tim-lawson/the-stack-v2-dedup-python-starcoder2-3b

blob_id stringlengths 40 40	formatted stringlengths 15 1.74M	input_ids listlengths 7 1.19M	tokens listlengths 7 1.19M	rows listlengths 7 1.19M	cols listlengths 7 1.19M
de925b8f6bd31bfdfd1f04628659847b0761899d	#!/usr/bin/env python # -- coding: utf-8 -- """ @author: Allen(Zifeng) An @course: @contact: [email protected] @file: 17. Letter Combinations of a Phone Number.py @time: 2020/2/2 21:18 """ from typing import List class Solution: def letterCombinations(self, digits: str) -> List[str]: d = {2: "abc", 3: "def", 4: "ghi", 5: "jkl", 6: "mno", 7: "pqrs", 8: "tuv", 9: "wxyz"} def merge(body, digits): if len(digits) == 0: ans.append(body) return else: for c in d[int(digits[0])]: merge(body + c, digits[1:]) # arr=[] ans = [] # for digit in digits: # arr.append(list(d[int(digit)])) # print(arr) merge("", digits) return ans if len(ans) != 1 else [] print(Solution().letterCombinations("")) # # class Solution: # def letterCombinations(self, digits: str) -> List[str]: # d={2:'abc', # 3:'def', # 4:'ghi', # 5:'jkl', # 6:'mno', # 7:'pqrs', # 8:'tuv', # 9:'wxyz' # } # # cmb=[''] if len(digits)!=0 else [] # # for digit in digits: # cmb=[p+q for p in cmb for q in d[int(digit)]] # # return cmb # print(Solution().letterCombinations('23'))	[ 8758, 3392, 52, 1793, 52, 2141, 4406, 222, 40, 8571, 10633, 63, 6471, 50, 61, 8571, 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359db73de2c2bb5967723dfb78f98fb84b337b9d	from math import degrees, sqrt, sin, cos, atan, radians from pygame import Surface, draw from pygame.sprite import Sprite from constants import ARROW_MAX_SPEED, FLOOR_Y from game_types import Radian, Degree class Arrow(Sprite): def __init__(self, color, screen, character, click_position): Sprite.__init__(self) self.color = color self.screen = screen self.character = character self.click_position = click_position width, height = 2, 2 self.image = Surface([width, height]) self.image.fill(color) draw.rect(self.image, color, [0, 0, width, height]) self.rect = self.image.get_rect() self.released = False self.release_speed = None self.release_position = None self.angle: Degree = None self.t = 0 self.is_moving_right = False self.stopped = False def set_center(self): if self.released: return x, y = self.character.get_center() self.rect.x = x self.rect.y = y def get_center(self): return self.rect.center def update(self): if self.rect.y >= FLOOR_Y: self.stopped = True if not self.released: return speed = self.release_speed t = self.t g = 0.980 vx = cos(self.angle) * speed vy = -sin(self.angle) * speed + 0.5 * g * t * t # print("angle {} -sin(angle) {:0<25} 0.5 * g * t * t {:0<25}".format(self.angle, -sin(self.angle), 0.5gtt)) self.rect.x += vx self.rect.y += vy self.t += 0.1 def release(self, release_position): if self.released: return self.released = True click_x, click_y = self.click_position self.release_position = release_x, release_y = release_position if release_x < click_x: self.is_moving_right = True adjacent, opposite = self.get_catheuses() angle: Degree = self.get_release_angle(adjacent, opposite) aiming_down = release_y < click_y self.angle = self.adjust_angle_to_aim_direction(angle, aiming_down, self.is_moving_right) length = self.get_line_length(adjacent, opposite) self.release_speed = self.get_release_speed(length) def get_catheuses(self): click_x, click_y = self.click_position release_x, release_y = 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97cc29e0d54e5d5e05dff16c92ecc4046363185f	from django.conf import settings from django.conf.urls.static import static from django.contrib import admin from django.urls import path, include from home import views from order import views as OV urlpatterns = [ path("user", include("user.urls")), path("order", include("order.urls")), path("shopcart/", OV.shopcart, name="shopcart"), path("product", include("product.urls")), path("", include("home.urls")), # '' - bu home path("faq/", views.faq, name="faq"), path("admin/", admin.site.urls), path("ckeditor", include("ckeditor_uploader.urls")), path("about/", views.about, name="about"), path("contact/", views.contact, name="about"), path("search/", views.search, name="search"), path("search_auto", views.search_auto, name="search_auto"), path("category/<int:id>/<slug:slug>/", views.category_products, name="category_products"), path("product/<int:id>/<slug:slug>/", views.product_detail, name="product_detail"), path("lic/", views.lic, name="lic"), path("post/", views.post, name="post"), path("post/<int:id>/", views.post_detail, name="post_detail"), path("lic/<int:id>/", views.lic_detail, name="lic_detail"), ] if settings.DEBUG: urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)	[ 1097, 8532, 51, 2982, 1220, 4086, 222, 1097, 8532, 51, 2982, 51, 11420, 51, 1592, 1220, 924, 222, 1097, 8532, 51, 12060, 1220, 5611, 222, 1097, 8532, 51, 11420, 1220, 1536, 49, 2323, 222, 1097, 6780, 1220, 11887, 222, 1097, 2554, 1220, 11887, 641, 565, 91, 222, 222, 983, 15735, 299, 447, 303, 1536, 459, 514, 411, 2323, 459, 514, 51, 11420, 12967, 303, 1536, 459, 1191, 411, 2323, 459, 1191, 51, 11420, 12967, 303, 1536, 459, 5579, 6835, 10974, 565, 91, 51, 5579, 6835, 49, 655, 366, 5579, 6835, 2132, 303, 1536, 459, 2463, 411, 2323, 459, 2463, 51, 11420, 12967, 303, 1536, 13963, 2323, 459, 2849, 51, 11420, 12967, 244, 607, 4267, 449, 1098, 6780, 303, 1536, 459, 20035, 10974, 11887, 51, 20035, 49, 655, 366, 20035, 2132, 303, 1536, 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35b24ffa14f8b3c2040d5becc8a35721e86d8b3d	total = totmil = cont = menor = 0 barato = " " print("-" * 40) print("LOJA SUPER BARATÃO") print("-" * 40) while True: produto = str(input("Nome do Produto: ")) preco = float(input("Preço: ")) cont += 1 total += preco if preco > 1000: totmil += 1 if cont == 1 or preco < menor: barato = produto menor = preco resp = " " while resp not in "SN": resp = str(input("Quer continuar? [S/N]")).strip().upper()[0] if resp == "N": break print("O total da compra foi R${:.2f}".format(total)) print("Temos {} produtos custando mais de R$1000,00".format(totmil)) print("O produto mais barato foi {} que custa {:.2f}".format(barato, menor))	[ 3025, 299, 14928, 22719, 299, 729, 299, 10466, 290, 299, 244, 53, 222, 1652, 6599, 299, 332, 332, 222, 1243, 10364, 39, 338, 244, 57, 53, 46, 222, 1243, 459, 1323, 28937, 28102, 546, 45441, 741, 39787, 84, 678, 222, 1243, 10364, 39, 338, 244, 57, 53, 46, 222, 5544, 2969, 63, 303, 18326, 1318, 299, 615, 45, 1151, 459, 23035, 764, 1330, 105, 1318, 63, 29583, 303, 1426, 352, 299, 1916, 45, 1151, 459, 2641, 17314, 63, 29583, 303, 729, 1475, 244, 54, 303, 3410, 1475, 1426, 352, 303, 434, 1426, 352, 868, 244, 54, 53, 53, 53, 63, 310, 14928, 22719, 1475, 244, 54, 303, 434, 729, 630, 244, 54, 575, 1426, 352, 350, 10466, 290, 63, 310, 5710, 6599, 299, 18326, 1318, 310, 10466, 290, 299, 1426, 352, 303, 5137, 299, 332, 332, 303, 2236, 5137, 666, 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709271b98fc2b40c763522c54488be36968f02d8	from base import * try: from .prod_local import * except: pass # we currently don't have an interface that allows an administrator # to create a repository for another user. Until we have added this # capability, allow users to create repos. ELEMENTARY_ALLOW_REPO_CREATION = True	[ 1097, 1712, 1220, 338, 222, 222, 1287, 63, 303, 664, 657, 8615, 100, 1717, 1220, 338, 222, 7364, 63, 303, 3119, 222, 222, 40, 1019, 5937, 2818, 1348, 1178, 619, 2586, 708, 6990, 619, 25486, 222, 40, 391, 1506, 331, 5347, 456, 4691, 1275, 51, 30688, 354, 1019, 1178, 3654, 477, 222, 40, 26511, 49, 2625, 4272, 391, 1506, 25187, 51, 222, 14360, 4530, 100, 18425, 100, 26949, 100, 3424, 2909, 299, 2969, 222 ]	[ "from", "Ġbase", "Ġimport", "Ġ", "Ċ", "Ċ", "try", ":", "ĊĠĠĠ", "Ġfrom", "Ġ.", "prod", "_", "local", "Ġimport", "Ġ", "Ċ", "except", ":", "ĊĠĠĠ", "Ġpass", "Ċ", "Ċ", "#", "Ġwe", "Ġcurrently", "Ġdon", "'t", "Ġhave", "Ġan", "Ġinterface", "Ġthat", "Ġallows", "Ġan", "Ġadministrator", "Ċ", "#", "Ġto", "Ġcreate", "Ġa", "Ġrepository", "Ġfor", "Ġanother", "Ġuser", ".", "ĠUnt", "il", "Ġwe", "Ġhave", "Ġadded", "Ġthis", "Ċ", "#", "Ġcapability", ",", "Ġallow", "Ġusers", "Ġto", "Ġcreate", "Ġrepos", ".", "Ċ", "ELEMENT", "ARY", "_", "ALLOW", "_", "REPO", "_", "CRE", "ATION", "Ġ=", "ĠTrue", "Ċ" ]	[ 1, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 6, 6, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11 ]	[ 1, 5, 10, 17, 19, 1, 1, 4, 5, 4, 9, 11, 15, 16, 21, 28, 30, 1, 7, 8, 4, 9, 1, 1, 2, 5, 15, 19, 21, 26, 29, 39, 44, 51, 54, 68, 1, 2, 5, 12, 14, 25, 29, 37, 42, 43, 47, 49, 52, 57, 63, 68, 1, 2, 13, 14, 20, 26, 29, 36, 42, 43, 1, 8, 11, 12, 17, 18, 22, 23, 26, 31, 33, 38 ]
edcccc673994a8de281a683b747de52d2115f89e	from configparser import ConfigParser from ef.config.components import * from ef.config.efconf import EfConf from ef.config.section import ConfigSection comp_list = [ BoundaryConditions, InnerRegion, OutputFile, ParticleInteractionModel, ParticleSource, SpatialMesh, TimeGrid, ExternalFieldUniform, ] def test_components_to_conf_and_back(): for Component in comp_list: x = Component() y = x.to_conf().make() assert x == y def test_conf_to_configparser_and_back(): confs = [C().to_conf() for C in comp_list] parser = ConfigParser() for c in confs: c.add_section_to_parser(parser) conf2 = ConfigSection.parser_to_confs(parser) assert conf2 == confs def test_minimal_example(): parser = ConfigParser() parser.read("examples/minimal_working_example/minimal_conf.conf") components = [conf.make() for conf in ConfigSection.parser_to_confs(parser)] assert components == [ TimeGrid(1e-7, 1e-9, 1e-9), SpatialMesh((5, 5, 15), (0.5, 0.5, 1.5)), ParticleInteractionModel("noninteracting"), BoundaryConditions(0), ExternalFieldUniform("mgn_uni", "magnetic"), ExternalFieldUniform("el_uni", "electric"), OutputFile("example_", ".h5"), ] class TestEfConf: def test_conf_export(self): conf = EfConf(sources=[ParticleSource()], inner_regions=(InnerRegion(),)) s = conf.export_to_string() c1 = EfConf.from_string(s) assert c1 == conf def test_conf_repr(self): from numpy import array # for use in eval conf = EfConf(sources=[ParticleSource()], inner_regions=(InnerRegion(),)) s = repr(conf) c1 = eval(s) assert c1 == conf	[ 1097, 1377, 3936, 1220, 3395, 2837, 222, 222, 1097, 28300, 51, 1180, 51, 3545, 1220, 338, 222, 1097, 28300, 51, 1180, 51, 2332, 2982, 1220, 535, 107, 5204, 222, 1097, 28300, 51, 1180, 51, 1923, 1220, 3395, 5434, 222, 222, 998, 100, 687, 299, 447, 303, 16243, 692, 17547, 49, 303, 29323, 6071, 49, 303, 5601, 991, 49, 303, 31908, 16755, 1262, 49, 303, 31908, 2044, 49, 303, 36392, 6941, 49, 303, 3734, 3567, 49, 303, 16933, 1195, 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38e167630519b73bffea4ff527bc7b7272a49f1a	# -- coding:utf-8 -- # Author: hankcs # Date: 2019-01-13 15:01 import pickle import numpy as np from bert_serving.client import BertClient from pyhanlp import * CharTable = JClass("com.hankcs.hanlp.dictionary.other.CharTable") # bc = BertClient(ip='192.168.1.88') # ip address of the server bc = BertClient(ip="127.0.0.1") # ip address of the GPU machine def embed_last_token(text): result = bc.encode(text, show_tokens=True) # print(result) batch = [] for sent, tensor, tokens in zip(text, result[0], result[1]): valid = [] tid = 0 buffer = "" words = sent.lower().split() for i, t in enumerate(tokens): if t == "[CLS]" or t == "[SEP]": continue else: if t.startswith("##"): t = t[2:] elif t == "[UNK]": t = words[tid][len(buffer)] buffer += t if buffer == words[tid]: valid.append(i) buffer = "" tid += 1 # print(len(valid)) # exit() if len(valid) != len(sent.split()) or tid != len(words): print(valid) print(sent.split()) print(result[1]) batch.append(tensor[valid, :]) return batch def embed_sum(text): result = bc.encode(text, show_tokens=True) # print(result) batch = [] for sent, tensor, tokens in zip(text, result[0], result[1]): token_tensor = [] sent_tensor = [] tid = 0 buffer = "" words = sent.lower().split() for i, t in enumerate(tokens): if t == "[CLS]" or t == "[SEP]": continue else: if t.startswith("##"): t = t[2:] elif t == "[UNK]": t = words[tid][len(buffer)] buffer += t token_tensor.append(tensor[i, :]) if buffer == words[tid]: sent_tensor.append(np.stack(token_tensor).mean(axis=0)) token_tensor = [] buffer = "" tid += 1 # print(len(valid)) # exit() if tid != len(words) or len(sent_tensor) != len(words): print(sent.split()) print(tokens) exit() batch.append(np.stack(sent_tensor)) return batch def generate_bert(path, output, embed_fun=embed_sum): print(output) total = 0 with open(path) as src: batch = [] tensor = [] for line in src: line = line.strip() if len(line) == 0: continue batch.append( CharTable.convert(line) .replace("—", "-") .replace("‘", "'") .replace("…", ".") .replace("坜", "壢") .replace("唛", "麦") .replace("ㄅㄆㄇㄈ", "呀呀") .replace("’", "'") ) if len(batch) and len(batch) % 100 == 0: tensor.extend(embed_fun(batch)) total += len(batch) print(total) batch = [] if len(batch): tensor.extend(embed_fun(batch)) total += len(batch) print(total) with open(output, "wb") as f: pickle.dump(tensor, f) if __name__ == "__main__": # generate_bert('data/SemEval-2016/news.test.sent.txt', 'data/SemEval-2016/news.test.bert', embed_fun=embed_sum) # generate_bert('data/SemEval-2016/news.valid.sent.txt', 'data/SemEval-2016/news.valid.bert', embed_fun=embed_sum) # generate_bert('data/SemEval-2016/news.train.sent.txt', 'data/SemEval-2016/news.train.bert', embed_fun=embed_sum) # # generate_bert('data/SemEval-2016/text.test.sent.txt', 'data/SemEval-2016/text.test.bert', embed_fun=embed_sum) # generate_bert('data/SemEval-2016/text.valid.sent.txt', 'data/SemEval-2016/text.valid.bert', embed_fun=embed_sum) # generate_bert('data/SemEval-2016/text.train.sent.txt', 'data/SemEval-2016/text.train.bert', embed_fun=embed_sum) generate_bert("data/semeval15/cz.pas.dev.sent.txt", "data/embedding/bert_base_sum/cz.pas.dev.bert", embed_fun=embed_sum) generate_bert("data/semeval15/cz.pas.train.sent.txt", "data/embedding/bert_base_sum/cz.pas.train.bert", embed_fun=embed_sum) generate_bert("data/semeval15/cz.id.pas.sent.txt", "data/embedding/bert_base_sum/cz.id.pas.bert", embed_fun=embed_sum) # generate_bert('data/ctb5.1-pos/dev.short.sent.txt', 'data/embedding/bert_base_sum/ctb.pos.dev.bert', # embed_fun=embed_sum) # generate_bert('data/ctb5.1-pos/test.short.sent.txt', 'data/embedding/bert_base_sum/ctb.pos.test.bert', # embed_fun=embed_sum) # generate_bert('data/ctb5.1-pos/train.short.sent.txt', 'data/embedding/bert_base_sum/ctb.pos.train.bert', # embed_fun=embed_sum) # generate_bert('data/msra/dev.short.sent.txt', 'data/embedding/bert_base_sum/msra.dev.bert', # embed_fun=embed_sum) # generate_bert('data/msra/test.short.sent.txt', 'data/embedding/bert_base_sum/msra.test.bert', # embed_fun=embed_sum) # generate_bert('data/msra/train.short.sent.txt', 'data/embedding/bert_base_sum/msra.train.bert', # embed_fun=embed_sum) # generate_bert('data/msra/test.auto.short.sent.txt', 'data/embedding/bert_base_sum/msra.test.auto.bert', # embed_fun=embed_sum) # generate_bert('data/msra/test.auto.short.sent.txt', 'data/embedding/bert_base_sum/msra.test.auto.bert', # embed_fun=embed_sum) # generate_bert('data/msra/dev.auto.short.sent.txt', 'data/embedding/bert_base_sum/msra.dev.auto.bert', # embed_fun=embed_sum) # generate_bert('data/msra/train.auto.short.sent.txt', 'data/embedding/bert_base_sum/msra.train.auto.bert', # embed_fun=embed_sum) # generate_bert('data/ctb5/dev.sent.txt', 'data/embedding/bert_base_sum/ctb.dev.bert', # embed_fun=embed_sum) # generate_bert('data/ctb5/test.sent.txt', 'data/embedding/bert_base_sum/ctb.test.bert', # embed_fun=embed_sum) # 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816b1a932208a4525230dd886adf8c67dec3af3e	# content of conftest.py # adapted from http://pytest.org/latest/example/special.html import pytest import requests def tear_down(): """conftest.py tear_down - the last to go....""" print("\nTEARDOWN after all tests") @pytest.fixture(scope="session", autouse=True) def set_up(request): """conftest.py set_up - the first to start....""" print("\nSETUP before all tests") request.addfinalizer(tear_down)	[ 40, 1813, 451, 476, 726, 464, 51, 997, 222, 40, 39251, 664, 1930, 574, 12418, 51, 1107, 52, 5341, 52, 2783, 52, 10105, 51, 935, 222, 222, 485, 13090, 222, 485, 5188, 499, 222, 610, 22143, 100, 1772, 2284, 303, 1547, 487, 726, 464, 51, 997, 22143, 100, 1772, 449, 341, 2419, 391, 2006, 2279, 3012, 303, 1489, 5564, 115, 1731, 802, 10453, 2703, 1187, 3326, 678, 499, 222, 69, 12418, 51, 12942, 45, 3488, 366, 2867, 411, 2283, 558, 66, 1844, 46, 222, 610, 758, 100, 436, 45, 1375, 731, 303, 1547, 487, 726, 464, 51, 997, 758, 100, 436, 449, 341, 1950, 391, 1496, 2279, 3012, 465, 1489, 5564, 115, 32426, 2688, 1187, 3326, 678, 303, 1343, 51, 688, 3242, 2292, 45, 538, 301, 100, 1772, 46, 222 ]	[ "#", "Ġcontent", "Ġof", "Ġcon", "ft", "est", ".", "py", "Ċ", "#", "Ġadapted", "Ġfrom", "Ġhttp", "://", "pytest", ".", "org", "/", "latest", "/", "example", "/", "special", ".", "html", "Ċ", "Ċ", "import", "Ġpytest", "Ċ", "import", "Ġrequests", "ĊĊ", "Ċ", "def", "Ġtear", "_", "down", "():", "ĊĠĠĠ", "Ġ\"\"\"", "con", "ft", "est", ".", "py", "Ġtear", "_", "down", "Ġ-", "Ġthe", "Ġlast", "Ġto", "Ġgo", "....", "\"\"\"", "ĊĠĠĠ", "Ġprint", "(\"\\", "n", "TE", "AR", "DOWN", "Ġafter", "Ġall", "Ġtests", "\")", "ĊĊ", "Ċ", "@", "pytest", ".", "fixture", "(", "scope", "=\"", "session", "\",", "Ġauto", "use", "=", "True", ")", "Ċ", "def", "Ġset", "_", "up", "(", "request", "):", "ĊĠĠĠ", "Ġ\"\"\"", "con", "ft", "est", ".", "py", "Ġset", "_", "up", "Ġ-", "Ġthe", "Ġfirst", "Ġto", "Ġstart", "....", "\"\"\"", "ĊĊĠĠĠ", "Ġprint", "(\"\\", "n", "SETUP", "Ġbefore", "Ġall", "Ġtests", "\")", "ĊĠĠĠ", "Ġrequest", ".", "add", "final", "izer", "(", "te", "ar", "_", "down", ")", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 4, 4, 4, 5, 5, 5, 7, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18 ]	[ 1, 2, 10, 13, 17, 19, 22, 23, 25, 1, 2, 10, 15, 20, 23, 29, 30, 33, 34, 40, 41, 48, 49, 56, 57, 61, 1, 1, 7, 14, 1, 7, 16, 1, 1, 4, 9, 10, 14, 17, 4, 8, 11, 13, 16, 17, 19, 24, 25, 29, 31, 35, 40, 43, 46, 50, 53, 4, 10, 13, 14, 16, 18, 22, 28, 32, 38, 40, 1, 1, 2, 8, 9, 16, 17, 22, 24, 31, 33, 38, 41, 42, 46, 47, 1, 4, 8, 9, 11, 12, 19, 21, 4, 8, 11, 13, 16, 17, 19, 23, 24, 26, 28, 32, 38, 41, 47, 51, 54, 4, 10, 13, 14, 19, 26, 30, 36, 38, 4, 12, 13, 16, 21, 25, 26, 28, 30, 31, 35, 36 ]
9096ed4b68d2bef92df7db98589e744ddf3efad0	import matplotlib.pyplot as plt from shapely.geometry import MultiLineString, Polygon mls = MultiLineString([[(0, 1), (5, 1)], [(1, 2), (1, 0)]]) p = Polygon([(0.5, 0.5), (0.5, 1.5), (2, 1.5), (2, 0.5)]) results = mls.intersection(p) plt.subplot(1, 2, 1) for ls in mls: plt.plot(ls.xy) plt.plot(p.boundary.xy, "-.k") plt.xlim([0, 5]) plt.ylim([0, 2]) plt.subplot(1, 2, 2) for ls in results: plt.plot(*ls.xy) plt.xlim([0, 5]) plt.ylim([0, 2]) plt.show()	[ 485, 13168, 51, 18553, 641, 6020, 222, 1097, 807, 386, 3151, 51, 10132, 1220, 10497, 1829, 671, 49, 30182, 222, 222, 713, 120, 299, 10497, 1829, 671, 2034, 9448, 53, 49, 244, 54, 490, 327, 58, 49, 244, 54, 14237, 10474, 54, 49, 244, 55, 490, 327, 54, 49, 244, 53, 2201, 1156, 222, 117, 299, 30182, 43611, 53, 51, 58, 49, 244, 53, 51, 58, 490, 327, 53, 51, 58, 49, 244, 54, 51, 58, 490, 327, 55, 49, 244, 54, 51, 58, 490, 327, 55, 49, 244, 53, 51, 58, 14935, 222, 4333, 299, 364, 3259, 51, 11238, 45, 117, 46, 222, 222, 6670, 51, 24934, 45, 54, 49, 244, 55, 49, 244, 54, 46, 222, 999, 12906, 347, 364, 3259, 63, 303, 6020, 51, 3570, 2761, 3259, 51, 2223, 46, 222, 6670, 51, 3570, 2761, 117, 51, 18908, 51, 2223, 49, 4840, 51, 112, 678, 222, 6670, 51, 34637, 2034, 53, 49, 244, 58, 1156, 222, 6670, 51, 29715, 2034, 53, 49, 244, 55, 1156, 222, 222, 6670, 51, 24934, 45, 54, 49, 244, 55, 49, 244, 55, 46, 222, 999, 12906, 347, 3300, 63, 303, 6020, 51, 3570, 2761, 3259, 51, 2223, 46, 222, 6670, 51, 34637, 2034, 53, 49, 244, 58, 1156, 222, 6670, 51, 29715, 2034, 53, 49, 244, 55, 1156, 222, 222, 6670, 51, 2276, 365, 222 ]	[ "import", "Ġmatplotlib", ".", "pyplot", "Ġas", "Ġplt", "Ċ", "from", "Ġsh", "ap", "ely", ".", "geometry", "Ġimport", "ĠMulti", "Line", "String", ",", "ĠPolygon", "Ċ", "Ċ", "ml", "s", "Ġ=", "ĠMulti", "Line", "String", "([", "[(", "0", ",", "Ġ", "1", "),", "Ġ(", "5", ",", "Ġ", "1", ")],", "Ġ[(", "1", ",", "Ġ", "2", "),", "Ġ(", "1", ",", "Ġ", "0", ")]", "])", "Ċ", "p", "Ġ=", "ĠPolygon", "([(", "0", ".", "5", ",", "Ġ", "0", ".", "5", "),", "Ġ(", "0", ".", "5", ",", "Ġ", "1", ".", "5", "),", "Ġ(", "2", ",", "Ġ", "1", ".", "5", "),", "Ġ(", "2", ",", "Ġ", "0", ".", "5", ")])", "Ċ", "results", "Ġ=", "Ġm", "ls", ".", "intersection", "(", "p", ")", "Ċ", "Ċ", "plt", ".", "subplot", "(", "1", ",", "Ġ", "2", ",", "Ġ", "1", ")", "Ċ", "for", "Ġls", "Ġin", "Ġm", "ls", ":", "ĊĠĠĠ", "Ġplt", ".", "plot", "(", "ls", ".", "xy", ")", "Ċ", "plt", ".", "plot", "(", "p", ".", "boundary", ".", "xy", ",", "Ġ\"-", ".", "k", "\")", "Ċ", "plt", ".", "xlim", "([", "0", ",", "Ġ", "5", "])", "Ċ", "plt", ".", "ylim", "([", "0", ",", "Ġ", "2", "])", "Ċ", "Ċ", "plt", ".", "subplot", "(", "1", ",", "Ġ", "2", ",", "Ġ", "2", ")", "Ċ", "for", "Ġls", "Ġin", "Ġresults", ":", "ĊĠĠĠ", "Ġplt", ".", "plot", "(*", "ls", ".", "xy", ")", "Ċ", "plt", ".", "xlim", "([", "0", ",", "Ġ", "5", "])", "Ċ", "plt", ".", "ylim", "([", "0", ",", "Ġ", "2", "])", "Ċ", "Ċ", "plt", ".", "show", "()", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 21, 21, 21, 21, 21 ]	[ 1, 7, 18, 19, 25, 28, 32, 1, 5, 8, 10, 13, 14, 22, 29, 35, 39, 45, 46, 54, 1, 1, 3, 4, 6, 12, 16, 22, 24, 26, 27, 28, 29, 30, 32, 34, 35, 36, 37, 38, 41, 44, 45, 46, 47, 48, 50, 52, 53, 54, 55, 56, 58, 60, 1, 2, 4, 12, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 39, 40, 41, 42, 43, 44, 45, 47, 49, 50, 51, 52, 53, 54, 55, 58, 1, 8, 10, 12, 14, 15, 27, 28, 29, 30, 1, 1, 4, 5, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 1, 4, 7, 10, 12, 14, 15, 4, 8, 9, 13, 15, 17, 18, 20, 21, 1, 4, 5, 9, 11, 12, 13, 21, 22, 24, 25, 28, 29, 30, 32, 1, 4, 5, 9, 11, 12, 13, 14, 15, 17, 1, 4, 5, 9, 11, 12, 13, 14, 15, 17, 1, 1, 4, 5, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 1, 4, 7, 10, 18, 19, 4, 8, 9, 13, 15, 17, 18, 20, 21, 1, 4, 5, 9, 11, 12, 13, 14, 15, 17, 1, 4, 5, 9, 11, 12, 13, 14, 15, 17, 1, 1, 4, 5, 9, 11 ]
c99f1333c5ca3221e9932d9a9ba1d95a77924f0d	import sys import math def get_max_sum(arr): max_sum = -math.inf for i in range(1, 5): for j in range(1, 5): temp = ( arr[i][j] + arr[i - 1][j - 1] + arr[i - 1][j] + arr[i - 1][j + 1] + arr[i + 1][j + 1] + arr[i + 1][j] + arr[i + 1][j - 1] ) max_sum = max(max_sum, temp) return max_sum def main(): sys_in = sys.stdin sys_out = sys.stdout arr = [] for _ in range(6): temp = list(map(int, sys.stdin.readline().split())) arr.append(temp) print(get_max_sum(arr)) if __name__ == "__main__": main()	[ 485, 5263, 222, 485, 7820, 499, 222, 610, 640, 100, 1472, 100, 1055, 45, 865, 731, 303, 1788, 100, 1055, 299, 449, 3851, 51, 5290, 303, 456, 613, 347, 2189, 45, 54, 49, 244, 58, 731, 310, 456, 614, 347, 2189, 45, 54, 49, 244, 58, 731, 343, 1798, 299, 327, 419, 5941, 96, 110, 1697, 111, 98, 419, 494, 5941, 96, 110, 449, 244, 54, 1697, 111, 449, 244, 54, 98, 419, 494, 5941, 96, 110, 449, 244, 54, 1697, 111, 98, 419, 494, 5941, 96, 110, 449, 244, 54, 1697, 111, 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78c9f92349ba834bc64dc84f884638c4316a9ea4	INPUT_MINBIAS = "/build/RAWReference/MinBias_RAW_320_STARTUP.root" INPUT_TTBAR = "/build/RAWReference/TTbar_RAW_320_STARTUP.root" puSTARTUP_TTBAR = "/build/RAWReference/TTbar_Tauola_PileUp_RAW_320_STARTUP.root" relval = { "step1": { "step": "GEN-HLT", "timesize": (100, ["MinBias", "TTbar"]), "igprof": (50, ["TTbar"]), "memcheck": (5, ["TTbar"]), "pileup": ["TTbar"], # ??? 'pileupInput': '', "cmsdriver": "--eventcontent RAWSIM --conditions auto:mc", }, "step2": { "step": "RAW2DIGI-RECO", "timesize": (8000, ["MinBias", "TTbar"]), "igprof": (200, ["TTbar"]), "memcheck": (5, ["TTbar"]), "pileup": ["TTbar"], "pileupInput": puSTARTUP_TTBAR, "fileInput": [INPUT_MINBIAS, INPUT_TTBAR], "cmsdriver": "--eventcontent RECOSIM --conditions auto:startup", }, "GENSIMDIGI": { "step": "GEN-SIM,DIGI", "timesize": (100, ["MinBias", "SingleElectronE1000", "SingleMuMinusPt10", "SinglePiMinusE1000", "TTbar"]), "igprof": (5, ["TTbar"]), "memcheck": (5, ["TTbar"]), "pileup": ["TTbar"], # ??? 'pileupInput': '', "fileInput": "", "cmsdriver": "--eventcontent FEVTDEBUG --conditions auto:mc", }, "HLT": { "step": "HLT", "timesize": (8000, ["MinBias", "TTbar"]), "igprof": (500, ["TTbar"]), "memcheck": (5, ["TTbar"]), "pileup": ["TTbar"], "pileupInput": puSTARTUP_TTBAR, "fileInput": [INPUT_MINBIAS, INPUT_TTBAR], "cmsdriver": "--eventcontent RAWSIM --conditions auto:startup --processName HLTFROMRAW", }, "FASTSIM": { "step": "GEN-FASTSIM", "timesize": (8000, ["MinBias", "TTbar"]), "igprof": (500, ["TTbar"]), "memcheck": (5, ["TTbar"]), "pileup": ["TTbar"], "cmsdriver": "--eventcontent RECOSIM --conditions auto:mc", }, }	[ 10834, 100, 4697, 10037, 1117, 299, 2450, 1848, 52, 20373, 3580, 52, 2944, 22134, 100, 20373, 100, 56, 55, 53, 100, 7393, 2430, 51, 2405, 39, 222, 10834, 100, 6992, 13495, 299, 2450, 1848, 52, 20373, 3580, 52, 6992, 1652, 100, 20373, 100, 56, 55, 53, 100, 7393, 2430, 51, 2405, 39, 222, 222, 4059, 7393, 2430, 100, 6992, 13495, 299, 2450, 1848, 52, 20373, 3580, 52, 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c6b261a09b2982e17704f847586bbf38d27cb786	from ._sinAction import * from ._sinActionFeedback import * from ._sinActionGoal import * from ._sinActionResult import * from ._sinFeedback import * from ._sinGoal import * from ._sinResult import *	[ 1097, 31830, 7137, 1728, 1220, 338, 222, 1097, 31830, 7137, 1728, 15362, 1220, 338, 222, 1097, 31830, 7137, 1728, 17350, 1220, 338, 222, 1097, 31830, 7137, 22575, 1220, 338, 222, 1097, 31830, 7137, 15362, 1220, 338, 222, 1097, 31830, 7137, 17350, 1220, 338, 222, 1097, 31830, 7137, 1413, 1220, 338, 222 ]	[ "from", "Ġ._", "sin", "Action", "Ġimport", "Ġ", "Ċ", "from", "Ġ._", "sin", "Action", "Feedback", "Ġimport", "Ġ", "Ċ", "from", "Ġ._", "sin", "Action", "Goal", "Ġimport", "Ġ", "Ċ", "from", "Ġ._", "sin", "ActionResult", "Ġimport", "Ġ", "Ċ", "from", "Ġ._", "sin", "Feedback", "Ġimport", "Ġ", "Ċ", "from", "Ġ._", "sin", "Goal", "Ġimport", "Ġ", "Ċ", "from", "Ġ._", "sin", "Result", "Ġimport", "Ġ*", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7 ]	[ 1, 5, 8, 11, 17, 24, 26, 1, 5, 8, 11, 17, 25, 32, 34, 1, 5, 8, 11, 17, 21, 28, 30, 1, 5, 8, 11, 23, 30, 32, 1, 5, 8, 11, 19, 26, 28, 1, 5, 8, 11, 15, 22, 24, 1, 5, 8, 11, 17, 24, 26 ]
f4306f80330850415b74d729384f360489644e39	import unittest import numpy import pandas as pd import fixtures.examples_validate as examples from cellxgene_schema.validate import Validator from cellxgene_schema.write_labels import AnnDataLabelAppender # Tests for schema compliance of an AnnData object class TestValidAnndata(unittest.TestCase): """ Tests a valid AnnData object. Most other tests below modify this AnnData object and test for failure cases. The valid AnnData object has all valid cases described in the schema. """ def setUp(self): self.validator = Validator() self.validator.adata = examples.adata.copy() def test_valid_anndata(self): self.validator.validate_adata() self.assertFalse(self.validator.errors) class TestH5adValidation(unittest.TestCase): """ Checks that validation from h5ad works, only does one invalid example as extensive testing is done in the classes below """ def setUp(self): self.h5ad_valid_file = examples.h5ad_valid self.h5ad_invalid_file = examples.h5ad_invalid self.validator = Validator() def test_validate(self): # Valid h5ad self.assertTrue(self.validator.validate_adata(self.h5ad_valid_file)) # Invalid h5ads self.assertFalse(self.validator.validate_adata(self.h5ad_invalid_file)) class TestExpressionMatrix(unittest.TestCase): """ Fail cases for expression matrices (anndata.X and anndata.raw.X) """ def setUp(self): self.validator = Validator() self.validator.adata = examples.adata.copy() def test_shapes(self): """ All matrix layers MUST have the same shape, and have the same cell labels and gene labels. """ # Creates a raw layer self.validator.adata.raw = self.validator.adata self.validator.adata.raw.var.drop("feature_is_filtered", axis=1, inplace=True) self.validator.adata.X = examples.adata_non_raw.X.copy() self.validator.adata.uns["X_normalization"] = "CPM" # remove one gene self.validator.adata = self.validator.adata[:, 1:] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: Number of genes in X (3) is different than raw.X (4)."], ) def test_sparsity(self): """ In any layer, if a matrix has 50% or more values that are zeros, it is STRONGLY RECOMMENDED that the matrix be encoded as a scipy.sparse.csr_matrix """ self.validator.adata.X = self.validator.adata.X.toarray() self.validator.validate_adata() self.assertEqual( self.validator.warnings, [ "WARNING: Sparsity of 'X' is 0.875 which is greater than 0.5, " "and it is not a 'scipy.sparse.csr_matrix'. It is " "STRONGLY RECOMMENDED to use this type of matrix for " "the given sparsity." ], ) def test_raw_existence(self): """ Except for ATAC-seq and methylation data, raw data is REQUIRED """ # RNA - raw layer required del self.validator.adata.raw self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: Raw data is missing: there is no 'raw.X' and 'X_normalization' is not 'none'."], ) # ATAC - raw layer not required # The assignment above makes X to not be raw: self.validator.adata.uns["X_normalization"] = "CPM" # The following line makes it to be scATAC-seq data (EFO:0010891) # Missing raw data in atac-seq data is allowed, thus the following should not return an error message self.validator.errors = [] self.validator.adata.obs["assay_ontology_term_id"] = "EFO:0010891" self.validator.validate_adata() self.assertEqual(self.validator.errors, []) def test_final_strongly_recommended(self): """ Except for ATAC-seq and methylation data, final matrix is STRONGLY RECOMMENDED """ # move raw to X amd: i.e. there is no final self.validator.adata.X = self.validator.adata.raw.X del self.validator.adata.raw self.validator.adata.uns["X_normalization"] = "none" self.validator.validate_adata() self.assertEqual( self.validator.warnings, [ "WARNING: Only raw data was found, i.e. there is no 'raw.X' and 'uns['X_normalization']' is 'none'. " "It is STRONGLY RECOMMENDED that 'final' (normalized) data is provided." ], ) class TestObs(unittest.TestCase): """ Fail cases in adata.uns """ def setUp(self): self.validator = Validator() self.validator.adata = examples.adata.copy() def test_column_presence(self): """ obs is a pandas.DataFrame. Curators MUST annotate the following columns in the obs dataframe. """ columns = [ "assay_ontology_term_id", "development_stage_ontology_term_id", "disease_ontology_term_id", "ethnicity_ontology_term_id", "is_primary_data", "sex_ontology_term_id", "tissue_ontology_term_id", ] for column in columns: with self.subTest(column=column): self.validator.errors = [] self.validator.adata = examples.adata.copy() self.validator.adata.obs.drop(column, axis=1, inplace=True) # Remove batch condition because it has a dependency with is_primary_data self.validator.adata.uns.pop("batch_condition") self.validator.validate_adata() self.assertEqual( self.validator.errors, [f"ERROR: Dataframe 'obs' is missing column '{column}'."], ) def test_column_presence_organism(self): """ obs is a pandas.DataFrame. Curators MUST annotate the following columns in the obs dataframe. A separate check is need for organism_ontology_term_id because removing from anndata results in multiple errors given that other columns depend on its presence """ self.validator.adata.obs.drop("organism_ontology_term_id", axis=1, inplace=True) self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: Dataframe 'obs' is missing column 'organism_ontology_term_id'.", "ERROR: Checking values with dependencies failed for " "adata.obs['ethnicity_ontology_term_id'], this is likely due " "to missing dependent column in adata.obs.", "ERROR: Checking values with dependencies failed for " "adata.obs['development_stage_ontology_term_id'], this is likely due " "to missing dependent column in adata.obs.", ], ) def test_obsolete_term_id(self): """ Terms documented as obsolete in an ontology MUST NOT be used. For example, EFO:0009310 for obsolete_10x v2 was marked as obsolete in EFO version 3.31.0 and replaced by EFO:0009899 for 10x 3' v2. https://www.ebi.ac.uk/ols/ontologies/efo/terms?short_form=EFO_0009310 """ # Not a valid term self.validator.adata.obs["assay_ontology_term_id"][0] = "EFO:0009310" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0009310' in 'assay_ontology_term_id' is a deprecated term id of 'EFO'.", "ERROR: 'EFO:0009310' in 'assay_ontology_term_id' is not a child term id " "of '[['EFO:0002772', 'EFO:0010183']]'.", ], ) def test_assay_ontology_term_id(self): """ assay_ontology_term_id categorical with str categories. This MUST be an EFO term and either child of "EFO:0002772" or "EFO:0010183" If there is not an exact match for the assay, clarifying text MAY be enclosed in parentheses and appended to the most accurate term. For example, the sci-plex assay could be curated as "EFO:0010183 (sci-plex)" """ # Not a valid term self.validator.adata.obs["assay_ontology_term_id"][0] = "CL:000001" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'CL:000001' in 'assay_ontology_term_id' is not a valid ontology term id of 'EFO'.", "ERROR: 'CL:000001' in 'assay_ontology_term_id' is not a child term id of '[['EFO:0002772', 'EFO:0010183']]'.", ], ) # Not a valid child self.validator.adata.obs["assay_ontology_term_id"][0] = "EFO:0000001" self.validator.errors = [] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'EFO:0000001' in 'assay_ontology_term_id' is not a child term id of '[['EFO:0002772', 'EFO:0010183']]'."], ) # Not a clarifying text self.validator.adata.obs["assay_ontology_term_id"][0] = "EFO:0010183 sci-plex" self.validator.errors = [] self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0010183 sci-plex' in 'assay_ontology_term_id' is not a valid ontology term id of 'EFO'.", "ERROR: 'EFO:0010183 sci-plex' in 'assay_ontology_term_id' is not a child term id of " "'[['EFO:0002772', 'EFO:0010183']]'.", ], ) def test_cell_type_ontology_term_id(self): """ cell_type_ontology_term_id categorical with str categories. This MUST be a CL term. """ # Not a valid term self.validator.adata.obs["cell_type_ontology_term_id"][0] = "EFO:0000001" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'EFO:0000001' in 'cell_type_ontology_term_id' is not a valid ontology term id of 'CL'."], ) def test_development_stage_ontology_term_id_human(self): """ development_stage_ontology_term_id categorical with str categories. If unavailable, this MUST be "unknown". If organism_ontolology_term_id is "NCBITaxon:9606" for Homo sapiens, this MUST be the most accurate HsapDv term. """ self.validator.adata.obs["organism_ontology_term_id"][0] = "NCBITaxon:9606" self.validator.adata.obs["development_stage_ontology_term_id"][0] = "EFO:0000001" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0000001' in 'development_stage_ontology_term_id' is " "not a valid ontology term id of 'HsapDv'. When 'organism_ontology_term_id' is 'NCBITaxon:9606' " "(Homo sapiens), 'development_stage_ontology_term_id' MUST be a term id of 'HsapDv' or unknown." ], ) def test_development_stage_ontology_term_id_mouse(self): """ If organism_ontolology_term_id is "NCBITaxon:10090" for Mus musculus, this MUST be the most accurate MmusDv term """ self.validator.adata.obs["organism_ontology_term_id"][0] = "NCBITaxon:10090" self.validator.adata.obs["development_stage_ontology_term_id"][0] = "EFO:0000001" self.validator.adata.obs["ethnicity_ontology_term_id"][0] = "na" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0000001' in 'development_stage_ontology_term_id' is " "not a valid ontology term id of 'MmusDv'. When 'organism_ontology_term_id' is 'NCBITaxon:10090' " "(Mus musculus), 'development_stage_ontology_term_id' MUST be a term id of 'MmusDv' or unknown." ], ) def test_development_stage_ontology_term_id_all_species(self): """ All other it MUST be children of UBERON:0000105 and not UBERON:0000071 """ # Fail case not an UBERON term self.validator.adata.obs["organism_ontology_term_id"][0] = "NCBITaxon:10114" self.validator.adata.obs["development_stage_ontology_term_id"][0] = "EFO:0000001" self.validator.adata.obs["ethnicity_ontology_term_id"][0] = "na" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0000001' in 'development_stage_ontology_term_id' is " "not a valid ontology term id of 'UBERON'. When 'organism_ontology_term_id' is not 'NCBITaxon:10090' " "nor 'NCBITaxon:9606', 'development_stage_ontology_term_id' MUST be a child term id of " "'UBERON:0000105' excluding 'UBERON:0000071', or unknown.", "ERROR: 'EFO:0000001' in 'development_stage_ontology_term_id' is not " "a child term id of '[['UBERON:0000105']]'. When 'organism_ontology_term_id' is not 'NCBITaxon:10090' " "nor 'NCBITaxon:9606', 'development_stage_ontology_term_id' MUST be a child term id of " "'UBERON:0000105' excluding 'UBERON:0000071', or unknown.", ], ) # All other it MUST be children of UBERON:0000105 and not UBERON:0000071 # Fail case UBERON:0000071 self.validator.errors = [] self.validator.adata.obs["organism_ontology_term_id"][0] = "NCBITaxon:10114" self.validator.adata.obs["development_stage_ontology_term_id"][0] = "UBERON:0000071" self.validator.adata.obs["ethnicity_ontology_term_id"][0] = "na" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'UBERON:0000071' in 'development_stage_ontology_term_id' is not allowed'. When " "'organism_ontology_term_id' is not 'NCBITaxon:10090' " "nor 'NCBITaxon:9606', 'development_stage_ontology_term_id' MUST be a child term id of " "'UBERON:0000105' excluding 'UBERON:0000071', or unknown.", ], ) def test_disease_ontology_term_id(self): """ disease_ontology_term_id categorical with str categories. This MUST be a MONDO term or PATO:0000461 for normal or healthy. """ # Invalid ontology self.validator.adata.obs["disease_ontology_term_id"][0] = "EFO:0000001" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0000001' in 'disease_ontology_term_id' is not a " "valid ontology term id of 'MONDO, PATO'. Only 'PATO:0000461' is allowed for 'PATO' term ids." ], ) # Invalid PATO term id self.validator.errors = [] self.validator.adata.obs["disease_ontology_term_id"][0] = "PATO:0001894" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'PATO:0001894' in 'disease_ontology_term_id' is not an allowed term: '[['PATO:0000461']]'. " "Only 'PATO:0000461' is allowed for 'PATO' term ids." ], ) def test_ethnicity_ontology_term_id(self): """ ethnicity_ontology_term_id categorical with str categories. If organism_ontolology_term_id is "NCBITaxon:9606" for Homo sapiens, this MUST be either a HANCESTRO term or "unknown" if unavailable. Otherwise, for all other organisms this MUST be "na". """ # If organism_ontolology_term_id is "NCBITaxon:9606" for Homo sapiens, # this MUST be either a HANCESTRO term or "unknown" if unavailable. self.validator.adata.obs["organism_ontology_term_id"][0] = "NCBITaxon:9606" self.validator.adata.obs["ethnicity_ontology_term_id"][0] = "EFO:0000001" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0000001' in 'ethnicity_ontology_term_id' is " "not a valid ontology term id of 'HANCESTRO'. When 'organism_ontology_term_id' is 'NCBITaxon:9606' " "(Homo sapiens), ethnicity_ontology_term_id MUST be a term id of 'HANCESTRO' or 'unknown'." ], ) # Otherwise, for all other organisms this MUST be "na". Below is the test case for mouse data. # development_stage_ontology_term_id has to be set to an appropriate mouse term id, otherwise there # will be an error in that field. self.validator.errors = [] self.validator.adata.obs["organism_ontology_term_id"][0] = "NCBITaxon:10090" self.validator.adata.obs["development_stage_ontology_term_id"][0] = "MmusDv:0000003" self.validator.adata.obs["ethnicity_ontology_term_id"][0] = "EFO:0000001" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0000001' in 'ethnicity_ontology_term_id' is not a " "valid value of 'ethnicity_ontology_term_id'. When 'organism_ontology_term_id' is NOT 'NCBITaxon:9606' " "(Homo sapiens), ethnicity_ontology_term_id MUST be 'na'." ], ) def test_organism_ontology_term_id(self): """ organism_ontology_term_id categorical with str categories. This MUST be a child of NCBITaxon:33208. """ # Setting "organism_ontology_term_id" to "EFO:0000001" is the fail case. However since this represents neither # human nor mouse, then two other columns that are dependent on it need to be set appropriately to avoid # other error messages: "development_stage_ontology_term_id" and "ethnicity_ontology_term_id" self.validator.adata.obs["organism_ontology_term_id"][0] = "EFO:0000001" self.validator.adata.obs["development_stage_ontology_term_id"][0] = "unknown" self.validator.adata.obs["ethnicity_ontology_term_id"][0] = "na" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0000001' in 'organism_ontology_term_id' is not a valid " "ontology term id of 'NCBITaxon'. Only children term ids of 'NCBITaxon:33208' for metazoan are allowed." ], ) def test_tissue_ontology_term_id_base(self): """ tissue_ontology_term_id categorical with str categories. This MUST be the term that best describes the tissue that this cell was derived from, depending on the type of biological sample: """ self.validator.adata.obs["tissue_ontology_term_id"][0] = "EFO:0000001" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'EFO:0000001' in 'tissue_ontology_term_id' is not a valid ontology term id of 'UBERON, CL'."], ) def test_tissue_ontology_term_id_cell_culture(self): """ Cell Culture - MUST be a CL term appended with " (cell culture)" """ self.validator.adata.obs["tissue_ontology_term_id"][0] = "CL:0000057 (CELL culture)" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'CL:0000057 (CELL culture)' in 'tissue_ontology_term_id' is " "not a valid ontology term id of 'UBERON, CL'." ], ) def test_tissue_ontology_term_id_organoid(self): """ Organoid - MUST be an UBERON term appended with " (organoid)" """ self.validator.adata.obs["tissue_ontology_term_id"][0] = "CL:0000057 (ORGANOID)" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'CL:0000057 (ORGANOID)' in 'tissue_ontology_term_id' is not a valid ontology term id of 'UBERON, CL'."], ) def test_sex_ontology_term_id(self): """ sex_ontology_term_id categorical with str categories. This MUST be a child of PATOPATO:0001894 for phenotypic sex or "unknown" if unavailable """ self.validator.adata.obs["sex_ontology_term_id"][0] = "EFO:0000001" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0000001' in 'sex_ontology_term_id' is " "not a valid ontology term id of 'PATO'. Only 'PATO:0000383', 'PATO:0000384', 'PATO:0001340', " "or 'unknown' are allowed." ], ) def test_is_primary_data(self): """ is_primary_data bool. This MUST be True if this is the canonical instance of this cellular observation and False if not. This is commonly False for meta-analyses reusing data or for secondary views of data. """ self.validator.adata.obs["is_primary_data"] = "FALSE" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: Column 'is_primary_data' in dataframe 'obs' must be boolean, not 'object'."], ) class TestVar(unittest.TestCase): """ Fail cases in adata.var and adata.raw.var """ def setUp(self): self.validator = Validator() self.validator.adata = examples.adata.copy() def test_var_and_raw_var_same_index(self): """ var.index MUST contain unique identifiers for features. raw.var.index MUST be identical to var.index. """ # Swap first row for second one var = Validator.getattr_anndata(self.validator.adata, "var") # First swap the index new_index = list(var.index) tmp = new_index[0] new_index[0] = new_index[1] new_index[1] = tmp var.set_index(pd.Index(new_index), inplace=True) # Then swap the actual rows tmp = var.iloc[0, :].copy() var.iloc[0, :] = var.iloc[1, :].copy() var.iloc[1, :] = tmp self.validator.validate_adata() print("FOO", self.validator.errors) self.assertEqual( self.validator.errors, ["ERROR: Index of 'raw.var' is not identical to index of 'var'."], ) def test_check_unique_var(self): """ var.index MUST contain unique ENSEMBL gene identifiers for features. """ for component_name in ["var", "raw.var"]: with self.subTest(component_name=component_name): # Resetting validator self.validator.adata = examples.adata.copy() self.validator.errors = [] # Duplicate 1st row in var and assign it to 2nd component = Validator.getattr_anndata(self.validator.adata, component_name) new_index = list(component.index) new_index[1] = new_index[0] component.set_index(pd.Index(new_index), inplace=True) component.iloc[1, :] = component.iloc[0, :] self.validator.validate_adata() self.assertEqual( self.validator.errors, [f"ERROR: Column 'index' in dataframe '{component_name}' is not unique."], ) def test_column_presence(self): """ var is a pandas.DataFrame. Curators MUST annotate the following columns in the obs dataframe. feature_is_filtered must not be in raw.var, and it's only checked in var """ columns = ["feature_is_filtered", "feature_biotype"] for component_name in ["var", "raw.var"]: for column in columns: if column == "feature_is_filtered" and component_name == "raw.var": continue with self.subTest(component_name=component_name, column=column): # Resetting validator self.validator.errors = [] self.validator.adata = examples.adata.copy() component = Validator.getattr_anndata(self.validator.adata, component_name) component.drop(column, axis=1, inplace=True) self.validator.validate_adata() self.assertEqual( self.validator.errors, [f"ERROR: Dataframe '{component_name}' is missing column '{column}'."], ) def test_feature_is_filtered(self): """ feature_is_filtered bool. This MUST be True if the feature was filtered out in the final matrix (X) but is present in the raw matrix (raw.X). The value for all cells of the given feature in the final matrix MUST be 0. Otherwise, this MUST be False. """ # Duplicate 1st row in var and assigned to 2nd self.validator.adata.var["feature_is_filtered"][0] = True for i in range(self.validator.adata.X.shape[0]): self.validator.adata.X[i, 0] = 0 self.validator.adata.X[0, 0] = 1 self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: Some features are 'True' in 'feature_is_filtered' of dataframe 'var', " "but there are 1 non-zero values in the corresponding columns of the matrix 'X'. " "All values for these features must be 0." ], ) def test_columns_not_in_raw_var(self): """ Curators MUST annotate the following column only in the var dataframe. This column MUST NOT be present in raw.var: feature_is_filtered """ self.validator.adata.raw = self.validator.adata self.validator.adata.uns["X_normalization"] = "CPM" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: Column 'feature_is_filtered' must not be present in 'raw.var'."], ) def test_feature_id_wrong_format(self): """ feature_id (var.index) str. If the feature_biotype is "gene" then this MUST be an ENSEMBL term. If the feature_biotype is "spike-in" then this MUST be an ERCC Spike-In identifier. This tests the case of an ID with an incorrect format "ENSEBML_NOGENE" """ for component_name in ["var", "raw.var"]: with self.subTest(component_name=component_name): # Resetting validator self.validator.adata = examples.adata.copy() self.validator.errors = [] component = Validator.getattr_anndata(self.validator.adata, component_name) new_index = list(component.index) new_index[0] = "ENSEBML_NOGENE" component.set_index(pd.Index(new_index), inplace=True) component["feature_biotype"][0] = "gene" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ f"ERROR: Could not infer organism from feature ID 'ENSEBML_NOGENE' " f"in '{component_name}', make sure it is a valid ID." ], ) def test_feature_id_non_existent_ensembl(self): """ feature_id (var.index) str. If the feature_biotype is "gene" then this MUST be an ENSEMBL term. If the feature_biotype is "spike-in" then this MUST be an ERCC Spike-In identifier. This tests the case of an ENSEMBL ID that has the right format but doesn't exist """ for component_name in ["var", "raw.var"]: with self.subTest(component_name=component_name): # Resetting validator self.validator.adata = examples.adata.copy() self.validator.errors = [] component = Validator.getattr_anndata(self.validator.adata, component_name) new_index = list(component.index) new_index[0] = "ENSG000" component.set_index(pd.Index(new_index), inplace=True) component["feature_biotype"][0] = "gene" self.validator.validate_adata() self.assertEqual( self.validator.errors, [f"ERROR: 'ENSG000' is not a valid feature ID in '{component_name}'."], ) def test_feature_id_non_existent_ercc(self): """ feature_id (var.index) str. If the feature_biotype is "gene" then this MUST be an ENSEMBL term. If the feature_biotype is "spike-in" then this MUST be an ERCC Spike-In identifier. This tests the case of an ERCC ID that has the right format but doesn't exist """ for component_name in ["var", "raw.var"]: with self.subTest(component_name=component_name): # Resetting validator self.validator.adata = examples.adata.copy() self.validator.errors = [] component = Validator.getattr_anndata(self.validator.adata, component_name) new_index = list(component.index) new_index[0] = "ERCC-000000" component.set_index(pd.Index(new_index), inplace=True) component["feature_biotype"][0] = "spike-in" self.validator.validate_adata() self.assertEqual( self.validator.errors, [f"ERROR: 'ERCC-000000' is not a valid feature ID in '{component_name}'."], ) class TestUns(unittest.TestCase): """ Fail cases in adata.uns """ def setUp(self): self.validator = Validator() self.validator.adata = examples.adata.copy() def test_required_fields_schema_version(self): """ Curators MUST annotate `schema_version` and values in uns (schema_version) """ del self.validator.adata.uns["schema_version"] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: adata has no schema definition in 'adata.uns'. Validation cannot be performed."], ) def test_required_fields_title(self): """ Curators MUST annotate `schema_version` and values in uns (title) """ del self.validator.adata.uns["title"] self.validator.validate_adata() self.assertEqual(self.validator.errors, ["ERROR: 'title' in 'uns' is not present."]) def test_required_fields_X_normalization(self): """ Curators MUST annotate `schema_version` and values in uns (X_normalization) """ del self.validator.adata.uns["X_normalization"] self.validator.validate_adata() self.assertEqual(self.validator.errors, ["ERROR: 'X_normalization' in 'uns' is not present."]) def test_leading_trailing_double_spaces_in_strings(self): """ The following sequences MUST NOT appear in str types documented in the schema: Leading control or space separators - ” This is an example” Trailing control or space separators - “This is an example ” Multiple (internal) control or space separators - "This is an example" """ self.validator.adata.uns["title"] = " There is a leading space" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: ' There is a leading space' in 'uns['title']' is not valid, it contains leading spaces."], ) self.validator.adata.uns["title"] = "There is a trailing space " self.validator.errors = [] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'There is a trailing space ' in 'uns['title']' is not valid, it contains trailing spaces."], ) self.validator.adata.uns["title"] = "There are double spaces" self.validator.errors = [] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'There are double spaces' in 'uns['title']' is not valid, it contains double spaces."], ) def test_schema_version(self): """ Schema_version, This MUST be "2.0.0". """ self.validator.adata.uns["schema_version"] = "1.0.0" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: Schema version '1.0.0' is not supported. Current supported versions: '['2.0.0']'. " "Validation cannot be performed." ], ) def test_title(self): """ Title MUST be a string """ # list instead of string self.validator.adata.uns["title"] = ["title"] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: '['title']' in 'uns['title']' is not valid, it must be a string."], ) def test_X_normalization_is_str(self): """ X_normalization str. """ # list instead of string self.validator.adata.uns["X_normalization"] = ["normalization"] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: '['normalization']' in 'uns['X_normalization']' is not valid, it must be a string."], ) def test_X_normalization_not_raw(self): """ X_normalization str. This SHOULD describe the method used to normalize the data stored in AnnData X. If data in X are raw, this SHOULD be "none". FAIL CASE for when X_normalization was set to "none" but X may not be raw data """ # Assign a real value to X while X_normalization is 'none' del self.validator.adata.raw self.validator.adata.uns["X_normalization"] = "none" self.validator.validate_adata() print("FOO", self.validator.warnings) self.assertEqual( self.validator.warnings, [ "WARNING: uns['X_normalization'] is 'none', there is no 'raw.X' and 'X' doesn't appear " "to have raw counts (integers)" ], ) def test_batch_condition_is_list(self): """ batch_condition list[str] """ # Check valid case of numpy array which is interchangeable with lists self.validator.adata.uns["batch_condition"] = numpy.array(self.validator.adata.uns["batch_condition"]) self.validator.validate_adata() self.assertEqual(self.validator.errors, []) # Check fail case: not a list nor numpy array self.validator.adata.uns["batch_condition"] = "cell_type_ontology_term_id" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'cell_type_ontology_term_id' in 'uns['batch_condition']' is not valid, it must be a list or numpy array."], ) def test_batch_condition_is_column_from_obs(self): """ batch_condition list[str]. str values MUST refer to cell metadata keys in obs. """ self.validator.adata.uns["batch_condition"] = ["NO_COLUMN"] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: Value 'NO_COLUMN' of list 'batch_condition' is not a column in 'adata.obs'."], ) def test_default_embedding_is_str(self): """ Default_embedding str. """ self.validator.adata.uns["default_embedding"] = ["X_umap"] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: '['X_umap']' in 'uns['default_embedding']' is not valid, it must be a string."], ) def test_default_embedding_is_key_from_obsm(self): """ Default_embedding str. The value MUST match a key to an embedding in obsm """ self.validator.adata.uns["default_embedding"] = "X_other" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'X_other' in 'uns['default_embedding']' is not valid, it must be a key of 'adata.obsm'."], ) def test_X_approximate_distribution_is_str(self): """ X_approximate_distribution str. The value MUST be "count" [...] or "normal". Note that `normal` is tested in the happy path test case using `good_uns`. """ # Check valid case of "count" which is not included in valid object self.validator.adata.uns["X_approximate_distribution"] = "count" self.validator.validate_adata() self.assertEqual(self.validator.errors, []) # Invalid type: list self.validator.adata.uns["X_approximate_distribution"] = ["count"] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: '['count']' in 'uns['X_approximate_distribution']' is not valid, it must be a string."], ) def test_X_approximate_distribution_is_valid(self): """ X_approximate_distribution str. The value MUST be "count" [...] or "normal" """ self.validator.adata.uns["X_approximate_distribution"] = "COUNT" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'COUNT' in 'uns['X_approximate_distribution']' is not valid. Allowed terms: ['count', 'normal']."], ) class TestObsm(unittest.TestCase): """ Fail cases for adata.obsm """ def setUp(self): self.validator = Validator() self.validator.adata = examples.adata.copy() def test_obsm_values_ara_numpy(self): """ values in obsm MUST be a numpy.ndarray """ self.validator.adata.obsm["X_tsne"] = pd.DataFrame( self.validator.adata.obsm["X_umap"], index=self.validator.adata.obs_names ) self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: All embeddings have to be of 'numpy.ndarray' type, " "'adata.obsm['X_tsne']' is <class 'pandas.core.frame.DataFrame'>')." ], ) def test_obsm_values_at_least_one_X(self): """ At least one key for the embedding MUST be prefixed with "X_" """ self.validator.adata.obsm["umap"] = self.validator.adata.obsm["X_umap"] self.validator.adata.uns["default_embedding"] = "umap" del self.validator.adata.obsm["X_umap"] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: At least one embedding in 'obsm' has to have a key with an 'X_' prefix."], ) def test_obsm_shape(self): """ Curators MUST annotate one or more two-dimensional (m >= 2) embeddings """ # Makes 1 column array self.validator.adata.obsm["X_umap"] = numpy.delete(self.validator.adata.obsm["X_umap"], 0, 1) self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: All embeddings must have as many rows as cells, and " "at least two columns.'adata.obsm['X_umap']' has shape " "of '(2, 1)'." ], ) class TestAddingLabels(unittest.TestCase): """ Tests the addition of labels from IDs based on schema specification. The test is done by comparing manually created dataframes (positive control) against the ones produced by the validator """ @classmethod def setUpClass(cls): # Manually created data (positive control) cls.adata_with_labels = examples.adata_with_labels # Validate test data validator = Validator() validator.adata = examples.adata.copy() validator.validate_adata() # Add labels through validator cls.label_writer = AnnDataLabelAppender(validator) cls.label_writer._add_labels() def test_var_added_labels(self): """ When a dataset is uploaded, cellxgene Data Portal MUST automatically add the matching human-readable name for the corresponding feature identifier and the inferred NCBITaxon term for the reference organism to the var dataframe. Curators MUST NOT annotate the following columns: - feature_name. If the feature_biotype is "gene" then this MUST be the human-readable ENSEMBL gene name assigned to the feature_id. If the feature_biotype is "spike-in" then this MUST be the ERCC Spike-In identifier appended with " spike-in control". - feature_reference. This MUST be the reference organism for a feature: Homo sapiens "NCBITaxon:9606" Mus musculus "NCBITaxon:10090" SARS-CoV-2 "NCBITaxon:2697049" ERCC Spike-Ins "NCBITaxon:32630" """ for column in ["feature_name", "feature_reference"]: expected_column = self.adata_with_labels.var[column] obtained_column = self.label_writer.adata.var[column] for i, j in zip(expected_column.tolist(), obtained_column.tolist()): with self.subTest(i=i, j=j): self.assertEqual(i, j) def test_obs_added_labels(self): """ When a dataset is uploaded, the cellxgene Data Portal MUST automatically add the matching human-readable name for the corresponding ontology term to the obs dataframe. Curators MUST NOT annotate the following columns. - assay. categorical with str categories. This MUST be the human-readable name assigned to the value of assay_ontology_term_id. Any clarifying text enclosed in parentheses and appended to assay_ontology_term_id MUST be appended to assay. - cell_type. categorical with str categories. This MUST be the human-readable name assigned to the value of cell_type_ontology_term_id. - development_stage. categorical with str categories. This MUST be "unknown" if set in development_stage_ontology_term_id; otherwise, this MUST be the human-readable name assigned to the value of development_stage_ontology_term_id. - disease. categorical with str categories. This MUST be the human-readable name assigned to the value of disease_ontology_term_id. - ethnicity. categorical with str categories. This MUST be "na" or "unknown" if set in ethnicity_ontology_term_id; otherwise, this MUST be the human-readable name assigned to the value of ethnicity_ontology_term_id. - organism. categorical with str categories. This MUST be the human-readable name assigned to the value of organism_ontology_term_id. - sex. categorical with str categories. This MUST be "unknown" if set in sex_ontology_term_id; otherwise, this MUST be the human-readable name assigned to the value of sex_ontology_term_id. - tissue. categorical with str categories. This MUST be the human-readable name assigned to the value of tissue_ontology_term_id. " (cell culture)" or " (organoid)" MUST be appended if present in tissue_ontology_term_id. """ for column in [ "assay", "cell_type", "development_stage", "disease", "ethnicity", "organism", "sex", "tissue", ]: expected_column = self.adata_with_labels.obs[column] obtained_column = self.label_writer.adata.obs[column] for i, j in zip(expected_column.tolist(), obtained_column.tolist()): with self.subTest(i=i, j=j): self.assertEqual(i, j)	[ 485, 15790, 222, 222, 485, 6610, 222, 485, 13247, 641, 6451, 222, 222, 485, 37705, 51, 7944, 100, 4647, 641, 8322, 222, 1097, 4046, 125, 16241, 100, 3982, 51, 4647, 1220, 19510, 222, 1097, 4046, 125, 16241, 100, 3982, 51, 1838, 100, 6007, 1220, 43759, 768, 2186, 34380, 499, 222, 40, 11399, 456, 5379, 4960, 451, 619, 43759, 768, 1319, 499, 222, 842, 2128, 2320, 25444, 624, 45, 18959, 51, 7246, 731, 303, 1547, 303, 11399, 331, 2101, 43759, 768, 1319, 51, 19576, 1623, 3326, 4834, 5370, 477, 43759, 768, 1319, 480, 913, 456, 7837, 6819, 51, 465, 906, 2101, 43759, 768, 1319, 1421, 1187, 2101, 6819, 10309, 347, 341, 5379, 51, 303, 1547, 465, 684, 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299, 332, 2973, 82, 39, 603, 607, 3365, 1611, 17849, 310, 649, 51, 9885, 51, 2020, 299, 649, 51, 9885, 51, 2020, 7175, 244, 54, 10209, 310, 649, 51, 9885, 51, 4647, 100, 2020, 365, 310, 649, 51, 5307, 45, 343, 649, 51, 9885, 51, 4312, 49, 343, 3059, 2946, 63, 5132, 451, 31376, 347, 1618, 327, 56, 46, 458, 3473, 2806, 5096, 51, 93, 327, 57, 566, 3461, 310, 848, 465, 684, 913, 100, 438, 4207, 562, 45, 803, 731, 310, 1547, 310, 719, 1364, 5458, 49, 434, 331, 5083, 1421, 244, 58, 53, 42, 575, 1851, 2102, 708, 904, 22738, 49, 580, 458, 9324, 586, 2079, 94, 3062, 6294, 35333, 708, 310, 341, 5083, 545, 11849, 641, 331, 18945, 51, 15504, 51, 23090, 100, 4948, 310, 1547, 603, 649, 51, 9885, 51, 2020, 51, 93, 299, 649, 51, 9885, 51, 2020, 51, 93, 51, 471, 977, 365, 310, 649, 51, 9885, 51, 4647, 100, 2020, 365, 310, 649, 51, 5307, 45, 343, 649, 51, 9885, 51, 20340, 49, 343, 447, 419, 332, 10436, 63, 377, 15889, 562, 451, 349, 93, 44, 458, 244, 53, 51, 61, 60, 58, 1532, 458, 10783, 2806, 244, 53, 51, 58, 49, 332, 419, 332, 382, 580, 458, 666, 331, 349, 36858, 51, 15504, 51, 23090, 100, 4948, 2503, 2048, 458, 332, 419, 332, 2393, 586, 2079, 94, 3062, 6294, 35333, 391, 813, 477, 847, 451, 5083, 456, 332, 419, 332, 1402, 2716, 328, 15889, 562, 3128, 343, 2520, 310, 848, 465, 684, 913, 100, 1312, 100, 346, 7454, 45, 803, 731, 310, 1547, 310, 48109, 456, 13089, 1205, 50, 4974, 480, 616, 23458, 3840, 727, 49, 5096, 727, 458, 38463, 310, 1547, 603, 607, 20648, 449, 5096, 5458, 2147, 310, 3579, 649, 51, 9885, 51, 2020, 51, 1312, 310, 649, 51, 9885, 51, 4647, 100, 2020, 365, 310, 649, 51, 5307, 45, 343, 649, 51, 9885, 51, 4312, 49, 343, 3059, 2946, 63, 16297, 727, 458, 5839, 63, 2038, 458, 1307, 349, 1312, 51, 93, 44, 480, 349, 93, 100, 4434, 1668, 44, 458, 666, 349, 4129, 2503, 3461, 310, 848, 603, 607, 13089, 1205, 449, 5096, 5458, 666, 2147, 310, 607, 906, 13830, 3713, 7437, 1618, 391, 666, 545, 5096, 63, 649, 51, 9885, 51, 2020, 51, 16349, 1388, 93, 100, 4434, 1668, 1465, 299, 332, 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ad3a7221883a847fc9d26097c3801973cbbda38e	from django.urls import path, include from Income import views urlpatterns = [ path("IncomeHome/", views.IncomeHome, name="IncomeHome"), path("IncomeCreate/", views.IncomeCreate.as_view(), name="IncomeCreate"), path("IncomeUpdate/<int:pk>", views.IncomeUpdate.as_view(), name="IncomeUpdate"), path("IncomeDelete/<int:pk>", views.IncomeDelete.as_view(), name="IncomeDelete"), path("Income/", views.IncomeView.as_view(), name="Income"), ]	[ 1097, 8532, 51, 11420, 1220, 1536, 49, 2323, 222, 222, 1097, 719, 3772, 1220, 11887, 222, 222, 983, 15735, 299, 447, 303, 1536, 459, 31779, 4637, 10974, 11887, 51, 31779, 4637, 49, 655, 366, 31779, 4637, 2132, 303, 1536, 459, 31779, 1940, 10974, 11887, 51, 31779, 1940, 51, 321, 100, 1090, 1046, 655, 366, 31779, 1940, 2132, 303, 1536, 459, 31779, 2346, 14612, 429, 63, 3198, 9603, 11887, 51, 31779, 2346, 51, 321, 100, 1090, 1046, 655, 366, 31779, 2346, 2132, 303, 1536, 459, 31779, 3422, 14612, 429, 63, 3198, 9603, 11887, 51, 31779, 3422, 51, 321, 100, 1090, 1046, 655, 366, 31779, 3422, 2132, 303, 1536, 459, 31779, 10974, 11887, 51, 31779, 1089, 51, 321, 100, 1090, 1046, 655, 366, 31779, 2132, 222, 98, 222 ]	[ "from", "Ġdjango", ".", "urls", "Ġimport", "Ġpath", ",", "Ġinclude", "Ċ", "Ċ", "from", "ĠIn", "come", "Ġimport", "Ġviews", "Ċ", "Ċ", "url", "patterns", "Ġ=", "Ġ[", "ĊĠĠĠ", "Ġpath", "(\"", "Income", "Home", "/\",", "Ġviews", ".", "Income", "Home", ",", "Ġname", "=\"", "Income", "Home", "\"),", "ĊĠĠĠ", "Ġpath", "(\"", "Income", "Create", "/\",", "Ġviews", ".", "Income", "Create", ".", "as", "_", "view", "(),", "Ġname", "=\"", "Income", "Create", "\"),", "ĊĠĠĠ", "Ġpath", "(\"", "Income", "Update", "/<", "int", ":", "pk", ">\",", "Ġviews", ".", "Income", "Update", ".", "as", "_", "view", "(),", "Ġname", "=\"", "Income", "Update", "\"),", "ĊĠĠĠ", "Ġpath", "(\"", "Income", "Delete", "/<", "int", ":", "pk", ">\",", "Ġviews", ".", "Income", "Delete", ".", "as", "_", "view", "(),", "Ġname", "=\"", "Income", "Delete", "\"),", "ĊĠĠĠ", "Ġpath", "(\"", "Income", "/\",", "Ġviews", ".", "Income", "View", ".", "as", "_", "view", "(),", "Ġname", "=\"", "Income", "\"),", "Ċ", "]", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11 ]	[ 1, 5, 12, 13, 17, 24, 29, 30, 38, 1, 1, 5, 8, 12, 19, 25, 1, 1, 4, 12, 14, 16, 4, 9, 11, 17, 21, 24, 30, 31, 37, 41, 42, 47, 49, 55, 59, 62, 4, 9, 11, 17, 23, 26, 32, 33, 39, 45, 46, 48, 49, 53, 56, 61, 63, 69, 75, 78, 4, 9, 11, 17, 23, 25, 28, 29, 31, 34, 40, 41, 47, 53, 54, 56, 57, 61, 64, 69, 71, 77, 83, 86, 4, 9, 11, 17, 23, 25, 28, 29, 31, 34, 40, 41, 47, 53, 54, 56, 57, 61, 64, 69, 71, 77, 83, 86, 4, 9, 11, 17, 20, 26, 27, 33, 37, 38, 40, 41, 45, 48, 53, 55, 61, 64, 1, 2 ]
180f7f0ade9770c6669680bd13ac8f2fd55cc8c7	def raizCubica(numero): r = pow(numero, (1 / 3)) return r numeros = [] raices = [] for x in range(5): numeros.insert(x, float(input("Ingrese Numero: "))) raices.insert(x, round(raizCubica(numeros[x]), 3)) print("Numeros: ", numeros) print("Raices: ", raices)	[ 610, 16535, 445, 36108, 5303, 45, 24391, 731, 303, 559, 299, 13157, 45, 24391, 49, 327, 54, 536, 244, 56, 509, 303, 461, 559, 499, 222, 1538, 14731, 299, 1627, 222, 1709, 1362, 299, 1627, 222, 222, 999, 837, 347, 2189, 45, 58, 731, 303, 1952, 14731, 51, 3233, 45, 125, 49, 1916, 45, 1151, 459, 402, 5013, 296, 10859, 2168, 63, 332, 2636, 303, 16535, 1362, 51, 3233, 45, 125, 49, 7880, 45, 1709, 445, 36108, 5303, 45, 1538, 14731, 96, 125, 5759, 244, 56, 509, 222, 222, 1243, 459, 2585, 14731, 63, 3021, 1952, 14731, 46, 222, 1243, 459, 28255, 1362, 63, 3021, 16535, 1362, 46, 222 ]	[ "def", "Ġra", "iz", "Cub", "ica", "(", "numero", "):", "ĊĠĠĠ", "Ġr", "Ġ=", "Ġpow", "(", "numero", ",", "Ġ(", "1", "Ġ/", "Ġ", "3", "))", "ĊĠĠĠ", "Ġreturn", "Ġr", "ĊĊ", "Ċ", "num", "eros", "Ġ=", "Ġ[]", "Ċ", "ra", "ices", "Ġ=", "Ġ[]", "Ċ", "Ċ", "for", "Ġx", "Ġin", "Ġrange", "(", "5", "):", "ĊĠĠĠ", "Ġnum", "eros", ".", "insert", "(", "x", ",", "Ġfloat", "(", "input", "(\"", "In", "gre", "se", "ĠNum", "ero", ":", "Ġ\"", ")))", "ĊĠĠĠ", "Ġra", "ices", ".", "insert", "(", "x", ",", "Ġround", "(", "ra", "iz", "Cub", "ica", "(", "num", "eros", "[", "x", "]),", "Ġ", "3", "))", "Ċ", "Ċ", "print", "(\"", "Num", "eros", ":", "Ġ\",", "Ġnum", "eros", ")", "Ċ", "print", "(\"", "Ra", "ices", ":", "Ġ\",", "Ġra", "ices", ")", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 5, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 8, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14 ]	[ 1, 4, 7, 9, 12, 15, 16, 22, 24, 4, 6, 8, 12, 13, 19, 20, 22, 23, 25, 26, 27, 29, 4, 11, 13, 1, 1, 4, 8, 10, 13, 1, 3, 7, 9, 12, 1, 1, 4, 6, 9, 15, 16, 17, 19, 4, 8, 12, 13, 19, 20, 21, 22, 28, 29, 34, 36, 38, 41, 43, 47, 50, 51, 53, 56, 4, 7, 11, 12, 18, 19, 20, 21, 27, 28, 30, 32, 35, 38, 39, 42, 46, 47, 48, 51, 52, 53, 55, 1, 1, 6, 8, 11, 15, 16, 19, 23, 27, 28, 1, 6, 8, 10, 14, 15, 18, 21, 25, 26 ]
97ebdeada3d797a971b5c3851b75f9754595f67c	""" Python package setup file. """ from setuptools import setup setup( name="TF_Speech", version="0.2.0", extras_require={"tensorflow": ["tensorflow"], "tensorflow with gpu": ["tensorflow-gpu"]}, )	[ 3012, 222, 8378, 2670, 4721, 822, 51, 222, 3012, 222, 222, 1097, 29910, 1220, 4721, 222, 222, 5536, 45, 303, 655, 366, 5771, 100, 22023, 411, 303, 1522, 366, 53, 51, 55, 51, 53, 411, 303, 35960, 100, 1999, 8492, 13535, 582, 3059, 13535, 3461, 332, 13535, 642, 20943, 582, 3059, 13535, 50, 9921, 1465, 855, 222, 46, 222 ]	[ "\"\"\"", "Ċ", "Python", "Ġpackage", "Ġsetup", "Ġfile", ".", "Ċ", "\"\"\"", "Ċ", "Ċ", "from", "Ġsetuptools", "Ġimport", "Ġsetup", "Ċ", "Ċ", "setup", "(", "ĊĠĠĠ", "Ġname", "=\"", "TF", "_", "Speech", "\",", "ĊĠĠĠ", "Ġversion", "=\"", "0", ".", "2", ".", "0", "\",", "ĊĠĠĠ", "Ġextras", "_", "require", "={\"", "tensorflow", "\":", "Ġ[\"", "tensorflow", "\"],", "Ġ\"", "tensorflow", "Ġwith", "Ġgpu", "\":", "Ġ[\"", "tensorflow", "-", "gpu", "\"]", "},", "Ċ", ")", "Ċ" ]	[ 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 5, 6, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11 ]	[ 1, 4, 1, 7, 15, 21, 26, 27, 1, 4, 1, 1, 5, 16, 23, 29, 1, 1, 6, 7, 4, 9, 11, 13, 14, 20, 22, 4, 12, 14, 15, 16, 17, 18, 19, 21, 4, 11, 12, 19, 22, 32, 34, 37, 47, 50, 52, 62, 67, 71, 73, 76, 86, 87, 90, 92, 94, 1, 2 ]
9f34bf3a0bb24db428b7af1a354aec1d3a72df98	from random import randrange from django.core.exceptions import ValidationError from django.contrib.auth import get_user_model from rest_framework import serializers from rest_framework_simplejwt.serializers import TokenObtainPairSerializer from .models import EmailValidation from ..emails.models import Email from ..users.serializers import FullUserSerializer User = get_user_model() def user_with_email_not_existing(email): try: User.objects.get(email=email) raise ValidationError(message="This email is taken") except User.DoesNotExist: return email def email_does_exist(email): try: User.objects.get(email=email) return email except User.DoesNotExist: raise ValidationError(message="User does not exist!") class CreatePasswordEmailValidationSerializer(serializers.Serializer): email = serializers.EmailField(validators=[email_does_exist]) def save(self): validation_code = randrange(10000000, 100000000) email = Email.objects.create( validation_code=validation_code, to=self.validated_data.get("email"), type=self.validated_data.get("type") ) new_validation = EmailValidation.objects.create( validation_code=validation_code, email=email, type=self.validated_data.get("type") ) return new_validation class CreateEmailValidationSerializer(serializers.Serializer): email = serializers.EmailField(validators=[user_with_email_not_existing]) def save(self): validation_code = randrange(10000000, 100000000) email = Email.objects.create( validation_code=validation_code, to=self.validated_data.get("email"), type=self.validated_data.get("type") ) new_validation = EmailValidation.objects.create( validation_code=validation_code, email=email, type=self.validated_data.get("type") ) return new_validation class EmailSerializer(serializers.ModelSerializer): email = serializers.EmailField() class Meta: model = EmailValidation fields = ["email"] class EmailValidationSerializer(serializers.ModelSerializer): email = serializers.EmailField() class Meta: model = EmailValidation fields = ["email", "validation_code"] class EmailValidationPasswordSerializer(serializers.ModelSerializer): email = serializers.EmailField() password = serializers.CharField(max_length=200) class Meta: model = EmailValidation fields = ["email", "validation_code", "password"] class NewUserSerializer(serializers.ModelSerializer): email = serializers.EmailField() class Meta: model = EmailValidation fields = ["email"] class TokenObtainPairViewWithUserProfileSerializer(TokenObtainPairSerializer): def validate(self, attrs): data = super().validate(attrs) refresh = self.get_token(self.user) data["refresh"] = str(refresh) data["access"] = str(refresh.access_token) data["user"] = FullUserSerializer(self.user).data return data	[ 1097, 4051, 1220, 10510, 3623, 222, 222, 1097, 8532, 51, 1284, 51, 11453, 1220, 40026, 222, 1097, 8532, 51, 12060, 51, 2408, 1220, 640, 100, 514, 100, 1184, 222, 222, 1097, 6125, 100, 2863, 1220, 34229, 222, 1097, 6125, 100, 2863, 100, 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2ca91c410b8c8d6306d5ed918783a4d77a091ba8	from typing import List from re import match from utility import ButtonGroup import rumps class RepeatWorkBreak(rumps.App): def __init__(self): rumps.debug_mode(True) self.config = { "app_title": "Repeat Work and Break", "start": "Start", "pause": "Pause Timer", "continue": "Continue Timer", "stop": "Stop Timer", "timeout_message": "Time is up! Take a break :)", "shift_time_in_seconds": 60 * 60 * 1, # 60 seconds * 60 = 1 hour "break_time_in_seconds": 60 * 5, "shift_setting_buttons": [ { "title": "1 hour", }, { "title": "4 hour", }, { "title": "8 hour", }, ], "break_setting_buttons": [ { "title": "5 minutes", }, { "title": "10 minutes", }, { "title": "15 minutes", }, ], } self.app = rumps.App(self.config["app_title"]) self.timer = rumps.Timer(self.on_tick, 1) self.shift_setting_button_group = ButtonGroup( self.config["shift_setting_buttons"], callback=self.handle_shift_setting_button ) self.break_setting_button_group = ButtonGroup( self.config["break_setting_buttons"], callback=self.handle_shift_setting_button ) self.shift_time_in_seconds = self.config["shift_time_in_seconds"] self.break_time_in_seconds = self.config["break_time_in_seconds"] self.elapsed_shift_time_in_hours = 0 self.progress_box = "◻︎" * (self.shift_time_in_seconds // 3600) self.start_pause_button = rumps.MenuItem(title=self.config["start"], callback=self.start_timer) self.stop_button = rumps.MenuItem(title=self.config["stop"], callback=None) self.app.menu = [ { "Preferences": { "Setting Shift": self.shift_setting_button_group.buttons, "Setting Break / hr": self.break_setting_button_group.buttons, } }, None, self.start_pause_button, self.stop_button, ] def set_up_menu(self): self.timer.stop() self.timer.count = 0 self.app.title = self.config["app_title"] def convert_seconds_to_time_string(self, seconds) -> str: seconds = seconds % (24 * 3600) hours, seconds = divmod(seconds, 3600) minutes, seconds = divmod(seconds, 60) return "%d:%02d:%02d" % (hours, minutes, seconds) def on_tick(self, sender): time_left_in_seconds = sender.end - sender.count time_left_in_string = self.convert_seconds_to_time_string(time_left_in_seconds) if sender.count != 0 and sender.count % 3600 == 0: self.elapsed_shift_time_in_hours += 1 self.update_progress_box() if time_left_in_seconds == 0: rumps.notification(title=self.config["app_title"], subtitle=self.config["timeout_message"], message="") self.stop_timer() self.stop_button.set_callback(None) else: self.stop_button.set_callback(self.stop_timer) self.app.title = self.progress_box + " \| " + time_left_in_string sender.count += 1 def update_progress_box(self): self.progress_box = ( self.elapsed_shift_time_in_hours * "☑︎" + (self.shift_time_in_seconds // 3600 - self.elapsed_shift_time_in_hours) * "◻︎" ) def start_timer(self, sender): if sender.title.lower().startswith(("start", "continue")): if sender.title == self.config["start"]: self.timer.count = 0 self.timer.end = self.shift_time_in_seconds sender.title = self.config["pause"] self.timer.start() else: sender.title = self.config["continue"] self.timer.stop() def stop_timer(self, sender=None): self.set_up_menu() self.stop_button.set_callback(None) self.start_pause_button.title = self.config["start"] def handle_shift_setting_button(self, sender): self.shift_setting_button_group.toggle(sender) selected_hours = int(match(r"^\d+\s{1}", sender.title)[0]) self.progress_box = "◻︎" * selected_hours # update empty progress box self.shift_time_in_seconds = selected_hours * 3600 # hours in seconds def handle_break_setting_button(self, sender): self.break_setting_button_group.toggle(sender) selected_minutes = int(match(r"^\d+\s{1}", sender.title)[0]) self.break_time_in_seconds = selected_minutes * 60 def run(self): self.app.run() if __name__ == "__main__": app = RepeatWorkBreak() app.run()	[ 1097, 12018, 1220, 1701, 222, 1097, 334, 1220, 2549, 222, 1097, 15479, 1220, 7413, 1582, 222, 485, 559, 2311, 120, 499, 222, 842, 39839, 3003, 10440, 45, 119, 2311, 120, 51, 1306, 731, 303, 684, 1176, 1683, 3232, 803, 731, 310, 559, 2311, 120, 51, 2824, 100, 2832, 45, 1844, 46, 603, 649, 51, 1180, 299, 320, 343, 332, 745, 100, 1333, 582, 332, 18282, 6352, 480, 19869, 411, 343, 332, 1384, 582, 332, 2148, 411, 343, 332, 12091, 582, 332, 13456, 16327, 411, 343, 332, 7514, 582, 332, 17737, 16327, 411, 343, 332, 4074, 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8bc40ed4fe1091ecdb40cd55ff9cf53010078823	import requests import json data = json.load(open("dummy_data/data.json")) for one in data: print(one) r = requests.post("http://localhost:8080/sumari", json=one) print(r.text)	[ 485, 5188, 222, 485, 2379, 222, 222, 624, 299, 2379, 51, 963, 45, 1693, 459, 11230, 100, 624, 52, 624, 51, 1279, 2152, 222, 222, 999, 1611, 347, 727, 63, 303, 1489, 45, 677, 46, 303, 559, 299, 5188, 51, 1545, 459, 544, 574, 5112, 63, 61, 53, 61, 53, 52, 1055, 2828, 411, 2379, 66, 677, 46, 303, 1489, 45, 119, 51, 897, 46, 222 ]	[ "import", "Ġrequests", "Ċ", "import", "Ġjson", "Ċ", "Ċ", "data", "Ġ=", "Ġjson", ".", "load", "(", "open", "(\"", "dummy", "_", "data", "/", "data", ".", "json", "\"))", "Ċ", "Ċ", "for", "Ġone", "Ġin", "Ġdata", ":", "ĊĠĠĠ", "Ġprint", "(", "one", ")", "ĊĠĠĠ", "Ġr", "Ġ=", "Ġrequests", ".", "post", "(\"", "http", "://", "localhost", ":", "8", "0", "8", "0", "/", "sum", "ari", "\",", "Ġjson", "=", "one", ")", "ĊĠĠĠ", "Ġprint", "(", "r", ".", "text", ")", "Ċ" ]	[ 1, 1, 1, 2, 2, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9 ]	[ 1, 7, 16, 1, 7, 12, 1, 1, 5, 7, 12, 13, 17, 18, 22, 24, 29, 30, 34, 35, 39, 40, 44, 47, 1, 1, 4, 8, 11, 16, 17, 4, 10, 11, 14, 15, 4, 6, 8, 17, 18, 22, 24, 28, 31, 40, 41, 42, 43, 44, 45, 46, 49, 52, 54, 59, 60, 63, 64, 4, 10, 11, 12, 13, 17, 18 ]
