value of the array at x[1]+1 is assigned to x[1] three times,
rather than being incremented 3 times.
Dealing with variable numbers of indices within programs
========================================================
The index syntax is very powerful but limiting when dealing with
a variable number of indices. For example, if you want to write
a function that can handle arguments with various numbers of
dimensions without having to write special case code for each
number of possible dimensions, how can that be done? If one
supplies to the index a tuple, the tuple will be interpreted
as a list of indices. For example (using the previous definition
for the array z): ::
>>> indices = (1,1,1,1)
>>> z[indices]
40
So one can use code to construct tuples of any number of indices
and then use these within an index.
Slices can be specified within programs by using the slice() function
in Python. For example: ::
>>> indices = (1,1,1,slice(0,2)) # same as [1,1,1,0:2]
>>> z[indices]
array([39, 40])
Likewise, ellipsis can be specified by code by using the Ellipsis
object: ::
>>> indices = (1, Ellipsis, 1) # same as [1,...,1]
>>> z[indices]
array([[28, 31, 34],
[37, 40, 43],
[46, 49, 52]])
For this reason it is possible to use the output from the np.where()
function directly as an index since it always returns a tuple of index
arrays.
Because the special treatment of tuples, they are not automatically
converted to an array as a list would be. As an example: ::
>>> z[[1,1,1,1]] # produces a large array
array([[[[27, 28, 29],
[30, 31, 32], ...
>>> z[(1,1,1,1)] # returns a single value
40
""" |
"""
Purpose: Linear Algebra Parser
Based on: SimpleCalc.py example (author NAME in pyparsing-1.3.3
Author: NAME Ellis & Grant, Inc. 2005
License: You may freely use, modify, and distribute this software.
Warranty: THIS SOFTWARE HAS NO WARRANTY WHATSOEVER. USE AT YOUR OWN RISK.
Notes: Parses infix linear algebra (LA) notation for vectors, matrices, and scalars.
Output is C code function calls. The parser can be run as an interactive
interpreter or included as module to use for in-place substitution into C files
containing LA equations.
Supported operations are:
OPERATION: INPUT OUTPUT
Scalar addition: "a = b+c" "a=(b+c)"
Scalar subtraction: "a = b-c" "a=(b-c)"
Scalar multiplication: "a = b*c" "a=b*c"
Scalar division: "a = b/c" "a=b/c"
Scalar exponentiation: "a = b^c" "a=pow(b,c)"
Vector scaling: "V3_a = V3_b * c" "vCopy(a,vScale(b,c))"
Vector addition: "V3_a = V3_b + V3_c" "vCopy(a,vAdd(b,c))"
Vector subtraction: "V3_a = V3_b - V3_c" "vCopy(a,vSubtract(b,c))"
Vector dot product: "a = V3_b * V3_c" "a=vDot(b,c)"
Vector outer product: "M3_a = V3_b @ V3_c" "a=vOuterProduct(b,c)"
Vector magn. squared: "a = V3_b^Mag2" "a=vMagnitude2(b)"
Vector magnitude: "a = V3_b^Mag" "a=sqrt(vMagnitude2(b))"
Matrix scaling: "M3_a = M3_b * c" "mCopy(a,mScale(b,c))"
Matrix addition: "M3_a = M3_b + M3_c" "mCopy(a,mAdd(b,c))"
Matrix subtraction: "M3_a = M3_b - M3_c" "mCopy(a,mSubtract(b,c))"
Matrix multiplication: "M3_a = M3_b * M3_c" "mCopy(a,mMultiply(b,c))"
Matrix by vector mult.: "V3_a = M3_b * V3_c" "vCopy(a,mvMultiply(b,c))"
Matrix inversion: "M3_a = M3_b^-1" "mCopy(a,mInverse(b))"
Matrix transpose: "M3_a = M3_b^T" "mCopy(a,mTranspose(b))"
Matrix determinant: "a = M3_b^Det" "a=mDeterminant(b)"
The parser requires the expression to be an equation. Each non-scalar variable
must be prefixed with a type tag, 'M3_' for 3x3 matrices and 'V3_' for 3-vectors.
For proper compilation of the C code, the variables need to be declared without
the prefix as float[3] for vectors and float[3][3] for matrices. The operations do
not modify any variables on the right-hand side of the equation.
Equations may include nested expressions within parentheses. The allowed binary
operators are '+-*/^' for scalars, and '+-*^@' for vectors and matrices with the
meanings defined in the table above.
Specifying an improper combination of operands, e.g. adding a vector to a matrix,
is detected by the parser and results in a Python TypeError Exception. The usual cause
of this is omitting one or more tag prefixes. The parser knows nothing about a
a variable's C declaration and relies entirely on the type tags. Errors in C
declarations are not caught until compile time.
Usage: To process LA equations embedded in source files, import this module and
pass input and output file objects to the fprocess() function. You can
can also invoke the parser from the command line, e.g. 'python LAparser.py',
to run a small test suite and enter an interactive loop where you can enter
LA equations and see the resulting C code.
""" |
"""
(GPL)
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along with this program. If not, see <http://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
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""" |
""" The core class bits of TileStache.
Two important classes can be found here.
Layer represents a set of tiles in TileStache. It keeps references to
providers, projections, a Configuration instance, and other details required
for to the storage and rendering of a tile set. Layers are represented in the
configuration file as a dictionary:
{
"cache": ...,
"layers":
{
"example-name":
{
"provider": { ... },
"metatile": { ... },
"preview": { ... },
"projection": ...,
"stale lock timeout": ...,
"cache lifespan": ...,
"write cache": ...,
"bounds": { ... },
"allowed origin": ...,
"maximum cache age": ...,
"redirects": ...,
"tile height": ...,
"jpeg options": ...,
"png options": ...
}
}
}
- "provider" refers to a Provider, explained in detail in TileStache.Providers.
- "metatile" optionally makes it possible for multiple individual tiles to be
rendered at one time, for greater speed and efficiency. This is commonly used
for the Mapnik provider. See below for more information on metatiles.
- "preview" optionally overrides the starting point for the built-in per-layer
slippy map preview, useful for image-based layers where appropriate.
See below for more information on the preview.
- "projection" names a geographic projection, explained in TileStache.Geography.
If omitted, defaults to spherical mercator.
- "stale lock timeout" is an optional number of seconds to wait before forcing
a lock that might be stuck. This is defined on a per-layer basis, rather than
for an entire cache at one time, because you may have different expectations
for the rendering speeds of different layer configurations. Defaults to 15.
- "cache lifespan" is an optional number of seconds that cached tiles should
be stored. This is defined on a per-layer basis. Defaults to forever if None,
0 or omitted.
- "write cache" is an optional boolean value to allow skipping cache write
altogether. This is defined on a per-layer basis. Defaults to true if omitted.
- "bounds" is an optional dictionary of six tile boundaries to limit the
rendered area: low (lowest zoom level), high (highest zoom level), north,
west, south, and east (all in degrees).
- "allowed origin" is an optional string that shows up in the response HTTP
header Access-Control-Allow-Origin, useful for when you need to provide
javascript direct access to response data such as GeoJSON or pixel values.
The header is part of a W3C working draft (http://www.w3.org/TR/cors/).
- "maximum cache age" is an optional number of seconds used to control behavior
of downstream caches. Causes TileStache responses to include Cache-Control
and Expires HTTP response headers. Useful when TileStache is itself hosted
behind an HTTP cache such as Squid, Cloudfront, or Akamai.
- "redirects" is an optional dictionary of per-extension HTTP redirects,
treated as lowercase. Useful in cases where your tile provider can support
many formats but you want to enforce limits to save on cache usage.
If a request is made for a tile with an extension in the dictionary keys,
a response can be generated that redirects the client to the same tile
with another extension.
- "tile height" gives the height of the image tile in pixels. You almost always
want to leave this at the default value of 256, but you can use a value of 512
to create double-size, double-resolution tiles for high-density phone screens.
- "jpeg options" is an optional dictionary of JPEG creation options, passed
through to PIL: http://effbot.org/imagingbook/format-jpeg.htm.
- "png options" is an optional dictionary of PNG creation options, passed
through to PIL: http://effbot.org/imagingbook/format-png.htm.
- "pixel effect" is an optional dictionary that defines an effect to be applied
for all tiles of this layer. Pixel effect can be any of these: blackwhite,
greyscale, desaturate, pixelate, halftone, or blur.
The public-facing URL of a single tile for this layer might look like this:
http://example.com/tilestache.cgi/example-name/0/0/0.png
Sample JPEG creation options:
{
"quality": 90,
"progressive": true,
"optimize": true
}
Sample PNG creation options:
{
"optimize": true,
"palette": "filename.act"
}
Sample pixel effect:
{
"name": "desaturate",
"factor": 0.85
}
Sample bounds:
{
"low": 9, "high": 15,
"south": 37.749, "west": -122.358,
"north": 37.860, "east": -122.113
}
Metatile represents a larger area to be rendered at one time. Metatiles are
represented in the configuration file as a dictionary:
{
"rows": 4,
"columns": 4,
"buffer": 64
}
- "rows" and "columns" are the height and width of the metatile measured in
tiles. This example metatile is four rows tall and four columns wide, so it
will render sixteen tiles simultaneously.
- "buffer" is a buffer area around the metatile, measured in pixels. This is
useful for providers with labels or icons, where it's necessary to draw a
bit extra around the edges to ensure that text is not cut off. This example
metatile has a buffer of 64 pixels, so the resulting metatile will be 1152
pixels square: 4 rows x 256 pixels + 2 x 64 pixel buffer.
The preview can be accessed through a URL like /<layer name>/preview.html:
{
"lat": 33.9901,
"lon": -116.1637,
"zoom": 16,
"ext": "jpg"
}
- "lat" and "lon" are the starting latitude and longitude in degrees.
- "zoom" is the starting zoom level.
- "ext" is the filename extension, e.g. "png".
""" |
"""
Implementation of the trigsimp algorithm by Fu et al.
The idea behind the ``fu`` algorithm is to use a sequence of rules, applied
in what is heuristically known to be a smart order, to select a simpler
expression that is equivalent to the input.
There are transform rules in which a single rule is applied to the
expression tree. The following are just mnemonic in nature; see the
docstrings for examples.
TR0 - simplify expression
TR1 - sec-csc to cos-sin
TR2 - tan-cot to sin-cos ratio
TR2i - sin-cos ratio to tan
TR3 - angle canonicalization
TR4 - functions at special angles
TR5 - powers of sin to powers of cos
TR6 - powers of cos to powers of sin
TR7 - reduce cos power (increase angle)
TR8 - expand products of sin-cos to sums
TR9 - contract sums of sin-cos to products
TR10 - separate sin-cos arguments
TR10i - collect sin-cos arguments
TR11 - reduce double angles
TR12 - separate tan arguments
TR12i - collect tan arguments
TR13 - expand product of tan-cot
TRmorrie - prod(cos(x*2**i), (i, 0, k - 1)) -> sin(2**k*x)/(2**k*sin(x))
TR14 - factored powers of sin or cos to cos or sin power
TR15 - negative powers of sin to cot power
TR16 - negative powers of cos to tan power
TR22 - tan-cot powers to negative powers of sec-csc functions
TR111 - negative sin-cos-tan powers to csc-sec-cot
There are 4 combination transforms (CTR1 - CTR4) in which a sequence of
transformations are applied and the simplest expression is selected from
a few options.
Finally, there are the 2 rule lists (RL1 and RL2), which apply a
sequence of transformations and combined transformations, and the ``fu``
algorithm itself, which applies rules and rule lists and selects the
best expressions. There is also a function ``L`` which counts the number
of trigonometric funcions that appear in the expression.
Other than TR0, re-writing of expressions is not done by the transformations.
e.g. TR10i finds pairs of terms in a sum that are in the form like
``cos(x)*cos(y) + sin(x)*sin(y)``. Such expression are targeted in a bottom-up
traversal of the expression, but no manipulation to make them appear is
attempted. For example,
Set-up for examples below:
>>> from sympy.simplify.fu import fu, L, TR9, TR10i, TR11
>>> from sympy import factor, sin, cos, powsimp
>>> from sympy.abc import x, y, z, a
>>> from time import time
>>> eq = cos(x + y)/cos(x)
>>> TR10i(eq.expand(trig=True))
-sin(x)*sin(y)/cos(x) + cos(y)
If the expression is put in "normal" form (with a common denominator) then
the transformation is successful:
>>> TR10i(_.normal())
cos(x + y)/cos(x)
TR11's behavior is similar. It rewrites double angles as smaller angles but
doesn't do any simplification of the result.
>>> TR11(sin(2)**a*cos(1)**(-a), 1)
(2*sin(1)*cos(1))**a*cos(1)**(-a)
>>> powsimp(_)
(2*sin(1))**a
The temptation is to try make these TR rules "smarter" but that should really
be done at a higher level; the TR rules should try maintain the "do one thing
well" principle. There is one exception, however. In TR10i and TR9 terms are
recognized even when they are each multiplied by a common factor:
>>> fu(a*cos(x)*cos(y) + a*sin(x)*sin(y))
a*cos(x - y)
Factoring with ``factor_terms`` is used but it it "JIT"-like, being delayed
until it is deemed necessary. Furthermore, if the factoring does not
help with the simplification, it is not retained, so
``a*cos(x)*cos(y) + a*sin(x)*sin(z)`` does not become the factored
(but unsimplified in the trigonometric sense) expression:
>>> fu(a*cos(x)*cos(y) + a*sin(x)*sin(z))
a*sin(x)*sin(z) + a*cos(x)*cos(y)
In some cases factoring might be a good idea, but the user is left
to make that decision. For example:
>>> expr=((15*sin(2*x) + 19*sin(x + y) + 17*sin(x + z) + 19*cos(x - z) +
... 25)*(20*sin(2*x) + 15*sin(x + y) + sin(y + z) + 14*cos(x - z) +
... 14*cos(y - z))*(9*sin(2*y) + 12*sin(y + z) + 10*cos(x - y) + 2*cos(y -
... z) + 18)).expand(trig=True).expand()
In the expanded state, there are nearly 1000 trig functions:
>>> L(expr)
932
If the expression where factored first, this would take time but the
resulting expression would be transformed very quickly:
>>> def clock(f, n=2):
... t=time(); f(); return round(time()-t, n)
...
>>> clock(lambda: factor(expr)) # doctest: +SKIP
0.86
>>> clock(lambda: TR10i(expr), 3) # doctest: +SKIP
0.016
If the unexpanded expression is used, the transformation takes longer but
not as long as it took to factor it and then transform it:
>>> clock(lambda: TR10i(expr), 2) # doctest: +SKIP
0.28
So neither expansion nor factoring is used in ``TR10i``: if the
expression is already factored (or partially factored) then expansion
with ``trig=True`` would destroy what is already known and take
longer; if the expression is expanded, factoring may take longer than
simply applying the transformation itself.
Although the algorithms should be canonical, always giving the same
result, they may not yield the best result. This, in general, is
the nature of simplification where searching all possible transformation
paths is very expensive. Here is a simple example. There are 6 terms
in the following sum:
>>> expr = (sin(x)**2*cos(y)*cos(z) + sin(x)*sin(y)*cos(x)*cos(z) +
... sin(x)*sin(z)*cos(x)*cos(y) + sin(y)*sin(z)*cos(x)**2 + sin(y)*sin(z) +
... cos(y)*cos(z))
>>> args = expr.args
Serendipitously, fu gives the best result:
>>> fu(expr)
3*cos(y - z)/2 - cos(2*x + y + z)/2
But if different terms were combined, a less-optimal result might be
obtained, requiring some additional work to get better simplification,
but still less than optimal. The following shows an alternative form
of ``expr`` that resists optimal simplification once a given step
is taken since it leads to a dead end:
>>> TR9(-cos(x)**2*cos(y + z) + 3*cos(y - z)/2 +
... cos(y + z)/2 + cos(-2*x + y + z)/4 - cos(2*x + y + z)/4)
sin(2*x)*sin(y + z)/2 - cos(x)**2*cos(y + z) + 3*cos(y - z)/2 + cos(y + z)/2
Here is a smaller expression that exhibits the same behavior:
>>> a = sin(x)*sin(z)*cos(x)*cos(y) + sin(x)*sin(y)*cos(x)*cos(z)
>>> TR10i(a)
sin(x)*sin(y + z)*cos(x)
>>> newa = _
>>> TR10i(expr - a) # this combines two more of the remaining terms
sin(x)**2*cos(y)*cos(z) + sin(y)*sin(z)*cos(x)**2 + cos(y - z)
>>> TR10i(_ + newa) == _ + newa # but now there is no more simplification
True
Without getting lucky or trying all possible pairings of arguments, the
final result may be less than optimal and impossible to find without
better heuristics or brute force trial of all possibilities.
Notes
=====
This work was started by NAME at the Technological School
"Electronic systems" (30.11.2011).
References
==========
http://rfdz.ph-noe.ac.at/fileadmin/Mathematik_Uploads/ACDCA/
DESTIME2006/DES_contribs/Fu/simplification.pdf
http://www.sosmath.com/trig/Trig5/trig5/pdf/pdf.html gives a formula sheet.
""" |
"""
This is a procedural interface to the matplotlib object-oriented
plotting library.
The following plotting commands are provided; the majority have
MATLAB |reg| [*]_ analogs and similar arguments.
.. |reg| unicode:: 0xAE
_Plotting commands
acorr - plot the autocorrelation function
annotate - annotate something in the figure
arrow - add an arrow to the axes
axes - Create a new axes
axhline - draw a horizontal line across axes
axvline - draw a vertical line across axes
axhspan - draw a horizontal bar across axes
axvspan - draw a vertical bar across axes
axis - Set or return the current axis limits
autoscale - turn axis autoscaling on or off, and apply it
bar - make a bar chart
barh - a horizontal bar chart
broken_barh - a set of horizontal bars with gaps
box - set the axes frame on/off state
boxplot - make a box and whisker plot
violinplot - make a violin plot
cla - clear current axes
clabel - label a contour plot
clf - clear a figure window
clim - adjust the color limits of the current image
close - close a figure window
colorbar - add a colorbar to the current figure
cohere - make a plot of coherence
contour - make a contour plot
contourf - make a filled contour plot
csd - make a plot of cross spectral density
delaxes - delete an axes from the current figure
draw - Force a redraw of the current figure
errorbar - make an errorbar graph
figlegend - make legend on the figure rather than the axes
figimage - make a figure image
figtext - add text in figure coords
figure - create or change active figure
fill - make filled polygons
findobj - recursively find all objects matching some criteria
gca - return the current axes
gcf - return the current figure
gci - get the current image, or None
getp - get a graphics property
grid - set whether gridding is on
hist - make a histogram
hold - set the axes hold state
ioff - turn interaction mode off
ion - turn interaction mode on
isinteractive - return True if interaction mode is on
imread - load image file into array
imsave - save array as an image file
imshow - plot image data
ishold - return the hold state of the current axes
legend - make an axes legend
locator_params - adjust parameters used in locating axis ticks
loglog - a log log plot
matshow - display a matrix in a new figure preserving aspect
margins - set margins used in autoscaling
pause - pause for a specified interval
pcolor - make a pseudocolor plot
pcolormesh - make a pseudocolor plot using a quadrilateral mesh
pie - make a pie chart
plot - make a line plot
plot_date - plot dates
plotfile - plot column data from an ASCII tab/space/comma delimited file
pie - pie charts
polar - make a polar plot on a PolarAxes
psd - make a plot of power spectral density
quiver - make a direction field (arrows) plot
rc - control the default params
rgrids - customize the radial grids and labels for polar
savefig - save the current figure
scatter - make a scatter plot
setp - set a graphics property
semilogx - log x axis
semilogy - log y axis
show - show the figures
specgram - a spectrogram plot
spy - plot sparsity pattern using markers or image
stem - make a stem plot
subplot - make one subplot (numrows, numcols, axesnum)
subplots - make a figure with a set of (numrows, numcols) subplots
subplots_adjust - change the params controlling the subplot positions of current figure
subplot_tool - launch the subplot configuration tool
suptitle - add a figure title
table - add a table to the plot
text - add some text at location x,y to the current axes
thetagrids - customize the radial theta grids and labels for polar
tick_params - control the appearance of ticks and tick labels
ticklabel_format - control the format of tick labels
title - add a title to the current axes
tricontour - make a contour plot on a triangular grid
tricontourf - make a filled contour plot on a triangular grid
tripcolor - make a pseudocolor plot on a triangular grid
triplot - plot a triangular grid
xcorr - plot the autocorrelation function of x and y
xlim - set/get the xlimits
ylim - set/get the ylimits
xticks - set/get the xticks
yticks - set/get the yticks
xlabel - add an xlabel to the current axes
ylabel - add a ylabel to the current axes
autumn - set the default colormap to autumn
bone - set the default colormap to bone
cool - set the default colormap to cool
copper - set the default colormap to copper
flag - set the default colormap to flag
gray - set the default colormap to gray
hot - set the default colormap to hot
hsv - set the default colormap to hsv
jet - set the default colormap to jet
pink - set the default colormap to pink
prism - set the default colormap to prism
spring - set the default colormap to spring
summer - set the default colormap to summer
winter - set the default colormap to winter
spectral - set the default colormap to spectral
_Event handling
connect - register an event handler
disconnect - remove a connected event handler
_Matrix commands
cumprod - the cumulative product along a dimension
cumsum - the cumulative sum along a dimension
detrend - remove the mean or besdt fit line from an array
diag - the k-th diagonal of matrix
diff - the n-th differnce of an array
eig - the eigenvalues and eigen vectors of v
eye - a matrix where the k-th diagonal is ones, else zero
find - return the indices where a condition is nonzero
fliplr - flip the rows of a matrix up/down
flipud - flip the columns of a matrix left/right
linspace - a linear spaced vector of N values from min to max inclusive
logspace - a log spaced vector of N values from min to max inclusive
meshgrid - repeat x and y to make regular matrices
ones - an array of ones
rand - an array from the uniform distribution [0,1]
randn - an array from the normal distribution
rot90 - rotate matrix k*90 degress counterclockwise
squeeze - squeeze an array removing any dimensions of length 1
tri - a triangular matrix
tril - a lower triangular matrix
triu - an upper triangular matrix
vander - the Vandermonde matrix of vector x
svd - singular value decomposition
zeros - a matrix of zeros
_Probability
normpdf - The Gaussian probability density function
rand - random numbers from the uniform distribution
randn - random numbers from the normal distribution
_Statistics
amax - the maximum along dimension m
amin - the minimum along dimension m
corrcoef - correlation coefficient
cov - covariance matrix
mean - the mean along dimension m
median - the median along dimension m
norm - the norm of vector x
prod - the product along dimension m
ptp - the max-min along dimension m
std - the standard deviation along dimension m
asum - the sum along dimension m
ksdensity - the kernel density estimate
_Time series analysis
bartlett - M-point Bartlett window
blackman - M-point Blackman window
cohere - the coherence using average periodiogram
csd - the cross spectral density using average periodiogram
fft - the fast Fourier transform of vector x
hamming - M-point Hamming window
hanning - M-point Hanning window
hist - compute the histogram of x
kaiser - M length Kaiser window
psd - the power spectral density using average periodiogram
sinc - the sinc function of array x
_Dates
date2num - convert python datetimes to numeric representation
drange - create an array of numbers for date plots
num2date - convert numeric type (float days since 0001) to datetime
_Other
angle - the angle of a complex array
griddata - interpolate irregularly distributed data to a regular grid
load - Deprecated--please use loadtxt.
loadtxt - load ASCII data into array.
polyfit - fit x, y to an n-th order polynomial
polyval - evaluate an n-th order polynomial
roots - the roots of the polynomial coefficients in p
save - Deprecated--please use savetxt.
savetxt - save an array to an ASCII file.
trapz - trapezoidal integration
__end
.. [*] MATLAB is a registered trademark of The MathWorks, Inc.
