paulo037/starcoder · Datasets at Hugging Face

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linkedin/__init__.py	ptwobrussell/python-linkedin	6	88616	<filename>linkedin/__init__.py __version__ = '2.0' VERSION = tuple(map(int, __version__.split('.')))	[ 1, 529, 9507, 29958, 2324, 287, 262, 29914, 1649, 2344, 26914, 2272, 13, 1649, 3259, 1649, 353, 525, 29906, 29889, 29900, 29915, 13, 16358, 353, 18761, 29898, 1958, 29898, 524, 29892, 4770, 3259, 26914, 5451, 877, 6169, 4961, 13, 2 ]
day15p1.py	minddrive/adventofcode2017	0	79774	<gh_stars>0 #!/usr/bin/env python3 def next_nums(): A = 703 B = 516 for _ in range(40000000): A = 16807 A %= 2147483647 B = 48271 B %= 2147483647 yield (A, B) count = 0 for newA, newB in next_nums(): if newA % 65536 == newB % 65536: count += 1 print(count)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 13, 13, 1753, 2446, 29918, 1949, 29879, 7295, 13, 1678, 319, 353, 29871, 29955, 29900, 29941, 13, 1678, 350, 353, 29871, 29945, 29896, 29953, 13, 13, 1678, 363, 903, 297, 3464, 29898, 29946, 29900, 29900, 29900, 29900, 29900, 29900, 29900, 1125, 13, 4706, 319, 334, 29922, 29871, 29896, 29953, 29947, 29900, 29955, 13, 4706, 319, 1273, 29922, 29871, 29906, 29896, 29946, 29955, 29946, 29947, 29941, 29953, 29946, 29955, 13, 4706, 350, 334, 29922, 29871, 29946, 29947, 29906, 29955, 29896, 13, 4706, 350, 1273, 29922, 29871, 29906, 29896, 29946, 29955, 29946, 29947, 29941, 29953, 29946, 29955, 13, 4706, 7709, 313, 29909, 29892, 350, 29897, 13, 13, 13, 2798, 353, 29871, 29900, 13, 13, 1454, 716, 29909, 29892, 716, 29933, 297, 2446, 29918, 1949, 29879, 7295, 13, 1678, 565, 716, 29909, 1273, 29871, 29953, 29945, 29945, 29941, 29953, 1275, 716, 29933, 1273, 29871, 29953, 29945, 29945, 29941, 29953, 29901, 13, 4706, 2302, 4619, 29871, 29896, 13, 13, 2158, 29898, 2798, 29897, 13, 2 ]
examples/optimization_applications/search_space_example.py	nevinadalal/Hyperactive	4	59738	<reponame>nevinadalal/Hyperactive<filename>examples/optimization_applications/search_space_example.py """ Hyperactive is very versatile, because it can handle not just numerical or string variables in the search space, but also functions. If you want to search for the best list, numpy array, dataframed or class you can put them into a function that returns them as shown in the example below. This enables many possibilities for more complex optimization applications. Neural architecture search, feature engineering, ensemble optimization and many other applications are only possible or much easier if you can put functions in the search space. """ from hyperactive import Hyperactive def function_0(): # do stuff in function0 return def function_1(): # do stuff in function1 return def function_2(): # do stuff in function2 return def list1(): return [1, 0, 0] def list2(): return [0, 1, 0] def list3(): return [0, 0, 1] # Hyperactive can handle python objects in the search space search_space = { "int": list(range(1, 10)), "float": [0.1, 0.01, 0.001], "string": ["string1", "string2"], "function": [function_0, function_1, function_2], "list": [list1, list2, list3], } def objective_function(para): # score must be a number score = 1 return score hyper = Hyperactive() hyper.add_search(objective_function, search_space, n_iter=20) hyper.run() search_data = hyper.search_data(objective_function) print("\n Search Data: \n", search_data)	[ 1, 529, 276, 1112, 420, 29958, 484, 3845, 27283, 284, 29914, 26322, 546, 4925, 29966, 9507, 29958, 19057, 29914, 20640, 2133, 29918, 932, 5795, 29914, 4478, 29918, 3493, 29918, 4773, 29889, 2272, 13, 15945, 29908, 13, 26322, 546, 4925, 338, 1407, 1224, 24285, 29892, 1363, 372, 508, 4386, 451, 925, 16259, 470, 29871, 13, 1807, 3651, 297, 278, 2740, 2913, 29892, 541, 884, 3168, 29889, 960, 366, 864, 304, 13, 4478, 363, 278, 1900, 1051, 29892, 12655, 1409, 29892, 848, 1341, 2795, 470, 770, 366, 508, 1925, 963, 964, 263, 13, 2220, 393, 3639, 963, 408, 4318, 297, 278, 1342, 2400, 29889, 13, 13, 4013, 28936, 1784, 24496, 363, 901, 4280, 13883, 8324, 29889, 13, 8139, 3631, 11258, 2740, 29892, 4682, 21639, 29892, 21285, 13883, 322, 1784, 916, 8324, 526, 29871, 13, 6194, 1950, 470, 1568, 6775, 565, 366, 508, 1925, 3168, 297, 278, 2740, 2913, 29889, 13, 15945, 29908, 13, 13, 3166, 11266, 4925, 1053, 26078, 4925, 13, 13, 13, 1753, 740, 29918, 29900, 7295, 13, 1678, 396, 437, 6433, 297, 740, 29900, 13, 1678, 736, 29871, 13, 13, 13, 1753, 740, 29918, 29896, 7295, 13, 1678, 396, 437, 6433, 297, 740, 29896, 13, 1678, 736, 29871, 13, 13, 13, 1753, 740, 29918, 29906, 7295, 13, 1678, 396, 437, 6433, 297, 740, 29906, 13, 1678, 736, 29871, 13, 13, 13, 1753, 1051, 29896, 7295, 13, 1678, 736, 518, 29896, 29892, 29871, 29900, 29892, 29871, 29900, 29962, 13, 13, 13, 1753, 1051, 29906, 7295, 13, 1678, 736, 518, 29900, 29892, 29871, 29896, 29892, 29871, 29900, 29962, 13, 13, 13, 1753, 1051, 29941, 7295, 13, 1678, 736, 518, 29900, 29892, 29871, 29900, 29892, 29871, 29896, 29962, 13, 13, 13, 29937, 26078, 4925, 508, 4386, 3017, 3618, 297, 278, 2740, 2913, 13, 4478, 29918, 3493, 353, 426, 13, 1678, 376, 524, 1115, 1051, 29898, 3881, 29898, 29896, 29892, 29871, 29896, 29900, 8243, 13, 1678, 376, 7411, 1115, 518, 29900, 29889, 29896, 29892, 29871, 29900, 29889, 29900, 29896, 29892, 29871, 29900, 29889, 29900, 29900, 29896, 1402, 13, 1678, 376, 1807, 1115, 6796, 1807, 29896, 613, 376, 1807, 29906, 12436, 13, 1678, 376, 2220, 1115, 518, 2220, 29918, 29900, 29892, 740, 29918, 29896, 29892, 740, 29918, 29906, 1402, 13, 1678, 376, 1761, 1115, 518, 1761, 29896, 29892, 1051, 29906, 29892, 1051, 29941, 1402, 13, 29913, 13, 13, 13, 1753, 12091, 29918, 2220, 29898, 22752, 1125, 13, 1678, 396, 8158, 1818, 367, 263, 1353, 13, 1678, 8158, 353, 29871, 29896, 13, 1678, 736, 8158, 13, 13, 13, 24947, 353, 26078, 4925, 580, 13, 24947, 29889, 1202, 29918, 4478, 29898, 3318, 573, 29918, 2220, 29892, 2740, 29918, 3493, 29892, 302, 29918, 1524, 29922, 29906, 29900, 29897, 13, 24947, 29889, 3389, 580, 13, 13, 4478, 29918, 1272, 353, 11266, 29889, 4478, 29918, 1272, 29898, 3318, 573, 29918, 2220, 29897, 13, 13, 2158, 14182, 29876, 11856, 3630, 29901, 320, 29876, 613, 2740, 29918, 1272, 29897, 13, 2 ]
code/improved method/SpatialColumnDetection.py	astrob3rry/spatial-data-profiling	0	40300	<filename>code/improved method/SpatialColumnDetection.py import pandas as pd import sys, os import numpy as np from snapy import MinHash, LSH from fuzzywuzzy import fuzz from fuzzywuzzy import process import random import re import Settings as Settings from pyspark.sql import SparkSession spark = SparkSession.builder.appName("project-test").config("spark.some.config.option", "some-value").getOrCreate() spark.sparkContext.addPyFile("/home/wh2099/project/mmh3.py") spark.sparkContext.addPyFile("/home/wh2099/project/snapy.zip") import mmh3 from snapy import MinHash, LSH class SpatialColumnDetection: def __init__(self, df, defaultFiles, index): self.types = Settings.types self.df = df # column names self.columnNames = list(df.columns) # lower case column names self.lcColumnNames = [] # lower case column name to orginal name map self.lowerUpperDict = {} self.upperLowerDict = {} # each column is what type self.colNameType = {} self.defaultFiles = defaultFiles self.index = index # get the dtype dictionary first self.dtypes = {} for keyVal in df.dtypes: self.dtypes[keyVal[0]] = keyVal[1] def initReturnResult(self): self.colNameType["file_index"] = str(self.index) self.colNameType["total_spatial_attributes"] = 0 self.colNameType["attributes"] = {} for colName in self.columnNames: self.colNameType["attributes"][colName] = {} # change all column names to lower cases def changeLowerCase(self, ): for colName in self.columnNames: lwColName = colName.lower() self.lcColumnNames.append(lwColName) self.lowerUpperDict[lwColName] = colName self.upperLowerDict[colName] = lwColName def lcs(self, X, Y): # find the length of the strings m = len(X) n = len(Y) # declaring the array for storing the dp values L = [[None] * (n + 1) for i in range(m + 1)] """Following steps build L[m + 1][n + 1] in bottom up fashion Note: L[i][j] contains length of LCS of X[0..i-1] and Y[0..j-1]""" for i in range(m + 1): for j in range(n + 1): if i == 0 or j == 0: L[i][j] = 0 elif X[i - 1] == Y[j - 1]: L[i][j] = L[i - 1][j - 1] + 1 else: L[i][j] = max(L[i - 1][j], L[i][j - 1]) # L[m][n] contains the length of LCS of X[0..n-1] & Y[0..m-1] return L[m][n] def editDistance(self, x, y): return len(x) + len(y) - 2 * self.lcs(x, y) def detect(self, ): self.changeLowerCase() self.initReturnResult() colIndex = 0 for colName in self.columnNames: # change to lower case and trip it colNameLwStrip = colName.lower().strip() if self.detectLongitude(colNameLwStrip, colName): type = "longitude" elif self.detectLatitude(colNameLwStrip, colName): type = "latitude" elif self.detectAddress(colNameLwStrip, colName): type = "address" # elif self.detectCounty(colNameLwStrip, colName): # type = "county" elif self.detectBorough(colNameLwStrip, colName): type = "borough" elif self.detectCity(colNameLwStrip, colName): type = "city" elif self.detectState(colNameLwStrip, colName): type = "state" elif self.detectCountry(colNameLwStrip, colName): type = "country" elif self.detectZipcode(colNameLwStrip, colName): type = "zipcode" elif self.detectOtherLocationAttribute(colNameLwStrip, colName): type = "other location attribute" else: type = "not spatial attribute" self.colNameType["attributes"][colName]["type"] = type self.colNameType["attributes"][colName]["index"] = colIndex colIndex += 1 count = 0 for colName in self.columnNames: if self.colNameType["attributes"][colName]["type"] != "not spatial attribute": count += 1 self.colNameType["attributes"][colName]["is_spatial"] = True else: self.colNameType["attributes"][colName]["is_spatial"] = False self.colNameType["total_spatial_attributes"] = count return self.colNameType def commonDetectMethod(self, colNameLw, names, thredshold): for name in names: if (colNameLw in name) or (name in colNameLw): return True # use fuzz ratio, which uses the idea of levestain distance, we can specify elsewhere for name in names: if fuzz.ratio(name, colNameLw) > thredshold: return True return False # TODO: to be finish, colName passed in is lowercase, colNameLw is the lower case and stip() def detectLongitude(self, colNameLw, colName): names = ["longitude", "lon"] thredshold = 75 if self.commonDetectMethod(colNameLw, names, thredshold): return True return False # TODO: use datamart def detectLatitude(self, colNameLw, colName): names = ["latitude"] thredshold = 75 if self.commonDetectMethod(colNameLw, names, thredshold): return True return False # list all the country out, count how many of the sample column values are in country list def detectCountry(self, colNameLw,colName): # quite similar word if "county" in colNameLw: return False names = ["country"] thredshold = 100 if self.commonDetectMethod(colNameLw, names, thredshold): return True # this not work for "yes" or "no" column because "no" stands for norway if (self.dtypes[colName] == object) or (self.dtypes[colName] == "string"): dfCountryNames = self.defaultFiles.dfCountryNames # sampling and pair wise comparison sampleSize = 500 sampleSize = min(sampleSize, self.df.count()) columnValuesSample = random.sample(self.df.select(colName).rdd.flatMap(lambda x: x).collect(), sampleSize) columnValuesSample = [x for x in columnValuesSample if type(x) == str] sampleLength = len(columnValuesSample) # meanning many are nan if sampleLength / sampleSize < 0.1: return False # get the average length of the values avgLen = sum(map(len, columnValuesSample)) / len(columnValuesSample) # compare with country code, other wise compare with country full name if avgLen <= 2.3: countries = dfCountryNames.select("Code").rdd.flatMap(lambda x: x).collect() countries = [code for code in countries if code != "NO"] else: countries = dfCountryNames.select("Name").rdd.flatMap(lambda x: x).collect() countries = [code for code in countries if code != "Norway"] # equality count count = 0 for value in columnValuesSample: for country in countries: if value.lower() == country.lower(): count += 1 break if count / sampleLength > 0.7: return True return False # list all the state out, fullname and abbreviation, can use sampling def detectState(self, colNameLw, colName): names = ["state"] thredshold = 90 if self.commonDetectMethod(colNameLw, names, thredshold): return True if (self.dtypes[colName] == object) or (self.dtypes[colName] == "string"): dfStateNames = self.defaultFiles.dfStateNames # sampling and pair wise comparison sampleSize = 500 sampleSize = min(sampleSize, self.df.count()) columnValuesSample = random.sample(self.df.select(colName).rdd.flatMap(lambda x: x).collect(), sampleSize) columnValuesSample = [x for x in columnValuesSample if type(x) == str] sampleLength = len(columnValuesSample) # meanning many are nan if sampleLength / sampleSize < 0.1: return False # get the average length of the values avgLen = sum(map(len, columnValuesSample)) / len(columnValuesSample) # compare with state code, other wise compare with state full name if avgLen <= 2.3: states = dfStateNames.select("Code").rdd.flatMap(lambda x: x).collect() else: states = dfStateNames.select("Name").rdd.flatMap(lambda x: x).collect() # equality count count = 0 for value in columnValuesSample: for state in states: if value.lower() == state.lower(): count += 1 break if count / sampleLength > 0.6: return True return False # can use sampling def detectCity(self, colNameLw, colName): names = ["city", "town"] thredshold = 90 # if self.commonDetectMethod(colNameLw, names, thredshold): # return True # avoid capcity, ethnicity, electricity words for name in names: if colNameLw == name: return True # some column name states, county but have New York inside if self.commonDetectMethod(colNameLw, ["state", "county"], thredshold): return False if (self.dtypes[colName] == object) or (self.dtypes[colName] == "string"): dfCityNames = self.defaultFiles.dfCityNames # sampling and pair wise comparison sampleSize = 500 sampleSize = min(sampleSize, self.df.count()) columnValuesSample = random.sample(self.df.select(colName).rdd.flatMap(lambda x: x).collect(), sampleSize) columnValuesSample = [x for x in columnValuesSample if type(x) == str] sampleLength = len(columnValuesSample) # meanning many are nan if sampleLength / sampleSize < 0.1: return False cities = dfCityNames.select("Name").rdd.flatMap(lambda x: x).collect() # equality count count = 0 for value in columnValuesSample: for city in cities: if value == city: count += 1 break if count / sampleLength > 0.6: return True return False # list all the county out, count how many of the sample column values are in county list. Not country!!! def detectCounty(self, colNameLw, colName): # quite similar word if "country" in colNameLw: return False names = ["county"] thredshold = 70 if self.commonDetectMethod(colNameLw, names, thredshold): return True if (self.dtypes[colName] == object) or (self.dtypes[colName] == "string"): dfCountyNames = self.defaultFiles.dfCountyNames # sampling and pair wise comparison sampleSize = 500 sampleSize = min(sampleSize, self.df.count()) columnValuesSample = random.sample(self.df.select(colName).rdd.flatMap(lambda x: x).collect(), sampleSize) columnValuesSample = [x for x in columnValuesSample if type(x) == str] sampleLength = len(columnValuesSample) # meaning many are nan if sampleLength / sampleSize < 0.1: return False counties = dfCountyNames.select("Name").rdd.flatMap(lambda x: x).collect() # equality count count = 0 for value in columnValuesSample: for county in counties: if value.lower() == county.lower(): count += 1 break if count / sampleLength > 0.6: return True return False def detectBorough(self, colNameLw, colName): names = ["borough", "boro", "borocode"] thredshold = 80 if self.commonDetectMethod(colNameLw, names, thredshold): return True return False # need to use sampling def detectAddress(self, colNameLw, colName): names = ["address", "street", "block"] thredshold = 80 if self.commonDetectMethod(colNameLw, names, thredshold): # add one more condition if (self.dtypes[colName] == object) or (self.dtypes[colName] == "string"): return True if (self.dtypes[colName] == object) or (self.dtypes[colName] == "string"): # sampling and pair wise comparison sampleSize = 500 sampleSize = min(sampleSize, self.df.count()) columnValuesSample = random.sample(self.df.select(colName).rdd.flatMap(lambda x: x).collect(), sampleSize) columnValuesSample = [x for x in columnValuesSample if type(x) == str] sampleLength = len(columnValuesSample) # meanning many are nan if sampleLength / sampleSize < 0.1: return False # get the average length of the values avgLen = sum(map(len, columnValuesSample)) / len(columnValuesSample) # probably not address if avgLen < 5: return False # use regex expression, detect full address regexPattern =""" \b\d{1,6} +.{2,25}\b(avenue\|ave\|court\|ct\|street\|st\| drive\|dr\|lane\|ln\|road\|rd\|blvd\|plaza\|parkway\|pkwy\| boulevard\|)[.,]?(.{0,25} +\b\d{5}\b)? """ count = 0 for x in columnValuesSample: result = re.match(regexPattern, x) if result != None: if result.group() > 10: count += 1 if count / sampleLength > 0.6: return True # use regex expression to detect street name like regexPattern2 =""" \d+[ ](?:[A-Za-z0-9.-]+[ ]?)+(?:Avenue\|Lane\|Road\| Boulevard\|Drive\|Street\|Ave\|Dr\|Rd\|Blvd\|Ln\|St)\.? """ count = 0 for x in columnValuesSample: result = re.match(regexPattern2, x) if result != None: if result.group() > 10: count += 1 if count / sampleLength > 0.7: return True # TODO use the addr_detection package # try: # clf2 = Postal_clf() # result = clf2.predict(columnValuesSample) # except: # return False return False # need to use sampling, and regex def detectZipcode(self, colNameLw, colName): names = ["zip", "zipcode", "zcode", "postcode"] thredshold = 70 if self.commonDetectMethod(colNameLw, names, thredshold): return True if (self.dtypes[colName] == object) or (self.dtypes[colName] == "string") or \ (self.dtypes[colName] == "int") or (self.dtypes[colName] == "bigint"): # sampling and pair wise comparison sampleSize = 500 sampleSize = min(sampleSize, self.df.count()) columnValuesSample = random.sample(self.df.select(colName).rdd.flatMap(lambda x: x).collect(), sampleSize) if (self.dtypes[colName] == "int") or (self.dtypes[colName] == "bigint"): columnValuesSample = [str(x) for x in columnValuesSample] else: columnValuesSample = [x for x in columnValuesSample if type(x) == str] sampleLength = len(columnValuesSample) # meanning many are nan if sampleLength / sampleSize < 0.1: return False # just us country here, can use other api? regexPattern = r"^[0-9]{5}(?:-[0-9]{3})?$" matches = [] for x in columnValuesSample: result = re.match(regexPattern, x) if result != None: matches.append(bool(x)) count = sum([bool(x) for x in matches]) if count / sampleLength > 0.6: return True return False def detectOtherLocationAttribute(self, colNameLw, colName): names = ["location", "home", "house", "lot", "bin", "bbl", "nta", "geom", "precinct", "census_tract", "community", "district", "building"] thredshold = 70 if self.commonDetectMethod(colNameLw, names, thredshold): return True return False	[ 1, 529, 9507, 29958, 401, 29914, 326, 771, 1490, 1158, 29914, 29903, 5031, 616, 4409, 29928, 2650, 428, 29889, 2272, 13, 5215, 11701, 408, 10518, 13, 5215, 10876, 29892, 2897, 13, 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openstates/openstates-master/openstates/de/legislators.py	Jgorsick/Advocacy_Angular	0	6029	import re import lxml.html from openstates.utils import LXMLMixin from billy.scrape.legislators import LegislatorScraper, Legislator class DELegislatorScraper(LegislatorScraper,LXMLMixin): jurisdiction = 'de' def scrape(self, chamber, term): url = { 'upper': 'http://legis.delaware.gov/legislature.nsf/sen?openview', 'lower': 'http://legis.delaware.gov/Legislature.nsf/Reps?openview', }[chamber] doc = self.lxmlize(url) if chamber == "upper": #for the senate, it's the same table #but the html is hard-coded in js. table_js = doc.xpath('.//script')[-1].text_content() table = None for line in table_js.split("\n"): if line.strip().startswith("var") and "sen=" in line: table = line.replace("var","") table = table.replace('sen="<','<') table = table.replace('>";','>') break assert table is not None, "Senate table could not be found" table = lxml.html.fromstring(table) table.make_links_absolute(url) trs = table.xpath('//tr') else: #same table for the house, but kindly in actual html trs = doc.xpath('//tr') base_url = "http://legis.delaware.gov" for tr in trs: name_and_url = tr.xpath('.//a')[0] bio_url = name_and_url.attrib["href"] bio_url = bio_url.replace("JavaScript:window.top.location.href=","") bio_url = bio_url.replace('"','') name = name_and_url.text_content() if name.strip() == "." or name.strip() == "": continue if name.strip().lower().startswith("vacant"): continue re_spaces=re.compile(r'\s{1,5}') name = ' '.join(re_spaces.split(name)) district = tr.xpath('.//td')[2].text_content() district = district.replace("District:","").strip() leg = self.scrape_bio(term, chamber, district, name, bio_url) leg.add_source(bio_url, page="legislator detail page") leg.add_source(url, page="legislator list page") self.save_legislator(leg) def scrape_bio(self, term, chamber, district, name, url): # this opens the committee section without having to do another request url += '&TableRow=1.5.5' frame_doc = self.lxmlize(url) actual_url = frame_doc.xpath("//frame[@name='right']/@src")[0] doc = self.lxmlize(actual_url) # party is in one of these party = doc.xpath('//div[@id="page_header"]')[0].text.strip()[-3:] if '(D)' in party: party = 'Democratic' elif '(R)' in party: party = 'Republican' else: raise AssertionError("No party found for {name}".format(name=name)) leg = Legislator(term, chamber, district, name, party=party) photo_url = doc.xpath('//img[contains(@src, "jpg")]/@src') if photo_url: leg['photo_url'] = photo_url[0] contact_info = self.scrape_contact_info(doc) leg.update(contact_info) return leg def scrape_contact_info(self, doc): # Email email = doc.xpath(".//a[contains(@href,'mailto')]") email = email[0].text_content().strip() leg_email = None dist_email = None try: emails = email.split(";") except AttributeError: pass else: for e in emails: e = e.strip() if e: if "state.de.us" in e: leg_email = e else: dist_email = e # Offices leg_office = dict(name="Capitol Office", type="capitol", phone=None, fax=None, email=leg_email, address=None) dist_office = dict(name="Outside Office", type="capitol", phone=None,fax=None, email=dist_email, address=None) #this is enormously painful, DE. office_list = doc.xpath("//tr") for office in office_list: title_td = 0 #in some trs the photo is the first td if len(office.xpath("./td/img")) > 0: title_td = 1 try: title_text = office.xpath("./td")[title_td].text_content().lower() content = office.xpath("./td")[title_td+1].text_content() except IndexError: continue leg_office = self.add_contact("legislative", title_text,content,leg_office) dist_office = self.add_contact("outside", title_text,content,dist_office) offices = [o for o in [leg_office,dist_office] if o["address"]] assert len(offices) > 0, "No offices with addresses found "\ "make sure we're not losing any data." return {"offices":offices} def add_contact(self,office_type, title_text,content,office): #office type is the name of the office #either "legislative" or "outside" if "{} office".format(office_type) in title_text: office["address"] = content.strip() if "{} phone".format(office_type) in title_text: phones = content.lower().split("\n") if len(phones) == 1: phone = self.clean_phone(phones[0]) if phone: office["phone"] = phone else: for line in phones: if "phone" in line: phone = self.clean_phone(line) if phone: office["phone"] = phone elif "fax" in line: phone = self.clean_phone(line) if phone: office["fax"] = phone return office def clean_phone(self,phone): if not phone.strip(): return if not re.search("\d",phone): return if not ":" in phone: return phone return phone.split(":")[1].strip()	[ 1, 1053, 337, 13, 5215, 301, 3134, 29889, 1420, 13, 3166, 1722, 28631, 29889, 13239, 1053, 365, 9165, 29924, 861, 262, 13, 13, 3166, 289, 9403, 29889, 1557, 336, 412, 29889, 1397, 7497, 4097, 1053, 18991, 1061, 4421, 336, 546, 29892, 18991, 1061, 13, 13, 13, 1990, 5012, 22988, 7497, 1061, 4421, 336, 546, 29898, 22988, 7497, 1061, 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tests/test_handler.py	CJSoldier/webssh	13	4247	<filename>tests/test_handler.py import unittest import paramiko from tornado.httputil import HTTPServerRequest from tests.utils import read_file, make_tests_data_path from webssh.handler import MixinHandler, IndexHandler, InvalidValueError class TestMixinHandler(unittest.TestCase): def test_get_real_client_addr(self): handler = MixinHandler() handler.request = HTTPServerRequest(uri='/') self.assertIsNone(handler.get_real_client_addr()) ip = '127.0.0.1' handler.request.headers.add('X-Real-Ip', ip) self.assertEqual(handler.get_real_client_addr(), False) handler.request.headers.add('X-Real-Port', '12345x') self.assertEqual(handler.get_real_client_addr(), False) handler.request.headers.update({'X-Real-Port': '12345'}) self.assertEqual(handler.get_real_client_addr(), (ip, 12345)) handler.request.headers.update({'X-Real-ip': None}) self.assertEqual(handler.get_real_client_addr(), False) handler.request.headers.update({'X-Real-Port': '12345x'}) self.assertEqual(handler.get_real_client_addr(), False) class TestIndexHandler(unittest.TestCase): def test_get_specific_pkey_with_plain_key(self): fname = 'test_rsa.key' cls = paramiko.RSAKey key = read_file(make_tests_data_path(fname)) pkey = IndexHandler.get_specific_pkey(cls, key, None) self.assertIsInstance(pkey, cls) pkey = IndexHandler.get_specific_pkey(cls, key, 'iginored') self.assertIsInstance(pkey, cls) pkey = IndexHandler.get_specific_pkey(cls, 'x'+key, None) self.assertIsNone(pkey) def test_get_specific_pkey_with_encrypted_key(self): fname = 'test_rsa_password.key' cls = paramiko.RSAKey password = '<PASSWORD>' key = read_file(make_tests_data_path(fname)) pkey = IndexHandler.get_specific_pkey(cls, key, password) self.assertIsInstance(pkey, cls) pkey = IndexHandler.get_specific_pkey(cls, 'x'+key, None) self.assertIsNone(pkey) with self.assertRaises(paramiko.PasswordRequiredException): pkey = IndexHandler.get_specific_pkey(cls, key, None) def test_get_pkey_obj_with_plain_key(self): fname = 'test_ed25519.key' cls = paramiko.Ed25519Key key = read_file(make_tests_data_path(fname)) pkey = IndexHandler.get_pkey_obj(key, None, fname) self.assertIsInstance(pkey, cls) pkey = IndexHandler.get_pkey_obj(key, 'iginored', fname) self.assertIsInstance(pkey, cls) with self.assertRaises(InvalidValueError) as exc: pkey = IndexHandler.get_pkey_obj('x'+key, None, fname) self.assertIn('Invalid private key', str(exc)) def test_get_pkey_obj_with_encrypted_key(self): fname = 'test_ed25519_password.key' password = '<PASSWORD>' cls = paramiko.Ed25519Key key = read_file(make_tests_data_path(fname)) pkey = IndexHandler.get_pkey_obj(key, password, fname) self.assertIsInstance(pkey, cls) with self.assertRaises(InvalidValueError) as exc: pkey = IndexHandler.get_pkey_obj(key, 'wrongpass', fname) self.assertIn('Wrong password', str(exc)) with self.assertRaises(InvalidValueError) as exc: pkey = IndexHandler.get_pkey_obj('x'+key, password, fname) 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chap3-3.py	mikidake/Ex3	0	27253	<gh_stars>0 # Chapter3 Ex.3) # Modification of 'Guess My Number' # Guess My Number # # The computer picks a random number between 1 and 100 # The player tries to guess it and the computer lets # the player know if the guess is too high, too low # or right on the money # The player can try to guess up to three times import random print("\tWelcome to 'Guess My Number'!") print("\nI'm thinking of a number between 1 and 100.") print("Try to guess it in as few attempts as possible.") print("You have three chances to try!\n") # set the initial values the_number = random.randint(1, 100) tries = 1 maxTries = 3 # guessing loop while tries <= maxTries: guess = int(input("Take a guess: ")) if guess == the_number: print("\nYou guessed it! The number was", the_number) if tries == 1: print("And it only took you", tries, "try!\n") break else: print("And it only took you", tries, "tries!\n") break elif guess > the_number: print("Guess Lower..") else: print("Guess Higher..") tries += 1 if tries > maxTries: print("\nSorry you ran out of tries! The random number was ", the_number) input("\n\nPress the enter key to exit.")	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 23040, 29941, 1222, 29889, 29941, 8443, 13, 29937, 3382, 2450, 310, 525, 9485, 404, 1619, 9681, 29915, 30004, 13, 30004, 13, 29937, 2088, 404, 1619, 9681, 30004, 13, 29937, 30004, 13, 29937, 450, 6601, 5839, 29879, 263, 4036, 1353, 1546, 29871, 29896, 322, 29871, 29896, 29900, 29900, 30004, 13, 29937, 450, 4847, 14335, 304, 4140, 372, 322, 278, 6601, 16869, 30004, 13, 29937, 278, 4847, 1073, 565, 278, 4140, 338, 2086, 1880, 29892, 2086, 4482, 30004, 13, 29937, 470, 1492, 373, 278, 6909, 30004, 13, 29937, 450, 4847, 508, 1018, 304, 4140, 701, 304, 2211, 3064, 30004, 13, 30004, 13, 5215, 4036, 29871, 6756, 13, 30004, 13, 2158, 14182, 29873, 28862, 2763, 304, 525, 9485, 404, 1619, 9681, 29915, 29991, 1159, 30004, 13, 2158, 14182, 29876, 29902, 29915, 29885, 7291, 310, 263, 1353, 1546, 29871, 29896, 322, 29871, 29896, 29900, 29900, 23157, 30004, 13, 2158, 703, 15870, 304, 4140, 372, 297, 408, 2846, 14734, 408, 1950, 23157, 30004, 13, 2158, 703, 3492, 505, 2211, 521, 2925, 304, 1018, 9903, 29876, 1159, 30004, 13, 30004, 13, 29937, 731, 278, 2847, 1819, 30004, 13, 1552, 29918, 4537, 353, 4036, 29889, 9502, 524, 29898, 29896, 29892, 29871, 29896, 29900, 29900, 8443, 13, 29873, 2722, 353, 29871, 29896, 30004, 13, 3317, 29911, 2722, 353, 29871, 29941, 30004, 13, 30004, 13, 29937, 20680, 2425, 30004, 13, 8000, 14335, 5277, 4236, 29911, 2722, 29901, 30004, 13, 1678, 4140, 353, 938, 29898, 2080, 703, 26772, 263, 4140, 29901, 376, 876, 30004, 13, 1678, 565, 4140, 1275, 278, 29918, 4537, 29901, 30004, 13, 4706, 1596, 14182, 29876, 3492, 4140, 287, 372, 29991, 450, 1353, 471, 613, 278, 29918, 4537, 8443, 13, 4706, 565, 14335, 1275, 29871, 29896, 29901, 30004, 13, 9651, 1596, 703, 2855, 372, 871, 3614, 366, 613, 14335, 29892, 376, 2202, 9903, 29876, 1159, 30004, 13, 9651, 2867, 30004, 13, 4706, 1683, 29901, 30004, 13, 9651, 1596, 703, 2855, 372, 871, 3614, 366, 613, 14335, 29892, 376, 29873, 2722, 9903, 29876, 1159, 30004, 13, 9651, 2867, 30004, 13, 1678, 25342, 4140, 1405, 278, 29918, 4537, 29901, 30004, 13, 4706, 1596, 703, 9485, 404, 27723, 636, 1159, 30004, 13, 1678, 1683, 29901, 30004, 13, 4706, 1596, 703, 9485, 404, 5057, 261, 636, 1159, 30004, 13, 1678, 14335, 4619, 29871, 29896, 30004, 13, 1678, 565, 14335, 1405, 4236, 29911, 2722, 29901, 30004, 13, 4706, 1596, 14182, 29876, 29903, 3818, 366, 6350, 714, 310, 14335, 29991, 450, 4036, 1353, 471, 9162, 278, 29918, 4537, 8443, 13, 4706, 6756, 13, 2080, 14182, 29876, 29905, 29876, 10923, 278, 3896, 1820, 304, 6876, 23157, 2 ]
tests/dce_models/test_dibem.py	michaelberks/madym_python	0	146402	''' Test file for the dce_models sub-module ''' import pytest import os import sys import numpy as np from tempfile import TemporaryDirectory sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'src'))) #------------------------------------------------------------------------------- # Tests for dibem module from QbiPy.dce_models import dibem, two_cxm_model, active_uptake_model from QbiPy.dce_models import dce_aif #------------------------------------------------------------------------------- def test_params_AUEM(): #Test consistency of conversion to/from DIBEM F_p = 1.0 v_ecs = 0.2 k_i = 0.25 k_ef = 0.05 dibem_params = active_uptake_model.params_to_DIBEM(F_p, v_ecs, k_i, k_ef, False) F_p2, v_ecs2, k_i2, k_ef2 = active_uptake_model.params_from_DIBEM(dibem_params, False) dibem_params = active_uptake_model.params_to_DIBEM(F_p, v_ecs, k_i, k_ef, True) F_p3, v_ecs3, k_i3, k_ef3 = active_uptake_model.params_from_DIBEM(dibem_params, True) assert F_p == pytest.approx(F_p2) assert v_ecs == pytest.approx(v_ecs2) assert k_i == pytest.approx(k_i2) assert k_ef == pytest.approx(k_ef2) assert F_p == pytest.approx(F_p3) assert v_ecs == pytest.approx(v_ecs3) assert k_i == pytest.approx(k_i3) assert k_ef == pytest.approx(k_ef3) def test_params_2CXM(): F_p = 1.0 PS = 0.2 v_e = 0.2 v_p = 0.1 dibem_params = two_cxm_model.params_to_DIBEM(F_p, PS, v_e, v_p, False) print('params_2CXM_to_DIBEM: ', dibem_params) F_p2, PS2, v_e2, v_p2 = two_cxm_model.params_from_DIBEM(dibem_params, False) dibem_params = two_cxm_model.params_to_DIBEM(F_p, PS, v_e, v_p, True) print('params_2CXM_to_DIBEM: ', dibem_params) F_p3, PS3, v_e3, v_p3 = two_cxm_model.params_from_DIBEM(dibem_params, True) assert F_p == pytest.approx(F_p2) assert PS == pytest.approx(PS2) assert v_e == pytest.approx(v_e2) assert v_p == pytest.approx(v_p2) assert F_p == pytest.approx(F_p3) assert PS == pytest.approx(PS3) assert v_e == pytest.approx(v_e3) assert v_p == pytest.approx(v_p3) def test_concentration_from_model_scalar(): times = np.linspace(0, 6, 50) aif = dce_aif.Aif(times = times) F_pos = 0.2 F_neg = 0.2 K_pos = 0.5 K_neg = 4.0 f_a = 0.5 tau_a = 0.1 tau_v = 0.05 C_t = dibem.concentration_from_model( aif, F_pos, F_neg, K_pos, K_neg, f_a, tau_a, tau_v ) assert C_t.size == times.size assert np.all(np.isfinite(C_t)) def test_concentration_from_array(): times = np.linspace(0, 6, 50) aif = dce_aif.Aif(times = times) F_pos = [0.2, 0.25] F_neg = [0.2, 0.25] K_pos = [0.5, 0.55] K_neg = [4.0, 4.5] f_a = 0.5 tau_a = 0.1 tau_v = 0.05 C_t = dibem.concentration_from_model( aif, F_pos, F_neg, K_pos, K_neg, f_a, tau_a, tau_v ) assert C_t.size == 2*times.size assert np.all(np.isfinite(C_t))	[ 1, 14550, 13, 3057, 934, 363, 278, 270, 346, 29918, 9794, 1014, 29899, 5453, 13, 12008, 13, 5215, 11451, 1688, 13, 5215, 2897, 13, 5215, 10876, 13, 5215, 12655, 408, 7442, 13, 3166, 5694, 1445, 1053, 6789, 1971, 653, 9882, 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hkl/user.py	NSLS-II/hklpy	1	168409	<filename>hkl/user.py """ Provide a simplified UI for hklpy diffractometer users. The user must define a diffractometer instance, then register that instance here calling `select_diffractometer(instance)`. FUNCTIONS .. autosummary:: ~cahkl ~cahkl_table ~calc_UB ~list_samples ~new_sample ~or_swap ~select_diffractometer ~set_energy ~setor ~show_sample ~show_selected_diffractometer ~update_sample ~wh ~pa """ __all__ = """ cahkl cahkl_table calc_UB change_sample list_samples new_sample or_swap pa select_diffractometer set_energy setor show_sample show_selected_diffractometer update_sample wh """.split() import logging import gi gi.require_version("Hkl", "5.0") logger = logging.getLogger(__name__) from .diffract import Diffractometer from .util import Lattice import numpy import pyRestTable _geom_ = None # selected diffractometer geometry def _check_geom_selected(args, kwargs): """Raise ValueError if no diffractometer geometry is selected.""" if _geom_ is None: raise ValueError( "No diffractometer selected." " Call 'select_diffractometer(diffr)' where" " 'diffr' is a diffractometer instance." ) def cahkl(h, k, l): """ Calculate motor positions for one reflection. Returns a namedtuple. Does not move motors. """ _check_geom_selected() # TODO: make certain this will not move the motors! return _geom_.forward(h, k, l) def cahkl_table(reflections, digits=5): """ Print a table with motor positions for each reflection given. Parameters ---------- reflections : list(tuple(number,number,number)) This is a list of reflections where each reflection is a tuple of 3 numbers specifying (h, k, l) of the reflection to compute the ``forward()`` computation. Example: ``[(1,0,0), (1,1,1)]`` digits : int Number of digits to roundoff each position value. Default is 5. """ _check_geom_selected() print(_geom_.forward_solutions_table(reflections, digits=digits)) # def calc_energy(): # # TODO: should this be added? # raise NotImplementedError def calc_UB(r1, r2, wavelength=None): """Compute the UB matrix with two reflections.""" _check_geom_selected() _geom_.calc.sample.compute_UB(r1, r2) print(_geom_.calc.sample.UB) def change_sample(sample): """Pick a known sample to be the current selection.""" _check_geom_selected() if sample not in _geom_.calc._samples: # fmt: off raise KeyError( f"Sample '{sample}' is unknown." f" Known samples: {list(_geom_.calc._samples.keys())}" ) # fmt: on _geom_.calc.sample = sample show_sample(sample) def list_samples(verbose=True): """List all defined crystal samples.""" _check_geom_selected() # always show the default sample first current_name = _geom_.calc.sample_name show_sample(current_name, verbose=verbose) # now, show any other samples for sample in _geom_.calc._samples.keys(): if sample != current_name: if verbose: print("") show_sample(sample, verbose=verbose) def new_sample(nm, a, b, c, alpha, beta, gamma): """Define a new crystal sample.""" _check_geom_selected() if nm in _geom_.calc._samples: logger.warning( ( "Sample '%s' is already defined." " Use 'update_sample()' to change lattice parameters" " on the current* sample." ), nm, ) else: lattice = Lattice(a=a, b=b, c=c, alpha=alpha, beta=beta, gamma=gamma) _geom_.calc.new_sample(nm, lattice=lattice) show_sample() def or_swap(): """Swap the 2 [UB] reflections, re-compute & return new [UB].""" # note: or_swap is how the community of SPEC users knows this function _check_geom_selected() return _geom_.calc.sample.swap_orientation_reflections() def select_diffractometer(instrument=None): """Name the diffractometer to be used.""" global _geom_ if instrument is None or isinstance(instrument, Diffractometer): _geom_ = instrument else: raise TypeError(f"{instrument} must be a 'Diffractometer' subclass") def set_energy(value, units=None, offset=None): """ Set the energy (thus wavelength) to be used. """ _check_geom_selected() if units is not None: _geom_.energy_units.put(units) if offset is not None: _geom_.energy_offset.put(offset) _geom_.energy.put(value) def setor(h, k, l, args, wavelength=None, kwargs): """Define a crystal reflection and its motor positions.""" _check_geom_selected() if len(args) == 0: if len(kwargs) == 0: pos = _geom_.real_position else: # fmt: off pos = [ kwargs[m] for m in _geom_.calc.physical_axis_names if m in kwargs ] # fmt: on else: pos = args # NOTE: libhkl gets the wavelength on a reflection from hkl.calc # when the wavelength is set, it calls libhkl directly # as self._geometry.wavelength_set(wavelength, self._units) # The code here uses that procedure. if wavelength not in (None, 0): _geom_.calc.wavelength = wavelength refl = _geom_.calc.sample.add_reflection(h, k, l, position=pos) return refl def show_sample(sample_name=None, verbose=True): """Print the default sample name and crystal lattice.""" _check_geom_selected() sample_name = sample_name or _geom_.calc.sample_name sample = _geom_.calc._samples[sample_name] title = sample_name if sample_name == _geom_.calc.sample.name: title += " ()" # Print Lattice more simply (than as a namedtuple). lattice = [getattr(sample.lattice, parm) for parm in sample.lattice._fields] if verbose: tbl = pyRestTable.Table() tbl.addLabel("key") tbl.addLabel("value") tbl.addRow(("name", sample_name)) tbl.addRow(("lattice", lattice)) for i, r in enumerate(sample.reflections): tbl.addRow((f"reflection {i+1}", r)) tbl.addRow(("U", numpy.round(sample.U, 5))) tbl.addRow(("UB", numpy.round(sample.UB, 5))) print(f"Sample: {title}\n") print(tbl) else: print(f"{title}: {lattice}") def show_selected_diffractometer(instrument=None): """Print the name of the selected diffractometer.""" if _geom_ is None: print("No diffractometer selected.") print(_geom_.name) def update_sample(a, b, c, alpha, beta, gamma): """Update current sample lattice.""" _check_geom_selected() _geom_.calc.sample.lattice = ( a, b, c, alpha, beta, gamma, ) # define the current sample show_sample(_geom_.calc.sample.name, verbose=False) def pa(): """Report (all) the diffractometer settings.""" _check_geom_selected() _geom_.pa() def wh(): """Report (brief) where is the diffractometer.""" _check_geom_selected() _geom_.wh()	[ 1, 529, 9507, 29958, 29882, 6321, 29914, 1792, 29889, 2272, 13, 15945, 29908, 13, 1184, 29894, 680, 263, 20875, 3740, 363, 298, 6321, 2272, 2923, 1461, 8328, 4160, 29889, 13, 13, 1576, 1404, 1818, 4529, 263, 2923, 1461, 8328, 2777, 29892, 769, 13, 9573, 393, 2777, 1244, 5432, 421, 2622, 29918, 12765, 1461, 8328, 29898, 8758, 14466, 13, 13, 29943, 28700, 29903, 13, 13, 636, 1120, 359, 398, 5219, 1057, 13, 13, 1678, 3695, 29883, 801, 6321, 13, 1678, 3695, 29883, 801, 6321, 29918, 2371, 13, 1678, 3695, 28667, 29918, 7466, 13, 1678, 3695, 1761, 29918, 27736, 13, 1678, 3695, 1482, 29918, 11249, 13, 1678, 3695, 272, 29918, 26276, 13, 1678, 3695, 2622, 29918, 12765, 1461, 8328, 13, 1678, 3695, 842, 29918, 27548, 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Network_and_802.11/scapy/check_if_rogue_dhcp.py	hacky1997/My-Gray-Hacker-Resources	14	128805	<gh_stars>10-100 #!/usr/bin/env python from scapy.all import * import threading from colorama import Back from sys import argv from time import sleep global lock lock = threading.Lock() def BootpPacket(): fm, hw = get_if_raw_hwaddr(scapy.all.conf.iface) ether=Ether(dst="ff:ff:ff:ff:ff:ff") ip=IP(src="0.0.0.0",dst="255.255.255.255") udp=UDP(sport=68,dport=67) bootp=BOOTP(chaddr=hw) dhcp=DHCP(options=[("message-type","discover"),"end"]) DHCPDiscover = ether/ip/udp/bootp/dhcp return DHCPDiscover def sendpkt(DHCPDiscover): srp(DHCPDiscover,timeout=2,inter=5, verbose=0) a = sniff(prn=test) # Thread2 for sniffing BOOTP Message on the network (DHCP OFFER MSG) t2 = threading.Thread(target=test) t2.start() def test(pkt): if pkt.haslayer(BOOTP): if pkt[IP].src != "0.0.0.0" and pkt[IP].src != LegalDHCPServer and pkt[BOOTP].op ==2: if pkt[IP].src not in dhcpservers: try: lock.acquire() print "Rogue DHCP Server(hacker) is at " + Back.RED+"%s"%pkt[IP].src+Back.RESET dhcpservers.add(pkt[IP].src) except : lock.acquire() finally: lock.release() else: pass else: if pkt[IP].src not in legalservers: try: lock.acquire() print "Legal DHCP Server:%s"%pkt[IP].src legalservers.add(pkt[IP].src) except: lock.acquire() finally: lock.release() else: pass else: pass if __name__ == '__main__': DHCPDiscover = BootpPacket() legalservers = set() dhcpservers = set() while True: if len(argv) != 2: print 'Usage: %s <LegalServersIP>'%argv[0] exit(0) else: LegalDHCPServer = argv[1] t = threading.Thread(target=sendpkt,args=DHCPDiscover) t.start() sleep(5)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 3166, 885, 27580, 29889, 497, 1053, 334, 13, 5215, 3244, 292, 13, 3166, 2927, 3304, 1053, 7437, 13, 3166, 10876, 1053, 1852, 29894, 13, 3166, 931, 1053, 8709, 13, 13, 10945, 7714, 13, 908, 353, 3244, 292, 29889, 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logistic_regression/logistic_regression.py	suitnecniv/dota2	127	194928	<filename>logistic_regression/logistic_regression.py import numpy as np import pickle, os NUM_HEROES = 108 NUM_FEATURES = NUM_HEROES * 2 class D2LogisticRegression: def __init__(self, model_root='logistic_regression'): model_path = os.path.join(model_root, 'model.pkl') with open(model_path, 'r') as input_file: self.model = pickle.load(input_file) def transform(self, my_team, their_team): X = np.zeros(NUM_FEATURES, dtype=np.int8) for hero_id in my_team: X[hero_id - 1] = 1 for hero_id in their_team: X[hero_id - 1 + NUM_HEROES] = 1 return X def recommend(self, my_team, their_team, hero_candidates): '''Returns a list of (hero, probablility of winning with hero added) recommended to complete my_team.''' team_possibilities = [(candidate, my_team + [candidate]) for candidate in hero_candidates] prob_candidate_pairs = [] for candidate, team in team_possibilities: query = self.transform(team, their_team) prob = self.score(query) #self.model.predict_proba(query)[0][1] prob_candidate_pairs.append((prob, candidate)) prob_candidate_pairs = sorted(prob_candidate_pairs, reverse=True)[0:5 - len(my_team)] return prob_candidate_pairs def score(self, query): '''Score the query using the model, considering both radiant and dire teams.''' radiant_query = query dire_query = np.concatenate((radiant_query[NUM_HEROES:NUM_FEATURES], radiant_query[0:NUM_HEROES])) rad_prob = self.model.predict_proba(radiant_query)[0][1] dire_prob = self.model.predict_proba(dire_query)[0][0] return (rad_prob + dire_prob) / 2 def predict(self, dream_team, their_team): '''Returns the probability of the dream_team winning against their_team.''' dream_team_query = self.transform(dream_team, their_team) return self.score(dream_team_query) #return self.model.predict_proba(dream_team_query)[0][1]	[ 1, 529, 9507, 29958, 1188, 4695, 29918, 276, 11476, 29914, 1188, 4695, 29918, 276, 11476, 29889, 2272, 13, 5215, 12655, 408, 7442, 13, 5215, 5839, 280, 29892, 2897, 13, 13, 13967, 29918, 4448, 29949, 2890, 353, 29871, 29896, 29900, 29947, 13, 13967, 29918, 16359, 1299, 11499, 29903, 353, 28019, 29918, 4448, 29949, 2890, 334, 29871, 29906, 13, 13, 1990, 360, 29906, 3403, 4695, 4597, 23881, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1904, 29918, 4632, 2433, 1188, 4695, 29918, 276, 11476, 29374, 13, 4706, 1904, 29918, 2084, 353, 2897, 29889, 2084, 29889, 7122, 29898, 4299, 29918, 4632, 29892, 525, 4299, 29889, 29886, 6321, 1495, 13, 4706, 411, 1722, 29898, 4299, 29918, 2084, 29892, 525, 29878, 1495, 408, 1881, 29918, 1445, 29901, 13, 9651, 1583, 29889, 4299, 353, 5839, 280, 29889, 1359, 29898, 2080, 29918, 1445, 29897, 13, 13, 1678, 822, 4327, 29898, 1311, 29892, 590, 29918, 14318, 29892, 1009, 29918, 14318, 1125, 13, 4706, 1060, 353, 7442, 29889, 3298, 359, 29898, 13967, 29918, 16359, 1299, 11499, 29903, 29892, 26688, 29922, 9302, 29889, 524, 29947, 29897, 13, 4706, 363, 13444, 29918, 333, 297, 590, 29918, 14318, 29901, 13, 9651, 1060, 29961, 29882, 1489, 29918, 333, 448, 29871, 29896, 29962, 353, 29871, 29896, 13, 4706, 363, 13444, 29918, 333, 297, 1009, 29918, 14318, 29901, 13, 9651, 1060, 29961, 29882, 1489, 29918, 333, 448, 29871, 29896, 718, 28019, 29918, 4448, 29949, 2890, 29962, 353, 29871, 29896, 13, 4706, 736, 1060, 13, 13, 1678, 822, 6907, 29898, 1311, 29892, 590, 29918, 14318, 29892, 1009, 29918, 14318, 29892, 13444, 29918, 29883, 5380, 1078, 1125, 13, 4706, 14550, 11609, 29879, 263, 1051, 310, 313, 29882, 1489, 29892, 2070, 12478, 1793, 310, 15613, 411, 13444, 2715, 29897, 13622, 304, 4866, 590, 29918, 14318, 29889, 12008, 13, 4706, 3815, 29918, 28802, 747, 9770, 353, 17288, 29883, 5380, 403, 29892, 590, 29918, 14318, 718, 518, 29883, 5380, 403, 2314, 363, 14020, 297, 13444, 29918, 29883, 5380, 1078, 29962, 13, 13, 4706, 2070, 29918, 29883, 5380, 403, 29918, 29886, 7121, 353, 5159, 13, 4706, 363, 14020, 29892, 3815, 297, 3815, 29918, 28802, 747, 9770, 29901, 13, 9651, 2346, 353, 1583, 29889, 9067, 29898, 14318, 29892, 1009, 29918, 14318, 29897, 13, 9651, 2070, 353, 1583, 29889, 13628, 29898, 1972, 29897, 396, 1311, 29889, 4299, 29889, 27711, 29918, 771, 2291, 29898, 1972, 9601, 29900, 3816, 29896, 29962, 13, 9651, 2070, 29918, 29883, 5380, 403, 29918, 29886, 7121, 29889, 4397, 3552, 22795, 29892, 14020, 876, 13, 4706, 2070, 29918, 29883, 5380, 403, 29918, 29886, 7121, 353, 12705, 29898, 22795, 29918, 29883, 5380, 403, 29918, 29886, 7121, 29892, 11837, 29922, 5574, 9601, 29900, 29901, 29945, 448, 7431, 29898, 1357, 29918, 14318, 4638, 13, 4706, 736, 2070, 29918, 29883, 5380, 403, 29918, 29886, 7121, 13, 13, 1678, 822, 8158, 29898, 1311, 29892, 2346, 1125, 13, 4706, 14550, 20097, 278, 2346, 773, 278, 1904, 29892, 13858, 1716, 17937, 424, 322, 2970, 10907, 29889, 12008, 13, 4706, 17937, 424, 29918, 1972, 353, 2346, 13, 4706, 2970, 29918, 1972, 353, 7442, 29889, 535, 29883, 2579, 403, 3552, 3665, 29875, 424, 29918, 1972, 29961, 13967, 29918, 4448, 29949, 2890, 29901, 13967, 29918, 16359, 1299, 11499, 29903, 1402, 17937, 424, 29918, 1972, 29961, 29900, 29901, 13967, 29918, 4448, 29949, 2890, 12622, 13, 4706, 2971, 29918, 22795, 353, 1583, 29889, 4299, 29889, 27711, 29918, 771, 2291, 29898, 3665, 29875, 424, 29918, 1972, 9601, 29900, 3816, 29896, 29962, 13, 4706, 2970, 29918, 22795, 353, 1583, 29889, 4299, 29889, 27711, 29918, 771, 2291, 29898, 20146, 29918, 1972, 9601, 29900, 3816, 29900, 29962, 13, 4706, 736, 313, 3665, 29918, 22795, 718, 2970, 29918, 22795, 29897, 847, 29871, 29906, 13, 13, 1678, 822, 8500, 29898, 1311, 29892, 12561, 29918, 14318, 29892, 1009, 29918, 14318, 1125, 13, 4706, 14550, 11609, 29879, 278, 6976, 310, 278, 12561, 29918, 14318, 15613, 2750, 1009, 29918, 14318, 29889, 12008, 13, 4706, 12561, 29918, 14318, 29918, 1972, 353, 1583, 29889, 9067, 29898, 29881, 1633, 29918, 14318, 29892, 1009, 29918, 14318, 29897, 13, 4706, 736, 1583, 29889, 13628, 29898, 29881, 1633, 29918, 14318, 29918, 1972, 29897, 13, 4706, 396, 2457, 1583, 29889, 4299, 29889, 27711, 29918, 771, 2291, 29898, 29881, 1633, 29918, 14318, 29918, 1972, 9601, 29900, 3816, 29896, 29962, 13, 2 ]
main/faq.py	vanessa-bell/hd-kiosk-v2	0	69045	<gh_stars>0 import flask import flask_wtf import wtforms import auth import model import util from google.appengine.ext import ndb from main import app class FaqUpdateForm(flask_wtf.FlaskForm): question = wtforms.TextAreaField('Question', [wtforms.validators.required()]) answer = wtforms.TextAreaField('Answer', [wtforms.validators.required()]) category = wtforms.SelectField('Category', [wtforms.validators.required()], choices=[('About/History','About/History'),('Financials','Financials'),('The Physical Space','The Physical Space'),('Other Hackerspaces','Other Hackerspaces'),('Paperwork and Logistics','Paperwork and Logistics'),('Miscellaneous','Miscellaneous')]) @app.route('/faqs/create/', methods=['GET', 'POST']) @auth.login_required def faq_create(): form = FaqUpdateForm() if form.validate_on_submit(): faq_db = model.Faq( user_key=auth.current_user_key(), question=form.question.data, answer=form.answer.data, category=form.category.data, ) faq_db.put() return flask.redirect(flask.url_for('welcome')) return flask.render_template( 'faq_create.html', html_class='faq-create', title='Create FAQ', form=form, ) @app.route('/faqs/') def faqs_list(): faq_dbs, faq_cursor = model.Faq.get_dbs() return flask.render_template( 'faqs_list.html', html_class='faqs-list', title='Frequently Asked Questions', faq_dbs=faq_dbs, next_url=util.generate_next_url(faq_cursor), ) @app.route('/faqs/stats') def statistics(): faq_dbs, faq_cursor = model.Faq.get_dbs() return flask.render_template( 'statistics.html', html_class='statistics', title='Statistics', faq_dbs=faq_dbs, next_url=util.generate_next_url(faq_cursor), ) @app.route('/faqs/<int:faq_id>/update/', methods=['GET', 'POST']) @auth.login_required def faq_update(faq_id): faq_db = model.Faq.get_by_id(faq_id) if not faq_db: # or faq_db.user_key != auth.current_user_key() flask.abort(404) form = FaqUpdateForm(obj=faq_db) if form.validate_on_submit(): form.populate_obj(faq_db) faq_db.put() return flask.redirect(flask.url_for('faqs_list')) return flask.render_template( 'faq_update.html', html_class='faq-update', title=faq_db.question, form=form, faq_db=faq_db, faq_id=faq_id ) @app.route('/faqs/<int:faq_id>/delete/', methods=['GET', 'POST']) @auth.login_required def faq_delete(faq_id): faq_key = ndb.Key("Faq",faq_id) faq_key.delete() faq_dbs = model.Faq.get_dbs() return flask.redirect(flask.url_for('faqs_list')) @app.route('/faqs/<int:faq_id>/count/', methods=['GET', 'POST']) def faq_count(faq_id): stat_db = model.Stat( faq_key=ndb.Key("Faq",faq_id), ) stat_db.put() faq_db = model.Faq.get_by_id(faq_id) faq_db.count = faq_db.count + 1 faq_db.put() return 'done' # return response, 200, {'Content-Type': 'text/plain'}	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 29784, 13, 5215, 29784, 29918, 29893, 13264, 13, 5215, 281, 29873, 9514, 13, 13, 5215, 4817, 13, 5215, 1904, 13, 5215, 3667, 13, 3166, 5386, 29889, 932, 10599, 29889, 1062, 1053, 29871, 299, 29890, 13, 13, 3166, 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morsecodetoolkit/data/__init__.py	1-800-BAD-CODE/MorseCodeToolkit	2	95666	<filename>morsecodetoolkit/data/__init__.py from morsecodetoolkit.data.synthetic_dataset import SyntheticMorseDataset __all__ = [ "SyntheticMorseDataset" ]	[ 1, 529, 9507, 29958, 12257, 3471, 397, 300, 1507, 7354, 29914, 1272, 29914, 1649, 2344, 26914, 2272, 13, 13, 3166, 3036, 3471, 397, 300, 1507, 7354, 29889, 1272, 29889, 19274, 386, 7492, 29918, 24713, 1053, 10829, 386, 7492, 28581, 344, 16390, 24541, 13, 13, 1649, 497, 1649, 353, 518, 13, 1678, 376, 29216, 386, 7492, 28581, 344, 16390, 24541, 29908, 13, 29962, 13, 2 ]
my_studyguide/math1.py	worldwidekatie/study_guide	0	135999	<filename>my_studyguide/math1.py class Math1(): def __init__(self): self.my_num1 = 10 self.my_num2 = 20 def addition(self): return self.my_num1 + self.my_num2 def subtraction(self): return self.my_num1 - self.my_num2 class Math_Plus(Math1): def __init__(self, my_num1=40, my_num2=90): self.my_num1 = my_num1 self.my_num2 = my_num2 def multiplication(self): return self.my_num1 * self.my_num2 def division(self): return self.my_num1 / self.my_num2 if __name__ == "__main__": math1 = Math1() print(math1.addition()) #30 print(math1.subtraction()) #-10 math_plus = Math_Plus() print(math_plus.addition()) #130 print(math_plus.subtraction()) #-50 print(math_plus.multiplication()) #3600 print(math_plus.division()) #0.-44444444	[ 1, 529, 9507, 29958, 1357, 29918, 18082, 29891, 13075, 29914, 755, 29896, 29889, 2272, 13, 13, 13, 1990, 5792, 29896, 7295, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 1583, 29889, 1357, 29918, 1949, 29896, 353, 29871, 29896, 29900, 13, 4706, 1583, 29889, 1357, 29918, 1949, 29906, 353, 29871, 29906, 29900, 13, 13, 1678, 822, 6124, 29898, 1311, 1125, 13, 4706, 736, 1583, 29889, 1357, 29918, 1949, 29896, 718, 1583, 29889, 1357, 29918, 1949, 29906, 13, 13, 1678, 822, 1014, 3018, 428, 29898, 1311, 1125, 13, 4706, 736, 1583, 29889, 1357, 29918, 1949, 29896, 448, 1583, 29889, 1357, 29918, 1949, 29906, 13, 13, 13, 1990, 5792, 29918, 29575, 29898, 11309, 29896, 1125, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 590, 29918, 1949, 29896, 29922, 29946, 29900, 29892, 590, 29918, 1949, 29906, 29922, 29929, 29900, 1125, 13, 4706, 1583, 29889, 1357, 29918, 1949, 29896, 353, 590, 29918, 1949, 29896, 13, 4706, 1583, 29889, 1357, 29918, 1949, 29906, 353, 590, 29918, 1949, 29906, 13, 13, 1678, 822, 21666, 29898, 1311, 1125, 13, 4706, 736, 1583, 29889, 1357, 29918, 1949, 29896, 334, 1583, 29889, 1357, 29918, 1949, 29906, 13, 13, 1678, 822, 8542, 29898, 1311, 1125, 13, 4706, 736, 1583, 29889, 1357, 29918, 1949, 29896, 847, 1583, 29889, 1357, 29918, 1949, 29906, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 5844, 29896, 353, 5792, 29896, 580, 13, 1678, 1596, 29898, 755, 29896, 29889, 1202, 654, 3101, 396, 29941, 29900, 13, 1678, 1596, 29898, 755, 29896, 29889, 1491, 3018, 428, 3101, 396, 29899, 29896, 29900, 13, 268, 13, 1678, 5844, 29918, 11242, 353, 5792, 29918, 29575, 580, 13, 1678, 1596, 29898, 755, 29918, 11242, 29889, 1202, 654, 3101, 396, 29896, 29941, 29900, 13, 1678, 1596, 29898, 755, 29918, 11242, 29889, 1491, 3018, 428, 3101, 396, 29899, 29945, 29900, 13, 1678, 1596, 29898, 755, 29918, 11242, 29889, 18056, 1414, 3101, 396, 29941, 29953, 29900, 29900, 13, 1678, 1596, 29898, 755, 29918, 11242, 29889, 4563, 2459, 3101, 396, 29900, 9229, 29946, 29946, 29946, 29946, 29946, 29946, 29946, 29946, 2 ]
txnintegration/stats_utils.py	ajenie/sawtooth-validator	4	1611745	<filename>txnintegration/stats_utils.py # Copyright 2016 Intel Corporation # # 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 time import csv import collections class CsvManager(object): def __init__(self): self.csvdata = [] self.file = None self.writer = None def open_csv_file(self, filename, filepath=""): self.file = open(filename, 'wt') self.writer = csv.writer(self.file) def close_csv_file(self): self.file.close() def csv_newline(self): self.csvdata = [] def csv_append(self, datalist): self.csvdata.extend(datalist) def csv_write_header(self, headerlist=None, add_time=True): if headerlist is not None: self.csvdata.extend(headerlist) if add_time: self.csvdata.insert(0, "time") self._csv_write() def csv_write_data(self, datalist=None, add_time=True): if datalist is not None: self.csvdata.extend(datalist) if add_time: self.csvdata.insert(0, time.time()) self._csv_write() def _csv_write(self): self.writer.writerow(self.csvdata) self.csvdata = [] class SummaryStatsCsvManager(object): def __init__(self, system_stats, platform_stats): self.csv_enabled = False self.csv_mgr = CsvManager() self.ss = system_stats self.ps = platform_stats def initialize(self): self.csv_enabled = True filename = "summary_stats_" + str(int(time.time())) + ".csv" self.csv_mgr.open_csv_file(filename) header = self.ss.get_names() self.csv_mgr.csv_append(header) header = self.ps.get_names() self.csv_mgr.csv_write_header(header) def write_stats(self): if self.csv_enabled: data = self.ss.get_data() self.csv_mgr.csv_append(data) data = self.ps.get_data() self.csv_mgr.csv_write_data(data) def stop(self): if self.csv_enabled: self.csv_mgr.close_csv_file() class ValidatorStatsCsvManager(object): def __init__(self, client_list): self.csv_enabled = False self.csv_mgr = CsvManager() self.get_header = False self.csv_stats = CsvManager() self.clients = client_list self.stat_names = [] self.dw = DictWalker() def initialize(self): self.csv_enabled = True filename = "validator_stats_" + str(int(time.time())) + ".csv" self.csv_mgr.open_csv_file(filename) # defer writing header until first instance of data dictionary # is available self.get_header = True def write_stats(self): current_time = time.time() if self.csv_enabled: for client in self.clients: if client.responding: self.dw.walk(client.vsm.val_stats) if self.get_header: names = self.dw.get_names() names.insert(0, "validator_name") names.insert(0, "time") self.csv_mgr.csv_write_header(names, add_time=False) self.get_header = False data = self.dw.get_data() data.insert(0, client.name) data.insert(0, current_time) self.csv_mgr.csv_write_data(data, add_time=False) def stop(self): if self.csv_enabled: self.csv_mgr.close_csv_file() class DictWalker(object): def __init__(self): self.name_list = [] self.unique_name_list = [] self.unique_name_with_comma_list = [] # generates a new set of key/value pairs # each time walk() is called self.data = dict() # contains a set of keys ordered by entry order # that persists across calls to walk() - this lets you discover # (and remember) new keys as the appear in the data stream self.names = collections.OrderedDict() self.value_type_error_count = 0 self.name_has_comma_error_count = 0 def walk(self, data_dict): self.data = dict() return self._traverse_dict(data_dict) def _traverse_dict(self, data_dict): for key, value in data_dict.iteritems(): self.name_list.append(key) if isinstance(value, dict): self._traverse_dict(value) else: if isinstance(value, (list, tuple)): self.value_type_error_count += 1 value = "value_type_error" if isinstance(value, str): if value.find(",") is not -1: self.value_has_comma_error += 1 value = "value_comma_error" unique_name = self._unique_name(self.name_list) if unique_name.find(",") is not -1: self.name_has_comma_error_count += 1 self.unique_name_with_comma_list.append(unique_name) else: self.unique_name_list.append(unique_name) self.data[unique_name] = value self.names[unique_name] = None self.name_list.pop() if len(self.name_list) > 0: self.name_list.pop() def get_data(self): # retrieve data using names so it is always reported in the same order # this is important when using get_data() and get_names() # to write header and data to csv file to ensure headers and data # are written in the same order even if the source dictionary changes # size (which it will - at minimum validators do not all process # the same messages data_list = [] for name, value in self.names.items(): value = self.data.get(name, "no_val") data_list.append(value) return data_list def get_names(self): name_list = [] for name in self.names: name_list.append(name) return name_list def _unique_name(self, name_list): s = "" for name in name_list: if len(s) == 0: s = name else: s = "{}-{}".format(s, name) return s class TransactionRate(object): def __init__(self): self.txn_history = collections.deque() self.previous_block_count = 0 self.avg_txn_rate = 0.0 self.avg_block_time = 0.0 self.window_time = 0.0 self.window_txn_count = 0 def calculate_txn_rate(self, current_block_count, current_txn_count, window_size=10): """ Args: current_block_count: current number of committed blocks current_txn_count: current number of committed transactions window_size: number of blocks to average over Synopsis: Each time the block count changes, a snapshot of the current number of committed txns and current time is placed in the queue. If there are two or more entries in the queue, the average txn rate and average block commit time is calculated. If there are more than window_size transactions in the queue, the oldest entry is popped from the queue. Returns: avg_txn_rate: average number of transactions per second avg_block_time: average block commit time """ if not current_block_count == self.previous_block_count: self.previous_block_count = current_block_count current_block_time = time.time() self.txn_history.append([current_txn_count, current_block_time]) # if less than 2 samples, can't do anything if len(self.txn_history) < 2: self.avg_txn_rate = 0.0 self.avg_block_time = 0.0 return self.avg_txn_rate, self.avg_block_time # otherwise calculate from tip to tail; current is tip, [0] is tail past_txn_count, past_block_time = self.txn_history[0] self.window_time = current_block_time - past_block_time self.window_txn_count = current_txn_count - past_txn_count self.avg_txn_rate = \ float(self.window_txn_count) / self.window_time self.avg_block_time = \ (self.window_time) / (len(self.txn_history) - 1) # if more than "window_size" samples, discard oldest if len(self.txn_history) > window_size: self.txn_history.popleft() return self.avg_txn_rate, self.avg_block_time class PlatformIntervalStats(object): def __init__(self): self.intv_net_bytes_sent = 0 self.intv_net_bytes_recv = 0 self.last_net_bytes_sent = 0 self.last_net_bytes_recv = 0 self.intv_disk_bytes_read = 0 self.intv_disk_bytes_write = 0 self.last_disk_bytes_read = 0 self.last_disk_bytes_write = 0 self.intv_disk_count_read = 0 self.intv_disk_count_write = 0 self.last_disk_count_read = 0 self.last_disk_count_write = 0 def calculate_interval_stats(self, val_stats): net_stats = val_stats["platform"]["snetio"] self.intv_net_bytes_sent = \ net_stats["bytes_sent"] - self.last_net_bytes_sent self.intv_net_bytes_recv = \ net_stats["bytes_recv"] - self.last_net_bytes_recv self.last_net_bytes_sent = net_stats["bytes_sent"] self.last_net_bytes_recv = net_stats["bytes_recv"] disk_stats = val_stats["platform"]["sdiskio"] self.intv_disk_bytes_write = \ disk_stats["write_bytes"] - self.last_disk_bytes_write self.intv_disk_bytes_read = \ disk_stats["read_bytes"] - self.last_disk_bytes_read self.last_disk_bytes_write = disk_stats["write_bytes"] self.last_disk_bytes_read = disk_stats["read_bytes"] self.intv_disk_count_write = \ disk_stats["write_count"] - self.last_disk_count_write self.intv_disk_count_read = \ disk_stats["read_count"] - self.last_disk_count_read self.last_disk_count_write = disk_stats["write_count"] self.last_disk_count_read = disk_stats["read_count"]	[ 1, 529, 9507, 29958, 7508, 29876, 27925, 29914, 16202, 29918, 13239, 29889, 2272, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29896, 29953, 18555, 15025, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 29937, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 13, 29937, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 13, 29937, 13, 29937, 268, 1732, 597, 1636, 29889, 4288, 29889, 990, 29914, 506, 11259, 29914, 27888, 1430, 1660, 29899, 29906, 29889, 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25/02/getattr.py	pylangstudy/201707	0	100962	<filename>25/02/getattr.py # getattr(object, name[, default]) class C: def A(self): pass print(getattr(C, 'A'))	[ 1, 529, 9507, 29958, 29906, 29945, 29914, 29900, 29906, 29914, 657, 5552, 29889, 2272, 13, 29937, 679, 5552, 29898, 3318, 29892, 1024, 21939, 2322, 2314, 13, 1990, 315, 29901, 13, 1678, 822, 319, 29898, 1311, 1125, 1209, 13, 2158, 29898, 657, 5552, 29898, 29907, 29892, 525, 29909, 8785, 13, 2 ]
handybelt/latex2img/__main__.py	charleskawczynski/handybelt	0	101608	<gh_stars>0 import argparse import os import subprocess def main(kwargs): filename = kwargs['filename'] dpi = kwargs['dpi'] filename = filename.replace('/',os.sep) if '\\'==os.sep else filename.replace('\\',os.sep) path = os.path.dirname(filename) path = '.' if path=='' else path p, ext = os.path.splitext(filename) subprocess.run('python -m buildlatex '+filename) subprocess.run('python -m pdf2img '+filename.replace(ext,'.pdf')) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Make an image from a pdf file.') parser.add_argument('filename', type=str, help='Filename of image to convert') parser.add_argument('--dpi', type=int, default=300, help='dpi for generated image') args = parser.parse_args() main(vars(args))	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 1852, 5510, 13, 5215, 2897, 13, 5215, 1014, 5014, 13, 13, 1753, 1667, 29898, 1068, 19290, 1125, 13, 29871, 10422, 353, 9049, 5085, 1839, 9507, 2033, 13, 29871, 270, 1631, 418, 353, 9049, 5085, 1839, 29881, 1631, 2033, 13, 29871, 10422, 353, 10422, 29889, 6506, 11219, 742, 359, 29889, 19570, 29897, 565, 525, 1966, 29915, 1360, 359, 29889, 19570, 1683, 10422, 29889, 6506, 877, 1966, 742, 359, 29889, 19570, 29897, 13, 29871, 2224, 353, 2897, 29889, 2084, 29889, 25721, 29898, 9507, 29897, 13, 29871, 2224, 353, 525, 6169, 565, 2224, 1360, 4907, 1683, 2224, 13, 29871, 282, 29892, 1294, 353, 2897, 29889, 2084, 29889, 23579, 568, 486, 29898, 9507, 29897, 13, 29871, 1014, 5014, 29889, 3389, 877, 4691, 448, 29885, 2048, 25694, 525, 29974, 9507, 29897, 13, 29871, 1014, 5014, 29889, 3389, 877, 4691, 448, 29885, 13552, 29906, 2492, 525, 29974, 9507, 29889, 6506, 29898, 1062, 29892, 4286, 5140, 8785, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 29871, 13812, 353, 1852, 5510, 29889, 15730, 11726, 29898, 8216, 2433, 9984, 385, 1967, 515, 263, 13552, 934, 29889, 1495, 13, 29871, 13812, 29889, 1202, 29918, 23516, 877, 9507, 742, 13, 1678, 1134, 29922, 710, 29892, 13, 1678, 1371, 2433, 3434, 3871, 310, 1967, 304, 3588, 1495, 13, 29871, 13812, 29889, 1202, 29918, 23516, 877, 489, 29881, 1631, 742, 13, 1678, 1134, 29922, 524, 29892, 13, 1678, 2322, 29922, 29941, 29900, 29900, 29892, 13, 1678, 1371, 2433, 29881, 1631, 363, 5759, 1967, 1495, 13, 29871, 6389, 353, 13812, 29889, 5510, 29918, 5085, 580, 13, 29871, 1667, 29898, 1068, 16908, 29898, 5085, 876, 13, 2 ]
securitybot/loader.py	gyrospectre/securitybot	3	125881	import yaml import logging from importlib import import_module from securitybot.auth.auth import BaseAuthClient from securitybot.chat.chat import BaseChatClient from securitybot.db.database import BaseDbClient from securitybot.secretsmgmt.secretsmgmt import BaseSecretsClient from securitybot.tasker import Tasker from securitybot.exceptions import ( InvalidAuthProvider, InvalidChatProvider, InvalidDatabaseProvider, InvalidSecretsProvider ) def load_secrets_client(secrets_provider): try: sanitized_provider = secrets_provider.lower() module_name = 'securitybot.secretsmgmt.{}'.format( sanitized_provider ) module = import_module(module_name) client = getattr(module, 'SecretsClient') if not issubclass(client, BaseSecretsClient): raise AttributeError( '{}.Client is not an SecretsClient Provider'.format( module_name ) ) return client except (ModuleNotFoundError, AttributeError) as e: raise InvalidSecretsProvider( 'Secrets Management Provider for "{}" is not available: {}'.format( secrets_provider, e ) ) def build_secrets_client(secrets_provider, connection_config): secrets_class = load_secrets_client(secrets_provider) return secrets_class( connection_config ) def add_secrets_to_config(smclient, secrets, config): for client_type, clients in secrets.items(): client = config[client_type]['provider'] if client in clients: logging.debug( "About to fetch secret 'securitybot/{}/{}'".format( client_type, client ) ) scrt_raw = smclient.get_secret( 'securitybot/{}/{}'.format( client_type, client ) ) for scrt_name in secrets[client_type][client]: config[client_type][client][scrt_name] = scrt_raw[scrt_name] else: logging.debug( 'No secrets for {} provider {}'.format( client_type, client ) ) return True def load_auth_client(auth_provider): try: sanitized_provider = auth_provider.lower() module_name = 'securitybot.auth.{}'.format( sanitized_provider ) module = import_module(module_name) client = getattr(module, 'AuthClient') if not issubclass(client, BaseAuthClient): raise AttributeError( '{}.Client is not an Auth Provider'.format(module_name) ) return client except (ModuleNotFoundError, AttributeError) as e: raise InvalidAuthProvider( 'Auth Provider for "{}" is not available: {}'.format( auth_provider, e ) ) def build_auth_client(auth_provider, connection_config, reauth_time, auth_attrib): auth_class = load_auth_client(auth_provider) return auth_class( connection_config, reauth_time, auth_attrib ) def load_chat_client(chat_provider): try: sanitized_provider = chat_provider.lower() module_name = 'securitybot.chat.{}'.format( sanitized_provider ) module = import_module(module_name) client = getattr(module, 'ChatClient') if not issubclass(client, BaseChatClient): raise AttributeError( '{}.Client is not a Chat Provider'.format(module_name) ) return client except (ModuleNotFoundError, AttributeError) as e: raise InvalidChatProvider( 'Chat Provider for "{}" is not available: {}'.format( chat_provider, e ) ) def build_chat_client(chat_provider, connection_config): chat_class = load_chat_client(chat_provider) return chat_class( connection_config ) def build_tasker(dbclient): return Tasker(dbclient) def load_db_client(db_provider): try: sanitized_provider = db_provider.lower() module_name = 'securitybot.db.{}'.format( sanitized_provider ) module = import_module(module_name) client = getattr(module, 'DbClient') if not issubclass(client, BaseDbClient): raise AttributeError( '{} is not a Db Provider'.format(module_name) ) return client except (ModuleNotFoundError, AttributeError) as e: raise InvalidDatabaseProvider( 'DB Provider for "{}" is not available: {}'.format( db_provider, e ) ) def build_db_client(db_provider, connection_config, tables): db_class = load_db_client(db_provider) connection_config['tables'] = tables return db_class( config=connection_config, queries=load_yaml(connection_config.get('queries_path', None)) ) def load_yaml(path): if path: return yaml.safe_load(open(path)) else: 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project/test.py	Lucklyric/hydra-pytorch-lightning-seed	4	157194	#!/usr/bin/env python3 # -- coding: utf-8 -- # File : test.py # Author: Alvin(<NAME> <<EMAIL>> # Date : 02.05.2021 import json import logging import hydra import pytorch_lightning as pl import torch from omegaconf import DictConfig, OmegaConf log = logging.getLogger(__name__) @hydra.main(config_path='config', config_name='test_config') def main(cfg: DictConfig): print(OmegaConf.to_yaml(cfg)) pl.seed_everything(cfg.seed) # ------------ # data # ------------ data_module = hydra.utils.instantiate(cfg.data) test_dataloader = data_module.test_dataloader() # ------------ # model # ------------ model = hydra.utils.instantiate(cfg.model) # model.load_from_checkpoint(cfg.checkpoint) model.load_state_dict(torch.load(cfg.checkpoint_path)['state_dict']) trainer = pl.Trainer(**(cfg.pl_trainer)) # ------------ # testing # ------------ result = trainer.test(model, test_dataloaders=test_dataloader) log.info(result) with open(f'{trainer.log_dir}/out', 'w') as f: f.write(json.dumps(str(result), indent=4)) if __name__ == '__main__': try: main() except Exception as e: log.error(e) exit(1)	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 3497, 29871, 584, 1243, 29889, 2272, 13, 29937, 13361, 29901, 838, 3845, 29898, 29966, 5813, 29958, 3532, 26862, 6227, 6778, 13, 29937, 4712, 29871, 584, 29871, 29900, 29906, 29889, 29900, 29945, 29889, 29906, 29900, 29906, 29896, 13, 13, 5215, 4390, 13, 5215, 12183, 13, 13, 5215, 27246, 336, 13, 5215, 282, 3637, 25350, 29918, 4366, 1076, 408, 715, 13, 5215, 4842, 305, 13, 3166, 2703, 2442, 5527, 1053, 360, 919, 3991, 29892, 13352, 2442, 16376, 13, 13, 1188, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 29897, 13, 13, 13, 29992, 29882, 2941, 336, 29889, 3396, 29898, 2917, 29918, 2084, 2433, 2917, 742, 2295, 29918, 978, 2433, 1688, 29918, 2917, 1495, 13, 1753, 1667, 29898, 16859, 29901, 360, 919, 3991, 1125, 13, 1678, 1596, 29898, 5981, 16376, 29889, 517, 29918, 25162, 29898, 16859, 876, 13, 1678, 715, 29889, 26776, 29918, 17991, 1918, 29898, 16859, 29889, 26776, 29897, 13, 13, 1678, 396, 448, 1378, 5634, 13, 1678, 396, 848, 13, 1678, 396, 448, 1378, 5634, 13, 1678, 848, 29918, 5453, 353, 27246, 336, 29889, 13239, 29889, 2611, 3656, 403, 29898, 16859, 29889, 1272, 29897, 13, 1678, 1243, 29918, 29881, 2075, 29877, 1664, 353, 848, 29918, 5453, 29889, 1688, 29918, 29881, 2075, 29877, 1664, 580, 13, 13, 1678, 396, 448, 1378, 5634, 13, 1678, 396, 1904, 13, 1678, 396, 448, 1378, 5634, 13, 1678, 1904, 353, 27246, 336, 29889, 13239, 29889, 2611, 3656, 403, 29898, 16859, 29889, 4299, 29897, 13, 1678, 396, 1904, 29889, 1359, 29918, 3166, 29918, 3198, 3149, 29898, 16859, 29889, 3198, 3149, 29897, 13, 1678, 1904, 29889, 1359, 29918, 3859, 29918, 8977, 29898, 7345, 305, 29889, 1359, 29898, 16859, 29889, 3198, 3149, 29918, 2084, 29897, 1839, 3859, 29918, 8977, 11287, 13, 13, 1678, 1020, 4983, 353, 715, 29889, 5323, 4983, 29898, 1068, 29898, 16859, 29889, 572, 29918, 3018, 4983, 876, 13, 13, 1678, 396, 448, 1378, 5634, 13, 1678, 396, 6724, 13, 1678, 396, 448, 1378, 5634, 13, 1678, 1121, 353, 1020, 4983, 29889, 1688, 29898, 4299, 29892, 1243, 29918, 29881, 2075, 29877, 24574, 29922, 1688, 29918, 29881, 2075, 29877, 1664, 29897, 13, 1678, 1480, 29889, 3888, 29898, 2914, 29897, 13, 1678, 411, 1722, 29898, 29888, 29915, 29912, 3018, 4983, 29889, 1188, 29918, 3972, 6822, 449, 742, 525, 29893, 1495, 408, 285, 29901, 13, 4706, 285, 29889, 3539, 29898, 3126, 29889, 29881, 17204, 29898, 710, 29898, 2914, 511, 29536, 29922, 29946, 876, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 1018, 29901, 13, 4706, 1667, 580, 13, 1678, 5174, 8960, 408, 321, 29901, 13, 4706, 1480, 29889, 2704, 29898, 29872, 29897, 13, 4706, 6876, 29898, 29896, 29897, 13, 2 ]
app/models.py	pitrho/motorsports-historic-data-api	5	60144	from flask.ext.sqlalchemy import SQLAlchemy from sqlalchemy.dialects.postgresql import HSTORE from sqlalchemy.ext.mutable import MutableDict db = SQLAlchemy() PersonType = db.Enum('driver', 'team-owner', 'crew-chief', 'vehicle-owner', 'team-principal', 'technical-chief', 'race-engineer', name='person_types') class Person(db.Model): __tablename__ = 'people' id = db.Column(db.Integer, primary_key=True, autoincrement=True) name = db.Column(db.String(100), nullable=False) country = db.Column(db.String(50), nullable=False) class Series(db.Model): __tablename__ = 'series' id = db.Column(db.String(5), primary_key=True) description = db.Column(db.String(50), nullable=True) class Team(db.Model): __tablename__ = 'teams' id = db.Column(db.String(50), primary_key=True) name = db.Column(db.String(50), nullable=False) alias = db.Column(db.String(50), nullable=False) owner_id = db.Column(db.Integer, db.ForeignKey('people.id'), nullable=False) races = db.relationship('Race', secondary='race_results') owner = db.relationship('Person', primaryjoin=owner_id == Person.id) class Vehicle(db.Model): __tablename__ = 'vehicles' id = db.Column(db.Integer, primary_key=True, autoincrement=True) number = db.Column(db.Integer, nullable=False) owner_id = db.Column(db.Integer, db.ForeignKey('people.id'), nullable=True) vehicle_metadata = db.Column(MutableDict.as_mutable(HSTORE), nullable=False) races = db.relationship('Race', secondary='race_results') owner = db.relationship('Person', primaryjoin=owner_id == Person.id) class DriverStanding(db.Model): __tablename__ = 'driver_standings' id = db.Column(db.Integer, primary_key=True, autoincrement=True) driver_id = db.Column(db.Integer, db.ForeignKey('people.id'), nullable=False) vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'), nullable=False) series = db.Column(db.String(5), db.ForeignKey('series.id'), nullable=False) season = db.Column(db.Integer, nullable=False) position = db.Column(db.Integer, nullable=False) points = db.Column(db.Integer, nullable=False) poles = db.Column(db.Integer, nullable=False) wins = db.Column(db.Integer, nullable=False) starts = db.Column(db.Integer, nullable=False) dnfs = db.Column(db.Integer, nullable=False) top5 = db.Column(db.Integer, nullable=False) top10 = db.Column(db.Integer, nullable=False) driver = db.relationship('Person') vehicle = db.relationship('Vehicle') class TeamStanding(db.Model): __tablename__ = 'team_standings' id = db.Column(db.Integer, primary_key=True, autoincrement=True) team_id = db.Column(db.String(50), db.ForeignKey('teams.id'), nullable=False) vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'), nullable=False) series = db.Column(db.String(5), db.ForeignKey('series.id'), nullable=False) season = db.Column(db.Integer, nullable=False) position = db.Column(db.Integer, nullable=False) points = db.Column(db.Integer, nullable=False) poles = db.Column(db.Integer, nullable=False) team = db.relationship('Team') vehicle = db.relationship('Vehicle') class OwnerStanding(db.Model): __tablename__ = 'owner_standings' id = db.Column(db.Integer, primary_key=True, autoincrement=True) vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'), nullable=False) series = db.Column(db.String(5), db.ForeignKey('series.id'), nullable=False) season = db.Column(db.Integer, nullable=False) position = db.Column(db.Integer, nullable=False) points = db.Column(db.Integer, nullable=False) vehicle = db.relationship('Vehicle') class RaceTrack(db.Model): __tablename__ = 'race_tracks' id = db.Column(db.Integer, primary_key=True, autoincrement=True) site = db.Column(db.String(50), nullable=False) circuit_name = db.Column(db.String(100), nullable=False) city = db.Column(db.String(50), nullable=False) state = db.Column(db.String(2), nullable=True) country = db.Column(db.String(50), nullable=False) class Race(db.Model): __tablename__ = 'races' id = db.Column(db.String(50), primary_key=True) round = db.Column(db.Integer, nullable=False) name = db.Column(db.String(100), nullable=False) season = db.Column(db.Integer, nullable=False) race_track_id = db.Column(db.Integer, db.ForeignKey('race_tracks.id'), nullable=False) date = db.Column(db.DateTime, nullable=False) laps = db.Column(db.Integer, nullable=False) length = db.Column(db.Numeric(5, 3), nullable=False) distance = db.Column(db.Numeric(5, 1), nullable=False) series = db.Column(db.String(5), db.ForeignKey('series.id'), nullable=False) race_types = db.relationship('RaceType', secondary='races_types') race_track = db.relationship('RaceTrack') class RaceType(db.Model): __tablename__ = 'race_types' id = db.Column(db.String(5), primary_key=True) description = db.Column(db.String(50), nullable=True) class RacesTypes(db.Model): __tablename__ = 'races_types' race_id = db.Column(db.String(50), db.ForeignKey('races.id'), primary_key=True) race_type = db.Column(db.String(5), db.ForeignKey('race_types.id'), primary_key=True) class RaceStanding(db.Model): __tablename__ = 'race_standings' id = db.Column(db.Integer, primary_key=True, autoincrement=True) race_id = db.Column(db.String(50), db.ForeignKey('races.id'), nullable=False) race_time = db.Column(db.Time, nullable=False) caution_flags = db.Column(db.Integer, nullable=False) caution_flag_laps = db.Column(db.Integer, nullable=False) lead_changes = db.Column(db.Integer, nullable=False) pole_speed = db.Column(db.Numeric(6, 3), nullable=False) avg_speed = db.Column(db.Numeric(6, 3), nullable=False) victory_margin = db.Column(db.Numeric(6, 3), nullable=False) class RaceEntryType(db.Model): __tablename__ = 'race_entry_types' id = db.Column(db.Integer, primary_key=True, autoincrement=True) entry_type = db.Column(db.String(50), nullable=False) class RaceEntry(db.Model): __tablename__ = 'race_entries' id = db.Column(db.Integer, primary_key=True, autoincrement=True) race_id = db.Column(db.String(50), db.ForeignKey('races.id'), nullable=False) team_id = db.Column(db.String(50), db.ForeignKey('teams.id'), nullable=False) vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'), nullable=False) entry_type_id = db.Column(db.Integer, db.ForeignKey('race_entry_types.id'), nullable=False) race = db.relationship('Race') team = db.relationship('Team') vehicle = db.relationship('Vehicle') entry_type = db.relationship('RaceEntryType') people = db.relationship('RaceEntryPerson') class RaceEntryPerson(db.Model): __tablename__ = 'race_entries_people' id = db.Column(db.Integer, primary_key=True, autoincrement=True) race_entry_id = db.Column(db.Integer, db.ForeignKey('race_entries.id'), nullable=False) person_id = db.Column(db.Integer, db.ForeignKey('people.id'), nullable=False) type = db.Column(PersonType, nullable=False) person = db.relationship('Person') race_entry = db.relationship('RaceEntry') class RaceResult(db.Model): __tablename__ = 'race_results' id = db.Column(db.Integer, primary_key=True, autoincrement=True) race_id = db.Column(db.String(50), db.ForeignKey('races.id'), nullable=False) team_id = db.Column(db.String(50), db.ForeignKey('teams.id'), nullable=False) vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'), nullable=False) sponsor = db.Column(db.String(100), nullable=False) grid = db.Column(db.Integer, nullable=False) position = db.Column(db.Integer, nullable=False) laps = db.Column(db.Integer, nullable=False) status = db.Column(db.String(50), nullable=False) laps_led = db.Column(db.Integer, nullable=False) points = db.Column(db.Integer, nullable=False) money = db.Column(db.Numeric(10, 2), nullable=False) race = db.relationship('Race') team = db.relationship('Team') vehicle = db.relationship('Vehicle') people = db.relationship('RaceResultPerson') class RaceResultPerson(db.Model): __tablename__ = 'race_results_people' id = db.Column(db.Integer, primary_key=True, autoincrement=True) race_result_id = db.Column(db.Integer, db.ForeignKey('race_results.id'), nullable=False) person_id = db.Column(db.Integer, db.ForeignKey('people.id'), nullable=False) type = db.Column(PersonType, nullable=False) person = db.relationship('Person') race_result = db.relationship('RaceResult') class QualifyingResult(db.Model): __tablename__ = 'qualifying_results' id = db.Column(db.Integer, primary_key=True, autoincrement=True) race_id = db.Column(db.String(50), db.ForeignKey('races.id'), nullable=False) team_id = db.Column(db.String(50), db.ForeignKey('teams.id'), nullable=False) vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'), nullable=False) session = db.Column(db.Integer, nullable=False) position = db.Column(db.Integer, nullable=False) lap_time = db.Column(db.Numeric(6, 3), nullable=False) race = db.relationship('Race') team = db.relationship('Team') vehicle = db.relationship('Vehicle') people = db.relationship('QualifyingResultPerson') class QualifyingResultPerson(db.Model): __tablename__ = 'qualifying_results_people' id = db.Column(db.Integer, primary_key=True, autoincrement=True) qualifying_result_id = db.Column(db.Integer, db.ForeignKey('qualifying_results.id'), nullable=False) person_id = db.Column(db.Integer, db.ForeignKey('people.id'), nullable=False) type = db.Column(PersonType, nullable=False) person = db.relationship('Person') qualifying_result = db.relationship('QualifyingResult') class PracticeResult(db.Model): ___tablename__ = 'practice_results' id = db.Column(db.Integer, primary_key=True, autoincrement=True) race_id = db.Column(db.String(50), db.ForeignKey('races.id'), nullable=False) team_id = db.Column(db.String(50), db.ForeignKey('teams.id'), nullable=False) vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'), nullable=False) session = db.Column(db.Integer, nullable=False) position = db.Column(db.Integer, nullable=False) lap_time = db.Column(db.Numeric(6, 3), nullable=False) race = db.relationship('Race') team = db.relationship('Team') vehicle = db.relationship('Vehicle') people = 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2674, 800, 4034, 877, 24399, 9215, 3591, 1495, 13, 13, 13, 1990, 29124, 625, 3591, 29898, 2585, 29889, 3195, 1125, 13, 13, 1678, 903, 1649, 3891, 2435, 420, 1649, 353, 525, 29886, 1461, 625, 29918, 9902, 29915, 13, 13, 1678, 1178, 353, 4833, 29889, 4409, 29898, 2585, 29889, 7798, 29892, 7601, 29918, 1989, 29922, 5574, 29892, 4469, 25629, 29922, 5574, 29897, 13, 1678, 8175, 29918, 333, 353, 4833, 29889, 4409, 29898, 2585, 29889, 1231, 29898, 29945, 29900, 511, 4833, 29889, 27755, 2558, 877, 336, 778, 29889, 333, 5477, 1870, 519, 29922, 8824, 29897, 13, 1678, 3815, 29918, 333, 353, 4833, 29889, 4409, 29898, 2585, 29889, 1231, 29898, 29945, 29900, 511, 4833, 29889, 27755, 2558, 877, 371, 2232, 29889, 333, 5477, 1870, 519, 29922, 8824, 29897, 13, 1678, 19716, 29918, 333, 353, 4833, 29889, 4409, 29898, 2585, 29889, 7798, 29892, 4833, 29889, 27755, 2558, 877, 345, 29882, 4027, 29889, 333, 5477, 1870, 519, 29922, 8824, 29897, 13, 1678, 4867, 353, 4833, 29889, 4409, 29898, 2585, 29889, 7798, 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4833, 29889, 4409, 29898, 2585, 29889, 7798, 29892, 4833, 29889, 27755, 2558, 29898, 29925, 1461, 625, 3591, 29889, 333, 511, 1870, 519, 29922, 8824, 29897, 13, 1678, 2022, 29918, 333, 353, 4833, 29889, 4409, 29898, 2585, 29889, 7798, 29892, 4833, 29889, 27755, 2558, 877, 25719, 29889, 333, 5477, 1870, 519, 29922, 8824, 29897, 13, 1678, 1134, 353, 4833, 29889, 4409, 29898, 7435, 1542, 29892, 1870, 519, 29922, 8824, 29897, 13, 13, 1678, 2022, 353, 4833, 29889, 2674, 800, 4034, 877, 7435, 1495, 13, 1678, 6944, 29918, 2914, 353, 4833, 29889, 2674, 800, 4034, 877, 29925, 1461, 625, 3591, 1495, 13, 2 ]
lib/harm_plot.py	pnlbwh/multi-shell-dMRIharmonization	4	33402	#!/usr/bin/env python import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import pandas as pd from conversion import read_imgs_masks from os.path import isfile, basename XERR=0.1 ELINEWIDTH=3 CAPSIZE=5 CAPTHICK=3 FMT='cD' def harm_plot(ydata, labels, outPrefix, bshell_b): ''' :param ydata: list of [y1, y2, y3, ...] where each yi is a list :param labels: list of strings :param outPrefix: :return: ''' outPrefix += f'_b{bshell_b}' labels= list(labels) num_series, num_sub= np.shape(ydata) iter_obj= [i for i in range(num_series)] # errorbar plot plt.figure(1) plt.grid(True) for i in iter_obj: x= list(inp.ones((num_sub,))) y= ydata[i] plt.plot(x, y, 'r') plt.errorbar([i], np.mean(y), xerr=XERR, yerr=np.std(y), ecolor='k', capsize=CAPSIZE, capthick=CAPTHICK, elinewidth=ELINEWIDTH, fmt=FMT) plt.xticks(iter_obj, labels) plt.title('Comparison of meanFA before and after harmonization') plt.ylabel('meanFA over IIT_mean_FA_skeleton') plt.savefig(outPrefix+'_ebarplot.png') # plt.show() # box plot # plt.figure(2) # plt.grid(True) # for i in iter_obj: # x = list(i * np.ones((num_sub,))) # y = ydata[i] # plt.plot(x, y, 'r') # # plt.boxplot(ydata, labels=labels, positions=iter_obj, # boxprops=dict(linewidth=4), # medianprops=dict(linewidth=4), # whiskerprops=dict(linewidth=2)) # # # plt.title(f'Comparison of boxplot before and after harmonization for b{bshell_b}') # plt.ylabel('meanFA over IIT_mean_FA_skeleton') # plt.savefig(outPrefix+'_boxplot.png') # plt.show() # return (outPrefix+'_ebarplot.png', outPrefix+'_boxplot.png') return outPrefix+'_ebarplot.png' def generate_csv(imgs, site_means, outPrefix, bshell_b): try: imgs, _= read_imgs_masks(imgs) except: pass statFile = outPrefix + '_stat.csv' if isfile(statFile): df= pd.read_csv(statFile) df= df.assign(*{f'meanFA b{bshell_b}':site_means}) else: stat = {'subject': [basename(f) for f in imgs], f'meanFA b{bshell_b}': site_means} df = pd.DataFrame(stat) df.to_csv(statFile, index=False) if __name__=='__main__': sub=['hi','hello','go','come'] ref_mean= [0.46, 0.49, 0.44, 0.40] target_mean_before= [0.42, 0.58, 0.43, 0.66] target_mean_after= [0.5 , 0.45, 0.40, 0.55] labels=['Reference','Target before','Target after'] bshell_b= 2000 harm_plot([ref_mean, target_mean_before, target_mean_after], labels, '/tmp/abc', bshell_b) # harm_plot([ref_mean], ['Reference'], '/tmp/abc', bshell_b) generate_csv(sub, ref_mean, '/tmp/abc', bshell_b)	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 5215, 12655, 408, 7442, 13, 5215, 22889, 13, 2922, 17357, 29889, 1509, 877, 29909, 1505, 1495, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 5215, 11701, 408, 10518, 13, 3166, 11301, 1053, 1303, 29918, 2492, 29879, 29918, 13168, 29879, 13, 3166, 2897, 29889, 2084, 1053, 338, 1445, 29892, 2362, 3871, 13, 13, 29990, 21662, 29922, 29900, 29889, 29896, 13, 6670, 8895, 22574, 29922, 29941, 13, 29907, 3301, 14226, 29922, 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29946, 511, 13, 1678, 396, 632, 377, 3873, 261, 11030, 29922, 8977, 29898, 16292, 29922, 29906, 876, 13, 1678, 396, 13, 1678, 396, 13, 1678, 396, 14770, 29889, 3257, 29898, 29888, 29915, 1523, 20941, 310, 3800, 5317, 1434, 322, 1156, 10311, 265, 2133, 363, 289, 29912, 29890, 15903, 29918, 29890, 29913, 1495, 13, 1678, 396, 14770, 29889, 29891, 1643, 877, 12676, 4519, 975, 306, 1806, 29918, 12676, 29918, 4519, 29918, 26050, 11285, 1495, 13, 1678, 396, 14770, 29889, 7620, 1003, 29898, 449, 23095, 29974, 15972, 1884, 5317, 29889, 2732, 1495, 13, 1678, 396, 14770, 29889, 4294, 580, 13, 1678, 396, 736, 313, 449, 23095, 29974, 15972, 774, 279, 5317, 29889, 2732, 742, 714, 23095, 29974, 15972, 1884, 5317, 29889, 2732, 1495, 13, 13, 1678, 736, 714, 23095, 29974, 15972, 774, 279, 5317, 29889, 2732, 29915, 13, 13, 13, 1753, 5706, 29918, 7638, 29898, 2492, 29879, 29892, 3268, 29918, 1004, 550, 29892, 714, 23095, 29892, 289, 15903, 29918, 29890, 1125, 13, 13, 1678, 1018, 29901, 13, 4706, 527, 3174, 29892, 903, 29922, 1303, 29918, 2492, 29879, 29918, 13168, 29879, 29898, 2492, 29879, 29897, 13, 1678, 5174, 29901, 13, 4706, 1209, 13, 13, 1678, 1002, 2283, 353, 714, 23095, 718, 22868, 6112, 29889, 7638, 29915, 13, 268, 13, 1678, 565, 338, 1445, 29898, 6112, 2283, 1125, 13, 4706, 4489, 29922, 10518, 29889, 949, 29918, 7638, 29898, 6112, 2283, 29897, 13, 4706, 4489, 29922, 4489, 29889, 16645, 29898, 1068, 29912, 29888, 29915, 12676, 4519, 289, 29912, 29890, 15903, 29918, 29890, 29913, 2396, 2746, 29918, 1004, 550, 1800, 13, 1678, 1683, 29901, 13, 4706, 1002, 353, 11117, 16009, 2396, 518, 6500, 3871, 29898, 29888, 29897, 363, 285, 297, 527, 3174, 1402, 285, 29915, 12676, 4519, 289, 29912, 29890, 15903, 29918, 29890, 29913, 2396, 3268, 29918, 1004, 550, 29913, 13, 4706, 4489, 353, 10518, 29889, 17271, 29898, 6112, 29897, 13, 268, 13, 1678, 4489, 29889, 517, 29918, 7638, 29898, 6112, 2283, 29892, 2380, 29922, 8824, 29897, 13, 13, 13, 361, 4770, 978, 1649, 1360, 29915, 1649, 3396, 1649, 2396, 13, 13, 1678, 1014, 29922, 1839, 2918, 3788, 12199, 3788, 1484, 3788, 2763, 2033, 13, 1678, 2143, 29918, 12676, 29922, 518, 29900, 29889, 29946, 29953, 29892, 29871, 29900, 29889, 29946, 29929, 29892, 29871, 29900, 29889, 29946, 29946, 29892, 29871, 29900, 29889, 29946, 29900, 29962, 13, 1678, 3646, 29918, 12676, 29918, 11083, 29922, 518, 29900, 29889, 29946, 29906, 29892, 29871, 29900, 29889, 29945, 29947, 29892, 29871, 29900, 29889, 29946, 29941, 29892, 29871, 29900, 29889, 29953, 29953, 29962, 13, 1678, 3646, 29918, 12676, 29918, 7045, 29922, 518, 29900, 29889, 29945, 1919, 29871, 29900, 29889, 29946, 29945, 29892, 29871, 29900, 29889, 29946, 29900, 29892, 29871, 29900, 29889, 29945, 29945, 29962, 13, 1678, 11073, 29922, 1839, 7422, 3788, 8667, 1434, 3788, 8667, 1156, 2033, 13, 1678, 289, 15903, 29918, 29890, 29922, 29871, 29906, 29900, 29900, 29900, 13, 268, 13, 1678, 10311, 29918, 5317, 4197, 999, 29918, 12676, 29892, 3646, 29918, 12676, 29918, 11083, 29892, 3646, 29918, 12676, 29918, 7045, 1402, 11073, 29892, 8207, 7050, 29914, 10736, 742, 289, 15903, 29918, 29890, 29897, 13, 1678, 396, 10311, 29918, 5317, 4197, 999, 29918, 12676, 1402, 6024, 7422, 7464, 8207, 7050, 29914, 10736, 742, 289, 15903, 29918, 29890, 29897, 13, 268, 13, 1678, 5706, 29918, 7638, 29898, 1491, 29892, 2143, 29918, 12676, 29892, 8207, 7050, 29914, 10736, 742, 289, 15903, 29918, 29890, 29897, 13, 268, 13, 268, 13, 2 ]
sparse_decomposition/decomposition/decomposition.py	bdpedigo/sparse_matrix_analysis	2	44909	# Some of the implementation inspired by: # REF: https://github.com/fchen365/epca import time from abc import abstractmethod import numpy as np from factor_analyzer import Rotator from sklearn.base import BaseEstimator from sklearn.preprocessing import StandardScaler from sklearn.utils import check_array from graspologic.embed import selectSVD from ..utils import calculate_explained_variance_ratio, soft_threshold from scipy.linalg import orthogonal_procrustes def _varimax(X): return Rotator(normalize=False).fit_transform(X) def _polar(X): # REF: https://en.wikipedia.org/wiki/Polar_decomposition#Relation_to_the_SVD U, D, Vt = selectSVD(X, n_components=X.shape[1], algorithm="full") return U @ Vt def _polar_rotate_shrink(X, gamma=0.1): # Algorithm 1 from the paper U, _, _ = selectSVD(X, n_components=X.shape[1], algorithm="full") # U = _polar(X) # R, _ = orthogonal_procrustes(U_old, U) # print(np.linalg.norm(U_old @ R - U)) U_rot = _varimax(U) U_thresh = soft_threshold(U_rot, gamma) return U_thresh def _reorder_components(X, Z_hat, Y_hat): score_norms = np.linalg.norm(X @ Y_hat, axis=0) sort_inds = np.argsort(-score_norms) return Z_hat[:, sort_inds], Y_hat[:, sort_inds] # import abc # class SuperclassMeta(type): # def __new__(mcls, classname, bases, cls_dict): # cls = super().__new__(mcls, classname, bases, cls_dict) # for name, member in cls_dict.items(): # if not getattr(member, "__doc__"): # member.__doc__ = getattr(bases[-1], name).__doc__ # return cls class BaseSparseDecomposition(BaseEstimator): def __init__( self, n_components=2, gamma=None, max_iter=10, scale=False, center=False, tol=1e-4, verbose=0, ): """Sparse matrix decomposition model. Parameters ---------- n_components : int, optional (default=2) Number of components or embedding dimensions. gamma : float, int or None, optional (default=None) Sparsity parameter, must be nonnegative. Lower values lead to more sparsity in the estimated components. If ``None``, will be set to ``sqrt(n_components * X.shape[1])`` where ``X`` is the matrix passed to ``fit``. max_iter : int, optional (default=10) Maximum number of iterations allowed, must be nonnegative. scale : bool, optional [description], by default False center : bool, optional [description], by default False tol : float or int, optional (default=1e-4) Tolerance for stopping iterative optimization. If the relative difference in score is less than this amount the algorithm will terminate. verbose : int, optional (default=0) Verbosity level. Higher values will result in more messages. """ self.n_components = n_components self.gamma = gamma self.max_iter = max_iter self.scale = scale self.center = center self.tol = tol self.verbose = verbose # TODO add random state def _initialize(self, X): """[summary] Parameters ---------- X : [type] [description] Returns ------- [type] [description] """ U, D, Vt = selectSVD(X, n_components=self.n_components) score = np.linalg.norm(D) return U, Vt.T, score def _validate_parameters(self, X): """[summary] Parameters ---------- X : [type] [description] """ if not self.gamma: gamma = np.sqrt(self.n_components * X.shape[1]) else: gamma = self.gamma self.gamma_ = gamma def _preprocess_data(self, X): """[summary] Parameters ---------- X : [type] [description] Returns ------- [type] [description] """ if self.scale or self.center: X = StandardScaler( with_mean=self.center, with_std=self.scale ).fit_transform(X) return X # def _compute_matrix_difference(X, metric='max'): # TODO better convergence criteria def fit_transform(self, X, y=None): """[summary] Parameters ---------- X : [type] [description] y : [type], optional [description], by default None Returns ------- [type] [description] """ self._validate_parameters(X) self._validate_data(X, copy=True, ensure_2d=True) # from sklearn BaseEstimator Z_hat, Y_hat, score = self._initialize(X) if self.gamma == np.inf: max_iter = 0 else: max_iter = self.max_iter # for keeping track of progress over iteration Z_diff = np.inf Y_diff = np.inf norm_score_diff = np.inf last_score = 0 # main loop i = 0 while (i < max_iter) and (norm_score_diff > self.tol): if self.verbose > 0: print(f"Iteration: {i}") iter_time = time.time() Z_hat_new, Y_hat_new = self._update_estimates(X, Z_hat, Y_hat) # Z_hat_new, Y_hat_new = _reorder_components(X, Z_hat_new, Y_hat_new) Z_diff = np.linalg.norm(Z_hat_new - Z_hat) Y_diff = np.linalg.norm(Y_hat_new - Y_hat) norm_Z_diff = Z_diff / np.linalg.norm(Z_hat_new) norm_Y_diff = Y_diff / np.linalg.norm(Y_hat_new) Z_hat = Z_hat_new Y_hat = Y_hat_new B_hat = Z_hat.T @ X @ Y_hat score = np.linalg.norm(B_hat) norm_score_diff = np.abs(score - last_score) / score last_score = score if self.verbose > 1: print(f"{time.time() - iter_time:.3f} seconds elapsed for iteration.") if self.verbose > 0: print(f"Difference in Z_hat: {Z_diff}") print(f"Difference in Y_hat: {Z_diff}") print(f"Normalized difference in Z_hat: {norm_Z_diff}") print(f"Normalized difference in Y_hat: {norm_Y_diff}") print(f"Total score: {score}") print(f"Normalized difference in score: {norm_score_diff}") print() i += 1 Z_hat, Y_hat = _reorder_components(X, Z_hat, Y_hat) # save attributes self.n_iter_ = i self.components_ = Y_hat.T # TODO this should not be cumulative by the sklearn definition self.explained_variance_ratio_ = calculate_explained_variance_ratio(X, Y_hat) self.score_ = score return Z_hat def fit(self, X): """[summary] Parameters ---------- X : [type] [description] Returns ------- [type] [description] """ self.fit_transform(X) return self def transform(self, X): """[summary] Parameters ---------- X : [type] [description] Returns ------- [type] [description] """ # TODO input checking return X @ self.components_.T @abstractmethod def _update_estimates(self, X, Z_hat, Y_hat): """[summary] Parameters ---------- X : [type] [description] Z_hat : [type] [description] Y_hat : [type] [description] """ pass class SparseComponentAnalysis(BaseSparseDecomposition): def _update_estimates(self, X, Z_hat, Y_hat): """[summary] Parameters ---------- X : [type] [description] Z_hat : [type] [description] Y_hat : [type] [description] Returns ------- [type] [description] """ Y_hat = _polar_rotate_shrink(X.T @ Z_hat, gamma=self.gamma) Z_hat = _polar(X @ Y_hat) return Z_hat, Y_hat def _save_attributes(self, X, Z_hat, Y_hat): """[summary] Parameters ---------- X : [type] [description] Z_hat : [type] [description] Y_hat : [type] [description] """ pass class SparseMatrixApproximation(BaseSparseDecomposition): def _update_estimates(self, X, Z_hat, Y_hat): """[summary] Parameters ---------- X : [type] [description] Z_hat : [type] [description] Y_hat : [type] [description] Returns ------- [type] [description] """ Z_hat = _polar_rotate_shrink(X @ Y_hat) Y_hat = _polar_rotate_shrink(X.T @ Z_hat) return Z_hat, Y_hat def _save_attributes(self, X, Z_hat, Y_hat): """[summary] Parameters ---------- X : [type] [description] Z_hat : [type] [description] Y_hat : [type] [description] """ B = Z_hat.T @ X @ Y_hat self.score_ = B self.right_latent_ = Y_hat self.left_latent_ = Z_hat	[ 1, 396, 3834, 310, 278, 5314, 20603, 491, 29901, 13, 29937, 5195, 29943, 29901, 2045, 597, 3292, 29889, 510, 29914, 29888, 2724, 29941, 29953, 29945, 29914, 1022, 1113, 13, 13, 5215, 931, 13, 3166, 25638, 1053, 9846, 5696, 13, 13, 5215, 12655, 408, 7442, 13, 3166, 7329, 29918, 24209, 3298, 1053, 9664, 1061, 13, 3166, 2071, 19668, 29889, 3188, 1053, 7399, 12787, 326, 1061, 13, 3166, 2071, 19668, 29889, 1457, 19170, 1053, 10117, 29636, 261, 13, 3166, 2071, 19668, 29889, 13239, 1053, 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PyUdemy/Day17/Start.py/quiz-game/main.py	JoseArtur/phyton-exercices	0	161139	<gh_stars>0 from question_model import Question from data import question_data from quiz_brain import QuizBrain question_bank = [] for i in question_data: question = Question(i["text"], i["answer"]) question_bank.append(question) quiz = QuizBrain(question_bank) while quiz.still_has_question() : quiz.next_question() print("You have completed the quiz") print(f"Your final score is {quiz.score}/{quiz.question_number}.")	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 1139, 29918, 4299, 1053, 894, 13, 3166, 848, 1053, 1139, 29918, 1272, 13, 3166, 439, 466, 29918, 2634, 262, 1053, 751, 466, 22097, 13, 12470, 29918, 9157, 353, 5159, 13, 1454, 474, 297, 1139, 29918, 1272, 29901, 13, 1678, 1139, 353, 894, 29898, 29875, 3366, 726, 12436, 474, 3366, 12011, 20068, 13, 1678, 1139, 29918, 9157, 29889, 4397, 29898, 12470, 29897, 13, 13, 13, 339, 466, 353, 751, 466, 22097, 29898, 12470, 29918, 9157, 29897, 13, 8000, 439, 466, 29889, 303, 453, 29918, 5349, 29918, 12470, 580, 584, 13, 1678, 439, 466, 29889, 4622, 29918, 12470, 580, 13, 2158, 703, 3492, 505, 8676, 278, 439, 466, 1159, 13, 2158, 29898, 29888, 29908, 10858, 2186, 8158, 338, 426, 339, 466, 29889, 13628, 6822, 29912, 339, 466, 29889, 12470, 29918, 4537, 1836, 1159, 13, 2 ]
tools/find-broken-edition-parm.py	ctheune/assembly-cms	0	24023	# Copyright (c) 2010 gocept gmbh & co. kg # See also LICENSE.txt import zope.traversing.api stack = [root['summer10']] while stack: page = stack.pop() for edition in page.editions: for tag in edition.parameters: if not ':' in tag: print zope.traversing.api.getPath(edition) stack.extend(page.subpages)	[ 1, 396, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29900, 748, 1547, 330, 8337, 29882, 669, 1302, 29889, 12118, 13, 29937, 2823, 884, 365, 2965, 1430, 1660, 29889, 3945, 13, 13, 5215, 503, 2300, 29889, 3018, 874, 292, 29889, 2754, 13, 13, 1429, 353, 518, 4632, 1839, 2083, 1050, 29896, 29900, 2033, 29962, 13, 13, 8000, 5096, 29901, 13, 1678, 1813, 353, 5096, 29889, 7323, 580, 13, 1678, 363, 12203, 297, 1813, 29889, 287, 2187, 29901, 13, 4706, 363, 4055, 297, 12203, 29889, 16744, 29901, 13, 9651, 565, 451, 525, 11283, 297, 4055, 29901, 13, 18884, 1596, 503, 2300, 29889, 3018, 874, 292, 29889, 2754, 29889, 657, 2605, 29898, 287, 654, 29897, 13, 1678, 5096, 29889, 21843, 29898, 3488, 29889, 1491, 12292, 29897, 13, 2 ]
src/extract_bcs_crops.py	pmh47/textured-mesh-gen	30	113420	<reponame>pmh47/textured-mesh-gen<filename>src/extract_bcs_crops.py import os import sys from itertools import islice, count from collections import deque import skvideo.io import cv2 from crop_extraction_common import * from bcs_common import * step = 10 median_count = 25 # should be odd; number of stepped-frames to include in median (half either side of current frame) crops_per_file = 256 score_threshold = 0.7 edge_distance = 20 min_box_area = 1500 # square raw-pixels crop_border_fraction = 0.075 # per side dataset_name = sys.argv[1] masks_path = './bcs_masks' def main(): crops_path = bcs_preprocessed_path + '/dataset/' + dataset_name + '/crops_{}x{}_{}-per-file'.format(crop_width, crop_height, crops_per_file) if not os.path.exists(crops_path): os.makedirs(crops_path) occluder_mask = cv2.imread(masks_path + '/' + dataset_name + '_occluders.png') occluder_mask = cv2.resize(occluder_mask, (raw_width, raw_height), interpolation=cv2.INTER_NEAREST) occluder_mask = np.max(occluder_mask, axis=2) rotations = cv2.imread(masks_path + '/' + dataset_name + '_rotations.png')[:, :, 1] # i.e. take just G channel, which is rotation rotations = cv2.resize(rotations, (raw_width, raw_height), interpolation=cv2.INTER_NEAREST) rotations = rotations.astype(np.float32) * 2. * np.pi / 255 projection_matrix, view_rotation_matrix = load_calibration_matrices(dataset_name) detectron = DetectronWrapper(score_threshold) def stepped_indices_and_frames(): reader = skvideo.io.vreader(bcs_raw_path + '/dataset/' + dataset_name + '/video.avi') buffer = deque() indices_and_frames = zip(count(), reader) for index, frame in islice(indices_and_frames, 0, None, step): buffer.append(frame[:, :, ::-1]) # convert BGR to RGB if len(buffer) == median_count: # i.e. start considering frames only when we have enough to compute a median yield ( index - median_count // 2 * step, buffer[median_count // 2], lambda box: np.median(np.asarray([extract_crop(other_frame, raw_width, raw_height, crop_width, crop_border_fraction, box[:-1])[0] for other_frame in buffer]), axis=0), ) buffer.popleft() with ShardedRecordWriter(crops_path + '/{:04d}_l.tfrecords', crops_per_file) as left_writer: with ShardedRecordWriter(crops_path + '/{:04d}_r.tfrecords', crops_per_file) as right_writer: left_crop_count = right_crop_count = 0 for frame_index, frame, get_median_crop in stepped_indices_and_frames(): # Note that boxes are indexed by x1/y1/x2/y2/score class_to_boxes_and_masks = detectron(frame) def touches_edge(box, mask): if box[0] < edge_distance or box[2] > frame.shape[1] - edge_distance: return True if box[1] < edge_distance or box[3] > frame.shape[0] - edge_distance: return True return False def touches_occluder(box, mask): dilated_mask = cv2.dilate(mask, np.ones([3, 3])) if np.count_nonzero(dilated_mask occluder_mask) > 0: return True return False def touches_other_instance(box, mask): dilated_mask = cv2.dilate(mask, np.ones([3, 3])) for boxes_and_masks in class_to_boxes_and_masks.values(): for _, other_mask in boxes_and_masks: if other_mask is mask: continue if np.count_nonzero(dilated_mask * other_mask) > 0: return True return False def is_large_enough(box, mask): return box_area(box) > min_box_area car_boxes_and_masks = list(filter( lambda box_and_mask: not touches_edge(box_and_mask) and not touches_occluder(box_and_mask) and not touches_other_instance(box_and_mask) and is_large_enough(box_and_mask), class_to_boxes_and_masks['car'] )) for box, mask in car_boxes_and_masks: assert crop_width == crop_height crop, crop_raw_size = extract_crop(frame, raw_width, raw_height, crop_width, crop_border_fraction, box[:-1]) cropped_mask, _ = extract_crop(mask, raw_width, raw_height, crop_width, crop_border_fraction, box[:-1]) median = get_median_crop(box) centre_x = int((box[0] + box[2]) // 2 + 0.5) centre_y = int((box[1] + box[3]) // 2 + 0.5) rotation = rotations[centre_y, centre_x] offset_x = centre_x - raw_width / 2. offset_y = raw_height / 2. - centre_y crop_offset_matrix = [ [1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [-offset_x, -offset_y, 0., 1.], ] xy_ndc_matrix = np.diag([2. / crop_raw_size, 2. / crop_raw_size, 1., 1.]) crop_projection_matrix = np.dot(np.dot(projection_matrix, crop_offset_matrix), xy_ndc_matrix) example = tf.train.Example( features=tf.train.Features( feature={ 'pixels': jpeg_feature(crop), 'background': jpeg_feature(median), 'mask': png_feature(cropped_mask), 'rotation': float32_feature(rotation), 'projection_matrix': float32_feature(crop_projection_matrix), 'view_rotation_matrix': float32_feature(view_rotation_matrix), 'dataset_name': string_feature(dataset_name) } ) ) if abs(rotation - np.pi) < 0.1: writer = right_writer right_crop_count += 1 else: writer = left_writer left_crop_count += 1 writer.write(example.SerializeToString()) if len(car_boxes_and_masks) > 0: print('{} left-lane / {} right-lane crops written at frame 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api/recomend/urls.py	JCTec/Recomender	0	1617604	from django.urls import path from django.conf.urls import url from .views import * urlpatterns = [ path('questions/', QuestionAPI.as_view()), path('answers/', AnswersAPI.as_view()), ]	[ 1, 515, 9557, 29889, 26045, 1053, 2224, 13, 3166, 9557, 29889, 5527, 29889, 26045, 1053, 3142, 13, 3166, 869, 7406, 1053, 334, 13, 13, 13, 2271, 11037, 29879, 353, 518, 13, 1678, 2224, 877, 2619, 29914, 742, 894, 8787, 29889, 294, 29918, 1493, 25739, 13, 1678, 2224, 877, 550, 17538, 29914, 742, 530, 5956, 8787, 29889, 294, 29918, 1493, 25739, 13, 29962, 13, 13, 2 ]
acoustic_scene_classification/test.py	GT-KIM/specmix	2	65361	import numpy as np import matplotlib.pyplot as plt augment_method = "Specaugment" d = np.load("result_{}/curve.npz".format(augment_method)) loss = d['loss'] acc = d['acc'] best_acc = d['best_acc'] print(best_acc) plt.plot(loss) plt.show() plt.clf() plt.plot(acc) plt.show() plt.clf()	[ 1, 1053, 12655, 408, 7442, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 13, 2987, 358, 29918, 5696, 353, 376, 10299, 2987, 358, 29908, 13, 13, 29881, 353, 7442, 29889, 1359, 703, 2914, 648, 6822, 2764, 345, 29889, 9302, 29920, 1642, 4830, 29898, 2987, 358, 29918, 5696, 876, 13, 6758, 353, 270, 1839, 6758, 2033, 13, 5753, 353, 270, 1839, 5753, 2033, 13, 13318, 29918, 5753, 353, 270, 1839, 13318, 29918, 5753, 2033, 13, 13, 2158, 29898, 13318, 29918, 5753, 29897, 13, 13, 572, 29873, 29889, 5317, 29898, 6758, 29897, 13, 572, 29873, 29889, 4294, 580, 13, 572, 29873, 29889, 695, 29888, 580, 13, 13, 572, 29873, 29889, 5317, 29898, 5753, 29897, 13, 572, 29873, 29889, 4294, 580, 13, 572, 29873, 29889, 695, 29888, 580, 2 ]
pyhmy/rpc/request.py	difengJ/pyhmy	37	12180	import json import requests from .exceptions import ( RequestsError, RequestsTimeoutError, RPCError ) _default_endpoint = 'http://localhost:9500' _default_timeout = 30 def base_request(method, params=None, endpoint=_default_endpoint, timeout=_default_timeout) -> str: """ Basic RPC request Parameters --------- method: str RPC Method to call params: :obj:`list`, optional Parameters for the RPC method endpoint: :obj:`str`, optional Endpoint to send request to timeout: :obj:`int`, optional Timeout in seconds Returns ------- str Raw output from the request Raises ------ TypeError If params is not a list or None RequestsTimeoutError If request timed out RequestsError If other request error occured """ if params is None: params = [] elif not isinstance(params, list): raise TypeError(f'invalid type {params.__class__}') try: payload = { "id": "1", "jsonrpc": "2.0", "method": method, "params": params } headers = { 'Content-Type': 'application/json' } resp = requests.request('POST', endpoint, headers=headers, data=json.dumps(payload), timeout=timeout, allow_redirects=True) return resp.content except requests.exceptions.Timeout as err: raise RequestsTimeoutError(endpoint) from err except requests.exceptions.RequestException as err: raise RequestsError(endpoint) from err def rpc_request(method, params=None, endpoint=_default_endpoint, timeout=_default_timeout) -> dict: """ RPC request Parameters --------- method: str RPC Method to call params: :obj:`list`, optional Parameters for the RPC method endpoint: :obj:`str`, optional Endpoint to send request to timeout: :obj:`int`, optional Timeout in seconds Returns ------- dict Returns dictionary representation of RPC response Example format: { "jsonrpc": "2.0", "id": 1, "result": ... } Raises ------ RPCError If RPC response returned a blockchain error See Also -------- base_request """ raw_resp = base_request(method, params, endpoint, timeout) try: resp = json.loads(raw_resp) if 'error' in resp: raise RPCError(method, endpoint, str(resp['error'])) return resp except json.decoder.JSONDecodeError as err: raise RPCError(method, endpoint, raw_resp) from err # 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Solutions/gigasecond.py	matthijskrul/PythonExercism	0	120728	<reponame>matthijskrul/PythonExercism<filename>Solutions/gigasecond.py import datetime def add(moment): return moment + datetime.timedelta(seconds=10**9)	[ 1, 529, 276, 1112, 420, 29958, 2922, 386, 823, 808, 29878, 352, 29914, 11980, 1252, 6269, 1608, 29966, 9507, 29958, 13296, 17925, 29914, 29887, 335, 559, 1116, 29889, 2272, 13, 5215, 12865, 13, 13, 13, 1753, 788, 29898, 29885, 2932, 1125, 13, 1678, 736, 3256, 718, 12865, 29889, 9346, 287, 2554, 29898, 23128, 29922, 29896, 29900, 1068, 29929, 29897, 13, 2 ]
