paulo037/starcoder · Datasets at Hugging Face

max_stars_repo_path stringlengths 4 237	max_stars_repo_name stringlengths 6 117	max_stars_count int64 0 95.2k	id stringlengths 1 7	content stringlengths 12 593k	input_ids sequencelengths 7 549k
basic/arithmetic.py	anuragarwalkar/basic-python	0	118477	<reponame>anuragarwalkar/basic-python # Arithmetic Operators import math print(math.ceil(2.9)) # 3 print(math.floor(2.9)) # 2 print(10+3) # 13 print(10-3) # 7 print(10/3) # 3.3 Floating point number print(10//3) # 3 Int print(10%3) # 1 print(103) # 30 print(103) # 1000 10 to the power of 3 print(abs(-2.9)) # 2.9 x = 10 x += 3 print(x) x = 10 + 3 2 print(x)	[ 1, 529, 276, 1112, 420, 29958, 273, 332, 28641, 20919, 279, 29914, 16121, 29899, 4691, 13, 29937, 826, 18542, 6607, 4097, 13, 5215, 5844, 13, 13, 2158, 29898, 755, 29889, 27696, 29898, 29906, 29889, 29929, 876, 396, 29871, 29941, 13, 13, 2158, 29898, 755, 29889, 14939, 29898, 29906, 29889, 29929, 876, 396, 29871, 29906, 13, 13, 2158, 29898, 29896, 29900, 29974, 29941, 29897, 396, 29871, 29896, 29941, 13, 2158, 29898, 29896, 29900, 29899, 29941, 29897, 396, 29871, 29955, 13, 2158, 29898, 29896, 29900, 29914, 29941, 29897, 396, 29871, 29941, 29889, 29941, 26043, 1218, 1298, 1353, 13, 2158, 29898, 29896, 29900, 458, 29941, 29897, 396, 29871, 29941, 3159, 13, 2158, 29898, 29896, 29900, 29995, 29941, 29897, 396, 29871, 29896, 13, 2158, 29898, 29896, 29900, 29930, 29941, 29897, 396, 29871, 29941, 29900, 13, 2158, 29898, 29896, 29900, 1068, 29941, 29897, 396, 29871, 29896, 29900, 29900, 29900, 29871, 29896, 29900, 304, 278, 3081, 310, 29871, 29941, 13, 13, 2158, 29898, 6897, 6278, 29906, 29889, 29929, 876, 396, 29871, 29906, 29889, 29929, 13, 13, 29916, 353, 29871, 29896, 29900, 13, 29916, 4619, 29871, 29941, 13, 2158, 29898, 29916, 29897, 13, 13, 29916, 353, 29871, 29896, 29900, 718, 29871, 29941, 334, 29871, 29906, 13, 13, 2158, 29898, 29916, 29897, 13, 2 ]
scripts/generate_ramachandran_statistics.py	leimao/Ramachandran	2	131836	<gh_stars>1-10 import ramachandran.statistics ramachandran.statistics.count_torsion_angles_from_directory(dir_path="./pdbx_collections_resolution_1p0", save_file_path="./data/probability.npz", b_factor_threshold=30, resolution=90, num_processes=12) ramachandran.statistics.compute_gaussian_kde_densities_from_directory(dir_path="./pdbx_collections_resolution_1p0", save_file_path="./data/gaussian_density.npz", b_factor_threshold=30, resolution=360, num_processes=12)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 13472, 496, 392, 661, 29889, 6112, 6765, 13, 13, 2572, 496, 392, 661, 29889, 6112, 6765, 29889, 2798, 29918, 29873, 943, 291, 29918, 19536, 29918, 3166, 29918, 12322, 29898, 3972, 29918, 2084, 543, 6904, 29886, 2585, 29916, 29918, 29027, 29918, 9778, 918, 29918, 29896, 29886, 29900, 613, 4078, 29918, 1445, 29918, 2084, 543, 6904, 1272, 29914, 22795, 3097, 29889, 9302, 29920, 613, 289, 29918, 19790, 29918, 386, 12268, 29922, 29941, 29900, 29892, 10104, 29922, 29929, 29900, 29892, 954, 29918, 5014, 267, 29922, 29896, 29906, 29897, 13, 13, 2572, 496, 392, 661, 29889, 6112, 6765, 29889, 26017, 29918, 29887, 17019, 29918, 29895, 311, 29918, 21518, 1907, 29918, 3166, 29918, 12322, 29898, 3972, 29918, 2084, 543, 6904, 29886, 2585, 29916, 29918, 29027, 29918, 9778, 918, 29918, 29896, 29886, 29900, 613, 4078, 29918, 1445, 29918, 2084, 543, 6904, 1272, 29914, 29887, 17019, 29918, 21518, 537, 29889, 9302, 29920, 613, 289, 29918, 19790, 29918, 386, 12268, 29922, 29941, 29900, 29892, 10104, 29922, 29941, 29953, 29900, 29892, 954, 29918, 5014, 267, 29922, 29896, 29906, 29897, 2 ]
calibrate.py	malcolmdimeglio/pow_alert	0	183798	<filename>calibrate.py #! /usr/bin/env python import os import cv2 import numpy as np from matplotlib import pyplot as plt from dotenv import load_dotenv, find_dotenv from logger import * load_dotenv(find_dotenv()) curr_dir = os.path.dirname(os.path.realpath(__file__)) cypress_cal_img =f"{curr_dir}/templates/SnowStake_Cypress_night0.jpg" cypress_top_template_img = f"{curr_dir}/templates/40.jpg" cypress_base_template_img = f"{curr_dir}/templates/base.jpg" # All the 6 methods for comparison methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR', 'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED'] def cypress_img(debug_option=False): log.debug("Calibrate Cypress webacam") log.debug(f"Template images used to perform calibration: \n" \ f" - Webcam: {cypress_cal_img}\n" \ f" - Top ROI: {cypress_top_template_img}\n" \ f" - Bottom ROI: {cypress_base_template_img}") # the following image will be used to calibrate the scale img = cv2.imread(cypress_cal_img, 0) img2 = img.copy() template1 = cv2.imread(cypress_top_template_img, 0) template2 = cv2.imread(cypress_base_template_img, 0) log.debug("Resize templates by a 1:2 factor to accomodate Apple grab tool effect") template1 = cv2.resize(template1, (0, 0), fx=0.5, fy=0.5) template2 = cv2.resize(template2, (0, 0), fx=0.5, fy=0.5) h1, w1 = template1.shape h2, w2 = template2.shape if debug_option: # allows to pick which method works best for meth in methods: img = img2.copy() method = eval(meth) # Apply template Matching res = cv2.matchTemplate(img, template1, method) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res) # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]: top_left = min_loc else: top_left = max_loc bottom_right = (top_left[0] + w1, top_left[1] + h1) cv2.rectangle(img, top_left, bottom_right, 255, 2) plt.subplot(111), plt.imshow(img, cmap='gray') plt.title(meth), plt.xticks([]), plt.yticks([]) plt.show() print("TM_CCOEFF_NORMED is best method (or equivalent to TM_CCORR_NORMED, TM_SQDIFF, TM_SQDIFF_NORMED)") print("TM_CCORR is the worst") debug.log("Use TM_CCOEFF_NORMED method to find template ROI images on calibration image") method = eval('cv2.TM_CCOEFF_NORMED') img = img2.copy() res1 = cv2.matchTemplate(img, template1, method) res2 = cv2.matchTemplate(img, template2, method) debug.log("Extract the 4 coordinates for the whole ROI") min_val1, max_val1, min_loc1, max_loc1 = cv2.minMaxLoc(res1) min_val2, max_val2, min_loc2, max_loc2 = cv2.minMaxLoc(res2) top_left1 = max_loc1 top_left2 = max_loc2 bottom_right1 = (top_left1[0] + w1, top_left1[1] + h1) bottom_right2 = (top_left2[0] + w2, top_left2[1] + h2) if debug_option: roi_top_left = top_left1 roi_top_right = (top_left1[0] + w1, top_left1[1]) roi_bottom_right = bottom_right2 roi_bottom_left = (bottom_right2[0] - w2, bottom_right2[1]) cv2.rectangle(img, top_left1, bottom_right1, 255, 2) cv2.rectangle(img, top_left2, bottom_right2, 255, 2) points = [ list(roi_top_left), list(roi_top_right), list(roi_bottom_right), list(roi_bottom_left) ] # Now plot the ROI pts = np.array(points, np.int32) cv2.polylines(img, [pts], True, (255, 255, 255), thickness=5) plt.subplot(111), plt.imshow(img, cmap='gray') plt.title('ROI'), plt.xticks([]), plt.yticks([]) plt.show() top_left_offset = (0, 0) top_right_offset = (w1, 0) bottom_left_offset = ((bottom_right2[0] - w2) - top_left1[0], bottom_right2[1] - top_left1[1]) bottom_right_offset = (bottom_right2[0] - top_left1[0], bottom_right2[1] - top_left1[1]) log.debug("Update .env file with the offset to apply to get the 4 corners of the ROI") log.debug("TOP_LEFT_OFFSET=\""+str(top_left_offset)+"\"\n" \ "TOP_RIGHT_OFFSET=\"" + str(top_right_offset) + "\"\n" \ "BOTTOM_LEFT_OFFSET=\"" + str(bottom_left_offset) + "\"\n" \ "BOTTOM_RIGHT_OFFSET=\"" + str(bottom_right_offset) + "\"\n") with open(".env", 'a') as env: env.write("\n") env.write("TOP_LEFT_OFFSET=\""+str(top_left_offset)+"\"\n") env.write("TOP_RIGHT_OFFSET=\"" + str(top_right_offset) + "\"\n") env.write("BOTTOM_LEFT_OFFSET=\"" + str(bottom_left_offset) + "\"\n") env.write("BOTTOM_RIGHT_OFFSET=\"" + str(bottom_right_offset) + "\"\n")	[ 1, 529, 9507, 29958, 1052, 4626, 403, 29889, 2272, 13, 29937, 29991, 847, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 5215, 2897, 13, 5215, 13850, 29906, 13, 5215, 12655, 408, 7442, 13, 3166, 22889, 1053, 11451, 5317, 408, 14770, 13, 3166, 8329, 6272, 1053, 2254, 29918, 6333, 6272, 29892, 1284, 29918, 6333, 6272, 13, 3166, 17927, 1053, 334, 13, 13, 13, 1359, 29918, 6333, 6272, 29898, 2886, 29918, 6333, 6272, 3101, 13, 21962, 29918, 3972, 353, 2897, 29889, 2084, 29889, 25721, 29898, 359, 29889, 2084, 29889, 6370, 2084, 22168, 1445, 1649, 876, 13, 13, 1270, 2139, 29918, 1052, 29918, 2492, 353, 29888, 29908, 29912, 21962, 29918, 3972, 6822, 20943, 29914, 29903, 3707, 855, 1296, 29918, 29733, 2139, 29918, 11147, 29900, 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growler/middleware/responsetime.py	pyGrowler/Growler	806	191468	<reponame>pyGrowler/Growler # # growler/middleware/responsetime.py # """ Provides middleware which adds a header indicating how long the request took to process """ import time import logging logger = logging.getLogger(__name__) class ResponseTime: """ Middleware which saves the time when initially called, and sets an 'on_headers' event to get the time difference which can be logged or sent to the client. """ UNIT_TO_FACTOR_MAP = { 's': 1, 'ms': 1000, 'us': 1000000, } def __init__(self, digits=3, log=None, units='ms', header="X-Response-Time", suffix=True, clobber_header=False): """ Construct ResponseTime middleware. Parameters: digits (int): precision log (Logger or None): Writes the time difference to the log units (str): Time units (default: milliseconds 'ms') header (str): Name of header to send response time as suffix (bool): Whether to format with """ self.units = units self.header_name = header self.digits = digits self.log = log if log else logger.getChild("id=%x" % id(self)) self.suffix = suffix self.clobber_header = clobber_header def __call__(self, req, res): start_time = time.monotonic() def on_header_send(): # if header already exists, do NOT clobber it if not self.clobber_header and self.header_name in res.headers: return dt = self.format_timediff(time.monotonic() - start_time) val = "{}{}".format(dt, self.units) if self.suffix else dt res.set(self.header_name, val) if self.log: self.log.info("-- timer %s", val) res.events.on('before_headers', on_header_send) def format_timediff(self, td): factor = self.UNIT_TO_FACTOR_MAP[self.units] return str(round(factor * td, self.digits))	[ 1, 529, 276, 1112, 420, 29958, 2272, 29954, 798, 1358, 29914, 29954, 798, 1358, 13, 29937, 13, 29937, 6548, 1358, 29914, 17662, 2519, 29914, 26679, 5410, 29889, 2272, 13, 29937, 13, 15945, 29908, 13, 1184, 29894, 2247, 7256, 2519, 607, 12778, 263, 4839, 23941, 920, 1472, 278, 2009, 3614, 304, 13, 5014, 13, 15945, 29908, 13, 13, 5215, 931, 13, 5215, 12183, 13, 13, 21707, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 29897, 13, 13, 13, 1990, 13291, 2481, 29901, 13, 1678, 9995, 13, 1678, 14253, 2519, 607, 27401, 278, 931, 746, 12919, 2000, 29892, 322, 6166, 385, 13, 1678, 525, 265, 29918, 13662, 29915, 1741, 304, 679, 278, 931, 4328, 607, 508, 367, 13817, 470, 2665, 13, 1678, 304, 278, 3132, 29889, 13, 1678, 9995, 13, 13, 1678, 8291, 1806, 29918, 4986, 29918, 4519, 1783, 1955, 29918, 23827, 353, 426, 13, 4706, 525, 29879, 2396, 29871, 29896, 29892, 13, 4706, 525, 1516, 2396, 29871, 29896, 29900, 29900, 29900, 29892, 13, 4706, 525, 375, 2396, 29871, 29896, 29900, 29900, 29900, 29900, 29900, 29900, 29892, 13, 1678, 500, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 13, 462, 13340, 29922, 29941, 29892, 13, 462, 1480, 29922, 8516, 29892, 13, 462, 10340, 2433, 1516, 742, 13, 462, 4839, 543, 29990, 29899, 5103, 29899, 2481, 613, 13, 462, 25557, 29922, 5574, 29892, 13, 462, 274, 2127, 495, 29918, 6672, 29922, 8824, 1125, 13, 4706, 9995, 13, 4706, 1281, 4984, 13291, 2481, 7256, 2519, 29889, 13, 13, 4706, 12662, 2699, 29901, 13, 9651, 13340, 313, 524, 1125, 16716, 13, 9651, 1480, 313, 16363, 470, 6213, 1125, 16849, 267, 278, 931, 4328, 304, 278, 1480, 13, 9651, 10340, 313, 710, 1125, 5974, 10340, 313, 4381, 29901, 3533, 21462, 525, 1516, 1495, 13, 9651, 4839, 313, 710, 1125, 4408, 310, 4839, 304, 3638, 2933, 931, 408, 13, 9651, 25557, 313, 11227, 1125, 26460, 304, 3402, 411, 13, 4706, 9995, 13, 4706, 1583, 29889, 348, 1169, 353, 10340, 13, 4706, 1583, 29889, 6672, 29918, 978, 353, 4839, 13, 4706, 1583, 29889, 7501, 1169, 353, 13340, 13, 4706, 1583, 29889, 1188, 353, 1480, 565, 1480, 1683, 17927, 29889, 657, 5938, 703, 333, 16328, 29916, 29908, 1273, 1178, 29898, 1311, 876, 13, 4706, 1583, 29889, 2146, 600, 861, 353, 25557, 13, 4706, 1583, 29889, 29883, 2127, 495, 29918, 6672, 353, 274, 2127, 495, 29918, 6672, 13, 13, 1678, 822, 4770, 4804, 12035, 1311, 29892, 12428, 29892, 620, 1125, 13, 4706, 1369, 29918, 2230, 353, 931, 29889, 3712, 327, 8927, 580, 13, 13, 4706, 822, 373, 29918, 6672, 29918, 6717, 7295, 13, 9651, 396, 565, 4839, 2307, 4864, 29892, 437, 6058, 274, 2127, 495, 372, 13, 9651, 565, 451, 1583, 29889, 29883, 2127, 495, 29918, 6672, 322, 1583, 29889, 6672, 29918, 978, 297, 620, 29889, 13662, 29901, 13, 18884, 736, 13, 13, 9651, 11636, 353, 1583, 29889, 4830, 29918, 9346, 287, 2593, 29898, 2230, 29889, 3712, 327, 8927, 580, 448, 1369, 29918, 2230, 29897, 13, 9651, 659, 353, 29850, 1157, 29913, 1642, 4830, 29898, 6008, 29892, 1583, 29889, 348, 1169, 29897, 565, 1583, 29889, 2146, 600, 861, 1683, 11636, 13, 9651, 620, 29889, 842, 29898, 1311, 29889, 6672, 29918, 978, 29892, 659, 29897, 13, 13, 9651, 565, 1583, 29889, 1188, 29901, 13, 18884, 1583, 29889, 1188, 29889, 3888, 703, 489, 12237, 1273, 29879, 613, 659, 29897, 13, 13, 4706, 620, 29889, 13604, 29889, 265, 877, 11083, 29918, 13662, 742, 373, 29918, 6672, 29918, 6717, 29897, 13, 13, 1678, 822, 3402, 29918, 9346, 287, 2593, 29898, 1311, 29892, 22599, 1125, 13, 4706, 7329, 353, 1583, 29889, 3904, 1806, 29918, 4986, 29918, 4519, 1783, 1955, 29918, 23827, 29961, 1311, 29889, 348, 1169, 29962, 13, 4706, 736, 851, 29898, 14486, 29898, 19790, 334, 22599, 29892, 1583, 29889, 7501, 1169, 876, 13, 2 ]
test_calculator.py	Kidatoy/Advanced-Calculator	0	11307	<reponame>Kidatoy/Advanced-Calculator import unittest # https://docs.python.org/3/library/unittest.html from modules.calculator import Calculator as Calc class TestCalculator(unittest.TestCase): """ Test Driven Development Unittest File Module: Calculator Updated: 12/16/2019 Author: <NAME> """ def test_addition(self): """ Evaluate addition corner cases """ self.assertEqual(2, Calc().eval('1+1')) self.assertEqual(2, Calc().eval('1.0+1.0')) self.assertEqual(0, Calc().eval('-1+1')) self.assertEqual(-2, Calc().eval('-1+-1')) def test_subtraction(self): """ Evaluate subtraction corner cases """ self.assertEqual(0, Calc().eval('1-1')) self.assertEqual(-2, Calc().eval('-1-1')) self.assertEqual(0, Calc().eval('-1--1')) def test_multiplication(self): """ Evaluate multiplication corner cases """ self.assertEqual(0, Calc().eval('10')) self.assertEqual(0, Calc().eval('0-1')) self.assertEqual(1, Calc().eval('11')) self.assertEqual(-1, Calc().eval('-11')) self.assertEqual(1, Calc().eval('-1-1')) self.assertEqual(1, Calc().eval('.254')) def test_division(self): """ Test division corner cases Note: division by zero is handled in test_exceptions """ self.assertEqual(1, Calc().eval('1/1')) self.assertEqual(.25, Calc().eval('1/4')) self.assertEqual(-1, Calc().eval('-1/1')) self.assertEqual(1, Calc().eval('-1/-1')) self.assertEqual(0, Calc().eval('0/-1')) def test_exponents(self): """ Test exponent corner cases """ self.assertEqual(1, Calc().eval('2^0')) self.assertEqual(2, Calc().eval('2^1')) self.assertEqual(4, Calc().eval('2^2')) self.assertEqual(.5, Calc().eval('2^-1')) self.assertEqual(4, Calc().eval('-2^2')) def test_parentheses(self): """ Test parentheses corner cases """ self.assertEqual(5.0, Calc().eval('(4.0)+1')) self.assertEqual(3.0, Calc().eval('(4+1)-2')) self.assertEqual(5.0, Calc().eval('(5+-5)+5')) self.assertEqual(-5.0, Calc().eval('(-10+3)+2')) self.assertEqual(-26.0, Calc().eval('10-(32)^2')) def test_pi(self): """ Test pi corner cases """ self.assertEqual(4.1415926535, Calc().eval('(pi)+1')) self.assertEqual(1.1415926535, Calc().eval('(pi)-2')) self.assertEqual(3.1415926535, Calc().eval('(pi+-5)+5')) self.assertEqual(1.8584073465, Calc().eval('(-pi+3)+2')) self.assertEqual(-29.478417602100684, Calc().eval('10-(pi2)^2')) self.assertEqual(1.57079632675, Calc().eval('pi/2')) def test_e(self): """ Test e corner cases """ self.assertEqual(3.7182818284, Calc().eval('(e)+1')) self.assertEqual(0.7182818283999999, Calc().eval('(e)-2')) self.assertEqual(2.7182818284, Calc().eval('(e+-5)+5')) self.assertEqual(2.2817181716, Calc().eval('(-e+3)+2')) self.assertEqual(-19.556224394438587, Calc().eval('10-(e2)^2')) self.assertEqual(1.3591409142, Calc().eval('e/2')) def test_phi(self): """ Test phi corner cases """ self.assertEqual(2.6180339886999997, Calc().eval('(phi)+1')) self.assertEqual(-0.3819660113000001, Calc().eval('(phi)-2')) self.assertEqual(1.6180339886999997, 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transform.py	ShrillP/ImageScanner	0	57770	<filename>transform.py<gh_stars>0 import numpy as np import cv2 def order_points(points): rectangle = np.zeros((4, 2), dtype="float32") s = points.sum(axis=1) rectangle[0] = points[np.argmin(s)] rectangle[2] = points[np.argmax(s)] difference = np.diff(points, axis=1) rectangle[1] = points[np.argmin(difference)] rectangle[3] = points[np.argmax(difference)] return rectangle def four_point_transform(image, points): rectangle = order_points(points) (tl, tr, br, bl) = rectangle width_A = np.sqrt(((br[0] - bl[0]) 2) + ((br[1] - bl[1]) 2)) width_B = np.sqrt(((tr[0] - tl[0]) 2) + ((tr[1] - tl[1]) 2)) max_width = max(int(width_A), int(width_B)) height_A = np.sqrt(((tr[0] - br[0]) 2) + ((tr[1] - br[1]) 2)) height_B = np.sqrt(((tl[0] - bl[0]) 2) + ((tl[1] - bl[1]) 2)) max_height = max(int(height_A), int(height_B)) destination = np.array([ [0, 0], [max_width - 1, 0], [max_width - 1, max_height - 1], [0, max_height - 1]], dtype="float32") transform = cv2.getPerspectiveTransform(rectangle, destination) warped = cv2.warpPerspective(image, transform, (max_width, max_height)) return warped	[ 1, 529, 9507, 29958, 9067, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 12655, 408, 7442, 13, 5215, 13850, 29906, 13, 13, 13, 1753, 1797, 29918, 9748, 29898, 9748, 1125, 13, 1678, 16701, 353, 7442, 29889, 3298, 359, 3552, 29946, 29892, 29871, 29906, 511, 26688, 543, 7411, 29941, 29906, 1159, 13, 13, 1678, 269, 353, 3291, 29889, 2083, 29898, 8990, 29922, 29896, 29897, 13, 1678, 16701, 29961, 29900, 29962, 353, 3291, 29961, 9302, 29889, 1191, 1195, 29898, 29879, 4638, 13, 1678, 16701, 29961, 29906, 29962, 353, 3291, 29961, 9302, 29889, 1191, 3317, 29898, 29879, 4638, 13, 13, 1678, 4328, 353, 7442, 29889, 12765, 29898, 9748, 29892, 9685, 29922, 29896, 29897, 13, 1678, 16701, 29961, 29896, 29962, 353, 3291, 29961, 9302, 29889, 1191, 1195, 29898, 29881, 17678, 4638, 13, 1678, 16701, 29961, 29941, 29962, 353, 3291, 29961, 9302, 29889, 1191, 3317, 29898, 29881, 17678, 4638, 13, 13, 1678, 736, 16701, 13, 13, 13, 1753, 3023, 29918, 3149, 29918, 9067, 29898, 3027, 29892, 3291, 1125, 13, 1678, 16701, 353, 1797, 29918, 9748, 29898, 9748, 29897, 13, 1678, 313, 15206, 29892, 534, 29892, 1506, 29892, 1999, 29897, 353, 16701, 13, 13, 1678, 2920, 29918, 29909, 353, 7442, 29889, 3676, 3552, 29898, 1182, 29961, 29900, 29962, 448, 1999, 29961, 29900, 2314, 3579, 29871, 29906, 29897, 718, 5135, 1182, 29961, 29896, 29962, 448, 1999, 29961, 29896, 2314, 3579, 29871, 29906, 876, 13, 1678, 2920, 29918, 29933, 353, 7442, 29889, 3676, 3552, 29898, 509, 29961, 29900, 29962, 448, 260, 29880, 29961, 29900, 2314, 3579, 29871, 29906, 29897, 718, 5135, 509, 29961, 29896, 29962, 448, 260, 29880, 29961, 29896, 2314, 3579, 29871, 29906, 876, 13, 1678, 4236, 29918, 2103, 353, 4236, 29898, 524, 29898, 2103, 29918, 29909, 511, 938, 29898, 2103, 29918, 29933, 876, 13, 13, 1678, 3171, 29918, 29909, 353, 7442, 29889, 3676, 3552, 29898, 509, 29961, 29900, 29962, 448, 1506, 29961, 29900, 2314, 3579, 29871, 29906, 29897, 718, 5135, 509, 29961, 29896, 29962, 448, 1506, 29961, 29896, 2314, 3579, 29871, 29906, 876, 13, 1678, 3171, 29918, 29933, 353, 7442, 29889, 3676, 3552, 29898, 15206, 29961, 29900, 29962, 448, 1999, 29961, 29900, 2314, 3579, 29871, 29906, 29897, 718, 5135, 15206, 29961, 29896, 29962, 448, 1999, 29961, 29896, 2314, 3579, 29871, 29906, 876, 13, 1678, 4236, 29918, 3545, 353, 4236, 29898, 524, 29898, 3545, 29918, 29909, 511, 938, 29898, 3545, 29918, 29933, 876, 13, 13, 1678, 12551, 353, 7442, 29889, 2378, 4197, 13, 4706, 518, 29900, 29892, 29871, 29900, 1402, 13, 4706, 518, 3317, 29918, 2103, 448, 29871, 29896, 29892, 29871, 29900, 1402, 13, 4706, 518, 3317, 29918, 2103, 448, 29871, 29896, 29892, 4236, 29918, 3545, 448, 29871, 29896, 1402, 13, 4706, 518, 29900, 29892, 4236, 29918, 3545, 448, 29871, 29896, 20526, 26688, 543, 7411, 29941, 29906, 1159, 13, 13, 1678, 4327, 353, 13850, 29906, 29889, 657, 15136, 12645, 13372, 29898, 1621, 2521, 29892, 12551, 29897, 13, 1678, 1370, 9795, 353, 13850, 29906, 29889, 4495, 29886, 15136, 12645, 29898, 3027, 29892, 4327, 29892, 313, 3317, 29918, 2103, 29892, 4236, 29918, 3545, 876, 13, 13, 1678, 736, 1370, 9795, 13, 13, 2 ]
hello_world_cpp/launch/server.launch.py	fjnkt98/hello_world	0	48931	import launch import launch_ros def generate_launch_description(): server_node_container = launch_ros.actions.ComposableNodeContainer( node_name='server_node_container', node_namespace='', package='rclcpp_components', node_executable='component_container', composable_node_descriptions=[ launch_ros.descriptions.ComposableNode( package='hello_world_cpp', node_plugin='hello_world_cpp::Server', node_name='server' ) ], output='screen' ) return launch.LaunchDescription([server_node_container])	[ 1, 1053, 6826, 13, 5215, 6826, 29918, 1883, 13, 13, 13, 1753, 5706, 29918, 15343, 29918, 8216, 7295, 13, 1678, 1923, 29918, 3177, 29918, 7611, 353, 6826, 29918, 1883, 29889, 7387, 29889, 1523, 1066, 519, 4247, 7895, 29898, 13, 9651, 2943, 29918, 978, 2433, 2974, 29918, 3177, 29918, 7611, 742, 13, 9651, 2943, 29918, 22377, 2433, 742, 13, 9651, 3577, 2433, 29878, 695, 8223, 29918, 14036, 742, 13, 9651, 2943, 29918, 4258, 9246, 2433, 9700, 29918, 7611, 742, 13, 9651, 5541, 519, 29918, 3177, 29918, 2783, 699, 1980, 11759, 13, 18884, 6826, 29918, 1883, 29889, 2783, 699, 1980, 29889, 1523, 1066, 519, 4247, 29898, 13, 462, 1678, 3577, 2433, 12199, 29918, 11526, 29918, 8223, 742, 13, 462, 1678, 2943, 29918, 8582, 2433, 12199, 29918, 11526, 29918, 8223, 1057, 6004, 742, 13, 462, 1678, 2943, 29918, 978, 2433, 2974, 29915, 13, 462, 1678, 1723, 13, 18884, 21251, 13, 9651, 1962, 2433, 10525, 29915, 13, 9651, 1723, 13, 1678, 736, 6826, 29889, 17641, 9868, 4197, 2974, 29918, 3177, 29918, 7611, 2314, 13, 2 ]
Vigenere/decoder.py	zF4ke/Ciphr	0	194004	from operator import sub word = str(input("Type the word to be decrypted: ")).strip().lower().replace(" ", "") key = str(input("Type the vigenere key: ")).strip().lower().replace(" ", "") nkey = 0 res, w, letters, srettel = "", "", " abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz ", " zyxwvutsrqponmlkjihgfedcbazyxwvutsrqponmlkjihgfedcba " if len(word) > len(key): nkey = len(word) - len(key) if nkey < len(key): w = key[:nkey] res = key + w elif nkey > len(key): if len(key) == 1: res = key * (nkey + 1) elif len(key) == 2: res = key * nkey else: w = key + key[:nkey] res = key + w else: res += key + key elif len(word) < len(key): nkey = len(key) - len(word) w = key[:-nkey] res += w else: res += key s, l, fin, enc = [], [], [], [] for i in word: s.append(letters.find(i)) for j in res: l.append(letters.find(j)) for n in range(0, len(s)): s[n] = s[n] + 1 fin = list(map(sub, s, l)) for x in fin: if x < 0: x = abs(x) + 1 enc.append(srettel[x]) else: enc.append(letters[x]) print("".join(enc))	[ 1, 515, 5455, 1053, 1014, 13, 13, 1742, 353, 851, 29898, 2080, 703, 1542, 278, 1734, 304, 367, 1602, 14740, 29901, 376, 8106, 17010, 2141, 13609, 2141, 6506, 703, 9162, 20569, 13, 1989, 353, 851, 29898, 2080, 703, 1542, 278, 325, 2101, 406, 1820, 29901, 376, 8106, 17010, 2141, 13609, 2141, 6506, 703, 9162, 20569, 13, 29876, 1989, 353, 29871, 29900, 13, 690, 29892, 281, 29892, 8721, 29892, 269, 13158, 295, 353, 12633, 12633, 376, 25638, 1753, 12443, 823, 6321, 23521, 459, 29939, 29878, 303, 4090, 29893, 20230, 10736, 1753, 12443, 823, 6321, 23521, 459, 29939, 29878, 303, 4090, 29893, 20230, 9162, 376, 503, 29891, 29916, 29893, 29894, 8842, 29878, 29939, 1112, 828, 29895, 27272, 29887, 29888, 287, 10702, 24683, 29916, 29893, 29894, 8842, 29878, 29939, 1112, 828, 29895, 27272, 29887, 29888, 287, 29883, 2291, 376, 13, 13, 361, 7431, 29898, 1742, 29897, 1405, 7431, 29898, 1989, 1125, 13, 1678, 302, 1989, 353, 7431, 29898, 1742, 29897, 448, 7431, 29898, 1989, 29897, 13, 1678, 565, 302, 1989, 529, 7431, 29898, 1989, 1125, 13, 4706, 281, 353, 1820, 7503, 29876, 1989, 29962, 13, 4706, 620, 353, 1820, 718, 281, 13, 1678, 25342, 302, 1989, 1405, 7431, 29898, 1989, 1125, 13, 4706, 565, 7431, 29898, 1989, 29897, 1275, 29871, 29896, 29901, 13, 9651, 620, 353, 1820, 334, 313, 29876, 1989, 718, 29871, 29896, 29897, 13, 4706, 25342, 7431, 29898, 1989, 29897, 1275, 29871, 29906, 29901, 13, 9651, 620, 353, 1820, 334, 302, 1989, 13, 4706, 1683, 29901, 13, 9651, 281, 353, 1820, 718, 1820, 7503, 29876, 1989, 29962, 13, 9651, 620, 353, 1820, 718, 281, 13, 1678, 1683, 29901, 13, 4706, 620, 4619, 1820, 718, 1820, 13, 13, 23681, 7431, 29898, 1742, 29897, 529, 7431, 29898, 1989, 1125, 13, 1678, 302, 1989, 353, 7431, 29898, 1989, 29897, 448, 7431, 29898, 1742, 29897, 13, 1678, 281, 353, 1820, 7503, 29899, 29876, 1989, 29962, 13, 1678, 620, 4619, 281, 13, 13, 2870, 29901, 13, 1678, 620, 4619, 1820, 13, 13, 29879, 29892, 301, 29892, 1436, 29892, 2094, 353, 19997, 19997, 19997, 5159, 13, 13, 1454, 474, 297, 1734, 29901, 13, 1678, 269, 29889, 4397, 29898, 1026, 2153, 29889, 2886, 29898, 29875, 876, 13, 13, 1454, 432, 297, 620, 29901, 13, 1678, 301, 29889, 4397, 29898, 1026, 2153, 29889, 2886, 29898, 29926, 876, 13, 13, 1454, 302, 297, 3464, 29898, 29900, 29892, 7431, 29898, 29879, 22164, 13, 1678, 269, 29961, 29876, 29962, 353, 269, 29961, 29876, 29962, 718, 29871, 29896, 13, 13, 4951, 353, 1051, 29898, 1958, 29898, 1491, 29892, 269, 29892, 301, 876, 13, 13, 1454, 921, 297, 1436, 29901, 13, 1678, 565, 921, 529, 29871, 29900, 29901, 13, 4706, 921, 353, 6425, 29898, 29916, 29897, 718, 29871, 29896, 13, 4706, 2094, 29889, 4397, 29898, 29879, 13158, 295, 29961, 29916, 2314, 13, 1678, 1683, 29901, 13, 4706, 2094, 29889, 4397, 29898, 1026, 2153, 29961, 29916, 2314, 13, 13, 2158, 703, 1642, 7122, 29898, 3977, 876, 13, 2 ]
Python/PythonIntro/Exercises.py	IQSS/workshops	30	102942	# --- # jupyter: # jupytext: # formats: ipynb,md,py:light # text_representation: # extension: .py # format_name: light # format_version: '1.3' # jupytext_version: 0.8.6 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Introduction to Python exercise solutions # ## Exercise: Reading text from a file and splitting # Alice's Adventures in Wonderland is full of memorable characters. The main characters from the story are listed, one-per-line, in the file named `Characters.txt`. # # NOTE: we will not always explicitly demonstrate everything you need to know in order to complete an exercise. Instead we focus on teaching you how to discover available methods and how use the help function to learn how to use them. It is expected that you will spend some time during the exercises looking for appropriate methods and perhaps reading documentation. # 1. Open the `Characters.txt` file and read its contents. # 2. Split text on newlines to produce a list with one element per line. # Store the result as "alice_characters". # ## Exercise: count the number of main characters # So far we've learned that there are 12 chapters, around 830 paragraphs, and about 26 thousand words in Alice's Adventures in Wonderland. Along the way we've also learned how to open a file and read its contents, split strings, calculate the length of objects, discover methods for string and list objects, and index/subset lists in Python. Now it is time for you to put these skills to use to learn something about the main characters in the story. # 1. Count the number of main characters in the story (i.e., get the length # of the list you created in previous exercise). # 2. Extract and print just the first character from the list you created in # the previous exercise. # 3. (BONUS, optional): Sort the list you created in step 2 alphabetically, and then extract the last element. # ## Exercise: Iterating and counting things # Now that we know how to iterate using for-loops and list comprehensions the possibilities really start to open up. For example, we can use these techniques to count the number of times each character appears in the story. # + # 1. Make sure you have both the text and the list of characters. # # Open and read both "Alice_in_wonderland.txt" and # "Characters.txt" if you have not already done so. # + # 2. Which chapter has the most words? # # Split the text into chaptes (i.e., split on "CHAPTER ") # and use a for-loop or list comprehension to iterate over # the chapters. For each chapter, split it into words and # calculate the length. # + # 3. How many times is each character mentioned in the text? # Iterate over the list of characters using a for-loop or # list comprehension. For each character, call the count method # with that character as the argument. # + # 4. (BONUS, optional): Put the character counts computed # above in a dictionary with character names as the keys and # counts as the values. # + # 5. (BONUS, optional): Use a nested for-loop or nested comprehension # to calculate the number of times each character is mentioned # in each chapter.	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examples/fire.py	pombreda/py-lepton	7	13028	<filename>examples/fire.py ############################################################################# # # Copyright (c) 2008 by <NAME> and contributors # All Rights Reserved. # # This software is subject to the provisions of the MIT License # A copy of the license should accompany this distribution. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # ############################################################################# """Fire simulation using point sprites""" __version__ = '$Id$' import os from pyglet import image from pyglet.gl import * from lepton import Particle, ParticleGroup, default_system from lepton.renderer import PointRenderer from lepton.texturizer import SpriteTexturizer, create_point_texture from lepton.emitter import StaticEmitter from lepton.domain import Line from lepton.controller import Gravity, Lifetime, Movement, Fader, ColorBlender win = pyglet.window.Window(resizable=True, visible=False) win.clear() glEnable(GL_BLEND) glShadeModel(GL_SMOOTH) glBlendFunc(GL_SRC_ALPHA,GL_ONE) glDisable(GL_DEPTH_TEST) flame = StaticEmitter( rate=500, template=Particle( position=(300,25,0), velocity=(0,0,0), color=(1,1,1,1), ), position=Line((win.width/2 - 85, -15, 0), (win.width/2 + 85, -15, 0)), deviation=Particle(position=(10,0,0), velocity=(7,50,0), age=0.75) ) default_system.add_global_controller( Lifetime(6), Gravity((0,20,0)), Movement(), ColorBlender( [(0, (0,0,0.5,0)), (0.5, (0,0,0.5,0.2)), (0.75, (0,0.5,1,0.6)), (1.5, (1,1,0,0.2)), (2.7, (0.9,0.2,0,0.4)), (3.2, (0.6,0.1,0.05,0.2)), (4.0, (0.8,0.8,0.8,0.1)), (6.0, (0.8,0.8,0.8,0)), ] ), ) group = ParticleGroup(controllers=[flame], renderer=PointRenderer(64, SpriteTexturizer(create_point_texture(64, 5)))) win.set_visible(True) pyglet.clock.schedule_interval(default_system.update, (1.0/30.0)) pyglet.clock.set_fps_limit(None) @win.event def on_draw(): win.clear() glLoadIdentity() default_system.draw() if __name__ == '__main__': default_system.run_ahead(2, 30) pyglet.app.run()	[ 1, 529, 9507, 29958, 19057, 29914, 8696, 29889, 2272, 13, 13383, 13383, 13383, 13383, 7346, 4136, 29937, 13, 29937, 13, 29937, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29900, 29947, 491, 529, 5813, 29958, 322, 17737, 29560, 13, 29937, 2178, 26863, 2538, 9841, 29889, 13, 29937, 13, 29937, 910, 7047, 338, 4967, 304, 278, 1326, 12112, 310, 278, 341, 1806, 19245, 13, 29937, 319, 3509, 310, 278, 19405, 881, 10259, 1384, 445, 4978, 29889, 13, 29937, 6093, 7791, 7818, 12982, 1525, 8519, 13756, 13044, 3352, 376, 3289, 8519, 613, 399, 1806, 8187, 2692, 399, 1718, 29934, 13566, 29979, 8079, 13764, 29979, 476, 22255, 29892, 8528, 15094, 1799, 6323, 13, 29937, 306, 3580, 5265, 3352, 29892, 2672, 6154, 15789, 4214, 350, 2692, 6058, 27848, 3352, 7495, 6093, 399, 1718, 29934, 13566, 29059, 8079, 341, 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server/ahj_app/tests/test_misc_views.py	btansy/ahj-registry	0	90490	<filename>server/ahj_app/tests/test_misc_views.py from django.urls import reverse from django.conf import settings from ahj_app.models import User, Edit, Comment from fixtures import * import pytest import datetime @pytest.mark.django_db def test_form_validator__username_exists(generate_client_with_webpage_credentials): client = generate_client_with_webpage_credentials(Username='someone') url = reverse('form-validator') response = client.get(url, {'Username':'someone'}) assert response.data['UsernameExists'] assert response.status_code == 200 @pytest.mark.django_db def test_form_validator__email_exists(generate_client_with_webpage_credentials): client = generate_client_with_webpage_credentials(Email='<EMAIL>') url = reverse('form-validator') response = client.get(url, {'Email':'a<EMAIL>'}) assert response.data['EmailExists'] assert response.status_code == 200 @pytest.mark.django_db def test_form_validator__no_params(client_with_webpage_credentials): url = reverse('form-validator') response = client_with_webpage_credentials.get(url, {}) assert not response.data['UsernameExists'] and not response.data['EmailExists'] assert response.status_code == 200 @pytest.mark.django_db def test_user_comments__comments_exist(ahj_obj, generate_client_with_webpage_credentials): client = generate_client_with_webpage_credentials(Username='someone') user = User.objects.get(Username='someone') comment1 = Comment.objects.create(UserID=user, AHJPK=ahj_obj.AHJPK, CommentText='This is one comment.', ReplyingTo=None) comment2 = Comment.objects.create(UserID=user, AHJPK=ahj_obj.AHJPK, CommentText='This is another.', ReplyingTo=comment1.CommentID) url = reverse('user-comments') response = client.get(url, {'UserID': user.UserID}) assert len(response.data) == 2 # 2 comments returned assert response.status_code == 200 @pytest.mark.django_db def test_user_comments__user_does_not_exist(client_with_webpage_credentials): url = reverse('user-comments') response = client_with_webpage_credentials.get(url, {'UserID': 99999999}) assert len(response.data) == 0 # no comments returned assert response.status_code == 200 @pytest.mark.django_db def test_comment_submit__normal_submission(ahj_obj, client_with_webpage_credentials): url = reverse('comment-submit') response = client_with_webpage_credentials.post(url, {'CommentText': 'This is a comment.', 'AHJPK':ahj_obj.AHJPK}) assert response.data['CommentText']['Value'] == 'This is a comment.' # check if comment object returned matches assert response.status_code == 200 @pytest.mark.django_db def test_comment_submit__no_comment(ahj_obj, client_with_webpage_credentials): url = reverse('comment-submit') response = client_with_webpage_credentials.post(url, {'AHJPK':ahj_obj.AHJPK}) assert response.status_code == 400 @pytest.mark.django_db def test_user_edits__edits_exist(ahj_obj, generate_client_with_webpage_credentials): client = generate_client_with_webpage_credentials(Username='someone') user = User.objects.get(Username='someone') edit1 = Edit.objects.create(ChangedBy= user, AHJPK=ahj_obj, SourceTable='Contact', SourceColumn='Title', SourceRow='143', DateRequested=datetime.datetime.now()) edit2 = Edit.objects.create(ChangedBy= user, AHJPK=ahj_obj, SourceTable='Contact', SourceColumn='Title', SourceRow='143', DateRequested=datetime.datetime.now()) url = reverse('user-edits') response = client.get(url, {'UserID': user.UserID}) assert len(response.data) == 2 # 2 edits returned assert response.status_code == 200 @pytest.mark.django_db def test_user_edits__user_does_not_exist(client_with_webpage_credentials): url = reverse('user-edits') response = client_with_webpage_credentials.get(url, {'UserID': 99999999}) assert len(response.data) == 0 # no edits returned assert response.status_code == 200 @pytest.mark.django_db def test_send_support_email__valid_usage(client_with_webpage_credentials): url = reverse('send-support-email') settings.SUNSPEC_SUPPORT_EMAIL = '<EMAIL>' response = client_with_webpage_credentials.post(url, {'Email': '<EMAIL>', 'Subject': 'A subject.', 'Message': 'A message.'}) assert response.status_code == 200	[ 1, 529, 9507, 29958, 2974, 29914, 801, 29926, 29918, 932, 29914, 21150, 29914, 1688, 29918, 29885, 10669, 29918, 7406, 29889, 2272, 13, 3166, 9557, 29889, 26045, 1053, 11837, 13, 3166, 9557, 29889, 5527, 1053, 6055, 13, 3166, 21023, 29926, 29918, 932, 29889, 9794, 1053, 4911, 29892, 7641, 29892, 461, 13, 3166, 5713, 486, 1973, 1053, 334, 13, 5215, 11451, 1688, 13, 5215, 12865, 13, 13, 29992, 2272, 1688, 29889, 3502, 29889, 14095, 29918, 2585, 13, 1753, 1243, 29918, 689, 29918, 3084, 1061, 1649, 6786, 29918, 9933, 29898, 17158, 29918, 4645, 29918, 2541, 29918, 2676, 3488, 29918, 11944, 9409, 1125, 13, 1678, 3132, 353, 5706, 29918, 4645, 29918, 2541, 29918, 2676, 3488, 29918, 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educative/course1/graphs/ch2_dfs.py	liveroot/ambition2020	0	51603	<gh_stars>0 import educative.course1.stacks_queues.stack as s import educative.course1.graphs.graph as g input_num_vertices = 5 input_edges = {0: [1, 2], 1: [3, 4]} expected_output = "02143 or 02134 or 01432 or 01342" # this code implements Depth First Traversal in a graph. Each element in the graph's adjacency list # represents a list of nodes directly connected to the current element. Each of these lists represent # levels in the graph. We traverse all the nodes from root node to last node first, then we back-track # to the last visited common node and then we take second branch from there, until we back-track to root node # and no branches are left unvisited. # -------------- # For traversal: # -------------- # 1. We first push the root node in a stack and mark it visited. # 2. Now we get the list of adjacent nodes from the root node and push all adjacent nodes in the list # to the stack. # 3. As we are pushing the adjacent nodes to the stack, we mark them as visited. # 4. Simultaneously, we pop the nodes from the stack and append the node's value to the result # -------------- def bfs_traversal(graph, root): result = "" visited = [False] * graph.num_vertices stack = s.Stack(graph.num_vertices, True) # suppress_printing = True stack.push(root) while not stack.is_empty(): g_node = stack.pop() result += str(g_node) dll = graph.adjacency_list[g_node] if dll is not None: current = dll.head while current: if not visited[current.value]: stack.push(current.value) visited[current.value] = True current = current.next return result def main(): # create a graph from input data graph = g.Graph(input_num_vertices, True) # suppress_printing = True for x in input_edges.keys(): for y in input_edges[x]: graph.add_edge(x, y) # set a starting point for traversal root = 0 print("Input:") print("Graph in Adjacency List Representation:") print(str(graph.prettify())) print("Expected: " + str(expected_output)) print("Output: " + str(bfs_traversal(graph, root))) if __name__ == 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hexun/hexun/items.py	judypol/pytonStudy	0	1612353	<filename>hexun/hexun/items.py # -- coding: utf-8 -- # Define here the models for your scraped items # # See documentation in: # http://doc.scrapy.org/en/latest/topics/items.html import scrapy from scrapy import Field class HexunItem(scrapy.Item): # define the fields for your item here like: product=Field() #产品 dateTime = Field() # 时间 price = Field() # 价格 amount = Field() # 成交额 volumn = Field() # 成交量 avePrice = Field() # 领先指标 openInterest = Field() # 持仓量	[ 1, 529, 9507, 29958, 20970, 348, 29914, 20970, 348, 29914, 7076, 29889, 2272, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 29937, 22402, 1244, 278, 4733, 363, 596, 885, 2390, 287, 4452, 13, 29937, 13, 29937, 2823, 5106, 297, 29901, 13, 29937, 1732, 597, 1514, 29889, 1557, 336, 2272, 29889, 990, 29914, 264, 29914, 12333, 29914, 3332, 1199, 29914, 7076, 29889, 1420, 13, 13, 5215, 24559, 2272, 13, 3166, 24559, 2272, 1053, 8989, 13, 13, 13, 1990, 379, 735, 348, 2001, 29898, 1557, 336, 2272, 29889, 2001, 1125, 13, 1678, 396, 4529, 278, 4235, 363, 596, 2944, 1244, 763, 29901, 13, 1678, 3234, 29922, 3073, 580, 268, 396, 231, 189, 170, 31399, 13, 1678, 2635, 2481, 353, 8989, 580, 29871, 396, 29871, 30594, 31016, 13, 1678, 8666, 353, 8989, 580, 29871, 396, 29871, 231, 190, 186, 31168, 13, 1678, 5253, 353, 8989, 580, 29871, 396, 29871, 30494, 31398, 236, 165, 160, 13, 1678, 1700, 1227, 353, 8989, 580, 29871, 396, 29871, 30494, 31398, 31180, 13, 1678, 8992, 13026, 353, 8989, 580, 29871, 396, 29871, 236, 165, 137, 31244, 31084, 31062, 13, 1678, 1722, 4074, 342, 353, 8989, 580, 29871, 396, 29871, 31695, 231, 190, 150, 31180, 13, 2 ]
python/cendalytics/report/core/dmo/record_retriever.py	jiportilla/ontology	0	120233	#!/usr/bin/env python # -- coding: utf-8 -- from tabulate import tabulate from base import BaseObject from datamongo import BaseMongoClient from datamongo import CendantCollection from datamongo import CendantXdm from datamongo import TransformCendantRecords class RecordRetriever(BaseObject): def __init__(self, key_field: str, mongo_client: BaseMongoClient, collection_names: dict, is_debug: bool = False): """ Created: 13-Nov-2019 <EMAIL> * refactored out of 'generate-feedback-report' Updated: 14-Nov-2019 <EMAIL> * added random record capability https://github.ibm.com/GTS-CDO/unstructured-analytics/issues/1331#issuecomment-16009658 :param key_field: the value of the keyfield to process (e.g. the actual Serial Number or Open Seat ID) :param collection_names: a dictionary containing a complete set of collection names Sample Input: { 'src': 'supply_src_20191025', 'tag': 'supply_tag_20191025', 'xdm': 'supply_xdm_20191029' } :param is_debug: """ BaseObject.__init__(self, __name__) self._is_debug = is_debug self._key_field = key_field self._mongo_client = mongo_client self._collection_names = collection_names def _retrieve_record(self, collection_name: str): collection = CendantCollection(is_debug=self._is_debug, some_base_client=self._mongo_client, some_collection_name=collection_name) if self._key_field.lower().strip() == "random": # GIT-1331-16009658 return collection.random(total_records=1)[0] return collection.by_key_field(self._key_field) def _tag_record(self) -> dict or None: tag_record = self._retrieve_record(self._collection_names["tag"]) if not self._collection_names["tag"]: self.logger.warning('\n'.join([ "TAG Collection Not Found", f"\t{self._collection_names}"])) return None if not tag_record: self.logger.warning('\n'.join([ f"TAG Record Not Found (" f"key-field={self._key_field})"])) return None if self._is_debug: df_record = TransformCendantRecords.to_dataframe(a_record=tag_record, include_text=False) self.logger.debug('\n'.join([ f"Retrieved TAG Record (" f"key-field={self._key_field})", tabulate(df_record, tablefmt='psql', headers='keys')])) return tag_record def _xdm_record(self) -> dict or None: if not self._collection_names["xdm"]: self.logger.warning('\n'.join([ "XDM Collection Not Found", f"\t{self._collection_names}"])) return None xdm_record = self._retrieve_record(self._collection_names["xdm"]) if not xdm_record: self.logger.warning('\n'.join([ f"XDM Record Not Found (" f"key-field={self._key_field})"])) return None if self._is_debug: self.logger.debug('\n'.join([ f"Retrieved XDM Record (" f"key-field={self._key_field})", tabulate(CendantXdm.dataframe(xdm_record), tablefmt='psql', headers='keys')])) return xdm_record def process(self) -> dict: svcresult = { "tag": self._tag_record(), "xdm": self._xdm_record()} if self._is_debug: self.logger.debug('\n'.join([ "Records Retrieved", f"\tKey Field: {self._key_field}", f"\tTAG Collection: {self._collection_names['tag']}", f"\tXDM Collection: {self._collection_names['xdm']}"])) return svcresult	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 13, 3166, 4434, 5987, 1053, 4434, 5987, 13, 13, 3166, 2967, 1053, 7399, 2061, 13, 3166, 1418, 314, 7443, 1053, 7399, 29924, 7443, 4032, 13, 3166, 1418, 314, 7443, 1053, 315, 5818, 7196, 13, 3166, 1418, 314, 7443, 1053, 315, 5818, 29990, 18933, 13, 3166, 1418, 314, 7443, 1053, 4103, 689, 29907, 5818, 4789, 4339, 13, 13, 13, 1990, 14164, 8015, 2546, 369, 29898, 5160, 2061, 1125, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 13, 462, 1820, 29918, 2671, 29901, 851, 29892, 13, 462, 19476, 29918, 4645, 29901, 7399, 29924, 7443, 4032, 29892, 13, 462, 4333, 29918, 7039, 29901, 9657, 29892, 13, 462, 338, 29918, 8382, 29901, 6120, 353, 7700, 1125, 13, 4706, 9995, 13, 4706, 6760, 630, 29901, 13, 632, 29896, 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tweet.py	m-r-moon/videographer	0	88864	<gh_stars>0 ############################### # Filename: tweet.py # # Author: <NAME> # ############################### import tweepy from time import sleep from credentials import * auth = tweepy.OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_secret) api = tweepy.API(auth) # text status update api.update_status('status') # file status update api.update_with_media(filename, status, video)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 13383, 7346, 4136, 2277, 29937, 13, 29937, 2514, 3871, 29901, 7780, 300, 29889, 2272, 3986, 396, 13, 29937, 13361, 29901, 529, 5813, 29958, 418, 396, 13, 13383, 7346, 4136, 2277, 29937, 13, 13, 5215, 7780, 1022, 29891, 13, 3166, 931, 1053, 8709, 13, 3166, 16140, 1053, 334, 13, 13, 5150, 353, 7780, 1022, 29891, 29889, 29949, 6444, 4598, 29898, 25978, 261, 29918, 1989, 29892, 21691, 29918, 19024, 29897, 13, 5150, 29889, 842, 29918, 5943, 29918, 6979, 29898, 5943, 29918, 6979, 29892, 2130, 29918, 19024, 29897, 13, 2754, 353, 7780, 1022, 29891, 29889, 8787, 29898, 5150, 29897, 13, 13, 29937, 1426, 4660, 2767, 13, 2754, 29889, 5504, 29918, 4882, 877, 4882, 1495, 13, 13, 29937, 934, 4660, 2767, 13, 2754, 29889, 5504, 29918, 2541, 29918, 9799, 29898, 9507, 29892, 4660, 29892, 4863, 29897, 13, 2 ]
tests/test_groups.py	daylinmorgan/click-rich-help	4	182754	<gh_stars>1-10 import click from click_rich_help import StyledCommand, StyledGroup def test_command_group(runner): @click.group( cls=StyledGroup, command_groups={"main": ["cmd1", "cmd2"]}, ) def cli(): pass @cli.command(cls=StyledCommand, styles={"header": "yellow", "option": "green"}) @click.option("--name", help="The person to greet.") def cmd1(count): pass @cli.command() def cmd2(): pass @cli.command() def othercmd(): pass result = runner.invoke(cli, ["--help"], color=True) assert not result.exception assert result.output.splitlines() == [ "\x1b[1;3;36mUsage\x1b[0m: \x1b[1mcli\x1b[0m \x1b[1m[OPTIONS] COMMAND [ARGS]...\x1b[0m", "", "\x1b[1;3;36mOptions\x1b[0m:", " \x1b[1;33m--help\x1b[0m Show this message and exit.", "", "\x1b[1;3;36mmain\x1b[0m:", " \x1b[1;33mcmd1\x1b[0m ", " \x1b[1;33mcmd2\x1b[0m ", "", "\x1b[1;3;36mCommands\x1b[0m:", " \x1b[1;33mothercmd\x1b[0m ", ] def test_unkown_command(runner): @click.group(cls=StyledGroup, command_groups={"main": ["cmd1", "unknowncmd"]}) def cli(): pass @cli.command(cls=StyledCommand, styles={"header": "yellow", "option": "green"}) @click.option("--name", help="The person to greet.") def cmd1(count): pass @cli.command() def cmd2(): pass @cli.command() def othercmd(): pass result = runner.invoke(cli, ["--help"], color=True) assert isinstance(result.exception, ValueError) assert ( str(result.exception) == "Unable to find command 'unknowncmd' in list of commands" ) def test_option_group(runner): @click.group(cls=StyledGroup, styles={"header": "yellow", "option": "green"}) def cli(): pass @cli.command(option_groups={"Config": ["--config", "--save-config"]}) @click.option("--name", help="The person to greet.") @click.option("--config", help="path to config") @click.option("--save-config", help="save config", is_flag=True) def command(name): pass result = runner.invoke(cli, ["command", "--help"], color=True) assert not result.exception assert result.output.splitlines() == [ "\x1b[33mUsage\x1b[0m: \x1b[1mcli command\x1b[0m \x1b[1m[OPTIONS]\x1b[0m", "", "\x1b[33mConfig\x1b[0m:", " \x1b[32m--config \x1b[0m\x1b[32mTEXT\x1b[0m path to config", " \x1b[32m--save-config\x1b[0m save config", "", "\x1b[33mOptions\x1b[0m:", " \x1b[32m--name \x1b[0m\x1b[32mTEXT\x1b[0m The person to greet.", " \x1b[32m--help\x1b[0m Show this message and exit.", ] def test_unknown_option(runner): @click.command( cls=StyledCommand, option_groups={"Config": ["--config", "--save-config", "--unknown-option"]}, ) @click.option("--name", help="The person to greet.") @click.option("--config", help="path to config") @click.option("--save-config", help="save config", is_flag=True) def cli(name): pass result = runner.invoke(cli, ["--help"], color=True) assert isinstance(result.exception, ValueError) assert ( str(result.exception) == "Unable to find option '--unknown-option' in list of options" )	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 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sigm/ia/views.py	mariobp/SIGM	0	106899	<reponame>mariobp/SIGM<gh_stars>0 # -- coding: utf-8 -- from __future__ import unicode_literals from django.shortcuts import render, redirect from django.http import HttpResponse from django.contrib.auth.decorators import login_required from django.contrib.auth import login, logout, authenticate import models import forms import json # Create your views here. @login_required(login_url='/login/') def index(request): periodo = models.Periodo.objects.last() return render(request, 'index.html', {'periodo': periodo}) # end def def logoutUsers(request): logout(request) return redirect('/') # end def def abrirPeriodo(request): old = models.Periodo.objects.last() if old: num = old.numero+1 nuevo = models.Periodo(numero=num, cerrado=False) nuevo.save() else: nuevo = models.Periodo(numero=1, cerrado=False) nuevo.save() # end if return redirect('/') # end def def cerrarPeriodo(request, pk): periodo = models.Periodo.objects.filter(id=pk).first() if periodo: periodo.cerrado = True periodo.save() return redirect('/') # end def def registrar_e(request): if request.method == "POST": form = forms.EstudianteForm(request.POST) if form.is_valid(): form.save() return redirect('/matricula/estudiante/') else: return render(request, 'registro_e.html', {'form': form}) # end if return render(request, 'registro_e.html', {'form': forms.EstudianteForm()}) # end if def matricula_e(request): identificacion = request.GET.get('identificacion', False) mensaje = "" if identificacion: estudiante = models.Estudiante.objects.filter(identificacion=identificacion).first() if estudiante: periodo = models.Periodo.objects.last() if periodo: matricula = models.Matricula.objects.filter(estudiante=estudiante, periodo=periodo) if matricula: mensaje = "Ya esta matriculado" else: if not periodo.cerrado: matricula = models.Matricula(periodo=periodo, estudiante=estudiante) matricula.save() mensaje = "Matriculado" else: mensaje = "Periodo cerrado, no se puede matricular" else: mensaje = "No se puede matricular" else: mensaje = "No hay ningun estudiante con este codigo" return render(request, 'matricula.html', {"mensaje": mensaje}) # end def def desertados(request): return render(request, 'desertados.html', {}) # end def def listaMatriculados(request): periodo = models.Periodo.objects.last() estudiante = models.Estudiante.objects.all().exclude(matricula__periodo=periodo).values('latitud', 'longitud') data = list(estudiante) return HttpResponse(json.dumps(data), content_type="text/json") # end def	[ 1, 529, 276, 1112, 420, 29958, 29885, 2628, 25288, 29914, 5425, 21576, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 3166, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 13, 3166, 9557, 29889, 12759, 7582, 29879, 1053, 4050, 29892, 6684, 13, 3166, 9557, 29889, 1124, 1053, 9056, 5103, 13, 3166, 9557, 29889, 21570, 29889, 5150, 29889, 19557, 4097, 1053, 6464, 29918, 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src/genui/settings/__init__.py	Tontolda/genui	15	60558	""" __init__.py Created by: <NAME> On: 4/27/20, 10:49 AM """	[ 1, 9995, 13, 1649, 2344, 26914, 2272, 13, 13, 20399, 491, 29901, 529, 5813, 29958, 13, 2951, 29901, 29871, 29946, 29914, 29906, 29955, 29914, 29906, 29900, 29892, 29871, 29896, 29900, 29901, 29946, 29929, 13862, 13, 15945, 29908, 13, 13, 2 ]
sandbox/decorator_def.py	olileger/sBeull	0	136106	import functools def FuncDecoratorOne(func): @functools.wraps(func) def wrapper(args, kwargs): print("Calling: ", func.__name__) return func(args, *kwargs) return wrapper def FuncDecoratorListProperties(func): @functools.wraps(func) def wrapper(args, *kwargs): print("Object's properties: ", args[0].__dict__) return func(args, *kwargs) return wrapper def ClassDecoratorOne(c): __init__ = c.__init__ @functools.wraps(c.__init__) def wrapper(_self_, args, *kwargs): __init__(_self_, args, **kwargs) c.__init__ = wrapper return c	[ 1, 1053, 2090, 312, 8789, 13, 13, 1753, 383, 4661, 6185, 272, 1061, 6716, 29898, 9891, 1125, 13, 1678, 732, 7692, 312, 8789, 29889, 29893, 336, 567, 29898, 9891, 29897, 13, 1678, 822, 14476, 10456, 5085, 29892, 3579, 19290, 1125, 13, 4706, 1596, 703, 5594, 292, 29901, 9162, 3653, 17255, 978, 1649, 29897, 13, 4706, 736, 3653, 10456, 5085, 29892, 3579, 19290, 29897, 13, 1678, 736, 14476, 13, 13, 13, 1753, 383, 4661, 6185, 272, 1061, 1293, 11857, 29898, 9891, 1125, 13, 1678, 732, 7692, 312, 8789, 29889, 29893, 336, 567, 29898, 9891, 29897, 13, 1678, 822, 14476, 10456, 5085, 29892, 3579, 19290, 1125, 13, 4706, 1596, 703, 2061, 29915, 29879, 4426, 29901, 9162, 6389, 29961, 29900, 1822, 1649, 8977, 1649, 29897, 13, 4706, 736, 3653, 10456, 5085, 29892, 3579, 19290, 29897, 13, 1678, 736, 14476, 13, 13, 13, 1753, 4134, 6185, 272, 1061, 6716, 29898, 29883, 1125, 13, 1678, 4770, 2344, 1649, 353, 274, 17255, 2344, 1649, 13, 1678, 732, 7692, 312, 8789, 29889, 29893, 336, 567, 29898, 29883, 17255, 2344, 1649, 29897, 13, 1678, 822, 14476, 7373, 1311, 3383, 334, 5085, 29892, 3579, 19290, 1125, 13, 4706, 4770, 2344, 1649, 7373, 1311, 3383, 334, 5085, 29892, 3579, 19290, 29897, 13, 1678, 274, 17255, 2344, 1649, 353, 14476, 13, 1678, 736, 274, 2 ]
RIPv2-Simulation/Router.py	vkmanojk/Networks-VirtualLAN	0	8171	''' Summary: Program that implements a routing deamon based on the RIP version 2 protocol from RFC2453. Usage: python3 Router.py <router_config_file> Configuration File: The user supplies a router configuration file of the format: [Settings] router-id = <router_number> input-ports = <input> [, <input>, ...] outputs = <output>-<metric>-<destination_router> [, <output>-<metric>-<destination_router>, ...] where, router_number: ID of router between 1 - 64000. input: port number between 1024 - 64000. output: port number between 1024 - 6400, not equal to any inputs. metric: metric of output between 1 - 16. destination_router: ID of destination router. Description: This program implements a basic RIPv2 routing protocol from RFC2453 for routing computations in computer networks. It takes a configuration file as shown above and sets up a router with a new socket for each input-port. The RIPv2 protocol uses a routing table to keep track of all reachable routers on the network along with their metric/cost and the direct next hop router ID along the route to that destination router. However, it can only send messages to the direct neighbours specified in outputs. The protocol uses the Bellman-Ford distance vector algorithm to compute the lowest cost route to each router in the network. If the metric is 16 or greater, the router is considered unreachable. The routing table initially starts with a single route entry (RTE) for itself with a metric of zero. The routing table is periodically transmitted too each of its direct output ports via an unsolicited response message as defined in RFC2453 section 3.9.2 and 4. This is performed on a separate thread so it does not interfere with other operations The receives messages from other routers by using the python select() function which blocks until a message is ready to be read. Once a message is received the header and contents are validated. If the message is valid each RTE is processed according to RFC2453 section 3.9.2. If a new router is found the RTE is added to the routing table, adding the cost to the metric for the output the message was received on. If the RTE already exists, but the metric is smaller, the metric is updated to the lower metric. If the lower metric is from a different next hop router, change the next hop. If nothing has changed, restart the timeout timer. If RTE metric >= max metric of 16, mark the entry for garbage collection and update the metric in the table. If any change has occurred in the routing table as a result of a received message, a triggered update (RFC2453 section 3.10.1) is sent to all outputs with the updated entries. Triggered updates are sent with a random delay between 1 - 5 seconds to prevent synchronized updates. Request messages are not implemented in this program. Timers (all timers are on separate threads) (RFC2453 section 3.8): Update timer - Periodic unsolicited response message sent to all outputs. The period is adjusted each time to a random value between 0.8 * BASE_TIMER and 1.2 * BASE_TIMER to prevent synchronized updates. Timeout - used to check the routing table for RTEs which have have not been updated within the ROUTE_TIMEOUT interval. If a router has not been heard from within this time, then set the metric to the max metric of 16 and start the garbage collection timer. Garbage timer - used to check the routing table for RTEs set for garbage collection. If the timeout >= DELETE_TIMEOUT, mark the RTE for deletion. Garbage Collection - used to check the routing table for RTEs marked for deletion, and removes those entries from the table. ''' import configparser import select import socket import sys import time import threading import struct import datetime from random import randint, randrange DEBUG = False HOST = '127.0.0.1' # localhost BASE_TIMER = 5 MAX_METRIC = 16 ROUTE_TIMEOUT = BASE_TIMER * 6 DELETE_TIMEOUT = BASE_TIMER * 4 AF_INET = 2 # =========================================================================== # TRANSITIONS class Transistion(): '''Class Representing a transition between states.''' def __init__(self, to_state): self.to_state = to_state def execute(self): '''Run the transition functions''' pass # =========================================================================== # STATES class State(): '''Class Representing a generic state''' def __init__(self, fsm): self.fsm = fsm def enter(self): '''Execute functions for entering a state''' pass def execute(self): '''Execute functions while in state''' pass def exit(self): '''Execute functions for leaving a state''' pass class StartUp(State): '''Class Representing the Start up state which reads the configuration file ''' def __init__(self, fsm): super(StartUp, self).__init__(fsm) def execute(self): '''Execute the configuration functions''' print_message("Loading Configuration File: '" + self.fsm.router.config_file + "'") config = configparser.ConfigParser() config.read(self.fsm.router.config_file) self.get_router_id(config) self.setup_inputs(config) self.get_outputs(config) self.setup_routing_table() self.fsm.router.print_routing_table() self.fsm.to_transition("toWaiting") def exit(self): '''Print complete message''' print_message("Router Setup Complete.") def get_router_id(self, config): '''Read the router id number from the configuration file''' if 1 <= int(config['Settings']['router-id']) <= 64000: self.fsm.router.router_settings['id'] = \ int(config['Settings']['router-id']) else: raise Exception('Invalid Router ID Number') def get_outputs(self, config): '''Return a dictionary of outputs containing port, cost and destination router id from the Configuration file''' outputs = config['Settings']['outputs'].split(', ') outputs = [i.split('-') for i in outputs] self.fsm.router.router_settings['outputs'] = {} existing_ports = [] for output in outputs: is_valid_port = 1024 <= int(output[0]) <= 64000 and not \ int(output[0]) in existing_ports is_valid_cost = 1 <= int(output[1]) < 16 is_valid_id = 1 <= int(output[2]) <= 64000 if is_valid_port and is_valid_cost and is_valid_id: existing_ports.append(int(output[0])) self.fsm.router.router_settings['outputs'][int(output[2])] = \ {'metric': int(output[1]), 'port': int(output[0])} else: raise Exception('Invalid Outputs') def setup_inputs(self, config): '''Create input sockets from the inputs specified in the config file''' # get inputs from configuration file ports = config['Settings']['input-ports'].split(', ') inputs = [] for port in ports: if 1024 <= int(port) <= 64000 and not int(port) in inputs: inputs.append(int(port)) else: raise Exception('Invalid Port Number') self.fsm.router.router_settings['inputs'] = {} # create socket for each input port for port in inputs: try: self.fsm.router.router_settings['inputs'][port] = \ socket.socket(socket.AF_INET, socket.SOCK_DGRAM) print_message('Socket ' + str(port) + ' Created.') except socket.error as msg: print('Failed to create socket. Message: ' + str(msg)) sys.exit() # bind port to socket try: self.fsm.router.router_settings['inputs'][port].bind( (HOST, port)) print_message('Socket ' + str(port) + ' Bind Complete.') except socket.error as msg: print('Failed to create socket. Message ' + str(msg)) sys.exit() def setup_routing_table(self): '''Setup routing table with the outputs specified in the config file''' self.fsm.router.routing_table[self.fsm.router.router_settings['id']] = \ RIPRouteEntry(address=self.fsm.router.router_settings['id'], nexthop=0, metric=0, imported=True) class Waiting(State): ''' Class representing the waiting state of the FSM where the router waits for messages to be received on its input sockets. When a message is received the state changes to the ReadMeassage state. ''' def __init__(self, fsm): super(Waiting, self).__init__(fsm) def enter(self): '''Display State entry message''' print_message("Entering idle state...") def execute(self): '''Waits for input sockets to be readable and then changes the state to process the received message.''' readable = select.select( self.fsm.router.router_settings['inputs'].values(), [], []) if readable[0]: self.fsm.router.readable_ports = readable[0] self.fsm.to_transition("toReadMessage") def exit(self): '''Display State exit message''' print_message("Message Received") class ReadMessage(State): '''Class representing the state for reading messages received on the input sockets''' def __init__(self, fsm): super(ReadMessage, self).__init__(fsm) def enter(self): print_message("Reading Messages...") def execute(self): for port in self.fsm.router.readable_ports: packet = RIPPacket(port.recvfrom(1024)[0]) self.fsm.router.update_routing_table(packet) if self.fsm.router.route_change: self.fsm.router.trigger_update() self.fsm.router.print_routing_table() self.fsm.to_transition("toWaiting") def exit(self): print_message("Messages Read.") # =========================================================================== # FINITE STATE MACHINE class RouterFSM(): '''Class representing the Router finite state machine''' def __init__(self, rip_router): self.router = rip_router self.states = {} self.transitions = {} self.cur_state = None self.trans = None def add_transistion(self, trans_name, transition): '''Add a new transition to the FSM''' self.transitions[trans_name] = transition def add_state(self, state_name, state): '''Add a new state to the FSM''' self.states[state_name] = state def set_state(self, state_name): '''Set the current state of the FSM''' self.cur_state = self.states[state_name] def to_transition(self, to_trans): '''Set the current transition of the FSM''' self.trans = self.transitions[to_trans] def execute(self): '''Run the FSM''' if self.trans: self.cur_state.exit() self.trans.execute() self.set_state(self.trans.to_state) self.cur_state.enter() self.trans = None self.cur_state.execute() # =========================================================================== # IMPLEMENTATION class RIPPacket: '''Class representing a RIP packet containing a header and body as defined in RFC2453 RIPv2 section 4.''' def __init__(self, data=None, header=None, rtes=None): if data: self._init_from_network(data) elif header and rtes: self._init_from_host(header, rtes) else: raise ValueError def __repr__(self): return "RIPPacket: Command {}, Ver. {}, number of RTEs {}.". \ format(self.header.cmd, self.header.ver, len(self.rtes)) def _init_from_network(self, data): '''Init for RIPPacket if data is from the network''' # Packet Validation datalen = len(data) if datalen < RIPHeader.SIZE: raise FormatException malformed_rtes = (datalen - RIPHeader.SIZE) % RIPRouteEntry.SIZE if malformed_rtes: raise FormatException # Convert bytes in packet to header and RTE data num_rtes = int((datalen - RIPHeader.SIZE) / RIPRouteEntry.SIZE) self.header = RIPHeader(data[0:RIPHeader.SIZE]) self.rtes = [] rte_start = RIPHeader.SIZE rte_end = RIPHeader.SIZE + RIPRouteEntry.SIZE # Loop over data packet to obtain each RTE for i in range(num_rtes): self.rtes.append(RIPRouteEntry(rawdata=data[rte_start:rte_end], src_id=self.header.src)) rte_start += RIPRouteEntry.SIZE rte_end += RIPRouteEntry.SIZE def _init_from_host(self, header, rtes): '''Init for imported data''' if header.ver != 2: raise ValueError("Only Version 2 is supported.") self.header = header self.rtes = rtes def serialize(self): '''Return the byte sting representing this packet for network transmission''' packed = self.header.serialize() for rte in self.rtes: packed += rte.serialize() return packed class RIPHeader: '''Class representing the header of a RIP packet''' FORMAT = "!BBH" SIZE = struct.calcsize(FORMAT) TYPE_RESPONSE = 2 VERSION = 2 def __init__(self, rawdata=None, router_id=None): self.packed = None if rawdata: self._init_from_network(rawdata) elif router_id: self._init_from_host(router_id) else: raise ValueError def __repr__(self): return "RIP Header (cmd = {}, ver = {}, src = {})".format(self.cmd, self.ver, self.src) def _init_from_network(self, rawdata): '''init for data from network''' header = struct.unpack(self.FORMAT, rawdata) self.cmd = header[0] self.ver = header[1] self.src = header[2] def _init_from_host(self, router_id): '''Init for data from host''' self.cmd = self.TYPE_RESPONSE self.ver = self.VERSION self.src = router_id def serialize(self): '''Return the byte sting representing this header for network transmission''' return struct.pack(self.FORMAT, self.cmd, self.ver, self.src) class RIPRouteEntry: '''Class representing a single RIP route entry (RTE)''' FORMAT = "!HHIII" SIZE = struct.calcsize(FORMAT) MIN_METRIC = 0 MAX_METRIC = 16 def __init__(self, rawdata=None, src_id=None, address=None, nexthop=None, metric=None, imported=False): self.changed = False self.imported = imported self.init_timeout() if rawdata and src_id != None: self._init_from_network(rawdata, src_id) elif address and nexthop != None and metric != None: self._init_from_host(address, nexthop, metric) else: raise ValueError def __repr__(self): template = "\|{:^11}\|{:^10}\|{:^11}\|{:^15}\|{:^10}\|{:^13}\|" # Check that timeout is set if self.timeout == None: return template.format(self.addr, self.metric, self.nexthop, self.changed, self.garbage, str(self.timeout)) else: timeout = (datetime.datetime.now() - self.timeout).total_seconds() return template.format(self.addr, self.metric, self.nexthop, self.changed, self.garbage, round(timeout, 1)) def _init_from_host(self, address, nexthop, metric): '''Init for data from host''' self.afi = AF_INET self.tag = 0 # not used self.addr = address self.nexthop = nexthop self.metric = metric def _init_from_network(self, rawdata, src_id): '''Init for data received from network''' rte = struct.unpack(self.FORMAT, rawdata) self.afi = rte[0] self.tag = rte[1] self.addr = rte[2] self.set_nexthop(rte[3]) self.metric = rte[4] if self.nexthop == 0: self.nexthop = src_id # Validation if not self.MIN_METRIC <= self.metric <= self.MAX_METRIC: raise FormatException def init_timeout(self): '''Initialize the timeout property''' if self.imported: self.timeout = None else: self.timeout = datetime.datetime.now() self.garbage = False self.marked_for_delection = False def __eq__(self, other): if self.afi == other.afi and \ self.addr == other.addr and \ self.tag == other.tag and \ self.nexthop == other.nexthop and \ self.metric == other.metric: return True else: return False def set_nexthop(self, nexthop): '''Set the nexthop property''' self.nexthop = nexthop def serialize(self): '''Pack entries into typical RIPv2 packet format for sending over the network. ''' return struct.pack(self.FORMAT, self.afi, self.tag, self.addr, self.nexthop, self.metric) class FormatException(Exception): '''Class representing the Format Exception''' def __init__(self, message=""): self.message = message class Router: '''Class representing a single router''' def __init__(self, config_file): self.fsm = RouterFSM(self) self.config_file = config_file # Dictionary of router settings, including router-id, inputs and # outputs self.router_settings = {} self.readable_ports = [] # Dictionary of routing table self.routing_table = {} self.route_change = False # STATES self.fsm.add_state("StartUp", StartUp(self.fsm)) self.fsm.add_state("Waiting", Waiting(self.fsm)) self.fsm.add_state("ReadMessage", ReadMessage(self.fsm)) # TRANSITIONS self.fsm.add_transistion("toWaiting", Transistion("Waiting")) self.fsm.add_transistion("toReadMessage", Transistion("ReadMessage")) self.fsm.set_state("StartUp") def execute(self): '''Run the router's finite state machine''' self.fsm.execute() def update_routing_table(self, packet): '''Update Routing table if new route info exist''' for rte in packet.rtes: # ignore RTEs of self if rte.addr != self.fsm.router.router_settings['id']: bestroute = self.routing_table.get(rte.addr) # set nexthop to source router and calculate metric rte.set_nexthop(packet.header.src) rte.metric = min(rte.metric + self.router_settings['outputs'][ packet.header.src]['metric'], RIPRouteEntry.MAX_METRIC) # Route dosn't yet exist if not bestroute: # ignore RTEs with a metric of MAX_METRIC if rte.metric == RIPRouteEntry.MAX_METRIC: return # Add new RTE to routing table rte.changed = True self.route_change = True self.routing_table[rte.addr] = rte print_message("RTE added for Router: " + str(rte.addr)) return else: # Route already exists if rte.nexthop == bestroute.nexthop: if bestroute.metric != rte.metric: if bestroute.metric != RIPRouteEntry.MAX_METRIC \ and rte.metric >= RIPRouteEntry.MAX_METRIC: # mark for garbage collection bestroute.metric = RIPRouteEntry.MAX_METRIC bestroute.garbage = True bestroute.changed = True self.route_change = True else: self.update_route(bestroute, rte) # Route still exists with same values elif not bestroute.garbage: bestroute.init_timeout() # Lower metric on existing route elif rte.metric < bestroute.metric: self.update_route(bestroute, rte) def update_route(self, bestroute, rte): '''Update an existing route entry with new route info''' bestroute.init_timeout() bestroute.garbage = False bestroute.changed = True bestroute.metric = rte.metric bestroute.nexthop = rte.nexthop self.route_change = True print_message("RTE for Router: " + str(rte.addr) + " updated with metric=" + str(rte.metric) + ", nexthop=" + str(rte.nexthop) + ".") def print_routing_table(self): '''Print the routing table to the terminal''' line = "+-----------+----------+-----------+---------------+----------+-------------+" print(line) print( "\| Routing Table (Router " + str(self.router_settings['id']) + ") \|") print(line) print( "\|Router ID \| Metric \| NextHop \| ChangedFlag \| Garbage \| Timeout(s) \|") print(line) print(self.routing_table[self.router_settings['id']]) print( "+===========+==========+===========+===============+==========+=============+") for entry in self.routing_table: if entry != self.router_settings['id']: print(self.routing_table[entry]) print(line) print('\n') def trigger_update(self): '''Send Routing update for only the routes which have changed''' changed_rtes = [] print_message("Sending Trigger update.") for rte in self.routing_table.values(): if rte.changed: changed_rtes.append(rte) rte.changed = False self.route_change = False # send update with random delay between 1 and 5 seconds delay = randint(1, 5) threading.Timer(delay, self.update, [changed_rtes]) def update(self, entries): '''Send a message to all output ports''' if self.router_settings != {}: sock = list(self.router_settings['inputs'].values())[1] local_header = RIPHeader(router_id=self.router_settings['id']) for output in self.router_settings['outputs']: # Split horizon # Remove RTES for which nexthop == output split_horizon_entries = [] for entry in entries: if entry.nexthop != output: split_horizon_entries.append(entry) else: # Poison reverse # Create new entry to get around some funky referencing # When doing poisoned_entry = entry poisoned_entry = RIPRouteEntry(rawdata=None, src_id=None, address=entry.addr, nexthop=entry.nexthop, metric= RIPRouteEntry.MAX_METRIC, imported=entry.imported) split_horizon_entries.append(poisoned_entry) # comment out to disable split horizon packet = RIPPacket( header=local_header, rtes=split_horizon_entries) # Uncomment to disable split horizon # packet = RIPPacket(header=local_header, rtes=entries) sock.sendto(packet.serialize(), (HOST, self.router_settings['outputs'][output]["port"])) print_message("Message Sent To Router: " + str(output)) def check_timeout(self): '''Check the current timeout value for each RTE in the routing table. If the time difference with now is greater than ROUTE_TIMEOUT, then set the metric to 16 and start the garbage collection timer.''' print_message("Checking timeout...") if self.routing_table != {}: for rte in self.routing_table.values(): if rte.timeout != None and \ (datetime.datetime.now() - rte.timeout).total_seconds() \ >= ROUTE_TIMEOUT: rte.garbage = True rte.changed = True self.route_change = True rte.metric = RIPRouteEntry.MAX_METRIC rte.timeout = datetime.datetime.now() self.print_routing_table() print_message("Router: " + str(rte.addr) + " timed out.") def garbage_timer(self): '''Check the status of the garbage property of each RTE. If true, and the timeout value difference with now is greater than DELETE_TIMEOUT, mark it for deletion''' print_message("Checking garbage timeout...") if self.routing_table != {}: for rte in self.routing_table.values(): if rte.garbage: if (datetime.datetime.now() - rte.timeout).total_seconds() \ >= DELETE_TIMEOUT: rte.marked_for_delection = True def garbage_collection(self): '''Check the routing table for RTE's that are marked for deletion and remove them.''' print_message("Collecting Garbage...") if self.routing_table != {}: delete_routes = [] for rte in self.routing_table.values(): if rte.marked_for_delection: delete_routes.append(rte.addr) print_message("Router: " + str(rte.addr) + " has been " + "removed from the routing table.") for entry in delete_routes: del self.routing_table[entry] self.print_routing_table() def timer(self, function, param=None): '''Start a periodic timer which calls a specified function''' if param != None: function(list(param.values())) period = BASE_TIMER * randrange(8, 12, 1) / 10 else: period = BASE_TIMER function() threading.Timer(period, self.timer, [function, param]).start() def start_timers(self): '''Start the timers on separate threads''' self.timer(self.update, param=self.routing_table) self.timer(self.check_timeout) self.timer(self.garbage_timer) self.timer(self.garbage_collection) def main_loop(self): '''Start the main loop for the program.''' while True: self.execute() # RUN THE PROGRAM def print_message(message): '''Print the given message with the current time before it''' if DEBUG: print("[" + time.strftime("%H:%M:%S") + "]: " + message) def main(): '''Main function to run the program.''' if __name__ == "__main__": router = Router(str(sys.argv[-1])) router.start_timers() router.main_loop() main()	[ 1, 14550, 30004, 13, 1678, 6991, 5219, 29901, 7835, 393, 10703, 263, 21398, 316, 314, 265, 2729, 373, 278, 6756, 13, 632, 390, 5690, 1873, 29871, 29906, 9608, 515, 390, 8610, 29906, 29946, 29945, 29941, 22993, 13, 1678, 6756, 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skylib/extraction/__init__.py	SkynetRTN/skylib	1	102625	""" SkyLib source extraction functions - a replacement for SExtractor. main: high-level source extraction interface. isophotal: a fast implementation of isophotal analysis. """ from .main import *	[ 1, 9995, 13, 29903, 3459, 14868, 2752, 4805, 428, 3168, 448, 263, 16920, 363, 3725, 486, 28891, 29889, 13, 13, 3396, 29901, 1880, 29899, 5563, 2752, 4805, 428, 5067, 29889, 13, 275, 3021, 7288, 29901, 263, 5172, 5314, 310, 338, 3021, 7288, 7418, 29889, 13, 15945, 29908, 13, 13, 3166, 869, 3396, 1053, 334, 13, 2 ]
scan/biotools.py	dubinnyi/rcsbscan	0	187622	<filename>scan/biotools.py import gzip import sys, os import warnings import time from Bio.PDB.PDBExceptions import PDBConstructionWarning import re from Bio.PDB.mmtf import MMTFParser from Bio.PDB import * from Bio.Seq import Seq from Bio.Data import IUPACData import Bio.SVDSuperimposer import multiprocessing as mp LOCK = mp.Lock() class MPCounter(object): def __init__(self, initval=0): self.val = mp.Value('i', initval) self.lock = mp.Lock() def increment(self): with self.lock: self.val.value += 1 def value(self): with self.lock: return self.val.value def eprint(args, kwargs): with LOCK: print(args, file=sys.stderr, *kwargs) def mpprint(args, *kwargs): with LOCK: print(args, file=sys.stdout, kwargs) def opengz(filename, args: 'r'): if filename[-2:] == "gz": openfunc = gzip.open openarg = args + 't' else: openfunc = open openarg = args return openfunc(filename, openarg) def parse_structure_filename(filename): id = os.path.basename(filename) format = 'auto' zip_flag = False if id.startswith("pdb"): id = id[3:] format = 'pdb' if id.endswith(".gz"): id = id[0:-3] zip_flag = True if id.endswith(".pdb"): id = id[0:-4] format = 'pdb' if id.endswith(".mmcif"): id = id[0:-6] format = 'cif' if id.endswith(".cif"): id = id[0:-4] format = 'cif' if id.endswith(".ent"): id = id[0:-4] if id.endswith(".mmtf"): id = id[0:-5] format = 'mmtf' id = id.upper() return (id, format, zip_flag) def get_structure_from_file(filename, kwargs): # format argument is unused force_format = format = 'auto' if 'format' in kwargs.keys(): force_format = kwargs['format'] (file_id, filename_format, zip_flag) = parse_structure_filename(filename) cif_parser = MMCIFParser() pdb_parser = PDBParser() mmtf_parser = MMTFParser() if force_format != 'auto': format = force_format if format == 'auto': format = filename_format if format == 'pdb' or format == 'auto': parsers = [pdb_parser, cif_parser] elif format == 'mmtf' or format == 'auto': parsers = [mmtf_parser] else: parsers = [cif_parser, pdb_parser, mmtf_parser] for parser in parsers: try: if zip_flag: open_function = opengz else: open_function = open if format == 'mmtf': structure = parser.get_structure(filename) if structure: return structure with open_function(filename, 'r') as handle: structure = parser.get_structure(file_id, handle) if structure: return structure except: continue eprint("File {} could not be read".format(filename)) return None def chain_sequence_ppbuild(chain): # Fails if chain is dicontinued ppb = PPBuilder() pp = ppb.build_peptides(chain) if pp and len(pp): return (pp[0].get_sequence()) else: return (None) def chain_sequence_aaselect(chain): # Ok if chain is discontinued res_list = chain.get_list() res_list_aa = [res for res in res_list if is_aa(res)] return res_list_aa def res_oneletter(res): #dict_3to1 = IUPACData.protein_letters_3to1.copy() dict_3to1 = {} dict_3to1['Mse'] = 'm' # Seleno methyonine https://www.rcsb.org/ligand/MSE dict_3to1['Mly'] = 'k' # N,N dimethyl lysine https://www.rcsb.org/ligand/MLY dict_3to1['Cso'] = 'c' # S-HYDROXYCYSTEINE https://www.rcsb.org/ligand/CSO dict_3to1['Csd'] = 'a' # 3-SULFINOALANINE https://www.rcsb.org/ligand/CSD dict_3to1['Cme'] = 'e' # S,S-(2-HYDROXYETHYL)THIOCYSTEINE # https://www.rcsb.org/ligand/CME dict_3to1['Tys'] = 'y' # O-SULFO-L-TYROSINE https://www.rcsb.org/ligand/TYS dict_3to1['Dah'] = 'f' # 3,4-DIHYDROXYPHENYLALANINE # https://www.rcsb.org/ligand/DAH rname3 = res.get_resname() rname3 = rname3[0].upper() + rname3[1:].lower() try: rname1 = IUPACData.protein_letters_3to1[rname3] except KeyError: try: rname1 = dict_3to1[rname3] except KeyError: rname1 = '.' return rname1 def res_list_to_one_letter_string(res_list): if not res_list: return (0, "") prev_id = None sequence_start = None seq = '' for r in res_list: this_id = r.get_id()[1] # residue number, may repeat o have gaps if prev_id: if this_id == prev_id or this_id == prev_id + 1: seq += res_oneletter(r) elif this_id > prev_id + 1: for r1 in range(prev_id + 1, this_id): seq += "." seq += res_oneletter(r) else: sequence_start = this_id seq += res_oneletter(r) prev_id = this_id return (sequence_start, Seq(seq)) def chain_one_letter_string(chain): res_list = chain_sequence_aaselect(chain) return res_list_to_one_letter_string(res_list) def res_insertions(res_list): res_list_ins = [res for res in res_list if res.get_id()[2] != ' '] return res_list_ins def chain_water(chain): # Ok if chain is discontinued res_list = chain.get_list() res_list_water = [res for res in res_list if res.get_id()[0] == 'W'] return res_list_water def chain_water_id(chain, water_id): # Ok if chain is discontinued res_list = chain.get_list() res_list_water = [res for res in res_list \ if res.get_id()[0] == 'W' and res.get_id()[1] == water_id] return res_list_water def bio_str_to_resid(string, hetfield =' '): # Return residue in biopython-compatible format # insertion character after number is converted to icode # hetfield is ' ' for ordinary residues # # examples: # bio_str_to_resid('10') # (' ', 10, ' ') # bio_str_to_resid('11A') # (' ', 11, 'A') p = re.compile('(?P<resseq>\d+)(?P<icode>[A-Z]?)$') m = p.match(string) if not m: return None resseq = int(m.group('resseq')) icode = m.group('icode') icode = ' ' if not icode else icode return (hetfield, resseq, icode) def bio_resid_to_str(resid): hetfield, resseq, icode = resid return "{}{}".format(resseq, icode.strip()) def range_str_to_tuple(range_str): if not range_str or range_str == '' or range_str == '.' or range_str.upper() == 'ALL': # Select all return None range_split = range_str.split('-') if len(range_split) == 0: # Select all return None elif len(range_split) == 1: res_start = bio_str_to_resid(range_split[0]) res_finish = res_start else: res_start = bio_str_to_resid(range_split[0]) res_finish = bio_str_to_resid(range_split[1]) #res_finish[1] += 1 return (res_start, res_finish) def select_residues_from_chain(chain, kwargs): # chain: biopython chain # first_res: first residue id to select, e.g. (' ',1,' ') # last_res: last residue to select, e.g. (' ',10, 'A') # gap_count: select 'gap_count' residues before first_res or after last_res # select all residues if no argumants provided res_list_aa = chain_sequence_aaselect(chain) id_list = [res.get_id() for res in res_list_aa] res_tuple = kwargs['res_tuple'] if 'res_tuple' in kwargs else None gap_count = kwargs['gap_count'] if 'gap_count' in kwargs else 0 if res_tuple: (first_res, last_res) = res_tuple else: first_res = kwargs['first_res'] if 'first_res' in kwargs else None last_res = kwargs['last_res'] if 'last_res' in kwargs else None first_index, last_index = 0, len(res_list_aa) if first_res: try: first_index = id_list.index(first_res) except: raise IndexError("Residue '{}' is out of sequence range".format(first_res)) if last_res: try: last_index = id_list.index(last_res) except: raise IndexError("Residue '{}' is out of sequence range".format(last_res)) if first_res and last_res: return res_list_aa[first_index: last_index + 1] elif first_res and ( gap_count is not None ): if first_index - gap_count < 0: gap_count = first_index return res_list_aa[first_index - gap_count : first_index] elif last_res and ( gap_count is not None ): return res_list_aa[last_index + 1 : last_index + 1 + gap_count] elif first_res and ( gap_count is None ): return res_list_aa[first_index :] elif last_res and ( gap_count is None ): return res_list_aa[: last_index + 1] else: return res_list_aa def atom_str_to_set(atom_str): atom_set = set() if not atom_str or atom_str == '' or atom_str == '.' or atom_str.upper() == 'ALL': return atom_set atom_list = atom_str.split(',') for a in atom_list: a = a.strip() atom_set.add(a) return atom_set def select_atoms_from_res_list(res_list, ref_atom_names_set): atom_list = [] for r in res_list: for a in r.get_list(): if ref_atom_names_set and a.get_name() in ref_atom_names_set: atom_list.append(a) if not ref_atom_names_set: # set is empty, append all atoms atom_list.append(a) if ref_atom_names_set and not atom_list: raise IndexError("Atoms not present in structure") return atom_list def check_res_list_is_continuous(res_list): prev_id = None for r in res_list: this_id = r.get_id()[1] # residue number, may repeat o have gaps if prev_id: if not (this_id == prev_id or this_id == prev_id + 1): return False prev_id = this_id return True def get_res_and_atoms(chain_res_aa, resno, ref_res_list_len, ref_atom_names_set): try: res_list_to_fit = chain_res_aa[resno: resno + ref_res_list_len] except: return (None, None) if check_res_list_is_continuous(res_list_to_fit): atom_list_to_fit = select_atoms_from_res_list(res_list_to_fit, ref_atom_names_set) return (res_list_to_fit, atom_list_to_fit) else: # eprint("Residue list is discontinous: {} - {}".format(res_list_to_fit[0].get_full_id(), # res_list_to_fit[-1].get_id())) return (None, None) def recursive_expand(list_of_files_and_dirs): result = [] for f in list_of_files_and_dirs: if os.path.isfile(f): result.append(f) elif os.path.isdir(f): subdirs = [os.path.join(f, d) for d in os.listdir(f)] result.extend(recursive_expand(subdirs)) return result	[ 1, 529, 9507, 29958, 16192, 29914, 5365, 327, 8789, 29889, 2272, 13, 5215, 330, 7554, 13, 5215, 10876, 29892, 2897, 13, 5215, 18116, 13, 5215, 931, 13, 3166, 21184, 29889, 29925, 4051, 29889, 29925, 4051, 2451, 29879, 1053, 349, 4051, 12075, 4080, 22709, 13, 5215, 337, 13, 13, 3166, 21184, 29889, 29925, 4051, 29889, 4317, 13264, 1053, 28880, 8969, 11726, 13, 3166, 21184, 29889, 29925, 4051, 1053, 334, 13, 3166, 21184, 29889, 23718, 1053, 25981, 13, 3166, 21184, 29889, 1469, 1053, 306, 4897, 2477, 1469, 13, 13, 5215, 21184, 29889, 7597, 29928, 19111, 326, 1066, 261, 13, 13, 5215, 6674, 307, 985, 292, 408, 22326, 13, 13, 21339, 353, 22326, 29889, 16542, 580, 13, 13, 13, 1990, 341, 9026, 5336, 29898, 3318, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 2069, 791, 29922, 29900, 1125, 13, 4706, 1583, 29889, 791, 353, 22326, 29889, 1917, 877, 29875, 742, 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29918, 1761, 29898, 690, 29918, 1761, 29918, 517, 29918, 9202, 29892, 2143, 29918, 8678, 29918, 7039, 29918, 842, 29897, 13, 4706, 736, 313, 690, 29918, 1761, 29918, 517, 29918, 9202, 29892, 12301, 29918, 1761, 29918, 517, 29918, 9202, 29897, 13, 1678, 1683, 29901, 13, 4706, 396, 321, 2158, 703, 1666, 333, 434, 1051, 338, 766, 1285, 262, 681, 29901, 6571, 448, 6571, 1642, 4830, 29898, 690, 29918, 1761, 29918, 517, 29918, 9202, 29961, 29900, 1822, 657, 29918, 8159, 29918, 333, 3285, 13, 4706, 396, 462, 462, 462, 418, 620, 29918, 1761, 29918, 517, 29918, 9202, 14352, 29896, 1822, 657, 29918, 333, 22130, 13, 4706, 736, 313, 8516, 29892, 6213, 29897, 13, 13, 13, 1753, 16732, 29918, 18837, 29898, 1761, 29918, 974, 29918, 5325, 29918, 392, 29918, 3972, 29879, 1125, 13, 1678, 1121, 353, 5159, 13, 1678, 363, 285, 297, 1051, 29918, 974, 29918, 5325, 29918, 392, 29918, 3972, 29879, 29901, 13, 4706, 565, 2897, 29889, 2084, 29889, 275, 1445, 29898, 29888, 1125, 13, 9651, 1121, 29889, 4397, 29898, 29888, 29897, 13, 4706, 25342, 2897, 29889, 2084, 29889, 275, 3972, 29898, 29888, 1125, 13, 9651, 1014, 3972, 29879, 353, 518, 359, 29889, 2084, 29889, 7122, 29898, 29888, 29892, 270, 29897, 363, 270, 297, 2897, 29889, 1761, 3972, 29898, 29888, 4638, 13, 9651, 1121, 29889, 21843, 29898, 3757, 25397, 29918, 18837, 29898, 1491, 3972, 29879, 876, 13, 1678, 736, 1121, 13, 268, 13, 2 ]
instance/config.py	Alweezy/cp2-bucketlist-api	1	69746	<reponame>Alweezy/cp2-bucketlist-api import os class Config(object): """Parent configuration class.""" DEBUG = False CSRF_ENABLED = True SECRET_KEY = os.getenv('SECRET_KEY') SQLALCHEMY_DATABASE_URI = os.getenv('DATABASE_URL') class DevelopmentConfig(Config): """Configurations for Development.""" DEBUG = True SQLALCHEMY_DATABASE_URI = 'postgresql://localhost/flask_db' class TestingConfig(Config): """Configurations for Testing, with a separate test database.""" TESTING = True SQLALCHEMY_DATABASE_URI = 'postgresql://localhost/test_db' DEBUG = True class StagingConfig(Config): """Configurations for Staging.""" DEBUG = True class ProductionConfig(Config): """Configurations for Production.""" DEBUG = False TESTING = False app_config = { 'development': DevelopmentConfig, 'testing': TestingConfig, 'staging': StagingConfig, 'production': ProductionConfig, }	[ 1, 529, 276, 1112, 420, 29958, 2499, 705, 29872, 1537, 29914, 6814, 29906, 29899, 21454, 1761, 29899, 2754, 13, 5215, 2897, 13, 13, 13, 1990, 12782, 29898, 3318, 1125, 13, 1678, 9995, 9780, 5285, 770, 1213, 15945, 13, 1678, 21681, 353, 7700, 13, 1678, 315, 14098, 29943, 29918, 1430, 6181, 29928, 353, 5852, 13, 1678, 3725, 22245, 29911, 29918, 10818, 353, 2897, 29889, 657, 6272, 877, 1660, 22245, 29911, 29918, 10818, 1495, 13, 1678, 3758, 1964, 3210, 12665, 29979, 29918, 25832, 27982, 29918, 15551, 353, 2897, 29889, 657, 6272, 877, 25832, 27982, 29918, 4219, 1495, 13, 13, 13, 1990, 14650, 3991, 29898, 3991, 1125, 13, 1678, 9995, 3991, 332, 800, 363, 14650, 1213, 15945, 13, 1678, 21681, 353, 5852, 13, 1678, 3758, 1964, 3210, 12665, 29979, 29918, 25832, 27982, 29918, 15551, 353, 525, 29272, 597, 7640, 29914, 1579, 1278, 29918, 2585, 29915, 13, 13, 13, 1990, 4321, 292, 3991, 29898, 3991, 1125, 13, 1678, 9995, 3991, 332, 800, 363, 4321, 292, 29892, 411, 263, 5004, 1243, 2566, 1213, 15945, 13, 1678, 17067, 1254, 4214, 353, 5852, 13, 1678, 3758, 1964, 3210, 12665, 29979, 29918, 25832, 27982, 29918, 15551, 353, 525, 29272, 597, 7640, 29914, 1688, 29918, 2585, 29915, 13, 1678, 21681, 353, 5852, 13, 13, 13, 1990, 624, 6751, 3991, 29898, 3991, 1125, 13, 1678, 9995, 3991, 332, 800, 363, 624, 6751, 1213, 15945, 13, 1678, 21681, 353, 5852, 13, 13, 13, 1990, 19561, 3991, 29898, 3991, 1125, 13, 1678, 9995, 3991, 332, 800, 363, 19561, 1213, 15945, 13, 1678, 21681, 353, 7700, 13, 1678, 17067, 1254, 4214, 353, 7700, 13, 13, 13, 932, 29918, 2917, 353, 426, 13, 1678, 525, 25431, 2396, 14650, 3991, 29892, 13, 1678, 525, 13424, 2396, 4321, 292, 3991, 29892, 13, 1678, 525, 303, 6751, 2396, 624, 6751, 3991, 29892, 13, 1678, 525, 24601, 2396, 19561, 3991, 29892, 13, 29913, 13, 2 ]
migrations/0018_auto_20180326_0331.py	tkhjp/editor_tkh	0	77708	<gh_stars>0 # Generated by Django 2.0.2 on 2018-03-26 03:31 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('editor', '0017_knowledge_point_list_grade'), ] operations = [ migrations.AlterField( model_name='question_upload', name='years_of_question', field=models.CharField(choices=[('p2', '小二'), ('p3', '小三'), ('p2', '小四'), ('p2', '小五'), ('p2', '小六')], default='p5', max_length=2), ), ]	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 3251, 630, 491, 15337, 29871, 29906, 29889, 29900, 29889, 29906, 373, 29871, 29906, 29900, 29896, 29947, 29899, 29900, 29941, 29899, 29906, 29953, 29871, 29900, 29941, 29901, 29941, 29896, 13, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 15204, 742, 525, 29900, 29900, 29896, 29955, 29918, 28385, 5485, 29918, 3149, 29918, 1761, 29918, 8228, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 2499, 357, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 12470, 29918, 9009, 742, 13, 9651, 1024, 2433, 6360, 29879, 29918, 974, 29918, 12470, 742, 13, 9651, 1746, 29922, 9794, 29889, 27890, 29898, 1859, 1575, 11759, 877, 29886, 29906, 742, 525, 30446, 30685, 5477, 6702, 29886, 29941, 742, 525, 30446, 30457, 5477, 6702, 29886, 29906, 742, 525, 30446, 30928, 5477, 6702, 29886, 29906, 742, 525, 30446, 30904, 5477, 6702, 29886, 29906, 742, 525, 30446, 31304, 1495, 1402, 2322, 2433, 29886, 29945, 742, 4236, 29918, 2848, 29922, 29906, 511, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
Oefeningen/standalone/list_comprehension_4.py	Seviran/Python_3	0	24049	string_input = "amazing" vowels = "aeiou" answer = [char for char in string_input if char not in vowels] print(answer)	[ 1, 29871, 13, 1807, 29918, 2080, 353, 376, 314, 834, 292, 29908, 13, 29894, 340, 1379, 353, 376, 3660, 29875, 283, 29908, 13, 12011, 353, 518, 3090, 363, 1373, 297, 1347, 29918, 2080, 565, 1373, 451, 297, 325, 340, 1379, 29962, 13, 2158, 29898, 12011, 29897, 13, 2 ]
source/methods_upload_workspace_stats.py	CheyenneNS/metrics	0	1603628	<reponame>CheyenneNS/metrics from pymongo import MongoClient from pymongo import ReadPreference from biokbase.workspace.client import Workspace import json as _json import os import mysql.connector as mysql import requests import time from datetime import date #import pprint requests.packages.urllib3.disable_warnings() #pp = pprint.PrettyPrinter(indent=4) # NOTE get_user_info_from_auth2 sets up the initial dict. #The following functions update certain fields in the dict. # So get_user_info_from_auth2 must be called before get_internal_users and get_user_orgs_count metrics_mysql_password = os.environ['METRICS_MYSQL_PWD'] mongoDB_metrics_connection = os.environ['MONGO_PATH'] sql_host = os.environ['SQL_HOST'] query_on = os.environ['QUERY_ON'] ws_url = os.environ['WS_URL'] ws_user_token = os.environ["METRICS_WS_USER_TOKEN"] to_workspace = os.environ['WRK_SUFFIX'] def get_workspaces(db): """ gets narrative workspaces information for non temporary workspaces """ workspaces_dict = {} #Get all the legitimate workspaces and their respective user (saved(not_temp)) workspaces_cursor = db.workspaces.find({"moddate": {"$exists":True}}, # IF NOT DELETED NARRATIVES {"del" : False, "moddate": {"$exists":True}}, # IF ONLY SAVED NARRATIVES "meta" : {"k" : "is_temporary", "v" : "false"} }, {"owner":1,"ws":1,"moddate":1,"del":1,"_id":0}) for record in workspaces_cursor: if record["ws"] != 615: is_deleted_ws = 0 if record["del"] == True: is_deleted_ws = 1 workspaces_dict[record["ws"]] = {"ws_id" : record["ws"], "username" : record["owner"], "mod_date" : record["moddate"], "initial_save_date" : None, "top_lvl_object_count" : 0, "total_object_count" : 0, "visible_app_cells_count" : 0, "code_cells_count" : 0, "narrative_version" : 0, "hidden_object_count" : 0, "deleted_object_count" : 0, "total_size" : 0, 'top_lvl_size' : 0, "is_public" : 0, "is_deleted" : is_deleted_ws, "is_temporary" : None, "number_of_shares" : 0 } workspace_is_temporary_cursor = db.workspaces.find({"moddate": {"$exists":True}, #{"del" : False,"moddate": {"$exists":True}, "meta" : {"k" : "is_temporary", "v" : "true"} }, {"ws":1,"_id":0}) for record in workspace_is_temporary_cursor: workspaces_dict[record["ws"]]["is_temporary"] = 1 workspace_not_temporary_cursor = db.workspaces.find({"moddate": {"$exists":True}, #{"del" : False, "moddate": {"$exists":True}, "meta" : {"k" : "is_temporary", "v" : "false"} }, {"ws":1,"_id":0}) for record in workspace_not_temporary_cursor: workspaces_dict[record["ws"]]["is_temporary"] = 0 return workspaces_dict def get_workspace_shares(db, workspaces_dict): """ gets number of shares per workspace """ aggregation_string=[ {"$match" : {"perm" : { "$in": [ 10,20,30 ]}}}, {"$group" : {"_id" : "$id", "shared_count" : { "$sum" : 1 }}}] all_shared_perms_cursor=db.workspaceACLs.aggregate(aggregation_string) for record in db.workspaceACLs.aggregate(aggregation_string): if record["_id"] in workspaces_dict: workspaces_dict[record["_id"]]["number_of_shares"] = record["shared_count"] return workspaces_dict def get_public_workspaces(db, workspaces_dict): """ Gets IDs of public workspaces """ public_workspaces_cursor = db.workspaceACLs.find({"user" : ""},{"id":1,"_id":0}) for record in public_workspaces_cursor: if record["id"] in workspaces_dict: workspaces_dict[record["id"]]["is_public"] = 1 return workspaces_dict """ OLD FOR APP CELL COUNTING NOW HANDLED BY NEW FUNCTION def get_app_cell_count(wsadmin, narrative_ref, workspaces_with_app_cell_oddities): # Gets the number of App Cells in the narrative See documentation for WS administer here https://github.com/kbase/workspace_deluxe/blob/02217e4d63da8442d9eed6611aaa790f173de58e/docsource/administrationinterface.rst # info = wsadmin.administer({'command': "getObjectInfo", 'params': {"objects": [{"ref": narrative_ref}], "includeMetadata": 1} })["infos"][0] meta = info[10] total_app_cells = 0 # print("META: " + str(meta)) for key in meta: if key.startswith("method."): # if(isinstance(meta[key], int)): try: total_app_cells += int(meta[key]) except ValueError: workspaces_with_app_cell_oddities.add(narrative_ref) print("META: " + str(meta)) return (total_app_cells, workspaces_with_app_cell_oddities) """ def get_narrative_cell_counts(wsadmin, narrative_ref): """ Gets the number of App Cells and Code Cells in the narrative See documentation for WS administer here https://github.com/kbase/workspace_deluxe/blob/02217e4d63da8442d9eed6611aaa790f173de58e/docsource/administrationinterface.rst """ app_count = 0 code_count = 0 is_suspect = 0 try: nar_obj = wsadmin.administer({'command': "getObjects", 'params': {"objects": [{"ref": narrative_ref}]}})["data"][0]["data"] if "cells" in nar_obj: for cell in nar_obj["cells"]: if(cell["metadata"].get("kbase", {}).get("type") == "app"): app_count += 1 elif(cell["metadata"].get("kbase", {}).get("type") == "code"): code_count += 1 return(app_count, code_count, is_suspect) except: # Shock node is suspect is_suspect = 1 print("Narrative Ref : " + narrative_ref + " has a shock node issue") #try running on the previous version of the narrative if exists #if previous version does not exist return zeros (ws_id, obj_id, obj_ver) = narrative_ref.split("/") if int(obj_ver) > 1: new_ref = ws_id + "/" + obj_id + "/" + str(int(obj_ver) - 1) (app_count, code_count, dummy) = get_narrative_cell_counts(wsadmin, new_ref) return(app_count, code_count, is_suspect) def get_kbase_staff(db_connection): """ get list of KBase staff """ cursor = db_connection.cursor() query = "use "+query_on cursor.execute(query) #get all existing users kbase_staff = set() query = "select username from metrics.user_info where kb_internal_user = 1;" cursor.execute(query) for (username) in cursor: kbase_staff.add(username[0]) return kbase_staff def get_objects(db, workspaces_dict, db_connection): """ get object counts as well as hidden del, size, is_narrative informationf for the workspace. """ wsadmin = Workspace(ws_url, token=ws_user_token) #OBJECT COUNTS ACROSS ALL WORKSPACES. object_counts_dict = dict() #OBJECT COUNTS ACROSS USER WORKSPACES users_object_counts_dict = dict() #SAME FOR USER AND ALL # {object_type_full : { "object_type" : ..., # "object_spec_version" : ..., # "last_mod_date" : max_date , # "top_lvl_object_count" : # , # "total_object_count" : # , # "public_object_count" : # , # "private_object_count" : # , # "hidden_object_count" : # , # "deleted_object_count" : # , # "copy_count" : # , # "total_size" : # , # "top_lvl_size" : #}} kbase_staff = get_kbase_staff(db_connection) # workspaces_with_app_cell_oddities = set() #keeping track of workspaces with odd metadata where app cell counting fails narrative_refs_with_shock_node_issues = set() #keep track of narrative refs with shocknode issues to keep track of later. ########################### #few debugging lines to do a smaller set of workspaces. ########################### # ws_limit = 1 #For debugging purposes, if ws_limit is None then it does all workspaces. # ws_counter = 0 # for ws_id in sorted(workspaces_dict.keys()): # ws_counter += 1 # if ws_counter > ws_limit : # del workspaces_dict[ws_id] ########################### #debugging lines to minimal Workspaces # temp_dict = dict() # temp_dict[49114] = workspaces_dict[49114] # temp_dict[3] = workspaces_dict[3] # temp_dict[5009] = workspaces_dict[5009] # temp_dict[1000] = workspaces_dict[1000] # temp_dict[53462] = workspaces_dict[53462] # temp_dict[324] = workspaces_dict[324] # workspaces_dict.clear() # workspaces_dict = temp_dict ############### for ws_id in sorted(workspaces_dict.keys()): print("PROCESSING WS : " + str(ws_id)) is_narrative = False top_level_lookup_dict = dict() is_public_flag = workspaces_dict[ws_id]["is_public"] tl_ws_obj_cursor = db.workspaceObjects.find({"ws":ws_id},{"id":1,"numver":1,"del":1,"hide":1,"_id":0}) for tl_object in tl_ws_obj_cursor: top_level_lookup_dict[tl_object["id"]] = {"numver" : tl_object["numver"], "del" : tl_object["del"], "hide" : tl_object["hide"]} ws_obj_vers_cursor = db.workspaceObjVersions.find({"ws":ws_id},{"type":1,"id":1,"ver":1,"size":1, "savedate":1,"copied":1,"_id":0}); for ws_obj_ver in ws_obj_vers_cursor: object_type_full = ws_obj_ver["type"] (object_type, object_spec_version) = object_type_full.split("-") obj_id = ws_obj_ver["id"] obj_ver = ws_obj_ver["ver"] obj_size = ws_obj_ver["size"] top_obj_size = 0 obj_save_date = ws_obj_ver["savedate"] obj_copied = 0 if ws_obj_ver["copied"] is not None: obj_copied = 1 is_hidden = 0 is_deleted = 0 is_top_level = 0 workspaces_dict[ws_id]["total_object_count"] += 1 workspaces_dict[ws_id]["total_size"] += obj_size if obj_ver == top_level_lookup_dict[obj_id]["numver"]: #means have maxed version of the object top_level is_top_level = 1 top_obj_size = obj_size workspaces_dict[ws_id]["top_lvl_size"] += obj_size #IF A NARRATIVE OBJECT, UPDATE THE WS dict if object_type == "KBaseNarrative.Narrative": workspaces_dict[ws_id]["narrative_version"] = obj_ver if top_level_lookup_dict[obj_id]["del"]: workspaces_dict[ws_id]["visible_app_cells_count"] = 0 workspaces_dict[ws_id]["code_cells_count"] = 0 # else: # (workspaces_dict[ws_id]["visible_app_cells_count"],workspaces_with_app_cell_oddities) = get_app_cell_count(wsadmin, # str(ws_id) + "/" + str(obj_id), # workspaces_with_app_cell_oddities) else: (workspaces_dict[ws_id]["visible_app_cells_count"], workspaces_dict[ws_id]["code_cells_count"], is_suspect) = \ get_narrative_cell_counts(wsadmin, str(ws_id) + "/" + str(obj_id) + "/" + str(obj_ver)) if is_suspect == 1: narrative_refs_with_shock_node_issues.add(str(ws_id) + "/" + str(obj_id) + "/" + str(obj_ver)) #Get Workspace numbers workspaces_dict[ws_id]["top_lvl_object_count"] += 1 if top_level_lookup_dict[obj_id]["hide"]: workspaces_dict[ws_id]["hidden_object_count"] += obj_ver if top_level_lookup_dict[obj_id]["del"]: workspaces_dict[ws_id]["deleted_object_count"] += obj_ver if obj_ver == 1 and object_type == "KBaseNarrative.Narrative": workspaces_dict[ws_id]["initial_save_date"] = obj_save_date if top_level_lookup_dict[obj_id]["hide"]: is_hidden = 1 if top_level_lookup_dict[obj_id]["del"]: is_deleted = 1 if object_type_full not in object_counts_dict: #First time seeing this object full type. Initialize in the dict object_counts_dict[object_type_full] = dict() object_counts_dict[object_type_full]["object_type"] = object_type object_counts_dict[object_type_full]["object_spec_version"] = object_spec_version object_counts_dict[object_type_full]["last_mod_date"] = obj_save_date object_counts_dict[object_type_full]["total_size"] = obj_size object_counts_dict[object_type_full]["top_lvl_size"] = top_obj_size object_counts_dict[object_type_full]["total_object_count"] = 1 object_counts_dict[object_type_full]["copy_count"] = obj_copied object_counts_dict[object_type_full]["top_lvl_object_count"] = is_top_level object_counts_dict[object_type_full]["hidden_object_count"] = is_hidden object_counts_dict[object_type_full]["deleted_object_count"] = is_deleted object_counts_dict[object_type_full]["public_object_count"] = is_public_flag object_counts_dict[object_type_full]["max_object_size"] = obj_size if is_public_flag == 1: object_counts_dict[object_type_full]["private_object_count"] = 0 else: object_counts_dict[object_type_full]["private_object_count"] = 1 else: #Exists, add up values accordingly. #check if date is greater than last_mod_date standing_date = object_counts_dict[object_type_full]["last_mod_date"] if obj_save_date > standing_date: object_counts_dict[object_type_full]["last_mod_date"] = obj_save_date standing_max_object_size = object_counts_dict[object_type_full]["max_object_size"] if obj_size > standing_max_object_size: object_counts_dict[object_type_full]["max_object_size"] = obj_size object_counts_dict[object_type_full]["total_size"] += obj_size object_counts_dict[object_type_full]["top_lvl_size"] += top_obj_size object_counts_dict[object_type_full]["total_object_count"] += 1 object_counts_dict[object_type_full]["copy_count"] += obj_copied object_counts_dict[object_type_full]["top_lvl_object_count"] += is_top_level object_counts_dict[object_type_full]["hidden_object_count"] += is_hidden object_counts_dict[object_type_full]["deleted_object_count"] += is_deleted object_counts_dict[object_type_full]["public_object_count"] += is_public_flag if is_public_flag == 0: object_counts_dict[object_type_full]["private_object_count"] += 1 # SAME THING AS ABOVE BUT FOR USER ONLY WORKSPACE OBJECTS if workspaces_dict[ws_id]["username"] not in kbase_staff: if object_type_full not in users_object_counts_dict: #First time seeing this object full type for users data. Initialize in the dict users_object_counts_dict[object_type_full] = dict() users_object_counts_dict[object_type_full]["object_type"] = object_type users_object_counts_dict[object_type_full]["object_spec_version"] = object_spec_version users_object_counts_dict[object_type_full]["last_mod_date"] = obj_save_date users_object_counts_dict[object_type_full]["total_size"] = obj_size users_object_counts_dict[object_type_full]["top_lvl_size"] = top_obj_size users_object_counts_dict[object_type_full]["total_object_count"] = 1 users_object_counts_dict[object_type_full]["copy_count"] = obj_copied users_object_counts_dict[object_type_full]["top_lvl_object_count"] = is_top_level users_object_counts_dict[object_type_full]["hidden_object_count"] = is_hidden users_object_counts_dict[object_type_full]["deleted_object_count"] = is_deleted users_object_counts_dict[object_type_full]["public_object_count"] = is_public_flag users_object_counts_dict[object_type_full]["max_object_size"] = obj_size if is_public_flag == 1: users_object_counts_dict[object_type_full]["private_object_count"] = 0 else: users_object_counts_dict[object_type_full]["private_object_count"] = 1 else: #Exists, add up values accordingly. #check if date is greater than last_mod_date standing_date = users_object_counts_dict[object_type_full]["last_mod_date"] if obj_save_date > standing_date: users_object_counts_dict[object_type_full]["last_mod_date"] = obj_save_date standing_max_object_size = users_object_counts_dict[object_type_full]["max_object_size"] if obj_size > standing_max_object_size: users_object_counts_dict[object_type_full]["max_object_size"] = obj_size users_object_counts_dict[object_type_full]["total_size"] += obj_size users_object_counts_dict[object_type_full]["top_lvl_size"] += top_obj_size users_object_counts_dict[object_type_full]["total_object_count"] += 1 users_object_counts_dict[object_type_full]["copy_count"] += obj_copied users_object_counts_dict[object_type_full]["top_lvl_object_count"] += is_top_level users_object_counts_dict[object_type_full]["hidden_object_count"] += is_hidden users_object_counts_dict[object_type_full]["deleted_object_count"] += is_deleted users_object_counts_dict[object_type_full]["public_object_count"] += is_public_flag if is_public_flag == 0: users_object_counts_dict[object_type_full]["private_object_count"] += 1 return (workspaces_dict, object_counts_dict, users_object_counts_dict, narrative_refs_with_shock_node_issues) def upload_workspace_stats(): """ Is the "main" function to get and upload both workspace data as well as workspace object summary stats """ start_time = time.time() #connect to mysql db_connection = mysql.connect( host = sql_host, user = "metrics", passwd = <PASSWORD>, database = "metrics" ) cursor = db_connection.cursor() query = "use "+query_on cursor.execute(query) #CHECK IF THIS WAS DONE THIS MONTH ALREADY. IT IS MEANT TO BE RUN ONCE PER MONTH (IDEALLY THE FIRST OF THE MONTH) today = date.today() current_month = str(today.year) + "-" + today.strftime('%m') query = "select DATE_FORMAT(max(record_date),'%Y-%m') from metrics.users_workspace_object_counts"; cursor.execute(query) for (db_date) in cursor: db_date_month = db_date[0] if db_date_month == current_month: print("THE WORKSPACE and WS OBJECTS COUNTS UPLOADER HAS BEEN RUN THIS MONTH. THE PROGRAM WILL EXIT") exit() else: print("IT HAS NOT BEEN RUN THIS MONTH, WE WILL RUN THE PROGRAM") client= MongoClient(mongoDB_metrics_connection+to_workspace) db = client.workspace workspaces_dict = get_workspaces(db) get_ws_top_info_time = time.time() - start_time workspaces_dict = get_workspace_shares(db, workspaces_dict) workspaces_dict = get_public_workspaces(db, workspaces_dict) (workspaces_dict, object_counts_dict, users_object_counts_dict, narrative_refs_with_shock_node_issues) = \ get_objects(db, workspaces_dict, db_connection) print("TOTAL WS Number : " + str(len(workspaces_dict))) gather_time = time.time() - start_time # print("--- gather data %s seconds ---" % (gather_time)) # print("WORKSPACE DICT : ") # pp.pprint(workspaces_dict) # print("OBJECT COUNTS DICT : ") # pp.pprint(object_counts_dict) #WORKSPACES UPLOADING prep_cursor = db_connection.cursor(prepared=True) workspaces_insert_statement = "insert into metrics.workspaces "\ "(ws_id, username, mod_date, initial_save_date, record_date, "\ "top_lvl_object_count, total_object_count, "\ "visible_app_cells_count, code_cells_count, narrative_version, "\ "hidden_object_count, deleted_object_count, "\ "total_size, top_lvl_size, is_public, "\ "is_temporary, is_deleted, number_of_shares) "\ "values(%s,%s, %s, %s, now(), %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s);" for ws_id in sorted(workspaces_dict.keys()): # print("PROCESSING WS: " + str(ws_id) + " USERNAME: " + workspaces_dict[ws_id]['username']) input = (ws_id, workspaces_dict[ws_id]['username'], workspaces_dict[ws_id]['mod_date'], workspaces_dict[ws_id]['initial_save_date'], workspaces_dict[ws_id]['top_lvl_object_count'], workspaces_dict[ws_id]['total_object_count'], workspaces_dict[ws_id]['visible_app_cells_count'],workspaces_dict[ws_id]['code_cells_count'], workspaces_dict[ws_id]['narrative_version'], workspaces_dict[ws_id]['hidden_object_count'], workspaces_dict[ws_id]['deleted_object_count'], workspaces_dict[ws_id]['total_size'], workspaces_dict[ws_id]['top_lvl_size'], workspaces_dict[ws_id]['is_public'], workspaces_dict[ws_id]['is_temporary'], workspaces_dict[ws_id]['is_deleted'], workspaces_dict[ws_id]['number_of_shares']) prep_cursor.execute(workspaces_insert_statement,input) print("TOTAL WS Number : " + str(len(workspaces_dict))) print("--- get ws top info time took %s seconds ---" % (get_ws_top_info_time)) print("--- gather data %s seconds ---" % (gather_time)) workspace_time = time.time() - (gather_time + start_time) print("--- workspaces uploading %s seconds ---" % (workspace_time)) #OBJECT COUNTS UPLOADING prep_cursor_obj = db_connection.cursor(prepared=True) obj_counts_insert_statement = "insert into metrics.workspace_object_counts "\ "(object_type, object_spec_version, object_type_full, "\ "record_date, last_mod_date, top_lvl_object_count, "\ "total_object_count, public_object_count, private_object_count, "\ "hidden_object_count, deleted_object_count, copy_count, "\ "total_size, top_lvl_size, max_object_size) "\ "values(%s,%s, %s, now(), %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s);" #print("OBJECT COUNTS DICT : " + str(object_counts_dict)) for obj_full in sorted(object_counts_dict): obj_input = (object_counts_dict[obj_full]["object_type"],object_counts_dict[obj_full]["object_spec_version"], obj_full, object_counts_dict[obj_full]["last_mod_date"], object_counts_dict[obj_full]["top_lvl_object_count"], object_counts_dict[obj_full]["total_object_count"], object_counts_dict[obj_full]["public_object_count"], object_counts_dict[obj_full]["private_object_count"], object_counts_dict[obj_full]["hidden_object_count"], object_counts_dict[obj_full]["deleted_object_count"], object_counts_dict[obj_full]["copy_count"], object_counts_dict[obj_full]["total_size"], object_counts_dict[obj_full]["top_lvl_size"], object_counts_dict[obj_full]["max_object_size"]) prep_cursor.execute(obj_counts_insert_statement,obj_input) object_time = time.time() - (workspace_time + gather_time + start_time) print("--- object counts uploading %s seconds ---" % (object_time)) #USERS_OBJECT COUNTS UPLOADING prep_cursor_obj = db_connection.cursor(prepared=True) users_obj_counts_insert_statement = "insert into metrics.users_workspace_object_counts "\ "(object_type, object_spec_version, object_type_full, "\ "record_date, last_mod_date, top_lvl_object_count, "\ "total_object_count, public_object_count, private_object_count, "\ "hidden_object_count, deleted_object_count, copy_count, "\ "total_size, top_lvl_size, max_object_size) "\ "values(%s,%s, %s, now(), %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s);" for obj_full in sorted(users_object_counts_dict): users_obj_input = (users_object_counts_dict[obj_full]["object_type"], users_object_counts_dict[obj_full]["object_spec_version"], obj_full, users_object_counts_dict[obj_full]["last_mod_date"], users_object_counts_dict[obj_full]["top_lvl_object_count"], users_object_counts_dict[obj_full]["total_object_count"], users_object_counts_dict[obj_full]["public_object_count"], users_object_counts_dict[obj_full]["private_object_count"], users_object_counts_dict[obj_full]["hidden_object_count"], users_object_counts_dict[obj_full]["deleted_object_count"], users_object_counts_dict[obj_full]["copy_count"], users_object_counts_dict[obj_full]["total_size"], users_object_counts_dict[obj_full]["top_lvl_size"], users_object_counts_dict[obj_full]["max_object_size"]) prep_cursor.execute(users_obj_counts_insert_statement,users_obj_input) users_object_time = time.time() - (workspace_time + gather_time + start_time + object_time) print("--- users object counts uploading %s seconds ---" % (users_object_time)) #SUSPECT SHOCK NODE INSERTING/UPDATING #If exists, update the last_seen_date #If does not exist make a new record. # query to see if it exists prep_cursor_ssn_exists = db_connection.cursor(prepared=True) ssn_exists_query = "select count() from metrics.suspect_shock_nodes "\ "where ws_obj_reference = %s and shock_node = %s" # query_to_update suspect_shock_nodes prep_cursor_ssn_update = db_connection.cursor(prepared=True) suspect_shock_node_update_statement = "update metrics.suspect_shock_nodes "\ "set last_seen_date = now() "\ "where ws_obj_reference = %s and shock_node = %s;" # query_to_insert suspect_shock_nodes prep_cursor_ssn_insert = db_connection.cursor(prepared=True) suspect_shock_node_insert_statement = "insert into metrics.suspect_shock_nodes "\ "(ws_obj_reference, shock_node, "\ "first_seen_date, last_seen_date, ws_id) "\ "values(%s,%s, now(), now(), %s);" # print("narrative_refs_with_shock_node_issues : " + str(narrative_refs_with_shock_node_issues)) for nar_ref in narrative_refs_with_shock_node_issues: # print("IN SHOCK NODES ISSUES : " + nar_ref) (ws_id, nar_id, nar_version) = nar_ref.split("/") chksum_cursor = db.workspaceObjVersions.find({"ws":int(ws_id),"id":int(nar_id),"ver":int(nar_version)},{"chksum":1,"_id":0}) chksum = None for chksum_arr in chksum_cursor: chksum = chksum_arr["chksum"] node_cursor =db.shock_nodeMap.find({"chksum":chksum},{"node":1,"_id":0}) node = None for node_arr in node_cursor: node = node_arr["node"] #check if nar_ref_node combo exists: prep_cursor_ssn_exists.execute(ssn_exists_query,(nar_ref, node)) exists_count_arr = prep_cursor_ssn_exists.fetchone() if exists_count_arr[0] == 0: #means a new problem narrative/node needs an insert prep_cursor_ssn_insert.execute(suspect_shock_node_insert_statement,(nar_ref, node, int(ws_id))) else: #means the nar_ref node existed . 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neviseh/persian/numbers.py	meyt/neviseh	0	189886	<filename>neviseh/persian/numbers.py from . import PersianTextBase class PersianNumbers(PersianTextBase): def translate_arabic_numbers(self): """ Translate Arabic numbers to Persian :return: """ characters_map = { '١': '۱', '٢': '۲', '٣': '۳', '٤': '۴', '٥': '۵', '٦': '۶', '٧': '۷', '٨': '۸', '٩': '۹', '٠': '۰', } self.replace_by_character_map('arabic_numbers_replace', characters_map) return self def translate_latin_numbers(self): """ Translate Latin numbers to Persian :return: """ characters_map = { '0': '۰', '1': '۱', '2': '۲', '3': '۳', '4': '۴', '5': '۵', '6': '۶', '7': '۷', '8': '۸', '9': '۹', '.': '.', '%': '٪' } self.replace_by_character_map('latin_numbers_replace', characters_map) return self	[ 1, 529, 9507, 29958, 484, 29894, 895, 29882, 29914, 6774, 713, 29914, 20326, 29889, 2272, 13, 3166, 869, 1053, 9034, 713, 1626, 5160, 13, 13, 13, 1990, 9034, 713, 29478, 29898, 15136, 713, 1626, 5160, 1125, 13, 13, 1678, 822, 14240, 29918, 25822, 293, 29918, 20326, 29898, 1311, 1125, 13, 4706, 9995, 13, 4706, 4103, 9632, 10387, 293, 3694, 304, 9034, 713, 13, 4706, 584, 2457, 29901, 13, 4706, 9995, 13, 4706, 4890, 29918, 1958, 353, 426, 13, 9651, 525, 220, 164, 2396, 525, 222, 180, 742, 13, 9651, 525, 220, 165, 2396, 525, 222, 181, 742, 13, 9651, 525, 220, 166, 2396, 525, 222, 182, 742, 13, 9651, 525, 220, 167, 2396, 525, 222, 183, 742, 13, 9651, 525, 220, 168, 2396, 525, 222, 184, 742, 13, 9651, 525, 220, 169, 2396, 525, 222, 185, 742, 13, 9651, 525, 220, 170, 2396, 525, 222, 186, 742, 13, 9651, 525, 220, 171, 2396, 525, 222, 187, 742, 13, 9651, 525, 220, 172, 2396, 525, 222, 188, 742, 13, 9651, 525, 220, 163, 2396, 525, 222, 179, 742, 13, 4706, 500, 13, 4706, 1583, 29889, 6506, 29918, 1609, 29918, 18609, 29918, 1958, 877, 25822, 293, 29918, 20326, 29918, 6506, 742, 4890, 29918, 1958, 29897, 13, 4706, 736, 1583, 13, 13, 1678, 822, 14240, 29918, 5066, 262, 29918, 20326, 29898, 1311, 1125, 13, 4706, 9995, 13, 4706, 4103, 9632, 13548, 3694, 304, 9034, 713, 13, 4706, 584, 2457, 29901, 13, 4706, 9995, 13, 4706, 4890, 29918, 1958, 353, 426, 13, 9651, 525, 29900, 2396, 525, 222, 179, 742, 13, 9651, 525, 29896, 2396, 525, 222, 180, 742, 13, 9651, 525, 29906, 2396, 525, 222, 181, 742, 13, 9651, 525, 29941, 2396, 525, 222, 182, 742, 13, 9651, 525, 29946, 2396, 525, 222, 183, 742, 13, 9651, 525, 29945, 2396, 525, 222, 184, 742, 13, 9651, 525, 29953, 2396, 525, 222, 185, 742, 13, 9651, 525, 29955, 2396, 525, 222, 186, 742, 13, 9651, 525, 29947, 2396, 525, 222, 187, 742, 13, 9651, 525, 29929, 2396, 525, 222, 188, 742, 13, 9651, 15300, 2396, 15300, 742, 13, 9651, 14210, 2396, 525, 220, 173, 29915, 13, 4706, 500, 13, 4706, 1583, 29889, 6506, 29918, 1609, 29918, 18609, 29918, 1958, 877, 5066, 262, 29918, 20326, 29918, 6506, 742, 4890, 29918, 1958, 29897, 13, 4706, 736, 1583, 13, 2 ]