4e9a968842c2b3eca79690f0b56c8e176b203138	print((9 * int(input()) / 5) + 32)	[ 1243, 1181, 62, 338, 648, 45, 1151, 1177, 536, 244, 58, 46, 494, 244, 56, 55, 46, 222 ]	[ "print", "((", "9", "Ġ*", "Ġint", "(", "input", "())", "Ġ/", "Ġ", "5", ")", "Ġ+", "Ġ", "3", "2", ")", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]	[ 1, 6, 8, 9, 11, 15, 16, 21, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35 ]
7a1bd2b4734527a414c6173ea8edb150221f8042	import numpy as np import pandas as pd from scipy.optimize import minimize from datetime import datetime import time from functions import weather_scraper def getData(): # # run weather_scraper.py to fetch new weather data # weather_scraper.getData() ## Read in csv file "weather_data.csv" weather_data = pd.read_csv("data/weather_data.csv") # Grab the current month & hour currentMonth = datetime.now().month currentHour = datetime.now().hour # Determine which month group the current month is [0,5] currentMonthGroup = currentMonth // 2 hoep_data = [] temp = weather_data.iloc[:, 2] # Change hour string to number from 0-23 for i in range(len(temp)): weather_data.iloc[i, 1] = (currentHour + i) % 24 # Convert temperature data to HOEP data if currentMonthGroup == 0: hoep_data = temp.apply(lambda x: (2.02887 * x + 39.633) / 100) elif currentMonthGroup == 1: hoep_data = temp.apply(lambda x: (0.453122 * x + 19.8276) / 100) elif currentMonthGroup == 2: hoep_data = temp.apply(lambda x: (1.13665 * x - 11.0085) / 100) elif currentMonthGroup == 3: hoep_data = temp.apply(lambda x: (1.90245 * x - 23.2826) / 100) elif currentMonthGroup == 4: hoep_data = temp.apply(lambda x: (1.39145 * x - 8.97971) / 100) else: hoep_data = temp.apply(lambda x: (1.72767 * x + 21.3536) / 100) # Load in the load_data load_data = pd.read_excel("data/load_data.xlsx", index_col=0, engine="openpyxl") # Create loading schedule based on current time of day and month load_sched = np.arange(48) for i in range(len(temp)): load_sched[i] = load_data.iloc[weather_data.iloc[i, 1], currentMonthGroup] WMST = 0.003499 ## x[0:48] = PCEA ## x[48:96] = ESB start_time = time.time() # Constraints to ensure that ESB falls within limits def constraint1(x): for i in range(48): if i == 0: x[48] = 0 else: x[48 + i] = x[48 + i - 1] + x[i] return x[0:48] + x[48:96] def constraint2(x): for i in range(48): if i == 0: x[48] = 0 else: x[48 + i] = x[48 + i - 1] + x[i] return 10000 - (x[0:48] + x[48:96]) power = ((-5000, 5000),) * 48 storage = ((0, 10000),) * 48 # Objective def MEC(x): # ( PDLL + PCEA ) x HOEP return sum(sum((load_sched + np.array([x[0:48]])) * (np.array(hoep_data) + WMST))) x0 = np.array([np.ones(48), np.ones(48)]) bounds = power + storage cons1 = {"type": "ineq", "fun": constraint1} cons2 = {"type": "ineq", "fun": constraint2} cons = [cons1, cons2] sol = minimize(MEC, x0, method="SLSQP", bounds=bounds, constraints=cons, options={"maxiter": 150, "disp": True}) input_var = {"EA_w_bill": round(sol.fun, 2)} return input_var	[ 485, 6610, 641, 2115, 222, 485, 13247, 641, 6451, 222, 1097, 18945, 51, 22990, 1220, 32949, 222, 1097, 7181, 1220, 7181, 222, 485, 1153, 222, 1097, 4465, 1220, 20104, 100, 564, 26877, 499, 222, 610, 22920, 2284, 303, 607, 607, 1441, 20104, 100, 564, 26877, 51, 997, 391, 5644, 556, 20104, 727, 303, 607, 20104, 100, 564, 26877, 51, 11574, 365, 465, 5533, 4858, 347, 11490, 822, 332, 15903, 100, 624, 51, 4849, 39, 303, 20104, 100, 624, 299, 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1bdb19373960e4f63d80d6ab73ec3c0939e40b7f	import contextlib import dask import dask.array as da import packaging.version import pandas import six import sklearn SK_VERSION = packaging.version.parse(sklearn.__version__) DASK_VERSION = packaging.version.parse(dask.__version__) PANDAS_VERSION = packaging.version.parse(pandas.__version__) @contextlib.contextmanager def dummy_context(args, *kwargs): yield if six.PY2: from collections import Mapping else: from collections.abc import Mapping # noqa if DASK_VERSION < packaging.version.parse("1.1.0"): blockwise = da.atop else: blockwise = da.blockwise	[ 485, 1637, 1379, 222, 222, 485, 362, 933, 222, 485, 362, 933, 51, 977, 641, 5301, 222, 485, 36413, 51, 1687, 222, 485, 13247, 222, 485, 18834, 222, 485, 14415, 222, 222, 9727, 100, 4539, 299, 36413, 51, 1687, 51, 2228, 45, 45974, 3688, 1687, 8238, 222, 73, 5159, 100, 4539, 299, 36413, 51, 1687, 51, 2228, 45, 105, 933, 3688, 1687, 8238, 222, 85, 3362, 1117, 100, 4539, 299, 36413, 51, 1687, 51, 2228, 45, 24848, 3688, 1687, 8238, 499, 222, 69, 1834, 1379, 51, 1834, 4031, 222, 610, 11657, 100, 1834, 2761, 1150, 49, 1137, 5350, 731, 303, 6785, 499, 222, 344, 18834, 51, 5189, 55, 63, 303, 664, 13722, 1220, 23518, 222, 728, 63, 303, 664, 13722, 51, 8198, 1220, 23518, 244, 607, 16390, 222, 222, 344, 493, 5159, 100, 4539, 350, 36413, 51, 1687, 51, 2228, 459, 54, 51, 54, 51, 53, 10320, 303, 2181, 3790, 299, 5301, 51, 288, 539, 222, 728, 63, 303, 2181, 3790, 299, 5301, 51, 1540, 3790, 222 ]	[ "import", "Ġcontext", "lib", "Ċ", "Ċ", "import", "Ġd", "ask", "Ċ", "import", "Ġd", "ask", ".", "array", "Ġas", "Ġda", "Ċ", "import", "Ġpackaging", ".", "version", "Ċ", "import", "Ġpandas", "Ċ", "import", "Ġsix", "Ċ", "import", "Ġsklearn", "Ċ", "Ċ", "SK", "_", "VERSION", "Ġ=", "Ġpackaging", ".", "version", ".", "parse", "(", "sklearn", ".__", "version", "__)", "Ċ", "D", "ASK", "_", "VERSION", "Ġ=", "Ġpackaging", ".", "version", ".", "parse", "(", "d", "ask", ".__", "version", "__)", "Ċ", "P", "AND", "AS", "_", "VERSION", "Ġ=", "Ġpackaging", ".", "version", ".", "parse", "(", "pandas", ".__", "version", "__)", "ĊĊ", "Ċ", "@", "context", "lib", ".", "context", "manager", "Ċ", "def", "Ġdummy", "_", "context", "(", "args", ",", "Ġ*", "kwargs", "):", "ĊĠĠĠ", "Ġyield", "ĊĊ", "Ċ", "if", "Ġsix", ".", "PY", "2", ":", "ĊĠĠĠ", "Ġfrom", "Ġcollections", "Ġimport", "ĠMapping", "Ċ", "else", ":", "ĊĠĠĠ", "Ġfrom", "Ġcollections", ".", "abc", "Ġimport", "ĠMapping", "Ġ", "Ġ#", "Ġnoqa", "Ċ", "Ċ", "if", "ĠD", "ASK", "_", "VERSION", "Ġ<", "Ġpackaging", ".", "version", ".", "parse", "(\"", "1", ".", "1", ".", "0", "\"):", "ĊĠĠĠ", "Ġblock", "wise", "Ġ=", "Ġda", ".", "at", "op", "Ċ", "else", ":", "ĊĠĠĠ", "Ġblock", "wise", "Ġ=", "Ġda", ".", "block", "wise", "Ċ" ]	[ 1, 1, 1, 1, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 14, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 19, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28 ]	[ 1, 7, 15, 18, 1, 1, 7, 9, 12, 1, 7, 9, 12, 13, 18, 21, 24, 1, 7, 17, 18, 25, 1, 7, 14, 1, 7, 11, 1, 7, 15, 1, 1, 3, 4, 11, 13, 23, 24, 31, 32, 37, 38, 45, 48, 55, 58, 1, 2, 5, 6, 13, 15, 25, 26, 33, 34, 39, 40, 41, 44, 47, 54, 57, 1, 2, 5, 7, 8, 15, 17, 27, 28, 35, 36, 41, 42, 48, 51, 58, 61, 1, 1, 2, 9, 12, 13, 20, 27, 1, 4, 10, 11, 18, 20, 24, 25, 28, 34, 36, 4, 10, 1, 1, 3, 7, 8, 10, 11, 12, 4, 9, 21, 28, 36, 1, 5, 6, 4, 9, 21, 22, 25, 32, 40, 41, 43, 48, 1, 1, 3, 5, 8, 9, 16, 18, 28, 29, 36, 37, 42, 44, 45, 46, 47, 48, 49, 52, 4, 10, 14, 16, 19, 20, 22, 24, 1, 5, 6, 4, 10, 14, 16, 19, 20, 25, 29 ]
a4f932a8566afe0265dc1057d0f6534a608697f7	""" LeetCode Problem: 242. Valid Anagram Link: https://leetcode.com/problems/valid-anagram/ Written by: Mostofa Adib Shakib Language: Python """ class Solution(object): def isAnagram(self, s, t): """ :type s: str :type t: str :rtype: bool """ length1 = len(s) length2 = len(t) if length1 != length2: return False s = sorted(s) # sorted the string in alphanumeric order t = sorted(t) # sorted the string in alphanumeric order for i in range(0, length1): if s[i] != t[i]: return False # return false if the two sorted strings are not the same. return True # if the sorted strings are same return True	[ 3012, 222, 43608, 18031, 63, 244, 55, 57, 55, 51, 5397, 2262, 8353, 222, 2375, 63, 1698, 574, 19403, 51, 527, 52, 23389, 52, 1346, 50, 297, 8353, 52, 222, 23361, 829, 63, 19576, 1048, 102, 3604, 947, 2432, 793, 947, 222, 5980, 63, 5006, 222, 3012, 499, 222, 842, 12219, 45, 1491, 731, 303, 684, 458, 1062, 8353, 45, 803, 49, 328, 49, 292, 731, 310, 1547, 310, 518, 700, 328, 63, 615, 310, 518, 700, 292, 63, 615, 310, 518, 18388, 63, 1578, 310, 1547, 603, 3124, 54, 299, 2095, 45, 120, 46, 310, 3124, 55, 299, 2095, 45, 121, 46, 603, 434, 3124, 54, 882, 3124, 55, 63, 343, 461, 3208, 603, 328, 299, 8635, 45, 120, 46, 244, 607, 8635, 341, 821, 347, 763, 40901, 2554, 310, 292, 299, 8635, 45, 121, 46, 244, 607, 8635, 341, 821, 347, 763, 40901, 2554, 603, 456, 613, 347, 2189, 45, 53, 49, 3124, 54, 731, 343, 434, 328, 96, 110, 98, 882, 292, 96, 110, 2805, 419, 461, 3208, 244, 607, 461, 920, 434, 341, 3161, 8635, 5872, 904, 666, 341, 2454, 51, 603, 461, 2969, 244, 607, 434, 341, 8635, 5872, 904, 2454, 461, 2969, 222 ]	[ "\"\"\"", "Ċ", "LeetCode", "ĠProblem", ":", "Ġ", "2", "4", "2", ".", "ĠValid", "ĠAn", "agram", "Ċ", "Link", ":", "Ġhttps", "://", "leetcode", ".", "com", "/", "problems", "/", "valid", "-", "an", "agram", "/", "Ċ", "Written", "Ġby", ":", "ĠMost", "of", "a", "ĠAd", "ib", "ĠSh", "ak", "ib", "Ċ", "Language", ":", "ĠPython", "Ċ", "\"\"\"", "ĊĊ", "Ċ", "class", "ĠSolution", "(", "object", "):", "ĊĠĠĠ", "Ġdef", "Ġis", "An", "agram", "(", "self", ",", "Ġs", ",", "Ġt", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġ:", "type", "Ġs", ":", "Ġstr", "ĊĠĠĠĠĠĠĠ", "Ġ:", "type", "Ġt", ":", "Ġstr", "ĊĠĠĠĠĠĠĠ", "Ġ:", "rtype", ":", "Ġbool", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĊĠĠĠĠĠĠĠ", "Ġlength", "1", "Ġ=", "Ġlen", "(", "s", ")", "ĊĠĠĠĠĠĠĠ", "Ġlength", "2", "Ġ=", "Ġlen", "(", "t", ")", "ĊĊĠĠĠĠĠĠĠ", "Ġif", "Ġlength", "1", "Ġ!=", "Ġlength", "2", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "ĠFalse", "ĊĊĠĠĠĠĠĠĠ", "Ġs", "Ġ=", "Ġsorted", "(", "s", ")", "Ġ", "Ġ#", "Ġsorted", "Ġthe", "Ġstring", "Ġin", "Ġal", "phanumeric", "Ġorder", "ĊĠĠĠĠĠĠĠ", "Ġt", "Ġ=", "Ġsorted", "(", "t", ")", "Ġ", "Ġ#", "Ġsorted", "Ġthe", "Ġstring", "Ġin", "Ġal", "phanumeric", "Ġorder", "ĊĊĠĠĠĠĠĠĠ", "Ġfor", "Ġi", "Ġin", "Ġrange", "(", "0", ",", "Ġlength", "1", "):", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġs", "[", "i", "]", "Ġ!=", "Ġt", "[", "i", "]:", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "ĠFalse", "Ġ", "Ġ#", "Ġreturn", "Ġfalse", "Ġif", "Ġthe", "Ġtwo", "Ġsorted", "Ġstrings", "Ġare", "Ġnot", "Ġthe", "Ġsame", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġreturn", "ĠTrue", "Ġ", "Ġ#", "Ġif", "Ġthe", "Ġsorted", "Ġstrings", "Ġare", "Ġsame", "Ġreturn", "ĠTrue", "Ċ" ]	[ 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 8, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 15, 15, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30 ]	[ 1, 4, 1, 9, 17, 18, 19, 20, 21, 22, 23, 29, 32, 37, 1, 5, 6, 12, 15, 23, 24, 27, 28, 36, 37, 42, 43, 45, 50, 51, 1, 8, 11, 12, 17, 19, 20, 23, 25, 28, 30, 32, 1, 9, 10, 17, 1, 4, 1, 1, 6, 15, 16, 22, 24, 4, 8, 11, 13, 18, 19, 23, 24, 26, 27, 29, 31, 8, 12, 8, 10, 14, 16, 17, 21, 8, 10, 14, 16, 17, 21, 8, 10, 15, 16, 21, 8, 12, 8, 15, 16, 18, 22, 23, 24, 25, 8, 15, 16, 18, 22, 23, 24, 25, 8, 11, 18, 19, 22, 29, 30, 31, 12, 19, 25, 8, 10, 12, 19, 20, 21, 22, 23, 25, 32, 36, 43, 46, 49, 59, 65, 8, 10, 12, 19, 20, 21, 22, 23, 25, 32, 36, 43, 46, 49, 59, 65, 8, 12, 14, 17, 23, 24, 25, 26, 33, 34, 36, 12, 15, 17, 18, 19, 20, 23, 25, 26, 27, 29, 16, 23, 29, 30, 32, 39, 45, 48, 52, 56, 63, 71, 75, 79, 83, 88, 89, 8, 15, 20, 21, 23, 26, 30, 37, 45, 49, 54, 61, 66 ]
6dd11f71e514a46462bf0b97ddac9ea474e86ad0	import os, glob import numpy as np from ..algorithms.utils import get_file_manager from ..algorithms.clustered_writes import * from ..exp_utils import create_empty_dir def test_get_entity_sizes(): # in C order bytes_per_voxel = 1 R = (10, 9, 10) cs = (5, 3, 2) partition = (2, 3, 5) bs, brs, bss = get_entity_sizes(cs, bytes_per_voxel, partition) assert bs == 5 * 3 * 2 assert brs == 5 * 3 * 2 * 5 assert bss == 5 * 3 * 2 * 5 * 3 def test_get_strategy(): # in C order bytes_per_voxel = 1 R = (20, 9, 10) cs = (5, 3, 2) partition = (4, 3, 5) bs, brs, bss = get_entity_sizes(cs, bytes_per_voxel, partition) test_case = { 5 * 2 * 3: 0, # 1 block 5 * 2 * 3 * 4: 0, # 4 blocks 5 * 2 * 3 * 5: 1, # 1 row 5 * 2 * 3 * 5 * 2: 1, # 2 rows 5 * 2 * 3 * 5 * 3: 2, # 1 slice 5 * 2 * 3 * 5 * 3 * 3: 2, # 3 slices 5 * 2 * 3 * 5 * 3 * 4: 2, # whole img 5 * 2 * 3 * 5 * 3 * 7: 2, # whole img (more mem than necessary) } for buffer_mem_size, expected in test_case.items(): strategy = get_strategy(buffer_mem_size, bs, brs, bss) assert strategy == expected def test_compute_buffers(): # in C order bytes_per_voxel = 1 R = (20, 9, 10) cs = (5, 3, 2) partition = (4, 3, 5) bs, brs, bss = get_entity_sizes(cs, bytes_per_voxel, partition) origarr_size = R[0] * R[1] * R[2] * bytes_per_voxel test_case = { 5 * 2 * 3: 4 * 3 * 5, # 1 block 5 * 2 * 3 * 4: 4 * 3 * 2, # 4 blocks 5 * 2 * 3 * 5: 4 * 3, # 1 row 5 * 2 * 3 * 5 * 2: 4 * 2, # 2 rows 5 * 2 * 3 * 5 * 3: 4, # 1 slice 5 * 2 * 3 * 5 * 3 * 3: 2, # 3 slices 5 * 2 * 3 * 5 * 3 * 4: 1, # whole img 5 * 2 * 3 * 5 * 3 * 7: 1, # whole img (more mem than necessary) } for buffer_mem_size, expected in test_case.items(): strategy = get_strategy(buffer_mem_size, bs, brs, bss) buffers = compute_buffers(buffer_mem_size, strategy, origarr_size, cs, bs, brs, bss, partition, R, bytes_per_voxel) # test number of buffers nb_buffers = len(buffers.values()) assert nb_buffers == expected def test_clustered_writes(): bpv = 1 R = (20, 9, 10) 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c09c02a36a64e9522cfc8c0951bd6c98f404f09c	# Random number guessing game. # 10 July 20 # CTI-110 P5HW1 - Random Number # Thelma Majette import random randomNumber = random.randint(1, 100) # main function def main(): # Create a variable to control the loop. keep_going = "y" while keep_going == "y": # Ask user for a number () guess = int(input("\nGuess a number between 1 and 100: ")) # Perform the selected action. if guess > randomNumber: print("\nToo high, try again.") elif guess < randomNumber: print("\nToo low, try again") else: print("\nCongratulations, you guessed the correct number!") keep_going = "n" main()	[ 40, 9607, 1470, 11107, 318, 4204, 51, 222, 40, 244, 54, 53, 32853, 244, 55, 53, 222, 40, 409, 1944, 50, 54, 54, 53, 466, 58, 14650, 54, 449, 9607, 5132, 222, 40, 906, 113, 954, 507, 2512, 15837, 222, 222, 485, 4051, 222, 222, 3917, 1936, 299, 4051, 51, 21732, 45, 54, 49, 244, 54, 53, 53, 46, 499, 222, 40, 2594, 686, 222, 610, 2594, 2284, 303, 607, 2673, 331, 2702, 391, 3518, 341, 5276, 51, 303, 5016, 100, 12403, 299, 332, 126, 39, 303, 2236, 5016, 100, 12403, 630, 332, 126, 582, 310, 607, 31749, 1275, 456, 331, 1470, 1423, 310, 11107, 299, 648, 45, 1151, 5564, 115, 38014, 331, 1470, 3761, 244, 54, 480, 244, 54, 53, 53, 63, 29583, 603, 607, 19810, 341, 4337, 2146, 51, 310, 434, 11107, 868, 4051, 1936, 63, 343, 1489, 5564, 115, 20564, 3883, 49, 1614, 4076, 7164, 310, 4378, 11107, 350, 4051, 1936, 63, 343, 1489, 5564, 115, 20564, 7487, 49, 1614, 4076, 678, 310, 832, 63, 343, 1489, 5564, 115, 628, 44796, 17536, 49, 863, 37755, 1265, 341, 3831, 1470, 16013, 343, 5016, 100, 12403, 299, 332, 115, 39, 499, 222, 1850, 365, 222 ]	[ "#", "ĠRandom", "Ġnumber", "Ġguess", "ing", "Ġgame", ".", "Ċ", "#", "Ġ", "1", "0", "ĠJuly", "Ġ", "2", "0", "Ċ", "#", "ĠC", "TI", "-", "1", "1", "0", "ĠP", "5", "HW", "1", "Ġ-", "ĠRandom", "ĠNumber", "Ċ", "#", "ĠThe", "l", "ma", "ĠM", "aj", "ette", "Ċ", "Ċ", "import", "Ġrandom", "Ċ", "Ċ", "random", "Number", "Ġ=", "Ġrandom", ".", "randint", "(", "1", ",", "Ġ", "1", "0", "0", ")", "ĊĊ", "Ċ", "#", "Ġmain", "Ġfunction", "Ċ", "def", "Ġmain", "():", "ĊĠĠĠ", "Ġ#", "ĠCreate", "Ġa", "Ġvariable", "Ġto", "Ġcontrol", "Ġthe", "Ġloop", ".", "ĊĠĠĠ", "Ġkeep", "_", "going", "Ġ=", "Ġ\"", "y", "\"", "ĊĠĠĠ", "Ġwhile", "Ġkeep", "_", "going", "Ġ==", "Ġ\"", "y", "\":", "ĊĠĠĠĠĠĠĠ", "Ġ#", "ĠAsk", "Ġuser", "Ġfor", "Ġa", "Ġnumber", "Ġ()", "ĊĠĠĠĠĠĠĠ", "Ġguess", "Ġ=", "Ġint", "(", "input", "(\"\\", "n", "Guess", "Ġa", "Ġnumber", "Ġbetween", "Ġ", "1", "Ġand", "Ġ", "1", "0", "0", ":", "Ġ\"))", "ĊĊĠĠĠĠĠĠĠ", "Ġ#", "ĠPerform", "Ġthe", "Ġselected", "Ġaction", ".", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġguess", "Ġ>", "Ġrandom", "Number", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"\\", "n", "Too", "Ġhigh", ",", "Ġtry", "Ġagain", ".\")", "ĊĠĠĠĠĠĠĠ", "Ġelif", "Ġguess", "Ġ<", "Ġrandom", "Number", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"\\", "n", "Too", "Ġlow", ",", "Ġtry", "Ġagain", "\")", "ĊĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"\\", "n", "Con", "grat", "ulations", ",", "Ġyou", "Ġgues", "sed", "Ġthe", "Ġcorrect", "Ġnumber", "!\")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġkeep", "_", "going", "Ġ=", "Ġ\"", "n", "\"", "ĊĊ", "Ċ", "main", "()", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 10, 11, 11, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 26, 28, 29, 29, 29 ]	[ 1, 2, 9, 16, 22, 25, 30, 31, 1, 2, 3, 4, 5, 10, 11, 12, 13, 1, 2, 4, 6, 7, 8, 9, 10, 12, 13, 15, 16, 18, 25, 32, 1, 2, 6, 7, 9, 11, 13, 17, 1, 1, 7, 14, 1, 1, 7, 13, 15, 22, 23, 30, 31, 32, 33, 34, 35, 36, 37, 38, 1, 1, 2, 7, 16, 1, 4, 9, 12, 4, 6, 13, 15, 24, 27, 35, 39, 44, 45, 4, 9, 10, 15, 17, 19, 20, 21, 4, 10, 15, 16, 21, 24, 26, 27, 29, 8, 10, 14, 19, 23, 25, 32, 35, 8, 14, 16, 20, 21, 26, 29, 30, 35, 37, 44, 52, 53, 54, 58, 59, 60, 61, 62, 63, 67, 8, 10, 18, 22, 31, 38, 39, 8, 11, 17, 19, 26, 32, 33, 12, 18, 21, 22, 25, 30, 31, 35, 41, 44, 8, 13, 19, 21, 28, 34, 35, 12, 18, 21, 22, 25, 29, 30, 34, 40, 42, 8, 13, 14, 12, 18, 21, 22, 25, 29, 37, 38, 42, 47, 50, 54, 62, 69, 72, 12, 17, 18, 23, 25, 27, 28, 29, 1, 1, 5, 7 ]
798ddd4a6e4febb4664bf1c973877628d1a45c71	from django.conf.urls import patterns, include, url # Uncomment the next two lines to enable the admin: # from django.contrib import admin # admin.autodiscover() urlpatterns = patterns( "accounts.views", url(r"^$", "home", name="home"), url(r"^login/$", "login", name="login"), url(r"^logout/$", "logout", name="logout"), url(r"^register/$", "register", name="register"), url(r"^dashboard/", "dashboard", name="dashboard"), url(r"^rewards/", "rewards", name="rewards"), url(r"get_all_data/", "get_all_data", name="get_all_data"), )	[ 1097, 8532, 51, 2982, 51, 11420, 1220, 15137, 49, 2323, 49, 2001, 222, 222, 40, 47234, 341, 2371, 3161, 5452, 391, 5362, 341, 5611, 63, 222, 40, 664, 8532, 51, 12060, 1220, 5611, 222, 40, 5611, 51, 4872, 450, 316, 4120, 365, 222, 222, 983, 15735, 299, 15137, 45, 303, 332, 12900, 51, 4361, 411, 303, 2001, 45, 119, 39, 99, 30201, 332, 2849, 411, 655, 366, 2849, 2132, 303, 2001, 45, 119, 39, 99, 3632, 9770, 411, 332, 3632, 411, 655, 366, 3632, 2132, 303, 2001, 45, 119, 39, 99, 12651, 9770, 411, 332, 12651, 411, 655, 366, 12651, 2132, 303, 2001, 45, 119, 39, 99, 3334, 9770, 411, 332, 3334, 411, 655, 366, 3334, 2132, 303, 2001, 45, 119, 39, 99, 7870, 10974, 332, 7870, 411, 655, 366, 7870, 2132, 303, 2001, 45, 119, 39, 99, 33753, 10974, 332, 33753, 411, 655, 366, 33753, 2132, 303, 2001, 45, 119, 39, 390, 100, 483, 100, 624, 10974, 332, 390, 100, 483, 100, 624, 411, 655, 366, 390, 100, 483, 100, 624, 2132, 222, 46, 222 ]	[ "from", "Ġdjango", ".", "conf", ".", "urls", "Ġimport", "Ġpatterns", ",", "Ġinclude", ",", "Ġurl", "Ċ", "Ċ", "#", "ĠUncomment", "Ġthe", "Ġnext", "Ġtwo", "Ġlines", "Ġto", "Ġenable", "Ġthe", "Ġadmin", ":", "Ċ", "#", "Ġfrom", "Ġdjango", ".", "contrib", "Ġimport", "Ġadmin", "Ċ", "#", "Ġadmin", ".", "aut", "od", "is", "cover", "()", "Ċ", "Ċ", "url", "patterns", "Ġ=", "Ġpatterns", "(", "ĊĠĠĠ", "Ġ\"", "accounts", ".", "views", "\",", "ĊĠĠĠ", "Ġurl", "(", "r", "\"", "^", "$\",", "Ġ\"", "home", "\",", "Ġname", "=\"", "home", "\"),", "ĊĠĠĠ", "Ġurl", "(", "r", "\"", "^", "login", "/$", "\",", "Ġ\"", "login", "\",", "Ġname", "=\"", "login", "\"),", "ĊĠĠĠ", "Ġurl", "(", "r", "\"", "^", "logout", "/$", "\",", "Ġ\"", "logout", "\",", "Ġname", "=\"", "logout", "\"),", "ĊĠĠĠ", "Ġurl", "(", "r", "\"", "^", "register", "/$", "\",", "Ġ\"", "register", "\",", "Ġname", "=\"", "register", "\"),", "ĊĠĠĠ", "Ġurl", "(", "r", "\"", "^", "dashboard", "/\",", "Ġ\"", "dashboard", "\",", "Ġname", "=\"", "dashboard", "\"),", "ĊĠĠĠ", "Ġurl", "(", "r", "\"", "^", "rewards", "/\",", "Ġ\"", "rewards", "\",", "Ġname", "=\"", "rewards", "\"),", "ĊĠĠĠ", "Ġurl", "(", "r", "\"", "get", "_", "all", "_", "data", "/\",", "Ġ\"", "get", "_", "all", "_", "data", "\",", "Ġname", "=\"", "get", "_", "all", "_", "data", "\"),", "Ċ", ")", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16 ]	[ 1, 5, 12, 13, 17, 18, 22, 29, 38, 39, 47, 48, 52, 1, 1, 2, 12, 16, 21, 25, 31, 34, 41, 45, 51, 52, 1, 2, 7, 14, 15, 22, 29, 35, 1, 2, 8, 9, 12, 14, 16, 21, 23, 1, 1, 4, 12, 14, 23, 24, 4, 6, 14, 15, 20, 22, 4, 8, 9, 10, 11, 12, 15, 17, 21, 23, 28, 30, 34, 37, 4, 8, 9, 10, 11, 12, 17, 19, 21, 23, 28, 30, 35, 37, 42, 45, 4, 8, 9, 10, 11, 12, 18, 20, 22, 24, 30, 32, 37, 39, 45, 48, 4, 8, 9, 10, 11, 12, 20, 22, 24, 26, 34, 36, 41, 43, 51, 54, 4, 8, 9, 10, 11, 12, 21, 24, 26, 35, 37, 42, 44, 53, 56, 4, 8, 9, 10, 11, 12, 19, 22, 24, 31, 33, 38, 40, 47, 50, 4, 8, 9, 10, 11, 14, 15, 18, 19, 23, 26, 28, 31, 32, 35, 36, 40, 42, 47, 49, 52, 53, 56, 57, 61, 64, 1, 2 ]
3340277df91f1421dab8d204eddce65b4604432b	from django.shortcuts import render, redirect from .models import Courses from django.views.generic import CreateView, ListView, UpdateView, DeleteView from .forms import CourceCreateForm from django.urls import reverse_lazy from django.urls import reverse class CourceListView(ListView): model = Courses template_name = "cources/cource_list.html" context_object_name = "cources" class CourceCreateView(CreateView): template_name = "cources/create_cource.html" form_class = CourceCreateForm success_url = reverse_lazy("cources:cource_list") class CourceUpdateView(UpdateView): model = Courses form_class = CourceCreateForm template_name = "cources/course_update.html" success_url = reverse_lazy("cources:cource_list") def DeleteView(request, pk): cource = Courses.objects.filter(pk=pk) cource.delete() return redirect(reverse("cources:cource_list"))	[ 1097, 8532, 51, 36264, 1220, 2922, 49, 5675, 222, 1097, 657, 3404, 1220, 26021, 1490, 222, 1097, 8532, 51, 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66444047f9e5eea845c8ac2dbaaf16fc2914d6ec	if answ[1] == "дата": apisay(datetime.date.today(), toho, torep)	[ 344, 619, 1605, 96, 54, 98, 630, 332, 4382, 40151, 582, 303, 1863, 316, 414, 45, 6703, 51, 787, 51, 16029, 1046, 391, 3493, 49, 391, 4332, 46, 222 ]	[ "if", "Ġan", "sw", "[", "1", "]", "Ġ==", "Ġ\"", "Ð´", "Ð°ÑĤÐ°", "\":", "ĊĠĠĠ", "Ġap", "is", "ay", "(", "datetime", ".", "date", ".", "today", "(),", "Ġto", "ho", ",", "Ġto", "rep", ")", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 ]	[ 1, 3, 6, 8, 9, 10, 11, 14, 16, 17, 20, 22, 4, 7, 9, 11, 12, 20, 21, 25, 26, 31, 34, 37, 39, 40, 43, 46, 47 ]
d2f6d7c779d3d6e61d9da7af01a2931fdabec828	import random choices = ["X", "O"] try: # Choice of X-O given to the player player_sym = input("Choose 'X' or 'O' : ") # raising an exception if the variable is not X or O if player_sym != "X" and player_sym != "O": raise Exception("Symbol not found") except Exception as e: print(e.args) else: # Allotting the other one as the computer symbol choices.remove(player_sym) comp_sym = choices[0] player_dict = {player_sym: "Player", comp_sym: "Computer"} # creating the board board = [" "] * 9 gameEnd = False # to track when the game ends unmarked = [i for i in range(9)] # to track all the blank boxes left # gameOver function check if the game already has a winner def gameOver(board, symbol): # below is the sequence of all the possible winning combinations if ( board[0] == board[3] == board[6] == symbol or board[1] == board[7] == board[4] == symbol or board[2] == board[5] == board[8] == symbol or board[0] == board[1] == board[2] == symbol or board[5] == board[3] == board[4] == symbol or board[6] == board[7] == board[8] == symbol or board[2] == board[4] == board[6] == symbol or board[0] == board[4] == board[8] == symbol ): # if there is a pattern match the game is over hence return True return True # function for marking the box with the symbol def mark(pos, symbol): board[pos] = symbol unmarked.remove(pos) # Used it for debugging : print(f"Unmarked : {unmarked}") # function to display the board at a particular time def displayBoard(): for i in range(len(board)): # formatting the output for the middle elements if i == 1 or i == 4 or i == 7: print(f"\|{board[i]}\|", end=" ") elif i == 2 or i == 5: print(f"{board[i]}\n--------") # marks the end of a line and hence bifurcates two lines else: print(f"{board[i]}", end=" ") if __name__ == "__main__": # this is where the game starts while not gameEnd: # loop until game ends try: player_pos = int(input("\n\nWhere would you mark? ")) # check if position index is on the board and is available for marking else raise Exception if player_pos < 0 or player_pos > 8 or (player_pos not in unmarked): raise Exception("Position out of Board") break except Exception as e: print(e.args) else: mark(player_pos, player_sym) # check if the game has already ended and if yes, declare the player as winner if gameOver(board, player_sym): displayBoard() print("\n\nPlayer Won!!!") break # computer will mark on some random square that is not marked yet comp_pos = unmarked[random.randint(0, len(unmarked) - 1)] mark(comp_pos, comp_sym) # check if the game has already ended and if yes, declare the computer as winner if gameOver(board, comp_sym): displayBoard() print("\n\nComputer WON!!!") break # display the board after each iteration displayBoard() # marks the end of the game print("GAME OVER")	[ 485, 4051, 222, 222, 19878, 299, 3059, 93, 411, 332, 84, 1465, 222, 1287, 63, 303, 607, 281, 37461, 451, 1618, 50, 84, 2716, 391, 341, 4376, 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",", "Ġplayer", "_", "sym", ")", "ĊĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "ĠĠĠĠĠĠĠĠ", "Ġcheck", "Ġif", "Ġthe", "Ġgame", "Ġhas", "Ġalready", "Ġended", "Ġand", "Ġif", "Ġyes", ",", "Ġdeclare", "Ġthe", "Ġplayer", "Ġas", "Ġwinner", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġgame", "Over", "(", "board", ",", "Ġplayer", "_", "sym", "):", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġdisplay", "Board", "()", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"\\", "n", "\\", "n", "Player", "ĠW", "on", "!!", "!\")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġbreak", "ĊĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "ĠĠĠĠĠĠĠĠ", "Ġcomputer", "Ġwill", "Ġmark", "Ġon", "Ġsome", "Ġrandom", "Ġsquare", "Ġthat", "Ġis", "Ġnot", "Ġmarked", "Ġyet", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġcomp", "_", "pos", "Ġ=", "Ġun", "marked", "[", "random", ".", "randint", "(", "0", ",", "Ġlen", "(", "un", "marked", ")", "Ġ-", "Ġ", "1", ")]", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġmark", "(", "comp", "_", "pos", ",", "Ġcomp", "_", "sym", ")", "ĊĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġ#", "ĠĠĠĠĠĠĠĠ", "Ġcheck", "Ġif", "Ġthe", "Ġgame", "Ġhas", "Ġalready", "Ġended", "Ġand", "Ġif", "Ġyes", ",", "Ġdeclare", 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b4c6075aabe833f6fe23471f608d928edd25ef63	from .base import paw_test class warning_test(paw_test): def test_warning_badchars(self): self.paw.cset_lookup(self.badchar) self.assertEqual(1, self.paw.wcount)	[ 1097, 657, 1460, 1220, 317, 831, 100, 881, 499, 222, 842, 5987, 100, 881, 45, 117, 831, 100, 881, 731, 303, 684, 913, 100, 5718, 100, 3387, 13004, 45, 803, 731, 310, 649, 51, 117, 831, 51, 104, 489, 100, 10124, 45, 803, 51, 3387, 1612, 46, 310, 649, 51, 5307, 45, 54, 49, 649, 51, 117, 831, 51, 124, 1016, 46, 222 ]	[ "from", "Ġ.", "base", "Ġimport", "Ġp", "aw", "_", "test", "ĊĊ", "Ċ", "class", "Ġwarning", "_", "test", "(", "p", "aw", "_", "test", "):", "ĊĠĠĠ", "Ġdef", "Ġtest", "_", "warning", "_", "bad", "chars", "(", "self", "):", "ĊĠĠĠĠĠĠĠ", "Ġself", ".", "p", "aw", ".", "c", "set", "_", "lookup", "(", "self", ".", "bad", "char", ")", "ĊĠĠĠĠĠĠĠ", "Ġself", ".", "assertEqual", "(", "1", ",", "Ġself", ".", "p", "aw", ".", "w", "count", ")", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7 ]	[ 1, 5, 7, 11, 18, 20, 22, 23, 27, 1, 1, 6, 14, 15, 19, 20, 21, 23, 24, 28, 30, 4, 8, 13, 14, 21, 22, 25, 30, 31, 35, 37, 8, 13, 14, 15, 17, 18, 19, 22, 23, 29, 30, 34, 35, 38, 42, 43, 8, 13, 14, 25, 26, 27, 28, 33, 34, 35, 37, 38, 39, 44, 45 ]
24c9b562411a63f0d3f2ee509bb60dafe7fbecd1	import os from flask import Flask # from flask_login import LoginManager from flask_sqlalchemy import SQLAlchemy # from flask_bcrypt import Bcrypt from flask_wtf.csrf import CSRFProtect app = Flask(__name__) csrf = CSRFProtect(app) # bcrypt = Bcrypt(app) app.config["SECRET_KEY"] = "v\xf9\xf7\x11\x13\x18\xfaMYp\xed_\xe8\xc9w\x06\x8e\xf0f\xd2\xba\xfd\x8c\xda" app.config["SQLALCHEMY_DATABASE_URI"] = ( "postgres://vwfeollskqmjyw:1d738da99074015b148d72cfd94ea584dcb39e81c1bb197fb9da65455c756b0f@ec2-50-17-227-28.compute-1.amazonaws.com:5432/dc8lr6j69aeqjt" ) # app.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql: //infoproj2:info3180@localhost/infoproj2' PRO_PIC_UPLOAD_FOLDER = "./app/static/profile_photos" POSTS_UPLOAD_FOLDER = "./app/static/posts_photos" app.config["SQLALCHEMY_TRACK_MODIFICATIONS"] = True # added just to suppress a warning db = SQLAlchemy(app) # Flask-Login LoginManager # login_manager = LoginManager() # login_manager.init_app(app) # login_manager.login_view = 'login' app.config.from_object(__name__) from app import views, models # intense-basin-58864 #name of project on heroku	[ 485, 2355, 222, 1097, 18958, 1220, 24033, 222, 222, 40, 664, 18958, 100, 3632, 1220, 10660, 1590, 222, 1097, 18958, 100, 41228, 1220, 4447, 43108, 222, 222, 40, 664, 18958, 100, 45139, 1220, 570, 3712, 222, 1097, 18958, 100, 124, 4274, 51, 14230, 1220, 37186, 38432, 222, 222, 745, 299, 24033, 4077, 444, 8238, 222, 14230, 299, 37186, 38432, 45, 745, 46, 222, 40, 31981, 299, 570, 3712, 45, 745, 46, 222, 222, 745, 51, 1180, 1388, 17231, 100, 3319, 1465, 299, 332, 123, 97, 2088, 62, 97, 2088, 60, 97, 125, 54, 54, 97, 125, 54, 56, 97, 125, 54, 61, 97, 12655, 11844, 117, 97, 14208, 20232, 2651, 61, 97, 3092, 62, 124, 97, 125, 53, 59, 97, 125, 61, 106, 97, 2088, 53, 107, 97, 3096, 55, 97, 18113, 97, 11604, 97, 125, 61, 104, 97, 16393, 39, 222, 745, 51, 1180, 1388, 3566, 744, 1424, 4565, 94, 100, 20536, 100, 6344, 1465, 299, 327, 303, 332, 12254, 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14a357f3dfb3d59f1d8cfd566edeaf8b0e5bb56d	#!/usr/bin/env python import rospy from geometry_msgs.msg import Twist from sensor_msgs.msg import Joy import serial from sys import platform if platform == "linux" or platform == "linux2": ser = serial.Serial("/dev/ttyACM0") elif platform == "darwin": pass elif platform == "win32": # Windows... ser = serial.Serial("COM16") """ In this test code we are testing basic vehicle control over the network we use ROS middleware to send the control commands This script runs at the remote driver end. Receives joystick messages (subscribed to Joy topic) then converts the joystick inputs into commands WE ARE NOT USING THIS METHOD NOW --- WE HAVE SEPERATED OUT ALL THE STREAMS FROM THE JOYSTICK """ oldvar = 0 first_a = 0 first_d = 0 # Configuatrion tuned for CAR in LOW speed base_throttle = 5500 peak_throttle = 6500 base_brake = 450 peak_brake = 600 button = 0 def callback(data): global first_a global first_d global oldvar global base_throttle global peak_throttle global base_brake global peak_brake global button # print data axis1 = -data.axes[1] axis3 = -data.axes[3] # in logitech axis 3 is axis 4 confirm with ashish button1 = data.buttons[1] button4 = data.buttons[4] button5 = data.buttons[5] button_ = button1 + button4 + button5 if axis1 > 0.1: bval = int((axis1) * (peak_brake - base_brake) + base_brake) print(bval) ser.write(str(bval).encode("utf-8")) ser.write("a".encode("utf-8")) #### ser.write("4000a".encode('utf-8')) #throttle released on braking print("Brake") elif axis1 < -0.1 and axis3 < 0.1: tval = int((axis1 * -1 + axis3 * -1) * (peak_throttle - base_throttle) * 0.5 + base_throttle) if abs(tval - oldvar) > 5: # print(tval) ser.write(str(tval).encode("utf-8")) ser.write("a".encode("utf-8")) ser.write("450a".encode("utf-8")) # brake released on acceleration print("Throttle") oldvar = tval elif axis1 > -0.1 and axis1 < 0.1: ser.write("4000a".encode("utf-8")) ser.write("450a".encode("utf-8")) # brake released print("Zero Throttle") print(axis1) print(axis3) if button1 == 1: print("Emergency Brake") ser.write("4600a".encode("utf-8")) # throttle released ser.write("600a".encode("utf-8")) # brake engaged if button4 and button5 == 0: if first_a == 0: ser.write("1000a".encode("utf-8")) print("Joystick button 4 pressed.") first_a = 1 if button5 and button4 == 0: if first_d == 0: ser.write("2000a".encode("utf-8")) print("Joystick button 5 pressed.") first_d = 1 if button - button_ != 0: if button4 == 0: first_a = 0 if button5 == 0: first_d = 0 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1ef9df43725196904ec6c0c881f4a1204174b176	import requests, shutil, os, glob from zipfile import ZipFile import pandas as pd from xlrd import open_workbook import csv # zipfilename = 'desiya_hotels' # try: # # downloading zip file # r = requests.get('http://staticstore.travelguru.com/testdump/1300001176/Excel.zip', auth=('testdump', 'testdump'), verify=False,stream=True) #Note web_link is https:// # r.raw.decode_content = True # with open(os.path.join(os.path.dirname(__file__), 'storage/{}.zip'.format(zipfilename)), 'wb') as f: # shutil.copyfileobj(r.raw, f) # # #extracting zip file as xls file # with ZipFile(glob.glob(os.path.join(os.path.dirname(__file__), 'storage/desiya.zip'))[0], 'r') as zip: # zip.extractall(os.path.join(os.path.dirname(__file__), 'storage/')) # #Rename xls file name as "desiya_hotels" # if glob.glob(os.path.join(os.path.dirname(__file__), 'storage/[0-9].xls')): # for filename in glob.glob(os.path.join(os.path.dirname(__file__), 'storage/[a-zA-z].xls')): # os.remove(filename) # os.rename(glob.glob(os.path.join(os.path.dirname(__file__), 'storage/[0-9].xls'))[0], os.path.join(os.path.dirname(__file__),'storage/{}.xls'.format(zipfilename))) # else: # print('unzipped xls file is not found in storare folder') # except Exception as e: # print("Error while downloading zip file") # read xls file # xls = pd.ExcelFile(glob.glob(os.path.join(os.path.dirname(__file__), 'storage/desiya.xls'))[0]) # df = pd.read_excel(xls, sheet_name=0, index_col=None) # print(df['Name']) # print(df.head(5)) # for index, row in df.iterrows(): # print(index, row[3]) # convert xls to csvc # df.to_csv(os.path.join(os.path.dirname(__file__),'storage/{}'.format('robot.csv')), encoding='utf-8', index=False) # convert xls file to csv using xlrd module xlsfile = glob.glob(os.path.join(os.path.dirname(__file__), "storage/robot.xls"))[0] wb = open_workbook(xlsfile) sheet = wb.sheet_by_name("robot_list") with open(os.path.join(os.path.dirname(__file__), "storage/robot_list.csv"), "w") as file: writer = csv.writer(file, delimiter=",") headers = [cell.value for cell in sheet.row(0)] writer.writerow(headers) for i in range(1, sheet.nrows): rowvalue_list = [str(cell.value).strip() if cell.value else None for cell in sheet.row(i)] writer.writerow(rowvalue_list)	[ 485, 5188, 49, 23074, 49, 2355, 49, 12542, 222, 1097, 8617, 781, 1220, 23049, 991, 222, 485, 13247, 641, 6451, 222, 1097, 32317, 5344, 1220, 2177, 100, 43049, 222, 485, 11490, 222, 222, 40, 8617, 3633, 299, 349, 305, 3450, 9491, 100, 8156, 9918, 44, 222, 40, 1614, 63, 222, 40, 281, 607, 24836, 8617, 822, 222, 40, 281, 559, 299, 5188, 51, 390, 482, 544, 574, 1592, 2173, 51, 24693, 43306, 51, 527, 52, 881, 5955, 52, 54, 56, 53, 53, 53, 53, 54, 54, 60, 59, 52, 12860, 51, 3992, 389, 3813, 37620, 881, 5955, 389, 349, 881, 5955, 1444, 6152, 66, 2737, 49, 2255, 66, 1844, 46, 280, 607, 5232, 2692, 100, 1222, 458, 1698, 574, 222, 40, 281, 559, 51, 1312, 51, 5480, 100, 1304, 299, 2969, 222, 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7a59c8c883a9aaa723175783e01aa62e23503fde	#!/C:\Program Files (x86)\Python35-32 # importar librarias necesarias from urllib.request import urlopen from bs4 import BeautifulSoup	[ 8758, 72, 3509, 5756, 11105, 327, 125, 61, 59, 5443, 8378, 56, 58, 50, 56, 55, 222, 222, 40, 1220, 301, 34950, 2828, 321, 29269, 2828, 321, 222, 1097, 22296, 51, 1375, 1220, 13387, 33875, 222, 1097, 14270, 57, 1220, 39693, 222 ]	[ "#!/", "C", ":\\", "Program", "ĠFiles", "Ġ(", "x", "8", "6", ")\\", "Python", "3", "5", "-", "3", "2", "Ċ", "Ċ", "#", "Ġimport", "ar", "Ġlibr", "ari", "as", "Ġneces", "ari", "as", "Ċ", "from", "Ġurllib", ".", "request", "Ġimport", "Ġur", "lopen", "Ċ", "from", "Ġbs", "4", "Ġimport", "ĠBeautifulSoup", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5 ]	[ 1, 4, 5, 7, 14, 20, 22, 23, 24, 25, 27, 33, 34, 35, 36, 37, 38, 1, 1, 2, 9, 11, 16, 19, 21, 27, 30, 32, 1, 5, 12, 13, 20, 27, 30, 35, 1, 5, 8, 9, 16, 30 ]
48d0bfdc607a4605ef82f5c7dc7fd6fc85c4255f	""" BMI=weight0.45259227/(hei0.0254)** """ wht = 2 if wht == 0: print("wht is", wht) else: print("whtsdsb") # 今天也完成了100波比跳 wei = float(input("wei=")) hei = float(input("hei=")) bmi = (wei * 0.45259227) / ((hei * 0.0254) ** 2) print("BMI=", bmi) if bmi < 18.5: print("too light") elif bmi < 25: print("normal") elif bmi < 30: print("over") else: print("wanghantangshidssb")	[ 3012, 222, 71, 5899, 66, 3327, 47, 53, 51, 57, 58, 55, 58, 62, 55, 55, 60, 13953, 311, 110, 47, 53, 51, 53, 55, 58, 57, 9642, 222, 3012, 222, 222, 124, 403, 299, 244, 55, 222, 344, 360, 403, 630, 244, 53, 63, 303, 1489, 459, 124, 403, 458, 411, 360, 403, 46, 222, 728, 63, 303, 1489, 459, 124, 403, 2728, 5692, 678, 222, 40, 244, 45697, 8268, 17030, 3956, 54, 53, 53, 34494, 10482, 24981, 222, 13558, 299, 1916, 45, 1151, 459, 13558, 366, 509, 222, 311, 110, 299, 1916, 45, 1151, 459, 311, 110, 366, 509, 222, 103, 4557, 299, 327, 13558, 338, 244, 53, 51, 57, 58, 55, 58, 62, 55, 55, 60, 46, 536, 1849, 311, 110, 338, 244, 53, 51, 53, 55, 58, 57, 46, 1137, 244, 55, 46, 222, 1243, 459, 71, 5899, 12633, 342, 4557, 46, 222, 344, 342, 4557, 350, 244, 54, 61, 51, 58, 63, 303, 1489, 459, 23846, 6314, 678, 222, 12168, 342, 4557, 350, 244, 55, 58, 63, 303, 1489, 459, 4434, 678, 222, 12168, 342, 4557, 350, 244, 56, 53, 63, 303, 1489, 459, 1515, 678, 222, 728, 63, 303, 1489, 459, 31943, 109, 710, 761, 635, 333, 441, 103, 678, 222 ]	[ "\"\"\"", "Ċ", "B", "MI", "=", "weight", "", "0", ".", "4", "5", "2", "5", "9", "2", "2", "7", "/(", "he", "i", "", "0", ".", "0", "2", "5", "4", ")*", "Ċ", "\"\"\"", "Ċ", "Ċ", "w", "ht", "Ġ=", "Ġ", "2", "Ċ", "if", "Ġw", "ht", "Ġ==", "Ġ", "0", ":", "ĊĠĠĠ", "Ġprint", "(\"", "w", "ht", "Ġis", "\",", "Ġw", "ht", ")", "Ċ", "else", ":", "ĊĠĠĠ", "Ġprint", "(\"", "w", "ht", "sd", "sb", "\")", "Ċ", "#", "Ġ", "ä»Ĭå¤©", "ä¹Ł", "å®ĮæĪĲ", "äºĨ", "1", "0", "0", "æ³¢", "æ¯Ķ", "è·³", "Ċ", "wei", "Ġ=", "Ġfloat", "(", "input", "(\"", "wei", "=\"", "))", "Ċ", "he", "i", "Ġ=", "Ġfloat", "(", "input", "(\"", "he", "i", "=\"", "))", "Ċ", "b", "mi", "Ġ=", "Ġ(", "wei", "Ġ", "Ġ", "0", ".", "4", "5", "2", "5", "9", "2", "2", "7", ")", "Ġ/", "Ġ((", "he", "i", "Ġ", "Ġ", "0", ".", "0", "2", "5", "4", ")", "Ġ*", "Ġ", "2", ")", "Ċ", "print", "(\"", "B", "MI", "=\",", "Ġb", "mi", ")", "Ċ", "if", "Ġb", "mi", "Ġ<", "Ġ", "1", "8", ".", "5", ":", "ĊĠĠĠ", "Ġprint", "(\"", "too", "Ġlight", "\")", "Ċ", "elif", "Ġb", "mi", "Ġ<", "Ġ", "2", "5", ":", "ĊĠĠĠ", "Ġprint", "(\"", "normal", "\")", "Ċ", "elif", "Ġb", "mi", "Ġ<", "Ġ", "3", "0", ":", "ĊĠĠĠ", "Ġprint", "(\"", "over", "\")", "Ċ", "else", ":", "ĊĠĠĠ", "Ġprint", "(\"", "wang", "h", "ant", "ang", "sh", "id", "ss", "b", "\")", "Ċ" ]	[ 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22 ]	[ 1, 4, 1, 2, 4, 5, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 37, 1, 4, 1, 1, 2, 4, 6, 7, 8, 1, 3, 5, 7, 10, 11, 12, 13, 4, 10, 12, 13, 15, 18, 20, 22, 24, 25, 1, 5, 6, 4, 10, 12, 13, 15, 17, 19, 21, 1, 2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 1, 4, 6, 12, 13, 18, 20, 23, 25, 27, 1, 3, 4, 6, 12, 13, 18, 20, 22, 23, 25, 27, 1, 2, 4, 6, 8, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 30, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 46, 47, 48, 49, 1, 6, 8, 9, 11, 14, 16, 18, 19, 1, 3, 5, 7, 9, 10, 11, 12, 13, 14, 15, 4, 10, 12, 15, 21, 23, 1, 5, 7, 9, 11, 12, 13, 14, 15, 4, 10, 12, 18, 20, 1, 5, 7, 9, 11, 12, 13, 14, 15, 4, 10, 12, 16, 18, 1, 5, 6, 4, 10, 12, 16, 17, 20, 23, 25, 27, 29, 30, 32 ]
05e4bcc7323b908a7b45d766ada463ce172e25c4	import graphene import f1hub.drivers.schema import f1hub.results.schema import f1hub.constructors.schema import f1hub.races.schema import f1hub.status.schema import f1hub.circuits.schema import f1hub.constructorresults.schema import f1hub.constructorstandings.schema import f1hub.driverstandings.schema import f1hub.laptimes.schema import f1hub.pitstops.schema import f1hub.qualifying.schema import f1hub.seasons.schema class Query( f1hub.drivers.schema.Query, f1hub.results.schema.Query, f1hub.constructors.schema.Query, f1hub.races.schema.Query, f1hub.status.schema.Query, f1hub.circuits.schema.Query, f1hub.constructorresults.schema.Query, f1hub.constructorstandings.schema.Query, f1hub.driverstandings.schema.Query, f1hub.laptimes.schema.Query, f1hub.pitstops.schema.Query, f1hub.qualifying.schema.Query, f1hub.seasons.schema.Query, graphene.ObjectType, ): pass schema = graphene.Schema(query=Query)	[ 485, 4023, 2126, 222, 222, 485, 315, 54, 8530, 51, 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7530c2c85f83d1714840ba97c1ec702f063658c5	from typing import List import glm import pxng import OpenGL.GL as gl class VertexArrayObject: def __init__(self, primitive): self._primitive = primitive self._buffers: List[pxng.BufferObject] = [] self._indices = pxng.BufferObject(data_type=self.index_data_type, array_type=gl.GL_ELEMENT_ARRAY_BUFFER) self._vao = gl.glGenVertexArrays(1) def attach_buffer(self, vbo: pxng.BufferObject): self._buffers.append(vbo) return len(self._buffers) - 1 def add_quad(self, p1, p2, p3, p4): i = self._buffers[0].index self._buffers[0].set_value(p1) self._buffers[0].set_value(p2) self._buffers[0].set_value(p3) self._buffers[0].set_value(p4) self._indices.set_value(glm.u16vec3(i, i + 1, i + 3)) self._indices.set_value(glm.u16vec3(i + 1, i + 2, i + 3)) def add_triangle(self, p1, p2, p3): i = self._buffers[0].index self._buffers[0].set_value(p1) self._buffers[0].set_value(p2) self._buffers[0].set_value(p3) self._indices.set_value(glm.u16vec3(i, i + 1, i + 2)) def add_line(self, p1, p2): i = self._buffers[0].index self._buffers[0].set_value(p1) self._buffers[0].set_value(p2) self._indices.set_value(glm.u16vec2(i, i + 1)) def add_point(self, p1): i = self._buffers[0].index self._buffers[0].set_value(p1) self._indices.set_value(glm.u16vec1(i)) def set_colors(self, args: glm.vec4, target=1): for c in args: self._buffers[target].set_value(c) def set_texture(self, args: glm.vec2 or glm.uvec2, target=1): for c in args: self._buffers[target].set_value(c) def create(self): gl.glBindVertexArray(self._vao) for index, vbo in enumerate(self._buffers): vbo.bind(index) self._indices.bind(None) def reset(self): self._indices.reset() for vbo in self._buffers: vbo.reset() def draw(self): index_count = len(self._indices) * self.primitive_component_count gl.glDrawElements(self._primitive, index_count, gl.GL_UNSIGNED_SHORT, None) @property def index_data_type(self): if self._primitive == gl.GL_TRIANGLES: return glm.u16vec3 elif self._primitive == gl.GL_LINES: return glm.u16vec2 elif self._primitive == gl.GL_POINTS: return glm.u16vec1 else: raise UserWarning(f"Unknown primitive type {self._primitive}") @property def primitive_component_count(self): if self._primitive == gl.GL_TRIANGLES: return 3 elif self._primitive == gl.GL_LINES: return 2 elif self._primitive == gl.GL_POINTS: return 1 else: raise UserWarning(f"Unknown primitive type {self._primitive}") def bind(self): gl.glBindVertexArray(self._vao) if self._indices.bind(None): if any(vbo.changed for vbo in self._buffers): self.create() return True gl.glBindVertexArray(0) return False	[ 1097, 12018, 1220, 1701, 222, 222, 485, 16793, 222, 485, 9249, 1159, 222, 222, 485, 26194, 51, 2079, 641, 3978, 499, 222, 842, 15657, 1280, 976, 63, 303, 684, 1176, 1683, 3232, 803, 49, 17135, 731, 310, 649, 1132, 19678, 299, 17135, 310, 649, 1132, 15647, 63, 1701, 96, 1100, 1159, 51, 2030, 976, 98, 299, 1627, 310, 649, 1132, 7746, 299, 9249, 1159, 51, 2030, 976, 45, 624, 100, 700, 66, 803, 51, 1076, 100, 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1f7d770106ea8e7d1c0bb90e1fc576b7ee2f0220	# Generated by Django 3.0.8 on 2020-08-28 17:37 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("shop", "0003_auto_20200828_1836"), ] operations = [ migrations.AddField( model_name="order", name="total", field=models.CharField(default=0, max_length=200), preserve_default=False, ), migrations.AlterField( model_name="order", name="items", field=models.CharField(max_length=300), ), ]	[ 40, 13046, 829, 18288, 244, 56, 51, 53, 51, 61, 563, 244, 55, 53, 55, 53, 50, 53, 61, 50, 55, 61, 244, 54, 60, 63, 56, 60, 222, 222, 1097, 8532, 51, 1219, 1220, 12520, 49, 4590, 499, 222, 842, 18572, 45, 19772, 51, 12071, 731, 303, 6450, 299, 447, 310, 2903, 5579, 411, 332, 53, 53, 53, 56, 100, 2853, 100, 55, 53, 55, 53, 53, 61, 55, 61, 100, 54, 61, 56, 59, 2132, 303, 1499, 465, 7392, 299, 447, 310, 12520, 51, 38109, 45, 343, 1573, 100, 444, 366, 1191, 411, 343, 655, 366, 3025, 411, 343, 1620, 66, 3404, 51, 16347, 45, 1555, 66, 53, 49, 1788, 100, 1340, 66, 55, 53, 53, 490, 343, 24081, 100, 1555, 66, 2737, 49, 310, 2604, 310, 12520, 51, 18835, 1195, 45, 343, 1573, 100, 444, 366, 1191, 411, 343, 655, 366, 3053, 411, 343, 1620, 66, 3404, 51, 16347, 45, 1472, 100, 1340, 66, 56, 53, 53, 490, 310, 2604, 303, 1499, 222 ]	[ "#", "ĠGenerated", "Ġby", "ĠDjango", "Ġ", "3", ".", "0", ".", "8", "Ġon", "Ġ", "2", "0", "2", "0", "-", "0", "8", "-", "2", "8", "Ġ", "1", "7", ":", "3", "7", "Ċ", "Ċ", "from", "Ġdjango", ".", "db", "Ġimport", "Ġmigrations", ",", "Ġmodels", "ĊĊ", "Ċ", "class", "ĠMigration", "(", "migrations", ".", "Migration", "):", "ĊĠĠĠ", "Ġdependencies", "Ġ=", "Ġ[", "ĊĠĠĠĠĠĠĠ", "Ġ(\"", "shop", "\",", "Ġ\"", "0", "0", "0", "3", "_", "auto", "_", "2", "0", "2", "0", "0", "8", "2", "8", "_", "1", "8", "3", "6", "\"),", "ĊĠĠĠ", "Ġ]", "ĊĊĠĠĠ", "Ġoperations", "Ġ=", "Ġ[", "ĊĠĠĠĠĠĠĠ", "Ġmigrations", ".", "AddField", "(", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġmodel", "_", "name", "=\"", "order", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġname", "=\"", "total", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfield", "=", "models", ".", "CharField", "(", "default", "=", "0", ",", "Ġmax", "_", "length", "=", "2", "0", "0", "),", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġpreserve", "_", "default", "=", "False", ",", "ĊĠĠĠĠĠĠĠ", "Ġ),", "ĊĠĠĠĠĠĠĠ", "Ġmigrations", ".", "Alter", "Field", "(", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġmodel", "_", "name", "=\"", "order", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġname", "=\"", "items", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfield", "=", "models", ".", "CharField", "(", "max", "_", "length", "=", "3", "0", "0", "),", "ĊĠĠĠĠĠĠĠ", "Ġ),", "ĊĠĠĠ", "Ġ]", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 11, 11, 11, 11, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 17, 17, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 23, 23 ]	[ 1, 2, 12, 15, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 1, 1, 5, 12, 13, 15, 22, 33, 34, 41, 1, 1, 6, 16, 17, 27, 28, 37, 39, 4, 17, 19, 21, 8, 11, 15, 17, 19, 20, 21, 22, 23, 24, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 45, 4, 6, 4, 15, 17, 19, 8, 19, 20, 28, 29, 12, 18, 19, 23, 25, 30, 32, 12, 17, 19, 24, 26, 12, 18, 19, 25, 26, 35, 36, 43, 44, 45, 46, 50, 51, 57, 58, 59, 60, 61, 63, 12, 21, 22, 29, 30, 35, 36, 8, 11, 8, 19, 20, 25, 30, 31, 12, 18, 19, 23, 25, 30, 32, 12, 17, 19, 24, 26, 12, 18, 19, 25, 26, 35, 36, 39, 40, 46, 47, 48, 49, 50, 52, 8, 11, 4, 6 ]
01847c9e601eae6775cd4324483740c30e344557	from django.apps import AppConfig class CfCoreConfig(AppConfig): name = "cf_core"	[ 1097, 8532, 51, 7520, 1220, 2013, 1081, 499, 222, 842, 409, 107, 2869, 1081, 45, 42728, 731, 303, 655, 299, 332, 2185, 100, 1284, 39, 222 ]	[ "from", "Ġdjango", ".", "apps", "Ġimport", "ĠApp", "Config", "ĊĊ", "Ċ", "class", "ĠC", "f", "Core", "Config", "(", "AppConfig", "):", "ĊĠĠĠ", "Ġname", "Ġ=", "Ġ\"", "cf", "_", "core", "\"", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5 ]	[ 1, 5, 12, 13, 17, 24, 28, 34, 1, 1, 6, 8, 9, 13, 19, 20, 29, 31, 4, 9, 11, 13, 15, 16, 20, 21 ]
ca11e9cf0bcfcbd714c45b5c95bd2c2044b65909	"""Woma objects for dealing with HTTP. Request and Response inherit from webob's Request and Response objects, so see http://docs.webob.org/en/latest/ for full documentation. The only things documented here are the customizations. """ from webob import Request as BaseRequest from webob import Response as BaseResponse class Client(object): """Make requests to a wsgi app and return the response.""" def __init__(self, app): self.app = app def request(self, path, method, body=None): path = path or "/" request = BaseRequest.blank(path) request.method = method request.text = body or "" return request.get_response(self.app) def get(self, path=None): return self.request(path, "GET") def post(self, path=None, body=None): return self.request(path, "POST", body) def put(self, path=None, body=None): return self.request(path, "PUT", body) class Request(BaseRequest): """A webob.Request with additional properties.""" @property def kwargs(self): """Returns 'router.kwargs' from environ if present, or {} otherwise.""" return self.environ.get("router.kwargs", {}) class Response(BaseResponse): """A webob.Response that can be initialized with defaults from request.""" @classmethod def for_request(cls, request): """Initialize a Response with defaults based on the request. >>> request = Request({}) >>> request.headers['Content-Type'] = 'text/html; charset=latin1' >>> response = Response.for_request(request) >>> response.content_type 'text/html' >>> response.charset 'latin1' """ return cls(status_code=200, content_type=request.content_type or "text/plain", charset=request.charset or "UTF-8") def write(self, text): """An alias for `response.text = text`. >>> response = Response() >>> response.write('some text') >>> response.text 'some text' """ self.text = text	[ 3012, 92, 15809, 4706, 456, 31459, 642, 4818, 51, 222, 222, 1123, 480, 5178, 11096, 664, 1019, 23259, 1200, 4463, 480, 5178, 4706, 49, 1278, 2337, 222, 544, 574, 2198, 51, 1399, 23259, 51, 1107, 52, 291, 52, 5341, 52, 456, 3559, 4349, 51, 906, 1773, 6397, 222, 2826, 337, 2464, 904, 341, 3137, 9429, 51, 222, 222, 3012, 222, 222, 1097, 1019, 23259, 1220, 4463, 641, 3787, 1123, 222, 1097, 1019, 23259, 1220, 5178, 641, 3787, 1416, 499, 222, 842, 5680, 45, 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2c22f891f30825bcb97987c78a98988ad2a92210	import os import sys import json import logging import argparse from glob import glob from pricewatcher.tools import ensure_mkdir from pricewatcher.parser.f21 import ForeverParser from pricewatcher.parser.jcrew import JcrewParser from pricewatcher.utils.load_es import bulk_load_es BRAND_PARSERS = {"forever21": ForeverParser, "jcrew": JcrewParser} # Set up logging FORMAT = "[%(asctime)s][%(levelname)s] %(message)s" logging.basicConfig(format=FORMAT, datefmt="%m-%d-%Y %H:%M:%S") logging.getLogger().setLevel(logging.INFO) def date_handler(obj): return obj.isoformat() if hasattr(obj, "isoformat") else obj def run(): parser = argparse.ArgumentParser(description="Process some integers.") parser.add_argument("--input-base", required=True, help="") parser.add_argument("--output-base", default="parsed_pages", help="") parser.add_argument("--datetime", required=True, help="YYYYMMDD") parser.add_argument("--hour", default="", help="HH") parser.add_argument("--brand", default="", choices=BRAND_PARSERS.keys(), help="") parser.add_argument("--load-es", action="store_true") parser.add_argument("--es-host", default="localhost", help="default to localhost") parser.add_argument("--es-port", default="9200", help="default to 9200") parser.add_argument("--es-cleanup", action="store_true", help="remove index before loading new data") args = parser.parse_args() # Argument parsing dt_str = args.datetime hour_str = args.hour brand_str = args.brand input_base = args.input_base output_base = args.output_base # ES arguments es_host, es_port = args.es_host, args.es_port load_es = args.load_es # Parsing Raw Pages input_files = glob(os.path.join(input_base, dt_str, hour_str, brand_str, "", "", "*")) for file_path in input_files: dt_str, hour_str, br, category, sub_category, filename = file_path.split("/")[-6:] parser = BRAND_PARSERS[brand_str](file_path) parsed_docs = parser.parse() if parsed_docs: doc_list, price_list = parsed_docs logging.info("[STATUS] parsed %s docs from %s" % (len(doc_list), file_path)) if not load_es: # Output Result output_dir = os.path.join(output_base, os.path.join(dt_str, hour_str, br, category)) ensure_mkdir(output_dir) output_path = os.path.join(output_dir, filename + ".json") logging.info("[WRITE] output to %s" % output_path) # Dump Product List with open(output_path + ".doc", "w") as ofile: ofile.write(json.dumps(doc_list, default=date_handler)) with open(output_path + ".price", "w") as ofile: ofile.write(json.dumps(price_list, default=date_handler)) else: # es_index, es_doctype = br, category logging.info("[LOAD ES] loading to ElasticSearch...") preprocessed_list = [] for doc in doc_list: preprocessed_list.append({"index": {"_index": br, "_type": category, "_id": doc["product_id"]}}) preprocessed_list.append(doc) bulk_load_es(es_host, es_port, br, category, preprocessed_list, opt_dict=None) bulk_load_es(es_host, es_port, br, "price", price_list)	[ 485, 2355, 222, 485, 5263, 222, 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3cd7abf9659fe1db0ef3aa58df8dd7fd959e10a6	import os import csv import re totWords = 0 wordLen = 0 totSentWithPunctuation = 0 sourceFile = os.path.join("Resources", "paragraph_2.txt") with open(sourceFile, "r") as paragraph: paragraph = paragraph.read().split("\n\n") for sentence in paragraph: # Remove punctuation from sentences sentWithPunctuation = sentence sentNoPunctuation = re.sub(r"[^\w\s]", "", sentence) # Split sentence with no punctuation by words using spaces words = sentNoPunctuation.split(" ") for word in words: wordLen = wordLen + len(word) # Compute totals for output message totWords = totWords + len(words) # Total words for all sentences avgSentLen_Words = round(totWords / len(paragraph), 2) # Average words for all sentences avgLetterCount = round(wordLen / totWords, 2) # Average letter by word for all sentences totSentWithPunctuation = totSentWithPunctuation + len(sentWithPunctuation) avgSentLen_chars = round(totSentWithPunctuation / len(paragraph), 2) # Validate output by printing a test line # print(f"words: {len(words)} S w Punct. len: {len(sentWithPunctuation)} Sentence: {sentWithPunctuation}") print(f"\n\nParagraph Analysis of '{sourceFile}' file") print(f"---------------------------------------------------------") print(f" Approximate Word Count: {totWords} ") print(f" Approximate Sentence Count: {len(paragraph)} ") print(f" Average Letter Count: {avgLetterCount} ") print(f" Average Sentence Length (words): {avgSentLen_Words} ") print(f" Average Sentence Length (chars): {avgSentLen_chars} ")	[ 485, 2355, 222, 485, 11490, 222, 485, 334, 222, 222, 9059, 13587, 299, 244, 53, 222, 1131, 5752, 299, 244, 53, 222, 9059, 10486, 1569, 85, 24768, 299, 244, 53, 222, 222, 1876, 991, 299, 2355, 51, 1005, 51, 2548, 459, 6061, 411, 332, 11662, 100, 55, 51, 2787, 678, 222, 222, 1814, 2177, 45, 1876, 991, 49, 332, 119, 678, 641, 18841, 63, 303, 18841, 299, 18841, 51, 870, 941, 3045, 5564, 115, 97, 115, 678, 499, 222, 999, 15492, 347, 18841, 63, 303, 607, 6491, 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b95619f3f52ff3747e38ecc153123962d0122a4d	# noinspection PyStatementEffect { "name": "ldap_user", "summary": "", "description": "域账号用户管理，登录及查询用户信息", "author": "", "website": "", "source": {"git": "https://github.com/LeiQiao/Parasite-Plugins.git", "branch": "master"}, "category": "", "version": "0.1", "api": { "/user/token": "user_api.gen_token", "/user/captcha": "user_api.gen_captcha", "/user/login": {"POST": "user_api.login"}, "/user/search": "user_api.search_users", }, # any plugin necessary for this one to work correctly "depends": ["base", "base_api_wrapper", "redis_client", "i18n"], }	[ 40, 1307, 28924, 4374, 5510, 5572, 222, 128, 303, 332, 444, 582, 332, 19406, 100, 514, 411, 303, 332, 1386, 582, 4343, 303, 332, 2328, 582, 332, 15113, 38074, 7840, 10177, 1140, 17602, 12325, 14768, 7840, 7853, 411, 303, 332, 2133, 582, 4343, 303, 332, 12790, 582, 4343, 303, 332, 1876, 582, 3479, 2929, 582, 332, 662, 574, 951, 51, 527, 52, 1494, 110, 86, 16872, 52, 945, 321, 676, 50, 15459, 51, 2929, 411, 332, 6606, 582, 332, 2611, 3933, 303, 332, 3168, 582, 4343, 303, 332, 1687, 582, 332, 53, 51, 54, 411, 303, 332, 1256, 582, 320, 310, 2450, 514, 52, 1977, 582, 332, 514, 100, 1256, 51, 2536, 100, 1977, 411, 310, 2450, 514, 52, 26488, 582, 332, 514, 100, 1256, 51, 2536, 100, 26488, 411, 310, 2450, 514, 52, 3632, 582, 3479, 3719, 582, 332, 514, 100, 1256, 51, 3632, 3933, 310, 2450, 514, 52, 1925, 582, 332, 514, 100, 1256, 51, 1925, 100, 3724, 411, 303, 820, 303, 607, 1364, 3985, 7527, 456, 477, 1611, 391, 1409, 8487, 303, 332, 27240, 582, 3059, 1460, 411, 332, 1460, 100, 1256, 100, 5106, 411, 332, 9557, 100, 1598, 411, 332, 110, 54, 61, 115, 3461, 222, 130, 222 ]	[ "#", "Ġno", "inspection", "ĠPy", "Statement", "Effect", "Ċ", "{", "ĊĠĠĠ", "Ġ\"", "name", "\":", "Ġ\"", "ldap", "_", "user", "\",", "ĊĠĠĠ", "Ġ\"", "summary", "\":", "Ġ\"\",", "ĊĠĠĠ", "Ġ\"", "description", "\":", "Ġ\"", "åŁŁ", "è´¦åı·", "çĶ¨æĪ·", "ç®¡çĲĨ", "ï¼Į", "çĻ»å½ķ", "åıĬ", "æŁ¥è¯¢", "çĶ¨æĪ·", "ä¿¡æģ¯", "\",", "ĊĠĠĠ", "Ġ\"", "author", "\":", "Ġ\"\",", "ĊĠĠĠ", "Ġ\"", "website", "\":", "Ġ\"\",", "ĊĠĠĠ", "Ġ\"", "source", "\":", "Ġ{\"", "git", "\":", "Ġ\"", "https", "://", "github", ".", "com", "/", "Le", "i", "Q", "iao", "/", "Par", "as", "ite", "-", "Plugins", ".", "git", "\",", "Ġ\"", "branch", "\":", "Ġ\"", "master", "\"},", "ĊĠĠĠ", "Ġ\"", "category", "\":", "Ġ\"\",", "ĊĠĠĠ", "Ġ\"", "version", "\":", "Ġ\"", "0", ".", "1", "\",", "ĊĠĠĠ", "Ġ\"", "api", "\":", "Ġ{", "ĊĠĠĠĠĠĠĠ", "Ġ\"/", "user", "/", "token", "\":", "Ġ\"", "user", "_", "api", ".", "gen", "_", "token", "\",", "ĊĠĠĠĠĠĠĠ", "Ġ\"/", "user", "/", "captcha", "\":", "Ġ\"", "user", "_", "api", ".", "gen", "_", "captcha", "\",", "ĊĠĠĠĠĠĠĠ", "Ġ\"/", "user", "/", "login", "\":", "Ġ{\"", "POST", "\":", "Ġ\"", "user", "_", "api", ".", "login", "\"},", "ĊĠĠĠĠĠĠĠ", "Ġ\"/", "user", "/", "search", "\":", "Ġ\"", "user", "_", "api", ".", "search", "_", "users", "\",", "ĊĠĠĠ", "Ġ},", "ĊĠĠĠ", "Ġ#", "Ġany", "Ġplugin", "Ġnecessary", "Ġfor", "Ġthis", "Ġone", "Ġto", "Ġwork", "Ġcorrectly", "ĊĠĠĠ", "Ġ\"", "depends", "\":", "Ġ[\"", "base", "\",", "Ġ\"", "base", "_", "api", "_", "wrapper", "\",", "Ġ\"", "redis", "_", "client", "\",", "Ġ\"", "i", "1", "8", "n", "\"],", "Ċ", "}", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 19 ]	[ 1, 2, 5, 15, 18, 27, 33, 1, 2, 4, 6, 10, 12, 14, 18, 19, 23, 25, 4, 6, 13, 15, 19, 4, 6, 17, 19, 21, 22, 24, 26, 28, 29, 31, 32, 34, 36, 38, 40, 4, 6, 12, 14, 18, 4, 6, 13, 15, 19, 4, 6, 12, 14, 17, 20, 22, 24, 29, 32, 38, 39, 42, 43, 45, 46, 47, 50, 51, 54, 56, 59, 60, 67, 68, 71, 73, 75, 81, 83, 85, 91, 94, 4, 6, 14, 16, 20, 4, 6, 13, 15, 17, 18, 19, 20, 22, 4, 6, 9, 11, 13, 8, 11, 15, 16, 21, 23, 25, 29, 30, 33, 34, 37, 38, 43, 45, 8, 11, 15, 16, 23, 25, 27, 31, 32, 35, 36, 39, 40, 47, 49, 8, 11, 15, 16, 21, 23, 26, 30, 32, 34, 38, 39, 42, 43, 48, 51, 8, 11, 15, 16, 22, 24, 26, 30, 31, 34, 35, 41, 42, 47, 49, 4, 7, 4, 6, 10, 17, 27, 31, 36, 40, 43, 48, 58, 4, 6, 13, 15, 18, 22, 24, 26, 30, 31, 34, 35, 42, 44, 46, 51, 52, 58, 60, 62, 63, 64, 65, 66, 69, 1, 2 ]
484d104a8481a707a187d0bcb30898c3459a88be	# -- coding: utf-8 -- from django.conf.urls import patterns, include, url from apps.virt.views import node, domain, device, cluster, home urlpatterns = patterns( "", # Home url(r"^$", home.HomeView.as_view(), name="home"), # Cluster url(r"^cluster/status/$", cluster.ClusterStatusView.as_view(), name="cluster_status"), # Node url(r"^node/list/$", node.NodeListView.as_view(), name="node_list"), url(r"^node/add/$", node.NodeCreateView.as_view(), name="node_add"), url(r"^node/(?P<pk>\d+)/libvirt/$", node.NodeLibvirtView.as_view(), name="node_libvirt"), url(r"^node/(?P<pk>\d+)/libvirt/update/$", node.UpdateCapabilitiesView.as_view(), name="node_libvirt_update"), url(r"^node/(?P<pk>\d+)/libvirt/update/domains/$", node.UpdateDomainsView.as_view(), name="node_libvirt_updatedomains"), url(r"^node/(?P<pk>\d+)/libvirt/create/domains/$", node.CreateALLDomainsView.as_view(), name="node_libvirt_createdomains"), url(r"^node/(?P<pk>\d+)/edit/$", node.NodeUpdateView.as_view(), name="node_edit"), url(r"^node/(?P<pk>\d+)/delete/$", node.NodeDeleteView.as_view(), name="node_delete"), # Domain url(r"^domain/list/$", domain.DomainListView.as_view(), name="domain_list"), url(r"^domain/add/$", domain.DomainCreateView.as_view(), name="domain_add"), url(r"^domain/(?P<pk>\d+)/libvirt/$", domain.DomainLibvirtView.as_view(), name="domain_libvirt"), url(r"^domain/(?P<pk>\d+)/edit/$", domain.DomainUpdateView.as_view(), name="domain_edit"), url(r"^domain/(?P<pk>\d+)/delete/$", domain.DomainDeleteView.as_view(), name="domain_delete"), url(r"^domain/(?P<pk>\d+)/libvirt/create/$", domain.LibvirtCreateView.as_view(), name="domain_libvirt_create"), url(r"^domain/(?P<pk>\d+)/libvirt/reboot/$", domain.LibvirtRebootView.as_view(), name="domain_libvirt_reboot"), url(r"^domain/(?P<pk>\d+)/libvirt/shutdown/$", domain.LibvirtShutdownView.as_view(), name="domain_libvirt_shutdown"), url(r"^domain/(?P<pk>\d+)/libvirt/destroy/$", domain.LibvirtDestroyView.as_view(), name="domain_libvirt_destroy"), url( r"^domain/(?P<pk>\d+)/libvirt/migrate/(?P<node_pk>\d+)/$", domain.LibvirtMigrateView.as_view(), name="domain_libvirt_migrate", ), url(r"^domain/(?P<pk>\d+)/libvirt/resume/$", domain.LibvirtResumeView.as_view(), name="domain_libvirt_resume"), url(r"^domain/(?P<pk>\d+)/libvirt/suspend/$", domain.LibvirtSuspendView.as_view(), name="domain_libvirt_suspend"), # Device url(r"^domain/(?P<pk>\d+)/device/(?P<type>\w+)/add/$", device.DeviceCreateView.as_view(), name="device_add"), url(r"^device/(?P<pk>\d+)/$", device.DeviceUpdateView.as_view(), name="device_edit"), url(r"^device/(?P<pk>\d+)/attach/$", device.DeviceAttachView.as_view(), name="device_attach"), url(r"^device/(?P<pk>\d+)/detach/$", device.DeviceDetachView.as_view(), name="device_detach"), url(r"^device/(?P<pk>\d+)/delete/$", device.DeviceDeleteView.as_view(), name="device_delete"), )	[ 40, 8571, 10633, 63, 6471, 50, 61, 8571, 222, 222, 1097, 8532, 51, 2982, 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545794cf4f0b2ab63b6a90951a78f8bdaca3c9e6	from collections import defaultdict as dd def grouping(w): d = dd(list) for k, v in ((len([y for y in x if y.isupper()]), x) for x in sorted(w, key=str.casefold)): d[k].append(v) return dict(sorted(d.items()))	[ 1097, 13722, 1220, 31701, 641, 14713, 499, 222, 610, 33006, 45, 124, 731, 303, 362, 299, 14713, 45, 687, 46, 303, 456, 851, 49, 373, 347, 1849, 1688, 2034, 126, 456, 553, 347, 837, 434, 553, 51, 316, 7647, 365, 5759, 837, 46, 456, 837, 347, 8635, 45, 124, 49, 1221, 66, 484, 51, 1749, 8727, 8485, 310, 362, 96, 112, 1015, 1713, 45, 123, 46, 303, 461, 5473, 45, 11980, 45, 105, 51, 3053, 5183, 222 ]	[ "from", "Ġcollections", "Ġimport", "Ġdefaultdict", "Ġas", "Ġdd", "ĊĊ", "Ċ", "def", "Ġgrouping", "(", "w", "):", "ĊĠĠĠ", "Ġd", "Ġ=", "Ġdd", "(", "list", ")", "ĊĠĠĠ", "Ġfor", "Ġk", ",", "Ġv", "Ġin", "Ġ((", "len", "([", "y", "Ġfor", "Ġy", "Ġin", "Ġx", "Ġif", "Ġy", ".", "is", "upper", "()", "]),", "Ġx", ")", "Ġfor", "Ġx", "Ġin", "Ġsorted", "(", "w", ",", "Ġkey", "=", "str", ".", "case", "fold", ")):", "ĊĠĠĠĠĠĠĠ", "Ġd", "[", "k", "].", "append", "(", "v", ")", "ĊĠĠĠ", "Ġreturn", "Ġdict", "(", "sorted", "(", "d", ".", "items", "()))", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 3, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8 ]	[ 1, 5, 17, 24, 36, 39, 42, 1, 1, 4, 13, 14, 15, 17, 4, 6, 8, 11, 12, 16, 17, 4, 8, 10, 11, 13, 16, 19, 22, 24, 25, 29, 31, 34, 36, 39, 41, 42, 44, 49, 51, 54, 56, 57, 61, 63, 66, 73, 74, 75, 76, 80, 81, 84, 85, 89, 93, 96, 8, 10, 11, 12, 14, 20, 21, 22, 23, 4, 11, 16, 17, 23, 24, 25, 26, 31, 35 ]
ab844143ceddf32982682f5092762af0c97db577	from ..translators.translator import Translator	[ 1097, 4390, 1568, 113, 4079, 51, 24802, 1220, 3574, 12044, 222 ]	[ "from", "Ġ..", "trans", "l", "ators", ".", "translator", "Ġimport", "ĠTrans", "lator", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]	[ 1, 5, 8, 13, 14, 19, 20, 30, 37, 43, 48 ]
0762c5bec2d796bb7888e3de45e29fb20f88f491	from starter2 import * from collections import defaultdict import scipy import colors import hair_dryer reload(hair_dryer) import three_loopers_u500 as TL import movie_frames def GE_pearson(this_looper, core_list=None): if core_list is None: core_list = np.unique(this_looper.tr.core_ids) name = this_looper.sim_name thtr = this_looper.tr mask = movie_frames.quantized_mask(this_looper).flatten() times = thtr.times[mask] + 0 # the zero makes a copy times.shape = times.size, 1 times = times / colors.tff G = colors.G # gx = thtr.track_dict['grav_x'] # gy = thtr.track_dict['grav_y'] # gz = thtr.track_dict['grav_z'] # GE2 = -1/(8np.pi)(gxgx+gygy+gzgz) # ge_min=GE2.min() # ge_max=GE2.max() PearsonR = np.zeros([len(core_list), len(times)]) PearsonP = np.zeros([len(core_list), len(times)]) PearsonRho = np.zeros([len(core_list), len(times)]) PeakRho = np.zeros([len(core_list), len(times)]) for nc, core_id in enumerate(core_list): print("GE pearson %s %d" % (name, core_id)) ms = trackage.mini_scrubber(thtr, core_id, do_velocity=False) # ms.particle_pos(core_id) if ms.nparticles < 1000: sl = slice(None) c = [0.5] 4 else: sl = slice(None, None, 10) # c=[0,0,0,0.1] c = [0.1] * 4 rho = ms.density[sl] rho = rho[:, mask] PeakRho[nc, :] = rho.max(axis=0) gx = thtr.c([core_id], "grav_x")[sl][:, mask] gy = thtr.c([core_id], "grav_y")[sl][:, mask] gz = thtr.c([core_id], "grav_z")[sl][:, mask] GE2 = 1 / (8 * np.pi * G) * (gx * gx + gy * gy + gz * gz) RRR = ms.r[sl][:, mask] for n in range(GE2.shape[1]): the_x = np.log(RRR[:, n]) the_y = np.log(GE2[:, n]) # the_y=rho[:,n] r, p = scipy.stats.pearsonr(the_x, the_y) PearsonR[nc, n] = r PearsonP[nc, n] = p the_y = np.log(rho[:, n]) r, p = scipy.stats.pearsonr(the_x, the_y) PearsonRho[nc, n] = r if 0: fig, ax = plt.subplots(1, 2) ax[0].plot(times, PearsonR) # ax[0].boxplot(PearsonR) # ax[1].boxplot(PearsonRho) fig.savefig("plots_to_sort/phi_box_%s.png" % name) return {"PR": PearsonR, "PP": PearsonP, "Prho": PearsonRho, "T": times, "PeakRho": PeakRho} if 0: fig, ax = plt.subplots(1, 1) ax.plot(times, GE2, c=c, linewidth=0.1) axbonk(ax, xlabel=r"$t/t_{ff}$", ylabel=r"$(\nabla \phi)^2/8 pi G$", yscale="log", ylim=[ge_min, ge_max]) ax2 = ax.twinx() c = [1.0, 0.1, 0.1, 0.1] ax2.plot(times, rho, c=c, linewidth=0.1) axbonk(ax2, xlabel=r"$t/t_{ff}$", ylabel=r"$\rho$", yscale="log") outname = "plots_to_sort/%s_GE_t_c%04d.png" % (this_looper.sim_name, core_id) fig.savefig(outname) print(outname) sims = ["u501", "u502", "u503"] if "stuff" not in dir(): stuff = {} for sim in sims: core_list = np.unique(TL.loops[sim].tr.core_ids) # core_list=core_list[:10] stuff[sim] = GE_pearson(TL.loops[sim], core_list=core_list) if 1: for sim in stuff: fig, ax = plt.subplots(1, 1) T = stuff[sim]["T"] rho = stuff[sim]["PeakRho"] Rphi = stuff[sim]["PR"] ax.plot(Rphi.transpose(), rho.transpose(), c=[0.1] * 4) axbonk(ax, xlabel="time", ylabel="rho max", yscale="log") fig.savefig("plots_to_sort/peak_rho_pearson_phi%s.png" % sim) if 1: for sim in stuff: fig, ax = plt.subplots(1, 1) T = stuff[sim]["T"] rho = stuff[sim]["PeakRho"] ax.plot(T, rho.transpose(), c=[0.1] * 4) axbonk(ax, xlabel="time", ylabel="rho max", yscale="log") fig.savefig("plots_to_sort/peak_rho_%s.png" % sim) if 0: for sim in stuff: fig, ax = plt.subplots(1, 1) c = [0.1] * 4 # ax.plot( stuff[sim]['T'], stuff[sim]['PR'].transpose(),c=c) # ax.scatter( stuff[sim]['Prho'].transpose(), stuff[sim]['PR'].transpose(),c=c) XX, YY = stuff[sim]["Prho"].flatten(), stuff[sim]["PR"].flatten() ok = (~np.isnan(XX)) * (~np.isnan(YY)) XX = XX[ok] YY = YY[ok] xbins = np.linspace(XX.min(), XX.max(), 64) ybins = np.linspace(YY.min(), YY.max(), 64) hist, xb, yb = np.histogram2d(XX, YY, bins=[xbins, ybins]) import pcolormesh_helper as pch pch.helper(hist, xb, yb, ax=ax) fig.savefig("plots_to_sort/RGE_Rrho_%s.png" % sim) if 1: for sim in stuff: fig, ax = plt.subplots(1, 2) Rphi = stuff[sim]["PR"] 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7bbbd30ba1578c1165ccf5c2fff22609c16dfd64	""" Cores no terminal """ a = 3 b = 5 print("Os valores são \033[32m{}\033[m e \033[31m{}\033[m !!!".format(a, b)) # Dicionário de cores: nome = "Kátia" cores = {"limpa": "\033]m", "azul": "\033[34m", "amarelo": "\033[33m", "pretoebranco": "\033[7;30m"} print("Prazer em te conhecer, {}{}{}!!!".format(cores["azul"], nome, cores["amarelo"])) # dá pra colocar as cores dentro das chaves tb.	[ 3012, 222, 2253, 595, 1307, 10733, 222, 3012, 222, 222, 102, 299, 244, 56, 222, 103, 299, 244, 58, 222, 1243, 459, 14587, 26065, 20976, 788, 53, 56, 56, 96, 56, 55, 114, 29189, 53, 56, 56, 96, 114, 503, 788, 53, 56, 56, 96, 56, 54, 114, 29189, 53, 56, 56, 96, 114, 33017, 2316, 1664, 45, 102, 49, 342, 509, 222, 222, 40, 493, 15454, 15567, 428, 28875, 63, 222, 15110, 299, 332, 80, 14002, 923, 39, 222, 7484, 299, 3479, 41709, 102, 582, 2724, 53, 56, 56, 98, 114, 411, 332, 1392, 357, 582, 2724, 53, 56, 56, 96, 56, 57, 114, 411, 332, 424, 605, 335, 582, 2724, 53, 56, 56, 96, 56, 56, 114, 411, 332, 1023, 471, 106, 858, 15460, 582, 2724, 53, 56, 56, 96, 60, 64, 56, 53, 114, 3667, 222, 222, 1243, 459, 2161, 48144, 2667, 2713, 476, 311, 10046, 49, 2172, 33380, 20882, 2316, 1664, 45, 7484, 1388, 1392, 357, 3461, 15443, 49, 28875, 1388, 424, 605, 335, 23830, 222, 40, 362, 1597, 1173, 102, 2118, 901, 301, 641, 28875, 38706, 9512, 683, 7777, 15317, 51, 222 ]	[ "\"\"\"", "Ċ", "Co", "res", "Ġno", "Ġterminal", "Ċ", "\"\"\"", "Ċ", "Ċ", "a", "Ġ=", "Ġ", "3", "Ċ", "b", "Ġ=", "Ġ", "5", "Ċ", "print", "(\"", "Os", "Ġvalores", "ĠsÃ£o", "Ġ\\", "0", "3", "3", "[", "3", "2", "m", "{}\\", "0", "3", "3", "[", "m", "Ġe", "Ġ\\", "0", "3", "3", "[", "3", "1", "m", "{}\\", "0", "3", "3", "[", "m", "Ġ!!!", "\".", "format", "(", "a", ",", "Ġb", "))", "Ċ", "Ċ", "#", "ĠD", "icion", "Ã¡rio", "Ġde", "Ġcores", ":", "Ċ", "nome", "Ġ=", "Ġ\"", "K", "Ã¡t", "ia", "\"", "Ċ", "cores", "Ġ=", "Ġ{\"", "limp", "a", "\":", "Ġ\"\\", "0", "3", "3", "]", "m", "\",", "Ġ\"", "az", "ul", "\":", "Ġ\"\\", "0", "3", "3", "[", "3", "4", "m", "\",", "Ġ\"", "am", "are", "lo", "\":", "Ġ\"\\", "0", "3", "3", "[", "3", "3", "m", "\",", "Ġ\"", "pre", "to", "e", "br", "anco", "\":", "Ġ\"\\", "0", "3", "3", "[", "7", ";", "3", "0", "m", "\"}", "Ċ", "Ċ", "print", "(\"", "Pr", "azer", "Ġem", "Ġte", "Ġcon", "he", "cer", ",", "Ġ{}", "{}{}", "!!!", "\".", "format", "(", "cores", "[\"", "az", "ul", "\"],", "Ġnome", ",", "Ġcores", "[\"", "am", "are", "lo", "\"]))", "Ċ", "#", "Ġd", "Ã¡", "Ġpr", "a", "Ġco", "loc", "ar", "Ġas", "Ġcores", "Ġdentro", "Ġdas", "Ġch", "aves", "Ġtb", ".", "Ċ" ]	[ 1, 1, 2, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14 ]	[ 1, 4, 1, 3, 6, 9, 18, 1, 4, 1, 1, 2, 4, 5, 6, 1, 2, 4, 5, 6, 1, 6, 8, 10, 18, 22, 24, 25, 26, 27, 28, 29, 30, 31, 34, 35, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 50, 53, 54, 55, 56, 57, 58, 62, 64, 70, 71, 72, 73, 75, 77, 1, 1, 2, 4, 9, 13, 16, 22, 23, 1, 5, 7, 9, 10, 12, 14, 15, 1, 6, 8, 11, 15, 16, 18, 21, 22, 23, 24, 25, 26, 28, 30, 32, 34, 36, 39, 40, 41, 42, 43, 44, 45, 46, 48, 50, 52, 55, 57, 59, 62, 63, 64, 65, 66, 67, 68, 69, 71, 73, 76, 78, 79, 81, 85, 87, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 101, 1, 1, 6, 8, 10, 14, 17, 20, 24, 26, 29, 30, 33, 37, 40, 42, 48, 49, 54, 56, 58, 60, 63, 68, 69, 75, 77, 79, 82, 84, 88, 1, 2, 4, 5, 8, 9, 12, 15, 17, 20, 26, 33, 37, 40, 44, 47, 48 ]
