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# -*- coding: utf-8 -*- """ Created on Mon Oct 23 16:31:30 2017 @author: ben """ import numpy as np def RDE(x): xs=x.copy() xs=np.isfinite(xs) # this changes xs from values to a boolean if np.sum(xs)<2 : return np.nan ind=np.arange(0.5, np.sum(xs)) LH=np.interp(np.array([0.16, 0.84])*np.sum(xs), ind, np.sort(x[xs])) #print('LH =',LH) return (LH[1]-LH[0])/2. # trying to get some kind of a width of the data ~variance #import scipy.stats as stats #def RDE(x): # return (stats.scoreatpercentile(x, 84 )-stats.scoreatpercentile(x, 16))/2.
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import os os.environ["CUDA_VISIBLE_DEVICES"]="4" from model import get_model from model import get_model_max from model import get_model_C_mul from model import get_model_C_sub import tensorflow as tf import numpy as np from sklearn.metrics import roc_auc_score,average_precision_score, f1_score from sklearn.metrics import accuracy_score,recall_score def stat(y_label,y_pred): # print('y_label=',y_label) # print('y_pred=',y_pred) threshold = 0.5 auc = roc_auc_score(y_label, y_pred) aupr = average_precision_score(y_label, y_pred) for i in range(len(y_pred)): if y_pred[i][0] >= threshold: y_pred[i][0] = 1 if y_pred[i][0] < threshold: y_pred[i][0] = 0 TP = 0 TN = 0 FP = 0 FN = 0 for i in range(len(y_pred)): if y_pred[i][0] == 0 and y_label[i] == 0: TN = TN + 1 if y_pred[i][0] == 1 and y_label[i] == 1: TP = TP + 1 if y_pred[i][0] == 0 and y_label[i] == 1: FN = FN + 1 if y_pred[i][0] == 1 and y_label[i] == 0: FP = FP + 1 specificity = TN/(TN+FP) recall = recall_score(y_label,y_pred) acc = accuracy_score(y_label,y_pred) f1 = f1_score(y_label, y_pred) acc = round(acc, 4) auc = round(auc,4) aupr = round(aupr, 4) f1 = round(f1,4) return acc,auc,aupr,f1,recall,specificity ########################## datatype = 2021 kmer = 3 ########################## for m in range(100): model=None model=get_model() model.load_weights('./model/3mer2021/Solanum lycopersicumModel%s.h5'%m) if datatype == 2020: names = ['Arabidopsis lyrata','Solanum lycopersicum'] elif datatype == 2021: names = ['aly','mtr','stu','bdi'] for name in names: Data_dir='/home/yxy/Project/002/processData/3mer/' if datatype == 2020: test=np.load(Data_dir+'5mer%s_test.npz'%name) elif datatype == 2021: test=np.load(Data_dir+'%s%stest2021.npz'%(name,kmer)) X_mi_tes,X_lnc_tes,y_tes=test['X_mi_tes'],test['X_lnc_tes'],test['y_tes'] print("****************Testing %s specific model on %s cell line****************"%(m,name)) y_pred = model.predict([X_mi_tes,X_lnc_tes]) auc = roc_auc_score(y_tes, y_pred) aupr = average_precision_score(y_tes, y_pred) f1 = f1_score(y_tes, np.round(y_pred.reshape(-1))) print("AUC : ", auc) print("AUPR : ", aupr) print("f1_score", f1) acc,auc,aupr,f1,recall,specificity = stat(y_tes, y_pred) print("ACC : ", acc,"auc : ", auc,"aupr :" , aupr,"f1 : ", f1,"recall : ",recall,"specificity : ",specificity)
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# Generated by Django 3.2.16 on 2022-12-14 10:35 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ("regimes", "0009_update_cwc_shortened_names"), ("regimes", "0009_update_nsg_regimes"), ] operations = []
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# Generated by Django 2.2.2 on 2019-06-07 06:46 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('example', '0006_auto_20181228_0752'), ] operations = [ migrations.AddField( model_name='artproject', name='description', field=models.CharField(max_length=100, null=True), ), ]
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import collections import cibblbibbl from cibblbibbl import bbcode from .plaintext import kreasontrans, nreasontrans def _diedstr(dRPP, killerId, reason): if killerId: killer = cibblbibbl.player.player(killerId) oppoTe = dRPP[killer]["team"] return ( f'{kreasontrans.get(reason, reason)}' f'{bbcode.player(killer)} ({_teamstr(killer, oppoTe)})' ) else: return f', {nreasontrans.get(reason, reason)}' def _playersseq(T, source_playersseq): StarPlayer = cibblbibbl.player.StarPlayer players = [] for Pl in sorted(source_playersseq, key=lambda Pl: Pl.name): if Pl.achievements: prestige = sum( A.prestige(T.season, maxtournament=T) for A in Pl.achievements ) if prestige or isinstance(Pl, StarPlayer): players.append([Pl, prestige]) elif isinstance(Pl, StarPlayer): players.append([Pl, 0]) return players def _teamstr(player, team): if isinstance(player, cibblbibbl.player.StarPlayer): return "Star Player" elif isinstance(player, cibblbibbl.player.MercenaryPlayer): return "Mercenary" else: return bbcode.team(team) def bbcode_section(s): return bbcode.size(bbcode.b(bbcode.i(s)), 12) def export(T): cls_StarPlayer = cibblbibbl.player.StarPlayer cls_RaisedDeadPlayer = cibblbibbl.player.RaisedDeadPlayer dTAv1 = T.teamachievementvalues(False, False, False, False) dPAv1 = T.playerachievementvalues() dRPP = T.rawplayerperformances() dPP = T.playerperformances() achievements = sorted(T.achievements) d_achievements = collections.defaultdict(dict) for A in achievements: d_achievements[A.clskey()][A.subject] = A prev_tournament = {} for Te in T.teams(): prev_tournament[Te] = Te.prev_tournament(T) parts = [] parts.append("[block=center]") nrsuffix = {1: "st", 2: "nd", 3: "rd"} for d in reversed(T.standings()): nr = d["nr"] if nr is None: continue Te = d["team"] nrstr = f'{nr}{nrsuffix.get(nr, "th")} place: ' nrstr = bbcode.i(nrstr) part = nrstr + bbcode.team(Te) if nr == 1: part = bbcode.size(bbcode.b(part), 12) parts.append(part + "\n") tp_keys = ("tp_admin", "tp_match", "tp_standings") dtp = {k: 0 for k in tp_keys} for k in dtp: A = d_achievements.get(k, {}).get(Te) if A: dtp[k] = A.prestige(T.season, maxtournament=T) prestige = sum(dtp.values()) if T.friendly == "no": preststr = f'Prestige Points Earned: {prestige}' dTTAv1 = dTAv1[Te] dTPAv1 = dPAv1[Te] T0 = prev_tournament[Te] if T0: dPAv0 = T0.playerachievementvalues() dTPAv0 = dPAv0[Te] else: dTPAv0 = 0 achiev = dTTAv1 + dTPAv1 - dTPAv0 if achiev: sign = ("+" if -1 < achiev else "") preststr += f' (and {sign}{achiev} Achiev.)' parts.append(preststr + "\n") parts.append("\n") parts.append("[/block]") parts.append("\n") As = sorted( A for A in T.achievements if not A.clskey().startswith("tp") and A.get("status", "proposed") in {"awarded", "proposed"} and not isinstance(A.subject, cls_RaisedDeadPlayer) ) if As: parts.append(bbcode_section("Achievements") + "\n") parts.append(bbcode.hr() + "\n") items = [] prev_clskey = None for A in As: item = A.export_bbcode() if item is None: continue clskey = A.clskey() if clskey != prev_clskey: if items: parts.append(bbcode.list_(items) + "") parts.append("\n") parts.append("[block=center]") logo_url = A.get("logo_url") if logo_url: parts.append(bbcode.img(logo_url) + "\n") parts.append(bbcode.b(bbcode.i(A["name"])) + "\n") parts.append("\n") descr = bbcode.i(A["description"]) parts.append( "[block=automargin width=67%]" + descr + "[/block]" ) parts.append("[/block]") prev_clskey = clskey items = [] items.append(item) else: if items: parts.append(bbcode.list_(items) + "") deadplayers = _playersseq(T, T.deadplayers()) transferred = T.transferredplayers() trplayers = _playersseq(T, transferred) retiredplayers = T.retiredplayers(dPP=dPP) retplayers = _playersseq(T, retiredplayers) if deadplayers or trplayers or retplayers: if As: parts.append("\n") parts.append("\n") stitle = ( "Players with achievements" " that changed their forms and/or teams" ) parts.append(bbcode_section(stitle) + "\n") parts.append(bbcode.hr() + "\n") if deadplayers: parts.append( bbcode.center( bbcode.img("/i/607211") + "\n" + bbcode.b(bbcode.i("Died")) ) ) items = [] for Pl, prestige in deadplayers: d = dPP[Pl] matchId, half, turn, reason, killerId = d["dead"] Ma = cibblbibbl.match.Match(matchId) Te = d["team"] s = "" s += f'{bbcode.player(Pl)} ({_teamstr(Pl, Te)})' if prestige: s += f' ({prestige} Achiev.)' s += _diedstr(dRPP, killerId, reason) s += f' [{bbcode.match(Ma, "match")}]' items.append(s) parts.append(bbcode.list_(items) + "") if trplayers: if deadplayers: parts.append("\n") parts.append( bbcode.center( bbcode.img("/i/607210") + "\n" + bbcode.b(bbcode.i( "Transferred and/or Transformed" )) ) ) items = [] for Pl, prestige in trplayers: matchId, half, turn, reason, killerId = transferred[Pl] Ma = cibblbibbl.match.Match(matchId) teams = Ma.teams Te = dRPP[Pl]["team"] s = "" s += f'{bbcode.player(Pl)} ({_teamstr(Pl, Te)})' if prestige: s += f' ({prestige} Achiev.)' s += _diedstr(dRPP, killerId, reason) nextsparts = [] for Pl1 in Pl.nexts: name = bbcode.player(Pl1) if isinstance(Pl1, cls_RaisedDeadPlayer): if Pl1.next is not None: Pl1 = Pl1.next name = bbcode.player(Pl1) else: plparts = str(Pl1).split() plparts[0] = Pl1.prevreason name = " ".join(plparts) try: nextTe = dRPP[Pl1]["team"] except KeyError: nextTe = Pl1.team nextsparts.append( f'to {bbcode.team(nextTe)}' f' as {name}' ) s += f', joined {" and ".join(nextsparts)}' items.append(s) parts.append(bbcode.list_(items)) if retplayers: if deadplayers or trplayers: parts.append("\n") parts.append( bbcode.center( bbcode.img("/i/607209") + "\n" + bbcode.b(bbcode.i("Retired")) ) ) items = [] for Pl, prestige in retplayers: d = retiredplayers[Pl] Te = d["team"] s = f'{bbcode.player(Pl)} ({bbcode.team(Te)})' s += f' ({prestige} Achiev.)' items.append(s) parts.append(bbcode.list_(items)) s = "".join(parts) return s
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri Jul 5 20:46:03 2019 @author: ymguo Combine image crop, color shift, rotation and perspective transform together to complete a data augmentation script. """ import skimage.io as io import numpy as np import cv2 import random import os import glob from matplotlib import pyplot as plt #from skimage import data_dir #from PIL import Image def data_augmentation(f): # img = io.imread(f) # 依次读取rgb图片 img = f # image crop img_crop = img[0:300, 0:450] # color shift def random_light_color(img): # brightness B, G, R = cv2.split(img) b_rand = random.randint(-50, 50) if b_rand == 0: pass elif b_rand > 0: lim = 255 - b_rand B[B > lim] = 255 # 防止超过255 越界 B[B <= lim] = (b_rand + B[B <= lim]).astype(img.dtype) elif b_rand < 0: lim = 0 - b_rand B[B < lim] = 0 # 防止小于0 越界 B[B >= lim] = (b_rand + B[B >= lim]).astype(img.dtype) g_rand = random.randint(-50, 50) if g_rand == 0: pass elif g_rand > 0: lim = 255 - g_rand G[G > lim] = 255 G[G <= lim] = (g_rand + G[G <= lim]).astype(img.dtype) elif g_rand < 0: lim = 0 - g_rand G[G < lim] = 0 G[G >= lim] = (g_rand + G[G >= lim]).astype(img.dtype) r_rand = random.randint(-50, 50) if r_rand == 0: pass elif r_rand > 0: lim = 255 - r_rand R[R > lim] = 255 R[R <= lim] = (r_rand + R[R <= lim]).astype(img.dtype) elif r_rand < 0: lim = 0 - r_rand R[R < lim] = 0 R[R >= lim] = (r_rand + R[R >= lim]).astype(img.dtype) img_merge = cv2.merge((B, G, R)) # 融合 # img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR) ? return img_merge img_color_shift = random_light_color(img_crop) # rotation M = cv2.getRotationMatrix2D((img_color_shift.shape[1] / 2, img_color_shift.shape[0] / 2), 30, 0.85) # center, angle, scale img_rotate = cv2.warpAffine(img_color_shift, M, (img_color_shift.shape[1], img_color_shift.shape[0])) # warpAffine函数:把旋转矩阵作用到图形上 # perspective transform def random_warp(img, row, col): height, width, channels = img.shape # warp: random_margin = 60 x1 = random.randint(-random_margin, random_margin) y1 = random.randint(-random_margin, random_margin) x2 = random.randint(width - random_margin - 1, width - 1) y2 = random.randint(-random_margin, random_margin) x3 = random.randint(width - random_margin - 1, width - 1) y3 = random.randint(height - random_margin - 1, height - 1) x4 = random.randint(-random_margin, random_margin) y4 = random.randint(height - random_margin - 1, height - 1) dx1 = random.randint(-random_margin, random_margin) dy1 = random.randint(-random_margin, random_margin) dx2 = random.randint(width - random_margin - 1, width - 1) dy2 = random.randint(-random_margin, random_margin) dx3 = random.randint(width - random_margin - 1, width - 1) dy3 = random.randint(height - random_margin - 1, height - 1) dx4 = random.randint(-random_margin, random_margin) dy4 = random.randint(height - random_margin - 1, height - 1) pts1 = np.float32([[x1, y1], [x2, y2], [x3, y3], [x4, y4]]) pts2 = np.float32([[dx1, dy1], [dx2, dy2], [dx3, dy3], [dx4, dy4]]) M_warp = cv2.getPerspectiveTransform(pts1, pts2) img_warp = cv2.warpPerspective(img, M_warp, (width, height)) return img_warp img_warp = random_warp(img_rotate, img_rotate.shape[0], img_rotate.shape[1]) return img_warp # 获取待处理文件夹下的所有图片 # glob.glob 返回所有匹配的文件路径列表,只有一个参数pathname。 paths = glob.glob(os.path.join('/Users/ymguo/CVsummer/jpg_before/','*.jpg')) paths.sort() # 排序 print(paths) i = 0 for path in paths: im = cv2.imread(path) # 依次读取图片 # pic_after = [] pic_after = data_augmentation(im) print(i) plt.imshow(pic_after) plt.show() # 依次存储处理后并重命名的图片到新的文件夹下 io.imsave("/Users/ymguo/CVsummer/pic_after/"+np.str(i)+'.jpg',pic_after) i += 1 #print(pic_after.dtype) #print(pic_after.shape) '''一些不太正确的尝试''' #def file_name(file_dir): # for root, dirs, files in os.walk(file_dir): # count = 1 # #当前文件夹所有文件 # for i in files: # im=Image.open(i) # out=data_augmentation(im) # out.save('/Users/ymguo/CVsummer/image/'+str(count)+'.png','PNG') # count+=1 # print(i) # #file_name("/Users/ymguo/CVsummer/coll_after/")#当前文件夹 #file_name('./')#当前文件夹 #srcImgFolder = "/Users/ymguo/CVsummer/coll_after" #def data(dir_proc): # for file in os.listdir(dir_proc): # fullFile = os.path.join(dir_proc, file) # if os.path.isdir(fullFile): # data_augmentation(fullFile) # # #if __name__ == "__main__": # data(srcImgFolder) #str=data_dir+'/*.png' #coll_before = io.ImageCollection(str) #coll_after = io.ImageCollection(str,load_func=data_augmentation) # coll = io.ImageCollection(str) # skimage.io.ImageCollection(load_pattern,load_func=None) # 回调函数默认为imread(),即批量读取图片。 #print(len(coll_after)) # 处理后的图片数量 #print(coll_before[1].shape) # #plt.imshow(coll_before[1]) #plt.show() #plt.imshow(coll_after[1]) #plt.show() #io.imshow(coll_before[10]) #io.imshow(coll_after[10]) #cv2.imshow('raw pic', coll_before[10]) #cv2.imshow('pic after data augmentation', coll_after[10]) #key = cv2.waitKey(0) #if key == 27: # cv2.destroyAllWindows() # 循环保存c处理后的图片 #for i in range(len(coll_after)): # io.imsave("/Users/ymguo/CVsummer/coll_after/"+np.str(i)+'.png',coll_after[i])
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#!/usr/bin/env python # -*- coding:utf-8 -*- # @Time : 2020-07-21 # @Author : Joey Jiang # @File : base7_3.py # @Software : PyCharm # @Description: python控制流语法 # 1.1、分支结构 import random a = 0 if a == 0: print("a=0") else: print("a!=0") # 1.2、多重分支 a = 1 if a == 1: print("a=1") elif a == 2: print("a=2") elif a == 3: print("a==3") else: print("a!=1、2、3") # 1.3、练习 # 分别使用分支嵌套以及多重分支去实现分段函数求值 # 3x - 5 (x>1) # f(x)= x + 2 (-1<=x<=1) # 5x + 3(x<-1) # 1.3.1分支嵌套 x = -2 if x > 1: print(3 * x - 5) else: if x >= -1: print(x + 2) else: print(5 * x + 3) # 1.3.2多重分支 if x > 1: print(3 * x - 5) elif x >= -1: print(x + 2) else: print(5 * x + 3) # 2.1练习 # 计算1~100的和 sum1 = 0 for i in range(1, 101): sum1 = sum1 + i print(sum1) # 2.2练习 # 加入分支结构实现1~100之间偶数的求和 sum2 = 0 for i in range(1, 101): if i % 2 == 0: sum2 = sum2 + i print(sum2) # 2.3练习 # 使用python实现1~100之间偶数求和 sum3 = 0 for i in range(2, 101): if i % 2 == 0: sum3 = sum3 + i print(sum3) # 3、While循环 # 3.1、While Else while_a = 1 while while_a == 1: print("while_a=1") while_a = while_a + 1 else: print("while_a!=1") print(while_a) # 3.2、简单语句组 flag = 10 while flag == 10: flag = flag + 1 else: print(flag) # 4、break语句 for i in range(4): if i == 2: break print("i=", i) # 5、continue语句 for j in range(4): if j == 2: continue print("j=", j) # 6、练习 """ 猜数字游戏,计算机出一个1~100之间的随机数由人来猜, 计算机根据人猜的数字分别给出提示大一点/小一点/猜对了 """ guess_number = random.randint(1, 100) print(guess_number) while True: number = int(input("请输入一个1~100之间的整数>")) if number == guess_number: print("猜对了") break elif number > guess_number: print("大一点") else: print("小一点")
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/lesson2_2_step8.py
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from selenium import webdriver import time import math import os try: link = "http://suninjuly.github.io/file_input.html" browser = webdriver.Chrome() browser.get(link) input1 = browser.find_element_by_name("firstname") input1.send_keys("test") input2 = browser.find_element_by_name("lastname") input2.send_keys("test") input3 = browser.find_element_by_name("email") input3.send_keys("test") fileButton = browser.find_element_by_id("file") current_dir = os.path.abspath(os.path.dirname(__file__)) file_path = os.path.join(current_dir, 'answer.txt') fileButton.send_keys(file_path) button = browser.find_element_by_tag_name("button") button.click() finally: # ожидание чтобы визуально оценить результаты прохождения скрипта time.sleep(10) # закрываем браузер после всех манипуляций browser.quit() file_path = os.path.join(current_dir, 'output.txt')
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/manage.py
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#!/usr/bin/env python import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "CallProgramNG.settings") try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv)
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/src/eposfederator/libs/base/schema.py
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import marshmallow class Schema(marshmallow.Schema): class Meta(object): strict = True dateformat = "%Y-%m-%dT%H:%M:%S"
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/pysnmp/IPV6-TCP-MIB.py
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# # PySNMP MIB module IPV6-TCP-MIB (http://snmplabs.com/pysmi) # ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/IPV6-TCP-MIB # Produced by pysmi-0.3.4 at Mon Apr 29 19:45:44 2019 # On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4 # Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) # ObjectIdentifier, OctetString, Integer = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "OctetString", "Integer") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ConstraintsIntersection, SingleValueConstraint, ConstraintsUnion, ValueRangeConstraint, ValueSizeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsIntersection", "SingleValueConstraint", "ConstraintsUnion", "ValueRangeConstraint", "ValueSizeConstraint") Ipv6Address, Ipv6IfIndexOrZero = mibBuilder.importSymbols("IPV6-TC", "Ipv6Address", "Ipv6IfIndexOrZero") ModuleCompliance, ObjectGroup, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "ObjectGroup", "NotificationGroup") MibScalar, MibTable, MibTableRow, MibTableColumn, experimental, ObjectIdentity, Gauge32, Counter64, Counter32, Bits, NotificationType, IpAddress, ModuleIdentity, Integer32, iso, TimeTicks, Unsigned32, mib_2, MibIdentifier = mibBuilder.importSymbols("SNMPv2-SMI", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "experimental", "ObjectIdentity", "Gauge32", "Counter64", "Counter32", "Bits", "NotificationType", "IpAddress", "ModuleIdentity", "Integer32", "iso", "TimeTicks", "Unsigned32", "mib-2", "MibIdentifier") DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention") ipv6TcpMIB = ModuleIdentity((1, 3, 6, 1, 3, 86)) ipv6TcpMIB.setRevisions(('2017-02-22 00:00', '1998-01-29 00:00',)) if mibBuilder.loadTexts: ipv6TcpMIB.setLastUpdated('201702220000Z') if mibBuilder.loadTexts: ipv6TcpMIB.setOrganization('IETF IPv6 MIB Working Group') tcp = MibIdentifier((1, 3, 6, 1, 2, 1, 6)) ipv6TcpConnTable = MibTable((1, 3, 6, 1, 2, 1, 6, 16), ) if mibBuilder.loadTexts: ipv6TcpConnTable.setStatus('obsolete') ipv6TcpConnEntry = MibTableRow((1, 3, 6, 1, 2, 1, 6, 16, 1), ).setIndexNames((0, "IPV6-TCP-MIB", "ipv6TcpConnLocalAddress"), (0, "IPV6-TCP-MIB", "ipv6TcpConnLocalPort"), (0, "IPV6-TCP-MIB", "ipv6TcpConnRemAddress"), (0, "IPV6-TCP-MIB", "ipv6TcpConnRemPort"), (0, "IPV6-TCP-MIB", "ipv6TcpConnIfIndex")) if mibBuilder.loadTexts: ipv6TcpConnEntry.setStatus('obsolete') ipv6TcpConnLocalAddress = MibTableColumn((1, 3, 6, 1, 2, 1, 6, 16, 1, 1), Ipv6Address()) if mibBuilder.loadTexts: ipv6TcpConnLocalAddress.setStatus('obsolete') ipv6TcpConnLocalPort = MibTableColumn((1, 3, 6, 1, 2, 1, 6, 16, 1, 2), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 65535))) if mibBuilder.loadTexts: ipv6TcpConnLocalPort.setStatus('obsolete') ipv6TcpConnRemAddress = MibTableColumn((1, 3, 6, 1, 2, 1, 6, 16, 1, 3), Ipv6Address()) if mibBuilder.loadTexts: ipv6TcpConnRemAddress.setStatus('obsolete') ipv6TcpConnRemPort = MibTableColumn((1, 3, 6, 1, 2, 1, 6, 16, 1, 4), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 65535))) if mibBuilder.loadTexts: ipv6TcpConnRemPort.setStatus('obsolete') ipv6TcpConnIfIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 6, 16, 1, 5), Ipv6IfIndexOrZero()) if mibBuilder.loadTexts: ipv6TcpConnIfIndex.setStatus('obsolete') ipv6TcpConnState = MibTableColumn((1, 3, 6, 1, 2, 1, 6, 16, 1, 6), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12))).clone(namedValues=NamedValues(("closed", 1), ("listen", 2), ("synSent", 3), ("synReceived", 4), ("established", 5), ("finWait1", 6), ("finWait2", 7), ("closeWait", 8), ("lastAck", 9), ("closing", 10), ("timeWait", 11), ("deleteTCB", 12)))).setMaxAccess("readwrite") if mibBuilder.loadTexts: ipv6TcpConnState.setStatus('obsolete') ipv6TcpConformance = MibIdentifier((1, 3, 6, 1, 3, 86, 2)) ipv6TcpCompliances = MibIdentifier((1, 3, 6, 1, 3, 86, 2, 1)) ipv6TcpGroups = MibIdentifier((1, 3, 6, 1, 3, 86, 2, 2)) ipv6TcpCompliance = ModuleCompliance((1, 3, 6, 1, 3, 86, 2, 1, 1)).setObjects(("IPV6-TCP-MIB", "ipv6TcpGroup")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): ipv6TcpCompliance = ipv6TcpCompliance.setStatus('obsolete') ipv6TcpGroup = ObjectGroup((1, 3, 6, 1, 3, 86, 2, 2, 1)).setObjects(("IPV6-TCP-MIB", "ipv6TcpConnState")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): ipv6TcpGroup = ipv6TcpGroup.setStatus('obsolete') mibBuilder.exportSymbols("IPV6-TCP-MIB", ipv6TcpConnTable=ipv6TcpConnTable, ipv6TcpConnEntry=ipv6TcpConnEntry, ipv6TcpMIB=ipv6TcpMIB, ipv6TcpGroups=ipv6TcpGroups, ipv6TcpConnIfIndex=ipv6TcpConnIfIndex, tcp=tcp, ipv6TcpConnRemPort=ipv6TcpConnRemPort, ipv6TcpConformance=ipv6TcpConformance, PYSNMP_MODULE_ID=ipv6TcpMIB, ipv6TcpConnState=ipv6TcpConnState, ipv6TcpConnRemAddress=ipv6TcpConnRemAddress, ipv6TcpConnLocalPort=ipv6TcpConnLocalPort, ipv6TcpCompliances=ipv6TcpCompliances, ipv6TcpConnLocalAddress=ipv6TcpConnLocalAddress, ipv6TcpCompliance=ipv6TcpCompliance, ipv6TcpGroup=ipv6TcpGroup)
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angeldsLee/miniblog
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from flask import render_template, flash, redirect, session, url_for, request, g from flask_login import login_user, logout_user, current_user, login_required from app import app, db, lm, oid from .forms import LoginForm, EditForm from .models import User from datetime import datetime @lm.user_loader def laod_user(id): return User.query.get(int(id)) @app.before_request def before_request(): g.user = current_user if g.user.is_authenticated: # before_request handler will update the time in the database. g.user.last_seen = datetime.utcnow() db.session.add(g.user) db.session.commit() @app.route('/') @app.route('/index') @login_required def index(): user = g.user posts = [ # fake array of posts { 'author': {'nickname': 'John'}, 'body': 'Beautiful day in Portland!' }, { 'author': {'nickname': 'Susan'}, 'body': 'The Avengers movie was so cool!' } ] return render_template('index.html', title='home', user=user, posts=posts) @app.route('/login', methods=['GET', 'POST']) @oid.loginhandler def login(): if g.user is not None and g.user.is_authenticated: # if a user is already logged in return redirect(url_for('index')) form = LoginForm() # print "form = LoginForm()" if form.validate_on_submit(): # print "validate_on_submit" session['remember_me'] = form.remember_me.data return oid.try_login(form.openid.data, ask_for=['nickname', 'email']) # trigger authentication # print "not pass validate_on_submit" return render_template('login.html', title='Sign In', form=form, providers=app.config['OPENID_PROVIDERS']) @app.route('/edit', methods=['GET', 'POST']) @login_required def edit(): form = EditForm(g.user.nickname) if form.validate_on_submit(): g.user.nickname = form.nickname.data g.user.about_me = form.about_me.data db.session.add(g.user) db.session.commit() flash('your changes have been saved') return redirect(url_for('edit')) else: form.nickname.data = g.user.nickname form.about_me.data = g.user.about_me return render_template('edit.html', form=form) @app.route('/logout') def logout(): logout_user() return redirect(url_for('index')) @app.route('/user/<nickname>') @login_required def user(nickname): user = User.query.filter_by(nickname=nickname).first() # print "dsli user" # print user if user == None: flash('User %s not found.' % nickname) return redirect(url_for('index')) posts = [ {'author' : user, 'body' : 'test post #1'}, {'author' : user, 'body' : 'test post #2'} ] return render_template('user.html', user=user, posts=posts) @oid.after_login def after_login(resp): if resp.email is None or resp.email == "": flash('Invalid login, please try again.') return redirect(url_for('login')) user = User.query.filter_by(email=resp.email).first() # search our database for the email provided if user is None: # add a new user to our database nickname = resp.nickname if nickname is None or nickname == "": nickname = resp.email.split('@')[0] nickname = User.make_unique_nickname(nickname) user = User(nickname=nickname, email=resp.email) db.session.add(user) db.session.commit() remember_me = False if 'remember_me' in session: remember_me = session['remember_me'] session.pop('remember_me', None) login_user(user, remember = remember_me) # return redirect(url_for('index')) return redirect(request.args.get('next') or url_for('index')) # redirect to the next page, or the index page if a next page was not provided in the request @app.errorhandler(404) def not_found_error(error): return render_template('404.html'), 404 @app.errorhandler(500) def internal_error(error): db.session.rollback() return render_template('500.html'), 500
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2022-05-15T03:29:56.903688
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# Lint as: python3 # Copyright 2021 The TensorFlow Authors. All Rights Reserved. # # 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. # ============================================================================== """Implementation of combination functions for dual-encoder models.""" from lingvo import compat as tf from lingvo.core import base_layer class DotProductScoreFunction(base_layer.BaseLayer): """Performs dot product combination between two encoded vectors.""" @classmethod def Params(cls): p = super().Params() p.name = 'dot_product_score_function' return p def FProp(self, theta, x, y): """Computes pair-wise dot product similarity. Args: theta: NestedMap of variables belonging to this layer and its children. x: batch of encoded representations from modality x. A float32 Tensor of shape [x_batch_size, encoded_dim] y: batch of encoded representations from modality y. A float32 Tensor of shape [y_batch_size, encoded_dim] Returns: Pairwise dot products. A float32 Tensor with shape `[x_batch_size, y_batch_size]`. """ return tf.matmul(x, y, transpose_b=True)
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/orchestrator/core/orc_server/orchestrator/migrations/0002_protocol_port.py
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# Generated by Django 2.2 on 2019-05-07 14:52 import django.core.validators from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('orchestrator', '0001_initial'), ] operations = [ migrations.AddField( model_name='protocol', name='port', field=models.IntegerField(default=8080, help_text='Port of the transport', validators=[django.core.validators.MinValueValidator(1), django.core.validators.MaxValueValidator(65535)]), ), ]
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import os, sys from flask import Flask, request, redirect, url_for, \ abort, render_template, jsonify, send_from_directory, \ Response, g, Blueprint, current_app import sonos-web site = Blueprint('site', __name__) @site.route('/humans.txt') def humans(): return send_from_directory(os.path.join(current_app.root_path, 'public'), 'humans.txt', mimetype='text/plain') @site.route('/robots.txt') def robots(): return send_from_directory(os.path.join(current_app.root_path, 'public'), 'robots.txt', mimetype='text/plain') @site.route('/favicon.ico') def favicon(): return send_from_directory(os.path.join(current_app.root_path, 'public'), 'favicon.ico', mimetype='image/vnd.microsoft.icon') @site.route('/', defaults={'path': 'index'}) @site.route('/<path:path>') def index(path): return render_template('index.html')
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#!/usr/bin/env python import os import sys if __name__ == '__main__': os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'loloAfya.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv)
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import pyaf.Bench.TS_datasets as tsds import pyaf.tests.artificial.process_artificial_dataset as art art.process_dataset(N = 128 , FREQ = 'D', seed = 0, trendtype = "LinearTrend", cycle_length = 30, transform = "Integration", sigma = 0.0, exog_count = 100, ar_order = 0);
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# 35. Special Sort(구글) # N개의 정수가 입력되면 양의 정수와 음의 정수가 섞인 숫자들을 음의 정수는 왼쪽으로 양의 정수는 오른족으로 나눠라 # 입력된 음과 양의 정수의 순서는 입력된 순서를 유지한다. a=[] n=int(input("정렬할 숫자의 갯수를 입력하시오:")) for i in range(0,n): a.append(int(input())) for i in range(0,n-1): for j in range(0,(n-i)-1): if(a[j]>0 and a[j+1]<0): temp=a[j] a[j]=a[j+1] a[j+1]=temp print(a)
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#! /usr/bin/env python # -*- coding: utf-8 -*- # Definition for a binary tree node. # class TreeNode(object): # def __init__(self, x): # self.val = x # self.left = None # self.right = None c.. Solution o.. ___ zigzagLevelOrder root __ n.. root: r_ [] left2right = 1 # 1. scan the level from left to right. -1 reverse. ans, stack, temp # list, [root], [] _____ stack: temp = [node.val ___ node __ stack] stack = [child ___ node __ stack ___ child __ (node.left, node.right) __ child] ans += [temp[::left2right]] # Pythonic way left2right *= -1 r_ ans """ [] [1] [1,2,3] [0,1,2,3,4,5,6,null,null,7,null,8,9,null,10] """
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#calss header class _SUFFERED(): def __init__(self,): self.name = "SUFFERED" self.definitions = suffer self.parents = [] self.childen = [] self.properties = [] self.jsondata = {} self.basic = ['suffer']
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class myclass(): myUrls = ['asdasd',] def addVals(self): for i in range(1,7): self.myUrls.append(i) def start(self): for i in self.myUrls: print(i) self.addVals() asda = myclass() asda.start()
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#1. Створити клас Calc, який буде мати атребут last_result та 4 методи. Методи повинні виконувати математичні операції з 2-ма числами, а саме додавання, віднімання, # множення, ділення. # - Якщо під час створення екземпляру класу звернутися до атребута last_result він повинен повернути пусте значення # - Якщо використати один з методів - last_result повенен повернути результат виконання попереднього методу. # - Додати документування в клас (можете почитати цю статтю: https://realpython.com/documenting-python-code/ ) class Calc(): def __init__(self,a,b,last_result = None): self.a=a self.b=b self.last_result = last_result def add(self): self.last_result = self.a+self.b return self.last_result def mul(self): self.last_result = self.a*self.b return self.last_result def div(self): self.last_result = self.a/self.b return self.last_result def sub(self): self.last_result = self.a-self.b return self.last_result a=int(input("Enter first number: ")) b=int(input("Enter second number: ")) obj=Calc(a,b) choice=1 while choice!=0: print("0. Exit") print("1. Add") print("2. Subtraction") print("3. Multiplication") print("4. Division") print("5. Last result") choice=int(input("Enter choice: ")) if choice==1: print("Result: ",obj.add()) elif choice==2: print("Result: ",obj.sub()) elif choice==3: print("Result: ",obj.mul()) elif choice==4: print("Result: ",round(obj.div(),2)) elif choice==5: print("Last Result: ",round(obj.last_result)) elif choice==0: print("Exiting!") else: print("Invalid choice!!")