pyrevolve/genotype/learning_body/crossover.py	braj29/robo_swimmers	0	72173	<reponame>braj29/robo_swimmers from typing import List from .config import Config from pyrevolve.genotype.genotype import Genotype def crossover(parents: List[Genotype], config: Config) -> Genotype: return parents[0]	[ 1, 529, 276, 1112, 420, 29958, 2634, 29926, 29906, 29929, 29914, 307, 833, 29918, 2774, 6727, 414, 13, 3166, 19229, 1053, 2391, 13, 3166, 869, 2917, 1053, 12782, 13, 3166, 11451, 276, 1555, 345, 29889, 1885, 327, 668, 29889, 1885, 327, 668, 1053, 5739, 327, 668, 13, 13, 13, 1753, 274, 1883, 578, 369, 29898, 862, 1237, 29901, 2391, 29961, 15462, 327, 668, 1402, 2295, 29901, 12782, 29897, 1599, 5739, 327, 668, 29901, 13, 1678, 736, 11825, 29961, 29900, 29962, 13, 2 ]
bot/main.py	EJH2/IQ-Discord	1	193132	# coding=utf-8 """Main bot file""" import aiohttp import time from collections import Counter, deque from pathlib import Path import discord from discord.ext import commands from pyppeteer import launch, errors from bot.utils.logging import setup_logger from bot.utils.over import send, _default_help_command discord.abc.Messageable.send = send class Bot(commands.AutoShardedBot): """Subclasses AutoShardedBot to give more flexibility with design""" def __init__(self, args, kwargs): self.config = kwargs.pop('config') self.start_time = time.time() super().__init__(args, kwargs) # pay no mind to this ugliness. self.remove_command('help') self.command(self.help_attrs)(_default_help_command) self.pm_help = None self.case_insensitive = True self.app_info = None shard = f"\| Shard {self.shard_id}" if self.shard_id else "" self.activity = discord.Game(name=f"{self.command_prefix}help {shard}") self.session = aiohttp.ClientSession(loop=self.loop, headers={"User-Agent": self.http.user_agent}) self.browser_page = None self.browser = self.loop.create_task(self.create_browser()) self.priv = self.config['extras'].get('privatebin', 'https://privatebin.net') self.polr = self.config['extras'].get('polr', None) self.commands_used = Counter() self.commands_used_in = Counter() self.errors = deque(maxlen=10) self.revisions = None discord_logger = setup_logger("discord") self.logger = setup_logger("Bot") self.command_logger = setup_logger("Commands") self.loggers = [discord_logger, self.logger, self.command_logger] _modules = [mod.stem for mod in Path("bot/cogs").glob("*.py")] self.load_extension(f"bot.cogs.core") self.load_extension(f"bot.cogs.owner") if 'bare' in kwargs.pop('argv'): # load the bot bare-bones to diagnose issues return for module in _modules: try: if module in ['core', 'errors']: pass self.load_extension(f"bot.cogs.{module}") except discord.DiscordException as exc: self.logger.error(f"{type(exc).__name__} occurred when loading {module}: {exc}") # make sure to only print ready text once self._loaded = False async def on_ready(self): """Function called when bot is ready or resumed""" if self._loaded is False: end_time = time.time() - self.start_time self.app_info = await self.application_info() self.logger.info(f"Loaded Bot:") self.logger.info(f"Logged in as {self.user}") self.logger.info(f"ID is {self.user.id}") self.logger.info(f"Owned by {self.app_info.owner}") self.description = f"Hello, this is the help menu for {self.user.name}!" self.logger.info(f"Bot started in {end_time} seconds") self._loaded = True return self.logger.info(f"Resumed bot session on shard {self.shard_id}!") async def create_browser(self): """Task to create browser for scraping purposes.""" await self.wait_until_ready() self.browser = await launch(args=["--no-sandbox"], headless=True) self.browser_page = await self.browser.newPage() # noinspection PyProtectedMember async def close(self): """Function called when closing the bot""" try: await self.browser_page.close() or self.logger.info("Browser page successfully closed!") except (errors.PageError, AttributeError): # browser was never created; edge case pass await self.browser.close() or self.logger.info("Browser successfully closed!") await super().close() await self.http._session.close() await self.session.close() for logger in self.loggers: for handler in logger.handlers: logger.removeHandler(handler)	[ 1, 396, 14137, 29922, 9420, 29899, 29947, 13, 15945, 29908, 6330, 9225, 934, 15945, 29908, 13, 5215, 263, 601, 1124, 13, 5215, 931, 13, 3166, 16250, 1053, 315, 5336, 29892, 316, 802, 13, 3166, 2224, 1982, 1053, 10802, 13, 13, 5215, 2313, 536, 13, 3166, 2313, 536, 29889, 1062, 1053, 8260, 13, 3166, 11451, 407, 2650, 261, 1053, 6826, 29892, 4436, 13, 13, 3166, 9225, 29889, 13239, 29889, 21027, 1053, 6230, 29918, 21707, 13, 3166, 9225, 29889, 13239, 29889, 957, 1053, 3638, 29892, 903, 4381, 29918, 8477, 29918, 6519, 13, 13, 13, 2218, 16090, 29889, 10736, 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dbaas/drivers/mysqlpercona.py	didindinn/database-as-a-service	303	78614	from . import mysqldb from physical.models import Instance class MySQLPercona(mysqldb.MySQL): def get_default_instance_type(self): return Instance.MYSQL_PERCONA @classmethod def topology_name(cls): return ['mysql_percona_single'] class MySQLPerconaFOXHA(mysqldb.MySQLFOXHA): def get_default_instance_type(self): return Instance.MYSQL_PERCONA @classmethod def topology_name(cls): return ['mysql_percona_foxha']	[ 1, 515, 869, 1053, 15534, 430, 29890, 13, 3166, 9128, 29889, 9794, 1053, 2799, 749, 13, 13, 13, 1990, 9254, 5894, 535, 29874, 29898, 19268, 430, 29890, 29889, 3421, 4176, 1125, 13, 13, 1678, 822, 679, 29918, 4381, 29918, 8758, 29918, 1853, 29898, 1311, 1125, 13, 4706, 736, 2799, 749, 29889, 17870, 4176, 29918, 13171, 6007, 29909, 13, 13, 1678, 732, 1990, 5696, 13, 1678, 822, 20159, 29918, 978, 29898, 25932, 1125, 13, 4706, 736, 6024, 7938, 29918, 546, 535, 29874, 29918, 14369, 2033, 13, 13, 13, 1990, 9254, 5894, 535, 29874, 5800, 29990, 15715, 29898, 19268, 430, 29890, 29889, 3421, 4176, 5800, 29990, 15715, 1125, 13, 13, 1678, 822, 679, 29918, 4381, 29918, 8758, 29918, 1853, 29898, 1311, 1125, 13, 4706, 736, 2799, 749, 29889, 17870, 4176, 29918, 13171, 6007, 29909, 13, 13, 1678, 732, 1990, 5696, 13, 1678, 822, 20159, 29918, 978, 29898, 25932, 1125, 13, 4706, 736, 6024, 7938, 29918, 546, 535, 29874, 29918, 8944, 2350, 2033, 13, 2 ]
examples/max_min.py	csunny/algorithm	21	70927	#!/usr/bin/env python # -- coding:utf-8 -- def max_min(v): m_min, m_max = min(v[0], v[-1]), max(v[0], v[-1]) for i in v[1:len(v)-1]: if i<m_min: m_min = i elif i > m_max: m_max = i return m_min, m_max if __name__ == '__main__': m_min, m_max = max_min([9, 3, 4, 7, 2, 0]) print(m_min, m_max)	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 14137, 29901, 9420, 29899, 29947, 448, 29930, 29899, 13, 13, 1753, 4236, 29918, 1195, 29898, 29894, 1125, 13, 1678, 286, 29918, 1195, 29892, 286, 29918, 3317, 353, 1375, 29898, 29894, 29961, 29900, 1402, 325, 14352, 29896, 11724, 4236, 29898, 29894, 29961, 29900, 1402, 325, 14352, 29896, 2314, 13, 1678, 363, 474, 297, 325, 29961, 29896, 29901, 2435, 29898, 29894, 6817, 29896, 5387, 13, 4706, 565, 474, 29966, 29885, 29918, 1195, 29901, 13, 9651, 286, 29918, 1195, 353, 474, 13, 4706, 25342, 474, 1405, 286, 29918, 3317, 29901, 13, 9651, 286, 29918, 3317, 353, 474, 13, 268, 13, 1678, 736, 286, 29918, 1195, 29892, 286, 29918, 3317, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 286, 29918, 1195, 29892, 286, 29918, 3317, 353, 4236, 29918, 1195, 4197, 29929, 29892, 29871, 29941, 29892, 29871, 29946, 29892, 29871, 29955, 29892, 29871, 29906, 29892, 29871, 29900, 2314, 13, 1678, 1596, 29898, 29885, 29918, 1195, 29892, 286, 29918, 3317, 29897, 13, 2 ]
workflows/pangenome-generate/sort_fasta_by_quality_and_len.py	AndreaGuarracino/bh20-seq-resource	2	138778	#!/usr/bin/env python3 # Sort the sequences by quality (percentage of number of N bases not called, descending) and by length (descending). # The best sequence is the longest one, with no uncalled bases. import os import sys import gzip #import xxhash # Faster library import hashlib def open_gzipsafe(path_file): if path_file.endswith('.gz'): return gzip.open(path_file, 'rt') else: return open(path_file) path_fasta = sys.argv[1] hash_to_count_and_headers_dict = {} header_to_seq_dict = {} header_percCalledBases_seqLength_list = [] with open_gzipsafe(path_fasta) as f: for fasta in f.read().strip('\n>').split('>'): header = fasta.strip('\n').split('\n')[0] sequence = ''.join(fasta.strip('\n').split('\n')[1:]) ##hash = xxhash.xxh64(sequence).hexdigest() # Faster library hash = hashlib.md5(sequence.encode('utf-8')).hexdigest() if hash not in hash_to_count_and_headers_dict: # New sequence hash_to_count_and_headers_dict[hash] = [0, []] header_to_seq_dict[header] = sequence seq_len = len(sequence) header_percCalledBases_seqLength_list.append([header, (seq_len - sequence.count('N'))/seq_len, seq_len]) hash_to_count_and_headers_dict[hash][0] += 1 hash_to_count_and_headers_dict[hash][1].append(header) with open('dups.txt', 'w') as fw: for count, header_list in hash_to_count_and_headers_dict.values(): fw.write('\t'.join([str(count), ', '.join(header_list)]) + '\n') for header, percCalledBases, seqLength_list in sorted(header_percCalledBases_seqLength_list, key=lambda x: (x[-2], x[-1]), reverse = True): sys.stdout.write('>{}\n{}\n'.format(header, header_to_seq_dict[header]))	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 13, 29937, 20025, 278, 15602, 491, 11029, 313, 25376, 482, 310, 1353, 310, 405, 22561, 451, 2000, 29892, 5153, 2548, 29897, 322, 491, 3309, 313, 14273, 2548, 467, 13, 29937, 450, 1900, 5665, 338, 278, 27217, 697, 29892, 411, 694, 443, 13998, 22561, 29889, 13, 13, 5215, 2897, 13, 5215, 10876, 13, 5215, 330, 7554, 13, 13, 29937, 5215, 15473, 8568, 396, 383, 1901, 3489, 13, 5215, 6608, 1982, 13, 13, 1753, 1722, 29918, 29887, 2526, 567, 29874, 1725, 29898, 2084, 29918, 1445, 1125, 13, 1678, 565, 2224, 29918, 1445, 29889, 1975, 2541, 12839, 18828, 29374, 13, 268, 12, 2457, 330, 7554, 29889, 3150, 29898, 2084, 29918, 1445, 29892, 525, 2273, 1495, 13, 1678, 1683, 29901, 13, 4706, 736, 1722, 29898, 2084, 29918, 1445, 29897, 13, 13, 2084, 29918, 29888, 5427, 353, 10876, 29889, 19218, 29961, 29896, 29962, 13, 13, 8568, 29918, 517, 29918, 2798, 29918, 392, 29918, 13662, 29918, 8977, 353, 6571, 13, 13, 6672, 29918, 517, 29918, 11762, 29918, 8977, 353, 6571, 13, 6672, 29918, 546, 29883, 29907, 4212, 29933, 2129, 29918, 11762, 6513, 29918, 1761, 353, 5159, 13, 13, 2541, 1722, 29918, 29887, 2526, 567, 29874, 1725, 29898, 2084, 29918, 29888, 5427, 29897, 408, 285, 29901, 13, 1678, 363, 5172, 29874, 297, 285, 29889, 949, 2141, 17010, 28909, 29876, 29958, 2824, 5451, 877, 16299, 1125, 13, 4706, 4839, 353, 5172, 29874, 29889, 17010, 28909, 29876, 2824, 5451, 28909, 29876, 29861, 29900, 29962, 13, 4706, 5665, 353, 525, 4286, 7122, 29898, 29888, 5427, 29889, 17010, 28909, 29876, 2824, 5451, 28909, 29876, 29861, 29896, 29901, 2314, 13, 13, 4706, 444, 8568, 353, 15473, 8568, 29889, 4419, 29882, 29953, 29946, 29898, 16506, 467, 20970, 7501, 342, 580, 396, 383, 1901, 3489, 13, 4706, 6608, 353, 6608, 1982, 29889, 3487, 29945, 29898, 16506, 29889, 12508, 877, 9420, 29899, 29947, 1495, 467, 20970, 7501, 342, 580, 13, 13, 4706, 565, 6608, 451, 297, 6608, 29918, 517, 29918, 2798, 29918, 392, 29918, 13662, 29918, 8977, 29901, 13, 9651, 396, 1570, 5665, 13, 9651, 6608, 29918, 517, 29918, 2798, 29918, 392, 29918, 13662, 29918, 8977, 29961, 8568, 29962, 353, 518, 29900, 29892, 5159, 29962, 13, 13, 9651, 4839, 29918, 517, 29918, 11762, 29918, 8977, 29961, 6672, 29962, 353, 5665, 13, 13, 9651, 19359, 29918, 2435, 353, 7431, 29898, 16506, 29897, 13, 9651, 4839, 29918, 546, 29883, 29907, 4212, 29933, 2129, 29918, 11762, 6513, 29918, 1761, 29889, 4397, 4197, 6672, 29892, 313, 11762, 29918, 2435, 448, 5665, 29889, 2798, 877, 29940, 8785, 29914, 11762, 29918, 2435, 29892, 19359, 29918, 2435, 2314, 13, 13, 4706, 6608, 29918, 517, 29918, 2798, 29918, 392, 29918, 13662, 29918, 8977, 29961, 8568, 3816, 29900, 29962, 4619, 29871, 29896, 13, 4706, 6608, 29918, 517, 29918, 2798, 29918, 392, 29918, 13662, 29918, 8977, 29961, 8568, 3816, 29896, 1822, 4397, 29898, 6672, 29897, 13, 13, 13, 2541, 1722, 877, 700, 567, 29889, 3945, 742, 525, 29893, 1495, 408, 285, 29893, 29901, 13, 1678, 363, 2302, 29892, 4839, 29918, 1761, 297, 6608, 29918, 517, 29918, 2798, 29918, 392, 29918, 13662, 29918, 8977, 29889, 5975, 7295, 13, 4706, 285, 29893, 29889, 3539, 28909, 29873, 4286, 7122, 4197, 710, 29898, 2798, 511, 13420, 15300, 7122, 29898, 6672, 29918, 1761, 29897, 2314, 718, 11297, 29876, 1495, 13, 13, 1454, 4839, 29892, 639, 29883, 29907, 4212, 29933, 2129, 29892, 19359, 6513, 29918, 1761, 297, 12705, 29898, 6672, 29918, 546, 29883, 29907, 4212, 29933, 2129, 29918, 11762, 6513, 29918, 1761, 29892, 1820, 29922, 2892, 921, 29901, 313, 29916, 14352, 29906, 1402, 921, 14352, 29896, 11724, 11837, 353, 5852, 1125, 13, 1678, 10876, 29889, 25393, 29889, 3539, 877, 26208, 1012, 29876, 29912, 1012, 29876, 4286, 4830, 29898, 6672, 29892, 4839, 29918, 517, 29918, 11762, 29918, 8977, 29961, 6672, 12622, 13, 2 ]
plugins/welcome.py	su226/IdhagnBot	2	1616563	<reponame>su226/IdhagnBot from util.config import BaseConfig, Field from nonebot.adapters.onebot.v11 import Bot, Event, Message import nonebot class Config(BaseConfig): __file__ = "welcome" welcome: dict[int, str] = Field(default_factory=dict) leave: set[int] = Field(default_factory=set) CONFIG = Config.load() async def check_welcome(event: Event): return event.notice_type == "group_increase" and event.group_id in CONFIG.welcome welcome = nonebot.on_notice(check_welcome) @welcome.handle() async def handle_welcome(event: Event): await welcome.send(Message(CONFIG.welcome[event.group_id].format(event.user_id))) async def check_leave(event: Event): return event.notice_type == "group_decrease" and event.group_id in CONFIG.leave leave = nonebot.on_notice(check_leave) @leave.handle() async def handle_leave(bot: Bot, event: Event): username = (await bot.get_stranger_info(user_id=event.user_id))["nickname"] if event.operator_id != event.user_id: operator_name = (await bot.get_group_member_info(group_id=event.group_id, user_id=event.operator_id))["nickname"] await leave.send(f"{operator_name}（{event.operator_id}）将 {username}（{event.user_id}）踢出了本群") else: await leave.send(f"{username}（{event.user_id}）退出了本群")	[ 1, 529, 276, 1112, 420, 29958, 2146, 29906, 29906, 29953, 29914, 1204, 29882, 4211, 29933, 327, 13, 3166, 3667, 29889, 2917, 1053, 7399, 3991, 29892, 8989, 13, 3166, 5642, 7451, 29889, 328, 481, 2153, 29889, 650, 7451, 29889, 29894, 29896, 29896, 1053, 11273, 29892, 6864, 29892, 7777, 13, 5215, 5642, 7451, 13, 13, 1990, 12782, 29898, 5160, 3991, 1125, 13, 29871, 4770, 1445, 1649, 353, 376, 20466, 2763, 29908, 13, 29871, 12853, 29901, 9657, 29961, 524, 29892, 851, 29962, 353, 8989, 29898, 4381, 29918, 14399, 29922, 8977, 29897, 13, 29871, 5967, 29901, 731, 29961, 524, 29962, 353, 8989, 29898, 4381, 29918, 14399, 29922, 842, 29897, 13, 13, 25903, 353, 12782, 29889, 1359, 580, 13, 13, 12674, 822, 1423, 29918, 20466, 2763, 29898, 3696, 29901, 6864, 1125, 13, 29871, 736, 1741, 29889, 1333, 625, 29918, 1853, 1275, 376, 2972, 29918, 262, 1037, 559, 29908, 322, 1741, 29889, 2972, 29918, 333, 297, 8707, 18667, 29889, 20466, 2763, 13, 20466, 2763, 353, 5642, 7451, 29889, 265, 29918, 1333, 625, 29898, 3198, 29918, 20466, 2763, 29897, 13, 29992, 20466, 2763, 29889, 8411, 580, 13, 12674, 822, 4386, 29918, 20466, 2763, 29898, 3696, 29901, 6864, 1125, 13, 29871, 7272, 12853, 29889, 6717, 29898, 3728, 29898, 25903, 29889, 20466, 2763, 29961, 3696, 29889, 2972, 29918, 333, 1822, 4830, 29898, 3696, 29889, 1792, 29918, 333, 4961, 13, 13, 12674, 822, 1423, 29918, 280, 1351, 29898, 3696, 29901, 6864, 1125, 13, 29871, 736, 1741, 29889, 1333, 625, 29918, 1853, 1275, 376, 2972, 29918, 311, 1037, 559, 29908, 322, 1741, 29889, 2972, 29918, 333, 297, 8707, 18667, 29889, 280, 1351, 13, 280, 1351, 353, 5642, 7451, 29889, 265, 29918, 1333, 625, 29898, 3198, 29918, 280, 1351, 29897, 13, 29992, 280, 1351, 29889, 8411, 580, 13, 12674, 822, 4386, 29918, 280, 1351, 29898, 7451, 29901, 11273, 29892, 1741, 29901, 6864, 1125, 13, 29871, 8952, 353, 313, 20675, 9225, 29889, 657, 29918, 710, 4600, 29918, 3888, 29898, 1792, 29918, 333, 29922, 3696, 29889, 1792, 29918, 333, 876, 3366, 19254, 978, 3108, 13, 29871, 565, 1741, 29889, 6891, 29918, 333, 2804, 1741, 29889, 1792, 29918, 333, 29901, 13, 1678, 5455, 29918, 978, 353, 313, 20675, 9225, 29889, 657, 29918, 2972, 29918, 14242, 29918, 3888, 29898, 2972, 29918, 333, 29922, 3696, 29889, 2972, 29918, 333, 29892, 1404, 29918, 333, 29922, 3696, 29889, 6891, 29918, 333, 876, 3366, 19254, 978, 3108, 13, 1678, 7272, 5967, 29889, 6717, 29898, 29888, 29908, 29912, 6891, 29918, 978, 29913, 30419, 29912, 3696, 29889, 6891, 29918, 333, 29913, 30409, 30998, 426, 6786, 29913, 30419, 29912, 3696, 29889, 1792, 29918, 333, 29913, 30409, 235, 187, 165, 30544, 30743, 30346, 31829, 1159, 13, 29871, 1683, 29901, 13, 1678, 7272, 5967, 29889, 6717, 29898, 29888, 29908, 29912, 6786, 29913, 30419, 29912, 3696, 29889, 1792, 29918, 333, 29913, 30409, 236, 131, 131, 30544, 30743, 30346, 31829, 1159, 13, 2 ]
interceptor/jobs_wrapper.py	fossabot/interceptor	0	132538	# Copyright 2018 getcarrier.io # # 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 docker from uuid import uuid4 from interceptor import constants as c class JobsWrapper(object): @staticmethod def dast(container, execution_params, job_name, redis_connection, args, kwargs): client = docker.from_env() required_keys = ['host', 'port', 'protocol', 'test_type'] if not all(k in execution_params for k in required_keys): return False, "Missing required params (%s)" % ",".join(required_keys) if not [container] in [k.tags for k in client.images.list()]: return False, "Please specify proper tag of security container e.g. getcarrier/dast:latest" host = execution_params.get('host') port = execution_params.get('port') protocol = execution_params.get('protocol') project = execution_params.get('project', job_name) environment = execution_params.get('environment', "default") # redis_connection = '' client.containers.run(container, name=f'{job_name}_{uuid4()}'[:36], nano_cpus=c.CONTAINER_CPU_QUOTA, mem_limit=c.CONTAINER_MEMORY_QUOTA, command=f"-s {execution_params['test_type']}", environment={"host": host, "port": port, "protocol": protocol, "project_name": project, "environment": environment, "redis_connection": redis_connection}, remove=True) return True, "Done" @staticmethod def sast(container, execution_params, job_name, redis_connection, args, *kwargs): pass @staticmethod def perfui(container, execution_params, job_name, redis_connection, args, *kwargs): pass @staticmethod def perfmeter(container, execution_params, job_name, redis_connection, args, *kwargs): client = docker.from_env() #redis_connection = '' client.containers.run(container, name=f'{job_name}_{uuid4()}'[:36], nano_cpus=c.CONTAINER_CPU_QUOTA, mem_limit=c.CONTAINER_MEMORY_QUOTA, command=f"{execution_params['jmeter_execution_string']}", environment={"redis_connection": redis_connection}, remove=True) return True, "Done" @staticmethod def perfgatling(container, execution_params, job_name, redis_connection, args, **kwargs): pass	[ 1, 396, 259, 14187, 1266, 29871, 29906, 29900, 29896, 29947, 679, 4287, 4336, 29889, 601, 13, 29937, 13, 29937, 259, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 29937, 259, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 13, 29937, 259, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 13, 29937, 13, 29937, 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9965, 1118, 13, 462, 795, 3349, 29922, 5574, 29897, 13, 4706, 736, 5852, 29892, 376, 25632, 29908, 13, 13, 1678, 732, 7959, 5696, 13, 1678, 822, 269, 579, 29898, 7611, 29892, 8225, 29918, 7529, 29892, 4982, 29918, 978, 29892, 29825, 29918, 9965, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 4706, 1209, 13, 13, 1678, 732, 7959, 5696, 13, 1678, 822, 23895, 1481, 29898, 7611, 29892, 8225, 29918, 7529, 29892, 4982, 29918, 978, 29892, 29825, 29918, 9965, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 4706, 1209, 13, 13, 1678, 732, 7959, 5696, 13, 1678, 822, 23895, 29391, 29898, 7611, 29892, 8225, 29918, 7529, 29892, 4982, 29918, 978, 29892, 29825, 29918, 9965, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 4706, 3132, 353, 10346, 29889, 3166, 29918, 6272, 580, 13, 4706, 396, 1127, 275, 29918, 9965, 353, 6629, 13, 4706, 3132, 29889, 1285, 475, 414, 29889, 3389, 29898, 7611, 29892, 1024, 29922, 29888, 29915, 29912, 9057, 29918, 978, 3227, 25118, 29946, 580, 10162, 7503, 29941, 29953, 1402, 13, 462, 795, 302, 1562, 29918, 6814, 375, 29922, 29883, 29889, 6007, 6040, 1177, 1001, 29918, 6271, 29965, 29918, 13356, 2891, 29909, 29892, 2626, 29918, 13400, 29922, 29883, 29889, 6007, 6040, 1177, 1001, 29918, 2303, 29924, 18929, 29918, 13356, 2891, 29909, 29892, 13, 462, 795, 1899, 29922, 29888, 29908, 29912, 22256, 29918, 7529, 1839, 21231, 1308, 29918, 22256, 29918, 1807, 2033, 17671, 13, 462, 795, 5177, 3790, 29908, 1127, 275, 29918, 9965, 1115, 29825, 29918, 9965, 1118, 13, 462, 795, 3349, 29922, 5574, 29897, 13, 4706, 736, 5852, 29892, 376, 25632, 29908, 13, 13, 1678, 732, 7959, 5696, 13, 1678, 822, 639, 16434, 271, 1847, 29898, 7611, 29892, 8225, 29918, 7529, 29892, 4982, 29918, 978, 29892, 29825, 29918, 9965, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 4706, 1209, 2 ]
FaceRecognitionWebsite/codeDesign/myDjango02/app01/migrations/0012_remove_getdatacss_copybook.py	ChunjunHu/FaceRecognitionLibraryWebsite	1	169962	# Generated by Django 2.0.1 on 2019-01-09 15:54 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('app01', '0011_auto_20190109_2351'), ] operations = [ migrations.RemoveField( model_name='getdatacss', name='CopyBook', ), ]	[ 1, 396, 3251, 630, 491, 15337, 29871, 29906, 29889, 29900, 29889, 29896, 373, 29871, 29906, 29900, 29896, 29929, 29899, 29900, 29896, 29899, 29900, 29929, 29871, 29896, 29945, 29901, 29945, 29946, 30004, 13, 30004, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 30004, 13, 30004, 13, 30004, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 30004, 13, 30004, 13, 1678, 9962, 353, 518, 30004, 13, 4706, 6702, 932, 29900, 29896, 742, 525, 29900, 29900, 29896, 29896, 29918, 6921, 29918, 29906, 29900, 29896, 29929, 29900, 29896, 29900, 29929, 29918, 29906, 29941, 29945, 29896, 5477, 30004, 13, 1678, 4514, 30004, 13, 30004, 13, 1678, 6931, 353, 518, 30004, 13, 4706, 9725, 800, 29889, 15941, 3073, 29898, 30004, 13, 9651, 1904, 29918, 978, 2433, 657, 1272, 4268, 23592, 13, 9651, 1024, 2433, 11882, 10967, 23592, 13, 4706, 10353, 30004, 13, 1678, 4514, 30004, 13, 2 ]
scripts/venv/lib/python2.7/site-packages/cogent/align/progressive.py	sauloal/cnidaria	3	31612	<gh_stars>1-10 #!/usr/bin/env python from __future__ import with_statement from cogent import LoadTree from cogent.phylo import nj as NJ from cogent.phylo.distance import EstimateDistances from cogent.core.info import Info from cogent.util import progress_display as UI __author__ = "<NAME>" __copyright__ = "Copyright 2007-2012, The Cogent Project" __credits__ = ["<NAME>", "<NAME>"] __license__ = "GPL" __version__ = "1.5.3" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" __status__ = "Production" @UI.display_wrap def TreeAlign(model, seqs, tree=None, indel_rate=0.01, indel_length=0.01, ui = None, ests_from_pairwise=True, param_vals=None): """Returns a multiple alignment and tree. Uses the provided substitution model and a tree for determining the progressive order. If a tree is not provided a Neighbour Joining tree is constructed from pairwise distances estimated from pairwise aligning the sequences. If running in parallel, only the distance estimation is parallelised and only the master CPU returns the alignment and tree, other CPU's return None, None. Arguments: - model: a substitution model - seqs: a sequence collection - indel_rate, indel_length: parameters for the progressive pair-HMM - ests_from_pairwise: if no tree provided and True, the median value of the substitution model parameters are used - param_vals: named key, value pairs for model parameters. These override ests_from_pairwise. """ _exclude_params = ['mprobs', 'rate', 'bin_switch'] if param_vals: param_vals = dict(param_vals) else: param_vals = {} if isinstance(seqs, dict): seq_names = seqs.keys() else: seq_names = seqs.getSeqNames() two_seqs = len(seq_names) == 2 if tree: tip_names = tree.getTipNames() tip_names.sort() seq_names.sort() assert tip_names == seq_names, \ "names don't match between seqs and tree: tree=%s; seqs=%s" % \ (tip_names, seq_names) ests_from_pairwise = False elif two_seqs: tree = LoadTree(tip_names=seqs.getSeqNames()) ests_from_pairwise = False else: if ests_from_pairwise: est_params = [param for param in model.getParamList() \ if param not in _exclude_params] else: est_params = None dcalc = EstimateDistances(seqs, model, do_pair_align=True, est_params=est_params) dcalc.run() dists = dcalc.getPairwiseDistances() tree = NJ.nj(dists) LF = model.makeLikelihoodFunction(tree.bifurcating(name_unnamed=True), aligned=False) if ests_from_pairwise and not param_vals: # we use the Median to avoid the influence of outlier pairs param_vals = {} for param in est_params: numbers = dcalc.getParamValues(param) print "Param Estimate Summary Stats: %s" % param print numbers.summarize() param_vals[param] = numbers.Median ui.display("Doing %s alignment" % ["progressive", "pairwise"][two_seqs]) with LF.updatesPostponed(): for param, val in param_vals.items(): LF.setParamRule(param, value=val, is_constant=True) LF.setParamRule('indel_rate', value=indel_rate, is_constant=True) LF.setParamRule('indel_length', value=indel_length, is_constant=True) LF.setSequences(seqs) edge = LF.getLogLikelihood().edge (vtLnL, align) = edge.getViterbiScoreAndAlignment(0.5) info = Info() info["AlignParams"] = param_vals info["AlignParams"].update(dict(indel_length=indel_length, indel_rate=indel_rate)) align.Info = info return align, tree	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 3166, 4770, 29888, 9130, 1649, 1053, 411, 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PythonServer/UnitTestCasesForWebSocket.py	Cyberlightning/2D-3DCapture	2	4916	''' Created on Mar 6, 2014 @author: tharanga ''' import unittest from time import sleep import EventService as es from EventService import WebSocketServer as ws from EventService import EventManager as em import socket from base64 import b64encode import struct import MySQLdb import json import EventService import flaskr import tempfile def encodeMessage( message): message = b64encode(message) b1 =0x80 \| 0x1 & 0x0f b2 = 0 header="" payload_len = len(message) if payload_len < 126 : header = struct.pack('>BB', b1, payload_len) message= header +message elif (payload_len < ((2 ** 16) - 1)): b2 \|= 126 header += chr(b1) header += chr(b2) l = struct.pack(">H", payload_len) header += l message = header +message else: b2 \|= 127 header += chr(b1) header += chr(b2) l = struct.pack(">Q", payload_len) header += l message = header +message return message class TestWebSockets(unittest.TestCase): def setUp(self): self.wsServer = ws('',12345,'127.0.0.1') self.wsServer.setRunning(True); sleep(1) self.testsocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.testsocket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) # Create a socket object host = 'localhost' # Get local machine name port = 12345 self.testsocket.connect((host, port)) def tearDown(self): self.wsServer.closeConnection(); self.testsocket.close() sleep(1) def test_webSocketServerOBject(self): self.assertEqual(self.wsServer.SERVER, '', "Server set to the desired value") self.assertEqual(self.wsServer.PORT, 12345, "Server port is set correctly") self.assertEqual(self.wsServer.LOCALHOST, "127.0.0.1", "Localhost set to 127.0.0.1") def test_invalid_Request(self): message= "Test Message" self.testsocket.send(message) data = repr(self.testsocket.recv(1024)) #print 'Response to invalid message<TestMessage> %s'%(data) self.assertEqual(data, '\'CONNECTION_REJECTED\'', "Invalid Message rejected") def test_valid_WS_Request(self): message = "GET /mychat HTTP/1.1\nHost: server.example.com\nUpgrade: websocket\nConnection: Upgrade\nSec-WebSocket-Key: <KEY>nSec-WebSocket-Protocol: chat\nSec-WebSocket-Version: 13\nOrigin: localhost\n\n" # message = "Test message" self.testsocket.sendall(message) wsresponse = repr(self.testsocket.recv(1024)) #print 'Response to valid ws request %s'%wsresponse self.assertNotEqual(wsresponse, '\'CONNECTION_REJECTED\'', "Connection is not rejected") self.assertIsNotNone(wsresponse, "Connection Response is not Empty") self.testsocket.sendall(("Test Message")) data = repr(self.testsocket.recv(1024)) #print 'Response to un encoded Request %s'%(data) self.assertEqual(data, "\'Un expected opcode\'", "In valid Message rejected") def test_invalid_Messge(self): message = "GET /mychat HTTP/1.1\nHost: server.example.com\nUpgrade: websocket\nConnection: Upgrade\nSec-WebSocket-Key: <KEY>==\nSec-WebSocket-Protocol: chat\nSec-WebSocket-Version: 13\nOrigin: localhost\n\n" self.testsocket.sendall(message) wsresponse = repr(self.testsocket.recv(1024)) sleep(1) self.testsocket.sendall("Test Message") data = repr(self.testsocket.recv(1024)) self.assertEqual(data, "\'Un expected opcode\'", "In valid Message rejected") def test_malformed_Message(self): message = "GET /mychat HTTP/1.1\nHost: server.example.com\nUpgrade: websocket\nConnection: Upgrade\nSec-WebSocket-Key: <KEY>==\nSec-WebSocket-Protocol: chat\nSec-WebSocket-Version: 13\nOrigin: localhost\n\n" self.testsocket.sendall(message) wsresponse = repr(self.testsocket.recv(1024)) # print wsresponse self.testsocket.send(encodeMessage("Test Message"))#This line seems to get stuck at times. Solution is to use sendAll, use \n at the end data = repr(self.testsocket.recv(1024)) self.assertEqual(data, "\'MISFORMATED MESSAGE\'", "Messages with out a type is rejected") def test_wellformed_Message_for_Text(self): message = "GET /mychat HTTP/1.1\nHost: server.example.com\nUpgrade: websocket\nConnection: Upgrade\nSec-WebSocket-Key: <KEY>nSec-WebSocket-Protocol: chat\nSec-WebSocket-Version: 13\nOrigin: localhost\n\n" self.testsocket.sendall(message) wsresponse = repr(self.testsocket.recv(1024)) # print wsresponse self.testsocket.send(encodeMessage("1<---->Test Message"))#This line seems to get stuck at times. Solution is to use sendAll, use \n at the end data = repr(self.testsocket.recv(1024)) print data self.assertEqual(data, "\'Text received\'", "Text Messages is identified and accepted") def test_wellformed_Message_for_Json(self): message = "GET /mychat HTTP/1.1\nHost: server.example.com\nUpgrade: websocket\nConnection: Upgrade\nSec-WebSocket-Key: x3JJHMbDL1EzLkh9GBhXDw==\nSec-WebSocket-Protocol: chat\nSec-WebSocket-Version: 13\nOrigin: localhost\n\n" self.testsocket.sendall(message) wsresponse = repr(self.testsocket.recv(1024)) self.testsocket.send(encodeMessage("2<---->Test Message"))#This line seems to get stuck at times. Solution is to use sendAll, use \n at the end data = repr(self.testsocket.recv(1024)) # print data self.assertEqual(data, "\'json is received\'", "json Messages is identified and accepted") ##TO RUN THE FOLLOWING UNIT TESTS IT IS EXPECTED HAVE THE DATABASE ##CREATED. DATABASE SCRIPT IS PROVIDED TO CREATE THE NECESSARY DATABASES AND TABLES ##ASSISCIATED DATA IS NOT PROVIDED. class TestDatabase(unittest.TestCase): def setUp(self): self.connection = es.dbConnect() def tearDown(self): self.connection.close() def test_data_insert_data_Read(self): self.assertIsInstance(self.connection, MySQLdb.connection, "Database connection accurately set") jsondata ={"type":"image", "time":"2014.3.4_14.40.30", "ext":"png", "deviceType":"Mobile", "deviceOS":"Badda", "browsertype":"Firefox", "position":{"lon":25.4583105, "lat":65.0600797, "alt":-1000, "acc":48.38800048828125}, "device":{"ax":0, "ay":0, "az":0, "gx":0, "gy":0, "gz":0, "ra":210.5637, "rb":47.5657, "rg":6.9698, "orientation":"potrait"}, "vwidth":480, "vheight":800} alt = str(jsondata["position"]["alt"]); if alt=="None": alt = '0' heading = '0' speed = '0' width = jsondata["vwidth"] height =jsondata["vheight"] if width > height : screenorientation= 1.00#landscape else : screenorientation= 0.00#potrait filename = jsondata["type"]+"_"+jsondata["time"]+"."+jsondata["ext"] sqlstring1 = "INSERT INTO Imagedata values (\'"+filename+"\',GeomFromText ('POINT("+ str(jsondata["position"]["lat"])+" "+str(jsondata["position"]["lon"])+")'),"+str(jsondata["position"]["alt"])+","+str(jsondata["position"]["acc"]) sqlstring2 =","+str(jsondata["device"]["gx"])+","+str(jsondata["device"]["gy"])+","+str(jsondata["device"]["gz"]) sqlstring3 = ","+str(jsondata["device"]["ra"])+","+str(jsondata["device"]["rb"])+","+str(jsondata["device"]["rg"])+","+str(screenorientation)+",\'"+jsondata["device"]["orientation"]+"\',now(),\'"+str(jsondata["deviceOS"])+"\',\'"+str(jsondata["browsertype"])+"\',\'"+str(jsondata["deviceType"])+"\');" sqlstring = sqlstring1 + sqlstring2+ sqlstring3 #print(sqlstring) es.dbInsert(sqlstring) sqlreadsting = 'select imagename, Browser,devicetype,X(location) as latitude, Y(location) as longitude from Imagedata where time=\'2014.3.4_14.40.31\'' result = es.dbRead(sqlreadsting) self.assertIsNotNone(result, "Inserted data is retrieved and it is not null") for row in result: self.assertEqual(row[0], "image_2014.3.4_14.40.30.png", "Image name is correctly set and saved") self.assertEqual(row[1], 65.0600797, "Latitudes are saved") self.assertEqual(row[2], 25.4583105, "Longitude are saved") HOST = '127.0.0.1' # The remote host PORT = 17322 class RestServerTestCase(unittest.TestCase): def setUp(self): self.db_fd, flaskr.app.config['DATABASE'] = tempfile.mkstemp() EventService.app.config['TESTING'] = True self.app = EventService.app.test_client() flaskr.init_db() #self.socketServer = self.app.WebSocketServer('',wsport,'127.0.0.1') def test_rootpath(self): rv = self.app.get('/') assert 'This is a REST Service for 2D3DCapture Server.' in rv.data def test_post_image(self): rv = self.app.post('/postImage') assert 'READY' in rv.data def test_clossing_websocket(self): rv =self.app.post('/closewebsocketserver') assert 'CLOSED' or 'ALREADY_CLOSSED' in rv.data def test_start_websocket(self): rv =self.app.get('/startwebsocketserver') # print rv.data assert 'READY' in rv.data def test_post_binary_image(self): rv =self.app.post('/postBinaryImage') assert 'READY' or '415 Unsupported Media Type' in rv.data def test_get_All_Image_Data(self): rv =self.app.get('/getAllImageData') jsonmsg = json.loads(rv.data) self.assertIsNotNone(jsonmsg['imageList'] , "getImageData returns a non None list") def test_get_location_Image_Data(self): rv =self.app.get('/getLocationImageData?lat=65.0600797&lon=25.4583105') jsonmsg = json.loads(rv.data) self.assertIsNotNone(jsonmsg['imageList'] , "getLocationImageData returns a non None list.This is a feature test for location based image data") def test_closest_Image_retrieval(self): jsondata1 ={"type":"image", "time":"2014.3.4_14.40.31", "ext":"png", "deviceType":"Mobile", "deviceOS":"Badda", "browsertype":"Firefox", "position":{"lon":25.4583105, "lat":65.0600797, "alt":-1000, "acc":48.38800048828125}, "device":{"ax":0, "ay":0, "az":0, "gx":0, "gy":0, "gz":0, "ra":210.5637, "rb":47.5657, "rg":6.9698, "orientation":"potrait"}, "vwidth":480, "vheight":800} jsondata2 ={"type":"image", "time":"2014.3.4_14.40.32", "ext":"png", "deviceType":"Mobile", "deviceOS":"Badda", "browsertype":"Firefox", "position":{"lon":25.4582115, "lat":65.0600797, "alt":-1000, "acc":48.38800048828125}, "device":{"ax":0, "ay":0, "az":0, "gx":0, "gy":0, "gz":0, "ra":210.5637, "rb":47.5657, "rg":6.9698, "orientation":"potrait"}, "vwidth":480, "vheight":800} jsondata3 ={"type":"image", "time":"2014.3.4_14.40.33", "ext":"png", "deviceType":"Mobile", "deviceOS":"Badda", "browsertype":"Firefox", "position":{"lon":25.4584104, "lat":65.0600797, "alt":-1000, "acc":48.38800048828125}, "device":{"ax":0, "ay":0, "az":0, "gx":0, "gy":0, "gz":0, "ra":210.5637, "rb":47.5657, "rg":6.9698, "orientation":"potrait"}, "vwidth":480, "vheight":800} jsondata4 ={"type":"image", "time":"2014.3.4_14.40.34", "ext":"png", "deviceType":"Mobile", "deviceOS":"Badda", "browsertype":"Firefox", "position":{"lon":25.4586115, "lat":65.0600797, "alt":-1000, "acc":48.38800048828125}, "device":{"ax":0, "ay":0, "az":0, "gx":0, "gy":0, "gz":0, "ra":210.5637, "rb":47.5657, "rg":6.9698, "orientation":"potrait"}, "vwidth":480, "vheight":800} jsondata5 ={"type":"image", "time":"2014.3.4_14.40.35", "ext":"png", "deviceType":"Mobile", "deviceOS":"Badda", "browsertype":"Firefox", "position":{"lon":25.4587125, "lat":65.0600797, "alt":-1000, "acc":48.38800048828125}, "device":{"ax":0, "ay":0, "az":0, "gx":0, "gy":0, "gz":0, "ra":210.5637, "rb":47.5657, "rg":6.9698, "orientation":"potrait"}, "vwidth":480, "vheight":800} jsondata6 ={"type":"image", "time":"2014.3.4_14.40.36", "ext":"png", "deviceType":"Mobile", "deviceOS":"Badda", "browsertype":"Firefox", "position":{"lon":25.4588125, "lat":65.0600797, "alt":-1000, "acc":48.38800048828125}, "device":{"ax":0, "ay":0, "az":0, "gx":0, "gy":0, "gz":0, "ra":210.5637, "rb":47.5657, "rg":6.9698, "orientation":"potrait"}, "vwidth":480, "vheight":800} es.saveData(jsondata1) es.saveData(jsondata2) es.saveData(jsondata3) es.saveData(jsondata4) es.saveData(jsondata5) es.saveData(jsondata6) radius = 0.0001 photoList = es.getClosestImages( 65.0601787, 25.4583107, radius ) self.assertEqual(len(photoList), 4, "Length of the list should be equal of the first test") for row in photoList: assert 'image_2014.3.4_14.40.32.png' or 'image_2014.3.4_14.40.31.png' in row[0] photoList2 = es.getClosestImages( 65.0601787, 25.4587107, radius ) self.assertEqual(len(photoList2), 2, "Length of the list should be equal of the second test") for row in photoList2: assert 'image_2014.3.4_14.40.34.png' or 'image_2014.3.4_14.40.35.png' in row[0] def suite(): testsuit =unittest.TestSuite() testsuit.addTest(TestWebSockets('test_webSocketServerOBject')) testsuit.addTest(TestWebSockets('test_valid_WS_Request')) testsuit.addTest(TestWebSockets('test_invalid_Messge')) testsuit.addTest(TestWebSockets('test_invalid_Request')) testsuit.addTest(TestWebSockets('test_malformed_Message')) testsuit.addTest(TestWebSockets('test_wellformed_Message_for_Text')) testsuit.addTest(TestWebSockets('test_wellformed_Message_for_Json')) testsuit.addTest(TestDatabase('test_data_insert_data_Read')) testsuit.addTest(RestServerTestCase('test_rootpath')) testsuit.addTest(RestServerTestCase('test_post_image')) testsuit.addTest(RestServerTestCase('test_start_websocket')) testsuit.addTest(RestServerTestCase('test_clossing_websocket')) testsuit.addTest(RestServerTestCase('test_post_binary_image')) testsuit.addTest(RestServerTestCase('test_get_All_Image_Data')) testsuit.addTest(RestServerTestCase('test_closest_Image_retrieval')) return testsuit suite = suite() runner = unittest.TextTestRunner(verbosity=3) runner.run(suite) # if __name__ == "__main__": # #import sys;sys.argv = ['', 'Test.testName'] # unittest.main()	[ 1, 14550, 13, 20399, 373, 1085, 29871, 29953, 29892, 29871, 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unionfind-component.py	surpace/shed	2	181020	class UnionFind: def __init__(self, size): self.parent = list(range(size)) self.component = [[i] for i in range(size)] def root(self, i): if self.parent[i] != i: self.parent[i] = self.root(self.parent[i]) return self.parent[i] def unite(self, i, j): i, j = self.root(i), self.root(j) if len(self.component[i]) < len(self.component[j]): i, j = j, i self.parent[j] = i self.component[i] += self.component[j]	[ 1, 770, 7761, 12542, 29901, 13, 29871, 822, 4770, 2344, 12035, 1311, 29892, 2159, 1125, 13, 1678, 1583, 29889, 3560, 353, 1051, 29898, 3881, 29898, 2311, 876, 13, 1678, 1583, 29889, 9700, 353, 5519, 29875, 29962, 363, 474, 297, 3464, 29898, 2311, 4638, 13, 259, 13, 29871, 822, 3876, 29898, 1311, 29892, 474, 1125, 13, 1678, 565, 1583, 29889, 3560, 29961, 29875, 29962, 2804, 474, 29901, 13, 418, 1583, 29889, 3560, 29961, 29875, 29962, 353, 1583, 29889, 4632, 29898, 1311, 29889, 3560, 29961, 29875, 2314, 13, 1678, 736, 1583, 29889, 3560, 29961, 29875, 29962, 13, 259, 13, 29871, 822, 443, 568, 29898, 1311, 29892, 474, 29892, 432, 1125, 13, 1678, 474, 29892, 432, 353, 1583, 29889, 4632, 29898, 29875, 511, 1583, 29889, 4632, 29898, 29926, 29897, 13, 1678, 565, 7431, 29898, 1311, 29889, 9700, 29961, 29875, 2314, 529, 7431, 29898, 1311, 29889, 9700, 29961, 29926, 29962, 1125, 13, 418, 474, 29892, 432, 353, 432, 29892, 474, 13, 1678, 1583, 29889, 3560, 29961, 29926, 29962, 353, 474, 13, 1678, 1583, 29889, 9700, 29961, 29875, 29962, 4619, 1583, 29889, 9700, 29961, 29926, 29962, 13, 2 ]
capsNet.py	zhaoaite/ASTCapsNet	0	33158	""" License: Apache-2.0 Author: <NAME> E-mail: <EMAIL> """ import tensorflow as tf import rnn_cell_GRU as rnn_cell import rnn import numpy as np from config import cfg from utils import get_batch_data from capsLayer import CapsLayer from sklearn import metrics import pickle from sklearn.cross_validation import train_test_split from sklearn import preprocessing from plot_confusion_matrix import plot_confusion_matrix from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt import os import tensorflow as tf from tensorflow.contrib.tensor_forest.python import tensor_forest from tensorflow.python.ops import resources class CapsNet(object): def __init__(self, is_training=True): self.graph = tf.Graph() with self.graph.as_default(): self.n_classes=52 self.X, self.labels = get_batch_data(cfg.dataset, cfg.batch_size, cfg.num_threads) self.Y = tf.one_hot(self.labels, depth=self.n_classes, axis=1, dtype=tf.float32) # LSTM Parameters self.n_input=45 self.n_steps=45 # tf Graph input self.lstm_x = tf.reshape(self.X, shape=(cfg.batch_size, self.n_steps, self.n_input)) self.lstm_y = tf.reshape(self.Y, shape=(cfg.batch_size, self.n_classes)) self.kernel_size1=13 self.kernel_size2=9 self.conv1_outa=self.n_input-self.kernel_size1+1 self.conv1_outb=self.n_steps-self.kernel_size1+1 # self.cap1_out=(self.conv1_outa-self.kernel_size+1)/2((self.conv1_outb-self.kernel_size+1)/2) # self.cap1_out=int((self.conv1_outa-self.kernel_size2+1)(self.conv1_outb-self.kernel_size2+1)32/4) self.cap1_out=5408 self.n_hidden= self.conv1_outb # Define weights self.lstm_weights = { 'out': tf.Variable(tf.random_normal([self.n_hidden, self.n_classes])) } self.lstm_biases = { 'out': tf.Variable(tf.random_normal([self.n_classes])) } if is_training: self.build_arch() self.loss() self._summary() # t_vars = tf.trainable_variables() self.global_step = tf.Variable(0, name='global_step', trainable=False) self.optimizer = tf.train.AdamOptimizer() self.train_op = self.optimizer.minimize(self.total_loss, global_step=self.global_step) # var_list=t_vars) self.train_c1 = self.optimizer.minimize(self.lstm_cost, global_step=self.global_step) self.train_c2 = self.optimizer.minimize(self.dense_cov1_cost, global_step=self.global_step) self.train_c3 = self.optimizer.minimize(self.dense_caps1_cost, global_step=self.global_step) print('end net') else: self.X = tf.placeholder(tf.float32, shape=(cfg.batch_size,self.n_input, self.n_steps, 1)) self.labels = tf.placeholder(tf.int32, shape=(cfg.batch_size, )) self.Y = tf.reshape(self.labels, shape=(cfg.batch_size, self.n_classes, 1)) self.build_arch() tf.logging.info('Seting up the main structure') def RNN(self): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, n_steps, n_input) # Required shape: 'n_steps' tensors list of shape (batch_size, n_input) # Permuting batch_size and n_steps x = tf.transpose(self.lstm_x, [1, 0, 2]) # Reshaping to (n_stepsbatch_size, n_input) x = tf.reshape(tensor=x, shape=[-1, self.n_input]) # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input) x = tf.split(value=x, num_or_size_splits=self.n_steps, axis=0) # Define a lstm cell with tensorflow #lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1) lstm_cell = rnn_cell.GRUCell(self.n_hidden) #lstm_cell = rnn_cell.LSTMCell(n_hidden,use_peepholes=True) # avoid overfitting lstm_cell = rnn_cell.DropoutWrapper(lstm_cell, output_keep_prob=0.5) # 2 layers lstm lstm_cell = rnn_cell.MultiRNNCell([lstm_cell]2) # Get lstm cell output outputs, states = rnn.rnn(cell=lstm_cell, inputs=x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], self.lstm_weights['out']) + self.lstm_biases['out'],outputs[-1] def build_arch(self): with tf.variable_scope('LSTM_layer'): #pred batch4d out batch128d pred,out = self.RNN() out=tf.reshape(out,(-1,1,self.n_hidden,1)) # cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=pred, labels=y)) # #Adam optimizer # optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Evaluate model # correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) # accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) with tf.variable_scope('Conv1_layer'): # Conv1, [batch_size, 20, 20, 256] conv1 = tf.contrib.layers.conv2d(self.X, num_outputs=256, kernel_size=self.kernel_size1, stride=1, padding='VALID') # print(conv1.get_shape(),[cfg.batch_size, self.conv1_outa,self.conv1_outb, 256]) assert conv1.get_shape() == [cfg.batch_size, self.conv1_outa,self.conv1_outb, 256] out=tf.tile(out,[1,self.conv1_outa,1,256]) self.conv1=tf.add(conv1,out) # out_temp= tf.placeholder(tf.float32, shape=(cfg.batch_size,self.conv1_outa+1,self.conv1_outb, 256)) # self.dense1 = tf.layers.dense(inputs=tf.reshape(self.conv1,(cfg.batch_size,-1)), units=self.n_classes, activation=tf.nn.relu) #全连接层 pool = tf.layers.max_pooling2d(inputs=self.conv1, pool_size=[2, 2], strides=2) fc1 = tf.layers.dense(inputs=pool, units=1024, activation=tf.nn.relu) fc2 = tf.layers.dense(inputs=fc1, units=512, activation=tf.nn.relu) self.dense1 = tf.layers.dense(inputs=tf.reshape(fc2,(cfg.batch_size,-1)), units=self.n_classes, activation=None) self.dense1_index = tf.to_int32(tf.argmax(tf.nn.softmax(self.dense1, axis=1), axis=1)) # Primary Capsules layer, return [batch_size, 1152, 8, 1] with tf.variable_scope('PrimaryCaps_layer'): primaryCaps = CapsLayer(num_outputs=32, vec_len=8, with_routing=False, layer_type='CONV', conv1_outa=self.conv1_outa, conv1_outb=self.conv1_outb, cap1_out=self.cap1_out,n_classes=self.n_classes) (self.caps1,pred) = primaryCaps(self.conv1, kernel_size=self.kernel_size2, stride=2) self.lstmpred=pred assert self.caps1.get_shape() == [cfg.batch_size, self.cap1_out, 8, 1] # self.dense2= tf.layers.dense(inputs=tf.reshape(self.caps1,(cfg.batch_size,-1)), units=self.n_classes, activation=tf.nn.relu) pool = tf.layers.max_pooling2d(inputs=self.caps1, pool_size=[2, 2], strides=2) fc1 = tf.layers.dense(inputs=pool, units=1024, activation=tf.nn.relu) fc2 = tf.layers.dense(inputs=fc1, units=512, activation=tf.nn.relu) self.dense2 = tf.layers.dense(inputs=tf.reshape(fc2,(cfg.batch_size,-1)), units=self.n_classes, activation=None) self.dense2_index = tf.to_int32(tf.argmax(tf.nn.softmax(self.dense2, axis=1), axis=1)) # DigitCaps layer, return [batch_size, 10, 16, 1] with tf.variable_scope('DigitCaps_layer'): digitCaps = CapsLayer(num_outputs=self.n_classes, vec_len=8, with_routing=True, layer_type='FC',conv1_outa=self.conv1_outa, conv1_outb=self.conv1_outb, cap1_out=self.cap1_out,n_classes=self.n_classes) self.caps2 = digitCaps(self.caps1) # self.caps2 = tf.add(tf.tile(tf.reshape(self.lstmpred,(cfg.batch_size,self.n_classes,1,1)),[1,1,16,1]),self.caps2) # Decoder structure in Fig. 2 # 1. Do masking, how: with tf.variable_scope('Masking'): # a). calc \|\|v_c\|\|, then do softmax(\|\|v_c\|\|) # [batch_size, 10, 16, 1] => [batch_size, 10, 1, 1] self.v_length = tf.sqrt(tf.reduce_sum(tf.square(self.caps2), axis=2, keepdims=True)) self.softmax_v = tf.nn.softmax(self.v_length, axis=1) assert self.softmax_v.get_shape() == [cfg.batch_size, self.n_classes, 1, 1] # b). pick out the index of max softmax val of the 10 caps # [batch_size, 10, 1, 1] => [batch_size] (index) self.argmax_idx = tf.to_int32(tf.argmax(self.softmax_v, axis=1)) assert self.argmax_idx.get_shape() == [cfg.batch_size, 1, 1] self.argmax_idx = tf.reshape(self.argmax_idx, shape=(cfg.batch_size, )) # self.max_index_list= tf.Variable(tf.ones([cfg.batch_size, ],dtype=tf.int32)) # index_list=tf.stack([self.dense1_index,self.dense2_index,self.argmax_idx],1) # # for i in range(cfg.batch_size): # max_index=tf.to_int32(tf.argmax(tf.bincount(index_list[i]))) # self.update_op=tf.assign(self.max_index_list[i],max_index) # # Method 1. # if not cfg.mask_with_y: # # c). indexing # # It's not easy to understand the indexing process with argmax_idx # # as we are 3-dim animal # masked_v = [] # for batch_size in range(cfg.batch_size): # v = self.caps2[batch_size][self.argmax_idx[batch_size], :] # masked_v.append(tf.reshape(v, shape=(1, 1, 8, 1))) # # self.masked_v = tf.concat(masked_v, axis=0) # assert self.masked_v.get_shape() == [cfg.batch_size, 1, 8, 1] # # Method 2. masking with true label, default mode # else: # # self.masked_v = tf.matmul(tf.squeeze(self.caps2), tf.reshape(self.Y, (-1, 10, 1)), transpose_a=True) # self.masked_v = tf.multiply(tf.squeeze(self.caps2), tf.reshape(self.Y, (-1, self.n_classes, 1))) # self.v_length = tf.sqrt(tf.reduce_sum(tf.square(self.caps2), axis=2, keepdims=True) + epsilon) # # # 2. Reconstructe the MNIST images with 3 FC layers # # [batch_size, 1, 16, 1] => [batch_size, 16] => [batch_size, 512] # with tf.variable_scope('Decoder'): # vector_j = tf.reshape(self.masked_v, shape=(cfg.batch_size, -1)) # fc1 = tf.contrib.layers.fully_connected(vector_j, num_outputs=512) # assert fc1.get_shape() == [cfg.batch_size, 512] # fc2 = tf.contrib.layers.fully_connected(fc1, num_outputs=1024) # assert fc2.get_shape() == [cfg.batch_size, 1024] # self.decoded = tf.contrib.layers.fully_connected(fc2, num_outputs=self.n_stepsself.n_input, activation_fn=tf.sigmoid) def loss(self): # 1. The margin loss # [batch_size, 10, 1, 1] # max_l = max(0, m_plus-\|\|v_c\|\|)^2 max_l = tf.square(tf.maximum(0., cfg.m_plus - self.v_length)) # max_r = max(0, \|\|v_c\|\|-m_minus)^2 max_r = tf.square(tf.maximum(0., self.v_length - cfg.m_minus)) assert max_l.get_shape() == [cfg.batch_size,self.n_classes, 1, 1] # reshape: [batch_size, 10, 1, 1] => [batch_size, 10] max_l = tf.reshape(max_l, shape=(cfg.batch_size, -1)) max_r = tf.reshape(max_r, shape=(cfg.batch_size, -1)) # calc T_c: [batch_size, 10] # T_c = Y, is my understanding correct? Try it. T_c = self.Y # [batch_size, 10], element-wise multiply L_c = T_c * max_l + cfg.lambda_val * (1 - T_c) * max_r self.margin_loss = tf.reduce_mean(tf.reduce_sum(L_c, axis=1)) # # 2. The reconstruction loss # orgin = tf.reshape(self.X, shape=(cfg.batch_size, -1)) # squared = tf.square(self.decoded - orgin) # self.reconstruction_err = tf.reduce_mean(squared) # lstm loss self.lstm_cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.lstmpred, labels=self.lstm_y)) self.lstm_index = tf.to_int32(tf.argmax(self.lstmpred, axis=1)) self.dense_cov1_cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.dense1, labels=self.lstm_y)) self.dense_caps1_cost= tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.dense2, labels=self.lstm_y)) # 3. Total loss # The paper uses sum of squared error as reconstruction error, but we # have used reduce_mean in `# 2 The reconstruction loss` to calculate # mean squared error. In order to keep in line with the paper,the # regularization scale should be 0.0005784=0.392 # self.total_loss = self.margin_loss + cfg.regularization_scale self.reconstruction_err+lstm_cost # self.total_loss = self.margin_loss+self.lstm_cost self.total_loss = self.margin_loss+self.lstm_cost+self.dense_cov1_cost+self.dense_caps1_cost # Summary def _summary(self): train_summary = [] train_summary.append(tf.summary.scalar('train/margin_loss', self.margin_loss)) # train_summary.append(tf.summary.scalar('train/reconstruction_loss', self.reconstruction_err)) train_summary.append(tf.summary.scalar('train/total_loss', self.total_loss)) # train_summary.append(tf.summary.scalar('train/rf_loss', self.rf_loss_op)) # recon_img = tf.reshape(self.decoded, shape=(cfg.batch_size, self.n_input,self.n_steps, 1)) # train_summary.append(tf.summary.image('reconstruction_img', recon_img)) self.train_summary = tf.summary.merge(train_summary) # correct_prediction = tf.equal(tf.to_int32(self.labels), 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vocoder/hifigan/env.py	10088/MockingBird	1	46337	import os import shutil def build_env(config, config_name, path): t_path = os.path.join(path, config_name) if config != t_path: os.makedirs(path, exist_ok=True) shutil.copyfile(config, os.path.join(path, config_name))	[ 1, 1053, 2897, 13, 5215, 528, 4422, 13, 13, 1753, 2048, 29918, 6272, 29898, 2917, 29892, 2295, 29918, 978, 29892, 2224, 1125, 13, 1678, 260, 29918, 2084, 353, 2897, 29889, 2084, 29889, 7122, 29898, 2084, 29892, 2295, 29918, 978, 29897, 13, 1678, 565, 2295, 2804, 260, 29918, 2084, 29901, 13, 4706, 2897, 29889, 29885, 12535, 12935, 29898, 2084, 29892, 1863, 29918, 554, 29922, 5574, 29897, 13, 4706, 528, 4422, 29889, 8552, 1445, 29898, 2917, 29892, 2897, 29889, 2084, 29889, 7122, 29898, 2084, 29892, 2295, 29918, 978, 876, 13, 2 ]
python/deploy-cpu-alarms.py	earthlab/aws-ops	1	37053	# Deploy idle CPU alarms to stop EC2 instances import boto3 account_id = boto3.client("sts").get_caller_identity().get("Account") region = boto3.session.Session().region_name client = boto3.client("cloudwatch") ec = boto3.client("ec2") reservations = ec.describe_instances() exceptions = ["prf-"] for r in reservations["Reservations"]: for i in r["Instances"]: instance_id = i["InstanceId"] for t in i["Tags"]: if t["Key"] == "Name": iname = t["Value"] name_excepted = any([e in iname for e in exceptions]) if name_excepted: continue else: alarm_name = "CPU Alarm " + iname + instance_id alarm = client.put_metric_alarm( AlarmName=alarm_name, MetricName="CPUUtilization", Namespace="AWS/EC2", Statistic="Maximum", ComparisonOperator="LessThanOrEqualToThreshold", Threshold=1.0, Period=60 * 60, # in seconds EvaluationPeriods=2, Dimensions=[{"Name": "InstanceId", "Value": instance_id}], Unit="Percent", ActionsEnabled=True, AlarmActions=[ ":".join( [ "arn:aws:swf", region, account_id, "action/actions/AWS_EC2.InstanceId.Stop/1.0", ] ) ], )	[ 1, 396, 10034, 2376, 28132, 10808, 394, 279, 1516, 304, 5040, 17522, 29906, 8871, 13, 5215, 289, 3747, 29941, 13, 13, 10149, 29918, 333, 353, 289, 3747, 29941, 29889, 4645, 703, 303, 29879, 2564, 657, 29918, 4804, 261, 29918, 22350, 2141, 657, 703, 10601, 1159, 13, 12803, 353, 289, 3747, 29941, 29889, 7924, 29889, 7317, 2141, 12803, 29918, 978, 13, 4645, 353, 289, 3747, 29941, 29889, 4645, 703, 9274, 12344, 1159, 13, 687, 353, 289, 3747, 29941, 29889, 4645, 703, 687, 29906, 1159, 13, 690, 6972, 800, 353, 21226, 29889, 2783, 29581, 29918, 2611, 2925, 580, 13, 13, 11739, 29879, 353, 6796, 558, 29888, 29899, 3108, 13, 13, 1454, 364, 297, 620, 6972, 800, 3366, 1666, 6972, 800, 3108, 29901, 13, 1678, 363, 474, 297, 364, 3366, 3379, 2925, 3108, 29901, 13, 4706, 2777, 29918, 333, 353, 474, 3366, 4998, 1204, 3108, 13, 4706, 363, 260, 297, 474, 3366, 28089, 3108, 29901, 13, 9651, 565, 260, 3366, 2558, 3108, 1275, 376, 1170, 1115, 13, 18884, 297, 420, 353, 260, 3366, 1917, 3108, 13, 18884, 1024, 29918, 19499, 287, 353, 738, 4197, 29872, 297, 297, 420, 363, 321, 297, 15283, 2314, 13, 18884, 565, 1024, 29918, 19499, 287, 29901, 13, 462, 1678, 6773, 13, 18884, 1683, 29901, 13, 462, 1678, 21200, 29918, 978, 353, 376, 6271, 29965, 838, 2817, 376, 718, 297, 420, 718, 2777, 29918, 333, 13, 462, 1678, 21200, 353, 3132, 29889, 649, 29918, 16414, 29918, 284, 2817, 29898, 13, 462, 4706, 838, 2817, 1170, 29922, 284, 2817, 29918, 978, 29892, 13, 462, 4706, 4737, 2200, 1170, 543, 6271, 29965, 7270, 2133, 613, 13, 462, 4706, 14706, 3535, 543, 29909, 7811, 29914, 11206, 29906, 613, 13, 462, 4706, 6666, 4695, 543, 7976, 12539, 613, 13, 462, 4706, 422, 20941, 26486, 543, 29931, 404, 1349, 273, 2816, 9843, 1762, 1349, 12268, 613, 13, 462, 4706, 498, 12268, 29922, 29896, 29889, 29900, 29892, 13, 462, 4706, 29498, 29922, 29953, 29900, 334, 29871, 29953, 29900, 29892, 29871, 396, 297, 6923, 13, 462, 4706, 382, 4387, 362, 29853, 29879, 29922, 29906, 29892, 13, 462, 4706, 4792, 5580, 11759, 6377, 1170, 1115, 376, 4998, 1204, 613, 376, 1917, 1115, 2777, 29918, 333, 29913, 1402, 13, 462, 4706, 13223, 543, 27933, 613, 13, 462, 4706, 319, 1953, 10861, 29922, 5574, 29892, 13, 462, 4706, 838, 2817, 26525, 11759, 13, 462, 9651, 29242, 1642, 7122, 29898, 13, 462, 18884, 518, 13, 462, 462, 1678, 376, 2753, 29901, 10467, 29901, 2774, 29888, 613, 13, 462, 462, 1678, 5120, 29892, 13, 462, 462, 1678, 3633, 29918, 333, 29892, 13, 462, 462, 1678, 376, 2467, 29914, 7387, 29914, 29909, 7811, 29918, 11206, 29906, 29889, 4998, 1204, 29889, 16329, 29914, 29896, 29889, 29900, 613, 13, 462, 18884, 4514, 13, 462, 9651, 1723, 13, 462, 4706, 21251, 13, 462, 1678, 1723, 13, 2 ]
wirepas_backend_client/tools/utils.py	bencorrado/backend-client	0	20802	<gh_stars>0 """ Utils ======= Contains multipurpose utilities for serializing objects and obtaining arguments from the command line. .. Copyright: Copyright 2019 Wirepas Ltd under Apache License, Version 2.0. See file LICENSE for full license details. """ import binascii import datetime import json import threading from google.protobuf import json_format def deferred_thread(fn): """ Decorator to handle a request on its own Thread to avoid blocking the calling Thread on I/O. It creates a new Thread but it shouldn't impact the performances as requests are not supposed to be really frequent (few per seconds) """ def wrapper(args, kwargs): thread = threading.Thread(target=fn, args=args, kwargs=kwargs) thread.start() return thread return wrapper class JsonSerializer(json.JSONEncoder): """ JsonSerializer Extends the JSONEncoder base class with object handlers for bytearrya, datetime and proto. """ proto_as_json = False sort_keys = True indent = 4 def __init__(self, proto_as_json: bool = False, kwargs): super(JsonSerializer, self).__init__(*kwargs) self.proto_as_json = proto_as_json if "indent" in kwargs: self.indent = kwargs["indent"] if "sort_keys" in kwargs: self.sort_keys = kwargs["sort_keys"] def default(self, obj) -> str: """ Lookup table for serializing objects Pylint complains about the method signature, but this is the recommended way of implementing a custom JSON serialization as seen in: https://docs.python.org/3/library/json.html#json.JSONEncoder """ # pylint: disable=locally-disabled, method-hidden, arguments-differ if isinstance(obj, (datetime.datetime, datetime.date)): return obj.isoformat() if isinstance(obj, (bytearray, bytes)): return binascii.hexlify(obj) if isinstance(obj, set): return str(obj) if hasattr(obj, "DESCRIPTOR"): if self.proto_as_json is True: pstr = json_format.MessageToJson( obj, including_default_value_fields=True ) else: pstr = json_format.MessageToDict( obj, including_default_value_fields=True ) return pstr return json.JSONEncoder.default(self, obj) def serialize(self, obj, flatten=False): """ returns a json representation of the object """ if flatten: temp = dict() for key, value in sorted(obj.items()): if isinstance(value, dict): for child_key, child_value in value.items(): temp[f"{key}.{child_key}"] = child_value obj = temp jobj = json.dumps( obj, cls=JsonSerializer, sort_keys=self.sort_keys, indent=self.indent, ) return jobj def flatten(input_dict, separator="/", prefix=""): """ Flattens a dictionary with nested dictionaries and lists into a single dictionary. The key compression is done using the chosen separator. """ output_dict = {} def step(member, parent_key=""): if isinstance(member, dict): for key, value in member.items(): step( value, f"{parent_key}{separator}{key}" if parent_key else str(key), ) elif isinstance(member, list): for index, sublist in enumerate(member, start=0): step( sublist, f"{parent_key}{separator}{index}" if parent_key else str(index), ) else: output_dict[f"{parent_key}"] = member step(input_dict) return output_dict class Signal: """Wrapper around and exit signal""" def __init__(self, signal=None): super(Signal, self).__init__() if signal is None: signal = False self.signal = signal def is_set(self) -> bool: """ Returns the state of the inner event or boolean """ try: ret = self.signal.is_set() except AttributeError: ret = self.signal return ret def set(self) -> bool: """ Sets the event or inner boolean """ try: ret = self.signal.set() except AttributeError: self.signal = True ret = True return ret def chunker(seq, size) -> list(): """ Splits a sequence in multiple 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mooda/util/iplot/iplot_timeseries.py	rbardaji/mooda	15	1606456	<reponame>rbardaji/mooda """ Implementation of WaterFrame.iplot_timeseries() """ import numpy as np import plotly.graph_objects as go def iplot_timeseries(list_wf, parameter_to_plot): """ It creates a Plotly figure with the time-series of the input parameter. Parameters ---------- list_wf: List of WaterFrames List of WaterFrames to be concatenated. parameter_to_plot: str Parameters to plot. Returns ------- figure: dict Plotly figure dictionary """ data = [] for wf in list_wf: # Extract data df = wf.data[parameter_to_plot].dropna().reset_index() df = df.groupby(['DEPTH', 'TIME'])[parameter_to_plot].mean() df = df.reset_index() df.set_index('TIME', inplace=True) # df.renane(columns={parameter_to_plot: wf.metadata.get('platform_code')}, inplace=True) data.append( go.Scatter(x=df.index, y=df[parameter_to_plot], # fill="tozeroy", name=wf.metadata.get('platform_code'))) # Layout y_label = None try: title = list_wf[0].vocabulary[parameter_to_plot]['long_name'] except KeyError: title = parameter_to_plot try: y_label = list_wf[0].vocabulary[parameter_to_plot]['units'] except KeyError: pass min_value = np.nanmin(list_wf[-1].data[parameter_to_plot]) max_value = np.nanmax(list_wf[0].data[parameter_to_plot]) layout = { 'title': title, 'yaxis': { # 'range': [min_value, max_value], 'title': y_label }, 'margin': {'l': 50, 'r': 10, 't': 45, 'b': 30} } figure = {"data": data, "layout": layout} return figure	[ 1, 529, 276, 1112, 420, 29958, 6050, 538, 1175, 29875, 29914, 29885, 2092, 29874, 13, 15945, 29908, 1954, 14607, 310, 13062, 4308, 29889, 29875, 5317, 29918, 3706, 6358, 580, 9995, 13, 5215, 12655, 408, 7442, 13, 5215, 6492, 368, 29889, 4262, 29918, 12650, 408, 748, 13, 13, 13, 1753, 474, 5317, 29918, 3706, 6358, 29898, 1761, 29918, 29893, 29888, 29892, 3443, 29918, 517, 29918, 5317, 1125, 13, 1678, 9995, 13, 1678, 739, 10017, 263, 18399, 368, 4377, 411, 278, 931, 29899, 13757, 310, 278, 1881, 3443, 29889, 13, 13, 1678, 12662, 2699, 13, 1678, 448, 1378, 29899, 13, 4706, 1051, 29918, 29893, 29888, 29901, 2391, 310, 13062, 14438, 1280, 13, 9651, 2391, 310, 13062, 14438, 1280, 304, 367, 16125, 630, 29889, 13, 4706, 3443, 29918, 517, 29918, 5317, 29901, 851, 13, 9651, 12662, 2699, 304, 6492, 29889, 13, 13, 1678, 16969, 13, 1678, 448, 22158, 13, 4706, 4377, 29901, 9657, 13, 9651, 18399, 368, 4377, 8600, 13, 1678, 9995, 13, 1678, 848, 353, 5159, 13, 1678, 363, 281, 29888, 297, 1051, 29918, 29893, 29888, 29901, 13, 4706, 396, 7338, 1461, 848, 13, 4706, 4489, 353, 281, 29888, 29889, 1272, 29961, 15501, 29918, 517, 29918, 5317, 1822, 8865, 1056, 2141, 12071, 29918, 2248, 580, 13, 4706, 4489, 353, 4489, 29889, 27789, 18959, 2287, 29925, 4690, 742, 525, 15307, 2033, 9601, 15501, 29918, 517, 29918, 5317, 1822, 12676, 580, 13, 4706, 4489, 353, 4489, 29889, 12071, 29918, 2248, 580, 13, 4706, 4489, 29889, 842, 29918, 2248, 877, 15307, 742, 297, 6689, 29922, 5574, 29897, 13, 4706, 396, 4489, 29889, 1267, 1662, 29898, 13099, 3790, 15501, 29918, 517, 29918, 5317, 29901, 281, 29888, 29889, 19635, 29889, 657, 877, 12120, 29918, 401, 1495, 1118, 297, 6689, 29922, 5574, 29897, 13, 13, 4706, 848, 29889, 4397, 29898, 13, 9651, 748, 29889, 4421, 2620, 29898, 29916, 29922, 2176, 29889, 2248, 29892, 343, 29922, 2176, 29961, 15501, 29918, 517, 29918, 5317, 1402, 13, 462, 539, 396, 5445, 543, 517, 9171, 29891, 613, 13, 462, 539, 1024, 29922, 29893, 29888, 29889, 19635, 29889, 657, 877, 12120, 29918, 401, 29915, 4961, 13, 13, 1678, 396, 20259, 13, 1678, 343, 29918, 1643, 353, 6213, 13, 13, 1678, 1018, 29901, 13, 4706, 3611, 353, 1051, 29918, 29893, 29888, 29961, 29900, 1822, 29894, 542, 370, 352, 653, 29961, 15501, 29918, 517, 29918, 5317, 22322, 5426, 29918, 978, 2033, 13, 1678, 5174, 7670, 2392, 29901, 13, 4706, 3611, 353, 3443, 29918, 517, 29918, 5317, 13, 1678, 1018, 29901, 13, 4706, 343, 29918, 1643, 353, 1051, 29918, 29893, 29888, 29961, 29900, 1822, 29894, 542, 370, 352, 653, 29961, 15501, 29918, 517, 29918, 5317, 22322, 348, 1169, 2033, 13, 1678, 5174, 7670, 2392, 29901, 13, 4706, 1209, 13, 13, 1678, 1375, 29918, 1767, 353, 7442, 29889, 13707, 1195, 29898, 1761, 29918, 29893, 29888, 14352, 29896, 1822, 1272, 29961, 15501, 29918, 517, 29918, 5317, 2314, 13, 1678, 4236, 29918, 1767, 353, 7442, 29889, 13707, 3317, 29898, 1761, 29918, 29893, 29888, 29961, 29900, 1822, 1272, 29961, 15501, 29918, 517, 29918, 5317, 2314, 13, 13, 1678, 5912, 353, 426, 13, 4706, 525, 3257, 2396, 3611, 29892, 13, 4706, 525, 29891, 8990, 2396, 426, 13, 9651, 396, 525, 3881, 2396, 518, 1195, 29918, 1767, 29892, 4236, 29918, 1767, 1402, 13, 9651, 525, 3257, 2396, 343, 29918, 1643, 13, 4706, 2981, 13, 4706, 525, 9264, 2396, 11117, 29880, 2396, 29871, 29945, 29900, 29892, 525, 29878, 2396, 29871, 29896, 29900, 29892, 525, 29873, 2396, 29871, 29946, 29945, 29892, 525, 29890, 2396, 29871, 29941, 29900, 29913, 13, 1678, 500, 13, 13, 1678, 4377, 353, 8853, 1272, 1115, 848, 29892, 376, 2680, 1115, 5912, 29913, 13, 13, 1678, 736, 4377, 13, 2 ]
IGTI - Bootcamp Desenvolvedor Python/04/cap4/pygame/Cap2 - desenhando/desenhando_retangulo.py	ruginski/DS	43	89395	import pygame from pygame.locals import * from sys import exit from random import * pygame.init() screen = pygame.display.set_mode((640, 480), 0, 32) screen.lock() for count in range(10): random_color = (randint(0,255), randint(0,255), randint(0,255)) random_pos = (randint(0,639), randint(0,479)) random_size = (639-randint(random_pos[0],639), 479-randint(random_pos[1],479)) pygame.draw.rect(screen, random_color, Rect(random_pos, random_size)) screen.unlock() pygame.display.update() while True: for event in pygame.event.get(): if event.type == QUIT: pygame.quit() exit()	[ 1, 1053, 22028, 13, 3166, 22028, 29889, 2997, 29879, 1053, 334, 13, 3166, 10876, 1053, 6876, 29871, 13, 13, 3166, 4036, 1053, 334, 13, 13, 2272, 11802, 29889, 2344, 580, 13, 10525, 353, 22028, 29889, 4990, 29889, 842, 29918, 8513, 3552, 29953, 29946, 29900, 29892, 29871, 29946, 29947, 29900, 511, 29871, 29900, 29892, 29871, 29941, 29906, 29897, 13, 13, 10525, 29889, 908, 580, 13, 1454, 2302, 297, 3464, 29898, 29896, 29900, 1125, 13, 1678, 4036, 29918, 2780, 353, 313, 9502, 524, 29898, 29900, 29892, 29906, 29945, 29945, 511, 20088, 524, 29898, 29900, 29892, 29906, 29945, 29945, 511, 20088, 524, 29898, 29900, 29892, 29906, 29945, 29945, 876, 13, 1678, 4036, 29918, 1066, 353, 313, 9502, 524, 29898, 29900, 29892, 29953, 29941, 29929, 511, 20088, 524, 29898, 29900, 29892, 29946, 29955, 29929, 876, 13, 1678, 4036, 29918, 2311, 353, 313, 29953, 29941, 29929, 29899, 9502, 524, 29898, 8172, 29918, 1066, 29961, 29900, 1402, 29953, 29941, 29929, 511, 29871, 29946, 29955, 29929, 29899, 9502, 524, 29898, 8172, 29918, 1066, 29961, 29896, 1402, 29946, 29955, 29929, 876, 13, 1678, 22028, 29889, 4012, 29889, 1621, 29898, 10525, 29892, 4036, 29918, 2780, 29892, 22914, 29898, 8172, 29918, 1066, 29892, 4036, 29918, 2311, 876, 13, 13, 10525, 29889, 348, 908, 580, 13, 13, 2272, 11802, 29889, 4990, 29889, 5504, 580, 13, 13, 8000, 5852, 29901, 13, 1678, 363, 1741, 297, 22028, 29889, 3696, 29889, 657, 7295, 13, 4706, 565, 1741, 29889, 1853, 1275, 660, 29965, 1806, 29901, 13, 9651, 22028, 29889, 28358, 580, 13, 9651, 6876, 580, 13, 2 ]
Simulator/tournament.py	apayne19/DoubleAuctionMarket	30	126936	<reponame>apayne19/DoubleAuctionMarket<gh_stars>10-100 from Trader.trader import Trader_Shaver, Trader_ZIU, Trader_ZIC, Trader_Kaplan, Trader_PS, Trader_AA, Trader_GD, Trader_ZIP import Institution.double_auction_institution as ins import random class Tournament(object): """ A class that makes a trader""" def __init__(self, name, iterations, auction): self.name = name self.iterations = iterations self.traders = [] self.auction = auction def show(self): print ("I am tournament {}.".format(self.name)) def prepare_traders(self, tn, mkt, limits): d = {} t = {} if len(tn) != mkt.num_buyers + mkt.num_sellers: print ("tn = {} does not have the right length".format(tn)) for k in range(mkt.num_buyers + mkt.num_sellers): t_id = "t" + str(k) # make trader id t[t_id] = globals()[tn[k]]() # create object t[t_id].name = t_id # set objects name t[t_id].da = ins.Auction("da", limits[0], limits[1]) d[t_id] = {} d[t_id]['units'] = 0 # keep track of units used d[t_id]['earn'] = 0 # keep track of earnings d[t_id]['strat'] = tn[k] # traders strategy if k < mkt.num_buyers: t[t_id].type = "buyer" # set objects type t[t_id].values = mkt.get_buyer_values(k) # set objects values d[t_id]['type'] = "B" # traders type d[t_id]['values'] = mkt.get_buyer_values(k) # trader's values else: # note seller index is trader_index - num_buyers t[t_id].type = "seller" # set objects type t[t_id].costs = mkt.get_seller_costs(k - mkt.num_buyers) # set objects values d[t_id]['type'] = "S" # traders type d[t_id]['costs'] = mkt.get_seller_costs(k - mkt.num_buyers) # trader's values d["strategies"] = [k for k in set(tn)] # get market efficiency eqv = mkt.get_equilibrium() # (Price low, Price High Quantity, Maximum Surplus) ep_low = eqv[0] ep_high = eqv[1] e_quantity = eqv[2] maximum_surplus = eqv[3] d["equilibrium"] = (ep_low, ep_high, e_quantity, maximum_surplus) traders = [t[t_id] for t_id in t.keys()] self.traders = [t[t_id] for t_id in t.keys()] return d def run(self, display): self.auction.open_board("tournament official") if display: print() print("Auction Open") print(self.auction.report_standing(), self.auction.report_contracts()) length_old_contracts = 0 for i in range(self.iterations): random.shuffle(self.traders) for trader in self.traders: trader.offer() contracts = self.auction.report_contracts() if len(contracts) > length_old_contracts: length_old_contracts = len(contracts) if display: print (i, contracts[len(contracts) - 1]) if display: print() def eval(self, display, d, result_header): # calculate market efficiency ep_low = d['equilibrium'][0] ep_high = d['equilibrium'][1] e_quantity = d['equilibrium'][2] maximum_surplus = d['equilibrium'][3] for trader in self.traders: trader_id = trader.name d[trader_id]['units'] = 0 d[trader_id]['earn'] = 0 # calculate actual surplus and earnings actual_surplus = 0 for contract in self.auction.report_contracts(): price = contract[0] buyer_id = contract[1] seller_id = contract[2] if d[buyer_id]['type'] == 'B': value = d[buyer_id]['values'][d[buyer_id]['units']] d[buyer_id]['earn'] += value - price d[buyer_id]['units'] += 1 else: print ("error, buyer id = {}, buyer type = {}".format(buyer_id, d[buyer_id]['type'])) if d[seller_id]['type'] == 'S': cost = d[seller_id]['costs'][d[seller_id]['units']] d[seller_id]['earn'] += price - cost d[seller_id]['units'] += 1 else: print ("error in contract {}, seller id = {}, seller type = {}".format(contract, seller_id, d[seller_id]['type'])) actual_surplus += value - cost efficiency = int((actual_surplus / maximum_surplus) * 100) result_header.extend([ep_low, ep_high, e_quantity, maximum_surplus, actual_surplus, efficiency]) if display: print ("actual surplus = {}, maximum surplus = {}.".format(actual_surplus, maximum_surplus)) print ("market efficiency = {} percent.".format(efficiency)) for k in range(len(self.traders)): t_id = "t" + str(k) t_strat = d[t_id]['strat'] earn = d[t_id]['earn'] if display: print ("Trader {}, using strategy {}, earned {}.".format(t_id, t_strat, earn)) result_header.extend([t_id, t_strat, earn]) if display: print() for k in d['strategies']: strat_earn = 0 count = 0 for l in range(len(self.traders)): t_id = "t" + str(l) if k == d[t_id]['strat']: count = count + 1 strat_earn += d[t_id]['earn'] if count > 0: avg_earn = int(strat_earn / count) result_header.extend([k, avg_earn]) if display: print ("Strategy {} earned an average of {}.".format(k, avg_earn)) return result_header def save_results(self, result_header, output_file): # write tournamnet results as .csv file output_file = open(output_file + '.csv', 'w', newline='') output_writer = csv.writer(output_file) # First write out result header record output_writer.writerow(result_header) # Second write out offers information orders = self.auction.report_orders() output_writer.writerow(["orders", "time, id, type, amt/null", len(orders)]) for element in orders: s = [] s.append(element[0]) s.append(element[1]) s.append(element[2]) s.append(element[3]) output_writer.writerow(s) # Third write out contract information contracts = self.auction.report_contracts() output_writer.writerow(["contracts", "price, buyer_id, seller_id", len(contracts)]) for element in contracts: s = [] s.append(element[0]) s.append(element[1]) s.append(element[2]) output_writer.writerow(s) output_file.close() def sim(self, display, num_trials, d): # initialize sum variables total_earnings = [0] * len(d['strategies']) sum_squared = [0] * len(d['strategies']) for k in range(num_trials): result = [] self.run(display) # TODO Put input and output files back in for blanks result_header = [" ", " ", len(self.traders)] result_header = self.eval(display, d, result_header) # result header = [input_file, output_file, num_traders, ep_low, ep_high, e_quantity, # max_surplus, actual_surplus, efficiency, # for all numtraders trader id, trader strategy, trader earnings] # print(result_header) result.append(result_header[8]) j = 9 + int(result_header[2]) * 3 # move to where strategy results are for k in d['strategies']: result.append(result_header[j + 1]) j = j + 2 if display: print("for run {} result {}".format(k, result)) for j in range(len(d['strategies'])): earn = result[j + 1] total_earnings[j] += earn sum_squared[j] += earn * earn avg_earning = [] var = [] sdev = [] for j in range(len(d['strategies'])): avg_earn = total_earnings[j] / num_trials variance = ((sum_squared[j] / num_trials) - (avg_earn * avg_earn)) standard_deviation = variance ** 0.5 avg_earning.append(avg_earn) var.append(variance) sdev.append(standard_deviation) print () print ("Average Earnings for {} rounds".format(num_trials)) print (d['strategies']) print(avg_earning) print(sdev) if __name__ == "__main__": trn = Tournament ("Tournamnet Name", 10, "placeholder for double auction")	[ 1, 529, 276, 1112, 420, 29958, 481, 388, 484, 29896, 29929, 29914, 11843, 29909, 29884, 428, 9802, 300, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 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akilib/raspberrypi/AKI_I2C_LPS25H.py	nonNoise/akilib	29	24428	<filename>akilib/raspberrypi/AKI_I2C_LPS25H.py #import mraa import smbus import time I2C_ADDR = 0x5C class AKI_I2C_LPS25H: def __init__(self): print "AKI_I2C_LPS25H" self.i2c = smbus.SMBus(1) def i2cReg(self,wr,addr=0x00,data=0x00): try: if(wr == "w"): return self.i2c.write_byte_data(I2C_ADDR,addr,data) elif(wr == "r"): return self.i2c.read_byte_data(I2C_ADDR,addr) else : return -1 except IOError, err: print "No ACK!" time.sleep(0.1) self.i2cReg(wr,addr,data) def WHO_AM_I(self): # "WHO_AM_I" return self.i2cReg("r",0x0F) def Init(self): return self.i2cReg("w",0x20,0x90) def Press(self): p =0 p = p \| self.i2cReg("r",0x28) <<0 p = p \| self.i2cReg("r",0x29) <<8 p = p \| self.i2cReg("r",0x2A) <<16 mbar = p/4096 return mbar def Temp (self): t = 0 t = t \| self.i2cReg("r",0x2B) <<0 t = t \| self.i2cReg("r",0x2C) <<8 t = -((t-1)^0xffff) return (42.5+(t/480.0))	[ 1, 529, 9507, 29958, 557, 15943, 29914, 3417, 29886, 16344, 1631, 29914, 22311, 29902, 29918, 29902, 29906, 29907, 29918, 29931, 7024, 29906, 29945, 29950, 29889, 2272, 13, 29937, 5215, 286, 336, 29874, 13, 13, 5215, 1560, 8262, 13, 5215, 931, 13, 13, 29902, 29906, 29907, 29918, 3035, 8353, 353, 29871, 29900, 29916, 29945, 29907, 13, 13, 1990, 319, 29968, 29902, 29918, 29902, 29906, 29907, 29918, 29931, 7024, 29906, 29945, 29950, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 1596, 376, 22311, 29902, 29918, 29902, 29906, 29907, 29918, 29931, 7024, 29906, 29945, 29950, 29908, 13, 4706, 1583, 29889, 29875, 29906, 29883, 353, 1560, 8262, 29889, 29903, 9486, 375, 29898, 29896, 29897, 13, 1678, 822, 474, 29906, 29883, 4597, 29898, 1311, 29892, 15866, 29892, 10030, 29922, 29900, 29916, 29900, 29900, 29892, 1272, 29922, 29900, 29916, 29900, 29900, 1125, 13, 4706, 1018, 29901, 13, 9651, 565, 29898, 15866, 1275, 376, 29893, 29908, 1125, 13, 18884, 736, 1583, 29889, 29875, 29906, 29883, 29889, 3539, 29918, 10389, 29918, 1272, 29898, 29902, 29906, 29907, 29918, 3035, 8353, 29892, 10030, 29892, 1272, 29897, 13, 9651, 25342, 29898, 15866, 1275, 376, 29878, 29908, 1125, 13, 18884, 736, 1583, 29889, 29875, 29906, 29883, 29889, 949, 29918, 10389, 29918, 1272, 29898, 29902, 29906, 29907, 29918, 3035, 8353, 29892, 10030, 29897, 13, 9651, 1683, 584, 13, 18884, 736, 448, 29896, 13, 4706, 5174, 10663, 2392, 29892, 4589, 29901, 13, 9651, 1596, 376, 3782, 319, 7077, 3850, 13, 9651, 931, 29889, 17059, 29898, 29900, 29889, 29896, 29897, 13, 9651, 1583, 29889, 29875, 29906, 29883, 4597, 29898, 15866, 29892, 10030, 29892, 1272, 29897, 13, 1678, 822, 399, 8187, 29918, 5194, 29918, 29902, 29898, 1311, 1125, 13, 4706, 396, 376, 29956, 8187, 29918, 5194, 29918, 29902, 29908, 13, 4706, 736, 1583, 29889, 29875, 29906, 29883, 4597, 703, 29878, 613, 29900, 29916, 29900, 29943, 29897, 13, 13, 1678, 822, 10886, 29898, 1311, 1125, 13, 4706, 736, 1583, 29889, 29875, 29906, 29883, 4597, 703, 29893, 613, 29900, 29916, 29906, 29900, 29892, 29900, 29916, 29929, 29900, 29897, 29871, 13, 29871, 13, 1678, 822, 5254, 29898, 1311, 1125, 13, 4706, 282, 353, 29900, 13, 4706, 282, 353, 282, 891, 1583, 29889, 29875, 29906, 29883, 4597, 703, 29878, 613, 29900, 29916, 29906, 29947, 29897, 3532, 29900, 13, 4706, 282, 353, 282, 891, 1583, 29889, 29875, 29906, 29883, 4597, 703, 29878, 613, 29900, 29916, 29906, 29929, 29897, 3532, 29947, 13, 4706, 282, 353, 282, 891, 1583, 29889, 29875, 29906, 29883, 4597, 703, 29878, 613, 29900, 29916, 29906, 29909, 29897, 3532, 29896, 29953, 13, 4706, 286, 1646, 353, 282, 29914, 29946, 29900, 29929, 29953, 13, 4706, 736, 286, 1646, 13, 13, 1678, 822, 21121, 313, 1311, 1125, 13, 4706, 260, 353, 29871, 29900, 13, 4706, 260, 353, 260, 891, 1583, 29889, 29875, 29906, 29883, 4597, 703, 29878, 613, 29900, 29916, 29906, 29933, 29897, 3532, 29900, 13, 4706, 260, 353, 260, 891, 1583, 29889, 29875, 29906, 29883, 4597, 703, 29878, 613, 29900, 29916, 29906, 29907, 29897, 3532, 29947, 13, 4706, 260, 353, 448, 3552, 29873, 29899, 29896, 4887, 29900, 29916, 17156, 29897, 13, 4706, 736, 313, 29946, 29906, 29889, 29945, 17108, 29873, 29914, 29946, 29947, 29900, 29889, 29900, 876, 2 ]
setup.py	MagnaXSoftware/popo	0	146881	<filename>setup.py<gh_stars>0 from setuptools import setup, find_packages setup( name="popo", version="0.0.1", url="https://github.com/MagnaXSoftware/popo", license="MIT", author="<NAME>", author_email="<EMAIL>", description="POPO gives you a simple, small, clean ORM with Plain Old Python Objects.", packages=find_packages(), classifiers=[ "Development Status :: 4 - Beta", "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Topic :: Database", ], )	[ 1, 529, 9507, 29958, 14669, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 731, 21245, 8789, 1053, 6230, 29892, 1284, 29918, 8318, 13, 13, 14669, 29898, 13, 1678, 1024, 543, 7323, 29877, 613, 13, 1678, 1873, 543, 29900, 29889, 29900, 29889, 29896, 613, 13, 1678, 3142, 543, 991, 597, 3292, 29889, 510, 29914, 29924, 15713, 29990, 6295, 14093, 29914, 7323, 29877, 613, 13, 1678, 19405, 543, 26349, 613, 13, 1678, 4148, 543, 29966, 5813, 28341, 13, 1678, 4148, 29918, 5269, 543, 29966, 26862, 6227, 28341, 13, 1678, 6139, 543, 29925, 4590, 29949, 4076, 366, 263, 2560, 29892, 2319, 29892, 5941, 6323, 29924, 411, 349, 7420, 8198, 5132, 4669, 29879, 19602, 13, 1678, 9741, 29922, 2886, 29918, 8318, 3285, 13, 1678, 770, 14903, 11759, 13, 4706, 376, 21956, 358, 16034, 4761, 29871, 29946, 448, 350, 1187, 613, 13, 4706, 376, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 613, 13, 4706, 376, 29931, 293, 1947, 4761, 438, 5425, 28268, 1490, 4761, 341, 1806, 19245, 613, 13, 4706, 376, 7094, 1218, 2184, 4761, 6570, 25266, 613, 13, 4706, 376, 7031, 293, 4761, 5470, 613, 13, 1678, 21251, 13, 29897, 13, 2 ]
et_micc2/templates/app-single-command/{{tmpl.project_name}}/{{tmpl.package_name}}/{{tmpl.cli_app_name}}.py	etijskens/et-micc2	0	184283	<filename>et_micc2/templates/app-single-command/{{tmpl.project_name}}/{{tmpl.package_name}}/{{tmpl.cli_app_name}}.py # -- coding: utf-8 -- """Command line interface {{tmpl.app_name}} (no sub-commands).""" import sys import click @click.command() @click.option('-v', '--verbosity', count=True , help="The verbosity of the program." , default=1 ) def main(verbosity): """Command line interface {{tmpl.app_name}}. A 'hello' world CLI example. """ for _ in range(verbosity): click.echo("running {{tmpl.app_name}}") if __name__ == "__main__": sys.exit(main()) # pragma: no cover #eodf	[ 1, 529, 9507, 29958, 300, 29918, 13076, 29883, 29906, 29914, 20943, 29914, 932, 29899, 14369, 29899, 6519, 29914, 6224, 18276, 572, 29889, 4836, 29918, 978, 930, 29914, 6224, 18276, 572, 29889, 5113, 29918, 978, 930, 29914, 6224, 18276, 572, 29889, 11303, 29918, 932, 29918, 978, 27243, 2272, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 15945, 29908, 6255, 1196, 5067, 8620, 18276, 572, 29889, 932, 29918, 978, 930, 313, 1217, 1014, 29899, 26381, 467, 15945, 29908, 13, 13, 5215, 10876, 13, 13, 5215, 2828, 13, 13, 13, 29992, 3808, 29889, 6519, 580, 13, 29992, 3808, 29889, 3385, 877, 29899, 29894, 742, 525, 489, 18248, 359, 537, 742, 2302, 29922, 5574, 13, 632, 1919, 1371, 543, 1576, 9750, 359, 537, 310, 278, 1824, 1213, 13, 632, 1919, 2322, 29922, 29896, 13, 632, 1723, 13, 1753, 1667, 29898, 18248, 359, 537, 1125, 13, 1678, 9995, 6255, 1196, 5067, 8620, 18276, 572, 29889, 932, 29918, 978, 27243, 13, 268, 13, 1678, 319, 525, 12199, 29915, 3186, 24492, 1342, 29889, 13, 1678, 9995, 13, 268, 13, 1678, 363, 903, 297, 3464, 29898, 18248, 359, 537, 1125, 13, 4706, 2828, 29889, 8057, 703, 21094, 8620, 18276, 572, 29889, 932, 29918, 978, 930, 1159, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 10876, 29889, 13322, 29898, 3396, 3101, 29871, 396, 282, 23929, 29901, 694, 4612, 13, 29937, 29872, 397, 29888, 13, 2 ]
pkg/unix/Python27/Lib/site-packages/klei/textureconverter.py	hexgear-studio/ds_mod_tools	112	199156	import sys, os, tempfile, shutil from PIL import Image import util import subprocess ''' TOOL_PATH = os.path.join( os.path.dirname( __file__ ), "../../../../../../tools/bin" ) TEXTURE_CONVERTER = os.path.abspath( os.path.join( TOOL_PATH, "TextureConverter.exe" ) ) def Convert( src_filenames, dest_filename, texture_format="bc3", no_premultiply=False, force=False, platform='opengl', generate_mips=False, width=None, height=None, verbose=False, ignore_exceptions=False): is_newer = False # If a list is passed in, concatenate the filenames with semi-colon separators, otherwise just use the filename src_filename_str = None if isinstance( src_filenames, list ): src_filename_str = ';'.join( src_filenames ) for filename in src_filenames: is_newer = is_newer or util.IsFileNewer( filename, dest_filename ) else: src_filename_str = src_filenames is_newer = is_newer or util.IsFileNewer( src_filename_str, dest_filename ) if force or is_newer: cmd_list = [ TEXTURE_CONVERTER, '--swizzle', '--format ' + texture_format, '--platform ' + platform, '-i ' + src_filename_str, '-o ' + dest_filename, ] if generate_mips: cmd_list.append( '--mipmap' ) if not no_premultiply: cmd_list.append( '--premultiply' ) if width: cmd_list.append( '-w {}'.format( width ) ) if height: cmd_list.append( '-h {}'.format( height ) ) cmd = " ".join( cmd_list ) if verbose: print( cmd ) if subprocess.call( cmd_list ) != 0: sys.stderr.write( "Error attempting to convert {} to {}\n".format( src_filenames, dest_filename ) ) sys.stderr.write( cmd + "\n" ) if not ignore_exceptions: raise ''' KTECH_LOCATION = "ktech" texture_format_translation = { 'bc1': 'dxt1', 'bc2': 'dxt3', 'bc3': 'dxt5', 'rgb': 'rgb', 'argb': 'rgba', } def Convert( src_filenames, dest_filename, texture_format="bc3", no_premultiply=False, force=False, platform='opengl', generate_mips=False, width=None, height=None, verbose=False, ignore_exceptions=False): is_newer = False assert texture_format_translation[texture_format], "Invalid texture format {}".format(texture_format) texture_format = texture_format_translation[texture_format] # ktech has multiplatform texture support as a stub, but disabled since only opengl is used. # (the support was built in, but the option to define in as a program parameter was left commented out) assert platform == "opengl", "Invalid platform {}".format(platform) # If a list is passed in, concatenate the filenames with semi-colon separators, otherwise just use the filename src_filename_str = None if isinstance( src_filenames, list ): src_filename_str = ','.join( src_filenames ) for filename in src_filenames: is_newer = is_newer or util.IsFileNewer( filename, dest_filename ) else: src_filename_str = src_filenames is_newer = is_newer or util.IsFileNewer( src_filename_str, dest_filename ) if force or is_newer: cmd_list = [ KTECH_LOCATION, '--quiet', '--compression ' + texture_format, ] if not generate_mips: cmd_list.append( '--no-mipmaps' ) if no_premultiply: cmd_list.append( '--no-premultiply' ) if width: cmd_list.append( '--width {}'.format( width ) ) if height: cmd_list.append( '--height {}'.format( height ) ) cmd_list.append( '--' ) cmd_list.append( src_filename_str ) cmd_list.append( dest_filename ) cmd = " ".join( cmd_list ) if verbose: print( cmd ) if subprocess.call( cmd_list ) != 0: sys.stderr.write( "Error attempting to convert {} to {}\n".format( src_filenames, dest_filename ) ) sys.stderr.write( cmd + "\n" ) if not ignore_exceptions: raise def GenerateMips( im ): mips = [] w, h = im.size while w >= 1 or h >= 1: mips.append( im ) w /= 2 h /= 2 im = im.resize( ( max( w, 1 ), max( h, 1 ) ), Image.ANTIALIAS ) return mips def SaveImagesToTemp( images, basename ): tempdir = tempfile.mkdtemp() idx = 0 filenames = [] for image in images: name = "{0}{1}.png".format( basename, idx ) filename = os.path.join( tempdir, name ) filenames.append( filename ) image.save( filename ) idx += 1 return ( tempdir, filenames ) def MipAndConvert( im, dest_filename, platform='opengl', texture_format="bc3", no_premultiply = False, force=False, ignore_exceptions=False ): if isinstance( im, str ): im = Image.open( im ) mips = GenerateMips( im ) tempdir, filenames = SaveImagesToTemp( mips, "mip" ) try: Convert( src_filenames=filenames, dest_filename=dest_filename, texture_format=texture_format, platform=platform, no_premultiply=no_premultiply, force=force, ignore_exceptions=ignore_exceptions ) finally: if os.path.exists( tempdir ): shutil.rmtree( tempdir )	[ 1, 1053, 10876, 29892, 2897, 29892, 5694, 1445, 29892, 528, 4422, 30004, 13, 3166, 349, 6227, 1053, 7084, 30004, 13, 5215, 3667, 30004, 13, 5215, 1014, 5014, 30004, 13, 30004, 13, 12008, 30004, 13, 4986, 5607, 29918, 10145, 353, 2897, 29889, 2084, 29889, 7122, 29898, 2897, 29889, 2084, 29889, 25721, 29898, 4770, 1445, 1649, 10353, 376, 21546, 21546, 21546, 8504, 29914, 2109, 29908, 1723, 30004, 13, 16975, 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lib/coginvasion/hood/TTSafeZoneLoader.py	theclashingfritz/Cog-Invasion-Online-Dump	1	1614361	# uncompyle6 version 3.2.4 # Python bytecode 2.7 (62211) # Decompiled from: Python 2.7.15 (v2.7.15:ca079a3ea3, Apr 30 2018, 16:30:26) [MSC v.1500 64 bit (AMD64)] # Embedded file name: lib.coginvasion.hood.TTSafeZoneLoader from lib.coginvasion.holiday.HolidayManager import HolidayType import SafeZoneLoader, TTPlayground from lib.coginvasion.globals import CIGlobals class TTSafeZoneLoader(SafeZoneLoader.SafeZoneLoader): def __init__(self, hood, parentFSM, doneEvent): SafeZoneLoader.SafeZoneLoader.__init__(self, hood, parentFSM, doneEvent) self.playground = TTPlayground.TTPlayground self.pgMusicFilename = 'phase_4/audio/bgm/TC_nbrhood.mid' self.interiorMusicFilename = 'phase_3.5/audio/bgm/TC_SZ_activity.mid' self.battleMusicFile = 'phase_3.5/audio/bgm/encntr_general_bg.mid' self.invasionMusicFiles = [ 'phase_12/audio/bgm/BossBot_CEO_v1.mid', 'phase_9/audio/bgm/encntr_suit_winning.mid'] self.tournamentMusicFiles = [ 'phase_3.5/audio/bgm/encntr_nfsmw_bg_1.ogg', 'phase_3.5/audio/bgm/encntr_nfsmw_bg_2.ogg', 'phase_3.5/audio/bgm/encntr_nfsmw_bg_3.ogg', 'phase_3.5/audio/bgm/encntr_nfsmw_bg_4.ogg'] self.bossBattleMusicFile = 'phase_7/audio/bgm/encntr_suit_winning_indoor.mid' self.dnaFile = 'phase_4/dna/new_ttc_sz.pdna' self.szStorageDNAFile = 'phase_4/dna/storage_TT_sz.pdna' self.szHolidayDNAFile = None if base.cr.holidayManager.getHoliday() == HolidayType.CHRISTMAS: self.szHolidayDNAFile = 'phase_4/dna/winter_storage_TT_sz.pdna' self.telescope = None self.birdNoises = [ 'phase_4/audio/sfx/SZ_TC_bird1.ogg', 'phase_4/audio/sfx/SZ_TC_bird2.ogg', 'phase_4/audio/sfx/SZ_TC_bird3.ogg'] return def load(self): SafeZoneLoader.SafeZoneLoader.load(self) self.geom.find('/toontown_central_beta_DNARoot').setTwoSided(1) self.geom.find('/ground_center').setBin('ground', 18) self.geom.find('/ground_sidewalk').setBin('ground', 18) self.geom.find('/ground').setBin('ground', 18) self.geom.find('/ground_center_coll').setCollideMask(CIGlobals.FloorBitmask) self.geom.find('/ground_sidewalk_coll').setCollideMask(CIGlobals.FloorBitmask) for tunnel in self.geom.findAllMatches('*/linktunnel_tt'): tunnel.find('/tunnel_floor_1').setTexture(loader.loadTexture('phase_4/models/neighborhoods/tex/sidewalkbrown.jpg'), 1) for tree in self.geom.findAllMatches('/prop_green_tree_*_DNARoot'): newShadow = loader.loadModel('phase_3/models/props/drop_shadow.bam') newShadow.reparentTo(tree) newShadow.setScale(1.5) newShadow.setColor(0, 0, 0, 0.5, 1)	[ 1, 396, 443, 2388, 1508, 29953, 1873, 29871, 29941, 29889, 29906, 29889, 29946, 13, 29937, 5132, 7023, 401, 29871, 29906, 29889, 29955, 313, 29953, 29906, 29906, 29896, 29896, 29897, 13, 29937, 897, 2388, 2356, 515, 29901, 5132, 29871, 29906, 29889, 29955, 29889, 29896, 29945, 313, 29894, 29906, 29889, 29955, 29889, 29896, 29945, 29901, 1113, 29900, 29955, 29929, 29874, 29941, 11248, 29941, 29892, 319, 558, 29871, 29941, 29900, 29871, 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util/__init__.py	TinDang97/face_recognition	6	64123	<gh_stars>1-10 import cv2 import numpy def normalize_L2(x): assert isinstance(x, numpy.ndarray) return x / numpy.sqrt(numpy.sum((x ** 2), keepdims=True, axis=1)) def cosine_similarity(x1, x2, skip_normalize=False): if type(x1) is list: x1 = numpy.array(x1) if type(x2) is list: x2 = numpy.array(x2) assert type(x1) is numpy.ndarray or type(x2) is numpy.ndarray assert x1.shape[-1] == x2.shape[-1] assert len(x1.shape) == 2 if not skip_normalize: x1 = normalize_L2(x1) x2 = normalize_L2(x2) return numpy.dot(x1, x2.T) def draw_square(img, position, color=(0, 255, 0)) -> numpy.ndarray: """ Draw text at position in image. - position: top-left, bottom-right of square - color: support tuple and hex_color :return: """ if not isinstance(position, tuple): position = tuple(position) return cv2.rectangle(img, position[0:2], position[2:4], color, 2) def show_image(img, windows_name, windows_size=(1280, 720), windows_mode=cv2.WINDOW_NORMAL, wait_time=1, key_press_exit="q"): """ Show image in RGB format Parameters ---------- img: numpy.ndarray image array windows_name: str Title of window windows_size: tuple[int, int] (Default: SD_RESOLUTION) size of window windows_mode: int (Default: cv2.WINDOW_NORMAL) Mode of window wait_time: int Block time. (-1: infinite) key_press_exit: str Key stop event. 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scout/server/blueprints/login/views.py	szilvajuhos/scout	0	1610778	# -- coding: utf-8 -- from datetime import datetime from flask import (abort, current_app, Blueprint, flash, redirect, request, session, url_for, render_template) from flask_login import login_user, logout_user from flask_oauthlib.client import OAuthException from flask_ldap3_login.forms import LDAPLoginForm from flask_ldap3_login import AuthenticationResponseStatus from scout.server.extensions import google, login_manager, store, ldap_manager from scout.server.utils import public_endpoint from . import controllers from .models import LoginUser, LdapUser import logging LOG = logging.getLogger(__name__) login_bp = Blueprint('login', __name__, template_folder='templates', static_folder='static', static_url_path='/login/static') login_manager.login_view = 'login.login' login_manager.login_message = 'Please log in to access this page.' login_manager.login_message_category = 'info' ldap_users = {} # used by ldap save_user @login_manager.user_loader def load_user(user_id): """Returns the currently active user as an object.""" user_obj = store.user(user_id) user_inst = LoginUser(user_obj) if user_obj else None return user_inst @google.tokengetter def get_google_token(): """Returns a tuple of Google tokens, if they exist""" return session.get('google_token') @login_bp.route('/login', methods=['GET', 'POST']) @public_endpoint def login(): """Login a user if they have access.""" if 'next' in request.args: session['next_url'] = request.args['next'] user_id = None if current_app.config.get('LDAP_HOST') and request.method=='POST': form = LDAPLoginForm() LOG.info('Validating LDAP user') if form.validate_on_submit(): user_id = form.username.data else: flash("username-password combination is not valid, plase try again", "warning") return redirect(url_for('public.index')) if current_app.config.get('GOOGLE'): if session.get('email'): user_id = session['email'] session.pop('email') else: LOG.info('Validating Google user login') callback_url = url_for('.authorized', _external=True) return google.authorize(callback=callback_url) if request.args.get('email'): # log in against Scout database user_id = request.args.get('email') LOG.info('Validating user {} against Scout database'.format(user_id)) user_obj = store.user(user_id) if user_obj is None: flash("User not whitelisted", 'warning') return redirect(url_for('public.index')) user_obj['accessed_at'] = datetime.now() if session.get('name'): # These args come from google auth user_obj['name'] = session.get('name') user_obj['locale'] = session.get('locale') store.update_user(user_obj) user_dict = LoginUser(user_obj) return perform_login(user_dict) @login_bp.route('/logout') def logout(): logout_user() flash('you logged out', 'success') return redirect(url_for('public.index')) @login_bp.route('/authorized') @public_endpoint def authorized(): oauth_response = None try: oauth_response = google.authorized_response() except OAuthException as error: current_app.logger.warning(oauth_response.message) flash("{} - try again!".format(oauth_response.message), 'warning') return redirect(url_for('public.index')) if oauth_response is None: flash("Access denied: reason={} error={}" .format(request.args.get['error_reason'], request.args['error_description']), 'danger') return abort(403) # add token to session, do it before validation to be able to fetch # additional data (like email) on the authenticated user session['google_token'] = (oauth_response['access_token'], '') # get additional user info with the access token google_user = google.get('userinfo') google_data = google_user.data session['email'] = google_data['email'].lower() session['name'] = google_data['name'] session['locale'] = google_data['locale'] return redirect(url_for('login.login')) @login_bp.route('/users') def users(): """Show all users in the system.""" data = controllers.users(store) return render_template('login/users.html', **data) def perform_login(user_dict): if 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feature/functions/feature_selection.py	manaranjoc/ResonanceImageClassification	0	182297	import numpy as np from mlxtend.feature_selection import SequentialFeatureSelector as SFS from sklearn.model_selection import KFold from sklearn.feature_selection import SelectPercentile, mutual_info_classif import sys from pathlib import Path sys.path[0] = str(Path(sys.path[0]).parent) from metrics import metrics, meanMetrics, stdMetrics, printMetrics import time def select_features(classifier, n_features, fwd, fltg): sfs = SFS(classifier, k_features=n_features, forward=fwd, floating=fltg, verbose=1, scoring='accuracy', cv=10, n_jobs=-1) return sfs def select_features_number(classifier, number_features, fwd, fltg, X, Y): tiempo_i = time.time() Errores = np.ones(10) Metrics = np.zeros((10,5)) j = 0 kf = KFold(n_splits=10) clf = classifier sf = select_features(clf, number_features, fwd, fltg) sf = sf.fit(X, Y) X_sf = sf.transform(X) for train_index, test_index in kf.split(X_sf): X_train, X_test = X_sf[train_index], X_sf[test_index] y_train, y_test = Y[train_index], Y[test_index] classifier.fit(X_train, y_train) y_pred = classifier.predict(X_test) Errores[j] = 1-metrics(y_test,y_pred)[0] Metrics[j,:] = metrics(y_test, y_pred) j+=1 print("\nError de validación aplicando SFS: "+str(np.mean(Errores))+"+/-"+str(np.std(Errores))) print("\nEficiencia en validación aplicando SFS: "+str((1-np.mean(Errores))100)+"%") print("\nTiempo total de ejecución: "+str(time.time()-tiempo_i)+" segundos.") MetricsMean = meanMetrics(Metrics) MetricsStd = stdMetrics(Metrics) printMetrics(MetricsMean) print("\nDesviaciones Estandard") printMetrics(MetricsStd) return sf def select_features_filter_percentage(classifier, percentage, X, Y): tiempo_i = time.time() Errores = np.ones(10) Metrics = np.zeros((10,5)) j = 0 kf = KFold(n_splits=10) filter_method = SelectPercentile(mutual_info_classif, percentile=percentage) filter_method.fit(X,Y) X_sf = filter_method.transform(X) for train_index, test_index in kf.split(X_sf): X_train, X_test = X_sf[train_index], X_sf[test_index] y_train, y_test = Y[train_index], Y[test_index] classifier.fit(X_train, y_train) y_pred = classifier.predict(X_test) Metrics[j,:] = metrics(y_test, y_pred) Errores[j] = 1-metrics(y_test,y_pred)[0] j+=1 print("\nError de validación aplicando at "+str(percentage)+"%: "+str(np.mean(Errores))+"+/-"+str(np.std(Errores))) print("\nEficiencia en validación aplicando at "+str(percentage)+"%: "+str((1-np.mean(Errores))100)+"%") print("\nTiempo total de ejecución: "+str(time.time()-tiempo_i)+" segundos.") MetricsMean = meanMetrics(Metrics) MetricsStd = stdMetrics(Metrics) printMetrics(MetricsMean) print("\nDesviaciones Estandard") printMetrics(MetricsStd) return filter_method	[ 1, 1053, 12655, 408, 7442, 13, 3166, 286, 29880, 486, 355, 29889, 14394, 29918, 21731, 1053, 922, 339, 2556, 19132, 10378, 408, 317, 9998, 13, 3166, 2071, 19668, 29889, 4299, 29918, 21731, 1053, 476, 29943, 1025, 13, 3166, 2071, 19668, 29889, 14394, 29918, 21731, 1053, 7605, 27933, 488, 29892, 5478, 950, 29918, 3888, 29918, 1990, 361, 13, 13, 5215, 10876, 13, 3166, 2224, 1982, 1053, 10802, 13, 9675, 29889, 2084, 29961, 29900, 29962, 353, 851, 29898, 2605, 29898, 9675, 29889, 2084, 29961, 29900, 14664, 3560, 29897, 13, 13, 3166, 21556, 1053, 21556, 29892, 2099, 10095, 10817, 29892, 3659, 10095, 10817, 29892, 1596, 10095, 10817, 13, 13, 5215, 931, 13, 13, 1753, 1831, 29918, 22100, 29898, 1990, 3709, 29892, 302, 29918, 22100, 29892, 285, 9970, 29892, 1652, 29873, 29887, 1125, 13, 13, 1678, 269, 5847, 353, 317, 9998, 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Bin/RepoTools/Scripts/GithubTools/github_preparation.py	stinvi/dava.engine	26	170170	#!/usr/bin/env python import sys import os import argparse import subprocess class Preparation: system_list = set(['.gitattributes', '.gitignore', '.gitmodules']) white_list = set() clear_list = set(['Bin/RepoTools/Scripts/GithubTools/white_list.txt', 'Bin/RepoTools/Scripts/GithubTools/github_preparation.py']) def __init__(self, base_path): self.base_path = os.path.abspath(base_path) self.read_white_list() self.read_white_list() self.do() def read_white_list(self): try: files_list = open(os.path.abspath(self.base_path + '/Bin/RepoTools/Scripts/GithubTools/white_list.txt')) for line in files_list: full_path = os.path.abspath(self.base_path + line.partition('#')[0].replace('\n', '')).replace("!", "") path = line.partition('#')[0].replace('\n', '').replace("!", "")[1:] if "!" in line: if path: self.clear_list.add(path) else: if os.path.exists(path): if path != self.base_path: self.white_list.add(path) except IOError: print "##teamcity[message text='Unable to open file white_list.txt' status='ERROR']" sys.exit(3) def execute_cmd(self, cmd): sys.stdout.write(cmd + '\n') sys.stdout.flush() try: pass cmd_log = subprocess.check_output(cmd, shell=True, cwd=self.base_path) sys.stdout.write(cmd_log) sys.stdout.flush() except subprocess.CalledProcessError as cmd_except: print "##teamcity[message text='CMD Error' errorDetails='CMD: {cmd} ERROR: {error}' status='ERROR']".format(cmd=cmd, error=cmd_except.output) sys.exit(3) def do(self): os.chdir(self.base_path) for path in self.system_list: cmd = "git add {path}".format(path=path) self.execute_cmd(cmd) for path in self.white_list: cmd = "git add -f {path}".format(path=path) self.execute_cmd(cmd) for path in self.clear_list: cmd = "git reset {path}".format(path=path) self.execute_cmd(cmd) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Utility add files to the repository from the whitelist.') parser.add_argument('--path', required=True, help='Example: --path /var/git/repo') args = parser.parse_args() preparation = Preparation(args.path)	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 5215, 10876, 13, 5215, 2897, 13, 5215, 1852, 5510, 13, 5215, 1014, 5014, 13, 13, 13, 1990, 4721, 862, 362, 29901, 13, 1678, 1788, 29918, 1761, 353, 731, 18959, 29889, 5559, 15697, 742, 15300, 5559, 17281, 742, 15300, 5559, 7576, 11287, 13, 1678, 4796, 29918, 1761, 353, 731, 580, 13, 1678, 2821, 29918, 1761, 353, 731, 18959, 29933, 262, 29914, 5612, 29877, 24183, 29914, 4081, 29879, 29914, 29954, 2985, 24183, 29914, 10921, 29918, 1761, 29889, 3945, 742, 525, 29933, 262, 29914, 5612, 29877, 24183, 29914, 4081, 29879, 29914, 29954, 2985, 24183, 29914, 3292, 29918, 1457, 862, 362, 29889, 2272, 11287, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 2967, 29918, 2084, 1125, 13, 4706, 1583, 29889, 3188, 29918, 2084, 353, 2897, 29889, 2084, 29889, 370, 1028, 493, 29898, 3188, 29918, 2084, 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packages/pyright-internal/src/tests/samples/paramInference1.py	Jasha10/pyright	3,934	143288	# This sample tests the logic that infers parameter types based on # default argument values or annotated base class methods. class Parent: def func1(self, a: int, b: str) -> float: ... class Child(Parent): def func1(self, a, b): reveal_type(self, expected_text="Self@Child") reveal_type(a, expected_text="int") reveal_type(b, expected_text="str") return a def func2(a, b=0, c=None): reveal_type(a, expected_text="Unknown") reveal_type(b, expected_text="int") reveal_type(c, expected_text="Unknown \| None") def func3(a=(1, 2), b=[1,2], c={1: 2}): reveal_type(a, expected_text="Unknown") reveal_type(b, expected_text="Unknown") reveal_type(c, expected_text="Unknown")	[ 1, 396, 910, 4559, 6987, 278, 5900, 393, 3041, 414, 3443, 4072, 2729, 373, 13, 29937, 2322, 2980, 1819, 470, 9732, 630, 2967, 770, 3519, 29889, 13, 13, 13, 1990, 22280, 29901, 13, 1678, 822, 3653, 29896, 29898, 1311, 29892, 263, 29901, 938, 29892, 289, 29901, 851, 29897, 1599, 5785, 29901, 13, 4706, 2023, 13, 13, 13, 1990, 10517, 29898, 9780, 1125, 13, 1678, 822, 3653, 29896, 29898, 1311, 29892, 263, 29892, 289, 1125, 13, 4706, 10320, 284, 29918, 1853, 29898, 1311, 29892, 3806, 29918, 726, 543, 24313, 29992, 5938, 1159, 13, 4706, 10320, 284, 29918, 1853, 29898, 29874, 29892, 3806, 29918, 726, 543, 524, 1159, 13, 4706, 10320, 284, 29918, 1853, 29898, 29890, 29892, 3806, 29918, 726, 543, 710, 1159, 13, 4706, 736, 263, 13, 13, 13, 1753, 3653, 29906, 29898, 29874, 29892, 289, 29922, 29900, 29892, 274, 29922, 8516, 1125, 13, 1678, 10320, 284, 29918, 1853, 29898, 29874, 29892, 3806, 29918, 726, 543, 14148, 1159, 13, 1678, 10320, 284, 29918, 1853, 29898, 29890, 29892, 3806, 29918, 726, 543, 524, 1159, 13, 1678, 10320, 284, 29918, 1853, 29898, 29883, 29892, 3806, 29918, 726, 543, 14148, 891, 6213, 1159, 13, 13, 13, 1753, 3653, 29941, 29898, 29874, 7607, 29896, 29892, 29871, 29906, 511, 289, 11759, 29896, 29892, 29906, 1402, 274, 3790, 29896, 29901, 29871, 29906, 29913, 1125, 13, 1678, 10320, 284, 29918, 1853, 29898, 29874, 29892, 3806, 29918, 726, 543, 14148, 1159, 13, 1678, 10320, 284, 29918, 1853, 29898, 29890, 29892, 3806, 29918, 726, 543, 14148, 1159, 13, 1678, 10320, 284, 29918, 1853, 29898, 29883, 29892, 3806, 29918, 726, 543, 14148, 1159, 13, 2 ]