b5ac3695a224d531f5baa53a07d3c894d44e8c4c	import matplotlib.pyplot as plt Ci_MSB = [32, 16, 8, 4, 2, 1] Ci_LSB = [16, 8, 4, 2, 1] CB = 1 CP_B = 0 CP_LSB = (32 - 1) * (CB + CP_B - 1) + 10 print(CP_LSB) CP_MSB = 0 Csum_LSB = sum(Ci_LSB) + CP_LSB Csum_MSB = sum(Ci_MSB) + CP_MSB Cx = Csum_LSB * Csum_MSB + (CB + CP_B) * Csum_LSB + (CB + CP_B) * Csum_MSB Wi_MSB = [Ci_MSB[i] * (CB + CP_B + Csum_LSB) / Cx for i in range(6)] Wi_LSB = [Ci_LSB[i] * (CB + CP_B) / Cx for i in range(5)] print(Wi_MSB) print(Wi_LSB) def AtoD(vin): code = [0 for i in range(12)] code[0] = 1 if vin > 0 else 0 for i in range(6): vin = vin - Wi_MSB[i] * (code[i] - 0.5) * 2 code[i + 1] = 1 if vin > 0 else 0 for i in range(5): vin = vin - Wi_LSB[i] * (code[i + 6] - 0.5) * 2 code[i + 7] = 1 if vin > 0 else 0 dec_num = 0 for b in code: dec_num = dec_num * 2 + b return dec_num print(AtoD(0.50)) def DtoA_ideal(code): v = -1.0 for i in range(12): v += 2 ** (11 - i) * code[i] / 2048 return v print(DtoA_ideal([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])) n = 1000000 x = [-1 + i / n for i in range(2 * n + 1)] y = [AtoD(v) for v in x] # print(y[int(n/6):int(n/6)+100]) bin_num = [i for i in range(4096)] bin_size = [0 for i in range(4096)] left = x[0] for i in range(2 * n): if y[i + 1] != y[i]: bin_size[y[i]] = x[i + 1] - left left = x[i + 1] # print(bin_size) DNL = [data * 2047 - 1 for data in bin_size] plt.plot(bin_num[1:4094], DNL[1:4094]) # plt.xlim(1000,1005) plt.show() # y = [DtoA_ideal(AtoD(v)) for v in x] # plt.plot(x,y) # plt.xlim(-0.01,0) # plt.ylim(-0.01,0) # plt.show() # def Vout(index): # V = 0.0 # for i in range(6): # V = V + Wi_MSB[i] * int(format(index,'b').zfill(11)[i])1 # for i in range(5): # V = V + Wi_LSB[i] int(format(index,'b').zfill(11)[i+6])1 # return V # print(Vout(2047)) # # x = [i for i in range(2048)] # y = [Vout(i) for i in range(2048)] # DNL = [0]+[y[i+1]-y[i]-Vout(2047)/2047 for i in range(2047)] # DNL = [data2048 for data in DNL] # INL = [y[i] -iVout(2047)/2047 for i in range (2048)] # INL = 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c9d12f14fa0e46e4590746d45862fe255b415a1d	# vim: expandtab # -- coding: utf-8 -- from poleno.utils.template import Library from chcemvediet.apps.obligees.models import Obligee register = Library() @register.simple_tag def gender(gender, masculine, feminine, neuter, plurale): if gender == Obligee.GENDERS.MASCULINE: return masculine elif gender == Obligee.GENDERS.FEMININE: return feminine elif gender == Obligee.GENDERS.NEUTER: return neuter elif gender == Obligee.GENDERS.PLURALE: return plurale else: return ""	[ 40, 19232, 63, 11782, 3019, 222, 40, 8571, 10633, 63, 6471, 50, 61, 8571, 222, 1097, 7757, 7899, 51, 2324, 51, 2402, 1220, 13395, 222, 1097, 683, 24192, 2256, 6675, 51, 7520, 51, 926, 393, 319, 326, 51, 3404, 1220, 5842, 393, 41067, 499, 222, 3334, 299, 13395, 365, 499, 222, 69, 3334, 51, 6681, 100, 2159, 222, 610, 17756, 45, 13816, 49, 19492, 2908, 501, 49, 315, 423, 285, 501, 49, 1188, 3082, 49, 1302, 324, 1572, 731, 303, 434, 17756, 630, 5842, 393, 41067, 51, 76, 3298, 9146, 51, 82, 17563, 809, 3142, 63, 310, 461, 19492, 2908, 501, 303, 4378, 17756, 630, 5842, 393, 41067, 51, 76, 3298, 9146, 51, 75, 4565, 540, 3142, 63, 310, 461, 315, 423, 285, 501, 303, 4378, 17756, 630, 5842, 393, 41067, 51, 76, 3298, 9146, 51, 3585, 1086, 546, 63, 310, 461, 1188, 3082, 303, 4378, 17756, 630, 5842, 393, 41067, 51, 76, 3298, 9146, 51, 2759, 1282, 14349, 63, 310, 461, 1302, 324, 1572, 303, 832, 63, 310, 461, 3347, 222 ]	[ "#", "Ġvim", ":", "Ġexpand", "tab", "Ċ", "#", "Ġ--", "Ġcoding", ":", "Ġutf", "-", "8", "Ġ--", "Ċ", "from", "Ġpol", "eno", ".", "utils", ".", "template", "Ġimport", "ĠLibrary", "Ċ", "from", "Ġch", "cem", "ved", "iet", ".", "apps", ".", "ob", "li", "ge", "es", ".", "models", "Ġimport", "ĠOb", "li", "gee", "ĊĊ", "Ċ", "register", "Ġ=", "ĠLibrary", "()", "ĊĊ", "Ċ", "@", "register", ".", "simple", "_", "tag", "Ċ", "def", "Ġgender", "(", "gender", ",", "Ġmas", "cul", "ine", ",", "Ġf", "em", "in", "ine", ",", "Ġne", "uter", ",", "Ġpl", "ur", "ale", "):", "ĊĠĠĠ", "Ġif", "Ġgender", "Ġ==", "ĠOb", "li", "gee", ".", "G", "END", "ERS", ".", "M", "ASC", "UL", "INE", ":", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġmas", "cul", "ine", "ĊĠĠĠ", "Ġelif", "Ġgender", "Ġ==", "ĠOb", "li", "gee", ".", "G", "END", "ERS", ".", "F", "EM", "IN", "INE", ":", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġf", "em", "in", "ine", "ĊĠĠĠ", "Ġelif", "Ġgender", "Ġ==", "ĠOb", "li", "gee", ".", "G", "END", "ERS", ".", "NE", "UT", "ER", ":", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġne", "uter", "ĊĠĠĠ", "Ġelif", "Ġgender", "Ġ==", "ĠOb", "li", "gee", ".", "G", "END", "ERS", ".", "PL", "UR", "ALE", ":", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġpl", "ur", "ale", "ĊĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġ\"\"", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 7, 7, 7, 7, 7, 9, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 20, 20, 20, 21, 21, 21 ]	[ 1, 2, 6, 7, 14, 17, 1, 2, 6, 13, 14, 18, 19, 20, 24, 1, 5, 9, 12, 13, 18, 19, 27, 34, 42, 1, 5, 8, 11, 14, 17, 18, 22, 23, 25, 27, 29, 31, 32, 38, 45, 48, 50, 53, 1, 1, 9, 11, 19, 21, 1, 1, 2, 10, 11, 17, 18, 21, 1, 4, 11, 12, 18, 19, 23, 26, 29, 30, 32, 34, 36, 39, 40, 43, 47, 48, 51, 53, 56, 58, 4, 7, 14, 17, 20, 22, 25, 26, 27, 30, 33, 34, 35, 38, 40, 43, 44, 8, 15, 19, 22, 25, 4, 9, 16, 19, 22, 24, 27, 28, 29, 32, 35, 36, 37, 39, 41, 44, 45, 8, 15, 17, 19, 21, 24, 4, 9, 16, 19, 22, 24, 27, 28, 29, 32, 35, 36, 38, 40, 42, 43, 8, 15, 18, 22, 4, 9, 16, 19, 22, 24, 27, 28, 29, 32, 35, 36, 38, 40, 43, 44, 8, 15, 18, 20, 23, 4, 9, 10, 8, 15, 18 ]
58e023c3c453d1e190fdb5bc457358f42d1bd93f	# https://leetcode.com/problems/how-many-numbers-are-smaller-than-the-current-number/ # BruteForce class BruteForceSolution: def smallerNumbersThanCurrent(self, nums): answer = [] for num in nums: counter = 0 for i in range(len(nums)): if nums[i] < num: counter += 1 answer.append(counter) return answer class Solution: def smallerNumbersThanCurrent(self, nums): answer = [] sortedNums = sorted(nums) for num in nums: answer.append(sortedNums.index(num)) return answer example = BruteForceSolution() exampleTwo = Solution() print(example.smallerNumbersThanCurrent([8, 1, 2, 2, 3])) print(exampleTwo.smallerNumbersThanCurrent([8, 1, 2, 2, 3]))	[ 40, 1698, 574, 19403, 51, 527, 52, 23389, 52, 6910, 50, 11412, 50, 14995, 50, 605, 50, 47727, 50, 20954, 50, 1402, 50, 1719, 50, 2188, 52, 222, 222, 40, 10360, 1161, 10500, 499, 222, 842, 10360, 1161, 10500, 13667, 63, 303, 684, 14788, 14847, 11548, 3075, 45, 803, 49, 12988, 731, 310, 7618, 299, 1627, 603, 456, 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b4b7e20c9558bd1b29a1c1fa24bfca8a2d292b27	import xml.etree.ElementTree as ET # tree = ET.parse('rutas/rutas_prueba.xml') # treeToAdd = ET.parse('rutas/rutas_prueba_agregar.xml') # root = tree.getroot() # git rootToAdd = treeToAdd.getroot() # for child in root: # for test in child: # print(test.tag, test.attrib) # for elem in root.iter(): # print(elem.tag) # prueba = [elem.tag for elem in root.iter()] # print(prueba) # print(ET.tostring(root, encoding='utf8').decode('utf8')) # for elem in rootToAdd: # root.append(elem) # # tree.write('rutas/probando_agregados.xml') # get the tree for each routes file rutas0k_10k = ET.parse("rutas/rutas0k-10k.xml") rutas10k_30k = ET.parse("rutas/rutas10k-30k.xml") rutas30k_50k = ET.parse("rutas/rutas30k-50k.xml") rutas50k_70k = ET.parse("rutas/rutas50k-70k.xml") rutas70k_90k = ET.parse("rutas/rutas70k-90k.xml") rutas90k_110k = ET.parse("rutas/rutas90k-110k.xml") # root for each routes tree root1 = rutas0k_10k.getroot() root2 = rutas10k_30k.getroot() root3 = rutas30k_50k.getroot() root4 = rutas50k_70k.getroot() root5 = rutas70k_90k.getroot() root6 = rutas90k_110k.getroot() # each root except first root rootsToAdd = [root2, root3, root4, root5, root6] # add each element to the first tree for root in rootsToAdd: for elem in root: root1.append(elem) # write the tree to a new file rutas0k_10k.write("rutas/rutas0k-110k.xml")	[ 485, 5604, 51, 48659, 51, 1325, 3371, 641, 20520, 222, 222, 40, 4369, 299, 20520, 51, 2228, 482, 24919, 321, 52, 24919, 321, 100, 796, 35917, 51, 2608, 704, 222, 40, 4369, 27099, 299, 20520, 51, 2228, 482, 24919, 321, 52, 24919, 321, 100, 796, 35917, 100, 500, 25420, 51, 2608, 704, 222, 222, 40, 3070, 299, 4369, 51, 390, 2405, 365, 222, 222, 40, 5569, 3070, 27099, 299, 244, 4369, 27099, 51, 390, 2405, 365, 222, 222, 40, 456, 2851, 347, 3070, 63, 222, 40, 283, 456, 913, 347, 2851, 63, 222, 40, 370, 1489, 45, 881, 51, 2159, 49, 913, 51, 22740, 46, 499, 222, 40, 456, 10318, 347, 3070, 51, 1751, 2284, 222, 40, 283, 1489, 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97bbb181cbc0f5bfbf0b2298133fc226b6217d91	import tensorflow as tf from tensorflow.python.framework import graph_util from net import siameseNet_batchnorm as siameseNet import dataset import numpy as np import cv2 import os batch_size = 64 input_height = 32 input_width = 32 total_epoch_num = 50 snapshot = 100 support_image_extensions = [".jpg", ".png", ".jpeg", ".bmp"] margin = 1.0 channals = 3 train_image_root = "D:/forTensorflow/charRecTrain/forMyDNNCode/train" test_image_root = "D:/forTensorflow/charRecTrain/forMyDNNCode/test" model_path = "models/" pb_path = os.path.join(model_path, "pb/") ckpt_path = os.path.join(model_path, "ckpt/") if not os.path.exists(pb_path): os.makedirs(pb_path) if not os.path.exists(ckpt_path): os.makedirs(ckpt_path) model_name = "siamese_triplet_28out_allloss_bn" if __name__ == "__main__": # image_paths,labels=get_images_path(test_image_root) # data=next_batch(True,None,image_paths,labels) # for left,right,label in zip(data): # cv2.imshow("left",left) # cv2.imshow("right", right) # print(label) # cv2.waitKey(0) first_shape = None anchor_placeholder = tf.placeholder(tf.float32, shape=[first_shape, input_height, input_width, channals], name="anchor") similar_placeholder = tf.placeholder(tf.float32, shape=[first_shape, input_height, input_width, channals], name="similar") dissimilar_placeholder = tf.placeholder( tf.float32, shape=[first_shape, input_height, input_width, channals], name="dissimilar" ) labels_placeholder = tf.placeholder( tf.float32, shape=[ None if first_shape is None else first_shape 3, ], name="labels", ) is_training_placeholder = tf.placeholder_with_default(False, shape=(), name="is_training") siamese_net = siameseNet.siameseNet() anchor = siamese_net.inference(anchor_placeholder, reuse=False, is_training=is_training_placeholder) similar = siamese_net.inference(similar_placeholder, reuse=True, is_training=is_training_placeholder) dissimilar = siamese_net.inference(dissimilar_placeholder, reuse=True, is_training=is_training_placeholder) loss, pos_dist, neg_dist = siamese_net.loss(anchor, similar, dissimilar, labels_placeholder, margin) flatten_out_anchor = tf.identity(anchor, name="flatten_anchor") flatten_out_similar = tf.identity(similar, name="flatten_similar") flatten_out_dissimilar = tf.identity(dissimilar, name="flatten_dissimilar") update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) global_step = tf.Variable(0, trainable=False) # learning_rate = tf.train.exponential_decay(0.01, global_step, 100, 0.9) # optimizer = tf.train.MomentumOptimizer(learning_rate, 0.9) with tf.control_dependencies([tf.group(*update_ops)]): # train_step = optimizer.minimize(loss, global_step) train_step = tf.train.MomentumOptimizer(0.01, 0.90).minimize(loss, global_step=global_step) var_list = tf.trainable_variables() if global_step is not None: var_list.append(global_step) g_list = tf.global_variables() # 从全局变量中获得batch norm的缩放和偏差 bn_moving_vars = [g for g in g_list if "moving_mean" in g.name] bn_moving_vars += [g for g in g_list if "moving_variance" in g.name] var_list += bn_moving_vars ckpt_saver = tf.train.Saver() train_dataset = dataset.dataset(train_image_root, batch_size, support_image_extensions, input_height, input_width, channals) test_dataset = dataset.dataset(test_image_root, batch_size, support_image_extensions, input_height, input_width, channals) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) # if os.path.exists(os.path.join(ckpt_path, "checkpoint")): # ckpt_saver.restore(sess, tf.train.latest_checkpoint(ckpt_path)) total_iters_num = 0 for epoch_num in range(total_epoch_num): train_images_num = train_dataset.sample_len cur_epoch_iters_num = train_images_num // batch_size for iters_num in range(cur_epoch_iters_num): train_anchor, train_similar, train_dissimilar, train_labels = train_dataset.next_triplet_batch() test_anchor, test_similar, test_dissimilar, test_labels = test_dataset.next_triplet_batch() if train_anchor is None or test_anchor is None: continue train_dict = { anchor_placeholder: train_anchor, similar_placeholder: train_similar, dissimilar_placeholder: train_dissimilar, labels_placeholder: train_labels, is_training_placeholder: True, } test_dict = { anchor_placeholder: test_anchor, similar_placeholder: test_similar, dissimilar_placeholder: test_dissimilar, labels_placeholder: test_labels, is_training_placeholder: False, } _, _global_step = sess.run([train_step, global_step], feed_dict=train_dict) anchor_out, similar_out, dissimilar_out = sess.run( [flatten_out_anchor, flatten_out_similar, flatten_out_dissimilar], feed_dict=train_dict ) _train_loss, _train_pos_dist, _train_neg_dist = sess.run([loss, pos_dist, neg_dist], feed_dict=train_dict) _test_loss, _test_pos_dist, _test_neg_dist = sess.run([loss, pos_dist, neg_dist], feed_dict=test_dict) print("distance:", list(zip(_train_pos_dist.flatten(), _train_neg_dist.flatten()))[:5]) one_moving_meaning_show = "No mean or variance" if len(bn_moving_vars) > 0: one_moving_meaning = sess.graph.get_tensor_by_name(bn_moving_vars[0].name) one_moving_meaning_show = "{}={}".format(bn_moving_vars[0].name, np.mean(one_moving_meaning.eval())) print(one_moving_meaning_show) show_text = "epoch:{},epoch-iters:{},total-iters:{},loss:{},lr:{},val:{}".format( epoch_num, iters_num + 1, _global_step, _train_loss, "0.99", _test_loss ) print(show_text) if _global_step % snapshot == 0: # 保存PB constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ["flatten_anchor"]) save_model_name = model_name + "-" + str(_global_step) + ".pb" with tf.gfile.FastGFile(pb_path + save_model_name, mode="wb") as fw: fw.write(constant_graph.SerializeToString()) # 保存CKPT ckpt_saver.save(sess, ckpt_path + model_name + ".ckpt", global_step=total_iters_num) print("Successfully saved model {}".format(save_model_name))	[ 485, 15196, 641, 5369, 222, 1097, 15196, 51, 2980, 51, 2863, 1220, 4023, 100, 1058, 222, 1097, 3723, 1220, 7087, 340, 296, 3598, 100, 4476, 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68f3d3fce52d08381adc522ee032ef3181aec82a	# Generated by Django 2.2.3 on 2019-07-27 10:41 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("beerFriends", "0006_auto_20190726_1504"), ] operations = [ migrations.AlterField( model_name="beer", name="rating", field=models.FloatField(blank=True, null=True), ), ]	[ 40, 13046, 829, 18288, 244, 55, 51, 55, 51, 56, 563, 244, 55, 53, 54, 62, 50, 53, 60, 50, 55, 60, 244, 54, 53, 63, 57, 54, 222, 222, 1097, 8532, 51, 1219, 1220, 12520, 49, 4590, 499, 222, 842, 18572, 45, 19772, 51, 12071, 731, 303, 6450, 299, 447, 310, 2903, 46761, 32272, 411, 332, 53, 53, 53, 59, 100, 2853, 100, 55, 53, 54, 62, 53, 60, 55, 59, 100, 54, 58, 53, 57, 2132, 303, 1499, 465, 7392, 299, 447, 310, 12520, 51, 18835, 1195, 45, 343, 1573, 100, 444, 366, 46761, 411, 343, 655, 366, 11748, 411, 343, 1620, 66, 3404, 51, 3690, 1195, 45, 5021, 66, 1844, 49, 762, 66, 1844, 490, 310, 2604, 303, 1499, 222 ]	[ "#", "ĠGenerated", "Ġby", "ĠDjango", "Ġ", "2", ".", "2", ".", "3", "Ġon", "Ġ", "2", "0", "1", "9", "-", "0", "7", "-", "2", "7", "Ġ", "1", "0", ":", "4", "1", "Ċ", "Ċ", "from", "Ġdjango", ".", "db", "Ġimport", "Ġmigrations", ",", "Ġmodels", "ĊĊ", "Ċ", "class", "ĠMigration", "(", "migrations", ".", "Migration", "):", "ĊĠĠĠ", "Ġdependencies", "Ġ=", "Ġ[", "ĊĠĠĠĠĠĠĠ", "Ġ(\"", "beer", "Friends", "\",", "Ġ\"", "0", "0", "0", "6", "_", "auto", "_", "2", "0", "1", "9", "0", "7", "2", "6", "_", "1", "5", "0", "4", "\"),", "ĊĠĠĠ", "Ġ]", "ĊĊĠĠĠ", "Ġoperations", "Ġ=", "Ġ[", "ĊĠĠĠĠĠĠĠ", "Ġmigrations", ".", "Alter", "Field", "(", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġmodel", "_", "name", "=\"", "beer", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġname", "=\"", "rating", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfield", "=", "models", ".", "Float", "Field", "(", "blank", "=", "True", ",", "Ġnull", "=", "True", "),", "ĊĠĠĠĠĠĠĠ", "Ġ),", "ĊĠĠĠ", "Ġ]", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 17, 17 ]	[ 1, 2, 12, 15, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 1, 1, 5, 12, 13, 15, 22, 33, 34, 41, 1, 1, 6, 16, 17, 27, 28, 37, 39, 4, 17, 19, 21, 8, 11, 15, 22, 24, 26, 27, 28, 29, 30, 31, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 52, 4, 6, 4, 15, 17, 19, 8, 19, 20, 25, 30, 31, 12, 18, 19, 23, 25, 29, 31, 12, 17, 19, 25, 27, 12, 18, 19, 25, 26, 31, 36, 37, 42, 43, 47, 48, 53, 54, 58, 60, 8, 11, 4, 6 ]
4775bef3623497e9bbe79ca2d4e9e9da0422c450	# # # # ------------------------------------------------------------------------------------------------------------------------------ # # This program have been developed by Hamed Noori and with citiation of the related publicaitons # can be used without permission. # This program is for a novel architecture for traffic light control system which can form and manipulate # vehicular platoons using clusters of traffic lights. This method, called Platoon-Based Intelligent Traffic Lights (PB-ITL) # is based on coordinated traffic lights that are connected and are also able to communicate with vehicles wirelessly. PB-ITL # groups traffic lights in clusters and each cluster tries to provide proper green status for the platoon of vehicles, using the # Platooning concept which is seeking to utilize close to full capacity of roads. # This lib is a Python-based program which can simulate a city with dynamic intelligent traffic lights. # The author can be reach at [email protected] # # ------------------------------------------------------------------------------------------------------------------------------ # # def start_simulation(sumo, scenario, network, begin, end, interval, output): logging.debug("Finding unused port") unused_port_lock = UnusedPortLock() unused_port_lock.__enter__() remote_port = find_unused_port() logging.debug("Port %d was found" % remote_port) logging.debug("Starting SUMO as a server") sumo = subprocess.Popen( [ "D:\\PATH\\sumo-gui.exe", "-c", "D:\\\PATH\\Your.sumo.cfg", "--tripinfo-output", output, "--device.emissions.probability", "1.0", "--remote-port", str(remote_port), 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d82412055affc96d634957c953a35ea69b7e702f	"""Turning on or off, toggling and checking the status' of a specific relay""" #!/bin/env python3 from time import sleep from gpiozero import LED RELAYS = [LED(23), LED(24), LED(25), LED(8), LED(7), LED(1), LED(12), LED(16)] def on_action(relay_option, number): """To turn on the chosen relay""" relay_option.on() print(f"relay {number} is turning on") def off_action(relay_option, number): """To turn off the chosen relay""" relay_option.off() print(f"relay {number} is turning off") def toggle_action(relay_option, number): """To toggle the chosen relay""" print(f"relay {number} is toggling") relay_option.on() sleep(0.5) relay_option.off() sleep(0.5) def print_help(): """Print/show help for informations of the required parameter""" print(""" Description Arguments: number number of relay 1 to 8 action on, off, or toggle optional arguments: h show this help message and exit """) def options(): """Input the relay number or show help and check the input""" input_str = input("Which relay? ") while True: if input_str == "h": print_help() return index = int(input_str) - 1 if 0 <= index <= 7: relay_status(RELAYS[index], input_str) relay_action(RELAYS[index], input_str) relay_status(RELAYS[index], input_str) return else: print("index out of range") return def relay_action(relay_number, num): """Do the given order(turn on, turn off, toggle) or raise error""" action = input("Which action? 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862b529741d9c3e6cf7ca50272c8af724c56ac62	from wasserstoff.wasserstoff import Config, Environment __all__ = ["Config", "Environment"]	[ 1097, 360, 40088, 289, 1739, 51, 124, 40088, 289, 1739, 1220, 3395, 49, 10584, 222, 222, 523, 483, 523, 299, 3059, 1081, 411, 332, 6342, 1465, 222 ]	[ "from", "Ġw", "asser", "st", "off", ".", "w", "asser", "st", "off", "Ġimport", "ĠConfig", ",", "ĠEnvironment", "Ċ", "Ċ", "__", "all", "__", "Ġ=", "Ġ[\"", "Config", "\",", "Ġ\"", "Environment", "\"]", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3 ]	[ 1, 5, 7, 12, 14, 17, 18, 19, 24, 26, 29, 36, 43, 44, 56, 1, 1, 3, 6, 8, 10, 13, 19, 21, 23, 34, 36 ]
6d25b0fedf0d5081a3a0a93ddacc49748464d9d0	# Required python libraries for attack.py import socket import os import sys from termcolor import colored import StringIO import time # need to find Python equivalent libraries for these import stdio import stdlib import unistd # need to find Python equivalent libraries for these import includes import killer import main import protocol import rand import resolv import scanner import table import util def checksum_tcp_udp(ip_header, buffer_name, data_length, lenth): return sum_checksum_tcp_udp def checksum_generic(address, count): return sum_checksum_generic	[ 40, 11529, 4406, 11382, 456, 11142, 51, 997, 222, 485, 6241, 222, 485, 2355, 222, 485, 5263, 222, 1097, 5626, 1507, 1220, 38714, 222, 485, 44614, 222, 485, 1153, 222, 222, 40, 1868, 391, 2309, 5006, 13765, 11382, 456, 3301, 222, 222, 485, 1248, 735, 222, 485, 47584, 222, 485, 725, 446, 105, 222, 40, 1868, 391, 2309, 5006, 13765, 11382, 456, 3301, 222, 222, 485, 8619, 222, 485, 17480, 284, 222, 485, 2594, 222, 485, 7088, 222, 485, 10510, 222, 485, 18767, 123, 222, 485, 20398, 222, 485, 1877, 222, 485, 4548, 499, 222, 610, 19343, 100, 9379, 100, 19020, 45, 658, 100, 2127, 49, 3129, 100, 444, 49, 727, 100, 1340, 49, 475, 48269, 731, 303, 461, 3668, 100, 21034, 100, 9379, 100, 19020, 499, 222, 610, 19343, 100, 9957, 45, 2485, 49, 2409, 731, 303, 461, 3668, 100, 21034, 100, 9957, 222 ]	[ "#", "ĠRequired", "Ġpython", "Ġlibraries", "Ġfor", "Ġattack", ".", "py", "Ċ", "import", "Ġsocket", "Ċ", "import", "Ġos", "Ċ", "import", "Ġsys", "Ċ", "from", "Ġterm", "color", "Ġimport", "Ġcolored", "Ċ", "import", "ĠStringIO", "Ċ", "import", "Ġtime", "Ċ", "Ċ", "#", "Ġneed", "Ġto", "Ġfind", "ĠPython", "Ġequivalent", "Ġlibraries", "Ġfor", "Ġthese", "Ċ", "Ċ", "import", "Ġstd", "io", "Ċ", "import", "Ġstdlib", "Ċ", "import", "Ġun", "ist", "d", "Ċ", "#", "Ġneed", "Ġto", "Ġfind", "ĠPython", "Ġequivalent", "Ġlibraries", "Ġfor", "Ġthese", "Ċ", "Ċ", "import", "Ġincludes", "Ċ", "import", "Ġkill", "er", "Ċ", "import", "Ġmain", "Ċ", "import", "Ġprotocol", "Ċ", "import", "Ġrand", "Ċ", "import", "Ġresol", "v", "Ċ", "import", "Ġscanner", "Ċ", "import", "Ġtable", "Ċ", "import", "Ġutil", "ĊĊ", "Ċ", "def", "Ġchecksum", "_", "tcp", "_", "udp", "(", "ip", "_", "header", ",", "Ġbuffer", "_", "name", ",", "Ġdata", "_", "length", ",", "Ġl", "enth", "):", "ĊĠĠĠ", "Ġreturn", "Ġsum", "_", "checksum", "_", "tcp", "_", "udp", "ĊĊ", "Ċ", "def", "Ġchecksum", "_", "generic", "(", "address", ",", "Ġcount", "):", "ĊĠĠĠ", "Ġreturn", "Ġsum", "_", "checksum", "_", "generic", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 11, 11, 11, 11, 12, 12, 12, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 16, 16, 16, 17, 17, 17, 17, 18, 18, 18, 19, 19, 19, 20, 20, 20, 21, 21, 21, 21, 22, 22, 22, 23, 23, 23, 24, 24, 24, 26, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28, 28, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 32 ]	[ 1, 2, 11, 18, 28, 32, 39, 40, 42, 1, 7, 14, 1, 7, 10, 1, 7, 11, 1, 5, 10, 15, 22, 30, 1, 7, 16, 1, 7, 12, 1, 1, 2, 7, 10, 15, 22, 33, 43, 47, 53, 1, 1, 7, 11, 13, 1, 7, 14, 1, 7, 10, 13, 14, 1, 2, 7, 10, 15, 22, 33, 43, 47, 53, 1, 1, 7, 16, 1, 7, 12, 14, 1, 7, 12, 1, 7, 16, 1, 7, 12, 1, 7, 13, 14, 1, 7, 15, 1, 7, 13, 1, 7, 12, 1, 1, 4, 13, 14, 17, 18, 21, 22, 24, 25, 31, 32, 39, 40, 44, 45, 50, 51, 57, 58, 60, 64, 66, 4, 11, 15, 16, 24, 25, 28, 29, 32, 1, 1, 4, 13, 14, 21, 22, 29, 30, 36, 38, 4, 11, 15, 16, 24, 25, 32 ]
e57b30a7a1cf987918abfb3cb7d612bdead2ddcd	from django.db import models # Create your models here. class Airlines(models.Model): flight_number = models.CharField(max_length=8, unique=True) airlines_id = models.CharField(max_length=10) source = models.CharField(max_length=20) destination = models.CharField(max_length=20) departure = models.TimeField() arrival = models.TimeField() base_price = models.DecimalField(decimal_places=2, max_digits=10) def __str__(self): return self.flight_number class Users(models.Model): user_id = models.CharField(max_length=16) email = models.EmailField(max_length=50, unique=True) password = models.CharField(max_length=20) phone_number = models.IntegerField() gender = models.CharField(max_length=10) def __str__(self): return self.email class Bookings(models.Model): booking_id = models.AutoField(primary_key=True) email = models.ForeignKey(Users, on_delete=models.CASCADE) flight_num = models.ForeignKey(Airlines, on_delete=models.CASCADE, default="00000", 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2827a56c12c1e15a6fe26ce182aa07d76735d77f	""" MDSANIMA Setup """ import sys import pathlib from setuptools import setup, find_packages HERE = pathlib.Path(__file__).parent CURRENT_PYTHON = sys.version_info[:2] REQUIRED_PYTHON = (3, 6) # This check and everything above must remain compatible with Python 2.7. if CURRENT_PYTHON < REQUIRED_PYTHON: sys.stderr.write( """========================== Unsupported Python Version ========================== This version of MDSANIMA requires Python {}.{} but you're trying to install it on Python {}.{} """.format(*(REQUIRED_PYTHON + CURRENT_PYTHON)) ) sys.exit(1) VERSION = "0.2.0" PACKAGE_NAME = "mdsanima" AUTHOR = "Marcin Rozewski" AUTHOR_EMAIL = "[email protected]" URL = "https://github.com/mdsanima/mdsanima" LICENSE = "MIT" DESCRIPTION = "The package contains modules that will help in calculating rendering time." LONG_DESCRIPTION = (HERE / "README.rst").read_text() LONG_DESC_TYPE = "text/x-rst" INSTALL_REQUIRES = ["humanfriendly"] KEYWORDS = [ "mdsanima", "render time", "calculator render time", "blender", "blener3d", "rendering", "houdini", "sidefx", "vfx", "cinema4d", "cycles", "redshift", "render engine", "octane render", "mantra", "vray", "clarisse ifx", ] setup( name=PACKAGE_NAME, version=VERSION, description=DESCRIPTION, long_description=LONG_DESCRIPTION, long_description_content_type=LONG_DESC_TYPE, author=AUTHOR, license=LICENSE, author_email=AUTHOR_EMAIL, url=URL, install_requires=INSTALL_REQUIRES, packages=find_packages(), extras_require={ "docs": [ "sphinx", "sphinx-autoapi", "sphinx-rtd-theme", "sphinx-bootstrap-theme", "sphinx-prompt", "sphinx-tabs", "recommonmark", ], }, python_requires=">=3.6", keywords=KEYWORDS, classifiers=[ "Development Status :: 5 - Production/Stable", "Environment :: Console", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", 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d4ac5c6f08e9baa458fbe0ca7aa90c4d9372844f	import h5py import numpy as np # import tracking dt = h5py.special_dtype(vlen=bytes) def stringDataset(group, name, data, system=None): dset = group.create_dataset(name, (1,), dtype=dt, data=data) if system: addSystemAttribute(dset, system) return dset def addStringAttribute(dset_or_group, name, data): # return dset_or_group.attrs.create(name, np.string_(data)) # , (1,), dtype=dt) dset_or_group.attrs[name] = bytes(data, "utf-8") def addSystemAttribute(dset_or_group, data): return addStringAttribute(dset_or_group, "system", data) def add_dataset(group, name, data, system=None, dtype=None): if type(data) is str: return stringDataset(group, name, data, system) else: if dtype: dset = group.create_dataset(name, data=data, dtype=dtype) else: dset = group.create_dataset(name, data=data) if system: addSystemAttribute(dset, system) return dset def saveH5Recursive(h5_filename, data_dict): # import here to prevent circular import try: import tracking except ImportError: from . import tracking def recurse_save(group, dict_or_data, dict_or_data_name, new_group=None): if dict_or_data is None: dict_or_data = "None" if group is None: print("'recurse_save' has been called with None") raise ValueError if type(dict_or_data) is tracking.BeamProfile or type(dict_or_data) is tracking.ScreenDistribution: dict_or_data = dict_or_data.to_dict() if type(dict_or_data) is tuple: dict_or_data = {"tuple_%i": x for i, x in enumerate(dict_or_data)} if type(dict_or_data) is dict: if type(dict_or_data_name) is int: inner_key = str(dict_or_data_name) try: new_group = group.create_group(dict_or_data_name) except Exception as e: print(e) print(dict_or_data_name, "error") # raise if new_group is None: raise ValueError for key, val in dict_or_data.items(): try: recurse_save(new_group, val, key) except ValueError as e: print(e) print("I called recurse_save with None") # import pdb; pdb.set_trace() else: mydata = dict_or_data inner_key = dict_or_data_name if type(mydata) is str: add_dataset(group, inner_key, mydata.encode("utf-8"), "unknown") # elif type(mydata) is tuple: # mydata2 = np.array(mydata) # add_dataset(group, inner_key, mydata2, 'unknown') elif (type(mydata) is list and type(mydata[0]) is str) or ( hasattr(mydata, "dtype") and mydata.dtype.type is np.str_ ): # For list of strings, we need this procedure try: if hasattr(mydata, "dtype") and mydata.dtype.type is np.str and len(mydata.shape) == 2: mydata = mydata.flatten() if len(mydata.shape) == 2: new_list = [[n.encode("ascii") for n in arr] for arr in mydata] max_str_size = max(max(len(n) for n in arr) for arr in mydata) elif len(mydata.shape) == 1: new_list = [n.encode("ascii") for n in mydata] max_str_size = max(len(n) for n in mydata) elif len(mydata.shape) == 0: new_list = [mydata.encode("ascii")] max_str_size = len(new_list[0]) # print('Max len %i' % max_str_size) dset = group.create_dataset(inner_key, mydata.shape, "S%i" % max_str_size, new_list) # print(np.array(dset)) dset.attrs.create("system", "unknown", (1,), dtype=dt) except: print("Error for key", inner_key) print(type(mydata)) if type(mydata) is list: print("type(mydata[0])") print(type(mydata[0])) print("mydata") print(mydata) elif hasattr(mydata, "dtype") and mydata.dtype == np.dtype("O"): if mydata.shape == (): add_dataset(group, inner_key, mydata, "unknown") elif len(mydata.shape) == 1: try: add_dataset(group, inner_key, mydata, "unknown") except Exception as e: print(e) print("Error for key", inner_key) print(group, inner_key) else: for i in range(mydata.shape[0]): for j in range(mydata.shape[1]): try: add_dataset(group, inner_key + "_%i_%i" % (i, j), mydata[i, j], "unknown") except: print("Error for key", inner_key) print(group, inner_key, i, j) else: try: add_dataset(group, inner_key, mydata, "unknown") except Exception as e: print("Error", e) print(inner_key, type(mydata)) with h5py.File(h5_filename, "w") as dataH5: for main_key, subdict in data_dict.items(): recurse_save(dataH5, subdict, main_key, None) # recurse_save(dataH5, data_dict, 'none', new_group=dataH5) def loadH5Recursive(h5_file): def recurse_load(group_or_val, key, saved_dict_curr): type_ = type(group_or_val) try: group_or_val[()] except: hasval = False else: hasval = True if type_ is h5py._hl.files.File: for new_key, new_group_or_val in group_or_val.items(): recurse_load(new_group_or_val, new_key, saved_dict_curr) elif type_ is h5py._hl.group.Group: saved_dict_curr[key] = new_dict = {} for new_key, new_group_or_val in group_or_val.items(): recurse_load(new_group_or_val, new_key, new_dict) elif type_ == np.dtype("O") and hasval and type(group_or_val[()]) is bytes: saved_dict_curr[key] = group_or_val[()].decode() elif type_ == h5py._hl.dataset.Dataset: dtype = group_or_val.dtype # if not hasattr(group_or_val, 'value'): # print('Could not store key %s with type %s in dict' % (key, dtype)) # return if dtype in ( np.dtype("int64"), np.dtype("int32"), np.dtype("int16"), np.dtype("int8"), np.dtype("uint32"), np.dtype("uint16"), np.dtype("uint8"), np.dtype("uint64"), ): saved_dict_curr[key] = np.array(group_or_val[()], dtype).squeeze() if saved_dict_curr[key].size == 1: saved_dict_curr[key] = int(saved_dict_curr[key]) elif dtype == np.dtype("bool"): try: saved_dict_curr[key] = bool(group_or_val[()]) except Exception as e: print(e) print("Could not store key %s with type %s in dict (1)" % (key, dtype)) elif dtype in (np.dtype("float64"), np.dtype("float32")): saved_dict_curr[key] = np.array(group_or_val[()]).squeeze() if saved_dict_curr[key].size == 1: saved_dict_curr[key] = float(saved_dict_curr[key]) elif dtype.str.startswith("\|S"): if group_or_val[()].shape == (1, 1): saved_dict_curr[key] = group_or_val[()][0, 0].decode() elif group_or_val[()].shape == (1,): saved_dict_curr[key] = group_or_val[()][0].decode() elif group_or_val[()].shape == (): saved_dict_curr[key] = group_or_val[()].decode() else: saved_dict_curr[key] = [x.decode() for x in group_or_val[()].squeeze()] elif dtype.str == "\|O": saved_dict_curr[key] = group_or_val[()] elif type(group_or_val[()]) is str: saved_dict_curr[key] = group_or_val[()] else: print("Could not store key %s with type %s in dict (2)" % (key, dtype)) else: print("Could not store key %s with type %s in dict (3)" % (key, type_)) saved_dict = {} with h5py.File(h5_file.strip(), "r") as f: if "none" in f: recurse_load(f["none"], "key", saved_dict) saved_dict = saved_dict["key"] else: recurse_load(f, "key", saved_dict) return saved_dict	[ 485, 439, 58, 997, 222, 485, 6610, 641, 2115, 222, 40, 1220, 15549, 222, 222, 2654, 299, 439, 58, 997, 51, 10105, 100, 12094, 45, 123, 1688, 66, 3669, 46, 499, 222, 610, 821, 9440, 45, 1382, 49, 655, 49, 727, 49, 2684, 66, 2545, 731, 303, 362, 489, 299, 2370, 51, 1315, 100, 5709, 45, 444, 49, 327, 54, 28345, 9799, 66, 2654, 49, 727, 66, 624, 46, 303, 434, 2684, 63, 310, 1035, 1615, 2230, 45, 105, 489, 49, 2684, 46, 303, 461, 362, 489, 499, 222, 610, 1035, 671, 2230, 45, 105, 489, 100, 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4d388c912915c3f1f9e433f1342289f0864b3a11	# /usr/bin/python # File: UdpClient.py # Author: David Zemon # Project: Project1 # # Created with: PyCharm Community Edition """ @description: """ __author__ = "david" import logging from src.UDP import UDPClient logging.basicConfig(level="DEBUG") serverName = "127.0.0.1" serverPort = 12000 client = UDPClient() message = input("Input lowercase sentence: ") client.sendto(message.encode("utf-8"), (serverName, serverPort)) modifiedMessage, serverAddress = client.recvfrom(2048) print(modifiedMessage.decode("utf-8")) client.close()	[ 40, 536, 3392, 52, 1793, 52, 2980, 222, 40, 2050, 63, 283, 650, 5994, 1503, 51, 997, 222, 40, 6265, 63, 244, 23241, 2538, 6004, 222, 40, 5426, 63, 5426, 54, 222, 40, 222, 40, 6956, 642, 63, 4374, 2674, 114, 20562, 24480, 222, 222, 3012, 222, 69, 2328, 63, 222, 3012, 222, 222, 523, 2133, 523, 299, 332, 27928, 39, 222, 222, 485, 5751, 222, 1097, 1581, 51, 20952, 1220, 24616, 1503, 222, 222, 7506, 51, 6404, 1081, 45, 2675, 366, 2913, 678, 222, 222, 1805, 577, 299, 332, 54, 55, 60, 51, 53, 51, 53, 51, 54, 39, 222, 1805, 3451, 299, 244, 54, 55, 53, 53, 53, 222, 222, 1598, 299, 24616, 1503, 365, 222, 222, 1454, 299, 1533, 459, 1630, 27104, 15492, 63, 7816, 222, 1598, 51, 2729, 471, 45, 1454, 51, 4458, 459, 3330, 50, 61, 2132, 327, 1805, 577, 49, 2160, 3451, 509, 222, 222, 10492, 1193, 49, 2160, 2455, 299, 2060, 51, 12085, 1097, 45, 55, 53, 57, 61, 46, 222, 1243, 45, 10492, 1193, 51, 5480, 459, 3330, 50, 61, 2152, 222, 1598, 51, 2698, 365, 222 ]	[ "#", "Ġ/", "usr", "/", "bin", "/", "python", "Ċ", "#", "ĠFile", ":", "ĠĠĠ", "ĠU", "dp", "Client", ".", "py", "Ċ", "#", "ĠAuthor", ":", "Ġ", "ĠDavid", "ĠZ", "emon", "Ċ", "#", "ĠProject", ":", "ĠProject", "1", "Ċ", "#", "Ċ", "#", "ĠCreated", "Ġwith", ":", "ĠPy", "Char", "m", "ĠCommunity", "ĠEdition", "Ċ", "Ċ", "\"\"\"", "Ċ", "@", "description", ":", "Ċ", "\"\"\"", "Ċ", "Ċ", "__", "author", "__", "Ġ=", "Ġ\"", "david", "\"", "Ċ", "Ċ", "import", "Ġlogging", "Ċ", "from", "Ġsrc", ".", "UDP", "Ġimport", "ĠUDP", "Client", "Ċ", "Ċ", "logging", ".", "basic", "Config", "(", "level", "=\"", "DEBUG", "\")", "Ċ", "Ċ", "server", "Name", "Ġ=", "Ġ\"", "1", "2", "7", ".", "0", ".", "0", ".", "1", "\"", "Ċ", "server", "Port", "Ġ=", "Ġ", "1", "2", "0", "0", "0", "Ċ", "Ċ", "client", "Ġ=", "ĠUDP", "Client", "()", "Ċ", "Ċ", "message", "Ġ=", "Ġinput", "(\"", "Input", "Ġlowercase", "Ġsentence", ":", "Ġ\")", "Ċ", "client", ".", "send", "to", "(", "message", ".", "encode", "(\"", "utf", "-", "8", "\"),", "Ġ(", "server", "Name", ",", "Ġserver", "Port", "))", "Ċ", "Ċ", "modified", "Message", ",", "Ġserver", "Address", "Ġ=", "Ġclient", ".", "recv", "from", "(", "2", "0", "4", "8", ")", "Ċ", "print", "(", "modified", "Message", ".", "decode", "(\"", "utf", "-", "8", "\"))", "Ċ", "client", ".", "close", "()", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 8, 8, 9, 9, 9, 9, 10, 10, 11, 12, 12, 12, 12, 12, 12, 12, 12, 13, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 22, 22, 22, 22, 22, 22, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29 ]	[ 1, 2, 4, 7, 8, 11, 12, 18, 1, 2, 7, 8, 11, 13, 15, 21, 22, 24, 1, 2, 9, 10, 11, 17, 19, 23, 1, 2, 10, 11, 19, 20, 1, 2, 1, 2, 10, 15, 16, 19, 23, 24, 34, 42, 1, 1, 4, 1, 2, 13, 14, 1, 4, 1, 1, 3, 9, 11, 13, 15, 20, 21, 1, 1, 7, 15, 1, 5, 9, 10, 13, 20, 24, 30, 1, 1, 8, 9, 14, 20, 21, 26, 28, 33, 35, 1, 1, 7, 11, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 1, 7, 11, 13, 14, 15, 16, 17, 18, 19, 1, 1, 7, 9, 13, 19, 21, 1, 1, 8, 10, 16, 18, 23, 33, 42, 43, 46, 1, 7, 8, 12, 14, 15, 22, 23, 29, 31, 34, 35, 36, 39, 41, 47, 51, 52, 59, 63, 65, 1, 1, 9, 16, 17, 24, 31, 33, 40, 41, 45, 49, 50, 51, 52, 53, 54, 55, 1, 6, 7, 15, 22, 23, 29, 31, 34, 35, 36, 39, 1, 7, 8, 13, 15 ]
cd5945631a9dd505bf67089bab8c5a37ad375129	import pandas as pd df1 = pd.read_csv("../final/your_no.tsv", "\t") df2 = pd.read_csv("../../Downloads/me.csv", "\t") final = pd.concat([df1, df2]) final.to_csv("../../Downloads/final_con_final.tsv", sep="\t", index=False)	[ 485, 13247, 641, 6451, 222, 222, 1519, 54, 299, 6451, 51, 870, 100, 4849, 15471, 3242, 52, 8772, 100, 1365, 51, 28729, 411, 2724, 121, 678, 222, 1519, 55, 299, 6451, 51, 870, 100, 4849, 33419, 27467, 52, 308, 51, 4849, 411, 2724, 121, 678, 222, 3242, 299, 6451, 51, 7289, 2034, 1519, 54, 49, 5247, 55, 1156, 222, 3242, 51, 471, 100, 4849, 33419, 27467, 52, 3242, 100, 487, 100, 3242, 51, 28729, 411, 19215, 38600, 121, 411, 1671, 66, 2737, 46, 222 ]	[ "import", "Ġpandas", "Ġas", "Ġpd", "Ċ", "Ċ", "df", "1", "Ġ=", "Ġpd", ".", "read", "_", "csv", "(\"../", "final", "/", "your", "_", "no", ".", "tsv", "\",", "Ġ\"\\", "t", "\")", "Ċ", "df", "2", "Ġ=", "Ġpd", ".", "read", "_", "csv", "(\"../../", "Downloads", "/", "me", ".", "csv", "\",", "Ġ\"\\", "t", "\")", "Ċ", "final", "Ġ=", "Ġpd", ".", "concat", "([", "df", "1", ",", "Ġdf", "2", "])", "Ċ", "final", ".", "to", "_", "csv", "(\"../../", "Downloads", "/", "final", "_", "con", "_", "final", ".", "tsv", "\",", "Ġsep", "=\"\\", "t", "\",", "Ġindex", "=", "False", ")", "Ċ" ]	[ 1, 1, 1, 1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 ]	[ 1, 7, 14, 17, 20, 1, 1, 3, 4, 6, 9, 10, 14, 15, 18, 23, 28, 29, 33, 34, 36, 37, 40, 42, 45, 46, 48, 1, 3, 4, 6, 9, 10, 14, 15, 18, 26, 35, 36, 38, 39, 42, 44, 47, 48, 50, 1, 6, 8, 11, 12, 18, 20, 22, 23, 24, 27, 28, 30, 1, 6, 7, 9, 10, 13, 21, 30, 31, 36, 37, 40, 41, 46, 47, 50, 52, 56, 59, 60, 62, 68, 69, 74, 75 ]
16b425d7b8cde1aabe038ccae6922091afb84415	# coding: utf-8 # In[1]: import matplotlib.pyplot as plt import matplotlib import pandas as pd import numpy as np from pandas import DataFrame, Series import os, re # In[2]: OUTPUT_EXCEL = "월별원내약품사용현황.xlsx" # In[3]: # 데이타셋 준비 data_source_dir = "사용량월별통계/원내" dfs = [] for fname in os.listdir(data_source_dir): fn, ext = os.path.splitext(fname) if ext in [".xls", ".xlsx"]: df = pd.read_excel(os.path.join(data_source_dir, fname)) df["사용(개시)년월"] = fn dfs.append(df) use_amount_df = pd.concat(dfs, ignore_index=True) # In[4]: drug_standard_df = pd.read_json("drug.json").T drug_info_df = pd.read_excel("약품정보.xls") use_amount_df = pd.merge(drug_info_df, use_amount_df[["사용량", "약품코드", "사용(개시)년월"]], on="약품코드", how="left") use_amount_df = pd.merge( use_amount_df, drug_standard_df[["보험코드", "제품명", "판매사", "성분/함량"]], left_on="EDI코드", right_on="보험코드", how="left", ) use_amount_df["제품명"] = use_amount_df["제품명"].fillna(use_amount_df["약품명(한글)"]) use_amount_df["사용개시년월"] = use_amount_df["수가시작일자"].map(lambda x: str(x)[0:4] + "-" + str(x)[4:6]) use_amount_df["사용(개시)년월"] = use_amount_df["사용(개시)년월"].fillna(use_amount_df["사용개시년월"]) use_amount_df["성분명"] = use_amount_df["성분명"].fillna(use_amount_df["성분/함량"]) use_amount_df["원내/원외 처방구분"] = use_amount_df["원내/원외 처방구분"].map({1: "원외", 2: "원외/원내", 3: "원내"}) use_amount_df["약품법적구분"] = use_amount_df["약품법적구분"].map( {0: "일반", 1: "마약", 2: "향정약", 3: "독약", 4: "한방약", 5: "고가약"} ) # In[5]: def get_last(s): try: return max(s) except: return s # In[6]: months = use_amount_df["사용(개시)년월"].unique() months = sorted(months.tolist(), reverse=1) use_amount_df["최후사용월"] = use_amount_df.groupby(["제품명"])["사용(개시)년월"].transform(get_last) use_amount_df["최근미사용월수"] = use_amount_df["최후사용월"].map(lambda x: months.index(x) if x in months else -1) # In[7]: use_amount_in_df = use_amount_df[use_amount_df["원내/원외 처방구분"] != "원외"] # In[8]: use_amount_in_df["사용량"] = use_amount_in_df["사용량"].fillna("오픈후미사용") # In[9]: pat = "(\(([^\d].?)\)+\s)\|퇴장방지\s\|생산원가보전,\s\|사용장려(비\s\d+원\|및\|비용지급,\s)" use_amount_in_df = use_amount_in_df.rename(columns={"제품명": "약품명(드럭인포)", "약품명(한글)": "약품명(원내)"}) use_amount_in_df["약품명(드럭인포)"] = use_amount_in_df["약품명(드럭인포)"].str.replace(pat, "") # In[10]: pvt = use_amount_in_df.pivot_table( index=["EDI코드", "약품명(드럭인포)", "성분명", "약품코드", "약품명(원내)", "효능코드명", "규격단위", "최근미사용월수"], columns=["사용(개시)년월"], values=["사용량"], aggfunc=sum, ) # In[11]: pvt.to_excel(OUTPUT_EXCEL) os.startfile(OUTPUT_EXCEL) # In[ ]:	[ 40, 10633, 63, 6471, 50, 61, 222, 222, 40, 719, 96, 54, 2805, 222, 222, 485, 13168, 51, 18553, 641, 6020, 222, 485, 13168, 222, 485, 13247, 641, 6451, 222, 485, 6610, 641, 2115, 222, 1097, 13247, 1220, 23069, 49, 20956, 222, 485, 2355, 49, 334, 499, 222, 40, 719, 96, 55, 2805, 222, 222, 10838, 100, 1478, 34336, 299, 332, 48719, 44023, 24525, 48550, 37465, 205, 264, 254, 18454, 7993, 25632, 12028, 140, 51, 22852, 39, 499, 222, 40, 719, 96, 56, 2805, 222, 222, 40, 1866, 14973, 31885, 19644, 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0eaaa81d3c8bc61368701e1916b42ede88b90d04	# EXERCISE: # Plotting distributions pairwise (2) # In this exercise, you will generate pairwise joint distributions again. This time, you will make two particular # additions: # - You will display regressions as well as scatter plots in the off-diagonal subplots. You will do this with the # argument kind='reg' (where 'reg' means 'regression'). Another option for kind is 'scatter' (the default) that # plots scatter plots in the off-diagonal subplots. # - You will also visualize the joint distributions separated by continent of origin. You will do this with the # keyword argument hue specifying the 'origin'. # INSTRUCTIONS: # - Plot the pairwise joint distributions separated by continent of origin and display the regressions. # CODE: # Print the first 5 rows of the DataFrame print(auto.head()) # Plot the pairwise joint distributions grouped by 'origin' along with regression lines sns.pairplot(auto, kind="reg", hue="origin") # Display the plot plt.show()	[ 40, 2800, 546, 4264, 825, 63, 222, 222, 40, 19171, 1073, 28654, 6120, 3790, 327, 55, 46, 222, 222, 40, 719, 477, 22467, 49, 863, 1118, 4468, 6120, 3790, 16234, 28654, 4076, 51, 1369, 1153, 49, 863, 1118, 1949, 3161, 8445, 222, 40, 42084, 63, 222, 222, 40, 449, 2469, 1118, 2540, 334, 2280, 1804, 641, 4509, 641, 27022, 25791, 347, 341, 2143, 50, 36223, 1381, 17685, 51, 2469, 1118, 764, 477, 642, 341, 222, 40, 2814, 6207, 1080, 1604, 44, 327, 2786, 349, 1604, 44, 6084, 349, 26753, 1833, 25359, 2489, 456, 6207, 458, 349, 18735, 44, 327, 1402, 1263, 46, 708, 222, 40, 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27e9635adf6109f3ab13b9d8dd5809973b61ca03	# Web Scraping # Make sure you have bs4, webbrowser and request installed as your third party modules import bs4, webbrowser, requests try: data = requests.get("http://en.wikipedia.org/wiki/Python") data.raise_for_status() my_data = bs4.BeautifulSoup(data.text, "lxml") print("List of all the header tags: \n\n") for the_data in my_data.find_all("a"): try: print(the_data.attrs["href"]) except Exception as err: print(err) except Exception as err: print(err) print("\nNo website matches your search.")	[ 40, 3718, 6918, 1085, 318, 222, 40, 6429, 3673, 863, 1178, 14270, 57, 49, 2692, 7138, 480, 1343, 6733, 641, 1390, 10999, 15280, 7912, 222, 222, 485, 14270, 57, 49, 2692, 7138, 49, 5188, 222, 222, 1287, 63, 303, 727, 299, 5188, 51, 390, 459, 544, 574, 291, 51, 12464, 51, 1107, 52, 6439, 52, 8378, 678, 303, 727, 51, 13227, 100, 999, 100, 1535, 365, 465, 1690, 100, 624, 299, 14270, 57, 51, 1483, 20054, 32415, 45, 624, 51, 897, 49, 332, 113, 2608, 678, 465, 1489, 459, 739, 451, 1187, 341, 3353, 5957, 63, 788, 115, 97, 115, 678, 303, 456, 341, 100, 624, 347, 1690, 100, 624, 51, 1828, 100, 483, 459, 102, 10320, 310, 1614, 63, 343, 1489, 45, 1402, 100, 624, 51, 10433, 1388, 6935, 6957, 310, 2959, 3284, 641, 1155, 63, 343, 1489, 45, 549, 46, 222, 222, 7364, 3284, 641, 1155, 63, 303, 1489, 45, 549, 46, 303, 1489, 5564, 115, 2042, 9581, 8012, 1390, 3252, 7164, 222 ]	[ "#", "ĠWeb", "ĠSc", "rap", "ing", "Ċ", "#", "ĠMake", "Ġsure", "Ġyou", "Ġhave", "Ġbs", "4", ",", "Ġweb", "browser", "Ġand", "Ġrequest", "Ġinstalled", "Ġas", "Ġyour", "Ġthird", "Ġparty", "Ġmodules", "Ċ", "Ċ", "import", "Ġbs", "4", ",", "Ġweb", "browser", ",", "Ġrequests", "Ċ", "Ċ", "try", ":", "ĊĠĠĠ", "Ġdata", "Ġ=", "Ġrequests", ".", "get", "(\"", "http", "://", "en", ".", "wikipedia", ".", "org", "/", "wiki", "/", "Python", "\")", "ĊĠĠĠ", "Ġdata", ".", "raise", "_", "for", "_", "status", "()", "ĊĊĠĠĠ", "Ġmy", "_", "data", "Ġ=", "Ġbs", "4", ".", "Be", "autiful", "Soup", "(", "data", ".", "text", ",", "Ġ\"", "l", "xml", "\")", "ĊĊĠĠĠ", "Ġprint", "(\"", "List", "Ġof", "Ġall", "Ġthe", "Ġheader", "Ġtags", ":", "Ġ\\", "n", "\\", "n", "\")", "ĊĠĠĠ", "Ġfor", "Ġthe", "_", "data", "Ġin", "Ġmy", "_", "data", ".", "find", "_", "all", "(\"", "a", "\"):", "ĊĠĠĠĠĠĠĠ", "Ġtry", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(", "the", "_", "data", ".", "attrs", "[\"", "href", "\"])", "ĊĠĠĠĠĠĠĠ", "Ġexcept", "ĠException", "Ġas", "Ġerr", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(", "err", ")", "Ċ", "Ċ", "except", "ĠException", "Ġas", "Ġerr", ":", "ĊĠĠĠ", "Ġprint", "(", "err", ")", "ĊĠĠĠ", "Ġprint", "(\"\\", "n", "No", "Ġwebsite", "Ġmatches", "Ġyour", "Ġsearch", ".\")", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 18, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21 ]	[ 1, 2, 6, 9, 12, 15, 1, 2, 7, 12, 16, 21, 24, 25, 26, 30, 37, 41, 49, 59, 62, 67, 73, 79, 87, 1, 1, 7, 10, 11, 12, 16, 23, 24, 33, 1, 1, 4, 5, 4, 9, 11, 20, 21, 24, 26, 30, 33, 35, 36, 45, 46, 49, 50, 54, 55, 61, 63, 4, 9, 10, 15, 16, 19, 20, 26, 28, 4, 7, 8, 12, 14, 17, 18, 19, 21, 28, 32, 33, 37, 38, 42, 43, 45, 46, 49, 51, 4, 10, 12, 16, 19, 23, 27, 34, 39, 40, 42, 43, 44, 45, 47, 4, 8, 12, 13, 17, 20, 23, 24, 28, 29, 33, 34, 37, 39, 40, 43, 8, 12, 13, 12, 18, 19, 22, 23, 27, 28, 33, 35, 39, 42, 8, 15, 25, 28, 32, 33, 12, 18, 19, 22, 23, 1, 1, 7, 17, 20, 24, 25, 4, 10, 11, 14, 15, 4, 10, 13, 14, 16, 24, 32, 37, 44, 47 ]
f5f26819be4b98fab3d46e57e1a5431e54342aed	# coding: utf-8 """Supporting model logic for predicting emotional content of user input.""" import pandas as pd import gensim from sklearn.model_selection import train_test_split from sklearn.multiclass import OneVsRestClassifier from sklearn.svm import LinearSVC # load data for emo2vec loc = "https://s3-us-west-1.amazonaws.com/victorsdatasets/NRCEmotionLexiconv092AnnotatorandSenseLevel.txt" print("loading & cleaning the data...") em_words = pd.read_csv( loc, sep="\t", names=[ "annotator_id", "remove", "word", "joy", "sadness", "fear", "anger", "trust", "disgust", "surprise", "anticipation", "POS", ], ) em_words.drop("remove", axis=1, inplace=True) em_words["word"], em_words["synonym"] = em_words["word"].str.split("--").str em_words["toss1"], em_words["joy"] = em_words["joy"].str.split("-").str em_words["toss2"], em_words["sadness"] = em_words["sadness"].str.split("-").str em_words["toss3"], em_words["fear"] = em_words["fear"].str.split("-").str em_words["toss4"], em_words["anger"] = em_words["anger"].str.split("-").str em_words["toss5"], em_words["trust"] = em_words["trust"].str.split("-").str em_words["toss6"], em_words["disgust"] = em_words["disgust"].str.split("-").str em_words["toss7"], em_words["surprise"] = em_words["surprise"].str.split("-").str em_words["toss8"], em_words["anticipation"] = em_words["anticipation"].str.split("-").str em_words["toss9"], em_words["POS"] = em_words["POS"].str.split("-").str em_words.drop(["toss1", "toss2", "toss3", "toss4", "toss5", "toss6", "toss7", "toss8", "toss9"], axis=1, inplace=True) new_cols = [ "annotator_id", "word", "synonym", "joy", "sadness", "fear", "anger", "trust", "disgust", "surprise", "anticipation", "POS", ] em_words = em_words.reindex_axis(new_cols, axis=1) emotions = em_words[["joy", "sadness", "fear", "anger", "trust", "disgust", "surprise", "anticipation"]] em_words[emotions.columns] = em_words[emotions.columns].apply(pd.to_numeric) affected = em_words[emotions.columns].groupby([em_words["word"]], sort=False).mean().reset_index() # Load Google's pre-trained Word2Vec model. print("training the word2vec model from google's corpus") model = gensim.models.Word2Vec.load_word2vec_format("../GoogleNews-vectors-negative300.bin", binary=True) # create list of word indicies to drop to avoid keyerrors with Google's pre-trained model. idx_to_drop = [] def dropper(): for ex in affected["word"]: if ex not in model.vocab: idx_to_drop.append(affected.loc[affected.word == ex].index[0]) # drop words from affected that are not in google's model dropper() affected = affected.drop(idx_to_drop, axis=0) G_vectors = lambda x: model[x] affected["word_vectors"] = affected["word"].apply(G_vectors) affected["label_vectors"] = affected[emotions.columns].values.tolist() affected["binary"] = (affected[emotions.columns] > 0).astype(int).values.tolist() df1 = affected[emotions.columns].rank(method="max", axis=1).rank(method="first", axis=1) ma = df1.max().max() affected["label"] = (df1 == ma).astype(int).values.tolist() affected["target"] = affected["label"].apply(lambda x: x.index(1)) label_dict = {0: "joy", 1: "sadness", 2: "fear", 3: "anger", 4: "trust", 5: "disgust", 6: "surprise", 7: "anticipation"} affected["label_name"] = affected["target"].apply(lambda x: label_dict[x]) emo2vec = affected[["word_vectors", "label_vectors", "binary", "label", "target", "label_name"]] # # Model Testing print("splitting into train/test groups...") emo_X, emo_y = list(emo2vec.word_vectors), list(emo2vec.target) emo_X_train, emo_X_test, emo_y_train, emo_y_test = train_test_split(emo_X, emo_y, random_state=42) # ### OnevsRest with LinearSVC (best score) print("creating a model with the best stuff we've got...") 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d2e46944ab05c5e8c1979101728b7b25900be342	import pytest from time import sleep from timeflux.helpers.background import Task class DummyWorker: def echo(self, message="hello", delay=0, fail=False): sleep(delay) if fail: raise Exception("failed") self.message = message return self.message def test_default(working_path): task = Task(DummyWorker(), "echo").start() while not task.done: status = task.status() assert status["result"] == "hello" assert status["instance"].message == "hello" def test_args(working_path): task = Task(DummyWorker(), "echo", "foobar").start() while not task.done: status = task.status() assert status["result"] == "foobar" def test_kwargs(working_path): task = Task(DummyWorker(), "echo", message="foobar").start() while not task.done: status = task.status() assert status["result"] == "foobar" def test_exception(working_path): task = Task(DummyWorker(), "echo", fail=True).start() while not task.done: status = task.status() assert status["success"] == False assert status["exception"].args[0] == "failed" def test_stop_running(working_path): task = Task(DummyWorker(), "echo", delay=5).start() sleep(0.5) assert task.done == False task.stop() assert task.done == True def test_stop_not_running(working_path): task = Task(DummyWorker(), "echo").start() while not task.done: status = task.status() task.stop() assert task.done == True	[ 485, 13090, 222, 1097, 1153, 1220, 9375, 222, 1097, 1153, 12045, 51, 8789, 51, 4175, 1220, 4211, 499, 222, 842, 25222, 8077, 63, 303, 684, 2264, 45, 803, 49, 1492, 366, 7670, 411, 7826, 66, 53, 49, 3562, 66, 2737, 731, 310, 9375, 45, 7875, 46, 310, 434, 3562, 63, 343, 3655, 3284, 459, 7824, 678, 310, 649, 51, 1454, 299, 1492, 310, 461, 649, 51, 1454, 499, 222, 610, 913, 100, 1555, 45, 13601, 100, 1005, 731, 303, 2918, 299, 4211, 45, 17292, 8077, 1046, 332, 3486, 1912, 1384, 365, 303, 2236, 666, 2918, 51, 5099, 63, 310, 2302, 299, 2918, 51, 1535, 365, 303, 1217, 2302, 1388, 1285, 1465, 630, 332, 7670, 39, 303, 1217, 2302, 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b2f2f1e4b7070ac867b71e538f759e527eb1ffb9	from pymouse import PyMouse m = PyMouse() w, h = m.screen_size() class base_controller: def __init__(self): pass def move(self, xy: list): """ 移动 """ m.move(xy[0] * w, xy[1] * h) def click(self, xy: list): """ 点击 """ m.click(xy[0] * w, xy[1] * h) def scroll(self, marks: list): """ 滚动 """ d = marks[0][1] - marks[-1][1] R = 0.2 print(d) if d > R: m.scroll(-1) elif d < -R: m.scroll(1) def press(self, xy: list, ones=True): """ 长按 """ if ones: m.press(xy[0] * w, xy[1] * h) else: m.drag(xy[0] * w, xy[1] * h) def release(self, xy: list): """ 松开 """ m.release(xy[0] * w, xy[1] * h) class mac_controller(base_controller): def __init__(self): super(mac_controller, self).__init__()	[ 1097, 3643, 7079, 1220, 4374, 6042, 222, 222, 114, 299, 4374, 6042, 365, 222, 124, 49, 439, 299, 364, 51, 4475, 100, 911, 365, 499, 222, 842, 1712, 100, 4641, 63, 303, 684, 1176, 1683, 3232, 803, 731, 310, 3119, 465, 684, 5187, 45, 803, 49, 13633, 63, 1168, 731, 310, 1547, 310, 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ec9efeca7eef7b8ee25c1e089e675bdb1e53413b	# -- coding:utf-8 -- # Author: washing # DateTime: 2022/5/18 10:28 # File: 0668.py # Desc: CV class Solution: def findKthNumber(self, m: int, n: int, k: int) -> int: return bisect_left(range(m * n), k, key=lambda x: x // n * n + sum(x // i for i in range(x // n + 1, m + 1)))	[ 40, 8571, 10633, 63, 3330, 50, 61, 8571, 222, 40, 6265, 63, 360, 22413, 222, 40, 6674, 63, 244, 55, 53, 55, 55, 52, 58, 52, 54, 61, 244, 54, 53, 63, 55, 61, 222, 40, 2050, 63, 244, 53, 59, 59, 61, 51, 997, 222, 40, 44152, 63, 18185, 499, 222, 842, 12219, 63, 303, 684, 2309, 80, 368, 1936, 45, 803, 49, 364, 63, 648, 49, 329, 63, 648, 49, 851, 63, 648, 46, 984, 648, 63, 310, 461, 342, 46911, 100, 1892, 45, 3623, 45, 114, 338, 329, 490, 851, 49, 1221, 66, 6034, 837, 63, 837, 453, 329, 338, 329, 494, 3668, 45, 125, 453, 613, 456, 613, 347, 2189, 45, 125, 453, 329, 494, 244, 54, 49, 364, 494, 244, 54, 2636, 222 ]	[ "#", "Ġ--", "Ġcoding", ":", "utf", "-", "8", "Ġ--", "Ċ", "#", "ĠAuthor", ":", "Ġw", "ashing", "Ċ", "#", "ĠDateTime", ":", "Ġ", "2", "0", "2", "2", "/", "5", "/", "1", "8", "Ġ", "1", "0", ":", "2", "8", "Ċ", "#", "ĠFile", ":", "Ġ", "0", "6", "6", "8", ".", "py", "Ċ", "#", "ĠDesc", ":", "ĠCV", "ĊĊ", "Ċ", "class", "ĠSolution", ":", "ĊĠĠĠ", "Ġdef", "Ġfind", "K", "th", "Number", "(", "self", ",", "Ġm", ":", "Ġint", ",", "Ġn", ":", "Ġint", ",", "Ġk", ":", "Ġint", ")", "Ġ->", "Ġint", ":", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġb", "isect", "_", "left", "(", "range", "(", "m", "Ġ", "Ġn", "),", "Ġk", ",", "Ġkey", "=", "lambda", "Ġx", ":", "Ġx", "Ġ//", "Ġn", "Ġ", "Ġn", "Ġ+", "Ġsum", "(", "x", "Ġ//", "Ġi", "Ġfor", "Ġi", "Ġin", "Ġrange", "(", "x", "Ġ//", "Ġn", "Ġ+", "Ġ", "1", ",", "Ġm", "Ġ+", "Ġ", "1", ")))", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 7, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10 ]	[ 1, 2, 6, 13, 14, 17, 18, 19, 23, 1, 2, 9, 10, 12, 18, 1, 2, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 1, 2, 7, 8, 9, 10, 11, 12, 13, 14, 16, 1, 2, 7, 8, 11, 1, 1, 6, 15, 16, 4, 8, 13, 14, 16, 22, 23, 27, 28, 30, 31, 35, 36, 38, 39, 43, 44, 46, 47, 51, 52, 55, 59, 60, 8, 15, 17, 22, 23, 27, 28, 33, 34, 35, 37, 39, 41, 43, 44, 48, 49, 55, 57, 58, 60, 63, 65, 67, 69, 71, 75, 76, 77, 80, 82, 86, 88, 91, 97, 98, 99, 102, 104, 106, 107, 108, 109, 111, 113, 114, 115, 118 ]
13342922022f0a0e8928c81c1c4716125af0b2c4	import matplotlib.pyplot as plt import numpy as np plt.rcParams["savefig.dpi"] = 300 # 图片像素 plt.rcParams["figure.dpi"] = 300 # 分辨率 plt.rcParams["font.sans-serif"] = ["SimHei"] plt.rcParams["axes.unicode_minus"] = False x_axis = [20, 40, 60, 80, 100] rf = [184, 174, 166, 159, 157.5] anns = [186, 179, 170, 164, 161] adaboost = [187.5, 176, 172, 163, 162] x = np.arange(len(x_axis)) # 首先用第一个的长度作为横坐标 width = 0.2 # 设置柱与柱之间的宽度 fig, ax = plt.subplots() p_rf = ax.bar( x - width, rf, width, alpha=0.9, ) p_anns = ax.bar(x, anns, width, alpha=0.9, color="red") p_adaboost = ax.bar(x + width, adaboost, width, alpha=0.9, color="green") ax.set_xticks(x + width / 2) # 将坐标设置在指定位置 ax.set_xticklabels(x_axis) # 将横坐标替换成 plt.legend((p_rf[0], p_anns[0], p_adaboost[0]), ("RF", "ANNs", "AdaBoost"), loc="best", fontsize=20) plt.xticks(fontsize=18) plt.yticks(fontsize=18) plt.ylim(150, 200) # 指定Y轴的高度 plt.xlabel("训练集大小(%)", fontsize=20) plt.ylabel("MAE(s)", fontsize=20) 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cb08b95e3b9c80fb74d4415b3798ddbb36cd76e7	import unittest """ Find the largest 0 to 9 pandigital that can be formed by concatenating products Take the number 6 and multiply it by each of 1273 and 9854: 6 × 1273 = 7638 6 × 9854 = 59124 By concatenating these products we get the 1 to 9 pandigital 763859124. We will call 763859124 the "concatenated product of 6 and (1273,9854)". Notice too, that the concatenation of the input numbers, 612739854, is also 1 to 9 pandigital. The same can be done for 0 to 9 pandigital numbers. What is the largest 0 to 9 pandigital 10-digit concatenated product of an integer with two or more other integers, such that the concatenation of the input numbers is also a 0 to 9 pandigital 10-digit number? """ class Test(unittest.TestCase): def test(self): pass	[ 485, 15790, 222, 222, 3012, 222, 4882, 341, 22936, 244, 53, 391, 244, 62, 317, 382, 384, 4729, 708, 902, 545, 38765, 829, 40920, 1761, 10062, 222, 222, 18305, 341, 1470, 244, 59, 480, 22277, 580, 829, 2531, 451, 244, 54, 55, 60, 56, 480, 244, 62, 61, 58, 57, 63, 222, 59, 29851, 244, 54, 55, 60, 56, 299, 280, 60, 59, 56, 61, 222, 59, 29851, 244, 62, 61, 58, 57, 299, 244, 58, 62, 54, 55, 57, 222, 1103, 40920, 1761, 3301, 10062, 1019, 640, 341, 244, 54, 391, 244, 62, 317, 382, 384, 4729, 244, 60, 59, 56, 61, 58, 62, 54, 55, 57, 51, 2726, 1118, 1495, 244, 60, 59, 56, 61, 58, 62, 54, 55, 57, 341, 332, 7289, 291, 751, 4072, 451, 244, 59, 480, 327, 54, 55, 60, 56, 49, 62, 61, 58, 57, 46, 2316, 21279, 5150, 49, 708, 341, 14406, 23633, 451, 341, 1533, 7560, 49, 244, 59, 54, 55, 60, 56, 62, 61, 58, 57, 49, 458, 2353, 244, 54, 391, 244, 62, 317, 382, 384, 4729, 51, 222, 1338, 2454, 902, 545, 3412, 456, 244, 53, 391, 244, 62, 317, 382, 384, 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e695b9458c0e98521e560dbb291f6f05bda1549f	from savers.saver import SaverInterface import os from config import SaverConfig import mysql.connector import json import logging class SQLSaver(SaverInterface): # This class takes in json files and will interpret the jsons as follows. # {'tablename':[{'columnname01':'somevalue','columnname02':'somevalue'},{'columnaname02':'somevalue'}]} def connect(self): self.logging.info( "Logging to %s@%s:%s -p %s", self.config.SQL_USER, self.config.SQL_HOST, self.config.SQL_DATABASE, self.config.SQL_PASSWD, ) self.db = mysql.connector.connect( host=self.config.SQL_HOST, user=self.config.SQL_USER, passwd=self.config.SQL_PASSWD, database=self.config.SQL_DATABASE, auth_plugin="mysql_native_password", ) self.logging.info("Log in is successful") def executesql(self, sqlstatement): self.logging.debug("Executing %s", sqlstatement) cursor = self.db.cursor() cursor.execute(sqlstatement) self.db.commit() rowcount = cursor.rowcount cursor.close() return rowcount def executesqlupdate(self, sqlstatement): try: self.logging.debug("Executing %s ", sqlstatement) cursor = self.db.cursor() cursor.execute(sqlstatement) self.db.commit() rowcount = cursor.rowcount cursor.close() except Exception as e: self.logging.error("Problem executing SQL: %s", str(e)) def executesql(self, sqlstatement, data): try: self.logging.debug("Executing %s with data as follows %s", sqlstatement, data) cursor = self.db.cursor() cursor.execute(sqlstatement, data) self.db.commit() rowcount = cursor.rowcount cursor.close() except Exception as e: self.logging.error("Problem executing SQL: %s", str(e)) rowcount = 0 return rowcount def querysql(self, sqlstatement): print("Statements to execute\n", sqlstatement) cursor = self.db.cursor() cursor.execute(sqlstatement) records = cursor.fetchall() cursor.close() return records def __init__(self, config=None): if config == None: self.config = SaverConfig() logging.basicConfig( level=logging.DEBUG, handlers=[logging.FileHandler("{0}/{1}.log".format("/logs", "saverservice-sqlsaver")), logging.StreamHandler()], format="%(asctime)-15s %(levelname)-8s %(message)s", ) self.logging = logging self.connect() def freequery(self, sqlstring): records = self.querysql(sqlstring) return records def create(self, jsonobject): try: self.logging.info("Starting create operations") if isinstance(jsonobject, str): self.logging.debug("data obj received is not json, manually converting") jsonobject = json.loads(jsonobject) dictkeys = jsonobject.keys() totalrowcount = 0 for tablename in dictkeys: rowstoadd = jsonobject[tablename] self.logging.debug("Table: %s Rows: %s", tablename, rowstoadd) sqlstatementcolnames = "" sqlstatementcolvalues = "" for row in rowstoadd: print("Row:{}".format(row)) dictcolsinrow = row.keys() print("ColumnNames: {}".format(dictcolsinrow)) colCount = 0 datalist = [] for col in dictcolsinrow: self.logging.debug("Col:%s,Val:%s", col, row[col]) if colCount == 0: sqlstatementcolnames = col # sqlstatementcolvalues="\'"+str(row[col])+"\'" sqlstatementcolvalues = "%s" datalist.append(str(row[col])) else: sqlstatementcolnames = sqlstatementcolnames + "," + col # sqlstatementcolvalues=sqlstatementcolvalues+','+"\'"+str(row[col])+"\'" sqlstatementcolvalues = sqlstatementcolvalues + ",%s" datalist.append(str(row[col])) colCount = colCount + 1 sqlstatement = ( "INSERT INTO " + tablename + "(" + sqlstatementcolnames + ") VALUES (" + sqlstatementcolvalues + ")" ) rowcount = self.executesql(sqlstatement, datalist) totalrowcount = totalrowcount + rowcount except Exception as e: totalrowcount = 0 return totalrowcount def update(self, jsonobject): # Expects the following json # {"tablename":[ # {"row": # {"data":{"columnname02":"01value","columnname02":"02value"},"condition":{"columnname03":"03value"}}}, # {"row": # {"data":{"columnname04":"04value","columnname05":"05value"},"condition":{"columnname06":"06value"}}} # ]} dictkeys = jsonobject.keys() print("Dict keys:", dictkeys) sqlstatements = [] for tablename in dictkeys: rowstoupdate = jsonobject[tablename] print("Table: {}\nRows:{}".format(tablename, rowstoupdate)) for rowtoupdate in rowstoupdate: rowdata = rowtoupdate["row"]["data"] rowcondition = rowtoupdate["row"]["condition"] print("ROW:\n\tRowData:{}\n\tRowCondition:{}".format(rowdata, rowcondition)) sqlstatementdata = "" colCount = 0 for colname in rowdata.keys(): if colCount == 0: sqlstatementdata = colname + "=" + "'" + str(rowdata[colname]) + "'" else: sqlstatementdata = sqlstatementdata + "," + colname + "=" + "'" + str(rowdata[colname]) + "'" colCount = colCount + 1 sqlstatementcondition = "" colCount = 0 for colname in rowcondition.keys(): if colCount == 0: sqlstatementcondition = colname + "=" + "'" + str(rowcondition[colname]) + "'" else: sqlstatementcondition = ( sqlstatementcondition + " AND " + colname + "=" + "'" + str(rowcondition[colname]) + "'" ) colCount = colCount + 1 sqlstatement = "UPDATE " + tablename + " SET " + sqlstatementdata + " WHERE " + sqlstatementcondition sqlstatements.append(sqlstatement) self.executesql(sqlstatements) def setreportstatus(self, filename, status): sqlstring = "UPDATE reports SET currentloc='" + status.upper() + "' WHERE filename='" + filename.lower() + "'" self.executesqlupdate(sqlstring) def get(self): super.get() def delete(self): super.delete() if __name__ == "__main__": jsonobj = json.load(open("create_report.json", "r")) saver = SQLSaver() saver.create(jsonobj) jsonobj = json.load(open("create_ingest.json", "r")) saver.create(jsonobj) jsonobj = json.load(open("update_ingest.json", "r")) saver.update(jsonobj) # rowsreportsaffected=saver.save({'reports':[{'filename':'file01.pdf','created_at':'2020-01-09 15:00:00','ingested_at':'2020-01-09 15:01:00','currentloc':'/home/shareddata'},{'filename':'file02.pdf','created_at':'2020-01-09 16:00:00','ingested_at':'2020-01-09 16:01:00','currentloc':'/home/shareddata'}]) # rowsingestsaffected=saver.save({'ingests':[{'text':'This is good weather?','section':'observation','created_at':'2020-01-02 12:33:33','ingest_id':'1','predicted_category':'DOCTRINE','annotated_category':'DOCTRINE'}]}) # rowsingestupdate=save.update({'ingests':[{'id':'1','annotated_category':'PERSONNEL'}]}) pass	[ 1097, 4276, 5257, 51, 2473, 443, 1220, 377, 15243, 3058, 222, 485, 2355, 222, 1097, 1377, 1220, 377, 15243, 1081, 222, 485, 13062, 51, 11636, 222, 485, 2379, 222, 485, 5751, 499, 222, 842, 4447, 42039, 45, 42039, 3058, 731, 303, 607, 1369, 462, 8756, 347, 2379, 2293, 480, 1118, 21715, 341, 2379, 120, 641, 11629, 51, 303, 607, 5095, 34480, 972, 8659, 44, 2628, 444, 53, 54, 11583, 6074, 872, 1216, 2628, 444, 53, 55, 11583, 6074, 872, 8947, 12164, 2628, 297, 340, 53, 55, 11583, 6074, 872, 6062, 6690, 465, 684, 5023, 45, 803, 731, 310, 649, 51, 7506, 51, 1357, 45, 343, 332, 11165, 391, 925, 120, 36688, 120, 6081, 120, 449, 117, 925, 120, 411, 343, 649, 51, 1180, 51, 3566, 100, 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c55991e738c89ee09dabd79d514e710e0fcbac85	from splinter import Browser from time import sleep from datetime import datetime, timedelta import os, sys import urllib import cv2 import numpy as np from PIL import Image import imutils import csv class Scraper: start_date = datetime(2018, 1, 8) url = "http://spaceweather.com/" def scrape(self): self.browser = Browser("firefox") self.browser.driver.set_page_load_timeout(60) self.browser.visit(self.url) for day in self.get_days(): self.scrape_day(day) def scrape_day(self, day): self.browser.select("month", day.strftime("%m")) self.browser.select("day", day.strftime("%d")) self.browser.select("year", day.strftime("%Y")) button = self.browser.find_by_name("view") button.click() text = self.browser.find_by_css(".solarWindText")[4].text number = int(text.split(" ")[2].strip()) link = self.browser.find_link_by_partial_href("images{}/".format(day.strftime("%Y")))["href"] folder_name = "data/{}{}{}".format(day.strftime("%Y"), day.strftime("%m"), day.strftime("%d")) image_name = "{}/image.gif".format(folder_name) txt_name = "{}/data.txt".format(folder_name) os.mkdir(folder_name) urllib.urlretrieve(link, image_name) img = Image.open(image_name) img.save("{}/image.png".format(folder_name), "png", optimize=True, quality=70) txt_file = open(txt_name, "w") txt_file.write(str(number)) txt_file.close() print("Downloaded data for {}, sunspots: {}".format(day.strftime("%m/%d/%Y"), number)) def get_days(self): days = [] for i in range(0, 8): base = self.start_date + timedelta(days=7 * i) first = base second = base + timedelta(days=2) third = base + timedelta(days=4) days.append(first) days.append(second) days.append(third) return days class Entry: folder = None date = None sunspots = -1 image_path = None counted_sunspots = 0 sections = [0, 0, 0, 0] def nothing(self, arg): pass def __init__(self, folder, date, sunspots, image_path): self.folder = folder self.date = date self.sunspots = sunspots self.image_path = image_path def process(self): frame = cv2.imread(self.image_path) height, width, channels = frame.shape frameBGR = cv2.GaussianBlur(frame, (1, 1), 0) hsv = cv2.cvtColor(frameBGR, cv2.COLOR_BGR2HSV) colorLow = np.array([0, 90, 80]) colorHigh = np.array([10, 255, 255]) mask = cv2.inRange(hsv, colorLow, colorHigh) result = cv2.bitwise_and(frame, frame, mask=mask) image_edged = cv2.Canny(mask, 50, 100) image_edged = cv2.dilate(image_edged, None, iterations=1) image_edged = cv2.erode(image_edged, None, iterations=1) cnts = cv2.findContours(image_edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] if imutils.is_cv2() else cnts[1] image_contours = cv2.bitwise_not(result) self.counted_sunspots = 0 self.sections = [0, 0, 0, 0] section_1_start, section_1_end = 0, height / 4 section_2_start, section_2_end = height / 4, height / 4 2 section_3_start, section_3_end = height / 4 * 2, height / 4 * 3 section_4_start, section_4_end = height / 4 * 3, height / 4 * 4 cv2.line(image_contours, (0, section_1_end), (width, section_1_end), (0, 0, 0), 5) cv2.line(image_contours, (0, section_2_end), (width, section_2_end), (0, 0, 0), 10) cv2.line(image_contours, (0, section_3_end), (width, section_3_end), (0, 0, 0), 5) cv2.circle(image_contours, (width / 2, height / 2), width / 2, (0, 0, 0), 5) font = cv2.FONT_HERSHEY_SIMPLEX cv2.putText(image_contours, self.date.strftime("%a %b %d"), (20, 50), font, 2, (0, 0, 0), 2, cv2.LINE_AA) cv2.putText( image_contours, self.date.strftime("SSN: {}".format(self.sunspots)), (20, 100), font, 1.5, (0, 0, 0), 2, cv2.LINE_AA ) for c in cnts: if cv2.contourArea(c) < 5: continue (x, y), radius = cv2.minEnclosingCircle(c) x = int(x) y = int(y) radius = int(radius) cv2.circle(image_contours, (x, y), radius, (100, 100, 255), -1) self.counted_sunspots = self.counted_sunspots + 1 if y >= section_1_start and y <= section_1_end: # cv2.putText(image_contours, '1', (x, y - 10), font, 0.8, (100, 100, 255), 2, cv2.LINE_AA) self.sections[0] = self.sections[0] + 1 elif y >= section_2_start and y <= section_2_end: # cv2.putText(image_contours, '2', (x, y - 10), font, 0.8, (100, 100, 255), 2, cv2.LINE_AA) self.sections[1] = self.sections[1] + 1 elif y >= section_3_start and y <= section_3_end: # cv2.putText(image_contours, '3', (x, y - 10), font, 0.8, (100, 100, 255), 2, cv2.LINE_AA) self.sections[2] = self.sections[2] + 1 elif y >= section_4_start and y <= section_4_end: # cv2.putText(image_contours, '4', (x, y - 10), font, 0.8, (100, 100, 255), 2, cv2.LINE_AA) self.sections[3] = self.sections[3] + 1 print("Counted sunspots: {}".format(self.counted_sunspots)) print(self.sections) cv2.putText(image_contours, "Section 1: {}".format(self.sections[0]), (20, 130), font, 1, (0, 0, 0), 2, cv2.LINE_AA) cv2.putText(image_contours, "Section 2: {}".format(self.sections[1]), (20, 160), font, 1, (0, 0, 0), 2, cv2.LINE_AA) cv2.putText(image_contours, "Section 3: {}".format(self.sections[2]), (20, 190), font, 1, (0, 0, 0), 2, cv2.LINE_AA) cv2.putText(image_contours, "Section 4: {}".format(self.sections[3]), (20, 220), font, 1, (0, 0, 0), 2, cv2.LINE_AA) colorLow = np.array([0, 0, 90]) colorHigh = np.array([0, 0, 255]) mask = cv2.inRange(hsv, colorLow, colorHigh) image_contours[mask > 0] = (0, 0, 0) vis = np.concatenate((frame, image_contours), axis=1) cv2.imwrite("out/images/{}.png".format(self.folder), vis) class Processor: entries = [] def load(self): folders = os.listdir("data") for folder in folders: year = int(folder[:4]) month = int(folder[4:6]) day = int(folder[6:8]) date = datetime(year, month, day) image_name = "data/{}/image.png".format(folder) txt_name = "data/{}/data.txt".format(folder) txt_file = open(txt_name, "r") content = txt_file.readlines() txt_file.close() number = int(content[0]) print(folder) entry = Entry(folder, date, number, image_name) entry.process() self.entries.append(entry) self.entries.sort(key=lambda x: x.date, reverse=False) def compute(self): for section in range(0, 4): total = 0 for entry in self.entries: total += entry.sections[section] average = float(total) / float(len(self.entries)) print("-------[Section {}]-------".format(section + 1)) print("Total: {}".format(total)) print("Average: {}".format(average)) total = 0 sections_data = [["date", "section_1", "section_2", "section_3", "section_4"]] numbers_data = [["date", "reported", "visible"]] for entry in self.entries: total += entry.counted_sunspots sections_data.append([entry.date.strftime("%Y/%m/%d")] + entry.sections) numbers_data.append([entry.date.strftime("%Y/%m/%d")] + [entry.sunspots, entry.counted_sunspots]) average = float(total) / float(len(self.entries)) print("---------[TOTAL]---------") print("Total: {}".format(total)) print("Average: {}".format(average)) csv_file = open("out/sections.csv", "w") writer = csv.writer(csv_file) writer.writerows(sections_data) csv_file.close() csv_file = open("out/numbers.csv", "w") writer = csv.writer(csv_file) writer.writerows(numbers_data) csv_file.close() scraper = Scraper() scraper.scrape() processor = Processor() processor.load() processor.compute()	[ 1097, 14804, 1164, 1220, 14154, 222, 1097, 1153, 1220, 9375, 222, 1097, 7181, 1220, 7181, 49, 30864, 222, 485, 2355, 49, 5263, 222, 485, 22296, 222, 485, 6781, 55, 222, 485, 6610, 641, 2115, 222, 1097, 35991, 1220, 4393, 222, 485, 3987, 2324, 222, 485, 11490, 499, 222, 842, 6918, 26877, 63, 303, 1496, 100, 787, 299, 7181, 45, 55, 53, 54, 61, 49, 244, 54, 49, 244, 61, 46, 303, 2001, 299, 332, 544, 574, 32145, 412, 106, 3814, 51, 527, 5694, 465, 684, 2240, 23253, 45, 803, 731, 310, 649, 51, 7138, 299, 14154, 459, 35148, 678, 310, 649, 51, 7138, 51, 5381, 51, 489, 100, 1655, 100, 963, 100, 5470, 45, 59, 53, 46, 310, 649, 51, 7138, 51, 8299, 45, 803, 51, 983, 46, 310, 456, 5830, 347, 649, 51, 390, 100, 6140, 2284, 343, 649, 51, 48617, 100, 3386, 45, 3386, 46, 465, 684, 2240, 23253, 100, 3386, 45, 803, 49, 5830, 731, 310, 649, 51, 7138, 51, 1632, 459, 5926, 411, 5830, 51, 20934, 4923, 114, 2152, 310, 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b767519229058b50183d78bb97121f050e5b6bad	# defining private variables class Privacy: def __init__(self, val): self.__val = 900 print("Private data member =", self.__val, "\n") value = Privacy(800) print("Value not changable\n") value.__val	[ 40, 20774, 964, 4918, 222, 842, 43630, 63, 303, 684, 1176, 1683, 3232, 803, 49, 928, 731, 310, 649, 3688, 718, 299, 244, 62, 53, 53, 310, 1489, 459, 6533, 727, 5817, 299, 411, 649, 3688, 718, 49, 2724, 115, 678, 499, 222, 872, 299, 43630, 45, 61, 53, 53, 46, 222, 1243, 459, 899, 666, 683, 761, 463, 97, 115, 678, 222, 872, 3688, 718, 222 ]	[ "#", "Ġdefining", "Ġprivate", "Ġvariables", "Ċ", "class", "ĠPrivacy", ":", "ĊĠĠĠ", "Ġdef", "Ġ__", "init", "__(", "self", ",", "Ġval", "):", "ĊĠĠĠĠĠĠĠ", "Ġself", ".__", "val", "Ġ=", "Ġ", "9", "0", "0", "ĊĠĠĠĠĠĠĠ", "Ġprint", "(\"", "Private", "Ġdata", "Ġmember", "Ġ=", "\",", "Ġself", ".__", "val", ",", "Ġ\"\\", "n", "\")", "ĊĊ", "Ċ", "value", "Ġ=", "ĠPrivacy", "(", "8", "0", "0", ")", "Ċ", "print", "(\"", "Value", "Ġnot", "Ġch", "ang", "able", "\\", "n", "\")", "Ċ", "value", ".__", "val", "Ċ" ]	[ 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10 ]	[ 1, 2, 11, 19, 29, 1, 6, 14, 15, 4, 8, 11, 15, 18, 22, 23, 27, 29, 8, 13, 16, 19, 21, 22, 23, 24, 25, 8, 14, 16, 23, 28, 35, 37, 39, 44, 47, 50, 51, 54, 55, 57, 1, 1, 6, 8, 16, 17, 18, 19, 20, 21, 1, 6, 8, 13, 17, 20, 23, 27, 28, 29, 31, 1, 6, 9, 12 ]
b679444fde7cd8eb819443922f37ee54c0f29de4	from pirates.teleport.AreaTeleportActor import AreaTeleportActor class DoorTeleportActor(AreaTeleportActor): pass	[ 1097, 317, 495, 1218, 51, 11448, 644, 51, 5585, 46432, 10459, 1220, 21725, 46432, 10459, 499, 222, 842, 47874, 46432, 10459, 45, 5585, 46432, 10459, 731, 303, 3119, 222 ]	[ "from", "Ġp", "ir", "ates", ".", "tele", "port", ".", "Area", "Teleport", "Actor", "Ġimport", "ĠArea", "Teleport", "Actor", "ĊĊ", "Ċ", "class", "ĠDoor", "Teleport", "Actor", "(", "Area", "Teleport", "Actor", "):", "ĊĠĠĠ", "Ġpass", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5 ]	[ 1, 5, 7, 9, 13, 14, 18, 22, 23, 27, 35, 40, 47, 52, 60, 65, 1, 1, 6, 11, 19, 24, 25, 29, 37, 42, 44, 4, 9 ]