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#!/usr/bin/env python # Read in the PSF map # FInd all the areas which are Galactic latitude less than 10 degrees # INterpolate the PSF # Write out the new map # Then when I rerun the flux-calibration, using the PSF map, it should be correct import numpy as np from astropy.io import fits from astropy import wcs from optparse import OptionParser from astropy.coordinates import SkyCoord from astropy import units as u from scipy.interpolate import griddata #import matplotlib.pyplot as plt #import matplotlib.image as mpimg usage="Usage: %prog [options] <file>\n" parser = OptionParser(usage=usage) parser.add_option('--psf',type="string", dest="psf", help="The filename of the psf image you want to read in.") parser.add_option('--output',type="string", dest="output", default="interpolated_GP_PSF.fits", help="The filename of the output interpolated PSF image.") (options, args) = parser.parse_args() latitude=-26.70331940 # Read in the PSF psf = fits.open(options.psf) a = psf[0].data[0] b = psf[0].data[1] pa = psf[0].data[2] blur = psf[0].data[3] # Diagnostic plot #plt.imshow(a,vmin=0.05,vmax=0.2) #plt.colorbar() #plt.savefig("original_a.png") w_psf = wcs.WCS(psf[0].header,naxis=2) #create an array but don't set the values (they are random) indexes = np.empty( (psf[0].data.shape[1]*psf[0].data.shape[2],2),dtype=int) #since I know exactly what the index array needs to look like I can construct # it faster than list comprehension would allow #we do this only once and then recycle it idx = np.array([ (j,0) for j in xrange(psf[0].data.shape[2])]) j=psf[0].data.shape[2] for i in xrange(psf[0].data.shape[1]): idx[:,1]=i indexes[i*j:(i+1)*j] = idx # The RA and Dec co-ordinates of each location in the PSF map # Each one is a 1D array of shape 64800 (from 180 (Dec) x 360 (RA)) ra_psf,dec_psf = w_psf.wcs_pix2world(indexes,1).transpose() # A 1D array of co-ordinates at each location c_psf = SkyCoord(ra=ra_psf, dec=dec_psf, unit=(u.degree, u.degree)) # A 1D list of indices referring to the locations where we want to use the data gal_indices = np.where(abs(c_psf.galactic.b.value)>10.) # A 1D list of pairs of co-ordinates ("points") referring to the locations where we want to use the data gin = gal_indices[0] idx = indexes[gin[:]] a_data = a[idx[:,1], idx[:,0]] b_data = b[idx[:,1], idx[:,0]] pa_data = pa[idx[:,1], idx[:,0]] blur_data = blur[idx[:,1], idx[:,0]] grid_x, grid_y = np.mgrid[0:179:180j, 0:359:360j] # Only interpolate over points which are not NaN a_cubic_interp = griddata(idx[np.logical_not(np.isnan(a_data))], a_data[np.logical_not(np.isnan(a_data))], (grid_y, grid_x), method="linear") b_cubic_interp = griddata(idx[np.logical_not(np.isnan(a_data))], b_data[np.logical_not(np.isnan(a_data))], (grid_y, grid_x), method="linear") pa_cubic_interp = griddata(idx[np.logical_not(np.isnan(a_data))], pa_data[np.logical_not(np.isnan(a_data))], (grid_y, grid_x), method="linear") blur_cubic_interp = griddata(idx[np.logical_not(np.isnan(a_data))], blur_data[np.logical_not(np.isnan(a_data))], (grid_y, grid_x), method="linear") # Diagnostic plot #plt.clf() #plt.imshow(a_cubic_interp,vmin=0.05,vmax=0.2) #plt.colorbar() #plt.savefig("cubicinterp_a.png") psf[0].data[0] = a_cubic_interp psf[0].data[1] = b_cubic_interp psf[0].data[2] = pa_cubic_interp psf[0].data[3] = blur_cubic_interp psf.writeto(options.output,clobber=True)
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#!/usr/bin/env python #coding: utf8 # Analisis Sintactico del lenguaje Brainiac. # Modulo: SintBrainiac # Autores: Wilthew, Patricia 09-10910 # Leopoldo Pimentel 06-40095 import ply.lex as lex import ply.yacc as yacc import sys import funciones from LexBrainiax import tokens contador = -1 # Clases utilizadas para imprimir el arbol sintactico # Clase para NUMERO class numero: def __init__(self,value): self.type = "Numero" self.value = value def __str__(self): global contador contador = contador + 1 tabs = " "*contador str_ = str(self.value) + " " contador = contador - 1 return str_ # Clase para IDENTIFICADOR class ident: def __init__(self,name): self.type = "Identificador" self.name = name def __str__(self): global contador contador = contador + 1 tabs = " "*contador str_ = str(self.name) + " " contador = contador - 1 return str_ # Clase para EXPRESION UNARIA class op_un: def __init__(self,pre,e): self.pre = pre self.e = e def __str__(self): global contador contador = contador + 1 tabs = " "*contador str_ = "EXPRESION_UNARIA\n" + tabs + "Operador: " + str(self.pre) + "\n" + tabs + "Valor: " + str(self.e) + " " contador = contador - 1 return str_ # Clase para EXPRESION BINARIA class op_bin: def __init__(self,left,right,op): self.left = left self.right = right self.op = op if op == '+': self.op = 'Suma' elif op == '-': self.op = 'Resta' elif op == '~': self.op = 'Negacion' elif op == '*': self.op = 'Multiplicacion' elif op == '%': self.op = 'Modulo' elif op == '/': self.op = 'Division' elif op == '=': self.op = 'Igual' elif op == '/=': self.op = 'Desigual' elif op == '<': self.op = 'Menor que' elif op == '>': self.op = 'Mayor que' elif op == '>=': self.op = 'Mayor o igual que' elif op == '<=': self.op = 'Menor o igual que' elif op == '&': self.op = 'Concatenacion' elif op == '#': self.op = 'Inspeccion' elif op == '\/': self.op = 'Or' else: self.op = 'And' def __str__(self): global contador contador = contador + 1 tabs = contador*" " tabs_plus = " " + tabs str_ = "EXPRESION_BINARIA\n" + tabs + "Operacion: " + str(self.op) + "\n" str_ = str_ + tabs + "Operador izquierdo: " + str(self.left) + "\n" + tabs + "Operador derecho: " + str(self.right) + " " contador = contador - 1 return str_ # Clase para ITERACION_INDETERMINADA class inst_while: def __init__(self,cond,inst): self.cond = cond self.inst = inst def __str__(self): global contador contador = contador + 1 tabs = " "*contador str_ = "ITERACION_INDETERMINADA\n" + tabs + "Condicion: " str_ = str_+ str(self.cond) + "\n" + tabs + "Instruccion: " + str(self.inst) + " " contador = contador - 1 return str_ # Clase para ITERACION_DETERMINADA class inst_for: def __init__(self,ident,inf,sup,inst): self.ident = ident self.inf = inf self.sup = sup self.inst = inst def __str__(self): global contador contador = contador + 1 tabs = " "*contador str_ = "ITERACION_DETERMINADA\n" + tabs + "Identificador: " + str(self.ident) str_ = str_ + "\n" + tabs + "Cota inf: " + str(self.inf) +", Cota sup: " str_ = str_ + str(self.sup) + "\n" + tabs + "Instruccion: " + str(self.inst) + " " contador = contador - 1 return str_ # Clase para CONDICIONAL class inst_if: def __init__(self,cond,instr0,instr1): self.cond = cond self.instr0 = instr0 self.instr1 = instr1 def __str__(self): global contador contador = contador + 1 tabs = " "*contador aux = "" if self.instr1 != None: aux = "\n" +tabs + "Else: " + str(self.instr1) + " " str_ = "CONDICIONAL\n" + tabs + "Guardia: " + str(self.cond) + "\n" + tabs + "Exito: " + str(self.instr0) + aux contador = contador - 1 return str_ # Clase para B-INSTRUCCION class inst_b: def __init__(self, slist, ident): self.slist = slist self.ident = ident def __pop__(self): return self.slist.pop() def __len__(self): return len(self.slist) def __str__(self): global contador contador = contador +1 tabs = " "*contador lista_simbolos = "" for elem in self.slist: lista_simbolos = lista_simbolos + str(elem) str_ = "B-INSTRUCCION\n" + tabs + "Lista de simbolos: " + lista_simbolos + "\n" straux = tabs + "Identificador: " + str(self.ident) + " " contador = contador - 1 return str_ + straux # Clase para ASIGNACION class inst_asig: def __init__(self,ident,val): self.ident = ident self.val = val def __str__(self): global contador contador = contador + 1 tabs = " "*contador str_ = "ASIGNACION\n" + tabs + "Identificador: " + str(self.ident) + "\n" + tabs + "Valor: " + str(self.val) + " " contador = contador - 1 return str_ # Clase para READ class inst_read: def __init__(self,ident): self.ident = ident def __str__(self): global contador contador = contador + 1 tabs = " "*contador str_ = "READ\n" + tabs + "Identificador: " + str(self.ident.name) + " " contador = contador - 1 return str_ # Clase para WRITE class inst_write: def __init__(self,expr): self.expr = expr def __str__(self): global contador contador += 1 tabs = contador*" " strw = "WRITE" + "\n" + tabs + "Contenido: " str1 = strw + str(self.expr) + " " contador = contador - 1 return str1 # Clase para SECUENCIACION class inst_list: def __init__(self): self.lista = [] def __len__(self): return len(self.lista) def __pop__(self): return self.lista.pop() def __str__(self): global contador contador = contador + 1 self.lista.reverse() str_ = "SECUENCIACION\n" contador = contador + 1 tabs = contador*" " while self.lista: elemento = self.lista.pop() str_ = str_ + tabs + str(elemento) if len(self.lista) != 0: str_ = str_ + "\n" + tabs + "\n" contador = contador - 1 return str_ def print_(self,contador): self.lista.reverse() while self.lista: elemento = self.lista.pop() elemento.print_(contador,0) tabs = contador*" " if len(self.lista) != 0: str_ = str_ + ";" return str_ # Clase para BLOQUE class bloque: def __init__(self,lista): self.lista = lista def __len__(self): return len(self.lista) def __str__(self): global contador contador = contador + 1 tabs = " "*contador str_ = "BLOQUE\n" str_ = str_ + str(self.lista) contador = contador - 1 return str_ def main(): # Se abre el archivo y se guarda su contenido en el string codigo file_name = sys.argv[1] fp = open(file_name) codigo = fp.read() # Manejo de gramática y construccion de arbol # Definicion del símbolo inicial start = 'programa' # Precedencia de los operadores precedence = ( ('left','TkDisyuncion'), ('left','TkConjuncion'), ('left','TkIgual','TkDesigual'), ('left','TkMenor','TkMayor','TkMayorIgual','TkMenorIgual'), ('left','TkMas','TkResta'), ('left','TkMult','TkDiv','TkMod'), ('left','TkConcat'), ('left','TkAt'), ('right','uminus','unot', 'uinspeccion'), ) # PROGRAMA def p_programa(p): ''' programa : declaracion TkExecute instlist TkDone | TkExecute instlist TkDone ''' if len(p) == 5: p[0] = p[3] elif len(p) == 4: p[0] = p[2] # TERMINO UNARIO def p_term_num(p): ''' term : TkNum ''' p[0] = numero(p[1]) str_ = "" tabs = (contador+1)*" " # IDENTIFICADOR def p_term_ident(p): ''' term : TkIdent ''' p[0] = ident(p[1]) str_ = "" tabs = (contador+1)*" " # EXPRESION UNARIA ARITMETICA def p_exp_un(p): ''' exp_un : TkResta exp %prec uminus | TkNegacion exp %prec unot | TkInspeccion exp %prec uinspeccion ''' p[0] = op_un(p[1],p[2]) # EXPRESION def p_exp(p): ''' exp : term | exp_un | TkParAbre exp TkParCierra | TkCorcheteAbre exp TkCorcheteCierra | TkLlaveAbre exp TkLlaveCierra | exp TkMas exp | exp TkMult exp | exp TkMod exp | exp TkDiv exp | exp TkResta exp | TkTrue | TkFalse | exp TkIgual exp | exp TkDesigual exp | exp TkMenor exp | exp TkMayor exp | exp TkMenorIgual exp | exp TkMayorIgual exp | exp TkDisyuncion exp | exp TkConjuncion exp | exp TkConcat exp ''' if len(p) == 2: p[0] = p[1] elif len(p) == 4 and p[1] != '(' and p[1] != '[' and p[1] != '{': p[0] = op_bin(p[1],p[3],p[2]) else: p[0] = p[2] # ASIGNACION def p_instruccion_asignacion(p): ''' instruccion : TkIdent TkAsignacion exp ''' p[0] = inst_asig(p[1],p[3]) # READ def p_instruccion_read(p): ''' instruccion : TkRead exp ''' p[0] = inst_read(p[2]) # WRITE def p_instruccion_write(p): ''' instruccion : TkWrite exp ''' p[0] = inst_write(p[2]) # WHILE def p_instruccion_while(p): ''' instruccion : TkWhile exp TkDo instlist TkDone ''' p[0] = inst_while(p[2],p[4]) # FOR def p_instruccion_for(p): ''' instruccion : TkFor TkIdent TkFrom exp TkTo exp TkDo instlist TkDone''' p[0] = inst_for(p[2],p[4],p[6],p[8]) # IF def p_instruccion_if(p): ''' instruccion : TkIf exp TkThen instlist TkDone | TkIf exp TkThen instlist TkElse instlist TkDone ''' if len(p) == 6: p[0] = inst_if(p[2],p[4],None) else: p[0] = inst_if(p[2],p[4],p[6]) # BLOQUE DE INSTRUCCIONES def p_instruccion_bloque(p): ''' instruccion : declaracion TkExecute instlist TkDone | TkExecute instlist TkDone ''' if len(p) == 4: p[0] = inst_bloque(p[2]) elif len(p) == 5: p[0] = inst_bloque(p[3]) # BLOQUE DE B-INSTRUCCION (Ej: {lista_tape} At [a] ) def p_instruccion_b(p): ''' instruccion : TkLlaveAbre lista_tape TkLlaveCierra TkAt ident_tape ''' p[0] = inst_b(p[2], p[5]) def p_ident_tape(p): ''' ident_tape : TkCorcheteAbre exp TkCorcheteCierra | TkIdent ''' if len(p) == 4: p[0] = p[2] elif len(p) == 2: p[0] = p[1] # LISTA DE SIMBOLOS DE B-INSTRUCCIONES (Ej: ++++--...>>><..) def p_lista_tape(p): ''' lista_tape : lista_tape simb_tape | simb_tape ''' if len(p) == 2: p[0] = [] p[0].append(p[1]) else: p[0] = p[1] p[0].append(p[2]) def p_simb_tape(p): '''simb_tape : TkPunto | TkMayor | TkMenor | TkMas | TkResta | TkComa ''' p[0] = p[1] # SECUENCIACION DE INSTRUCCIONES def p_instlist(p): ''' instlist : instlist semicoloninst | instruccion ''' if len(p) == 2: p[0] = inst_list() p[0].lista.append(p[1]) elif len(p) == 3: p[0] = p[1] p[0].lista.append(p[2]) def p_commainst(p): ''' semicoloninst : TkPuntoYComa instruccion ''' p[0] = p[2] # DECLARACION def p_declaracion(p): ''' declaracion : TkDeclare declist ''' def p_declist(p): ''' declist : dec TkPuntoYComa declist | dec ''' def p_dec(p): ''' dec : varlist TkType tipo ''' def p_varlist(p): '''varlist : TkIdent TkComa varlist | TkIdent ''' def p_tipo_int(p): 'tipo : TkInteger' def p_tipo_bool(p): 'tipo : TkBoolean' def p_tipo_tape(p): 'tipo : TkTape' #Funcion de error del parser def p_error(p): c = funciones.hallar_columna(codigo,p) print "Error de sintaxis en linea %s, columna %s: token \'%s\' inesperado." % (p.lineno,c,p.value[0]) sys.exit(0) # Se construye la funcion del parser parser = yacc.yacc() # LOGGER # Set up a logging object import logging logging.basicConfig( level = logging.DEBUG, filename = "parselog.txt", filemode = "w", format = "%(filename)10s:%(lineno)4d:%(message)s" ) log = logging.getLogger() # Se construye el árbol arbol = parser.parse(codigo,debug=log) # Se imprime el árbol print funciones.print_arbol(arbol) if __name__ == "__main__": main()
[ "patwilthew@cookie.(none)" ]
patwilthew@cookie.(none)
eba5b10fdb01d5e9de0a691c5d7012932098fcb9
b8b0a29b6f5bac70c408e46e6df1d6583e9ad8c0
/portdata/serializers.py
83fafe1145f099424288819777404e25e9f5cc1e
[]
no_license
varunsak/sdgindia
20c41575a6f0c638662f1df6bd7a121ce3da8cf8
a7fe9f6770e7b6ba628c376e773b11a19f58ccf4
refs/heads/master
2020-04-08T02:33:04.252409
2019-01-19T19:56:43
2019-01-19T19:56:43
158,939,063
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py
from rest_framework import serializers from .models import PortData class DataSerializer(serializers.ModelSerializer): id = serializers.IntegerField(read_only=True) class Meta: model = PortData fields = ( 'id', 'product', 'quantity', 'unit', 'item_rate_inv', 'currency', 'total_amount', 'fob_inr', 'item_rate_inr', 'fob_usd', 'foreign_port', 'foreign_country', 'india_port', 'india_company', 'foreign_company', 'invoice_number', 'hs_code' )
c33e6baaffb8face637be67783a763a26bfa8b9a
f13988ddd8623c3c3df09c9ed4d8fce837281624
/ps4/segmentation.py
7ec68d6f3dc228a0fa538d2ed0d85b4efb9a9b8a
[]
no_license
jingxa/cs231a_my
aee8e5aafe2d5654dfde2ea827038397fdaafb53
ddb70703bf31ecc7ae8aa715ec603ab007935cb7
refs/heads/master
2020-03-15T15:11:35.998984
2018-11-07T14:19:56
2018-11-07T14:19:56
132,206,754
0
0
null
null
null
null
UTF-8
Python
false
false
4,966
py
import numpy as np import matplotlib.pyplot as plt import scipy.io as sio from imageio import imread from scipy.spatial.distance import cdist def kmeans_segmentation(im, features, num_clusters): H, W = im.shape[0], im.shape[1] N = features.shape[0] # 第一步: 随机选择num_clusters个种子 center_idx = np.random.randint(N, size=num_clusters) centriods = features[center_idx] matrixes = np.zeros((H, W)) # 第二步: 迭代器划分 while True: # 每个像素到cneter的距离 dist = np.zeros((N, num_clusters)) for i in range(num_clusters): dist[:, i] = np.linalg.norm(features - centriods[i, :], axis=1) # 距离 # 寻找最近中心 nearest = np.argmin(dist, axis=1) # (N,1) # 更新 prev_centriods = centriods for i in range(num_clusters): pixels_idx = np.where(nearest == i) # 和 第 i 个中心邻近的像素集合 cluster = features[pixels_idx] # (M,5) centriods[i, :] = np.mean(cluster, axis=0) # 重新计算平均值 # 收敛 if np.array_equal(prev_centriods, centriods): break pixels_clusters = np.reshape(nearest, (H, W)) return pixels_clusters def meanshift_segmentation(im, features, bandwidth): H, W = im.shape[0], im.shape[1] N, M = features.shape # 数量, 特征维度 mask = np.ones(N) clusters = [] while np.sum(mask) > 0 : # 当前还有像素未被遍历 loc = np.argwhere(mask > 0) idx = loc[int(np.random.choice(loc.shape[0], 1)[0])][0] # 随扈挑选一个像素 mask[idx] = 0 # 标记 current_mean = features[idx] prev_mean = current_mean while True: dist = np.linalg.norm(features - prev_mean, axis=1) incircle = dist < bandwidth # 距离小于半径的点 mask[incircle] = 0 current_mean = np.mean(features[incircle], axis=0) # 新的中心 # 稳定,收敛 if np.linalg.norm(current_mean - prev_mean) < 0.01 * bandwidth: break prev_mean = current_mean isValid = True for cluster in clusters: if np.linalg.norm(cluster - current_mean) < 0.5 * bandwidth: # 两个划分为一个cluster isValid = False if isValid: # 添加一个新cluster clusters.append(current_mean) pixels_clusters = np.zeros((H, W)) clusters = np.array(clusters) for i in range(N): # 计算每个像素点的最近中心 idx = np.argmin(np.linalg.norm(features[i, :] - clusters, axis=1)) h = int(i/W) w = i % W pixels_clusters[h, w] = idx return pixels_clusters.astype(int) def draw_clusters_on_image(im, pixel_clusters): num_clusters = int(pixel_clusters.max()) + 1 average_color = np.zeros((num_clusters, 3)) cluster_count = np.zeros(num_clusters) for i in range(im.shape[0]): for j in range(im.shape[1]): c = pixel_clusters[i,j] cluster_count[c] += 1 average_color[c, :] += im[i, j, :] for c in range(num_clusters): average_color[c,:] /= float(cluster_count[c]) out_im = np.zeros_like(im) for i in range(im.shape[0]): for j in range(im.shape[1]): c = pixel_clusters[i,j] out_im[i,j,:] = average_color[c,:] return out_im if __name__ == '__main__': # Change these parameters to see the effects of K-means and Meanshift num_clusters = [5] bandwidths = [0.3] for filename in ['lake', 'rocks', 'plates']: img = imread('data/%s.jpeg' % filename) # Create the feature vector for the images features = np.zeros((img.shape[0] * img.shape[1], 5)) for row in range(img.shape[0]): for col in range(img.shape[1]): features[row*img.shape[1] + col, :] = np.array([row, col, img[row, col, 0], img[row, col, 1], img[row, col, 2]]) # features_normalized = features / features.max(axis = 0) # Part I: Segmentation using K-Means # for nc in num_clusters: # clustered_pixels = kmeans_segmentation(img, features_normalized, nc) # cluster_im = draw_clusters_on_image(img, clustered_pixels) # plt.imshow(cluster_im) # plt.title('K-means with %d clusters on %s.jpeg' % (int(nc), filename)) # plt.show() # # Part II: Segmentation using Meanshift for bandwidth in bandwidths: clustered_pixels = meanshift_segmentation(img, features_normalized, bandwidth) cluster_im = draw_clusters_on_image(img, clustered_pixels) plt.imshow(cluster_im) plt.title('Meanshift with bandwidth %.2f on %s.jpeg' % (bandwidth, filename)) plt.show()
9e134e9dc6bdf1edb51087e18635b3916beb92af
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/2016_11_Python/GUI/PyQt/firstPyQt.py
a9e43e6c73e48b32eb7fe9e9d5f87578f6fe1759
[]
no_license
JasonatWang/LearnToProgram
fb5d6a0ade9732312cf8d257d70537af76fcb891
677872a940bfe635901460385d22d4ee45818c08
refs/heads/master
2020-12-03T05:21:00.315712
2016-12-23T06:12:58
2016-12-23T06:13:17
68,612,446
0
0
null
null
null
null
UTF-8
Python
false
false
2,597
py
# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'firstPyQt.ui' # # Created by: PyQt5 UI code generator 5.7 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets class Ui_MainWindow(object): def setupUi(self, MainWindow): MainWindow.setObjectName("MainWindow") MainWindow.resize(802, 592) self.centralwidget = QtWidgets.QWidget(MainWindow) self.centralwidget.setObjectName("centralwidget") self.verticalLayoutWidget = QtWidgets.QWidget(self.centralwidget) self.verticalLayoutWidget.setGeometry(QtCore.QRect(0, 0, 801, 391)) self.verticalLayoutWidget.setObjectName("verticalLayoutWidget") self.verticalLayout = QtWidgets.QVBoxLayout(self.verticalLayoutWidget) self.verticalLayout.setContentsMargins(0, 0, 0, 0) self.verticalLayout.setObjectName("verticalLayout") self.label = QtWidgets.QLabel(self.verticalLayoutWidget) self.label.setObjectName("label") self.verticalLayout.addWidget(self.label) self.horizontalLayout = QtWidgets.QHBoxLayout() self.horizontalLayout.setObjectName("horizontalLayout") self.pushButton_2 = QtWidgets.QPushButton(self.verticalLayoutWidget) self.pushButton_2.setObjectName("pushButton_2") self.horizontalLayout.addWidget(self.pushButton_2) self.pushButton = QtWidgets.QPushButton(self.verticalLayoutWidget) self.pushButton.setObjectName("pushButton") self.horizontalLayout.addWidget(self.pushButton) self.verticalLayout.addLayout(self.horizontalLayout) self.verticalLayoutWidget.raise_() self.label.raise_() MainWindow.setCentralWidget(self.centralwidget) self.menubar = QtWidgets.QMenuBar(MainWindow) self.menubar.setGeometry(QtCore.QRect(0, 0, 802, 30)) self.menubar.setObjectName("menubar") MainWindow.setMenuBar(self.menubar) self.statusbar = QtWidgets.QStatusBar(MainWindow) self.statusbar.setObjectName("statusbar") MainWindow.setStatusBar(self.statusbar) self.retranslateUi(MainWindow) QtCore.QMetaObject.connectSlotsByName(MainWindow) def retranslateUi(self, MainWindow): _translate = QtCore.QCoreApplication.translate MainWindow.setWindowTitle(_translate("MainWindow", "MainWindow")) self.label.setText(_translate("MainWindow", "Hello World!")) self.pushButton_2.setText(_translate("MainWindow", "OK")) self.pushButton.setText(_translate("MainWindow", "Cancel"))
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f51d53650185500e379805b15855b0330b6a0e7f
/src/pdf_routines.py
beb1450a8ccec7c9e6ce6b9fd2a9fb344aaa8a09
[ "MIT" ]
permissive
olrodrig/SNII_ETOS
0888dfcadd450c93a24f22462ebb4ac37d40854d
a11afd49c9c32bd249a11c935880d132ac17849a
refs/heads/master
2022-05-28T20:45:29.794411
2020-04-25T08:06:43
2020-04-25T08:06:43
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import numpy as np from sklearn import mixture def snii_templates_epochs(): JD_ln_template, JD_fd_template = {}, {} JD_ln_template['1986L'] , JD_fd_template['1986L'] = 2446705.5 , 2446711.1 JD_ln_template['1990E'] , JD_fd_template['1990E'] = 2447932.5 , 2447937.62 JD_ln_template['1999br'] , JD_fd_template['1999br'] = 2451272.9 , 2451280.9 JD_ln_template['1999em'] , JD_fd_template['1999em'] = 2451471.95 , 2451479.51 JD_ln_template['1999gi'] , JD_fd_template['1999gi'] = 2451515.68 , 2451522.32 JD_ln_template['1999go'] , JD_fd_template['1999go'] = 2451527.7 , 2451535.7 JD_ln_template['2000dc'] , JD_fd_template['2000dc'] = 2451758.8 , 2451765.8 JD_ln_template['2000dj'] , JD_fd_template['2000dj'] = 2451785.487, 2451795.9 JD_ln_template['2000el'] , JD_fd_template['2000el'] = 2451835.7 , 2451840.6 JD_ln_template['2001X'] , JD_fd_template['2001X'] = 2451958.0 , 2451968.3 JD_ln_template['2001do'] , JD_fd_template['2001do'] = 2452131.7 , 2452135.7 JD_ln_template['2001fa'] , JD_fd_template['2001fa'] = 2452195.9 , 2452200.9 JD_ln_template['2002an'] , JD_fd_template['2002an'] = 2452292.04 , 2452297.02 JD_ln_template['2002ce'] , JD_fd_template['2002ce'] = 2452369.7 , 2452375.378 JD_ln_template['2002gd'] , JD_fd_template['2002gd'] = 2452549.28 , 2452550.53 JD_ln_template['2003Z'] , JD_fd_template['2003Z'] = 2452660.2 , 2452669.2 JD_ln_template['2003bn'] , JD_fd_template['2003bn'] = 2452691.5 , 2452692.83 JD_ln_template['2003ej'] , JD_fd_template['2003ej'] = 2452770.8 , 2452779.8 JD_ln_template['2003hg'] , JD_fd_template['2003hg'] = 2452860.9 , 2452869.9 JD_ln_template['2003hl'] , JD_fd_template['2003hl'] = 2452863.0 , 2452872.0 JD_ln_template['2003iq'] , JD_fd_template['2003iq'] = 2452918.47 , 2452921.458 JD_ln_template['2004ci'] , JD_fd_template['2004ci'] = 2453168.9 , 2453171.8 JD_ln_template['2004er'] , JD_fd_template['2004er'] = 2453269.88 , 2453273.9 JD_ln_template['2004et'] , JD_fd_template['2004et'] = 2453270.517, 2453271.483 JD_ln_template['2004fc'] , JD_fd_template['2004fc'] = 2453292.89 , 2453295.124 JD_ln_template['2004fx'] , JD_fd_template['2004fx'] = 2453300.92 , 2453306.93 JD_ln_template['2005ay'] , JD_fd_template['2005ay'] = 2453449.121, 2453456.58 JD_ln_template['2005cs'] , JD_fd_template['2005cs'] = 2453548.43 , 2453549.41 JD_ln_template['2005dz'] , JD_fd_template['2005dz'] = 2453615.8 , 2453623.71 JD_ln_template['2006Y'] , JD_fd_template['2006Y'] = 2453763.09 , 2453770.08 JD_ln_template['2006bc'] , JD_fd_template['2006bc'] = 2453811.087, 2453819.15 JD_ln_template['2006bp'] , JD_fd_template['2006bp'] = 2453833.677, 2453834.647 JD_ln_template['2006it'] , JD_fd_template['2006it'] = 2454004.69 , 2454009.67 JD_ln_template['2006iw'] , JD_fd_template['2006iw'] = 2454009.737, 2454011.798 JD_ln_template['2007hv'] , JD_fd_template['2007hv'] = 2454342.5 , 2454352.87 JD_ln_template['2007il'] , JD_fd_template['2007il'] = 2454345.94 , 2454353.95 JD_ln_template['2007pk'] , JD_fd_template['2007pk'] = 2454409.83 , 2454414.81 JD_ln_template['2008bh'] , JD_fd_template['2008bh'] = 2454538.57 , 2454548.66 JD_ln_template['2008br'] , JD_fd_template['2008br'] = 2454559.323, 2454564.265 JD_ln_template['2008ho'] , JD_fd_template['2008ho'] = 2454787.77 , 2454796.61 JD_ln_template['2008if'] , JD_fd_template['2008if'] = 2454802.73 , 2454812.71 JD_ln_template['2008il'] , JD_fd_template['2008il'] = 2454822.69 , 2454827.64 JD_ln_template['2008in'] , JD_fd_template['2008in'] = 2454824.45 , 2454824.95 JD_ln_template['2009ao'] , JD_fd_template['2009ao'] = 2454886.62 , 2454894.62 JD_ln_template['2009bz'] , JD_fd_template['2009bz'] = 2454912.03 , 2454919.98 JD_ln_template['2010id'] , JD_fd_template['2010id'] = 2455450.82 , 2455454.743 JD_ln_template['2012aw'] , JD_fd_template['2012aw'] = 2456001.769, 2456003.349 JD_ln_template['2013am'] , JD_fd_template['2013am'] = 2456371.698, 2456373.138 JD_ln_template['2013by'] , JD_fd_template['2013by'] = 2456402.872, 2456403.752 JD_ln_template['2013ej'] , JD_fd_template['2013ej'] = 2456497.04 , 2456497.625 JD_ln_template['2013fs'] , JD_fd_template['2013fs'] = 2456570.82 , 2456571.737 JD_ln_template['2013hj'] , JD_fd_template['2013hj'] = 2456635.7 , 2456638.8 JD_ln_template['2014G'] , JD_fd_template['2014G'] = 2456668.35 , 2456671.111 JD_ln_template['LSQ14gv'], JD_fd_template['LSQ14gv'] = 2456670.7 , 2456674.8 JD_ln_template['2014cx'] , JD_fd_template['2014cx'] = 2456901.89 , 2456902.90 JD_ln_template['2014cy'] , JD_fd_template['2014cy'] = 2456898.8 , 2456900.5 JD_ln_template['2015bs'] , JD_fd_template['2015bs'] = 2456915.5 , 2456925.5 JD_ln_template['2016esw'], JD_fd_template['2016esw'] = 2457607.802, 2457608.814 return JD_ln_template, JD_fd_template #compute weighted average def weighted_average(x, sigma_x, with_intrinsic_error=True): if len(x) > 1: if with_intrinsic_error: residuals = x - np.mean(x) rms = np.sqrt(np.sum(residuals**2)/float(len(residuals)-1)) sigma_0s = np.linspace(0.0, rms, 100) else: sigma_0s = np.array([0.0]) m2lnL_min = 1.e90 for sigma_0 in sigma_0s: Var = sigma_x**2 + sigma_0**2 w_ave = np.sum(x/Var)/np.sum(1.0/Var) m2lnL = np.sum(np.log(Var)+(x-w_ave)**2/Var) if m2lnL < m2lnL_min: m2lnL_min = m2lnL best_x = w_ave best_error = np.sqrt(1.0/np.sum(1.0/Var)) else: best_x, best_error = x[0], sigma_x[0] return best_x, best_error #pick rangom values given a pdf def values_from_distribution(x, pdf, N): x_sample = np.random.choice(x, N, p=pdf/np.sum(pdf)) #sum of probabilities must to be 1 return x_sample #Simpson's rule def simpson(x,f): integral = (f[0] + f[-1]) / 3.0 #extremes n = len(x) four = "o" for i in range(1, n - 1): if four == "o": integral += f[i] * 4.0 / 3.0 four = "x" else: integral += f[i] * 2.0 / 3.0 four = "o" integral = (x[1] - x[0]) * integral return integral #discard possible outliers through the Tukey's rule def tukey_rule(x, k=1.5): Q1, Q3 = np.quantile(x, [0.25, 0.75]) IQR = Q3 - Q1 x = x[x>=Q1-k*IQR] x = x[x<=Q3+k*IQR] return x #return a normalized gaussian pdf def gaussian_pdf(mu, sigma, x_sampled): g_sampled = np.exp(-0.5*(mu-x_sampled)**2/sigma**2) g_sampled = g_sampled / simpson(x_sampled, g_sampled) return g_sampled #return a uniform pdf def uniform_pdf(x_min, x_max, x): h = 1.0/(x_max-x_min) pdf = np.linspace(h, h, len(x)) for i in range(0, len(x)): if x[i] < x_min or x[i] > x_max: pdf[i] = 0.0 return pdf #return a pdf computed as a mixture of Gaussians def get_pdf(y, y_sampled, max_components=2): x, x_sampled = y.reshape(-1,1), y_sampled.reshape(-1,1) BIC_min = 1.e90 for n_components in range(1, max_components+1): gmm = mixture.GaussianMixture(n_components=n_components) model = gmm.fit(x) BIC = model.bic(x) if BIC < BIC_min: BIC_min = BIC model_min = model ln_pdf = model_min.score_samples(x_sampled) pdf = np.exp(ln_pdf) return pdf #return different pdf's def final_pdfs(z, JD_ln, JD_fd, pdfs_per_sn, x_sampled, N_sample, rms_t0): #define the uniform pdf's given by the JD_fd and JD_fd+JD_ln ln, fd = (JD_ln - JD_fd)/(1.0+z), 0.0 pdf_fd = uniform_pdf(-9999.0, fd, x_sampled) #fd as prior pdf_fd_ln = uniform_pdf(ln, fd, x_sampled) #fd and ln as prior #combine the pdf of different sne pdf_snid = np.linspace(1.0, 1.0, len(x_sampled)) for pdf_per_sn in pdfs_per_sn: pdf_snid = pdf_snid*pdf_per_sn #add typical rms(t0) error err_0 = np.random.normal(0.0, rms_t0, N_sample) err_0 = np.random.choice(tukey_rule(err_0), N_sample) err_0 = err_0 - np.median(err_0) t0s_snid = values_from_distribution(x_sampled, pdf_snid, N_sample) t0s_snid = t0s_snid + err_0 t0s_snid = np.random.choice(tukey_rule(t0s_snid),N_sample) #compute pdf's pdf_snid = get_pdf(t0s_snid, x_sampled, max_components=1) pdf_snid_fd = pdf_snid*pdf_fd pdf_snid_fd_ln = pdf_snid*pdf_fd_ln #normalize pdf's pdf_snid = pdf_snid / simpson(x_sampled, pdf_snid) pdf_snid_fd = pdf_snid_fd / simpson(x_sampled, pdf_snid_fd) pdf_snid_fd_ln = pdf_snid_fd_ln / simpson(x_sampled, pdf_snid_fd_ln) return pdf_fd_ln, pdf_snid, pdf_snid_fd, pdf_snid_fd_ln def average_pdf_per_sn_bm_with_t0_error(sne_bm, t0s_bm, rms_t0s_bm, x_pdf, N_sample): JD_ln_template, JD_fd_template = snii_templates_epochs() pdfs_per_sn = [] for sn_bm, spec_phase, err_spec_phase in zip(sne_bm, t0s_bm, rms_t0s_bm): delta = round(JD_fd_template[sn_bm]-JD_ln_template[sn_bm],3) rms_uniform = delta/np.sqrt(12.0) if rms_uniform < 0.3*err_spec_phase: pdf_per_sn = gaussian_pdf(spec_phase, err_spec_phase, x_pdf) else: err_t0_template = np.random.uniform(-0.5*delta,0.5*delta, N_sample) err_t0_template = err_t0_template - np.median(err_t0_template) #center the distibution to zero x1 = np.random.normal(spec_phase, err_spec_phase, N_sample) x1 = np.random.choice(tukey_rule(x1), N_sample) x1 = x1 - np.median(x1) + spec_phase #center the distibution to the phase #include values from the uniform distrution x= x1 + err_t0_template pdf_per_sn = get_pdf(x, x_pdf) pdf_per_sn = pdf_per_sn / simpson(x_pdf, pdf_per_sn) pdfs_per_sn.append(pdf_per_sn) return pdfs_per_sn def average_pdf_per_sn_bm(t0s_best, rms_t0s_best, sne_best): #best matching SNe sne_bm = list(set(sne_best)) t0s_bm, rms_t0s_bm = [], [] for sn_bm in sne_bm: phases, err_phases = np.array([]), np.array([]) for sn_i, spec_phase, err_spec_phase in zip(sne_best, t0s_best, rms_t0s_best): if sn_i == sn_bm: phases = np.append(phases, spec_phase) err_phases = np.append(err_phases, err_spec_phase) t0_best, rms_t0_best = weighted_average(phases, err_phases) t0s_bm.append(t0_best) rms_t0s_bm.append(rms_t0_best) return sne_bm, t0s_bm, rms_t0s_bm def typical_pdf_per_sn_bm_per_spectrum(t0s_best, rms_t0s_best, sne_best, t_spec_best): epochs = list(set(t_spec_best)) new_sne_best, new_t0_best, new_rms_t0_best = [], [], [] for epoch in epochs: #number of templates at the epoch templates = [] for t, sn_best in zip(t_spec_best, sne_best): if t == epoch: templates.append(sn_best) templates = list(set(templates)) for template in templates: phases, err_phases = np.array([]), np.array([]) for t, sn_best, spec_phase, err_spec_phase in zip(t_spec_best, sne_best, t0s_best, rms_t0s_best): if t == epoch and sn_best == template: phases = np.append(phases, spec_phase) err_phases = np.append(err_phases, err_spec_phase) t0_best, rms_t0_best = weighted_average(phases, err_phases) new_sne_best.append(template) new_t0_best.append(t0_best) new_rms_t0_best.append(rms_t0_best*np.sqrt(float(len(phases)))) sne_best, t0_best, rms_t0_best = np.array(new_sne_best), np.array(new_t0_best), np.array(new_rms_t0_best) return sne_best, t0_best, rms_t0_best
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/stems/gis/convert.py
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""" GIS variable conversion library Functions here are convenient ways of going from various representations of GIS information used in this stack (e.g., WKT) to the following representations: * Coordinate Reference System * :py:class:`rasterio.crs.CRS` * Geotransform * :py:class:`affine.Affine` * Bounding Box * :py:class:`rasterio.coords.BoundingBox` * Bounds * :py:class:`shapely.geom.Polygon` """ from functools import singledispatch import logging from affine import Affine import numpy as np from osgeo import osr from rasterio.coords import BoundingBox from rasterio.crs import CRS from rasterio.errors import CRSError import shapely.geometry from ..utils import (find_subclasses, register_multi_singledispatch) logger = logging.getLogger() LIST_TYPE = (tuple, list, np.ndarray, ) # XARRAY_TYPE = (xr.Dataset, xr.DataArray) GEOM_TYPE = find_subclasses(shapely.geometry.base.BaseGeometry) # ============================================================================ # Affine geotransform @singledispatch def to_transform(value, from_gdal=False): """ Convert input into an :py:class:`affine.Affine` transform Parameters ---------- value : Affine or iterable 6 numbers representing affine transform from_gdal : bool, optional If `value` is a tuple or list, specifies if transform is GDAL variety (True) or rasterio/affine (False) Returns ------- affine.Affine Affine transform """ raise _CANT_CONVERT(value) @to_transform.register(Affine) def _to_transform_affine(value, from_gdal=False): return value @register_multi_singledispatch(to_transform, LIST_TYPE) def _to_transform_iter(value, from_gdal=False): if from_gdal: return Affine.from_gdal(*value[:6]) else: return Affine(*value[:6]) @to_transform.register(str) def _to_transform_str(value, from_gdal=False, sep=','): return _to_transform_iter([float(v) for v in value.split(sep)]) # ============================================================================ # CRS # TODO: Dispatch function for Cartopy @singledispatch def to_crs(value): """ Convert a CRS representation to a :py:class:`rasterio.crs.CRS` Parameters ---------- value : str, int, dict, or osr.SpatialReference Coordinate reference system as WKT, Proj.4 string, EPSG code, rasterio-compatible proj4 attributes in a dict, or OSR definition Returns ------- rasterio.crs.CRS CRS """ raise _CANT_CONVERT(value) @to_crs.register(CRS) def _to_crs_crs(value): return value @to_crs.register(str) def _to_crs_str(value): # After rasterio=1.0.14 WKT is backbone so try it first try: crs_ = CRS.from_wkt(value) crs_.is_valid except CRSError as err: logger.debug('Could not parse CRS as WKT', err) try: crs_ = CRS.from_string(value) crs_.is_valid except CRSError as err: logger.debug('Could not parse CRS as Proj4', err) raise CRSError('Could not interpret CRS input as ' 'either WKT or Proj4') return crs_ @to_crs.register(int) def _to_crs_epsg(value): return CRS.from_epsg(value) @to_crs.register(dict) def _to_crs_dict(value): return CRS(value) @to_crs.register(osr.SpatialReference) def _to_crs_osr(value): return CRS.from_wkt(value.ExportToWkt()) # ============================================================================ # BoundingBox @singledispatch def to_bounds(value): """ Convert input to a :py:class:`rasterio.coords.BoundingBox` Parameters ---------- value : iterable, or Polygon Input containing some geographic information Returns ------- BoundingBox Bounding box (left, bottom, right, top). Also described as (minx, miny, maxx, maxy) """ raise _CANT_CONVERT(value) @to_bounds.register(BoundingBox) def _to_bounds_bounds(value): return value @register_multi_singledispatch(to_bounds, LIST_TYPE) def _to_bounds_iter(value): return BoundingBox(*value) @register_multi_singledispatch(to_bounds, GEOM_TYPE) def _to_bounds_geom(value): return BoundingBox(*value.bounds) # ============================================================================ # Polygon @singledispatch def to_bbox(value): """ Convert input a bounding box :py:class:`shapely.geometry.Polygon` Parameters ---------- value : BoundingBox Object representing a bounding box, or an xarray object with coords we can use to calculate one from Returns ------- shapely.geometry.Polygon BoundingBox as a polygon """ raise _CANT_CONVERT(value) @register_multi_singledispatch(to_bbox, GEOM_TYPE) def _to_bbox_geom(value): return _to_bbox_bounds(BoundingBox(*value.bounds)) @to_bbox.register(BoundingBox) def _to_bbox_bounds(value): return shapely.geometry.box(*value) # ============================================================================ # UTILITIES def _CANT_CONVERT(obj): return TypeError(f"Don't know how to convert this type: {type(obj)}")
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from __future__ import unicode_literals import io import json import os import re import sys import onnx from onnx.backend.test.case import collect_snippets snippets = collect_snippets() categories = { 'Constant': 'Constant', 'Conv': 'Layer', 'ConvInteger': 'Layer', 'ConvTranspose': 'Layer', 'FC': 'Layer', 'RNN': 'Layer', 'LSTM': 'Layer', 'GRU': 'Layer', 'Gemm': 'Layer', 'Dropout': 'Dropout', 'Elu': 'Activation', 'HardSigmoid': 'Activation', 'LeakyRelu': 'Activation', 'PRelu': 'Activation', 'ThresholdedRelu': 'Activation', 'Relu': 'Activation', 'Selu': 'Activation', 'Sigmoid': 'Activation', 'Tanh': 'Activation', 'LogSoftmax': 'Activation', 'Softmax': 'Activation', 'Softplus': 'Activation', 'Softsign': 'Activation', 'BatchNormalization': 'Normalization', 'InstanceNormalization': 'Normalization', 'LpNormalization': 'Normalization', 'LRN': 'Normalization', 'Flatten': 'Shape', 'Reshape': 'Shape', 'Tile': 'Shape', 'Xor': 'Logic', 'Not': 'Logic', 'Or': 'Logic', 'Less': 'Logic', 'And': 'Logic', 'Greater': 'Logic', 'Equal': 'Logic', 'AveragePool': 'Pool', 'GlobalAveragePool': 'Pool', 'GlobalLpPool': 'Pool', 'GlobalMaxPool': 'Pool', 'LpPool': 'Pool', 'MaxPool': 'Pool', 'MaxRoiPool': 'Pool', 'Concat': 'Tensor', 'Slice': 'Tensor', 'Split': 'Tensor', 'Pad': 'Tensor', 'ImageScaler': 'Data', 'Crop': 'Data', 'Upsample': 'Data', 'Transpose': 'Transform', 'Gather': 'Transform', 'Unsqueeze': 'Transform', 'Squeeze': 'Transform', } attribute_type_table = { 'undefined': None, 'float': 'float32', 'int': 'int64', 'string': 'string', 'tensor': 'tensor', 'graph': 'graph', 'floats': 'float32[]', 'ints': 'int64[]', 'strings': 'string[]', 'tensors': 'tensor[]', 'graphs': 'graph[]', } def generate_json_attr_type(attribute_type, attribute_name, op_type, op_domain): assert isinstance(attribute_type, onnx.defs.OpSchema.AttrType) key = op_domain + ':' + op_type + ':' + attribute_name if key == ':Cast:to' or key == ':EyeLike:dtype' or key == ':RandomNormal:dtype': return 'DataType' s = str(attribute_type) s = s[s.rfind('.')+1:].lower() if s in attribute_type_table: return attribute_type_table[s] return None def generate_json_attr_default_value(attr_value): if not str(attr_value): return None if attr_value.HasField('i'): return attr_value.i if attr_value.HasField('s'): return attr_value.s.decode('utf8') if attr_value.HasField('f'): return attr_value.f return None def generate_json_support_level_name(support_level): assert isinstance(support_level, onnx.defs.OpSchema.SupportType) s = str(support_level) return s[s.rfind('.')+1:].lower() def generate_json_types(types): r = [] for type in types: r.append(type) r = sorted(r) return r def format_range(value): if value == 2147483647: return '&#8734;' return str(value) def format_description(description): def replace_line(match): link = match.group(1) url = match.group(2) if not url.startswith("http://") and not url.startswith("https://"): url = "https://github.com/onnx/onnx/blob/master/docs/" + url return "[" + link + "](" + url + ")" description = re.sub("\\[(.+)\\]\\(([^ ]+?)( \"(.+)\")?\\)", replace_line, description) return description def generate_json(schemas, json_file): json_root = [] for schema in schemas: json_schema = {} json_schema['name'] = schema.name if schema.domain: json_schema['module'] = schema.domain else: json_schema['module'] = 'ai.onnx' json_schema['version'] = schema.since_version json_schema['support_level'] = generate_json_support_level_name(schema.support_level) if schema.doc: json_schema['description'] = format_description(schema.doc.lstrip()) if schema.attributes: json_schema['attributes'] = [] for _, attribute in sorted(schema.attributes.items()): json_attribute = {} json_attribute['name'] = attribute.name attribute_type = generate_json_attr_type(attribute.type, attribute.name, schema.name, schema.domain) if attribute_type: json_attribute['type'] = attribute_type elif 'type' in json_attribute: del json_attribute['type'] json_attribute['required'] = attribute.required default_value = generate_json_attr_default_value(attribute.default_value) if default_value: json_attribute['default'] = default_value json_attribute['description'] = format_description(attribute.description) json_schema['attributes'].append(json_attribute) if schema.inputs: json_schema['inputs'] = [] for input in schema.inputs: json_input = {} json_input['name'] = input.name json_input['type'] = input.typeStr if input.option == onnx.defs.OpSchema.FormalParameterOption.Optional: json_input['option'] = 'optional' elif input.option == onnx.defs.OpSchema.FormalParameterOption.Variadic: json_input['list'] = True json_input['description'] = format_description(input.description) json_schema['inputs'].append(json_input) json_schema['min_input'] = schema.min_input json_schema['max_input'] = schema.max_input if schema.outputs: json_schema['outputs'] = [] for output in schema.outputs: json_output = {} json_output['name'] = output.name json_output['type'] = output.typeStr if output.option == onnx.defs.OpSchema.FormalParameterOption.Optional: json_output['option'] = 'optional' elif output.option == onnx.defs.OpSchema.FormalParameterOption.Variadic: json_output['list'] = True json_output['description'] = format_description(output.description) json_schema['outputs'].append(json_output) json_schema['min_output'] = schema.min_output json_schema['max_output'] = schema.max_output if schema.min_input != schema.max_input: json_schema['inputs_range'] = format_range(schema.min_input) + ' - ' + format_range(schema.max_input) if schema.min_output != schema.max_output: json_schema['outputs_range'] = format_range(schema.min_output) + ' - ' + format_range(schema.max_output) if schema.type_constraints: json_schema['type_constraints'] = [] for type_constraint in schema.type_constraints: json_schema['type_constraints'].append({ 'description': type_constraint.description, 'type_param_str': type_constraint.type_param_str, 'allowed_type_strs': type_constraint.allowed_type_strs }) if schema.name in snippets: def update_code(code): lines = code.splitlines() while len(lines) > 0 and re.search("\\s*#", lines[-1]): lines.pop() if len(lines) > 0 and len(lines[-1]) == 0: lines.pop() return '\n'.join(lines) json_schema['examples'] = [] for summary, code in sorted(snippets[schema.name]): json_schema['examples'].append({ 'summary': summary, 'code': update_code(code) }) if schema.name in categories: json_schema['category'] = categories[schema.name] json_root.append(json_schema); json_root = sorted(json_root, key=lambda item: item['name'] + ':' + str(item['version'] if 'version' in item else 0).zfill(4)) with io.open(json_file, 'w', newline='') as fout: json_root = json.dumps(json_root, indent=2) for line in json_root.splitlines(): line = line.rstrip() if sys.version_info[0] < 3: line = str(line) fout.write(line) fout.write('\n') def metadata(): json_file = os.path.join(os.path.dirname(__file__), '../source/onnx-metadata.json') all_schemas_with_history = onnx.defs.get_all_schemas_with_history() generate_json(all_schemas_with_history, json_file) def optimize(): import onnx from onnx import optimizer file = sys.argv[2] base = os.path.splitext(file) onnx_model = onnx.load(file) passes = optimizer.get_available_passes() optimized_model = optimizer.optimize(onnx_model, passes) onnx.save(optimized_model, base + '.optimized.onnx') def infer(): import onnx import onnx.shape_inference from onnx import shape_inference file = sys.argv[2] base = os.path.splitext(file)[0] onnx_model = onnx.load(base + '.onnx') onnx_model = onnx.shape_inference.infer_shapes(onnx_model) onnx.save(onnx_model, base + '.shape.onnx') if __name__ == '__main__': command_table = { 'metadata': metadata, 'optimize': optimize, 'infer': infer } command = sys.argv[1] command_table[command]()
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/Week7/After14.py
f051a0b47f89f4fb9463f9bece77e23caaf0f586
[]
no_license
Chudvan/Python_osnovy_programmirovaniya-Coursera-
304925397d3e7f4b49bc3f62dc89f782d36a1f76
19117cb198ed50bb90ff8082efc0dad4e80bce13
refs/heads/master
2020-07-07T13:49:14.504232
2019-08-21T02:00:01
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from sys import stdin numberWordsDict = dict() for line in stdin: lineList = line.split() for word in lineList: if word not in numberWordsDict: numberWordsDict[word] = 0 numberWordsDict[word] += 1 tupleList = [] for word in numberWordsDict: tupleList.append((numberWordsDict[word], word)) tupleList.sort(key=lambda curTuple: (-curTuple[0], curTuple[1])) for curTuple in tupleList: print(curTuple[1])
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/contactbook.py
05a5715d3a06a40a21e502278f0cf56788ca7c36
[]
no_license
Ajit1999/ContactBook-API
de6f51d0e1fcf49b5c8b8bfacf4b7750b64b9356
df64583db98eb3421f07177f3c7dbb771c218ac4
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2023-07-12T00:12:38.396876
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from flask import Flask from flask_pymongo import PyMongo from bson.json_util import dumps from bson.objectid import ObjectId from flask import jsonify, request app = Flask(__name__) app.secret_key = "secretkey" app.config['MONGO_URI'] = "mongodb://localhost:27017/User" mongo = PyMongo(app) @app.route('/add',methods=['POST']) def add_user(): _json = request.json _name = _json['name'] _address = _json['address'] _contactno = _json['contact'] _email = _json['email'] if _name and _address and _contactno and _email and request.method == 'POST': id = mongo.db.user.insert({'name':_name,'address':_address,'contact':_contactno,'email':_email}) resp = jsonify("Contact added sucessfully") resp.status_code = 200 return resp else: return not_found() @app.route('/users') def users(): users = mongo.db.user.find() resp = dumps(users) return resp @app.route('/user/<id>') def user(id): user = mongo.db.user.find_one({'_id':ObjectId(id)}) resp = dumps(user) return resp @app.route('/delete/<id>',methods=['DELETE']) def delete_user(id): delete_user = mongo.db.user.delete_one({'_id': ObjectId(id)}) resp = jsonify("Contact deleted successfully") resp.status_code = 200 return resp @app.route('/update/<id>', methods =['PUT']) def update(id): _id = id _json = request.json _name = _json['name'] _address = _json['address'] _contactno = _json['contact'] _email = _json['email'] if _name and _address and _contactno and _email and _id and request.method == 'PUT': mongo.db.user.update({'_id':ObjectId(_id['$oid']) if '$oid' in _id else ObjectId(_id)}, {'$set': {'name':_name,'address':_address,'contact':_contactno,'email':_email,}}) resp = jsonify("Contact updated Successfully") resp.status_code = 200 return resp else: return not_found() @app.errorhandler(404) def not_found(error=None): message = { 'status': 404, 'message':'Not Found' + request.url } resp = jsonify(message) resp.status_code = 404 return resp if __name__ =="__main__": app.run(debug = True)
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/tests/test_utils.py
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coras-io/lint-review
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refs/heads/master
2020-12-25T22:28:52.698909
2019-11-28T15:56:53
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import lintreview.utils as utils import os from unittest import skipIf js_hint_installed = os.path.exists( os.path.join(os.getcwd(), 'node_modules', '.bin', 'jshint')) def test_in_path(): assert utils.in_path('python'), 'No python in path' assert not utils.in_path('bad_cmd_name') @skipIf(not js_hint_installed, 'Missing local jshint. Skipping') def test_npm_exists(): assert utils.npm_exists('jshint'), 'Should be there.' assert not utils.npm_exists('not there'), 'Should not be there.'