lib/send_file.py	collinahn/file_name_changer	1	150898	import requests import json from requests.exceptions import ( ConnectionError, Timeout, HTTPError, ) from lib.json_parser import TextParser from lib.log_gongik import Logger from .__PRIVATE import IP, PORT_API, DOWNLOAD_KEY class LogFileSender(object): def __init__(self, target_file_dir: str) -> None: self.log = Logger() host = IP port = PORT_API self.key = DOWNLOAD_KEY self.server_url: str = f'http://{host}:{port}/api/v1/log-report' self.target_dir: str = target_file_dir def _send_server(self) -> requests.Response or None: try: self.log.INFO(f'putting {self.target_dir} to memory') whole_log_file: str = TextParser(self.target_dir).value self.log.INFO(f'sending {self.target_dir} to server, size = {len(whole_log_file)}') post_data = json.dumps({ 'pw': self.key, 'log': whole_log_file }) return requests.post(self.server_url, data=post_data) except (requests.exceptions.ChunkedEncodingError, ConnectionError, Timeout, HTTPError) as networkerror: self.log.ERROR(f'network error occurred {networkerror}') return None def report(self) -> bool: res = self._send_server() if res.status_code != 200: self.log.ERROR(f'{res.content = }') return False self.log.INFO('log report complete') return True	[ 1, 1053, 7274, 13, 5215, 4390, 13, 3166, 7274, 29889, 11739, 29879, 1053, 313, 13, 1678, 15160, 2392, 29892, 13, 1678, 5974, 449, 29892, 13, 1678, 7331, 2392, 29892, 13, 29897, 13, 13, 3166, 4303, 29889, 3126, 29918, 16680, 1053, 3992, 11726, 13, 3166, 4303, 29889, 1188, 29918, 29887, 549, 638, 1053, 28468, 13, 3166, 869, 1649, 29829, 29963, 3040, 1053, 5641, 29892, 349, 8476, 29918, 8787, 29892, 360, 9806, 29940, 29428, 29918, 10818, 13, 13, 1990, 4522, 2283, 29615, 29898, 3318, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 3646, 29918, 1445, 29918, 3972, 29901, 851, 29897, 1599, 6213, 29901, 13, 4706, 1583, 29889, 1188, 353, 28468, 580, 13, 13, 4706, 3495, 353, 5641, 13, 4706, 2011, 353, 349, 8476, 29918, 8787, 13, 4706, 1583, 29889, 1989, 353, 360, 9806, 29940, 29428, 29918, 10818, 13, 13, 4706, 1583, 29889, 2974, 29918, 2271, 29901, 851, 353, 285, 29915, 1124, 597, 29912, 3069, 6177, 29912, 637, 6822, 2754, 29914, 29894, 29896, 29914, 1188, 29899, 12276, 29915, 13, 4706, 1583, 29889, 5182, 29918, 3972, 29901, 851, 353, 3646, 29918, 1445, 29918, 3972, 13, 13, 1678, 822, 903, 6717, 29918, 2974, 29898, 1311, 29897, 1599, 7274, 29889, 5103, 470, 6213, 29901, 13, 4706, 1018, 29901, 13, 9651, 1583, 29889, 1188, 29889, 11690, 29898, 29888, 29915, 649, 1259, 426, 1311, 29889, 5182, 29918, 3972, 29913, 304, 3370, 1495, 13, 9651, 3353, 29918, 1188, 29918, 1445, 29901, 851, 353, 3992, 11726, 29898, 1311, 29889, 5182, 29918, 3972, 467, 1767, 13, 9651, 1583, 29889, 1188, 29889, 11690, 29898, 29888, 29915, 29879, 2548, 426, 1311, 29889, 5182, 29918, 3972, 29913, 304, 1923, 29892, 2159, 353, 426, 2435, 29898, 15970, 280, 29918, 1188, 29918, 1445, 2915, 1495, 13, 13, 9651, 1400, 29918, 1272, 353, 4390, 29889, 29881, 17204, 3319, 13, 18884, 525, 29886, 29893, 2396, 1583, 29889, 1989, 29892, 13, 18884, 525, 1188, 2396, 3353, 29918, 1188, 29918, 1445, 13, 9651, 5615, 13, 13, 9651, 736, 7274, 29889, 2490, 29898, 1311, 29889, 2974, 29918, 2271, 29892, 848, 29922, 2490, 29918, 1272, 29897, 13, 4706, 5174, 313, 24830, 29889, 11739, 29879, 29889, 1451, 2960, 287, 14934, 2392, 29892, 15160, 2392, 29892, 5974, 449, 29892, 7331, 2392, 29897, 408, 3564, 2704, 29901, 13, 9651, 1583, 29889, 1188, 29889, 11432, 29898, 29888, 29915, 11618, 1059, 10761, 426, 11618, 2704, 29913, 1495, 13, 9651, 736, 6213, 13, 13, 1678, 822, 3461, 29898, 1311, 29897, 1599, 6120, 29901, 13, 4706, 620, 353, 1583, 3032, 6717, 29918, 2974, 580, 13, 4706, 565, 620, 29889, 4882, 29918, 401, 2804, 29871, 29906, 29900, 29900, 29901, 13, 9651, 1583, 29889, 1188, 29889, 11432, 29898, 29888, 29915, 29912, 690, 29889, 3051, 353, 500, 1495, 13, 9651, 736, 7700, 13, 13, 4706, 1583, 29889, 1188, 29889, 11690, 877, 1188, 3461, 4866, 1495, 13, 4706, 736, 5852, 2 ]
temparature.py	Shrijan21/hacktoberfest2021-2	0	164989	<reponame>Shrijan21/hacktoberfest2021-2 print("Choose from below options:") print("1.Celsius to Fahrenheit.") print("2.Fahrenheit to Celsius.") o=int(input("option(1/2):")) if(o==1): c=float(input("Temperature in Celsius:")) f=1.8*(c)+32.0 f=round(f,1) #temperature in fahrenheit precise to 1 decimal place print("Temperature in Fahrenheit:",f) elif(o==2): f=float(input("Temperature in Fahrenheit:")) c=(f-32)/1.8 c=round(c,1) #temperature in celsius precise to 1 decimal place print("Temperature in Celsius:",c) else: print("Choose 1 or 2.")	[ 1, 529, 276, 1112, 420, 29958, 2713, 4927, 273, 29906, 29896, 29914, 29882, 547, 6264, 29613, 29906, 29900, 29906, 29896, 29899, 29906, 13, 2158, 703, 15954, 852, 515, 2400, 3987, 29901, 1159, 13, 2158, 703, 29896, 29889, 29907, 295, 1039, 375, 304, 383, 21446, 6884, 23157, 13, 2158, 703, 29906, 29889, 29943, 21446, 6884, 304, 14227, 1039, 375, 23157, 13, 29877, 29922, 524, 29898, 2080, 703, 3385, 29898, 29896, 29914, 29906, 1125, 5783, 13, 361, 29898, 29877, 1360, 29896, 1125, 13, 1678, 274, 29922, 7411, 29898, 2080, 703, 5776, 546, 1535, 297, 14227, 1039, 375, 29901, 5783, 13, 1678, 285, 29922, 29896, 29889, 29947, 16395, 29883, 7240, 29941, 29906, 29889, 29900, 13, 1678, 285, 29922, 14486, 29898, 29888, 29892, 29896, 29897, 396, 12863, 1535, 297, 285, 21446, 6884, 18378, 304, 29871, 29896, 13677, 2058, 13, 1678, 1596, 703, 5776, 546, 1535, 297, 383, 21446, 6884, 29901, 613, 29888, 29897, 13, 23681, 29898, 29877, 1360, 29906, 1125, 13, 1678, 285, 29922, 7411, 29898, 2080, 703, 5776, 546, 1535, 297, 383, 21446, 6884, 29901, 5783, 13, 1678, 274, 7607, 29888, 29899, 29941, 29906, 6802, 29896, 29889, 29947, 13, 1678, 274, 29922, 14486, 29898, 29883, 29892, 29896, 29897, 396, 12863, 1535, 297, 6432, 1039, 375, 18378, 304, 29871, 29896, 13677, 2058, 13, 1678, 1596, 703, 5776, 546, 1535, 297, 14227, 1039, 375, 29901, 613, 29883, 29897, 13, 2870, 29901, 13, 1678, 1596, 703, 15954, 852, 29871, 29896, 470, 29871, 29906, 23157, 2 ]
old_playground/minimal_induction.py	sterin/pyzz	0	143713	from pyzz import * def not_equal( N, ws1, ws2 ): return disjunction( N, ( w1 ^ w2 for w1, w2 in zip(ws1, ws2) ) ) def not_equal_states(U, i, j): return not_equal( U.F, U[U.N.get_Flops(), i], U[U.N.get_Flops(), j] ) def unique(U): return conjunction( U.F, ( not_equal_states(U, i, j) for i,j in i_lt_j(U.frames) ) ) def induction(N, max): po = disjunction( N, N.get_POs() ) UB = unroll(N, init=True) SB = solver(UB.F) US = unroll(N, unroll=1, init=False) SS = solver(US.F) SS.clause( [ US[~po,0] ] ) for frame in xrange(max): print "Base %5d: "%frame, UB.unroll(1) fbpo = UB[po, frame] rc = SB.solve( [ fbpo ] ) if rc == solver.l_True: print "SAT" return rc elif rc == solver.l_False: print "UNSAT" SB.clause( [ ~fbpo ] ) print "Step %5d: "%frame, US.unroll(1) fspo = US[ po, frame+1 ] rc = SS.solve( [ fspo, unique(US) ] ) if rc == solver.l_True: SS.clause( [~fspo] ) print "SAT" elif rc == solver.l_False: print "UNSAT" return rc return solver.l_Undef print induction( arg_netlist() , 50)	[ 1, 515, 11451, 5617, 1053, 334, 13, 13, 1753, 451, 29918, 11745, 29898, 405, 29892, 16904, 29896, 29892, 16904, 29906, 29871, 1125, 13, 1678, 736, 766, 29926, 651, 29898, 405, 29892, 313, 281, 29896, 6228, 281, 29906, 363, 281, 29896, 29892, 281, 29906, 297, 14319, 29898, 5652, 29896, 29892, 16904, 29906, 29897, 1723, 1723, 13, 13, 1753, 451, 29918, 11745, 29918, 28631, 29898, 29965, 29892, 474, 29892, 432, 1125, 13, 1678, 736, 451, 29918, 11745, 29898, 501, 29889, 29943, 29892, 501, 29961, 29965, 29889, 29940, 29889, 657, 29918, 29943, 417, 567, 3285, 474, 1402, 501, 29961, 29965, 29889, 29940, 29889, 657, 29918, 29943, 417, 567, 3285, 432, 29962, 1723, 13, 13, 1753, 5412, 29898, 29965, 1125, 13, 1678, 736, 9589, 651, 29898, 501, 29889, 29943, 29892, 313, 451, 29918, 11745, 29918, 28631, 29898, 29965, 29892, 474, 29892, 432, 29897, 363, 474, 29892, 29926, 297, 474, 29918, 1896, 29918, 29926, 29898, 29965, 29889, 19935, 29897, 1723, 1723, 13, 13, 1753, 21445, 29898, 29940, 29892, 4236, 1125, 13, 13, 1678, 772, 353, 766, 29926, 651, 29898, 405, 29892, 405, 29889, 657, 29918, 13152, 29879, 580, 1723, 13, 29871, 13, 1678, 501, 29933, 353, 443, 1245, 29898, 29940, 29892, 2069, 29922, 5574, 29897, 13, 1678, 317, 29933, 353, 899, 369, 29898, 7466, 29889, 29943, 29897, 13, 268, 13, 1678, 3148, 353, 443, 1245, 29898, 29940, 29892, 443, 1245, 29922, 29896, 29892, 2069, 29922, 8824, 29897, 13, 1678, 5886, 353, 899, 369, 29898, 3308, 29889, 29943, 29897, 13, 13, 1678, 5886, 29889, 16398, 1509, 29898, 518, 3148, 29961, 30022, 1129, 29892, 29900, 29962, 4514, 1723, 13, 268, 13, 1678, 363, 3515, 297, 921, 3881, 29898, 3317, 1125, 13, 308, 13, 4706, 1596, 376, 5160, 1273, 29945, 29881, 29901, 11860, 2557, 29892, 13, 308, 13, 4706, 501, 29933, 29889, 348, 1245, 29898, 29896, 29897, 13, 268, 13, 4706, 285, 29890, 1129, 353, 501, 29933, 29961, 1129, 29892, 3515, 29962, 13, 268, 13, 4706, 364, 29883, 353, 317, 29933, 29889, 2929, 345, 29898, 518, 285, 29890, 1129, 4514, 1723, 13, 308, 13, 4706, 565, 364, 29883, 1275, 899, 369, 29889, 29880, 29918, 5574, 29901, 13, 9651, 1596, 376, 29903, 1299, 29908, 13, 9651, 736, 364, 29883, 13, 632, 13, 4706, 25342, 364, 29883, 1275, 899, 369, 29889, 29880, 29918, 8824, 29901, 13, 9651, 1596, 376, 29965, 3059, 1299, 29908, 13, 9651, 317, 29933, 29889, 16398, 1509, 29898, 518, 3695, 14943, 1129, 4514, 1723, 13, 13, 4706, 1596, 376, 14448, 1273, 29945, 29881, 29901, 11860, 2557, 29892, 13, 308, 13, 4706, 3148, 29889, 348, 1245, 29898, 29896, 29897, 13, 308, 13, 4706, 285, 1028, 29877, 353, 3148, 29961, 772, 29892, 3515, 29974, 29896, 4514, 13, 268, 13, 4706, 364, 29883, 353, 5886, 29889, 2929, 345, 29898, 518, 285, 1028, 29877, 29892, 5412, 29898, 3308, 29897, 4514, 1723, 13, 308, 13, 4706, 565, 364, 29883, 1275, 899, 369, 29889, 29880, 29918, 5574, 29901, 13, 9651, 5886, 29889, 16398, 1509, 29898, 518, 30022, 29888, 1028, 29877, 29962, 1723, 13, 9651, 1596, 376, 29903, 1299, 29908, 13, 632, 13, 4706, 25342, 364, 29883, 1275, 899, 369, 29889, 29880, 29918, 8824, 29901, 13, 9651, 1596, 376, 29965, 3059, 1299, 29908, 13, 9651, 736, 364, 29883, 13, 13, 1678, 736, 899, 369, 29889, 29880, 29918, 25263, 1389, 13, 268, 13, 13, 2158, 21445, 29898, 1852, 29918, 1212, 1761, 580, 1919, 29871, 29945, 29900, 29897, 13, 2 ]
valida/errors.py	hpcflow/valida	0	1614863	class RuleValidationFailure: def __init__( self, rule, ): pass class DuplicateRule(Exception): pass class IncompatibleRules(Exception): pass class ValidationError(Exception): pass class InvalidCallable(Exception): pass class MalformedConditionLikeSpec(Exception): pass class MalformedContainerItemSpec(Exception): pass class MalformedDataPathSpec(Exception): pass class MalformedRuleSpec(Exception): pass	[ 1, 770, 27308, 19448, 24155, 29901, 13, 1678, 822, 4770, 2344, 12035, 13, 4706, 1583, 29892, 13, 4706, 5751, 29892, 13, 268, 1125, 13, 4706, 1209, 13, 13, 13, 1990, 18733, 5926, 10740, 29898, 2451, 1125, 13, 1678, 1209, 13, 13, 13, 1990, 512, 23712, 29934, 2540, 29898, 2451, 1125, 13, 1678, 1209, 13, 13, 13, 1990, 15758, 362, 2392, 29898, 2451, 1125, 13, 1678, 1209, 13, 13, 13, 1990, 21403, 5594, 519, 29898, 2451, 1125, 13, 1678, 1209, 13, 13, 13, 1990, 3792, 15628, 25255, 27552, 10299, 29898, 2451, 1125, 13, 1678, 1209, 13, 13, 13, 1990, 3792, 15628, 7895, 2001, 10299, 29898, 2451, 1125, 13, 1678, 1209, 13, 13, 13, 1990, 3792, 15628, 1469, 2605, 10299, 29898, 2451, 1125, 13, 1678, 1209, 13, 13, 13, 1990, 3792, 15628, 10740, 10299, 29898, 2451, 1125, 13, 1678, 1209, 13, 2 ]
jupyter/password.py	panchohumeres/state-of-open-data	4	170265	from notebook.auth import passwd import os PASSWORD=os.environ['PASSWORD'] with open('/tmp/sha1-psswd', 'w') as f: f.write(passwd(PASSWORD))	[ 1, 515, 451, 19273, 29889, 5150, 1053, 1209, 9970, 13, 5215, 2897, 13, 13, 25711, 17013, 29922, 359, 29889, 21813, 1839, 25711, 17013, 2033, 13, 13, 2541, 1722, 11219, 7050, 29914, 17051, 29896, 29899, 567, 2774, 29881, 742, 525, 29893, 1495, 408, 285, 29901, 13, 1678, 285, 29889, 3539, 29898, 3364, 9970, 29898, 25711, 17013, 876, 2 ]
modeling/src/load_data.py	alyildiz/covid_19_xray	0	41043	<reponame>alyildiz/covid_19_xray from sklearn.model_selection import train_test_split from src.utils import load_dataset def get_data(): train_list_file, train_list_class = load_dataset(data_part="train") test_x, test_y = load_dataset(data_part="test") train_x, val_x, train_y, val_y = train_test_split( train_list_file, train_list_class, stratify=train_list_class, test_size=0.3 ) return train_x, val_x, test_x, train_y, val_y, test_y	[ 1, 529, 276, 1112, 420, 29958, 14997, 789, 466, 29914, 24542, 333, 29918, 29896, 29929, 29918, 29916, 764, 13, 3166, 2071, 19668, 29889, 4299, 29918, 21731, 1053, 7945, 29918, 1688, 29918, 5451, 13, 3166, 4765, 29889, 13239, 1053, 2254, 29918, 24713, 13, 13, 13, 1753, 679, 29918, 1272, 7295, 13, 1678, 7945, 29918, 1761, 29918, 1445, 29892, 7945, 29918, 1761, 29918, 1990, 353, 2254, 29918, 24713, 29898, 1272, 29918, 1595, 543, 14968, 1159, 13, 1678, 1243, 29918, 29916, 29892, 1243, 29918, 29891, 353, 2254, 29918, 24713, 29898, 1272, 29918, 1595, 543, 1688, 1159, 13, 13, 1678, 7945, 29918, 29916, 29892, 659, 29918, 29916, 29892, 7945, 29918, 29891, 29892, 659, 29918, 29891, 353, 7945, 29918, 1688, 29918, 5451, 29898, 13, 4706, 7945, 29918, 1761, 29918, 1445, 29892, 7945, 29918, 1761, 29918, 1990, 29892, 26742, 1598, 29922, 14968, 29918, 1761, 29918, 1990, 29892, 1243, 29918, 2311, 29922, 29900, 29889, 29941, 13, 1678, 1723, 13, 13, 1678, 736, 7945, 29918, 29916, 29892, 659, 29918, 29916, 29892, 1243, 29918, 29916, 29892, 7945, 29918, 29891, 29892, 659, 29918, 29891, 29892, 1243, 29918, 29891, 13, 2 ]
Exercicios - Mundo1/Ex023.py	BrianMath/ExerciciosPythonCeV	0	57794	# Ex. 023 num = str(input("Digite um número: ")) # .zfill(4) -> preenche com 0 até completar 4 casas while len(num) < 4: num = "0" + num print("Unidade: ", num[3]) print("Dezena: ", num[2]) print("Centena: ", num[1]) print("Unidade de milhar: ", num[0])	[ 1, 396, 1222, 29889, 29871, 29900, 29906, 29941, 13, 13, 1949, 353, 851, 29898, 2080, 703, 14991, 568, 1922, 13831, 29901, 376, 876, 29871, 396, 869, 29920, 5589, 29898, 29946, 29897, 1599, 758, 264, 1173, 419, 29871, 29900, 16659, 3315, 279, 29871, 29946, 3209, 294, 13, 8000, 7431, 29898, 1949, 29897, 529, 29871, 29946, 29901, 13, 1678, 954, 353, 376, 29900, 29908, 718, 954, 13, 2158, 703, 2525, 5558, 29901, 9162, 954, 29961, 29941, 2314, 13, 2158, 703, 2772, 2256, 29874, 29901, 9162, 954, 29961, 29906, 2314, 13, 2158, 703, 23369, 2386, 29901, 9162, 954, 29961, 29896, 2314, 13, 2158, 703, 2525, 5558, 316, 2316, 8222, 29901, 9162, 954, 29961, 29900, 2314, 13, 2 ]
dotnet.py	rrezartbuzhala/ca-cloner	0	111293	import os import sys import shutil import constants from os import path from helpers import read_config app_name = sys.argv[1] #Copying the template try: src = 'templates/'+ sys.argv[2] except: src = 'templates/' + read_config(constants.default_template) dir_dest = read_config("DESTINATION_OUTPUT") + "\\" dest = dir_dest + app_name+'/' shutil.copytree(src, dest) project_dir = dest project_dir_src = project_dir + 'src/' os.chdir(project_dir) file_ext = lambda file : os.path.splitext(file)[1] def change_keywords_in_file(filepath): file = open(filepath,'r') filedata = file.read() file.close() newdata = filedata.replace(constants.keyword,app_name) file = open(filepath,'w') file.write(newdata) file.close() def change_files_rec(current_path): for i in os.listdir(current_path): new_name = i if constants.keyword in i: new_name = i.replace(constants.keyword,app_name) os.rename(current_path+i,current_path+new_name) if path.isfile(current_path+new_name): change_keywords_in_file(current_path+new_name) else: change_files_rec(current_path+new_name+'/') #Recuresively changing all solution name references for all files change_files_rec(project_dir) print('Application created at: '+dest) print("Enjoy codeing")	[ 1, 1053, 2897, 13, 5215, 10876, 13, 5215, 528, 4422, 13, 5215, 17727, 13, 3166, 2897, 1053, 2224, 13, 3166, 1371, 414, 1053, 1303, 29918, 2917, 13, 13, 932, 29918, 978, 353, 10876, 29889, 19218, 29961, 29896, 29962, 13, 13, 13, 29937, 11882, 292, 278, 4472, 13, 2202, 29901, 13, 259, 4765, 353, 525, 20943, 29914, 18717, 10876, 29889, 19218, 29961, 29906, 29962, 13, 19499, 29901, 13, 1678, 4765, 353, 525, 20943, 22208, 718, 1303, 29918, 2917, 29898, 3075, 1934, 29889, 4381, 29918, 6886, 29897, 13, 268, 13, 3972, 29918, 7854, 353, 1303, 29918, 2917, 703, 2287, 1254, 1177, 8098, 29918, 12015, 12336, 1159, 718, 376, 1966, 29908, 13, 7854, 353, 4516, 29918, 7854, 718, 623, 29918, 978, 23097, 22208, 13, 845, 4422, 29889, 8552, 8336, 29898, 4351, 29892, 2731, 29897, 29871, 13, 13, 13, 4836, 29918, 3972, 353, 2731, 13, 4836, 29918, 3972, 29918, 4351, 353, 2060, 29918, 3972, 718, 525, 4351, 22208, 13, 359, 29889, 305, 3972, 29898, 4836, 29918, 3972, 29897, 13, 13, 1445, 29918, 1062, 353, 14013, 934, 584, 2897, 29889, 2084, 29889, 23579, 568, 486, 29898, 1445, 9601, 29896, 29962, 13, 13, 13, 1753, 1735, 29918, 1989, 9303, 29918, 262, 29918, 1445, 29898, 1445, 2084, 1125, 13, 1678, 934, 353, 1722, 29898, 1445, 2084, 5501, 29878, 1495, 13, 1678, 934, 1272, 353, 934, 29889, 949, 580, 13, 1678, 934, 29889, 5358, 580, 13, 13, 1678, 716, 1272, 353, 934, 1272, 29889, 6506, 29898, 3075, 1934, 29889, 26766, 29892, 932, 29918, 978, 29897, 13, 13, 1678, 934, 353, 1722, 29898, 1445, 2084, 5501, 29893, 1495, 13, 1678, 934, 29889, 3539, 29898, 1482, 1272, 29897, 13, 1678, 934, 29889, 5358, 580, 13, 13, 1753, 1735, 29918, 5325, 29918, 3757, 29898, 3784, 29918, 2084, 1125, 13, 1678, 363, 474, 297, 2897, 29889, 1761, 3972, 29898, 3784, 29918, 2084, 1125, 13, 4706, 716, 29918, 978, 353, 474, 13, 4706, 565, 17727, 29889, 26766, 297, 474, 29901, 13, 9651, 716, 29918, 978, 353, 474, 29889, 6506, 29898, 3075, 1934, 29889, 26766, 29892, 932, 29918, 978, 29897, 13, 9651, 2897, 29889, 1267, 420, 29898, 3784, 29918, 2084, 29974, 29875, 29892, 3784, 29918, 2084, 29974, 1482, 29918, 978, 29897, 13, 4706, 565, 2224, 29889, 275, 1445, 29898, 3784, 29918, 2084, 29974, 1482, 29918, 978, 1125, 13, 9651, 1735, 29918, 1989, 9303, 29918, 262, 29918, 1445, 29898, 3784, 29918, 2084, 29974, 1482, 29918, 978, 29897, 13, 4706, 1683, 29901, 13, 9651, 1735, 29918, 5325, 29918, 3757, 29898, 3784, 29918, 2084, 29974, 1482, 29918, 978, 23097, 29914, 1495, 13, 13, 13, 29937, 4789, 1973, 3598, 6480, 599, 1650, 1024, 9282, 363, 599, 2066, 13, 3167, 29918, 5325, 29918, 3757, 29898, 4836, 29918, 3972, 29897, 13, 13, 2158, 877, 4873, 2825, 472, 29901, 525, 29974, 7854, 29897, 13, 2158, 703, 2369, 2212, 29891, 775, 292, 1159, 13, 2 ]
buzzmobile/tools/image_const/image_const.py	gtagency/buzzmobile	25	138176	<gh_stars>10-100 #!/usr/bin/env python """image_const: node that continuously publishes a given image from file. Publishes: image_filename Image with topic name same as file name """ from __future__ import print_function # TODO(irapha): remove this when #126 is fixed. import sys import rospy import cv2 from cv_bridge import CvBridge from sensor_msgs.msg import Image bridge = CvBridge() def continuously_publish_image(image_filename): """Publishes the image given at a constant rate.""" cv2_img = cv2.imread(image_filename) gray_im = cv2.cvtColor(cv2_img, cv2.COLOR_BGR2GRAY) img_msg = bridge.cv2_to_imgmsg(gray_im, encoding="mono8") topic_name = sys.argv[1].split('/')[-1][:-4] pub = rospy.Publisher(topic_name, Image, queue_size=1) rospy.init_node('image_const', anonymous=True) rate = rospy.Rate(1) # 1hz while not rospy.is_shutdown(): pub.publish(img_msg) rate.sleep() if __name__ == '__main__': if len(sys.argv) < 2: print("Please provide a image filename") else: continuously_publish_image(sys.argv[1])	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 15945, 29908, 3027, 29918, 3075, 29901, 2943, 393, 3133, 5794, 9805, 267, 263, 2183, 1967, 515, 934, 29889, 13, 13, 21076, 1674, 267, 29901, 13, 1678, 1967, 29918, 9507, 7084, 411, 11261, 1024, 1021, 408, 934, 1024, 13, 15945, 29908, 13, 13, 3166, 4770, 29888, 9130, 1649, 1053, 1596, 29918, 2220, 13, 29937, 14402, 29898, 29875, 1140, 29874, 1125, 3349, 445, 746, 396, 29896, 29906, 29953, 338, 4343, 29889, 13, 13, 5215, 10876, 13, 5215, 696, 1028, 29891, 13, 5215, 13850, 29906, 13, 13, 3166, 13850, 29918, 18419, 1053, 315, 29894, 29933, 8605, 13, 3166, 23530, 29918, 1516, 3174, 29889, 7645, 1053, 7084, 13, 13, 13, 18419, 353, 315, 29894, 29933, 8605, 580, 13, 13, 13, 1753, 3133, 5794, 29918, 23679, 29918, 3027, 29898, 3027, 29918, 9507, 1125, 13, 1678, 9995, 21076, 1674, 267, 278, 1967, 2183, 472, 263, 4868, 6554, 1213, 15945, 13, 1678, 13850, 29906, 29918, 2492, 353, 13850, 29906, 29889, 326, 949, 29898, 3027, 29918, 9507, 29897, 13, 1678, 16749, 29918, 326, 353, 13850, 29906, 29889, 11023, 29873, 3306, 29898, 11023, 29906, 29918, 2492, 29892, 13850, 29906, 29889, 15032, 1955, 29918, 29933, 14345, 29906, 29954, 22800, 29897, 13, 1678, 10153, 29918, 7645, 353, 12945, 29889, 11023, 29906, 29918, 517, 29918, 2492, 7645, 29898, 21012, 29918, 326, 29892, 8025, 543, 29885, 3231, 29947, 1159, 13, 13, 1678, 11261, 29918, 978, 353, 10876, 29889, 19218, 29961, 29896, 1822, 5451, 11219, 1495, 14352, 29896, 3816, 13018, 29946, 29962, 13, 1678, 2529, 353, 696, 1028, 29891, 29889, 21076, 1674, 261, 29898, 13010, 29918, 978, 29892, 7084, 29892, 9521, 29918, 2311, 29922, 29896, 29897, 13, 13, 1678, 696, 1028, 29891, 29889, 2344, 29918, 3177, 877, 3027, 29918, 3075, 742, 21560, 29922, 5574, 29897, 13, 13, 1678, 6554, 353, 696, 1028, 29891, 29889, 19907, 29898, 29896, 29897, 396, 29871, 29896, 29882, 29920, 13, 13, 1678, 1550, 451, 696, 1028, 29891, 29889, 275, 29918, 845, 329, 3204, 7295, 13, 4706, 2529, 29889, 23679, 29898, 2492, 29918, 7645, 29897, 13, 4706, 6554, 29889, 17059, 580, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 565, 7431, 29898, 9675, 29889, 19218, 29897, 529, 29871, 29906, 29901, 13, 4706, 1596, 703, 12148, 3867, 263, 1967, 10422, 1159, 13, 1678, 1683, 29901, 13, 4706, 3133, 5794, 29918, 23679, 29918, 3027, 29898, 9675, 29889, 19218, 29961, 29896, 2314, 13, 2 ]
test/test_examples.py	VDBWRAIR/pyjip	18	151065	#!/usr/bin/env python """Test some of the examples pipelines and tools""" import os import jip import unittest import jip.tools class BWAPipelineTest(unittest.TestCase): def testPipelineStructure(self): # load the pipeline tool = jip.find("examples/bwa/pileup.jip") assert tool is not None # create a pipeline p = jip.Pipeline() # create a new pipeline node and configure id p.run(tool, input="setup.py", reference="Makefile", output="out.txt") # expand the pipeline such that the internal pipeline is resolved p.expand(validate=False) # after expansion with this setuo, the pipeline should have 7 nodes assert len(p) == 7 # the graph should consist of 6 edges assert len(p.edges) == 6 # get out the nodes. we have to use indexes here # because the names might have changed after expansion ref = p.get("ref") align = p.get("align") sam = p.get("sam") bam = p.get("bam") dups = p.get("dups") index = p.get("index") pileup = p.get("pileup") # check the connections assert not ref.has_incoming() assert align.has_incoming(ref) assert sam.has_incoming(align) assert bam.has_incoming(sam) assert dups.has_incoming(bam) assert index.has_incoming(dups) assert pileup.has_incoming(index) assert not pileup.has_outgoing() def testPipelineStructureMultiplexed(self): # load the pipeline tool = jip.find("examples/bwa/pileup.jip") assert tool is not None # create a pipeline p = jip.Pipeline() # create a new pipeline node and configure id p.run(tool, input=["setup.py", "README.rst"], reference="Makefile", output="${input\|ext}_out.txt") # expand the pipeline such that the internal pipeline is resolved # this will also validate all nodes and raise an exception # if one of the nodes validations failed p.expand(validate=False) # after expansion with this setuo, the pipeline should have 7 nodes assert len(p) == 13 # the graph should consist of 6 edges assert len(p.edges) == 12 # get out the nodes. we have to use indexes here # because the names might have changed after expansion ref = p.get("ref") align = p.get("align.0") sam = p.get("sam.0") bam = p.get("bam.0") dups = p.get("dups.0") index = p.get("index.0") pileup = p.get("pileup.0") # check the connections assert not ref.has_incoming() assert align.has_incoming(ref) assert sam.has_incoming(align) assert bam.has_incoming(sam) assert dups.has_incoming(bam) assert index.has_incoming(dups) assert pileup.has_incoming(index) assert not pileup.has_outgoing() # test second set ref = p.get("ref") align = p.get("align.1") sam = p.get("sam.1") bam = p.get("bam.1") dups = p.get("dups.1") index = p.get("index.1") pileup = p.get("pileup.1") # check the connections assert not ref.has_incoming() assert align.has_incoming(ref) assert sam.has_incoming(align) assert bam.has_incoming(sam) assert dups.has_incoming(bam) assert index.has_incoming(dups) assert pileup.has_incoming(index) assert not pileup.has_outgoing() @jip.tool('gem_index') class GemIndex(object): """ The GEM Indexer tool Usage: gem_index -i <genome> [-o <genome_index>] [-t <threads>] [--no-hash] Options: --help Show this help message -o, --output-dir <output_dir> The folder where the output GEM index is created -t, --threads <threads> The number of execution threads [default: 1] --no-hash Do not produce the hash file [default: false] Inputs: -i, --input <genome> The fasta file for the genome """ def init(self): self.add_output('output', "${input\|name\|ext}.gem") def setup(self): out = "${input\|name\|ext}.gem" if self.options['output_dir']: out = "${output_dir}/" + out self.options['output'].set(out) def get_command(self): return "gemtools index -i ${input} -o ${output} -t ${threads} "\ "${no_hash\|arg}" @jip.tool('gem_t_index') class GemTranscriptomeIndex(object): """ The GEM Transcrptome Indexer tool Usage: gem_t_index -i <genome_index> -a <annotation> [-m <max_read_length>] [-o <output_folder>] [-p <output_prefix>] [-t <threads>] Options: --help Show this help message -o, --output-dir <output_dir> The folder where the output files are created [default: ${annotation\|parent}] -p, --prefix <output_prefix> The name to be used for the output files [default: ${annotation\|name}] -t, --threads <threads> The number of execution threads [default: 1] -m, --max-length <max_read_length> Maximum read length [default: 150] Inputs: -i, --index <genome_index> The GEM index file for the genome -a, --annotation <annotation> The reference annotation in GTF format """ def init(self): #if not self.options['output_dir']: # self.options['output_dir'] = "." #if not self.options['prefix']: # self.options['prefix'] = "${annotation}" self.add_output('gem', "${output_dir}/${prefix}.junctions.gem") self.add_output('keys', "${output_dir}/${prefix}.junctions.keys") def get_command(self): return 'bash', 'gemtools t-index -i ${index} -a ${annotation} ' \ '-o ${output_dir\|abs}/${prefix} -t ${threads} ' \ '-m ${max_length}' def test_gemtools_index_command_rendering_for_options(): p = jip.Pipeline() p.run('gem_index', input="Makefile", output_dir='test') p.expand(validate=True) node = p.get('gem_index') print node._tool.options job = jip.create_jobs(p)[0] infile = os.path.abspath("Makefile") base = os.path.dirname(infile) print ">>>", job.command assert job.command == 'gemtools index -i %s -o %s.gem -t 1 ' % ( infile, os.path.join(base, "test/Makefile")) outfiles = list(job.get_output_files()) assert len(outfiles) == 1 assert outfiles[0] == os.path.join(base, "test/Makefile.gem") def test_gemtools_t_index_inputs(): p = jip.Pipeline() p.run('gem_t_index', index="Makefile", annotation='setup.py', output_dir='test') p.expand(validate=False) job = jip.create_jobs(p)[0] infile = os.path.abspath("Makefile") annotation = os.path.abspath("setup.py") base = os.path.dirname(infile) print ">>>", job.command assert job.command == 'gemtools t-index -i %s -a %s -o %s -t 1 -m 150' % ( infile, annotation, os.path.join(base, "test/setup.py")) outfiles = list(job.get_output_files()) assert len(outfiles) == 2 assert outfiles[0] == os.path.join(base, "test/setup.py.junctions.gem") assert outfiles[1] == os.path.join(base, "test/setup.py.junctions.keys") @jip.tool('grape_gem_rnatool') class gem(object): """ The GEMTools RNAseq Mapping Pipeline Usage: gem -f <fastq_file>... -i <genome_index> -a <annotation> -q <quality> [-n <name>] [-o <output_dir>] [-t <threads>] Options: --help Show this help message -q, --quality <quality> The fastq offset quality -n, --name <name> The output prefix name [default: ${fastq.raw()[0]\|name\|ext\|ext\|re("_[12]","")}] -o, --output-dir <output_dir> The output folder -t, --threads <threads> The number of execution threads [default: 1] Inputs: -f, --fastq <fastq_file>... The input fastq -i, --index <genome_index> The GEM index file for the genome -a, --annotation <annotation> The reference annotation in GTF format """ def init(self): self.add_output('map', "${output_dir}/${name}.map.gz") self.add_output('bam', "${output_dir}/${name}.bam") self.add_output('bai', "${output_dir}/${name}.bam.bai") self.add_option('single_end', False, long="--single-end", hidden=False) def setup(self): if len(self.fastq) == 1: self.options['single_end'].set(True) def get_command(self): return 'bash', 'gemtools rna-pipeline ${options()}' @jip.tool('grape_flux') class flux(object): """ The Flux Capacitor Usage: flux -i <input> -a <annotation> [-o <output_dir>] Options: --help Show this help message -o, --output-dir <output_dir> The output folder Inputs: -i, --input <input> The input file with mappings -a, --annotation <annotation> The reference annotation in GTF format """ def init(self): self.add_option('name', "${input\|name\|ext}") self.add_output('gtf', "${output_dir}/${name}.gtf") def get_command(self): return 'bash', 'flux-capacitor ${options()}' @jip.pipeline('grape_gem_rnapipeline') class GrapePipeline(object): """ Run the default RNAseq pipeline usage: rnaseq -f <fastq_file>... -q <quality> -i <genome_index> -a <annotation> [-o <output_dir>] Inputs: -f, --fastq <fastq_file>... The input reference genome -i, --index <genome_index> The input reference genome -a, --annotation <annotation The input reference annotation Options: -q, --quality <quality> The fatq offset quality [default: 33] -o, --output-dir <output_dir> The output prefix [default: ${fastq.raw()[0]\|abs\|parent}] """ def pipeline(self): p = jip.Pipeline() gem = p.run( 'grape_gem_rnatool', index=self.index, annotation=self.annotation, fastq=self.fastq, quality=self.quality, output_dir=self.output_dir ) p.run( 'grape_flux', input=gem.bam, annotation=self.annotation, output_dir=self.output_dir ) p.context(locals()) return p def test_gem_name_option_delegation(): p = jip.Pipeline() p.run('grape_gem_rnapipeline', fastq='reads_1.fastq.gz', index='index.gem', annotation='gencode.gtf') jobs = jip.create_jobs(p, validate=False) ldir = os.getcwd() j = os.path.join assert len(jobs) == 2 assert jobs[0].configuration['index'].get() == j(ldir, 'index.gem') assert jobs[0].configuration['fastq'].get() == j(ldir, 'reads_1.fastq.gz') assert jobs[0].configuration['annotation'].get() == j(ldir, 'gencode.gtf') assert jobs[0].configuration['quality'].get() == '33' assert jobs[0].configuration['output_dir'].get() == ldir assert jobs[0].configuration['name'].get() == 'reads' assert jobs[0].configuration['bam'].get() == j(ldir, 'reads.bam') assert jobs[0].configuration['bai'].get() == j(ldir, 'reads.bam.bai') assert jobs[0].configuration['map'].get() == j(ldir, 'reads.map.gz') assert jobs[1].configuration['input'].get() == j(ldir, 'reads.bam') assert jobs[1].configuration['name'].get() == 'reads' assert jobs[1].configuration['annotation'].get() == j(ldir, 'gencode.gtf') assert jobs[1].configuration['output_dir'].get() == ldir assert jobs[1].configuration['gtf'].get() == j(ldir, 'reads.gtf') assert len(jobs[0].children) == 1 assert len(jobs[1].dependencies) == 1 assert jobs[0].children[0] == jobs[1] def test_gem_name_option_delegation_with_output_dir(): p = jip.Pipeline() p.run('grape_gem_rnapipeline', fastq='reads_1.fastq.gz', index='index.gem', annotation='gencode.gtf', output_dir="mydir") jobs = jip.create_jobs(p, validate=False) ldir = os.getcwd() j = os.path.join assert len(jobs) == 2 assert jobs[0].configuration['index'].get() == j(ldir, 'index.gem') assert jobs[0].configuration['fastq'].get() == j(ldir, 'reads_1.fastq.gz') assert jobs[0].configuration['annotation'].get() == j(ldir, 'gencode.gtf') assert jobs[0].configuration['quality'].get() == '33' assert jobs[0].configuration['output_dir'].get() == "mydir" assert jobs[0].configuration['name'].get() == 'reads' assert jobs[0].configuration['bam'].get() == j(ldir, 'mydir/reads.bam') assert jobs[0].configuration['bai'].get() == j(ldir, 'mydir/reads.bam.bai') assert jobs[0].configuration['map'].get() == j(ldir, 'mydir/reads.map.gz') assert jobs[1].configuration['input'].get() == j(ldir, 'mydir/reads.bam') assert jobs[1].configuration['name'].get() == 'reads' assert jobs[1].configuration['annotation'].get() == j(ldir, 'gencode.gtf') assert jobs[1].configuration['output_dir'].get() == "mydir" assert jobs[1].configuration['gtf'].get() == j(ldir, 'mydir/reads.gtf') assert len(jobs[0].children) == 1 assert len(jobs[1].dependencies) == 1 assert jobs[0].children[0] == jobs[1] def test_multiple_pipelines_with_delegated_outputs(): @jip.tool('grape_gem_index') class GemIndex(object): """\ The GEM Indexer tool Usage: gem_index -i <genome> [-o <genome_index>] Options: -o, --output <genome_index> The output GEM index file [default: ${input\|ext}.gem] -i, --input <genome> The fasta file for the genome """ def get_command(self): return "bash", "gemtools index ${options()}" @jip.tool('grape_gem_rnatool') class gem(object): """\ The GEMtools RNAseq Mapping Pipeline Usage: gem -f <fastq_file> -i <genome_index> Inputs: -f, --fastq <fastq_file> The input fastq -i, --index <genome_index> The GEM index file for the genome """ def get_command(self): return 'bash', 'gemtools rna-pipeline ${options()}' @jip.pipeline('grape_gem_setup') class SetupPipeline(object): """\ The GEM indexes setup pipeline usage: setup -i <genome> Options: -i, --input <genome> The input reference genome """ def init(self): self.add_output('index', '${input\|ext}.gem') def pipeline(self): p = jip.Pipeline() index = p.run('grape_gem_index', input=self.input, output=self.index) p.context(locals()) return p @jip.pipeline('grape_gem_rnapipeline') class GrapePipeline(object): """\ The default GRAPE RNAseq pipeline usage: rnaseq -f <fastq_file> -g <genome> Inputs: -f, --fastq <fastq_file> The input reference genome -g, --genome <genome_index> The input reference genome """ def pipeline(self): p = jip.Pipeline() gem_setup = p.run('grape_gem_setup', input=self.genome) gem = p.run('grape_gem_rnatool', index=gem_setup.index, fastq=self.fastq) p.context(locals()) return p p = jip.Pipeline() node = p.run('grape_gem_rnapipeline') node.fastq = 'reads_1.fastq.gz' node.genome = 'genome.fa' jobs = jip.create_jobs(p, validate=False) assert len(jobs) == 2 gem_job = filter(lambda x: x.name == 'gem', jobs)[0] assert gem_job is not None assert gem_job.configuration['index'].get().endswith('genome.gem') def test_embedded_pipelines_stage_one(tmpdir): tmpdir = str(tmpdir) # laod teh embedded example jip.scanner.add_module('examples/embedded_submission/embedded.py') jip.scanner.scan_modules() p = jip.Pipeline() p.job(dir=tmpdir).run('example_embedded') jobs = jip.create_jobs(p) assert jobs[0].configuration['prefix'] == 'test' assert jobs[0].configuration['output'] == [ os.path.join(tmpdir, 'test.*') ] assert len(jobs) == 1 def test_embedded_pipelines_stage_two(tmpdir): tmpdir = str(tmpdir) # create stage one output open(os.path.join(tmpdir, 'test.1'), 'a').close() open(os.path.join(tmpdir, 'test.2'), 'a').close() open(os.path.join(tmpdir, 'test.3'), 'a').close() open(os.path.join(tmpdir, 'test.4'), 'a').close() open(os.path.join(tmpdir, 'test.5'), 'a').close() # laod teh embedded example jip.scanner.add_module('examples/embedded_submission/embedded.py') jip.scanner.scan_modules() p = jip.Pipeline() p.job(dir=tmpdir).run('example_embedded') jobs = jip.create_jobs(p) assert len(jobs) == 8 assert jobs[0].configuration['prefix'] == 'test' print ">>>OUTPUT", jobs[0].configuration['output'].raw() print ">>>TMPDIR", tmpdir assert jobs[0].configuration['output'] == [ os.path.join(tmpdir, 'test.1'), os.path.join(tmpdir, 'test.2'), os.path.join(tmpdir, 'test.3'), os.path.join(tmpdir, 'test.4'), os.path.join(tmpdir, 'test.5'), ] assert jobs[1].configuration['input'] == os.path.join(tmpdir, 'test.1') assert jobs[2].configuration['input'] == os.path.join(tmpdir, 'test.2') assert jobs[3].configuration['input'] == os.path.join(tmpdir, 'test.3') assert jobs[4].configuration['input'] == os.path.join(tmpdir, 'test.4') assert jobs[5].configuration['input'] == os.path.join(tmpdir, 'test.5') print jobs[6].configuration['input'].raw() assert jobs[6].configuration['input'] == [ os.path.join(tmpdir, 'consumed_test.1'), os.path.join(tmpdir, 'consumed_test.2'), os.path.join(tmpdir, 'consumed_test.3'), os.path.join(tmpdir, 'consumed_test.4'), os.path.join(tmpdir, 'consumed_test.5'), ] def test_dynamic_options(): script = '''#!/usr/bin/env jip # Touch a number of files with a common prefix # # usage: # touch --prefix <prefix> --count <count> #%begin init add_output('output') #%end #%begin setup options['output'].set(["%s_%s" % (prefix, i) for i in range(1, count.get(int) + 1)]) #%end #%begin command for x in ${output}; do touch $x done ''' tool = jip.tools.ScriptTool.from_string(script) tool.init() assert tool is not None p = jip.Pipeline() node = p.job('test').run(tool, prefix='test', count=5) assert node is not None p.expand() assert len(p) == 1 node = p.get('test') assert node.prefix == 'test' cwd = os.getcwd() assert node.output == [os.path.join(cwd, x) for x in ['test_1', 'test_2', 'test_3', 'test_4', 'test_5']] def test_dynamic_options_multiplex(): script = '''#!/usr/bin/env jip # Touch a number of files with a common prefix # # usage: # touch --prefix <prefix> --count <count> #%begin init add_output('output') #%end #%begin setup options['output'].set(["%s_%s" % (prefix, i) for i in range(1, count.get(int) + 1)]) #%end #%begin command for x in ${output}; do touch $x done ''' tool = jip.tools.ScriptTool.from_string(script) tool.init() assert tool is not None p = jip.Pipeline() node = p.job('test').run(tool, prefix='test', count=[1, 2]) assert node is not None p.expand() cwd = os.getcwd() assert len(p) == 2 node_1 = p.get('test.0') node_2 = p.get('test.1') assert node_1.output == os.path.join(cwd, 'test_1') assert node_2.output == [os.path.join(cwd, x) for x in ['test_1', 'test_2']] def test_hello_world_py_fun(tmpdir): tmpdir = str(tmpdir) jip.scanner.add_module('examples/hello_world/hello_world.py') jip.scanner.scan_modules() p = jip.Pipeline() p.job(dir=tmpdir).run('fun_hello_world_py') jobs = jip.create_jobs(p) assert len(jobs) == 1 def test_hello_world_py_cls(tmpdir): tmpdir = str(tmpdir) jip.scanner.add_module('examples/hello_world/hello_world.py') jip.scanner.scan_modules() p = jip.Pipeline() p.job(dir=tmpdir).run('cls_hello_world_py') jobs = jip.create_jobs(p) assert len(jobs) == 1 def test_file_touch(): p = jip.Pipeline() node = p.run('examples/file_touch.jip', p='test', c=5) p.expand() cwd = os.getcwd() j = os.path.join assert node.output == [ j(cwd, 'test_' + x) for x in ['1', '2', '3', '4', '5'] ] def test_pipeline_to_pipeline_edge_delegation(): @jip.tool('grape_gem_index') class GemIndex(object): """\ The GEM Indexer tool Usage: gem_index -i <genome> [-o <genome_index>] Options: -o, --output <genome_index> The output GEM index file [default: ${input\|ext}.gem] -i, --input <genome> The fasta file for the genome """ def get_command(self): return "bash", "gemtools index ${options()}" @jip.tool('grape_gem_rnatool') class gem(object): """\ The GEMtools RNAseq Mapping Pipeline Usage: gem -f <fastq_file> -i <genome_index> Inputs: -f, --fastq <fastq_file> The input fastq -i, --index <genome_index> The GEM index file for the genome """ def get_command(self): return 'bash', 'gemtools rna-pipeline ${options()}' @jip.pipeline('grape_gem_setup') class SetupPipeline(object): """\ The GEM indexes setup pipeline usage: setup -i <genome> Options: -i, --input <genome> The input reference genome """ def init(self): self.add_output('index', '${input\|ext}.gem') def pipeline(self): p = jip.Pipeline() index = p.run('grape_gem_index', input=self.input, output=self.index) p.context(locals()) return p @jip.pipeline('grape_gem_rnapipeline') class GrapePipeline(object): """\ The default GRAPE RNAseq pipeline usage: rnaseq -f <fastq_file> -g <genome> Inputs: -f, --fastq <fastq_file> The input reference genome -g, --genome <genome_index> The input reference genome """ def pipeline(self): p = jip.Pipeline() gem_setup = p.run('grape_gem_setup', input=self.genome) gem = p.run('grape_gem_rnatool', index=gem_setup.index, fastq=self.fastq) p.context(locals()) return p p = jip.Pipeline() node = p.run('grape_gem_rnapipeline') node.fastq = 'reads_1.fastq.gz' node.genome = 'genome.fa' p.expand(validate=False) index = p.get('index') gem_node = p.get('gem') cwd = os.getcwd() j = os.path.join assert index.has_outgoing(gem_node, link=('output', 'index'), value=j(cwd, 'genome.gem')) def test_subpipe_incoming_edge_resolve(): @jip.pipeline() def subedge_pipe(tool): """Subedge usage: subedge --input <input> """ p = jip.Pipeline() p.job('ls').bash('ls ${input}', input=tool.input) return p p = jip.Pipeline() produce = p.job('touch').bash('touch ${outfile}', outfile='out.dat') p.run('subedge_pipe', input=produce.outfile) p.expand(validate=False) touch = p.get('touch') ls = p.get('ls') cwd = os.getcwd() j = os.path.join assert touch.has_outgoing(ls, link=('outfile', 'input'), value=j(cwd, 'out.dat')) def test_subpipe_incoming_edge_resolve_pipe_to_pipe(): @jip.pipeline() def subedge_pipe_1(tool): """Subedge usage: subedge --input <input> --output <output> """ p = jip.Pipeline() p.job('p1').bash('touch', input=tool.input, output=tool.output) return p @jip.pipeline() def subedge_pipe_2(tool): """Subedge usage: subedge --input <input> --output <output> """ p = jip.Pipeline() p.job('p2').bash('touch', input=tool.input, output=tool.output) return p @jip.pipeline() def subedge_pipe_combine(tool): """Subedge usage: subedge --input <input> --output <output> """ p = jip.Pipeline() p1 = p.run('subedge_pipe_1', input=tool.input, output='p1.out') p.run('subedge_pipe_2', input=p1, output=tool.output) return p p = jip.Pipeline() p.run('subedge_pipe_combine', input='a.txt', output="out.dat") p.expand(validate=False) assert len(p) == 2 p1 = p.get('p1') p2 = p.get('p2') cwd = os.getcwd() j = os.path.join assert p1.has_outgoing(p2) assert p1.has_outgoing(p2, value=j(cwd, 'out.dat')) assert p1.has_outgoing(p2, link=('output', 'input'), value=j(cwd, 'p1.out')) if __name__ == '__main__': 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freecodecamp/brownie/brownie_simple/scripts/deploy.py	People-Help-People/contract-collection	0	192094	from brownie import accounts, config, SimpleStorage, network import os def get_account(): if network.show_active() =="developement": account = accounts[0] else: # account = accounts.add(os.getenv("PRIVATE_KEY")) account = accounts.add(config["wallets"]["from_key"]) # account = accounts.load("test") return account def deploy_simple_storage(): account=get_account() simple_storage = SimpleStorage.deploy({"from": account}) transaction = simple_storage.store(1, {"from": account}) transaction.wait(1) print(simple_storage.retrieve()) def main(): deploy_simple_storage()	[ 1, 515, 3347, 2786, 1053, 15303, 29892, 2295, 29892, 12545, 10486, 29892, 3564, 30004, 13, 30004, 13, 5215, 2897, 30004, 13, 30004, 13, 30004, 13, 1753, 679, 29918, 10149, 7295, 1678, 6756, 13, 1678, 565, 3564, 29889, 4294, 29918, 4925, 580, 353, 543, 4888, 882, 1115, 30004, 13, 4706, 3633, 353, 15303, 29961, 29900, 29962, 30004, 13, 1678, 1683, 29901, 30004, 13, 4706, 396, 3633, 353, 15303, 29889, 1202, 29898, 359, 29889, 657, 6272, 703, 29829, 29963, 3040, 29918, 10818, 5783, 30004, 13, 4706, 3633, 353, 15303, 29889, 1202, 29898, 2917, 3366, 14625, 10376, 3108, 3366, 3166, 29918, 1989, 20068, 30004, 13, 4706, 396, 3633, 353, 15303, 29889, 1359, 703, 1688, 1159, 30004, 13, 1678, 736, 3633, 30004, 13, 30004, 13, 30004, 13, 1753, 7246, 29918, 12857, 29918, 12925, 7295, 30004, 13, 1678, 3633, 29922, 657, 29918, 10149, 26471, 13, 1678, 2560, 29918, 12925, 353, 12545, 10486, 29889, 16519, 3319, 29908, 3166, 1115, 3633, 1800, 30004, 13, 1678, 10804, 353, 2560, 29918, 12925, 29889, 8899, 29898, 29896, 29892, 8853, 3166, 1115, 3633, 1800, 30004, 13, 1678, 10804, 29889, 10685, 29898, 29896, 8443, 13, 1678, 1596, 29898, 12857, 29918, 12925, 29889, 276, 509, 2418, 3101, 30004, 13, 30004, 13, 30004, 13, 1753, 1667, 7295, 30004, 13, 1678, 7246, 29918, 12857, 29918, 12925, 26471, 13, 2 ]
Data_Sending_raspberrypi/pl1.py	rsk97/Web-GIS-smart-irrigation	0	94463	<gh_stars>0 #/usr/bin/python import serial import requests from selenium import webdriver import urllib mail = 0 def sendmail(SM1,SM2,WL): import smtplib from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText fromaddr = "<EMAIL>" toaddr = "<EMAIL>" msg = MIMEMultipart() msg['From'] = fromaddr msg['To'] = toaddr msg['Subject'] = "Farm details" body="" m1="PLANT 1 IS DYING!!!" m2="PLANT 2 IS DYING!!!" m3="PLANT 1 IS HEALTHY" m4="PLANT 2 IS HEALTHY" if mail==1: body=m1+m4 elif mail==2: body=m2+m3 elif mail==3: body=m1+m2 elif mail==0: body=m3+m4 msg.attach(MIMEText(body, 'plain')) server = smtplib.SMTP('smtp.gmail.com', 587) server.starttls() server.login(fromaddr, "arshad9991") text = msg.as_string() server.sendmail(fromaddr, toaddr, text) server.quit() ser=serial.Serial('/dev/ttyACM0', 9600) while(1): data=ser.readline() f = open("/home/pi/Desktop/check","a") f.write(data+"\n") x=data.split(" ") print(x) print(data) sm1=int(x[3]) sm2=int(x[4]) wlev=float(x[5]) status = 0 if(sm1<0 and sm2 >0): status = 1 if(sm1>0 and sm2<0): status = 2 if(sm1<0 and sm2<0): status =3 if(status != mail): mail=status sendmail(int(x[3]),int(x[4]),int(x[2])) if(len(x)==6): mark=0 for i in range(6): try: d=float(x[i]) except: print(i) mark=1 break if(mark==1): print("ERROR") continue print("SENT") try: urllib.urlopen("http://ad07.pythonanywhere.com/plants/getWaterLevel?level="+x[5]+"&rain="+x[4]); urllib.urlopen("http://ad07.pythonanywhere.com/plants/getdata?t="+str(x[1])+"&plantID=1&h="+x[0]+"&s="+x[2]); urllib.urlopen("http://ad07.pythonanywhere.com/plants/getdata?t="+str(x[1])+"&plantID=2&h="+x[0]+"&s="+x[3]); except: pass #browser.get("http://10.0.3.23:9090/plants/getdata?t="+str(x[1])+"&plantID=2&h="+x[0]+"&s="+x[-2] ); #print("http://10.0.3.23:9090/plants/getdata?t="+str(x[1])+"&plantID=2&h="+x[0]+"&s="+x[-2]); #browser.get("http://10.0.3.23:9090/plants/getdata?t="+str(x[1])+"&plantID=1&h="+x[0]+"&s="+x[2]); #browser.get("http://10.0.3.23:9090/plants/getdata?t="+str(x[1])+"&plantID=2&h="+x[0]+"&s="+x[-2]); #browser.get("http://10.0.3.23:9090/plants/getWaterLevel?level="+x[5]+"&rain="+x[4]); print(x[-1])	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 29914, 4855, 29914, 2109, 29914, 4691, 13, 5215, 7797, 13, 5215, 7274, 13, 13, 3166, 18866, 1053, 1856, 9465, 13, 5215, 3142, 1982, 13, 13, 2549, 353, 29871, 29900, 13, 13, 13, 1753, 3638, 2549, 29898, 17061, 29896, 29892, 17061, 29906, 29892, 29956, 29931, 1125, 13, 13, 12, 5215, 1560, 9392, 1982, 12, 13, 12, 3166, 4876, 29889, 29885, 603, 29889, 18056, 442, 1053, 341, 8890, 6857, 27494, 13, 12, 3166, 4876, 29889, 29885, 603, 29889, 726, 1053, 341, 8890, 1626, 13, 12, 3166, 10030, 353, 9872, 26862, 6227, 11903, 13, 12, 517, 10030, 353, 9872, 26862, 6227, 11903, 13, 12, 7645, 353, 341, 8890, 6857, 27494, 580, 13, 12, 7645, 1839, 4591, 2033, 353, 515, 10030, 13, 12, 7645, 1839, 1762, 2033, 353, 304, 10030, 13, 12, 7645, 1839, 20622, 2033, 353, 376, 29943, 2817, 4902, 29908, 13, 12, 2587, 13776, 13, 13, 12, 29885, 29896, 543, 7390, 13566, 29871, 29896, 8519, 360, 29979, 4214, 6824, 3850, 13, 12, 29885, 29906, 543, 7390, 13566, 29871, 29906, 8519, 360, 29979, 4214, 6824, 3850, 13, 12, 29885, 29941, 543, 7390, 13566, 29871, 29896, 8519, 17714, 1964, 4690, 29979, 29908, 13, 12, 29885, 29946, 543, 7390, 13566, 29871, 29906, 8519, 17714, 1964, 4690, 29979, 29908, 268, 13, 13, 12, 361, 10524, 1360, 29896, 29901, 13, 12, 12, 2587, 29922, 29885, 29896, 29974, 29885, 29946, 13, 12, 23681, 10524, 1360, 29906, 29901, 13, 12, 12, 2587, 29922, 29885, 29906, 29974, 29885, 29941, 13, 12, 23681, 10524, 1360, 29941, 29901, 13, 12, 12, 2587, 29922, 29885, 29896, 29974, 29885, 29906, 13, 12, 23681, 10524, 1360, 29900, 29901, 13, 12, 12, 2587, 29922, 29885, 29941, 29974, 29885, 29946, 13, 13, 12, 7645, 29889, 14930, 29898, 29924, 8890, 1626, 29898, 2587, 29892, 525, 24595, 8785, 13, 12, 2974, 353, 1560, 9392, 1982, 29889, 17061, 3557, 877, 3844, 9392, 29889, 21980, 29889, 510, 742, 29871, 29945, 29947, 29955, 29897, 13, 12, 2974, 29889, 2962, 29873, 3137, 580, 13, 12, 2974, 29889, 7507, 29898, 3166, 10030, 29892, 376, 279, 845, 328, 29929, 29929, 29929, 29896, 1159, 13, 12, 726, 353, 10191, 29889, 294, 29918, 1807, 580, 13, 12, 2974, 29889, 6717, 2549, 29898, 3166, 10030, 29892, 304, 10030, 29892, 1426, 29897, 13, 12, 2974, 29889, 28358, 580, 13, 13, 643, 29922, 15550, 29889, 9125, 11219, 3359, 29914, 4349, 2477, 29924, 29900, 742, 29871, 29929, 29953, 29900, 29900, 29897, 13, 13, 13, 13, 8000, 29898, 29896, 1125, 13, 12, 1272, 29922, 643, 29889, 949, 1220, 580, 13, 12, 29888, 353, 1722, 11974, 5184, 29914, 1631, 29914, 17600, 29914, 3198, 3284, 29874, 1159, 13, 12, 29888, 29889, 3539, 29898, 1272, 13578, 29905, 29876, 1159, 13, 12, 29916, 29922, 1272, 29889, 5451, 703, 16521, 13, 12, 2158, 29898, 29916, 29897, 13, 12, 2158, 29898, 1272, 29897, 13, 12, 3844, 29896, 29922, 524, 29898, 29916, 29961, 29941, 2314, 13, 12, 3844, 29906, 29922, 524, 29898, 29916, 29961, 29946, 2314, 13, 12, 29893, 2608, 29922, 7411, 29898, 29916, 29961, 29945, 2314, 13, 12, 4882, 353, 29871, 29900, 13, 12, 361, 29898, 3844, 29896, 29966, 29900, 322, 1560, 29906, 1405, 29900, 1125, 13, 12, 12, 4882, 353, 29871, 29896, 13, 12, 361, 29898, 3844, 29896, 29958, 29900, 322, 1560, 29906, 29966, 29900, 1125, 13, 12, 12, 4882, 353, 29871, 29906, 13, 12, 361, 29898, 3844, 29896, 29966, 29900, 322, 1560, 29906, 29966, 29900, 1125, 13, 12, 12, 4882, 353, 29941, 13, 12, 361, 29898, 4882, 2804, 10524, 1125, 13, 12, 12, 2549, 29922, 4882, 13, 12, 12, 6717, 2549, 29898, 524, 29898, 29916, 29961, 29941, 11724, 524, 29898, 29916, 29961, 29946, 11724, 524, 29898, 29916, 29961, 29906, 12622, 13, 12, 361, 29898, 2435, 29898, 29916, 29897, 1360, 29953, 1125, 13, 12, 12, 3502, 29922, 29900, 13, 12, 12, 1454, 474, 297, 3464, 29898, 29953, 1125, 13, 12, 12, 12, 2202, 29901, 13, 12, 12, 12, 12, 29881, 29922, 7411, 29898, 29916, 29961, 29875, 2314, 13, 12, 12, 12, 19499, 29901, 13, 12, 12, 12, 12, 2158, 29898, 29875, 29897, 13, 12, 12, 12, 12, 3502, 29922, 29896, 13, 12, 12, 12, 12, 8690, 13, 12, 12, 361, 29898, 3502, 1360, 29896, 1125, 13, 12, 12, 12, 2158, 703, 11432, 1159, 13, 12, 12, 12, 19878, 13, 12, 12, 2158, 703, 29903, 3919, 1159, 13, 12, 12, 2202, 29901, 13, 12, 12, 12, 2271, 1982, 29889, 332, 417, 2238, 703, 1124, 597, 328, 29900, 29955, 29889, 4691, 1384, 3062, 29889, 510, 29914, 572, 1934, 29914, 657, 29956, 1008, 10108, 29973, 5563, 543, 29974, 29916, 29961, 29945, 10062, 29908, 29987, 6038, 543, 29974, 29916, 29961, 29946, 5691, 13, 12, 12, 12, 2271, 1982, 29889, 332, 417, 2238, 703, 1124, 597, 328, 29900, 29955, 29889, 4691, 1384, 3062, 29889, 510, 29914, 572, 1934, 29914, 657, 1272, 29973, 29873, 543, 29974, 710, 29898, 29916, 29961, 29896, 2314, 13578, 29987, 24389, 1367, 29922, 29896, 29987, 29882, 543, 29974, 29916, 29961, 29900, 10062, 29908, 29987, 29879, 543, 29974, 29916, 29961, 29906, 5691, 13, 12, 12, 12, 2271, 1982, 29889, 332, 417, 2238, 703, 1124, 597, 328, 29900, 29955, 29889, 4691, 1384, 3062, 29889, 510, 29914, 572, 1934, 29914, 657, 1272, 29973, 29873, 543, 29974, 710, 29898, 29916, 29961, 29896, 2314, 13578, 29987, 24389, 1367, 29922, 29906, 29987, 29882, 543, 29974, 29916, 29961, 29900, 10062, 29908, 29987, 29879, 543, 29974, 29916, 29961, 29941, 5691, 13, 12, 12, 19499, 29901, 13, 12, 12, 12, 3364, 13, 12, 12, 29937, 15965, 29889, 657, 703, 1124, 597, 29896, 29900, 29889, 29900, 29889, 29941, 29889, 29906, 29941, 29901, 29929, 29900, 29929, 29900, 29914, 572, 1934, 29914, 657, 1272, 29973, 29873, 543, 29974, 710, 29898, 29916, 29961, 29896, 2314, 13578, 29987, 24389, 1367, 29922, 29906, 29987, 29882, 543, 29974, 29916, 29961, 29900, 10062, 29908, 29987, 29879, 543, 29974, 29916, 14352, 29906, 29962, 3482, 13, 12, 12, 29937, 2158, 703, 1124, 597, 29896, 29900, 29889, 29900, 29889, 29941, 29889, 29906, 29941, 29901, 29929, 29900, 29929, 29900, 29914, 572, 1934, 29914, 657, 1272, 29973, 29873, 543, 29974, 710, 29898, 29916, 29961, 29896, 2314, 13578, 29987, 24389, 1367, 29922, 29906, 29987, 29882, 543, 29974, 29916, 29961, 29900, 10062, 29908, 29987, 29879, 543, 29974, 29916, 14352, 29906, 5691, 13, 12, 12, 29937, 15965, 29889, 657, 703, 1124, 597, 29896, 29900, 29889, 29900, 29889, 29941, 29889, 29906, 29941, 29901, 29929, 29900, 29929, 29900, 29914, 572, 1934, 29914, 657, 1272, 29973, 29873, 543, 29974, 710, 29898, 29916, 29961, 29896, 2314, 13578, 29987, 24389, 1367, 29922, 29896, 29987, 29882, 543, 29974, 29916, 29961, 29900, 10062, 29908, 29987, 29879, 543, 29974, 29916, 29961, 29906, 5691, 13, 12, 12, 29937, 15965, 29889, 657, 703, 1124, 597, 29896, 29900, 29889, 29900, 29889, 29941, 29889, 29906, 29941, 29901, 29929, 29900, 29929, 29900, 29914, 572, 1934, 29914, 657, 1272, 29973, 29873, 543, 29974, 710, 29898, 29916, 29961, 29896, 2314, 13578, 29987, 24389, 1367, 29922, 29906, 29987, 29882, 543, 29974, 29916, 29961, 29900, 10062, 29908, 29987, 29879, 543, 29974, 29916, 14352, 29906, 5691, 13, 12, 12, 29937, 15965, 29889, 657, 703, 1124, 597, 29896, 29900, 29889, 29900, 29889, 29941, 29889, 29906, 29941, 29901, 29929, 29900, 29929, 29900, 29914, 572, 1934, 29914, 657, 29956, 1008, 10108, 29973, 5563, 543, 29974, 29916, 29961, 29945, 10062, 29908, 29987, 6038, 543, 29974, 29916, 29961, 29946, 5691, 13, 12, 12, 2158, 29898, 29916, 14352, 29896, 2314, 13, 2 ]
main.py	ARCJ137442/makeFolderTenser	0	54508	<gh_stars>0 ''' README: 本程序用于创建一系列的「索引文件夹」，其相当于一个索引矩阵（可能是「张量」），其包含的值（本质上是集合，但一般是文件）使用一个特定的序列（等价于一个向量）进行访问；原理/作用/用途：通过增大其他用户逐一检索文件夹之难度以「隐藏」部分文件夹或文件待补 ''' import os #此函数来源于网络 def mkdir(path): # 引入模块 import os # 去除首尾空格 path=path.strip() # 去除尾部 \ 符号 path=path.rstrip("\\") # 判断路径是否存在 # 存在 True # 不存在 False isExists=os.path.exists(path) # 判断结果 if not isExists: # 如果不存在则创建目录 # 创建目录操作函数 os.makedirs(path) print(path+' 创建成功') return True else: # 如果目录存在则不创建，并提示目录已存在 print(path+'目录已存在') return False #此函数用于按字符批量生成文件夹，根目录末尾可以不带「/」 #示例：path='C:\dir\',nums='gbv^4%faw1' def makeDirsByChar(path,chars): root=path.strip().rstrip("\\") for i in chars: mkdir(root+'\\'+str(i)) #此函数用于按照整数序列生成n个文件夹 #示例：path='C:\',num=9 def makeDirVector(path,num): root=path.strip().rstrip("\\") for i in range(num): mkdir(root+'\\'+str(i)) #此函数用于按照0~9的整数序列生成文件夹「张量」，使用递归以支持任意长度（多起来仍然很恐怖，建议少于5层） #示例：path='C:\',nums='1321' def makeDirTenser(path,nums): root=path.strip().rstrip("\\") #print("正在尝试用%s生成%s"%(nums,root)) if len(nums)>1: for i in range(int(nums[0])): makeDirTenser(path+'\\%s\\'%i,nums[1:]) else: makeDirVector(path,int(nums)) #直接打开程序的预设功能（分别对应前面三个函数：字串、向量、张量） if __name__ == "__main__": # 字串 print("切换到字串模式。。。") root=input("输入根路径（默认为chars）：") if root=='':root=r"chars" value=input("输入字串参数（留空以跳过）：") if value!='': makeDirsByChar(root,value) # 向量 print("切换到向量模式。。。") root=input("输入根路径（默认为vecter）：") if root=='':root=r"vecter" value=input("输入文件夹数量（留空以跳过）：") if value!='': makeDirVector(root,int(value)) # 张量 print("切换到张量模式。。。") root=input("输入根路径（默认为tenser）：") if root=='':root=r"tenser" value=input("输入张量阶数序列（留空以跳过）：") if value!='': makeDirTenser(root,value) ''' -并没有什么用的末尾注释- '''	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 12008, 13, 16310, 2303, 29901, 13, 30346, 31101, 31463, 30406, 30909, 31441, 30886, 30287, 31185, 31025, 30210, 30481, 31836, 31674, 30333, 30631, 232, 167, 188, 30482, 30214, 13, 31149, 30990, 30948, 30909, 30287, 30502, 31836, 31674, 234, 162, 172, 236, 155, 184, 30419, 30682, 30815, 30392, 30481, 31328, 31180, 30482, 30409, 30214, 31149, 31473, 232, 147, 174, 30210, 30959, 30419, 30346, 235, 183, 171, 30429, 30392, 30893, 30733, 30214, 231, 192, 137, 30287, 235, 139, 175, 30392, 30333, 30631, 30409, 30785, 30406, 30287, 30502, 31141, 30495, 30210, 31463, 31025, 30419, 31184, 231, 190, 186, 30909, 30287, 30502, 31331, 31180, 30409, 31174, 30448, 235, 177, 194, 31658, 31608, 13, 30667, 30687, 29914, 30732, 30406, 29914, 30406, 236, 131, 151, 30383, 30768, 31138, 232, 165, 161, 30257, 31149, 31221, 30406, 31229, 236, 131, 147, 30287, 233, 166, 131, 31836, 30333, 30631, 232, 167, 188, 30577, 236, 157, 193, 30898, 30651, 30481, 236, 157, 147, 31707, 30482, 30636, 30748, 30333, 30631, 232, 167, 188, 31391, 30333, 30631, 13, 13, 232, 193, 136, 235, 164, 168, 13, 12008, 13, 13, 5215, 2897, 13, 13, 29937, 31389, 31629, 30354, 30805, 31193, 30909, 31222, 234, 190, 159, 13, 1753, 29356, 29898, 2084, 1125, 13, 1678, 396, 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var/spack/repos/builtin/packages/ferret/package.py	padamson/spack	2	74594	<reponame>padamson/spack<filename>var/spack/repos/builtin/packages/ferret/package.py # Copyright 2013-2021 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) import os from spack import * class Ferret(Package): """Ferret is an interactive computer visualization and analysis environment designed to meet the needs of oceanographers and meteorologists analyzing large and complex gridded data sets.""" homepage = "http://ferret.pmel.noaa.gov/Ferret/home" url = "https://github.com/NOAA-PMEL/Ferret/archive/v7.6.0.tar.gz" maintainers = ['RemiLacroix-IDRIS'] version('7.6.0', sha256='69832d740bd44c9eadd198a5de4d96c4c01ae90ae28c2c3414c1bb9f43e475d1') version('7.5.0', sha256='2a038c547e6e80e6bd0645a374c3247360cf8c94ea56f6f3444b533257eb16db') version('7.4', sha256='5167bb9e6ef441ae9cf90da555203d2155e3fcf929e7b8dddb237de0d58c5e5f') version('7.3', sha256='ae80a732c34156b5287a23696cf4ae4faf4de1dd705ff43cbb4168b05c6faaf4') version('7.2', sha256='21c339b1bafa6939fc869428d906451f130f7e77e828c532ab9488d51cf43095') version('6.96', sha256='7eb87156aa586cfe838ab83f08b2102598f9ab62062d540a5da8c9123816331a') variant('datasets', default=False, description="Install Ferret standard datasets") depends_on("hdf5+hl") depends_on("netcdf-c") depends_on("netcdf-fortran") depends_on("readline") depends_on("zlib") depends_on("libx11") depends_on("curl") # Make Java dependency optional with older versions of Ferret patch('https://github.com/NOAA-PMEL/Ferret/commit/c7eb70a0b17045c8ca7207d586bfea77a5340668.patch', sha256='5bd581db4578c013faed375844b206fbe71f93fe9ce60f8f9f41d64abc6a5972', level=1, working_dir='FERRET', when='@:6.99') resource(name='datasets', url='https://github.com/NOAA-PMEL/FerretDatasets/archive/v7.6.tar.gz', sha256='b2fef758ec1817c1c19e6225857ca3a82c727d209ed7fd4697d45c5533bb2c72', placement='fer_dsets', when='+datasets') def url_for_version(self, version): if version <= Version('7.2'): return 'ftp://ftp.pmel.noaa.gov/ferret/pub/source/fer_source.v{0}.tar.gz'.format( version.joined) else: return 'https://github.com/NOAA-PMEL/Ferret/archive/v{0}.tar.gz'.format(version) def patch(self): spec = self.spec hdf5_prefix = spec['hdf5'].prefix netcdff_prefix = spec['netcdf-fortran'].prefix readline_prefix = spec['readline'].prefix libz_prefix = spec['zlib'].prefix work_dir = 'FERRET' if '@:7.2' in spec else '.' with working_dir(work_dir, create=False): if '@7.3:' in spec: copy('site_specific.mk.in', 'site_specific.mk') copy('external_functions/ef_utility/site_specific.mk.in', 'external_functions/ef_utility/site_specific.mk') filter_file(r'^DIR_PREFIX.+', 'DIR_PREFIX = %s' % self.stage.source_path, 'site_specific.mk') # Setting this to blank not to force # using the static version of readline filter_file(r'^(READLINE_(LIB)?DIR).+', '\\1 = ', 'site_specific.mk') else: filter_file(r'^LIBZ_DIR.+', 'LIBZ_DIR = %s' % libz_prefix, 'site_specific.mk') filter_file(r'^JAVA_HOME.+', ' ', 'site_specific.mk') filter_file(r'^READLINE_DIR.+', 'READLINE_DIR = %s' % readline_prefix, 'site_specific.mk') filter_file(r'^BUILDTYPE.+', 'BUILDTYPE = x86_64-linux', 'site_specific.mk') filter_file(r'^INSTALL_FER_DIR.+', 'INSTALL_FER_DIR = %s' % spec.prefix, 'site_specific.mk') filter_file(r'^(HDF5_(LIB)?DIR).+', '\\1 = %s' % hdf5_prefix, 'site_specific.mk') filter_file(r'^(NETCDF4?_(LIB)?DIR).+', '\\1 = %s' % netcdff_prefix, 'site_specific.mk') if '@:7.3' in spec: # Don't force using the static version of libz filter_file(r'\$\(LIBZ_DIR\)/lib64/libz.a', '-lz', 'platform_specific.mk.x86_64-linux') # Don't force using the static version of libgfortran filter_file(r'-Wl,-Bstatic -lgfortran -Wl,-Bdynamic', '-lgfortran', 'platform_specific.mk.x86_64-linux') # This prevents the rpaths to be properly set # by Spack's compiler wrappers filter_file(r'-v --verbose', '', 'platform_specific.mk.x86_64-linux') filter_file(r'^[ \t]LD[ \t]=.+', 'LD = %s' % spack_cc, 'platform_specific.mk.x86_64-linux') else: # Don't force using the static version of libgfortran filter_file(r'-static-libgfortran', '', 'platform_specific.mk.x86_64-linux') if '@:7.4' in spec: compilers_spec_file = 'platform_specific.mk.x86_64-linux' else: compilers_spec_file = 'site_specific.mk' # Make sure Ferret uses Spack's compiler wrappers filter_file(r'^[ \t]CC[ \t]=.+', 'CC = %s' % spack_cc, compilers_spec_file) filter_file(r'^[ \t]CXX[ \t]=.+', 'CXX = %s' % spack_cxx, compilers_spec_file) filter_file(r'^[ \t]FC[ \t]=.+', 'FC = %s' % spack_fc, compilers_spec_file) filter_file(r'^[ \t]F77[ \t]=.+', 'F77 = %s' % spack_f77, compilers_spec_file) filter_file(r'\$\(NETCDF4?_(LIB)?DIR\)./libnetcdff.a', "-L%s -lnetcdff" % spec['netcdf-fortran'].prefix.lib, 'platform_specific.mk.x86_64-linux') filter_file(r'\$\(NETCDF4?_(LIB)?DIR\)./libnetcdf.a', "-L%s -lnetcdf" % spec['netcdf-c'].prefix.lib, 'platform_specific.mk.x86_64-linux') filter_file(r'\$\(HDF5_(LIB)?DIR\)./libhdf5_hl.a', "-L%s -lhdf5_hl" % spec['hdf5'].prefix.lib, 'platform_specific.mk.x86_64-linux') filter_file(r'\$\(HDF5_(LIB)?DIR\)./libhdf5.a', "-L%s -lhdf5" % spec['hdf5'].prefix.lib, 'platform_specific.mk.x86_64-linux') def install(self, spec, prefix): if 'LDFLAGS' in env and env['LDFLAGS']: env['LDFLAGS'] += ' ' + '-lquadmath' else: env['LDFLAGS'] = '-lquadmath' work_dir = 'FERRET' if '@:7.2' in self.spec else '.' with working_dir(work_dir, create=False): os.environ['LD_X11'] = '-L%s -lX11' % spec['libx11'].prefix.lib os.environ['HOSTTYPE'] = 'x86_64-linux' make(parallel=False) make("install") if '+datasets' in self.spec: mkdir(self.prefix.fer_dsets) install_tree('fer_dsets', self.prefix.fer_dsets) def setup_run_environment(self, env): env.set('FER_DIR', self.prefix) env.set('FER_GO', ' '.join(['.', self.prefix.go, self.prefix.examples, self.prefix.contrib])) env.set('FER_EXTERNAL_FUNCTIONS', self.prefix.ext_func.libs) env.set('FER_PALETTE', ' '.join(['.', self.prefix.ppl])) env.set('FER_FONTS', self.prefix.ppl.fonts) fer_data = ['.'] fer_descr = ['.'] fer_grids = ['.'] if '+datasets' in self.spec: env.set('FER_DSETS', self.prefix.fer_dsets) fer_data.append(self.prefix.fer_dsets.data) fer_descr.append(self.prefix.fer_dsets.descr) fer_grids.append(self.prefix.fer_dsets.grids) fer_data.extend([self.prefix.go, self.prefix.examples]) env.set('FER_DATA', ' '.join(fer_data)) env.set('FER_DESCR', ' '.join(fer_descr)) env.set('FER_GRIDS', ' '.join(fer_grids))	[ 1, 529, 276, 1112, 420, 29958, 8305, 314, 1100, 29914, 1028, 547, 29966, 9507, 29958, 1707, 29914, 1028, 547, 29914, 276, 1066, 29914, 16145, 262, 29914, 8318, 29914, 571, 2267, 29914, 5113, 29889, 2272, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29896, 29941, 29899, 29906, 29900, 29906, 29896, 19520, 22469, 5514, 3086, 14223, 29892, 365, 12182, 322, 916, 13, 29937, 1706, 547, 8010, 10682, 414, 29889, 2823, 278, 2246, 29899, 5563, 315, 4590, 29979, 22789, 3912, 934, 363, 4902, 29889, 13, 29937, 13, 29937, 10937, 29928, 29990, 29899, 29931, 293, 1947, 29899, 12889, 29901, 313, 17396, 1829, 29899, 29906, 29889, 29900, 6323, 341, 1806, 29897, 13, 13, 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style_transfer/layer.py	haonguyen1107/style_transfer	0	159688	import numpy as np import tensorflow as tf def upsample_nearest(inputs, scale): shape = tf.shape(input=inputs) n, h, w, c = shape[0], shape[1], shape[2], shape[3] return tf.image.resize(inputs, tf.stack([hscale, wscale]), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)	[ 1, 1053, 12655, 408, 7442, 13, 5215, 26110, 408, 15886, 13, 13, 13, 1753, 24081, 981, 29918, 28502, 342, 29898, 2080, 29879, 29892, 6287, 1125, 13, 1678, 8267, 353, 15886, 29889, 12181, 29898, 2080, 29922, 2080, 29879, 29897, 13, 1678, 302, 29892, 298, 29892, 281, 29892, 274, 353, 8267, 29961, 29900, 1402, 8267, 29961, 29896, 1402, 8267, 29961, 29906, 1402, 8267, 29961, 29941, 29962, 13, 1678, 736, 15886, 29889, 3027, 29889, 21476, 29898, 2080, 29879, 29892, 15886, 29889, 1429, 4197, 29882, 29930, 7052, 29892, 281, 29930, 7052, 11724, 1158, 29922, 13264, 29889, 3027, 29889, 1666, 675, 4062, 29889, 8186, 29909, 1525, 1254, 29918, 8186, 6259, 29950, 29933, 1955, 29897, 2 ]
src/visions/lib/relations/string_to_datetime.py	sweersr/visions	0	36897	import pandas as pd from visions import visions_string, visions_datetime from visions.core.model import TypeRelation from visions.core.model.relations import InferenceRelation from visions.utils.coercion import test_utils def to_datetime_year_week(series): """Convert a series of the format YYYY/UU (year, week) to datetime. A '0' is added as day dummy value, as pandas requires a day value to parse. Args: series: the Series to parse Returns: A datetime series Examples: >>> series = pd.Series(['2018/47', '2018/12', '2018/03']) >>> parsed_series = to_datetime_year_week(series) >>> print(parsed_series.dt.week) 0 47 1 12 2 3 dtype: int64 """ return pd.to_datetime(series + "0", format="%Y/%U%w") def to_datetime_year_month_day(series): """Convert a series of the format YYYYMMDD (year, month, day) to datetime. Args: series: the Series to parse Returns: A datetime series Examples: >>> series = pd.Series(['20181201', '20181202', '20181203']) >>> parsed_series = to_datetime_year_week(series) >>> print(parsed_series.dt.day) 0 1 1 2 2 3 dtype: int64 """ return pd.to_datetime(series, format="%Y%m%d") def get_string_datetime_type_relation(func): return InferenceRelation( relationship=test_utils.coercion_test(func), transformer=func, related_type=visions_string, type=visions_datetime, ) def string_to_datetime_year_week(): return get_string_datetime_type_relation(to_datetime_year_week) def string_to_datetime_year_month_day(): return get_string_datetime_type_relation(to_datetime_year_month_day)	[ 1, 1053, 11701, 408, 10518, 13, 3166, 1998, 1080, 1053, 1998, 1080, 29918, 1807, 29892, 1998, 1080, 29918, 12673, 13, 13, 3166, 1998, 1080, 29889, 3221, 29889, 4299, 1053, 5167, 9662, 362, 13, 3166, 1998, 1080, 29889, 3221, 29889, 4299, 29889, 2674, 800, 1053, 512, 1659, 9662, 362, 13, 3166, 1998, 1080, 29889, 13239, 29889, 1111, 6269, 291, 1053, 1243, 29918, 13239, 13, 13, 13, 1753, 304, 29918, 12673, 29918, 6360, 29918, 18448, 29898, 13757, 1125, 13, 1678, 9995, 18455, 263, 3652, 310, 278, 3402, 612, 14995, 29979, 29914, 29965, 29965, 313, 6360, 29892, 4723, 29897, 304, 12865, 29889, 13, 1678, 319, 525, 29900, 29915, 338, 2715, 408, 2462, 20254, 995, 29892, 408, 11701, 6858, 263, 2462, 995, 304, 6088, 29889, 13, 13, 1678, 826, 3174, 29901, 13, 4706, 3652, 29901, 278, 10488, 304, 6088, 13, 13, 1678, 16969, 29901, 13, 4706, 319, 12865, 3652, 13, 13, 1678, 1222, 9422, 29901, 13, 4706, 8653, 3652, 353, 10518, 29889, 19204, 18959, 29906, 29900, 29896, 29947, 29914, 29946, 29955, 742, 525, 29906, 29900, 29896, 29947, 29914, 29896, 29906, 742, 525, 29906, 29900, 29896, 29947, 29914, 29900, 29941, 11287, 13, 4706, 8653, 21213, 29918, 13757, 353, 304, 29918, 12673, 29918, 6360, 29918, 18448, 29898, 13757, 29897, 13, 4706, 8653, 1596, 29898, 862, 8485, 29918, 13757, 29889, 6008, 29889, 18448, 29897, 13, 308, 29900, 268, 29946, 29955, 13, 308, 29896, 268, 29896, 29906, 13, 308, 29906, 418, 29941, 13, 4706, 26688, 29901, 938, 29953, 29946, 13, 1678, 9995, 13, 1678, 736, 10518, 29889, 517, 29918, 12673, 29898, 13757, 718, 376, 29900, 613, 3402, 543, 29995, 29979, 22584, 29965, 29995, 29893, 1159, 13, 13, 13, 1753, 304, 29918, 12673, 29918, 6360, 29918, 10874, 29918, 3250, 29898, 13757, 1125, 13, 1678, 9995, 18455, 263, 3652, 310, 278, 3402, 612, 14995, 29979, 29924, 5773, 29928, 313, 6360, 29892, 4098, 29892, 2462, 29897, 304, 12865, 29889, 13, 13, 1678, 826, 3174, 29901, 13, 4706, 3652, 29901, 278, 10488, 304, 6088, 13, 13, 1678, 16969, 29901, 13, 4706, 319, 12865, 3652, 13, 13, 1678, 1222, 9422, 29901, 13, 4706, 8653, 3652, 353, 10518, 29889, 19204, 18959, 29906, 29900, 29896, 29947, 29896, 29906, 29900, 29896, 742, 525, 29906, 29900, 29896, 29947, 29896, 29906, 29900, 29906, 742, 525, 29906, 29900, 29896, 29947, 29896, 29906, 29900, 29941, 11287, 13, 4706, 8653, 21213, 29918, 13757, 353, 304, 29918, 12673, 29918, 6360, 29918, 18448, 29898, 13757, 29897, 13, 4706, 8653, 1596, 29898, 862, 8485, 29918, 13757, 29889, 6008, 29889, 3250, 29897, 13, 308, 29900, 268, 29896, 13, 308, 29896, 268, 29906, 13, 308, 29906, 268, 29941, 13, 4706, 26688, 29901, 938, 29953, 29946, 13, 1678, 9995, 13, 1678, 736, 10518, 29889, 517, 29918, 12673, 29898, 13757, 29892, 3402, 543, 29995, 29979, 29995, 29885, 29995, 29881, 1159, 13, 13, 13, 1753, 679, 29918, 1807, 29918, 12673, 29918, 1853, 29918, 23445, 29898, 9891, 1125, 13, 1678, 736, 512, 1659, 9662, 362, 29898, 13, 4706, 9443, 29922, 1688, 29918, 13239, 29889, 1111, 6269, 291, 29918, 1688, 29898, 9891, 511, 13, 4706, 4327, 261, 29922, 9891, 29892, 13, 4706, 4475, 29918, 1853, 29922, 1730, 1080, 29918, 1807, 29892, 13, 4706, 1134, 29922, 1730, 1080, 29918, 12673, 29892, 13, 1678, 1723, 13, 13, 13, 1753, 1347, 29918, 517, 29918, 12673, 29918, 6360, 29918, 18448, 7295, 13, 1678, 736, 679, 29918, 1807, 29918, 12673, 29918, 1853, 29918, 23445, 29898, 517, 29918, 12673, 29918, 6360, 29918, 18448, 29897, 13, 13, 13, 1753, 1347, 29918, 517, 29918, 12673, 29918, 6360, 29918, 10874, 29918, 3250, 7295, 13, 1678, 736, 679, 29918, 1807, 29918, 12673, 29918, 1853, 29918, 23445, 29898, 517, 29918, 12673, 29918, 6360, 29918, 10874, 29918, 3250, 29897, 13, 2 ]
minder_lastreview.py	hodea/hodea-review-minder	0	37407	<gh_stars>0 # -- coding: utf-8 -- """ Created on Mon Feb 26 19:38:29 2018 @author: Daniel """ import argparse import os from minder_config import minder_cfg from minder_database import minder_db from minder_htmlreport import minder_report import time import hashlib import uuid class get_lastreview: def __init__(self, minder_dict, topdir, cfg_exclude, cfg_type): print(topdir) print(cfg_exclude) print(cfg_type) print(minder_dict) filecnt = 0 for root, dirs, files in os.walk(topdir): for name in files: find = False for i in range(0,len(cfg_exclude)): if os.path.join(root, name).startswith(os.path.dirname(cfg_exclude[i])): find = True if find is True: continue for j in range(0,len(cfg_type)): if name.lower().endswith(cfg_type[j]): try: flog = open(os.path.join(root, name), "rb") flog.close() flog = open(os.path.join(root, name), 'r+') flog.close() filecnt+=1 except: print("ERROR: No Access to: " + os.path.join(root, name)) raise Exception print(filecnt)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 15945, 29908, 13, 20399, 373, 2598, 26319, 29871, 29906, 29953, 29871, 29896, 29929, 29901, 29941, 29947, 29901, 29906, 29929, 29871, 29906, 29900, 29896, 29947, 13, 13, 29992, 8921, 29901, 8432, 13, 15945, 29908, 13, 13, 5215, 1852, 5510, 13, 5215, 2897, 13, 3166, 3458, 261, 29918, 2917, 1053, 3458, 261, 29918, 16859, 259, 13, 3166, 3458, 261, 29918, 9803, 1053, 3458, 261, 29918, 2585, 13, 3166, 3458, 261, 29918, 1420, 12276, 1053, 3458, 261, 29918, 12276, 13, 5215, 931, 13, 5215, 6608, 1982, 13, 5215, 318, 5416, 13, 13, 13, 1990, 679, 29918, 4230, 27828, 29901, 13, 268, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 3458, 261, 29918, 8977, 29892, 2246, 3972, 29892, 274, 16434, 29918, 735, 2325, 29892, 274, 16434, 29918, 1853, 1125, 13, 308, 13, 308, 13, 4706, 1596, 29898, 3332, 3972, 29897, 13, 4706, 1596, 29898, 16859, 29918, 735, 2325, 29897, 13, 4706, 1596, 29898, 16859, 29918, 1853, 29897, 13, 4706, 1596, 29898, 29885, 4995, 29918, 8977, 29897, 13, 4706, 934, 20047, 353, 29871, 29900, 13, 4706, 363, 3876, 29892, 4516, 29879, 29892, 2066, 297, 2897, 29889, 20919, 29898, 3332, 3972, 1125, 13, 9651, 363, 1024, 297, 2066, 29901, 13, 18884, 1284, 353, 7700, 13, 18884, 363, 474, 297, 3464, 29898, 29900, 29892, 2435, 29898, 16859, 29918, 735, 2325, 22164, 13, 13, 462, 1678, 565, 2897, 29889, 2084, 29889, 7122, 29898, 4632, 29892, 1024, 467, 27382, 2541, 29898, 359, 29889, 2084, 29889, 25721, 29898, 16859, 29918, 735, 2325, 29961, 29875, 12622, 29901, 13, 462, 4706, 1284, 353, 5852, 13, 18884, 565, 1284, 338, 5852, 29901, 13, 462, 1678, 6773, 13, 18884, 363, 432, 297, 3464, 29898, 29900, 29892, 2435, 29898, 16859, 29918, 1853, 22164, 29871, 13, 462, 1678, 565, 1024, 29889, 13609, 2141, 1975, 2541, 29898, 16859, 29918, 1853, 29961, 29926, 29962, 1125, 13, 462, 4706, 1018, 29901, 13, 462, 9651, 285, 1188, 353, 1722, 29898, 359, 29889, 2084, 29889, 7122, 29898, 4632, 29892, 1024, 511, 376, 6050, 1159, 13, 462, 9651, 285, 1188, 29889, 5358, 580, 259, 13, 462, 9651, 285, 1188, 353, 1722, 29898, 359, 29889, 2084, 29889, 7122, 29898, 4632, 29892, 1024, 511, 525, 29878, 29974, 1495, 259, 13, 462, 9651, 285, 1188, 29889, 5358, 580, 29871, 13, 462, 9651, 934, 20047, 23661, 29896, 13, 462, 4706, 5174, 29901, 13, 462, 9651, 1596, 703, 11432, 29901, 1939, 11028, 304, 29901, 376, 718, 2897, 29889, 2084, 29889, 7122, 29898, 4632, 29892, 1024, 876, 13, 462, 9651, 12020, 8960, 13, 4706, 1596, 29898, 1445, 20047, 29897, 13, 308, 2 ]
networkx/classes/tests/test_filters.py	jmmcd/networkx	3	134079	import pytest import networkx as nx class TestFilterFactory(object): def test_no_filter(self): nf = nx.filters.no_filter assert nf() assert nf(1) assert nf(2, 1) def test_hide_nodes(self): f = nx.classes.filters.hide_nodes([1, 2, 3]) assert not f(1) assert not f(2) assert not f(3) assert f(4) assert f(0) assert f('a') pytest.raises(TypeError, f, 1, 2) pytest.raises(TypeError, f) def test_show_nodes(self): f = nx.classes.filters.show_nodes([1, 2, 3]) assert f(1) assert f(2) assert f(3) assert not f(4) assert not f(0) assert not f('a') pytest.raises(TypeError, f, 1, 2) pytest.raises(TypeError, f) def test_hide_edges(self): factory = nx.classes.filters.hide_edges f = factory([(1, 2), (3, 4)]) assert not f(1, 2) assert not f(3, 4) assert not f(4, 3) assert f(2, 3) assert f(0, -1) assert f('a', 'b') pytest.raises(TypeError, f, 1, 2, 3) pytest.raises(TypeError, f, 1) pytest.raises(TypeError, f) pytest.raises(TypeError, factory, [1, 2, 3]) pytest.raises(ValueError, factory, [(1, 2, 3)]) def test_show_edges(self): factory = nx.classes.filters.show_edges f = factory([(1, 2), (3, 4)]) assert f(1, 2) assert f(3, 4) assert f(4, 3) assert not f(2, 3) assert not f(0, -1) assert not f('a', 'b') pytest.raises(TypeError, f, 1, 2, 3) pytest.raises(TypeError, f, 1) pytest.raises(TypeError, f) pytest.raises(TypeError, factory, [1, 2, 3]) pytest.raises(ValueError, factory, [(1, 2, 3)]) def test_hide_diedges(self): factory = nx.classes.filters.hide_diedges f = factory([(1, 2), (3, 4)]) assert not f(1, 2) assert not f(3, 4) assert f(4, 3) assert f(2, 3) assert f(0, -1) assert f('a', 'b') pytest.raises(TypeError, f, 1, 2, 3) pytest.raises(TypeError, f, 1) pytest.raises(TypeError, f) pytest.raises(TypeError, factory, [1, 2, 3]) pytest.raises(ValueError, factory, [(1, 2, 3)]) def test_show_diedges(self): factory = nx.classes.filters.show_diedges f = factory([(1, 2), (3, 4)]) assert f(1, 2) assert f(3, 4) assert not f(4, 3) assert not f(2, 3) assert not f(0, -1) assert not f('a', 'b') pytest.raises(TypeError, f, 1, 2, 3) pytest.raises(TypeError, f, 1) pytest.raises(TypeError, f) pytest.raises(TypeError, factory, [1, 2, 3]) pytest.raises(ValueError, factory, [(1, 2, 3)]) def test_hide_multiedges(self): factory = nx.classes.filters.hide_multiedges f = factory([(1, 2, 0), (3, 4, 1), (1, 2, 1)]) assert not f(1, 2, 0) assert not f(1, 2, 1) assert f(1, 2, 2) assert f(3, 4, 0) assert not f(3, 4, 1) assert not f(4, 3, 1) assert f(4, 3, 0) assert f(2, 3, 0) assert f(0, -1, 0) assert f('a', 'b', 0) pytest.raises(TypeError, f, 1, 2, 3, 4) pytest.raises(TypeError, f, 1, 2) pytest.raises(TypeError, f, 1) pytest.raises(TypeError, f) pytest.raises(TypeError, factory, [1, 2, 3]) pytest.raises(ValueError, factory, [(1, 2)]) pytest.raises(ValueError, factory, [(1, 2, 3, 4)]) def test_show_multiedges(self): factory = nx.classes.filters.show_multiedges f = factory([(1, 2, 0), (3, 4, 1), (1, 2, 1)]) assert f(1, 2, 0) assert f(1, 2, 1) assert not f(1, 2, 2) assert not f(3, 4, 0) assert f(3, 4, 1) assert f(4, 3, 1) assert not f(4, 3, 0) assert not f(2, 3, 0) assert not f(0, -1, 0) assert not f('a', 'b', 0) pytest.raises(TypeError, f, 1, 2, 3, 4) pytest.raises(TypeError, f, 1, 2) pytest.raises(TypeError, f, 1) pytest.raises(TypeError, f) pytest.raises(TypeError, factory, [1, 2, 3]) pytest.raises(ValueError, factory, [(1, 2)]) pytest.raises(ValueError, factory, [(1, 2, 3, 4)]) def test_hide_multidiedges(self): factory = nx.classes.filters.hide_multidiedges f = factory([(1, 2, 0), (3, 4, 1), (1, 2, 1)]) assert not f(1, 2, 0) assert not f(1, 2, 1) assert f(1, 2, 2) assert f(3, 4, 0) assert not f(3, 4, 1) assert f(4, 3, 1) assert f(4, 3, 0) assert f(2, 3, 0) assert f(0, -1, 0) assert f('a', 'b', 0) pytest.raises(TypeError, f, 1, 2, 3, 4) pytest.raises(TypeError, f, 1, 2) pytest.raises(TypeError, f, 1) pytest.raises(TypeError, f) pytest.raises(TypeError, factory, [1, 2, 3]) pytest.raises(ValueError, factory, [(1, 2)]) pytest.raises(ValueError, factory, [(1, 2, 3, 4)]) def test_show_multidiedges(self): factory = nx.classes.filters.show_multidiedges f = factory([(1, 2, 0), (3, 4, 1), (1, 2, 1)]) assert f(1, 2, 0) assert f(1, 2, 1) assert not f(1, 2, 2) assert not f(3, 4, 0) assert f(3, 4, 1) assert not f(4, 3, 1) assert not f(4, 3, 0) assert not f(2, 3, 0) assert not f(0, -1, 0) assert not f('a', 'b', 0) pytest.raises(TypeError, f, 1, 2, 3, 4) pytest.raises(TypeError, f, 1, 2) pytest.raises(TypeError, f, 1) pytest.raises(TypeError, f) pytest.raises(TypeError, factory, [1, 2, 3]) pytest.raises(ValueError, factory, [(1, 2)]) pytest.raises(ValueError, factory, [(1, 2, 3, 4)])	[ 1, 1053, 11451, 1688, 13, 5215, 3564, 29916, 408, 302, 29916, 13, 13, 13, 1990, 4321, 5072, 5126, 29898, 3318, 1125, 13, 1678, 822, 1243, 29918, 1217, 29918, 4572, 29898, 1311, 1125, 13, 4706, 302, 29888, 353, 302, 29916, 29889, 26705, 29889, 1217, 29918, 4572, 13, 4706, 4974, 302, 29888, 580, 13, 4706, 4974, 302, 29888, 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scripts/CNN_Unet_cluster0.py	kevinshen56714/NucleiTracker	2	95103	<gh_stars>1-10 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import os from os.path import join import glob import sys import random import warnings from tqdm import tqdm import itertools from itertools import chain from skimage.io import imread, imshow, imread_collection, concatenate_images from skimage.transform import resize from skimage.morphology import label from sklearn.model_selection import train_test_split import tensorflow as tf import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D, BatchNormalization, Conv2D, UpSampling2D, Lambda from keras.models import Model, load_model from keras.layers import Input from keras.layers.core import Dropout, Lambda from keras.layers.convolutional import Conv2D, Conv2DTranspose from keras.layers.pooling import MaxPooling2D from keras.layers.merge import concatenate from keras import backend as K from keras.callbacks import Callback, EarlyStopping, ReduceLROnPlateau, ModelCheckpoint from keras import initializers, layers, models from keras.utils import to_categorical from keras.utils.vis_utils import plot_model from keras.preprocessing.image import ImageDataGenerator from keras import callbacks IMG_WIDTH = 128 IMG_HEIGHT = 128 IMG_CHANNELS = 3 TRAIN_PATH = 'stage1_train/' TEST_PATH = 'stage1_test/' warnings.filterwarnings('ignore', category=UserWarning, module='skimage') seed = 42 random.seed = seed np.random.seed = seed df = pd.read_csv('C:\\Users\\liukevin\\Desktop\\project_DM\\clustered_stage1_train.csv',sep=',') df1 = pd.read_csv('C:\\Users\\liukevin\\Desktop\\project_DM\\clustered_stage1_test.csv',sep=',') cluster=[[],[],[],[]] for i in range(len(df)): if df['cluster-id'][i]==0: cluster[0].append(df['ImageId'][i]) elif df['cluster-id'][i]==1: cluster[1].append(df['ImageId'][i]) elif df['cluster-id'][i]==2: cluster[2].append(df['ImageId'][i]) elif df['cluster-id'][i]==3: cluster[3].append(df['ImageId'][i]) cluster_test=[[],[],[],[]] for i in range(len(df1)): if df1['cluster-id'][i]==0: cluster_test[0].append(df1['ImageId'][i]) elif df1['cluster-id'][i]==1: cluster_test[1].append(df1['ImageId'][i]) elif df1['cluster-id'][i]==2: cluster_test[2].append(df1['ImageId'][i]) IdInCluster=0 # Get train and test IDs train_ids = cluster[IdInCluster] test_ids = cluster_test[IdInCluster] # Get and resize train images and masks X_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8) Y_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool) print('Getting and resizing train images and masks ... ') sys.stdout.flush() for n, id_ in tqdm(enumerate(train_ids), total=len(train_ids)): path = TRAIN_PATH + id_ img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS] img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True) X_train[n] = img mask = np.zeros((IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool) for mask_file in next(os.walk(path + '/masks/'))[2]: mask_ = imread(path + '/masks/' + mask_file) mask_ = np.expand_dims(resize(mask_, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True), axis=-1) mask = np.maximum(mask, mask_) Y_train[n] = mask # Get and resize test images X_test = np.zeros((len(test_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8) sizes_test = [] print('Getting and resizing test images ... ') sys.stdout.flush() for n, id_ in tqdm(enumerate(test_ids), total=len(test_ids)): path = TEST_PATH + id_ img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS] sizes_test.append([img.shape[0], img.shape[1]]) img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True) X_test[n] = img print('Done!') x_train, x_test, y_train, y_test = train_test_split(X_train, Y_train, test_size=0.2) print('\nx_train',x_train.shape) print('x_test',x_test.shape) print('y_train',y_train.shape) print('y_test',y_test.shape) def plotTrainData(a,b): for i in range(5): ix = random.randint(0, len(train_ids)) plt.subplot(1,2,1) plt.title("X_train") imshow(a[ix]) plt.axis('off') plt.subplot(1,2,2) plt.title("Y_train") imshow(np.squeeze(b[ix])) plt.axis('off') plt.show() plotTrainData(X_train,Y_train) class MetricsCheckpoint(Callback): """Callback that saves metrics after each epoch""" def __init__(self, savepath): super(MetricsCheckpoint, self).__init__() self.savepath = savepath self.history = {} def on_epoch_end(self, epoch, logs=None): for k, v in logs.items(): self.history.setdefault(k, []).append(v) np.save(self.savepath, self.history) def plotKerasLearningCurve(): plt.figure(figsize=(10,5)) metrics = np.load('logs.npy')[()] filt = ['acc'] # try to add 'loss' to see the loss learning curve for k in filter(lambda x : np.any([kk in x for kk in filt]), metrics.keys()): l = np.array(metrics[k]) plt.plot(l, c= 'r' if 'val' not in k else 'b', label='val' if 'val' in k else 'train') x = np.argmin(l) if 'loss' in k else np.argmax(l) y = l[x] plt.scatter(x,y, lw=0, alpha=0.25, s=100, c='r' if 'val' not in k else 'b') plt.text(x, y, '{} = {:.4f}'.format(x,y), size='15', color= 'r' if 'val' not in k else 'b') plt.legend(loc=4) plt.axis([0, None, None, None]); plt.grid() plt.xlabel('Number of epochs') def plot_learning_curve(history): plt.figure(figsize=(8,8)) plt.subplot(1,2,1) plt.plot(history.history['acc']) plt.plot(history.history['val_acc']) plt.title('model accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.savefig('./accuracy_curve.png') #plt.clf() # summarize history for loss plt.subplot(1,2,2) 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.savefig('./loss_curve.png') # Define IoU metric def mean_iou(y_true, y_pred): prec = [] for t in np.arange(0.5, 1.0, 0.05): y_pred_ = tf.to_int32(y_pred > t) score, up_opt = tf.metrics.mean_iou(y_true, y_pred_, 2) K.get_session().run(tf.local_variables_initializer()) with tf.control_dependencies([up_opt]): score = tf.identity(score) prec.append(score) return K.mean(K.stack(prec), axis=0) #RLE encoding for submission def rle_encoding(x): dots = np.where(x.T.flatten() == 1)[0] run_lengths = [] prev = -2 for b in dots: if (b>prev+1): run_lengths.extend((b + 1, 0)) run_lengths[-1] += 1 prev = b return run_lengths def prob_to_rles(x, cutoff=0.5): lab_img = label(x > cutoff) for i in range(1, lab_img.max() + 1): yield rle_encoding(lab_img == i) def SIMPLE(a,b,c,d): smooth = 1. def dice_coef(y_true, y_pred): y_true_f = K.flatten(y_true) y_pred_f = K.flatten(y_pred) intersection = K.sum(y_true_f * y_pred_f) return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth) def dice_coef_loss(y_true, y_pred): return -dice_coef(y_true, y_pred) simple_cnn = Sequential() simple_cnn.add(BatchNormalization(input_shape = (None, None, IMG_CHANNELS),name = 'NormalizeInput')) simple_cnn.add(Conv2D(8, kernel_size = (3,3), padding = 'same')) simple_cnn.add(Conv2D(8, kernel_size = (3,3), padding = 'same')) # use dilations to get a slightly larger field of view simple_cnn.add(Conv2D(16, kernel_size = (3,3), dilation_rate = 2, padding = 'same')) simple_cnn.add(Conv2D(16, kernel_size = (3,3), dilation_rate = 2, padding = 'same')) simple_cnn.add(Conv2D(32, kernel_size = (3,3), dilation_rate = 3, padding = 'same')) # the final processing simple_cnn.add(Conv2D(16, kernel_size = (1,1), padding = 'same')) simple_cnn.add(Conv2D(1, kernel_size = (1,1), padding = 'same', activation = 'sigmoid')) simple_cnn.summary() earlystopper = EarlyStopping(patience=5, verbose=1) checkpointer = ModelCheckpoint('model-dsbowl2018-1.h5', verbose=1, save_best_only=True) simple_cnn.compile(optimizer = 'adam', loss = dice_coef_loss, metrics = [dice_coef, 'acc', 'mse']) history = simple_cnn.fit(x_train,y_train, validation_data=(x_test,y_test),callbacks = [earlystopper, checkpointer, MetricsCheckpoint('logs')],epochs = 30) plot_learning_curve(history) plt.show() plotKerasLearningCurve() plt.show() global modelY modelY = simple_cnn return modelY SIMPLE(x_train, y_train,x_test,y_test) def UNET(a,b,c,d): # Build U-Net model inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS)) s = Lambda(lambda x: x / 255) (inputs) c1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (s) c1 = Dropout(0.1) (c1) c1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c1) p1 = MaxPooling2D((2, 2)) (c1) c2 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p1) c2 = Dropout(0.1) (c2) c2 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c2) p2 = MaxPooling2D((2, 2)) (c2) c3 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p2) c3 = Dropout(0.2) (c3) c3 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c3) p3 = MaxPooling2D((2, 2)) (c3) c4 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p3) c4 = Dropout(0.2) (c4) c4 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c4) p4 = MaxPooling2D(pool_size=(2, 2)) (c4) c5 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p4) c5 = Dropout(0.3) (c5) c5 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c5) u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (c5) u6 = concatenate([u6, c4]) c6 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u6) c6 = Dropout(0.2) (c6) c6 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c6) u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (c6) u7 = concatenate([u7, c3]) c7 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u7) c7 = Dropout(0.2) (c7) c7 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c7) u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same') (c7) u8 = concatenate([u8, c2]) c8 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u8) c8 = Dropout(0.1) (c8) c8 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c8) u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same') (c8) u9 = concatenate([u9, c1], axis=3) c9 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u9) c9 = Dropout(0.1) (c9) c9 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c9) outputs = Conv2D(1, (1, 1), activation='sigmoid') (c9) model = Model(inputs=[inputs], outputs=[outputs]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=[mean_iou,'accuracy']) #model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc']) model.summary() # Fit model earlystopper = EarlyStopping(patience=5, verbose=1) checkpointer = ModelCheckpoint('model-dsbowl2018-2.h5', verbose=1, save_best_only=True) results = model.fit(a,b,batch_size=16,verbose=1,epochs=30,validation_data=(c,d),callbacks = [earlystopper, checkpointer, MetricsCheckpoint('logs')]) plot_learning_curve(results) plt.show() plotKerasLearningCurve() plt.show() global modelZ modelZ = model return modelZ UNET(x_train, y_train,x_test,y_test) def plotPredictions(a,b,c,d,e): model = e # Threshold predictions preds_train = model.predict(a[:int(a.shape[0]0.9)], verbose=1) preds_val = model.predict(a[int(a.shape[0]0.9):], verbose=1) preds_test = model.predict(c, verbose=1) preds_train_t = (preds_train > 0.5).astype(np.uint8) preds_val_t = (preds_val > 0.5).astype(np.uint8) preds_test_t = (preds_test > 0.5).astype(np.uint8) # Perform a sanity check on some random training samples ix = random.randint(0, len(preds_train_t)) plt.subplot(1,3,1) plt.title("X_train") plt.axis('off') imshow(a[ix]) plt.subplot(1,3,2) plt.title("Y_train") plt.axis('off') imshow(np.squeeze(b[ix])) plt.subplot(1,3,3) plt.title("Prediction") plt.axis('off') imshow(np.squeeze(preds_train_t[ix])) plt.show() # Perform a sanity check on some random validation samples ix = random.randint(0, len(preds_val_t)) plt.subplot(1,3,1) plt.title("X_test") plt.axis('off') imshow(a[int(a.shape[0]0.9):][ix]) plt.subplot(1,3,2) plt.title("Y_test") plt.axis('off') imshow(np.squeeze(b[int(b.shape[0]0.9):][ix])) plt.subplot(1,3,3) plt.title("Prediction") plt.axis('off') imshow(np.squeeze(preds_val_t[ix])) plt.show() plotPredictions(x_train,y_train,x_test,y_test,modelZ) test_image_paths = [glob.glob(join(TEST_PATH, test_id, "images", ""))[0] for test_id in test_ids] def threshold(image_gray): image_gray = cv2.GaussianBlur(image_gray, (7, 7), 1) ret, thresh = cv2.threshold(image_gray, 0, 255, cv2.THRESH_OTSU) _, cnts, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) cnts = sorted(cnts, key=cv2.contourArea, reverse=True) max_cnt_area = cv2.contourArea(cnts[0]) if max_cnt_area > 50000: ret, thresh = cv2.threshold(image_gray, 0, 255, cv2.THRESH_OTSU \| cv2.THRESH_BINARY_INV) return thresh def apply_morphology(thresh): mask = cv2.dilate(thresh, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))) mask = cv2.erode(mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))) return mask segmented = [] for test_image_path in tqdm(test_image_paths): tmp_image = cv2.imread(test_image_path, cv2.IMREAD_GRAYSCALE) thresh = threshold(tmp_image) mask = apply_morphology(thresh) segmented.append(mask) def saveResults(a,b): preds_test = a.predict(b, verbose=1) # Create list of upsampled test masks preds_test_upsampled = [] for i in range(len(preds_test)): preds_test_upsampled.append(resize(np.squeeze(preds_test[i]), (sizes_test[i][0], sizes_test[i][1]), mode='constant', preserve_range=True)) new_test_ids = [] rles = [] for n, id_ in enumerate(test_ids): rle = list(prob_to_rles(preds_test_upsampled[n])) rles.extend(rle) new_test_ids.extend([id_] len(rle)) # Create submission DataFrame sub = pd.DataFrame() sub['ImageId'] = new_test_ids sub['EncodedPixels'] = pd.Series(rles).apply(lambda x: ' '.join(str(y) for y in x)) sub.to_csv(a.name+'.csv', index=False) print("Results Saved") print("\nMethod 1: Simple CNN") saveResults(modelY,X_test) print("\nMethod 2: Unet CNN") saveResults(modelZ,X_test)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 12655, 408, 7442, 30004, 13, 5215, 11701, 408, 10518, 30004, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 30004, 13, 5215, 13850, 29906, 30004, 13, 5215, 2897, 30004, 13, 3166, 2897, 29889, 2084, 1053, 5988, 30004, 13, 5215, 13149, 30004, 13, 5215, 10876, 30004, 13, 5215, 4036, 30004, 13, 5215, 18116, 30004, 13, 3166, 260, 29939, 18933, 1053, 260, 29939, 18933, 30004, 13, 5215, 4256, 8504, 30004, 13, 3166, 4256, 8504, 1053, 9704, 30004, 13, 3166, 2071, 3027, 29889, 601, 1053, 527, 949, 29892, 527, 4294, 29892, 527, 949, 29918, 10855, 29892, 16125, 403, 29918, 8346, 30004, 13, 3166, 2071, 3027, 29889, 9067, 1053, 19490, 30004, 13, 3166, 2071, 3027, 29889, 29885, 5676, 3002, 1053, 3858, 30004, 13, 3166, 2071, 19668, 29889, 4299, 29918, 21731, 1053, 7945, 29918, 1688, 29918, 5451, 30004, 13, 5215, 26110, 408, 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test/login.py	hongren798911/haha	0	26645	<reponame>hongren798911/haha num1 = 100 num2 = 200 num3 = 300 num4 = 400 num5 = 500 mum6 = 600 num7 = 700 num8 = 800	[ 1, 529, 276, 1112, 420, 29958, 29882, 549, 1267, 29955, 29929, 29947, 29929, 29896, 29896, 29914, 29882, 25613, 13, 1949, 29896, 353, 29871, 29896, 29900, 29900, 13, 1949, 29906, 353, 29871, 29906, 29900, 29900, 13, 1949, 29941, 353, 29871, 29941, 29900, 29900, 13, 1949, 29946, 353, 29871, 29946, 29900, 29900, 13, 13, 13, 1949, 29945, 353, 29871, 29945, 29900, 29900, 13, 13, 29885, 398, 29953, 353, 29871, 29953, 29900, 29900, 13, 13, 1949, 29955, 353, 29871, 29955, 29900, 29900, 13, 13, 1949, 29947, 353, 29871, 29947, 29900, 29900, 13, 2 ]
pytype/tests/py2/test_stdlib.py	souravbadami/pytype	1	3896	<reponame>souravbadami/pytype """Tests of selected stdlib functions.""" from pytype.tests import test_base class StdlibTests(test_base.TargetPython27FeatureTest): """Tests for files in typeshed/stdlib.""" def testPosix(self): ty = self.Infer(""" import posix x = posix.urandom(10) """) self.assertTypesMatchPytd(ty, """ posix = ... # type: module x = ... # type: str """) def testXRange(self): self.Check(""" import random random.sample(xrange(10), 5) """) def testStringTypes(self): ty = self.Infer(""" import types if isinstance("", types.StringTypes): x = 42 if isinstance(False, types.StringTypes): y = 42 if isinstance(u"", types.StringTypes): z = 42 """, deep=False) self.assertTypesMatchPytd(ty, """ types = ... # type: module x = ... # type: int z = ... # type: int """) def testDefaultDict(self): self.Check(""" import collections import itertools ids = collections.defaultdict(itertools.count(17).next) """) def testSysVersionInfoLt(self): ty = self.Infer(""" import sys if sys.version_info[0] < 3: v = 42 else: v = "hello world" """) self.assertTypesMatchPytd(ty, """ sys = ... # type: module v = ... # type: int """) def testSysVersionInfoLe(self): ty = self.Infer(""" import sys if sys.version_info[0] <= 2: v = 42 else: v = "hello world" """) self.assertTypesMatchPytd(ty, """ sys = ... # type: module v = ... # type: int """) def testSysVersionInfoEq(self): ty = self.Infer(""" import sys if sys.version_info[0] == 2: v = 42 elif sys.version_info[0] == 3: v = "hello world" else: v = None """) self.assertTypesMatchPytd(ty, """ sys = ... # type: module v = ... # type: int """) def testSysVersionInfoGe(self): ty = self.Infer(""" import sys if sys.version_info[0] >= 3: v = 42 else: v = "hello world" """) self.assertTypesMatchPytd(ty, """ sys = ... # type: module v = ... # type: str """) def testSysVersionInfoGt(self): ty = self.Infer(""" import sys if sys.version_info[0] > 2: v = 42 else: v = "hello world" """) self.assertTypesMatchPytd(ty, """ sys = ... # type: module v = ... # type: str """) def testSysVersionInfoNamedAttribute(self): ty = self.Infer(""" import sys if sys.version_info.major == 2: v = 42 else: v = "hello world" """) self.assertTypesMatchPytd(ty, """ sys: module v: int """) test_base.main(globals(), __name__ == "__main__")	[ 1, 529, 276, 1112, 420, 29958, 29879, 283, 5705, 12313, 4479, 29914, 2272, 1853, 13, 15945, 29908, 24376, 310, 4629, 3659, 1982, 3168, 1213, 15945, 13, 13, 3166, 11451, 1853, 29889, 21150, 1053, 1243, 29918, 3188, 13, 13, 13, 1990, 624, 29881, 1982, 24376, 29898, 1688, 29918, 3188, 29889, 8667, 11980, 29906, 29955, 19132, 3057, 1125, 13, 29871, 9995, 24376, 363, 2066, 297, 4072, 17143, 29914, 4172, 1982, 1213, 15945, 13, 13, 29871, 822, 1243, 9135, 861, 29898, 1311, 1125, 13, 1678, 7911, 353, 1583, 29889, 797, 571, 703, 15945, 13, 418, 1053, 926, 861, 13, 418, 921, 353, 926, 861, 29889, 332, 2685, 29898, 29896, 29900, 29897, 13, 1678, 5124, 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server/test.py	marcosmuto/IBMCloudAppDevelopment_Capstone	0	111519	import requests import json from datetime import datetime from requests.auth import HTTPBasicAuth review = dict() review["id"] = int(datetime.now().timestamp()) review["name"] = "<NAME>" review["dealership"] = 1 review["review"] = "Good deal" review["purchase"] = True review["purchase_date"] = "02/16/2020" review["car_make"] = "Honda" review["car_model"] = "Fit" review["car_year"] = 2020 print(review) json_payload = dict() json_payload["review"] = review url="https://43bb4759.us-south.apigw.appdomain.cloud/api/review" response = requests.post(url, json=json_payload) print(response)	[ 1, 1053, 7274, 13, 5215, 4390, 13, 3166, 12865, 1053, 12865, 13, 3166, 7274, 29889, 5150, 1053, 7331, 16616, 6444, 13, 13, 27828, 353, 9657, 580, 13, 27828, 3366, 333, 3108, 353, 938, 29898, 12673, 29889, 3707, 2141, 16394, 3101, 13, 27828, 3366, 978, 3108, 353, 9872, 5813, 11903, 13, 27828, 3366, 311, 284, 10475, 3108, 353, 29871, 29896, 13, 27828, 3366, 27828, 3108, 353, 376, 18420, 5376, 29908, 13, 27828, 3366, 29886, 27574, 3108, 353, 5852, 13, 27828, 3366, 29886, 27574, 29918, 1256, 3108, 353, 376, 29900, 29906, 29914, 29896, 29953, 29914, 29906, 29900, 29906, 29900, 29908, 13, 27828, 3366, 4287, 29918, 5675, 3108, 353, 376, 29950, 14287, 29908, 13, 27828, 3366, 4287, 29918, 4299, 3108, 353, 376, 29943, 277, 29908, 13, 27828, 3366, 4287, 29918, 6360, 3108, 353, 29871, 29906, 29900, 29906, 29900, 13, 13, 2158, 29898, 27828, 29897, 13, 13, 3126, 29918, 23813, 353, 9657, 580, 13, 3126, 29918, 23813, 3366, 27828, 3108, 353, 9076, 13, 13, 2271, 543, 991, 597, 29946, 29941, 1327, 29946, 29955, 29945, 29929, 29889, 375, 29899, 29879, 2438, 29889, 481, 335, 29893, 29889, 932, 7247, 29889, 9274, 29914, 2754, 29914, 27828, 29908, 13, 268, 13, 5327, 353, 7274, 29889, 2490, 29898, 2271, 29892, 4390, 29922, 3126, 29918, 23813, 29897, 13, 2158, 29898, 5327, 29897, 2 ]
credit-bias/src/data/make_dataset.py	tteofili/benchmark-models	0	127034	# -- coding: utf-8 -- import click import colorlog import logging from pathlib import Path from dotenv import find_dotenv, load_dotenv import pandas as pd import numpy as np import os handler = colorlog.StreamHandler() handler.setFormatter( colorlog.ColoredFormatter("%(log_color)s%(levelname)s:%(name)s:%(message)s") ) logger = colorlog.getLogger(__name__) def filter_columns(df_: pd.DataFrame) -> pd.DataFrame: selected_columns = [ "LoanNumber", "ListedOnUTC", "UserName", "NewCreditCustomer", "LoanDate", "MaturityDate_Original", "MaturityDate_Last", "Age", "DateOfBirth", "Gender", "Country", "AppliedAmount", "Amount", "Interest", "LoanDuration", "MonthlyPayment", "UseOfLoan", "Education", "MaritalStatus", "NrOfDependants", "EmploymentStatus", "EmploymentDurationCurrentEmployer", "WorkExperience", "OccupationArea", "HomeOwnershipType", "IncomeFromPrincipalEmployer", "IncomeFromPension", "IncomeFromFamilyAllowance", "IncomeFromSocialWelfare", "IncomeFromLeavePay", "IncomeFromChildSupport", "IncomeOther", "IncomeTotal", "ExistingLiabilities", "RefinanceLiabilities", "DebtToIncome", "FreeCash", "DefaultDate", "Status", "CreditScoreEeMini", "NoOfPreviousLoansBeforeLoan", "AmountOfPreviousLoansBeforeLoan", "PreviousRepaymentsBeforeLoan", "PreviousEarlyRepaymentsBefoleLoan", "PreviousEarlyRepaymentsCountBeforeLoan", ] return df_[selected_columns] def filter_rows(df_: pd.DataFrame) -> pd.DataFrame: return df_[(df_["Country"] == "EE") & (df_["Status"] != "Current")] def rename_columns(df_: pd.DataFrame) -> pd.DataFrame: df_ = df_.replace(-1, np.nan) zero_replacements = [ "Age", "Education", "MaritalStatus", "EmploymentStatus", "OccupationArea", "CreditScoreEeMini", ] df_[zero_replacements] = df_[zero_replacements].replace(0.0, np.nan) value_replacements = { "UseOfLoan": { 0: "Loan_consolidation", 1: "Real_estate", 2: "Home_improvement", 3: "Business", 4: "Education", 5: "Travel", 6: "Vehicle", 7: "Other", 8: "Health", 101: "Working_capital_financing", 102: "Purchase_of_machinery_equipment", 103: "Renovation_of_real_estate", 104: "Accounts_receivable_financing ", 105: "Acquisition_of_means_of_transport", 106: "Construction_finance", 107: "Acquisition_of_stocks", 108: "Acquisition_of_real_estate", 109: "Guaranteeing_obligation ", 110: "Other_business", }, "Education": { 1: "Primary", 2: "Basic", 3: "Vocational", 4: "Secondary", 5: "Higher", }, "MaritalStatus": { 1: "Married", 2: "Cohabitant", 3: "Single", 4: "Divorced", 5: "Widow", }, "EmploymentStatus": { 1: "Unemployed", 2: "Partially", 3: "Fully", 4: "Self_employed", 5: "Entrepreneur", 6: "Retiree", }, "NewCreditCustomer": {0: "Existing_credit_customer", 1: "New_credit_Customer"}, "OccupationArea": { 1: "Other", 2: "Mining", 3: "Processing", 4: "Energy", 5: "Utilities", 6: "Construction", 7: "Retail_and_wholesale", 8: "Transport_and_warehousing", 9: "Hospitality_and_catering", 10: "Info_and_telecom", 11: "Finance_and_insurance", 12: "Real_estate", 13: "Research", 14: "Administrative", 15: "Civil_service_and_military", 16: "Education", 17: "Healthcare_and_social_help", 18: "Art_and_entertainment", 19: "Agriculture_forestry_and_fishing", }, "HomeOwnershipType": { 0: "Homeless", 1: "Owner", 2: "Living_with_parents", 3: "Tenant_pre_furnished_property", 4: "Tenant_unfurnished_property", 5: "Council_house", 6: "Joint_tenant", 7: "Joint_ownership", 8: "Mortgage", 9: "Owner_with_encumbrance", 10: "Other", }, "NrOfDependants": {"10Plus": 11}, "Gender": {0: "Male", 1: "Female", 2: "Unknown"}, } df_ = df_.replace(value_replacements) return df_ def add_new_columns(df_: pd.DataFrame) -> pd.DataFrame: df_["Defaulted"] = df_["DefaultDate"].apply(lambda x: 0 if pd.isnull(x) else 1) df_["PaidLoan"] = df_["Defaulted"].replace({0: 1, 1: 0}) df_["LoanStatus"] = df_["PaidLoan"].apply( lambda x: "Paid back" if x == 1 else "Defaulted" ) df_["AgeGroup"] = df_["Age"].apply(lambda x: "Under 40" if x < 40 else "Over 40") return df_ def reformat_columns(df_: pd.DataFrame) -> pd.DataFrame: df_["ListedOnUTC"] = pd.to_datetime(df_["ListedOnUTC"]) df_["LoanDate"] = pd.to_datetime(df_["LoanDate"]) df_["MaturityDate_Original"] = pd.to_datetime(df_["MaturityDate_Original"]) df_["MaturityDate_Last"] = pd.to_datetime(df_["MaturityDate_Last"]) df_["DateOfBirth"] = pd.to_datetime(df_["DateOfBirth"]) df_["DefaultDate"] = pd.to_datetime(df_["DefaultDate"]) df_["LoanDuration"] = pd.to_numeric(df_["LoanDuration"]) df_["NrOfDependants"] = pd.to_numeric(df_["NrOfDependants"]) df_["CreditScoreEeMini"] = df_["CreditScoreEeMini"].astype(str) df_["Defaulted"] = df_["Defaulted"].astype(bool) df_["PaidLoan"] = df_["PaidLoan"].astype(bool) return df_ @click.command() @click.argument("input_filepath", type=click.Path(exists=True)) @click.argument("output_filepath", type=click.Path()) def main(input_filepath, output_filepath): """Runs data processing scripts to turn raw data from (../raw) into cleaned data ready to be analyzed (saved in ../processed). """ logger.info("making interim data set from raw data") # load raw dataset _df = pd.read_csv(os.path.join(input_filepath, "LoanData.zip")) # Filter not used columns _df = filter_columns(_df) # Filter not used rows _df = filter_rows(_df) # Rename columns _df = rename_columns(_df) # Add new columns _df = add_new_columns(_df) # Reformat columns _df = reformat_columns(_df) _df.to_csv(os.path.join(output_filepath, "data.csv"), index=False) if __name__ == "__main__": log_fmt = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" logging.basicConfig(level=logging.INFO, format=log_fmt) # not used in this stub but often useful for finding various files project_dir = Path(__file__).resolve().parents[2] # find .env automagically by walking up directories until it's found, then # load up the .env entries as environment variables load_dotenv(find_dotenv()) main()	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 5215, 2828, 13, 5215, 2927, 1188, 13, 5215, 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Code/VB/demo.py	CaptainE/Non-negative-latent-space-models	0	130024	<gh_stars>0 import numpy as np from scipy.stats import gamma,poisson from gnmf import nmf_vb import matplotlib.pyplot as plt if __name__ == '__main__': W=40 K=30 I=3 a_tm=np.ones((W,I))10 b_tm=np.ones((W,I)) a_ve=np.ones((I,K))0.1 b_ve=np.ones((I,K)) T = gamma.rvs(a_tm,b_tm) V = gamma.rvs(a_ve,b_ve) X = poisson.rvs(T@V) X = X + 1000np.random.randn(X.shape) (tie_a_tm, tie_b_tm, tie_a_ve, tie_b_ve)=('free','tie_all','free','tie_all') init_mat_factor = nmf_vb(a_tm, b_tm, a_ve, b_ve,tie_a_tm, tie_b_tm, tie_a_ve, tie_b_ve) g=init_mat_factor.VB_parm_calc(X,epoch=1000,update=10,print_period=500) (g_E_T,g_E_logT,g_E_V,g_E_logV,g_Bound,g_a_ve,g_b_ve,g_a_tm,g_b_tm)=g X_reconstruct=g_E_T@g_E_V #upper left plot is the reconstructed matrix plt.subplot(211) plt.imshow(X_reconstruct) #lower left plot is the original matrix plt.subplot(212) plt.imshow(X) #the right subplot is a colorbar plt.subplots_adjust(bottom=0.1,right=0.8,top=0.9) cax=plt.axes([0.6,0.1,0.075,0.8]) plt.colorbar(cax=cax) plt.show()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 12655, 408, 7442, 30004, 13, 3166, 4560, 2272, 29889, 16202, 1053, 330, 2735, 29892, 1129, 17387, 30004, 13, 3166, 330, 22882, 29888, 1053, 302, 29885, 29888, 29918, 24666, 30004, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 30004, 13, 30004, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 30004, 13, 12, 29956, 29922, 29946, 29900, 30004, 13, 12, 29968, 29922, 29941, 29900, 30004, 13, 12, 29902, 29922, 29941, 30004, 13, 12, 29874, 29918, 18276, 29922, 9302, 29889, 2873, 3552, 29956, 29892, 29902, 876, 29930, 29896, 29900, 30004, 13, 12, 29890, 29918, 18276, 29922, 9302, 29889, 2873, 3552, 29956, 29892, 29902, 876, 30004, 13, 12, 29874, 29918, 345, 29922, 9302, 29889, 2873, 3552, 29902, 29892, 29968, 876, 29930, 29900, 29889, 29896, 30004, 13, 12, 29890, 29918, 345, 29922, 9302, 29889, 2873, 3552, 29902, 29892, 29968, 876, 30004, 13, 12, 29911, 353, 330, 2735, 29889, 29878, 4270, 29898, 29874, 29918, 18276, 29892, 29890, 29918, 18276, 8443, 13, 12, 29963, 353, 330, 2735, 29889, 29878, 4270, 29898, 29874, 29918, 345, 29892, 29890, 29918, 345, 8443, 13, 12, 29990, 353, 772, 17387, 29889, 29878, 4270, 29898, 29911, 29992, 29963, 8443, 13, 12, 29990, 353, 1060, 718, 29871, 29896, 29900, 29900, 29900, 29930, 9302, 29889, 8172, 29889, 9502, 29876, 10456, 29990, 29889, 12181, 8443, 13, 12, 29898, 29873, 347, 29918, 29874, 29918, 18276, 29892, 22134, 29918, 29890, 29918, 18276, 29892, 22134, 29918, 29874, 29918, 345, 29892, 22134, 29918, 29890, 29918, 345, 3892, 877, 9021, 3788, 29873, 347, 29918, 497, 3788, 9021, 3788, 29873, 347, 29918, 497, 1495, 30004, 13, 12, 2344, 29918, 2922, 29918, 19790, 353, 302, 29885, 29888, 29918, 24666, 29898, 29874, 29918, 18276, 29892, 289, 29918, 18276, 29892, 263, 29918, 345, 29892, 289, 29918, 345, 29892, 29873, 347, 29918, 29874, 29918, 18276, 29892, 22134, 29918, 29890, 29918, 18276, 29892, 22134, 29918, 29874, 29918, 345, 29892, 22134, 29918, 29890, 29918, 345, 8443, 13, 12, 29887, 29922, 2344, 29918, 2922, 29918, 19790, 29889, 24281, 29918, 862, 29885, 29918, 28667, 29898, 29990, 29892, 1022, 2878, 29922, 29896, 29900, 29900, 29900, 29892, 5504, 29922, 29896, 29900, 29892, 2158, 29918, 19145, 29922, 29945, 29900, 29900, 8443, 13, 12, 29898, 29887, 29918, 29923, 29918, 29911, 29892, 29887, 29918, 29923, 29918, 1188, 29911, 29892, 29887, 29918, 29923, 29918, 29963, 29892, 29887, 29918, 29923, 29918, 1188, 29963, 29892, 29887, 29918, 17109, 29892, 29887, 29918, 29874, 29918, 345, 29892, 29887, 29918, 29890, 29918, 345, 29892, 29887, 29918, 29874, 29918, 18276, 29892, 29887, 29918, 29890, 29918, 18276, 3892, 29887, 30004, 13, 12, 29990, 29918, 276, 11433, 29922, 29887, 29918, 29923, 29918, 29911, 29992, 29887, 29918, 29923, 29918, 29963, 30004, 13, 12, 29937, 21064, 2175, 6492, 338, 278, 337, 11433, 287, 4636, 30004, 13, 12, 572, 29873, 29889, 1491, 5317, 29898, 29906, 29896, 29896, 8443, 13, 12, 572, 29873, 29889, 326, 4294, 29898, 29990, 29918, 276, 11433, 8443, 13, 12, 29937, 13609, 2175, 6492, 338, 278, 2441, 4636, 30004, 13, 12, 572, 29873, 29889, 1491, 5317, 29898, 29906, 29896, 29906, 8443, 13, 12, 572, 29873, 29889, 326, 4294, 29898, 29990, 8443, 13, 12, 29937, 1552, 1492, 1014, 5317, 338, 263, 2927, 1646, 30004, 13, 12, 572, 29873, 29889, 1491, 26762, 29918, 328, 5143, 29898, 8968, 29922, 29900, 29889, 29896, 29892, 1266, 29922, 29900, 29889, 29947, 29892, 3332, 29922, 29900, 29889, 29929, 8443, 13, 12, 1113, 29916, 29922, 572, 29873, 29889, 1165, 267, 4197, 29900, 29889, 29953, 29892, 29900, 29889, 29896, 29892, 29900, 29889, 29900, 29955, 29945, 29892, 29900, 29889, 29947, 2314, 30004, 13, 12, 572, 29873, 29889, 2780, 1646, 29898, 1113, 29916, 29922, 1113, 29916, 8443, 13, 12, 572, 29873, 29889, 4294, 580, 2 ]