f714c7006f50379cc7508a13d710d902d38d2d1f	import torch import torch.nn as nn import torch.nn.functional as F # Const. low-rank version class xCNNlow(torch.nn.Module): def __init__(self, channels, filters, kernel_size, padding=0, stride=1, groups=1, rank=1, bias=True): super(xCNNlow, self).__init__() self.filters = filters self.times = 2 self.kernel_size = kernel_size self.channels = channels // groups self.padding = padding self.stride = stride self.biasTrue = bias self.rank = rank self.groups = groups self.conv_weights = nn.Parameter(torch.Tensor(filters // self.times, channels, kernel_size, kernel_size).to(device)) self.column_weights = nn.Parameter(torch.Tensor(filters - filters // self.times, self.rank).to(device)) self.row_weights = nn.Parameter(torch.Tensor(self.rank, filters // self.times).to(device)) torch.nn.init.xavier_uniform(self.conv_weights) self.column_weights.data.uniform_(-0.1, 0.1) self.row_weights.data.uniform_(-0.1, 0.1) if self.biasTrue: self.bias = nn.Parameter(torch.Tensor(filters).to(device)) self.bias.data.uniform_(-0.1, 0.1) def forward(self, input): self.correlated_weights = torch.mm( self.column_weights, torch.mm(self.row_weights, self.conv_weights.reshape(self.filters // self.times, -1)) ).reshape(self.filters - self.filters // self.times, self.channels, self.kernel_size, self.kernel_size) if self.biasTrue: return F.conv2d( input, torch.cat((self.conv_weights, self.correlated_weights), dim=0), bias=self.bias, padding=self.padding, stride=self.stride, ) else: return F.conv2d( input, torch.cat((self.conv_weights, self.correlated_weights), dim=0), padding=self.padding, stride=self.stride ) # count FLOPs def count_op_xCNNlow(m, x, y): x = x[0] multiply_adds = 1 cin = m.channels cout = m.filters kh, kw = m.kernel_size, m.kernel_size batch_size = x.size()[0] out_h = y.size(2) out_w = y.size(3) # ops per output element # kernel_mul = kh * kw * cin # kernel_add = kh * kw * cin - 1 kernel_ops = multiply_adds * kh * kw bias_ops = 1 if m.biasTrue is True else 0 ops_per_element = kernel_ops + bias_ops # total ops # num_out_elements = y.numel() output_elements = batch_size * out_w * out_h * cout conv_ops = output_elements * ops_per_element * cin // m.groups # per output element total_mul_1 = m.filters // m.times total_add_1 = total_mul_1 - 1 num_elements_1 = m.rank * (cin * kh * kw) # (m.filters - m.filters//m.times) total_mul_2 = m.rank total_add_2 = total_mul_2 - 1 num_elements_2 = (m.filters - m.filters // m.times) * (cin * kh * kw) # (m.filters - m.filters//m.times) lin_ops = (total_mul_1 + total_add_1) * num_elements_1 + (total_mul_2 + total_add_2) * num_elements_2 total_ops = lin_ops + conv_ops print(lin_ops, conv_ops) m.total_ops = torch.Tensor([int(total_ops)])	[ 485, 5665, 222, 485, 5665, 51, 4042, 641, 8507, 222, 485, 5665, 51, 4042, 51, 18678, 641, 525, 499, 222, 40, 7967, 51, 7487, 50, 6571, 1522, 222, 842, 837, 38852, 760, 45, 7808, 51, 4042, 51, 2478, 731, 303, 684, 1176, 1683, 3232, 803, 49, 11043, 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726f133bcf592315c42f8701be8308422ffbf0d9	from flask import Flask, render_template from flask_ask import Ask, statement, question, session import reverse_geocoder as rg from geopy import distance from geopy.geocoders import Nominatim import requests import time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app = Flask(__name__) ask = Ask(app, "/space_walk") def find_ordinals(city, iss): """ Take tuple coordinates (lat, lon) for City and ISS and find the cardinal direction of NE, SE, SW, NW """ if iss[0] - city[0] > 0: a = "North" else: a = "South" if iss[1] - city[1] > 0: b = "East" else: b = "West" return "".join([a, b]) def where_is_the_iss_now(): iss_now_website = "http://api.open-notify.org/iss-now.json" webby = requests.get(iss_now_website) data = webby.json() if data["iss_position"]: longitude = data["iss_position"].get("longitude") latitude = data["iss_position"].get("latitude") results = rg.search((latitude, longitude), mode=1) lat, lon, name, admin1, admin2, cc = results[0].values() ordinal = find_ordinals(city=(float(lat), float(lon)), iss=(float(latitude), float(longitude))) country_cc = requests.get( "https://pkgstore.datahub.io/core/country-list/data_json/data/8c458f2d15d9f2119654b29ede6e45b8/data_json.json" ) country_cc = country_cc.json() iss_coordinates = (latitude, longitude) k_nearest_coordinates = (lat, lon) distance_miles = distance.distance(k_nearest_coordinates, iss_coordinates).miles country_name = "" for i in filter(lambda d: d.get("Code") == cc, country_cc): country_name = i.get("Name") location_text = ", ".join([name, admin1, country_name]) if distance_miles > 150: answer = "The International Space Station is {} miles {} off the coast of {}".format( int(distance_miles), ordinal, location_text ) else: answer = "the International Space Station is {} miles {} near {}".format(int(distance_miles), ordinal, location_text) return answer, latitude, longitude, distance_miles, ordinal, name, admin1, country_name @app.route("/") def homepage(): return "" @ask.launch def start_skill(): # welcome_message = 'Welcome to the Fleet Feet Journal! What is your name?' welcome_message_reprompt = render_template("welcome_message_reprompt") welcome_message = render_template("welcome_message") return question(welcome_message).reprompt(welcome_message_reprompt) @ask.intent("YourLocation") def pass_over(my_location): geolocator = Nominatim(user_agent="my-application") print(my_location) location = geolocator.geocode(my_location, language="en-US") try: city = location.address.split(",")[0] state = location.address.split(",")[2] country = location.address.split(",")[-1] location_name = ", ".join([city, state, country]) except IndexError: location_name = location.address.split(",")[-1] fly_over = requests.get( "http://api.open-notify.org/iss-pass.json?lat={}&lon={}".format(location.latitude, location.longitude) ) fly_over = fly_over.json() if fly_over["message"] == "success": rise = fly_over["response"][0] answer = time.strftime("%A, %B %d, %Y at %I:%M %p GMT", time.localtime(rise.get("risetime"))) a = rise.get("risetime") # last epoch recorded b = time.time() # current epoch time c = a - b # returns seconds hours = c // 3600 % 24 minutes = c // 60 % 60 minutes = int(minutes) hours = int(hours) if minutes == 1: minorminutes = "minute" else: minorminutes = "minutes" if hours == 1: hour_or_hours = "hour" else: hour_or_hours = "hours" if hours == 0: time_til_rise = "{} {}".format(minutes, minorminutes) else: time_til_rise = "{} {} and {} {}".format(hours, hour_or_hours, minutes, minorminutes) else: answer = "failure" return statement("the next flyover for {} will begin in {} on {}".format(location_name, time_til_rise, answer)) @ask.intent("WhereISS") def share_location(): iss_location, latitude, longitude, distance_miles, ordinal, name, admin1, country_name = where_is_the_iss_now() latitude, longitude, distance_miles = float(latitude), float(longitude), float(distance_miles) return statement(iss_location).standard_card( title="Location of the International Space Station", text="Latitude {} and Longitude {},\n {} miles {} of {}, {} in {}".format( round(latitude, 2), round(longitude, 2), round(distance_miles, 0), ordinal, name, admin1, country_name ), ) @ask.intent("AMAZON.FallbackIntent") def fallback(): to_continue = render_template("to_continue") return question("Sorry, I am not sure what you asked me...{}".format(to_continue)) @ask.intent("AMAZON.NavigateHomeIntent") def go_home(): return question("et - phone home") @ask.intent("AMAZON.HelpIntent") def help_me(): help_me_text = render_template("help") return question(help_me_text) @ask.intent("Credits") def speak_credits(): credits_ = render_template("credits") return statement(credits_) @ask.intent("AMAZON.StopIntent") def stop(): bye_text = render_template("bye") return statement(bye_text) @ask.intent("AMAZON.CancelIntent") def cancel(): bye_text = render_template("bye") return statement(bye_text) @ask.session_ended def session_ended(): return "{}", 200 if __name__ == "__main__": app.run(debug=True)	[ 1097, 18958, 1220, 24033, 49, 2922, 100, 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cae49da8dd436fc51b472c4a88703d8bc6c79bda	import SCons.Util import xml.dom.minidom, re, os.path ################################################################################ # DocBook pseudobuilder # TODO: Only generate the output formats that are known ################################################################################ def generate(env): def remove_doctype(target, source, env): f = open(str(target[0])) output = [] for line in f.readlines(): output.append(re.sub("^<!DOCTYPE .*", "", line)) f.close() f = open(str(target[0]), "wb") for line in output: f.write(line) f.close() def buildDocBook(env, source): db_env = env.Clone() db_env["XMLCATALOGS"] = [db_env["DOCBOOK_XML"]] # PDF generation fo = db_env.XSLT(os.path.splitext(source)[0] + ".fo", source, XSLTSTYLESHEET=db_env["DOCBOOK_XSL_FO"]) pdf = db_env.FO(fo) # HTML generation db_env.XSLT(os.path.splitext(source)[0] + ".html", source, XSLTSTYLESHEET=db_env["DOCBOOK_XSL_HTML"]) # WordPress generation wp_params = 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c139cbc3e693d75ad196e10257ff3028aa835709	# Complete the hurdleRace function below. def hurdleRace(k, height): if k < max(height): return max(height) - k return 0 print(hurdleRace(2, [2, 5, 4, 5, 2]))	[ 40, 22309, 341, 36381, 105, 293, 31548, 686, 4834, 51, 222, 610, 36381, 105, 293, 31548, 45, 112, 49, 2401, 731, 303, 434, 851, 350, 1788, 45, 2296, 731, 310, 461, 1788, 45, 2296, 46, 449, 851, 303, 461, 244, 53, 499, 222, 1243, 45, 44788, 105, 293, 31548, 45, 55, 49, 447, 55, 49, 244, 58, 49, 244, 57, 49, 244, 58, 49, 244, 55, 5310, 222 ]	[ "#", "ĠComplete", "Ġthe", "Ġhur", "d", "le", "Race", "Ġfunction", "Ġbelow", ".", "Ċ", "def", "Ġhur", "d", "le", "Race", "(", "k", ",", "Ġheight", "):", "ĊĠĠĠ", "Ġif", "Ġk", "Ġ<", "Ġmax", "(", "height", "):", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġmax", "(", "height", ")", "Ġ-", "Ġk", "ĊĠĠĠ", "Ġreturn", "Ġ", "0", "ĊĊ", "Ċ", "print", "(", "hur", "d", "le", "Race", "(", "2", ",", "Ġ[", "2", ",", "Ġ", "5", ",", "Ġ", "4", ",", "Ġ", "5", ",", "Ġ", "2", "]))", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8 ]	[ 1, 2, 11, 15, 19, 20, 22, 26, 35, 41, 42, 1, 4, 8, 9, 11, 15, 16, 17, 18, 25, 27, 4, 7, 9, 11, 15, 16, 22, 24, 8, 15, 19, 20, 26, 27, 29, 31, 4, 11, 12, 13, 1, 1, 6, 7, 10, 11, 13, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 38 ]
77971b088a7e076e3bf6d7aa320981a50e7756ce	from flask import Flask from flask_ask import Ask, statement, question, session # import json, requests import random app = Flask(__name__) ask = Ask(app, "/") def get_cat_fact(): myFacts = [ "Cats should not be fed tuna exclusively, as it lacks taurine, an essential nutrient required for good feline health. Make sure you have the proper Pet supplies to keep your cat happy and healthy.", "The strongest climber among the big cats, a leopard can carry prey twice its weight up a tree.", "A cat’s hearing is better than a dog’s. A cat can hear high-frequency sounds up to two octaves higher than a human.", "Tylenol and chocolate are both poisionous to cats.", "Cats have 30 teeth (12 incisors, 10 premolars, 4 canines, and 4 molars), while dogs have 42. Kittens have baby teeth, which are replaced by permanent teeth around the age of 7 months.", "It has been scientifically proven that owning cats is good for our health and can decrease the occurrence of high blood pressure and other illnesses.", "A cat can’t climb head first down a tree because every claw on a cat’s paw points the same way. To get down from a tree, a cat must back down.", "Cats are subject to gum disease and to dental caries. They should have their teeth cleaned by the vet or the cat dentist once a year.", "A domestic cat can run at speeds of 30 mph.", "Cat families usually play best in even numbers. Cats and kittens should be aquired in pairs whenever possible.", "A cat’s back is extremely flexible because it has up to 53 loosely fitting vertebrae. Humans only have 34.", "The claws on the cat’s back paws aren’t as sharp as the claws on the front paws because the claws in the back don’t retract and, consequently, become worn.", "Cat paws act as tempetature regulators, shock absorbers, hunting and grooming tools, sensors, and more", "Cats see six times better in the dark and at night than humans.", "The cat's tail is used to maintain balance.", "Cats have 300 million neurons; dogs have about 160 million", "Both humans and cats have identical regions in the brain responsible for emotion.", "The lightest cat on record is a blue point Himalayan called Tinker Toy, who weighed 1 pound, 6 ounces (616 g). Tinker Toy was 2.75 inches (7 cm) tall and 7.5 inches (19 cm) long.", "An adult lion's roar can be heard up to five miles (eight kilometers) away.", "You check your cats pulse on the inside of the back thigh, where the leg joins to the body. Normal for cats: 110-170 beats per minute.", "The largest cat breed is the Ragdoll. Male Ragdolls weigh between 12 and 20 lbs (5.4-9.0 k). Females weigh between 10 and 15 lbs (4.5-6.8 k).", "A cat's normal temperature varies around 101 degrees Fahrenheit.", "Unlike other cats, lions have a tuft of hair at the end of their tails.", "Cats don’t have sweat glands over their bodies like humans do. Instead, they sweat only through their paws.", "The average cat food meal is the equivalent to about five mice.", "The first official cat show in the UK was organised at Crystal Palace in 1871.", "In just seven years, a single pair of cats and their offspring could produce a staggering total of 420,000 kittens.", ] fact = myFacts[random.ranint(0, len(myFacts) - 1)] return fact @ask.launch def start_skill(): welcome_message = "Hello there, would you like to hear a cat fact?" return question(welcome_message) @ask.intent("YesIntent") def share_headlines(): fact = get_cat_fact() cat_fact = "Did you know, " + fact return statement(cat_fact) @ask.intent("NoIntent") def no_intent(): bye_text = "Ok! 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124d7da330aa7c869320e10f4f89cc1c872f85f2	import matplotlib.pyplot as plt import sys sys.path.append("coin_flipping_src") from monte_carlo import monte_carlo from probability import probability plt.style.use("bmh") x_coords = range(10) probablility_results = [probability(x, 10) for x in x_coords] plt.plot(x_coords, probablility_results, linewidth=2.5) # plt.plot([0,1,2,3,4],[0.1, 0.3, 0.5, 0.1, 0.1],linewidth=2.5) for _ in range(5): plt.plot(x_coords, [monte_carlo(x, 10, 100) for x in x_coords], linewidth=0.75) # plt.plot([0,1,2,3,4],[0.3, 0.1, 0.4, 0.2, 0.1],linewidth=0.75) # plt.plot([0,1,2,3,4],[0.2, 0.2, 0.3, 0.3, 0.2],linewidth=0.75) plt.legend(["True", "MC 1", "MC 2", "MC 3", "MC 4", "MC 5"]) plt.xlabel("Number of Heads") plt.ylabel("Probability") plt.title("True Distribution vs Monte Carlo Simulations for 10 Coin Flips") plt.savefig("plot.png") plt.show()	[ 485, 13168, 51, 18553, 641, 6020, 222, 485, 5263, 222, 222, 3750, 51, 1005, 51, 1713, 459, 8546, 100, 107, 393, 11787, 100, 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502da0f0dafe42d3464fabb1d92ae1b0d7ef11f3	# Check given matrix is valid sudoku or Not.	[ 40, 3471, 2716, 5083, 458, 2101, 328, 33269, 575, 3204, 51, 222 ]	[ "#", "ĠCheck", "Ġgiven", "Ġmatrix", "Ġis", "Ġvalid", "Ġs", "udoku", "Ġor", "ĠNot", ".", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]	[ 1, 2, 8, 14, 21, 24, 30, 32, 37, 40, 44, 45 ]
4e31c2a80bec77a1f5aafc8a91617fb4b2941788	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Author: André Pacheco E-mail: [email protected] This file implements the methods and functions to load the image as a PyTorch dataset If you find any bug or have some suggestion, please, email me. """ from PIL import Image from torch.utils import data import torchvision.transforms as transforms class BuildDataset(data.Dataset): """ This the standard way to implement a dataset pipeline in PyTorch. We need to extend the torch.utils.data.Dataset class and implement the following methods: __len__, __getitem__ and the constructor __init__ """ def __init__(self, imgs_path, labels, extra_info=None, transform=None): """ The constructor gets the images path and their respectively labels and extra information (if it exists). In addition, you can specify some transform operation to be carry out on the images. It's important to note the images must match with the labels (an extra information if exist). For example, the imgs_path[x]'s label must take place on labels[x]. Parameters: :param imgs_path (list): a list of string containing the image paths :param labels (list) a list of labels for each image :param extra_info (list): a list of extra information regarding each image. If None, there is no information. Defaul is None. :param transform (torchvision.transforms.transforms.Compose): transform operations to be carry out on the images """ self.imgs_path = imgs_path self.labels = labels self.extra_info = extra_info # if transform is None, we need to ensure that the PIL image will be transformed to tensor, otherwise we'll got # an exception if transform is not None: self.transform = transform else: self.transform = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()]) def __len__(self): """This method just returns the dataset size""" return len(self.imgs_path) def __getitem__(self, item): """ It gets the image, labels and extra information (if it exists) according to the index informed in `item`. It also performs the transform on the image. :param item (int): an index in the interval [0, ..., len(img_paths)-1] :return (tuple): a tuple containing the image, its label and extra information (if it exists) """ image = Image.open(self.imgs_path[item]).convert("RGB") # Applying the transformations image = self.transform(image) img_name = self.imgs_path[item].split("/")[-1].split(".")[0] # print(self.labels[item]) # print(self.extra_info[item]) if self.extra_info is None: extra_info = [] else: extra_info = self.extra_info[item] if self.labels is None: labels = [] else: labels = self.labels[item] return image, labels, extra_info, img_name def get_data_loader(imgs_path, labels, extra_info=None, transform=None, params=None): """ This function gets a list og images path, their labels and extra information (if it exists) and returns a DataLoader for these files. You also can set some transformations using torchvision.transforms in order to perform data augmentation. Lastly, params is a dictionary that you can set the following parameters: batch_size (int): the batch size for the dataset. If it's not informed the default is 30 shuf (bool): set it true if wanna shuffe the dataset. If it's not informed the default is True num_workers (int): the number thread in CPU to load the dataset. If it's not informed the default is 0 (which :param imgs_path (list): a list of string containing the images path :param labels (list): a list of labels for each image :param extra_info (list, optional): a list of extra information regarding each image. If it's None, it means there's no extra information. Default is None :param transform (torchvision.transforms, optional): use the torchvision.transforms.compose to perform the data augmentation for the dataset. Alternatively, you can use the jedy.pytorch.utils.augmentation to perform the augmentation. If it's None, none augmentation will be perform. Default is None :param params (dictionary, optional): this dictionary contains the following parameters: batch_size: the batch size. If the key is not informed or params = None, the default value will be 30 shuf: if you'd like to shuffle the dataset. If the key is not informed or params = None, the default value will be True num_workers: the number of threads to be used in CPU. If the key is not informed or params = None, the default value will be 4 pin_memory = set it to True to Pytorch preload the images on GPU. If the key is not informed or params = None, the default value will be True :return (torch.utils.data.DataLoader): a dataloader with the dataset and the chose params """ dt = BuildDataset(imgs_path, labels, extra_info, transform) # Checking the params values. If it's not defined in params of if params is None, the default values are described # below: batch_size = 30 shuf = True num_workers = 4 pin_memory = True # However, if the params is defined, we used the values described on it: if params is not None: if "batch_size" in params.keys(): batch_size = params["batch_size"] if "shuf" in params.keys(): shuf = params["shuf"] if "num_workers" in params.keys(): num_workers = params["num_workers"] if "pin_memory" in params.keys(): pin_memory = params["pin_memory"] # Calling the dataloader dl = data.DataLoader(dataset=dt, batch_size=batch_size, shuffle=shuf, num_workers=num_workers, pin_memory=pin_memory) return dl	[ 8758, 3392, 52, 1793, 52, 2141, 4406, 56, 222, 40, 8571, 10633, 63, 6471, 50, 61, 8571, 222, 222, 3012, 222, 4372, 63, 4262, 42773, 466, 914, 352, 222, 74, 50, 2215, 63, 317, 914, 352, 51, 998, 69, 7265, 51, 527, 222, 222, 2287, 822, 4584, 341, 4457, 480, 4465, 391, 2480, 341, 1802, 641, 331, 40790, 5984, 222, 222, 2811, 863, 2309, 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75133dd924f8f3f028075c5d2109bb79ddc7fe87	import pymysql def testeSelect(db): # 创建查询游标 cur1 = db.cursor() # 使用 execute() 方法执行 SQL 查询 cur1.execute("SELECT VERSION()") # 使用 fetchone() 方法获取单条数据. data = cur1.fetchone() print(dir(data)) print("cur1 : %s " % cur1) print("Database version : %s " % data) def dropTable(db): # 创建查询游标 cur1 = db.cursor() cur1.execute("drop table if exists python_demo") print("dropTable", cur1) def createTable(db): # 创建查询游标 cur1 = db.cursor() sql = """ CREATE TABLE IF NOT EXISTS python_demo ( MEMBER_ID INT(20) NOT NULL AUTO_INCREMENT COMMENT '会员ID', MEMBER_CODE VARCHAR(20) NOT NULL COMMENT '会员代码', MEMBER_NAME VARCHAR(128) NOT NULL COMMENT '公司中文名称', MEMBER_NAME_SHORT VARCHAR(128) NULL DEFAULT NULL COMMENT '公司简称', COMPANY_NAME_EN VARCHAR(128) NULL DEFAULT NULL COMMENT '公司英文名称', REG_PLACE VARCHAR(20) NULL DEFAULT NULL COMMENT '公司注册地址', REG_ADDRESS VARCHAR(128) NULL DEFAULT NULL COMMENT '公司注册地址', ENT_TYPE VARCHAR(20) NULL DEFAULT NULL COMMENT '公司性质', REGCAPITAL DOUBLE(12,2) NULL DEFAULT NULL COMMENT '注册资本', REG_CURRENCY VARCHAR(20) NULL DEFAULT NULL COMMENT '注册资本币种', JUDICIAL_PERSON VARCHAR(32) NULL DEFAULT NULL COMMENT '法人名称', BUSINESS_SCOPE VARCHAR(128) NULL DEFAULT NULL COMMENT '公司经营范围', COM_TEL VARCHAR(20) NULL DEFAULT NULL COMMENT '公司电话', COM_FAX VARCHAR(64) NULL DEFAULT NULL COMMENT '公司传真', PERSON_INCHARGE VARCHAR(64) NULL DEFAULT NULL COMMENT '公司负责人', ZIP_CODE VARCHAR(6) NULL DEFAULT NULL COMMENT '邮编', CON_NAME VARCHAR(32) NULL DEFAULT NULL COMMENT '联系人姓名', CON_MOBILE VARCHAR(20) NULL DEFAULT NULL COMMENT '联系人手机', CON_EMAIL VARCHAR(32) NULL DEFAULT NULL COMMENT '联系人邮箱', CON_FAX VARCHAR(32) NULL DEFAULT NULL COMMENT '联系人传真', CON_CERT_TYPE VARCHAR(20) NULL DEFAULT NULL COMMENT '联系人证件类型', CON_CERT_NO VARCHAR(20) NULL DEFAULT NULL COMMENT '联系人证件号', CON_CERT_DATE DATE NULL DEFAULT NULL COMMENT '联系人证件失效时间', CON_CER1_ID INT(20) NULL DEFAULT NULL COMMENT '联系人身份证正面ID', CON_CER2_ID INT(20) NULL DEFAULT NULL COMMENT '联系人身份证反面ID', THERECER_INTGRATED VARCHAR(20) NULL DEFAULT NULL COMMENT '三证合一标志', BIZLIC_ID INT(20) NULL DEFAULT NULL COMMENT '营业执照ID', BIZLIC_NO VARCHAR(20) NULL DEFAULT NULL COMMENT '营业执照代码', BIZLIC_DATE DATE NULL DEFAULT NULL COMMENT '营业执照失效时间', TAXREGISTER_ID INT(20) NULL DEFAULT NULL COMMENT '税务等级证书ID', TAXREGISTER_NO VARCHAR(20) NULL DEFAULT NULL COMMENT '税务登记号', TAXREGISTER_DATE DATE NULL DEFAULT NULL COMMENT '税务登记失效时间', ORGREG_ID INT(20) NULL DEFAULT NULL COMMENT '组织机构代码证ID', ORGREG_NO VARCHAR(20) NULL DEFAULT NULL COMMENT '组织机构代码', ORGREG_DATE DATE NULL DEFAULT NULL COMMENT '组织机构失效时间', BANK_ID INT(20) NULL DEFAULT NULL COMMENT '银行开户许可证ID', BANK_TYPE VARCHAR(20) NULL DEFAULT NULL COMMENT '开户银行类别', BANK_DEPOSIT VARCHAR(128) NULL DEFAULT NULL COMMENT '开户银行', BANK_DEPOSIT_CODE VARCHAR(32) NULL DEFAULT NULL COMMENT '开户银行编码', BANK_ACCOUNTNO VARCHAR(32) NULL DEFAULT NULL COMMENT '银行账号', BANK_HOUSEHOULD VARCHAR(32) NULL DEFAULT NULL COMMENT '银行户主', INVOICE_TITLE VARCHAR(128) NULL DEFAULT NULL COMMENT '开票台头', INVOICE_ADDRESS VARCHAR(20) NULL DEFAULT NULL COMMENT '开票地址', INVOICE_ADDRESS_DT VARCHAR(128) NULL DEFAULT NULL COMMENT '开票详细地址', APPLY_SELLER_ID INT(20) NULL DEFAULT NULL COMMENT '申请审核机构', BUYER_FLAG VARCHAR(20) NULL DEFAULT NULL COMMENT '买家标识', SELLER_FLAG VARCHAR(20) NULL DEFAULT NULL COMMENT '卖家标识', THIRD_PART_FLAG VARCHAR(20) NULL DEFAULT NULL COMMENT '第三方标识', MAIN_USER_ID INT(20) NULL DEFAULT NULL COMMENT '主账号ID', MDM_DATA_CODE VARCHAR(20) NULL DEFAULT NULL COMMENT 'MDM主数据CODE', ERP_DATA_CODE VARCHAR(20) NULL DEFAULT NULL COMMENT 'ERP会员CODE', REG_TYPE VARCHAR(20) NULL DEFAULT NULL COMMENT '注册类型', STATUS VARCHAR(20) NULL DEFAULT NULL COMMENT '会员状态', AUDITOR VARCHAR(128) NULL DEFAULT NULL COMMENT '审核人', AUDIT_DATE DATETIME NULL DEFAULT NULL COMMENT '审核时间', AUDIT_RESULT VARCHAR(20) NULL DEFAULT NULL COMMENT '审核结果', AUDIT_OPINION VARCHAR(128) NULL DEFAULT NULL COMMENT '审核意见', MDM_AUDITOR VARCHAR(32) NULL DEFAULT NULL COMMENT 'MDM审核人', MDM_AUDIT_DATE DATETIME NULL DEFAULT NULL COMMENT 'MDM审核时间', MDM_AUDIT_OPINION VARCHAR(128) NULL DEFAULT NULL COMMENT 'MDM审核意见', MDM_AUDIT_RESULT VARCHAR(20) NULL DEFAULT NULL COMMENT 'MDM审核结果', MEMBER_CHG_ID INT(20) NULL DEFAULT NULL COMMENT '变更ID', CHANGE_STATUS VARCHAR(20) NULL DEFAULT NULL COMMENT '变更状态', ALIVE_FLAG VARCHAR(1) NOT NULL COMMENT '当前有效状态', LANG_VER VARCHAR(10) NULL DEFAULT NULL COMMENT '语言类型', CREATE_USER INT(20) NOT NULL COMMENT '创建者', CREATE_DATE DATETIME NOT NULL COMMENT '创建时间', UPDATE_USER INT(20) NULL DEFAULT NULL COMMENT '修改者', UPDATE_DATE DATETIME NULL DEFAULT NULL COMMENT '修改时间', DELETE_USER INT(20) NULL DEFAULT NULL COMMENT '删除者', DELETE_DATE DATETIME NULL DEFAULT NULL COMMENT '删除时间', BUYER_TYPE VARCHAR(20) NULL DEFAULT NULL COMMENT '买家类型(01：个人买家；02：公司买家)', AUDIT_OPTION_FLAG VARCHAR(20) NULL DEFAULT NULL COMMENT '审核身份标识(01：平台；02：卖家)', AUDIT_SELLER_ID INT(20) NULL DEFAULT NULL COMMENT '审核卖家ID(当审核身份标识为卖家审核时，审核的卖家ID)', SELLER_MDM VARCHAR(20) NULL DEFAULT NULL COMMENT '卖家MDM系统', SELLER_SAP VARCHAR(20) NULL DEFAULT NULL COMMENT '卖家SAP系统', SELLER_MDM_DATA_CODE VARCHAR(20) NULL DEFAULT NULL COMMENT '卖家MDM系统数据CODE', IS_PLAT_BLACK VARCHAR(2) NULL DEFAULT NULL COMMENT '黑名单状态(41：是；0：否)', INVOCIE_ADDRESS_LEFT3 VARCHAR(10) NULL DEFAULT NULL COMMENT '用户所属区域-省', INVOCIE_ADDRESS_RIGHT5 VARCHAR(10) NULL DEFAULT NULL COMMENT '用户所属区域-市', PRIMARY KEY (MEMBER_ID) ) COMMENT='会员信息表' COLLATE='utf8_general_ci' ENGINE=InnoDB """ cur1.execute(sql) print("createTabl", cur1) def selectTable(db): # 创建查询游标 cur1 = db.cursor() cur1.execute("select member_name,MEMBER_CODE,member_id from python_demo limit 10") # 使用 fetchall() 接收全部的返回结果行 data = cur1.fetchall() for index, item in enumerate(data): print(index, sep=" ", end=" ") for index2, item2 in enumerate(item): print(item2, sep=" ", end=" ") print("") def insertTable(db): # 创建查询游标 cur1 = db.cursor() cur1.execute( "INSERT INTO python_demo (MEMBER_CODE, MEMBER_NAME, MEMBER_NAME_SHORT, COMPANY_NAME_EN, REG_PLACE, REG_ADDRESS, ENT_TYPE, REGCAPITAL, REG_CURRENCY, JUDICIAL_PERSON, BUSINESS_SCOPE, COM_TEL, COM_FAX, PERSON_INCHARGE, ZIP_CODE, CON_NAME, CON_MOBILE, CON_EMAIL, CON_FAX, CON_CERT_TYPE, CON_CERT_NO, CON_CERT_DATE, CON_CER1_ID, CON_CER2_ID, THERECER_INTGRATED, BIZLIC_ID, BIZLIC_NO, BIZLIC_DATE, TAXREGISTER_ID, TAXREGISTER_NO, TAXREGISTER_DATE, ORGREG_ID, ORGREG_NO, ORGREG_DATE, BANK_ID, BANK_TYPE, BANK_DEPOSIT, BANK_DEPOSIT_CODE, BANK_ACCOUNTNO, BANK_HOUSEHOULD, INVOICE_TITLE, INVOICE_ADDRESS, INVOICE_ADDRESS_DT, APPLY_SELLER_ID, BUYER_FLAG, SELLER_FLAG, THIRD_PART_FLAG, MAIN_USER_ID, MDM_DATA_CODE, ERP_DATA_CODE, REG_TYPE, STATUS, AUDITOR, AUDIT_DATE, AUDIT_RESULT, AUDIT_OPINION, MDM_AUDITOR, MDM_AUDIT_DATE, MDM_AUDIT_OPINION, MDM_AUDIT_RESULT, MEMBER_CHG_ID, CHANGE_STATUS, ALIVE_FLAG, LANG_VER, CREATE_USER, CREATE_DATE, UPDATE_USER, UPDATE_DATE, DELETE_USER, DELETE_DATE, BUYER_TYPE, AUDIT_OPTION_FLAG, AUDIT_SELLER_ID, SELLER_MDM, SELLER_SAP, SELLER_MDM_DATA_CODE, IS_PLAT_BLACK, INVOCIE_ADDRESS_LEFT3, INVOCIE_ADDRESS_RIGHT5) VALUES ('A000001', '中国有限公司', '中国有限公司', NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, 'xinming', '15136378930', NULL, NULL, NULL, NULL, NULL, NULL, NULL, '0', NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, '0', '1', '0', 2, 'M0000001', '00M0000001', '10', '01', NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, 2, '01', '1', 'ZH-CN', 179143, '2016-05-28 12:16:23', NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL)" ) # 使用 fetchall() 接收全部的返回结果行 # data = cur1.rowcount() # print('insertTable',data) # 打开数据库连接 db = pymysql.connect(host="127.0.0.1", user="pu3147", password="1qaz@WSX", database="demo", port=3306, charset="UTF8") dropTable(db) createTable(db) insertTable(db) insertTable(db) insertTable(db) insertTable(db) insertTable(db) testeSelect(db) selectTable(db) # 关闭数据库连接 db.close()	[ 485, 317, 2225, 5547, 499, 222, 610, 913, 106, 1926, 45, 1219, 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de347b41cd88947690cb42e043880a80d81e2c5c	# Generated by Django 3.2.7 on 2021-09-11 19:38 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("cryptocurrency", "0012_rename_cancel_exists_order_cancel_exist"), ] operations = [ migrations.AlterField( model_name="order", name="created_at", field=models.IntegerField(blank=True, null=True), ), ]	[ 40, 13046, 829, 18288, 244, 56, 51, 55, 51, 60, 563, 244, 55, 53, 55, 54, 50, 53, 62, 50, 54, 54, 244, 54, 62, 63, 56, 61, 222, 222, 1097, 8532, 51, 1219, 1220, 12520, 49, 4590, 499, 222, 842, 18572, 45, 19772, 51, 12071, 731, 303, 6450, 299, 447, 310, 2903, 3712, 561, 4595, 411, 332, 53, 53, 54, 55, 100, 13287, 100, 8158, 100, 4678, 100, 1191, 100, 8158, 100, 12531, 2132, 303, 1499, 465, 7392, 299, 447, 310, 12520, 51, 18835, 1195, 45, 343, 1573, 100, 444, 366, 1191, 411, 343, 655, 366, 4536, 100, 288, 411, 343, 1620, 66, 3404, 51, 30363, 45, 5021, 66, 1844, 49, 762, 66, 1844, 490, 310, 2604, 303, 1499, 222 ]	[ "#", "ĠGenerated", "Ġby", "ĠDjango", "Ġ", "3", ".", "2", ".", "7", "Ġon", "Ġ", "2", "0", "2", "1", "-", "0", "9", "-", "1", "1", "Ġ", "1", "9", ":", "3", "8", "Ċ", "Ċ", "from", "Ġdjango", ".", "db", "Ġimport", "Ġmigrations", ",", "Ġmodels", "ĊĊ", "Ċ", "class", "ĠMigration", "(", "migrations", ".", "Migration", "):", "ĊĠĠĠ", "Ġdependencies", "Ġ=", "Ġ[", "ĊĠĠĠĠĠĠĠ", "Ġ(\"", "crypt", "oc", "urrency", "\",", "Ġ\"", "0", "0", "1", "2", "_", "rename", "_", "cancel", "_", "exists", "_", "order", "_", "cancel", "_", "exist", "\"),", "ĊĠĠĠ", "Ġ]", "ĊĊĠĠĠ", "Ġoperations", "Ġ=", "Ġ[", "ĊĠĠĠĠĠĠĠ", "Ġmigrations", ".", "Alter", "Field", "(", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġmodel", "_", "name", "=\"", "order", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġname", "=\"", "created", "_", "at", "\",", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfield", "=", "models", ".", "IntegerField", "(", "blank", "=", "True", ",", "Ġnull", "=", "True", "),", "ĊĠĠĠĠĠĠĠ", "Ġ),", "ĊĠĠĠ", "Ġ]", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 17, 17 ]	[ 1, 2, 12, 15, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 1, 1, 5, 12, 13, 15, 22, 33, 34, 41, 1, 1, 6, 16, 17, 27, 28, 37, 39, 4, 17, 19, 21, 8, 11, 16, 18, 25, 27, 29, 30, 31, 32, 33, 34, 40, 41, 47, 48, 54, 55, 60, 61, 67, 68, 73, 76, 4, 6, 4, 15, 17, 19, 8, 19, 20, 25, 30, 31, 12, 18, 19, 23, 25, 30, 32, 12, 17, 19, 26, 27, 29, 31, 12, 18, 19, 25, 26, 38, 39, 44, 45, 49, 50, 55, 56, 60, 62, 8, 11, 4, 6 ]
8e85740123467889bdeb6b27d5eaa4b39df280ed	from .celery import app from home.models import Banner from settings.const import BANNER_COUNT from home.serializers import BannerModelSerializer from django.core.cache import cache from django.conf import settings @app.task def update_banner_list(): # 获取最新内容 banner_query = Banner.objects.filter(is_delete=False, is_show=True).order_by("-orders")[:BANNER_COUNT] # 序列化 banner_data = BannerModelSerializer(banner_query, many=True).data for banner in banner_data: banner["image"] = settings.END_BASE_URL + banner["image"] # 更新缓存 cache.set("banner_list", banner_data) return True	[ 1097, 657, 3331, 852, 1220, 1142, 222, 222, 1097, 6780, 51, 3404, 1220, 46512, 222, 1097, 4086, 51, 931, 1220, 570, 946, 13726, 100, 7516, 222, 1097, 6780, 51, 34191, 1220, 46512, 1262, 7271, 222, 1097, 8532, 51, 1284, 51, 2630, 1220, 3337, 222, 1097, 8532, 51, 2982, 1220, 4086, 499, 222, 69, 745, 51, 2810, 222, 610, 1991, 100, 12012, 100, 687, 2284, 303, 607, 13614, 41582, 12339, 303, 26558, 100, 1234, 299, 46512, 51, 5754, 51, 2049, 45, 316, 100, 2577, 66, 2737, 49, 458, 100, 2276, 66, 1844, 566, 1191, 100, 1186, 10364, 10424, 678, 2934, 38934, 13726, 100, 7516, 98, 303, 607, 244, 44884, 303, 26558, 100, 624, 299, 46512, 1262, 7271, 45, 12012, 100, 1234, 49, 5110, 66, 1844, 566, 624, 303, 456, 26558, 347, 26558, 100, 624, 63, 310, 26558, 1388, 915, 1465, 299, 4086, 51, 3298, 100, 6185, 100, 2427, 494, 26558, 1388, 915, 1465, 303, 607, 37605, 20139, 303, 3337, 51, 489, 459, 12012, 100, 687, 411, 26558, 100, 624, 46, 303, 461, 2969, 222 ]	[ "from", "Ġ.", "cel", "ery", "Ġimport", "Ġapp", "Ċ", "Ċ", "from", "Ġhome", ".", "models", "Ġimport", "ĠBanner", "Ċ", "from", "Ġsettings", ".", "const", "Ġimport", "ĠB", "AN", "NER", "_", "COUNT", "Ċ", "from", "Ġhome", ".", "serializers", "Ġimport", "ĠBanner", "Model", "Serializer", "Ċ", "from", "Ġdjango", ".", "core", ".", "cache", "Ġimport", "Ġcache", "Ċ", "from", "Ġdjango", ".", "conf", "Ġimport", "Ġsettings", "ĊĊ", "Ċ", "@", "app", ".", "task", "Ċ", "def", "Ġupdate", "_", "banner", "_", "list", "():", "ĊĠĠĠ", "Ġ#", "Ġèİ·åıĸ", "æľĢæĸ°", "åĨħå®¹", "ĊĠĠĠ", "Ġbanner", "_", "query", "Ġ=", "ĠBanner", ".", "objects", ".", "filter", "(", "is", "_", "delete", "=", "False", ",", "Ġis", "_", "show", "=", "True", ").", "order", "_", "by", "(\"-", "orders", "\")", "[:", "BAN", "NER", "_", "COUNT", "]", "ĊĠĠĠ", "Ġ#", "Ġ", "åºıåĪĹåĮĸ", "ĊĠĠĠ", "Ġbanner", "_", "data", "Ġ=", "ĠBanner", "Model", "Serializer", "(", "banner", "_", "query", ",", "Ġmany", "=", "True", ").", "data", "ĊĠĠĠ", "Ġfor", "Ġbanner", "Ġin", "Ġbanner", "_", "data", ":", "ĊĠĠĠĠĠĠĠ", "Ġbanner", "[\"", "image", "\"]", "Ġ=", "Ġsettings", ".", "END", "_", "BASE", "_", "URL", "Ġ+", "Ġbanner", "[\"", "image", "\"]", "ĊĠĠĠ", "Ġ#", "ĠæĽ´æĸ°", "ç¼ĵåŃĺ", "ĊĠĠĠ", "Ġcache", ".", "set", "(\"", "banner", "_", "list", "\",", "Ġbanner", "_", "data", ")", "ĊĠĠĠ", "Ġreturn", "ĠTrue", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 9, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20 ]	[ 1, 5, 7, 10, 13, 20, 24, 1, 1, 5, 10, 11, 17, 24, 31, 1, 5, 14, 15, 20, 27, 29, 31, 34, 35, 40, 1, 5, 10, 11, 22, 29, 36, 41, 51, 1, 5, 12, 13, 17, 18, 23, 30, 36, 1, 5, 12, 13, 17, 24, 33, 1, 1, 2, 5, 6, 10, 1, 4, 11, 12, 18, 19, 23, 26, 4, 6, 9, 11, 13, 4, 11, 12, 17, 19, 26, 27, 34, 35, 41, 42, 44, 45, 51, 52, 57, 58, 61, 62, 66, 67, 71, 73, 78, 79, 81, 84, 90, 92, 94, 97, 100, 101, 106, 107, 4, 6, 7, 10, 4, 11, 12, 16, 18, 25, 30, 40, 41, 47, 48, 53, 54, 59, 60, 64, 66, 70, 4, 8, 15, 18, 25, 26, 30, 31, 8, 15, 17, 22, 24, 26, 35, 36, 39, 40, 44, 45, 48, 50, 57, 59, 64, 66, 4, 6, 9, 11, 4, 10, 11, 14, 16, 22, 23, 27, 29, 36, 37, 41, 42, 4, 11, 16 ]
a2421a8673a524c32539555596711a71a8e00dbf	import os import argparse import torch import model.model as module_arch from utils.util import remove_weight_norms from train import get_instance from librosa import load from librosa.output import write_wav from time import time def main(config, resume, infile, outfile, sigma, dur, half): # build model architecture model = get_instance(module_arch, "arch", config) model.summary() # load state dict checkpoint = torch.load(resume) state_dict = checkpoint["state_dict"] if config["n_gpu"] > 1: model = torch.nn.DataParallel(model) model.load_state_dict(state_dict) if config["n_gpu"] > 1: model = model.module model.apply(remove_weight_norms) # prepare model for testing device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = model.to(device) model.eval() sr = config["arch"]["args"]["sr"] y, _ = load(infile, sr=sr, duration=dur) y = torch.Tensor(y).to(device) # get mel before turn to half, because sparse.half is not implement yet mel = model.get_mel(y[None, :]) if half: model = model.half() mel = mel.half() start = time() x = model.infer(mel, sigma) cost = time() - start print("Time cost: {:.4f}, Speed: {:.4f} kHz".format(cost, x.numel() / cost / 1000)) # print(x.max(), x.min()) write_wav(outfile, x.cpu().float().numpy(), sr, False) if __name__ == "__main__": parser = argparse.ArgumentParser(description="WaveGlow inference") parser.add_argument("infile", type=str, help="wave file to generate mel-spectrogram") parser.add_argument("outfile", type=str, help="output file name") parser.add_argument("--duration", type=float, help="duration of audio, in seconds") parser.add_argument("--half", action="store_true") parser.add_argument("-s", "--sigma", type=float, default=1.0) parser.add_argument("-r", "--resume", default=None, type=str, help="path to latest checkpoint (default: None)") parser.add_argument("-d", "--device", default=None, type=str, help="indices of GPUs to enable (default: all)") args = 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6154979cd2853dd2bd26d1ae5df7365efa0141c2	from sqlalchemy import Column, MetaData, Table, BigInteger, String, DateTime, Integer from migrate import * meta = MetaData() table = Table( "accesses", meta, Column("id", BigInteger, primary_key=True, nullable=False), Column("uuid", String(255), nullable=False), Column("created_at", DateTime), ) def upgrade(migrate_engine): meta.bind = migrate_engine table.create() def downgrade(migrate_engine): meta.bind = migrate_engine table.drop()	[ 1097, 30936, 1220, 7747, 49, 11990, 768, 49, 5565, 49, 21721, 49, 910, 49, 6674, 49, 4418, 222, 1097, 22631, 1220, 338, 222, 222, 2874, 299, 11990, 768, 365, 222, 1292, 299, 5565, 45, 303, 332, 348, 8278, 411, 303, 6096, 49, 303, 7747, 459, 333, 411, 21721, 49, 7470, 100, 697, 66, 1844, 49, 12610, 66, 2737, 490, 303, 7747, 459, 6615, 411, 910, 45, 55, 58, 58, 490, 12610, 66, 2737, 490, 303, 7747, 459, 4536, 100, 288, 411, 6674, 490, 222, 46, 499, 222, 610, 10751, 45, 20101, 100, 3502, 731, 303, 6096, 51, 2555, 299, 22631, 100, 3502, 303, 1877, 51, 1315, 365, 499, 222, 610, 2850, 4966, 45, 20101, 100, 3502, 731, 303, 6096, 51, 2555, 299, 22631, 100, 3502, 303, 1877, 51, 3850, 365, 222 ]	[ "from", "Ġsqlalchemy", "Ġimport", "ĠColumn", ",", "ĠMeta", "Data", ",", "ĠTable", ",", "ĠBigInteger", ",", "ĠString", ",", "ĠDateTime", ",", "ĠInteger", "Ċ", "from", "Ġmigrate", "Ġimport", "Ġ*", "Ċ", "Ċ", "meta", "Ġ=", "ĠMeta", "Data", "()", "Ċ", "table", "Ġ=", "ĠTable", "(", "ĊĠĠĠ", "Ġ\"", "ac", "cesses", "\",", "ĊĠĠĠ", "Ġmeta", ",", "ĊĠĠĠ", "ĠColumn", "(\"", "id", "\",", "ĠBigInteger", ",", "Ġprimary", "_", "key", "=", "True", ",", "Ġnullable", "=", "False", "),", "ĊĠĠĠ", "ĠColumn", "(\"", "uuid", "\",", "ĠString", "(", "2", "5", "5", "),", "Ġnullable", "=", "False", "),", "ĊĠĠĠ", "ĠColumn", "(\"", "created", "_", "at", "\",", "ĠDateTime", "),", "Ċ", ")", "ĊĊ", "Ċ", "def", "Ġupgrade", "(", "migrate", "_", "engine", "):", "ĊĠĠĠ", "Ġmeta", ".", "bind", "Ġ=", "Ġmigrate", "_", "engine", "ĊĠĠĠ", "Ġtable", ".", "create", "()", "ĊĊ", "Ċ", "def", "Ġdown", "grade", "(", "migrate", "_", "engine", "):", "ĊĠĠĠ", "Ġmeta", ".", "bind", "Ġ=", "Ġmigrate", "_", "engine", "ĊĠĠĠ", "Ġtable", ".", "drop", "()", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 13, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21 ]	[ 1, 5, 16, 23, 30, 31, 36, 40, 41, 47, 48, 59, 60, 67, 68, 77, 78, 86, 1, 5, 13, 20, 22, 1, 1, 5, 7, 12, 16, 18, 1, 6, 8, 14, 15, 4, 6, 8, 14, 16, 4, 9, 10, 4, 11, 13, 15, 17, 28, 29, 37, 38, 41, 42, 46, 47, 56, 57, 62, 64, 4, 11, 13, 17, 19, 26, 27, 28, 29, 30, 32, 41, 42, 47, 49, 4, 11, 13, 20, 21, 23, 25, 34, 36, 1, 2, 1, 1, 4, 12, 13, 20, 21, 27, 29, 4, 9, 10, 14, 16, 24, 25, 31, 4, 10, 11, 17, 19, 1, 1, 4, 9, 14, 15, 22, 23, 29, 31, 4, 9, 10, 14, 16, 24, 25, 31, 4, 10, 11, 15, 17 ]
813354c9c294c0323c1b54cda7074fbffa49cdb3	from django.utils import timezone from factory import DjangoModelFactory from djtriggers.tests.models import DummyTrigger class DummyTriggerFactory(DjangoModelFactory): class Meta: model = DummyTrigger trigger_type = "dummy_trigger_test" source = "tests" date_received = timezone.now() date_processed = None process_after = None number_of_tries = 0	[ 1097, 8532, 51, 2324, 1220, 22145, 222, 1097, 7769, 1220, 18288, 1262, 2232, 222, 222, 1097, 362, 23834, 13464, 51, 4895, 51, 3404, 1220, 25222, 7903, 499, 222, 842, 25222, 7903, 2232, 45, 36075, 1262, 2232, 731, 303, 462, 11990, 63, 310, 1573, 299, 25222, 7903, 465, 6604, 100, 700, 299, 332, 11230, 100, 7513, 100, 881, 39, 303, 1932, 299, 332, 4895, 39, 303, 2361, 100, 15642, 299, 22145, 51, 2192, 365, 303, 2361, 100, 15299, 299, 1686, 303, 2183, 100, 5497, 299, 1686, 303, 1470, 100, 1048, 100, 2774, 326, 299, 244, 53, 222 ]	[ "from", "Ġdjango", ".", "utils", "Ġimport", "Ġtimezone", "Ċ", "from", "Ġfactory", "Ġimport", "ĠDjango", "Model", "Factory", "Ċ", "Ċ", "from", "Ġd", "jt", "riggers", ".", "tests", ".", "models", "Ġimport", "ĠDummy", "Trigger", "ĊĊ", "Ċ", "class", "ĠDummy", "Trigger", "Factory", "(", "Django", "Model", "Factory", "):", "ĊĠĠĠ", "Ġclass", "ĠMeta", ":", "ĊĠĠĠĠĠĠĠ", "Ġmodel", "Ġ=", "ĠDummy", "Trigger", "ĊĊĠĠĠ", "Ġtrigger", "_", "type", "Ġ=", "Ġ\"", "dummy", "_", "trigger", "_", "test", "\"", "ĊĠĠĠ", "Ġsource", "Ġ=", "Ġ\"", "tests", "\"", "ĊĠĠĠ", "Ġdate", "_", "received", "Ġ=", "Ġtimezone", ".", "now", "()", "ĊĠĠĠ", "Ġdate", "_", "processed", "Ġ=", "ĠNone", "ĊĠĠĠ", "Ġprocess", "_", "after", "Ġ=", "ĠNone", "ĊĠĠĠ", "Ġnumber", "_", "of", "_", "tri", "es", "Ġ=", "Ġ", "0", "Ċ" ]	[ 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9, 9, 9, 9, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16 ]	[ 1, 5, 12, 13, 18, 25, 34, 1, 5, 13, 20, 27, 32, 39, 1, 1, 5, 7, 9, 16, 17, 22, 23, 29, 36, 42, 49, 1, 1, 6, 12, 19, 26, 27, 33, 38, 45, 47, 4, 10, 15, 16, 8, 14, 16, 22, 29, 4, 12, 13, 17, 19, 21, 26, 27, 34, 35, 39, 40, 4, 11, 13, 15, 20, 21, 4, 9, 10, 18, 20, 29, 30, 33, 35, 4, 9, 10, 19, 21, 26, 4, 12, 13, 18, 20, 25, 4, 11, 12, 14, 15, 18, 20, 22, 23, 24 ]
aebc918d6a1d1d2473f74d77b8a915ac25548e3a	import cachetools cache = cachetools.LRUCache(maxsize=3) cache["PyCon"] = "India" cache["year"] = "2017" print("Older: " + cache["year"]) cache["year"] = "2018" print("Newer: " + cache["year"]) print(cache) cache["sdate"] = "05/09/2018" print(cache) cache["edate"] = "09/09/2018" print(cache)	[ 485, 3337, 4303, 222, 222, 2630, 299, 3337, 4303, 51, 14909, 90, 2610, 45, 1472, 911, 66, 56, 46, 222, 2630, 1388, 5019, 628, 1465, 299, 332, 402, 5600, 39, 222, 2630, 1388, 4614, 1465, 299, 332, 55, 53, 54, 60, 39, 222, 1243, 459, 25606, 512, 63, 332, 494, 3337, 1388, 4614, 6957, 222, 222, 2630, 1388, 4614, 1465, 299, 332, 55, 53, 54, 61, 39, 222, 1243, 459, 1827, 284, 63, 332, 494, 3337, 1388, 4614, 6957, 222, 1243, 45, 2630, 46, 222, 222, 2630, 1388, 120, 787, 1465, 299, 332, 53, 58, 52, 53, 62, 52, 55, 53, 54, 61, 39, 222, 1243, 45, 2630, 46, 222, 222, 2630, 1388, 337, 351, 1465, 299, 332, 53, 62, 52, 53, 62, 52, 55, 53, 54, 61, 39, 222, 1243, 45, 2630, 46, 222 ]	[ "import", "Ġcache", "tools", "Ċ", "Ċ", "cache", "Ġ=", "Ġcache", "tools", ".", "LR", "U", "Cache", "(", "max", "size", "=", "3", ")", "Ċ", "cache", "[\"", "Py", "Con", "\"]", "Ġ=", "Ġ\"", "In", "dia", "\"", "Ċ", "cache", "[\"", "year", "\"]", "Ġ=", "Ġ\"", "2", "0", "1", "7", "\"", "Ċ", "print", "(\"", "Ol", "der", ":", "Ġ\"", "Ġ+", "Ġcache", "[\"", "year", "\"])", "Ċ", "Ċ", "cache", "[\"", "year", "\"]", "Ġ=", "Ġ\"", "2", "0", "1", "8", "\"", "Ċ", "print", "(\"", "New", "er", ":", "Ġ\"", "Ġ+", "Ġcache", "[\"", "year", "\"])", "Ċ", "print", "(", "cache", ")", "Ċ", "Ċ", "cache", "[\"", "s", "date", "\"]", "Ġ=", "Ġ\"", "0", "5", "/", "0", "9", "/", "2", "0", "1", "8", "\"", "Ċ", "print", "(", "cache", ")", "Ċ", "Ċ", "cache", "[\"", "ed", "ate", "\"]", "Ġ=", "Ġ\"", "0", "9", "/", "0", "9", "/", "2", "0", "1", "8", "\"", "Ċ", "print", "(", "cache", ")", "Ċ" ]	[ 1, 1, 1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16 ]	[ 1, 7, 13, 18, 1, 1, 6, 8, 14, 19, 20, 22, 23, 28, 29, 32, 36, 37, 38, 39, 1, 6, 8, 10, 13, 15, 17, 19, 21, 24, 25, 1, 6, 8, 12, 14, 16, 18, 19, 20, 21, 22, 23, 1, 6, 8, 10, 13, 14, 16, 18, 24, 26, 30, 33, 1, 1, 6, 8, 12, 14, 16, 18, 19, 20, 21, 22, 23, 1, 6, 8, 11, 13, 14, 16, 18, 24, 26, 30, 33, 1, 6, 7, 12, 13, 1, 1, 6, 8, 9, 13, 15, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 1, 6, 7, 12, 13, 1, 1, 6, 8, 10, 13, 15, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 1, 6, 7, 12, 13 ]
cd234911c1f990b8029dfa792d132847bf39a6aa	import math def vol_shell(r1, r2): a = abs((4 / 3) * math.pi * ((r13) - (r23))) return round(a, 3) print(vol_shell(3, 3))	[ 485, 7820, 499, 222, 610, 3937, 100, 7890, 45, 119, 54, 49, 559, 55, 731, 303, 331, 299, 11397, 1181, 57, 536, 244, 56, 46, 338, 7820, 51, 1314, 338, 1849, 119, 54, 345, 56, 46, 449, 327, 119, 55, 345, 56, 2636, 303, 461, 7880, 45, 102, 49, 244, 56, 46, 499, 222, 1243, 45, 3088, 100, 7890, 45, 56, 49, 244, 56, 509, 222 ]	[ "import", "Ġmath", "ĊĊ", "Ċ", "def", "Ġvol", "_", "shell", "(", "r", "1", ",", "Ġr", "2", "):", "ĊĠĠĠ", "Ġa", "Ġ=", "Ġabs", "((", "4", "Ġ/", "Ġ", "3", ")", "Ġ", "Ġmath", ".", "pi", "Ġ", "Ġ((", "r", "1", "", "3", ")", "Ġ-", "Ġ(", "r", "2", "", "3", ")))", "ĊĠĠĠ", "Ġreturn", "Ġround", "(", "a", ",", "Ġ", "3", ")", "ĊĊ", "Ċ", "print", "(", "vol", "_", "shell", "(", "3", ",", "Ġ", "3", "))", "Ċ" ]	[ 1, 1, 1, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9 ]	[ 1, 7, 12, 1, 1, 4, 8, 9, 14, 15, 16, 17, 18, 20, 21, 23, 4, 6, 8, 12, 14, 15, 17, 18, 19, 20, 22, 27, 28, 30, 32, 35, 36, 37, 39, 40, 41, 43, 45, 46, 47, 49, 50, 53, 4, 11, 17, 18, 19, 20, 21, 22, 23, 1, 1, 6, 7, 10, 11, 16, 17, 18, 19, 20, 21, 23 ]
7b5713c9a5afa911df1c2939751de30412162f15	from collections import OrderedDict import copy import numpy as np from scipy.optimize import curve_fit from ... import Operation as opmod from ...Operation import Operation from ....tools import saxstools class SpectrumFit(Operation): """ Use a measured SAXS spectrum (I(q) vs. q), to optimize the parameters of a theoretical SAXS spectrum for one or several populations of scatterers. Works by minimizing an objective function that compares the measured spectrum against the theoretical result. TODO: document the algorithm here. Input arrays of q and I(q), a string indicating choice of objective function, a dict of features describing the spectrum, and a list of strings indicating which keys in the dict should be used as optimization parameters. The input features dict includes initial fit parameters as well as the flags indicating which populations to include. The features dict is of the same format as SpectrumProfiler and SpectrumParameterization outputs. Outputs a return code and the features dict, with entries updated for the optimized parameters. Also returns the theoretical result for I(q), and a renormalized measured spectrum for visual comparison. """ def __init__(self): input_names = ["q", "I", "flags", "params", "fit_params", "objfun"] output_names = ["params", "q_I_opt"] super(SpectrumFit, self).__init__(input_names, output_names) self.input_doc["q"] = "1d array of wave vector values in 1/Angstrom units" self.input_doc["I"] = "1d array of intensity values I(q)" self.input_doc["flags"] = "dict of flags indicating what populations to fit" self.input_doc["params"] = ( "dict of initial values for the scattering equation parameters " "for each of the populations specified in the input flags" ) self.input_doc["fit_params"] = "list of strings (keys) indicating which parameters to optimize" self.input_doc["objfun"] = ( "string indicating objective function for optimization: " + "see documentation of saxstools.fit_spectrum() for supported objective functions" ) self.output_doc["params"] = ( "dict of scattering equation parameters copied from inputs, " "with values optimized for all keys specified in fit_params" ) self.output_doc["q_I_opt"] = "n-by-2 array of q and the optimized computed intensity spectrum" self.input_type["q"] = opmod.workflow_item self.input_type["I"] = opmod.workflow_item self.input_type["flags"] = opmod.workflow_item self.input_type["params"] = opmod.workflow_item self.inputs["objfun"] = "chi2log" def run(self): f = self.inputs["flags"] if f["bad_data"] or not any([f["precursor_scattering"], f["form_factor_scattering"], f["diffraction_peaks"]]): self.outputs["params"] = {} return if f["diffraction_peaks"]: self.outputs["params"] = {"ERROR_MESSAGE": "diffraction peak fitting not yet supported"} return q, I = self.inputs["q"], self.inputs["I"] m = self.inputs["objfun"] p = self.inputs["params"] fitkeys = self.inputs["fit_params"] # p_opt = copy.deepcopy(p) # Set up constraints as needed c = [] if f["form_factor_scattering"] or f["diffraction_peaks"]: c = ["fix_I0"] # Fitting happens here p_opt = saxstools.fit_spectrum(q, I, m, f, p, fitkeys, c) I_opt = saxstools.compute_saxs(q, f, p_opt) nz = (I > 0) & (I_opt > 0) logI_nz = np.log(I[nz]) logIopt_nz = np.log(I_opt[nz]) Imean = np.mean(logI_nz) Istd = np.std(logI_nz) logI_nz_s = (logI_nz - Imean) / Istd logIopt_nz_s = (logIopt_nz - Imean) / Istd f["R2log_fit"] = saxstools.compute_Rsquared(np.log(I[nz]), np.log(I_opt[nz])) f["chi2log_fit"] = saxstools.compute_chi2(logI_nz_s, logIopt_nz_s) q_I_opt = np.array([q, I_opt]).T self.outputs["features"] = f self.outputs["q_I_opt"] = q_I_opt	[ 1097, 13722, 1220, 34279, 222, 485, 1948, 222, 222, 485, 6610, 641, 2115, 222, 1097, 18945, 51, 22990, 1220, 14574, 100, 4803, 222, 222, 1097, 2437, 1220, 11957, 641, 3107, 1820, 222, 1097, 2437, 3786, 1220, 11957, 222, 1097, 23249, 4303, 1220, 328, 668, 289, 7570, 499, 222, 842, 32615, 10538, 14088, 45, 3786, 731, 303, 1547, 303, 4443, 331, 26560, 45334, 88, 29588, 327, 78, 45, 118, 46, 8563, 51, 2706, 490, 303, 391, 22394, 341, 3406, 451, 331, 47941, 1599, 45334, 88, 29588, 303, 456, 1611, 575, 10241, 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595912753d778a0fa8332f0df00e06a9da5cde93	################################################################################ # # # This file is part of the Potato Engine (PE). # # # # Copyright (C) 2007-2010 ElectroMagnetic Potatoes (EMP). # # See the AUTHORS file for more information. # # # # This library is free software; you can redistribute it and/or # # modify it under the terms of the GNU Lesser General Public # # License as published by the Free Software Foundation; either # # version 2.1 of the License, or (at your option) any later version. # # # # This library is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # # Lesser General Public License for more details. # # # # You should have received a copy of the GNU Lesser General Public License # # along with this program. If not, see <http://www.gnu.org/licenses/>. # # # ################################################################################ import os import build ################################################################ # Default options (will be overriden by command line switches) # ################################################################ # Parallel build SetOption("num_jobs", 4) # include cache SetOption("implicit_cache", 1) ########################################################## # Command-line parameters (overriden by localconfig.py) # ########################################################## buildVariables = Variables("localconfig.py") buildVariables.Add(PathVariable("QTDIR", "Qt4 root directory", "/usr/share/qt4", PathVariable.PathIsDir)) buildVariables.Add(PathVariable("OGRE_HOME", "Ogre1.6 root directory (windows only)", None, PathVariable.PathIsDir)) buildVariables.Add( PathVariable("PTHREADWIN32_HOME", "PthreadWin32 root directory (windows only)", None, PathVariable.PathIsDir) ) buildVariables.Add(PathVariable("ODE_HOME", "ODE 0.11 root directory", None, PathVariable.PathIsDir)) buildVariables.Add(BoolVariable("DEBUG", "If true, build in debug configuration", False)) buildVariables.Add( BoolVariable( "FORCE_MINGW", "When both MinGW and VC++ are installed, force the use of the MinGW compiler instead of the default (windows only)", False, ) ) buildVariables.Add(BoolVariable("DISABLE_GRAPH", "Disable dependency graph generation", False)) ############################################################################## # Variable value extraction (nasty, should be updated when the API evolves) # # The reason for having this here is that we have to access variables before # # we can create the real construction environment (for tools selection) # ############################################################################## currentVariables = Environment(variables=buildVariables).Dictionary() #################### # Base environment # #################### baseTools = ["qt"] if os.name == "nt": if currentVariables["FORCE_MINGW"]: baseTools.append("mingw") else: baseTools.append("default") else: baseTools.append("default") baseEnvironment = Environment(tools=baseTools, variables=buildVariables) # additional variables baseEnvironment["OSNAME"] = os.name baseEnvironment["SYSPATH"] = os.environ["PATH"].split(os.pathsep) if baseEnvironment["CC"] == "cl": baseEnvironment.AppendUnique(CPPFLAGS=["/EHsc"]) # debug symbols vs. optimization if baseEnvironment["DEBUG"]: if baseEnvironment["CC"] == "cl": baseEnvironment.AppendUnique(CPPFLAGS=["/Z7"]) else: baseEnvironment.AppendUnique(CPPFLAGS=["-g"]) else: if baseEnvironment["CC"] == "cl": baseEnvironment.AppendUnique(CPPFLAGS=["/Ox"]) else: baseEnvironment.AppendUnique(CPPFLAGS=["-O2"]) # Qt tool workaround baseEnvironment.Replace(LIBS=[]) baseEnvironment.Replace(LIBPATH=[]) baseEnvironment.Replace(CPPPATH=[]) # Qt UI builder uiBuilder = Builder(action="$QT_UIC $QT_UICDECLFLAGS -o ${TARGETS[0]} $SOURCE") baseEnvironment.Append(BUILDERS={"Ui": uiBuilder}) # Qt RC builder rcBuilder = Builder(action="$QT_BINPATH/rcc $QT_RCCDECLFLAGS -o ${TARGETS[0]} $SOURCE") baseEnvironment.Append(BUILDERS={"Rc": rcBuilder}) # Under windows, add the platform SDK if os.name == "nt" and baseEnvironment["CC"] == "cl": import _winreg key = _winreg.OpenKey(_winreg.HKEY_CURRENT_USER, "Software\\Microsoft\\Microsoft SDKs\\Windows") winSdkHome = _winreg.QueryValueEx(key, "CurrentInstallFolder")[0] _winreg.CloseKey(key) baseEnvironment["WINSDK_HOME"] = winSdkHome baseEnvironment.AppendUnique(CPPPATH=["$WINSDK_HOME/Include"]) baseEnvironment.AppendUnique(LIBPATH=["$WINSDK_HOME/Lib"]) # Do not rely on VC++ runtime library if os.name == "nt" and baseEnvironment["CC"] == "cl": baseEnvironment.AppendUnique(CPPFLAGS=["/MD"]) # Speed up change analysis baseEnvironment.Decider("MD5-timestamp") ##################### # Command-line help # ##################### Help(buildVariables.GenerateHelpText(baseEnvironment)) ################################## # SCons environment declarations # ################################## walker = build.DependencyWalker() # external component database for script in Glob("components.*.py"): SConscript(script, exports="walker", variant_dir="build", duplicate=0) walker.makeEnvironments(baseEnvironment) if not baseEnvironment["DISABLE_GRAPH"]: walker.makeDependencyGraph("dependencies.png")	[ 31362, 222, 40, 48565, 607, 222, 40, 1369, 822, 458, 1760, 451, 341, 23678, 6599, 10092, 327, 1321, 566, 1229, 607, 222, 40, 48565, 607, 222, 40, 2657, 327, 72, 46, 244, 55, 53, 53, 60, 50, 55, 53, 54, 53, 45106, 309, 18601, 25768, 23678, 6599, 326, 327, 7871, 566, 7223, 607, 222, 40, 2819, 341, 14721, 822, 456, 1851, 2490, 51, 19054, 607, 222, 40, 48565, 607, 222, 40, 1369, 4277, 458, 3760, 2951, 64, 863, 902, 14081, 580, 480, 52, 290, 2087, 607, 222, 40, 5370, 580, 1549, 341, 5974, 451, 341, 6994, 16227, 6153, 5062, 6031, 607, 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13b69ec61d6b2129f1974ce7cae91c84100b3b58	import tensorflow.keras from PIL import Image, ImageOps from os import listdir from os.path import isfile, join import numpy as np import glob import cv2 np.set_printoptions(suppress=True) # Load the model model = tensorflow.keras.models.load_model("./converted_keras/keras_model.h5") # Create the array of the right shape to feed into the keras model # The 'length' or number of images you can put into the array is # determined by the first position in the shape tuple, in this case 1. data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32) path = glob.glob("/Users/zjisuoo/Documents/zjisuoo_git/OurChord/00_NOTE_DATA/TEST/*.png") images = [] for image in path: n1 = cv2.imread(image) n2 = cv2.resize(n1, (244, 244)) images.append(n2) print(image) # turn the image int a numpy array image_array = np.array(n2) # Normalize the image normalized_image_array = (image_array.astype(dtype=np.float32) / 127.0) - 1 # Load the image into the array data = normalized_image_array # run the inference prediction = model.predict(data) # print(prediction) if prediction[0][0] > 0.8: print("2분음표") elif prediction[0][1] > 0.8: print("4분음표") elif prediction[0][2] > 0.8: print("8분음표") elif prediction[0][3] > 0.8: print("16분음표") else: print("음표아님")	[ 485, 15196, 51, 16350, 222, 1097, 35991, 1220, 4393, 49, 4393, 11200, 222, 1097, 2355, 1220, 1168, 1537, 222, 1097, 2355, 51, 1005, 1220, 458, 781, 49, 7163, 222, 485, 6610, 641, 2115, 222, 485, 12542, 222, 485, 6781, 55, 222, 222, 2399, 51, 489, 100, 1243, 2200, 45, 33933, 66, 1844, 46, 222, 222, 40, 6859, 341, 1573, 222, 1184, 299, 15196, 51, 16350, 51, 3404, 51, 963, 100, 1184, 12031, 36042, 100, 16350, 52, 16350, 100, 1184, 51, 109, 58, 678, 222, 222, 40, 2673, 341, 1437, 451, 341, 2109, 6073, 391, 7006, 2012, 341, 21144, 1573, 222, 40, 906, 349, 1340, 44, 575, 1470, 451, 5753, 863, 902, 4180, 2012, 341, 1437, 458, 222, 40, 19886, 829, 341, 1950, 2764, 347, 341, 6073, 8871, 49, 347, 477, 1152, 244, 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68d537cb8488ae4f2c8300e885be78540952dec0	#!/usr/bin/env python2 # coding=utf8 from __future__ import absolute_import, division, print_function from sqlalchemy import func from walis.model.walis import walis_session from walis.model.zeus import zeus_session, zeus_db_handler from walis.model.zeus.activity import ( SubsidyProcessRecord, SubsidyPayRecord, ActivityStats, ) from walis.model.walis.activity import PaymentNoticeRecord as NoticeRecord from walis.utils.time import get_today_begin_time, get_today_end_time MAX_LIST_SIZE = 1000 DEFAULT_LIST_SIZE = 200 def get_new_pay_records(process_at, limit=200): with zeus_session() as session: result = ( session.query( SubsidyPayRecord.id, SubsidyPayRecord.restaurant_id, SubsidyProcessRecord.card_id, SubsidyProcessRecord.processed_at, SubsidyPayRecord.status, ) .outerjoin(SubsidyProcessRecord, SubsidyProcessRecord.pay_record_id == SubsidyPayRecord.id) .filter(SubsidyPayRecord.id > process_at) .filter(SubsidyProcessRecord.status != SubsidyProcessRecord.STATUS_FAIL) .order_by(SubsidyPayRecord.id.asc()) .limit(limit) .all() ) return result def get_success_pay_records(record_ids): with zeus_session() as session: result = ( session.query( SubsidyPayRecord.id, SubsidyPayRecord.restaurant_id, SubsidyProcessRecord.card_id, SubsidyProcessRecord.processed_at, ) .outerjoin(SubsidyProcessRecord, SubsidyProcessRecord.pay_record_id == SubsidyPayRecord.id) .filter(SubsidyPayRecord.status == SubsidyPayRecord.STATUS_SUCCESS) .filter(SubsidyProcessRecord.status != SubsidyProcessRecord.STATUS_FAIL) .filter(SubsidyPayRecord.id.in_(record_ids)) .all() ) return result def get_activity_stats(pay_record_id): with zeus_session() as session: results = ( session.query( ActivityStats.activity_id, ActivityStats.activity_category_id, func.sum(ActivityStats.total_subsidy), func.min(ActivityStats.date), func.max(ActivityStats.date), func.sum(ActivityStats.quantity), ) .group_by(ActivityStats.restaurant_id, ActivityStats.activity_id, ActivityStats.activity_category_id) .filter(ActivityStats.pay_record_id == pay_record_id) .filter(ActivityStats.status == ActivityStats.STATUS_PAY_SUCCESS) .all() ) return results def get_success_record_ids_by_restaurant(restaurant_id, activity_id=None, activity_category_id=None): with zeus_session() as session: query = ( session.query(SubsidyPayRecord.id) .filter(SubsidyPayRecord.restaurant_id == restaurant_id) .filter(SubsidyPayRecord.status == SubsidyPayRecord.STATUS_SUCCESS) ) if activity_id is not None: query.filter(SubsidyPayRecord.activity_id == activity_id) if activity_category_id is not None: query.filter(SubsidyPayRecord.activity_category_id == activity_category_id) record_ids = query.all() return [r[0] for r in record_ids] PAYLOG_STATUS_LIST = { ActivityStats.STATUS_PAY_RECORD_GENERATED, ActivityStats.STATUS_PAY_SUCCESS, ActivityStats.STATUS_PAY_FAIL, } @zeus_db_handler def query_paylog_by_rst(restaurant_id, activity_id=None, activity_category_id=None, offset=None, limit=None): """Except ActivityStats.STATUS_PENDING （未审核状态）""" q = ( session.query( ActivityStats.pay_record_id, ActivityStats.activity_id, ActivityStats.activity_category_id, ActivityStats.status, func.min(ActivityStats.date), func.max(ActivityStats.date), func.sum(ActivityStats.quantity), func.sum(ActivityStats.total_subsidy), SubsidyPayRecord.created_at, func.max(SubsidyProcessRecord.id), ) .group_by(ActivityStats.pay_record_id, ActivityStats.activity_id, ActivityStats.activity_category_id) .outerjoin(SubsidyPayRecord, SubsidyPayRecord.id == ActivityStats.pay_record_id) .outerjoin(SubsidyProcessRecord, SubsidyProcessRecord.pay_record_id == SubsidyPayRecord.id) .filter(ActivityStats.restaurant_id == restaurant_id) .filter(ActivityStats.status.in_(PAYLOG_STATUS_LIST)) .order_by(SubsidyPayRecord.created_at.desc()) ) if activity_id is not None: q = q.filter(ActivityStats.activity_id == activity_id) if activity_category_id is not None: q = q.filter(ActivityStats.activity_category_id == activity_category_id) if limit is not None: q = q.limit(min(limit, MAX_LIST_SIZE)) else: q = q.limit(DEFAULT_LIST_SIZE) if offset is not None: q = q.offset(offset) return q @zeus_db_handler def query_pay_records(restaurant_id, offset=None, limit=None): q = ( session.query(SubsidyPayRecord) .filter(SubsidyPayRecord.restaurant_id == restaurant_id) .order_by(SubsidyPayRecord.created_at.desc()) ) if limit is not None: q = q.limit(min(limit, MAX_LIST_SIZE)) else: q = q.limit(DEFAULT_LIST_SIZE) if offset is not None: q = q.offset(offset) return q.all() @zeus_db_handler def query_paylog(pay_record_ids, activity_id=None, activity_category_id=None, offset=None, limit=None): q = ( session.query( ActivityStats.pay_record_id, ActivityStats.activity_id, ActivityStats.activity_category_id, ActivityStats.status, func.min(ActivityStats.date), func.max(ActivityStats.date), func.sum(ActivityStats.quantity), func.sum(ActivityStats.total_subsidy), ) .group_by(ActivityStats.pay_record_id, ActivityStats.activity_id, ActivityStats.activity_category_id) .filter(ActivityStats.pay_record_id.in_(pay_record_ids)) .filter(ActivityStats.status.in_(PAYLOG_STATUS_LIST)) .order_by(ActivityStats.created_at.desc()) ) if activity_id is not None: q = q.filter(ActivityStats.activity_id == activity_id) if activity_category_id is not None: q = q.filter(ActivityStats.activity_category_id == activity_category_id) if limit is not None: q = q.limit(min(limit, MAX_LIST_SIZE)) else: q = q.limit(DEFAULT_LIST_SIZE) if offset is not None: q = q.offset(offset) return q @zeus_db_handler def get_max_subsidy_process_record_ids(pay_record_ids): q = ( session.query(func.max(SubsidyProcessRecord.id)) .group_by(SubsidyProcessRecord.pay_record_id) .filter(SubsidyProcessRecord.pay_record_id.in_(pay_record_ids)) ) return q @zeus_db_handler def count_paylog_by_rst(restaurant_id, activity_id=None, activity_category_id=None): """Except ActivityStats.STATUS_PENDING （未审核状态）""" q = ( session.query(ActivityStats.id) .group_by(ActivityStats.pay_record_id, ActivityStats.activity_id, ActivityStats.activity_category_id) .filter(ActivityStats.restaurant_id == restaurant_id) .filter(ActivityStats.status.in_(PAYLOG_STATUS_LIST)) ) if activity_id is not None: q = q.filter(ActivityStats.activity_id == activity_id) if activity_category_id is not None: q = q.filter(ActivityStats.activity_category_id == activity_category_id) return len(q.all()) @zeus_db_handler def query_process_records_by_ids(process_ids): query = session.query(SubsidyProcessRecord).filter(SubsidyProcessRecord.id.in_(process_ids)) return query.all() @zeus_db_handler def get_subsidy_record_process_time(record_ids, status): return ( session.query(SubsidyProcessRecord.pay_record_id, SubsidyProcessRecord.processed_at) .filter(SubsidyProcessRecord.pay_record_id.in_(record_ids)) .filter(SubsidyProcessRecord.status == status) .all() ) def get_pay_activities_by_restaurant(rst_id): with zeus_session() as session: query = ( session.query( ActivityStats.activity_id, ActivityStats.activity_category_id, ) .group_by(ActivityStats.activity_id, ActivityStats.activity_category_id) .filter(ActivityStats.restaurant_id == rst_id) ) return query.all() # javis model begins def query_sms_send_info(start_time=None, end_time=None, phone=None, restaurant_id=None, card_num_tail=None, status=None): with walis_session() as session: query = session.query(NoticeRecord) if phone: query = query.filter(NoticeRecord.phone == phone) if restaurant_id: query = query.filter(NoticeRecord.restaurant_id == restaurant_id) if card_num_tail: query = query.filter(NoticeRecord.card_num_tail == card_num_tail) if status: query = query.filter(NoticeRecord.status == status) if not start_time: start_time = get_today_begin_time() if not end_time: end_time = get_today_end_time() query = query.filter(NoticeRecord.created_at > start_time).filter(NoticeRecord.created_at < end_time) return query.all() def query_sms_send_count(start_time=None, end_time=None, status=None): with walis_session() as session: if not start_time: start_time = get_today_begin_time() if not end_time: end_time = get_today_end_time() query = ( session.query(func.count(NoticeRecord.record_id)) .filter(NoticeRecord.created_at > start_time) .filter(NoticeRecord.created_at < end_time) ) if status is not None: query = query.filter(NoticeRecord.status == status) return query.scalar() @zeus_db_handler def query_auto_pay_activity_stats_result( city_ids=None, restaurant_ids=None, activity_id=None, activity_category_id=None, from_date=None, to_date=None, statuses=None, offset=None, limit=None, with_subsidy=None, ): q = ( session.query( ActivityStats.restaurant_id, ActivityStats.activity_id, ActivityStats.activity_category_id, func.sum(ActivityStats.quantity), func.sum(ActivityStats.total_subsidy), func.min(ActivityStats.date), func.max(ActivityStats.date), ) .group_by(ActivityStats.restaurant_id, ActivityStats.activity_id, ActivityStats.activity_category_id) .order_by(ActivityStats.restaurant_id.desc()) ) return _query_activity_stats( q, city_ids, restaurant_ids, activity_id, activity_category_id, from_date, to_date, statuses, with_subsidy, offset, limit, ) def _query_activity_stats( q, city_ids=None, restaurant_ids=None, activity_id=None, activity_category_id=None, from_date=None, to_date=None, statuses=None, with_subsidy=None, offset=None, limit=None, ): if activity_id is not None: q = q.filter(ActivityStats.activity_id == activity_id) if activity_category_id is not None: q = q.filter(ActivityStats.activity_category_id == activity_category_id) # noqa if city_ids is not None: q = q.filter(ActivityStats.city_id.in_(city_ids)) if restaurant_ids is not None: q = q.filter(ActivityStats.restaurant_id.in_(restaurant_ids)) if from_date is not None: q = q.filter(ActivityStats.date >= from_date) if to_date is not None: q = q.filter(ActivityStats.date <= to_date) if statuses is not None: q = q.filter(ActivityStats.status.in_(statuses)) if 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a32fb683f8d46f901e8dcd2d075ace22ee81e076	import base64 import string def hexStringtoBytes(hexstring): byteArray = bytes.fromhex(hexstring) return byteArray def xorBytes(bytes1, bytes2): xored = bytes([x ^ bytes2[i] for i, x in enumerate(bytes1)]) return xored def xorAgainstCharacter(byteArray, character): str2 = [ord(character)] * len(byteArray) return xorBytes(byteArray, str2) def scoreString(input): arr = [(chr(x) in string.printable) for x in input] return arr.count(True) if __name__ == "__main__": hexstring = "1b37373331363f78151b7f2b783431333d78397828372d363c78373e783a393b3736" bytes1 = hexStringtoBytes(hexstring) scores = [] for x in string.printable: temp = xorAgainstCharacter(bytes1, x) print(str(x), str(temp)) scores.append(scoreString(temp))	[ 485, 1712, 59, 57, 222, 485, 821, 499, 222, 610, 9965, 671, 471, 3656, 45, 6961, 819, 731, 303, 41558, 299, 3534, 51, 1097, 6961, 45, 6961, 819, 46, 303, 461, 41558, 499, 222, 610, 22090, 3656, 45, 3669, 54, 49, 3534, 55, 731, 303, 837, 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9b581df505765e895047584c5bb586faef95295f	import dash import dash_core_components as dcc import dash_html_components as html import dash_table as dt import plotly.express as px import pandas as pd import plotly.graph_objects as go import numpy as np from datetime import datetime as dat from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn import metrics from sklearn.feature_selection import f_regression from sklearn.feature_selection import SelectKBest # TODO: # The model doesn't really take the opponent into consideration when calculating the win percentage. As you would expect, this is not ideal and is something that needs to be fixed # # The bar charts only graph 2019 data. Allowing the user to choose the year would be an easy addition. Future implemenatations could also include a previous X games option instead # # The bar chart only graphs stats correctly if they are selected in order. For example, if the set of possible stats are ['assists', 'rebounds', 'blocks'], they must all be selected # in order to show all the values correctly. If the user selects only 'assists' and 'blocks' then 'assists' graphs correctly. 'blocks' is graphed but is given the value assigned # to 'rebounds' because it assumes the second position in the array of stats to be graphed. # # The model doesn't run well (and generally fails) for small schools due to a lack of data for those teams. Error checking needs to be implemented to eliminate this problem. def getStatsByYear(teamID, year, data): """Returns the stats for a chosen team for a specific year. Choices are 2016 - 2019""" teamStats = data[data["team_id"] == teamID] for index, row in teamStats.iterrows(): if row["season"] == year: teamStatsForGivenYear = teamStats[data["season"] == row["season"]] return teamStatsForGivenYear def generate_bar_chart(team, opponent, stats, stat_names, data): """Generates a bar chart for a the user selected team, opponent and stats""" teamStats = getStatsByYear(team, 2019, data) opponentStats = getStatsByYear(opponent, 2019, data) teamStatValues = teamStats[ [ "assists", "assists_turnover_ratio", "blocked_att", "blocks", "defensive_rebounds", "fast_break_pts", "field_goals_att", "field_goals_pct", "field_goals_made", "free_throws_att", "free_throws_pct", "free_throws_made", "offensive_rebounds", "personal_fouls", "points", "points_against", "points_in_paint", "points_off_turnovers", "rebounds", "second_chance_pts", "steals", "team_rebounds", "three_points_att", "three_points_pct", "three_points_made", "turnovers", "two_points_att", "two_points_pct", "two_points_made", ] ] opponentStatValues = opponentStats[ [ "assists", "assists_turnover_ratio", "blocked_att", "blocks", "defensive_rebounds", "fast_break_pts", "field_goals_att", "field_goals_pct", "field_goals_made", "free_throws_att", "free_throws_pct", "free_throws_made", "offensive_rebounds", "personal_fouls", "points", "points_against", "points_in_paint", "points_off_turnovers", "rebounds", "second_chance_pts", "steals", "team_rebounds", "three_points_att", "three_points_pct", "three_points_made", "turnovers", "two_points_att", "two_points_pct", "two_points_made", ] ] stats_to_be_graphed = [] for i in range(len(stat_names)): if i in stats: stats_to_be_graphed.append(stat_names[i]) # Graphs average stat values for the user's chosen team teamVals = go.Bar(x=stats_to_be_graphed, y=teamStatValues.mean(), name=data[(data.team_id == team)]["market"].iloc[0]) # Graphs average stat values for the opponent's team opponentVals = go.Bar( x=stats_to_be_graphed, y=opponentStatValues.mean(), name=data[(data.team_id == opponent)]["market"].iloc[0] ) data = [teamVals, opponentVals] layout = go.Layout(barmode="group") fig = go.Figure(data=data, layout=layout) return fig def getAllTeamMatchRecords(teamID, df): """Returns all game records for a given teamID""" return df[df["team_id"] == teamID] def select_features(X_train, y_train, X_test): """Selects features""" # configure to select all features fs = SelectKBest(score_func=f_regression, k="all") # learn relationship from training data fs.fit(X_train, y_train) # transform train input data X_train_fs = fs.transform(X_train) # transform test input data X_test_fs = fs.transform(X_test) return X_train_fs, X_test_fs, fs def overallFeatures(df): """Return list of top four features""" datasetForFS = df datasetForFS.fillna(0) X1 = datasetForFS[ [ "assists", "blocks", "defensive_rebounds", "opponent_drb", "fast_break_pts", "points_in_paint", "points_off_turnovers", "rebounds", "steals", "turnovers", "efg", "tov_pct", "orb_pct", "ftr", ] ] y1 = datasetForFS["win"] X_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size=0.2, random_state=0) X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test) colList = X1.columns.values.tolist() statScoreDF = pd.DataFrame(data={"Stat": pd.Series(colList), "Score": pd.Series(fs.scores_.tolist())}) statScoreDF = statScoreDF.sort_values(by=["Score"], ascending=False) return statScoreDF.head(n=4)["Stat"].tolist() def avgDataRow(df): """Returns the average values of a dataFrame""" df1 = dict() for columnName, columnData in df.iteritems(): df1[columnName] = [df[columnName].mean()] return pd.DataFrame(df1) def updateWinPct(dfMain, dfWin, reg): """Return new win percentage""" dfPred = reg.predict(dfMain) return pd.DataFrame({"Actual": dfWin.mean(), "Predicted (int)": np.around(dfPred), "Predicted (float)": dfPred}) def filterRowsFS(df): """Return dataframe with selected features""" return df[ [ "assists", "blocks", "defensive_rebounds", "opponent_drb", "fast_break_pts", "points_in_paint", "points_off_turnovers", "rebounds", "steals", "turnovers", "efg", "tov_pct", "orb_pct", "ftr", ] ] def learn(dataset): """Trains the model""" dataset = pd.read_csv("team_boxscores_v3.csv") dataset = dataset.fillna(0) # Shuffle dataset = dataset.sample(frac=1) X1 = dataset[ [ "assists", "blocks", "defensive_rebounds", "opponent_drb", "fast_break_pts", "points_in_paint", "points_off_turnovers", "rebounds", "steals", "turnovers", "efg", "tov_pct", "orb_pct", "ftr", ] ] y1 = dataset["win"] # No shuffle # X_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size=0.2, random_state=0) # W/ shuffle X_train = X1[int(len(X1) / 5) :] X_test = X1[: int(len(X1) / 5)] y_train = y1[int(len(y1) / 5) :] y_test = y1[: int(len(y1) / 5)] regressor = LinearRegression() regressor.fit(X_train, y_train) coeff_df = pd.DataFrame(regressor.coef_, X1.columns, columns=["Coefficient"]) y_pred = regressor.predict(X_test) y_pred_round = np.around(regressor.predict(X_test)) return regressor, pd.DataFrame({"Actual": y_test, "Predicted (int)": y_pred_round, "Predicted (float)": y_pred}) def calculate_win_percentage(team, stat1, stat2, stat3, stat4, regressor, data): """Calculates the win percentage for a team and the 4 selected stat values""" temp = getAllTeamMatchRecords(team, data) changed_stat1 = overallFeatures(temp)[0] changed_stat2 = overallFeatures(temp)[1] changed_stat3 = overallFeatures(temp)[2] changed_stat4 = overallFeatures(temp)[3] average_team_stats = avgDataRow(filterRowsFS(temp)) dfWin = temp["win"] dfFinal = pd.DataFrame( { "Actual": dfWin.mean(), "Predicted (int)": np.around(regressor.predict(average_team_stats)), "Predicted (float)": regressor.predict(average_team_stats), } ) origWinPct = dfFinal.at[0, "Predicted (float)"] average_team_stats.at[0, changed_stat1] = stat1 average_team_stats.at[0, changed_stat2] = stat2 average_team_stats.at[0, changed_stat3] = stat3 average_team_stats.at[0, changed_stat4] = stat4 win_percentage = updateWinPct(average_team_stats, dfWin, regressor).at[0, "Predicted (float)"] # Makes sure you can't have a win percentage of > 100% or < 0% if win_percentage > 1: win_percentage = 1 elif win_percentage < 0: win_percentage = 0 win_percentage = win_percentage * 100 win_percentage = round(win_percentage, 2) win_percentage_text = "Projected Win Percentage: " + str(win_percentage) + "%" return win_percentage_text def get_default_slider_values(team, data): """Gets the values the each of the 4 sliders should display. 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de925b8f6bd31bfdfd1f04628659847b0761899d