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43e788ee824ce1f6611d42690688136e5840af0e
/Video.py
5727fe4166addad073efc4954296de4a11e5ee5a
[]
no_license
Karthik8396/lrn_opencv2
3b9c9d824bee26c5d3c5c8ab54fb12e5a9bf145e
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2020-07-10T05:09:03.104573
2019-08-31T14:23:17
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import cv2 import numpy cap=cv2.VideoCapture(0) #first webcam fourcc =cv2.VideoWriter_fourcc(*'XVID') # for saving the video and fourcc is codec out=cv2.VideoWriter('output.avi',fourcc,20.0,(640,480)) # adding codec and size of video cv2.VideoWriter() while True : ret,frame = cap.read() gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) cv2.imshow('frame',frame) cv2.imshow('gray',gray) out.write(frame) if cv2.waitKey(1) & 0xFF == ord('q'): #waitkey return 32 bit value(32 ones) 0xFF is 11111111(8 bit value),logical and makes it true and if executes break #ord is for getting key value cap.release() out.release() cv2.destroyAllWindows()
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/controlledEnviroment/GUIpackage/Classes/LetterToCharactersClass.py
ab0de4a4b3da873f5bdf638a9426c5ee6cd8f359
[]
no_license
carolyn-brodie/Summer2021
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refs/heads/master
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class LetterToCharacters(): def __init__(self): self.letters = ["ch", "sh", "th", "wh", "ng", "nk", "wr", "str", "spr", "bl", "cl", "fl", "gl", "pl", "br", "cr", "dr", "fr", "gr", "pr", "tr", "sk", "sl", "sp", "st", "sw"] self.alphabet = {"a": 0, "b": 0, "c": 0, "d": 0, "e": 0, "f": 0, "g": 0, "h": 0, "i": 0, "j": 0, "k": 0, "l": 0, "m": 0, "n": 0, "o": 0, "p": 0, "q": 0, "r": 0, "s": 0, "t": 0, "u": 0, "v": 0, "w": 0, "x": 0, "y": 0, "z": 0, "!": 0, "@": 0, "#": 0, "$": 0, "%": 0, "^": 0, ")": 0, "*": 0, "(": 0, "_": 0} self.digraph_dict = {"ch": "!", "sh": "@", "th": "#", "wh": "$", "ng": "%", "nk": "^", "wr": ")"} self.blend_dict = {"str": "*", "spr": "(", "bl": "[", "cl": "]", "fl": "|", "gl": ":", "pl": "<", "br": ">", "cr": "?", "dr": "~", "fr": "`", "gr": "\u00d8", "pr": "\u00d9", "tr": "\u00da", "sk": "\u00db", "sl": "\u00dd", "sp": "\u00de", "st": "\u00df", "sw": "\u00e0"} self.vowel_dict = {"ai": "\u00e1", "au": "\u00e2", "aw": "\u00e3", "ay": "\u00e4", "ea": "\u00e5", "ee": "\u00e6", "ei": "\u00e7", "eo": "\u00e8", "eu": "\u00e9", "ew": "\u00ea", "ey": "\u00eb", "ie": "\u00ec", "oa": "\u00ed", "oe": "\u00ee", "oi": "\u00ef", "oo": "\u00f0", "ou": "\u00f1", "ow": "\u00f2", "oy": "\u00f3", "ue": "\u00f4", "ui": "\u00f5"} self.combined_dict = {} self.combined_dict.update(self.digraph_dict) self.combined_dict.update(self.blend_dict) self.combined_dict.update(self.vowel_dict) self.reverse_dict = {value: key for (key, value) in self.combined_dict.items()} self.allCombined = self.returnAllCombined() def lettersToCharacters(self, word): for item in self.letters: if item in word: var = word.index(item) word = word.replace(word[var: var + len(item)], self.combined_dict[item]) return word def charactersToLetters(self, word): for item in self.reverse_dict.keys(): if item in word: var = word.index(item) word = word.replace(word[var], self.reverse_dict[item]) return word def returnCombined(self): return self.combined_dict def returnReversed(self): return self.reverse_dict def returnAllCombined(self): temp = self.alphabet temp.update(self.reverse_dict) return temp def formatDictForReturn(self, dict1): temp = dict1 for char in temp: temp[char] = 0 return temp def nestDict(self, dict1): temp = {} temp.update(dict1) for char1 in temp: temp1 = {} temp1.update(dict1) temp[char1] = temp1 return temp def returnFormated(self): temp = self.nestDict(self.formatDictForReturn(self.returnAllCombined())) return temp
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/pythonapp/imgtxt/admin.py
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no_license
mogilivishal/Verzeo-OCR-Project
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refs/heads/master
2022-04-17T20:32:45.724447
2020-02-16T17:38:52
2020-02-16T17:38:52
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from django.contrib import admin from .models import Document admin.site.register(Document)
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/pysweng/tests/test_oop.py
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lopezpdvn/pysweng
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refs/heads/master
2021-01-18T23:42:55.054505
2016-12-30T09:43:18
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import unittest from pysweng.oop import (dummy_function, DUMMY_GLOBAL_CONSTANT_0, DUMMY_GLOBAL_CONSTANT_1) class TestDummies(unittest.TestCase): def test_global_variables(self): self.assertEqual(DUMMY_GLOBAL_CONSTANT_0, 'FOO') self.assertEqual(DUMMY_GLOBAL_CONSTANT_1, 'BAR') def test_dummy_funcion(self): self.assertEqual(dummy_function('a'), 'a'); self.assertEqual(dummy_function(555), 555); if __name__ == '__main__': unittest.main()
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/Python/SImplifyPline/CleanUpPolyline.py
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[ "LicenseRef-scancode-warranty-disclaimer" ]
no_license
pgolay/PG_Scripts
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2021-01-19T16:53:41.525879
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import Rhino import scriptcontext as sc """ Cleans up by collapsing tiny segments in a polyline. """ def CleanUpPolyline(): while True: tol = sc.doc.ModelAbsoluteTolerance if sc.sticky.has_key("PLineSimplifyTol"): tol = sc.sticky["PLineSimplifyTol"] go = Rhino.Input.Custom.GetObject() go.AcceptNumber(True, False) go.GeometryFilter = Rhino.DocObjects.ObjectType.Curve opDblTol = Rhino.Input.Custom.OptionDouble(tol) go.AddOptionDouble("SegmentTolerance",opDblTol) result = go.Get() if( go.CommandResult() != Rhino.Commands.Result.Success ): return if result == Rhino.Input.GetResult.Object: if type(go.Object(0).Geometry()) == Rhino.Geometry.PolylineCurve: curve = go.Object(0).Geometry() rc, pLine = curve.TryGetPolyline() pLineId = go.Object(0).ObjectId else: sc.doc.Objects.UnselectAll() sc.doc.Views.Redraw() print "Sorry, that was not a polyline." continue break elif result == Rhino.Input.GetResult.Option: tol = opDblTol.CurrentValue sc.sticky["PLineSimplifyTol"] = tol continue elif result == Rhino.Input.GetResult.Number: tol = go.Number() sc.sticky["PLineSimplifyTol"] = tol continue break count = pLine.CollapseShortSegments(tol) if count !=0: sc.doc.Objects.Replace(pLineId, pLine) sc.doc.Views.Redraw() print str(count) + " short segments were collapsed." else: print "No short segments were collapsed." pass if __name__ == "__main__": CleanUpPolyline()
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/code/configuration.py
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no_license
HankTsai/Sales_Forecast_Retailer
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import os import logging from pymssql import connect from datetime import datetime from configparser import ConfigParser config = ConfigParser() config.read('setting.ini') class CodeLogger: """log儲存設定模組""" def __init__(self): self.logger = logging.getLogger(os.path.basename(__file__)) self.formatter = logging.Formatter( '["%(asctime)s - %(levelname)s - %(name)s - %(message)s" - function:%(funcName)s - line:%(lineno)d]') self.log_name = config['filepath']['log_path'] + datetime.now().strftime("forecast_%Y-%m-%d_%H-%M-%S.log") logging.basicConfig(level=logging.INFO, datefmt='%Y%m%d_%H:%M:%S',) def store_logger(self): """設定log儲存""" handler = logging.FileHandler(self.log_name, "w", encoding = "UTF-8") handler.setFormatter(self.formatter) self.logger.addHandler(handler) self.logger.propagate = False def show_logger(self): """設定log在終端機顯示""" console = logging.StreamHandler() console.setLevel(logging.FATAL) console.setFormatter(self.formatter) self.logger.addHandler(console) class DBConnect: """繼承並設計DB連線處理""" def __init__(self): self.host = config['connect']['server'] self.user = config['connect']['username'] self.password = config['connect']['password'] self.database = config['connect']['database'] self.conn = connect(host=self.host, user=self.user, password=self.password, database=self.database, autocommit=True) def query(self, sql, as_dict=False, para=()): """查詢DB數據""" # as_dict 是讓數據呈現key/value型態 try: cursor = self.conn.cursor(as_dict) if para: cursor.execute(sql,para) return cursor else: cursor.execute(sql) return cursor except Exception as me: CodeLogger().logger.error(me) def insert(self, sql, para=()): """新增DB數據""" try: cursor = self.conn.cursor() cursor.execute(sql,para) except Exception as me: CodeLogger().logger.error(me) def delete(self, sql, para=()): """刪除DB數據""" try: cursor = self.conn.cursor() cursor.execute(sql,para) except Exception as me: CodeLogger().logger.error(me) def commit(self): self.conn.commit() def close(self): self.conn.close()
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/py_controls/MemoryManager.py
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CyberCrunch/DU_AI_Gov
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refs/heads/master
2021-06-20T12:46:35.360703
2017-08-08T19:18:14
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# -*- coding: utf-8 -*- """ Created on Fri Dec 30 15:52:43 2016 @author: robin """ import json from enum import Enum #testing possible enums for readability...(not implemeted) class NrH(Enum): #human data formtat for Json name = 0 human = 1 job = 2 status = 3 position = 4 money = 5 class NrL(Enum): #location data formtat for Json name = 0 location = 1 planet = 2 structure = 3 longitude = 4 latitude = 5 resource = 6 reward = 7 class SpH(Enum): #human string formtat for registration name = 0 job = 1 class SpL(Enum): #location string formtat for registration name = 0 planet = 1 structure = 2 longitude = 3 latitude = 4 def regHuman(msg): splitStr = msg.split() if(len(splitStr) != 2): return "Invalid Parameters, please use Format: !reg YourName YourJob" with open('memoryDB.json', 'r+') as json_file: json_data = json.load(json_file) json_data[splitStr[SpH.name.value]] = ['Human', splitStr[SpH.job.value],"idle", "unknownPos", 0] json_file.seek(0, 0) json_file.write(json.dumps(json_data, indent=4)) json_file.truncate() return ("New human registered: " +msg) def regLocation(msg): splitStr = msg.split() if(len(splitStr) != 5): return ("Invalid Parameters, please use Format: !geodata name planet type longitude latitude") with open('memoryDB.json', 'r+') as json_file: json_data = json.load(json_file) json_data[splitStr[SpL.name.value]] = ['Location', splitStr[SpL.planet.value], splitStr[SpL.structure.value], splitStr[SpL.longitude.value], splitStr[SpL.latitude.value], "default", 0] json_file.seek(0, 0) json_file.write(json.dumps(json_data, indent=4)) json_file.truncate() return ("New location registered: " +msg) def getDatabase(): with open('memoryDB.json', 'r') as json_file: json_data = json.load(json_file) return(json.dumps(json_data, indent=4, sort_keys=True))
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/src/coefSubset/evaluate/ranks/tenth/rank_2p49_Q.py
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no_license
TanemuraKiyoto/PPI-native-detection-via-LR
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refs/heads/master
2022-12-05T11:59:01.014309
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# 9 July 2019 # Kiyoto Aramis Tanemura # Several metrics are used to assess the performance of the trained RF model, notably native ranking. This script returns a ranking of the native protein-protein complex among a decoy set. For convenience, I will define as a function and will call in a general performance assessment script. # Modified 11 July 2019 by Kiyoto Aramis Tanemura. To parallelize the process, I will replace the for loop for the testFileList to a multiprocessing pool. # Modified 9 September 2019 by Kiyoto Aramis Tanemura. I will use the function to perform the calculation on one CSV file only. Thus instead of a function to import in other scripts, they will be individual jobs parallelized as individual jobs in the queue. import os import pandas as pd import numpy as np import pickle os.chdir('/mnt/scratch/tanemur1/') # Read the model and trainFile testFile = '2p49.csv' identifier = 'Q' thresholdCoef = 0.1 testFilePath = '/mnt/scratch/tanemur1/CASF-PPI/nonb_descriptors/complete/' modelPath = '/mnt/home/tanemur1/6May2019/2019-11-11/results/coefSubset/tenth/' outputPath = '/mnt/home/tanemur1/6May2019/2019-11-11/results/coefSubset/evaluate/tenth/ranks/' pdbID = testFile[:4] with open(modelPath + 'model' + identifier + '.pkl', 'rb') as f: clf = pickle.load(f) result = pd.DataFrame() scoreList = [] df1 = pd.read_csv(testFilePath + testFile) dropList = ['Unnamed: 0', 'Unnamed: 0.1', 'ref'] df1 = df1.drop(dropList, axis = 1) df1 = df1.set_index('Pair_name') df1 = pd.DataFrame(df1.values.T, columns = df1.index, index = df1.columns) df1.fillna(0.0, inplace = True) df1 = df1.reindex(sorted(df1.columns), axis = 1) # Drop features with coefficients below threshold coefs = pd.read_csv('/mnt/home/tanemur1/6May2019/2019-11-11/results/medianCoefs.csv', index_col = 0, header = None, names = ['coefficients']) coefs = coefs[np.abs(coefs['coefficients']) < thresholdCoef] dropList = list(coefs.index) del coefs df1.drop(dropList, axis = 1, inplace = True) with open(modelPath + 'standardScaler' + identifier + '.pkl', 'rb') as g: scaler = pickle.load(g) for i in range(len(df1)): # subtract from one row each row of the dataframe, then remove the trivial row[[i]] - row[[i]]. Also some input files have 'class' column. This is erroneous and is removed. df2 = pd.DataFrame(df1.iloc[[i]].values - df1.values, index = df1.index, columns = df1.columns) df2 = df2.drop(df1.iloc[[i]].index[0], axis = 0) # Standardize inut DF using the standard scaler used for training data. df2 = scaler.transform(df2) # Predict class of each comparison descriptor and sum the classes to obtain score. Higher score corresponds to more native-like complex predictions = clf.predict(df2) score = sum(predictions) scoreList.append(score) # Make a new DataFrame to store the score and corresponding descriptorID. Add rank as column. Note: lower rank corresponds to more native-like complex result = pd.DataFrame(data = {'score': scoreList}, index = df1.index.tolist()).sort_values(by = 'score', ascending = False) result['rank'] = range(1, len(result) + 1) with open(outputPath + pdbID + identifier + '.csv', 'w') as h: result.to_csv(h)
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c9aa19a4d46b5c5357121e76e2e9784f2140ba41
/cashonly/management/commands/debtreminder.py
09a10f922fe66a1bb31ef740723ed9ab65469d2c
[]
no_license
klonfed/cashonly
2e617094ad95b82be62808fbbb781e9a2250b8a6
514e1c9cd8814e38b518b0be382940d1cb229725
refs/heads/master
2021-01-19T18:30:35.317250
2015-11-20T22:20:00
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from cashonly.models import * from django.conf import settings from django.core.mail import send_mass_mail from django.core.management.base import NoArgsCommand from django.template import Context from django.template.loader import get_template from django.utils import translation from django.utils.translation import ugettext as _ class Command(NoArgsCommand): help = 'Sends a reminder mail to every with a negative credit' def handle_noargs(self, **options): translation.activate('de') tpl = get_template('cashonly/debt_reminder.txt') messages = [] for a in Account.objects.all(): if a.credit < 0: name = '%s %s' % (a.user.first_name, a.user.last_name) context = {'name': name, 'credit': a.credit} rcpts = ['%s <%s>' % (name, a.user.email)] messages.append(('%s%s' % (settings.EMAIL_SUBJECT_PREFIX, _('Debt Reminder')), tpl.render(Context(context)), settings.DEFAULT_FROM_EMAIL, rcpts)) send_mass_mail(tuple(messages))
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/auto_feature.py
de319e49c20d33cfabc61c32af47395ae90da9f0
[]
no_license
CheneyYin/Motor
ecaab18e084ed4083c9ccb980a2d9b4310bf0637
f3009e0335a9a70d5299b3814f7df4f43b03eff4
refs/heads/master
2020-05-07T12:40:15.447944
2019-08-12T03:28:22
2019-08-12T03:28:22
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2019-04-17T07:00:08
2019-04-10T06:21:22
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import numpy as np import pandas as pd from sklearn.preprocessing import MinMaxScaler import tsfresh.feature_extraction.feature_calculators as fc import matplotlib.pyplot as plt import warnings train_path1 = '../Motor-Data/Motor_tain/N/00aab5a5-e096-4e4e-803f-a8525506cbd8_F.csv' train_path1 = '../Motor-Data/Motor_tain/N/00aab5a5-e096-4e4e-803f-a8525506cbd8_B.csv' df1 = pd.read_csv(train_path1, header = 0) df2 = pd.read_csv(train_path2, header = 0) df = pd.DataFrame(data = np.column_stack([df1['ai1'],df1['ai2'], df2['ai1'], df2['ai2'], range(79999), '1']), columns = ['F_ai1','F_ai2', 'B_ai1', 'B_ai2', 'time', 'id'])
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/MPK261/__init__.py
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[]
no_license
maratbakirov/AbletonLive10_MIDIRemoteScripts
bf0749c5c4cce8e83b23f14f671e52752702539d
ed1174d9959b20ed05fb099f0461bbc006bfbb79
refs/heads/master
2021-06-16T19:58:34.038163
2021-05-09T11:46:46
2021-05-09T11:46:46
203,174,328
0
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2019-08-19T13:04:23
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# Embedded file name: /Users/versonator/Jenkins/live/output/mac_64_static/Release/python-bundle/MIDI Remote Scripts/MPK261/__init__.py # Compiled at: 2018-04-23 20:27:04 from __future__ import absolute_import, print_function, unicode_literals from .MPK261 import MPK261 from _Framework.Capabilities import controller_id, inport, outport, CONTROLLER_ID_KEY, PORTS_KEY, NOTES_CC, SCRIPT, REMOTE def get_capabilities(): return {CONTROLLER_ID_KEY: controller_id(vendor_id=2536, product_ids=[ 37], model_name='MPK261'), PORTS_KEY: [ inport(props=[NOTES_CC, SCRIPT, REMOTE]), outport(props=[SCRIPT, REMOTE])]} def create_instance(c_instance): return MPK261(c_instance)
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/xiaojian/second_phase/day12/http_sever2.0.py
12ccde8198046391e24f9698efd843eacb0c011c
[]
no_license
Wellsjian/20180826
424b65f828f0174e4d568131da01dafc2a36050a
0156ad4db891a2c4b06711748d2624080578620c
refs/heads/master
2021-06-18T12:16:08.466177
2019-09-01T10:06:44
2019-09-01T10:06:44
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""" HTTP 2.0 接口设计: 1.提供句柄,通过句柄调用属性和方法 obj = open() lock = Lock() 2.实例化对象,通过对象设置,启动服务 t = Thread() p = Process() 3.根据功能需求,无法帮助用户决定的内容,通过参数传递 4.能够解决的问题,不要让用户去解决,需要用户解决的问题可以用重写的方法去解决 技术分析: HTTP 协议 思路分析 1.使用类进行封装 2.从用户的角度决定代码的编写 """ # 具体HTTP sever功能. from socket import * from select import * class HTTPSever: def __init__(self, host, port, dir): self.addrss = (host, port) self.host = host self.port = port self.dir = dir self.rlist = [] self.wlist = [] self.xlist = [] self.create_socket() self.bind() # 创建套接字 def create_socket(self): self.sockfd = socket() self.sockfd.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1) # 绑定地址 def bind(self): self.sockfd.bind(self.addrss) # 启动服务 def server_forver(self): self.sockfd.listen(5) print("listen the port %d" % self.port) self.rlist.append(self.sockfd) while True: rs, ws, xs = select(self.rlist, self.wlist, self.xlist) self.do_rlist(rs) # 具体处理请求 def handle(self, connfd): request = connfd.recv(1024) if not request: connfd.close() self.rlist.remove(connfd) return # 提取请求内容 request_line = request.splitlines()[0] info = request_line.decode().split(" ")[1] print(connfd.getpeername(), ":", info) if info == "/" or info[-5:] == ".html": self.get_html(connfd, info) else: self.get_data(connfd,info) def get_data(self,connfd,info): response = "HTTP/1.1 200 ok\r\n" response += "\r\n" response += "<h1>Waiting for the HTTPSEVER 3.0<h1>" connfd.send(response.encode()) def get_html(self,connfd,info): if info == "/": html_name = self.dir + "/index.html" else: html_name = self.dir + info try: obj = open(html_name) except Exception: response = "HTTP/1.1 404 not found\r\n" response += "Content_Type:text/html\r\n" response += "\r\n" response += "<h1>sorry.....<h1>" else: response = "HTTP/1.1 200 OK\r\n" response += "Content_Type:text/html\r\n" response += "\r\n" response += obj.read() finally: connfd.send(response.encode()) # 具体处理rlist里的监控信号 def do_rlist(self, rs): for r in rs: if r is self.sockfd: connfd, addr = self.sockfd.accept() print("Connect from ", addr) self.rlist.append(connfd) else: self.handle(r) if __name__ == "__main__": # 希望通过HTTPSever类快速搭建http服务,用以展示自己的网页 # HOST = "0.0.0.0" # PORT = 22222 # ADDR = (HOST, PORT) # DIR = "./static" HOST = "172.40.74.151" PORT = 8888 DIR ="./hfklswn" # 实例化对象 httpfd = HTTPSever(HOST, PORT, DIR) # 启动HTTP服务 httpfd.server_forver()
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1d605dbc4b6ff943ac3fffd2f610b698534bcdd2
/trainShallowClassifier_tttt_highlevel.py
bdf0ae16ab6d26a275edadd551fff3285699bfcd
[]
no_license
emilbols/EFT4Tops
fec75b9b4b97f2e1c7611694445e07c1c23038ab
4ce00b4c0d2d75af56c677709e83de0e41bce6d7
refs/heads/master
2020-04-10T16:27:03.309960
2019-04-11T12:50:09
2019-04-11T12:50:09
161,145,658
0
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from ROOT import TFile, TTree, TChain, TCanvas, TH1D, TLegend, gROOT, gStyle import sys import ROOT import os import time from argparse import ArgumentParser from array import array from math import * import numpy as np from collections import Counter import root_numpy as rootnp import matplotlib.pyplot as plt from keras import initializers from keras.models import Sequential, Model from keras.layers import Dense, Activation, Dropout, Input, Convolution1D, Concatenate, Flatten from keras.utils import np_utils from keras.callbacks import EarlyStopping, ModelCheckpoint, LearningRateScheduler from keras.optimizers import SGD,Adam from keras.regularizers import l1, l2 from keras.regularizers import l1, l2 from keras.utils import to_categorical from keras.layers.normalization import BatchNormalization #from keras.utils.visualize_util import plot from numpy.lib.recfunctions import stack_arrays from sklearn.preprocessing import StandardScaler from keras.models import load_model from sklearn.metrics import roc_curve,roc_auc_score from sklearn.model_selection import train_test_split import pickle from rootpy.plotting import Hist from rootpy.plotting import Hist2D from sklearn.neural_network import MLPClassifier from keras import backend as K from keras.engine.topology import Layer class SortLayer(Layer): def __init__(self, kernel_initializer='glorot_uniform', **kwargs): self.output_dim = output_dim self.kernel_initializer = initializers.get(kernel_initializer) self.kernel_size = conv_utils.normalize_tuple(1, 1, 'kernel_size') super(SortLayer, self).__init__(**kwargs) def build(self, input_shape): # Create a trainable weight variable for this layer. channel_axis = 1 input_dim = input_shape[channel_axis] kernel_shape = self.kernel_size + (input_dim, 1) self.kernel = self.add_weight(shape=kernel_shape, initializer=self.kernel_initializer, name='kernel') super(SortLayer, self).build(input_shape) # Be sure to call this at the end def call(self, x): values = K.conv1d(x, self.kernel, strides = 1, padding = "valid", data_format = NULL, dilation_rate = 1) order = tf.contrib.framework.argsort(values, direction='ASCENDING') print order.shape x = x[order] return x def compute_output_shape(self, input_shape): return (input_shape[0], self.output_dim) def draw_roc(df, df2, label, color, draw_unc=False, ls='-', draw_auc=True, flavour = False): newx = np.logspace(-3, 0, 100) tprs = pd.DataFrame() scores = [] if flavour: cs = ( (df['isC'] == 0) & (df['isCC'] == 0) & (df['isGCC'] == 0) ) else: cs = ( (df['isUD'] == 0) & (df['isS'] == 0) & (df['isG'] == 0) ) df = df[cs] df2 = df2[cs] tmp_fpr, tmp_tpr, _ = roc_curve(np.clip(df['isB']+df['isBB']+df['isLeptonicB_C']+df['isLeptonicB']+df['isGBB'],0,1), df2['prob_isBB']+df2['prob_isB']) scores.append( roc_auc_score(np.clip(df['isB']+df['isBB']+df['isLeptonicB_C']+df['isLeptonicB']+df['isGBB'],0,1), df2['prob_isB']+df2['prob_isBB']) ) coords = pd.DataFrame() coords['fpr'] = tmp_fpr coords['tpr'] = tmp_tpr clean = coords.drop_duplicates(subset=['fpr']) spline = InterpolatedUnivariateSpline(clean.fpr, clean.tpr,k=1) tprs = spline(newx) scores = np.array(scores) auc = ' AUC: %.3f +/- %.3f' % (scores.mean(), scores.std()) if draw_auc else '' plt.plot(tprs, newx, label=label + auc, c=color, ls=ls) def makeROC(fpr, tpr, thresholds,AUC,outfile,signal_label, background_label): c = TCanvas("c","c",700,600) ROOT.gPad.SetMargin(0.15,0.07,0.15,0.05) ROOT.gPad.SetLogy(0) ROOT.gPad.SetGrid(1,1) ROOT.gStyle.SetGridColor(17) roc = ROOT.TGraph(len(fpr),tpr,fpr) roc.SetLineColor(2) roc.SetLineWidth(2) roc.SetTitle(";Signal efficiency (%s); Background efficiency (%s)"%(signal_label, background_label)) roc.GetXaxis().SetTitleOffset(1.4) roc.GetXaxis().SetTitleSize(0.045) roc.GetYaxis().SetTitleOffset(1.4) roc.GetYaxis().SetTitleSize(0.045) roc.GetXaxis().SetRangeUser(0,1) roc.GetYaxis().SetRangeUser(0.000,1) roc.Draw("AL") ROOT.gStyle.SetTextFont(42) t = ROOT.TPaveText(0.2,0.84,0.4,0.94,"NBNDC") t.SetTextAlign(11) t.SetFillStyle(0) t.SetBorderSize(0) t.AddText('AUC = %.3f'%AUC) t.Draw('same') c.SaveAs(outfile) def makeDiscr(discr_dict,outfile,xtitle="discriminator"): c = ROOT.TCanvas("c","c",800,500) ROOT.gStyle.SetOptStat(0) ROOT.gPad.SetMargin(0.15,0.1,0.2,0.1) #ROOT.gPad.SetLogy(1) #ROOT.gPad.SetGrid(1,1) ROOT.gStyle.SetGridColor(17) l = TLegend(0.17,0.75,0.88,0.88) l.SetTextSize(0.055) l.SetBorderSize(0) l.SetFillStyle(0) l.SetNColumns(2) colors = [2,4,8,ROOT.kCyan+2] counter = 0 for leg,discr in discr_dict.iteritems(): a = Hist(30, 0, 1) #fill_hist_with_ndarray(a, discr) a.fill_array(discr) a.SetLineColor(colors[counter]) a.SetLineWidth(2) a.GetXaxis().SetTitle(xtitle) a.GetXaxis().SetLabelSize(0.05) a.GetXaxis().SetTitleSize(0.05) a.GetXaxis().SetTitleOffset(1.45) a.GetYaxis().SetTitle("a.u.") a.GetYaxis().SetTickSize(0) a.GetYaxis().SetLabelSize(0) a.GetYaxis().SetTitleSize(0.06) a.GetYaxis().SetTitleOffset(0.9) a.Scale(1./a.Integral()) #a.GetYaxis().SetRangeUser(0.00001,100) a.GetYaxis().SetRangeUser(0,0.2) if counter == 0: a.draw("hist") else: a.draw("same hist") l.AddEntry(a,leg,"l") counter += 1 l.Draw("same") c.SaveAs(outfile) def drawTrainingCurve(input,output): hist = pickle.load(open(input,"rb")) tr_acc = hist["acc"] tr_loss = hist["loss"] val_acc = hist["val_acc"] val_loss = hist["val_loss"] epochs = range(len(tr_acc)) plt.figure(1) plt.subplot(211) plt.plot(epochs, tr_acc,label="training") plt.plot(epochs, val_acc, label="validation") plt.legend(loc='best') plt.grid(True) #plt.xlabel("number of epochs") plt.ylabel("accuracy") plt.subplot(212) plt.plot(epochs, tr_loss, label="training") plt.plot(epochs, val_loss, label="validation") plt.legend(loc='best') plt.grid(True) plt.xlabel("number of epochs") plt.ylabel("loss") plt.savefig(output) gROOT.SetBatch(1) OutputDir = 'Model_Shallow_highlevel_LO' Y = np.load('LO_highlevel_train/truth.npy') X_flat = np.load('LO_highlevel_train/features_flat.npy') print Y.shape SM = (Y == 0) left = ((Y == 1) | (Y == 2)) leftright = ((Y == 3) | (Y == 4) ) right = (Y == 5) Y[left] = 1 Y[leftright] = 2 Y[right] = 3 cut = len(Y[SM])/2 Y = Y[cut:] SM = (Y == 0) left = ((Y == 1)) right = ((Y == 2)) X_flat = X_flat[cut:] print len(Y) print len(Y[left]) print len(Y[SM]) print len(Y[right]) labels = Y Y = to_categorical(labels, num_classes=4) X_flat_train, X_flat_test, Y_train, Y_test, y_train, y_test = train_test_split(X_flat, Y, labels, test_size=0.2,random_state = 930607) adam = Adam(lr=0.0005, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) nclasses = 4 dropoutRate = 0.1 Inputs = Input(shape=(22,)) x = BatchNormalization(momentum=0.6,name='globalvars_input_batchnorm') (Inputs) x = Dense(50,activation='relu',kernel_initializer='lecun_uniform',name='dense_0')(x) x = Dropout(dropoutRate)(x) pred=Dense(nclasses, activation='softmax',kernel_initializer='lecun_uniform',name='ID_pred')(x) model = Model(inputs=Inputs,outputs=pred) model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy']) print model.summary() X_train = X_flat_train X_test = X_flat_test train_history = model.fit(X_train, Y_train, batch_size=512, epochs=200, validation_data=(X_test, Y_test), callbacks = [ModelCheckpoint(OutputDir + "/model_checkpoint_save.hdf5")], shuffle=True,verbose=1) pickle.dump(train_history.history,open(OutputDir + "/loss_and_acc.pkl",'wb')) drawTrainingCurve(OutputDir+"/loss_and_acc.pkl",OutputDir+"/training_curve.pdf") discr_dict = model.predict(X_test) SM_discr = [(1-discr_dict[jdx,0]) for jdx in range(0,len(discr_dict[:,0])) if y_test[jdx] == 0] EFT_discr = [(1-discr_dict[jdx,0]) for jdx in range(0,len(discr_dict[:,0])) if y_test[jdx] ==1 or y_test[jdx] == 2 or y_test[jdx] == 3] fpr, tpr, thres = roc_curve(np.concatenate((np.zeros(len(SM_discr)),np.ones(len(EFT_discr)))),np.concatenate((SM_discr,EFT_discr))) AUC = 1-roc_auc_score(np.concatenate((np.zeros(len(SM_discr)),np.ones(len(EFT_discr)))),np.concatenate((SM_discr,EFT_discr))) makeROC(fpr, tpr, thres,AUC,OutputDir+"/roc_SMvsEFT.pdf","EFT","SM") makeDiscr({"EFT":EFT_discr,"SM":SM_discr},OutputDir+"/discr_SMvsEFT.pdf","discriminator P(t_{L}) + P(t_{R})") tL_discr = [discr_dict[jdx,1]/(1-discr_dict[jdx,0]) for jdx in range(0,len(discr_dict[:,0])) if y_test[jdx] == 1] tLR_discr = [discr_dict[jdx,1]/(1-discr_dict[jdx,0]) for jdx in range(0,len(discr_dict[:,0])) if y_test[jdx] == 2] tR_discr = [discr_dict[jdx,1]/(1-discr_dict[jdx,0]) for jdx in range(0,len(discr_dict[:,0])) if y_test[jdx] == 3] fpr, tpr, thres = roc_curve(np.concatenate((np.zeros(len(tR_discr)),np.ones(len(tL_discr)))),np.concatenate((tR_discr,tL_discr))) AUC = 1-roc_auc_score(np.concatenate((np.zeros(len(tR_discr)),np.ones(len(tL_discr)))),np.concatenate((tR_discr,tL_discr))) makeROC(fpr, tpr, thres,AUC,OutputDir+"/roc_tLvstR.pdf","t_{L}","t_{R}") makeDiscr({"tL":tL_discr,"tR":tR_discr},OutputDir+"/discr_tLvstR.pdf","discriminator #frac{P(t_{L})}{P(t_{L}) + P(t_{R})}")
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/models/CaptionModel.py
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[]
no_license
sunyuxi/RobustChangeCaptioning
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c3ea1206a34cae8879a2accffc11c15b8fce0181
refs/heads/master
2023-08-17T16:02:22.527198
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# This file contains ShowAttendTell and AllImg model # ShowAttendTell is from Show, Attend and Tell: Neural Image Caption Generation with Visual Attention # https://arxiv.org/abs/1502.03044 # AllImg is a model where # img feature is concatenated with word embedding at every time step as the input of lstm from __future__ import absolute_import from __future__ import division from __future__ import print_function from functools import reduce import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import * class CaptionModel(nn.Module): def __init__(self): super(CaptionModel, self).__init__() # implements beam search # calls beam_step and returns the final set of beams # augments log-probabilities with diversity terms when number of groups > 1 def forward(self, *args, **kwargs): mode = kwargs.get('mode', 'forward') if 'mode' in kwargs: del kwargs['mode'] return getattr(self, '_'+mode)(*args, **kwargs) def beam_search(self, init_state, init_logprobs, *args, **kwargs): # function computes the similarity score to be augmented def add_diversity(beam_seq_table, logprobsf, t, divm, diversity_lambda, bdash): local_time = t - divm unaug_logprobsf = logprobsf.clone() for prev_choice in range(divm): prev_decisions = beam_seq_table[prev_choice][local_time] for sub_beam in range(bdash): for prev_labels in range(bdash): logprobsf[sub_beam][prev_decisions[prev_labels]] = logprobsf[sub_beam][prev_decisions[prev_labels]] - diversity_lambda return unaug_logprobsf # does one step of classical beam search def beam_step(logprobsf, unaug_logprobsf, beam_size, t, beam_seq, beam_seq_logprobs, beam_logprobs_sum, state): #INPUTS: #logprobsf: probabilities augmented after diversity #beam_size: obvious #t : time instant #beam_seq : tensor contanining the beams #beam_seq_logprobs: tensor contanining the beam logprobs #beam_logprobs_sum: tensor contanining joint logprobs #OUPUTS: #beam_seq : tensor containing the word indices of the decoded captions #beam_seq_logprobs : log-probability of each decision made, same size as beam_seq #beam_logprobs_sum : joint log-probability of each beam ys,ix = torch.sort(logprobsf,1,True) candidates = [] cols = min(beam_size, ys.size(1)) rows = beam_size if t == 0: rows = 1 for c in range(cols): # for each column (word, essentially) for q in range(rows): # for each beam expansion #compute logprob of expanding beam q with word in (sorted) position c local_logprob = ys[q,c].item() candidate_logprob = beam_logprobs_sum[q] + local_logprob local_unaug_logprob = unaug_logprobsf[q,ix[q,c]] candidates.append({'c':ix[q,c], 'q':q, 'p':candidate_logprob, 'r':local_unaug_logprob}) candidates = sorted(candidates, key=lambda x: -x['p']) new_state = [_.clone() for _ in state] #beam_seq_prev, beam_seq_logprobs_prev if t >= 1: #we''ll need these as reference when we fork beams around beam_seq_prev = beam_seq[:t].clone() beam_seq_logprobs_prev = beam_seq_logprobs[:t].clone() for vix in range(beam_size): v = candidates[vix] #fork beam index q into index vix if t >= 1: beam_seq[:t, vix] = beam_seq_prev[:, v['q']] beam_seq_logprobs[:t, vix] = beam_seq_logprobs_prev[:, v['q']] #rearrange recurrent states for state_ix in range(len(new_state)): # copy over state in previous beam q to new beam at vix new_state[state_ix][:, vix] = state[state_ix][:, v['q']] # dimension one is time step #append new end terminal at the end of this beam beam_seq[t, vix] = v['c'] # c'th word is the continuation beam_seq_logprobs[t, vix] = v['r'] # the raw logprob here beam_logprobs_sum[vix] = v['p'] # the new (sum) logprob along this beam state = new_state return beam_seq,beam_seq_logprobs,beam_logprobs_sum,state,candidates # Start diverse_beam_search cfg = kwargs['cfg'] gpu_ids = cfg.gpu_id device = torch.device("cuda:%d" % gpu_ids[0]) beam_size = cfg.model.speaker.get('beam_size', 10) group_size = cfg.model.speaker.get('group_size', 1) diversity_lambda = cfg.model.speaker.get('diversity_lambda', 0.5) decoding_constraint = cfg.model.speaker.get('decoding_constraint', 0) max_ppl = cfg.model.speaker.get('max_ppl', 0) bdash = beam_size // group_size # beam per group # INITIALIZATIONS beam_seq_table = [torch.LongTensor(self.seq_length, bdash).zero_() for _ in range(group_size)] beam_seq_logprobs_table = [torch.FloatTensor(self.seq_length, bdash).zero_() for _ in range(group_size)] beam_logprobs_sum_table = [torch.zeros(bdash) for _ in range(group_size)] # logprobs # logprobs predicted in last time step, shape (beam_size, vocab_size) done_beams_table = [[] for _ in range(group_size)] state_table = [list(torch.unbind(_)) for _ in torch.stack(init_state).chunk(group_size, 2)] logprobs_table = list(init_logprobs.chunk(group_size, 0)) # END INIT # Chunk elements in the args args = list(args) args = [_.chunk(group_size) if _ is not None else [None]*group_size for _ in args] args = [[args[i][j] for i in range(len(args))] for j in range(group_size)] for t in range(self.seq_length + group_size - 1): for divm in range(group_size): if t >= divm and t <= self.seq_length + divm - 1: # add diversity logprobsf = logprobs_table[divm].data.float() # suppress previous word if decoding_constraint and t-divm > 0: logprobsf.scatter_(1, beam_seq_table[divm][t-divm-1].unsqueeze(1).to(device), float('-inf')) # suppress UNK tokens in the decoding (here <UNK> has an index of 1) logprobsf[:, 1] = logprobsf[:, 1] - 1000 # diversity is added here # the function directly modifies the logprobsf values and hence, we need to return # the unaugmented ones for sorting the candidates in the end. # for historical # reasons :-) unaug_logprobsf = add_diversity(beam_seq_table,logprobsf,t,divm,diversity_lambda,bdash) # infer new beams beam_seq_table[divm],\ beam_seq_logprobs_table[divm],\ beam_logprobs_sum_table[divm],\ state_table[divm],\ candidates_divm = beam_step(logprobsf, unaug_logprobsf, bdash, t-divm, beam_seq_table[divm], beam_seq_logprobs_table[divm], beam_logprobs_sum_table[divm], state_table[divm]) # if time's up... or if end token is reached then copy beams for vix in range(bdash): if beam_seq_table[divm][t-divm,vix] == 0 or t == self.seq_length + divm - 1: final_beam = { 'seq': beam_seq_table[divm][:, vix].clone(), 'logps': beam_seq_logprobs_table[divm][:, vix].clone(), 'unaug_p': beam_seq_logprobs_table[divm][:, vix].sum().item(), 'p': beam_logprobs_sum_table[divm][vix].item() } if max_ppl: final_beam['p'] = final_beam['p'] / (t-divm+1) done_beams_table[divm].append(final_beam) # don't continue beams from finished sequences beam_logprobs_sum_table[divm][vix] = -1000 # move the current group one step forward in time it = beam_seq_table[divm][t-divm] logprobs_table[divm], state_table[divm] = self.get_logprobs_state(it.to(device), *(args[divm] + [state_table[divm]])) # all beams are sorted by their log-probabilities done_beams_table = [sorted(done_beams_table[i], key=lambda x: -x['p'])[:bdash] for i in range(group_size)] done_beams = reduce(lambda a,b:a+b, done_beams_table) return done_beams
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/factor_catalog.py
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''' To download pickled instances for FFHQ and LSUN-Bedrooms, visit: https://drive.google.com/open?id=1GYzEzOCaI8FUS6JHdt6g9UfNTmpO08Tt ''' import torch import ptutils from spherical_kmeans import MiniBatchSphericalKMeans def one_hot(a, n): import numpy as np b = np.zeros((a.size, n)) b[np.arange(a.size), a] = 1 return b class FactorCatalog: def __init__(self, k, random_state=0, factorization=None, **kwargs): if factorization is None: factorization = MiniBatchSphericalKMeans self._factorization = factorization(n_clusters=k, random_state=random_state, **kwargs) self.annotations = {} def _preprocess(self, X): X_flat = ptutils.partial_flat(X) return X_flat def _postprocess(self, labels, X, raw): heatmaps = torch.from_numpy(one_hot(labels, self._factorization.cluster_centers_.shape[0])).float() heatmaps = ptutils.partial_unflat(heatmaps, N=X.shape[0], H=X.shape[-1]) if raw: heatmaps = ptutils.MultiResolutionStore(heatmaps, 'nearest') return heatmaps else: heatmaps = ptutils.MultiResolutionStore(torch.cat([(heatmaps[:, v].sum(1, keepdim=True)) for v in self.annotations.values()], 1), 'nearest') labels = list(self.annotations.keys()) return heatmaps, labels def fit_predict(self, X, raw=False): self._factorization.fit(self._preprocess(X)) labels = self._factorization.labels_ return self._postprocess(labels, X, raw) def predict(self, X, raw=False): labels = self._factorization.predict(self._preprocess(X)) return self._postprocess(labels, X, raw) def __repr__(self): header = '{} catalog:'.format(type(self._factorization)) return '{}\n\t{}'.format(header, self.annotations)
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/static_frame/test/unit/test_frame_iter.py
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import unittest import typing as tp import numpy as np import frame_fixtures as ff import static_frame as sf # from static_frame import Index from static_frame import IndexHierarchy # from static_frame import IndexHierarchyGO # from static_frame import IndexYearMonth # from static_frame import IndexYearGO # from static_frame import IndexYear from static_frame import IndexDate # from static_frame import IndexDateGO from static_frame import Series from static_frame import Frame from static_frame import FrameGO from static_frame import TypeBlocks # from static_frame import mloc # from static_frame import ILoc from static_frame import HLoc # from static_frame import DisplayConfig # from static_frame import IndexAutoFactory from static_frame.test.test_case import TestCase # from static_frame.test.test_case import skip_win # from static_frame.test.test_case import skip_linux_no_display # from static_frame.test.test_case import skip_pylt37 # from static_frame.test.test_case import temp_file # from static_frame.core.exception import ErrorInitFrame # from static_frame.core.exception import ErrorInitIndex from static_frame.core.exception import AxisInvalid nan = np.nan class TestUnit(TestCase): #--------------------------------------------------------------------------- def test_frame_iter_a(self) -> None: records = ( (1, 2, 'a', False, True), (30, 50, 'b', True, False)) f1 = Frame.from_records(records, columns=('p', 'q', 'r', 's', 't'), index=('x','y')) self.assertEqual((f1.keys() == f1.columns).all(), True) self.assertEqual([x for x in f1.columns], ['p', 'q', 'r', 's', 't']) self.assertEqual([x for x in f1], ['p', 'q', 'r', 's', 't']) def test_frame_iter_array_a(self) -> None: records = ( (1, 2, 'a', False, True), (30, 50, 'b', True, False)) f1 = Frame.from_records(records, columns=('p', 'q', 'r', 's', 't'), index=('x','y')) self.assertEqual( next(iter(f1.iter_array(axis=0))).tolist(), [1, 30]) self.assertEqual( next(iter(f1.iter_array(axis=1))).tolist(), [1, 2, 'a', False, True]) def test_frame_iter_array_b(self) -> None: arrays = list(np.random.rand(1000) for _ in range(100)) f1 = Frame.from_items( zip(range(100), arrays) ) # iter columns post = f1.iter_array(axis=0).apply_pool(np.sum, max_workers=4, use_threads=True) self.assertEqual(post.shape, (100,)) self.assertAlmostEqual(f1.sum().sum(), post.sum()) post = f1.iter_array(axis=0).apply_pool(np.sum, max_workers=4, use_threads=False) self.assertEqual(post.shape, (100,)) self.assertAlmostEqual(f1.sum().sum(), post.sum()) def test_frame_iter_array_c(self) -> None: arrays = [] for _ in range(8): arrays.append(list(range(8))) f1 = Frame.from_items( zip(range(8), arrays) ) func = {x: chr(x+65) for x in range(8)} # iter columns post = f1.iter_element().apply_pool(func, max_workers=4, use_threads=True) self.assertEqual(post.to_pairs(0), ((0, ((0, 'A'), (1, 'B'), (2, 'C'), (3, 'D'), (4, 'E'), (5, 'F'), (6, 'G'), (7, 'H'))), (1, ((0, 'A'), (1, 'B'), (2, 'C'), (3, 'D'), (4, 'E'), (5, 'F'), (6, 'G'), (7, 'H'))), (2, ((0, 'A'), (1, 'B'), (2, 'C'), (3, 'D'), (4, 'E'), (5, 'F'), (6, 'G'), (7, 'H'))), (3, ((0, 'A'), (1, 'B'), (2, 'C'), (3, 'D'), (4, 'E'), (5, 'F'), (6, 'G'), (7, 'H'))), (4, ((0, 'A'), (1, 'B'), (2, 'C'), (3, 'D'), (4, 'E'), (5, 'F'), (6, 'G'), (7, 'H'))), (5, ((0, 'A'), (1, 'B'), (2, 'C'), (3, 'D'), (4, 'E'), (5, 'F'), (6, 'G'), (7, 'H'))), (6, ((0, 'A'), (1, 'B'), (2, 'C'), (3, 'D'), (4, 'E'), (5, 'F'), (6, 'G'), (7, 'H'))), (7, ((0, 'A'), (1, 'B'), (2, 'C'), (3, 'D'), (4, 'E'), (5, 'F'), (6, 'G'), (7, 'H')))) ) def test_frame_iter_array_d(self) -> None: arrays = [] for _ in range(8): arrays.append(list(range(8))) f1 = Frame.from_items( zip(range(8), arrays) ) # when called with a pool, values are gien the func as a single argument, which for an element iteration is a tuple of coord, value func = lambda arg: arg[0][1] # iter columns post = f1.iter_element_items().apply_pool(func, max_workers=4, use_threads=True) self.assertEqual(post.to_pairs(0), ((0, ((0, 0), (1, 0), (2, 0), (3, 0), (4, 0), (5, 0), (6, 0), (7, 0))), (1, ((0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1))), (2, ((0, 2), (1, 2), (2, 2), (3, 2), (4, 2), (5, 2), (6, 2), (7, 2))), (3, ((0, 3), (1, 3), (2, 3), (3, 3), (4, 3), (5, 3), (6, 3), (7, 3))), (4, ((0, 4), (1, 4), (2, 4), (3, 4), (4, 4), (5, 4), (6, 4), (7, 4))), (5, ((0, 5), (1, 5), (2, 5), (3, 5), (4, 5), (5, 5), (6, 5), (7, 5))), (6, ((0, 6), (1, 6), (2, 6), (3, 6), (4, 6), (5, 6), (6, 6), (7, 6))), (7, ((0, 7), (1, 7), (2, 7), (3, 7), (4, 7), (5, 7), (6, 7), (7, 7)))) ) def test_frame_iter_array_e(self) -> None: f = sf.Frame.from_dict( dict(diameter=(12756, 6792, 142984), mass=(5.97, 0.642, 1898)), index=('Earth', 'Mars', 'Jupiter'), dtypes=dict(diameter=np.int64)) post = f.iter_array(axis=0).apply(np.sum) self.assertTrue(post.dtype == float) def test_frame_iter_array_f(self) -> None: f = sf.Frame(np.arange(12).reshape(3,4), index=IndexDate.from_date_range('2020-01-01', '2020-01-03')) post = f.iter_array(axis=0).apply(np.sum, name='foo') self.assertEqual(post.name, 'foo') self.assertEqual( f.iter_array(axis=0).apply(np.sum).to_pairs(), ((0, 12), (1, 15), (2, 18), (3, 21)) ) self.assertEqual( f.iter_array(axis=1).apply(np.sum).to_pairs(), ((np.datetime64('2020-01-01'), 6), (np.datetime64('2020-01-02'), 22), (np.datetime64('2020-01-03'), 38)) ) def test_frame_iter_array_g(self) -> None: f = sf.FrameGO(index=IndexDate.from_date_range('2020-01-01', '2020-01-03')) post = list(f.iter_array(axis=0)) self.assertEqual(post, []) post = list(f.iter_array(axis=1)) self.assertEqual([x.tolist() for x in post], [[], [], []]) #--------------------------------------------------------------------------- def test_frame_iter_tuple_a(self) -> None: post = tuple(sf.Frame.from_elements(range(5)).iter_tuple(axis=0, constructor=tuple)) self.assertEqual(post, ((0, 1, 2, 3, 4),)) def test_frame_iter_tuple_b(self) -> None: post = tuple(sf.Frame.from_elements(range(3), index=tuple('abc')).iter_tuple(axis=0)) self.assertEqual(post, ((0, 1, 2),)) self.assertEqual(tuple(post[0]._asdict().items()), (('a', 0), ('b', 1), ('c', 2)) ) def test_frame_iter_tuple_c(self) -> None: with self.assertRaises(AxisInvalid): post = tuple(sf.Frame.from_elements(range(5)).iter_tuple(axis=2)) def test_frame_iter_tuple_d(self) -> None: f = sf.FrameGO(index=IndexDate.from_date_range('2020-01-01', '2020-01-03')) post = list(f.iter_tuple(constructor=tuple, axis=0)) self.assertEqual(post, []) post = list(f.iter_tuple(axis=1)) self.assertEqual([len(x) for x in post], [0, 0, 0]) def test_frame_iter_tuple_e(self) -> None: records = ( (1, 2, 'a', False, True), (30, 50, 'b', True, False)) f1 = FrameGO.from_records(records, columns=('p', 'q', 'r', 's', 't'), index=('x','y')) class Record(tp.NamedTuple): x: object y: object post1 = list(f1.iter_tuple(constructor=Record)) self.assertTrue(all(isinstance(x, Record) for x in post1)) post2 = list(f1.iter_tuple(constructor=tuple)) self.assertEqual(post2, [(1, 30), (2, 50), ('a', 'b'), (False, True), (True, False)]) #--------------------------------------------------------------------------- def test_frame_iter_series_a(self) -> None: f1 = ff.parse('f(Fg)|s(2,8)|i(I,str)|c(Ig,str)|v(int)') post1 = tuple(f1.iter_series(axis=0)) self.assertEqual(len(post1), 8) self.assertEqual(post1[0].to_pairs(), (('zZbu', -88017), ('ztsv', 92867))) post2 = tuple(f1.iter_series(axis=1)) self.assertEqual(len(post2), 2) self.assertEqual(post2[0].to_pairs(), (('zZbu', -88017), ('ztsv', 162197), ('zUvW', -3648), ('zkuW', 129017), ('zmVj', 58768), ('z2Oo', 84967), ('z5l6', 146284), ('zCE3', 137759))) #--------------------------------------------------------------------------- def test_frame_iter_tuple_items_a(self) -> None: records = ( (1, 2, 'a', False, True), (30, 50, 'b', True, False)) f1 = FrameGO.from_records(records, columns=('p', 'q', 'r', 's', 't'), index=('x','y')) post1 = list(f1.iter_tuple_items(constructor=list)) self.assertEqual(post1, [('p', [1, 30]), ('q', [2, 50]), ('r', ['a', 'b']), ('s', [False, True]), ('t', [True, False])]) #--------------------------------------------------------------------------- def test_frame_iter_element_a(self) -> None: # reindex both axis records = ( (2, 2, 'a', False, False), (30, 34, 'b', True, False), (2, 95, 'c', False, False), (30, 73, 'd', True, True), ) f1 = Frame.from_records(records, columns=('p', 'q', 'r', 's', 't'), index=('w', 'x', 'y', 'z')) self.assertEqual( [x for x in f1.iter_element()], [2, 2, 'a', False, False, 30, 34, 'b', True, False, 2, 95, 'c', False, False, 30, 73, 'd', True, True]) self.assertEqual(list(f1.iter_element(axis=1)), [2, 30, 2, 30, 2, 34, 95, 73, 'a', 'b', 'c', 'd', False, True, False, True, False, False, False, True]) self.assertEqual([x for x in f1.iter_element_items()], [(('w', 'p'), 2), (('w', 'q'), 2), (('w', 'r'), 'a'), (('w', 's'), False), (('w', 't'), False), (('x', 'p'), 30), (('x', 'q'), 34), (('x', 'r'), 'b'), (('x', 's'), True), (('x', 't'), False), (('y', 'p'), 2), (('y', 'q'), 95), (('y', 'r'), 'c'), (('y', 's'), False), (('y', 't'), False), (('z', 'p'), 30), (('z', 'q'), 73), (('z', 'r'), 'd'), (('z', 's'), True), (('z', 't'), True)]) post1 = f1.iter_element().apply(lambda x: '_' + str(x) + '_') self.assertEqual(post1.to_pairs(0), (('p', (('w', '_2_'), ('x', '_30_'), ('y', '_2_'), ('z', '_30_'))), ('q', (('w', '_2_'), ('x', '_34_'), ('y', '_95_'), ('z', '_73_'))), ('r', (('w', '_a_'), ('x', '_b_'), ('y', '_c_'), ('z', '_d_'))), ('s', (('w', '_False_'), ('x', '_True_'), ('y', '_False_'), ('z', '_True_'))), ('t', (('w', '_False_'), ('x', '_False_'), ('y', '_False_'), ('z', '_True_'))))) post2 = f1.iter_element(axis=1).apply(lambda x: '_' + str(x) + '_') self.assertEqual(post2.to_pairs(0), (('p', (('w', '_2_'), ('x', '_30_'), ('y', '_2_'), ('z', '_30_'))), ('q', (('w', '_2_'), ('x', '_34_'), ('y', '_95_'), ('z', '_73_'))), ('r', (('w', '_a_'), ('x', '_b_'), ('y', '_c_'), ('z', '_d_'))), ('s', (('w', '_False_'), ('x', '_True_'), ('y', '_False_'), ('z', '_True_'))), ('t', (('w', '_False_'), ('x', '_False_'), ('y', '_False_'), ('z', '_True_'))))) def test_frame_iter_element_b(self) -> None: # reindex both axis records = ( (2, 2, 'a', False, False), (30, 34, 'b', True, False), (2, 95, 'c', False, False), (30, 73, 'd', True, True), ) f1 = Frame.from_records(records, columns=('p', 'q', 'r', 's', 't'), index=('w', 'x', 'y', 'z')) # support working with mappings post = f1.iter_element().map_any({2: 200, False: 200}) self.assertEqual(post.to_pairs(0), (('p', (('w', 200), ('x', 30), ('y', 200), ('z', 30))), ('q', (('w', 200), ('x', 34), ('y', 95), ('z', 73))), ('r', (('w', 'a'), ('x', 'b'), ('y', 'c'), ('z', 'd'))), ('s', (('w', 200), ('x', True), ('y', 200), ('z', True))), ('t', (('w', 200), ('x', 200), ('y', 200), ('z', True)))) ) def test_frame_iter_element_c(self) -> None: a2 = np.array([ [None, None], [None, 1], [None, 5] ], dtype=object) a1 = np.array([True, False, True]) a3 = np.array([['a'], ['b'], ['c']]) tb1 = TypeBlocks.from_blocks((a3, a1, a2)) f1 = Frame(tb1, index=self.get_letters(None, tb1.shape[0]), columns=IndexHierarchy.from_product(('i', 'ii'), ('a', 'b')) ) values = list(f1.iter_element()) self.assertEqual(values, ['a', True, None, None, 'b', False, None, 1, 'c', True, None, 5] ) f2 = f1.iter_element().apply(lambda x: str(x).lower().replace('e', '')) self.assertEqual(f1.columns.