tests/integration/RoyaltiesPayment/test_withdrawals.py	MiracleCorunus/nft_royalties_market	37	179164	<reponame>MiracleCorunus/nft_royalties_market<gh_stars>10-100 import brownie from brownie.test import strategy import math class StateMachine: value = strategy("uint256", min_value=1, max_value="100 ether") withdrawal = strategy("uint256", min_value=1, max_value="20 ether") address = strategy("address", length=5) def __init__(cls, accounts, RoyaltiesPayment): cls.accounts = accounts cls.contract = RoyaltiesPayment.deploy(accounts[1:], {"from": accounts[0]}) def setup(self): self.balance = 0 self.payouts = 0 self.total_deposits = 0 self.user_balances = {account: 0 for account in self.accounts[1:]} def rule_add_user(self, address): if self.contract.balances(address)["userIndex"] == 0: self.contract.addPayee(address, {"from": self.accounts[0]}) self.user_balances = {account: 0 for account in self.accounts[1:]} self.user_balances[address] = 0 self.payouts += 1 def rule_deposit(self, value): self.accounts[0].transfer(to=self.contract, amount=value) self.balance += value self.total_deposits += value self.user_balances = { k: v + (value // len(self.user_balances)) for k, v in self.user_balances.items() } def rule_withdraw(self, address, withdrawal): if (address in self.user_balances) and self.user_balances[address] >= withdrawal: self.contract.withdraw(withdrawal, {"from": address}) self.user_balances[address] -= withdrawal self.balance -= withdrawal else: with brownie.reverts(): self.contract.withdraw(withdrawal, {"from": address}) def rule_withdraw_all(self, address): if (address in self.user_balances) and self.user_balances[address] > 0: self.contract.withdrawAll({"from": address}) self.balance -= self.user_balances[address] self.user_balances[address] = 0 else: with brownie.reverts(): self.contract.withdrawAll({"from": address}) def invariant(self): math.isclose(self.contract.balance(), self.balance, rel_tol=self.payouts) for user, amount in self.user_balances.items(): assert math.isclose( self.contract.balances(user)["balance"], amount, rel_tol=self.payouts ) assert amount <= (self.total_deposits // len(self.user_balances)) def test_withdrawals(state_machine, RoyaltiesPayment, accounts): state_machine( StateMachine, accounts, RoyaltiesPayment, settings={"max_examples": 30} )	[ 1, 529, 276, 1112, 420, 29958, 29924, 29875, 10792, 12521, 348, 375, 29914, 29876, 615, 29918, 4727, 1997, 583, 29918, 28549, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 5215, 3347, 2786, 13, 3166, 3347, 2786, 29889, 1688, 1053, 13705, 13, 5215, 5844, 13, 13, 13, 1990, 4306, 29076, 29901, 13, 13, 1678, 995, 353, 13705, 703, 13470, 29906, 29945, 29953, 613, 1375, 29918, 1767, 29922, 29896, 29892, 4236, 29918, 1767, 543, 29896, 29900, 29900, 29871, 1979, 1159, 13, 1678, 28679, 284, 353, 13705, 703, 13470, 29906, 29945, 29953, 613, 1375, 29918, 1767, 29922, 29896, 29892, 4236, 29918, 1767, 543, 29906, 29900, 29871, 1979, 1159, 13, 1678, 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hackathon/column1/problem_10_4_LCA.py	abdurahmanadilovic/elements-of-programming-interviews	0	84656	from collections import defaultdict class Node: def __init__(self, value): self.parent = None self.value = value def get_height(node): height = 0 while node is not None: height += 1 node = node.parent return height def solution2(node1, node2): height1, height2 = get_height(node1), get_height(node2) node_to_correct = node1 if height1 < height2: node_to_correct = node2 diff = abs(height1 - height2) while diff > 0: node_to_correct = node_to_correct.parent diff -= 1 if height1 < height2: node2 = node_to_correct else: node1 = node_to_correct while node1.parent != node2.parent: node1 = node1.parent node2 = node2.parent return node1.parent # less efficient, takes O(n) space def solution(node1, node2): visited = defaultdict(bool) while True: if node1 and node1.parent: if visited[node1.parent.value]: return node1.parent visited[node1.parent.value] = True if node2 and node2.parent: if visited[node2.parent.value]: return node2.parent visited[node2.parent.value] = True node1 = node1.parent node2 = node2.parent	[ 1, 515, 16250, 1053, 2322, 8977, 13, 13, 13, 1990, 9071, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 995, 1125, 13, 4706, 1583, 29889, 3560, 353, 6213, 13, 4706, 1583, 29889, 1767, 353, 995, 13, 13, 13, 1753, 679, 29918, 3545, 29898, 3177, 1125, 13, 1678, 3171, 353, 29871, 29900, 13, 13, 1678, 1550, 2943, 338, 451, 6213, 29901, 13, 4706, 3171, 4619, 29871, 29896, 13, 4706, 2943, 353, 2943, 29889, 3560, 13, 13, 1678, 736, 3171, 13, 13, 13, 1753, 1650, 29906, 29898, 3177, 29896, 29892, 2943, 29906, 1125, 13, 1678, 3171, 29896, 29892, 3171, 29906, 353, 679, 29918, 3545, 29898, 3177, 29896, 511, 679, 29918, 3545, 29898, 3177, 29906, 29897, 13, 13, 1678, 2943, 29918, 517, 29918, 15728, 353, 2943, 29896, 13, 13, 1678, 565, 3171, 29896, 529, 3171, 29906, 29901, 13, 4706, 2943, 29918, 517, 29918, 15728, 353, 2943, 29906, 13, 13, 1678, 2923, 353, 6425, 29898, 3545, 29896, 448, 3171, 29906, 29897, 13, 13, 1678, 1550, 2923, 1405, 29871, 29900, 29901, 13, 4706, 2943, 29918, 517, 29918, 15728, 353, 2943, 29918, 517, 29918, 15728, 29889, 3560, 13, 4706, 2923, 22361, 29871, 29896, 13, 13, 1678, 565, 3171, 29896, 529, 3171, 29906, 29901, 13, 4706, 2943, 29906, 353, 2943, 29918, 517, 29918, 15728, 13, 1678, 1683, 29901, 13, 4706, 2943, 29896, 353, 2943, 29918, 517, 29918, 15728, 13, 13, 1678, 1550, 2943, 29896, 29889, 3560, 2804, 2943, 29906, 29889, 3560, 29901, 13, 4706, 2943, 29896, 353, 2943, 29896, 29889, 3560, 13, 4706, 2943, 29906, 353, 2943, 29906, 29889, 3560, 13, 13, 1678, 736, 2943, 29896, 29889, 3560, 13, 13, 13, 29937, 3109, 8543, 29892, 4893, 438, 29898, 29876, 29897, 2913, 13, 1753, 1650, 29898, 3177, 29896, 29892, 2943, 29906, 1125, 13, 13, 1678, 16669, 353, 2322, 8977, 29898, 11227, 29897, 13, 13, 1678, 1550, 5852, 29901, 13, 4706, 565, 2943, 29896, 322, 2943, 29896, 29889, 3560, 29901, 13, 9651, 565, 16669, 29961, 3177, 29896, 29889, 3560, 29889, 1767, 5387, 13, 18884, 736, 2943, 29896, 29889, 3560, 13, 9651, 16669, 29961, 3177, 29896, 29889, 3560, 29889, 1767, 29962, 353, 5852, 13, 13, 4706, 565, 2943, 29906, 322, 2943, 29906, 29889, 3560, 29901, 13, 9651, 565, 16669, 29961, 3177, 29906, 29889, 3560, 29889, 1767, 5387, 13, 18884, 736, 2943, 29906, 29889, 3560, 13, 13, 9651, 16669, 29961, 3177, 29906, 29889, 3560, 29889, 1767, 29962, 353, 5852, 13, 13, 4706, 2943, 29896, 353, 2943, 29896, 29889, 3560, 13, 4706, 2943, 29906, 353, 2943, 29906, 29889, 3560, 13, 2 ]
train.py	HirataYurina/yoloV4-keras-techi	6	140196	# -- coding:utf-8 -- # author:平手友梨奈ii # e-mail:<EMAIL> # datetime:1993/12/01 # filename:configs.py # software: PyCharm import numpy as np import tensorflow as tf import keras.backend as K from keras.layers import Input, Lambda from keras.models import Model from keras.optimizers import Adam from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping from nets.yolo4 import yolo_body from nets.yolo4_loss import yolo_loss from keras.backend.tensorflow_backend import set_session from utils.utils import get_random_data, get_random_mosaic_data, get_random_mosaic_data_v2 from my_queue import GeneratorEnqueuer import time import math from cosine_anneal import WarmUpCosineDecayScheduler from config.configs import CONFIG def get_classes(classes_path): """loads the classes""" with open(classes_path) as f: class_names = f.readlines() # use list expression to make your code more concise class_names = [c.strip() for c in class_names] return class_names def get_anchors(anchors_path): """loads the anchors from a file""" with open(anchors_path) as f: anchors = f.readline() # use list expression to make your code more concise anchors = [float(x) for x in anchors.split(',')] return np.array(anchors).reshape(-1, 2) def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes): """data generator for fit_generator the assignment strategy: one gt ---> one anchor 1.find which anchor(9 anchors) gt belongs to 2.find which grid gt belongs to Args: annotation_lines: a list [anno1, anno2, ...] batch_size: batch size input_shape: resolution [h, w] anchors: anchor boxes num_classes: the number of class max_boxes: box_data: [max_boxes, 5] when have a lot of gt to predict, need to set max_boxes bigger. Returns: batch data: [image_data, y_true], np.zeros(batch_size) """ n = len(annotation_lines) i = 0 while True: image_data = [] box_data = [] for b in range(batch_size): if i == 0: # shuffle dataset at begin of epoch np.random.shuffle(annotation_lines) image, box = get_random_data(annotation_lines[i], input_shape) image_data.append(image) box_data.append(box) i = (i + 1) % n image_data = np.array(image_data) box_data = np.array(box_data) # get true_boxes # y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes) y_true = preprocess_true_boxes_iou_thres(box_data, input_shape, anchors, num_classes, iou_threshold=CONFIG.TRAIN.IOU_THRESHOLD) # use yield to get generator yield [image_data, y_true], np.zeros(batch_size) def data_generator_mosaic_iou_thres(annotation_lines, batch_size, input_shape, anchors, num_classes): """data generator for fit_generator the assignment strategy: one gt ---> more anchor(iou > iou_threshold) Args: annotation_lines: a list [anno1, anno2, ...] batch_size: batch size input_shape: resolution [h, w] anchors: anchor boxes num_classes: the number of class max_boxes: box_data: [max_boxes, 5] when have a lot of gt to predict, must set max_boxes bigger. iou_threshold: if iou > iou_threshold, the anchor is responsible for this gt. Returns: batch data: [image_data, y_true], np.zeros(batch_size) """ n = len(annotation_lines) shuffle_num = n // 4 i = 0 while True: image_data = [] box_data = [] for b in range(batch_size): if i == 0: # shuffle dataset at begin of epoch np.random.shuffle(annotation_lines) image, box = get_random_mosaic_data(annotation_lines[4 i:4 * i + 4], input_shape) image_data.append(image) box_data.append(box) i = (i + 1) % shuffle_num image_data = np.array(image_data) box_data = np.array(box_data) y_true = preprocess_true_boxes_iou_thres(box_data, input_shape, anchors, num_classes, iou_threshold=CONFIG.TRAIN.IOU_THRESHOLD) # use yield to get generator yield [image_data, y_true], np.zeros(batch_size) def preprocess_true_boxes(true_boxes, input_shape, anchors, num_classes): assert (true_boxes[..., 4] < num_classes).all(), 'class id must be less than num_classes' num_layers = len(anchors) // 3 anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]] if num_layers == 3 else [[3, 4, 5], [1, 2, 3]] true_boxes = np.array(true_boxes, dtype='float32') input_shape = np.array(input_shape, dtype='int32') # 416,416 boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2 boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2] true_boxes[..., 0:2] = boxes_xy / input_shape[:] true_boxes[..., 2:4] = boxes_wh / input_shape[:] m = true_boxes.shape[0] grid_shapes = [input_shape // {0: 32, 1: 16, 2: 8}[l] for l in range(num_layers)] # [(m, 13, 13, 3, 85), (m, 26, 26, 3, 85), (m, 52, 52, 3, 85)] y_true = [np.zeros((m, grid_shapes[l][0], grid_shapes[l][1], len(anchor_mask[l]), 5 + num_classes), dtype='float32') for l in range(num_layers)] # (1, 9, 2) anchors = np.expand_dims(anchors, 0) anchor_maxes = anchors / 2. anchor_mins = -anchor_maxes # filter invalid boxes valid_mask = boxes_wh[..., 0] > 0 for b in range(m): wh = boxes_wh[b, valid_mask[b]] if len(wh) == 0: continue # [n, 1, 2] wh = np.expand_dims(wh, -2) box_maxes = wh / 2. box_mins = -box_maxes # get iou intersect_mins = np.maximum(box_mins, anchor_mins) intersect_maxes = np.minimum(box_maxes, anchor_maxes) intersect_wh = np.maximum(intersect_maxes - intersect_mins, 0.) intersect_area = intersect_wh[..., 0] intersect_wh[..., 1] box_area = wh[..., 0] * wh[..., 1] anchor_area = anchors[..., 0] * anchors[..., 1] iou = intersect_area / (box_area + anchor_area - intersect_area) best_anchor = np.argmax(iou, axis=-1) for t, n in enumerate(best_anchor): for l in range(num_layers): if n in anchor_mask[l]: # assign gt to one grid i = np.floor(true_boxes[b, t, 0] * grid_shapes[l][1]).astype('int32') j = np.floor(true_boxes[b, t, 1] * grid_shapes[l][0]).astype('int32') # assign gt to one anchor k = anchor_mask[l].index(n) c = true_boxes[b, t, 4].astype('int32') y_true[l][b, j, i, k, 0:4] = true_boxes[b, t, 0:4] # score = 1 and get one hot class label y_true[l][b, j, i, k, 4] = 1 y_true[l][b, j, i, k, 5 + c] = 1 return y_true def preprocess_true_boxes_iou_thres(true_boxes, input_shape, anchors, num_classes, iou_threshold=0.3): """get true boxes with iou threshold""" assert (true_boxes[..., 4] < num_classes).all(), 'class id must be less than num_classes' num_layers = len(anchors) // 3 anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]] if num_layers == 3 else [[3, 4, 5], [1, 2, 3]] true_boxes = np.array(true_boxes, dtype='float32') input_shape = np.array(input_shape, dtype='int32') # 416,416 boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2 boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2] true_boxes[..., 0:2] = boxes_xy / input_shape[:] true_boxes[..., 2:4] = boxes_wh / input_shape[:] m = true_boxes.shape[0] grid_shapes = [input_shape // {0: 32, 1: 16, 2: 8}[l] for l in range(num_layers)] y_true = [np.zeros((m, grid_shapes[l][0], grid_shapes[l][1], len(anchor_mask[l]), 5 + num_classes), dtype='float32') for l in range(num_layers)] # [1, 9, 2] anchors = np.expand_dims(anchors, 0) anchor_maxes = anchors / 2. anchor_mins = -anchor_maxes # filter invalid boxes valid_mask = boxes_wh[..., 0] > 0 for b in range(m): wh = boxes_wh[b, valid_mask[b]] if len(wh) == 0: continue # [n, 1, 2] wh = np.expand_dims(wh, -2) box_maxes = wh / 2. box_mins = -box_maxes intersect_mins = np.maximum(box_mins, anchor_mins) intersect_maxes = np.minimum(box_maxes, anchor_maxes) intersect_wh = np.maximum(intersect_maxes - intersect_mins, 0.) intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1] box_area = wh[..., 0] * wh[..., 1] anchor_area = anchors[..., 0] * anchors[..., 1] iou = intersect_area / (box_area + anchor_area - intersect_area) # 1.iou > iou_threshold positive = iou > iou_threshold # [num_true_boxes, num_anchors] for t, n in enumerate(positive): n = np.array(n, dtype=np.int32) pos_index = np.argwhere(n == 1) if len(pos_index): continue for id in pos_index: id = id[0] for l in range(num_layers): if id in anchor_mask[l]: i = np.floor(true_boxes[b, t, 0] * grid_shapes[l][1]).astype('int32') j = np.floor(true_boxes[b, t, 1] * grid_shapes[l][0]).astype('int32') k = anchor_mask[l].index(id) c = true_boxes[b, t, 4].astype('int32') y_true[l][b, j, i, k, 0:4] = true_boxes[b, t, 0:4] y_true[l][b, j, i, k, 4] = 1 y_true[l][b, j, i, k, 5 + c] = 1 # 2.if no positive anchor, just choose the best one to be the positive. best_anchor = np.argmax(iou, axis=-1) for t, n in enumerate(best_anchor): for l in range(num_layers): if n in anchor_mask[l]: i = np.floor(true_boxes[b, t, 0] * grid_shapes[l][1]).astype('int32') j = np.floor(true_boxes[b, t, 1] * grid_shapes[l][0]).astype('int32') k = anchor_mask[l].index(n) c = true_boxes[b, t, 4].astype('int32') y_true[l][b, j, i, k, 0:4] = true_boxes[b, t, 0:4] y_true[l][b, j, i, k, 4] = 1 y_true[l][b, j, i, k, 5 + c] = 1 return y_true def get_batch(num_workers, max_queue_size=32, use_mosaic_iout_generator=CONFIG.DATASET.MOSAIC_AUG, multiprocessing=CONFIG.DATASET.MULTIPROCESS, kwargs): """ Args: num_workers: number of workers max_queue_size: max queue size multiprocessing: true in linux and false in windows use_mosaic_iout_generator: use mosaic_iou_thres_generator or not kwargs: args used in data generator """ enqueuer = None try: if use_mosaic_iout_generator: enqueuer = GeneratorEnqueuer(data_generator_mosaic_iou_thres(kwargs), use_multiprocessing=multiprocessing) else: enqueuer = GeneratorEnqueuer(data_generator(kwargs), use_multiprocessing=multiprocessing) enqueuer.start(max_queue_size=max_queue_size, workers=num_workers) generator_output = None while True: while enqueuer.is_running(): if not enqueuer.queue.empty(): generator_output = enqueuer.queue.get() break else: time.sleep(0.01) yield generator_output generator_output = None finally: if enqueuer is not None: enqueuer.stop() config = tf.ConfigProto() # A "Best-fit with coalescing" algorithm, simplified from a version of dlmalloc. config.gpu_options.allocator_type = 'BFC' config.gpu_options.per_process_gpu_memory_fraction = 1 config.gpu_options.allow_growth = True set_session(tf.Session(config=config)) if __name__ == "__main__": annotation_path = CONFIG.TRAIN.ANNO_PATH valid_anno_path = CONFIG.TRAIN.VALID_PATH classes_path = CONFIG.TRAIN.CLASS_PATH anchors_path = CONFIG.TRAIN.ANCHOR_PATH # pretrained model path weights_path = CONFIG.TRAIN.PRE_TRAINED_MODEL class_names = get_classes(classes_path) anchors = get_anchors(anchors_path) num_classes = len(class_names) num_anchors = len(anchors) # checkpoint path log_dir = CONFIG.TRAIN.SAVE_PATH # resolution input_shape = CONFIG.TRAIN.RESOLUTION # clear previous tf graph K.clear_session() image_input = Input(shape=(None, None, 3)) h, w = input_shape # create model print('Create YOLOv4 model with {} anchors and {} classes.'.format(num_anchors, num_classes)) model_body = yolo_body(image_input, num_anchors // 3, num_classes) print('Load weights {}.'.format(weights_path)) model_body.load_weights(weights_path, by_name=True, skip_mismatch=True) y_true = [Input(shape=(h // {0: 32, 1: 16, 2: 8}[l], w // {0: 32, 1: 16, 2: 8}[l], num_anchors // 3, num_classes + 5)) for l in range(3)] loss_input = [model_body.output, y_true] # merge custom loss layer into model model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss', arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': CONFIG.TRAIN.IGNORE_THRES, 'use_focal_confidence_loss': CONFIG.TRAIN.CONFIDENCE_FOCAL, 'use_focal_class_loss': CONFIG.TRAIN.CLASS_FOCAL, 'use_diou': CONFIG.TRAIN.DIOU, 'use_ciou': CONFIG.TRAIN.CIOU, 'print_loss': False})(loss_input) # create model_loss model = Model([model_body.input, y_true], model_loss) # freeze_layers = 249 freeze_layers = CONFIG.TRAIN.FREEZE_LAYERS for i in range(freeze_layers): model_body.layers[i].trainable = False print('Freeze the first {} layers of total {} layers.'.format(freeze_layers, len(model_body.layers))) # checkpoint logging = TensorBoard(log_dir=log_dir) checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}.h5', monitor='loss', save_weights_only=True, save_best_only=False, period=CONFIG.TRAIN.SAVE_PERIOD) # reduce lr on plateau # this lr decay is worse than cosine anneal reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.1, patience=5, verbose=1) # i don't use early stopping frequently because it is not orthogonal. early_stopping = EarlyStopping(monitor='loss', min_delta=0, patience=10, verbose=1) # get training annotations with open(annotation_path) as f: lines = f.readlines() np.random.seed(10101) np.random.shuffle(lines) num_train = len(lines) # get validating annotations with open(valid_anno_path) as f: valid_lines = f.readlines() np.random.shuffle(valid_lines) num_val = len(valid_lines) np.random.seed(None) # one stage training if CONFIG.TRAIN.TRANSFER: model.compile(optimizer=Adam(lr=CONFIG.TRAIN.LR_STAGE1), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) batch_size = CONFIG.TRAIN.BATCH1 print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size)) model.fit_generator(get_batch(num_workers=CONFIG.DATASET.WORKERS, max_queue_size=CONFIG.DATASET.MAX_QUEUE, annotation_lines=lines, batch_size=batch_size, input_shape=input_shape, anchors=anchors, num_classes=num_classes), steps_per_epoch=max(1, num_train // batch_size), # validation_data=get_batch(1, annotation_lines=valid_lines, batch_size=batch_size, # input_shape=input_shape, anchors=anchors, # num_classes=num_classes), # validation steps: at the end of epoch, generate validation_steps batch data # validation_steps=max(1, num_val // batch_size), epochs=CONFIG.TRAIN.EPOCH1, initial_epoch=0, callbacks=[checkpoint]) model.save_weights(log_dir + 'trained_weights_stage_1.h5') # unfreeze in second stage for i in range(freeze_layers): model_body.layers[i].trainable = True print('layers have been unfrozen!!') # training in second stage # fine tune if True: model.compile(optimizer=Adam(lr=CONFIG.TRAIN.LR_STAGE2), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) batch_size = CONFIG.TRAIN.BATCH2 # cosine anneal total_epoch = CONFIG.TRAIN.EPOCH2 - CONFIG.TRAIN.EPOCH1 cosine_anneal = 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katana-nbi/katana/api/nfvo.py	afoteas/katana-slice_manager	0	21295	# -- coding: utf-8 -- import logging from logging import handlers import pickle import time import uuid from bson.binary import Binary from bson.json_util import dumps from flask import request from flask_classful import FlaskView import pymongo from requests import ConnectTimeout, ConnectionError from katana.shared_utils.mongoUtils import mongoUtils from katana.shared_utils.nfvoUtils import osmUtils # Logging Parameters logger = logging.getLogger(__name__) file_handler = handlers.RotatingFileHandler("katana.log", maxBytes=10000, backupCount=5) stream_handler = logging.StreamHandler() formatter = logging.Formatter("%(asctime)s %(name)s %(levelname)s %(message)s") stream_formatter = logging.Formatter("%(asctime)s %(name)s %(levelname)s %(message)s") file_handler.setFormatter(formatter) stream_handler.setFormatter(stream_formatter) logger.setLevel(logging.DEBUG) logger.addHandler(file_handler) logger.addHandler(stream_handler) class NFVOView(FlaskView): route_prefix = "/api/" req_fields = ["id", "nfvousername", "nfvopassword", "nfvoip", "tenantname"] def index(self): """ Returns a list of nfvo and their details, used by: `katana nfvo ls` """ nfvo_data = mongoUtils.index("nfvo") return_data = [] for infvo in nfvo_data: return_data.append( dict( _id=infvo["_id"], nfvo_id=infvo["id"], created_at=infvo["created_at"], type=infvo["type"], ) ) return dumps(return_data), 200 # @route('/all/') #/nfvo/all def all(self): """ Same with index(self) above, but returns all nfvo details """ return dumps(mongoUtils.index("nfvo")), 200 def get(self, uuid): """ Returns the details of specific nfvo, used by: `katana nfvo inspect [uuid]` """ data = mongoUtils.get("nfvo", uuid) if data: return dumps(data), 200 else: return "Not Found", 404 def post(self): """ Add a new nfvo. The request must provide the nfvo details. used by: `katana nfvo add -f [yaml file]` """ new_uuid = str(uuid.uuid4()) request.json["_id"] = new_uuid request.json["created_at"] = time.time() # unix epoch request.json["tenants"] = {} if request.json["type"] == "OSM": # Create the NFVO object try: osm_username = request.json["nfvousername"] osm_password = request.json["<PASSWORD>"] osm_ip = request.json["nfvoip"] osm_project_name = request.json["tenantname"] nfvo_id = request.json["id"] except KeyError: return f"Error: Required fields: {self.req_fields}", 400 else: osm = osmUtils.Osm(nfvo_id, osm_ip, osm_username, osm_password, osm_project_name) try: osm.getToken() except ConnectTimeout as e: logger.exception("Connection Timeout: {}".format(e)) response = dumps({"error": "Unable to connect to NFVO"}) return (response, 400) except ConnectionError as e: logger.exception("Connection Error: {}".format(e)) response = dumps({"error": "Unable to connect to NFVO"}) return (response, 400) else: # Store the osm object to the mongo db thebytes = pickle.dumps(osm) obj_json = {"_id": new_uuid, "id": request.json["id"], "obj": Binary(thebytes)} try: new_uuid = mongoUtils.add("nfvo", request.json) except pymongo.errors.DuplicateKeyError: return f"NFVO with id {nfvo_id} already exists", 400 mongoUtils.add("nfvo_obj", obj_json) # Get information regarding VNFDs and NSDs osm.bootstrapNfvo() return f"Created {new_uuid}", 201 else: response = dumps({"error": "This type nfvo is not supported"}) return response, 400 def delete(self, uuid): """ Delete a specific nfvo. used by: `katana nfvo rm [uuid]` """ del_nfvo = mongoUtils.get("nfvo", uuid) if del_nfvo: if del_nfvo["tenants"]: return "Cannot delete nfvo {} - In use".format(uuid), 400 mongoUtils.delete("nfvo_obj", uuid) mongoUtils.delete_all("nsd", {"nfvo_id": del_nfvo["id"]}) mongoUtils.delete_all("vnfd", {"nfvoid": del_nfvo["id"]}) mongoUtils.delete("nfvo", uuid) return "Deleted NFVO {}".format(uuid), 200 else: # if uuid is not found, return error return "Error: No such nfvo: {}".format(uuid), 404 def put(self, uuid): """ Update the details of a specific nfvo. used by: `katana nfvo update -f [yaml file] [uuid]` """ data = request.json data["_id"] = uuid old_data = mongoUtils.get("nfvo", uuid) if old_data: data["created_at"] = old_data["created_at"] data["tenants"] = old_data["tenants"] try: for entry in self.req_fields: if data[entry] != old_data[entry]: return "Cannot update field: " + entry, 400 except KeyError: return f"Error: Required fields: {self.req_fields}", 400 else: mongoUtils.update("nfvo", uuid, data) return f"Modified {uuid}", 200 else: new_uuid = uuid data = request.json data["_id"] = new_uuid data["created_at"] = time.time() # unix epoch data["tenants"] = {} if request.json["type"] == "OSM": # Create the NFVO object try: osm_username = request.json["nfvousername"] osm_password = request.json["nfvopassword"] osm_ip = request.json["nfvoip"] osm_project_name = request.json["tenantname"] nfvo_id = request.json["id"] except KeyError: return f"Error: Required fields: {self.req_fields}", 400 else: osm = osmUtils.Osm( nfvo_id, osm_ip, osm_username, osm_password, osm_project_name ) try: osm.getToken() except ConnectTimeout as e: logger.exception("Connection Timeout: {}".format(e)) response = dumps({"error": "Unable to connect to NFVO"}) return (response, 400) except ConnectionError as e: logger.exception("Connection Error: {}".format(e)) response = dumps({"error": "Unable to connect to NFVO"}) return (response, 400) else: # Store the osm object to the mongo db thebytes = pickle.dumps(osm) obj_json = {"_id": new_uuid, "id": data["id"], "obj": Binary(thebytes)} try: new_uuid = mongoUtils.add("nfvo", data) except pymongo.errors.DuplicateKeyError: return f"NFVO with id {nfvo_id} already exists", 400 mongoUtils.add("nfvo_obj", obj_json) # Get information regarding VNFDs and NSDs osm.bootstrapNfvo() else: response = dumps({"error": "This type nfvo is not supported"}) return response, 400 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mtr/sync/lib/converters.py	mtrgroup/django-mtr-import-export	0	1614403	from decimal import Decimal from django.utils.six import text_type from django.utils.encoding import smart_text from .manager import manager from ..translation import gettext_lazy as _ @manager.register('converter', label=_('Decimal')) def decimal(value, action): if value: if isinstance(value, text_type): value = value.replace(',', '.') return Decimal(value) else: return Decimal(0) @manager.register('converter', label=_('String')) def string(value, action): if value: return smart_text(value) else: return '' @manager.register('converter', label=_('Coma separated list')) def comalist(value, action): if action == 'export': if isinstance(value, list): return ','.join(smart_text(v) for v in value) else: if isinstance(value, text_type): return value.split(',') return [value] @manager.register('converter', label=_('Boolean')) def boolean(value, action): if isinstance(value, str): if value.isdigit(): value = int(value) elif not isinstance(value, int): value = 0 return bool(value) @manager.register('converter', label=_('File path')) def filepath(value, action): if value: return value.path else: return value @manager.register('converter', label=_('Integer')) def integer(value, action): if value: return int(value) else: return value @manager.register('converter', label=_('None')) def none(value, action): return None	[ 1, 515, 13677, 1053, 3826, 3039, 13, 13, 3166, 9557, 29889, 13239, 29889, 28319, 1053, 1426, 29918, 1853, 13, 3166, 9557, 29889, 13239, 29889, 22331, 1053, 15040, 29918, 726, 13, 13, 3166, 869, 12847, 1053, 8455, 13, 3166, 6317, 3286, 18411, 1053, 679, 726, 29918, 433, 1537, 408, 903, 13, 13, 13, 29992, 12847, 29889, 9573, 877, 535, 13549, 742, 3858, 29922, 29918, 877, 23307, 8785, 13, 1753, 13677, 29898, 1767, 29892, 3158, 1125, 13, 1678, 565, 995, 29901, 13, 4706, 565, 338, 8758, 29898, 1767, 29892, 1426, 29918, 1853, 1125, 13, 9651, 995, 353, 995, 29889, 6506, 29317, 742, 15300, 1495, 13, 4706, 736, 3826, 3039, 29898, 1767, 29897, 13, 1678, 1683, 29901, 13, 4706, 736, 3826, 3039, 29898, 29900, 29897, 13, 13, 13, 29992, 12847, 29889, 9573, 877, 535, 13549, 742, 3858, 29922, 29918, 877, 1231, 8785, 13, 1753, 1347, 29898, 1767, 29892, 3158, 1125, 13, 1678, 565, 995, 29901, 13, 4706, 736, 15040, 29918, 726, 29898, 1767, 29897, 13, 1678, 1683, 29901, 13, 4706, 736, 6629, 13, 13, 13, 29992, 12847, 29889, 9573, 877, 535, 13549, 742, 3858, 29922, 29918, 877, 1523, 29874, 13055, 1051, 8785, 13, 1753, 419, 284, 391, 29898, 1767, 29892, 3158, 1125, 13, 1678, 565, 3158, 1275, 525, 15843, 2396, 13, 4706, 565, 338, 8758, 29898, 1767, 29892, 1051, 1125, 13, 9651, 736, 13420, 4286, 7122, 29898, 3844, 442, 29918, 726, 29898, 29894, 29897, 363, 325, 297, 995, 29897, 13, 1678, 1683, 29901, 13, 4706, 565, 338, 8758, 29898, 1767, 29892, 1426, 29918, 1853, 1125, 13, 9651, 736, 995, 29889, 5451, 29317, 1495, 13, 1678, 736, 518, 1767, 29962, 13, 13, 13, 29992, 12847, 29889, 9573, 877, 535, 13549, 742, 3858, 29922, 29918, 877, 18146, 8785, 13, 1753, 7223, 29898, 1767, 29892, 3158, 1125, 13, 1678, 565, 338, 8758, 29898, 1767, 29892, 851, 1125, 13, 4706, 565, 995, 29889, 275, 26204, 7295, 13, 9651, 995, 353, 938, 29898, 1767, 29897, 13, 1678, 25342, 451, 338, 8758, 29898, 1767, 29892, 938, 1125, 13, 4706, 995, 353, 29871, 29900, 13, 1678, 736, 6120, 29898, 1767, 29897, 13, 13, 13, 29992, 12847, 29889, 9573, 877, 535, 13549, 742, 3858, 29922, 29918, 877, 2283, 2224, 8785, 13, 1753, 934, 2084, 29898, 1767, 29892, 3158, 1125, 13, 1678, 565, 995, 29901, 13, 4706, 736, 995, 29889, 2084, 13, 1678, 1683, 29901, 13, 4706, 736, 995, 13, 13, 13, 29992, 12847, 29889, 9573, 877, 535, 13549, 742, 3858, 29922, 29918, 877, 7798, 8785, 13, 1753, 6043, 29898, 1767, 29892, 3158, 1125, 13, 1678, 565, 995, 29901, 13, 4706, 736, 938, 29898, 1767, 29897, 13, 1678, 1683, 29901, 13, 4706, 736, 995, 13, 13, 13, 29992, 12847, 29889, 9573, 877, 535, 13549, 742, 3858, 29922, 29918, 877, 8516, 8785, 13, 1753, 5642, 29898, 1767, 29892, 3158, 1125, 13, 1678, 736, 6213, 13, 2 ]
omicexperiment/transforms/filters/taxonomy.py	bassio/omicexperiment	7	190878	import pandas as pd from omicexperiment.transforms.transform import Filter, AttributeFilter, AttributeFlexibleOperatorMixin class TaxonomyAttributeFilter(AttributeFilter, AttributeFlexibleOperatorMixin): def __dapply__(self, experiment): _op = self._op_function(experiment._counts_with_tax()) criteria = _op(self.value) return experiment.data_df[criteria] def __eapply__(self, experiment): filtered_df = self.__dapply__(experiment) return experiment.with_data_df(filtered_df)	[ 1, 1053, 11701, 408, 10518, 13, 3166, 2703, 625, 29916, 15362, 29889, 9067, 29879, 29889, 9067, 1053, 19916, 29892, 23833, 5072, 29892, 23833, 29943, 2506, 1821, 26486, 29924, 861, 262, 13, 13, 13, 1990, 17847, 21926, 6708, 5072, 29898, 6708, 5072, 29892, 23833, 29943, 2506, 1821, 26486, 29924, 861, 262, 1125, 13, 1678, 822, 4770, 29881, 7302, 12035, 1311, 29892, 7639, 1125, 13, 4706, 903, 459, 353, 1583, 3032, 459, 29918, 2220, 29898, 735, 15362, 3032, 2798, 29879, 29918, 2541, 29918, 20725, 3101, 13, 4706, 16614, 353, 903, 459, 29898, 1311, 29889, 1767, 29897, 13, 4706, 736, 7639, 29889, 1272, 29918, 2176, 29961, 29883, 21977, 29962, 13, 268, 13, 1678, 822, 4770, 29872, 7302, 12035, 1311, 29892, 7639, 1125, 13, 4706, 22289, 29918, 2176, 353, 1583, 17255, 29881, 7302, 12035, 735, 15362, 29897, 13, 4706, 736, 7639, 29889, 2541, 29918, 1272, 29918, 2176, 29898, 4572, 287, 29918, 2176, 29897, 13, 308, 13, 2 ]
scqubits/io_utils/fileio_backends.py	IlyaLSMmisis/scqubits-1	0	119290	# fileio_backends.py # # This file is part of scqubits. # # Copyright (c) 2019, <NAME> and <NAME> # All rights reserved. # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. ############################################################################ """ Helper routines for writing data to h5 files. """ import ast import csv import os import re from abc import ABC, abstractmethod import numpy as np try: import h5py except ImportError: _HAS_H5PY = False else: _HAS_H5PY = True import scqubits.io_utils.fileio as io import scqubits.utils.misc as utils class IOWriter(ABC): """ ABC for writing class instance data to file. Parameters ---------- filename: str file_handle: h5.Group, optional """ def __init__(self, filename, file_handle=None): self.filename = filename self.io_data = None self.file_handle = file_handle @abstractmethod def to_file(self, io_data, *kwargs): pass @abstractmethod def write_attributes(self, args, *kwargs): pass @abstractmethod def write_ndarrays(self, args, *kwargs): pass @abstractmethod def write_objects(self, args, *kwargs): pass class H5Writer(IOWriter): """Writes IOData to a custom-format h5 file""" def write_attributes(self, h5file_group): """ Attribute data consists of 1. `__init__` parameters that are of type str or numerical. These are directly written into `h5py.Group.attrs` 2. lists are stored under `<h5py.Group>/__lists` 3. dicts are stored under `<h5py.Group>/__dicts` Parameters ---------- h5file_group: h5py.Group """ h5file_group.attrs.create("__type", self.io_data.typename) # Record the type of the current class instance attributes = self.io_data.attributes for attr_name, attr_value in attributes.items(): if isinstance(attr_value, dict): # h5py does not serialize dicts automatically, so have to do it manually group_name = "__dicts/" + attr_name h5file_group.create_group(group_name) io.write(attr_value, self.filename, file_handle=h5file_group[group_name]) elif isinstance(attr_value, (list, tuple)): group_name = "__lists/" + attr_name h5file_group.create_group(group_name) io.write(attr_value, self.filename, file_handle=h5file_group[group_name]) else: h5file_group.attrs[attr_name] = attr_value def write_ndarrays(self, h5file_group): """ Writes ndarray (float or complex) data contained in `self.iodata` to the provided `h5py.Group` as a `h5py.Dataset`, using gzip compression. Parameters ---------- h5file_group: h5py.Group """ for name, array in self.io_data.ndarrays.items(): h5file_group.create_dataset(name, data=array, dtype=array.dtype, compression="gzip") def write_objects(self, h5file_group): """ Writes data representing a Python object other than ndarray, list and dict, contained in `self.iodata` to the provided `h5py.Group` und `<h5py.Group>/__objects`. Parameters ---------- h5file_group: h5py.Group """ h5file_group = h5file_group.create_group("__objects") for obj_name in self.io_data.objects.keys(): new_h5group = h5file_group.create_group(obj_name) io.write(self.io_data.objects[obj_name], self.filename, file_handle=new_h5group) @utils.Required(h5py=_HAS_H5PY) def to_file(self, io_data, file_handle=None): """ Takes the serialized IOData and writes it either to a new h5 file with file name given by `self.filename` to to the given h5py.Group of an open h5 file. Parameters ---------- io_data: IOData file_handle: h5py.Group, optional """ self.io_data = io_data if file_handle is None: h5file_group = h5py.File(self.filename, 'w') else: h5file_group = file_handle self.write_attributes(h5file_group) self.write_ndarrays(h5file_group) self.write_objects(h5file_group) class H5Reader: """ Enables reading h5 files generated with scqubits. Parameters ---------- filename: str file_handle: h5py.Group, optional """ def __init__(self, filename, file_handle=None): self.filename = filename self.io_data = None self.file_handle = file_handle @staticmethod def h5_attrs_to_dict(h5_attrs): """ Converts h5 attribute data to a Python dictionary. Parameters ---------- h5_attrs: h5py.AttributeManager as obtained by accessing `<h5py.Group>.attrs` Returns ------- dict [str, str or Number] """ return {attr_name: attr_value for attr_name, attr_value in h5_attrs.items()} def read_attributes(self, h5file_group): """ Read data from h5 file group that is stored directly as `<h5py.Group>.attrs`, or saved in subgroups titled `<h5py.Group>/__lists` and `<h5py.Group>/__dicts`. Parameters ---------- h5file_group: h5py.Group Returns ------- dict [str, dict or list] """ attributes = self.h5_attrs_to_dict(h5file_group.attrs) if '__dicts' in h5file_group: for dict_name in h5file_group['__dicts']: attributes[dict_name] = io.read(self.filename, h5file_group['__dicts/' + dict_name]) if '__lists' in h5file_group: for list_name in h5file_group['__lists']: attributes[list_name] = io.read(self.filename, h5file_group['__lists/' + list_name]) return attributes def read_ndarrays(self, h5file_group): """ Read numpy array data from h5 file group. Parameters ---------- h5file_group: h5py.Group Returns ------- dict [str, ndarray] """ ndarrays = {name: array[:] for name, array in h5file_group.items() if isinstance(array, h5py.Dataset)} return ndarrays def read_objects(self, h5file_group): """ Read data from the given h5 file group that represents a Python object other than an ndarray, list, or dict. Parameters ---------- h5file_group: h5py.Group Returns ------- dict [str, IOData] """ inner_objects = {} h5file_group = h5file_group["__objects"] for obj_name in h5file_group: inner_objects[obj_name] = io.read(self.filename, h5file_group[obj_name]) return inner_objects @utils.Required(h5py=_HAS_H5PY) def from_file(self, filename, file_handle=None): """ Either opens a new h5 file for reading or accesses an already opened file via the given h5.Group handle. Reads all data from the three categories of attributes (incl. lists and dicts), ndarrays, and objects. Parameters ---------- filename: str file_handle: h5.Group, optional Returns ------- IOData """ if file_handle is None: h5file_group = h5py.File(filename, 'r') else: h5file_group = file_handle attributes = self.read_attributes(h5file_group) typename = attributes['__type'] del attributes['__type'] ndarrays = self.read_ndarrays(h5file_group) inner_objects = self.read_objects(h5file_group) return io.IOData(typename, attributes, ndarrays, inner_objects) class CSVWriter(IOWriter): """ Given filename='somename.csv', write initdata into somename.csv Then, additional csv files are written for each dataset, with filenames: 'somename_' + dataname0 + '.csv' etc. """ def append_ndarray_info(self, attributes): """Add data set information to attributes, so that dataset names and dimensions are available in attributes CSV file.""" for index, dataname in enumerate(self.io_data.ndarrays.keys()): data = self.io_data.ndarrays[dataname] attributes['dataset' + str(index)] = dataname if data.ndim == 3: slice_count = len(data) else: slice_count = 1 attributes['dataset' + str(index) + '.slices'] = slice_count return attributes def write_attributes(self, filename): attributes = self.io_data.attributes attributes["__type"] = self.io_data.typename attributes = self.append_ndarray_info(attributes) with open(filename, mode='w', newline='') as meta_file: file_writer = csv.writer(meta_file, delimiter=',') file_writer.writerow(attributes.keys()) file_writer.writerow(attributes.values()) def write_ndarrays(self, filename): filename_stub, _ = os.path.splitext(filename) for dataname, dataset in self.io_data.ndarrays.items(): filename = filename_stub + '_' + dataname + '.csv' self.write_data(filename, dataset) def write_data(self, filename, dataset): if dataset.ndim <= 2: np.savetxt(filename, dataset) elif dataset.ndim == 3: np_savetxt_3d(dataset, filename) else: raise Exception("Dataset has dimensions > 3. Cannot write to CSV file.") def write_objects(self, args, kwargs): raise NotImplementedError def to_file(self, io_data, kwargs): self.io_data = io_data self.write_attributes(self.filename) self.write_ndarrays(self.filename) # no support for write_objects in CSV format class CSVReader: @staticmethod def read_attributes(filename): with open(filename, mode='r') as meta_file: file_reader = csv.reader(meta_file, delimiter=',') meta_keys = file_reader.__next__() meta_values = file_reader.__next__() return dict(zip(meta_keys, meta_values)) def process_metadict(self, meta_dict): attributes = { attr_name: utils.to_expression_or_string(attr_value) for attr_name, attr_value in meta_dict.items() if not re.match(r'dataset\d+', attr_name) } data_names = [dataname for datalabel, dataname in meta_dict.items() if re.match(r'dataset\d+$', datalabel)] data_slices = [ast.literal_eval(value) for key, value in meta_dict.items() if re.match(r'dataset\d+.slices', key)] return attributes, data_names, data_slices @staticmethod def read_data(filename, slices): try: data_array = np.loadtxt(filename) except ValueError: data_array = np.loadtxt(filename, dtype=np.complex_) if slices > 1: nrows, ncols = data_array.shape return data_array.reshape((slices, nrows//slices, ncols)) return data_array def from_file(self, filename, **kwargs): """ Parameters ---------- filename: str Returns ------- class instance generated from file data """ ext_attributes = self.read_attributes(filename) typename = ext_attributes['__type'] del ext_attributes['__type'] attributes, data_names, data_slices = self.process_metadict(ext_attributes) filename_stub, _ = os.path.splitext(filename) ndarrays = {} for index, dataname in enumerate(data_names): data_filename = filename_stub + '_' + dataname + '.csv' slices = data_slices[index] ndarrays[dataname] = self.read_data(data_filename, slices) return io.IOData(typename, attributes, ndarrays, objects=None) def np_savetxt_3d(array3d, filename): """ Helper function that splits a 3d numpy array into 2d slices for writing as csv data to a new file. 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db/settings.py	liuansen/python-utils-class	3	163973	# -- coding:utf-8 -- # author：Anson from __future__ import unicode_literals MYSQL_DATABASES = { 'default': { 'ENGINE': 'mysql', 'NAME': '', 'USER': '', 'PASSWORD': '', 'HOST': '', 'PORT': '3306', 'STORAGE_ENGINE': 'INNODB', } } REDIS_CONNECTION = { 'default': { 'HOST': '', 'PORT': '', 'PASSWD': '', 'DB': 0, } } try: from local_settings import * except ImportError: pass	[ 1, 396, 448, 29930, 29899, 14137, 29901, 9420, 29899, 29947, 448, 29930, 29899, 30004, 13, 29937, 4148, 30383, 2744, 1100, 30004, 13, 3166, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 30004, 13, 30004, 13, 30004, 13, 17870, 4176, 29918, 25832, 27982, 29903, 353, 3336, 13, 1678, 525, 4381, 2396, 3336, 13, 4706, 525, 1430, 29954, 8895, 2396, 525, 7938, 23592, 13, 4706, 525, 5813, 2396, 15516, 30004, 13, 4706, 525, 11889, 2396, 15516, 30004, 13, 4706, 525, 25711, 17013, 2396, 15516, 30004, 13, 4706, 525, 20832, 2396, 15516, 30004, 13, 4706, 525, 15082, 2396, 525, 29941, 29941, 29900, 29953, 23592, 13, 4706, 525, 1254, 1955, 10461, 29918, 1430, 29954, 8895, 2396, 525, 1177, 6632, 4051, 23592, 13, 1678, 4970, 13, 8117, 13, 30004, 13, 19386, 3235, 29918, 6007, 8186, 9838, 353, 3336, 13, 1678, 525, 4381, 2396, 3336, 13, 4706, 525, 20832, 2396, 15516, 30004, 13, 4706, 525, 15082, 2396, 15516, 30004, 13, 4706, 525, 25711, 24668, 2396, 15516, 30004, 13, 4706, 525, 4051, 2396, 29871, 29900, 11167, 13, 1678, 4970, 13, 8117, 13, 30004, 13, 2202, 29901, 30004, 13, 1678, 515, 1887, 29918, 11027, 1053, 334, 30004, 13, 19499, 16032, 2392, 29901, 30004, 13, 1678, 1209, 30004, 13, 2 ]
anstosstools/__init__.py	ericziethen/anstosstools	0	56126	"""Root package for anstoss tools."""	[ 1, 9995, 10303, 3577, 363, 385, 303, 2209, 8492, 1213, 15945, 13, 2 ]
faker/providers/company/ro_RO/__init__.py	jeffwright13/faker	3	139815	from .. import Provider as CompanyProvider class Provider(CompanyProvider): formats = ( '{{last_name}} {{company_suffix}}', '{{last_name}} {{last_name}} {{company_suffix}}', '{{last_name}}', ) company_suffixes = ( 'SRL', 'SA', 'SCA', 'SNC', 'SCS', 'AFJ', 'ASF', 'CON', 'CRL', 'INC', 'LOC', 'OC1', 'OC2', 'OC3', 'PFA', 'RA', 'SCS', 'SPI', 'URL', ) def company_suffix(self): return self.random_element(self.company_suffixes)	[ 1, 515, 6317, 1053, 1019, 5489, 408, 6938, 6980, 13, 13, 13, 1990, 1019, 5489, 29898, 21410, 6980, 1125, 13, 13, 1678, 21971, 353, 313, 13, 4706, 525, 6224, 4230, 29918, 978, 930, 8620, 14518, 29918, 2146, 600, 861, 930, 742, 13, 4706, 525, 6224, 4230, 29918, 978, 930, 8620, 4230, 29918, 978, 930, 8620, 14518, 29918, 2146, 600, 861, 930, 742, 13, 4706, 525, 6224, 4230, 29918, 978, 930, 742, 13, 1678, 1723, 13, 13, 1678, 5001, 29918, 2146, 600, 861, 267, 353, 313, 13, 4706, 525, 29903, 2241, 742, 525, 8132, 742, 525, 29903, 5454, 742, 525, 29903, 15868, 742, 525, 7187, 29903, 742, 525, 5098, 29967, 742, 525, 3289, 29943, 742, 525, 6007, 742, 525, 29907, 2241, 742, 525, 1177, 29907, 742, 13, 4706, 525, 16652, 742, 525, 20166, 29896, 742, 525, 20166, 29906, 742, 525, 20166, 29941, 742, 525, 29925, 4519, 742, 525, 4717, 742, 525, 7187, 29903, 742, 525, 29903, 2227, 742, 525, 4219, 742, 13, 1678, 1723, 13, 13, 1678, 822, 5001, 29918, 2146, 600, 861, 29898, 1311, 1125, 13, 4706, 736, 1583, 29889, 8172, 29918, 5029, 29898, 1311, 29889, 14518, 29918, 2146, 600, 861, 267, 29897, 13, 2 ]
Task/Non-decimal-radices-Convert/Python/non-decimal-radices-convert-2.py	djgoku/RosettaCodeData	5	77439	digits = "0123456789abcdefghijklmnopqrstuvwxyz" def baseN(num,b): return (((num == 0) and "0" ) or ( baseN(num // b, b).lstrip("0") + digits[num % b])) # alternatively: def baseN(num,b): if num == 0: return "0" result = "" while num != 0: num, d = divmod(num, b) result += digits[d] return result[::-1] # reverse k = 26 s = baseN(k,16) # returns the string 1a	[ 1, 13340, 353, 376, 29900, 29896, 29906, 29941, 29946, 29945, 29953, 29955, 29947, 29929, 10736, 1753, 12443, 823, 6321, 23521, 459, 29939, 29878, 303, 4090, 29893, 20230, 29908, 13, 1753, 2967, 29940, 29898, 1949, 29892, 29890, 1125, 13, 259, 736, 313, 3552, 1949, 1275, 29871, 29900, 29897, 322, 29871, 376, 29900, 29908, 1723, 13, 965, 470, 313, 2967, 29940, 29898, 1949, 849, 289, 29892, 289, 467, 29880, 17010, 703, 29900, 1159, 13, 18884, 718, 13340, 29961, 1949, 1273, 289, 12622, 13, 13, 29937, 5136, 6703, 29901, 13, 1753, 2967, 29940, 29898, 1949, 29892, 29890, 1125, 13, 29871, 565, 954, 1275, 29871, 29900, 29901, 736, 376, 29900, 29908, 13, 29871, 1121, 353, 5124, 13, 29871, 1550, 954, 2804, 29871, 29900, 29901, 13, 1678, 954, 29892, 270, 353, 1933, 1545, 29898, 1949, 29892, 289, 29897, 13, 1678, 1121, 4619, 13340, 29961, 29881, 29962, 13, 29871, 736, 1121, 29961, 1057, 29899, 29896, 29962, 396, 11837, 13, 13, 29895, 353, 29871, 29906, 29953, 13, 29879, 353, 2967, 29940, 29898, 29895, 29892, 29896, 29953, 29897, 396, 3639, 278, 1347, 29871, 29896, 29874, 13, 2 ]
demo.py	geerk/toradbapi	1	170332	<reponame>geerk/toradbapi from __future__ import (absolute_import, division, print_function, unicode_literals) import tornado.httpserver import tornado.ioloop import tornado.options import tornado.web import tornado.gen import tornado.platform.twisted import mysql.connector tornado.platform.twisted.install() import toradbapi DB_NAME = 'test_toradbapi' DB_HOST = '127.0.0.1' DB_PORT = 3306 DB_USER = 'root' DB_PASSWORD = '' class MainHandler(tornado.web.RequestHandler): def initialize(self, db): self.db = db @tornado.gen.coroutine def get(self): people = yield self.db.run_query('SELECT `name` FROM `person`') self.finish('<html><head><title>Demo toradbapi</title></head>' '<body><h3>Type new name:</h3>' '<form method="post"><input name="name" type="text"/>' '<input type="submit" value="Submit"/></form>' '<h3>Already inserted names:</h3>' '<ul>%s</ul></body></html>' % ''.join( '<li>%s</li>' % name for name in people)) @tornado.gen.coroutine def post(self): name = self.get_argument('name') try: yield self.db.run_operation( # always use escaping functionality to avoid sql-injection 'INSERT INTO `person` (`name`) VALUES (%s)', (name,)) except mysql.connector.errors.DatabaseError as e: self.finish('<html><head><title>Demo toradbapi</title></head>' '<body><h3>Error inserting new name: %s</h3>' '<br/><a href="/">Main page</a></body></html>' % e) else: self.redirect('/') def setup_database(): # just to ensure that database and table exist cnx = mysql.connector.connect( user=DB_USER, passwd=DB_PASSWORD, host=DB_HOST, port=DB_PORT) cursor = cnx.cursor() try: cursor.execute('CREATE DATABASE `%s` CHARACTER SET utf8' % DB_NAME) except mysql.connector.errors.DatabaseError: # do nothing if database exists pass cursor.execute('USE `%s`' % DB_NAME) cursor.execute('CREATE TABLE IF NOT EXISTS `person` (' ' `name` varchar(255) NOT NULL,' ' `dob` date DEFAULT NULL,' ' UNIQUE KEY (`name`))') cursor.close() cnx.close() def main(): setup_database() tornado.options.parse_command_line() pool = toradbapi.ConnectionPool( 'mysql.connector', database=DB_NAME, user=DB_USER, port=DB_PORT, password=<PASSWORD>, host=DB_HOST) application = tornado.web.Application([(r'/', MainHandler, {'db': pool})]) http_server = tornado.httpserver.HTTPServer(application) http_server.listen(8888, '127.0.0.1') try: tornado.ioloop.IOLoop.instance().start() except KeyboardInterrupt: pass finally: pool.close() tornado.ioloop.IOLoop.instance().stop() if __name__ == '__main__': main()	[ 1, 529, 276, 1112, 420, 29958, 479, 5968, 29914, 7345, 328, 29890, 2754, 13, 3166, 4770, 29888, 9130, 1649, 1053, 313, 23552, 29918, 5215, 29892, 8542, 29892, 13, 462, 4706, 1596, 29918, 2220, 29892, 29104, 29918, 20889, 1338, 29897, 13, 13, 5215, 10146, 912, 29889, 991, 261, 369, 13, 5215, 10146, 912, 29889, 29875, 3543, 459, 13, 5215, 10146, 912, 29889, 6768, 13, 5215, 10146, 912, 29889, 2676, 13, 5215, 10146, 912, 29889, 1885, 13, 5215, 10146, 912, 29889, 12120, 29889, 7516, 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PolTools/main_programs/align.py	GeoffSCollins/PolTools	0	1613892	import os import sys import subprocess import argparse from PolTools.utils.make_random_filename import generate_random_filename from PolTools.utils.remove_files import remove_files from multiprocessing import Pool from collections import defaultdict from statistics import mean from shutil import rmtree # TODO: Move the files to pricenas global MAIN_DIRECTORY # Get input def parse_args(args): def positive_int(num): try: val = int(num) if val <= 0: raise Exception("Go to the except") except: raise argparse.ArgumentTypeError(num + " must be positive") return val parser = argparse.ArgumentParser(prog='PolTools align', description='Align raw data to generate bed and bigwig files.\n' + "More information can be found at " + "https://geoffscollins.github.io/PolTools/align.html") parser.add_argument('-u', '--url', dest='url', metavar='url', type=str) parser.add_argument('-f', '--folder-name', dest='folder_name', metavar='folder_name', type=str) parser.add_argument('-l', '--umi-length', dest='umi_length', metavar='umi_length', type=positive_int, default=8) parser.add_argument('--min-insert', dest='min_insert', metavar='min_insert', type=positive_int, default=32) parser.add_argument('--max-insert', dest='max_insert', metavar='max_insert', type=positive_int, default=600) parser.add_argument('-t', '--threads', dest='threads', metavar='threads', type=positive_int, nargs='?', default=10) parser.add_argument('-i', '--index', dest='index', metavar='index', type=str) parser.add_argument('-c', '--chrom-sizes-file', dest='chrom_sizes_file', metavar='chrom_sizes_file', type=str) parser.add_argument('-s', '--server-name', dest='server_name', metavar='server_name', type=str, default=None) parser.add_argument('--spike-in-genome', dest='spike_in_genome', metavar='spike_in_genome', type=str, default=None) parser.add_argument('-g', '--genome', dest='genomes', metavar='genome', type=str, nargs="+", action="extend") parser.add_argument('-x', '--conversion-file', dest='sample_name_conversion_filename', metavar='conversion_file', type=str, default=None) return parser.parse_args(args) def read_sample_name_conversion_table(filename): if not filename or not os.path.isfile(filename): return None, ['.fastq.gz'] downloading_prefixes = [] sample_name_conversion = {} with open(filename) as file: for i, line in enumerate(file): if i != 0: download_prefix, prefix, new_name, group_name = line.split() downloading_prefixes.append(download_prefix) sample_name_conversion[prefix] = [new_name, group_name] return sample_name_conversion, downloading_prefixes def build_directory_structure(): os.mkdir(MAIN_DIRECTORY) os.mkdir(MAIN_DIRECTORY + "original_files") os.mkdir(MAIN_DIRECTORY + "trimmed") os.mkdir(MAIN_DIRECTORY + "sam") os.mkdir(MAIN_DIRECTORY + "bed") os.mkdir(MAIN_DIRECTORY + "bigwig") os.mkdir(MAIN_DIRECTORY + "logs") def download_data(url, prefixes): # Download the data in the original_files folder # -q quiet, -nH is no host directories, -P is the directory prefix original_files_directory = MAIN_DIRECTORY + "original_files" # Comma separated list to -A prefixes = [prefix + ".fastq.gz" for prefix in prefixes] accepted_file_string = ",".join(prefixes) os.system("wget -q --cut-dirs=100 -nH -r -A " + accepted_file_string + " -P " + original_files_directory + " " + url) def _trim_helper(prefix): # Do the trimming and write the stdout and stderr to the logging file result = subprocess.check_output( "trim_galore --paired --small_rna --dont_gzip --quality 0 --length 34 --output_dir " + MAIN_DIRECTORY + "trimmed" + " " + MAIN_DIRECTORY + "original_files/" + prefix + "", shell=True, stderr=subprocess.STDOUT, text=True ) logs_directory = MAIN_DIRECTORY + "logs" with open(logs_directory + "/trimming.log", 'a') as file: file.write(result) def trim_adapters(threads): # Group the samples by the prefix -- string until the first underscore prefixes = set() for filename in os.listdir(MAIN_DIRECTORY + "original_files"): prefixes.add(filename.split("_lane")[0]) # Trim adapters for each group with the same prefix with Pool(threads) as pool: pool.map(_trim_helper, prefixes) def _align_and_dedup(read_one_file, read_two_file, sample_name, align_params, threads): trim5 = align_params['trim5'] trim3 = align_params['trim3'] minins = align_params['minins'] maxins = align_params['maxins'] index = align_params['index'] sam_directory = MAIN_DIRECTORY + "sam" bed_directory = MAIN_DIRECTORY + "bed" sam_file = sam_directory + "/" + sample_name + ".sam" result = subprocess.check_output( "bowtie --trim5 " + trim5 + " --trim3 " + trim3 + " --minins " + minins + " --maxins " + maxins + " --fr --best --allow-contain --sam --fullref --chunkmbs 5000 --threads " + str(threads) + " " + index + " -1 " + read_one_file + " -2 " + read_two_file + " > " + sam_file, shell=True, stderr=subprocess.STDOUT, text=True ) logs_directory = MAIN_DIRECTORY + "logs" with open(logs_directory + "/align.log", 'a') as file: file.write(sample_name + "\n") file.write(result + "\n\n") # Also dedup and log result = subprocess.check_output( "dedup -n " + trim5 + " -f -p -t " + str(threads) + " -1 " + read_one_file + " -2 " + read_two_file + " -S " + sam_file, shell=True, stderr=subprocess.STDOUT, text=True ) with open(logs_directory + "/dedup.log", 'a') as file: file.write(result + "\n\n") # Move the bed file to the bed directory os.system('mv ' + sam_file.replace(".sam", "-dedup.bed") + ' ' + bed_directory) def align(align_params, sample_name_conversion, threads): trimmed_directory = MAIN_DIRECTORY + "trimmed" bed_directory = MAIN_DIRECTORY + "bed" # Group the trimmed files by prefix prefixes = defaultdict(list) for filename in os.listdir(trimmed_directory): if 'trimming_report' not in filename: prefix = '_'.join(filename.split('_')[:-6]) # Remove the lane string if there is one if 'lane' in prefix: location = prefix.find('lane') prefix = prefix[:location] + prefix[location + 6:] prefixes[prefix].append(filename) # If there is more than one lane with the prefix, then we align and dedup separately but combine the bed files at the end for prefix, filenames in prefixes.items(): if len(filenames) == 2: prefixes[prefix] = [sorted(filenames)] else: # Group by lane lane_strings = defaultdict(list) for filename in filenames: lane_string = filename.split('_')[-3] lane_strings[lane_string].append(filename) new_list = [] # Verify that there are two files for each lane string and make sure we add them to the prefixes dict for lane_filenames in lane_strings.values(): if len(lane_filenames) != 2: print("Something odd happened with lanes. Here are the samples grouped: " + str(lane_filenames)) else: new_list.append(lane_filenames) prefixes[prefix] = sorted(new_list) for prefix, groups in prefixes.items(): need_to_combine_bed_files = False if len(groups) == 1 else True # If the sample name is being converted, do it if sample_name_conversion and prefix in sample_name_conversion: sample_name = sample_name_conversion[prefix][0] else: sample_name = prefix lane_specific_bed_files = [] for i, group in enumerate(groups): read_one_file, read_two_file = group read_one_file = trimmed_directory + "/" + read_one_file read_two_file = trimmed_directory + "/" + read_two_file # If the lanes need to be combined, then we will add a number to the end of the sample name if need_to_combine_bed_files: lane_specific_bed_files.append(bed_directory + "/" + sample_name + str(i) + ".bed") _align_and_dedup(read_one_file, read_two_file, sample_name + str(i), align_params, threads) else: _align_and_dedup(read_one_file, read_two_file, sample_name, align_params, threads) if need_to_combine_bed_files: # Combine the bed files that begin with the sample name and write a new file without the numbers at the end os.system( 'cat ' + bed_directory + "/" + sample_name + ".bed" + ' > ' + bed_directory + "/" + sample_name + ".bed" ) # Remove all the lane specific files remove_files(lane_specific_bed_files) def _calculate_spike_in_normalization_table(sample_name_conversion, spike_in_genome, genomes): def get_group_name(filename): sample_name = filename.replace('-dedup.bed', '') for prefix in sample_name_conversion: if sample_name_conversion[prefix][0] == sample_name: return sample_name_conversion[prefix][1] return "unknown" bed_directory = MAIN_DIRECTORY + "bed" normalization_group_data = defaultdict(dict) # Loop through each bed file for filename in os.listdir(bed_directory): # Get the total number of reads and split it for each genome group_name = get_group_name(filename) normalization_group_data[group_name][filename] = { 'total reads': 0, 'library size normalization factor': 0.0, 'spike in normalization factor': 0.0 } for genome in genomes: normalization_group_data[group_name][filename][genome] = 0 with open(bed_directory + "/" + filename) as file: for line in file: chrom, left, right, name, score, strand = line.split() # Add the read to total counts and the specific genome normalization_group_data[group_name][filename]['total reads'] += 1 if 'chr' in chrom: normalization_group_data[group_name][filename]['hg38'] += 1 else: normalization_group_data[group_name][filename][chrom] += 1 # For each group, calculate the normalization factors and write it to the file with open(MAIN_DIRECTORY + "normalization_table.tsv", 'w') as file: file.write("\t".join( ["Sample", "Total Reads"] + genomes + ["Library Size Correction Factor", "Spike-in Correction Factor", "Final Correction Factor", "Group"] ) + "\n") for group_name, group_data in normalization_group_data.items(): # Now calculate the normalization factors average_counts = mean([sample_data['total reads'] for sample_data in group_data.values()]) for sample_data in group_data.values(): sample_data['library size normalization factor'] = average_counts / sample_data['total reads'] average_normalized_spike_in_reads = mean( [sample_data['library size normalization factor'] sample_data[spike_in_genome] for sample_data in group_data.values()] ) for sample_data in group_data.values(): sample_data['spike in normalization factor'] = average_normalized_spike_in_reads / sample_data[spike_in_genome] # Write the data for each sample for sample_filename, sample_data in group_data.items(): final_correction_factor = sample_data['library size normalization factor'] * \ sample_data['spike in normalization factor'] file.write( "\t".join( [sample_filename.replace("-dedup.bed", ""), str(sample_data['total reads'])] + [str(sample_data[genome]) for genome in genomes] + [str(sample_data['library size normalization factor'])] + [str(sample_data['spike in normalization factor'])] + [str(final_correction_factor), group_name] ) + "\n" ) def _calculate_library_size_normalization_table(sample_name_conversion): def get_group_name(filename): sample_name = filename.replace('-dedup.bed', '') for prefix in sample_name_conversion: if sample_name_conversion[prefix][0] == sample_name: return sample_name_conversion[prefix][1] return "unknown" bed_directory = MAIN_DIRECTORY + "bed" normalization_group_data = defaultdict(dict) for filename in os.listdir(bed_directory): group_name = get_group_name(filename) with open(bed_directory + "/" + filename) as file: for i, _ in enumerate(file): pass normalization_group_data[group_name][filename] = i + 1 # Write the data to a file with open(MAIN_DIRECTORY + "normalization_table.tsv", 'w') as file: for group_name, group_data in normalization_group_data.items(): average_library_size = mean([count for count in group_data.values()]) # Write the headers file.write( "\t".join( ["Sample", "Total Reads", "Correction Factor"] ) + "\n" ) for filename, counts in group_data.items(): file.write( "\t".join( [filename.replace("-dedup.bed", ""), str(counts), str(average_library_size / counts), group_name] ) + "\n" ) def calculate_normalization_table(sample_name_conversion, spike_in_genome=None, genomes=None): # If they provide arguments, then it must be spike_in normalization if spike_in_genome and genomes: _calculate_spike_in_normalization_table(sample_name_conversion, spike_in_genome, genomes) else: # Calculate based on total library size _calculate_library_size_normalization_table(sample_name_conversion) def _generate_bigwig(filename, normalization_factor, chrom_sizes_file, hosting_location): bigwig_directory = MAIN_DIRECTORY + "bigwig" bedgraph_files = [] links = [] link_template = "track type=bigWig visibility=full name='<name>' autoScale=on alwaysZero=on " + \ "windowingFunction=maximum negateValues=off color=0,0,0 altColor=0,0,0 " + \ "bigDataUrl=<hostLocation>/<file>" tmp_file = generate_random_filename(".tmp") os.system('cat ' + filename + """ \| awk '$1 != "chrEBV" {print $0}' > """ + tmp_file) os.system('bedSort ' + tmp_file + ' ' + filename) for strand, strand_string in [("+", "FW"), ("-", "RV")]: os.system( 'bedtools genomecov -scale ' + normalization_factor + ' -i ' + filename + ' -g ' + chrom_sizes_file + ' -bg -strand ' + strand + ' > ' + filename.replace('.bed', '-' + strand_string + '.bedGraph') ) os.system( 'bedtools genomecov -scale ' + normalization_factor + ' -i ' + filename + ' -g ' + chrom_sizes_file + ' -bg -strand ' + strand + ' -5' + ' > ' + filename.replace('.bed', '-' + strand_string + '-5.bedGraph') ) os.system( 'bedtools genomecov -scale ' + normalization_factor + ' -i ' + filename + ' -g ' + chrom_sizes_file + ' -bg -strand ' + strand + ' -3' + ' > ' + filename.replace('.bed', '-' + strand_string + '-3.bedGraph') ) bedgraph_files.append(filename.replace('.bed', '-' + strand_string + '.bedGraph')) bedgraph_files.append(filename.replace('.bed', '-' + strand_string + '-5.bedGraph')) bedgraph_files.append(filename.replace('.bed', '-' + strand_string + '-3.bedGraph')) # Convert bedGraphs to bigwigs and add track links for file in bedgraph_files: os.system('bedSort ' + file + ' ' + file) bigwig_file = bigwig_directory + "/" + file.split('/')[-1].replace('.bedGraph', '.bw') os.system('bedGraphToBigWig ' + file + ' ' + chrom_sizes_file + ' ' + bigwig_file) curr_link = link_template if "-RV.bw" in bigwig_file or "-RV-5.bw" in bigwig_file or "-RV-3.bw" in bigwig_file: curr_link = curr_link.replace("negateValues=off", "negateValues=on") file_basename = file.split("/")[-1] # Get the sample names sample_name = file_basename sample_name = sample_name.replace(".bw", "").replace("_", " ").replace("-", " ") # Now put the sample name in curr_link = curr_link.replace("<name>", sample_name).replace("<file>", file_basename) if hosting_location: curr_link = curr_link.replace("<hostLocation>", hosting_location) links.append(curr_link) # Remove all the bedgraph and tmp files remove_files(bedgraph_files, tmp_file) # Sort the links so they are in the order of FW, RV, FW5, RV 5, FW3, RV3 links.sort() final_links = [ links[2], # Fw links[5], # Rv links[1], # Fw 5 links[4], # Rv 5 links[0], # Fw 3 links[3], # Rv 3 ] return final_links def generate_bigwigs(chrom_sizes_filename, threads, hosting_location): # First get the normalization factors normalization_factors = {} with open(MAIN_DIRECTORY + "normalization_table.tsv") as file: for i, line in enumerate(file): if i != 0: sample_name, *_, normalization_factor, _ = line.split() normalization_factors[sample_name] = normalization_factor bed_directory = MAIN_DIRECTORY + "bed" bigwig_directory = MAIN_DIRECTORY + "bigwig" args = [] for filename in os.listdir(bed_directory): sample_name = filename.split("/")[-1].replace("-dedup.bed", "") args.append( [ bed_directory + "/" + filename, normalization_factors[sample_name], chrom_sizes_filename, hosting_location ] ) with Pool(threads) as pool: all_links = pool.starmap(_generate_bigwig, args) # Write the links to a file with open(bigwig_directory + "/links.txt", 'w') as file: for dataset_links in all_links: for link in dataset_links: file.write(link + "\n") def clean_directories(): # Remove the trimmed and sam directories rmtree(MAIN_DIRECTORY + "trimmed") rmtree(MAIN_DIRECTORY + "sam") def main(args): args = parse_args(args) url = args.url folder_name = args.folder_name threads = args.threads chrom_sizes_file = args.chrom_sizes_file server_name = args.server_name spike_in_genome = args.spike_in_genome genomes = args.genomes + [spike_in_genome] sample_name_conversion_filename = args.sample_name_conversion_filename align_params = { 'trim5': str(args.umi_length), 'trim3': str(args.umi_length), 'minins': str(args.min_insert), 'maxins': str(args.max_insert), 'index': args.index } hosting_location = server_name + folder_name if server_name else '' global MAIN_DIRECTORY MAIN_DIRECTORY = "/home/geoff/.trial/" + folder_name + "/" # If given a 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phase/jump_ge.py	ryos36/retro-for-FPGA	0	133973	class JUMP_GE: def __init__(self, stack): self.phase = 0 self.ip = 0 self.stack = stack self.a = 0 def exec(self, ip, data): if (self.phase == 0): self.ip = ip + 1 self.a = self.stack.pop() self.phase = 1 return False elif (self.phase == 1): b = self.stack.pop() if b > self.a: self.phase = 2 return False else: self.phase = 0 return True elif (self.phase == 2): self.ip = data - 1 self.phase = 3 return False else: assert(self.phase == 3) self.phase = 0 return True	[ 1, 770, 435, 29965, 3580, 29918, 1692, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 5096, 1125, 13, 4706, 1583, 29889, 21646, 353, 29871, 29900, 13, 4706, 1583, 29889, 666, 353, 29871, 29900, 13, 4706, 1583, 29889, 1429, 353, 5096, 13, 4706, 1583, 29889, 29874, 353, 29871, 29900, 13, 13, 1678, 822, 2279, 29898, 1311, 29892, 10377, 29892, 848, 1125, 13, 4706, 565, 313, 1311, 29889, 21646, 1275, 29871, 29900, 1125, 13, 9651, 1583, 29889, 666, 353, 10377, 718, 29871, 29896, 13, 9651, 1583, 29889, 29874, 353, 1583, 29889, 1429, 29889, 7323, 580, 13, 9651, 1583, 29889, 21646, 353, 29871, 29896, 13, 9651, 736, 7700, 13, 4706, 25342, 313, 1311, 29889, 21646, 1275, 29871, 29896, 1125, 13, 9651, 289, 353, 1583, 29889, 1429, 29889, 7323, 580, 13, 9651, 565, 289, 1405, 1583, 29889, 29874, 29901, 13, 18884, 1583, 29889, 21646, 353, 29871, 29906, 13, 18884, 736, 7700, 13, 9651, 1683, 29901, 13, 18884, 1583, 29889, 21646, 353, 29871, 29900, 13, 18884, 736, 5852, 13, 4706, 25342, 313, 1311, 29889, 21646, 1275, 29871, 29906, 1125, 13, 9651, 1583, 29889, 666, 353, 848, 448, 29871, 29896, 13, 9651, 1583, 29889, 21646, 353, 29871, 29941, 13, 9651, 736, 7700, 13, 4706, 1683, 29901, 13, 9651, 4974, 29898, 1311, 29889, 21646, 1275, 29871, 29941, 29897, 13, 9651, 1583, 29889, 21646, 353, 29871, 29900, 13, 9651, 736, 5852, 13, 2 ]
nrw/viersen.py	risklayer/corona-landkreis-crawler	12	1601807	<filename>nrw/viersen.py #!/usr/bin/python3 from botbase import * _viersen_cc = re.compile(r"([0-9.]+) neue Infektionen") _viersen_s = re.compile(r"([0-9.]+\|\w+) Menschen werden stationär behandelt", re.U) _viersen_i = re.compile(r"([0-9.]+\|\w+) befinden sich auf der Intensiv", re.U) _viersen_q = re.compile(r"([0-9.]+) Kontaktpersonen befinden") def viersen(sheets): soup = get_soup("https://www.presse-service.de/meldungen.aspx?ps_id=1381") articles = soup.find(class_="ps_meldungsliste").findAll("li") #print([a.get_text() for a in articles], sep"\n") article = next(a for a in articles if "Sieben-Tage-Inzidenz" in a.get_text() or "Neuinfektionen" in a.get_text()) if not today().strftime("%d.%m.%Y") in article.get_text(): raise NotYetAvailableException("Viersen noch alt: "+article.get_text().strip().split("\n")[0]) url = urljoin("https://www.presse-service.de/meldungen.aspx?ps_id=1381", article.find("a")["href"]) print("Getting", url) assert url soup = get_soup(url) main = soup.find(class_="ps_content") text = main.get_text(" ").strip() #print(text) table = next(t for t in main.findAll("table") if "bestätigte Fälle" in t.get_text()) rows = [[x.get_text().strip() for x in y] for y in table.findAll("tr")] #print(rows, sep="\n") assert "Gesamt" in rows[-1][1] assert "bestätigte Fälle" in rows[0][3] #assert "Genesene" in rows[0][7] assert "verstorben" in rows[0][7] #c, g, d = map(force_int, [rows[-1][3], rows[-1][7], rows[-1][9]]) c, d = map(force_int, [rows[-1][3], rows[-1][7]]) g = None cc = force_int(_viersen_cc.search(text).group(1)) s, i, q = None, None, None m = _viersen_q.search(text) if m: q = force_int(m.group(1)) + c - d - g m = _viersen_s.search(text) if m: s = force_int(m.group(1)) m = _viersen_i.search(text) if m: i = force_int(m.group(1)) update(sheets, 5166, c=c, cc=cc, d=d, g=g, q=q, s=s, i=i, sig="Bot", ignore_delta="mon") return True schedule.append(Task(15, 30, 18, 35, 360, 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alexaskillz.py	willchis/alexaskillz	0	65182	import os import time import urllib, json from random import randint from flask import Flask from flask_ask import Ask, statement, question app = Flask(__name__) ask = Ask(app, '/') MAGIC_SEAWEED_KEY = 'e96ca9b1f03a627aaab136eafe4f83ee' SPOT_ID = '1449' @ask.launch def launched(): return question("This is a surf forecast skill for rest bay, porth call, South Wales. Would you like today's surf report?").reprompt("I didn't get that. Would you like the current conditions?") @ask.intent('YesOrNoFIrstIntent', mapping = { 'answer': 'yesorno' }) def yes_or_no(answer): statement('Okay') if (answer == 'yes'): return surf_report('today') else: return @ask.intent('SwellIntent', mapping = { 'report_time': 'timeofday' }) def current_surf(report_time): api_url = 'http://magicseaweed.com/api/%s/forecast/?spot_id=%s&fields=timestamp,swell.minBreakingHeight,swell.maxBreakingHeight,wind.speed, wind.compassDirection' % (MAGIC_SEAWEED_KEY, SPOT_ID) print 'Calling Magic Seaweed via API URL: ' + api_url json_reponse = read_from_url(api_url) unix_now = time.time() time_difference = 0 closest_time = json_reponse[0] for item in json_reponse: item_time_difference = abs(item['timestamp'] - unix_now) if (item_time_difference <= time_difference): time_difference = item_time_difference closest_time = item min_swell = closest_time['swell']['minBreakingHeight'] max_swell = closest_time['swell']['maxBreakingHeight'] wind_speed = closest_time['wind']['speed'] print 'Got swell heights: %d to %d ft and wind speed %d' % (min_swell, max_swell, wind_speed) return statement('Currently the swell height is between %d and %d foot at rest bay. The wind speed is %d miles per hour.' \ % (min_swell, max_swell, wind_speed)) @ask.intent('BuildStatusIntent', mapping = { 'plan': 'Plan' }) def build_status(plan): # Use Bamboo API to get build status(es) or trigger build etc. return statement('The latest %s build is red, with 4 failing tests.' % plan) @ask.intent('CurrentAdvertIntent') def current_advert(): # E.g. match advert based on DAR info/IP and return related promo and alexa 'card' return statement('That was the new Budweiser summer cider, available summer 2017. Visit the Alexa app for a free sample.') \ .standard_card(title='Budweiser Summer Cider', text='Claim your free sample..', large_image_url='https://upload.wikimedia.org/wikipedia/commons/thumb/7/71/Bud_and_Budvar.jpg/1200px-Bud_and_Budvar.jpg') #<EMAIL>('GiveRandomInfo') def politics_fact(): #TODO add headers to ensure wiki api allows access title = get_random_politics_title() statement(title.encode('utf-8')) title = urllib.quote(title) # encode to include in url wiki_api = 'https://en.wikipedia.org/w/api.php?format=json&action=query&prop=extracts&exintro=&explaintext=&titles=' json_reponse = read_from_url(wiki_api + title) introduction = '' try: pages = json_reponse['query']['pages'] for key in pages.keys(): print 'wiki page ref: ' + key introduction = pages[key]['extract'] except: print 'Error thrown when using title: ' + title politics_fact() return return statement(introduction.encode('utf-8')) def read_from_url(url): response = urllib.urlopen(url) return json.loads(response.read()) def get_random_politics_title(): options = ['Anarchism', 'Anarcho-capitalism', 'Anarcho-primitivism', 'Anarcho-syndicalism', 'Anarchist communism', 'Anti-communism', 'Antidisestablishmentarianism', 'Asian values', 'Authoritarianism', 'Bioregional democracy', 'Black populism', 'Black supremacy', 'Bolivarian Revolution', 'Capitalism', 'Christian democracy', 'Christian socialism', 'Classical liberalism', 'Classical republicanism', 'Collectivism', 'Communism', 'Communitarianism', 'Conservatism', 'Corporatocracy', 'Demarchy', 'Democracy (varieties)', 'Democratic egalitarianism', 'Democratic peace theory', 'Democratic socialism', 'Democratic transhumanism', 'Eco-socialism', 'Egalitarianism', 'Fascism', 'Federalism', 'Green anarchism', 'Green politics', 'Green syndicalism', 'Hindu nationalism', 'Individualist anarchism', 'Integral humanism', 'Islamism', 'Isocracy', 'Kemalist ideology', 'Leftism', 'Liberalism', 'Libertarianism', 'Libertarian socialism', 'Localism', 'Majoritarianism', 'Marxism', 'Marxism-Leninism', 'Marxist philosophy', 'Meritocracy', 'Minarchism', 'Minoritarianism', 'Miscegenation', 'Moderate Libertarianism','Monarchism','Nationalism','National Socialism','Neoliberalism','Pacifism','Paleolibertarianism','Patriotism','Political symbolism','Panislamism','Peronism','Populism','Post-Communism','Progressivism','Putinism','Racial purity','Racial segregation','Racialism','Radical centrism','Radical liberalism','Rationalism','Republicanism','Screen theory','Slavophile','Small-l libertarianism','Social Credit','Social democracy','Socialism','Social liberalism','Social philosophy','Sphere of influence','Stalinism','Thatcherism','Third Position','Totalitarianism','Tribalism','Trotskyism', 'Utilitarianism' ] return options[randint(0, len(options) - 1)] if __name__ == '__main__': #politics_fact() current_surf('today') port = int(os.environ.get('PORT', 5000)) app.run(host='0.0.0.0', port=port)	[ 1, 1053, 2897, 13, 5215, 931, 13, 5215, 3142, 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tests/test_notary.py	sxn/notary	7	71345	from pathlib import Path from click.testing import CliRunner import notary from notary import cli def test_add_license_no_preexisting(): runner = CliRunner() with runner.isolated_filesystem(): license_file = Path("LICENSE") assert not license_file.exists() result = runner.invoke(cli.add, input="\n".join(["mit", "<NAME>", "y"])) assert result.exit_code == 0 assert "License name: mit\n" in result.output assert "Author: <NAME>\n" in result.output assert "Adding MIT License as the project's license. Continue? [y/N]: y\n" assert "Added MIT License" in result.output assert license_file.exists() def test_add_license_no_preexisting_multiple_found(): runner = CliRunner() with runner.isolated_filesystem(): license_file = Path("LICENSE") assert not license_file.exists() result = runner.invoke(cli.add, input="\n".join(["m", "1", "<NAME>", "y"])) assert result.exit_code == 0 assert "License name: m\n" in result.output assert "Found the following matching licenses:\n" in result.output assert "1: MIT License\n" in result.output assert "2: Mozilla Public License Version 2.0\n" in result.output assert "Please choose which one you'd like to add [1]: 1\n" assert "Author: <NAME>\n" in result.output assert "Adding MIT License as the project's license. Continue? [y/N]: y\n" assert "Added MIT License to /" in result.output assert "/LICENSE.\n" in result.output assert license_file.exists() def test_add_license_no_preexisting_no_author_needed(): runner = CliRunner() with runner.isolated_filesystem(): license_file = Path("LICENSE") assert not license_file.exists() result = runner.invoke(cli.add, input="\n".join(["agpl", "y"])) assert result.exit_code == 0 assert "License name: agpl\n" in result.output assert "Adding GNU Affero General Public License version 3 as the project's license. Continue? [y/N]: y\n" in result.output assert "Added GNU Affero General Public License version 3 to /" in result.output assert "/LICENSE.\n" in result.output assert license_file.exists() def test_add_license_no_preexisting_no_license_matched(): runner = CliRunner() with runner.isolated_filesystem(): license_file = Path("LICENSE") assert not license_file.exists() result = runner.invoke(cli.add, input="\n".join(["gibberish", "3", "y"])) assert result.exit_code == 0 assert "License name: gibberish\n" in result.output assert "Found the following matching licenses:\n" in result.output assert "1: GNU Affero General Public License version 3\n" in result.output assert "2: Apache License 2.0\n" in result.output assert "3: GNU General Public License v3.0\n" in result.output assert "4: GNU Lesser General Public License v3.0\n" in result.output assert "5: MIT License\n" in result.output assert "6: Mozilla Public License Version 2.0\n" in result.output assert "7: The Unlicense\n" in result.output assert "Please choose which one you'd like to add [1]: 3\n" in result.output assert "Adding GNU General Public License v3.0 as the project's license. Continue? [y/N]: y\n" in result.output assert "Added GNU General Public License v3.0 to /" in result.output assert "/LICENSE.\n" in result.output assert license_file.exists() def test_add_license_preexisting_license(): runner = CliRunner() with runner.isolated_filesystem(): old_license_file = Path("LICENSE.rst") with old_license_file.open("w") as f: f.write("") new_license_file = Path("LICENSE") assert not new_license_file.exists() result = runner.invoke(cli.add, input="\n".join(["y", "mit", "<NAME>", "y"])) assert result.exit_code == 0 assert "The following license file(s) already exist:\n" in result.output assert "/LICENSE.rst\n" in result.output assert "Remove /" in result.output assert "/LICENSE.rst? [y/N]: y\n" in result.output assert "Removed /" in result.output assert "/LICENSE.rst.\n" in result.output assert "License name: mit\n" in result.output assert "Author: <NAME>\n" in result.output assert "Adding MIT License as the project's license. Continue? [y/N]: y\n" assert "Added MIT License" in result.output assert new_license_file.exists() def test_remove_no_license(): runner = CliRunner() with runner.isolated_filesystem(): result = runner.invoke(cli.remove) assert result.exit_code == 0 assert "No license file found in the current directory." in result.output def test_remove_existing_license_answer_yes(): runner = CliRunner() with runner.isolated_filesystem(): license_file = Path("LICENSE.rst") with license_file.open("w") as f: f.write("") assert license_file.exists() result = runner.invoke(cli.remove, input="y") assert result.exit_code == 0 assert "The following license file(s) already exist:" in result.output assert "/LICENSE.rst? [y/N]: y\n" in result.output assert "Removed /" in result.output assert "/LICENSE.rst.\n" in result.output assert not license_file.exists() def test_remove_existing_license_answer_no(): runner = CliRunner() with runner.isolated_filesystem(): license_file = Path("LICENSE.rst") with license_file.open("w") as f: f.write("") assert license_file.exists() result = runner.invoke(cli.remove, input="n") assert result.exit_code == 0 assert "The following license file(s) already exist:" in result.output assert "/LICENSE.rst? 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src/utils.py	jaejun-lee/amzbinimg	1	176628	<gh_stars>1-10 import tensorflow.keras.backend as K import matplotlib.pyplot as plt import numpy as np ### Custome Metrics def soft_rmse(y_true, y_pred): '''caculate RMSE for categorical predictions. ''' return K.sqrt( K.mean(K.cast_to_floatx ( K.square( K.argmax(y_true) - K.argmax(y_pred) )), axis=-1) ) def soft_acc(y_true, y_pred): '''caculate accuracy for RMSE loss function prediction. ''' return K.mean(K.equal(K.round(y_true), K.round(y_pred))) ### Plot def plot_history(history): plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.show() def visualize(img, encoder, decoder): """Draws original, encoded and decoded images""" # img[None] will have shape of (1, 32, 32, 3) which is the same as the model input code = encoder.predict(img[None])[0] reco = decoder.predict(code[None])[0] plt.subplot(1,3,1) plt.title("Original") plt.imshow(img) plt.subplot(1,3,2) plt.title("Code") plt.imshow(code.reshape([code.shape[-1]//2,-1])) plt.subplot(1,3,3) plt.title("Reconstructed") plt.imshow(reco) plt.show() def visualize_features(img, encoder, decoder): code = encoder.predict(img[None])[0] images_per_row = 16 num_of_features = code.shape[0] n_cols = num_of_features // images_per_row display_grid = np.zeros(images_per_row, n_cols) for row in range(images_per_row): for col in range(n_cols): mask = np.zeros(num_of_features) mask[row * n_cols + cos] = 1 new_code = code.copy() new_code = new_code * mask reco = decoder.predict(new_code[None])[0] display_grid[row, col] = reco plt.figure(figsize= (images_per_row, n_cols)) plt.title("Feature Representation by Decoder") plt.grid(False) plt.imshow(display_grid, aspect='auto', cmap='viridis') ### configure training layer def change_trainable_layers(model, trainable_index): for layer in model.layers[:trainable_index]: layer.trainable = False for layer in model.layers[trainable_index:]: layer.trainable = True def print_model_properties(model, indices = 0): for i, layer in enumerate(model.layers[indices:]): print(f"Layer {i+indices} \| Name: {layer.name} \| Trainable: {layer.trainable}") ### Image Processing def plot_image_and_histogram(img): fig = plt.figure(figsize=(8,4)) ax1 = fig.add_subplot(121) ax1.imshow(img) ax1.set_title('Image') ax2 = fig.add_subplot(122) ax2.hist(img[:,:,0].flatten(), color='red', bins=25) ax2.hist(img[:,:,1].flatten(), color='green', bins=25) ax2.hist(img[:,:,2].flatten(), color='blue', bins=25) ax2.set_title('Histogram of pixel intensities') ax2.set_xlabel('Pixel intensity') ax2.set_ylabel('Count') plt.tight_layout(pad=1)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 26110, 29889, 3946, 294, 29889, 27852, 408, 476, 13, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 5215, 12655, 408, 7442, 13, 13, 13, 2277, 29937, 315, 504, 608, 4737, 10817, 13, 1753, 4964, 29918, 1758, 344, 29898, 29891, 29918, 3009, 29892, 343, 29918, 11965, 1125, 13, 1678, 14550, 29883, 562, 5987, 390, 29924, 1660, 363, 11608, 936, 27303, 29889, 29871, 13, 1678, 14550, 13, 1678, 736, 476, 29889, 3676, 29898, 29871, 476, 29889, 12676, 29898, 29968, 29889, 4384, 29918, 517, 29918, 7411, 29916, 313, 476, 29889, 17619, 29898, 476, 29889, 1191, 3317, 29898, 29891, 29918, 3009, 29897, 448, 476, 29889, 1191, 3317, 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SimModel_Python_API/simmodel_swig/Release/SimSpace_Occupied_Default.py	EnEff-BIM/EnEffBIM-Framework	3	79655	# This file was automatically generated by SWIG (http://www.swig.org). # Version 3.0.7 # # Do not make changes to this file unless you know what you are doing--modify # the SWIG interface file instead. from sys import version_info if version_info >= (2, 6, 0): def swig_import_helper(): from os.path import dirname import imp fp = None try: fp, pathname, description = imp.find_module('_SimSpace_Occupied_Default', [dirname(__file__)]) except ImportError: import _SimSpace_Occupied_Default return _SimSpace_Occupied_Default if fp is not None: try: _mod = imp.load_module('_SimSpace_Occupied_Default', fp, pathname, description) finally: fp.close() return _mod _SimSpace_Occupied_Default = swig_import_helper() del swig_import_helper else: import _SimSpace_Occupied_Default del version_info try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self, class_type, name, value, static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'SwigPyObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name, None) if method: return method(self, value) if (not static): if _newclass: object.__setattr__(self, name, value) else: self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self, class_type, name, value): return _swig_setattr_nondynamic(self, class_type, name, value, 0) def _swig_getattr_nondynamic(self, class_type, name, static=1): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name, None) if method: return method(self) if (not static): return object.__getattr__(self, name) else: raise AttributeError(name) def _swig_getattr(self, class_type, name): return _swig_getattr_nondynamic(self, class_type, name, 0) def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) try: _object = object _newclass = 1 except AttributeError: class _object: pass _newclass = 0 try: import weakref weakref_proxy = weakref.proxy except: weakref_proxy = lambda x: x import base class SimSpace(base.SimSpatialStructureElement): __swig_setmethods__ = {} for _s in [base.SimSpatialStructureElement]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimSpace, name, value) __swig_getmethods__ = {} for _s in [base.SimSpatialStructureElement]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimSpace, name) __repr__ = _swig_repr def SpaceZoneAssignments(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceZoneAssignments(self, args) def SpaceNumber(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceNumber(self, args) def SpaceName(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceName(self, args) def SpaceInteriorOrExterior(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceInteriorOrExterior(self, args) def SpaceDatumElevation(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceDatumElevation(self, args) def SpaceThermalSimulationType(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceThermalSimulationType(self, args) def SpaceConditioningRequirement(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceConditioningRequirement(self, args) def SpaceOccupantDensity(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceOccupantDensity(self, args) def SpaceOccupantHeatRateLatent(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceOccupantHeatRateLatent(self, args) def SpaceOccupantHeatRateSensible(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceOccupantHeatRateSensible(self, args) def SpaceOccupantLoad(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceOccupantLoad(self, args) def SpaceEquipmentLoad(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceEquipmentLoad(self, args) def SpaceLightingLoad(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceLightingLoad(self, args) def InsideDryBulbTempHeating(self, args): return _SimSpace_Occupied_Default.SimSpace_InsideDryBulbTempHeating(self, args) def InsideRelHumidityHeating(self, args): return _SimSpace_Occupied_Default.SimSpace_InsideRelHumidityHeating(self, args) def InsideDryBulbTempCooling(self, args): return _SimSpace_Occupied_Default.SimSpace_InsideDryBulbTempCooling(self, args) def InsideRelHumidityCooling(self, args): return _SimSpace_Occupied_Default.SimSpace_InsideRelHumidityCooling(self, args) def IncludesReturnAirPlenum(self, args): return _SimSpace_Occupied_Default.SimSpace_IncludesReturnAirPlenum(self, args) def PeakAirFlowCooling(self, args): return _SimSpace_Occupied_Default.SimSpace_PeakAirFlowCooling(self, args) def PeakAirFlowHeating(self, args): return _SimSpace_Occupied_Default.SimSpace_PeakAirFlowHeating(self, args) def ExhaustAirFlowRate(self, args): return _SimSpace_Occupied_Default.SimSpace_ExhaustAirFlowRate(self, args) def NaturalAirChangeRate(self, args): return _SimSpace_Occupied_Default.SimSpace_NaturalAirChangeRate(self, args) def MechanicalAirChangeRate(self, args): return _SimSpace_Occupied_Default.SimSpace_MechanicalAirChangeRate(self, args) def VentilationType(self, args): return _SimSpace_Occupied_Default.SimSpace_VentilationType(self, args) def OutsideAirPerPerson(self, args): return _SimSpace_Occupied_Default.SimSpace_OutsideAirPerPerson(self, args) def SpaceHeight(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceHeight(self, args) def SpaceGrossPerimeter(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceGrossPerimeter(self, args) def SpaceGrossFloorArea(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceGrossFloorArea(self, args) def SpaceNetFloorArea(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceNetFloorArea(self, args) def SpaceGrossVolume(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceGrossVolume(self, args) def SpaceNetVolume(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceNetVolume(self, args) def SpaceNetFloorAreaBOMA(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceNetFloorAreaBOMA(self, args) def SpaceUsableFloorAreaBOMA(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceUsableFloorAreaBOMA(self, args) def ClassRef_SpaceByFunction(self, args): return _SimSpace_Occupied_Default.SimSpace_ClassRef_SpaceByFunction(self, args) def ClassRef_SpaceTypeOwner(self, args): return _SimSpace_Occupied_Default.SimSpace_ClassRef_SpaceTypeOwner(self, args) def ClassRef_SpaceCategoryOwner(self, args): return _SimSpace_Occupied_Default.SimSpace_ClassRef_SpaceCategoryOwner(self, args) def ClassRef_SpaceCategoryBOMA(self, args): return _SimSpace_Occupied_Default.SimSpace_ClassRef_SpaceCategoryBOMA(self, args) def SpaceOccupants(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceOccupants(self, args) def OccupancyScheduleAssignment(self, args): return _SimSpace_Occupied_Default.SimSpace_OccupancyScheduleAssignment(self, args) def LightingScheduleAssignment(self, args): return _SimSpace_Occupied_Default.SimSpace_LightingScheduleAssignment(self, args) def EquipmentScheduleAssignment(self, args): return _SimSpace_Occupied_Default.SimSpace_EquipmentScheduleAssignment(self, args) def GeometricRepresentations(self, args): return _SimSpace_Occupied_Default.SimSpace_GeometricRepresentations(self, args) def SpaceInSpatialContainer(self, args): return _SimSpace_Occupied_Default.SimSpace_SpaceInSpatialContainer(self, args) def AssociatedPlena(self, args): return _SimSpace_Occupied_Default.SimSpace_AssociatedPlena(self, args) def AssociatedElements(self, args): return _SimSpace_Occupied_Default.SimSpace_AssociatedElements(self, args) def __init__(self, args): this = _SimSpace_Occupied_Default.new_SimSpace(args) try: self.this.append(this) except: self.this = this def _clone(self, f=0, c=None): return _SimSpace_Occupied_Default.SimSpace__clone(self, f, c) __swig_destroy__ = _SimSpace_Occupied_Default.delete_SimSpace __del__ = lambda self: None SimSpace_swigregister = _SimSpace_Occupied_Default.SimSpace_swigregister SimSpace_swigregister(SimSpace) class SimSpace_Occupied(SimSpace): __swig_setmethods__ = {} for _s in [SimSpace]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimSpace_Occupied, name, value) __swig_getmethods__ = {} for _s in [SimSpace]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimSpace_Occupied, name) __repr__ = _swig_repr def T24CommRefrigEPD(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24CommRefrigEPD(self, args) def T24CommRefrigEqmtSchedRef(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24CommRefrigEqmtSchedRef(self, args) def T24CommRefrigLatentFraction(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24CommRefrigLatentFraction(self, args) def T24CommRefrigLostFraction(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24CommRefrigLostFraction(self, args) def T24CommRefrigRadFraction(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24CommRefrigRadFraction(self, args) def T24EnvelopeStatus(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24EnvelopeStatus(self, args) def T24ExhaustAirChangesPerHour(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24ExhaustAirChangesPerHour(self, args) def T24ExhaustPerArea(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24ExhaustPerArea(self, args) def T24ExhaustPerSpace(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24ExhaustPerSpace(self, args) def T24HasProcessExhaust(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24HasProcessExhaust(self, args) def T24IntLightingSpecMethod(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24IntLightingSpecMethod(self, args) def T24KitchExhHoodDutyList(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24KitchExhHoodDutyList(self, args) def T24KitchExhHoodFlowList(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24KitchExhHoodFlowList(self, args) def T24KitchExhHoodLengthList(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24KitchExhHoodLengthList(self, args) def T24KitchExhHoodStyleList(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24KitchExhHoodStyleList(self, args) def T24LabExhRateType(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24LabExhRateType(self, args) def T24LightingStatus(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24LightingStatus(self, args) def T24MandLightCntrlCntRpt(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24MandLightCntrlCntRpt(self, args) def T24MandLightCntrlDescRpt(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24MandLightCntrlDescRpt(self, args) def T24MandLightCntrlAccepReqRpt(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24MandLightCntrlAccepReqRpt(self, args) def T24MandLightCntrlIsAutoShOffCntrlRpt(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24MandLightCntrlIsAutoShOffCntrlRpt(self, args) def T24MandLightCntrlIsDayltngCntrlRpt(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24MandLightCntrlIsDayltngCntrlRpt(self, args) def T24MandLightCntrlIsDmndRespCntrlRpt(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24MandLightCntrlIsDmndRespCntrlRpt(self, args) def T24MandLightCntrlIsManAreaCntrlRpt(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24MandLightCntrlIsManAreaCntrlRpt(self, args) def T24MandLightCntrlIsMultLvlCntrlRpt(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24MandLightCntrlIsMultLvlCntrlRpt(self, args) def T24SkylightRqmtExcep(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24SkylightRqmtExcep(self, args) def T24SpaceFunction(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24SpaceFunction(self, args) def T24ConstructStatus3(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24ConstructStatus3(self, args) def __init__(self, args): this = _SimSpace_Occupied_Default.new_SimSpace_Occupied(args) try: self.this.append(this) except: self.this = this def _clone(self, f=0, c=None): return _SimSpace_Occupied_Default.SimSpace_Occupied__clone(self, f, c) __swig_destroy__ = _SimSpace_Occupied_Default.delete_SimSpace_Occupied __del__ = lambda self: None SimSpace_Occupied_swigregister = _SimSpace_Occupied_Default.SimSpace_Occupied_swigregister SimSpace_Occupied_swigregister(SimSpace_Occupied) class SimSpace_Occupied_Default(SimSpace_Occupied): __swig_setmethods__ = {} for _s in [SimSpace_Occupied]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimSpace_Occupied_Default, name, value) __swig_getmethods__ = {} for _s in [SimSpace_Occupied]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimSpace_Occupied_Default, name) __repr__ = _swig_repr def __init__(self, args): this = _SimSpace_Occupied_Default.new_SimSpace_Occupied_Default(args) try: self.this.append(this) except: self.this = this def _clone(self, f=0, c=None): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default__clone(self, f, c) __swig_destroy__ = _SimSpace_Occupied_Default.delete_SimSpace_Occupied_Default __del__ = lambda self: None SimSpace_Occupied_Default_swigregister = _SimSpace_Occupied_Default.SimSpace_Occupied_Default_swigregister SimSpace_Occupied_Default_swigregister(SimSpace_Occupied_Default) class SimSpace_Occupied_Default_sequence(base.sequence_common): __swig_setmethods__ = {} for _s in [base.sequence_common]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimSpace_Occupied_Default_sequence, name, value) __swig_getmethods__ = {} for _s in [base.sequence_common]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimSpace_Occupied_Default_sequence, name) __repr__ = _swig_repr def __init__(self, args): this = _SimSpace_Occupied_Default.new_SimSpace_Occupied_Default_sequence(args) try: self.this.append(this) except: self.this = this def assign(self, n, x): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_assign(self, n, x) def begin(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_begin(self, args) def end(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_end(self, args) def rbegin(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_rbegin(self, args) def rend(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_rend(self, args) def at(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_at(self, args) def front(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_front(self, args) def back(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_back(self, args) def push_back(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_push_back(self, args) def pop_back(self): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_pop_back(self) def detach_back(self, pop=True): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_detach_back(self, pop) def insert(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_insert(self, args) def erase(self, args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_erase(self, args) def detach(self, position, r, erase=True): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_detach(self, position, r, erase) def swap(self, x): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_swap(self, x) __swig_destroy__ = _SimSpace_Occupied_Default.delete_SimSpace_Occupied_Default_sequence __del__ = lambda self: None SimSpace_Occupied_Default_sequence_swigregister = _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_swigregister SimSpace_Occupied_Default_sequence_swigregister(SimSpace_Occupied_Default_sequence) # This file is compatible with both classic and new-style classes.	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chj/index/JType.py	aemcgraw/CodeHawk-Java	0	194700	<filename>chj/index/JType.py<gh_stars>0 # ------------------------------------------------------------------------------ # CodeHawk Java Analyzer # Author: <NAME> # ------------------------------------------------------------------------------ # The MIT License (MIT) # # Copyright (c) 2016-2019 Kestrel Technology LLC # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # ------------------------------------------------------------------------------ import chj.index.JDictionaryRecord as JD class JavaTypesBase(JD.JDictionaryRecord): def __init__(self,tpd,index,tags,args): JD.JDictionaryRecord.__init__(self,index,tags,args) self.tpd = tpd def get_scalar_size(self): return 4 def is_scalar(self): return False def is_array(self): return False def is_object(self): return False def __str__(self): return 'javatypesbase' class StringConstant(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_string(self): if len(self.tags) > 0: return self.tags[0] else: return '' def get_string_length(self): return int(self.args[0]) def is_hex(self): return len(self.tags) > 1 def __str__(self): if self.is_hex(): return ('(' + str(self.get_string_length()) + '-char-string' +')') else: return self.get_string() class ClassObjectType(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_class(self): return self.tpd.jd.get_cn(int(self.args[0])) def is_object(self): return True def __str__(self): return str(self.get_class()) class ArrayObjectType(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def is_object_array_type(self): return True def is_array(self): return True def get_value_type(self): return self.tpd.get_value_type(int(self.args[0])) def __str__(self): return str(self.get_value_type()) class ObjectValueType(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def is_object_value_type(self): return True def is_object_type(self): return True def is_object(self): return True def is_array_type(self): return self.get_object_type().is_object_array_type() def get_object_type(self): return self.tpd.get_object_type(int(self.args[0])) def get_class(self): return self.get_object_type().get_class() def __str__(self): return str(self.get_object_type()) class BasicValueType(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_scalar_size(self): if self.is_long() or self.is_double(): return 8 else: return 4 def is_basic_type(self): return True def is_scalara(self): return True def is_long(self): return self.tags[1] == 'L' def is_double(self): return self.tags[1] == 'D' def get_basic_type(self): return self.tags[1] def __str__(self): return str(self.get_basic_type()) class MethodDescriptor(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def has_return_value(self): return int(self.args[0]) == 1 def get_return_type(self): if self.has_return_value(): return self.tpd.get_value_type(int(self.args[1])) def get_argument_types(self): if self.has_return_value(): return [ self.tpd.get_value_type(int(x)) for x in self.args[2:] ] else: return [ self.tpd.get_value_type(int(x)) for x in self.args[1:] ] def __str__(self): sreturn = '' if self.get_return_type() is None else str(self.get_return_type()) return ('(' + ','.join([ str(x) for x in self.get_argument_types()]) + ')' + sreturn ) class ValueDescriptor(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_value_type(self): return self.tpd.get_value_type(int(self.args[0])) def __str__(self): return 'descr:' + str(self.get_value_type()) class ConstString(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_string(self): return self.tpd.get_string(int(self.args[0])) def __str__(self): return self.get_string() class ConstInt(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_int(self): return int(self.args[0]) def __str__(self): return str(self.get_int()) class ConstFloat(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_float(self): return float(self.tags[1]) def __str__(self): return self.tags[1] def ConstLong(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_long(self): return int(self.tags[1]) def __str__(self): return self.tags[1] class ConstDouble(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_double(self): return float(tags[1]) def __str__(self): return tags[1] class ConstClass(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_class(self): return self.tpd.get_object_type(int(self.args[0])) def __str__(self): return str(self.get_class()) class FieldHandle(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_class_name(self): return self.tpd.jd.get_class(int(self.args[0])) def get_field_signature(self): return self.tpd.jd.get_field_signature(int(self.args[1])) def __str__(self): return str(self.get_class_name()) + ':' + str(self.get_field_signature()) class MethodHandle(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_object_type(self): return self.tpd.get_object_type(int(self.args[0])) def get_method_signature(self): return self.tpd.jd.get_method_signature(int(self.args[1])) def __str__(self): return str(self.get_object_type()) + ':' + str(self.get_method_signature()) class InterfaceHandle(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_class_name(self): return self.tpd.jd.get_class(int(self.args[0])) def get_method_signature(self): return self.tpd.jd.get_method+signature(int(self.args[1])) def __str__(self): return str(self.get_class_name()) + ':' + str(self.get_method_signature()) class ConstValue(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_constant_value(self): return self.jd.get_constant_value(self.args[0]) def __str__(self): return 'C:' + str(self.get_constant_value()) class ConstField(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_class_name(self): return self.tpd.jd.get_cn(int(self.args[0])) def get_field_signature(self): return self.tpd.jd.get_field_signature(int(self.args[1])) def __str__(self): return 'C:' + str(self.get_class_name()) + '.' + str(self.get_field_signature()) class ConstMethod(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_object_type(self): return self.tpd.get_object_type(int(args[0])) def get_method_signature(self): return self.tpd.jd.get_method_signature(int(args[1])) def __str__(self): return 'C:' + str(self.get_object_type()) + '.' + str(self.get_method_signature()) class ConstInterfaceMethod(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_class_name(self): return self.tpd.jd.get_cn(int(self.args[0])) def get_method_signature(self): return self.tpd.jd.get_method_signature(int(self.args[1])) def __str__(self): return 'C:' + str(self.get_class_name()) + '.' + str(self.get_method_signature()) class ConstDynamicMethod(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_bootstrap_method_index(self): return int(self.args[0]) def get_method_signature(self): return self.tpd.jd.get_method_signature(int(self.args[1])) def __str__(self): return ('C:Dynamic(' + str(self.get_bootstrap_mehtod_index()) + ').' + str(self.get_method_signature())) class ConstNameAndType(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_name(self): return self.tpd.get_string(int(self.args[0])) def get_type(self): return self.tpd.get_descriptor(int(self.args[1])) def __str__(self): return 'CNT:' + self.get_name() + ':' + str(self.get_type()) class ConstStringUTF8(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_string(self): return self.tpd.get_string(int(args[0])) def __str__(self): return 'C:' + self.get_string() class ConstMethodHandle(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_reference_kind(self): return self.tags[1] def get_method_handle_type(self): return self.tpd.get_method_handle_type(int(self.args[0])) def __str__(self): return ('C:' + str(self.get_method_handle_type()) + '(' + self.get_reference_kind() + ')') class ConstMethodType(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_method_descriptor(self): return self.tpd.get_method_descriptor(int(self.args[0])) def __str__(self): return 'C:' + str(self.get_method_descriptor()) class ConstUnusable(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def __str__(self): return 'unusable' class BootstrapArgConstantValue(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_constant_value(self): return self.tpd.get_constant_value(int(self.args[0])) def __str__(self): return str(self.get_constant_value()) class BootstrapArgMethodHandle(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_reference_kind(self): return self.tags[1] def get_method_handle_type(self): return self.jd.get_method_handle_type(int(self.args[0])) def __str__(self): return (str(self.get_method_handle_type()) + '(' + self.get_reference_kind() + ')') class BootstrapArgMethodType(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_method_descriptor(self): return self.tpd.get_method_descriptor(int(self.args[0])) def __str__(self): return str(self.get_method_descriptor()) class BootstrapMethodData(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_reference_kind(self): return self.tags[1] def get_method_handle_type(self): return self.tpd.get_method_handle_type(int(self.args[0])) def get_arguments(self): return [ self.tpd.get_bootstrap_argument(int(x)) for x in self.args[1:] ] def __str__(self): return (str(self.get_method_handle_types()) + '(' + ','.join([ str(x) for x in self.get_arguments() ]) + ')')	[ 1, 529, 9507, 29958, 305, 29926, 29914, 2248, 29914, 29967, 1542, 29889, 2272, 29966, 12443, 29918, 303, 1503, 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code/df_data_struct.py	jrihds/pdat.github.io	0	170392	#!/usr/bin/env python3 """Creating a dataframe from a dict or list.""" import pandas as pd d = {"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]} l = [[1,4,7], [2,5,8], [3,6,9]] df = pd.DataFrame(d) print(df) df = pd.DataFrame(l) df.columns = ['a', 'b', 'c'] print(df) # a b c # 0 1 4 7 # 1 2 5 8 # 2 3 6 9	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 15945, 29908, 9832, 1218, 263, 12205, 515, 263, 9657, 470, 1051, 1213, 15945, 13, 13, 5215, 11701, 408, 10518, 13, 13, 29881, 353, 8853, 29874, 1115, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 1402, 13, 268, 376, 29890, 1115, 518, 29946, 29892, 29871, 29945, 29892, 29871, 29953, 1402, 13, 268, 376, 29883, 1115, 518, 29955, 29892, 29871, 29947, 29892, 29871, 29929, 12258, 13, 13, 29880, 353, 5519, 29896, 29892, 29946, 29892, 29955, 1402, 13, 268, 518, 29906, 29892, 29945, 29892, 29947, 1402, 13, 268, 518, 29941, 29892, 29953, 29892, 29929, 5262, 13, 13, 2176, 353, 10518, 29889, 17271, 29898, 29881, 29897, 13, 2158, 29898, 2176, 29897, 13, 13, 2176, 353, 10518, 29889, 17271, 29898, 29880, 29897, 13, 2176, 29889, 13099, 353, 6024, 29874, 742, 525, 29890, 742, 525, 29883, 2033, 13, 2158, 29898, 2176, 29897, 13, 13, 29937, 1678, 263, 29871, 289, 29871, 274, 13, 29937, 29871, 29900, 259, 29896, 259, 29946, 259, 29955, 13, 29937, 29871, 29896, 259, 29906, 259, 29945, 259, 29947, 13, 29937, 29871, 29906, 259, 29941, 259, 29953, 259, 29929, 13, 2 ]