#!/usr/bin/env python # -*- coding: utf-8 -*- """ @author: Allen(Zifeng) An @course: @contact: [email protected] @file: 17. Letter Combinations of a Phone Number.py @time: 2020/2/2 21:18 """ from typing import List class Solution: def letterCombinations(self, digits: str) -> List[str]: d = {2: "abc", 3: "def", 4: "ghi", 5: "jkl", 6: "mno", 7: "pqrs", 8: "tuv", 9: "wxyz"} def merge(body, digits): if len(digits) == 0: ans.append(body) return else: for c in d[int(digits[0])]: merge(body + c, digits[1:]) # arr=[] ans = [] # for digit in digits: # arr.append(list(d[int(digit)])) # print(arr) merge("", digits) return ans if len(ans) != 1 else [] print(Solution().letterCombinations("")) # # class Solution: # def letterCombinations(self, digits: str) -> List[str]: # d={2:'abc', # 3:'def', # 4:'ghi', # 5:'jkl', # 6:'mno', # 7:'pqrs', # 8:'tuv', # 9:'wxyz' # } # # cmb=[''] if len(digits)!=0 else [] # # for digit in digits: # cmb=[p+q for p in cmb for q in d[int(digit)]] # # return cmb # print(Solution().letterCombinations('23'))