__class__, f2.columns.__class__,) self.assertEqual(f2.to_pairs(0), ((('i', 'a'), (('a', 'a'), ('b', 'b'), ('c', 'c'))), (('i', 'b'), (('a', 'tru'), ('b', 'fals'), ('c', 'tru'))), (('ii', 'a'), (('a', 'non'), ('b', 'non'), ('c', 'non'))), (('ii', 'b'), (('a', 'non'), ('b', '1'), ('c', '5')))) ) def test_frame_iter_element_d(self) -> None: f1 = sf.Frame.from_elements(['I', 'II', 'III'], columns=('A',)) f2 = sf.Frame.from_elements([67, 28, 99], columns=('B',), index=('I', 'II', 'IV')) post = f1['A'].iter_element().map_any(f2['B']) # if we do not match the mapping, we keep the value. self.assertEqual(post.to_pairs(), ((0, 67), (1, 28), (2, 'III'))) def test_frame_iter_element_e(self) -> None: f1 = Frame.from_records(np.arange(9).reshape(3, 3)) self.assertEqual(list(f1.iter_element(axis=1)), [0, 3, 6, 1, 4, 7, 2, 5, 8]) mapping = {x: x*3 for x in range(9)} f2 = f1.iter_element(axis=1).map_all(mapping) self.assertEqual([d.kind for d in f2.dtypes.values], ['i', 'i', 'i']) #--------------------------------------------------------------------------- def test_frame_iter_group_a(self) -> None: columns = tuple('pqrst') index = tuple('zxwy') records = (('A', 1, 'a', False, False), ('A', 2, 'b', True, False), ('B', 1, 'c', False, False), ('B', 2, 'd', True, True)) f = Frame.from_records( records, columns=columns, index=index,name='foo') f = f.set_index_hierarchy(('p', 'q'), drop=True) with self.assertRaises(AxisInvalid): _ = f.iter_group('s', axis=-1).apply(lambda x: x.shape) post = f.iter_group('s').apply(lambda x: x.shape) self.assertEqual(post.to_pairs(), ((False, (2, 3)), (True, (2, 3))) ) def test_frame_iter_group_b(self) -> None: columns = tuple('pqrst') index = tuple('zxwy') records = (('A', 1, 'a', False, False), ('A', 2, 'b', True, False), ('B', 1, 'c', False, False), ('B', 2, 'd', True, True)) f = Frame.from_records( records, columns=columns, index=index, name='foo') post = f.iter_group(['p', 'q']).apply(len) self.assertEqual(post.to_pairs(), ((('A', 1), 1), (('A', 2), 1), (('B', 1), 1), (('B', 2), 1)) ) def test_frame_iter_group_c(self) -> None: columns = tuple('pqrst') index = tuple('zxwy') records = (('A', 1, 'a', False, False), ('A', 2, 'b', True, False), ('B', 1, 'c', False, False), ('B', 2, 'd', True, True)) f = Frame.from_records( records, columns=columns, index=index, name='foo') with self.assertRaises(TypeError): next(iter(f.iter_group(foo='x'))) with self.assertRaises(TypeError): next(iter(f.iter_group(3, 5))) self.assertEqual(next(iter(f.iter_group('q'))).to_pairs(0), (('p', (('z', 'A'), ('w', 'B'))), ('q', (('z', 1), ('w', 1))), ('r', (('z', 'a'), ('w', 'c'))), ('s', (('z', False), ('w', False))), ('t', (('z', False), ('w', False)))) ) def test_frame_iter_group_d(self) -> None: f = sf.Frame.from_element(1, columns=[1,2,3], index=['a']) empty = f.reindex([]) self.assertEqual(list(empty.iter_element()), []) self.assertEqual(list(empty.iter_group(key=1)), []) def test_frame_iter_group_e(self) -> None: f = sf.Frame.from_element(None, columns=[1,2,3], index=['a']) empty = f.reindex([]) self.assertEqual(list(empty.iter_element()), []) self.assertEqual(list(empty.iter_group(key=1)), []) def test_frame_iter_group_f(self) -> None: f = sf.Frame(np.arange(3).reshape(1,3), columns=tuple('abc')) f = f.drop.loc[0] post1 = tuple(f.iter_group(['b','c'])) self.assertEqual(post1, ()) post2 = tuple(f.iter_group('a')) self.assertEqual(post2, ()) #--------------------------------------------------------------------------- def test_frame_iter_group_items_a(self) -> None: # testing a hierarchical index and columns, selecting column with a tuple records = ( ('a', 999999, 0.1), ('a', 201810, 0.1), ('b', 999999, 0.4), ('b', 201810, 0.4)) f1 = Frame.from_records(records, columns=list('abc')) f1 = f1.set_index_hierarchy(['a', 'b'], drop=False) f1 = f1.relabel_level_add(columns='i') groups = list(f1.iter_group_items(('i', 'a'), axis=0)) self.assertEqual(groups[0][0], 'a') self.assertEqual(groups[0][1].to_pairs(0), ((('i', 'a'), ((('a', 999999), 'a'), (('a', 201810), 'a'))), (('i', 'b'), ((('a', 999999), 999999), (('a', 201810), 201810))), (('i', 'c'), ((('a', 999999), 0.1), (('a', 201810), 0.1))))) self.assertEqual(groups[1][0], 'b') self.assertEqual(groups[1][1].to_pairs(0), ((('i', 'a'), ((('b', 999999), 'b'), (('b', 201810), 'b'))), (('i', 'b'), ((('b', 999999), 999999), (('b', 201810), 201810))), (('i', 'c'), ((('b', 999999), 0.4), (('b', 201810), 0.4))))) def test_frame_iter_group_items_b(self) -> None: columns = tuple('pqrst') index = tuple('zxwy') records = (('A', 1, 'a', False, False), ('A', 2, 'b', True, False), ('B', 1, 'c', False, False), ('B', 2, 'd', True, True)) f = Frame.from_records( records, columns=columns, index=index,name='foo') f = f.set_index_hierarchy(('p', 'q'), drop=True) post = f.iter_group_items('s').apply( lambda k, x: f'{k}: {len(x)}') self.assertEqual(post.to_pairs(), ((False, 'False: 2'), (True, 'True: 2')) ) def test_frame_iter_group_items_c(self) -> None: # Test optimized sorting approach. Data must have a non-object dtype and key must be single data = np.array([[0, 1, 1, 3], [3, 3, 2, 3], [5, 5, 1, 3], [7, 2, 2, 4]]) frame = sf.Frame(data, columns=tuple('abcd'), index=tuple('wxyz')) # Column groups = list(frame.iter_group_items('c', axis=0)) expected_pairs = [ (('a', (('w', 0), ('y', 5))), ('b', (('w', 1), ('y', 5))), ('c', (('w', 1), ('y', 1))), ('d', (('w', 3), ('y', 3)))), (('a', (('x', 3), ('z', 7))), ('b', (('x', 3), ('z', 2))), ('c', (('x', 2), ('z', 2))), ('d', (('x', 3), ('z', 4))))] self.assertEqual([1, 2], [group[0] for group in groups]) self.assertEqual(expected_pairs, [group[1].to_pairs(axis=0) for group in groups]) # Index groups = list(frame.iter_group_items('w', axis=1)) expected_pairs = [ (('a', (('w', 0), ('x', 3), ('y', 5), ('z', 7))),), #type: ignore (('b', (('w', 1), ('x', 3), ('y', 5), ('z', 2))), #type: ignore ('c', (('w', 1), ('x', 2), ('y', 1), ('z', 2)))), (('d', (('w', 3), ('x', 3), ('y', 3), ('z', 4))),)] #type: ignore self.assertEqual([0, 1, 3], [group[0] for group in groups]) self.assertEqual(expected_pairs, [group[1].to_pairs(axis=0) for group in groups]) def test_frame_iter_group_items_d(self) -> None: # Test iterating with multiple key selection data = np.array([[0, 1, 1, 3], [3, 3, 2, 3], [5, 5, 1, 3], [7, 2, 2, 4]]) frame = sf.Frame(data, columns=tuple('abcd'), index=tuple('wxyz')) # Column groups = list(frame.iter_group_items(['c', 'd'], axis=0)) expected_pairs = [ (('a', (('w', 0), ('y', 5))), ('b', (('w', 1), ('y', 5))), ('c', (('w', 1), ('y', 1))), ('d', (('w', 3), ('y', 3)))), (('a', (('x', 3),)), ('b', (('x', 3),)), ('c', (('x', 2),)), ('d', (('x', 3),))), (('a', (('z', 7),)), ('b', (('z', 2),)), ('c', (('z', 2),)), ('d', (('z', 4),)))] self.assertEqual([(1, 3), (2, 3), (2, 4)], [group[0] for group in groups]) self.assertEqual(expected_pairs, [group[1].to_pairs(axis=0) for group in groups]) # Index groups = list(frame.iter_group_items(['x', 'y'], axis=1)) expected_pairs = [ (('c', (('w', 1), ('x', 2), ('y', 1), ('z', 2))),), #type: ignore (('d', (('w', 3), ('x', 3), ('y', 3), ('z', 4))),), #type: ignore (('a', (('w', 0), ('x', 3), ('y', 5), ('z', 7))), #type: ignore ('b', (('w', 1), ('x', 3), ('y', 5), ('z', 2)))), ] self.assertEqual([(2, 1), (3, 3), (3, 5)], [group[0] for group in groups]) self.assertEqual(expected_pairs, [group[1].to_pairs(axis=0) for group in groups]) def test_frame_iter_group_items_e(self) -> None: columns = tuple('pqrst') index = tuple('zxwy') records = (('A', 1, 'a', False, False), ('A', 2, 'b', True, False), ('B', 1, 'c', False, False), ('B', 2, 'd', True, True)) f = Frame.from_records( records, columns=columns, index=index,name='foo') # using an array to select self.assertEqual( tuple(k for k, v in f.iter_group_items(f.columns == 's')), ((False,), (True,)) ) self.assertEqual( tuple(k for k, v in f.iter_group_items(f.columns.isin(('p', 't')))), (('A', False), ('B', False), ('B', True)) ) self.assertEqual( tuple(k for k, v in f.iter_group_items(['s', 't'])), ((False, False), (True, False), (True, True)) ) self.assertEqual( tuple(k for k, v in f.iter_group_items(slice('s','t'))), ((False, False), (True, False), (True, True)) ) def test_frame_iter_group_items_f(self) -> None: objs = [object() for _ in range(2)] data = [[1, 2, objs[0]], [3, 4, objs[0]], [5, 6, objs[1]]] f = sf.Frame.from_records(data, columns=tuple('abc')) post1 = {k: v for k, v in f.iter_group_items('c')} post2 = {k[0]: v for k, v in f.iter_group_items(['c'])} # as a list, this gets a multiple key self.assertEqual(len(post1), 2) self.assertEqual(len(post1), len(post2)) obj_a = objs[0] obj_b = objs[1] self.assertEqual(post1[obj_a].shape, (2, 3)) self.assertEqual(post1[obj_a].shape, post2[obj_a].shape) self.assertEqual(post1[obj_a].to_pairs(0), (('a', ((0, 1), (1, 3))), ('b', ((0, 2), (1, 4))), ('c', ((0, obj_a), (1, obj_a))))) self.assertEqual(post2[obj_a].to_pairs(0), (('a', ((0, 1), (1, 3))), ('b', ((0, 2), (1, 4))), ('c', ((0, obj_a), (1, obj_a))))) self.assertEqual(post1[obj_b].shape, (1, 3)) self.assertEqual(post1[obj_b].shape, post2[obj_b].shape) self.assertEqual(post1[obj_b].to_pairs(0), (('a', ((2, 5),)), ('b', ((2, 6),)), ('c', ((2, obj_b),)))) self.assertEqual(post2[obj_b].to_pairs(0), (('a', ((2, 5),)), ('b', ((2, 6),)), ('c', ((2, obj_b),)))) #--------------------------------------------------------------------------- def test_frame_iter_group_index_a(self) -> None: records = ( (2, 2, 'a', False, False), (30, 34, 'b', True, False), (2, 95, 'c', False, False), ) f1 = Frame.from_records(records, columns=('p', 'q', 'r', 's', 't'), index=('x', 'y', 'z')) with self.assertRaises(TypeError): f1.iter_group_labels(3, 4) with self.assertRaises(TypeError): f1.iter_group_labels(foo=4) post = tuple(f1.iter_group_labels(0, axis=0)) self.assertEqual(len(post), 3) self.assertEqual( f1.iter_group_labels(0, axis=0).apply(lambda x: x[['p', 'q']].values.sum()).to_pairs(), (('x', 4), ('y', 64), ('z', 97)) ) def test_frame_iter_group_index_b(self) -> None: records = ( (2, 2, 'a', 'q', False, False), (30, 34, 'b', 'c', True, False), (2, 95, 'c', 'd', False, False), ) f1 = Frame.from_records(records, columns=IndexHierarchy.from_product((1, 2, 3), ('a', 'b')), index=('x', 'y', 'z')) # with axis 1, we are grouping based on columns while maintain the index post_tuple = tuple(f1.iter_group_labels(1, axis=1)) self.assertEqual(len(post_tuple), 2) post = f1[HLoc[f1.columns[0]]] self.assertEqual(post.__class__, Series) self.assertEqual(post.to_pairs(), (('x', 2), ('y', 30), ('z', 2)) ) post = f1.loc[:, HLoc[f1.columns[0]]] self.assertEqual(post.__class__, Series) self.assertEqual(post.to_pairs(), (('x', 2), ('y', 30), ('z', 2)) ) self.assertEqual( f1.iter_group_labels(1, axis=1).apply(lambda x: x.iloc[:, 0].sum()).to_pairs(), (('a', 34), ('b', 131)) ) def test_frame_iter_group_index_c(self) -> None: columns = tuple('pqrst') index = tuple('zxwy') records = (('A', 1, 'a', False, False), ('A', 2, 'b', True, False), ('B', 1, 'c', False, False), ('B', 2, 'd', True, True)) f = Frame.from_records( records, columns=columns, index=index,name='foo') f = f.set_index_hierarchy(('p', 'q'), drop=True) with self.assertRaises(AxisInvalid): _ = f.iter_group_labels_items(0, axis=-1).apply(lambda k, x: f'{k}:{x.size}') post = f.iter_group_labels_items(0).apply(lambda k, x: f'{k}:{x.size}') self.assertEqual(post.to_pairs(), (('A', 'A:6'), ('B', 'B:6')) ) #--------------------------------------------------------------------------- def test_frame_reversed(self) -> None: columns = tuple('pqrst') index = tuple('zxwy') records = ((2, 2, 'a', False, False), (30, 34, 'b', True, False), (2, 95, 'c', False, False), (30, 73, 'd', True, True)) f = Frame.from_records( records, columns=columns, index=index,name='foo') self.assertTrue(tuple(reversed(f)) == tuple(reversed(columns))) #--------------------------------------------------------------------------- def test_frame_axis_window_items_a(self) -> None: base = np.array([1, 2, 3, 4]) records = (base * n for n in range(1, 21)) f1 = Frame.from_records(records, columns=list('ABCD'), index=self.get_letters(20)) post0 = tuple(f1._axis_window_items(size=2, axis=0)) self.assertEqual(len(post0), 19) self.assertEqual(post0[0][0], 'b') self.assertEqual(post0[0][1].__class__, Frame) self.assertEqual(post0[0][1].shape, (2, 4)) self.assertEqual(post0[-1][0], 't') self.assertEqual(post0[-1][1].__class__, Frame) self.assertEqual(post0[-1][1].shape, (2, 4)) post1 = tuple(f1._axis_window_items(size=2, axis=1)) self.assertEqual(len(post1), 3) self.assertEqual(post1[0][0], 'B') self.assertEqual(post1[0][1].__class__, Frame) self.assertEqual(post1[0][1].shape, (20, 2)) self.assertEqual(post1[-1][0], 'D') self.assertEqual(post1[-1][1].__class__, Frame) self.assertEqual(post1[-1][1].shape, (20, 2)) def test_frame_axis_window_items_b(self) -> None: base = np.array([1, 2, 3, 4]) records = (base * n for n in range(1, 21)) f1 = Frame.from_records(records, columns=list('ABCD'), index=self.get_letters(20)) post0 = tuple(f1._axis_window_items(size=2, axis=0, as_array=True)) self.assertEqual(len(post0), 19) self.assertEqual(post0[0][0], 'b') self.assertEqual(post0[0][1].__class__, np.ndarray) self.assertEqual(post0[0][1].shape, (2, 4)) self.assertEqual(post0[-1][0], 't') self.assertEqual(post0[-1][1].__class__, np.ndarray) self.assertEqual(post0[-1][1].shape, (2, 4)) post1 = tuple(f1._axis_window_items(size=2, axis=1, as_array=True)) self.assertEqual(len(post1), 3) self.assertEqual(post1[0][0], 'B') self.assertEqual(post1[0][1].__class__, np.ndarray) self.assertEqual(post1[0][1].shape, (20, 2)) self.assertEqual(post1[-1][0], 'D') self.assertEqual(post1[-1][1].__class__, np.ndarray) self.assertEqual(post1[-1][1].shape, (20, 2)) def test_frame_iter_window_a(self) -> None: base = np.array([1, 2, 3, 4]) records = (base * n for n in range(1, 21)) f1 = Frame.from_records(records, columns=list('ABCD'), index=self.get_letters(20)) self.assertEqual( f1.iter_window(size=3).apply(lambda f: f['B'].sum()).to_pairs(), (('c', 12), ('d', 18), ('e', 24), ('f', 30), ('g', 36), ('h', 42), ('i', 48), ('j', 54), ('k', 60), ('l', 66), ('m', 72), ('n', 78), ('o', 84), ('p', 90), ('q', 96), ('r', 102), ('s', 108), ('t', 114)) ) post = list(f1.iter_window(size=3)) self.assertEqual(len(post), 18) self.assertTrue(all(f.shape == (3, 4) for f in post)) #--------------------------------------------------------------------------- def test_frame_axis_interface_a(self) -> None: # reindex both axis records = ( (1, 2, 'a', False, True), (30, 34, 'b', True, False), (54, 95, 'c', False, False), (65, 73, 'd', True, True), ) f1 = Frame.from_records(records, columns=('p', 'q', 'r', 's', 't'), index=('w', 'x', 'y', 'z')) self.assertEqual(f1.to_pairs(1), (('w', (('p', 1), ('q', 2), ('r', 'a'), ('s', False), ('t', True))), ('x', (('p', 30), ('q', 34), ('r', 'b'), ('s', True), ('t', False))), ('y', (('p', 54), ('q', 95), ('r', 'c'), ('s', False), ('t', False))), ('z', (('p', 65), ('q', 73), ('r', 'd'), ('s', True), ('t', True))))) for x in f1.iter_tuple(axis=0): self.assertTrue(len(x), 4) for x in f1.iter_tuple(axis=1): self.assertTrue(len(x), 5) f2 = f1[['p', 'q']] s1 = f2.iter_array(axis=0).apply(np.sum) self.assertEqual(list(s1.items()), [('p', 150), ('q', 204)]) s2 = f2.iter_array(axis=1).apply(np.sum) self.assertEqual(list(s2.items()), [('w', 3), ('x', 64), ('y', 149), ('z', 138)]) def sum_if(idx: tp.Hashable, vals: tp.Iterable[int]) -> tp.Optional[int]: if idx in ('x', 'z'): return tp.cast(int, np.sum(vals)) return None s3 = f2.iter_array_items(axis=1).apply(sum_if) self.assertEqual(list(s3.items()), [('w', None), ('x', 64), ('y', None), ('z', 138)]) #--------------------------------------------------------------------------- def test_frame_group_a(self) -> None: # reindex both axis records = ( (2, 2, 'a', False, False), (30, 34, 'b', True, False), (2, 95, 'c', False, False), (30, 73, 'd', True, True), ) f1 = Frame.from_records(records, columns=('p', 'q', 'r', 's', 't'), index=('w', 'x', 'y', 'z')) with self.assertRaises(AxisInvalid): post = tuple(f1._axis_group_iloc_items(4, axis=-1)) post = tuple(f1._axis_group_iloc_items(4, axis=0)) # row iter, group by column 4 group1, group_frame_1 = post[0] group2, group_frame_2 = post[1] self.assertEqual(group1, False) self.assertEqual(group2, True) self.assertEqual(group_frame_1.to_pairs(0), (('p', (('w', 2), ('x', 30), ('y', 2))), ('q', (('w', 2), ('x', 34), ('y', 95))), ('r', (('w', 'a'), ('x', 'b'), ('y', 'c'))), ('s', (('w', False), ('x', True), ('y', False))), ('t', (('w', False), ('x', False), ('y', False))))) self.assertEqual(group_frame_2.to_pairs(0), (('p', (('z', 30),)), ('q', (('z', 73),)), ('r', (('z', 'd'),)), ('s', (('z', True),)), ('t', (('z', True),)))) def test_frame_group_b(self) -> None: # reindex both axis records = ( (2, 2, 'a', False, False), (30, 34, 'b', True, False), (2, 95, 'c', False, False), (30, 73, 'd', True, True), ) f1 = Frame.from_records(records, columns=('p', 'q', 'r', 's', 't'), index=('w', 'x', 'y', 'z')) # column iter, group by row 0 post = list(f1._axis_group_iloc_items(0, axis=1)) self.assertEqual(post[0][0], 2) self.assertEqual(post[0][1].to_pairs(0), (('p', (('w', 2), ('x', 30), ('y', 2), ('z', 30))), ('q', (('w', 2), ('x', 34), ('y', 95), ('z', 73))))) self.assertEqual(post[1][0], False) self.assertEqual(post[1][1].to_pairs(0), (('s', (('w', False), ('x', True), ('y', False), ('z', True))), ('t', (('w', False), ('x', False), ('y', False), ('z', True))))) self.assertEqual(post[2][0], 'a') self.assertEqual(post[2][1].to_pairs(0), (('r', (('w', 'a'), ('x', 'b'), ('y', 'c'), ('z', 'd'))),)) def test_frame_axis_interface_b(self) -> None: # reindex both axis records = ( (2, 2, 'a', False, False), (30, 34, 'b', True, False), (2, 95, 'c', False, False), (30, 73, 'd', True, True), ) f1 = Frame.from_records(records, columns=('p', 'q', 'r', 's', 't'), index=('w', 'x', 'y', 'z')) post = list(f1.iter_group_items('s', axis=0)) self.assertEqual(post[0][1].to_pairs(0), (('p', (('w', 2), ('y', 2))), ('q', (('w', 2), ('y', 95))), ('r', (('w', 'a'), ('y', 'c'))), ('s', (('w', False), ('y', False))), ('t', (('w', False), ('y', False))))) self.assertEqual(post[1][1].to_pairs(0), (('p', (('x', 30), ('z', 30))), ('q', (('x', 34), ('z', 73))), ('r', (('x', 'b'), ('z', 'd'))), ('s', (('x', True), ('z', True))), ('t', (('x', False), ('z', True))))) s1 = f1.iter_group('p', axis=0).apply(lambda f: f['q'].values.sum()) self.assertEqual(list(s1.items()), [(2, 97), (30, 107)]) if __name__ == '__main__': unittest.main()
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import subprocess import os import shutil DEST_DIRECTORY = '.' if os.path.isdir("upx"): upx_string = "--upx-dir=upx" else: upx_string = "" if os.path.isdir("build"): shutil.rmtree("build") subprocess.run(" ".join(["pyinstaller Gui.spec ", upx_string, "-y ", "--onefile ", f"--distpath {DEST_DIRECTORY} ", ]), shell=True)
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def level(resp): """ Args: resp: level: string Returns: [level 1: 배송준비중, 2: 집화완료, 3: 배송중, 4: 지점 도착, 5: 배송출발, 6:배송 완료] """ if resp['level'] == 1: return { "code": 1, "level": "배송 준비중" } elif resp['level'] == 2: return { "code": 2, "level": "집화 완료" } elif resp['level'] == 3: return { "code": 3, "level": "배송중" } elif resp['level'] == 4: return { "code": 4, "level": "지점 도착" } elif resp['level'] == 5: return { "code": 5, "level": "배송 출발" } elif resp['level'] == 6: return { "code": 6, "level": "배송 완료" } else: return { "code": 0, "level": "Internal System Error" }
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# -*- mode: python -*- block_cipher = None a = Analysis(['Jobbtider.py'], pathex=['C:\\Users\\Nicki\\Documents\\Programmering\\LearnPython\\Jobb'], binaries=[], datas=[], hiddenimports=[], hookspath=[], runtime_hooks=[], excludes=[], win_no_prefer_redirects=False, win_private_assemblies=False, cipher=block_cipher) pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher) exe = EXE(pyz, a.scripts, exclude_binaries=True, name='Jobbtider', debug=False, strip=False, upx=True, console=True ) coll = COLLECT(exe, a.binaries, a.zipfiles, a.datas, strip=False, upx=True, name='Jobbtider')
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#!/usr/bin/python """ Defines the document class that is used with the docgen system. """ # define authorship information __authors__ = ['Eric Hulser'] __author__ = ','.join(__authors__) __credits__ = [] __copyright__ = 'Copyright (c) 2011, Projex Software' __license__ = 'LGPL' # maintanence information __maintainer__ = 'Projex Software' __email__ = '[email protected]' #------------------------------------------------------------------------------ import inspect import logging import new import os import re import xml.sax.saxutils from projex import text from projex import wikitext from projex.docgen import templates from projex.docgen import commands logger = logging.getLogger(__name__) DATA_TYPE_ORDER = [ 'module', 'class', 'variable', 'member', 'property', 'enum', 'function', 'method', 'signal', 'slot', 'abstract method', 'class method', 'static method', 'deprecated method', 'built-in', ] DATA_PRIVACY_ORDER = [ 'public', 'imported public', 'protected', 'imported protected', 'private', 'imported private', 'built-in', 'imported built-in', ] DATA_ORDER = [] for privacy in DATA_PRIVACY_ORDER: for typ in DATA_TYPE_ORDER: DATA_ORDER.append('%s %s' % (privacy, typ)) class Attribute(tuple): """ Used to map tuple returns to support different python versions. """ def __init__( self, member_tuple ): super(Attribute, self).__init__(member_tuple) self.name = member_tuple[0] self.kind = member_tuple[1] self.defining_class = member_tuple[2] self.object = member_tuple[3] if ( hasattr(self.object, 'func_type') ): self.kind = self.object.func_type #------------------------------------------------------------------------------ class DocumentData(object): """ Struct to hold data about a document object. """ name = None value = None dataType = None privacy = None def section( self ): """ Returns the section type for this data by joining the privacy and \ type information. :return <str> """ return (self.privacy + ' ' + self.dataType) @staticmethod def create( name, value, kind = 'data', defaultVarType = 'variable', defaultFuncType ='function' ): """ Creates a new document data instance. :return <DocumentData> """ # look for private members results = re.match('^(_\w+)__.+', name) if ( results ): name = name.replace(results.group(1), '') # determine the privacy level for this data privacy = 'public' if ( name.startswith('__') and name.endswith('__') ): privacy = 'built-in' elif ( name.startswith('__') ): privacy = 'private' elif ( name.startswith('_') ): privacy = 'protected' docdata = DocumentData() docdata.name = name docdata.value = value # look for specific kinds of methods if ( kind == 'method' ): type_name = type(value).__name__ if ( type_name == 'pyqtSignal' ): kind = 'signal' elif ( type_name == 'pyqtSlot' ): kind = 'slot' elif ( type_name == 'pyqtProperty' ): kind = 'property' elif ( hasattr(value, 'func_type') ): kind = getattr(value, 'func_type') if ( kind != 'data' ): docdata.dataType = kind else: docdata.dataType = commands.defaultValueType( value, defaultVarType, defaultFuncType ) docdata.privacy = privacy return docdata #------------------------------------------------------------------------------ class Document(object): """ Defines the class that collects all documentation for a python object. """ cache = {} aliases = {} reverseAliases = {} def __init__( self ): self._object = None self._parent = None self._objectName = '' self._html = '' self._allMembersHtml = '' self._title = '' self._data = {} self._sourceHtml = {} self._children = [] # protected methods def _bases( self, cls, recursive = False ): """ Looks up the bases for the inputed obj instance. :param obj | <object> :param recursive | <bool> :return [<cls>, ..] """ if ( not inspect.isclass( cls ) ): return [] output = list(cls.__bases__[:]) if ( not recursive ): return output for basecls in output: output += self._bases(basecls, recursive = recursive) return list(set(output)) def _collectMembers( self, obj ): if ( not inspect.isclass( obj ) ): return [] try: members = inspect.classify_class_attrs(self._object) except AttributeError: members = [] # support python25- if ( members and type(members[0]) == tuple ): members = [ Attribute(member) for member in members ] return members def _generateAllMemberSummary( self, member ): """ Generates the member summary documentation. :param member <Attribute> :return <str> """ try: obj = getattr(member.defining_class, member.name) except AttributeError: return '' key = member.name cls = member.defining_class if ( 'method' in member.kind ): docname = cls.__module__ + '-' + cls.__name__ doc = Document.cache.get(docname) if ( doc ): opts = (doc.url(relativeTo = self), key, key) href = '<a href="%s#%s">%s</a>' % opts else: href = key kind = member.kind if ( hasattr(obj, 'func_type') ): kind = obj.func_type templ = '%s::%s%s' if ( 'static' in kind ): templ += ' [static]' elif ( 'class' in kind ): templ += ' [class]' elif ( 'abstract' in kind ): templ += ' [abstract]' elif ( 'deprecated' in kind ): templ += ' [deprecated]' return templ % (cls.__name__, href, self._generateArgs(obj)) else: opts = (cls.__name__, key, type(member.object).__name__) return '%s::%s : %s' % opts def _generateAllMembersDocs(self): """ Generates the all members documentation for this document. :return <str> """ if ( not inspect.isclass(self._object) ): return '' members = self._collectMembers(self._object) member_docs = [] members.sort( lambda x, y: cmp( x.name, y.name ) ) for member in members: if ( member.name.startswith('__') and member.name.endswith('__') ): continue member_doc = self._generateAllMemberSummary(member) if ( member_doc ): member_docs.append('<li>%s</li>' % member_doc) environ = commands.ENVIRON.copy() environ['members_left'] = '\n'.join( member_docs[:len(member_docs)/2]) environ['members_right'] = '\n'.join( member_docs[len(member_docs)/2:]) environ['title'] = self.title() environ['base_url'] = self.baseurl() environ['static_url'] = environ['base_url'] + '/_static' environ['navigation'] %= environ return templates.template('allmembers.html') % environ def _generateArgs(self, obj): """ Generates the argument information for the inputed object. :param obj | <variant> :return <str> """ try: return inspect.formatargspec( *inspect.getargspec( obj ) ) except TypeError: try: return self._generateArgs( obj.im_func ) except AttributeError: pass if ( isinstance( obj, new.instancemethod ) and hasattr( obj.im_func, 'func_args' ) ): return obj.im_func.func_args return '(*args, **kwds) [unknown]' def _generateHtml( self ): """ Generates the HTML documentation for this document. :return <str> """ if ( self.isNull() or self._html ): return self._html # generate module docs if ( inspect.ismodule( self._object ) ): return self._generateModuleDocs() # generate class docs elif ( inspect.isclass( self._object ) ): return self._generateClassDocs() # not sure what this is return '' def _generateClassDocs( self ): """ Generates class documentation for this object. """ html = [] self.parseData() # determine the inheritance bases = [] for base in self._bases( self._object ): doc = commands.findDocument(base) if ( doc ): opt = {} opt['text'] = base.__name__ opt['url'] = doc.url( relativeTo = self ) bases.append( templates.template('link_standard.html') % opt ) else: bases.append( base.__name__ ) if ( len(bases) > 1 ): basestxt = ', '.join(bases[:-1]) inherits = 'Inherits %s and %s.' % (basestxt, bases[-1]) elif (len(bases) == 1): inherits = 'Inherits %s.' % bases[0] else: inherits = '' # determine the subclasses subclasses = [] for subcls in self._subclasses( self._object ): doc = commands.findDocument(subcls) if ( doc ): opt = {} opt['text'] = subcls.__name__ opt['url'] = doc.url( relativeTo = self ) subclasses.append( templates.template('link_standard.html') % opt ) else: subclasses.append( subcls.__name__ ) if ( len(subclasses) > 1 ): subs = ', '.join(subclasses[:-1]) inherited_by = 'Inherited by %s and %s.' % (subs, subclasses[-1]) elif ( len(subclasses) == 1 ): inherited_by = 'Inherited by %s.' % (subclasses[0]) else: inherited_by = '' allmembers = self.objectName().split('.')[-1] + '-allmembers.html' # generate the module environ environ = commands.ENVIRON.copy() environ['title'] = self.title() environ['allmembers'] = './' + allmembers environ['breadcrumbs'] = self.breadcrumbs() environ['url'] = self.url() environ['doctype'] = 'Class' environ['inherits'] = inherits environ['inherited_by'] = inherited_by modname = self._object.__module__ moddoc = Document.cache.get(modname) if ( moddoc ): modurl = moddoc.url(relativeTo = self) environ['module'] = '<a href="%s">%s</a>' % (modurl, modname) else: environ['module'] = modname html.append( templates.template('header_class.html') % environ ) # generate the summary report gdata = self.groupedData() keys = [key for key in gdata.keys() if key in DATA_ORDER] keys.sort(lambda x, y: cmp(DATA_ORDER.index(x), DATA_ORDER.index(y))) for key in keys: html.append( self._generateSummary( key, gdata[key] ) ) # generate the main documentation maindocs = self._generateObjectDocs( self._object ) if ( maindocs ): environ = commands.ENVIRON.copy() environ['type'] = 'Class' environ['contents'] = maindocs html.append( templates.template('docs_main.html') % environ ) # generate the member documentation funcs = self.data().values() html.append( self._generateMemberDocs( 'Member Documentation', funcs)) # generate the document environ return '\n'.join(html) def _generateMemberDocs( self, title, data ): """ Generates the member documentation for the inputed set of data. :param title | <str> :param data | [ <DocumentData>, .. ] """ if ( not data ): return '' bases = [] subclasses = [] # generate the html html = [] data.sort(lambda x, y: cmp(x.name, y.name)) for entry in data: # generate function information if ( 'function' in entry.dataType or 'method' in entry.dataType ): # lookup base methods for reimplimintation reimpliments = [] for base in bases: if ( entry.name in base.__dict__ ): doc = commands.findDocument(base) if ( doc ): opt = {} opt['text'] = base.__name__ opt['url'] = doc.url( relativeTo = self ) opt['url'] += '#' + entry.name href = templates.template('link_standard.html') % opt reimpliments.append( href ) else: reimpliments.append( entry.name ) reimpliment_doc = '' if ( reimpliments ): urls = ','.join(reimpliments) reimpliment_doc = 'Reimpliments from %s.' % urls # lookup submodules for reimplimentation reimplimented = [] for subcls in subclasses: if ( entry.name in subcls.__dict__ ): doc = commands.findDocument(subcls) if ( doc ): opt = {} opt['text'] = subcls.__name__ opt['url'] = doc.url( relativeTo = self ) opt['url'] += '#' + entry.name href = templates.template('link_standard.html') % opt reimplimented.append( href ) else: reimplimented.append( entry.name ) reimplimented_doc = '' if ( reimplimented ): urls = ','.join(reimplimented) reimplimented_doc = 'Reimplimented by %s.' % urls func_split = entry.dataType.split(' ') desc = '' if ( len(func_split) > 1 ): desc = '[%s]' % func_split[0] # add the function to the documentation environ = commands.ENVIRON.copy() environ['type'] = entry.dataType environ['name'] = entry.name environ['args'] = self._generateArgs( entry.value ) environ['desc'] = desc environ['contents'] = self._generateObjectDocs(entry.value) environ['reimpliments'] = reimpliment_doc environ['reimplimented'] = reimplimented_doc html.append( templates.template('docs_function.html') % environ ) elif ( entry.dataType == 'enum' ): environ = commands.ENVIRON.copy() environ['name'] = entry.name value_contents = [] values = entry.value.values() values.sort() for value in values: value_opts = {} value_opts['key'] = entry.value[value] value_opts['value'] = value value_templ = templates.template('docs_enum_value.html') value_item = value_templ % value_opts value_contents.append( value_item ) environ['contents'] = '\n'.join(value_contents) html.append( templates.template('docs_enum.html') % environ ) environ = {} environ['title'] = title environ['contents'] = '\n'.join( html ) return templates.template('docs_members.html') % environ def _generateModuleDocs( self ): """ Generates module documentation for this object. """ html = [] # generate the module environ environ = commands.ENVIRON.copy() environ['title'] = self.title() environ['base_url'] = self.baseurl() environ['static_url'] = environ['base_url'] + '/_static' environ['breadcrumbs'] = self.breadcrumbs() environ['url'] = self.url() environ['doctype'] = 'Module' if ( '__init__' in self._object.__file__ ): environ['doctype'] = 'Package' url_split = environ['url'].split('/') sources_url = './%s-source.html' % url_split[-1].split('.')[0] environ['sources'] = sources_url environ['navigation'] %= environ html.append( templates.template('header_module.html') % environ ) # generate the summary report gdata = self.groupedData() for key in sorted( gdata.keys(), key = lambda x: DATA_ORDER.index(x)): value = gdata[key] html.append( self._generateSummary( key, gdata[key] ) ) # generate the main documentation maindocs = self._generateObjectDocs( self._object ) if ( maindocs ): environ = commands.ENVIRON.copy() environ['type'] = 'Module' environ['contents'] = maindocs html.append( templates.template('docs_main.html') % environ ) # generate the member documentation html.append( self._generateMemberDocs('Module Function Documentation', self.data().values())) return '\n'.join(html) def _generateObjectDocs( self, obj ): """ Generates documentation based on the inputed object's docstring and member variable information. :param obj | <str> :return <str> html """ # get the documentation try: docs = inspect.getdoc(obj) except AttributeError: pass if ( docs == None ): try: docs = inspect.getcomments(obj) except AttributeError: docs = '' return wikitext.render(docs, commands.url_handler, options=commands.RENDER_OPTIONS) def _generateSourceDocs( self ): """ Return the documentation containing the source code. :return <str> """ if ( not inspect.ismodule(self._object) ): return '' # load the code file codefilename = os.path.splitext( self._object.__file__ )[0] codefilename += '.py' codefile = open(codefilename, 'r') code = codefile.read() codefile.close() environ = commands.ENVIRON.copy() environ['code'] = xml.sax.saxutils.escape(code) environ['title'] = self.title() environ['base_url'] = self.baseurl() environ['static_url'] = environ['base_url'] + '/_static' environ['breadcrumbs'] = self.breadcrumbs(includeSelf = True) environ['navigation'] %= environ return templates.template('source.html') % environ def _generateSummary( self, section, values, columns = 1 ): """ Generates summary information for the inputed section and value data. :param section | <str> :param values | [ <DocumentData>, .. ] :param columns | <int> :return <str> """ # strip out built-in variables newvalues = [] for value in values: if ( not (value.privacy == 'built-in' and value.dataType == 'variable' )): newvalues.append(value) values = newvalues if ( not values ): return '' # split the data into columns values.sort( lambda x, y: cmp( x.name.lower(), y.name.lower() ) ) url = self.url() coldata = [] if ( columns > 1 ): pass else: coldata = [values] html = [] processed = [] for colitem in coldata: for data in colitem: data_environ = {} data_environ['url'] = url data_environ['name'] = data.name data_environ['type'] = data.dataType processed.append( data.name ) if ( 'function' in data.dataType or 'method' in data.dataType ): data_environ['args'] = self._generateArgs( data.value ) templ = templates.template('summary_function.html') html.append( templ % data_environ ) elif ( data.dataType == 'enum' ): templ = templates.template('summary_enum.html') html.append( templ % data_environ ) elif ( 'variable' in data.dataType or 'member' in data.dataType ): try: value = getattr(self._object, data.name) except AttributeError: value = None data_environ['value_type'] = type(value).__name__ templ = templates.template('summary_variable.html') html.append( templ % data_environ ) else: datadoc = commands.findDocument(data.value) if ( datadoc ): opts = {} opts['text'] = data.name opts['url'] = datadoc.url( relativeTo = self ) contents = templates.template('link_standard.html') % opts else: contents = data.name data_environ['contents'] = contents templ = templates.template('summary_item.html') html.append( templ % data_environ ) # update the bases environ members = self._collectMembers(self._object) inherited_members = {} for member in members: mem_name = member.name mem_kind = member.kind mem_cls = member.defining_class mem_value = member.object if ( hasattr(member.object, 'func_type') ): mem_kind = member.object.func_type if ( mem_cls == self._object ): continue data = DocumentData.create( mem_name, mem_value, mem_kind, 'member', 'method' ) if ( section != data.section() ): continue inherited_members.setdefault( mem_cls, 0 ) inherited_members[mem_cls] += 1 inherit_summaries = [] templ = templates.template('summary_inherit.html') bases = self._bases( self._object, True ) inherits = inherited_members.keys() inherits.sort( lambda x, y: cmp( bases.index(x), bases.index(y) ) ) for inherited in inherits: count = inherited_members[inherited] doc = commands.findDocument( inherited ) if ( not doc ): continue opt = {} opt['count'] = count opt['base'] = inherited.__name__ opt['url'] = doc.url( relativeTo = self ) opt['type'] = section inherit_summaries.append( templ % opt ) # generate the summary information words = [word.capitalize() for word in text.words(section)] words[-1] = text.pluralize(words[-1]) summary_environ = {} summary_environ['contents'] = '\n'.join(html) summary_environ['section'] = ' '.join(words) summary_environ['inherits'] = '\n'.join(inherit_summaries) return templates.template('summary.html') % summary_environ def _subclasses( self, obj ): """ Looks up all the classes that inherit from this object. :param obj | <object> :return [<cls>, ..] """ output = [] for doc in Document.cache.values(): doc_obj = doc.object() if ( inspect.isclass( doc_obj ) and obj in doc_obj.__bases__ ): output.append( doc_obj ) return output #------------------------------------------------------------------------------ # public methods def addChild( self, child ): """ Adds the inputed document as a sub-child for this document. :param child | <Document> """ child._parent = self self._children.append(child) def allMembersHtml( self ): """ Returns the documentation for all the members linked to this document. This method only applies to class objects. :return <str> """ if ( not inspect.isclass( self._object ) ): return '' if ( not self._allMembersHtml ): self._allMembersHtml = self._generateAllMembersDocs() return self._allMembersHtml def baseurl( self ): """ Returns the relative url to get back to the root of the documentation api. :return <str> """ baseurl = self.url() count = len(baseurl.split('/')) return ('../' * count).strip('/') def breadcrumbs(self, relativeTo = None, first = True, includeSelf = False): """ Creates a link to all of the previous modules for this item. :param relativeTo | <Document> | Relative to another document. first | <bool> includeSelf | <bool> | Create a link to this doc. :return <str> """ basecrumbs = '' if ( not relativeTo ): relativeTo = self basecrumbs = self.title().split('.')[-1] if ( includeSelf ): opts = { 'url': './' + os.path.split(self.url())[1], 'text': self.title().split('.')[-1] } basecrumbs = templates.template('link_breadcrumbs.html') % opts if ( inspect.isclass( self._object ) ): doc = Document.cache.get( self._object.__module__ ) elif ( inspect.ismodule( self._object ) ): parent_mod = '.'.join( self._object.__name__.split('.')[:-1] ) doc = Document.cache.get( parent_mod ) else: doc = None if ( doc ): opts = {} opts['url'] = doc.url(relativeTo) opts['text' ] = doc.title().split('.')[-1] link = templates.template('link_breadcrumbs.html') % opts subcrumbs = doc.breadcrumbs(relativeTo, first = False) else: subcrumbs = '' link = '' parts = [] if ( first ): # add the home url baseurl = self.baseurl() home_url = '%s/index.html' % baseurl home_opts = { 'text': 'Home', 'url': home_url } home_part = templates.template('link_breadcrumbs.html') % home_opts parts.append(home_part) # add the api url api_url = '%s/api/index.html' % baseurl api_opts = { 'text': 'API', 'url': api_url } api_part = templates.template('link_breadcrumbs.html') % api_opts parts.append(api_part) if ( subcrumbs ): parts.append( subcrumbs ) if ( link ): parts.append( link ) if ( basecrumbs ): parts.append( basecrumbs ) return ''.join( parts ) def children( self ): """ Returns the child documents for this instance. :return [ <Document>, .. ] """ return self._children def data( self ): """ Returns the data that has been loaded for this document. :return <dict> """ return self._data def export( self, basepath, page = None ): """ Exports the html files for this document and its children to the given basepath. :param basepath | <str> :param page | <str> || None :return <bool> success """ # make sure the base path exists if ( not os.path.exists( basepath ) ): return False basepath = os.path.normpath(basepath) url = self.url() filename = os.path.join(basepath, url) docpath = os.path.dirname(filename) # add the doc path if ( not os.path.exists(docpath) ): os.makedirs(docpath) if ( not page ): page = templates.template('page.html') # setup the default environ commands.url_handler.setRootUrl(self.baseurl()) doc_environ = commands.ENVIRON.copy() doc_environ['title'] = self.title() doc_environ['base_url'] = self.baseurl() doc_environ['static_url'] = doc_environ['base_url'] + '/_static' doc_environ['contents'] = self.html() doc_environ['breadcrumbs'] = self.breadcrumbs(includeSelf = True) doc_environ['navigation'] %= doc_environ # generate the main html file exportfile = open(filename, 'w') exportfile.write( page % doc_environ ) exportfile.close() # generate the all members html file allmember_html = self.allMembersHtml() if ( allmember_html ): fpath, fname = os.path.split(filename) fname = fname.split('.')[0] + '-allmembers.html' afilesource = os.path.join(fpath, fname) doc_environ['contents'] = allmember_html # create the crumbs crumbs = self.breadcrumbs(includeSelf = True) opts = {'url': '#', 'text': 'All Members'} crumbs += templates.template('link_breadcrumbs.html') % opts doc_environ['breadcrumbs'] = crumbs # save the all members file membersfile = open(afilesource, 'w') membersfile.write( page % doc_environ ) membersfile.close() # generate the source code file source_html = self.sourceHtml() if ( source_html ): fpath, fname = os.path.split(filename) fname = fname.split('.')[0] + '-source.html' sfilesource = os.path.join(fpath, fname) doc_environ['contents'] = source_html # create the crumbs crumbs = self.breadcrumbs(includeSelf = True) opts = {'url': '#', 'text': 'Source Code'} crumbs += templates.template('link_breadcrumbs.html') % opts doc_environ['breadcrumbs'] = crumbs # save the source file sourcefile = open(sfilesource, 'w') sourcefile.write( page % doc_environ ) sourcefile.close() # generate the children for child in self.children(): child.export(basepath, page) def findData( self, dtype ): """ Looks up the inputed data objects based on the given data type. :param dataType | <str> :return <str> """ self.parseData() output = [] for data in self._data.values(): if ( data.dataType == dtype or (data.privacy + ' ' + data.dataType) == dtype ): output.append(data) return output def groupedData( self ): """ Groups the data together based on their data types and returns it. :return { <str> grp: [ <DocumentData>, .. ], .. } """ output = {} values = self._data.values() values.sort( lambda x, y: cmp(x.name, y.name) ) for data in values: dtype = '%s %s' % (data.privacy, data.dataType) output.setdefault(dtype, []) output[dtype].append(data) return output def html( self ): """ Returns the generated html for this document. :return <str> """ if ( not self._html ): self._html = self._generateHtml() return self._html def isNull( self ): """ Returns whether or not this document has any data associated with it. :return <bool> """ return self._object == None def object( self ): """ Returns the object that this document represents. :return <object> || None """ return self._object def objectName( self ): """ Returns the object name that this object will represent. This will be similar to a URL, should be unique per document. :return <str> """ return self._objectName def parent( self ): """ Returns the parent document of this instance. :return <Document> || None """ return self._parent def parseData( self ): """ Parses out all the information that is part of this item's object. This is the method that does the bulk of the processing for the documents. :return <bool> success """ if ( self.isNull() or self._data ): return False class_attrs = [] obj = self.object() # parse out class information cls_kind_map = {} if ( inspect.isclass( obj ) ): contents = self._collectMembers(obj) for const in contents: if ( const[2] == obj ): class_attrs.append( const[0] ) cls_kind_map[const.name] = const.kind # try to load all the items try: members = dict(inspect.getmembers(obj)) except AttributeError: members = {} for key in dir(obj): if ( not key in members ): try: members[key] = getattr(obj, key) except AttributeError: pass modname = '' if ( inspect.ismodule(obj) ): modname = obj.__name__ for name, value in members.items(): # ignore inherited items if ( class_attrs and not name in class_attrs ): continue varType = 'variable' funcType = 'function' kind = 'data' if ( inspect.isclass( self._object ) ): varType = 'member' funcType = 'static method' kind = cls_kind_map.get(name, 'data') docdata = DocumentData.create( name, value, kind, varType, funcType ) if ( modname and hasattr(value, '__module__') and modname != getattr(value, '__module__') ): docdata.privacy = 'imported ' + docdata.privacy self._data[name] = docdata def setObject( self, obj ): """ Sets the object instance for this document to the inputed object. This will be either a module, package, class, or enum instance. This will clear the html information and title data. :param obj | <variant> """ self._object = obj self._html = '' self._allMembersHtml = '' self._title = str(obj.__name__) if ( inspect.isclass( obj ) ): self.setObjectName( '%s-%s' % (obj.__module__, obj.__name__) ) else: self.setObjectName( obj.__name__ ) def setObjectName( self, objectName ): """ Sets the object name for this document to the given name. :param objectName | <str> """ self._objectName = objectName def setTitle( self, title ): """ Sets the title string for this document to the inputed string. :param title | <str> """ self._title = title def sourceHtml( self ): """ Returns the source file html for this document. This method only applies to module documents. :return <str> """ if ( not inspect.ismodule(self._object) ): return '' if ( not self._sourceHtml ): self._sourceHtml = self._generateSourceDocs() return self._sourceHtml def title( self ): """ Returns the title string for this document. :return <str> """ return self._title def url( self, relativeTo = None ): """ Returns the path to this document's html file. If the optional relativeTo keyword is specified, then the generated url will be made in relation to the local path for the current document. :param relativeTo <Document> || None :return <str> """ modname = self.objectName() if ( inspect.ismodule( self._object ) ): if ( '__init__' in self._object.__file__ ): modname += '.__init__' if ( not relativeTo ): return modname.replace('.','/') + '.html' relmodule = relativeTo.objectName() relobject = relativeTo.object() if ( inspect.ismodule( relobject ) ): if ( '__init__' in relobject.__file__ ): relmodule += '.__init__' relpath = relmodule.split('.') mypath = modname.split('.') go_up = '/..' * (len(relpath)-1) go_down = '/'.join([ part for part in mypath if part ]) return (go_up + '/' + go_down + '.html').strip('/')