""" |
# from .. import feature_extraction
# import collections
# import numpy as np
# import scipy.stats
# import ml.utils as utils
# import ml.system as system
# import ml.opensmile
# import ml.parsing.arff
#
#
# class PitchExtractor(feature_extraction.FeatureExtractor):
# def __init__(self, config):
# self.params = utils.read_config(config, require=["pitch_config"])
# self.temp_folder = config["temp_folder"] if "temp_folder" in config else "/tmp/opensmile_arffs/"
# system.mkdir_p(self.temp_folder)
#
# def extract(self, instance):
# # last_seconds_values = self.params["extract_on_last_seconds"]
# windows = self.params["extract_on_windows"]
#
# times_pitch, pitch = self.pitch_track(instance)
#
# all_values = {}
#
# all_values["pitch_slope"] = collections.defaultdict(lambda: np.nan)
# all_values["mean_pitch"] = collections.defaultdict(lambda: np.nan)
#
# times_pitch = times_pitch - times_pitch.max() # Alineando a 0.
#
# for (w_from, w_to) in windows:
# window = (w_from, w_to)
# if not w_from:
# indices = np.array([True] * len(times_pitch))
# else:
# indices = (times_pitch >= w_from) & (times_pitch < w_to)
#
# all_values["mean_pitch"][window] = np.mean(pitch[indices])
#
# if sum(indices) > 2: # suficientes valores para calcular slope
# all_values["pitch_slope"][window] = scipy.stats.linregress(times_pitch[indices], pitch[indices])[0] # [0] => slope (m)
#
# feat = {}
# for (w_from, w_to) in windows:
# window = (w_from, w_to)
# if not w_from:
# in_ms = "all_ipu"
# else:
# in_ms = "({},{})".format(int(w_from * 1000), int(w_to * 1000))
#
# for feat_name in ["pitch_slope", "mean_pitch"]:
# feat["{}_{}".format(feat_name, in_ms)] = all_values[feat_name][window]
#
# if self.params["extended_features"]:
# feat["pitch"] = pitch
# feat["times_pitch"] = times_pitch
#
# return feat
#
# def pitch_track(self, instance):
# data = ml.opensmile.call_script(self.params["smile_extract_path"], self.temp_folder, self.params["pitch_config"], instance.filename)
#
# times = ml.parsing.arff.get_column(data, "frameTime")
# pitch = ml.parsing.arff.get_column(data, "F0final_sma")
#
# times = times[pitch > 0]
# pitch = pitch[pitch > 0]
#
# return times, pitch
# #
# # fs = instance.audio.frame_rate
# # ms_10_in_frames = math.ceil(fs / 100) # Extract every 10 ms.
# # min_pitch = 50 if instance.speaker_male() else 75
# # max_pitch = 300 if instance.speaker_male() else 500
# # x = np.array(instance.audio.get_array_of_samples())
# # pitch_swipe = pysptk.swipe(x.astype(np.float64), fs=fs, hopsize=ms_10_in_frames, min=min_pitch, max=max_pitch, otype="pitch", threshold=0.27)
# #
# # times = np.arange(0, len(pitch_swipe)) / 100.0
# # filtered_times = times[pitch_swipe > 0]
# # filtered_pitch_swipe = pitch_swipe[pitch_swipe > 0]
# #
# # return filtered_times, filtered_pitch_swipe
#
# def batch_extract(self, instances):
# return [self.extract(instance) for instance in instances]
|
"""Configuration file parser.
A configuration file consists of sections, lead by a "[section]" header,
and followed by "name: value" entries, with continuations and such in
the style of RFC 822.
Intrinsic defaults can be specified by passing them into the
ConfigParser constructor as a dictionary.
class:
ConfigParser -- responsible for parsing a list of
configuration files, and managing the parsed database.
methods:
__init__(defaults=None, dict_type=_default_dict, allow_no_value=False,
delimiters=('=', ':'), comment_prefixes=('#', ';'),
inline_comment_prefixes=None, strict=True,
empty_lines_in_values=True):
Create the parser. When `defaults' is given, it is initialized into the
dictionary or intrinsic defaults. The keys must be strings, the values
must be appropriate for %()s string interpolation.
When `dict_type' is given, it will be used to create the dictionary
objects for the list of sections, for the options within a section, and
for the default values.
When `delimiters' is given, it will be used as the set of substrings
that divide keys from values.
When `comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in empty lines. Comments can be
indented.
When `inline_comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in non-empty lines.
When `strict` is True, the parser won't allow for any section or option
duplicates while reading from a single source (file, string or
dictionary). Default is True.
When `empty_lines_in_values' is False (default: True), each empty line
marks the end of an option. Otherwise, internal empty lines of
a multiline option are kept as part of the value.
When `allow_no_value' is True (default: False), options without
values are accepted; the value presented for these is None.
sections()
Return all the configuration section names, sans DEFAULT.
has_section(section)
Return whether the given section exists.
has_option(section, option)
Return whether the given option exists in the given section.
options(section)
Return list of configuration options for the named section.
read(filenames, encoding=None)
Read and parse the list of named configuration files, given by
name. A single filename is also allowed. Non-existing files
are ignored. Return list of successfully read files.
read_file(f, filename=None)
Read and parse one configuration file, given as a file object.
The filename defaults to f.name; it is only used in error
messages (if f has no `name' attribute, the string `<???>' is used).
read_string(string)
Read configuration from a given string.
read_dict(dictionary)
Read configuration from a dictionary. Keys are section names,
values are dictionaries with keys and values that should be present
in the section. If the used dictionary type preserves order, sections
and their keys will be added in order. Values are automatically
converted to strings.
get(section, option, raw=False, vars=None, fallback=_UNSET)
Return a string value for the named option. All % interpolations are
expanded in the return values, based on the defaults passed into the
constructor and the DEFAULT section. Additional substitutions may be
provided using the `vars' argument, which must be a dictionary whose
contents override any pre-existing defaults. If `option' is a key in
`vars', the value from `vars' is used.
getint(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to an integer.
getfloat(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a float.
getboolean(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a boolean (currently case
insensitively defined as 0, false, no, off for False, and 1, true,
yes, on for True). Returns False or True.
items(section=_UNSET, raw=False, vars=None)
If section is given, return a list of tuples with (section_name,
section_proxy) for each section, including DEFAULTSECT. Otherwise,
return a list of tuples with (name, value) for each option
in the section.
remove_section(section)
Remove the given file section and all its options.
remove_option(section, option)
Remove the given option from the given section.
set(section, option, value)
Set the given option.
write(fp, space_around_delimiters=True)
Write the configuration state in .ini format. If
`space_around_delimiters' is True (the default), delimiters
between keys and values are surrounded by spaces.
""" |
"""
Algorithms and classes to support enumerative combinatorics.
Currently just multiset partitions, but more could be added.
Terminology (following Knuth, algorithm IP_ADDRESSM TAOCP)
*multiset* aaabbcccc has a *partition* aaabc | bccc
The submultisets, aaabc and bccc of the partition are called
*parts*, or sometimes *vectors*. (Knuth notes that multiset
partitions can be thought of as partitions of vectors of integers,
where the ith element of the vector gives the multiplicity of
element i.)
The values a, b and c are *components* of the multiset. These
correspond to elements of a set, but in a multiset can be present
with a multiplicity greater than 1.
The algorithm deserves some explanation.
Think of the part aaabc from the multiset above. If we impose an
ordering on the components of the multiset, we can represent a part
with a vector, in which the value of the first element of the vector
corresponds to the multiplicity of the first component in that
part. Thus, aaabc can be represented by the vector [3, 1, 1]. We
can also define an ordering on parts, based on the lexicographic
ordering of the vector (leftmost vector element, i.e., the element
with the smallest component number, is the most significant), so
that [3, 1, 1] > [3, 1, 0] and [3, 1, 1] > [2, 1, 4]. The ordering
on parts can be extended to an ordering on partitions: First, sort
the parts in each partition, left-to-right in decreasing order. Then
partition A is greater than partition B if A's leftmost/greatest
part is greater than B's leftmost part. If the leftmost parts are
equal, compare the second parts, and so on.
In this ordering, the greatest partition of a given multiset has only
one part. The least partition is the one in which the components
are spread out, one per part.
The enumeration algorithms in this file yield the partitions of the
argument multiset in decreasing order. The main data structure is a
stack of parts, corresponding to the current partition. An
important invariant is that the parts on the stack are themselves in
decreasing order. This data structure is decremented to find the
next smaller partition. Most often, decrementing the partition will
only involve adjustments to the smallest parts at the top of the
stack, much as adjacent integers *usually* differ only in their last
few digits.
Knuth's algorithm uses two main operations on parts:
Decrement - change the part so that it is smaller in the
(vector) lexicographic order, but reduced by the smallest amount possible.
For example, if the multiset has vector [5,
3, 1], and the bottom/greatest part is [4, 2, 1], this part would
decrement to [4, 2, 0], while [4, 0, 0] would decrement to [3, 3,
1]. A singleton part is never decremented -- [1, 0, 0] is not
decremented to [0, 3, 1]. Instead, the decrement operator needs
to fail for this case. In Knuth's psuedocode, the decrement
operator is step m5.
Spread unallocated multiplicity - Once a part has been decremented,
it cannot be the rightmost part in the partition. There is some
multiplicity that has not been allocated, and new parts must be
created above it in the stack to use up this multiplicity. To
maintain the invariant that the parts on the stack are in
decreasing order, these new parts must be less than or equal to
the decremented part.
For example, if the multiset is [5, 3, 1], and its most
significant part has just been decremented to [5, 3, 0], the
spread operation will add a new part so that the stack becomes
[[5, 3, 0], [0, 0, 1]]. If the most significant part (for the
same multiset) has been decremented to [2, 0, 0] the stack becomes
[[2, 0, 0], [2, 0, 0], [1, 3, 1]]. In the psuedocode, the spread
operation for one part is step m2. The complete spread operation
is a loop of steps m2 and m3.
In order to facilitate the spread operation, Knuth stores, for each
component of each part, not just the multiplicity of that component
in the part, but also the total multiplicity available for this
component in this part or any lesser part above it on the stack.
One added twist is that Knuth does not represent the part vectors as
arrays. Instead, he uses a sparse representation, in which a
component of a part is represented as a component number (c), plus
the multiplicity of the component in that part (v) as well as the
total multiplicity available for that component (u). This saves
time that would be spent skipping over zeros.
""" |
"""
==============
Array Creation
==============
Introduction
============
There are 5 general mechanisms for creating arrays:
1) Conversion from other Python structures (e.g., lists, tuples)
2) Intrinsic numpy array array creation objects (e.g., arange, ones, zeros,
etc.)
3) Reading arrays from disk, either from standard or custom formats
4) Creating arrays from raw bytes through the use of strings or buffers
5) Use of special library functions (e.g., random)
This section will not cover means of replicating, joining, or otherwise
expanding or mutating existing arrays. Nor will it cover creating object
arrays or record arrays. Both of those are covered in their own sections.
Converting Python array_like Objects to Numpy Arrays
====================================================
In general, numerical data arranged in an array-like structure in Python can
be converted to arrays through the use of the array() function. The most
obvious examples are lists and tuples. See the documentation for array() for
details for its use. Some objects may support the array-protocol and allow
conversion to arrays this way. A simple way to find out if the object can be
converted to a numpy array using array() is simply to try it interactively and
see if it works! (The Python Way).
Examples: ::
>>> x = np.array([2,3,1,0])
>>> x = np.array([2, 3, 1, 0])
>>> x = np.array([[1,2.0],[0,0],(1+1j,3.)]) # note mix of tuple and lists,
and types
>>> x = np.array([[ 1.+0.j, 2.+0.j], [ 0.+0.j, 0.+0.j], [ 1.+1.j, 3.+0.j]])
Intrinsic Numpy Array Creation
==============================
Numpy has built-in functions for creating arrays from scratch:
zeros(shape) will create an array filled with 0 values with the specified
shape. The default dtype is float64.
``>>> np.zeros((2, 3))
array([[ 0., 0., 0.], [ 0., 0., 0.]])``
ones(shape) will create an array filled with 1 values. It is identical to
zeros in all other respects.
arange() will create arrays with regularly incrementing values. Check the
docstring for complete information on the various ways it can be used. A few
examples will be given here: ::
>>> np.arange(10)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.arange(2, 10, dtype=np.float)
array([ 2., 3., 4., 5., 6., 7., 8., 9.])
>>> np.arange(2, 3, 0.1)
array([ 2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9])
Note that there are some subtleties regarding the last usage that the user
should be aware of that are described in the arange docstring.
linspace() will create arrays with a specified number of elements, and
spaced equally between the specified beginning and end values. For
example: ::
>>> np.linspace(1., 4., 6)
array([ 1. , 1.6, 2.2, 2.8, 3.4, 4. ])
The advantage of this creation function is that one can guarantee the
number of elements and the starting and end point, which arange()
generally will not do for arbitrary start, stop, and step values.
indices() will create a set of arrays (stacked as a one-higher dimensioned
array), one per dimension with each representing variation in that dimension.
An example illustrates much better than a verbal description: ::
>>> np.indices((3,3))
array([[[0, 0, 0], [1, 1, 1], [2, 2, 2]], [[0, 1, 2], [0, 1, 2], [0, 1, 2]]])
This is particularly useful for evaluating functions of multiple dimensions on
a regular grid.
Reading Arrays From Disk
========================
This is presumably the most common case of large array creation. The details,
of course, depend greatly on the format of data on disk and so this section
can only give general pointers on how to handle various formats.
Standard Binary Formats
-----------------------
Various fields have standard formats for array data. The following lists the
ones with known python libraries to read them and return numpy arrays (there
may be others for which it is possible to read and convert to numpy arrays so
check the last section as well)
::
HDF5: PyTables
FITS: PyFITS
Examples of formats that cannot be read directly but for which it is not hard
to convert are libraries like PIL (able to read and write many image formats
such as jpg, png, etc).
Common ASCII Formats
------------------------
Comma Separated Value files (CSV) are widely used (and an export and import
option for programs like Excel). There are a number of ways of reading these
files in Python. There are CSV functions in Python and functions in pylab
(part of matplotlib).
More generic ascii files can be read using the io package in scipy.
Custom Binary Formats
---------------------
There are a variety of approaches one can use. If the file has a relatively
simple format then one can write a simple I/O library and use the numpy
fromfile() function and .tofile() method to read and write numpy arrays
directly (mind your byteorder though!) If a good C or C++ library exists that
read the data, one can wrap that library with a variety of techniques though
that certainly is much more work and requires significantly more advanced
knowledge to interface with C or C++.
Use of Special Libraries
------------------------
There are libraries that can be used to generate arrays for special purposes
and it isn't possible to enumerate all of them. The most common uses are use
of the many array generation functions in random that can generate arrays of
random values, and some utility functions to generate special matrices (e.g.
diagonal).
""" |
"""
Cython optimize zeros API
=========================
The underlying C functions for the following root finders can be accessed
directly using Cython:
- `~scipy.optimize.bisect`
- `~scipy.optimize.ridder`
- `~scipy.optimize.brenth`
- `~scipy.optimize.brentq`
The Cython API for the zeros functions is similar except there is no ``disp``
argument. Import the zeros functions using ``cimport`` from
`scipy.optimize.cython_optimize`. ::
from scipy.optimize.cython_optimize cimport bisect, ridder, brentq, brenth
Callback signature
------------------
The zeros functions in `~scipy.optimize.cython_optimize` expect a callback that
takes a double for the scalar independent variable as the 1st argument and a
user defined ``struct`` with any extra parameters as the 2nd argument. ::
double (*callback_type)(double, void*)
Examples
--------
Usage of `~scipy.optimize.cython_optimize` requires Cython to write callbacks
that are compiled into C. For more information on compiling Cython, see the
`Cython Documentation <http://docs.cython.org/en/latest/index.html>`_.
These are the basic steps:
1. Create a Cython ``.pyx`` file, for example: ``myexample.pyx``.
2. Import the desired root finder from `~scipy.optimize.cython_optimize`.
3. Write the callback function, and call the selected zeros function passing
the callback, any extra arguments, and the other solver parameters. ::
from scipy.optimize.cython_optimize cimport brentq
# import math from Cython
from libc cimport math
myargs = {'C0': 1.0, 'C1': 0.7} # a dictionary of extra arguments
XLO, XHI = 0.5, 1.0 # lower and upper search boundaries
XTOL, RTOL, MITR = 1e-3, 1e-3, 10 # other solver parameters
# user-defined struct for extra parameters
ctypedef struct test_params:
double C0
double C1
# user-defined callback
cdef double f(double x, void *args):
cdef test_params *myargs = <test_params *> args
return myargs.C0 - math.exp(-(x - myargs.C1))
# Cython wrapper function
cdef double brentq_wrapper_example(dict args, double xa, double xb,
double xtol, double rtol, int mitr):
# Cython automatically casts dictionary to struct
cdef test_params myargs = args
return brentq(
f, xa, xb, <test_params *> &myargs, xtol, rtol, mitr, NULL)
# Python function
def brentq_example(args=myargs, xa=XLO, xb=XHI, xtol=XTOL, rtol=RTOL,
mitr=MITR):
'''Calls Cython wrapper from Python.'''
return brentq_wrapper_example(args, xa, xb, xtol, rtol, mitr)
4. If you want to call your function from Python, create a Cython wrapper, and
a Python function that calls the wrapper, or use ``cpdef``. Then, in Python,
you can import and run the example. ::
from myexample import brentq_example
x = brentq_example()
# 0.6999942848231314
5. Create a Cython ``.pxd`` file if you need to export any Cython functions.
Full output
-----------
The functions in `~scipy.optimize.cython_optimize` can also copy the full
output from the solver to a C ``struct`` that is passed as its last argument.
If you don't want the full output, just pass ``NULL``. The full output
``struct`` must be type ``zeros_full_output``, which is defined in
`scipy.optimize.cython_optimize` with the following fields:
- ``int funcalls``: number of function calls
- ``int iterations``: number of iterations
- ``int error_num``: error number
- ``double root``: root of function
The root is copied by `~scipy.optimize.cython_optimize` to the full output
``struct``. An error number of -1 means a sign error, -2 means a convergence
error, and 0 means the solver converged. Continuing from the previous example::
from scipy.optimize.cython_optimize cimport zeros_full_output
# cython brentq solver with full output
cdef brent_full_output brentq_full_output_wrapper_example(
dict args, double xa, double xb, double xtol, double rtol,
int mitr):
cdef test_params myargs = args
cdef zeros_full_output my_full_output
# use my_full_output instead of NULL
brentq(f, xa, xb, &myargs, xtol, rtol, mitr, &my_full_output)
return my_full_output
# Python function
def brent_full_output_example(args=myargs, xa=XLO, xb=XHI, xtol=XTOL,
rtol=RTOL, mitr=MITR):
'''Returns full output'''
return brentq_full_output_wrapper_example(args, xa, xb, xtol, rtol,
mitr)
result = brent_full_output_example()