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b3d9013ab6facb8dd9361e2a0715a8ed0cdfeaba

from setuptools import setup import imp def get_version(): ver_file = None try: ver_file, pathname, description = imp.find_module("__version__", ["cmakelint"]) vermod = imp.load_module("__version__", ver_file, pathname, description) version = vermod.VERSION return version finally: if ver_file is not None: ver_file.close() setup( name="cmakelint", version=get_version(), packages=["cmakelint"], scripts=["bin/cmakelint"], entry_points={"console_scripts": ["cmakelint = cmakelint.main:main"]}, install_requires=[""], author="Richard Quirk", author_email="[email protected]", url="https://github.com/richq/cmake-lint", download_url="https://github.com/richq/cmake-lint", keywords=["cmake", "lint"], classifiers=[ "Topic :: Software Development", "Programming Language :: Other", "Programming Language :: Python", "License :: OSI Approved :: Apache Software License", ], description="Static code checker for CMake files", long_description="""cmakelint parses CMake files and reports style issues.""", license="Apache 2.0", )

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359db73de2c2bb5967723dfb78f98fb84b337b9d

from math import degrees, sqrt, sin, cos, atan, radians from pygame import Surface, draw from pygame.sprite import Sprite from constants import ARROW_MAX_SPEED, FLOOR_Y from game_types import Radian, Degree class Arrow(Sprite): def __init__(self, color, screen, character, click_position): Sprite.__init__(self) self.color = color self.screen = screen self.character = character self.click_position = click_position width, height = 2, 2 self.image = Surface([width, height]) self.image.fill(color) draw.rect(self.image, color, [0, 0, width, height]) self.rect = self.image.get_rect() self.released = False self.release_speed = None self.release_position = None self.angle: Degree = None self.t = 0 self.is_moving_right = False self.stopped = False def set_center(self): if self.released: return x, y = self.character.get_center() self.rect.x = x self.rect.y = y def get_center(self): return self.rect.center def update(self): if self.rect.y >= FLOOR_Y: self.stopped = True if not self.released: return speed = self.release_speed t = self.t g = 0.980 vx = cos(self.angle) * speed vy = -sin(self.angle) * speed + 0.5 * g * t * t # print("angle {} -sin(angle) {:0<25} 0.5 * g * t * t {:0<25}".format(self.angle, -sin(self.angle), 0.5*g*t*t)) self.rect.x += vx self.rect.y += vy self.t += 0.1 def release(self, release_position): if self.released: return self.released = True click_x, click_y = self.click_position self.release_position = release_x, release_y = release_position if release_x < click_x: self.is_moving_right = True adjacent, opposite = self.get_catheuses() angle: Degree = self.get_release_angle(adjacent, opposite) aiming_down = release_y < click_y self.angle = self.adjust_angle_to_aim_direction(angle, aiming_down, self.is_moving_right) length = self.get_line_length(adjacent, opposite) self.release_speed = self.get_release_speed(length) def get_catheuses(self): click_x, click_y = self.click_position release_x, release_y = self.release_position adjacent = abs(release_x - click_x) opposite = abs(release_y - click_y) return adjacent, opposite def get_release_angle(self, adjacent: float, opposite: float) -> Degree: if adjacent == 0: return 90 return degrees(atan(opposite / adjacent)) def adjust_angle_to_aim_direction(self, angle: Degree, aiming_down: bool, is_moving_right: bool) -> Radian: adjustment: Degree = 0 if is_moving_right: if aiming_down: adjustment = 180 + 2 * (90 - angle) else: if aiming_down: adjustment = 180 else: adjustment = 2 * (90 - angle) return radians(angle + adjustment) def get_line_length(self, adjacent, opposite) -> float: return sqrt(adjacent**2 + opposite**2) def get_release_speed(self, length) -> float: if length > 100: return ARROW_MAX_SPEED # Ex.: 80 / 100 = 80% * ARROW_MAX_SPEED return ARROW_MAX_SPEED * (length / 100) def hit(self): self.stopped = True

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97cc29e0d54e5d5e05dff16c92ecc4046363185f

from django.conf import settings from django.conf.urls.static import static from django.contrib import admin from django.urls import path, include from home import views from order import views as OV urlpatterns = [ path("user", include("user.urls")), path("order", include("order.urls")), path("shopcart/", OV.shopcart, name="shopcart"), path("product", include("product.urls")), path("", include("home.urls")), # '' - bu home path("faq/", views.faq, name="faq"), path("admin/", admin.site.urls), path("ckeditor", include("ckeditor_uploader.urls")), path("about/", views.about, name="about"), path("contact/", views.contact, name="about"), path("search/", views.search, name="search"), path("search_auto", views.search_auto, name="search_auto"), path("category/<int:id>/<slug:slug>/", views.category_products, name="category_products"), path("product/<int:id>/<slug:slug>/", views.product_detail, name="product_detail"), path("lic/", views.lic, name="lic"), path("post/", views.post, name="post"), path("post/<int:id>/", views.post_detail, name="post_detail"), path("lic/<int:id>/", views.lic_detail, name="lic_detail"), ] if settings.DEBUG: urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

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35b24ffa14f8b3c2040d5becc8a35721e86d8b3d

total = totmil = cont = menor = 0 barato = " " print("-" * 40) print("LOJA SUPER BARATÃO") print("-" * 40) while True: produto = str(input("Nome do Produto: ")) preco = float(input("Preço: ")) cont += 1 total += preco if preco > 1000: totmil += 1 if cont == 1 or preco < menor: barato = produto menor = preco resp = " " while resp not in "SN": resp = str(input("Quer continuar? [S/N]")).strip().upper()[0] if resp == "N": break print("O total da compra foi R${:.2f}".format(total)) print("Temos {} produtos custando mais de R$1000,00".format(totmil)) print("O produto mais barato foi {} que custa {:.2f}".format(barato, menor))

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709271b98fc2b40c763522c54488be36968f02d8

from base import * try: from .prod_local import * except: pass # we currently don't have an interface that allows an administrator # to create a repository for another user. Until we have added this # capability, allow users to create repos. ELEMENTARY_ALLOW_REPO_CREATION = True

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edcccc673994a8de281a683b747de52d2115f89e

from configparser import ConfigParser from ef.config.components import * from ef.config.efconf import EfConf from ef.config.section import ConfigSection comp_list = [ BoundaryConditions, InnerRegion, OutputFile, ParticleInteractionModel, ParticleSource, SpatialMesh, TimeGrid, ExternalFieldUniform, ] def test_components_to_conf_and_back(): for Component in comp_list: x = Component() y = x.to_conf().make() assert x == y def test_conf_to_configparser_and_back(): confs = [C().to_conf() for C in comp_list] parser = ConfigParser() for c in confs: c.add_section_to_parser(parser) conf2 = ConfigSection.parser_to_confs(parser) assert conf2 == confs def test_minimal_example(): parser = ConfigParser() parser.read("examples/minimal_working_example/minimal_conf.conf") components = [conf.make() for conf in ConfigSection.parser_to_confs(parser)] assert components == [ TimeGrid(1e-7, 1e-9, 1e-9), SpatialMesh((5, 5, 15), (0.5, 0.5, 1.5)), ParticleInteractionModel("noninteracting"), BoundaryConditions(0), ExternalFieldUniform("mgn_uni", "magnetic"), ExternalFieldUniform("el_uni", "electric"), OutputFile("example_", ".h5"), ] class TestEfConf: def test_conf_export(self): conf = EfConf(sources=[ParticleSource()], inner_regions=(InnerRegion(),)) s = conf.export_to_string() c1 = EfConf.from_string(s) assert c1 == conf def test_conf_repr(self): from numpy import array # for use in eval conf = EfConf(sources=[ParticleSource()], inner_regions=(InnerRegion(),)) s = repr(conf) c1 = eval(s) assert c1 == conf

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38e167630519b73bffea4ff527bc7b7272a49f1a

# -*- coding:utf-8 -*- # Author: hankcs # Date: 2019-01-13 15:01 import pickle import numpy as np from bert_serving.client import BertClient from pyhanlp import * CharTable = JClass("com.hankcs.hanlp.dictionary.other.CharTable") # bc = BertClient(ip='192.168.1.88') # ip address of the server bc = BertClient(ip="127.0.0.1") # ip address of the GPU machine def embed_last_token(text): result = bc.encode(text, show_tokens=True) # print(result) batch = [] for sent, tensor, tokens in zip(text, result[0], result[1]): valid = [] tid = 0 buffer = "" words = sent.lower().split() for i, t in enumerate(tokens): if t == "[CLS]" or t == "[SEP]": continue else: if t.startswith("##"): t = t[2:] elif t == "[UNK]": t = words[tid][len(buffer)] buffer += t if buffer == words[tid]: valid.append(i) buffer = "" tid += 1 # print(len(valid)) # exit() if len(valid) != len(sent.split()) or tid != len(words): print(valid) print(sent.split()) print(result[1]) batch.append(tensor[valid, :]) return batch def embed_sum(text): result = bc.encode(text, show_tokens=True) # print(result) batch = [] for sent, tensor, tokens in zip(text, result[0], result[1]): token_tensor = [] sent_tensor = [] tid = 0 buffer = "" words = sent.lower().split() for i, t in enumerate(tokens): if t == "[CLS]" or t == "[SEP]": continue else: if t.startswith("##"): t = t[2:] elif t == "[UNK]": t = words[tid][len(buffer)] buffer += t token_tensor.append(tensor[i, :]) if buffer == words[tid]: sent_tensor.append(np.stack(token_tensor).mean(axis=0)) token_tensor = [] buffer = "" tid += 1 # print(len(valid)) # exit() if tid != len(words) or len(sent_tensor) != len(words): print(sent.split()) print(tokens) exit() batch.append(np.stack(sent_tensor)) return batch def generate_bert(path, output, embed_fun=embed_sum): print(output) total = 0 with open(path) as src: batch = [] tensor = [] for line in src: line = line.strip() if len(line) == 0: continue batch.append( CharTable.convert(line) .replace("—", "-") .replace("‘", "'") .replace("…", ".") .replace("坜", "壢") .replace("唛", "麦") .replace("ㄅㄆㄇㄈ", "呀呀") .replace("’", "'") ) if len(batch) and len(batch) % 100 == 0: tensor.extend(embed_fun(batch)) total += len(batch) print(total) batch = [] if len(batch): tensor.extend(embed_fun(batch)) total += len(batch) print(total) with open(output, "wb") as f: pickle.dump(tensor, f) if __name__ == "__main__": # generate_bert('data/SemEval-2016/news.test.sent.txt', 'data/SemEval-2016/news.test.bert', embed_fun=embed_sum) # generate_bert('data/SemEval-2016/news.valid.sent.txt', 'data/SemEval-2016/news.valid.bert', embed_fun=embed_sum) # generate_bert('data/SemEval-2016/news.train.sent.txt', 'data/SemEval-2016/news.train.bert', embed_fun=embed_sum) # # generate_bert('data/SemEval-2016/text.test.sent.txt', 'data/SemEval-2016/text.test.bert', embed_fun=embed_sum) # generate_bert('data/SemEval-2016/text.valid.sent.txt', 'data/SemEval-2016/text.valid.bert', embed_fun=embed_sum) # generate_bert('data/SemEval-2016/text.train.sent.txt', 'data/SemEval-2016/text.train.bert', embed_fun=embed_sum) generate_bert("data/semeval15/cz.pas.dev.sent.txt", "data/embedding/bert_base_sum/cz.pas.dev.bert", embed_fun=embed_sum) generate_bert("data/semeval15/cz.pas.train.sent.txt", "data/embedding/bert_base_sum/cz.pas.train.bert", embed_fun=embed_sum) generate_bert("data/semeval15/cz.id.pas.sent.txt", "data/embedding/bert_base_sum/cz.id.pas.bert", embed_fun=embed_sum) # generate_bert('data/ctb5.1-pos/dev.short.sent.txt', 'data/embedding/bert_base_sum/ctb.pos.dev.bert', # embed_fun=embed_sum) # generate_bert('data/ctb5.1-pos/test.short.sent.txt', 'data/embedding/bert_base_sum/ctb.pos.test.bert', # embed_fun=embed_sum) # generate_bert('data/ctb5.1-pos/train.short.sent.txt', 'data/embedding/bert_base_sum/ctb.pos.train.bert', # embed_fun=embed_sum) # generate_bert('data/msra/dev.short.sent.txt', 'data/embedding/bert_base_sum/msra.dev.bert', # embed_fun=embed_sum) # generate_bert('data/msra/test.short.sent.txt', 'data/embedding/bert_base_sum/msra.test.bert', # embed_fun=embed_sum) # generate_bert('data/msra/train.short.sent.txt', 'data/embedding/bert_base_sum/msra.train.bert', # embed_fun=embed_sum) # generate_bert('data/msra/test.auto.short.sent.txt', 'data/embedding/bert_base_sum/msra.test.auto.bert', # embed_fun=embed_sum) # generate_bert('data/msra/test.auto.short.sent.txt', 'data/embedding/bert_base_sum/msra.test.auto.bert', # embed_fun=embed_sum) # generate_bert('data/msra/dev.auto.short.sent.txt', 'data/embedding/bert_base_sum/msra.dev.auto.bert', # embed_fun=embed_sum) # generate_bert('data/msra/train.auto.short.sent.txt', 'data/embedding/bert_base_sum/msra.train.auto.bert', # embed_fun=embed_sum) # generate_bert('data/ctb5/dev.sent.txt', 'data/embedding/bert_base_sum/ctb.dev.bert', # embed_fun=embed_sum) # generate_bert('data/ctb5/test.sent.txt', 'data/embedding/bert_base_sum/ctb.test.bert', # embed_fun=embed_sum) # generate_bert('data/ctb5/train.sent.txt', 'data/embedding/bert_base_sum/ctb.train.bert', # embed_fun=embed_sum)

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119, 4, 6, 15, 16, 20, 22, 26, 27, 30, 34, 35, 36, 37, 38, 39, 40, 44, 45, 50, 51, 55, 56, 59, 61, 63, 67, 68, 71, 75, 76, 77, 78, 79, 80, 81, 85, 86, 91, 92, 96, 98, 104, 105, 108, 109, 114, 115, 118, 119, 4, 6, 4, 6, 15, 16, 20, 22, 26, 27, 30, 34, 35, 36, 37, 38, 39, 40, 44, 45, 49, 50, 54, 55, 58, 60, 62, 66, 67, 70, 74, 75, 76, 77, 78, 79, 80, 84, 85, 89, 90, 94, 96, 102, 103, 106, 107, 112, 113, 116, 117, 4, 6, 15, 16, 20, 22, 26, 27, 30, 34, 35, 36, 37, 38, 39, 40, 44, 45, 50, 51, 55, 56, 59, 61, 63, 67, 68, 71, 75, 76, 77, 78, 79, 80, 81, 85, 86, 91, 92, 96, 98, 104, 105, 108, 109, 114, 115, 118, 119, 4, 6, 15, 16, 20, 22, 26, 27, 30, 34, 35, 36, 37, 38, 39, 40, 44, 45, 50, 51, 55, 56, 59, 61, 63, 67, 68, 71, 75, 76, 77, 78, 79, 80, 81, 85, 86, 91, 92, 96, 98, 104, 105, 108, 109, 114, 115, 118, 119, 4, 13, 14, 18, 20, 24, 25, 27, 29, 32, 33, 34, 35, 37, 38, 41, 42, 45, 46, 50, 51, 54, 56, 58, 62, 63, 72, 73, 77, 78, 82, 83, 86, 87, 89, 90, 93, 94, 97, 98, 102, 104, 110, 111, 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82, 83, 88, 89, 93, 95, 4, 6, 20, 26, 27, 30, 31, 36, 37, 40, 41 ]

816b1a932208a4525230dd886adf8c67dec3af3e

# content of conftest.py # adapted from http://pytest.org/latest/example/special.html import pytest import requests def tear_down(): """conftest.py tear_down - the last to go....""" print("\nTEARDOWN after all tests") @pytest.fixture(scope="session", autouse=True) def set_up(request): """conftest.py set_up - the first to start....""" print("\nSETUP before all tests") request.addfinalizer(tear_down)

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9096ed4b68d2bef92df7db98589e744ddf3efad0

import matplotlib.pyplot as plt from shapely.geometry import MultiLineString, Polygon mls = MultiLineString([[(0, 1), (5, 1)], [(1, 2), (1, 0)]]) p = Polygon([(0.5, 0.5), (0.5, 1.5), (2, 1.5), (2, 0.5)]) results = mls.intersection(p) plt.subplot(1, 2, 1) for ls in mls: plt.plot(*ls.xy) plt.plot(*p.boundary.xy, "-.k") plt.xlim([0, 5]) plt.ylim([0, 2]) plt.subplot(1, 2, 2) for ls in results: plt.plot(*ls.xy) plt.xlim([0, 5]) plt.ylim([0, 2]) plt.show()

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c99f1333c5ca3221e9932d9a9ba1d95a77924f0d

import sys import math def get_max_sum(arr): max_sum = -math.inf for i in range(1, 5): for j in range(1, 5): temp = ( arr[i][j] + arr[i - 1][j - 1] + arr[i - 1][j] + arr[i - 1][j + 1] + arr[i + 1][j + 1] + arr[i + 1][j] + arr[i + 1][j - 1] ) max_sum = max(max_sum, temp) return max_sum def main(): sys_in = sys.stdin sys_out = sys.stdout arr = [] for _ in range(6): temp = list(map(int, sys.stdin.readline().split())) arr.append(temp) print(get_max_sum(arr)) if __name__ == "__main__": main()

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78c9f92349ba834bc64dc84f884638c4316a9ea4

INPUT_MINBIAS = "/build/RAWReference/MinBias_RAW_320_STARTUP.root" INPUT_TTBAR = "/build/RAWReference/TTbar_RAW_320_STARTUP.root" puSTARTUP_TTBAR = "/build/RAWReference/TTbar_Tauola_PileUp_RAW_320_STARTUP.root" relval = { "step1": { "step": "GEN-HLT", "timesize": (100, ["MinBias", "TTbar"]), "igprof": (50, ["TTbar"]), "memcheck": (5, ["TTbar"]), "pileup": ["TTbar"], # ??? 'pileupInput': '', "cmsdriver": "--eventcontent RAWSIM --conditions auto:mc", }, "step2": { "step": "RAW2DIGI-RECO", "timesize": (8000, ["MinBias", "TTbar"]), "igprof": (200, ["TTbar"]), "memcheck": (5, ["TTbar"]), "pileup": ["TTbar"], "pileupInput": puSTARTUP_TTBAR, "fileInput": [INPUT_MINBIAS, INPUT_TTBAR], "cmsdriver": "--eventcontent RECOSIM --conditions auto:startup", }, "GENSIMDIGI": { "step": "GEN-SIM,DIGI", "timesize": (100, ["MinBias", "SingleElectronE1000", "SingleMuMinusPt10", "SinglePiMinusE1000", "TTbar"]), "igprof": (5, ["TTbar"]), "memcheck": (5, ["TTbar"]), "pileup": ["TTbar"], # ??? 'pileupInput': '', "fileInput": "", "cmsdriver": "--eventcontent FEVTDEBUG --conditions auto:mc", }, "HLT": { "step": "HLT", "timesize": (8000, ["MinBias", "TTbar"]), "igprof": (500, ["TTbar"]), "memcheck": (5, ["TTbar"]), "pileup": ["TTbar"], "pileupInput": puSTARTUP_TTBAR, "fileInput": [INPUT_MINBIAS, INPUT_TTBAR], "cmsdriver": "--eventcontent RAWSIM --conditions auto:startup --processName HLTFROMRAW", }, "FASTSIM": { "step": "GEN-FASTSIM", "timesize": (8000, ["MinBias", "TTbar"]), "igprof": (500, ["TTbar"]), "memcheck": (5, ["TTbar"]), "pileup": ["TTbar"], "cmsdriver": "--eventcontent RECOSIM --conditions auto:mc", }, }

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c6b261a09b2982e17704f847586bbf38d27cb786

from ._sinAction import * from ._sinActionFeedback import * from ._sinActionGoal import * from ._sinActionResult import * from ._sinFeedback import * from ._sinGoal import * from ._sinResult import *

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f4306f80330850415b74d729384f360489644e39

import unittest import numpy import pandas as pd import fixtures.examples_validate as examples from cellxgene_schema.validate import Validator from cellxgene_schema.write_labels import AnnDataLabelAppender # Tests for schema compliance of an AnnData object class TestValidAnndata(unittest.TestCase): """ Tests a valid AnnData object. Most other tests below modify this AnnData object and test for failure cases. The valid AnnData object has all valid cases described in the schema. """ def setUp(self): self.validator = Validator() self.validator.adata = examples.adata.copy() def test_valid_anndata(self): self.validator.validate_adata() self.assertFalse(self.validator.errors) class TestH5adValidation(unittest.TestCase): """ Checks that validation from h5ad works, only does one invalid example as extensive testing is done in the classes below """ def setUp(self): self.h5ad_valid_file = examples.h5ad_valid self.h5ad_invalid_file = examples.h5ad_invalid self.validator = Validator() def test_validate(self): # Valid h5ad self.assertTrue(self.validator.validate_adata(self.h5ad_valid_file)) # Invalid h5ads self.assertFalse(self.validator.validate_adata(self.h5ad_invalid_file)) class TestExpressionMatrix(unittest.TestCase): """ Fail cases for expression matrices (anndata.X and anndata.raw.X) """ def setUp(self): self.validator = Validator() self.validator.adata = examples.adata.copy() def test_shapes(self): """ All matrix layers MUST have the same shape, and have the same cell labels and gene labels. """ # Creates a raw layer self.validator.adata.raw = self.validator.adata self.validator.adata.raw.var.drop("feature_is_filtered", axis=1, inplace=True) self.validator.adata.X = examples.adata_non_raw.X.copy() self.validator.adata.uns["X_normalization"] = "CPM" # remove one gene self.validator.adata = self.validator.adata[:, 1:] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: Number of genes in X (3) is different than raw.X (4)."], ) def test_sparsity(self): """ In any layer, if a matrix has 50% or more values that are zeros, it is STRONGLY RECOMMENDED that the matrix be encoded as a scipy.sparse.csr_matrix """ self.validator.adata.X = self.validator.adata.X.toarray() self.validator.validate_adata() self.assertEqual( self.validator.warnings, [ "WARNING: Sparsity of 'X' is 0.875 which is greater than 0.5, " "and it is not a 'scipy.sparse.csr_matrix'. It is " "STRONGLY RECOMMENDED to use this type of matrix for " "the given sparsity." ], ) def test_raw_existence(self): """ Except for ATAC-seq and methylation data, raw data is REQUIRED """ # RNA - raw layer required del self.validator.adata.raw self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: Raw data is missing: there is no 'raw.X' and 'X_normalization' is not 'none'."], ) # ATAC - raw layer not required # The assignment above makes X to not be raw: self.validator.adata.uns["X_normalization"] = "CPM" # The following line makes it to be scATAC-seq data (EFO:0010891) # Missing raw data in atac-seq data is allowed, thus the following should not return an error message self.validator.errors = [] self.validator.adata.obs["assay_ontology_term_id"] = "EFO:0010891" self.validator.validate_adata() self.assertEqual(self.validator.errors, []) def test_final_strongly_recommended(self): """ Except for ATAC-seq and methylation data, final matrix is STRONGLY RECOMMENDED """ # move raw to X amd: i.e. there is no final self.validator.adata.X = self.validator.adata.raw.X del self.validator.adata.raw self.validator.adata.uns["X_normalization"] = "none" self.validator.validate_adata() self.assertEqual( self.validator.warnings, [ "WARNING: Only raw data was found, i.e. there is no 'raw.X' and 'uns['X_normalization']' is 'none'. " "It is STRONGLY RECOMMENDED that 'final' (normalized) data is provided." ], ) class TestObs(unittest.TestCase): """ Fail cases in adata.uns """ def setUp(self): self.validator = Validator() self.validator.adata = examples.adata.copy() def test_column_presence(self): """ obs is a pandas.DataFrame. Curators MUST annotate the following columns in the obs dataframe. """ columns = [ "assay_ontology_term_id", "development_stage_ontology_term_id", "disease_ontology_term_id", "ethnicity_ontology_term_id", "is_primary_data", "sex_ontology_term_id", "tissue_ontology_term_id", ] for column in columns: with self.subTest(column=column): self.validator.errors = [] self.validator.adata = examples.adata.copy() self.validator.adata.obs.drop(column, axis=1, inplace=True) # Remove batch condition because it has a dependency with is_primary_data self.validator.adata.uns.pop("batch_condition") self.validator.validate_adata() self.assertEqual( self.validator.errors, [f"ERROR: Dataframe 'obs' is missing column '{column}'."], ) def test_column_presence_organism(self): """ obs is a pandas.DataFrame. Curators MUST annotate the following columns in the obs dataframe. A separate check is need for organism_ontology_term_id because removing from anndata results in multiple errors given that other columns depend on its presence """ self.validator.adata.obs.drop("organism_ontology_term_id", axis=1, inplace=True) self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: Dataframe 'obs' is missing column 'organism_ontology_term_id'.", "ERROR: Checking values with dependencies failed for " "adata.obs['ethnicity_ontology_term_id'], this is likely due " "to missing dependent column in adata.obs.", "ERROR: Checking values with dependencies failed for " "adata.obs['development_stage_ontology_term_id'], this is likely due " "to missing dependent column in adata.obs.", ], ) def test_obsolete_term_id(self): """ Terms documented as obsolete in an ontology MUST NOT be used. For example, EFO:0009310 for obsolete_10x v2 was marked as obsolete in EFO version 3.31.0 and replaced by EFO:0009899 for 10x 3' v2. https://www.ebi.ac.uk/ols/ontologies/efo/terms?short_form=EFO_0009310 """ # Not a valid term self.validator.adata.obs["assay_ontology_term_id"][0] = "EFO:0009310" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0009310' in 'assay_ontology_term_id' is a deprecated term id of 'EFO'.", "ERROR: 'EFO:0009310' in 'assay_ontology_term_id' is not a child term id " "of '[['EFO:0002772', 'EFO:0010183']]'.", ], ) def test_assay_ontology_term_id(self): """ assay_ontology_term_id categorical with str categories. This MUST be an EFO term and either child of "EFO:0002772" or "EFO:0010183" If there is not an exact match for the assay, clarifying text MAY be enclosed in parentheses and appended to the most accurate term. For example, the sci-plex assay could be curated as "EFO:0010183 (sci-plex)" """ # Not a valid term self.validator.adata.obs["assay_ontology_term_id"][0] = "CL:000001" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'CL:000001' in 'assay_ontology_term_id' is not a valid ontology term id of 'EFO'.", "ERROR: 'CL:000001' in 'assay_ontology_term_id' is not a child term id of '[['EFO:0002772', 'EFO:0010183']]'.", ], ) # Not a valid child self.validator.adata.obs["assay_ontology_term_id"][0] = "EFO:0000001" self.validator.errors = [] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'EFO:0000001' in 'assay_ontology_term_id' is not a child term id of '[['EFO:0002772', 'EFO:0010183']]'."], ) # Not a clarifying text self.validator.adata.obs["assay_ontology_term_id"][0] = "EFO:0010183 sci-plex" self.validator.errors = [] self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0010183 sci-plex' in 'assay_ontology_term_id' is not a valid ontology term id of 'EFO'.", "ERROR: 'EFO:0010183 sci-plex' in 'assay_ontology_term_id' is not a child term id of " "'[['EFO:0002772', 'EFO:0010183']]'.", ], ) def test_cell_type_ontology_term_id(self): """ cell_type_ontology_term_id categorical with str categories. This MUST be a CL term. """ # Not a valid term self.validator.adata.obs["cell_type_ontology_term_id"][0] = "EFO:0000001" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'EFO:0000001' in 'cell_type_ontology_term_id' is not a valid ontology term id of 'CL'."], ) def test_development_stage_ontology_term_id_human(self): """ development_stage_ontology_term_id categorical with str categories. If unavailable, this MUST be "unknown". If organism_ontolology_term_id is "NCBITaxon:9606" for Homo sapiens, this MUST be the most accurate HsapDv term. """ self.validator.adata.obs["organism_ontology_term_id"][0] = "NCBITaxon:9606" self.validator.adata.obs["development_stage_ontology_term_id"][0] = "EFO:0000001" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0000001' in 'development_stage_ontology_term_id' is " "not a valid ontology term id of 'HsapDv'. When 'organism_ontology_term_id' is 'NCBITaxon:9606' " "(Homo sapiens), 'development_stage_ontology_term_id' MUST be a term id of 'HsapDv' or unknown." ], ) def test_development_stage_ontology_term_id_mouse(self): """ If organism_ontolology_term_id is "NCBITaxon:10090" for Mus musculus, this MUST be the most accurate MmusDv term """ self.validator.adata.obs["organism_ontology_term_id"][0] = "NCBITaxon:10090" self.validator.adata.obs["development_stage_ontology_term_id"][0] = "EFO:0000001" self.validator.adata.obs["ethnicity_ontology_term_id"][0] = "na" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0000001' in 'development_stage_ontology_term_id' is " "not a valid ontology term id of 'MmusDv'. When 'organism_ontology_term_id' is 'NCBITaxon:10090' " "(Mus musculus), 'development_stage_ontology_term_id' MUST be a term id of 'MmusDv' or unknown." ], ) def test_development_stage_ontology_term_id_all_species(self): """ All other it MUST be children of UBERON:0000105 and not UBERON:0000071 """ # Fail case not an UBERON term self.validator.adata.obs["organism_ontology_term_id"][0] = "NCBITaxon:10114" self.validator.adata.obs["development_stage_ontology_term_id"][0] = "EFO:0000001" self.validator.adata.obs["ethnicity_ontology_term_id"][0] = "na" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0000001' in 'development_stage_ontology_term_id' is " "not a valid ontology term id of 'UBERON'. When 'organism_ontology_term_id' is not 'NCBITaxon:10090' " "nor 'NCBITaxon:9606', 'development_stage_ontology_term_id' MUST be a child term id of " "'UBERON:0000105' excluding 'UBERON:0000071', or unknown.", "ERROR: 'EFO:0000001' in 'development_stage_ontology_term_id' is not " "a child term id of '[['UBERON:0000105']]'. When 'organism_ontology_term_id' is not 'NCBITaxon:10090' " "nor 'NCBITaxon:9606', 'development_stage_ontology_term_id' MUST be a child term id of " "'UBERON:0000105' excluding 'UBERON:0000071', or unknown.", ], ) # All other it MUST be children of UBERON:0000105 and not UBERON:0000071 # Fail case UBERON:0000071 self.validator.errors = [] self.validator.adata.obs["organism_ontology_term_id"][0] = "NCBITaxon:10114" self.validator.adata.obs["development_stage_ontology_term_id"][0] = "UBERON:0000071" self.validator.adata.obs["ethnicity_ontology_term_id"][0] = "na" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'UBERON:0000071' in 'development_stage_ontology_term_id' is not allowed'. When " "'organism_ontology_term_id' is not 'NCBITaxon:10090' " "nor 'NCBITaxon:9606', 'development_stage_ontology_term_id' MUST be a child term id of " "'UBERON:0000105' excluding 'UBERON:0000071', or unknown.", ], ) def test_disease_ontology_term_id(self): """ disease_ontology_term_id categorical with str categories. This MUST be a MONDO term or PATO:0000461 for normal or healthy. """ # Invalid ontology self.validator.adata.obs["disease_ontology_term_id"][0] = "EFO:0000001" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0000001' in 'disease_ontology_term_id' is not a " "valid ontology term id of 'MONDO, PATO'. Only 'PATO:0000461' is allowed for 'PATO' term ids." ], ) # Invalid PATO term id self.validator.errors = [] self.validator.adata.obs["disease_ontology_term_id"][0] = "PATO:0001894" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'PATO:0001894' in 'disease_ontology_term_id' is not an allowed term: '[['PATO:0000461']]'. " "Only 'PATO:0000461' is allowed for 'PATO' term ids." ], ) def test_ethnicity_ontology_term_id(self): """ ethnicity_ontology_term_id categorical with str categories. If organism_ontolology_term_id is "NCBITaxon:9606" for Homo sapiens, this MUST be either a HANCESTRO term or "unknown" if unavailable. Otherwise, for all other organisms this MUST be "na". """ # If organism_ontolology_term_id is "NCBITaxon:9606" for Homo sapiens, # this MUST be either a HANCESTRO term or "unknown" if unavailable. self.validator.adata.obs["organism_ontology_term_id"][0] = "NCBITaxon:9606" self.validator.adata.obs["ethnicity_ontology_term_id"][0] = "EFO:0000001" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0000001' in 'ethnicity_ontology_term_id' is " "not a valid ontology term id of 'HANCESTRO'. When 'organism_ontology_term_id' is 'NCBITaxon:9606' " "(Homo sapiens), ethnicity_ontology_term_id MUST be a term id of 'HANCESTRO' or 'unknown'." ], ) # Otherwise, for all other organisms this MUST be "na". Below is the test case for mouse data. # development_stage_ontology_term_id has to be set to an appropriate mouse term id, otherwise there # will be an error in that field. self.validator.errors = [] self.validator.adata.obs["organism_ontology_term_id"][0] = "NCBITaxon:10090" self.validator.adata.obs["development_stage_ontology_term_id"][0] = "MmusDv:0000003" self.validator.adata.obs["ethnicity_ontology_term_id"][0] = "EFO:0000001" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0000001' in 'ethnicity_ontology_term_id' is not a " "valid value of 'ethnicity_ontology_term_id'. When 'organism_ontology_term_id' is NOT 'NCBITaxon:9606' " "(Homo sapiens), ethnicity_ontology_term_id MUST be 'na'." ], ) def test_organism_ontology_term_id(self): """ organism_ontology_term_id categorical with str categories. This MUST be a child of NCBITaxon:33208. """ # Setting "organism_ontology_term_id" to "EFO:0000001" is the fail case. However since this represents neither # human nor mouse, then two other columns that are dependent on it need to be set appropriately to avoid # other error messages: "development_stage_ontology_term_id" and "ethnicity_ontology_term_id" self.validator.adata.obs["organism_ontology_term_id"][0] = "EFO:0000001" self.validator.adata.obs["development_stage_ontology_term_id"][0] = "unknown" self.validator.adata.obs["ethnicity_ontology_term_id"][0] = "na" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0000001' in 'organism_ontology_term_id' is not a valid " "ontology term id of 'NCBITaxon'. Only children term ids of 'NCBITaxon:33208' for metazoan are allowed." ], ) def test_tissue_ontology_term_id_base(self): """ tissue_ontology_term_id categorical with str categories. This MUST be the term that best describes the tissue that this cell was derived from, depending on the type of biological sample: """ self.validator.adata.obs["tissue_ontology_term_id"][0] = "EFO:0000001" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'EFO:0000001' in 'tissue_ontology_term_id' is not a valid ontology term id of 'UBERON, CL'."], ) def test_tissue_ontology_term_id_cell_culture(self): """ Cell Culture - MUST be a CL term appended with " (cell culture)" """ self.validator.adata.obs["tissue_ontology_term_id"][0] = "CL:0000057 (CELL culture)" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'CL:0000057 (CELL culture)' in 'tissue_ontology_term_id' is " "not a valid ontology term id of 'UBERON, CL'." ], ) def test_tissue_ontology_term_id_organoid(self): """ Organoid - MUST be an UBERON term appended with " (organoid)" """ self.validator.adata.obs["tissue_ontology_term_id"][0] = "CL:0000057 (ORGANOID)" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'CL:0000057 (ORGANOID)' in 'tissue_ontology_term_id' is not a valid ontology term id of 'UBERON, CL'."], ) def test_sex_ontology_term_id(self): """ sex_ontology_term_id categorical with str categories. This MUST be a child of PATOPATO:0001894 for phenotypic sex or "unknown" if unavailable """ self.validator.adata.obs["sex_ontology_term_id"][0] = "EFO:0000001" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: 'EFO:0000001' in 'sex_ontology_term_id' is " "not a valid ontology term id of 'PATO'. Only 'PATO:0000383', 'PATO:0000384', 'PATO:0001340', " "or 'unknown' are allowed." ], ) def test_is_primary_data(self): """ is_primary_data bool. This MUST be True if this is the canonical instance of this cellular observation and False if not. This is commonly False for meta-analyses reusing data or for secondary views of data. """ self.validator.adata.obs["is_primary_data"] = "FALSE" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: Column 'is_primary_data' in dataframe 'obs' must be boolean, not 'object'."], ) class TestVar(unittest.TestCase): """ Fail cases in adata.var and adata.raw.var """ def setUp(self): self.validator = Validator() self.validator.adata = examples.adata.copy() def test_var_and_raw_var_same_index(self): """ var.index MUST contain unique identifiers for features. raw.var.index MUST be identical to var.index. """ # Swap first row for second one var = Validator.getattr_anndata(self.validator.adata, "var") # First swap the index new_index = list(var.index) tmp = new_index[0] new_index[0] = new_index[1] new_index[1] = tmp var.set_index(pd.Index(new_index), inplace=True) # Then swap the actual rows tmp = var.iloc[0, :].copy() var.iloc[0, :] = var.iloc[1, :].copy() var.iloc[1, :] = tmp self.validator.validate_adata() print("FOO", self.validator.errors) self.assertEqual( self.validator.errors, ["ERROR: Index of 'raw.var' is not identical to index of 'var'."], ) def test_check_unique_var(self): """ var.index MUST contain unique ENSEMBL gene identifiers for features. """ for component_name in ["var", "raw.var"]: with self.subTest(component_name=component_name): # Resetting validator self.validator.adata = examples.adata.copy() self.validator.errors = [] # Duplicate 1st row in var and assign it to 2nd component = Validator.getattr_anndata(self.validator.adata, component_name) new_index = list(component.index) new_index[1] = new_index[0] component.set_index(pd.Index(new_index), inplace=True) component.iloc[1, :] = component.iloc[0, :] self.validator.validate_adata() self.assertEqual( self.validator.errors, [f"ERROR: Column 'index' in dataframe '{component_name}' is not unique."], ) def test_column_presence(self): """ var is a pandas.DataFrame. Curators MUST annotate the following columns in the obs dataframe. feature_is_filtered must not be in raw.var, and it's only checked in var """ columns = ["feature_is_filtered", "feature_biotype"] for component_name in ["var", "raw.var"]: for column in columns: if column == "feature_is_filtered" and component_name == "raw.var": continue with self.subTest(component_name=component_name, column=column): # Resetting validator self.validator.errors = [] self.validator.adata = examples.adata.copy() component = Validator.getattr_anndata(self.validator.adata, component_name) component.drop(column, axis=1, inplace=True) self.validator.validate_adata() self.assertEqual( self.validator.errors, [f"ERROR: Dataframe '{component_name}' is missing column '{column}'."], ) def test_feature_is_filtered(self): """ feature_is_filtered bool. This MUST be True if the feature was filtered out in the final matrix (X) but is present in the raw matrix (raw.X). The value for all cells of the given feature in the final matrix MUST be 0. Otherwise, this MUST be False. """ # Duplicate 1st row in var and assigned to 2nd self.validator.adata.var["feature_is_filtered"][0] = True for i in range(self.validator.adata.X.shape[0]): self.validator.adata.X[i, 0] = 0 self.validator.adata.X[0, 0] = 1 self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: Some features are 'True' in 'feature_is_filtered' of dataframe 'var', " "but there are 1 non-zero values in the corresponding columns of the matrix 'X'. " "All values for these features must be 0." ], ) def test_columns_not_in_raw_var(self): """ Curators MUST annotate the following column only in the var dataframe. This column MUST NOT be present in raw.var: feature_is_filtered """ self.validator.adata.raw = self.validator.adata self.validator.adata.uns["X_normalization"] = "CPM" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: Column 'feature_is_filtered' must not be present in 'raw.var'."], ) def test_feature_id_wrong_format(self): """ feature_id (var.index) str. If the feature_biotype is "gene" then this MUST be an ENSEMBL term. If the feature_biotype is "spike-in" then this MUST be an ERCC Spike-In identifier. This tests the case of an ID with an incorrect format "ENSEBML_NOGENE" """ for component_name in ["var", "raw.var"]: with self.subTest(component_name=component_name): # Resetting validator self.validator.adata = examples.adata.copy() self.validator.errors = [] component = Validator.getattr_anndata(self.validator.adata, component_name) new_index = list(component.index) new_index[0] = "ENSEBML_NOGENE" component.set_index(pd.Index(new_index), inplace=True) component["feature_biotype"][0] = "gene" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ f"ERROR: Could not infer organism from feature ID 'ENSEBML_NOGENE' " f"in '{component_name}', make sure it is a valid ID." ], ) def test_feature_id_non_existent_ensembl(self): """ feature_id (var.index) str. If the feature_biotype is "gene" then this MUST be an ENSEMBL term. If the feature_biotype is "spike-in" then this MUST be an ERCC Spike-In identifier. This tests the case of an ENSEMBL ID that has the right format but doesn't exist """ for component_name in ["var", "raw.var"]: with self.subTest(component_name=component_name): # Resetting validator self.validator.adata = examples.adata.copy() self.validator.errors = [] component = Validator.getattr_anndata(self.validator.adata, component_name) new_index = list(component.index) new_index[0] = "ENSG000" component.set_index(pd.Index(new_index), inplace=True) component["feature_biotype"][0] = "gene" self.validator.validate_adata() self.assertEqual( self.validator.errors, [f"ERROR: 'ENSG000' is not a valid feature ID in '{component_name}'."], ) def test_feature_id_non_existent_ercc(self): """ feature_id (var.index) str. If the feature_biotype is "gene" then this MUST be an ENSEMBL term. If the feature_biotype is "spike-in" then this MUST be an ERCC Spike-In identifier. This tests the case of an ERCC ID that has the right format but doesn't exist """ for component_name in ["var", "raw.var"]: with self.subTest(component_name=component_name): # Resetting validator self.validator.adata = examples.adata.copy() self.validator.errors = [] component = Validator.getattr_anndata(self.validator.adata, component_name) new_index = list(component.index) new_index[0] = "ERCC-000000" component.set_index(pd.Index(new_index), inplace=True) component["feature_biotype"][0] = "spike-in" self.validator.validate_adata() self.assertEqual( self.validator.errors, [f"ERROR: 'ERCC-000000' is not a valid feature ID in '{component_name}'."], ) class TestUns(unittest.TestCase): """ Fail cases in adata.uns """ def setUp(self): self.validator = Validator() self.validator.adata = examples.adata.copy() def test_required_fields_schema_version(self): """ Curators MUST annotate `schema_version` and values in uns (schema_version) """ del self.validator.adata.uns["schema_version"] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: adata has no schema definition in 'adata.uns'. Validation cannot be performed."], ) def test_required_fields_title(self): """ Curators MUST annotate `schema_version` and values in uns (title) """ del self.validator.adata.uns["title"] self.validator.validate_adata() self.assertEqual(self.validator.errors, ["ERROR: 'title' in 'uns' is not present."]) def test_required_fields_X_normalization(self): """ Curators MUST annotate `schema_version` and values in uns (X_normalization) """ del self.validator.adata.uns["X_normalization"] self.validator.validate_adata() self.assertEqual(self.validator.errors, ["ERROR: 'X_normalization' in 'uns' is not present."]) def test_leading_trailing_double_spaces_in_strings(self): """ The following sequences MUST NOT appear in str types documented in the schema: Leading control or space separators - ” This is an example” Trailing control or space separators - “This is an example ” Multiple (internal) control or space separators - "This is an example" """ self.validator.adata.uns["title"] = " There is a leading space" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: ' There is a leading space' in 'uns['title']' is not valid, it contains leading spaces."], ) self.validator.adata.uns["title"] = "There is a trailing space " self.validator.errors = [] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'There is a trailing space ' in 'uns['title']' is not valid, it contains trailing spaces."], ) self.validator.adata.uns["title"] = "There are double spaces" self.validator.errors = [] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'There are double spaces' in 'uns['title']' is not valid, it contains double spaces."], ) def test_schema_version(self): """ Schema_version, This MUST be "2.0.0". """ self.validator.adata.uns["schema_version"] = "1.0.0" self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: Schema version '1.0.0' is not supported. Current supported versions: '['2.0.0']'. " "Validation cannot be performed." ], ) def test_title(self): """ Title MUST be a string """ # list instead of string self.validator.adata.uns["title"] = ["title"] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: '['title']' in 'uns['title']' is not valid, it must be a string."], ) def test_X_normalization_is_str(self): """ X_normalization str. """ # list instead of string self.validator.adata.uns["X_normalization"] = ["normalization"] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: '['normalization']' in 'uns['X_normalization']' is not valid, it must be a string."], ) def test_X_normalization_not_raw(self): """ X_normalization str. This SHOULD describe the method used to normalize the data stored in AnnData X. If data in X are raw, this SHOULD be "none". FAIL CASE for when X_normalization was set to "none" but X may not be raw data """ # Assign a real value to X while X_normalization is 'none' del self.validator.adata.raw self.validator.adata.uns["X_normalization"] = "none" self.validator.validate_adata() print("FOO", self.validator.warnings) self.assertEqual( self.validator.warnings, [ "WARNING: uns['X_normalization'] is 'none', there is no 'raw.X' and 'X' doesn't appear " "to have raw counts (integers)" ], ) def test_batch_condition_is_list(self): """ batch_condition list[str] """ # Check valid case of numpy array which is interchangeable with lists self.validator.adata.uns["batch_condition"] = numpy.array(self.validator.adata.uns["batch_condition"]) self.validator.validate_adata() self.assertEqual(self.validator.errors, []) # Check fail case: not a list nor numpy array self.validator.adata.uns["batch_condition"] = "cell_type_ontology_term_id" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'cell_type_ontology_term_id' in 'uns['batch_condition']' is not valid, it must be a list or numpy array."], ) def test_batch_condition_is_column_from_obs(self): """ batch_condition list[str]. str values MUST refer to cell metadata keys in obs. """ self.validator.adata.uns["batch_condition"] = ["NO_COLUMN"] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: Value 'NO_COLUMN' of list 'batch_condition' is not a column in 'adata.obs'."], ) def test_default_embedding_is_str(self): """ Default_embedding str. """ self.validator.adata.uns["default_embedding"] = ["X_umap"] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: '['X_umap']' in 'uns['default_embedding']' is not valid, it must be a string."], ) def test_default_embedding_is_key_from_obsm(self): """ Default_embedding str. The value MUST match a key to an embedding in obsm """ self.validator.adata.uns["default_embedding"] = "X_other" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'X_other' in 'uns['default_embedding']' is not valid, it must be a key of 'adata.obsm'."], ) def test_X_approximate_distribution_is_str(self): """ X_approximate_distribution str. The value MUST be "count" [...] or "normal". Note that `normal` is tested in the happy path test case using `good_uns`. """ # Check valid case of "count" which is not included in valid object self.validator.adata.uns["X_approximate_distribution"] = "count" self.validator.validate_adata() self.assertEqual(self.validator.errors, []) # Invalid type: list self.validator.adata.uns["X_approximate_distribution"] = ["count"] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: '['count']' in 'uns['X_approximate_distribution']' is not valid, it must be a string."], ) def test_X_approximate_distribution_is_valid(self): """ X_approximate_distribution str. The value MUST be "count" [...] or "normal" """ self.validator.adata.uns["X_approximate_distribution"] = "COUNT" self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: 'COUNT' in 'uns['X_approximate_distribution']' is not valid. Allowed terms: ['count', 'normal']."], ) class TestObsm(unittest.TestCase): """ Fail cases for adata.obsm """ def setUp(self): self.validator = Validator() self.validator.adata = examples.adata.copy() def test_obsm_values_ara_numpy(self): """ values in obsm MUST be a numpy.ndarray """ self.validator.adata.obsm["X_tsne"] = pd.DataFrame( self.validator.adata.obsm["X_umap"], index=self.validator.adata.obs_names ) self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: All embeddings have to be of 'numpy.ndarray' type, " "'adata.obsm['X_tsne']' is <class 'pandas.core.frame.DataFrame'>')." ], ) def test_obsm_values_at_least_one_X(self): """ At least one key for the embedding MUST be prefixed with "X_" """ self.validator.adata.obsm["umap"] = self.validator.adata.obsm["X_umap"] self.validator.adata.uns["default_embedding"] = "umap" del self.validator.adata.obsm["X_umap"] self.validator.validate_adata() self.assertEqual( self.validator.errors, ["ERROR: At least one embedding in 'obsm' has to have a key with an 'X_' prefix."], ) def test_obsm_shape(self): """ Curators MUST annotate one or more two-dimensional (m >= 2) embeddings """ # Makes 1 column array self.validator.adata.obsm["X_umap"] = numpy.delete(self.validator.adata.obsm["X_umap"], 0, 1) self.validator.validate_adata() self.assertEqual( self.validator.errors, [ "ERROR: All embeddings must have as many rows as cells, and " "at least two columns.'adata.obsm['X_umap']' has shape " "of '(2, 1)'." ], ) class TestAddingLabels(unittest.TestCase): """ Tests the addition of labels from IDs based on schema specification. The test is done by comparing manually created dataframes (positive control) against the ones produced by the validator """ @classmethod def setUpClass(cls): # Manually created data (positive control) cls.adata_with_labels = examples.adata_with_labels # Validate test data validator = Validator() validator.adata = examples.adata.copy() validator.validate_adata() # Add labels through validator cls.label_writer = AnnDataLabelAppender(validator) cls.label_writer._add_labels() def test_var_added_labels(self): """ When a dataset is uploaded, cellxgene Data Portal MUST automatically add the matching human-readable name for the corresponding feature identifier and the inferred NCBITaxon term for the reference organism to the var dataframe. Curators MUST NOT annotate the following columns: - feature_name. If the feature_biotype is "gene" then this MUST be the human-readable ENSEMBL gene name assigned to the feature_id. If the feature_biotype is "spike-in" then this MUST be the ERCC Spike-In identifier appended with " spike-in control". - feature_reference. This MUST be the reference organism for a feature: Homo sapiens "NCBITaxon:9606" Mus musculus "NCBITaxon:10090" SARS-CoV-2 "NCBITaxon:2697049" ERCC Spike-Ins "NCBITaxon:32630" """ for column in ["feature_name", "feature_reference"]: expected_column = self.adata_with_labels.var[column] obtained_column = self.label_writer.adata.var[column] for i, j in zip(expected_column.tolist(), obtained_column.tolist()): with self.subTest(i=i, j=j): self.assertEqual(i, j) def test_obs_added_labels(self): """ When a dataset is uploaded, the cellxgene Data Portal MUST automatically add the matching human-readable name for the corresponding ontology term to the obs dataframe. Curators MUST NOT annotate the following columns. - assay. categorical with str categories. This MUST be the human-readable name assigned to the value of assay_ontology_term_id. Any clarifying text enclosed in parentheses and appended to assay_ontology_term_id MUST be appended to assay. - cell_type. categorical with str categories. This MUST be the human-readable name assigned to the value of cell_type_ontology_term_id. - development_stage. categorical with str categories. This MUST be "unknown" if set in development_stage_ontology_term_id; otherwise, this MUST be the human-readable name assigned to the value of development_stage_ontology_term_id. - disease. categorical with str categories. This MUST be the human-readable name assigned to the value of disease_ontology_term_id. - ethnicity. categorical with str categories. This MUST be "na" or "unknown" if set in ethnicity_ontology_term_id; otherwise, this MUST be the human-readable name assigned to the value of ethnicity_ontology_term_id. - organism. categorical with str categories. This MUST be the human-readable name assigned to the value of organism_ontology_term_id. - sex. categorical with str categories. This MUST be "unknown" if set in sex_ontology_term_id; otherwise, this MUST be the human-readable name assigned to the value of sex_ontology_term_id. - tissue. categorical with str categories. This MUST be the human-readable name assigned to the value of tissue_ontology_term_id. " (cell culture)" or " (organoid)" MUST be appended if present in tissue_ontology_term_id. """ for column in [ "assay", "cell_type", "development_stage", "disease", "ethnicity", "organism", "sex", "tissue", ]: expected_column = self.adata_with_labels.obs[column] obtained_column = self.label_writer.adata.obs[column] for i, j in zip(expected_column.tolist(), obtained_column.tolist()): with self.subTest(i=i, j=j): self.assertEqual(i, j)

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41, 46, 52, 54, 58, 61, 64, 74, 77, 80, 82, 8, 12, 8, 13, 14, 23, 24, 29, 30, 33, 35, 42, 43, 52, 54, 56, 58, 59, 60, 65, 66, 8, 13, 14, 23, 24, 32, 33, 38, 40, 8, 13, 14, 25, 26, 12, 17, 18, 27, 28, 34, 35, 12, 15, 20, 21, 23, 24, 25, 30, 31, 34, 36, 39, 41, 48, 49, 58, 60, 61, 64, 68, 74, 75, 78, 83, 86, 88, 92, 95, 97, 102, 103, 105, 107, 109, 112, 8, 10, 4, 8, 13, 14, 15, 16, 22, 27, 28, 40, 41, 43, 44, 47, 48, 52, 54, 8, 12, 8, 10, 11, 17, 22, 23, 35, 39, 40, 44, 50, 55, 58, 60, 65, 66, 71, 72, 75, 77, 83, 85, 8, 13, 18, 20, 26, 27, 30, 37, 40, 44, 50, 55, 60, 65, 71, 73, 77, 78, 81, 83, 8, 12, 8, 10, 16, 22, 27, 30, 32, 37, 38, 44, 47, 51, 60, 63, 69, 76, 8, 13, 14, 23, 24, 29, 30, 33, 35, 36, 37, 43, 48, 49, 61, 63, 65, 67, 72, 73, 8, 13, 14, 23, 24, 32, 33, 38, 40, 8, 13, 14, 25, 26, 30, 31, 40, 41, 47, 48, 52, 8, 10, 18, 23, 24, 29, 8, 13, 14, 23, 24, 29, 30, 33, 35, 36, 37, 43, 48, 49, 61, 63, 65, 68, 73, 75, 8, 13, 14, 23, 24, 32, 33, 38, 40, 8, 13, 14, 25, 26, 12, 17, 18, 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60, 12, 17, 18, 27, 28, 33, 34, 36, 38, 40, 41, 42, 44, 46, 49, 55, 56, 60, 61, 70, 71, 76, 77, 80, 81, 86, 8, 10, 8, 13, 14, 23, 24, 32, 33, 38, 40, 8, 13, 14, 25, 26, 12, 17, 18, 27, 28, 34, 35, 12, 14, 16, 18, 23, 24, 28, 39, 44, 47, 50, 53, 55, 60, 61, 68, 69, 74, 75, 77, 16, 19, 24, 25, 27, 29, 31, 32, 33, 35, 37, 39, 40, 43, 45, 50, 52, 58, 59, 63, 64, 69, 70, 79, 81, 84, 85, 12, 15, 8, 10, 4, 8, 13, 14, 16, 18, 19, 25, 26, 28, 29, 34, 35, 38, 39, 40, 41, 45, 47, 8, 12, 8, 11, 17, 21, 25, 29, 33, 43, 48, 51, 60, 65, 67, 68, 70, 8, 12, 8, 13, 14, 23, 24, 29, 30, 32, 34, 36, 38, 40, 42, 44, 49, 50, 59, 60, 65, 66, 68, 70, 72, 73, 74, 76, 78, 80, 8, 13, 14, 23, 24, 29, 30, 33, 35, 42, 43, 52, 54, 56, 58, 60, 62, 63, 8, 12, 17, 18, 27, 28, 33, 34, 36, 38, 40, 41, 42, 44, 46, 48, 8, 13, 14, 23, 24, 32, 33, 38, 40, 8, 13, 14, 25, 26, 12, 17, 18, 27, 28, 34, 35, 12, 15, 20, 21, 24, 30, 34, 44, 47, 49, 51, 53, 54, 58, 61, 66, 68, 72, 77, 80, 82, 83, 85, 92, 94, 96, 8, 10, 4, 8, 13, 14, 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40, 46, 47, 55, 60, 69, 72, 76, 82, 85, 89, 90, 98, 99, 103, 104, 106, 107, 12, 14, 16, 21, 22, 34, 39, 43, 54, 55, 60, 65, 68, 72, 78, 79, 87, 92, 101, 104, 108, 12, 18, 21, 23, 28, 29, 37, 38, 42, 43, 45, 46, 48, 50, 54, 62, 64, 67, 69, 71, 76, 79, 81, 86, 12, 15, 24, 27, 35, 38, 40, 45, 46, 54, 55, 59, 60, 62, 63, 8, 12, 8, 12, 19, 22, 24, 12, 14, 17, 19, 21, 12, 14, 18, 19, 23, 25, 12, 14, 25, 26, 31, 33, 12, 14, 15, 21, 23, 12, 14, 17, 20, 23, 25, 12, 14, 19, 22, 24, 12, 14, 17, 19, 12, 14, 15, 20, 22, 8, 10, 11, 12, 21, 22, 28, 30, 35, 36, 41, 42, 46, 47, 53, 54, 57, 58, 64, 65, 12, 21, 22, 28, 30, 35, 36, 41, 42, 48, 49, 54, 55, 58, 59, 65, 66, 12, 16, 18, 19, 21, 24, 28, 29, 37, 38, 44, 45, 51, 54, 63, 64, 70, 71, 77, 81, 16, 21, 26, 27, 30, 34, 35, 36, 37, 38, 39, 41, 42, 43, 45, 20, 25, 26, 37, 38, 39, 40, 42, 43 ]

ad3a7221883a847fc9d26097c3801973cbbda38e

from django.urls import path, include from Income import views urlpatterns = [ path("IncomeHome/", views.IncomeHome, name="IncomeHome"), path("IncomeCreate/", views.IncomeCreate.as_view(), name="IncomeCreate"), path("IncomeUpdate/<int:pk>", views.IncomeUpdate.as_view(), name="IncomeUpdate"), path("IncomeDelete/<int:pk>", views.IncomeDelete.as_view(), name="IncomeDelete"), path("Income/", views.IncomeView.as_view(), name="Income"), ]

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180f7f0ade9770c6669680bd13ac8f2fd55cc8c7

def raizCubica(numero): r = pow(numero, (1 / 3)) return r numeros = [] raices = [] for x in range(5): numeros.insert(x, float(input("Ingrese Numero: "))) raices.insert(x, round(raizCubica(numeros[x]), 3)) print("Numeros: ", numeros) print("Raices: ", raices)

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97ebdeada3d797a971b5c3851b75f9754595f67c

""" Python package setup file. """ from setuptools import setup setup( name="TF_Speech", version="0.2.0", extras_require={"tensorflow": ["tensorflow"], "tensorflow with gpu": ["tensorflow-gpu"]}, )

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9f34bf3a0bb24db428b7af1a354aec1d3a72df98

from random import randrange from django.core.exceptions import ValidationError from django.contrib.auth import get_user_model from rest_framework import serializers from rest_framework_simplejwt.serializers import TokenObtainPairSerializer from .models import EmailValidation from ..emails.models import Email from ..users.serializers import FullUserSerializer User = get_user_model() def user_with_email_not_existing(email): try: User.objects.get(email=email) raise ValidationError(message="This email is taken") except User.DoesNotExist: return email def email_does_exist(email): try: User.objects.get(email=email) return email except User.DoesNotExist: raise ValidationError(message="User does not exist!") class CreatePasswordEmailValidationSerializer(serializers.Serializer): email = serializers.EmailField(validators=[email_does_exist]) def save(self): validation_code = randrange(10000000, 100000000) email = Email.objects.create( validation_code=validation_code, to=self.validated_data.get("email"), type=self.validated_data.get("type") ) new_validation = EmailValidation.objects.create( validation_code=validation_code, email=email, type=self.validated_data.get("type") ) return new_validation class CreateEmailValidationSerializer(serializers.Serializer): email = serializers.EmailField(validators=[user_with_email_not_existing]) def save(self): validation_code = randrange(10000000, 100000000) email = Email.objects.create( validation_code=validation_code, to=self.validated_data.get("email"), type=self.validated_data.get("type") ) new_validation = EmailValidation.objects.create( validation_code=validation_code, email=email, type=self.validated_data.get("type") ) return new_validation class EmailSerializer(serializers.ModelSerializer): email = serializers.EmailField() class Meta: model = EmailValidation fields = ["email"] class EmailValidationSerializer(serializers.ModelSerializer): email = serializers.EmailField() class Meta: model = EmailValidation fields = ["email", "validation_code"] class EmailValidationPasswordSerializer(serializers.ModelSerializer): email = serializers.EmailField() password = serializers.CharField(max_length=200) class Meta: model = EmailValidation fields = ["email", "validation_code", "password"] class NewUserSerializer(serializers.ModelSerializer): email = serializers.EmailField() class Meta: model = EmailValidation fields = ["email"] class TokenObtainPairViewWithUserProfileSerializer(TokenObtainPairSerializer): def validate(self, attrs): data = super().validate(attrs) refresh = self.get_token(self.user) data["refresh"] = str(refresh) data["access"] = str(refresh.access_token) data["user"] = FullUserSerializer(self.user).data return data

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2ca91c410b8c8d6306d5ed918783a4d77a091ba8

from typing import List from re import match from utility import ButtonGroup import rumps class RepeatWorkBreak(rumps.App): def __init__(self): rumps.debug_mode(True) self.config = { "app_title": "Repeat Work and Break", "start": "Start", "pause": "Pause Timer", "continue": "Continue Timer", "stop": "Stop Timer", "timeout_message": "Time is up! Take a break :)", "shift_time_in_seconds": 60 * 60 * 1, # 60 seconds * 60 = 1 hour "break_time_in_seconds": 60 * 5, "shift_setting_buttons": [ { "title": "1 hour", }, { "title": "4 hour", }, { "title": "8 hour", }, ], "break_setting_buttons": [ { "title": "5 minutes", }, { "title": "10 minutes", }, { "title": "15 minutes", }, ], } self.app = rumps.App(self.config["app_title"]) self.timer = rumps.Timer(self.on_tick, 1) self.shift_setting_button_group = ButtonGroup( self.config["shift_setting_buttons"], callback=self.handle_shift_setting_button ) self.break_setting_button_group = ButtonGroup( self.config["break_setting_buttons"], callback=self.handle_shift_setting_button ) self.shift_time_in_seconds = self.config["shift_time_in_seconds"] self.break_time_in_seconds = self.config["break_time_in_seconds"] self.elapsed_shift_time_in_hours = 0 self.progress_box = "◻︎" * (self.shift_time_in_seconds // 3600) self.start_pause_button = rumps.MenuItem(title=self.config["start"], callback=self.start_timer) self.stop_button = rumps.MenuItem(title=self.config["stop"], callback=None) self.app.menu = [ { "Preferences": { "Setting Shift": self.shift_setting_button_group.buttons, "Setting Break / hr": self.break_setting_button_group.buttons, } }, None, self.start_pause_button, self.stop_button, ] def set_up_menu(self): self.timer.stop() self.timer.count = 0 self.app.title = self.config["app_title"] def convert_seconds_to_time_string(self, seconds) -> str: seconds = seconds % (24 * 3600) hours, seconds = divmod(seconds, 3600) minutes, seconds = divmod(seconds, 60) return "%d:%02d:%02d" % (hours, minutes, seconds) def on_tick(self, sender): time_left_in_seconds = sender.end - sender.count time_left_in_string = self.convert_seconds_to_time_string(time_left_in_seconds) if sender.count != 0 and sender.count % 3600 == 0: self.elapsed_shift_time_in_hours += 1 self.update_progress_box() if time_left_in_seconds == 0: rumps.notification(title=self.config["app_title"], subtitle=self.config["timeout_message"], message="") self.stop_timer() self.stop_button.set_callback(None) else: self.stop_button.set_callback(self.stop_timer) self.app.title = self.progress_box + " | " + time_left_in_string sender.count += 1 def update_progress_box(self): self.progress_box = ( self.elapsed_shift_time_in_hours * "☑︎" + (self.shift_time_in_seconds // 3600 - self.elapsed_shift_time_in_hours) * "◻︎" ) def start_timer(self, sender): if sender.title.lower().startswith(("start", "continue")): if sender.title == self.config["start"]: self.timer.count = 0 self.timer.end = self.shift_time_in_seconds sender.title = self.config["pause"] self.timer.start() else: sender.title = self.config["continue"] self.timer.stop() def stop_timer(self, sender=None): self.set_up_menu() self.stop_button.set_callback(None) self.start_pause_button.title = self.config["start"] def handle_shift_setting_button(self, sender): self.shift_setting_button_group.toggle(sender) selected_hours = int(match(r"^\d+\s{1}", sender.title)[0]) self.progress_box = "◻︎" * selected_hours # update empty progress box self.shift_time_in_seconds = selected_hours * 3600 # hours in seconds def handle_break_setting_button(self, sender): self.break_setting_button_group.toggle(sender) selected_minutes = int(match(r"^\d+\s{1}", sender.title)[0]) self.break_time_in_seconds = selected_minutes * 60 def run(self): self.app.run() if __name__ == "__main__": app = RepeatWorkBreak() app.run()

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8bc40ed4fe1091ecdb40cd55ff9cf53010078823

import requests import json data = json.load(open("dummy_data/data.json")) for one in data: print(one) r = requests.post("http://localhost:8080/sumari", json=one) print(r.text)

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4e9a968842c2b3eca79690f0b56c8e176b203138

print((9 * int(input()) / 5) + 32)

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[ 1, 6, 8, 9, 11, 15, 16, 21, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35 ]

7a1bd2b4734527a414c6173ea8edb150221f8042

import numpy as np import pandas as pd from scipy.optimize import minimize from datetime import datetime import time from functions import weather_scraper def getData(): # # run weather_scraper.py to fetch new weather data # weather_scraper.getData() ## Read in csv file "weather_data.csv" weather_data = pd.read_csv("data/weather_data.csv") # Grab the current month & hour currentMonth = datetime.now().month currentHour = datetime.now().hour # Determine which month group the current month is [0,5] currentMonthGroup = currentMonth // 2 hoep_data = [] temp = weather_data.iloc[:, 2] # Change hour string to number from 0-23 for i in range(len(temp)): weather_data.iloc[i, 1] = (currentHour + i) % 24 # Convert temperature data to HOEP data if currentMonthGroup == 0: hoep_data = temp.apply(lambda x: (2.02887 * x + 39.633) / 100) elif currentMonthGroup == 1: hoep_data = temp.apply(lambda x: (0.453122 * x + 19.8276) / 100) elif currentMonthGroup == 2: hoep_data = temp.apply(lambda x: (1.13665 * x - 11.0085) / 100) elif currentMonthGroup == 3: hoep_data = temp.apply(lambda x: (1.90245 * x - 23.2826) / 100) elif currentMonthGroup == 4: hoep_data = temp.apply(lambda x: (1.39145 * x - 8.97971) / 100) else: hoep_data = temp.apply(lambda x: (1.72767 * x + 21.3536) / 100) # Load in the load_data load_data = pd.read_excel("data/load_data.xlsx", index_col=0, engine="openpyxl") # Create loading schedule based on current time of day and month load_sched = np.arange(48) for i in range(len(temp)): load_sched[i] = load_data.iloc[weather_data.iloc[i, 1], currentMonthGroup] WMST = 0.003499 ## x[0:48] = PCEA ## x[48:96] = ESB start_time = time.time() # Constraints to ensure that ESB falls within limits def constraint1(x): for i in range(48): if i == 0: x[48] = 0 else: x[48 + i] = x[48 + i - 1] + x[i] return x[0:48] + x[48:96] def constraint2(x): for i in range(48): if i == 0: x[48] = 0 else: x[48 + i] = x[48 + i - 1] + x[i] return 10000 - (x[0:48] + x[48:96]) power = ((-5000, 5000),) * 48 storage = ((0, 10000),) * 48 # Objective def MEC(x): # ( PDLL + PCEA ) x HOEP return sum(sum((load_sched + np.array([x[0:48]])) * (np.array(hoep_data) + WMST))) x0 = np.array([np.ones(48), np.ones(48)]) bounds = power + storage cons1 = {"type": "ineq", "fun": constraint1} cons2 = {"type": "ineq", "fun": constraint2} cons = [cons1, cons2] sol = minimize(MEC, x0, method="SLSQP", bounds=bounds, constraints=cons, options={"maxiter": 150, "disp": True}) input_var = {"EA_w_bill": round(sol.fun, 2)} return input_var

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1bdb19373960e4f63d80d6ab73ec3c0939e40b7f

import contextlib import dask import dask.array as da import packaging.version import pandas import six import sklearn SK_VERSION = packaging.version.parse(sklearn.__version__) DASK_VERSION = packaging.version.parse(dask.__version__) PANDAS_VERSION = packaging.version.parse(pandas.__version__) @contextlib.contextmanager def dummy_context(*args, **kwargs): yield if six.PY2: from collections import Mapping else: from collections.abc import Mapping # noqa if DASK_VERSION < packaging.version.parse("1.1.0"): blockwise = da.atop else: blockwise = da.blockwise

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a4f932a8566afe0265dc1057d0f6534a608697f7

""" LeetCode Problem: 242. Valid Anagram Link: https://leetcode.com/problems/valid-anagram/ Written by: Mostofa Adib Shakib Language: Python """ class Solution(object): def isAnagram(self, s, t): """ :type s: str :type t: str :rtype: bool """ length1 = len(s) length2 = len(t) if length1 != length2: return False s = sorted(s) # sorted the string in alphanumeric order t = sorted(t) # sorted the string in alphanumeric order for i in range(0, length1): if s[i] != t[i]: return False # return false if the two sorted strings are not the same. return True # if the sorted strings are same return True

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6dd11f71e514a46462bf0b97ddac9ea474e86ad0

import os, glob import numpy as np from ..algorithms.utils import get_file_manager from ..algorithms.clustered_writes import * from ..exp_utils import create_empty_dir def test_get_entity_sizes(): # in C order bytes_per_voxel = 1 R = (10, 9, 10) cs = (5, 3, 2) partition = (2, 3, 5) bs, brs, bss = get_entity_sizes(cs, bytes_per_voxel, partition) assert bs == 5 * 3 * 2 assert brs == 5 * 3 * 2 * 5 assert bss == 5 * 3 * 2 * 5 * 3 def test_get_strategy(): # in C order bytes_per_voxel = 1 R = (20, 9, 10) cs = (5, 3, 2) partition = (4, 3, 5) bs, brs, bss = get_entity_sizes(cs, bytes_per_voxel, partition) test_case = { 5 * 2 * 3: 0, # 1 block 5 * 2 * 3 * 4: 0, # 4 blocks 5 * 2 * 3 * 5: 1, # 1 row 5 * 2 * 3 * 5 * 2: 1, # 2 rows 5 * 2 * 3 * 5 * 3: 2, # 1 slice 5 * 2 * 3 * 5 * 3 * 3: 2, # 3 slices 5 * 2 * 3 * 5 * 3 * 4: 2, # whole img 5 * 2 * 3 * 5 * 3 * 7: 2, # whole img (more mem than necessary) } for buffer_mem_size, expected in test_case.items(): strategy = get_strategy(buffer_mem_size, bs, brs, bss) assert strategy == expected def test_compute_buffers(): # in C order bytes_per_voxel = 1 R = (20, 9, 10) cs = (5, 3, 2) partition = (4, 3, 5) bs, brs, bss = get_entity_sizes(cs, bytes_per_voxel, partition) origarr_size = R[0] * R[1] * R[2] * bytes_per_voxel test_case = { 5 * 2 * 3: 4 * 3 * 5, # 1 block 5 * 2 * 3 * 4: 4 * 3 * 2, # 4 blocks 5 * 2 * 3 * 5: 4 * 3, # 1 row 5 * 2 * 3 * 5 * 2: 4 * 2, # 2 rows 5 * 2 * 3 * 5 * 3: 4, # 1 slice 5 * 2 * 3 * 5 * 3 * 3: 2, # 3 slices 5 * 2 * 3 * 5 * 3 * 4: 1, # whole img 5 * 2 * 3 * 5 * 3 * 7: 1, # whole img (more mem than necessary) } for buffer_mem_size, expected in test_case.items(): strategy = get_strategy(buffer_mem_size, bs, brs, bss) buffers = compute_buffers(buffer_mem_size, strategy, origarr_size, cs, bs, brs, bss, partition, R, bytes_per_voxel) # test number of buffers nb_buffers = len(buffers.values()) assert nb_buffers == expected def test_clustered_writes(): bpv = 1 R = (20, 9, 10) cs = (5, 3, 2) ff = "HDF5" outdir_path = "./outdir" test_case = [ 5 * 3 * 2, # 1 block 5 * 3 * 2 * 4, # 4 blocks 5 * 3 * 2 * 5, # 1 row 5 * 3 * 2 * 5 * 2, # 2 rows 5 * 3 * 2 * 5 * 3, # 1 slice 5 * 3 * 2 * 5 * 3 * 3, # 3 slices 5 * 3 * 2 * 5 * 3 * 4, # whole img 5 * 3 * 2 * 5 * 3 * 7, # whole img (more mem than necessary) ] nb_chunks = 4 * 3 * 5 # create input array origarr_filepath = "./original_array.hdf5" data = np.random.normal(size=R) fm = get_file_manager(ff) if os.path.isfile(origarr_filepath): os.remove(origarr_filepath) fm.write(origarr_filepath, data, R, _slices=None) for m in test_case: create_empty_dir(outdir_path) clustered_writes(origarr_filepath, R, cs, bpv, m, ff, outdir_path) workdir = os.getcwd() os.chdir(outdir_path) filenames = list() for filename in glob.glob("*.hdf5"): arr = fm.read_all(filename) assert arr.shape == cs filenames.append(filename) assert len(filenames) == nb_chunks os.chdir(workdir)