source/energy_function.py	Bolt64/protein_lab	0	59333	<gh_stars>0 #!/usr/bin/env python import get_atom_distances as gad import itertools as it import pdb_parser as parser lj_potential = lambda distance, rm: (rm/distance)*12 - 2(rm/distance)**6 def get_energy(pdb_file, rm_file): energy=0 rm_values=gad.read_labeled_pairs(rm_file) for atom1,atom2 in it.combinations(parser.parse_file(pdb_file), 2): key=tuple(sorted(((atom1['resName'], atom1['name']),(atom2['resName'], atom2['name'])))) if key in rm_values: energy+=lj_potential(gad.get_distance(atom1, atom2), rm_values[key]) return energy	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 5215, 679, 29918, 8678, 29918, 5721, 2925, 408, 330, 328, 13, 5215, 4256, 8504, 408, 372, 13, 5215, 282, 2585, 29918, 16680, 408, 13812, 13, 13, 14042, 29918, 17765, 2556, 353, 14013, 5418, 29892, 20241, 29901, 313, 1758, 29914, 19244, 29897, 1068, 29896, 29906, 448, 29871, 29906, 16395, 1758, 29914, 19244, 29897, 1068, 29953, 13, 13, 1753, 679, 29918, 27548, 29898, 29886, 2585, 29918, 1445, 29892, 20241, 29918, 1445, 1125, 13, 1678, 5864, 29922, 29900, 13, 1678, 20241, 29918, 5975, 29922, 29887, 328, 29889, 949, 29918, 29880, 24025, 29918, 29886, 7121, 29898, 1758, 29918, 1445, 29897, 13, 1678, 363, 12301, 29896, 29892, 8678, 29906, 297, 372, 29889, 510, 2109, 800, 29898, 16680, 29889, 5510, 29918, 1445, 29898, 29886, 2585, 29918, 1445, 511, 29871, 29906, 1125, 13, 4706, 1820, 29922, 23583, 29898, 24582, 3552, 29898, 8678, 29896, 1839, 690, 1170, 7464, 12301, 29896, 1839, 978, 2033, 21336, 8678, 29906, 1839, 690, 1170, 7464, 12301, 29906, 1839, 978, 2033, 13697, 13, 4706, 565, 1820, 297, 20241, 29918, 5975, 29901, 13, 9651, 5864, 23661, 14042, 29918, 17765, 2556, 29898, 29887, 328, 29889, 657, 29918, 19244, 29898, 8678, 29896, 29892, 12301, 29906, 511, 20241, 29918, 5975, 29961, 1989, 2314, 13, 1678, 736, 5864, 13, 2 ]
lib/Site.py	EthanZeigler/pastebin_scraper	0	36989	from queue import Queue import time from settings import USE_DB, DB_DB, DB_DUMP_TABLE, DB_ACCT_TABLE, REQUEST_SPACING import logging from . import helper import sqlite3 import threading class Site(object): ''' Site - parent class used for a generic 'Queue' structure with a few helper methods and features. Implements the following methods: empty() - Is the Queue empty get(): Get the next item in the queue put(item): Puts an item in the queue tail(): Shows the last item in the queue peek(): Shows the next item in the queue length(): Returns the length of the queue clear(): Clears the queue list(): Lists the contents of the Queue download(url): Returns the content from the URL ''' # Note from Jordan (original author) # I would have used the built-in queue, but there is no support for a peek() method # that I could find... So, I decided to implement my own queue with a few # changes def __init__(self, queue=None): if queue is None: self.queue = [] def empty(self): return len(self.queue) == 0 def get(self): if not self.empty(): result = self.queue[0] del self.queue[0] else: result = None return result def put(self, item): self.queue.append(item) def peek(self): return self.queue[0] if not self.empty() else None def tail(self): return self.queue[-1] if not self.empty() else None def length(self): return len(self.queue) def clear(self): self.queue = [] def list(self): print('\n'.join(url for url in self.queue)) def monitor(self, t_lock, db_lock, db_client): self.update() while(1): while not self.empty(): paste = self.get() self.ref_id = paste.id logging.info('[] Checking + Spacer ' + paste.url) paste.text = self.get_paste_text(paste) time.sleep(REQUEST_SPACING) interesting = helper.run_match(paste) if interesting: logging.info('[] FOUND ' + (paste.type).upper() + ' ' + paste.url) if USE_DB: db_lock.acquire() try: cursor = db_client.cursor() cursor.execute('''INSERT INTO %s ( text, emails, hashes, num_emails, num_hashes, type, db_keywords, url, author ) VALUES ( ?, ?, ?, ?, ?, ?, ?, ?, ? )''' % (DB_DUMP_TABLE), (paste.text, str(paste.emails), str(paste.hashes), paste.num_emails, paste.num_hashes, paste.type, str(paste.db_keywords), str("https://pastebin.com/"+paste.id), paste.author,)) except: logging.info('[] ERROR: Failed to save paste. Manual review: ' + paste.url) db_lock.release() self.update() while self.empty(): logging.debug('[] No results... sleeping') time.sleep(self.sleep) self.update()	[ 1, 515, 9521, 1053, 5462, 434, 30004, 13, 5215, 931, 30004, 13, 3166, 6055, 1053, 501, 1660, 29918, 4051, 29892, 6535, 29918, 4051, 29892, 6535, 29918, 14849, 3580, 29918, 21009, 29892, 6535, 29918, 2477, 1783, 29918, 21009, 29892, 5195, 14130, 29918, 5550, 2477, 4214, 30004, 13, 5215, 12183, 30004, 13, 3166, 869, 1053, 16876, 30004, 13, 5215, 21120, 29941, 30004, 13, 5215, 3244, 292, 30004, 13, 30004, 13, 30004, 13, 30004, 13, 30004, 13, 1990, 10781, 29898, 3318, 1125, 30004, 13, 1678, 14550, 30004, 13, 1678, 10781, 448, 3847, 770, 1304, 363, 263, 10035, 30004, 13, 1678, 525, 10620, 29915, 3829, 411, 263, 2846, 16876, 3519, 30004, 13, 1678, 322, 5680, 29889, 1954, 9711, 278, 1494, 3519, 29901, 30004, 13, 30004, 13, 9651, 4069, 580, 448, 1317, 278, 5462, 434, 4069, 30004, 13, 9651, 679, 7295, 3617, 278, 2446, 2944, 297, 278, 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mission_cannibal_Mike_Boodoo.py	BluedragonXVI/missionary_cannibal	0	69325	import heapq # <NAME> # Artificial Intelligence Assign 01 # DFS and A* algorithms acting on Node class who's state is represented as a dictionary # DFS's fringe/frontier is a list and A* is a min-priority queue, using the heapq module # Since dictionaries aren't hashable, the closed/reached set contains string representation of state, str(Node.state) # Node class has global Node class counter that is cleared after DFS or A* are run # Main work happens in the Node.perform_action method that takes an action from the action tuple and ensures it is valid # New node is only generated if resulting new state is valid and satifies constraints of the problem # Output and discussion at end class Node: node_count = 0 def __init__(self, state:dict, parent, prev_action, depth:int, total_cost:int): self.state = state self.parent:Node = parent self.prev_action:tuple = prev_action self.depth = depth Node.node_count += 1 def A_star_cost(self, depth): # f(n) used in A* # need total number on left for both side heuristics num_left = self.state[ML] + self.state[CL] if self.state["position"] == left and num_left < 3: return 1 + depth elif self.state["position"] == left and num_left > 2: return ((num_left-2)2)+1+depth elif self.state["position"] == right: return (2num_left) + depth def print_path(self, flag:bool): num_miss:int num_cann:int direction = "" while self.parent is not None: for miss, cann in self.prev_action.items(): num_miss, num_cann = miss, cann if (self.state["position"] == left): direction = right else: direction = left if flag != True: print("DFS SOLUTION NODE -", str(self.state), "at depth: ",self.depth, "by bringing", num_miss, "miss and", num_cann, " cann from the", direction) else: print("A* SOLUTION NODE -", str(self.state), "at depth: ",self.depth, "by bringing", num_miss, "miss and", num_cann, " cann from the", direction, "f(n) =", self.total_cost) self = self.parent print("INITIAL NODE -", str(self.state), "at depth: ",self.depth) def perform_action(self, action:dict, fringe:list, reached:set): num_miss:int num_cann:int for miss, cann in action.items(): num_miss, num_cann = miss, cann # check if action is actually possible (enough miss or cann on that side) # for initial state, {3,3,L,0,0} gave me no missed actions as expected if self.state["position"] == left: # all actions will be applied to left -> right if num_miss > self.state[ML]: #print("Action not performed, not enough miss to move, num_miss: ", num_miss) return # ensure there are enough miss and cann to transfer if num_cann > self.state[CL]: #print("Action not performed, not enough cann to move, num_cann: ", num_cann) return # now ensure that the state is legal (cann don't outnumber miss) next_miss_left, next_miss_right = self.state[ML] - num_miss, self.state[MR] + num_miss next_cann_left, next_cann_right = self.state[CL] - num_cann, self.state[CR] + num_cann if next_cann_left > next_miss_left and next_miss_left > 0: #print("Illegal move detected, cannibals:", next_cann_left, " outnumber missionaries:", next_miss_left, "on left!") #print("The attemped move was sending", num_cann, "cannibals and", num_miss, "missionaries to the right") return if next_cann_right > next_miss_right and next_miss_right > 0: #print("Illegal move detected, cannibals:", next_cann_right, " outnumber missionaries:", next_miss_right, "on right!") #print("The attemped move was sending", num_cann, "cannibals and", num_miss, " missionaries to the right") return # legal nodes can now be added to frontier/fringe # create the state of the new node new_state:dict = {ML:next_miss_left, CL:next_cann_left, "position":right, MR:next_miss_right, CR:next_cann_right} state_string = str(new_state) if state_string not in reached: new_node:Node = Node(new_state, self, action, self.depth+1, None) new_node.total_cost = new_node.A_star_cost(self.depth) print("New unique node created with state:", new_state, "with depth:", new_node.depth) fringe.append(new_node) reached.add(state_string) #print("state string added to reached with value:", state_string) if self.state["position"] == right: # all actions will be applied to right -> left if num_miss > self.state[MR]: #print("Action not performed, not enough miss to move, num_miss: ", num_miss) return # ensure there are enough miss and cann to transfer if num_cann > self.state[CR]: #print("Action not performed, not enough cann to move, num_cann: ", num_cann) return # now ensure that the state is legal (cann don't outnumber miss) next_miss_left, next_miss_right = self.state[ML] + num_miss, self.state[MR] - num_miss next_cann_left, next_cann_right = self.state[CL] + num_cann, self.state[CR] - num_cann if next_cann_left > next_miss_left and next_miss_left > 0: #print("Illegal move detected, cannibals:", next_cann_left, " outnumber missionaries:", next_miss_left, "on left!") #print("The attemped move was sending", num_cann, "cannibals and", num_miss, "missionaries to the right") return if next_cann_right > next_miss_right and next_miss_right > 0: #print("Illegal move detected, cannibals:", next_cann_right, " outnumber missionaries:", next_miss_right, "on right!") #print("The attemped move was sending", num_cann, "cannibals and", num_miss, " missionaries to the right") return # legal nodes can now be added to frontier/fringe # create the state of the new node new_state:dict = {ML:next_miss_left, CL:next_cann_left, "position":left, MR:next_miss_right, CR:next_cann_right} state_string = str(new_state) if state_string not in reached: new_node:Node = Node(new_state, self, action, self.depth+1, None) print("New unique node created with state:", new_state, "with depth:", new_node.depth) fringe.append(new_node) reached.add(state_string) #print("state string added to reached with value:", state_string) # A_star action def perform_action_a_star(self, action:dict, priority_queue, reached:set): num_miss:int num_cann:int for miss, cann in action.items(): num_miss, num_cann = miss, cann # check if action is actually possible (enough miss or cann on that side) # for initial state, {3,3,L,0,0} gave me no missed actions as expected if self.state["position"] == left: # all actions will be applied to left -> right if num_miss > self.state[ML]: #print("Action not performed, not enough miss to move, num_miss: ", num_miss) return # ensure there are enough miss and cann to transfer if num_cann > self.state[CL]: #print("Action not performed, not enough cann to move, num_cann: ", num_cann) return # now ensure that the state is legal (cann don't outnumber miss) next_miss_left, next_miss_right = self.state[ML] - num_miss, self.state[MR] + num_miss next_cann_left, next_cann_right = self.state[CL] - num_cann, self.state[CR] + num_cann if next_cann_left > next_miss_left and next_miss_left > 0: #print("Illegal move detected, cannibals:", next_cann_left, " outnumber missionaries:", next_miss_left, "on left!") #print("The attemped move was sending", num_cann, "cannibals and", num_miss, "missionaries to the right") return if next_cann_right > next_miss_right and next_miss_right > 0: #print("Illegal move detected, cannibals:", next_cann_right, " outnumber missionaries:", next_miss_right, "on right!") #print("The attemped move was sending", num_cann, "cannibals and", num_miss, " missionaries to the right") return # legal nodes can now be added to frontier/fringe # create the state of the new node new_state:dict = {ML:next_miss_left, CL:next_cann_left, "position":right, MR:next_miss_right, CR:next_cann_right} state_string = str(new_state) if state_string not in reached: new_node:Node = Node(new_state, self, action, self.depth+1, None) new_node.total_cost = new_node.A_star_cost(new_node.depth) print("New unique node created with state:", new_state, "with depth:", new_node.depth, "and A* cost:", new_node.total_cost) #fringe.append(new_node) heapq.heappush(priority_queue, (new_node.total_cost, state_string, new_node)) reached.add(state_string) #print("state string added to reached with value:", state_string) if self.state["position"] == right: # all actions will be applied to right -> left if num_miss > self.state[MR]: #print("Action not performed, not enough miss to move, num_miss: ", num_miss) return # ensure there are enough miss and cann to transfer if num_cann > self.state[CR]: #print("Action not performed, not enough cann to move, num_cann: ", num_cann) return # now ensure that the state is legal (cann don't outnumber miss) next_miss_left, next_miss_right = self.state[ML] + num_miss, self.state[MR] - num_miss next_cann_left, next_cann_right = self.state[CL] + num_cann, self.state[CR] - num_cann if next_cann_left > next_miss_left and next_miss_left > 0: #print("Illegal move detected, cannibals:", next_cann_left, " outnumber missionaries:", next_miss_left, "on left!") #print("The attemped move was sending", num_cann, "cannibals and", num_miss, "missionaries to the right") return if next_cann_right > next_miss_right and next_miss_right > 0: #print("Illegal move detected, cannibals:", next_cann_right, " outnumber missionaries:", next_miss_right, "on right!") #print("The attemped move was sending", num_cann, "cannibals and", num_miss, " missionaries to the right") return # legal nodes can now be added to frontier/fringe # create the state of the new node new_state:dict = {ML:next_miss_left, CL:next_cann_left, "position":left, MR:next_miss_right, CR:next_cann_right} state_string = str(new_state) if state_string not in reached: new_node:Node = Node(new_state, self, action, self.depth+1, None) new_node.total_cost = new_node.A_star_cost(new_node.depth) print("New unique node created with state:", new_state, "with depth:", new_node.depth, "and A* cost:", new_node.total_cost) #fringe.append(new_node) heapq.heappush(priority_queue, (new_node.total_cost, state_string, new_node)) reached.add(state_string) #print("state string added to reached with value:", state_string) if __name__ == "__main__": ML, MR = "miss_left", "miss_right" CL, CR = "cann_left", "cann_right" left, right = "left", "right" frontier:list = [] actions:tuple = ({2:0}, {0:2}, {1:1}, {1:0}, {0:1}) # tuple of {miss:cann} entries. key of tuple entry is miss, value of tuple entry is cann initial_state = {ML:3, CL:3, "position":left, MR:0, CR:0} goal_state = {ML:0, CL:0, "position":right, MR:3, CR:3} reached:set = set() Node.node_count = 0 inital_node = Node(initial_state, None, None, 0, None) for action in actions: # each item is a miss:cann dictionary a_star_flag = False inital_node.perform_action(action, frontier, reached) found_solution = False while frontier and (found_solution != True): current_node:Node = frontier.pop() if current_node.state == goal_state: print("VALID STATE REACHED at depth:", current_node.depth) found_solution = True current_node.print_path(a_star_flag) print("Number of nodes created:", Node.node_count) break else: for action_2 in actions: current_node.perform_action(action_2, frontier, reached) print("End reached...") reached.clear() # A* version of above which is same except for min_priority_queue operations Node.node_count = 0 reached_a_star:set = set() priority_queue = [] for action in actions: # each item is a miss:cann dictionary a_star_flag = True inital_node.perform_action_a_star(action, priority_queue, reached_a_star) found_solution = False while priority_queue and (found_solution != True): popped_node_tuple = heapq.heappop(priority_queue) current_node:Node = popped_node_tuple[2] if current_node.state == goal_state: print("VALID STATE REACHED at depth:", current_node.depth) found_solution = True current_node.print_path(a_star_flag) print("Number of nodes created:", Node.node_count) break else: for action_2 in actions: current_node.perform_action_a_star(action_2, priority_queue, reached_a_star) print("End reached...") reached_a_star.clear() # Code output and analysis below: # Without using a reached set, DFS never stopped and generated nodes at depth > 40,000 until manually stopping program # Using a reached set, DFS found a solution at depth 11 while generating 15 legal nodes, output below: # Solution nodes output in reverse order (Goal_state -> ... -> Initial_state) along with action previously applied to get to that state """ VALID STATE REACHED at depth: 11 DFS SOLUTION NODE - {'miss_left': 0, 'cann_left': 0, 'position': 'right', 'miss_right': 3, 'cann_right': 3} at depth: 11 by bringing 0 miss and 2 cann from the left DFS SOLUTION NODE - {'miss_left': 0, 'cann_left': 2, 'position': 'left', 'miss_right': 3, 'cann_right': 1} at depth: 10 by bringing 0 miss and 1 cann from the right DFS SOLUTION NODE - {'miss_left': 0, 'cann_left': 1, 'position': 'right', 'miss_right': 3, 'cann_right': 2} at depth: 9 by bringing 0 miss and 2 cann from the left DFS SOLUTION NODE - {'miss_left': 0, 'cann_left': 3, 'position': 'left', 'miss_right': 3, 'cann_right': 0} at depth: 8 by bringing 0 miss and 1 cann from the right DFS SOLUTION NODE - {'miss_left': 0, 'cann_left': 2, 'position': 'right', 'miss_right': 3, 'cann_right': 1} at depth: 7 by bringing 2 miss and 0 cann from the left DFS SOLUTION NODE - {'miss_left': 2, 'cann_left': 2, 'position': 'left', 'miss_right': 1, 'cann_right': 1} at depth: 6 by bringing 1 miss and 1 cann from the right DFS SOLUTION NODE - {'miss_left': 1, 'cann_left': 1, 'position': 'right', 'miss_right': 2, 'cann_right': 2} at depth: 5 by bringing 2 miss and 0 cann from the left DFS SOLUTION NODE - {'miss_left': 3, 'cann_left': 1, 'position': 'left', 'miss_right': 0, 'cann_right': 2} at depth: 4 by bringing 0 miss and 1 cann from the right DFS SOLUTION NODE - {'miss_left': 3, 'cann_left': 0, 'position': 'right', 'miss_right': 0, 'cann_right': 3} at depth: 3 by bringing 0 miss and 2 cann from the left DFS SOLUTION NODE - {'miss_left': 3, 'cann_left': 2, 'position': 'left', 'miss_right': 0, 'cann_right': 1} at depth: 2 by bringing 0 miss and 1 cann from the right DFS SOLUTION NODE - {'miss_left': 3, 'cann_left': 1, 'position': 'right', 'miss_right': 0, 'cann_right': 2} at depth: 1 by bringing 0 miss and 2 cann from the left INITIAL NODE - {'miss_left': 3, 'cann_left': 3, 'position': 'left', 'miss_right': 0, 'cann_right': 0} at depth: 0 """ # Without using a reached set, A* found a solution at depth 11 while generating 23 legal nodes, output below: """ VALID STATE REACHED at depth: 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 0, 'position': 'right', 'miss_right': 3, 'cann_right': 3} at depth: 11 by bringing 0 miss and 2 cann from the left f(n) = 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 2, 'position': 'left', 'miss_right': 3, 'cann_right': 1} at depth: 10 by bringing 0 miss and 1 cann from the right f(n) = 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 1, 'position': 'right', 'miss_right': 3, 'cann_right': 2} at depth: 9 by bringing 0 miss and 2 cann from the left f(n) = 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 3, 'position': 'left', 'miss_right': 3, 'cann_right': 0} at depth: 8 by bringing 0 miss and 1 cann from the right f(n) = 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 2, 'position': 'right', 'miss_right': 3, 'cann_right': 1} at depth: 7 by bringing 2 miss and 0 cann from the left f(n) = 11 A* SOLUTION NODE - {'miss_left': 2, 'cann_left': 2, 'position': 'left', 'miss_right': 1, 'cann_right': 1} at depth: 6 by bringing 1 miss and 1 cann from the right f(n) = 11 A* SOLUTION NODE - {'miss_left': 1, 'cann_left': 1, 'position': 'right', 'miss_right': 2, 'cann_right': 2} at depth: 5 by bringing 2 miss and 0 cann from the left f(n) = 9 A* SOLUTION NODE - {'miss_left': 3, 'cann_left': 1, 'position': 'left', 'miss_right': 0, 'cann_right': 2} at depth: 4 by bringing 0 miss and 1 cann from the right f(n) = 9 A* SOLUTION NODE - {'miss_left': 3, 'cann_left': 0, 'position': 'right', 'miss_right': 0, 'cann_right': 3} at depth: 3 by bringing 0 miss and 2 cann from the left f(n) = 9 A* SOLUTION NODE - {'miss_left': 3, 'cann_left': 2, 'position': 'left', 'miss_right': 0, 'cann_right': 1} at depth: 2 by bringing 0 miss and 1 cann from the right f(n) = 9 A* SOLUTION NODE - {'miss_left': 3, 'cann_left': 1, 'position': 'right', 'miss_right': 0, 'cann_right': 2} at depth: 1 by bringing 0 miss and 2 cann from the left f(n) = 9 INITIAL NODE - {'miss_left': 3, 'cann_left': 3, 'position': 'left', 'miss_right': 0, 'cann_right': 0} at depth: 0 Number of nodes created: 23 ------------------------------------------------------------------------------------------------------------------- """ # Using a reached set to avoid cycles, A* found a solution at depth 11 while generating 14 nodes, output below: """ VALID STATE REACHED at depth: 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 0, 'position': 'right', 'miss_right': 3, 'cann_right': 3} at depth: 11 by bringing 0 miss and 2 cann from the left f(n) = 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 2, 'position': 'left', 'miss_right': 3, 'cann_right': 1} at depth: 10 by bringing 0 miss and 1 cann from the right f(n) = 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 1, 'position': 'right', 'miss_right': 3, 'cann_right': 2} at depth: 9 by bringing 0 miss and 2 cann from the left f(n) = 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 3, 'position': 'left', 'miss_right': 3, 'cann_right': 0} at depth: 8 by bringing 0 miss and 1 cann from the right f(n) = 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 2, 'position': 'right', 'miss_right': 3, 'cann_right': 1} at depth: 7 by bringing 2 miss and 0 cann from the left f(n) = 11 A* SOLUTION NODE - {'miss_left': 2, 'cann_left': 2, 'position': 'left', 'miss_right': 1, 'cann_right': 1} at depth: 6 by bringing 1 miss and 1 cann from the right f(n) = 11 A* SOLUTION NODE - {'miss_left': 1, 'cann_left': 1, 'position': 'right', 'miss_right': 2, 'cann_right': 2} at depth: 5 by bringing 2 miss and 0 cann from the left f(n) = 9 A* SOLUTION NODE - {'miss_left': 3, 'cann_left': 1, 'position': 'left', 'miss_right': 0, 'cann_right': 2} at depth: 4 by bringing 0 miss and 1 cann from the right f(n) = 9 A* SOLUTION NODE - {'miss_left': 3, 'cann_left': 0, 'position': 'right', 'miss_right': 0, 'cann_right': 3} at depth: 3 by bringing 0 miss and 2 cann from the left f(n) = 9 A* SOLUTION NODE - {'miss_left': 3, 'cann_left': 2, 'position': 'left', 'miss_right': 0, 'cann_right': 1} at depth: 2 by bringing 0 miss and 1 cann from the right f(n) = 9 A* SOLUTION NODE - {'miss_left': 3, 'cann_left': 1, 'position': 'right', 'miss_right': 0, 'cann_right': 2} at depth: 1 by bringing 0 miss and 2 cann from the left f(n) = 9 INITIAL NODE - {'miss_left': 3, 'cann_left': 3, 'position': 'left', 'miss_right': 0, 'cann_right': 0} at depth: 0 Number of nodes created: 14 """ # DFS found solution at same depth as A, I believe this is due to the constraints of the problem severely reducing the amount of nodes being made. # The branching factor ends up being less than 5 many times because of the constraints # Should test with bigger initial state with more miss and cann to see if algorithms diverge # Unlike DFS, A found the same solution with and without a closed/reached set where DFS only worked with a closed list since # due to being trapped in a cycle. # If the heuristic function is admissible and consistent this would then suggest depth 11 is the optimal solution depth.	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PyTorch/UOSBT/Supervised/uossl_1.py	agcdke/UOS	0	145110	import os import sys import PIL import math import time import json import random import numpy as np import pandas as pd import matplotlib.pyplot as plt import torch import torchvision from pathlib import Path from PIL import Image, ImageOps, ImageFilter from torch import nn, optim from torchvision import transforms, utils import torchvision.models as models from torchvision.transforms import ToTensor import torchvision.transforms.functional as VisionF from torchvision.utils import make_grid from torch.utils.data import Dataset, DataLoader import torchvision.transforms as transforms from torch.utils.data.sampler import SubsetRandomSampler torch.cuda.empty_cache() torch.manual_seed(42) class DoctableDataset(Dataset): def __init__(self, root, image_dir, csv_file, train=True, transform=None): self.root = root self.image_dir = image_dir self.image_files = os.listdir(image_dir) self.data = pd.read_csv(csv_file, header=None) self.transform = transform self.train = train def __len__(self): return len(self.data) def __getitem__(self, idx): if torch.is_tensor(idx): idx = idx.tolist() img_name = self.data.iloc[idx,0] label = self.data.iloc[idx,1] image_name = os.path.join(self.image_dir, img_name) image = PIL.Image.open(image_name).convert('RGB') if self.transform: image = self.transform(image) return (image, label) def uossl_doctab_main(): # check if CUDA is available train_on_gpu = torch.cuda.is_available() if not train_on_gpu: print('CUDA is not available. Training on CPU ...') else: print('CUDA is available! Training on GPU ...') num_workers = 2 batch_size = 8 valid_size = 0.2 n_epochs = 400 root = "data/ssldoctable" train_image_dir = "data/ssldoctable/train" test_image_dir = "data/ssldoctable/test" train_csv_file = "data/ssldoctable/train_labels.csv" test_csv_file = "data/ssldoctable/test_labels.csv" checkpoint_dir = Path("chkpt/") start_time = time.time() checkpoint_dir.mkdir(parents=True, exist_ok=True) stats_file = open(checkpoint_dir / 'stats.txt', 'a', buffering=1) val_stats_file = open(checkpoint_dir / 'val_stats.txt', 'a', buffering=1) print(' '.join(sys.argv)) print(' '.join(sys.argv), file=stats_file) print(' '.join(sys.argv), file=val_stats_file) # convert data to a normalized torch.FloatTensor transform = transforms.Compose([ transforms.Resize(256, interpolation=Image.BICUBIC), transforms.RandomResizedCrop(224, interpolation=Image.BICUBIC), transforms.RandomHorizontalFlip(p=0.5), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) # choose the training and test datasets train_data = DoctableDataset(root, train_image_dir, train_csv_file, train=True, transform= transform) test_data = DoctableDataset(root, test_image_dir, test_csv_file, train=False, transform= transform) # obtain training indices that will be used for validation num_train = len(train_data) indices = list(range(num_train)) np.random.shuffle(indices) split = int(np.floor(valid_size * num_train)) train_idx, valid_idx = indices[split:], indices[:split] # define samplers for obtaining training and validation batches train_sampler = SubsetRandomSampler(train_idx) valid_sampler = SubsetRandomSampler(valid_idx) # prepare data loaders (combine dataset and sampler) train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers) valid_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, sampler=valid_sampler, num_workers=num_workers) test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, num_workers=num_workers) # specify the image classes classes = ['bargraph', 'doctable'] model = models.resnet50(pretrained=True) #print(model) # move tensors to GPU if CUDA is available if train_on_gpu: model.cuda() # specify loss function (categorical cross-entropy) criterion = nn.CrossEntropyLoss() # specify optimizer optimizer = torch.optim.Adam(model.parameters(),lr = 0.001) # number of epochs to train the model valid_loss_min = np.Inf # track change in validation loss for epoch in range(1, n_epochs+1): # keep track of training and validation loss train_loss = 0.0 valid_loss = 0.0 ################### # train the model # ################### model.train() for data, target in train_loader: # move tensors to GPU if CUDA is available if train_on_gpu: data, target = data.cuda(), target.cuda() # clear the gradients of all optimized variables optimizer.zero_grad() # forward pass: compute predicted outputs by passing inputs to the model output = model(data) # calculate the batch loss loss = criterion(output, target) # backward pass: compute gradient of the loss with respect to model parameters loss.backward() # perform a single optimization step (parameter update) optimizer.step() # update training loss train_loss += loss.item()data.size(0) ###################### # validate the model # ###################### model.eval() for data, target in valid_loader: # move tensors to GPU if CUDA is available if train_on_gpu: data, target = data.cuda(), target.cuda() # forward pass: compute predicted outputs by passing inputs to the model output = model(data) # calculate the batch loss loss = criterion(output, target) # update average validation loss valid_loss += loss.item()data.size(0) # calculate average losses train_loss = train_loss/len(train_loader.sampler) valid_loss = valid_loss/len(valid_loader.sampler) stats = dict(epoch=epoch, train_loss=train_loss, valid_loss=valid_loss, time=int(time.time() - start_time)) print(json.dumps(stats)) print(json.dumps(stats), file=stats_file) # print training/validation statistics print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format( epoch, train_loss, valid_loss)) # save model if validation loss has decreased if valid_loss <= valid_loss_min: val_stats = dict(valid_loss_min=valid_loss_min,valid_loss=valid_loss) print(json.dumps(val_stats)) print(json.dumps(val_stats), file=val_stats_file) print('Validation loss decreased ({:.6f} --> {:.6f}). 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7_kyu/Strings_swap_vowels_case.py	UlrichBerntien/Codewars-Katas	0	127786	<gh_stars>0 VOWELS = { c for c in "aeouiAEOUI" } def swap_vowel_case_char(c :str) -> str: return c.swapcase() if c in VOWELS else c def swap_vowel_case(st: str) -> str: return "".join(swap_vowel_case_char(c) for c in st)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29963, 9806, 6670, 29903, 353, 426, 274, 363, 274, 297, 376, 3660, 283, 29875, 16036, 29949, 3120, 29908, 500, 13, 13, 1753, 17945, 29918, 29894, 27531, 29918, 4878, 29918, 3090, 29898, 29883, 584, 710, 29897, 1599, 851, 29901, 13, 1678, 736, 274, 29889, 26276, 4878, 580, 565, 274, 297, 478, 9806, 6670, 29903, 1683, 274, 13, 13, 1753, 17945, 29918, 29894, 27531, 29918, 4878, 29898, 303, 29901, 851, 29897, 1599, 851, 29901, 29871, 13, 1678, 736, 376, 1642, 7122, 29898, 26276, 29918, 29894, 27531, 29918, 4878, 29918, 3090, 29898, 29883, 29897, 363, 274, 297, 380, 29897, 13, 2 ]
DesignPattern/CompositePattern/__init__.py	Oliver-Yan-2019/algorithm	1	57452	""" 组合模式 """ from DataStructure.Link import FavoritesList if __name__ == '__main__': _favorites_list = FavoritesList()	[ 1, 9995, 13, 31263, 30733, 31382, 30607, 13, 15945, 29908, 13, 13, 3166, 3630, 5015, 12425, 29889, 6595, 1053, 383, 17118, 3246, 1293, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 903, 29888, 17118, 3246, 29918, 1761, 353, 383, 17118, 3246, 1293, 580, 13, 2 ]
examples/passwd_example.py	ZhukovGreen/aiohttp-json-rpc	0	99315	<gh_stars>0 #!/usr/bin/env python # -- coding: utf-8 -- # Author: <NAME> <<EMAIL>> from aiohttp_json_rpc.auth.passwd import PasswdAuthBackend from aiohttp_json_rpc.auth import login_required from aiohttp_json_rpc import JsonRpc from aiohttp.web import Application import asyncio import os @asyncio.coroutine def ping(request): return 'pong' @login_required @asyncio.coroutine def pong(request): return 'ping' if __name__ == '__main__': PASSWD_FILE = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'user.db') # login: admin:admin loop = asyncio.get_event_loop() rpc = JsonRpc(auth_backend=PasswdAuthBackend(PASSWD_FILE)) rpc.add_methods( ('', ping), ('', pong), ) app = Application(loop=loop) app.router.add_route('*', '/', rpc) handler = app.make_handler() server = loop.run_until_complete( loop.create_server(handler, '0.0.0.0', 8080)) loop.run_forever()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 13361, 29901, 529, 5813, 29958, 3532, 26862, 6227, 6778, 13, 13, 3166, 263, 601, 1124, 29918, 3126, 29918, 29878, 6739, 29889, 5150, 29889, 3364, 9970, 1053, 6978, 9970, 6444, 5841, 355, 13, 3166, 263, 601, 1124, 29918, 3126, 29918, 29878, 6739, 29889, 5150, 1053, 6464, 29918, 12403, 13, 3166, 263, 601, 1124, 29918, 3126, 29918, 29878, 6739, 1053, 14355, 29934, 6739, 13, 3166, 263, 601, 1124, 29889, 2676, 1053, 8427, 13, 5215, 408, 948, 3934, 13, 5215, 2897, 13, 13, 13, 29992, 294, 948, 3934, 29889, 2616, 449, 457, 13, 1753, 24543, 29898, 3827, 1125, 13, 1678, 736, 525, 29886, 549, 29915, 13, 13, 13, 29992, 7507, 29918, 12403, 13, 29992, 294, 948, 3934, 29889, 2616, 449, 457, 13, 1753, 282, 549, 29898, 3827, 1125, 13, 1678, 736, 525, 15702, 29915, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 17687, 1799, 24668, 29918, 7724, 353, 2897, 29889, 2084, 29889, 7122, 29898, 359, 29889, 2084, 29889, 25721, 29898, 359, 29889, 2084, 29889, 370, 1028, 493, 22168, 1445, 1649, 8243, 13, 462, 1669, 525, 1792, 29889, 2585, 1495, 29871, 396, 6464, 29901, 4113, 29901, 6406, 13, 13, 1678, 2425, 353, 408, 948, 3934, 29889, 657, 29918, 3696, 29918, 7888, 580, 13, 1678, 364, 6739, 353, 14355, 29934, 6739, 29898, 5150, 29918, 27852, 29922, 7129, 9970, 6444, 5841, 355, 29898, 25711, 24668, 29918, 7724, 876, 13, 13, 1678, 364, 6739, 29889, 1202, 29918, 23515, 29898, 13, 4706, 6702, 742, 24543, 511, 13, 4706, 6702, 742, 282, 549, 511, 13, 1678, 1723, 13, 13, 1678, 623, 353, 8427, 29898, 7888, 29922, 7888, 29897, 13, 1678, 623, 29889, 15140, 29889, 1202, 29918, 13134, 877, 29930, 742, 8207, 742, 364, 6739, 29897, 13, 13, 1678, 7834, 353, 623, 29889, 5675, 29918, 13789, 580, 13, 13, 1678, 1923, 353, 2425, 29889, 3389, 29918, 29305, 29918, 8835, 29898, 13, 4706, 2425, 29889, 3258, 29918, 2974, 29898, 13789, 29892, 525, 29900, 29889, 29900, 29889, 29900, 29889, 29900, 742, 29871, 29947, 29900, 29947, 29900, 876, 13, 13, 1678, 2425, 29889, 3389, 29918, 1079, 369, 580, 13, 2 ]
api/admin.py	titans55/pizza-ordering-service	1	44719	from django.contrib import admin from django import forms from . import models class PizzaFlavorAdminForm(forms.ModelForm): class Meta: model = models.PizzaFlavor fields = "__all__" class PizzaFlavorAdmin(admin.ModelAdmin): form = PizzaFlavorAdminForm list_display = [ "id", "name", "s_size_price", "m_size_price", "l_size_price", ] readonly_fields = [ "m_size_price", "s_size_price", "l_size_price", "id", "name", ] class OrderedPizzaAdminForm(forms.ModelForm): class Meta: model = models.OrderedPizza fields = "__all__" class OrderedPizzaAdmin(admin.ModelAdmin): form = OrderedPizzaAdminForm list_display = [ "id", "size", "created", "count", ] readonly_fields = [ "id", "size", "created", "count", ] class OrderAdminForm(forms.ModelForm): class Meta: model = models.Order fields = "__all__" class OrderAdmin(admin.ModelAdmin): form = OrderAdminForm list_display = [ "id", "customer_id", "last_updated", "created", "is_paid", ] readonly_fields = 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api/portfolio/utils.py	lucasblazzi/insights-project	0	1601653	import os import grpc from google.protobuf.json_format import MessageToDict from insights_pb2_grpc import MetricsStub from insights_pb2 import PortfolioRequest, Product import redis import ast metrics_port = os.environ.get("METRICS_PORT", "9999") def message_encoder(message): for k, v in message.items(): if isinstance(v, list): message[k] = str(v) return message def build_request(payload): request = PortfolioRequest() request.start_date = payload["start_date"] request.end_date = payload["end_date"] request.amount = float(payload["amount"]) for p in payload.get("products", []): product = request.products.add() product.id = p["id"] product.proportion = float(p["proportion"]) product.amount = float(p["amount"]) return request def get_portfolio_metrics(payload): try: with grpc.insecure_channel(f"metrics_service:{metrics_port}") as channel: stub = MetricsStub(channel) request = build_request(payload) metrics = stub.GetPortfolioMetrics(request) return MessageToDict(metrics) except Exception as e: raise e def send_notifications(portfolio, clients): try: client = redis.Redis(host="redis_db", port=6379) _portfolio = ast.literal_eval(portfolio) for c in clients: _portfolio["email"] = c["email"] _portfolio["name"] = c["name"] client.xadd("portfolio", message_encoder(_portfolio)) except Exception as e: raise e	[ 1, 1053, 2897, 13, 5215, 867, 6739, 13, 3166, 5386, 29889, 17529, 9721, 29889, 3126, 29918, 4830, 1053, 7777, 1762, 21533, 13, 3166, 1663, 5861, 29918, 24381, 29906, 29918, 629, 6739, 1053, 4737, 10817, 855, 431, 13, 3166, 1663, 5861, 29918, 24381, 29906, 1053, 3371, 25648, 3089, 29892, 10969, 13, 5215, 29825, 13, 5215, 8717, 13, 13, 2527, 10817, 29918, 637, 353, 2897, 29889, 21813, 29889, 657, 703, 2303, 5659, 2965, 29903, 29918, 15082, 613, 376, 29929, 29929, 29929, 29929, 1159, 13, 13, 13, 1753, 2643, 29918, 3977, 6119, 29898, 4906, 1125, 13, 1678, 363, 413, 29892, 325, 297, 2643, 29889, 7076, 7295, 13, 4706, 565, 338, 8758, 29898, 29894, 29892, 1051, 1125, 13, 9651, 2643, 29961, 29895, 29962, 353, 851, 29898, 29894, 29897, 13, 1678, 736, 2643, 13, 13, 13, 1753, 2048, 29918, 3827, 29898, 23813, 1125, 13, 1678, 2009, 353, 3371, 25648, 3089, 580, 13, 1678, 2009, 29889, 2962, 29918, 1256, 353, 20092, 3366, 2962, 29918, 1256, 3108, 13, 1678, 2009, 29889, 355, 29918, 1256, 353, 20092, 3366, 355, 29918, 1256, 3108, 13, 1678, 2009, 29889, 14506, 353, 5785, 29898, 23813, 3366, 14506, 20068, 13, 1678, 363, 282, 297, 20092, 29889, 657, 703, 14456, 613, 5159, 1125, 13, 4706, 3234, 353, 2009, 29889, 14456, 29889, 1202, 580, 13, 4706, 3234, 29889, 333, 353, 282, 3366, 333, 3108, 13, 4706, 3234, 29889, 771, 637, 291, 353, 5785, 29898, 29886, 3366, 771, 637, 291, 20068, 13, 4706, 3234, 29889, 14506, 353, 5785, 29898, 29886, 3366, 14506, 20068, 13, 1678, 736, 2009, 13, 13, 13, 1753, 679, 29918, 637, 25648, 29918, 2527, 10817, 29898, 23813, 1125, 13, 1678, 1018, 29901, 13, 4706, 411, 867, 6739, 29889, 262, 24216, 29918, 12719, 29898, 29888, 29908, 2527, 10817, 29918, 5509, 26254, 2527, 10817, 29918, 637, 27195, 408, 8242, 29901, 13, 9651, 19281, 353, 4737, 10817, 855, 431, 29898, 12719, 29897, 13, 9651, 2009, 353, 2048, 29918, 3827, 29898, 23813, 29897, 13, 9651, 21556, 353, 19281, 29889, 2577, 2290, 25648, 10095, 10817, 29898, 3827, 29897, 13, 9651, 736, 7777, 1762, 21533, 29898, 2527, 10817, 29897, 13, 1678, 5174, 8960, 408, 321, 29901, 13, 4706, 12020, 321, 13, 13, 13, 1753, 3638, 29918, 1333, 8232, 29898, 637, 25648, 29892, 13154, 1125, 13, 1678, 1018, 29901, 13, 4706, 3132, 353, 29825, 29889, 9039, 275, 29898, 3069, 543, 1127, 275, 29918, 2585, 613, 2011, 29922, 29953, 29941, 29955, 29929, 29897, 13, 4706, 903, 637, 25648, 353, 8717, 29889, 20889, 284, 29918, 14513, 29898, 637, 25648, 29897, 13, 4706, 363, 274, 297, 13154, 29901, 13, 9651, 903, 637, 25648, 3366, 5269, 3108, 353, 274, 3366, 5269, 3108, 13, 9651, 903, 637, 25648, 3366, 978, 3108, 353, 274, 3366, 978, 3108, 13, 9651, 3132, 29889, 29916, 1202, 703, 637, 25648, 613, 2643, 29918, 3977, 6119, 7373, 637, 25648, 876, 13, 1678, 5174, 8960, 408, 321, 29901, 13, 4706, 12020, 321, 13, 13, 2 ]
floreal/views/view_purchases.py	caracole-io/circuitscourts	0	18364	<reponame>caracole-io/circuitscourts<filename>floreal/views/view_purchases.py #!/usr/bin/python # -- coding: utf-8 -- import os from django.http import HttpResponse, HttpResponseForbidden from django.shortcuts import render_to_response from django.contrib.auth.decorators import login_required from caracole import settings from .decorators import nw_admin_required from .getters import get_delivery, get_subgroup from . import latex from .spreadsheet import spreadsheet from .delivery_description import delivery_description MIME_TYPE = { 'pdf': "application/pdf", 'xlsx': "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"} def non_html_response(name_bits, name_extension, content): """Common helper to serve PDF and Excel content.""" filename = ("_".join(name_bits) + "." + name_extension).replace(" ", "_") mime_type = MIME_TYPE[name_extension] response = HttpResponse(content_type=mime_type) response['Content-Disposition'] = 'attachment; filename="%s"' % filename response.write(content) return response @login_required() def view_purchases_html(request, delivery, subgroup=None): """View purchases for a given delivery, possibly restricted to a subgroup. (subgroup) staff only.""" dv = get_delivery(delivery) if subgroup: sg = get_subgroup(subgroup) if request.user not in sg.staff.all() and request.user not in sg.network.staff.all(): return HttpResponseForbidden("Réservé aux admins") subgroups = [sg] else: if request.user not in dv.network.staff.all(): return HttpResponseForbidden("Réservé aux admins") subgroups = dv.network.subgroup_set.all() return render_to_response('view_purchases.html', delivery_description(dv, subgroups)) @login_required() def view_purchases_xlsx(request, delivery, subgroup=None): """View purchases for a given delivery as an MS-Excel spreadsheet, possibly restricted to a subgroup. (subgroup) staff only.""" dv = get_delivery(delivery) if subgroup: sg = get_subgroup(subgroup) if request.user not in sg.staff.all() and request.user not in sg.network.staff.all(): return HttpResponseForbidden("Réservé aux admins") subgroups = [sg] else: if request.user not in dv.network.staff.all(): return HttpResponseForbidden("Réservé aux admins") subgroups = dv.network.subgroup_set.all() return non_html_response((dv.network.name, dv.name), "xlsx", spreadsheet(dv, subgroups)) @login_required() def view_purchases_latex(request, delivery, subgroup=None): """View purchases for a given delivery as a PDF table, generated through LaTeX, possibly restricted to a subgroup. (subgroup) staff only.""" dv = get_delivery(delivery) if subgroup: sg = get_subgroup(subgroup) if request.user not in sg.staff.all() and request.user not in sg.network.staff.all(): return HttpResponseForbidden("Réservé aux admins") content = latex.subgroup(dv, sg) name_bits = (dv.network.name, dv.name, sg.name) else: if request.user not in dv.network.staff.all(): return HttpResponseForbidden("Réservé aux admins") content = latex.delivery_table(dv) name_bits = (dv.network.name, dv.name) return non_html_response(name_bits, "pdf", content) @login_required() def view_cards_latex(request, delivery, subgroup=None): """View purchases for a given delivery as a PDF table, generated through LaTeX, possibly restricted to a subgroup. Subgroups are presented as ready-to-cut tables, whole deliveries as list per subgroup. (subgroup) staff only.""" dv = get_delivery(delivery) if subgroup: sg = get_subgroup(subgroup) if request.user not in sg.staff.all() and request.user not in sg.network.staff.all(): return HttpResponseForbidden("Réservé aux admins") content = latex.cards(dv, sg) name_bits = (dv.network.name, dv.name, sg.name) else: content = latex.delivery_cards(dv) name_bits = (dv.network.name, dv.name) return non_html_response(name_bits, "pdf", content) @nw_admin_required(lambda a: get_delivery(a['delivery']).network) def get_archive(request, delivery, suffix): """Retrieve the PDF/MS-Excel file versions of a terminated delivery which have been saved upontermination.""" dv = get_delivery(delivery) file_name = os.path.join(settings.DELIVERY_ARCHIVE_DIR, "dv-%d.%s" % (dv.id, suffix)) with open(file_name) as f: content = f.read() name_bits = (dv.network.name, dv.name) return non_html_response(name_bits, suffix, content)	[ 1, 529, 276, 1112, 420, 29958, 4287, 562, 1772, 29899, 601, 29914, 6034, 19544, 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mindspore/parallel/mpi/_mpi_config.py	GuoSuiming/mindspore	55	131048	<reponame>GuoSuiming/mindspore<filename>mindspore/parallel/mpi/_mpi_config.py # Copyright 2020 Huawei Technologies Co., Ltd # # 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. # ============================================================================ """ The MPI config, used to configure the MPI environment. """ import threading from mindspore._c_expression import MpiConfig from mindspore._checkparam import args_type_check class _MpiConfig: """ _MpiConfig is the config tool for controlling MPI Note: Create a config through instantiating MpiConfig object is not recommended. should use MpiConfig() to get the config since MpiConfig is singleton. """ _instance = None _instance_lock = threading.Lock() def __init__(self): self._mpiconfig_handle = MpiConfig.get_instance() def __new__(cls, args, kwargs): if cls._instance is None: cls._instance_lock.acquire() cls._instance = object.__new__(cls) cls._instance_lock.release() return cls._instance def __getattribute__(self, attr): value = object.__getattribute__(self, attr) if attr == "_mpiconfig_handle" and value is None: raise ValueError("mpiconfig handle is none in MpiConfig!!!") return value @property def enable_mpi(self): return self._mpiconfig_handle.get_enable_mpi() @enable_mpi.setter def enable_mpi(self, enable_mpi): self._mpiconfig_handle.set_enable_mpi(enable_mpi) _k_mpi_config = None def _mpi_config(): """ Get the global mpi config, if mpi config is not created, create a new one. Returns: _MpiConfig, the global mpi config. """ global _k_mpi_config if _k_mpi_config is None: _k_mpi_config = _MpiConfig() return _k_mpi_config @args_type_check(enable_mpi=bool) def _set_mpi_config(*kwargs): """ Sets mpi config for running environment. mpi config should be configured before running your program. If there is no configuration, mpi moudle will be disabled by default. Note: Attribute name is required for setting attributes. Args: enable_mpi (bool): Whether to enable mpi. Default: False. Raises: ValueError: If input key is not an attribute in mpi config. Examples: >>> mpiconfig.set_mpi_config(enable_mpi=True) """ for key, value in kwargs.items(): if not hasattr(_mpi_config(), key): raise ValueError("Set mpi config keyword %s is not recognized!" % key) setattr(_mpi_config(), key, value) def _get_mpi_config(attr_key): """ Gets mpi config attribute value according to the input key. Args: attr_key (str): The key of the attribute. Returns: Object, The value of given attribute key. Raises: ValueError: If input key is not an attribute in config. 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DP/ValueIteration.py	davidbrown3/reinforcement-learning	1	127823	import numpy as np def value_iteration(env, theta=0.0001, discount_factor=1.0): """ Value Iteration Algorithm. Args: env: OpenAI environment. env.P represents the transition probabilities of the environment. theta: Stopping threshold. If the value of all states changes less than theta in one iteration we are done. discount_factor: lambda time discount factor. Returns: A tuple (policy, V) of the optimal policy and the optimal value function. """ V = np.zeros(env.nS) Policy = np.zeros([env.nS, env.nA]) while True: ConvergeFlag = True for StateIdx, StateName in enumerate(env.P.keys()): ActionValues = np.zeros(env.nA) StateInfo = env.P[StateName] for ActionIdx, ActionName in enumerate(StateInfo.keys()): # For now assume that all probabilities are 1 ActionInfo = StateInfo[ActionName][0] Reward = ActionInfo[2] NextState = ActionInfo[1] NextStateValue = V[NextState] ActionValues[ActionIdx] = Reward + discount_factorNextStateValue UpdatedValue = np.max(ActionValues) ConvergeFlag = ConvergeFlag & (np.abs(V[StateIdx]-UpdatedValue) < theta) V[StateIdx] = UpdatedValue if ConvergeFlag: break for StateIdx, StateName in enumerate(env.P.keys()): StateInfo = env.P[StateName] ActionValues = np.zeros(env.nA) for ActionIdx, ActionName in enumerate(StateInfo.keys()): # For now assume that all probabilities are 1 ActionInfo = StateInfo[ActionName][0] Reward = ActionInfo[2] NextState = ActionInfo[1] NextStateValue = V[NextState] ActionValues[ActionIdx] = Reward + discount_factorNextStateValue MaxValueIdx = np.argmax(ActionValues) Policy[StateIdx,:] = 0 Policy[StateIdx,MaxValueIdx] = 1 return Policy, V	[ 1, 1053, 12655, 408, 7442, 13, 13, 1753, 995, 29918, 1524, 362, 29898, 6272, 29892, 278, 941, 29922, 29900, 29889, 29900, 29900, 29900, 29896, 29892, 2313, 792, 29918, 19790, 29922, 29896, 29889, 29900, 1125, 13, 1678, 9995, 13, 1678, 7865, 20504, 362, 29068, 29889, 13, 268, 13, 1678, 826, 3174, 29901, 13, 4706, 8829, 29901, 4673, 23869, 5177, 29889, 8829, 29889, 29925, 11524, 278, 9558, 2070, 11614, 310, 278, 5177, 29889, 13, 4706, 278, 941, 29901, 6639, 3262, 16897, 29889, 960, 278, 995, 310, 599, 5922, 3620, 3109, 1135, 278, 941, 13, 9651, 297, 697, 12541, 591, 526, 2309, 29889, 13, 4706, 2313, 792, 29918, 19790, 29901, 14013, 931, 2313, 792, 7329, 29889, 13, 308, 13, 1678, 16969, 29901, 13, 4706, 319, 18761, 313, 22197, 29892, 478, 29897, 310, 278, 14413, 8898, 322, 278, 14413, 995, 740, 29889, 308, 13, 1678, 9995, 13, 13, 1678, 478, 353, 7442, 29889, 3298, 359, 29898, 6272, 29889, 29876, 29903, 29897, 13, 1678, 25219, 353, 7442, 29889, 3298, 359, 4197, 6272, 29889, 29876, 29903, 29892, 8829, 29889, 29876, 29909, 2314, 13, 268, 13, 1678, 1550, 5852, 29901, 13, 13, 4706, 1281, 369, 479, 21979, 353, 5852, 13, 13, 4706, 363, 4306, 1204, 29916, 29892, 4306, 1170, 297, 26985, 29898, 6272, 29889, 29925, 29889, 8149, 580, 1125, 13, 13, 9651, 9123, 9065, 353, 7442, 29889, 3298, 359, 29898, 6272, 29889, 29876, 29909, 29897, 13, 632, 13, 9651, 4306, 3401, 353, 8829, 29889, 29925, 29961, 2792, 1170, 29962, 13, 632, 13, 9651, 363, 9123, 1204, 29916, 29892, 9123, 1170, 297, 26985, 29898, 2792, 3401, 29889, 8149, 580, 1125, 13, 462, 13, 18884, 396, 1152, 1286, 5251, 393, 599, 2070, 11614, 526, 29871, 29896, 13, 18884, 9123, 3401, 353, 4306, 3401, 29961, 4276, 1170, 3816, 29900, 29962, 13, 462, 13, 18884, 390, 809, 538, 353, 9123, 3401, 29961, 29906, 29962, 13, 18884, 8084, 2792, 353, 9123, 3401, 29961, 29896, 29962, 13, 18884, 8084, 2792, 1917, 353, 478, 29961, 9190, 2792, 29962, 13, 18884, 9123, 9065, 29961, 4276, 1204, 29916, 29962, 353, 390, 809, 538, 718, 2313, 792, 29918, 19790, 29930, 9190, 2792, 1917, 13, 632, 13, 9651, 25723, 1917, 353, 7442, 29889, 3317, 29898, 4276, 9065, 29897, 13, 9651, 1281, 369, 479, 21979, 353, 1281, 369, 479, 21979, 669, 313, 9302, 29889, 6897, 29898, 29963, 29961, 2792, 1204, 29916, 29962, 29899, 29248, 1917, 29897, 529, 278, 941, 29897, 13, 9651, 478, 29961, 2792, 1204, 29916, 29962, 353, 25723, 1917, 13, 13, 4706, 565, 1281, 369, 479, 21979, 29901, 2867, 13, 13, 1678, 363, 4306, 1204, 29916, 29892, 4306, 1170, 297, 26985, 29898, 6272, 29889, 29925, 29889, 8149, 580, 1125, 13, 13, 4706, 4306, 3401, 353, 8829, 29889, 29925, 29961, 2792, 1170, 29962, 13, 4706, 9123, 9065, 353, 7442, 29889, 3298, 359, 29898, 6272, 29889, 29876, 29909, 29897, 13, 13, 4706, 363, 9123, 1204, 29916, 29892, 9123, 1170, 297, 26985, 29898, 2792, 3401, 29889, 8149, 580, 1125, 13, 632, 13, 9651, 396, 1152, 1286, 5251, 393, 599, 2070, 11614, 526, 29871, 29896, 13, 9651, 9123, 3401, 353, 4306, 3401, 29961, 4276, 1170, 3816, 29900, 29962, 13, 632, 13, 9651, 390, 809, 538, 353, 9123, 3401, 29961, 29906, 29962, 13, 9651, 8084, 2792, 353, 9123, 3401, 29961, 29896, 29962, 13, 9651, 8084, 2792, 1917, 353, 478, 29961, 9190, 2792, 29962, 13, 9651, 9123, 9065, 29961, 4276, 1204, 29916, 29962, 353, 390, 809, 538, 718, 2313, 792, 29918, 19790, 29930, 9190, 2792, 1917, 13, 308, 13, 4706, 5918, 1917, 1204, 29916, 353, 7442, 29889, 1191, 3317, 29898, 4276, 9065, 29897, 13, 4706, 25219, 29961, 2792, 1204, 29916, 29892, 17531, 353, 29871, 29900, 13, 4706, 25219, 29961, 2792, 1204, 29916, 29892, 7976, 1917, 1204, 29916, 29962, 353, 29871, 29896, 13, 632, 13, 1678, 736, 25219, 29892, 478, 2 ]
survae/tests/transforms/bijections/coupling/coupling_linear.py	alisiahkoohi/survae_flows	262	99673	import numpy as np import torch import torch.nn as nn import torchtestcase import unittest from survae.transforms.bijections.coupling import * from survae.nn.layers import ElementwiseParams, ElementwiseParams2d from survae.tests.transforms.bijections import BijectionTest class AdditiveCouplingBijectionTest(BijectionTest): def test_bijection_is_well_behaved(self): batch_size = 10 self.eps = 1e-6 for shape in [(6,), (6,8,8)]: for num_condition in [None, 1]: with self.subTest(shape=shape, num_condition=num_condition): x = torch.randn(batch_size, shape) if num_condition is None: if len(shape) == 1: net = nn.Linear(3,3) if len(shape) == 3: net = nn.Conv2d(3,3, kernel_size=3, padding=1) else: if len(shape) == 1: net = nn.Linear(1,5) if len(shape) == 3: net = nn.Conv2d(1,5, kernel_size=3, padding=1) bijection = AdditiveCouplingBijection(net, num_condition=num_condition) self.assert_bijection_is_well_behaved(bijection, x, z_shape=(batch_size, shape)) z, _ = bijection.forward(x) if num_condition is None: self.assertEqual(x[:,:3], z[:,:3]) else: self.assertEqual(x[:,:1], z[:,:1]) class AffineCouplingBijectionTest(BijectionTest): def test_bijection_is_well_behaved(self): batch_size = 10 self.eps = 5e-6 for shape in [(6,), (6,8,8)]: for num_condition in [None, 1]: with self.subTest(shape=shape, num_condition=num_condition): x = torch.randn(batch_size, shape) if num_condition is None: if len(shape) == 1: net = nn.Sequential(nn.Linear(3,32), ElementwiseParams(2)) if len(shape) == 3: net = nn.Sequential(nn.Conv2d(3,32, kernel_size=3, padding=1), ElementwiseParams2d(2)) else: if len(shape) == 1: net = nn.Sequential(nn.Linear(1,52), ElementwiseParams(2)) if len(shape) == 3: net = nn.Sequential(nn.Conv2d(1,52, kernel_size=3, padding=1), ElementwiseParams2d(2)) bijection = AffineCouplingBijection(net, num_condition=num_condition) 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tests/bdd/steps/test_calculate_season.py	nora-ns/allseasons	4	1614974	from pytest_bdd import scenario, given, when, then from pytest_bdd import parsers from urllib.parse import urljoin import pytest import sys # pytestmark means it this mark will apply to all tests in the module pytestmark = pytest.mark.skipif(sys.platform in ('win32', 'cygwin', 'darwin'), reason="firefox tests don't work in Windows/OSX") # @pytest.mark.django_db tells the tests that we intend to access the DB @pytest.mark.django_db @scenario('../features/calculate_season.feature', 'First form') def test_form1(): pass @pytest.mark.django_db @scenario('../features/calculate_season.feature', 'Second form') def test_form2(): pass # browser fixture comes from pytest-splinter # live_server fixture comes from pytest-django @when('I go to the convert page') def go_to_convert(browser, live_server): browser.visit(urljoin(str(live_server), '/convert/')) @when(parsers.parse('I fill out the name "{name}"')) def fill_out_name(browser, name): browser.fill_form({ '0-name': name, }) @when(parsers.parse('I fill out the date as [{day}] [{month}] [{year}]')) def fill_out_date(browser, day, month, year): browser.fill_form({ '0-date_day': day, '0-date_month': month, '0-date_year': year, }) @when(parsers.parse('I select the location as Latitude {lat} Longitude {lng}')) def select_the_location(browser, lat, lng): browser.fill_form({ '0-location_0': lat, '0-location_1': lng, }) @when('I submit the form') def submit_form(browser): browser.find_by_css('button[type=submit]').first.click() @then(parsers.parse('I should see the following available calendars:\n{text}')) def see_calendars(browser, text): expected = text.split('\n') elements = [el.text for el in browser.find_by_css('.radio')] assert elements == expected @given(parsers.parse('I have submitted the first form as follows:\n{text}')) def submitted_first_form(browser, live_server, text): items = dict([item.split(': ') for item in text.split('\n')]) go_to_convert(browser, live_server) fill_out_name(browser, items['name']) fill_out_date(browser, items['day'], items['month'], items['year']) select_the_location(browser, items['lat'], items['lng']) submit_form(browser) @when(parsers.parse('I select the calendar "{calendar}"')) def select_the_seasonset(browser, calendar): browser.choose('1-calendar', calendar) @then(parsers.parse('I should see "{expected}"')) def see_text(browser, expected): el = browser.find_by_css('#result').first assert el.text == expected	[ 1, 515, 11451, 1688, 29918, 29890, 1289, 1053, 10483, 29892, 2183, 29892, 746, 29892, 769, 13, 3166, 11451, 1688, 29918, 29890, 1289, 1053, 610, 4253, 13, 3166, 3142, 1982, 29889, 5510, 1053, 3142, 7122, 13, 5215, 11451, 1688, 13, 5215, 10876, 13, 13, 29937, 11451, 1688, 3502, 2794, 372, 445, 2791, 674, 3394, 304, 599, 6987, 297, 278, 3883, 13, 13, 2272, 1688, 3502, 353, 11451, 1688, 29889, 3502, 29889, 11014, 361, 29898, 9675, 29889, 12120, 297, 6702, 5080, 29941, 29906, 742, 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maskrcnn_benchmark/modeling/roi_heads/box_head/loss.py	RyanXLi/OneshotDet	16	25466	<reponame>RyanXLi/OneshotDet<gh_stars>10-100 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. import torch from torch.nn import functional as F from maskrcnn_benchmark.layers import smooth_l1_loss, SigmoidFocalLoss from maskrcnn_benchmark.modeling.box_coder import BoxCoder from maskrcnn_benchmark.modeling.matcher import Matcher from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou from maskrcnn_benchmark.modeling.balanced_positive_negative_sampler import ( BalancedPositiveNegativeSampler ) from maskrcnn_benchmark.modeling.utils import cat class FastRCNNLossComputation(object): """ Computes the loss for Faster R-CNN. Also supports FPN """ def __init__( self, cfg, proposal_matcher, fg_bg_sampler, box_coder, cls_agnostic_bbox_reg=False ): """ Arguments: proposal_matcher (Matcher) fg_bg_sampler (BalancedPositiveNegativeSampler) box_coder (BoxCoder) """ self.cfg = cfg self.proposal_matcher = proposal_matcher self.fg_bg_sampler = fg_bg_sampler self.box_coder = box_coder self.cls_agnostic_bbox_reg = cls_agnostic_bbox_reg if cfg.FEW_SHOT.SECOND_STAGE_CLS_LOSS == 'focal_loss': self.cls_loss_func = SigmoidFocalLoss( cfg.MODEL.FCOS.LOSS_GAMMA, cfg.FEW_SHOT.SECOND_STAGE_LOSS_ALPHA ) def match_targets_to_proposals(self, proposal, target): match_quality_matrix = boxlist_iou(target, proposal) # (M, N) matched_idxs = self.proposal_matcher(match_quality_matrix) # -1, -2 (N) ############################### ### create soft labels ### currently only assume 1 class ############################### if self.cfg.FEW_SHOT.SOFT_LABELING: match_iou_matrix = match_quality_matrix.t() # (N, M) # assert match_iou_matrix.size(1) == 2, 'only supporting 1 classes right now, but received {} classes'.format(match_iou_matrix.size(1)) # assert torch.sum(match_iou_matrix[torch.nonzero(matched_idxs<1), 1]) == 0, \ # ['positive class column of non-positive prediction should all be zero ! ', # torch.sum(match_iou_matrix[torch.nonzero(matched_idxs>0), 1])] matched_idxs_temp = matched_idxs.clone() matched_idxs_temp[matched_idxs_temp<0] = 0 match_iou = match_iou_matrix[torch.arange(len(matched_idxs_temp)).long(), matched_idxs_temp].clone() matched_idxs_invalid_inds = torch.nonzero(matched_idxs<0) match_iou[matched_idxs_invalid_inds] = 0 # match_iou_matrix_max, _ = match_iou_matrix.max(dim=1) # match_iou_big_iou = torch.nonzero(match_iou_matrix_max>0.5).squeeze(1) # print(match_iou_big_iou) # print('iiou', torch.cat([match_iou[match_iou_big_iou].unsqueeze(1), match_iou_matrix[match_iou_big_iou]], dim=1)) # Fast RCNN only need "labels" field for selecting the targets target = target.copy_with_fields("labels") # get the targets corresponding GT for each proposal # NB: need to clamp the indices because we can have a single # GT in the image, and matched_idxs can be -2, which goes # out of bounds matched_targets = target[matched_idxs.clamp(min=0)] if self.cfg.FEW_SHOT.SOFT_LABELING: matched_targets.add_field('soft_labels', match_iou) matched_targets.add_field("matched_idxs", matched_idxs) return matched_targets def soft_labeling_function(self, t): ''' discrete: t >= 0.5 linear : t transLinear: (0.2t+0.8) (t>=0.5) + 0.5 ~ 1 --> 0.9 ~ 1 (2.25t-0.225) (t>=0.1) * (t<0.5) + 0.1 ~ 0.5 --> 0 ~ 0.9 0 0 ~ 0.1 trans4thLinear: (0.2t + 0.8) (t>=0.5) + 0.5 ~ 1 --> 0.9 ~ 1 0.9(2t)*4 0 ~ 0.5 --> 0 ~ 0.9 ''' if self.cfg.FEW_SHOT.SOFT_LABELING_FUNC == 'discrete': return (t>=0.5).float() elif self.cfg.FEW_SHOT.SOFT_LABELING_FUNC == 'linear': return t elif self.cfg.FEW_SHOT.SOFT_LABELING_FUNC == 'transLinear': # transitional linear upper = (0.2t+0.8) * (t>=0.5).float() middle = (2.25t-0.225) (t>=0.1).float() * (t<0.5).float() lower = 0 return upper+middle+lower elif self.cfg.FEW_SHOT.SOFT_LABELING_FUNC == 'trans4thLinear': # transitional 4th order linear upper = (0.2t+0.8) (t>=0.5).float() lower = 0.9((2t)*4) (t<0.5).float() return upper+lower def prepare_targets(self, proposals, targets): labels = [] regression_targets = [] if self.cfg.FEW_SHOT.SOFT_LABELING: soft_labels = [] for proposals_per_image, targets_per_image in zip(proposals, targets): matched_targets = self.match_targets_to_proposals( # -1, -2 proposals_per_image, targets_per_image ) matched_idxs = matched_targets.get_field("matched_idxs") if self.cfg.FEW_SHOT.SOFT_LABELING: soft_labels_per_image = self.soft_labeling_function(matched_targets.get_field("soft_labels")) soft_labels.append(soft_labels_per_image) labels_per_image = matched_targets.get_field("labels") labels_per_image = labels_per_image.to(dtype=torch.int64) # Label background (below the low threshold) bg_inds = matched_idxs == Matcher.BELOW_LOW_THRESHOLD labels_per_image[bg_inds] = 0 # Label ignore proposals (between low and high thresholds) ignore_inds = matched_idxs == Matcher.BETWEEN_THRESHOLDS labels_per_image[ignore_inds] = -1 # -1 is ignored by sampler # compute regression targets regression_targets_per_image = self.box_coder.encode( matched_targets.bbox, proposals_per_image.bbox ) labels.append(labels_per_image) regression_targets.append(regression_targets_per_image) if self.cfg.FEW_SHOT.SOFT_LABELING: return labels, soft_labels, regression_targets return labels, regression_targets # def sample_proposal_per_box(self, box, image_size, iou_range=(0.5, 0.9), num=100): # ''' # receive box (x1, y1, x2, y2) # ''' # def uniSample(a, b): # return torch.rand(1).squeeze()(b-a) + a # def b_area(box): # return (box[2]-box[0])(box[3]-box[1]) # def b_iou(box1, box2): # l = torch.max(box1[0], box2[0]) # t = torch.max(box1[1], box2[1]) # r = torch.min(box1[2], box2[2]) # b = torch.min(box1[3], box2[3]) # area = (r-l)(b-t) # return area / ( b_area(box1) + b_area(box2) - area ) # x1, y1, x2, y2 = box # W, H = image_size # assert x1>=0 and x1<=x2 and x2<W and \ # y1>=0 and y1<=y2 and y2<H, 'input box not valid {} {} {} {}'.format(x1, y1, x2, y2) # center_x, center_y = (x1+x2)/2, (y1+y2)/2 # box_w = x2-x1+1 # box_h = y2-y1+1 # proposals = [] # while len(proposals) < num: # rand_iou = uniSample(iou_range) # restrict to iou range # min_iou_term = 2rand_iou/(rand_iou+1) # xc_prop = uniSample(0, 0.3333) # restrict to 0~1/3 # yc_prop_min = torch.max(0., min_iou_term) # yc_prop_max = torch.min(0.3333, min_iou_term) # if yc_prop_max <= yc_prop_min: # continue # yc_prop = uniSample(yc_prop_min, yc_prop_max) # xc_dir = torch.randint(2).float()2-1 # xc = center_x + xc_dirbox_wxc_prop # yc_dir = torch.randint(2).float()2-1 # yc = center_y + yc_dirbox_hyc_prop # for i in range(100): # each center location only try at most 100 times, resort another center location if not # width_min = rand_ioubox_w, # width_max = box_w/rand_iou # width = uniSample(width_min, width_max) # height_min = rand_ioubox_h / min(width, box_w) # height_max = box_h/rand_iou # height = uniSample(height_min, height_max) # rand_box = torch.tensor( [int(xc-width/2), int(yc-height/2), int(xc+width/2), int(yc+height/2)]).float() # rand_box.clamp_(0, image_size-1) # if b_iou(rand_box, box) >= rand_iou: # break # proposals.append(rand_box) # proposals = torch.stack(proposals, dim=0) # (num, 4) xyxy # return proposals # def handcraft_sample_proposals(self, targets, num_per_image=100): # proposals = [] # labels = [] # num_sample_per_box = 100 # for targets_per_image in targets: # image_size = targets_per_image.size # bboxes_per_image = targets_per_image.bbox # labels_per_image = targets.get_field('labels') # proposals_per_image = [] # labels_per_image = [] # for bbox, label in zip(bboxes_per_image, labels_per_image): # sampled_bboxes = self.sample_proposal_per_box(bbox, image_size, iou_range=(0.5, 0.9), num=num_sample_per_box) # sampled_labels = label.unsqueeze().repeat(len(sampled_bboxes)) # proposals_per_image.append(sampled_bboxes) # labels_per_image.append(sampled_labels) # proposals_per_image = torch.cat(proposals_per_image, dim=0) # labels_per_image = torch.cat(labels_per_image, dim=0) # if len(proposals_per_image) > num_per_image: # inds = torch.randperm(len(proposals_per_image)) # selected_inds = inds[:num_per_image] # proposals_per_image = proposals_per_image[selected_inds] # labels_per_image = labels_per_image[selected_inds] # assert len(proposals_per_image) == num_per_image, [ len(proposals_per_image), num_per_image ] # proposals.append(proposals_per_image) # labels.append(labels_per_image) # return proposals, labels def subsample(self, proposals, targets): """ This method performs the positive/negative sampling, and return the sampled proposals. Note: this function keeps a state. Arguments: proposals (list[BoxList]) targets (list[BoxList]) """ # handcrafted_proposals, handcrafted_labels = self.handcraft_sample_proposals(proposals, targets) # assert len(handcrafted_proposals) == len(proposals), [len(handcrafted_proposals), len(proposals)] # proposals = [torch.cat([proposals[i], handcrafted_proposals[i]]) for i in range(len(proposals))] if self.cfg.FEW_SHOT.SOFT_LABELING: labels, soft_labels, regression_targets = self.prepare_targets(proposals, targets) else: labels, regression_targets = self.prepare_targets(proposals, targets) sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels, neg_supp=self.cfg.FEW_SHOT.NEG_SUPPORT.TURN_ON) # print('pos:', len(sampled_pos_inds[0].nonzero()), 'neg', len(sampled_neg_inds[0].nonzero())) proposals = list(proposals) # add corresponding label and regression_targets information to the bounding boxes if self.cfg.FEW_SHOT.SOFT_LABELING: for labels_per_image, soft_labels_per_image, regression_targets_per_image, proposals_per_image in zip( labels, soft_labels, regression_targets, proposals ): proposals_per_image.add_field("labels", labels_per_image) proposals_per_image.add_field("soft_labels", soft_labels_per_image) proposals_per_image.add_field( "regression_targets", regression_targets_per_image ) else: for labels_per_image, regression_targets_per_image, proposals_per_image in zip( labels, regression_targets, proposals ): proposals_per_image.add_field("labels", labels_per_image) proposals_per_image.add_field( "regression_targets", regression_targets_per_image ) # distributed sampled proposals, that were obtained on all feature maps # concatenated via the fg_bg_sampler, into individual feature map levels for img_idx, (pos_inds_img, neg_inds_img) in enumerate( zip(sampled_pos_inds, sampled_neg_inds) ): img_sampled_inds = torch.nonzero(pos_inds_img \| neg_inds_img).squeeze(1) proposals_per_image = proposals[img_idx][img_sampled_inds] # assert torch.sum(proposals_per_image.get_field('labels').float() - proposals_per_image.get_field('soft_labels'))==0, \ # [proposals_per_image.get_field('labels'), proposals_per_image.get_field('soft_labels')] proposals[img_idx] = proposals_per_image self._proposals = proposals return proposals def CXE(self, predicted, target): my_target = torch.stack([1-target, target], dim=1) return -(my_target torch.log(predicted)).mean() def FOCAL_LOSS(self, predicted_diff): ''' multi cls focal loss ''' EPISILON = 1e-6 log_pt = torch.log(1-predicted_diff + EPISILON) return - ((predicted_diff) * log_pt).mean() def __call__(self, class_logits, box_regression, neg_class_logits=None, rev_class_logits=None, gt_label=-1): """ Computes the loss for Faster R-CNN. This requires that the subsample method has been called beforehand. Arguments: class_logits (list[Tensor]) box_regression (list[Tensor]) Returns: classification_loss (Tensor) box_loss (Tensor) """ class_logits = cat(class_logits, dim=0) box_regression = cat(box_regression, dim=0) if self.cfg.FEW_SHOT.REVERSE_ORDER: assert rev_class_logits is not None rev_class_logits = cat(rev_class_logits, dim=0) device = class_logits.device if not hasattr(self, "_proposals"): raise RuntimeError("subsample needs to be called before") proposals = self._proposals if self.cfg.FEW_SHOT.SOFT_LABELING: soft_labels = cat([proposal.get_field("soft_labels") for proposal in proposals], dim=0) labels = cat([proposal.get_field("labels") for proposal in proposals], dim=0) regression_targets = cat( [proposal.get_field("regression_targets") for proposal in proposals], dim=0 ) if gt_label == -1: N = labels.size(0) pos_inds = torch.nonzero(labels > 0) if self.cfg.FEW_SHOT.SECOND_STAGE_CLS_LOSS == 'focal_loss': classification_loss = self.cls_loss_func( class_logits, labels.int() ) / max(pos_inds.numel(), 1) elif self.cfg.FEW_SHOT.SECOND_STAGE_CLS_LOSS == 'ce_loss': if self.cfg.FEW_SHOT.LOSS_WEIGHTED: fg_weight = 0.75 if class_logits.size(1) == 2: weight = torch.tensor([1-fg_weight, fg_weight]).cuda(class_logits.get_device()).float() elif class_logits.size(1) == 3: weight = torch.tensor([1-fg_weight, fg_weight, fg_weight]).cuda(class_logits.get_device()).float() else: raise Exception('class logits dimention wrong, can only be 2 or 3 for softmax ce loss') classification_loss = F.cross_entropy(class_logits, labels, weight=weight) # change to focal loss else: classification_loss = F.cross_entropy(class_logits, labels) # change to focal loss elif self.cfg.FEW_SHOT.SECOND_STAGE_CLS_LOSS == 'mse_loss' and self.cfg.FEW_SHOT.SOFT_LABELING: classification_loss = torch.mean((class_logits.sigmoid()-soft_labels)2) elif self.cfg.FEW_SHOT.SECOND_STAGE_CLS_LOSS == 'mse_loss' and not self.cfg.FEW_SHOT.SOFT_LABELING: classification_loss = torch.mean((class_logits.sigmoid()-labels.float())2) elif self.cfg.FEW_SHOT.SECOND_STAGE_CLS_LOSS == 'l1_loss' and self.cfg.FEW_SHOT.SOFT_LABELING: classification_loss = torch.mean(torch.abs(class_logits.sigmoid()-soft_labels)) elif self.cfg.FEW_SHOT.SECOND_STAGE_CLS_LOSS == 'cxe_loss' and self.cfg.FEW_SHOT.SOFT_LABELING: classification_loss = self.CXE(class_logits.softmax(dim=1), soft_labels) else: raise Exception('clasification loss of second stage not valid') if self.cfg.FEW_SHOT.REVERSE_ORDER: reverse_cls_loss = self.FOCAL_LOSS( torch.abs(class_logits.softmax(dim=-1)-rev_class_logits.softmax(dim=-1)) ) # get indices that correspond to the regression targets for # the corresponding ground truth labels, to be used with # advanced indexing sampled_pos_inds_subset = torch.nonzero(labels > 0).squeeze(1) labels_pos = labels[sampled_pos_inds_subset] if self.cls_agnostic_bbox_reg: map_inds = torch.tensor([4, 5, 6, 7], device=device) else: map_inds = 4 * labels_pos[:, None] + torch.tensor( [0, 1, 2, 3], device=device) box_loss = smooth_l1_loss( box_regression[sampled_pos_inds_subset[:, None], map_inds], regression_targets[sampled_pos_inds_subset], size_average=False, beta=1, ) box_loss = box_loss / labels.numel() else: # only calculating loss of bboxes belonging to gt_label and mse loss N = labels.size(0) cls_labels = labels.clone() cls_labels[torch.nonzero(cls_labels!=gt_label)] = 0 cls_labels[torch.nonzero(cls_labels==gt_label)] = 1 pos_inds = torch.nonzero(cls_labels > 0) if self.cfg.FEW_SHOT.SECOND_STAGE_CLS_LOSS == 'focal_loss': classification_loss = self.cls_loss_func( class_logits, cls_labels.int() ) / ( pos_inds.numel() + N ) else: classification_loss = F.cross_entropy(class_logits, cls_labels) # change to focal loss # get indices that correspond to the regression targets for # the corresponding ground truth labels, to be used with # advanced indexing if cls_labels.numel() == 0: box_loss = torch.tensor(0).float().cuda(device) else: sampled_pos_inds_subset = torch.nonzero(cls_labels > 0).squeeze(1) labels_pos = cls_labels[sampled_pos_inds_subset] if self.cls_agnostic_bbox_reg: map_inds = torch.tensor([4, 5, 6, 7], device=device) else: map_inds = 4 * labels_pos[:, None] + torch.tensor( [0, 1, 2, 3], device=device) box_loss = smooth_l1_loss( box_regression[sampled_pos_inds_subset[:, None], map_inds], regression_targets[sampled_pos_inds_subset], size_average=False, beta=1, ) box_loss = box_loss / cls_labels.numel() if self.cfg.FEW_SHOT.REVERSE_ORDER: return classification_loss, box_loss, reverse_cls_loss # add in new neg support linz if neg_class_logits is not None: neg_class_logits = cat(neg_class_logits, dim=0) focus_neg_class_logits = neg_class_logits[labels==1] focus_pos_class_logits = class_logits[labels==1] # focus_labels = labels[labels==1] focus_neg_class_scores = focus_neg_class_logits.softmax(dim=1)[:,1] focus_pos_class_scores = focus_pos_class_logits.softmax(dim=1)[:,1] cls_suppress_loss = F.relu(focus_neg_class_scores-focus_pos_class_scores+0.3).mean() # cls_suppress_loss = F.cross_entropy(focus_neg_class_logits, 1-focus_labels) # change to focal loss return classification_loss, box_loss, cls_suppress_loss return classification_loss, box_loss def make_roi_box_loss_evaluator(cfg): matcher = Matcher( cfg.MODEL.ROI_HEADS.FG_IOU_THRESHOLD, cfg.MODEL.ROI_HEADS.BG_IOU_THRESHOLD, allow_low_quality_matches=False, ) bbox_reg_weights = cfg.MODEL.ROI_HEADS.BBOX_REG_WEIGHTS box_coder = BoxCoder(weights=bbox_reg_weights) fg_bg_sampler = BalancedPositiveNegativeSampler( cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE, cfg.MODEL.ROI_HEADS.POSITIVE_FRACTION ) cls_agnostic_bbox_reg = cfg.MODEL.CLS_AGNOSTIC_BBOX_REG loss_evaluator = FastRCNNLossComputation( cfg, matcher, fg_bg_sampler, box_coder, cls_agnostic_bbox_reg ) return loss_evaluator	[ 1, 529, 276, 1112, 420, 29958, 29934, 10094, 29990, 23410, 29914, 2951, 267, 8711, 6362, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 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databass/exprutil.py	ivychen/databass-public	6	86626	from itertools import chain from .exprs import * def predicate_to_cnf(e): """ Split AND tree into a list of conditions """ if not e: return [] if e.is_type("Expr") and e.op.lower() == "and": return list(chain(*map(predicate_to_cnf, [e.l, e.r]))) if e.is_type("Paren"): return [e.c] return [e] def cnf_to_predicate(conds): """ turn list of conditions into an AND tree """ if not conds: return None cond = conds[0] for c in conds[1:]: cond = Expr("and", cond, c) return cond	[ 1, 515, 4256, 8504, 1053, 9704, 13, 3166, 869, 13338, 29879, 1053, 334, 13, 13, 1753, 24384, 29918, 517, 29918, 18038, 29888, 29898, 29872, 1125, 13, 29871, 9995, 13, 29871, 26178, 5300, 5447, 964, 263, 1051, 310, 5855, 13, 29871, 9995, 13, 29871, 565, 451, 321, 29901, 13, 1678, 736, 5159, 13, 29871, 565, 321, 29889, 275, 29918, 1853, 703, 21176, 1159, 322, 321, 29889, 459, 29889, 13609, 580, 1275, 376, 392, 1115, 13, 1678, 736, 1051, 29898, 14153, 10456, 1958, 29898, 11965, 9593, 29918, 517, 29918, 18038, 29888, 29892, 518, 29872, 29889, 29880, 29892, 321, 29889, 29878, 29962, 4961, 13, 29871, 565, 321, 29889, 275, 29918, 1853, 703, 2177, 264, 29908, 1125, 13, 1678, 736, 518, 29872, 29889, 29883, 29962, 13, 29871, 736, 518, 29872, 29962, 13, 13, 1753, 274, 29876, 29888, 29918, 517, 29918, 11965, 9593, 29898, 1116, 29879, 1125, 13, 29871, 9995, 13, 29871, 2507, 1051, 310, 5855, 964, 385, 5300, 5447, 13, 29871, 9995, 13, 29871, 565, 451, 2148, 29879, 29901, 736, 6213, 13, 29871, 2148, 353, 2148, 29879, 29961, 29900, 29962, 13, 29871, 363, 274, 297, 2148, 29879, 29961, 29896, 29901, 5387, 13, 1678, 2148, 353, 1222, 558, 703, 392, 613, 2148, 29892, 274, 29897, 13, 29871, 736, 2148, 13, 13, 13, 2 ]
nesta/packages/worldbank/collect_worldbank.py	anniyanvr/nesta	13	1604553	""" Collect worldbank ================= Collect worldbank sociodemographic data by country. """ import requests from retrying import retry import json from collections import defaultdict import re import math import pandas as pd WORLDBANK_ENDPOINT = "http://api.worldbank.org/v2/{}" DEAD_RESPONSE = (None, None) # tuple to match the default python return type def worldbank_request(suffix, page, per_page=10000, data_key_path=None): """Hit the worldbank API and extract metadata and data from the response. Args: suffix (str): Suffix to append to :obj:`WORLDBANK_ENDPOINT`. page (int): Pagination number in API request. per_page (int): Number of results to return per request. data_key_path (list): List specifying json path to data object. Returns: metadata, data (dict, list): Metadata and data from API response. """ response = _worldbank_request(suffix=suffix, page=page, per_page=per_page) metadata, data = data_from_response(response=response, data_key_path=data_key_path) return metadata, data @retry(stop_max_attempt_number=3, wait_fixed=2000) def _worldbank_request(suffix, page, per_page): """Hit the worldbank API and return the response. Args: suffix (str): Suffix to append to :obj:`WORLDBANK_ENDPOINT`. page (int): Pagination number in API request. per_page (int): Number of results to return per request. Returns: response (:obj:`requests.Response`) """ # Hit the API r = requests.get(WORLDBANK_ENDPOINT.format(suffix), params=dict(per_page=per_page, format="json", page=page)) # There are some non-404 status codes which indicate invalid API request if r.status_code == 400: return DEAD_RESPONSE r.raise_for_status() # There are even some 200 status codes which indicate invalid API request # purely by returning non-json data response = DEAD_RESPONSE try: response = r.json() except json.JSONDecodeError: pass finally: return response def data_from_response(response, data_key_path=None): """Split up the response from the worldbank API. Args: response (tuple): Response from worldbank API, expected to be a tuple of two json items. data_key_path (list): List specifying json path to data object. Returns: metadata, data (dict, list): Metadata and data from API response. """ # If the data is stored ({metadata}, [datarows]) if data_key_path is None or response == DEAD_RESPONSE: metadata, datarows = response # Otherwise if the data is stored as {metadata, path:{[to:data]}} # (or similar) else: metadata = response datarows = response.copy() for key in data_key_path: datarows = datarows[key] if key != data_key_path[-1] and type(datarows) is list: datarows = datarows[0] return metadata, datarows def calculate_number_of_api_pages(suffix, per_page=10000, data_key_path=None): """Calculate the number of API scrolls required to paginate through this request. Args: suffix (str): Suffix to append to :obj:`WORLDBANK_ENDPOINT`. per_page (int): Number of results to return per request. data_key_path (list): List specifying json path to data object. Returns: n_pages (int): Number of API scrolls required. """ # Discover the shape of the data by inspecting the metadata with # a tiny request (1 result, 1 page) metadata, _ = worldbank_request(suffix=suffix, page=1, per_page=1, data_key_path=data_key_path) # If the request was invalid, there are no pages if metadata is None: return 0 # Calculate the number of pages required total = int(metadata["total"]) n_pages = math.floor(total / per_page) + int(total % per_page > 0) return n_pages def worldbank_data_interval(suffix, first_page, last_page, per_page=10000, data_key_path=None): """Yield a row of data from worldbank API in a page interval. Args: suffix (str): Suffix to append to :obj:`WORLDBANK_ENDPOINT`. {first, last}_page (int): First (last) page number of the API request. per_page (int): Number of results to return per request. data_key_path (list): List specifying json path to data object. Yields: row (dict): A row of data from the worldbank API. """ for page in range(first_page, last_page+1): _, datarows = worldbank_request(suffix=suffix, page=page, per_page=per_page, data_key_path=data_key_path) if datarows is None: continue for row in datarows: yield row def worldbank_data(suffix, per_page=10000, data_key_path=None): """Yield a row of data from worldbank API in a page interval. Args: suffix (str): Suffix to append to :obj:`WORLDBANK_ENDPOINT`. per_page (int): Number of results to return per request. data_key_path (list): List specifying json path to data object. Yields: row (dict): A row of data from the worldbank API. """ n_pages = calculate_number_of_api_pages(suffix=suffix, per_page=per_page, data_key_path=data_key_path) return worldbank_data_interval(suffix, first_page=1, last_page=n_pages, per_page=per_page, data_key_path=data_key_path) def get_worldbank_resource(resource): """Extract and flatten all data for one worldbank resource. Args: resource (str): One of "countries", "series" or "source" Returns: collection (list): A list of resource data. """ collection = [] for row in worldbank_data(resource): # Flatten out any data stored by a key named "value" data = {} for k, v in row.items(): if type(v) is dict: v = v["value"] data[k] = v collection.append(data) return collection def get_variables_by_code(codes): """Discover all dataset locations for each variable id, by variable code. Note: one variable may exist in many datasets, which is handy in the case of missing data. Args: codes (list): The codes of all variables to be discovered. Returns: variables (dict): Mapping of variable id --> dataset names. """ key_path = ["source", "concept", "variable"] # Mapping variable id --> dataset names variables = defaultdict(list) sources = get_worldbank_resource("source") for source in sources: # Extract variables in this "source" (dataset) suffix = f"sources/{source['id']}/series/data" data = worldbank_data(suffix, data_key_path=key_path) # Filter out variables that we don't want filtered_data = filter(lambda row: (row['id'] in codes), data) # Assign remaining datasets to this variable for row in filtered_data: variables[row['id']].append(source['id']) return variables def unpack_quantity(row, concept, value): """Unpack row like {"variable": [{"concept":<concept>, <value>:_i_want_this_}]} Args: row (dict): Row of Worldbank API data. concept (str): The name of the dataset containing the variable. value (str): The name of the variable to unpack. Returns: A value. """ for quantity in row['variable']: if quantity['concept'] == concept: return quantity[value] raise NameError(f"No item found in {row['variable']} with " f"concept = {concept}") def unpack_data(row): """Unpack an entire row of Worldbank API data. Args: row (dict): Row of Worldbank API data. Returns: country, variable, time, value """ country = unpack_quantity(row, 'Country', 'id') #variable = unpack_quantity(row, 'Series', 'value') time = unpack_quantity(row, 'Time', 'value') value = row['value'] return country, time, value def get_country_data(variables, aliases, time="all"): """Extract data for specified variables for all available countries, in a specified year. Args: variables (dict): Mapping of variable --> dataset ids. aliases (dict): Mapping of dirty -> clean variable name. time (str): String to identify time period to request. Returns: country_data (dict): Mapping of country --> variable name --> value """ # Iterate through datasets country_data = defaultdict(dict) # lambda: defaultdict(list)) kwargs_list = get_country_data_kwargs(variables=variables, aliases=aliases, time=time) for kwargs in kwargs_list: _country_data = country_data_single_request(kwargs) for country, data in _country_data.items(): for var_name, data_row in data.items(): country_data[country][var_name] = data_row return country_data def get_country_data_kwargs(variables, aliases, time="all", per_page=10000, max_pages=None): """Generate every set of kwargs required to make single requests. Designed to be used with :obj:`country_data_single_request` in order to be batched. Args: variables (dict): Mapping of variable --> dataset ids. aliases (dict): Mapping of variable name aliases, to ensure consistent variable naming between data collections. per_page (int): Number of results to return per request. Returns: kwargs_list (list): kwargs list for :obj:`country_data_single_request`. """ # Iterate through datasets kwargs_list = [] key_path = ["source", "data"] for series, sources in variables.items(): # The name of a given variable varies subtlely across multiple # datasets, so we extract the variable name the first time for # consistency across datasets. alias = aliases[series] for source in sources: suffix = (f"sources/{source}/country/all/" f"series/{series}/time/{time}/data") n_pages = calculate_number_of_api_pages(suffix=suffix, per_page=per_page, data_key_path=key_path) for page in range(1, n_pages+1): if max_pages is not None and page > max_pages: break parameters = dict(alias=alias, suffix=suffix, first_page=page, last_page=page, per_page=per_page, data_key_path=key_path) kwargs_list.append(parameters) return kwargs_list def country_data_single_request(alias, kwargs): """Extract data for all countries using kwargs generated by :obj:`get_country_data_kwargs`. Args: kwargs (dict): An item returned by :obj:`get_country_data_kwargs`. Returns: country_data (dict): Mapping of country --> variable name --> value """ country_data = defaultdict(lambda: defaultdict(list)) done_pkeys = set() data = worldbank_data_interval(kwargs) for country, time, value in map(unpack_data, data): if value is None: # Missing data for this country continue pkey = (country, time) if pkey in done_pkeys: # Already done this country continue done_pkeys.add(pkey) new_row = {"value": value, "time": time} country_data[country][alias].append(new_row) return country_data # def flatten_country_data(country_data, country_metadata): # """Merge and flatten country data and metadata together. # Args: # country_data (dict): Mapping of country --> variable name --> value # country_metadata (list): List of country metadata. # Returns: # flat_country_data (list): Flattened country data and metadata. # """ # flat_country_data = [dict(country_data[metadata['id']], metadata) # for metadata in country_metadata # if metadata['id'] in country_data] # return flat_country_data def discover_variable_name(series): """Discover variable names from each series [short hand code]. Args: series (str): The short hand code for the variable name, according to Worldbank API. Returns: alias (str): The variable name for the given series. """ _, data = worldbank_request(f"en/indicator/{series}", page=1) alias = data[0]["name"] return alias def clean_variable_name(var_name): """Clean a single variable name ready for DB storage. Args: var_name (str): Variable name to be cleaned Returns: new_var_name (str): A MySQL compliant variable name. """ # Lower, replace '%', remove non-alphanums and use '_' new_var_name = var_name.lower().replace("%", "pc") new_var_name = re.sub('[^0-9a-zA-Z]+', ' ', new_var_name) new_var_name = new_var_name.lstrip().rstrip().replace(" ", "_") # Recursively shorten from middle character until less than 64 chars long # (this is the default MySQL limit) # Middle character has been chosen to allow some readability. while len(new_var_name) > 64: # Find the longest term longest_term = "" for term in new_var_name.split("_"): if len(term) <= len(longest_term): continue longest_term = term # Remove the middle character from the longest term middle = len(longest_term) - 1 new_term = longest_term[:middle] + longest_term[middle+1:] new_var_name = new_var_name.replace(longest_term, new_term) return new_var_name def clean_variable_names(flat_country_data): """Clean variable names ready for DB storage. Args: flat_country_data (list): Flattened country data. Returns: out_data (list): Same as input data, with MySQL compliant field names. """ out_data = [] for row in flat_country_data: new_row = {} for key, v in row.items(): # Only clean names containing spaces if " " not in key: new_row[key] = v continue new_key = clean_variable_name(key) new_row[new_key] = v out_data.append(new_row) return out_data def is_bad_quarter(x, bad_quarters): return any(q in x for q in bad_quarters) def flatten_country_data(country_data, country_metadata, bad_quarters=("Q1", "Q3", "Q4")): # Discover the year from the time variable. # Expected to be in the formats "XXXX" and "XXXX QX" country_metadata = {metadata["id"]: metadata for metadata in country_metadata} fairly_flat_data = [] for iso3, _ in country_metadata.items(): for variable, data in country_data[iso3].items(): for row in data: year = re.findall(r'(\d{4})', row["time"])[0] flatter_row = dict(country=iso3, variable=variable, year=year, row) fairly_flat_data.append(flatter_row) # Group by country and year and remove quarters we don't want. very_flat_data = [] df = pd.DataFrame(fairly_flat_data) for (country, year), _df in df.groupby(["country", "year"]): # Identify quarters we don't want condition = _df["time"].apply(is_bad_quarter, bad_quarters=bad_quarters) if (~condition).sum() == 0: continue row = {r["variable"]: r["value"] for _, r in _df.loc[~condition].iterrows()} row = dict(year=year, row, country_metadata[country]) very_flat_data.append(row) return very_flat_data if __name__ == "__main__": # DO ALL OF THE FOLLOWING IN A SINGLE LUIGI TASK variables = get_variables_by_code(["SP.RUR.TOTL.ZS", "SP.URB.TOTL.IN.ZS", "SP.POP.DPND", "SP.POP.TOTL", "SP.DYN.LE00.IN", "SP.DYN.IMRT.IN", "BAR.NOED.25UP.ZS", "BAR.TER.CMPT.25UP.ZS", "NYGDPMKTPSAKD", "SI.POV.NAHC", "SI.POV.GINI"]) aliases = {series: discover_variable_name(series) for series, sources in variables.items()} # (EXCEPT THIS ONE) # country_data = get_country_data(variables, aliases) # ALSO DO THIS kwargs_list = get_country_data_kwargs(variables=variables, aliases=aliases, max_pages=2) # <== Test mode # THEN BATCH, FOR CHUNKS OF KWARGS country_data = defaultdict(dict) for kwargs in kwargs_list: _country_data = country_data_single_request(**kwargs) for country, data in _country_data.items(): for var_name, data_row in data.items(): country_data[country][var_name] = data_row # THEN A FINAL TASK GETS THE ABOVE RESULTS BY COUNTRY # AND WRITES TO DATABASE country_metadata = get_worldbank_resource("countries") flat_country_data = flatten_country_data(country_data, country_metadata) cleaned_data = clean_variable_names(flat_country_data) # STEP AFTER PUTS THESE IN ES	[ 1, 9995, 13, 28916, 3186, 9157, 13, 9166, 29922, 13, 13, 28916, 3186, 9157, 5374, 356, 29885, 12122, 848, 491, 4234, 29889, 13, 15945, 29908, 13, 13, 5215, 7274, 13, 3166, 337, 2202, 292, 1053, 337, 2202, 13, 5215, 4390, 13, 3166, 16250, 1053, 2322, 8977, 13, 5215, 337, 13, 5215, 5844, 13, 5215, 11701, 408, 10518, 13, 13, 11686, 29931, 4051, 2190, 29968, 29918, 1430, 11191, 6992, 29911, 353, 376, 1124, 597, 2754, 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src/ctc/protocols/fei_utils/analytics/metric_groups/buyback_metrics.py	fei-protocol/checkthechain	94	50674	from __future__ import annotations import typing from ctc import spec from ctc.protocols import balancer_utils from .. import analytics_spec async def async_compute_buybacks( blocks: list[int], verbose: bool = False ) -> analytics_spec.MetricGroup: return { 'name': 'Buybacks', 'metrics': { 'buybacks_usd': (await async_compute_tribe_buybacks_usd(blocks)), }, } async def async_compute_tribe_buybacks_usd( blocks: list[int], swaps: typing.Optional[spec.DataFrame] = None ) -> analytics_spec.MetricData: from ctc.toolbox import pd_utils # load swaps if swaps is None: swaps = await balancer_utils.async_get_pool_swaps( pool_address='0xc1382fe6e17bcdbc3d35f73f5317fbf261ebeecd' ) swaps = typing.cast( spec.DataFrame, swaps.droplevel('transaction_index').droplevel('log_index'), ) # filter tribe buys fei = '0x956f47f50a910163d8bf957cf5846d573e7f87ca' tribe_buys: typing.Any = swaps[swaps['arg__tokenOut'] == fei] # type: ignore tribe_buys = tribe_buys['arg__amountOut'].map(float) / 1e18 cummulative_tribe_buys = tribe_buys.cumsum() # cummulative_tribe_buys = evm.interpolate_block_series( # start_block=min(blocks), # pre_fill_value=0, # series=cummulative_tribe_buys, # end_block=max(blocks), # ) cummulative_tribe_buys = pd_utils.interpolate_series( series=cummulative_tribe_buys, start_index=min(blocks), end_index=max(blocks), pre_fill_value=0, ) # filter tribe sells tribe_sells_df = swaps[swaps['arg__tokenIn'] == fei] # type: ignore if len(tribe_sells_df) > 0: tribe_sells = tribe_sells_df['arg__amountIn'].map(float) / 1e18 cummulative_tribe_sells = tribe_sells.cumsum() # cummulative_tribe_sells = evm.interpolate_block_series( # start_block=min(blocks), # pre_fill_value=0, # series=cummulative_tribe_sells, # end_block=max(blocks), # ) cummulative_tribe_sells = pd_utils.interpolate_series( series=cummulative_tribe_sells, start_index=min(blocks), end_index=max(blocks), pre_fill_value=0, ) net_tribe_buys = cummulative_tribe_buys - cummulative_tribe_sells else: net_tribe_buys = cummulative_tribe_buys return { 'name': 'Buybacks USD', 'values': [net_tribe_buys[block] for block in blocks], 'units': 'FEI', }	[ 1, 515, 4770, 29888, 9130, 1649, 1053, 25495, 13, 13, 5215, 19229, 13, 13, 3166, 274, 14246, 1053, 1580, 13, 3166, 274, 14246, 29889, 20464, 29879, 1053, 6411, 25856, 29918, 13239, 13, 13, 3166, 6317, 1053, 16114, 1199, 29918, 6550, 13, 13, 13, 12674, 822, 7465, 29918, 26017, 29918, 2423, 29891, 1627, 29879, 29898, 13, 1678, 10930, 29901, 1051, 29961, 524, 1402, 26952, 29901, 6120, 353, 7700, 13, 29897, 1599, 16114, 1199, 29918, 6550, 29889, 10095, 2200, 4782, 29901, 13, 13, 1678, 736, 426, 13, 4706, 525, 978, 2396, 525, 3727, 29891, 1627, 29879, 742, 13, 4706, 525, 2527, 10817, 2396, 426, 13, 9651, 525, 2423, 29891, 1627, 29879, 29918, 375, 29881, 2396, 313, 20675, 7465, 29918, 26017, 29918, 3626, 915, 29918, 2423, 29891, 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huTools/http/engine_httplib2.py	gadventures/huTools	0	72186	#!/usr/bin/env python # encoding: utf-8 """ engine_httplib2.py implements httplib2 based queries for huTools.http Created by <NAME> on 2010-10-24. Copyright (c) 2010, 2011 HUDORA. All rights reserved. """ from __future__ import unicode_literals from builtins import object import socket import huTools.http.tools from huTools.http import exceptions _http = None def request(url, method, content, headers, timeout=50, caching=None): """Does a HTTP Request via httplib2.""" global _http if not _http: import huTools.http._httplib2 _http = huTools.http._httplib2.Http(timeout=timeout) headers['User-Agent'] = headers.get('User-Agent', '') + ' (httplib2)' _http.clear_credentials() _http.forward_authorization_headers = True # Do not re-use the global Http object after a timeout. # To achieve this, it is set to None. try: resp, content = _http.request(url, method, content, headers=headers) except socket.timeout: _http = None raise exceptions.Timeout replyheaders = {} replyheaders.update(resp) return int(resp.status), replyheaders, content class AsyncHttpResult(object): """Syncronous emulation for plain Python. See `engine_appengine.AsyncHttpResult()` for the thinking behind it. `huTools.http.fetch_async()` is a somewhat more high-level interface. """ def __init__(self, caching=None): self.url, self.method, self.content, self.headers, self.timeout = None, None, None, None, None self._result = None def fetch(self, url, content='', method='GET', credentials=None, headers=None, multipart=False, ua='', timeout=25, returnhandler=lambda x, y, z: (x, y, z)): """Save parameters but delay request execution until get_result() is called.""" self.url, self.method, self.content, self.headers, self.timeout, _dummy = \ huTools.http.tools.prepare_headers(url, content, method, credentials, headers, multipart, ua, timeout) self.returnhandler = returnhandler def get_result(self): """Execute request pass it to returnhandler and return.""" # Cache the result because we mght get called more than once if not self._result: self._result = self.returnhandler(*request(self.url, self.method, self.content, self.headers, self.timeout)) return self._result	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 8025, 29901, 23616, 29899, 29947, 13, 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lunespy/wallet/assembly.py	lunes-platform/LunesPy	4	167408	def random_seed(len: int) -> str: from lunespy.wallet.constants import word_list from os import urandom def f(): word_count = 2048 r: bytes = urandom(4) x: int = r[3] + (r[2] << 8) + (r[1] << 16) + (r[0] << 24) w1: int = x % word_count w2: int = ((int(x / word_count) >> 0) + w1) % word_count w3: int = ((int((int(x / word_count) >> 0) / word_count) >> 0) + w2) % word_count return w1, w2, w3 n_words_multiple_of_3: int = len // 3 return " ".join([ word_list[n] for _ in range(n_words_multiple_of_3) for n in f() ]) def from_seed(seed: str, nonce: int, chain: int): from lunespy.crypto import bytes_to_b58, to_address, to_private_key, to_public_key, hidden_seed from lunespy.wallet import Wallet hash_seed: bytes = hidden_seed(nonce, seed) private_key: bytes = to_private_key(hash_seed) public_key: bytes = to_public_key(private_key) address: bytes = to_address(public_key, chain, 1) return Wallet( private_key=bytes_to_b58(private_key), public_key=bytes_to_b58(public_key), address=bytes_to_b58(address), nonce=nonce, chain=chain, seed=seed ) def from_private_key(private_key: str, chain: int = 1): from lunespy.crypto import bytes_to_b58, b58_to_bytes, to_address, to_private_key, to_public_key from lunespy.wallet import Wallet private_key: bytes = to_private_key(b58_to_bytes(private_key)) public_key: bytes = to_public_key(private_key) address: bytes = to_address(public_key, chain, 1) return Wallet( private_key=bytes_to_b58(private_key), public_key=bytes_to_b58(public_key), address=bytes_to_b58(address), seed_len=0, nonce=0, chain=chain, seed="" )	[ 1, 822, 4036, 29918, 26776, 29898, 2435, 29901, 938, 29897, 1599, 851, 29901, 13, 1678, 515, 301, 7844, 2272, 29889, 14625, 1026, 29889, 3075, 1934, 1053, 1734, 29918, 1761, 13, 1678, 515, 2897, 1053, 5065, 2685, 13, 13, 1678, 822, 285, 7295, 13, 4706, 1734, 29918, 2798, 353, 29871, 29906, 29900, 29946, 29947, 13, 4706, 364, 29901, 6262, 353, 5065, 2685, 29898, 29946, 29897, 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github_oauth_gateway/auth.py	HDI-Project/ballet-oauth-gateway	1	169868	import requests GITHUB_OAUTH_URL = 'https://github.com/login/oauth/access_token' def request_token(client_id, client_secret, code, redirect_uri, state): url = GITHUB_OAUTH_URL data = { 'client_id': client_id, 'client_secret': client_secret, 'code': code, 'state': state, } headers = { 'Accept': 'application/json' } response = requests.post(url, data=data, headers=headers) response.raise_for_status() return response.json()	[ 1, 1053, 7274, 13, 13, 29954, 13054, 7466, 29918, 29949, 20656, 29950, 29918, 4219, 353, 525, 991, 597, 3292, 29889, 510, 29914, 7507, 29914, 23106, 29914, 5943, 29918, 6979, 29915, 13, 13, 13, 1753, 2009, 29918, 6979, 29898, 4645, 29918, 333, 29892, 3132, 29918, 19024, 29892, 775, 29892, 6684, 29918, 5338, 29892, 2106, 1125, 13, 1678, 3142, 353, 402, 13054, 7466, 29918, 29949, 20656, 29950, 29918, 4219, 13, 13, 1678, 848, 353, 426, 13, 4706, 525, 4645, 29918, 333, 2396, 3132, 29918, 333, 29892, 13, 4706, 525, 4645, 29918, 19024, 2396, 3132, 29918, 19024, 29892, 13, 4706, 525, 401, 2396, 775, 29892, 13, 4706, 525, 3859, 2396, 2106, 29892, 13, 1678, 500, 13, 13, 1678, 9066, 353, 426, 13, 4706, 525, 23965, 2396, 525, 6214, 29914, 3126, 29915, 13, 1678, 500, 13, 13, 1678, 2933, 353, 7274, 29889, 2490, 29898, 2271, 29892, 848, 29922, 1272, 29892, 9066, 29922, 13662, 29897, 13, 1678, 2933, 29889, 22692, 29918, 1454, 29918, 4882, 580, 13, 13, 1678, 736, 2933, 29889, 3126, 580, 13, 2 ]
cap9/ex3/index1.py	felipesch92/livroPython	0	137595	with open('multiplos4.txt', 'w') as multiplos: pares = open('pares.txt') for l in pares.readlines(): num = l.replace('\n', '') if int(num) % 4 == 0: multiplos.write(f'{num}\n')	[ 1, 411, 1722, 877, 9910, 572, 359, 29946, 29889, 3945, 742, 525, 29893, 1495, 408, 2473, 572, 359, 29901, 13, 1678, 610, 267, 353, 1722, 877, 862, 267, 29889, 3945, 1495, 13, 1678, 363, 301, 297, 610, 267, 29889, 949, 9012, 7295, 13, 4706, 954, 353, 301, 29889, 6506, 28909, 29876, 742, 27255, 13, 4706, 565, 938, 29898, 1949, 29897, 1273, 29871, 29946, 1275, 29871, 29900, 29901, 13, 9651, 2473, 572, 359, 29889, 3539, 29898, 29888, 29915, 29912, 1949, 1012, 29876, 1495, 13, 2 ]
packages/mccomponents/tests/mccomponentsbpmodule/sample/Broadened_E_Q_Kernel_TestCase.py	mcvine/mcvine	5	5432	#!/usr/bin/env python # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # <NAME> # California Institute of Technology # (C) 2006-2010 All Rights Reserved # # {LicenseText} # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # standalone = True import unittestX as unittest import journal debug = journal.debug( "Broadened_E_Q_Kernel_TestCase" ) warning = journal.warning( "Broadened_E_Q_Kernel_TestCase" ) import mcni from mccomposite import mccompositebp from mccomponents import mccomponentsbp class TestCase(unittest.TestCase): def test(self): E_Q = "Q*Q/3." S_Q = "1" sigma_Q = "Q/2." Qmin = 0; Qmax = 10 absorption_coefficient = scattering_coefficient = 1. kernel = mccomponentsbp.create_Broadened_E_Q_Kernel( E_Q, S_Q, sigma_Q, Qmin, Qmax, absorption_coefficient, scattering_coefficient, ) ei = 500 # meV from mcni.utils import conversion vil = conversion.e2v(ei) vi = (0,0,vil) import numpy.linalg as nl import numpy as np for i in range(10): event = mcni.neutron( r = (0,0,0), v = vi, prob = 1, time = 0 ) kernel.scatter( event ); vf = np.array(event.state.velocity) diffv = vi - vf Q = conversion.v2k(nl.norm(diffv)) ef = conversion.v2e(nl.norm(vf)) E = ei - ef # print E, Q, event E1 = eval(E_Q) continue return pass # end of TestCase def main(): unittest.main() return if __name__ == "__main__": main() # version __id__ = "$Id: TestCase.py 696 2010-11-09 06:23:06Z linjiao $" # End of file	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 13, 29937, 3695, 26594, 26594, 26594, 26594, 26594, 26594, 26594, 26594, 26594, 14087, 30022, 13, 29937, 13, 29937, 462, 462, 259, 529, 5813, 29958, 13, 29937, 462, 418, 8046, 8907, 310, 17968, 13, 29937, 462, 418, 313, 29907, 29897, 29871, 29906, 29900, 29900, 29953, 29899, 29906, 29900, 29896, 29900, 2178, 26863, 2538, 9841, 259, 13, 29937, 13, 29937, 426, 29931, 293, 1947, 1626, 29913, 13, 29937, 13, 29937, 3695, 26594, 26594, 26594, 26594, 26594, 26594, 26594, 26594, 26594, 14087, 30022, 13, 29937, 13, 13, 13, 1689, 18785, 353, 5852, 13, 13, 13, 5215, 443, 27958, 29990, 408, 443, 27958, 13, 5215, 8955, 13, 13, 8382, 353, 8955, 29889, 8382, 29898, 376, 29857, 4858, 287, 29918, 29923, 29918, 29984, 29918, 29968, 5851, 29918, 3057, 8259, 29908, 1723, 13, 27392, 353, 8955, 29889, 27392, 29898, 376, 29857, 4858, 287, 29918, 29923, 29918, 29984, 29918, 29968, 5851, 29918, 3057, 8259, 29908, 1723, 13, 13, 13, 5215, 286, 29883, 1240, 13, 3166, 286, 29883, 22410, 568, 1053, 286, 29883, 22410, 568, 25288, 29871, 13, 3166, 286, 29883, 14036, 1053, 286, 29883, 14036, 25288, 13, 13, 1990, 4321, 8259, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 13, 1678, 822, 1243, 29898, 1311, 1125, 13, 4706, 382, 29918, 29984, 353, 376, 29984, 29930, 29984, 29914, 29941, 1213, 13, 4706, 317, 29918, 29984, 353, 376, 29896, 29908, 13, 4706, 269, 2934, 29918, 29984, 353, 376, 29984, 29914, 29906, 1213, 13, 4706, 660, 1195, 353, 29871, 29900, 29936, 660, 3317, 353, 29871, 29896, 29900, 13, 4706, 17977, 683, 29918, 1111, 8462, 353, 14801, 292, 29918, 1111, 8462, 353, 29871, 29896, 29889, 13, 4706, 8466, 353, 286, 29883, 14036, 25288, 29889, 3258, 29918, 29857, 4858, 287, 29918, 29923, 29918, 29984, 29918, 29968, 5851, 29898, 13, 9651, 382, 29918, 29984, 29892, 317, 29918, 29984, 29892, 269, 2934, 29918, 29984, 29892, 13, 9651, 660, 1195, 29892, 660, 3317, 29892, 13, 9651, 17977, 683, 29918, 1111, 8462, 29892, 13, 9651, 14801, 292, 29918, 1111, 8462, 29892, 13, 9651, 1723, 13, 13, 4706, 321, 29875, 353, 29871, 29945, 29900, 29900, 396, 592, 29963, 13, 4706, 515, 286, 29883, 1240, 29889, 13239, 1053, 11301, 13, 4706, 11928, 353, 11301, 29889, 29872, 29906, 29894, 29898, 10096, 29897, 13, 4706, 3516, 353, 313, 29900, 29892, 29900, 29892, 17191, 29897, 13, 13, 4706, 1053, 12655, 29889, 29880, 979, 29887, 408, 302, 29880, 13, 4706, 1053, 12655, 408, 7442, 13, 4706, 363, 474, 297, 3464, 29898, 29896, 29900, 1125, 13, 9651, 1741, 353, 286, 29883, 1240, 29889, 17821, 1617, 29898, 29871, 13, 18884, 364, 353, 313, 29900, 29892, 29900, 29892, 29900, 511, 325, 353, 3516, 29892, 29871, 13, 18884, 2070, 353, 29871, 29896, 29892, 931, 353, 29871, 29900, 1723, 13, 9651, 8466, 29889, 1557, 2620, 29898, 1741, 3482, 13, 9651, 325, 29888, 353, 7442, 29889, 2378, 29898, 3696, 29889, 3859, 29889, 955, 25245, 29897, 13, 9651, 2923, 29894, 353, 3516, 448, 325, 29888, 13, 9651, 660, 353, 11301, 29889, 29894, 29906, 29895, 29898, 12938, 29889, 12324, 29898, 12765, 29894, 876, 13, 9651, 321, 29888, 353, 11301, 29889, 29894, 29906, 29872, 29898, 12938, 29889, 12324, 29898, 29894, 29888, 876, 13, 9651, 382, 353, 321, 29875, 448, 321, 29888, 13, 9651, 396, 1596, 382, 29892, 660, 29892, 1741, 13, 9651, 382, 29896, 353, 19745, 29898, 29923, 29918, 29984, 29897, 13, 9651, 6773, 13, 13, 4706, 736, 13, 268, 13, 268, 13, 1678, 1209, 29871, 396, 1095, 310, 4321, 8259, 13, 13, 268, 13, 1753, 1667, 7295, 13, 1678, 443, 27958, 29889, 3396, 580, 13, 1678, 736, 13, 268, 13, 268, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 1667, 580, 13, 268, 13, 29937, 1873, 13, 1649, 333, 1649, 353, 3908, 1204, 29901, 4321, 8259, 29889, 2272, 29871, 29953, 29929, 29953, 29871, 29906, 29900, 29896, 29900, 29899, 29896, 29896, 29899, 29900, 29929, 29871, 29900, 29953, 29901, 29906, 29941, 29901, 29900, 29953, 29999, 6276, 29926, 29653, 395, 29908, 13, 13, 29937, 2796, 310, 934, 29871, 13, 2 ]
cyk/cnf/linear_cnf.py	azoimide/cyk	0	93569	<reponame>azoimide/cyk import sys def read_linear_grammar(f=sys.stdin): lines = f.readlines() # rule_lines = lines[:-1] rule_lines = lines # s = map(lambda c: ord(c) - ord('a'), lines[-1].strip()) ups = [] rnups = [] lnups = [] nt_count = 0 t_count = 0 t_tab = {} nt_tab = {} for line in rule_lines: if line[0] == '#': continue lhs, rhs = line.strip().split(' ') # New N in LHS if lhs not in nt_tab: nt_tab[lhs] = len(nt_tab) lnups.append([]) rnups.append([]) ups.append([]) # N -> T (up) if len(rhs) == 1: if rhs.istitle(): raise ValueError("Invalid terminal: " + rhs) if rhs not in t_tab: t_tab[rhs] = len(t_tab) ups[nt_tab[lhs]].append(t_tab[rhs]) else: # N -> NT (lnup) if rhs[0].istitle() and not rhs[1].istitle(): nt = rhs[0] t = rhs[1] # New N in RHS if nt not in nt_tab: nt_tab[nt] = len(nt_tab) ups.append([]) rnups.append([]) lnups.append([]) # New T in RHS if t not in t_tab: t_tab[t] = len(t_tab) lnups[nt_tab[lhs]].append((nt_tab[nt],t_tab[t])) # N -> TN (rnup) elif not rhs[0].istitle() and rhs[1].istitle(): t = rhs[0] nt = rhs[1] # New N in RHS if nt not in nt_tab: nt_tab[nt] = len(nt_tab) ups.append([]) rnups.append([]) lnups.append([]) # New T in RHS if t not in t_tab: t_tab[t] = len(t_tab) rnups[nt_tab[lhs]].append((t_tab[t],nt_tab[nt])) else: raise ValueError("Invalid rule: " + line) return lnups, rnups, ups, nt_tab, t_tab, 0	[ 1, 529, 276, 1112, 420, 29958, 834, 29877, 326, 680, 29914, 25923, 13, 5215, 10876, 13, 13, 1753, 1303, 29918, 10660, 29918, 1393, 3034, 29898, 29888, 29922, 9675, 29889, 4172, 262, 1125, 13, 1678, 3454, 353, 285, 29889, 949, 9012, 580, 13, 1678, 396, 5751, 29918, 9012, 353, 3454, 7503, 29899, 29896, 29962, 13, 1678, 5751, 29918, 9012, 353, 3454, 13, 1678, 396, 269, 353, 2910, 29898, 2892, 274, 29901, 4356, 29898, 29883, 29897, 448, 4356, 877, 29874, 5477, 3454, 14352, 29896, 1822, 17010, 3101, 13, 1678, 24081, 353, 5159, 13, 1678, 364, 3433, 567, 353, 5159, 13, 1678, 301, 3433, 567, 353, 5159, 13, 1678, 302, 29873, 29918, 2798, 353, 29871, 29900, 13, 1678, 260, 29918, 2798, 353, 29871, 29900, 13, 1678, 260, 29918, 3891, 353, 6571, 13, 1678, 302, 29873, 29918, 3891, 353, 6571, 13, 1678, 363, 1196, 297, 5751, 29918, 9012, 29901, 13, 4706, 565, 1196, 29961, 29900, 29962, 1275, 16321, 2396, 13, 9651, 6773, 13, 4706, 301, 9499, 29892, 29365, 353, 1196, 29889, 17010, 2141, 5451, 877, 25710, 13, 13, 4706, 396, 1570, 405, 297, 365, 14851, 13, 4706, 565, 301, 9499, 451, 297, 302, 29873, 29918, 3891, 29901, 13, 9651, 302, 29873, 29918, 3891, 29961, 29880, 9499, 29962, 353, 7431, 29898, 593, 29918, 3891, 29897, 13, 9651, 301, 3433, 567, 29889, 4397, 4197, 2314, 13, 9651, 364, 3433, 567, 29889, 4397, 4197, 2314, 13, 9651, 24081, 29889, 4397, 4197, 2314, 13, 13, 4706, 396, 405, 1599, 323, 313, 786, 29897, 13, 4706, 565, 7431, 29898, 29878, 9499, 29897, 1275, 29871, 29896, 29901, 29871, 13, 9651, 565, 29365, 29889, 391, 1740, 7295, 13, 18884, 12020, 7865, 2392, 703, 13919, 8638, 29901, 376, 718, 29365, 29897, 13, 9651, 565, 29365, 451, 297, 260, 29918, 3891, 29901, 13, 18884, 260, 29918, 3891, 29961, 29878, 9499, 29962, 353, 7431, 29898, 29873, 29918, 3891, 29897, 13, 9651, 24081, 29961, 593, 29918, 3891, 29961, 29880, 9499, 29962, 1822, 4397, 29898, 29873, 29918, 3891, 29961, 29878, 9499, 2314, 13, 4706, 1683, 29901, 13, 13, 9651, 396, 405, 1599, 405, 29911, 313, 3083, 786, 29897, 13, 9651, 565, 29365, 29961, 29900, 1822, 391, 1740, 580, 322, 451, 29365, 29961, 29896, 1822, 391, 1740, 7295, 29871, 13, 18884, 302, 29873, 353, 29365, 29961, 29900, 29962, 13, 18884, 260, 353, 29365, 29961, 29896, 29962, 13, 13, 18884, 396, 1570, 405, 297, 390, 14851, 13, 18884, 565, 302, 29873, 451, 297, 302, 29873, 29918, 3891, 29901, 13, 462, 1678, 302, 29873, 29918, 3891, 29961, 593, 29962, 353, 7431, 29898, 593, 29918, 3891, 29897, 13, 462, 1678, 24081, 29889, 4397, 4197, 2314, 13, 462, 1678, 364, 3433, 567, 29889, 4397, 4197, 2314, 13, 462, 1678, 301, 3433, 567, 29889, 4397, 4197, 2314, 13, 13, 18884, 396, 1570, 323, 297, 390, 14851, 13, 18884, 565, 260, 451, 297, 260, 29918, 3891, 29901, 13, 462, 1678, 260, 29918, 3891, 29961, 29873, 29962, 353, 7431, 29898, 29873, 29918, 3891, 29897, 13, 13, 18884, 301, 3433, 567, 29961, 593, 29918, 3891, 29961, 29880, 9499, 29962, 1822, 4397, 3552, 593, 29918, 3891, 29961, 593, 1402, 29873, 29918, 3891, 29961, 29873, 12622, 13, 13, 9651, 396, 405, 1599, 323, 29940, 313, 27539, 786, 29897, 13, 9651, 25342, 451, 29365, 29961, 29900, 1822, 391, 1740, 580, 322, 29365, 29961, 29896, 1822, 391, 1740, 7295, 29871, 13, 18884, 260, 353, 29365, 29961, 29900, 29962, 13, 18884, 302, 29873, 353, 29365, 29961, 29896, 29962, 13, 13, 18884, 396, 1570, 405, 297, 390, 14851, 13, 18884, 565, 302, 29873, 451, 297, 302, 29873, 29918, 3891, 29901, 13, 462, 1678, 302, 29873, 29918, 3891, 29961, 593, 29962, 353, 7431, 29898, 593, 29918, 3891, 29897, 13, 462, 1678, 24081, 29889, 4397, 4197, 2314, 13, 462, 1678, 364, 3433, 567, 29889, 4397, 4197, 2314, 13, 462, 1678, 301, 3433, 567, 29889, 4397, 4197, 2314, 13, 13, 18884, 396, 1570, 323, 297, 390, 14851, 13, 18884, 565, 260, 451, 297, 260, 29918, 3891, 29901, 13, 462, 1678, 260, 29918, 3891, 29961, 29873, 29962, 353, 7431, 29898, 29873, 29918, 3891, 29897, 13, 13, 18884, 364, 3433, 567, 29961, 593, 29918, 3891, 29961, 29880, 9499, 29962, 1822, 4397, 3552, 29873, 29918, 3891, 29961, 29873, 1402, 593, 29918, 3891, 29961, 593, 12622, 13, 9651, 1683, 29901, 13, 18884, 12020, 7865, 2392, 703, 13919, 5751, 29901, 376, 718, 1196, 29897, 13, 1678, 736, 301, 3433, 567, 29892, 364, 3433, 567, 29892, 24081, 29892, 302, 29873, 29918, 3891, 29892, 260, 29918, 3891, 29892, 29871, 29900, 13, 13, 2 ]