# {'error_num': 0,
# 'funcalls': 6,
# 'iterations': 5,
# 'root': 0.6999942848231314}
""" |
# Test 64-bit COMPARE LOGICAL IMMEDIATE AND BRANCH in cases where the sheer
# number of instructions causes some branches to be out of range.
# RUN: python %s | llc -mtriple=s390x-linux-gnu | FileCheck %s
# Construct:
#
# before0:
# conditional branch to after0
# ...
# beforeN:
# conditional branch to after0
# main:
# 0xffb4 bytes, from MVIY instructions
# conditional branch to main
# after0:
# ...
# conditional branch to main
# afterN:
#
# Each conditional branch sequence occupies 18 bytes if it uses a short
# branch and 24 if it uses a long one. The ones before "main:" have to
# take the branch length into account, which is 6 for short branches,
# so the final (0x4c - 6) / 18 == 3 blocks can use short branches.
# The ones after "main:" do not, so the first 0x4c / 18 == 4 blocks
# can use short branches. The conservative algorithm we use makes
# one of the forward branches unnecessarily long, as noted in the
# check output below.
#
# CHECK: lg [[REG:%r[0-5]]], 0(%r3)
# CHECK: sg [[REG]], 0(%r4)
# CHECK: clgfi [[REG]], 50
# CHECK: jgl [[LABEL:\.L[^ ]*]]
# CHECK: lg [[REG:%r[0-5]]], 0(%r3)
# CHECK: sg [[REG]], 0(%r4)
# CHECK: clgfi [[REG]], 51
# CHECK: jgl [[LABEL]]
# CHECK: lg [[REG:%r[0-5]]], 0(%r3)
# CHECK: sg [[REG]], 0(%r4)
# CHECK: clgfi [[REG]], 52
# CHECK: jgl [[LABEL]]
# CHECK: lg [[REG:%r[0-5]]], 0(%r3)
# CHECK: sg [[REG]], 0(%r4)
# CHECK: clgfi [[REG]], 53
# CHECK: jgl [[LABEL]]
# CHECK: lg [[REG:%r[0-5]]], 0(%r3)
# CHECK: sg [[REG]], 0(%r4)
# CHECK: clgfi [[REG]], 54
# CHECK: jgl [[LABEL]]
# ...as mentioned above, the next one could be a CLGIJL instead...
# CHECK: lg [[REG:%r[0-5]]], 0(%r3)
# CHECK: sg [[REG]], 0(%r4)
# CHECK: clgfi [[REG]], 55
# CHECK: jgl [[LABEL]]
# CHECK: lg [[REG:%r[0-5]]], 0(%r3)
# CHECK: sg [[REG]], 0(%r4)
# CHECK: clgijl [[REG]], 56, [[LABEL]]
# CHECK: lg [[REG:%r[0-5]]], 0(%r3)
# CHECK: sg [[REG]], 0(%r4)
# CHECK: clgijl [[REG]], 57, [[LABEL]]
# ...main goes here...
# CHECK: lg [[REG:%r[0-5]]], 0(%r3)
# CHECK: sg [[REG]], 0(%r4)
# CHECK: clgijl [[REG]], 100, [[LABEL:\.L[^ ]*]]
# CHECK: lg [[REG:%r[0-5]]], 0(%r3)
# CHECK: sg [[REG]], 0(%r4)
# CHECK: clgijl [[REG]], 101, [[LABEL]]
# CHECK: lg [[REG:%r[0-5]]], 0(%r3)
# CHECK: sg [[REG]], 0(%r4)
# CHECK: clgijl [[REG]], 102, [[LABEL]]
# CHECK: lg [[REG:%r[0-5]]], 0(%r3)
# CHECK: sg [[REG]], 0(%r4)
# CHECK: clgijl [[REG]], 103, [[LABEL]]
# CHECK: lg [[REG:%r[0-5]]], 0(%r3)
# CHECK: sg [[REG]], 0(%r4)
# CHECK: clgfi [[REG]], 104
# CHECK: jgl [[LABEL]]
# CHECK: lg [[REG:%r[0-5]]], 0(%r3)
# CHECK: sg [[REG]], 0(%r4)
# CHECK: clgfi [[REG]], 105
# CHECK: jgl [[LABEL]]
# CHECK: lg [[REG:%r[0-5]]], 0(%r3)
# CHECK: sg [[REG]], 0(%r4)
# CHECK: clgfi [[REG]], 106
# CHECK: jgl [[LABEL]]
# CHECK: lg [[REG:%r[0-5]]], 0(%r3)
# CHECK: sg [[REG]], 0(%r4)
# CHECK: clgfi [[REG]], 107
# CHECK: jgl [[LABEL]]
|
#
# XML-RPC CLIENT LIBRARY
# $Id$
#
# an XML-RPC client interface for Python.
#
# the marshalling and response parser code can also be used to
# implement XML-RPC servers.
#
# Notes:
# this version is designed to work with Python 2.1 or newer.
#
# History:
# 1999-01-14 fl Created
# 1999-01-15 fl Changed dateTime to use localtime
# 1999-01-16 fl Added Binary/base64 element, default to RPC2 service
# 1999-01-19 fl Fixed array data element (from Skip Montanaro)
# 1999-01-21 fl Fixed dateTime constructor, etc.
# 1999-02-02 fl Added fault handling, handle empty sequences, etc.
# 1999-02-10 fl Fixed problem with empty responses (from Skip Montanaro)
# 1999-06-20 fl Speed improvements, pluggable parsers/transports (0.9.8)
# 2000-11-28 fl Changed boolean to check the truth value of its argument
# 2001-02-24 fl Added encoding/Unicode/SafeTransport patches
# 2001-02-26 fl Added compare support to wrappers (0.9.9/1.0b1)
# 2001-03-28 fl Make sure response tuple is a singleton
# 2001-03-29 fl Don't require empty params element (from NAME 2001-06-10 fl Folded in _xmlrpclib accelerator support (1.0b2)
# 2001-08-20 fl Base xmlrpclib.Error on built-in Exception (from NAME 2001-09-03 fl Allow Transport subclass to override getparser
# 2001-09-10 fl Lazy import of urllib, cgi, xmllib (20x import speedup)
# 2001-10-01 fl Remove containers from memo cache when done with them
# 2001-10-01 fl Use faster escape method (80% dumps speedup)
# 2001-10-02 fl More dumps microtuning
# 2001-10-04 fl Make sure import expat gets a parser (from NAME 2001-10-10 sm Allow long ints to be passed as ints if they don't overflow
# 2001-10-17 sm Test for int and long overflow (allows use on 64-bit systems)
# 2001-11-12 fl Use repr() to marshal doubles (from NAME 2002-03-17 fl Avoid buffered read when possible (from NAME 2002-04-07 fl Added pythondoc comments
# 2002-04-16 fl Added __str__ methods to datetime/binary wrappers
# 2002-05-15 fl Added error constants (from NAME 2002-06-27 fl Merged with Python CVS version
# 2002-10-22 fl Added basic authentication (based on code from NAME 2003-01-22 sm Add support for the bool type
# 2003-02-27 gvr Remove apply calls
# 2003-04-24 sm Use cStringIO if available
# 2003-04-25 ak Add support for nil
# 2003-06-15 gn Add support for time.struct_time
# 2003-07-12 gp Correct marshalling of Faults
# 2003-10-31 mvl Add multicall support
# 2004-08-20 mvl Bump minimum supported Python version to 2.1
#
# Copyright (c) 1999-2002 by Secret Labs AB.
# Copyright (c) 1999-2002 by NAME EMAIL http://www.pythonware.com
#
# --------------------------------------------------------------------
# The XML-RPC client interface is
#
# Copyright (c) 1999-2002 by Secret Labs AB
# Copyright (c) 1999-2002 by NAME By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
|
"""
===================
Universal Functions
===================
Ufuncs are, generally speaking, mathematical functions or operations that are
applied element-by-element to the contents of an array. That is, the result
in each output array element only depends on the value in the corresponding
input array (or arrays) and on no other array elements. NumPy comes with a
large suite of ufuncs, and scipy extends that suite substantially. The simplest
example is the addition operator: ::
>>> np.array([0,2,3,4]) + np.array([1,1,-1,2])
array([1, 3, 2, 6])
The unfunc module lists all the available ufuncs in numpy. Documentation on
the specific ufuncs may be found in those modules. This documentation is
intended to address the more general aspects of unfuncs common to most of
them. All of the ufuncs that make use of Python operators (e.g., +, -, etc.)
have equivalent functions defined (e.g. add() for +)
Type coercion
=============
What happens when a binary operator (e.g., +,-,\\*,/, etc) deals with arrays of
two different types? What is the type of the result? Typically, the result is
the higher of the two types. For example: ::
float32 + float64 -> float64
int8 + int32 -> int32
int16 + float32 -> float32
float32 + complex64 -> complex64
There are some less obvious cases generally involving mixes of types
(e.g. uints, ints and floats) where equal bit sizes for each are not
capable of saving all the information in a different type of equivalent
bit size. Some examples are int32 vs float32 or uint32 vs int32.
Generally, the result is the higher type of larger size than both
(if available). So: ::
int32 + float32 -> float64
uint32 + int32 -> int64
Finally, the type coercion behavior when expressions involve Python
scalars is different than that seen for arrays. Since Python has a
limited number of types, combining a Python int with a dtype=np.int8
array does not coerce to the higher type but instead, the type of the
array prevails. So the rules for Python scalars combined with arrays is
that the result will be that of the array equivalent the Python scalar
if the Python scalar is of a higher 'kind' than the array (e.g., float
vs. int), otherwise the resultant type will be that of the array.
For example: ::
Python int + int8 -> int8
Python float + int8 -> float64
ufunc methods
=============
Binary ufuncs support 4 methods.
**.reduce(arr)** applies the binary operator to elements of the array in
sequence. For example: ::
>>> np.add.reduce(np.arange(10)) # adds all elements of array
45
For multidimensional arrays, the first dimension is reduced by default: ::
>>> np.add.reduce(np.arange(10).reshape(2,5))
array([ 5, 7, 9, 11, 13])
The axis keyword can be used to specify different axes to reduce: ::
>>> np.add.reduce(np.arange(10).reshape(2,5),axis=1)
array([10, 35])
**.accumulate(arr)** applies the binary operator and generates an an
equivalently shaped array that includes the accumulated amount for each
element of the array. A couple examples: ::
>>> np.add.accumulate(np.arange(10))
array([ 0, 1, 3, 6, 10, 15, 21, 28, 36, 45])
>>> np.multiply.accumulate(np.arange(1,9))
array([ 1, 2, 6, 24, 120, 720, 5040, 40320])
The behavior for multidimensional arrays is the same as for .reduce(),
as is the use of the axis keyword).
**.reduceat(arr,indices)** allows one to apply reduce to selected parts
of an array. It is a difficult method to understand. See the documentation
at:
**.outer(arr1,arr2)** generates an outer operation on the two arrays arr1 and
arr2. It will work on multidimensional arrays (the shape of the result is
the concatenation of the two input shapes.: ::
>>> np.multiply.outer(np.arange(3),np.arange(4))
array([[0, 0, 0, 0],
[0, 1, 2, 3],
[0, 2, 4, 6]])
Output arguments
================
All ufuncs accept an optional output array. The array must be of the expected
output shape. Beware that if the type of the output array is of a different
(and lower) type than the output result, the results may be silently truncated
or otherwise corrupted in the downcast to the lower type. This usage is useful
when one wants to avoid creating large temporary arrays and instead allows one
to reuse the same array memory repeatedly (at the expense of not being able to
use more convenient operator notation in expressions). Note that when the
output argument is used, the ufunc still returns a reference to the result.
>>> x = np.arange(2)
>>> np.add(np.arange(2),np.arange(2.),x)
array([0, 2])
>>> x
array([0, 2])
and & or as ufuncs
==================
Invariably people try to use the python 'and' and 'or' as logical operators
(and quite understandably). But these operators do not behave as normal
operators since Python treats these quite differently. They cannot be
overloaded with array equivalents. Thus using 'and' or 'or' with an array
results in an error. There are two alternatives:
1) use the ufunc functions logical_and() and logical_or().
2) use the bitwise operators & and \\|. The drawback of these is that if
the arguments to these operators are not boolean arrays, the result is
likely incorrect. On the other hand, most usages of logical_and and
logical_or are with boolean arrays. As long as one is careful, this is
a convenient way to apply these operators.
""" |
#!/usr/bin/env python
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"""
========
Glossary
========
along an axis
Axes are defined for arrays with more than one dimension. A
2-dimensional array has two corresponding axes: the first running
vertically downwards across rows (axis 0), and the second running
horizontally across columns (axis 1).
Many operation can take place along one of these axes. For example,
we can sum each row of an array, in which case we operate along
columns, or axis 1::
>>> x = np.arange(12).reshape((3,4))
>>> x
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.sum(axis=1)
array([ 6, 22, 38])
array
A homogeneous container of numerical elements. Each element in the
array occupies a fixed amount of memory (hence homogeneous), and
can be a numerical element of a single type (such as float, int
or complex) or a combination (such as ``(float, int, float)``). Each
array has an associated data-type (or ``dtype``), which describes
the numerical type of its elements::
>>> x = np.array([1, 2, 3], float)
>>> x
array([ 1., 2., 3.])
>>> x.dtype # floating point number, 64 bits of memory per element
dtype('float64')
# More complicated data type: each array element is a combination of
# and integer and a floating point number
>>> np.array([(1, 2.0), (3, 4.0)], dtype=[('x', int), ('y', float)])
array([(1, 2.0), (3, 4.0)],
dtype=[('x', '<i4'), ('y', '<f8')])
Fast element-wise operations, called `ufuncs`_, operate on arrays.
array_like
Any sequence that can be interpreted as an ndarray. This includes
nested lists, tuples, scalars and existing arrays.
attribute
A property of an object that can be accessed using ``obj.attribute``,
e.g., ``shape`` is an attribute of an array::
>>> x = np.array([1, 2, 3])
>>> x.shape
(3,)
BLAS
`Basic Linear Algebra Subprograms <http://en.wikipedia.org/wiki/BLAS>`_
broadcast
NumPy can do operations on arrays whose shapes are mismatched::
>>> x = np.array([1, 2])
>>> y = np.array([[3], [4]])
>>> x
array([1, 2])
>>> y
array([[3],
[4]])
>>> x + y
array([[4, 5],
[5, 6]])
See `doc.broadcasting`_ for more information.
C order
See `row-major`
column-major
A way to represent items in a N-dimensional array in the 1-dimensional
computer memory. In column-major order, the leftmost index "varies the
fastest": for example the array::
[[1, 2, 3],
[4, 5, 6]]
is represented in the column-major order as::
[1, 4, 2, 5, 3, 6]
Column-major order is also known as the Fortran order, as the Fortran
programming language uses it.
decorator
An operator that transforms a function. For example, a ``log``
decorator may be defined to print debugging information upon
function execution::
>>> def log(f):
... def new_logging_func(*args, **kwargs):
... print "Logging call with parameters:", args, kwargs
... return f(*args, **kwargs)
...
... return new_logging_func
Now, when we define a function, we can "decorate" it using ``log``::
>>> @log
... def add(a, b):
... return a + b
Calling ``add`` then yields:
>>> add(1, 2)
Logging call with parameters: (1, 2) {}
3
dictionary
Resembling a language dictionary, which provides a mapping between
words and descriptions thereof, a Python dictionary is a mapping
between two objects::
>>> x = {1: 'one', 'two': [1, 2]}
Here, `x` is a dictionary mapping keys to values, in this case
the integer 1 to the string "one", and the string "two" to
the list ``[1, 2]``. The values may be accessed using their
corresponding keys::
>>> x[1]
'one'
>>> x['two']
[1, 2]
Note that dictionaries are not stored in any specific order. Also,
most mutable (see *immutable* below) objects, such as lists, may not
be used as keys.
For more information on dictionaries, read the
`Python tutorial <http://docs.python.org/tut>`_.
Fortran order
See `column-major`
flattened
Collapsed to a one-dimensional array. See `ndarray.flatten`_ for details.
immutable
An object that cannot be modified after execution is called
immutable. Two common examples are strings and tuples.
instance
A class definition gives the blueprint for constructing an object::
>>> class House(object):
... wall_colour = 'white'
Yet, we have to *build* a house before it exists::
>>> h = House() # build a house
Now, ``h`` is called a ``House`` instance. An instance is therefore
a specific realisation of a class.
iterable
A sequence that allows "walking" (iterating) over items, typically
using a loop such as::
>>> x = [1, 2, 3]
>>> [item**2 for item in x]
[1, 4, 9]
It is often used in combintion with ``enumerate``::
>>> keys = ['a','b','c']
>>> for n, k in enumerate(keys):
... print "Key %d: %s" % (n, k)
...
Key 0: a
Key 1: b
Key 2: c
list
A Python container that can hold any number of objects or items.
The items do not have to be of the same type, and can even be
lists themselves::
>>> x = [2, 2.0, "two", [2, 2.0]]
The list `x` contains 4 items, each which can be accessed individually::
>>> x[2] # the string 'two'
'two'
>>> x[3] # a list, containing an integer 2 and a float 2.0
[2, 2.0]
It is also possible to select more than one item at a time,
using *slicing*::
>>> x[0:2] # or, equivalently, x[:2]
[2, 2.0]
In code, arrays are often conveniently expressed as nested lists::
>>> np.array([[1, 2], [3, 4]])
array([[1, 2],
[3, 4]])
For more information, read the section on lists in the `Python
tutorial <http://docs.python.org/tut>`_. For a mapping
type (key-value), see *dictionary*.
mask
A boolean array, used to select only certain elements for an operation::
>>> x = np.arange(5)
>>> x
array([0, 1, 2, 3, 4])
>>> mask = (x > 2)
>>> mask
array([False, False, False, True, True], dtype=bool)
>>> x[mask] = -1
>>> x
array([ 0, 1, 2, -1, -1])
masked array
Array that suppressed values indicated by a mask::
>>> x = np.ma.masked_array([np.nan, 2, np.nan], [True, False, True])
>>> x
masked_array(data = [-- 2.0 --],
mask = [ True False True],
fill_value = 1e+20)
<BLANKLINE>
>>> x + [1, 2, 3]
masked_array(data = [-- 4.0 --],
mask = [ True False True],
fill_value = 1e+20)
<BLANKLINE>
Masked arrays are often used when operating on arrays containing
missing or invalid entries.
matrix
A 2-dimensional ndarray that preserves its two-dimensional nature
throughout operations. It has certain special operations, such as ``*``
(matrix multiplication) and ``**`` (matrix power), defined::
>>> x = np.mat([[1, 2], [3, 4]])
>>> x
matrix([[1, 2],
[3, 4]])
>>> x**2
matrix([[ 7, 10],
[15, 22]])
method
A function associated with an object. For example, each ndarray has a
method called ``repeat``::
>>> x = np.array([1, 2, 3])
>>> x.repeat(2)
array([1, 1, 2, 2, 3, 3])
ndarray
See *array*.
reference
If ``a`` is a reference to ``b``, then ``(a is b) == True``. Therefore,
``a`` and ``b`` are different names for the same Python object.
row-major
A way to represent items in a N-dimensional array in the 1-dimensional
computer memory. In row-major order, the rightmost index "varies
the fastest": for example the array::
[[1, 2, 3],
[4, 5, 6]]
is represented in the row-major order as::
[1, 2, 3, 4, 5, 6]
Row-major order is also known as the C order, as the C programming
language uses it. New Numpy arrays are by default in row-major order.
self
Often seen in method signatures, ``self`` refers to the instance
of the associated class. For example:
>>> class Paintbrush(object):
... color = 'blue'
...
... def paint(self):
... print "Painting the city %s!" % self.color
...
>>> p = Paintbrush()
>>> p.color = 'red'
>>> p.paint() # self refers to 'p'
Painting the city red!
slice
Used to select only certain elements from a sequence::
>>> x = range(5)
>>> x
[0, 1, 2, 3, 4]
>>> x[1:3] # slice from 1 to 3 (excluding 3 itself)
[1, 2]
>>> x[1:5:2] # slice from 1 to 5, but skipping every second element
[1, 3]
>>> x[::-1] # slice a sequence in reverse
[4, 3, 2, 1, 0]
Arrays may have more than one dimension, each which can be sliced
individually::
>>> x = np.array([[1, 2], [3, 4]])
>>> x
array([[1, 2],
[3, 4]])
>>> x[:, 1]
array([2, 4])
tuple
A sequence that may contain a variable number of types of any
kind. A tuple is immutable, i.e., once constructed it cannot be
changed. Similar to a list, it can be indexed and sliced::
>>> x = (1, 'one', [1, 2])
>>> x
(1, 'one', [1, 2])
>>> x[0]
1
>>> x[:2]
(1, 'one')
A useful concept is "tuple unpacking", which allows variables to
be assigned to the contents of a tuple::
>>> x, y = (1, 2)
>>> x, y = 1, 2
This is often used when a function returns multiple values:
>>> def return_many():
... return 1, 'alpha', None
>>> a, b, c = return_many()
>>> a, b, c
(1, 'alpha', None)
>>> a
1
>>> b
'alpha'
ufunc
Universal function. A fast element-wise array operation. Examples include
``add``, ``sin`` and ``logical_or``.
view
An array that does not own its data, but refers to another array's
data instead. For example, we may create a view that only shows
every second element of another array::
>>> x = np.arange(5)
>>> x
array([0, 1, 2, 3, 4])
>>> y = x[::2]
>>> y
array([0, 2, 4])
>>> x[0] = 3 # changing x changes y as well, since y is a view on x
>>> y
array([3, 2, 4])
wrapper
Python is a high-level (highly abstracted, or English-like) language.
This abstraction comes at a price in execution speed, and sometimes
it becomes necessary to use lower level languages to do fast
computations. A wrapper is code that provides a bridge between
high and the low level languages, allowing, e.g., Python to execute
code written in C or Fortran.
Examples include ctypes, SWIG and Cython (which wraps C and C++)
and f2py (which wraps Fortran).
""" |
"""
TestCmd.py: a testing framework for commands and scripts.
The TestCmd module provides a framework for portable automated testing
of executable commands and scripts (in any language, not just Python),
especially commands and scripts that require file system interaction.
In addition to running tests and evaluating conditions, the TestCmd
module manages and cleans up one or more temporary workspace
directories, and provides methods for creating files and directories in
those workspace directories from in-line data, here-documents), allowing
tests to be completely self-contained.
A TestCmd environment object is created via the usual invocation:
import TestCmd
test = TestCmd.TestCmd()
There are a bunch of keyword arguments available at instantiation:
test = TestCmd.TestCmd(description = 'string',
program = 'program_or_script_to_test',
interpreter = 'script_interpreter',
workdir = 'prefix',
subdir = 'subdir',
verbose = Boolean,
match = default_match_function,
diff = default_diff_function,
combine = Boolean)
There are a bunch of methods that let you do different things:
test.verbose_set(1)
test.description_set('string')
test.program_set('program_or_script_to_test')
test.interpreter_set('script_interpreter')
test.interpreter_set(['script_interpreter', 'arg'])
test.workdir_set('prefix')
test.workdir_set('')
test.workpath('file')
test.workpath('subdir', 'file')
test.subdir('subdir', ...)
test.rmdir('subdir', ...)
test.write('file', "contents\n")
test.write(['subdir', 'file'], "contents\n")
test.read('file')
test.read(['subdir', 'file'])
test.read('file', mode)
test.read(['subdir', 'file'], mode)
test.writable('dir', 1)
test.writable('dir', None)
test.preserve(condition, ...)
test.cleanup(condition)
test.command_args(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program')
test.run(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
chdir = 'directory_to_chdir_to',
stdin = 'input to feed to the program\n')
universal_newlines = True)
p = test.start(program = 'program_or_script_to_run',
interpreter = 'script_interpreter',
arguments = 'arguments to pass to program',
universal_newlines = None)
test.finish(self, p)
test.pass_test()
test.pass_test(condition)
test.pass_test(condition, function)
test.fail_test()
test.fail_test(condition)
test.fail_test(condition, function)
test.fail_test(condition, function, skip)
test.no_result()
test.no_result(condition)
test.no_result(condition, function)
test.no_result(condition, function, skip)
test.stdout()
test.stdout(run)
test.stderr()
test.stderr(run)
test.symlink(target, link)
test.banner(string)
test.banner(string, width)
test.diff(actual, expected)
test.match(actual, expected)
test.match_exact("actual 1\nactual 2\n", "expected 1\nexpected 2\n")
test.match_exact(["actual 1\n", "actual 2\n"],
["expected 1\n", "expected 2\n"])
test.match_re("actual 1\nactual 2\n", regex_string)
test.match_re(["actual 1\n", "actual 2\n"], list_of_regexes)
test.match_re_dotall("actual 1\nactual 2\n", regex_string)
test.match_re_dotall(["actual 1\n", "actual 2\n"], list_of_regexes)
test.tempdir()
test.tempdir('temporary-directory')
test.sleep()
test.sleep(seconds)
test.where_is('foo')
test.where_is('foo', 'PATH1:PATH2')
test.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
test.unlink('file')
test.unlink('subdir', 'file')
The TestCmd module provides pass_test(), fail_test(), and no_result()
unbound functions that report test results for use with the Aegis change
management system. These methods terminate the test immediately,
reporting PASSED, FAILED, or NO RESULT respectively, and exiting with
status 0 (success), 1 or 2 respectively. This allows for a distinction
between an actual failed test and a test that could not be properly
evaluated because of an external condition (such as a full file system
or incorrect permissions).
import TestCmd
TestCmd.pass_test()
TestCmd.pass_test(condition)
TestCmd.pass_test(condition, function)
TestCmd.fail_test()
TestCmd.fail_test(condition)
TestCmd.fail_test(condition, function)
TestCmd.fail_test(condition, function, skip)
TestCmd.no_result()
TestCmd.no_result(condition)
TestCmd.no_result(condition, function)
TestCmd.no_result(condition, function, skip)
The TestCmd module also provides unbound functions that handle matching
in the same way as the match_*() methods described above.
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_exact)
test = TestCmd.TestCmd(match = TestCmd.match_re)
test = TestCmd.TestCmd(match = TestCmd.match_re_dotall)
The TestCmd module provides unbound functions that can be used for the
"diff" argument to TestCmd.TestCmd instantiation:
import TestCmd
test = TestCmd.TestCmd(match = TestCmd.match_re,
diff = TestCmd.diff_re)
test = TestCmd.TestCmd(diff = TestCmd.simple_diff)
The "diff" argument can also be used with standard difflib functions:
import difflib
test = TestCmd.TestCmd(diff = difflib.context_diff)
test = TestCmd.TestCmd(diff = difflib.unified_diff)
Lastly, the where_is() method also exists in an unbound function
version.
import TestCmd
TestCmd.where_is('foo')
TestCmd.where_is('foo', 'PATH1:PATH2')
TestCmd.where_is('foo', 'PATH1;PATH2', '.suffix3;.suffix4')
""" |
"""Drag-and-drop support for Tkinter.
This is very preliminary. I currently only support dnd *within* one
application, between different windows (or within the same window).
I an trying to make this as generic as possible -- not dependent on
the use of a particular widget or icon type, etc. I also hope that
this will work with Pmw.
To enable an object to be dragged, you must create an event binding
for it that starts the drag-and-drop process. Typically, you should
bind <ButtonPress> to a callback function that you write. The function
should call Tkdnd.dnd_start(source, event), where 'source' is the
object to be dragged, and 'event' is the event that invoked the call
(the argument to your callback function). Even though this is a class
instantiation, the returned instance should not be stored -- it will
be kept alive automatically for the duration of the drag-and-drop.
When a drag-and-drop is already in process for the Tk interpreter, the
call is *ignored*; this normally averts starting multiple simultaneous
dnd processes, e.g. because different button callbacks all
dnd_start().
The object is *not* necessarily a widget -- it can be any
application-specific object that is meaningful to potential
drag-and-drop targets.