second-sem/genPlotsBetweenCoverageAndFPKM.py	piyush-kansal/masters-project	1	159694	#!/usr/bin/env python # Main script for generating plots between coverage and FPKM values # <NAME> import sys import os import subprocess import csv import rpy2.robjects as robjects import pickle # Validate number of i/p arguments def validateNumberOfArguments(): if len(sys.argv) != 5: print "Invalid number of arguments" print "Usage:" print "genPlotsBetweenCoverageAndFPKM.py <number-of-experiments> <cufflinks-op-files-path-on-hdfs> <expression-matrix-path> <ref-ptt-file>" sys.exit(1) # Accepts user i/p, validate them and initialize required params def acceptUserInput(): validateNumberOfArguments() FNULL = open(os.devnull, 'w') # Declare params global global EXP_CONT, CUFF_PATH, EXPM_PATH, REF_FILE, PLOT_FILE global coverageVals, fpkmVals, ranges # Initialize i/p arguments EXP_CONT = int(sys.argv[1]) CUFF_PATH = sys.argv[2] cmd = 'hadoop dfs -ls %s' % (CUFF_PATH) try: subprocess.check_call(cmd.split(), stdout=FNULL, stderr=subprocess.STDOUT) except subprocess.CalledProcessError: print 'Directory %s does not exits on HDFS.' % (CUFF_PATH) sys.exit(1) FNULL.close() EXPM_PATH = sys.argv[3] if not os.path.isfile(EXPM_PATH): print 'File %s does not exits on file system.' % (EXPM_PATH) sys.exit(1) REF_FILE = sys.argv[4] if not os.path.isfile(REF_FILE): print 'File %s does not exits on file system.' % (REF_FILE) sys.exit(1) PLOT_FILE = 'XY.plot' def copyFilesInCurrentDir(): i = 1 while i <= EXP_CONT: cmd = 'hadoop dfs -copyToLocal %s/%d/genes.fpkm_tracking genes.fpkm_tracking.%d' % (CUFF_PATH, i, i) r = os.system(cmd) if r: print 'Some error occured while copying Cufflinks output on local file system for i=%d. Exiting ...' % (i) sys.exit(1) i += 1 cmd = 'cp %s .' % (EXPM_PATH) os.system(cmd) cmd = 'cp %s .' % (REF_FILE) os.system(cmd) def rangeExists(s, e): for index, r in enumerate(ranges): if s >= long(r[0]) and s < long(r[1]): return index if s < long(r[0]): return None return None def genValues(): coverageFile = open(EXPM_PATH, 'r') records = csv.reader(coverageFile, delimiter=' ') i = 0 coverageVals = [] while i < EXP_CONT: coverageVals.append(','.join([r[i] for r in records])) coverageFile.seek(0) i += 1 coverageFile.close() refFile = open(REF_FILE, 'r') records = csv.reader(refFile, delimiter='\t') ranges = [tuple(r[0].split('..')) for r in records] refFile.close() i = 1 fpkmVals = [] while i <= EXP_CONT: fpkmFileName = 'genes.fpkm_tracking.%d' % (i) fpkmFile = open(fpkmFileName, 'r') records = csv.reader(fpkmFile, delimiter='\t') j = 1 row = ['0']len(ranges) for r in records: if j == 1: j = 2 continue else: s,e = r[6].split(':')[1].split('-') ret = rangeExists(long(s), e) if ret: row[ret] = r[9] fpkmVals.append(','.join(row)) fpkmFile.close() i += 1 def writeValsToFile(): oFile = open(PLOT_FILE, 'wb') pickle.dump(coverageVals, oFile) pickle.dump(fpkmVals, oFile) oFile.close() def genPlots(): iFile = open(PLOT_FILE, 'rb') coverageVals = pickle.load(iFile) fpkmVals = pickle.load(iFile) iFile.close() r = robjects.r i = 0 while i < EXP_CONT: x = robjects.IntVector([long(item) for item in coverageVals[i].split(',')]) y = robjects.FloatVector([float(item) for item in fpkmVals[i].split(',')]) r.jpeg('t%d.jpg' % (i+1)) r.plot(x, y, xlab="Coverage", ylab="FPKM") i += 1 def cleanup(): cmd = 'rm -f genes.fpkm_tracking' os.system(cmd) # Main program if __name__ == "__main__": acceptUserInput() copyFilesInCurrentDir() genValues() writeValsToFile() cleanup() genPlots() print "Program finished successfully" sys.exit(0)	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 29937, 4241, 2471, 363, 14655, 24580, 1546, 23746, 322, 383, 21738, 29924, 1819, 13, 29937, 529, 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nsst_translate_corpus.py	AlexanderJenke/nsst	0	2672	from argparse import ArgumentParser from tqdm import tqdm import NSST from nsst_translate import best_transition_sequence if __name__ == '__main__': parser = ArgumentParser() parser.add_argument("--nsst_file", default="output/nsst_tss20_th4_nSt100_Q0.pkl", help="nsst file") parser.add_argument("--src_lang", default="output/europarl-v7.de-en.de.clean") parser.add_argument("--tgt_lang", default="output/europarl-v7.de-en.en.clean") parser.add_argument("--enforce_n_reg", default=True) parser.add_argument("--output", default=f"output/nsst_stat_nreg_100Q0.csv") args = parser.parse_args() args.enforce_n_final_reg = False # load NSST nsst = NSST.NSST() nsst.load(args.nsst_file) args.nsst = nsst # open files src_file = open(args.src_lang, 'r') tgt_file = open(args.tgt_lang, 'r') output_file = open(args.output, 'w') # iterate over sentences, first 4096 -> test sentences for src, tgt, _ in tqdm(list(zip(src_file, tgt_file, range(4096))), desc="Processing sentences"): # remove line breaks src = src[:-1] tgt = tgt[:-1] # try to translate try: # prepare tokenisations token_src = [nsst.tokenization_src[word] if word in nsst.tokenization_src else 0 for word in src.split(" ") if len(word)] token_tgt = [nsst.tokenization_tgt[word] if word in nsst.tokenization_tgt else 0 for word in tgt.split(" ") if len(word)] # run nsst args.input = src args.token_src = token_src result = best_transition_sequence(args) # get best result pred = sorted((k for k in result if ('Qf' in args.nsst_file or not args.enforce_n_final_reg or len(k[1]) == 1) and ('Q0' in args.nsst_file or k[0] == -1) ), key=lambda x: x[2], reverse=True)[0] n_res = len(result) q, reg, prob = pred # write to csv if not len(reg): # catch empty registers continue token_pred = [w for w in reg[0].split(' ') if len(w)] pred_str = "" for t in token_pred: pred_str += f"{nsst.tokenization_tgt_lut[int(t)]} " token_src_str = "" for t in token_src: token_src_str += f"{t} " token_tgt_str = "" for t in token_tgt: token_tgt_str += f"{t} " token_pred_str = "" for t in token_pred: token_pred_str += f"{t} " print(f"{src};{token_src_str[:-1]};" f"{tgt};{token_tgt_str[:-1]};" f"{pred_str};{token_pred_str[:-1]};" f"{prob};{len(reg)};{n_res}", file=output_file) output_file.flush() except RuntimeError: pass # close files src_file.close() tgt_file.close() output_file.close()	[ 1, 515, 1852, 5510, 1053, 23125, 11726, 13, 13, 3166, 260, 29939, 18933, 1053, 260, 29939, 18933, 13, 13, 5215, 3865, 1254, 13, 3166, 17534, 303, 29918, 21652, 1053, 1900, 29918, 20543, 29918, 16506, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 13812, 353, 23125, 11726, 580, 13, 1678, 13812, 29889, 1202, 29918, 23516, 703, 489, 1983, 303, 29918, 1445, 613, 2322, 543, 4905, 29914, 1983, 303, 29918, 29873, 893, 29906, 29900, 29918, 386, 29946, 29918, 29876, 855, 29896, 29900, 29900, 29918, 29984, 29900, 29889, 29886, 6321, 613, 1371, 543, 1983, 303, 934, 1159, 13, 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src/example_package/additions.py	energyinpython/packaging_tutorial	0	156466	import numpy as np # reverse = True: descending order (TOPSIS, CODAS), False: ascending order (VIKOR, SPOTIS) def rank_preferences(pref, reverse = True): """ Rank alternatives according to MCDM preference function values. Parameters ---------- pref : ndarray vector with MCDM preference function values for alternatives reverse : bool Boolean variable which is True for MCDM methods which rank alternatives in descending order and False for MCDM methods which rank alternatives in ascending order Returns ------- ndarray vector with alternatives ranking. Alternative with 1 value is the best. """ rank = np.zeros(len(pref)) sorted_pref = sorted(pref, reverse = reverse) pos = 1 for i in range(len(sorted_pref) - 1): ind = np.where(sorted_pref[i] == pref)[0] rank[ind] = pos if sorted_pref[i] != sorted_pref[i + 1]: pos += 1 ind = np.where(sorted_pref[i + 1] == pref)[0] rank[ind] = pos return rank.astype(int)	[ 1, 1053, 12655, 408, 7442, 13, 13, 29937, 11837, 353, 5852, 29901, 5153, 2548, 1797, 313, 29911, 4590, 29903, 3235, 29892, 4810, 29928, 3289, 511, 7700, 29901, 12066, 2548, 1797, 313, 18118, 29968, 1955, 29892, 10937, 2891, 3235, 29897, 13, 1753, 7115, 29918, 1457, 10662, 29898, 29886, 999, 29892, 11837, 353, 5852, 1125, 13, 1678, 9995, 13, 1678, 22125, 27809, 5034, 304, 341, 6530, 29924, 24583, 740, 1819, 29889, 13, 13, 1678, 12662, 2699, 13, 1678, 448, 1378, 29899, 13, 4706, 758, 29888, 584, 29871, 299, 2378, 13, 9651, 4608, 411, 341, 6530, 29924, 24583, 740, 1819, 363, 27809, 13, 4706, 11837, 584, 6120, 13, 9651, 11185, 2286, 607, 338, 5852, 363, 341, 6530, 29924, 3519, 607, 7115, 27809, 297, 5153, 2548, 13, 9651, 1797, 322, 7700, 363, 341, 6530, 29924, 3519, 607, 7115, 27809, 297, 12066, 2548, 13, 9651, 1797, 13, 13, 1678, 16969, 13, 1678, 448, 22158, 13, 308, 299, 2378, 13, 9651, 4608, 411, 27809, 24034, 29889, 12440, 1230, 411, 29871, 29896, 995, 338, 278, 1900, 29889, 13, 1678, 9995, 13, 1678, 7115, 353, 7442, 29889, 3298, 359, 29898, 2435, 29898, 29886, 999, 876, 13, 1678, 12705, 29918, 29886, 999, 353, 12705, 29898, 29886, 999, 29892, 11837, 353, 11837, 29897, 13, 1678, 926, 353, 29871, 29896, 13, 1678, 363, 474, 297, 3464, 29898, 2435, 29898, 24582, 29918, 29886, 999, 29897, 448, 29871, 29896, 1125, 13, 4706, 1399, 353, 7442, 29889, 3062, 29898, 24582, 29918, 29886, 999, 29961, 29875, 29962, 1275, 758, 29888, 9601, 29900, 29962, 13, 4706, 7115, 29961, 513, 29962, 353, 926, 13, 4706, 565, 12705, 29918, 29886, 999, 29961, 29875, 29962, 2804, 12705, 29918, 29886, 999, 29961, 29875, 718, 29871, 29896, 5387, 13, 9651, 926, 4619, 29871, 29896, 13, 1678, 1399, 353, 7442, 29889, 3062, 29898, 24582, 29918, 29886, 999, 29961, 29875, 718, 29871, 29896, 29962, 1275, 758, 29888, 9601, 29900, 29962, 13, 1678, 7115, 29961, 513, 29962, 353, 926, 13, 1678, 736, 7115, 29889, 579, 668, 29898, 524, 29897, 13, 2 ]
cogandmem/writing.py	aemacdermid/cogandmem	0	94858	<gh_stars>0 """ Functions for writing to files. To the best of my knowledge, the functions in this module work. However, the module can be improved. At present, it does not take advantage of Python's csv module. Since data is usually written to csv files, this is a major oversight. This module contains the following functions (see the docstrings for more information): ready_file_for_writing: given a string, returns a file object ready for writing. list_or_tuple: write a list or tuple to a file. write_dict: write a dictionary to a file. study_phase: write a study phase to a file. paired_study: write a study phase with WordPair objects to a file. cued_recall_results: write the results of a cued-recall test to a file. """ import os def ready_file_for_writing(f): """Make sure a file is ready for writing. f should be a string or file.""" try: extant = os.path.isfile(f) except TypeError: # Presumably, this is already a file. return f if extant: # Don't overwrite the extant file. f = open(f, "a") else: f = open(f, "w") return f def list_or_tuple(f, w, sep = "\n", c = True, end_string = "\n"): """ Write a list or tuple to a file. Parameters: f: the file to which to write w; can be a file open for writing, a string pointing to an extant file, or a string pointing to a to-be-created file. w: the to-be-written list or tuple. Keyword Parameters: sep: the string separating each list element; defaults to "\n". c: Boolean indicating whether the file should be closed before the function ends; defaults to True. end_string: a string to write at the end; defaults to "\n". """ f = ready_file_for_writing(f) for i in range(len(w)): e = w[i] if isinstance(e, (list, tuple)): list_or_tuple(f, e, c = False, end_string = "") elif isinstance(e, dict): write_dict(e, f, False, end_string = "") else: f.write(str(e)) if i < len(w)-1: f.write(sep) elif end_string: f.write(end_string) if c: f.close() def write_dict(d, f, c = True, end_string = "\n"): """ Write a dictionary (d) to a file (f). f can be a file open for writing or a string pointing to an extant or to-be-created file. Entries in d are written one by one to f. Each key is written first, followed by the entry. Two blank lines separate keys. Dicts, lists, and tuples are written with each entry/element on a separate line. All other types are converted to strings and written on the same line as the key. If c is True, f is closed before the function ends. The end_string parameter defaults to "\n". It is written after d is written. """ f = ready_file_for_writing(f) for k in d: f.write(str(k)+": ") w = d[k] if isinstance(w, (list, tuple)): f.write("[\n") list_or_tuple(f, w, c = False) f.write("]\n") elif isinstance(w, dict): f.write("{\n") write_dict(w, f, False) f.write("}\n") else: f.write(str(w)+"\n") if end_string: f.write(end_string) if c: f.close() def study_phase(stimuli, f, sep = ",", close_when_finished = True): """ Write a study list to a file. If the Stimulus objects passed to the function contain information in their condition attribute, then a three-column table is written to f (serial position, word, condition). If no condition information is included, then the condition column is omitted. Parameters: stimuli: a list of Stimulus objects representing the study list. f: the file to which to write stimuli; this can be a file opened for writing, a string pointing to an extant file, or a string pointing to a to-be-created file. If f is a file, writing starts wherever the file is currently positioned for writing; if f is a string pointing to an extant file, stimuli are written to the end; and if f is a to-be-created file, stimuli are written in the file at the top. Keyword Parameters: sep: string denoting what separates the values; defaults to ",". close_when_finished: Boolean indicating whether f should be closed before ending the function; defaults to True. """ f = ready_file_for_writing(f) f.write("serial_position{:s}word".format(sep)) if stimuli[0].condition: write_condition = True f.write("{:s}condition\n".format(sep)) else: write_condition = False f.write("\n") for i in range(len(stimuli)): f.write("{:d}{:s}{:s}".format(i+1, sep, stimuli[i].word)) if write_condition: f.write("{:s}{:s}\n".format(sep, str(stimuli[i].condition))) else: f.write("\n") if close_when_finished: f.close() def paired_study(stimuli, f, sep = ",", close_when_finished = True): """ Write a paired study list to a file. If the WordPair objects passed to the function contain information in their condition attribute, then a four-column table is written to f (serial position, cue, target, condition). If no condition information is included, then the condition column is omitted. Parameters: stimuli: a list of WordPair objects representing the study list. f: the file to which to write stimuli; this can be a file opened for writing, a string pointing to an extant file, or a string pointing to a to-be-created file. If f is a file, writing starts wherever the file is currently positioned for writing; if f is a string pointing to an extant file, stimuli are written to the end; and if f is a to-be-created file, stimuli are written in the file at the top. Keyword Parameters: sep: string denoting what separates the values; defaults to ",". close_when_finished: Boolean indicating whether f should be closed before ending the function; defaults to True. """ f = ready_file_for_writing(f) f.write("serial_position"+sep+"cue"+sep+"target") if stimuli[0].condition: write_condition = True f.write(sep+"condition\n") else: write_condition = False f.write("\n") for i in range(len(stimuli)): pair = stimuli[i] f.write(str(i+1)+sep+pair.cue+sep+pair.target) if write_condition: f.write(sep+str(pair.condition)+"\n") else: f.write("\n") if close_when_finished: f.close() def cued_recall_results(word_pairs, f, sep = ",", close_when_finished = True): """ Write the results of a cued-recall test to a file. If the WordPair objects passed to the function contain information in their condition attribute, then a five-column table is written to f (test position, cue, target, response, condition). If no condition information is included, then the condition column is omitted. Parameters: word_pairs: the WordPair objects to write. f: the file to which to write stimuli; this can be a file opened for writing, a string pointing to an extant file, or a string pointing to a to-be-created file. If f is a file, writing starts wherever the file is currently positioned for writing; if f is a string pointing to an extant file, stimuli are written to the end; and if f is a to-be-created file, stimuli are written in the file at the top. Keyword Parameters: sep: string denoting what separates the values; defaults to ",". close_when_finished: Boolean indicating whether f should be closed before ending the function; defaults to True. 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interview-preparation-kit/minimum-time-required.py	gajubadge11/HackerRank-1	340	117103	<filename>interview-preparation-kit/minimum-time-required.py #!/bin/python3 import math import os import random import re import sys from collections import Counter def calculate_days(day, cnt): res = 0 for el in cnt.items(): res += el[1] * (day // el[0]) return res # Complete the minTime function below. def minTime(machines, goal): cnt = Counter(machines) day_limit = 10**13 curgoal = 0 curday = 0 res = 0 prev_big = day_limit prev_low = 0 curday = day_limit // 2 while True: # detect if we are bouncing around in a loop if curday == prev_big or curday == prev_low: #print("curday = {} prev_low = {} prev_big = {}".format(curday, prev_low, prev_big)) prev_low_res = calculate_days(prev_low, cnt) prev_big_res = calculate_days(prev_big, cnt) if prev_low_res >= goal: return prev_low else: return prev_big curgoal = calculate_days(curday, cnt) if curgoal < goal: prev_low = curday #print("micro curday = {} curgoal = {} < {}, diff = {}".format(curday, curgoal, goal, abs(curgoal - goal))) curday = curday + (1 + day_limit - curday)//2 res = curday else: #print("micro curday = {} curgoal = {} > {}, diff = {}".format(curday, curgoal, goal, abs(curgoal - goal))) prev_big = curday day_limit = curday curday = curday // 2 res = curday return res if __name__ == '__main__': fptr = open(os.environ['OUTPUT_PATH'], 'w') nGoal = input().split() n = int(nGoal[0]) goal = int(nGoal[1]) machines = list(map(int, input().rstrip().split())) ans = minTime(machines, goal) fptr.write(str(ans) + '\n') fptr.close()	[ 1, 529, 9507, 29958, 1639, 1493, 29899, 1457, 862, 362, 29899, 7354, 29914, 1195, 12539, 29899, 2230, 29899, 12403, 29889, 2272, 13, 29937, 14708, 2109, 29914, 4691, 29941, 13, 13, 5215, 5844, 13, 5215, 2897, 13, 5215, 4036, 13, 5215, 337, 13, 5215, 10876, 13, 3166, 16250, 1053, 315, 5336, 13, 13, 1753, 8147, 29918, 16700, 29898, 3250, 29892, 274, 593, 1125, 13, 1678, 620, 353, 29871, 29900, 13, 13, 1678, 363, 560, 297, 274, 593, 29889, 7076, 7295, 13, 4706, 620, 4619, 560, 29961, 29896, 29962, 334, 313, 3250, 849, 560, 29961, 29900, 2314, 13, 13, 1678, 736, 620, 13, 13, 13, 29937, 25034, 278, 1375, 2481, 740, 2400, 29889, 13, 1753, 1375, 2481, 29898, 29885, 496, 1475, 29892, 7306, 1125, 13, 1678, 274, 593, 353, 315, 5336, 29898, 29885, 496, 1475, 29897, 13, 1678, 2462, 29918, 13400, 353, 29871, 29896, 29900, 1068, 29896, 29941, 13, 1678, 3151, 28111, 353, 29871, 29900, 13, 1678, 3151, 3250, 353, 29871, 29900, 13, 1678, 620, 353, 29871, 29900, 13, 1678, 12379, 29918, 3752, 353, 2462, 29918, 13400, 13, 1678, 12379, 29918, 677, 353, 29871, 29900, 13, 13, 1678, 3151, 3250, 353, 2462, 29918, 13400, 849, 29871, 29906, 13, 13, 1678, 1550, 5852, 29901, 13, 4706, 396, 6459, 565, 591, 526, 289, 1309, 3277, 2820, 297, 263, 2425, 13, 4706, 565, 3151, 3250, 1275, 12379, 29918, 3752, 470, 3151, 3250, 1275, 12379, 29918, 677, 29901, 13, 9651, 396, 2158, 703, 2764, 3250, 353, 6571, 12379, 29918, 677, 353, 6571, 12379, 29918, 3752, 353, 6571, 1642, 4830, 29898, 2764, 3250, 29892, 12379, 29918, 677, 29892, 12379, 29918, 3752, 876, 13, 9651, 12379, 29918, 677, 29918, 690, 353, 8147, 29918, 16700, 29898, 16304, 29918, 677, 29892, 274, 593, 29897, 13, 9651, 12379, 29918, 3752, 29918, 690, 353, 8147, 29918, 16700, 29898, 16304, 29918, 3752, 29892, 274, 593, 29897, 13, 9651, 565, 12379, 29918, 677, 29918, 690, 6736, 7306, 29901, 13, 18884, 736, 12379, 29918, 677, 13, 9651, 1683, 29901, 13, 18884, 736, 12379, 29918, 3752, 13, 13, 4706, 3151, 28111, 353, 8147, 29918, 16700, 29898, 2764, 3250, 29892, 274, 593, 29897, 13, 13, 4706, 565, 3151, 28111, 529, 7306, 29901, 13, 9651, 12379, 29918, 677, 353, 3151, 3250, 13, 9651, 396, 2158, 703, 29885, 2357, 3151, 3250, 353, 6571, 3151, 28111, 353, 6571, 529, 24335, 2923, 353, 6571, 1642, 4830, 29898, 2764, 3250, 29892, 3151, 28111, 29892, 7306, 29892, 6425, 29898, 2764, 28111, 448, 7306, 4961, 13, 9651, 3151, 3250, 353, 3151, 3250, 718, 313, 29896, 718, 2462, 29918, 13400, 448, 3151, 3250, 29897, 458, 29906, 13, 9651, 620, 353, 3151, 3250, 13, 4706, 1683, 29901, 13, 9651, 396, 2158, 703, 29885, 2357, 3151, 3250, 353, 6571, 3151, 28111, 353, 6571, 1405, 24335, 2923, 353, 6571, 1642, 4830, 29898, 2764, 3250, 29892, 3151, 28111, 29892, 7306, 29892, 6425, 29898, 2764, 28111, 448, 7306, 4961, 13, 9651, 12379, 29918, 3752, 353, 3151, 3250, 13, 9651, 2462, 29918, 13400, 353, 3151, 3250, 13, 9651, 3151, 3250, 353, 3151, 3250, 849, 29871, 29906, 13, 9651, 620, 353, 3151, 3250, 13, 13, 1678, 736, 620, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 285, 7414, 353, 1722, 29898, 359, 29889, 21813, 1839, 12015, 12336, 29918, 10145, 7464, 525, 29893, 1495, 13, 13, 1678, 302, 8120, 284, 353, 1881, 2141, 5451, 580, 13, 13, 1678, 302, 353, 938, 29898, 29876, 8120, 284, 29961, 29900, 2314, 13, 13, 1678, 7306, 353, 938, 29898, 29876, 8120, 284, 29961, 29896, 2314, 13, 13, 1678, 14884, 353, 1051, 29898, 1958, 29898, 524, 29892, 1881, 2141, 29878, 17010, 2141, 5451, 22130, 13, 13, 1678, 6063, 353, 1375, 2481, 29898, 29885, 496, 1475, 29892, 7306, 29897, 13, 13, 1678, 285, 7414, 29889, 3539, 29898, 710, 29898, 550, 29897, 718, 11297, 29876, 1495, 13, 13, 1678, 285, 7414, 29889, 5358, 580, 13, 2 ]
tarea2c/mod/game_over.py	camilo-nb/CC3501-tareas	0	29869	<gh_stars>0 import os from collections import deque import numpy as np from OpenGL.GL import * import lib.basic_shapes as bs import lib.easy_shaders as es import lib.transformations as tr class GameOver: def __init__(self): self.GPU = deque([ es.toGPUShape(bs.createTextureCube(os.path.join('mod', 'tex', 'game_over_1.png')), GL_REPEAT, GL_NEAREST), es.toGPUShape(bs.createTextureCube(os.path.join('mod', 'tex', 'game_over_2.png')), GL_REPEAT, GL_NEAREST), es.toGPUShape(bs.createTextureCube(os.path.join('mod', 'tex', 'game_over_3.png')), GL_REPEAT, GL_NEAREST), es.toGPUShape(bs.createTextureCube(os.path.join('mod', 'tex', 'game_over_4.png')), GL_REPEAT, GL_NEAREST) ]) self.x, self.y, self.z = 0, 310, 100 self.phi = 0 self.tick = 0 self.s = 10 self.transform = tr.matmul([tr.translate(self.x, self.y, self.z), tr.scale(self.s, self.s, 0.0001)]) def draw(self, pipeline, projection, view): glUseProgram(pipeline.shaderProgram) 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echr/steps/prepare_database.py	nlopuszynska/ECHR-OD_process	20	85441	import argparse import json import os from os import listdir from os.path import isfile import shutil from genson import SchemaBuilder from enum import Enum import copy import flatdict import pandas as pd import numpy as np from collections import OrderedDict from functools import reduce # forward compatibility for Python 3 import operator import sys from echr.utils.folders import make_build_folder from echr.utils.cli import TAB from echr.utils.logger import getlogger from rich.markdown import Markdown from rich.console import Console log = getlogger() __console = Console(record=True) DELIMITER = '.' type_priority = OrderedDict([ ('number', float), ('integer', int), ('string', str) ]) class COL_HINT(str, Enum): HOT_ONE = 'hot_one' POSITIONAL = 'positional' def format_structured_json(cases_list): res = [] representents = {} extractedapp = {} scl = {} decision_body = {} for name in cases_list: with open(name, 'r') as f: c = json.load(f) c['representedby'] = [r for r in c['representedby'] if r != 'N/A'] representents[c['appno']] = {'representedby': c['representedby']} extractedapp[c['appno']] = {'appnos': c['extractedappno']} decision_body[c['appno']] = { 'name': [e['name'] for e in c['decision_body']], 'role': {e['name']: e['role'] for e in c['decision_body'] if 'role' in e} } scl[c['appno']] = {'scl': c['scl']} c['respondent'] = c['respondent'].split(';') # c['applicability'] = c['applicability'].strip().split(';') c['appno'] = c['appno'].split(';')[0] c['decisiondate'] = c['decisiondate'].split(' ')[0] c['judgementdate'] = c['judgementdate'].split(' ')[0] c['introductiondate'] = c['introductiondate'].split(' ')[0] c['kpdate'] = c['kpdate'].split(' ')[0] c['separateopinion'] = True if c['separateopinion'] == 'TRUE' else False del c['representedby'] del c['extractedappno'] del c['decision_body'] del c['scl'] del c['documents'] del c['content'] del c['externalsources'] del c['kpthesaurus'] del c['__conclusion'] del c['__articles'] if not len(c['issue']): del c['issue'] else: c['issue'] = sorted(c['issue']) if not len(c['applicability']): del c['applicability'] res.append(c) return res, representents, extractedapp, scl, decision_body def get_by_path(root, items): return reduce(operator.getitem, items, root) def set_by_path(root, items, value): get_by_path(root, items[:-1])[items[-1]] = value def determine_schema(X): builder = SchemaBuilder() for x in X: builder.add_object(x) schema = builder return schema def get_flat_type_mapping(flat_schema): flat_type_mapping = {} for k in flat_schema.keys(): if k.endswith(DELIMITER + 'type'): key = k.replace('properties' + DELIMITER, '').replace(DELIMITER + 'type', '') flat_type_mapping[key] = flat_schema[k] return flat_type_mapping def get_flat_domain_mapping(X, flat_type_mapping): flat_domain_mapping = {} for x in X: flat = flatdict.FlatterDict(x, delimiter='.') for k in flat_type_mapping.keys(): v = flat.get(k) if v is not None: if k not in flat_domain_mapping: flat_domain_mapping[k] = set() type_ = flat_type_mapping[k] try: if type_ == 'array': flat_domain_mapping[k].update(get_by_path(x, k.split('.'))) else: flat_domain_mapping[k].add(get_by_path(x, k.split('.'))) except: if not flat_domain_mapping[k]: del flat_domain_mapping[k] for k in flat_domain_mapping: flat_domain_mapping[k] = list(flat_domain_mapping[k]) return flat_domain_mapping def flatten_dataset(X, flat_type_mapping, schema_hints=None): if schema_hints is None: schema_hints = {} flat_X = [] for x in X: flat = flatdict.FlatterDict(x, delimiter=DELIMITER) c_x = copy.deepcopy(x) for k in flat_type_mapping.keys(): col_type = schema_hints.get(k, {}).get('col_type') if col_type not in [None, COL_HINT.POSITIONAL]: continue v = flat.get(k) if v is not None: sort = schema_hints.get(k, {}).get('sort', False) if sort: type_ = flat_type_mapping[k] if type_ == 'array': item_types = flat_type_mapping.get(k + '.items') a = get_by_path(c_x, k.split('.')) if isinstance(item_types, list): try: a = sorted(a) except: print('# Warning: mix-type array with types: {}'.format(', '.join(item_types))) print('# Warning; no comparison operator provided. Try to assess the proper cast...') for t in type_priority: try: a = list(map(type_priority[t], a)) print('# Casting \'{}\' to {}'.format(k, t)) break except: log.error('Could not cast \'{}\' to {}'.format(k, t)) else: print('# Error: Could not find any way to sort {}'.format(k)) raise Exception('Could not find any way to sort {}'.format(k)) set_by_path(c_x, k.split('.'), sorted(a)) flat = flatdict.FlatterDict(c_x, delimiter=DELIMITER) flat_X.append(flat) return flat_X def hot_one_encoder_on_list(df, column): v = [x if isinstance(x, list) else [] for x in df[column].values] l = [len(x) for x in v] f, u = pd.factorize(np.concatenate(v)) n, m = len(v), u.size i = np.arange(n).repeat(l) dummies = pd.DataFrame( np.bincount(i * m + f, minlength=n * m).reshape(n, m), df.index, map(lambda x: str(column) + '=' + str(x), u) ) return df.drop(column, 1).join(dummies) def normalize(X, schema_hints=None): if schema_hints is None: schema_hints = {} def hot_one_encoder(df, columns): return pd.get_dummies(df, prefix_sep="=", columns=columns) schema = determine_schema(X) flat_schema = flatdict.FlatDict(schema.to_schema(), delimiter=DELIMITER) flat_type_mapping = get_flat_type_mapping(flat_schema) flat_domain_mapping = get_flat_domain_mapping(X, flat_type_mapping) flat_X = flatten_dataset(X, flat_type_mapping, schema_hints) columns_to_encode = [k for k, v in schema_hints.items() if v['col_type'] == COL_HINT.HOT_ONE] df = pd.DataFrame(flat_X) for c in df.columns: f = next((k for k in columns_to_encode if c.startswith(k)), None) if f: df = df.drop(c, 1) encoded = [] for c in columns_to_encode: type_ = flat_type_mapping[c] if type_ == 'array': if c == 'conclusion': articles = set() for x in X: for e in x[c]: if 'article' in e: articles.add(e['article']) articles = sorted(articles) df2 = [] for x in X: e = [] xart = {v['article']: v['type'] for v in x['conclusion'] if 'article' in v} for a in articles: v = 0 if a in xart: if xart[a] == 'violation': v = 1 else: v = -1 e.append(v) df2.append(e) df2 = pd.DataFrame(df2, columns=list(map(lambda x: 'ccl_article={}'.format(x), articles))) encoded.append(df2) else: df2 = pd.DataFrame(X)[[c]] e = hot_one_encoder_on_list(df2, c) encoded.append(e) else: df2 = pd.DataFrame(X)[c] e = hot_one_encoder(df2, [c]) encoded.append(e) df = pd.concat([df] + encoded, axis=1) return df, schema, flat_schema, flat_type_mapping, flat_domain_mapping def run(console, build, title, output_prefix='cases', force=False): __console = console global print print = __console.print print(Markdown("- Step configuration")) print(TAB + "> Prepare release folder structure") paths = ['unstructured', 'structured', 'raw'] for p in paths: make_build_folder(console, os.path.join(build, p), force, strict=False) print(Markdown("- Normalize database")) input_folder = os.path.join(build, 'raw', 'preprocessed_documents') cases_files = [os.path.join(input_folder, f) for f in listdir(input_folder) if isfile(os.path.join(input_folder, f)) and '.json' in f] print(TAB + "> Prepare unstructured cases [green][DONE]") # Unstructured with open(os.path.join(build, 'unstructured', 'cases.json'), 'w') as outfile: outfile.write('[\n') for i, f in enumerate(cases_files): with open(f) as json_file: data = json.load(json_file) json.dump(data, outfile, indent=4) if i != len(cases_files) - 1: outfile.write(',\n') outfile.write('\n]') # Structured print(TAB + "> Generate flat cases [green][DONE]") flat_cases , representatives, extractedapp, scl, decision_body = format_structured_json(cases_files) print(TAB + "> Flat cases size: {}MiB".format(sys.getsizeof(flat_cases) / 1000)) schema_hints = { 'article': { 'col_type': COL_HINT.HOT_ONE }, 'documentcollectionid': { 'col_type': COL_HINT.HOT_ONE }, 'applicability': { 'col_type': COL_HINT.HOT_ONE }, 'paragraphs': { 'col_type': COL_HINT.HOT_ONE }, 'conclusion': { 'col_type': COL_HINT.HOT_ONE, 'sub_element': 'flatten' } } output_path = os.path.join(build, 'structured') with open(os.path.join(output_path, 'flat_cases.json'), 'w') as outfile: json.dump(flat_cases, outfile, indent=4) with open(os.path.join(output_path, 'schema_hint.json'), 'w') as outfile: json.dump(schema_hints, outfile, indent=4) X = flat_cases df, schema, flat_schema, flat_type_mapping, flat_domain_mapping = normalize(X, schema_hints) df.to_json(os.path.join(output_path, '{}.json'.format(output_prefix)), orient='records') df.to_csv(os.path.join(output_path, '{}.csv'.format(output_prefix))) json_files = [ ('schema', schema.to_schema()), ('flat_schema', flat_schema.as_dict()), ('flat_type_mapping', flat_type_mapping), ('flat_domain_mapping', flat_domain_mapping) ] for f in json_files: with open(os.path.join(output_path, '{}_{}.json'.format(output_prefix, f[0])), 'w') as outfile: json.dump(f[1], outfile, indent=4) os.remove(os.path.join(output_path, 'flat_cases.json')) os.remove(os.path.join(output_path, 'cases_flat_schema.json')) os.remove(os.path.join(output_path, 'cases_flat_type_mapping.json')) print(TAB + '> Generate appnos matrice [green][DONE]') matrice_appnos = {} for k, v in extractedapp.items(): matrice_appnos[k] = {e:1 for e in v['appnos']} with open(os.path.join(output_path, 'matrice_appnos.json'), 'w') as outfile: json.dump(matrice_appnos, outfile, indent=4) print(TAB + '> Generate scl matrice [green][DONE]') matrice_scl = {} for k, v in scl.items(): matrice_scl[k] = {e: 1 for e in v['scl']} with open(os.path.join(output_path, 'matrice_scl.json'), 'w') as outfile: json.dump(matrice_scl, outfile, indent=4) print(TAB + '> Generate representatives matrice [green][DONE]') matrice_representedby = {} for k, v in representatives.items(): matrice_representedby[k] = {e: 1 for e in v['representedby']} with open(os.path.join(output_path, 'matrice_representatives.json'), 'w') as outfile: json.dump(matrice_representedby, outfile, indent=4) print(TAB + '> Generate decision body matrice [green][DONE]') matrice_decision_body = {} for k, v in decision_body.items(): matrice_decision_body[k] = {k:v for k,v in v['role'].items()} with open(os.path.join(output_path, 'matrice_decision_body.json'), 'w') as outfile: json.dump(matrice_decision_body, outfile, indent=4) print(TAB + '> Create archives [green][DONE]') # Raw shutil.make_archive(os.path.join(build, 'raw', 'judgments'), 'zip', os.path.join(build, 'raw', 'judgments')) # All from zipfile import ZipFile with ZipFile(os.path.join(build, 'all.zip'), 'w') as zipObj: # Iterate over all the files in directory folders = ['unstructured', 'raw', 'structured'] for f in folders: for folderName, _, filenames in os.walk(os.path.join(build, f)): for filename in filenames: if not filename.endswith('.zip'): filePath = os.path.join(folderName, filename) zipObj.write(filePath) def main(args): console = Console(record=True) run(console, build=args.build, title=args.title, force=args.f) def parse_args(parser): args = parser.parse_args() # Check path return args if __name__ == "__main__": parser = argparse.ArgumentParser(description='Normalize any databse of 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vathos/trainer/tpu_trainer.py	satyajitghana/ProjektDepth	2	44250	from .base_trainer import BaseTrainer import torch import os class TPUTrainer(BaseTrainer): r"""TPUTrainer: Trains the vathos model on TPU """ def __init__(self, args, kwargs): super(TPUTrainer, self).__init__(args, **kwargs) import torch_xla import torch_xla.core.xla_model as xm import torch_xla.distributed.parallel_loader as pl import torch_xla.distributed.xla_multiprocessing as xmp os.environ['XLA_USE_BF16'] = 1 @staticmethod def _xla_train(index): device = xm.xla_device() para_loader = pl.ParallelLoader(train_loader, [device]) model = MNIST().train().to(device) loss_fn = nn.NLLLoss() optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum) for data, target in para_loader.per_device_loader(device): optimizer.zero_grad() output = model(data) loss = loss_fn(output, target) loss.backward() xm.optimizer_step(optimizer) def train_epoch(self, epoch): return 0 def test_epoch(self, epoch): device = xm.xla_device() xm.mark_step() return 0	[ 1, 515, 869, 3188, 29918, 3018, 4983, 1053, 7399, 5323, 4983, 13, 13, 5215, 4842, 305, 13, 5215, 2897, 13, 13, 13, 1990, 323, 12336, 336, 4983, 29898, 5160, 5323, 4983, 1125, 13, 1678, 364, 15945, 29908, 3557, 2692, 336, 4983, 29901, 3201, 1144, 278, 325, 493, 359, 1904, 373, 323, 7056, 13, 1678, 9995, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 13, 4706, 2428, 29898, 3557, 2692, 336, 4983, 29892, 1583, 467, 1649, 2344, 1649, 10456, 5085, 29892, 3579, 19290, 29897, 13, 4706, 1053, 4842, 305, 29918, 29916, 433, 13, 4706, 1053, 4842, 305, 29918, 29916, 433, 29889, 3221, 29889, 29916, 433, 29918, 4299, 408, 921, 29885, 13, 4706, 1053, 4842, 305, 29918, 29916, 433, 29889, 5721, 7541, 29889, 23482, 29918, 12657, 408, 715, 13, 4706, 1053, 4842, 305, 29918, 29916, 433, 29889, 5721, 7541, 29889, 29916, 433, 29918, 18056, 307, 985, 292, 408, 921, 1526, 13, 13, 4706, 2897, 29889, 21813, 1839, 29990, 4375, 29918, 17171, 29918, 28062, 29896, 29953, 2033, 353, 29871, 29896, 13, 13, 1678, 732, 7959, 5696, 13, 1678, 822, 903, 29916, 433, 29918, 14968, 29898, 2248, 1125, 13, 4706, 4742, 353, 921, 29885, 29889, 29916, 433, 29918, 10141, 580, 13, 4706, 1702, 29918, 12657, 353, 715, 29889, 2177, 6553, 10036, 29898, 14968, 29918, 12657, 29892, 518, 10141, 2314, 13, 13, 4706, 1904, 353, 341, 29940, 9047, 2141, 14968, 2141, 517, 29898, 10141, 29897, 13, 4706, 6410, 29918, 9144, 353, 302, 29876, 29889, 29940, 2208, 29931, 2209, 580, 13, 4706, 5994, 3950, 353, 5994, 29889, 26016, 29928, 29898, 4299, 29889, 16744, 3285, 301, 29878, 29922, 29212, 29892, 19399, 29922, 29885, 2932, 398, 29897, 13, 13, 4706, 363, 848, 29892, 3646, 297, 1702, 29918, 12657, 29889, 546, 29918, 10141, 29918, 12657, 29898, 10141, 1125, 13, 9651, 5994, 3950, 29889, 9171, 29918, 5105, 580, 13, 9651, 1962, 353, 1904, 29898, 1272, 29897, 13, 9651, 6410, 353, 6410, 29918, 9144, 29898, 4905, 29892, 3646, 29897, 13, 9651, 6410, 29889, 1627, 1328, 580, 13, 9651, 921, 29885, 29889, 20640, 3950, 29918, 10568, 29898, 20640, 3950, 29897, 13, 13, 1678, 822, 7945, 29918, 1022, 2878, 29898, 1311, 29892, 21502, 305, 1125, 13, 13, 4706, 736, 29871, 29900, 13, 13, 1678, 822, 1243, 29918, 1022, 2878, 29898, 1311, 29892, 21502, 305, 1125, 13, 13, 4706, 4742, 353, 921, 29885, 29889, 29916, 433, 29918, 10141, 580, 13, 13, 4706, 921, 29885, 29889, 3502, 29918, 10568, 580, 13, 13, 4706, 736, 29871, 29900, 13, 2 ]
examples/linear_wake/analysis.py	SaVicente/hipace	19	49538	<gh_stars>10-100 #! /usr/bin/env python3 # This Python analysis script is part of the code Hipace # # It compares the transverse field By with the theoretical value, plots both # the simulation result and the theory on the same plot, and asserts that the # difference is small. # # To use it, run the simulation and execute this script with # > ../../build/bin/hipace inputs_SI # > python analysis.py # Note: the simulation may take some time, as the box size must be high to have # decent agreement import matplotlib.pyplot as plt import scipy.constants as scc from scipy.stats import norm import matplotlib import sys import numpy as np import math import argparse from openpmd_viewer import OpenPMDTimeSeries parser = argparse.ArgumentParser(description='Script to analyze the correctness of the beam in vacuum') parser.add_argument('--normalized-units', dest='norm_units', action='store_true', default=False, help='Run the analysis in normalized units') parser.add_argument('--do-plot', dest='do_plot', action='store_true', default=False, help='Plot figures and save them to file') parser.add_argument('--gaussian-beam', dest='gaussian_beam', action='store_true', default=False, help='Run the analysis on the Gaussian beam') parser.add_argument('--output-dir', dest='output_dir', default='diags/hdf5', help='Path to the directory containing output files') args = parser.parse_args() ts = OpenPMDTimeSeries(args.output_dir) if args.norm_units: kp = 1. ne = 1. q_e = 1. else: kp = 1./10.e-6 ne = scc.c2 / scc.e2 * scc.m_e * scc.epsilon_0 * kp*2 q_e = scc.e rho_along_z, rho_meta = ts.get_field(field='rho', iteration=ts.iterations[-1], slice_across=['x','y'], slice_relative_position=[0,0]) zeta_array = rho_meta.z dzeta = rho_meta.dz nz = len(rho_meta.z) # generating the array with the beam density nb_array = np.zeros(nz) beam_starting_position = 1 / kp distance_to_start_pos = rho_meta.zmax - beam_starting_position index_beam_head = np.int(distance_to_start_pos / dzeta) beam_length = 2 / kp beam_length_i = np.int(beam_length / dzeta) if (args.gaussian_beam): sigma_z = 1.41 / kp peak_density = 0.01ne nb_array = peak_densitynp.sqrt(2np.pi)norm.pdf(np.linspace(-nz/2,nz/2,nz)dzeta/sigma_z) else: nb_array[nz-index_beam_head-beam_length_i:nz-index_beam_head] = 0.01 * ne # calculating the second derivative of the beam density array nb_dzdz = np.zeros(nz) nb_dzdz[1:nz-1] = (nb_array[0:nz-2] - 2nb_array[1:nz-1] + nb_array[2:nz])/dzeta2 # calculating the theoretical plasma density (see Timon Mehrling's thesis page 41) n_th = np.zeros(nz) tmp = np.zeros([nz,nz],dtype=float) for i in np.arange(nz-1,-1,-1): for j in range(nz-i): tmp[i,j]= i-(nz-1-j) tmp = (dzeta/kpnp.sin(kpdzetatmp) * np.full([nz,nz],1) * nb_dzdz[np.linspace(nz-1,0,nz,dtype=int)]) n_th = np.sum(tmp,axis = 1) + nb_array rho_th = n_th * q_e if args.do_plot: fig, ax = plt.subplots() ax.plot(zeta_array, rho_along_z) ax.plot(zeta_array, rho_th, linestyle='--') 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flatlib/chart.py	D-Vaillant/flatlib	196	72486	<reponame>D-Vaillant/flatlib """ This file is part of flatlib - (C) FlatAngle Author: <NAME> (<EMAIL>) This module implements a class to represent an astrology Chart. It provides methods to handle the chart, as well as three relevant properties: - objects: a list with the chart's objects - houses: a list with the chart's houses - angles: a list with the chart's angles Since houses 1 and 10 may not match the Asc and MC in some house systems, the Chart class includes the list of angles. The angles should be used when you want to deal with angle's longitudes. There are also methods to access fixed stars. """ from . import angle from . import const from . import utils from .ephem import ephem from .datetime import Datetime # ------------------ # # Chart Class # # ------------------ # class Chart: """ This class represents an astrology chart. """ def __init__(self, date, pos, **kwargs): """ Creates an astrology chart for a given date and location. Optional arguments are: - hsys: house system - IDs: list of objects to include """ # Handle optional arguments hsys = kwargs.get('hsys', const.HOUSES_DEFAULT) IDs = kwargs.get('IDs', const.LIST_OBJECTS_TRADITIONAL) self.date = date self.pos = pos self.hsys = hsys self.objects = ephem.getObjectList(IDs, date, pos) self.houses, self.angles = ephem.getHouses(date, pos, hsys) def copy(self): """ Returns a deep copy of this chart. """ chart = Chart.__new__(Chart) chart.date = self.date chart.pos = self.pos chart.hsys = self.hsys chart.objects = self.objects.copy() chart.houses = self.houses.copy() chart.angles = self.angles.copy() return chart # === Properties === # def getObject(self, ID): """ Returns an object from the chart. """ return self.objects.get(ID) def getHouse(self, ID): """ Returns an house from the chart. """ return self.houses.get(ID) def getAngle(self, ID): """ Returns an angle from the chart. """ return self.angles.get(ID) def get(self, ID): """ Returns an object, house or angle from the chart. """ if ID.startswith('House'): return self.getHouse(ID) elif ID in const.LIST_ANGLES: return self.getAngle(ID) else: return self.getObject(ID) # === Fixed stars === # # The computation of fixed stars is inefficient, # so the access must be made directly to the # ephemeris only when needed. def getFixedStar(self, ID): """ Returns a fixed star from the ephemeris. """ return ephem.getFixedStar(ID, self.date) def getFixedStars(self): """ Returns a list with all fixed stars. """ IDs = const.LIST_FIXED_STARS return ephem.getFixedStarList(IDs, self.date) # === Houses and angles === # def isHouse1Asc(self): """ Returns true if House1 is the same as the Asc. """ house1 = self.getHouse(const.HOUSE1) asc = self.getAngle(const.ASC) dist = angle.closestdistance(house1.lon, asc.lon) return abs(dist) < 0.0003 # 1 arc-second def isHouse10MC(self): """ Returns true if House10 is the same as the MC. """ house10 = self.getHouse(const.HOUSE10) mc = self.getAngle(const.MC) dist = angle.closestdistance(house10.lon, mc.lon) return abs(dist) < 0.0003 # 1 arc-second # === Other properties === # def isDiurnal(self): """ Returns true if this chart is diurnal. """ sun = self.getObject(const.SUN) mc = self.getAngle(const.MC) # Get ecliptical positions and check if the # sun is above the horizon. lat = self.pos.lat sunRA, sunDecl = utils.eqCoords(sun.lon, sun.lat) mcRA, mcDecl = utils.eqCoords(mc.lon, 0) return utils.isAboveHorizon(sunRA, sunDecl, mcRA, lat) def getMoonPhase(self): """ Returns the phase of the moon. """ sun = self.getObject(const.SUN) moon = self.getObject(const.MOON) dist = angle.distance(sun.lon, moon.lon) if dist < 90: return const.MOON_FIRST_QUARTER elif dist < 180: return const.MOON_SECOND_QUARTER elif dist < 270: return const.MOON_THIRD_QUARTER else: return const.MOON_LAST_QUARTER # === Solar returns === # def solarReturn(self, year): """ Returns this chart's solar return for a given year. 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app/fedgraphnn/recsys_subgraph_link_pred/torch_subgraph_link_prediction.py	ray-ruisun/FedML	0	75942	<reponame>ray-ruisun/FedML import fedml from data.data_loader import * from model.gcn_link import GCNLinkPred from model.gat_link import GATLinkPred from model.sage_link import SAGELinkPred from trainer.fed_subgraph_lp_trainer import FedSubgraphLPTrainer from fedml.simulation import SimulatorMPI def load_data(args): if args.dataset not in ["ciao", "epinions"]: raise Exception("no such dataset!") args.pred_task = "link_prediction" args.metric = "MAE" if args.model == "gcn": args.normalize_features = True args.normalize_adjacency = True ( train_data_num, val_data_num, test_data_num, train_data_global, val_data_global, test_data_global, data_local_num_dict, train_data_local_dict, val_data_local_dict, test_data_local_dict, feature_dim, ) = load_partition_data(args, args.data_cache_dir, args.client_num_in_total) dataset = [ train_data_num, test_data_num, train_data_global, test_data_global, data_local_num_dict, train_data_local_dict, test_data_local_dict, feature_dim, ] return dataset def create_model(args, model_name, feature_dim): logging.info("create_model. model_name = %s" % (model_name)) if model_name == "gcn": model = GCNLinkPred(feature_dim, args.hidden_size, args.node_embedding_dim) elif model_name == "gat": model = GATLinkPred( feature_dim, args.hidden_size, args.node_embedding_dim, args.num_heads ) elif model_name == "sage": model = SAGELinkPred(feature_dim, args.hidden_size, args.node_embedding_dim) else: raise Exception("such model does not exist !") trainer = FedSubgraphLPTrainer(model) logging.info("Model and Trainer - done") return model, if __name__ == "__main__": # init FedML framework args = fedml.init() # init device device = fedml.device.get_device(args) # load data dataset, output_dim = load_data(args) # load model model, trainer = create_model(args, args.model_name) # start training simulator = SimulatorMPI(args, device, dataset, model, trainer) simulator.run()	[ 1, 529, 276, 1112, 420, 29958, 764, 29899, 582, 275, 348, 29914, 29943, 287, 1988, 13, 5215, 21242, 828, 13, 3166, 848, 29889, 1272, 29918, 12657, 1053, 334, 13, 3166, 1904, 29889, 29887, 18038, 29918, 2324, 1053, 402, 13778, 6595, 23084, 13, 3166, 1904, 29889, 28818, 29918, 2324, 1053, 402, 1299, 6595, 23084, 13, 3166, 1904, 29889, 29879, 482, 29918, 2324, 1053, 317, 10461, 6595, 23084, 13, 13, 3166, 1020, 4983, 29889, 29888, 287, 29918, 1491, 4262, 29918, 22833, 29918, 3018, 4983, 1053, 17972, 4035, 4262, 13208, 5323, 4983, 13, 3166, 21242, 828, 29889, 3601, 2785, 1053, 3439, 9183, 29924, 2227, 13, 13, 13, 1753, 2254, 29918, 1272, 29898, 5085, 1125, 13, 1678, 565, 6389, 29889, 24713, 451, 297, 6796, 1512, 29877, 613, 376, 1022, 262, 1080, 3108, 29901, 13, 4706, 12020, 8960, 703, 1217, 1316, 8783, 29991, 1159, 13, 13, 1678, 6389, 29889, 11965, 29918, 7662, 353, 376, 2324, 29918, 11965, 2463, 29908, 13, 13, 1678, 6389, 29889, 16414, 353, 376, 1529, 29923, 29908, 13, 13, 1678, 565, 6389, 29889, 4299, 1275, 376, 29887, 18038, 1115, 13, 4706, 6389, 29889, 8945, 675, 29918, 22100, 353, 5852, 13, 4706, 6389, 29889, 8945, 675, 29918, 26859, 562, 3819, 353, 5852, 13, 13, 1678, 313, 13, 4706, 7945, 29918, 1272, 29918, 1949, 29892, 13, 4706, 659, 29918, 1272, 29918, 1949, 29892, 13, 4706, 1243, 29918, 1272, 29918, 1949, 29892, 13, 4706, 7945, 29918, 1272, 29918, 10945, 29892, 13, 4706, 659, 29918, 1272, 29918, 10945, 29892, 13, 4706, 1243, 29918, 1272, 29918, 10945, 29892, 13, 4706, 848, 29918, 2997, 29918, 1949, 29918, 8977, 29892, 13, 4706, 7945, 29918, 1272, 29918, 2997, 29918, 8977, 29892, 13, 4706, 659, 29918, 1272, 29918, 2997, 29918, 8977, 29892, 13, 4706, 1243, 29918, 1272, 29918, 2997, 29918, 8977, 29892, 13, 4706, 4682, 29918, 6229, 29892, 13, 1678, 1723, 353, 2254, 29918, 16707, 29918, 1272, 29898, 5085, 29892, 6389, 29889, 1272, 29918, 8173, 29918, 3972, 29892, 6389, 29889, 4645, 29918, 1949, 29918, 262, 29918, 7827, 29897, 13, 13, 1678, 8783, 353, 518, 13, 4706, 7945, 29918, 1272, 29918, 1949, 29892, 13, 4706, 1243, 29918, 1272, 29918, 1949, 29892, 13, 4706, 7945, 29918, 1272, 29918, 10945, 29892, 13, 4706, 1243, 29918, 1272, 29918, 10945, 29892, 13, 4706, 848, 29918, 2997, 29918, 1949, 29918, 8977, 29892, 13, 4706, 7945, 29918, 1272, 29918, 2997, 29918, 8977, 29892, 13, 4706, 1243, 29918, 1272, 29918, 2997, 29918, 8977, 29892, 13, 4706, 4682, 29918, 6229, 29892, 13, 1678, 4514, 13, 13, 1678, 736, 8783, 29871, 13, 13, 13, 1753, 1653, 29918, 4299, 29898, 5085, 29892, 1904, 29918, 978, 29892, 4682, 29918, 6229, 1125, 13, 1678, 12183, 29889, 3888, 703, 3258, 29918, 4299, 29889, 1904, 29918, 978, 353, 1273, 29879, 29908, 1273, 313, 4299, 29918, 978, 876, 13, 1678, 565, 1904, 29918, 978, 1275, 376, 29887, 18038, 1115, 13, 4706, 1904, 353, 402, 13778, 6595, 23084, 29898, 14394, 29918, 6229, 29892, 6389, 29889, 10892, 29918, 2311, 29892, 6389, 29889, 3177, 29918, 17987, 8497, 29918, 6229, 29897, 13, 1678, 25342, 1904, 29918, 978, 1275, 376, 28818, 1115, 13, 4706, 1904, 353, 402, 1299, 6595, 23084, 29898, 13, 9651, 4682, 29918, 6229, 29892, 6389, 29889, 10892, 29918, 2311, 29892, 6389, 29889, 3177, 29918, 17987, 8497, 29918, 6229, 29892, 6389, 29889, 1949, 29918, 2813, 29879, 13, 4706, 1723, 13, 1678, 25342, 1904, 29918, 978, 1275, 376, 29879, 482, 1115, 13, 4706, 1904, 353, 317, 10461, 6595, 23084, 29898, 14394, 29918, 6229, 29892, 6389, 29889, 10892, 29918, 2311, 29892, 6389, 29889, 3177, 29918, 17987, 8497, 29918, 6229, 29897, 13, 1678, 1683, 29901, 13, 4706, 12020, 8960, 703, 14565, 1904, 947, 451, 1863, 1738, 1159, 13, 1678, 1020, 4983, 353, 17972, 4035, 4262, 13208, 5323, 4983, 29898, 4299, 29897, 13, 1678, 12183, 29889, 3888, 703, 3195, 322, 3201, 4983, 29871, 448, 2309, 1159, 13, 1678, 736, 1904, 29892, 29871, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 396, 2069, 17972, 1988, 6890, 13, 1678, 6389, 353, 21242, 828, 29889, 2344, 580, 13, 13, 1678, 396, 2069, 4742, 13, 1678, 4742, 353, 21242, 828, 29889, 10141, 29889, 657, 29918, 10141, 29898, 5085, 29897, 13, 13, 1678, 396, 2254, 848, 13, 1678, 8783, 29892, 1962, 29918, 6229, 353, 2254, 29918, 1272, 29898, 5085, 29897, 13, 13, 1678, 396, 2254, 1904, 13, 1678, 1904, 29892, 1020, 4983, 353, 1653, 29918, 4299, 29898, 5085, 29892, 6389, 29889, 4299, 29918, 978, 29897, 13, 13, 1678, 396, 1369, 6694, 13, 1678, 1027, 9183, 353, 3439, 9183, 29924, 2227, 29898, 5085, 29892, 4742, 29892, 8783, 29892, 1904, 29892, 1020, 4983, 29897, 13, 1678, 1027, 9183, 29889, 3389, 580, 2 ]
chapter_06/03_line_numbers.py	SergeHall/Tony-Gaddis-Python-4th	2	44752	<reponame>SergeHall/Tony-Gaddis-Python-4th # 3. Номера строк. Напишите программу, которая запрашивает у пользователя # имя файла. Программа должна вывести на экран содержимое файла, при этом # каждая строка должна предваряться ее номером и двоеточием. Нумерация строк # должна начинаться с 1. number_list.txt def main(): try: name_file = input('Name file + .file extension: ') open_file = open(str(name_file), 'r') count = 1 for content in open_file: content = content.rstrip('\n') print(count, ': ', content, sep='') count += 1 open_file.close() except ValueError: print('ValueError') except FileNotFoundError: print('FileNotFoundError') main()	[ 1, 529, 276, 1112, 420, 29958, 1748, 479, 29950, 497, 29914, 29911, 2592, 29899, 29954, 1202, 275, 29899, 11980, 29899, 29946, 386, 13, 29937, 259, 29941, 29889, 16571, 1488, 494, 2091, 16635, 29889, 2372, 1668, 1911, 730, 20796, 1805, 29892, 21105, 1077, 8821, 1911, 20120, 863, 18636, 9718, 11146, 13, 29937, 29871, 606, 5010, 6725, 29977, 684, 29889, 6807, 2194, 29959, 1155, 19611, 477, 2771, 1521, 1415, 665, 18796, 3048, 1778, 6620, 1969, 1630, 29919, 6725, 29977, 684, 29892, 1695, 17045, 13, 29937, 29871, 1787, 12393, 29970, 2091, 576, 642, 19611, 477, 4570, 846, 2359, 4199, 1694, 29919, 4228, 1488, 4416, 606, 10332, 25930, 1499, 3098, 29889, 1392, 29960, 1488, 494, 5274, 2091, 16635, 13, 29937, 29871, 19611, 477, 665, 14005, 4199, 531, 29871, 29896, 29889, 1353, 29918, 1761, 29889, 3945, 13, 13, 1753, 1667, 7295, 13, 1678, 1018, 29901, 13, 4706, 1024, 29918, 1445, 353, 1881, 877, 1170, 934, 718, 869, 1445, 6081, 29901, 25710, 13, 4706, 1722, 29918, 1445, 353, 1722, 29898, 710, 29898, 978, 29918, 1445, 511, 525, 29878, 1495, 13, 4706, 2302, 353, 29871, 29896, 13, 4706, 363, 2793, 297, 1722, 29918, 1445, 29901, 13, 9651, 2793, 353, 2793, 29889, 29878, 17010, 28909, 29876, 1495, 13, 9651, 1596, 29898, 2798, 29892, 525, 29901, 13420, 2793, 29892, 16345, 2433, 1495, 13, 9651, 2302, 4619, 29871, 29896, 13, 4706, 1722, 29918, 1445, 29889, 5358, 580, 13, 13, 1678, 5174, 7865, 2392, 29901, 13, 4706, 1596, 877, 1917, 2392, 1495, 13, 1678, 5174, 3497, 17413, 2392, 29901, 13, 4706, 1596, 877, 2283, 17413, 2392, 1495, 13, 13, 13, 3396, 580, 13, 2 ]
kde/cudakde.py	atfienberg/kde	0	161768	# pylint: disable=invalid-name from __future__ import absolute_import, division __license__ = """MIT License Copyright (c) 2014-2019 <NAME> and <NAME> 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 warnings import numpy as n from .classes import KDE class gaussian_kde(KDE): def __init__(self, data, weights=None, kde_values=None, use_cuda=True, adaptive=False, weight_adaptive_bw=False, alpha=0.3, bw_method='silverman'): if kde_values != None: raise NotImplementedError("`kde_values` is not supported for" " cudakde.") KDE.__init__(self, data, use_cuda, weights=weights, alpha=alpha, method=bw_method) self.weighted = False if weights is None or len(weights) == 0 else True if adaptive: if not self.weighted and weight_adaptive_bw: warnings.warn("Since `weights` aren't given" " `weight_adaptive_bw` will have no effect!") self.calcLambdas(weights=weight_adaptive_bw, weightedCov=weight_adaptive_bw) else: self.lambdas = n.ones(self.n) def __call__(self, points): points = n.atleast_2d(points) self.kde(points, weights=self.weighted, weightedCov=self.weighted) return n.array(self.values) class bootstrap_kde(object): def __init__(self, data, niter=10, weights=None, kwargs): assert int(niter) == float(niter) niter = int(niter) self.kernels = [] self.bootstrap_indices = [] self.data = n.atleast_2d(data) self.d, self.n = self.data.shape self.weighted = False if weights is None or len(weights) == 0 else True for _ in xrange(niter): indices = n.array(self.get_bootstrap_indices()) self.bootstrap_indices.append(indices) if self.weighted: kernel = gaussian_kde(data[..., indices], weights=weights[indices], kwargs) else: kernel = gaussian_kde(data[..., indices], kwargs) self.kernels.append(kernel) def __call__(self, points): return self.evaluate(points) def evaluate(self, points): points = n.atleast_2d(points) _, m = points.shape means, sqmeans = n.zeros(m), n.zeros(m) for kernel in self.kernels: values = kernel(points) means += values sqmeans += values2 means /= len(self.kernels) sqmeans /= len(self.kernels) errors = n.sqrt(sqmeans - means**2) return means, errors def get_bootstrap_indices(self): bootstrap_indices = n.random.choice(self.n, size=self.n, replace=True) return bootstrap_indices	[ 1, 396, 282, 2904, 524, 29901, 11262, 29922, 20965, 29899, 978, 13, 13, 13, 3166, 4770, 29888, 9130, 1649, 1053, 8380, 29918, 5215, 29892, 8542, 13, 13, 1649, 506, 1947, 1649, 353, 9995, 26349, 19245, 13, 13, 11882, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29946, 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alipay/aop/api/domain/SsdataDataserviceRiskAudioVerifyModel.py	articuly/alipay-sdk-python-all	0	196028	<gh_stars>0 #!/usr/bin/env python # -- coding: utf-8 -- import simplejson as json from alipay.aop.api.constant.ParamConstants import * class SsdataDataserviceRiskAudioVerifyModel(object): def __init__(self): self._audio_content = None self._audio_type = None self._checker = None self._risk_type = None self._sample_rate = None @property def audio_content(self): return self._audio_content @audio_content.setter def audio_content(self, value): self._audio_content = value @property def audio_type(self): return self._audio_type @audio_type.setter def audio_type(self, value): self._audio_type = value @property def checker(self): return self._checker @checker.setter def checker(self, value): self._checker = value @property def risk_type(self): return self._risk_type @risk_type.setter def risk_type(self, value): if isinstance(value, list): self._risk_type = list() for i in value: self._risk_type.append(i) @property def sample_rate(self): return self._sample_rate @sample_rate.setter def sample_rate(self, value): self._sample_rate = value def to_alipay_dict(self): params = dict() if self.audio_content: if hasattr(self.audio_content, 'to_alipay_dict'): params['audio_content'] = self.audio_content.to_alipay_dict() else: params['audio_content'] = self.audio_content if self.audio_type: if hasattr(self.audio_type, 'to_alipay_dict'): params['audio_type'] = self.audio_type.to_alipay_dict() else: params['audio_type'] = self.audio_type if self.checker: if hasattr(self.checker, 'to_alipay_dict'): params['checker'] = self.checker.to_alipay_dict() else: params['checker'] = self.checker if self.risk_type: if isinstance(self.risk_type, list): for i in range(0, len(self.risk_type)): element = self.risk_type[i] if hasattr(element, 'to_alipay_dict'): self.risk_type[i] = element.to_alipay_dict() if hasattr(self.risk_type, 'to_alipay_dict'): params['risk_type'] = 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bp_data.py	basf/bscience-backend	1	192186	#from pprint import pprint import json from unidecode import unidecode from flask import Blueprint, current_app, request, Response from i_structures.struct_utils import detect_format, poscar_to_ase, optimade_to_ase, refine, get_formula from i_structures.cif_utils import cif_to_ase from i_data import Data_type from utils import get_data_storage, fmt_msg, key_auth, is_plain_text, html_formula, is_valid_uuid, ase_serialize bp_data = Blueprint('data', __name__, url_prefix='/data') @bp_data.route("/create", methods=['POST']) @key_auth def create(): """ Data item recognition and saving logics Expects content: string Returns JSON->error: string or confirmation object {object->uuid, object->type, object->name} """ content = request.values.get('content') if not content: return fmt_msg('Empty request') if not 0 < len(content) < 200000: return fmt_msg('Request size is invalid') if not is_plain_text(content): #return fmt_msg('Request contains unsupported (non-latin) characters') content = unidecode(content) fmt = detect_format(content) if fmt == 'cif': ase_obj, error = cif_to_ase(content) elif fmt == 'poscar': ase_obj, error = poscar_to_ase(content) elif fmt == 'optimade': ase_obj, error = optimade_to_ase(content) else: return fmt_msg('Provided data format unsuitable or not recognized') if error: return fmt_msg(error) if 'disordered' in ase_obj.info: return fmt_msg('Structural disorder is currently not supported') ase_obj, error = refine(ase_obj, conventional_cell=True) if error: return fmt_msg(error) formula = get_formula(ase_obj) content = ase_serialize(ase_obj) db = get_data_storage() uuid = db.put_item(formula, content, Data_type.structure) db.close() return Response(json.dumps(dict( uuid=uuid, type=Data_type.structure, name=html_formula(formula), ), indent=4), content_type='application/json', status=200) @bp_data.route("/listing", methods=['POST']) @key_auth def listing(): """ Expects uuid: uuid or uuid[] Returns JSON->error: string or listing [ {object->uuid, object->type, object->name}, ... ] """ uuid = request.values.get('uuid') #current_app.logger.warning(uuid) if not uuid: return fmt_msg('Empty request') db = get_data_storage() if ':' in uuid: uuids = set( uuid.split(':') ) for uuid in uuids: if not is_valid_uuid(uuid): return fmt_msg('Invalid request') items = db.get_items(list(uuids)) #found_uuids = set( [item['uuid'] for item in items] ) #if found_uuids != uuids: # return fmt_msg('No such content', 204) else: if not is_valid_uuid(uuid): return fmt_msg('Invalid request') item = db.get_item(uuid) items = [item] if item else [] if not items: return fmt_msg('No such content', 204) db.close() items = [ dict( uuid=item['uuid'], name=html_formula(item['name']), type=item['type']) for item in items ] return Response(json.dumps(items, indent=4), content_type='application/json', status=200) @bp_data.route("/delete", methods=['POST']) @key_auth def delete(): """ Expects uuid: uuid Returns JSON->error: string or JSON empty dict """ uuid = request.values.get('uuid') if not uuid or not is_valid_uuid(uuid): return fmt_msg('Empty or invalid request') db = get_data_storage() result = db.drop_item(uuid) db.close() if result: return Response('{}', content_type='application/json', status=200) return fmt_msg('No such content', 204)	[ 1, 29871, 13, 29937, 3166, 282, 2158, 1053, 282, 2158, 13, 5215, 4390, 13, 3166, 443, 680, 401, 1053, 443, 680, 401, 13, 13, 3166, 29784, 1053, 10924, 2158, 29892, 1857, 29918, 932, 29892, 2009, 29892, 13291, 13, 13, 3166, 474, 29918, 4984, 1973, 29889, 4984, 29918, 13239, 1053, 6459, 29918, 4830, 29892, 926, 4287, 29918, 517, 29918, 559, 29892, 5994, 1943, 29918, 517, 29918, 559, 29892, 2143, 457, 29892, 679, 29918, 689, 2497, 13, 3166, 474, 29918, 4984, 1973, 29889, 29883, 361, 29918, 13239, 1053, 274, 361, 29918, 517, 29918, 559, 13, 3166, 474, 29918, 1272, 1053, 3630, 29918, 1853, 13, 13, 3166, 3667, 29879, 1053, 679, 29918, 1272, 29918, 12925, 29892, 19200, 29918, 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telluride_decoding/utils.py	RULCSoft/telluride_decoding	8	1610821	# Copyright 2020 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Utility routines when decoding brain signals. More to come.. the CCA functions need to get the Pearson correlation too. """ import tensorflow.compat.v2 as tf # TODO Check to see if we can use (might need to get into core) # contrib/streaming_pearson_correlation def pearson_correlation_graph(x, y): """Use TF to compute the Pearson correlation between two sets of data. This works between two (multi-dimensional) feature arrays. Like np.corrcoef, x and y are 1-D or 2-D arrays containing multiple variables and observations. Each column of x represents a variable, and each row a single observation of all those variables. From: https://stackoverflow.com/questions/43834830/tensorflow-equivalent-of-np-corrcoef-on-a-specific-axis Args: x: First multidimensional array, n_observations x n_vars y: Second multidimensional array, n_observations x n_vars Returns: n_vars dimensional vector with the correlation of each pair of columns. """ def t(x): return tf.transpose(x) with tf.compat.v1.variable_scope('pearson'): if len(x.shape) == 1: x = tf.reshape(x, (-1, 1), name='pearson_x_reshape') if len(y.shape) == 1: y = tf.reshape(y, (-1, 1), name='pearson_y_reshape') xy_t = tf.concat([tf.cast(x, tf.float32), tf.cast(y, tf.float32)], axis=1) mean_t = tf.reduce_mean(xy_t, axis=0, keepdims=True) dsize = tf.cast(tf.shape(x)[0], tf.float32) cov_t = tf.matmul(t(xy_t - mean_t), (xy_t - mean_t) / (dsize - 1)) cov2_t = tf.linalg.tensor_diag(1/tf.sqrt(tf.linalg.tensor_diag_part(cov_t))) cor = tf.matmul(cov2_t, tf.matmul(cov_t, cov2_t)) return cor	[ 1, 396, 14187, 1266, 29871, 29906, 29900, 29906, 29900, 5087, 9266, 29889, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 29937, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 13, 29937, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 13, 29937, 13, 29937, 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SectorGen.py	ShawnDriscoll/Traveller-RPG-SectorGen	5	142849	<reponame>ShawnDriscoll/Traveller-RPG-SectorGen # # Python 3.9.11 Sector Generator # ######################################################## """ SectorGen 0.4.0 Beta ----------------------------------------------------------------------- This program generates sectors using rules from Mongoose Traveller 2nd Edition, and a smidgen from Traveller 5.10 """ from colorama import init from colorama import Fore, Back, Style init() # initialize colorama #import win32com.client as win32 import sys #sys.path.append('C:\Users\Shonner\Documents\My Files\Python Programs\Mongoose Traveller') from random import randint from PyQt5.QtCore import * from PyQt5.QtGui import * from PyQt5.QtWidgets import * #import PyQt5.QtMultimedia as MM import time from mainwindow import Ui_MainWindow from aboutdialog import Ui_aboutDialog from completeddialog import Ui_completedDialog from rpg_tools.PyDiceroll import roll import os import logging import csv import json import datetime __author__ = '<NAME> <<EMAIL>>\n<EMAIL>' __app__ = 'SectorGen 0.4.0 (Beta)' __version__ = '0.4.0b' class aboutDialog(QDialog, Ui_aboutDialog): def __init__(self): super().__init__() log.info('PyQt5 aboutDialog initializing...') self.setWindowFlags(Qt.Drawer \| Qt.WindowStaysOnTopHint) self.setupUi(self) self.aboutOKButton.clicked.connect(self.acceptOKButtonClicked) log.info('PyQt5 aboutDialog initialized.') def acceptOKButtonClicked(self): log.info('PyQt5 aboutDialog closing...') self.close() class completedDialog(QDialog, Ui_completedDialog): def __init__(self): ''' Open the Completed dialog window ''' super().__init__() log.info('PyQt5 completedDialog initializing...') self.setWindowFlags(Qt.Drawer \| Qt.WindowStaysOnTopHint) self.setupUi(self) self.completedOKButton.clicked.connect(self.acceptOKButtonClicked) self.numworldsDisplay.setText('') log.info('PyQt5 completedDialog initialized.') def acceptOKButtonClicked(self): ''' Close the PyQt5 Completed dialog window ''' log.info('PyQt5 completedDialog closing...') self.close() def showNumworlds(self): ''' Show the number of worlds generated in the PyQt5 Completed dialog window ''' self.show() class MainWindow(QMainWindow, Ui_MainWindow): def __init__(self): super().__init__() log.info('PyQt5 MainWindow initializing...') self.setupUi(self) self.genButton.clicked.connect(self.generateSector) self.actionGen.triggered.connect(self.generateSector) self.actionAbout_SectorGen.triggered.connect(self.actionAbout_triggered) self.actionQuitProg.triggered.connect(self.actionQuitProg_triggered) # Options self.hydro_calc_method = 'Based on Atmosphere' self.hydroBox.addItem('Based on Atmosphere') self.hydroBox.addItem('Based on Size') self.hydroBox.setCurrentIndex(0) self.hydroBox.currentIndexChanged.connect(self.hydroBox_changed) self.stellar_densityBox.addItem('Extra Galactic: 1%') self.stellar_densityBox.addItem('Rift: 3%') self.stellar_densityBox.addItem('Sparse: 17%') self.stellar_densityBox.addItem('Scattered: 33%') self.stellar_densityBox.addItem('Standard: 50%') self.stellar_densityBox.addItem('Dense: 66%') self.stellar_densityBox.addItem('Cluster: 83%') self.stellar_densityBox.addItem('Core: 91%') self.stellar_densityBox.setCurrentIndex(4) self.stellar_densityBox.currentIndexChanged.connect(self.stellar_densityBox_changed) self.stellar_density = 50 log.info('Default Stellar Density is Standard ' + str(self.stellar_density) + '%.') self.allegianceBox.addItem('As: Aslan Hierate') self.allegianceBox.addItem('Cs: Client State') self.allegianceBox.addItem('Im: Third Imperium') self.allegianceBox.addItem('Na: Non-Aligned, Human-dominated') self.allegianceBox.addItem('So: Solomani Confederation') self.allegianceBox.addItem('Va: Non-Aligned, Vargr-dominated') self.allegianceBox.addItem('Zh: Zhodani Consulate') self.allegianceBox.addItem('Random') self.allegianceBox.setCurrentIndex(2) self.allegiance = 'Im' log.info('Default Allegiance is Im: Third Imperium.') self.random_allegiance = False self.allegianceBox.currentIndexChanged.connect(self.allegianceBox_changed) self.super_earth_chance = False self.super_earth_checkBox.toggled.connect(self.super_earth_checkBox_changed) self.super_earth_checkBox.setChecked(self.super_earth_chance) self.popAboutDialog = aboutDialog() self.popCompletedDialog = completedDialog() # UPP Code Table self.hex_code = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'] self.core_code = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P'] # World Data Tables self.world_temperature_dm = [0, 0, -2, -2, -1, -1, 0, 0, 1, 1, 2, 6, 6, 2, -1, 2] self.size_value = [ 2, 2, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] self.atmosphere_value = [ 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1] self.hydro_value = [ 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0] self.population_value = [ 0, 1, 1, 1, 1, 1, 0, 0, 1, 2, 4, 0, 0, 0, 0, 0] self.government_value = [ 1, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, -2, -2, 0] self.minimum_TL = [ 8, 8, 5, 5, 3, -1, -1, 3, -1, 3, 8, 9, 10, 5, 5, 8] self.world_starport_class = ['X', 'X', 'X', 'E', 'E', 'D', 'D', 'C', 'C', 'B', 'B', 'A', 'A', 'A', 'A'] self.starport_value = [ -4, -4, -4, 0, 0, 0, 0, 2, 2, 4, 4, 6, 6, 6, 6] self.gas_giant_quantity = [' ', ' ', '1', '1', '2', '2', '3', '3', '4', '4', '4', '5', '5'] self.planetoid_belt_quantity = [' ', ' ', '1', '1', '1', '1', '1', '1', '2', '2', '2', '2', '3'] self.sn = [0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3] self.primary_star_type = [' ', ' ', 'A', 'M', 'M', 'M', 'M', 'M', 'K', 'G', 'F', 'F', 'F'] self.companion_star_type = [' ', ' ', 'A', 'F', 'F', 'G', 'G', 'K', 'K', 'M', 'M', 'M', 'M'] self.star_size = [' ', ' ', 'II ', 'III', 'IV ', 'V ', 'V ', 'V ', 'V ', 'V ', 'V ', 'VI ', 'D '] # Sound Tables self.V = 1 self.CV = 2 self.VC = 3 self.CVC = 4 self.CC = 5 self.ic_sound = ['b','br','c','ch','d','g', 'h','j','k','l','m','p', 'r','s','st','sh', 't','v','w','z'] self.ic_freq = [28,12,20,16,27,9,20,20,13, 28,24,27,24,30,13,25, 20,6,16,4] self.v_sound = ['a','e','i','o','u'] self.v_freq = [16,20,10,7,3] self.mc_sound = ['g','lt','ns','nst','ls','ll','nn'] self.mc_freq = [20,3,18,16,18,4,3] self.fc_sound = ['ch','ck','d','dy','dyne', 'hl','li','la','le','ler', 'nn','m','man','ma','mer','ny', 'me','n','nas','ne','ng', 'ner','nor','nie', 'rie','rlie','rly','rie','rt', 'ry','sa','sha','nshi','nski','son', 'nson','th','ta','ti','t','v', 'za','ue','than', 'lam','lis','lus','ton','tis','tus', 'love','se','nter','ll'] self.fc_freq = [6,13,22,12,3,3,3,10,6,10,7, 25,10,4,13,12,5,27,11,4,14,13,17,7,6,5,5,6,3, 21,10,3,8,3,20,9,14,10,16,11,8,6,8,10,7,6,5,7,7,4, 4,12,5,4] for i in range(len(self.ic_sound)): log.debug(self.ic_sound[i] + ' ' + str(self.ic_freq[i])) for i in range(len(self.v_sound)): log.debug(self.v_sound[i] + ' ' + str(self.v_freq[i])) for i in range(len(self.mc_sound)): log.debug(self.mc_sound[i] + ' ' + str(self.mc_freq[i])) for i in range(len(self.fc_sound)): log.debug(self.fc_sound[i] + ' ' + str(self.fc_freq[i])) self.syllable_type = [self.V,self.V,self.V,self.V,self.V,self.V,self.V,self.V, self.VC,self.VC,self.VC,self.VC, self.CV,self.CV,self.CV,self.CV, self.CVC,self.CVC,self.CVC,self.CVC,self.CVC,self.CVC, self.CC,self.CC] #print self.syllable_type self.ic_sounds = [] for i in range(len(self.ic_freq)): for j in range(self.ic_freq[i]): self.ic_sounds.append(self.ic_sound[i]) self.v_sounds = [] for i in range(len(self.v_freq)): for j in range(self.v_freq[i]): self.v_sounds.append(self.v_sound[i]) self.mc_sounds = [] for i in range(len(self.mc_freq)): for j in range(self.mc_freq[i]): self.mc_sounds.append(self.mc_sound[i]) self.fc_sounds = [] for i in range(len(self.fc_freq)): for j in range(self.fc_freq[i]): self.fc_sounds.append(self.fc_sound[i]) log.info('PyQt5 MainWindow initialized.') def pick_sound(self): if self.s_type == self.V: self.sound = self.v_sounds[randint(1, len(self.v_sounds)) - 1] if self.s_type == self.CV: self.sound = self.ic_sounds[randint(1, len(self.ic_sounds)) - 1] + self.v_sounds[randint(1, len(self.v_sounds)) - 1] if self.s_type == self.VC: self.sound = self.v_sounds[randint(1, len(self.v_sounds)) - 1] + self.fc_sounds[randint(1, len(self.fc_sounds)) - 1] if self.s_type == self.CVC: self.sound = self.ic_sounds[randint(1, len(self.ic_sounds)) - 1] + self.v_sounds[randint(1, len(self.v_sounds)) - 1] \ + self.fc_sounds[randint(1, len(self.fc_sounds)) - 1] if self.s_type == self.CC: self.sound = self.mc_sounds[randint(1, len(self.mc_sounds)) - 1] def generateSector(self): if not os.path.exists('data'): os.mkdir('data') csv_file_out = open('data/raw_sector_data.csv', 'w', newline='') csv_writer = csv.writer(csv_file_out) csv_writer.writerow(['Location', 'World_Name', 'Starport', 'Size', 'Atmosphere', 'Hydrographics', 'Population', 'Government', 'Law_Level', 'Tech_Level', 'Trade_Codes', 'Travel_Code', 'Base', 'Pop_M', 'Belts', 'Gas_giants', 'Worlds', 'Allegiance', 'Stellar_Data', 'Temperature']) json_file_out = open('data/raw_sector_data.json', 'w') mapper_file_out = open('data/sec_p00_p00.dat', 'w', newline='') mapper_file_out.write('# Generated by https://github.com/ShawnDriscoll/Traveller-RPG-SectorGen') mapper_file_out.write('\n# ' + str(datetime.datetime.now())) world_list = [] self.main_world_tech_level = -1 self.main_world_travel_code = '' # Name the sector proper = False while not(proper): temp = self.CC while temp == self.CC: temp = self.syllable_type[randint(1, len(self.syllable_type)) - 1] self.s_type = temp self.pick_sound() self.word = self.sound building = True while building: syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.V and (syllable == self.V or syllable == self.VC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CV and (syllable == self.V or syllable == self.VC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.VC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CVC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] if temp == self.VC or temp == self.CVC: building = False else: self.s_type = syllable self.pick_sound() self.word += self.sound temp = syllable if len(self.word) > 3 and len(self.word) < 14: proper = True self.sector_name = chr(ord(self.word[0]) - 32) + self.word[1:len(self.word)] proper = False while not(proper): temp = self.CC while temp == self.CC: temp = self.syllable_type[randint(1, len(self.syllable_type)) - 1] self.s_type = temp self.pick_sound() self.word = self.sound building = True while building: syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.V and (syllable == self.V or syllable == self.VC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CV and (syllable == self.V or syllable == self.VC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.VC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CVC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] if temp == self.VC or temp == self.CVC: building = False else: self.s_type = syllable self.pick_sound() self.word += self.sound temp = syllable if len(self.word) > 3 and len(self.word) < 14: proper = True name_variance = roll('d100') #print(name_variance) if name_variance <= 60: if name_variance >= 45: self.sector_name += ' ' + chr(ord(self.word[0]) - 32) + self.word[1:len(self.word)] elif name_variance >= 35: self.sector_name += ' ' + 'Alpha' elif name_variance >= 25: self.sector_name += ' ' + 'Beta' elif name_variance >= 15: self.sector_name += ' ' + 'Gamma' elif name_variance >= 10: self.sector_name += ' ' + 'Reaches' elif name_variance >= 7: self.sector_name += ' ' + 'Sector' elif name_variance >= 6: self.sector_name += ' ' + 'Frontier' elif name_variance >= 4: self.sector_name += ' ' + 'UNEXPLORED' elif name_variance >= 2: self.sector_name += ' ' + 'UNKNOWN' #print(self.sector_name) mapper_file_out.write('\n\n# ' + self.sector_name) mapper_file_out.write('\n# 0,0') mapper_file_out.write('\n\n# Name: ' + self.sector_name) mapper_file_out.write('\n\n# Milieu: M1105') mapper_file_out.write('\n\n# Credits: ' + self.sector_name + ' sector was randomly generated by ' + __app__) mapper_file_out.write('\n\n# Author: <NAME> https://www.youtube.com/user/ShawnDriscollCG') mapper_file_out.write('\n# Source: Rules from Mongoose Traveller 2nd Edition, and a smidgen from Traveller 5.10\n') # Name the subsectors #self.subsect_name = [] for subs in range(16): proper = False while not(proper): temp = self.CC while temp == self.CC: temp = self.syllable_type[randint(1, len(self.syllable_type)) - 1] self.s_type = temp self.pick_sound() self.word = self.sound building = True while building: syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.V and (syllable == self.V or syllable == self.VC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CV and (syllable == self.V or syllable == self.VC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.VC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CVC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] if temp == self.VC or temp == self.CVC: building = False else: self.s_type = syllable self.pick_sound() self.word += self.sound temp = syllable if len(self.word) > 3 and len(self.word) < 14: proper = True mapper_file_out.write('\n# Subsector ' + self.core_code[subs] + ': ' + chr(ord(self.word[0]) - 32) + self.word[1:len(self.word)]) #self.subsect_name.append(chr(ord(self.word[0]) - 32) + self.word[1:len(self.word)]) #print(self.subsect_name) mapper_file_out.write('\n\n# Alleg: As: "<NAME>"') mapper_file_out.write('\n# Alleg: Cs: "Client State"') mapper_file_out.write('\n# Alleg: Im: "Third Imperium"') mapper_file_out.write('\n# Alleg: Na: "Non-Aligned, Human-dominated"') mapper_file_out.write('\n# Alleg: So: "Solomani Confederation"') mapper_file_out.write('\n# Alleg: Va: "Non-Aligned, Vargr-dominated"') mapper_file_out.write('\n# Alleg: Zh: "Zhodani Consulate"') mapper_file_out.write('\n\nHex Name UWP Remarks {Ix} (Ex) [Cx] N B Z PBG W A Stellar') mapper_file_out.write( '\n---- -------------------- --------- -------------------- ------ ------- ------ - -- - --- -- ---- --------------') self.worlds_rolled = 0 # Make the worlds for hex_grid_col in range(32): for hex_grid_row in range(40): if roll('d100') <= self.stellar_density: # Get World Name proper = False while not(proper): temp = self.CC while temp == self.CC: temp = self.syllable_type[randint(1, len(self.syllable_type)) - 1] self.s_type = temp self.pick_sound() self.word = self.sound building = True while building: syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.V and (syllable == self.V or syllable == self.VC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CV and (syllable == self.V or syllable == self.VC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.VC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CVC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] if temp == self.VC or temp == self.CVC: building = False else: self.s_type = syllable self.pick_sound() self.word += self.sound temp = syllable if len(self.word) > 3 and len(self.word) < 14: proper = True self.main_world_name = chr(ord(self.word[0]) - 32) + self.word[1:len(self.word)] # Find Location and Subsector for Main World self.main_world_location = self.core_code[(hex_grid_col) // 8 + ((hex_grid_row) // 10) * 4] #print(self.main_world_location) self.main_world_location = '' if hex_grid_col + 1 < 10: self.main_world_location += '0' + self.hex_code[hex_grid_col + 1] else: self.main_world_location += str(hex_grid_col + 1) if hex_grid_row + 1 < 10: self.main_world_location += '0' + self.hex_code[hex_grid_row + 1] else: self.main_world_location += str(hex_grid_row + 1) #print(self.main_world_location) # Roll for Main World Size self.main_world_size = roll('2D-2') # from Traveller5 if self.main_world_size == 10: if self.super_earth_chance: self.main_world_size = roll('1D+9') #print(self.main_world_size) # Roll for Main World Atmosphere self.main_world_atmosphere = roll('FLUX') + self.main_world_size # from Traveller5 if self.main_world_atmosphere < 0 or self.main_world_size == 0: self.main_world_atmosphere = 0 if self.main_world_atmosphere > 15: self.main_world_atmosphere = 15 # Roll for Main World Hydrographics if self.hydro_calc_method == 'Based on Atmosphere': # from Mongoose Traveller 2nd Edition and Traveller5 self.main_world_hydrographics = roll('FLUX') + self.main_world_atmosphere else: # from something else self.main_world_hydrographics = roll('FLUX') + self.main_world_size if self.main_world_hydrographics < 0: self.main_world_hydrographics = 0 if self.main_world_hydrographics > 10: self.main_world_hydrographics = 10 # Size and Atmosphere DMs added to Hydrographics if self.main_world_size < 2: self.main_world_hydrographics = 0 if self.main_world_atmosphere < 2 or \ self.main_world_atmosphere == 10 or \ self.main_world_atmosphere == 11 or \ self.main_world_atmosphere == 12: self.main_world_hydrographics += -4 # Main World Temperature? self.main_world_temperature = roll('2d6') + self.world_temperature_dm[self.main_world_atmosphere] # Temperature DMs added to Hydrographics if self.main_world_atmosphere != 13 and self.main_world_atmosphere != 15: if self.main_world_temperature >= 10 and self.main_world_temperature <= 11: self.main_world_hydrographics += -2 if self.main_world_temperature >= 12: self.main_world_hydrographics += -6 if self.main_world_hydrographics < 0: self.main_world_hydrographics = 0 # Roll for Main World Population #self.main_world_population = roll('2D6-2') #self.main_world_population = roll('4D4-4') self.main_world_population = roll('2d6-2') # Roll for Main World Government if self.main_world_population > 0: self.main_world_government = roll('FLUX') + self.main_world_population if self.main_world_government < 0: self.main_world_government = 0 if self.main_world_government > 15: self.main_world_government = 15 else: self.main_world_government = 0 # Roll for Law Level if self.main_world_population > 0: self.main_world_law_level = roll('FLUX') + self.main_world_government if self.main_world_law_level < 0: self.main_world_law_level = 0 if self.main_world_law_level > 15: self.main_world_law_level = 15 else: self.main_world_law_level = 0 # Roll for Starport self.main_world_starport = roll('2D6') if self.main_world_population >= 10: self.main_world_starport += 2 elif self.main_world_population >= 8: self.main_world_starport += 1 elif self.main_world_population <= 2: self.main_world_starport += -2 elif self.main_world_population <= 4: self.main_world_starport += -1 # Roll for Technology Level if self.main_world_population > 0: self.main_world_tech_level = roll('1D6') # Add DMs self.main_world_tech_level += self.starport_value[self.main_world_starport] self.main_world_tech_level += self.size_value[self.main_world_size] self.main_world_tech_level += self.atmosphere_value[self.main_world_atmosphere] self.main_world_tech_level += self.hydro_value[self.main_world_hydrographics] self.main_world_tech_level += self.population_value[self.main_world_population] self.main_world_tech_level += self.government_value[self.main_world_government] # Adjust Minimum TL for Environmental Limits if self.minimum_TL[self.main_world_atmosphere] != -1: if self.main_world_tech_level < self.minimum_TL[self.main_world_atmosphere]: self.main_world_tech_level = self.minimum_TL[self.main_world_atmosphere] if self.main_world_tech_level < 0: self.main_world_tech_level = 0 if self.main_world_tech_level > 15: self.main_world_tech_level = 15 else: self.main_world_tech_level = 0 # Calculate Travel Code self.main_world_travel_code = '' if (self.main_world_atmosphere >= 10 or (self.main_world_government == 0 or self.main_world_government == 7 \ or self.main_world_government == 10) or (self.main_world_law_level == 0 or self.main_world_law_level >= 9)) and roll('d6') == 6: self.main_world_travel_code = ' A' # Lookup Trade Code(s) self.trade_index = 0 self.main_world_trade_code = [' ',' ',' ',' ',' ',' ', ' ', ' ', ' ', ' '] self.main_world_trade_class = [' ',' ',' ',' ',' ',' ', ' ', ' ', ' ', ' '] if self.main_world_atmosphere >= 4 \ and self.main_world_atmosphere <= 9 \ and self.main_world_hydrographics >= 4 \ and self.main_world_hydrographics <= 8 \ and self.main_world_population >= 5 \ and self.main_world_population <= 7: self.main_world_trade_code[self.trade_index] = 'Ag' self.main_world_trade_class[self.trade_index] = 'Agricultural' self.trade_index += 1 if (self.main_world_atmosphere == 2 \ or self.main_world_atmosphere == 3 \ or self.main_world_atmosphere == 10 \ or self.main_world_atmosphere == 11) \ and self.main_world_hydrographics >= 1 \ and self.main_world_hydrographics <= 5 \ and self.main_world_population >= 3 \ and self.main_world_population <= 6 \ and self.main_world_law_level >= 6 \ and self.main_world_law_level <= 9: self.main_world_trade_code[self.trade_index] = 'Px' self.main_world_trade_class[self.trade_index] = 'Prison' self.main_world_travel_code = ' A' self.trade_index += 1 if self.main_world_size == 0 \ and self.main_world_atmosphere == 0 \ and self.main_world_hydrographics == 0: self.main_world_trade_code[self.trade_index] = 'As' self.main_world_trade_class[self.trade_index] = 'Asteroid' self.trade_index += 1 if self.main_world_population == 0 \ and self.main_world_government == 0 \ and self.main_world_law_level == 0: if self.world_starport_class[self.main_world_starport] == 'E' \ or self.world_starport_class[self.main_world_starport] == 'X': if roll('1D3') > 1: self.main_world_tech_level = 0 self.main_world_trade_code[self.trade_index] = 'Ba' self.main_world_trade_class[self.trade_index] = 'Barren' else: self.main_world_tech_level = roll('3D-3') self.main_world_trade_code[self.trade_index] = 'Di' self.main_world_trade_class[self.trade_index] = 'Dieback' self.main_world_travel_code = ' R' self.trade_index += 1 if self.world_starport_class[self.main_world_starport] == 'A': if roll('1D3') == 1: self.main_world_trade_code[self.trade_index] = 'Cp' self.main_world_trade_class[self.trade_index] = 'Subsector Capital' self.trade_index += 1 if self.main_world_population >=5 \ and self.main_world_population <= 10 \ and self.main_world_government == 6 \ and self.main_world_law_level >= 0 \ and self.main_world_law_level <= 3: self.main_world_trade_code[self.trade_index] = 'Cy' self.main_world_trade_class[self.trade_index] = 'Colony' self.trade_index += 1 if self.main_world_atmosphere >= 2 \ and self.main_world_hydrographics == 0: self.main_world_trade_code[self.trade_index] = 'De' self.main_world_trade_class[self.trade_index] = 'Desert' self.trade_index += 1 if self.main_world_atmosphere >= 10 \ and self.main_world_hydrographics >= 1: self.main_world_trade_code[self.trade_index] = 'Fl' self.main_world_trade_class[self.trade_index] = 'Fluid Oceans' self.trade_index += 1 if self.main_world_size >= 6 \ and self.main_world_size <= 8 \ and (self.main_world_atmosphere == 5 \ or self.main_world_atmosphere == 6 \ or self.main_world_atmosphere == 8) \ and self.main_world_hydrographics >= 5 \ and self.main_world_hydrographics <= 7: self.main_world_trade_code[self.trade_index] = 'Ga' self.main_world_trade_class[self.trade_index] = 'Garden' self.trade_index += 1 if self.main_world_population >= 9: self.main_world_trade_code[self.trade_index] = 'Hi' self.main_world_trade_class[self.trade_index] = 'High Population' self.trade_index += 1 if self.main_world_tech_level >= 12: self.main_world_trade_code[self.trade_index] = 'Ht' self.main_world_trade_class[self.trade_index] = 'High Tech' self.trade_index += 1 if self.main_world_atmosphere >= 0 \ and self.main_world_atmosphere <= 1 \ and self.main_world_hydrographics >= 1: self.main_world_trade_code[self.trade_index] = 'Ic' self.main_world_trade_class[self.trade_index] = 'Ice-Capped' self.trade_index += 1 if (self.main_world_atmosphere >= 0 \ and self.main_world_atmosphere <= 2 \ or self.main_world_atmosphere == 4 \ or self.main_world_atmosphere == 7 \ or self.main_world_atmosphere == 9) \ and self.main_world_population >= 9: self.main_world_trade_code[self.trade_index] = 'In' self.main_world_trade_class[self.trade_index] = 'Industrial' self.trade_index += 1 if self.main_world_population >= 1 \ and self.main_world_population <= 3: self.main_world_trade_code[self.trade_index] = 'Lo' self.main_world_trade_class[self.trade_index] = 'Low Population' self.trade_index += 1 if self.main_world_tech_level <= 5: self.main_world_trade_code[self.trade_index] = 'Lt' self.main_world_trade_class[self.trade_index] = 'Low Tech' self.trade_index += 1 if self.main_world_atmosphere >= 0 \ and self.main_world_atmosphere <= 3 \ and self.main_world_hydrographics >= 0 \ and self.main_world_hydrographics <= 3 \ and self.main_world_population >= 6: self.main_world_trade_code[self.trade_index] = 'Na' self.main_world_trade_class[self.trade_index] = 'Non-Agricultural' self.trade_index += 1 if self.main_world_population <= 6: self.main_world_trade_code[self.trade_index] = 'Ni' self.main_world_trade_class[self.trade_index] = 'Non-Industrial' self.trade_index += 1 if self.main_world_atmosphere >= 2 \ and self.main_world_atmosphere <= 5 \ and self.main_world_hydrographics >= 0 \ and self.main_world_hydrographics <= 3: self.main_world_trade_code[self.trade_index] = 'Po' self.main_world_trade_class[self.trade_index] = 'Poor' self.trade_index += 1 if (self.main_world_atmosphere == 6 \ or self.main_world_atmosphere == 8) \ and self.main_world_population >= 6 \ and self.main_world_population <= 8 \ and self.main_world_government >= 4 \ and self.main_world_government <= 9: self.main_world_trade_code[self.trade_index] = 'Ri' self.main_world_trade_class[self.trade_index] = 'Rich' self.trade_index += 1 if self.main_world_hydrographics == 10 \ and self.main_world_atmosphere >= 1 \ and self.main_world_atmosphere <= 9: self.main_world_trade_code[self.trade_index] = 'Wa' self.main_world_trade_class[self.trade_index] = 'Water World' self.trade_index += 1 if self.main_world_atmosphere == 0: self.main_world_trade_code[self.trade_index] = 'Va' self.main_world_trade_class[self.trade_index] = 'Vacuum' self.trade_index += 1 if self.main_world_government + self.main_world_law_level >= 30: self.main_world_trade_code[self.trade_index] = 'Fo' self.main_world_trade_class[self.trade_index] = 'Forbidden' self.trade_index += 1 self.main_world_travel_code = ' R' # Any bases? Naval? Research, etc? self.base = '' if self.world_starport_class[self.main_world_starport] == 'A': if roll('2D6') >= 8: self.base = 'N' elif roll('2D6') >= 10: self.base = 'S' elif roll('2D6') >= 8: self.base = 'R' elif roll('2D6') >= 6: self.base = 'I' if self.world_starport_class[self.main_world_starport] == 'B': if roll('2D6') >= 8: self.base = 'N' elif roll('2D6') >= 8: self.base = 'S' elif roll('2D6') >= 10: self.base = 'R' elif roll('2D6') >= 8: self.base = 'I' elif roll('2D6') >= 12: self.base = 'P' if self.world_starport_class[self.main_world_starport] == 'C': if roll('2D6') >= 8: self.base = 'S' elif roll('2D6') >= 10: self.base = 'R' elif roll('2D6') >= 8: self.base = 'W' elif roll('2D6') >= 10: self.base = 'I' elif roll('2D6') >= 10: self.base = 'P' if self.world_starport_class[self.main_world_starport] == 'D': if roll('2D6') >= 7: self.base = 'S' elif roll('2D6') >= 12: self.base = 'P' if self.world_starport_class[self.main_world_starport] == 'E': if roll('2D6') >= 12: self.base = 'P' # Population Multiplier? if roll('1D6') > 3: self.population_multiplier = roll('1D6-1') while self.population_multiplier == 5: self.population_multiplier = roll('1D6-1') else: self.population_multiplier = roll('1D6+4') while self.population_multiplier == 10: self.population_multiplier = roll('1D6+4') if self.population_multiplier < 1: self.population_multiplier = 1 # Planetoid Belts? self.planetoid_belts = '0' if roll('2D6') >= 8: self.planetoid_belts = self.planetoid_belt_quantity[roll('2D')] # Gas Giants? self.gas_giants = '0' if roll('2D6') >= 5: self.gas_giants = self.gas_giant_quantity[roll('2D')] # Worlds? self.worlds = str(1 + int(self.planetoid_belts) + int(self.gas_giants) + roll('2D')) # Allegiance? if self.random_allegiance is True: self.allegiance = 'Im' temp = roll('D100') if temp > 50: self.allegiance = 'Cs' if temp > 80: self.allegiance = 'Na' if temp > 90: self.allegiance = 'Va' if temp > 94: self.allegiance = 'So' if temp > 97: self.allegiance = 'As' if temp == 100: self.allegiance = 'Zh' # System Nature self.stellar_data = '' star_count = self.sn[roll('2D6')] if self.main_world_atmosphere >= 4 and self.main_world_atmosphere <= 9 \ or self.main_world_population >= 8: star_DM = 4 else: star_DM = 0 star_rolled = roll('2D6') + star_DM if star_rolled > 12: star_rolled = 12 previous_star_rolled = star_rolled self.stellar_data += self.primary_star_type[star_rolled] if roll('1D6') > 3: classification = roll('1D6-1') while classification == 5: classification = roll('1D6-1') else: classification = roll('1D6+4') while classification == 10: classification = roll('1D6+4') self.stellar_data += str(classification) star_rolled = roll('2D6') + star_DM if star_rolled > 12: star_rolled = 12 star_size_rolled = self.star_size[star_rolled] if (self.stellar_data == 'K5' or self.stellar_data == 'M9') \ and star_size_rolled == 'IV ': star_size_rolled = 'V ' if (self.stellar_data == 'B0' or self.stellar_data == 'F4') \ and star_size_rolled == 'IV ': star_size_rolled = 'V ' self.stellar_data += ' ' + star_size_rolled previous_star_size_rolled = star_rolled star_count += -1 if star_count > 1: star_rolled = roll('2D6') + previous_star_rolled if star_rolled > 12: star_rolled = 12 companion_data = self.companion_star_type[star_rolled] if roll('1D6') > 3: classification = roll('1D6-1') while classification == 5: classification = roll('1D6-1') else: classification = roll('1D6+4') while classification == 10: classification = roll('1D6+4') companion_data += str(classification) star_rolled = roll('2D6') + previous_star_size_rolled if star_rolled > 12: star_rolled = 12 star_size_rolled = self.star_size[star_rolled] if (companion_data == 'K5' or companion_data == 'M9') \ and star_size_rolled == 'IV ': star_size_rolled = 'V ' if (companion_data == 'B0' or companion_data == 'F4') \ and star_size_rolled == 'IV ': star_size_rolled = 'V ' self.stellar_data += ' ' + companion_data + ' ' + star_size_rolled # Maybe World has no name? Just a UWP. if roll('D100') > 95: self.main_world_name = self.world_starport_class[self.main_world_starport] + \ self.hex_code[self.main_world_size] + \ self.hex_code[self.main_world_atmosphere] + \ self.hex_code[self.main_world_hydrographics] + \ self.hex_code[self.main_world_population] + \ self.hex_code[self.main_world_government] + \ self.hex_code[self.main_world_law_level] + '-' + \ self.hex_code[self.main_world_tech_level] self.main_world_trade_codes = '' self.main_world_trade_classes = '' for i in range(self.trade_index): self.main_world_trade_codes += ' ' + self.main_world_trade_code[i] self.main_world_trade_classes += self.main_world_trade_class[i] + ', ' # Print Main World print(self.main_world_name + ' (%s %s%s%s%s%s%s%s-%s %s%s)' % (self.main_world_location, \ self.world_starport_class[self.main_world_starport], \ self.hex_code[self.main_world_size], self.hex_code[self.main_world_atmosphere], \ self.hex_code[self.main_world_hydrographics], self.hex_code[self.main_world_population], \ self.hex_code[self.main_world_government], self.hex_code[self.main_world_law_level], \ self.hex_code[self.main_world_tech_level], self.main_world_trade_codes, self.main_world_travel_code)) self.worlds_rolled += 1 csv_writer.writerow([self.main_world_location, self.main_world_name, self.world_starport_class[self.main_world_starport], self.hex_code[self.main_world_size], self.hex_code[self.main_world_atmosphere], self.hex_code[self.main_world_hydrographics], self.hex_code[self.main_world_population], self.hex_code[self.main_world_government], self.hex_code[self.main_world_law_level], self.hex_code[self.main_world_tech_level], self.main_world_trade_codes[1:], self.main_world_travel_code[len(self.main_world_travel_code) - 1:len(self.main_world_travel_code)], self.base, self.population_multiplier, self.planetoid_belts, self.gas_giants, self.worlds, self.allegiance, self.stellar_data, self.main_world_temperature]) trav_rec = {} trav_rec['Location'] = self.main_world_location trav_rec['World_Name'] = self.main_world_name trav_rec['Starport'] = self.world_starport_class[self.main_world_starport] trav_rec['Size'] = self.hex_code[self.main_world_size] trav_rec['Atmosphere'] = self.hex_code[self.main_world_atmosphere] trav_rec['Hydrographics'] = self.hex_code[self.main_world_hydrographics] trav_rec['Population'] = self.hex_code[self.main_world_population] trav_rec['Government'] = self.hex_code[self.main_world_government] trav_rec['Law_Level'] = self.hex_code[self.main_world_law_level] trav_rec['Tech_Level'] = self.hex_code[self.main_world_tech_level] trav_rec['Trade_Codes'] = self.main_world_trade_codes[1:] trav_rec['Travel_Code'] = self.main_world_travel_code[len(self.main_world_travel_code) - 1:len(self.main_world_travel_code)] trav_rec['Base'] = self.base trav_rec['Pop_M'] = self.population_multiplier trav_rec['Belts'] = self.planetoid_belts trav_rec['Gas_Giants'] = self.gas_giants trav_rec['Worlds'] = self.worlds trav_rec['Allegiance'] = self.allegiance trav_rec['Stellar_Data'] = self.stellar_data trav_rec['Temperature'] = self.main_world_temperature world_list.append(trav_rec) detail_line = self.main_world_location detail_line += ' ' + self.main_world_name while len(detail_line) < 26: detail_line += ' ' detail_line += self.world_starport_class[self.main_world_starport] + \ self.hex_code[self.main_world_size] + \ self.hex_code[self.main_world_atmosphere] + \ self.hex_code[self.main_world_hydrographics] + \ self.hex_code[self.main_world_population] + \ self.hex_code[self.main_world_government] + \ self.hex_code[self.main_world_law_level] + '-' + \ self.hex_code[self.main_world_tech_level] detail_line += ' ' + self.main_world_trade_codes[1:] while len(detail_line) < 81: detail_line += ' ' detail_line += self.base while len(detail_line) < 84: detail_line += ' ' detail_line += self.main_world_travel_code[len(self.main_world_travel_code) - 1:len(self.main_world_travel_code)] while len(detail_line) < 85: detail_line += ' ' detail_line += ' ' + str(self.population_multiplier) + str(self.planetoid_belts) + str(self.gas_giants) while len(detail_line) < 90: detail_line += ' ' detail_line += self.worlds while len(detail_line) < 93: detail_line += ' ' detail_line += self.allegiance while len(detail_line) < 98: detail_line += ' ' detail_line += self.stellar_data mapper_file_out.write('\n' + detail_line) #json.dump(world_list, json_file_out, indent=4, ensure_ascii=True) json.dump(world_list, json_file_out, ensure_ascii=True) csv_file_out.close() json_file_out.close() mapper_file_out.close() print('Worlds rolled:', self.worlds_rolled) self.passingNumworlds() def hydroBox_changed(self): self.hydro_calc_choices = ['Based on Atmosphere', 'Based on Size'] self.hydro_calc_method = self.hydro_calc_choices[self.hydroBox.currentIndex()] #print(self.hydro_calc_method) log.info("'" + self.hydro_calc_method + "'" + ' was selected.') def stellar_densityBox_changed(self): self.density_choices = [1, 3, 17, 33, 50, 66, 83, 91] self.stellar_density = self.density_choices[self.stellar_densityBox.currentIndex()] #print(self.stellar_density) log.info('A Stellar Density of ' + str(self.stellar_density) + '% was selected.') def allegianceBox_changed(self): self.allegiance_choices = ['As', 'Cs', 'Im', 'Na', 'So', 'Va', 'Zh'] if self.allegianceBox.currentIndex() == 7: self.random_allegiance = True log.info('Random Allegiance was selected.') else: self.allegiance = self.allegiance_choices[self.allegianceBox.currentIndex()] self.random_allegiance = False log.info('Allegiance "' + self.allegiance + '" was selected.') def super_earth_checkBox_changed(self): self.super_earth_chance = self.super_earth_checkBox.isChecked() #print(self.super_earth_chance) log.info('Chance for Super-Earth: ' + str(self.super_earth_chance)) def actionAbout_triggered(self): log.info(__app__ + ' show about...') self.popAboutDialog.show() def actionQuitProg_triggered(self): log.info(__app__ + ' quiting...') log.info(__app__ + ' DONE.') log.info('Logging ended.') self.close() def passingNumworlds(self): log.info('Number of worlds: ' + str(self.worlds_rolled)) self.popCompletedDialog.numworldsDisplay.setText('Number of worlds: ' + str(self.worlds_rolled)) self.popCompletedDialog.showNumworlds() if __name__ == '__main__': log = logging.getLogger('SectorGen') log.setLevel(logging.DEBUG) if not os.path.exists('Logs'): os.mkdir('Logs') fh = logging.FileHandler('Logs/SectorGen.log', 'w') formatter = logging.Formatter('%(asctime)s %(levelname)s %(name)s - %(message)s', datefmt = '%a, %d %b %Y %H:%M:%S') fh.setFormatter(formatter) log.addHandler(fh) log.info('Logging started.') log.info(__app__ + ' starting...') trange = time.localtime() creation_time = datetime.datetime.now() if trange[0] > 2022 or trange[1] > 11: log.warning('Beta time period has expired!') print() print(Fore.RED + Style.BRIGHT + __app__, 'EXPIRED.') print(Style.RESET_ALL) print() print(__author__) print() s = input('Press ENTER: ') log.info(__app__ + ' stopping...') print('OK') log.info(__app__ + ' stopped.') log.info('Logging ended.') else: print() print('Thank you for giving', __app__, 'a try.') log.info(__app__ + ' looking for PyDiceroll...') vernum, release = roll('info') print('This program uses', release) log.info(__app__ + ' found ' + release + '.') print() print('----------------------------') print(__author__) print() print('The Traveller game in all forms is owned by Far Future Enterprises.') print('Copyright 1977 - 2022 Far Future Enterprises.') print('Traveller is a registered trademark of Far Future Enterprises.') print() log.info(__app__ + ' started, and running...') app = QApplication(sys.argv) #Use print(QStyleFactory.keys()) to find a setStyle you like, instead of 'Fusion' app.setStyle('Fusion') darkPalette = QPalette() darkPalette.setColor(QPalette.Window, QColor(53, 53, 53)) darkPalette.setColor(QPalette.WindowText, Qt.white) darkPalette.setColor(QPalette.Disabled, QPalette.WindowText, QColor(127, 127, 127)) darkPalette.setColor(QPalette.Base, QColor(42, 42, 42)) darkPalette.setColor(QPalette.AlternateBase, QColor(66, 66, 66)) darkPalette.setColor(QPalette.ToolTipBase, Qt.white) darkPalette.setColor(QPalette.ToolTipText, Qt.white) darkPalette.setColor(QPalette.Text, Qt.white) darkPalette.setColor(QPalette.Disabled, QPalette.Text, QColor(127, 127, 127)) darkPalette.setColor(QPalette.Dark, QColor(35, 35, 35)) darkPalette.setColor(QPalette.Shadow, QColor(20, 20, 20)) darkPalette.setColor(QPalette.Button, QColor(53, 53, 53)) darkPalette.setColor(QPalette.ButtonText, Qt.white) darkPalette.setColor(QPalette.Disabled, QPalette.ButtonText, QColor(127, 127, 127)) darkPalette.setColor(QPalette.BrightText, Qt.red) darkPalette.setColor(QPalette.Link, QColor(42, 130, 218)) darkPalette.setColor(QPalette.Highlight, QColor(42, 130, 218)) darkPalette.setColor(QPalette.Disabled, QPalette.Highlight, QColor(80, 80, 80)) darkPalette.setColor(QPalette.HighlightedText, Qt.white) darkPalette.setColor(QPalette.Disabled, QPalette.HighlightedText, QColor(127, 127, 127)) mainApp = MainWindow() mainApp.show() app.setPalette(darkPalette) CURRENT_DIR = os.path.dirname(os.path.realpath(__file__)) #print(CURRENT_DIR) app.exec_()	[ 1, 529, 276, 1112, 420, 29958, 29903, 2350, 1233, 29928, 3780, 22017, 29914, 5323, 29894, 4539, 29899, 29934, 16903, 29899, 29903, 3019, 15462, 13, 29937, 13, 29937, 259, 5132, 29871, 29941, 29889, 29929, 29889, 29896, 29896, 317, 3019, 3251, 1061, 13, 29937, 13, 13383, 13383, 13383, 7346, 13, 13, 15945, 29908, 13, 29903, 3019, 15462, 29871, 29900, 29889, 29946, 29889, 29900, 350, 1187, 13, 2683, 2683, 2683, 2683, 26589, 13, 13, 4013, 1824, 16785, 409, 14359, 773, 6865, 515, 13, 29924, 7443, 852, 3201, 29894, 4539, 29871, 29906, 299, 17138, 29892, 322, 263, 1560, 333, 1885, 515, 3201, 29894, 4539, 29871, 29945, 29889, 29896, 29900, 13, 15945, 29908, 13, 13, 3166, 2927, 3304, 1053, 2069, 13, 3166, 2927, 3304, 1053, 28297, 29892, 7437, 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welcome.py	MadhavVij/File-Uploader-Bluemix	0	162931	import base64 import json import pymysql import swiftclient import os from cloudant import Cloudant from cryptography.hazmat.backends import default_backend from cryptography.hazmat.primitives import hashes from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC from flask import Flask, render_template, jsonify from cryptography.fernet import Fernet #Object Storage Credentials auth_url = projectId = region = userId = password = #Cloudant Credentials cloud_username = cloud_password = cloud_url = client = Cloudant(cloud_username, cloud_password, url=cloud_url) client.connect() session = client.session() app = Flask(__name__) def genKey(): passWord= input('Enter key: ') salt = os.urandom(16) kdf = PBKDF2HMAC( algorithm=hashes.SHA256(), length=32, salt=salt, iterations=100000, backend=default_backend() ) passWord = <PASSWORD>() return base64.urlsafe_b64encode(kdf.derive(passWord)) def authenticate(): return swiftclient.Connection(key=password, authurl=auth_url, auth_version='3', os_options={"project_id": projectId, "user_id": userId, "region_name": region}) def createContainer(container_name): #container_name = 'file-container' #container_name2 = 'file-container2' #container_name3 = 'file-container3' Obj_conn = authenticate() Obj_conn.put_container(container_name) #Obj_conn.put_container(container_name2) return Obj_conn def getContainerName(Obj_conn): for container in Obj_conn.get_account()[1]: return container['name'] def createDB(): dbName = "datafiles" return client.create_database(dbName) @app.route('/toCloudUp') def cloudList(): root = app.root_path + '\container/' filenames = [] for (dirName, subdirList, fileList) in os.walk(root): filenames.extend(fileList) return render_template('toCloudUp.html', files=filenames) @app.route('/cloudUp') def cloudantUpload(): #dbName = "datafiles" #dbName = client[dbName] #filePath = app.root_path+'\container/'+filename #f = open(filePath,'rb') #data = f.read() dbName = "datafiles" dbName = client[dbName] data = ListLocal() #jdata = json.load(data) #jdata = jdata[0] print(data[0]) dbName.create_document({data[0]}) #dbName.create_document({zerocount:[{filename: data.decode()}]}) return 'Uploaded Successful!' @app.route('/upload') def upload(): file_type1 = ['txt', 'TXT', 'text'] file_type2 = ['jpg','jpeg','JPG','JPEG','png','PNG','gif'] root = app.root_path + '\container/' for dirName, subdirList, fileList in os.walk(root): for fname in fileList: file_location = (dirName + "/" + fname) size = os.path.getsize(file_location) if fname[-3:] in file_type1: container_name = 'file-container' Obj_conn = createContainer(container_name) with open(file_location, 'rb') as load_file: Obj_conn.put_object(container_name, fname, contents=load_file.read(), content_type='application/octet-stream') load_file.close() elif fname[-3:] in file_type2 or fname[-4:] in file_type2: if size <= 200000: container_name = 'small_img' Obj_conn = createContainer(container_name) file_location = (dirName + "/" + fname) with open(file_location, 'rb') as load_file: Obj_conn.put_object(container_name, fname, contents=load_file.read(), content_type='application/octet-stream') load_file.close() elif size>200000: container_name = 'large_img' Obj_conn = createContainer(container_name) file_location = (dirName + "/" + fname) with open(file_location, 'rb') as load_file: Obj_conn.put_object(container_name, fname, contents=load_file.read(), content_type='application/octet-stream') load_file.close() return 'Upload Successful!' @app.route('/files') def list_files(): print(app.root_path) root = app.root_path+'\container/' filenames = [] for (dirName, subdirList, fileList) in os.walk(root): filenames.extend(fileList) print(filenames) return render_template('files.html', files=filenames) @app.route('/toDownload') def downloadList(): Obj_conn = createContainer() filenames = [] container_name = getContainerName(Obj_conn) for data in Obj_conn.get_container(container_name)[1]: filenames.append(data['name']) return render_template('toDownload.html', files=filenames) @app.route('/download/<filename>') def download(filename): Obj_conn = createContainer() container_name = getContainerName(Obj_conn) file_details = Obj_conn.get_object(container_name, filename) with open(app.root_path+'\download.txt', 'wb') as my_copy: my_copy.write(file_details[1]) return filename + ' Downloaded!' @app.route('/toEncrypt') def encryptList(): root = app.root_path + '\container/' filenames = [] for (dirName, subdirList, fileList) in os.walk(root): filenames.extend(fileList) return render_template('toEncrypt.html', files=filenames) @app.route('/encrypt/<userFile>') def encryptFile(userFile): key = genKey() root = app.root_path f = open(root+'/'+userFile[:-4]+'_key.txt','wb') f.write(key) f = Fernet(key) for dirName, subdirList, fileList in os.walk(root+'/container/'): for fname in fileList: if userFile == fname: file_location = (dirName + "/" + fname) with open(file_location, 'rb') as load_file: token = f.encrypt(load_file.read()) load_file.close() fname = fname[:-4]+'-crypted'+fname[-4:] new_location = (root + "/encrypted/" + fname) with open(new_location, 'wb') as write_file: write_file.write(token) write_file.close() return 'file ' +fname+ ' encrypted' @app.route('/toDecrypt') def decryptionList(): root = app.root_path + '\encrypted/' filenames = [] for (dirName, subdirList, fileList) in os.walk(root): filenames.extend(fileList) return render_template('toDecrypt.html', files=filenames) @app.route('/decrypt/<userFile>') def decryptFile(userFile): root = app.root_path userFile = userFile[:-12]+userFile[-4:] f = open(root+'/'+userFile[:-4]+'_key.txt', 'rb') key = f.read() g = Fernet(key) for dirName, subdirList, fileList in os.walk(root+'/encrypted/'): for fname in fileList: if userFile[:-4]+'-crypted'+userFile[-4:] == fname: file_location = (dirName + "/" + fname) with open(file_location, 'rb') as load_file: token = g.decrypt(load_file.read()) load_file.close() fname = userFile[:-4]+'-decrypted'+userFile[-4:] new_location = (root + "/decrypted/" + fname) print(new_location) with open(new_location, 'wb') as write_file: write_file.write(token) write_file.close() return 'file ' +fname+ ' decrypted' @app.route('/') def Welcome(): return render_template('index.html') def connectDB(): host = '' port = 3306 user = '' password = '' db = '' conn = pymysql.connect( host=host, port=port, user=user, password=password, db=db) return conn @app.route('/clearUp') def uploadFile(): conn = connectDB() curr = conn.cursor() file_type1 = ['txt', 'TXT', 'text'] file_type2 = ['jpg', 'jpeg', 'JPG', 'JPEG', 'png', 'PNG', 'gif'] root = app.root_path + '\container/' for dirName, subdirList, fileList in os.walk(root): for fname in fileList: file_location = (dirName + "/" + fname) fsize = os.path.getsize(file_location) if fname[-3:] in file_type1: with open(file_location, 'rb') as load_file: fdata = load_file.read() query = 'insert into file_container (fname,fsize, fdata) VALUES (%s,%s,%s)' args = (fname,fsize,fdata) curr.execute(query,args) conn.commit() load_file.close() # results = curr.fetchall() # for row in results: # print(row[1]) # conn.commit() curr.close() conn.close() return 'DB Updated!' @app.route('/clearDown/<filename>') def downloadFile(filename): conn = connectDB() curr = conn.cursor() query = 'select fdata from file_container WHERE fname = %s' args = (filename) curr.execute(query, args) fdata = curr.fetchone()[0] with open(app.root_path + '\download.txt', 'wb') as my_copy: my_copy.write(fdata) conn.commit() curr.close() conn.close() return 'Downloaded '+filename #################################################### @app.route('/ListLocal') def ListLocal(): root = app.root_path filenames = [] zerocount = 0 for dirName, subdirList, fileList in os.walk(root+'/container/'): for fname in fileList: file_location = (dirName + "/" + fname) size = os.path.getsize(file_location) if size == 0: 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graph_rl/envs/flatten_wrapper.py	nicoguertler/graphrl	1	41443	<filename>graph_rl/envs/flatten_wrapper.py from gym import Wrapper from ..spaces import space_from_gym_space class FlattenWrapper(Wrapper): """Flattens observation and action space.""" def __init__(self, env): super().__init__(env) self._action_space = space_from_gym_space(env.action_space) self._observation_space = space_from_gym_space(env.observation_space) self.action_space = self._action_space.get_flat_space().get_gym_space() self.observation_space = self._observation_space.get_flat_space().get_gym_space() def step(self, action): orig_action = self._action_space.unflatten_value(action) orig_obs, reward, done, info = self.env.step(orig_action) obs = self._observation_space.flatten_value(orig_obs) return obs, reward, done, info def reset(self, kwargs): orig_obs = self.env.reset(kwargs) obs = self._observation_space.flatten_value(orig_obs) return obs	[ 1, 529, 9507, 29958, 4262, 29918, 2096, 29914, 264, 4270, 29914, 1579, 8606, 29918, 17699, 29889, 2272, 13, 3166, 330, 962, 1053, 399, 6794, 13, 13, 3166, 6317, 22854, 1053, 2913, 29918, 3166, 29918, 29887, 962, 29918, 3493, 13, 13, 13, 1990, 2379, 8606, 15646, 29898, 15646, 1125, 13, 1678, 9995, 8754, 1131, 575, 15500, 322, 3158, 2913, 1213, 15945, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 8829, 1125, 13, 4706, 2428, 2141, 1649, 2344, 12035, 6272, 29897, 13, 4706, 1583, 3032, 2467, 29918, 3493, 353, 2913, 29918, 3166, 29918, 29887, 962, 29918, 3493, 29898, 6272, 29889, 2467, 29918, 3493, 29897, 13, 4706, 1583, 3032, 26739, 362, 29918, 3493, 353, 2913, 29918, 3166, 29918, 29887, 962, 29918, 3493, 29898, 6272, 29889, 26739, 362, 29918, 3493, 29897, 13, 13, 4706, 1583, 29889, 2467, 29918, 3493, 353, 1583, 3032, 2467, 29918, 3493, 29889, 657, 29918, 20620, 29918, 3493, 2141, 657, 29918, 29887, 962, 29918, 3493, 580, 13, 4706, 1583, 29889, 26739, 362, 29918, 3493, 353, 1583, 3032, 26739, 362, 29918, 3493, 29889, 657, 29918, 20620, 29918, 3493, 2141, 657, 29918, 29887, 962, 29918, 3493, 580, 13, 13, 1678, 822, 4331, 29898, 1311, 29892, 3158, 1125, 13, 4706, 1677, 29918, 2467, 353, 1583, 3032, 2467, 29918, 3493, 29889, 348, 1579, 8606, 29918, 1767, 29898, 2467, 29897, 13, 4706, 1677, 29918, 26290, 29892, 20751, 29892, 2309, 29892, 5235, 353, 1583, 29889, 6272, 29889, 10568, 29898, 12683, 29918, 2467, 29897, 13, 4706, 20881, 353, 1583, 3032, 26739, 362, 29918, 3493, 29889, 1579, 8606, 29918, 1767, 29898, 12683, 29918, 26290, 29897, 13, 4706, 736, 20881, 29892, 20751, 29892, 2309, 29892, 5235, 13, 13, 1678, 822, 10092, 29898, 1311, 29892, 3579, 19290, 1125, 13, 4706, 1677, 29918, 26290, 353, 1583, 29889, 6272, 29889, 12071, 29898, 1068, 19290, 29897, 13, 4706, 20881, 353, 1583, 3032, 26739, 362, 29918, 3493, 29889, 1579, 8606, 29918, 1767, 29898, 12683, 29918, 26290, 29897, 13, 4706, 736, 20881, 13, 13, 13, 13, 2 ]