basic/arithmetic.py

anuragarwalkar/basic-python

0

118477

<reponame>anuragarwalkar/basic-python # Arithmetic Operators import math print(math.ceil(2.9)) # 3 print(math.floor(2.9)) # 2 print(10+3) # 13 print(10-3) # 7 print(10/3) # 3.3 Floating point number print(10//3) # 3 Int print(10%3) # 1 print(10*3) # 30 print(10**3) # 1000 10 to the power of 3 print(abs(-2.9)) # 2.9 x = 10 x += 3 print(x) x = 10 + 3 * 2 print(x)

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scripts/generate_ramachandran_statistics.py

leimao/Ramachandran

2

131836

<gh_stars>1-10 import ramachandran.statistics ramachandran.statistics.count_torsion_angles_from_directory(dir_path="./pdbx_collections_resolution_1p0", save_file_path="./data/probability.npz", b_factor_threshold=30, resolution=90, num_processes=12) ramachandran.statistics.compute_gaussian_kde_densities_from_directory(dir_path="./pdbx_collections_resolution_1p0", save_file_path="./data/gaussian_density.npz", b_factor_threshold=30, resolution=360, num_processes=12)

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calibrate.py

malcolmdimeglio/pow_alert

0

183798

<filename>calibrate.py #! /usr/bin/env python import os import cv2 import numpy as np from matplotlib import pyplot as plt from dotenv import load_dotenv, find_dotenv from logger import * load_dotenv(find_dotenv()) curr_dir = os.path.dirname(os.path.realpath(__file__)) cypress_cal_img =f"{curr_dir}/templates/SnowStake_Cypress_night0.jpg" cypress_top_template_img = f"{curr_dir}/templates/40.jpg" cypress_base_template_img = f"{curr_dir}/templates/base.jpg" # All the 6 methods for comparison methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR', 'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED'] def cypress_img(debug_option=False): log.debug("Calibrate Cypress webacam") log.debug(f"Template images used to perform calibration: \n" \ f" - Webcam: {cypress_cal_img}\n" \ f" - Top ROI: {cypress_top_template_img}\n" \ f" - Bottom ROI: {cypress_base_template_img}") # the following image will be used to calibrate the scale img = cv2.imread(cypress_cal_img, 0) img2 = img.copy() template1 = cv2.imread(cypress_top_template_img, 0) template2 = cv2.imread(cypress_base_template_img, 0) log.debug("Resize templates by a 1:2 factor to accomodate Apple grab tool effect") template1 = cv2.resize(template1, (0, 0), fx=0.5, fy=0.5) template2 = cv2.resize(template2, (0, 0), fx=0.5, fy=0.5) h1, w1 = template1.shape h2, w2 = template2.shape if debug_option: # allows to pick which method works best for meth in methods: img = img2.copy() method = eval(meth) # Apply template Matching res = cv2.matchTemplate(img, template1, method) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res) # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]: top_left = min_loc else: top_left = max_loc bottom_right = (top_left[0] + w1, top_left[1] + h1) cv2.rectangle(img, top_left, bottom_right, 255, 2) plt.subplot(111), plt.imshow(img, cmap='gray') plt.title(meth), plt.xticks([]), plt.yticks([]) plt.show() print("TM_CCOEFF_NORMED is best method (or equivalent to TM_CCORR_NORMED, TM_SQDIFF, TM_SQDIFF_NORMED)") print("TM_CCORR is the worst") debug.log("Use TM_CCOEFF_NORMED method to find template ROI images on calibration image") method = eval('cv2.TM_CCOEFF_NORMED') img = img2.copy() res1 = cv2.matchTemplate(img, template1, method) res2 = cv2.matchTemplate(img, template2, method) debug.log("Extract the 4 coordinates for the whole ROI") min_val1, max_val1, min_loc1, max_loc1 = cv2.minMaxLoc(res1) min_val2, max_val2, min_loc2, max_loc2 = cv2.minMaxLoc(res2) top_left1 = max_loc1 top_left2 = max_loc2 bottom_right1 = (top_left1[0] + w1, top_left1[1] + h1) bottom_right2 = (top_left2[0] + w2, top_left2[1] + h2) if debug_option: roi_top_left = top_left1 roi_top_right = (top_left1[0] + w1, top_left1[1]) roi_bottom_right = bottom_right2 roi_bottom_left = (bottom_right2[0] - w2, bottom_right2[1]) cv2.rectangle(img, top_left1, bottom_right1, 255, 2) cv2.rectangle(img, top_left2, bottom_right2, 255, 2) points = [ list(roi_top_left), list(roi_top_right), list(roi_bottom_right), list(roi_bottom_left) ] # Now plot the ROI pts = np.array(points, np.int32) cv2.polylines(img, [pts], True, (255, 255, 255), thickness=5) plt.subplot(111), plt.imshow(img, cmap='gray') plt.title('ROI'), plt.xticks([]), plt.yticks([]) plt.show() top_left_offset = (0, 0) top_right_offset = (w1, 0) bottom_left_offset = ((bottom_right2[0] - w2) - top_left1[0], bottom_right2[1] - top_left1[1]) bottom_right_offset = (bottom_right2[0] - top_left1[0], bottom_right2[1] - top_left1[1]) log.debug("Update .env file with the offset to apply to get the 4 corners of the ROI") log.debug("TOP_LEFT_OFFSET=\""+str(top_left_offset)+"\"\n" \ "TOP_RIGHT_OFFSET=\"" + str(top_right_offset) + "\"\n" \ "BOTTOM_LEFT_OFFSET=\"" + str(bottom_left_offset) + "\"\n" \ "BOTTOM_RIGHT_OFFSET=\"" + str(bottom_right_offset) + "\"\n") with open(".env", 'a') as env: env.write("\n") env.write("TOP_LEFT_OFFSET=\""+str(top_left_offset)+"\"\n") env.write("TOP_RIGHT_OFFSET=\"" + str(top_right_offset) + "\"\n") env.write("BOTTOM_LEFT_OFFSET=\"" + str(bottom_left_offset) + "\"\n") env.write("BOTTOM_RIGHT_OFFSET=\"" + str(bottom_right_offset) + "\"\n")

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growler/middleware/responsetime.py

pyGrowler/Growler

806

191468

<reponame>pyGrowler/Growler # # growler/middleware/responsetime.py # """ Provides middleware which adds a header indicating how long the request took to process """ import time import logging logger = logging.getLogger(__name__) class ResponseTime: """ Middleware which saves the time when initially called, and sets an 'on_headers' event to get the time difference which can be logged or sent to the client. """ UNIT_TO_FACTOR_MAP = { 's': 1, 'ms': 1000, 'us': 1000000, } def __init__(self, digits=3, log=None, units='ms', header="X-Response-Time", suffix=True, clobber_header=False): """ Construct ResponseTime middleware. Parameters: digits (int): precision log (Logger or None): Writes the time difference to the log units (str): Time units (default: milliseconds 'ms') header (str): Name of header to send response time as suffix (bool): Whether to format with """ self.units = units self.header_name = header self.digits = digits self.log = log if log else logger.getChild("id=%x" % id(self)) self.suffix = suffix self.clobber_header = clobber_header def __call__(self, req, res): start_time = time.monotonic() def on_header_send(): # if header already exists, do NOT clobber it if not self.clobber_header and self.header_name in res.headers: return dt = self.format_timediff(time.monotonic() - start_time) val = "{}{}".format(dt, self.units) if self.suffix else dt res.set(self.header_name, val) if self.log: self.log.info("-- timer %s", val) res.events.on('before_headers', on_header_send) def format_timediff(self, td): factor = self.UNIT_TO_FACTOR_MAP[self.units] return str(round(factor * td, self.digits))

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test_calculator.py

Kidatoy/Advanced-Calculator

0

11307

<reponame>Kidatoy/Advanced-Calculator import unittest # https://docs.python.org/3/library/unittest.html from modules.calculator import Calculator as Calc class TestCalculator(unittest.TestCase): """ Test Driven Development Unittest File Module: Calculator Updated: 12/16/2019 Author: <NAME> """ def test_addition(self): """ Evaluate addition corner cases """ self.assertEqual(2, Calc().eval('1+1')) self.assertEqual(2, Calc().eval('1.0+1.0')) self.assertEqual(0, Calc().eval('-1+1')) self.assertEqual(-2, Calc().eval('-1+-1')) def test_subtraction(self): """ Evaluate subtraction corner cases """ self.assertEqual(0, Calc().eval('1-1')) self.assertEqual(-2, Calc().eval('-1-1')) self.assertEqual(0, Calc().eval('-1--1')) def test_multiplication(self): """ Evaluate multiplication corner cases """ self.assertEqual(0, Calc().eval('1*0')) self.assertEqual(0, Calc().eval('0*-1')) self.assertEqual(1, Calc().eval('1*1')) self.assertEqual(-1, Calc().eval('-1*1')) self.assertEqual(1, Calc().eval('-1*-1')) self.assertEqual(1, Calc().eval('.25*4')) def test_division(self): """ Test division corner cases Note: division by zero is handled in test_exceptions """ self.assertEqual(1, Calc().eval('1/1')) self.assertEqual(.25, Calc().eval('1/4')) self.assertEqual(-1, Calc().eval('-1/1')) self.assertEqual(1, Calc().eval('-1/-1')) self.assertEqual(0, Calc().eval('0/-1')) def test_exponents(self): """ Test exponent corner cases """ self.assertEqual(1, Calc().eval('2^0')) self.assertEqual(2, Calc().eval('2^1')) self.assertEqual(4, Calc().eval('2^2')) self.assertEqual(.5, Calc().eval('2^-1')) self.assertEqual(4, Calc().eval('-2^2')) def test_parentheses(self): """ Test parentheses corner cases """ self.assertEqual(5.0, Calc().eval('(4.0)+1')) self.assertEqual(3.0, Calc().eval('(4+1)-2')) self.assertEqual(5.0, Calc().eval('(5+-5)+5')) self.assertEqual(-5.0, Calc().eval('(-10+3)+2')) self.assertEqual(-26.0, Calc().eval('10-(3*2)^2')) def test_pi(self): """ Test pi corner cases """ self.assertEqual(4.1415926535, Calc().eval('(pi)+1')) self.assertEqual(1.1415926535, Calc().eval('(pi)-2')) self.assertEqual(3.1415926535, Calc().eval('(pi+-5)+5')) self.assertEqual(1.8584073465, Calc().eval('(-pi+3)+2')) self.assertEqual(-29.478417602100684, Calc().eval('10-(pi*2)^2')) self.assertEqual(1.57079632675, Calc().eval('pi/2')) def test_e(self): """ Test e corner cases """ self.assertEqual(3.7182818284, Calc().eval('(e)+1')) self.assertEqual(0.7182818283999999, Calc().eval('(e)-2')) self.assertEqual(2.7182818284, Calc().eval('(e+-5)+5')) self.assertEqual(2.2817181716, Calc().eval('(-e+3)+2')) self.assertEqual(-19.556224394438587, Calc().eval('10-(e*2)^2')) self.assertEqual(1.3591409142, Calc().eval('e/2')) def test_phi(self): """ Test phi corner cases """ self.assertEqual(2.6180339886999997, Calc().eval('(phi)+1')) self.assertEqual(-0.3819660113000001, Calc().eval('(phi)-2')) self.assertEqual(1.6180339886999997, Calc().eval('(phi+-5)+5')) self.assertEqual(3.3819660113000003, Calc().eval('(-phi+3)+2')) self.assertEqual(-0.47213595435372646, Calc().eval('10-(phi*2)^2')) self.assertEqual(0.80901699435, Calc().eval('phi/2'))

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transform.py

ShrillP/ImageScanner

0

57770

<filename>transform.py<gh_stars>0 import numpy as np import cv2 def order_points(points): rectangle = np.zeros((4, 2), dtype="float32") s = points.sum(axis=1) rectangle[0] = points[np.argmin(s)] rectangle[2] = points[np.argmax(s)] difference = np.diff(points, axis=1) rectangle[1] = points[np.argmin(difference)] rectangle[3] = points[np.argmax(difference)] return rectangle def four_point_transform(image, points): rectangle = order_points(points) (tl, tr, br, bl) = rectangle width_A = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) width_B = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) max_width = max(int(width_A), int(width_B)) height_A = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) height_B = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) max_height = max(int(height_A), int(height_B)) destination = np.array([ [0, 0], [max_width - 1, 0], [max_width - 1, max_height - 1], [0, max_height - 1]], dtype="float32") transform = cv2.getPerspectiveTransform(rectangle, destination) warped = cv2.warpPerspective(image, transform, (max_width, max_height)) return warped

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hello_world_cpp/launch/server.launch.py

fjnkt98/hello_world

0

48931

import launch import launch_ros def generate_launch_description(): server_node_container = launch_ros.actions.ComposableNodeContainer( node_name='server_node_container', node_namespace='', package='rclcpp_components', node_executable='component_container', composable_node_descriptions=[ launch_ros.descriptions.ComposableNode( package='hello_world_cpp', node_plugin='hello_world_cpp::Server', node_name='server' ) ], output='screen' ) return launch.LaunchDescription([server_node_container])

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Vigenere/decoder.py

zF4ke/Ciphr

0

194004

from operator import sub word = str(input("Type the word to be decrypted: ")).strip().lower().replace(" ", "") key = str(input("Type the vigenere key: ")).strip().lower().replace(" ", "") nkey = 0 res, w, letters, srettel = "", "", " abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz ", " zyxwvutsrqponmlkjihgfedcbazyxwvutsrqponmlkjihgfedcba " if len(word) > len(key): nkey = len(word) - len(key) if nkey < len(key): w = key[:nkey] res = key + w elif nkey > len(key): if len(key) == 1: res = key * (nkey + 1) elif len(key) == 2: res = key * nkey else: w = key + key[:nkey] res = key + w else: res += key + key elif len(word) < len(key): nkey = len(key) - len(word) w = key[:-nkey] res += w else: res += key s, l, fin, enc = [], [], [], [] for i in word: s.append(letters.find(i)) for j in res: l.append(letters.find(j)) for n in range(0, len(s)): s[n] = s[n] + 1 fin = list(map(sub, s, l)) for x in fin: if x < 0: x = abs(x) + 1 enc.append(srettel[x]) else: enc.append(letters[x]) print("".join(enc))

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Python/PythonIntro/Exercises.py

IQSS/workshops

30

102942

# --- # jupyter: # jupytext: # formats: ipynb,md,py:light # text_representation: # extension: .py # format_name: light # format_version: '1.3' # jupytext_version: 0.8.6 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Introduction to Python exercise solutions # ## Exercise: Reading text from a file and splitting # *Alice's Adventures in Wonderland* is full of memorable characters. The main characters from the story are listed, one-per-line, in the file named `Characters.txt`. # # NOTE: we will not always explicitly demonstrate everything you need to know in order to complete an exercise. Instead we focus on teaching you how to discover available methods and how use the help function to learn how to use them. It is expected that you will spend some time during the exercises looking for appropriate methods and perhaps reading documentation. # 1. Open the `Characters.txt` file and read its contents. # 2. Split text on newlines to produce a list with one element per line. # Store the result as "alice_characters". # ## Exercise: count the number of main characters # So far we've learned that there are 12 chapters, around 830 paragraphs, and about 26 thousand words in *Alice's Adventures in Wonderland*. Along the way we've also learned how to open a file and read its contents, split strings, calculate the length of objects, discover methods for string and list objects, and index/subset lists in Python. Now it is time for you to put these skills to use to learn something about the main characters in the story. # 1. Count the number of main characters in the story (i.e., get the length # of the list you created in previous exercise). # 2. Extract and print just the first character from the list you created in # the previous exercise. # 3. (BONUS, optional): Sort the list you created in step 2 alphabetically, and then extract the last element. # ## Exercise: Iterating and counting things # Now that we know how to iterate using for-loops and list comprehensions the possibilities really start to open up. For example, we can use these techniques to count the number of times each character appears in the story. # + # 1. Make sure you have both the text and the list of characters. # # Open and read both "Alice_in_wonderland.txt" and # "Characters.txt" if you have not already done so. # + # 2. Which chapter has the most words? # # Split the text into chaptes (i.e., split on "CHAPTER ") # and use a for-loop or list comprehension to iterate over # the chapters. For each chapter, split it into words and # calculate the length. # + # 3. How many times is each character mentioned in the text? # Iterate over the list of characters using a for-loop or # list comprehension. For each character, call the count method # with that character as the argument. # + # 4. (BONUS, optional): Put the character counts computed # above in a dictionary with character names as the keys and # counts as the values. # + # 5. (BONUS, optional): Use a nested for-loop or nested comprehension # to calculate the number of times each character is mentioned # in each chapter.

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examples/fire.py

pombreda/py-lepton

7

13028

<filename>examples/fire.py ############################################################################# # # Copyright (c) 2008 by <NAME> and contributors # All Rights Reserved. # # This software is subject to the provisions of the MIT License # A copy of the license should accompany this distribution. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # ############################################################################# """Fire simulation using point sprites""" __version__ = '$Id$' import os from pyglet import image from pyglet.gl import * from lepton import Particle, ParticleGroup, default_system from lepton.renderer import PointRenderer from lepton.texturizer import SpriteTexturizer, create_point_texture from lepton.emitter import StaticEmitter from lepton.domain import Line from lepton.controller import Gravity, Lifetime, Movement, Fader, ColorBlender win = pyglet.window.Window(resizable=True, visible=False) win.clear() glEnable(GL_BLEND) glShadeModel(GL_SMOOTH) glBlendFunc(GL_SRC_ALPHA,GL_ONE) glDisable(GL_DEPTH_TEST) flame = StaticEmitter( rate=500, template=Particle( position=(300,25,0), velocity=(0,0,0), color=(1,1,1,1), ), position=Line((win.width/2 - 85, -15, 0), (win.width/2 + 85, -15, 0)), deviation=Particle(position=(10,0,0), velocity=(7,50,0), age=0.75) ) default_system.add_global_controller( Lifetime(6), Gravity((0,20,0)), Movement(), ColorBlender( [(0, (0,0,0.5,0)), (0.5, (0,0,0.5,0.2)), (0.75, (0,0.5,1,0.6)), (1.5, (1,1,0,0.2)), (2.7, (0.9,0.2,0,0.4)), (3.2, (0.6,0.1,0.05,0.2)), (4.0, (0.8,0.8,0.8,0.1)), (6.0, (0.8,0.8,0.8,0)), ] ), ) group = ParticleGroup(controllers=[flame], renderer=PointRenderer(64, SpriteTexturizer(create_point_texture(64, 5)))) win.set_visible(True) pyglet.clock.schedule_interval(default_system.update, (1.0/30.0)) pyglet.clock.set_fps_limit(None) @win.event def on_draw(): win.clear() glLoadIdentity() default_system.draw() if __name__ == '__main__': default_system.run_ahead(2, 30) pyglet.app.run()

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server/ahj_app/tests/test_misc_views.py

btansy/ahj-registry

0

90490

<filename>server/ahj_app/tests/test_misc_views.py from django.urls import reverse from django.conf import settings from ahj_app.models import User, Edit, Comment from fixtures import * import pytest import datetime @pytest.mark.django_db def test_form_validator__username_exists(generate_client_with_webpage_credentials): client = generate_client_with_webpage_credentials(Username='someone') url = reverse('form-validator') response = client.get(url, {'Username':'someone'}) assert response.data['UsernameExists'] assert response.status_code == 200 @pytest.mark.django_db def test_form_validator__email_exists(generate_client_with_webpage_credentials): client = generate_client_with_webpage_credentials(Email='<EMAIL>') url = reverse('form-validator') response = client.get(url, {'Email':'a<EMAIL>'}) assert response.data['EmailExists'] assert response.status_code == 200 @pytest.mark.django_db def test_form_validator__no_params(client_with_webpage_credentials): url = reverse('form-validator') response = client_with_webpage_credentials.get(url, {}) assert not response.data['UsernameExists'] and not response.data['EmailExists'] assert response.status_code == 200 @pytest.mark.django_db def test_user_comments__comments_exist(ahj_obj, generate_client_with_webpage_credentials): client = generate_client_with_webpage_credentials(Username='someone') user = User.objects.get(Username='someone') comment1 = Comment.objects.create(UserID=user, AHJPK=ahj_obj.AHJPK, CommentText='This is one comment.', ReplyingTo=None) comment2 = Comment.objects.create(UserID=user, AHJPK=ahj_obj.AHJPK, CommentText='This is another.', ReplyingTo=comment1.CommentID) url = reverse('user-comments') response = client.get(url, {'UserID': user.UserID}) assert len(response.data) == 2 # 2 comments returned assert response.status_code == 200 @pytest.mark.django_db def test_user_comments__user_does_not_exist(client_with_webpage_credentials): url = reverse('user-comments') response = client_with_webpage_credentials.get(url, {'UserID': 99999999}) assert len(response.data) == 0 # no comments returned assert response.status_code == 200 @pytest.mark.django_db def test_comment_submit__normal_submission(ahj_obj, client_with_webpage_credentials): url = reverse('comment-submit') response = client_with_webpage_credentials.post(url, {'CommentText': 'This is a comment.', 'AHJPK':ahj_obj.AHJPK}) assert response.data['CommentText']['Value'] == 'This is a comment.' # check if comment object returned matches assert response.status_code == 200 @pytest.mark.django_db def test_comment_submit__no_comment(ahj_obj, client_with_webpage_credentials): url = reverse('comment-submit') response = client_with_webpage_credentials.post(url, {'AHJPK':ahj_obj.AHJPK}) assert response.status_code == 400 @pytest.mark.django_db def test_user_edits__edits_exist(ahj_obj, generate_client_with_webpage_credentials): client = generate_client_with_webpage_credentials(Username='someone') user = User.objects.get(Username='someone') edit1 = Edit.objects.create(ChangedBy= user, AHJPK=ahj_obj, SourceTable='Contact', SourceColumn='Title', SourceRow='143', DateRequested=datetime.datetime.now()) edit2 = Edit.objects.create(ChangedBy= user, AHJPK=ahj_obj, SourceTable='Contact', SourceColumn='Title', SourceRow='143', DateRequested=datetime.datetime.now()) url = reverse('user-edits') response = client.get(url, {'UserID': user.UserID}) assert len(response.data) == 2 # 2 edits returned assert response.status_code == 200 @pytest.mark.django_db def test_user_edits__user_does_not_exist(client_with_webpage_credentials): url = reverse('user-edits') response = client_with_webpage_credentials.get(url, {'UserID': 99999999}) assert len(response.data) == 0 # no edits returned assert response.status_code == 200 @pytest.mark.django_db def test_send_support_email__valid_usage(client_with_webpage_credentials): url = reverse('send-support-email') settings.SUNSPEC_SUPPORT_EMAIL = '<EMAIL>' response = client_with_webpage_credentials.post(url, {'Email': '<EMAIL>', 'Subject': 'A subject.', 'Message': 'A message.'}) assert response.status_code == 200

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educative/course1/graphs/ch2_dfs.py

liveroot/ambition2020

0

51603

<gh_stars>0 import educative.course1.stacks_queues.stack as s import educative.course1.graphs.graph as g input_num_vertices = 5 input_edges = {0: [1, 2], 1: [3, 4]} expected_output = "02143 or 02134 or 01432 or 01342" # this code implements Depth First Traversal in a graph. Each element in the graph's adjacency list # represents a list of nodes directly connected to the current element. Each of these lists represent # levels in the graph. We traverse all the nodes from root node to last node first, then we back-track # to the last visited common node and then we take second branch from there, until we back-track to root node # and no branches are left unvisited. # -------------- # For traversal: # -------------- # 1. We first push the root node in a stack and mark it visited. # 2. Now we get the list of adjacent nodes from the root node and push all adjacent nodes in the list # to the stack. # 3. As we are pushing the adjacent nodes to the stack, we mark them as visited. # 4. Simultaneously, we pop the nodes from the stack and append the node's value to the result # -------------- def bfs_traversal(graph, root): result = "" visited = [False] * graph.num_vertices stack = s.Stack(graph.num_vertices, True) # suppress_printing = True stack.push(root) while not stack.is_empty(): g_node = stack.pop() result += str(g_node) dll = graph.adjacency_list[g_node] if dll is not None: current = dll.head while current: if not visited[current.value]: stack.push(current.value) visited[current.value] = True current = current.next return result def main(): # create a graph from input data graph = g.Graph(input_num_vertices, True) # suppress_printing = True for x in input_edges.keys(): for y in input_edges[x]: graph.add_edge(x, y) # set a starting point for traversal root = 0 print("Input:") print("Graph in Adjacency List Representation:") print(str(graph.prettify())) print("Expected: " + str(expected_output)) print("Output: " + str(bfs_traversal(graph, root))) if __name__ == '__main__': main()

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hexun/hexun/items.py

judypol/pytonStudy

0

1612353

<filename>hexun/hexun/items.py # -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # http://doc.scrapy.org/en/latest/topics/items.html import scrapy from scrapy import Field class HexunItem(scrapy.Item): # define the fields for your item here like: product=Field() #产品 dateTime = Field() # 时间 price = Field() # 价格 amount = Field() # 成交额 volumn = Field() # 成交量 avePrice = Field() # 领先指标 openInterest = Field() # 持仓量

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python/cendalytics/report/core/dmo/record_retriever.py

jiportilla/ontology

0

120233

#!/usr/bin/env python # -*- coding: utf-8 -*- from tabulate import tabulate from base import BaseObject from datamongo import BaseMongoClient from datamongo import CendantCollection from datamongo import CendantXdm from datamongo import TransformCendantRecords class RecordRetriever(BaseObject): def __init__(self, key_field: str, mongo_client: BaseMongoClient, collection_names: dict, is_debug: bool = False): """ Created: 13-Nov-2019 <EMAIL> * refactored out of 'generate-feedback-report' Updated: 14-Nov-2019 <EMAIL> * added random record capability https://github.ibm.com/GTS-CDO/unstructured-analytics/issues/1331#issuecomment-16009658 :param key_field: the value of the keyfield to process (e.g. the actual Serial Number or Open Seat ID) :param collection_names: a dictionary containing a complete set of collection names Sample Input: { 'src': 'supply_src_20191025', 'tag': 'supply_tag_20191025', 'xdm': 'supply_xdm_20191029' } :param is_debug: """ BaseObject.__init__(self, __name__) self._is_debug = is_debug self._key_field = key_field self._mongo_client = mongo_client self._collection_names = collection_names def _retrieve_record(self, collection_name: str): collection = CendantCollection(is_debug=self._is_debug, some_base_client=self._mongo_client, some_collection_name=collection_name) if self._key_field.lower().strip() == "random": # GIT-1331-16009658 return collection.random(total_records=1)[0] return collection.by_key_field(self._key_field) def _tag_record(self) -> dict or None: tag_record = self._retrieve_record(self._collection_names["tag"]) if not self._collection_names["tag"]: self.logger.warning('\n'.join([ "TAG Collection Not Found", f"\t{self._collection_names}"])) return None if not tag_record: self.logger.warning('\n'.join([ f"TAG Record Not Found (" f"key-field={self._key_field})"])) return None if self._is_debug: df_record = TransformCendantRecords.to_dataframe(a_record=tag_record, include_text=False) self.logger.debug('\n'.join([ f"Retrieved TAG Record (" f"key-field={self._key_field})", tabulate(df_record, tablefmt='psql', headers='keys')])) return tag_record def _xdm_record(self) -> dict or None: if not self._collection_names["xdm"]: self.logger.warning('\n'.join([ "XDM Collection Not Found", f"\t{self._collection_names}"])) return None xdm_record = self._retrieve_record(self._collection_names["xdm"]) if not xdm_record: self.logger.warning('\n'.join([ f"XDM Record Not Found (" f"key-field={self._key_field})"])) return None if self._is_debug: self.logger.debug('\n'.join([ f"Retrieved XDM Record (" f"key-field={self._key_field})", tabulate(CendantXdm.dataframe(xdm_record), tablefmt='psql', headers='keys')])) return xdm_record def process(self) -> dict: svcresult = { "tag": self._tag_record(), "xdm": self._xdm_record()} if self._is_debug: self.logger.debug('\n'.join([ "Records Retrieved", f"\tKey Field: {self._key_field}", f"\tTAG Collection: {self._collection_names['tag']}", f"\tXDM Collection: {self._collection_names['xdm']}"])) return svcresult

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tweet.py

m-r-moon/videographer

0

88864

<gh_stars>0 ############################### # Filename: tweet.py # # Author: <NAME> # ############################### import tweepy from time import sleep from credentials import * auth = tweepy.OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_secret) api = tweepy.API(auth) # text status update api.update_status('status') # file status update api.update_with_media(filename, status, video)

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tests/test_groups.py

daylinmorgan/click-rich-help

4

182754

<gh_stars>1-10 import click from click_rich_help import StyledCommand, StyledGroup def test_command_group(runner): @click.group( cls=StyledGroup, command_groups={"main": ["cmd1", "cmd2"]}, ) def cli(): pass @cli.command(cls=StyledCommand, styles={"header": "yellow", "option": "green"}) @click.option("--name", help="The person to greet.") def cmd1(count): pass @cli.command() def cmd2(): pass @cli.command() def othercmd(): pass result = runner.invoke(cli, ["--help"], color=True) assert not result.exception assert result.output.splitlines() == [ "\x1b[1;3;36mUsage\x1b[0m: \x1b[1mcli\x1b[0m \x1b[1m[OPTIONS] COMMAND [ARGS]...\x1b[0m", "", "\x1b[1;3;36mOptions\x1b[0m:", " \x1b[1;33m--help\x1b[0m Show this message and exit.", "", "\x1b[1;3;36mmain\x1b[0m:", " \x1b[1;33mcmd1\x1b[0m ", " \x1b[1;33mcmd2\x1b[0m ", "", "\x1b[1;3;36mCommands\x1b[0m:", " \x1b[1;33mothercmd\x1b[0m ", ] def test_unkown_command(runner): @click.group(cls=StyledGroup, command_groups={"main": ["cmd1", "unknowncmd"]}) def cli(): pass @cli.command(cls=StyledCommand, styles={"header": "yellow", "option": "green"}) @click.option("--name", help="The person to greet.") def cmd1(count): pass @cli.command() def cmd2(): pass @cli.command() def othercmd(): pass result = runner.invoke(cli, ["--help"], color=True) assert isinstance(result.exception, ValueError) assert ( str(result.exception) == "Unable to find command 'unknowncmd' in list of commands" ) def test_option_group(runner): @click.group(cls=StyledGroup, styles={"header": "yellow", "option": "green"}) def cli(): pass @cli.command(option_groups={"Config": ["--config", "--save-config"]}) @click.option("--name", help="The person to greet.") @click.option("--config", help="path to config") @click.option("--save-config", help="save config", is_flag=True) def command(name): pass result = runner.invoke(cli, ["command", "--help"], color=True) assert not result.exception assert result.output.splitlines() == [ "\x1b[33mUsage\x1b[0m: \x1b[1mcli command\x1b[0m \x1b[1m[OPTIONS]\x1b[0m", "", "\x1b[33mConfig\x1b[0m:", " \x1b[32m--config \x1b[0m\x1b[32mTEXT\x1b[0m path to config", " \x1b[32m--save-config\x1b[0m save config", "", "\x1b[33mOptions\x1b[0m:", " \x1b[32m--name \x1b[0m\x1b[32mTEXT\x1b[0m The person to greet.", " \x1b[32m--help\x1b[0m Show this message and exit.", ] def test_unknown_option(runner): @click.command( cls=StyledCommand, option_groups={"Config": ["--config", "--save-config", "--unknown-option"]}, ) @click.option("--name", help="The person to greet.") @click.option("--config", help="path to config") @click.option("--save-config", help="save config", is_flag=True) def cli(name): pass result = runner.invoke(cli, ["--help"], color=True) assert isinstance(result.exception, ValueError) assert ( str(result.exception) == "Unable to find option '--unknown-option' in list of options" )

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sigm/ia/views.py

mariobp/SIGM

0

106899

<reponame>mariobp/SIGM<gh_stars>0 # -*- coding: utf-8 -*- from __future__ import unicode_literals from django.shortcuts import render, redirect from django.http import HttpResponse from django.contrib.auth.decorators import login_required from django.contrib.auth import login, logout, authenticate import models import forms import json # Create your views here. @login_required(login_url='/login/') def index(request): periodo = models.Periodo.objects.last() return render(request, 'index.html', {'periodo': periodo}) # end def def logoutUsers(request): logout(request) return redirect('/') # end def def abrirPeriodo(request): old = models.Periodo.objects.last() if old: num = old.numero+1 nuevo = models.Periodo(numero=num, cerrado=False) nuevo.save() else: nuevo = models.Periodo(numero=1, cerrado=False) nuevo.save() # end if return redirect('/') # end def def cerrarPeriodo(request, pk): periodo = models.Periodo.objects.filter(id=pk).first() if periodo: periodo.cerrado = True periodo.save() return redirect('/') # end def def registrar_e(request): if request.method == "POST": form = forms.EstudianteForm(request.POST) if form.is_valid(): form.save() return redirect('/matricula/estudiante/') else: return render(request, 'registro_e.html', {'form': form}) # end if return render(request, 'registro_e.html', {'form': forms.EstudianteForm()}) # end if def matricula_e(request): identificacion = request.GET.get('identificacion', False) mensaje = "" if identificacion: estudiante = models.Estudiante.objects.filter(identificacion=identificacion).first() if estudiante: periodo = models.Periodo.objects.last() if periodo: matricula = models.Matricula.objects.filter(estudiante=estudiante, periodo=periodo) if matricula: mensaje = "Ya esta matriculado" else: if not periodo.cerrado: matricula = models.Matricula(periodo=periodo, estudiante=estudiante) matricula.save() mensaje = "Matriculado" else: mensaje = "Periodo cerrado, no se puede matricular" else: mensaje = "No se puede matricular" else: mensaje = "No hay ningun estudiante con este codigo" return render(request, 'matricula.html', {"mensaje": mensaje}) # end def def desertados(request): return render(request, 'desertados.html', {}) # end def def listaMatriculados(request): periodo = models.Periodo.objects.last() estudiante = models.Estudiante.objects.all().exclude(matricula__periodo=periodo).values('latitud', 'longitud') data = list(estudiante) return HttpResponse(json.dumps(data), content_type="text/json") # end def

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src/genui/settings/__init__.py

Tontolda/genui

15

60558

""" __init__.py Created by: <NAME> On: 4/27/20, 10:49 AM """

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sandbox/decorator_def.py

olileger/sBeull

0

136106

import functools def FuncDecoratorOne(func): @functools.wraps(func) def wrapper(*args, **kwargs): print("Calling: ", func.__name__) return func(*args, **kwargs) return wrapper def FuncDecoratorListProperties(func): @functools.wraps(func) def wrapper(*args, **kwargs): print("Object's properties: ", args[0].__dict__) return func(*args, **kwargs) return wrapper def ClassDecoratorOne(c): __init__ = c.__init__ @functools.wraps(c.__init__) def wrapper(_self_, *args, **kwargs): __init__(_self_, *args, **kwargs) c.__init__ = wrapper return c