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c09c02a36a64e9522cfc8c0951bd6c98f404f09c

# Random number guessing game. # 10 July 20 # CTI-110 P5HW1 - Random Number # Thelma Majette import random randomNumber = random.randint(1, 100) # main function def main(): # Create a variable to control the loop. keep_going = "y" while keep_going == "y": # Ask user for a number () guess = int(input("\nGuess a number between 1 and 100: ")) # Perform the selected action. if guess > randomNumber: print("\nToo high, try again.") elif guess < randomNumber: print("\nToo low, try again") else: print("\nCongratulations, you guessed the correct number!") keep_going = "n" main()

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798ddd4a6e4febb4664bf1c973877628d1a45c71

from django.conf.urls import patterns, include, url # Uncomment the next two lines to enable the admin: # from django.contrib import admin # admin.autodiscover() urlpatterns = patterns( "accounts.views", url(r"^$", "home", name="home"), url(r"^login/$", "login", name="login"), url(r"^logout/$", "logout", name="logout"), url(r"^register/$", "register", name="register"), url(r"^dashboard/", "dashboard", name="dashboard"), url(r"^rewards/", "rewards", name="rewards"), url(r"get_all_data/", "get_all_data", name="get_all_data"), )

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3340277df91f1421dab8d204eddce65b4604432b

from django.shortcuts import render, redirect from .models import Courses from django.views.generic import CreateView, ListView, UpdateView, DeleteView from .forms import CourceCreateForm from django.urls import reverse_lazy from django.urls import reverse class CourceListView(ListView): model = Courses template_name = "cources/cource_list.html" context_object_name = "cources" class CourceCreateView(CreateView): template_name = "cources/create_cource.html" form_class = CourceCreateForm success_url = reverse_lazy("cources:cource_list") class CourceUpdateView(UpdateView): model = Courses form_class = CourceCreateForm template_name = "cources/course_update.html" success_url = reverse_lazy("cources:cource_list") def DeleteView(request, pk): cource = Courses.objects.filter(pk=pk) cource.delete() return redirect(reverse("cources:cource_list"))

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66444047f9e5eea845c8ac2dbaaf16fc2914d6ec

if answ[1] == "дата": apisay(datetime.date.today(), toho, torep)

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d2f6d7c779d3d6e61d9da7af01a2931fdabec828

import random choices = ["X", "O"] try: # Choice of X-O given to the player player_sym = input("Choose 'X' or 'O' : ") # raising an exception if the variable is not X or O if player_sym != "X" and player_sym != "O": raise Exception("Symbol not found") except Exception as e: print(e.args) else: # Allotting the other one as the computer symbol choices.remove(player_sym) comp_sym = choices[0] player_dict = {player_sym: "Player", comp_sym: "Computer"} # creating the board board = [" "] * 9 gameEnd = False # to track when the game ends unmarked = [i for i in range(9)] # to track all the blank boxes left # gameOver function check if the game already has a winner def gameOver(board, symbol): # below is the sequence of all the possible winning combinations if ( board[0] == board[3] == board[6] == symbol or board[1] == board[7] == board[4] == symbol or board[2] == board[5] == board[8] == symbol or board[0] == board[1] == board[2] == symbol or board[5] == board[3] == board[4] == symbol or board[6] == board[7] == board[8] == symbol or board[2] == board[4] == board[6] == symbol or board[0] == board[4] == board[8] == symbol ): # if there is a pattern match the game is over hence return True return True # function for marking the box with the symbol def mark(pos, symbol): board[pos] = symbol unmarked.remove(pos) # Used it for debugging : print(f"Unmarked : {unmarked}") # function to display the board at a particular time def displayBoard(): for i in range(len(board)): # formatting the output for the middle elements if i == 1 or i == 4 or i == 7: print(f"|{board[i]}|", end=" ") elif i == 2 or i == 5: print(f"{board[i]}\n--------") # marks the end of a line and hence bifurcates two lines else: print(f"{board[i]}", end=" ") if __name__ == "__main__": # this is where the game starts while not gameEnd: # loop until game ends try: player_pos = int(input("\n\nWhere would you mark? ")) # check if position index is on the board and is available for marking else raise Exception if player_pos < 0 or player_pos > 8 or (player_pos not in unmarked): raise Exception("Position out of Board") break except Exception as e: print(e.args) else: mark(player_pos, player_sym) # check if the game has already ended and if yes, declare the player as winner if gameOver(board, player_sym): displayBoard() print("\n\nPlayer Won!!!") break # computer will mark on some random square that is not marked yet comp_pos = unmarked[random.randint(0, len(unmarked) - 1)] mark(comp_pos, comp_sym) # check if the game has already ended and if yes, declare the computer as winner if gameOver(board, comp_sym): displayBoard() print("\n\nComputer WON!!!") break # display the board after each iteration displayBoard() # marks the end of the game print("GAME OVER")

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"Ġthe", "Ġgame", "ĊĠĠĠ", "Ġprint", "(\"", "GAME", "ĠOVER", "\")", "Ċ" ]

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b4c6075aabe833f6fe23471f608d928edd25ef63

from .base import paw_test class warning_test(paw_test): def test_warning_badchars(self): self.paw.cset_lookup(self.badchar) self.assertEqual(1, self.paw.wcount)

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24c9b562411a63f0d3f2ee509bb60dafe7fbecd1

import os from flask import Flask # from flask_login import LoginManager from flask_sqlalchemy import SQLAlchemy # from flask_bcrypt import Bcrypt from flask_wtf.csrf import CSRFProtect app = Flask(__name__) csrf = CSRFProtect(app) # bcrypt = Bcrypt(app) app.config["SECRET_KEY"] = "v\xf9\xf7\x11\x13\x18\xfaMYp\xed_\xe8\xc9w\x06\x8e\xf0f\xd2\xba\xfd\x8c\xda" app.config["SQLALCHEMY_DATABASE_URI"] = ( "postgres://vwfeollskqmjyw:1d738da99074015b148d72cfd94ea584dcb39e81c1bb197fb9da65455c756b0f@ec2-50-17-227-28.compute-1.amazonaws.com:5432/dc8lr6j69aeqjt" ) # app.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql: //infoproj2:info3180@localhost/infoproj2' PRO_PIC_UPLOAD_FOLDER = "./app/static/profile_photos" POSTS_UPLOAD_FOLDER = "./app/static/posts_photos" app.config["SQLALCHEMY_TRACK_MODIFICATIONS"] = True # added just to suppress a warning db = SQLAlchemy(app) # Flask-Login LoginManager # login_manager = LoginManager() # login_manager.init_app(app) # login_manager.login_view = 'login' app.config.from_object(__name__) from app import views, models # intense-basin-58864 #name of project on heroku

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14a357f3dfb3d59f1d8cfd566edeaf8b0e5bb56d

#!/usr/bin/env python import rospy from geometry_msgs.msg import Twist from sensor_msgs.msg import Joy import serial from sys import platform if platform == "linux" or platform == "linux2": ser = serial.Serial("/dev/ttyACM0") elif platform == "darwin": pass elif platform == "win32": # Windows... ser = serial.Serial("COM16") """ In this test code we are testing basic vehicle control over the network we use ROS middleware to send the control commands This script runs at the remote driver end. Receives joystick messages (subscribed to Joy topic) then converts the joystick inputs into commands WE ARE NOT USING THIS METHOD NOW --- WE HAVE SEPERATED OUT ALL THE STREAMS FROM THE JOYSTICK """ oldvar = 0 first_a = 0 first_d = 0 # Configuatrion tuned for CAR in LOW speed base_throttle = 5500 peak_throttle = 6500 base_brake = 450 peak_brake = 600 button = 0 def callback(data): global first_a global first_d global oldvar global base_throttle global peak_throttle global base_brake global peak_brake global button # print data axis1 = -data.axes[1] axis3 = -data.axes[3] # in logitech axis 3 is axis 4 confirm with ashish button1 = data.buttons[1] button4 = data.buttons[4] button5 = data.buttons[5] button_ = button1 + button4 + button5 if axis1 > 0.1: bval = int((axis1) * (peak_brake - base_brake) + base_brake) print(bval) ser.write(str(bval).encode("utf-8")) ser.write("a".encode("utf-8")) #### ser.write("4000a".encode('utf-8')) #throttle released on braking print("Brake") elif axis1 < -0.1 and axis3 < 0.1: tval = int((axis1 * -1 + axis3 * -1) * (peak_throttle - base_throttle) * 0.5 + base_throttle) if abs(tval - oldvar) > 5: # print(tval) ser.write(str(tval).encode("utf-8")) ser.write("a".encode("utf-8")) ser.write("450a".encode("utf-8")) # brake released on acceleration print("Throttle") oldvar = tval elif axis1 > -0.1 and axis1 < 0.1: ser.write("4000a".encode("utf-8")) ser.write("450a".encode("utf-8")) # brake released print("Zero Throttle") print(axis1) print(axis3) if button1 == 1: print("Emergency Brake") ser.write("4600a".encode("utf-8")) # throttle released ser.write("600a".encode("utf-8")) # brake engaged if button4 and button5 == 0: if first_a == 0: ser.write("1000a".encode("utf-8")) print("Joystick button 4 pressed.") first_a = 1 if button5 and button4 == 0: if first_d == 0: ser.write("2000a".encode("utf-8")) print("Joystick button 5 pressed.") first_d = 1 if button - button_ != 0: if button4 == 0: first_a = 0 if button5 == 0: first_d = 0 ser.write("3000a".encode("utf-8")) print("Joystick button released.") button = button_ # Intializes everything def start(): rospy.Subscriber("joy", Joy, callback) # starts the node rospy.init_node("Joy2Turtle") rospy.spin() if __name__ == "__main__": start()

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1ef9df43725196904ec6c0c881f4a1204174b176

import requests, shutil, os, glob from zipfile import ZipFile import pandas as pd from xlrd import open_workbook import csv # zipfilename = 'desiya_hotels' # try: # # downloading zip file # r = requests.get('http://staticstore.travelguru.com/testdump/1300001176/Excel.zip', auth=('testdump', 'testdump'), verify=False,stream=True) #Note web_link is https:// # r.raw.decode_content = True # with open(os.path.join(os.path.dirname(__file__), 'storage/{}.zip'.format(zipfilename)), 'wb') as f: # shutil.copyfileobj(r.raw, f) # # #extracting zip file as xls file # with ZipFile(glob.glob(os.path.join(os.path.dirname(__file__), 'storage/desiya*.zip'))[0], 'r') as zip: # zip.extractall(os.path.join(os.path.dirname(__file__), 'storage/')) # #Rename xls file name as "desiya_hotels" # if glob.glob(os.path.join(os.path.dirname(__file__), 'storage/*[0-9].xls')): # for filename in glob.glob(os.path.join(os.path.dirname(__file__), 'storage/*[a-zA-z].xls')): # os.remove(filename) # os.rename(glob.glob(os.path.join(os.path.dirname(__file__), 'storage/*[0-9].xls'))[0], os.path.join(os.path.dirname(__file__),'storage/{}.xls'.format(zipfilename))) # else: # print('unzipped xls file is not found in storare folder') # except Exception as e: # print("Error while downloading zip file") # read xls file # xls = pd.ExcelFile(glob.glob(os.path.join(os.path.dirname(__file__), 'storage/desiya*.xls'))[0]) # df = pd.read_excel(xls, sheet_name=0, index_col=None) # print(df['Name']) # print(df.head(5)) # for index, row in df.iterrows(): # print(index, row[3]) # convert xls to csvc # df.to_csv(os.path.join(os.path.dirname(__file__),'storage/{}'.format('robot.csv')), encoding='utf-8', index=False) # convert xls file to csv using xlrd module xlsfile = glob.glob(os.path.join(os.path.dirname(__file__), "storage/robot*.xls"))[0] wb = open_workbook(xlsfile) sheet = wb.sheet_by_name("robot_list") with open(os.path.join(os.path.dirname(__file__), "storage/robot_list.csv"), "w") as file: writer = csv.writer(file, delimiter=",") headers = [cell.value for cell in sheet.row(0)] writer.writerow(headers) for i in range(1, sheet.nrows): rowvalue_list = [str(cell.value).strip() if cell.value else None for cell in sheet.row(i)] writer.writerow(rowvalue_list)

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7a59c8c883a9aaa723175783e01aa62e23503fde

#!/C:\Program Files (x86)\Python35-32 # importar librarias necesarias from urllib.request import urlopen from bs4 import BeautifulSoup

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48d0bfdc607a4605ef82f5c7dc7fd6fc85c4255f

""" BMI=weight*0.45259227/(hei*0.0254)** """ wht = 2 if wht == 0: print("wht is", wht) else: print("whtsdsb") # 今天也完成了100波比跳 wei = float(input("wei=")) hei = float(input("hei=")) bmi = (wei * 0.45259227) / ((hei * 0.0254) ** 2) print("BMI=", bmi) if bmi < 18.5: print("too light") elif bmi < 25: print("normal") elif bmi < 30: print("over") else: print("wanghantangshidssb")

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05e4bcc7323b908a7b45d766ada463ce172e25c4

import graphene import f1hub.drivers.schema import f1hub.results.schema import f1hub.constructors.schema import f1hub.races.schema import f1hub.status.schema import f1hub.circuits.schema import f1hub.constructorresults.schema import f1hub.constructorstandings.schema import f1hub.driverstandings.schema import f1hub.laptimes.schema import f1hub.pitstops.schema import f1hub.qualifying.schema import f1hub.seasons.schema class Query( f1hub.drivers.schema.Query, f1hub.results.schema.Query, f1hub.constructors.schema.Query, f1hub.races.schema.Query, f1hub.status.schema.Query, f1hub.circuits.schema.Query, f1hub.constructorresults.schema.Query, f1hub.constructorstandings.schema.Query, f1hub.driverstandings.schema.Query, f1hub.laptimes.schema.Query, f1hub.pitstops.schema.Query, f1hub.qualifying.schema.Query, f1hub.seasons.schema.Query, graphene.ObjectType, ): pass schema = graphene.Schema(query=Query)

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7530c2c85f83d1714840ba97c1ec702f063658c5

from typing import List import glm import pxng import OpenGL.GL as gl class VertexArrayObject: def __init__(self, primitive): self._primitive = primitive self._buffers: List[pxng.BufferObject] = [] self._indices = pxng.BufferObject(data_type=self.index_data_type, array_type=gl.GL_ELEMENT_ARRAY_BUFFER) self._vao = gl.glGenVertexArrays(1) def attach_buffer(self, vbo: pxng.BufferObject): self._buffers.append(vbo) return len(self._buffers) - 1 def add_quad(self, p1, p2, p3, p4): i = self._buffers[0].index self._buffers[0].set_value(p1) self._buffers[0].set_value(p2) self._buffers[0].set_value(p3) self._buffers[0].set_value(p4) self._indices.set_value(glm.u16vec3(i, i + 1, i + 3)) self._indices.set_value(glm.u16vec3(i + 1, i + 2, i + 3)) def add_triangle(self, p1, p2, p3): i = self._buffers[0].index self._buffers[0].set_value(p1) self._buffers[0].set_value(p2) self._buffers[0].set_value(p3) self._indices.set_value(glm.u16vec3(i, i + 1, i + 2)) def add_line(self, p1, p2): i = self._buffers[0].index self._buffers[0].set_value(p1) self._buffers[0].set_value(p2) self._indices.set_value(glm.u16vec2(i, i + 1)) def add_point(self, p1): i = self._buffers[0].index self._buffers[0].set_value(p1) self._indices.set_value(glm.u16vec1(i)) def set_colors(self, *args: glm.vec4, target=1): for c in args: self._buffers[target].set_value(c) def set_texture(self, *args: glm.vec2 or glm.uvec2, target=1): for c in args: self._buffers[target].set_value(c) def create(self): gl.glBindVertexArray(self._vao) for index, vbo in enumerate(self._buffers): vbo.bind(index) self._indices.bind(None) def reset(self): self._indices.reset() for vbo in self._buffers: vbo.reset() def draw(self): index_count = len(self._indices) * self.primitive_component_count gl.glDrawElements(self._primitive, index_count, gl.GL_UNSIGNED_SHORT, None) @property def index_data_type(self): if self._primitive == gl.GL_TRIANGLES: return glm.u16vec3 elif self._primitive == gl.GL_LINES: return glm.u16vec2 elif self._primitive == gl.GL_POINTS: return glm.u16vec1 else: raise UserWarning(f"Unknown primitive type {self._primitive}") @property def primitive_component_count(self): if self._primitive == gl.GL_TRIANGLES: return 3 elif self._primitive == gl.GL_LINES: return 2 elif self._primitive == gl.GL_POINTS: return 1 else: raise UserWarning(f"Unknown primitive type {self._primitive}") def bind(self): gl.glBindVertexArray(self._vao) if self._indices.bind(None): if any(vbo.changed for vbo in self._buffers): self.create() return True gl.glBindVertexArray(0) return False

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1f7d770106ea8e7d1c0bb90e1fc576b7ee2f0220

# Generated by Django 3.0.8 on 2020-08-28 17:37 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("shop", "0003_auto_20200828_1836"), ] operations = [ migrations.AddField( model_name="order", name="total", field=models.CharField(default=0, max_length=200), preserve_default=False, ), migrations.AlterField( model_name="order", name="items", field=models.CharField(max_length=300), ), ]

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01847c9e601eae6775cd4324483740c30e344557

from django.apps import AppConfig class CfCoreConfig(AppConfig): name = "cf_core"

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ca11e9cf0bcfcbd714c45b5c95bd2c2044b65909

"""Woma objects for dealing with HTTP. Request and Response inherit from webob's Request and Response objects, so see http://docs.webob.org/en/latest/ for full documentation. The only things documented here are the customizations. """ from webob import Request as BaseRequest from webob import Response as BaseResponse class Client(object): """Make requests to a wsgi app and return the response.""" def __init__(self, app): self.app = app def request(self, path, method, body=None): path = path or "/" request = BaseRequest.blank(path) request.method = method request.text = body or "" return request.get_response(self.app) def get(self, path=None): return self.request(path, "GET") def post(self, path=None, body=None): return self.request(path, "POST", body) def put(self, path=None, body=None): return self.request(path, "PUT", body) class Request(BaseRequest): """A webob.Request with additional properties.""" @property def kwargs(self): """Returns 'router.kwargs' from environ if present, or {} otherwise.""" return self.environ.get("router.kwargs", {}) class Response(BaseResponse): """A webob.Response that can be initialized with defaults from request.""" @classmethod def for_request(cls, request): """Initialize a Response with defaults based on the request. >>> request = Request({}) >>> request.headers['Content-Type'] = 'text/html; charset=latin1' >>> response = Response.for_request(request) >>> response.content_type 'text/html' >>> response.charset 'latin1' """ return cls(status_code=200, content_type=request.content_type or "text/plain", charset=request.charset or "UTF-8") def write(self, text): """An alias for `response.text = text`. >>> response = Response() >>> response.write('some text') >>> response.text 'some text' """ self.text = text

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2c22f891f30825bcb97987c78a98988ad2a92210

import os import sys import json import logging import argparse from glob import glob from pricewatcher.tools import ensure_mkdir from pricewatcher.parser.f21 import ForeverParser from pricewatcher.parser.jcrew import JcrewParser from pricewatcher.utils.load_es import bulk_load_es BRAND_PARSERS = {"forever21": ForeverParser, "jcrew": JcrewParser} # Set up logging FORMAT = "[%(asctime)s][%(levelname)s] %(message)s" logging.basicConfig(format=FORMAT, datefmt="%m-%d-%Y %H:%M:%S") logging.getLogger().setLevel(logging.INFO) def date_handler(obj): return obj.isoformat() if hasattr(obj, "isoformat") else obj def run(): parser = argparse.ArgumentParser(description="Process some integers.") parser.add_argument("--input-base", required=True, help="") parser.add_argument("--output-base", default="parsed_pages", help="") parser.add_argument("--datetime", required=True, help="YYYYMMDD") parser.add_argument("--hour", default="*", help="HH") parser.add_argument("--brand", default="*", choices=BRAND_PARSERS.keys(), help="") parser.add_argument("--load-es", action="store_true") parser.add_argument("--es-host", default="localhost", help="default to localhost") parser.add_argument("--es-port", default="9200", help="default to 9200") parser.add_argument("--es-cleanup", action="store_true", help="remove index before loading new data") args = parser.parse_args() # Argument parsing dt_str = args.datetime hour_str = args.hour brand_str = args.brand input_base = args.input_base output_base = args.output_base # ES arguments es_host, es_port = args.es_host, args.es_port load_es = args.load_es # Parsing Raw Pages input_files = glob(os.path.join(input_base, dt_str, hour_str, brand_str, "*", "*", "*")) for file_path in input_files: dt_str, hour_str, br, category, sub_category, filename = file_path.split("/")[-6:] parser = BRAND_PARSERS[brand_str](file_path) parsed_docs = parser.parse() if parsed_docs: doc_list, price_list = parsed_docs logging.info("[STATUS] parsed %s docs from %s" % (len(doc_list), file_path)) if not load_es: # Output Result output_dir = os.path.join(output_base, os.path.join(dt_str, hour_str, br, category)) ensure_mkdir(output_dir) output_path = os.path.join(output_dir, filename + ".json") logging.info("[WRITE] output to %s" % output_path) # Dump Product List with open(output_path + ".doc", "w") as ofile: ofile.write(json.dumps(doc_list, default=date_handler)) with open(output_path + ".price", "w") as ofile: ofile.write(json.dumps(price_list, default=date_handler)) else: # es_index, es_doctype = br, category logging.info("[LOAD ES] loading to ElasticSearch...") preprocessed_list = [] for doc in doc_list: preprocessed_list.append({"index": {"_index": br, "_type": category, "_id": doc["product_id"]}}) preprocessed_list.append(doc) bulk_load_es(es_host, es_port, br, category, preprocessed_list, opt_dict=None) bulk_load_es(es_host, es_port, br, "price", price_list)

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3cd7abf9659fe1db0ef3aa58df8dd7fd959e10a6

import os import csv import re totWords = 0 wordLen = 0 totSentWithPunctuation = 0 sourceFile = os.path.join("Resources", "paragraph_2.txt") with open(sourceFile, "r") as paragraph: paragraph = paragraph.read().split("\n\n") for sentence in paragraph: # Remove punctuation from sentences sentWithPunctuation = sentence sentNoPunctuation = re.sub(r"[^\w\s]", "", sentence) # Split sentence with no punctuation by words using spaces words = sentNoPunctuation.split(" ") for word in words: wordLen = wordLen + len(word) # Compute totals for output message totWords = totWords + len(words) # Total words for all sentences avgSentLen_Words = round(totWords / len(paragraph), 2) # Average words for all sentences avgLetterCount = round(wordLen / totWords, 2) # Average letter by word for all sentences totSentWithPunctuation = totSentWithPunctuation + len(sentWithPunctuation) avgSentLen_chars = round(totSentWithPunctuation / len(paragraph), 2) # Validate output by printing a test line # print(f"words: {len(words)} S w Punct. len: {len(sentWithPunctuation)} Sentence: {sentWithPunctuation}") print(f"\n\nParagraph Analysis of '{sourceFile}' file") print(f"---------------------------------------------------------") print(f" Approximate Word Count: {totWords} ") print(f" Approximate Sentence Count: {len(paragraph)} ") print(f" Average Letter Count: {avgLetterCount} ") print(f" Average Sentence Length (words): {avgSentLen_Words} ") print(f" Average Sentence Length (chars): {avgSentLen_chars} ")

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b95619f3f52ff3747e38ecc153123962d0122a4d

# noinspection PyStatementEffect { "name": "ldap_user", "summary": "", "description": "域账号用户管理，登录及查询用户信息", "author": "", "website": "", "source": {"git": "https://github.com/LeiQiao/Parasite-Plugins.git", "branch": "master"}, "category": "", "version": "0.1", "api": { "/user/token": "user_api.gen_token", "/user/captcha": "user_api.gen_captcha", "/user/login": {"POST": "user_api.login"}, "/user/search": "user_api.search_users", }, # any plugin necessary for this one to work correctly "depends": ["base", "base_api_wrapper", "redis_client", "i18n"], }

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484d104a8481a707a187d0bcb30898c3459a88be

# -*- coding: utf-8 -*- from django.conf.urls import patterns, include, url from apps.virt.views import node, domain, device, cluster, home urlpatterns = patterns( "", # Home url(r"^$", home.HomeView.as_view(), name="home"), # Cluster url(r"^cluster/status/$", cluster.ClusterStatusView.as_view(), name="cluster_status"), # Node url(r"^node/list/$", node.NodeListView.as_view(), name="node_list"), url(r"^node/add/$", node.NodeCreateView.as_view(), name="node_add"), url(r"^node/(?P<pk>\d+)/libvirt/$", node.NodeLibvirtView.as_view(), name="node_libvirt"), url(r"^node/(?P<pk>\d+)/libvirt/update/$", node.UpdateCapabilitiesView.as_view(), name="node_libvirt_update"), url(r"^node/(?P<pk>\d+)/libvirt/update/domains/$", node.UpdateDomainsView.as_view(), name="node_libvirt_updatedomains"), url(r"^node/(?P<pk>\d+)/libvirt/create/domains/$", node.CreateALLDomainsView.as_view(), name="node_libvirt_createdomains"), url(r"^node/(?P<pk>\d+)/edit/$", node.NodeUpdateView.as_view(), name="node_edit"), url(r"^node/(?P<pk>\d+)/delete/$", node.NodeDeleteView.as_view(), name="node_delete"), # Domain url(r"^domain/list/$", domain.DomainListView.as_view(), name="domain_list"), url(r"^domain/add/$", domain.DomainCreateView.as_view(), name="domain_add"), url(r"^domain/(?P<pk>\d+)/libvirt/$", domain.DomainLibvirtView.as_view(), name="domain_libvirt"), url(r"^domain/(?P<pk>\d+)/edit/$", domain.DomainUpdateView.as_view(), name="domain_edit"), url(r"^domain/(?P<pk>\d+)/delete/$", domain.DomainDeleteView.as_view(), name="domain_delete"), url(r"^domain/(?P<pk>\d+)/libvirt/create/$", domain.LibvirtCreateView.as_view(), name="domain_libvirt_create"), url(r"^domain/(?P<pk>\d+)/libvirt/reboot/$", domain.LibvirtRebootView.as_view(), name="domain_libvirt_reboot"), url(r"^domain/(?P<pk>\d+)/libvirt/shutdown/$", domain.LibvirtShutdownView.as_view(), name="domain_libvirt_shutdown"), url(r"^domain/(?P<pk>\d+)/libvirt/destroy/$", domain.LibvirtDestroyView.as_view(), name="domain_libvirt_destroy"), url( r"^domain/(?P<pk>\d+)/libvirt/migrate/(?P<node_pk>\d+)/$", domain.LibvirtMigrateView.as_view(), name="domain_libvirt_migrate", ), url(r"^domain/(?P<pk>\d+)/libvirt/resume/$", domain.LibvirtResumeView.as_view(), name="domain_libvirt_resume"), url(r"^domain/(?P<pk>\d+)/libvirt/suspend/$", domain.LibvirtSuspendView.as_view(), name="domain_libvirt_suspend"), # Device url(r"^domain/(?P<pk>\d+)/device/(?P<type>\w+)/add/$", device.DeviceCreateView.as_view(), name="device_add"), url(r"^device/(?P<pk>\d+)/$", device.DeviceUpdateView.as_view(), name="device_edit"), url(r"^device/(?P<pk>\d+)/attach/$", device.DeviceAttachView.as_view(), name="device_attach"), url(r"^device/(?P<pk>\d+)/detach/$", device.DeviceDetachView.as_view(), name="device_detach"), url(r"^device/(?P<pk>\d+)/delete/$", device.DeviceDeleteView.as_view(), name="device_delete"), )

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545794cf4f0b2ab63b6a90951a78f8bdaca3c9e6

from collections import defaultdict as dd def grouping(w): d = dd(list) for k, v in ((len([y for y in x if y.isupper()]), x) for x in sorted(w, key=str.casefold)): d[k].append(v) return dict(sorted(d.items()))

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ab844143ceddf32982682f5092762af0c97db577

from ..translators.translator import Translator

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0762c5bec2d796bb7888e3de45e29fb20f88f491

from starter2 import * from collections import defaultdict import scipy import colors import hair_dryer reload(hair_dryer) import three_loopers_u500 as TL import movie_frames def GE_pearson(this_looper, core_list=None): if core_list is None: core_list = np.unique(this_looper.tr.core_ids) name = this_looper.sim_name thtr = this_looper.tr mask = movie_frames.quantized_mask(this_looper).flatten() times = thtr.times[mask] + 0 # the zero makes a copy times.shape = times.size, 1 times = times / colors.tff G = colors.G # gx = thtr.track_dict['grav_x'] # gy = thtr.track_dict['grav_y'] # gz = thtr.track_dict['grav_z'] # GE2 = -1/(8*np.pi)*(gx*gx+gy*gy+gz*gz) # ge_min=GE2.min() # ge_max=GE2.max() PearsonR = np.zeros([len(core_list), len(times)]) PearsonP = np.zeros([len(core_list), len(times)]) PearsonRho = np.zeros([len(core_list), len(times)]) PeakRho = np.zeros([len(core_list), len(times)]) for nc, core_id in enumerate(core_list): print("GE pearson %s %d" % (name, core_id)) ms = trackage.mini_scrubber(thtr, core_id, do_velocity=False) # ms.particle_pos(core_id) if ms.nparticles < 1000: sl = slice(None) c = [0.5] * 4 else: sl = slice(None, None, 10) # c=[0,0,0,0.1] c = [0.1] * 4 rho = ms.density[sl] rho = rho[:, mask] PeakRho[nc, :] = rho.max(axis=0) gx = thtr.c([core_id], "grav_x")[sl][:, mask] gy = thtr.c([core_id], "grav_y")[sl][:, mask] gz = thtr.c([core_id], "grav_z")[sl][:, mask] GE2 = 1 / (8 * np.pi * G) * (gx * gx + gy * gy + gz * gz) RRR = ms.r[sl][:, mask] for n in range(GE2.shape[1]): the_x = np.log(RRR[:, n]) the_y = np.log(GE2[:, n]) # the_y=rho[:,n] r, p = scipy.stats.pearsonr(the_x, the_y) PearsonR[nc, n] = r PearsonP[nc, n] = p the_y = np.log(rho[:, n]) r, p = scipy.stats.pearsonr(the_x, the_y) PearsonRho[nc, n] = r if 0: fig, ax = plt.subplots(1, 2) ax[0].plot(times, PearsonR) # ax[0].boxplot(PearsonR) # ax[1].boxplot(PearsonRho) fig.savefig("plots_to_sort/phi_box_%s.png" % name) return {"PR": PearsonR, "PP": PearsonP, "Prho": PearsonRho, "T": times, "PeakRho": PeakRho} if 0: fig, ax = plt.subplots(1, 1) ax.plot(times, GE2, c=c, linewidth=0.1) axbonk(ax, xlabel=r"$t/t_{ff}$", ylabel=r"$(\nabla \phi)^2/8 pi G$", yscale="log", ylim=[ge_min, ge_max]) ax2 = ax.twinx() c = [1.0, 0.1, 0.1, 0.1] ax2.plot(times, rho, c=c, linewidth=0.1) axbonk(ax2, xlabel=r"$t/t_{ff}$", ylabel=r"$\rho$", yscale="log") outname = "plots_to_sort/%s_GE_t_c%04d.png" % (this_looper.sim_name, core_id) fig.savefig(outname) print(outname) sims = ["u501", "u502", "u503"] if "stuff" not in dir(): stuff = {} for sim in sims: core_list = np.unique(TL.loops[sim].tr.core_ids) # core_list=core_list[:10] stuff[sim] = GE_pearson(TL.loops[sim], core_list=core_list) if 1: for sim in stuff: fig, ax = plt.subplots(1, 1) T = stuff[sim]["T"] rho = stuff[sim]["PeakRho"] Rphi = stuff[sim]["PR"] ax.plot(Rphi.transpose(), rho.transpose(), c=[0.1] * 4) axbonk(ax, xlabel="time", ylabel="rho max", yscale="log") fig.savefig("plots_to_sort/peak_rho_pearson_phi%s.png" % sim) if 1: for sim in stuff: fig, ax = plt.subplots(1, 1) T = stuff[sim]["T"] rho = stuff[sim]["PeakRho"] ax.plot(T, rho.transpose(), c=[0.1] * 4) axbonk(ax, xlabel="time", ylabel="rho max", yscale="log") fig.savefig("plots_to_sort/peak_rho_%s.png" % sim) if 0: for sim in stuff: fig, ax = plt.subplots(1, 1) c = [0.1] * 4 # ax.plot( stuff[sim]['T'], stuff[sim]['PR'].transpose(),c=c) # ax.scatter( stuff[sim]['Prho'].transpose(), stuff[sim]['PR'].transpose(),c=c) XX, YY = stuff[sim]["Prho"].flatten(), stuff[sim]["PR"].flatten() ok = (~np.isnan(XX)) * (~np.isnan(YY)) XX = XX[ok] YY = YY[ok] xbins = np.linspace(XX.min(), XX.max(), 64) ybins = np.linspace(YY.min(), YY.max(), 64) hist, xb, yb = np.histogram2d(XX, YY, bins=[xbins, ybins]) import pcolormesh_helper as pch pch.helper(hist, xb, yb, ax=ax) fig.savefig("plots_to_sort/RGE_Rrho_%s.png" % sim) if 1: for sim in stuff: fig, ax = plt.subplots(1, 2) Rphi = stuff[sim]["PR"] ax[0].boxplot(Rphi) ax[0].plot(Rphi.mean(axis=0)) ax[1].boxplot(stuff[sim]["Prho"]) axbonk(ax[0], xlabel="frame", ylabel="Rgrad phi") axbonk(ax[1], xlabel="frame", ylabel="R rho") fig.savefig("plots_to_sort/Boxes_%s.png" % (sim)) if 0: from scipy.ndimage import gaussian_filter fig, ax = plt.subplots() for sim in stuff: Rphi = stuff[sim]["PR"] Rrho = stuff[sim]["Prho"] ax.plot(gaussian_filter(Rphi.mean(axis=0), 1), colors.color[sim] + "--") ax.plot(Rrho.mean(axis=0), colors.color[sim]) axbonk(ax, xlabel="frame", ylabel="Rgrad phi") fig.savefig("plots_to_sort/MeanR_%s.png" % (sim))

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17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 8, 11, 12, 13, 17, 18, 23, 24, 28, 29, 31, 32, 33, 34, 44, 45, 46, 47, 48, 49, 8, 10, 12, 14, 15, 16, 18, 19, 20, 22, 27, 28, 29, 31, 32, 33, 34, 36, 38, 40, 42, 44, 49, 50, 51, 52, 54, 57, 60, 62, 67, 69, 72, 74, 8, 12, 16, 18, 20, 25, 26, 28, 29, 33, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 51, 52, 54, 56, 60, 61, 63, 67, 68, 71, 72, 76, 77, 82, 83, 85, 86, 8, 12, 13, 20, 21, 24, 28, 29, 8, 14, 15, 18, 22, 23, 1, 1, 4, 5, 7, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 24, 26, 27, 28, 29, 30, 32, 1, 3, 5, 10, 11, 15, 18, 22, 25, 4, 10, 12, 15, 4, 8, 12, 15, 19, 20, 21, 8, 13, 14, 18, 20, 23, 24, 30, 31, 33, 34, 39, 40, 43, 45, 47, 48, 52, 53, 56, 57, 8, 10, 15, 16, 20, 21, 25, 26, 30, 32, 33, 34, 35, 8, 14, 15, 18, 19, 21, 24, 25, 29, 32, 33, 35, 36, 41, 42, 45, 47, 52, 53, 57, 58, 62, 63, 67, 68, 1, 1, 3, 4, 5, 6, 4, 8, 12, 15, 21, 22, 8, 12, 13, 16, 18, 22, 23, 31, 32, 33, 34, 35, 36, 37, 8, 10, 12, 18, 19, 22, 25, 26, 28, 8, 12, 14, 20, 21, 24, 27, 31, 32, 34, 36, 8, 10, 13, 15, 21, 22, 25, 28, 30, 32, 8, 11, 12, 16, 17, 18, 21, 22, 31, 34, 38, 39, 48, 51, 53, 55, 56, 57, 58, 59, 61, 62, 63, 64, 8, 10, 12, 14, 15, 16, 18, 19, 21, 26, 28, 32, 34, 36, 41, 43, 46, 50, 52, 54, 59, 61, 64, 66, 8, 12, 13, 20, 22, 27, 28, 30, 31, 35, 36, 40, 41, 44, 45, 49, 52, 53, 56, 57, 58, 59, 62, 63, 65, 69, 70, 1, 1, 3, 4, 5, 6, 4, 8, 12, 15, 21, 22, 8, 12, 13, 16, 18, 22, 23, 31, 32, 33, 34, 35, 36, 37, 8, 10, 12, 18, 19, 22, 25, 26, 28, 8, 12, 14, 20, 21, 24, 27, 31, 32, 34, 36, 8, 11, 12, 16, 17, 18, 19, 23, 24, 33, 36, 38, 40, 41, 42, 43, 44, 46, 47, 48, 49, 8, 10, 12, 14, 15, 16, 18, 19, 21, 26, 28, 32, 34, 36, 41, 43, 46, 50, 52, 54, 59, 61, 64, 66, 8, 12, 13, 20, 22, 27, 28, 30, 31, 35, 36, 40, 41, 44, 46, 47, 48, 51, 52, 54, 58, 59, 1, 1, 3, 4, 5, 6, 4, 8, 12, 15, 21, 22, 8, 12, 13, 16, 18, 22, 23, 31, 32, 33, 34, 35, 36, 37, 8, 10, 12, 14, 15, 16, 17, 18, 20, 21, 22, 8, 10, 13, 14, 18, 19, 25, 26, 29, 32, 33, 36, 42, 43, 46, 49, 51, 54, 63, 66, 67, 68, 69, 70, 8, 10, 13, 14, 21, 22, 28, 29, 32, 35, 37, 39, 42, 51, 54, 60, 61, 64, 67, 69, 72, 81, 84, 85, 86, 87, 88, 8, 11, 12, 15, 17, 23, 24, 27, 30, 32, 34, 37, 44, 47, 53, 54, 57, 60, 62, 65, 72, 74, 8, 11, 13, 16, 18, 19, 24, 25, 27, 29, 31, 34, 36, 37, 42, 43, 45, 47, 8, 11, 13, 16, 17, 19, 20, 8, 11, 13, 16, 17, 19, 20, 8, 10, 14, 16, 19, 20, 28, 29, 31, 32, 35, 38, 41, 42, 45, 48, 49, 50, 51, 52, 8, 10, 14, 16, 19, 20, 28, 29, 31, 32, 35, 38, 41, 42, 45, 48, 49, 50, 51, 52, 8, 13, 14, 16, 17, 18, 20, 21, 23, 26, 27, 36, 37, 38, 39, 41, 42, 45, 46, 51, 53, 54, 58, 59, 61, 65, 67, 8, 15, 17, 22, 26, 27, 33, 36, 38, 40, 8, 10, 12, 13, 19, 20, 24, 25, 27, 28, 29, 31, 32, 33, 36, 37, 39, 40, 8, 12, 13, 20, 22, 27, 28, 30, 31, 35, 36, 37, 39, 40, 41, 44, 46, 47, 48, 51, 52, 54, 58, 59, 1, 1, 3, 4, 5, 6, 4, 8, 12, 15, 21, 22, 8, 12, 13, 16, 18, 22, 23, 31, 32, 33, 34, 35, 36, 37, 8, 10, 13, 15, 21, 22, 25, 28, 30, 32, 8, 11, 12, 13, 15, 18, 22, 23, 24, 27, 28, 8, 11, 12, 13, 15, 19, 20, 21, 24, 25, 29, 30, 34, 35, 36, 38, 8, 11, 12, 13, 15, 18, 22, 23, 28, 29, 32, 35, 37, 39, 42, 8, 10, 12, 14, 15, 16, 18, 19, 20, 22, 24, 29, 31, 36, 38, 40, 45, 47, 48, 52, 56, 58, 8, 10, 12, 14, 15, 16, 18, 19, 20, 22, 24, 29, 31, 36, 38, 40, 45, 47, 48, 52, 54, 8, 12, 13, 20, 22, 27, 28, 30, 31, 35, 36, 41, 43, 44, 45, 48, 49, 51, 53, 56, 58, 1, 1, 3, 4, 5, 6, 4, 9, 15, 16, 18, 23, 30, 39, 40, 46, 4, 8, 9, 12, 14, 18, 19, 27, 29, 4, 8, 12, 15, 21, 22, 8, 10, 13, 15, 21, 22, 25, 28, 30, 32, 8, 10, 13, 15, 21, 22, 25, 28, 30, 32, 34, 8, 11, 12, 16, 17, 25, 26, 32, 33, 34, 37, 38, 42, 43, 47, 48, 49, 51, 52, 53, 55, 62, 63, 68, 69, 72, 73, 75, 79, 81, 8, 11, 12, 16, 17, 18, 21, 22, 26, 27, 31, 32, 33, 35, 42, 43, 48, 49, 52, 54, 8, 10, 12, 14, 15, 16, 18, 19, 21, 26, 28, 33, 35, 37, 42, 44, 45, 49, 53, 55, 8, 12, 13, 20, 22, 27, 28, 30, 31, 35, 36, 40, 41, 43, 44, 45, 48, 49, 51, 53, 56, 58 ]

7bbbd30ba1578c1165ccf5c2fff22609c16dfd64

""" Cores no terminal """ a = 3 b = 5 print("Os valores são \033[32m{}\033[m e \033[31m{}\033[m !!!".format(a, b)) # Dicionário de cores: nome = "Kátia" cores = {"limpa": "\033]m", "azul": "\033[34m", "amarelo": "\033[33m", "pretoebranco": "\033[7;30m"} print("Prazer em te conhecer, {}{}{}!!!".format(cores["azul"], nome, cores["amarelo"])) # dá pra colocar as cores dentro das chaves tb.

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b5ac3695a224d531f5baa53a07d3c894d44e8c4c

import matplotlib.pyplot as plt Ci_MSB = [32, 16, 8, 4, 2, 1] Ci_LSB = [16, 8, 4, 2, 1] CB = 1 CP_B = 0 CP_LSB = (32 - 1) * (CB + CP_B - 1) + 10 print(CP_LSB) CP_MSB = 0 Csum_LSB = sum(Ci_LSB) + CP_LSB Csum_MSB = sum(Ci_MSB) + CP_MSB Cx = Csum_LSB * Csum_MSB + (CB + CP_B) * Csum_LSB + (CB + CP_B) * Csum_MSB Wi_MSB = [Ci_MSB[i] * (CB + CP_B + Csum_LSB) / Cx for i in range(6)] Wi_LSB = [Ci_LSB[i] * (CB + CP_B) / Cx for i in range(5)] print(Wi_MSB) print(Wi_LSB) def AtoD(vin): code = [0 for i in range(12)] code[0] = 1 if vin > 0 else 0 for i in range(6): vin = vin - Wi_MSB[i] * (code[i] - 0.5) * 2 code[i + 1] = 1 if vin > 0 else 0 for i in range(5): vin = vin - Wi_LSB[i] * (code[i + 6] - 0.5) * 2 code[i + 7] = 1 if vin > 0 else 0 dec_num = 0 for b in code: dec_num = dec_num * 2 + b return dec_num print(AtoD(0.50)) def DtoA_ideal(code): v = -1.0 for i in range(12): v += 2 ** (11 - i) * code[i] / 2048 return v print(DtoA_ideal([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])) n = 1000000 x = [-1 + i / n for i in range(2 * n + 1)] y = [AtoD(v) for v in x] # print(y[int(n/6):int(n/6)+100]) bin_num = [i for i in range(4096)] bin_size = [0 for i in range(4096)] left = x[0] for i in range(2 * n): if y[i + 1] != y[i]: bin_size[y[i]] = x[i + 1] - left left = x[i + 1] # print(bin_size) DNL = [data * 2047 - 1 for data in bin_size] plt.plot(bin_num[1:4094], DNL[1:4094]) # plt.xlim(1000,1005) plt.show() # y = [DtoA_ideal(AtoD(v)) for v in x] # plt.plot(x,y) # plt.xlim(-0.01,0) # plt.ylim(-0.01,0) # plt.show() # def Vout(index): # V = 0.0 # for i in range(6): # V = V + Wi_MSB[i] * int(format(index,'b').zfill(11)[i])*1 # for i in range(5): # V = V + Wi_LSB[i] * int(format(index,'b').zfill(11)[i+6])*1 # return V # print(Vout(2047)) # # x = [i for i in range(2048)] # y = [Vout(i) for i in range(2048)] # DNL = [0]+[y[i+1]-y[i]-Vout(2047)/2047 for i in range(2047)] # DNL = [data*2048 for data in DNL] # INL = [y[i] -i*Vout(2047)/2047 for i in range (2048)] # INL = [data*2048 for data in INL] # # plt.plot(x,DNL) # plt.show()

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c9d12f14fa0e46e4590746d45862fe255b415a1d

# vim: expandtab # -*- coding: utf-8 -*- from poleno.utils.template import Library from chcemvediet.apps.obligees.models import Obligee register = Library() @register.simple_tag def gender(gender, masculine, feminine, neuter, plurale): if gender == Obligee.GENDERS.MASCULINE: return masculine elif gender == Obligee.GENDERS.FEMININE: return feminine elif gender == Obligee.GENDERS.NEUTER: return neuter elif gender == Obligee.GENDERS.PLURALE: return plurale else: return ""

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58e023c3c453d1e190fdb5bc457358f42d1bd93f

# https://leetcode.com/problems/how-many-numbers-are-smaller-than-the-current-number/ # BruteForce class BruteForceSolution: def smallerNumbersThanCurrent(self, nums): answer = [] for num in nums: counter = 0 for i in range(len(nums)): if nums[i] < num: counter += 1 answer.append(counter) return answer class Solution: def smallerNumbersThanCurrent(self, nums): answer = [] sortedNums = sorted(nums) for num in nums: answer.append(sortedNums.index(num)) return answer example = BruteForceSolution() exampleTwo = Solution() print(example.smallerNumbersThanCurrent([8, 1, 2, 2, 3])) print(exampleTwo.smallerNumbersThanCurrent([8, 1, 2, 2, 3]))

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b4b7e20c9558bd1b29a1c1fa24bfca8a2d292b27

import xml.etree.ElementTree as ET # tree = ET.parse('rutas/rutas_prueba.xml') # treeToAdd = ET.parse('rutas/rutas_prueba_agregar.xml') # root = tree.getroot() # git rootToAdd = treeToAdd.getroot() # for child in root: # for test in child: # print(test.tag, test.attrib) # for elem in root.iter(): # print(elem.tag) # prueba = [elem.tag for elem in root.iter()] # print(prueba) # print(ET.tostring(root, encoding='utf8').decode('utf8')) # for elem in rootToAdd: # root.append(elem) # # tree.write('rutas/probando_agregados.xml') # get the tree for each routes file rutas0k_10k = ET.parse("rutas/rutas0k-10k.xml") rutas10k_30k = ET.parse("rutas/rutas10k-30k.xml") rutas30k_50k = ET.parse("rutas/rutas30k-50k.xml") rutas50k_70k = ET.parse("rutas/rutas50k-70k.xml") rutas70k_90k = ET.parse("rutas/rutas70k-90k.xml") rutas90k_110k = ET.parse("rutas/rutas90k-110k.xml") # root for each routes tree root1 = rutas0k_10k.getroot() root2 = rutas10k_30k.getroot() root3 = rutas30k_50k.getroot() root4 = rutas50k_70k.getroot() root5 = rutas70k_90k.getroot() root6 = rutas90k_110k.getroot() # each root except first root rootsToAdd = [root2, root3, root4, root5, root6] # add each element to the first tree for root in rootsToAdd: for elem in root: root1.append(elem) # write the tree to a new file rutas0k_10k.write("rutas/rutas0k-110k.xml")

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97bbb181cbc0f5bfbf0b2298133fc226b6217d91

import tensorflow as tf from tensorflow.python.framework import graph_util from net import siameseNet_batchnorm as siameseNet import dataset import numpy as np import cv2 import os batch_size = 64 input_height = 32 input_width = 32 total_epoch_num = 50 snapshot = 100 support_image_extensions = [".jpg", ".png", ".jpeg", ".bmp"] margin = 1.0 channals = 3 train_image_root = "D:/forTensorflow/charRecTrain/forMyDNNCode/train" test_image_root = "D:/forTensorflow/charRecTrain/forMyDNNCode/test" model_path = "models/" pb_path = os.path.join(model_path, "pb/") ckpt_path = os.path.join(model_path, "ckpt/") if not os.path.exists(pb_path): os.makedirs(pb_path) if not os.path.exists(ckpt_path): os.makedirs(ckpt_path) model_name = "siamese_triplet_28out_allloss_bn" if __name__ == "__main__": # image_paths,labels=get_images_path(test_image_root) # data=next_batch(True,None,image_paths,labels) # for left,right,label in zip(*data): # cv2.imshow("left",left) # cv2.imshow("right", right) # print(label) # cv2.waitKey(0) first_shape = None anchor_placeholder = tf.placeholder(tf.float32, shape=[first_shape, input_height, input_width, channals], name="anchor") similar_placeholder = tf.placeholder(tf.float32, shape=[first_shape, input_height, input_width, channals], name="similar") dissimilar_placeholder = tf.placeholder( tf.float32, shape=[first_shape, input_height, input_width, channals], name="dissimilar" ) labels_placeholder = tf.placeholder( tf.float32, shape=[ None if first_shape is None else first_shape * 3, ], name="labels", ) is_training_placeholder = tf.placeholder_with_default(False, shape=(), name="is_training") siamese_net = siameseNet.siameseNet() anchor = siamese_net.inference(anchor_placeholder, reuse=False, is_training=is_training_placeholder) similar = siamese_net.inference(similar_placeholder, reuse=True, is_training=is_training_placeholder) dissimilar = siamese_net.inference(dissimilar_placeholder, reuse=True, is_training=is_training_placeholder) loss, pos_dist, neg_dist = siamese_net.loss(anchor, similar, dissimilar, labels_placeholder, margin) flatten_out_anchor = tf.identity(anchor, name="flatten_anchor") flatten_out_similar = tf.identity(similar, name="flatten_similar") flatten_out_dissimilar = tf.identity(dissimilar, name="flatten_dissimilar") update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) global_step = tf.Variable(0, trainable=False) # learning_rate = tf.train.exponential_decay(0.01, global_step, 100, 0.9) # optimizer = tf.train.MomentumOptimizer(learning_rate, 0.9) with tf.control_dependencies([tf.group(*update_ops)]): # train_step = optimizer.minimize(loss, global_step) train_step = tf.train.MomentumOptimizer(0.01, 0.90).minimize(loss, global_step=global_step) var_list = tf.trainable_variables() if global_step is not None: var_list.append(global_step) g_list = tf.global_variables() # 从全局变量中获得batch norm的缩放和偏差 bn_moving_vars = [g for g in g_list if "moving_mean" in g.name] bn_moving_vars += [g for g in g_list if "moving_variance" in g.name] var_list += bn_moving_vars ckpt_saver = tf.train.Saver() train_dataset = dataset.dataset(train_image_root, batch_size, support_image_extensions, input_height, input_width, channals) test_dataset = dataset.dataset(test_image_root, batch_size, support_image_extensions, input_height, input_width, channals) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) # if os.path.exists(os.path.join(ckpt_path, "checkpoint")): # ckpt_saver.restore(sess, tf.train.latest_checkpoint(ckpt_path)) total_iters_num = 0 for epoch_num in range(total_epoch_num): train_images_num = train_dataset.sample_len cur_epoch_iters_num = train_images_num // batch_size for iters_num in range(cur_epoch_iters_num): train_anchor, train_similar, train_dissimilar, train_labels = train_dataset.next_triplet_batch() test_anchor, test_similar, test_dissimilar, test_labels = test_dataset.next_triplet_batch() if train_anchor is None or test_anchor is None: continue train_dict = { anchor_placeholder: train_anchor, similar_placeholder: train_similar, dissimilar_placeholder: train_dissimilar, labels_placeholder: train_labels, is_training_placeholder: True, } test_dict = { anchor_placeholder: test_anchor, similar_placeholder: test_similar, dissimilar_placeholder: test_dissimilar, labels_placeholder: test_labels, is_training_placeholder: False, } _, _global_step = sess.run([train_step, global_step], feed_dict=train_dict) anchor_out, similar_out, dissimilar_out = sess.run( [flatten_out_anchor, flatten_out_similar, flatten_out_dissimilar], feed_dict=train_dict ) _train_loss, _train_pos_dist, _train_neg_dist = sess.run([loss, pos_dist, neg_dist], feed_dict=train_dict) _test_loss, _test_pos_dist, _test_neg_dist = sess.run([loss, pos_dist, neg_dist], feed_dict=test_dict) print("distance:", list(zip(_train_pos_dist.flatten(), _train_neg_dist.flatten()))[:5]) one_moving_meaning_show = "No mean or variance" if len(bn_moving_vars) > 0: one_moving_meaning = sess.graph.get_tensor_by_name(bn_moving_vars[0].name) one_moving_meaning_show = "{}={}".format(bn_moving_vars[0].name, np.mean(one_moving_meaning.eval())) print(one_moving_meaning_show) show_text = "epoch:{},epoch-iters:{},total-iters:{},loss:{},lr:{},val:{}".format( epoch_num, iters_num + 1, _global_step, _train_loss, "0.99", _test_loss ) print(show_text) if _global_step % snapshot == 0: # 保存PB constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ["flatten_anchor"]) save_model_name = model_name + "-" + str(_global_step) + ".pb" with tf.gfile.FastGFile(pb_path + save_model_name, mode="wb") as fw: fw.write(constant_graph.SerializeToString()) # 保存CKPT ckpt_saver.save(sess, ckpt_path + model_name + ".ckpt", global_step=total_iters_num) print("Successfully saved model {}".format(save_model_name))

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68f3d3fce52d08381adc522ee032ef3181aec82a

# Generated by Django 2.2.3 on 2019-07-27 10:41 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("beerFriends", "0006_auto_20190726_1504"), ] operations = [ migrations.AlterField( model_name="beer", name="rating", field=models.FloatField(blank=True, null=True), ), ]

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4775bef3623497e9bbe79ca2d4e9e9da0422c450

# # # # ------------------------------------------------------------------------------------------------------------------------------ # # This program have been developed by Hamed Noori and with citiation of the related publicaitons # can be used without permission. # This program is for a novel architecture for traffic light control system which can form and manipulate # vehicular platoons using clusters of traffic lights. This method, called Platoon-Based Intelligent Traffic Lights (PB-ITL) # is based on coordinated traffic lights that are connected and are also able to communicate with vehicles wirelessly. PB-ITL # groups traffic lights in clusters and each cluster tries to provide proper green status for the platoon of vehicles, using the # Platooning concept which is seeking to utilize close to full capacity of roads. # This lib is a Python-based program which can simulate a city with dynamic intelligent traffic lights. # The author can be reach at [email protected] # # ------------------------------------------------------------------------------------------------------------------------------ # # def start_simulation(sumo, scenario, network, begin, end, interval, output): logging.debug("Finding unused port") unused_port_lock = UnusedPortLock() unused_port_lock.__enter__() remote_port = find_unused_port() logging.debug("Port %d was found" % remote_port) logging.debug("Starting SUMO as a server") sumo = subprocess.Popen( [ "D:\\PATH\\sumo-gui.exe", "-c", "D:\\\PATH\\Your.sumo.cfg", "--tripinfo-output", output, "--device.emissions.probability", "1.0", "--remote-port", str(remote_port), ], stdout=sys.stdout, stderr=sys.stderr, ) unused_port_lock.release() try: traci.init(remote_port) run(network, begin, end, interval) except Exception: logging.exception("Something bad happened") finally: logging.exception("Terminating SUMO") terminate_sumo(sumo) unused_port_lock.__exit__()

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d82412055affc96d634957c953a35ea69b7e702f

"""Turning on or off, toggling and checking the status' of a specific relay""" #!/bin/env python3 from time import sleep from gpiozero import LED RELAYS = [LED(23), LED(24), LED(25), LED(8), LED(7), LED(1), LED(12), LED(16)] def on_action(relay_option, number): """To turn on the chosen relay""" relay_option.on() print(f"relay {number} is turning on") def off_action(relay_option, number): """To turn off the chosen relay""" relay_option.off() print(f"relay {number} is turning off") def toggle_action(relay_option, number): """To toggle the chosen relay""" print(f"relay {number} is toggling") relay_option.on() sleep(0.5) relay_option.off() sleep(0.5) def print_help(): """Print/show help for informations of the required parameter""" print(""" Description Arguments: number number of relay 1 to 8 action on, off, or toggle optional arguments: h show this help message and exit """) def options(): """Input the relay number or show help and check the input""" input_str = input("Which relay? ") while True: if input_str == "h": print_help() return index = int(input_str) - 1 if 0 <= index <= 7: relay_status(RELAYS[index], input_str) relay_action(RELAYS[index], input_str) relay_status(RELAYS[index], input_str) return else: print("index out of range") return def relay_action(relay_number, num): """Do the given order(turn on, turn off, toggle) or raise error""" action = input("Which action? ") while True: try: return {"on": on_action, "off": off_action, "toggle": toggle_action}[action](relay_number, num) except KeyError: print("Try again") return relay_action(relay_number, num) def relay_status(relay_number, number): """Check initial relay's status""" if relay_number.value == 1: print(f"relay {number} is on") else: print(f"relay {number} is off") while True: options() sleep(1)

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862b529741d9c3e6cf7ca50272c8af724c56ac62

from wasserstoff.wasserstoff import Config, Environment __all__ = ["Config", "Environment"]

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6d25b0fedf0d5081a3a0a93ddacc49748464d9d0

# Required python libraries for attack.py import socket import os import sys from termcolor import colored import StringIO import time # need to find Python equivalent libraries for these import stdio import stdlib import unistd # need to find Python equivalent libraries for these import includes import killer import main import protocol import rand import resolv import scanner import table import util def checksum_tcp_udp(ip_header, buffer_name, data_length, lenth): return sum_checksum_tcp_udp def checksum_generic(address, count): return sum_checksum_generic

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e57b30a7a1cf987918abfb3cb7d612bdead2ddcd

from django.db import models # Create your models here. class Airlines(models.Model): flight_number = models.CharField(max_length=8, unique=True) airlines_id = models.CharField(max_length=10) source = models.CharField(max_length=20) destination = models.CharField(max_length=20) departure = models.TimeField() arrival = models.TimeField() base_price = models.DecimalField(decimal_places=2, max_digits=10) def __str__(self): return self.flight_number class Users(models.Model): user_id = models.CharField(max_length=16) email = models.EmailField(max_length=50, unique=True) password = models.CharField(max_length=20) phone_number = models.IntegerField() gender = models.CharField(max_length=10) def __str__(self): return self.email class Bookings(models.Model): booking_id = models.AutoField(primary_key=True) email = models.ForeignKey(Users, on_delete=models.CASCADE) flight_num = models.ForeignKey(Airlines, on_delete=models.CASCADE, default="00000", editable=True)

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2827a56c12c1e15a6fe26ce182aa07d76735d77f

""" MDSANIMA Setup """ import sys import pathlib from setuptools import setup, find_packages HERE = pathlib.Path(__file__).parent CURRENT_PYTHON = sys.version_info[:2] REQUIRED_PYTHON = (3, 6) # This check and everything above must remain compatible with Python 2.7. if CURRENT_PYTHON < REQUIRED_PYTHON: sys.stderr.write( """========================== Unsupported Python Version ========================== This version of MDSANIMA requires Python {}.{} but you're trying to install it on Python {}.{} """.format(*(REQUIRED_PYTHON + CURRENT_PYTHON)) ) sys.exit(1) VERSION = "0.2.0" PACKAGE_NAME = "mdsanima" AUTHOR = "Marcin Rozewski" AUTHOR_EMAIL = "[email protected]" URL = "https://github.com/mdsanima/mdsanima" LICENSE = "MIT" DESCRIPTION = "The package contains modules that will help in calculating rendering time." LONG_DESCRIPTION = (HERE / "README.rst").read_text() LONG_DESC_TYPE = "text/x-rst" INSTALL_REQUIRES = ["humanfriendly"] KEYWORDS = [ "mdsanima", "render time", "calculator render time", "blender", "blener3d", "rendering", "houdini", "sidefx", "vfx", "cinema4d", "cycles", "redshift", "render engine", "octane render", "mantra", "vray", "clarisse ifx", ] setup( name=PACKAGE_NAME, version=VERSION, description=DESCRIPTION, long_description=LONG_DESCRIPTION, long_description_content_type=LONG_DESC_TYPE, author=AUTHOR, license=LICENSE, author_email=AUTHOR_EMAIL, url=URL, install_requires=INSTALL_REQUIRES, packages=find_packages(), extras_require={ "docs": [ "sphinx", "sphinx-autoapi", "sphinx-rtd-theme", "sphinx-bootstrap-theme", "sphinx-prompt", "sphinx-tabs", "recommonmark", ], }, python_requires=">=3.6", keywords=KEYWORDS, classifiers=[ "Development Status :: 5 - Production/Stable", "Environment :: Console", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Natural Language :: English", "Operating System :: POSIX :: Linux", "Operating System :: Microsoft :: Windows :: Windows 10", "Operating System :: MacOS", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Multimedia :: Graphics :: 3D Rendering", "Topic :: Multimedia :: Graphics :: 3D Modeling", ], )

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d4ac5c6f08e9baa458fbe0ca7aa90c4d9372844f

import h5py import numpy as np # import tracking dt = h5py.special_dtype(vlen=bytes) def stringDataset(group, name, data, system=None): dset = group.create_dataset(name, (1,), dtype=dt, data=data) if system: addSystemAttribute(dset, system) return dset def addStringAttribute(dset_or_group, name, data): # return dset_or_group.attrs.create(name, np.string_(data)) # , (1,), dtype=dt) dset_or_group.attrs[name] = bytes(data, "utf-8") def addSystemAttribute(dset_or_group, data): return addStringAttribute(dset_or_group, "system", data) def add_dataset(group, name, data, system=None, dtype=None): if type(data) is str: return stringDataset(group, name, data, system) else: if dtype: dset = group.create_dataset(name, data=data, dtype=dtype) else: dset = group.create_dataset(name, data=data) if system: addSystemAttribute(dset, system) return dset def saveH5Recursive(h5_filename, data_dict): # import here to prevent circular import try: import tracking except ImportError: from . import tracking def recurse_save(group, dict_or_data, dict_or_data_name, new_group=None): if dict_or_data is None: dict_or_data = "None" if group is None: print("'recurse_save' has been called with None") raise ValueError if type(dict_or_data) is tracking.BeamProfile or type(dict_or_data) is tracking.ScreenDistribution: dict_or_data = dict_or_data.to_dict() if type(dict_or_data) is tuple: dict_or_data = {"tuple_%i": x for i, x in enumerate(dict_or_data)} if type(dict_or_data) is dict: if type(dict_or_data_name) is int: inner_key = str(dict_or_data_name) try: new_group = group.create_group(dict_or_data_name) except Exception as e: print(e) print(dict_or_data_name, "error") # raise if new_group is None: raise ValueError for key, val in dict_or_data.items(): try: recurse_save(new_group, val, key) except ValueError as e: print(e) print("I called recurse_save with None") # import pdb; pdb.set_trace() else: mydata = dict_or_data inner_key = dict_or_data_name if type(mydata) is str: add_dataset(group, inner_key, mydata.encode("utf-8"), "unknown") # elif type(mydata) is tuple: # mydata2 = np.array(mydata) # add_dataset(group, inner_key, mydata2, 'unknown') elif (type(mydata) is list and type(mydata[0]) is str) or ( hasattr(mydata, "dtype") and mydata.dtype.type is np.str_ ): # For list of strings, we need this procedure try: if hasattr(mydata, "dtype") and mydata.dtype.type is np.str and len(mydata.shape) == 2: mydata = mydata.flatten() if len(mydata.shape) == 2: new_list = [[n.encode("ascii") for n in arr] for arr in mydata] max_str_size = max(max(len(n) for n in arr) for arr in mydata) elif len(mydata.shape) == 1: new_list = [n.encode("ascii") for n in mydata] max_str_size = max(len(n) for n in mydata) elif len(mydata.shape) == 0: new_list = [mydata.encode("ascii")] max_str_size = len(new_list[0]) # print('Max len %i' % max_str_size) dset = group.create_dataset(inner_key, mydata.shape, "S%i" % max_str_size, new_list) # print(np.array(dset)) dset.attrs.create("system", "unknown", (1,), dtype=dt) except: print("Error for key", inner_key) print(type(mydata)) if type(mydata) is list: print("type(mydata[0])") print(type(mydata[0])) print("mydata") print(mydata) elif hasattr(mydata, "dtype") and mydata.dtype == np.dtype("O"): if mydata.shape == (): add_dataset(group, inner_key, mydata, "unknown") elif len(mydata.shape) == 1: try: add_dataset(group, inner_key, mydata, "unknown") except Exception as e: print(e) print("Error for key", inner_key) print(group, inner_key) else: for i in range(mydata.shape[0]): for j in range(mydata.shape[1]): try: add_dataset(group, inner_key + "_%i_%i" % (i, j), mydata[i, j], "unknown") except: print("Error for key", inner_key) print(group, inner_key, i, j) else: try: add_dataset(group, inner_key, mydata, "unknown") except Exception as e: print("Error", e) print(inner_key, type(mydata)) with h5py.File(h5_filename, "w") as dataH5: for main_key, subdict in data_dict.items(): recurse_save(dataH5, subdict, main_key, None) # recurse_save(dataH5, data_dict, 'none', new_group=dataH5) def loadH5Recursive(h5_file): def recurse_load(group_or_val, key, saved_dict_curr): type_ = type(group_or_val) try: group_or_val[()] except: hasval = False else: hasval = True if type_ is h5py._hl.files.File: for new_key, new_group_or_val in group_or_val.items(): recurse_load(new_group_or_val, new_key, saved_dict_curr) elif type_ is h5py._hl.group.Group: saved_dict_curr[key] = new_dict = {} for new_key, new_group_or_val in group_or_val.items(): recurse_load(new_group_or_val, new_key, new_dict) elif type_ == np.dtype("O") and hasval and type(group_or_val[()]) is bytes: saved_dict_curr[key] = group_or_val[()].decode() elif type_ == h5py._hl.dataset.Dataset: dtype = group_or_val.dtype # if not hasattr(group_or_val, 'value'): # print('Could not store key %s with type %s in dict' % (key, dtype)) # return if dtype in ( np.dtype("int64"), np.dtype("int32"), np.dtype("int16"), np.dtype("int8"), np.dtype("uint32"), np.dtype("uint16"), np.dtype("uint8"), np.dtype("uint64"), ): saved_dict_curr[key] = np.array(group_or_val[()], dtype).squeeze() if saved_dict_curr[key].size == 1: saved_dict_curr[key] = int(saved_dict_curr[key]) elif dtype == np.dtype("bool"): try: saved_dict_curr[key] = bool(group_or_val[()]) except Exception as e: print(e) print("Could not store key %s with type %s in dict (1)" % (key, dtype)) elif dtype in (np.dtype("float64"), np.dtype("float32")): saved_dict_curr[key] = np.array(group_or_val[()]).squeeze() if saved_dict_curr[key].size == 1: saved_dict_curr[key] = float(saved_dict_curr[key]) elif dtype.str.startswith("|S"): if group_or_val[()].shape == (1, 1): saved_dict_curr[key] = group_or_val[()][0, 0].decode() elif group_or_val[()].shape == (1,): saved_dict_curr[key] = group_or_val[()][0].decode() elif group_or_val[()].shape == (): saved_dict_curr[key] = group_or_val[()].decode() else: saved_dict_curr[key] = [x.decode() for x in group_or_val[()].squeeze()] elif dtype.str == "|O": saved_dict_curr[key] = group_or_val[()] elif type(group_or_val[()]) is str: saved_dict_curr[key] = group_or_val[()] else: print("Could not store key %s with type %s in dict (2)" % (key, dtype)) else: print("Could not store key %s with type %s in dict (3)" % (key, type_)) saved_dict = {} with h5py.File(h5_file.strip(), "r") as f: if "none" in f: recurse_load(f["none"], "key", saved_dict) saved_dict = saved_dict["key"] else: recurse_load(f, "key", saved_dict) return saved_dict

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4d388c912915c3f1f9e433f1342289f0864b3a11

# /usr/bin/python # File: UdpClient.py # Author: David Zemon # Project: Project1 # # Created with: PyCharm Community Edition """ @description: """ __author__ = "david" import logging from src.UDP import UDPClient logging.basicConfig(level="DEBUG") serverName = "127.0.0.1" serverPort = 12000 client = UDPClient() message = input("Input lowercase sentence: ") client.sendto(message.encode("utf-8"), (serverName, serverPort)) modifiedMessage, serverAddress = client.recvfrom(2048) print(modifiedMessage.decode("utf-8")) client.close()

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cd5945631a9dd505bf67089bab8c5a37ad375129

import pandas as pd df1 = pd.read_csv("../final/your_no.tsv", "\t") df2 = pd.read_csv("../../Downloads/me.csv", "\t") final = pd.concat([df1, df2]) final.to_csv("../../Downloads/final_con_final.tsv", sep="\t", index=False)

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16b425d7b8cde1aabe038ccae6922091afb84415

# coding: utf-8 # In[1]: import matplotlib.pyplot as plt import matplotlib import pandas as pd import numpy as np from pandas import DataFrame, Series import os, re # In[2]: OUTPUT_EXCEL = "월별원내약품사용현황.xlsx" # In[3]: # 데이타셋 준비 data_source_dir = "사용량월별통계/원내" dfs = [] for fname in os.listdir(data_source_dir): fn, ext = os.path.splitext(fname) if ext in [".xls", ".xlsx"]: df = pd.read_excel(os.path.join(data_source_dir, fname)) df["사용(개시)년월"] = fn dfs.append(df) use_amount_df = pd.concat(dfs, ignore_index=True) # In[4]: drug_standard_df = pd.read_json("drug.json").T drug_info_df = pd.read_excel("약품정보.xls") use_amount_df = pd.merge(drug_info_df, use_amount_df[["사용량", "약품코드", "사용(개시)년월"]], on="약품코드", how="left") use_amount_df = pd.merge( use_amount_df, drug_standard_df[["보험코드", "제품명", "판매사", "성분/함량"]], left_on="EDI코드", right_on="보험코드", how="left", ) use_amount_df["제품명"] = use_amount_df["제품명"].fillna(use_amount_df["약품명(한글)"]) use_amount_df["사용개시년월"] = use_amount_df["수가시작일자"].map(lambda x: str(x)[0:4] + "-" + str(x)[4:6]) use_amount_df["사용(개시)년월"] = use_amount_df["사용(개시)년월"].fillna(use_amount_df["사용개시년월"]) use_amount_df["성분명"] = use_amount_df["성분명"].fillna(use_amount_df["성분/함량"]) use_amount_df["원내/원외 처방구분"] = use_amount_df["원내/원외 처방구분"].map({1: "원외", 2: "원외/원내", 3: "원내"}) use_amount_df["약품법적구분"] = use_amount_df["약품법적구분"].map( {0: "일반", 1: "마약", 2: "향정약", 3: "독약", 4: "한방약", 5: "고가약"} ) # In[5]: def get_last(s): try: return max(s) except: return s # In[6]: months = use_amount_df["사용(개시)년월"].unique() months = sorted(months.tolist(), reverse=1) use_amount_df["최후사용월"] = use_amount_df.groupby(["제품명"])["사용(개시)년월"].transform(get_last) use_amount_df["최근미사용월수"] = use_amount_df["최후사용월"].map(lambda x: months.index(x) if x in months else -1) # In[7]: use_amount_in_df = use_amount_df[use_amount_df["원내/원외 처방구분"] != "원외"] # In[8]: use_amount_in_df["사용량"] = use_amount_in_df["사용량"].fillna("오픈후미사용") # In[9]: pat = "($([^\d].*?)$+\s*)|퇴장방지\s*|생산원가보전,*\s*|사용장려(비\s*\d+원|및|비용지급,*\s*)" use_amount_in_df = use_amount_in_df.rename(columns={"제품명": "약품명(드럭인포)", "약품명(한글)": "약품명(원내)"}) use_amount_in_df["약품명(드럭인포)"] = use_amount_in_df["약품명(드럭인포)"].str.replace(pat, "") # In[10]: pvt = use_amount_in_df.pivot_table( index=["EDI코드", "약품명(드럭인포)", "성분명", "약품코드", "약품명(원내)", "효능코드명", "규격단위", "최근미사용월수"], columns=["사용(개시)년월"], values=["사용량"], aggfunc=sum, ) # In[11]: pvt.to_excel(OUTPUT_EXCEL) os.startfile(OUTPUT_EXCEL) # In[ ]:

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0eaaa81d3c8bc61368701e1916b42ede88b90d04

# EXERCISE: # Plotting distributions pairwise (2) # In this exercise, you will generate pairwise joint distributions again. This time, you will make two particular # additions: # - You will display regressions as well as scatter plots in the off-diagonal subplots. You will do this with the # argument kind='reg' (where 'reg' means 'regression'). Another option for kind is 'scatter' (the default) that # plots scatter plots in the off-diagonal subplots. # - You will also visualize the joint distributions separated by continent of origin. You will do this with the # keyword argument hue specifying the 'origin'. # INSTRUCTIONS: # - Plot the pairwise joint distributions separated by continent of origin and display the regressions. # CODE: # Print the first 5 rows of the DataFrame print(auto.head()) # Plot the pairwise joint distributions grouped by 'origin' along with regression lines sns.pairplot(auto, kind="reg", hue="origin") # Display the plot plt.show()

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27e9635adf6109f3ab13b9d8dd5809973b61ca03

# Web Scraping # Make sure you have bs4, webbrowser and request installed as your third party modules import bs4, webbrowser, requests try: data = requests.get("http://en.wikipedia.org/wiki/Python") data.raise_for_status() my_data = bs4.BeautifulSoup(data.text, "lxml") print("List of all the header tags: \n\n") for the_data in my_data.find_all("a"): try: print(the_data.attrs["href"]) except Exception as err: print(err) except Exception as err: print(err) print("\nNo website matches your search.")