publishconf.py	siongui/pali-chanting	2	192935	<reponame>siongui/pali-chanting<gh_stars>1-10 #!/usr/bin/env python # -- coding: utf-8 -- # from __future__ import unicode_literals # This file is only used if you use `make publish` or # explicitly specify it as your config file. import os import sys sys.path.append(os.curdir) from pelicanconf import * # no trailing / SITEURL = 'https://siongui.github.io/pali-chanting' RELATIVE_URLS = False FEED_ALL_ATOM = None CATEGORY_FEED_ATOM = None TRANSLATION_FEED_ATOM = None AUTHOR_FEED_ATOM = None AUTHOR_FEED_RSS = None DELETE_OUTPUT_DIRECTORY = True # Following items are often useful when publishing #DISQUS_SITENAME = "" #GOOGLE_ANALYTICS = "" # Google Custom Search #GOOGLE_CSE = '000759460633137666077:43yuu4nvb0c'	[ 1, 529, 276, 1112, 420, 29958, 29879, 291, 23569, 29914, 29886, 2606, 29899, 13775, 292, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 396, 13, 3166, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 13, 29937, 910, 934, 338, 871, 1304, 565, 366, 671, 421, 5675, 9805, 29952, 470, 13, 29937, 9479, 6084, 372, 408, 596, 2295, 934, 29889, 13, 13, 5215, 2897, 13, 5215, 10876, 13, 9675, 29889, 2084, 29889, 4397, 29898, 359, 29889, 2764, 3972, 29897, 13, 3166, 4639, 2185, 5527, 1053, 334, 13, 13, 29937, 694, 25053, 847, 13, 29903, 9094, 4219, 353, 525, 991, 597, 29879, 291, 23569, 29889, 3292, 29889, 601, 29914, 29886, 2606, 29899, 13775, 292, 29915, 13, 1525, 29931, 1299, 18474, 29918, 4219, 29903, 353, 7700, 13, 13, 16359, 3352, 29918, 9818, 29918, 1299, 6488, 353, 6213, 13, 29907, 3040, 29954, 18929, 29918, 16359, 3352, 29918, 1299, 6488, 353, 6213, 13, 26813, 12750, 8098, 29918, 16359, 3352, 29918, 1299, 6488, 353, 6213, 13, 20656, 29950, 1955, 29918, 16359, 3352, 29918, 1299, 6488, 353, 6213, 13, 20656, 29950, 1955, 29918, 16359, 3352, 29918, 29934, 1799, 353, 6213, 13, 13, 2287, 18476, 29918, 12015, 12336, 29918, 4571, 26282, 18929, 353, 5852, 13, 13, 29937, 12206, 4452, 526, 4049, 5407, 746, 27256, 13, 13, 29937, 23711, 29984, 3308, 29918, 29903, 1806, 1430, 25797, 353, 5124, 13, 29937, 17080, 29949, 29954, 1307, 29918, 2190, 1964, 29979, 29911, 2965, 29903, 353, 5124, 13, 13, 29937, 5087, 8701, 11856, 13, 29937, 17080, 29949, 29954, 1307, 29918, 29907, 1660, 353, 525, 29900, 29900, 29900, 29955, 29945, 29929, 29946, 29953, 29900, 29953, 29941, 29941, 29896, 29941, 29955, 29953, 29953, 29953, 29900, 29955, 29955, 29901, 29946, 29941, 29891, 29884, 29884, 29946, 29876, 24666, 29900, 29883, 29915, 13, 2 ]
serving_patterns/src/app/apps/app_image.py	shibuiwilliam/ml-system-in-action	10	1611156	<gh_stars>1-10 import os from fastapi import FastAPI import logging from src.configurations import PlatformConfigurations from src.app.routers import health, predict_image from src.app.configurations import APIConfigurations logger = logging.getLogger(__name__) logger.info(f"starts {APIConfigurations.title}:{APIConfigurations.version}") logger.info(f"platform: {PlatformConfigurations.platform}") app = FastAPI( title=APIConfigurations.title, description=APIConfigurations.description, version=APIConfigurations.version, ) app.include_router(health.router, prefix="/health", tags=["health"]) app.include_router(predict_image.router, prefix="/predict", tags=["predict"])	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 2897, 13, 3166, 5172, 2754, 1053, 23786, 8787, 13, 5215, 12183, 13, 13, 3166, 4765, 29889, 2917, 332, 800, 1053, 28096, 3991, 332, 800, 13, 3166, 4765, 29889, 932, 29889, 27537, 2153, 1053, 9045, 29892, 8500, 29918, 3027, 13, 3166, 4765, 29889, 932, 29889, 2917, 332, 800, 1053, 3450, 3991, 332, 800, 13, 13, 21707, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 29897, 13, 21707, 29889, 3888, 29898, 29888, 29908, 27382, 426, 8787, 3991, 332, 800, 29889, 3257, 6177, 29912, 8787, 3991, 332, 800, 29889, 3259, 27195, 13, 21707, 29889, 3888, 29898, 29888, 29908, 12120, 29901, 426, 21889, 3991, 332, 800, 29889, 12120, 27195, 13, 13, 13, 932, 353, 23786, 8787, 29898, 13, 1678, 3611, 29922, 8787, 3991, 332, 800, 29889, 3257, 29892, 13, 1678, 6139, 29922, 8787, 3991, 332, 800, 29889, 8216, 29892, 13, 1678, 1873, 29922, 8787, 3991, 332, 800, 29889, 3259, 29892, 13, 29897, 13, 13, 932, 29889, 2856, 29918, 15140, 29898, 354, 4298, 29889, 15140, 29892, 10944, 13802, 354, 4298, 613, 8282, 29922, 3366, 354, 4298, 20068, 13, 13, 932, 29889, 2856, 29918, 15140, 29898, 27711, 29918, 3027, 29889, 15140, 29892, 10944, 13802, 27711, 613, 8282, 29922, 3366, 27711, 20068, 13, 2 ]
hello.py	jinheebee/github_test	0	57569	<filename>hello.py<gh_stars>0 print("hello world") a = 3 b = 4 c = a * b print(c)	[ 1, 529, 9507, 29958, 12199, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 2158, 703, 12199, 3186, 1159, 13, 29874, 353, 29871, 29941, 13, 29890, 353, 29871, 29946, 13, 29883, 353, 263, 334, 289, 13, 2158, 29898, 29883, 29897, 13, 2 ]
msg_capsgan.py	MahdiyarMM/MSG-CapsGAN	5	156261	<filename>msg_capsgan.py #Image lib import cv2 # math libraries import numpy as np import scipy.misc # ml libraries import tensorflow as tf from keras import layers, models, optimizers from keras import backend as K from keras.utils import to_categorical from keras.datasets import mnist, cifar10 from keras.layers import Input, Dense, Reshape, Flatten, Dropout, Lambda, concatenate, Multiply from keras.layers import BatchNormalization, Activation, ZeroPadding2D, Add from keras.layers.advanced_activations import LeakyReLU from keras.layers.convolutional import UpSampling2D, Conv2D from keras.models import Sequential, Model from keras.optimizers import Adam from keras.preprocessing.image import ImageDataGenerator from keras import callbacks import datetime # visualization import skimage from skimage import data, color, exposure from skimage.transform import resize import matplotlib.pyplot as plt import math from skimage.measure import compare_ssim as ssim from skimage.measure import compare_psnr as psnr from keras.applications import VGG19 # sys and helpers import sys import os import glob from tqdm import tqdm print('Modules imported.') class DataLoader3_all(): def __init__(self, dataset_name, img_res=(128, 128)): self.dataset_name = dataset_name self.img_res = img_res def load_data(self, batch_size=1, is_testing=False): data_type = "train" if not is_testing else "test" path = glob.glob('/content/drive/My Drive/CelebA_sample/CelebA/') batch_images = np.random.choice(path, size=batch_size) imgs_hr = [] imgs_lr = [] imgs_32 = [] imgs_64 = [] for img_path in batch_images: imgl = self.imread(img_path) img = imgl[20:218-20,:] h, w = self.img_res low_h, low_w = int(h / 8), int(w / 8) # img_hr = scipy.misc.imresize(img, self.img_res) # img_lr = scipy.misc.imresize(img, (low_h, low_w)) # img_32 = scipy.misc.imresize(img, (32, 32)) # img_64 = scipy.misc.imresize(img, (64, 64)) img_hr = cv2.resize(img, self.img_res) img_lr = cv2.resize(img, (low_h, low_w)) img_32 = cv2.resize(img, (32, 32)) img_64 = cv2.resize(img, (64, 64)) # If training => do random flip if not is_testing and np.random.random() < 0.5: img_hr = np.fliplr(img_hr) img_lr = np.fliplr(img_lr) img_32 = np.fliplr(img_32) img_64 = np.fliplr(img_64) imgs_hr.append(img_hr) imgs_lr.append(img_lr) imgs_32.append(img_32) imgs_64.append(img_64) imgs_hr = np.array(imgs_hr) / 127.5 - 1. imgs_lr = np.array(imgs_lr) / 127.5 - 1. imgs_32 = np.array(imgs_32) / 127.5 - 1. imgs_64 = np.array(imgs_64) / 127.5 - 1. return imgs_hr, imgs_lr , imgs_32, imgs_64 def imread(self, path): return plt.imread(path).astype(np.float) def load_test(self,path): img = self.imread(path) img_hr = scipy.misc.imresize(img, (256,256)) # Configure data loader dataset_name = 'img_align_celeba' hr_height = 128 hr_width = 128 data_loader = DataLoader3_all(dataset_name=dataset_name, img_res=(hr_height, hr_width)) def all_psnr(imageA, imageB): psnrs = [] for ii in range(len(imageA)): psnrs.append(psnr(imageA[ii],imageB[ii])) return np.mean(psnrs) def all_ssim(imageA, imageB): psnrs = [] for ii in range(len(imageA)): psnrs.append(ssim(imageA[ii],imageB[ii],multichannel =True)) return np.mean(psnrs) # squash function of capsule layers, borrowed from <NAME>'s implementation of Keras CapsNet `https://github.com/XifengGuo/CapsNet-Keras` def squash(vectors, axis=-1): """ The non-linear activation used in Capsule. It drives the length of a large vector to near 1 and small vector to 0 :param vectors: some vectors to be squashed, N-dim tensor :param axis: the axis to squash :return: a Tensor with same shape as input vectors """ s_squared_norm = K.sum(K.square(vectors), axis, keepdims=True) scale = s_squared_norm / (1 + s_squared_norm) / K.sqrt(s_squared_norm + K.epsilon()) return scale vectors # device check from tensorflow.python.client import device_lib print('Devices:', device_lib.list_local_devices()) # GPU check if not tf.test.gpu_device_name(): print('No GPU found.') else: print('Default GPU Device: {}'.format(tf.test.gpu_device_name())) def build_vgg(hr_shape): """ Builds a pre-trained VGG19 model that outputs image features extracted at the third block of the model """ vgg = VGG19(weights="imagenet") # Set outputs to outputs of last conv. layer in block 3 # See architecture at: https://github.com/keras-team/keras/blob/master/keras/applications/vgg19.py vgg.outputs = [vgg.layers[9].output] img = Input(hr_shape) # Extract image features img_features = vgg(img) return Model(img, img_features) optimizer = Adam(0.0003, 0.5) vgg = build_vgg((128,128,3)) vgg.trainable = False vgg.compile(loss='mse', optimizer=optimizer, metrics=['accuracy']) vgg64 = build_vgg((64,64,3)) vgg64.trainable = False vgg64.compile(loss='mse', optimizer=optimizer, metrics=['accuracy']) vgg32 = build_vgg((32,32,3)) vgg32.trainable = False vgg32.compile(loss='mse', optimizer=optimizer, metrics=['accuracy']) # discriminator structure def build_discriminator(): # depending on dataset we define input shape for our network img = Input(shape=(128, 128, 3)) img64 = Input(shape=(64, 64, 3)) img32 = Input(shape=(32, 32, 3)) x1 = Conv2D(filters=128, kernel_size=9, strides=2, padding='same', name='conv0')(img) img64f = Conv2D(filters=128, kernel_size=9, strides=1, padding='same', name='convimg0')(img64) x2 = concatenate([x1,img64f],axis=-1) x3 = Conv2D(filters=128, kernel_size=9, strides=2, padding='same', name='conv1')(x2) img32f = Conv2D(filters=128, kernel_size=9, strides=1, padding='same', name='convimg1')(img32) x4 = concatenate([x3,img32f],axis=-1) # first typical convlayer outputs a 20x20x256 matrix x5 = Conv2D(filters=256, kernel_size=9, strides=1, padding='valid', name='conv2')(x4) x6 = LeakyReLU()(x5) # original 'Dynamic Routing Between Capsules' paper does not include the batch norm layer after the first conv group x = BatchNormalization(momentum=0.8)(x6) # # primarycaps coming first # # filters 256 (n_vectors=8 * channels=32) x = Conv2D(filters=8 * 32, kernel_size=9, strides=2, padding='valid', name='primarycap_conv2')(x) #Flatten to add dense layer x = Flatten()(x) # reshape into the 8D vector for all 32 feature maps combined # (primary capsule has collections of activations which denote orientation of the digit # while intensity of the vector which denotes the presence of the digit) x = Reshape(target_shape=[-1, 8], name='primarycap_reshape')(x) # the purpose is to output a number between 0 and 1 for each capsule where the length of the input decides the amount x = Lambda(squash, name='primarycap_squash')(x) x = BatchNormalization(momentum=0.8)(x) # # digitcaps are here # x = Flatten()(x) uhat = Dense(160, kernel_initializer='he_normal', bias_initializer='zeros', name='uhat_digitcaps')(x) # c = coupling coefficient (softmax over the bias weights, log prior) \| "the coupling coefficients between capsule (i) and all the capsules in the layer above sum to 1" # we treat the coupling coefficiant as a softmax over bias weights from the previous dense layer c = Activation('softmax', name='softmax_digitcaps1')(uhat) # softmax will make sure that each weight c_ij is a non-negative number and their sum equals to one # s_j (output of the current capsule level) = uhat * c c = Dense(160)(c) # compute s_j x = Multiply()([uhat, c]) """ NOTE: Squashing the capsule outputs creates severe blurry artifacts, thus we replace it with Leaky ReLu. """ s_j = LeakyReLU()(x) # # we will repeat the routing part 2 more times (num_routing=3) to unfold the loop # c = Activation('softmax', name='softmax_digitcaps2')(s_j) # softmax will make sure that each weight c_ij is a non-negative number and their sum equals to one c = Dense(160)(c) # compute s_j x = Multiply()([uhat, c]) s_j = LeakyReLU()(x) c = Activation('softmax', name='softmax_digitcaps3')(s_j) # softmax will make sure that each weight c_ij is a non-negative number and their sum equals to one c = Dense(160)(c) # compute s_j x = Multiply()([uhat, c]) s_j = LeakyReLU()(x) pred = Dense(2525, activation='sigmoid')(s_j) patch = Reshape(target_shape=[-1, 25], name='last_out_reshape')(pred) return Model([img,img64,img32], patch) # build and compile the discriminator discriminator = build_discriminator() print('DISCRIMINATOR:') discriminator.summary() sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) #discriminator.load_weights('images_MSG/wdis6400.h5') discriminator.compile(loss='binary_crossentropy', optimizer=sgd) # generator structure def build_generator(): from keras.applications import VGG19 vgg = VGG19(weights="imagenet") vgg.outputs = [vgg.layers[9].output] vgg.trainable =False def deconv2d(layer_input,num=256): """Layers used during upsampling""" u = UpSampling2D(size=2)(layer_input) u = Conv2D(num, kernel_size=3, strides=1, padding='same')(u) u = Activation('relu')(u) return u def residual_block(layer_input, filters): """Residual block described in paper""" d = Conv2D(filters, kernel_size=3, strides=1, padding='same')(layer_input) d = Activation('relu')(d) d = BatchNormalization(momentum=0.8)(d) d = Conv2D(filters, kernel_size=3, strides=1, padding='same')(d) d = BatchNormalization(momentum=0.8)(d) d = Add()([d, layer_input]) return d img_lr_in = Input(shape=(16,16,3)) img_bilinear = Lambda(lambda image: tf.image.resize_images(image,(128, 128),method = tf.image.ResizeMethod.BICUBIC,align_corners = True, preserve_aspect_ratio = True))(img_lr_in) img_bilinear_64 = Lambda(lambda image: tf.image.resize_images(image,(64, 64),method = tf.image.ResizeMethod.BICUBIC,align_corners = True, preserve_aspect_ratio = True))(img_lr_in) img_bilinear_32 = Lambda(lambda image: tf.image.resize_images(image,(32, 32),method = tf.image.ResizeMethod.BICUBIC,align_corners = True, preserve_aspect_ratio = True))(img_lr_in) vgg_features = vgg(img_bilinear) vgg_features_cnn = Conv2D(64, kernel_size=3, strides=1, padding='same',name = "vgg_features_cnn")(vgg_features) pd = Conv2D(64, kernel_size=3, strides=1, padding='same')(img_lr_in) x = residual_block(pd,64) x = deconv2d(x,num=64) xconcat = concatenate([x,vgg_features_cnn],axis=-1,name='concatenation') pd = Conv2D(64, kernel_size=3, strides=1, padding='same')(xconcat) x = residual_block(pd,64) x_out_32 = Conv2D(3, kernel_size=3, strides=1, padding='same')(x) x_out_32 = Activation('tanh')(x_out_32) x_32_add = Add()([x_out_32, img_bilinear_32]) x_32 = Activation('tanh')(x_32_add) x = deconv2d(x) pd = Conv2D(64, kernel_size=3, strides=1, padding='same')(x) x = residual_block(pd,64) x_out_64 = Conv2D(3, kernel_size=3, strides=1, padding='same')(x) x_out_64 = Activation('tanh')(x_out_64) x_64_add = Add()([x_out_64, img_bilinear_64]) x_64 = Activation('tanh')(x_64_add) x = deconv2d(x) x = Conv2D(3, kernel_size=3, strides=1, padding='same')(x) x_out_pre = Activation('tanh')(x) x_out_add = Add()([x_out_pre, img_bilinear]) x_out = Activation('tanh')(x_out_add) return Model([img_lr_in],[x_out,x_64,x_32]) # build and compile the generator generator = build_generator() print('GENERATOR:') generator.summary() #generator.load_weights('images_MSG/wgen6400.h5') generator.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5)) model_json = generator.to_json() with open("model.json", "w") as json_file: json_file.write(model_json) model_json = generator.to_json() with open("model.json", "w") as json_file: json_file.write(model_json) # feeding noise to generator z = Input(shape=(16,16,3)) imgf = generator(z) fake_features = vgg(imgf[0]) fake_features_1 = vgg64(imgf[1]) fake_features_2 = vgg32(imgf[2]) # for the combined model we will only train the generator discriminator.trainable = False # try to discriminate generated images valid = discriminator(imgf) # the combined model (stacked generator and discriminator) takes # noise as input => generates images => determines validity combined = Model(z, [valid,fake_features,fake_features_1,fake_features_2]) print('COMBINED:') combined.summary() combined.compile(loss=['binary_crossentropy', 'mse', 'mse', 'mse'], loss_weights=[3e-3, 0.001,0.001,1], optimizer=Adam(0.0002, 0.5)) # loss values for further plotting D_L_REAL = [] D_L_FAKE = [] D_L = [] D_ACC = [] G_L = [] def train(dataset_title, epochs, batch_size=32, save_interval=50): start_time = datetime.datetime.now() half_batch = int(batch_size / 2) for epoch in range(0,50000): ''' #Progressive Weight Loss Adjustment if epoch == 600: combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1e-2, 1], optimizer=Adam(0.0002, 0.5)) # --------------------- if epoch == 1500: combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1e-1, 1], optimizer=Adam(0.0002, 0.5)) if epoch == 2500: combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1e-2, 1], optimizer=Adam(0.0002, 0.5)) if epoch == 3500: combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1e-1, 1], optimizer=Adam(0.0002, 0.5)) ''' # Train Discriminator # --------------------- # select a random half batch of images imgs_hr, imgs_lr, imgs_32, imgs_64 = data_loader.load_data(half_batch) # generate a half batch of new images gen_imgs = generator.predict(imgs_lr) # train the discriminator by feeding both real and fake (generated) images one by one d_loss_real = discriminator.train_on_batch([imgs_hr,imgs_64,imgs_32], np.random.uniform(low=0.8, high=1.2, size=(half_batch, 25,25))) # 0.9 for label smoothing d_loss_fake = discriminator.train_on_batch(gen_imgs, np.random.uniform(low=0.0, high=0.3, size=(half_batch, 25,25))) d_loss = 0.5 np.add(d_loss_real, d_loss_fake) # --------------------- # Train Generator # --------------------- imgs_hr, imgs_lr, imgs_32, imgs_64 = data_loader.load_data(batch_size) # the generator wants the discriminator to label the generated samples # as valid (ones) image_features = vgg.predict(imgs_hr) image_features_1 = vgg64.predict(imgs_64) image_features_2 = vgg32.predict(imgs_32) # train the generator g_loss = combined.train_on_batch(imgs_lr, [np.ones((batch_size,25,25)),image_features,image_features_1,image_features_2]) elapsed_time = datetime.datetime.now() - start_time # Plot the progress print ("%d time: %s ,[D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch,elapsed_time, d_loss, 100d_loss, g_loss[0])) D_L_REAL.append(d_loss_real) D_L_FAKE.append(d_loss_fake) D_L.append(d_loss) D_ACC.append(d_loss) G_L.append(g_loss) # if at save interval => save generated image samples if epoch % save_interval == 0: sample_images(epoch) generator.save_weights("images_MSG/wgen%d.h5" % (epoch)) discriminator.save_weights("images_MSG/wdis%d.h5" % (epoch)) def sample_images(epoch): os.makedirs('images_MSG/%s' % dataset_name, exist_ok=True) r, c = 2, 5 imgs_hr, imgs_lr,_,_ = data_loader.load_data(batch_size=2, is_testing=True) fake_hr = generator.predict(imgs_lr) imgs_hr_inp = imgs_hr imgs_lr_inp = imgs_lr # Rescale images 0 - 1 imgs_lr = 0.5 imgs_lr + 0.5 fake_hr[0] = 0.5 * fake_hr[0] + 0.5 fake_hr[1] = 0.5 * fake_hr[1] + 0.5 fake_hr[2] = 0.5 * fake_hr[2] + 0.5 imgs_hr = 0.5 * imgs_hr + 0.5 # Save generated images and the high resolution originals titles = ['Original','128x128'] fig, axs = plt.subplots(r, c) cnt = 0 for row in range(r): for col, image in enumerate([imgs_hr,fake_hr[0]]): axs[row, col+3].imshow(imgs_lr[row]) axs[row, col+2].imshow(fake_hr[2][row]) axs[row, col+1].imshow(fake_hr[1][row]) axs[row, col].imshow(image[row]) axs[row, col+1].set_title('64x64') axs[row, col+2].set_title('32x32') axs[row, col+3].set_title('input') axs[row, col].set_title(titles[col]) axs[row, col].axis('off') axs[row, col+1].axis('off') axs[row, col+2].axis('off') axs[row, col+3].axis('off') cnt += 1 txt = ("psnr = %f - ssim = %f" % (all_psnr(fake_hr[0],imgs_hr), all_ssim(fake_hr[0],imgs_hr))) fig.text(.5, .05, txt, ha='center') fig.savefig("images_MSG/%s/%d.png" % (dataset_name, epoch)) plt.close() # Save low resolution images for comparison for i in range(r): fig = plt.figure() plt.imshow(imgs_lr[i]) fig.savefig('images_MSG/%s/%d_lowres%d.png' % (dataset_name, epoch, i)) plt.close() print(all_psnr(fake_hr[0],imgs_hr)) print(all_ssim(fake_hr[0],imgs_hr)) if epoch<5000 and epoch>1000: combined.compile(loss=['binary_crossentropy', 'mse', 'mse', 'mse'], loss_weights=[3e-3, 0.001,(epoch - 999)/4000,1], optimizer=Adam(0.0002, 0.5)) if epoch<10000 and epoch>5000: combined.compile(loss=['binary_crossentropy', 'mse', 'mse', 'mse'], loss_weights=[3e-3, (epoch - 4999)/5000,1,1], optimizer=Adam(0.0002, 0.5)) if epoch<15000 and epoch>10000: combined.compile(loss=['binary_crossentropy', 'mse', 'mse', 'mse'], loss_weights=[3e-3 + 2e-3*(epoch - 9999)/5000, 1,1,1], optimizer=Adam(0.0002, 0.5)) return imgs_lr_inp,imgs_hr_inp history = train('cifar10', epochs=30000, batch_size=32, save_interval=50) #generator.save('mnist_model.h5') #generator.save('cifar10_model.h5') plt.plot(D_L) plt.title('Discriminator results (MNIST)') plt.xlabel('Epochs') plt.ylabel('Discriminator Loss (blue), Discriminator Accuracy (orange)') plt.legend(['Discriminator Loss', 'Discriminator Accuracy']) plt.show() plt.plot(G_L) plt.title('Generator results (MNIST)') plt.xlabel('Epochs') plt.ylabel('Generator Loss (blue)') plt.legend('Generator Loss') plt.show() plt.plot(D_L) plt.title('Discriminator results (CIFAR10)') plt.xlabel('Epochs') plt.ylabel('Discriminator Loss (blue), Discriminator Accuracy (orange)') plt.legend(['Discriminator Loss', 'Discriminator Accuracy']) plt.show() plt.plot(G_L) plt.title('Generator results (CIFAR10)') plt.xlabel('Epochs') plt.ylabel('Generator Loss (blue)') plt.legend('Generator Loss') plt.show()	[ 1, 529, 9507, 29958, 7645, 29918, 29883, 2547, 6249, 29889, 2272, 13, 13, 13, 29937, 2940, 4303, 13, 5215, 13850, 29906, 13, 29937, 5844, 9562, 13, 5215, 12655, 408, 7442, 13, 5215, 4560, 2272, 29889, 29885, 10669, 13, 29937, 286, 29880, 9562, 13, 5215, 26110, 408, 15886, 13, 3166, 13023, 294, 1053, 15359, 29892, 4733, 29892, 5994, 19427, 13, 3166, 13023, 294, 1053, 14998, 408, 476, 13, 3166, 13023, 294, 29889, 13239, 1053, 304, 29918, 29883, 20440, 936, 13, 3166, 13023, 294, 29889, 14538, 1691, 1053, 28597, 391, 29892, 274, 361, 279, 29896, 29900, 13, 3166, 13023, 294, 29889, 29277, 1053, 10567, 29892, 360, 1947, 29892, 2538, 14443, 29892, 2379, 8606, 29892, 20724, 449, 29892, 365, 2269, 29892, 16125, 403, 29892, 9683, 666, 368, 13, 3166, 13023, 294, 29889, 29277, 1053, 350, 905, 19077, 2133, 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monolithic/order_transaction.py	liks79/sample-saga-with-step-functions	3	31659	<reponame>liks79/sample-saga-with-step-functions """ Database model for Monolithic application ~~~~~~~~~~~~~~~~~~~~~~~ :created date: Thursday, March 12, 2020 :description: Monolithic application implementation of simple order transaction scenario :copyright: © 2020 written by sungshik (<EMAIL>) :license: BSD 3-Clause License, see LICENSE for more details. """ import config import traceback from model import db from model.user import User from model.order import Order from model.membership import Membership from model.payment import Payment from model.inventory import Inventory from faker import Faker from pprint import pformat from sqlalchemy_utc import utcnow from exception import OutOfStockError, PaymentFailureError fake = Faker(config.FAKER_LOCALE) logger = config.logger try: """ Data Preparation """ # Create tables, if it is not existed. db.Base.metadata.create_all(bind=db.engine) # T0.TRANSACTION BEGIN # Transaction is already begun by SQLalchemy logger.info('#### T0.TRANSACTION BEGIN ####') # T1.Initiate User model logger.info('#### T1.INITIATE USER MODEL ####') user = db.session.query(User).first() logger.info(pformat(user.to_json())) # T2.Initiate Inventory model logger.info('#### T2.INITIATE INVENTORY MODEL ####') item = db.session.query(Inventory).first() logger.info(pformat(item.to_json())) # T3.Initiate Payment model logger.info('#### T3.INITIATE PAYMENT MODEL ####') payment = Payment(pay_type='credit_card', user_id=user.user_id, allowed=fake.boolean(), date=utcnow()) logger.info(pformat(payment.to_json())) db.session.add(payment) # db.session.commit() # T4.Initiate Order model logger.info('#### T4.INITIATE ORDER MODEL ####') ordered_item_qty = fake.random_int(1, 100) order = Order(user_id=user.user_id, item_id=item.item_id, item_qty=ordered_item_qty, date=utcnow(), deliver_phone=fake.phone_number(), deliver_address=fake.address(), total_price=fake.random_int(100, 1000000)) logger.info(pformat(order.to_json())) db.session.add(order) # db.session.commit() # T5.Update Inventory model logger.info('#### T5.UPDATE INVENTORY MODEL ####') logger.info('UPDATE INVENTORY QTY: {} - {} = {}'. format(item.qty, ordered_item_qty, item.qty - ordered_item_qty)) item.qty = item.qty - ordered_item_qty # T6.Update Membership model membership = Membership(user_id=user.user_id, date=utcnow(), mileage=fake.random_int(10, 400)) db.session.add(membership) logger.info('#### T6.UPDATE MEMBERSHIP ####') logger.info(pformat(membership.to_json())) # OutOfStockError Exception handling logger.info(pformat(item.to_json())) if item.qty < 0: logger.error('ORDERED ITEM IS OUT OF STOCK: %s' % item.qty) logger.error('THIS IS AN INTENDED ERROR.') raise OutOfStockError # PaymentFailureError Exception handling logger.info(pformat(payment.to_json())) if payment.allowed is not True: logger.error('PAYMENT TRANSACTION IS FAILED: %s' % payment.allowed) logger.error('THIS IS AN INTENDED ERROR.') raise PaymentFailureError # T7.Commit db.session.commit() logger.info('#### T7.TRANSACTION COMPLETED! ####') except Exception as e: logger.error(e) print(traceback.format_exc()) db.session.rollback() finally: db.session.close()	[ 1, 529, 276, 1112, 420, 29958, 492, 2039, 29955, 29929, 29914, 11249, 29899, 29879, 7781, 29899, 2541, 29899, 10568, 29899, 12171, 13, 15945, 29908, 13, 1678, 5470, 1904, 363, 2598, 324, 389, 293, 2280, 13, 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reaver/utils/experiment.py	XinyiYS/reaver	0	1612175	<filename>reaver/utils/experiment.py import os import gin import json from datetime import datetime as dt class Experiment: def __init__(self, results_dir, env_name, agent_name, name=None, restore=False, restore_mix=False, args=None): if not name: if restore: experiments = [e for e in os.listdir(results_dir) if env_name in e and agent_name in e] assert len(experiments) > 0, 'No experiment to restore' name = max(experiments, key=lambda p: os.path.getmtime(results_dir+'/'+p)) name = '_'.join(name.split('_')[2:]) else: name = dt.now().strftime("%y-%m-%d_%H-%M-%S") else: if restore_mix: assert name is not None, "Must give a experiment name in order to <restore_mix>." experiments = [e for e in os.listdir(results_dir) if name in e and agent_name in e] assert len(experiments) > 0, 'No experiment to restore' name = dt.now().strftime("%y-%m-%d_%H-%M-%S") self.name = name self.restore = restore self.env_name = env_name self.agent_name = agent_name self.results_dir = results_dir self.args = args os.makedirs(self.path, exist_ok=True) os.makedirs(self.path + '/summaries', exist_ok=True) os.makedirs(self.results_dir + '/summaries', exist_ok=True) if not os.path.exists(self.summaries_path): os.symlink('../%s/summaries' % self.full_name, self.summaries_path) @property def full_name(self): if self.args.HRL and "HRL-{}".format(self.args.HRL) not in self.name: return '%s_%s_HRL-%s_%s' % (self.env_name, self.agent_name, self.args.HRL, self.name) else: return '%s_%s_%s' % (self.env_name, self.agent_name, self.name) @property def path(self): return '%s/%s' % (self.results_dir, self.full_name) @property def config_path(self): return '%s/%s' % (self.path, 'config.gin') @property def log_path(self): return '%s/%s' % (self.path, 'train.log') @property def checkpoints_path(self): return self.path + '/checkpoints' @property def summaries_path(self): return '%s/summaries/%s' % (self.results_dir, self.full_name) @property def experiment_config_path(self): return '%s/%s' % (self.path, 'experiment_config') def save_gin_config(self): with open(self.config_path, 'w') as cfg_file: cfg_file.write(gin.operative_config_str()) def save_experiment_config(self): ex_config_dict = {"name": self.name, "restore": self.restore, "env": self.env_name, "n_envs": self.args.n_envs, "agent": self.agent_name, "results_dir": self.results_dir, "time": dt.now().strftime("%y-%m-%d_%H-%M-%S"), "HRL": self.args.HRL, "max_ep_len": self.args.max_ep_len, } with open(self.experiment_config_path, 'a') as ex_cfg_file: ex_cfg_file.write(json.dumps(ex_config_dict)) def save_model_summary(self, model, **kwargs): if model: with open(self.path + '/' + 'model_summary.txt', 'w') as fl: model.summary(print_fn=lambda line: print(line, file=fl)) else: assert 'models' in kwargs and 'subenvs' in kwargs, "Missing models and subenvs" models = kwargs['models'] subenvs = kwargs['subenvs'] for model, subenv in zip(models, subenvs): with open(self.path + '/' 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repository/spraytime_repo.py	kbj2060/plantpointNutrients-backend	0	142950	<reponame>kbj2060/plantpointNutrients-backend<gh_stars>0 from domain.entities.spraytime import SprayTime as eSprayTime from repository.repo import BaseRepo from repository import models from controllers.app import session class SprayTimeRepository(BaseRepo): def __init__(self) -> None: super().__init__() self.model = models.SprayTime self.entity = eSprayTime def create(self, period): new_spraytime: models.SprayTime = models.SprayTime(period=period) session.add(new_spraytime) session.commit() sprayTimeRepository = SprayTimeRepository()	[ 1, 529, 276, 1112, 420, 29958, 21066, 29926, 29906, 29900, 29953, 29900, 29914, 24389, 3149, 29940, 329, 374, 1237, 29899, 27852, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 5354, 29889, 296, 1907, 29889, 1028, 764, 2230, 1053, 1706, 764, 2481, 408, 321, 5592, 764, 2481, 13, 3166, 9810, 29889, 20095, 1053, 7399, 5612, 29877, 13, 3166, 9810, 1053, 4733, 13, 3166, 21385, 29889, 932, 1053, 4867, 13, 13, 13, 1990, 1706, 764, 2481, 11481, 29898, 5160, 5612, 29877, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29897, 1599, 6213, 29901, 13, 4706, 2428, 2141, 1649, 2344, 1649, 580, 13, 4706, 1583, 29889, 4299, 353, 4733, 29889, 5592, 764, 2481, 13, 4706, 1583, 29889, 10041, 353, 321, 5592, 764, 2481, 13, 13, 1678, 822, 1653, 29898, 1311, 29892, 3785, 1125, 13, 4706, 716, 29918, 1028, 764, 2230, 29901, 4733, 29889, 5592, 764, 2481, 353, 4733, 29889, 5592, 764, 2481, 29898, 19145, 29922, 19145, 29897, 13, 4706, 4867, 29889, 1202, 29898, 1482, 29918, 1028, 764, 2230, 29897, 13, 4706, 4867, 29889, 15060, 580, 13, 13, 1028, 764, 2481, 11481, 353, 1706, 764, 2481, 11481, 580, 13, 2 ]
tests/conf.py	xncbf/django-dynamodb-cache	21	22252	<gh_stars>10-100 from random import random TABLE_NAME = f"test-django-dynamodb-cache-{random()}"	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 3166, 4036, 1053, 4036, 13, 13, 21009, 29918, 5813, 353, 285, 29908, 1688, 29899, 14095, 29899, 29881, 2926, 10396, 29899, 8173, 29899, 29912, 8172, 580, 5038, 13, 2 ]
src/tests/test_workflow.py	marcusianlevine/compose-flow	10	135910	import shlex from unittest import TestCase, mock from compose_flow.commands import Workflow from tests import BaseTestCase TEST_PROJECT_NAME = "test_project_name" @mock.patch("compose_flow.commands.subcommands.env.utils") @mock.patch("compose_flow.commands.subcommands.env.get_backend") class WorkflowTestCase(BaseTestCase): @staticmethod def _setup_docker_config_mock(mocks): get_backend_mock = mocks[-2] get_backend_mock.return_value.read.return_value = "FOO=1\nBAR=2" @staticmethod def _setup_utils_mock(mocks): utils_mock = mocks[-1] utils_mock.get_tag_version.return_value = "0.0.0" utils_mock.render.side_effect = lambda x, *kwargs: x def test_config_name_with_different_env(self, mocks): """Ensure the config can have a different env prefix than the current env This allows storage of environments other than the current one, for example, storing a default local config in the dev remote so that multiple devs can access it to seed their environment """ command = shlex.split("-e dev -c local-test-project env cat") workflow = Workflow(argv=command) self.assertEquals("local-test-project", workflow.config_name) def test_default_env_when_no_env_specified(self, mocks): self._setup_docker_config_mock(mocks) self._setup_utils_mock(mocks) command = shlex.split("env cat") workflow = Workflow(argv=command) env = workflow.environment self.assertEqual([], sorted(env.data.keys())) @mock.patch("compose_flow.commands.workflow.os") def test_docker_image_prefix_default(self, mocks): """ Ensure the default image name will not accidentally push the image to a remote registry """ os_mock = mocks[0] os_mock.environ = {} os_mock.path.exists.return_value = False workflow = Workflow(argv=[]) self.assertEqual(workflow.docker_image_prefix, "localhost.localdomain") @mock.patch("compose_flow.commands.workflow.settings") @mock.patch("compose_flow.commands.workflow.os") def test_docker_image_prefix_from_os_settings(self, mocks): os_mock = mocks[0] os_mock.path.exists.return_value = False settings_mock = mocks[1] settings_mock.DOCKER_IMAGE_PREFIX = "foo" workflow = Workflow(argv=[]) self.assertEqual(workflow.docker_image_prefix, "foo") @mock.patch( "compose_flow.commands.workflow.Workflow.app_config", new_callable=mock.PropertyMock, ) def test_docker_image_prefix_from_app_config(self, mocks): image_prefix = "registry.prefix.com/foo" app_config_mock = mocks[0] app_config_mock.return_value = {"build": {"image_prefix": image_prefix}} workflow = Workflow(argv=[]) self.assertEqual(workflow.docker_image_prefix, image_prefix) def test_load_env_when_env_specified(self, mocks): self._setup_docker_config_mock(mocks) self._setup_utils_mock(mocks) command = shlex.split("-e dev env cat") workflow = Workflow(argv=command) env = workflow.environment self.assertEqual(["BAR", "FOO"], sorted(env.data.keys())) self.assertEqual("1", env.data["FOO"]) self.assertEqual("2", env.data["BAR"]) @mock.patch( "compose_flow.commands.workflow.Workflow.subcommand", new_callable=mock.PropertyMock, ) def test_setup_environment_flag(self, mocks): """ Ensures the environment cache is set to an empty dictionary when a workflow environment should not be setup """ subcommand_mock = mocks[0] subcommand_mock.return_value.setup_environment = False command = shlex.split("-e dev env cat") workflow = Workflow(argv=command) workflow._setup_environment() self.assertEqual({}, workflow.environment._data) @mock.patch("compose_flow.commands.workflow.print") @mock.patch("compose_flow.commands.workflow.pkg_resources") def test_version(self, mocks): """ Ensure the --version arg just returns the version """ version = "0.0.0-test" pkg_resources_mock = mocks[0] pkg_resources_mock.require.return_value = [mock.Mock(version=version)] command = shlex.split("--version") workflow = Workflow(argv=command) workflow.run() print_mock = mocks[1] print_mock.assert_called_with(version) # noinspection PyUnusedLocal @mock.patch("compose_flow.commands.workflow.PROJECT_NAME", new=TEST_PROJECT_NAME) class WorkflowArgsTestCase(TestCase): """ Tests for parsing command line arguments """ def test_sensible_defaults_no_env(self, mocks): """ Test sensible defaults when no environment is defined """ command = shlex.split("publish") workflow = Workflow(argv=command) self.assertEqual(None, workflow.args.environment) self.assertEqual(None, workflow.args.remote) self.assertEqual(TEST_PROJECT_NAME, workflow.config_name) self.assertEqual(TEST_PROJECT_NAME, workflow.project_name) def test_sensible_defaults_with_env(self, mocks): """ Test sensible defaults when an environment is defined """ env = "dev" command = shlex.split(f"-e {env} publish") workflow = Workflow(argv=command) self.assertEqual(env, workflow.args.environment) self.assertEqual(env, workflow.args.remote) self.assertEqual(f"{env}-{TEST_PROJECT_NAME}", workflow.config_name) self.assertEqual(TEST_PROJECT_NAME, workflow.project_name) def 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python/ceasarCipher.py	majid-T/snippets	1	187621	<reponame>majid-T/snippets # Simplified Ceasar Cipher example works on alphabetic charachtors only. # Numbers provide a small pre-image since we are ciphering on each word. So numbers are not # accepted and instead of 1 you should type one to increese security import tkinter as tk # All alphabet chars in a list chars = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'] # Function to cipher a charcahter by provided shift def getCipherChar(inCh, shift): # For space only return space if inCh == ' ': return ' ' # Get the current index of charachter in our list index = chars.index(inCh) # shift the index newIndex = index+shift # Check if new index is bigger than lenght of list -1 to avoid out of range index, # if it is deduct lenght to cycle from begining if(newIndex > len(chars)-1): newIndex -= len(chars) return chars[newIndex] # Function to iterate n a phrase and chipher charachters # Uses the lenght of the first word as shift def cipherPhrase(): phrase = originalPhrase.get() shift = len(phrase.split(' ')[0]) result = '' for ch in phrase: result += getCipherChar(ch, shift) data.delete(1.0, tk.END) data.insert(tk.END, result) # GUI part of code win = tk.Tk() win.title('Ceasar Cipher code') originalPhrase = tk.Entry(win, width=40) originalPhrase.grid(row=0, column=0, padx=3, pady=3) button = tk.Button(win, text='Encrypt text', command=cipherPhrase) button.grid(row=0, column=1, pady=5) data = tk.Text(win, height=20, width=100) data.grid(row=1, column=0, columnspan=2) labelFooter = tk.Label( win, text='Sample non official Copy- Under dev version <EMAIL>') labelFooter.grid(row=2, column=0, columnspan=2, padx=10, pady=1) win.mainloop()	[ 1, 529, 276, 1112, 420, 29958, 655, 29926, 333, 29899, 29911, 29914, 29879, 1240, 27421, 13, 29937, 3439, 572, 2164, 7747, 294, 279, 11402, 8096, 1342, 1736, 373, 394, 561, 370, 7492, 1373, 5860, 943, 871, 29889, 13, 29937, 11848, 2596, 3867, 263, 2319, 758, 29899, 3027, 1951, 591, 526, 4583, 561, 3241, 373, 1269, 1734, 29889, 1105, 3694, 526, 451, 13, 29937, 9259, 322, 2012, 310, 29871, 29896, 366, 881, 1134, 697, 304, 3079, 968, 6993, 13, 13, 5215, 18883, 1639, 408, 18883, 13, 13, 29937, 2178, 22968, 22524, 297, 263, 1051, 13, 305, 1503, 353, 6024, 29874, 742, 525, 29890, 742, 525, 29883, 742, 525, 29881, 742, 525, 29872, 742, 525, 29888, 742, 525, 29887, 742, 525, 29882, 742, 525, 29875, 742, 525, 29926, 742, 525, 29895, 742, 525, 29880, 742, 525, 29885, 742, 525, 29876, 742, 525, 29877, 742, 525, 29886, 742, 525, 29939, 742, 525, 29878, 742, 525, 29879, 742, 13, 308, 525, 29873, 742, 525, 29884, 742, 525, 29894, 742, 525, 29893, 742, 525, 29916, 742, 525, 29891, 742, 525, 29920, 742, 525, 29909, 742, 525, 29933, 742, 525, 29907, 742, 525, 29928, 742, 525, 29923, 742, 525, 29943, 742, 525, 29954, 742, 525, 29950, 742, 525, 29902, 742, 525, 29967, 742, 525, 29968, 742, 525, 29931, 742, 525, 29924, 742, 525, 29940, 742, 13, 308, 525, 29949, 742, 525, 29925, 742, 525, 29984, 742, 525, 29934, 742, 525, 29903, 742, 525, 29911, 742, 525, 29965, 742, 525, 29963, 742, 525, 29956, 742, 525, 29990, 742, 525, 29979, 742, 525, 29999, 2033, 13, 13, 13, 29937, 6680, 304, 4583, 8096, 263, 1373, 29883, 801, 357, 491, 4944, 9500, 13, 1753, 679, 29907, 29875, 8096, 5914, 29898, 262, 1451, 29892, 9500, 1125, 13, 1678, 396, 1152, 2913, 871, 736, 2913, 13, 1678, 565, 297, 1451, 1275, 525, 525, 29901, 13, 4706, 736, 525, 525, 13, 13, 1678, 396, 3617, 278, 1857, 2380, 310, 1373, 496, 357, 297, 1749, 1051, 13, 1678, 2380, 353, 22524, 29889, 2248, 29898, 262, 1451, 29897, 13, 13, 1678, 396, 9500, 278, 2380, 13, 1678, 716, 3220, 353, 2380, 29974, 10889, 13, 13, 1678, 396, 5399, 565, 716, 2380, 338, 16600, 1135, 28537, 400, 310, 1051, 448, 29896, 304, 4772, 714, 310, 3464, 2380, 29892, 13, 1678, 396, 565, 372, 338, 28262, 5313, 28537, 400, 304, 11412, 515, 1812, 2827, 13, 1678, 565, 29898, 1482, 3220, 1405, 7431, 29898, 305, 1503, 6817, 29896, 1125, 13, 4706, 716, 3220, 22361, 7431, 29898, 305, 1503, 29897, 13, 13, 1678, 736, 22524, 29961, 1482, 3220, 29962, 13, 13, 29937, 6680, 304, 13649, 302, 263, 16549, 322, 18558, 8096, 1373, 496, 2153, 13, 29937, 10783, 267, 278, 28537, 400, 310, 278, 937, 1734, 408, 9500, 13, 13, 13, 1753, 4583, 8096, 29925, 1092, 559, 7295, 13, 1678, 16549, 353, 2441, 29925, 1092, 559, 29889, 657, 580, 13, 1678, 9500, 353, 7431, 29898, 24588, 559, 29889, 5451, 877, 525, 9601, 29900, 2314, 13, 1678, 1121, 353, 6629, 13, 1678, 363, 521, 297, 16549, 29901, 13, 4706, 1121, 4619, 679, 29907, 29875, 8096, 5914, 29898, 305, 29892, 9500, 29897, 13, 13, 1678, 848, 29889, 8143, 29898, 29896, 29889, 29900, 29892, 18883, 29889, 11794, 29897, 13, 1678, 848, 29889, 7851, 29898, 11178, 29889, 11794, 29892, 1121, 29897, 13, 13, 13, 29937, 14839, 760, 310, 775, 13, 5080, 353, 18883, 29889, 29911, 29895, 580, 13, 5080, 29889, 3257, 877, 29907, 29872, 294, 279, 11402, 8096, 775, 1495, 13, 13, 13, 13492, 29925, 1092, 559, 353, 18883, 29889, 9634, 29898, 5080, 29892, 2920, 29922, 29946, 29900, 29897, 13, 13492, 29925, 1092, 559, 29889, 7720, 29898, 798, 29922, 29900, 29892, 1897, 29922, 29900, 29892, 17132, 29916, 29922, 29941, 29892, 282, 3714, 29922, 29941, 29897, 13, 13, 13, 3092, 353, 18883, 29889, 3125, 29898, 5080, 29892, 1426, 2433, 8566, 4641, 1426, 742, 1899, 29922, 455, 8096, 29925, 1092, 559, 29897, 13, 3092, 29889, 7720, 29898, 798, 29922, 29900, 29892, 1897, 29922, 29896, 29892, 282, 3714, 29922, 29945, 29897, 13, 13, 1272, 353, 18883, 29889, 1626, 29898, 5080, 29892, 3171, 29922, 29906, 29900, 29892, 2920, 29922, 29896, 29900, 29900, 29897, 13, 1272, 29889, 7720, 29898, 798, 29922, 29896, 29892, 1897, 29922, 29900, 29892, 1897, 9653, 29922, 29906, 29897, 13, 13, 1643, 13440, 261, 353, 18883, 29889, 4775, 29898, 13, 1678, 5401, 29892, 1426, 2433, 17708, 1661, 6221, 14187, 29899, 7634, 2906, 1873, 529, 26862, 6227, 29958, 1495, 13, 1643, 13440, 261, 29889, 7720, 29898, 798, 29922, 29906, 29892, 1897, 29922, 29900, 29892, 1897, 9653, 29922, 29906, 29892, 17132, 29916, 29922, 29896, 29900, 29892, 282, 3714, 29922, 29896, 29897, 13, 13, 5080, 29889, 3396, 7888, 580, 13, 2 ]
recipes/recipe_modules/cloudbuildhelper/__init__.py	NDevTK/chromium-infra	0	193345	# Copyright 2019 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. PYTHON_VERSION_COMPATIBILITY = 'PY2+3' DEPS = [ 'recipe_engine/cipd', 'recipe_engine/commit_position', 'recipe_engine/context', 'recipe_engine/file', 'recipe_engine/golang', 'recipe_engine/json', 'recipe_engine/nodejs', 'recipe_engine/path', 'recipe_engine/raw_io', 'recipe_engine/step', 'depot_tools/depot_tools', 'depot_tools/git', 'depot_tools/git_cl', ]	[ 1, 396, 14187, 1266, 29871, 29906, 29900, 29896, 29929, 450, 678, 456, 1974, 13189, 943, 29889, 2178, 10462, 21676, 29889, 13, 29937, 4803, 310, 445, 2752, 775, 338, 4095, 287, 491, 263, 350, 7230, 29899, 3293, 19405, 393, 508, 367, 13, 29937, 1476, 297, 278, 365, 2965, 1430, 1660, 934, 29889, 13, 13, 20055, 4690, 1164, 29918, 16358, 29918, 21514, 1299, 8979, 6227, 11937, 353, 525, 20055, 29906, 29974, 29941, 29915, 13, 13, 2287, 7024, 353, 518, 13, 29871, 525, 4361, 412, 29918, 10599, 29914, 7334, 29881, 742, 13, 29871, 525, 4361, 412, 29918, 10599, 29914, 15060, 29918, 3283, 742, 13, 29871, 525, 4361, 412, 29918, 10599, 29914, 4703, 742, 13, 29871, 525, 4361, 412, 29918, 10599, 29914, 1445, 742, 13, 29871, 525, 4361, 412, 29918, 10599, 29914, 29887, 324, 574, 742, 13, 29871, 525, 4361, 412, 29918, 10599, 29914, 3126, 742, 13, 29871, 525, 4361, 412, 29918, 10599, 29914, 3177, 1315, 742, 13, 29871, 525, 4361, 412, 29918, 10599, 29914, 2084, 742, 13, 29871, 525, 4361, 412, 29918, 10599, 29914, 1610, 29918, 601, 742, 13, 29871, 525, 4361, 412, 29918, 10599, 29914, 10568, 742, 13, 13, 29871, 525, 2716, 327, 29918, 8504, 29914, 2716, 327, 29918, 8504, 742, 13, 29871, 525, 2716, 327, 29918, 8504, 29914, 5559, 742, 13, 29871, 525, 2716, 327, 29918, 8504, 29914, 5559, 29918, 695, 742, 13, 29962, 13, 2 ]
elpis/engines/common/input/clean_json.py	guillaume-wisniewski/elpis	118	119213	<gh_stars>100-1000 #!/usr/bin/python3 """ Given a json file with transcript information these tools can perform - manipulations including generating word lists or filtering output to - exclude english/punctuation. - Optionally provide the output json file name with -j Usage: python3 clean_json.py [-h] [--i INFILE] [--o OUTFILE] [-r] [-u] Copyright: University of Queensland, 2019 Contributors: <NAME> - (The University of Queensland, 2017) <NAME> - (The University of Queensland, 2018) <NAME> - (The University of Queensland, 2019) """ import os import re import sys import nltk from argparse import ArgumentParser from langid.langid import LanguageIdentifier, model from nltk.corpus import words from typing import Dict, List, Set from ..utilities import load_json_file, write_data_to_json_file def clean_utterance(utterance: Dict[str, str], punctuation_to_collapse_by: str = '', punctuation_to_explode_by: str = '', special_cases: set = {None}, translation_tags: set = {None}, remove_english: bool = False, english_words: set = None) -> (List[str], int): """ Takes an utterance and cleans it based on the rules established by the provided parameters. :param utterance: a dictionary with a "transcript" key-value pair. :param punctuation_to_collapse_by: punctuation marks to strip. :param punctuation_to_explode_by: punctuation marks to replace with spaces. :param special_cases: a list of words to always remove from the output. :param translation_tags: a list of tags to always remove from the output. :param remove_english: whether or not to remove English words. :param english_words: a list of english words to remove from the transcript (we suggest the nltk corpus). :return: a tuple with a list of 'cleaned' words and the number of English to remove. """ # TODO add interface setting to include user specific tags # translation_tags = {"@eng@", "<ind:", "<eng:"} # TODO add interface setting to skip this as caps are significant in some languages utterance_string = utterance.get("transcript").lower() dirty_words = utterance_string.split() clean_words = [] english_word_count = 0 for word in dirty_words: if word in special_cases: continue if remove_english and len(word) > 3 and word in english_words: english_word_count += 1 continue if word in translation_tags: return [], 0 # Word is ok to use, now clean it word = deal_with_punctuation(text=word, punctuation_to_collapse_by=punctuation_to_collapse_by, punctuation_to_explode_by=punctuation_to_explode_by) clean_words.append(word) return clean_words, english_word_count def get_english_words() -> Set[str]: """ Gets a list of English words from the nltk corpora (~235k words). N.B: will download the word list if not already available (~740kB), requires internet. :return: a set containing the English words """ nltk.download("words") # Will only download if not locally available. return set(words.words()) def are_words_valid(clean_words: List[str], english_word_count: int, remove_english: bool, use_langid: bool) -> bool: """ Determines whether a list of words is valid based on the provided parameters. :param clean_words: a list of clean word strings. :param english_word_count: the number of english words removed from the string during cleaning. :param remove_english: whether or not to remove english words. :param use_langid: whether or not to use the langid library to determine if a word is English. :return: True if utterance is valid, False otherwise. """ # Exclude utterance if empty after cleaning cleaned_transcription = " ".join(clean_words).strip() if cleaned_transcription == "": return False # Exclude utterance if > 10% english if remove_english and len(clean_words) > 0 and english_word_count / len(clean_words) > 0.1: # print(round(english_word_count / len(clean_words)), trans, file=sys.stderr) return False # Exclude utterance if langid thinks its english if remove_english and use_langid: langid_identifier = LanguageIdentifier.from_modelstring(model, norm_probs=True) lang, prob = langid_identifier.classify(cleaned_transcription) if lang == "en" and prob > 0.5: return False return True def clean_json_utterance(utterance: Dict[str, str], punctuation_to_collapse_by: str = '', punctuation_to_explode_by: str = '', special_cases: set = {None}, translation_tags: set = {None}, remove_english: bool = False, use_langid: bool = False) -> Dict[str, str]: """ Clean an utterance (Python dictionary) based on the given parameters. :param utterance: Python dictionary, must have a 'transcript' key-value. :param punctuation_to_collapse_by: punctuation marks to strip. :param punctuation_to_explode_by: punctuation marks to replace with spaces. :param special_cases: a list of words to always remove from the output. :param translation_tags: a list of tags to always remove from the output. :param remove_english: whether or not to remove English from the utterances. :param use_langid: whether or not to use the langid library to identify English to remove. :return: cleaned utterance (dictionary). """ # TODO make this an interface setting # special_cases = ["<silence>"] # Any words you want to ignore if remove_english: english_words = get_english_words() # pre-load English corpus else: english_words = set() # Clean the text in the dict, returns a list of cleaned words clean_words, english_word_count = \ clean_utterance(utterance=utterance, punctuation_to_collapse_by=punctuation_to_collapse_by, punctuation_to_explode_by=punctuation_to_explode_by, special_cases=special_cases, translation_tags=translation_tags, remove_english=remove_english, english_words=english_words ) # Check that the cleaned words are valid (ie, not null, not English etc) if are_words_valid(clean_words, english_word_count, remove_english, use_langid): cleaned_transcript = " ".join(clean_words).strip() else: # TODO is it best to return an empty str here or raise an error? cleaned_transcript = "" utterance['transcript'] = cleaned_transcript return utterance def clean_json_data(json_data: List[Dict[str, str]], punctuation_to_collapse_by: str = '', punctuation_to_explode_by: str = '', special_cases: set = {None}, translation_tags: set = {None}, remove_english: bool = False, use_langid: bool = False, ) -> List[Dict[str, str]]: """ Clean a list of utterances (Python dictionaries) based on the given parameters. :param json_data: List of Python dictionaries, each must have a 'transcript' key-value. :param punctuation_to_collapse_by: punctuation marks to strip. :param punctuation_to_explode_by: punctuation marks to replace with spaces. :param special_cases: a list of words to always remove from the output. :param translation_tags: a list of tags to always remove from the output. :param remove_english: whether or not to remove English from the utterances. :param use_langid: whether or not to use the langid library to identify English to remove. :return: list of cleaned utterances (dictionaries). """ json_data_cleaned = [] for utterance in json_data: utterance_cleaned = clean_json_utterance(utterance, punctuation_to_collapse_by=punctuation_to_collapse_by, punctuation_to_explode_by=punctuation_to_explode_by, special_cases=special_cases, translation_tags=translation_tags, remove_english=remove_english, use_langid=use_langid) json_data_cleaned.append(utterance_cleaned) return json_data_cleaned def extract_additional_corpora(additional_corpus: str = '', corpus_txt: str = '', punctuation_to_collapse_by: str = '', punctuation_to_explode_by: str = '') -> None: """ Takes a text file, extracts all sentences and writes them to the main corpus file. :param additional_corpus: the path to a plaintext file to extract additional sentences/lines from :param corpus_txt: the path to the compiled corpus.txt file :param punctuation_to_collapse_by: punctuation marks to strip :param punctuation_to_explode_by: punctuation marks to replace with spaces """ print("corpus_txt", corpus_txt) if os.path.exists(corpus_txt): write_mode = 'a' # append if already exists else: write_mode = 'w' # make a new file if not with open(corpus_txt, write_mode) as corpus_txt_file: if os.path.exists(additional_corpus): print(f"Extracting corpus examples from: {additional_corpus}") with open(additional_corpus, "r", encoding="utf-8", ) as file_: for line in file_.readlines(): # clean the text along the way line = \ deal_with_punctuation(text=line, punctuation_to_collapse_by=punctuation_to_collapse_by, punctuation_to_explode_by=punctuation_to_explode_by) if not line.endswith('\n'): line = line + '\n' corpus_txt_file.writelines(line) else: print(f"Provided additional text additional_corpus file path invalid: " f"{additional_corpus}") def deal_with_punctuation(text: str = '', punctuation_to_collapse_by: str = '', punctuation_to_explode_by: str = '') -> str: """ Removes punctuation from a string :param text: original text :param punctuation_to_collapse_by: punctuation marks to strip :param punctuation_to_explode_by: punctuation marks to replace with spaces :return: cleaned text """ new_text: str = text # Prioritise exploding first, these are punctuation marks that the user sets if punctuation_to_explode_by: pattern_to_explode_by = re.escape(punctuation_to_explode_by) new_text = re.sub(rf"[{pattern_to_explode_by}]", " ", new_text) # Then strip the rest if punctuation_to_collapse_by: pattern_to_collapse_by = re.escape(punctuation_to_collapse_by) new_text = re.sub(rf"[{pattern_to_collapse_by}]", "", new_text) return new_text def main() -> None: """ Run the entire clean_json process as a command line utility. Usage: python3 clean_json.py [--i INFILE] [--o OUTFILE] [-r] [-u] """ parser: ArgumentParser = ArgumentParser() parser.add_argument("-i", "--infile", type=str, help="The path to the dirty json file to clean.", required=True) parser.add_argument("-o", "--outfile", type=str, help="The path to the clean json file to write to", required=True) parser.add_argument("-r", "--remove_english", help="Remove english-like utterances", action="store_true") parser.add_argument("-u", "--use_langid", help="Use langid library to detect English", action="store_true") # TODO add defaults parser.add_argument("-c", "--punctuation_to_collapse_by", type=str, help="Chars to strip") parser.add_argument("-e", "--punctuation_to_explode_by", type=str, help="Chars to strip and replace with spaces") arguments = parser.parse_args() dirty_json_data: List[Dict[str, str]] = load_json_file(arguments.infile) outfile = arguments.outfile if arguments.outfile else sys.stdout print(f"Filtering dirty json data {arguments.infile}...") filtered_data = clean_json_data(json_data=dirty_json_data, remove_english=arguments.remove_english, use_langid=arguments.use_langid, punctuation_to_collapse_by=arguments.punctuation_to_collapse_by, punctuation_to_explode_by=arguments.punctuation_to_explode_by) write_data_to_json_file(data=list(filtered_data), file_name=outfile) print(f"Finished! 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server.py	vitorpamplona/relayserver	0	140114	import socket # for connecting to the server import _thread # to manage multiple clients import json # to enconde and decode the data host = '127.0.0.1' port = 5000 # initiate port no above 1024 clients = {} def server_program(): print('Starting Relay Server at ' + host + ':' + str(port)) # show in terminal print('Waiting for clients...') server_socket = socket.socket() # get instance server_socket.bind((host, port)) # bind host address and port together # configure how many clients the server can listen simultaneously server_socket.listen(2) while True: conn, address = server_socket.accept() # accept new connection _thread.start_new_thread(on_new_client,(conn, address)) #puts into a thread. server_socket.close() # close the connection def on_new_client(clientsocket, addr): # print("Connection from: " + str(addr)) # print("Waiting for client name...") # wait for the first message. msgRaw = clientsocket.recv(1024).decode() # print("Message received: " + msgRaw) # Register the socket name at a list. msgDict = json.loads(msgRaw) print(msgDict['client_id'] + " connected") clients[msgDict['client_id']] = clientsocket wait_for_next_message(clientsocket, msgDict['client_id']) def wait_for_next_message(clientsocket, client_id): # wait for a message. msgRaw = clientsocket.recv(1024).decode() while msgRaw: print("Message received from " + client_id + ": " + msgRaw) msgDict = json.loads(msgRaw) if msgDict['to'] in clients.keys(): #reformat the message formattedMsg = json.dumps({'from': client_id, 'data': msgDict['data']}) # sending it to the next partner clients[msgDict['to']].send(formattedMsg.encode()) # send data to the client else: print(msgDict['to'] + ' is not connected') # could not find the other socket formattedMsg = json.dumps({'error':msgDict['to']+ ' not found'}) # sending error message back. clientsocket.send(formattedMsg.encode()) # send data to the client #wait for next message msgRaw = clientsocket.recv(1024).decode() clientsocket.close() if __name__ == '__main__': server_program()	[ 1, 1053, 9909, 396, 363, 16791, 304, 278, 1923, 13, 5215, 903, 7097, 396, 304, 10933, 2999, 13154, 13, 5215, 4390, 396, 304, 427, 15973, 322, 21822, 278, 848, 13, 13, 3069, 353, 525, 29896, 29906, 29955, 29889, 29900, 29889, 29900, 29889, 29896, 29915, 13, 637, 353, 29871, 29945, 29900, 29900, 29900, 29871, 396, 14511, 403, 2011, 694, 2038, 29871, 29896, 29900, 29906, 29946, 13, 13, 11303, 1237, 353, 6571, 13, 13, 1753, 1923, 29918, 8860, 7295, 13, 1678, 1596, 877, 4763, 292, 6376, 388, 5656, 472, 525, 718, 3495, 718, 525, 11283, 718, 851, 29898, 637, 876, 29871, 396, 1510, 297, 8638, 13, 1678, 1596, 877, 15716, 292, 363, 13154, 856, 1495, 13, 13, 1678, 1923, 29918, 11514, 353, 9909, 29889, 11514, 580, 29871, 396, 679, 2777, 13, 1678, 1923, 29918, 11514, 29889, 5355, 3552, 3069, 29892, 2011, 876, 29871, 396, 7868, 3495, 3211, 322, 2011, 4208, 13, 13, 1678, 396, 10822, 920, 1784, 13154, 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Algorithms/LCP/29/math1.py	M-Quadra/LeetCode-problems	0	17971	<reponame>M-Quadra/LeetCode-problems class Solution: def orchestraLayout(self, num: int, xPos: int, yPos: int) -> int: a, b = (min(xPos, num-1-yPos), 1) if yPos >= xPos else (min(yPos, num-1-xPos), -1) return (4numa - 4aa - 2a + b(xPos+yPos) + (b>>1&1)4(num-a-1))%9 + 1	[ 1, 529, 276, 1112, 420, 29958, 29924, 29899, 2182, 328, 336, 29914, 3226, 300, 3399, 29899, 17199, 29879, 13, 1990, 24380, 29901, 13, 1678, 822, 470, 15554, 3453, 29898, 1311, 29892, 954, 29901, 938, 29892, 921, 9135, 29901, 938, 29892, 343, 9135, 29901, 938, 29897, 1599, 938, 29901, 13, 4706, 263, 29892, 289, 353, 313, 1195, 29898, 29916, 9135, 29892, 954, 29899, 29896, 29899, 29891, 9135, 511, 29871, 29896, 29897, 565, 343, 9135, 6736, 921, 9135, 1683, 313, 1195, 29898, 29891, 9135, 29892, 954, 29899, 29896, 29899, 29916, 9135, 511, 448, 29896, 29897, 13, 4706, 736, 313, 29946, 29930, 1949, 29930, 29874, 448, 29871, 29946, 29930, 29874, 29930, 29874, 448, 29871, 29906, 29930, 29874, 718, 289, 16395, 29916, 9135, 29974, 29891, 9135, 29897, 718, 313, 29890, 6778, 29896, 29987, 29896, 11877, 29946, 16395, 1949, 29899, 29874, 29899, 29896, 876, 29995, 29929, 718, 29871, 29896, 2 ]
lecture_04/312_plan_motion_ros_artist.py	farzanehesk/COMPAS-II-FS2022	11	17499	import math import time from compas_fab.backends import RosClient from compas.artists import Artist from compas.geometry import Frame with RosClient("localhost") as client: robot = client.load_robot(load_geometry=True) group = robot.main_group_name frame = Frame((0.4, 0.3, 0.05), (-1, 0, 0), (0, 1, 0)) tolerance_position = 0.001 tolerance_axes = [math.radians(1)] * 3 start_configuration = robot.zero_configuration() start_configuration.joint_values = (-0.106, 5.351, 2.231, -2.869, 4.712, 1.465) # create goal constraints from frame goal_constraints = robot.constraints_from_frame(frame, tolerance_position, tolerance_axes, group) trajectory = robot.plan_motion(goal_constraints, start_configuration, group, options=dict(planner_id="RRT")) print("Computed kinematic path with %d configurations." % len(trajectory.points)) print("Executing this path at full speed would take approx. %.3f seconds." % trajectory.time_from_start) artist = Artist(robot.model) for tp in trajectory.points: config = robot.zero_configuration() config.joint_values = tp.joint_values artist.update(config) artist.draw_visual() artist.redraw() time.sleep(0.02)	[ 1, 1053, 5844, 13, 5215, 931, 13, 13, 3166, 752, 294, 29918, 16582, 29889, 1627, 1975, 1053, 5678, 4032, 13, 13, 3166, 752, 294, 29889, 442, 2879, 1053, 3012, 391, 13, 3166, 752, 294, 29889, 19156, 1053, 12218, 13, 13, 2541, 5678, 4032, 703, 7640, 1159, 408, 3132, 29901, 13, 1678, 19964, 353, 3132, 29889, 1359, 29918, 307, 7451, 29898, 1359, 29918, 19156, 29922, 5574, 29897, 13, 1678, 2318, 353, 19964, 29889, 3396, 29918, 2972, 29918, 978, 13, 13, 1678, 3515, 353, 12218, 3552, 29900, 29889, 29946, 29892, 29871, 29900, 29889, 29941, 29892, 29871, 29900, 29889, 29900, 29945, 511, 8521, 29896, 29892, 29871, 29900, 29892, 29871, 29900, 511, 313, 29900, 29892, 29871, 29896, 29892, 29871, 29900, 876, 13, 1678, 20341, 749, 29918, 3283, 353, 29871, 29900, 29889, 29900, 29900, 29896, 13, 1678, 20341, 749, 29918, 1165, 267, 353, 518, 755, 29889, 3665, 5834, 29898, 29896, 4638, 334, 29871, 29941, 13, 13, 1678, 1369, 29918, 13305, 353, 19964, 29889, 9171, 29918, 13305, 580, 13, 1678, 1369, 29918, 13305, 29889, 12090, 29918, 5975, 353, 8521, 29900, 29889, 29896, 29900, 29953, 29892, 29871, 29945, 29889, 29941, 29945, 29896, 29892, 29871, 29906, 29889, 29906, 29941, 29896, 29892, 448, 29906, 29889, 29947, 29953, 29929, 29892, 29871, 29946, 29889, 29955, 29896, 29906, 29892, 29871, 29896, 29889, 29946, 29953, 29945, 29897, 13, 13, 1678, 396, 1653, 7306, 11938, 515, 3515, 13, 1678, 7306, 29918, 13646, 29879, 353, 19964, 29889, 13646, 29879, 29918, 3166, 29918, 2557, 29898, 2557, 29892, 20341, 749, 29918, 3283, 29892, 20341, 749, 29918, 1165, 267, 29892, 2318, 29897, 13, 13, 1678, 23324, 706, 353, 19964, 29889, 9018, 29918, 29885, 8194, 29898, 28111, 29918, 13646, 29879, 29892, 1369, 29918, 13305, 29892, 2318, 29892, 3987, 29922, 8977, 29898, 572, 7310, 29918, 333, 543, 29934, 13079, 5783, 13, 13, 1678, 1596, 703, 20606, 287, 19015, 19217, 2224, 411, 1273, 29881, 22920, 1213, 1273, 7431, 29898, 3018, 622, 706, 29889, 9748, 876, 13, 1678, 1596, 703, 5379, 17068, 445, 2224, 472, 2989, 6210, 723, 2125, 2134, 29916, 29889, 18695, 29941, 29888, 6923, 1213, 1273, 23324, 706, 29889, 2230, 29918, 3166, 29918, 2962, 29897, 13, 13, 1678, 7664, 353, 3012, 391, 29898, 307, 7451, 29889, 4299, 29897, 13, 13, 1678, 363, 260, 29886, 297, 23324, 706, 29889, 9748, 29901, 13, 4706, 2295, 353, 19964, 29889, 9171, 29918, 13305, 580, 13, 4706, 2295, 29889, 12090, 29918, 5975, 353, 260, 29886, 29889, 12090, 29918, 5975, 13, 4706, 7664, 29889, 5504, 29898, 2917, 29897, 13, 4706, 7664, 29889, 4012, 29918, 20119, 580, 13, 4706, 7664, 29889, 1127, 1610, 580, 13, 4706, 931, 29889, 17059, 29898, 29900, 29889, 29900, 29906, 29897, 13, 2 ]
pymath/vampire_numbers/vampire_numbers.py	JASTYN/pythonmaster	3	82769	def vampire_test(x, y): product = x * y if len(str(x) + str(y)) != len(str(product)): return False for i in str(x) + str(y): if i in str(product): return True else: return False	[ 1, 822, 325, 1160, 533, 29918, 1688, 29898, 29916, 29892, 343, 1125, 13, 1678, 3234, 353, 921, 334, 343, 13, 1678, 565, 7431, 29898, 710, 29898, 29916, 29897, 718, 851, 29898, 29891, 876, 2804, 7431, 29898, 710, 29898, 4704, 22164, 13, 4706, 736, 7700, 13, 13, 1678, 363, 474, 297, 851, 29898, 29916, 29897, 718, 851, 29898, 29891, 1125, 13, 4706, 565, 474, 297, 851, 29898, 4704, 1125, 13, 9651, 736, 5852, 13, 4706, 1683, 29901, 13, 9651, 736, 7700, 13, 2 ]
sandbox/decorator_use.py	olileger/sBeull	0	1615107	<gh_stars>0 from decorator_def import FuncDecoratorOne, FuncDecoratorListProperties, ClassDecoratorOne class FuncDecoratorUse: def __init__(_self_): _self_.var1 = "test" _self_.var2 = "abc" _self_.var3 = 12345 @FuncDecoratorListProperties def FunctionOne(_self_, v): print("FunctionOne: ", v) t = FuncDecoratorUse() t.FunctionOne("Test") @ClassDecoratorOne class ClassDecoratorUser: def __init__(_self_): print("Hello, World !") t = ClassDecoratorUser()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 10200, 1061, 29918, 1753, 1053, 383, 4661, 6185, 272, 1061, 6716, 29892, 383, 4661, 6185, 272, 1061, 1293, 11857, 29892, 4134, 6185, 272, 1061, 6716, 13, 13, 1990, 383, 4661, 6185, 272, 1061, 11403, 29901, 13, 13, 1678, 822, 4770, 2344, 1649, 7373, 1311, 29918, 1125, 13, 4706, 903, 1311, 5396, 1707, 29896, 353, 376, 1688, 29908, 13, 4706, 903, 1311, 5396, 1707, 29906, 353, 376, 10736, 29908, 13, 4706, 903, 1311, 5396, 1707, 29941, 353, 29871, 29896, 29906, 29941, 29946, 29945, 13, 13, 1678, 732, 14400, 6185, 272, 1061, 1293, 11857, 13, 1678, 822, 6680, 6716, 7373, 1311, 3383, 325, 1125, 13, 4706, 1596, 703, 6678, 6716, 29901, 9162, 325, 29897, 13, 13, 13, 29873, 353, 383, 4661, 6185, 272, 1061, 11403, 580, 13, 29873, 29889, 6678, 6716, 703, 3057, 1159, 13, 13, 29992, 2385, 6185, 272, 1061, 6716, 13, 1990, 4134, 6185, 272, 1061, 2659, 29901, 13, 1678, 822, 4770, 2344, 1649, 7373, 1311, 29918, 1125, 13, 4706, 1596, 703, 10994, 29892, 2787, 1738, 1159, 13, 13, 29873, 353, 4134, 6185, 272, 1061, 2659, 580, 2 ]
pyglobi/config.py	nicholac/pyglobi	0	92040	<gh_stars>0 # -- coding: utf-8 -- """ pyglobi.config ~~~~~~~~~~~~ Configuration used by pyglobi :copyright: (c) 2019 by <NAME>. :license: MIT, see LICENSE for more details. """ # Base URL's globi_neo_url = "https://neo4j.globalbioticinteractions.org/db/data/cypher" eol_url = "https://eol.org/pages" eol_geo_url = "https://eol.org/data/map_data_dwca"	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 15945, 29908, 13, 2272, 23705, 29875, 29889, 2917, 13, 26594, 14087, 13, 8614, 1304, 491, 19484, 2127, 29875, 13, 29901, 8552, 1266, 29901, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29929, 491, 529, 5813, 15513, 13, 29901, 506, 1947, 29901, 341, 1806, 29892, 1074, 365, 2965, 1430, 1660, 363, 901, 4902, 29889, 13, 15945, 29908, 13, 13, 29937, 7399, 3988, 29915, 29879, 13, 23705, 29875, 29918, 11496, 29918, 2271, 353, 376, 991, 597, 11496, 29946, 29926, 29889, 10945, 5365, 13574, 1639, 7387, 29889, 990, 29914, 2585, 29914, 1272, 29914, 1270, 8096, 29908, 13, 13, 29872, 324, 29918, 2271, 353, 376, 991, 597, 29872, 324, 29889, 990, 29914, 12292, 29908, 13, 13, 29872, 324, 29918, 24756, 29918, 2271, 353, 376, 991, 597, 29872, 324, 29889, 990, 29914, 1272, 29914, 1958, 29918, 1272, 29918, 28012, 1113, 29908, 13, 2 ]
pset_4/tasks/stylize.py	GabrielBG010/PSET4	0	155053	<reponame>GabrielBG010/PSET4<gh_stars>0 import os import luigi from luigi import ExternalTask, Parameter, Task, LocalTarget from luigi.format import FileWrapper from csci_utils.luigi.target import SuffixPreservingLocalTarget from neural_style.neural_style import stylize from pset_4.tasks.data import DownloadImage, DownloadModel class Stylize(Task): """ Luigi Taks that stylize images according to a model """ model = Parameter(default=r"rain_princess.pth") image = Parameter(default=r"luigi.jpg") def requires(self): return { 'image': self.clone(DownloadImage), 'model': self.clone(DownloadModel) } def output(self): # return SuffixPreservingLocalTarget of the stylized image return SuffixPreservingLocalTarget(os.path.join('images_out', "luigiout.jpg"), format=luigi.format.Nop) def run(self): inputs = self.input() with self.output().temporary_path() as temp_output_path: class args: content_image = inputs['image'].path output_image = temp_output_path model = inputs['model'].path cuda = 0 content_scale = None export_onnx = None stylize(args)	[ 1, 529, 276, 1112, 420, 29958, 29954, 370, 12836, 29933, 29954, 29900, 29896, 29900, 29914, 29925, 10490, 29946, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 2897, 13, 13, 5215, 8092, 10091, 13, 3166, 8092, 10091, 1053, 3985, 5398, 29892, 24953, 29892, 9330, 29892, 9959, 8667, 13, 13, 3166, 8092, 10091, 29889, 4830, 1053, 3497, 15646, 13, 3166, 5939, 455, 29918, 13239, 29889, 6092, 10091, 29889, 5182, 1053, 2166, 600, 861, 13504, 29530, 7717, 8667, 13, 3166, 19677, 29918, 3293, 29889, 484, 3631, 29918, 3293, 1053, 380, 2904, 675, 13, 3166, 282, 842, 29918, 29946, 29889, 20673, 29889, 1272, 1053, 25553, 2940, 29892, 25553, 3195, 13, 13, 13, 1990, 624, 2904, 675, 29898, 5398, 1125, 13, 1678, 9995, 13, 1678, 22487, 323, 10327, 393, 380, 2904, 675, 4558, 5034, 304, 263, 1904, 13, 1678, 9995, 13, 13, 1678, 1904, 353, 24953, 29898, 4381, 29922, 29878, 29908, 6038, 29918, 558, 262, 985, 29889, 29886, 386, 1159, 13, 1678, 1967, 353, 24953, 29898, 4381, 29922, 29878, 29908, 6092, 10091, 29889, 6173, 1159, 13, 13, 1678, 822, 6858, 29898, 1311, 1125, 13, 4706, 736, 426, 13, 9651, 525, 3027, 2396, 1583, 29889, 16513, 29898, 22954, 2940, 511, 13, 9651, 525, 4299, 2396, 1583, 29889, 16513, 29898, 22954, 3195, 29897, 13, 4706, 500, 13, 13, 1678, 822, 1962, 29898, 1311, 1125, 13, 4706, 396, 736, 2166, 600, 861, 13504, 29530, 7717, 8667, 310, 278, 380, 2904, 1891, 1967, 13, 4706, 736, 2166, 600, 861, 13504, 29530, 7717, 8667, 29898, 359, 29889, 2084, 29889, 7122, 877, 8346, 29918, 449, 742, 376, 6092, 10091, 449, 29889, 6173, 4968, 3402, 29922, 6092, 10091, 29889, 4830, 29889, 29940, 459, 29897, 13, 13, 1678, 822, 1065, 29898, 1311, 1125, 13, 4706, 10970, 353, 1583, 29889, 2080, 580, 13, 4706, 411, 1583, 29889, 4905, 2141, 1356, 1971, 653, 29918, 2084, 580, 408, 5694, 29918, 4905, 29918, 2084, 29901, 13, 9651, 770, 6389, 29901, 13, 18884, 2793, 29918, 3027, 353, 10970, 1839, 3027, 13359, 2084, 13, 18884, 1962, 29918, 3027, 353, 5694, 29918, 4905, 29918, 2084, 13, 18884, 1904, 353, 10970, 1839, 4299, 13359, 2084, 13, 18884, 274, 6191, 353, 29871, 29900, 13, 18884, 2793, 29918, 7052, 353, 6213, 13, 18884, 5609, 29918, 3409, 29916, 353, 6213, 13, 13, 9651, 380, 2904, 675, 29898, 5085, 29897, 13, 2 ]
movements.py	sxnnytripathi/GestureGaming	0	105598	<gh_stars>0 import numpy as np import cv2 import time import math import imutils from imutils.video import FileVideoStream, VideoStream import keyboard from pynput.keyboard import Key, Controller # keyboard=Controller() size_height = 600 size_width = 600 last = 0 # last extreme of steer chng = 3 # change in last, to remove small changes fulucations chngt = 3 # to restart change to 0, if moved towards centre by some value COLOR_RED = (0, 0, 255) COLOR_BLACK = (0, 0, 0) THRESH_ACTION = 60 # t def get_centroid(bbox): # center point of rectangle x, y, w, h = bbox centx = (2 * x + w) // 2 centy = (2 * y + h) // 2 return (centx, centy) def draw_circle(frame, center, radius=THRESH_ACTION, color=COLOR_BLACK, thickness=3): cv2.circle(frame, center, radius, color, thickness) def drawbox(ret, bbox, frame): # draws rectangle from bbox global q if ret: # Tracking Success p1 = (int(bbox[0]), int(bbox[1])) p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3])) cv2.rectangle(frame, p1, p2, (255, 0, 0), 2, 1) if p1[0] < 10: # quits program if taken hand to left top corner q = True # quit color = {"blue": (255, 0, 0), "red": (0, 0, 255), "green": (0, 255, 0), "white": (255, 255, 255)} def detect_center(img, hand): # convert image to gray-scale # drawing rectangle around the feature and labeling it (x, y, w, h) = [int(v) for v in hand] # cv2.rectangle(img, (x,y), (x+w, y+h), color['green'], 2) #uncomment if you want to see face boundary cv2.circle(img, ((2 * x + w) // 2, (2 * y + h) // 2), 2, color['green'], 2) hand = ((2 * x + w) // 2, (2 * y + h) // 2) return img, hand # cord value will be decided by uh by assigning the cirle on screen def draw_controller_left(img, cords): size = 40 # x1 = cords[0] - size y1 = cords[1] - size # x2 = cords[0] + size y2 = cords[1] + size cv2.circle(img, cords, size, color['blue'], 2) return (y1, y2) def draw_controller_right(img, cords): size = 40 x1 = cords[0] - size # y1 = cords[1] - size x2 = cords[0] + size # y2 = cords[1] + size cv2.circle(img, cords, size, color['red'], 2) return (x1, x2) def keyboard_events_l(lcord, cord_left, cmd): try: # x1,x2 = cord_right y1, y2 = cord_left xl, yl = lcord # xr,yr=rcord except Exception as e: print(e) return # if xr < x1: # cmd = "left" # elif(xr > x2): # cmd = "right" if (yl < y1): cmd = "z" elif (yl > y2): cmd = "down" if cmd: print("Detected movement: ", cmd, "\n") # if cmd=="left": # keyboard.press(cmd) # elif cmd=="right": # keyboard.press(cmd) keyboard.press(cmd) # keyboard.press(cmd) return cmd def keyboard_events_r(rcord, cord_right, cmd2): try: x1, x2 = cord_right # y1,y2 = cord_left # xl,yl=lcord xr, yr = rcord except Exception as e: print(e) return if xr < x1: cmd2 = "left" elif (xr > x2): cmd2 = "right" # elif(yl<y1): # cmd = "up" # elif(yl > y2): # cmd = "down" if cmd2: print("Detected another movement: ", cmd2, "\n") keyboard.press(cmd2) # elif cmd=="up": # keyboard.press(cmd2) # elif cmd=="down": # keyboard.press(cmd2) # keyboard.press(cmd) return cmd2 def reset_press_flag(lcord, rcord, cord_left, cord_right, cmd, cmd2): try: x1, x2 = cord_right y1, y2 = cord_left xl, yl = lcord xr, yr = rcord # xc, yc = nose_cords except: return True, cmd, cmd2 if x1 < xr < x2 or y1 < yl < y2: if x1 < xr < x2 and (cmd2 != None and cmd2 != ""): keyboard.release(cmd2) if y1 < yl < y2 and (cmd != None and cmd != ""): keyboard.release(cmd) if x1 < xr < x2 and y1 < yl < y2: # keyboard.release(cmd) return True, None, None elif x1 < xr < x2: return True, cmd, None elif y1 < yl < y2: return True, None, cmd2 return False, cmd, cmd2 def get_frame(): # to get frame by reading fvs, init fvs first res, frame = fvs.read() if frame is None: raise Exception("Frame Not Found") return frame = cv2.flip(frame, 1) frame = imutils.resize(frame, width=size_width, height=size_height) return frame fvs = cv2.VideoCapture(0) # 0 for web cam time.sleep(2.0) # to allow web cam to open TIMER_SETUP = 3 # timer for capturing base image t = time.time() while True: frame = get_frame() curr = (time.time() - t) if curr > TIMER_SETUP: break cv2.putText(frame, str(int(TIMER_SETUP - curr) + 1), (225, 255), cv2.FONT_HERSHEY_SIMPLEX, 1.5, COLOR_RED, 4) cv2.imshow("Setup", frame) cv2.waitKey(1) FRAME = frame.copy() cv2.destroyAllWindows() # Make box around hand frame = FRAME.copy() cv2.putText(frame, 'Select Left Hand for steering', (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.75, COLOR_RED, 2) bboxleft = cv2.selectROI(frame, False) # bounding box for left hand frame = FRAME.copy() cv2.putText(frame, 'Select Right Hand for acceleration', (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.75, COLOR_RED, 2) bboxright = cv2.selectROI(frame, False) # bounding box for right hand # centleft = get_centroid(bboxleft) # centright = get_centroid(bboxright) # draw_circle(frame, centleft) # circle, outside this movements will happend # draw_circle(frame, centright) BBL, BBR = bboxleft, bboxright # saving it for later cv2.destroyAllWindows() # fvs.release() # BBR ''' cv2.destroyAllWindows() ''' # fvs = cv2.VideoCapture(0) #0 for web cam # time.sleep(2.0) bboxleft, bboxright = BBL, BBR # Creating the CSRT Tracker trackerleft = cv2.TrackerCSRT_create() # left hand for steering trackerright = cv2.TrackerCSRT_create() # right hand for acceleration (acc) # Initialize tracker with first frame and bounding box trackerleft.init(FRAME, bboxleft) trackerright.init(FRAME, bboxright) # cv2.putText(frame.copy(), 'Put both your hands in Postion', (100,70), \ cv2.FONT_HERSHEY_SIMPLEX, 0.75, COLOR_BLACK, 2 press_flag = False cmd = "" cmd2 = "" while True: frame = get_frame() # if curr > TIMER_SETUP or frame is None: # break # cv2.putText(frame, str(int(TIMER_SETUP - curr)+1), (225,255), cv2.FONT_HERSHEY_SIMPLEX, 1.5, COLOR_RED, 4) if BBL is not None: # grab the new bounding box coordinates of the object (success, box) = trackerleft.update(frame) (success1, box1) = trackerright.update(frame) # check to see if the tracking was a success if success: (x, y, w, h) = [int(v) for v in box] cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) (a, b, c, d) = [int(f) for f in box1] cv2.rectangle(frame, (a, b), (a + c, b + d), (0, 255, 0), 2) # cv2.putText(img, cmd, (10,50), cv2.FONT_HERSHEY_SIMPLEX, 1, color['red'], 1, cv2.LINE_AA) frame, lcord = detect_center(frame, box) frame, rcord = detect_center(frame, box1) # print("detect_center func called...") # draw boundary circle cord_left = draw_controller_left(frame, (110, 189)) cord_right = draw_controller_right(frame, (479, 189)) # print("circle draw done") if press_flag and (cmd == None or cmd == ""): # print("calling keyboard event") cmd = keyboard_events_l(lcord, cord_left, cmd) if press_flag and (cmd2 == None or cmd2 == ""): cmd2 = keyboard_events_r(rcord, cord_right, cmd2) press_flag, cmd, cmd2 = reset_press_flag(lcord, rcord, cord_left, cord_right, cmd, cmd2) # print("press flag: ",press_flag) # Writing processed image in a new window cv2.imshow("Tracking", frame) if cv2.waitKey(1) == 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tests/util/alert_server.py	grayfallstown/greendoge-blockchain	44	64852	<gh_stars>10-100 import argparse import asyncio import logging from pathlib import Path from typing import Any from aiohttp import web log = logging.getLogger(__name__) class AlertServer: shut_down: bool shut_down_event: asyncio.Event log: Any app: Any alert_file_path: Path port: int @staticmethod async def create_alert_server(alert_file_path: Path, port): self = AlertServer() self.log = log self.shut_down = False self.app = web.Application() self.shut_down_event = asyncio.Event() self.port = port routes = [ web.get("/status", self.status), ] self.alert_file_path = alert_file_path self.app.add_routes(routes) return self async def status(self, request): file_text = self.alert_file_path.read_text() return web.Response(body=file_text, content_type="text/plain") async def stop(self): self.shut_down_event.set() async def run(self): runner = web.AppRunner(self.app, access_log=None) await runner.setup() site = web.TCPSite(runner, None, self.port) await site.start() async def run_and_wait(file_path, port): server = await AlertServer.create_alert_server(Path(file_path), port) await server.run() await server.shut_down_event.wait() def main(): parser = argparse.ArgumentParser() parser.add_argument("-file_path", type=str, dest="file_path") parser.add_argument("-port", type=str, dest="port") port = None file_path = None for key, value in vars(parser.parse_args()).items(): if key == "port": port = value elif key == "file_path": file_path = value else: print(f"Invalid argument {key}") if port is None or file_path is None: print( "Missing arguments, example usage:\n\n" "python greendoge/util/alert_server.py -p 4000 -file_path /home/user/alert.txt\n" ) quit() return asyncio.get_event_loop().run_until_complete(run_and_wait(file_path, port)) if __name__ == "__main__": main()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 5215, 1852, 5510, 13, 5215, 408, 948, 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streamkit/database/core/orm.py	glentner/streamkit	2	117420	# This program is free software: you can redistribute it and/or modify it under the # terms of the Apache License (v2.0) as published by the Apache Software Foundation. # # This program is distributed in the hope that it will be useful, but WITHOUT ANY # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A # PARTICULAR PURPOSE. See the Apache License for more details. # # You should have received a copy of the Apache License along with this program. # If not, see <https://www.apache.org/licenses/LICENSE-2.0>. """Database ORM for StreamKit.""" # type annotations from __future__ import annotations from typing import Tuple, Dict, Any # standard libs import logging # external libs from sqlalchemy.ext.declarative import declarative_base from sqlalchemy import Column, ForeignKey, Index, Integer, BigInteger, String, DateTime from sqlalchemy.schema import Sequence, CheckConstraint from sqlalchemy.orm import relationship # internal libs from .engine import schema, db_config # initialize module level logger log = logging.getLogger(__name__) Table = declarative_base() class Level(Table): """ A level relates a name and its identifier. """ __tablename__ = 'level' __table_args__ = {'schema': schema} id = Column('id', Integer, primary_key=True) name = Column('name', String, unique=True, nullable=False) def __repr__(self) -> str: return f'<Level(id={repr(self.id)}, name={repr(self.name)})>' def to_dict(self) -> Dict[str, Any]: """Python dictionary representation.""" return {'id': self.id, 'name': self.name} def values(self, joined: bool = False) -> Tuple: # noqa: unused parameter """ Tuple representation of values. Args: joined (bool): Join relations instead of identifiers. Not used by `Level`. Returns: row (tuple): The `Level.id` and `Level.name`. """ return self.id, self.name @staticmethod def keys(joined: bool = False) -> Tuple[str, ...]: # noqa: unused parameter """ Tuple representation of keys. Args: joined (bool): Join relations instead of identifiers. Not used by `Level`. Returns: keys (tuple): ('id', 'name') """ return 'id', 'name' def embedded(self) -> Dict[str, Any]: """Re-map keys and include join relationships.""" return {'level_id': self.id, 'level_name': self.name} class Topic(Table): """ A topic relates a name and its identifier. """ __tablename__ = 'topic' __table_args__ = {'schema': schema} id = Column('id', Integer, primary_key=True) name = Column('name', String, unique=True, nullable=False) def __repr__(self) -> str: return f'<Topic(id={repr(self.id)}, name={repr(self.name)})>' def to_dict(self) -> Dict[str, Any]: """Python dictionary representation.""" return {'id': self.id, 'name': self.name} def values(self, joined: bool = False) -> Tuple: # noqa: unused parameter """ Tuple representation of values. Args: joined (bool): Join relations instead of identifiers. Not used by `Topic`. Returns: row (tuple): The `Topic.id` and `Topic.name`. """ return self.id, self.name @staticmethod def keys(joined: bool = False) -> Tuple[str, ...]: # noqa: unused parameter """ Tuple representation of keys. Args: joined (bool): Join relations instead of identifiers. Not used by `Topic`. Returns: keys (tuple): ('id', 'name') """ return 'id', 'name' def embedded(self) -> Dict[str, Any]: """Re-map keys and include join relationships.""" return {'topic_id': self.id, 'topic_name': self.name} class Host(Table): """ A host relates a name and its identifier. """ __tablename__ = 'host' __table_args__ = {'schema': schema} id = Column('id', Integer, primary_key=True) name = Column('name', String, unique=True, nullable=False) def __repr__(self) -> str: return f'<Host(id={repr(self.id)}, name={repr(self.name)})>' def to_dict(self) -> Dict[str, Any]: """Python dictionary representation.""" return {'id': self.id, 'name': self.name} def values(self, joined: bool = False) -> Tuple: # noqa: unused parameter """ Tuple representation of values. Args: joined (bool): Join relations instead of identifiers. Not used by `Host`. Returns: row (tuple): The `Host.id` and `Host.name`. """ return self.id, self.name @staticmethod def keys(joined: bool = False) -> Tuple[str, ...]: # noqa: unused parameter """ Tuple representation of keys. Args: joined (bool): Join relations instead of identifiers. Not used by `Host`. Returns: keys (tuple): ('id', 'name') """ return 'id', 'name' def embedded(self) -> Dict[str, Any]: """Re-map keys and include join relationships.""" return {'host_id': self.id, 'host_name': self.name} class Message(Table): __tablename__ = 'message' __table_args__ = {'schema': schema} id = Column('id', BigInteger().with_variant(Integer, 'sqlite'), primary_key=True) time = Column('time', DateTime(timezone=True), nullable=False) topic_id = Column('topic_id', Integer, ForeignKey(Topic.id), nullable=False) level_id = Column('level_id', Integer, ForeignKey(Level.id), nullable=False) host_id = Column('host_id', Integer, ForeignKey(Host.id), nullable=False) text = Column('text', String, nullable=False) topic = relationship('Topic', backref='message') level = relationship('Level', backref='message') host = relationship('Host', backref='message') # conditionally redefine for time-based partitioning if db_config['backend'] in ('timescale', ): # NOTE: The primary key is (`time`, `topic_id`) NOT `id`. # This is weird but important for automatic hyper-table partitioning # on the `time` values for TimeScaleDB (PostgreSQL). id = Column('id', BigInteger, Sequence('message_id_seq', start=1, increment=1, schema=schema), CheckConstraint('id > 0', name='message_id_check'), nullable=False) time = Column('time', DateTime(timezone=True), nullable=False, primary_key=True) topic_id = Column('topic_id', Integer, nullable=False, primary_key=True) # NOTE: remove explicit foreign keys? # level_id = Column('level_id', Integer, nullable=False) # host_id = Column('host_id', Integer, nullable=False) # # topic = relationship('Topic', backref='message', foreign_keys=[topic_id], # primaryjoin='Message.topic_id == Topic.id') # level = relationship('Level', backref='message', foreign_keys=[level_id], # primaryjoin='Message.level_id == Level.id') # host = relationship('Host', backref='message', foreign_keys=[host_id], # primaryjoin='Message.host_id == Host.id') def __repr__(self) -> str: return (f'<Message(id={repr(self.id)},' f' time={repr(self.time)},' f' topic_id={repr(self.topic_id)},' f' level_id={repr(self.level_id)},' f' host_id={repr(self.host_id)},' f' text={repr(self.text)})>') def to_dict(self) -> Dict[str, Any]: """Python dictionary representation.""" return {'id': self.id, 'time': self.time, 'topic_id': self.topic_id, 'level_id': self.level_id, 'host_id': self.host_id, 'text': self.text} def values(self, joined: bool = False) -> Tuple: """ Tuple representation of values. Args: joined (bool): Join relations instead of identifiers (default: False). """ if not joined: return self.id, self.time, self.topic_id, self.level_id, self.host_id, self.text else: return self.id, self.time, self.topic.name, self.level.name, self.host.name, self.text @staticmethod def keys(joined: bool = False) -> Tuple[str, ...]: """ Tuple representation of keys. Args: joined (bool): Join relations instead of identifiers (default: False). """ if not joined: return 'id', 'time', 'topic_id', 'level_id', 'host_id', 'text' else: return 'id', 'time', 'topic', 'level', 'host', 'text' def embedded(self) -> Dict[str, Any]: """Re-map keys and include join relationships.""" message = {'message_time': self.time.strftime('%Y-%m-%d %H:%M:%S.%f'), 'message_text': self.text} return {message, self.topic.embedded(), self.level.embedded(), self.host.embedded()} if db_config['backend'] in ('timescale', ): # we use time-topic PK and need to index ID message_id_index = Index('message_id_index', Message.id) else: message_time_topic_index = Index('message_time_topic_index', Message.time, Message.topic_id) # efficient filtering on host or level message_level_index = Index('message_level_index', Message.level_id) message_host_index = Index('message_host_index', Message.host_id) class Subscriber(Table): """ A subscriber relates a name and its identifier. """ __tablename__ = 'subscriber' __table_args__ = {'schema': schema} id = Column('id', Integer, primary_key=True) name = Column('name', String, unique=True, nullable=False) def __repr__(self) -> str: return f'<Subscriber(id={repr(self.id)}, name={repr(self.name)})>' def to_dict(self) -> Dict[str, Any]: """Python dictionary representation.""" return {'id': self.id, 'name': self.name} def values(self, joined: bool = False) -> Tuple: # noqa: unused parameter """ Tuple representation of values. Args: joined (bool): Join relations instead of identifiers. Not used by `Subscriber`. Returns: row (tuple): The `Subscriber.id` and `Subscriber.name`. """ return self.id, self.name @staticmethod def keys(joined: bool = False) -> Tuple[str, ...]: # noqa: unused parameter """ Tuple representation of keys. Args: joined (bool): Join relations instead of identifiers. Not used by `Subscriber`. Returns: keys (tuple): ('id', 'name') """ return 'id', 'name' def embedded(self) -> Dict[str, Any]: """Re-map keys and include join relationships.""" return {'subscriber_id': self.id, 'subscriber_name': self.name} class Access(Table): __tablename__ = 'access' __table_args__ = {'schema': schema} subscriber_id = Column('subscriber_id', Integer, ForeignKey(Subscriber.id), nullable=False, primary_key=True) topic_id = Column('topic_id', Integer, ForeignKey(Topic.id), nullable=False, primary_key=True) time = Column('time', DateTime(timezone=True), nullable=False) subscriber = relationship('Subscriber', backref='access') topic = relationship('Topic', backref='access') def __repr__(self) -> str: return (f'<Access(subscriber_id={repr(self.subscriber_id)},' f' topic_id={repr(self.topic_id)},' f' time={repr(self.time)})>') def to_dict(self) -> Dict[str, Any]: """Python dictionary representation.""" return {'subscriber_id': self.subscriber_id, 'topic_id': self.topic_id, 'time': self.time} def values(self, joined: bool = False) -> Tuple: """ Tuple representation of values. Args: joined (bool): Join relations instead of identifiers (default: False). """ if not joined: return self.subscriber_id, self.topic_id, self.time else: return self.subscriber.name, self.topic.name, self.time @staticmethod def keys(joined: bool = False) -> Tuple[str, ...]: """ Tuple representation of keys. Args: joined (bool): Join relations instead of identifiers (default: False). """ if not joined: return 'subscriber_id', 'topic_id', 'time' else: return 'subscriber', 'topic', 'time' def embedded(self) -> Dict[str, Any]: """Re-map keys and include join relationships.""" access = {'access_time': self.time.strftime('%Y-%m-%d %H:%M:%S.%f')} return {access, self.subscriber.embedded(), **self.topic.embedded()} tables: Dict[str, Table] = { 'level': Level, 'topic': Topic, 'host': Host, 'message': Message, 'subscriber': Subscriber, 'access': Access }	[ 1, 396, 910, 1824, 338, 3889, 7047, 29901, 366, 508, 2654, 391, 2666, 372, 322, 29914, 272, 6623, 372, 1090, 278, 13, 29937, 4958, 310, 278, 13380, 19245, 313, 29894, 29906, 29889, 29900, 29897, 408, 6369, 491, 278, 13380, 18540, 10606, 29889, 13, 29937, 13, 29937, 910, 1824, 338, 13235, 297, 278, 4966, 393, 372, 674, 367, 5407, 29892, 541, 399, 1806, 8187, 2692, 13764, 29979, 13, 29937, 399, 1718, 29934, 13566, 29979, 29936, 1728, 1584, 278, 2411, 2957, 1370, 21867, 29891, 310, 341, 1001, 3210, 13566, 2882, 6227, 11937, 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napari_plugin_engine/__init__.py	tlambert-forks/napari-plugin-engine	6	75160	<filename>napari_plugin_engine/__init__.py try: from ._version import version as __version__ except ImportError: # broken installation, we don't even try # unknown only works because we do poor mans version compare __version__ = "unknown" __all__ = [ "get_metadata", "HookCaller", "HookCallError", "HookImplementation", "HookImplementationMarker", "HookResult", "HookSpecification", "HookSpecificationMarker", "napari_hook_implementation", "napari_hook_specification", "PluginCallError", "PluginError", "PluginImplementationError", "PluginImportError", "PluginManager", "PluginRegistrationError", "PluginValidationError", "standard_metadata", ] from .callers import HookResult from .dist import get_metadata, standard_metadata from .exceptions import ( HookCallError, PluginCallError, PluginError, PluginImplementationError, PluginImportError, PluginRegistrationError, PluginValidationError, ) from .hooks import HookCaller from .implementation import HookImplementation, HookSpecification from .manager import PluginManager from .markers import HookImplementationMarker, HookSpecificationMarker napari_hook_implementation = HookImplementationMarker("napari") napari_hook_specification = HookSpecificationMarker("napari")	[ 1, 529, 9507, 29958, 8971, 1306, 29918, 8582, 29918, 10599, 29914, 1649, 2344, 26914, 2272, 13, 2202, 29901, 13, 1678, 515, 869, 29918, 3259, 1053, 1873, 408, 4770, 3259, 1649, 13, 19499, 16032, 2392, 29901, 13, 1678, 396, 9391, 11161, 29892, 591, 1016, 29915, 29873, 1584, 1018, 13, 1678, 396, 9815, 871, 1736, 1363, 591, 437, 6460, 286, 550, 1873, 7252, 13, 1678, 4770, 3259, 1649, 353, 376, 26690, 29908, 13, 13, 1649, 497, 1649, 353, 518, 13, 1678, 376, 657, 29918, 19635, 613, 13, 1678, 376, 29950, 2550, 5594, 261, 613, 13, 1678, 376, 29950, 2550, 5594, 2392, 613, 13, 1678, 376, 29950, 2550, 1888, 14607, 613, 13, 1678, 376, 29950, 2550, 1888, 14607, 24619, 613, 13, 1678, 376, 29950, 2550, 3591, 613, 13, 1678, 376, 29950, 2550, 10299, 2450, 613, 13, 1678, 376, 29950, 2550, 10299, 2450, 24619, 613, 13, 1678, 376, 8971, 1306, 29918, 20849, 29918, 21382, 613, 13, 1678, 376, 8971, 1306, 29918, 20849, 29918, 6550, 2450, 613, 13, 1678, 376, 16288, 5594, 2392, 613, 13, 1678, 376, 16288, 2392, 613, 13, 1678, 376, 16288, 1888, 14607, 2392, 613, 13, 1678, 376, 16288, 17518, 2392, 613, 13, 1678, 376, 16288, 3260, 613, 13, 1678, 376, 16288, 4597, 8306, 2392, 613, 13, 1678, 376, 16288, 19448, 2392, 613, 13, 1678, 376, 15770, 29918, 19635, 613, 13, 29962, 13, 13, 3166, 869, 4804, 414, 1053, 29612, 3591, 13, 3166, 869, 5721, 1053, 679, 29918, 19635, 29892, 3918, 29918, 19635, 13, 3166, 869, 11739, 29879, 1053, 313, 13, 1678, 29612, 5594, 2392, 29892, 13, 1678, 1858, 3851, 5594, 2392, 29892, 13, 1678, 1858, 3851, 2392, 29892, 13, 1678, 1858, 3851, 1888, 14607, 2392, 29892, 13, 1678, 1858, 3851, 17518, 2392, 29892, 13, 1678, 1858, 3851, 4597, 8306, 2392, 29892, 13, 1678, 1858, 3851, 19448, 2392, 29892, 13, 29897, 13, 3166, 869, 1251, 12117, 1053, 29612, 5594, 261, 13, 3166, 869, 21382, 1053, 29612, 1888, 14607, 29892, 29612, 10299, 2450, 13, 3166, 869, 12847, 1053, 1858, 3851, 3260, 13, 3166, 869, 3502, 414, 1053, 29612, 1888, 14607, 24619, 29892, 29612, 10299, 2450, 24619, 13, 13, 8971, 1306, 29918, 20849, 29918, 21382, 353, 29612, 1888, 14607, 24619, 703, 8971, 1306, 1159, 13, 8971, 1306, 29918, 20849, 29918, 6550, 2450, 353, 29612, 10299, 2450, 24619, 703, 8971, 1306, 1159, 13, 2 ]
tests/test_tipc/bigru_crf/export_model.py	mukaiu/PaddleNLP	0	83592	# -- coding: UTF-8 -- # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os import paddle from paddle.static import InputSpec import paddlenlp as ppnlp from paddlenlp.data import Vocab from data import load_vocab from model import BiGruCrf # yapf: disable parser = argparse.ArgumentParser(__doc__) parser.add_argument("--data_dir", type=str, default=None, help="The folder where the dataset is located.") parser.add_argument("--params_path", type=str, default='./checkpoints/final.pdparams', help="The path of model parameter to be loaded.") parser.add_argument("--output_path", type=str, default='./infer_model', help="The path of model parameter in static graph to be saved.") parser.add_argument("--emb_dim", type=int, default=128, help="The dimension in which a word is embedded.") parser.add_argument("--hidden_size", type=int, default=128, help="The number of hidden nodes in the GRU layer.") args = parser.parse_args() # yapf: enable def main(): word_vocab = load_vocab(os.path.join(args.data_dir, 'word.dic')) label_vocab = load_vocab(os.path.join(args.data_dir, 'tag.dic')) model = BiGruCrf(args.emb_dim, args.hidden_size, len(word_vocab), len(label_vocab)) state_dict = paddle.load(args.params_path) model.set_dict(state_dict) model.eval() model = paddle.jit.to_static(model, input_spec=[ InputSpec(shape=[None, None], dtype="int64", name='token_ids'), InputSpec(shape=[None], dtype="int64", name='length') ]) # Save in static graph model. 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test/test_bch.py	timoi-Lucypher/npCrypto	0	139832	<reponame>timoi-Lucypher/npCrypto import numpy as np from npcrypto.codes.bch import BCH from npcrypto.codes.poly_gf2 import a2p, strip_zeros def test_encode(): #http://www.comlab.hut.fi/studies/3410/slides_08_6_4.pdf n = 7 m = 3 k = 3 t = 2 msg = np.array([1,0,1], dtype=np.uint8) expected = np.array([1, 1, 0, 0, 1, 0, 1], dtype=np.uint8) bch = BCH(n, m, k, t) gen = np.array([1, 0, 1, 1, 1], dtype=np.uint8) bch.set_generator(gen) cdw = bch.encode(msg) assert np.all(strip_zeros(cdw) == expected)	[ 1, 529, 276, 1112, 420, 29958, 29873, 4200, 29875, 29899, 24126, 1270, 8096, 29914, 9302, 29907, 17929, 13, 5215, 12655, 408, 7442, 13, 3166, 302, 6739, 17929, 29889, 18137, 29889, 29890, 305, 1053, 350, 3210, 13, 3166, 302, 6739, 17929, 29889, 18137, 29889, 22678, 29918, 29887, 29888, 29906, 1053, 263, 29906, 29886, 29892, 17820, 29918, 3298, 359, 13, 13, 1753, 1243, 29918, 12508, 7295, 13, 1678, 396, 1124, 597, 1636, 29889, 510, 8205, 29889, 29882, 329, 29889, 7241, 29914, 18082, 583, 29914, 29941, 29946, 29896, 29900, 29914, 2536, 2247, 29918, 29900, 29947, 29918, 29953, 29918, 29946, 29889, 5140, 13, 1678, 302, 353, 29871, 29955, 13, 1678, 286, 353, 29871, 29941, 13, 1678, 413, 353, 29871, 29941, 13, 1678, 260, 353, 29871, 29906, 13, 13, 1678, 10191, 353, 7442, 29889, 2378, 4197, 29896, 29892, 29900, 29892, 29896, 1402, 26688, 29922, 9302, 29889, 13470, 29947, 29897, 13, 1678, 3806, 353, 7442, 29889, 2378, 4197, 29896, 29892, 29871, 29896, 29892, 29871, 29900, 29892, 29871, 29900, 29892, 29871, 29896, 29892, 29871, 29900, 29892, 29871, 29896, 1402, 26688, 29922, 9302, 29889, 13470, 29947, 29897, 13, 1678, 289, 305, 353, 350, 3210, 29898, 29876, 29892, 286, 29892, 413, 29892, 260, 29897, 13, 1678, 2531, 353, 7442, 29889, 2378, 4197, 29896, 29892, 29871, 29900, 29892, 29871, 29896, 29892, 29871, 29896, 29892, 29871, 29896, 1402, 26688, 29922, 9302, 29889, 13470, 29947, 29897, 13, 1678, 289, 305, 29889, 842, 29918, 27959, 29898, 1885, 29897, 259, 13, 1678, 14965, 29893, 353, 289, 305, 29889, 12508, 29898, 7645, 29897, 13, 1678, 4974, 7442, 29889, 497, 29898, 17010, 29918, 3298, 359, 29898, 2252, 29893, 29897, 1275, 3806, 29897, 13, 268, 13, 13, 2 ]
stest/pylibs/lazy_libs.py	hotswwkyo/stest	0	128206	<reponame>hotswwkyo/stest #!/usr/bin/env python # -- encoding: utf-8 -- ''' @Author: 思文伟 @Date: 2022/03/23 ''' class LazyLibs(object): class Appium(object): @property def MobileBy(self): from appium.webdriver.common.mobileby import MobileBy return MobileBy @property def webdriver(self): from appium import webdriver return webdriver class Selenium(object): @property def exceptions(cls): from selenium.common import exceptions return exceptions @property def ActionChains(self): from selenium.webdriver import ActionChains return ActionChains @property def Keys(self): from selenium.webdriver.common.keys import Keys return Keys @property def By(self): from selenium.webdriver.common.by import By return By @property def WebDriverWait(self): from selenium.webdriver.support.wait import WebDriverWait return WebDriverWait @property def webdriver(self): from selenium import webdriver return webdriver @property def WebElement(self): from selenium.webdriver.remote.webelement import WebElement return WebElement @property def Select(self): from selenium.webdriver.support.ui import Select return Select class Wechat(object): @property def minium(self): import minium return minium @property def miniconfig(self): from minium.framework import miniconfig return miniconfig	[ 1, 529, 276, 1112, 420, 29958, 29882, 1862, 1615, 3459, 29877, 29914, 303, 342, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 8025, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 12008, 13, 29992, 13720, 29901, 29871, 31579, 30333, 231, 191, 162, 13, 29992, 2539, 29901, 29871, 29906, 29900, 29906, 29906, 29914, 29900, 29941, 29914, 29906, 29941, 13, 12008, 13, 13, 13, 1990, 19575, 29891, 14868, 29879, 29898, 3318, 1125, 13, 1678, 770, 2401, 1974, 29898, 3318, 1125, 13, 4706, 732, 6799, 13, 4706, 822, 21600, 2059, 29898, 1311, 1125, 13, 9651, 515, 623, 1974, 29889, 29813, 29889, 9435, 29889, 16769, 1609, 1053, 21600, 2059, 13, 9651, 736, 21600, 2059, 13, 13, 4706, 732, 6799, 13, 4706, 822, 1856, 9465, 29898, 1311, 1125, 13, 9651, 515, 623, 1974, 1053, 1856, 9465, 13, 9651, 736, 1856, 9465, 13, 13, 1678, 770, 317, 13462, 29898, 3318, 1125, 13, 4706, 732, 6799, 13, 4706, 822, 15283, 29898, 25932, 1125, 13, 9651, 515, 18866, 29889, 9435, 1053, 15283, 13, 9651, 736, 15283, 13, 13, 4706, 732, 6799, 13, 4706, 822, 9123, 1451, 2708, 29898, 1311, 1125, 13, 9651, 515, 18866, 29889, 29813, 1053, 9123, 1451, 2708, 13, 9651, 736, 9123, 1451, 2708, 13, 13, 4706, 732, 6799, 13, 4706, 822, 4813, 952, 29898, 1311, 1125, 13, 9651, 515, 18866, 29889, 29813, 29889, 9435, 29889, 8149, 1053, 4813, 952, 13, 9651, 736, 4813, 952, 13, 13, 4706, 732, 6799, 13, 4706, 822, 2648, 29898, 1311, 1125, 13, 9651, 515, 18866, 29889, 29813, 29889, 9435, 29889, 1609, 1053, 2648, 13, 9651, 736, 2648, 13, 13, 4706, 732, 6799, 13, 4706, 822, 2563, 12376, 15716, 29898, 1311, 1125, 13, 9651, 515, 18866, 29889, 29813, 29889, 5924, 29889, 10685, 1053, 2563, 12376, 15716, 13, 9651, 736, 2563, 12376, 15716, 13, 13, 4706, 732, 6799, 13, 4706, 822, 1856, 9465, 29898, 1311, 1125, 13, 9651, 515, 18866, 1053, 1856, 9465, 13, 9651, 736, 1856, 9465, 13, 13, 4706, 732, 6799, 13, 4706, 822, 2563, 2642, 29898, 1311, 1125, 13, 9651, 515, 18866, 29889, 29813, 29889, 16674, 29889, 705, 915, 944, 1053, 2563, 2642, 13, 9651, 736, 2563, 2642, 13, 13, 4706, 732, 6799, 13, 4706, 822, 7605, 29898, 1311, 1125, 13, 9651, 515, 18866, 29889, 29813, 29889, 5924, 29889, 1481, 1053, 7605, 13, 9651, 736, 7605, 13, 13, 1678, 770, 1334, 13496, 29898, 3318, 1125, 13, 4706, 732, 6799, 13, 4706, 822, 1375, 1974, 29898, 1311, 1125, 13, 9651, 1053, 1375, 1974, 13, 9651, 736, 1375, 1974, 13, 13, 4706, 732, 6799, 13, 4706, 822, 1375, 4144, 1003, 29898, 1311, 1125, 13, 9651, 515, 1375, 1974, 29889, 4468, 1053, 1375, 4144, 1003, 13, 9651, 736, 1375, 4144, 1003, 13, 2 ]
reprounzip-qt/reprounzip_qt/qt_terminal.py	kyleam/reprozip	0	69081	<filename>reprounzip-qt/reprounzip_qt/qt_terminal.py # Copyright (C) 2014-2017 New York University # This file is part of ReproZip which is released under the Revised BSD License # See file LICENSE for full license details. from __future__ import division, print_function, unicode_literals import cgi import locale from PyQt4 import QtCore, QtGui import sys class Terminal(QtGui.QWidget): finished = QtCore.pyqtSignal(int) def __init__(self, cmdline, env, input_enabled=False, success_msg=None, fail_msg=None, kwargs): super(Terminal, self).__init__(kwargs) self.success_msg = success_msg or "Command finished" self.fail_msg = fail_msg or "Command failed" layout = QtGui.QVBoxLayout() self.text = QtGui.QTextEdit(readOnly=True) layout.addWidget(self.text) if input_enabled: self.input = QtGui.QLineEdit() self.input.returnPressed.connect(self._enter) layout.addWidget(self.input) else: self.input = None self.setLayout(layout) self.process = QtCore.QProcess(self) # Dodge py2app issues environ = QtCore.QProcessEnvironment.systemEnvironment() if environ.contains('PYTHONHOME'): environ.remove('PYTHONPATH') environ.remove('PYTHONHOME') if sys.platform == 'darwin': environ.insert( 'PATH', (environ.value('PATH', '/usr/bin:/bin:/usr/sbin:/sbin') + ':/usr/local/bin:/opt/reprounzip')) # Unset TERM to avoid ansi escapes environ.remove('TERM') # Add additional environment variables for k, v in env.items(): environ.insert(k, v) self.process.setProcessEnvironment(environ) self.process.setProcessChannelMode(QtCore.QProcess.SeparateChannels) if input_enabled: mode = QtCore.QIODevice.ReadWrite else: mode = QtCore.QIODevice.ReadOnly self.process.start(cmdline[0], cmdline[1:], mode) if not input_enabled: self.process.closeWriteChannel() self.process.readyReadStandardOutput.connect(self._read_stdout) self.process.readyReadStandardError.connect(self._read_stderr) self.process.finished.connect(self._finished) self.text.setHtml('''\ <style> body { font: Consolas, "Liberation Mono", Menlo, Courier, monospace; } .err { color: red; } </style> ''') self.text.append('<span style="color: blue;">%s</span>' % cgi.escape(' '.join(cmdline))) def _enter(self): cmd = self.input.text() self.input.setText('') self.process.write(cmd + '\n') def _read_stdout(self): out = self.process.readAllStandardOutput() out = bytes(out).decode(locale.getpreferredencoding() or 'UTF-8', 'replace') self.text.append('<span>%s</span>' % cgi.escape(out)) def _read_stderr(self): out = self.process.readAllStandardError() out = bytes(out).decode(locale.getpreferredencoding() or 'UTF-8', 'replace') self.text.append('<span class="err">%s</span>' % cgi.escape(out)) def _finished(self, code, status): good = False if status == QtCore.QProcess.NormalExit: msg = "returned %d" % code self.finished.emit(code) good = code == 0 else: msg = "crashed" self.finished.emit(-1) if good: msg = self.success_msg else: msg = "%s (%s)" % (self.fail_msg, msg) self.text.append('<br><span style="color: blue;">%s</span>' % msg) def run_in_builtin_terminal(cmd, env, text=None, success_msg=None, fail_msg=None): result = [False] def store_result(code): result[:] = [code] dialog = QtGui.QDialog() layout = QtGui.QVBoxLayout() if text is not None: layout.addWidget(QtGui.QLabel(text)) terminal = Terminal(cmd, env, input_enabled=False, success_msg=success_msg, fail_msg=fail_msg) terminal.finished.connect(store_result) layout.addWidget(terminal) buttons = QtGui.QHBoxLayout() buttons.addStretch(1) accept = QtGui.QPushButton("Close", enabled=False) accept.clicked.connect(dialog.accept) terminal.finished.connect(lambda _: accept.setEnabled(True)) buttons.addWidget(accept) layout.addLayout(buttons) dialog.setLayout(layout) dialog.exec_() return result[0]	[ 1, 529, 9507, 29958, 276, 558, 1309, 7554, 29899, 17915, 29914, 276, 558, 1309, 7554, 29918, 17915, 29914, 17915, 29918, 8489, 979, 29889, 2272, 13, 29937, 14187, 1266, 313, 29907, 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1125, 13, 4706, 1121, 7503, 29962, 353, 518, 401, 29962, 13, 13, 1678, 7928, 353, 14705, 28707, 29889, 29984, 7647, 580, 13, 1678, 5912, 353, 14705, 28707, 29889, 29984, 29963, 3313, 3453, 580, 13, 1678, 565, 1426, 338, 451, 6213, 29901, 13, 4706, 5912, 29889, 1202, 8801, 29898, 17303, 28707, 29889, 2239, 1107, 29898, 726, 876, 13, 1678, 8638, 353, 29175, 29898, 9006, 29892, 8829, 29892, 1881, 29918, 17590, 29922, 8824, 29892, 13, 462, 4706, 2551, 29918, 7645, 29922, 8698, 29918, 7645, 29892, 4418, 29918, 7645, 29922, 14057, 29918, 7645, 29897, 13, 1678, 8638, 29889, 4951, 3276, 29889, 6915, 29898, 8899, 29918, 2914, 29897, 13, 1678, 5912, 29889, 1202, 8801, 29898, 8489, 979, 29897, 13, 1678, 9828, 353, 14705, 28707, 29889, 29984, 29950, 3313, 3453, 580, 13, 1678, 9828, 29889, 1202, 855, 10301, 29898, 29896, 29897, 13, 1678, 3544, 353, 14705, 28707, 29889, 29984, 27031, 3125, 703, 11123, 613, 9615, 29922, 8824, 29897, 13, 1678, 3544, 29889, 3808, 287, 29889, 6915, 29898, 15901, 29889, 16044, 29897, 13, 1678, 8638, 29889, 4951, 3276, 29889, 6915, 29898, 2892, 903, 29901, 3544, 29889, 842, 10861, 29898, 5574, 876, 13, 1678, 9828, 29889, 1202, 8801, 29898, 16044, 29897, 13, 1678, 5912, 29889, 1202, 3453, 29898, 4187, 7453, 29897, 13, 1678, 7928, 29889, 842, 3453, 29898, 2680, 29897, 13, 1678, 7928, 29889, 4258, 29918, 580, 13, 13, 1678, 736, 1121, 29961, 29900, 29962, 13, 2 ]
weather_alarm/main.py	Cs4r/weather_alarm	0	23459	<filename>weather_alarm/main.py #!/usr/bin/env python3 # -- coding: utf-8 -- import datetime import os from apscheduler.schedulers.blocking import BlockingScheduler from weather_alarm.constants import * from weather_alarm.forecaster import Forecaster from weather_alarm.sender import NotificationSender sender = NotificationSender(BOT_TOKEN, TELEGRAM_USER_ID) forecaster = Forecaster(OWM_API_KEY) def send_tomorrow_forecast(hour, time): sender.send_message(forecaster.tomorrow_forecast_at(CITY, hour, time)) def send_current_observed_weather(): sender.send_message(forecaster.current_observed_weather(CITY)) now = datetime.datetime.now() nightly_alarm_time = datetime.datetime(now.year, now.month, now.day, NIGHTLY_ALARM_TIME) daily_alarm_time = datetime.datetime(now.year, now.month, now.day, DAILY_ALARM_TIME) scheduler = BlockingScheduler() scheduler.add_job(func=send_tomorrow_forecast, args=FORECAST_TIME, trigger='interval', next_run_time=nightly_alarm_time, misfire_grace_time=30, days=1) scheduler.add_job(func=send_current_observed_weather, trigger='interval', next_run_time=daily_alarm_time, misfire_grace_time=30, days=1) print('Press Ctrl+{0} to exit'.format('Break' if os.name == 'nt' else 'C')) try: scheduler.start() except (KeyboardInterrupt, SystemExit): pass	[ 1, 529, 9507, 29958, 705, 1624, 29918, 284, 2817, 29914, 3396, 29889, 2272, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 5215, 12865, 13, 5215, 2897, 13, 3166, 263, 567, 305, 14952, 29889, 816, 287, 352, 414, 29889, 1271, 292, 1053, 15658, 292, 4504, 14952, 13, 3166, 14826, 29918, 284, 2817, 29889, 3075, 1934, 1053, 334, 13, 3166, 14826, 29918, 284, 2817, 29889, 1079, 29883, 1901, 1053, 28297, 29883, 1901, 13, 3166, 14826, 29918, 284, 2817, 29889, 15452, 1053, 28578, 29615, 13, 13, 13, 15452, 353, 28578, 29615, 29898, 29933, 2891, 29918, 4986, 29968, 1430, 29892, 17067, 1307, 29954, 25058, 29918, 11889, 29918, 1367, 29897, 13, 1079, 29883, 1901, 353, 28297, 29883, 1901, 29898, 9806, 29924, 29918, 8787, 29918, 10818, 29897, 13, 13, 13, 1753, 3638, 29918, 15135, 22396, 29918, 1079, 4384, 29898, 18721, 29892, 931, 1125, 13, 1678, 10004, 29889, 6717, 29918, 4906, 29898, 1079, 29883, 1901, 29889, 15135, 22396, 29918, 1079, 4384, 29918, 271, 29898, 29907, 11937, 29892, 7234, 29892, 931, 876, 13, 13, 13, 1753, 3638, 29918, 3784, 29918, 711, 643, 1490, 29918, 705, 1624, 7295, 13, 1678, 10004, 29889, 6717, 29918, 4906, 29898, 1079, 29883, 1901, 29889, 3784, 29918, 711, 643, 1490, 29918, 705, 1624, 29898, 29907, 11937, 876, 13, 13, 13, 3707, 353, 12865, 29889, 12673, 29889, 3707, 580, 13, 11147, 368, 29918, 284, 2817, 29918, 2230, 353, 12865, 29889, 12673, 29898, 3707, 29889, 6360, 29892, 1286, 29889, 10874, 29892, 1286, 29889, 3250, 29892, 334, 29940, 22530, 16786, 29918, 1964, 1718, 29924, 29918, 15307, 29897, 13, 29881, 8683, 29918, 284, 2817, 29918, 2230, 353, 12865, 29889, 12673, 29898, 3707, 29889, 6360, 29892, 1286, 29889, 10874, 29892, 1286, 29889, 3250, 29892, 334, 7698, 6227, 29979, 29918, 1964, 1718, 29924, 29918, 15307, 29897, 13, 13, 816, 14952, 353, 15658, 292, 4504, 14952, 580, 13, 13, 816, 14952, 29889, 1202, 29918, 9057, 29898, 9891, 29922, 6717, 29918, 15135, 22396, 29918, 1079, 4384, 29892, 6389, 29922, 5800, 1525, 5454, 1254, 29918, 15307, 29892, 7135, 2433, 19207, 742, 2446, 29918, 3389, 29918, 2230, 29922, 11147, 368, 29918, 284, 2817, 29918, 2230, 29892, 13, 462, 29871, 3984, 8696, 29918, 3874, 346, 29918, 2230, 29922, 29941, 29900, 29892, 3841, 29922, 29896, 29897, 13, 13, 816, 14952, 29889, 1202, 29918, 9057, 29898, 9891, 29922, 6717, 29918, 3784, 29918, 711, 643, 1490, 29918, 705, 1624, 29892, 7135, 2433, 19207, 742, 2446, 29918, 3389, 29918, 2230, 29922, 29881, 8683, 29918, 284, 2817, 29918, 2230, 29892, 13, 462, 29871, 3984, 8696, 29918, 3874, 346, 29918, 2230, 29922, 29941, 29900, 29892, 3841, 29922, 29896, 29897, 13, 13, 2158, 877, 10923, 315, 11742, 29974, 29912, 29900, 29913, 304, 6876, 4286, 4830, 877, 20130, 557, 29915, 565, 2897, 29889, 978, 1275, 525, 593, 29915, 1683, 525, 29907, 8785, 13, 13, 2202, 29901, 13, 1678, 1364, 14952, 29889, 2962, 580, 13, 19499, 313, 2558, 3377, 4074, 6685, 29892, 2184, 24365, 1125, 13, 1678, 1209, 2 ]