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RIPv2-Simulation/Router.py

vkmanojk/Networks-VirtualLAN

0

8171

''' Summary: Program that implements a routing deamon based on the RIP version 2 protocol from RFC2453. Usage: python3 Router.py <router_config_file> Configuration File: The user supplies a router configuration file of the format: [Settings] router-id = <router_number> input-ports = <input> [, <input>, ...] outputs = <output>-<metric>-<destination_router> [, <output>-<metric>-<destination_router>, ...] where, router_number: ID of router between 1 - 64000. input: port number between 1024 - 64000. output: port number between 1024 - 6400, not equal to any inputs. metric: metric of output between 1 - 16. destination_router: ID of destination router. Description: This program implements a basic RIPv2 routing protocol from RFC2453 for routing computations in computer networks. It takes a configuration file as shown above and sets up a router with a new socket for each input-port. The RIPv2 protocol uses a routing table to keep track of all reachable routers on the network along with their metric/cost and the direct next hop router ID along the route to that destination router. However, it can only send messages to the direct neighbours specified in outputs. The protocol uses the Bellman-Ford distance vector algorithm to compute the lowest cost route to each router in the network. If the metric is 16 or greater, the router is considered unreachable. The routing table initially starts with a single route entry (RTE) for itself with a metric of zero. The routing table is periodically transmitted too each of its direct output ports via an unsolicited response message as defined in RFC2453 section 3.9.2 and 4. This is performed on a separate thread so it does not interfere with other operations The receives messages from other routers by using the python select() function which blocks until a message is ready to be read. Once a message is received the header and contents are validated. If the message is valid each RTE is processed according to RFC2453 section 3.9.2. If a new router is found the RTE is added to the routing table, adding the cost to the metric for the output the message was received on. If the RTE already exists, but the metric is smaller, the metric is updated to the lower metric. If the lower metric is from a different next hop router, change the next hop. If nothing has changed, restart the timeout timer. If RTE metric >= max metric of 16, mark the entry for garbage collection and update the metric in the table. If any change has occurred in the routing table as a result of a received message, a triggered update (RFC2453 section 3.10.1) is sent to all outputs with the updated entries. Triggered updates are sent with a random delay between 1 - 5 seconds to prevent synchronized updates. Request messages are not implemented in this program. Timers (all timers are on separate threads) (RFC2453 section 3.8): Update timer - Periodic unsolicited response message sent to all outputs. The period is adjusted each time to a random value between 0.8 * BASE_TIMER and 1.2 * BASE_TIMER to prevent synchronized updates. Timeout - used to check the routing table for RTEs which have have not been updated within the ROUTE_TIMEOUT interval. If a router has not been heard from within this time, then set the metric to the max metric of 16 and start the garbage collection timer. Garbage timer - used to check the routing table for RTEs set for garbage collection. If the timeout >= DELETE_TIMEOUT, mark the RTE for deletion. Garbage Collection - used to check the routing table for RTEs marked for deletion, and removes those entries from the table. ''' import configparser import select import socket import sys import time import threading import struct import datetime from random import randint, randrange DEBUG = False HOST = '127.0.0.1' # localhost BASE_TIMER = 5 MAX_METRIC = 16 ROUTE_TIMEOUT = BASE_TIMER * 6 DELETE_TIMEOUT = BASE_TIMER * 4 AF_INET = 2 # =========================================================================== # TRANSITIONS class Transistion(): '''Class Representing a transition between states.''' def __init__(self, to_state): self.to_state = to_state def execute(self): '''Run the transition functions''' pass # =========================================================================== # STATES class State(): '''Class Representing a generic state''' def __init__(self, fsm): self.fsm = fsm def enter(self): '''Execute functions for entering a state''' pass def execute(self): '''Execute functions while in state''' pass def exit(self): '''Execute functions for leaving a state''' pass class StartUp(State): '''Class Representing the Start up state which reads the configuration file ''' def __init__(self, fsm): super(StartUp, self).__init__(fsm) def execute(self): '''Execute the configuration functions''' print_message("Loading Configuration File: '" + self.fsm.router.config_file + "'") config = configparser.ConfigParser() config.read(self.fsm.router.config_file) self.get_router_id(config) self.setup_inputs(config) self.get_outputs(config) self.setup_routing_table() self.fsm.router.print_routing_table() self.fsm.to_transition("toWaiting") def exit(self): '''Print complete message''' print_message("Router Setup Complete.") def get_router_id(self, config): '''Read the router id number from the configuration file''' if 1 <= int(config['Settings']['router-id']) <= 64000: self.fsm.router.router_settings['id'] = \ int(config['Settings']['router-id']) else: raise Exception('Invalid Router ID Number') def get_outputs(self, config): '''Return a dictionary of outputs containing port, cost and destination router id from the Configuration file''' outputs = config['Settings']['outputs'].split(', ') outputs = [i.split('-') for i in outputs] self.fsm.router.router_settings['outputs'] = {} existing_ports = [] for output in outputs: is_valid_port = 1024 <= int(output[0]) <= 64000 and not \ int(output[0]) in existing_ports is_valid_cost = 1 <= int(output[1]) < 16 is_valid_id = 1 <= int(output[2]) <= 64000 if is_valid_port and is_valid_cost and is_valid_id: existing_ports.append(int(output[0])) self.fsm.router.router_settings['outputs'][int(output[2])] = \ {'metric': int(output[1]), 'port': int(output[0])} else: raise Exception('Invalid Outputs') def setup_inputs(self, config): '''Create input sockets from the inputs specified in the config file''' # get inputs from configuration file ports = config['Settings']['input-ports'].split(', ') inputs = [] for port in ports: if 1024 <= int(port) <= 64000 and not int(port) in inputs: inputs.append(int(port)) else: raise Exception('Invalid Port Number') self.fsm.router.router_settings['inputs'] = {} # create socket for each input port for port in inputs: try: self.fsm.router.router_settings['inputs'][port] = \ socket.socket(socket.AF_INET, socket.SOCK_DGRAM) print_message('Socket ' + str(port) + ' Created.') except socket.error as msg: print('Failed to create socket. Message: ' + str(msg)) sys.exit() # bind port to socket try: self.fsm.router.router_settings['inputs'][port].bind( (HOST, port)) print_message('Socket ' + str(port) + ' Bind Complete.') except socket.error as msg: print('Failed to create socket. Message ' + str(msg)) sys.exit() def setup_routing_table(self): '''Setup routing table with the outputs specified in the config file''' self.fsm.router.routing_table[self.fsm.router.router_settings['id']] = \ RIPRouteEntry(address=self.fsm.router.router_settings['id'], nexthop=0, metric=0, imported=True) class Waiting(State): ''' Class representing the waiting state of the FSM where the router waits for messages to be received on its input sockets. When a message is received the state changes to the ReadMeassage state. ''' def __init__(self, fsm): super(Waiting, self).__init__(fsm) def enter(self): '''Display State entry message''' print_message("Entering idle state...") def execute(self): '''Waits for input sockets to be readable and then changes the state to process the received message.''' readable = select.select( self.fsm.router.router_settings['inputs'].values(), [], []) if readable[0]: self.fsm.router.readable_ports = readable[0] self.fsm.to_transition("toReadMessage") def exit(self): '''Display State exit message''' print_message("Message Received") class ReadMessage(State): '''Class representing the state for reading messages received on the input sockets''' def __init__(self, fsm): super(ReadMessage, self).__init__(fsm) def enter(self): print_message("Reading Messages...") def execute(self): for port in self.fsm.router.readable_ports: packet = RIPPacket(port.recvfrom(1024)[0]) self.fsm.router.update_routing_table(packet) if self.fsm.router.route_change: self.fsm.router.trigger_update() self.fsm.router.print_routing_table() self.fsm.to_transition("toWaiting") def exit(self): print_message("Messages Read.") # =========================================================================== # FINITE STATE MACHINE class RouterFSM(): '''Class representing the Router finite state machine''' def __init__(self, rip_router): self.router = rip_router self.states = {} self.transitions = {} self.cur_state = None self.trans = None def add_transistion(self, trans_name, transition): '''Add a new transition to the FSM''' self.transitions[trans_name] = transition def add_state(self, state_name, state): '''Add a new state to the FSM''' self.states[state_name] = state def set_state(self, state_name): '''Set the current state of the FSM''' self.cur_state = self.states[state_name] def to_transition(self, to_trans): '''Set the current transition of the FSM''' self.trans = self.transitions[to_trans] def execute(self): '''Run the FSM''' if self.trans: self.cur_state.exit() self.trans.execute() self.set_state(self.trans.to_state) self.cur_state.enter() self.trans = None self.cur_state.execute() # =========================================================================== # IMPLEMENTATION class RIPPacket: '''Class representing a RIP packet containing a header and body as defined in RFC2453 RIPv2 section 4.''' def __init__(self, data=None, header=None, rtes=None): if data: self._init_from_network(data) elif header and rtes: self._init_from_host(header, rtes) else: raise ValueError def __repr__(self): return "RIPPacket: Command {}, Ver. {}, number of RTEs {}.". \ format(self.header.cmd, self.header.ver, len(self.rtes)) def _init_from_network(self, data): '''Init for RIPPacket if data is from the network''' # Packet Validation datalen = len(data) if datalen < RIPHeader.SIZE: raise FormatException malformed_rtes = (datalen - RIPHeader.SIZE) % RIPRouteEntry.SIZE if malformed_rtes: raise FormatException # Convert bytes in packet to header and RTE data num_rtes = int((datalen - RIPHeader.SIZE) / RIPRouteEntry.SIZE) self.header = RIPHeader(data[0:RIPHeader.SIZE]) self.rtes = [] rte_start = RIPHeader.SIZE rte_end = RIPHeader.SIZE + RIPRouteEntry.SIZE # Loop over data packet to obtain each RTE for i in range(num_rtes): self.rtes.append(RIPRouteEntry(rawdata=data[rte_start:rte_end], src_id=self.header.src)) rte_start += RIPRouteEntry.SIZE rte_end += RIPRouteEntry.SIZE def _init_from_host(self, header, rtes): '''Init for imported data''' if header.ver != 2: raise ValueError("Only Version 2 is supported.") self.header = header self.rtes = rtes def serialize(self): '''Return the byte sting representing this packet for network transmission''' packed = self.header.serialize() for rte in self.rtes: packed += rte.serialize() return packed class RIPHeader: '''Class representing the header of a RIP packet''' FORMAT = "!BBH" SIZE = struct.calcsize(FORMAT) TYPE_RESPONSE = 2 VERSION = 2 def __init__(self, rawdata=None, router_id=None): self.packed = None if rawdata: self._init_from_network(rawdata) elif router_id: self._init_from_host(router_id) else: raise ValueError def __repr__(self): return "RIP Header (cmd = {}, ver = {}, src = {})".format(self.cmd, self.ver, self.src) def _init_from_network(self, rawdata): '''init for data from network''' header = struct.unpack(self.FORMAT, rawdata) self.cmd = header[0] self.ver = header[1] self.src = header[2] def _init_from_host(self, router_id): '''Init for data from host''' self.cmd = self.TYPE_RESPONSE self.ver = self.VERSION self.src = router_id def serialize(self): '''Return the byte sting representing this header for network transmission''' return struct.pack(self.FORMAT, self.cmd, self.ver, self.src) class RIPRouteEntry: '''Class representing a single RIP route entry (RTE)''' FORMAT = "!HHIII" SIZE = struct.calcsize(FORMAT) MIN_METRIC = 0 MAX_METRIC = 16 def __init__(self, rawdata=None, src_id=None, address=None, nexthop=None, metric=None, imported=False): self.changed = False self.imported = imported self.init_timeout() if rawdata and src_id != None: self._init_from_network(rawdata, src_id) elif address and nexthop != None and metric != None: self._init_from_host(address, nexthop, metric) else: raise ValueError def __repr__(self): template = "|{:^11}|{:^10}|{:^11}|{:^15}|{:^10}|{:^13}|" # Check that timeout is set if self.timeout == None: return template.format(self.addr, self.metric, self.nexthop, self.changed, self.garbage, str(self.timeout)) else: timeout = (datetime.datetime.now() - self.timeout).total_seconds() return template.format(self.addr, self.metric, self.nexthop, self.changed, self.garbage, round(timeout, 1)) def _init_from_host(self, address, nexthop, metric): '''Init for data from host''' self.afi = AF_INET self.tag = 0 # not used self.addr = address self.nexthop = nexthop self.metric = metric def _init_from_network(self, rawdata, src_id): '''Init for data received from network''' rte = struct.unpack(self.FORMAT, rawdata) self.afi = rte[0] self.tag = rte[1] self.addr = rte[2] self.set_nexthop(rte[3]) self.metric = rte[4] if self.nexthop == 0: self.nexthop = src_id # Validation if not self.MIN_METRIC <= self.metric <= self.MAX_METRIC: raise FormatException def init_timeout(self): '''Initialize the timeout property''' if self.imported: self.timeout = None else: self.timeout = datetime.datetime.now() self.garbage = False self.marked_for_delection = False def __eq__(self, other): if self.afi == other.afi and \ self.addr == other.addr and \ self.tag == other.tag and \ self.nexthop == other.nexthop and \ self.metric == other.metric: return True else: return False def set_nexthop(self, nexthop): '''Set the nexthop property''' self.nexthop = nexthop def serialize(self): '''Pack entries into typical RIPv2 packet format for sending over the network. ''' return struct.pack(self.FORMAT, self.afi, self.tag, self.addr, self.nexthop, self.metric) class FormatException(Exception): '''Class representing the Format Exception''' def __init__(self, message=""): self.message = message class Router: '''Class representing a single router''' def __init__(self, config_file): self.fsm = RouterFSM(self) self.config_file = config_file # Dictionary of router settings, including router-id, inputs and # outputs self.router_settings = {} self.readable_ports = [] # Dictionary of routing table self.routing_table = {} self.route_change = False # STATES self.fsm.add_state("StartUp", StartUp(self.fsm)) self.fsm.add_state("Waiting", Waiting(self.fsm)) self.fsm.add_state("ReadMessage", ReadMessage(self.fsm)) # TRANSITIONS self.fsm.add_transistion("toWaiting", Transistion("Waiting")) self.fsm.add_transistion("toReadMessage", Transistion("ReadMessage")) self.fsm.set_state("StartUp") def execute(self): '''Run the router's finite state machine''' self.fsm.execute() def update_routing_table(self, packet): '''Update Routing table if new route info exist''' for rte in packet.rtes: # ignore RTEs of self if rte.addr != self.fsm.router.router_settings['id']: bestroute = self.routing_table.get(rte.addr) # set nexthop to source router and calculate metric rte.set_nexthop(packet.header.src) rte.metric = min(rte.metric + self.router_settings['outputs'][ packet.header.src]['metric'], RIPRouteEntry.MAX_METRIC) # Route dosn't yet exist if not bestroute: # ignore RTEs with a metric of MAX_METRIC if rte.metric == RIPRouteEntry.MAX_METRIC: return # Add new RTE to routing table rte.changed = True self.route_change = True self.routing_table[rte.addr] = rte print_message("RTE added for Router: " + str(rte.addr)) return else: # Route already exists if rte.nexthop == bestroute.nexthop: if bestroute.metric != rte.metric: if bestroute.metric != RIPRouteEntry.MAX_METRIC \ and rte.metric >= RIPRouteEntry.MAX_METRIC: # mark for garbage collection bestroute.metric = RIPRouteEntry.MAX_METRIC bestroute.garbage = True bestroute.changed = True self.route_change = True else: self.update_route(bestroute, rte) # Route still exists with same values elif not bestroute.garbage: bestroute.init_timeout() # Lower metric on existing route elif rte.metric < bestroute.metric: self.update_route(bestroute, rte) def update_route(self, bestroute, rte): '''Update an existing route entry with new route info''' bestroute.init_timeout() bestroute.garbage = False bestroute.changed = True bestroute.metric = rte.metric bestroute.nexthop = rte.nexthop self.route_change = True print_message("RTE for Router: " + str(rte.addr) + " updated with metric=" + str(rte.metric) + ", nexthop=" + str(rte.nexthop) + ".") def print_routing_table(self): '''Print the routing table to the terminal''' line = "+-----------+----------+-----------+---------------+----------+-------------+" print(line) print( "| Routing Table (Router " + str(self.router_settings['id']) + ") |") print(line) print( "|Router ID | Metric | NextHop | ChangedFlag | Garbage | Timeout(s) |") print(line) print(self.routing_table[self.router_settings['id']]) print( "+===========+==========+===========+===============+==========+=============+") for entry in self.routing_table: if entry != self.router_settings['id']: print(self.routing_table[entry]) print(line) print('\n') def trigger_update(self): '''Send Routing update for only the routes which have changed''' changed_rtes = [] print_message("Sending Trigger update.") for rte in self.routing_table.values(): if rte.changed: changed_rtes.append(rte) rte.changed = False self.route_change = False # send update with random delay between 1 and 5 seconds delay = randint(1, 5) threading.Timer(delay, self.update, [changed_rtes]) def update(self, entries): '''Send a message to all output ports''' if self.router_settings != {}: sock = list(self.router_settings['inputs'].values())[1] local_header = RIPHeader(router_id=self.router_settings['id']) for output in self.router_settings['outputs']: # Split horizon # Remove RTES for which nexthop == output split_horizon_entries = [] for entry in entries: if entry.nexthop != output: split_horizon_entries.append(entry) else: # Poison reverse # Create new entry to get around some funky referencing # When doing poisoned_entry = entry poisoned_entry = RIPRouteEntry(rawdata=None, src_id=None, address=entry.addr, nexthop=entry.nexthop, metric= RIPRouteEntry.MAX_METRIC, imported=entry.imported) split_horizon_entries.append(poisoned_entry) # comment out to disable split horizon packet = RIPPacket( header=local_header, rtes=split_horizon_entries) # Uncomment to disable split horizon # packet = RIPPacket(header=local_header, rtes=entries) sock.sendto(packet.serialize(), (HOST, self.router_settings['outputs'][output]["port"])) print_message("Message Sent To Router: " + str(output)) def check_timeout(self): '''Check the current timeout value for each RTE in the routing table. If the time difference with now is greater than ROUTE_TIMEOUT, then set the metric to 16 and start the garbage collection timer.''' print_message("Checking timeout...") if self.routing_table != {}: for rte in self.routing_table.values(): if rte.timeout != None and \ (datetime.datetime.now() - rte.timeout).total_seconds() \ >= ROUTE_TIMEOUT: rte.garbage = True rte.changed = True self.route_change = True rte.metric = RIPRouteEntry.MAX_METRIC rte.timeout = datetime.datetime.now() self.print_routing_table() print_message("Router: " + str(rte.addr) + " timed out.") def garbage_timer(self): '''Check the status of the garbage property of each RTE. If true, and the timeout value difference with now is greater than DELETE_TIMEOUT, mark it for deletion''' print_message("Checking garbage timeout...") if self.routing_table != {}: for rte in self.routing_table.values(): if rte.garbage: if (datetime.datetime.now() - rte.timeout).total_seconds() \ >= DELETE_TIMEOUT: rte.marked_for_delection = True def garbage_collection(self): '''Check the routing table for RTE's that are marked for deletion and remove them.''' print_message("Collecting Garbage...") if self.routing_table != {}: delete_routes = [] for rte in self.routing_table.values(): if rte.marked_for_delection: delete_routes.append(rte.addr) print_message("Router: " + str(rte.addr) + " has been " + "removed from the routing table.") for entry in delete_routes: del self.routing_table[entry] self.print_routing_table() def timer(self, function, param=None): '''Start a periodic timer which calls a specified function''' if param != None: function(list(param.values())) period = BASE_TIMER * randrange(8, 12, 1) / 10 else: period = BASE_TIMER function() threading.Timer(period, self.timer, [function, param]).start() def start_timers(self): '''Start the timers on separate threads''' self.timer(self.update, param=self.routing_table) self.timer(self.check_timeout) self.timer(self.garbage_timer) self.timer(self.garbage_collection) def main_loop(self): '''Start the main loop for the program.''' while True: self.execute() # RUN THE PROGRAM def print_message(message): '''Print the given message with the current time before it''' if DEBUG: print("[" + time.strftime("%H:%M:%S") + "]: " + message) def main(): '''Main function to run the program.''' if __name__ == "__main__": router = Router(str(sys.argv[-1])) router.start_timers() router.main_loop() main()

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skylib/extraction/__init__.py

SkynetRTN/skylib

1

102625

""" SkyLib source extraction functions - a replacement for SExtractor. main: high-level source extraction interface. isophotal: a fast implementation of isophotal analysis. """ from .main import *

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scan/biotools.py

dubinnyi/rcsbscan

0

187622

<filename>scan/biotools.py import gzip import sys, os import warnings import time from Bio.PDB.PDBExceptions import PDBConstructionWarning import re from Bio.PDB.mmtf import MMTFParser from Bio.PDB import * from Bio.Seq import Seq from Bio.Data import IUPACData import Bio.SVDSuperimposer import multiprocessing as mp LOCK = mp.Lock() class MPCounter(object): def __init__(self, initval=0): self.val = mp.Value('i', initval) self.lock = mp.Lock() def increment(self): with self.lock: self.val.value += 1 def value(self): with self.lock: return self.val.value def eprint(*args, **kwargs): with LOCK: print(*args, file=sys.stderr, **kwargs) def mpprint(*args, **kwargs): with LOCK: print(*args, file=sys.stdout, **kwargs) def opengz(filename, args: 'r'): if filename[-2:] == "gz": openfunc = gzip.open openarg = args + 't' else: openfunc = open openarg = args return openfunc(filename, openarg) def parse_structure_filename(filename): id = os.path.basename(filename) format = 'auto' zip_flag = False if id.startswith("pdb"): id = id[3:] format = 'pdb' if id.endswith(".gz"): id = id[0:-3] zip_flag = True if id.endswith(".pdb"): id = id[0:-4] format = 'pdb' if id.endswith(".mmcif"): id = id[0:-6] format = 'cif' if id.endswith(".cif"): id = id[0:-4] format = 'cif' if id.endswith(".ent"): id = id[0:-4] if id.endswith(".mmtf"): id = id[0:-5] format = 'mmtf' id = id.upper() return (id, format, zip_flag) def get_structure_from_file(filename, **kwargs): # format argument is unused force_format = format = 'auto' if 'format' in kwargs.keys(): force_format = kwargs['format'] (file_id, filename_format, zip_flag) = parse_structure_filename(filename) cif_parser = MMCIFParser() pdb_parser = PDBParser() mmtf_parser = MMTFParser() if force_format != 'auto': format = force_format if format == 'auto': format = filename_format if format == 'pdb' or format == 'auto': parsers = [pdb_parser, cif_parser] elif format == 'mmtf' or format == 'auto': parsers = [mmtf_parser] else: parsers = [cif_parser, pdb_parser, mmtf_parser] for parser in parsers: try: if zip_flag: open_function = opengz else: open_function = open if format == 'mmtf': structure = parser.get_structure(filename) if structure: return structure with open_function(filename, 'r') as handle: structure = parser.get_structure(file_id, handle) if structure: return structure except: continue eprint("File {} could not be read".format(filename)) return None def chain_sequence_ppbuild(chain): # Fails if chain is dicontinued ppb = PPBuilder() pp = ppb.build_peptides(chain) if pp and len(pp): return (pp[0].get_sequence()) else: return (None) def chain_sequence_aaselect(chain): # Ok if chain is discontinued res_list = chain.get_list() res_list_aa = [res for res in res_list if is_aa(res)] return res_list_aa def res_oneletter(res): #dict_3to1 = IUPACData.protein_letters_3to1.copy() dict_3to1 = {} dict_3to1['Mse'] = 'm' # Seleno methyonine https://www.rcsb.org/ligand/MSE dict_3to1['Mly'] = 'k' # N,N dimethyl lysine https://www.rcsb.org/ligand/MLY dict_3to1['Cso'] = 'c' # S-HYDROXYCYSTEINE https://www.rcsb.org/ligand/CSO dict_3to1['Csd'] = 'a' # 3-SULFINOALANINE https://www.rcsb.org/ligand/CSD dict_3to1['Cme'] = 'e' # S,S-(2-HYDROXYETHYL)THIOCYSTEINE # https://www.rcsb.org/ligand/CME dict_3to1['Tys'] = 'y' # O-SULFO-L-TYROSINE https://www.rcsb.org/ligand/TYS dict_3to1['Dah'] = 'f' # 3,4-DIHYDROXYPHENYLALANINE # https://www.rcsb.org/ligand/DAH rname3 = res.get_resname() rname3 = rname3[0].upper() + rname3[1:].lower() try: rname1 = IUPACData.protein_letters_3to1[rname3] except KeyError: try: rname1 = dict_3to1[rname3] except KeyError: rname1 = '.' return rname1 def res_list_to_one_letter_string(res_list): if not res_list: return (0, "") prev_id = None sequence_start = None seq = '' for r in res_list: this_id = r.get_id()[1] # residue number, may repeat o have gaps if prev_id: if this_id == prev_id or this_id == prev_id + 1: seq += res_oneletter(r) elif this_id > prev_id + 1: for r1 in range(prev_id + 1, this_id): seq += "." seq += res_oneletter(r) else: sequence_start = this_id seq += res_oneletter(r) prev_id = this_id return (sequence_start, Seq(seq)) def chain_one_letter_string(chain): res_list = chain_sequence_aaselect(chain) return res_list_to_one_letter_string(res_list) def res_insertions(res_list): res_list_ins = [res for res in res_list if res.get_id()[2] != ' '] return res_list_ins def chain_water(chain): # Ok if chain is discontinued res_list = chain.get_list() res_list_water = [res for res in res_list if res.get_id()[0] == 'W'] return res_list_water def chain_water_id(chain, water_id): # Ok if chain is discontinued res_list = chain.get_list() res_list_water = [res for res in res_list \ if res.get_id()[0] == 'W' and res.get_id()[1] == water_id] return res_list_water def bio_str_to_resid(string, hetfield =' '): # Return residue in biopython-compatible format # insertion character after number is converted to icode # hetfield is ' ' for ordinary residues # # examples: # bio_str_to_resid('10') # (' ', 10, ' ') # bio_str_to_resid('11A') # (' ', 11, 'A') p = re.compile('(?P<resseq>\d+)(?P<icode>[A-Z]?)$') m = p.match(string) if not m: return None resseq = int(m.group('resseq')) icode = m.group('icode') icode = ' ' if not icode else icode return (hetfield, resseq, icode) def bio_resid_to_str(resid): hetfield, resseq, icode = resid return "{}{}".format(resseq, icode.strip()) def range_str_to_tuple(range_str): if not range_str or range_str == '*' or range_str == '.' or range_str.upper() == 'ALL': # Select all return None range_split = range_str.split('-') if len(range_split) == 0: # Select all return None elif len(range_split) == 1: res_start = bio_str_to_resid(range_split[0]) res_finish = res_start else: res_start = bio_str_to_resid(range_split[0]) res_finish = bio_str_to_resid(range_split[1]) #res_finish[1] += 1 return (res_start, res_finish) def select_residues_from_chain(chain, **kwargs): # chain: biopython chain # first_res: first residue id to select, e.g. (' ',1,' ') # last_res: last residue to select, e.g. (' ',10, 'A') # gap_count: select 'gap_count' residues before first_res or after last_res # select all residues if no argumants provided res_list_aa = chain_sequence_aaselect(chain) id_list = [res.get_id() for res in res_list_aa] res_tuple = kwargs['res_tuple'] if 'res_tuple' in kwargs else None gap_count = kwargs['gap_count'] if 'gap_count' in kwargs else 0 if res_tuple: (first_res, last_res) = res_tuple else: first_res = kwargs['first_res'] if 'first_res' in kwargs else None last_res = kwargs['last_res'] if 'last_res' in kwargs else None first_index, last_index = 0, len(res_list_aa) if first_res: try: first_index = id_list.index(first_res) except: raise IndexError("Residue '{}' is out of sequence range".format(first_res)) if last_res: try: last_index = id_list.index(last_res) except: raise IndexError("Residue '{}' is out of sequence range".format(last_res)) if first_res and last_res: return res_list_aa[first_index: last_index + 1] elif first_res and ( gap_count is not None ): if first_index - gap_count < 0: gap_count = first_index return res_list_aa[first_index - gap_count : first_index] elif last_res and ( gap_count is not None ): return res_list_aa[last_index + 1 : last_index + 1 + gap_count] elif first_res and ( gap_count is None ): return res_list_aa[first_index :] elif last_res and ( gap_count is None ): return res_list_aa[: last_index + 1] else: return res_list_aa def atom_str_to_set(atom_str): atom_set = set() if not atom_str or atom_str == '*' or atom_str == '.' or atom_str.upper() == 'ALL': return atom_set atom_list = atom_str.split(',') for a in atom_list: a = a.strip() atom_set.add(a) return atom_set def select_atoms_from_res_list(res_list, ref_atom_names_set): atom_list = [] for r in res_list: for a in r.get_list(): if ref_atom_names_set and a.get_name() in ref_atom_names_set: atom_list.append(a) if not ref_atom_names_set: # set is empty, append all atoms atom_list.append(a) if ref_atom_names_set and not atom_list: raise IndexError("Atoms not present in structure") return atom_list def check_res_list_is_continuous(res_list): prev_id = None for r in res_list: this_id = r.get_id()[1] # residue number, may repeat o have gaps if prev_id: if not (this_id == prev_id or this_id == prev_id + 1): return False prev_id = this_id return True def get_res_and_atoms(chain_res_aa, resno, ref_res_list_len, ref_atom_names_set): try: res_list_to_fit = chain_res_aa[resno: resno + ref_res_list_len] except: return (None, None) if check_res_list_is_continuous(res_list_to_fit): atom_list_to_fit = select_atoms_from_res_list(res_list_to_fit, ref_atom_names_set) return (res_list_to_fit, atom_list_to_fit) else: # eprint("Residue list is discontinous: {} - {}".format(res_list_to_fit[0].get_full_id(), # res_list_to_fit[-1].get_id())) return (None, None) def recursive_expand(list_of_files_and_dirs): result = [] for f in list_of_files_and_dirs: if os.path.isfile(f): result.append(f) elif os.path.isdir(f): subdirs = [os.path.join(f, d) for d in os.listdir(f)] result.extend(recursive_expand(subdirs)) return result

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instance/config.py

Alweezy/cp2-bucketlist-api

1

69746

<reponame>Alweezy/cp2-bucketlist-api import os class Config(object): """Parent configuration class.""" DEBUG = False CSRF_ENABLED = True SECRET_KEY = os.getenv('SECRET_KEY') SQLALCHEMY_DATABASE_URI = os.getenv('DATABASE_URL') class DevelopmentConfig(Config): """Configurations for Development.""" DEBUG = True SQLALCHEMY_DATABASE_URI = 'postgresql://localhost/flask_db' class TestingConfig(Config): """Configurations for Testing, with a separate test database.""" TESTING = True SQLALCHEMY_DATABASE_URI = 'postgresql://localhost/test_db' DEBUG = True class StagingConfig(Config): """Configurations for Staging.""" DEBUG = True class ProductionConfig(Config): """Configurations for Production.""" DEBUG = False TESTING = False app_config = { 'development': DevelopmentConfig, 'testing': TestingConfig, 'staging': StagingConfig, 'production': ProductionConfig, }

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migrations/0018_auto_20180326_0331.py

tkhjp/editor_tkh

0

77708

<gh_stars>0 # Generated by Django 2.0.2 on 2018-03-26 03:31 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('editor', '0017_knowledge_point_list_grade'), ] operations = [ migrations.AlterField( model_name='question_upload', name='years_of_question', field=models.CharField(choices=[('p2', '小二'), ('p3', '小三'), ('p2', '小四'), ('p2', '小五'), ('p2', '小六')], default='p5', max_length=2), ), ]

[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 3251, 630, 491, 15337, 29871, 29906, 29889, 29900, 29889, 29906, 373, 29871, 29906, 29900, 29896, 29947, 29899, 29900, 29941, 29899, 29906, 29953, 29871, 29900, 29941, 29901, 29941, 29896, 13, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 15204, 742, 525, 29900, 29900, 29896, 29955, 29918, 28385, 5485, 29918, 3149, 29918, 1761, 29918, 8228, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 2499, 357, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 12470, 29918, 9009, 742, 13, 9651, 1024, 2433, 6360, 29879, 29918, 974, 29918, 12470, 742, 13, 9651, 1746, 29922, 9794, 29889, 27890, 29898, 1859, 1575, 11759, 877, 29886, 29906, 742, 525, 30446, 30685, 5477, 6702, 29886, 29941, 742, 525, 30446, 30457, 5477, 6702, 29886, 29906, 742, 525, 30446, 30928, 5477, 6702, 29886, 29906, 742, 525, 30446, 30904, 5477, 6702, 29886, 29906, 742, 525, 30446, 31304, 1495, 1402, 2322, 2433, 29886, 29945, 742, 4236, 29918, 2848, 29922, 29906, 511, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]

Oefeningen/standalone/list_comprehension_4.py

Seviran/Python_3

0

24049

string_input = "amazing" vowels = "aeiou" answer = [char for char in string_input if char not in vowels] print(answer)

[ 1, 29871, 13, 1807, 29918, 2080, 353, 376, 314, 834, 292, 29908, 13, 29894, 340, 1379, 353, 376, 3660, 29875, 283, 29908, 13, 12011, 353, 518, 3090, 363, 1373, 297, 1347, 29918, 2080, 565, 1373, 451, 297, 325, 340, 1379, 29962, 13, 2158, 29898, 12011, 29897, 13, 2 ]

source/methods_upload_workspace_stats.py

CheyenneNS/metrics

0

1603628

<reponame>CheyenneNS/metrics from pymongo import MongoClient from pymongo import ReadPreference from biokbase.workspace.client import Workspace import json as _json import os import mysql.connector as mysql import requests import time from datetime import date #import pprint requests.packages.urllib3.disable_warnings() #pp = pprint.PrettyPrinter(indent=4) # NOTE get_user_info_from_auth2 sets up the initial dict. #The following functions update certain fields in the dict. # So get_user_info_from_auth2 must be called before get_internal_users and get_user_orgs_count metrics_mysql_password = os.environ['METRICS_MYSQL_PWD'] mongoDB_metrics_connection = os.environ['MONGO_PATH'] sql_host = os.environ['SQL_HOST'] query_on = os.environ['QUERY_ON'] ws_url = os.environ['WS_URL'] ws_user_token = os.environ["METRICS_WS_USER_TOKEN"] to_workspace = os.environ['WRK_SUFFIX'] def get_workspaces(db): """ gets narrative workspaces information for non temporary workspaces """ workspaces_dict = {} #Get all the legitimate workspaces and their respective user (saved(not_temp)) workspaces_cursor = db.workspaces.find({"moddate": {"$exists":True}}, # IF NOT DELETED NARRATIVES {"del" : False, "moddate": {"$exists":True}}, # IF ONLY SAVED NARRATIVES "meta" : {"k" : "is_temporary", "v" : "false"} }, {"owner":1,"ws":1,"moddate":1,"del":1,"_id":0}) for record in workspaces_cursor: if record["ws"] != 615: is_deleted_ws = 0 if record["del"] == True: is_deleted_ws = 1 workspaces_dict[record["ws"]] = {"ws_id" : record["ws"], "username" : record["owner"], "mod_date" : record["moddate"], "initial_save_date" : None, "top_lvl_object_count" : 0, "total_object_count" : 0, "visible_app_cells_count" : 0, "code_cells_count" : 0, "narrative_version" : 0, "hidden_object_count" : 0, "deleted_object_count" : 0, "total_size" : 0, 'top_lvl_size' : 0, "is_public" : 0, "is_deleted" : is_deleted_ws, "is_temporary" : None, "number_of_shares" : 0 } workspace_is_temporary_cursor = db.workspaces.find({"moddate": {"$exists":True}, #{"del" : False,"moddate": {"$exists":True}, "meta" : {"k" : "is_temporary", "v" : "true"} }, {"ws":1,"_id":0}) for record in workspace_is_temporary_cursor: workspaces_dict[record["ws"]]["is_temporary"] = 1 workspace_not_temporary_cursor = db.workspaces.find({"moddate": {"$exists":True}, #{"del" : False, "moddate": {"$exists":True}, "meta" : {"k" : "is_temporary", "v" : "false"} }, {"ws":1,"_id":0}) for record in workspace_not_temporary_cursor: workspaces_dict[record["ws"]]["is_temporary"] = 0 return workspaces_dict def get_workspace_shares(db, workspaces_dict): """ gets number of shares per workspace """ aggregation_string=[ {"$match" : {"perm" : { "$in": [ 10,20,30 ]}}}, {"$group" : {"_id" : "$id", "shared_count" : { "$sum" : 1 }}}] all_shared_perms_cursor=db.workspaceACLs.aggregate(aggregation_string) for record in db.workspaceACLs.aggregate(aggregation_string): if record["_id"] in workspaces_dict: workspaces_dict[record["_id"]]["number_of_shares"] = record["shared_count"] return workspaces_dict def get_public_workspaces(db, workspaces_dict): """ Gets IDs of public workspaces """ public_workspaces_cursor = db.workspaceACLs.find({"user" : "*"},{"id":1,"_id":0}) for record in public_workspaces_cursor: if record["id"] in workspaces_dict: workspaces_dict[record["id"]]["is_public"] = 1 return workspaces_dict """ OLD FOR APP CELL COUNTING NOW HANDLED BY NEW FUNCTION def get_app_cell_count(wsadmin, narrative_ref, workspaces_with_app_cell_oddities): # Gets the number of App Cells in the narrative See documentation for WS administer here https://github.com/kbase/workspace_deluxe/blob/02217e4d63da8442d9eed6611aaa790f173de58e/docsource/administrationinterface.rst # info = wsadmin.administer({'command': "getObjectInfo", 'params': {"objects": [{"ref": narrative_ref}], "includeMetadata": 1} })["infos"][0] meta = info[10] total_app_cells = 0 # print("META: " + str(meta)) for key in meta: if key.startswith("method."): # if(isinstance(meta[key], int)): try: total_app_cells += int(meta[key]) except ValueError: workspaces_with_app_cell_oddities.add(narrative_ref) print("META: " + str(meta)) return (total_app_cells, workspaces_with_app_cell_oddities) """ def get_narrative_cell_counts(wsadmin, narrative_ref): """ Gets the number of App Cells and Code Cells in the narrative See documentation for WS administer here https://github.com/kbase/workspace_deluxe/blob/02217e4d63da8442d9eed6611aaa790f173de58e/docsource/administrationinterface.rst """ app_count = 0 code_count = 0 is_suspect = 0 try: nar_obj = wsadmin.administer({'command': "getObjects", 'params': {"objects": [{"ref": narrative_ref}]}})["data"][0]["data"] if "cells" in nar_obj: for cell in nar_obj["cells"]: if(cell["metadata"].get("kbase", {}).get("type") == "app"): app_count += 1 elif(cell["metadata"].get("kbase", {}).get("type") == "code"): code_count += 1 return(app_count, code_count, is_suspect) except: # Shock node is suspect is_suspect = 1 print("Narrative Ref : " + narrative_ref + " has a shock node issue") #try running on the previous version of the narrative if exists #if previous version does not exist return zeros (ws_id, obj_id, obj_ver) = narrative_ref.split("/") if int(obj_ver) > 1: new_ref = ws_id + "/" + obj_id + "/" + str(int(obj_ver) - 1) (app_count, code_count, dummy) = get_narrative_cell_counts(wsadmin, new_ref) return(app_count, code_count, is_suspect) def get_kbase_staff(db_connection): """ get list of KBase staff """ cursor = db_connection.cursor() query = "use "+query_on cursor.execute(query) #get all existing users kbase_staff = set() query = "select username from metrics.user_info where kb_internal_user = 1;" cursor.execute(query) for (username) in cursor: kbase_staff.add(username[0]) return kbase_staff def get_objects(db, workspaces_dict, db_connection): """ get object counts as well as hidden del, size, is_narrative informationf for the workspace. """ wsadmin = Workspace(ws_url, token=ws_user_token) #OBJECT COUNTS ACROSS ALL WORKSPACES. object_counts_dict = dict() #OBJECT COUNTS ACROSS USER WORKSPACES users_object_counts_dict = dict() #SAME FOR USER AND ALL # {object_type_full : { "object_type" : ..., # "object_spec_version" : ..., # "last_mod_date" : max_date , # "top_lvl_object_count" : # , # "total_object_count" : # , # "public_object_count" : # , # "private_object_count" : # , # "hidden_object_count" : # , # "deleted_object_count" : # , # "copy_count" : # , # "total_size" : # , # "top_lvl_size" : #}} kbase_staff = get_kbase_staff(db_connection) # workspaces_with_app_cell_oddities = set() #keeping track of workspaces with odd metadata where app cell counting fails narrative_refs_with_shock_node_issues = set() #keep track of narrative refs with shocknode issues to keep track of later. ########################### #few debugging lines to do a smaller set of workspaces. ########################### # ws_limit = 1 #For debugging purposes, if ws_limit is None then it does all workspaces. # ws_counter = 0 # for ws_id in sorted(workspaces_dict.keys()): # ws_counter += 1 # if ws_counter > ws_limit : # del workspaces_dict[ws_id] ########################### #debugging lines to minimal Workspaces # temp_dict = dict() # temp_dict[49114] = workspaces_dict[49114] # temp_dict[3] = workspaces_dict[3] # temp_dict[5009] = workspaces_dict[5009] # temp_dict[1000] = workspaces_dict[1000] # temp_dict[53462] = workspaces_dict[53462] # temp_dict[324] = workspaces_dict[324] # workspaces_dict.clear() # workspaces_dict = temp_dict ############### for ws_id in sorted(workspaces_dict.keys()): print("PROCESSING WS : " + str(ws_id)) is_narrative = False top_level_lookup_dict = dict() is_public_flag = workspaces_dict[ws_id]["is_public"] tl_ws_obj_cursor = db.workspaceObjects.find({"ws":ws_id},{"id":1,"numver":1,"del":1,"hide":1,"_id":0}) for tl_object in tl_ws_obj_cursor: top_level_lookup_dict[tl_object["id"]] = {"numver" : tl_object["numver"], "del" : tl_object["del"], "hide" : tl_object["hide"]} ws_obj_vers_cursor = db.workspaceObjVersions.find({"ws":ws_id},{"type":1,"id":1,"ver":1,"size":1, "savedate":1,"copied":1,"_id":0}); for ws_obj_ver in ws_obj_vers_cursor: object_type_full = ws_obj_ver["type"] (object_type, object_spec_version) = object_type_full.split("-") obj_id = ws_obj_ver["id"] obj_ver = ws_obj_ver["ver"] obj_size = ws_obj_ver["size"] top_obj_size = 0 obj_save_date = ws_obj_ver["savedate"] obj_copied = 0 if ws_obj_ver["copied"] is not None: obj_copied = 1 is_hidden = 0 is_deleted = 0 is_top_level = 0 workspaces_dict[ws_id]["total_object_count"] += 1 workspaces_dict[ws_id]["total_size"] += obj_size if obj_ver == top_level_lookup_dict[obj_id]["numver"]: #means have maxed version of the object top_level is_top_level = 1 top_obj_size = obj_size workspaces_dict[ws_id]["top_lvl_size"] += obj_size #IF A NARRATIVE OBJECT, UPDATE THE WS dict if object_type == "KBaseNarrative.Narrative": workspaces_dict[ws_id]["narrative_version"] = obj_ver if top_level_lookup_dict[obj_id]["del"]: workspaces_dict[ws_id]["visible_app_cells_count"] = 0 workspaces_dict[ws_id]["code_cells_count"] = 0 # else: # (workspaces_dict[ws_id]["visible_app_cells_count"],workspaces_with_app_cell_oddities) = get_app_cell_count(wsadmin, # str(ws_id) + "/" + str(obj_id), # workspaces_with_app_cell_oddities) else: (workspaces_dict[ws_id]["visible_app_cells_count"], workspaces_dict[ws_id]["code_cells_count"], is_suspect) = \ get_narrative_cell_counts(wsadmin, str(ws_id) + "/" + str(obj_id) + "/" + str(obj_ver)) if is_suspect == 1: narrative_refs_with_shock_node_issues.add(str(ws_id) + "/" + str(obj_id) + "/" + str(obj_ver)) #Get Workspace numbers workspaces_dict[ws_id]["top_lvl_object_count"] += 1 if top_level_lookup_dict[obj_id]["hide"]: workspaces_dict[ws_id]["hidden_object_count"] += obj_ver if top_level_lookup_dict[obj_id]["del"]: workspaces_dict[ws_id]["deleted_object_count"] += obj_ver if obj_ver == 1 and object_type == "KBaseNarrative.Narrative": workspaces_dict[ws_id]["initial_save_date"] = obj_save_date if top_level_lookup_dict[obj_id]["hide"]: is_hidden = 1 if top_level_lookup_dict[obj_id]["del"]: is_deleted = 1 if object_type_full not in object_counts_dict: #First time seeing this object full type. Initialize in the dict object_counts_dict[object_type_full] = dict() object_counts_dict[object_type_full]["object_type"] = object_type object_counts_dict[object_type_full]["object_spec_version"] = object_spec_version object_counts_dict[object_type_full]["last_mod_date"] = obj_save_date object_counts_dict[object_type_full]["total_size"] = obj_size object_counts_dict[object_type_full]["top_lvl_size"] = top_obj_size object_counts_dict[object_type_full]["total_object_count"] = 1 object_counts_dict[object_type_full]["copy_count"] = obj_copied object_counts_dict[object_type_full]["top_lvl_object_count"] = is_top_level object_counts_dict[object_type_full]["hidden_object_count"] = is_hidden object_counts_dict[object_type_full]["deleted_object_count"] = is_deleted object_counts_dict[object_type_full]["public_object_count"] = is_public_flag object_counts_dict[object_type_full]["max_object_size"] = obj_size if is_public_flag == 1: object_counts_dict[object_type_full]["private_object_count"] = 0 else: object_counts_dict[object_type_full]["private_object_count"] = 1 else: #Exists, add up values accordingly. #check if date is greater than last_mod_date standing_date = object_counts_dict[object_type_full]["last_mod_date"] if obj_save_date > standing_date: object_counts_dict[object_type_full]["last_mod_date"] = obj_save_date standing_max_object_size = object_counts_dict[object_type_full]["max_object_size"] if obj_size > standing_max_object_size: object_counts_dict[object_type_full]["max_object_size"] = obj_size object_counts_dict[object_type_full]["total_size"] += obj_size object_counts_dict[object_type_full]["top_lvl_size"] += top_obj_size object_counts_dict[object_type_full]["total_object_count"] += 1 object_counts_dict[object_type_full]["copy_count"] += obj_copied object_counts_dict[object_type_full]["top_lvl_object_count"] += is_top_level object_counts_dict[object_type_full]["hidden_object_count"] += is_hidden object_counts_dict[object_type_full]["deleted_object_count"] += is_deleted object_counts_dict[object_type_full]["public_object_count"] += is_public_flag if is_public_flag == 0: object_counts_dict[object_type_full]["private_object_count"] += 1 # SAME THING AS ABOVE BUT FOR USER ONLY WORKSPACE OBJECTS if workspaces_dict[ws_id]["username"] not in kbase_staff: if object_type_full not in users_object_counts_dict: #First time seeing this object full type for users data. Initialize in the dict users_object_counts_dict[object_type_full] = dict() users_object_counts_dict[object_type_full]["object_type"] = object_type users_object_counts_dict[object_type_full]["object_spec_version"] = object_spec_version users_object_counts_dict[object_type_full]["last_mod_date"] = obj_save_date users_object_counts_dict[object_type_full]["total_size"] = obj_size users_object_counts_dict[object_type_full]["top_lvl_size"] = top_obj_size users_object_counts_dict[object_type_full]["total_object_count"] = 1 users_object_counts_dict[object_type_full]["copy_count"] = obj_copied users_object_counts_dict[object_type_full]["top_lvl_object_count"] = is_top_level users_object_counts_dict[object_type_full]["hidden_object_count"] = is_hidden users_object_counts_dict[object_type_full]["deleted_object_count"] = is_deleted users_object_counts_dict[object_type_full]["public_object_count"] = is_public_flag users_object_counts_dict[object_type_full]["max_object_size"] = obj_size if is_public_flag == 1: users_object_counts_dict[object_type_full]["private_object_count"] = 0 else: users_object_counts_dict[object_type_full]["private_object_count"] = 1 else: #Exists, add up values accordingly. #check if date is greater than last_mod_date standing_date = users_object_counts_dict[object_type_full]["last_mod_date"] if obj_save_date > standing_date: users_object_counts_dict[object_type_full]["last_mod_date"] = obj_save_date standing_max_object_size = users_object_counts_dict[object_type_full]["max_object_size"] if obj_size > standing_max_object_size: users_object_counts_dict[object_type_full]["max_object_size"] = obj_size users_object_counts_dict[object_type_full]["total_size"] += obj_size users_object_counts_dict[object_type_full]["top_lvl_size"] += top_obj_size users_object_counts_dict[object_type_full]["total_object_count"] += 1 users_object_counts_dict[object_type_full]["copy_count"] += obj_copied users_object_counts_dict[object_type_full]["top_lvl_object_count"] += is_top_level users_object_counts_dict[object_type_full]["hidden_object_count"] += is_hidden users_object_counts_dict[object_type_full]["deleted_object_count"] += is_deleted users_object_counts_dict[object_type_full]["public_object_count"] += is_public_flag if is_public_flag == 0: users_object_counts_dict[object_type_full]["private_object_count"] += 1 return (workspaces_dict, object_counts_dict, users_object_counts_dict, narrative_refs_with_shock_node_issues) def upload_workspace_stats(): """ Is the "main" function to get and upload both workspace data as well as workspace object summary stats """ start_time = time.time() #connect to mysql db_connection = mysql.connect( host = sql_host, user = "metrics", passwd = <PASSWORD>, database = "metrics" ) cursor = db_connection.cursor() query = "use "+query_on cursor.execute(query) #CHECK IF THIS WAS DONE THIS MONTH ALREADY. IT IS MEANT TO BE RUN ONCE PER MONTH (IDEALLY THE FIRST OF THE MONTH) today = date.today() current_month = str(today.year) + "-" + today.strftime('%m') query = "select DATE_FORMAT(max(record_date),'%Y-%m') from metrics.users_workspace_object_counts"; cursor.execute(query) for (db_date) in cursor: db_date_month = db_date[0] if db_date_month == current_month: print("THE WORKSPACE and WS OBJECTS COUNTS UPLOADER HAS BEEN RUN THIS MONTH. THE PROGRAM WILL EXIT") exit() else: print("IT HAS NOT BEEN RUN THIS MONTH, WE WILL RUN THE PROGRAM") client= MongoClient(mongoDB_metrics_connection+to_workspace) db = client.workspace workspaces_dict = get_workspaces(db) get_ws_top_info_time = time.time() - start_time workspaces_dict = get_workspace_shares(db, workspaces_dict) workspaces_dict = get_public_workspaces(db, workspaces_dict) (workspaces_dict, object_counts_dict, users_object_counts_dict, narrative_refs_with_shock_node_issues) = \ get_objects(db, workspaces_dict, db_connection) print("TOTAL WS Number : " + str(len(workspaces_dict))) gather_time = time.time() - start_time # print("--- gather data %s seconds ---" % (gather_time)) # print("WORKSPACE DICT : ") # pp.pprint(workspaces_dict) # print("OBJECT COUNTS DICT : ") # pp.pprint(object_counts_dict) #WORKSPACES UPLOADING prep_cursor = db_connection.cursor(prepared=True) workspaces_insert_statement = "insert into metrics.workspaces "\ "(ws_id, username, mod_date, initial_save_date, record_date, "\ "top_lvl_object_count, total_object_count, "\ "visible_app_cells_count, code_cells_count, narrative_version, "\ "hidden_object_count, deleted_object_count, "\ "total_size, top_lvl_size, is_public, "\ "is_temporary, is_deleted, number_of_shares) "\ "values(%s,%s, %s, %s, now(), %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s);" for ws_id in sorted(workspaces_dict.keys()): # print("PROCESSING WS: " + str(ws_id) + " USERNAME: " + workspaces_dict[ws_id]['username']) input = (ws_id, workspaces_dict[ws_id]['username'], workspaces_dict[ws_id]['mod_date'], workspaces_dict[ws_id]['initial_save_date'], workspaces_dict[ws_id]['top_lvl_object_count'], workspaces_dict[ws_id]['total_object_count'], workspaces_dict[ws_id]['visible_app_cells_count'],workspaces_dict[ws_id]['code_cells_count'], workspaces_dict[ws_id]['narrative_version'], workspaces_dict[ws_id]['hidden_object_count'], workspaces_dict[ws_id]['deleted_object_count'], workspaces_dict[ws_id]['total_size'], workspaces_dict[ws_id]['top_lvl_size'], workspaces_dict[ws_id]['is_public'], workspaces_dict[ws_id]['is_temporary'], workspaces_dict[ws_id]['is_deleted'], workspaces_dict[ws_id]['number_of_shares']) prep_cursor.execute(workspaces_insert_statement,input) print("TOTAL WS Number : " + str(len(workspaces_dict))) print("--- get ws top info time took %s seconds ---" % (get_ws_top_info_time)) print("--- gather data %s seconds ---" % (gather_time)) workspace_time = time.time() - (gather_time + start_time) print("--- workspaces uploading %s seconds ---" % (workspace_time)) #OBJECT COUNTS UPLOADING prep_cursor_obj = db_connection.cursor(prepared=True) obj_counts_insert_statement = "insert into metrics.workspace_object_counts "\ "(object_type, object_spec_version, object_type_full, "\ "record_date, last_mod_date, top_lvl_object_count, "\ "total_object_count, public_object_count, private_object_count, "\ "hidden_object_count, deleted_object_count, copy_count, "\ "total_size, top_lvl_size, max_object_size) "\ "values(%s,%s, %s, now(), %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s);" #print("OBJECT COUNTS DICT : " + str(object_counts_dict)) for obj_full in sorted(object_counts_dict): obj_input = (object_counts_dict[obj_full]["object_type"],object_counts_dict[obj_full]["object_spec_version"], obj_full, object_counts_dict[obj_full]["last_mod_date"], object_counts_dict[obj_full]["top_lvl_object_count"], object_counts_dict[obj_full]["total_object_count"], object_counts_dict[obj_full]["public_object_count"], object_counts_dict[obj_full]["private_object_count"], object_counts_dict[obj_full]["hidden_object_count"], object_counts_dict[obj_full]["deleted_object_count"], object_counts_dict[obj_full]["copy_count"], object_counts_dict[obj_full]["total_size"], object_counts_dict[obj_full]["top_lvl_size"], object_counts_dict[obj_full]["max_object_size"]) prep_cursor.execute(obj_counts_insert_statement,obj_input) object_time = time.time() - (workspace_time + gather_time + start_time) print("--- object counts uploading %s seconds ---" % (object_time)) #USERS_OBJECT COUNTS UPLOADING prep_cursor_obj = db_connection.cursor(prepared=True) users_obj_counts_insert_statement = "insert into metrics.users_workspace_object_counts "\ "(object_type, object_spec_version, object_type_full, "\ "record_date, last_mod_date, top_lvl_object_count, "\ "total_object_count, public_object_count, private_object_count, "\ "hidden_object_count, deleted_object_count, copy_count, "\ "total_size, top_lvl_size, max_object_size) "\ "values(%s,%s, %s, now(), %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s);" for obj_full in sorted(users_object_counts_dict): users_obj_input = (users_object_counts_dict[obj_full]["object_type"], users_object_counts_dict[obj_full]["object_spec_version"], obj_full, users_object_counts_dict[obj_full]["last_mod_date"], users_object_counts_dict[obj_full]["top_lvl_object_count"], users_object_counts_dict[obj_full]["total_object_count"], users_object_counts_dict[obj_full]["public_object_count"], users_object_counts_dict[obj_full]["private_object_count"], users_object_counts_dict[obj_full]["hidden_object_count"], users_object_counts_dict[obj_full]["deleted_object_count"], users_object_counts_dict[obj_full]["copy_count"], users_object_counts_dict[obj_full]["total_size"], users_object_counts_dict[obj_full]["top_lvl_size"], users_object_counts_dict[obj_full]["max_object_size"]) prep_cursor.execute(users_obj_counts_insert_statement,users_obj_input) users_object_time = time.time() - (workspace_time + gather_time + start_time + object_time) print("--- users object counts uploading %s seconds ---" % (users_object_time)) #SUSPECT SHOCK NODE INSERTING/UPDATING #If exists, update the last_seen_date #If does not exist make a new record. # query to see if it exists prep_cursor_ssn_exists = db_connection.cursor(prepared=True) ssn_exists_query = "select count(*) from metrics.suspect_shock_nodes "\ "where ws_obj_reference = %s and shock_node = %s" # query_to_update suspect_shock_nodes prep_cursor_ssn_update = db_connection.cursor(prepared=True) suspect_shock_node_update_statement = "update metrics.suspect_shock_nodes "\ "set last_seen_date = now() "\ "where ws_obj_reference = %s and shock_node = %s;" # query_to_insert suspect_shock_nodes prep_cursor_ssn_insert = db_connection.cursor(prepared=True) suspect_shock_node_insert_statement = "insert into metrics.suspect_shock_nodes "\ "(ws_obj_reference, shock_node, "\ "first_seen_date, last_seen_date, ws_id) "\ "values(%s,%s, now(), now(), %s);" # print("narrative_refs_with_shock_node_issues : " + str(narrative_refs_with_shock_node_issues)) for nar_ref in narrative_refs_with_shock_node_issues: # print("IN SHOCK NODES ISSUES : " + nar_ref) (ws_id, nar_id, nar_version) = nar_ref.split("/") chksum_cursor = db.workspaceObjVersions.find({"ws":int(ws_id),"id":int(nar_id),"ver":int(nar_version)},{"chksum":1,"_id":0}) chksum = None for chksum_arr in chksum_cursor: chksum = chksum_arr["chksum"] node_cursor =db.shock_nodeMap.find({"chksum":chksum},{"node":1,"_id":0}) node = None for node_arr in node_cursor: node = node_arr["node"] #check if nar_ref_node combo exists: prep_cursor_ssn_exists.execute(ssn_exists_query,(nar_ref, node)) exists_count_arr = prep_cursor_ssn_exists.fetchone() if exists_count_arr[0] == 0: #means a new problem narrative/node needs an insert prep_cursor_ssn_insert.execute(suspect_shock_node_insert_statement,(nar_ref, node, int(ws_id))) else: #means the nar_ref node existed . Update the last seen date. prep_cursor_ssn_insert.execute(suspect_shock_node_update_statement,(nar_ref, node)) db_connection.commit() #upload_workspace_stats()

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neviseh/persian/numbers.py

meyt/neviseh

0

189886

<filename>neviseh/persian/numbers.py from . import PersianTextBase class PersianNumbers(PersianTextBase): def translate_arabic_numbers(self): """ Translate Arabic numbers to Persian :return: """ characters_map = { '١': '۱', '٢': '۲', '٣': '۳', '٤': '۴', '٥': '۵', '٦': '۶', '٧': '۷', '٨': '۸', '٩': '۹', '٠': '۰', } self.replace_by_character_map('arabic_numbers_replace', characters_map) return self def translate_latin_numbers(self): """ Translate Latin numbers to Persian :return: """ characters_map = { '0': '۰', '1': '۱', '2': '۲', '3': '۳', '4': '۴', '5': '۵', '6': '۶', '7': '۷', '8': '۸', '9': '۹', '.': '.', '%': '٪' } self.replace_by_character_map('latin_numbers_replace', characters_map) return self

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pyrevolve/genotype/learning_body/crossover.py

braj29/robo_swimmers

0

72173

<reponame>braj29/robo_swimmers from typing import List from .config import Config from pyrevolve.genotype.genotype import Genotype def crossover(parents: List[Genotype], config: Config) -> Genotype: return parents[0]

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bot/main.py

EJH2/IQ-Discord

1

193132

# coding=utf-8 """Main bot file""" import aiohttp import time from collections import Counter, deque from pathlib import Path import discord from discord.ext import commands from pyppeteer import launch, errors from bot.utils.logging import setup_logger from bot.utils.over import send, _default_help_command discord.abc.Messageable.send = send class Bot(commands.AutoShardedBot): """Subclasses AutoShardedBot to give more flexibility with design""" def __init__(self, *args, **kwargs): self.config = kwargs.pop('config') self.start_time = time.time() super().__init__(*args, **kwargs) # pay no mind to this ugliness. self.remove_command('help') self.command(**self.help_attrs)(_default_help_command) self.pm_help = None self.case_insensitive = True self.app_info = None shard = f"| Shard {self.shard_id}" if self.shard_id else "" self.activity = discord.Game(name=f"{self.command_prefix}help {shard}") self.session = aiohttp.ClientSession(loop=self.loop, headers={"User-Agent": self.http.user_agent}) self.browser_page = None self.browser = self.loop.create_task(self.create_browser()) self.priv = self.config['extras'].get('privatebin', 'https://privatebin.net') self.polr = self.config['extras'].get('polr', None) self.commands_used = Counter() self.commands_used_in = Counter() self.errors = deque(maxlen=10) self.revisions = None discord_logger = setup_logger("discord") self.logger = setup_logger("Bot") self.command_logger = setup_logger("Commands") self.loggers = [discord_logger, self.logger, self.command_logger] _modules = [mod.stem for mod in Path("bot/cogs").glob("*.py")] self.load_extension(f"bot.cogs.core") self.load_extension(f"bot.cogs.owner") if 'bare' in kwargs.pop('argv'): # load the bot bare-bones to diagnose issues return for module in _modules: try: if module in ['core', 'errors']: pass self.load_extension(f"bot.cogs.{module}") except discord.DiscordException as exc: self.logger.error(f"{type(exc).__name__} occurred when loading {module}: {exc}") # make sure to only print ready text once self._loaded = False async def on_ready(self): """Function called when bot is ready or resumed""" if self._loaded is False: end_time = time.time() - self.start_time self.app_info = await self.application_info() self.logger.info(f"Loaded Bot:") self.logger.info(f"Logged in as {self.user}") self.logger.info(f"ID is {self.user.id}") self.logger.info(f"Owned by {self.app_info.owner}") self.description = f"Hello, this is the help menu for {self.user.name}!" self.logger.info(f"Bot started in {end_time} seconds") self._loaded = True return self.logger.info(f"Resumed bot session on shard {self.shard_id}!") async def create_browser(self): """Task to create browser for scraping purposes.""" await self.wait_until_ready() self.browser = await launch(args=["--no-sandbox"], headless=True) self.browser_page = await self.browser.newPage() # noinspection PyProtectedMember async def close(self): """Function called when closing the bot""" try: await self.browser_page.close() or self.logger.info("Browser page successfully closed!") except (errors.PageError, AttributeError): # browser was never created; edge case pass await self.browser.close() or self.logger.info("Browser successfully closed!") await super().close() await self.http._session.close() await self.session.close() for logger in self.loggers: for handler in logger.handlers: logger.removeHandler(handler)

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dbaas/drivers/mysqlpercona.py

didindinn/database-as-a-service

303

78614

from . import mysqldb from physical.models import Instance class MySQLPercona(mysqldb.MySQL): def get_default_instance_type(self): return Instance.MYSQL_PERCONA @classmethod def topology_name(cls): return ['mysql_percona_single'] class MySQLPerconaFOXHA(mysqldb.MySQLFOXHA): def get_default_instance_type(self): return Instance.MYSQL_PERCONA @classmethod def topology_name(cls): return ['mysql_percona_foxha']

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examples/max_min.py

csunny/algorithm

21

70927

#!/usr/bin/env python # -*- coding:utf-8 -*- def max_min(v): m_min, m_max = min(v[0], v[-1]), max(v[0], v[-1]) for i in v[1:len(v)-1]: if i<m_min: m_min = i elif i > m_max: m_max = i return m_min, m_max if __name__ == '__main__': m_min, m_max = max_min([9, 3, 4, 7, 2, 0]) print(m_min, m_max)

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workflows/pangenome-generate/sort_fasta_by_quality_and_len.py

AndreaGuarracino/bh20-seq-resource

2

138778

#!/usr/bin/env python3 # Sort the sequences by quality (percentage of number of N bases not called, descending) and by length (descending). # The best sequence is the longest one, with no uncalled bases. import os import sys import gzip #import xxhash # Faster library import hashlib def open_gzipsafe(path_file): if path_file.endswith('.gz'): return gzip.open(path_file, 'rt') else: return open(path_file) path_fasta = sys.argv[1] hash_to_count_and_headers_dict = {} header_to_seq_dict = {} header_percCalledBases_seqLength_list = [] with open_gzipsafe(path_fasta) as f: for fasta in f.read().strip('\n>').split('>'): header = fasta.strip('\n').split('\n')[0] sequence = ''.join(fasta.strip('\n').split('\n')[1:]) ##hash = xxhash.xxh64(sequence).hexdigest() # Faster library hash = hashlib.md5(sequence.encode('utf-8')).hexdigest() if hash not in hash_to_count_and_headers_dict: # New sequence hash_to_count_and_headers_dict[hash] = [0, []] header_to_seq_dict[header] = sequence seq_len = len(sequence) header_percCalledBases_seqLength_list.append([header, (seq_len - sequence.count('N'))/seq_len, seq_len]) hash_to_count_and_headers_dict[hash][0] += 1 hash_to_count_and_headers_dict[hash][1].append(header) with open('dups.txt', 'w') as fw: for count, header_list in hash_to_count_and_headers_dict.values(): fw.write('\t'.join([str(count), ', '.join(header_list)]) + '\n') for header, percCalledBases, seqLength_list in sorted(header_percCalledBases_seqLength_list, key=lambda x: (x[-2], x[-1]), reverse = True): sys.stdout.write('>{}\n{}\n'.format(header, header_to_seq_dict[header]))

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plugins/welcome.py

su226/IdhagnBot

2

1616563

<reponame>su226/IdhagnBot from util.config import BaseConfig, Field from nonebot.adapters.onebot.v11 import Bot, Event, Message import nonebot class Config(BaseConfig): __file__ = "welcome" welcome: dict[int, str] = Field(default_factory=dict) leave: set[int] = Field(default_factory=set) CONFIG = Config.load() async def check_welcome(event: Event): return event.notice_type == "group_increase" and event.group_id in CONFIG.welcome welcome = nonebot.on_notice(check_welcome) @welcome.handle() async def handle_welcome(event: Event): await welcome.send(Message(CONFIG.welcome[event.group_id].format(event.user_id))) async def check_leave(event: Event): return event.notice_type == "group_decrease" and event.group_id in CONFIG.leave leave = nonebot.on_notice(check_leave) @leave.handle() async def handle_leave(bot: Bot, event: Event): username = (await bot.get_stranger_info(user_id=event.user_id))["nickname"] if event.operator_id != event.user_id: operator_name = (await bot.get_group_member_info(group_id=event.group_id, user_id=event.operator_id))["nickname"] await leave.send(f"{operator_name}（{event.operator_id}）将 {username}（{event.user_id}）踢出了本群") else: await leave.send(f"{username}（{event.user_id}）退出了本群")

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interceptor/jobs_wrapper.py

fossabot/interceptor

0

132538

# Copyright 2018 getcarrier.io # # 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 docker from uuid import uuid4 from interceptor import constants as c class JobsWrapper(object): @staticmethod def dast(container, execution_params, job_name, redis_connection, *args, **kwargs): client = docker.from_env() required_keys = ['host', 'port', 'protocol', 'test_type'] if not all(k in execution_params for k in required_keys): return False, "Missing required params (%s)" % ",".join(required_keys) if not [container] in [k.tags for k in client.images.list()]: return False, "Please specify proper tag of security container e.g. getcarrier/dast:latest" host = execution_params.get('host') port = execution_params.get('port') protocol = execution_params.get('protocol') project = execution_params.get('project', job_name) environment = execution_params.get('environment', "default") # redis_connection = '' client.containers.run(container, name=f'{job_name}_{uuid4()}'[:36], nano_cpus=c.CONTAINER_CPU_QUOTA, mem_limit=c.CONTAINER_MEMORY_QUOTA, command=f"-s {execution_params['test_type']}", environment={"host": host, "port": port, "protocol": protocol, "project_name": project, "environment": environment, "redis_connection": redis_connection}, remove=True) return True, "Done" @staticmethod def sast(container, execution_params, job_name, redis_connection, *args, **kwargs): pass @staticmethod def perfui(container, execution_params, job_name, redis_connection, *args, **kwargs): pass @staticmethod def perfmeter(container, execution_params, job_name, redis_connection, *args, **kwargs): client = docker.from_env() #redis_connection = '' client.containers.run(container, name=f'{job_name}_{uuid4()}'[:36], nano_cpus=c.CONTAINER_CPU_QUOTA, mem_limit=c.CONTAINER_MEMORY_QUOTA, command=f"{execution_params['jmeter_execution_string']}", environment={"redis_connection": redis_connection}, remove=True) return True, "Done" @staticmethod def perfgatling(container, execution_params, job_name, redis_connection, *args, **kwargs): pass

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FaceRecognitionWebsite/codeDesign/myDjango02/app01/migrations/0012_remove_getdatacss_copybook.py

ChunjunHu/FaceRecognitionLibraryWebsite

1

169962

# Generated by Django 2.0.1 on 2019-01-09 15:54 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('app01', '0011_auto_20190109_2351'), ] operations = [ migrations.RemoveField( model_name='getdatacss', name='CopyBook', ), ]

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scripts/venv/lib/python2.7/site-packages/cogent/align/progressive.py

sauloal/cnidaria

3

31612

<gh_stars>1-10 #!/usr/bin/env python from __future__ import with_statement from cogent import LoadTree from cogent.phylo import nj as NJ from cogent.phylo.distance import EstimateDistances from cogent.core.info import Info from cogent.util import progress_display as UI __author__ = "<NAME>" __copyright__ = "Copyright 2007-2012, The Cogent Project" __credits__ = ["<NAME>", "<NAME>"] __license__ = "GPL" __version__ = "1.5.3" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" __status__ = "Production" @UI.display_wrap def TreeAlign(model, seqs, tree=None, indel_rate=0.01, indel_length=0.01, ui = None, ests_from_pairwise=True, param_vals=None): """Returns a multiple alignment and tree. Uses the provided substitution model and a tree for determining the progressive order. If a tree is not provided a Neighbour Joining tree is constructed from pairwise distances estimated from pairwise aligning the sequences. If running in parallel, only the distance estimation is parallelised and only the master CPU returns the alignment and tree, other CPU's return None, None. Arguments: - model: a substitution model - seqs: a sequence collection - indel_rate, indel_length: parameters for the progressive pair-HMM - ests_from_pairwise: if no tree provided and True, the median value of the substitution model parameters are used - param_vals: named key, value pairs for model parameters. These override ests_from_pairwise. """ _exclude_params = ['mprobs', 'rate', 'bin_switch'] if param_vals: param_vals = dict(param_vals) else: param_vals = {} if isinstance(seqs, dict): seq_names = seqs.keys() else: seq_names = seqs.getSeqNames() two_seqs = len(seq_names) == 2 if tree: tip_names = tree.getTipNames() tip_names.sort() seq_names.sort() assert tip_names == seq_names, \ "names don't match between seqs and tree: tree=%s; seqs=%s" % \ (tip_names, seq_names) ests_from_pairwise = False elif two_seqs: tree = LoadTree(tip_names=seqs.getSeqNames()) ests_from_pairwise = False else: if ests_from_pairwise: est_params = [param for param in model.getParamList() \ if param not in _exclude_params] else: est_params = None dcalc = EstimateDistances(seqs, model, do_pair_align=True, est_params=est_params) dcalc.run() dists = dcalc.getPairwiseDistances() tree = NJ.nj(dists) LF = model.makeLikelihoodFunction(tree.bifurcating(name_unnamed=True), aligned=False) if ests_from_pairwise and not param_vals: # we use the Median to avoid the influence of outlier pairs param_vals = {} for param in est_params: numbers = dcalc.getParamValues(param) print "Param Estimate Summary Stats: %s" % param print numbers.summarize() param_vals[param] = numbers.Median ui.display("Doing %s alignment" % ["progressive", "pairwise"][two_seqs]) with LF.updatesPostponed(): for param, val in param_vals.items(): LF.setParamRule(param, value=val, is_constant=True) LF.setParamRule('indel_rate', value=indel_rate, is_constant=True) LF.setParamRule('indel_length', value=indel_length, is_constant=True) LF.setSequences(seqs) edge = LF.getLogLikelihood().edge (vtLnL, align) = edge.getViterbiScoreAndAlignment(0.5) info = Info() info["AlignParams"] = param_vals info["AlignParams"].update(dict(indel_length=indel_length, indel_rate=indel_rate)) align.Info = info return align, tree

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PythonServer/UnitTestCasesForWebSocket.py

Cyberlightning/2D-3DCapture

2

4916

''' Created on Mar 6, 2014 @author: tharanga ''' import unittest from time import sleep import EventService as es from EventService import WebSocketServer as ws from EventService import EventManager as em import socket from base64 import b64encode import struct import MySQLdb import json import EventService import flaskr import tempfile def encodeMessage( message): message = b64encode(message) b1 =0x80 | 0x1 & 0x0f b2 = 0 header="" payload_len = len(message) if payload_len < 126 : header = struct.pack('>BB', b1, payload_len) message= header +message elif (payload_len < ((2 ** 16) - 1)): b2 |= 126 header += chr(b1) header += chr(b2) l = struct.pack(">H", payload_len) header += l message = header +message else: b2 |= 127 header += chr(b1) header += chr(b2) l = struct.pack(">Q", payload_len) header += l message = header +message return message class TestWebSockets(unittest.TestCase): def setUp(self): self.wsServer = ws('',12345,'127.0.0.1') self.wsServer.setRunning(True); sleep(1) self.testsocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.testsocket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) # Create a socket object host = 'localhost' # Get local machine name port = 12345 self.testsocket.connect((host, port)) def tearDown(self): self.wsServer.closeConnection(); self.testsocket.close() sleep(1) def test_webSocketServerOBject(self): self.assertEqual(self.wsServer.SERVER, '', "Server set to the desired value") self.assertEqual(self.wsServer.PORT, 12345, "Server port is set correctly") self.assertEqual(self.wsServer.LOCALHOST, "127.0.0.1", "Localhost set to 127.0.0.1") def test_invalid_Request(self): message= "Test Message" self.testsocket.send(message) data = repr(self.testsocket.recv(1024)) #print 'Response to invalid message<TestMessage> %s'%(data) self.assertEqual(data, '\'CONNECTION_REJECTED\'', "Invalid Message rejected") def test_valid_WS_Request(self): message = "GET /mychat HTTP/1.1\nHost: server.example.com\nUpgrade: websocket\nConnection: Upgrade\nSec-WebSocket-Key: <KEY>nSec-WebSocket-Protocol: chat\nSec-WebSocket-Version: 13\nOrigin: localhost\n\n" # message = "Test message" self.testsocket.sendall(message) wsresponse = repr(self.testsocket.recv(1024)) #print 'Response to valid ws request %s'%wsresponse self.assertNotEqual(wsresponse, '\'CONNECTION_REJECTED\'', "Connection is not rejected") self.assertIsNotNone(wsresponse, "Connection Response is not Empty") self.testsocket.sendall(("Test Message")) data = repr(self.testsocket.recv(1024)) #print 'Response to un encoded Request %s'%(data) self.assertEqual(data, "\'Un expected opcode\'", "In valid Message rejected") def test_invalid_Messge(self): message = "GET /mychat HTTP/1.1\nHost: server.example.com\nUpgrade: websocket\nConnection: Upgrade\nSec-WebSocket-Key: <KEY>==\nSec-WebSocket-Protocol: chat\nSec-WebSocket-Version: 13\nOrigin: localhost\n\n" self.testsocket.sendall(message) wsresponse = repr(self.testsocket.recv(1024)) sleep(1) self.testsocket.sendall("Test Message") data = repr(self.testsocket.recv(1024)) self.assertEqual(data, "\'Un expected opcode\'", "In valid Message rejected") def test_malformed_Message(self): message = "GET /mychat HTTP/1.1\nHost: server.example.com\nUpgrade: websocket\nConnection: Upgrade\nSec-WebSocket-Key: <KEY>==\nSec-WebSocket-Protocol: chat\nSec-WebSocket-Version: 13\nOrigin: localhost\n\n" self.testsocket.sendall(message) wsresponse = repr(self.testsocket.recv(1024)) # print wsresponse self.testsocket.send(encodeMessage("Test Message"))#This line seems to get stuck at times. Solution is to use sendAll, use \n at the end data = repr(self.testsocket.recv(1024)) self.assertEqual(data, "\'MISFORMATED MESSAGE\'", "Messages with out a type is rejected") def test_wellformed_Message_for_Text(self): message = "GET /mychat HTTP/1.1\nHost: server.example.com\nUpgrade: websocket\nConnection: Upgrade\nSec-WebSocket-Key: <KEY>nSec-WebSocket-Protocol: chat\nSec-WebSocket-Version: 13\nOrigin: localhost\n\n" self.testsocket.sendall(message) wsresponse = repr(self.testsocket.recv(1024)) # print wsresponse self.testsocket.send(encodeMessage("1<---->Test Message"))#This line seems to get stuck at times. Solution is to use sendAll, use \n at the end data = repr(self.testsocket.recv(1024)) print data self.assertEqual(data, "\'Text received\'", "Text Messages is identified and accepted") def test_wellformed_Message_for_Json(self): message = "GET /mychat HTTP/1.1\nHost: server.example.com\nUpgrade: websocket\nConnection: Upgrade\nSec-WebSocket-Key: x3JJHMbDL1EzLkh9GBhXDw==\nSec-WebSocket-Protocol: chat\nSec-WebSocket-Version: 13\nOrigin: localhost\n\n" self.testsocket.sendall(message) wsresponse = repr(self.testsocket.recv(1024)) self.testsocket.send(encodeMessage("2<---->Test Message"))#This line seems to get stuck at times. Solution is to use sendAll, use \n at the end data = repr(self.testsocket.recv(1024)) # print data self.assertEqual(data, "\'json is received\'", "json Messages is identified and accepted") ##TO RUN THE FOLLOWING UNIT TESTS IT IS EXPECTED HAVE THE DATABASE ##CREATED. DATABASE SCRIPT IS PROVIDED TO CREATE THE NECESSARY DATABASES AND TABLES ##ASSISCIATED DATA IS NOT PROVIDED. class TestDatabase(unittest.TestCase): def setUp(self): self.connection = es.dbConnect() def tearDown(self): self.connection.close() def test_data_insert_data_Read(self): self.assertIsInstance(self.connection, MySQLdb.connection, "Database connection accurately set") jsondata ={"type":"image", "time":"2014.3.4_14.40.30", "ext":"png", "deviceType":"Mobile", "deviceOS":"Badda", "browsertype":"Firefox", "position":{"lon":25.4583105, "lat":65.0600797, "alt":-1000, "acc":48.38800048828125}, "device":{"ax":0, "ay":0, "az":0, "gx":0, "gy":0, "gz":0, "ra":210.5637, "rb":47.5657, "rg":6.9698, "orientation":"potrait"}, "vwidth":480, "vheight":800} alt = str(jsondata["position"]["alt"]); if alt=="None": alt = '0' heading = '0' speed = '0' width = jsondata["vwidth"] height =jsondata["vheight"] if width > height : screenorientation= 1.00#landscape else : screenorientation= 0.00#potrait filename = jsondata["type"]+"_"+jsondata["time"]+"."+jsondata["ext"] sqlstring1 = "INSERT INTO Imagedata values (\'"+filename+"\',GeomFromText ('POINT("+ str(jsondata["position"]["lat"])+" "+str(jsondata["position"]["lon"])+")'),"+str(jsondata["position"]["alt"])+","+str(jsondata["position"]["acc"]) sqlstring2 =","+str(jsondata["device"]["gx"])+","+str(jsondata["device"]["gy"])+","+str(jsondata["device"]["gz"]) sqlstring3 = ","+str(jsondata["device"]["ra"])+","+str(jsondata["device"]["rb"])+","+str(jsondata["device"]["rg"])+","+str(screenorientation)+",\'"+jsondata["device"]["orientation"]+"\',now(),\'"+str(jsondata["deviceOS"])+"\',\'"+str(jsondata["browsertype"])+"\',\'"+str(jsondata["deviceType"])+"\');" sqlstring = sqlstring1 + sqlstring2+ sqlstring3 #print(sqlstring) es.dbInsert(sqlstring) sqlreadsting = 'select imagename, Browser,devicetype,X(location) as latitude, Y(location) as longitude from Imagedata where time=\'2014.3.4_14.40.31\'' result = es.dbRead(sqlreadsting) self.assertIsNotNone(result, "Inserted data is retrieved and it is not null") for row in result: self.assertEqual(row[0], "image_2014.3.4_14.40.30.png", "Image name is correctly set and saved") self.assertEqual(row[1], 65.0600797, "Latitudes are saved") self.assertEqual(row[2], 25.4583105, "Longitude are saved") HOST = '127.0.0.1' # The remote host PORT = 17322 class RestServerTestCase(unittest.TestCase): def setUp(self): self.db_fd, flaskr.app.config['DATABASE'] = tempfile.mkstemp() EventService.app.config['TESTING'] = True self.app = EventService.app.test_client() flaskr.init_db() #self.socketServer = self.app.WebSocketServer('',wsport,'127.0.0.1') def test_rootpath(self): rv = self.app.get('/') assert 'This is a REST Service for 2D3DCapture Server.' in rv.data def test_post_image(self): rv = self.app.post('/postImage') assert 'READY' in rv.data def test_clossing_websocket(self): rv =self.app.post('/closewebsocketserver') assert 'CLOSED' or 'ALREADY_CLOSSED' in rv.data def test_start_websocket(self): rv =self.app.get('/startwebsocketserver') # print rv.data assert 'READY' in rv.data def test_post_binary_image(self): rv =self.app.post('/postBinaryImage') assert 'READY' or '415 Unsupported Media Type' in rv.data def test_get_All_Image_Data(self): rv =self.app.get('/getAllImageData') jsonmsg = json.loads(rv.data) self.assertIsNotNone(jsonmsg['imageList'] , "getImageData returns a non None list") def test_get_location_Image_Data(self): rv =self.app.get('/getLocationImageData?lat=65.0600797&lon=25.4583105') jsonmsg = json.loads(rv.data) self.assertIsNotNone(jsonmsg['imageList'] , "getLocationImageData returns a non None list.This is a feature test for location based image data") def test_closest_Image_retrieval(self): jsondata1 ={"type":"image", "time":"2014.3.4_14.40.31", "ext":"png", "deviceType":"Mobile", "deviceOS":"Badda", "browsertype":"Firefox", "position":{"lon":25.4583105, "lat":65.0600797, "alt":-1000, "acc":48.38800048828125}, "device":{"ax":0, "ay":0, "az":0, "gx":0, "gy":0, "gz":0, "ra":210.5637, "rb":47.5657, "rg":6.9698, "orientation":"potrait"}, "vwidth":480, "vheight":800} jsondata2 ={"type":"image", "time":"2014.3.4_14.40.32", "ext":"png", "deviceType":"Mobile", "deviceOS":"Badda", "browsertype":"Firefox", "position":{"lon":25.4582115, "lat":65.0600797, "alt":-1000, "acc":48.38800048828125}, "device":{"ax":0, "ay":0, "az":0, "gx":0, "gy":0, "gz":0, "ra":210.5637, "rb":47.5657, "rg":6.9698, "orientation":"potrait"}, "vwidth":480, "vheight":800} jsondata3 ={"type":"image", "time":"2014.3.4_14.40.33", "ext":"png", "deviceType":"Mobile", "deviceOS":"Badda", "browsertype":"Firefox", "position":{"lon":25.4584104, "lat":65.0600797, "alt":-1000, "acc":48.38800048828125}, "device":{"ax":0, "ay":0, "az":0, "gx":0, "gy":0, "gz":0, "ra":210.5637, "rb":47.5657, "rg":6.9698, "orientation":"potrait"}, "vwidth":480, "vheight":800} jsondata4 ={"type":"image", "time":"2014.3.4_14.40.34", "ext":"png", "deviceType":"Mobile", "deviceOS":"Badda", "browsertype":"Firefox", "position":{"lon":25.4586115, "lat":65.0600797, "alt":-1000, "acc":48.38800048828125}, "device":{"ax":0, "ay":0, "az":0, "gx":0, "gy":0, "gz":0, "ra":210.5637, "rb":47.5657, "rg":6.9698, "orientation":"potrait"}, "vwidth":480, "vheight":800} jsondata5 ={"type":"image", "time":"2014.3.4_14.40.35", "ext":"png", "deviceType":"Mobile", "deviceOS":"Badda", "browsertype":"Firefox", "position":{"lon":25.4587125, "lat":65.0600797, "alt":-1000, "acc":48.38800048828125}, "device":{"ax":0, "ay":0, "az":0, "gx":0, "gy":0, "gz":0, "ra":210.5637, "rb":47.5657, "rg":6.9698, "orientation":"potrait"}, "vwidth":480, "vheight":800} jsondata6 ={"type":"image", "time":"2014.3.4_14.40.36", "ext":"png", "deviceType":"Mobile", "deviceOS":"Badda", "browsertype":"Firefox", "position":{"lon":25.4588125, "lat":65.0600797, "alt":-1000, "acc":48.38800048828125}, "device":{"ax":0, "ay":0, "az":0, "gx":0, "gy":0, "gz":0, "ra":210.5637, "rb":47.5657, "rg":6.9698, "orientation":"potrait"}, "vwidth":480, "vheight":800} es.saveData(jsondata1) es.saveData(jsondata2) es.saveData(jsondata3) es.saveData(jsondata4) es.saveData(jsondata5) es.saveData(jsondata6) radius = 0.0001 photoList = es.getClosestImages( 65.0601787, 25.4583107, radius ) self.assertEqual(len(photoList), 4, "Length of the list should be equal of the first test") for row in photoList: assert 'image_2014.3.4_14.40.32.png' or 'image_2014.3.4_14.40.31.png' in row[0] photoList2 = es.getClosestImages( 65.0601787, 25.4587107, radius ) self.assertEqual(len(photoList2), 2, "Length of the list should be equal of the second test") for row in photoList2: assert 'image_2014.3.4_14.40.34.png' or 'image_2014.3.4_14.40.35.png' in row[0] def suite(): testsuit =unittest.TestSuite() testsuit.addTest(TestWebSockets('test_webSocketServerOBject')) testsuit.addTest(TestWebSockets('test_valid_WS_Request')) testsuit.addTest(TestWebSockets('test_invalid_Messge')) testsuit.addTest(TestWebSockets('test_invalid_Request')) testsuit.addTest(TestWebSockets('test_malformed_Message')) testsuit.addTest(TestWebSockets('test_wellformed_Message_for_Text')) testsuit.addTest(TestWebSockets('test_wellformed_Message_for_Json')) testsuit.addTest(TestDatabase('test_data_insert_data_Read')) testsuit.addTest(RestServerTestCase('test_rootpath')) testsuit.addTest(RestServerTestCase('test_post_image')) testsuit.addTest(RestServerTestCase('test_start_websocket')) testsuit.addTest(RestServerTestCase('test_clossing_websocket')) testsuit.addTest(RestServerTestCase('test_post_binary_image')) testsuit.addTest(RestServerTestCase('test_get_All_Image_Data')) testsuit.addTest(RestServerTestCase('test_closest_Image_retrieval')) return testsuit suite = suite() runner = unittest.TextTestRunner(verbosity=3) runner.run(suite) # if __name__ == "__main__": # #import sys;sys.argv = ['', 'Test.testName'] # unittest.main()

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unionfind-component.py

surpace/shed

2

181020

class UnionFind: def __init__(self, size): self.parent = list(range(size)) self.component = [[i] for i in range(size)] def root(self, i): if self.parent[i] != i: self.parent[i] = self.root(self.parent[i]) return self.parent[i] def unite(self, i, j): i, j = self.root(i), self.root(j) if len(self.component[i]) < len(self.component[j]): i, j = j, i self.parent[j] = i self.component[i] += self.component[j]

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capsNet.py

zhaoaite/ASTCapsNet

0

33158

""" License: Apache-2.0 Author: <NAME> E-mail: <EMAIL> """ import tensorflow as tf import rnn_cell_GRU as rnn_cell import rnn import numpy as np from config import cfg from utils import get_batch_data from capsLayer import CapsLayer from sklearn import metrics import pickle from sklearn.cross_validation import train_test_split from sklearn import preprocessing from plot_confusion_matrix import plot_confusion_matrix from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt import os import tensorflow as tf from tensorflow.contrib.tensor_forest.python import tensor_forest from tensorflow.python.ops import resources class CapsNet(object): def __init__(self, is_training=True): self.graph = tf.Graph() with self.graph.as_default(): self.n_classes=52 self.X, self.labels = get_batch_data(cfg.dataset, cfg.batch_size, cfg.num_threads) self.Y = tf.one_hot(self.labels, depth=self.n_classes, axis=1, dtype=tf.float32) # LSTM Parameters self.n_input=45 self.n_steps=45 # tf Graph input self.lstm_x = tf.reshape(self.X, shape=(cfg.batch_size, self.n_steps, self.n_input)) self.lstm_y = tf.reshape(self.Y, shape=(cfg.batch_size, self.n_classes)) self.kernel_size1=13 self.kernel_size2=9 self.conv1_outa=self.n_input-self.kernel_size1+1 self.conv1_outb=self.n_steps-self.kernel_size1+1 # self.cap1_out=(self.conv1_outa-self.kernel_size+1)/2*((self.conv1_outb-self.kernel_size+1)/2) # self.cap1_out=int((self.conv1_outa-self.kernel_size2+1)*(self.conv1_outb-self.kernel_size2+1)*32/4) self.cap1_out=5408 self.n_hidden= self.conv1_outb # Define weights self.lstm_weights = { 'out': tf.Variable(tf.random_normal([self.n_hidden, self.n_classes])) } self.lstm_biases = { 'out': tf.Variable(tf.random_normal([self.n_classes])) } if is_training: self.build_arch() self.loss() self._summary() # t_vars = tf.trainable_variables() self.global_step = tf.Variable(0, name='global_step', trainable=False) self.optimizer = tf.train.AdamOptimizer() self.train_op = self.optimizer.minimize(self.total_loss, global_step=self.global_step) # var_list=t_vars) self.train_c1 = self.optimizer.minimize(self.lstm_cost, global_step=self.global_step) self.train_c2 = self.optimizer.minimize(self.dense_cov1_cost, global_step=self.global_step) self.train_c3 = self.optimizer.minimize(self.dense_caps1_cost, global_step=self.global_step) print('end net') else: self.X = tf.placeholder(tf.float32, shape=(cfg.batch_size,self.n_input, self.n_steps, 1)) self.labels = tf.placeholder(tf.int32, shape=(cfg.batch_size, )) self.Y = tf.reshape(self.labels, shape=(cfg.batch_size, self.n_classes, 1)) self.build_arch() tf.logging.info('Seting up the main structure') def RNN(self): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, n_steps, n_input) # Required shape: 'n_steps' tensors list of shape (batch_size, n_input) # Permuting batch_size and n_steps x = tf.transpose(self.lstm_x, [1, 0, 2]) # Reshaping to (n_steps*batch_size, n_input) x = tf.reshape(tensor=x, shape=[-1, self.n_input]) # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input) x = tf.split(value=x, num_or_size_splits=self.n_steps, axis=0) # Define a lstm cell with tensorflow #lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1) lstm_cell = rnn_cell.GRUCell(self.n_hidden) #lstm_cell = rnn_cell.LSTMCell(n_hidden,use_peepholes=True) # avoid overfitting lstm_cell = rnn_cell.DropoutWrapper(lstm_cell, output_keep_prob=0.5) # 2 layers lstm lstm_cell = rnn_cell.MultiRNNCell([lstm_cell]*2) # Get lstm cell output outputs, states = rnn.rnn(cell=lstm_cell, inputs=x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], self.lstm_weights['out']) + self.lstm_biases['out'],outputs[-1] def build_arch(self): with tf.variable_scope('LSTM_layer'): #pred batch*4d out batch*128d pred,out = self.RNN() out=tf.reshape(out,(-1,1,self.n_hidden,1)) # cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=pred, labels=y)) # #Adam optimizer # optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Evaluate model # correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) # accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) with tf.variable_scope('Conv1_layer'): # Conv1, [batch_size, 20, 20, 256] conv1 = tf.contrib.layers.conv2d(self.X, num_outputs=256, kernel_size=self.kernel_size1, stride=1, padding='VALID') # print(conv1.get_shape(),[cfg.batch_size, self.conv1_outa,self.conv1_outb, 256]) assert conv1.get_shape() == [cfg.batch_size, self.conv1_outa,self.conv1_outb, 256] out=tf.tile(out,[1,self.conv1_outa,1,256]) self.conv1=tf.add(conv1,out) # out_temp= tf.placeholder(tf.float32, shape=(cfg.batch_size,self.conv1_outa+1,self.conv1_outb, 256)) # self.dense1 = tf.layers.dense(inputs=tf.reshape(self.conv1,(cfg.batch_size,-1)), units=self.n_classes, activation=tf.nn.relu) #全连接层 pool = tf.layers.max_pooling2d(inputs=self.conv1, pool_size=[2, 2], strides=2) fc1 = tf.layers.dense(inputs=pool, units=1024, activation=tf.nn.relu) fc2 = tf.layers.dense(inputs=fc1, units=512, activation=tf.nn.relu) self.dense1 = tf.layers.dense(inputs=tf.reshape(fc2,(cfg.batch_size,-1)), units=self.n_classes, activation=None) self.dense1_index = tf.to_int32(tf.argmax(tf.nn.softmax(self.dense1, axis=1), axis=1)) # Primary Capsules layer, return [batch_size, 1152, 8, 1] with tf.variable_scope('PrimaryCaps_layer'): primaryCaps = CapsLayer(num_outputs=32, vec_len=8, with_routing=False, layer_type='CONV', conv1_outa=self.conv1_outa, conv1_outb=self.conv1_outb, cap1_out=self.cap1_out,n_classes=self.n_classes) (self.caps1,pred) = primaryCaps(self.conv1, kernel_size=self.kernel_size2, stride=2) self.lstmpred=pred assert self.caps1.get_shape() == [cfg.batch_size, self.cap1_out, 8, 1] # self.dense2= tf.layers.dense(inputs=tf.reshape(self.caps1,(cfg.batch_size,-1)), units=self.n_classes, activation=tf.nn.relu) pool = tf.layers.max_pooling2d(inputs=self.caps1, pool_size=[2, 2], strides=2) fc1 = tf.layers.dense(inputs=pool, units=1024, activation=tf.nn.relu) fc2 = tf.layers.dense(inputs=fc1, units=512, activation=tf.nn.relu) self.dense2 = tf.layers.dense(inputs=tf.reshape(fc2,(cfg.batch_size,-1)), units=self.n_classes, activation=None) self.dense2_index = tf.to_int32(tf.argmax(tf.nn.softmax(self.dense2, axis=1), axis=1)) # DigitCaps layer, return [batch_size, 10, 16, 1] with tf.variable_scope('DigitCaps_layer'): digitCaps = CapsLayer(num_outputs=self.n_classes, vec_len=8, with_routing=True, layer_type='FC',conv1_outa=self.conv1_outa, conv1_outb=self.conv1_outb, cap1_out=self.cap1_out,n_classes=self.n_classes) self.caps2 = digitCaps(self.caps1) # self.caps2 = tf.add(tf.tile(tf.reshape(self.lstmpred,(cfg.batch_size,self.n_classes,1,1)),[1,1,16,1]),self.caps2) # Decoder structure in Fig. 2 # 1. Do masking, how: with tf.variable_scope('Masking'): # a). calc ||v_c||, then do softmax(||v_c||) # [batch_size, 10, 16, 1] => [batch_size, 10, 1, 1] self.v_length = tf.sqrt(tf.reduce_sum(tf.square(self.caps2), axis=2, keepdims=True)) self.softmax_v = tf.nn.softmax(self.v_length, axis=1) assert self.softmax_v.get_shape() == [cfg.batch_size, self.n_classes, 1, 1] # b). pick out the index of max softmax val of the 10 caps # [batch_size, 10, 1, 1] => [batch_size] (index) self.argmax_idx = tf.to_int32(tf.argmax(self.softmax_v, axis=1)) assert self.argmax_idx.get_shape() == [cfg.batch_size, 1, 1] self.argmax_idx = tf.reshape(self.argmax_idx, shape=(cfg.batch_size, )) # self.max_index_list= tf.Variable(tf.ones([cfg.batch_size, ],dtype=tf.int32)) # index_list=tf.stack([self.dense1_index,self.dense2_index,self.argmax_idx],1) # # for i in range(cfg.batch_size): # max_index=tf.to_int32(tf.argmax(tf.bincount(index_list[i]))) # self.update_op=tf.assign(self.max_index_list[i],max_index) # # Method 1. # if not cfg.mask_with_y: # # c). indexing # # It's not easy to understand the indexing process with argmax_idx # # as we are 3-dim animal # masked_v = [] # for batch_size in range(cfg.batch_size): # v = self.caps2[batch_size][self.argmax_idx[batch_size], :] # masked_v.append(tf.reshape(v, shape=(1, 1, 8, 1))) # # self.masked_v = tf.concat(masked_v, axis=0) # assert self.masked_v.get_shape() == [cfg.batch_size, 1, 8, 1] # # Method 2. masking with true label, default mode # else: # # self.masked_v = tf.matmul(tf.squeeze(self.caps2), tf.reshape(self.Y, (-1, 10, 1)), transpose_a=True) # self.masked_v = tf.multiply(tf.squeeze(self.caps2), tf.reshape(self.Y, (-1, self.n_classes, 1))) # self.v_length = tf.sqrt(tf.reduce_sum(tf.square(self.caps2), axis=2, keepdims=True) + epsilon) # # # 2. Reconstructe the MNIST images with 3 FC layers # # [batch_size, 1, 16, 1] => [batch_size, 16] => [batch_size, 512] # with tf.variable_scope('Decoder'): # vector_j = tf.reshape(self.masked_v, shape=(cfg.batch_size, -1)) # fc1 = tf.contrib.layers.fully_connected(vector_j, num_outputs=512) # assert fc1.get_shape() == [cfg.batch_size, 512] # fc2 = tf.contrib.layers.fully_connected(fc1, num_outputs=1024) # assert fc2.get_shape() == [cfg.batch_size, 1024] # self.decoded = tf.contrib.layers.fully_connected(fc2, num_outputs=self.n_steps*self.n_input, activation_fn=tf.sigmoid) def loss(self): # 1. The margin loss # [batch_size, 10, 1, 1] # max_l = max(0, m_plus-||v_c||)^2 max_l = tf.square(tf.maximum(0., cfg.m_plus - self.v_length)) # max_r = max(0, ||v_c||-m_minus)^2 max_r = tf.square(tf.maximum(0., self.v_length - cfg.m_minus)) assert max_l.get_shape() == [cfg.batch_size,self.n_classes, 1, 1] # reshape: [batch_size, 10, 1, 1] => [batch_size, 10] max_l = tf.reshape(max_l, shape=(cfg.batch_size, -1)) max_r = tf.reshape(max_r, shape=(cfg.batch_size, -1)) # calc T_c: [batch_size, 10] # T_c = Y, is my understanding correct? Try it. T_c = self.Y # [batch_size, 10], element-wise multiply L_c = T_c * max_l + cfg.lambda_val * (1 - T_c) * max_r self.margin_loss = tf.reduce_mean(tf.reduce_sum(L_c, axis=1)) # # 2. The reconstruction loss # orgin = tf.reshape(self.X, shape=(cfg.batch_size, -1)) # squared = tf.square(self.decoded - orgin) # self.reconstruction_err = tf.reduce_mean(squared) # lstm loss self.lstm_cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.lstmpred, labels=self.lstm_y)) self.lstm_index = tf.to_int32(tf.argmax(self.lstmpred, axis=1)) self.dense_cov1_cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.dense1, labels=self.lstm_y)) self.dense_caps1_cost= tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.dense2, labels=self.lstm_y)) # 3. Total loss # The paper uses sum of squared error as reconstruction error, but we # have used reduce_mean in `# 2 The reconstruction loss` to calculate # mean squared error. In order to keep in line with the paper,the # regularization scale should be 0.0005*784=0.392 # self.total_loss = self.margin_loss + cfg.regularization_scale * self.reconstruction_err+lstm_cost # self.total_loss = self.margin_loss+self.lstm_cost self.total_loss = self.margin_loss+self.lstm_cost+self.dense_cov1_cost+self.dense_caps1_cost # Summary def _summary(self): train_summary = [] train_summary.append(tf.summary.scalar('train/margin_loss', self.margin_loss)) # train_summary.append(tf.summary.scalar('train/reconstruction_loss', self.reconstruction_err)) train_summary.append(tf.summary.scalar('train/total_loss', self.total_loss)) # train_summary.append(tf.summary.scalar('train/rf_loss', self.rf_loss_op)) # recon_img = tf.reshape(self.decoded, shape=(cfg.batch_size, self.n_input,self.n_steps, 1)) # train_summary.append(tf.summary.image('reconstruction_img', recon_img)) self.train_summary = tf.summary.merge(train_summary) # correct_prediction = tf.equal(tf.to_int32(self.labels), self.max_index_list) # self.accuracy = tf.reduce_sum(tf.cast(correct_prediction, tf.float32))

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vocoder/hifigan/env.py

10088/MockingBird

1

46337

import os import shutil def build_env(config, config_name, path): t_path = os.path.join(path, config_name) if config != t_path: os.makedirs(path, exist_ok=True) shutil.copyfile(config, os.path.join(path, config_name))

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python/deploy-cpu-alarms.py

earthlab/aws-ops

1

37053

# Deploy idle CPU alarms to stop EC2 instances import boto3 account_id = boto3.client("sts").get_caller_identity().get("Account") region = boto3.session.Session().region_name client = boto3.client("cloudwatch") ec = boto3.client("ec2") reservations = ec.describe_instances() exceptions = ["prf-"] for r in reservations["Reservations"]: for i in r["Instances"]: instance_id = i["InstanceId"] for t in i["Tags"]: if t["Key"] == "Name": iname = t["Value"] name_excepted = any([e in iname for e in exceptions]) if name_excepted: continue else: alarm_name = "CPU Alarm " + iname + instance_id alarm = client.put_metric_alarm( AlarmName=alarm_name, MetricName="CPUUtilization", Namespace="AWS/EC2", Statistic="Maximum", ComparisonOperator="LessThanOrEqualToThreshold", Threshold=1.0, Period=60 * 60, # in seconds EvaluationPeriods=2, Dimensions=[{"Name": "InstanceId", "Value": instance_id}], Unit="Percent", ActionsEnabled=True, AlarmActions=[ ":".join( [ "arn:aws:swf", region, account_id, "action/actions/AWS_EC2.InstanceId.Stop/1.0", ] ) ], )

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wirepas_backend_client/tools/utils.py

bencorrado/backend-client

0

20802

<gh_stars>0 """ Utils ======= Contains multipurpose utilities for serializing objects and obtaining arguments from the command line. .. Copyright: Copyright 2019 Wirepas Ltd under Apache License, Version 2.0. See file LICENSE for full license details. """ import binascii import datetime import json import threading from google.protobuf import json_format def deferred_thread(fn): """ Decorator to handle a request on its own Thread to avoid blocking the calling Thread on I/O. It creates a new Thread but it shouldn't impact the performances as requests are not supposed to be really frequent (few per seconds) """ def wrapper(*args, **kwargs): thread = threading.Thread(target=fn, args=args, kwargs=kwargs) thread.start() return thread return wrapper class JsonSerializer(json.JSONEncoder): """ JsonSerializer Extends the JSONEncoder base class with object handlers for bytearrya, datetime and proto. """ proto_as_json = False sort_keys = True indent = 4 def __init__(self, proto_as_json: bool = False, **kwargs): super(JsonSerializer, self).__init__(**kwargs) self.proto_as_json = proto_as_json if "indent" in kwargs: self.indent = kwargs["indent"] if "sort_keys" in kwargs: self.sort_keys = kwargs["sort_keys"] def default(self, obj) -> str: """ Lookup table for serializing objects Pylint complains about the method signature, but this is the recommended way of implementing a custom JSON serialization as seen in: https://docs.python.org/3/library/json.html#json.JSONEncoder """ # pylint: disable=locally-disabled, method-hidden, arguments-differ if isinstance(obj, (datetime.datetime, datetime.date)): return obj.isoformat() if isinstance(obj, (bytearray, bytes)): return binascii.hexlify(obj) if isinstance(obj, set): return str(obj) if hasattr(obj, "DESCRIPTOR"): if self.proto_as_json is True: pstr = json_format.MessageToJson( obj, including_default_value_fields=True ) else: pstr = json_format.MessageToDict( obj, including_default_value_fields=True ) return pstr return json.JSONEncoder.default(self, obj) def serialize(self, obj, flatten=False): """ returns a json representation of the object """ if flatten: temp = dict() for key, value in sorted(obj.items()): if isinstance(value, dict): for child_key, child_value in value.items(): temp[f"{key}.{child_key}"] = child_value obj = temp jobj = json.dumps( obj, cls=JsonSerializer, sort_keys=self.sort_keys, indent=self.indent, ) return jobj def flatten(input_dict, separator="/", prefix=""): """ Flattens a dictionary with nested dictionaries and lists into a single dictionary. The key compression is done using the chosen separator. """ output_dict = {} def step(member, parent_key=""): if isinstance(member, dict): for key, value in member.items(): step( value, f"{parent_key}{separator}{key}" if parent_key else str(key), ) elif isinstance(member, list): for index, sublist in enumerate(member, start=0): step( sublist, f"{parent_key}{separator}{index}" if parent_key else str(index), ) else: output_dict[f"{parent_key}"] = member step(input_dict) return output_dict class Signal: """Wrapper around and exit signal""" def __init__(self, signal=None): super(Signal, self).__init__() if signal is None: signal = False self.signal = signal def is_set(self) -> bool: """ Returns the state of the inner event or boolean """ try: ret = self.signal.is_set() except AttributeError: ret = self.signal return ret def set(self) -> bool: """ Sets the event or inner boolean """ try: ret = self.signal.set() except AttributeError: self.signal = True ret = True return ret def chunker(seq, size) -> list(): """ Splits a sequence in multiple parts Args: seq ([]) : an array size (int) : length of each array part Returns: array ([]) : a chunk of SEQ with given SIZE """ return (seq[pos : pos + size] for pos in range(0, len(seq), size))

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mooda/util/iplot/iplot_timeseries.py

rbardaji/mooda

15

1606456

<reponame>rbardaji/mooda """ Implementation of WaterFrame.iplot_timeseries() """ import numpy as np import plotly.graph_objects as go def iplot_timeseries(list_wf, parameter_to_plot): """ It creates a Plotly figure with the time-series of the input parameter. Parameters ---------- list_wf: List of WaterFrames List of WaterFrames to be concatenated. parameter_to_plot: str Parameters to plot. Returns ------- figure: dict Plotly figure dictionary """ data = [] for wf in list_wf: # Extract data df = wf.data[parameter_to_plot].dropna().reset_index() df = df.groupby(['DEPTH', 'TIME'])[parameter_to_plot].mean() df = df.reset_index() df.set_index('TIME', inplace=True) # df.renane(columns={parameter_to_plot: wf.metadata.get('platform_code')}, inplace=True) data.append( go.Scatter(x=df.index, y=df[parameter_to_plot], # fill="tozeroy", name=wf.metadata.get('platform_code'))) # Layout y_label = None try: title = list_wf[0].vocabulary[parameter_to_plot]['long_name'] except KeyError: title = parameter_to_plot try: y_label = list_wf[0].vocabulary[parameter_to_plot]['units'] except KeyError: pass min_value = np.nanmin(list_wf[-1].data[parameter_to_plot]) max_value = np.nanmax(list_wf[0].data[parameter_to_plot]) layout = { 'title': title, 'yaxis': { # 'range': [min_value, max_value], 'title': y_label }, 'margin': {'l': 50, 'r': 10, 't': 45, 'b': 30} } figure = {"data": data, "layout": layout} return figure

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IGTI - Bootcamp Desenvolvedor Python/04/cap4/pygame/Cap2 - desenhando/desenhando_retangulo.py

ruginski/DS

43

89395

import pygame from pygame.locals import * from sys import exit from random import * pygame.init() screen = pygame.display.set_mode((640, 480), 0, 32) screen.lock() for count in range(10): random_color = (randint(0,255), randint(0,255), randint(0,255)) random_pos = (randint(0,639), randint(0,479)) random_size = (639-randint(random_pos[0],639), 479-randint(random_pos[1],479)) pygame.draw.rect(screen, random_color, Rect(random_pos, random_size)) screen.unlock() pygame.display.update() while True: for event in pygame.event.get(): if event.type == QUIT: pygame.quit() exit()