Potential drag-and-drop targets are discovered as follows. Whenever
the mouse moves, and at the start and end of a drag-and-drop move, the
Tk widget directly under the mouse is inspected. This is the target
widget (not to be confused with the target object, yet to be
determined). If there is no target widget, there is no dnd target
object. If there is a target widget, and it has an attribute
dnd_accept, this should be a function (or any callable object). The
function is called as dnd_accept(source, event), where 'source' is the
object being dragged (the object passed to dnd_start() above), and
'event' is the most recent event object (generally a <Motion> event;
it can also be <ButtonPress> or <ButtonRelease>). If the dnd_accept()
function returns something other than None, this is the new dnd target
object. If dnd_accept() returns None, or if the target widget has no
dnd_accept attribute, the target widget's parent is considered as the
target widget, and the search for a target object is repeated from
there. If necessary, the search is repeated all the way up to the
root widget. If none of the target widgets can produce a target
object, there is no target object (the target object is None).
The target object thus produced, if any, is called the new target
object. It is compared with the old target object (or None, if there
was no old target widget). There are several cases ('source' is the
source object, and 'event' is the most recent event object):
- Both the old and new target objects are None. Nothing happens.
- The old and new target objects are the same object. Its method
dnd_motion(source, event) is called.
- The old target object was None, and the new target object is not
None. The new target object's method dnd_enter(source, event) is
called.
- The new target object is None, and the old target object is not
None. The old target object's method dnd_leave(source, event) is
called.
- The old and new target objects differ and neither is None. The old
target object's method dnd_leave(source, event), and then the new
target object's method dnd_enter(source, event) is called.
Once this is done, the new target object replaces the old one, and the
Tk mainloop proceeds. The return value of the methods mentioned above
is ignored; if they raise an exception, the normal exception handling
mechanisms take over.
The drag-and-drop processes can end in two ways: a final target object
is selected, or no final target object is selected. When a final
target object is selected, it will always have been notified of the
potential drop by a call to its dnd_enter() method, as described
above, and possibly one or more calls to its dnd_motion() method; its
dnd_leave() method has not been called since the last call to
dnd_enter(). The target is notified of the drop by a call to its
method dnd_commit(source, event).
If no final target object is selected, and there was an old target
object, its dnd_leave(source, event) method is called to complete the
dnd sequence.
Finally, the source object is notified that the drag-and-drop process
is over, by a call to source.dnd_end(target, event), specifying either
the selected target object, or None if no target object was selected.
The source object can use this to implement the commit action; this is
sometimes simpler than to do it in the target's dnd_commit(). The
target's dnd_commit() method could then simply be aliased to
dnd_leave().
At any time during a dnd sequence, the application can cancel the
sequence by calling the cancel() method on the object returned by
dnd_start(). This will call dnd_leave() if a target is currently
active; it will never call dnd_commit().
""" |
#!/usr/bin/env python
#python mapk/plot_mean.py 09/data/mapk3_1e-15_0.25_fixed_0_normal_ALL_tc.dat 09/data/mapk3_1e-15_0.5_fixed_0_normal_ALL_tc.dat 09/data/mapk3_1e-15_1_fixed_0_normal_ALL_tc.dat 09/data/mapk3_1e-15_2_fixed_0_normal_ALL_tc.dat 09/data/mapk3_1e-15_4_fixed_0_normal_ALL_tc.dat
# python mapk/plot_mean.py 09/data/mapk3_1e-15_0.25_fixed_1e-2_normal_ALL_tc.dat 09/data/mapk3_1e-15_0.5_fixed_1e-2_normal_ALL_tc.dat 09/data/mapk3_1e-15_1_fixed_1e-2_normal_ALL_tc.dat 09/data/mapk3_1e-15_2_fixed_1e-2_normal_ALL_tc.dat 09/data/mapk3_1e-15_4_fixed_1e-2_normal_ALL_tc.dat
# python mapk/plot_mean.py 09/data/mapk3_1e-15_0.25_fixed_1e-6_normal_ALL_tc.dat 09/data/mapk3_1e-15_0.5_fixed_1e-6_normal_ALL_tc.dat 09/data/mapk3_1e-15_1_fixed_1e-6_normal_ALL_tc.dat 09/data/mapk3_1e-15_2_fixed_1e-6_normal_ALL_tc.dat 09/data/mapk3_1e-15_4_fixed_1e-6_normal_ALL_tc.dat
# python mapk/plot_mean.py 09/data/mapk3_1e-15_4_fixed_0_normal_ALL_tc.dat 09/data/mapk3_1e-15_4_fixed_1e-6_normal_ALL_tc.dat 09/data/mapk3_1e-15_4_fixed_1e-5_normal_ALL_tc.dat 09/data/mapk3_1e-15_4_fixed_1e-4_normal_ALL_tc.dat 09/data/mapk3_1e-15_4_fixed_1e-3_normal_ALL_tc.dat 09/data/mapk3_1e-15_4_fixed_1e-2_normal_ALL_tc.dat 09/data/mapk3_1e-15_4_fixed_1e-1_normal_ALL_tc.dat
# with lower D, ti=1e-6
# python plot_mean.py 09-3/data/mapk3_1e-15_0.03125_fixed_1e-6_normal_ALL_tc.dat 09-3/data/mapk3_1e-15_0.0625_fixed_1e-6_normal_ALL_tc.dat 09-3/data/mapk3_1e-15_0.125_fixed_1e-6_normal_ALL_tc.dat 09/data/mapk3_1e-15_0.25_fixed_1e-6_normal_ALL_tc.dat 09/data/mapk3_1e-15_0.5_fixed_1e-6_normal_ALL_tc.dat 09/data/mapk3_1e-15_1_fixed_1e-6_normal_ALL_tc.dat 09/data/mapk3_1e-15_2_fixed_1e-6_normal_ALL_tc.dat 09/data/mapk3_1e-15_4_fixed_1e-6_normal_ALL_tc.dat
# less # of data set
# python plot_mean.py 09-3/data/mapk3_1e-15_0.03125_fixed_1e-6_normal_ALL_tc.dat 09-3/data/mapk3_1e-15_0.0625_fixed_1e-6_normal_ALL_tc.dat 09/data/mapk3_1e-15_0.25_fixed_1e-6_normal_ALL_tc.dat 09/data/mapk3_1e-15_1_fixed_1e-6_normal_ALL_tc.dat 09/data/mapk3_1e-15_4_fixed_1e-6_normal_ALL_tc.dat
# with lower D, ti=1e-2
# python plot_mean.py 09-3/data/mapk3_1e-15_0.03125_fixed_1e-2_normal_ALL_tc.dat 09-3/data/mapk3_1e-15_0.0625_fixed_1e-2_normal_ALL_tc.dat 09-3/data/mapk3_1e-15_0.125_fixed_1e-2_normal_ALL_tc.dat 09/data/mapk3_1e-15_0.25_fixed_1e-2_normal_ALL_tc.dat 09/data/mapk3_1e-15_0.5_fixed_1e-2_normal_ALL_tc.dat 09/data/mapk3_1e-15_1_fixed_1e-2_normal_ALL_tc.dat 09/data/mapk3_1e-15_2_fixed_1e-2_normal_ALL_tc.dat 09/data/mapk3_1e-15_4_fixed_1e-2_normal_ALL_tc.dat
# less # of data set
# python plot_mean.py 09-3/data/mapk3_1e-15_0.03125_fixed_1e-2_normal_ALL_tc.dat 09-3/data/mapk3_1e-15_0.0625_fixed_1e-2_normal_ALL_tc.dat 09/data/mapk3_1e-15_0.25_fixed_1e-2_normal_ALL_tc.dat 09/data/mapk3_1e-15_1_fixed_1e-2_normal_ALL_tc.dat 09/data/mapk3_1e-15_4_fixed_1e-2_normal_ALL_tc.dat
|
# vim: set fileencoding=utf-8 :
# ***********************IMPORTANT NMAP LICENSE TERMS************************
# * *
# * The Nmap Security Scanner is (C) 1996-2013 Insecure.Com LLC. Nmap is *
# * also a registered trademark of Insecure.Com LLC. This program is free *
# * software; you may redistribute and/or modify it under the terms of the *
# * GNU General Public License as published by the Free Software *
# * Foundation; Version 2 ("GPL"), BUT ONLY WITH ALL OF THE CLARIFICATIONS *
# * AND EXCEPTIONS DESCRIBED HEREIN. This guarantees your right to use, *
# * modify, and redistribute this software under certain conditions. If *
# * you wish to embed Nmap technology into proprietary software, we sell *
# * alternative licenses (contact EMAIL Dozens of software *
# * vendors already license Nmap technology such as host discovery, port *
# * scanning, OS detection, version detection, and the Nmap Scripting *
# * Engine. *
# * *
# * Note that the GPL places important restrictions on "derivative works", *
# * yet it does not provide a detailed definition of that term. To avoid *
# * misunderstandings, we interpret that term as broadly as copyright law *
# * allows. For example, we consider an application to constitute a *
# * derivative work for the purpose of this license if it does any of the *
# * following with any software or content covered by this license *
# * ("Covered Software"): *
# * *
# * o Integrates source code from Covered Software. *
# * *
# * o Reads or includes copyrighted data files, such as Nmap's nmap-os-db *
# * or nmap-service-probes. *
# * *
# * o Is designed specifically to execute Covered Software and parse the *
# * results (as opposed to typical shell or execution-menu apps, which will *
# * execute anything you tell them to). *
# * *
# * o Includes Covered Software in a proprietary executable installer. The *
# * installers produced by InstallShield are an example of this. Including *
# * Nmap with other software in compressed or archival form does not *
# * trigger this provision, provided appropriate open source decompression *
# * or de-archiving software is widely available for no charge. For the *
# * purposes of this license, an installer is considered to include Covered *
# * Software even if it actually retrieves a copy of Covered Software from *
# * another source during runtime (such as by downloading it from the *
# * Internet). *
# * *
# * o Links (statically or dynamically) to a library which does any of the *
# * above. *
# * *
# * o Executes a helper program, module, or script to do any of the above. *
# * *
# * This list is not exclusive, but is meant to clarify our interpretation *
# * of derived works with some common examples. Other people may interpret *
# * the plain GPL differently, so we consider this a special exception to *
# * the GPL that we apply to Covered Software. Works which meet any of *
# * these conditions must conform to all of the terms of this license, *
# * particularly including the GPL Section 3 requirements of providing *
# * source code and allowing free redistribution of the work as a whole. *
# * *
# * As another special exception to the GPL terms, Insecure.Com LLC grants *
# * permission to link the code of this program with any version of the *
# * OpenSSL library which is distributed under a license identical to that *
# * listed in the included docs/licenses/OpenSSL.txt file, and distribute *
# * linked combinations including the two. *
# * *
# * Any redistribution of Covered Software, including any derived works, *
# * must obey and carry forward all of the terms of this license, including *
# * obeying all GPL rules and restrictions. For example, source code of *
# * the whole work must be provided and free redistribution must be *
# * allowed. All GPL references to "this License", are to be treated as *
# * including the special and conditions of the license text as well. *
# * *
# * Because this license imposes special exceptions to the GPL, Covered *
# * Work may not be combined (even as part of a larger work) with plain GPL *
# * software. The terms, conditions, and exceptions of this license must *
# * be included as well. This license is incompatible with some other open *
# * source licenses as well. In some cases we can relicense portions of *
# * Nmap or grant special permissions to use it in other open source *
# * software. Please contact EMAIL with any such requests. *
# * Similarly, we don't incorporate incompatible open source software into *
# * Covered Software without special permission from the copyright holders. *
# * *
# * If you have any questions about the licensing restrictions on using *
# * Nmap in other works, are happy to help. As mentioned above, we also *
# * offer alternative license to integrate Nmap into proprietary *
# * applications and appliances. These contracts have been sold to dozens *
# * of software vendors, and generally include a perpetual license as well *
# * as providing for priority support and updates. They also fund the *
# * continued development of Nmap. Please email EMAIL for *
# * further information. *
# * *
# * If you received these files with a written license agreement or *
# * contract stating terms other than the terms above, then that *
# * alternative license agreement takes precedence over these comments. *
# * *
# * Source is provided to this software because we believe users have a *
# * right to know exactly what a program is going to do before they run it. *
# * This also allows you to audit the software for security holes (none *
# * have been found so far). *
# * *
# * Source code also allows you to port Nmap to new platforms, fix bugs, *
# * and add new features. You are highly encouraged to send your changes *
# * to the EMAIL mailing list for possible incorporation into the *
# * main distribution. By sending these changes to Fyodor or one of the *
# * Insecure.Org development mailing lists, or checking them into the Nmap *
# * source code repository, it is understood (unless you specify otherwise) *
# * that you are offering the Nmap Project (Insecure.Com LLC) the *
# * unlimited, non-exclusive right to reuse, modify, and relicense the *
# * code. Nmap will always be available Open Source, but this is important *
# * because the inability to relicense code has caused devastating problems *
# * for other Free Software projects (such as KDE and NASM). We also *
# * occasionally relicense the code to third parties as discussed above. *
# * If you wish to specify special license conditions of your *
# * contributions, just say so when you send them. *
# * *
# * This program is distributed in the hope that it will be useful, but *
# * WITHOUT ANY WARRANTY; without even the implied warranty of *
# * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Nmap *
# * license file for more details (it's in a COPYING file included with *
# * Nmap, and also available from https://svn.nmap.org/nmap/COPYING *
# * *
# ***************************************************************************/
|
# -*- coding: utf-8 -*-
#
# Picard, the next-generation MusicBrainz tagger
#
# Copyright (C) 2004 NAME Copyright (C) 2006 NAME Copyright (C) 2014 Sophist-UK
# Copyright (C) 2014, 2018, 2020 NAME Copyright (C) 2017 NAME Copyright (C) 2018-2019 NAME Copyright (C) 2020 NAME Copyright (C) 2020 Undearius
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
# This modules provides functionality for simplifying unicode strings.
# The unicode character set (of over 1m codepoints and 24,000 characters) includes:
# Normal ascii (latin) non-accented characters
# Combined latin characters e.g. ae in normal usage
# Compatibility combined latin characters (retained for compatibility with other character sets)
# These can look very similar to normal characters and can be confusing for searches, sort orders etc.
# Non-latin (e.g. japanese, greek, hebrew etc.) characters
# Both latin and non-latin characters can be accented. Accents can be either:
# Provided by separate nonspacing_mark characters which are visually overlaid (visually 1 character is actually 2); or
# Integrated accented characters (i.e. non-accented characters combined with a nonspace_mark into a single character)
# Again these can be confusing for searches, sort orders etc.
# Punctuation can also be confusing in unicode e.g. several types of single or double quote mark.
# For latin script:
# Combined characters, accents and punctuation can be visually similar but look different to search engines,
# sort orders etc. and the number of ways to use similar looking characters can (does) result in inconsistent
# usage inside Music metadata.
#
# Simplifying # the unicode character sets by many-to-one mappings can improve consistency and reduce confusion,
# however sometimes the choice of specific characters can be a deliberate part of an album, song title or artist name
# (and should not therefore be changed without careful thought) and occasionally the choice of characters can be
# malicious (i.e. to defeat firewalls or spam filters or to appear to be something else).
#
# Finally, given the size of the unicode character set, fonts are unlikely to display all characters,
# making simplification a necessity.
#
# Simplification may also be needed to make tags conform to ISO-8859-1 (extended ascii) or to make tags or filenames
# into ascii, perhaps because the file system or player cannot support unicode.
#
# Non-latin scripts may also need to be converted to latin scripts through:
# Translation (e.g. hebrew word for mother is translated to "mother"); or
# Transliteration (e.g. the SOUND of the hebrew letter or word is spelt out in latin)
# These are non-trivial, and the software to do these is far from comprehensive.
# This module provides utility functions to enable simplification of latin and punctuation unicode:
# 1. simplify compatibility characters;
# 2. split combined characters;
# 3. remove accents (entirely or if not in ISO-8859-1 as applicable);
# 4. replace remaining non-ascii or non-ISO-8859-1 characters with a default character
# This module also provides an extension infrastructure to allow translation and / or transliteration plugins to be added.
|
"""
[2015-12-16] Challenge #245 [Intermediate] Ggggggg gggg Ggggg-ggggg!
https://www.reddit.com/r/dailyprogrammer/comments/3x3hqa/20151216_challenge_245_intermediate_ggggggg_gggg/
We have discovered a new species of aliens! They look like
[this](https://www.redditstatic.com/about/assets/reddit-alien.png) and are trying to communicate with us using the
/r/ggggg subreddit! As you might have been able to tell, though, it is awfully hard to understand what they're saying
since their super-advanced alphabet only makes use of two letters: "g" and "G". Fortunately, their numbers, spacing and
punctuation are the same.
We are going to write a program to translate to and from our alphabet to theirs, so we can be enlightened by their
intelligence.
Feel free to code either the encoding program, the decoding program, or both.
^Also, ^please ^do ^not ^actually ^harass ^the ^residents ^of ^/r/ggggg.
# Part 1: Decoding
First, we need to be able to understand what the Ggggg aliens are saying. Fortunately, they are cooperative in this
matter, and they helpfully include a "key" to translate between their g-based letters and our Latin letters. Your
decoder program needs to read this key from the first line of the input, then use it to translate the rest of the input.
## Sample decoder input 1
H GgG d gGg e ggG l GGg o gGG r Ggg w ggg
GgGggGGGgGGggGG, ggggGGGggGGggGg!
## Sample decoder output 1
Hello, world!
**Explanation:** Reading the input, the key is:
* H = GgG
* d = gGg
* e = ggG
* l = GGg
* o = gGG
* r = Ggg
* w = ggg
When we read the message from left to right, we can divide it into letters like so (alternating letters bolded):
> **GgG**ggG**GGg**GGg**gGG**, **ggg**gGG**Ggg**GGg**gGg**!
Take those letter groups and turn them into letters using the key, and you get "Hello, world!"
## Sample decoder input 2
a GgG d GggGg e GggGG g GGGgg h GGGgG i GGGGg l GGGGG m ggg o GGg p Gggg r gG y ggG
GGGgGGGgGGggGGgGggG /gG/GggGgGgGGGGGgGGGGGggGGggggGGGgGGGgggGGgGggggggGggGGgG!
Note that the letters are *not* guaranteed to be of equal length.
## Sample decoder output 2
hooray /r/dailyprogrammer!
# Part 2: Encoding
Next, we will go in the other direction. Come up with a key based on the letters "g" and "G" that maps all the letters
in a given message to Ggggg equivalents, use it to translate the message, then output both the key and the translated
message. You can double-check your work using the decoding script from part 1.
## Sample input
Hello, world!
## Sample output
H GgG d gGg e ggG l GGg o gGG r Ggg w ggg
GgGggGGGgGGggGG, ggggGGGggGGggGg!
Your key (and thus message) may end up being completely different than the one provided here. That's fine, as long as
it can be translated back.
# Part 2.1 (Bonus points): Compression
Just as it annoys us to see someone typing "llliiiiikkkeeee ttttthhhiiiisssss", the Ggggg aliens don't actually enjoy
unnecessary verbosity. Modify your encoding script to create a key that results in the *shortest possible Ggggg
message*. You should be able to decode the output using the same decoder used in part 1 (the second sample input/output
in part 1 is actually compressed).
Here's a [hint](https://en.wikipedia.org/wiki/Variable-length_code).
## Sample input:
Here's the thing. You said a "jackdaw is a crow."
Is it in the same family? Yes. No one's arguing that.
As someone who is a scientist who studies crows, I am telling you, specifically, in science, no one calls jackdaws
crows. If you want to be "specific" like you said, then you shouldn't either. They're not the same thing.
If you're saying "crow family" you're referring to the taxonomic grouping of Corvidae, which includes things from
nutcrackers to blue jays to ravens.
So your reasoning for calling a jackdaw a crow is because random people "call the black ones crows?" Let's get
grackles and blackbirds in there, then, too.
Also, calling someone a human or an ape? It's not one or the other, that's not how taxonomy works. They're both. A
jackdaw is a jackdaw and a member of the crow family. But that's not what you said. You said a jackdaw is a crow, which
is not true unless you're okay with calling all members of the crow family crows, which means you'd call blue jays,
ravens, and other birds crows, too. Which you said you don't.
It's okay to just admit you're wrong, you know?
## Sample output:
Found here (a bit too big to paste in the challenge itself): http://www.hastebin.com/raw/inihibehux.txt
Remember you can test your decoder on this message, too!
--------
C GgggGgg H GgggGgG T GgggGGg a gGg c GGggG d GggG e GgG g ggGgG h GGgGg i gGGg j GgggGGG l gGGG m ggGGg n GGgGG o ggg
p ggGGG r GGGg s GGGG t GGgggG u ggGgg v Ggggg w GGggggg y GGggggG
GgggGGgGGgGggGGgGGGG GGggGGGgGggGggGGGgGGGGgGGGgGGggGgGGgG GGggggggGgGGGG ggGGGGGGggggggGGGgggGGGGGgGGggG gGgGGgGGGggG
GggGgGGgGGGGGGggGggGggGGGGGGGGGgGGggG gggGggggGgGGGGg gGgGGgggG
/GGGg/GggGgGggGGggGGGGGggggGggGGGGGGggggggGgGGGGggGgggGGgggGGgGgGGGGg_gGGgGggGGgGgGgGGGG. GgggGgGgGgGggggGgG gGg
GGggGgggggggGGG GGggGGGgGggGggGGGgGGGGgGGGgGGggGgGGgG gGGgGggGGgGgGg? GgggGgggggggGGgGgG GgggGGGggggGGgGGgGG ggGggGGGG
gggGggggGgGGGGg GGgggGGGgGgGgGGGGgGgG!
""" |
#import unittest
#
#class TestCase_Unit( unittest.TestCase ):
#
# def test_export_insert_nok( self ):
#
# res = self.handler.export_insert( 1, 2 )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_insert( ( 1, ), 2 )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_insert( 1, ( 2, ) )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_insert( 1, { 2: 2} )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_insert( ( 1, ), ( 2, ) )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_insert( ( 1, ), { 2: 2 } )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_insert( [ 1, ], 2 )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_insert( 1, [ 2, ] )
# self.assertEquals( res['OK'], False )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_insert( [ 1, ], [ 2, ] )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_insert( [ 1, ], { 2: 2 } )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_insert( [ 1, ], { 2: 2 } )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_insert( [1,], {'2':2} )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_insert( (1,), {'2':2} )
# self.assertEquals( res[ 'OK' ], False )
#
# def test_export_insert_ok( self ):
#
# res = self.handler.export_insert( {'1':1}, {'2':2} )
# self.assertEquals( res[ 'OK' ], True )
#
##################################################################################
#
# def test_export_update_nok( self ):
#
# res = self.handler.export_update( 1, 2 )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_update( ( 1, ), 2 )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_update( 1, ( 2, ) )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_update( 1, { 2: 2} )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_update( ( 1, ), ( 2, ) )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_update( ( 1, ), { 2: 2 } )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_update( [ 1, ], 2 )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_update( 1, [ 2, ] )
# self.assertEquals( res['OK'], False )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_update( [ 1, ], [ 2, ] )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_update( [ 1, ], { 2: 2 } )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_update( [ 1, ], { 2: 2 } )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_update( [1,], {'2':2} )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_update( (1,), {'2':2} )
# self.assertEquals( res[ 'OK' ], False )
#
# def test_export_update_ok( self ):
#
# res = self.handler.export_update( {'1':1}, {'2':2} )
# self.assertEquals( res[ 'OK' ], True )
#
##################################################################################
#
# def test_export_get_nok( self ):
#
# res = self.handler.export_get( 1, 2 )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_get( ( 1, ), 2 )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_get( 1, ( 2, ) )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_get( 1, { 2: 2} )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_get( ( 1, ), ( 2, ) )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_get( ( 1, ), { 2: 2 } )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_get( [ 1, ], 2 )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_get( 1, [ 2, ] )
# self.assertEquals( res['OK'], False )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_get( [ 1, ], [ 2, ] )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_get( [ 1, ], { 2: 2 } )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_get( [ 1, ], { 2: 2 } )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_get( [1,], {'2':2} )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_get( (1,), {'2':2} )
# self.assertEquals( res[ 'OK' ], False )
#
# def test_export_get_ok( self ):
#
# res = self.handler.export_get( {'1':1}, {'2':2} )
# self.assertEquals( res[ 'OK' ], True )
#
##################################################################################
#
# def test_export_delete_nok( self ):
#
# res = self.handler.export_delete( 1, 2 )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_delete( ( 1, ), 2 )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_delete( 1, ( 2, ) )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_delete( 1, { 2: 2} )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_delete( ( 1, ), ( 2, ) )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_delete( ( 1, ), { 2: 2 } )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_delete( [ 1, ], 2 )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_delete( 1, [ 2, ] )
# self.assertEquals( res['OK'], False )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_delete( [ 1, ], [ 2, ] )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_delete( [ 1, ], { 2: 2 } )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_delete( [ 1, ], { 2: 2 } )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_delete( [1,], {'2':2} )
# self.assertEquals( res['OK'], False )
# res = self.handler.export_delete( (1,), {'2':2} )
# self.assertEquals( res[ 'OK' ], False )
#
# def test_export_delete_ok( self ):
#
# res = self.handler.export_delete( {'1':1}, {'2':2} )
# self.assertEquals( res[ 'OK' ], True )
#
#################################################################################
|
"""Configuration file parser.