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from random import randint # Внутренняя логика игры — корабли, игровая доска и вся логика связанная с ней. # Внешняя логика игры — пользовательский интерфейс, искусственный интеллект, игровой контроллер, который считает побитые корабли. # В начале имеет смысл написать классы исключений, которые будет использовать наша программа. Например, когда игрок пытается выстрелить в клетку за пределами поля, во внутренней логике должно выбрасываться соответствующее исключение BoardOutException, а потом отлавливаться во внешней логике, выводя сообщение об этой ошибке пользователю. class BoardException(Exception): pass class BoardOutException(BoardException): def __str__(self): return "Вы пытаетесь выстрелить за доску!" class BoardUsedException(BoardException): def __str__(self): return "Вы уже стреляли в эту клетку" class BoardWrongShipException(BoardException): def __str__(self): return "Корабль вышел за границы поля" pass # Далее нужно реализовать класс Dot — класс точек на поле. Каждая точка описывается параметрами: # # Координата по оси x . # Координата по оси y . # В программе мы будем часто обмениваться информацией о точках на поле, поэтому имеет смысле сделать отдельный тип данных дня них. # Очень удобно будет реализовать в этом классе метод __eq__, чтобы точки можно было проверять на равенство. # Тогда, чтобы проверить, находится ли точка в списке, достаточно просто использовать оператор in, как мы делали это с числами . class Dot: def __init__(self,x,y): self.x=x self.y=y def __eq__(self, other): return self.x == other.x and self.y == other.y def __repr__(self): return f"Dot({self.x},{self.y})" # Следующим идёт класс Ship — корабль на игровом поле, который описывается параметрами: # # Длина. # Точка, где размещён нос корабля. # Направление корабля (вертикальное/горизонтальное). # Количеством жизней (сколько точек корабля еще не подбито). # И имеет методы: # # Метод dots, который возвращает список всех точек корабля. class Ship: def __init__(self, bow, long, orientation): self.bow = bow self.long = long self.orientation = orientation self.lives = long @property def dots(self): ship_dots = [] for i in range(self.long): cur_x = self.bow.x cur_y = self.bow.y if self.orientation == 0: cur_x += i elif self.orientation == 1: cur_y += i ship_dots.append(Dot(cur_x, cur_y)) return ship_dots def shooten(self, shot): return shot in self.dots # Самый важный класс во внутренней логике — класс Board — игровая доска. Доска описывается параметрами: # # Двумерный список, в котором хранятся состояния каждой из клеток. # Список кораблей доски. # Параметр hid типа bool — информация о том, нужно ли скрывать корабли на доске (для вывода доски врага) или нет (для своей доски). # Количество живых кораблей на доске. class Board: def __init__(self, hid=False, size=6): self.size = size self.hid = hid self.count = 0 self.field = [["O"] * size for _ in range(size)] self.busy = [] self.ships = [] # И имеет методы: # # Метод add_ship, который ставит корабль на доску (если ставить не получается, выбрасываем исключения). def add_ship(self, ship): for d in ship.dots: if self.out(d) or d in self.busy: raise BoardWrongShipException() for d in ship.dots: self.field[d.x][d.y] = "■" self.busy.append(d) self.ships.append(ship) self.contour(ship) # Метод contour, который обводит корабль по контуру. Он будет полезен и в ходе самой игры, и в при расстановке кораблей (помечает соседние точки, # где корабля по правилам быть не может). def contour(self, ship, verb=False): near = [ (-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 0), (0, 1), (1, -1), (1, 0), (1, 1) ] for d in ship.dots: for dx, dy in near: cur = Dot(d.x + dx, d.y + dy) if not (self.out(cur)) and cur not in self.busy: if verb: self.field[cur.x][cur.y] = "." self.busy.append(cur) # Метод, который выводит доску в консоль в зависимости от параметра hid. def __str__(self): res = "" res += " | 1 | 2 | 3 | 4 | 5 | 6 |" for i, row in enumerate(self.field): res += f"\n{i + 1} | " + " | ".join(row) + " |" if self.hid: res = res.replace("■", "O") return res # Метод out, который для точки (объекта класса Dot) возвращает True, если точка выходит за пределы поля, и False, если не выходит. def out(self, d): return not ((0 <= d.x < self.size) and (0 <= d.y < self.size)) # Метод shot, который делает выстрел по доске (если есть попытка выстрелить за пределы и в использованную точку, нужно выбрасывать исключения). def shot(self, d): if self.out(d): raise BoardOutException() if d in self.busy: raise BoardUsedException() self.busy.append(d) for ship in self.ships: if d in ship.dots: ship.lives -= 1 self.field[d.x][d.y] = "X" if ship.lives == 0: self.count += 1 self.contour(ship, verb=True) print("Корабль уничтожен!") return False else: print("Корабль ранен!") return True self.field[d.x][d.y] = "." print("Мимо!") return False def begin(self): self.busy = [] class All_board(): def __init__(self, board_1=None, board_2=None): self.board_1 = board_1 self.board_2 = board_2 def __str__(self): res = "" res2 = "" res += " Доска пользователя Доска компьютера " res += f"\n | 1 | 2 | 3 | 4 | 5 | 6 | ... | 1 | 2 | 3 | 4 | 5 | 6 |" for i, row in enumerate(self.board_1.field): for j, row2 in enumerate(self.board_2.field): if i == j: res2 = " | ".join(row2).replace("■", "O") res += f"\n{i + 1} | " + " | ".join(row) + " | " +"..."+ f"{i + 1} | " + res2 + " | " return res # Теперь нужно заняться внешней логикой: Класс Player — класс игрока в игру (и AI, и пользователь). Этот класс будет родителем для классов с AI и с пользователем. # Игрок описывается параметрами: # Собственная доска (объект класса Board) # Доска врага. # И имеет следующие методы: # # ask — метод, который «спрашивает» игрока, в какую клетку он делает выстрел. # Пока мы делаем общий для AI и пользователя класс, этот метод мы описать не можем. # Оставим этот метод пустым. Тем самым обозначим, что потомки должны реализовать этот метод. # move — метод, который делает ход в игре. # Тут мы вызываем метод ask, делаем выстрел по вражеской доске (метод Board.shot), отлавливаем исключения, и если они есть, пытаемся повторить ход. # Метод должен возвращать True, если этому игроку нужен повторный ход (например если он выстрелом подбил корабль). class Player: def __init__(self, board, enemy): self.board = board self.enemy = enemy self.last_shoot = None def ask(self): raise NotImplementedError() def move(self,shoot_near): while True: try: target = self.ask(shoot_near) repeat = self.enemy.shot(target) self.last_shoot = target # if repeat: print ("после попадания вторая попытка",last_shoot) return repeat except BoardException as e: print(e) # Теперь нам остаётся унаследовать классы AI и User от Player и переопределить в них метод ask. # Для AI это будет выбор случайной точка, а для User этот метод будет спрашивать координаты точки из консоли. class AI(Player): def ask(self, shoot_near): if self.last_shoot is not None: print("Последний выстрел компьютера ",self.last_shoot.x+1,self.last_shoot.y+1) # Учтим стрелять рядом if shoot_near: while True: try: print("стреляю рядом 1") d = Dot(self.last_shoot.x, self.last_shoot.y + 1) break except BoardException as e: print(e) try: print("стреляю рядом 2") d = Dot(self.last_shoot.x, self.last_shoot.y - 1) break except BoardException as e: print(e) try: print("стреляю рядом 3") d = Dot(self.last_shoot.x + 1, self.last_shoot.y) break except BoardException as e: print(e) try: print("стреляю рядом 4") d = Dot(self.last_shoot.x - 1, self.last_shoot.y) break except BoardException as e: print(e) else: d = Dot(randint(0, 5), randint(0, 5)) print(f"Ход компьютера: {d.x + 1} {d.y + 1}") return d class User(Player): def ask(self,shoot_near): if self.last_shoot is not None: print("Последний выстрел игрока ", self.last_shoot.x+1,self.last_shoot.y+1) while True: cords = input("Ваш ход: ").split() if len(cords) != 2: print(" Введите 2 координаты! ") continue x, y = cords if not (x.isdigit()) or not (y.isdigit()): print(" Введите числа! ") continue x, y = int(x), int(y) return Dot(x - 1, y - 1) # После создаём наш главный класс — класс Game. Игра описывается параметрами: # # Игрок-пользователь, объект класса User. # Доска пользователя. # Игрок-компьютер, объект класса Ai. # Доска компьютера. # И имеет методы: # # random_board — метод генерирует случайную доску. Для этого мы просто пытаемся в случайные клетки изначально пустой доски расставлять корабли (в бесконечном цикле пытаемся поставить корабль в случайную току, пока наша попытка не окажется успешной). Лучше расставлять сначала длинные корабли, а потом короткие. Если было сделано много (несколько тысяч) попыток установить корабль, но это не получилось, значит доска неудачная и на неё корабль уже не добавить. В таком случае нужно начать генерировать новую доску. # greet — метод, который в консоли приветствует пользователя и рассказывает о формате ввода. # loop — метод с самим игровым циклом. Там мы просто последовательно вызываем метод mode для игроков и делаем проверку, сколько живых кораблей осталось на досках, чтобы определить победу. # start — запуск игры. Сначала вызываем greet, а потом loop. class Game: def __init__(self, size=6): self.size = size choice = None pl = None while choice is None: # Запускаем выбор расстановки кораблей choice = int(input("0 - случайная расстановка кораблей, 1 - раставить самостоятельно :")) if choice == 0: pl = self.random_board() break elif choice == 1: pl = self.self_board() break else: choice = None print("Неверно выбрано значение") co = self.random_board() co.hid = True self.ai = AI(co, pl) self.us = User(pl, co) self.all = All_board(self.us.board, self.ai.board) def random_board(self): board = None while board is None: board = self.random_place() return board def random_place(self): lens = [3, 2, 2, 1, 1, 1, 1] board = Board(size=self.size) attempts = 0 for l in lens: while True: attempts += 1 if attempts > 2000: return None ship = Ship(Dot(randint(0, self.size), randint(0, self.size)), l, randint(0, 1)) try: board.add_ship(ship) break except BoardWrongShipException: pass board.begin() return board # Даем игроку самому расставить корабли def self_board(self): lens = [3, 2, 2, 1, 1, 1, 1] board = Board(size=self.size) print("--------------------") print("-Установите корабли-") print(" формат ввода: x y z") print(" x - номер строки ") print(" y - номер столбца ") print(" z - направление корабля (1-горизонтально, 0-вертикально)") for l in lens: while True: print("-" * 20) print("Доска пользователя:") print(board) bows = input(f"Введите координаты и направление для корабля длинной {l}: ").split() if len(bows) != 3: print(" Введите 3 значения! координтаы носа и направление ") continue x, y, z = bows if not (x.isdigit()) or not (y.isdigit()) or not (z.isdigit()): print(" Введите числа! ") continue x, y, z = int(x), int(y), int(z) ship = Ship(Dot(x-1, y-1), l, z) try: board.add_ship(ship) break except BoardWrongShipException: pass board.begin() return board def greet(self): print("-------------------") print(" Приветсвуем вас ") print(" в игре ") print(" морской бой ") print("-------------------") print(" формат ввода: x y ") print(" x - номер строки ") print(" y - номер столбца ") def loop(self): num = 0 shoot_near = False while True: print("-" * 20) # print("Доска пользователя:") # print(self.us.board) # print("-" * 20) # print("Доска компьютера:") # print(self.ai.board) print(self.all) if num % 2 == 0: print("-" * 20) print("Ходит пользователь!") repeat = self.us.move(shoot_near) else: print("-" * 20) print("Ходит компьютер!") repeat = self.ai.move(shoot_near) if repeat: num -= 1 shoot_near = True else: shoot_near = False if self.ai.board.count == 7: print("-" * 20) print("Пользователь выиграл!") break if self.us.board.count == 7: print("-" * 20) print("Компьютер выиграл!") break num += 1 def start(self): self.greet() self.loop() # И останется просто создать экземпляр класса Game и вызвать метод start. g = Game() g.start()
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/triple_net_tensorboard_random_multiGpus/multi_gpu_demo.py
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[]
no_license
Continue7777/DSSM-
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# -*- coding: utf-8 -*- from datetime import datetime import os import time import tensorflow as tf import mnist_inference # 定义训练神经网络时需要用到的配置。这些配置与5.5节中定义的配置类似。 BATCH_SIZE = 100 LEARNING_RATE_BASE = 0.001 LEARNING_RATE_DECAY = 0.99 REGULARAZTION_RATE = 0.0001 TRAINING_STEPS = 1000 MOVING_AVERAGE_DECAY = 0.99 N_GPU = 4 # 定义日志和模型输出的路径。 MODEL_SAVE_PATH = "/path/to/logs_and_models/" MODEL_NAME = "model.ckpt" # 定义数据存储的路径。因为需要为不同的GPU提供不同的训练数据,所以通过placerholder # 的方式就需要手动准备多份数据。为了方便训练数据的获取过程,可以采用第7章中介绍的输 # 入队列的方式从TFRecord中读取数据。于是在这里提供的数据文件路径为将MNIST训练数据 # 转化为TFRecords格式之后的路径。如何将MNIST数据转化为TFRecord格式在第7章中有 # 详细介绍,这里不再赘述。 DATA_PATH = "/path/to/data.tfrecords" # 定义输入队列得到训练数据,具体细节可以参考第7章。 def get_input(): filename_queue = tf.train.string_input_producer([DATA_PATH]) reader = tf.TFRecordReader() _, serialized_example = reader.read(filename_queue) # 定义数据解析格式。 features = tf.parse_single_example( serialized_example, features={ 'image_raw': tf.FixedLenFeature([], tf.string), 'pixels': tf.FixedLenFeature([], tf.int64), 'label': tf.FixedLenFeature([], tf.int64), }) # 解析图片和标签信息。 decoded_image = tf.decode_raw(features['image_raw'], tf.uint8) reshaped_image = tf.reshape(decoded_image, [784]) retyped_image = tf.cast(reshaped_image, tf.float32) label = tf.cast(features['label'], tf.int32) # 定义输入队列并返回。 min_after_dequeue = 10000 capacity = min_after_dequeue + 3 * BATCH_SIZE return tf.train.shuffle_batch( [retyped_image, label], batch_size=BATCH_SIZE, capacity=capacity, min_after_dequeue=min_after_dequeue) # 定义损失函数。对于给定的训练数据、正则化损失计算规则和命名空间,计算在这个命名空间 # 下的总损失。之所以需要给定命名空间是因为不同的GPU上计算得出的正则化损失都会加入名为 # loss的集合,如果不通过命名空间就会将不同GPU上的正则化损失都加进来。 def get_loss(x, y_, regularizer, scope): # 沿用5.5节中定义的函数来计算神经网络的前向传播结果。 y = mnist_inference.inference(x, regularizer) # 计算交叉熵损失。 cross_entropy = tf.reduce_mean( tf.nn.sparse_softmax_cross_entropy_with_logits(y, y_)) # 计算当前GPU上计算得到的正则化损失。 regularization_loss = tf.add_n(tf.get_collection('losses', scope)) # 计算最终的总损失。 loss = cross_entropy + regularization_loss return loss # 计算每一个变量梯度的平均值。 def average_gradients(tower_grads): average_grads = [] # 枚举所有的变量和变量在不同GPU上计算得出的梯度。 for grad_and_vars in zip(*tower_grads): # 计算所有GPU上的梯度平均值。 grads = [] for g, _ in grad_and_vars: expanded_g = tf.expand_dims(g, 0) grads.append(expanded_g) grad = tf.concat(0, grads) grad = tf.reduce_mean(grad, 0) v = grad_and_vars[0][1] grad_and_var = (grad, v) # 将变量和它的平均梯度对应起来。 average_grads.append(grad_and_var) # 返回所有变量的平均梯度,这将被用于变量更新。 return average_grads # 主训练过程。 def main(argv=None): # 将简单的运算放在CPU上,只有神经网络的训练过程放在GPU上。 with tf.Graph().as_default(), tf.device('/cpu:0'): # 获取训练batch。 x, y_ = get_input() regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE) # 定义训练轮数和指数衰减的学习率。 global_step = tf.get_variable( 'global_step', [], initializer=tf.constant_initializer(0), trainable=False) learning_rate = tf.train.exponential_decay( LEARNING_RATE_BASE, global_step, 60000 / BATCH_SIZE, LEARNING_ RATE_DECAY) # 定义优化方法。 opt = tf.train.GradientDescentOptimizer(learning_rate) tower_grads = [] # 将神经网络的优化过程跑在不同的GPU上。 for i in range(N_GPU): # 将优化过程指定在一个GPU上。 with tf.device('/gpu:%d' % i): with tf.name_scope('GPU_%d' % i) as scope: cur_loss = get_loss(x, y_, regularizer, scope) # 在第一次声明变量之后,将控制变量重用的参数设置为True。这样可以 # 让不同的GPU更新同一组参数。注意tf.name_scope函数并不会影响 # tf.get_ variable的命名空间。 tf.get_variable_scope().reuse_variables() # 使用当前GPU计算所有变量的梯度。 grads = opt.compute_gradients(cur_loss) tower_grads.append(grads) # 计算变量的平均梯度,并输出到TensorBoard日志中。 grads = average_gradients(tower_grads) for grad, var in grads: if grad is not None: tf.histogram_summary( 'gradients_on_average/%s' % var.op.name, grad) # 使用平均梯度更新参数。 apply_gradient_op = opt.apply_gradients( grads, global_step=global_ step) for var in tf.trainable_variables(): tf.histogram_summary(var.op.name, var) # 计算变量的滑动平均值。 variable_averages = tf.train.ExponentialMovingAverage( MOVING_AVERAGE_DECAY, global_step) variables_averages_op = variable_averages.apply( tf.trainable_variables()) # 每一轮迭代需要更新变量的取值并更新变量的滑动平均值。 train_op = tf.group(apply_gradient_op, variables_averages_op) saver = tf.train.Saver(tf.all_variables()) summary_op = tf.merge_all_summaries() init = tf.initialize_all_variables() # 训练过程。 with tf.Session(config=tf.ConfigProto( allow_soft_placement=True, log_device_placement=True)) as sess: # 初始化所有变量并启动队列。 init.run() coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) summary_writer = tf.train.SummaryWriter( MODEL_SAVE_PATH, sess.graph) for step in range(TRAINING_STEPS): # 执行神经网络训练操作,并记录训练操作的运行时间。 start_time = time.time() _, loss_value = sess.run([train_op, cur_loss]) duration = time.time() - start_time # 每隔一段时间展示当前的训练进度,并统计训练速度。 if step != 0 and step % 10 == 0: # 计算使用过的训练数据个数。因为在每一次运行训练操作时,每一个GPU # 都会使用一个batch的训练数据,所以总共用到的训练数据个数为 # batch大小×GPU个数。 num_examples_per_step = BATCH_SIZE * N_GPU # num_examples_per_step为本次迭代使用到的训练数据个数, # duration为运行当前训练过程使用的时间,于是平均每秒可以处理的训 # 练数据个数为num_examples_per_step / duration。 examples_per_sec = num_examples_per_step / duration # duration为运行当前训练过程使用的时间,因为在每一个训练过程中, # 每一个GPU都会使用一个batch的训练数据,所以在单个batch上的训 # 练所需要时间为duration / GPU个数。 sec_per_batch = duration / N_GPU # 输出训练信息。 format_str = ('step %d, loss = %.2f (%.1f examples/ ' ' sec; %.3f sec/batch)') print(format_str % (step, loss_value, examples_per_sec, sec_per_batch)) # 通过TensorBoard可视化训练过程。 summary = sess.run(summary_op) summary_writer.add_summary(summary, step) # 每隔一段时间保存当前的模型。 if step % 1000 == 0 or (step + 1) == TRAINING_STEPS: checkpoint_path = os.path.join( MODEL_SAVE_PATH, MODEL_ NAME) saver.save(sess, checkpoint_path, global_step=step) coord.request_stop() coord.join(threads) if __name__ == '__main__': tf.app.run()
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/yelpCNN.py
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[]
no_license
wanaaaa/yelpCNN1D
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# https://chriskhanhtran.github.io/posts/cnn-sentence-classification/ from functionClass import * from gensim.models import Word2Vec import torch import torch.optim as optim device = 'cuda' rateReviewTrainList, rateReviewTestList, maxListCount = dataRead() xyDataLoader = DataLoaderFun(rateReviewTrainList, maxListCount, batchSize=2500) textCNNmodel = trainFun(xyDataLoader, maxListCount, epochs=20) # textCNNmodel = TextCnn(maxListCount).cuda(device=device) textCNNmodel = TextCnn(maxListCount).cpu() textCNNmodel.load_state_dict(torch.load('traindTextCNNmodel.model')) textCNNmodel.eval() # ================================================ # ================================================ # ================================================ xyTestDataLoader = DataLoaderFun(rateReviewTestList, maxListCount, batchSize=1) for epoch in range(1): # print("num of epochs->", epoch) for step, batch in enumerate(xyTestDataLoader): x_test, y_test = tuple(t.to('cpu') for t in batch) y_pridict = textCNNmodel(x_test) print("y_pridict->", y_pridict, 'y_test->', y_test) # break torch.cuda.empty_cache()
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/attractions/__init__.py
4be87a5da7791f1c059468e21ff1aacb5221f3c6
[]
no_license
kirksudduth/petting_zoo
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ce9fb52ca0aff0cb640a2041b3996156f8bb8ca1
refs/heads/master
2022-11-20T19:22:15.611061
2020-07-21T20:21:55
2020-07-21T20:21:55
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from .attraction import Attraction from .petting_zoo import Petting_zoo from .snake_pit import Snake_pit from .wetlands import Wetlands from .attractions_instances import creature_culdesac from .attractions_instances import no_feet_knoll from .attractions_instances import swimmy_jazz
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/Python/menu.py
66e3ba4c5b15a961c7e3ea0fd84e0ebe95f018a3
[]
no_license
HolbertonSchoolTun/HackDay_mastermind
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92c5bbb0d01bae8dfaae3015195db6f33942c5a5
refs/heads/master
2022-12-24T04:42:43.966128
2020-09-19T02:35:39
2020-09-19T02:35:39
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#!/usr/bin/python3 """ """ import pygame import pygame_menu from main import start_game class Game(): pygame.init() surface = pygame.display.set_mode((450, 600)) def set_difficulty(value, difficulty): if value == 1: return(1) else: return (2) def start_the_game(): # Do the job here ! start_game() def Play_Mode(mode, value): pass pygame.display.set_caption("Mastermind") menu = pygame_menu.Menu(600, 450, 'MasterMind', theme=pygame_menu.themes.THEME_DARK) menu.add_selector('Difficulty : ', [('Hard', 1), ('Easy', 2)], onchange=set_difficulty) menu.add_selector('Play Mode : ', [('Single Player', 1), ('Two Players', 2)], onchange=Play_Mode) menu.add_button('Play', start_the_game) menu.add_button('Quit', pygame_menu.events.EXIT) menu.mainloop(surface)
[ "achrefbs" ]
achrefbs
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/impala/tests/test_impala.py
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permissive
attilajeges/impyla
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2023-07-15T17:15:48.683389
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2020-10-01T23:10:16
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# Copyright 2019 Cloudera Inc. # # 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. import pytest from pytest import yield_fixture BIGGER_TABLE_NUM_ROWS = 100 @yield_fixture(scope='module') def bigger_table(cur): table_name = 'tmp_bigger_table' ddl = """CREATE TABLE {0} (s string) STORED AS PARQUET""".format(table_name) cur.execute(ddl) dml = """INSERT INTO {0} VALUES {1}""".format(table_name, ",".join(["('row{0}')".format(i) for i in xrange(BIGGER_TABLE_NUM_ROWS)])) # Disable codegen and expr rewrites so query runs faster. cur.execute("set disable_codegen=1") cur.execute("set enable_expr_rewrites=0") cur.execute(dml) try: yield table_name finally: cur.execute("DROP TABLE {0}".format(table_name)) def test_has_more_rows(cur, bigger_table): """Test that impyla correctly handles empty row batches returned with the hasMoreRows flag.""" # Set the fetch timeout very low and add sleeps so that Impala will return # empty batches. Run on a single node with a single thread to make as predictable # as possible. cur.execute("set fetch_rows_timeout_ms=1") cur.execute("set num_nodes=1") cur.execute("set mt_dop=1") cur.execute("""select * from {0} where s != cast(sleep(2) as string)""".format(bigger_table)) expected_rows = [("row{0}".format(i),) for i in xrange(BIGGER_TABLE_NUM_ROWS)] assert sorted(cur.fetchall()) == sorted(expected_rows)
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/upload_this_on_arduino/pyduino.py
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[]
no_license
rouanro/PS
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a474d5ac9d23d50388c1811ddf256efa408b33d6
refs/heads/master
2020-03-18T21:57:12.402332
2018-05-29T15:19:15
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""" A library to interface Arduino through serial connection """ import serial import smtplib from email.message import EmailMessage class Arduino(): """ Models an Arduino connection """ def __init__(self, serial_port='/dev/ttyACM0', baud_rate=9600, read_timeout=5): """ Initializes the serial connection to the Arduino board """ self.conn = serial.Serial(serial_port, baud_rate) self.conn.timeout = read_timeout # Timeout for readline() def set_pin_mode(self, pin_number, mode): """ Performs a pinMode() operation on pin_number Internally sends b'M{mode}{pin_number} where mode could be: - I for INPUT - O for OUTPUT - P for INPUT_PULLUP MO13 """ # command = (''.join(('M',mode,str(pin_number)))).encode() #print 'set_pin_mode =',command,(''.join(('M',mode,str(pin_number)))) # self.conn.write(command) def digital_read(self, pin_number): """ Performs a digital read on pin_number and returns the value (1 or 0) Internally sends b'RD{pin_number}' over the serial connection """ command = (''.join(('RD', str(pin_number)))).encode() #self.conn.write(command) line_received = self.conn.readline().decode().strip() header, value = line_received.split(':') # e.g. D13:1 if header == ('D'+ str(pin_number)): # If header matches return int(value) def digital_write(self, pin_number, digital_value): """ Writes the digital_value on pin_number Internally sends b'WD{pin_number}:{digital_value}' over the serial connection """ command = (''.join(('WD', str(pin_number), ':', str(digital_value)))).encode() #self.conn.write(command) def analog_read(self, pin_number): """ Performs an analog read on pin_number and returns the value (0 to 1023) Internally sends b'RA{pin_number}' over the serial connection """ command = (''.join(('RA', str(pin_number)))).encode() self.conn.write(command) print(command) line_received = self.conn.readline().decode().strip() #header, value = line_received.split(':') # e.g. A4:1 if line_received[0:2] == ("A0"): value = line_received[3:] # If header matches return int(value) if line_received[0:2] == ("A4"): value = line_received[3:] return value # me == the sender's email address # you == the recipient's email address # msg = EmailMessage() # msg['Subject'] = 'Teeeeeeeeeeest' # msg['From'] = '[email protected]' # msg['To'] = '[email protected]' # Send the message via our own SMTP server. # s = smtplib.SMTP('localhost') # s.send_message(msg) # s.quit() def analog_write(self, pin_number, analog_value): """ Writes the analog value (0 to 255) on pin_number Internally sends b'WA{pin_number}:{analog_value}' over the serial connection """ command = (''.join(('WA', str(pin_number), ':', str(analog_value)))).encode() #self.conn.write(command) def send_message(self, message): command = message.encode() self.conn.write(command) def send_email(self, user, pwd, recipient, subject, body): FROM = user TO = recipient if isinstance(recipient, list) else [recipient] SUBJECT = subject TEXT = body # Prepare actual message message = """From: %s\nTo: %s\nSubject: %s\n\n%s """ % (FROM, ", ".join(TO), SUBJECT, TEXT) try: server = smtplib.SMTP("smtp.gmail.com", 587) server.ehlo() server.starttls() server.login(user, pwd) server.sendmail(FROM, TO, message) server.close() print('successfully sent the mail') except: print("failed to send mail") def close(self): """ To ensure we are properly closing our connection to the Arduino device. """ self.conn.close() print ('Connection to Arduino closed')
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/issegm1/solve_ST.py
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from __future__ import print_function from sklearn.datasets import fetch_mldata import logging import copy from datetime import datetime import argparse import cPickle import os import os.path as osp import re import sys import math import time from functools import partial from PIL import Image from multiprocessing import Pool from sklearn.metrics import log_loss import numpy as np import mxnet as mx import scipy.io from util1 import mxutil from util1 import transformer as ts from util1 import util from util1.lr_scheduler import FixedScheduler, LinearScheduler, PolyScheduler from data1 import FileIter, make_divisible #from data_src import FileIter, make_divisible, parse_split_file def parse_split_file_tgt(dataset_tgt, split_tgt, data_root=''): split_filename = 'issegm1/data_list/{}/{}.lst'.format(dataset_tgt, split_tgt) image_list = [] label_gt_list = [] image_data_list = [] with open(split_filename) as f: for item in f.readlines(): fields = item.strip().split('\t') image_list.append(os.path.join(data_root, fields[0])) image_data_list.append(fields[0]) label_gt_list.append(os.path.join(data_root, fields[1])) return image_list, label_gt_list,image_data_list def parse_model_label(args): assert args.model is not None fields = [_.strip() for _ in osp.basename(args.model).split('_')] # parse fields i = 0 num_fields = len(fields) # database dataset = fields[i] if args.dataset is None else args.dataset dataset_tgt = args.dataset_tgt i += 1 ######################## network structure assert fields[i].startswith('rn') net_type = re.compile('rn[a-z]*').findall(fields[i])[0] net_name = fields[i][len(net_type):].strip('-') i += 1 # number of classes assert fields[i].startswith('cls') classes = int(fields[i][len('cls'):]) i += 1 ######################## feature resolution #feat_stride = 32 feat_stride = 8 if i < num_fields and fields[i].startswith('s'): feat_stride = int(fields[i][len('s'):]) i += 1 # learning rate lr_params = { 'type': 'fixed', 'base': 0.1, 'args': None, } if args.base_lr is not None: lr_params['base'] = args.base_lr if args.lr_type in ('linear',): lr_params['type'] = args.lr_type elif args.lr_type in ('poly',): lr_params['type'] = args.lr_type elif args.lr_type == 'step': lr_params['args'] = {'step': [int(_) for _ in args.lr_steps.split(',')], 'factor': 0.1} model_specs = { # model 'lr_params': lr_params, 'net_type': net_type, 'net_name': net_name, 'classes': classes, 'feat_stride': feat_stride, # data 'dataset': dataset, 'dataset_tgt': dataset_tgt } return model_specs def parse_args(): parser = argparse.ArgumentParser(description='Tune FCRNs from ResNets.') parser.add_argument('--dataset', default=None, help='The source dataset to use, e.g. cityscapes, voc.') parser.add_argument('--dataset-tgt', dest='dataset_tgt', default=None, help='The target dataset to use, e.g. cityscapes, GM.') parser.add_argument('--split', dest='split', default='train', help='The split to use, e.g. train, trainval.') parser.add_argument('--split-tgt', dest='split_tgt', default='val', help='The split to use in target domain e.g. train, trainval.') parser.add_argument('--data-root', dest='data_root', help='The root data dir. for source domain', default=None, type=str) parser.add_argument('--data-root-tgt', dest='data_root_tgt', help='The root data dir. for target domain', default=None, type=str) parser.add_argument('--output', default=None, help='The output dir.') parser.add_argument('--model', default=None, help='The unique label of this model.') parser.add_argument('--batch-images', dest='batch_images', help='The number of images per batch.', default=None, type=int) parser.add_argument('--crop-size', dest='crop_size', help='The size of network input during training.', default=None, type=int) parser.add_argument('--origin-size', dest='origin_size', help='The size of images to crop from in source domain', default=2048, type=int) parser.add_argument('--origin-size-tgt', dest='origin_size_tgt', help='The size of images to crop from in target domain', default=2048, type=int) parser.add_argument('--scale-rate-range', dest='scale_rate_range', help='The range of rescaling', default='0.7,1.3', type=str) parser.add_argument('--weights', default=None, help='The path of a pretrained model.') parser.add_argument('--gpus', default='0', help='The devices to use, e.g. 0,1,2,3') # parser.add_argument('--lr-type', dest='lr_type', help='The learning rate scheduler, e.g., fixed(default)/step/linear', default=None, type=str) parser.add_argument('--base-lr', dest='base_lr', help='The lr to start from.', default=None, type=float) parser.add_argument('--lr-steps', dest='lr_steps', help='The steps when to reduce lr.', default=None, type=str) parser.add_argument('--weight-decay', dest='weight_decay', help='The weight decay in sgd.', default=0.0005, type=float) # parser.add_argument('--from-epoch', dest='from_epoch', help='The epoch to start from.', default=None, type=int) parser.add_argument('--stop-epoch', dest='stop_epoch', help='The index of epoch to stop.', default=None, type=int) parser.add_argument('--to-epoch', dest='to_epoch', help='The number of epochs to run.', default=None, type=int) # how many rounds to generate pseudo labels parser.add_argument('--idx-round', dest='idx_round', help='The current number of rounds to generate pseudo labels', default=0, type=int) # initial portion of selected pseudo labels in target domain parser.add_argument('--init-tgt-port', dest='init_tgt_port', help='The initial portion of pixels selected in target dataset, both by global and class-wise threshold', default=0.3, type=float) parser.add_argument('--init-src-port', dest='init_src_port', help='The initial portion of images selected in source dataset', default=0.3, type=float) parser.add_argument('--seed-int', dest='seed_int', help='The random seed', default=0, type=int) parser.add_argument('--mine-port', dest='mine_port', help='The portion of data being mined', default=0.5, type=float) # parser.add_argument('--mine-id-number', dest='mine_id_number', help='Thresholding value for deciding mine id', default=3, type=int) parser.add_argument('--mine-thresh', dest='mine_thresh', help='The threshold to determine the mine id', default=0.001, type=float) parser.add_argument('--mine-id-address', dest='mine_id_address', help='The address of mine id', default=None, type=str) # parser.add_argument('--phase', help='Phase of this call, e.g., train/val.', default='train', type=str) parser.add_argument('--with-prior', dest='with_prior', help='with prior', default='True', type=str) # for testing parser.add_argument('--test-scales', dest='test_scales', help='Lengths of the longer side to resize an image into, e.g., 224,256.', default=None, type=str) parser.add_argument('--test-flipping', dest='test_flipping', help='If average predictions of original and flipped images.', default=False, action='store_true') parser.add_argument('--test-steps', dest='test_steps', help='The number of steps to take, for predictions at a higher resolution.', default=1, type=int) # parser.add_argument('--kvstore', dest='kvstore', help='The type of kvstore, e.g., local/device.', default='local', type=str) parser.add_argument('--prefetch-threads', dest='prefetch_threads', help='The number of threads to fetch data.', default=1, type=int) parser.add_argument('--prefetcher', dest='prefetcher', help='The type of prefetercher, e.g., process/thread.', default='thread', type=str) parser.add_argument('--cache-images', dest='cache_images', help='If cache images, e.g., 0/1', default=None, type=int) parser.add_argument('--log-file', dest='log_file', default=None, type=str) parser.add_argument('--check-start', dest='check_start', help='The first epoch to snapshot.', default=1, type=int) parser.add_argument('--check-step', dest='check_step', help='The steps between adjacent snapshots.', default=4, type=int) parser.add_argument('--debug', help='True means logging debug info.', default=False, action='store_true') parser.add_argument('--backward-do-mirror', dest='backward_do_mirror', help='True means less gpu memory usage.', default=False, action='store_true') parser.add_argument('--no-cudnn', dest='no_mxnet_cudnn_autotune_default', help='True means deploy cudnn.', default=False, action='store_true') parser.add_argument('--kc-policy', dest='kc_policy', help='The kc determination policy, currently only "global" and "cb" (class-balanced)', default='cb', type=str) if len(sys.argv) == 1: parser.print_help() sys.exit(1) args = parser.parse_args() if args.debug: os.environ['MXNET_ENGINE_TYPE'] = 'NaiveEngine' if args.backward_do_mirror: os.environ['MXNET_BACKWARD_DO_MIRROR'] = '1' if args.no_mxnet_cudnn_autotune_default: os.environ['MXNET_CUDNN_AUTOTUNE_DEFAULT'] = '0' if args.output is None: if args.phase == 'val': args.output = osp.dirname(args.weights) else: args.output = 'output' if args.weights is not None: if args.model is None: assert '_ep-' in args.weights parts = osp.basename(args.weights).split('_ep-') args.model = '_'.join(parts[:-1]) if args.phase == 'train': if args.from_epoch is None: assert '_ep-' in args.weights parts = os.path.basename(args.weights).split('_ep-') assert len(parts) == 2 from_model = parts[0] if from_model == args.model: parts = os.path.splitext(os.path.basename(args.weights))[0].split('-') args.from_epoch = int(parts[-1]) if args.model is None: raise NotImplementedError('Missing argument: args.model') if args.from_epoch is None: args.from_epoch = 0 if args.log_file is None: if args.phase == 'train': args.log_file = '{}.log'.format(args.model) elif args.phase == 'val': suffix = '' if args.split_tgt != 'val': suffix = '_{}'.format(args.split_tgt) args.log_file = '{}{}.log'.format(osp.splitext(osp.basename(args.weights))[0], suffix) else: raise NotImplementedError('Unknown phase: {}'.format(args.phase)) model_specs = parse_model_label(args) if args.data_root is None: args.data_root = osp.join('data', model_specs['dataset']) return args, model_specs def get_dataset_specs_tgt(args, model_specs): dataset = args.dataset dataset_tgt = args.dataset_tgt meta = {} mine_id = None mine_id_priority = None mine_port = args.mine_port mine_th = args.mine_thresh cmap_path = 'data/shared/cmap.pkl' cache_images = args.phase == 'train' mx_workspace = 1650 sys.path.insert(0, 'data/cityscapesscripts/helpers') if args.phase == 'train': mine_id = np.load(args.mine_id_address + '/mine_id.npy') mine_id_priority = np.load(args.mine_id_address + '/mine_id_priority.npy') mine_th = np.zeros(len(mine_id)) # trainId starts from 0 if dataset == 'gta' and dataset_tgt == 'cityscapes': from labels import id2label, trainId2label # label_2_id_tgt = 255 * np.ones((256,)) for l in id2label: if l in (-1, 255): continue label_2_id_tgt[l] = id2label[l].trainId id_2_label_tgt = np.array([trainId2label[_].id for _ in trainId2label if _ not in (-1, 255)]) valid_labels_tgt = sorted(set(id_2_label_tgt.ravel())) id_2_label_src = id_2_label_tgt label_2_id_src = label_2_id_tgt valid_labels_src = valid_labels_tgt # cmap = np.zeros((256, 3), dtype=np.uint8) for i in id2label.keys(): cmap[i] = id2label[i].color # ident_size = True # #max_shape_src = np.array((1052, 1914)) max_shape_src = np.array((1024, 2048)) max_shape_tgt = np.array((1024, 2048)) # if args.split in ('train+', 'trainval+'): cache_images = False # if args.phase in ('val',): mx_workspace = 8192 elif dataset == 'synthia' and dataset_tgt == 'cityscapes': from labels_cityscapes_synthia import id2label as id2label_tgt from labels_cityscapes_synthia import trainId2label as trainId2label_tgt from labels_synthia import id2label as id2label_src label_2_id_src = 255 * np.ones((256,)) for l in id2label_src: if l in (-1, 255): continue label_2_id_src[l] = id2label_src[l].trainId label_2_id_tgt = 255 * np.ones((256,)) for l in id2label_tgt: if l in (-1, 255): continue label_2_id_tgt[l] = id2label_tgt[l].trainId id_2_label_tgt = np.array([trainId2label_tgt[_].id for _ in trainId2label_tgt if _ not in (-1, 255)]) valid_labels_tgt = sorted(set(id_2_label_tgt.ravel())) id_2_label_src = None valid_labels_src = None # cmap = np.zeros((256, 3), dtype=np.uint8) for i in id2label_tgt.keys(): cmap[i] = id2label_tgt[i].color # ident_size = True # max_shape_src = np.array((760, 1280)) max_shape_tgt = np.array((1024, 2048)) # if args.split in ('train+', 'trainval+'): cache_images = False # if args.phase in ('val',): mx_workspace = 8192 else: raise NotImplementedError('Unknow dataset: {}'.format(args.dataset)) if cmap is None and cmap_path is not None: if osp.isfile(cmap_path): with open(cmap_path) as f: cmap = cPickle.load(f) meta['gpus'] = args.gpus meta['mine_port'] = mine_port meta['mine_id'] = mine_id meta['mine_id_priority'] = mine_id_priority meta['mine_th'] = mine_th meta['label_2_id_tgt'] = label_2_id_tgt meta['id_2_label_tgt'] = id_2_label_tgt meta['valid_labels_tgt'] = valid_labels_tgt meta['label_2_id_src'] = label_2_id_src meta['id_2_label_src'] = id_2_label_src meta['valid_labels_src'] = valid_labels_src meta['cmap'] = cmap meta['ident_size'] = ident_size meta['max_shape_src'] = meta.get('max_shape_src', max_shape_src) meta['max_shape_tgt'] = meta.get('max_shape_tgt', max_shape_tgt) meta['cache_images'] = args.cache_images if args.cache_images is not None else cache_images meta['mx_workspace'] = mx_workspace return meta '''def _get_metric(): def _eval_func(label, pred): # global sxloss gt_label = label.ravel() valid_flag = gt_label != 255 labels = gt_label[valid_flag].astype(int) n,c,h,w = pred.shape valid_inds = np.where(valid_flag)[0] probmap = np.rollaxis(pred.astype(np.float32),1).reshape((c, -1)) valid_probmap = probmap[labels, valid_inds] log_valid_probmap = -np.log(valid_probmap+1e-32) sum_metric = log_valid_probmap.sum() num_inst = valid_flag.sum() return (sum_metric, num_inst + (num_inst == 0)) return mx.metric.CustomMetric(_eval_func, 'loss')''' class Multi_Accuracy(mx.metric.EvalMetric): """Calculate accuracies of multi label""" def __init__(self, num=None): self.num = num super(Multi_Accuracy, self).