linkedin/__init__.py

ptwobrussell/python-linkedin

6

88616

<filename>linkedin/__init__.py __version__ = '2.0' VERSION = tuple(map(int, __version__.split('.')))

[ 1, 529, 9507, 29958, 2324, 287, 262, 29914, 1649, 2344, 26914, 2272, 13, 1649, 3259, 1649, 353, 525, 29906, 29889, 29900, 29915, 13, 16358, 353, 18761, 29898, 1958, 29898, 524, 29892, 4770, 3259, 26914, 5451, 877, 6169, 4961, 13, 2 ]

day15p1.py

minddrive/adventofcode2017

0

79774

<gh_stars>0 #!/usr/bin/env python3 def next_nums(): A = 703 B = 516 for _ in range(40000000): A *= 16807 A %= 2147483647 B *= 48271 B %= 2147483647 yield (A, B) count = 0 for newA, newB in next_nums(): if newA % 65536 == newB % 65536: count += 1 print(count)

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examples/optimization_applications/search_space_example.py

nevinadalal/Hyperactive

4

59738

<reponame>nevinadalal/Hyperactive<filename>examples/optimization_applications/search_space_example.py """ Hyperactive is very versatile, because it can handle not just numerical or string variables in the search space, but also functions. If you want to search for the best list, numpy array, dataframed or class you can put them into a function that returns them as shown in the example below. This enables many possibilities for more complex optimization applications. Neural architecture search, feature engineering, ensemble optimization and many other applications are only possible or much easier if you can put functions in the search space. """ from hyperactive import Hyperactive def function_0(): # do stuff in function0 return def function_1(): # do stuff in function1 return def function_2(): # do stuff in function2 return def list1(): return [1, 0, 0] def list2(): return [0, 1, 0] def list3(): return [0, 0, 1] # Hyperactive can handle python objects in the search space search_space = { "int": list(range(1, 10)), "float": [0.1, 0.01, 0.001], "string": ["string1", "string2"], "function": [function_0, function_1, function_2], "list": [list1, list2, list3], } def objective_function(para): # score must be a number score = 1 return score hyper = Hyperactive() hyper.add_search(objective_function, search_space, n_iter=20) hyper.run() search_data = hyper.search_data(objective_function) print("\n Search Data: \n", search_data)

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code/improved method/SpatialColumnDetection.py

astrob3rry/spatial-data-profiling

0

40300

<filename>code/improved method/SpatialColumnDetection.py import pandas as pd import sys, os import numpy as np from snapy import MinHash, LSH from fuzzywuzzy import fuzz from fuzzywuzzy import process import random import re import Settings as Settings from pyspark.sql import SparkSession spark = SparkSession.builder.appName("project-test").config("spark.some.config.option", "some-value").getOrCreate() spark.sparkContext.addPyFile("/home/wh2099/project/mmh3.py") spark.sparkContext.addPyFile("/home/wh2099/project/snapy.zip") import mmh3 from snapy import MinHash, LSH class SpatialColumnDetection: def __init__(self, df, defaultFiles, index): self.types = Settings.types self.df = df # column names self.columnNames = list(df.columns) # lower case column names self.lcColumnNames = [] # lower case column name to orginal name map self.lowerUpperDict = {} self.upperLowerDict = {} # each column is what type self.colNameType = {} self.defaultFiles = defaultFiles self.index = index # get the dtype dictionary first self.dtypes = {} for keyVal in df.dtypes: self.dtypes[keyVal[0]] = keyVal[1] def initReturnResult(self): self.colNameType["file_index"] = str(self.index) self.colNameType["total_spatial_attributes"] = 0 self.colNameType["attributes"] = {} for colName in self.columnNames: self.colNameType["attributes"][colName] = {} # change all column names to lower cases def changeLowerCase(self, ): for colName in self.columnNames: lwColName = colName.lower() self.lcColumnNames.append(lwColName) self.lowerUpperDict[lwColName] = colName self.upperLowerDict[colName] = lwColName def lcs(self, X, Y): # find the length of the strings m = len(X) n = len(Y) # declaring the array for storing the dp values L = [[None] * (n + 1) for i in range(m + 1)] """Following steps build L[m + 1][n + 1] in bottom up fashion Note: L[i][j] contains length of LCS of X[0..i-1] and Y[0..j-1]""" for i in range(m + 1): for j in range(n + 1): if i == 0 or j == 0: L[i][j] = 0 elif X[i - 1] == Y[j - 1]: L[i][j] = L[i - 1][j - 1] + 1 else: L[i][j] = max(L[i - 1][j], L[i][j - 1]) # L[m][n] contains the length of LCS of X[0..n-1] & Y[0..m-1] return L[m][n] def editDistance(self, x, y): return len(x) + len(y) - 2 * self.lcs(x, y) def detect(self, ): self.changeLowerCase() self.initReturnResult() colIndex = 0 for colName in self.columnNames: # change to lower case and trip it colNameLwStrip = colName.lower().strip() if self.detectLongitude(colNameLwStrip, colName): type = "longitude" elif self.detectLatitude(colNameLwStrip, colName): type = "latitude" elif self.detectAddress(colNameLwStrip, colName): type = "address" # elif self.detectCounty(colNameLwStrip, colName): # type = "county" elif self.detectBorough(colNameLwStrip, colName): type = "borough" elif self.detectCity(colNameLwStrip, colName): type = "city" elif self.detectState(colNameLwStrip, colName): type = "state" elif self.detectCountry(colNameLwStrip, colName): type = "country" elif self.detectZipcode(colNameLwStrip, colName): type = "zipcode" elif self.detectOtherLocationAttribute(colNameLwStrip, colName): type = "other location attribute" else: type = "not spatial attribute" self.colNameType["attributes"][colName]["type"] = type self.colNameType["attributes"][colName]["index"] = colIndex colIndex += 1 count = 0 for colName in self.columnNames: if self.colNameType["attributes"][colName]["type"] != "not spatial attribute": count += 1 self.colNameType["attributes"][colName]["is_spatial"] = True else: self.colNameType["attributes"][colName]["is_spatial"] = False self.colNameType["total_spatial_attributes"] = count return self.colNameType def commonDetectMethod(self, colNameLw, names, thredshold): for name in names: if (colNameLw in name) or (name in colNameLw): return True # use fuzz ratio, which uses the idea of levestain distance, we can specify elsewhere for name in names: if fuzz.ratio(name, colNameLw) > thredshold: return True return False # TODO: to be finish, colName passed in is lowercase, colNameLw is the lower case and stip() def detectLongitude(self, colNameLw, colName): names = ["longitude", "lon"] thredshold = 75 if self.commonDetectMethod(colNameLw, names, thredshold): return True return False # TODO: use datamart def detectLatitude(self, colNameLw, colName): names = ["latitude"] thredshold = 75 if self.commonDetectMethod(colNameLw, names, thredshold): return True return False # list all the country out, count how many of the sample column values are in country list def detectCountry(self, colNameLw,colName): # quite similar word if "county" in colNameLw: return False names = ["country"] thredshold = 100 if self.commonDetectMethod(colNameLw, names, thredshold): return True # this not work for "yes" or "no" column because "no" stands for norway if (self.dtypes[colName] == object) or (self.dtypes[colName] == "string"): dfCountryNames = self.defaultFiles.dfCountryNames # sampling and pair wise comparison sampleSize = 500 sampleSize = min(sampleSize, self.df.count()) columnValuesSample = random.sample(self.df.select(colName).rdd.flatMap(lambda x: x).collect(), sampleSize) columnValuesSample = [x for x in columnValuesSample if type(x) == str] sampleLength = len(columnValuesSample) # meanning many are nan if sampleLength / sampleSize < 0.1: return False # get the average length of the values avgLen = sum(map(len, columnValuesSample)) / len(columnValuesSample) # compare with country code, other wise compare with country full name if avgLen <= 2.3: countries = dfCountryNames.select("Code").rdd.flatMap(lambda x: x).collect() countries = [code for code in countries if code != "NO"] else: countries = dfCountryNames.select("Name").rdd.flatMap(lambda x: x).collect() countries = [code for code in countries if code != "Norway"] # equality count count = 0 for value in columnValuesSample: for country in countries: if value.lower() == country.lower(): count += 1 break if count / sampleLength > 0.7: return True return False # list all the state out, fullname and abbreviation, can use sampling def detectState(self, colNameLw, colName): names = ["state"] thredshold = 90 if self.commonDetectMethod(colNameLw, names, thredshold): return True if (self.dtypes[colName] == object) or (self.dtypes[colName] == "string"): dfStateNames = self.defaultFiles.dfStateNames # sampling and pair wise comparison sampleSize = 500 sampleSize = min(sampleSize, self.df.count()) columnValuesSample = random.sample(self.df.select(colName).rdd.flatMap(lambda x: x).collect(), sampleSize) columnValuesSample = [x for x in columnValuesSample if type(x) == str] sampleLength = len(columnValuesSample) # meanning many are nan if sampleLength / sampleSize < 0.1: return False # get the average length of the values avgLen = sum(map(len, columnValuesSample)) / len(columnValuesSample) # compare with state code, other wise compare with state full name if avgLen <= 2.3: states = dfStateNames.select("Code").rdd.flatMap(lambda x: x).collect() else: states = dfStateNames.select("Name").rdd.flatMap(lambda x: x).collect() # equality count count = 0 for value in columnValuesSample: for state in states: if value.lower() == state.lower(): count += 1 break if count / sampleLength > 0.6: return True return False # can use sampling def detectCity(self, colNameLw, colName): names = ["city", "town"] thredshold = 90 # if self.commonDetectMethod(colNameLw, names, thredshold): # return True # avoid capcity, ethnicity, electricity words for name in names: if colNameLw == name: return True # some column name states, county but have New York inside if self.commonDetectMethod(colNameLw, ["state", "county"], thredshold): return False if (self.dtypes[colName] == object) or (self.dtypes[colName] == "string"): dfCityNames = self.defaultFiles.dfCityNames # sampling and pair wise comparison sampleSize = 500 sampleSize = min(sampleSize, self.df.count()) columnValuesSample = random.sample(self.df.select(colName).rdd.flatMap(lambda x: x).collect(), sampleSize) columnValuesSample = [x for x in columnValuesSample if type(x) == str] sampleLength = len(columnValuesSample) # meanning many are nan if sampleLength / sampleSize < 0.1: return False cities = dfCityNames.select("Name").rdd.flatMap(lambda x: x).collect() # equality count count = 0 for value in columnValuesSample: for city in cities: if value == city: count += 1 break if count / sampleLength > 0.6: return True return False # list all the county out, count how many of the sample column values are in county list. Not country!!! def detectCounty(self, colNameLw, colName): # quite similar word if "country" in colNameLw: return False names = ["county"] thredshold = 70 if self.commonDetectMethod(colNameLw, names, thredshold): return True if (self.dtypes[colName] == object) or (self.dtypes[colName] == "string"): dfCountyNames = self.defaultFiles.dfCountyNames # sampling and pair wise comparison sampleSize = 500 sampleSize = min(sampleSize, self.df.count()) columnValuesSample = random.sample(self.df.select(colName).rdd.flatMap(lambda x: x).collect(), sampleSize) columnValuesSample = [x for x in columnValuesSample if type(x) == str] sampleLength = len(columnValuesSample) # meaning many are nan if sampleLength / sampleSize < 0.1: return False counties = dfCountyNames.select("Name").rdd.flatMap(lambda x: x).collect() # equality count count = 0 for value in columnValuesSample: for county in counties: if value.lower() == county.lower(): count += 1 break if count / sampleLength > 0.6: return True return False def detectBorough(self, colNameLw, colName): names = ["borough", "boro", "borocode"] thredshold = 80 if self.commonDetectMethod(colNameLw, names, thredshold): return True return False # need to use sampling def detectAddress(self, colNameLw, colName): names = ["address", "street", "block"] thredshold = 80 if self.commonDetectMethod(colNameLw, names, thredshold): # add one more condition if (self.dtypes[colName] == object) or (self.dtypes[colName] == "string"): return True if (self.dtypes[colName] == object) or (self.dtypes[colName] == "string"): # sampling and pair wise comparison sampleSize = 500 sampleSize = min(sampleSize, self.df.count()) columnValuesSample = random.sample(self.df.select(colName).rdd.flatMap(lambda x: x).collect(), sampleSize) columnValuesSample = [x for x in columnValuesSample if type(x) == str] sampleLength = len(columnValuesSample) # meanning many are nan if sampleLength / sampleSize < 0.1: return False # get the average length of the values avgLen = sum(map(len, columnValuesSample)) / len(columnValuesSample) # probably not address if avgLen < 5: return False # use regex expression, detect full address regexPattern =""" \b\d{1,6} +.{2,25}\b(avenue|ave|court|ct|street|st| drive|dr|lane|ln|road|rd|blvd|plaza|parkway|pkwy| boulevard|)[.,]?(.{0,25} +\b\d{5}\b)? """ count = 0 for x in columnValuesSample: result = re.match(regexPattern, x) if result != None: if result.group() > 10: count += 1 if count / sampleLength > 0.6: return True # use regex expression to detect street name like regexPattern2 =""" \d+[ ](?:[A-Za-z0-9.-]+[ ]?)+(?:Avenue|Lane|Road| Boulevard|Drive|Street|Ave|Dr|Rd|Blvd|Ln|St)\.? """ count = 0 for x in columnValuesSample: result = re.match(regexPattern2, x) if result != None: if result.group() > 10: count += 1 if count / sampleLength > 0.7: return True # TODO use the addr_detection package # try: # clf2 = Postal_clf() # result = clf2.predict(columnValuesSample) # except: # return False return False # need to use sampling, and regex def detectZipcode(self, colNameLw, colName): names = ["zip", "zipcode", "zcode", "postcode"] thredshold = 70 if self.commonDetectMethod(colNameLw, names, thredshold): return True if (self.dtypes[colName] == object) or (self.dtypes[colName] == "string") or \ (self.dtypes[colName] == "int") or (self.dtypes[colName] == "bigint"): # sampling and pair wise comparison sampleSize = 500 sampleSize = min(sampleSize, self.df.count()) columnValuesSample = random.sample(self.df.select(colName).rdd.flatMap(lambda x: x).collect(), sampleSize) if (self.dtypes[colName] == "int") or (self.dtypes[colName] == "bigint"): columnValuesSample = [str(x) for x in columnValuesSample] else: columnValuesSample = [x for x in columnValuesSample if type(x) == str] sampleLength = len(columnValuesSample) # meanning many are nan if sampleLength / sampleSize < 0.1: return False # just us country here, can use other api? regexPattern = r"^[0-9]{5}(?:-[0-9]{3})?$" matches = [] for x in columnValuesSample: result = re.match(regexPattern, x) if result != None: matches.append(bool(x)) count = sum([bool(x) for x in matches]) if count / sampleLength > 0.6: return True return False def detectOtherLocationAttribute(self, colNameLw, colName): names = ["location", "home", "house", "lot", "bin", "bbl", "nta", "geom", "precinct", "census_tract", "community", "district", "building"] thredshold = 70 if self.commonDetectMethod(colNameLw, names, thredshold): return True return False

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openstates/openstates-master/openstates/de/legislators.py

Jgorsick/Advocacy_Angular

0

6029

import re import lxml.html from openstates.utils import LXMLMixin from billy.scrape.legislators import LegislatorScraper, Legislator class DELegislatorScraper(LegislatorScraper,LXMLMixin): jurisdiction = 'de' def scrape(self, chamber, term): url = { 'upper': 'http://legis.delaware.gov/legislature.nsf/sen?openview', 'lower': 'http://legis.delaware.gov/Legislature.nsf/Reps?openview', }[chamber] doc = self.lxmlize(url) if chamber == "upper": #for the senate, it's the same table #but the html is hard-coded in js. table_js = doc.xpath('.//script')[-1].text_content() table = None for line in table_js.split("\n"): if line.strip().startswith("var") and "sen=" in line: table = line.replace("var","") table = table.replace('sen="<','<') table = table.replace('>";','>') break assert table is not None, "Senate table could not be found" table = lxml.html.fromstring(table) table.make_links_absolute(url) trs = table.xpath('//tr') else: #same table for the house, but kindly in actual html trs = doc.xpath('//tr') base_url = "http://legis.delaware.gov" for tr in trs: name_and_url = tr.xpath('.//a')[0] bio_url = name_and_url.attrib["href"] bio_url = bio_url.replace("JavaScript:window.top.location.href=","") bio_url = bio_url.replace('"','') name = name_and_url.text_content() if name.strip() == "." or name.strip() == "": continue if name.strip().lower().startswith("vacant"): continue re_spaces=re.compile(r'\s{1,5}') name = ' '.join(re_spaces.split(name)) district = tr.xpath('.//td')[2].text_content() district = district.replace("District:","").strip() leg = self.scrape_bio(term, chamber, district, name, bio_url) leg.add_source(bio_url, page="legislator detail page") leg.add_source(url, page="legislator list page") self.save_legislator(leg) def scrape_bio(self, term, chamber, district, name, url): # this opens the committee section without having to do another request url += '&TableRow=1.5.5' frame_doc = self.lxmlize(url) actual_url = frame_doc.xpath("//frame[@name='right']/@src")[0] doc = self.lxmlize(actual_url) # party is in one of these party = doc.xpath('//div[@id="page_header"]')[0].text.strip()[-3:] if '(D)' in party: party = 'Democratic' elif '(R)' in party: party = 'Republican' else: raise AssertionError("No party found for {name}".format(name=name)) leg = Legislator(term, chamber, district, name, party=party) photo_url = doc.xpath('//img[contains(@src, "jpg")]/@src') if photo_url: leg['photo_url'] = photo_url[0] contact_info = self.scrape_contact_info(doc) leg.update(contact_info) return leg def scrape_contact_info(self, doc): # Email email = doc.xpath(".//a[contains(@href,'mailto')]") email = email[0].text_content().strip() leg_email = None dist_email = None try: emails = email.split(";") except AttributeError: pass else: for e in emails: e = e.strip() if e: if "state.de.us" in e: leg_email = e else: dist_email = e # Offices leg_office = dict(name="Capitol Office", type="capitol", phone=None, fax=None, email=leg_email, address=None) dist_office = dict(name="Outside Office", type="capitol", phone=None,fax=None, email=dist_email, address=None) #this is enormously painful, DE. office_list = doc.xpath("//tr") for office in office_list: title_td = 0 #in some trs the photo is the first td if len(office.xpath("./td/img")) > 0: title_td = 1 try: title_text = office.xpath("./td")[title_td].text_content().lower() content = office.xpath("./td")[title_td+1].text_content() except IndexError: continue leg_office = self.add_contact("legislative", title_text,content,leg_office) dist_office = self.add_contact("outside", title_text,content,dist_office) offices = [o for o in [leg_office,dist_office] if o["address"]] assert len(offices) > 0, "No offices with addresses found "\ "make sure we're not losing any data." return {"offices":offices} def add_contact(self,office_type, title_text,content,office): #office type is the name of the office #either "legislative" or "outside" if "{} office".format(office_type) in title_text: office["address"] = content.strip() if "{} phone".format(office_type) in title_text: phones = content.lower().split("\n") if len(phones) == 1: phone = self.clean_phone(phones[0]) if phone: office["phone"] = phone else: for line in phones: if "phone" in line: phone = self.clean_phone(line) if phone: office["phone"] = phone elif "fax" in line: phone = self.clean_phone(line) if phone: office["fax"] = phone return office def clean_phone(self,phone): if not phone.strip(): return if not re.search("\d",phone): return if not ":" in phone: return phone return phone.split(":")[1].strip()

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tests/test_handler.py

CJSoldier/webssh

13

4247

<filename>tests/test_handler.py import unittest import paramiko from tornado.httputil import HTTPServerRequest from tests.utils import read_file, make_tests_data_path from webssh.handler import MixinHandler, IndexHandler, InvalidValueError class TestMixinHandler(unittest.TestCase): def test_get_real_client_addr(self): handler = MixinHandler() handler.request = HTTPServerRequest(uri='/') self.assertIsNone(handler.get_real_client_addr()) ip = '127.0.0.1' handler.request.headers.add('X-Real-Ip', ip) self.assertEqual(handler.get_real_client_addr(), False) handler.request.headers.add('X-Real-Port', '12345x') self.assertEqual(handler.get_real_client_addr(), False) handler.request.headers.update({'X-Real-Port': '12345'}) self.assertEqual(handler.get_real_client_addr(), (ip, 12345)) handler.request.headers.update({'X-Real-ip': None}) self.assertEqual(handler.get_real_client_addr(), False) handler.request.headers.update({'X-Real-Port': '12345x'}) self.assertEqual(handler.get_real_client_addr(), False) class TestIndexHandler(unittest.TestCase): def test_get_specific_pkey_with_plain_key(self): fname = 'test_rsa.key' cls = paramiko.RSAKey key = read_file(make_tests_data_path(fname)) pkey = IndexHandler.get_specific_pkey(cls, key, None) self.assertIsInstance(pkey, cls) pkey = IndexHandler.get_specific_pkey(cls, key, 'iginored') self.assertIsInstance(pkey, cls) pkey = IndexHandler.get_specific_pkey(cls, 'x'+key, None) self.assertIsNone(pkey) def test_get_specific_pkey_with_encrypted_key(self): fname = 'test_rsa_password.key' cls = paramiko.RSAKey password = '<PASSWORD>' key = read_file(make_tests_data_path(fname)) pkey = IndexHandler.get_specific_pkey(cls, key, password) self.assertIsInstance(pkey, cls) pkey = IndexHandler.get_specific_pkey(cls, 'x'+key, None) self.assertIsNone(pkey) with self.assertRaises(paramiko.PasswordRequiredException): pkey = IndexHandler.get_specific_pkey(cls, key, None) def test_get_pkey_obj_with_plain_key(self): fname = 'test_ed25519.key' cls = paramiko.Ed25519Key key = read_file(make_tests_data_path(fname)) pkey = IndexHandler.get_pkey_obj(key, None, fname) self.assertIsInstance(pkey, cls) pkey = IndexHandler.get_pkey_obj(key, 'iginored', fname) self.assertIsInstance(pkey, cls) with self.assertRaises(InvalidValueError) as exc: pkey = IndexHandler.get_pkey_obj('x'+key, None, fname) self.assertIn('Invalid private key', str(exc)) def test_get_pkey_obj_with_encrypted_key(self): fname = 'test_ed25519_password.key' password = '<PASSWORD>' cls = paramiko.Ed25519Key key = read_file(make_tests_data_path(fname)) pkey = IndexHandler.get_pkey_obj(key, password, fname) self.assertIsInstance(pkey, cls) with self.assertRaises(InvalidValueError) as exc: pkey = IndexHandler.get_pkey_obj(key, 'wrongpass', fname) self.assertIn('Wrong password', str(exc)) with self.assertRaises(InvalidValueError) as exc: pkey = IndexHandler.get_pkey_obj('x'+key, password, fname) self.assertIn('Invalid private key', str(exc)) with self.assertRaises(paramiko.PasswordRequiredException): pkey = IndexHandler.get_pkey_obj(key, '', fname)

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chap3-3.py

mikidake/Ex3

0

27253

<gh_stars>0 # Chapter3 Ex.3) # Modification of 'Guess My Number' # Guess My Number # # The computer picks a random number between 1 and 100 # The player tries to guess it and the computer lets # the player know if the guess is too high, too low # or right on the money # The player can try to guess up to three times import random print("\tWelcome to 'Guess My Number'!") print("\nI'm thinking of a number between 1 and 100.") print("Try to guess it in as few attempts as possible.") print("You have three chances to try!\n") # set the initial values the_number = random.randint(1, 100) tries = 1 maxTries = 3 # guessing loop while tries <= maxTries: guess = int(input("Take a guess: ")) if guess == the_number: print("\nYou guessed it! The number was", the_number) if tries == 1: print("And it only took you", tries, "try!\n") break else: print("And it only took you", tries, "tries!\n") break elif guess > the_number: print("Guess Lower..") else: print("Guess Higher..") tries += 1 if tries > maxTries: print("\nSorry you ran out of tries! The random number was ", the_number) input("\n\nPress the enter key to exit.")