A configuration file consists of sections, lead by a "[section]" header,
and followed by "name: value" entries, with continuations and such in
the style of RFC 822.
Intrinsic defaults can be specified by passing them into the
ConfigParser constructor as a dictionary.
class:
ConfigParser -- responsible for parsing a list of
configuration files, and managing the parsed database.
methods:
__init__(defaults=None, dict_type=_default_dict, allow_no_value=False,
delimiters=('=', ':'), comment_prefixes=('#', ';'),
inline_comment_prefixes=None, strict=True,
empty_lines_in_values=True):
Create the parser. When `defaults' is given, it is initialized into the
dictionary or intrinsic defaults. The keys must be strings, the values
must be appropriate for %()s string interpolation.
When `dict_type' is given, it will be used to create the dictionary
objects for the list of sections, for the options within a section, and
for the default values.
When `delimiters' is given, it will be used as the set of substrings
that divide keys from values.
When `comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in empty lines. Comments can be
indented.
When `inline_comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in non-empty lines.
When `strict` is True, the parser won't allow for any section or option
duplicates while reading from a single source (file, string or
dictionary). Default is True.
When `empty_lines_in_values' is False (default: True), each empty line
marks the end of an option. Otherwise, internal empty lines of
a multiline option are kept as part of the value.
When `allow_no_value' is True (default: False), options without
values are accepted; the value presented for these is None.
sections()
Return all the configuration section names, sans DEFAULT.
has_section(section)
Return whether the given section exists.
has_option(section, option)
Return whether the given option exists in the given section.
options(section)
Return list of configuration options for the named section.
read(filenames, encoding=None)
Read and parse the list of named configuration files, given by
name. A single filename is also allowed. Non-existing files
are ignored. Return list of successfully read files.
read_file(f, filename=None)
Read and parse one configuration file, given as a file object.
The filename defaults to f.name; it is only used in error
messages (if f has no `name' attribute, the string `<???>' is used).
read_string(string)
Read configuration from a given string.
read_dict(dictionary)
Read configuration from a dictionary. Keys are section names,
values are dictionaries with keys and values that should be present
in the section. If the used dictionary type preserves order, sections
and their keys will be added in order. Values are automatically
converted to strings.
get(section, option, raw=False, vars=None, fallback=_UNSET)
Return a string value for the named option. All % interpolations are
expanded in the return values, based on the defaults passed into the
constructor and the DEFAULT section. Additional substitutions may be
provided using the `vars' argument, which must be a dictionary whose
contents override any pre-existing defaults. If `option' is a key in
`vars', the value from `vars' is used.
getint(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to an integer.
getfloat(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a float.
getboolean(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a boolean (currently case
insensitively defined as 0, false, no, off for False, and 1, true,
yes, on for True). Returns False or True.
items(section=_UNSET, raw=False, vars=None)
If section is given, return a list of tuples with (name, value) for
each option in the section. Otherwise, return a list of tuples with
(section_name, section_proxy) for each section, including DEFAULTSECT.
remove_section(section)
Remove the given file section and all its options.
remove_option(section, option)
Remove the given option from the given section.
set(section, option, value)
Set the given option.
write(fp, space_around_delimiters=True)
Write the configuration state in .ini format. If
`space_around_delimiters' is True (the default), delimiters
between keys and values are surrounded by spaces.
""" |
"""Configuration file parser.
A configuration file consists of sections, lead by a "[section]" header,
and followed by "name: value" entries, with continuations and such in
the style of RFC 822.
Intrinsic defaults can be specified by passing them into the
ConfigParser constructor as a dictionary.
class:
ConfigParser -- responsible for parsing a list of
configuration files, and managing the parsed database.
methods:
__init__(defaults=None, dict_type=_default_dict, allow_no_value=False,
delimiters=('=', ':'), comment_prefixes=('#', ';'),
inline_comment_prefixes=None, strict=True,
empty_lines_in_values=True):
Create the parser. When `defaults' is given, it is initialized into the
dictionary or intrinsic defaults. The keys must be strings, the values
must be appropriate for %()s string interpolation.
When `dict_type' is given, it will be used to create the dictionary
objects for the list of sections, for the options within a section, and
for the default values.
When `delimiters' is given, it will be used as the set of substrings
that divide keys from values.
When `comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in empty lines. Comments can be
indented.
When `inline_comment_prefixes' is given, it will be used as the set of
substrings that prefix comments in non-empty lines.
When `strict` is True, the parser won't allow for any section or option
duplicates while reading from a single source (file, string or
dictionary). Default is True.
When `empty_lines_in_values' is False (default: True), each empty line
marks the end of an option. Otherwise, internal empty lines of
a multiline option are kept as part of the value.
When `allow_no_value' is True (default: False), options without
values are accepted; the value presented for these is None.
sections()
Return all the configuration section names, sans DEFAULT.
has_section(section)
Return whether the given section exists.
has_option(section, option)
Return whether the given option exists in the given section.
options(section)
Return list of configuration options for the named section.
read(filenames, encoding=None)
Read and parse the list of named configuration files, given by
name. A single filename is also allowed. Non-existing files
are ignored. Return list of successfully read files.
read_file(f, filename=None)
Read and parse one configuration file, given as a file object.
The filename defaults to f.name; it is only used in error
messages (if f has no `name' attribute, the string `<???>' is used).
read_string(string)
Read configuration from a given string.
read_dict(dictionary)
Read configuration from a dictionary. Keys are section names,
values are dictionaries with keys and values that should be present
in the section. If the used dictionary type preserves order, sections
and their keys will be added in order. Values are automatically
converted to strings.
get(section, option, raw=False, vars=None, fallback=_UNSET)
Return a string value for the named option. All % interpolations are
expanded in the return values, based on the defaults passed into the
constructor and the DEFAULT section. Additional substitutions may be
provided using the `vars' argument, which must be a dictionary whose
contents override any pre-existing defaults. If `option' is a key in
`vars', the value from `vars' is used.
getint(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to an integer.
getfloat(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a float.
getboolean(section, options, raw=False, vars=None, fallback=_UNSET)
Like get(), but convert value to a boolean (currently case
insensitively defined as 0, false, no, off for False, and 1, true,
yes, on for True). Returns False or True.
items(section=_UNSET, raw=False, vars=None)
If section is given, return a list of tuples with (name, value) for
each option in the section. Otherwise, return a list of tuples with
(section_name, section_proxy) for each section, including DEFAULTSECT.
remove_section(section)
Remove the given file section and all its options.
remove_option(section, option)
Remove the given option from the given section.
set(section, option, value)
Set the given option.
write(fp, space_around_delimiters=True)
Write the configuration state in .ini format. If
`space_around_delimiters' is True (the default), delimiters
between keys and values are surrounded by spaces.
""" |
# This code is part of Ansible, but is an independent component.
# This particular file snippet, and this file snippet only, is BSD licensed.
# Modules you write using this snippet, which is embedded dynamically by Ansible
# still belong to the author of the module, and may assign their own license
# to the complete work.
#
# Copyright (c), NAME <EMAIL>, 2012-2013
# Copyright (c), NAME <EMAIL>, 2015
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * 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.
#
# THIS 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.
#
# The match_hostname function and supporting code is under the terms and
# conditions of the Python Software Foundation License. They were taken from
# the Python3 standard library and adapted for use in Python2. See comments in the
# source for which code precisely is under this License. PSF License text
# follows:
#
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# FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
# A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON,
# OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
#
# 6. This License Agreement will automatically terminate upon a material
# breach of its terms and conditions.
#
# 7. Nothing in this License Agreement shall be deemed to create any
# relationship of agency, partnership, or joint venture between PSF and
# Licensee. This License Agreement does not grant permission to use PSF
# trademarks or trade name in a trademark sense to endorse or promote
# products or services of Licensee, or any third party.
#
# 8. By copying, installing or otherwise using Python, Licensee
# agrees to be bound by the terms and conditions of this License
# Agreement.
|
"""
Wrappers to LAPACK library
==========================
flapack -- wrappers for Fortran [*] LAPACK routines
clapack -- wrappers for ATLAS LAPACK routines
calc_lwork -- calculate optimal lwork parameters
get_lapack_funcs -- query for wrapper functions.
[*] If ATLAS libraries are available then Fortran routines
actually use ATLAS routines and should perform equally
well to ATLAS routines.
Module flapack
++++++++++++++
In the following all function names are shown without
type prefix (s,d,c,z). Optimal values for lwork can
be computed using calc_lwork module.
Linear Equations
----------------
Drivers::
lu,piv,x,info = gesv(a,b,overwrite_a=0,overwrite_b=0)
lub,piv,x,info = gbsv(kl,ku,ab,b,overwrite_ab=0,overwrite_b=0)
c,x,info = posv(a,b,lower=0,overwrite_a=0,overwrite_b=0)
Computational routines::
lu,piv,info = getrf(a,overwrite_a=0)
x,info = getrs(lu,piv,b,trans=0,overwrite_b=0)
inv_a,info = getri(lu,piv,lwork=min_lwork,overwrite_lu=0)
c,info = potrf(a,lower=0,clean=1,overwrite_a=0)
x,info = potrs(c,b,lower=0,overwrite_b=0)
inv_a,info = potri(c,lower=0,overwrite_c=0)
inv_c,info = trtri(c,lower=0,unitdiag=0,overwrite_c=0)
Linear Least Squares (LLS) Problems
-----------------------------------
Drivers::
v,x,s,rank,info = gelss(a,b,cond=-1.0,lwork=min_lwork,overwrite_a=0,overwrite_b=0)
Computational routines::
qr,tau,info = geqrf(a,lwork=min_lwork,overwrite_a=0)
q,info = orgqr|ungqr(qr,tau,lwork=min_lwork,overwrite_qr=0,overwrite_tau=1)
Generalized Linear Least Squares (LSE and GLM) Problems
-------------------------------------------------------
Standard Eigenvalue and Singular Value Problems
-----------------------------------------------
Drivers::
w,v,info = syev|heev(a,compute_v=1,lower=0,lwork=min_lwork,overwrite_a=0)
w,v,info = syevd|heevd(a,compute_v=1,lower=0,lwork=min_lwork,overwrite_a=0)
w,v,info = syevr|heevr(a,compute_v=1,lower=0,vrange=,irange=,atol=-1.0,lwork=min_lwork,overwrite_a=0)
t,sdim,(wr,wi|w),vs,info = gees(select,a,compute_v=1,sort_t=0,lwork=min_lwork,select_extra_args=(),overwrite_a=0)
wr,(wi,vl|w),vr,info = geev(a,compute_vl=1,compute_vr=1,lwork=min_lwork,overwrite_a=0)
u,s,vt,info = gesdd(a,compute_uv=1,lwork=min_lwork,overwrite_a=0)
Computational routines::
ht,tau,info = gehrd(a,lo=0,hi=n-1,lwork=min_lwork,overwrite_a=0)
ba,lo,hi,pivscale,info = gebal(a,scale=0,permute=0,overwrite_a=0)
Generalized Eigenvalue and Singular Value Problems
--------------------------------------------------
Drivers::
w,v,info = sygv|hegv(a,b,itype=1,compute_v=1,lower=0,lwork=min_lwork,overwrite_a=0,overwrite_b=0)
w,v,info = sygvd|hegvd(a,b,itype=1,compute_v=1,lower=0,lwork=min_lwork,overwrite_a=0,overwrite_b=0)
(alphar,alphai|alpha),beta,vl,vr,info = ggev(a,b,compute_vl=1,compute_vr=1,lwork=min_lwork,overwrite_a=0,overwrite_b=0)
Auxiliary routines
------------------
a,info = lauum(c,lower=0,overwrite_c=0)
a = laswp(a,piv,k1=0,k2=len(piv)-1,off=0,inc=1,overwrite_a=0)
Module clapack
++++++++++++++
Linear Equations
----------------
Drivers::
lu,piv,x,info = gesv(a,b,rowmajor=1,overwrite_a=0,overwrite_b=0)
c,x,info = posv(a,b,lower=0,rowmajor=1,overwrite_a=0,overwrite_b=0)
Computational routines::
lu,piv,info = getrf(a,rowmajor=1,overwrite_a=0)
x,info = getrs(lu,piv,b,trans=0,rowmajor=1,overwrite_b=0)
inv_a,info = getri(lu,piv,rowmajor=1,overwrite_lu=0)
c,info = potrf(a,lower=0,clean=1,rowmajor=1,overwrite_a=0)
x,info = potrs(c,b,lower=0,rowmajor=1,overwrite_b=0)
inv_a,info = potri(c,lower=0,rowmajor=1,overwrite_c=0)
inv_c,info = trtri(c,lower=0,unitdiag=0,rowmajor=1,overwrite_c=0)
Auxiliary routines
------------------
a,info = lauum(c,lower=0,rowmajor=1,overwrite_c=0)
Module calc_lwork
+++++++++++++++++
Optimal lwork is maxwrk. Default is minwrk.
minwrk,maxwrk = gehrd(prefix,n,lo=0,hi=n-1)
minwrk,maxwrk = gesdd(prefix,m,n,compute_uv=1)
minwrk,maxwrk = gelss(prefix,m,n,nrhs)
minwrk,maxwrk = getri(prefix,n)
minwrk,maxwrk = geev(prefix,n,compute_vl=1,compute_vr=1)
minwrk,maxwrk = heev(prefix,n,lower=0)
minwrk,maxwrk = syev(prefix,n,lower=0)
minwrk,maxwrk = gees(prefix,n,compute_v=1)
minwrk,maxwrk = geqrf(prefix,m,n)
minwrk,maxwrk = gqr(prefix,m,n)
""" |
# functions and views for supporting AJAX requests and responses
#
# this is a high-level package that exports most symbols from
# sub-modules; see those packages for implementation details
# AJAX is pretty simple. Requests look like ordinary HTTP requests
# (and must be validated the same way) but the response needs the
# correct MIME type, and the body is just JSON data, XML, or HTML.
#
# On the browser side, though, there are a couple of wrinkles.
# If the server returns a redirect response, it won't reload the
# host page in the browser, it will just cause the browser to
# re-issue the AJAX request to the redirected URL. Also, we would
# like to be able to return error messages that could be presented
# in modal dialogs, rather than forcing the host page to be
# redirected to an error page (thus potentially losing state).
#
# We therefore define some standards around how WE will be doing
# AJAX:
#
# 1. All AJAX requests are POST requests.
#
# AJAX allows GET but the browser will cache responses. This
# is only appropriate if we are absolutely, positively sure
# the request will never, ever fail. And since we're not really
# going to be that sure, we should always preserve our ability
# to process errors in an intelligent fashion.
#
# 2. All AJAX responses will be JSON, not HTML or XML.
#
# If we return raw HTML, we have to try to guess when errors
# happen and how to present them to the user. Also, the server
# would have to know the context of the request in order to
# format such an error in a way that makes sense. Skip it;
# return HTML responses in a JSON wrapper (see below) so that
# errors can be handled the same way for all AJAX calls.
#
# 3. All AJAX responses will adhere to a common format.
#
# {
# # mark this as a properly-formatted response
# 'sculpt': 'ajax',
#
# # mutually-exclusive error-ish responses
# 'location': <url>,
# 'exception': {
# 'code': <error_code>,
# 'title': <error_title>, # optional
# 'message': <error_message>
# },
# 'error': {
# 'code': <error_code>,
# 'title': <error_title>, # optional
# 'message': <error_message>
# },
# 'form_error': [
# [ <field_name>, <field_label>, [ <error_message>, ... ] ],
# ...
# ],
#
# # non-exclusive success-ish responses
# 'results': <app-specific data>
# 'modal': {
# 'code': <error_code>,
# 'title': <error_title>, # optional
# 'message': <error_message>
# },
# 'toast': [
# {
# 'duration': <time_in_milliseconds>,
# 'class_name': <css_class>,
# 'message': <toast_message>,
# 'delay': <delay> # optional
# },
# ...
# ],
# 'html': [
# {
# 'id': <DOM_node_id>,
# 'html': <replacement_html>,
# 'class_add': <classes_to_add_to_DOM_node>, # optional
# 'class_remove': <classesto_remove_from_DOM_node> # optional
# },
# ...
# ],
# }
#
# For error-ish responses, only ONE of the top-level keys
# (location, exception, error, form_error) will be present.
# For success-ish responses, at least one (but possibly more)
# of the top-level keys is required. For details, see the
# client-side code in sculpt_ajax.js.
#
# NOTE: ALL formatted responses are returned with HTTP status
# 200 (OK), including errors. Other errors will be interpreted
# by the JavaScript AJAX handler as hard server errors and
# generate a canned response.
#
# NOTE: we extract exceptions as a different type because they
# indicate a problem with the server code, and they're handled
# differently on the client side (styled).
#
# 4. Forms will always be submitted via AJAX.
#
# The normal pattern is for a GET request to a form page to
# return an empty (or perhaps pre-populated) form, and the POST
# request to be the submission of that form data; successful
# POST returns a redirect to the next page, and a failed POST
# returns HTML with form errors embedded.
#
# Instead, we want the POST form submission to happen via AJAX
# so that we can return a lighter-weight JSON blob of errors
# that can then be used to do some dynamic error highlighting.
# We could certainly submit the data in a non-AJAX form, but
# that makes the resulting display of errors take longer because
# we're not just sending the error data, but the whole page
# around it. That's not a great user experience, so we submit
# via AJAX and return lighter-weight data.
#
# This means that for form handlers, GET returns the regular
# HTML and is not an AJAX request, but POST returns JSON data
# because it IS an AJAX request.
|
"""Generic socket server classes.
This module tries to capture the various aspects of defining a server:
For socket-based servers:
- address family:
- AF_INET{,6}: IP (Internet Protocol) sockets (default)
- AF_UNIX: Unix domain sockets
- others, e.g. AF_DECNET are conceivable (see <socket.h>
- socket type:
- SOCK_STREAM (reliable stream, e.g. TCP)
- SOCK_DGRAM (datagrams, e.g. UDP)
For request-based servers (including socket-based):
- client address verification before further looking at the request
(This is actually a hook for any processing that needs to look
at the request before anything else, e.g. logging)
- how to handle multiple requests:
- synchronous (one request is handled at a time)
- forking (each request is handled by a new process)
- threading (each request is handled by a new thread)
The classes in this module favor the server type that is simplest to
write: a synchronous TCP/IP server. This is bad class design, but
save some typing. (There's also the issue that a deep class hierarchy
slows down method lookups.)
There are five classes in an inheritance diagram, four of which represent
synchronous servers of four types:
+------------+
| BaseServer |
+------------+
|
v
+-----------+ +------------------+
| TCPServer |------->| UnixStreamServer |
+-----------+ +------------------+
|
v
+-----------+ +--------------------+
| UDPServer |------->| UnixDatagramServer |
+-----------+ +--------------------+
Note that UnixDatagramServer derives from UDPServer, not from
UnixStreamServer -- the only difference between an IP and a Unix
stream server is the address family, which is simply repeated in both
unix server classes.
Forking and threading versions of each type of server can be created
using the ForkingMixIn and ThreadingMixIn mix-in classes. For
instance, a threading UDP server class is created as follows:
class ThreadingUDPServer(ThreadingMixIn, UDPServer): pass
The Mix-in class must come first, since it overrides a method defined
in UDPServer! Setting the various member variables also changes
the behavior of the underlying server mechanism.
To implement a service, you must derive a class from
BaseRequestHandler and redefine its handle() method. You can then run
various versions of the service by combining one of the server classes
with your request handler class.
The request handler class must be different for datagram or stream
services. This can be hidden by using the request handler
subclasses StreamRequestHandler or DatagramRequestHandler.
Of course, you still have to use your head!
For instance, it makes no sense to use a forking server if the service
contains state in memory that can be modified by requests (since the
modifications in the child process would never reach the initial state
kept in the parent process and passed to each child). In this case,
you can use a threading server, but you will probably have to use
locks to avoid two requests that come in nearly simultaneous to apply
conflicting changes to the server state.
On the other hand, if you are building e.g. an HTTP server, where all
data is stored externally (e.g. in the file system), a synchronous
class will essentially render the service "deaf" while one request is
being handled -- which may be for a very long time if a client is slow
to read all the data it has requested. Here a threading or forking
server is appropriate.
In some cases, it may be appropriate to process part of a request
synchronously, but to finish processing in a forked child depending on
the request data. This can be implemented by using a synchronous
server and doing an explicit fork in the request handler class
handle() method.
Another approach to handling multiple simultaneous requests in an
environment that supports neither threads nor fork (or where these are
too expensive or inappropriate for the service) is to maintain an
explicit table of partially finished requests and to use select() to
decide which request to work on next (or whether to handle a new
incoming request). This is particularly important for stream services
where each client can potentially be connected for a long time (if
threads or subprocesses cannot be used).
Future work:
- Standard classes for Sun RPC (which uses either UDP or TCP)
- Standard mix-in classes to implement various authentication
and encryption schemes
- Standard framework for select-based multiplexing
XXX Open problems:
- What to do with out-of-band data?
BaseServer:
- split generic "request" functionality out into BaseServer class.
Copyright (C) 2000 NAME <EMAIL>
example: read entries from a SQL database (requires overriding
get_request() to return a table entry from the database).
entry is processed by a RequestHandlerClass.
""" |
"""This module contains the tools needed for satellite detection
within an ACS/WFC image as published in
`ACS ISR 2016-01 <http://www.stsci.edu/hst/acs/documents/isrs/isr1601.pdf>`_.
.. note::
Only tested for ACS/WFC FLT and FLC images, but it should
theoretically work for any instrument.
Requires skimage version 0.11.x or later.
:func:`skimage.transform.probabilistic_hough_line` gives
slightly different results from run to run, but this should
not matter since :func:`detsat` only provides crude
approximation of the actual trail(s).
Performance is faster when ``plot=False``, where applicable.