__init__('multi-accuracy') def reset(self): """Resets the internal evaluation result to initial state.""" self.num_inst = 0 if self.num is None else [0] * self.num self.sum_metric = 0.0 if self.num is None else [0.0] * self.num def update(self, labels, preds): mx.metric.check_label_shapes(labels, preds) if self.num is not None: assert len(labels) == self.num for i in range(len(labels)): #print ('I am here in accuracy') #pred_label = mx.nd.argmax_channel(preds[i]).asnumpy().astype('int32') pred_label = preds[i].asnumpy().astype('float') label = labels[i].asnumpy().astype('int32') mx.metric.check_label_shapes(label, pred_label) if self.num is None: #self.sum_metric += (pred_label.flat == label.flat).sum() #self.num_inst += len(pred_label.flat) outEval = _eval_func(label, pred_label) self.sum_metric = outEval[0] self.num_inst = outEval[1] else: if i==0: outEval = _eval_func(label, pred_label) self.sum_metric[i] = outEval[0] self.num_inst[i] = outEval[1] else: #self.sum_metric[i] = (pred_label.flat == label.flat).sum() #print(label.shape, pred_label.shape, label, pred_label) #self.sum_metric[i] = log_loss(label.flat, pred_label.flat) self.sum_metric[i] = cross_entropy(label.flatten(), pred_label.flatten()) self.num_inst[i] = len(pred_label.flat) #print self.sum_metric[i], self.num_inst[i] def get(self): """Gets the current evaluation result. Returns ------- names : list of str Name of the metrics. values : list of float Value of the evaluations. """ if self.num is None: return super(Multi_Accuracy, self).get() else: return zip(*(('%s-task%d'%(self.name, i), float('nan') if self.num_inst[i] == 0 else self.sum_metric[i] / self.num_inst[i]) for i in range(self.num))) def get_name_value(self): """Returns zipped name and value pairs. Returns ------- list of tuples A (name, value) tuple list. """ if self.num is None: return super(Multi_Accuracy, self).get_name_value() name, value = self.get() return list(zip(name, value)) def _eval_func(label, pred): # global sxloss gt_label = label.ravel() valid_flag = gt_label != 255 labels = gt_label[valid_flag].astype(int) n,c,h,w = pred.shape valid_inds = np.where(valid_flag)[0] probmap = np.rollaxis(pred.astype(np.float32),1).reshape((c, -1)) valid_probmap = probmap[labels, valid_inds] log_valid_probmap = -np.log(valid_probmap+1e-32) sum_metric = log_valid_probmap.sum() num_inst = valid_flag.sum() return (sum_metric, num_inst + (num_inst == 0)) def cross_entropy(targets, predictions): N = predictions.shape[0] lo = np.log(predictions+ 1e-6) #print predictions,lo ce = -np.sum(targets*lo)/N return ce def _get_scalemeanstd(): if model_specs['net_type'] in ('rna',): return (1.0 / 255, np.array([0.485, 0.456, 0.406]).reshape((1, 1, 3)), np.array([0.229, 0.224, 0.225]).reshape((1, 1, 3))) return None, None, None def _get_transformer_image(): scale, mean_, std_ = _get_scalemeanstd() transformers = [] if scale > 0: transformers.append(ts.ColorScale(np.single(scale))) transformers.append(ts.ColorNormalize(mean_, std_)) return transformers def _get_module(args, margs, dargs, net=None): if net is None: # the following lines show how to create symbols for our networks if model_specs['net_type'] == 'rna': from util1.symbol.symbol import cfg as symcfg symcfg['lr_type'] = 'alex' symcfg['workspace'] = dargs.mx_workspace symcfg['bn_use_global_stats'] = True if model_specs['net_name'] == 'a1': from util1.symbol.resnet_v2 import fcrna_model_a1, fcrna_model_a1_1 #net = fcrna_model_a1(margs.classes, margs.feat_stride, bootstrapping=False) net = fcrna_model_a1_1(margs.classes, margs.feat_stride, bootstrapping=False) if net is None: raise NotImplementedError('Unknown network: {}'.format(vars(margs))) contexts = [mx.gpu(int(_)) for _ in args.gpus.split(',')] #mod = mx.mod.Module(net, context=contexts) mod = mx.mod.Module(net, context=contexts, label_names=['softmax_label', 'sigmoid_label']) return mod def _make_dirs(path): if not osp.isdir(path): os.makedirs(path) def facc(label, pred): pred = pred.argmax(1).ravel() label = label.ravel() return (pred == label).mean() def fentropy(label, pred): pred_source = pred[:, 1, :, :].ravel() label = label.ravel() return -(label * np.log(pred_source + 1e-12) + (1. - label) * np.log(1. - pred_source + 1e-12)).mean() def _interp_preds_as_impl(num_classes, im_size, pred_stride, imh, imw, pred): imh0, imw0 = im_size pred = pred.astype(np.single, copy=False) input_h, input_w = pred.shape[0] * pred_stride, pred.shape[1] * pred_stride assert pred_stride >= 1. this_interp_pred = np.array(Image.fromarray(pred).resize((input_w, input_h), Image.CUBIC)) if imh0 == imh: interp_pred = this_interp_pred[:imh, :imw] else: interp_method = util.get_interp_method(imh, imw, imh0, imw0) interp_pred = np.array(Image.fromarray(this_interp_pred[:imh, :imw]).resize((imw0, imh0), interp_method)) return interp_pred def interp_preds_as(im_size, net_preds, pred_stride, imh, imw, threads=4): num_classes = net_preds.shape[0] worker = partial(_interp_preds_as_impl, num_classes, im_size, pred_stride, imh, imw) if threads == 1: ret = [worker(_) for _ in net_preds] else: pool = Pool(threads) ret = pool.map(worker, net_preds) pool.close() return np.array(ret) class ScoreUpdater(object): def __init__(self, valid_labels, c_num, x_num, logger=None, label=None, info=None): self._valid_labels = valid_labels self._confs = np.zeros((c_num, c_num, x_num)) self._pixels = np.zeros((c_num, x_num)) self._logger = logger self._label = label self._info = info @property def info(self): return self._info def reset(self): self._start = time.time() self._computed = np.zeros((self._pixels.shape[1],)) self._confs[:] = 0 self._pixels[:] = 0 @staticmethod def calc_updates(valid_labels, pred_label, label): num_classes = len(valid_labels) pred_flags = [set(np.where((pred_label == _).ravel())[0]) for _ in valid_labels] class_flags = [set(np.where((label == _).ravel())[0]) for _ in valid_labels] conf = [len(class_flags[j].intersection(pred_flags[k])) for j in xrange(num_classes) for k in xrange(num_classes)] pixel = [len(class_flags[j]) for j in xrange(num_classes)] return np.single(conf).reshape((num_classes, num_classes)), np.single(pixel) def do_updates(self, conf, pixel, i, computed=True): if computed: self._computed[i] = 1 self._confs[:, :, i] = conf self._pixels[:, i] = pixel def update(self, pred_label, label, i, computed=True): conf, pixel = ScoreUpdater.calc_updates(self._valid_labels, pred_label, label) self.do_updates(conf, pixel, i, computed) self.scores(i) def scores(self, i=None, logger=None): confs = self._confs pixels = self._pixels num_classes = pixels.shape[0] x_num = pixels.shape[1] class_pixels = pixels.sum(1) class_pixels += class_pixels == 0 scores = confs[xrange(num_classes), xrange(num_classes), :].sum(1) acc = scores.sum() / pixels.sum() cls_accs = scores / class_pixels class_preds = confs.sum(0).sum(1) ious = scores / (class_pixels + class_preds - scores) logger = self._logger if logger is None else logger if logger is not None: if i is not None: speed = 1. * self._computed.sum() / (time.time() - self._start) logger.info('Done {}/{} with speed: {:.2f}/s'.format(i + 1, x_num, speed)) name = '' if self._label is None else '{}, '.format(self._label) logger.info('{}pixel acc: {:.2f}%, mean acc: {:.2f}%, mean iou: {:.2f}%'. \ format(name, acc * 100, cls_accs.mean() * 100, ious.mean() * 100)) with util.np_print_options(formatter={'float': '{:5.2f}'.format}): logger.info('\n{}'.format(cls_accs * 100)) logger.info('\n{}'.format(ious * 100)) return acc, cls_accs, ious def overall_scores(self, logger=None): acc, cls_accs, ious = self.scores(None, logger) return acc, cls_accs.mean(), ious.mean() def _train_impl(args, model_specs, logger): if len(args.output) > 0: _make_dirs(args.output) # dataiter dataset_specs_tgt = get_dataset_specs_tgt(args, model_specs) scale, mean_, _ = _get_scalemeanstd() if scale > 0: mean_ /= scale margs = argparse.Namespace(**model_specs) dargs = argparse.Namespace(**dataset_specs_tgt) # number of list_lines split_filename = 'issegm1/data_list/{}/{}.lst'.format(margs.dataset, args.split) num_source = 0 with open(split_filename) as f: for item in f.readlines(): num_source = num_source + 1 # batches_per_epoch = num_source // args.batch_images # optimizer assert args.to_epoch is not None if args.stop_epoch is not None: assert args.stop_epoch > args.from_epoch and args.stop_epoch <= args.to_epoch else: args.stop_epoch = args.to_epoch from_iter = args.from_epoch * batches_per_epoch to_iter = args.to_epoch * batches_per_epoch lr_params = model_specs['lr_params'] base_lr = lr_params['base'] if lr_params['type'] == 'fixed': scheduler = FixedScheduler() elif lr_params['type'] == 'step': left_step = [] for step in lr_params['args']['step']: if from_iter > step: base_lr *= lr_params['args']['factor'] continue left_step.append(step - from_iter) model_specs['lr_params']['step'] = left_step scheduler = mx.lr_scheduler.MultiFactorScheduler(**lr_params['args']) elif lr_params['type'] == 'linear': scheduler = LinearScheduler(updates=to_iter + 1, frequency=50, stop_lr=min(base_lr / 100., 1e-6), offset=from_iter) elif lr_params['type'] == 'poly': scheduler = PolyScheduler(updates=to_iter + 1, frequency=50, stop_lr=min(base_lr / 100., 1e-8), power=0.9, offset=from_iter) initializer = mx.init.Xavier(rnd_type='gaussian', factor_type='in', magnitude=2) optimizer_params = { 'learning_rate': base_lr, 'momentum': 0.9, 'wd': args.weight_decay, 'lr_scheduler': scheduler, 'rescale_grad': 1.0 / len(args.gpus.split(',')), } data_src_port = args.init_src_port data_src_num = int(num_source * data_src_port) mod = _get_module(args, margs, dargs) addr_weights = args.weights # first weights should be xxxx_ep-0000.params! addr_output = args.output # initializer net_args = None net_auxs = None ### if addr_weights is not None: net_args, net_auxs = mxutil.load_params_from_file(addr_weights) print ('feat_stride', margs.feat_stride) ####################################### training model to_model = osp.join(addr_output, str(args.idx_round), '{}_ep'.format(args.model)) dataiter = FileIter(dataset=margs.dataset, split=args.split, data_root=args.data_root, num_sel_source=data_src_num, num_source=num_source, seed_int=args.seed_int, dataset_tgt=args.dataset_tgt, split_tgt=args.split_tgt, data_root_tgt=args.data_root_tgt, sampler='random', batch_images=args.batch_images, meta=dataset_specs_tgt, rgb_mean=mean_, feat_stride=margs.feat_stride, label_stride=margs.feat_stride, origin_size=args.origin_size, origin_size_tgt=args.origin_size_tgt, crop_size=args.crop_size, scale_rate_range=[float(_) for _ in args.scale_rate_range.split(',')], transformer=None, transformer_image=ts.Compose(_get_transformer_image()), prefetch_threads=args.prefetch_threads, prefetcher_type=args.prefetcher, ) dataiter.reset() #ad = dataiter.next() #label_shapes = [x if isinstance(x, DataDesc) else DataDesc(*x) for x in label_shapes] #print (ad) mod.fit( dataiter, eval_metric=Multi_Accuracy(2), #eval_metric=_get_metric(), batch_end_callback=mx.callback.log_train_metric(10, auto_reset=False), epoch_end_callback=mx.callback.do_checkpoint(to_model), kvstore=args.kvstore, optimizer='sgd', optimizer_params=optimizer_params, initializer=initializer, arg_params=net_args, aux_params=net_auxs, allow_missing=args.from_epoch == 0, begin_epoch=args.from_epoch, num_epoch=args.stop_epoch, ) # @profile # MST: def _val_impl(args, model_specs, logger): if len(args.output) > 0: _make_dirs(args.output) # dataiter dataset_specs_tgt = get_dataset_specs_tgt(args, model_specs) scale, mean_, _ = _get_scalemeanstd() if scale > 0: mean_ /= scale #print (model_specs) margs = argparse.Namespace(**model_specs) dargs = argparse.Namespace(**dataset_specs_tgt) mod = _get_module(args, margs, dargs) addr_weights = args.weights # first weights should be xxxx_ep-0000.params! addr_output = args.output # current round index cround = args.idx_round net_args = None net_auxs = None ### if addr_weights is not None: net_args, net_auxs = mxutil.load_params_from_file(addr_weights) ###### save_dir = osp.join(args.output, str(cround), 'results') save_dir_self_train = osp.join(args.output, str(cround), 'self_train') # pseudo labels save_dir_pseudo_labelIds = osp.join(save_dir_self_train, 'pseudo_labelIds') save_dir_pseudo_color = osp.join(save_dir_self_train, 'pseudo_color') # without sp save_dir_nplabelIds = osp.join(save_dir, 'nplabelIds') save_dir_npcolor = osp.join(save_dir, 'npcolor') # probability map save_dir_probmap = osp.join(args.output, 'probmap') save_dir_stats = osp.join(args.output, 'stats') _make_dirs(save_dir) _make_dirs(save_dir_pseudo_labelIds) _make_dirs(save_dir_pseudo_color) _make_dirs(save_dir_nplabelIds) _make_dirs(save_dir_npcolor) _make_dirs(save_dir_probmap) _make_dirs(save_dir_stats) if args.with_prior == 'True': # with sp save_dir_splabelIds = osp.join(save_dir_self_train, 'splabelIds') save_dir_spcolor = osp.join(save_dir_self_train, 'spcolor') _make_dirs(save_dir_splabelIds) _make_dirs(save_dir_spcolor) if args.kc_policy == 'cb': # reweighted prediction map save_dir_rwlabelIds = osp.join(save_dir_self_train, 'rwlabelIds') save_dir_rwcolor = osp.join(save_dir_self_train, 'rwcolor') _make_dirs(save_dir_rwlabelIds) _make_dirs(save_dir_rwcolor) ###### dataset_tgt = model_specs['dataset_tgt'] image_list_tgt, label_gt_list_tgt,image_tgt_list = parse_split_file_tgt(margs.dataset_tgt, args.split_tgt) has_gt = args.split_tgt in ('train', 'val',) crop_sizes = sorted([int(_) for _ in args.test_scales.split(',')])[::-1] crop_size = crop_sizes[0] assert len(crop_sizes) == 1, 'multi-scale testing not implemented' label_stride = margs.feat_stride x_num = len(image_list_tgt) do_forward = True # for all images that has the same resolution if do_forward: batch = None transformers = [ts.Scale(crop_size, Image.CUBIC, False)] transformers += _get_transformer_image() transformer = ts.Compose(transformers) scorer_np = ScoreUpdater(dargs.valid_labels_tgt, margs.classes, x_num, logger) scorer_np.reset() # with prior if args.with_prior == 'True': scorer = ScoreUpdater(dargs.valid_labels_tgt, margs.classes, x_num, logger) scorer.reset() done_count = 0 # for multi-scale testing num_classes = margs.classes init_tgt_port = float(args.init_tgt_port) # class-wise cls_exist_array = np.zeros([1, num_classes], dtype=int) cls_thresh = np.zeros([num_classes]) # confidence thresholds for all classes cls_size = np.zeros([num_classes]) # number of predictions in each class array_pixel = 0.0 # prior if args.with_prior == 'True': in_path_prior = 'spatial_prior/{}/prior_array.mat'.format(args.dataset) sprior = scipy.io.loadmat(in_path_prior) prior_array = sprior["prior_array"].astype(np.float32) #prior_array = np.maximum(prior_array,0) ############################ network forward for i in xrange(x_num): start = time.time() ############################ network forward on single image (from official ResNet-38 implementation) sample_name = osp.splitext(osp.basename(image_list_tgt[i]))[0] im_path = osp.join(args.data_root_tgt, image_list_tgt[i]) rim = np.array(Image.open(im_path).convert('RGB'), np.uint8) if do_forward: im = transformer(rim) imh, imw = im.shape[:2] # init if batch is None: if dargs.ident_size: input_h = make_divisible(imh, margs.feat_stride) input_w = make_divisible(imw, margs.feat_stride) else: input_h = input_w = make_divisible(crop_size, margs.feat_stride) label_h, label_w = input_h / label_stride, input_w / label_stride test_steps = args.test_steps pred_stride = label_stride / test_steps pred_h, pred_w = label_h * test_steps, label_w * test_steps input_data = np.zeros((1, 3, input_h, input_w), np.single) input_label = 255 * np.ones((1, label_h * label_w), np.single) #dataiter_tgt = mx.io.NDArrayIter(input_data, input_label) input_label2 = np.ones((1, 19), np.single) label = {'softmax_label':input_label, 'sigmoid_label':input_label2} dataiter_tgt = mx.io.NDArrayIter(input_data, label) batch = dataiter_tgt.next() mod.bind(dataiter_tgt.provide_data, dataiter_tgt.provide_label, for_training=False, force_rebind=True) if not mod.params_initialized: mod.init_params(arg_params=net_args, aux_params=net_auxs) nim = np.zeros((3, imh + label_stride, imw + label_stride), np.single) sy = sx = label_stride // 2 nim[:, sy:sy + imh, sx:sx + imw] = im.transpose(2, 0, 1) net_preds = np.zeros((margs.classes, pred_h, pred_w), np.single) sy = sx = pred_stride // 2 + np.arange(test_steps) * pred_stride for ix in xrange(test_steps): for iy in xrange(test_steps): input_data = np.zeros((1, 3, input_h, input_w), np.single) input_data[0, :, :imh, :imw] = nim[:, sy[iy]:sy[iy] + imh, sx[ix]:sx[ix] + imw] batch.data[0] = mx.nd.array(input_data) mod.forward(batch, is_train=False) this_call_preds = mod.get_outputs()[0].asnumpy()[0] if args.test_flipping: batch.data[0] = mx.nd.array(input_data[:, :, :, ::-1]) mod.forward(batch, is_train=False) # average the original and flipped image prediction this_call_preds = 0.5 * ( this_call_preds + mod.get_outputs()[0].asnumpy()[0][:, :, ::-1]) net_preds[:, iy:iy + pred_h:test_steps, ix:ix + pred_w:test_steps] = this_call_preds interp_preds_np = interp_preds_as(rim.shape[:2], net_preds, pred_stride, imh, imw) ########################### #save predicted labels and confidence score vectors in target domains # no prior prediction with trainIDs pred_label_np = interp_preds_np.argmax(0) # no prior prediction with labelIDs if dargs.id_2_label_tgt is not None: pred_label_np = dargs.id_2_label_tgt[pred_label_np] # no prior color prediction im_to_save_np = Image.fromarray(pred_label_np.astype(np.uint8)) im_to_save_npcolor = im_to_save_np.copy() if dargs.cmap is not None: im_to_save_npcolor.putpalette(dargs.cmap.ravel()) # save no prior prediction with labelIDs and colors out_path_np = osp.join(save_dir_nplabelIds, '{}.png'.format(sample_name)) out_path_npcolor = osp.join(save_dir_npcolor, '{}.png'.format(sample_name)) im_to_save_np.save(out_path_np) im_to_save_npcolor.save(out_path_npcolor) # with prior if args.with_prior == 'True': probmap = np.multiply(prior_array,interp_preds_np).astype(np.float32) elif args.with_prior == 'False': probmap = interp_preds_np.copy().astype(np.float32) pred_label = probmap.argmax(0) probmap_max = np.amax(probmap, axis=0) ############################ save confidence scores of target domain as class-wise vectors for idx_cls in np.arange(0, num_classes): idx_temp = pred_label == idx_cls sname = 'array_cls' + str(idx_cls) if not (sname in locals()): exec ("%s = np.float32(0)" % sname) if idx_temp.any(): cls_exist_array[0, idx_cls] = 1 probmap_max_cls_temp = probmap_max[idx_temp].astype(np.float32) len_cls = probmap_max_cls_temp.size # downsampling by rate 4 probmap_cls = probmap_max_cls_temp[0:len_cls:4] exec ("%s = np.append(%s,probmap_cls)" % (sname, sname)) ############################ save prediction # save prediction probablity map out_path_probmap = osp.join(save_dir_probmap, '{}.npy'.format(sample_name)) np.save(out_path_probmap, probmap.astype(np.float32)) # save predictions with spatial priors, if sp exist. if args.with_prior == 'True': if dargs.id_2_label_tgt is not None: pred_label = dargs.id_2_label_tgt[pred_label] im_to_save_sp = Image.fromarray(pred_label.astype(np.uint8)) im_to_save_spcolor = im_to_save_sp.copy() if dargs.cmap is not None: # save color seg map im_to_save_spcolor.putpalette(dargs.cmap.ravel()) out_path_sp = osp.join(save_dir_splabelIds, '{}.png'.format(sample_name)) out_path_spcolor = osp.join(save_dir_spcolor, '{}.png'.format(sample_name)) im_to_save_sp.save(out_path_sp) im_to_save_spcolor.save(out_path_spcolor) # log information done_count += 1 if not has_gt: logger.info( 'Done {}/{} with speed: {:.2f}/s'.format(i + 1, x_num, 1. * done_count / (time.time() - start))) continue if args.split_tgt in ('train', 'val'): # evaluate with ground truth label_path = osp.join(args.data_root_tgt, label_gt_list_tgt[i]) label = np.array(Image.open(label_path), np.uint8) if args.with_prior == 'True': scorer.update(pred_label, label, i) scorer_np.update(pred_label_np, label, i) # save target training list fout = 'issegm1/data_list/{}/{}_training_gpu{}.lst'.format(args.dataset_tgt,args.split_tgt,args.gpus) fo = open(fout, "w") for idx_image in range(x_num): sample_name = osp.splitext(osp.basename(image_list_tgt[idx_image]))[0] fo.write(image_tgt_list[idx_image] + '\t' + osp.join(save_dir_pseudo_labelIds, '{}.png'.format(sample_name)) + '\n') fo.close() ############################ kc generation start_sort = time.time() # threshold for each class if args.kc_policy == 'global': for idx_cls in np.arange(0,num_classes): tname = 'array_cls' + str(idx_cls) exec ("array_pixel = np.append(array_pixel,%s)" % tname) # reverse=False for ascending losses and reverse=True for descending confidence array_pixel = sorted(array_pixel, reverse = True) len_cls = len(array_pixel) len_thresh = int(math.floor(len_cls * init_tgt_port)) cls_size[:] = len_cls cls_thresh[:] = array_pixel[len_thresh-1].copy() array_pixel = 0.0 if args.kc_policy == 'cb': for idx_cls in np.arange(0, num_classes): tname = 'array_cls' + str(idx_cls) if cls_exist_array[0, idx_cls] == 1: exec("%s = sorted(%s,reverse=True)" % (tname, tname)) # reverse=False for ascending losses and reverse=True for descending confidence exec("len_cls = len(%s)" % tname) cls_size[idx_cls] = len_cls len_thresh = int(math.floor(len_cls * init_tgt_port)) if len_thresh != 0: exec("cls_thresh[idx_cls] = %s[len_thresh-1].copy()" % tname) exec("%s = %d" % (tname, 0.0)) # threshold for mine_id with priority mine_id_priority = np.nonzero(cls_size / np.sum(cls_size) < args.mine_thresh)[0] # chosen mine_id mine_id_all = np.argsort(cls_size / np.sum(cls_size)) mine_id = mine_id_all[:args.mine_id_number] print(mine_id) np.save(save_dir_stats + '/mine_id.npy', mine_id) np.save(save_dir_stats + '/mine_id_priority.npy', mine_id_priority) np.save(save_dir_stats + '/cls_thresh.npy', cls_thresh) np.save(save_dir_stats + '/cls_size.npy', cls_size) logger.info('Kc determination done in %.2f s.', time.time() - start_sort) ############################ pseudo-label generation for i in xrange(x_num): sample_name = osp.splitext(osp.basename(image_list_tgt[i]))[0] sample_pseudo_label_name = osp.join(save_dir_pseudo_labelIds, '{}.png'.format(sample_name)) sample_pseudocolor_label_name = osp.join(save_dir_pseudo_color, '{}.png'.format(sample_name)) out_path_probmap = osp.join(save_dir_probmap, '{}.npy'.format(sample_name)) probmap = np.load(out_path_probmap) rw_probmap = np.zeros(probmap.shape, np.single) cls_thresh[cls_thresh == 0] = 1.0 # cls_thresh = 0 means there is no prediction in this class ############# pseudo-label assignment for idx_cls in np.arange(0, num_classes): cls_thresh_temp = cls_thresh[idx_cls] cls_probmap = probmap[idx_cls,:,:] cls_rw_probmap = np.true_divide(cls_probmap,cls_thresh_temp) rw_probmap[idx_cls,:,:] = cls_rw_probmap.copy() rw_probmap_max = np.amax(rw_probmap, axis=0) pseudo_label = np.argmax(rw_probmap,axis=0) ############# pseudo-label selection idx_unconfid = rw_probmap_max < 1 idx_confid = rw_probmap_max >= 1 # pseudo-labels with labelID pseudo_label = pseudo_label.astype(np.uint8) pseudo_label_labelID = dargs.id_2_label_tgt[pseudo_label] rw_pred_label = pseudo_label_labelID.copy() # ignore label assignment, compatible with labelIDs pseudo_label_labelID[idx_unconfid] = 0 ############# save pseudo-label im_to_save_pseudo = Image.fromarray(pseudo_label_labelID.astype(np.uint8)) im_to_save_pseudocol = im_to_save_pseudo.copy() if dargs.cmap is not None: # save segmentation prediction with color im_to_save_pseudocol.putpalette(dargs.cmap.ravel()) out_path_pseudo = osp.join(save_dir_pseudo_labelIds, '{}.png'.format(sample_name)) out_path_colpseudo = osp.join(save_dir_pseudo_color, '{}.png'.format(sample_name)) im_to_save_pseudo.save(out_path_pseudo) im_to_save_pseudocol.save(out_path_colpseudo) ############# save reweighted pseudo-label in cbst if args.kc_policy == 'cb': im_to_save_rw = Image.fromarray(rw_pred_label.astype(np.uint8)) im_to_save_rwcolor = im_to_save_rw.copy() if dargs.cmap is not None: im_to_save_rwcolor.putpalette(dargs.cmap.ravel()) out_path_rw = osp.join(save_dir_rwlabelIds, '{}.png'.format(sample_name)) out_path_rwcolor = osp.join(save_dir_rwcolor, '{}.png'.format(sample_name)) # save no prior prediction with labelIDs and colors im_to_save_rw.save(out_path_rw) im_to_save_rwcolor.save(out_path_rwcolor) ## remove probmap folder import shutil shutil.rmtree(save_dir_probmap) ## if __name__ == "__main__": util.cfg['choose_interpolation_method'] = True args, model_specs = parse_args() if len(args.output) > 0: _make_dirs(args.output) logger = util.set_logger(args.output, args.log_file, args.debug) logger.info('start with arguments %s', args) logger.info('and model specs %s', model_specs) if args.phase == 'train': _train_impl(args, model_specs, logger) elif args.phase == 'val': _val_impl(args, model_specs, logger) else: raise NotImplementedError('Unknown phase: {}'.format(args.phase))
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import sys from PyQt5.QtWidgets import QWidget, QListWidget, QLabel, QComboBox from PyQt5.QtGui import QFont from PyQt5.QtCore import QUrl path = 'C:/MedRec' sys.path.append(path + '/GUI/') from autocompletecombo import Autocomplete class ViewRecord(QWidget): def __init__(self, parent = None): super(ViewRecord, self).__init__(parent) self.initViewRecordUI() def initViewRecordUI(self): self.setGeometry(525, 225, 1080, 720) #initialize labels self.patient_name_label = QLabel('Patient Name : ', self) self.case_name_label = QLabel('Case Name : ', self) #initialize fields self.patient_name_entry = Autocomplete(self) self.case_name_entry = Autocomplete(self) #initi
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/res/scripts/common/offers.py
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wanyancan/WOTDecompiled
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import time from collections import namedtuple import BigWorld from constants import IS_BASEAPP from debug_utils import * ENTITY_TYPE_ACCOUNT = 0 ENTITY_TYPE_CLAN = 1 ENTITY_TYPE_NAMES_BY_IDS = ('Account', 'Clan') ENTITY_TYPE_IDS_BY_NAMES = {'Account': ENTITY_TYPE_ACCOUNT, 'Clan': ENTITY_TYPE_CLAN} ENTITY_TYPE_IDS = (ENTITY_TYPE_ACCOUNT, ENTITY_TYPE_CLAN) OFFER_SELL = 0 _OFFER_KIND_MASK = 192 SRC_WARE_GOLD = 0 SRC_WARE_CREDITS = 256 SRC_WARE_ITEMS = 512 SRC_WARE_VEHICLE = 768 SRC_WARE_TANKMAN = 1024 SRC_WARE_KINDS = (SRC_WARE_GOLD, SRC_WARE_CREDITS, SRC_WARE_ITEMS, SRC_WARE_VEHICLE, SRC_WARE_TANKMAN) SRC_WARE_MONEY_KINDS = (SRC_WARE_GOLD, SRC_WARE_CREDITS) _SRC_WARE_KIND_MASK = 3840 DST_WARE_GOLD = 0 DST_WARE_CREDITS = 4096 DST_WARE_KINDS = (DST_WARE_GOLD, DST_WARE_CREDITS) _DST_WARE_KIND_MASK = 61440 def makeOfferFlags(offerKind, srcWareKind, dstWareKind, srcEntityType, dstEntityType): return offerKind | srcWareKind | dstWareKind | srcEntityType | dstEntityType << 3 ParsedOfferFlags = namedtuple('ParsedOfferFlags', 'offerKind srcWareKind dstWareKind srcEntityType dstEntityType') def parseOfferFlags(flags): raw = (flags & _OFFER_KIND_MASK, flags & _SRC_WARE_KIND_MASK, flags & _DST_WARE_KIND_MASK, flags & 7, flags >> 3 & 7) return ParsedOfferFlags._make(raw) def parseSrcEntityTypeFromFlags(flags): return flags & 7 def parseDstEntityTypeFromFlags(flags): return flags >> 3 & 7 class OutOffers(object): Offer = namedtuple('Offer', 'flags dstDBID dstName srcWares dstWares validTill fee') def __init__(self, offersDict, outWriterGetter = None): offersDict.setdefault('nextID', 0) offersDict.setdefault('done', {}) offersDict.setdefault('out', {}) self.__data = offersDict self.__outWriter = outWriterGetter if outWriterGetter is not None else _WriterGetter(offersDict['out']) return def __getitem__(self, offerID): return _makeOutOffer(self.__data['out'][offerID]) def get(self, offerID): offer = self.__data['out'].get(offerID) if offer is not None: return _makeOutOffer(offer) else: return def getExt(self, offerID, default = None): outExt = self.__data.get('outExt') if outExt is None: return default else: return outExt.get(offerID, default) def items(self): return [ (id, _makeOutOffer(data)) for id, data in self.__data['out'].iteritems() ] def clear(self): self.__data['out'].clear() self.__data['done'].clear() self.__data.pop('outExt', None) self.__data['nextID'] += 1 return def count(self): return len(self.__data['out']) def doneOffers(self): return self.__data['done'] def timedOutOffers(self): res = [] currTime = int(time.time()) for offerID, offer in self.__data['out'].iteritems(): if offer[5] <= currTime: res.append(offerID) return res def inventorySlots(self): vehs = [] numTmen = 0 for offer in self.__data['out'].itervalues(): srcWareKind = offer[0] & _SRC_WARE_KIND_MASK if srcWareKind == SRC_WARE_VEHICLE: vehs.append(offer[3][0]) elif srcWareKind == SRC_WARE_TANKMAN: numTmen += 1 return (vehs, numTmen) def moveToDone(self, offerID): data = self.__data data['done'][offerID] = self.__outWriter().pop(offerID) outExt = data.get('outExt') if outExt is not None: outExt.pop(offerID, None) data['nextID'] += 1 return len(data['done']) def remove(self, offerID): if self.__outWriter().pop(offerID, None) is not None: self.__data['nextID'] += 1 outExt = self.__data.get('outExt') if outExt is not None: outExt.pop(offerID, None) return def removeDone(self, offerID): self.__data['done'].pop(offerID, None) return def updateDestination(self, offerID, dstEntityType, dstEntityDBID, dstEntityName): raise self.__data['out'][offerID][1] == dstEntityDBID or AssertionError def createOffer(self, flags, srcDBID, srcName, dstDBID, dstName, validSec, srcWares, srcFee, dstWares, dstFee, ext = None): currTime = int(time.time()) validTill = currTime + int(validSec) offer = (flags, dstDBID, dstName, srcWares, dstWares, validTill, srcFee) data = self.__data offerID = ((currTime & 1048575) << 12) + (data['nextID'] & 4095) data['nextID'] += 1 if not (offerID not in data['out'] and offerID not in data['done']): raise AssertionError self.__outWriter()[offerID] = offer data.setdefault('outExt', {})[offerID] = ext is not None and ext return (offerID, (offerID, flags, srcDBID, srcName, srcWares, dstWares, validTill, dstFee)) class InOffers(object): Offer = namedtuple('Offer', 'srcOfferID flags srcDBID srcName srcWares dstWares validTill fee') def __init__(self, offersDict, inWriterGetter = None): offersDict.setdefault('nextID', 0) offersDict.setdefault('in', {}) self.__data = offersDict self.__inWriter = inWriterGetter if inWriterGetter is not None else _WriterGetter(offersDict['in']) return def __getitem__(self, offerID): return _makeInOffer(self.__data['in'][offerID]) def get(self, offerID): offer = self.__data['in'].get(offerID) if offer is not None: return _makeInOffer(offer) else: return def items(self): return [ (id, _makeOutOffer(data)) for id, data in self.__data['in'].iteritems() ] def clear(self): self.__data['in'].clear() self.__data['nextID'] += 1 def count(self): return len(self.__data['in']) def timedOutOffers(self): res = [] currTime = int(time.time()) for offerID, offer in self.__data['in'].iteritems(): if offer[6] <= currTime: res.append(offerID) return res def findOfferBySource(self, srcEntityType, srcEntityDBID, srcOfferID): for inOfferID, offer in self.__data['in'].iteritems(): if offer[0] == srcOfferID and offer[2] == srcEntityDBID and parseSrcEntityTypeFromFlags(offer[1]) == srcEntityType: return inOfferID return None def add(self, offer): data = self.__data offerID = data['nextID'] data['nextID'] += 1 self.__inWriter()[offerID] = tuple(offer) return offerID def remove(self, offerID): if self.__inWriter().pop(offerID, None) is not None: self.__data['nextID'] += 1 return def collectOutOfferResults(outOffer): offerFlags = parseOfferFlags(outOffer.flags) gold = 0 credits = 0 items = None if offerFlags.srcWareKind == SRC_WARE_GOLD: gold -= outOffer.srcWares + outOffer.fee elif offerFlags.srcWareKind == SRC_WARE_CREDITS: credits -= outOffer.srcWares + outOffer.fee else: items = outOffer.srcWares if offerFlags.dstWareKind == DST_WARE_GOLD: gold += outOffer.dstWares else: credits += outOffer.dstWares return (offerFlags, gold, credits, items) def collectInOfferResults(inOffer): offerFlags = parseOfferFlags(inOffer.flags) gold = 0 credits = 0 items = None if offerFlags.srcWareKind == SRC_WARE_GOLD: gold += inOffer.srcWares elif offerFlags.srcWareKind == SRC_WARE_CREDITS: credits += inOffer.srcWares else: items = inOffer.srcWares if offerFlags.dstWareKind == DST_WARE_GOLD: gold -= inOffer.dstWares + inOffer.fee else: credits -= inOffer.dstWares + inOffer.fee return (offerFlags, gold, credits, items) _makeOutOffer = OutOffers.Offer._make _makeInOffer = InOffers.Offer._make class _WriterGetter(object): def __init__(self, dict): self.__d = dict def __call__(self): return self.__d
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/weapp/wapi/mall/__init__.py
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chengdg/weizoom
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# -*- coding: utf-8 -*- import product import promotion
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# cs212 ; Unit 4 ; 28 # ----------------- # User Instructions # # In this problem, you will generalize the bridge problem # by writing a function bridge_problem3, that makes a call # to lowest_cost_search. def bridge_problem3(here): """Find the fastest (least elapsed time) path to the goal in the bridge problem.""" # your code here return lowest_cost_search() # <== your arguments here # your code here if necessary def lowest_cost_search(start, successors, is_goal, action_cost): """Return the lowest cost path, starting from start state, and considering successors(state) => {state:action,...}, that ends in a state for which is_goal(state) is true, where the cost of a path is the sum of action costs, which are given by action_cost(action).""" explored = set() # set of states we have visited frontier = [ [start] ] # ordered list of paths we have blazed while frontier: path = frontier.pop(0) state1 = final_state(path) if is_goal(state1): return path explored.add(state1) pcost = path_cost(path) for (state, action) in successors(state1).items(): if state not in explored: total_cost = pcost + action_cost(action) path2 = path + [(action, total_cost), state] add_to_frontier(frontier, path2) return Fail def final_state(path): return path[-1] def path_cost(path): "The total cost of a path (which is stored in a tuple with the final action)." if len(path) < 3: return 0 else: action, total_cost = path[-2] return total_cost def add_to_frontier(frontier, path): "Add path to frontier, replacing costlier path if there is one." # (This could be done more efficiently.) # Find if there is an old path to the final state of this path. old = None for i,p in enumerate(frontier): if final_state(p) == final_state(path): old = i break if old is not None and path_cost(frontier[old]) < path_cost(path): return # Old path was better; do nothing elif old is not None: del frontier[old] # Old path was worse; delete it ## Now add the new path and re-sort frontier.append(path) frontier.sort(key=path_cost) def bsuccessors2(state): """Return a dict of {state:action} pairs. A state is a (here, there) tuple, where here and there are frozensets of people (indicated by their times) and/or the light.""" here, there = state if 'light' in here: return dict(((here - frozenset([a, b, 'light']), there | frozenset([a, b, 'light'])), (a, b, '->')) for a in here if a is not 'light' for b in here if b is not 'light') else: return dict(((here | frozenset([a, b, 'light']), there - frozenset([a, b, 'light'])), (a, b, '<-')) for a in there if a is not 'light' for b in there if b is not 'light') def bcost(action): "Returns the cost (a number) of an action in the bridge problem." # An action is an (a, b, arrow) tuple; a and b are times; arrow is a string a, b, arrow = action return max(a, b) def test(): here = [1, 2, 5, 10] assert bridge_problem3(here) == [ (frozenset([1, 2, 'light', 10, 5]), frozenset([])), ((2, 1, '->'), 2), (frozenset([10, 5]), frozenset([1, 2, 'light'])), ((2, 2, '<-'), 4), (frozenset(['light', 10, 2, 5]), frozenset([1])), ((5, 10, '->'), 14), (frozenset([2]), frozenset([1, 10, 5, 'light'])), ((1, 1, '<-'), 15), (frozenset([1, 2, 'light']), frozenset([10, 5])), ((2, 1, '->'), 17), (frozenset([]), frozenset([1, 10, 2, 5, 'light']))] return 'test passes' print test()
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# -*- coding: utf-8 -*- # Licensed under the Apache License: http://www.apache.org/licenses/LICENSE-2.0 # For details: https://bitbucket.org/ned/coveragepy/src/default/NOTICE.txt # # coverage.py documentation build configuration file, created by # sphinx-quickstart on Wed May 13 22:18:33 2009. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys, os # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.append(os.path.abspath('.')) # on_rtd is whether we are on readthedocs.org on_rtd = os.environ.get('READTHEDOCS', None) == 'True' # -- General configuration ----------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.todo', 'sphinx.ext.ifconfig', 'sphinxcontrib.spelling', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8' # The master toctree document. master_doc = 'index' # General information about the project. project = u'Coverage.py' copyright = u'2009\N{EN DASH}2017, Ned Batchelder' # CHANGEME # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '4.3.4' # CHANGEME # The full version, including alpha/beta/rc tags. release = '4.3.4' # CHANGEME # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of documents that shouldn't be included in the build. #unused_docs = [] # List of directories, relative to source directory, that shouldn't be searched # for source files. exclude_trees = ['_build'] # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. Major themes that come with # Sphinx are currently 'default' and 'sphinxdoc'. #html_theme = 'default' if not on_rtd: # only import and set the theme if we're building docs locally import sphinx_rtd_theme html_theme = 'sphinx_rtd_theme' html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # otherwise, readthedocs.org uses their theme by default, so no need to specify it # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} #html_style = "neds.css" #html_add_permalinks = "" # Add any paths that contain custom themes here, relative to this directory. html_theme_path = ['_templates'] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. html_use_modindex = False # If false, no index is generated. html_use_index = False # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. html_show_sourcelink = False # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # If nonempty, this is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = '.htm' # Output file base name for HTML help builder. htmlhelp_basename = 'coveragepydoc' # -- Spelling --- spelling_word_list_filename = 'dict.txt' spelling_show_suggestions = False # When auto-doc'ing a class, write the class' docstring and the __init__ docstring # into the class docs. autoclass_content = "class" prerelease = bool(max(release).isalpha()) def setup(app): app.add_stylesheet('coverage.css') app.add_config_value('prerelease', False, 'env') app.info("** Prerelease = %r" % prerelease)
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/src/hackerrank/algo/implementation/kangaroo.py
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no_license
nikhilkuria/algo
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#!/bin/python3 import math import os import random import re import sys # Complete the kangaroo function below. def kangaroo(x1, v1, x2, v2): kangaroo_one_pos = x1 kangaroo_two_pos = x2 while True: if kangaroo_one_pos == kangaroo_two_pos: return "YES" if kangaroo_one_pos > kangaroo_two_pos and v1 >= v2: break if kangaroo_two_pos > kangaroo_one_pos and v2 >= v1: break kangaroo_one_pos = kangaroo_one_pos + v1 kangaroo_two_pos = kangaroo_two_pos + v2 return "NO" print(kangaroo(0,2,5,3))
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/20-01-21 while홀.py
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q=1 while q <=100: print(q) q=q+2
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/propertyestimator/properties/solvation.py
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MSchauperl/propertyestimator
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""" A collection of physical property definitions relating to solvation free energies. """ from propertyestimator import unit from propertyestimator.properties import PhysicalProperty from propertyestimator.properties.plugins import register_estimable_property from propertyestimator.protocols import coordinates, forcefield, miscellaneous, yank, simulation, groups from propertyestimator.substances import Substance from propertyestimator.thermodynamics import Ensemble from propertyestimator.workflow import WorkflowOptions from propertyestimator.workflow.schemas import WorkflowSchema from propertyestimator.workflow.utils import ProtocolPath @register_estimable_property() class SolvationFreeEnergy(PhysicalProperty): """A class representation of a solvation free energy property.""" @staticmethod def get_default_workflow_schema(calculation_layer, options=None): if calculation_layer == 'SimulationLayer': # Currently reweighting is not supported. return SolvationFreeEnergy.get_default_simulation_workflow_schema(options) return None @staticmethod def get_default_simulation_workflow_schema(options=None): """Returns the default workflow to use when estimating this property from direct simulations. Parameters ---------- options: WorkflowOptions The default options to use when setting up the estimation workflow. Returns ------- WorkflowSchema The schema to follow when estimating this property. """ # Setup the fully solvated systems. build_full_coordinates = coordinates.BuildCoordinatesPackmol('build_solvated_coordinates') build_full_coordinates.substance = ProtocolPath('substance', 'global') build_full_coordinates.max_molecules = 2000 assign_full_parameters = forcefield.BuildSmirnoffSystem(f'assign_solvated_parameters') assign_full_parameters.force_field_path = ProtocolPath('force_field_path', 'global') assign_full_parameters.substance = ProtocolPath('substance', 'global') assign_full_parameters.coordinate_file_path = ProtocolPath('coordinate_file_path', build_full_coordinates.id) # Perform a quick minimisation of the full system to give # YANK a better starting point for its minimisation. energy_minimisation = simulation.RunEnergyMinimisation('energy_minimisation') energy_minimisation.system_path = ProtocolPath('system_path', assign_full_parameters.id) energy_minimisation.input_coordinate_file = ProtocolPath('coordinate_file_path', build_full_coordinates.id) equilibration_simulation = simulation.RunOpenMMSimulation('equilibration_simulation') equilibration_simulation.ensemble = Ensemble.NPT equilibration_simulation.steps_per_iteration = 100000 equilibration_simulation.output_frequency = 10000 equilibration_simulation.timestep = 2.0 * unit.femtosecond equilibration_simulation.thermodynamic_state = ProtocolPath('thermodynamic_state', 'global') equilibration_simulation.system_path = ProtocolPath('system_path', assign_full_parameters.id) equilibration_simulation.input_coordinate_file = ProtocolPath('output_coordinate_file', energy_minimisation.id) # Create a substance which only contains the solute (e.g. for the # vacuum phase simulations). filter_solvent = miscellaneous.FilterSubstanceByRole('filter_solvent') filter_solvent.input_substance = ProtocolPath('substance', 'global') filter_solvent.component_role = Substance.ComponentRole.Solvent filter_solute = miscellaneous.FilterSubstanceByRole('filter_solute') filter_solute.input_substance = ProtocolPath('substance', 'global') filter_solute.component_role = Substance.ComponentRole.Solute # Setup the solute in vacuum system. build_vacuum_coordinates = coordinates.BuildCoordinatesPackmol('build_vacuum_coordinates') build_vacuum_coordinates.substance = ProtocolPath('filtered_substance', filter_solute.id) build_vacuum_coordinates.max_molecules = 1 assign_vacuum_parameters = forcefield.BuildSmirnoffSystem(f'assign_parameters') assign_vacuum_parameters.force_field_path = ProtocolPath('force_field_path', 'global') assign_vacuum_parameters.substance = ProtocolPath('filtered_substance', filter_solute.id) assign_vacuum_parameters.coordinate_file_path = ProtocolPath('coordinate_file_path', build_vacuum_coordinates.id) # Set up the protocol to run yank. run_yank = yank.SolvationYankProtocol('run_solvation_yank') run_yank.solute = ProtocolPath('filtered_substance', filter_solute.id) run_yank.solvent_1 = ProtocolPath('filtered_substance', filter_solvent.id) run_yank.solvent_2 = Substance() run_yank.thermodynamic_state = ProtocolPath('thermodynamic_state', 'global') run_yank.steps_per_iteration = 500 run_yank.checkpoint_interval = 50 run_yank.solvent_1_coordinates = ProtocolPath('output_coordinate_file', equilibration_simulation.id) run_yank.solvent_1_system = ProtocolPath('system_path', assign_full_parameters.id) run_yank.solvent_2_coordinates = ProtocolPath('coordinate_file_path', build_vacuum_coordinates.id) run_yank.solvent_2_system = ProtocolPath('system_path', assign_vacuum_parameters.id) # Set up the group which will run yank until the free energy has been determined to within # a given uncertainty conditional_group = groups.ConditionalGroup(f'conditional_group') conditional_group.max_iterations = 20 if options.convergence_mode != WorkflowOptions.ConvergenceMode.NoChecks: condition = groups.ConditionalGroup.Condition() condition.condition_type = groups.ConditionalGroup.ConditionType.LessThan condition.right_hand_value = ProtocolPath('target_uncertainty', 'global') condition.left_hand_value = ProtocolPath('estimated_free_energy.uncertainty', conditional_group.id, run_yank.id) conditional_group.add_condition(condition) # Define the total number of iterations that yank should run for. total_iterations = miscellaneous.MultiplyValue('total_iterations') total_iterations.value = 2000 total_iterations.multiplier = ProtocolPath('current_iteration', conditional_group.id) # Make sure the simulations gets extended after each iteration. run_yank.number_of_iterations = ProtocolPath('result', total_iterations.id) conditional_group.add_protocols(total_iterations, run_yank) # Define the full workflow schema. schema = WorkflowSchema(property_type=SolvationFreeEnergy.__name__) schema.id = '{}{}'.format(SolvationFreeEnergy.__name__, 'Schema') schema.protocols = { build_full_coordinates.id: build_full_coordinates.schema, assign_full_parameters.id: assign_full_parameters.schema, energy_minimisation.id: energy_minimisation.schema, equilibration_simulation.id: equilibration_simulation.schema, filter_solvent.id: filter_solvent.schema, filter_solute.id: filter_solute.schema, build_vacuum_coordinates.id: build_vacuum_coordinates.schema, assign_vacuum_parameters.id: assign_vacuum_parameters.schema, conditional_group.id: conditional_group.schema } schema.final_value_source = ProtocolPath('estimated_free_energy', conditional_group.id, run_yank.id) return schema