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tests/dce_models/test_dibem.py

michaelberks/madym_python

0

146402

''' Test file for the dce_models sub-module ''' import pytest import os import sys import numpy as np from tempfile import TemporaryDirectory sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'src'))) #------------------------------------------------------------------------------- # Tests for dibem module from QbiPy.dce_models import dibem, two_cxm_model, active_uptake_model from QbiPy.dce_models import dce_aif #------------------------------------------------------------------------------- def test_params_AUEM(): #Test consistency of conversion to/from DIBEM F_p = 1.0 v_ecs = 0.2 k_i = 0.25 k_ef = 0.05 dibem_params = active_uptake_model.params_to_DIBEM(F_p, v_ecs, k_i, k_ef, False) F_p2, v_ecs2, k_i2, k_ef2 = active_uptake_model.params_from_DIBEM(*dibem_params, False) dibem_params = active_uptake_model.params_to_DIBEM(F_p, v_ecs, k_i, k_ef, True) F_p3, v_ecs3, k_i3, k_ef3 = active_uptake_model.params_from_DIBEM(*dibem_params, True) assert F_p == pytest.approx(F_p2) assert v_ecs == pytest.approx(v_ecs2) assert k_i == pytest.approx(k_i2) assert k_ef == pytest.approx(k_ef2) assert F_p == pytest.approx(F_p3) assert v_ecs == pytest.approx(v_ecs3) assert k_i == pytest.approx(k_i3) assert k_ef == pytest.approx(k_ef3) def test_params_2CXM(): F_p = 1.0 PS = 0.2 v_e = 0.2 v_p = 0.1 dibem_params = two_cxm_model.params_to_DIBEM(F_p, PS, v_e, v_p, False) print('params_2CXM_to_DIBEM: ', dibem_params) F_p2, PS2, v_e2, v_p2 = two_cxm_model.params_from_DIBEM(*dibem_params, False) dibem_params = two_cxm_model.params_to_DIBEM(F_p, PS, v_e, v_p, True) print('params_2CXM_to_DIBEM: ', dibem_params) F_p3, PS3, v_e3, v_p3 = two_cxm_model.params_from_DIBEM(*dibem_params, True) assert F_p == pytest.approx(F_p2) assert PS == pytest.approx(PS2) assert v_e == pytest.approx(v_e2) assert v_p == pytest.approx(v_p2) assert F_p == pytest.approx(F_p3) assert PS == pytest.approx(PS3) assert v_e == pytest.approx(v_e3) assert v_p == pytest.approx(v_p3) def test_concentration_from_model_scalar(): times = np.linspace(0, 6, 50) aif = dce_aif.Aif(times = times) F_pos = 0.2 F_neg = 0.2 K_pos = 0.5 K_neg = 4.0 f_a = 0.5 tau_a = 0.1 tau_v = 0.05 C_t = dibem.concentration_from_model( aif, F_pos, F_neg, K_pos, K_neg, f_a, tau_a, tau_v ) assert C_t.size == times.size assert np.all(np.isfinite(C_t)) def test_concentration_from_array(): times = np.linspace(0, 6, 50) aif = dce_aif.Aif(times = times) F_pos = [0.2, 0.25] F_neg = [0.2, 0.25] K_pos = [0.5, 0.55] K_neg = [4.0, 4.5] f_a = 0.5 tau_a = 0.1 tau_v = 0.05 C_t = dibem.concentration_from_model( aif, F_pos, F_neg, K_pos, K_neg, f_a, tau_a, tau_v ) assert C_t.size == 2*times.size assert np.all(np.isfinite(C_t))

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hkl/user.py

NSLS-II/hklpy

1

168409

<filename>hkl/user.py """ Provide a simplified UI for hklpy diffractometer users. The user must define a diffractometer instance, then register that instance here calling `select_diffractometer(instance)`. FUNCTIONS .. autosummary:: ~cahkl ~cahkl_table ~calc_UB ~list_samples ~new_sample ~or_swap ~select_diffractometer ~set_energy ~setor ~show_sample ~show_selected_diffractometer ~update_sample ~wh ~pa """ __all__ = """ cahkl cahkl_table calc_UB change_sample list_samples new_sample or_swap pa select_diffractometer set_energy setor show_sample show_selected_diffractometer update_sample wh """.split() import logging import gi gi.require_version("Hkl", "5.0") logger = logging.getLogger(__name__) from .diffract import Diffractometer from .util import Lattice import numpy import pyRestTable _geom_ = None # selected diffractometer geometry def _check_geom_selected(*args, **kwargs): """Raise ValueError if no diffractometer geometry is selected.""" if _geom_ is None: raise ValueError( "No diffractometer selected." " Call 'select_diffractometer(diffr)' where" " 'diffr' is a diffractometer instance." ) def cahkl(h, k, l): """ Calculate motor positions for one reflection. Returns a namedtuple. Does not move motors. """ _check_geom_selected() # TODO: make certain this will not move the motors! return _geom_.forward(h, k, l) def cahkl_table(reflections, digits=5): """ Print a table with motor positions for each reflection given. Parameters ---------- reflections : list(tuple(number,number,number)) This is a list of reflections where each reflection is a tuple of 3 numbers specifying (h, k, l) of the reflection to compute the ``forward()`` computation. Example: ``[(1,0,0), (1,1,1)]`` digits : int Number of digits to roundoff each position value. Default is 5. """ _check_geom_selected() print(_geom_.forward_solutions_table(reflections, digits=digits)) # def calc_energy(): # # TODO: should this be added? # raise NotImplementedError def calc_UB(r1, r2, wavelength=None): """Compute the UB matrix with two reflections.""" _check_geom_selected() _geom_.calc.sample.compute_UB(r1, r2) print(_geom_.calc.sample.UB) def change_sample(sample): """Pick a known sample to be the current selection.""" _check_geom_selected() if sample not in _geom_.calc._samples: # fmt: off raise KeyError( f"Sample '{sample}' is unknown." f" Known samples: {list(_geom_.calc._samples.keys())}" ) # fmt: on _geom_.calc.sample = sample show_sample(sample) def list_samples(verbose=True): """List all defined crystal samples.""" _check_geom_selected() # always show the default sample first current_name = _geom_.calc.sample_name show_sample(current_name, verbose=verbose) # now, show any other samples for sample in _geom_.calc._samples.keys(): if sample != current_name: if verbose: print("") show_sample(sample, verbose=verbose) def new_sample(nm, a, b, c, alpha, beta, gamma): """Define a new crystal sample.""" _check_geom_selected() if nm in _geom_.calc._samples: logger.warning( ( "Sample '%s' is already defined." " Use 'update_sample()' to change lattice parameters" " on the *current* sample." ), nm, ) else: lattice = Lattice(a=a, b=b, c=c, alpha=alpha, beta=beta, gamma=gamma) _geom_.calc.new_sample(nm, lattice=lattice) show_sample() def or_swap(): """Swap the 2 [UB] reflections, re-compute & return new [UB].""" # note: or_swap is how the community of SPEC users knows this function _check_geom_selected() return _geom_.calc.sample.swap_orientation_reflections() def select_diffractometer(instrument=None): """Name the diffractometer to be used.""" global _geom_ if instrument is None or isinstance(instrument, Diffractometer): _geom_ = instrument else: raise TypeError(f"{instrument} must be a 'Diffractometer' subclass") def set_energy(value, units=None, offset=None): """ Set the energy (thus wavelength) to be used. """ _check_geom_selected() if units is not None: _geom_.energy_units.put(units) if offset is not None: _geom_.energy_offset.put(offset) _geom_.energy.put(value) def setor(h, k, l, *args, wavelength=None, **kwargs): """Define a crystal reflection and its motor positions.""" _check_geom_selected() if len(args) == 0: if len(kwargs) == 0: pos = _geom_.real_position else: # fmt: off pos = [ kwargs[m] for m in _geom_.calc.physical_axis_names if m in kwargs ] # fmt: on else: pos = args # NOTE: libhkl gets the wavelength on a reflection from hkl.calc # when the wavelength is set, it calls libhkl directly # as self._geometry.wavelength_set(wavelength, self._units) # The code here uses that procedure. if wavelength not in (None, 0): _geom_.calc.wavelength = wavelength refl = _geom_.calc.sample.add_reflection(h, k, l, position=pos) return refl def show_sample(sample_name=None, verbose=True): """Print the default sample name and crystal lattice.""" _check_geom_selected() sample_name = sample_name or _geom_.calc.sample_name sample = _geom_.calc._samples[sample_name] title = sample_name if sample_name == _geom_.calc.sample.name: title += " (*)" # Print Lattice more simply (than as a namedtuple). lattice = [getattr(sample.lattice, parm) for parm in sample.lattice._fields] if verbose: tbl = pyRestTable.Table() tbl.addLabel("key") tbl.addLabel("value") tbl.addRow(("name", sample_name)) tbl.addRow(("lattice", lattice)) for i, r in enumerate(sample.reflections): tbl.addRow((f"reflection {i+1}", r)) tbl.addRow(("U", numpy.round(sample.U, 5))) tbl.addRow(("UB", numpy.round(sample.UB, 5))) print(f"Sample: {title}\n") print(tbl) else: print(f"{title}: {lattice}") def show_selected_diffractometer(instrument=None): """Print the name of the selected diffractometer.""" if _geom_ is None: print("No diffractometer selected.") print(_geom_.name) def update_sample(a, b, c, alpha, beta, gamma): """Update current sample lattice.""" _check_geom_selected() _geom_.calc.sample.lattice = ( a, b, c, alpha, beta, gamma, ) # define the current sample show_sample(_geom_.calc.sample.name, verbose=False) def pa(): """Report (all) the diffractometer settings.""" _check_geom_selected() _geom_.pa() def wh(): """Report (brief) where is the diffractometer.""" _check_geom_selected() _geom_.wh()

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Network_and_802.11/scapy/check_if_rogue_dhcp.py

hacky1997/My-Gray-Hacker-Resources

14

128805

<gh_stars>10-100 #!/usr/bin/env python from scapy.all import * import threading from colorama import Back from sys import argv from time import sleep global lock lock = threading.Lock() def BootpPacket(): fm, hw = get_if_raw_hwaddr(scapy.all.conf.iface) ether=Ether(dst="ff:ff:ff:ff:ff:ff") ip=IP(src="0.0.0.0",dst="255.255.255.255") udp=UDP(sport=68,dport=67) bootp=BOOTP(chaddr=hw) dhcp=DHCP(options=[("message-type","discover"),"end"]) DHCPDiscover = ether/ip/udp/bootp/dhcp return DHCPDiscover def sendpkt(DHCPDiscover): srp(DHCPDiscover,timeout=2,inter=5, verbose=0) a = sniff(prn=test) # Thread2 for sniffing BOOTP Message on the network (DHCP OFFER MSG) t2 = threading.Thread(target=test) t2.start() def test(pkt): if pkt.haslayer(BOOTP): if pkt[IP].src != "0.0.0.0" and pkt[IP].src != LegalDHCPServer and pkt[BOOTP].op ==2: if pkt[IP].src not in dhcpservers: try: lock.acquire() print "Rogue DHCP Server(hacker) is at " + Back.RED+"%s"%pkt[IP].src+Back.RESET dhcpservers.add(pkt[IP].src) except : lock.acquire() finally: lock.release() else: pass else: if pkt[IP].src not in legalservers: try: lock.acquire() print "Legal DHCP Server:%s"%pkt[IP].src legalservers.add(pkt[IP].src) except: lock.acquire() finally: lock.release() else: pass else: pass if __name__ == '__main__': DHCPDiscover = BootpPacket() legalservers = set() dhcpservers = set() while True: if len(argv) != 2: print 'Usage: %s <LegalServersIP>'%argv[0] exit(0) else: LegalDHCPServer = argv[1] t = threading.Thread(target=sendpkt,args=DHCPDiscover) t.start() sleep(5)

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logistic_regression/logistic_regression.py

suitnecniv/dota2

127

194928

<filename>logistic_regression/logistic_regression.py import numpy as np import pickle, os NUM_HEROES = 108 NUM_FEATURES = NUM_HEROES * 2 class D2LogisticRegression: def __init__(self, model_root='logistic_regression'): model_path = os.path.join(model_root, 'model.pkl') with open(model_path, 'r') as input_file: self.model = pickle.load(input_file) def transform(self, my_team, their_team): X = np.zeros(NUM_FEATURES, dtype=np.int8) for hero_id in my_team: X[hero_id - 1] = 1 for hero_id in their_team: X[hero_id - 1 + NUM_HEROES] = 1 return X def recommend(self, my_team, their_team, hero_candidates): '''Returns a list of (hero, probablility of winning with hero added) recommended to complete my_team.''' team_possibilities = [(candidate, my_team + [candidate]) for candidate in hero_candidates] prob_candidate_pairs = [] for candidate, team in team_possibilities: query = self.transform(team, their_team) prob = self.score(query) #self.model.predict_proba(query)[0][1] prob_candidate_pairs.append((prob, candidate)) prob_candidate_pairs = sorted(prob_candidate_pairs, reverse=True)[0:5 - len(my_team)] return prob_candidate_pairs def score(self, query): '''Score the query using the model, considering both radiant and dire teams.''' radiant_query = query dire_query = np.concatenate((radiant_query[NUM_HEROES:NUM_FEATURES], radiant_query[0:NUM_HEROES])) rad_prob = self.model.predict_proba(radiant_query)[0][1] dire_prob = self.model.predict_proba(dire_query)[0][0] return (rad_prob + dire_prob) / 2 def predict(self, dream_team, their_team): '''Returns the probability of the dream_team winning against their_team.''' dream_team_query = self.transform(dream_team, their_team) return self.score(dream_team_query) #return self.model.predict_proba(dream_team_query)[0][1]

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main/faq.py

vanessa-bell/hd-kiosk-v2

0

69045

<gh_stars>0 import flask import flask_wtf import wtforms import auth import model import util from google.appengine.ext import ndb from main import app class FaqUpdateForm(flask_wtf.FlaskForm): question = wtforms.TextAreaField('Question', [wtforms.validators.required()]) answer = wtforms.TextAreaField('Answer', [wtforms.validators.required()]) category = wtforms.SelectField('Category', [wtforms.validators.required()], choices=[('About/History','About/History'),('Financials','Financials'),('The Physical Space','The Physical Space'),('Other Hackerspaces','Other Hackerspaces'),('Paperwork and Logistics','Paperwork and Logistics'),('Miscellaneous','Miscellaneous')]) @app.route('/faqs/create/', methods=['GET', 'POST']) @auth.login_required def faq_create(): form = FaqUpdateForm() if form.validate_on_submit(): faq_db = model.Faq( user_key=auth.current_user_key(), question=form.question.data, answer=form.answer.data, category=form.category.data, ) faq_db.put() return flask.redirect(flask.url_for('welcome')) return flask.render_template( 'faq_create.html', html_class='faq-create', title='Create FAQ', form=form, ) @app.route('/faqs/') def faqs_list(): faq_dbs, faq_cursor = model.Faq.get_dbs() return flask.render_template( 'faqs_list.html', html_class='faqs-list', title='Frequently Asked Questions', faq_dbs=faq_dbs, next_url=util.generate_next_url(faq_cursor), ) @app.route('/faqs/stats') def statistics(): faq_dbs, faq_cursor = model.Faq.get_dbs() return flask.render_template( 'statistics.html', html_class='statistics', title='Statistics', faq_dbs=faq_dbs, next_url=util.generate_next_url(faq_cursor), ) @app.route('/faqs/<int:faq_id>/update/', methods=['GET', 'POST']) @auth.login_required def faq_update(faq_id): faq_db = model.Faq.get_by_id(faq_id) if not faq_db: # or faq_db.user_key != auth.current_user_key() flask.abort(404) form = FaqUpdateForm(obj=faq_db) if form.validate_on_submit(): form.populate_obj(faq_db) faq_db.put() return flask.redirect(flask.url_for('faqs_list')) return flask.render_template( 'faq_update.html', html_class='faq-update', title=faq_db.question, form=form, faq_db=faq_db, faq_id=faq_id ) @app.route('/faqs/<int:faq_id>/delete/', methods=['GET', 'POST']) @auth.login_required def faq_delete(faq_id): faq_key = ndb.Key("Faq",faq_id) faq_key.delete() faq_dbs = model.Faq.get_dbs() return flask.redirect(flask.url_for('faqs_list')) @app.route('/faqs/<int:faq_id>/count/', methods=['GET', 'POST']) def faq_count(faq_id): stat_db = model.Stat( faq_key=ndb.Key("Faq",faq_id), ) stat_db.put() faq_db = model.Faq.get_by_id(faq_id) faq_db.count = faq_db.count + 1 faq_db.put() return 'done' # return response, 200, {'Content-Type': 'text/plain'}

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333, 20690, 5504, 29914, 742, 3519, 29922, 1839, 7194, 742, 525, 5438, 11287, 13, 29992, 5150, 29889, 7507, 29918, 12403, 13, 13, 1753, 2258, 29939, 29918, 5504, 29898, 5444, 29939, 29918, 333, 1125, 13, 29871, 2258, 29939, 29918, 2585, 353, 1904, 29889, 14206, 29939, 29889, 657, 29918, 1609, 29918, 333, 29898, 5444, 29939, 29918, 333, 29897, 13, 29871, 565, 451, 2258, 29939, 29918, 2585, 29901, 29871, 13, 1678, 396, 470, 2258, 29939, 29918, 2585, 29889, 1792, 29918, 1989, 2804, 4817, 29889, 3784, 29918, 1792, 29918, 1989, 580, 13, 1678, 29784, 29889, 370, 441, 29898, 29946, 29900, 29946, 29897, 13, 29871, 883, 353, 7748, 29939, 6422, 2500, 29898, 5415, 29922, 5444, 29939, 29918, 2585, 29897, 13, 29871, 565, 883, 29889, 15480, 29918, 265, 29918, 7892, 7295, 13, 1678, 883, 29889, 7323, 5987, 29918, 5415, 29898, 5444, 29939, 29918, 2585, 29897, 13, 1678, 2258, 29939, 29918, 2585, 29889, 649, 580, 13, 1678, 736, 29784, 29889, 17886, 29898, 1579, 1278, 29889, 2271, 29918, 1454, 877, 5444, 29939, 29879, 29918, 1761, 8785, 13, 29871, 736, 29784, 29889, 9482, 29918, 6886, 29898, 13, 418, 525, 5444, 29939, 29918, 5504, 29889, 1420, 742, 13, 418, 3472, 29918, 1990, 2433, 5444, 29939, 29899, 5504, 742, 13, 418, 3611, 29922, 5444, 29939, 29918, 2585, 29889, 12470, 29892, 13, 418, 883, 29922, 689, 29892, 13, 418, 2258, 29939, 29918, 2585, 29922, 5444, 29939, 29918, 2585, 29892, 13, 418, 2258, 29939, 29918, 333, 29922, 5444, 29939, 29918, 333, 13, 1678, 1723, 13, 13, 29992, 932, 29889, 13134, 11219, 5444, 29939, 29879, 29914, 29966, 524, 29901, 5444, 29939, 29918, 333, 20690, 8143, 29914, 742, 3519, 29922, 1839, 7194, 742, 525, 5438, 11287, 13, 29992, 5150, 29889, 7507, 29918, 12403, 13, 13, 1753, 2258, 29939, 29918, 8143, 29898, 5444, 29939, 29918, 333, 1125, 13, 29871, 2258, 29939, 29918, 1989, 353, 29871, 299, 29890, 29889, 2558, 703, 14206, 29939, 613, 5444, 29939, 29918, 333, 29897, 13, 29871, 2258, 29939, 29918, 1989, 29889, 8143, 580, 13, 29871, 2258, 29939, 29918, 2585, 29879, 353, 1904, 29889, 14206, 29939, 29889, 657, 29918, 2585, 29879, 580, 13, 29871, 736, 29784, 29889, 17886, 29898, 1579, 1278, 29889, 2271, 29918, 1454, 877, 5444, 29939, 29879, 29918, 1761, 8785, 13, 259, 13, 13, 29992, 932, 29889, 13134, 11219, 5444, 29939, 29879, 29914, 29966, 524, 29901, 5444, 29939, 29918, 333, 20690, 2798, 29914, 742, 3519, 29922, 1839, 7194, 742, 525, 5438, 11287, 13, 1753, 2258, 29939, 29918, 2798, 29898, 5444, 29939, 29918, 333, 1125, 13, 1678, 1002, 29918, 2585, 353, 1904, 29889, 9513, 29898, 13, 418, 2258, 29939, 29918, 1989, 29922, 299, 29890, 29889, 2558, 703, 14206, 29939, 613, 5444, 29939, 29918, 333, 511, 13, 1678, 1723, 13, 1678, 1002, 29918, 2585, 29889, 649, 580, 13, 13, 1678, 2258, 29939, 29918, 2585, 353, 1904, 29889, 14206, 29939, 29889, 657, 29918, 1609, 29918, 333, 29898, 5444, 29939, 29918, 333, 29897, 13, 1678, 2258, 29939, 29918, 2585, 29889, 2798, 353, 2258, 29939, 29918, 2585, 29889, 2798, 718, 29871, 29896, 13, 1678, 2258, 29939, 29918, 2585, 29889, 649, 580, 13, 1678, 736, 525, 15091, 29915, 13, 1678, 396, 736, 2933, 29892, 29871, 29906, 29900, 29900, 29892, 11117, 3916, 29899, 1542, 2396, 525, 726, 29914, 24595, 10827, 2 ]

morsecodetoolkit/data/__init__.py

1-800-BAD-CODE/MorseCodeToolkit

2

95666

<filename>morsecodetoolkit/data/__init__.py from morsecodetoolkit.data.synthetic_dataset import SyntheticMorseDataset __all__ = [ "SyntheticMorseDataset" ]

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my_studyguide/math1.py

worldwidekatie/study_guide

0

135999

<filename>my_studyguide/math1.py class Math1(): def __init__(self): self.my_num1 = 10 self.my_num2 = 20 def addition(self): return self.my_num1 + self.my_num2 def subtraction(self): return self.my_num1 - self.my_num2 class Math_Plus(Math1): def __init__(self, my_num1=40, my_num2=90): self.my_num1 = my_num1 self.my_num2 = my_num2 def multiplication(self): return self.my_num1 * self.my_num2 def division(self): return self.my_num1 / self.my_num2 if __name__ == "__main__": math1 = Math1() print(math1.addition()) #30 print(math1.subtraction()) #-10 math_plus = Math_Plus() print(math_plus.addition()) #130 print(math_plus.subtraction()) #-50 print(math_plus.multiplication()) #3600 print(math_plus.division()) #0.-44444444

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txnintegration/stats_utils.py

ajenie/sawtooth-validator

4

1611745

<filename>txnintegration/stats_utils.py # Copyright 2016 Intel Corporation # # 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 time import csv import collections class CsvManager(object): def __init__(self): self.csvdata = [] self.file = None self.writer = None def open_csv_file(self, filename, filepath=""): self.file = open(filename, 'wt') self.writer = csv.writer(self.file) def close_csv_file(self): self.file.close() def csv_newline(self): self.csvdata = [] def csv_append(self, datalist): self.csvdata.extend(datalist) def csv_write_header(self, headerlist=None, add_time=True): if headerlist is not None: self.csvdata.extend(headerlist) if add_time: self.csvdata.insert(0, "time") self._csv_write() def csv_write_data(self, datalist=None, add_time=True): if datalist is not None: self.csvdata.extend(datalist) if add_time: self.csvdata.insert(0, time.time()) self._csv_write() def _csv_write(self): self.writer.writerow(self.csvdata) self.csvdata = [] class SummaryStatsCsvManager(object): def __init__(self, system_stats, platform_stats): self.csv_enabled = False self.csv_mgr = CsvManager() self.ss = system_stats self.ps = platform_stats def initialize(self): self.csv_enabled = True filename = "summary_stats_" + str(int(time.time())) + ".csv" self.csv_mgr.open_csv_file(filename) header = self.ss.get_names() self.csv_mgr.csv_append(header) header = self.ps.get_names() self.csv_mgr.csv_write_header(header) def write_stats(self): if self.csv_enabled: data = self.ss.get_data() self.csv_mgr.csv_append(data) data = self.ps.get_data() self.csv_mgr.csv_write_data(data) def stop(self): if self.csv_enabled: self.csv_mgr.close_csv_file() class ValidatorStatsCsvManager(object): def __init__(self, client_list): self.csv_enabled = False self.csv_mgr = CsvManager() self.get_header = False self.csv_stats = CsvManager() self.clients = client_list self.stat_names = [] self.dw = DictWalker() def initialize(self): self.csv_enabled = True filename = "validator_stats_" + str(int(time.time())) + ".csv" self.csv_mgr.open_csv_file(filename) # defer writing header until first instance of data dictionary # is available self.get_header = True def write_stats(self): current_time = time.time() if self.csv_enabled: for client in self.clients: if client.responding: self.dw.walk(client.vsm.val_stats) if self.get_header: names = self.dw.get_names() names.insert(0, "validator_name") names.insert(0, "time") self.csv_mgr.csv_write_header(names, add_time=False) self.get_header = False data = self.dw.get_data() data.insert(0, client.name) data.insert(0, current_time) self.csv_mgr.csv_write_data(data, add_time=False) def stop(self): if self.csv_enabled: self.csv_mgr.close_csv_file() class DictWalker(object): def __init__(self): self.name_list = [] self.unique_name_list = [] self.unique_name_with_comma_list = [] # generates a new set of key/value pairs # each time walk() is called self.data = dict() # contains a set of keys ordered by entry order # that persists across calls to walk() - this lets you discover # (and remember) new keys as the appear in the data stream self.names = collections.OrderedDict() self.value_type_error_count = 0 self.name_has_comma_error_count = 0 def walk(self, data_dict): self.data = dict() return self._traverse_dict(data_dict) def _traverse_dict(self, data_dict): for key, value in data_dict.iteritems(): self.name_list.append(key) if isinstance(value, dict): self._traverse_dict(value) else: if isinstance(value, (list, tuple)): self.value_type_error_count += 1 value = "value_type_error" if isinstance(value, str): if value.find(",") is not -1: self.value_has_comma_error += 1 value = "value_comma_error" unique_name = self._unique_name(self.name_list) if unique_name.find(",") is not -1: self.name_has_comma_error_count += 1 self.unique_name_with_comma_list.append(unique_name) else: self.unique_name_list.append(unique_name) self.data[unique_name] = value self.names[unique_name] = None self.name_list.pop() if len(self.name_list) > 0: self.name_list.pop() def get_data(self): # retrieve data using names so it is always reported in the same order # this is important when using get_data() and get_names() # to write header and data to csv file to ensure headers and data # are written in the same order even if the source dictionary changes # size (which it will - at minimum validators do not all process # the same messages data_list = [] for name, value in self.names.items(): value = self.data.get(name, "no_val") data_list.append(value) return data_list def get_names(self): name_list = [] for name in self.names: name_list.append(name) return name_list def _unique_name(self, name_list): s = "" for name in name_list: if len(s) == 0: s = name else: s = "{}-{}".format(s, name) return s class TransactionRate(object): def __init__(self): self.txn_history = collections.deque() self.previous_block_count = 0 self.avg_txn_rate = 0.0 self.avg_block_time = 0.0 self.window_time = 0.0 self.window_txn_count = 0 def calculate_txn_rate(self, current_block_count, current_txn_count, window_size=10): """ Args: current_block_count: current number of committed blocks current_txn_count: current number of committed transactions window_size: number of blocks to average over Synopsis: Each time the block count changes, a snapshot of the current number of committed txns and current time is placed in the queue. If there are two or more entries in the queue, the average txn rate and average block commit time is calculated. If there are more than window_size transactions in the queue, the oldest entry is popped from the queue. Returns: avg_txn_rate: average number of transactions per second avg_block_time: average block commit time """ if not current_block_count == self.previous_block_count: self.previous_block_count = current_block_count current_block_time = time.time() self.txn_history.append([current_txn_count, current_block_time]) # if less than 2 samples, can't do anything if len(self.txn_history) < 2: self.avg_txn_rate = 0.0 self.avg_block_time = 0.0 return self.avg_txn_rate, self.avg_block_time # otherwise calculate from tip to tail; current is tip, [0] is tail past_txn_count, past_block_time = self.txn_history[0] self.window_time = current_block_time - past_block_time self.window_txn_count = current_txn_count - past_txn_count self.avg_txn_rate = \ float(self.window_txn_count) / self.window_time self.avg_block_time = \ (self.window_time) / (len(self.txn_history) - 1) # if more than "window_size" samples, discard oldest if len(self.txn_history) > window_size: self.txn_history.popleft() return self.avg_txn_rate, self.avg_block_time class PlatformIntervalStats(object): def __init__(self): self.intv_net_bytes_sent = 0 self.intv_net_bytes_recv = 0 self.last_net_bytes_sent = 0 self.last_net_bytes_recv = 0 self.intv_disk_bytes_read = 0 self.intv_disk_bytes_write = 0 self.last_disk_bytes_read = 0 self.last_disk_bytes_write = 0 self.intv_disk_count_read = 0 self.intv_disk_count_write = 0 self.last_disk_count_read = 0 self.last_disk_count_write = 0 def calculate_interval_stats(self, val_stats): net_stats = val_stats["platform"]["snetio"] self.intv_net_bytes_sent = \ net_stats["bytes_sent"] - self.last_net_bytes_sent self.intv_net_bytes_recv = \ net_stats["bytes_recv"] - self.last_net_bytes_recv self.last_net_bytes_sent = net_stats["bytes_sent"] self.last_net_bytes_recv = net_stats["bytes_recv"] disk_stats = val_stats["platform"]["sdiskio"] self.intv_disk_bytes_write = \ disk_stats["write_bytes"] - self.last_disk_bytes_write self.intv_disk_bytes_read = \ disk_stats["read_bytes"] - self.last_disk_bytes_read self.last_disk_bytes_write = disk_stats["write_bytes"] self.last_disk_bytes_read = disk_stats["read_bytes"] self.intv_disk_count_write = \ disk_stats["write_count"] - self.last_disk_count_write self.intv_disk_count_read = \ disk_stats["read_count"] - self.last_disk_count_read self.last_disk_count_write = disk_stats["write_count"] self.last_disk_count_read = disk_stats["read_count"]

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25/02/getattr.py

pylangstudy/201707

0

100962

<filename>25/02/getattr.py # getattr(object, name[, default]) class C: def A(self): pass print(getattr(C, 'A'))

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handybelt/latex2img/__main__.py

charleskawczynski/handybelt

0

101608

<gh_stars>0 import argparse import os import subprocess def main(**kwargs): filename = kwargs['filename'] dpi = kwargs['dpi'] filename = filename.replace('/',os.sep) if '\\'==os.sep else filename.replace('\\',os.sep) path = os.path.dirname(filename) path = '.' if path=='' else path p, ext = os.path.splitext(filename) subprocess.run('python -m buildlatex '+filename) subprocess.run('python -m pdf2img '+filename.replace(ext,'.pdf')) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Make an image from a pdf file.') parser.add_argument('filename', type=str, help='Filename of image to convert') parser.add_argument('--dpi', type=int, default=300, help='dpi for generated image') args = parser.parse_args() main(**vars(args))

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securitybot/loader.py

gyrospectre/securitybot

3

125881

import yaml import logging from importlib import import_module from securitybot.auth.auth import BaseAuthClient from securitybot.chat.chat import BaseChatClient from securitybot.db.database import BaseDbClient from securitybot.secretsmgmt.secretsmgmt import BaseSecretsClient from securitybot.tasker import Tasker from securitybot.exceptions import ( InvalidAuthProvider, InvalidChatProvider, InvalidDatabaseProvider, InvalidSecretsProvider ) def load_secrets_client(secrets_provider): try: sanitized_provider = secrets_provider.lower() module_name = 'securitybot.secretsmgmt.{}'.format( sanitized_provider ) module = import_module(module_name) client = getattr(module, 'SecretsClient') if not issubclass(client, BaseSecretsClient): raise AttributeError( '{}.Client is not an SecretsClient Provider'.format( module_name ) ) return client except (ModuleNotFoundError, AttributeError) as e: raise InvalidSecretsProvider( 'Secrets Management Provider for "{}" is not available: {}'.format( secrets_provider, e ) ) def build_secrets_client(secrets_provider, connection_config): secrets_class = load_secrets_client(secrets_provider) return secrets_class( connection_config ) def add_secrets_to_config(smclient, secrets, config): for client_type, clients in secrets.items(): client = config[client_type]['provider'] if client in clients: logging.debug( "About to fetch secret 'securitybot/{}/{}'".format( client_type, client ) ) scrt_raw = smclient.get_secret( 'securitybot/{}/{}'.format( client_type, client ) ) for scrt_name in secrets[client_type][client]: config[client_type][client][scrt_name] = scrt_raw[scrt_name] else: logging.debug( 'No secrets for {} provider {}'.format( client_type, client ) ) return True def load_auth_client(auth_provider): try: sanitized_provider = auth_provider.lower() module_name = 'securitybot.auth.{}'.format( sanitized_provider ) module = import_module(module_name) client = getattr(module, 'AuthClient') if not issubclass(client, BaseAuthClient): raise AttributeError( '{}.Client is not an Auth Provider'.format(module_name) ) return client except (ModuleNotFoundError, AttributeError) as e: raise InvalidAuthProvider( 'Auth Provider for "{}" is not available: {}'.format( auth_provider, e ) ) def build_auth_client(auth_provider, connection_config, reauth_time, auth_attrib): auth_class = load_auth_client(auth_provider) return auth_class( connection_config, reauth_time, auth_attrib ) def load_chat_client(chat_provider): try: sanitized_provider = chat_provider.lower() module_name = 'securitybot.chat.{}'.format( sanitized_provider ) module = import_module(module_name) client = getattr(module, 'ChatClient') if not issubclass(client, BaseChatClient): raise AttributeError( '{}.Client is not a Chat Provider'.format(module_name) ) return client except (ModuleNotFoundError, AttributeError) as e: raise InvalidChatProvider( 'Chat Provider for "{}" is not available: {}'.format( chat_provider, e ) ) def build_chat_client(chat_provider, connection_config): chat_class = load_chat_client(chat_provider) return chat_class( connection_config ) def build_tasker(dbclient): return Tasker(dbclient) def load_db_client(db_provider): try: sanitized_provider = db_provider.lower() module_name = 'securitybot.db.{}'.format( sanitized_provider ) module = import_module(module_name) client = getattr(module, 'DbClient') if not issubclass(client, BaseDbClient): raise AttributeError( '{} is not a Db Provider'.format(module_name) ) return client except (ModuleNotFoundError, AttributeError) as e: raise InvalidDatabaseProvider( 'DB Provider for "{}" is not available: {}'.format( db_provider, e ) ) def build_db_client(db_provider, connection_config, tables): db_class = load_db_client(db_provider) connection_config['tables'] = tables return db_class( config=connection_config, queries=load_yaml(connection_config.get('queries_path', None)) ) def load_yaml(path): if path: return yaml.safe_load(open(path)) else: return None

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project/test.py

Lucklyric/hydra-pytorch-lightning-seed

4

157194

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # File : test.py # Author: Alvin(<NAME> <<EMAIL>> # Date : 02.05.2021 import json import logging import hydra import pytorch_lightning as pl import torch from omegaconf import DictConfig, OmegaConf log = logging.getLogger(__name__) @hydra.main(config_path='config', config_name='test_config') def main(cfg: DictConfig): print(OmegaConf.to_yaml(cfg)) pl.seed_everything(cfg.seed) # ------------ # data # ------------ data_module = hydra.utils.instantiate(cfg.data) test_dataloader = data_module.test_dataloader() # ------------ # model # ------------ model = hydra.utils.instantiate(cfg.model) # model.load_from_checkpoint(cfg.checkpoint) model.load_state_dict(torch.load(cfg.checkpoint_path)['state_dict']) trainer = pl.Trainer(**(cfg.pl_trainer)) # ------------ # testing # ------------ result = trainer.test(model, test_dataloaders=test_dataloader) log.info(result) with open(f'{trainer.log_dir}/out', 'w') as f: f.write(json.dumps(str(result), indent=4)) if __name__ == '__main__': try: main() except Exception as e: log.error(e) exit(1)

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app/models.py

pitrho/motorsports-historic-data-api

5

60144

from flask.ext.sqlalchemy import SQLAlchemy from sqlalchemy.dialects.postgresql import HSTORE from sqlalchemy.ext.mutable import MutableDict db = SQLAlchemy() PersonType = db.Enum('driver', 'team-owner', 'crew-chief', 'vehicle-owner', 'team-principal', 'technical-chief', 'race-engineer', name='person_types') class Person(db.Model): __tablename__ = 'people' id = db.Column(db.Integer, primary_key=True, autoincrement=True) name = db.Column(db.String(100), nullable=False) country = db.Column(db.String(50), nullable=False) class Series(db.Model): __tablename__ = 'series' id = db.Column(db.String(5), primary_key=True) description = db.Column(db.String(50), nullable=True) class Team(db.Model): __tablename__ = 'teams' id = db.Column(db.String(50), primary_key=True) name = db.Column(db.String(50), nullable=False) alias = db.Column(db.String(50), nullable=False) owner_id = db.Column(db.Integer, db.ForeignKey('people.id'), nullable=False) races = db.relationship('Race', secondary='race_results') owner = db.relationship('Person', primaryjoin=owner_id == Person.id) class Vehicle(db.Model): __tablename__ = 'vehicles' id = db.Column(db.Integer, primary_key=True, autoincrement=True) number = db.Column(db.Integer, nullable=False) owner_id = db.Column(db.Integer, db.ForeignKey('people.id'), nullable=True) vehicle_metadata = db.Column(MutableDict.as_mutable(HSTORE), nullable=False) races = db.relationship('Race', secondary='race_results') owner = db.relationship('Person', primaryjoin=owner_id == Person.id) class DriverStanding(db.Model): __tablename__ = 'driver_standings' id = db.Column(db.Integer, primary_key=True, autoincrement=True) driver_id = db.Column(db.Integer, db.ForeignKey('people.id'), nullable=False) vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'), nullable=False) series = db.Column(db.String(5), db.ForeignKey('series.id'), nullable=False) season = db.Column(db.Integer, nullable=False) position = db.Column(db.Integer, nullable=False) points = db.Column(db.Integer, nullable=False) poles = db.Column(db.Integer, nullable=False) wins = db.Column(db.Integer, nullable=False) starts = db.Column(db.Integer, nullable=False) dnfs = db.Column(db.Integer, nullable=False) top5 = db.Column(db.Integer, nullable=False) top10 = db.Column(db.Integer, nullable=False) driver = db.relationship('Person') vehicle = db.relationship('Vehicle') class TeamStanding(db.Model): __tablename__ = 'team_standings' id = db.Column(db.Integer, primary_key=True, autoincrement=True) team_id = db.Column(db.String(50), db.ForeignKey('teams.id'), nullable=False) vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'), nullable=False) series = db.Column(db.String(5), db.ForeignKey('series.id'), nullable=False) season = db.Column(db.Integer, nullable=False) position = db.Column(db.Integer, nullable=False) points = db.Column(db.Integer, nullable=False) poles = db.Column(db.Integer, nullable=False) team = db.relationship('Team') vehicle = db.relationship('Vehicle') class OwnerStanding(db.Model): __tablename__ = 'owner_standings' id = db.Column(db.Integer, primary_key=True, autoincrement=True) vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'), nullable=False) series = db.Column(db.String(5), db.ForeignKey('series.id'), nullable=False) season = db.Column(db.Integer, nullable=False) position = db.Column(db.Integer, nullable=False) points = db.Column(db.Integer, nullable=False) vehicle = db.relationship('Vehicle') class RaceTrack(db.Model): __tablename__ = 'race_tracks' id = db.Column(db.Integer, primary_key=True, autoincrement=True) site = db.Column(db.String(50), nullable=False) circuit_name = db.Column(db.String(100), nullable=False) city = db.Column(db.String(50), nullable=False) state = db.Column(db.String(2), nullable=True) country = db.Column(db.String(50), nullable=False) class Race(db.Model): __tablename__ = 'races' id = db.Column(db.String(50), primary_key=True) round = db.Column(db.Integer, nullable=False) name = db.Column(db.String(100), nullable=False) season = db.Column(db.Integer, nullable=False) race_track_id = db.Column(db.Integer, db.ForeignKey('race_tracks.id'), nullable=False) date = db.Column(db.DateTime, nullable=False) laps = db.Column(db.Integer, nullable=False) length = db.Column(db.Numeric(5, 3), nullable=False) distance = db.Column(db.Numeric(5, 1), nullable=False) series = db.Column(db.String(5), db.ForeignKey('series.id'), nullable=False) race_types = db.relationship('RaceType', secondary='races_types') race_track = db.relationship('RaceTrack') class RaceType(db.Model): __tablename__ = 'race_types' id = db.Column(db.String(5), primary_key=True) description = db.Column(db.String(50), nullable=True) class RacesTypes(db.Model): __tablename__ = 'races_types' race_id = db.Column(db.String(50), db.ForeignKey('races.id'), primary_key=True) race_type = db.Column(db.String(5), db.ForeignKey('race_types.id'), primary_key=True) class RaceStanding(db.Model): __tablename__ = 'race_standings' id = db.Column(db.Integer, primary_key=True, autoincrement=True) race_id = db.Column(db.String(50), db.ForeignKey('races.id'), nullable=False) race_time = db.Column(db.Time, nullable=False) caution_flags = db.Column(db.Integer, nullable=False) caution_flag_laps = db.Column(db.Integer, nullable=False) lead_changes = db.Column(db.Integer, nullable=False) pole_speed = db.Column(db.Numeric(6, 3), nullable=False) avg_speed = db.Column(db.Numeric(6, 3), nullable=False) victory_margin = db.Column(db.Numeric(6, 3), nullable=False) class RaceEntryType(db.Model): __tablename__ = 'race_entry_types' id = db.Column(db.Integer, primary_key=True, autoincrement=True) entry_type = db.Column(db.String(50), nullable=False) class RaceEntry(db.Model): __tablename__ = 'race_entries' id = db.Column(db.Integer, primary_key=True, autoincrement=True) race_id = db.Column(db.String(50), db.ForeignKey('races.id'), nullable=False) team_id = db.Column(db.String(50), db.ForeignKey('teams.id'), nullable=False) vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'), nullable=False) entry_type_id = db.Column(db.Integer, db.ForeignKey('race_entry_types.id'), nullable=False) race = db.relationship('Race') team = db.relationship('Team') vehicle = db.relationship('Vehicle') entry_type = db.relationship('RaceEntryType') people = db.relationship('RaceEntryPerson') class RaceEntryPerson(db.Model): __tablename__ = 'race_entries_people' id = db.Column(db.Integer, primary_key=True, autoincrement=True) race_entry_id = db.Column(db.Integer, db.ForeignKey('race_entries.id'), nullable=False) person_id = db.Column(db.Integer, db.ForeignKey('people.id'), nullable=False) type = db.Column(PersonType, nullable=False) person = db.relationship('Person') race_entry = db.relationship('RaceEntry') class RaceResult(db.Model): __tablename__ = 'race_results' id = db.Column(db.Integer, primary_key=True, autoincrement=True) race_id = db.Column(db.String(50), db.ForeignKey('races.id'), nullable=False) team_id = db.Column(db.String(50), db.ForeignKey('teams.id'), nullable=False) vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'), nullable=False) sponsor = db.Column(db.String(100), nullable=False) grid = db.Column(db.Integer, nullable=False) position = db.Column(db.Integer, nullable=False) laps = db.Column(db.Integer, nullable=False) status = db.Column(db.String(50), nullable=False) laps_led = db.Column(db.Integer, nullable=False) points = db.Column(db.Integer, nullable=False) money = db.Column(db.Numeric(10, 2), nullable=False) race = db.relationship('Race') team = db.relationship('Team') vehicle = db.relationship('Vehicle') people = db.relationship('RaceResultPerson') class RaceResultPerson(db.Model): __tablename__ = 'race_results_people' id = db.Column(db.Integer, primary_key=True, autoincrement=True) race_result_id = db.Column(db.Integer, db.ForeignKey('race_results.id'), nullable=False) person_id = db.Column(db.Integer, db.ForeignKey('people.id'), nullable=False) type = db.Column(PersonType, nullable=False) person = db.relationship('Person') race_result = db.relationship('RaceResult') class QualifyingResult(db.Model): __tablename__ = 'qualifying_results' id = db.Column(db.Integer, primary_key=True, autoincrement=True) race_id = db.Column(db.String(50), db.ForeignKey('races.id'), nullable=False) team_id = db.Column(db.String(50), db.ForeignKey('teams.id'), nullable=False) vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'), nullable=False) session = db.Column(db.Integer, nullable=False) position = db.Column(db.Integer, nullable=False) lap_time = db.Column(db.Numeric(6, 3), nullable=False) race = db.relationship('Race') team = db.relationship('Team') vehicle = db.relationship('Vehicle') people = db.relationship('QualifyingResultPerson') class QualifyingResultPerson(db.Model): __tablename__ = 'qualifying_results_people' id = db.Column(db.Integer, primary_key=True, autoincrement=True) qualifying_result_id = db.Column(db.Integer, db.ForeignKey('qualifying_results.id'), nullable=False) person_id = db.Column(db.Integer, db.ForeignKey('people.id'), nullable=False) type = db.Column(PersonType, nullable=False) person = db.relationship('Person') qualifying_result = db.relationship('QualifyingResult') class PracticeResult(db.Model): ___tablename__ = 'practice_results' id = db.Column(db.Integer, primary_key=True, autoincrement=True) race_id = db.Column(db.String(50), db.ForeignKey('races.id'), nullable=False) team_id = db.Column(db.String(50), db.ForeignKey('teams.id'), nullable=False) vehicle_id = db.Column(db.Integer, db.ForeignKey('vehicles.id'), nullable=False) session = db.Column(db.Integer, nullable=False) position = db.Column(db.Integer, nullable=False) lap_time = db.Column(db.Numeric(6, 3), nullable=False) race = db.relationship('Race') team = db.relationship('Team') vehicle = db.relationship('Vehicle') people = db.relationship('PracticeResultPerson') class PracticeResultPerson(db.Model): __tablename__ = 'practice_results_people' id = db.Column(db.Integer, primary_key=True, autoincrement=True) practice_result_id = db.Column(db.Integer, db.ForeignKey(PracticeResult.id), nullable=False) person_id = db.Column(db.Integer, db.ForeignKey('people.id'), nullable=False) type = db.Column(PersonType, nullable=False) person = db.relationship('Person') practice_result = db.relationship('PracticeResult')

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lib/harm_plot.py

pnlbwh/multi-shell-dMRIharmonization

4

33402

#!/usr/bin/env python import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import pandas as pd from conversion import read_imgs_masks from os.path import isfile, basename XERR=0.1 ELINEWIDTH=3 CAPSIZE=5 CAPTHICK=3 FMT='cD' def harm_plot(ydata, labels, outPrefix, bshell_b): ''' :param ydata: list of [y1, y2, y3, ...] where each yi is a list :param labels: list of strings :param outPrefix: :return: ''' outPrefix += f'_b{bshell_b}' labels= list(labels) num_series, num_sub= np.shape(ydata) iter_obj= [i for i in range(num_series)] # errorbar plot plt.figure(1) plt.grid(True) for i in iter_obj: x= list(i*np.ones((num_sub,))) y= ydata[i] plt.plot(x, y, 'r*') plt.errorbar([i], np.mean(y), xerr=XERR, yerr=np.std(y), ecolor='k', capsize=CAPSIZE, capthick=CAPTHICK, elinewidth=ELINEWIDTH, fmt=FMT) plt.xticks(iter_obj, labels) plt.title('Comparison of meanFA before and after harmonization') plt.ylabel('meanFA over IIT_mean_FA_skeleton') plt.savefig(outPrefix+'_ebarplot.png') # plt.show() # box plot # plt.figure(2) # plt.grid(True) # for i in iter_obj: # x = list(i * np.ones((num_sub,))) # y = ydata[i] # plt.plot(x, y, 'r*') # # plt.boxplot(ydata, labels=labels, positions=iter_obj, # boxprops=dict(linewidth=4), # medianprops=dict(linewidth=4), # whiskerprops=dict(linewidth=2)) # # # plt.title(f'Comparison of boxplot before and after harmonization for b{bshell_b}') # plt.ylabel('meanFA over IIT_mean_FA_skeleton') # plt.savefig(outPrefix+'_boxplot.png') # plt.show() # return (outPrefix+'_ebarplot.png', outPrefix+'_boxplot.png') return outPrefix+'_ebarplot.png' def generate_csv(imgs, site_means, outPrefix, bshell_b): try: imgs, _= read_imgs_masks(imgs) except: pass statFile = outPrefix + '_stat.csv' if isfile(statFile): df= pd.read_csv(statFile) df= df.assign(**{f'meanFA b{bshell_b}':site_means}) else: stat = {'subject': [basename(f) for f in imgs], f'meanFA b{bshell_b}': site_means} df = pd.DataFrame(stat) df.to_csv(statFile, index=False) if __name__=='__main__': sub=['hi','hello','go','come'] ref_mean= [0.46, 0.49, 0.44, 0.40] target_mean_before= [0.42, 0.58, 0.43, 0.66] target_mean_after= [0.5 , 0.45, 0.40, 0.55] labels=['Reference','Target before','Target after'] bshell_b= 2000 harm_plot([ref_mean, target_mean_before, target_mean_after], labels, '/tmp/abc', bshell_b) # harm_plot([ref_mean], ['Reference'], '/tmp/abc', bshell_b) generate_csv(sub, ref_mean, '/tmp/abc', bshell_b)

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sparse_decomposition/decomposition/decomposition.py

bdpedigo/sparse_matrix_analysis

2

44909

# Some of the implementation inspired by: # REF: https://github.com/fchen365/epca import time from abc import abstractmethod import numpy as np from factor_analyzer import Rotator from sklearn.base import BaseEstimator from sklearn.preprocessing import StandardScaler from sklearn.utils import check_array from graspologic.embed import selectSVD from ..utils import calculate_explained_variance_ratio, soft_threshold from scipy.linalg import orthogonal_procrustes def _varimax(X): return Rotator(normalize=False).fit_transform(X) def _polar(X): # REF: https://en.wikipedia.org/wiki/Polar_decomposition#Relation_to_the_SVD U, D, Vt = selectSVD(X, n_components=X.shape[1], algorithm="full") return U @ Vt def _polar_rotate_shrink(X, gamma=0.1): # Algorithm 1 from the paper U, _, _ = selectSVD(X, n_components=X.shape[1], algorithm="full") # U = _polar(X) # R, _ = orthogonal_procrustes(U_old, U) # print(np.linalg.norm(U_old @ R - U)) U_rot = _varimax(U) U_thresh = soft_threshold(U_rot, gamma) return U_thresh def _reorder_components(X, Z_hat, Y_hat): score_norms = np.linalg.norm(X @ Y_hat, axis=0) sort_inds = np.argsort(-score_norms) return Z_hat[:, sort_inds], Y_hat[:, sort_inds] # import abc # class SuperclassMeta(type): # def __new__(mcls, classname, bases, cls_dict): # cls = super().__new__(mcls, classname, bases, cls_dict) # for name, member in cls_dict.items(): # if not getattr(member, "__doc__"): # member.__doc__ = getattr(bases[-1], name).__doc__ # return cls class BaseSparseDecomposition(BaseEstimator): def __init__( self, n_components=2, gamma=None, max_iter=10, scale=False, center=False, tol=1e-4, verbose=0, ): """Sparse matrix decomposition model. Parameters ---------- n_components : int, optional (default=2) Number of components or embedding dimensions. gamma : float, int or None, optional (default=None) Sparsity parameter, must be nonnegative. Lower values lead to more sparsity in the estimated components. If ``None``, will be set to ``sqrt(n_components * X.shape[1])`` where ``X`` is the matrix passed to ``fit``. max_iter : int, optional (default=10) Maximum number of iterations allowed, must be nonnegative. scale : bool, optional [description], by default False center : bool, optional [description], by default False tol : float or int, optional (default=1e-4) Tolerance for stopping iterative optimization. If the relative difference in score is less than this amount the algorithm will terminate. verbose : int, optional (default=0) Verbosity level. Higher values will result in more messages. """ self.n_components = n_components self.gamma = gamma self.max_iter = max_iter self.scale = scale self.center = center self.tol = tol self.verbose = verbose # TODO add random state def _initialize(self, X): """[summary] Parameters ---------- X : [type] [description] Returns ------- [type] [description] """ U, D, Vt = selectSVD(X, n_components=self.n_components) score = np.linalg.norm(D) return U, Vt.T, score def _validate_parameters(self, X): """[summary] Parameters ---------- X : [type] [description] """ if not self.gamma: gamma = np.sqrt(self.n_components * X.shape[1]) else: gamma = self.gamma self.gamma_ = gamma def _preprocess_data(self, X): """[summary] Parameters ---------- X : [type] [description] Returns ------- [type] [description] """ if self.scale or self.center: X = StandardScaler( with_mean=self.center, with_std=self.scale ).fit_transform(X) return X # def _compute_matrix_difference(X, metric='max'): # TODO better convergence criteria def fit_transform(self, X, y=None): """[summary] Parameters ---------- X : [type] [description] y : [type], optional [description], by default None Returns ------- [type] [description] """ self._validate_parameters(X) self._validate_data(X, copy=True, ensure_2d=True) # from sklearn BaseEstimator Z_hat, Y_hat, score = self._initialize(X) if self.gamma == np.inf: max_iter = 0 else: max_iter = self.max_iter # for keeping track of progress over iteration Z_diff = np.inf Y_diff = np.inf norm_score_diff = np.inf last_score = 0 # main loop i = 0 while (i < max_iter) and (norm_score_diff > self.tol): if self.verbose > 0: print(f"Iteration: {i}") iter_time = time.time() Z_hat_new, Y_hat_new = self._update_estimates(X, Z_hat, Y_hat) # Z_hat_new, Y_hat_new = _reorder_components(X, Z_hat_new, Y_hat_new) Z_diff = np.linalg.norm(Z_hat_new - Z_hat) Y_diff = np.linalg.norm(Y_hat_new - Y_hat) norm_Z_diff = Z_diff / np.linalg.norm(Z_hat_new) norm_Y_diff = Y_diff / np.linalg.norm(Y_hat_new) Z_hat = Z_hat_new Y_hat = Y_hat_new B_hat = Z_hat.T @ X @ Y_hat score = np.linalg.norm(B_hat) norm_score_diff = np.abs(score - last_score) / score last_score = score if self.verbose > 1: print(f"{time.time() - iter_time:.3f} seconds elapsed for iteration.") if self.verbose > 0: print(f"Difference in Z_hat: {Z_diff}") print(f"Difference in Y_hat: {Z_diff}") print(f"Normalized difference in Z_hat: {norm_Z_diff}") print(f"Normalized difference in Y_hat: {norm_Y_diff}") print(f"Total score: {score}") print(f"Normalized difference in score: {norm_score_diff}") print() i += 1 Z_hat, Y_hat = _reorder_components(X, Z_hat, Y_hat) # save attributes self.n_iter_ = i self.components_ = Y_hat.T # TODO this should not be cumulative by the sklearn definition self.explained_variance_ratio_ = calculate_explained_variance_ratio(X, Y_hat) self.score_ = score return Z_hat def fit(self, X): """[summary] Parameters ---------- X : [type] [description] Returns ------- [type] [description] """ self.fit_transform(X) return self def transform(self, X): """[summary] Parameters ---------- X : [type] [description] Returns ------- [type] [description] """ # TODO input checking return X @ self.components_.T @abstractmethod def _update_estimates(self, X, Z_hat, Y_hat): """[summary] Parameters ---------- X : [type] [description] Z_hat : [type] [description] Y_hat : [type] [description] """ pass class SparseComponentAnalysis(BaseSparseDecomposition): def _update_estimates(self, X, Z_hat, Y_hat): """[summary] Parameters ---------- X : [type] [description] Z_hat : [type] [description] Y_hat : [type] [description] Returns ------- [type] [description] """ Y_hat = _polar_rotate_shrink(X.T @ Z_hat, gamma=self.gamma) Z_hat = _polar(X @ Y_hat) return Z_hat, Y_hat def _save_attributes(self, X, Z_hat, Y_hat): """[summary] Parameters ---------- X : [type] [description] Z_hat : [type] [description] Y_hat : [type] [description] """ pass class SparseMatrixApproximation(BaseSparseDecomposition): def _update_estimates(self, X, Z_hat, Y_hat): """[summary] Parameters ---------- X : [type] [description] Z_hat : [type] [description] Y_hat : [type] [description] Returns ------- [type] [description] """ Z_hat = _polar_rotate_shrink(X @ Y_hat) Y_hat = _polar_rotate_shrink(X.T @ Z_hat) return Z_hat, Y_hat def _save_attributes(self, X, Z_hat, Y_hat): """[summary] Parameters ---------- X : [type] [description] Z_hat : [type] [description] Y_hat : [type] [description] """ B = Z_hat.T @ X @ Y_hat self.score_ = B self.right_latent_ = Y_hat self.left_latent_ = Z_hat

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PyUdemy/Day17/Start.py/quiz-game/main.py

JoseArtur/phyton-exercices

0

161139

<gh_stars>0 from question_model import Question from data import question_data from quiz_brain import QuizBrain question_bank = [] for i in question_data: question = Question(i["text"], i["answer"]) question_bank.append(question) quiz = QuizBrain(question_bank) while quiz.still_has_question() : quiz.next_question() print("You have completed the quiz") print(f"Your final score is {quiz.score}/{quiz.question_number}.")

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tools/find-broken-edition-parm.py

ctheune/assembly-cms

0

24023

# Copyright (c) 2010 gocept gmbh & co. kg # See also LICENSE.txt import zope.traversing.api stack = [root['summer10']] while stack: page = stack.pop() for edition in page.editions: for tag in edition.parameters: if not ':' in tag: print zope.traversing.api.getPath(edition) stack.extend(page.subpages)

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src/extract_bcs_crops.py

pmh47/textured-mesh-gen

30

113420

<reponame>pmh47/textured-mesh-gen<filename>src/extract_bcs_crops.py import os import sys from itertools import islice, count from collections import deque import skvideo.io import cv2 from crop_extraction_common import * from bcs_common import * step = 10 median_count = 25 # should be odd; number of stepped-frames to include in median (half either side of current frame) crops_per_file = 256 score_threshold = 0.7 edge_distance = 20 min_box_area = 1500 # square raw-pixels crop_border_fraction = 0.075 # per side dataset_name = sys.argv[1] masks_path = './bcs_masks' def main(): crops_path = bcs_preprocessed_path + '/dataset/' + dataset_name + '/crops_{}x{}_{}-per-file'.format(crop_width, crop_height, crops_per_file) if not os.path.exists(crops_path): os.makedirs(crops_path) occluder_mask = cv2.imread(masks_path + '/' + dataset_name + '_occluders.png') occluder_mask = cv2.resize(occluder_mask, (raw_width, raw_height), interpolation=cv2.INTER_NEAREST) occluder_mask = np.max(occluder_mask, axis=2) rotations = cv2.imread(masks_path + '/' + dataset_name + '_rotations.png')[:, :, 1] # i.e. take just G channel, which is rotation rotations = cv2.resize(rotations, (raw_width, raw_height), interpolation=cv2.INTER_NEAREST) rotations = rotations.astype(np.float32) * 2. * np.pi / 255 projection_matrix, view_rotation_matrix = load_calibration_matrices(dataset_name) detectron = DetectronWrapper(score_threshold) def stepped_indices_and_frames(): reader = skvideo.io.vreader(bcs_raw_path + '/dataset/' + dataset_name + '/video.avi') buffer = deque() indices_and_frames = zip(count(), reader) for index, frame in islice(indices_and_frames, 0, None, step): buffer.append(frame[:, :, ::-1]) # convert BGR to RGB if len(buffer) == median_count: # i.e. start considering frames only when we have enough to compute a median yield ( index - median_count // 2 * step, buffer[median_count // 2], lambda box: np.median(np.asarray([extract_crop(other_frame, raw_width, raw_height, crop_width, crop_border_fraction, *box[:-1])[0] for other_frame in buffer]), axis=0), ) buffer.popleft() with ShardedRecordWriter(crops_path + '/{:04d}_l.tfrecords', crops_per_file) as left_writer: with ShardedRecordWriter(crops_path + '/{:04d}_r.tfrecords', crops_per_file) as right_writer: left_crop_count = right_crop_count = 0 for frame_index, frame, get_median_crop in stepped_indices_and_frames(): # Note that boxes are indexed by x1/y1/x2/y2/score class_to_boxes_and_masks = detectron(frame) def touches_edge(box, mask): if box[0] < edge_distance or box[2] > frame.shape[1] - edge_distance: return True if box[1] < edge_distance or box[3] > frame.shape[0] - edge_distance: return True return False def touches_occluder(box, mask): dilated_mask = cv2.dilate(mask, np.ones([3, 3])) if np.count_nonzero(dilated_mask * occluder_mask) > 0: return True return False def touches_other_instance(box, mask): dilated_mask = cv2.dilate(mask, np.ones([3, 3])) for boxes_and_masks in class_to_boxes_and_masks.values(): for _, other_mask in boxes_and_masks: if other_mask is mask: continue if np.count_nonzero(dilated_mask * other_mask) > 0: return True return False def is_large_enough(box, mask): return box_area(box) > min_box_area car_boxes_and_masks = list(filter( lambda box_and_mask: not touches_edge(*box_and_mask) and not touches_occluder(*box_and_mask) and not touches_other_instance(*box_and_mask) and is_large_enough(*box_and_mask), class_to_boxes_and_masks['car'] )) for box, mask in car_boxes_and_masks: assert crop_width == crop_height crop, crop_raw_size = extract_crop(frame, raw_width, raw_height, crop_width, crop_border_fraction, *box[:-1]) cropped_mask, _ = extract_crop(mask, raw_width, raw_height, crop_width, crop_border_fraction, *box[:-1]) median = get_median_crop(box) centre_x = int((box[0] + box[2]) // 2 + 0.5) centre_y = int((box[1] + box[3]) // 2 + 0.5) rotation = rotations[centre_y, centre_x] offset_x = centre_x - raw_width / 2. offset_y = raw_height / 2. - centre_y crop_offset_matrix = [ [1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [-offset_x, -offset_y, 0., 1.], ] xy_ndc_matrix = np.diag([2. / crop_raw_size, 2. / crop_raw_size, 1., 1.]) crop_projection_matrix = np.dot(np.dot(projection_matrix, crop_offset_matrix), xy_ndc_matrix) example = tf.train.Example( features=tf.train.Features( feature={ 'pixels': jpeg_feature(crop), 'background': jpeg_feature(median), 'mask': png_feature(cropped_mask), 'rotation': float32_feature(rotation), 'projection_matrix': float32_feature(crop_projection_matrix), 'view_rotation_matrix': float32_feature(view_rotation_matrix), 'dataset_name': string_feature(dataset_name) } ) ) if abs(rotation - np.pi) < 0.1: writer = right_writer right_crop_count += 1 else: writer = left_writer left_crop_count += 1 writer.write(example.SerializeToString()) if len(car_boxes_and_masks) > 0: print('{} left-lane / {} right-lane crops written at frame {}'.format(left_crop_count, right_crop_count, frame_index)) if __name__ == '__main__': main()

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api/recomend/urls.py

JCTec/Recomender

0

1617604

from django.urls import path from django.conf.urls import url from .views import * urlpatterns = [ path('questions/', QuestionAPI.as_view()), path('answers/', AnswersAPI.as_view()), ]

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acoustic_scene_classification/test.py

GT-KIM/specmix

2

65361

import numpy as np import matplotlib.pyplot as plt augment_method = "Specaugment" d = np.load("result_{}/curve.npz".format(augment_method)) loss = d['loss'] acc = d['acc'] best_acc = d['best_acc'] print(best_acc) plt.plot(loss) plt.show() plt.clf() plt.plot(acc) plt.show() plt.clf()

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pyhmy/rpc/request.py

difengJ/pyhmy

37

12180

import json import requests from .exceptions import ( RequestsError, RequestsTimeoutError, RPCError ) _default_endpoint = 'http://localhost:9500' _default_timeout = 30 def base_request(method, params=None, endpoint=_default_endpoint, timeout=_default_timeout) -> str: """ Basic RPC request Parameters --------- method: str RPC Method to call params: :obj:`list`, optional Parameters for the RPC method endpoint: :obj:`str`, optional Endpoint to send request to timeout: :obj:`int`, optional Timeout in seconds Returns ------- str Raw output from the request Raises ------ TypeError If params is not a list or None RequestsTimeoutError If request timed out RequestsError If other request error occured """ if params is None: params = [] elif not isinstance(params, list): raise TypeError(f'invalid type {params.__class__}') try: payload = { "id": "1", "jsonrpc": "2.0", "method": method, "params": params } headers = { 'Content-Type': 'application/json' } resp = requests.request('POST', endpoint, headers=headers, data=json.dumps(payload), timeout=timeout, allow_redirects=True) return resp.content except requests.exceptions.Timeout as err: raise RequestsTimeoutError(endpoint) from err except requests.exceptions.RequestException as err: raise RequestsError(endpoint) from err def rpc_request(method, params=None, endpoint=_default_endpoint, timeout=_default_timeout) -> dict: """ RPC request Parameters --------- method: str RPC Method to call params: :obj:`list`, optional Parameters for the RPC method endpoint: :obj:`str`, optional Endpoint to send request to timeout: :obj:`int`, optional Timeout in seconds Returns ------- dict Returns dictionary representation of RPC response Example format: { "jsonrpc": "2.0", "id": 1, "result": ... } Raises ------ RPCError If RPC response returned a blockchain error See Also -------- base_request """ raw_resp = base_request(method, params, endpoint, timeout) try: resp = json.loads(raw_resp) if 'error' in resp: raise RPCError(method, endpoint, str(resp['error'])) return resp except json.decoder.JSONDecodeError as err: raise RPCError(method, endpoint, raw_resp) from err # TODO: Add GET requests

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Solutions/gigasecond.py

matthijskrul/PythonExercism

0

120728

<reponame>matthijskrul/PythonExercism<filename>Solutions/gigasecond.py import datetime def add(moment): return moment + datetime.timedelta(seconds=10**9)

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lib/send_file.py

collinahn/file_name_changer

1

150898

import requests import json from requests.exceptions import ( ConnectionError, Timeout, HTTPError, ) from lib.json_parser import TextParser from lib.log_gongik import Logger from .__PRIVATE import IP, PORT_API, DOWNLOAD_KEY class LogFileSender(object): def __init__(self, target_file_dir: str) -> None: self.log = Logger() host = IP port = PORT_API self.key = DOWNLOAD_KEY self.server_url: str = f'http://{host}:{port}/api/v1/log-report' self.target_dir: str = target_file_dir def _send_server(self) -> requests.Response or None: try: self.log.INFO(f'putting {self.target_dir} to memory') whole_log_file: str = TextParser(self.target_dir).value self.log.INFO(f'sending {self.target_dir} to server, size = {len(whole_log_file)}') post_data = json.dumps({ 'pw': self.key, 'log': whole_log_file }) return requests.post(self.server_url, data=post_data) except (requests.exceptions.ChunkedEncodingError, ConnectionError, Timeout, HTTPError) as networkerror: self.log.ERROR(f'network error occurred {networkerror}') return None def report(self) -> bool: res = self._send_server() if res.status_code != 200: self.log.ERROR(f'{res.content = }') return False self.log.INFO('log report complete') return True

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temparature.py

Shrijan21/hacktoberfest2021-2

0

164989

<reponame>Shrijan21/hacktoberfest2021-2 print("Choose from below options:") print("1.Celsius to Fahrenheit.") print("2.Fahrenheit to Celsius.") o=int(input("option(1/2):")) if(o==1): c=float(input("Temperature in Celsius:")) f=1.8*(c)+32.0 f=round(f,1) #temperature in fahrenheit precise to 1 decimal place print("Temperature in Fahrenheit:",f) elif(o==2): f=float(input("Temperature in Fahrenheit:")) c=(f-32)/1.8 c=round(c,1) #temperature in celsius precise to 1 decimal place print("Temperature in Celsius:",c) else: print("Choose 1 or 2.")

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old_playground/minimal_induction.py

sterin/pyzz

0

143713

from pyzz import * def not_equal( N, ws1, ws2 ): return disjunction( N, ( w1 ^ w2 for w1, w2 in zip(ws1, ws2) ) ) def not_equal_states(U, i, j): return not_equal( U.F, U[U.N.get_Flops(), i], U[U.N.get_Flops(), j] ) def unique(U): return conjunction( U.F, ( not_equal_states(U, i, j) for i,j in i_lt_j(U.frames) ) ) def induction(N, max): po = disjunction( N, N.get_POs() ) UB = unroll(N, init=True) SB = solver(UB.F) US = unroll(N, unroll=1, init=False) SS = solver(US.F) SS.clause( [ US[~po,0] ] ) for frame in xrange(max): print "Base %5d: "%frame, UB.unroll(1) fbpo = UB[po, frame] rc = SB.solve( [ fbpo ] ) if rc == solver.l_True: print "SAT" return rc elif rc == solver.l_False: print "UNSAT" SB.clause( [ ~fbpo ] ) print "Step %5d: "%frame, US.unroll(1) fspo = US[ po, frame+1 ] rc = SS.solve( [ fspo, unique(US) ] ) if rc == solver.l_True: SS.clause( [~fspo] ) print "SAT" elif rc == solver.l_False: print "UNSAT" return rc return solver.l_Undef print induction( arg_netlist() , 50)

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valida/errors.py

hpcflow/valida

0

1614863

class RuleValidationFailure: def __init__( self, rule, ): pass class DuplicateRule(Exception): pass class IncompatibleRules(Exception): pass class ValidationError(Exception): pass class InvalidCallable(Exception): pass class MalformedConditionLikeSpec(Exception): pass class MalformedContainerItemSpec(Exception): pass class MalformedDataPathSpec(Exception): pass class MalformedRuleSpec(Exception): pass

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jupyter/password.py

panchohumeres/state-of-open-data

4

170265

from notebook.auth import passwd import os PASSWORD=os.environ['PASSWORD'] with open('/tmp/sha1-psswd', 'w') as f: f.write(passwd(PASSWORD))

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modeling/src/load_data.py

alyildiz/covid_19_xray

0

41043

<reponame>alyildiz/covid_19_xray from sklearn.model_selection import train_test_split from src.utils import load_dataset def get_data(): train_list_file, train_list_class = load_dataset(data_part="train") test_x, test_y = load_dataset(data_part="test") train_x, val_x, train_y, val_y = train_test_split( train_list_file, train_list_class, stratify=train_list_class, test_size=0.3 ) return train_x, val_x, test_x, train_y, val_y, test_y

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Exercicios - Mundo1/Ex023.py

BrianMath/ExerciciosPythonCeV

0

57794

# Ex. 023 num = str(input("Digite um número: ")) # .zfill(4) -> preenche com 0 até completar 4 casas while len(num) < 4: num = "0" + num print("Unidade: ", num[3]) print("Dezena: ", num[2]) print("Centena: ", num[1]) print("Unidade de milhar: ", num[0])

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dotnet.py

rrezartbuzhala/ca-cloner

0

111293

import os import sys import shutil import constants from os import path from helpers import read_config app_name = sys.argv[1] #Copying the template try: src = 'templates/'+ sys.argv[2] except: src = 'templates/' + read_config(constants.default_template) dir_dest = read_config("DESTINATION_OUTPUT") + "\\" dest = dir_dest + app_name+'/' shutil.copytree(src, dest) project_dir = dest project_dir_src = project_dir + 'src/' os.chdir(project_dir) file_ext = lambda file : os.path.splitext(file)[1] def change_keywords_in_file(filepath): file = open(filepath,'r') filedata = file.read() file.close() newdata = filedata.replace(constants.keyword,app_name) file = open(filepath,'w') file.write(newdata) file.close() def change_files_rec(current_path): for i in os.listdir(current_path): new_name = i if constants.keyword in i: new_name = i.replace(constants.keyword,app_name) os.rename(current_path+i,current_path+new_name) if path.isfile(current_path+new_name): change_keywords_in_file(current_path+new_name) else: change_files_rec(current_path+new_name+'/') #Recuresively changing all solution name references for all files change_files_rec(project_dir) print('Application created at: '+dest) print("Enjoy codeing")

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buzzmobile/tools/image_const/image_const.py

gtagency/buzzmobile

25

138176

<gh_stars>10-100 #!/usr/bin/env python """image_const: node that continuously publishes a given image from file. Publishes: image_filename Image with topic name same as file name """ from __future__ import print_function # TODO(irapha): remove this when #126 is fixed. import sys import rospy import cv2 from cv_bridge import CvBridge from sensor_msgs.msg import Image bridge = CvBridge() def continuously_publish_image(image_filename): """Publishes the image given at a constant rate.""" cv2_img = cv2.imread(image_filename) gray_im = cv2.cvtColor(cv2_img, cv2.COLOR_BGR2GRAY) img_msg = bridge.cv2_to_imgmsg(gray_im, encoding="mono8") topic_name = sys.argv[1].split('/')[-1][:-4] pub = rospy.Publisher(topic_name, Image, queue_size=1) rospy.init_node('image_const', anonymous=True) rate = rospy.Rate(1) # 1hz while not rospy.is_shutdown(): pub.publish(img_msg) rate.sleep() if __name__ == '__main__': if len(sys.argv) < 2: print("Please provide a image filename") else: continuously_publish_image(sys.argv[1])

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test/test_examples.py

VDBWRAIR/pyjip

18

151065

#!/usr/bin/env python """Test some of the examples pipelines and tools""" import os import jip import unittest import jip.tools class BWAPipelineTest(unittest.TestCase): def testPipelineStructure(self): # load the pipeline tool = jip.find("examples/bwa/pileup.jip") assert tool is not None # create a pipeline p = jip.Pipeline() # create a new pipeline node and configure id p.run(tool, input="setup.py", reference="Makefile", output="out.txt") # expand the pipeline such that the internal pipeline is resolved p.expand(validate=False) # after expansion with this setuo, the pipeline should have 7 nodes assert len(p) == 7 # the graph should consist of 6 edges assert len(p.edges) == 6 # get out the nodes. we have to use indexes here # because the names might have changed after expansion ref = p.get("ref") align = p.get("align") sam = p.get("sam") bam = p.get("bam") dups = p.get("dups") index = p.get("index") pileup = p.get("pileup") # check the connections assert not ref.has_incoming() assert align.has_incoming(ref) assert sam.has_incoming(align) assert bam.has_incoming(sam) assert dups.has_incoming(bam) assert index.has_incoming(dups) assert pileup.has_incoming(index) assert not pileup.has_outgoing() def testPipelineStructureMultiplexed(self): # load the pipeline tool = jip.find("examples/bwa/pileup.jip") assert tool is not None # create a pipeline p = jip.Pipeline() # create a new pipeline node and configure id p.run(tool, input=["setup.py", "README.rst"], reference="Makefile", output="${input|ext}_out.txt") # expand the pipeline such that the internal pipeline is resolved # this will also validate all nodes and raise an exception # if one of the nodes validations failed p.expand(validate=False) # after expansion with this setuo, the pipeline should have 7 nodes assert len(p) == 13 # the graph should consist of 6 edges assert len(p.edges) == 12 # get out the nodes. we have to use indexes here # because the names might have changed after expansion ref = p.get("ref") align = p.get("align.0") sam = p.get("sam.0") bam = p.get("bam.0") dups = p.get("dups.0") index = p.get("index.0") pileup = p.get("pileup.0") # check the connections assert not ref.has_incoming() assert align.has_incoming(ref) assert sam.has_incoming(align) assert bam.has_incoming(sam) assert dups.has_incoming(bam) assert index.has_incoming(dups) assert pileup.has_incoming(index) assert not pileup.has_outgoing() # test second set ref = p.get("ref") align = p.get("align.1") sam = p.get("sam.1") bam = p.get("bam.1") dups = p.get("dups.1") index = p.get("index.1") pileup = p.get("pileup.1") # check the connections assert not ref.has_incoming() assert align.has_incoming(ref) assert sam.has_incoming(align) assert bam.has_incoming(sam) assert dups.has_incoming(bam) assert index.has_incoming(dups) assert pileup.has_incoming(index) assert not pileup.has_outgoing() @jip.tool('gem_index') class GemIndex(object): """ The GEM Indexer tool Usage: gem_index -i <genome> [-o <genome_index>] [-t <threads>] [--no-hash] Options: --help Show this help message -o, --output-dir <output_dir> The folder where the output GEM index is created -t, --threads <threads> The number of execution threads [default: 1] --no-hash Do not produce the hash file [default: false] Inputs: -i, --input <genome> The fasta file for the genome """ def init(self): self.add_output('output', "${input|name|ext}.gem") def setup(self): out = "${input|name|ext}.gem" if self.options['output_dir']: out = "${output_dir}/" + out self.options['output'].set(out) def get_command(self): return "gemtools index -i ${input} -o ${output} -t ${threads} "\ "${no_hash|arg}" @jip.tool('gem_t_index') class GemTranscriptomeIndex(object): """ The GEM Transcrptome Indexer tool Usage: gem_t_index -i <genome_index> -a <annotation> [-m <max_read_length>] [-o <output_folder>] [-p <output_prefix>] [-t <threads>] Options: --help Show this help message -o, --output-dir <output_dir> The folder where the output files are created [default: ${annotation|parent}] -p, --prefix <output_prefix> The name to be used for the output files [default: ${annotation|name}] -t, --threads <threads> The number of execution threads [default: 1] -m, --max-length <max_read_length> Maximum read length [default: 150] Inputs: -i, --index <genome_index> The GEM index file for the genome -a, --annotation <annotation> The reference annotation in GTF format """ def init(self): #if not self.options['output_dir']: # self.options['output_dir'] = "." #if not self.options['prefix']: # self.options['prefix'] = "${annotation}" self.add_output('gem', "${output_dir}/${prefix}.junctions.gem") self.add_output('keys', "${output_dir}/${prefix}.junctions.keys") def get_command(self): return 'bash', 'gemtools t-index -i ${index} -a ${annotation} ' \ '-o ${output_dir|abs}/${prefix} -t ${threads} ' \ '-m ${max_length}' def test_gemtools_index_command_rendering_for_options(): p = jip.Pipeline() p.run('gem_index', input="Makefile", output_dir='test') p.expand(validate=True) node = p.get('gem_index') print node._tool.options job = jip.create_jobs(p)[0] infile = os.path.abspath("Makefile") base = os.path.dirname(infile) print ">>>", job.command assert job.command == 'gemtools index -i %s -o %s.gem -t 1 ' % ( infile, os.path.join(base, "test/Makefile")) outfiles = list(job.get_output_files()) assert len(outfiles) == 1 assert outfiles[0] == os.path.join(base, "test/Makefile.gem") def test_gemtools_t_index_inputs(): p = jip.Pipeline() p.run('gem_t_index', index="Makefile", annotation='setup.py', output_dir='test') p.expand(validate=False) job = jip.create_jobs(p)[0] infile = os.path.abspath("Makefile") annotation = os.path.abspath("setup.py") base = os.path.dirname(infile) print ">>>", job.command assert job.command == 'gemtools t-index -i %s -a %s -o %s -t 1 -m 150' % ( infile, annotation, os.path.join(base, "test/setup.py")) outfiles = list(job.get_output_files()) assert len(outfiles) == 2 assert outfiles[0] == os.path.join(base, "test/setup.py.junctions.gem") assert outfiles[1] == os.path.join(base, "test/setup.py.junctions.keys") @jip.tool('grape_gem_rnatool') class gem(object): """ The GEMTools RNAseq Mapping Pipeline Usage: gem -f <fastq_file>... -i <genome_index> -a <annotation> -q <quality> [-n <name>] [-o <output_dir>] [-t <threads>] Options: --help Show this help message -q, --quality <quality> The fastq offset quality -n, --name <name> The output prefix name [default: ${fastq.raw()[0]|name|ext|ext|re("_[12]","")}] -o, --output-dir <output_dir> The output folder -t, --threads <threads> The number of execution threads [default: 1] Inputs: -f, --fastq <fastq_file>... The input fastq -i, --index <genome_index> The GEM index file for the genome -a, --annotation <annotation> The reference annotation in GTF format """ def init(self): self.add_output('map', "${output_dir}/${name}.map.gz") self.add_output('bam', "${output_dir}/${name}.bam") self.add_output('bai', "${output_dir}/${name}.bam.bai") self.add_option('single_end', False, long="--single-end", hidden=False) def setup(self): if len(self.fastq) == 1: self.options['single_end'].set(True) def get_command(self): return 'bash', 'gemtools rna-pipeline ${options()}' @jip.tool('grape_flux') class flux(object): """ The Flux Capacitor Usage: flux -i <input> -a <annotation> [-o <output_dir>] Options: --help Show this help message -o, --output-dir <output_dir> The output folder Inputs: -i, --input <input> The input file with mappings -a, --annotation <annotation> The reference annotation in GTF format """ def init(self): self.add_option('name', "${input|name|ext}") self.add_output('gtf', "${output_dir}/${name}.gtf") def get_command(self): return 'bash', 'flux-capacitor ${options()}' @jip.pipeline('grape_gem_rnapipeline') class GrapePipeline(object): """ Run the default RNAseq pipeline usage: rnaseq -f <fastq_file>... -q <quality> -i <genome_index> -a <annotation> [-o <output_dir>] Inputs: -f, --fastq <fastq_file>... The input reference genome -i, --index <genome_index> The input reference genome -a, --annotation <annotation The input reference annotation Options: -q, --quality <quality> The fatq offset quality [default: 33] -o, --output-dir <output_dir> The output prefix [default: ${fastq.raw()[0]|abs|parent}] """ def pipeline(self): p = jip.Pipeline() gem = p.run( 'grape_gem_rnatool', index=self.index, annotation=self.annotation, fastq=self.fastq, quality=self.quality, output_dir=self.output_dir ) p.run( 'grape_flux', input=gem.bam, annotation=self.annotation, output_dir=self.output_dir ) p.context(locals()) return p def test_gem_name_option_delegation(): p = jip.Pipeline() p.run('grape_gem_rnapipeline', fastq='reads_1.fastq.gz', index='index.gem', annotation='gencode.gtf') jobs = jip.create_jobs(p, validate=False) ldir = os.getcwd() j = os.path.join assert len(jobs) == 2 assert jobs[0].configuration['index'].get() == j(ldir, 'index.gem') assert jobs[0].configuration['fastq'].get() == j(ldir, 'reads_1.fastq.gz') assert jobs[0].configuration['annotation'].get() == j(ldir, 'gencode.gtf') assert jobs[0].configuration['quality'].get() == '33' assert jobs[0].configuration['output_dir'].get() == ldir assert jobs[0].configuration['name'].get() == 'reads' assert jobs[0].configuration['bam'].get() == j(ldir, 'reads.bam') assert jobs[0].configuration['bai'].get() == j(ldir, 'reads.bam.bai') assert jobs[0].configuration['map'].get() == j(ldir, 'reads.map.gz') assert jobs[1].configuration['input'].get() == j(ldir, 'reads.bam') assert jobs[1].configuration['name'].get() == 'reads' assert jobs[1].configuration['annotation'].get() == j(ldir, 'gencode.gtf') assert jobs[1].configuration['output_dir'].get() == ldir assert jobs[1].configuration['gtf'].get() == j(ldir, 'reads.gtf') assert len(jobs[0].children) == 1 assert len(jobs[1].dependencies) == 1 assert jobs[0].children[0] == jobs[1] def test_gem_name_option_delegation_with_output_dir(): p = jip.Pipeline() p.run('grape_gem_rnapipeline', fastq='reads_1.fastq.gz', index='index.gem', annotation='gencode.gtf', output_dir="mydir") jobs = jip.create_jobs(p, validate=False) ldir = os.getcwd() j = os.path.join assert len(jobs) == 2 assert jobs[0].configuration['index'].get() == j(ldir, 'index.gem') assert jobs[0].configuration['fastq'].get() == j(ldir, 'reads_1.fastq.gz') assert jobs[0].configuration['annotation'].get() == j(ldir, 'gencode.gtf') assert jobs[0].configuration['quality'].get() == '33' assert jobs[0].configuration['output_dir'].get() == "mydir" assert jobs[0].configuration['name'].get() == 'reads' assert jobs[0].configuration['bam'].get() == j(ldir, 'mydir/reads.bam') assert jobs[0].configuration['bai'].get() == j(ldir, 'mydir/reads.bam.bai') assert jobs[0].configuration['map'].get() == j(ldir, 'mydir/reads.map.gz') assert jobs[1].configuration['input'].get() == j(ldir, 'mydir/reads.bam') assert jobs[1].configuration['name'].get() == 'reads' assert jobs[1].configuration['annotation'].get() == j(ldir, 'gencode.gtf') assert jobs[1].configuration['output_dir'].get() == "mydir" assert jobs[1].configuration['gtf'].get() == j(ldir, 'mydir/reads.gtf') assert len(jobs[0].children) == 1 assert len(jobs[1].dependencies) == 1 assert jobs[0].children[0] == jobs[1] def test_multiple_pipelines_with_delegated_outputs(): @jip.tool('grape_gem_index') class GemIndex(object): """\ The GEM Indexer tool Usage: gem_index -i <genome> [-o <genome_index>] Options: -o, --output <genome_index> The output GEM index file [default: ${input|ext}.gem] -i, --input <genome> The fasta file for the genome """ def get_command(self): return "bash", "gemtools index ${options()}" @jip.tool('grape_gem_rnatool') class gem(object): """\ The GEMtools RNAseq Mapping Pipeline Usage: gem -f <fastq_file> -i <genome_index> Inputs: -f, --fastq <fastq_file> The input fastq -i, --index <genome_index> The GEM index file for the genome """ def get_command(self): return 'bash', 'gemtools rna-pipeline ${options()}' @jip.pipeline('grape_gem_setup') class SetupPipeline(object): """\ The GEM indexes setup pipeline usage: setup -i <genome> Options: -i, --input <genome> The input reference genome """ def init(self): self.add_output('index', '${input|ext}.gem') def pipeline(self): p = jip.Pipeline() index = p.run('grape_gem_index', input=self.input, output=self.index) p.context(locals()) return p @jip.pipeline('grape_gem_rnapipeline') class GrapePipeline(object): """\ The default GRAPE RNAseq pipeline usage: rnaseq -f <fastq_file> -g <genome> Inputs: -f, --fastq <fastq_file> The input reference genome -g, --genome <genome_index> The input reference genome """ def pipeline(self): p = jip.Pipeline() gem_setup = p.run('grape_gem_setup', input=self.genome) gem = p.run('grape_gem_rnatool', index=gem_setup.index, fastq=self.fastq) p.context(locals()) return p p = jip.Pipeline() node = p.run('grape_gem_rnapipeline') node.fastq = 'reads_1.fastq.gz' node.genome = 'genome.fa' jobs = jip.create_jobs(p, validate=False) assert len(jobs) == 2 gem_job = filter(lambda x: x.name == 'gem', jobs)[0] assert gem_job is not None assert gem_job.configuration['index'].get().endswith('genome.gem') def test_embedded_pipelines_stage_one(tmpdir): tmpdir = str(tmpdir) # laod teh embedded example jip.scanner.add_module('examples/embedded_submission/embedded.py') jip.scanner.scan_modules() p = jip.Pipeline() p.job(dir=tmpdir).run('example_embedded') jobs = jip.create_jobs(p) assert jobs[0].configuration['prefix'] == 'test' assert jobs[0].configuration['output'] == [ os.path.join(tmpdir, 'test.*') ] assert len(jobs) == 1 def test_embedded_pipelines_stage_two(tmpdir): tmpdir = str(tmpdir) # create stage one output open(os.path.join(tmpdir, 'test.1'), 'a').close() open(os.path.join(tmpdir, 'test.2'), 'a').close() open(os.path.join(tmpdir, 'test.3'), 'a').close() open(os.path.join(tmpdir, 'test.4'), 'a').close() open(os.path.join(tmpdir, 'test.5'), 'a').close() # laod teh embedded example jip.scanner.add_module('examples/embedded_submission/embedded.py') jip.scanner.scan_modules() p = jip.Pipeline() p.job(dir=tmpdir).run('example_embedded') jobs = jip.create_jobs(p) assert len(jobs) == 8 assert jobs[0].configuration['prefix'] == 'test' print ">>>OUTPUT", jobs[0].configuration['output'].raw() print ">>>TMPDIR", tmpdir assert jobs[0].configuration['output'] == [ os.path.join(tmpdir, 'test.1'), os.path.join(tmpdir, 'test.2'), os.path.join(tmpdir, 'test.3'), os.path.join(tmpdir, 'test.4'), os.path.join(tmpdir, 'test.5'), ] assert jobs[1].configuration['input'] == os.path.join(tmpdir, 'test.1') assert jobs[2].configuration['input'] == os.path.join(tmpdir, 'test.2') assert jobs[3].configuration['input'] == os.path.join(tmpdir, 'test.3') assert jobs[4].configuration['input'] == os.path.join(tmpdir, 'test.4') assert jobs[5].configuration['input'] == os.path.join(tmpdir, 'test.5') print jobs[6].configuration['input'].raw() assert jobs[6].configuration['input'] == [ os.path.join(tmpdir, 'consumed_test.1'), os.path.join(tmpdir, 'consumed_test.2'), os.path.join(tmpdir, 'consumed_test.3'), os.path.join(tmpdir, 'consumed_test.4'), os.path.join(tmpdir, 'consumed_test.5'), ] def test_dynamic_options(): script = '''#!/usr/bin/env jip # Touch a number of files with a common prefix # # usage: # touch --prefix <prefix> --count <count> #%begin init add_output('output') #%end #%begin setup options['output'].set(["%s_%s" % (prefix, i) for i in range(1, count.get(int) + 1)]) #%end #%begin command for x in ${output}; do touch $x done ''' tool = jip.tools.ScriptTool.from_string(script) tool.init() assert tool is not None p = jip.Pipeline() node = p.job('test').run(tool, prefix='test', count=5) assert node is not None p.expand() assert len(p) == 1 node = p.get('test') assert node.prefix == 'test' cwd = os.getcwd() assert node.output == [os.path.join(cwd, x) for x in ['test_1', 'test_2', 'test_3', 'test_4', 'test_5']] def test_dynamic_options_multiplex(): script = '''#!/usr/bin/env jip # Touch a number of files with a common prefix # # usage: # touch --prefix <prefix> --count <count> #%begin init add_output('output') #%end #%begin setup options['output'].set(["%s_%s" % (prefix, i) for i in range(1, count.get(int) + 1)]) #%end #%begin command for x in ${output}; do touch $x done ''' tool = jip.tools.ScriptTool.from_string(script) tool.init() assert tool is not None p = jip.Pipeline() node = p.job('test').run(tool, prefix='test', count=[1, 2]) assert node is not None p.expand() cwd = os.getcwd() assert len(p) == 2 node_1 = p.get('test.0') node_2 = p.get('test.1') assert node_1.output == os.path.join(cwd, 'test_1') assert node_2.output == [os.path.join(cwd, x) for x in ['test_1', 'test_2']] def test_hello_world_py_fun(tmpdir): tmpdir = str(tmpdir) jip.scanner.add_module('examples/hello_world/hello_world.py') jip.scanner.scan_modules() p = jip.Pipeline() p.job(dir=tmpdir).run('fun_hello_world_py') jobs = jip.create_jobs(p) assert len(jobs) == 1 def test_hello_world_py_cls(tmpdir): tmpdir = str(tmpdir) jip.scanner.add_module('examples/hello_world/hello_world.py') jip.scanner.scan_modules() p = jip.Pipeline() p.job(dir=tmpdir).run('cls_hello_world_py') jobs = jip.create_jobs(p) assert len(jobs) == 1 def test_file_touch(): p = jip.Pipeline() node = p.run('examples/file_touch.jip', p='test', c=5) p.expand() cwd = os.getcwd() j = os.path.join assert node.output == [ j(cwd, 'test_' + x) for x in ['1', '2', '3', '4', '5'] ] def test_pipeline_to_pipeline_edge_delegation(): @jip.tool('grape_gem_index') class GemIndex(object): """\ The GEM Indexer tool Usage: gem_index -i <genome> [-o <genome_index>] Options: -o, --output <genome_index> The output GEM index file [default: ${input|ext}.gem] -i, --input <genome> The fasta file for the genome """ def get_command(self): return "bash", "gemtools index ${options()}" @jip.tool('grape_gem_rnatool') class gem(object): """\ The GEMtools RNAseq Mapping Pipeline Usage: gem -f <fastq_file> -i <genome_index> Inputs: -f, --fastq <fastq_file> The input fastq -i, --index <genome_index> The GEM index file for the genome """ def get_command(self): return 'bash', 'gemtools rna-pipeline ${options()}' @jip.pipeline('grape_gem_setup') class SetupPipeline(object): """\ The GEM indexes setup pipeline usage: setup -i <genome> Options: -i, --input <genome> The input reference genome """ def init(self): self.add_output('index', '${input|ext}.gem') def pipeline(self): p = jip.Pipeline() index = p.run('grape_gem_index', input=self.input, output=self.index) p.context(locals()) return p @jip.pipeline('grape_gem_rnapipeline') class GrapePipeline(object): """\ The default GRAPE RNAseq pipeline usage: rnaseq -f <fastq_file> -g <genome> Inputs: -f, --fastq <fastq_file> The input reference genome -g, --genome <genome_index> The input reference genome """ def pipeline(self): p = jip.Pipeline() gem_setup = p.run('grape_gem_setup', input=self.genome) gem = p.run('grape_gem_rnatool', index=gem_setup.index, fastq=self.fastq) p.context(locals()) return p p = jip.Pipeline() node = p.run('grape_gem_rnapipeline') node.fastq = 'reads_1.fastq.gz' node.genome = 'genome.fa' p.expand(validate=False) index = p.get('index') gem_node = p.get('gem') cwd = os.getcwd() j = os.path.join assert index.has_outgoing(gem_node, link=('output', 'index'), value=j(cwd, 'genome.gem')) def test_subpipe_incoming_edge_resolve(): @jip.pipeline() def subedge_pipe(tool): """Subedge usage: subedge --input <input> """ p = jip.Pipeline() p.job('ls').bash('ls ${input}', input=tool.input) return p p = jip.Pipeline() produce = p.job('touch').bash('touch ${outfile}', outfile='out.dat') p.run('subedge_pipe', input=produce.outfile) p.expand(validate=False) touch = p.get('touch') ls = p.get('ls') cwd = os.getcwd() j = os.path.join assert touch.has_outgoing(ls, link=('outfile', 'input'), value=j(cwd, 'out.dat')) def test_subpipe_incoming_edge_resolve_pipe_to_pipe(): @jip.pipeline() def subedge_pipe_1(tool): """Subedge usage: subedge --input <input> --output <output> """ p = jip.Pipeline() p.job('p1').bash('touch', input=tool.input, output=tool.output) return p @jip.pipeline() def subedge_pipe_2(tool): """Subedge usage: subedge --input <input> --output <output> """ p = jip.Pipeline() p.job('p2').bash('touch', input=tool.input, output=tool.output) return p @jip.pipeline() def subedge_pipe_combine(tool): """Subedge usage: subedge --input <input> --output <output> """ p = jip.Pipeline() p1 = p.run('subedge_pipe_1', input=tool.input, output='p1.out') p.run('subedge_pipe_2', input=p1, output=tool.output) return p p = jip.Pipeline() p.run('subedge_pipe_combine', input='a.txt', output="out.dat") p.expand(validate=False) assert len(p) == 2 p1 = p.get('p1') p2 = p.get('p2') cwd = os.getcwd() j = os.path.join assert p1.has_outgoing(p2) assert p1.has_outgoing(p2, value=j(cwd, 'out.dat')) assert p1.has_outgoing(p2, link=('output', 'input'), value=j(cwd, 'p1.out')) if __name__ == '__main__': unittest.main()

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freecodecamp/brownie/brownie_simple/scripts/deploy.py

People-Help-People/contract-collection

0

192094

from brownie import accounts, config, SimpleStorage, network import os def get_account(): if network.show_active() =="developement": account = accounts[0] else: # account = accounts.add(os.getenv("PRIVATE_KEY")) account = accounts.add(config["wallets"]["from_key"]) # account = accounts.load("test") return account def deploy_simple_storage(): account=get_account() simple_storage = SimpleStorage.deploy({"from": account}) transaction = simple_storage.store(1, {"from": account}) transaction.wait(1) print(simple_storage.retrieve()) def main(): deploy_simple_storage()

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Data_Sending_raspberrypi/pl1.py

rsk97/Web-GIS-smart-irrigation

0

94463

<gh_stars>0 #/usr/bin/python import serial import requests from selenium import webdriver import urllib mail = 0 def sendmail(SM1,SM2,WL): import smtplib from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText fromaddr = "<EMAIL>" toaddr = "<EMAIL>" msg = MIMEMultipart() msg['From'] = fromaddr msg['To'] = toaddr msg['Subject'] = "Farm details" body="" m1="PLANT 1 IS DYING!!!" m2="PLANT 2 IS DYING!!!" m3="PLANT 1 IS HEALTHY" m4="PLANT 2 IS HEALTHY" if mail==1: body=m1+m4 elif mail==2: body=m2+m3 elif mail==3: body=m1+m2 elif mail==0: body=m3+m4 msg.attach(MIMEText(body, 'plain')) server = smtplib.SMTP('smtp.gmail.com', 587) server.starttls() server.login(fromaddr, "arshad9991") text = msg.as_string() server.sendmail(fromaddr, toaddr, text) server.quit() ser=serial.Serial('/dev/ttyACM0', 9600) while(1): data=ser.readline() f = open("/home/pi/Desktop/check","a") f.write(data+"\n") x=data.split(" ") print(x) print(data) sm1=int(x[3]) sm2=int(x[4]) wlev=float(x[5]) status = 0 if(sm1<0 and sm2 >0): status = 1 if(sm1>0 and sm2<0): status = 2 if(sm1<0 and sm2<0): status =3 if(status != mail): mail=status sendmail(int(x[3]),int(x[4]),int(x[2])) if(len(x)==6): mark=0 for i in range(6): try: d=float(x[i]) except: print(i) mark=1 break if(mark==1): print("ERROR") continue print("SENT") try: urllib.urlopen("http://ad07.pythonanywhere.com/plants/getWaterLevel?level="+x[5]+"&rain="+x[4]); urllib.urlopen("http://ad07.pythonanywhere.com/plants/getdata?t="+str(x[1])+"&plantID=1&h="+x[0]+"&s="+x[2]); urllib.urlopen("http://ad07.pythonanywhere.com/plants/getdata?t="+str(x[1])+"&plantID=2&h="+x[0]+"&s="+x[3]); except: pass #browser.get("http://10.0.3.23:9090/plants/getdata?t="+str(x[1])+"&plantID=2&h="+x[0]+"&s="+x[-2] ); #print("http://10.0.3.23:9090/plants/getdata?t="+str(x[1])+"&plantID=2&h="+x[0]+"&s="+x[-2]); #browser.get("http://10.0.3.23:9090/plants/getdata?t="+str(x[1])+"&plantID=1&h="+x[0]+"&s="+x[2]); #browser.get("http://10.0.3.23:9090/plants/getdata?t="+str(x[1])+"&plantID=2&h="+x[0]+"&s="+x[-2]); #browser.get("http://10.0.3.23:9090/plants/getWaterLevel?level="+x[5]+"&rain="+x[4]); print(x[-1])

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main.py

ARCJ137442/makeFolderTenser

0

54508

<gh_stars>0 ''' README: 本程序用于创建一系列的「索引文件夹」，其相当于一个索引矩阵（可能是「张量」），其包含的值（本质上是集合，但一般是文件）使用一个特定的序列（等价于一个向量）进行访问；原理/作用/用途：通过增大其他用户逐一检索文件夹之难度以「隐藏」部分文件夹或文件待补 ''' import os #此函数来源于网络 def mkdir(path): # 引入模块 import os # 去除首尾空格 path=path.strip() # 去除尾部 \ 符号 path=path.rstrip("\\") # 判断路径是否存在 # 存在 True # 不存在 False isExists=os.path.exists(path) # 判断结果 if not isExists: # 如果不存在则创建目录 # 创建目录操作函数 os.makedirs(path) print(path+' 创建成功') return True else: # 如果目录存在则不创建，并提示目录已存在 print(path+'目录已存在') return False #此函数用于按字符批量生成文件夹，根目录末尾可以不带「/」 #示例：path='C:\dir\',nums='gbv^4%faw1' def makeDirsByChar(path,chars): root=path.strip().rstrip("\\") for i in chars: mkdir(root+'\\'+str(i)) #此函数用于按照整数序列生成n个文件夹 #示例：path='C:\',num=9 def makeDirVector(path,num): root=path.strip().rstrip("\\") for i in range(num): mkdir(root+'\\'+str(i)) #此函数用于按照0~9的整数序列生成文件夹「张量」，使用递归以支持任意长度（多起来仍然很恐怖，建议少于5层） #示例：path='C:\',nums='1321' def makeDirTenser(path,nums): root=path.strip().rstrip("\\") #print("正在尝试用%s生成%s"%(nums,root)) if len(nums)>1: for i in range(int(nums[0])): makeDirTenser(path+'\\%s\\'%i,nums[1:]) else: makeDirVector(path,int(nums)) #直接打开程序的预设功能（分别对应前面三个函数：字串、向量、张量） if __name__ == "__main__": # 字串 print("切换到字串模式。。。") root=input("输入根路径（默认为chars）：") if root=='':root=r"chars" value=input("输入字串参数（留空以跳过）：") if value!='': makeDirsByChar(root,value) # 向量 print("切换到向量模式。。。") root=input("输入根路径（默认为vecter）：") if root=='':root=r"vecter" value=input("输入文件夹数量（留空以跳过）：") if value!='': makeDirVector(root,int(value)) # 张量 print("切换到张量模式。。。") root=input("输入根路径（默认为tenser）：") if root=='':root=r"tenser" value=input("输入张量阶数序列（留空以跳过）：") if value!='': makeDirTenser(root,value) ''' -并没有什么用的末尾注释- '''

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var/spack/repos/builtin/packages/ferret/package.py

padamson/spack

2

74594

<reponame>padamson/spack<filename>var/spack/repos/builtin/packages/ferret/package.py # Copyright 2013-2021 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) import os from spack import * class Ferret(Package): """Ferret is an interactive computer visualization and analysis environment designed to meet the needs of oceanographers and meteorologists analyzing large and complex gridded data sets.""" homepage = "http://ferret.pmel.noaa.gov/Ferret/home" url = "https://github.com/NOAA-PMEL/Ferret/archive/v7.6.0.tar.gz" maintainers = ['RemiLacroix-IDRIS'] version('7.6.0', sha256='69832d740bd44c9eadd198a5de4d96c4c01ae90ae28c2c3414c1bb9f43e475d1') version('7.5.0', sha256='2a038c547e6e80e6bd0645a374c3247360cf8c94ea56f6f3444b533257eb16db') version('7.4', sha256='5167bb9e6ef441ae9cf90da555203d2155e3fcf929e7b8dddb237de0d58c5e5f') version('7.3', sha256='ae80a732c34156b5287a23696cf4ae4faf4de1dd705ff43cbb4168b05c6faaf4') version('7.2', sha256='21c339b1bafa6939fc869428d906451f130f7e77e828c532ab9488d51cf43095') version('6.96', sha256='7eb87156aa586cfe838ab83f08b2102598f9ab62062d540a5da8c9123816331a') variant('datasets', default=False, description="Install Ferret standard datasets") depends_on("hdf5+hl") depends_on("netcdf-c") depends_on("netcdf-fortran") depends_on("readline") depends_on("zlib") depends_on("libx11") depends_on("curl") # Make Java dependency optional with older versions of Ferret patch('https://github.com/NOAA-PMEL/Ferret/commit/c7eb70a0b17045c8ca7207d586bfea77a5340668.patch', sha256='5bd581db4578c013faed375844b206fbe71f93fe9ce60f8f9f41d64abc6a5972', level=1, working_dir='FERRET', when='@:6.99') resource(name='datasets', url='https://github.com/NOAA-PMEL/FerretDatasets/archive/v7.6.tar.gz', sha256='b2fef758ec1817c1c19e6225857ca3a82c727d209ed7fd4697d45c5533bb2c72', placement='fer_dsets', when='+datasets') def url_for_version(self, version): if version <= Version('7.2'): return 'ftp://ftp.pmel.noaa.gov/ferret/pub/source/fer_source.v{0}.tar.gz'.format( version.joined) else: return 'https://github.com/NOAA-PMEL/Ferret/archive/v{0}.tar.gz'.format(version) def patch(self): spec = self.spec hdf5_prefix = spec['hdf5'].prefix netcdff_prefix = spec['netcdf-fortran'].prefix readline_prefix = spec['readline'].prefix libz_prefix = spec['zlib'].prefix work_dir = 'FERRET' if '@:7.2' in spec else '.' with working_dir(work_dir, create=False): if '@7.3:' in spec: copy('site_specific.mk.in', 'site_specific.mk') copy('external_functions/ef_utility/site_specific.mk.in', 'external_functions/ef_utility/site_specific.mk') filter_file(r'^DIR_PREFIX.+', 'DIR_PREFIX = %s' % self.stage.source_path, 'site_specific.mk') # Setting this to blank not to force # using the static version of readline filter_file(r'^(READLINE_(LIB)?DIR).+', '\\1 = ', 'site_specific.mk') else: filter_file(r'^LIBZ_DIR.+', 'LIBZ_DIR = %s' % libz_prefix, 'site_specific.mk') filter_file(r'^JAVA_HOME.+', ' ', 'site_specific.mk') filter_file(r'^READLINE_DIR.+', 'READLINE_DIR = %s' % readline_prefix, 'site_specific.mk') filter_file(r'^BUILDTYPE.+', 'BUILDTYPE = x86_64-linux', 'site_specific.mk') filter_file(r'^INSTALL_FER_DIR.+', 'INSTALL_FER_DIR = %s' % spec.prefix, 'site_specific.mk') filter_file(r'^(HDF5_(LIB)?DIR).+', '\\1 = %s' % hdf5_prefix, 'site_specific.mk') filter_file(r'^(NETCDF4?_(LIB)?DIR).+', '\\1 = %s' % netcdff_prefix, 'site_specific.mk') if '@:7.3' in spec: # Don't force using the static version of libz filter_file(r'\$$LIBZ_DIR$/lib64/libz.a', '-lz', 'platform_specific.mk.x86_64-linux') # Don't force using the static version of libgfortran filter_file(r'-Wl,-Bstatic -lgfortran -Wl,-Bdynamic', '-lgfortran', 'platform_specific.mk.x86_64-linux') # This prevents the rpaths to be properly set # by Spack's compiler wrappers filter_file(r'-v --verbose', '', 'platform_specific.mk.x86_64-linux') filter_file(r'^[ \t]*LD[ \t]*=.+', 'LD = %s' % spack_cc, 'platform_specific.mk.x86_64-linux') else: # Don't force using the static version of libgfortran filter_file(r'-static-libgfortran', '', 'platform_specific.mk.x86_64-linux') if '@:7.4' in spec: compilers_spec_file = 'platform_specific.mk.x86_64-linux' else: compilers_spec_file = 'site_specific.mk' # Make sure Ferret uses Spack's compiler wrappers filter_file(r'^[ \t]*CC[ \t]*=.+', 'CC = %s' % spack_cc, compilers_spec_file) filter_file(r'^[ \t]*CXX[ \t]*=.+', 'CXX = %s' % spack_cxx, compilers_spec_file) filter_file(r'^[ \t]*FC[ \t]*=.+', 'FC = %s' % spack_fc, compilers_spec_file) filter_file(r'^[ \t]*F77[ \t]*=.+', 'F77 = %s' % spack_f77, compilers_spec_file) filter_file(r'\$$NETCDF4?_(LIB)?DIR$.*/libnetcdff.a', "-L%s -lnetcdff" % spec['netcdf-fortran'].prefix.lib, 'platform_specific.mk.x86_64-linux') filter_file(r'\$$NETCDF4?_(LIB)?DIR$.*/libnetcdf.a', "-L%s -lnetcdf" % spec['netcdf-c'].prefix.lib, 'platform_specific.mk.x86_64-linux') filter_file(r'\$$HDF5_(LIB)?DIR$.*/libhdf5_hl.a', "-L%s -lhdf5_hl" % spec['hdf5'].prefix.lib, 'platform_specific.mk.x86_64-linux') filter_file(r'\$$HDF5_(LIB)?DIR$.*/libhdf5.a', "-L%s -lhdf5" % spec['hdf5'].prefix.lib, 'platform_specific.mk.x86_64-linux') def install(self, spec, prefix): if 'LDFLAGS' in env and env['LDFLAGS']: env['LDFLAGS'] += ' ' + '-lquadmath' else: env['LDFLAGS'] = '-lquadmath' work_dir = 'FERRET' if '@:7.2' in self.spec else '.' with working_dir(work_dir, create=False): os.environ['LD_X11'] = '-L%s -lX11' % spec['libx11'].prefix.lib os.environ['HOSTTYPE'] = 'x86_64-linux' make(parallel=False) make("install") if '+datasets' in self.spec: mkdir(self.prefix.fer_dsets) install_tree('fer_dsets', self.prefix.fer_dsets) def setup_run_environment(self, env): env.set('FER_DIR', self.prefix) env.set('FER_GO', ' '.join(['.', self.prefix.go, self.prefix.examples, self.prefix.contrib])) env.set('FER_EXTERNAL_FUNCTIONS', self.prefix.ext_func.libs) env.set('FER_PALETTE', ' '.join(['.', self.prefix.ppl])) env.set('FER_FONTS', self.prefix.ppl.fonts) fer_data = ['.'] fer_descr = ['.'] fer_grids = ['.'] if '+datasets' in self.spec: env.set('FER_DSETS', self.prefix.fer_dsets) fer_data.append(self.prefix.fer_dsets.data) fer_descr.append(self.prefix.fer_dsets.descr) fer_grids.append(self.prefix.fer_dsets.grids) fer_data.extend([self.prefix.go, self.prefix.examples]) env.set('FER_DATA', ' '.join(fer_data)) env.set('FER_DESCR', ' '.join(fer_descr)) env.set('FER_GRIDS', ' '.join(fer_grids))

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style_transfer/layer.py

haonguyen1107/style_transfer

0

159688

import numpy as np import tensorflow as tf def upsample_nearest(inputs, scale): shape = tf.shape(input=inputs) n, h, w, c = shape[0], shape[1], shape[2], shape[3] return tf.image.resize(inputs, tf.stack([h*scale, w*scale]), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)

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src/visions/lib/relations/string_to_datetime.py

sweersr/visions

0

36897

import pandas as pd from visions import visions_string, visions_datetime from visions.core.model import TypeRelation from visions.core.model.relations import InferenceRelation from visions.utils.coercion import test_utils def to_datetime_year_week(series): """Convert a series of the format YYYY/UU (year, week) to datetime. A '0' is added as day dummy value, as pandas requires a day value to parse. Args: series: the Series to parse Returns: A datetime series Examples: >>> series = pd.Series(['2018/47', '2018/12', '2018/03']) >>> parsed_series = to_datetime_year_week(series) >>> print(parsed_series.dt.week) 0 47 1 12 2 3 dtype: int64 """ return pd.to_datetime(series + "0", format="%Y/%U%w") def to_datetime_year_month_day(series): """Convert a series of the format YYYYMMDD (year, month, day) to datetime. Args: series: the Series to parse Returns: A datetime series Examples: >>> series = pd.Series(['20181201', '20181202', '20181203']) >>> parsed_series = to_datetime_year_week(series) >>> print(parsed_series.dt.day) 0 1 1 2 2 3 dtype: int64 """ return pd.to_datetime(series, format="%Y%m%d") def get_string_datetime_type_relation(func): return InferenceRelation( relationship=test_utils.coercion_test(func), transformer=func, related_type=visions_string, type=visions_datetime, ) def string_to_datetime_year_week(): return get_string_datetime_type_relation(to_datetime_year_week) def string_to_datetime_year_month_day(): return get_string_datetime_type_relation(to_datetime_year_month_day)