Examples
--------
>>> from acstools.satdet import detsat, make_mask, update_dq
Find trail segments for a single image and extension without multiprocessing,
and display plots (not shown) and verbose information:
>>> results, errors = detsat(
... 'jc8m10syq_flc.fits', chips=[4], n_processes=1,
... plot=True, verbose=True)
1 file(s) found...
Processing jc8m10syq_flc.fits[4]...
Rescale intensity percentiles: 110.161376953, 173.693756409
Length of PHT result: 42
min(x0)= 1, min(x1)= 269, min(y0)= 827, min(y1)= 780
max(x0)=3852, max(x1)=4094, max(y0)=1611, max(y1)=1545
buf=200
topx=3896, topy=1848
...
Trail Direction: Right to Left
42 trail segment(s) detected
...
End point list:
1. (1256, 1345), (2770, 1037)
2. ( 11, 1598), ( 269, 1545)
...
Total run time: 22.4326269627 s
>>> results[('jc8m10syq_flc.fits', 4)]
array([[[1242, 1348],
[2840, 1023]],
[[1272, 1341],
[2688, 1053]],
...
[[2697, 1055],
[2967, 1000]]])
>>> errors
{}
Find trail segments for multiple images and all ACS/WFC science extensions with
multiprocessing:
>>> results, errors = detsat(
... '*_flc.fits', chips=[1, 4], n_processes=12, verbose=True)
6 file(s) found...
Using 12 processes
Number of trail segment(s) found:
abell2744-hffpar_acs-wfc_f814w_13495_11_01_jc8n11q9q_flc.fits[1]: 0
abell2744-hffpar_acs-wfc_f814w_13495_11_01_jc8n11q9q_flc.fits[4]: 4
abell2744_acs-wfc_f814w_13495_51_04_jc8n51hoq_flc.fits[1]: 2
abell2744_acs-wfc_f814w_13495_51_04_jc8n51hoq_flc.fits[4]: 34
abell2744_acs-wfc_f814w_13495_93_02_jc8n93a7q_flc.fits[1]: 20
abell2744_acs-wfc_f814w_13495_93_02_jc8n93a7q_flc.fits[4]: 20
j8oc01sxq_flc.fits[1]: 0
j8oc01sxq_flc.fits[4]: 0
jc8m10syq_flc.fits[1]: 0
jc8m10syq_flc.fits[4]: 38
jc8m32j5q_flc.fits[1]: 42
jc8m32j5q_flc.fits[4]: 12
Total run time: 34.6021330357 s
>>> results[('jc8m10syq_flc.fits', 4)]
array([[[1242, 1348],
[2840, 1023]],
[[1272, 1341],
[2688, 1053]],
...
[[2697, 1055],
[2967, 1000]]])
>>> errors
{}
For a given image and extension, create a DQ mask for a satellite trail using
the first segment (other segments should give similar masks) based on the
results from above (plots not shown):
>>> trail_coords = results[('jc8m10syq_flc.fits', 4)]
>>> trail_segment = trail_coords[0]
>>> trail_segment
array([[1199, 1357],
[2841, 1023]])
>>> mask = make_mask('jc8m10syq_flc.fits', 4, trail_segment,
... plot=True, verbose=True)
Rotation: -11.4976988695
Hit image edge at counter=26
Hit rotate edge at counter=38
Run time: 19.476323843 s
Update the corresponding DQ array using the mask from above:
>>> update_dq('jc8m10syq_flc.fits', 6, mask, verbose=True)
DQ flag value is 16384
Input... flagged NPIX=156362
Existing flagged NPIX=0
Newly... flagged NPIX=156362
jc8m10syq_flc.fits[6] updated
""" |
"""
=====================================
Sparse matrices (:mod:`scipy.sparse`)
=====================================
.. currentmodule:: scipy.sparse
SciPy 2-D sparse matrix package for numeric data.
Contents
========
Sparse matrix classes
---------------------
.. autosummary::
:toctree: generated/
bsr_matrix - Block Sparse Row matrix
coo_matrix - A sparse matrix in COOrdinate format
csc_matrix - Compressed Sparse Column matrix
csr_matrix - Compressed Sparse Row matrix
dia_matrix - Sparse matrix with DIAgonal storage
dok_matrix - Dictionary Of Keys based sparse matrix
lil_matrix - Row-based list of lists sparse matrix
spmatrix - Sparse matrix base class
Functions
---------
Building sparse matrices:
.. autosummary::
:toctree: generated/
eye - Sparse MxN matrix whose k-th diagonal is all ones
identity - Identity matrix in sparse format
kron - kronecker product of two sparse matrices
kronsum - kronecker sum of sparse matrices
diags - Return a sparse matrix from diagonals
spdiags - Return a sparse matrix from diagonals
block_diag - Build a block diagonal sparse matrix
tril - Lower triangular portion of a matrix in sparse format
triu - Upper triangular portion of a matrix in sparse format
bmat - Build a sparse matrix from sparse sub-blocks
hstack - Stack sparse matrices horizontally (column wise)
vstack - Stack sparse matrices vertically (row wise)
rand - Random values in a given shape
random - Random values in a given shape
Save and load sparse matrices:
.. autosummary::
:toctree: generated/
save_npz - Save a sparse matrix to a file using ``.npz`` format.
load_npz - Load a sparse matrix from a file using ``.npz`` format.
Sparse matrix tools:
.. autosummary::
:toctree: generated/
find
Identifying sparse matrices:
.. autosummary::
:toctree: generated/
issparse
isspmatrix
isspmatrix_csc
isspmatrix_csr
isspmatrix_bsr
isspmatrix_lil
isspmatrix_dok
isspmatrix_coo
isspmatrix_dia
Submodules
----------
.. autosummary::
csgraph - Compressed sparse graph routines
linalg - sparse linear algebra routines
Exceptions
----------
.. autosummary::
:toctree: generated/
SparseEfficiencyWarning
SparseWarning
Usage information
=================
There are seven available sparse matrix types:
1. csc_matrix: Compressed Sparse Column format
2. csr_matrix: Compressed Sparse Row format
3. bsr_matrix: Block Sparse Row format
4. lil_matrix: List of Lists format
5. dok_matrix: Dictionary of Keys format
6. coo_matrix: COOrdinate format (aka IJV, triplet format)
7. dia_matrix: DIAgonal format
To construct a matrix efficiently, use either dok_matrix or lil_matrix.
The lil_matrix class supports basic slicing and fancy indexing with a
similar syntax to NumPy arrays. As illustrated below, the COO format
may also be used to efficiently construct matrices. Despite their
similarity to NumPy arrays, it is **strongly discouraged** to use NumPy
functions directly on these matrices because NumPy may not properly convert
them for computations, leading to unexpected (and incorrect) results. If you
do want to apply a NumPy function to these matrices, first check if SciPy has
its own implementation for the given sparse matrix class, or **convert the
sparse matrix to a NumPy array** (e.g., using the `toarray()` method of the
class) first before applying the method.
To perform manipulations such as multiplication or inversion, first
convert the matrix to either CSC or CSR format. The lil_matrix format is
row-based, so conversion to CSR is efficient, whereas conversion to CSC
is less so.
All conversions among the CSR, CSC, and COO formats are efficient,
linear-time operations.
Matrix vector product
---------------------
To do a vector product between a sparse matrix and a vector simply use
the matrix `dot` method, as described in its docstring:
>>> import numpy as np
>>> from scipy.sparse import csr_matrix
>>> A = csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]])
>>> v = np.array([1, 0, -1])
>>> A.dot(v)
array([ 1, -3, -1], dtype=int64)
.. warning:: As of NumPy 1.7, `np.dot` is not aware of sparse matrices,
therefore using it will result on unexpected results or errors.
The corresponding dense array should be obtained first instead:
>>> np.dot(A.toarray(), v)
array([ 1, -3, -1], dtype=int64)
but then all the performance advantages would be lost.
The CSR format is specially suitable for fast matrix vector products.
Example 1
---------
Construct a 1000x1000 lil_matrix and add some values to it:
>>> from scipy.sparse import lil_matrix
>>> from scipy.sparse.linalg import spsolve
>>> from numpy.linalg import solve, norm
>>> from numpy.random import rand
>>> A = lil_matrix((1000, 1000))
>>> A[0, :100] = rand(100)
>>> A[1, 100:200] = A[0, :100]
>>> A.setdiag(rand(1000))
Now convert it to CSR format and solve A x = b for x:
>>> A = A.tocsr()
>>> b = rand(1000)
>>> x = spsolve(A, b)
Convert it to a dense matrix and solve, and check that the result
is the same:
>>> x_ = solve(A.toarray(), b)
Now we can compute norm of the error with:
>>> err = norm(x-x_)
>>> err < 1e-10
True
It should be small :)
Example 2
---------
Construct a matrix in COO format:
>>> from scipy import sparse
>>> from numpy import array
>>> I = array([0,3,1,0])
>>> J = array([0,3,1,2])
>>> V = array([4,5,7,9])
>>> A = sparse.coo_matrix((V,(I,J)),shape=(4,4))
Notice that the indices do not need to be sorted.
Duplicate (i,j) entries are summed when converting to CSR or CSC.
>>> I = array([0,0,1,3,1,0,0])
>>> J = array([0,2,1,3,1,0,0])
>>> V = array([1,1,1,1,1,1,1])
>>> B = sparse.coo_matrix((V,(I,J)),shape=(4,4)).tocsr()
This is useful for constructing finite-element stiffness and mass matrices.
Further details
---------------
CSR column indices are not necessarily sorted. Likewise for CSC row
indices. Use the .sorted_indices() and .sort_indices() methods when
sorted indices are required (e.g., when passing data to other libraries).
""" |
# import unittest
# import os
# import tempfile,shutil,copy
# from pyramid import testing
# from pyramid.paster import get_appsettings
# from askomics.libaskomics.TripleStoreExplorer import TripleStoreExplorer
# import json
# from interface_tps import InterfaceTPS
# class tripleStoreExplorerTests(unittest.TestCase):
# def setUp( self ):
# self.settings = get_appsettings('configs/development.virtuoso.ini', name='main')
# self.request = testing.DummyRequest()
# self.request.session['upload_directory'] = os.path.join( os.path.dirname( __file__ ), "..", "test-data")
# self.request.session['username'] = 'jdoe'
# self.request.session['group'] = 'base'
# self.temp_directory = tempfile.mkdtemp()
# self.it = InterfaceTPS(self.settings,self.request)
# self.it.empty()
# self.it.load_test1()
# def tearDown( self ):
# shutil.rmtree( self.temp_directory )
# self.it.empty()
# def test_start_points(self):
# tse = TripleStoreExplorer(self.settings, self.request.session)
# tse.get_start_points()
# def test_build_sparql_query_from_json(self):
# variates = ['?Personne1', '?label1', '?Age1', '?ID1', '?Sexe1']
# constraintesRelations = [['?URIPersonne1 rdf:type <http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#Personne>',
# '?URIPersonne1 rdfs:label ?Personne1',
# '?URIPersonne1 <http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#label> ?label1',
# '<http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#label> rdfs:domain <http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#Personne>',
# '<http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#label> rdfs:range <http://www.w3.org/2001/XMLSchema#string>',
# '<http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#label> rdf:type owl:DatatypeProperty',
# '?URIPersonne1 <http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#Age> ?Age1',
# '<http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#Age> rdfs:domain <http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#Personne>',
# '<http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#Age> rdfs:range <http://www.w3.org/2001/XMLSchema#decimal>',
# '<http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#Age> rdf:type owl:DatatypeProperty',
# '?URIPersonne1 <http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#ID> ?ID1',
# '<http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#ID> rdfs:domain <http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#Personne>',
# '<http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#ID> rdfs:range <http://www.w3.org/2001/XMLSchema#string>',
# '<http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#ID> rdf:type owl:DatatypeProperty',
# '?URIPersonne1 <http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#Sexe> ?Sexe1'],'']
# limit = 10
# tse = TripleStoreExplorer(self.settings, self.request.session)
# results,query = tse.build_sparql_query_from_json(variates,constraintesRelations,limit,True)
# a = {'Age1': '23',
# 'ID1': 'AZERTY',
# 'Personne1': 'A',
# 'Sexe1': 'http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#F',
# 'label1': 'Alice'}
# b = {'Age1': '25',
# 'ID1': 'QSDFG',
# 'Personne1': 'B',
# 'Sexe1': 'http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#M',
# 'label1': 'Bob'}
# c = {'Age1': '34',
# 'ID1': 'WXCVB',
# 'Personne1': 'C',
# 'Sexe1': 'http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#M',
# 'label1': 'Charles'}
# d = {'Age1': '45',
# 'ID1': 'RTYU',
# 'Personne1': 'D',
# 'Sexe1': 'http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#M',
# 'label1': 'Denis'}
# e = {'Age1': '55',
# 'ID1': 'RTYUIO',
# 'Personne1': 'E',
# 'Sexe1': 'http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#F','label1': 'Elise'}
# f = {'Age1': '77',
# 'ID1': 'DFGH',
# 'Personne1': 'F',
# 'Sexe1': 'http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#M',
# 'label1': 'Fred'}
# g = {'Age1': '99',
# 'ID1': 'BNHJK',
# 'Personne1': 'G',
# 'Sexe1': 'http://www.semanticweb.org/irisa/ontologies/2016/1/igepp-ontology#M',
# 'label1': 'Georges'}
# for elt in results:
# self.assertTrue(elt in [a,b,c,d,e,f,g])
# assert len(results) == 7
# constraintesR1 = copy.deepcopy(constraintesRelations)
# constraintesR1[0].append('VALUES ?Sexe1 { :F }')
# #results,query = tse.build_sparql_query_from_json(variates,constraintesR1,limit,True)
# #for elt in results:
# # print(elt)
# # self.assertTrue(elt in [a,e])
# #assert len(results) == 2
# #constraintesR2 = copy.deepcopy(constraintesRelations)
# #constraintesR2[0].append('FILTER ( ?Age1 < 25)')
# #results,query = tse.build_sparql_query_from_json(variates,constraintesR2,limit,True)
# #assert results == [a]
|
"""
LsDisk - Command ``ls -lanR /dev/disk/by-*``
============================================
The ``ls -lanR /dev/disk/by-*`` command provides information for the listing of the
directories under ``/dev/disk/`` .
Sample input is shown in the Examples. See ``FileListing`` class for
additional information.
Examples:
>>> LS_DISK = '''
... /dev/disk/by-id:
... total 0
... drwxr-xr-x. 2 0 0 360 Sep 20 09:36 .
... drwxr-xr-x. 5 0 0 100 Sep 20 09:36 ..
... lrwxrwxrwx. 1 0 0 9 Sep 20 09:36 ata-VBOX_CD-ROM_VB2-01700376 -> ../../sr0
... lrwxrwxrwx. 1 0 0 9 Sep 20 09:36 ata-VBOX_HARDDISK_VB4c56cb04-26932e6a -> ../../sdb
... lrwxrwxrwx. 1 0 0 10 Sep 20 09:36 ata-VBOX_HARDDISK_VB4c56cb04-26932e6a-part1 -> ../../sdb1
... lrwxrwxrwx. 1 0 0 10 Sep 20 09:36 scsi-SATA_VBOX_HARDDISK_VB4c56cb04-26932e6a-part1 -> ../../sdb1
...
... /dev/disk/by-path:
... total 0
... drwxr-xr-x. 2 0 0 160 Sep 20 09:36 .
... drwxr-xr-x. 5 0 0 100 Sep 20 09:36 ..
... lrwxrwxrwx. 1 0 0 9 Sep 20 09:36 pci-0000:00:0d.0-scsi-1:0:0:0 -> ../../sdb
... lrwxrwxrwx. 1 0 0 10 Sep 20 09:36 pci-0000:00:0d.0-scsi-1:0:0:0-part1 -> ../../sdb1
...
... /dev/disk/by-uuid:
... total 0
... drwxr-xr-x. 2 0 0 100 Sep 20 09:36 .
... drwxr-xr-x. 5 0 0 100 Sep 20 09:36 ..
... lrwxrwxrwx. 1 0 0 10 Sep 20 09:36 3ab50b34-d0b9-4518-9f21-05307d895f81 -> ../../dm-1
... lrwxrwxrwx. 1 0 0 10 Sep 20 09:36 51c5cf12-a577-441e-89da-bc93a73a1ba3 -> ../../sda1
... lrwxrwxrwx. 1 0 0 10 Sep 20 09:36 7b0068d4-1399-4ce7-a54a-3e2fc1232299 -> ../../dm-0
... '''
>>> from insights.tests import context_wrap
>>> ls_disk = LsDisk(context_wrap(LS_DISK))
<__main__.LsDisk object at 0x7f674914c690>
>>> "/dev/disk/by-path" in ls_disk
True
>>> ls_disk.files_of("/dev/disk/by-path")
['pci-0000:00:0d.0-scsi-1:0:0:0', 'pci-0000:00:0d.0-scsi-1:0:0:0-part1']
>>> ls_disk.dirs_of("/dev/disk/by-path")
['.', '..']
>>> ls_disk.specials_of("/dev/disk/by-path")
[]
>>> ls_disk.listing_of("/dev/disk/by-path").keys()
['pci-0000:00:0d.0-scsi-1:0:0:0-part1', 'pci-0000:00:0d.0-scsi-1:0:0:0', '..', '.']
>>> ls_disk.dir_entry("/dev/disk/by-path", "pci-0000:00:0d.0-scsi-1:0:0:0")
{'group': '0', 'name': 'pci-0000:00:0d.0-scsi-1:0:0:0', 'links': 1, 'perms': 'rwxrwxrwx.',
'raw_entry': 'lrwxrwxrwx. 1 0 0 9 Sep 20 09:36 pci-0000:00:0d.0-scsi-1:0:0:0 -> ../../sdb', 'owner': '0',
'link': '../../sdb', 'date': 'Sep 20 09:36', 'type': 'l', 'size': 9}
>>> ls_disk.listing_of('/dev/disk/by-path')['.']['type'] == 'd'
True
>>> ls_disk.listing_of('/dev/disk/by-path')['pci-0000:00:0d.0-scsi-1:0:0:0']['link']
'../../sdb'
""" |
"""Doctest for method/function calls.
We're going the use these types for extra testing
>>> from UserList import UserList
>>> from UserDict import UserDict
We're defining four helper functions
>>> def e(a,b):
... print a, b
>>> def f(*a, **k):
... print a, test_support.sortdict(k)
>>> def g(x, *y, **z):
... print x, y, test_support.sortdict(z)
>>> def h(j=1, a=2, h=3):
... print j, a, h
Argument list examples
>>> f()
() {}
>>> f(1)
(1,) {}
>>> f(1, 2)
(1, 2) {}
>>> f(1, 2, 3)
(1, 2, 3) {}
>>> f(1, 2, 3, *(4, 5))
(1, 2, 3, 4, 5) {}
>>> f(1, 2, 3, *[4, 5])
(1, 2, 3, 4, 5) {}
>>> f(1, 2, 3, *UserList([4, 5]))
(1, 2, 3, 4, 5) {}
Here we add keyword arguments
>>> f(1, 2, 3, **{'a':4, 'b':5})
(1, 2, 3) {'a': 4, 'b': 5}
>>> f(1, 2, 3, *[4, 5], **{'a':6, 'b':7})
(1, 2, 3, 4, 5) {'a': 6, 'b': 7}
>>> f(1, 2, 3, x=4, y=5, *(6, 7), **{'a':8, 'b': 9})
(1, 2, 3, 6, 7) {'a': 8, 'b': 9, 'x': 4, 'y': 5}
>>> f(1, 2, 3, **UserDict(a=4, b=5))
(1, 2, 3) {'a': 4, 'b': 5}
>>> f(1, 2, 3, *(4, 5), **UserDict(a=6, b=7))
(1, 2, 3, 4, 5) {'a': 6, 'b': 7}
>>> f(1, 2, 3, x=4, y=5, *(6, 7), **UserDict(a=8, b=9))
(1, 2, 3, 6, 7) {'a': 8, 'b': 9, 'x': 4, 'y': 5}
Examples with invalid arguments (TypeErrors). We're also testing the function
names in the exception messages.
Verify clearing of SF bug #733667
>>> e(c=4)
Traceback (most recent call last):
...
TypeError: e() got an unexpected keyword argument 'c'
>>> g()
Traceback (most recent call last):
...
TypeError: g() takes at least 1 argument (0 given)
>>> g(*())
Traceback (most recent call last):
...
TypeError: g() takes at least 1 argument (0 given)
>>> g(*(), **{})
Traceback (most recent call last):
...
TypeError: g() takes at least 1 argument (0 given)
>>> g(1)
1 () {}
>>> g(1, 2)
1 (2,) {}
>>> g(1, 2, 3)
1 (2, 3) {}
>>> g(1, 2, 3, *(4, 5))
1 (2, 3, 4, 5) {}
>>> class Nothing: pass
...
>>> g(*Nothing())
Traceback (most recent call last):
...
TypeError: g() argument after * must be a sequence, not instance
>>> class Nothing:
... def __len__(self): return 5
...
>>> g(*Nothing())
Traceback (most recent call last):
...
TypeError: g() argument after * must be a sequence, not instance
>>> class Nothing():
... def __len__(self): return 5
... def __getitem__(self, i):
... if i<3: return i
... else: raise IndexError(i)
...
>>> g(*Nothing())
0 (1, 2) {}
>>> class Nothing:
... def __init__(self): self.c = 0
... def __iter__(self): return self
... def next(self):
... if self.c == 4:
... raise StopIteration
... c = self.c
... self.c += 1
... return c
...
>>> g(*Nothing())
0 (1, 2, 3) {}
Make sure that the function doesn't stomp the dictionary
>>> d = {'a': 1, 'b': 2, 'c': 3}
>>> d2 = d.copy()
>>> g(1, d=4, **d)
1 () {'a': 1, 'b': 2, 'c': 3, 'd': 4}
>>> d == d2
True
What about willful misconduct?
>>> def saboteur(**kw):
... kw['x'] = 'm'
... return kw
>>> d = {}
>>> kw = saboteur(a=1, **d)
>>> d
{}
>>> g(1, 2, 3, **{'x': 4, 'y': 5})
Traceback (most recent call last):
...
TypeError: g() got multiple values for keyword argument 'x'
>>> f(**{1:2})
Traceback (most recent call last):
...
TypeError: f() keywords must be strings
>>> h(**{'e': 2})
Traceback (most recent call last):
...
TypeError: h() got an unexpected keyword argument 'e'
>>> h(*h)
Traceback (most recent call last):
...
TypeError: h() argument after * must be a sequence, not function
>>> dir(*h)
Traceback (most recent call last):
...
TypeError: dir() argument after * must be a sequence, not function
>>> None(*h)
Traceback (most recent call last):
...
TypeError: NoneType object argument after * must be a sequence, \
not function
>>> h(**h)
Traceback (most recent call last):
...
TypeError: h() argument after ** must be a mapping, not function
>>> dir(**h)
Traceback (most recent call last):
...
TypeError: dir() argument after ** must be a mapping, not function
>>> None(**h)
Traceback (most recent call last):
...
TypeError: NoneType object argument after ** must be a mapping, \
not function
>>> dir(b=1, **{'b': 1})
Traceback (most recent call last):
...
TypeError: dir() got multiple values for keyword argument 'b'
Another helper function
>>> def f2(*a, **b):
... return a, b
>>> d = {}
>>> for i in xrange(512):
... key = 'k%d' % i
... d[key] = i
>>> a, b = f2(1, *(2,3), **d)
>>> len(a), len(b), b == d
(3, 512, True)
>>> class Foo:
... def method(self, arg1, arg2):
... return arg1+arg2
>>> x = Foo()
>>> Foo.method(*(x, 1, 2))
3
>>> Foo.method(x, *(1, 2))
3
>>> Foo.method(*(1, 2, 3))
Traceback (most recent call last):
...