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Simulator/tournament.py

apayne19/DoubleAuctionMarket

30

126936

<reponame>apayne19/DoubleAuctionMarket<gh_stars>10-100 from Trader.trader import Trader_Shaver, Trader_ZIU, Trader_ZIC, Trader_Kaplan, Trader_PS, Trader_AA, Trader_GD, Trader_ZIP import Institution.double_auction_institution as ins import random class Tournament(object): """ A class that makes a trader""" def __init__(self, name, iterations, auction): self.name = name self.iterations = iterations self.traders = [] self.auction = auction def show(self): print ("I am tournament {}.".format(self.name)) def prepare_traders(self, tn, mkt, limits): d = {} t = {} if len(tn) != mkt.num_buyers + mkt.num_sellers: print ("tn = {} does not have the right length".format(tn)) for k in range(mkt.num_buyers + mkt.num_sellers): t_id = "t" + str(k) # make trader id t[t_id] = globals()[tn[k]]() # create object t[t_id].name = t_id # set objects name t[t_id].da = ins.Auction("da", limits[0], limits[1]) d[t_id] = {} d[t_id]['units'] = 0 # keep track of units used d[t_id]['earn'] = 0 # keep track of earnings d[t_id]['strat'] = tn[k] # traders strategy if k < mkt.num_buyers: t[t_id].type = "buyer" # set objects type t[t_id].values = mkt.get_buyer_values(k) # set objects values d[t_id]['type'] = "B" # traders type d[t_id]['values'] = mkt.get_buyer_values(k) # trader's values else: # note seller index is trader_index - num_buyers t[t_id].type = "seller" # set objects type t[t_id].costs = mkt.get_seller_costs(k - mkt.num_buyers) # set objects values d[t_id]['type'] = "S" # traders type d[t_id]['costs'] = mkt.get_seller_costs(k - mkt.num_buyers) # trader's values d["strategies"] = [k for k in set(tn)] # get market efficiency eqv = mkt.get_equilibrium() # (Price low, Price High Quantity, Maximum Surplus) ep_low = eqv[0] ep_high = eqv[1] e_quantity = eqv[2] maximum_surplus = eqv[3] d["equilibrium"] = (ep_low, ep_high, e_quantity, maximum_surplus) traders = [t[t_id] for t_id in t.keys()] self.traders = [t[t_id] for t_id in t.keys()] return d def run(self, display): self.auction.open_board("tournament official") if display: print() print("Auction Open") print(self.auction.report_standing(), self.auction.report_contracts()) length_old_contracts = 0 for i in range(self.iterations): random.shuffle(self.traders) for trader in self.traders: trader.offer() contracts = self.auction.report_contracts() if len(contracts) > length_old_contracts: length_old_contracts = len(contracts) if display: print (i, contracts[len(contracts) - 1]) if display: print() def eval(self, display, d, result_header): # calculate market efficiency ep_low = d['equilibrium'][0] ep_high = d['equilibrium'][1] e_quantity = d['equilibrium'][2] maximum_surplus = d['equilibrium'][3] for trader in self.traders: trader_id = trader.name d[trader_id]['units'] = 0 d[trader_id]['earn'] = 0 # calculate actual surplus and earnings actual_surplus = 0 for contract in self.auction.report_contracts(): price = contract[0] buyer_id = contract[1] seller_id = contract[2] if d[buyer_id]['type'] == 'B': value = d[buyer_id]['values'][d[buyer_id]['units']] d[buyer_id]['earn'] += value - price d[buyer_id]['units'] += 1 else: print ("error, buyer id = {}, buyer type = {}".format(buyer_id, d[buyer_id]['type'])) if d[seller_id]['type'] == 'S': cost = d[seller_id]['costs'][d[seller_id]['units']] d[seller_id]['earn'] += price - cost d[seller_id]['units'] += 1 else: print ("error in contract {}, seller id = {}, seller type = {}".format(contract, seller_id, d[seller_id]['type'])) actual_surplus += value - cost efficiency = int((actual_surplus / maximum_surplus) * 100) result_header.extend([ep_low, ep_high, e_quantity, maximum_surplus, actual_surplus, efficiency]) if display: print ("actual surplus = {}, maximum surplus = {}.".format(actual_surplus, maximum_surplus)) print ("market efficiency = {} percent.".format(efficiency)) for k in range(len(self.traders)): t_id = "t" + str(k) t_strat = d[t_id]['strat'] earn = d[t_id]['earn'] if display: print ("Trader {}, using strategy {}, earned {}.".format(t_id, t_strat, earn)) result_header.extend([t_id, t_strat, earn]) if display: print() for k in d['strategies']: strat_earn = 0 count = 0 for l in range(len(self.traders)): t_id = "t" + str(l) if k == d[t_id]['strat']: count = count + 1 strat_earn += d[t_id]['earn'] if count > 0: avg_earn = int(strat_earn / count) result_header.extend([k, avg_earn]) if display: print ("Strategy {} earned an average of {}.".format(k, avg_earn)) return result_header def save_results(self, result_header, output_file): # write tournamnet results as .csv file output_file = open(output_file + '.csv', 'w', newline='') output_writer = csv.writer(output_file) # First write out result header record output_writer.writerow(result_header) # Second write out offers information orders = self.auction.report_orders() output_writer.writerow(["orders", "time, id, type, amt/null", len(orders)]) for element in orders: s = [] s.append(element[0]) s.append(element[1]) s.append(element[2]) s.append(element[3]) output_writer.writerow(s) # Third write out contract information contracts = self.auction.report_contracts() output_writer.writerow(["contracts", "price, buyer_id, seller_id", len(contracts)]) for element in contracts: s = [] s.append(element[0]) s.append(element[1]) s.append(element[2]) output_writer.writerow(s) output_file.close() def sim(self, display, num_trials, d): # initialize sum variables total_earnings = [0] * len(d['strategies']) sum_squared = [0] * len(d['strategies']) for k in range(num_trials): result = [] self.run(display) # TODO Put input and output files back in for blanks result_header = [" ", " ", len(self.traders)] result_header = self.eval(display, d, result_header) # result header = [input_file, output_file, num_traders, ep_low, ep_high, e_quantity, # max_surplus, actual_surplus, efficiency, # ** for all numtraders** trader id, trader strategy, trader earnings] # print(result_header) result.append(result_header[8]) j = 9 + int(result_header[2]) * 3 # move to where strategy results are for k in d['strategies']: result.append(result_header[j + 1]) j = j + 2 if display: print("for run {} result {}".format(k, result)) for j in range(len(d['strategies'])): earn = result[j + 1] total_earnings[j] += earn sum_squared[j] += earn * earn avg_earning = [] var = [] sdev = [] for j in range(len(d['strategies'])): avg_earn = total_earnings[j] / num_trials variance = ((sum_squared[j] / num_trials) - (avg_earn * avg_earn)) standard_deviation = variance ** 0.5 avg_earning.append(avg_earn) var.append(variance) sdev.append(standard_deviation) print () print ("Average Earnings for {} rounds".format(num_trials)) print (d['strategies']) print(avg_earning) print(sdev) if __name__ == "__main__": trn = Tournament ("Tournamnet Name", 10, "placeholder for double auction")

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akilib/raspberrypi/AKI_I2C_LPS25H.py

nonNoise/akilib

29

24428

<filename>akilib/raspberrypi/AKI_I2C_LPS25H.py #import mraa import smbus import time I2C_ADDR = 0x5C class AKI_I2C_LPS25H: def __init__(self): print "AKI_I2C_LPS25H" self.i2c = smbus.SMBus(1) def i2cReg(self,wr,addr=0x00,data=0x00): try: if(wr == "w"): return self.i2c.write_byte_data(I2C_ADDR,addr,data) elif(wr == "r"): return self.i2c.read_byte_data(I2C_ADDR,addr) else : return -1 except IOError, err: print "No ACK!" time.sleep(0.1) self.i2cReg(wr,addr,data) def WHO_AM_I(self): # "WHO_AM_I" return self.i2cReg("r",0x0F) def Init(self): return self.i2cReg("w",0x20,0x90) def Press(self): p =0 p = p | self.i2cReg("r",0x28) <<0 p = p | self.i2cReg("r",0x29) <<8 p = p | self.i2cReg("r",0x2A) <<16 mbar = p/4096 return mbar def Temp (self): t = 0 t = t | self.i2cReg("r",0x2B) <<0 t = t | self.i2cReg("r",0x2C) <<8 t = -((t-1)^0xffff) return (42.5+(t/480.0))

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setup.py

MagnaXSoftware/popo

0

146881

<filename>setup.py<gh_stars>0 from setuptools import setup, find_packages setup( name="popo", version="0.0.1", url="https://github.com/MagnaXSoftware/popo", license="MIT", author="<NAME>", author_email="<EMAIL>", description="POPO gives you a simple, small, clean ORM with Plain Old Python Objects.", packages=find_packages(), classifiers=[ "Development Status :: 4 - Beta", "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Topic :: Database", ], )

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et_micc2/templates/app-single-command/{{tmpl.project_name}}/{{tmpl.package_name}}/{{tmpl.cli_app_name}}.py

etijskens/et-micc2

0

184283

<filename>et_micc2/templates/app-single-command/{{tmpl.project_name}}/{{tmpl.package_name}}/{{tmpl.cli_app_name}}.py # -*- coding: utf-8 -*- """Command line interface {{tmpl.app_name}} (no sub-commands).""" import sys import click @click.command() @click.option('-v', '--verbosity', count=True , help="The verbosity of the program." , default=1 ) def main(verbosity): """Command line interface {{tmpl.app_name}}. A 'hello' world CLI example. """ for _ in range(verbosity): click.echo("running {{tmpl.app_name}}") if __name__ == "__main__": sys.exit(main()) # pragma: no cover #eodf

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pkg/unix/Python27/Lib/site-packages/klei/textureconverter.py

hexgear-studio/ds_mod_tools

112

199156

import sys, os, tempfile, shutil from PIL import Image import util import subprocess ''' TOOL_PATH = os.path.join( os.path.dirname( __file__ ), "../../../../../../tools/bin" ) TEXTURE_CONVERTER = os.path.abspath( os.path.join( TOOL_PATH, "TextureConverter.exe" ) ) def Convert( src_filenames, dest_filename, texture_format="bc3", no_premultiply=False, force=False, platform='opengl', generate_mips=False, width=None, height=None, verbose=False, ignore_exceptions=False): is_newer = False # If a list is passed in, concatenate the filenames with semi-colon separators, otherwise just use the filename src_filename_str = None if isinstance( src_filenames, list ): src_filename_str = ';'.join( src_filenames ) for filename in src_filenames: is_newer = is_newer or util.IsFileNewer( filename, dest_filename ) else: src_filename_str = src_filenames is_newer = is_newer or util.IsFileNewer( src_filename_str, dest_filename ) if force or is_newer: cmd_list = [ TEXTURE_CONVERTER, '--swizzle', '--format ' + texture_format, '--platform ' + platform, '-i ' + src_filename_str, '-o ' + dest_filename, ] if generate_mips: cmd_list.append( '--mipmap' ) if not no_premultiply: cmd_list.append( '--premultiply' ) if width: cmd_list.append( '-w {}'.format( width ) ) if height: cmd_list.append( '-h {}'.format( height ) ) cmd = " ".join( cmd_list ) if verbose: print( cmd ) if subprocess.call( cmd_list ) != 0: sys.stderr.write( "Error attempting to convert {} to {}\n".format( src_filenames, dest_filename ) ) sys.stderr.write( cmd + "\n" ) if not ignore_exceptions: raise ''' KTECH_LOCATION = "ktech" texture_format_translation = { 'bc1': 'dxt1', 'bc2': 'dxt3', 'bc3': 'dxt5', 'rgb': 'rgb', 'argb': 'rgba', } def Convert( src_filenames, dest_filename, texture_format="bc3", no_premultiply=False, force=False, platform='opengl', generate_mips=False, width=None, height=None, verbose=False, ignore_exceptions=False): is_newer = False assert texture_format_translation[texture_format], "Invalid texture format {}".format(texture_format) texture_format = texture_format_translation[texture_format] # ktech has multiplatform texture support as a stub, but disabled since only opengl is used. # (the support was built in, but the option to define in as a program parameter was left commented out) assert platform == "opengl", "Invalid platform {}".format(platform) # If a list is passed in, concatenate the filenames with semi-colon separators, otherwise just use the filename src_filename_str = None if isinstance( src_filenames, list ): src_filename_str = ','.join( src_filenames ) for filename in src_filenames: is_newer = is_newer or util.IsFileNewer( filename, dest_filename ) else: src_filename_str = src_filenames is_newer = is_newer or util.IsFileNewer( src_filename_str, dest_filename ) if force or is_newer: cmd_list = [ KTECH_LOCATION, '--quiet', '--compression ' + texture_format, ] if not generate_mips: cmd_list.append( '--no-mipmaps' ) if no_premultiply: cmd_list.append( '--no-premultiply' ) if width: cmd_list.append( '--width {}'.format( width ) ) if height: cmd_list.append( '--height {}'.format( height ) ) cmd_list.append( '--' ) cmd_list.append( src_filename_str ) cmd_list.append( dest_filename ) cmd = " ".join( cmd_list ) if verbose: print( cmd ) if subprocess.call( cmd_list ) != 0: sys.stderr.write( "Error attempting to convert {} to {}\n".format( src_filenames, dest_filename ) ) sys.stderr.write( cmd + "\n" ) if not ignore_exceptions: raise def GenerateMips( im ): mips = [] w, h = im.size while w >= 1 or h >= 1: mips.append( im ) w /= 2 h /= 2 im = im.resize( ( max( w, 1 ), max( h, 1 ) ), Image.ANTIALIAS ) return mips def SaveImagesToTemp( images, basename ): tempdir = tempfile.mkdtemp() idx = 0 filenames = [] for image in images: name = "{0}{1}.png".format( basename, idx ) filename = os.path.join( tempdir, name ) filenames.append( filename ) image.save( filename ) idx += 1 return ( tempdir, filenames ) def MipAndConvert( im, dest_filename, platform='opengl', texture_format="bc3", no_premultiply = False, force=False, ignore_exceptions=False ): if isinstance( im, str ): im = Image.open( im ) mips = GenerateMips( im ) tempdir, filenames = SaveImagesToTemp( mips, "mip" ) try: Convert( src_filenames=filenames, dest_filename=dest_filename, texture_format=texture_format, platform=platform, no_premultiply=no_premultiply, force=force, ignore_exceptions=ignore_exceptions ) finally: if os.path.exists( tempdir ): shutil.rmtree( tempdir )

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lib/coginvasion/hood/TTSafeZoneLoader.py

theclashingfritz/Cog-Invasion-Online-Dump

1

1614361

# uncompyle6 version 3.2.4 # Python bytecode 2.7 (62211) # Decompiled from: Python 2.7.15 (v2.7.15:ca079a3ea3, Apr 30 2018, 16:30:26) [MSC v.1500 64 bit (AMD64)] # Embedded file name: lib.coginvasion.hood.TTSafeZoneLoader from lib.coginvasion.holiday.HolidayManager import HolidayType import SafeZoneLoader, TTPlayground from lib.coginvasion.globals import CIGlobals class TTSafeZoneLoader(SafeZoneLoader.SafeZoneLoader): def __init__(self, hood, parentFSM, doneEvent): SafeZoneLoader.SafeZoneLoader.__init__(self, hood, parentFSM, doneEvent) self.playground = TTPlayground.TTPlayground self.pgMusicFilename = 'phase_4/audio/bgm/TC_nbrhood.mid' self.interiorMusicFilename = 'phase_3.5/audio/bgm/TC_SZ_activity.mid' self.battleMusicFile = 'phase_3.5/audio/bgm/encntr_general_bg.mid' self.invasionMusicFiles = [ 'phase_12/audio/bgm/BossBot_CEO_v1.mid', 'phase_9/audio/bgm/encntr_suit_winning.mid'] self.tournamentMusicFiles = [ 'phase_3.5/audio/bgm/encntr_nfsmw_bg_1.ogg', 'phase_3.5/audio/bgm/encntr_nfsmw_bg_2.ogg', 'phase_3.5/audio/bgm/encntr_nfsmw_bg_3.ogg', 'phase_3.5/audio/bgm/encntr_nfsmw_bg_4.ogg'] self.bossBattleMusicFile = 'phase_7/audio/bgm/encntr_suit_winning_indoor.mid' self.dnaFile = 'phase_4/dna/new_ttc_sz.pdna' self.szStorageDNAFile = 'phase_4/dna/storage_TT_sz.pdna' self.szHolidayDNAFile = None if base.cr.holidayManager.getHoliday() == HolidayType.CHRISTMAS: self.szHolidayDNAFile = 'phase_4/dna/winter_storage_TT_sz.pdna' self.telescope = None self.birdNoises = [ 'phase_4/audio/sfx/SZ_TC_bird1.ogg', 'phase_4/audio/sfx/SZ_TC_bird2.ogg', 'phase_4/audio/sfx/SZ_TC_bird3.ogg'] return def load(self): SafeZoneLoader.SafeZoneLoader.load(self) self.geom.find('**/toontown_central_beta_DNARoot').setTwoSided(1) self.geom.find('**/ground_center').setBin('ground', 18) self.geom.find('**/ground_sidewalk').setBin('ground', 18) self.geom.find('**/ground').setBin('ground', 18) self.geom.find('**/ground_center_coll').setCollideMask(CIGlobals.FloorBitmask) self.geom.find('**/ground_sidewalk_coll').setCollideMask(CIGlobals.FloorBitmask) for tunnel in self.geom.findAllMatches('**/linktunnel_tt*'): tunnel.find('**/tunnel_floor_1').setTexture(loader.loadTexture('phase_4/models/neighborhoods/tex/sidewalkbrown.jpg'), 1) for tree in self.geom.findAllMatches('**/prop_green_tree_*_DNARoot'): newShadow = loader.loadModel('phase_3/models/props/drop_shadow.bam') newShadow.reparentTo(tree) newShadow.setScale(1.5) newShadow.setColor(0, 0, 0, 0.5, 1)

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util/__init__.py

TinDang97/face_recognition

6

64123

<gh_stars>1-10 import cv2 import numpy def normalize_L2(x): assert isinstance(x, numpy.ndarray) return x / numpy.sqrt(numpy.sum((x ** 2), keepdims=True, axis=1)) def cosine_similarity(x1, x2, skip_normalize=False): if type(x1) is list: x1 = numpy.array(x1) if type(x2) is list: x2 = numpy.array(x2) assert type(x1) is numpy.ndarray or type(x2) is numpy.ndarray assert x1.shape[-1] == x2.shape[-1] assert len(x1.shape) == 2 if not skip_normalize: x1 = normalize_L2(x1) x2 = normalize_L2(x2) return numpy.dot(x1, x2.T) def draw_square(img, position, color=(0, 255, 0)) -> numpy.ndarray: """ Draw text at position in image. - position: top-left, bottom-right of square - color: support tuple and hex_color :return: """ if not isinstance(position, tuple): position = tuple(position) return cv2.rectangle(img, position[0:2], position[2:4], color, 2) def show_image(img, windows_name, windows_size=(1280, 720), windows_mode=cv2.WINDOW_NORMAL, wait_time=1, key_press_exit="q"): """ Show image in RGB format Parameters ---------- img: numpy.ndarray image array windows_name: str Title of window windows_size: tuple[int, int] (Default: SD_RESOLUTION) size of window windows_mode: int (Default: cv2.WINDOW_NORMAL) Mode of window wait_time: int Block time. (-1: infinite) key_press_exit: str Key stop event. Returns ------- bool True - Stop event from user """ cv2.namedWindow(windows_name, windows_mode) cv2.imshow(windows_name, img[:, :, ::-1]) cv2.resizeWindow(windows_name, *windows_size) if cv2.waitKey(wait_time) & 0xFF == ord(key_press_exit): cv2.destroyWindow(windows_name) return False return True

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scout/server/blueprints/login/views.py

szilvajuhos/scout

0

1610778

# -*- coding: utf-8 -*- from datetime import datetime from flask import (abort, current_app, Blueprint, flash, redirect, request, session, url_for, render_template) from flask_login import login_user, logout_user from flask_oauthlib.client import OAuthException from flask_ldap3_login.forms import LDAPLoginForm from flask_ldap3_login import AuthenticationResponseStatus from scout.server.extensions import google, login_manager, store, ldap_manager from scout.server.utils import public_endpoint from . import controllers from .models import LoginUser, LdapUser import logging LOG = logging.getLogger(__name__) login_bp = Blueprint('login', __name__, template_folder='templates', static_folder='static', static_url_path='/login/static') login_manager.login_view = 'login.login' login_manager.login_message = 'Please log in to access this page.' login_manager.login_message_category = 'info' ldap_users = {} # used by ldap save_user @login_manager.user_loader def load_user(user_id): """Returns the currently active user as an object.""" user_obj = store.user(user_id) user_inst = LoginUser(user_obj) if user_obj else None return user_inst @google.tokengetter def get_google_token(): """Returns a tuple of Google tokens, if they exist""" return session.get('google_token') @login_bp.route('/login', methods=['GET', 'POST']) @public_endpoint def login(): """Login a user if they have access.""" if 'next' in request.args: session['next_url'] = request.args['next'] user_id = None if current_app.config.get('LDAP_HOST') and request.method=='POST': form = LDAPLoginForm() LOG.info('Validating LDAP user') if form.validate_on_submit(): user_id = form.username.data else: flash("username-password combination is not valid, plase try again", "warning") return redirect(url_for('public.index')) if current_app.config.get('GOOGLE'): if session.get('email'): user_id = session['email'] session.pop('email') else: LOG.info('Validating Google user login') callback_url = url_for('.authorized', _external=True) return google.authorize(callback=callback_url) if request.args.get('email'): # log in against Scout database user_id = request.args.get('email') LOG.info('Validating user {} against Scout database'.format(user_id)) user_obj = store.user(user_id) if user_obj is None: flash("User not whitelisted", 'warning') return redirect(url_for('public.index')) user_obj['accessed_at'] = datetime.now() if session.get('name'): # These args come from google auth user_obj['name'] = session.get('name') user_obj['locale'] = session.get('locale') store.update_user(user_obj) user_dict = LoginUser(user_obj) return perform_login(user_dict) @login_bp.route('/logout') def logout(): logout_user() flash('you logged out', 'success') return redirect(url_for('public.index')) @login_bp.route('/authorized') @public_endpoint def authorized(): oauth_response = None try: oauth_response = google.authorized_response() except OAuthException as error: current_app.logger.warning(oauth_response.message) flash("{} - try again!".format(oauth_response.message), 'warning') return redirect(url_for('public.index')) if oauth_response is None: flash("Access denied: reason={} error={}" .format(request.args.get['error_reason'], request.args['error_description']), 'danger') return abort(403) # add token to session, do it before validation to be able to fetch # additional data (like email) on the authenticated user session['google_token'] = (oauth_response['access_token'], '') # get additional user info with the access token google_user = google.get('userinfo') google_data = google_user.data session['email'] = google_data['email'].lower() session['name'] = google_data['name'] session['locale'] = google_data['locale'] return redirect(url_for('login.login')) @login_bp.route('/users') def users(): """Show all users in the system.""" data = controllers.users(store) return render_template('login/users.html', **data) def perform_login(user_dict): if login_user(user_dict, remember=True): flash("you logged in as: {}".format(user_dict.name), 'success') next_url = session.pop('next_url', None) return redirect(request.args.get('next') or next_url or url_for('cases.index')) else: flash('sorry, you could not log in', 'warning') return redirect(url_for('public.index')) @ldap_manager.save_user def save_user(dn, username, data, memberships): user = LdapUser(dn, username, data) ldap_users[dn] = user return user

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feature/functions/feature_selection.py

manaranjoc/ResonanceImageClassification

0

182297

import numpy as np from mlxtend.feature_selection import SequentialFeatureSelector as SFS from sklearn.model_selection import KFold from sklearn.feature_selection import SelectPercentile, mutual_info_classif import sys from pathlib import Path sys.path[0] = str(Path(sys.path[0]).parent) from metrics import metrics, meanMetrics, stdMetrics, printMetrics import time def select_features(classifier, n_features, fwd, fltg): sfs = SFS(classifier, k_features=n_features, forward=fwd, floating=fltg, verbose=1, scoring='accuracy', cv=10, n_jobs=-1) return sfs def select_features_number(classifier, number_features, fwd, fltg, X, Y): tiempo_i = time.time() Errores = np.ones(10) Metrics = np.zeros((10,5)) j = 0 kf = KFold(n_splits=10) clf = classifier sf = select_features(clf, number_features, fwd, fltg) sf = sf.fit(X, Y) X_sf = sf.transform(X) for train_index, test_index in kf.split(X_sf): X_train, X_test = X_sf[train_index], X_sf[test_index] y_train, y_test = Y[train_index], Y[test_index] classifier.fit(X_train, y_train) y_pred = classifier.predict(X_test) Errores[j] = 1-metrics(y_test,y_pred)[0] Metrics[j,:] = metrics(y_test, y_pred) j+=1 print("\nError de validación aplicando SFS: "+str(np.mean(Errores))+"+/-"+str(np.std(Errores))) print("\nEficiencia en validación aplicando SFS: "+str((1-np.mean(Errores))*100)+"%") print("\nTiempo total de ejecución: "+str(time.time()-tiempo_i)+" segundos.") MetricsMean = meanMetrics(Metrics) MetricsStd = stdMetrics(Metrics) printMetrics(MetricsMean) print("\nDesviaciones Estandard") printMetrics(MetricsStd) return sf def select_features_filter_percentage(classifier, percentage, X, Y): tiempo_i = time.time() Errores = np.ones(10) Metrics = np.zeros((10,5)) j = 0 kf = KFold(n_splits=10) filter_method = SelectPercentile(mutual_info_classif, percentile=percentage) filter_method.fit(X,Y) X_sf = filter_method.transform(X) for train_index, test_index in kf.split(X_sf): X_train, X_test = X_sf[train_index], X_sf[test_index] y_train, y_test = Y[train_index], Y[test_index] classifier.fit(X_train, y_train) y_pred = classifier.predict(X_test) Metrics[j,:] = metrics(y_test, y_pred) Errores[j] = 1-metrics(y_test,y_pred)[0] j+=1 print("\nError de validación aplicando at "+str(percentage)+"%: "+str(np.mean(Errores))+"+/-"+str(np.std(Errores))) print("\nEficiencia en validación aplicando at "+str(percentage)+"%: "+str((1-np.mean(Errores))*100)+"%") print("\nTiempo total de ejecución: "+str(time.time()-tiempo_i)+" segundos.") MetricsMean = meanMetrics(Metrics) MetricsStd = stdMetrics(Metrics) printMetrics(MetricsMean) print("\nDesviaciones Estandard") printMetrics(MetricsStd) return filter_method

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Bin/RepoTools/Scripts/GithubTools/github_preparation.py

stinvi/dava.engine

26

170170

#!/usr/bin/env python import sys import os import argparse import subprocess class Preparation: system_list = set(['.gitattributes', '.gitignore', '.gitmodules']) white_list = set() clear_list = set(['Bin/RepoTools/Scripts/GithubTools/white_list.txt', 'Bin/RepoTools/Scripts/GithubTools/github_preparation.py']) def __init__(self, base_path): self.base_path = os.path.abspath(base_path) self.read_white_list() self.read_white_list() self.do() def read_white_list(self): try: files_list = open(os.path.abspath(self.base_path + '/Bin/RepoTools/Scripts/GithubTools/white_list.txt')) for line in files_list: full_path = os.path.abspath(self.base_path + line.partition('#')[0].replace('\n', '')).replace("!", "") path = line.partition('#')[0].replace('\n', '').replace("!", "")[1:] if "!" in line: if path: self.clear_list.add(path) else: if os.path.exists(path): if path != self.base_path: self.white_list.add(path) except IOError: print "##teamcity[message text='Unable to open file white_list.txt' status='ERROR']" sys.exit(3) def execute_cmd(self, cmd): sys.stdout.write(cmd + '\n') sys.stdout.flush() try: pass cmd_log = subprocess.check_output(cmd, shell=True, cwd=self.base_path) sys.stdout.write(cmd_log) sys.stdout.flush() except subprocess.CalledProcessError as cmd_except: print "##teamcity[message text='CMD Error' errorDetails='CMD: {cmd} ERROR: {error}' status='ERROR']".format(cmd=cmd, error=cmd_except.output) sys.exit(3) def do(self): os.chdir(self.base_path) for path in self.system_list: cmd = "git add {path}".format(path=path) self.execute_cmd(cmd) for path in self.white_list: cmd = "git add -f {path}".format(path=path) self.execute_cmd(cmd) for path in self.clear_list: cmd = "git reset {path}".format(path=path) self.execute_cmd(cmd) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Utility add files to the repository from the whitelist.') parser.add_argument('--path', required=True, help='Example: --path /var/git/repo') args = parser.parse_args() preparation = Preparation(args.path)

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packages/pyright-internal/src/tests/samples/paramInference1.py

Jasha10/pyright

3,934

143288

# This sample tests the logic that infers parameter types based on # default argument values or annotated base class methods. class Parent: def func1(self, a: int, b: str) -> float: ... class Child(Parent): def func1(self, a, b): reveal_type(self, expected_text="Self@Child") reveal_type(a, expected_text="int") reveal_type(b, expected_text="str") return a def func2(a, b=0, c=None): reveal_type(a, expected_text="Unknown") reveal_type(b, expected_text="int") reveal_type(c, expected_text="Unknown | None") def func3(a=(1, 2), b=[1,2], c={1: 2}): reveal_type(a, expected_text="Unknown") reveal_type(b, expected_text="Unknown") reveal_type(c, expected_text="Unknown")

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tests/integration/RoyaltiesPayment/test_withdrawals.py

MiracleCorunus/nft_royalties_market

37

179164

<reponame>MiracleCorunus/nft_royalties_market<gh_stars>10-100 import brownie from brownie.test import strategy import math class StateMachine: value = strategy("uint256", min_value=1, max_value="100 ether") withdrawal = strategy("uint256", min_value=1, max_value="20 ether") address = strategy("address", length=5) def __init__(cls, accounts, RoyaltiesPayment): cls.accounts = accounts cls.contract = RoyaltiesPayment.deploy(accounts[1:], {"from": accounts[0]}) def setup(self): self.balance = 0 self.payouts = 0 self.total_deposits = 0 self.user_balances = {account: 0 for account in self.accounts[1:]} def rule_add_user(self, address): if self.contract.balances(address)["userIndex"] == 0: self.contract.addPayee(address, {"from": self.accounts[0]}) self.user_balances = {account: 0 for account in self.accounts[1:]} self.user_balances[address] = 0 self.payouts += 1 def rule_deposit(self, value): self.accounts[0].transfer(to=self.contract, amount=value) self.balance += value self.total_deposits += value self.user_balances = { k: v + (value // len(self.user_balances)) for k, v in self.user_balances.items() } def rule_withdraw(self, address, withdrawal): if (address in self.user_balances) and self.user_balances[address] >= withdrawal: self.contract.withdraw(withdrawal, {"from": address}) self.user_balances[address] -= withdrawal self.balance -= withdrawal else: with brownie.reverts(): self.contract.withdraw(withdrawal, {"from": address}) def rule_withdraw_all(self, address): if (address in self.user_balances) and self.user_balances[address] > 0: self.contract.withdrawAll({"from": address}) self.balance -= self.user_balances[address] self.user_balances[address] = 0 else: with brownie.reverts(): self.contract.withdrawAll({"from": address}) def invariant(self): math.isclose(self.contract.balance(), self.balance, rel_tol=self.payouts) for user, amount in self.user_balances.items(): assert math.isclose( self.contract.balances(user)["balance"], amount, rel_tol=self.payouts ) assert amount <= (self.total_deposits // len(self.user_balances)) def test_withdrawals(state_machine, RoyaltiesPayment, accounts): state_machine( StateMachine, accounts, RoyaltiesPayment, settings={"max_examples": 30} )

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6736, 28679, 284, 29901, 13, 9651, 1583, 29889, 1285, 1461, 29889, 2541, 4012, 29898, 2541, 4012, 284, 29892, 8853, 3166, 1115, 3211, 1800, 13, 9651, 1583, 29889, 1792, 29918, 5521, 2925, 29961, 7328, 29962, 22361, 28679, 284, 13, 9651, 1583, 29889, 5521, 749, 22361, 28679, 284, 13, 4706, 1683, 29901, 13, 9651, 411, 3347, 2786, 29889, 276, 369, 1372, 7295, 13, 18884, 1583, 29889, 1285, 1461, 29889, 2541, 4012, 29898, 2541, 4012, 284, 29892, 8853, 3166, 1115, 3211, 1800, 13, 13, 1678, 822, 5751, 29918, 2541, 4012, 29918, 497, 29898, 1311, 29892, 3211, 1125, 13, 4706, 565, 313, 7328, 297, 1583, 29889, 1792, 29918, 5521, 2925, 29897, 322, 1583, 29889, 1792, 29918, 5521, 2925, 29961, 7328, 29962, 1405, 29871, 29900, 29901, 13, 9651, 1583, 29889, 1285, 1461, 29889, 2541, 4012, 3596, 3319, 29908, 3166, 1115, 3211, 1800, 13, 9651, 1583, 29889, 5521, 749, 22361, 1583, 29889, 1792, 29918, 5521, 2925, 29961, 7328, 29962, 13, 9651, 1583, 29889, 1792, 29918, 5521, 2925, 29961, 7328, 29962, 353, 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358, 29892, 15303, 1125, 13, 1678, 2106, 29918, 23523, 29898, 13, 4706, 4306, 29076, 29892, 15303, 29892, 15793, 1997, 583, 15467, 358, 29892, 6055, 3790, 29908, 3317, 29918, 19057, 1115, 29871, 29941, 29900, 29913, 13, 1678, 1723, 13, 2 ]

hackathon/column1/problem_10_4_LCA.py

abdurahmanadilovic/elements-of-programming-interviews

0

84656

from collections import defaultdict class Node: def __init__(self, value): self.parent = None self.value = value def get_height(node): height = 0 while node is not None: height += 1 node = node.parent return height def solution2(node1, node2): height1, height2 = get_height(node1), get_height(node2) node_to_correct = node1 if height1 < height2: node_to_correct = node2 diff = abs(height1 - height2) while diff > 0: node_to_correct = node_to_correct.parent diff -= 1 if height1 < height2: node2 = node_to_correct else: node1 = node_to_correct while node1.parent != node2.parent: node1 = node1.parent node2 = node2.parent return node1.parent # less efficient, takes O(n) space def solution(node1, node2): visited = defaultdict(bool) while True: if node1 and node1.parent: if visited[node1.parent.value]: return node1.parent visited[node1.parent.value] = True if node2 and node2.parent: if visited[node2.parent.value]: return node2.parent visited[node2.parent.value] = True node1 = node1.parent node2 = node2.parent

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train.py

HirataYurina/yoloV4-keras-techi

6

140196

# -*- coding:utf-8 -*- # author:平手友梨奈ii # e-mail:<EMAIL> # datetime:1993/12/01 # filename:configs.py # software: PyCharm import numpy as np import tensorflow as tf import keras.backend as K from keras.layers import Input, Lambda from keras.models import Model from keras.optimizers import Adam from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping from nets.yolo4 import yolo_body from nets.yolo4_loss import yolo_loss from keras.backend.tensorflow_backend import set_session from utils.utils import get_random_data, get_random_mosaic_data, get_random_mosaic_data_v2 from my_queue import GeneratorEnqueuer import time import math from cosine_anneal import WarmUpCosineDecayScheduler from config.configs import CONFIG def get_classes(classes_path): """loads the classes""" with open(classes_path) as f: class_names = f.readlines() # use list expression to make your code more concise class_names = [c.strip() for c in class_names] return class_names def get_anchors(anchors_path): """loads the anchors from a file""" with open(anchors_path) as f: anchors = f.readline() # use list expression to make your code more concise anchors = [float(x) for x in anchors.split(',')] return np.array(anchors).reshape(-1, 2) def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes): """data generator for fit_generator the assignment strategy: one gt ---> one anchor 1.find which anchor(9 anchors) gt belongs to 2.find which grid gt belongs to Args: annotation_lines: a list [anno1, anno2, ...] batch_size: batch size input_shape: resolution [h, w] anchors: anchor boxes num_classes: the number of class max_boxes: box_data: [max_boxes, 5] when have a lot of gt to predict, need to set max_boxes bigger. Returns: batch data: [image_data, *y_true], np.zeros(batch_size) """ n = len(annotation_lines) i = 0 while True: image_data = [] box_data = [] for b in range(batch_size): if i == 0: # shuffle dataset at begin of epoch np.random.shuffle(annotation_lines) image, box = get_random_data(annotation_lines[i], input_shape) image_data.append(image) box_data.append(box) i = (i + 1) % n image_data = np.array(image_data) box_data = np.array(box_data) # get true_boxes # y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes) y_true = preprocess_true_boxes_iou_thres(box_data, input_shape, anchors, num_classes, iou_threshold=CONFIG.TRAIN.IOU_THRESHOLD) # use yield to get generator yield [image_data, *y_true], np.zeros(batch_size) def data_generator_mosaic_iou_thres(annotation_lines, batch_size, input_shape, anchors, num_classes): """data generator for fit_generator the assignment strategy: one gt ---> more anchor(iou > iou_threshold) Args: annotation_lines: a list [anno1, anno2, ...] batch_size: batch size input_shape: resolution [h, w] anchors: anchor boxes num_classes: the number of class max_boxes: box_data: [max_boxes, 5] when have a lot of gt to predict, must set max_boxes bigger. iou_threshold: if iou > iou_threshold, the anchor is responsible for this gt. Returns: batch data: [image_data, *y_true], np.zeros(batch_size) """ n = len(annotation_lines) shuffle_num = n // 4 i = 0 while True: image_data = [] box_data = [] for b in range(batch_size): if i == 0: # shuffle dataset at begin of epoch np.random.shuffle(annotation_lines) image, box = get_random_mosaic_data(annotation_lines[4 * i:4 * i + 4], input_shape) image_data.append(image) box_data.append(box) i = (i + 1) % shuffle_num image_data = np.array(image_data) box_data = np.array(box_data) y_true = preprocess_true_boxes_iou_thres(box_data, input_shape, anchors, num_classes, iou_threshold=CONFIG.TRAIN.IOU_THRESHOLD) # use yield to get generator yield [image_data, *y_true], np.zeros(batch_size) def preprocess_true_boxes(true_boxes, input_shape, anchors, num_classes): assert (true_boxes[..., 4] < num_classes).all(), 'class id must be less than num_classes' num_layers = len(anchors) // 3 anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]] if num_layers == 3 else [[3, 4, 5], [1, 2, 3]] true_boxes = np.array(true_boxes, dtype='float32') input_shape = np.array(input_shape, dtype='int32') # 416,416 boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2 boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2] true_boxes[..., 0:2] = boxes_xy / input_shape[:] true_boxes[..., 2:4] = boxes_wh / input_shape[:] m = true_boxes.shape[0] grid_shapes = [input_shape // {0: 32, 1: 16, 2: 8}[l] for l in range(num_layers)] # [(m, 13, 13, 3, 85), (m, 26, 26, 3, 85), (m, 52, 52, 3, 85)] y_true = [np.zeros((m, grid_shapes[l][0], grid_shapes[l][1], len(anchor_mask[l]), 5 + num_classes), dtype='float32') for l in range(num_layers)] # (1, 9, 2) anchors = np.expand_dims(anchors, 0) anchor_maxes = anchors / 2. anchor_mins = -anchor_maxes # filter invalid boxes valid_mask = boxes_wh[..., 0] > 0 for b in range(m): wh = boxes_wh[b, valid_mask[b]] if len(wh) == 0: continue # [n, 1, 2] wh = np.expand_dims(wh, -2) box_maxes = wh / 2. box_mins = -box_maxes # get iou intersect_mins = np.maximum(box_mins, anchor_mins) intersect_maxes = np.minimum(box_maxes, anchor_maxes) intersect_wh = np.maximum(intersect_maxes - intersect_mins, 0.) intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1] box_area = wh[..., 0] * wh[..., 1] anchor_area = anchors[..., 0] * anchors[..., 1] iou = intersect_area / (box_area + anchor_area - intersect_area) best_anchor = np.argmax(iou, axis=-1) for t, n in enumerate(best_anchor): for l in range(num_layers): if n in anchor_mask[l]: # assign gt to one grid i = np.floor(true_boxes[b, t, 0] * grid_shapes[l][1]).astype('int32') j = np.floor(true_boxes[b, t, 1] * grid_shapes[l][0]).astype('int32') # assign gt to one anchor k = anchor_mask[l].index(n) c = true_boxes[b, t, 4].astype('int32') y_true[l][b, j, i, k, 0:4] = true_boxes[b, t, 0:4] # score = 1 and get one hot class label y_true[l][b, j, i, k, 4] = 1 y_true[l][b, j, i, k, 5 + c] = 1 return y_true def preprocess_true_boxes_iou_thres(true_boxes, input_shape, anchors, num_classes, iou_threshold=0.3): """get true boxes with iou threshold""" assert (true_boxes[..., 4] < num_classes).all(), 'class id must be less than num_classes' num_layers = len(anchors) // 3 anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]] if num_layers == 3 else [[3, 4, 5], [1, 2, 3]] true_boxes = np.array(true_boxes, dtype='float32') input_shape = np.array(input_shape, dtype='int32') # 416,416 boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2 boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2] true_boxes[..., 0:2] = boxes_xy / input_shape[:] true_boxes[..., 2:4] = boxes_wh / input_shape[:] m = true_boxes.shape[0] grid_shapes = [input_shape // {0: 32, 1: 16, 2: 8}[l] for l in range(num_layers)] y_true = [np.zeros((m, grid_shapes[l][0], grid_shapes[l][1], len(anchor_mask[l]), 5 + num_classes), dtype='float32') for l in range(num_layers)] # [1, 9, 2] anchors = np.expand_dims(anchors, 0) anchor_maxes = anchors / 2. anchor_mins = -anchor_maxes # filter invalid boxes valid_mask = boxes_wh[..., 0] > 0 for b in range(m): wh = boxes_wh[b, valid_mask[b]] if len(wh) == 0: continue # [n, 1, 2] wh = np.expand_dims(wh, -2) box_maxes = wh / 2. box_mins = -box_maxes intersect_mins = np.maximum(box_mins, anchor_mins) intersect_maxes = np.minimum(box_maxes, anchor_maxes) intersect_wh = np.maximum(intersect_maxes - intersect_mins, 0.) intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1] box_area = wh[..., 0] * wh[..., 1] anchor_area = anchors[..., 0] * anchors[..., 1] iou = intersect_area / (box_area + anchor_area - intersect_area) # 1.iou > iou_threshold positive = iou > iou_threshold # [num_true_boxes, num_anchors] for t, n in enumerate(positive): n = np.array(n, dtype=np.int32) pos_index = np.argwhere(n == 1) if len(pos_index): continue for id in pos_index: id = id[0] for l in range(num_layers): if id in anchor_mask[l]: i = np.floor(true_boxes[b, t, 0] * grid_shapes[l][1]).astype('int32') j = np.floor(true_boxes[b, t, 1] * grid_shapes[l][0]).astype('int32') k = anchor_mask[l].index(id) c = true_boxes[b, t, 4].astype('int32') y_true[l][b, j, i, k, 0:4] = true_boxes[b, t, 0:4] y_true[l][b, j, i, k, 4] = 1 y_true[l][b, j, i, k, 5 + c] = 1 # 2.if no positive anchor, just choose the best one to be the positive. best_anchor = np.argmax(iou, axis=-1) for t, n in enumerate(best_anchor): for l in range(num_layers): if n in anchor_mask[l]: i = np.floor(true_boxes[b, t, 0] * grid_shapes[l][1]).astype('int32') j = np.floor(true_boxes[b, t, 1] * grid_shapes[l][0]).astype('int32') k = anchor_mask[l].index(n) c = true_boxes[b, t, 4].astype('int32') y_true[l][b, j, i, k, 0:4] = true_boxes[b, t, 0:4] y_true[l][b, j, i, k, 4] = 1 y_true[l][b, j, i, k, 5 + c] = 1 return y_true def get_batch(num_workers, max_queue_size=32, use_mosaic_iout_generator=CONFIG.DATASET.MOSAIC_AUG, multiprocessing=CONFIG.DATASET.MULTIPROCESS, **kwargs): """ Args: num_workers: number of workers max_queue_size: max queue size multiprocessing: true in linux and false in windows use_mosaic_iout_generator: use mosaic_iou_thres_generator or not **kwargs: args used in data generator """ enqueuer = None try: if use_mosaic_iout_generator: enqueuer = GeneratorEnqueuer(data_generator_mosaic_iou_thres(**kwargs), use_multiprocessing=multiprocessing) else: enqueuer = GeneratorEnqueuer(data_generator(**kwargs), use_multiprocessing=multiprocessing) enqueuer.start(max_queue_size=max_queue_size, workers=num_workers) generator_output = None while True: while enqueuer.is_running(): if not enqueuer.queue.empty(): generator_output = enqueuer.queue.get() break else: time.sleep(0.01) yield generator_output generator_output = None finally: if enqueuer is not None: enqueuer.stop() config = tf.ConfigProto() # A "Best-fit with coalescing" algorithm, simplified from a version of dlmalloc. config.gpu_options.allocator_type = 'BFC' config.gpu_options.per_process_gpu_memory_fraction = 1 config.gpu_options.allow_growth = True set_session(tf.Session(config=config)) if __name__ == "__main__": annotation_path = CONFIG.TRAIN.ANNO_PATH valid_anno_path = CONFIG.TRAIN.VALID_PATH classes_path = CONFIG.TRAIN.CLASS_PATH anchors_path = CONFIG.TRAIN.ANCHOR_PATH # pretrained model path weights_path = CONFIG.TRAIN.PRE_TRAINED_MODEL class_names = get_classes(classes_path) anchors = get_anchors(anchors_path) num_classes = len(class_names) num_anchors = len(anchors) # checkpoint path log_dir = CONFIG.TRAIN.SAVE_PATH # resolution input_shape = CONFIG.TRAIN.RESOLUTION # clear previous tf graph K.clear_session() image_input = Input(shape=(None, None, 3)) h, w = input_shape # create model print('Create YOLOv4 model with {} anchors and {} classes.'.format(num_anchors, num_classes)) model_body = yolo_body(image_input, num_anchors // 3, num_classes) print('Load weights {}.'.format(weights_path)) model_body.load_weights(weights_path, by_name=True, skip_mismatch=True) y_true = [Input(shape=(h // {0: 32, 1: 16, 2: 8}[l], w // {0: 32, 1: 16, 2: 8}[l], num_anchors // 3, num_classes + 5)) for l in range(3)] loss_input = [*model_body.output, *y_true] # merge custom loss layer into model model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss', arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': CONFIG.TRAIN.IGNORE_THRES, 'use_focal_confidence_loss': CONFIG.TRAIN.CONFIDENCE_FOCAL, 'use_focal_class_loss': CONFIG.TRAIN.CLASS_FOCAL, 'use_diou': CONFIG.TRAIN.DIOU, 'use_ciou': CONFIG.TRAIN.CIOU, 'print_loss': False})(loss_input) # create model_loss model = Model([model_body.input, *y_true], model_loss) # freeze_layers = 249 freeze_layers = CONFIG.TRAIN.FREEZE_LAYERS for i in range(freeze_layers): model_body.layers[i].trainable = False print('Freeze the first {} layers of total {} layers.'.format(freeze_layers, len(model_body.layers))) # checkpoint logging = TensorBoard(log_dir=log_dir) checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}.h5', monitor='loss', save_weights_only=True, save_best_only=False, period=CONFIG.TRAIN.SAVE_PERIOD) # reduce lr on plateau # this lr decay is worse than cosine anneal reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.1, patience=5, verbose=1) # i don't use early stopping frequently because it is not orthogonal. early_stopping = EarlyStopping(monitor='loss', min_delta=0, patience=10, verbose=1) # get training annotations with open(annotation_path) as f: lines = f.readlines() np.random.seed(10101) np.random.shuffle(lines) num_train = len(lines) # get validating annotations with open(valid_anno_path) as f: valid_lines = f.readlines() np.random.shuffle(valid_lines) num_val = len(valid_lines) np.random.seed(None) # one stage training if CONFIG.TRAIN.TRANSFER: model.compile(optimizer=Adam(lr=CONFIG.TRAIN.LR_STAGE1), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) batch_size = CONFIG.TRAIN.BATCH1 print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size)) model.fit_generator(get_batch(num_workers=CONFIG.DATASET.WORKERS, max_queue_size=CONFIG.DATASET.MAX_QUEUE, annotation_lines=lines, batch_size=batch_size, input_shape=input_shape, anchors=anchors, num_classes=num_classes), steps_per_epoch=max(1, num_train // batch_size), # validation_data=get_batch(1, annotation_lines=valid_lines, batch_size=batch_size, # input_shape=input_shape, anchors=anchors, # num_classes=num_classes), # validation steps: at the end of epoch, generate validation_steps * batch data # validation_steps=max(1, num_val // batch_size), epochs=CONFIG.TRAIN.EPOCH1, initial_epoch=0, callbacks=[checkpoint]) model.save_weights(log_dir + 'trained_weights_stage_1.h5') # unfreeze in second stage for i in range(freeze_layers): model_body.layers[i].trainable = True print('layers have been unfrozen!!') # training in second stage # fine tune if True: model.compile(optimizer=Adam(lr=CONFIG.TRAIN.LR_STAGE2), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) batch_size = CONFIG.TRAIN.BATCH2 # cosine anneal total_epoch = CONFIG.TRAIN.EPOCH2 - CONFIG.TRAIN.EPOCH1 cosine_anneal = WarmUpCosineDecayScheduler(learning_rate_base=CONFIG.TRAIN.LR_STAGE2, total_steps=total_epoch * math.ceil(num_train / batch_size), interval_epoch=CONFIG.TRAIN.COS_INTERVAL) print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size)) model.fit_generator(get_batch(num_workers=CONFIG.DATASET.WORKERS, max_queue_size=CONFIG.DATASET.MAX_QUEUE, annotation_lines=lines, batch_size=batch_size, input_shape=input_shape, anchors=anchors, num_classes=num_classes), steps_per_epoch=max(1, num_train // batch_size), # validation_data=get_batch(annotation_lines=valid_lines, batch_size=batch_size, # input_shape=input_shape, anchors=anchors, # num_classes=num_classes), # validation_steps=max(1, num_val // batch_size), epochs=CONFIG.TRAIN.EPOCH2, initial_epoch=CONFIG.TRAIN.EPOCH1, callbacks=[checkpoint, cosine_anneal]) model.save_weights(log_dir + 'last1.h5')

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katana-nbi/katana/api/nfvo.py

afoteas/katana-slice_manager

0

21295

# -*- coding: utf-8 -*- import logging from logging import handlers import pickle import time import uuid from bson.binary import Binary from bson.json_util import dumps from flask import request from flask_classful import FlaskView import pymongo from requests import ConnectTimeout, ConnectionError from katana.shared_utils.mongoUtils import mongoUtils from katana.shared_utils.nfvoUtils import osmUtils # Logging Parameters logger = logging.getLogger(__name__) file_handler = handlers.RotatingFileHandler("katana.log", maxBytes=10000, backupCount=5) stream_handler = logging.StreamHandler() formatter = logging.Formatter("%(asctime)s %(name)s %(levelname)s %(message)s") stream_formatter = logging.Formatter("%(asctime)s %(name)s %(levelname)s %(message)s") file_handler.setFormatter(formatter) stream_handler.setFormatter(stream_formatter) logger.setLevel(logging.DEBUG) logger.addHandler(file_handler) logger.addHandler(stream_handler) class NFVOView(FlaskView): route_prefix = "/api/" req_fields = ["id", "nfvousername", "nfvopassword", "nfvoip", "tenantname"] def index(self): """ Returns a list of nfvo and their details, used by: `katana nfvo ls` """ nfvo_data = mongoUtils.index("nfvo") return_data = [] for infvo in nfvo_data: return_data.append( dict( _id=infvo["_id"], nfvo_id=infvo["id"], created_at=infvo["created_at"], type=infvo["type"], ) ) return dumps(return_data), 200 # @route('/all/') #/nfvo/all def all(self): """ Same with index(self) above, but returns all nfvo details """ return dumps(mongoUtils.index("nfvo")), 200 def get(self, uuid): """ Returns the details of specific nfvo, used by: `katana nfvo inspect [uuid]` """ data = mongoUtils.get("nfvo", uuid) if data: return dumps(data), 200 else: return "Not Found", 404 def post(self): """ Add a new nfvo. The request must provide the nfvo details. used by: `katana nfvo add -f [yaml file]` """ new_uuid = str(uuid.uuid4()) request.json["_id"] = new_uuid request.json["created_at"] = time.time() # unix epoch request.json["tenants"] = {} if request.json["type"] == "OSM": # Create the NFVO object try: osm_username = request.json["nfvousername"] osm_password = request.json["<PASSWORD>"] osm_ip = request.json["nfvoip"] osm_project_name = request.json["tenantname"] nfvo_id = request.json["id"] except KeyError: return f"Error: Required fields: {self.req_fields}", 400 else: osm = osmUtils.Osm(nfvo_id, osm_ip, osm_username, osm_password, osm_project_name) try: osm.getToken() except ConnectTimeout as e: logger.exception("Connection Timeout: {}".format(e)) response = dumps({"error": "Unable to connect to NFVO"}) return (response, 400) except ConnectionError as e: logger.exception("Connection Error: {}".format(e)) response = dumps({"error": "Unable to connect to NFVO"}) return (response, 400) else: # Store the osm object to the mongo db thebytes = pickle.dumps(osm) obj_json = {"_id": new_uuid, "id": request.json["id"], "obj": Binary(thebytes)} try: new_uuid = mongoUtils.add("nfvo", request.json) except pymongo.errors.DuplicateKeyError: return f"NFVO with id {nfvo_id} already exists", 400 mongoUtils.add("nfvo_obj", obj_json) # Get information regarding VNFDs and NSDs osm.bootstrapNfvo() return f"Created {new_uuid}", 201 else: response = dumps({"error": "This type nfvo is not supported"}) return response, 400 def delete(self, uuid): """ Delete a specific nfvo. used by: `katana nfvo rm [uuid]` """ del_nfvo = mongoUtils.get("nfvo", uuid) if del_nfvo: if del_nfvo["tenants"]: return "Cannot delete nfvo {} - In use".format(uuid), 400 mongoUtils.delete("nfvo_obj", uuid) mongoUtils.delete_all("nsd", {"nfvo_id": del_nfvo["id"]}) mongoUtils.delete_all("vnfd", {"nfvoid": del_nfvo["id"]}) mongoUtils.delete("nfvo", uuid) return "Deleted NFVO {}".format(uuid), 200 else: # if uuid is not found, return error return "Error: No such nfvo: {}".format(uuid), 404 def put(self, uuid): """ Update the details of a specific nfvo. used by: `katana nfvo update -f [yaml file] [uuid]` """ data = request.json data["_id"] = uuid old_data = mongoUtils.get("nfvo", uuid) if old_data: data["created_at"] = old_data["created_at"] data["tenants"] = old_data["tenants"] try: for entry in self.req_fields: if data[entry] != old_data[entry]: return "Cannot update field: " + entry, 400 except KeyError: return f"Error: Required fields: {self.req_fields}", 400 else: mongoUtils.update("nfvo", uuid, data) return f"Modified {uuid}", 200 else: new_uuid = uuid data = request.json data["_id"] = new_uuid data["created_at"] = time.time() # unix epoch data["tenants"] = {} if request.json["type"] == "OSM": # Create the NFVO object try: osm_username = request.json["nfvousername"] osm_password = request.json["nfvopassword"] osm_ip = request.json["nfvoip"] osm_project_name = request.json["tenantname"] nfvo_id = request.json["id"] except KeyError: return f"Error: Required fields: {self.req_fields}", 400 else: osm = osmUtils.Osm( nfvo_id, osm_ip, osm_username, osm_password, osm_project_name ) try: osm.getToken() except ConnectTimeout as e: logger.exception("Connection Timeout: {}".format(e)) response = dumps({"error": "Unable to connect to NFVO"}) return (response, 400) except ConnectionError as e: logger.exception("Connection Error: {}".format(e)) response = dumps({"error": "Unable to connect to NFVO"}) return (response, 400) else: # Store the osm object to the mongo db thebytes = pickle.dumps(osm) obj_json = {"_id": new_uuid, "id": data["id"], "obj": Binary(thebytes)} try: new_uuid = mongoUtils.add("nfvo", data) except pymongo.errors.DuplicateKeyError: return f"NFVO with id {nfvo_id} already exists", 400 mongoUtils.add("nfvo_obj", obj_json) # Get information regarding VNFDs and NSDs osm.bootstrapNfvo() else: response = dumps({"error": "This type nfvo is not supported"}) return response, 400 return f"Created {new_uuid}", 201

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mtr/sync/lib/converters.py

mtrgroup/django-mtr-import-export

0

1614403

from decimal import Decimal from django.utils.six import text_type from django.utils.encoding import smart_text from .manager import manager from ..translation import gettext_lazy as _ @manager.register('converter', label=_('Decimal')) def decimal(value, action): if value: if isinstance(value, text_type): value = value.replace(',', '.') return Decimal(value) else: return Decimal(0) @manager.register('converter', label=_('String')) def string(value, action): if value: return smart_text(value) else: return '' @manager.register('converter', label=_('Coma separated list')) def comalist(value, action): if action == 'export': if isinstance(value, list): return ','.join(smart_text(v) for v in value) else: if isinstance(value, text_type): return value.split(',') return [value] @manager.register('converter', label=_('Boolean')) def boolean(value, action): if isinstance(value, str): if value.isdigit(): value = int(value) elif not isinstance(value, int): value = 0 return bool(value) @manager.register('converter', label=_('File path')) def filepath(value, action): if value: return value.path else: return value @manager.register('converter', label=_('Integer')) def integer(value, action): if value: return int(value) else: return value @manager.register('converter', label=_('None')) def none(value, action): return None

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omicexperiment/transforms/filters/taxonomy.py

bassio/omicexperiment

7

190878

import pandas as pd from omicexperiment.transforms.transform import Filter, AttributeFilter, AttributeFlexibleOperatorMixin class TaxonomyAttributeFilter(AttributeFilter, AttributeFlexibleOperatorMixin): def __dapply__(self, experiment): _op = self._op_function(experiment._counts_with_tax()) criteria = _op(self.value) return experiment.data_df[criteria] def __eapply__(self, experiment): filtered_df = self.__dapply__(experiment) return experiment.with_data_df(filtered_df)

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scqubits/io_utils/fileio_backends.py

IlyaLSMmisis/scqubits-1

0

119290

# fileio_backends.py # # This file is part of scqubits. # # Copyright (c) 2019, <NAME> and <NAME> # All rights reserved. # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. ############################################################################ """ Helper routines for writing data to h5 files. """ import ast import csv import os import re from abc import ABC, abstractmethod import numpy as np try: import h5py except ImportError: _HAS_H5PY = False else: _HAS_H5PY = True import scqubits.io_utils.fileio as io import scqubits.utils.misc as utils class IOWriter(ABC): """ ABC for writing class instance data to file. Parameters ---------- filename: str file_handle: h5.Group, optional """ def __init__(self, filename, file_handle=None): self.filename = filename self.io_data = None self.file_handle = file_handle @abstractmethod def to_file(self, io_data, **kwargs): pass @abstractmethod def write_attributes(self, *args, **kwargs): pass @abstractmethod def write_ndarrays(self, *args, **kwargs): pass @abstractmethod def write_objects(self, *args, **kwargs): pass class H5Writer(IOWriter): """Writes IOData to a custom-format h5 file""" def write_attributes(self, h5file_group): """ Attribute data consists of 1. `__init__` parameters that are of type str or numerical. These are directly written into `h5py.Group.attrs` 2. lists are stored under `<h5py.Group>/__lists` 3. dicts are stored under `<h5py.Group>/__dicts` Parameters ---------- h5file_group: h5py.Group """ h5file_group.attrs.create("__type", self.io_data.typename) # Record the type of the current class instance attributes = self.io_data.attributes for attr_name, attr_value in attributes.items(): if isinstance(attr_value, dict): # h5py does not serialize dicts automatically, so have to do it manually group_name = "__dicts/" + attr_name h5file_group.create_group(group_name) io.write(attr_value, self.filename, file_handle=h5file_group[group_name]) elif isinstance(attr_value, (list, tuple)): group_name = "__lists/" + attr_name h5file_group.create_group(group_name) io.write(attr_value, self.filename, file_handle=h5file_group[group_name]) else: h5file_group.attrs[attr_name] = attr_value def write_ndarrays(self, h5file_group): """ Writes ndarray (float or complex) data contained in `self.iodata` to the provided `h5py.Group` as a `h5py.Dataset`, using gzip compression. Parameters ---------- h5file_group: h5py.Group """ for name, array in self.io_data.ndarrays.items(): h5file_group.create_dataset(name, data=array, dtype=array.dtype, compression="gzip") def write_objects(self, h5file_group): """ Writes data representing a Python object other than ndarray, list and dict, contained in `self.iodata` to the provided `h5py.Group` und `<h5py.Group>/__objects`. Parameters ---------- h5file_group: h5py.Group """ h5file_group = h5file_group.create_group("__objects") for obj_name in self.io_data.objects.keys(): new_h5group = h5file_group.create_group(obj_name) io.write(self.io_data.objects[obj_name], self.filename, file_handle=new_h5group) @utils.Required(h5py=_HAS_H5PY) def to_file(self, io_data, file_handle=None): """ Takes the serialized IOData and writes it either to a new h5 file with file name given by `self.filename` to to the given h5py.Group of an open h5 file. Parameters ---------- io_data: IOData file_handle: h5py.Group, optional """ self.io_data = io_data if file_handle is None: h5file_group = h5py.File(self.filename, 'w') else: h5file_group = file_handle self.write_attributes(h5file_group) self.write_ndarrays(h5file_group) self.write_objects(h5file_group) class H5Reader: """ Enables reading h5 files generated with scqubits. Parameters ---------- filename: str file_handle: h5py.Group, optional """ def __init__(self, filename, file_handle=None): self.filename = filename self.io_data = None self.file_handle = file_handle @staticmethod def h5_attrs_to_dict(h5_attrs): """ Converts h5 attribute data to a Python dictionary. Parameters ---------- h5_attrs: h5py.AttributeManager as obtained by accessing `<h5py.Group>.attrs` Returns ------- dict [str, str or Number] """ return {attr_name: attr_value for attr_name, attr_value in h5_attrs.items()} def read_attributes(self, h5file_group): """ Read data from h5 file group that is stored directly as `<h5py.Group>.attrs`, or saved in subgroups titled `<h5py.Group>/__lists` and `<h5py.Group>/__dicts`. Parameters ---------- h5file_group: h5py.Group Returns ------- dict [str, dict or list] """ attributes = self.h5_attrs_to_dict(h5file_group.attrs) if '__dicts' in h5file_group: for dict_name in h5file_group['__dicts']: attributes[dict_name] = io.read(self.filename, h5file_group['__dicts/' + dict_name]) if '__lists' in h5file_group: for list_name in h5file_group['__lists']: attributes[list_name] = io.read(self.filename, h5file_group['__lists/' + list_name]) return attributes def read_ndarrays(self, h5file_group): """ Read numpy array data from h5 file group. Parameters ---------- h5file_group: h5py.Group Returns ------- dict [str, ndarray] """ ndarrays = {name: array[:] for name, array in h5file_group.items() if isinstance(array, h5py.Dataset)} return ndarrays def read_objects(self, h5file_group): """ Read data from the given h5 file group that represents a Python object other than an ndarray, list, or dict. Parameters ---------- h5file_group: h5py.Group Returns ------- dict [str, IOData] """ inner_objects = {} h5file_group = h5file_group["__objects"] for obj_name in h5file_group: inner_objects[obj_name] = io.read(self.filename, h5file_group[obj_name]) return inner_objects @utils.Required(h5py=_HAS_H5PY) def from_file(self, filename, file_handle=None): """ Either opens a new h5 file for reading or accesses an already opened file via the given h5.Group handle. Reads all data from the three categories of attributes (incl. lists and dicts), ndarrays, and objects. Parameters ---------- filename: str file_handle: h5.Group, optional Returns ------- IOData """ if file_handle is None: h5file_group = h5py.File(filename, 'r') else: h5file_group = file_handle attributes = self.read_attributes(h5file_group) typename = attributes['__type'] del attributes['__type'] ndarrays = self.read_ndarrays(h5file_group) inner_objects = self.read_objects(h5file_group) return io.IOData(typename, attributes, ndarrays, inner_objects) class CSVWriter(IOWriter): """ Given filename='somename.csv', write initdata into somename.csv Then, additional csv files are written for each dataset, with filenames: 'somename_' + dataname0 + '.csv' etc. """ def append_ndarray_info(self, attributes): """Add data set information to attributes, so that dataset names and dimensions are available in attributes CSV file.""" for index, dataname in enumerate(self.io_data.ndarrays.keys()): data = self.io_data.ndarrays[dataname] attributes['dataset' + str(index)] = dataname if data.ndim == 3: slice_count = len(data) else: slice_count = 1 attributes['dataset' + str(index) + '.slices'] = slice_count return attributes def write_attributes(self, filename): attributes = self.io_data.attributes attributes["__type"] = self.io_data.typename attributes = self.append_ndarray_info(attributes) with open(filename, mode='w', newline='') as meta_file: file_writer = csv.writer(meta_file, delimiter=',') file_writer.writerow(attributes.keys()) file_writer.writerow(attributes.values()) def write_ndarrays(self, filename): filename_stub, _ = os.path.splitext(filename) for dataname, dataset in self.io_data.ndarrays.items(): filename = filename_stub + '_' + dataname + '.csv' self.write_data(filename, dataset) def write_data(self, filename, dataset): if dataset.ndim <= 2: np.savetxt(filename, dataset) elif dataset.ndim == 3: np_savetxt_3d(dataset, filename) else: raise Exception("Dataset has dimensions > 3. Cannot write to CSV file.") def write_objects(self, *args, **kwargs): raise NotImplementedError def to_file(self, io_data, **kwargs): self.io_data = io_data self.write_attributes(self.filename) self.write_ndarrays(self.filename) # no support for write_objects in CSV format class CSVReader: @staticmethod def read_attributes(filename): with open(filename, mode='r') as meta_file: file_reader = csv.reader(meta_file, delimiter=',') meta_keys = file_reader.__next__() meta_values = file_reader.__next__() return dict(zip(meta_keys, meta_values)) def process_metadict(self, meta_dict): attributes = { attr_name: utils.to_expression_or_string(attr_value) for attr_name, attr_value in meta_dict.items() if not re.match(r'dataset\d+', attr_name) } data_names = [dataname for datalabel, dataname in meta_dict.items() if re.match(r'dataset\d+$', datalabel)] data_slices = [ast.literal_eval(value) for key, value in meta_dict.items() if re.match(r'dataset\d+.slices', key)] return attributes, data_names, data_slices @staticmethod def read_data(filename, slices): try: data_array = np.loadtxt(filename) except ValueError: data_array = np.loadtxt(filename, dtype=np.complex_) if slices > 1: nrows, ncols = data_array.shape return data_array.reshape((slices, nrows//slices, ncols)) return data_array def from_file(self, filename, **kwargs): """ Parameters ---------- filename: str Returns ------- class instance generated from file data """ ext_attributes = self.read_attributes(filename) typename = ext_attributes['__type'] del ext_attributes['__type'] attributes, data_names, data_slices = self.process_metadict(ext_attributes) filename_stub, _ = os.path.splitext(filename) ndarrays = {} for index, dataname in enumerate(data_names): data_filename = filename_stub + '_' + dataname + '.csv' slices = data_slices[index] ndarrays[dataname] = self.read_data(data_filename, slices) return io.IOData(typename, attributes, ndarrays, objects=None) def np_savetxt_3d(array3d, filename): """ Helper function that splits a 3d numpy array into 2d slices for writing as csv data to a new file. Slices are separated by a comment row `# New slice`. Parameters ---------- array3d: ndarray with ndim = 3 filename: str """ with open(filename, mode='w', newline='') as datafile: datafile.write('# Array shape: {0}\n'.format(array3d.shape)) for data_slice in array3d: np.savetxt(datafile, data_slice) datafile.write('# New slice\n')

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db/settings.py

liuansen/python-utils-class

3

163973

# -*- coding:utf-8 -*- # author：Anson from __future__ import unicode_literals MYSQL_DATABASES = { 'default': { 'ENGINE': 'mysql', 'NAME': '', 'USER': '', 'PASSWORD': '', 'HOST': '', 'PORT': '3306', 'STORAGE_ENGINE': 'INNODB', } } REDIS_CONNECTION = { 'default': { 'HOST': '', 'PORT': '', 'PASSWD': '', 'DB': 0, } } try: from local_settings import * except ImportError: pass

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anstosstools/__init__.py

ericziethen/anstosstools

0

56126

"""Root package for anstoss tools."""

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faker/providers/company/ro_RO/__init__.py

jeffwright13/faker

3

139815

from .. import Provider as CompanyProvider class Provider(CompanyProvider): formats = ( '{{last_name}} {{company_suffix}}', '{{last_name}} {{last_name}} {{company_suffix}}', '{{last_name}}', ) company_suffixes = ( 'SRL', 'SA', 'SCA', 'SNC', 'SCS', 'AFJ', 'ASF', 'CON', 'CRL', 'INC', 'LOC', 'OC1', 'OC2', 'OC3', 'PFA', 'RA', 'SCS', 'SPI', 'URL', ) def company_suffix(self): return self.random_element(self.company_suffixes)

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Task/Non-decimal-radices-Convert/Python/non-decimal-radices-convert-2.py

djgoku/RosettaCodeData

5

77439

digits = "0123456789abcdefghijklmnopqrstuvwxyz" def baseN(num,b): return (((num == 0) and "0" ) or ( baseN(num // b, b).lstrip("0") + digits[num % b])) # alternatively: def baseN(num,b): if num == 0: return "0" result = "" while num != 0: num, d = divmod(num, b) result += digits[d] return result[::-1] # reverse k = 26 s = baseN(k,16) # returns the string 1a

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demo.py

geerk/toradbapi

1

170332

<reponame>geerk/toradbapi from __future__ import (absolute_import, division, print_function, unicode_literals) import tornado.httpserver import tornado.ioloop import tornado.options import tornado.web import tornado.gen import tornado.platform.twisted import mysql.connector tornado.platform.twisted.install() import toradbapi DB_NAME = 'test_toradbapi' DB_HOST = '127.0.0.1' DB_PORT = 3306 DB_USER = 'root' DB_PASSWORD = '' class MainHandler(tornado.web.RequestHandler): def initialize(self, db): self.db = db @tornado.gen.coroutine def get(self): people = yield self.db.run_query('SELECT `name` FROM `person`') self.finish('<html><head><title>Demo toradbapi</title></head>' '<body><h3>Type new name:</h3>' '<form method="post"><input name="name" type="text"/>' '<input type="submit" value="Submit"/></form>' '<h3>Already inserted names:</h3>' '<ul>%s</ul></body></html>' % ''.join( '<li>%s</li>' % name for name in people)) @tornado.gen.coroutine def post(self): name = self.get_argument('name') try: yield self.db.run_operation( # always use escaping functionality to avoid sql-injection 'INSERT INTO `person` (`name`) VALUES (%s)', (name,)) except mysql.connector.errors.DatabaseError as e: self.finish('<html><head><title>Demo toradbapi</title></head>' '<body><h3>Error inserting new name: %s</h3>' '<br/><a href="/">Main page</a></body></html>' % e) else: self.redirect('/') def setup_database(): # just to ensure that database and table exist cnx = mysql.connector.connect( user=DB_USER, passwd=DB_PASSWORD, host=DB_HOST, port=DB_PORT) cursor = cnx.cursor() try: cursor.execute('CREATE DATABASE `%s` CHARACTER SET utf8' % DB_NAME) except mysql.connector.errors.DatabaseError: # do nothing if database exists pass cursor.execute('USE `%s`' % DB_NAME) cursor.execute('CREATE TABLE IF NOT EXISTS `person` (' ' `name` varchar(255) NOT NULL,' ' `dob` date DEFAULT NULL,' ' UNIQUE KEY (`name`))') cursor.close() cnx.close() def main(): setup_database() tornado.options.parse_command_line() pool = toradbapi.ConnectionPool( 'mysql.connector', database=DB_NAME, user=DB_USER, port=DB_PORT, password=<PASSWORD>, host=DB_HOST) application = tornado.web.Application([(r'/', MainHandler, {'db': pool})]) http_server = tornado.httpserver.HTTPServer(application) http_server.listen(8888, '127.0.0.1') try: tornado.ioloop.IOLoop.instance().start() except KeyboardInterrupt: pass finally: pool.close() tornado.ioloop.IOLoop.instance().stop() if __name__ == '__main__': main()

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PolTools/main_programs/align.py

GeoffSCollins/PolTools

0

1613892

import os import sys import subprocess import argparse from PolTools.utils.make_random_filename import generate_random_filename from PolTools.utils.remove_files import remove_files from multiprocessing import Pool from collections import defaultdict from statistics import mean from shutil import rmtree # TODO: Move the files to pricenas global MAIN_DIRECTORY # Get input def parse_args(args): def positive_int(num): try: val = int(num) if val <= 0: raise Exception("Go to the except") except: raise argparse.ArgumentTypeError(num + " must be positive") return val parser = argparse.ArgumentParser(prog='PolTools align', description='Align raw data to generate bed and bigwig files.\n' + "More information can be found at " + "https://geoffscollins.github.io/PolTools/align.html") parser.add_argument('-u', '--url', dest='url', metavar='url', type=str) parser.add_argument('-f', '--folder-name', dest='folder_name', metavar='folder_name', type=str) parser.add_argument('-l', '--umi-length', dest='umi_length', metavar='umi_length', type=positive_int, default=8) parser.add_argument('--min-insert', dest='min_insert', metavar='min_insert', type=positive_int, default=32) parser.add_argument('--max-insert', dest='max_insert', metavar='max_insert', type=positive_int, default=600) parser.add_argument('-t', '--threads', dest='threads', metavar='threads', type=positive_int, nargs='?', default=10) parser.add_argument('-i', '--index', dest='index', metavar='index', type=str) parser.add_argument('-c', '--chrom-sizes-file', dest='chrom_sizes_file', metavar='chrom_sizes_file', type=str) parser.add_argument('-s', '--server-name', dest='server_name', metavar='server_name', type=str, default=None) parser.add_argument('--spike-in-genome', dest='spike_in_genome', metavar='spike_in_genome', type=str, default=None) parser.add_argument('-g', '--genome', dest='genomes', metavar='genome', type=str, nargs="+", action="extend") parser.add_argument('-x', '--conversion-file', dest='sample_name_conversion_filename', metavar='conversion_file', type=str, default=None) return parser.parse_args(args) def read_sample_name_conversion_table(filename): if not filename or not os.path.isfile(filename): return None, ['.fastq.gz'] downloading_prefixes = [] sample_name_conversion = {} with open(filename) as file: for i, line in enumerate(file): if i != 0: download_prefix, prefix, new_name, group_name = line.split() downloading_prefixes.append(download_prefix) sample_name_conversion[prefix] = [new_name, group_name] return sample_name_conversion, downloading_prefixes def build_directory_structure(): os.mkdir(MAIN_DIRECTORY) os.mkdir(MAIN_DIRECTORY + "original_files") os.mkdir(MAIN_DIRECTORY + "trimmed") os.mkdir(MAIN_DIRECTORY + "sam") os.mkdir(MAIN_DIRECTORY + "bed") os.mkdir(MAIN_DIRECTORY + "bigwig") os.mkdir(MAIN_DIRECTORY + "logs") def download_data(url, prefixes): # Download the data in the original_files folder # -q quiet, -nH is no host directories, -P is the directory prefix original_files_directory = MAIN_DIRECTORY + "original_files" # Comma separated list to -A prefixes = [prefix + "*.fastq.gz" for prefix in prefixes] accepted_file_string = ",".join(prefixes) os.system("wget -q --cut-dirs=100 -nH -r -A " + accepted_file_string + " -P " + original_files_directory + " " + url) def _trim_helper(prefix): # Do the trimming and write the stdout and stderr to the logging file result = subprocess.check_output( "trim_galore --paired --small_rna --dont_gzip --quality 0 --length 34 --output_dir " + MAIN_DIRECTORY + "trimmed" + " " + MAIN_DIRECTORY + "original_files/" + prefix + "*", shell=True, stderr=subprocess.STDOUT, text=True ) logs_directory = MAIN_DIRECTORY + "logs" with open(logs_directory + "/trimming.log", 'a') as file: file.write(result) def trim_adapters(threads): # Group the samples by the prefix -- string until the first underscore prefixes = set() for filename in os.listdir(MAIN_DIRECTORY + "original_files"): prefixes.add(filename.split("_lane")[0]) # Trim adapters for each group with the same prefix with Pool(threads) as pool: pool.map(_trim_helper, prefixes) def _align_and_dedup(read_one_file, read_two_file, sample_name, align_params, threads): trim5 = align_params['trim5'] trim3 = align_params['trim3'] minins = align_params['minins'] maxins = align_params['maxins'] index = align_params['index'] sam_directory = MAIN_DIRECTORY + "sam" bed_directory = MAIN_DIRECTORY + "bed" sam_file = sam_directory + "/" + sample_name + ".sam" result = subprocess.check_output( "bowtie --trim5 " + trim5 + " --trim3 " + trim3 + " --minins " + minins + " --maxins " + maxins + " --fr --best --allow-contain --sam --fullref --chunkmbs 5000 --threads " + str(threads) + " " + index + " -1 " + read_one_file + " -2 " + read_two_file + " > " + sam_file, shell=True, stderr=subprocess.STDOUT, text=True ) logs_directory = MAIN_DIRECTORY + "logs" with open(logs_directory + "/align.log", 'a') as file: file.write(sample_name + "\n") file.write(result + "\n\n") # Also dedup and log result = subprocess.check_output( "dedup -n " + trim5 + " -f -p -t " + str(threads) + " -1 " + read_one_file + " -2 " + read_two_file + " -S " + sam_file, shell=True, stderr=subprocess.STDOUT, text=True ) with open(logs_directory + "/dedup.log", 'a') as file: file.write(result + "\n\n") # Move the bed file to the bed directory os.system('mv ' + sam_file.replace(".sam", "-dedup.bed") + ' ' + bed_directory) def align(align_params, sample_name_conversion, threads): trimmed_directory = MAIN_DIRECTORY + "trimmed" bed_directory = MAIN_DIRECTORY + "bed" # Group the trimmed files by prefix prefixes = defaultdict(list) for filename in os.listdir(trimmed_directory): if 'trimming_report' not in filename: prefix = '_'.join(filename.split('_')[:-6]) # Remove the lane string if there is one if 'lane' in prefix: location = prefix.find('lane') prefix = prefix[:location] + prefix[location + 6:] prefixes[prefix].append(filename) # If there is more than one lane with the prefix, then we align and dedup separately but combine the bed files at the end for prefix, filenames in prefixes.items(): if len(filenames) == 2: prefixes[prefix] = [sorted(filenames)] else: # Group by lane lane_strings = defaultdict(list) for filename in filenames: lane_string = filename.split('_')[-3] lane_strings[lane_string].append(filename) new_list = [] # Verify that there are two files for each lane string and make sure we add them to the prefixes dict for lane_filenames in lane_strings.values(): if len(lane_filenames) != 2: print("Something odd happened with lanes. Here are the samples grouped: " + str(lane_filenames)) else: new_list.append(lane_filenames) prefixes[prefix] = sorted(new_list) for prefix, groups in prefixes.items(): need_to_combine_bed_files = False if len(groups) == 1 else True # If the sample name is being converted, do it if sample_name_conversion and prefix in sample_name_conversion: sample_name = sample_name_conversion[prefix][0] else: sample_name = prefix lane_specific_bed_files = [] for i, group in enumerate(groups): read_one_file, read_two_file = group read_one_file = trimmed_directory + "/" + read_one_file read_two_file = trimmed_directory + "/" + read_two_file # If the lanes need to be combined, then we will add a number to the end of the sample name if need_to_combine_bed_files: lane_specific_bed_files.append(bed_directory + "/" + sample_name + str(i) + ".bed") _align_and_dedup(read_one_file, read_two_file, sample_name + str(i), align_params, threads) else: _align_and_dedup(read_one_file, read_two_file, sample_name, align_params, threads) if need_to_combine_bed_files: # Combine the bed files that begin with the sample name and write a new file without the numbers at the end os.system( 'cat ' + bed_directory + "/" + sample_name + "*.bed" + ' > ' + bed_directory + "/" + sample_name + ".bed" ) # Remove all the lane specific files remove_files(lane_specific_bed_files) def _calculate_spike_in_normalization_table(sample_name_conversion, spike_in_genome, genomes): def get_group_name(filename): sample_name = filename.replace('-dedup.bed', '') for prefix in sample_name_conversion: if sample_name_conversion[prefix][0] == sample_name: return sample_name_conversion[prefix][1] return "unknown" bed_directory = MAIN_DIRECTORY + "bed" normalization_group_data = defaultdict(dict) # Loop through each bed file for filename in os.listdir(bed_directory): # Get the total number of reads and split it for each genome group_name = get_group_name(filename) normalization_group_data[group_name][filename] = { 'total reads': 0, 'library size normalization factor': 0.0, 'spike in normalization factor': 0.0 } for genome in genomes: normalization_group_data[group_name][filename][genome] = 0 with open(bed_directory + "/" + filename) as file: for line in file: chrom, left, right, name, score, strand = line.split() # Add the read to total counts and the specific genome normalization_group_data[group_name][filename]['total reads'] += 1 if 'chr' in chrom: normalization_group_data[group_name][filename]['hg38'] += 1 else: normalization_group_data[group_name][filename][chrom] += 1 # For each group, calculate the normalization factors and write it to the file with open(MAIN_DIRECTORY + "normalization_table.tsv", 'w') as file: file.write("\t".join( ["Sample", "Total Reads"] + genomes + ["Library Size Correction Factor", "Spike-in Correction Factor", "Final Correction Factor", "Group"] ) + "\n") for group_name, group_data in normalization_group_data.items(): # Now calculate the normalization factors average_counts = mean([sample_data['total reads'] for sample_data in group_data.values()]) for sample_data in group_data.values(): sample_data['library size normalization factor'] = average_counts / sample_data['total reads'] average_normalized_spike_in_reads = mean( [sample_data['library size normalization factor'] * sample_data[spike_in_genome] for sample_data in group_data.values()] ) for sample_data in group_data.values(): sample_data['spike in normalization factor'] = average_normalized_spike_in_reads / sample_data[spike_in_genome] # Write the data for each sample for sample_filename, sample_data in group_data.items(): final_correction_factor = sample_data['library size normalization factor'] * \ sample_data['spike in normalization factor'] file.write( "\t".join( [sample_filename.replace("-dedup.bed", ""), str(sample_data['total reads'])] + [str(sample_data[genome]) for genome in genomes] + [str(sample_data['library size normalization factor'])] + [str(sample_data['spike in normalization factor'])] + [str(final_correction_factor), group_name] ) + "\n" ) def _calculate_library_size_normalization_table(sample_name_conversion): def get_group_name(filename): sample_name = filename.replace('-dedup.bed', '') for prefix in sample_name_conversion: if sample_name_conversion[prefix][0] == sample_name: return sample_name_conversion[prefix][1] return "unknown" bed_directory = MAIN_DIRECTORY + "bed" normalization_group_data = defaultdict(dict) for filename in os.listdir(bed_directory): group_name = get_group_name(filename) with open(bed_directory + "/" + filename) as file: for i, _ in enumerate(file): pass normalization_group_data[group_name][filename] = i + 1 # Write the data to a file with open(MAIN_DIRECTORY + "normalization_table.tsv", 'w') as file: for group_name, group_data in normalization_group_data.items(): average_library_size = mean([count for count in group_data.values()]) # Write the headers file.write( "\t".join( ["Sample", "Total Reads", "Correction Factor"] ) + "\n" ) for filename, counts in group_data.items(): file.write( "\t".join( [filename.replace("-dedup.bed", ""), str(counts), str(average_library_size / counts), group_name] ) + "\n" ) def calculate_normalization_table(sample_name_conversion, spike_in_genome=None, genomes=None): # If they provide arguments, then it must be spike_in normalization if spike_in_genome and genomes: _calculate_spike_in_normalization_table(sample_name_conversion, spike_in_genome, genomes) else: # Calculate based on total library size _calculate_library_size_normalization_table(sample_name_conversion) def _generate_bigwig(filename, normalization_factor, chrom_sizes_file, hosting_location): bigwig_directory = MAIN_DIRECTORY + "bigwig" bedgraph_files = [] links = [] link_template = "track type=bigWig visibility=full name='<name>' autoScale=on alwaysZero=on " + \ "windowingFunction=maximum negateValues=off color=0,0,0 altColor=0,0,0 " + \ "bigDataUrl=<hostLocation>/<file>" tmp_file = generate_random_filename(".tmp") os.system('cat ' + filename + """ | awk '$1 != "chrEBV" {print $0}' > """ + tmp_file) os.system('bedSort ' + tmp_file + ' ' + filename) for strand, strand_string in [("+", "FW"), ("-", "RV")]: os.system( 'bedtools genomecov -scale ' + normalization_factor + ' -i ' + filename + ' -g ' + chrom_sizes_file + ' -bg -strand ' + strand + ' > ' + filename.replace('.bed', '-' + strand_string + '.bedGraph') ) os.system( 'bedtools genomecov -scale ' + normalization_factor + ' -i ' + filename + ' -g ' + chrom_sizes_file + ' -bg -strand ' + strand + ' -5' + ' > ' + filename.replace('.bed', '-' + strand_string + '-5.bedGraph') ) os.system( 'bedtools genomecov -scale ' + normalization_factor + ' -i ' + filename + ' -g ' + chrom_sizes_file + ' -bg -strand ' + strand + ' -3' + ' > ' + filename.replace('.bed', '-' + strand_string + '-3.bedGraph') ) bedgraph_files.append(filename.replace('.bed', '-' + strand_string + '.bedGraph')) bedgraph_files.append(filename.replace('.bed', '-' + strand_string + '-5.bedGraph')) bedgraph_files.append(filename.replace('.bed', '-' + strand_string + '-3.bedGraph')) # Convert bedGraphs to bigwigs and add track links for file in bedgraph_files: os.system('bedSort ' + file + ' ' + file) bigwig_file = bigwig_directory + "/" + file.split('/')[-1].replace('.bedGraph', '.bw') os.system('bedGraphToBigWig ' + file + ' ' + chrom_sizes_file + ' ' + bigwig_file) curr_link = link_template if "-RV.bw" in bigwig_file or "-RV-5.bw" in bigwig_file or "-RV-3.bw" in bigwig_file: curr_link = curr_link.replace("negateValues=off", "negateValues=on") file_basename = file.split("/")[-1] # Get the sample names sample_name = file_basename sample_name = sample_name.replace(".bw", "").replace("_", " ").replace("-", " ") # Now put the sample name in curr_link = curr_link.replace("<name>", sample_name).replace("<file>", file_basename) if hosting_location: curr_link = curr_link.replace("<hostLocation>", hosting_location) links.append(curr_link) # Remove all the bedgraph and tmp files remove_files(bedgraph_files, tmp_file) # Sort the links so they are in the order of FW, RV, FW5, RV 5, FW3, RV3 links.sort() final_links = [ links[2], # Fw links[5], # Rv links[1], # Fw 5 links[4], # Rv 5 links[0], # Fw 3 links[3], # Rv 3 ] return final_links def generate_bigwigs(chrom_sizes_filename, threads, hosting_location): # First get the normalization factors normalization_factors = {} with open(MAIN_DIRECTORY + "normalization_table.tsv") as file: for i, line in enumerate(file): if i != 0: sample_name, *_, normalization_factor, _ = line.split() normalization_factors[sample_name] = normalization_factor bed_directory = MAIN_DIRECTORY + "bed" bigwig_directory = MAIN_DIRECTORY + "bigwig" args = [] for filename in os.listdir(bed_directory): sample_name = filename.split("/")[-1].replace("-dedup.bed", "") args.append( [ bed_directory + "/" + filename, normalization_factors[sample_name], chrom_sizes_filename, hosting_location ] ) with Pool(threads) as pool: all_links = pool.starmap(_generate_bigwig, args) # Write the links to a file with open(bigwig_directory + "/links.txt", 'w') as file: for dataset_links in all_links: for link in dataset_links: file.write(link + "\n") def clean_directories(): # Remove the trimmed and sam directories rmtree(MAIN_DIRECTORY + "trimmed") rmtree(MAIN_DIRECTORY + "sam") def main(args): args = parse_args(args) url = args.url folder_name = args.folder_name threads = args.threads chrom_sizes_file = args.chrom_sizes_file server_name = args.server_name spike_in_genome = args.spike_in_genome genomes = args.genomes + [spike_in_genome] sample_name_conversion_filename = args.sample_name_conversion_filename align_params = { 'trim5': str(args.umi_length), 'trim3': str(args.umi_length), 'minins': str(args.min_insert), 'maxins': str(args.max_insert), 'index': args.index } hosting_location = server_name + folder_name if server_name else '' global MAIN_DIRECTORY MAIN_DIRECTORY = "/home/geoff/.trial/" + folder_name + "/" # If given a conversion file, then only download the ones in the conversion file sample_name_conversion, prefixes = read_sample_name_conversion_table(sample_name_conversion_filename) build_directory_structure() download_data(url, prefixes) trim_adapters(threads) align(align_params, sample_name_conversion, threads) calculate_normalization_table(sample_name_conversion, spike_in_genome, genomes) generate_bigwigs(chrom_sizes_file, threads, hosting_location) clean_directories() if __name__ == '__main__': main(sys.argv[1:])

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phase/jump_ge.py

ryos36/retro-for-FPGA

0

133973

class JUMP_GE: def __init__(self, stack): self.phase = 0 self.ip = 0 self.stack = stack self.a = 0 def exec(self, ip, data): if (self.phase == 0): self.ip = ip + 1 self.a = self.stack.pop() self.phase = 1 return False elif (self.phase == 1): b = self.stack.pop() if b > self.a: self.phase = 2 return False else: self.phase = 0 return True elif (self.phase == 2): self.ip = data - 1 self.phase = 3 return False else: assert(self.phase == 3) self.phase = 0 return True

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nrw/viersen.py

risklayer/corona-landkreis-crawler

12

1601807

<filename>nrw/viersen.py #!/usr/bin/python3 from botbase import * _viersen_cc = re.compile(r"([0-9.]+) neue Infektionen") _viersen_s = re.compile(r"([0-9.]+|\w+) Menschen werden stationär behandelt", re.U) _viersen_i = re.compile(r"([0-9.]+|\w+) befinden sich auf der Intensiv", re.U) _viersen_q = re.compile(r"([0-9.]+) Kontaktpersonen befinden") def viersen(sheets): soup = get_soup("https://www.presse-service.de/meldungen.aspx?ps_id=1381") articles = soup.find(class_="ps_meldungsliste").findAll("li") #print(*[a.get_text() for a in articles], sep"\n") article = next(a for a in articles if "Sieben-Tage-Inzidenz" in a.get_text() or "Neuinfektionen" in a.get_text()) if not today().strftime("%d.%m.%Y") in article.get_text(): raise NotYetAvailableException("Viersen noch alt: "+article.get_text().strip().split("\n")[0]) url = urljoin("https://www.presse-service.de/meldungen.aspx?ps_id=1381", article.find("a")["href"]) print("Getting", url) assert url soup = get_soup(url) main = soup.find(class_="ps_content") text = main.get_text(" ").strip() #print(text) table = next(t for t in main.findAll("table") if "bestätigte Fälle" in t.get_text()) rows = [[x.get_text().strip() for x in y] for y in table.findAll("tr")] #print(*rows, sep="\n") assert "Gesamt" in rows[-1][1] assert "bestätigte Fälle" in rows[0][3] #assert "Genesene" in rows[0][7] assert "verstorben" in rows[0][7] #c, g, d = map(force_int, [rows[-1][3], rows[-1][7], rows[-1][9]]) c, d = map(force_int, [rows[-1][3], rows[-1][7]]) g = None cc = force_int(_viersen_cc.search(text).group(1)) s, i, q = None, None, None m = _viersen_q.search(text) if m: q = force_int(m.group(1)) + c - d - g m = _viersen_s.search(text) if m: s = force_int(m.group(1)) m = _viersen_i.search(text) if m: i = force_int(m.group(1)) update(sheets, 5166, c=c, cc=cc, d=d, g=g, q=q, s=s, i=i, sig="Bot", ignore_delta="mon") return True schedule.append(Task(15, 30, 18, 35, 360, viersen, 5166)) if __name__ == '__main__': viersen(googlesheets())

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alexaskillz.py

willchis/alexaskillz

0

65182

import os import time import urllib, json from random import randint from flask import Flask from flask_ask import Ask, statement, question app = Flask(__name__) ask = Ask(app, '/') MAGIC_SEAWEED_KEY = 'e96ca9b1f03a627aaab136eafe4f83ee' SPOT_ID = '1449' @ask.launch def launched(): return question("This is a surf forecast skill for rest bay, porth call, South Wales. Would you like today's surf report?").reprompt("I didn't get that. Would you like the current conditions?") @ask.intent('YesOrNoFIrstIntent', mapping = { 'answer': 'yesorno' }) def yes_or_no(answer): statement('Okay') if (answer == 'yes'): return surf_report('today') else: return @ask.intent('SwellIntent', mapping = { 'report_time': 'timeofday' }) def current_surf(report_time): api_url = 'http://magicseaweed.com/api/%s/forecast/?spot_id=%s&fields=timestamp,swell.minBreakingHeight,swell.maxBreakingHeight,wind.speed, wind.compassDirection' % (MAGIC_SEAWEED_KEY, SPOT_ID) print 'Calling Magic Seaweed via API URL: ' + api_url json_reponse = read_from_url(api_url) unix_now = time.time() time_difference = 0 closest_time = json_reponse[0] for item in json_reponse: item_time_difference = abs(item['timestamp'] - unix_now) if (item_time_difference <= time_difference): time_difference = item_time_difference closest_time = item min_swell = closest_time['swell']['minBreakingHeight'] max_swell = closest_time['swell']['maxBreakingHeight'] wind_speed = closest_time['wind']['speed'] print 'Got swell heights: %d to %d ft and wind speed %d' % (min_swell, max_swell, wind_speed) return statement('Currently the swell height is between %d and %d foot at rest bay. The wind speed is %d miles per hour.' \ % (min_swell, max_swell, wind_speed)) @ask.intent('BuildStatusIntent', mapping = { 'plan': 'Plan' }) def build_status(plan): # Use Bamboo API to get build status(es) or trigger build etc. return statement('The latest %s build is red, with 4 failing tests.' % plan) @ask.intent('CurrentAdvertIntent') def current_advert(): # E.g. match advert based on DAR info/IP and return related promo and alexa 'card' return statement('That was the new Budweiser summer cider, available summer 2017. Visit the Alexa app for a free sample.') \ .standard_card(title='Budweiser Summer Cider', text='Claim your free sample..', large_image_url='https://upload.wikimedia.org/wikipedia/commons/thumb/7/71/Bud_and_Budvar.jpg/1200px-Bud_and_Budvar.jpg') #<EMAIL>('GiveRandomInfo') def politics_fact(): #TODO add headers to ensure wiki api allows access title = get_random_politics_title() statement(title.encode('utf-8')) title = urllib.quote(title) # encode to include in url wiki_api = 'https://en.wikipedia.org/w/api.php?format=json&action=query&prop=extracts&exintro=&explaintext=&titles=' json_reponse = read_from_url(wiki_api + title) introduction = '' try: pages = json_reponse['query']['pages'] for key in pages.keys(): print 'wiki page ref: ' + key introduction = pages[key]['extract'] except: print 'Error thrown when using title: ' + title politics_fact() return return statement(introduction.encode('utf-8')) def read_from_url(url): response = urllib.urlopen(url) return json.loads(response.read()) def get_random_politics_title(): options = ['Anarchism', 'Anarcho-capitalism', 'Anarcho-primitivism', 'Anarcho-syndicalism', 'Anarchist communism', 'Anti-communism', 'Antidisestablishmentarianism', 'Asian values', 'Authoritarianism', 'Bioregional democracy', 'Black populism', 'Black supremacy', 'Bolivarian Revolution', 'Capitalism', 'Christian democracy', 'Christian socialism', 'Classical liberalism', 'Classical republicanism', 'Collectivism', 'Communism', 'Communitarianism', 'Conservatism', 'Corporatocracy', 'Demarchy', 'Democracy (varieties)', 'Democratic egalitarianism', 'Democratic peace theory', 'Democratic socialism', 'Democratic transhumanism', 'Eco-socialism', 'Egalitarianism', 'Fascism', 'Federalism', 'Green anarchism', 'Green politics', 'Green syndicalism', 'Hindu nationalism', 'Individualist anarchism', 'Integral humanism', 'Islamism', 'Isocracy', 'Kemalist ideology', 'Leftism', 'Liberalism', 'Libertarianism', 'Libertarian socialism', 'Localism', 'Majoritarianism', 'Marxism', 'Marxism-Leninism', 'Marxist philosophy', 'Meritocracy', 'Minarchism', 'Minoritarianism', 'Miscegenation', 'Moderate Libertarianism','Monarchism','Nationalism','National Socialism','Neoliberalism','Pacifism','Paleolibertarianism','Patriotism','Political symbolism','Panislamism','Peronism','Populism','Post-Communism','Progressivism','Putinism','Racial purity','Racial segregation','Racialism','Radical centrism','Radical liberalism','Rationalism','Republicanism','Screen theory','Slavophile','Small-l libertarianism','Social Credit','Social democracy','Socialism','Social liberalism','Social philosophy','Sphere of influence','Stalinism','Thatcherism','Third Position','Totalitarianism','Tribalism','Trotskyism', 'Utilitarianism' ] return options[randint(0, len(options) - 1)] if __name__ == '__main__': #politics_fact() current_surf('today') port = int(os.environ.get('PORT', 5000)) app.run(host='0.0.0.0', port=port)

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tests/test_notary.py

sxn/notary

7

71345

from pathlib import Path from click.testing import CliRunner import notary from notary import cli def test_add_license_no_preexisting(): runner = CliRunner() with runner.isolated_filesystem(): license_file = Path("LICENSE") assert not license_file.exists() result = runner.invoke(cli.add, input="\n".join(["mit", "<NAME>", "y"])) assert result.exit_code == 0 assert "License name: mit\n" in result.output assert "Author: <NAME>\n" in result.output assert "Adding MIT License as the project's license. Continue? [y/N]: y\n" assert "Added MIT License" in result.output assert license_file.exists() def test_add_license_no_preexisting_multiple_found(): runner = CliRunner() with runner.isolated_filesystem(): license_file = Path("LICENSE") assert not license_file.exists() result = runner.invoke(cli.add, input="\n".join(["m", "1", "<NAME>", "y"])) assert result.exit_code == 0 assert "License name: m\n" in result.output assert "Found the following matching licenses:\n" in result.output assert "1: MIT License\n" in result.output assert "2: Mozilla Public License Version 2.0\n" in result.output assert "Please choose which one you'd like to add [1]: 1\n" assert "Author: <NAME>\n" in result.output assert "Adding MIT License as the project's license. Continue? [y/N]: y\n" assert "Added MIT License to /" in result.output assert "/LICENSE.\n" in result.output assert license_file.exists() def test_add_license_no_preexisting_no_author_needed(): runner = CliRunner() with runner.isolated_filesystem(): license_file = Path("LICENSE") assert not license_file.exists() result = runner.invoke(cli.add, input="\n".join(["agpl", "y"])) assert result.exit_code == 0 assert "License name: agpl\n" in result.output assert "Adding GNU Affero General Public License version 3 as the project's license. Continue? [y/N]: y\n" in result.output assert "Added GNU Affero General Public License version 3 to /" in result.output assert "/LICENSE.\n" in result.output assert license_file.exists() def test_add_license_no_preexisting_no_license_matched(): runner = CliRunner() with runner.isolated_filesystem(): license_file = Path("LICENSE") assert not license_file.exists() result = runner.invoke(cli.add, input="\n".join(["gibberish", "3", "y"])) assert result.exit_code == 0 assert "License name: gibberish\n" in result.output assert "Found the following matching licenses:\n" in result.output assert "1: GNU Affero General Public License version 3\n" in result.output assert "2: Apache License 2.0\n" in result.output assert "3: GNU General Public License v3.0\n" in result.output assert "4: GNU Lesser General Public License v3.0\n" in result.output assert "5: MIT License\n" in result.output assert "6: Mozilla Public License Version 2.0\n" in result.output assert "7: The Unlicense\n" in result.output assert "Please choose which one you'd like to add [1]: 3\n" in result.output assert "Adding GNU General Public License v3.0 as the project's license. Continue? [y/N]: y\n" in result.output assert "Added GNU General Public License v3.0 to /" in result.output assert "/LICENSE.\n" in result.output assert license_file.exists() def test_add_license_preexisting_license(): runner = CliRunner() with runner.isolated_filesystem(): old_license_file = Path("LICENSE.rst") with old_license_file.open("w") as f: f.write("") new_license_file = Path("LICENSE") assert not new_license_file.exists() result = runner.invoke(cli.add, input="\n".join(["y", "mit", "<NAME>", "y"])) assert result.exit_code == 0 assert "The following license file(s) already exist:\n" in result.output assert "/LICENSE.rst\n" in result.output assert "Remove /" in result.output assert "/LICENSE.rst? [y/N]: y\n" in result.output assert "Removed /" in result.output assert "/LICENSE.rst.\n" in result.output assert "License name: mit\n" in result.output assert "Author: <NAME>\n" in result.output assert "Adding MIT License as the project's license. Continue? [y/N]: y\n" assert "Added MIT License" in result.output assert new_license_file.exists() def test_remove_no_license(): runner = CliRunner() with runner.isolated_filesystem(): result = runner.invoke(cli.remove) assert result.exit_code == 0 assert "No license file found in the current directory." in result.output def test_remove_existing_license_answer_yes(): runner = CliRunner() with runner.isolated_filesystem(): license_file = Path("LICENSE.rst") with license_file.open("w") as f: f.write("") assert license_file.exists() result = runner.invoke(cli.remove, input="y") assert result.exit_code == 0 assert "The following license file(s) already exist:" in result.output assert "/LICENSE.rst? [y/N]: y\n" in result.output assert "Removed /" in result.output assert "/LICENSE.rst.\n" in result.output assert not license_file.exists() def test_remove_existing_license_answer_no(): runner = CliRunner() with runner.isolated_filesystem(): license_file = Path("LICENSE.rst") with license_file.open("w") as f: f.write("") assert license_file.exists() result = runner.invoke(cli.remove, input="n") assert result.exit_code == 0 assert "The following license file(s) already exist:" in result.output assert "/LICENSE.rst? [y/N]: n\n" in result.output assert "Not removing license.\n" in result.output assert license_file.exists()

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src/utils.py

jaejun-lee/amzbinimg

1

176628

<gh_stars>1-10 import tensorflow.keras.backend as K import matplotlib.pyplot as plt import numpy as np ### Custome Metrics def soft_rmse(y_true, y_pred): '''caculate RMSE for categorical predictions. ''' return K.sqrt( K.mean(K.cast_to_floatx ( K.square( K.argmax(y_true) - K.argmax(y_pred) )), axis=-1) ) def soft_acc(y_true, y_pred): '''caculate accuracy for RMSE loss function prediction. ''' return K.mean(K.equal(K.round(y_true), K.round(y_pred))) ### Plot def plot_history(history): plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.show() def visualize(img, encoder, decoder): """Draws original, encoded and decoded images""" # img[None] will have shape of (1, 32, 32, 3) which is the same as the model input code = encoder.predict(img[None])[0] reco = decoder.predict(code[None])[0] plt.subplot(1,3,1) plt.title("Original") plt.imshow(img) plt.subplot(1,3,2) plt.title("Code") plt.imshow(code.reshape([code.shape[-1]//2,-1])) plt.subplot(1,3,3) plt.title("Reconstructed") plt.imshow(reco) plt.show() def visualize_features(img, encoder, decoder): code = encoder.predict(img[None])[0] images_per_row = 16 num_of_features = code.shape[0] n_cols = num_of_features // images_per_row display_grid = np.zeros(images_per_row, n_cols) for row in range(images_per_row): for col in range(n_cols): mask = np.zeros(num_of_features) mask[row * n_cols + cos] = 1 new_code = code.copy() new_code = new_code * mask reco = decoder.predict(new_code[None])[0] display_grid[row, col] = reco plt.figure(figsize= (images_per_row, n_cols)) plt.title("Feature Representation by Decoder") plt.grid(False) plt.imshow(display_grid, aspect='auto', cmap='viridis') ### configure training layer def change_trainable_layers(model, trainable_index): for layer in model.layers[:trainable_index]: layer.trainable = False for layer in model.layers[trainable_index:]: layer.trainable = True def print_model_properties(model, indices = 0): for i, layer in enumerate(model.layers[indices:]): print(f"Layer {i+indices} | Name: {layer.name} | Trainable: {layer.trainable}") ### Image Processing def plot_image_and_histogram(img): fig = plt.figure(figsize=(8,4)) ax1 = fig.add_subplot(121) ax1.imshow(img) ax1.set_title('Image') ax2 = fig.add_subplot(122) ax2.hist(img[:,:,0].flatten(), color='red', bins=25) ax2.hist(img[:,:,1].flatten(), color='green', bins=25) ax2.hist(img[:,:,2].flatten(), color='blue', bins=25) ax2.set_title('Histogram of pixel intensities') ax2.set_xlabel('Pixel intensity') ax2.set_ylabel('Count') plt.tight_layout(pad=1)

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SimModel_Python_API/simmodel_swig/Release/SimSpace_Occupied_Default.py

EnEff-BIM/EnEffBIM-Framework

3

79655

# This file was automatically generated by SWIG (http://www.swig.org). # Version 3.0.7 # # Do not make changes to this file unless you know what you are doing--modify # the SWIG interface file instead. from sys import version_info if version_info >= (2, 6, 0): def swig_import_helper(): from os.path import dirname import imp fp = None try: fp, pathname, description = imp.find_module('_SimSpace_Occupied_Default', [dirname(__file__)]) except ImportError: import _SimSpace_Occupied_Default return _SimSpace_Occupied_Default if fp is not None: try: _mod = imp.load_module('_SimSpace_Occupied_Default', fp, pathname, description) finally: fp.close() return _mod _SimSpace_Occupied_Default = swig_import_helper() del swig_import_helper else: import _SimSpace_Occupied_Default del version_info try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self, class_type, name, value, static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'SwigPyObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name, None) if method: return method(self, value) if (not static): if _newclass: object.__setattr__(self, name, value) else: self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self, class_type, name, value): return _swig_setattr_nondynamic(self, class_type, name, value, 0) def _swig_getattr_nondynamic(self, class_type, name, static=1): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name, None) if method: return method(self) if (not static): return object.__getattr__(self, name) else: raise AttributeError(name) def _swig_getattr(self, class_type, name): return _swig_getattr_nondynamic(self, class_type, name, 0) def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) try: _object = object _newclass = 1 except AttributeError: class _object: pass _newclass = 0 try: import weakref weakref_proxy = weakref.proxy except: weakref_proxy = lambda x: x import base class SimSpace(base.SimSpatialStructureElement): __swig_setmethods__ = {} for _s in [base.SimSpatialStructureElement]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimSpace, name, value) __swig_getmethods__ = {} for _s in [base.SimSpatialStructureElement]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimSpace, name) __repr__ = _swig_repr def SpaceZoneAssignments(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceZoneAssignments(self, *args) def SpaceNumber(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceNumber(self, *args) def SpaceName(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceName(self, *args) def SpaceInteriorOrExterior(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceInteriorOrExterior(self, *args) def SpaceDatumElevation(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceDatumElevation(self, *args) def SpaceThermalSimulationType(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceThermalSimulationType(self, *args) def SpaceConditioningRequirement(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceConditioningRequirement(self, *args) def SpaceOccupantDensity(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceOccupantDensity(self, *args) def SpaceOccupantHeatRateLatent(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceOccupantHeatRateLatent(self, *args) def SpaceOccupantHeatRateSensible(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceOccupantHeatRateSensible(self, *args) def SpaceOccupantLoad(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceOccupantLoad(self, *args) def SpaceEquipmentLoad(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceEquipmentLoad(self, *args) def SpaceLightingLoad(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceLightingLoad(self, *args) def InsideDryBulbTempHeating(self, *args): return _SimSpace_Occupied_Default.SimSpace_InsideDryBulbTempHeating(self, *args) def InsideRelHumidityHeating(self, *args): return _SimSpace_Occupied_Default.SimSpace_InsideRelHumidityHeating(self, *args) def InsideDryBulbTempCooling(self, *args): return _SimSpace_Occupied_Default.SimSpace_InsideDryBulbTempCooling(self, *args) def InsideRelHumidityCooling(self, *args): return _SimSpace_Occupied_Default.SimSpace_InsideRelHumidityCooling(self, *args) def IncludesReturnAirPlenum(self, *args): return _SimSpace_Occupied_Default.SimSpace_IncludesReturnAirPlenum(self, *args) def PeakAirFlowCooling(self, *args): return _SimSpace_Occupied_Default.SimSpace_PeakAirFlowCooling(self, *args) def PeakAirFlowHeating(self, *args): return _SimSpace_Occupied_Default.SimSpace_PeakAirFlowHeating(self, *args) def ExhaustAirFlowRate(self, *args): return _SimSpace_Occupied_Default.SimSpace_ExhaustAirFlowRate(self, *args) def NaturalAirChangeRate(self, *args): return _SimSpace_Occupied_Default.SimSpace_NaturalAirChangeRate(self, *args) def MechanicalAirChangeRate(self, *args): return _SimSpace_Occupied_Default.SimSpace_MechanicalAirChangeRate(self, *args) def VentilationType(self, *args): return _SimSpace_Occupied_Default.SimSpace_VentilationType(self, *args) def OutsideAirPerPerson(self, *args): return _SimSpace_Occupied_Default.SimSpace_OutsideAirPerPerson(self, *args) def SpaceHeight(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceHeight(self, *args) def SpaceGrossPerimeter(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceGrossPerimeter(self, *args) def SpaceGrossFloorArea(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceGrossFloorArea(self, *args) def SpaceNetFloorArea(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceNetFloorArea(self, *args) def SpaceGrossVolume(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceGrossVolume(self, *args) def SpaceNetVolume(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceNetVolume(self, *args) def SpaceNetFloorAreaBOMA(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceNetFloorAreaBOMA(self, *args) def SpaceUsableFloorAreaBOMA(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceUsableFloorAreaBOMA(self, *args) def ClassRef_SpaceByFunction(self, *args): return _SimSpace_Occupied_Default.SimSpace_ClassRef_SpaceByFunction(self, *args) def ClassRef_SpaceTypeOwner(self, *args): return _SimSpace_Occupied_Default.SimSpace_ClassRef_SpaceTypeOwner(self, *args) def ClassRef_SpaceCategoryOwner(self, *args): return _SimSpace_Occupied_Default.SimSpace_ClassRef_SpaceCategoryOwner(self, *args) def ClassRef_SpaceCategoryBOMA(self, *args): return _SimSpace_Occupied_Default.SimSpace_ClassRef_SpaceCategoryBOMA(self, *args) def SpaceOccupants(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceOccupants(self, *args) def OccupancyScheduleAssignment(self, *args): return _SimSpace_Occupied_Default.SimSpace_OccupancyScheduleAssignment(self, *args) def LightingScheduleAssignment(self, *args): return _SimSpace_Occupied_Default.SimSpace_LightingScheduleAssignment(self, *args) def EquipmentScheduleAssignment(self, *args): return _SimSpace_Occupied_Default.SimSpace_EquipmentScheduleAssignment(self, *args) def GeometricRepresentations(self, *args): return _SimSpace_Occupied_Default.SimSpace_GeometricRepresentations(self, *args) def SpaceInSpatialContainer(self, *args): return _SimSpace_Occupied_Default.SimSpace_SpaceInSpatialContainer(self, *args) def AssociatedPlena(self, *args): return _SimSpace_Occupied_Default.SimSpace_AssociatedPlena(self, *args) def AssociatedElements(self, *args): return _SimSpace_Occupied_Default.SimSpace_AssociatedElements(self, *args) def __init__(self, *args): this = _SimSpace_Occupied_Default.new_SimSpace(*args) try: self.this.append(this) except: self.this = this def _clone(self, f=0, c=None): return _SimSpace_Occupied_Default.SimSpace__clone(self, f, c) __swig_destroy__ = _SimSpace_Occupied_Default.delete_SimSpace __del__ = lambda self: None SimSpace_swigregister = _SimSpace_Occupied_Default.SimSpace_swigregister SimSpace_swigregister(SimSpace) class SimSpace_Occupied(SimSpace): __swig_setmethods__ = {} for _s in [SimSpace]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimSpace_Occupied, name, value) __swig_getmethods__ = {} for _s in [SimSpace]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimSpace_Occupied, name) __repr__ = _swig_repr def T24CommRefrigEPD(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24CommRefrigEPD(self, *args) def T24CommRefrigEqmtSchedRef(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24CommRefrigEqmtSchedRef(self, *args) def T24CommRefrigLatentFraction(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24CommRefrigLatentFraction(self, *args) def T24CommRefrigLostFraction(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24CommRefrigLostFraction(self, *args) def T24CommRefrigRadFraction(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24CommRefrigRadFraction(self, *args) def T24EnvelopeStatus(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24EnvelopeStatus(self, *args) def T24ExhaustAirChangesPerHour(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24ExhaustAirChangesPerHour(self, *args) def T24ExhaustPerArea(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24ExhaustPerArea(self, *args) def T24ExhaustPerSpace(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24ExhaustPerSpace(self, *args) def T24HasProcessExhaust(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24HasProcessExhaust(self, *args) def T24IntLightingSpecMethod(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24IntLightingSpecMethod(self, *args) def T24KitchExhHoodDutyList(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24KitchExhHoodDutyList(self, *args) def T24KitchExhHoodFlowList(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24KitchExhHoodFlowList(self, *args) def T24KitchExhHoodLengthList(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24KitchExhHoodLengthList(self, *args) def T24KitchExhHoodStyleList(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24KitchExhHoodStyleList(self, *args) def T24LabExhRateType(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24LabExhRateType(self, *args) def T24LightingStatus(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24LightingStatus(self, *args) def T24MandLightCntrlCntRpt(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24MandLightCntrlCntRpt(self, *args) def T24MandLightCntrlDescRpt(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24MandLightCntrlDescRpt(self, *args) def T24MandLightCntrlAccepReqRpt(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24MandLightCntrlAccepReqRpt(self, *args) def T24MandLightCntrlIsAutoShOffCntrlRpt(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24MandLightCntrlIsAutoShOffCntrlRpt(self, *args) def T24MandLightCntrlIsDayltngCntrlRpt(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24MandLightCntrlIsDayltngCntrlRpt(self, *args) def T24MandLightCntrlIsDmndRespCntrlRpt(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24MandLightCntrlIsDmndRespCntrlRpt(self, *args) def T24MandLightCntrlIsManAreaCntrlRpt(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24MandLightCntrlIsManAreaCntrlRpt(self, *args) def T24MandLightCntrlIsMultLvlCntrlRpt(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24MandLightCntrlIsMultLvlCntrlRpt(self, *args) def T24SkylightRqmtExcep(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24SkylightRqmtExcep(self, *args) def T24SpaceFunction(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24SpaceFunction(self, *args) def T24ConstructStatus3(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_T24ConstructStatus3(self, *args) def __init__(self, *args): this = _SimSpace_Occupied_Default.new_SimSpace_Occupied(*args) try: self.this.append(this) except: self.this = this def _clone(self, f=0, c=None): return _SimSpace_Occupied_Default.SimSpace_Occupied__clone(self, f, c) __swig_destroy__ = _SimSpace_Occupied_Default.delete_SimSpace_Occupied __del__ = lambda self: None SimSpace_Occupied_swigregister = _SimSpace_Occupied_Default.SimSpace_Occupied_swigregister SimSpace_Occupied_swigregister(SimSpace_Occupied) class SimSpace_Occupied_Default(SimSpace_Occupied): __swig_setmethods__ = {} for _s in [SimSpace_Occupied]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimSpace_Occupied_Default, name, value) __swig_getmethods__ = {} for _s in [SimSpace_Occupied]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimSpace_Occupied_Default, name) __repr__ = _swig_repr def __init__(self, *args): this = _SimSpace_Occupied_Default.new_SimSpace_Occupied_Default(*args) try: self.this.append(this) except: self.this = this def _clone(self, f=0, c=None): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default__clone(self, f, c) __swig_destroy__ = _SimSpace_Occupied_Default.delete_SimSpace_Occupied_Default __del__ = lambda self: None SimSpace_Occupied_Default_swigregister = _SimSpace_Occupied_Default.SimSpace_Occupied_Default_swigregister SimSpace_Occupied_Default_swigregister(SimSpace_Occupied_Default) class SimSpace_Occupied_Default_sequence(base.sequence_common): __swig_setmethods__ = {} for _s in [base.sequence_common]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimSpace_Occupied_Default_sequence, name, value) __swig_getmethods__ = {} for _s in [base.sequence_common]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimSpace_Occupied_Default_sequence, name) __repr__ = _swig_repr def __init__(self, *args): this = _SimSpace_Occupied_Default.new_SimSpace_Occupied_Default_sequence(*args) try: self.this.append(this) except: self.this = this def assign(self, n, x): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_assign(self, n, x) def begin(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_begin(self, *args) def end(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_end(self, *args) def rbegin(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_rbegin(self, *args) def rend(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_rend(self, *args) def at(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_at(self, *args) def front(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_front(self, *args) def back(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_back(self, *args) def push_back(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_push_back(self, *args) def pop_back(self): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_pop_back(self) def detach_back(self, pop=True): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_detach_back(self, pop) def insert(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_insert(self, *args) def erase(self, *args): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_erase(self, *args) def detach(self, position, r, erase=True): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_detach(self, position, r, erase) def swap(self, x): return _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_swap(self, x) __swig_destroy__ = _SimSpace_Occupied_Default.delete_SimSpace_Occupied_Default_sequence __del__ = lambda self: None SimSpace_Occupied_Default_sequence_swigregister = _SimSpace_Occupied_Default.SimSpace_Occupied_Default_sequence_swigregister SimSpace_Occupied_Default_sequence_swigregister(SimSpace_Occupied_Default_sequence) # This file is compatible with both classic and new-style classes.