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minder_lastreview.py

hodea/hodea-review-minder

0

37407

<gh_stars>0 # -*- coding: utf-8 -*- """ Created on Mon Feb 26 19:38:29 2018 @author: Daniel """ import argparse import os from minder_config import minder_cfg from minder_database import minder_db from minder_htmlreport import minder_report import time import hashlib import uuid class get_lastreview: def __init__(self, minder_dict, topdir, cfg_exclude, cfg_type): print(topdir) print(cfg_exclude) print(cfg_type) print(minder_dict) filecnt = 0 for root, dirs, files in os.walk(topdir): for name in files: find = False for i in range(0,len(cfg_exclude)): if os.path.join(root, name).startswith(os.path.dirname(cfg_exclude[i])): find = True if find is True: continue for j in range(0,len(cfg_type)): if name.lower().endswith(cfg_type[j]): try: flog = open(os.path.join(root, name), "rb") flog.close() flog = open(os.path.join(root, name), 'r+') flog.close() filecnt+=1 except: print("ERROR: No Access to: " + os.path.join(root, name)) raise Exception print(filecnt)

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networkx/classes/tests/test_filters.py

jmmcd/networkx

3

134079

import pytest import networkx as nx class TestFilterFactory(object): def test_no_filter(self): nf = nx.filters.no_filter assert nf() assert nf(1) assert nf(2, 1) def test_hide_nodes(self): f = nx.classes.filters.hide_nodes([1, 2, 3]) assert not f(1) assert not f(2) assert not f(3) assert f(4) assert f(0) assert f('a') pytest.raises(TypeError, f, 1, 2) pytest.raises(TypeError, f) def test_show_nodes(self): f = nx.classes.filters.show_nodes([1, 2, 3]) assert f(1) assert f(2) assert f(3) assert not f(4) assert not f(0) assert not f('a') pytest.raises(TypeError, f, 1, 2) pytest.raises(TypeError, f) def test_hide_edges(self): factory = nx.classes.filters.hide_edges f = factory([(1, 2), (3, 4)]) assert not f(1, 2) assert not f(3, 4) assert not f(4, 3) assert f(2, 3) assert f(0, -1) assert f('a', 'b') pytest.raises(TypeError, f, 1, 2, 3) pytest.raises(TypeError, f, 1) pytest.raises(TypeError, f) pytest.raises(TypeError, factory, [1, 2, 3]) pytest.raises(ValueError, factory, [(1, 2, 3)]) def test_show_edges(self): factory = nx.classes.filters.show_edges f = factory([(1, 2), (3, 4)]) assert f(1, 2) assert f(3, 4) assert f(4, 3) assert not f(2, 3) assert not f(0, -1) assert not f('a', 'b') pytest.raises(TypeError, f, 1, 2, 3) pytest.raises(TypeError, f, 1) pytest.raises(TypeError, f) pytest.raises(TypeError, factory, [1, 2, 3]) pytest.raises(ValueError, factory, [(1, 2, 3)]) def test_hide_diedges(self): factory = nx.classes.filters.hide_diedges f = factory([(1, 2), (3, 4)]) assert not f(1, 2) assert not f(3, 4) assert f(4, 3) assert f(2, 3) assert f(0, -1) assert f('a', 'b') pytest.raises(TypeError, f, 1, 2, 3) pytest.raises(TypeError, f, 1) pytest.raises(TypeError, f) pytest.raises(TypeError, factory, [1, 2, 3]) pytest.raises(ValueError, factory, [(1, 2, 3)]) def test_show_diedges(self): factory = nx.classes.filters.show_diedges f = factory([(1, 2), (3, 4)]) assert f(1, 2) assert f(3, 4) assert not f(4, 3) assert not f(2, 3) assert not f(0, -1) assert not f('a', 'b') pytest.raises(TypeError, f, 1, 2, 3) pytest.raises(TypeError, f, 1) pytest.raises(TypeError, f) pytest.raises(TypeError, factory, [1, 2, 3]) pytest.raises(ValueError, factory, [(1, 2, 3)]) def test_hide_multiedges(self): factory = nx.classes.filters.hide_multiedges f = factory([(1, 2, 0), (3, 4, 1), (1, 2, 1)]) assert not f(1, 2, 0) assert not f(1, 2, 1) assert f(1, 2, 2) assert f(3, 4, 0) assert not f(3, 4, 1) assert not f(4, 3, 1) assert f(4, 3, 0) assert f(2, 3, 0) assert f(0, -1, 0) assert f('a', 'b', 0) pytest.raises(TypeError, f, 1, 2, 3, 4) pytest.raises(TypeError, f, 1, 2) pytest.raises(TypeError, f, 1) pytest.raises(TypeError, f) pytest.raises(TypeError, factory, [1, 2, 3]) pytest.raises(ValueError, factory, [(1, 2)]) pytest.raises(ValueError, factory, [(1, 2, 3, 4)]) def test_show_multiedges(self): factory = nx.classes.filters.show_multiedges f = factory([(1, 2, 0), (3, 4, 1), (1, 2, 1)]) assert f(1, 2, 0) assert f(1, 2, 1) assert not f(1, 2, 2) assert not f(3, 4, 0) assert f(3, 4, 1) assert f(4, 3, 1) assert not f(4, 3, 0) assert not f(2, 3, 0) assert not f(0, -1, 0) assert not f('a', 'b', 0) pytest.raises(TypeError, f, 1, 2, 3, 4) pytest.raises(TypeError, f, 1, 2) pytest.raises(TypeError, f, 1) pytest.raises(TypeError, f) pytest.raises(TypeError, factory, [1, 2, 3]) pytest.raises(ValueError, factory, [(1, 2)]) pytest.raises(ValueError, factory, [(1, 2, 3, 4)]) def test_hide_multidiedges(self): factory = nx.classes.filters.hide_multidiedges f = factory([(1, 2, 0), (3, 4, 1), (1, 2, 1)]) assert not f(1, 2, 0) assert not f(1, 2, 1) assert f(1, 2, 2) assert f(3, 4, 0) assert not f(3, 4, 1) assert f(4, 3, 1) assert f(4, 3, 0) assert f(2, 3, 0) assert f(0, -1, 0) assert f('a', 'b', 0) pytest.raises(TypeError, f, 1, 2, 3, 4) pytest.raises(TypeError, f, 1, 2) pytest.raises(TypeError, f, 1) pytest.raises(TypeError, f) pytest.raises(TypeError, factory, [1, 2, 3]) pytest.raises(ValueError, factory, [(1, 2)]) pytest.raises(ValueError, factory, [(1, 2, 3, 4)]) def test_show_multidiedges(self): factory = nx.classes.filters.show_multidiedges f = factory([(1, 2, 0), (3, 4, 1), (1, 2, 1)]) assert f(1, 2, 0) assert f(1, 2, 1) assert not f(1, 2, 2) assert not f(3, 4, 0) assert f(3, 4, 1) assert not f(4, 3, 1) assert not f(4, 3, 0) assert not f(2, 3, 0) assert not f(0, -1, 0) assert not f('a', 'b', 0) pytest.raises(TypeError, f, 1, 2, 3, 4) pytest.raises(TypeError, f, 1, 2) pytest.raises(TypeError, f, 1) pytest.raises(TypeError, f) pytest.raises(TypeError, factory, [1, 2, 3]) pytest.raises(ValueError, factory, [(1, 2)]) pytest.raises(ValueError, factory, [(1, 2, 3, 4)])

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scripts/CNN_Unet_cluster0.py

kevinshen56714/NucleiTracker

2

95103

<gh_stars>1-10 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import os from os.path import join import glob import sys import random import warnings from tqdm import tqdm import itertools from itertools import chain from skimage.io import imread, imshow, imread_collection, concatenate_images from skimage.transform import resize from skimage.morphology import label from sklearn.model_selection import train_test_split import tensorflow as tf import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D, BatchNormalization, Conv2D, UpSampling2D, Lambda from keras.models import Model, load_model from keras.layers import Input from keras.layers.core import Dropout, Lambda from keras.layers.convolutional import Conv2D, Conv2DTranspose from keras.layers.pooling import MaxPooling2D from keras.layers.merge import concatenate from keras import backend as K from keras.callbacks import Callback, EarlyStopping, ReduceLROnPlateau, ModelCheckpoint from keras import initializers, layers, models from keras.utils import to_categorical from keras.utils.vis_utils import plot_model from keras.preprocessing.image import ImageDataGenerator from keras import callbacks IMG_WIDTH = 128 IMG_HEIGHT = 128 IMG_CHANNELS = 3 TRAIN_PATH = 'stage1_train/' TEST_PATH = 'stage1_test/' warnings.filterwarnings('ignore', category=UserWarning, module='skimage') seed = 42 random.seed = seed np.random.seed = seed df = pd.read_csv('C:\\Users\\liukevin\\Desktop\\project_DM\\clustered_stage1_train.csv',sep=',') df1 = pd.read_csv('C:\\Users\\liukevin\\Desktop\\project_DM\\clustered_stage1_test.csv',sep=',') cluster=[[],[],[],[]] for i in range(len(df)): if df['cluster-id'][i]==0: cluster[0].append(df['ImageId'][i]) elif df['cluster-id'][i]==1: cluster[1].append(df['ImageId'][i]) elif df['cluster-id'][i]==2: cluster[2].append(df['ImageId'][i]) elif df['cluster-id'][i]==3: cluster[3].append(df['ImageId'][i]) cluster_test=[[],[],[],[]] for i in range(len(df1)): if df1['cluster-id'][i]==0: cluster_test[0].append(df1['ImageId'][i]) elif df1['cluster-id'][i]==1: cluster_test[1].append(df1['ImageId'][i]) elif df1['cluster-id'][i]==2: cluster_test[2].append(df1['ImageId'][i]) IdInCluster=0 # Get train and test IDs train_ids = cluster[IdInCluster] test_ids = cluster_test[IdInCluster] # Get and resize train images and masks X_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8) Y_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool) print('Getting and resizing train images and masks ... ') sys.stdout.flush() for n, id_ in tqdm(enumerate(train_ids), total=len(train_ids)): path = TRAIN_PATH + id_ img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS] img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True) X_train[n] = img mask = np.zeros((IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool) for mask_file in next(os.walk(path + '/masks/'))[2]: mask_ = imread(path + '/masks/' + mask_file) mask_ = np.expand_dims(resize(mask_, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True), axis=-1) mask = np.maximum(mask, mask_) Y_train[n] = mask # Get and resize test images X_test = np.zeros((len(test_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8) sizes_test = [] print('Getting and resizing test images ... ') sys.stdout.flush() for n, id_ in tqdm(enumerate(test_ids), total=len(test_ids)): path = TEST_PATH + id_ img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS] sizes_test.append([img.shape[0], img.shape[1]]) img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True) X_test[n] = img print('Done!') x_train, x_test, y_train, y_test = train_test_split(X_train, Y_train, test_size=0.2) print('\nx_train',x_train.shape) print('x_test',x_test.shape) print('y_train',y_train.shape) print('y_test',y_test.shape) def plotTrainData(a,b): for i in range(5): ix = random.randint(0, len(train_ids)) plt.subplot(1,2,1) plt.title("X_train") imshow(a[ix]) plt.axis('off') plt.subplot(1,2,2) plt.title("Y_train") imshow(np.squeeze(b[ix])) plt.axis('off') plt.show() plotTrainData(X_train,Y_train) class MetricsCheckpoint(Callback): """Callback that saves metrics after each epoch""" def __init__(self, savepath): super(MetricsCheckpoint, self).__init__() self.savepath = savepath self.history = {} def on_epoch_end(self, epoch, logs=None): for k, v in logs.items(): self.history.setdefault(k, []).append(v) np.save(self.savepath, self.history) def plotKerasLearningCurve(): plt.figure(figsize=(10,5)) metrics = np.load('logs.npy')[()] filt = ['acc'] # try to add 'loss' to see the loss learning curve for k in filter(lambda x : np.any([kk in x for kk in filt]), metrics.keys()): l = np.array(metrics[k]) plt.plot(l, c= 'r' if 'val' not in k else 'b', label='val' if 'val' in k else 'train') x = np.argmin(l) if 'loss' in k else np.argmax(l) y = l[x] plt.scatter(x,y, lw=0, alpha=0.25, s=100, c='r' if 'val' not in k else 'b') plt.text(x, y, '{} = {:.4f}'.format(x,y), size='15', color= 'r' if 'val' not in k else 'b') plt.legend(loc=4) plt.axis([0, None, None, None]); plt.grid() plt.xlabel('Number of epochs') def plot_learning_curve(history): plt.figure(figsize=(8,8)) plt.subplot(1,2,1) plt.plot(history.history['acc']) plt.plot(history.history['val_acc']) plt.title('model accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.savefig('./accuracy_curve.png') #plt.clf() # summarize history for loss plt.subplot(1,2,2) 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.savefig('./loss_curve.png') # Define IoU metric def mean_iou(y_true, y_pred): prec = [] for t in np.arange(0.5, 1.0, 0.05): y_pred_ = tf.to_int32(y_pred > t) score, up_opt = tf.metrics.mean_iou(y_true, y_pred_, 2) K.get_session().run(tf.local_variables_initializer()) with tf.control_dependencies([up_opt]): score = tf.identity(score) prec.append(score) return K.mean(K.stack(prec), axis=0) #RLE encoding for submission def rle_encoding(x): dots = np.where(x.T.flatten() == 1)[0] run_lengths = [] prev = -2 for b in dots: if (b>prev+1): run_lengths.extend((b + 1, 0)) run_lengths[-1] += 1 prev = b return run_lengths def prob_to_rles(x, cutoff=0.5): lab_img = label(x > cutoff) for i in range(1, lab_img.max() + 1): yield rle_encoding(lab_img == i) def SIMPLE(a,b,c,d): smooth = 1. def dice_coef(y_true, y_pred): y_true_f = K.flatten(y_true) y_pred_f = K.flatten(y_pred) intersection = K.sum(y_true_f * y_pred_f) return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth) def dice_coef_loss(y_true, y_pred): return -dice_coef(y_true, y_pred) simple_cnn = Sequential() simple_cnn.add(BatchNormalization(input_shape = (None, None, IMG_CHANNELS),name = 'NormalizeInput')) simple_cnn.add(Conv2D(8, kernel_size = (3,3), padding = 'same')) simple_cnn.add(Conv2D(8, kernel_size = (3,3), padding = 'same')) # use dilations to get a slightly larger field of view simple_cnn.add(Conv2D(16, kernel_size = (3,3), dilation_rate = 2, padding = 'same')) simple_cnn.add(Conv2D(16, kernel_size = (3,3), dilation_rate = 2, padding = 'same')) simple_cnn.add(Conv2D(32, kernel_size = (3,3), dilation_rate = 3, padding = 'same')) # the final processing simple_cnn.add(Conv2D(16, kernel_size = (1,1), padding = 'same')) simple_cnn.add(Conv2D(1, kernel_size = (1,1), padding = 'same', activation = 'sigmoid')) simple_cnn.summary() earlystopper = EarlyStopping(patience=5, verbose=1) checkpointer = ModelCheckpoint('model-dsbowl2018-1.h5', verbose=1, save_best_only=True) simple_cnn.compile(optimizer = 'adam', loss = dice_coef_loss, metrics = [dice_coef, 'acc', 'mse']) history = simple_cnn.fit(x_train,y_train, validation_data=(x_test,y_test),callbacks = [earlystopper, checkpointer, MetricsCheckpoint('logs')],epochs = 30) plot_learning_curve(history) plt.show() plotKerasLearningCurve() plt.show() global modelY modelY = simple_cnn return modelY SIMPLE(x_train, y_train,x_test,y_test) def UNET(a,b,c,d): # Build U-Net model inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS)) s = Lambda(lambda x: x / 255) (inputs) c1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (s) c1 = Dropout(0.1) (c1) c1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c1) p1 = MaxPooling2D((2, 2)) (c1) c2 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p1) c2 = Dropout(0.1) (c2) c2 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c2) p2 = MaxPooling2D((2, 2)) (c2) c3 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p2) c3 = Dropout(0.2) (c3) c3 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c3) p3 = MaxPooling2D((2, 2)) (c3) c4 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p3) c4 = Dropout(0.2) (c4) c4 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c4) p4 = MaxPooling2D(pool_size=(2, 2)) (c4) c5 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p4) c5 = Dropout(0.3) (c5) c5 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c5) u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (c5) u6 = concatenate([u6, c4]) c6 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u6) c6 = Dropout(0.2) (c6) c6 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c6) u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (c6) u7 = concatenate([u7, c3]) c7 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u7) c7 = Dropout(0.2) (c7) c7 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c7) u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same') (c7) u8 = concatenate([u8, c2]) c8 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u8) c8 = Dropout(0.1) (c8) c8 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c8) u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same') (c8) u9 = concatenate([u9, c1], axis=3) c9 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u9) c9 = Dropout(0.1) (c9) c9 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c9) outputs = Conv2D(1, (1, 1), activation='sigmoid') (c9) model = Model(inputs=[inputs], outputs=[outputs]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=[mean_iou,'accuracy']) #model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc']) model.summary() # Fit model earlystopper = EarlyStopping(patience=5, verbose=1) checkpointer = ModelCheckpoint('model-dsbowl2018-2.h5', verbose=1, save_best_only=True) results = model.fit(a,b,batch_size=16,verbose=1,epochs=30,validation_data=(c,d),callbacks = [earlystopper, checkpointer, MetricsCheckpoint('logs')]) plot_learning_curve(results) plt.show() plotKerasLearningCurve() plt.show() global modelZ modelZ = model return modelZ UNET(x_train, y_train,x_test,y_test) def plotPredictions(a,b,c,d,e): model = e # Threshold predictions preds_train = model.predict(a[:int(a.shape[0]*0.9)], verbose=1) preds_val = model.predict(a[int(a.shape[0]*0.9):], verbose=1) preds_test = model.predict(c, verbose=1) preds_train_t = (preds_train > 0.5).astype(np.uint8) preds_val_t = (preds_val > 0.5).astype(np.uint8) preds_test_t = (preds_test > 0.5).astype(np.uint8) # Perform a sanity check on some random training samples ix = random.randint(0, len(preds_train_t)) plt.subplot(1,3,1) plt.title("X_train") plt.axis('off') imshow(a[ix]) plt.subplot(1,3,2) plt.title("Y_train") plt.axis('off') imshow(np.squeeze(b[ix])) plt.subplot(1,3,3) plt.title("Prediction") plt.axis('off') imshow(np.squeeze(preds_train_t[ix])) plt.show() # Perform a sanity check on some random validation samples ix = random.randint(0, len(preds_val_t)) plt.subplot(1,3,1) plt.title("X_test") plt.axis('off') imshow(a[int(a.shape[0]*0.9):][ix]) plt.subplot(1,3,2) plt.title("Y_test") plt.axis('off') imshow(np.squeeze(b[int(b.shape[0]*0.9):][ix])) plt.subplot(1,3,3) plt.title("Prediction") plt.axis('off') imshow(np.squeeze(preds_val_t[ix])) plt.show() plotPredictions(x_train,y_train,x_test,y_test,modelZ) test_image_paths = [glob.glob(join(TEST_PATH, test_id, "images", "*"))[0] for test_id in test_ids] def threshold(image_gray): image_gray = cv2.GaussianBlur(image_gray, (7, 7), 1) ret, thresh = cv2.threshold(image_gray, 0, 255, cv2.THRESH_OTSU) _, cnts, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) cnts = sorted(cnts, key=cv2.contourArea, reverse=True) max_cnt_area = cv2.contourArea(cnts[0]) if max_cnt_area > 50000: ret, thresh = cv2.threshold(image_gray, 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY_INV) return thresh def apply_morphology(thresh): mask = cv2.dilate(thresh, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))) mask = cv2.erode(mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))) return mask segmented = [] for test_image_path in tqdm(test_image_paths): tmp_image = cv2.imread(test_image_path, cv2.IMREAD_GRAYSCALE) thresh = threshold(tmp_image) mask = apply_morphology(thresh) segmented.append(mask) def saveResults(a,b): preds_test = a.predict(b, verbose=1) # Create list of upsampled test masks preds_test_upsampled = [] for i in range(len(preds_test)): preds_test_upsampled.append(resize(np.squeeze(preds_test[i]), (sizes_test[i][0], sizes_test[i][1]), mode='constant', preserve_range=True)) new_test_ids = [] rles = [] for n, id_ in enumerate(test_ids): rle = list(prob_to_rles(preds_test_upsampled[n])) rles.extend(rle) new_test_ids.extend([id_] * len(rle)) # Create submission DataFrame sub = pd.DataFrame() sub['ImageId'] = new_test_ids sub['EncodedPixels'] = pd.Series(rles).apply(lambda x: ' '.join(str(y) for y in x)) sub.to_csv(a.name+'.csv', index=False) print("Results Saved") print("\nMethod 1: Simple CNN") saveResults(modelY,X_test) print("\nMethod 2: Unet CNN") saveResults(modelZ,X_test)

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test/login.py

hongren798911/haha

0

26645

<reponame>hongren798911/haha num1 = 100 num2 = 200 num3 = 300 num4 = 400 num5 = 500 mum6 = 600 num7 = 700 num8 = 800

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pytype/tests/py2/test_stdlib.py

souravbadami/pytype

1

3896

<reponame>souravbadami/pytype """Tests of selected stdlib functions.""" from pytype.tests import test_base class StdlibTests(test_base.TargetPython27FeatureTest): """Tests for files in typeshed/stdlib.""" def testPosix(self): ty = self.Infer(""" import posix x = posix.urandom(10) """) self.assertTypesMatchPytd(ty, """ posix = ... # type: module x = ... # type: str """) def testXRange(self): self.Check(""" import random random.sample(xrange(10), 5) """) def testStringTypes(self): ty = self.Infer(""" import types if isinstance("", types.StringTypes): x = 42 if isinstance(False, types.StringTypes): y = 42 if isinstance(u"", types.StringTypes): z = 42 """, deep=False) self.assertTypesMatchPytd(ty, """ types = ... # type: module x = ... # type: int z = ... # type: int """) def testDefaultDict(self): self.Check(""" import collections import itertools ids = collections.defaultdict(itertools.count(17).next) """) def testSysVersionInfoLt(self): ty = self.Infer(""" import sys if sys.version_info[0] < 3: v = 42 else: v = "hello world" """) self.assertTypesMatchPytd(ty, """ sys = ... # type: module v = ... # type: int """) def testSysVersionInfoLe(self): ty = self.Infer(""" import sys if sys.version_info[0] <= 2: v = 42 else: v = "hello world" """) self.assertTypesMatchPytd(ty, """ sys = ... # type: module v = ... # type: int """) def testSysVersionInfoEq(self): ty = self.Infer(""" import sys if sys.version_info[0] == 2: v = 42 elif sys.version_info[0] == 3: v = "hello world" else: v = None """) self.assertTypesMatchPytd(ty, """ sys = ... # type: module v = ... # type: int """) def testSysVersionInfoGe(self): ty = self.Infer(""" import sys if sys.version_info[0] >= 3: v = 42 else: v = "hello world" """) self.assertTypesMatchPytd(ty, """ sys = ... # type: module v = ... # type: str """) def testSysVersionInfoGt(self): ty = self.Infer(""" import sys if sys.version_info[0] > 2: v = 42 else: v = "hello world" """) self.assertTypesMatchPytd(ty, """ sys = ... # type: module v = ... # type: str """) def testSysVersionInfoNamedAttribute(self): ty = self.Infer(""" import sys if sys.version_info.major == 2: v = 42 else: v = "hello world" """) self.assertTypesMatchPytd(ty, """ sys: module v: int """) test_base.main(globals(), __name__ == "__main__")

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server/test.py

marcosmuto/IBMCloudAppDevelopment_Capstone

0

111519

import requests import json from datetime import datetime from requests.auth import HTTPBasicAuth review = dict() review["id"] = int(datetime.now().timestamp()) review["name"] = "<NAME>" review["dealership"] = 1 review["review"] = "Good deal" review["purchase"] = True review["purchase_date"] = "02/16/2020" review["car_make"] = "Honda" review["car_model"] = "Fit" review["car_year"] = 2020 print(review) json_payload = dict() json_payload["review"] = review url="https://43bb4759.us-south.apigw.appdomain.cloud/api/review" response = requests.post(url, json=json_payload) print(response)

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credit-bias/src/data/make_dataset.py

tteofili/benchmark-models

0

127034

# -*- coding: utf-8 -*- import click import colorlog import logging from pathlib import Path from dotenv import find_dotenv, load_dotenv import pandas as pd import numpy as np import os handler = colorlog.StreamHandler() handler.setFormatter( colorlog.ColoredFormatter("%(log_color)s%(levelname)s:%(name)s:%(message)s") ) logger = colorlog.getLogger(__name__) def filter_columns(df_: pd.DataFrame) -> pd.DataFrame: selected_columns = [ "LoanNumber", "ListedOnUTC", "UserName", "NewCreditCustomer", "LoanDate", "MaturityDate_Original", "MaturityDate_Last", "Age", "DateOfBirth", "Gender", "Country", "AppliedAmount", "Amount", "Interest", "LoanDuration", "MonthlyPayment", "UseOfLoan", "Education", "MaritalStatus", "NrOfDependants", "EmploymentStatus", "EmploymentDurationCurrentEmployer", "WorkExperience", "OccupationArea", "HomeOwnershipType", "IncomeFromPrincipalEmployer", "IncomeFromPension", "IncomeFromFamilyAllowance", "IncomeFromSocialWelfare", "IncomeFromLeavePay", "IncomeFromChildSupport", "IncomeOther", "IncomeTotal", "ExistingLiabilities", "RefinanceLiabilities", "DebtToIncome", "FreeCash", "DefaultDate", "Status", "CreditScoreEeMini", "NoOfPreviousLoansBeforeLoan", "AmountOfPreviousLoansBeforeLoan", "PreviousRepaymentsBeforeLoan", "PreviousEarlyRepaymentsBefoleLoan", "PreviousEarlyRepaymentsCountBeforeLoan", ] return df_[selected_columns] def filter_rows(df_: pd.DataFrame) -> pd.DataFrame: return df_[(df_["Country"] == "EE") & (df_["Status"] != "Current")] def rename_columns(df_: pd.DataFrame) -> pd.DataFrame: df_ = df_.replace(-1, np.nan) zero_replacements = [ "Age", "Education", "MaritalStatus", "EmploymentStatus", "OccupationArea", "CreditScoreEeMini", ] df_[zero_replacements] = df_[zero_replacements].replace(0.0, np.nan) value_replacements = { "UseOfLoan": { 0: "Loan_consolidation", 1: "Real_estate", 2: "Home_improvement", 3: "Business", 4: "Education", 5: "Travel", 6: "Vehicle", 7: "Other", 8: "Health", 101: "Working_capital_financing", 102: "Purchase_of_machinery_equipment", 103: "Renovation_of_real_estate", 104: "Accounts_receivable_financing ", 105: "Acquisition_of_means_of_transport", 106: "Construction_finance", 107: "Acquisition_of_stocks", 108: "Acquisition_of_real_estate", 109: "Guaranteeing_obligation ", 110: "Other_business", }, "Education": { 1: "Primary", 2: "Basic", 3: "Vocational", 4: "Secondary", 5: "Higher", }, "MaritalStatus": { 1: "Married", 2: "Cohabitant", 3: "Single", 4: "Divorced", 5: "Widow", }, "EmploymentStatus": { 1: "Unemployed", 2: "Partially", 3: "Fully", 4: "Self_employed", 5: "Entrepreneur", 6: "Retiree", }, "NewCreditCustomer": {0: "Existing_credit_customer", 1: "New_credit_Customer"}, "OccupationArea": { 1: "Other", 2: "Mining", 3: "Processing", 4: "Energy", 5: "Utilities", 6: "Construction", 7: "Retail_and_wholesale", 8: "Transport_and_warehousing", 9: "Hospitality_and_catering", 10: "Info_and_telecom", 11: "Finance_and_insurance", 12: "Real_estate", 13: "Research", 14: "Administrative", 15: "Civil_service_and_military", 16: "Education", 17: "Healthcare_and_social_help", 18: "Art_and_entertainment", 19: "Agriculture_forestry_and_fishing", }, "HomeOwnershipType": { 0: "Homeless", 1: "Owner", 2: "Living_with_parents", 3: "Tenant_pre_furnished_property", 4: "Tenant_unfurnished_property", 5: "Council_house", 6: "Joint_tenant", 7: "Joint_ownership", 8: "Mortgage", 9: "Owner_with_encumbrance", 10: "Other", }, "NrOfDependants": {"10Plus": 11}, "Gender": {0: "Male", 1: "Female", 2: "Unknown"}, } df_ = df_.replace(value_replacements) return df_ def add_new_columns(df_: pd.DataFrame) -> pd.DataFrame: df_["Defaulted"] = df_["DefaultDate"].apply(lambda x: 0 if pd.isnull(x) else 1) df_["PaidLoan"] = df_["Defaulted"].replace({0: 1, 1: 0}) df_["LoanStatus"] = df_["PaidLoan"].apply( lambda x: "Paid back" if x == 1 else "Defaulted" ) df_["AgeGroup"] = df_["Age"].apply(lambda x: "Under 40" if x < 40 else "Over 40") return df_ def reformat_columns(df_: pd.DataFrame) -> pd.DataFrame: df_["ListedOnUTC"] = pd.to_datetime(df_["ListedOnUTC"]) df_["LoanDate"] = pd.to_datetime(df_["LoanDate"]) df_["MaturityDate_Original"] = pd.to_datetime(df_["MaturityDate_Original"]) df_["MaturityDate_Last"] = pd.to_datetime(df_["MaturityDate_Last"]) df_["DateOfBirth"] = pd.to_datetime(df_["DateOfBirth"]) df_["DefaultDate"] = pd.to_datetime(df_["DefaultDate"]) df_["LoanDuration"] = pd.to_numeric(df_["LoanDuration"]) df_["NrOfDependants"] = pd.to_numeric(df_["NrOfDependants"]) df_["CreditScoreEeMini"] = df_["CreditScoreEeMini"].astype(str) df_["Defaulted"] = df_["Defaulted"].astype(bool) df_["PaidLoan"] = df_["PaidLoan"].astype(bool) return df_ @click.command() @click.argument("input_filepath", type=click.Path(exists=True)) @click.argument("output_filepath", type=click.Path()) def main(input_filepath, output_filepath): """Runs data processing scripts to turn raw data from (../raw) into cleaned data ready to be analyzed (saved in ../processed). """ logger.info("making interim data set from raw data") # load raw dataset _df = pd.read_csv(os.path.join(input_filepath, "LoanData.zip")) # Filter not used columns _df = filter_columns(_df) # Filter not used rows _df = filter_rows(_df) # Rename columns _df = rename_columns(_df) # Add new columns _df = add_new_columns(_df) # Reformat columns _df = reformat_columns(_df) _df.to_csv(os.path.join(output_filepath, "data.csv"), index=False) if __name__ == "__main__": log_fmt = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" logging.basicConfig(level=logging.INFO, format=log_fmt) # not used in this stub but often useful for finding various files project_dir = Path(__file__).resolve().parents[2] # find .env automagically by walking up directories until it's found, then # load up the .env entries as environment variables load_dotenv(find_dotenv()) main()

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Code/VB/demo.py

CaptainE/Non-negative-latent-space-models

0

130024

<gh_stars>0 import numpy as np from scipy.stats import gamma,poisson from gnmf import nmf_vb import matplotlib.pyplot as plt if __name__ == '__main__': W=40 K=30 I=3 a_tm=np.ones((W,I))*10 b_tm=np.ones((W,I)) a_ve=np.ones((I,K))*0.1 b_ve=np.ones((I,K)) T = gamma.rvs(a_tm,b_tm) V = gamma.rvs(a_ve,b_ve) X = poisson.rvs(T@V) X = X + 1000*np.random.randn(*X.shape) (tie_a_tm, tie_b_tm, tie_a_ve, tie_b_ve)=('free','tie_all','free','tie_all') init_mat_factor = nmf_vb(a_tm, b_tm, a_ve, b_ve,tie_a_tm, tie_b_tm, tie_a_ve, tie_b_ve) g=init_mat_factor.VB_parm_calc(X,epoch=1000,update=10,print_period=500) (g_E_T,g_E_logT,g_E_V,g_E_logV,g_Bound,g_a_ve,g_b_ve,g_a_tm,g_b_tm)=g X_reconstruct=g_E_T@g_E_V #upper left plot is the reconstructed matrix plt.subplot(211) plt.imshow(X_reconstruct) #lower left plot is the original matrix plt.subplot(212) plt.imshow(X) #the right subplot is a colorbar plt.subplots_adjust(bottom=0.1,right=0.8,top=0.9) cax=plt.axes([0.6,0.1,0.075,0.8]) plt.colorbar(cax=cax) plt.show()

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second-sem/genPlotsBetweenCoverageAndFPKM.py

piyush-kansal/masters-project

1

159694

#!/usr/bin/env python # Main script for generating plots between coverage and FPKM values # <NAME> import sys import os import subprocess import csv import rpy2.robjects as robjects import pickle # Validate number of i/p arguments def validateNumberOfArguments(): if len(sys.argv) != 5: print "Invalid number of arguments" print "Usage:" print "genPlotsBetweenCoverageAndFPKM.py <number-of-experiments> <cufflinks-op-files-path-on-hdfs> <expression-matrix-path> <ref-ptt-file>" sys.exit(1) # Accepts user i/p, validate them and initialize required params def acceptUserInput(): validateNumberOfArguments() FNULL = open(os.devnull, 'w') # Declare params global global EXP_CONT, CUFF_PATH, EXPM_PATH, REF_FILE, PLOT_FILE global coverageVals, fpkmVals, ranges # Initialize i/p arguments EXP_CONT = int(sys.argv[1]) CUFF_PATH = sys.argv[2] cmd = 'hadoop dfs -ls %s' % (CUFF_PATH) try: subprocess.check_call(cmd.split(), stdout=FNULL, stderr=subprocess.STDOUT) except subprocess.CalledProcessError: print 'Directory %s does not exits on HDFS.' % (CUFF_PATH) sys.exit(1) FNULL.close() EXPM_PATH = sys.argv[3] if not os.path.isfile(EXPM_PATH): print 'File %s does not exits on file system.' % (EXPM_PATH) sys.exit(1) REF_FILE = sys.argv[4] if not os.path.isfile(REF_FILE): print 'File %s does not exits on file system.' % (REF_FILE) sys.exit(1) PLOT_FILE = 'XY.plot' def copyFilesInCurrentDir(): i = 1 while i <= EXP_CONT: cmd = 'hadoop dfs -copyToLocal %s/%d/genes.fpkm_tracking genes.fpkm_tracking.%d' % (CUFF_PATH, i, i) r = os.system(cmd) if r: print 'Some error occured while copying Cufflinks output on local file system for i=%d. Exiting ...' % (i) sys.exit(1) i += 1 cmd = 'cp %s .' % (EXPM_PATH) os.system(cmd) cmd = 'cp %s .' % (REF_FILE) os.system(cmd) def rangeExists(s, e): for index, r in enumerate(ranges): if s >= long(r[0]) and s < long(r[1]): return index if s < long(r[0]): return None return None def genValues(): coverageFile = open(EXPM_PATH, 'r') records = csv.reader(coverageFile, delimiter=' ') i = 0 coverageVals = [] while i < EXP_CONT: coverageVals.append(','.join([r[i] for r in records])) coverageFile.seek(0) i += 1 coverageFile.close() refFile = open(REF_FILE, 'r') records = csv.reader(refFile, delimiter='\t') ranges = [tuple(r[0].split('..')) for r in records] refFile.close() i = 1 fpkmVals = [] while i <= EXP_CONT: fpkmFileName = 'genes.fpkm_tracking.%d' % (i) fpkmFile = open(fpkmFileName, 'r') records = csv.reader(fpkmFile, delimiter='\t') j = 1 row = ['0']*len(ranges) for r in records: if j == 1: j = 2 continue else: s,e = r[6].split(':')[1].split('-') ret = rangeExists(long(s), e) if ret: row[ret] = r[9] fpkmVals.append(','.join(row)) fpkmFile.close() i += 1 def writeValsToFile(): oFile = open(PLOT_FILE, 'wb') pickle.dump(coverageVals, oFile) pickle.dump(fpkmVals, oFile) oFile.close() def genPlots(): iFile = open(PLOT_FILE, 'rb') coverageVals = pickle.load(iFile) fpkmVals = pickle.load(iFile) iFile.close() r = robjects.r i = 0 while i < EXP_CONT: x = robjects.IntVector([long(item) for item in coverageVals[i].split(',')]) y = robjects.FloatVector([float(item) for item in fpkmVals[i].split(',')]) r.jpeg('t%d.jpg' % (i+1)) r.plot(x, y, xlab="Coverage", ylab="FPKM") i += 1 def cleanup(): cmd = 'rm -f genes.fpkm_tracking*' os.system(cmd) # Main program if __name__ == "__main__": acceptUserInput() copyFilesInCurrentDir() genValues() writeValsToFile() cleanup() genPlots() print "Program finished successfully" sys.exit(0)

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nsst_translate_corpus.py

AlexanderJenke/nsst

0

2672

from argparse import ArgumentParser from tqdm import tqdm import NSST from nsst_translate import best_transition_sequence if __name__ == '__main__': parser = ArgumentParser() parser.add_argument("--nsst_file", default="output/nsst_tss20_th4_nSt100_Q0.pkl", help="nsst file") parser.add_argument("--src_lang", default="output/europarl-v7.de-en.de.clean") parser.add_argument("--tgt_lang", default="output/europarl-v7.de-en.en.clean") parser.add_argument("--enforce_n_reg", default=True) parser.add_argument("--output", default=f"output/nsst_stat_nreg_100Q0.csv") args = parser.parse_args() args.enforce_n_final_reg = False # load NSST nsst = NSST.NSST() nsst.load(args.nsst_file) args.nsst = nsst # open files src_file = open(args.src_lang, 'r') tgt_file = open(args.tgt_lang, 'r') output_file = open(args.output, 'w') # iterate over sentences, first 4096 -> test sentences for src, tgt, _ in tqdm(list(zip(src_file, tgt_file, range(4096))), desc="Processing sentences"): # remove line breaks src = src[:-1] tgt = tgt[:-1] # try to translate try: # prepare tokenisations token_src = [nsst.tokenization_src[word] if word in nsst.tokenization_src else 0 for word in src.split(" ") if len(word)] token_tgt = [nsst.tokenization_tgt[word] if word in nsst.tokenization_tgt else 0 for word in tgt.split(" ") if len(word)] # run nsst args.input = src args.token_src = token_src result = best_transition_sequence(args) # get best result pred = sorted((k for k in result if ('Qf' in args.nsst_file or not args.enforce_n_final_reg or len(k[1]) == 1) and ('Q0' in args.nsst_file or k[0] == -1) ), key=lambda x: x[2], reverse=True)[0] n_res = len(result) q, reg, prob = pred # write to csv if not len(reg): # catch empty registers continue token_pred = [w for w in reg[0].split(' ') if len(w)] pred_str = "" for t in token_pred: pred_str += f"{nsst.tokenization_tgt_lut[int(t)]} " token_src_str = "" for t in token_src: token_src_str += f"{t} " token_tgt_str = "" for t in token_tgt: token_tgt_str += f"{t} " token_pred_str = "" for t in token_pred: token_pred_str += f"{t} " print(f"{src};{token_src_str[:-1]};" f"{tgt};{token_tgt_str[:-1]};" f"{pred_str};{token_pred_str[:-1]};" f"{prob};{len(reg)};{n_res}", file=output_file) output_file.flush() except RuntimeError: pass # close files src_file.close() tgt_file.close() output_file.close()

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src/example_package/additions.py

energyinpython/packaging_tutorial

0

156466

import numpy as np # reverse = True: descending order (TOPSIS, CODAS), False: ascending order (VIKOR, SPOTIS) def rank_preferences(pref, reverse = True): """ Rank alternatives according to MCDM preference function values. Parameters ---------- pref : ndarray vector with MCDM preference function values for alternatives reverse : bool Boolean variable which is True for MCDM methods which rank alternatives in descending order and False for MCDM methods which rank alternatives in ascending order Returns ------- ndarray vector with alternatives ranking. Alternative with 1 value is the best. """ rank = np.zeros(len(pref)) sorted_pref = sorted(pref, reverse = reverse) pos = 1 for i in range(len(sorted_pref) - 1): ind = np.where(sorted_pref[i] == pref)[0] rank[ind] = pos if sorted_pref[i] != sorted_pref[i + 1]: pos += 1 ind = np.where(sorted_pref[i + 1] == pref)[0] rank[ind] = pos return rank.astype(int)

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cogandmem/writing.py

aemacdermid/cogandmem

0

94858

<gh_stars>0 """ Functions for writing to files. To the best of my knowledge, the functions in this module work. However, the module can be improved. At present, it does not take advantage of Python's csv module. Since data is usually written to csv files, this is a major oversight. This module contains the following functions (see the docstrings for more information): ready_file_for_writing: given a string, returns a file object ready for writing. list_or_tuple: write a list or tuple to a file. write_dict: write a dictionary to a file. study_phase: write a study phase to a file. paired_study: write a study phase with WordPair objects to a file. cued_recall_results: write the results of a cued-recall test to a file. """ import os def ready_file_for_writing(f): """Make sure a file is ready for writing. f should be a string or file.""" try: extant = os.path.isfile(f) except TypeError: # Presumably, this is already a file. return f if extant: # Don't overwrite the extant file. f = open(f, "a") else: f = open(f, "w") return f def list_or_tuple(f, w, sep = "\n", c = True, end_string = "\n"): """ Write a list or tuple to a file. Parameters: f: the file to which to write w; can be a file open for writing, a string pointing to an extant file, or a string pointing to a to-be-created file. w: the to-be-written list or tuple. Keyword Parameters: sep: the string separating each list element; defaults to "\n". c: Boolean indicating whether the file should be closed before the function ends; defaults to True. end_string: a string to write at the end; defaults to "\n". """ f = ready_file_for_writing(f) for i in range(len(w)): e = w[i] if isinstance(e, (list, tuple)): list_or_tuple(f, e, c = False, end_string = "") elif isinstance(e, dict): write_dict(e, f, False, end_string = "") else: f.write(str(e)) if i < len(w)-1: f.write(sep) elif end_string: f.write(end_string) if c: f.close() def write_dict(d, f, c = True, end_string = "\n"): """ Write a dictionary (d) to a file (f). f can be a file open for writing or a string pointing to an extant or to-be-created file. Entries in d are written one by one to f. Each key is written first, followed by the entry. Two blank lines separate keys. Dicts, lists, and tuples are written with each entry/element on a separate line. All other types are converted to strings and written on the same line as the key. If c is True, f is closed before the function ends. The end_string parameter defaults to "\n". It is written after d is written. """ f = ready_file_for_writing(f) for k in d: f.write(str(k)+": ") w = d[k] if isinstance(w, (list, tuple)): f.write("[\n") list_or_tuple(f, w, c = False) f.write("]\n") elif isinstance(w, dict): f.write("{\n") write_dict(w, f, False) f.write("}\n") else: f.write(str(w)+"\n") if end_string: f.write(end_string) if c: f.close() def study_phase(stimuli, f, sep = ",", close_when_finished = True): """ Write a study list to a file. If the Stimulus objects passed to the function contain information in their condition attribute, then a three-column table is written to f (serial position, word, condition). If no condition information is included, then the condition column is omitted. Parameters: stimuli: a list of Stimulus objects representing the study list. f: the file to which to write stimuli; this can be a file opened for writing, a string pointing to an extant file, or a string pointing to a to-be-created file. If f is a file, writing starts wherever the file is currently positioned for writing; if f is a string pointing to an extant file, stimuli are written to the end; and if f is a to-be-created file, stimuli are written in the file at the top. Keyword Parameters: sep: string denoting what separates the values; defaults to ",". close_when_finished: Boolean indicating whether f should be closed before ending the function; defaults to True. """ f = ready_file_for_writing(f) f.write("serial_position{:s}word".format(sep)) if stimuli[0].condition: write_condition = True f.write("{:s}condition\n".format(sep)) else: write_condition = False f.write("\n") for i in range(len(stimuli)): f.write("{:d}{:s}{:s}".format(i+1, sep, stimuli[i].word)) if write_condition: f.write("{:s}{:s}\n".format(sep, str(stimuli[i].condition))) else: f.write("\n") if close_when_finished: f.close() def paired_study(stimuli, f, sep = ",", close_when_finished = True): """ Write a paired study list to a file. If the WordPair objects passed to the function contain information in their condition attribute, then a four-column table is written to f (serial position, cue, target, condition). If no condition information is included, then the condition column is omitted. Parameters: stimuli: a list of WordPair objects representing the study list. f: the file to which to write stimuli; this can be a file opened for writing, a string pointing to an extant file, or a string pointing to a to-be-created file. If f is a file, writing starts wherever the file is currently positioned for writing; if f is a string pointing to an extant file, stimuli are written to the end; and if f is a to-be-created file, stimuli are written in the file at the top. Keyword Parameters: sep: string denoting what separates the values; defaults to ",". close_when_finished: Boolean indicating whether f should be closed before ending the function; defaults to True. """ f = ready_file_for_writing(f) f.write("serial_position"+sep+"cue"+sep+"target") if stimuli[0].condition: write_condition = True f.write(sep+"condition\n") else: write_condition = False f.write("\n") for i in range(len(stimuli)): pair = stimuli[i] f.write(str(i+1)+sep+pair.cue+sep+pair.target) if write_condition: f.write(sep+str(pair.condition)+"\n") else: f.write("\n") if close_when_finished: f.close() def cued_recall_results(word_pairs, f, sep = ",", close_when_finished = True): """ Write the results of a cued-recall test to a file. If the WordPair objects passed to the function contain information in their condition attribute, then a five-column table is written to f (test position, cue, target, response, condition). If no condition information is included, then the condition column is omitted. Parameters: word_pairs: the WordPair objects to write. f: the file to which to write stimuli; this can be a file opened for writing, a string pointing to an extant file, or a string pointing to a to-be-created file. If f is a file, writing starts wherever the file is currently positioned for writing; if f is a string pointing to an extant file, stimuli are written to the end; and if f is a to-be-created file, stimuli are written in the file at the top. Keyword Parameters: sep: string denoting what separates the values; defaults to ",". close_when_finished: Boolean indicating whether f should be closed before ending the function; defaults to True. """ f = ready_file_for_writing(f) f.write("test_position"+sep+"cue"+sep+"target"+sep+"response") if word_pairs[0].condition: write_condition = True f.write(sep+"condition"+sep+"\n") else: write_condition = False f.write("\n") for i in range(len(word_pairs)): pair = word_pairs[i] f.write(str(i+1)+sep+pair.cue+sep+pair.target+sep+pair.response) if write_condition: f.write(sep+str(pair.condition)+"\n") else: f.write("\n") if close_when_finished: f.close()

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interview-preparation-kit/minimum-time-required.py

gajubadge11/HackerRank-1

340

117103

<filename>interview-preparation-kit/minimum-time-required.py #!/bin/python3 import math import os import random import re import sys from collections import Counter def calculate_days(day, cnt): res = 0 for el in cnt.items(): res += el[1] * (day // el[0]) return res # Complete the minTime function below. def minTime(machines, goal): cnt = Counter(machines) day_limit = 10**13 curgoal = 0 curday = 0 res = 0 prev_big = day_limit prev_low = 0 curday = day_limit // 2 while True: # detect if we are bouncing around in a loop if curday == prev_big or curday == prev_low: #print("curday = {} prev_low = {} prev_big = {}".format(curday, prev_low, prev_big)) prev_low_res = calculate_days(prev_low, cnt) prev_big_res = calculate_days(prev_big, cnt) if prev_low_res >= goal: return prev_low else: return prev_big curgoal = calculate_days(curday, cnt) if curgoal < goal: prev_low = curday #print("micro curday = {} curgoal = {} < {}, diff = {}".format(curday, curgoal, goal, abs(curgoal - goal))) curday = curday + (1 + day_limit - curday)//2 res = curday else: #print("micro curday = {} curgoal = {} > {}, diff = {}".format(curday, curgoal, goal, abs(curgoal - goal))) prev_big = curday day_limit = curday curday = curday // 2 res = curday return res if __name__ == '__main__': fptr = open(os.environ['OUTPUT_PATH'], 'w') nGoal = input().split() n = int(nGoal[0]) goal = int(nGoal[1]) machines = list(map(int, input().rstrip().split())) ans = minTime(machines, goal) fptr.write(str(ans) + '\n') fptr.close()

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tarea2c/mod/game_over.py

camilo-nb/CC3501-tareas

0

29869

<gh_stars>0 import os from collections import deque import numpy as np from OpenGL.GL import * import lib.basic_shapes as bs import lib.easy_shaders as es import lib.transformations as tr class GameOver: def __init__(self): self.GPU = deque([ es.toGPUShape(bs.createTextureCube(os.path.join('mod', 'tex', 'game_over_1.png')), GL_REPEAT, GL_NEAREST), es.toGPUShape(bs.createTextureCube(os.path.join('mod', 'tex', 'game_over_2.png')), GL_REPEAT, GL_NEAREST), es.toGPUShape(bs.createTextureCube(os.path.join('mod', 'tex', 'game_over_3.png')), GL_REPEAT, GL_NEAREST), es.toGPUShape(bs.createTextureCube(os.path.join('mod', 'tex', 'game_over_4.png')), GL_REPEAT, GL_NEAREST) ]) self.x, self.y, self.z = 0, 310, 100 self.phi = 0 self.tick = 0 self.s = 10 self.transform = tr.matmul([tr.translate(self.x, self.y, self.z), tr.scale(self.s, self.s, 0.0001)]) def draw(self, pipeline, projection, view): glUseProgram(pipeline.shaderProgram) glUniformMatrix4fv(glGetUniformLocation(pipeline.shaderProgram, "model"), 1, GL_TRUE, self.transform) glUniformMatrix4fv(glGetUniformLocation(pipeline.shaderProgram, "projection"), 1, GL_TRUE, projection) glUniformMatrix4fv(glGetUniformLocation(pipeline.shaderProgram, "view"), 1, GL_TRUE, view) pipeline.drawShape(self.GPU[0]) def update(self): self.GPU.append(self.GPU.popleft()) self.tick -= 0.1 * np.exp(self.tick/10) self.phi = np.exp(self.tick) self.transform = tr.matmul([tr.translate(self.x, self.y, self.z), tr.rotationX(2 * self.phi), tr.rotationZ(self.phi), tr.scale(self.s, self.s, 0.0001)])

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echr/steps/prepare_database.py

nlopuszynska/ECHR-OD_process

20

85441

import argparse import json import os from os import listdir from os.path import isfile import shutil from genson import SchemaBuilder from enum import Enum import copy import flatdict import pandas as pd import numpy as np from collections import OrderedDict from functools import reduce # forward compatibility for Python 3 import operator import sys from echr.utils.folders import make_build_folder from echr.utils.cli import TAB from echr.utils.logger import getlogger from rich.markdown import Markdown from rich.console import Console log = getlogger() __console = Console(record=True) DELIMITER = '.' type_priority = OrderedDict([ ('number', float), ('integer', int), ('string', str) ]) class COL_HINT(str, Enum): HOT_ONE = 'hot_one' POSITIONAL = 'positional' def format_structured_json(cases_list): res = [] representents = {} extractedapp = {} scl = {} decision_body = {} for name in cases_list: with open(name, 'r') as f: c = json.load(f) c['representedby'] = [r for r in c['representedby'] if r != 'N/A'] representents[c['appno']] = {'representedby': c['representedby']} extractedapp[c['appno']] = {'appnos': c['extractedappno']} decision_body[c['appno']] = { 'name': [e['name'] for e in c['decision_body']], 'role': {e['name']: e['role'] for e in c['decision_body'] if 'role' in e} } scl[c['appno']] = {'scl': c['scl']} c['respondent'] = c['respondent'].split(';') # c['applicability'] = c['applicability'].strip().split(';') c['appno'] = c['appno'].split(';')[0] c['decisiondate'] = c['decisiondate'].split(' ')[0] c['judgementdate'] = c['judgementdate'].split(' ')[0] c['introductiondate'] = c['introductiondate'].split(' ')[0] c['kpdate'] = c['kpdate'].split(' ')[0] c['separateopinion'] = True if c['separateopinion'] == 'TRUE' else False del c['representedby'] del c['extractedappno'] del c['decision_body'] del c['scl'] del c['documents'] del c['content'] del c['externalsources'] del c['kpthesaurus'] del c['__conclusion'] del c['__articles'] if not len(c['issue']): del c['issue'] else: c['issue'] = sorted(c['issue']) if not len(c['applicability']): del c['applicability'] res.append(c) return res, representents, extractedapp, scl, decision_body def get_by_path(root, items): return reduce(operator.getitem, items, root) def set_by_path(root, items, value): get_by_path(root, items[:-1])[items[-1]] = value def determine_schema(X): builder = SchemaBuilder() for x in X: builder.add_object(x) schema = builder return schema def get_flat_type_mapping(flat_schema): flat_type_mapping = {} for k in flat_schema.keys(): if k.endswith(DELIMITER + 'type'): key = k.replace('properties' + DELIMITER, '').replace(DELIMITER + 'type', '') flat_type_mapping[key] = flat_schema[k] return flat_type_mapping def get_flat_domain_mapping(X, flat_type_mapping): flat_domain_mapping = {} for x in X: flat = flatdict.FlatterDict(x, delimiter='.') for k in flat_type_mapping.keys(): v = flat.get(k) if v is not None: if k not in flat_domain_mapping: flat_domain_mapping[k] = set() type_ = flat_type_mapping[k] try: if type_ == 'array': flat_domain_mapping[k].update(get_by_path(x, k.split('.'))) else: flat_domain_mapping[k].add(get_by_path(x, k.split('.'))) except: if not flat_domain_mapping[k]: del flat_domain_mapping[k] for k in flat_domain_mapping: flat_domain_mapping[k] = list(flat_domain_mapping[k]) return flat_domain_mapping def flatten_dataset(X, flat_type_mapping, schema_hints=None): if schema_hints is None: schema_hints = {} flat_X = [] for x in X: flat = flatdict.FlatterDict(x, delimiter=DELIMITER) c_x = copy.deepcopy(x) for k in flat_type_mapping.keys(): col_type = schema_hints.get(k, {}).get('col_type') if col_type not in [None, COL_HINT.POSITIONAL]: continue v = flat.get(k) if v is not None: sort = schema_hints.get(k, {}).get('sort', False) if sort: type_ = flat_type_mapping[k] if type_ == 'array': item_types = flat_type_mapping.get(k + '.items') a = get_by_path(c_x, k.split('.')) if isinstance(item_types, list): try: a = sorted(a) except: print('# Warning: mix-type array with types: {}'.format(', '.join(item_types))) print('# Warning; no comparison operator provided. Try to assess the proper cast...') for t in type_priority: try: a = list(map(type_priority[t], a)) print('# Casting \'{}\' to {}'.format(k, t)) break except: log.error('Could not cast \'{}\' to {}'.format(k, t)) else: print('# Error: Could not find any way to sort {}'.format(k)) raise Exception('Could not find any way to sort {}'.format(k)) set_by_path(c_x, k.split('.'), sorted(a)) flat = flatdict.FlatterDict(c_x, delimiter=DELIMITER) flat_X.append(flat) return flat_X def hot_one_encoder_on_list(df, column): v = [x if isinstance(x, list) else [] for x in df[column].values] l = [len(x) for x in v] f, u = pd.factorize(np.concatenate(v)) n, m = len(v), u.size i = np.arange(n).repeat(l) dummies = pd.DataFrame( np.bincount(i * m + f, minlength=n * m).reshape(n, m), df.index, map(lambda x: str(column) + '=' + str(x), u) ) return df.drop(column, 1).join(dummies) def normalize(X, schema_hints=None): if schema_hints is None: schema_hints = {} def hot_one_encoder(df, columns): return pd.get_dummies(df, prefix_sep="=", columns=columns) schema = determine_schema(X) flat_schema = flatdict.FlatDict(schema.to_schema(), delimiter=DELIMITER) flat_type_mapping = get_flat_type_mapping(flat_schema) flat_domain_mapping = get_flat_domain_mapping(X, flat_type_mapping) flat_X = flatten_dataset(X, flat_type_mapping, schema_hints) columns_to_encode = [k for k, v in schema_hints.items() if v['col_type'] == COL_HINT.HOT_ONE] df = pd.DataFrame(flat_X) for c in df.columns: f = next((k for k in columns_to_encode if c.startswith(k)), None) if f: df = df.drop(c, 1) encoded = [] for c in columns_to_encode: type_ = flat_type_mapping[c] if type_ == 'array': if c == 'conclusion': articles = set() for x in X: for e in x[c]: if 'article' in e: articles.add(e['article']) articles = sorted(articles) df2 = [] for x in X: e = [] xart = {v['article']: v['type'] for v in x['conclusion'] if 'article' in v} for a in articles: v = 0 if a in xart: if xart[a] == 'violation': v = 1 else: v = -1 e.append(v) df2.append(e) df2 = pd.DataFrame(df2, columns=list(map(lambda x: 'ccl_article={}'.format(x), articles))) encoded.append(df2) else: df2 = pd.DataFrame(X)[[c]] e = hot_one_encoder_on_list(df2, c) encoded.append(e) else: df2 = pd.DataFrame(X)[c] e = hot_one_encoder(df2, [c]) encoded.append(e) df = pd.concat([df] + encoded, axis=1) return df, schema, flat_schema, flat_type_mapping, flat_domain_mapping def run(console, build, title, output_prefix='cases', force=False): __console = console global print print = __console.print print(Markdown("- **Step configuration**")) print(TAB + "> Prepare release folder structure") paths = ['unstructured', 'structured', 'raw'] for p in paths: make_build_folder(console, os.path.join(build, p), force, strict=False) print(Markdown("- **Normalize database**")) input_folder = os.path.join(build, 'raw', 'preprocessed_documents') cases_files = [os.path.join(input_folder, f) for f in listdir(input_folder) if isfile(os.path.join(input_folder, f)) and '.json' in f] print(TAB + "> Prepare unstructured cases [green][DONE]") # Unstructured with open(os.path.join(build, 'unstructured', 'cases.json'), 'w') as outfile: outfile.write('[\n') for i, f in enumerate(cases_files): with open(f) as json_file: data = json.load(json_file) json.dump(data, outfile, indent=4) if i != len(cases_files) - 1: outfile.write(',\n') outfile.write('\n]') # Structured print(TAB + "> Generate flat cases [green][DONE]") flat_cases , representatives, extractedapp, scl, decision_body = format_structured_json(cases_files) print(TAB + "> Flat cases size: {}MiB".format(sys.getsizeof(flat_cases) / 1000)) schema_hints = { 'article': { 'col_type': COL_HINT.HOT_ONE }, 'documentcollectionid': { 'col_type': COL_HINT.HOT_ONE }, 'applicability': { 'col_type': COL_HINT.HOT_ONE }, 'paragraphs': { 'col_type': COL_HINT.HOT_ONE }, 'conclusion': { 'col_type': COL_HINT.HOT_ONE, 'sub_element': 'flatten' } } output_path = os.path.join(build, 'structured') with open(os.path.join(output_path, 'flat_cases.json'), 'w') as outfile: json.dump(flat_cases, outfile, indent=4) with open(os.path.join(output_path, 'schema_hint.json'), 'w') as outfile: json.dump(schema_hints, outfile, indent=4) X = flat_cases df, schema, flat_schema, flat_type_mapping, flat_domain_mapping = normalize(X, schema_hints) df.to_json(os.path.join(output_path, '{}.json'.format(output_prefix)), orient='records') df.to_csv(os.path.join(output_path, '{}.csv'.format(output_prefix))) json_files = [ ('schema', schema.to_schema()), ('flat_schema', flat_schema.as_dict()), ('flat_type_mapping', flat_type_mapping), ('flat_domain_mapping', flat_domain_mapping) ] for f in json_files: with open(os.path.join(output_path, '{}_{}.json'.format(output_prefix, f[0])), 'w') as outfile: json.dump(f[1], outfile, indent=4) os.remove(os.path.join(output_path, 'flat_cases.json')) os.remove(os.path.join(output_path, 'cases_flat_schema.json')) os.remove(os.path.join(output_path, 'cases_flat_type_mapping.json')) print(TAB + '> Generate appnos matrice [green][DONE]') matrice_appnos = {} for k, v in extractedapp.items(): matrice_appnos[k] = {e:1 for e in v['appnos']} with open(os.path.join(output_path, 'matrice_appnos.json'), 'w') as outfile: json.dump(matrice_appnos, outfile, indent=4) print(TAB + '> Generate scl matrice [green][DONE]') matrice_scl = {} for k, v in scl.items(): matrice_scl[k] = {e: 1 for e in v['scl']} with open(os.path.join(output_path, 'matrice_scl.json'), 'w') as outfile: json.dump(matrice_scl, outfile, indent=4) print(TAB + '> Generate representatives matrice [green][DONE]') matrice_representedby = {} for k, v in representatives.items(): matrice_representedby[k] = {e: 1 for e in v['representedby']} with open(os.path.join(output_path, 'matrice_representatives.json'), 'w') as outfile: json.dump(matrice_representedby, outfile, indent=4) print(TAB + '> Generate decision body matrice [green][DONE]') matrice_decision_body = {} for k, v in decision_body.items(): matrice_decision_body[k] = {k:v for k,v in v['role'].items()} with open(os.path.join(output_path, 'matrice_decision_body.json'), 'w') as outfile: json.dump(matrice_decision_body, outfile, indent=4) print(TAB + '> Create archives [green][DONE]') # Raw shutil.make_archive(os.path.join(build, 'raw', 'judgments'), 'zip', os.path.join(build, 'raw', 'judgments')) # All from zipfile import ZipFile with ZipFile(os.path.join(build, 'all.zip'), 'w') as zipObj: # Iterate over all the files in directory folders = ['unstructured', 'raw', 'structured'] for f in folders: for folderName, _, filenames in os.walk(os.path.join(build, f)): for filename in filenames: if not filename.endswith('.zip'): filePath = os.path.join(folderName, filename) zipObj.write(filePath) def main(args): console = Console(record=True) run(console, build=args.build, title=args.title, force=args.f) def parse_args(parser): args = parser.parse_args() # Check path return args if __name__ == "__main__": parser = argparse.ArgumentParser(description='Normalize any databse of arbitrarily nested documents.') parser.add_argument('--build', type=str, default="./build/echr_database/") parser.add_argument('--title', type=str) parser.add_argument('--schema_hints', type=str) parser.add_argument('--output_prefix', type=str) parser.add_argument('-f', action='store_true') parser.add_argument('-u', action='store_true') args = parse_args(parser) main(args)

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vathos/trainer/tpu_trainer.py

satyajitghana/ProjektDepth

2

44250

from .base_trainer import BaseTrainer import torch import os class TPUTrainer(BaseTrainer): r"""TPUTrainer: Trains the vathos model on TPU """ def __init__(self, *args, **kwargs): super(TPUTrainer, self).__init__(*args, **kwargs) import torch_xla import torch_xla.core.xla_model as xm import torch_xla.distributed.parallel_loader as pl import torch_xla.distributed.xla_multiprocessing as xmp os.environ['XLA_USE_BF16'] = 1 @staticmethod def _xla_train(index): device = xm.xla_device() para_loader = pl.ParallelLoader(train_loader, [device]) model = MNIST().train().to(device) loss_fn = nn.NLLLoss() optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum) for data, target in para_loader.per_device_loader(device): optimizer.zero_grad() output = model(data) loss = loss_fn(output, target) loss.backward() xm.optimizer_step(optimizer) def train_epoch(self, epoch): return 0 def test_epoch(self, epoch): device = xm.xla_device() xm.mark_step() return 0

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examples/linear_wake/analysis.py

SaVicente/hipace

19

49538

<gh_stars>10-100 #! /usr/bin/env python3 # This Python analysis script is part of the code Hipace # # It compares the transverse field By with the theoretical value, plots both # the simulation result and the theory on the same plot, and asserts that the # difference is small. # # To use it, run the simulation and execute this script with # > ../../build/bin/hipace inputs_SI # > python analysis.py # Note: the simulation may take some time, as the box size must be high to have # decent agreement import matplotlib.pyplot as plt import scipy.constants as scc from scipy.stats import norm import matplotlib import sys import numpy as np import math import argparse from openpmd_viewer import OpenPMDTimeSeries parser = argparse.ArgumentParser(description='Script to analyze the correctness of the beam in vacuum') parser.add_argument('--normalized-units', dest='norm_units', action='store_true', default=False, help='Run the analysis in normalized units') parser.add_argument('--do-plot', dest='do_plot', action='store_true', default=False, help='Plot figures and save them to file') parser.add_argument('--gaussian-beam', dest='gaussian_beam', action='store_true', default=False, help='Run the analysis on the Gaussian beam') parser.add_argument('--output-dir', dest='output_dir', default='diags/hdf5', help='Path to the directory containing output files') args = parser.parse_args() ts = OpenPMDTimeSeries(args.output_dir) if args.norm_units: kp = 1. ne = 1. q_e = 1. else: kp = 1./10.e-6 ne = scc.c**2 / scc.e**2 * scc.m_e * scc.epsilon_0 * kp**2 q_e = scc.e rho_along_z, rho_meta = ts.get_field(field='rho', iteration=ts.iterations[-1], slice_across=['x','y'], slice_relative_position=[0,0]) zeta_array = rho_meta.z dzeta = rho_meta.dz nz = len(rho_meta.z) # generating the array with the beam density nb_array = np.zeros(nz) beam_starting_position = 1 / kp distance_to_start_pos = rho_meta.zmax - beam_starting_position index_beam_head = np.int(distance_to_start_pos / dzeta) beam_length = 2 / kp beam_length_i = np.int(beam_length / dzeta) if (args.gaussian_beam): sigma_z = 1.41 / kp peak_density = 0.01*ne nb_array = peak_density*np.sqrt(2*np.pi)*norm.pdf(np.linspace(-nz/2,nz/2,nz)*dzeta/sigma_z) else: nb_array[nz-index_beam_head-beam_length_i:nz-index_beam_head] = 0.01 * ne # calculating the second derivative of the beam density array nb_dzdz = np.zeros(nz) nb_dzdz[1:nz-1] = (nb_array[0:nz-2] - 2*nb_array[1:nz-1] + nb_array[2:nz])/dzeta**2 # calculating the theoretical plasma density (see Timon Mehrling's thesis page 41) n_th = np.zeros(nz) tmp = np.zeros([nz,nz],dtype=float) for i in np.arange(nz-1,-1,-1): for j in range(nz-i): tmp[i,j]= i-(nz-1-j) tmp = (dzeta/kp*np.sin(kp*dzeta*tmp) * np.full([nz,nz],1) * nb_dzdz[np.linspace(nz-1,0,nz,dtype=int)]) n_th = np.sum(tmp,axis = 1) + nb_array rho_th = n_th * q_e if args.do_plot: fig, ax = plt.subplots() ax.plot(zeta_array, rho_along_z) ax.plot(zeta_array, rho_th, linestyle='--') ax.set_xlabel('x') ax.set_ylabel('rho') plt.savefig('rho_z.png') # Assert that the simulation result is close enough to theory error_rho = np.sum((rho_along_z-rho_th)**2) / np.sum((rho_th)**2) print("total relative error rho: " + str(error_rho) + " (tolerance = 0.016)") assert(error_rho < .025)

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flatlib/chart.py

D-Vaillant/flatlib

196

72486

<reponame>D-Vaillant/flatlib """ This file is part of flatlib - (C) FlatAngle Author: <NAME> (<EMAIL>) This module implements a class to represent an astrology Chart. It provides methods to handle the chart, as well as three relevant properties: - objects: a list with the chart's objects - houses: a list with the chart's houses - angles: a list with the chart's angles Since houses 1 and 10 may not match the Asc and MC in some house systems, the Chart class includes the list of angles. The angles should be used when you want to deal with angle's longitudes. There are also methods to access fixed stars. """ from . import angle from . import const from . import utils from .ephem import ephem from .datetime import Datetime # ------------------ # # Chart Class # # ------------------ # class Chart: """ This class represents an astrology chart. """ def __init__(self, date, pos, **kwargs): """ Creates an astrology chart for a given date and location. Optional arguments are: - hsys: house system - IDs: list of objects to include """ # Handle optional arguments hsys = kwargs.get('hsys', const.HOUSES_DEFAULT) IDs = kwargs.get('IDs', const.LIST_OBJECTS_TRADITIONAL) self.date = date self.pos = pos self.hsys = hsys self.objects = ephem.getObjectList(IDs, date, pos) self.houses, self.angles = ephem.getHouses(date, pos, hsys) def copy(self): """ Returns a deep copy of this chart. """ chart = Chart.__new__(Chart) chart.date = self.date chart.pos = self.pos chart.hsys = self.hsys chart.objects = self.objects.copy() chart.houses = self.houses.copy() chart.angles = self.angles.copy() return chart # === Properties === # def getObject(self, ID): """ Returns an object from the chart. """ return self.objects.get(ID) def getHouse(self, ID): """ Returns an house from the chart. """ return self.houses.get(ID) def getAngle(self, ID): """ Returns an angle from the chart. """ return self.angles.get(ID) def get(self, ID): """ Returns an object, house or angle from the chart. """ if ID.startswith('House'): return self.getHouse(ID) elif ID in const.LIST_ANGLES: return self.getAngle(ID) else: return self.getObject(ID) # === Fixed stars === # # The computation of fixed stars is inefficient, # so the access must be made directly to the # ephemeris only when needed. def getFixedStar(self, ID): """ Returns a fixed star from the ephemeris. """ return ephem.getFixedStar(ID, self.date) def getFixedStars(self): """ Returns a list with all fixed stars. """ IDs = const.LIST_FIXED_STARS return ephem.getFixedStarList(IDs, self.date) # === Houses and angles === # def isHouse1Asc(self): """ Returns true if House1 is the same as the Asc. """ house1 = self.getHouse(const.HOUSE1) asc = self.getAngle(const.ASC) dist = angle.closestdistance(house1.lon, asc.lon) return abs(dist) < 0.0003 # 1 arc-second def isHouse10MC(self): """ Returns true if House10 is the same as the MC. """ house10 = self.getHouse(const.HOUSE10) mc = self.getAngle(const.MC) dist = angle.closestdistance(house10.lon, mc.lon) return abs(dist) < 0.0003 # 1 arc-second # === Other properties === # def isDiurnal(self): """ Returns true if this chart is diurnal. """ sun = self.getObject(const.SUN) mc = self.getAngle(const.MC) # Get ecliptical positions and check if the # sun is above the horizon. lat = self.pos.lat sunRA, sunDecl = utils.eqCoords(sun.lon, sun.lat) mcRA, mcDecl = utils.eqCoords(mc.lon, 0) return utils.isAboveHorizon(sunRA, sunDecl, mcRA, lat) def getMoonPhase(self): """ Returns the phase of the moon. """ sun = self.getObject(const.SUN) moon = self.getObject(const.MOON) dist = angle.distance(sun.lon, moon.lon) if dist < 90: return const.MOON_FIRST_QUARTER elif dist < 180: return const.MOON_SECOND_QUARTER elif dist < 270: return const.MOON_THIRD_QUARTER else: return const.MOON_LAST_QUARTER # === Solar returns === # def solarReturn(self, year): """ Returns this chart's solar return for a given year. """ sun = self.getObject(const.SUN) date = Datetime('{0}/01/01'.format(year), '00:00', self.date.utcoffset) srDate = ephem.nextSolarReturn(date, sun.lon) return Chart(srDate, self.pos, hsys=self.hsys)

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app/fedgraphnn/recsys_subgraph_link_pred/torch_subgraph_link_prediction.py

ray-ruisun/FedML

0

75942

<reponame>ray-ruisun/FedML import fedml from data.data_loader import * from model.gcn_link import GCNLinkPred from model.gat_link import GATLinkPred from model.sage_link import SAGELinkPred from trainer.fed_subgraph_lp_trainer import FedSubgraphLPTrainer from fedml.simulation import SimulatorMPI def load_data(args): if args.dataset not in ["ciao", "epinions"]: raise Exception("no such dataset!") args.pred_task = "link_prediction" args.metric = "MAE" if args.model == "gcn": args.normalize_features = True args.normalize_adjacency = True ( train_data_num, val_data_num, test_data_num, train_data_global, val_data_global, test_data_global, data_local_num_dict, train_data_local_dict, val_data_local_dict, test_data_local_dict, feature_dim, ) = load_partition_data(args, args.data_cache_dir, args.client_num_in_total) dataset = [ train_data_num, test_data_num, train_data_global, test_data_global, data_local_num_dict, train_data_local_dict, test_data_local_dict, feature_dim, ] return dataset def create_model(args, model_name, feature_dim): logging.info("create_model. model_name = %s" % (model_name)) if model_name == "gcn": model = GCNLinkPred(feature_dim, args.hidden_size, args.node_embedding_dim) elif model_name == "gat": model = GATLinkPred( feature_dim, args.hidden_size, args.node_embedding_dim, args.num_heads ) elif model_name == "sage": model = SAGELinkPred(feature_dim, args.hidden_size, args.node_embedding_dim) else: raise Exception("such model does not exist !") trainer = FedSubgraphLPTrainer(model) logging.info("Model and Trainer - done") return model, if __name__ == "__main__": # init FedML framework args = fedml.init() # init device device = fedml.device.get_device(args) # load data dataset, output_dim = load_data(args) # load model model, trainer = create_model(args, args.model_name) # start training simulator = SimulatorMPI(args, device, dataset, model, trainer) simulator.run()

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chapter_06/03_line_numbers.py

SergeHall/Tony-Gaddis-Python-4th

2

44752

<reponame>SergeHall/Tony-Gaddis-Python-4th # 3. Номера строк. Напишите программу, которая запрашивает у пользователя # имя файла. Программа должна вывести на экран содержимое файла, при этом # каждая строка должна предваряться ее номером и двоеточием. Нумерация строк # должна начинаться с 1. number_list.txt def main(): try: name_file = input('Name file + .file extension: ') open_file = open(str(name_file), 'r') count = 1 for content in open_file: content = content.rstrip('\n') print(count, ': ', content, sep='') count += 1 open_file.close() except ValueError: print('ValueError') except FileNotFoundError: print('FileNotFoundError') main()

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kde/cudakde.py

atfienberg/kde

0

161768

# pylint: disable=invalid-name from __future__ import absolute_import, division __license__ = """MIT License Copyright (c) 2014-2019 <NAME> and <NAME> 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 warnings import numpy as n from .classes import KDE class gaussian_kde(KDE): def __init__(self, data, weights=None, kde_values=None, use_cuda=True, adaptive=False, weight_adaptive_bw=False, alpha=0.3, bw_method='silverman'): if kde_values != None: raise NotImplementedError("`kde_values` is not supported for" " cudakde.") KDE.__init__(self, data, use_cuda, weights=weights, alpha=alpha, method=bw_method) self.weighted = False if weights is None or len(weights) == 0 else True if adaptive: if not self.weighted and weight_adaptive_bw: warnings.warn("Since `weights` aren't given" " `weight_adaptive_bw` will have no effect!") self.calcLambdas(weights=weight_adaptive_bw, weightedCov=weight_adaptive_bw) else: self.lambdas = n.ones(self.n) def __call__(self, points): points = n.atleast_2d(points) self.kde(points, weights=self.weighted, weightedCov=self.weighted) return n.array(self.values) class bootstrap_kde(object): def __init__(self, data, niter=10, weights=None, **kwargs): assert int(niter) == float(niter) niter = int(niter) self.kernels = [] self.bootstrap_indices = [] self.data = n.atleast_2d(data) self.d, self.n = self.data.shape self.weighted = False if weights is None or len(weights) == 0 else True for _ in xrange(niter): indices = n.array(self.get_bootstrap_indices()) self.bootstrap_indices.append(indices) if self.weighted: kernel = gaussian_kde(data[..., indices], weights=weights[indices], **kwargs) else: kernel = gaussian_kde(data[..., indices], **kwargs) self.kernels.append(kernel) def __call__(self, points): return self.evaluate(points) def evaluate(self, points): points = n.atleast_2d(points) _, m = points.shape means, sqmeans = n.zeros(m), n.zeros(m) for kernel in self.kernels: values = kernel(points) means += values sqmeans += values**2 means /= len(self.kernels) sqmeans /= len(self.kernels) errors = n.sqrt(sqmeans - means**2) return means, errors def get_bootstrap_indices(self): bootstrap_indices = n.random.choice(self.n, size=self.n, replace=True) return bootstrap_indices

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alipay/aop/api/domain/SsdataDataserviceRiskAudioVerifyModel.py

articuly/alipay-sdk-python-all

0

196028

<gh_stars>0 #!/usr/bin/env python # -*- coding: utf-8 -*- import simplejson as json from alipay.aop.api.constant.ParamConstants import * class SsdataDataserviceRiskAudioVerifyModel(object): def __init__(self): self._audio_content = None self._audio_type = None self._checker = None self._risk_type = None self._sample_rate = None @property def audio_content(self): return self._audio_content @audio_content.setter def audio_content(self, value): self._audio_content = value @property def audio_type(self): return self._audio_type @audio_type.setter def audio_type(self, value): self._audio_type = value @property def checker(self): return self._checker @checker.setter def checker(self, value): self._checker = value @property def risk_type(self): return self._risk_type @risk_type.setter def risk_type(self, value): if isinstance(value, list): self._risk_type = list() for i in value: self._risk_type.append(i) @property def sample_rate(self): return self._sample_rate @sample_rate.setter def sample_rate(self, value): self._sample_rate = value def to_alipay_dict(self): params = dict() if self.audio_content: if hasattr(self.audio_content, 'to_alipay_dict'): params['audio_content'] = self.audio_content.to_alipay_dict() else: params['audio_content'] = self.audio_content if self.audio_type: if hasattr(self.audio_type, 'to_alipay_dict'): params['audio_type'] = self.audio_type.to_alipay_dict() else: params['audio_type'] = self.audio_type if self.checker: if hasattr(self.checker, 'to_alipay_dict'): params['checker'] = self.checker.to_alipay_dict() else: params['checker'] = self.checker if self.risk_type: if isinstance(self.risk_type, list): for i in range(0, len(self.risk_type)): element = self.risk_type[i] if hasattr(element, 'to_alipay_dict'): self.risk_type[i] = element.to_alipay_dict() if hasattr(self.risk_type, 'to_alipay_dict'): params['risk_type'] = self.risk_type.to_alipay_dict() else: params['risk_type'] = self.risk_type if self.sample_rate: if hasattr(self.sample_rate, 'to_alipay_dict'): params['sample_rate'] = self.sample_rate.to_alipay_dict() else: params['sample_rate'] = self.sample_rate return params @staticmethod def from_alipay_dict(d): if not d: return None o = SsdataDataserviceRiskAudioVerifyModel() if 'audio_content' in d: o.audio_content = d['audio_content'] if 'audio_type' in d: o.audio_type = d['audio_type'] if 'checker' in d: o.checker = d['checker'] if 'risk_type' in d: o.risk_type = d['risk_type'] if 'sample_rate' in d: o.sample_rate = d['sample_rate'] return o

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bp_data.py

basf/bscience-backend

1

192186

#from pprint import pprint import json from unidecode import unidecode from flask import Blueprint, current_app, request, Response from i_structures.struct_utils import detect_format, poscar_to_ase, optimade_to_ase, refine, get_formula from i_structures.cif_utils import cif_to_ase from i_data import Data_type from utils import get_data_storage, fmt_msg, key_auth, is_plain_text, html_formula, is_valid_uuid, ase_serialize bp_data = Blueprint('data', __name__, url_prefix='/data') @bp_data.route("/create", methods=['POST']) @key_auth def create(): """ Data item recognition and saving logics Expects content: string Returns JSON->error: string or confirmation object {object->uuid, object->type, object->name} """ content = request.values.get('content') if not content: return fmt_msg('Empty request') if not 0 < len(content) < 200000: return fmt_msg('Request size is invalid') if not is_plain_text(content): #return fmt_msg('Request contains unsupported (non-latin) characters') content = unidecode(content) fmt = detect_format(content) if fmt == 'cif': ase_obj, error = cif_to_ase(content) elif fmt == 'poscar': ase_obj, error = poscar_to_ase(content) elif fmt == 'optimade': ase_obj, error = optimade_to_ase(content) else: return fmt_msg('Provided data format unsuitable or not recognized') if error: return fmt_msg(error) if 'disordered' in ase_obj.info: return fmt_msg('Structural disorder is currently not supported') ase_obj, error = refine(ase_obj, conventional_cell=True) if error: return fmt_msg(error) formula = get_formula(ase_obj) content = ase_serialize(ase_obj) db = get_data_storage() uuid = db.put_item(formula, content, Data_type.structure) db.close() return Response(json.dumps(dict( uuid=uuid, type=Data_type.structure, name=html_formula(formula), ), indent=4), content_type='application/json', status=200) @bp_data.route("/listing", methods=['POST']) @key_auth def listing(): """ Expects uuid: uuid or uuid[] Returns JSON->error: string or listing [ {object->uuid, object->type, object->name}, ... ] """ uuid = request.values.get('uuid') #current_app.logger.warning(uuid) if not uuid: return fmt_msg('Empty request') db = get_data_storage() if ':' in uuid: uuids = set( uuid.split(':') ) for uuid in uuids: if not is_valid_uuid(uuid): return fmt_msg('Invalid request') items = db.get_items(list(uuids)) #found_uuids = set( [item['uuid'] for item in items] ) #if found_uuids != uuids: # return fmt_msg('No such content', 204) else: if not is_valid_uuid(uuid): return fmt_msg('Invalid request') item = db.get_item(uuid) items = [item] if item else [] if not items: return fmt_msg('No such content', 204) db.close() items = [ dict( uuid=item['uuid'], name=html_formula(item['name']), type=item['type']) for item in items ] return Response(json.dumps(items, indent=4), content_type='application/json', status=200) @bp_data.route("/delete", methods=['POST']) @key_auth def delete(): """ Expects uuid: uuid Returns JSON->error: string or JSON empty dict """ uuid = request.values.get('uuid') if not uuid or not is_valid_uuid(uuid): return fmt_msg('Empty or invalid request') db = get_data_storage() result = db.drop_item(uuid) db.close() if result: return Response('{}', content_type='application/json', status=200) return fmt_msg('No such content', 204)

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736, 19200, 29918, 7645, 877, 8915, 470, 8340, 2009, 1495, 13, 13, 1678, 4833, 353, 679, 29918, 1272, 29918, 12925, 580, 13, 1678, 1121, 353, 4833, 29889, 8865, 29918, 667, 29898, 25118, 29897, 13, 1678, 4833, 29889, 5358, 580, 13, 13, 1678, 565, 1121, 29901, 13, 4706, 736, 13291, 877, 8875, 742, 2793, 29918, 1853, 2433, 6214, 29914, 3126, 742, 4660, 29922, 29906, 29900, 29900, 29897, 13, 1678, 736, 19200, 29918, 7645, 877, 3782, 1316, 2793, 742, 29871, 29906, 29900, 29946, 29897, 13, 2 ]

telluride_decoding/utils.py

RULCSoft/telluride_decoding

8

1610821

# Copyright 2020 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Utility routines when decoding brain signals. More to come.. the CCA functions need to get the Pearson correlation too. """ import tensorflow.compat.v2 as tf # TODO Check to see if we can use (might need to get into core) # contrib/streaming_pearson_correlation def pearson_correlation_graph(x, y): """Use TF to compute the Pearson correlation between two sets of data. This works between two (multi-dimensional) feature arrays. Like np.corrcoef, x and y are 1-D or 2-D arrays containing multiple variables and observations. Each column of x represents a variable, and each row a single observation of all those variables. From: https://stackoverflow.com/questions/43834830/tensorflow-equivalent-of-np-corrcoef-on-a-specific-axis Args: x: First multidimensional array, n_observations x n_vars y: Second multidimensional array, n_observations x n_vars Returns: n_vars dimensional vector with the correlation of each pair of columns. """ def t(x): return tf.transpose(x) with tf.compat.v1.variable_scope('pearson'): if len(x.shape) == 1: x = tf.reshape(x, (-1, 1), name='pearson_x_reshape') if len(y.shape) == 1: y = tf.reshape(y, (-1, 1), name='pearson_y_reshape') xy_t = tf.concat([tf.cast(x, tf.float32), tf.cast(y, tf.float32)], axis=1) mean_t = tf.reduce_mean(xy_t, axis=0, keepdims=True) dsize = tf.cast(tf.shape(x)[0], tf.float32) cov_t = tf.matmul(t(xy_t - mean_t), (xy_t - mean_t) / (dsize - 1)) cov2_t = tf.linalg.tensor_diag(1/tf.sqrt(tf.linalg.tensor_diag_part(cov_t))) cor = tf.matmul(cov2_t, tf.matmul(cov_t, cov2_t)) return cor

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SectorGen.py

ShawnDriscoll/Traveller-RPG-SectorGen

5

142849

<reponame>ShawnDriscoll/Traveller-RPG-SectorGen # # Python 3.9.11 Sector Generator # ######################################################## """ SectorGen 0.4.0 Beta ----------------------------------------------------------------------- This program generates sectors using rules from Mongoose Traveller 2nd Edition, and a smidgen from Traveller 5.10 """ from colorama import init from colorama import Fore, Back, Style init() # initialize colorama #import win32com.client as win32 import sys #sys.path.append('C:\Users\Shonner\Documents\My Files\Python Programs\Mongoose Traveller') from random import randint from PyQt5.QtCore import * from PyQt5.QtGui import * from PyQt5.QtWidgets import * #import PyQt5.QtMultimedia as MM import time from mainwindow import Ui_MainWindow from aboutdialog import Ui_aboutDialog from completeddialog import Ui_completedDialog from rpg_tools.PyDiceroll import roll import os import logging import csv import json import datetime __author__ = '<NAME> <<EMAIL>>\n<EMAIL>' __app__ = 'SectorGen 0.4.0 (Beta)' __version__ = '0.4.0b' class aboutDialog(QDialog, Ui_aboutDialog): def __init__(self): super().__init__() log.info('PyQt5 aboutDialog initializing...') self.setWindowFlags(Qt.Drawer | Qt.WindowStaysOnTopHint) self.setupUi(self) self.aboutOKButton.clicked.connect(self.acceptOKButtonClicked) log.info('PyQt5 aboutDialog initialized.') def acceptOKButtonClicked(self): log.info('PyQt5 aboutDialog closing...') self.close() class completedDialog(QDialog, Ui_completedDialog): def __init__(self): ''' Open the Completed dialog window ''' super().__init__() log.info('PyQt5 completedDialog initializing...') self.setWindowFlags(Qt.Drawer | Qt.WindowStaysOnTopHint) self.setupUi(self) self.completedOKButton.clicked.connect(self.acceptOKButtonClicked) self.numworldsDisplay.setText('') log.info('PyQt5 completedDialog initialized.') def acceptOKButtonClicked(self): ''' Close the PyQt5 Completed dialog window ''' log.info('PyQt5 completedDialog closing...') self.close() def showNumworlds(self): ''' Show the number of worlds generated in the PyQt5 Completed dialog window ''' self.show() class MainWindow(QMainWindow, Ui_MainWindow): def __init__(self): super().__init__() log.info('PyQt5 MainWindow initializing...') self.setupUi(self) self.genButton.clicked.connect(self.generateSector) self.actionGen.triggered.connect(self.generateSector) self.actionAbout_SectorGen.triggered.connect(self.actionAbout_triggered) self.actionQuitProg.triggered.connect(self.actionQuitProg_triggered) # Options self.hydro_calc_method = 'Based on Atmosphere' self.hydroBox.addItem('Based on Atmosphere') self.hydroBox.addItem('Based on Size') self.hydroBox.setCurrentIndex(0) self.hydroBox.currentIndexChanged.connect(self.hydroBox_changed) self.stellar_densityBox.addItem('Extra Galactic: 1%') self.stellar_densityBox.addItem('Rift: 3%') self.stellar_densityBox.addItem('Sparse: 17%') self.stellar_densityBox.addItem('Scattered: 33%') self.stellar_densityBox.addItem('Standard: 50%') self.stellar_densityBox.addItem('Dense: 66%') self.stellar_densityBox.addItem('Cluster: 83%') self.stellar_densityBox.addItem('Core: 91%') self.stellar_densityBox.setCurrentIndex(4) self.stellar_densityBox.currentIndexChanged.connect(self.stellar_densityBox_changed) self.stellar_density = 50 log.info('Default Stellar Density is Standard ' + str(self.stellar_density) + '%.') self.allegianceBox.addItem('As: Aslan Hierate') self.allegianceBox.addItem('Cs: Client State') self.allegianceBox.addItem('Im: Third Imperium') self.allegianceBox.addItem('Na: Non-Aligned, Human-dominated') self.allegianceBox.addItem('So: Solomani Confederation') self.allegianceBox.addItem('Va: Non-Aligned, Vargr-dominated') self.allegianceBox.addItem('Zh: Zhodani Consulate') self.allegianceBox.addItem('Random') self.allegianceBox.setCurrentIndex(2) self.allegiance = 'Im' log.info('Default Allegiance is Im: Third Imperium.') self.random_allegiance = False self.allegianceBox.currentIndexChanged.connect(self.allegianceBox_changed) self.super_earth_chance = False self.super_earth_checkBox.toggled.connect(self.super_earth_checkBox_changed) self.super_earth_checkBox.setChecked(self.super_earth_chance) self.popAboutDialog = aboutDialog() self.popCompletedDialog = completedDialog() # UPP Code Table self.hex_code = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'] self.core_code = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P'] # World Data Tables self.world_temperature_dm = [0, 0, -2, -2, -1, -1, 0, 0, 1, 1, 2, 6, 6, 2, -1, 2] self.size_value = [ 2, 2, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] self.atmosphere_value = [ 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1] self.hydro_value = [ 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0] self.population_value = [ 0, 1, 1, 1, 1, 1, 0, 0, 1, 2, 4, 0, 0, 0, 0, 0] self.government_value = [ 1, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, -2, -2, 0] self.minimum_TL = [ 8, 8, 5, 5, 3, -1, -1, 3, -1, 3, 8, 9, 10, 5, 5, 8] self.world_starport_class = ['X', 'X', 'X', 'E', 'E', 'D', 'D', 'C', 'C', 'B', 'B', 'A', 'A', 'A', 'A'] self.starport_value = [ -4, -4, -4, 0, 0, 0, 0, 2, 2, 4, 4, 6, 6, 6, 6] self.gas_giant_quantity = [' ', ' ', '1', '1', '2', '2', '3', '3', '4', '4', '4', '5', '5'] self.planetoid_belt_quantity = [' ', ' ', '1', '1', '1', '1', '1', '1', '2', '2', '2', '2', '3'] self.sn = [0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3] self.primary_star_type = [' ', ' ', 'A', 'M', 'M', 'M', 'M', 'M', 'K', 'G', 'F', 'F', 'F'] self.companion_star_type = [' ', ' ', 'A', 'F', 'F', 'G', 'G', 'K', 'K', 'M', 'M', 'M', 'M'] self.star_size = [' ', ' ', 'II ', 'III', 'IV ', 'V ', 'V ', 'V ', 'V ', 'V ', 'V ', 'VI ', 'D '] # Sound Tables self.V = 1 self.CV = 2 self.VC = 3 self.CVC = 4 self.CC = 5 self.ic_sound = ['b','br','c','ch','d','g', 'h','j','k','l','m','p', 'r','s','st','sh', 't','v','w','z'] self.ic_freq = [28,12,20,16,27,9,20,20,13, 28,24,27,24,30,13,25, 20,6,16,4] self.v_sound = ['a','e','i','o','u'] self.v_freq = [16,20,10,7,3] self.mc_sound = ['g','lt','ns','nst','ls','ll','nn'] self.mc_freq = [20,3,18,16,18,4,3] self.fc_sound = ['ch','ck','d','dy','dyne', 'hl','li','la','le','ler', 'nn','m','man','ma','mer','ny', 'me','n','nas','ne','ng', 'ner','nor','nie', 'rie','rlie','rly','rie','rt', 'ry','sa','sha','nshi','nski','son', 'nson','th','ta','ti','t','v', 'za','ue','than', 'lam','lis','lus','ton','tis','tus', 'love','se','nter','ll'] self.fc_freq = [6,13,22,12,3,3,3,10,6,10,7, 25,10,4,13,12,5,27,11,4,14,13,17,7,6,5,5,6,3, 21,10,3,8,3,20,9,14,10,16,11,8,6,8,10,7,6,5,7,7,4, 4,12,5,4] for i in range(len(self.ic_sound)): log.debug(self.ic_sound[i] + ' ' + str(self.ic_freq[i])) for i in range(len(self.v_sound)): log.debug(self.v_sound[i] + ' ' + str(self.v_freq[i])) for i in range(len(self.mc_sound)): log.debug(self.mc_sound[i] + ' ' + str(self.mc_freq[i])) for i in range(len(self.fc_sound)): log.debug(self.fc_sound[i] + ' ' + str(self.fc_freq[i])) self.syllable_type = [self.V,self.V,self.V,self.V,self.V,self.V,self.V,self.V, self.VC,self.VC,self.VC,self.VC, self.CV,self.CV,self.CV,self.CV, self.CVC,self.CVC,self.CVC,self.CVC,self.CVC,self.CVC, self.CC,self.CC] #print self.syllable_type self.ic_sounds = [] for i in range(len(self.ic_freq)): for j in range(self.ic_freq[i]): self.ic_sounds.append(self.ic_sound[i]) self.v_sounds = [] for i in range(len(self.v_freq)): for j in range(self.v_freq[i]): self.v_sounds.append(self.v_sound[i]) self.mc_sounds = [] for i in range(len(self.mc_freq)): for j in range(self.mc_freq[i]): self.mc_sounds.append(self.mc_sound[i]) self.fc_sounds = [] for i in range(len(self.fc_freq)): for j in range(self.fc_freq[i]): self.fc_sounds.append(self.fc_sound[i]) log.info('PyQt5 MainWindow initialized.') def pick_sound(self): if self.s_type == self.V: self.sound = self.v_sounds[randint(1, len(self.v_sounds)) - 1] if self.s_type == self.CV: self.sound = self.ic_sounds[randint(1, len(self.ic_sounds)) - 1] + self.v_sounds[randint(1, len(self.v_sounds)) - 1] if self.s_type == self.VC: self.sound = self.v_sounds[randint(1, len(self.v_sounds)) - 1] + self.fc_sounds[randint(1, len(self.fc_sounds)) - 1] if self.s_type == self.CVC: self.sound = self.ic_sounds[randint(1, len(self.ic_sounds)) - 1] + self.v_sounds[randint(1, len(self.v_sounds)) - 1] \ + self.fc_sounds[randint(1, len(self.fc_sounds)) - 1] if self.s_type == self.CC: self.sound = self.mc_sounds[randint(1, len(self.mc_sounds)) - 1] def generateSector(self): if not os.path.exists('data'): os.mkdir('data') csv_file_out = open('data/raw_sector_data.csv', 'w', newline='') csv_writer = csv.writer(csv_file_out) csv_writer.writerow(['Location', 'World_Name', 'Starport', 'Size', 'Atmosphere', 'Hydrographics', 'Population', 'Government', 'Law_Level', 'Tech_Level', 'Trade_Codes', 'Travel_Code', 'Base', 'Pop_M', 'Belts', 'Gas_giants', 'Worlds', 'Allegiance', 'Stellar_Data', 'Temperature']) json_file_out = open('data/raw_sector_data.json', 'w') mapper_file_out = open('data/sec_p00_p00.dat', 'w', newline='') mapper_file_out.write('# Generated by https://github.com/ShawnDriscoll/Traveller-RPG-SectorGen') mapper_file_out.write('\n# ' + str(datetime.datetime.now())) world_list = [] self.main_world_tech_level = -1 self.main_world_travel_code = '' # Name the sector proper = False while not(proper): temp = self.CC while temp == self.CC: temp = self.syllable_type[randint(1, len(self.syllable_type)) - 1] self.s_type = temp self.pick_sound() self.word = self.sound building = True while building: syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.V and (syllable == self.V or syllable == self.VC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CV and (syllable == self.V or syllable == self.VC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.VC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CVC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] if temp == self.VC or temp == self.CVC: building = False else: self.s_type = syllable self.pick_sound() self.word += self.sound temp = syllable if len(self.word) > 3 and len(self.word) < 14: proper = True self.sector_name = chr(ord(self.word[0]) - 32) + self.word[1:len(self.word)] proper = False while not(proper): temp = self.CC while temp == self.CC: temp = self.syllable_type[randint(1, len(self.syllable_type)) - 1] self.s_type = temp self.pick_sound() self.word = self.sound building = True while building: syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.V and (syllable == self.V or syllable == self.VC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CV and (syllable == self.V or syllable == self.VC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.VC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CVC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] if temp == self.VC or temp == self.CVC: building = False else: self.s_type = syllable self.pick_sound() self.word += self.sound temp = syllable if len(self.word) > 3 and len(self.word) < 14: proper = True name_variance = roll('d100') #print(name_variance) if name_variance <= 60: if name_variance >= 45: self.sector_name += ' ' + chr(ord(self.word[0]) - 32) + self.word[1:len(self.word)] elif name_variance >= 35: self.sector_name += ' ' + 'Alpha' elif name_variance >= 25: self.sector_name += ' ' + 'Beta' elif name_variance >= 15: self.sector_name += ' ' + 'Gamma' elif name_variance >= 10: self.sector_name += ' ' + 'Reaches' elif name_variance >= 7: self.sector_name += ' ' + 'Sector' elif name_variance >= 6: self.sector_name += ' ' + 'Frontier' elif name_variance >= 4: self.sector_name += ' ' + 'UNEXPLORED' elif name_variance >= 2: self.sector_name += ' ' + 'UNKNOWN' #print(self.sector_name) mapper_file_out.write('\n\n# ' + self.sector_name) mapper_file_out.write('\n# 0,0') mapper_file_out.write('\n\n# Name: ' + self.sector_name) mapper_file_out.write('\n\n# Milieu: M1105') mapper_file_out.write('\n\n# Credits: ' + self.sector_name + ' sector was randomly generated by ' + __app__) mapper_file_out.write('\n\n# Author: <NAME> https://www.youtube.com/user/ShawnDriscollCG') mapper_file_out.write('\n# Source: Rules from Mongoose Traveller 2nd Edition, and a smidgen from Traveller 5.10\n') # Name the subsectors #self.subsect_name = [] for subs in range(16): proper = False while not(proper): temp = self.CC while temp == self.CC: temp = self.syllable_type[randint(1, len(self.syllable_type)) - 1] self.s_type = temp self.pick_sound() self.word = self.sound building = True while building: syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.V and (syllable == self.V or syllable == self.VC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CV and (syllable == self.V or syllable == self.VC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.VC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CVC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] if temp == self.VC or temp == self.CVC: building = False else: self.s_type = syllable self.pick_sound() self.word += self.sound temp = syllable if len(self.word) > 3 and len(self.word) < 14: proper = True mapper_file_out.write('\n# Subsector ' + self.core_code[subs] + ': ' + chr(ord(self.word[0]) - 32) + self.word[1:len(self.word)]) #self.subsect_name.append(chr(ord(self.word[0]) - 32) + self.word[1:len(self.word)]) #print(self.subsect_name) mapper_file_out.write('\n\n# Alleg: As: "<NAME>"') mapper_file_out.write('\n# Alleg: Cs: "Client State"') mapper_file_out.write('\n# Alleg: Im: "Third Imperium"') mapper_file_out.write('\n# Alleg: Na: "Non-Aligned, Human-dominated"') mapper_file_out.write('\n# Alleg: So: "Solomani Confederation"') mapper_file_out.write('\n# Alleg: Va: "Non-Aligned, Vargr-dominated"') mapper_file_out.write('\n# Alleg: Zh: "Zhodani Consulate"') mapper_file_out.write('\n\nHex Name UWP Remarks {Ix} (Ex) [Cx] N B Z PBG W A Stellar') mapper_file_out.write( '\n---- -------------------- --------- -------------------- ------ ------- ------ - -- - --- -- ---- --------------') self.worlds_rolled = 0 # Make the worlds for hex_grid_col in range(32): for hex_grid_row in range(40): if roll('d100') <= self.stellar_density: # Get World Name proper = False while not(proper): temp = self.CC while temp == self.CC: temp = self.syllable_type[randint(1, len(self.syllable_type)) - 1] self.s_type = temp self.pick_sound() self.word = self.sound building = True while building: syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.V and (syllable == self.V or syllable == self.VC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CV and (syllable == self.V or syllable == self.VC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.VC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] while temp == self.CVC and (syllable == self.CV or syllable == self.CVC or syllable == self.CC): syllable = self.syllable_type[randint(1, len(self.syllable_type)) - 1] if temp == self.VC or temp == self.CVC: building = False else: self.s_type = syllable self.pick_sound() self.word += self.sound temp = syllable if len(self.word) > 3 and len(self.word) < 14: proper = True self.main_world_name = chr(ord(self.word[0]) - 32) + self.word[1:len(self.word)] # Find Location and Subsector for Main World self.main_world_location = self.core_code[(hex_grid_col) // 8 + ((hex_grid_row) // 10) * 4] #print(self.main_world_location) self.main_world_location = '' if hex_grid_col + 1 < 10: self.main_world_location += '0' + self.hex_code[hex_grid_col + 1] else: self.main_world_location += str(hex_grid_col + 1) if hex_grid_row + 1 < 10: self.main_world_location += '0' + self.hex_code[hex_grid_row + 1] else: self.main_world_location += str(hex_grid_row + 1) #print(self.main_world_location) # Roll for Main World Size self.main_world_size = roll('2D-2') # from Traveller5 if self.main_world_size == 10: if self.super_earth_chance: self.main_world_size = roll('1D+9') #print(self.main_world_size) # Roll for Main World Atmosphere self.main_world_atmosphere = roll('FLUX') + self.main_world_size # from Traveller5 if self.main_world_atmosphere < 0 or self.main_world_size == 0: self.main_world_atmosphere = 0 if self.main_world_atmosphere > 15: self.main_world_atmosphere = 15 # Roll for Main World Hydrographics if self.hydro_calc_method == 'Based on Atmosphere': # from Mongoose Traveller 2nd Edition and Traveller5 self.main_world_hydrographics = roll('FLUX') + self.main_world_atmosphere else: # from something else self.main_world_hydrographics = roll('FLUX') + self.main_world_size if self.main_world_hydrographics < 0: self.main_world_hydrographics = 0 if self.main_world_hydrographics > 10: self.main_world_hydrographics = 10 # Size and Atmosphere DMs added to Hydrographics if self.main_world_size < 2: self.main_world_hydrographics = 0 if self.main_world_atmosphere < 2 or \ self.main_world_atmosphere == 10 or \ self.main_world_atmosphere == 11 or \ self.main_world_atmosphere == 12: self.main_world_hydrographics += -4 # Main World Temperature? self.main_world_temperature = roll('2d6') + self.world_temperature_dm[self.main_world_atmosphere] # Temperature DMs added to Hydrographics if self.main_world_atmosphere != 13 and self.main_world_atmosphere != 15: if self.main_world_temperature >= 10 and self.main_world_temperature <= 11: self.main_world_hydrographics += -2 if self.main_world_temperature >= 12: self.main_world_hydrographics += -6 if self.main_world_hydrographics < 0: self.main_world_hydrographics = 0 # Roll for Main World Population #self.main_world_population = roll('2D6-2') #self.main_world_population = roll('4D4-4') self.main_world_population = roll('2d6-2') # Roll for Main World Government if self.main_world_population > 0: self.main_world_government = roll('FLUX') + self.main_world_population if self.main_world_government < 0: self.main_world_government = 0 if self.main_world_government > 15: self.main_world_government = 15 else: self.main_world_government = 0 # Roll for Law Level if self.main_world_population > 0: self.main_world_law_level = roll('FLUX') + self.main_world_government if self.main_world_law_level < 0: self.main_world_law_level = 0 if self.main_world_law_level > 15: self.main_world_law_level = 15 else: self.main_world_law_level = 0 # Roll for Starport self.main_world_starport = roll('2D6') if self.main_world_population >= 10: self.main_world_starport += 2 elif self.main_world_population >= 8: self.main_world_starport += 1 elif self.main_world_population <= 2: self.main_world_starport += -2 elif self.main_world_population <= 4: self.main_world_starport += -1 # Roll for Technology Level if self.main_world_population > 0: self.main_world_tech_level = roll('1D6') # Add DMs self.main_world_tech_level += self.starport_value[self.main_world_starport] self.main_world_tech_level += self.size_value[self.main_world_size] self.main_world_tech_level += self.atmosphere_value[self.main_world_atmosphere] self.main_world_tech_level += self.hydro_value[self.main_world_hydrographics] self.main_world_tech_level += self.population_value[self.main_world_population] self.main_world_tech_level += self.government_value[self.main_world_government] # Adjust Minimum TL for Environmental Limits if self.minimum_TL[self.main_world_atmosphere] != -1: if self.main_world_tech_level < self.minimum_TL[self.main_world_atmosphere]: self.main_world_tech_level = self.minimum_TL[self.main_world_atmosphere] if self.main_world_tech_level < 0: self.main_world_tech_level = 0 if self.main_world_tech_level > 15: self.main_world_tech_level = 15 else: self.main_world_tech_level = 0 # Calculate Travel Code self.main_world_travel_code = '' if (self.main_world_atmosphere >= 10 or (self.main_world_government == 0 or self.main_world_government == 7 \ or self.main_world_government == 10) or (self.main_world_law_level == 0 or self.main_world_law_level >= 9)) and roll('d6') == 6: self.main_world_travel_code = ' A' # Lookup Trade Code(s) self.trade_index = 0 self.main_world_trade_code = [' ',' ',' ',' ',' ',' ', ' ', ' ', ' ', ' '] self.main_world_trade_class = [' ',' ',' ',' ',' ',' ', ' ', ' ', ' ', ' '] if self.main_world_atmosphere >= 4 \ and self.main_world_atmosphere <= 9 \ and self.main_world_hydrographics >= 4 \ and self.main_world_hydrographics <= 8 \ and self.main_world_population >= 5 \ and self.main_world_population <= 7: self.main_world_trade_code[self.trade_index] = 'Ag' self.main_world_trade_class[self.trade_index] = 'Agricultural' self.trade_index += 1 if (self.main_world_atmosphere == 2 \ or self.main_world_atmosphere == 3 \ or self.main_world_atmosphere == 10 \ or self.main_world_atmosphere == 11) \ and self.main_world_hydrographics >= 1 \ and self.main_world_hydrographics <= 5 \ and self.main_world_population >= 3 \ and self.main_world_population <= 6 \ and self.main_world_law_level >= 6 \ and self.main_world_law_level <= 9: self.main_world_trade_code[self.trade_index] = 'Px' self.main_world_trade_class[self.trade_index] = 'Prison' self.main_world_travel_code = ' A' self.trade_index += 1 if self.main_world_size == 0 \ and self.main_world_atmosphere == 0 \ and self.main_world_hydrographics == 0: self.main_world_trade_code[self.trade_index] = 'As' self.main_world_trade_class[self.trade_index] = 'Asteroid' self.trade_index += 1 if self.main_world_population == 0 \ and self.main_world_government == 0 \ and self.main_world_law_level == 0: if self.world_starport_class[self.main_world_starport] == 'E' \ or self.world_starport_class[self.main_world_starport] == 'X': if roll('1D3') > 1: self.main_world_tech_level = 0 self.main_world_trade_code[self.trade_index] = 'Ba' self.main_world_trade_class[self.trade_index] = 'Barren' else: self.main_world_tech_level = roll('3D-3') self.main_world_trade_code[self.trade_index] = 'Di' self.main_world_trade_class[self.trade_index] = 'Dieback' self.main_world_travel_code = ' R' self.trade_index += 1 if self.world_starport_class[self.main_world_starport] == 'A': if roll('1D3') == 1: self.main_world_trade_code[self.trade_index] = 'Cp' self.main_world_trade_class[self.trade_index] = 'Subsector Capital' self.trade_index += 1 if self.main_world_population >=5 \ and self.main_world_population <= 10 \ and self.main_world_government == 6 \ and self.main_world_law_level >= 0 \ and self.main_world_law_level <= 3: self.main_world_trade_code[self.trade_index] = 'Cy' self.main_world_trade_class[self.trade_index] = 'Colony' self.trade_index += 1 if self.main_world_atmosphere >= 2 \ and self.main_world_hydrographics == 0: self.main_world_trade_code[self.trade_index] = 'De' self.main_world_trade_class[self.trade_index] = 'Desert' self.trade_index += 1 if self.main_world_atmosphere >= 10 \ and self.main_world_hydrographics >= 1: self.main_world_trade_code[self.trade_index] = 'Fl' self.main_world_trade_class[self.trade_index] = 'Fluid Oceans' self.trade_index += 1 if self.main_world_size >= 6 \ and self.main_world_size <= 8 \ and (self.main_world_atmosphere == 5 \ or self.main_world_atmosphere == 6 \ or self.main_world_atmosphere == 8) \ and self.main_world_hydrographics >= 5 \ and self.main_world_hydrographics <= 7: self.main_world_trade_code[self.trade_index] = 'Ga' self.main_world_trade_class[self.trade_index] = 'Garden' self.trade_index += 1 if self.main_world_population >= 9: self.main_world_trade_code[self.trade_index] = 'Hi' self.main_world_trade_class[self.trade_index] = 'High Population' self.trade_index += 1 if self.main_world_tech_level >= 12: self.main_world_trade_code[self.trade_index] = 'Ht' self.main_world_trade_class[self.trade_index] = 'High Tech' self.trade_index += 1 if self.main_world_atmosphere >= 0 \ and self.main_world_atmosphere <= 1 \ and self.main_world_hydrographics >= 1: self.main_world_trade_code[self.trade_index] = 'Ic' self.main_world_trade_class[self.trade_index] = 'Ice-Capped' self.trade_index += 1 if (self.main_world_atmosphere >= 0 \ and self.main_world_atmosphere <= 2 \ or self.main_world_atmosphere == 4 \ or self.main_world_atmosphere == 7 \ or self.main_world_atmosphere == 9) \ and self.main_world_population >= 9: self.main_world_trade_code[self.trade_index] = 'In' self.main_world_trade_class[self.trade_index] = 'Industrial' self.trade_index += 1 if self.main_world_population >= 1 \ and self.main_world_population <= 3: self.main_world_trade_code[self.trade_index] = 'Lo' self.main_world_trade_class[self.trade_index] = 'Low Population' self.trade_index += 1 if self.main_world_tech_level <= 5: self.main_world_trade_code[self.trade_index] = 'Lt' self.main_world_trade_class[self.trade_index] = 'Low Tech' self.trade_index += 1 if self.main_world_atmosphere >= 0 \ and self.main_world_atmosphere <= 3 \ and self.main_world_hydrographics >= 0 \ and self.main_world_hydrographics <= 3 \ and self.main_world_population >= 6: self.main_world_trade_code[self.trade_index] = 'Na' self.main_world_trade_class[self.trade_index] = 'Non-Agricultural' self.trade_index += 1 if self.main_world_population <= 6: self.main_world_trade_code[self.trade_index] = 'Ni' self.main_world_trade_class[self.trade_index] = 'Non-Industrial' self.trade_index += 1 if self.main_world_atmosphere >= 2 \ and self.main_world_atmosphere <= 5 \ and self.main_world_hydrographics >= 0 \ and self.main_world_hydrographics <= 3: self.main_world_trade_code[self.trade_index] = 'Po' self.main_world_trade_class[self.trade_index] = 'Poor' self.trade_index += 1 if (self.main_world_atmosphere == 6 \ or self.main_world_atmosphere == 8) \ and self.main_world_population >= 6 \ and self.main_world_population <= 8 \ and self.main_world_government >= 4 \ and self.main_world_government <= 9: self.main_world_trade_code[self.trade_index] = 'Ri' self.main_world_trade_class[self.trade_index] = 'Rich' self.trade_index += 1 if self.main_world_hydrographics == 10 \ and self.main_world_atmosphere >= 1 \ and self.main_world_atmosphere <= 9: self.main_world_trade_code[self.trade_index] = 'Wa' self.main_world_trade_class[self.trade_index] = 'Water World' self.trade_index += 1 if self.main_world_atmosphere == 0: self.main_world_trade_code[self.trade_index] = 'Va' self.main_world_trade_class[self.trade_index] = 'Vacuum' self.trade_index += 1 if self.main_world_government + self.main_world_law_level >= 30: self.main_world_trade_code[self.trade_index] = 'Fo' self.main_world_trade_class[self.trade_index] = 'Forbidden' self.trade_index += 1 self.main_world_travel_code = ' R' # Any bases? Naval? Research, etc? self.base = '' if self.world_starport_class[self.main_world_starport] == 'A': if roll('2D6') >= 8: self.base = 'N' elif roll('2D6') >= 10: self.base = 'S' elif roll('2D6') >= 8: self.base = 'R' elif roll('2D6') >= 6: self.base = 'I' if self.world_starport_class[self.main_world_starport] == 'B': if roll('2D6') >= 8: self.base = 'N' elif roll('2D6') >= 8: self.base = 'S' elif roll('2D6') >= 10: self.base = 'R' elif roll('2D6') >= 8: self.base = 'I' elif roll('2D6') >= 12: self.base = 'P' if self.world_starport_class[self.main_world_starport] == 'C': if roll('2D6') >= 8: self.base = 'S' elif roll('2D6') >= 10: self.base = 'R' elif roll('2D6') >= 8: self.base = 'W' elif roll('2D6') >= 10: self.base = 'I' elif roll('2D6') >= 10: self.base = 'P' if self.world_starport_class[self.main_world_starport] == 'D': if roll('2D6') >= 7: self.base = 'S' elif roll('2D6') >= 12: self.base = 'P' if self.world_starport_class[self.main_world_starport] == 'E': if roll('2D6') >= 12: self.base = 'P' # Population Multiplier? if roll('1D6') > 3: self.population_multiplier = roll('1D6-1') while self.population_multiplier == 5: self.population_multiplier = roll('1D6-1') else: self.population_multiplier = roll('1D6+4') while self.population_multiplier == 10: self.population_multiplier = roll('1D6+4') if self.population_multiplier < 1: self.population_multiplier = 1 # Planetoid Belts? self.planetoid_belts = '0' if roll('2D6') >= 8: self.planetoid_belts = self.planetoid_belt_quantity[roll('2D')] # Gas Giants? self.gas_giants = '0' if roll('2D6') >= 5: self.gas_giants = self.gas_giant_quantity[roll('2D')] # Worlds? self.worlds = str(1 + int(self.planetoid_belts) + int(self.gas_giants) + roll('2D')) # Allegiance? if self.random_allegiance is True: self.allegiance = 'Im' temp = roll('D100') if temp > 50: self.allegiance = 'Cs' if temp > 80: self.allegiance = 'Na' if temp > 90: self.allegiance = 'Va' if temp > 94: self.allegiance = 'So' if temp > 97: self.allegiance = 'As' if temp == 100: self.allegiance = 'Zh' # System Nature self.stellar_data = '' star_count = self.sn[roll('2D6')] if self.main_world_atmosphere >= 4 and self.main_world_atmosphere <= 9 \ or self.main_world_population >= 8: star_DM = 4 else: star_DM = 0 star_rolled = roll('2D6') + star_DM if star_rolled > 12: star_rolled = 12 previous_star_rolled = star_rolled self.stellar_data += self.primary_star_type[star_rolled] if roll('1D6') > 3: classification = roll('1D6-1') while classification == 5: classification = roll('1D6-1') else: classification = roll('1D6+4') while classification == 10: classification = roll('1D6+4') self.stellar_data += str(classification) star_rolled = roll('2D6') + star_DM if star_rolled > 12: star_rolled = 12 star_size_rolled = self.star_size[star_rolled] if (self.stellar_data == 'K5' or self.stellar_data == 'M9') \ and star_size_rolled == 'IV ': star_size_rolled = 'V ' if (self.stellar_data == 'B0' or self.stellar_data == 'F4') \ and star_size_rolled == 'IV ': star_size_rolled = 'V ' self.stellar_data += ' ' + star_size_rolled previous_star_size_rolled = star_rolled star_count += -1 if star_count > 1: star_rolled = roll('2D6') + previous_star_rolled if star_rolled > 12: star_rolled = 12 companion_data = self.companion_star_type[star_rolled] if roll('1D6') > 3: classification = roll('1D6-1') while classification == 5: classification = roll('1D6-1') else: classification = roll('1D6+4') while classification == 10: classification = roll('1D6+4') companion_data += str(classification) star_rolled = roll('2D6') + previous_star_size_rolled if star_rolled > 12: star_rolled = 12 star_size_rolled = self.star_size[star_rolled] if (companion_data == 'K5' or companion_data == 'M9') \ and star_size_rolled == 'IV ': star_size_rolled = 'V ' if (companion_data == 'B0' or companion_data == 'F4') \ and star_size_rolled == 'IV ': star_size_rolled = 'V ' self.stellar_data += ' ' + companion_data + ' ' + star_size_rolled # Maybe World has no name? Just a UWP. if roll('D100') > 95: self.main_world_name = self.world_starport_class[self.main_world_starport] + \ self.hex_code[self.main_world_size] + \ self.hex_code[self.main_world_atmosphere] + \ self.hex_code[self.main_world_hydrographics] + \ self.hex_code[self.main_world_population] + \ self.hex_code[self.main_world_government] + \ self.hex_code[self.main_world_law_level] + '-' + \ self.hex_code[self.main_world_tech_level] self.main_world_trade_codes = '' self.main_world_trade_classes = '' for i in range(self.trade_index): self.main_world_trade_codes += ' ' + self.main_world_trade_code[i] self.main_world_trade_classes += self.main_world_trade_class[i] + ', ' # Print Main World print(self.main_world_name + ' (%s %s%s%s%s%s%s%s-%s %s%s)' % (self.main_world_location, \ self.world_starport_class[self.main_world_starport], \ self.hex_code[self.main_world_size], self.hex_code[self.main_world_atmosphere], \ self.hex_code[self.main_world_hydrographics], self.hex_code[self.main_world_population], \ self.hex_code[self.main_world_government], self.hex_code[self.main_world_law_level], \ self.hex_code[self.main_world_tech_level], self.main_world_trade_codes, self.main_world_travel_code)) self.worlds_rolled += 1 csv_writer.writerow([self.main_world_location, self.main_world_name, self.world_starport_class[self.main_world_starport], self.hex_code[self.main_world_size], self.hex_code[self.main_world_atmosphere], self.hex_code[self.main_world_hydrographics], self.hex_code[self.main_world_population], self.hex_code[self.main_world_government], self.hex_code[self.main_world_law_level], self.hex_code[self.main_world_tech_level], self.main_world_trade_codes[1:], self.main_world_travel_code[len(self.main_world_travel_code) - 1:len(self.main_world_travel_code)], self.base, self.population_multiplier, self.planetoid_belts, self.gas_giants, self.worlds, self.allegiance, self.stellar_data, self.main_world_temperature]) trav_rec = {} trav_rec['Location'] = self.main_world_location trav_rec['World_Name'] = self.main_world_name trav_rec['Starport'] = self.world_starport_class[self.main_world_starport] trav_rec['Size'] = self.hex_code[self.main_world_size] trav_rec['Atmosphere'] = self.hex_code[self.main_world_atmosphere] trav_rec['Hydrographics'] = self.hex_code[self.main_world_hydrographics] trav_rec['Population'] = self.hex_code[self.main_world_population] trav_rec['Government'] = self.hex_code[self.main_world_government] trav_rec['Law_Level'] = self.hex_code[self.main_world_law_level] trav_rec['Tech_Level'] = self.hex_code[self.main_world_tech_level] trav_rec['Trade_Codes'] = self.main_world_trade_codes[1:] trav_rec['Travel_Code'] = self.main_world_travel_code[len(self.main_world_travel_code) - 1:len(self.main_world_travel_code)] trav_rec['Base'] = self.base trav_rec['Pop_M'] = self.population_multiplier trav_rec['Belts'] = self.planetoid_belts trav_rec['Gas_Giants'] = self.gas_giants trav_rec['Worlds'] = self.worlds trav_rec['Allegiance'] = self.allegiance trav_rec['Stellar_Data'] = self.stellar_data trav_rec['Temperature'] = self.main_world_temperature world_list.append(trav_rec) detail_line = self.main_world_location detail_line += ' ' + self.main_world_name while len(detail_line) < 26: detail_line += ' ' detail_line += self.world_starport_class[self.main_world_starport] + \ self.hex_code[self.main_world_size] + \ self.hex_code[self.main_world_atmosphere] + \ self.hex_code[self.main_world_hydrographics] + \ self.hex_code[self.main_world_population] + \ self.hex_code[self.main_world_government] + \ self.hex_code[self.main_world_law_level] + '-' + \ self.hex_code[self.main_world_tech_level] detail_line += ' ' + self.main_world_trade_codes[1:] while len(detail_line) < 81: detail_line += ' ' detail_line += self.base while len(detail_line) < 84: detail_line += ' ' detail_line += self.main_world_travel_code[len(self.main_world_travel_code) - 1:len(self.main_world_travel_code)] while len(detail_line) < 85: detail_line += ' ' detail_line += ' ' + str(self.population_multiplier) + str(self.planetoid_belts) + str(self.gas_giants) while len(detail_line) < 90: detail_line += ' ' detail_line += self.worlds while len(detail_line) < 93: detail_line += ' ' detail_line += self.allegiance while len(detail_line) < 98: detail_line += ' ' detail_line += self.stellar_data mapper_file_out.write('\n' + detail_line) #json.dump(world_list, json_file_out, indent=4, ensure_ascii=True) json.dump(world_list, json_file_out, ensure_ascii=True) csv_file_out.close() json_file_out.close() mapper_file_out.close() print('Worlds rolled:', self.worlds_rolled) self.passingNumworlds() def hydroBox_changed(self): self.hydro_calc_choices = ['Based on Atmosphere', 'Based on Size'] self.hydro_calc_method = self.hydro_calc_choices[self.hydroBox.currentIndex()] #print(self.hydro_calc_method) log.info("'" + self.hydro_calc_method + "'" + ' was selected.') def stellar_densityBox_changed(self): self.density_choices = [1, 3, 17, 33, 50, 66, 83, 91] self.stellar_density = self.density_choices[self.stellar_densityBox.currentIndex()] #print(self.stellar_density) log.info('A Stellar Density of ' + str(self.stellar_density) + '% was selected.') def allegianceBox_changed(self): self.allegiance_choices = ['As', 'Cs', 'Im', 'Na', 'So', 'Va', 'Zh'] if self.allegianceBox.currentIndex() == 7: self.random_allegiance = True log.info('Random Allegiance was selected.') else: self.allegiance = self.allegiance_choices[self.allegianceBox.currentIndex()] self.random_allegiance = False log.info('Allegiance "' + self.allegiance + '" was selected.') def super_earth_checkBox_changed(self): self.super_earth_chance = self.super_earth_checkBox.isChecked() #print(self.super_earth_chance) log.info('Chance for Super-Earth: ' + str(self.super_earth_chance)) def actionAbout_triggered(self): log.info(__app__ + ' show about...') self.popAboutDialog.show() def actionQuitProg_triggered(self): log.info(__app__ + ' quiting...') log.info(__app__ + ' DONE.') log.info('Logging ended.') self.close() def passingNumworlds(self): log.info('Number of worlds: ' + str(self.worlds_rolled)) self.popCompletedDialog.numworldsDisplay.setText('Number of worlds: ' + str(self.worlds_rolled)) self.popCompletedDialog.showNumworlds() if __name__ == '__main__': log = logging.getLogger('SectorGen') log.setLevel(logging.DEBUG) if not os.path.exists('Logs'): os.mkdir('Logs') fh = logging.FileHandler('Logs/SectorGen.log', 'w') formatter = logging.Formatter('%(asctime)s %(levelname)s %(name)s - %(message)s', datefmt = '%a, %d %b %Y %H:%M:%S') fh.setFormatter(formatter) log.addHandler(fh) log.info('Logging started.') log.info(__app__ + ' starting...') trange = time.localtime() creation_time = datetime.datetime.now() if trange[0] > 2022 or trange[1] > 11: log.warning('Beta time period has expired!') print() print(Fore.RED + Style.BRIGHT + __app__, 'EXPIRED.') print(Style.RESET_ALL) print() print(__author__) print() s = input('Press ENTER: ') log.info(__app__ + ' stopping...') print('OK') log.info(__app__ + ' stopped.') log.info('Logging ended.') else: print() print('Thank you for giving', __app__, 'a try.') log.info(__app__ + ' looking for PyDiceroll...') vernum, release = roll('info') print('This program uses', release) log.info(__app__ + ' found ' + release + '.') print() print('----------------------------') print(__author__) print() print('The Traveller game in all forms is owned by Far Future Enterprises.') print('Copyright 1977 - 2022 Far Future Enterprises.') print('Traveller is a registered trademark of Far Future Enterprises.') print() log.info(__app__ + ' started, and running...') app = QApplication(sys.argv) #Use print(QStyleFactory.keys()) to find a setStyle you like, instead of 'Fusion' app.setStyle('Fusion') darkPalette = QPalette() darkPalette.setColor(QPalette.Window, QColor(53, 53, 53)) darkPalette.setColor(QPalette.WindowText, Qt.white) darkPalette.setColor(QPalette.Disabled, QPalette.WindowText, QColor(127, 127, 127)) darkPalette.setColor(QPalette.Base, QColor(42, 42, 42)) darkPalette.setColor(QPalette.AlternateBase, QColor(66, 66, 66)) darkPalette.setColor(QPalette.ToolTipBase, Qt.white) darkPalette.setColor(QPalette.ToolTipText, Qt.white) darkPalette.setColor(QPalette.Text, Qt.white) darkPalette.setColor(QPalette.Disabled, QPalette.Text, QColor(127, 127, 127)) darkPalette.setColor(QPalette.Dark, QColor(35, 35, 35)) darkPalette.setColor(QPalette.Shadow, QColor(20, 20, 20)) darkPalette.setColor(QPalette.Button, QColor(53, 53, 53)) darkPalette.setColor(QPalette.ButtonText, Qt.white) darkPalette.setColor(QPalette.Disabled, QPalette.ButtonText, QColor(127, 127, 127)) darkPalette.setColor(QPalette.BrightText, Qt.red) darkPalette.setColor(QPalette.Link, QColor(42, 130, 218)) darkPalette.setColor(QPalette.Highlight, QColor(42, 130, 218)) darkPalette.setColor(QPalette.Disabled, QPalette.Highlight, QColor(80, 80, 80)) darkPalette.setColor(QPalette.HighlightedText, Qt.white) darkPalette.setColor(QPalette.Disabled, QPalette.HighlightedText, QColor(127, 127, 127)) mainApp = MainWindow() mainApp.show() app.setPalette(darkPalette) CURRENT_DIR = os.path.dirname(os.path.realpath(__file__)) #print(CURRENT_DIR) app.exec_()