TypeError: unbound method method() must be called with Foo instance as \
first argument (got int instance instead)
>>> Foo.method(1, *[2, 3])
Traceback (most recent call last):
...
TypeError: unbound method method() must be called with Foo instance as \
first argument (got int instance instead)
A PyCFunction that takes only positional parameters shoud allow an
empty keyword dictionary to pass without a complaint, but raise a
TypeError if te dictionary is not empty
>>> try:
... silence = id(1, *{})
... True
... except:
... False
True
>>> id(1, **{'foo': 1})
Traceback (most recent call last):
...
TypeError: id() takes no keyword arguments
""" |
"""Generic socket server classes.
This module tries to capture the various aspects of defining a server:
For socket-based servers:
- address family:
- AF_INET{,6}: IP (Internet Protocol) sockets (default)
- AF_UNIX: Unix domain sockets
- others, e.g. AF_DECNET are conceivable (see <socket.h>
- socket type:
- SOCK_STREAM (reliable stream, e.g. TCP)
- SOCK_DGRAM (datagrams, e.g. UDP)
For request-based servers (including socket-based):
- client address verification before further looking at the request
(This is actually a hook for any processing that needs to look
at the request before anything else, e.g. logging)
- how to handle multiple requests:
- synchronous (one request is handled at a time)
- forking (each request is handled by a new process)
- threading (each request is handled by a new thread)
The classes in this module favor the server type that is simplest to
write: a synchronous TCP/IP server. This is bad class design, but
save some typing. (There's also the issue that a deep class hierarchy
slows down method lookups.)
There are five classes in an inheritance diagram, four of which represent
synchronous servers of four types:
+------------+
| BaseServer |
+------------+
|
v
+-----------+ +------------------+
| TCPServer |------->| UnixStreamServer |
+-----------+ +------------------+
|
v
+-----------+ +--------------------+
| UDPServer |------->| UnixDatagramServer |
+-----------+ +--------------------+
Note that UnixDatagramServer derives from UDPServer, not from
UnixStreamServer -- the only difference between an IP and a Unix
stream server is the address family, which is simply repeated in both
unix server classes.
Forking and threading versions of each type of server can be created
using the ForkingMixIn and ThreadingMixIn mix-in classes. For
instance, a threading UDP server class is created as follows:
class ThreadingUDPServer(ThreadingMixIn, UDPServer): pass
The Mix-in class must come first, since it overrides a method defined
in UDPServer! Setting the various member variables also changes
the behavior of the underlying server mechanism.
To implement a service, you must derive a class from
BaseRequestHandler and redefine its handle() method. You can then run
various versions of the service by combining one of the server classes
with your request handler class.
The request handler class must be different for datagram or stream
services. This can be hidden by using the request handler
subclasses StreamRequestHandler or DatagramRequestHandler.
Of course, you still have to use your head!
For instance, it makes no sense to use a forking server if the service
contains state in memory that can be modified by requests (since the
modifications in the child process would never reach the initial state
kept in the parent process and passed to each child). In this case,
you can use a threading server, but you will probably have to use
locks to avoid two requests that come in nearly simultaneous to apply
conflicting changes to the server state.
On the other hand, if you are building e.g. an HTTP server, where all
data is stored externally (e.g. in the file system), a synchronous
class will essentially render the service "deaf" while one request is
being handled -- which may be for a very long time if a client is slow
to read all the data it has requested. Here a threading or forking
server is appropriate.
In some cases, it may be appropriate to process part of a request
synchronously, but to finish processing in a forked child depending on
the request data. This can be implemented by using a synchronous
server and doing an explicit fork in the request handler class
handle() method.
Another approach to handling multiple simultaneous requests in an
environment that supports neither threads nor fork (or where these are
too expensive or inappropriate for the service) is to maintain an
explicit table of partially finished requests and to use select() to
decide which request to work on next (or whether to handle a new
incoming request). This is particularly important for stream services
where each client can potentially be connected for a long time (if
threads or subprocesses cannot be used).
Future work:
- Standard classes for Sun RPC (which uses either UDP or TCP)
- Standard mix-in classes to implement various authentication
and encryption schemes
- Standard framework for select-based multiplexing
XXX Open problems:
- What to do with out-of-band data?
BaseServer:
- split generic "request" functionality out into BaseServer class.
Copyright (C) 2000 NAME <EMAIL>
example: read entries from a SQL database (requires overriding
get_request() to return a table entry from the database).
entry is processed by a RequestHandlerClass.
""" |
"""
=============
Miscellaneous
=============
IEEE 754 Floating Point Special Values
--------------------------------------
Special values defined in numpy: nan, inf,
NaNs can be used as a poor-man's mask (if you don't care what the
original value was)
Note: cannot use equality to test NaNs. E.g.: ::
>>> myarr = np.array([1., 0., np.nan, 3.])
>>> np.where(myarr == np.nan)
>>> np.nan == np.nan # is always False! Use special numpy functions instead.
False
>>> myarr[myarr == np.nan] = 0. # doesn't work
>>> myarr
array([ 1., 0., NaN, 3.])
>>> myarr[np.isnan(myarr)] = 0. # use this instead find
>>> myarr
array([ 1., 0., 0., 3.])
Other related special value functions: ::
isinf(): True if value is inf
isfinite(): True if not nan or inf
nan_to_num(): Map nan to 0, inf to max float, -inf to min float
The following corresponds to the usual functions except that nans are excluded
from the results: ::
nansum()
nanmax()
nanmin()
nanargmax()
nanargmin()
>>> x = np.arange(10.)
>>> x[3] = np.nan
>>> x.sum()
nan
>>> np.nansum(x)
42.0
How numpy handles numerical exceptions
--------------------------------------
The default is to ``'warn'`` for ``invalid``, ``divide``, and ``overflow``
and ``'ignore'`` for ``underflow``. But this can be changed, and it can be
set individually for different kinds of exceptions. The different behaviors
are:
- 'ignore' : Take no action when the exception occurs.
- 'warn' : Print a `RuntimeWarning` (via the Python `warnings` module).
- 'raise' : Raise a `FloatingPointError`.
- 'call' : Call a function specified using the `seterrcall` function.
- 'print' : Print a warning directly to ``stdout``.
- 'log' : Record error in a Log object specified by `seterrcall`.
These behaviors can be set for all kinds of errors or specific ones:
- all : apply to all numeric exceptions
- invalid : when NaNs are generated
- divide : divide by zero (for integers as well!)
- overflow : floating point overflows
- underflow : floating point underflows
Note that integer divide-by-zero is handled by the same machinery.
These behaviors are set on a per-thread basis.
Examples
--------
::
>>> oldsettings = np.seterr(all='warn')
>>> np.zeros(5,dtype=np.float32)/0.
invalid value encountered in divide
>>> j = np.seterr(under='ignore')
>>> np.array([1.e-100])**10
>>> j = np.seterr(invalid='raise')
>>> np.sqrt(np.array([-1.]))
FloatingPointError: invalid value encountered in sqrt
>>> def errorhandler(errstr, errflag):
... print "saw stupid error!"
>>> np.seterrcall(errorhandler)
<function err_handler at 0x...>
>>> j = np.seterr(all='call')
>>> np.zeros(5, dtype=np.int32)/0
FloatingPointError: invalid value encountered in divide
saw stupid error!
>>> j = np.seterr(**oldsettings) # restore previous
... # error-handling settings
Interfacing to C
----------------
Only a survey of the choices. Little detail on how each works.
1) Bare metal, wrap your own C-code manually.
- Plusses:
- Efficient
- No dependencies on other tools
- Minuses:
- Lots of learning overhead:
- need to learn basics of Python C API
- need to learn basics of numpy C API
- need to learn how to handle reference counting and love it.
- Reference counting often difficult to get right.
- getting it wrong leads to memory leaks, and worse, segfaults
- API will change for Python 3.0!
2) Cython
- Plusses:
- avoid learning C API's
- no dealing with reference counting
- can code in pseudo python and generate C code
- can also interface to existing C code
- should shield you from changes to Python C api
- has become the de-facto standard within the scientific Python community
- fast indexing support for arrays
- Minuses:
- Can write code in non-standard form which may become obsolete
- Not as flexible as manual wrapping
4) ctypes
- Plusses:
- part of Python standard library
- good for interfacing to existing sharable libraries, particularly
Windows DLLs
- avoids API/reference counting issues
- good numpy support: arrays have all these in their ctypes
attribute: ::
a.ctypes.data a.ctypes.get_strides
a.ctypes.data_as a.ctypes.shape
a.ctypes.get_as_parameter a.ctypes.shape_as
a.ctypes.get_data a.ctypes.strides
a.ctypes.get_shape a.ctypes.strides_as
- Minuses:
- can't use for writing code to be turned into C extensions, only a wrapper
tool.
5) SWIG (automatic wrapper generator)
- Plusses:
- around a long time
- multiple scripting language support
- C++ support
- Good for wrapping large (many functions) existing C libraries
- Minuses:
- generates lots of code between Python and the C code
- can cause performance problems that are nearly impossible to optimize
out
- interface files can be hard to write
- doesn't necessarily avoid reference counting issues or needing to know
API's
7) scipy.weave
- Plusses:
- can turn many numpy expressions into C code
- dynamic compiling and loading of generated C code
- can embed pure C code in Python module and have weave extract, generate
interfaces and compile, etc.
- Minuses:
- Future very uncertain: it's the only part of Scipy not ported to Python 3
and is effectively deprecated in favor of Cython.
8) Psyco
- Plusses:
- Turns pure python into efficient machine code through jit-like
optimizations
- very fast when it optimizes well
- Minuses:
- Only on intel (windows?)
- Doesn't do much for numpy?
Interfacing to Fortran:
-----------------------
The clear choice to wrap Fortran code is
`f2py <http://docs.scipy.org/doc/numpy-dev/f2py/>`_.
Pyfort is an older alternative, but not supported any longer.
Fwrap is a newer project that looked promising but isn't being developed any
longer.
Interfacing to C++:
-------------------
1) Cython
2) CXX
3) Boost.python
4) SWIG
5) SIP (used mainly in PyQT)
""" |
"""
Work out the first ten digits of the sum
of the following one-hundred 50-digit numbers.
37107287533902102798797998220837590246510135740250
46376937677490009712648124896970078050417018260538
74324986199524741059474233309513058123726617309629
91942213363574161572522430563301811072406154908250
23067588207539346171171980310421047513778063246676
89261670696623633820136378418383684178734361726757
28112879812849979408065481931592621691275889832738
44274228917432520321923589422876796487670272189318
47451445736001306439091167216856844588711603153276
70386486105843025439939619828917593665686757934951
62176457141856560629502157223196586755079324193331
64906352462741904929101432445813822663347944758178
92575867718337217661963751590579239728245598838407
58203565325359399008402633568948830189458628227828
80181199384826282014278194139940567587151170094390
35398664372827112653829987240784473053190104293586
86515506006295864861532075273371959191420517255829
71693888707715466499115593487603532921714970056938
54370070576826684624621495650076471787294438377604
53282654108756828443191190634694037855217779295145
36123272525000296071075082563815656710885258350721
45876576172410976447339110607218265236877223636045
17423706905851860660448207621209813287860733969412
81142660418086830619328460811191061556940512689692
51934325451728388641918047049293215058642563049483
62467221648435076201727918039944693004732956340691
15732444386908125794514089057706229429197107928209
55037687525678773091862540744969844508330393682126
18336384825330154686196124348767681297534375946515
80386287592878490201521685554828717201219257766954
78182833757993103614740356856449095527097864797581
16726320100436897842553539920931837441497806860984
48403098129077791799088218795327364475675590848030
87086987551392711854517078544161852424320693150332
59959406895756536782107074926966537676326235447210
69793950679652694742597709739166693763042633987085
41052684708299085211399427365734116182760315001271
65378607361501080857009149939512557028198746004375
35829035317434717326932123578154982629742552737307
94953759765105305946966067683156574377167401875275
88902802571733229619176668713819931811048770190271
25267680276078003013678680992525463401061632866526
36270218540497705585629946580636237993140746255962
24074486908231174977792365466257246923322810917141
91430288197103288597806669760892938638285025333403
34413065578016127815921815005561868836468420090470
23053081172816430487623791969842487255036638784583
11487696932154902810424020138335124462181441773470
63783299490636259666498587618221225225512486764533
67720186971698544312419572409913959008952310058822
95548255300263520781532296796249481641953868218774
76085327132285723110424803456124867697064507995236
37774242535411291684276865538926205024910326572967
23701913275725675285653248258265463092207058596522
29798860272258331913126375147341994889534765745501
18495701454879288984856827726077713721403798879715
38298203783031473527721580348144513491373226651381
34829543829199918180278916522431027392251122869539
40957953066405232632538044100059654939159879593635
29746152185502371307642255121183693803580388584903
41698116222072977186158236678424689157993532961922
62467957194401269043877107275048102390895523597457
23189706772547915061505504953922979530901129967519
86188088225875314529584099251203829009407770775672
11306739708304724483816533873502340845647058077308
82959174767140363198008187129011875491310547126581
97623331044818386269515456334926366572897563400500
42846280183517070527831839425882145521227251250327
55121603546981200581762165212827652751691296897789
32238195734329339946437501907836945765883352399886
75506164965184775180738168837861091527357929701337
62177842752192623401942399639168044983993173312731
32924185707147349566916674687634660915035914677504
99518671430235219628894890102423325116913619626622
73267460800591547471830798392868535206946944540724
76841822524674417161514036427982273348055556214818
97142617910342598647204516893989422179826088076852
87783646182799346313767754307809363333018982642090
10848802521674670883215120185883543223812876952786
71329612474782464538636993009049310363619763878039
62184073572399794223406235393808339651327408011116
66627891981488087797941876876144230030984490851411
60661826293682836764744779239180335110989069790714
85786944089552990653640447425576083659976645795096
66024396409905389607120198219976047599490197230297
64913982680032973156037120041377903785566085089252
16730939319872750275468906903707539413042652315011
94809377245048795150954100921645863754710598436791
78639167021187492431995700641917969777599028300699
15368713711936614952811305876380278410754449733078
40789923115535562561142322423255033685442488917353
44889911501440648020369068063960672322193204149535
41503128880339536053299340368006977710650566631954
81234880673210146739058568557934581403627822703280
82616570773948327592232845941706525094512325230608
22918802058777319719839450180888072429661980811197
77158542502016545090413245809786882778948721859617
72107838435069186155435662884062257473692284509516
20849603980134001723930671666823555245252804609722
53503534226472524250874054075591789781264330331690
""" |
"""
Simple config
=============
Although CherryPy uses the :mod:`Python logging module <logging>`, it does so
behind the scenes so that simple logging is simple, but complicated logging
is still possible. "Simple" logging means that you can log to the screen
(i.e. console/stdout) or to a file, and that you can easily have separate
error and access log files.
Here are the simplified logging settings. You use these by adding lines to
your config file or dict. You should set these at either the global level or
per application (see next), but generally not both.
* ``log.screen``: Set this to True to have both "error" and "access" messages
printed to stdout.
* ``log.access_file``: Set this to an absolute filename where you want
"access" messages written.
* ``log.error_file``: Set this to an absolute filename where you want "error"
messages written.
Many events are automatically logged; to log your own application events, call
:func:`cherrypy.log`.
Architecture
============
Separate scopes
---------------
CherryPy provides log managers at both the global and application layers.
This means you can have one set of logging rules for your entire site,
and another set of rules specific to each application. The global log
manager is found at :func:`cherrypy.log`, and the log manager for each
application is found at :attr:`app.log<cherrypy._cptree.Application.log>`.
If you're inside a request, the latter is reachable from
``cherrypy.request.app.log``; if you're outside a request, you'll have to
obtain a reference to the ``app``: either the return value of
:func:`tree.mount()<cherrypy._cptree.Tree.mount>` or, if you used
:func:`quickstart()<cherrypy.quickstart>` instead, via
``cherrypy.tree.apps['/']``.
By default, the global logs are named "cherrypy.error" and "cherrypy.access",
and the application logs are named "cherrypy.error.2378745" and
"cherrypy.access.2378745" (the number is the id of the Application object).
This means that the application logs "bubble up" to the site logs, so if your
application has no log handlers, the site-level handlers will still log the
messages.
Errors vs. Access
-----------------
Each log manager handles both "access" messages (one per HTTP request) and
"error" messages (everything else). Note that the "error" log is not just for
errors! The format of access messages is highly formalized, but the error log
isn't--it receives messages from a variety of sources (including full error
tracebacks, if enabled).
If you are logging the access log and error log to the same source, then there
is a possibility that a specially crafted error message may replicate an access
log message as described in CWE-117. In this case it is the application
developer's responsibility to manually escape data before using CherryPy's log()
functionality, or they may create an application that is vulnerable to CWE-117.
This would be achieved by using a custom handler escape any special characters,
and attached as described below.
Custom Handlers
===============
The simple settings above work by manipulating Python's standard :mod:`logging`
module. So when you need something more complex, the full power of the standard
module is yours to exploit. You can borrow or create custom handlers, formats,
filters, and much more. Here's an example that skips the standard FileHandler
and uses a RotatingFileHandler instead:
::
#python
log = app.log
# Remove the default FileHandlers if present.
log.error_file = ""
log.access_file = ""
maxBytes = getattr(log, "rot_maxBytes", 10000000)
backupCount = getattr(log, "rot_backupCount", 1000)
# Make a new RotatingFileHandler for the error log.
fname = getattr(log, "rot_error_file", "error.log")
h = handlers.RotatingFileHandler(fname, 'a', maxBytes, backupCount)
h.setLevel(DEBUG)
h.setFormatter(_cplogging.logfmt)
log.error_log.addHandler(h)
# Make a new RotatingFileHandler for the access log.
fname = getattr(log, "rot_access_file", "access.log")
h = handlers.RotatingFileHandler(fname, 'a', maxBytes, backupCount)
h.setLevel(DEBUG)
h.setFormatter(_cplogging.logfmt)
log.access_log.addHandler(h)
The ``rot_*`` attributes are pulled straight from the application log object.
Since "log.*" config entries simply set attributes on the log object, you can
add custom attributes to your heart's content. Note that these handlers are
used ''instead'' of the default, simple handlers outlined above (so don't set
the "log.error_file" config entry, for example).
""" |
#!/usr/bin/env python
# ***** BEGIN LICENSE BLOCK *****
# Version: MPL 1.1/GPL 2.0/LGPL 2.1
#
# The contents of this file are subject to the Mozilla Public License Version
# 1.1 (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
# http://www.mozilla.org/MPL/
#
# Software distributed under the License is distributed on an "AS IS" basis,
# WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License
# for the specific language governing rights and limitations under the
# License.
#
# The Original Code is font utility code.
#
# The Initial Developer of the Original Code is Mozilla Corporation.
# Portions created by the Initial Developer are Copyright (C) 2009
# the Initial Developer. All Rights Reserved.
#
# Contributor(s):
# NAME <EMAIL>
#
# Alternatively, the contents of this file may be used under the terms of
# either the GNU General Public License Version 2 or later (the "GPL"), or
# the GNU Lesser General Public License Version 2.1 or later (the "LGPL"),
# in which case the provisions of the GPL or the LGPL are applicable instead
# of those above. If you wish to allow use of your version of this file only
# under the terms of either the GPL or the LGPL, and not to allow others to
# use your version of this file under the terms of the MPL, indicate your
# decision by deleting the provisions above and replace them with the notice
# and other provisions required by the GPL or the LGPL. If you do not delete
# the provisions above, a recipient may use your version of this file under
# the terms of any one of the MPL, the GPL or the LGPL.
#
# ***** END LICENSE BLOCK ***** */
# eotlitetool.py - create EOT version of OpenType font for use with IE
#
# Usage: eotlitetool.py [-o output-filename] font1 [font2 ...]
#
# OpenType file structure
# http://www.microsoft.com/typography/otspec/otff.htm
#
# Types:
#
# BYTE 8-bit unsigned integer.
# CHAR 8-bit signed integer.
# USHORT 16-bit unsigned integer.
# SHORT 16-bit signed integer.
# ULONG 32-bit unsigned integer.
# Fixed 32-bit signed fixed-point number (16.16)
# LONGDATETIME Date represented in number of seconds since 12:00 midnight, January 1, 1904. The value is represented as a signed 64-bit integer.
#
# SFNT Header
#
# Fixed sfnt version // 0x00010000 for version 1.0.
# USHORT numTables // Number of tables.
# USHORT searchRange // (Maximum power of 2 <= numTables) x 16.
# USHORT entrySelector // Log2(maximum power of 2 <= numTables).
# USHORT rangeShift // NumTables x 16-searchRange.
#
# Table Directory
#
# ULONG tag // 4-byte identifier.
# ULONG checkSum // CheckSum for this table.
# ULONG offset // Offset from beginning of TrueType font file.
# ULONG length // Length of this table.
#
# OS/2 Table (Version 4)
#
# USHORT version // 0x0004
# SHORT xAvgCharWidth
# USHORT usWeightClass
# USHORT usWidthClass
# USHORT fsType
# SHORT ySubscriptXSize
# SHORT ySubscriptYSize
# SHORT ySubscriptXOffset
# SHORT ySubscriptYOffset
# SHORT ySuperscriptXSize
# SHORT ySuperscriptYSize
# SHORT ySuperscriptXOffset
# SHORT ySuperscriptYOffset
# SHORT yStrikeoutSize
# SHORT yStrikeoutPosition
# SHORT sFamilyClass
# BYTE panose[10]
# ULONG ulUnicodeRange1 // Bits 0-31
# ULONG ulUnicodeRange2 // Bits 32-63
# ULONG ulUnicodeRange3 // Bits 64-95
# ULONG ulUnicodeRange4 // Bits 96-127
# CHAR achVendID[4]
# USHORT fsSelection
# USHORT usFirstCharIndex
# USHORT usLastCharIndex
# SHORT sTypoAscender
# SHORT sTypoDescender
# SHORT sTypoLineGap
# USHORT usWinAscent
# USHORT usWinDescent
# ULONG ulCodePageRange1 // Bits 0-31
# ULONG ulCodePageRange2 // Bits 32-63
# SHORT sxHeight
# SHORT sCapHeight
# USHORT usDefaultChar
# USHORT usBreakChar
# USHORT usMaxContext
#
#
# The Naming Table is organized as follows:
#
# [name table header]
# [name records]
# [string data]
#
# Name Table Header
#
# USHORT format // Format selector (=0).
# USHORT count // Number of name records.
# USHORT stringOffset // Offset to start of string storage (from start of table).
#
# Name Record
#
# USHORT platformID // Platform ID.
# USHORT encodingID // Platform-specific encoding ID.
# USHORT languageID // Language ID.
# USHORT nameID // Name ID.
# USHORT length // String length (in bytes).
# USHORT offset // String offset from start of storage area (in bytes).
#
# head Table
#
# Fixed tableVersion // Table version number 0x00010000 for version 1.0.
# Fixed fontRevision // Set by font manufacturer.
# ULONG checkSumAdjustment // To compute: set it to 0, sum the entire font as ULONG, then store 0xB1B0AFBA - sum.
# ULONG magicNumber // Set to 0x5F0F3CF5.
# USHORT flags
# USHORT unitsPerEm // Valid range is from 16 to 16384. This value should be a power of 2 for fonts that have TrueType outlines.