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/python/python_27357.py
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AK-1121/code_extraction
cc812b6832b112e3ffcc2bb7eb4237fd85c88c01
5297a4a3aab3bb37efa24a89636935da04a1f8b6
refs/heads/master
2020-05-23T08:04:11.789141
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# pyplot.savefig with empty export plt.show()
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/Log-Parsers/Recognition_Long_Talks/general_classes.py
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[]
no_license
jrweis01/Rubidium
89b27b8376891b42eb6b8bf952f70d92dd81768c
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refs/heads/master
2020-05-30T05:29:11.649283
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from templates_data import * import openpyxl import os import sys import shutil import datetime class Utils(object): def fetch_files_from_folder(self, pathToFolder): _pathToFiles = [] _fileNames = [] for dirPath, dirNames, fileNames in os.walk(pathToFolder): selected_path = [os.path.join(dirPath, item) for item in fileNames] _pathToFiles.extend(selected_path) selectedFile = [item for item in fileNames] _fileNames.extend(selectedFile) # Try to remove empty entries if none of the required files are in directory try: _pathToFiles.remove('') _imageFiles.remove('') except ValueError: pass # Warn if nothing was found in the given path if selectedFile == []: print 'No files with given parameters were found in:\n', dirPath, '\n' print len(_fileNames), 'files were found is searched folder(s)' return _pathToFiles, _fileNames def get_excel_worksheet(self): pass @staticmethod def insertion_sort(items): for i in range(1, len(items)): j = i while j > 0 and items[j] > items[j - 1]: items[j - 1], items[j] = items[j], items[j - 1] j = j - 1 return items def sort_order_dict(self,order_dict): for key in order_dict: items = order_dict[key] items = self.insertion_sort(items) def sorting_headers(self,sorting_dict,order_dict): sorted_list = [] for m in order_dict["noise_file_name"]: for i in order_dict["trig_to_ASR_delay"]: for j in order_dict["signal_dB"]: for k in order_dict["noise_dB"]: for key in sorting_dict: if (sorting_dict[key]["noise_file_name"] == str(m) and sorting_dict[key]["trig_to_ASR_delay"] == str(int(i)) and sorting_dict[key]["signal_dB"] == str(int(j)) and sorting_dict[key]["noise_dB"] == str(int(k))): sorted_list.append(key) return sorted_list def clear_dict_values(self,dict): for key in dict: dict[key].clear() def get_folder_location_path(self,folder): program_path = os.path.dirname(sys.argv[0]) template_path = program_path + '\\' + folder return template_path class ExcelHandler(object): def __init__(self, workbook_name): self.wb_name = workbook_name self.wb_name_with_dt = self._creat_new_excel_from_template_with_name_and_datetime(workbook_name) self.wb = openpyxl.load_workbook(str(self.wb_name_with_dt)) self.template_info = {} self.template_indexes = {'TRIG_ONLY': 4, 'MP_mTRIG_sASR': 4 ,'LJ_sTRIG_mASR' : 4} self.sheet_MP = None self.sheet_trig_only = None self.sheet_LJ_sTRIG_mASR = None def run_log_printing_LJ_sTRIG_mASR(self,log_dict): ''' for 'LJ_sTRIG_mASR' SHEET TEMPLATE''' asr_section = log_dict['asr_results_dict'] trig_section = log_dict['trig_results_dict_format'] if self.sheet_LJ_sTRIG_mASR is None: self.sheet_LJ_sTRIG_mASR = self._open_sheet('LJ_sTRIG_mASR') ROW = self.template_indexes['LJ_sTRIG_mASR'] ''' printing header section''' self._write_line_to_excel_sheet(self.sheet_LJ_sTRIG_mASR, ROW, 1, log_dict,EXCEL_LJ_sTRIG_mASR_TEMPLATE_HEADER_SECTION) ''' printing trig section''' self._write_line_to_excel_sheet(self.sheet_LJ_sTRIG_mASR,ROW,27,trig_section,EXCEL_LJ_sTRIG_mASR_TEMPLATE_TRIG_SECTION) ''' printing asr section''' cmd_template_order = ['volume_down' , 'volume_up' , 'next_song', 'pause' , 'resume', 'what_distance_have_i_done'] cmd_template_dict = {'volume_down': 'empty1.wav' , 'volume_up' : 'empty2.wav' , 'next_song' : 'empty3.wav', 'pause' : 'empty4.wav', 'resume' : 'empty5.wav' , 'what_distance_have_i_done' : 'empty6.wav'} for command in cmd_template_order: curr_key = cmd_template_dict[command] if curr_key in asr_section.keys(): curr_cmd_dict = asr_section[curr_key] self._write_line_to_excel_sheet(self.sheet_LJ_sTRIG_mASR, ROW, 10, curr_cmd_dict, EXCEL_LJ_sTRIG_mASR_TEMPLATE_ASR_SECTION) else: pass ROW += 1 self.template_indexes['LJ_sTRIG_mASR']+=6 def run_log_printing_TRIG_ONLY(self,log_dict,exl_tab_name): ''' for 'TRIG_ONLY' SHEET TEMPLATE''' if self.sheet_trig_only is None: self.sheet_trig_only = self._open_sheet(exl_tab_name) ROW = self.template_indexes[exl_tab_name] self._write_line_to_excel_sheet(self.sheet_trig_only,ROW,1,log_dict,EXCEL_TRIG_TEMPLATE_TUPLE) self.template_indexes[exl_tab_name] += 1 def run_log_printing_TRIG_ASR_MP(self,log_dict): ''' for 'MP_mTrig_sASR' SHEET TEMPLATE''' if self.sheet_MP is None: self.sheet_MP = self._open_sheet("MP_mTRIG_sASR") ROW = self.template_indexes["MP_mTRIG_sASR"] self._write_line_to_excel_sheet(self.sheet_MP,ROW,1,log_dict,EXCEL_MP_CMD_TEMPLATE) self.template_indexes['MP_mTRIG_sASR']+=1 def get_new_wb_name(self): return self.wb_name_with_dt def _creat_new_excel_from_template_with_name_and_datetime(self,project_name): program_path = os.path.dirname(sys.argv[0]) template_path = program_path + '\\template\exl.xlsx' shutil.copy2(str(template_path), str(program_path)) date_time = datetime.datetime.strftime(datetime.datetime.now(), '_%Y-%m-%d__%H_%M_%S') exl_file_name = str(project_name) + str(date_time) + ".xlsx" os.rename("exl.xlsx", str(exl_file_name)) return str(exl_file_name) def _write_line_to_excel_sheet(self,sheet,row,column,val_dict,template_list): row = str(row) start_col = column for i, key in enumerate(template_list): col = self._num_to_excel_alphabeit_colms(i+start_col) try: # sheet[col + row] = str(val_dict[key]) sheet[col + row] = val_dict[key] except : print key def _open_sheet(self,sheet_name): sheet = self.wb.get_sheet_by_name(sheet_name) return sheet def _num_to_excel_alphabeit_colms(self,index_num): cal1 = index_num % 27 cal2 = index_num // 26 new = index_num - cal2 * 26 if new == 0: new = 26 cal2 -= 1 if cal2: mychar = chr(cal2 + 64) + chr(new + 64) else: mychar = chr(index_num + 64) return mychar def save_workbook(self): self.wb.save(str(self.wb_name_with_dt))
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/caesar_cipher.py
f4a48db54a62b7b6068e748444f02a88f468a015
[]
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rongoodbin/secret_messages
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import string from ciphers import Cipher class Caesar(Cipher): FORWARD = string.ascii_uppercase * 3 def __init__(self, keyword=None, offset=3): self.offset = offset self.FORWARD = string.ascii_uppercase + string.ascii_uppercase[:self.offset+1] self.BACKWARD = string.ascii_uppercase[:self.offset+1] + string.ascii_uppercase def encrypt(self, text): output = [] text = text.upper() for char in text: try: index = self.FORWARD.index(char) except ValueError: output.append(char) else: output.append(self.FORWARD[index+self.offset]) return ''.join(output) def decrypt(self, text): output = [] text = text.upper() for char in text: try: index = self.BACKWARD.index(char) except ValueError: output.append(char) else: output.append(self.BACKWARD[index-self.offset]) return ''.join(output) if __name__ == "__main__": atbash = Caesar() encrypted_text = atbash.encrypt("testing this code! 2pm") print(encrypted_text) decrypted_text = atbash.decrypt(encrypted_text) print(decrypted_text)
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/ironic_python_agent/config.py
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2020-07-24T13:10:22.269466
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# Copyright 2016 Cisco Systems, Inc. # # 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. from oslo_config import cfg from ironic_python_agent import inspector from ironic_python_agent import netutils from ironic_python_agent import utils CONF = cfg.CONF APARAMS = utils.get_agent_params() cli_opts = [ cfg.StrOpt('api_url', default=APARAMS.get('ipa-api-url'), deprecated_name='api-url', regex='^http(s?):\/\/.+', help='URL of the Ironic API. ' 'Can be supplied as "ipa-api-url" kernel parameter.' 'The value must start with either http:// or https://.'), cfg.StrOpt('listen_host', default=APARAMS.get('ipa-listen-host', netutils.get_wildcard_address()), sample_default='::', deprecated_name='listen-host', help='The IP address to listen on. ' 'Can be supplied as "ipa-listen-host" kernel parameter.'), cfg.IntOpt('listen_port', default=int(APARAMS.get('ipa-listen-port', 9999)), deprecated_name='listen-port', help='The port to listen on. ' 'Can be supplied as "ipa-listen-port" kernel parameter.'), cfg.StrOpt('advertise_host', default=APARAMS.get('ipa-advertise-host', None), deprecated_name='advertise_host', help='The host to tell Ironic to reply and send ' 'commands to. ' 'Can be supplied as "ipa-advertise-host" ' 'kernel parameter.'), cfg.IntOpt('advertise_port', default=int(APARAMS.get('ipa-advertise-port', 9999)), deprecated_name='advertise-port', help='The port to tell Ironic to reply and send ' 'commands to. ' 'Can be supplied as "ipa-advertise-port" ' 'kernel parameter.'), cfg.IntOpt('ip_lookup_attempts', default=int(APARAMS.get('ipa-ip-lookup-attempts', 3)), deprecated_name='ip-lookup-attempts', help='The number of times to try and automatically ' 'determine the agent IPv4 address. ' 'Can be supplied as "ipa-ip-lookup-attempts" ' 'kernel parameter.'), cfg.IntOpt('ip_lookup_sleep', default=int(APARAMS.get('ipa-ip-lookup-timeout', 10)), deprecated_name='ip-lookup-sleep', help='The amount of time to sleep between attempts ' 'to determine IP address. ' 'Can be supplied as "ipa-ip-lookup-timeout" ' 'kernel parameter.'), cfg.StrOpt('network_interface', default=APARAMS.get('ipa-network-interface', None), deprecated_name='network-interface', help='The interface to use when looking for an IP address. ' 'Can be supplied as "ipa-network-interface" ' 'kernel parameter.'), cfg.IntOpt('lookup_timeout', default=int(APARAMS.get('ipa-lookup-timeout', 300)), deprecated_name='lookup-timeout', help='The amount of time to retry the initial lookup ' 'call to Ironic. After the timeout, the agent ' 'will exit with a non-zero exit code. ' 'Can be supplied as "ipa-lookup-timeout" ' 'kernel parameter.'), cfg.IntOpt('lookup_interval', default=int(APARAMS.get('ipa-lookup-interval', 1)), deprecated_name='lookup-interval', help='The initial interval for retries on the initial ' 'lookup call to Ironic. The interval will be ' 'doubled after each failure until timeout is ' 'exceeded. ' 'Can be supplied as "ipa-lookup-interval" ' 'kernel parameter.'), cfg.FloatOpt('lldp_timeout', default=APARAMS.get('ipa-lldp-timeout', APARAMS.get('lldp-timeout', 30.0)), help='The amount of seconds to wait for LLDP packets. ' 'Can be supplied as "ipa-lldp-timeout" ' 'kernel parameter.'), cfg.BoolOpt('collect_lldp', default=APARAMS.get('ipa-collect-lldp', False), help='Whether IPA should attempt to receive LLDP packets for ' 'each network interface it discovers in the inventory. ' 'Can be supplied as "ipa-collect-lldp" ' 'kernel parameter.'), cfg.BoolOpt('standalone', default=APARAMS.get('ipa-standalone', False), help='Note: for debugging only. Start the Agent but suppress ' 'any calls to Ironic API. ' 'Can be supplied as "ipa-standalone" ' 'kernel parameter.'), cfg.StrOpt('inspection_callback_url', default=APARAMS.get('ipa-inspection-callback-url'), help='Endpoint of ironic-inspector. If set, hardware inventory ' 'will be collected and sent to ironic-inspector ' 'on start up. ' 'Can be supplied as "ipa-inspection-callback-url" ' 'kernel parameter.'), cfg.StrOpt('inspection_collectors', default=APARAMS.get('ipa-inspection-collectors', inspector.DEFAULT_COLLECTOR), help='Comma-separated list of plugins providing additional ' 'hardware data for inspection, empty value gives ' 'a minimum required set of plugins. ' 'Can be supplied as "ipa-inspection-collectors" ' 'kernel parameter.'), cfg.IntOpt('inspection_dhcp_wait_timeout', default=APARAMS.get('ipa-inspection-dhcp-wait-timeout', inspector.DEFAULT_DHCP_WAIT_TIMEOUT), help='Maximum time (in seconds) to wait for the PXE NIC ' '(or all NICs if inspection_dhcp_all_interfaces is True) ' 'to get its IP address via DHCP before inspection. ' 'Set to 0 to disable waiting completely. ' 'Can be supplied as "ipa-inspection-dhcp-wait-timeout" ' 'kernel parameter.'), cfg.BoolOpt('inspection_dhcp_all_interfaces', default=APARAMS.get('ipa-inspection-dhcp-all-interfaces', False), help='Whether to wait for all interfaces to get their IP ' 'addresses before inspection. If set to false ' '(the default), only waits for the PXE interface. ' 'Can be supplied as ' '"ipa-inspection-dhcp-all-interfaces" ' 'kernel parameter.'), cfg.IntOpt('hardware_initialization_delay', default=APARAMS.get('ipa-hardware-initialization-delay', 0), help='How much time (in seconds) to wait for hardware to ' 'initialize before proceeding with any actions. ' 'Can be supplied as "ipa-hardware-initialization-delay" ' 'kernel parameter.'), cfg.IntOpt('disk_wait_attempts', default=APARAMS.get('ipa-disk-wait-attempts', 10), help='The number of times to try and check to see if ' 'at least one suitable disk has appeared in inventory ' 'before proceeding with any actions. ' 'Can be supplied as "ipa-disk-wait-attempts" ' 'kernel parameter.'), cfg.IntOpt('disk_wait_delay', default=APARAMS.get('ipa-disk-wait-delay', 3), help='How much time (in seconds) to wait between attempts ' 'to check if at least one suitable disk has appeared ' 'in inventory. Set to zero to disable. ' 'Can be supplied as "ipa-disk-wait-delay" ' 'kernel parameter.'), cfg.BoolOpt('insecure', default=APARAMS.get('ipa-insecure', False), help='Verify HTTPS connections. Can be supplied as ' '"ipa-insecure" kernel parameter.'), cfg.StrOpt('cafile', help='Path to PEM encoded Certificate Authority file ' 'to use when verifying HTTPS connections. ' 'Default is to use available system-wide configured CAs.'), cfg.StrOpt('certfile', help='Path to PEM encoded client certificate cert file. ' 'Must be provided together with "keyfile" option. ' 'Default is to not present any client certificates to ' 'the server.'), cfg.StrOpt('keyfile', help='Path to PEM encoded client certificate key file. ' 'Must be provided together with "certfile" option. ' 'Default is to not present any client certificates to ' 'the server.'), ] CONF.register_cli_opts(cli_opts) def list_opts(): return [('DEFAULT', cli_opts)]
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from details import spy, friends,ChatMessage,Spy from steganography.steganography import Steganography from datetime import datetime status_message = ['on work','updating....','on mood to learn'] print 'Hello let\s get started' existing = raw_input(" Do You Want continue as " + spy.salutation + " " + spy.name + " (Y/N)? ").upper() def add_status(current_status_message) : updated_status_message = None if current_status_message != None : print 'your current status message is %s \n' % (current_status_message) else : print 'you don\'t have any status message..\n' default = raw_input("do you want to select from the older status message(y/n)? Or want to write new?(n)") if default.upper() == "N" : new_status_message = raw_input("what stauts do you want to set?") if len(new_status_message) > 0: status_message.append(new_status_message) updated_status_message = new_status_message if updated_status_message.isspace(): print 'you don\'t have any status..' else: updated_status_message = updated_status_message.strip() print updated_status_message elif default.upper() == 'Y' : item_position = 1 for message in status_message : print '%d. %s' % (item_position, message) item_position = item_position + 1 message_selection = int(raw_input("\n choose from the above message")) if len(status_message) >= message_selection : updated_status_message = status_message[message_selection - 1] else: print 'the option you choose not available' if updated_status_message: print 'Your updated status message is: %s' % (updated_status_message) else: updated_status_message.startswith(" ") print 'You current don\'t have a status update' return updated_status_message def add_friend() : present_friend = spy('','',0,0.0) present_friend.name = raw_input("please add your friend's name") present_friend.salutation = raw_input("are they mr. or miss.?") present_friend.name = present_friend.salutation + " " + present_friend.name present_friend.age = raw_input("age?") present_friend.age = int(present_friend.age) present_friend.rating = raw_input("rating?") present_friend.rating = float(present_friend.rating) if len(present_friend.name) > 0 and present_friend.age >= 20 and present_friend.rating >= 2.0: friends.append(present_friend) print 'Friend Added!' else: print 'sorry! unable to add..invalid entry!' return len(friends) def select_friend(): item_number = 0 for friend in friends: print '%d %s with age %d with rating %.2f is online' % (item_number + 1, friend.name, friend.age, friend.rating) item_number = item_number + 1 friend_choice = raw_input("Choose from your friends") friend_choice_position = int(friend_choice) - 1 return friend_choice_position def send_message(): friend_choice = select_friend() original_image = raw_input("What is the name of image?") output_path = "output.jpg " text = raw_input("what do you want to say? ") Steganography.encode(original_image , output_path, text) new_chat = ChatMessage(text,True) friends[friend_choice].chats.append(new_chat) print "Your secret message image is ready!" def read_message(): sender = select_friend() output_path = raw_input("What is the name of the file?") secret_text = Steganography.decode(output_path) new_chat = ChatMessage(secret_text,False) friends[sender].chats.append(new_chat) print "Your secret message has been saved!" def read_chat_history(): read_for = select_friend() print '\n5' for chat in friends[read_for].chats: if chat.sent_by_me: print '[%s] %s: %s' % (chat.time.strftime("%d %B %Y"), 'You said:', chat.message) else: print '[%s] %s said: %s' % (chat.time.strftime("%d %B %Y"), friends[read_for].name, chat.message) def start_chat(spy) : current_status_message = None spy.name = spy.salutation + " " + spy.name if spy.age >=20 and spy.age <=50 : print "Authentication Complete. Welcome " + spy.name + " age: " + str(spy.age) + " and rating of spy:" + str( spy.rating) \ + " Proud to Have You onboard.." show_menu = True while show_menu : menu_choices = "What do you want to do?\n 1. Add a Status\n 2. Add a Friend\n 3. Send a Secret Message\n 4. Read a Secret Message\n" \ " 5. Read chat history\n 6. show status \n 7. show friends list\n 8. exit apllication\n" menu_choice = raw_input(menu_choices) if len(menu_choice) > 0 : menu_choice = int(menu_choice) if menu_choice == 1 : print 'you choose to Status Update' current_status_message = add_status(current_status_message) elif menu_choice == 2 : print 'you can add a friend now!' number_of_friends = add_friend() print 'You have %d friends' % (number_of_friends) elif menu_choice == 3 : print 'you can send a secret message here!' send_message() elif menu_choice == 4 : print 'you can read a secret message here!' read_message() elif menu_choice == 5 : print 'Your chat history' read_chat_history() elif menu_choice == 6: print 'your staus message here!\n' if current_status_message.startswith(" "): print 'you don\'t have status.. ' elif current_status_message.isspace(): print'you don\'t have any status..' else: current_status_message = add_status(current_status_message) elif menu_choice == 7 : print 'your friends are..\n' for i in friends: print i.name elif menu_choice == 8 : exit() else : show_menu = False else: print 'sorry You are not eligible to be a spy' if existing == "Y": start_chat(spy) else: spy = Spy('','',0,0.0) spy.name = raw_input("welcome to spy chat,tou need to tell your name first:") if len (spy.name) > 0: spy.salutation = raw_input("Should I call you Mr. or Ms.?: ") spy.age = int(raw_input("What is your Age?")) spy.age = int(spy.age) spy.rating = float(raw_input("what is your rating:")) if spy.rating >= 4.5: print "wow! Great Ace." elif spy.rating >= 4.0 and spy.rating < 4.5 : print "you are good." elif spy.rating >= 3.0 and spy.rating < 4.0 : print "you can do better." else: print 'We can always need to help in Office..' spy_rating = float(spy.rating) spy_is_online = True start_chat(spy) else : print "A Spy needs a valid Name!"
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#!/usr/bin/env python # # Copyright 2010 Membase, Inc. # # 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. # # PYTHONPATH needs to be set up to point to mc_bin_client import os import subprocess DEF_USERNAME = "Administrator" DEF_PASSWORD = "password" DEF_KIND = "json" DEF_MOXI_PORT = 11211 DEF_HTTP_PORT = 8091 DEF_RAMSIZE = 256 DEF_REPLICA = 1 CLI_EXE_LOC = "../membase-cli/membase" SSH_EXE_LOC = "/opt/membase/bin/cli/membase" class CLIInterface(object): def __init__(self, server, http_port=DEF_HTTP_PORT, username=DEF_USERNAME, password=DEF_PASSWORD, kind=DEF_KIND, debug=False, ssh=False, sshkey=None): self.server = server self.http_port = http_port self.username = username self.password = password self.kind = kind self.debug = debug self.ssh = ssh self.sshkey = sshkey if (debug): self.acting_server_args = "-c %s:%d -u %s -p %s -o %s -d" % (self.server, self.http_port, self.username, self.password, self.kind) else: self.acting_server_args = "-c %s:%d -u %s -p %s -o %s" % (self.server, self.http_port, self.username, self.password, self.kind) def server_list(self): cmd = " server-list " + self.acting_server_args return self.execute_command(cmd) def server_info(self): cmd = " server-info " + self.acting_server_args return self.execute_command(cmd) def server_add(self, server_to_add, rebalance=False): if (rebalance): cmd = " rebalance " + self.acting_server_args + " --server-add=%s:%d --server-add-username=%s --server-add-password=%s"\ % (server_to_add, self.http_port, self.username, self.password) else: cmd = " server-add " + self.acting_server_args + " --server-add=%s:%d --server-add-username=%s --server-add-password=%s"\ % (server_to_add, self.http_port, self.username, self.password) return self.execute_command(cmd) def server_readd(self, server_to_readd): cmd = " server-readd " + self.acting_server_args + " --server-add=%s:%d --server-add-username=%s --server-add-password=%s"\ % (server_to_readd, self.http_port, self.username, self.password) return self.execute_command(cmd) def rebalance(self): cmd = " rebalance " + self.acting_server_args return self.execute_command(cmd) def rebalance_stop(self): cmd = " reblance-stop " + self.acting_server_args return self.execute_command(cmd) def rebalance_status(self): cmd = " rebalance-status " + self.acting_server_args return self.execute_command(cmd) def failover(self, server_to_failover): cmd = " failover " + self.acting_server_args + " --server-failover %s" % (server_to_failover) return self.execute_command(cmd) def cluster_init(self, c_username=DEF_USERNAME, c_password=DEF_PASSWORD, c_port=DEF_HTTP_PORT, c_ramsize=DEF_RAMSIZE): cmd = " cluster-init " + self.acting_server_args\ + " --cluster-init-username=%s --cluster-init-password=%s --cluster-init-port=%d --cluster-init-ramsize=%d"\ % (c_username, c_password, c_port, c_ramsize) return self.execute_command(cmd) def node_init(self, path): cmd = " node-init " + self.acting_server_args + " --node-init-data-path=%s" % (path) return self.execute_command(cmd) def bucket_list(self): cmd = " bucket-list " + self.acting_server_args return self.execute_command(cmd) def bucket_create(self, bucket_name, bucket_type, bucket_port, bucket_password="", bucket_ramsize=DEF_RAMSIZE, replica_count=DEF_REPLICA): cmd = " bucket-create " + self.acting_server_args\ + " --bucket=%s --bucket-type=%s --bucket-port=%d --bucket-password=%s --bucket-ramsize=%d --bucket-replica=%d"\ % (bucket_name, bucket_type, bucket_port, bucket_password, bucket_ramsize, replica_count) return self.execute_command(cmd) def bucket_edit(self, bucket_name, bucket_type, bucket_port, bucket_password, bucket_ramsize, replica_count): cmd = " bucket-edit " + self.acting_server_args\ + " --bucket=%s --bucket-type=%s --bucket-port=%d --bucket-password=%s --bucket-ramsize=%d --bucket-replica=%d"\ % (bucket_name, bucket_type, bucket_port, bucket_password, bucket_ramsize, replica_count) return self.execute_command(cmd) def bucket_delete(self, bucket_name): cmd = " bucket-delete " + self.acting_server_args + " --bucket=%s" % (bucket_name) return self.execute_command(cmd) def bucket_flush(self): return "I don't work yet :-(" def execute_command(self, cmd): if (self.ssh): return self.execute_ssh(SSH_EXE_LOC + cmd) else: return self.execute_local(CLI_EXE_LOC + cmd) def execute_local(self, cmd): rtn = "" process = subprocess.Popen(cmd ,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE) stdoutdata,stderrdata=process.communicate() rtn += stdoutdata return rtn def execute_ssh(self, cmd): rtn="" if (self.sshkey == None): process = subprocess.Popen("ssh root@%s \"%s\"" % (self.server,cmd),shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE) else: process = subprocess.Popen("ssh -i %s root@%s \"%s\"" % (self.sshkey, self.server, cmd),shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE) stdoutdata,stderrdata=process.communicate() rtn += stdoutdata return rtn
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#Recall last exercise that you wrote a function, word_lengths, #which took in a string and returned a dictionary where each #word of the string was mapped to an integer value of how #long it was. # #This time, write a new function called length_words so that #the returned dictionary maps an integer, the length of a #word, to a list of words from the sentence with that length. #If a word occurs more than once, add it more than once. The #words in the list should appear in the same order in which #they appeared in the sentence. # #For example: # # length_words("I ate a bowl of cereal out of a dog bowl today.") # -> {3: ['ate', 'dog', 'out'], 1: ['a', 'a', 'i'], # 5: ['today'], 2: ['of', 'of'], 4: ['bowl'], 6: ['cereal']} # #As before, you should remove any punctuation and make the #string lowercase. # #Hint: To create a new list as the value for a dictionary key, #use empty brackets: lengths[wordLength] = []. Then, you would #be able to call lengths[wordLength].append(word). Note that #if you try to append to the list before creating it for that #key, you'll receive a KeyError. #Write your function here! def length_words(string): to_replace = ".,'!?" for mark in to_replace: string = string.replace(mark, "") string=string.lower() word_list=string.split() len_words={} for word in word_list: if not len(word)in len_words: len_words[len(word)] = [] len_words[len(word)].append(word) return len_words #Below are some lines of code that will test your function. #You can change the value of the variable(s) to test your #function with different inputs. # #If your function works correctly, this will originally #print: #{1: ['i', 'a', 'a'], 2: ['of', 'of'], 3: ['ate', 'out', 'dog'], 4: ['bowl', 'bowl'], 5: ['today'], 6: ['cereal']} # #The keys may appear in a different order, but within each #list the words should appear in the order shown above. print(length_words("I ate a bowl of cereal out of a dog bowl today."))
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import pickle import gdata.spreadsheet.text_db client = gdata.spreadsheet.text_db.DatabaseClient() client.SetCredentials('wolfgang.schuessel','iybnrxaseld') #client.SetCredentials('ohramweltgeschehen','kidman') databases=client.GetDatabases(name='imported-from-query') tables=databases[0].GetTables(name='mhs') target=tables[0] source=tables[1] print 'target table is ' + target.name print 'source table is ' + source.name databases=client.GetDatabases(name='geo20080813') db=databases[0] tables=db.GetTables(name='') table=tables[0] records=table.GetRecords(1,100) print [r.content for r in records] print [r.content for r in records if r.content['pickled']!=None] ap=[r.content['pickled'] for r in records] print len(ap) print ap au=[pickle.loads(i) for i in ap] print au #['', '', {'test': 'true', 'name': 'show'}, '', {'hausnummer': 5, 'has_content': False}, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', {'items': {'lokal': 'Asia Cooking'}, 'wifi': True}, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''] print len(au) #50 for i in range(0,len(au)): print i,au[i] print records[30].content #{'fundstelle': 'TRUE', 'hausnummer': '31', 'pickled': "(dp0\nS'items'\np1\n(dp2\nS'lokal'\np3\nS'Asia Cooking'\np4\nssS'wifi'\np5\nI01\ns.", 'address': 'mariahilferstrasse 31 wien', 'name': 'mhs:31'}
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from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import sys import tempfile import os from six.moves import urllib import pandas as pd import tensorflow as tf from featureloader import featureloader # load training features train_data = featureloader('TRAIN', 'ECG5000') df_train, feature_column = train_data.featureloader_UCR() # df_train.to_csv('tmp_1.csv') # load test training test_data = featureloader('TEST', 'ECG5000') df_test, feature_column = test_data.featureloader_UCR() # df_test.to_csv('tmp_2.csv') # remove \n in feature_column feature_column[-1] = feature_column[-1].strip() print(feature_column) def input_fn(df, feature_column): feature_cols = {k: tf.constant(df[k].values, shape=[df[k].size, 1]) for k in feature_column} label = tf.constant(df["label"].values) print(df["label"]) return feature_cols, label def train_input_fn(): return input_fn(df_train, feature_column) def eval_input_fn(): return input_fn(df_test, feature_column) # crossed_columns = tf.contrib.layers.crossed_columns(feature_column) index = 0 layer=[] for feature in feature_column: layer.append(tf.contrib.layers.real_valued_column(feature)) index+= 1 model_dir = tempfile.mkdtemp() m = tf.contrib.learn.LinearClassifier(feature_columns=layer, model_dir=model_dir) # m = tf.contrib.learn.DNNClassifier(feature_columns=layer, # model_dir=model_dir, # hidden_units=[100,50]) m.fit(input_fn = train_input_fn, steps=200) results = m.evaluate(input_fn=eval_input_fn, steps=1) for key in sorted(results): print("%s: %s" % (key, results[key]))
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from chesscorpy.helpers import get_player_colors, determine_player_colors def test_get_player_colors(): assert get_player_colors(5, 5) == ('White', 'black') assert get_player_colors(5, 2) == ('Black', 'white') def test_determine_player_colors(): # TODO: Test 'random' color assert determine_player_colors('white', 1, 2) == (1, 2) assert determine_player_colors('black', 1, 2) == (2, 1)
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import torch from torch import nn from torch.autograd import Variable import torch.nn.functional as F # extremely simple network to do basic science with training methods class BasicNetwork(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(784, 100) self.fc2 = nn.Linear(100, 10) def forward(self, x): x = x.view(-1, 784) x = F.relu(self.fc1(x)) out = self.fc2(x) return out # simple CNN for experiments on CIFAR10 class KrizhevskyNet(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(3, 64, 5) self.pool1 = nn.MaxPool2d(3, 2) self.conv2 = nn.Conv2d(64, 64, 5) self.pool2 = nn.MaxPool2d(3, 2) self.fc1 = nn.Linear(64*3*3, 384) self.fc2 = nn.Linear(384, 192) self.fc3 = nn.Linear(192, 10) def forward(self, x): x = F.relu(self.conv1(x)) x = self.pool1(x) x = F.relu(self.conv2(x)) x = self.pool2(x) x = x.view(x.size(0), -1) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) logits = self.fc3(x) return logits
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# Copyright (C) 2002-2017 CERN for the benefit of the ATLAS collaboration from DataQualityUtils.DQWebDisplayConfig import DQWebDisplayConfig dqconfig = DQWebDisplayConfig() dqconfig.config = "TCT" dqconfig.hcfg = "/afs/cern.ch/user/a/atlasdqm/dqmdisk/tier0/han_config/Collisions/collisions_run.1.41.hcfg" dqconfig.hcfg_min10 = "/afs/cern.ch/user/a/atlasdqm/dqmdisk/tier0/han_config/Collisions/collisions_minutes10.1.9.hcfg" dqconfig.hcfg_min30 = "/afs/cern.ch/user/a/atlasdqm/dqmdisk/tier0/han_config/Collisions/collisions_minutes30.1.5.hcfg" dqconfig.hanResultsDir = "/afs/cern.ch/atlas/offline/external/FullChainTest/tier0/dqm/han_results" dqconfig.htmlDir = "/afs/cern.ch/atlas/offline/external/FullChainTest/tier0/dqm/www" dqconfig.htmlWeb = "http://atlas-project-fullchaintest.web.cern.ch/atlas-project-FullChainTest/tier0/dqm/www" dqconfig.runlist = "runlist_TCT.xml" dqconfig.indexFile = "results_TCT.html" dqconfig.lockFile = "DQWebDisplay_TCT.lock" dqconfig.dbConnection = "sqlite://;schema=MyCOOL_histo.db;dbname=OFLP200" dqconfig.dqmfOfl = "/GLOBAL/DETSTATUS/DQMFOFL" dqconfig.dbConnectionHisto = "sqlite://;schema=MyCOOL_histo.db;dbname=OFLP200" dqconfig.dqmfOflHisto = "/GLOBAL/DETSTATUS/DQMFOFLH" dqconfig.dbTagName = "DetStatusDQMFOFL-TCT"
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#!/usr/bin/env python # coding: utf-8 # Design and Programming by Lead TA: Mojtaba Valipour @ Data Analytics Lab - UWaterloo.ca # COURSE: CS 486/686 - Artificial Intelligence - University of Waterloo - Spring 2020 - Alice Gao # Please let me know if you find any bugs in the code: [email protected] # The code will be available at https://github.com/mojivalipour/nnscratch # Version: 0.9.0 # Implement a neural network from scratch ''' Sources: - http://neuralnetworksanddeeplearning.com/chap2.html ''' print('Life is easy, you just need to do your best to find your place!') # Libraries import numpy as np import matplotlib.pyplot as plt from tqdm import tqdm from sklearn import datasets from sklearn.manifold import TSNE # visualization for data with more than two features from os import path import pandas as pd import csv import copy import random # Helper functions def fixSeed(seed=1010): np.random.seed(seed) random.seed(seed) # The hyper-parameters for the neural network nSamples = None # use None if you want to use full sample size # frogsSmall is the same dataset in Q1 that you have to use for comparision dataset = '2moons' # 2moons/frogsSmall/frogs noise = 0.05 # Noise in artificial datasets visNumSamples = 500 # number of samples to visualize # for regression, we use mean squared error. # for classification, we use cross entropy. # for now only mse is supported! lossFunction = 'mse' gdMethod = 'batch' # batch gradient descent method batchSize = 64 # only for minibatch gradient descent numEpochs = 200 # number of epochs learningRate = [0.5,0.05,0.005] # learning rates # for now only relu and sigmoid is supported lastActivationFunc = 'sigmoid' # relu/sigmoid/softmax # last layer activation function, this one is important # because we need to use it for classification later crossValidationFlag = True # if you like to run cross validation, set this flag to True kFold = 3 # k-fold cross validation, at least need to be 2 seed = 6565 # Do not change the seed for Assignment fixSeed(seed=seed) # fix the seed of random generator to make sure comparision is possible # Some Useful Notes for those students who are interested to know more: ''' - Neural networks are prone to overfitting. Increasing the number of parameters could lead to models that have complexity bigger than data. - Regularization, Normalization and Dropout are popular solutions to overfitting! - In a neural network, we usually use the softmax function as last layer activation for multi-class classification and sigmoid for single class classification. - For regression problems, we usually use Relu as last layer activation function and MSE as the loss function that we want to minimize. - Cross-entropy is the most useful loss function for multi-class classification. - Sometimes we need to use multiple neurons in the output layer, which means that we consider a neuron for each class. In this case, we need to use one-hot vectors to encode the labels. - Weight initialization is important! Gradient descent is not robust to weight initialization! Xavier initialization is the most popular method to initialize weights in neural networks. ''' # Load data colorBox = ['#377eb8','#FA0000','#344AA7', '#1EFA39','#00FBFF','#C500FF','#000000','#FFB600'] if dataset == '2moons': nSamples = 1000 if nSamples is None else nSamples X,y = datasets.make_moons(n_samples=nSamples, noise=noise, random_state=seed) numSamples, numFeatures, numClasses = X.shape[0], X.shape[1], 2 # shuffle X,y idxList = list(range(nSamples)) random.shuffle(idxList) # inplace X, y = X[idxList,:], y[idxList] elif dataset == 'frogsSmall' or dataset == 'frogs': if dataset == 'frogs': # original dataset name = 'Frogs_MFCCs.csv' else: # a small subset of frogs original dataset, same as A2Q1 name = 'frogs-small.csv' # check if we already have the file in the directory if not path.isfile(name): # otherwise ask user to upload it print("Please put this {} file in the current directory using choose files ...".format(name)) # just load the csv file X = pd.read_csv(name, sep=',') X["Family"] = X["Family"].astype('category') X["FamilyCat"] = X["Family"].cat.codes # added to the last column X, y = X.iloc[:,0:22].to_numpy(), X.iloc[:,-1].to_numpy() nSamples = X.shape[0] if nSamples is None else nSamples X, y = X[:nSamples,:], y[:nSamples] # filter number of samples numSamples, numFeatures, numClasses = X.shape[0], X.shape[1], len(np.unique(y)) print('#INFO: N (Number of Samples): {}, D (Number of Features): {}, C (Number of Classes): {}'.format(numSamples, numFeatures, numClasses)) plt.figure() # if y min is not zero, make it zero y = y - y.min() assert y.min() == 0 # sample required sample for visualization indices = list(range(numSamples)) selectedIndices = np.random.choice(indices, visNumSamples) colors = [colorBox[y[idx]] for idx in selectedIndices] if numFeatures == 2: XR = X[selectedIndices, :] else: # use tsne to reduce dimensionality for visualization XR = TSNE(n_components=2).fit_transform(X[selectedIndices,:]) plt.scatter(XR[:, 0], XR[:, 1], s=10, color=colors) plt.savefig('dataset.png') if len(y.shape) < 2: y = np.expand_dims(y,-1) # shape of y should be N x 1 # Define the network structure # # 2-Layer Network # config = { # # Layer Name: [Number of Nodes (in and out), Bias, Activation Function] # 'Hidden Layer 0': [[numFeatures, 30], True, 'relu'], # w1 # 'Fully Connected': [[30, 1], True, lastActivationFunc] # w2 # } # overfit network example config = { # Layer Name: [Number of Nodes (in and out), Bias, Activation Function] 'Hidden Layer 0': [[numFeatures, 1000], True, 'sigmoid'], # w1 'Fully Connected': [[1000, 1], True, lastActivationFunc] # w2 } # 3-Layer Network # config = { # # Layer Name: [Number of Nodes (in and out), Bias, Activation Function] # 'Hidden Layer 0': [[numFeatures, 3], True, 'sigmoid'], # w1 # 'Hidden Layer 1': [[3, 5], True, 'sigmoid'], # w2 # 'Fully Connected': [[5, 1], True, lastActivationFunc] # w2 # } # 4-layer Network # config = { # # Layer Name: [Number of Nodes (in and out), Bias, Activation Function] # 'Hidden Layer 0': [[numFeatures, 100], True, 'relu'], # w1 # 'Hidden Layer 1': [[100, 50], True, 'relu'], # w2 # 'Hidden Layer 2': [[50, 5], True, 'relu'], # w3 # 'Fully Connected': [[5, 1], True, lastActivationFunc] # w4 # } # Fully Connected Neural Network Class class neuralNetwork(): # initializing network def __init__(self, config=None, numClass=2, learningRate=0.005, numEpochs=10, batchSize= 64, lossFunction='mse'): self.config = config self.configKeyList = list(self.config.keys()) self.lossFunction = lossFunction self.numLayers = len(self.config) self.layers = {} self.layerShapes = {} self.learningRate = learningRate self.numEpochs = numEpochs self.loss = [] self.lossT = [] self.acc = [] self.accT = [] self.batchSize = batchSize self.numClass = numClass self.initWeights() # random init def initWeights(self): self.loss = [] self.lossT = [] self.acc = [] self.accT = [] if self.config != None: for key in config: # w is parameters, b is bias, a is activation function self.layers[key] = {'W':np.random.randn(self.config[key][0][0], self.config[key][0][1])/np.sqrt(self.config[key][0][1]), 'b':np.random.randn(self.config[key][0][1], ) if self.config[key][1]==True else [], 'a':self.config[key][2]} # keep track of shape only for better understanding self.layerShapes[key] = {'IS':self.config[key][0][0],'OS':self.config[key][0][1], 'NP':np.prod(self.layers[key]['W'].shape)+len(self.layers[key]['b'])} else: raise '#Err: Make sure you set a configuration correctly!' # activation functions def relu(self, X): return np.maximum(0, X) def sigmoid(self, X): #TODO: fix the overflow problem in Numpy exp function return 1./(1. + np.exp(-X)) def activationFunc(self, X, type='sigmoid'): if type == 'sigmoid': return self.sigmoid(X) elif type == 'relu': return self.relu(X) elif type == 'None': return X # do nothing else: raise '#Err: Not implemented activation function!' # objective/loss/cost functions def mse(self, y, yPred): # mean square error return np.mean(np.power(y-yPred,2)) def lossFunc(self, y, yPred, type='mse'): if type == 'mse': return self.mse(y, yPred) else: raise '#Err: Not implemented objective function!' # back-propagation learning # forward pass def forward(self, X): # apply a(W.T x X + b) for each layer for key in config: #print(X.shape, self.layers[key]['W'].shape) # save input of each layer for backward pass self.layers[key]['i'] = X z = np.dot(X, self.layers[key]['W']) z = z + self.layers[key]['b'] if len(self.layers[key]['b'])!=0 else z # save middle calculation for backward pass self.layers[key]['z'] = z X = self.activationFunc(z, type=self.layers[key]['a']) # save middle calculation for backward pass self.layers[key]['o'] = X return X # yPred # backward pass def backward(self, y, yPred): # derivative of sigmoid def sigmoidPrime(x): return self.sigmoid(x) * (1-self.sigmoid(x)) # derivative of relu def reluPrime(x): return np.where(x <= 0, 0, 1) def identity(x): return x #TODO: It's not necessary to use double for, # it is possible to implement faster and more efficient version # for each parameter (weights and bias) in each layer for idx, key in enumerate(config): # calculate derivatives if self.layers[key]['a'] == 'sigmoid': fPrime = sigmoidPrime elif self.layers[key]['a'] == 'relu': fPrime = reluPrime elif self.layers[key]['a'] == 'softmax': fPrime = softmaxPrime else: # None fPrime = identity deWRTdyPred = -(y-yPred) if self.lossFunction == 'mse' else 1 # de/dyPred # print('de/dy') # dyPred/dyPredBeforeActivation # in case of sigmoid g(x) x (1-g(x)) dyPredWRTdyPredPre = fPrime(self.layers[self.configKeyList[-1]]['o']) # print('dy/dz') # element wise multiplication/ hadamard product delta = np.multiply(deWRTdyPred, dyPredWRTdyPredPre) for idxW in range(len(config),idx,-1): # reverse if idxW-1 == idx: # calculating the derivative for the last one is different # because it is respected to that specific weight #print('\nWeights of layer',idx) deltaB = delta dxWRTdW = self.layers[key]['i'].T # dxWRTdW delta = np.dot(dxWRTdW,delta) #print('dz/dw') else: # this loop is depended to the number of layers in the configuration # print('\nWeights of layer',idxW-1) # the weights of current layer # how fast the cost is changing as a function of the output activation dxWRTdh = self.layers[self.configKeyList[idxW-1]]['W'].T # dxPreWRTdx-1 # print('dz/da') # print('output of layer',idxW-1-1) # the output of previous layer # how fast the activation function is changing dhWRTdhPre = fPrime(self.layers[self.configKeyList[idxW-1-1]]['o']) # dx-1WRTdx-1Pre # print('da/dz') delta = np.dot(delta, dxWRTdh) * dhWRTdhPre # sanity check: Numerical Gradient Checking # f'(x) = lim (f(x+deltax)-f(x))/deltax when deltax -> 0 # update parameters # W = W - Gamma * dL/dW self.layers[key]['djWRTdw'] = delta self.layers[key]['W'] = self.layers[key]['W'] - self.learningRate/y.shape[0] * delta # b = b - Gamma * dL/db self.layers[key]['djWRTdb'] = deltaB if len(self.layers[key]['b'])!=0: self.layers[key]['b'] = self.layers[key]['b'] - self.learningRate/y.shape[0] * np.sum(deltaB, axis=0) # Utility Functions def summary(self, space=20): print('{: <{}} | {: <{}} | {: <{}} | {: <{}}'.format("Layer Name", space, "Input Shape", space, "Output Shape", space, "Number of Parameters",space)) for key in config: print('{: <{}} | {: <{}} | {: <{}} | {: <{}}'.format(key, space, self.layerShapes[key]['IS'], space, self.layerShapes[key]['OS'], space, self.layerShapes[key]['NP'], space)) def fit(self, X, y, XT=None, yT=None, method='batch', batchSize=None, numEpochs=None, learningRate=None, initialState=None): if numEpochs is None: # overwrite numEpochs = self.numEpochs if learningRate is not None: self.learningRate = learningRate if batchSize is not None: self.batchSize = batchSize # if initialState is not None: # # use the given initial parameters (weights and bias) # self.layers = initialState if method == 'batch': # this is infact mini-batch gradient descent, just for consistency in course material # same as batched gradient descent in class to make it easier for you pBar = tqdm(range(numEpochs)) for edx in pBar: for idx in range(0, X.shape[0], self.batchSize): start = idx end = start + self.batchSize end = end if end < X.shape[0] else X.shape[0] #TODO: Support variable batchsize if end-start != self.batchSize: continue x_, y_ = X[start:end, :], y[start:end, :] yPred = self.forward(x_) loss = self.lossFunc(y_, yPred, type=self.lossFunction) self.backward(y_, yPred) yPred,yPredOrig = self.predict(X) loss = self.lossFunc(y, yPredOrig, type=self.lossFunction) self.loss.append(loss) acc = self.accuracy(y, yPred) self.acc.append(acc) if XT is not None: yPred, yPredOrig = self.predict(XT) loss = self.lossFunc(yT, yPredOrig, type=self.lossFunction) self.lossT.append(loss) acc = self.accuracy(yT, yPred) self.accT.append(acc) else: raise '#Err: {} Gradient Descent Method is Not implemented!'.format(method) def predict(self, X): yPred = self.forward(X) yPredOrigin = copy.deepcopy(yPred) # last layer activation function, class prediction should be single # and the output is between zero and one if self.config[self.configKeyList[-1]][-1] == 'sigmoid': yPred[yPred < 0.5] = 0 yPred[yPred >= 0.5] = 1 # multi-class problem elif self.config[self.configKeyList[-1]][-1] == 'softmax': raise '#Err: Prediction is not supported for softmax yet!' # single/multi class problem, single node and it can be anything greater than 0 elif self.config[self.configKeyList[-1]][-1] == 'relu': yPred = np.round(yPred) yPred = np.clip(yPred, 0, self.numClass-1) # sanity check return yPred, yPredOrigin def error(self, y, yPred): return self.lossFunc(y, yPred, type=self.lossFunction) def accuracy(self, y, yPred): return 100*np.sum(y==yPred)/y.shape[0] def plotLoss(self, loss=None, ax=None): if loss is None: loss = self.loss if ax is None: plt.plot(loss) plt.xlabel("Epochs") plt.ylabel("Loss") plt.title("Loss Per Epoch") plt.show() else: ax.plot(loss) ax.set_xlabel("Epochs") ax.set_ylabel("Loss") ax.set_title("Loss Per Epoch") def crossValidationIndices(self, index, k=5): # index is a list of indexes cvList = [] for idx in range(k): # iterate over k-folds interval = int(len(index)/k) start = idx * interval end = start + interval testIndexes = list(range(start,end)) trainIndexes = list(range(0,start)) + list(range(end,len(index))) cvList.append((trainIndexes, testIndexes)) return cvList if crossValidationFlag: if len(learningRate) == 1: fig, ax = plt.subplots(3,len(learningRate),figsize=(8,15)) else: fig, ax = plt.subplots(3,len(learningRate),figsize=(30,3*(len(learningRate)+2))) else: fig, ax = plt.subplots(1,1+len(learningRate),figsize=(30,1+len(learningRate))) for ldx, lr in enumerate(learningRate): nn = neuralNetwork(config=config, numClass=numClasses, numEpochs=numEpochs, learningRate=lr, lossFunction=lossFunction) # Initialize the network and the weights nn.initWeights() if crossValidationFlag: indexes = list(range(X.shape[0])) cvIndices = nn.crossValidationIndices(indexes, k=kFold) accList = [] accTList = [] lossList = [] lossTList = [] for k in range(kFold): nn.initWeights() XTrain, yTrain = X[cvIndices[k][0],:], y[cvIndices[k][0],:] XTest, yTest = X[cvIndices[k][1],:], y[cvIndices[k][1],:] # Train the network nn.fit(XTrain, yTrain, XTest, yTest, method=gdMethod, batchSize=batchSize, numEpochs=numEpochs, learningRate=lr) accList.append(nn.acc) accTList.append(nn.accT) lossList.append(nn.loss) lossTList.append(nn.lossT) acc = np.mean(accList, axis=0) accT = np.mean(accTList, axis=0) loss = np.mean(lossList, axis=0) lossT = np.mean(lossTList, axis=0) # print the network structure nn.summary() yPred, yPredOrig = nn.predict(X) print('#INFO: Mean squared error is {}'.format(nn.error(y,yPred))) colors = [colorBox[int(yPred[idx])] for idx in selectedIndices] if len(learningRate) == 1: ax[2].scatter(XR[:, 0], XR[:, 1], s=10, color=colors) ax[2].set_xlabel("X1") ax[2].set_ylabel("X2") ax[2].set_title("Data, LR: {}".format(lr)) ax[0].plot(acc) ax[0].plot(accT) ax[0].legend(['Train','Test']) ax[0].set_xlabel("Epochs") ax[0].set_ylabel("Accuracy") ax[0].set_title("Accuracy Per Epoch"+", LR: {}".format(lr)) ax[1].plot(loss) ax[1].plot(lossT) ax[1].legend(['Train','Test']) ax[1].set_xlabel("Epochs") ax[1].set_ylabel("Loss") ax[1].set_title("Loss Per Epoch"+", LR: {}".format(lr)) else: ax[2,ldx].scatter(XR[:, 0], XR[:, 1], s=10, color=colors) ax[2,ldx].set_xlabel("X1") ax[2,ldx].set_ylabel("X2") ax[2,ldx].set_title("Data, LR: {}".format(lr)) ax[0,ldx].plot(acc) ax[0,ldx].plot(accT) ax[0,ldx].legend(['Train','Test']) ax[0,ldx].set_xlabel("Epochs") ax[0,ldx].set_ylabel("Accuracy") ax[0,ldx].set_title("Accuracy Per Epoch"+", LR: {}".format(lr)) ax[1,ldx].plot(loss) ax[1,ldx].plot(lossT) ax[1,ldx].legend(['Train','Test']) ax[1,ldx].set_xlabel("Epochs") ax[1,ldx].set_ylabel("Loss") ax[1,ldx].set_title("Loss Per Epoch"+", LR: {}".format(lr)) else: # Perform a single run for visualization. nn.fit(X, y, method=gdMethod, batchSize=batchSize, numEpochs=numEpochs, learningRate=lr) # print the network structure nn.summary() yPred, yPredOrig = nn.predict(X) print('#INFO: Mean squared error is {}'.format(nn.error(y,yPred))) colors = [colorBox[int(yPred[idx])] for idx in selectedIndices] ax[ldx+1].scatter(XR[:, 0], XR[:, 1], s=10, color=colors) ax[ldx+1].set_xlabel("X1") ax[ldx+1].set_ylabel("X2") ax[ldx+1].set_title("LR: {}".format(lr)) # Plot the mean squared error with respect to the nu nn.plotLoss(ax=ax[0]) # train accuracy acc = nn.accuracy(y.squeeze(-1),yPred.squeeze(-1)) print('#INFO: Train Accuracy is {}'.format(acc)) if not crossValidationFlag: ax[0].legend(["LR: "+str(lr) for lr in learningRate]) # please feel free to save subplots for a better report fig.savefig('results.png')