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chj/index/JType.py

aemcgraw/CodeHawk-Java

0

194700

<filename>chj/index/JType.py<gh_stars>0 # ------------------------------------------------------------------------------ # CodeHawk Java Analyzer # Author: <NAME> # ------------------------------------------------------------------------------ # The MIT License (MIT) # # Copyright (c) 2016-2019 Kestrel Technology LLC # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # ------------------------------------------------------------------------------ import chj.index.JDictionaryRecord as JD class JavaTypesBase(JD.JDictionaryRecord): def __init__(self,tpd,index,tags,args): JD.JDictionaryRecord.__init__(self,index,tags,args) self.tpd = tpd def get_scalar_size(self): return 4 def is_scalar(self): return False def is_array(self): return False def is_object(self): return False def __str__(self): return 'javatypesbase' class StringConstant(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_string(self): if len(self.tags) > 0: return self.tags[0] else: return '' def get_string_length(self): return int(self.args[0]) def is_hex(self): return len(self.tags) > 1 def __str__(self): if self.is_hex(): return ('(' + str(self.get_string_length()) + '-char-string' +')') else: return self.get_string() class ClassObjectType(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_class(self): return self.tpd.jd.get_cn(int(self.args[0])) def is_object(self): return True def __str__(self): return str(self.get_class()) class ArrayObjectType(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def is_object_array_type(self): return True def is_array(self): return True def get_value_type(self): return self.tpd.get_value_type(int(self.args[0])) def __str__(self): return str(self.get_value_type()) class ObjectValueType(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def is_object_value_type(self): return True def is_object_type(self): return True def is_object(self): return True def is_array_type(self): return self.get_object_type().is_object_array_type() def get_object_type(self): return self.tpd.get_object_type(int(self.args[0])) def get_class(self): return self.get_object_type().get_class() def __str__(self): return str(self.get_object_type()) class BasicValueType(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_scalar_size(self): if self.is_long() or self.is_double(): return 8 else: return 4 def is_basic_type(self): return True def is_scalara(self): return True def is_long(self): return self.tags[1] == 'L' def is_double(self): return self.tags[1] == 'D' def get_basic_type(self): return self.tags[1] def __str__(self): return str(self.get_basic_type()) class MethodDescriptor(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def has_return_value(self): return int(self.args[0]) == 1 def get_return_type(self): if self.has_return_value(): return self.tpd.get_value_type(int(self.args[1])) def get_argument_types(self): if self.has_return_value(): return [ self.tpd.get_value_type(int(x)) for x in self.args[2:] ] else: return [ self.tpd.get_value_type(int(x)) for x in self.args[1:] ] def __str__(self): sreturn = '' if self.get_return_type() is None else str(self.get_return_type()) return ('(' + ','.join([ str(x) for x in self.get_argument_types()]) + ')' + sreturn ) class ValueDescriptor(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_value_type(self): return self.tpd.get_value_type(int(self.args[0])) def __str__(self): return 'descr:' + str(self.get_value_type()) class ConstString(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_string(self): return self.tpd.get_string(int(self.args[0])) def __str__(self): return self.get_string() class ConstInt(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_int(self): return int(self.args[0]) def __str__(self): return str(self.get_int()) class ConstFloat(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_float(self): return float(self.tags[1]) def __str__(self): return self.tags[1] def ConstLong(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_long(self): return int(self.tags[1]) def __str__(self): return self.tags[1] class ConstDouble(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_double(self): return float(tags[1]) def __str__(self): return tags[1] class ConstClass(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_class(self): return self.tpd.get_object_type(int(self.args[0])) def __str__(self): return str(self.get_class()) class FieldHandle(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_class_name(self): return self.tpd.jd.get_class(int(self.args[0])) def get_field_signature(self): return self.tpd.jd.get_field_signature(int(self.args[1])) def __str__(self): return str(self.get_class_name()) + ':' + str(self.get_field_signature()) class MethodHandle(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_object_type(self): return self.tpd.get_object_type(int(self.args[0])) def get_method_signature(self): return self.tpd.jd.get_method_signature(int(self.args[1])) def __str__(self): return str(self.get_object_type()) + ':' + str(self.get_method_signature()) class InterfaceHandle(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_class_name(self): return self.tpd.jd.get_class(int(self.args[0])) def get_method_signature(self): return self.tpd.jd.get_method+signature(int(self.args[1])) def __str__(self): return str(self.get_class_name()) + ':' + str(self.get_method_signature()) class ConstValue(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_constant_value(self): return self.jd.get_constant_value(self.args[0]) def __str__(self): return 'C:' + str(self.get_constant_value()) class ConstField(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_class_name(self): return self.tpd.jd.get_cn(int(self.args[0])) def get_field_signature(self): return self.tpd.jd.get_field_signature(int(self.args[1])) def __str__(self): return 'C:' + str(self.get_class_name()) + '.' + str(self.get_field_signature()) class ConstMethod(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_object_type(self): return self.tpd.get_object_type(int(args[0])) def get_method_signature(self): return self.tpd.jd.get_method_signature(int(args[1])) def __str__(self): return 'C:' + str(self.get_object_type()) + '.' + str(self.get_method_signature()) class ConstInterfaceMethod(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_class_name(self): return self.tpd.jd.get_cn(int(self.args[0])) def get_method_signature(self): return self.tpd.jd.get_method_signature(int(self.args[1])) def __str__(self): return 'C:' + str(self.get_class_name()) + '.' + str(self.get_method_signature()) class ConstDynamicMethod(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_bootstrap_method_index(self): return int(self.args[0]) def get_method_signature(self): return self.tpd.jd.get_method_signature(int(self.args[1])) def __str__(self): return ('C:Dynamic(' + str(self.get_bootstrap_mehtod_index()) + ').' + str(self.get_method_signature())) class ConstNameAndType(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_name(self): return self.tpd.get_string(int(self.args[0])) def get_type(self): return self.tpd.get_descriptor(int(self.args[1])) def __str__(self): return 'CNT:' + self.get_name() + ':' + str(self.get_type()) class ConstStringUTF8(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_string(self): return self.tpd.get_string(int(args[0])) def __str__(self): return 'C:' + self.get_string() class ConstMethodHandle(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_reference_kind(self): return self.tags[1] def get_method_handle_type(self): return self.tpd.get_method_handle_type(int(self.args[0])) def __str__(self): return ('C:' + str(self.get_method_handle_type()) + '(' + self.get_reference_kind() + ')') class ConstMethodType(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_method_descriptor(self): return self.tpd.get_method_descriptor(int(self.args[0])) def __str__(self): return 'C:' + str(self.get_method_descriptor()) class ConstUnusable(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def __str__(self): return 'unusable' class BootstrapArgConstantValue(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_constant_value(self): return self.tpd.get_constant_value(int(self.args[0])) def __str__(self): return str(self.get_constant_value()) class BootstrapArgMethodHandle(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_reference_kind(self): return self.tags[1] def get_method_handle_type(self): return self.jd.get_method_handle_type(int(self.args[0])) def __str__(self): return (str(self.get_method_handle_type()) + '(' + self.get_reference_kind() + ')') class BootstrapArgMethodType(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_method_descriptor(self): return self.tpd.get_method_descriptor(int(self.args[0])) def __str__(self): return str(self.get_method_descriptor()) class BootstrapMethodData(JavaTypesBase): def __init__(self,tpd,index,tags,args): JavaTypesBase.__init__(self,tpd,index,tags,args) def get_reference_kind(self): return self.tags[1] def get_method_handle_type(self): return self.tpd.get_method_handle_type(int(self.args[0])) def get_arguments(self): return [ self.tpd.get_bootstrap_argument(int(x)) for x in self.args[1:] ] def __str__(self): return (str(self.get_method_handle_types()) + '(' + ','.join([ str(x) for x in self.get_arguments() ]) + ')')

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code/df_data_struct.py

jrihds/pdat.github.io

0

170392

#!/usr/bin/env python3 """Creating a dataframe from a dict or list.""" import pandas as pd d = {"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]} l = [[1,4,7], [2,5,8], [3,6,9]] df = pd.DataFrame(d) print(df) df = pd.DataFrame(l) df.columns = ['a', 'b', 'c'] print(df) # a b c # 0 1 4 7 # 1 2 5 8 # 2 3 6 9

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source/energy_function.py

Bolt64/protein_lab

0

59333

<gh_stars>0 #!/usr/bin/env python import get_atom_distances as gad import itertools as it import pdb_parser as parser lj_potential = lambda distance, rm: (rm/distance)**12 - 2*(rm/distance)**6 def get_energy(pdb_file, rm_file): energy=0 rm_values=gad.read_labeled_pairs(rm_file) for atom1,atom2 in it.combinations(parser.parse_file(pdb_file), 2): key=tuple(sorted(((atom1['resName'], atom1['name']),(atom2['resName'], atom2['name'])))) if key in rm_values: energy+=lj_potential(gad.get_distance(atom1, atom2), rm_values[key]) return energy

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lib/Site.py

EthanZeigler/pastebin_scraper

0

36989

from queue import Queue import time from settings import USE_DB, DB_DB, DB_DUMP_TABLE, DB_ACCT_TABLE, REQUEST_SPACING import logging from . import helper import sqlite3 import threading class Site(object): ''' Site - parent class used for a generic 'Queue' structure with a few helper methods and features. Implements the following methods: empty() - Is the Queue empty get(): Get the next item in the queue put(item): Puts an item in the queue tail(): Shows the last item in the queue peek(): Shows the next item in the queue length(): Returns the length of the queue clear(): Clears the queue list(): Lists the contents of the Queue download(url): Returns the content from the URL ''' # Note from Jordan (original author) # I would have used the built-in queue, but there is no support for a peek() method # that I could find... So, I decided to implement my own queue with a few # changes def __init__(self, queue=None): if queue is None: self.queue = [] def empty(self): return len(self.queue) == 0 def get(self): if not self.empty(): result = self.queue[0] del self.queue[0] else: result = None return result def put(self, item): self.queue.append(item) def peek(self): return self.queue[0] if not self.empty() else None def tail(self): return self.queue[-1] if not self.empty() else None def length(self): return len(self.queue) def clear(self): self.queue = [] def list(self): print('\n'.join(url for url in self.queue)) def monitor(self, t_lock, db_lock, db_client): self.update() while(1): while not self.empty(): paste = self.get() self.ref_id = paste.id logging.info('[*] Checking + Spacer ' + paste.url) paste.text = self.get_paste_text(paste) time.sleep(REQUEST_SPACING) interesting = helper.run_match(paste) if interesting: logging.info('[*] FOUND ' + (paste.type).upper() + ' ' + paste.url) if USE_DB: db_lock.acquire() try: cursor = db_client.cursor() cursor.execute('''INSERT INTO %s ( text, emails, hashes, num_emails, num_hashes, type, db_keywords, url, author ) VALUES ( ?, ?, ?, ?, ?, ?, ?, ?, ? )''' % (DB_DUMP_TABLE), (paste.text, str(paste.emails), str(paste.hashes), paste.num_emails, paste.num_hashes, paste.type, str(paste.db_keywords), str("https://pastebin.com/"+paste.id), paste.author,)) except: logging.info('[*] ERROR: Failed to save paste. Manual review: ' + paste.url) db_lock.release() self.update() while self.empty(): logging.debug('[*] No results... sleeping') time.sleep(self.sleep) self.update()

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mission_cannibal_Mike_Boodoo.py

BluedragonXVI/missionary_cannibal

0

69325

import heapq # <NAME> # Artificial Intelligence Assign 01 # DFS and A* algorithms acting on Node class who's state is represented as a dictionary # DFS's fringe/frontier is a list and A* is a min-priority queue, using the heapq module # Since dictionaries aren't hashable, the closed/reached set contains string representation of state, str(Node.state) # Node class has global Node class counter that is cleared after DFS or A* are run # Main work happens in the Node.perform_action method that takes an action from the action tuple and ensures it is valid # New node is only generated if resulting new state is valid and satifies constraints of the problem # Output and discussion at end class Node: node_count = 0 def __init__(self, state:dict, parent, prev_action, depth:int, total_cost:int): self.state = state self.parent:Node = parent self.prev_action:tuple = prev_action self.depth = depth Node.node_count += 1 def A_star_cost(self, depth): # f(n) used in A* # need total number on left for both side heuristics num_left = self.state[ML] + self.state[CL] if self.state["position"] == left and num_left < 3: return 1 + depth elif self.state["position"] == left and num_left > 2: return ((num_left-2)*2)+1+depth elif self.state["position"] == right: return (2*num_left) + depth def print_path(self, flag:bool): num_miss:int num_cann:int direction = "" while self.parent is not None: for miss, cann in self.prev_action.items(): num_miss, num_cann = miss, cann if (self.state["position"] == left): direction = right else: direction = left if flag != True: print("DFS SOLUTION NODE -", str(self.state), "at depth: ",self.depth, "by bringing", num_miss, "miss and", num_cann, " cann from the", direction) else: print("A* SOLUTION NODE -", str(self.state), "at depth: ",self.depth, "by bringing", num_miss, "miss and", num_cann, " cann from the", direction, "f(n) =", self.total_cost) self = self.parent print("INITIAL NODE -", str(self.state), "at depth: ",self.depth) def perform_action(self, action:dict, fringe:list, reached:set): num_miss:int num_cann:int for miss, cann in action.items(): num_miss, num_cann = miss, cann # check if action is actually possible (enough miss or cann on that side) # for initial state, {3,3,L,0,0} gave me no missed actions as expected if self.state["position"] == left: # all actions will be applied to left -> right if num_miss > self.state[ML]: #print("Action not performed, not enough miss to move, num_miss: ", num_miss) return # ensure there are enough miss and cann to transfer if num_cann > self.state[CL]: #print("Action not performed, not enough cann to move, num_cann: ", num_cann) return # now ensure that the state is legal (cann don't outnumber miss) next_miss_left, next_miss_right = self.state[ML] - num_miss, self.state[MR] + num_miss next_cann_left, next_cann_right = self.state[CL] - num_cann, self.state[CR] + num_cann if next_cann_left > next_miss_left and next_miss_left > 0: #print("Illegal move detected, cannibals:", next_cann_left, " outnumber missionaries:", next_miss_left, "on left!") #print("The attemped move was sending", num_cann, "cannibals and", num_miss, "missionaries to the right") return if next_cann_right > next_miss_right and next_miss_right > 0: #print("Illegal move detected, cannibals:", next_cann_right, " outnumber missionaries:", next_miss_right, "on right!") #print("The attemped move was sending", num_cann, "cannibals and", num_miss, " missionaries to the right") return # legal nodes can now be added to frontier/fringe # create the state of the new node new_state:dict = {ML:next_miss_left, CL:next_cann_left, "position":right, MR:next_miss_right, CR:next_cann_right} state_string = str(new_state) if state_string not in reached: new_node:Node = Node(new_state, self, action, self.depth+1, None) new_node.total_cost = new_node.A_star_cost(self.depth) print("New unique node created with state:", new_state, "with depth:", new_node.depth) fringe.append(new_node) reached.add(state_string) #print("state string added to reached with value:", state_string) if self.state["position"] == right: # all actions will be applied to right -> left if num_miss > self.state[MR]: #print("Action not performed, not enough miss to move, num_miss: ", num_miss) return # ensure there are enough miss and cann to transfer if num_cann > self.state[CR]: #print("Action not performed, not enough cann to move, num_cann: ", num_cann) return # now ensure that the state is legal (cann don't outnumber miss) next_miss_left, next_miss_right = self.state[ML] + num_miss, self.state[MR] - num_miss next_cann_left, next_cann_right = self.state[CL] + num_cann, self.state[CR] - num_cann if next_cann_left > next_miss_left and next_miss_left > 0: #print("Illegal move detected, cannibals:", next_cann_left, " outnumber missionaries:", next_miss_left, "on left!") #print("The attemped move was sending", num_cann, "cannibals and", num_miss, "missionaries to the right") return if next_cann_right > next_miss_right and next_miss_right > 0: #print("Illegal move detected, cannibals:", next_cann_right, " outnumber missionaries:", next_miss_right, "on right!") #print("The attemped move was sending", num_cann, "cannibals and", num_miss, " missionaries to the right") return # legal nodes can now be added to frontier/fringe # create the state of the new node new_state:dict = {ML:next_miss_left, CL:next_cann_left, "position":left, MR:next_miss_right, CR:next_cann_right} state_string = str(new_state) if state_string not in reached: new_node:Node = Node(new_state, self, action, self.depth+1, None) print("New unique node created with state:", new_state, "with depth:", new_node.depth) fringe.append(new_node) reached.add(state_string) #print("state string added to reached with value:", state_string) # A_star action def perform_action_a_star(self, action:dict, priority_queue, reached:set): num_miss:int num_cann:int for miss, cann in action.items(): num_miss, num_cann = miss, cann # check if action is actually possible (enough miss or cann on that side) # for initial state, {3,3,L,0,0} gave me no missed actions as expected if self.state["position"] == left: # all actions will be applied to left -> right if num_miss > self.state[ML]: #print("Action not performed, not enough miss to move, num_miss: ", num_miss) return # ensure there are enough miss and cann to transfer if num_cann > self.state[CL]: #print("Action not performed, not enough cann to move, num_cann: ", num_cann) return # now ensure that the state is legal (cann don't outnumber miss) next_miss_left, next_miss_right = self.state[ML] - num_miss, self.state[MR] + num_miss next_cann_left, next_cann_right = self.state[CL] - num_cann, self.state[CR] + num_cann if next_cann_left > next_miss_left and next_miss_left > 0: #print("Illegal move detected, cannibals:", next_cann_left, " outnumber missionaries:", next_miss_left, "on left!") #print("The attemped move was sending", num_cann, "cannibals and", num_miss, "missionaries to the right") return if next_cann_right > next_miss_right and next_miss_right > 0: #print("Illegal move detected, cannibals:", next_cann_right, " outnumber missionaries:", next_miss_right, "on right!") #print("The attemped move was sending", num_cann, "cannibals and", num_miss, " missionaries to the right") return # legal nodes can now be added to frontier/fringe # create the state of the new node new_state:dict = {ML:next_miss_left, CL:next_cann_left, "position":right, MR:next_miss_right, CR:next_cann_right} state_string = str(new_state) if state_string not in reached: new_node:Node = Node(new_state, self, action, self.depth+1, None) new_node.total_cost = new_node.A_star_cost(new_node.depth) print("New unique node created with state:", new_state, "with depth:", new_node.depth, "and A* cost:", new_node.total_cost) #fringe.append(new_node) heapq.heappush(priority_queue, (new_node.total_cost, state_string, new_node)) reached.add(state_string) #print("state string added to reached with value:", state_string) if self.state["position"] == right: # all actions will be applied to right -> left if num_miss > self.state[MR]: #print("Action not performed, not enough miss to move, num_miss: ", num_miss) return # ensure there are enough miss and cann to transfer if num_cann > self.state[CR]: #print("Action not performed, not enough cann to move, num_cann: ", num_cann) return # now ensure that the state is legal (cann don't outnumber miss) next_miss_left, next_miss_right = self.state[ML] + num_miss, self.state[MR] - num_miss next_cann_left, next_cann_right = self.state[CL] + num_cann, self.state[CR] - num_cann if next_cann_left > next_miss_left and next_miss_left > 0: #print("Illegal move detected, cannibals:", next_cann_left, " outnumber missionaries:", next_miss_left, "on left!") #print("The attemped move was sending", num_cann, "cannibals and", num_miss, "missionaries to the right") return if next_cann_right > next_miss_right and next_miss_right > 0: #print("Illegal move detected, cannibals:", next_cann_right, " outnumber missionaries:", next_miss_right, "on right!") #print("The attemped move was sending", num_cann, "cannibals and", num_miss, " missionaries to the right") return # legal nodes can now be added to frontier/fringe # create the state of the new node new_state:dict = {ML:next_miss_left, CL:next_cann_left, "position":left, MR:next_miss_right, CR:next_cann_right} state_string = str(new_state) if state_string not in reached: new_node:Node = Node(new_state, self, action, self.depth+1, None) new_node.total_cost = new_node.A_star_cost(new_node.depth) print("New unique node created with state:", new_state, "with depth:", new_node.depth, "and A* cost:", new_node.total_cost) #fringe.append(new_node) heapq.heappush(priority_queue, (new_node.total_cost, state_string, new_node)) reached.add(state_string) #print("state string added to reached with value:", state_string) if __name__ == "__main__": ML, MR = "miss_left", "miss_right" CL, CR = "cann_left", "cann_right" left, right = "left", "right" frontier:list = [] actions:tuple = ({2:0}, {0:2}, {1:1}, {1:0}, {0:1}) # tuple of {miss:cann} entries. key of tuple entry is miss, value of tuple entry is cann initial_state = {ML:3, CL:3, "position":left, MR:0, CR:0} goal_state = {ML:0, CL:0, "position":right, MR:3, CR:3} reached:set = set() Node.node_count = 0 inital_node = Node(initial_state, None, None, 0, None) for action in actions: # each item is a miss:cann dictionary a_star_flag = False inital_node.perform_action(action, frontier, reached) found_solution = False while frontier and (found_solution != True): current_node:Node = frontier.pop() if current_node.state == goal_state: print("VALID STATE REACHED at depth:", current_node.depth) found_solution = True current_node.print_path(a_star_flag) print("Number of nodes created:", Node.node_count) break else: for action_2 in actions: current_node.perform_action(action_2, frontier, reached) print("End reached...") reached.clear() # A* version of above which is same except for min_priority_queue operations Node.node_count = 0 reached_a_star:set = set() priority_queue = [] for action in actions: # each item is a miss:cann dictionary a_star_flag = True inital_node.perform_action_a_star(action, priority_queue, reached_a_star) found_solution = False while priority_queue and (found_solution != True): popped_node_tuple = heapq.heappop(priority_queue) current_node:Node = popped_node_tuple[2] if current_node.state == goal_state: print("VALID STATE REACHED at depth:", current_node.depth) found_solution = True current_node.print_path(a_star_flag) print("Number of nodes created:", Node.node_count) break else: for action_2 in actions: current_node.perform_action_a_star(action_2, priority_queue, reached_a_star) print("End reached...") reached_a_star.clear() # Code output and analysis below: # Without using a reached set, DFS never stopped and generated nodes at depth > 40,000 until manually stopping program # Using a reached set, DFS found a solution at depth 11 while generating 15 legal nodes, output below: # Solution nodes output in reverse order (Goal_state -> ... -> Initial_state) along with action previously applied to get to that state """ VALID STATE REACHED at depth: 11 DFS SOLUTION NODE - {'miss_left': 0, 'cann_left': 0, 'position': 'right', 'miss_right': 3, 'cann_right': 3} at depth: 11 by bringing 0 miss and 2 cann from the left DFS SOLUTION NODE - {'miss_left': 0, 'cann_left': 2, 'position': 'left', 'miss_right': 3, 'cann_right': 1} at depth: 10 by bringing 0 miss and 1 cann from the right DFS SOLUTION NODE - {'miss_left': 0, 'cann_left': 1, 'position': 'right', 'miss_right': 3, 'cann_right': 2} at depth: 9 by bringing 0 miss and 2 cann from the left DFS SOLUTION NODE - {'miss_left': 0, 'cann_left': 3, 'position': 'left', 'miss_right': 3, 'cann_right': 0} at depth: 8 by bringing 0 miss and 1 cann from the right DFS SOLUTION NODE - {'miss_left': 0, 'cann_left': 2, 'position': 'right', 'miss_right': 3, 'cann_right': 1} at depth: 7 by bringing 2 miss and 0 cann from the left DFS SOLUTION NODE - {'miss_left': 2, 'cann_left': 2, 'position': 'left', 'miss_right': 1, 'cann_right': 1} at depth: 6 by bringing 1 miss and 1 cann from the right DFS SOLUTION NODE - {'miss_left': 1, 'cann_left': 1, 'position': 'right', 'miss_right': 2, 'cann_right': 2} at depth: 5 by bringing 2 miss and 0 cann from the left DFS SOLUTION NODE - {'miss_left': 3, 'cann_left': 1, 'position': 'left', 'miss_right': 0, 'cann_right': 2} at depth: 4 by bringing 0 miss and 1 cann from the right DFS SOLUTION NODE - {'miss_left': 3, 'cann_left': 0, 'position': 'right', 'miss_right': 0, 'cann_right': 3} at depth: 3 by bringing 0 miss and 2 cann from the left DFS SOLUTION NODE - {'miss_left': 3, 'cann_left': 2, 'position': 'left', 'miss_right': 0, 'cann_right': 1} at depth: 2 by bringing 0 miss and 1 cann from the right DFS SOLUTION NODE - {'miss_left': 3, 'cann_left': 1, 'position': 'right', 'miss_right': 0, 'cann_right': 2} at depth: 1 by bringing 0 miss and 2 cann from the left INITIAL NODE - {'miss_left': 3, 'cann_left': 3, 'position': 'left', 'miss_right': 0, 'cann_right': 0} at depth: 0 """ # Without using a reached set, A* found a solution at depth 11 while generating 23 legal nodes, output below: """ VALID STATE REACHED at depth: 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 0, 'position': 'right', 'miss_right': 3, 'cann_right': 3} at depth: 11 by bringing 0 miss and 2 cann from the left f(n) = 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 2, 'position': 'left', 'miss_right': 3, 'cann_right': 1} at depth: 10 by bringing 0 miss and 1 cann from the right f(n) = 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 1, 'position': 'right', 'miss_right': 3, 'cann_right': 2} at depth: 9 by bringing 0 miss and 2 cann from the left f(n) = 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 3, 'position': 'left', 'miss_right': 3, 'cann_right': 0} at depth: 8 by bringing 0 miss and 1 cann from the right f(n) = 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 2, 'position': 'right', 'miss_right': 3, 'cann_right': 1} at depth: 7 by bringing 2 miss and 0 cann from the left f(n) = 11 A* SOLUTION NODE - {'miss_left': 2, 'cann_left': 2, 'position': 'left', 'miss_right': 1, 'cann_right': 1} at depth: 6 by bringing 1 miss and 1 cann from the right f(n) = 11 A* SOLUTION NODE - {'miss_left': 1, 'cann_left': 1, 'position': 'right', 'miss_right': 2, 'cann_right': 2} at depth: 5 by bringing 2 miss and 0 cann from the left f(n) = 9 A* SOLUTION NODE - {'miss_left': 3, 'cann_left': 1, 'position': 'left', 'miss_right': 0, 'cann_right': 2} at depth: 4 by bringing 0 miss and 1 cann from the right f(n) = 9 A* SOLUTION NODE - {'miss_left': 3, 'cann_left': 0, 'position': 'right', 'miss_right': 0, 'cann_right': 3} at depth: 3 by bringing 0 miss and 2 cann from the left f(n) = 9 A* SOLUTION NODE - {'miss_left': 3, 'cann_left': 2, 'position': 'left', 'miss_right': 0, 'cann_right': 1} at depth: 2 by bringing 0 miss and 1 cann from the right f(n) = 9 A* SOLUTION NODE - {'miss_left': 3, 'cann_left': 1, 'position': 'right', 'miss_right': 0, 'cann_right': 2} at depth: 1 by bringing 0 miss and 2 cann from the left f(n) = 9 INITIAL NODE - {'miss_left': 3, 'cann_left': 3, 'position': 'left', 'miss_right': 0, 'cann_right': 0} at depth: 0 Number of nodes created: 23 ------------------------------------------------------------------------------------------------------------------- """ # Using a reached set to avoid cycles, A* found a solution at depth 11 while generating 14 nodes, output below: """ VALID STATE REACHED at depth: 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 0, 'position': 'right', 'miss_right': 3, 'cann_right': 3} at depth: 11 by bringing 0 miss and 2 cann from the left f(n) = 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 2, 'position': 'left', 'miss_right': 3, 'cann_right': 1} at depth: 10 by bringing 0 miss and 1 cann from the right f(n) = 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 1, 'position': 'right', 'miss_right': 3, 'cann_right': 2} at depth: 9 by bringing 0 miss and 2 cann from the left f(n) = 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 3, 'position': 'left', 'miss_right': 3, 'cann_right': 0} at depth: 8 by bringing 0 miss and 1 cann from the right f(n) = 11 A* SOLUTION NODE - {'miss_left': 0, 'cann_left': 2, 'position': 'right', 'miss_right': 3, 'cann_right': 1} at depth: 7 by bringing 2 miss and 0 cann from the left f(n) = 11 A* SOLUTION NODE - {'miss_left': 2, 'cann_left': 2, 'position': 'left', 'miss_right': 1, 'cann_right': 1} at depth: 6 by bringing 1 miss and 1 cann from the right f(n) = 11 A* SOLUTION NODE - {'miss_left': 1, 'cann_left': 1, 'position': 'right', 'miss_right': 2, 'cann_right': 2} at depth: 5 by bringing 2 miss and 0 cann from the left f(n) = 9 A* SOLUTION NODE - {'miss_left': 3, 'cann_left': 1, 'position': 'left', 'miss_right': 0, 'cann_right': 2} at depth: 4 by bringing 0 miss and 1 cann from the right f(n) = 9 A* SOLUTION NODE - {'miss_left': 3, 'cann_left': 0, 'position': 'right', 'miss_right': 0, 'cann_right': 3} at depth: 3 by bringing 0 miss and 2 cann from the left f(n) = 9 A* SOLUTION NODE - {'miss_left': 3, 'cann_left': 2, 'position': 'left', 'miss_right': 0, 'cann_right': 1} at depth: 2 by bringing 0 miss and 1 cann from the right f(n) = 9 A* SOLUTION NODE - {'miss_left': 3, 'cann_left': 1, 'position': 'right', 'miss_right': 0, 'cann_right': 2} at depth: 1 by bringing 0 miss and 2 cann from the left f(n) = 9 INITIAL NODE - {'miss_left': 3, 'cann_left': 3, 'position': 'left', 'miss_right': 0, 'cann_right': 0} at depth: 0 Number of nodes created: 14 """ # DFS found solution at same depth as A*, I believe this is due to the constraints of the problem severely reducing the amount of nodes being made. # The branching factor ends up being less than 5 many times because of the constraints # Should test with bigger initial state with more miss and cann to see if algorithms diverge # Unlike DFS, A* found the same solution with and without a closed/reached set where DFS only worked with a closed list since # due to being trapped in a cycle. # If the heuristic function is admissible and consistent this would then suggest depth 11 is the optimal solution depth.

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PyTorch/UOSBT/Supervised/uossl_1.py

agcdke/UOS

0

145110

import os import sys import PIL import math import time import json import random import numpy as np import pandas as pd import matplotlib.pyplot as plt import torch import torchvision from pathlib import Path from PIL import Image, ImageOps, ImageFilter from torch import nn, optim from torchvision import transforms, utils import torchvision.models as models from torchvision.transforms import ToTensor import torchvision.transforms.functional as VisionF from torchvision.utils import make_grid from torch.utils.data import Dataset, DataLoader import torchvision.transforms as transforms from torch.utils.data.sampler import SubsetRandomSampler torch.cuda.empty_cache() torch.manual_seed(42) class DoctableDataset(Dataset): def __init__(self, root, image_dir, csv_file, train=True, transform=None): self.root = root self.image_dir = image_dir self.image_files = os.listdir(image_dir) self.data = pd.read_csv(csv_file, header=None) self.transform = transform self.train = train def __len__(self): return len(self.data) def __getitem__(self, idx): if torch.is_tensor(idx): idx = idx.tolist() img_name = self.data.iloc[idx,0] label = self.data.iloc[idx,1] image_name = os.path.join(self.image_dir, img_name) image = PIL.Image.open(image_name).convert('RGB') if self.transform: image = self.transform(image) return (image, label) def uossl_doctab_main(): # check if CUDA is available train_on_gpu = torch.cuda.is_available() if not train_on_gpu: print('CUDA is not available. Training on CPU ...') else: print('CUDA is available! Training on GPU ...') num_workers = 2 batch_size = 8 valid_size = 0.2 n_epochs = 400 root = "data/ssldoctable" train_image_dir = "data/ssldoctable/train" test_image_dir = "data/ssldoctable/test" train_csv_file = "data/ssldoctable/train_labels.csv" test_csv_file = "data/ssldoctable/test_labels.csv" checkpoint_dir = Path("chkpt/") start_time = time.time() checkpoint_dir.mkdir(parents=True, exist_ok=True) stats_file = open(checkpoint_dir / 'stats.txt', 'a', buffering=1) val_stats_file = open(checkpoint_dir / 'val_stats.txt', 'a', buffering=1) print(' '.join(sys.argv)) print(' '.join(sys.argv), file=stats_file) print(' '.join(sys.argv), file=val_stats_file) # convert data to a normalized torch.FloatTensor transform = transforms.Compose([ transforms.Resize(256, interpolation=Image.BICUBIC), transforms.RandomResizedCrop(224, interpolation=Image.BICUBIC), transforms.RandomHorizontalFlip(p=0.5), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) # choose the training and test datasets train_data = DoctableDataset(root, train_image_dir, train_csv_file, train=True, transform= transform) test_data = DoctableDataset(root, test_image_dir, test_csv_file, train=False, transform= transform) # obtain training indices that will be used for validation num_train = len(train_data) indices = list(range(num_train)) np.random.shuffle(indices) split = int(np.floor(valid_size * num_train)) train_idx, valid_idx = indices[split:], indices[:split] # define samplers for obtaining training and validation batches train_sampler = SubsetRandomSampler(train_idx) valid_sampler = SubsetRandomSampler(valid_idx) # prepare data loaders (combine dataset and sampler) train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers) valid_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, sampler=valid_sampler, num_workers=num_workers) test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, num_workers=num_workers) # specify the image classes classes = ['bargraph', 'doctable'] model = models.resnet50(pretrained=True) #print(model) # move tensors to GPU if CUDA is available if train_on_gpu: model.cuda() # specify loss function (categorical cross-entropy) criterion = nn.CrossEntropyLoss() # specify optimizer optimizer = torch.optim.Adam(model.parameters(),lr = 0.001) # number of epochs to train the model valid_loss_min = np.Inf # track change in validation loss for epoch in range(1, n_epochs+1): # keep track of training and validation loss train_loss = 0.0 valid_loss = 0.0 ################### # train the model # ################### model.train() for data, target in train_loader: # move tensors to GPU if CUDA is available if train_on_gpu: data, target = data.cuda(), target.cuda() # clear the gradients of all optimized variables optimizer.zero_grad() # forward pass: compute predicted outputs by passing inputs to the model output = model(data) # calculate the batch loss loss = criterion(output, target) # backward pass: compute gradient of the loss with respect to model parameters loss.backward() # perform a single optimization step (parameter update) optimizer.step() # update training loss train_loss += loss.item()*data.size(0) ###################### # validate the model # ###################### model.eval() for data, target in valid_loader: # move tensors to GPU if CUDA is available if train_on_gpu: data, target = data.cuda(), target.cuda() # forward pass: compute predicted outputs by passing inputs to the model output = model(data) # calculate the batch loss loss = criterion(output, target) # update average validation loss valid_loss += loss.item()*data.size(0) # calculate average losses train_loss = train_loss/len(train_loader.sampler) valid_loss = valid_loss/len(valid_loader.sampler) stats = dict(epoch=epoch, train_loss=train_loss, valid_loss=valid_loss, time=int(time.time() - start_time)) print(json.dumps(stats)) print(json.dumps(stats), file=stats_file) # print training/validation statistics print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format( epoch, train_loss, valid_loss)) # save model if validation loss has decreased if valid_loss <= valid_loss_min: val_stats = dict(valid_loss_min=valid_loss_min,valid_loss=valid_loss) print(json.dumps(val_stats)) print(json.dumps(val_stats), file=val_stats_file) print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format( valid_loss_min, valid_loss)) torch.save(model.state_dict(), 'chkpt/model_sl_doctable.pt') valid_loss_min = valid_loss ###################### #model.load_state_dict(torch.load('chkpt/model_sl_doctable.pt')) if __name__ == "__main__": uossl_doctab_main()

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7_kyu/Strings_swap_vowels_case.py

UlrichBerntien/Codewars-Katas

0

127786

<gh_stars>0 VOWELS = { c for c in "aeouiAEOUI" } def swap_vowel_case_char(c :str) -> str: return c.swapcase() if c in VOWELS else c def swap_vowel_case(st: str) -> str: return "".join(swap_vowel_case_char(c) for c in st)

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DesignPattern/CompositePattern/__init__.py

Oliver-Yan-2019/algorithm

1

57452

""" 组合模式 """ from DataStructure.Link import FavoritesList if __name__ == '__main__': _favorites_list = FavoritesList()

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examples/passwd_example.py

ZhukovGreen/aiohttp-json-rpc

0

99315

<gh_stars>0 #!/usr/bin/env python # -*- coding: utf-8 -*- # Author: <NAME> <<EMAIL>> from aiohttp_json_rpc.auth.passwd import PasswdAuthBackend from aiohttp_json_rpc.auth import login_required from aiohttp_json_rpc import JsonRpc from aiohttp.web import Application import asyncio import os @asyncio.coroutine def ping(request): return 'pong' @login_required @asyncio.coroutine def pong(request): return 'ping' if __name__ == '__main__': PASSWD_FILE = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'user.db') # login: admin:admin loop = asyncio.get_event_loop() rpc = JsonRpc(auth_backend=PasswdAuthBackend(PASSWD_FILE)) rpc.add_methods( ('', ping), ('', pong), ) app = Application(loop=loop) app.router.add_route('*', '/', rpc) handler = app.make_handler() server = loop.run_until_complete( loop.create_server(handler, '0.0.0.0', 8080)) loop.run_forever()

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api/admin.py

titans55/pizza-ordering-service

1

44719

from django.contrib import admin from django import forms from . import models class PizzaFlavorAdminForm(forms.ModelForm): class Meta: model = models.PizzaFlavor fields = "__all__" class PizzaFlavorAdmin(admin.ModelAdmin): form = PizzaFlavorAdminForm list_display = [ "id", "name", "s_size_price", "m_size_price", "l_size_price", ] readonly_fields = [ "m_size_price", "s_size_price", "l_size_price", "id", "name", ] class OrderedPizzaAdminForm(forms.ModelForm): class Meta: model = models.OrderedPizza fields = "__all__" class OrderedPizzaAdmin(admin.ModelAdmin): form = OrderedPizzaAdminForm list_display = [ "id", "size", "created", "count", ] readonly_fields = [ "id", "size", "created", "count", ] class OrderAdminForm(forms.ModelForm): class Meta: model = models.Order fields = "__all__" class OrderAdmin(admin.ModelAdmin): form = OrderAdminForm list_display = [ "id", "customer_id", "last_updated", "created", "is_paid", ] readonly_fields = [ "customer_id", "last_updated", "created", "is_paid", "id", ] class DeliveryDetailAdminForm(forms.ModelForm): class Meta: model = models.DeliveryDetail fields = "__all__" class DeliveryDetailAdmin(admin.ModelAdmin): form = DeliveryDetailAdminForm list_display = [ "id", "assigned_courier_id", "status", "created", ] readonly_fields = [ "assigned_courier_id", "status", "id", "created", ] admin.site.register(models.PizzaFlavor, PizzaFlavorAdmin) admin.site.register(models.OrderedPizza, OrderedPizzaAdmin) admin.site.register(models.Order, OrderAdmin) admin.site.register(models.DeliveryDetail, DeliveryDetailAdmin)

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api/portfolio/utils.py

lucasblazzi/insights-project

0

1601653

import os import grpc from google.protobuf.json_format import MessageToDict from insights_pb2_grpc import MetricsStub from insights_pb2 import PortfolioRequest, Product import redis import ast metrics_port = os.environ.get("METRICS_PORT", "9999") def message_encoder(message): for k, v in message.items(): if isinstance(v, list): message[k] = str(v) return message def build_request(payload): request = PortfolioRequest() request.start_date = payload["start_date"] request.end_date = payload["end_date"] request.amount = float(payload["amount"]) for p in payload.get("products", []): product = request.products.add() product.id = p["id"] product.proportion = float(p["proportion"]) product.amount = float(p["amount"]) return request def get_portfolio_metrics(payload): try: with grpc.insecure_channel(f"metrics_service:{metrics_port}") as channel: stub = MetricsStub(channel) request = build_request(payload) metrics = stub.GetPortfolioMetrics(request) return MessageToDict(metrics) except Exception as e: raise e def send_notifications(portfolio, clients): try: client = redis.Redis(host="redis_db", port=6379) _portfolio = ast.literal_eval(portfolio) for c in clients: _portfolio["email"] = c["email"] _portfolio["name"] = c["name"] client.xadd("portfolio", message_encoder(_portfolio)) except Exception as e: raise e

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floreal/views/view_purchases.py

caracole-io/circuitscourts

0

18364

<reponame>caracole-io/circuitscourts<filename>floreal/views/view_purchases.py #!/usr/bin/python # -*- coding: utf-8 -*- import os from django.http import HttpResponse, HttpResponseForbidden from django.shortcuts import render_to_response from django.contrib.auth.decorators import login_required from caracole import settings from .decorators import nw_admin_required from .getters import get_delivery, get_subgroup from . import latex from .spreadsheet import spreadsheet from .delivery_description import delivery_description MIME_TYPE = { 'pdf': "application/pdf", 'xlsx': "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"} def non_html_response(name_bits, name_extension, content): """Common helper to serve PDF and Excel content.""" filename = ("_".join(name_bits) + "." + name_extension).replace(" ", "_") mime_type = MIME_TYPE[name_extension] response = HttpResponse(content_type=mime_type) response['Content-Disposition'] = 'attachment; filename="%s"' % filename response.write(content) return response @login_required() def view_purchases_html(request, delivery, subgroup=None): """View purchases for a given delivery, possibly restricted to a subgroup. (subgroup) staff only.""" dv = get_delivery(delivery) if subgroup: sg = get_subgroup(subgroup) if request.user not in sg.staff.all() and request.user not in sg.network.staff.all(): return HttpResponseForbidden("Réservé aux admins") subgroups = [sg] else: if request.user not in dv.network.staff.all(): return HttpResponseForbidden("Réservé aux admins") subgroups = dv.network.subgroup_set.all() return render_to_response('view_purchases.html', delivery_description(dv, subgroups)) @login_required() def view_purchases_xlsx(request, delivery, subgroup=None): """View purchases for a given delivery as an MS-Excel spreadsheet, possibly restricted to a subgroup. (subgroup) staff only.""" dv = get_delivery(delivery) if subgroup: sg = get_subgroup(subgroup) if request.user not in sg.staff.all() and request.user not in sg.network.staff.all(): return HttpResponseForbidden("Réservé aux admins") subgroups = [sg] else: if request.user not in dv.network.staff.all(): return HttpResponseForbidden("Réservé aux admins") subgroups = dv.network.subgroup_set.all() return non_html_response((dv.network.name, dv.name), "xlsx", spreadsheet(dv, subgroups)) @login_required() def view_purchases_latex(request, delivery, subgroup=None): """View purchases for a given delivery as a PDF table, generated through LaTeX, possibly restricted to a subgroup. (subgroup) staff only.""" dv = get_delivery(delivery) if subgroup: sg = get_subgroup(subgroup) if request.user not in sg.staff.all() and request.user not in sg.network.staff.all(): return HttpResponseForbidden("Réservé aux admins") content = latex.subgroup(dv, sg) name_bits = (dv.network.name, dv.name, sg.name) else: if request.user not in dv.network.staff.all(): return HttpResponseForbidden("Réservé aux admins") content = latex.delivery_table(dv) name_bits = (dv.network.name, dv.name) return non_html_response(name_bits, "pdf", content) @login_required() def view_cards_latex(request, delivery, subgroup=None): """View purchases for a given delivery as a PDF table, generated through LaTeX, possibly restricted to a subgroup. Subgroups are presented as ready-to-cut tables, whole deliveries as list per subgroup. (subgroup) staff only.""" dv = get_delivery(delivery) if subgroup: sg = get_subgroup(subgroup) if request.user not in sg.staff.all() and request.user not in sg.network.staff.all(): return HttpResponseForbidden("Réservé aux admins") content = latex.cards(dv, sg) name_bits = (dv.network.name, dv.name, sg.name) else: content = latex.delivery_cards(dv) name_bits = (dv.network.name, dv.name) return non_html_response(name_bits, "pdf", content) @nw_admin_required(lambda a: get_delivery(a['delivery']).network) def get_archive(request, delivery, suffix): """Retrieve the PDF/MS-Excel file versions of a terminated delivery which have been saved upontermination.""" dv = get_delivery(delivery) file_name = os.path.join(settings.DELIVERY_ARCHIVE_DIR, "dv-%d.%s" % (dv.id, suffix)) with open(file_name) as f: content = f.read() name_bits = (dv.network.name, dv.name) return non_html_response(name_bits, suffix, content)

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mindspore/parallel/mpi/_mpi_config.py

GuoSuiming/mindspore

55

131048

<reponame>GuoSuiming/mindspore<filename>mindspore/parallel/mpi/_mpi_config.py # Copyright 2020 Huawei Technologies Co., Ltd # # 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. # ============================================================================ """ The MPI config, used to configure the MPI environment. """ import threading from mindspore._c_expression import MpiConfig from mindspore._checkparam import args_type_check class _MpiConfig: """ _MpiConfig is the config tool for controlling MPI Note: Create a config through instantiating MpiConfig object is not recommended. should use MpiConfig() to get the config since MpiConfig is singleton. """ _instance = None _instance_lock = threading.Lock() def __init__(self): self._mpiconfig_handle = MpiConfig.get_instance() def __new__(cls, *args, **kwargs): if cls._instance is None: cls._instance_lock.acquire() cls._instance = object.__new__(cls) cls._instance_lock.release() return cls._instance def __getattribute__(self, attr): value = object.__getattribute__(self, attr) if attr == "_mpiconfig_handle" and value is None: raise ValueError("mpiconfig handle is none in MpiConfig!!!") return value @property def enable_mpi(self): return self._mpiconfig_handle.get_enable_mpi() @enable_mpi.setter def enable_mpi(self, enable_mpi): self._mpiconfig_handle.set_enable_mpi(enable_mpi) _k_mpi_config = None def _mpi_config(): """ Get the global mpi config, if mpi config is not created, create a new one. Returns: _MpiConfig, the global mpi config. """ global _k_mpi_config if _k_mpi_config is None: _k_mpi_config = _MpiConfig() return _k_mpi_config @args_type_check(enable_mpi=bool) def _set_mpi_config(**kwargs): """ Sets mpi config for running environment. mpi config should be configured before running your program. If there is no configuration, mpi moudle will be disabled by default. Note: Attribute name is required for setting attributes. Args: enable_mpi (bool): Whether to enable mpi. Default: False. Raises: ValueError: If input key is not an attribute in mpi config. Examples: >>> mpiconfig.set_mpi_config(enable_mpi=True) """ for key, value in kwargs.items(): if not hasattr(_mpi_config(), key): raise ValueError("Set mpi config keyword %s is not recognized!" % key) setattr(_mpi_config(), key, value) def _get_mpi_config(attr_key): """ Gets mpi config attribute value according to the input key. Args: attr_key (str): The key of the attribute. Returns: Object, The value of given attribute key. Raises: ValueError: If input key is not an attribute in config. """ if not hasattr(_mpi_config(), attr_key): raise ValueError("Get context keyword %s is not recognized!" % attr_key) return getattr(_mpi_config(), attr_key)

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DP/ValueIteration.py

davidbrown3/reinforcement-learning

1

127823

import numpy as np def value_iteration(env, theta=0.0001, discount_factor=1.0): """ Value Iteration Algorithm. Args: env: OpenAI environment. env.P represents the transition probabilities of the environment. theta: Stopping threshold. If the value of all states changes less than theta in one iteration we are done. discount_factor: lambda time discount factor. Returns: A tuple (policy, V) of the optimal policy and the optimal value function. """ V = np.zeros(env.nS) Policy = np.zeros([env.nS, env.nA]) while True: ConvergeFlag = True for StateIdx, StateName in enumerate(env.P.keys()): ActionValues = np.zeros(env.nA) StateInfo = env.P[StateName] for ActionIdx, ActionName in enumerate(StateInfo.keys()): # For now assume that all probabilities are 1 ActionInfo = StateInfo[ActionName][0] Reward = ActionInfo[2] NextState = ActionInfo[1] NextStateValue = V[NextState] ActionValues[ActionIdx] = Reward + discount_factor*NextStateValue UpdatedValue = np.max(ActionValues) ConvergeFlag = ConvergeFlag & (np.abs(V[StateIdx]-UpdatedValue) < theta) V[StateIdx] = UpdatedValue if ConvergeFlag: break for StateIdx, StateName in enumerate(env.P.keys()): StateInfo = env.P[StateName] ActionValues = np.zeros(env.nA) for ActionIdx, ActionName in enumerate(StateInfo.keys()): # For now assume that all probabilities are 1 ActionInfo = StateInfo[ActionName][0] Reward = ActionInfo[2] NextState = ActionInfo[1] NextStateValue = V[NextState] ActionValues[ActionIdx] = Reward + discount_factor*NextStateValue MaxValueIdx = np.argmax(ActionValues) Policy[StateIdx,:] = 0 Policy[StateIdx,MaxValueIdx] = 1 return Policy, V

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survae/tests/transforms/bijections/coupling/coupling_linear.py

alisiahkoohi/survae_flows

262

99673

import numpy as np import torch import torch.nn as nn import torchtestcase import unittest from survae.transforms.bijections.coupling import * from survae.nn.layers import ElementwiseParams, ElementwiseParams2d from survae.tests.transforms.bijections import BijectionTest class AdditiveCouplingBijectionTest(BijectionTest): def test_bijection_is_well_behaved(self): batch_size = 10 self.eps = 1e-6 for shape in [(6,), (6,8,8)]: for num_condition in [None, 1]: with self.subTest(shape=shape, num_condition=num_condition): x = torch.randn(batch_size, *shape) if num_condition is None: if len(shape) == 1: net = nn.Linear(3,3) if len(shape) == 3: net = nn.Conv2d(3,3, kernel_size=3, padding=1) else: if len(shape) == 1: net = nn.Linear(1,5) if len(shape) == 3: net = nn.Conv2d(1,5, kernel_size=3, padding=1) bijection = AdditiveCouplingBijection(net, num_condition=num_condition) self.assert_bijection_is_well_behaved(bijection, x, z_shape=(batch_size, *shape)) z, _ = bijection.forward(x) if num_condition is None: self.assertEqual(x[:,:3], z[:,:3]) else: self.assertEqual(x[:,:1], z[:,:1]) class AffineCouplingBijectionTest(BijectionTest): def test_bijection_is_well_behaved(self): batch_size = 10 self.eps = 5e-6 for shape in [(6,), (6,8,8)]: for num_condition in [None, 1]: with self.subTest(shape=shape, num_condition=num_condition): x = torch.randn(batch_size, *shape) if num_condition is None: if len(shape) == 1: net = nn.Sequential(nn.Linear(3,3*2), ElementwiseParams(2)) if len(shape) == 3: net = nn.Sequential(nn.Conv2d(3,3*2, kernel_size=3, padding=1), ElementwiseParams2d(2)) else: if len(shape) == 1: net = nn.Sequential(nn.Linear(1,5*2), ElementwiseParams(2)) if len(shape) == 3: net = nn.Sequential(nn.Conv2d(1,5*2, kernel_size=3, padding=1), ElementwiseParams2d(2)) bijection = AffineCouplingBijection(net, num_condition=num_condition) self.assert_bijection_is_well_behaved(bijection, x, z_shape=(batch_size, *shape)) z, _ = bijection.forward(x) if num_condition is None: self.assertEqual(x[:,:3], z[:,:3]) else: self.assertEqual(x[:,:1], z[:,:1]) if __name__ == '__main__': unittest.main()

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tests/bdd/steps/test_calculate_season.py

nora-ns/allseasons

4

1614974

from pytest_bdd import scenario, given, when, then from pytest_bdd import parsers from urllib.parse import urljoin import pytest import sys # pytestmark means it this mark will apply to all tests in the module pytestmark = pytest.mark.skipif(sys.platform in ('win32', 'cygwin', 'darwin'), reason="firefox tests don't work in Windows/OSX") # @pytest.mark.django_db tells the tests that we intend to access the DB @pytest.mark.django_db @scenario('../features/calculate_season.feature', 'First form') def test_form1(): pass @pytest.mark.django_db @scenario('../features/calculate_season.feature', 'Second form') def test_form2(): pass # browser fixture comes from pytest-splinter # live_server fixture comes from pytest-django @when('I go to the convert page') def go_to_convert(browser, live_server): browser.visit(urljoin(str(live_server), '/convert/')) @when(parsers.parse('I fill out the name "{name}"')) def fill_out_name(browser, name): browser.fill_form({ '0-name': name, }) @when(parsers.parse('I fill out the date as [{day}] [{month}] [{year}]')) def fill_out_date(browser, day, month, year): browser.fill_form({ '0-date_day': day, '0-date_month': month, '0-date_year': year, }) @when(parsers.parse('I select the location as Latitude {lat} Longitude {lng}')) def select_the_location(browser, lat, lng): browser.fill_form({ '0-location_0': lat, '0-location_1': lng, }) @when('I submit the form') def submit_form(browser): browser.find_by_css('button[type=submit]').first.click() @then(parsers.parse('I should see the following available calendars:\n{text}')) def see_calendars(browser, text): expected = text.split('\n') elements = [el.text for el in browser.find_by_css('.radio')] assert elements == expected @given(parsers.parse('I have submitted the first form as follows:\n{text}')) def submitted_first_form(browser, live_server, text): items = dict([item.split(': ') for item in text.split('\n')]) go_to_convert(browser, live_server) fill_out_name(browser, items['name']) fill_out_date(browser, items['day'], items['month'], items['year']) select_the_location(browser, items['lat'], items['lng']) submit_form(browser) @when(parsers.parse('I select the calendar "{calendar}"')) def select_the_seasonset(browser, calendar): browser.choose('1-calendar', calendar) @then(parsers.parse('I should see "{expected}"')) def see_text(browser, expected): el = browser.find_by_css('#result').first assert el.text == expected

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maskrcnn_benchmark/modeling/roi_heads/box_head/loss.py

RyanXLi/OneshotDet

16

25466

<reponame>RyanXLi/OneshotDet<gh_stars>10-100 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. import torch from torch.nn import functional as F from maskrcnn_benchmark.layers import smooth_l1_loss, SigmoidFocalLoss from maskrcnn_benchmark.modeling.box_coder import BoxCoder from maskrcnn_benchmark.modeling.matcher import Matcher from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou from maskrcnn_benchmark.modeling.balanced_positive_negative_sampler import ( BalancedPositiveNegativeSampler ) from maskrcnn_benchmark.modeling.utils import cat class FastRCNNLossComputation(object): """ Computes the loss for Faster R-CNN. Also supports FPN """ def __init__( self, cfg, proposal_matcher, fg_bg_sampler, box_coder, cls_agnostic_bbox_reg=False ): """ Arguments: proposal_matcher (Matcher) fg_bg_sampler (BalancedPositiveNegativeSampler) box_coder (BoxCoder) """ self.cfg = cfg self.proposal_matcher = proposal_matcher self.fg_bg_sampler = fg_bg_sampler self.box_coder = box_coder self.cls_agnostic_bbox_reg = cls_agnostic_bbox_reg if cfg.FEW_SHOT.SECOND_STAGE_CLS_LOSS == 'focal_loss': self.cls_loss_func = SigmoidFocalLoss( cfg.MODEL.FCOS.LOSS_GAMMA, cfg.FEW_SHOT.SECOND_STAGE_LOSS_ALPHA ) def match_targets_to_proposals(self, proposal, target): match_quality_matrix = boxlist_iou(target, proposal) # (M, N) matched_idxs = self.proposal_matcher(match_quality_matrix) # -1, -2 (N) ############################### ### create soft labels ### currently only assume 1 class ############################### if self.cfg.FEW_SHOT.SOFT_LABELING: match_iou_matrix = match_quality_matrix.t() # (N, M) # assert match_iou_matrix.size(1) == 2, 'only supporting 1 classes right now, but received {} classes'.format(match_iou_matrix.size(1)) # assert torch.sum(match_iou_matrix[torch.nonzero(matched_idxs<1), 1]) == 0, \ # ['positive class column of non-positive prediction should all be zero ! ', # torch.sum(match_iou_matrix[torch.nonzero(matched_idxs>0), 1])] matched_idxs_temp = matched_idxs.clone() matched_idxs_temp[matched_idxs_temp<0] = 0 match_iou = match_iou_matrix[torch.arange(len(matched_idxs_temp)).long(), matched_idxs_temp].clone() matched_idxs_invalid_inds = torch.nonzero(matched_idxs<0) match_iou[matched_idxs_invalid_inds] = 0 # match_iou_matrix_max, _ = match_iou_matrix.max(dim=1) # match_iou_big_iou = torch.nonzero(match_iou_matrix_max>0.5).squeeze(1) # print(match_iou_big_iou) # print('iiou', torch.cat([match_iou[match_iou_big_iou].unsqueeze(1), match_iou_matrix[match_iou_big_iou]], dim=1)) # Fast RCNN only need "labels" field for selecting the targets target = target.copy_with_fields("labels") # get the targets corresponding GT for each proposal # NB: need to clamp the indices because we can have a single # GT in the image, and matched_idxs can be -2, which goes # out of bounds matched_targets = target[matched_idxs.clamp(min=0)] if self.cfg.FEW_SHOT.SOFT_LABELING: matched_targets.add_field('soft_labels', match_iou) matched_targets.add_field("matched_idxs", matched_idxs) return matched_targets def soft_labeling_function(self, t): ''' discrete: t >= 0.5 linear : t transLinear: (0.2*t+0.8) * (t>=0.5) + 0.5 ~ 1 --> 0.9 ~ 1 (2.25*t-0.225) * (t>=0.1) * (t<0.5) + 0.1 ~ 0.5 --> 0 ~ 0.9 0 0 ~ 0.1 trans4thLinear: (0.2*t + 0.8) * (t>=0.5) + 0.5 ~ 1 --> 0.9 ~ 1 0.9*(2*t)**4 0 ~ 0.5 --> 0 ~ 0.9 ''' if self.cfg.FEW_SHOT.SOFT_LABELING_FUNC == 'discrete': return (t>=0.5).float() elif self.cfg.FEW_SHOT.SOFT_LABELING_FUNC == 'linear': return t elif self.cfg.FEW_SHOT.SOFT_LABELING_FUNC == 'transLinear': # transitional linear upper = (0.2*t+0.8) * (t>=0.5).float() middle = (2.25*t-0.225) * (t>=0.1).float() * (t<0.5).float() lower = 0 return upper+middle+lower elif self.cfg.FEW_SHOT.SOFT_LABELING_FUNC == 'trans4thLinear': # transitional 4th order linear upper = (0.2*t+0.8) * (t>=0.5).float() lower = 0.9*((2*t)**4) * (t<0.5).float() return upper+lower def prepare_targets(self, proposals, targets): labels = [] regression_targets = [] if self.cfg.FEW_SHOT.SOFT_LABELING: soft_labels = [] for proposals_per_image, targets_per_image in zip(proposals, targets): matched_targets = self.match_targets_to_proposals( # -1, -2 proposals_per_image, targets_per_image ) matched_idxs = matched_targets.get_field("matched_idxs") if self.cfg.FEW_SHOT.SOFT_LABELING: soft_labels_per_image = self.soft_labeling_function(matched_targets.get_field("soft_labels")) soft_labels.append(soft_labels_per_image) labels_per_image = matched_targets.get_field("labels") labels_per_image = labels_per_image.to(dtype=torch.int64) # Label background (below the low threshold) bg_inds = matched_idxs == Matcher.BELOW_LOW_THRESHOLD labels_per_image[bg_inds] = 0 # Label ignore proposals (between low and high thresholds) ignore_inds = matched_idxs == Matcher.BETWEEN_THRESHOLDS labels_per_image[ignore_inds] = -1 # -1 is ignored by sampler # compute regression targets regression_targets_per_image = self.box_coder.encode( matched_targets.bbox, proposals_per_image.bbox ) labels.append(labels_per_image) regression_targets.append(regression_targets_per_image) if self.cfg.FEW_SHOT.SOFT_LABELING: return labels, soft_labels, regression_targets return labels, regression_targets # def sample_proposal_per_box(self, box, image_size, iou_range=(0.5, 0.9), num=100): # ''' # receive box (x1, y1, x2, y2) # ''' # def uniSample(a, b): # return torch.rand(1).squeeze()*(b-a) + a # def b_area(box): # return (box[2]-box[0])*(box[3]-box[1]) # def b_iou(box1, box2): # l = torch.max(box1[0], box2[0]) # t = torch.max(box1[1], box2[1]) # r = torch.min(box1[2], box2[2]) # b = torch.min(box1[3], box2[3]) # area = (r-l)*(b-t) # return area / ( b_area(box1) + b_area(box2) - area ) # x1, y1, x2, y2 = box # W, H = image_size # assert x1>=0 and x1<=x2 and x2<W and \ # y1>=0 and y1<=y2 and y2<H, 'input box not valid {} {} {} {}'.format(x1, y1, x2, y2) # center_x, center_y = (x1+x2)/2, (y1+y2)/2 # box_w = x2-x1+1 # box_h = y2-y1+1 # proposals = [] # while len(proposals) < num: # rand_iou = uniSample(*iou_range) # restrict to iou range # min_iou_term = 2*rand_iou/(rand_iou+1) # xc_prop = uniSample(0, 0.3333) # restrict to 0~1/3 # yc_prop_min = torch.max(0., min_iou_term) # yc_prop_max = torch.min(0.3333, min_iou_term) # if yc_prop_max <= yc_prop_min: # continue # yc_prop = uniSample(yc_prop_min, yc_prop_max) # xc_dir = torch.randint(2).float()*2-1 # xc = center_x + xc_dir*box_w*xc_prop # yc_dir = torch.randint(2).float()*2-1 # yc = center_y + yc_dir*box_h*yc_prop # for i in range(100): # each center location only try at most 100 times, resort another center location if not # width_min = rand_iou*box_w, # width_max = box_w/rand_iou # width = uniSample(width_min, width_max) # height_min = rand_iou*box_h / min(width, box_w) # height_max = box_h/rand_iou # height = uniSample(height_min, height_max) # rand_box = torch.tensor( [int(xc-width/2), int(yc-height/2), int(xc+width/2), int(yc+height/2)]).float() # rand_box.clamp_(0, image_size-1) # if b_iou(rand_box, box) >= rand_iou: # break # proposals.append(rand_box) # proposals = torch.stack(proposals, dim=0) # (num, 4) xyxy # return proposals # def handcraft_sample_proposals(self, targets, num_per_image=100): # proposals = [] # labels = [] # num_sample_per_box = 100 # for targets_per_image in targets: # image_size = targets_per_image.size # bboxes_per_image = targets_per_image.bbox # labels_per_image = targets.get_field('labels') # proposals_per_image = [] # labels_per_image = [] # for bbox, label in zip(bboxes_per_image, labels_per_image): # sampled_bboxes = self.sample_proposal_per_box(bbox, image_size, iou_range=(0.5, 0.9), num=num_sample_per_box) # sampled_labels = label.unsqueeze().repeat(len(sampled_bboxes)) # proposals_per_image.append(sampled_bboxes) # labels_per_image.append(sampled_labels) # proposals_per_image = torch.cat(proposals_per_image, dim=0) # labels_per_image = torch.cat(labels_per_image, dim=0) # if len(proposals_per_image) > num_per_image: # inds = torch.randperm(len(proposals_per_image)) # selected_inds = inds[:num_per_image] # proposals_per_image = proposals_per_image[selected_inds] # labels_per_image = labels_per_image[selected_inds] # assert len(proposals_per_image) == num_per_image, [ len(proposals_per_image), num_per_image ] # proposals.append(proposals_per_image) # labels.append(labels_per_image) # return proposals, labels def subsample(self, proposals, targets): """ This method performs the positive/negative sampling, and return the sampled proposals. Note: this function keeps a state. Arguments: proposals (list[BoxList]) targets (list[BoxList]) """ # handcrafted_proposals, handcrafted_labels = self.handcraft_sample_proposals(proposals, targets) # assert len(handcrafted_proposals) == len(proposals), [len(handcrafted_proposals), len(proposals)] # proposals = [torch.cat([proposals[i], handcrafted_proposals[i]]) for i in range(len(proposals))] if self.cfg.FEW_SHOT.SOFT_LABELING: labels, soft_labels, regression_targets = self.prepare_targets(proposals, targets) else: labels, regression_targets = self.prepare_targets(proposals, targets) sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels, neg_supp=self.cfg.FEW_SHOT.NEG_SUPPORT.TURN_ON) # print('pos:', len(sampled_pos_inds[0].nonzero()), 'neg', len(sampled_neg_inds[0].nonzero())) proposals = list(proposals) # add corresponding label and regression_targets information to the bounding boxes if self.cfg.FEW_SHOT.SOFT_LABELING: for labels_per_image, soft_labels_per_image, regression_targets_per_image, proposals_per_image in zip( labels, soft_labels, regression_targets, proposals ): proposals_per_image.add_field("labels", labels_per_image) proposals_per_image.add_field("soft_labels", soft_labels_per_image) proposals_per_image.add_field( "regression_targets", regression_targets_per_image ) else: for labels_per_image, regression_targets_per_image, proposals_per_image in zip( labels, regression_targets, proposals ): proposals_per_image.add_field("labels", labels_per_image) proposals_per_image.add_field( "regression_targets", regression_targets_per_image ) # distributed sampled proposals, that were obtained on all feature maps # concatenated via the fg_bg_sampler, into individual feature map levels for img_idx, (pos_inds_img, neg_inds_img) in enumerate( zip(sampled_pos_inds, sampled_neg_inds) ): img_sampled_inds = torch.nonzero(pos_inds_img | neg_inds_img).squeeze(1) proposals_per_image = proposals[img_idx][img_sampled_inds] # assert torch.sum(proposals_per_image.get_field('labels').float() - proposals_per_image.get_field('soft_labels'))==0, \ # [proposals_per_image.get_field('labels'), proposals_per_image.get_field('soft_labels')] proposals[img_idx] = proposals_per_image self._proposals = proposals return proposals def CXE(self, predicted, target): my_target = torch.stack([1-target, target], dim=1) return -(my_target * torch.log(predicted)).mean() def FOCAL_LOSS(self, predicted_diff): ''' multi cls focal loss ''' EPISILON = 1e-6 log_pt = torch.log(1-predicted_diff + EPISILON) return - ((predicted_diff) * log_pt).mean() def __call__(self, class_logits, box_regression, neg_class_logits=None, rev_class_logits=None, gt_label=-1): """ Computes the loss for Faster R-CNN. This requires that the subsample method has been called beforehand. Arguments: class_logits (list[Tensor]) box_regression (list[Tensor]) Returns: classification_loss (Tensor) box_loss (Tensor) """ class_logits = cat(class_logits, dim=0) box_regression = cat(box_regression, dim=0) if self.cfg.FEW_SHOT.REVERSE_ORDER: assert rev_class_logits is not None rev_class_logits = cat(rev_class_logits, dim=0) device = class_logits.device if not hasattr(self, "_proposals"): raise RuntimeError("subsample needs to be called before") proposals = self._proposals if self.cfg.FEW_SHOT.SOFT_LABELING: soft_labels = cat([proposal.get_field("soft_labels") for proposal in proposals], dim=0) labels = cat([proposal.get_field("labels") for proposal in proposals], dim=0) regression_targets = cat( [proposal.get_field("regression_targets") for proposal in proposals], dim=0 ) if gt_label == -1: N = labels.size(0) pos_inds = torch.nonzero(labels > 0) if self.cfg.FEW_SHOT.SECOND_STAGE_CLS_LOSS == 'focal_loss': classification_loss = self.cls_loss_func( class_logits, labels.int() ) / max(pos_inds.numel(), 1) elif self.cfg.FEW_SHOT.SECOND_STAGE_CLS_LOSS == 'ce_loss': if self.cfg.FEW_SHOT.LOSS_WEIGHTED: fg_weight = 0.75 if class_logits.size(1) == 2: weight = torch.tensor([1-fg_weight, fg_weight]).cuda(class_logits.get_device()).float() elif class_logits.size(1) == 3: weight = torch.tensor([1-fg_weight, fg_weight, fg_weight]).cuda(class_logits.get_device()).float() else: raise Exception('class logits dimention wrong, can only be 2 or 3 for softmax ce loss') classification_loss = F.cross_entropy(class_logits, labels, weight=weight) # change to focal loss else: classification_loss = F.cross_entropy(class_logits, labels) # change to focal loss elif self.cfg.FEW_SHOT.SECOND_STAGE_CLS_LOSS == 'mse_loss' and self.cfg.FEW_SHOT.SOFT_LABELING: classification_loss = torch.mean((class_logits.sigmoid()-soft_labels)**2) elif self.cfg.FEW_SHOT.SECOND_STAGE_CLS_LOSS == 'mse_loss' and not self.cfg.FEW_SHOT.SOFT_LABELING: classification_loss = torch.mean((class_logits.sigmoid()-labels.float())**2) elif self.cfg.FEW_SHOT.SECOND_STAGE_CLS_LOSS == 'l1_loss' and self.cfg.FEW_SHOT.SOFT_LABELING: classification_loss = torch.mean(torch.abs(class_logits.sigmoid()-soft_labels)) elif self.cfg.FEW_SHOT.SECOND_STAGE_CLS_LOSS == 'cxe_loss' and self.cfg.FEW_SHOT.SOFT_LABELING: classification_loss = self.CXE(class_logits.softmax(dim=1), soft_labels) else: raise Exception('clasification loss of second stage not valid') if self.cfg.FEW_SHOT.REVERSE_ORDER: reverse_cls_loss = self.FOCAL_LOSS( torch.abs(class_logits.softmax(dim=-1)-rev_class_logits.softmax(dim=-1)) ) # get indices that correspond to the regression targets for # the corresponding ground truth labels, to be used with # advanced indexing sampled_pos_inds_subset = torch.nonzero(labels > 0).squeeze(1) labels_pos = labels[sampled_pos_inds_subset] if self.cls_agnostic_bbox_reg: map_inds = torch.tensor([4, 5, 6, 7], device=device) else: map_inds = 4 * labels_pos[:, None] + torch.tensor( [0, 1, 2, 3], device=device) box_loss = smooth_l1_loss( box_regression[sampled_pos_inds_subset[:, None], map_inds], regression_targets[sampled_pos_inds_subset], size_average=False, beta=1, ) box_loss = box_loss / labels.numel() else: # only calculating loss of bboxes belonging to gt_label and mse loss N = labels.size(0) cls_labels = labels.clone() cls_labels[torch.nonzero(cls_labels!=gt_label)] = 0 cls_labels[torch.nonzero(cls_labels==gt_label)] = 1 pos_inds = torch.nonzero(cls_labels > 0) if self.cfg.FEW_SHOT.SECOND_STAGE_CLS_LOSS == 'focal_loss': classification_loss = self.cls_loss_func( class_logits, cls_labels.int() ) / ( pos_inds.numel() + N ) else: classification_loss = F.cross_entropy(class_logits, cls_labels) # change to focal loss # get indices that correspond to the regression targets for # the corresponding ground truth labels, to be used with # advanced indexing if cls_labels.numel() == 0: box_loss = torch.tensor(0).float().cuda(device) else: sampled_pos_inds_subset = torch.nonzero(cls_labels > 0).squeeze(1) labels_pos = cls_labels[sampled_pos_inds_subset] if self.cls_agnostic_bbox_reg: map_inds = torch.tensor([4, 5, 6, 7], device=device) else: map_inds = 4 * labels_pos[:, None] + torch.tensor( [0, 1, 2, 3], device=device) box_loss = smooth_l1_loss( box_regression[sampled_pos_inds_subset[:, None], map_inds], regression_targets[sampled_pos_inds_subset], size_average=False, beta=1, ) box_loss = box_loss / cls_labels.numel() if self.cfg.FEW_SHOT.REVERSE_ORDER: return classification_loss, box_loss, reverse_cls_loss # add in new neg support linz if neg_class_logits is not None: neg_class_logits = cat(neg_class_logits, dim=0) focus_neg_class_logits = neg_class_logits[labels==1] focus_pos_class_logits = class_logits[labels==1] # focus_labels = labels[labels==1] focus_neg_class_scores = focus_neg_class_logits.softmax(dim=1)[:,1] focus_pos_class_scores = focus_pos_class_logits.softmax(dim=1)[:,1] cls_suppress_loss = F.relu(focus_neg_class_scores-focus_pos_class_scores+0.3).mean() # cls_suppress_loss = F.cross_entropy(focus_neg_class_logits, 1-focus_labels) # change to focal loss return classification_loss, box_loss, cls_suppress_loss return classification_loss, box_loss def make_roi_box_loss_evaluator(cfg): matcher = Matcher( cfg.MODEL.ROI_HEADS.FG_IOU_THRESHOLD, cfg.MODEL.ROI_HEADS.BG_IOU_THRESHOLD, allow_low_quality_matches=False, ) bbox_reg_weights = cfg.MODEL.ROI_HEADS.BBOX_REG_WEIGHTS box_coder = BoxCoder(weights=bbox_reg_weights) fg_bg_sampler = BalancedPositiveNegativeSampler( cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE, cfg.MODEL.ROI_HEADS.POSITIVE_FRACTION ) cls_agnostic_bbox_reg = cfg.MODEL.CLS_AGNOSTIC_BBOX_REG loss_evaluator = FastRCNNLossComputation( cfg, matcher, fg_bg_sampler, box_coder, cls_agnostic_bbox_reg ) return loss_evaluator

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databass/exprutil.py

ivychen/databass-public

6

86626

from itertools import chain from .exprs import * def predicate_to_cnf(e): """ Split AND tree into a list of conditions """ if not e: return [] if e.is_type("Expr") and e.op.lower() == "and": return list(chain(*map(predicate_to_cnf, [e.l, e.r]))) if e.is_type("Paren"): return [e.c] return [e] def cnf_to_predicate(conds): """ turn list of conditions into an AND tree """ if not conds: return None cond = conds[0] for c in conds[1:]: cond = Expr("and", cond, c) return cond

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nesta/packages/worldbank/collect_worldbank.py

anniyanvr/nesta

13

1604553

""" Collect worldbank ================= Collect worldbank sociodemographic data by country. """ import requests from retrying import retry import json from collections import defaultdict import re import math import pandas as pd WORLDBANK_ENDPOINT = "http://api.worldbank.org/v2/{}" DEAD_RESPONSE = (None, None) # tuple to match the default python return type def worldbank_request(suffix, page, per_page=10000, data_key_path=None): """Hit the worldbank API and extract metadata and data from the response. Args: suffix (str): Suffix to append to :obj:`WORLDBANK_ENDPOINT`. page (int): Pagination number in API request. per_page (int): Number of results to return per request. data_key_path (list): List specifying json path to data object. Returns: metadata, data (dict, list): Metadata and data from API response. """ response = _worldbank_request(suffix=suffix, page=page, per_page=per_page) metadata, data = data_from_response(response=response, data_key_path=data_key_path) return metadata, data @retry(stop_max_attempt_number=3, wait_fixed=2000) def _worldbank_request(suffix, page, per_page): """Hit the worldbank API and return the response. Args: suffix (str): Suffix to append to :obj:`WORLDBANK_ENDPOINT`. page (int): Pagination number in API request. per_page (int): Number of results to return per request. Returns: response (:obj:`requests.Response`) """ # Hit the API r = requests.get(WORLDBANK_ENDPOINT.format(suffix), params=dict(per_page=per_page, format="json", page=page)) # There are some non-404 status codes which indicate invalid API request if r.status_code == 400: return DEAD_RESPONSE r.raise_for_status() # There are even some 200 status codes which indicate invalid API request # purely by returning non-json data response = DEAD_RESPONSE try: response = r.json() except json.JSONDecodeError: pass finally: return response def data_from_response(response, data_key_path=None): """Split up the response from the worldbank API. Args: response (tuple): Response from worldbank API, expected to be a tuple of two json items. data_key_path (list): List specifying json path to data object. Returns: metadata, data (dict, list): Metadata and data from API response. """ # If the data is stored ({metadata}, [datarows]) if data_key_path is None or response == DEAD_RESPONSE: metadata, datarows = response # Otherwise if the data is stored as {metadata, path:{[to:data]}} # (or similar) else: metadata = response datarows = response.copy() for key in data_key_path: datarows = datarows[key] if key != data_key_path[-1] and type(datarows) is list: datarows = datarows[0] return metadata, datarows def calculate_number_of_api_pages(suffix, per_page=10000, data_key_path=None): """Calculate the number of API scrolls required to paginate through this request. Args: suffix (str): Suffix to append to :obj:`WORLDBANK_ENDPOINT`. per_page (int): Number of results to return per request. data_key_path (list): List specifying json path to data object. Returns: n_pages (int): Number of API scrolls required. """ # Discover the shape of the data by inspecting the metadata with # a tiny request (1 result, 1 page) metadata, _ = worldbank_request(suffix=suffix, page=1, per_page=1, data_key_path=data_key_path) # If the request was invalid, there are no pages if metadata is None: return 0 # Calculate the number of pages required total = int(metadata["total"]) n_pages = math.floor(total / per_page) + int(total % per_page > 0) return n_pages def worldbank_data_interval(suffix, first_page, last_page, per_page=10000, data_key_path=None): """Yield a row of data from worldbank API in a page interval. Args: suffix (str): Suffix to append to :obj:`WORLDBANK_ENDPOINT`. {first, last}_page (int): First (last) page number of the API request. per_page (int): Number of results to return per request. data_key_path (list): List specifying json path to data object. Yields: row (dict): A row of data from the worldbank API. """ for page in range(first_page, last_page+1): _, datarows = worldbank_request(suffix=suffix, page=page, per_page=per_page, data_key_path=data_key_path) if datarows is None: continue for row in datarows: yield row def worldbank_data(suffix, per_page=10000, data_key_path=None): """Yield a row of data from worldbank API in a page interval. Args: suffix (str): Suffix to append to :obj:`WORLDBANK_ENDPOINT`. per_page (int): Number of results to return per request. data_key_path (list): List specifying json path to data object. Yields: row (dict): A row of data from the worldbank API. """ n_pages = calculate_number_of_api_pages(suffix=suffix, per_page=per_page, data_key_path=data_key_path) return worldbank_data_interval(suffix, first_page=1, last_page=n_pages, per_page=per_page, data_key_path=data_key_path) def get_worldbank_resource(resource): """Extract and flatten all data for one worldbank resource. Args: resource (str): One of "countries", "series" or "source" Returns: collection (list): A list of resource data. """ collection = [] for row in worldbank_data(resource): # Flatten out any data stored by a key named "value" data = {} for k, v in row.items(): if type(v) is dict: v = v["value"] data[k] = v collection.append(data) return collection def get_variables_by_code(codes): """Discover all dataset locations for each variable id, by variable code. Note: one variable may exist in many datasets, which is handy in the case of missing data. Args: codes (list): The codes of all variables to be discovered. Returns: variables (dict): Mapping of variable id --> dataset names. """ key_path = ["source", "concept", "variable"] # Mapping variable id --> dataset names variables = defaultdict(list) sources = get_worldbank_resource("source") for source in sources: # Extract variables in this "source" (dataset) suffix = f"sources/{source['id']}/series/data" data = worldbank_data(suffix, data_key_path=key_path) # Filter out variables that we don't want filtered_data = filter(lambda row: (row['id'] in codes), data) # Assign remaining datasets to this variable for row in filtered_data: variables[row['id']].append(source['id']) return variables def unpack_quantity(row, concept, value): """Unpack row like {"variable": [{"concept":<concept>, <value>:_i_want_this_}]} Args: row (dict): Row of Worldbank API data. concept (str): The name of the dataset containing the variable. value (str): The name of the variable to unpack. Returns: A value. """ for quantity in row['variable']: if quantity['concept'] == concept: return quantity[value] raise NameError(f"No item found in {row['variable']} with " f"concept = {concept}") def unpack_data(row): """Unpack an entire row of Worldbank API data. Args: row (dict): Row of Worldbank API data. Returns: country, variable, time, value """ country = unpack_quantity(row, 'Country', 'id') #variable = unpack_quantity(row, 'Series', 'value') time = unpack_quantity(row, 'Time', 'value') value = row['value'] return country, time, value def get_country_data(variables, aliases, time="all"): """Extract data for specified variables for all available countries, in a specified year. Args: variables (dict): Mapping of variable --> dataset ids. aliases (dict): Mapping of dirty -> clean variable name. time (str): String to identify time period to request. Returns: country_data (dict): Mapping of country --> variable name --> value """ # Iterate through datasets country_data = defaultdict(dict) # lambda: defaultdict(list)) kwargs_list = get_country_data_kwargs(variables=variables, aliases=aliases, time=time) for kwargs in kwargs_list: _country_data = country_data_single_request(**kwargs) for country, data in _country_data.items(): for var_name, data_row in data.items(): country_data[country][var_name] = data_row return country_data def get_country_data_kwargs(variables, aliases, time="all", per_page=10000, max_pages=None): """Generate every set of kwargs required to make single requests. Designed to be used with :obj:`country_data_single_request` in order to be batched. Args: variables (dict): Mapping of variable --> dataset ids. aliases (dict): Mapping of variable name aliases, to ensure consistent variable naming between data collections. per_page (int): Number of results to return per request. Returns: kwargs_list (list): kwargs list for :obj:`country_data_single_request`. """ # Iterate through datasets kwargs_list = [] key_path = ["source", "data"] for series, sources in variables.items(): # The name of a given variable varies subtlely across multiple # datasets, so we extract the variable name the first time for # consistency across datasets. alias = aliases[series] for source in sources: suffix = (f"sources/{source}/country/all/" f"series/{series}/time/{time}/data") n_pages = calculate_number_of_api_pages(suffix=suffix, per_page=per_page, data_key_path=key_path) for page in range(1, n_pages+1): if max_pages is not None and page > max_pages: break parameters = dict(alias=alias, suffix=suffix, first_page=page, last_page=page, per_page=per_page, data_key_path=key_path) kwargs_list.append(parameters) return kwargs_list def country_data_single_request(alias, **kwargs): """Extract data for all countries using kwargs generated by :obj:`get_country_data_kwargs`. Args: kwargs (dict): An item returned by :obj:`get_country_data_kwargs`. Returns: country_data (dict): Mapping of country --> variable name --> value """ country_data = defaultdict(lambda: defaultdict(list)) done_pkeys = set() data = worldbank_data_interval(**kwargs) for country, time, value in map(unpack_data, data): if value is None: # Missing data for this country continue pkey = (country, time) if pkey in done_pkeys: # Already done this country continue done_pkeys.add(pkey) new_row = {"value": value, "time": time} country_data[country][alias].append(new_row) return country_data # def flatten_country_data(country_data, country_metadata): # """Merge and flatten country data and metadata together. # Args: # country_data (dict): Mapping of country --> variable name --> value # country_metadata (list): List of country metadata. # Returns: # flat_country_data (list): Flattened country data and metadata. # """ # flat_country_data = [dict(**country_data[metadata['id']], **metadata) # for metadata in country_metadata # if metadata['id'] in country_data] # return flat_country_data def discover_variable_name(series): """Discover variable names from each series [short hand code]. Args: series (str): The short hand code for the variable name, according to Worldbank API. Returns: alias (str): The variable name for the given series. """ _, data = worldbank_request(f"en/indicator/{series}", page=1) alias = data[0]["name"] return alias def clean_variable_name(var_name): """Clean a single variable name ready for DB storage. Args: var_name (str): Variable name to be cleaned Returns: new_var_name (str): A MySQL compliant variable name. """ # Lower, replace '%', remove non-alphanums and use '_' new_var_name = var_name.lower().replace("%", "pc") new_var_name = re.sub('[^0-9a-zA-Z]+', ' ', new_var_name) new_var_name = new_var_name.lstrip().rstrip().replace(" ", "_") # Recursively shorten from middle character until less than 64 chars long # (this is the default MySQL limit) # Middle character has been chosen to allow some readability. while len(new_var_name) > 64: # Find the longest term longest_term = "" for term in new_var_name.split("_"): if len(term) <= len(longest_term): continue longest_term = term # Remove the middle character from the longest term middle = len(longest_term) - 1 new_term = longest_term[:middle] + longest_term[middle+1:] new_var_name = new_var_name.replace(longest_term, new_term) return new_var_name def clean_variable_names(flat_country_data): """Clean variable names ready for DB storage. Args: flat_country_data (list): Flattened country data. Returns: out_data (list): Same as input data, with MySQL compliant field names. """ out_data = [] for row in flat_country_data: new_row = {} for key, v in row.items(): # Only clean names containing spaces if " " not in key: new_row[key] = v continue new_key = clean_variable_name(key) new_row[new_key] = v out_data.append(new_row) return out_data def is_bad_quarter(x, bad_quarters): return any(q in x for q in bad_quarters) def flatten_country_data(country_data, country_metadata, bad_quarters=("Q1", "Q3", "Q4")): # Discover the year from the time variable. # Expected to be in the formats "XXXX" and "XXXX QX" country_metadata = {metadata["id"]: metadata for metadata in country_metadata} fairly_flat_data = [] for iso3, _ in country_metadata.items(): for variable, data in country_data[iso3].items(): for row in data: year = re.findall(r'(\d{4})', row["time"])[0] flatter_row = dict(country=iso3, variable=variable, year=year, **row) fairly_flat_data.append(flatter_row) # Group by country and year and remove quarters we don't want. very_flat_data = [] df = pd.DataFrame(fairly_flat_data) for (country, year), _df in df.groupby(["country", "year"]): # Identify quarters we don't want condition = _df["time"].apply(is_bad_quarter, bad_quarters=bad_quarters) if (~condition).sum() == 0: continue row = {r["variable"]: r["value"] for _, r in _df.loc[~condition].iterrows()} row = dict(year=year, **row, **country_metadata[country]) very_flat_data.append(row) return very_flat_data if __name__ == "__main__": # DO ALL OF THE FOLLOWING IN A SINGLE LUIGI TASK variables = get_variables_by_code(["SP.RUR.TOTL.ZS", "SP.URB.TOTL.IN.ZS", "SP.POP.DPND", "SP.POP.TOTL", "SP.DYN.LE00.IN", "SP.DYN.IMRT.IN", "BAR.NOED.25UP.ZS", "BAR.TER.CMPT.25UP.ZS", "NYGDPMKTPSAKD", "SI.POV.NAHC", "SI.POV.GINI"]) aliases = {series: discover_variable_name(series) for series, sources in variables.items()} # (EXCEPT THIS ONE) # country_data = get_country_data(variables, aliases) # ALSO DO THIS kwargs_list = get_country_data_kwargs(variables=variables, aliases=aliases, max_pages=2) # <== Test mode # THEN BATCH, FOR CHUNKS OF KWARGS country_data = defaultdict(dict) for kwargs in kwargs_list: _country_data = country_data_single_request(**kwargs) for country, data in _country_data.items(): for var_name, data_row in data.items(): country_data[country][var_name] = data_row # THEN A FINAL TASK GETS THE ABOVE RESULTS BY COUNTRY # AND WRITES TO DATABASE country_metadata = get_worldbank_resource("countries") flat_country_data = flatten_country_data(country_data, country_metadata) cleaned_data = clean_variable_names(flat_country_data) # STEP AFTER PUTS THESE IN ES

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src/ctc/protocols/fei_utils/analytics/metric_groups/buyback_metrics.py

fei-protocol/checkthechain

94

50674

from __future__ import annotations import typing from ctc import spec from ctc.protocols import balancer_utils from .. import analytics_spec async def async_compute_buybacks( blocks: list[int], verbose: bool = False ) -> analytics_spec.MetricGroup: return { 'name': 'Buybacks', 'metrics': { 'buybacks_usd': (await async_compute_tribe_buybacks_usd(blocks)), }, } async def async_compute_tribe_buybacks_usd( blocks: list[int], swaps: typing.Optional[spec.DataFrame] = None ) -> analytics_spec.MetricData: from ctc.toolbox import pd_utils # load swaps if swaps is None: swaps = await balancer_utils.async_get_pool_swaps( pool_address='0xc1382fe6e17bcdbc3d35f73f5317fbf261ebeecd' ) swaps = typing.cast( spec.DataFrame, swaps.droplevel('transaction_index').droplevel('log_index'), ) # filter tribe buys fei = '0x956f47f50a910163d8bf957cf5846d573e7f87ca' tribe_buys: typing.Any = swaps[swaps['arg__tokenOut'] == fei] # type: ignore tribe_buys = tribe_buys['arg__amountOut'].map(float) / 1e18 cummulative_tribe_buys = tribe_buys.cumsum() # cummulative_tribe_buys = evm.interpolate_block_series( # start_block=min(blocks), # pre_fill_value=0, # series=cummulative_tribe_buys, # end_block=max(blocks), # ) cummulative_tribe_buys = pd_utils.interpolate_series( series=cummulative_tribe_buys, start_index=min(blocks), end_index=max(blocks), pre_fill_value=0, ) # filter tribe sells tribe_sells_df = swaps[swaps['arg__tokenIn'] == fei] # type: ignore if len(tribe_sells_df) > 0: tribe_sells = tribe_sells_df['arg__amountIn'].map(float) / 1e18 cummulative_tribe_sells = tribe_sells.cumsum() # cummulative_tribe_sells = evm.interpolate_block_series( # start_block=min(blocks), # pre_fill_value=0, # series=cummulative_tribe_sells, # end_block=max(blocks), # ) cummulative_tribe_sells = pd_utils.interpolate_series( series=cummulative_tribe_sells, start_index=min(blocks), end_index=max(blocks), pre_fill_value=0, ) net_tribe_buys = cummulative_tribe_buys - cummulative_tribe_sells else: net_tribe_buys = cummulative_tribe_buys return { 'name': 'Buybacks USD', 'values': [net_tribe_buys[block] for block in blocks], 'units': 'FEI', }

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huTools/http/engine_httplib2.py

gadventures/huTools

0

72186

#!/usr/bin/env python # encoding: utf-8 """ engine_httplib2.py implements httplib2 based queries for huTools.http Created by <NAME> on 2010-10-24. Copyright (c) 2010, 2011 HUDORA. All rights reserved. """ from __future__ import unicode_literals from builtins import object import socket import huTools.http.tools from huTools.http import exceptions _http = None def request(url, method, content, headers, timeout=50, caching=None): """Does a HTTP Request via httplib2.""" global _http if not _http: import huTools.http._httplib2 _http = huTools.http._httplib2.Http(timeout=timeout) headers['User-Agent'] = headers.get('User-Agent', '') + ' (httplib2)' _http.clear_credentials() _http.forward_authorization_headers = True # Do not re-use the global Http object after a timeout. # To achieve this, it is set to None. try: resp, content = _http.request(url, method, content, headers=headers) except socket.timeout: _http = None raise exceptions.Timeout replyheaders = {} replyheaders.update(resp) return int(resp.status), replyheaders, content class AsyncHttpResult(object): """Syncronous emulation for plain Python. See `engine_appengine.AsyncHttpResult()` for the thinking behind it. `huTools.http.fetch_async()` is a somewhat more high-level interface. """ def __init__(self, caching=None): self.url, self.method, self.content, self.headers, self.timeout = None, None, None, None, None self._result = None def fetch(self, url, content='', method='GET', credentials=None, headers=None, multipart=False, ua='', timeout=25, returnhandler=lambda x, y, z: (x, y, z)): """Save parameters but delay request execution until get_result() is called.""" self.url, self.method, self.content, self.headers, self.timeout, _dummy = \ huTools.http.tools.prepare_headers(url, content, method, credentials, headers, multipart, ua, timeout) self.returnhandler = returnhandler def get_result(self): """Execute request pass it to returnhandler and return.""" # Cache the result because we mght get called more than once if not self._result: self._result = self.returnhandler(*request(self.url, self.method, self.content, self.headers, self.timeout)) return self._result

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lunespy/wallet/assembly.py

lunes-platform/LunesPy

4

167408

def random_seed(len: int) -> str: from lunespy.wallet.constants import word_list from os import urandom def f(): word_count = 2048 r: bytes = urandom(4) x: int = r[3] + (r[2] << 8) + (r[1] << 16) + (r[0] << 24) w1: int = x % word_count w2: int = ((int(x / word_count) >> 0) + w1) % word_count w3: int = ((int((int(x / word_count) >> 0) / word_count) >> 0) + w2) % word_count return w1, w2, w3 n_words_multiple_of_3: int = len // 3 return " ".join([ word_list[n] for _ in range(n_words_multiple_of_3) for n in f() ]) def from_seed(seed: str, nonce: int, chain: int): from lunespy.crypto import bytes_to_b58, to_address, to_private_key, to_public_key, hidden_seed from lunespy.wallet import Wallet hash_seed: bytes = hidden_seed(nonce, seed) private_key: bytes = to_private_key(hash_seed) public_key: bytes = to_public_key(private_key) address: bytes = to_address(public_key, chain, 1) return Wallet( private_key=bytes_to_b58(private_key), public_key=bytes_to_b58(public_key), address=bytes_to_b58(address), nonce=nonce, chain=chain, seed=seed ) def from_private_key(private_key: str, chain: int = 1): from lunespy.crypto import bytes_to_b58, b58_to_bytes, to_address, to_private_key, to_public_key from lunespy.wallet import Wallet private_key: bytes = to_private_key(b58_to_bytes(private_key)) public_key: bytes = to_public_key(private_key) address: bytes = to_address(public_key, chain, 1) return Wallet( private_key=bytes_to_b58(private_key), public_key=bytes_to_b58(public_key), address=bytes_to_b58(address), seed_len=0, nonce=0, chain=chain, seed="" )

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github_oauth_gateway/auth.py

HDI-Project/ballet-oauth-gateway

1

169868

import requests GITHUB_OAUTH_URL = 'https://github.com/login/oauth/access_token' def request_token(client_id, client_secret, code, redirect_uri, state): url = GITHUB_OAUTH_URL data = { 'client_id': client_id, 'client_secret': client_secret, 'code': code, 'state': state, } headers = { 'Accept': 'application/json' } response = requests.post(url, data=data, headers=headers) response.raise_for_status() return response.json()

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cap9/ex3/index1.py

felipesch92/livroPython

0

137595

with open('multiplos4.txt', 'w') as multiplos: pares = open('pares.txt') for l in pares.readlines(): num = l.replace('\n', '') if int(num) % 4 == 0: multiplos.write(f'{num}\n')

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packages/mccomponents/tests/mccomponentsbpmodule/sample/Broadened_E_Q_Kernel_TestCase.py

mcvine/mcvine

5

5432

#!/usr/bin/env python # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # <NAME> # California Institute of Technology # (C) 2006-2010 All Rights Reserved # # {LicenseText} # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # standalone = True import unittestX as unittest import journal debug = journal.debug( "Broadened_E_Q_Kernel_TestCase" ) warning = journal.warning( "Broadened_E_Q_Kernel_TestCase" ) import mcni from mccomposite import mccompositebp from mccomponents import mccomponentsbp class TestCase(unittest.TestCase): def test(self): E_Q = "Q*Q/3." S_Q = "1" sigma_Q = "Q/2." Qmin = 0; Qmax = 10 absorption_coefficient = scattering_coefficient = 1. kernel = mccomponentsbp.create_Broadened_E_Q_Kernel( E_Q, S_Q, sigma_Q, Qmin, Qmax, absorption_coefficient, scattering_coefficient, ) ei = 500 # meV from mcni.utils import conversion vil = conversion.e2v(ei) vi = (0,0,vil) import numpy.linalg as nl import numpy as np for i in range(10): event = mcni.neutron( r = (0,0,0), v = vi, prob = 1, time = 0 ) kernel.scatter( event ); vf = np.array(event.state.velocity) diffv = vi - vf Q = conversion.v2k(nl.norm(diffv)) ef = conversion.v2e(nl.norm(vf)) E = ei - ef # print E, Q, event E1 = eval(E_Q) continue return pass # end of TestCase def main(): unittest.main() return if __name__ == "__main__": main() # version __id__ = "$Id: TestCase.py 696 2010-11-09 06:23:06Z linjiao $" # End of file

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cyk/cnf/linear_cnf.py

azoimide/cyk

0

93569

<reponame>azoimide/cyk import sys def read_linear_grammar(f=sys.stdin): lines = f.readlines() # rule_lines = lines[:-1] rule_lines = lines # s = map(lambda c: ord(c) - ord('a'), lines[-1].strip()) ups = [] rnups = [] lnups = [] nt_count = 0 t_count = 0 t_tab = {} nt_tab = {} for line in rule_lines: if line[0] == '#': continue lhs, rhs = line.strip().split(' ') # New N in LHS if lhs not in nt_tab: nt_tab[lhs] = len(nt_tab) lnups.append([]) rnups.append([]) ups.append([]) # N -> T (up) if len(rhs) == 1: if rhs.istitle(): raise ValueError("Invalid terminal: " + rhs) if rhs not in t_tab: t_tab[rhs] = len(t_tab) ups[nt_tab[lhs]].append(t_tab[rhs]) else: # N -> NT (lnup) if rhs[0].istitle() and not rhs[1].istitle(): nt = rhs[0] t = rhs[1] # New N in RHS if nt not in nt_tab: nt_tab[nt] = len(nt_tab) ups.append([]) rnups.append([]) lnups.append([]) # New T in RHS if t not in t_tab: t_tab[t] = len(t_tab) lnups[nt_tab[lhs]].append((nt_tab[nt],t_tab[t])) # N -> TN (rnup) elif not rhs[0].istitle() and rhs[1].istitle(): t = rhs[0] nt = rhs[1] # New N in RHS if nt not in nt_tab: nt_tab[nt] = len(nt_tab) ups.append([]) rnups.append([]) lnups.append([]) # New T in RHS if t not in t_tab: t_tab[t] = len(t_tab) rnups[nt_tab[lhs]].append((t_tab[t],nt_tab[nt])) else: raise ValueError("Invalid rule: " + line) return lnups, rnups, ups, nt_tab, t_tab, 0

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