# LONGDATETIME created // Number of seconds since 12:00 midnight, January 1, 1904. 64-bit integer
# LONGDATETIME modified // Number of seconds since 12:00 midnight, January 1, 1904. 64-bit integer
# SHORT xMin // For all glyph bounding boxes.
# SHORT yMin
# SHORT xMax
# SHORT yMax
# USHORT macStyle
# USHORT lowestRecPPEM // Smallest readable size in pixels.
# SHORT fontDirectionHint
# SHORT indexToLocFormat // 0 for short offsets, 1 for long.
# SHORT glyphDataFormat // 0 for current format.
#
#
#
# Embedded OpenType (EOT) file format
# http://www.w3.org/Submission/EOT/
#
# EOT version 0x00020001
#
# An EOT font consists of a header with the original OpenType font
# appended at the end. Most of the data in the EOT header is simply a
# copy of data from specific tables within the font data. The exceptions
# are the 'Flags' field and the root string name field. The root string
# is a set of names indicating domains for which the font data can be
# used. A null root string implies the font data can be used anywhere.
# The EOT header is in little-endian byte order but the font data remains
# in big-endian order as specified by the OpenType spec.
#
# Overall structure:
#
# [EOT header]
# [EOT name records]
# [font data]
#
# EOT header
#
# ULONG eotSize // Total structure length in bytes (including string and font data)
# ULONG fontDataSize // Length of the OpenType font (FontData) in bytes
# ULONG version // Version number of this format - 0x00020001
# ULONG flags // Processing Flags (0 == no special processing)
# BYTE fontPANOSE[10] // OS/2 Table panose
# BYTE charset // DEFAULT_CHARSET (0x01)
# BYTE italic // 0x01 if ITALIC in OS/2 Table fsSelection is set, 0 otherwise
# ULONG weight // OS/2 Table usWeightClass
# USHORT fsType // OS/2 Table fsType (specifies embedding permission flags)
# USHORT magicNumber // Magic number for EOT file - 0x504C.
# ULONG unicodeRange1 // OS/2 Table ulUnicodeRange1
# ULONG unicodeRange2 // OS/2 Table ulUnicodeRange2
# ULONG unicodeRange3 // OS/2 Table ulUnicodeRange3
# ULONG unicodeRange4 // OS/2 Table ulUnicodeRange4
# ULONG codePageRange1 // OS/2 Table ulCodePageRange1
# ULONG codePageRange2 // OS/2 Table ulCodePageRange2
# ULONG checkSumAdjustment // head Table CheckSumAdjustment
# ULONG reserved[4] // Reserved - must be 0
# USHORT padding1 // Padding - must be 0
#
# EOT name records
#
# USHORT FamilyNameSize // Font family name size in bytes
# BYTE FamilyName[FamilyNameSize] // Font family name (name ID = 1), little-endian UTF-16
# USHORT Padding2 // Padding - must be 0
#
# USHORT StyleNameSize // Style name size in bytes
# BYTE StyleName[StyleNameSize] // Style name (name ID = 2), little-endian UTF-16
# USHORT Padding3 // Padding - must be 0
#
# USHORT VersionNameSize // Version name size in bytes
# bytes VersionName[VersionNameSize] // Version name (name ID = 5), little-endian UTF-16
# USHORT Padding4 // Padding - must be 0
#
# USHORT FullNameSize // Full name size in bytes
# BYTE FullName[FullNameSize] // Full name (name ID = 4), little-endian UTF-16
# USHORT Padding5 // Padding - must be 0
#
# USHORT RootStringSize // Root string size in bytes
# BYTE RootString[RootStringSize] // Root string, little-endian UTF-16
|
#!/usr/bin/env python3
# Compatible with Python 2.7 and 3.2+, can be used either as a module
# or a standalone executable.
#
# Copyright 2017, 2018 Institute of Formal and Applied Linguistics (UFAL),
# Faculty of Mathematics and Physics, Charles University, Czech Republic.
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
#
# Authors: NAME NAME <EMAIL>
#
# Changelog:
# - [12 Apr 2018] Version 0.9: Initial release.
# - [19 Apr 2018] Version 1.0: Fix bug in MLAS (duplicate entries in functional_children).
# Add --counts option.
# - [02 May 2018] Version 1.1: When removing spaces to match gold and system characters,
# consider all Unicode characters of category Zs instead of
# just ASCII space.
# - [25 Jun 2018] Version 1.2: Use python3 in the she-bang (instead of python).
# In Python2, make the whole computation use `unicode` strings.
# Command line usage
# ------------------
# conll18_ud_eval.py [-v] gold_conllu_file system_conllu_file
#
# - if no -v is given, only the official CoNLL18 UD Shared Task evaluation metrics
# are printed
# - if -v is given, more metrics are printed (as precision, recall, F1 score,
# and in case the metric is computed on aligned words also accuracy on these):
# - Tokens: how well do the gold tokens match system tokens
# - Sentences: how well do the gold sentences match system sentences
# - Words: how well can the gold words be aligned to system words
# - UPOS: using aligned words, how well does UPOS match
# - XPOS: using aligned words, how well does XPOS match
# - UFeats: using aligned words, how well does universal FEATS match
# - AllTags: using aligned words, how well does UPOS+XPOS+FEATS match
# - Lemmas: using aligned words, how well does LEMMA match
# - UAS: using aligned words, how well does HEAD match
# - LAS: using aligned words, how well does HEAD+DEPREL(ignoring subtypes) match
# - CLAS: using aligned words with content DEPREL, how well does
# HEAD+DEPREL(ignoring subtypes) match
# - MLAS: using aligned words with content DEPREL, how well does
# HEAD+DEPREL(ignoring subtypes)+UPOS+UFEATS+FunctionalChildren(DEPREL+UPOS+UFEATS) match
# - BLEX: using aligned words with content DEPREL, how well does
# HEAD+DEPREL(ignoring subtypes)+LEMMAS match
# - if -c is given, raw counts of correct/gold_total/system_total/aligned words are printed
# instead of precision/recall/F1/AlignedAccuracy for all metrics.
# API usage
# ---------
# - load_conllu(file)
# - loads CoNLL-U file from given file object to an internal representation
# - the file object should return str in both Python 2 and Python 3
# - raises UDError exception if the given file cannot be loaded
# - evaluate(gold_ud, system_ud)
# - evaluate the given gold and system CoNLL-U files (loaded with load_conllu)
# - raises UDError if the concatenated tokens of gold and system file do not match
# - returns a dictionary with the metrics described above, each metric having
# three fields: precision, recall and f1
# Description of token matching
# -----------------------------
# In order to match tokens of gold file and system file, we consider the text
# resulting from concatenation of gold tokens and text resulting from
# concatenation of system tokens. These texts should match -- if they do not,
# the evaluation fails.
#
# If the texts do match, every token is represented as a range in this original
# text, and tokens are equal only if their range is the same.
# Description of word matching
# ----------------------------
# When matching words of gold file and system file, we first match the tokens.
# The words which are also tokens are matched as tokens, but words in multi-word
# tokens have to be handled differently.
#
# To handle multi-word tokens, we start by finding "multi-word spans".
# Multi-word span is a span in the original text such that
# - it contains at least one multi-word token
# - all multi-word tokens in the span (considering both gold and system ones)
# are completely inside the span (i.e., they do not "stick out")
# - the multi-word span is as small as possible
#
# For every multi-word span, we align the gold and system words completely
# inside this span using LCS on their FORMs. The words not intersecting
# (even partially) any multi-word span are then aligned as tokens.
|
"""
==========================================
Statistical functions (:mod:`scipy.stats`)
==========================================
.. module:: scipy.stats
This module contains a large number of probability distributions as
well as a growing library of statistical functions.
Each univariate distribution is an instance of a subclass of `rv_continuous`
(`rv_discrete` for discrete distributions):
.. autosummary::
:toctree: generated/
rv_continuous
rv_discrete
Continuous distributions
========================
.. autosummary::
:toctree: generated/
alpha -- Alpha
anglit -- Anglit
arcsine -- Arcsine
beta -- Beta
betaprime -- Beta Prime
bradford -- Bradford
burr -- Burr (Type III)
burr12 -- Burr (Type XII)
cauchy -- Cauchy
chi -- Chi
chi2 -- Chi-squared
cosine -- Cosine
dgamma -- Double Gamma
dweibull -- Double Weibull
erlang -- Erlang
expon -- Exponential
exponnorm -- Exponentially Modified Normal
exponweib -- Exponentiated Weibull
exponpow -- Exponential Power
f -- F (Snecdor F)
fatiguelife -- Fatigue Life (Birnbaum-Saunders)
fisk -- Fisk
foldcauchy -- Folded Cauchy
foldnorm -- Folded Normal
frechet_r -- Frechet Right Sided, Extreme Value Type II (Extreme LB) or weibull_min
frechet_l -- Frechet Left Sided, Weibull_max
genlogistic -- Generalized Logistic
gennorm -- Generalized normal
genpareto -- Generalized Pareto
genexpon -- Generalized Exponential
genextreme -- Generalized Extreme Value
gausshyper -- Gauss Hypergeometric
gamma -- Gamma
gengamma -- Generalized gamma
genhalflogistic -- Generalized Half Logistic
gilbrat -- Gilbrat
gompertz -- Gompertz (Truncated Gumbel)
gumbel_r -- Right Sided Gumbel, Log-Weibull, Fisher-Tippett, Extreme Value Type I
gumbel_l -- Left Sided Gumbel, etc.
halfcauchy -- Half Cauchy
halflogistic -- Half Logistic
halfnorm -- Half Normal
halfgennorm -- Generalized Half Normal
hypsecant -- Hyperbolic Secant
invgamma -- Inverse Gamma
invgauss -- Inverse Gaussian
invweibull -- Inverse Weibull
johnsonsb -- NAME
johnsonsu -- NAME
kappa4 -- Kappa 4 parameter
kappa3 -- Kappa 3 parameter
ksone -- Kolmogorov-Smirnov one-sided (no stats)
kstwobign -- Kolmogorov-Smirnov two-sided test for Large N (no stats)
laplace -- Laplace
levy -- Levy
levy_l
levy_stable
logistic -- Logistic
loggamma -- Log-Gamma
loglaplace -- Log-Laplace (Log Double Exponential)
lognorm -- Log-Normal
lomax -- Lomax (Pareto of the second kind)
maxwell -- Maxwell
mielke -- Mielke's Beta-Kappa
nakagami -- Nakagami
ncx2 -- Non-central chi-squared
ncf -- Non-central F
nct -- Non-central Student's T
norm -- Normal (Gaussian)
pareto -- Pareto
pearson3 -- Pearson type III
powerlaw -- Power-function
powerlognorm -- Power log normal
powernorm -- Power normal
rdist -- R-distribution
reciprocal -- Reciprocal
rayleigh -- Rayleigh
rice -- Rice
recipinvgauss -- Reciprocal Inverse Gaussian
semicircular -- Semicircular
skewnorm -- Skew normal
t -- Student's T
trapz -- Trapezoidal
triang -- Triangular
truncexpon -- Truncated Exponential
truncnorm -- Truncated Normal
tukeylambda -- Tukey-Lambda
uniform -- Uniform
vonmises -- Von-Mises (Circular)
vonmises_line -- Von-Mises (Line)
wald -- Wald
weibull_min -- Minimum Weibull (see Frechet)
weibull_max -- Maximum Weibull (see Frechet)
wrapcauchy -- Wrapped Cauchy
Multivariate distributions
==========================
.. autosummary::
:toctree: generated/
multivariate_normal -- Multivariate normal distribution
matrix_normal -- Matrix normal distribution
dirichlet -- Dirichlet
wishart -- Wishart
invwishart -- Inverse Wishart
multinomial -- Multinomial distribution
special_ortho_group -- SO(N) group
ortho_group -- O(N) group
random_correlation -- random correlation matrices
Discrete distributions
======================
.. autosummary::
:toctree: generated/
bernoulli -- Bernoulli
binom -- Binomial
boltzmann -- Boltzmann (Truncated Discrete Exponential)
dlaplace -- Discrete Laplacian
geom -- Geometric
hypergeom -- Hypergeometric
logser -- Logarithmic (Log-Series, Series)
nbinom -- Negative Binomial
planck -- Planck (Discrete Exponential)
poisson -- Poisson
randint -- Discrete Uniform
skellam -- Skellam
zipf -- Zipf
Statistical functions
=====================
Several of these functions have a similar version in scipy.stats.mstats
which work for masked arrays.
.. autosummary::
:toctree: generated/
describe -- Descriptive statistics
gmean -- Geometric mean
hmean -- Harmonic mean
kurtosis -- Fisher or Pearson kurtosis
kurtosistest --
mode -- Modal value
moment -- Central moment
normaltest --
skew -- Skewness
skewtest --
kstat --
kstatvar --
tmean -- Truncated arithmetic mean
tvar -- Truncated variance
tmin --
tmax --
tstd --
tsem --
variation -- Coefficient of variation
find_repeats
trim_mean
.. autosummary::
:toctree: generated/
cumfreq
histogram2
histogram
itemfreq
percentileofscore
scoreatpercentile
relfreq
.. autosummary::
:toctree: generated/
binned_statistic -- Compute a binned statistic for a set of data.
binned_statistic_2d -- Compute a 2-D binned statistic for a set of data.
binned_statistic_dd -- Compute a d-D binned statistic for a set of data.
.. autosummary::
:toctree: generated/
obrientransform
signaltonoise
bayes_mvs
mvsdist
sem
zmap
zscore
iqr
.. autosummary::
:toctree: generated/
sigmaclip
threshold
trimboth
trim1
.. autosummary::
:toctree: generated/
f_oneway
pearsonr
spearmanr
pointbiserialr
kendalltau
linregress
theilslopes
f_value
.. autosummary::
:toctree: generated/
ttest_1samp
ttest_ind
ttest_ind_from_stats
ttest_rel
kstest
chisquare
power_divergence
ks_2samp
mannwhitneyu
tiecorrect
rankdata
ranksums
wilcoxon
kruskal
friedmanchisquare
combine_pvalues
ss
square_of_sums
jarque_bera
.. autosummary::
:toctree: generated/
ansari
bartlett
levene
shapiro
anderson
anderson_ksamp
binom_test
fligner
median_test
mood
.. autosummary::
:toctree: generated/
boxcox
boxcox_normmax
boxcox_llf
entropy
.. autosummary::
:toctree: generated/
chisqprob
betai
Circular statistical functions
==============================
.. autosummary::
:toctree: generated/
circmean
circvar
circstd
Contingency table functions
===========================
.. autosummary::
:toctree: generated/
chi2_contingency
contingency.expected_freq
contingency.margins
fisher_exact
Plot-tests
==========
.. autosummary::
:toctree: generated/
ppcc_max
ppcc_plot
probplot
boxcox_normplot
Masked statistics functions
===========================
.. toctree::
stats.mstats
Univariate and multivariate kernel density estimation (:mod:`scipy.stats.kde`)
==============================================================================
.. autosummary::
:toctree: generated/
gaussian_kde
For many more stat related functions install the software R and the
interface package rpy.
""" |
#!/usr/bin/env python
# ***** BEGIN LICENSE BLOCK *****
# Version: MPL 1.1/GPL 2.0/LGPL 2.1
#
# The contents of this file are subject to the Mozilla Public License Version
# 1.1 (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
# http://www.mozilla.org/MPL/
#
# Software distributed under the License is distributed on an "AS IS" basis,
# WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License
# for the specific language governing rights and limitations under the
# License.
#
# The Original Code is font utility code.
#
# The Initial Developer of the Original Code is Mozilla Corporation.
# Portions created by the Initial Developer are Copyright (C) 2009
# the Initial Developer. All Rights Reserved.
#
# Contributor(s):
# NAME <EMAIL>
#
# Alternatively, the contents of this file may be used under the terms of
# either the GNU General Public License Version 2 or later (the "GPL"), or
# the GNU Lesser General Public License Version 2.1 or later (the "LGPL"),
# in which case the provisions of the GPL or the LGPL are applicable instead
# of those above. If you wish to allow use of your version of this file only
# under the terms of either the GPL or the LGPL, and not to allow others to
# use your version of this file under the terms of the MPL, indicate your
# decision by deleting the provisions above and replace them with the notice
# and other provisions required by the GPL or the LGPL. If you do not delete
# the provisions above, a recipient may use your version of this file under
# the terms of any one of the MPL, the GPL or the LGPL.
#
# ***** END LICENSE BLOCK ***** */
# eotlitetool.py - create EOT version of OpenType font for use with IE
#
# Usage: eotlitetool.py [-o output-filename] font1 [font2 ...]
#
# OpenType file structure
# http://www.microsoft.com/typography/otspec/otff.htm
#
# Types:
#
# BYTE 8-bit unsigned integer.
# CHAR 8-bit signed integer.
# USHORT 16-bit unsigned integer.
# SHORT 16-bit signed integer.
# ULONG 32-bit unsigned integer.
# Fixed 32-bit signed fixed-point number (16.16)
# LONGDATETIME Date represented in number of seconds since 12:00 midnight, January 1, 1904. The value is represented as a signed 64-bit integer.
#
# SFNT Header
#
# Fixed sfnt version // 0x00010000 for version 1.0.
# USHORT numTables // Number of tables.
# USHORT searchRange // (Maximum power of 2 <= numTables) x 16.
# USHORT entrySelector // Log2(maximum power of 2 <= numTables).
# USHORT rangeShift // NumTables x 16-searchRange.
#
# Table Directory
#
# ULONG tag // 4-byte identifier.
# ULONG checkSum // CheckSum for this table.
# ULONG offset // Offset from beginning of TrueType font file.
# ULONG length // Length of this table.
#
# OS/2 Table (Version 4)
#
# USHORT version // 0x0004
# SHORT xAvgCharWidth
# USHORT usWeightClass
# USHORT usWidthClass
# USHORT fsType
# SHORT ySubscriptXSize
# SHORT ySubscriptYSize
# SHORT ySubscriptXOffset
# SHORT ySubscriptYOffset
# SHORT ySuperscriptXSize
# SHORT ySuperscriptYSize
# SHORT ySuperscriptXOffset
# SHORT ySuperscriptYOffset
# SHORT yStrikeoutSize
# SHORT yStrikeoutPosition
# SHORT sFamilyClass
# BYTE panose[10]
# ULONG ulUnicodeRange1 // Bits 0-31
# ULONG ulUnicodeRange2 // Bits 32-63
# ULONG ulUnicodeRange3 // Bits 64-95
# ULONG ulUnicodeRange4 // Bits 96-127
# CHAR achVendID[4]
# USHORT fsSelection
# USHORT usFirstCharIndex
# USHORT usLastCharIndex
# SHORT sTypoAscender
# SHORT sTypoDescender
# SHORT sTypoLineGap
# USHORT usWinAscent
# USHORT usWinDescent
# ULONG ulCodePageRange1 // Bits 0-31
# ULONG ulCodePageRange2 // Bits 32-63
# SHORT sxHeight
# SHORT sCapHeight
# USHORT usDefaultChar
# USHORT usBreakChar
# USHORT usMaxContext
#
#
# The Naming Table is organized as follows:
#
# [name table header]
# [name records]
# [string data]
#
# Name Table Header
#
# USHORT format // Format selector (=0).
# USHORT count // Number of name records.
# USHORT stringOffset // Offset to start of string storage (from start of table).
#
# Name Record
#
# USHORT platformID // Platform ID.
# USHORT encodingID // Platform-specific encoding ID.
# USHORT languageID // Language ID.
# USHORT nameID // Name ID.
# USHORT length // String length (in bytes).
# USHORT offset // String offset from start of storage area (in bytes).
#
# head Table
#
# Fixed tableVersion // Table version number 0x00010000 for version 1.0.
# Fixed fontRevision // Set by font manufacturer.
# ULONG checkSumAdjustment // To compute: set it to 0, sum the entire font as ULONG, then store 0xB1B0AFBA - sum.
# ULONG magicNumber // Set to 0x5F0F3CF5.
# USHORT flags
# USHORT unitsPerEm // Valid range is from 16 to 16384. This value should be a power of 2 for fonts that have TrueType outlines.
# LONGDATETIME created // Number of seconds since 12:00 midnight, January 1, 1904. 64-bit integer
# LONGDATETIME modified // Number of seconds since 12:00 midnight, January 1, 1904. 64-bit integer
# SHORT xMin // For all glyph bounding boxes.
# SHORT yMin
# SHORT xMax
# SHORT yMax
# USHORT macStyle
# USHORT lowestRecPPEM // Smallest readable size in pixels.
# SHORT fontDirectionHint
# SHORT indexToLocFormat // 0 for short offsets, 1 for long.
# SHORT glyphDataFormat // 0 for current format.
#
#
#
# Embedded OpenType (EOT) file format
# http://www.w3.org/Submission/EOT/
#
# EOT version 0x00020001
#
# An EOT font consists of a header with the original OpenType font
# appended at the end. Most of the data in the EOT header is simply a
# copy of data from specific tables within the font data. The exceptions
# are the 'Flags' field and the root string name field. The root string
# is a set of names indicating domains for which the font data can be
# used. A null root string implies the font data can be used anywhere.
# The EOT header is in little-endian byte order but the font data remains
# in big-endian order as specified by the OpenType spec.
#
# Overall structure:
#
# [EOT header]
# [EOT name records]
# [font data]
#
# EOT header
#
# ULONG eotSize // Total structure length in bytes (including string and font data)
# ULONG fontDataSize // Length of the OpenType font (FontData) in bytes
# ULONG version // Version number of this format - 0x00020001
# ULONG flags // Processing Flags (0 == no special processing)
# BYTE fontPANOSE[10] // OS/2 Table panose
# BYTE charset // DEFAULT_CHARSET (0x01)
# BYTE italic // 0x01 if ITALIC in OS/2 Table fsSelection is set, 0 otherwise
# ULONG weight // OS/2 Table usWeightClass
# USHORT fsType // OS/2 Table fsType (specifies embedding permission flags)
# USHORT magicNumber // Magic number for EOT file - 0x504C.
# ULONG unicodeRange1 // OS/2 Table ulUnicodeRange1
# ULONG unicodeRange2 // OS/2 Table ulUnicodeRange2
# ULONG unicodeRange3 // OS/2 Table ulUnicodeRange3
# ULONG unicodeRange4 // OS/2 Table ulUnicodeRange4
# ULONG codePageRange1 // OS/2 Table ulCodePageRange1
# ULONG codePageRange2 // OS/2 Table ulCodePageRange2
# ULONG checkSumAdjustment // head Table CheckSumAdjustment
# ULONG reserved[4] // Reserved - must be 0
# USHORT padding1 // Padding - must be 0
#
# EOT name records
#
# USHORT FamilyNameSize // Font family name size in bytes
# BYTE FamilyName[FamilyNameSize] // Font family name (name ID = 1), little-endian UTF-16
# USHORT Padding2 // Padding - must be 0
#
# USHORT StyleNameSize // Style name size in bytes
# BYTE StyleName[StyleNameSize] // Style name (name ID = 2), little-endian UTF-16
# USHORT Padding3 // Padding - must be 0
#
# USHORT VersionNameSize // Version name size in bytes
# bytes VersionName[VersionNameSize] // Version name (name ID = 5), little-endian UTF-16
# USHORT Padding4 // Padding - must be 0
#
# USHORT FullNameSize // Full name size in bytes
# BYTE FullName[FullNameSize] // Full name (name ID = 4), little-endian UTF-16
# USHORT Padding5 // Padding - must be 0
#
# USHORT RootStringSize // Root string size in bytes
# BYTE RootString[RootStringSize] // Root string, little-endian UTF-16
|