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import mxnet as mx from mxnet.gluon import nn, HybridBlock, Parameter from mxnet.initializer import Xavier class Vgg16(HybridBlock): def __init__(self): super(Vgg16, self).__init__() self.conv1_1 = nn.Conv2D(in_channels=3, channels=64, kernel_size=3, strides=1, padding=1) self.conv1_2 = nn.Conv2D(in_channels=64, channels=64, kernel_size=3, strides=1, padding=1) self.conv2_1 = nn.Conv2D(in_channels=64, channels=128, kernel_size=3, strides=1, padding=1) self.conv2_2 = nn.Conv2D(in_channels=128, channels=128, kernel_size=3, strides=1, padding=1) self.conv3_1 = nn.Conv2D(in_channels=128, channels=256, kernel_size=3, strides=1, padding=1) self.conv3_2 = nn.Conv2D(in_channels=256, channels=256, kernel_size=3, strides=1, padding=1) self.conv3_3 = nn.Conv2D(in_channels=256, channels=256, kernel_size=3, strides=1, padding=1) self.conv4_1 = nn.Conv2D(in_channels=256, channels=512, kernel_size=3, strides=1, padding=1) self.conv4_2 = nn.Conv2D(in_channels=512, channels=512, kernel_size=3, strides=1, padding=1) self.conv4_3 = nn.Conv2D(in_channels=512, channels=512, kernel_size=3, strides=1, padding=1) self.conv5_1 = nn.Conv2D(in_channels=512, channels=512, kernel_size=3, strides=1, padding=1) self.conv5_2 = nn.Conv2D(in_channels=512, channels=512, kernel_size=3, strides=1, padding=1) self.conv5_3 = nn.Conv2D(in_channels=512, channels=512, kernel_size=3, strides=1, padding=1) def hybrid_forward(self,F, X): h = F.Activation(self.conv1_1(X), act_type='relu') h = F.Activation(self.conv1_2(h), act_type='relu') relu1_2 = h h = F.Pooling(h, pool_type='max', kernel=(2, 2), stride=(2, 2)) h = F.Activation(self.conv2_1(h), act_type='relu') h = F.Activation(self.conv2_2(h), act_type='relu') relu2_2 = h h = F.Pooling(h, pool_type='max', kernel=(2, 2), stride=(2, 2)) h = F.Activation(self.conv3_1(h), act_type='relu') h = F.Activation(self.conv3_2(h), act_type='relu') h = F.Activation(self.conv3_3(h), act_type='relu') relu3_3 = h h = F.Pooling(h, pool_type='max', kernel=(2, 2), stride=(2, 2)) h = F.Activation(self.conv4_1(h), act_type='relu') h = F.Activation(self.conv4_2(h), act_type='relu') h = F.Activation(self.conv4_3(h), act_type='relu') relu4_3 = h return [relu1_2, relu2_2, relu3_3, relu4_3] def _init_weights(self, fixed=False, pretrain_path=None, ctx=None): if pretrain_path is not None: print('Loading parameters from {} ...'.format(pretrain_path)) self.collect_params().load(pretrain_path, ctx=ctx) if fixed: print('Setting parameters of VGG16 to fixed ...') for param in self.collect_params().values(): param.grad_req = 'null' else: self.initialize(mx.initializer.Xavier(), ctx=ctx) return_layers_id = { 11: [6, 13, 20, 27], 16: [5, 12, 22, 42] } vgg_spec = {11: ([1, 1, 2, 2, 2], [64, 128, 256, 512, 512]), 13: ([2, 2, 2, 2, 2], [64, 128, 256, 512, 512]), 16: ([2, 2, 3, 3, 3], [64, 128, 256, 512, 512]), 19: ([2, 2, 4, 4, 4], [64, 128, 256, 512, 512])} class VGG(HybridBlock): r"""VGG model from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition" <https://arxiv.org/abs/1409.1556>`_ paper. Parameters ---------- layers : list of int Numbers of layers in each feature block. filters : list of int Numbers of filters in each feature block. List length should match the layers. classes : int, default 1000 Number of classification classes. batch_norm : bool, default False Use batch normalization. """ def __init__(self, num_layers, batch_norm=True, pretrain_path=None, ctx=None, **kwargs): super(VGG, self).__init__(**kwargs) layers, filters = vgg_spec[num_layers] self.features = self._make_features(layers, filters, batch_norm) self.features.add(nn.Dense(4096, activation='relu', weight_initializer='normal', bias_initializer='zeros')) self.features.add(nn.Dropout(rate=0.5)) self.features.add(nn.Dense(4096, activation='relu', weight_initializer='normal', bias_initializer='zeros')) self.features.add(nn.Dropout(rate=0.5)) self.output = nn.Dense(1000, weight_initializer='normal', bias_initializer='zeros') self.return_id_list = return_layers_id[num_layers] if pretrain_path is not None and os.path.isfile(pretrain_path): self.pretrained = True self.load_pretrained_param(pretrain_path, ctx) def _make_features(self, layers, filters, batch_norm): featurizer = nn.HybridSequential() for i, num in enumerate(layers): for _ in range(num): featurizer.add(nn.Conv2D(filters[i], kernel_size=3, padding=1, weight_initializer=Xavier(rnd_type='gaussian', factor_type='out', magnitude=2), bias_initializer='zeros')) if batch_norm: featurizer.add(nn.BatchNorm()) featurizer.add(nn.Activation('relu')) featurizer.add(nn.MaxPool2D(strides=2)) return featurizer def hybrid_forward(self, F, x): return_ = [] for id, layer in enumerate(self.features): if isinstance(layer, nn.basic_layers.Dense): break x = layer(x) if id in self.return_id_list: return_.append(x) #x = self.features(x) #x = self.output(x) return return_ def load_pretrained_param(self, pretrain_path, ctx): print('Loading Parameters from {}'.format(pretrain_path)) self.load_parameters(pretrain_path, ctx=ctx)
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fca01c1f424e8554841fcc221a613fb0bd0a0114
/zespol/admin.py
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[]
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Bartoszmleczko/GigTicketsApp
3bae86cb4cb8d17b90ebed2afa7dd5645b117f51
9fa013da7ec8a73aebca7ec00658470b067dee4a
refs/heads/master
2021-01-26T08:20:47.629696
2020-02-26T22:54:46
2020-02-26T22:54:46
243,381,072
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py
from django.contrib import admin from .models import * # Register your models here. class ClubAdmin(admin.ModelAdmin): list_display = ('name','address') admin.site.register(Band) admin.site.register(Club,ClubAdmin) admin.site.register(Concert) admin.site.register(Ticket) admin.site.register(Profile) admin.site.register(Genre)
cfd644d146385683734341f86b5e62a3ee4cd227
d5a196acb7531c89d930ba51e33e2319fab0972d
/150/A.py
220217dd59ad3170a30a2c1ee380094618c0dce1
[]
no_license
mido1003/atcorder
f1a073a850557c6f18176ad9ff3dfcfe5414afdf
92639b15d982f29042883621c2fb874e1813a447
refs/heads/master
2020-09-20T16:12:53.708315
2020-05-25T09:48:16
2020-05-25T09:48:16
224,533,793
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py
k,x = (int(x) for x in input().split()) if k * 500 >= x: print("Yes") else: print("No")
affbdc260006818519072805edce1e7247140a64
12db36eaad77c99b97878e96f2c4924dcf2ed83f
/exception/__init__.py
1847580c2e336851ee2594b864bd64590bf076c2
[]
no_license
sevenler/orange
0c442bc09dda1c811fd5e996bf240a1e98e788b7
370c04317a4f538f679deb7cab8f6d7a9c9b1d02
refs/heads/master
2021-01-11T22:41:25.748658
2017-01-17T18:13:40
2017-01-17T18:13:40
79,017,934
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py
from error_status import ErrorStatusException from authority import AuthorityException
ef82571b3a9d413818632a92cb1e3edb2d75dab3
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/DrivingTDM_SetupMatlabOOP/headerAndFunctionsMotor/ximc/python-profiles/STANDA/8MT195X-540-4.py
391e7db3d811458155873424999b6ceb86b43093
[ "BSD-2-Clause" ]
permissive
Rasedujjaman/matlabOOP
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e1f025ab9b00a3646719df23852079736d2b5701
refs/heads/main
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2021-08-31T16:12:39
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def set_profile_8MT195X_540_4(lib, id): worst_result = Result.Ok result = Result.Ok feedback_settings = feedback_settings_t() feedback_settings.IPS = 4000 class FeedbackType_: FEEDBACK_ENCODER_MEDIATED = 6 FEEDBACK_NONE = 5 FEEDBACK_EMF = 4 FEEDBACK_ENCODER = 1 feedback_settings.FeedbackType = FeedbackType_.FEEDBACK_EMF class FeedbackFlags_: FEEDBACK_ENC_TYPE_BITS = 192 FEEDBACK_ENC_TYPE_DIFFERENTIAL = 128 FEEDBACK_ENC_TYPE_SINGLE_ENDED = 64 FEEDBACK_ENC_REVERSE = 1 FEEDBACK_ENC_TYPE_AUTO = 0 feedback_settings.FeedbackFlags = FeedbackFlags_.FEEDBACK_ENC_TYPE_SINGLE_ENDED | FeedbackFlags_.FEEDBACK_ENC_TYPE_AUTO feedback_settings.CountsPerTurn = 4000 result = lib.set_feedback_settings(id, byref(feedback_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result home_settings = home_settings_t() home_settings.FastHome = 500 home_settings.uFastHome = 0 home_settings.SlowHome = 500 home_settings.uSlowHome = 0 home_settings.HomeDelta = 500 home_settings.uHomeDelta = 0 class HomeFlags_: HOME_USE_FAST = 256 HOME_STOP_SECOND_BITS = 192 HOME_STOP_SECOND_LIM = 192 HOME_STOP_SECOND_SYN = 128 HOME_STOP_SECOND_REV = 64 HOME_STOP_FIRST_BITS = 48 HOME_STOP_FIRST_LIM = 48 HOME_STOP_FIRST_SYN = 32 HOME_STOP_FIRST_REV = 16 HOME_HALF_MV = 8 HOME_MV_SEC_EN = 4 HOME_DIR_SECOND = 2 HOME_DIR_FIRST = 1 home_settings.HomeFlags = HomeFlags_.HOME_USE_FAST | HomeFlags_.HOME_STOP_SECOND_REV | HomeFlags_.HOME_STOP_FIRST_BITS | HomeFlags_.HOME_DIR_SECOND result = lib.set_home_settings(id, byref(home_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result move_settings = move_settings_t() move_settings.Speed = 1000 move_settings.uSpeed = 0 move_settings.Accel = 2000 move_settings.Decel = 4000 move_settings.AntiplaySpeed = 1000 move_settings.uAntiplaySpeed = 0 class MoveFlags_: RPM_DIV_1000 = 1 result = lib.set_move_settings(id, byref(move_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result engine_settings = engine_settings_t() engine_settings.NomVoltage = 1 engine_settings.NomCurrent = 2100 engine_settings.NomSpeed = 2000 engine_settings.uNomSpeed = 0 class EngineFlags_: ENGINE_LIMIT_RPM = 128 ENGINE_LIMIT_CURR = 64 ENGINE_LIMIT_VOLT = 32 ENGINE_ACCEL_ON = 16 ENGINE_ANTIPLAY = 8 ENGINE_MAX_SPEED = 4 ENGINE_CURRENT_AS_RMS = 2 ENGINE_REVERSE = 1 engine_settings.EngineFlags = EngineFlags_.ENGINE_LIMIT_RPM | EngineFlags_.ENGINE_ACCEL_ON | EngineFlags_.ENGINE_REVERSE engine_settings.Antiplay = 575 class MicrostepMode_: MICROSTEP_MODE_FRAC_256 = 9 MICROSTEP_MODE_FRAC_128 = 8 MICROSTEP_MODE_FRAC_64 = 7 MICROSTEP_MODE_FRAC_32 = 6 MICROSTEP_MODE_FRAC_16 = 5 MICROSTEP_MODE_FRAC_8 = 4 MICROSTEP_MODE_FRAC_4 = 3 MICROSTEP_MODE_FRAC_2 = 2 MICROSTEP_MODE_FULL = 1 engine_settings.MicrostepMode = MicrostepMode_.MICROSTEP_MODE_FRAC_256 engine_settings.StepsPerRev = 200 result = lib.set_engine_settings(id, byref(engine_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result entype_settings = entype_settings_t() class EngineType_: ENGINE_TYPE_BRUSHLESS = 5 ENGINE_TYPE_TEST = 4 ENGINE_TYPE_STEP = 3 ENGINE_TYPE_2DC = 2 ENGINE_TYPE_DC = 1 ENGINE_TYPE_NONE = 0 entype_settings.EngineType = EngineType_.ENGINE_TYPE_STEP | EngineType_.ENGINE_TYPE_NONE class DriverType_: DRIVER_TYPE_EXTERNAL = 3 DRIVER_TYPE_INTEGRATE = 2 DRIVER_TYPE_DISCRETE_FET = 1 entype_settings.DriverType = DriverType_.DRIVER_TYPE_INTEGRATE result = lib.set_entype_settings(id, byref(entype_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result power_settings = power_settings_t() power_settings.HoldCurrent = 50 power_settings.CurrReductDelay = 1000 power_settings.PowerOffDelay = 60 power_settings.CurrentSetTime = 300 class PowerFlags_: POWER_SMOOTH_CURRENT = 4 POWER_OFF_ENABLED = 2 POWER_REDUCT_ENABLED = 1 power_settings.PowerFlags = PowerFlags_.POWER_SMOOTH_CURRENT | PowerFlags_.POWER_OFF_ENABLED | PowerFlags_.POWER_REDUCT_ENABLED result = lib.set_power_settings(id, byref(power_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result secure_settings = secure_settings_t() secure_settings.LowUpwrOff = 800 secure_settings.CriticalIpwr = 4000 secure_settings.CriticalUpwr = 5500 secure_settings.CriticalT = 800 secure_settings.CriticalIusb = 450 secure_settings.CriticalUusb = 520 secure_settings.MinimumUusb = 420 class Flags_: ALARM_ENGINE_RESPONSE = 128 ALARM_WINDING_MISMATCH = 64 USB_BREAK_RECONNECT = 32 ALARM_FLAGS_STICKING = 16 ALARM_ON_BORDERS_SWAP_MISSET = 8 H_BRIDGE_ALERT = 4 LOW_UPWR_PROTECTION = 2 ALARM_ON_DRIVER_OVERHEATING = 1 secure_settings.Flags = Flags_.ALARM_ENGINE_RESPONSE | Flags_.ALARM_FLAGS_STICKING | Flags_.ALARM_ON_BORDERS_SWAP_MISSET | Flags_.H_BRIDGE_ALERT | Flags_.ALARM_ON_DRIVER_OVERHEATING result = lib.set_secure_settings(id, byref(secure_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result edges_settings = edges_settings_t() class BorderFlags_: BORDERS_SWAP_MISSET_DETECTION = 8 BORDER_STOP_RIGHT = 4 BORDER_STOP_LEFT = 2 BORDER_IS_ENCODER = 1 edges_settings.BorderFlags = BorderFlags_.BORDER_STOP_RIGHT | BorderFlags_.BORDER_STOP_LEFT class EnderFlags_: ENDER_SW2_ACTIVE_LOW = 4 ENDER_SW1_ACTIVE_LOW = 2 ENDER_SWAP = 1 edges_settings.EnderFlags = EnderFlags_.ENDER_SWAP edges_settings.LeftBorder = 175 edges_settings.uLeftBorder = 0 edges_settings.RightBorder = 25825 edges_settings.uRightBorder = 0 result = lib.set_edges_settings(id, byref(edges_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result pid_settings = pid_settings_t() pid_settings.KpU = 0 pid_settings.KiU = 0 pid_settings.KdU = 0 pid_settings.Kpf = 0.003599999938160181 pid_settings.Kif = 0.03799999877810478 pid_settings.Kdf = 2.8000000384054147e-05 result = lib.set_pid_settings(id, byref(pid_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result sync_in_settings = sync_in_settings_t() class SyncInFlags_: SYNCIN_GOTOPOSITION = 4 SYNCIN_INVERT = 2 SYNCIN_ENABLED = 1 sync_in_settings.ClutterTime = 4 sync_in_settings.Position = 0 sync_in_settings.uPosition = 0 sync_in_settings.Speed = 0 sync_in_settings.uSpeed = 0 result = lib.set_sync_in_settings(id, byref(sync_in_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result sync_out_settings = sync_out_settings_t() class SyncOutFlags_: SYNCOUT_ONPERIOD = 64 SYNCOUT_ONSTOP = 32 SYNCOUT_ONSTART = 16 SYNCOUT_IN_STEPS = 8 SYNCOUT_INVERT = 4 SYNCOUT_STATE = 2 SYNCOUT_ENABLED = 1 sync_out_settings.SyncOutFlags = SyncOutFlags_.SYNCOUT_ONSTOP | SyncOutFlags_.SYNCOUT_ONSTART sync_out_settings.SyncOutPulseSteps = 100 sync_out_settings.SyncOutPeriod = 2000 sync_out_settings.Accuracy = 0 sync_out_settings.uAccuracy = 0 result = lib.set_sync_out_settings(id, byref(sync_out_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result extio_settings = extio_settings_t() class EXTIOSetupFlags_: EXTIO_SETUP_INVERT = 2 EXTIO_SETUP_OUTPUT = 1 extio_settings.EXTIOSetupFlags = EXTIOSetupFlags_.EXTIO_SETUP_OUTPUT class EXTIOModeFlags_: EXTIO_SETUP_MODE_OUT_BITS = 240 EXTIO_SETUP_MODE_OUT_MOTOR_ON = 64 EXTIO_SETUP_MODE_OUT_ALARM = 48 EXTIO_SETUP_MODE_OUT_MOVING = 32 EXTIO_SETUP_MODE_OUT_ON = 16 EXTIO_SETUP_MODE_IN_BITS = 15 EXTIO_SETUP_MODE_IN_ALARM = 5 EXTIO_SETUP_MODE_IN_HOME = 4 EXTIO_SETUP_MODE_IN_MOVR = 3 EXTIO_SETUP_MODE_IN_PWOF = 2 EXTIO_SETUP_MODE_IN_STOP = 1 EXTIO_SETUP_MODE_IN_NOP = 0 EXTIO_SETUP_MODE_OUT_OFF = 0 extio_settings.EXTIOModeFlags = EXTIOModeFlags_.EXTIO_SETUP_MODE_IN_STOP | EXTIOModeFlags_.EXTIO_SETUP_MODE_IN_NOP | EXTIOModeFlags_.EXTIO_SETUP_MODE_OUT_OFF result = lib.set_extio_settings(id, byref(extio_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result brake_settings = brake_settings_t() brake_settings.t1 = 300 brake_settings.t2 = 500 brake_settings.t3 = 300 brake_settings.t4 = 400 class BrakeFlags_: BRAKE_ENG_PWROFF = 2 BRAKE_ENABLED = 1 brake_settings.BrakeFlags = BrakeFlags_.BRAKE_ENG_PWROFF result = lib.set_brake_settings(id, byref(brake_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result control_settings = control_settings_t() control_settings.MaxSpeed[0] = 100 control_settings.MaxSpeed[1] = 1000 control_settings.MaxSpeed[2] = 0 control_settings.MaxSpeed[3] = 0 control_settings.MaxSpeed[4] = 0 control_settings.MaxSpeed[5] = 0 control_settings.MaxSpeed[6] = 0 control_settings.MaxSpeed[7] = 0 control_settings.MaxSpeed[8] = 0 control_settings.MaxSpeed[9] = 0 control_settings.uMaxSpeed[0] = 0 control_settings.uMaxSpeed[1] = 0 control_settings.uMaxSpeed[2] = 0 control_settings.uMaxSpeed[3] = 0 control_settings.uMaxSpeed[4] = 0 control_settings.uMaxSpeed[5] = 0 control_settings.uMaxSpeed[6] = 0 control_settings.uMaxSpeed[7] = 0 control_settings.uMaxSpeed[8] = 0 control_settings.uMaxSpeed[9] = 0 control_settings.Timeout[0] = 1000 control_settings.Timeout[1] = 1000 control_settings.Timeout[2] = 1000 control_settings.Timeout[3] = 1000 control_settings.Timeout[4] = 1000 control_settings.Timeout[5] = 1000 control_settings.Timeout[6] = 1000 control_settings.Timeout[7] = 1000 control_settings.Timeout[8] = 1000 control_settings.MaxClickTime = 300 class Flags_: CONTROL_BTN_RIGHT_PUSHED_OPEN = 8 CONTROL_BTN_LEFT_PUSHED_OPEN = 4 CONTROL_MODE_BITS = 3 CONTROL_MODE_LR = 2 CONTROL_MODE_JOY = 1 CONTROL_MODE_OFF = 0 control_settings.Flags = Flags_.CONTROL_MODE_LR | Flags_.CONTROL_MODE_OFF control_settings.DeltaPosition = 1 control_settings.uDeltaPosition = 0 result = lib.set_control_settings(id, byref(control_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result joystick_settings = joystick_settings_t() joystick_settings.JoyLowEnd = 0 joystick_settings.JoyCenter = 5000 joystick_settings.JoyHighEnd = 10000 joystick_settings.ExpFactor = 100 joystick_settings.DeadZone = 50 class JoyFlags_: JOY_REVERSE = 1 result = lib.set_joystick_settings(id, byref(joystick_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result ctp_settings = ctp_settings_t() ctp_settings.CTPMinError = 3 class CTPFlags_: CTP_ERROR_CORRECTION = 16 REV_SENS_INV = 8 CTP_ALARM_ON_ERROR = 4 CTP_BASE = 2 CTP_ENABLED = 1 ctp_settings.CTPFlags = CTPFlags_.CTP_ERROR_CORRECTION result = lib.set_ctp_settings(id, byref(ctp_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result uart_settings = uart_settings_t() uart_settings.Speed = 115200 class UARTSetupFlags_: UART_STOP_BIT = 8 UART_PARITY_BIT_USE = 4 UART_PARITY_BITS = 3 UART_PARITY_BIT_MARK = 3 UART_PARITY_BIT_SPACE = 2 UART_PARITY_BIT_ODD = 1 UART_PARITY_BIT_EVEN = 0 uart_settings.UARTSetupFlags = UARTSetupFlags_.UART_PARITY_BIT_EVEN result = lib.set_uart_settings(id, byref(uart_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result controller_name = controller_name_t() controller_name.ControllerName = bytes([0, 113, 252, 118, 36, 0, 72, 0, 3, 0, 0, 0, 104, 101, 103, 0]) class CtrlFlags_: EEPROM_PRECEDENCE = 1 result = lib.set_controller_name(id, byref(controller_name)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result emf_settings = emf_settings_t() emf_settings.L = 0.013000000268220901 emf_settings.R = 2.5999999046325684 emf_settings.Km = 0.015599999576807022 class BackEMFFlags_: BACK_EMF_KM_AUTO = 4 BACK_EMF_RESISTANCE_AUTO = 2 BACK_EMF_INDUCTANCE_AUTO = 1 result = lib.set_emf_settings(id, byref(emf_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result engine_advansed_setup = engine_advansed_setup_t() engine_advansed_setup.stepcloseloop_Kw = 50 engine_advansed_setup.stepcloseloop_Kp_low = 1000 engine_advansed_setup.stepcloseloop_Kp_high = 33 result = lib.set_engine_advansed_setup(id, byref(engine_advansed_setup)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result extended_settings = extended_settings_t() extended_settings.Param1 = 0 result = lib.set_extended_settings(id, byref(extended_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result stage_name = stage_name_t() stage_name.PositionerName = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_stage_name(id, byref(stage_name)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result stage_information = stage_information_t() stage_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) stage_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_stage_information(id, byref(stage_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result stage_settings = stage_settings_t() stage_settings.LeadScrewPitch = 0 stage_settings.Units = bytes([0, 0, 0, 0, 0, 0, 0, 0]) stage_settings.MaxSpeed = 0 stage_settings.TravelRange = 0 stage_settings.SupplyVoltageMin = 0 stage_settings.SupplyVoltageMax = 0 stage_settings.MaxCurrentConsumption = 0 stage_settings.HorizontalLoadCapacity = 0 stage_settings.VerticalLoadCapacity = 0 result = lib.set_stage_settings(id, byref(stage_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result motor_information = motor_information_t() motor_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) motor_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_motor_information(id, byref(motor_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result motor_settings = motor_settings_t() class MotorType_: MOTOR_TYPE_BLDC = 3 MOTOR_TYPE_DC = 2 MOTOR_TYPE_STEP = 1 MOTOR_TYPE_UNKNOWN = 0 motor_settings.MotorType = MotorType_.MOTOR_TYPE_UNKNOWN motor_settings.ReservedField = 0 motor_settings.Poles = 0 motor_settings.Phases = 0 motor_settings.NominalVoltage = 0 motor_settings.NominalCurrent = 0 motor_settings.NominalSpeed = 0 motor_settings.NominalTorque = 0 motor_settings.NominalPower = 0 motor_settings.WindingResistance = 0 motor_settings.WindingInductance = 0 motor_settings.RotorInertia = 0 motor_settings.StallTorque = 0 motor_settings.DetentTorque = 0 motor_settings.TorqueConstant = 0 motor_settings.SpeedConstant = 0 motor_settings.SpeedTorqueGradient = 0 motor_settings.MechanicalTimeConstant = 0 motor_settings.MaxSpeed = 0 motor_settings.MaxCurrent = 0 motor_settings.MaxCurrentTime = 0 motor_settings.NoLoadCurrent = 0 motor_settings.NoLoadSpeed = 0 result = lib.set_motor_settings(id, byref(motor_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result encoder_information = encoder_information_t() encoder_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) encoder_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_encoder_information(id, byref(encoder_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result encoder_settings = encoder_settings_t() encoder_settings.MaxOperatingFrequency = 0 encoder_settings.SupplyVoltageMin = 0 encoder_settings.SupplyVoltageMax = 0 encoder_settings.MaxCurrentConsumption = 0 encoder_settings.PPR = 0 class EncoderSettings_: ENCSET_REVOLUTIONSENSOR_ACTIVE_HIGH = 256 ENCSET_REVOLUTIONSENSOR_PRESENT = 64 ENCSET_INDEXCHANNEL_PRESENT = 16 ENCSET_PUSHPULL_OUTPUT = 4 ENCSET_DIFFERENTIAL_OUTPUT = 1 result = lib.set_encoder_settings(id, byref(encoder_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result hallsensor_information = hallsensor_information_t() hallsensor_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) hallsensor_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_hallsensor_information(id, byref(hallsensor_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result hallsensor_settings = hallsensor_settings_t() hallsensor_settings.MaxOperatingFrequency = 0 hallsensor_settings.SupplyVoltageMin = 0 hallsensor_settings.SupplyVoltageMax = 0 hallsensor_settings.MaxCurrentConsumption = 0 hallsensor_settings.PPR = 0 result = lib.set_hallsensor_settings(id, byref(hallsensor_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result gear_information = gear_information_t() gear_information.Manufacturer = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) gear_information.PartNumber = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) result = lib.set_gear_information(id, byref(gear_information)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result gear_settings = gear_settings_t() gear_settings.ReductionIn = 0 gear_settings.ReductionOut = 0 gear_settings.RatedInputTorque = 0 gear_settings.RatedInputSpeed = 0 gear_settings.MaxOutputBacklash = 0 gear_settings.InputInertia = 0 gear_settings.Efficiency = 0 result = lib.set_gear_settings(id, byref(gear_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result accessories_settings = accessories_settings_t() accessories_settings.MagneticBrakeInfo = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) accessories_settings.MBRatedVoltage = 0 accessories_settings.MBRatedCurrent = 0 accessories_settings.MBTorque = 0 class MBSettings_: MB_POWERED_HOLD = 2 MB_AVAILABLE = 1 accessories_settings.TemperatureSensorInfo = bytes([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) accessories_settings.TSMin = 0 accessories_settings.TSMax = 0 accessories_settings.TSGrad = 0 class TSSettings_: TS_AVAILABLE = 8 TS_TYPE_BITS = 7 TS_TYPE_SEMICONDUCTOR = 2 TS_TYPE_THERMOCOUPLE = 1 TS_TYPE_UNKNOWN = 0 accessories_settings.TSSettings = TSSettings_.TS_TYPE_UNKNOWN class LimitSwitchesSettings_: LS_SHORTED = 16 LS_SW2_ACTIVE_LOW = 8 LS_SW1_ACTIVE_LOW = 4 LS_ON_SW2_AVAILABLE = 2 LS_ON_SW1_AVAILABLE = 1 result = lib.set_accessories_settings(id, byref(accessories_settings)) if result != Result.Ok: if worst_result == Result.Ok or worst_result == Result.ValueError: worst_result = result return worst_result
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permissive
MisinformedDNA/pulumi-azure-native
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de974fd984f7e98649951dbe80b4fc0603d03356
refs/heads/master
2023-03-24T22:02:03.842935
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# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from .. import _utilities, _tables from ._enums import * __all__ = [ 'PrincipalsResponse', ] @pulumi.output_type class PrincipalsResponse(dict): """ User principals. """ def __init__(__self__, *, object_id: Optional[str] = None, upn: Optional[str] = None): """ User principals. :param str object_id: Object Id for the user :param str upn: UPN of the user. """ if object_id is not None: pulumi.set(__self__, "object_id", object_id) if upn is not None: pulumi.set(__self__, "upn", upn) @property @pulumi.getter(name="objectId") def object_id(self) -> Optional[str]: """ Object Id for the user """ return pulumi.get(self, "object_id") @property @pulumi.getter def upn(self) -> Optional[str]: """ UPN of the user. """ return pulumi.get(self, "upn") def _translate_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
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/Count of Matches in Tournament.py
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[]
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Kuehar/LeetCode
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4555c20455f181f9dd7b3aba2a8779dea795edfb
refs/heads/master
2023-04-16T10:13:03.584541
2023-04-06T11:47:21
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243,361,421
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class Solution: def numberOfMatches(self, n: int) -> int: return n-1 # O(1) Solution. # Always this answer is n-1. Sum of matches are always equals to sum of loser. # Runtime: 28 ms, faster than 82.44% of Python3 online submissions for Count of Matches in Tournament. # Memory Usage: 14.3 MB, less than 40.04% of Python3 online submissions for Count of Matches in Tournament.
3e849edd794f2c41729ac050618dd2fa4f7ccd80
31d43b73e8104cd8aef3d97e39666022f2946223
/test.py
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[]
no_license
kgelber1/SSX-Python
2ed6b5e6b7b3775779464a7f624a70155ec8f657
4f5cded3acec68e24206af90ef5611db9adb1ac3
refs/heads/master
2020-06-24T07:08:33.486962
2019-10-24T18:11:18
2019-10-24T18:11:18
198,890,544
1
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import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation fig, ax = plt.subplots(1,1) x=np.linspace(np.pi,4*np.pi,100) N=len(x) ax.set_xlim(len(x)) ax.set_ylim(-1.5,1.5) line, = ax.plot([],[],'o-') def init(): line.set_ydata(np.ma.array(x[:], mask=True)) return line, def animate(i, *args, **kwargs): y=np.sin(x*i) line.set_data(np.arange(N),y) # update the data return line, ani = animation.FuncAnimation(fig, animate, init_func=init, frames=100, interval=10, blit= False, repeat = False) ani.save('2osc.mp4', writer="ffmpeg") fig.show()
1f24bf6dac22f50aece5a8dd643a221f8618bfc3
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/preprocess_dataset.py
b60128ef686b4fc795595ba89976d40b64300b89
[]
no_license
Jonlenes/clusters-news-headlines
92c623a5a214ea21d5e66dc2ff8a984e268374c3
39d54337ef28476a82bd44d39958534a6f4e7368
refs/heads/master
2021-10-19T20:41:54.808979
2019-02-23T11:36:32
2019-02-23T11:36:32
null
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null
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Python
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import pandas import string from nltk.stem.snowball import SnowballStemmer from load_dataset import path_dataset def remove_pnt_and_stemming(text_arr): """ Remove pontuação e executa o o stemming de todo o dataset""" stemmer = SnowballStemmer("english", ignore_stopwords=True) for i in range(0, text_arr.shape[0]): x[i] = x[i].translate(str.maketrans('', '', string.punctuation)) # removendo todas as pontuaçoes words = x[i].split() x[i] = "" for word in words: x[i] += stemmer.stem(word) + " " x[i] = x[i].strip() x[i] = re.sub(r'[^A-Za-z]+', ' ', x[i]) return text_final def split_dataset_by_year(dataset, save_dataset=True): """ Split dataset por ano - retorna/salva 1 dataset para cada ano no arquivo ogirinal """ key = str(dataset[0][0])[:4] datasets = [] current_dataset = [] for data in dataset: if key == str(data[0])[:4]: current_dataset.append(data[1]) else: datasets.append(current_dataset.copy()) key = str(data[0])[:4] current_dataset.clear() current_dataset.append(data[1]) datasets.append(current_dataset.copy()) if save_dataset: for i in range(0, len(datasets)): pandas.DataFrame(datasets[i]).to_csv("dataset_" + str(i + 1) + ".csv", index=False) return datasets if __name__ == '__main__': split_dataset_by_year(path_dataset)
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9fc5dd13e0595bd5796cd7ec109e3b7c290e2692
/wikipedia-scape.py
a54f56c6c75b06d0d4069f56a187c27ded4d5b68
[]
no_license
ronandoolan2/python-webscraping
812d5190dfe5f24029b4737438c80e8d40716971
4dc83a331415c3e55f06b1a8d0de47710db5ccd0
refs/heads/master
2021-01-19T00:54:22.801053
2017-04-16T09:10:47
2017-04-16T09:10:47
87,218,764
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py
from bs4 import BeautifulSoup import urllib2 import re wiki = "http://en.wikipedia.org/wiki/Mad_Max:_Fury_Road" header = {'User-Agent': 'Mozilla/5.0'} #Needed to prevent 403 error on Wikipedia req = urllib2.Request(wiki,headers=header) page = urllib2.urlopen(req) soup = BeautifulSoup(page) rnd = "" pick = "" NFL = "" player = "" pos = "" college = "" conf = "" notes = "" table = soup.find("table", { "class" : "wikitable sortable" }) print table #output = open('output.csv','w') for row in table.findAll("tr"): cells = row.findAll("href") for cell in cells: # search-term = re.search(r'director',cell) # if search-term: # print search-term #print "---" print cell.text print cells.text #print "---"
ec50df0aa2a320ce0f88bb7eea72f3ddae60e3a7
476768e5629340efcbc11fd175c7db12e09c2d52
/python/006.py
be26addbbddf5f50f6e7fff97a4484130aab1bf1
[]
no_license
zero1hac/projecteuler
fb8ded5de8d4126865c11081e4b407e0ae35e304
7dc00e89c9870d5c7d9c6364f1e80e19d69655e5
refs/heads/master
2020-04-23T20:10:51.375485
2019-03-25T08:38:59
2019-03-25T08:38:59
171,430,763
0
0
null
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null
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155
py
if __name__ == "__main__": n = 100 sum_of_squares = (n*(n+1)*(2*n+1))/6 square_of_sum = (n*(n+1)/2)**2 print square_of_sum - sum_of_squares
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/python/practise/带你学Django资料及源码/课堂与博客代码/peace_blog/blog/admin.py
9c1fb6228842fe4ec5d8931dc4a0aad2aa044aa9
[]
no_license
anzhihe/learning
503ab9a58f280227011da5eaa4b14b46c678e6f3
66f7f801e1395207778484e1543ea26309d4b354
refs/heads/master
2023-08-08T11:42:11.983677
2023-07-29T09:19:47
2023-07-29T09:19:47
188,768,643
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null
2023-08-24T02:10:34
2019-05-27T04:04:10
Python
UTF-8
Python
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py
from django.contrib import admin from .models import * # Register your models here. admin.site.register(Banner) admin.site.register(Category) admin.site.register(Tag) admin.site.register(Article) admin.site.register(FriendLink) admin.site.register(Comment) admin.site.register(BlogUser)
abaf5bf0704250f8f6056f02c645210cc6095283
33b3029d6efaa195a0530e8bafbbdc82e7aea697
/scripts/test_01.py
1cd1fbd9755fc06808f6eb20be588a2e5622a120
[]
no_license
wuyun19890323/lesson001
333bc2239151c6337a797d57926f683c05fa0c60
aa2e202b846664adfa5c1af8312b89000311ba8d
refs/heads/master
2020-03-19T11:11:58.829176
2018-06-08T12:53:05
2018-06-08T12:53:05
136,438,645
0
0
null
null
null
null
UTF-8
Python
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6,639
py
from selenium.webdriver.common.by import By from base.base_driver import browser_fire from page.page_load import PageLoad import unittest class TestLoad(unittest.TestCase): # def get_text(self,loc): # return self.scr_load.get_att(self.load_text) def get_ass(self): self.scr_load.get_scr(self.scr_load.load_get()) # 网址 url = "http://localhost/iwebshop/" # 定位登录链接 load_mark = By.XPATH, "//a[@href='/iwebshop/index.php?controller=simple&action=login']" # 定位用户名 username = By.XPATH, "//input[@type='text']" # 定位密码 password = By.XPATH, "//input[@type='password']" # 定位登录按钮 load_click = By.XPATH, "//input[@type='submit']" # 定位登录后文本域 load_text = By.XPATH, "//p[@class='loginfo']" # 定位退出按钮 load_quit = By.XPATH, "//a[@class='reg']" # 定位登录前账户或错误提示 load_wrong = By.XPATH, "//div[@class ='prompt']" # 定位登录前账户为空是提示填写用户名或邮箱 load_username_null = By.XPATH, "//tbody/tr[1]/td/label[@class='invalid-msg']" # 定位登录前密码为空是提示填写密码 load_password_null = By.XPATH, "//tbody/tr[2]/td/label[@class='invalid-msg']" def setUp(self): self.driver = browser_fire() self.scr_load = PageLoad(self.driver) self.scr_load.get_url(self.url) self.scr_load.maxi_wait(30) # 正确账户正确密码 def test_load001(self): # 点击登录链接 self.scr_load.click_load(self.load_mark) # 输入用户名 self.scr_load.input_text(self.username, "admin") # 输入密码 self.scr_load.input_text(self.password, "123456") # 点击登录按钮 self.scr_load.click_load(self.load_click) try: self.assertIn("admin", self.scr_load.get_att(self.load_text)) except AssertionError: self.get_ass() raise self.scr_load.click_load(self.load_quit) def tearDown(self): self.driver.quit() # 正确账户错误密码 def test_load002(self): # 点击登录链接 self.scr_load.click_load(self.load_mark) # 输入用户名 self.scr_load.input_text(self.username, "admin") # 输入密码 self.scr_load.input_text(self.password, "1234567") # 点击登录按钮 self.scr_load.click_load(self.load_click) try: self.assertIn("用户名和密码不匹配", self.scr_load.get_att(self.load_wrong)) except AssertionError: self.get_ass() raise # 正确账户密码为空 def test_load003(self): # 点击登录链接 self.scr_load.click_load(self.load_mark) # 输入用户名 self.scr_load.input_text(self.username, "admin") # 输入密码 self.scr_load.input_text(self.password, "") # 点击登录按钮 self.scr_load.click_load(self.load_click) try: self.assertIn("填写密码", self.scr_load.get_att(self.load_password_null)) except AssertionError: self.get_ass() raise # 错误账户正确密码 def test_load004(self): # 点击登录链接 self.scr_load.click_load(self.load_mark) # 输入用户名 self.scr_load.input_text(self.username, "admin1") # 输入密码 self.scr_load.input_text(self.password, "123456") # 点击登录按钮 self.scr_load.click_load(self.load_click) try: self.assertIn("用户名和密码不匹配", self.scr_load.get_att(self.load_wrong)) except AssertionError: self.get_ass() raise # 错误账户错误密码 def test_load005(self): # 点击登录链接 self.scr_load.click_load(self.load_mark) # 输入用户名 self.scr_load.input_text(self.username, "admin1") # 输入密码 self.scr_load.input_text(self.password, "1234567") # 点击登录按钮 self.scr_load.click_load(self.load_click) try: self.assertIn("用户名和密码不匹配", self.scr_load.get_att(self.load_wrong)) except AssertionError: self.get_ass() raise # 错误账户密码为空 def test_load006(self): # 点击登录链接 self.scr_load.click_load(self.load_mark) # 输入用户名 self.scr_load.input_text(self.username, "admin1") # 输入密码 self.scr_load.input_text(self.password, "") # 点击登录按钮 self.scr_load.click_load(self.load_click) try: self.assertIn("填写密码", self.scr_load.get_att(self.load_password_null)) except AssertionError: self.get_ass() raise # 空账户正确密码 def test_load007(self): # 点击登录链接 self.scr_load.click_load(self.load_mark) # 输入用户名 self.scr_load.input_text(self.username, "") # 输入密码 self.scr_load.input_text(self.password, "123456") # 点击登录按钮 self.scr_load.click_load(self.load_click) try: self.assertIn("填写用户名或邮箱", self.scr_load.get_att(self.load_username_null)) except AssertionError: self.get_ass() raise # 空账户错误密码 def test_load008(self): # 点击登录链接 self.scr_load.click_load(self.load_mark) # 输入用户名 self.scr_load.input_text(self.username, "") # 输入密码 self.scr_load.input_text(self.password, "1234567") # 点击登录按钮 self.scr_load.click_load(self.load_click) try: self.assertIn("填写用户名或邮箱", self.scr_load.get_att(self.load_username_null)) except AssertionError: self.get_ass() raise # 空账户空密码 def test_load009(self): # 点击登录链接 self.scr_load.click_load(self.load_mark) # 输入用户名 self.scr_load.input_text(self.username, "") # 输入密码 self.scr_load.input_text(self.password, "") # 点击登录按钮 self.scr_load.click_load(self.load_click) try: self.assertIn("填写用户名或邮箱", self.scr_load.get_att(self.load_username_null)) except AssertionError: self.get_ass() raise if __name__ == '__main__': unittest.main()
acc15361e8370b7ba0ae6a4582e8d0fc9c912c4d
f30f6672702591c2d0adad5a2f57af8afd493117
/todo/migrations/0004_auto_20190612_1151.py
cdf7922646dffb5a2af9d82cfc9a58c456b4640d
[]
no_license
MedMekss/Listed
0f294ecc16d2db4a9ee37f408b1a7a11229409f4
06ac0bb5140b11aaa704a6cd0f60bb2c15eb6449
refs/heads/master
2020-05-20T03:25:51.047936
2019-06-18T09:20:49
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# Generated by Django 2.2.1 on 2019-06-12 09:51 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('todo', '0003_auto_20190606_1243'), ] operations = [ migrations.AlterField( model_name='item', name='title', field=models.CharField(max_length=32), ), ]
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/sphinx_rstbuilder/builders/rst.py
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# -*- coding: utf-8 -*- from sphinx.builders.text import TextBuilder from ..writers.rst import RstWriter class RstBuilder(TextBuilder): name = 'rst' format = 'rst' out_suffix = '.rst' def get_target_uri(self, docname, typ=None): return docname + self.out_suffix def prepare_writing(self, docnames): self.writer = RstWriter(self)
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/venv/Scripts/pip-script.py
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#!D:\GitHub\venv\Scripts\python.exe # EASY-INSTALL-ENTRY-SCRIPT: 'pip==19.0.3','console_scripts','pip' __requires__ = 'pip==19.0.3' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('pip==19.0.3', 'console_scripts', 'pip')() )
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/mysite/blog/models.py
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SARTHAKKRSHARMA/Blog-Application
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from django.db import models from django.http import HttpResponseRedirect from django.urls import reverse from django.contrib.auth.admin import User from django.utils import timezone # Create your models here. class Blog_Detail(models.Model): author = models.ForeignKey(to=User,on_delete=models.CASCADE,related_name='author') title = models.CharField(max_length=200) body = models.TextField() creation_date = models.DateTimeField(default=timezone.now()) pub_date = models.DateTimeField(blank=True,null=True) likes = models.IntegerField(default=0) dislikes = models.IntegerField(default=0) like_user_reaction = models.ManyToManyField(to=User,blank=True,related_name='like_user') dislike_user_reaction = models.ManyToManyField(to=User,blank=True,related_name='dislike_user') def __str__(self): return self.title class Comments(models.Model): author = models.CharField(max_length=250,blank=True) blog = models.ForeignKey(Blog_Detail,on_delete=models.CASCADE,blank=True,null=True,related_name='comments') body = models.TextField(blank=True) creation_date = models.DateTimeField(default=timezone.now(),blank=True) likes = models.IntegerField(default = 0,blank=True) dislikes = models.IntegerField(default=0,blank=True) like_user_reaction = models.ManyToManyField(to=User,blank=True,related_name='like_comment_user') dislike_user_reaction = models.ManyToManyField(to=User,blank=True,related_name='dislike_comment_user') def __str__(self): return self.author
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/pythonvideos/napalm_mac_Address.py
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narkalya/git-demo
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from napalm import get_network_driver driver = get_network_driver('ios') iosvl2 = driver('192.168.122.72', 'david', 'cisco') iosvl2.open() print iosvl2.get_facts() ios_output = iosvl2.get_mac_address_table() print (json.dumps(ios_output, sort_keys=True, indent=4)) iosvl2.close()