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welcome.py

MadhavVij/File-Uploader-Bluemix

0

162931

import base64 import json import pymysql import swiftclient import os from cloudant import Cloudant from cryptography.hazmat.backends import default_backend from cryptography.hazmat.primitives import hashes from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC from flask import Flask, render_template, jsonify from cryptography.fernet import Fernet #Object Storage Credentials auth_url = projectId = region = userId = password = #Cloudant Credentials cloud_username = cloud_password = cloud_url = client = Cloudant(cloud_username, cloud_password, url=cloud_url) client.connect() session = client.session() app = Flask(__name__) def genKey(): passWord= input('Enter key: ') salt = os.urandom(16) kdf = PBKDF2HMAC( algorithm=hashes.SHA256(), length=32, salt=salt, iterations=100000, backend=default_backend() ) passWord = <PASSWORD>() return base64.urlsafe_b64encode(kdf.derive(passWord)) def authenticate(): return swiftclient.Connection(key=password, authurl=auth_url, auth_version='3', os_options={"project_id": projectId, "user_id": userId, "region_name": region}) def createContainer(container_name): #container_name = 'file-container' #container_name2 = 'file-container2' #container_name3 = 'file-container3' Obj_conn = authenticate() Obj_conn.put_container(container_name) #Obj_conn.put_container(container_name2) return Obj_conn def getContainerName(Obj_conn): for container in Obj_conn.get_account()[1]: return container['name'] def createDB(): dbName = "datafiles" return client.create_database(dbName) @app.route('/toCloudUp') def cloudList(): root = app.root_path + '\container/' filenames = [] for (dirName, subdirList, fileList) in os.walk(root): filenames.extend(fileList) return render_template('toCloudUp.html', files=filenames) @app.route('/cloudUp') def cloudantUpload(): #dbName = "datafiles" #dbName = client[dbName] #filePath = app.root_path+'\container/'+filename #f = open(filePath,'rb') #data = f.read() dbName = "datafiles" dbName = client[dbName] data = ListLocal() #jdata = json.load(data) #jdata = jdata[0] print(data[0]) dbName.create_document({data[0]}) #dbName.create_document({zerocount:[{filename: data.decode()}]}) return 'Uploaded Successful!' @app.route('/upload') def upload(): file_type1 = ['txt', 'TXT', 'text'] file_type2 = ['jpg','jpeg','JPG','JPEG','png','PNG','gif'] root = app.root_path + '\container/' for dirName, subdirList, fileList in os.walk(root): for fname in fileList: file_location = (dirName + "/" + fname) size = os.path.getsize(file_location) if fname[-3:] in file_type1: container_name = 'file-container' Obj_conn = createContainer(container_name) with open(file_location, 'rb') as load_file: Obj_conn.put_object(container_name, fname, contents=load_file.read(), content_type='application/octet-stream') load_file.close() elif fname[-3:] in file_type2 or fname[-4:] in file_type2: if size <= 200000: container_name = 'small_img' Obj_conn = createContainer(container_name) file_location = (dirName + "/" + fname) with open(file_location, 'rb') as load_file: Obj_conn.put_object(container_name, fname, contents=load_file.read(), content_type='application/octet-stream') load_file.close() elif size>200000: container_name = 'large_img' Obj_conn = createContainer(container_name) file_location = (dirName + "/" + fname) with open(file_location, 'rb') as load_file: Obj_conn.put_object(container_name, fname, contents=load_file.read(), content_type='application/octet-stream') load_file.close() return 'Upload Successful!' @app.route('/files') def list_files(): print(app.root_path) root = app.root_path+'\container/' filenames = [] for (dirName, subdirList, fileList) in os.walk(root): filenames.extend(fileList) print(filenames) return render_template('files.html', files=filenames) @app.route('/toDownload') def downloadList(): Obj_conn = createContainer() filenames = [] container_name = getContainerName(Obj_conn) for data in Obj_conn.get_container(container_name)[1]: filenames.append(data['name']) return render_template('toDownload.html', files=filenames) @app.route('/download/<filename>') def download(filename): Obj_conn = createContainer() container_name = getContainerName(Obj_conn) file_details = Obj_conn.get_object(container_name, filename) with open(app.root_path+'\download.txt', 'wb') as my_copy: my_copy.write(file_details[1]) return filename + ' Downloaded!' @app.route('/toEncrypt') def encryptList(): root = app.root_path + '\container/' filenames = [] for (dirName, subdirList, fileList) in os.walk(root): filenames.extend(fileList) return render_template('toEncrypt.html', files=filenames) @app.route('/encrypt/<userFile>') def encryptFile(userFile): key = genKey() root = app.root_path f = open(root+'/'+userFile[:-4]+'_key.txt','wb') f.write(key) f = Fernet(key) for dirName, subdirList, fileList in os.walk(root+'/container/'): for fname in fileList: if userFile == fname: file_location = (dirName + "/" + fname) with open(file_location, 'rb') as load_file: token = f.encrypt(load_file.read()) load_file.close() fname = fname[:-4]+'-crypted'+fname[-4:] new_location = (root + "/encrypted/" + fname) with open(new_location, 'wb') as write_file: write_file.write(token) write_file.close() return 'file ' +fname+ ' encrypted' @app.route('/toDecrypt') def decryptionList(): root = app.root_path + '\encrypted/' filenames = [] for (dirName, subdirList, fileList) in os.walk(root): filenames.extend(fileList) return render_template('toDecrypt.html', files=filenames) @app.route('/decrypt/<userFile>') def decryptFile(userFile): root = app.root_path userFile = userFile[:-12]+userFile[-4:] f = open(root+'/'+userFile[:-4]+'_key.txt', 'rb') key = f.read() g = Fernet(key) for dirName, subdirList, fileList in os.walk(root+'/encrypted/'): for fname in fileList: if userFile[:-4]+'-crypted'+userFile[-4:] == fname: file_location = (dirName + "/" + fname) with open(file_location, 'rb') as load_file: token = g.decrypt(load_file.read()) load_file.close() fname = userFile[:-4]+'-decrypted'+userFile[-4:] new_location = (root + "/decrypted/" + fname) print(new_location) with open(new_location, 'wb') as write_file: write_file.write(token) write_file.close() return 'file ' +fname+ ' decrypted' @app.route('/') def Welcome(): return render_template('index.html') def connectDB(): host = '' port = 3306 user = '' password = '' db = '' conn = pymysql.connect( host=host, port=port, user=user, password=password, db=db) return conn @app.route('/clearUp') def uploadFile(): conn = connectDB() curr = conn.cursor() file_type1 = ['txt', 'TXT', 'text'] file_type2 = ['jpg', 'jpeg', 'JPG', 'JPEG', 'png', 'PNG', 'gif'] root = app.root_path + '\container/' for dirName, subdirList, fileList in os.walk(root): for fname in fileList: file_location = (dirName + "/" + fname) fsize = os.path.getsize(file_location) if fname[-3:] in file_type1: with open(file_location, 'rb') as load_file: fdata = load_file.read() query = 'insert into file_container (fname,fsize, fdata) VALUES (%s,%s,%s)' args = (fname,fsize,fdata) curr.execute(query,args) conn.commit() load_file.close() # results = curr.fetchall() # for row in results: # print(row[1]) # conn.commit() curr.close() conn.close() return 'DB Updated!' @app.route('/clearDown/<filename>') def downloadFile(filename): conn = connectDB() curr = conn.cursor() query = 'select fdata from file_container WHERE fname = %s' args = (filename) curr.execute(query, args) fdata = curr.fetchone()[0] with open(app.root_path + '\download.txt', 'wb') as my_copy: my_copy.write(fdata) conn.commit() curr.close() conn.close() return 'Downloaded '+filename #################################################### @app.route('/ListLocal') def ListLocal(): root = app.root_path filenames = [] zerocount = 0 for dirName, subdirList, fileList in os.walk(root+'/container/'): for fname in fileList: file_location = (dirName + "/" + fname) size = os.path.getsize(file_location) if size == 0: zerocount += 1 filenames.append(tuple((fname,size))) dbName = "datafiles" dbName = client[dbName] zerocount = [zerocount] for filename,size in filenames: file_location = (root+'/container/' + filename) with open(file_location, 'rb') as load_file: data = load_file.read() data = data.decode('utf-8') zerocount.append({filename: data}) return jsonify(zerocount) port = os.getenv('PORT', '5000') if __name__ == '__main__': app.run(host='127.0.0.1', port=int(port))

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graph_rl/envs/flatten_wrapper.py

nicoguertler/graphrl

1

41443

<filename>graph_rl/envs/flatten_wrapper.py from gym import Wrapper from ..spaces import space_from_gym_space class FlattenWrapper(Wrapper): """Flattens observation and action space.""" def __init__(self, env): super().__init__(env) self._action_space = space_from_gym_space(env.action_space) self._observation_space = space_from_gym_space(env.observation_space) self.action_space = self._action_space.get_flat_space().get_gym_space() self.observation_space = self._observation_space.get_flat_space().get_gym_space() def step(self, action): orig_action = self._action_space.unflatten_value(action) orig_obs, reward, done, info = self.env.step(orig_action) obs = self._observation_space.flatten_value(orig_obs) return obs, reward, done, info def reset(self, **kwargs): orig_obs = self.env.reset(**kwargs) obs = self._observation_space.flatten_value(orig_obs) return obs

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publishconf.py

siongui/pali-chanting

2

192935

<reponame>siongui/pali-chanting<gh_stars>1-10 #!/usr/bin/env python # -*- coding: utf-8 -*- # from __future__ import unicode_literals # This file is only used if you use `make publish` or # explicitly specify it as your config file. import os import sys sys.path.append(os.curdir) from pelicanconf import * # no trailing / SITEURL = 'https://siongui.github.io/pali-chanting' RELATIVE_URLS = False FEED_ALL_ATOM = None CATEGORY_FEED_ATOM = None TRANSLATION_FEED_ATOM = None AUTHOR_FEED_ATOM = None AUTHOR_FEED_RSS = None DELETE_OUTPUT_DIRECTORY = True # Following items are often useful when publishing #DISQUS_SITENAME = "" #GOOGLE_ANALYTICS = "" # Google Custom Search #GOOGLE_CSE = '000759460633137666077:43yuu4nvb0c'

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serving_patterns/src/app/apps/app_image.py

shibuiwilliam/ml-system-in-action

10

1611156

<gh_stars>1-10 import os from fastapi import FastAPI import logging from src.configurations import PlatformConfigurations from src.app.routers import health, predict_image from src.app.configurations import APIConfigurations logger = logging.getLogger(__name__) logger.info(f"starts {APIConfigurations.title}:{APIConfigurations.version}") logger.info(f"platform: {PlatformConfigurations.platform}") app = FastAPI( title=APIConfigurations.title, description=APIConfigurations.description, version=APIConfigurations.version, ) app.include_router(health.router, prefix="/health", tags=["health"]) app.include_router(predict_image.router, prefix="/predict", tags=["predict"])

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hello.py

jinheebee/github_test

0

57569

<filename>hello.py<gh_stars>0 print("hello world") a = 3 b = 4 c = a * b print(c)

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msg_capsgan.py

MahdiyarMM/MSG-CapsGAN

5

156261

<filename>msg_capsgan.py #Image lib import cv2 # math libraries import numpy as np import scipy.misc # ml libraries import tensorflow as tf from keras import layers, models, optimizers from keras import backend as K from keras.utils import to_categorical from keras.datasets import mnist, cifar10 from keras.layers import Input, Dense, Reshape, Flatten, Dropout, Lambda, concatenate, Multiply from keras.layers import BatchNormalization, Activation, ZeroPadding2D, Add from keras.layers.advanced_activations import LeakyReLU from keras.layers.convolutional import UpSampling2D, Conv2D from keras.models import Sequential, Model from keras.optimizers import Adam from keras.preprocessing.image import ImageDataGenerator from keras import callbacks import datetime # visualization import skimage from skimage import data, color, exposure from skimage.transform import resize import matplotlib.pyplot as plt import math from skimage.measure import compare_ssim as ssim from skimage.measure import compare_psnr as psnr from keras.applications import VGG19 # sys and helpers import sys import os import glob from tqdm import tqdm print('Modules imported.') class DataLoader3_all(): def __init__(self, dataset_name, img_res=(128, 128)): self.dataset_name = dataset_name self.img_res = img_res def load_data(self, batch_size=1, is_testing=False): data_type = "train" if not is_testing else "test" path = glob.glob('/content/drive/My Drive/CelebA_sample/CelebA/*') batch_images = np.random.choice(path, size=batch_size) imgs_hr = [] imgs_lr = [] imgs_32 = [] imgs_64 = [] for img_path in batch_images: imgl = self.imread(img_path) img = imgl[20:218-20,:] h, w = self.img_res low_h, low_w = int(h / 8), int(w / 8) # img_hr = scipy.misc.imresize(img, self.img_res) # img_lr = scipy.misc.imresize(img, (low_h, low_w)) # img_32 = scipy.misc.imresize(img, (32, 32)) # img_64 = scipy.misc.imresize(img, (64, 64)) img_hr = cv2.resize(img, self.img_res) img_lr = cv2.resize(img, (low_h, low_w)) img_32 = cv2.resize(img, (32, 32)) img_64 = cv2.resize(img, (64, 64)) # If training => do random flip if not is_testing and np.random.random() < 0.5: img_hr = np.fliplr(img_hr) img_lr = np.fliplr(img_lr) img_32 = np.fliplr(img_32) img_64 = np.fliplr(img_64) imgs_hr.append(img_hr) imgs_lr.append(img_lr) imgs_32.append(img_32) imgs_64.append(img_64) imgs_hr = np.array(imgs_hr) / 127.5 - 1. imgs_lr = np.array(imgs_lr) / 127.5 - 1. imgs_32 = np.array(imgs_32) / 127.5 - 1. imgs_64 = np.array(imgs_64) / 127.5 - 1. return imgs_hr, imgs_lr , imgs_32, imgs_64 def imread(self, path): return plt.imread(path).astype(np.float) def load_test(self,path): img = self.imread(path) img_hr = scipy.misc.imresize(img, (256,256)) # Configure data loader dataset_name = 'img_align_celeba' hr_height = 128 hr_width = 128 data_loader = DataLoader3_all(dataset_name=dataset_name, img_res=(hr_height, hr_width)) def all_psnr(imageA, imageB): psnrs = [] for ii in range(len(imageA)): psnrs.append(psnr(imageA[ii],imageB[ii])) return np.mean(psnrs) def all_ssim(imageA, imageB): psnrs = [] for ii in range(len(imageA)): psnrs.append(ssim(imageA[ii],imageB[ii],multichannel =True)) return np.mean(psnrs) # squash function of capsule layers, borrowed from <NAME>'s implementation of Keras CapsNet `https://github.com/XifengGuo/CapsNet-Keras` def squash(vectors, axis=-1): """ The non-linear activation used in Capsule. It drives the length of a large vector to near 1 and small vector to 0 :param vectors: some vectors to be squashed, N-dim tensor :param axis: the axis to squash :return: a Tensor with same shape as input vectors """ s_squared_norm = K.sum(K.square(vectors), axis, keepdims=True) scale = s_squared_norm / (1 + s_squared_norm) / K.sqrt(s_squared_norm + K.epsilon()) return scale * vectors # device check from tensorflow.python.client import device_lib print('Devices:', device_lib.list_local_devices()) # GPU check if not tf.test.gpu_device_name(): print('No GPU found.') else: print('Default GPU Device: {}'.format(tf.test.gpu_device_name())) def build_vgg(hr_shape): """ Builds a pre-trained VGG19 model that outputs image features extracted at the third block of the model """ vgg = VGG19(weights="imagenet") # Set outputs to outputs of last conv. layer in block 3 # See architecture at: https://github.com/keras-team/keras/blob/master/keras/applications/vgg19.py vgg.outputs = [vgg.layers[9].output] img = Input(hr_shape) # Extract image features img_features = vgg(img) return Model(img, img_features) optimizer = Adam(0.0003, 0.5) vgg = build_vgg((128,128,3)) vgg.trainable = False vgg.compile(loss='mse', optimizer=optimizer, metrics=['accuracy']) vgg64 = build_vgg((64,64,3)) vgg64.trainable = False vgg64.compile(loss='mse', optimizer=optimizer, metrics=['accuracy']) vgg32 = build_vgg((32,32,3)) vgg32.trainable = False vgg32.compile(loss='mse', optimizer=optimizer, metrics=['accuracy']) # discriminator structure def build_discriminator(): # depending on dataset we define input shape for our network img = Input(shape=(128, 128, 3)) img64 = Input(shape=(64, 64, 3)) img32 = Input(shape=(32, 32, 3)) x1 = Conv2D(filters=128, kernel_size=9, strides=2, padding='same', name='conv0')(img) img64f = Conv2D(filters=128, kernel_size=9, strides=1, padding='same', name='convimg0')(img64) x2 = concatenate([x1,img64f],axis=-1) x3 = Conv2D(filters=128, kernel_size=9, strides=2, padding='same', name='conv1')(x2) img32f = Conv2D(filters=128, kernel_size=9, strides=1, padding='same', name='convimg1')(img32) x4 = concatenate([x3,img32f],axis=-1) # first typical convlayer outputs a 20x20x256 matrix x5 = Conv2D(filters=256, kernel_size=9, strides=1, padding='valid', name='conv2')(x4) x6 = LeakyReLU()(x5) # original 'Dynamic Routing Between Capsules' paper does not include the batch norm layer after the first conv group x = BatchNormalization(momentum=0.8)(x6) # # primarycaps coming first # # filters 256 (n_vectors=8 * channels=32) x = Conv2D(filters=8 * 32, kernel_size=9, strides=2, padding='valid', name='primarycap_conv2')(x) #Flatten to add dense layer x = Flatten()(x) # reshape into the 8D vector for all 32 feature maps combined # (primary capsule has collections of activations which denote orientation of the digit # while intensity of the vector which denotes the presence of the digit) x = Reshape(target_shape=[-1, 8], name='primarycap_reshape')(x) # the purpose is to output a number between 0 and 1 for each capsule where the length of the input decides the amount x = Lambda(squash, name='primarycap_squash')(x) x = BatchNormalization(momentum=0.8)(x) # # digitcaps are here # x = Flatten()(x) uhat = Dense(160, kernel_initializer='he_normal', bias_initializer='zeros', name='uhat_digitcaps')(x) # c = coupling coefficient (softmax over the bias weights, log prior) | "the coupling coefficients between capsule (i) and all the capsules in the layer above sum to 1" # we treat the coupling coefficiant as a softmax over bias weights from the previous dense layer c = Activation('softmax', name='softmax_digitcaps1')(uhat) # softmax will make sure that each weight c_ij is a non-negative number and their sum equals to one # s_j (output of the current capsule level) = uhat * c c = Dense(160)(c) # compute s_j x = Multiply()([uhat, c]) """ NOTE: Squashing the capsule outputs creates severe blurry artifacts, thus we replace it with Leaky ReLu. """ s_j = LeakyReLU()(x) # # we will repeat the routing part 2 more times (num_routing=3) to unfold the loop # c = Activation('softmax', name='softmax_digitcaps2')(s_j) # softmax will make sure that each weight c_ij is a non-negative number and their sum equals to one c = Dense(160)(c) # compute s_j x = Multiply()([uhat, c]) s_j = LeakyReLU()(x) c = Activation('softmax', name='softmax_digitcaps3')(s_j) # softmax will make sure that each weight c_ij is a non-negative number and their sum equals to one c = Dense(160)(c) # compute s_j x = Multiply()([uhat, c]) s_j = LeakyReLU()(x) pred = Dense(25*25, activation='sigmoid')(s_j) patch = Reshape(target_shape=[-1, 25], name='last_out_reshape')(pred) return Model([img,img64,img32], patch) # build and compile the discriminator discriminator = build_discriminator() print('DISCRIMINATOR:') discriminator.summary() sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) #discriminator.load_weights('images_MSG/wdis6400.h5') discriminator.compile(loss='binary_crossentropy', optimizer=sgd) # generator structure def build_generator(): from keras.applications import VGG19 vgg = VGG19(weights="imagenet") vgg.outputs = [vgg.layers[9].output] vgg.trainable =False def deconv2d(layer_input,num=256): """Layers used during upsampling""" u = UpSampling2D(size=2)(layer_input) u = Conv2D(num, kernel_size=3, strides=1, padding='same')(u) u = Activation('relu')(u) return u def residual_block(layer_input, filters): """Residual block described in paper""" d = Conv2D(filters, kernel_size=3, strides=1, padding='same')(layer_input) d = Activation('relu')(d) d = BatchNormalization(momentum=0.8)(d) d = Conv2D(filters, kernel_size=3, strides=1, padding='same')(d) d = BatchNormalization(momentum=0.8)(d) d = Add()([d, layer_input]) return d img_lr_in = Input(shape=(16,16,3)) img_bilinear = Lambda(lambda image: tf.image.resize_images(image,(128, 128),method = tf.image.ResizeMethod.BICUBIC,align_corners = True, preserve_aspect_ratio = True))(img_lr_in) img_bilinear_64 = Lambda(lambda image: tf.image.resize_images(image,(64, 64),method = tf.image.ResizeMethod.BICUBIC,align_corners = True, preserve_aspect_ratio = True))(img_lr_in) img_bilinear_32 = Lambda(lambda image: tf.image.resize_images(image,(32, 32),method = tf.image.ResizeMethod.BICUBIC,align_corners = True, preserve_aspect_ratio = True))(img_lr_in) vgg_features = vgg(img_bilinear) vgg_features_cnn = Conv2D(64, kernel_size=3, strides=1, padding='same',name = "vgg_features_cnn")(vgg_features) pd = Conv2D(64, kernel_size=3, strides=1, padding='same')(img_lr_in) x = residual_block(pd,64) x = deconv2d(x,num=64) xconcat = concatenate([x,vgg_features_cnn],axis=-1,name='concatenation') pd = Conv2D(64, kernel_size=3, strides=1, padding='same')(xconcat) x = residual_block(pd,64) x_out_32 = Conv2D(3, kernel_size=3, strides=1, padding='same')(x) x_out_32 = Activation('tanh')(x_out_32) x_32_add = Add()([x_out_32, img_bilinear_32]) x_32 = Activation('tanh')(x_32_add) x = deconv2d(x) pd = Conv2D(64, kernel_size=3, strides=1, padding='same')(x) x = residual_block(pd,64) x_out_64 = Conv2D(3, kernel_size=3, strides=1, padding='same')(x) x_out_64 = Activation('tanh')(x_out_64) x_64_add = Add()([x_out_64, img_bilinear_64]) x_64 = Activation('tanh')(x_64_add) x = deconv2d(x) x = Conv2D(3, kernel_size=3, strides=1, padding='same')(x) x_out_pre = Activation('tanh')(x) x_out_add = Add()([x_out_pre, img_bilinear]) x_out = Activation('tanh')(x_out_add) return Model([img_lr_in],[x_out,x_64,x_32]) # build and compile the generator generator = build_generator() print('GENERATOR:') generator.summary() #generator.load_weights('images_MSG/wgen6400.h5') generator.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5)) model_json = generator.to_json() with open("model.json", "w") as json_file: json_file.write(model_json) model_json = generator.to_json() with open("model.json", "w") as json_file: json_file.write(model_json) # feeding noise to generator z = Input(shape=(16,16,3)) imgf = generator(z) fake_features = vgg(imgf[0]) fake_features_1 = vgg64(imgf[1]) fake_features_2 = vgg32(imgf[2]) # for the combined model we will only train the generator discriminator.trainable = False # try to discriminate generated images valid = discriminator(imgf) # the combined model (stacked generator and discriminator) takes # noise as input => generates images => determines validity combined = Model(z, [valid,fake_features,fake_features_1,fake_features_2]) print('COMBINED:') combined.summary() combined.compile(loss=['binary_crossentropy', 'mse', 'mse', 'mse'], loss_weights=[3e-3, 0.001,0.001,1], optimizer=Adam(0.0002, 0.5)) # loss values for further plotting D_L_REAL = [] D_L_FAKE = [] D_L = [] D_ACC = [] G_L = [] def train(dataset_title, epochs, batch_size=32, save_interval=50): start_time = datetime.datetime.now() half_batch = int(batch_size / 2) for epoch in range(0,50000): ''' #Progressive Weight Loss Adjustment if epoch == 600: combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1e-2, 1], optimizer=Adam(0.0002, 0.5)) # --------------------- if epoch == 1500: combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1e-1, 1], optimizer=Adam(0.0002, 0.5)) if epoch == 2500: combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1e-2, 1], optimizer=Adam(0.0002, 0.5)) if epoch == 3500: combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1e-1, 1], optimizer=Adam(0.0002, 0.5)) ''' # Train Discriminator # --------------------- # select a random half batch of images imgs_hr, imgs_lr, imgs_32, imgs_64 = data_loader.load_data(half_batch) # generate a half batch of new images gen_imgs = generator.predict(imgs_lr) # train the discriminator by feeding both real and fake (generated) images one by one d_loss_real = discriminator.train_on_batch([imgs_hr,imgs_64,imgs_32], np.random.uniform(low=0.8, high=1.2, size=(half_batch, 25,25))) # 0.9 for label smoothing d_loss_fake = discriminator.train_on_batch(gen_imgs, np.random.uniform(low=0.0, high=0.3, size=(half_batch, 25,25))) d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) # --------------------- # Train Generator # --------------------- imgs_hr, imgs_lr, imgs_32, imgs_64 = data_loader.load_data(batch_size) # the generator wants the discriminator to label the generated samples # as valid (ones) image_features = vgg.predict(imgs_hr) image_features_1 = vgg64.predict(imgs_64) image_features_2 = vgg32.predict(imgs_32) # train the generator g_loss = combined.train_on_batch(imgs_lr, [np.ones((batch_size,25,25)),image_features,image_features_1,image_features_2]) elapsed_time = datetime.datetime.now() - start_time # Plot the progress print ("%d time: %s ,[D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch,elapsed_time, d_loss, 100*d_loss, g_loss[0])) D_L_REAL.append(d_loss_real) D_L_FAKE.append(d_loss_fake) D_L.append(d_loss) D_ACC.append(d_loss) G_L.append(g_loss) # if at save interval => save generated image samples if epoch % save_interval == 0: sample_images(epoch) generator.save_weights("images_MSG/wgen%d.h5" % (epoch)) discriminator.save_weights("images_MSG/wdis%d.h5" % (epoch)) def sample_images(epoch): os.makedirs('images_MSG/%s' % dataset_name, exist_ok=True) r, c = 2, 5 imgs_hr, imgs_lr,_,_ = data_loader.load_data(batch_size=2, is_testing=True) fake_hr = generator.predict(imgs_lr) imgs_hr_inp = imgs_hr imgs_lr_inp = imgs_lr # Rescale images 0 - 1 imgs_lr = 0.5 * imgs_lr + 0.5 fake_hr[0] = 0.5 * fake_hr[0] + 0.5 fake_hr[1] = 0.5 * fake_hr[1] + 0.5 fake_hr[2] = 0.5 * fake_hr[2] + 0.5 imgs_hr = 0.5 * imgs_hr + 0.5 # Save generated images and the high resolution originals titles = ['Original','128x128'] fig, axs = plt.subplots(r, c) cnt = 0 for row in range(r): for col, image in enumerate([imgs_hr,fake_hr[0]]): axs[row, col+3].imshow(imgs_lr[row]) axs[row, col+2].imshow(fake_hr[2][row]) axs[row, col+1].imshow(fake_hr[1][row]) axs[row, col].imshow(image[row]) axs[row, col+1].set_title('64x64') axs[row, col+2].set_title('32x32') axs[row, col+3].set_title('input') axs[row, col].set_title(titles[col]) axs[row, col].axis('off') axs[row, col+1].axis('off') axs[row, col+2].axis('off') axs[row, col+3].axis('off') cnt += 1 txt = ("psnr = %f - ssim = %f" % (all_psnr(fake_hr[0],imgs_hr), all_ssim(fake_hr[0],imgs_hr))) fig.text(.5, .05, txt, ha='center') fig.savefig("images_MSG/%s/%d.png" % (dataset_name, epoch)) plt.close() # Save low resolution images for comparison for i in range(r): fig = plt.figure() plt.imshow(imgs_lr[i]) fig.savefig('images_MSG/%s/%d_lowres%d.png' % (dataset_name, epoch, i)) plt.close() print(all_psnr(fake_hr[0],imgs_hr)) print(all_ssim(fake_hr[0],imgs_hr)) if epoch<5000 and epoch>1000: combined.compile(loss=['binary_crossentropy', 'mse', 'mse', 'mse'], loss_weights=[3e-3, 0.001,(epoch - 999)/4000,1], optimizer=Adam(0.0002, 0.5)) if epoch<10000 and epoch>5000: combined.compile(loss=['binary_crossentropy', 'mse', 'mse', 'mse'], loss_weights=[3e-3, (epoch - 4999)/5000,1,1], optimizer=Adam(0.0002, 0.5)) if epoch<15000 and epoch>10000: combined.compile(loss=['binary_crossentropy', 'mse', 'mse', 'mse'], loss_weights=[3e-3 + 2e-3*(epoch - 9999)/5000, 1,1,1], optimizer=Adam(0.0002, 0.5)) return imgs_lr_inp,imgs_hr_inp history = train('cifar10', epochs=30000, batch_size=32, save_interval=50) #generator.save('mnist_model.h5') #generator.save('cifar10_model.h5') plt.plot(D_L) plt.title('Discriminator results (MNIST)') plt.xlabel('Epochs') plt.ylabel('Discriminator Loss (blue), Discriminator Accuracy (orange)') plt.legend(['Discriminator Loss', 'Discriminator Accuracy']) plt.show() plt.plot(G_L) plt.title('Generator results (MNIST)') plt.xlabel('Epochs') plt.ylabel('Generator Loss (blue)') plt.legend('Generator Loss') plt.show() plt.plot(D_L) plt.title('Discriminator results (CIFAR10)') plt.xlabel('Epochs') plt.ylabel('Discriminator Loss (blue), Discriminator Accuracy (orange)') plt.legend(['Discriminator Loss', 'Discriminator Accuracy']) plt.show() plt.plot(G_L) plt.title('Generator results (CIFAR10)') plt.xlabel('Epochs') plt.ylabel('Generator Loss (blue)') plt.legend('Generator Loss') plt.show()

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monolithic/order_transaction.py

liks79/sample-saga-with-step-functions

3

31659

<reponame>liks79/sample-saga-with-step-functions """ Database model for Monolithic application ~~~~~~~~~~~~~~~~~~~~~~~ :created date: Thursday, March 12, 2020 :description: Monolithic application implementation of simple order transaction scenario :copyright: © 2020 written by sungshik (<EMAIL>) :license: BSD 3-Clause License, see LICENSE for more details. """ import config import traceback from model import db from model.user import User from model.order import Order from model.membership import Membership from model.payment import Payment from model.inventory import Inventory from faker import Faker from pprint import pformat from sqlalchemy_utc import utcnow from exception import OutOfStockError, PaymentFailureError fake = Faker(config.FAKER_LOCALE) logger = config.logger try: """ Data Preparation """ # Create tables, if it is not existed. db.Base.metadata.create_all(bind=db.engine) # T0.TRANSACTION BEGIN # Transaction is already begun by SQLalchemy logger.info('#### T0.TRANSACTION BEGIN ####') # T1.Initiate User model logger.info('#### T1.INITIATE USER MODEL ####') user = db.session.query(User).first() logger.info(pformat(user.to_json())) # T2.Initiate Inventory model logger.info('#### T2.INITIATE INVENTORY MODEL ####') item = db.session.query(Inventory).first() logger.info(pformat(item.to_json())) # T3.Initiate Payment model logger.info('#### T3.INITIATE PAYMENT MODEL ####') payment = Payment(pay_type='credit_card', user_id=user.user_id, allowed=fake.boolean(), date=utcnow()) logger.info(pformat(payment.to_json())) db.session.add(payment) # db.session.commit() # T4.Initiate Order model logger.info('#### T4.INITIATE ORDER MODEL ####') ordered_item_qty = fake.random_int(1, 100) order = Order(user_id=user.user_id, item_id=item.item_id, item_qty=ordered_item_qty, date=utcnow(), deliver_phone=fake.phone_number(), deliver_address=fake.address(), total_price=fake.random_int(100, 1000000)) logger.info(pformat(order.to_json())) db.session.add(order) # db.session.commit() # T5.Update Inventory model logger.info('#### T5.UPDATE INVENTORY MODEL ####') logger.info('UPDATE INVENTORY QTY: {} - {} = {}'. format(item.qty, ordered_item_qty, item.qty - ordered_item_qty)) item.qty = item.qty - ordered_item_qty # T6.Update Membership model membership = Membership(user_id=user.user_id, date=utcnow(), mileage=fake.random_int(10, 400)) db.session.add(membership) logger.info('#### T6.UPDATE MEMBERSHIP ####') logger.info(pformat(membership.to_json())) # OutOfStockError Exception handling logger.info(pformat(item.to_json())) if item.qty < 0: logger.error('ORDERED ITEM IS OUT OF STOCK: %s' % item.qty) logger.error('THIS IS AN INTENDED ERROR.') raise OutOfStockError # PaymentFailureError Exception handling logger.info(pformat(payment.to_json())) if payment.allowed is not True: logger.error('PAYMENT TRANSACTION IS FAILED: %s' % payment.allowed) logger.error('THIS IS AN INTENDED ERROR.') raise PaymentFailureError # T7.Commit db.session.commit() logger.info('#### T7.TRANSACTION COMPLETED! ####') except Exception as e: logger.error(e) print(traceback.format_exc()) db.session.rollback() finally: db.session.close()

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reaver/utils/experiment.py

XinyiYS/reaver

0

1612175

<filename>reaver/utils/experiment.py import os import gin import json from datetime import datetime as dt class Experiment: def __init__(self, results_dir, env_name, agent_name, name=None, restore=False, restore_mix=False, args=None): if not name: if restore: experiments = [e for e in os.listdir(results_dir) if env_name in e and agent_name in e] assert len(experiments) > 0, 'No experiment to restore' name = max(experiments, key=lambda p: os.path.getmtime(results_dir+'/'+p)) name = '_'.join(name.split('_')[2:]) else: name = dt.now().strftime("%y-%m-%d_%H-%M-%S") else: if restore_mix: assert name is not None, "Must give a experiment name in order to <restore_mix>." experiments = [e for e in os.listdir(results_dir) if name in e and agent_name in e] assert len(experiments) > 0, 'No experiment to restore' name = dt.now().strftime("%y-%m-%d_%H-%M-%S") self.name = name self.restore = restore self.env_name = env_name self.agent_name = agent_name self.results_dir = results_dir self.args = args os.makedirs(self.path, exist_ok=True) os.makedirs(self.path + '/summaries', exist_ok=True) os.makedirs(self.results_dir + '/summaries', exist_ok=True) if not os.path.exists(self.summaries_path): os.symlink('../%s/summaries' % self.full_name, self.summaries_path) @property def full_name(self): if self.args.HRL and "HRL-{}".format(self.args.HRL) not in self.name: return '%s_%s_HRL-%s_%s' % (self.env_name, self.agent_name, self.args.HRL, self.name) else: return '%s_%s_%s' % (self.env_name, self.agent_name, self.name) @property def path(self): return '%s/%s' % (self.results_dir, self.full_name) @property def config_path(self): return '%s/%s' % (self.path, 'config.gin') @property def log_path(self): return '%s/%s' % (self.path, 'train.log') @property def checkpoints_path(self): return self.path + '/checkpoints' @property def summaries_path(self): return '%s/summaries/%s' % (self.results_dir, self.full_name) @property def experiment_config_path(self): return '%s/%s' % (self.path, 'experiment_config') def save_gin_config(self): with open(self.config_path, 'w') as cfg_file: cfg_file.write(gin.operative_config_str()) def save_experiment_config(self): ex_config_dict = {"name": self.name, "restore": self.restore, "env": self.env_name, "n_envs": self.args.n_envs, "agent": self.agent_name, "results_dir": self.results_dir, "time": dt.now().strftime("%y-%m-%d_%H-%M-%S"), "HRL": self.args.HRL, "max_ep_len": self.args.max_ep_len, } with open(self.experiment_config_path, 'a') as ex_cfg_file: ex_cfg_file.write(json.dumps(ex_config_dict)) def save_model_summary(self, model, **kwargs): if model: with open(self.path + '/' + 'model_summary.txt', 'w') as fl: model.summary(print_fn=lambda line: print(line, file=fl)) else: assert 'models' in kwargs and 'subenvs' in kwargs, "Missing models and subenvs" models = kwargs['models'] subenvs = kwargs['subenvs'] for model, subenv in zip(models, subenvs): with open(self.path + '/' + subenv + '_model_summary.txt', 'w') as fl: model.summary(print_fn=lambda line: print(line, file=fl))

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repository/spraytime_repo.py

kbj2060/plantpointNutrients-backend

0

142950

<reponame>kbj2060/plantpointNutrients-backend<gh_stars>0 from domain.entities.spraytime import SprayTime as eSprayTime from repository.repo import BaseRepo from repository import models from controllers.app import session class SprayTimeRepository(BaseRepo): def __init__(self) -> None: super().__init__() self.model = models.SprayTime self.entity = eSprayTime def create(self, period): new_spraytime: models.SprayTime = models.SprayTime(period=period) session.add(new_spraytime) session.commit() sprayTimeRepository = SprayTimeRepository()

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tests/conf.py

xncbf/django-dynamodb-cache

21

22252

<gh_stars>10-100 from random import random TABLE_NAME = f"test-django-dynamodb-cache-{random()}"

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src/tests/test_workflow.py

marcusianlevine/compose-flow

10

135910

import shlex from unittest import TestCase, mock from compose_flow.commands import Workflow from tests import BaseTestCase TEST_PROJECT_NAME = "test_project_name" @mock.patch("compose_flow.commands.subcommands.env.utils") @mock.patch("compose_flow.commands.subcommands.env.get_backend") class WorkflowTestCase(BaseTestCase): @staticmethod def _setup_docker_config_mock(*mocks): get_backend_mock = mocks[-2] get_backend_mock.return_value.read.return_value = "FOO=1\nBAR=2" @staticmethod def _setup_utils_mock(*mocks): utils_mock = mocks[-1] utils_mock.get_tag_version.return_value = "0.0.0" utils_mock.render.side_effect = lambda x, **kwargs: x def test_config_name_with_different_env(self, *mocks): """Ensure the config can have a different env prefix than the current env This allows storage of environments other than the current one, for example, storing a default local config in the dev remote so that multiple devs can access it to seed their environment """ command = shlex.split("-e dev -c local-test-project env cat") workflow = Workflow(argv=command) self.assertEquals("local-test-project", workflow.config_name) def test_default_env_when_no_env_specified(self, *mocks): self._setup_docker_config_mock(*mocks) self._setup_utils_mock(*mocks) command = shlex.split("env cat") workflow = Workflow(argv=command) env = workflow.environment self.assertEqual([], sorted(env.data.keys())) @mock.patch("compose_flow.commands.workflow.os") def test_docker_image_prefix_default(self, *mocks): """ Ensure the default image name will not accidentally push the image to a remote registry """ os_mock = mocks[0] os_mock.environ = {} os_mock.path.exists.return_value = False workflow = Workflow(argv=[]) self.assertEqual(workflow.docker_image_prefix, "localhost.localdomain") @mock.patch("compose_flow.commands.workflow.settings") @mock.patch("compose_flow.commands.workflow.os") def test_docker_image_prefix_from_os_settings(self, *mocks): os_mock = mocks[0] os_mock.path.exists.return_value = False settings_mock = mocks[1] settings_mock.DOCKER_IMAGE_PREFIX = "foo" workflow = Workflow(argv=[]) self.assertEqual(workflow.docker_image_prefix, "foo") @mock.patch( "compose_flow.commands.workflow.Workflow.app_config", new_callable=mock.PropertyMock, ) def test_docker_image_prefix_from_app_config(self, *mocks): image_prefix = "registry.prefix.com/foo" app_config_mock = mocks[0] app_config_mock.return_value = {"build": {"image_prefix": image_prefix}} workflow = Workflow(argv=[]) self.assertEqual(workflow.docker_image_prefix, image_prefix) def test_load_env_when_env_specified(self, *mocks): self._setup_docker_config_mock(*mocks) self._setup_utils_mock(*mocks) command = shlex.split("-e dev env cat") workflow = Workflow(argv=command) env = workflow.environment self.assertEqual(["BAR", "FOO"], sorted(env.data.keys())) self.assertEqual("1", env.data["FOO"]) self.assertEqual("2", env.data["BAR"]) @mock.patch( "compose_flow.commands.workflow.Workflow.subcommand", new_callable=mock.PropertyMock, ) def test_setup_environment_flag(self, *mocks): """ Ensures the environment cache is set to an empty dictionary when a workflow environment should not be setup """ subcommand_mock = mocks[0] subcommand_mock.return_value.setup_environment = False command = shlex.split("-e dev env cat") workflow = Workflow(argv=command) workflow._setup_environment() self.assertEqual({}, workflow.environment._data) @mock.patch("compose_flow.commands.workflow.print") @mock.patch("compose_flow.commands.workflow.pkg_resources") def test_version(self, *mocks): """ Ensure the --version arg just returns the version """ version = "0.0.0-test" pkg_resources_mock = mocks[0] pkg_resources_mock.require.return_value = [mock.Mock(version=version)] command = shlex.split("--version") workflow = Workflow(argv=command) workflow.run() print_mock = mocks[1] print_mock.assert_called_with(version) # noinspection PyUnusedLocal @mock.patch("compose_flow.commands.workflow.PROJECT_NAME", new=TEST_PROJECT_NAME) class WorkflowArgsTestCase(TestCase): """ Tests for parsing command line arguments """ def test_sensible_defaults_no_env(self, *mocks): """ Test sensible defaults when no environment is defined """ command = shlex.split("publish") workflow = Workflow(argv=command) self.assertEqual(None, workflow.args.environment) self.assertEqual(None, workflow.args.remote) self.assertEqual(TEST_PROJECT_NAME, workflow.config_name) self.assertEqual(TEST_PROJECT_NAME, workflow.project_name) def test_sensible_defaults_with_env(self, *mocks): """ Test sensible defaults when an environment is defined """ env = "dev" command = shlex.split(f"-e {env} publish") workflow = Workflow(argv=command) self.assertEqual(env, workflow.args.environment) self.assertEqual(env, workflow.args.remote) self.assertEqual(f"{env}-{TEST_PROJECT_NAME}", workflow.config_name) self.assertEqual(TEST_PROJECT_NAME, workflow.project_name) def test_sensible_defaults_with_env_and_project(self, *mocks): """ Test sensible defaults when an environment and project name is defined """ env = "dev" command = shlex.split(f"-e {env} --project-name foo publish") workflow = Workflow(argv=command) self.assertEqual(env, workflow.args.environment) self.assertEqual(env, workflow.args.remote) self.assertEqual(f"{env}-foo", workflow.config_name) self.assertEqual("foo", workflow.project_name)

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python/ceasarCipher.py

majid-T/snippets

1

187621

<reponame>majid-T/snippets # Simplified Ceasar Cipher example works on alphabetic charachtors only. # Numbers provide a small pre-image since we are ciphering on each word. So numbers are not # accepted and instead of 1 you should type one to increese security import tkinter as tk # All alphabet chars in a list chars = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'] # Function to cipher a charcahter by provided shift def getCipherChar(inCh, shift): # For space only return space if inCh == ' ': return ' ' # Get the current index of charachter in our list index = chars.index(inCh) # shift the index newIndex = index+shift # Check if new index is bigger than lenght of list -1 to avoid out of range index, # if it is deduct lenght to cycle from begining if(newIndex > len(chars)-1): newIndex -= len(chars) return chars[newIndex] # Function to iterate n a phrase and chipher charachters # Uses the lenght of the first word as shift def cipherPhrase(): phrase = originalPhrase.get() shift = len(phrase.split(' ')[0]) result = '' for ch in phrase: result += getCipherChar(ch, shift) data.delete(1.0, tk.END) data.insert(tk.END, result) # GUI part of code win = tk.Tk() win.title('Ceasar Cipher code') originalPhrase = tk.Entry(win, width=40) originalPhrase.grid(row=0, column=0, padx=3, pady=3) button = tk.Button(win, text='Encrypt text', command=cipherPhrase) button.grid(row=0, column=1, pady=5) data = tk.Text(win, height=20, width=100) data.grid(row=1, column=0, columnspan=2) labelFooter = tk.Label( win, text='Sample non official Copy- Under dev version <EMAIL>') labelFooter.grid(row=2, column=0, columnspan=2, padx=10, pady=1) win.mainloop()

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recipes/recipe_modules/cloudbuildhelper/__init__.py

NDevTK/chromium-infra

0

193345

# Copyright 2019 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. PYTHON_VERSION_COMPATIBILITY = 'PY2+3' DEPS = [ 'recipe_engine/cipd', 'recipe_engine/commit_position', 'recipe_engine/context', 'recipe_engine/file', 'recipe_engine/golang', 'recipe_engine/json', 'recipe_engine/nodejs', 'recipe_engine/path', 'recipe_engine/raw_io', 'recipe_engine/step', 'depot_tools/depot_tools', 'depot_tools/git', 'depot_tools/git_cl', ]

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elpis/engines/common/input/clean_json.py

guillaume-wisniewski/elpis

118

119213

<gh_stars>100-1000 #!/usr/bin/python3 """ Given a json file with transcript information these tools can perform - manipulations including generating word lists or filtering output to - exclude english/punctuation. - Optionally provide the output json file name with -j Usage: python3 clean_json.py [-h] [--i INFILE] [--o OUTFILE] [-r] [-u] Copyright: University of Queensland, 2019 Contributors: <NAME> - (The University of Queensland, 2017) <NAME> - (The University of Queensland, 2018) <NAME> - (The University of Queensland, 2019) """ import os import re import sys import nltk from argparse import ArgumentParser from langid.langid import LanguageIdentifier, model from nltk.corpus import words from typing import Dict, List, Set from ..utilities import load_json_file, write_data_to_json_file def clean_utterance(utterance: Dict[str, str], punctuation_to_collapse_by: str = '', punctuation_to_explode_by: str = '', special_cases: set = {None}, translation_tags: set = {None}, remove_english: bool = False, english_words: set = None) -> (List[str], int): """ Takes an utterance and cleans it based on the rules established by the provided parameters. :param utterance: a dictionary with a "transcript" key-value pair. :param punctuation_to_collapse_by: punctuation marks to strip. :param punctuation_to_explode_by: punctuation marks to replace with spaces. :param special_cases: a list of words to always remove from the output. :param translation_tags: a list of tags to always remove from the output. :param remove_english: whether or not to remove English words. :param english_words: a list of english words to remove from the transcript (we suggest the nltk corpus). :return: a tuple with a list of 'cleaned' words and the number of English to remove. """ # TODO add interface setting to include user specific tags # translation_tags = {"@eng@", "<ind:", "<eng:"} # TODO add interface setting to skip this as caps are significant in some languages utterance_string = utterance.get("transcript").lower() dirty_words = utterance_string.split() clean_words = [] english_word_count = 0 for word in dirty_words: if word in special_cases: continue if remove_english and len(word) > 3 and word in english_words: english_word_count += 1 continue if word in translation_tags: return [], 0 # Word is ok to use, now clean it word = deal_with_punctuation(text=word, punctuation_to_collapse_by=punctuation_to_collapse_by, punctuation_to_explode_by=punctuation_to_explode_by) clean_words.append(word) return clean_words, english_word_count def get_english_words() -> Set[str]: """ Gets a list of English words from the nltk corpora (~235k words). N.B: will download the word list if not already available (~740kB), requires internet. :return: a set containing the English words """ nltk.download("words") # Will only download if not locally available. return set(words.words()) def are_words_valid(clean_words: List[str], english_word_count: int, remove_english: bool, use_langid: bool) -> bool: """ Determines whether a list of words is valid based on the provided parameters. :param clean_words: a list of clean word strings. :param english_word_count: the number of english words removed from the string during cleaning. :param remove_english: whether or not to remove english words. :param use_langid: whether or not to use the langid library to determine if a word is English. :return: True if utterance is valid, False otherwise. """ # Exclude utterance if empty after cleaning cleaned_transcription = " ".join(clean_words).strip() if cleaned_transcription == "": return False # Exclude utterance if > 10% english if remove_english and len(clean_words) > 0 and english_word_count / len(clean_words) > 0.1: # print(round(english_word_count / len(clean_words)), trans, file=sys.stderr) return False # Exclude utterance if langid thinks its english if remove_english and use_langid: langid_identifier = LanguageIdentifier.from_modelstring(model, norm_probs=True) lang, prob = langid_identifier.classify(cleaned_transcription) if lang == "en" and prob > 0.5: return False return True def clean_json_utterance(utterance: Dict[str, str], punctuation_to_collapse_by: str = '', punctuation_to_explode_by: str = '', special_cases: set = {None}, translation_tags: set = {None}, remove_english: bool = False, use_langid: bool = False) -> Dict[str, str]: """ Clean an utterance (Python dictionary) based on the given parameters. :param utterance: Python dictionary, must have a 'transcript' key-value. :param punctuation_to_collapse_by: punctuation marks to strip. :param punctuation_to_explode_by: punctuation marks to replace with spaces. :param special_cases: a list of words to always remove from the output. :param translation_tags: a list of tags to always remove from the output. :param remove_english: whether or not to remove English from the utterances. :param use_langid: whether or not to use the langid library to identify English to remove. :return: cleaned utterance (dictionary). """ # TODO make this an interface setting # special_cases = ["<silence>"] # Any words you want to ignore if remove_english: english_words = get_english_words() # pre-load English corpus else: english_words = set() # Clean the text in the dict, returns a list of cleaned words clean_words, english_word_count = \ clean_utterance(utterance=utterance, punctuation_to_collapse_by=punctuation_to_collapse_by, punctuation_to_explode_by=punctuation_to_explode_by, special_cases=special_cases, translation_tags=translation_tags, remove_english=remove_english, english_words=english_words ) # Check that the cleaned words are valid (ie, not null, not English etc) if are_words_valid(clean_words, english_word_count, remove_english, use_langid): cleaned_transcript = " ".join(clean_words).strip() else: # TODO is it best to return an empty str here or raise an error? cleaned_transcript = "" utterance['transcript'] = cleaned_transcript return utterance def clean_json_data(json_data: List[Dict[str, str]], punctuation_to_collapse_by: str = '', punctuation_to_explode_by: str = '', special_cases: set = {None}, translation_tags: set = {None}, remove_english: bool = False, use_langid: bool = False, ) -> List[Dict[str, str]]: """ Clean a list of utterances (Python dictionaries) based on the given parameters. :param json_data: List of Python dictionaries, each must have a 'transcript' key-value. :param punctuation_to_collapse_by: punctuation marks to strip. :param punctuation_to_explode_by: punctuation marks to replace with spaces. :param special_cases: a list of words to always remove from the output. :param translation_tags: a list of tags to always remove from the output. :param remove_english: whether or not to remove English from the utterances. :param use_langid: whether or not to use the langid library to identify English to remove. :return: list of cleaned utterances (dictionaries). """ json_data_cleaned = [] for utterance in json_data: utterance_cleaned = clean_json_utterance(utterance, punctuation_to_collapse_by=punctuation_to_collapse_by, punctuation_to_explode_by=punctuation_to_explode_by, special_cases=special_cases, translation_tags=translation_tags, remove_english=remove_english, use_langid=use_langid) json_data_cleaned.append(utterance_cleaned) return json_data_cleaned def extract_additional_corpora(additional_corpus: str = '', corpus_txt: str = '', punctuation_to_collapse_by: str = '', punctuation_to_explode_by: str = '') -> None: """ Takes a text file, extracts all sentences and writes them to the main corpus file. :param additional_corpus: the path to a plaintext file to extract additional sentences/lines from :param corpus_txt: the path to the compiled corpus.txt file :param punctuation_to_collapse_by: punctuation marks to strip :param punctuation_to_explode_by: punctuation marks to replace with spaces """ print("corpus_txt", corpus_txt) if os.path.exists(corpus_txt): write_mode = 'a' # append if already exists else: write_mode = 'w' # make a new file if not with open(corpus_txt, write_mode) as corpus_txt_file: if os.path.exists(additional_corpus): print(f"Extracting corpus examples from: {additional_corpus}") with open(additional_corpus, "r", encoding="utf-8", ) as file_: for line in file_.readlines(): # clean the text along the way line = \ deal_with_punctuation(text=line, punctuation_to_collapse_by=punctuation_to_collapse_by, punctuation_to_explode_by=punctuation_to_explode_by) if not line.endswith('\n'): line = line + '\n' corpus_txt_file.writelines(line) else: print(f"Provided additional text additional_corpus file path invalid: " f"{additional_corpus}") def deal_with_punctuation(text: str = '', punctuation_to_collapse_by: str = '', punctuation_to_explode_by: str = '') -> str: """ Removes punctuation from a string :param text: original text :param punctuation_to_collapse_by: punctuation marks to strip :param punctuation_to_explode_by: punctuation marks to replace with spaces :return: cleaned text """ new_text: str = text # Prioritise exploding first, these are punctuation marks that the user sets if punctuation_to_explode_by: pattern_to_explode_by = re.escape(punctuation_to_explode_by) new_text = re.sub(rf"[{pattern_to_explode_by}]", " ", new_text) # Then strip the rest if punctuation_to_collapse_by: pattern_to_collapse_by = re.escape(punctuation_to_collapse_by) new_text = re.sub(rf"[{pattern_to_collapse_by}]", "", new_text) return new_text def main() -> None: """ Run the entire clean_json process as a command line utility. Usage: python3 clean_json.py [--i INFILE] [--o OUTFILE] [-r] [-u] """ parser: ArgumentParser = ArgumentParser() parser.add_argument("-i", "--infile", type=str, help="The path to the dirty json file to clean.", required=True) parser.add_argument("-o", "--outfile", type=str, help="The path to the clean json file to write to", required=True) parser.add_argument("-r", "--remove_english", help="Remove english-like utterances", action="store_true") parser.add_argument("-u", "--use_langid", help="Use langid library to detect English", action="store_true") # TODO add defaults parser.add_argument("-c", "--punctuation_to_collapse_by", type=str, help="Chars to strip") parser.add_argument("-e", "--punctuation_to_explode_by", type=str, help="Chars to strip and replace with spaces") arguments = parser.parse_args() dirty_json_data: List[Dict[str, str]] = load_json_file(arguments.infile) outfile = arguments.outfile if arguments.outfile else sys.stdout print(f"Filtering dirty json data {arguments.infile}...") filtered_data = clean_json_data(json_data=dirty_json_data, remove_english=arguments.remove_english, use_langid=arguments.use_langid, punctuation_to_collapse_by=arguments.punctuation_to_collapse_by, punctuation_to_explode_by=arguments.punctuation_to_explode_by) write_data_to_json_file(data=list(filtered_data), file_name=outfile) print(f"Finished! Wrote {str(len(filtered_data))} transcriptions.") if __name__ == "__main__": main()

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server.py

vitorpamplona/relayserver

0

140114

import socket # for connecting to the server import _thread # to manage multiple clients import json # to enconde and decode the data host = '127.0.0.1' port = 5000 # initiate port no above 1024 clients = {} def server_program(): print('Starting Relay Server at ' + host + ':' + str(port)) # show in terminal print('Waiting for clients...') server_socket = socket.socket() # get instance server_socket.bind((host, port)) # bind host address and port together # configure how many clients the server can listen simultaneously server_socket.listen(2) while True: conn, address = server_socket.accept() # accept new connection _thread.start_new_thread(on_new_client,(conn, address)) #puts into a thread. server_socket.close() # close the connection def on_new_client(clientsocket, addr): # print("Connection from: " + str(addr)) # print("Waiting for client name...") # wait for the first message. msgRaw = clientsocket.recv(1024).decode() # print("Message received: " + msgRaw) # Register the socket name at a list. msgDict = json.loads(msgRaw) print(msgDict['client_id'] + " connected") clients[msgDict['client_id']] = clientsocket wait_for_next_message(clientsocket, msgDict['client_id']) def wait_for_next_message(clientsocket, client_id): # wait for a message. msgRaw = clientsocket.recv(1024).decode() while msgRaw: print("Message received from " + client_id + ": " + msgRaw) msgDict = json.loads(msgRaw) if msgDict['to'] in clients.keys(): #reformat the message formattedMsg = json.dumps({'from': client_id, 'data': msgDict['data']}) # sending it to the next partner clients[msgDict['to']].send(formattedMsg.encode()) # send data to the client else: print(msgDict['to'] + ' is not connected') # could not find the other socket formattedMsg = json.dumps({'error':msgDict['to']+ ' not found'}) # sending error message back. clientsocket.send(formattedMsg.encode()) # send data to the client #wait for next message msgRaw = clientsocket.recv(1024).decode() clientsocket.close() if __name__ == '__main__': server_program()

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Algorithms/LCP/29/math1.py

M-Quadra/LeetCode-problems

0

17971

<reponame>M-Quadra/LeetCode-problems class Solution: def orchestraLayout(self, num: int, xPos: int, yPos: int) -> int: a, b = (min(xPos, num-1-yPos), 1) if yPos >= xPos else (min(yPos, num-1-xPos), -1) return (4*num*a - 4*a*a - 2*a + b*(xPos+yPos) + (b>>1&1)*4*(num-a-1))%9 + 1

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lecture_04/312_plan_motion_ros_artist.py

farzanehesk/COMPAS-II-FS2022

11

17499

import math import time from compas_fab.backends import RosClient from compas.artists import Artist from compas.geometry import Frame with RosClient("localhost") as client: robot = client.load_robot(load_geometry=True) group = robot.main_group_name frame = Frame((0.4, 0.3, 0.05), (-1, 0, 0), (0, 1, 0)) tolerance_position = 0.001 tolerance_axes = [math.radians(1)] * 3 start_configuration = robot.zero_configuration() start_configuration.joint_values = (-0.106, 5.351, 2.231, -2.869, 4.712, 1.465) # create goal constraints from frame goal_constraints = robot.constraints_from_frame(frame, tolerance_position, tolerance_axes, group) trajectory = robot.plan_motion(goal_constraints, start_configuration, group, options=dict(planner_id="RRT")) print("Computed kinematic path with %d configurations." % len(trajectory.points)) print("Executing this path at full speed would take approx. %.3f seconds." % trajectory.time_from_start) artist = Artist(robot.model) for tp in trajectory.points: config = robot.zero_configuration() config.joint_values = tp.joint_values artist.update(config) artist.draw_visual() artist.redraw() time.sleep(0.02)

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pymath/vampire_numbers/vampire_numbers.py

JASTYN/pythonmaster

3

82769

def vampire_test(x, y): product = x * y if len(str(x) + str(y)) != len(str(product)): return False for i in str(x) + str(y): if i in str(product): return True else: return False

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sandbox/decorator_use.py

olileger/sBeull

0

1615107

<gh_stars>0 from decorator_def import FuncDecoratorOne, FuncDecoratorListProperties, ClassDecoratorOne class FuncDecoratorUse: def __init__(_self_): _self_.var1 = "test" _self_.var2 = "abc" _self_.var3 = 12345 @FuncDecoratorListProperties def FunctionOne(_self_, v): print("FunctionOne: ", v) t = FuncDecoratorUse() t.FunctionOne("Test") @ClassDecoratorOne class ClassDecoratorUser: def __init__(_self_): print("Hello, World !") t = ClassDecoratorUser()

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pyglobi/config.py

nicholac/pyglobi

0

92040

<gh_stars>0 # -*- coding: utf-8 -*- """ pyglobi.config ~~~~~~~~~~~~ Configuration used by pyglobi :copyright: (c) 2019 by <NAME>. :license: MIT, see LICENSE for more details. """ # Base URL's globi_neo_url = "https://neo4j.globalbioticinteractions.org/db/data/cypher" eol_url = "https://eol.org/pages" eol_geo_url = "https://eol.org/data/map_data_dwca"

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pset_4/tasks/stylize.py

GabrielBG010/PSET4

0

155053

<reponame>GabrielBG010/PSET4<gh_stars>0 import os import luigi from luigi import ExternalTask, Parameter, Task, LocalTarget from luigi.format import FileWrapper from csci_utils.luigi.target import SuffixPreservingLocalTarget from neural_style.neural_style import stylize from pset_4.tasks.data import DownloadImage, DownloadModel class Stylize(Task): """ Luigi Taks that stylize images according to a model """ model = Parameter(default=r"rain_princess.pth") image = Parameter(default=r"luigi.jpg") def requires(self): return { 'image': self.clone(DownloadImage), 'model': self.clone(DownloadModel) } def output(self): # return SuffixPreservingLocalTarget of the stylized image return SuffixPreservingLocalTarget(os.path.join('images_out', "luigiout.jpg"), format=luigi.format.Nop) def run(self): inputs = self.input() with self.output().temporary_path() as temp_output_path: class args: content_image = inputs['image'].path output_image = temp_output_path model = inputs['model'].path cuda = 0 content_scale = None export_onnx = None stylize(args)

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movements.py

sxnnytripathi/GestureGaming

0

105598

<gh_stars>0 import numpy as np import cv2 import time import math import imutils from imutils.video import FileVideoStream, VideoStream import keyboard from pynput.keyboard import Key, Controller # keyboard=Controller() size_height = 600 size_width = 600 last = 0 # last extreme of steer chng = 3 # change in last, to remove small changes fulucations chngt = 3 # to restart change to 0, if moved towards centre by some value COLOR_RED = (0, 0, 255) COLOR_BLACK = (0, 0, 0) THRESH_ACTION = 60 # t def get_centroid(bbox): # center point of rectangle x, y, w, h = bbox centx = (2 * x + w) // 2 centy = (2 * y + h) // 2 return (centx, centy) def draw_circle(frame, center, radius=THRESH_ACTION, color=COLOR_BLACK, thickness=3): cv2.circle(frame, center, radius, color, thickness) def drawbox(ret, bbox, frame): # draws rectangle from bbox global q if ret: # Tracking Success p1 = (int(bbox[0]), int(bbox[1])) p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3])) cv2.rectangle(frame, p1, p2, (255, 0, 0), 2, 1) if p1[0] < 10: # quits program if taken hand to left top corner q = True # quit color = {"blue": (255, 0, 0), "red": (0, 0, 255), "green": (0, 255, 0), "white": (255, 255, 255)} def detect_center(img, hand): # convert image to gray-scale # drawing rectangle around the feature and labeling it (x, y, w, h) = [int(v) for v in hand] # cv2.rectangle(img, (x,y), (x+w, y+h), color['green'], 2) #uncomment if you want to see face boundary cv2.circle(img, ((2 * x + w) // 2, (2 * y + h) // 2), 2, color['green'], 2) hand = ((2 * x + w) // 2, (2 * y + h) // 2) return img, hand # cord value will be decided by uh by assigning the cirle on screen def draw_controller_left(img, cords): size = 40 # x1 = cords[0] - size y1 = cords[1] - size # x2 = cords[0] + size y2 = cords[1] + size cv2.circle(img, cords, size, color['blue'], 2) return (y1, y2) def draw_controller_right(img, cords): size = 40 x1 = cords[0] - size # y1 = cords[1] - size x2 = cords[0] + size # y2 = cords[1] + size cv2.circle(img, cords, size, color['red'], 2) return (x1, x2) def keyboard_events_l(lcord, cord_left, cmd): try: # x1,x2 = cord_right y1, y2 = cord_left xl, yl = lcord # xr,yr=rcord except Exception as e: print(e) return # if xr < x1: # cmd = "left" # elif(xr > x2): # cmd = "right" if (yl < y1): cmd = "z" elif (yl > y2): cmd = "down" if cmd: print("Detected movement: ", cmd, "\n") # if cmd=="left": # keyboard.press(cmd) # elif cmd=="right": # keyboard.press(cmd) keyboard.press(cmd) # keyboard.press(cmd) return cmd def keyboard_events_r(rcord, cord_right, cmd2): try: x1, x2 = cord_right # y1,y2 = cord_left # xl,yl=lcord xr, yr = rcord except Exception as e: print(e) return if xr < x1: cmd2 = "left" elif (xr > x2): cmd2 = "right" # elif(yl<y1): # cmd = "up" # elif(yl > y2): # cmd = "down" if cmd2: print("Detected another movement: ", cmd2, "\n") keyboard.press(cmd2) # elif cmd=="up": # keyboard.press(cmd2) # elif cmd=="down": # keyboard.press(cmd2) # keyboard.press(cmd) return cmd2 def reset_press_flag(lcord, rcord, cord_left, cord_right, cmd, cmd2): try: x1, x2 = cord_right y1, y2 = cord_left xl, yl = lcord xr, yr = rcord # xc, yc = nose_cords except: return True, cmd, cmd2 if x1 < xr < x2 or y1 < yl < y2: if x1 < xr < x2 and (cmd2 != None and cmd2 != ""): keyboard.release(cmd2) if y1 < yl < y2 and (cmd != None and cmd != ""): keyboard.release(cmd) if x1 < xr < x2 and y1 < yl < y2: # keyboard.release(cmd) return True, None, None elif x1 < xr < x2: return True, cmd, None elif y1 < yl < y2: return True, None, cmd2 return False, cmd, cmd2 def get_frame(): # to get frame by reading fvs, init fvs first res, frame = fvs.read() if frame is None: raise Exception("Frame Not Found") return frame = cv2.flip(frame, 1) frame = imutils.resize(frame, width=size_width, height=size_height) return frame fvs = cv2.VideoCapture(0) # 0 for web cam time.sleep(2.0) # to allow web cam to open TIMER_SETUP = 3 # timer for capturing base image t = time.time() while True: frame = get_frame() curr = (time.time() - t) if curr > TIMER_SETUP: break cv2.putText(frame, str(int(TIMER_SETUP - curr) + 1), (225, 255), cv2.FONT_HERSHEY_SIMPLEX, 1.5, COLOR_RED, 4) cv2.imshow("Setup", frame) cv2.waitKey(1) FRAME = frame.copy() cv2.destroyAllWindows() # Make box around hand frame = FRAME.copy() cv2.putText(frame, 'Select Left Hand for steering', (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.75, COLOR_RED, 2) bboxleft = cv2.selectROI(frame, False) # bounding box for left hand frame = FRAME.copy() cv2.putText(frame, 'Select Right Hand for acceleration', (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.75, COLOR_RED, 2) bboxright = cv2.selectROI(frame, False) # bounding box for right hand # centleft = get_centroid(bboxleft) # centright = get_centroid(bboxright) # draw_circle(frame, centleft) # circle, outside this movements will happend # draw_circle(frame, centright) BBL, BBR = bboxleft, bboxright # saving it for later cv2.destroyAllWindows() # fvs.release() # BBR ''' cv2.destroyAllWindows() ''' # fvs = cv2.VideoCapture(0) #0 for web cam # time.sleep(2.0) bboxleft, bboxright = BBL, BBR # Creating the CSRT Tracker trackerleft = cv2.TrackerCSRT_create() # left hand for steering trackerright = cv2.TrackerCSRT_create() # right hand for acceleration (acc) # Initialize tracker with first frame and bounding box trackerleft.init(FRAME, bboxleft) trackerright.init(FRAME, bboxright) # cv2.putText(frame.copy(), 'Put both your hands in Postion', (100,70), \ cv2.FONT_HERSHEY_SIMPLEX, 0.75, COLOR_BLACK, 2 press_flag = False cmd = "" cmd2 = "" while True: frame = get_frame() # if curr > TIMER_SETUP or frame is None: # break # cv2.putText(frame, str(int(TIMER_SETUP - curr)+1), (225,255), cv2.FONT_HERSHEY_SIMPLEX, 1.5, COLOR_RED, 4) if BBL is not None: # grab the new bounding box coordinates of the object (success, box) = trackerleft.update(frame) (success1, box1) = trackerright.update(frame) # check to see if the tracking was a success if success: (x, y, w, h) = [int(v) for v in box] cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) (a, b, c, d) = [int(f) for f in box1] cv2.rectangle(frame, (a, b), (a + c, b + d), (0, 255, 0), 2) # cv2.putText(img, cmd, (10,50), cv2.FONT_HERSHEY_SIMPLEX, 1, color['red'], 1, cv2.LINE_AA) frame, lcord = detect_center(frame, box) frame, rcord = detect_center(frame, box1) # print("detect_center func called...") # draw boundary circle cord_left = draw_controller_left(frame, (110, 189)) cord_right = draw_controller_right(frame, (479, 189)) # print("circle draw done") if press_flag and (cmd == None or cmd == ""): # print("calling keyboard event") cmd = keyboard_events_l(lcord, cord_left, cmd) if press_flag and (cmd2 == None or cmd2 == ""): cmd2 = keyboard_events_r(rcord, cord_right, cmd2) press_flag, cmd, cmd2 = reset_press_flag(lcord, rcord, cord_left, cord_right, cmd, cmd2) # print("press flag: ",press_flag) # Writing processed image in a new window cv2.imshow("Tracking", frame) if cv2.waitKey(1) == 13: break cv2.destroyAllWindows() fvs.release() # fvs.release() # BBL

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tests/util/alert_server.py

grayfallstown/greendoge-blockchain

44

64852

<gh_stars>10-100 import argparse import asyncio import logging from pathlib import Path from typing import Any from aiohttp import web log = logging.getLogger(__name__) class AlertServer: shut_down: bool shut_down_event: asyncio.Event log: Any app: Any alert_file_path: Path port: int @staticmethod async def create_alert_server(alert_file_path: Path, port): self = AlertServer() self.log = log self.shut_down = False self.app = web.Application() self.shut_down_event = asyncio.Event() self.port = port routes = [ web.get("/status", self.status), ] self.alert_file_path = alert_file_path self.app.add_routes(routes) return self async def status(self, request): file_text = self.alert_file_path.read_text() return web.Response(body=file_text, content_type="text/plain") async def stop(self): self.shut_down_event.set() async def run(self): runner = web.AppRunner(self.app, access_log=None) await runner.setup() site = web.TCPSite(runner, None, self.port) await site.start() async def run_and_wait(file_path, port): server = await AlertServer.create_alert_server(Path(file_path), port) await server.run() await server.shut_down_event.wait() def main(): parser = argparse.ArgumentParser() parser.add_argument("-file_path", type=str, dest="file_path") parser.add_argument("-port", type=str, dest="port") port = None file_path = None for key, value in vars(parser.parse_args()).items(): if key == "port": port = value elif key == "file_path": file_path = value else: print(f"Invalid argument {key}") if port is None or file_path is None: print( "Missing arguments, example usage:\n\n" "python greendoge/util/alert_server.py -p 4000 -file_path /home/user/alert.txt\n" ) quit() return asyncio.get_event_loop().run_until_complete(run_and_wait(file_path, port)) if __name__ == "__main__": main()

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streamkit/database/core/orm.py

glentner/streamkit

2

117420

# This program is free software: you can redistribute it and/or modify it under the # terms of the Apache License (v2.0) as published by the Apache Software Foundation. # # This program is distributed in the hope that it will be useful, but WITHOUT ANY # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A # PARTICULAR PURPOSE. See the Apache License for more details. # # You should have received a copy of the Apache License along with this program. # If not, see <https://www.apache.org/licenses/LICENSE-2.0>. """Database ORM for StreamKit.""" # type annotations from __future__ import annotations from typing import Tuple, Dict, Any # standard libs import logging # external libs from sqlalchemy.ext.declarative import declarative_base from sqlalchemy import Column, ForeignKey, Index, Integer, BigInteger, String, DateTime from sqlalchemy.schema import Sequence, CheckConstraint from sqlalchemy.orm import relationship # internal libs from .engine import schema, db_config # initialize module level logger log = logging.getLogger(__name__) Table = declarative_base() class Level(Table): """ A level relates a name and its identifier. """ __tablename__ = 'level' __table_args__ = {'schema': schema} id = Column('id', Integer, primary_key=True) name = Column('name', String, unique=True, nullable=False) def __repr__(self) -> str: return f'<Level(id={repr(self.id)}, name={repr(self.name)})>' def to_dict(self) -> Dict[str, Any]: """Python dictionary representation.""" return {'id': self.id, 'name': self.name} def values(self, joined: bool = False) -> Tuple: # noqa: unused parameter """ Tuple representation of values. Args: joined (bool): Join relations instead of identifiers. Not used by `Level`. Returns: row (tuple): The `Level.id` and `Level.name`. """ return self.id, self.name @staticmethod def keys(joined: bool = False) -> Tuple[str, ...]: # noqa: unused parameter """ Tuple representation of keys. Args: joined (bool): Join relations instead of identifiers. Not used by `Level`. Returns: keys (tuple): ('id', 'name') """ return 'id', 'name' def embedded(self) -> Dict[str, Any]: """Re-map keys and include join relationships.""" return {'level_id': self.id, 'level_name': self.name} class Topic(Table): """ A topic relates a name and its identifier. """ __tablename__ = 'topic' __table_args__ = {'schema': schema} id = Column('id', Integer, primary_key=True) name = Column('name', String, unique=True, nullable=False) def __repr__(self) -> str: return f'<Topic(id={repr(self.id)}, name={repr(self.name)})>' def to_dict(self) -> Dict[str, Any]: """Python dictionary representation.""" return {'id': self.id, 'name': self.name} def values(self, joined: bool = False) -> Tuple: # noqa: unused parameter """ Tuple representation of values. Args: joined (bool): Join relations instead of identifiers. Not used by `Topic`. Returns: row (tuple): The `Topic.id` and `Topic.name`. """ return self.id, self.name @staticmethod def keys(joined: bool = False) -> Tuple[str, ...]: # noqa: unused parameter """ Tuple representation of keys. Args: joined (bool): Join relations instead of identifiers. Not used by `Topic`. Returns: keys (tuple): ('id', 'name') """ return 'id', 'name' def embedded(self) -> Dict[str, Any]: """Re-map keys and include join relationships.""" return {'topic_id': self.id, 'topic_name': self.name} class Host(Table): """ A host relates a name and its identifier. """ __tablename__ = 'host' __table_args__ = {'schema': schema} id = Column('id', Integer, primary_key=True) name = Column('name', String, unique=True, nullable=False) def __repr__(self) -> str: return f'<Host(id={repr(self.id)}, name={repr(self.name)})>' def to_dict(self) -> Dict[str, Any]: """Python dictionary representation.""" return {'id': self.id, 'name': self.name} def values(self, joined: bool = False) -> Tuple: # noqa: unused parameter """ Tuple representation of values. Args: joined (bool): Join relations instead of identifiers. Not used by `Host`. Returns: row (tuple): The `Host.id` and `Host.name`. """ return self.id, self.name @staticmethod def keys(joined: bool = False) -> Tuple[str, ...]: # noqa: unused parameter """ Tuple representation of keys. Args: joined (bool): Join relations instead of identifiers. Not used by `Host`. Returns: keys (tuple): ('id', 'name') """ return 'id', 'name' def embedded(self) -> Dict[str, Any]: """Re-map keys and include join relationships.""" return {'host_id': self.id, 'host_name': self.name} class Message(Table): __tablename__ = 'message' __table_args__ = {'schema': schema} id = Column('id', BigInteger().with_variant(Integer, 'sqlite'), primary_key=True) time = Column('time', DateTime(timezone=True), nullable=False) topic_id = Column('topic_id', Integer, ForeignKey(Topic.id), nullable=False) level_id = Column('level_id', Integer, ForeignKey(Level.id), nullable=False) host_id = Column('host_id', Integer, ForeignKey(Host.id), nullable=False) text = Column('text', String, nullable=False) topic = relationship('Topic', backref='message') level = relationship('Level', backref='message') host = relationship('Host', backref='message') # conditionally redefine for time-based partitioning if db_config['backend'] in ('timescale', ): # NOTE: The primary key is (`time`, `topic_id`) NOT `id`. # This is weird but important for automatic hyper-table partitioning # on the `time` values for TimeScaleDB (PostgreSQL). id = Column('id', BigInteger, Sequence('message_id_seq', start=1, increment=1, schema=schema), CheckConstraint('id > 0', name='message_id_check'), nullable=False) time = Column('time', DateTime(timezone=True), nullable=False, primary_key=True) topic_id = Column('topic_id', Integer, nullable=False, primary_key=True) # NOTE: remove explicit foreign keys? # level_id = Column('level_id', Integer, nullable=False) # host_id = Column('host_id', Integer, nullable=False) # # topic = relationship('Topic', backref='message', foreign_keys=[topic_id], # primaryjoin='Message.topic_id == Topic.id') # level = relationship('Level', backref='message', foreign_keys=[level_id], # primaryjoin='Message.level_id == Level.id') # host = relationship('Host', backref='message', foreign_keys=[host_id], # primaryjoin='Message.host_id == Host.id') def __repr__(self) -> str: return (f'<Message(id={repr(self.id)},' f' time={repr(self.time)},' f' topic_id={repr(self.topic_id)},' f' level_id={repr(self.level_id)},' f' host_id={repr(self.host_id)},' f' text={repr(self.text)})>') def to_dict(self) -> Dict[str, Any]: """Python dictionary representation.""" return {'id': self.id, 'time': self.time, 'topic_id': self.topic_id, 'level_id': self.level_id, 'host_id': self.host_id, 'text': self.text} def values(self, joined: bool = False) -> Tuple: """ Tuple representation of values. Args: joined (bool): Join relations instead of identifiers (default: False). """ if not joined: return self.id, self.time, self.topic_id, self.level_id, self.host_id, self.text else: return self.id, self.time, self.topic.name, self.level.name, self.host.name, self.text @staticmethod def keys(joined: bool = False) -> Tuple[str, ...]: """ Tuple representation of keys. Args: joined (bool): Join relations instead of identifiers (default: False). """ if not joined: return 'id', 'time', 'topic_id', 'level_id', 'host_id', 'text' else: return 'id', 'time', 'topic', 'level', 'host', 'text' def embedded(self) -> Dict[str, Any]: """Re-map keys and include join relationships.""" message = {'message_time': self.time.strftime('%Y-%m-%d %H:%M:%S.%f'), 'message_text': self.text} return {**message, **self.topic.embedded(), **self.level.embedded(), **self.host.embedded()} if db_config['backend'] in ('timescale', ): # we use time-topic PK and need to index ID message_id_index = Index('message_id_index', Message.id) else: message_time_topic_index = Index('message_time_topic_index', Message.time, Message.topic_id) # efficient filtering on host or level message_level_index = Index('message_level_index', Message.level_id) message_host_index = Index('message_host_index', Message.host_id) class Subscriber(Table): """ A subscriber relates a name and its identifier. """ __tablename__ = 'subscriber' __table_args__ = {'schema': schema} id = Column('id', Integer, primary_key=True) name = Column('name', String, unique=True, nullable=False) def __repr__(self) -> str: return f'<Subscriber(id={repr(self.id)}, name={repr(self.name)})>' def to_dict(self) -> Dict[str, Any]: """Python dictionary representation.""" return {'id': self.id, 'name': self.name} def values(self, joined: bool = False) -> Tuple: # noqa: unused parameter """ Tuple representation of values. Args: joined (bool): Join relations instead of identifiers. Not used by `Subscriber`. Returns: row (tuple): The `Subscriber.id` and `Subscriber.name`. """ return self.id, self.name @staticmethod def keys(joined: bool = False) -> Tuple[str, ...]: # noqa: unused parameter """ Tuple representation of keys. Args: joined (bool): Join relations instead of identifiers. Not used by `Subscriber`. Returns: keys (tuple): ('id', 'name') """ return 'id', 'name' def embedded(self) -> Dict[str, Any]: """Re-map keys and include join relationships.""" return {'subscriber_id': self.id, 'subscriber_name': self.name} class Access(Table): __tablename__ = 'access' __table_args__ = {'schema': schema} subscriber_id = Column('subscriber_id', Integer, ForeignKey(Subscriber.id), nullable=False, primary_key=True) topic_id = Column('topic_id', Integer, ForeignKey(Topic.id), nullable=False, primary_key=True) time = Column('time', DateTime(timezone=True), nullable=False) subscriber = relationship('Subscriber', backref='access') topic = relationship('Topic', backref='access') def __repr__(self) -> str: return (f'<Access(subscriber_id={repr(self.subscriber_id)},' f' topic_id={repr(self.topic_id)},' f' time={repr(self.time)})>') def to_dict(self) -> Dict[str, Any]: """Python dictionary representation.""" return {'subscriber_id': self.subscriber_id, 'topic_id': self.topic_id, 'time': self.time} def values(self, joined: bool = False) -> Tuple: """ Tuple representation of values. Args: joined (bool): Join relations instead of identifiers (default: False). """ if not joined: return self.subscriber_id, self.topic_id, self.time else: return self.subscriber.name, self.topic.name, self.time @staticmethod def keys(joined: bool = False) -> Tuple[str, ...]: """ Tuple representation of keys. Args: joined (bool): Join relations instead of identifiers (default: False). """ if not joined: return 'subscriber_id', 'topic_id', 'time' else: return 'subscriber', 'topic', 'time' def embedded(self) -> Dict[str, Any]: """Re-map keys and include join relationships.""" access = {'access_time': self.time.strftime('%Y-%m-%d %H:%M:%S.%f')} return {**access, **self.subscriber.embedded(), **self.topic.embedded()} tables: Dict[str, Table] = { 'level': Level, 'topic': Topic, 'host': Host, 'message': Message, 'subscriber': Subscriber, 'access': Access }

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napari_plugin_engine/__init__.py

tlambert-forks/napari-plugin-engine

6

75160

<filename>napari_plugin_engine/__init__.py try: from ._version import version as __version__ except ImportError: # broken installation, we don't even try # unknown only works because we do poor mans version compare __version__ = "unknown" __all__ = [ "get_metadata", "HookCaller", "HookCallError", "HookImplementation", "HookImplementationMarker", "HookResult", "HookSpecification", "HookSpecificationMarker", "napari_hook_implementation", "napari_hook_specification", "PluginCallError", "PluginError", "PluginImplementationError", "PluginImportError", "PluginManager", "PluginRegistrationError", "PluginValidationError", "standard_metadata", ] from .callers import HookResult from .dist import get_metadata, standard_metadata from .exceptions import ( HookCallError, PluginCallError, PluginError, PluginImplementationError, PluginImportError, PluginRegistrationError, PluginValidationError, ) from .hooks import HookCaller from .implementation import HookImplementation, HookSpecification from .manager import PluginManager from .markers import HookImplementationMarker, HookSpecificationMarker napari_hook_implementation = HookImplementationMarker("napari") napari_hook_specification = HookSpecificationMarker("napari")

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tests/test_tipc/bigru_crf/export_model.py

mukaiu/PaddleNLP

0

83592

# -*- coding: UTF-8 -*- # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os import paddle from paddle.static import InputSpec import paddlenlp as ppnlp from paddlenlp.data import Vocab from data import load_vocab from model import BiGruCrf # yapf: disable parser = argparse.ArgumentParser(__doc__) parser.add_argument("--data_dir", type=str, default=None, help="The folder where the dataset is located.") parser.add_argument("--params_path", type=str, default='./checkpoints/final.pdparams', help="The path of model parameter to be loaded.") parser.add_argument("--output_path", type=str, default='./infer_model', help="The path of model parameter in static graph to be saved.") parser.add_argument("--emb_dim", type=int, default=128, help="The dimension in which a word is embedded.") parser.add_argument("--hidden_size", type=int, default=128, help="The number of hidden nodes in the GRU layer.") args = parser.parse_args() # yapf: enable def main(): word_vocab = load_vocab(os.path.join(args.data_dir, 'word.dic')) label_vocab = load_vocab(os.path.join(args.data_dir, 'tag.dic')) model = BiGruCrf(args.emb_dim, args.hidden_size, len(word_vocab), len(label_vocab)) state_dict = paddle.load(args.params_path) model.set_dict(state_dict) model.eval() model = paddle.jit.to_static(model, input_spec=[ InputSpec(shape=[None, None], dtype="int64", name='token_ids'), InputSpec(shape=[None], dtype="int64", name='length') ]) # Save in static graph model. paddle.jit.save(model, os.path.join(args.output_path, "inference")) if __name__ == "__main__": main()

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test/test_bch.py

timoi-Lucypher/npCrypto

0

139832

<reponame>timoi-Lucypher/npCrypto import numpy as np from npcrypto.codes.bch import BCH from npcrypto.codes.poly_gf2 import a2p, strip_zeros def test_encode(): #http://www.comlab.hut.fi/studies/3410/slides_08_6_4.pdf n = 7 m = 3 k = 3 t = 2 msg = np.array([1,0,1], dtype=np.uint8) expected = np.array([1, 1, 0, 0, 1, 0, 1], dtype=np.uint8) bch = BCH(n, m, k, t) gen = np.array([1, 0, 1, 1, 1], dtype=np.uint8) bch.set_generator(gen) cdw = bch.encode(msg) assert np.all(strip_zeros(cdw) == expected)

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stest/pylibs/lazy_libs.py

hotswwkyo/stest

0

128206

<reponame>hotswwkyo/stest #!/usr/bin/env python # -*- encoding: utf-8 -*- ''' @Author: 思文伟 @Date: 2022/03/23 ''' class LazyLibs(object): class Appium(object): @property def MobileBy(self): from appium.webdriver.common.mobileby import MobileBy return MobileBy @property def webdriver(self): from appium import webdriver return webdriver class Selenium(object): @property def exceptions(cls): from selenium.common import exceptions return exceptions @property def ActionChains(self): from selenium.webdriver import ActionChains return ActionChains @property def Keys(self): from selenium.webdriver.common.keys import Keys return Keys @property def By(self): from selenium.webdriver.common.by import By return By @property def WebDriverWait(self): from selenium.webdriver.support.wait import WebDriverWait return WebDriverWait @property def webdriver(self): from selenium import webdriver return webdriver @property def WebElement(self): from selenium.webdriver.remote.webelement import WebElement return WebElement @property def Select(self): from selenium.webdriver.support.ui import Select return Select class Wechat(object): @property def minium(self): import minium return minium @property def miniconfig(self): from minium.framework import miniconfig return miniconfig

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reprounzip-qt/reprounzip_qt/qt_terminal.py

kyleam/reprozip

0

69081

<filename>reprounzip-qt/reprounzip_qt/qt_terminal.py # Copyright (C) 2014-2017 New York University # This file is part of ReproZip which is released under the Revised BSD License # See file LICENSE for full license details. from __future__ import division, print_function, unicode_literals import cgi import locale from PyQt4 import QtCore, QtGui import sys class Terminal(QtGui.QWidget): finished = QtCore.pyqtSignal(int) def __init__(self, cmdline, env, input_enabled=False, success_msg=None, fail_msg=None, **kwargs): super(Terminal, self).__init__(**kwargs) self.success_msg = success_msg or "Command finished" self.fail_msg = fail_msg or "Command failed" layout = QtGui.QVBoxLayout() self.text = QtGui.QTextEdit(readOnly=True) layout.addWidget(self.text) if input_enabled: self.input = QtGui.QLineEdit() self.input.returnPressed.connect(self._enter) layout.addWidget(self.input) else: self.input = None self.setLayout(layout) self.process = QtCore.QProcess(self) # Dodge py2app issues environ = QtCore.QProcessEnvironment.systemEnvironment() if environ.contains('PYTHONHOME'): environ.remove('PYTHONPATH') environ.remove('PYTHONHOME') if sys.platform == 'darwin': environ.insert( 'PATH', (environ.value('PATH', '/usr/bin:/bin:/usr/sbin:/sbin') + ':/usr/local/bin:/opt/reprounzip')) # Unset TERM to avoid ansi escapes environ.remove('TERM') # Add additional environment variables for k, v in env.items(): environ.insert(k, v) self.process.setProcessEnvironment(environ) self.process.setProcessChannelMode(QtCore.QProcess.SeparateChannels) if input_enabled: mode = QtCore.QIODevice.ReadWrite else: mode = QtCore.QIODevice.ReadOnly self.process.start(cmdline[0], cmdline[1:], mode) if not input_enabled: self.process.closeWriteChannel() self.process.readyReadStandardOutput.connect(self._read_stdout) self.process.readyReadStandardError.connect(self._read_stderr) self.process.finished.connect(self._finished) self.text.setHtml('''\ <style> body { font: Consolas, "Liberation Mono", Menlo, Courier, monospace; } .err { color: red; } </style> ''') self.text.append('<span style="color: blue;">%s</span>' % cgi.escape(' '.join(cmdline))) def _enter(self): cmd = self.input.text() self.input.setText('') self.process.write(cmd + '\n') def _read_stdout(self): out = self.process.readAllStandardOutput() out = bytes(out).decode(locale.getpreferredencoding() or 'UTF-8', 'replace') self.text.append('<span>%s</span>' % cgi.escape(out)) def _read_stderr(self): out = self.process.readAllStandardError() out = bytes(out).decode(locale.getpreferredencoding() or 'UTF-8', 'replace') self.text.append('<span class="err">%s</span>' % cgi.escape(out)) def _finished(self, code, status): good = False if status == QtCore.QProcess.NormalExit: msg = "returned %d" % code self.finished.emit(code) good = code == 0 else: msg = "crashed" self.finished.emit(-1) if good: msg = self.success_msg else: msg = "%s (%s)" % (self.fail_msg, msg) self.text.append('<br><span style="color: blue;">%s</span>' % msg) def run_in_builtin_terminal(cmd, env, text=None, success_msg=None, fail_msg=None): result = [False] def store_result(code): result[:] = [code] dialog = QtGui.QDialog() layout = QtGui.QVBoxLayout() if text is not None: layout.addWidget(QtGui.QLabel(text)) terminal = Terminal(cmd, env, input_enabled=False, success_msg=success_msg, fail_msg=fail_msg) terminal.finished.connect(store_result) layout.addWidget(terminal) buttons = QtGui.QHBoxLayout() buttons.addStretch(1) accept = QtGui.QPushButton("Close", enabled=False) accept.clicked.connect(dialog.accept) terminal.finished.connect(lambda _: accept.setEnabled(True)) buttons.addWidget(accept) layout.addLayout(buttons) dialog.setLayout(layout) dialog.exec_() return result[0]

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weather_alarm/main.py

Cs4r/weather_alarm

0

23459

<filename>weather_alarm/main.py #!/usr/bin/env python3 # -*- coding: utf-8 -*- import datetime import os from apscheduler.schedulers.blocking import BlockingScheduler from weather_alarm.constants import * from weather_alarm.forecaster import Forecaster from weather_alarm.sender import NotificationSender sender = NotificationSender(BOT_TOKEN, TELEGRAM_USER_ID) forecaster = Forecaster(OWM_API_KEY) def send_tomorrow_forecast(hour, time): sender.send_message(forecaster.tomorrow_forecast_at(CITY, hour, time)) def send_current_observed_weather(): sender.send_message(forecaster.current_observed_weather(CITY)) now = datetime.datetime.now() nightly_alarm_time = datetime.datetime(now.year, now.month, now.day, *NIGHTLY_ALARM_TIME) daily_alarm_time = datetime.datetime(now.year, now.month, now.day, *DAILY_ALARM_TIME) scheduler = BlockingScheduler() scheduler.add_job(func=send_tomorrow_forecast, args=FORECAST_TIME, trigger='interval', next_run_time=nightly_alarm_time, misfire_grace_time=30, days=1) scheduler.add_job(func=send_current_observed_weather, trigger='interval', next_run_time=daily_alarm_time, misfire_grace_time=30, days=1) print('Press Ctrl+{0} to exit'.format('Break' if os.name == 'nt' else 'C')) try: scheduler.start() except (KeyboardInterrupt, SystemExit): pass

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