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f5f26819be4b98fab3d46e57e1a5431e54342aed

# coding: utf-8 """Supporting model logic for predicting emotional content of user input.""" import pandas as pd import gensim from sklearn.model_selection import train_test_split from sklearn.multiclass import OneVsRestClassifier from sklearn.svm import LinearSVC # load data for emo2vec loc = "https://s3-us-west-1.amazonaws.com/victorsdatasets/NRCEmotionLexiconv092AnnotatorandSenseLevel.txt" print("loading & cleaning the data...") em_words = pd.read_csv( loc, sep="\t", names=[ "annotator_id", "remove", "word", "joy", "sadness", "fear", "anger", "trust", "disgust", "surprise", "anticipation", "POS", ], ) em_words.drop("remove", axis=1, inplace=True) em_words["word"], em_words["synonym"] = em_words["word"].str.split("--").str em_words["toss1"], em_words["joy"] = em_words["joy"].str.split("-").str em_words["toss2"], em_words["sadness"] = em_words["sadness"].str.split("-").str em_words["toss3"], em_words["fear"] = em_words["fear"].str.split("-").str em_words["toss4"], em_words["anger"] = em_words["anger"].str.split("-").str em_words["toss5"], em_words["trust"] = em_words["trust"].str.split("-").str em_words["toss6"], em_words["disgust"] = em_words["disgust"].str.split("-").str em_words["toss7"], em_words["surprise"] = em_words["surprise"].str.split("-").str em_words["toss8"], em_words["anticipation"] = em_words["anticipation"].str.split("-").str em_words["toss9"], em_words["POS"] = em_words["POS"].str.split("-").str em_words.drop(["toss1", "toss2", "toss3", "toss4", "toss5", "toss6", "toss7", "toss8", "toss9"], axis=1, inplace=True) new_cols = [ "annotator_id", "word", "synonym", "joy", "sadness", "fear", "anger", "trust", "disgust", "surprise", "anticipation", "POS", ] em_words = em_words.reindex_axis(new_cols, axis=1) emotions = em_words[["joy", "sadness", "fear", "anger", "trust", "disgust", "surprise", "anticipation"]] em_words[emotions.columns] = em_words[emotions.columns].apply(pd.to_numeric) affected = em_words[emotions.columns].groupby([em_words["word"]], sort=False).mean().reset_index() # Load Google's pre-trained Word2Vec model. print("training the word2vec model from google's corpus") model = gensim.models.Word2Vec.load_word2vec_format("../GoogleNews-vectors-negative300.bin", binary=True) # create list of word indicies to drop to avoid keyerrors with Google's pre-trained model. idx_to_drop = [] def dropper(): for ex in affected["word"]: if ex not in model.vocab: idx_to_drop.append(affected.loc[affected.word == ex].index[0]) # drop words from affected that are not in google's model dropper() affected = affected.drop(idx_to_drop, axis=0) G_vectors = lambda x: model[x] affected["word_vectors"] = affected["word"].apply(G_vectors) affected["label_vectors"] = affected[emotions.columns].values.tolist() affected["binary"] = (affected[emotions.columns] > 0).astype(int).values.tolist() df1 = affected[emotions.columns].rank(method="max", axis=1).rank(method="first", axis=1) ma = df1.max().max() affected["label"] = (df1 == ma).astype(int).values.tolist() affected["target"] = affected["label"].apply(lambda x: x.index(1)) label_dict = {0: "joy", 1: "sadness", 2: "fear", 3: "anger", 4: "trust", 5: "disgust", 6: "surprise", 7: "anticipation"} affected["label_name"] = affected["target"].apply(lambda x: label_dict[x]) emo2vec = affected[["word_vectors", "label_vectors", "binary", "label", "target", "label_name"]] # # Model Testing print("splitting into train/test groups...") emo_X, emo_y = list(emo2vec.word_vectors), list(emo2vec.target) emo_X_train, emo_X_test, emo_y_train, emo_y_test = train_test_split(emo_X, emo_y, random_state=42) # ### OnevsRest with LinearSVC (best score) print("creating a model with the best stuff we've got...") OVR = OneVsRestClassifier(LinearSVC(random_state=0), n_jobs=-1) emo_model = OVR.fit(emo_X_train, emo_y_train)

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d2e46944ab05c5e8c1979101728b7b25900be342

import pytest from time import sleep from timeflux.helpers.background import Task class DummyWorker: def echo(self, message="hello", delay=0, fail=False): sleep(delay) if fail: raise Exception("failed") self.message = message return self.message def test_default(working_path): task = Task(DummyWorker(), "echo").start() while not task.done: status = task.status() assert status["result"] == "hello" assert status["instance"].message == "hello" def test_args(working_path): task = Task(DummyWorker(), "echo", "foobar").start() while not task.done: status = task.status() assert status["result"] == "foobar" def test_kwargs(working_path): task = Task(DummyWorker(), "echo", message="foobar").start() while not task.done: status = task.status() assert status["result"] == "foobar" def test_exception(working_path): task = Task(DummyWorker(), "echo", fail=True).start() while not task.done: status = task.status() assert status["success"] == False assert status["exception"].args[0] == "failed" def test_stop_running(working_path): task = Task(DummyWorker(), "echo", delay=5).start() sleep(0.5) assert task.done == False task.stop() assert task.done == True def test_stop_not_running(working_path): task = Task(DummyWorker(), "echo").start() while not task.done: status = task.status() task.stop() assert task.done == True

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b2f2f1e4b7070ac867b71e538f759e527eb1ffb9

from pymouse import PyMouse m = PyMouse() w, h = m.screen_size() class base_controller: def __init__(self): pass def move(self, xy: list): """ 移动 """ m.move(xy[0] * w, xy[1] * h) def click(self, xy: list): """ 点击 """ m.click(xy[0] * w, xy[1] * h) def scroll(self, marks: list): """ 滚动 """ d = marks[0][1] - marks[-1][1] R = 0.2 print(d) if d > R: m.scroll(-1) elif d < -R: m.scroll(1) def press(self, xy: list, ones=True): """ 长按 """ if ones: m.press(xy[0] * w, xy[1] * h) else: m.drag(xy[0] * w, xy[1] * h) def release(self, xy: list): """ 松开 """ m.release(xy[0] * w, xy[1] * h) class mac_controller(base_controller): def __init__(self): super(mac_controller, self).__init__()

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ec9efeca7eef7b8ee25c1e089e675bdb1e53413b

# -*- coding:utf-8 -*- # Author: washing # DateTime: 2022/5/18 10:28 # File: 0668.py # Desc: CV class Solution: def findKthNumber(self, m: int, n: int, k: int) -> int: return bisect_left(range(m * n), k, key=lambda x: x // n * n + sum(x // i for i in range(x // n + 1, m + 1)))

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13342922022f0a0e8928c81c1c4716125af0b2c4

import matplotlib.pyplot as plt import numpy as np plt.rcParams["savefig.dpi"] = 300 # 图片像素 plt.rcParams["figure.dpi"] = 300 # 分辨率 plt.rcParams["font.sans-serif"] = ["SimHei"] plt.rcParams["axes.unicode_minus"] = False x_axis = [20, 40, 60, 80, 100] rf = [184, 174, 166, 159, 157.5] anns = [186, 179, 170, 164, 161] adaboost = [187.5, 176, 172, 163, 162] x = np.arange(len(x_axis)) # 首先用第一个的长度作为横坐标 width = 0.2 # 设置柱与柱之间的宽度 fig, ax = plt.subplots() p_rf = ax.bar( x - width, rf, width, alpha=0.9, ) p_anns = ax.bar(x, anns, width, alpha=0.9, color="red") p_adaboost = ax.bar(x + width, adaboost, width, alpha=0.9, color="green") ax.set_xticks(x + width / 2) # 将坐标设置在指定位置 ax.set_xticklabels(x_axis) # 将横坐标替换成 plt.legend((p_rf[0], p_anns[0], p_adaboost[0]), ("RF", "ANNs", "AdaBoost"), loc="best", fontsize=20) plt.xticks(fontsize=18) plt.yticks(fontsize=18) plt.ylim(150, 200) # 指定Y轴的高度 plt.xlabel("训练集大小(%)", fontsize=20) plt.ylabel("MAE(s)", fontsize=20) plt.show() # plt.savefig('MAE.png', dpi=3600)

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cb08b95e3b9c80fb74d4415b3798ddbb36cd76e7

import unittest """ Find the largest 0 to 9 pandigital that can be formed by concatenating products Take the number 6 and multiply it by each of 1273 and 9854: 6 × 1273 = 7638 6 × 9854 = 59124 By concatenating these products we get the 1 to 9 pandigital 763859124. We will call 763859124 the "concatenated product of 6 and (1273,9854)". Notice too, that the concatenation of the input numbers, 612739854, is also 1 to 9 pandigital. The same can be done for 0 to 9 pandigital numbers. What is the largest 0 to 9 pandigital 10-digit concatenated product of an integer with two or more other integers, such that the concatenation of the input numbers is also a 0 to 9 pandigital 10-digit number? """ class Test(unittest.TestCase): def test(self): pass

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e695b9458c0e98521e560dbb291f6f05bda1549f

from savers.saver import SaverInterface import os from config import SaverConfig import mysql.connector import json import logging class SQLSaver(SaverInterface): # This class takes in json files and will interpret the jsons as follows. # {'tablename':[{'columnname01':'somevalue','columnname02':'somevalue'},{'columnaname02':'somevalue'}]} def connect(self): self.logging.info( "Logging to %s@%s:%s -p %s", self.config.SQL_USER, self.config.SQL_HOST, self.config.SQL_DATABASE, self.config.SQL_PASSWD, ) self.db = mysql.connector.connect( host=self.config.SQL_HOST, user=self.config.SQL_USER, passwd=self.config.SQL_PASSWD, database=self.config.SQL_DATABASE, auth_plugin="mysql_native_password", ) self.logging.info("Log in is successful") def executesql(self, sqlstatement): self.logging.debug("Executing %s", sqlstatement) cursor = self.db.cursor() cursor.execute(sqlstatement) self.db.commit() rowcount = cursor.rowcount cursor.close() return rowcount def executesqlupdate(self, sqlstatement): try: self.logging.debug("Executing %s ", sqlstatement) cursor = self.db.cursor() cursor.execute(sqlstatement) self.db.commit() rowcount = cursor.rowcount cursor.close() except Exception as e: self.logging.error("Problem executing SQL: %s", str(e)) def executesql(self, sqlstatement, data): try: self.logging.debug("Executing %s with data as follows %s", sqlstatement, data) cursor = self.db.cursor() cursor.execute(sqlstatement, data) self.db.commit() rowcount = cursor.rowcount cursor.close() except Exception as e: self.logging.error("Problem executing SQL: %s", str(e)) rowcount = 0 return rowcount def querysql(self, sqlstatement): print("Statements to execute\n", sqlstatement) cursor = self.db.cursor() cursor.execute(sqlstatement) records = cursor.fetchall() cursor.close() return records def __init__(self, config=None): if config == None: self.config = SaverConfig() logging.basicConfig( level=logging.DEBUG, handlers=[logging.FileHandler("{0}/{1}.log".format("/logs", "saverservice-sqlsaver")), logging.StreamHandler()], format="%(asctime)-15s %(levelname)-8s %(message)s", ) self.logging = logging self.connect() def freequery(self, sqlstring): records = self.querysql(sqlstring) return records def create(self, jsonobject): try: self.logging.info("Starting create operations") if isinstance(jsonobject, str): self.logging.debug("data obj received is not json, manually converting") jsonobject = json.loads(jsonobject) dictkeys = jsonobject.keys() totalrowcount = 0 for tablename in dictkeys: rowstoadd = jsonobject[tablename] self.logging.debug("Table: %s Rows: %s", tablename, rowstoadd) sqlstatementcolnames = "" sqlstatementcolvalues = "" for row in rowstoadd: print("Row:{}".format(row)) dictcolsinrow = row.keys() print("ColumnNames: {}".format(dictcolsinrow)) colCount = 0 datalist = [] for col in dictcolsinrow: self.logging.debug("Col:%s,Val:%s", col, row[col]) if colCount == 0: sqlstatementcolnames = col # sqlstatementcolvalues="\'"+str(row[col])+"\'" sqlstatementcolvalues = "%s" datalist.append(str(row[col])) else: sqlstatementcolnames = sqlstatementcolnames + "," + col # sqlstatementcolvalues=sqlstatementcolvalues+','+"\'"+str(row[col])+"\'" sqlstatementcolvalues = sqlstatementcolvalues + ",%s" datalist.append(str(row[col])) colCount = colCount + 1 sqlstatement = ( "INSERT INTO " + tablename + "(" + sqlstatementcolnames + ") VALUES (" + sqlstatementcolvalues + ")" ) rowcount = self.executesql(sqlstatement, datalist) totalrowcount = totalrowcount + rowcount except Exception as e: totalrowcount = 0 return totalrowcount def update(self, jsonobject): # Expects the following json # {"tablename":[ # {"row": # {"data":{"columnname02":"01value","columnname02":"02value"},"condition":{"columnname03":"03value"}}}, # {"row": # {"data":{"columnname04":"04value","columnname05":"05value"},"condition":{"columnname06":"06value"}}} # ]} dictkeys = jsonobject.keys() print("Dict keys:", dictkeys) sqlstatements = [] for tablename in dictkeys: rowstoupdate = jsonobject[tablename] print("Table: {}\nRows:{}".format(tablename, rowstoupdate)) for rowtoupdate in rowstoupdate: rowdata = rowtoupdate["row"]["data"] rowcondition = rowtoupdate["row"]["condition"] print("ROW:\n\tRowData:{}\n\tRowCondition:{}".format(rowdata, rowcondition)) sqlstatementdata = "" colCount = 0 for colname in rowdata.keys(): if colCount == 0: sqlstatementdata = colname + "=" + "'" + str(rowdata[colname]) + "'" else: sqlstatementdata = sqlstatementdata + "," + colname + "=" + "'" + str(rowdata[colname]) + "'" colCount = colCount + 1 sqlstatementcondition = "" colCount = 0 for colname in rowcondition.keys(): if colCount == 0: sqlstatementcondition = colname + "=" + "'" + str(rowcondition[colname]) + "'" else: sqlstatementcondition = ( sqlstatementcondition + " AND " + colname + "=" + "'" + str(rowcondition[colname]) + "'" ) colCount = colCount + 1 sqlstatement = "UPDATE " + tablename + " SET " + sqlstatementdata + " WHERE " + sqlstatementcondition sqlstatements.append(sqlstatement) self.executesql(sqlstatements) def setreportstatus(self, filename, status): sqlstring = "UPDATE reports SET currentloc='" + status.upper() + "' WHERE filename='" + filename.lower() + "'" self.executesqlupdate(sqlstring) def get(self): super.get() def delete(self): super.delete() if __name__ == "__main__": jsonobj = json.load(open("create_report.json", "r")) saver = SQLSaver() saver.create(jsonobj) jsonobj = json.load(open("create_ingest.json", "r")) saver.create(jsonobj) jsonobj = json.load(open("update_ingest.json", "r")) saver.update(jsonobj) # rowsreportsaffected=saver.save({'reports':[{'filename':'file01.pdf','created_at':'2020-01-09 15:00:00','ingested_at':'2020-01-09 15:01:00','currentloc':'/home/shareddata'},{'filename':'file02.pdf','created_at':'2020-01-09 16:00:00','ingested_at':'2020-01-09 16:01:00','currentloc':'/home/shareddata'}]) # rowsingestsaffected=saver.save({'ingests':[{'text':'This is good weather?','section':'observation','created_at':'2020-01-02 12:33:33','ingest_id':'1','predicted_category':'DOCTRINE','annotated_category':'DOCTRINE'}]}) # rowsingestupdate=save.update({'ingests':[{'id':'1','annotated_category':'PERSONNEL'}]}) pass

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c55991e738c89ee09dabd79d514e710e0fcbac85

from splinter import Browser from time import sleep from datetime import datetime, timedelta import os, sys import urllib import cv2 import numpy as np from PIL import Image import imutils import csv class Scraper: start_date = datetime(2018, 1, 8) url = "http://spaceweather.com/" def scrape(self): self.browser = Browser("firefox") self.browser.driver.set_page_load_timeout(60) self.browser.visit(self.url) for day in self.get_days(): self.scrape_day(day) def scrape_day(self, day): self.browser.select("month", day.strftime("%m")) self.browser.select("day", day.strftime("%d")) self.browser.select("year", day.strftime("%Y")) button = self.browser.find_by_name("view") button.click() text = self.browser.find_by_css(".solarWindText")[4].text number = int(text.split(" ")[2].strip()) link = self.browser.find_link_by_partial_href("images{}/".format(day.strftime("%Y")))["href"] folder_name = "data/{}{}{}".format(day.strftime("%Y"), day.strftime("%m"), day.strftime("%d")) image_name = "{}/image.gif".format(folder_name) txt_name = "{}/data.txt".format(folder_name) os.mkdir(folder_name) urllib.urlretrieve(link, image_name) img = Image.open(image_name) img.save("{}/image.png".format(folder_name), "png", optimize=True, quality=70) txt_file = open(txt_name, "w") txt_file.write(str(number)) txt_file.close() print("Downloaded data for {}, sunspots: {}".format(day.strftime("%m/%d/%Y"), number)) def get_days(self): days = [] for i in range(0, 8): base = self.start_date + timedelta(days=7 * i) first = base second = base + timedelta(days=2) third = base + timedelta(days=4) days.append(first) days.append(second) days.append(third) return days class Entry: folder = None date = None sunspots = -1 image_path = None counted_sunspots = 0 sections = [0, 0, 0, 0] def nothing(self, *arg): pass def __init__(self, folder, date, sunspots, image_path): self.folder = folder self.date = date self.sunspots = sunspots self.image_path = image_path def process(self): frame = cv2.imread(self.image_path) height, width, channels = frame.shape frameBGR = cv2.GaussianBlur(frame, (1, 1), 0) hsv = cv2.cvtColor(frameBGR, cv2.COLOR_BGR2HSV) colorLow = np.array([0, 90, 80]) colorHigh = np.array([10, 255, 255]) mask = cv2.inRange(hsv, colorLow, colorHigh) result = cv2.bitwise_and(frame, frame, mask=mask) image_edged = cv2.Canny(mask, 50, 100) image_edged = cv2.dilate(image_edged, None, iterations=1) image_edged = cv2.erode(image_edged, None, iterations=1) cnts = cv2.findContours(image_edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] if imutils.is_cv2() else cnts[1] image_contours = cv2.bitwise_not(result) self.counted_sunspots = 0 self.sections = [0, 0, 0, 0] section_1_start, section_1_end = 0, height / 4 section_2_start, section_2_end = height / 4, height / 4 * 2 section_3_start, section_3_end = height / 4 * 2, height / 4 * 3 section_4_start, section_4_end = height / 4 * 3, height / 4 * 4 cv2.line(image_contours, (0, section_1_end), (width, section_1_end), (0, 0, 0), 5) cv2.line(image_contours, (0, section_2_end), (width, section_2_end), (0, 0, 0), 10) cv2.line(image_contours, (0, section_3_end), (width, section_3_end), (0, 0, 0), 5) cv2.circle(image_contours, (width / 2, height / 2), width / 2, (0, 0, 0), 5) font = cv2.FONT_HERSHEY_SIMPLEX cv2.putText(image_contours, self.date.strftime("%a %b %d"), (20, 50), font, 2, (0, 0, 0), 2, cv2.LINE_AA) cv2.putText( image_contours, self.date.strftime("SSN: {}".format(self.sunspots)), (20, 100), font, 1.5, (0, 0, 0), 2, cv2.LINE_AA ) for c in cnts: if cv2.contourArea(c) < 5: continue (x, y), radius = cv2.minEnclosingCircle(c) x = int(x) y = int(y) radius = int(radius) cv2.circle(image_contours, (x, y), radius, (100, 100, 255), -1) self.counted_sunspots = self.counted_sunspots + 1 if y >= section_1_start and y <= section_1_end: # cv2.putText(image_contours, '1', (x, y - 10), font, 0.8, (100, 100, 255), 2, cv2.LINE_AA) self.sections[0] = self.sections[0] + 1 elif y >= section_2_start and y <= section_2_end: # cv2.putText(image_contours, '2', (x, y - 10), font, 0.8, (100, 100, 255), 2, cv2.LINE_AA) self.sections[1] = self.sections[1] + 1 elif y >= section_3_start and y <= section_3_end: # cv2.putText(image_contours, '3', (x, y - 10), font, 0.8, (100, 100, 255), 2, cv2.LINE_AA) self.sections[2] = self.sections[2] + 1 elif y >= section_4_start and y <= section_4_end: # cv2.putText(image_contours, '4', (x, y - 10), font, 0.8, (100, 100, 255), 2, cv2.LINE_AA) self.sections[3] = self.sections[3] + 1 print("Counted sunspots: {}".format(self.counted_sunspots)) print(self.sections) cv2.putText(image_contours, "Section 1: {}".format(self.sections[0]), (20, 130), font, 1, (0, 0, 0), 2, cv2.LINE_AA) cv2.putText(image_contours, "Section 2: {}".format(self.sections[1]), (20, 160), font, 1, (0, 0, 0), 2, cv2.LINE_AA) cv2.putText(image_contours, "Section 3: {}".format(self.sections[2]), (20, 190), font, 1, (0, 0, 0), 2, cv2.LINE_AA) cv2.putText(image_contours, "Section 4: {}".format(self.sections[3]), (20, 220), font, 1, (0, 0, 0), 2, cv2.LINE_AA) colorLow = np.array([0, 0, 90]) colorHigh = np.array([0, 0, 255]) mask = cv2.inRange(hsv, colorLow, colorHigh) image_contours[mask > 0] = (0, 0, 0) vis = np.concatenate((frame, image_contours), axis=1) cv2.imwrite("out/images/{}.png".format(self.folder), vis) class Processor: entries = [] def load(self): folders = os.listdir("data") for folder in folders: year = int(folder[:4]) month = int(folder[4:6]) day = int(folder[6:8]) date = datetime(year, month, day) image_name = "data/{}/image.png".format(folder) txt_name = "data/{}/data.txt".format(folder) txt_file = open(txt_name, "r") content = txt_file.readlines() txt_file.close() number = int(content[0]) print(folder) entry = Entry(folder, date, number, image_name) entry.process() self.entries.append(entry) self.entries.sort(key=lambda x: x.date, reverse=False) def compute(self): for section in range(0, 4): total = 0 for entry in self.entries: total += entry.sections[section] average = float(total) / float(len(self.entries)) print("-------[Section {}]-------".format(section + 1)) print("Total: {}".format(total)) print("Average: {}".format(average)) total = 0 sections_data = [["date", "section_1", "section_2", "section_3", "section_4"]] numbers_data = [["date", "reported", "visible"]] for entry in self.entries: total += entry.counted_sunspots sections_data.append([entry.date.strftime("%Y/%m/%d")] + entry.sections) numbers_data.append([entry.date.strftime("%Y/%m/%d")] + [entry.sunspots, entry.counted_sunspots]) average = float(total) / float(len(self.entries)) print("---------[TOTAL]---------") print("Total: {}".format(total)) print("Average: {}".format(average)) csv_file = open("out/sections.csv", "w") writer = csv.writer(csv_file) writer.writerows(sections_data) csv_file.close() csv_file = open("out/numbers.csv", "w") writer = csv.writer(csv_file) writer.writerows(numbers_data) csv_file.close() scraper = Scraper() scraper.scrape() processor = Processor() processor.load() processor.compute()

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b767519229058b50183d78bb97121f050e5b6bad

# defining private variables class Privacy: def __init__(self, val): self.__val = 900 print("Private data member =", self.__val, "\n") value = Privacy(800) print("Value not changable\n") value.__val

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b679444fde7cd8eb819443922f37ee54c0f29de4

from pirates.teleport.AreaTeleportActor import AreaTeleportActor class DoorTeleportActor(AreaTeleportActor): pass

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f714c7006f50379cc7508a13d710d902d38d2d1f

import torch import torch.nn as nn import torch.nn.functional as F # Const. low-rank version class xCNNlow(torch.nn.Module): def __init__(self, channels, filters, kernel_size, padding=0, stride=1, groups=1, rank=1, bias=True): super(xCNNlow, self).__init__() self.filters = filters self.times = 2 self.kernel_size = kernel_size self.channels = channels // groups self.padding = padding self.stride = stride self.biasTrue = bias self.rank = rank self.groups = groups self.conv_weights = nn.Parameter(torch.Tensor(filters // self.times, channels, kernel_size, kernel_size).to(device)) self.column_weights = nn.Parameter(torch.Tensor(filters - filters // self.times, self.rank).to(device)) self.row_weights = nn.Parameter(torch.Tensor(self.rank, filters // self.times).to(device)) torch.nn.init.xavier_uniform(self.conv_weights) self.column_weights.data.uniform_(-0.1, 0.1) self.row_weights.data.uniform_(-0.1, 0.1) if self.biasTrue: self.bias = nn.Parameter(torch.Tensor(filters).to(device)) self.bias.data.uniform_(-0.1, 0.1) def forward(self, input): self.correlated_weights = torch.mm( self.column_weights, torch.mm(self.row_weights, self.conv_weights.reshape(self.filters // self.times, -1)) ).reshape(self.filters - self.filters // self.times, self.channels, self.kernel_size, self.kernel_size) if self.biasTrue: return F.conv2d( input, torch.cat((self.conv_weights, self.correlated_weights), dim=0), bias=self.bias, padding=self.padding, stride=self.stride, ) else: return F.conv2d( input, torch.cat((self.conv_weights, self.correlated_weights), dim=0), padding=self.padding, stride=self.stride ) # count FLOPs def count_op_xCNNlow(m, x, y): x = x[0] multiply_adds = 1 cin = m.channels cout = m.filters kh, kw = m.kernel_size, m.kernel_size batch_size = x.size()[0] out_h = y.size(2) out_w = y.size(3) # ops per output element # kernel_mul = kh * kw * cin # kernel_add = kh * kw * cin - 1 kernel_ops = multiply_adds * kh * kw bias_ops = 1 if m.biasTrue is True else 0 ops_per_element = kernel_ops + bias_ops # total ops # num_out_elements = y.numel() output_elements = batch_size * out_w * out_h * cout conv_ops = output_elements * ops_per_element * cin // m.groups # per output element total_mul_1 = m.filters // m.times total_add_1 = total_mul_1 - 1 num_elements_1 = m.rank * (cin * kh * kw) # (m.filters - m.filters//m.times) total_mul_2 = m.rank total_add_2 = total_mul_2 - 1 num_elements_2 = (m.filters - m.filters // m.times) * (cin * kh * kw) # (m.filters - m.filters//m.times) lin_ops = (total_mul_1 + total_add_1) * num_elements_1 + (total_mul_2 + total_add_2) * num_elements_2 total_ops = lin_ops + conv_ops print(lin_ops, conv_ops) m.total_ops = torch.Tensor([int(total_ops)])

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726f133bcf592315c42f8701be8308422ffbf0d9

from flask import Flask, render_template from flask_ask import Ask, statement, question, session import reverse_geocoder as rg from geopy import distance from geopy.geocoders import Nominatim import requests import time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app = Flask(__name__) ask = Ask(app, "/space_walk") def find_ordinals(city, iss): """ Take tuple coordinates (lat, lon) for City and ISS and find the cardinal direction of NE, SE, SW, NW """ if iss[0] - city[0] > 0: a = "North" else: a = "South" if iss[1] - city[1] > 0: b = "East" else: b = "West" return "".join([a, b]) def where_is_the_iss_now(): iss_now_website = "http://api.open-notify.org/iss-now.json" webby = requests.get(iss_now_website) data = webby.json() if data["iss_position"]: longitude = data["iss_position"].get("longitude") latitude = data["iss_position"].get("latitude") results = rg.search((latitude, longitude), mode=1) lat, lon, name, admin1, admin2, cc = results[0].values() ordinal = find_ordinals(city=(float(lat), float(lon)), iss=(float(latitude), float(longitude))) country_cc = requests.get( "https://pkgstore.datahub.io/core/country-list/data_json/data/8c458f2d15d9f2119654b29ede6e45b8/data_json.json" ) country_cc = country_cc.json() iss_coordinates = (latitude, longitude) k_nearest_coordinates = (lat, lon) distance_miles = distance.distance(k_nearest_coordinates, iss_coordinates).miles country_name = "" for i in filter(lambda d: d.get("Code") == cc, country_cc): country_name = i.get("Name") location_text = ", ".join([name, admin1, country_name]) if distance_miles > 150: answer = "The International Space Station is {} miles {} off the coast of {}".format( int(distance_miles), ordinal, location_text ) else: answer = "the International Space Station is {} miles {} near {}".format(int(distance_miles), ordinal, location_text) return answer, latitude, longitude, distance_miles, ordinal, name, admin1, country_name @app.route("/") def homepage(): return "" @ask.launch def start_skill(): # welcome_message = 'Welcome to the Fleet Feet Journal! What is your name?' welcome_message_reprompt = render_template("welcome_message_reprompt") welcome_message = render_template("welcome_message") return question(welcome_message).reprompt(welcome_message_reprompt) @ask.intent("YourLocation") def pass_over(my_location): geolocator = Nominatim(user_agent="my-application") print(my_location) location = geolocator.geocode(my_location, language="en-US") try: city = location.address.split(",")[0] state = location.address.split(",")[2] country = location.address.split(",")[-1] location_name = ", ".join([city, state, country]) except IndexError: location_name = location.address.split(",")[-1] fly_over = requests.get( "http://api.open-notify.org/iss-pass.json?lat={}&lon={}".format(location.latitude, location.longitude) ) fly_over = fly_over.json() if fly_over["message"] == "success": rise = fly_over["response"][0] answer = time.strftime("%A, %B %d, %Y at %I:%M %p GMT", time.localtime(rise.get("risetime"))) a = rise.get("risetime") # last epoch recorded b = time.time() # current epoch time c = a - b # returns seconds hours = c // 3600 % 24 minutes = c // 60 % 60 minutes = int(minutes) hours = int(hours) if minutes == 1: minorminutes = "minute" else: minorminutes = "minutes" if hours == 1: hour_or_hours = "hour" else: hour_or_hours = "hours" if hours == 0: time_til_rise = "{} {}".format(minutes, minorminutes) else: time_til_rise = "{} {} and {} {}".format(hours, hour_or_hours, minutes, minorminutes) else: answer = "failure" return statement("the next flyover for {} will begin in {} on {}".format(location_name, time_til_rise, answer)) @ask.intent("WhereISS") def share_location(): iss_location, latitude, longitude, distance_miles, ordinal, name, admin1, country_name = where_is_the_iss_now() latitude, longitude, distance_miles = float(latitude), float(longitude), float(distance_miles) return statement(iss_location).standard_card( title="Location of the International Space Station", text="Latitude {} and Longitude {},\n {} miles {} of {}, {} in {}".format( round(latitude, 2), round(longitude, 2), round(distance_miles, 0), ordinal, name, admin1, country_name ), ) @ask.intent("AMAZON.FallbackIntent") def fallback(): to_continue = render_template("to_continue") return question("Sorry, I am not sure what you asked me...{}".format(to_continue)) @ask.intent("AMAZON.NavigateHomeIntent") def go_home(): return question("et - phone home") @ask.intent("AMAZON.HelpIntent") def help_me(): help_me_text = render_template("help") return question(help_me_text) @ask.intent("Credits") def speak_credits(): credits_ = render_template("credits") return statement(credits_) @ask.intent("AMAZON.StopIntent") def stop(): bye_text = render_template("bye") return statement(bye_text) @ask.intent("AMAZON.CancelIntent") def cancel(): bye_text = render_template("bye") return statement(bye_text) @ask.session_ended def session_ended(): return "{}", 200 if __name__ == "__main__": app.run(debug=True)

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cae49da8dd436fc51b472c4a88703d8bc6c79bda

import SCons.Util import xml.dom.minidom, re, os.path ################################################################################ # DocBook pseudobuilder # TODO: Only generate the output formats that are known ################################################################################ def generate(env): def remove_doctype(target, source, env): f = open(str(target[0])) output = [] for line in f.readlines(): output.append(re.sub("^<!DOCTYPE .*", "", line)) f.close() f = open(str(target[0]), "wb") for line in output: f.write(line) f.close() def buildDocBook(env, source): db_env = env.Clone() db_env["XMLCATALOGS"] = [db_env["DOCBOOK_XML"]] # PDF generation fo = db_env.XSLT(os.path.splitext(source)[0] + ".fo", source, XSLTSTYLESHEET=db_env["DOCBOOK_XSL_FO"]) pdf = db_env.FO(fo) # HTML generation db_env.XSLT(os.path.splitext(source)[0] + ".html", source, XSLTSTYLESHEET=db_env["DOCBOOK_XSL_HTML"]) # WordPress generation wp_params = [("wordpress.dir", env.get("DOCBOOK_WP_DIR", "../../wordpress"))] wp_pdf_url = env.get("DOCBOOK_WP_PDF_URL", pdf[0].name) if len(wp_pdf_url) > 0: wp_params.append(("pdf.url", wp_pdf_url)) wp_params.append(("pdf.icon", env.get("DOCBOOK_WP_PDF_ICON", "/icons/pdf.png"))) wp = db_env.XSLT( os.path.splitext(source)[0] + ".wp.php", source, XSLTSTYLESHEET=db_env["DOCBOOK_XSL_WP"], XSLTPARAMS=wp_params + env.get("XSLTPARAMS", []), ) db_env.AddPostAction(wp, SCons.Action.Action(remove_doctype, cmdstr="$FIXCOMSTR")) env.AddMethod(buildDocBook, "DocBook") def exists(env): return True

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c139cbc3e693d75ad196e10257ff3028aa835709

# Complete the hurdleRace function below. def hurdleRace(k, height): if k < max(height): return max(height) - k return 0 print(hurdleRace(2, [2, 5, 4, 5, 2]))

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77971b088a7e076e3bf6d7aa320981a50e7756ce

from flask import Flask from flask_ask import Ask, statement, question, session # import json, requests import random app = Flask(__name__) ask = Ask(app, "/") def get_cat_fact(): myFacts = [ "Cats should not be fed tuna exclusively, as it lacks taurine, an essential nutrient required for good feline health. Make sure you have the proper Pet supplies to keep your cat happy and healthy.", "The strongest climber among the big cats, a leopard can carry prey twice its weight up a tree.", "A cat’s hearing is better than a dog’s. A cat can hear high-frequency sounds up to two octaves higher than a human.", "Tylenol and chocolate are both poisionous to cats.", "Cats have 30 teeth (12 incisors, 10 premolars, 4 canines, and 4 molars), while dogs have 42. Kittens have baby teeth, which are replaced by permanent teeth around the age of 7 months.", "It has been scientifically proven that owning cats is good for our health and can decrease the occurrence of high blood pressure and other illnesses.", "A cat can’t climb head first down a tree because every claw on a cat’s paw points the same way. To get down from a tree, a cat must back down.", "Cats are subject to gum disease and to dental caries. They should have their teeth cleaned by the vet or the cat dentist once a year.", "A domestic cat can run at speeds of 30 mph.", "Cat families usually play best in even numbers. Cats and kittens should be aquired in pairs whenever possible.", "A cat’s back is extremely flexible because it has up to 53 loosely fitting vertebrae. Humans only have 34.", "The claws on the cat’s back paws aren’t as sharp as the claws on the front paws because the claws in the back don’t retract and, consequently, become worn.", "Cat paws act as tempetature regulators, shock absorbers, hunting and grooming tools, sensors, and more", "Cats see six times better in the dark and at night than humans.", "The cat's tail is used to maintain balance.", "Cats have 300 million neurons; dogs have about 160 million", "Both humans and cats have identical regions in the brain responsible for emotion.", "The lightest cat on record is a blue point Himalayan called Tinker Toy, who weighed 1 pound, 6 ounces (616 g). Tinker Toy was 2.75 inches (7 cm) tall and 7.5 inches (19 cm) long.", "An adult lion's roar can be heard up to five miles (eight kilometers) away.", "You check your cats pulse on the inside of the back thigh, where the leg joins to the body. Normal for cats: 110-170 beats per minute.", "The largest cat breed is the Ragdoll. Male Ragdolls weigh between 12 and 20 lbs (5.4-9.0 k). Females weigh between 10 and 15 lbs (4.5-6.8 k).", "A cat's normal temperature varies around 101 degrees Fahrenheit.", "Unlike other cats, lions have a tuft of hair at the end of their tails.", "Cats don’t have sweat glands over their bodies like humans do. Instead, they sweat only through their paws.", "The average cat food meal is the equivalent to about five mice.", "The first official cat show in the UK was organised at Crystal Palace in 1871.", "In just seven years, a single pair of cats and their offspring could produce a staggering total of 420,000 kittens.", ] fact = myFacts[random.ranint(0, len(myFacts) - 1)] return fact @ask.launch def start_skill(): welcome_message = "Hello there, would you like to hear a cat fact?" return question(welcome_message) @ask.intent("YesIntent") def share_headlines(): fact = get_cat_fact() cat_fact = "Did you know, " + fact return statement(cat_fact) @ask.intent("NoIntent") def no_intent(): bye_text = "Ok! Have a wonderful day!" return statement(bye_text) if __name__ == "__main__": app.run(debug=True)

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124d7da330aa7c869320e10f4f89cc1c872f85f2

import matplotlib.pyplot as plt import sys sys.path.append("coin_flipping_src") from monte_carlo import monte_carlo from probability import probability plt.style.use("bmh") x_coords = range(10) probablility_results = [probability(x, 10) for x in x_coords] plt.plot(x_coords, probablility_results, linewidth=2.5) # plt.plot([0,1,2,3,4],[0.1, 0.3, 0.5, 0.1, 0.1],linewidth=2.5) for _ in range(5): plt.plot(x_coords, [monte_carlo(x, 10, 100) for x in x_coords], linewidth=0.75) # plt.plot([0,1,2,3,4],[0.3, 0.1, 0.4, 0.2, 0.1],linewidth=0.75) # plt.plot([0,1,2,3,4],[0.2, 0.2, 0.3, 0.3, 0.2],linewidth=0.75) plt.legend(["True", "MC 1", "MC 2", "MC 3", "MC 4", "MC 5"]) plt.xlabel("Number of Heads") plt.ylabel("Probability") plt.title("True Distribution vs Monte Carlo Simulations for 10 Coin Flips") plt.savefig("plot.png") plt.show()

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502da0f0dafe42d3464fabb1d92ae1b0d7ef11f3

# Check given matrix is valid sudoku or Not.

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4e31c2a80bec77a1f5aafc8a91617fb4b2941788

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Author: André Pacheco E-mail: [email protected] This file implements the methods and functions to load the image as a PyTorch dataset If you find any bug or have some suggestion, please, email me. """ from PIL import Image from torch.utils import data import torchvision.transforms as transforms class BuildDataset(data.Dataset): """ This the standard way to implement a dataset pipeline in PyTorch. We need to extend the torch.utils.data.Dataset class and implement the following methods: __len__, __getitem__ and the constructor __init__ """ def __init__(self, imgs_path, labels, extra_info=None, transform=None): """ The constructor gets the images path and their respectively labels and extra information (if it exists). In addition, you can specify some transform operation to be carry out on the images. It's important to note the images must match with the labels (an extra information if exist). For example, the imgs_path[x]'s label must take place on labels[x]. Parameters: :param imgs_path (list): a list of string containing the image paths :param labels (list) a list of labels for each image :param extra_info (list): a list of extra information regarding each image. If None, there is no information. Defaul is None. :param transform (torchvision.transforms.transforms.Compose): transform operations to be carry out on the images """ self.imgs_path = imgs_path self.labels = labels self.extra_info = extra_info # if transform is None, we need to ensure that the PIL image will be transformed to tensor, otherwise we'll got # an exception if transform is not None: self.transform = transform else: self.transform = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()]) def __len__(self): """This method just returns the dataset size""" return len(self.imgs_path) def __getitem__(self, item): """ It gets the image, labels and extra information (if it exists) according to the index informed in `item`. It also performs the transform on the image. :param item (int): an index in the interval [0, ..., len(img_paths)-1] :return (tuple): a tuple containing the image, its label and extra information (if it exists) """ image = Image.open(self.imgs_path[item]).convert("RGB") # Applying the transformations image = self.transform(image) img_name = self.imgs_path[item].split("/")[-1].split(".")[0] # print(self.labels[item]) # print(self.extra_info[item]) if self.extra_info is None: extra_info = [] else: extra_info = self.extra_info[item] if self.labels is None: labels = [] else: labels = self.labels[item] return image, labels, extra_info, img_name def get_data_loader(imgs_path, labels, extra_info=None, transform=None, params=None): """ This function gets a list og images path, their labels and extra information (if it exists) and returns a DataLoader for these files. You also can set some transformations using torchvision.transforms in order to perform data augmentation. Lastly, params is a dictionary that you can set the following parameters: batch_size (int): the batch size for the dataset. If it's not informed the default is 30 shuf (bool): set it true if wanna shuffe the dataset. If it's not informed the default is True num_workers (int): the number thread in CPU to load the dataset. If it's not informed the default is 0 (which :param imgs_path (list): a list of string containing the images path :param labels (list): a list of labels for each image :param extra_info (list, optional): a list of extra information regarding each image. If it's None, it means there's no extra information. Default is None :param transform (torchvision.transforms, optional): use the torchvision.transforms.compose to perform the data augmentation for the dataset. Alternatively, you can use the jedy.pytorch.utils.augmentation to perform the augmentation. If it's None, none augmentation will be perform. Default is None :param params (dictionary, optional): this dictionary contains the following parameters: batch_size: the batch size. If the key is not informed or params = None, the default value will be 30 shuf: if you'd like to shuffle the dataset. If the key is not informed or params = None, the default value will be True num_workers: the number of threads to be used in CPU. If the key is not informed or params = None, the default value will be 4 pin_memory = set it to True to Pytorch preload the images on GPU. If the key is not informed or params = None, the default value will be True :return (torch.utils.data.DataLoader): a dataloader with the dataset and the chose params """ dt = BuildDataset(imgs_path, labels, extra_info, transform) # Checking the params values. If it's not defined in params of if params is None, the default values are described # below: batch_size = 30 shuf = True num_workers = 4 pin_memory = True # However, if the params is defined, we used the values described on it: if params is not None: if "batch_size" in params.keys(): batch_size = params["batch_size"] if "shuf" in params.keys(): shuf = params["shuf"] if "num_workers" in params.keys(): num_workers = params["num_workers"] if "pin_memory" in params.keys(): pin_memory = params["pin_memory"] # Calling the dataloader dl = data.DataLoader(dataset=dt, batch_size=batch_size, shuffle=shuf, num_workers=num_workers, pin_memory=pin_memory) return dl

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75133dd924f8f3f028075c5d2109bb79ddc7fe87

import pymysql def testeSelect(db): # 创建查询游标 cur1 = db.cursor() # 使用 execute() 方法执行 SQL 查询 cur1.execute("SELECT VERSION()") # 使用 fetchone() 方法获取单条数据. data = cur1.fetchone() print(dir(data)) print("cur1 : %s " % cur1) print("Database version : %s " % data) def dropTable(db): # 创建查询游标 cur1 = db.cursor() cur1.execute("drop table if exists python_demo") print("dropTable", cur1) def createTable(db): # 创建查询游标 cur1 = db.cursor() sql = """ CREATE TABLE IF NOT EXISTS python_demo ( MEMBER_ID INT(20) NOT NULL AUTO_INCREMENT COMMENT '会员ID', MEMBER_CODE VARCHAR(20) NOT NULL COMMENT '会员代码', MEMBER_NAME VARCHAR(128) NOT NULL COMMENT '公司中文名称', MEMBER_NAME_SHORT VARCHAR(128) NULL DEFAULT NULL COMMENT '公司简称', COMPANY_NAME_EN VARCHAR(128) NULL DEFAULT NULL COMMENT '公司英文名称', REG_PLACE VARCHAR(20) NULL DEFAULT NULL COMMENT '公司注册地址', REG_ADDRESS VARCHAR(128) NULL DEFAULT NULL COMMENT '公司注册地址', ENT_TYPE VARCHAR(20) NULL DEFAULT NULL COMMENT '公司性质', REGCAPITAL DOUBLE(12,2) NULL DEFAULT NULL COMMENT '注册资本', REG_CURRENCY VARCHAR(20) NULL DEFAULT NULL COMMENT '注册资本币种', JUDICIAL_PERSON VARCHAR(32) NULL DEFAULT NULL COMMENT '法人名称', BUSINESS_SCOPE VARCHAR(128) NULL DEFAULT NULL COMMENT '公司经营范围', COM_TEL VARCHAR(20) NULL DEFAULT NULL COMMENT '公司电话', COM_FAX VARCHAR(64) NULL DEFAULT NULL COMMENT '公司传真', PERSON_INCHARGE VARCHAR(64) NULL DEFAULT NULL COMMENT '公司负责人', ZIP_CODE VARCHAR(6) NULL DEFAULT NULL COMMENT '邮编', CON_NAME VARCHAR(32) NULL DEFAULT NULL COMMENT '联系人姓名', CON_MOBILE VARCHAR(20) NULL DEFAULT NULL COMMENT '联系人手机', CON_EMAIL VARCHAR(32) NULL DEFAULT NULL COMMENT '联系人邮箱', CON_FAX VARCHAR(32) NULL DEFAULT NULL COMMENT '联系人传真', CON_CERT_TYPE VARCHAR(20) NULL DEFAULT NULL COMMENT '联系人证件类型', CON_CERT_NO VARCHAR(20) NULL DEFAULT NULL COMMENT '联系人证件号', CON_CERT_DATE DATE NULL DEFAULT NULL COMMENT '联系人证件失效时间', CON_CER1_ID INT(20) NULL DEFAULT NULL COMMENT '联系人身份证正面ID', CON_CER2_ID INT(20) NULL DEFAULT NULL COMMENT '联系人身份证反面ID', THERECER_INTGRATED VARCHAR(20) NULL DEFAULT NULL COMMENT '三证合一标志', BIZLIC_ID INT(20) NULL DEFAULT NULL COMMENT '营业执照ID', BIZLIC_NO VARCHAR(20) NULL DEFAULT NULL COMMENT '营业执照代码', BIZLIC_DATE DATE NULL DEFAULT NULL COMMENT '营业执照失效时间', TAXREGISTER_ID INT(20) NULL DEFAULT NULL COMMENT '税务等级证书ID', TAXREGISTER_NO VARCHAR(20) NULL DEFAULT NULL COMMENT '税务登记号', TAXREGISTER_DATE DATE NULL DEFAULT NULL COMMENT '税务登记失效时间', ORGREG_ID INT(20) NULL DEFAULT NULL COMMENT '组织机构代码证ID', ORGREG_NO VARCHAR(20) NULL DEFAULT NULL COMMENT '组织机构代码', ORGREG_DATE DATE NULL DEFAULT NULL COMMENT '组织机构失效时间', BANK_ID INT(20) NULL DEFAULT NULL COMMENT '银行开户许可证ID', BANK_TYPE VARCHAR(20) NULL DEFAULT NULL COMMENT '开户银行类别', BANK_DEPOSIT VARCHAR(128) NULL DEFAULT NULL COMMENT '开户银行', BANK_DEPOSIT_CODE VARCHAR(32) NULL DEFAULT NULL COMMENT '开户银行编码', BANK_ACCOUNTNO VARCHAR(32) NULL DEFAULT NULL COMMENT '银行账号', BANK_HOUSEHOULD VARCHAR(32) NULL DEFAULT NULL COMMENT '银行户主', INVOICE_TITLE VARCHAR(128) NULL DEFAULT NULL COMMENT '开票台头', INVOICE_ADDRESS VARCHAR(20) NULL DEFAULT NULL COMMENT '开票地址', INVOICE_ADDRESS_DT VARCHAR(128) NULL DEFAULT NULL COMMENT '开票详细地址', APPLY_SELLER_ID INT(20) NULL DEFAULT NULL COMMENT '申请审核机构', BUYER_FLAG VARCHAR(20) NULL DEFAULT NULL COMMENT '买家标识', SELLER_FLAG VARCHAR(20) NULL DEFAULT NULL COMMENT '卖家标识', THIRD_PART_FLAG VARCHAR(20) NULL DEFAULT NULL COMMENT '第三方标识', MAIN_USER_ID INT(20) NULL DEFAULT NULL COMMENT '主账号ID', MDM_DATA_CODE VARCHAR(20) NULL DEFAULT NULL COMMENT 'MDM主数据CODE', ERP_DATA_CODE VARCHAR(20) NULL DEFAULT NULL COMMENT 'ERP会员CODE', REG_TYPE VARCHAR(20) NULL DEFAULT NULL COMMENT '注册类型', STATUS VARCHAR(20) NULL DEFAULT NULL COMMENT '会员状态', AUDITOR VARCHAR(128) NULL DEFAULT NULL COMMENT '审核人', AUDIT_DATE DATETIME NULL DEFAULT NULL COMMENT '审核时间', AUDIT_RESULT VARCHAR(20) NULL DEFAULT NULL COMMENT '审核结果', AUDIT_OPINION VARCHAR(128) NULL DEFAULT NULL COMMENT '审核意见', MDM_AUDITOR VARCHAR(32) NULL DEFAULT NULL COMMENT 'MDM审核人', MDM_AUDIT_DATE DATETIME NULL DEFAULT NULL COMMENT 'MDM审核时间', MDM_AUDIT_OPINION VARCHAR(128) NULL DEFAULT NULL COMMENT 'MDM审核意见', MDM_AUDIT_RESULT VARCHAR(20) NULL DEFAULT NULL COMMENT 'MDM审核结果', MEMBER_CHG_ID INT(20) NULL DEFAULT NULL COMMENT '变更ID', CHANGE_STATUS VARCHAR(20) NULL DEFAULT NULL COMMENT '变更状态', ALIVE_FLAG VARCHAR(1) NOT NULL COMMENT '当前有效状态', LANG_VER VARCHAR(10) NULL DEFAULT NULL COMMENT '语言类型', CREATE_USER INT(20) NOT NULL COMMENT '创建者', CREATE_DATE DATETIME NOT NULL COMMENT '创建时间', UPDATE_USER INT(20) NULL DEFAULT NULL COMMENT '修改者', UPDATE_DATE DATETIME NULL DEFAULT NULL COMMENT '修改时间', DELETE_USER INT(20) NULL DEFAULT NULL COMMENT '删除者', DELETE_DATE DATETIME NULL DEFAULT NULL COMMENT '删除时间', BUYER_TYPE VARCHAR(20) NULL DEFAULT NULL COMMENT '买家类型(01：个人买家；02：公司买家)', AUDIT_OPTION_FLAG VARCHAR(20) NULL DEFAULT NULL COMMENT '审核身份标识(01：平台；02：卖家)', AUDIT_SELLER_ID INT(20) NULL DEFAULT NULL COMMENT '审核卖家ID(当审核身份标识为卖家审核时，审核的卖家ID)', SELLER_MDM VARCHAR(20) NULL DEFAULT NULL COMMENT '卖家MDM系统', SELLER_SAP VARCHAR(20) NULL DEFAULT NULL COMMENT '卖家SAP系统', SELLER_MDM_DATA_CODE VARCHAR(20) NULL DEFAULT NULL COMMENT '卖家MDM系统数据CODE', IS_PLAT_BLACK VARCHAR(2) NULL DEFAULT NULL COMMENT '黑名单状态(41：是；0：否)', INVOCIE_ADDRESS_LEFT3 VARCHAR(10) NULL DEFAULT NULL COMMENT '用户所属区域-省', INVOCIE_ADDRESS_RIGHT5 VARCHAR(10) NULL DEFAULT NULL COMMENT '用户所属区域-市', PRIMARY KEY (MEMBER_ID) ) COMMENT='会员信息表' COLLATE='utf8_general_ci' ENGINE=InnoDB """ cur1.execute(sql) print("createTabl", cur1) def selectTable(db): # 创建查询游标 cur1 = db.cursor() cur1.execute("select member_name,MEMBER_CODE,member_id from python_demo limit 10") # 使用 fetchall() 接收全部的返回结果行 data = cur1.fetchall() for index, item in enumerate(data): print(index, sep=" ", end=" ") for index2, item2 in enumerate(item): print(item2, sep=" ", end=" ") print("") def insertTable(db): # 创建查询游标 cur1 = db.cursor() cur1.execute( "INSERT INTO python_demo (MEMBER_CODE, MEMBER_NAME, MEMBER_NAME_SHORT, COMPANY_NAME_EN, REG_PLACE, REG_ADDRESS, ENT_TYPE, REGCAPITAL, REG_CURRENCY, JUDICIAL_PERSON, BUSINESS_SCOPE, COM_TEL, COM_FAX, PERSON_INCHARGE, ZIP_CODE, CON_NAME, CON_MOBILE, CON_EMAIL, CON_FAX, CON_CERT_TYPE, CON_CERT_NO, CON_CERT_DATE, CON_CER1_ID, CON_CER2_ID, THERECER_INTGRATED, BIZLIC_ID, BIZLIC_NO, BIZLIC_DATE, TAXREGISTER_ID, TAXREGISTER_NO, TAXREGISTER_DATE, ORGREG_ID, ORGREG_NO, ORGREG_DATE, BANK_ID, BANK_TYPE, BANK_DEPOSIT, BANK_DEPOSIT_CODE, BANK_ACCOUNTNO, BANK_HOUSEHOULD, INVOICE_TITLE, INVOICE_ADDRESS, INVOICE_ADDRESS_DT, APPLY_SELLER_ID, BUYER_FLAG, SELLER_FLAG, THIRD_PART_FLAG, MAIN_USER_ID, MDM_DATA_CODE, ERP_DATA_CODE, REG_TYPE, STATUS, AUDITOR, AUDIT_DATE, AUDIT_RESULT, AUDIT_OPINION, MDM_AUDITOR, MDM_AUDIT_DATE, MDM_AUDIT_OPINION, MDM_AUDIT_RESULT, MEMBER_CHG_ID, CHANGE_STATUS, ALIVE_FLAG, LANG_VER, CREATE_USER, CREATE_DATE, UPDATE_USER, UPDATE_DATE, DELETE_USER, DELETE_DATE, BUYER_TYPE, AUDIT_OPTION_FLAG, AUDIT_SELLER_ID, SELLER_MDM, SELLER_SAP, SELLER_MDM_DATA_CODE, IS_PLAT_BLACK, INVOCIE_ADDRESS_LEFT3, INVOCIE_ADDRESS_RIGHT5) VALUES ('A000001', '中国有限公司', '中国有限公司', NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, 'xinming', '15136378930', NULL, NULL, NULL, NULL, NULL, NULL, NULL, '0', NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, '0', '1', '0', 2, 'M0000001', '00M0000001', '10', '01', NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, 2, '01', '1', 'ZH-CN', 179143, '2016-05-28 12:16:23', NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL)" ) # 使用 fetchall() 接收全部的返回结果行 # data = cur1.rowcount() # print('insertTable',data) # 打开数据库连接 db = pymysql.connect(host="127.0.0.1", user="pu3147", password="1qaz@WSX", database="demo", port=3306, charset="UTF8") dropTable(db) createTable(db) insertTable(db) insertTable(db) insertTable(db) insertTable(db) insertTable(db) testeSelect(db) selectTable(db) # 关闭数据库连接 db.close()

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de347b41cd88947690cb42e043880a80d81e2c5c

# Generated by Django 3.2.7 on 2021-09-11 19:38 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("cryptocurrency", "0012_rename_cancel_exists_order_cancel_exist"), ] operations = [ migrations.AlterField( model_name="order", name="created_at", field=models.IntegerField(blank=True, null=True), ), ]

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8e85740123467889bdeb6b27d5eaa4b39df280ed

from .celery import app from home.models import Banner from settings.const import BANNER_COUNT from home.serializers import BannerModelSerializer from django.core.cache import cache from django.conf import settings @app.task def update_banner_list(): # 获取最新内容 banner_query = Banner.objects.filter(is_delete=False, is_show=True).order_by("-orders")[:BANNER_COUNT] # 序列化 banner_data = BannerModelSerializer(banner_query, many=True).data for banner in banner_data: banner["image"] = settings.END_BASE_URL + banner["image"] # 更新缓存 cache.set("banner_list", banner_data) return True

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a2421a8673a524c32539555596711a71a8e00dbf

import os import argparse import torch import model.model as module_arch from utils.util import remove_weight_norms from train import get_instance from librosa import load from librosa.output import write_wav from time import time def main(config, resume, infile, outfile, sigma, dur, half): # build model architecture model = get_instance(module_arch, "arch", config) model.summary() # load state dict checkpoint = torch.load(resume) state_dict = checkpoint["state_dict"] if config["n_gpu"] > 1: model = torch.nn.DataParallel(model) model.load_state_dict(state_dict) if config["n_gpu"] > 1: model = model.module model.apply(remove_weight_norms) # prepare model for testing device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = model.to(device) model.eval() sr = config["arch"]["args"]["sr"] y, _ = load(infile, sr=sr, duration=dur) y = torch.Tensor(y).to(device) # get mel before turn to half, because sparse.half is not implement yet mel = model.get_mel(y[None, :]) if half: model = model.half() mel = mel.half() start = time() x = model.infer(mel, sigma) cost = time() - start print("Time cost: {:.4f}, Speed: {:.4f} kHz".format(cost, x.numel() / cost / 1000)) # print(x.max(), x.min()) write_wav(outfile, x.cpu().float().numpy(), sr, False) if __name__ == "__main__": parser = argparse.ArgumentParser(description="WaveGlow inference") parser.add_argument("infile", type=str, help="wave file to generate mel-spectrogram") parser.add_argument("outfile", type=str, help="output file name") parser.add_argument("--duration", type=float, help="duration of audio, in seconds") parser.add_argument("--half", action="store_true") parser.add_argument("-s", "--sigma", type=float, default=1.0) parser.add_argument("-r", "--resume", default=None, type=str, help="path to latest checkpoint (default: None)") parser.add_argument("-d", "--device", default=None, type=str, help="indices of GPUs to enable (default: all)") args = parser.parse_args() if args.resume: config = torch.load(args.resume)["config"] if args.device: os.environ["CUDA_VISIBLE_DEVICES"] = args.device main(config, args.resume, args.infile, args.outfile, args.sigma, args.duration, args.half)

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6154979cd2853dd2bd26d1ae5df7365efa0141c2

from sqlalchemy import Column, MetaData, Table, BigInteger, String, DateTime, Integer from migrate import * meta = MetaData() table = Table( "accesses", meta, Column("id", BigInteger, primary_key=True, nullable=False), Column("uuid", String(255), nullable=False), Column("created_at", DateTime), ) def upgrade(migrate_engine): meta.bind = migrate_engine table.create() def downgrade(migrate_engine): meta.bind = migrate_engine table.drop()

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813354c9c294c0323c1b54cda7074fbffa49cdb3

from django.utils import timezone from factory import DjangoModelFactory from djtriggers.tests.models import DummyTrigger class DummyTriggerFactory(DjangoModelFactory): class Meta: model = DummyTrigger trigger_type = "dummy_trigger_test" source = "tests" date_received = timezone.now() date_processed = None process_after = None number_of_tries = 0

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aebc918d6a1d1d2473f74d77b8a915ac25548e3a

import cachetools cache = cachetools.LRUCache(maxsize=3) cache["PyCon"] = "India" cache["year"] = "2017" print("Older: " + cache["year"]) cache["year"] = "2018" print("Newer: " + cache["year"]) print(cache) cache["sdate"] = "05/09/2018" print(cache) cache["edate"] = "09/09/2018" print(cache)

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cd234911c1f990b8029dfa792d132847bf39a6aa

import math def vol_shell(r1, r2): a = abs((4 / 3) * math.pi * ((r1**3) - (r2**3))) return round(a, 3) print(vol_shell(3, 3))

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7b5713c9a5afa911df1c2939751de30412162f15

from collections import OrderedDict import copy import numpy as np from scipy.optimize import curve_fit from ... import Operation as opmod from ...Operation import Operation from ....tools import saxstools class SpectrumFit(Operation): """ Use a measured SAXS spectrum (I(q) vs. q), to optimize the parameters of a theoretical SAXS spectrum for one or several populations of scatterers. Works by minimizing an objective function that compares the measured spectrum against the theoretical result. TODO: document the algorithm here. Input arrays of q and I(q), a string indicating choice of objective function, a dict of features describing the spectrum, and a list of strings indicating which keys in the dict should be used as optimization parameters. The input features dict includes initial fit parameters as well as the flags indicating which populations to include. The features dict is of the same format as SpectrumProfiler and SpectrumParameterization outputs. Outputs a return code and the features dict, with entries updated for the optimized parameters. Also returns the theoretical result for I(q), and a renormalized measured spectrum for visual comparison. """ def __init__(self): input_names = ["q", "I", "flags", "params", "fit_params", "objfun"] output_names = ["params", "q_I_opt"] super(SpectrumFit, self).__init__(input_names, output_names) self.input_doc["q"] = "1d array of wave vector values in 1/Angstrom units" self.input_doc["I"] = "1d array of intensity values I(q)" self.input_doc["flags"] = "dict of flags indicating what populations to fit" self.input_doc["params"] = ( "dict of initial values for the scattering equation parameters " "for each of the populations specified in the input flags" ) self.input_doc["fit_params"] = "list of strings (keys) indicating which parameters to optimize" self.input_doc["objfun"] = ( "string indicating objective function for optimization: " + "see documentation of saxstools.fit_spectrum() for supported objective functions" ) self.output_doc["params"] = ( "dict of scattering equation parameters copied from inputs, " "with values optimized for all keys specified in fit_params" ) self.output_doc["q_I_opt"] = "n-by-2 array of q and the optimized computed intensity spectrum" self.input_type["q"] = opmod.workflow_item self.input_type["I"] = opmod.workflow_item self.input_type["flags"] = opmod.workflow_item self.input_type["params"] = opmod.workflow_item self.inputs["objfun"] = "chi2log" def run(self): f = self.inputs["flags"] if f["bad_data"] or not any([f["precursor_scattering"], f["form_factor_scattering"], f["diffraction_peaks"]]): self.outputs["params"] = {} return if f["diffraction_peaks"]: self.outputs["params"] = {"ERROR_MESSAGE": "diffraction peak fitting not yet supported"} return q, I = self.inputs["q"], self.inputs["I"] m = self.inputs["objfun"] p = self.inputs["params"] fitkeys = self.inputs["fit_params"] # p_opt = copy.deepcopy(p) # Set up constraints as needed c = [] if f["form_factor_scattering"] or f["diffraction_peaks"]: c = ["fix_I0"] # Fitting happens here p_opt = saxstools.fit_spectrum(q, I, m, f, p, fitkeys, c) I_opt = saxstools.compute_saxs(q, f, p_opt) nz = (I > 0) & (I_opt > 0) logI_nz = np.log(I[nz]) logIopt_nz = np.log(I_opt[nz]) Imean = np.mean(logI_nz) Istd = np.std(logI_nz) logI_nz_s = (logI_nz - Imean) / Istd logIopt_nz_s = (logIopt_nz - Imean) / Istd f["R2log_fit"] = saxstools.compute_Rsquared(np.log(I[nz]), np.log(I_opt[nz])) f["chi2log_fit"] = saxstools.compute_chi2(logI_nz_s, logIopt_nz_s) q_I_opt = np.array([q, I_opt]).T self.outputs["features"] = f self.outputs["q_I_opt"] = q_I_opt

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595912753d778a0fa8332f0df00e06a9da5cde93

################################################################################ # # # This file is part of the Potato Engine (PE). # # # # Copyright (C) 2007-2010 ElectroMagnetic Potatoes (EMP). # # See the AUTHORS file for more information. # # # # This library is free software; you can redistribute it and/or # # modify it under the terms of the GNU Lesser General Public # # License as published by the Free Software Foundation; either # # version 2.1 of the License, or (at your option) any later version. # # # # This library is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # # Lesser General Public License for more details. # # # # You should have received a copy of the GNU Lesser General Public License # # along with this program. If not, see <http://www.gnu.org/licenses/>. # # # ################################################################################ import os import build ################################################################ # Default options (will be overriden by command line switches) # ################################################################ # Parallel build SetOption("num_jobs", 4) # include cache SetOption("implicit_cache", 1) ########################################################## # Command-line parameters (overriden by localconfig.py) # ########################################################## buildVariables = Variables("localconfig.py") buildVariables.Add(PathVariable("QTDIR", "Qt4 root directory", "/usr/share/qt4", PathVariable.PathIsDir)) buildVariables.Add(PathVariable("OGRE_HOME", "Ogre1.6 root directory (windows only)", None, PathVariable.PathIsDir)) buildVariables.Add( PathVariable("PTHREADWIN32_HOME", "PthreadWin32 root directory (windows only)", None, PathVariable.PathIsDir) ) buildVariables.Add(PathVariable("ODE_HOME", "ODE 0.11 root directory", None, PathVariable.PathIsDir)) buildVariables.Add(BoolVariable("DEBUG", "If true, build in debug configuration", False)) buildVariables.Add( BoolVariable( "FORCE_MINGW", "When both MinGW and VC++ are installed, force the use of the MinGW compiler instead of the default (windows only)", False, ) ) buildVariables.Add(BoolVariable("DISABLE_GRAPH", "Disable dependency graph generation", False)) ############################################################################## # Variable value extraction (nasty, should be updated when the API evolves) # # The reason for having this here is that we have to access variables before # # we can create the real construction environment (for tools selection) # ############################################################################## currentVariables = Environment(variables=buildVariables).Dictionary() #################### # Base environment # #################### baseTools = ["qt"] if os.name == "nt": if currentVariables["FORCE_MINGW"]: baseTools.append("mingw") else: baseTools.append("default") else: baseTools.append("default") baseEnvironment = Environment(tools=baseTools, variables=buildVariables) # additional variables baseEnvironment["OSNAME"] = os.name baseEnvironment["SYSPATH"] = os.environ["PATH"].split(os.pathsep) if baseEnvironment["CC"] == "cl": baseEnvironment.AppendUnique(CPPFLAGS=["/EHsc"]) # debug symbols vs. optimization if baseEnvironment["DEBUG"]: if baseEnvironment["CC"] == "cl": baseEnvironment.AppendUnique(CPPFLAGS=["/Z7"]) else: baseEnvironment.AppendUnique(CPPFLAGS=["-g"]) else: if baseEnvironment["CC"] == "cl": baseEnvironment.AppendUnique(CPPFLAGS=["/Ox"]) else: baseEnvironment.AppendUnique(CPPFLAGS=["-O2"]) # Qt tool workaround baseEnvironment.Replace(LIBS=[]) baseEnvironment.Replace(LIBPATH=[]) baseEnvironment.Replace(CPPPATH=[]) # Qt UI builder uiBuilder = Builder(action="$QT_UIC $QT_UICDECLFLAGS -o ${TARGETS[0]} $SOURCE") baseEnvironment.Append(BUILDERS={"Ui": uiBuilder}) # Qt RC builder rcBuilder = Builder(action="$QT_BINPATH/rcc $QT_RCCDECLFLAGS -o ${TARGETS[0]} $SOURCE") baseEnvironment.Append(BUILDERS={"Rc": rcBuilder}) # Under windows, add the platform SDK if os.name == "nt" and baseEnvironment["CC"] == "cl": import _winreg key = _winreg.OpenKey(_winreg.HKEY_CURRENT_USER, "Software\\Microsoft\\Microsoft SDKs\\Windows") winSdkHome = _winreg.QueryValueEx(key, "CurrentInstallFolder")[0] _winreg.CloseKey(key) baseEnvironment["WINSDK_HOME"] = winSdkHome baseEnvironment.AppendUnique(CPPPATH=["$WINSDK_HOME/Include"]) baseEnvironment.AppendUnique(LIBPATH=["$WINSDK_HOME/Lib"]) # Do not rely on VC++ runtime library if os.name == "nt" and baseEnvironment["CC"] == "cl": baseEnvironment.AppendUnique(CPPFLAGS=["/MD"]) # Speed up change analysis baseEnvironment.Decider("MD5-timestamp") ##################### # Command-line help # ##################### Help(buildVariables.GenerateHelpText(baseEnvironment)) ################################## # SCons environment declarations # ################################## walker = build.DependencyWalker() # external component database for script in Glob("components.*.py"): SConscript(script, exports="walker", variant_dir="build", duplicate=0) walker.makeEnvironments(baseEnvironment) if not baseEnvironment["DISABLE_GRAPH"]: walker.makeDependencyGraph("dependencies.png")

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3b42e218acf1c93fab3a0893efa8bf32a274eb23

# Generated by Django 2.2.6 on 2019-11-13 13:11 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("interface", "0010_auto_20191104_2107"), ] operations = [ migrations.AlterField( model_name="submission", name="review_score", field=models.DecimalField(decimal_places=2, editable=False, max_digits=5, null=True), ), migrations.AlterField( model_name="submission", name="total_score", field=models.DecimalField(decimal_places=2, editable=False, max_digits=5, null=True), ), ]

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13b69ec61d6b2129f1974ce7cae91c84100b3b58

import tensorflow.keras from PIL import Image, ImageOps from os import listdir from os.path import isfile, join import numpy as np import glob import cv2 np.set_printoptions(suppress=True) # Load the model model = tensorflow.keras.models.load_model("./converted_keras/keras_model.h5") # Create the array of the right shape to feed into the keras model # The 'length' or number of images you can put into the array is # determined by the first position in the shape tuple, in this case 1. data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32) path = glob.glob("/Users/zjisuoo/Documents/zjisuoo_git/OurChord/00_NOTE_DATA/TEST/*.png") images = [] for image in path: n1 = cv2.imread(image) n2 = cv2.resize(n1, (244, 244)) images.append(n2) print(image) # turn the image int a numpy array image_array = np.array(n2) # Normalize the image normalized_image_array = (image_array.astype(dtype=np.float32) / 127.0) - 1 # Load the image into the array data = normalized_image_array # run the inference prediction = model.predict(data) # print(prediction) if prediction[0][0] > 0.8: print("2분음표") elif prediction[0][1] > 0.8: print("4분음표") elif prediction[0][2] > 0.8: print("8분음표") elif prediction[0][3] > 0.8: print("16분음표") else: print("음표아님")

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68d537cb8488ae4f2c8300e885be78540952dec0

#!/usr/bin/env python2 # coding=utf8 from __future__ import absolute_import, division, print_function from sqlalchemy import func from walis.model.walis import walis_session from walis.model.zeus import zeus_session, zeus_db_handler from walis.model.zeus.activity import ( SubsidyProcessRecord, SubsidyPayRecord, ActivityStats, ) from walis.model.walis.activity import PaymentNoticeRecord as NoticeRecord from walis.utils.time import get_today_begin_time, get_today_end_time MAX_LIST_SIZE = 1000 DEFAULT_LIST_SIZE = 200 def get_new_pay_records(process_at, limit=200): with zeus_session() as session: result = ( session.query( SubsidyPayRecord.id, SubsidyPayRecord.restaurant_id, SubsidyProcessRecord.card_id, SubsidyProcessRecord.processed_at, SubsidyPayRecord.status, ) .outerjoin(SubsidyProcessRecord, SubsidyProcessRecord.pay_record_id == SubsidyPayRecord.id) .filter(SubsidyPayRecord.id > process_at) .filter(SubsidyProcessRecord.status != SubsidyProcessRecord.STATUS_FAIL) .order_by(SubsidyPayRecord.id.asc()) .limit(limit) .all() ) return result def get_success_pay_records(record_ids): with zeus_session() as session: result = ( session.query( SubsidyPayRecord.id, SubsidyPayRecord.restaurant_id, SubsidyProcessRecord.card_id, SubsidyProcessRecord.processed_at, ) .outerjoin(SubsidyProcessRecord, SubsidyProcessRecord.pay_record_id == SubsidyPayRecord.id) .filter(SubsidyPayRecord.status == SubsidyPayRecord.STATUS_SUCCESS) .filter(SubsidyProcessRecord.status != SubsidyProcessRecord.STATUS_FAIL) .filter(SubsidyPayRecord.id.in_(record_ids)) .all() ) return result def get_activity_stats(pay_record_id): with zeus_session() as session: results = ( session.query( ActivityStats.activity_id, ActivityStats.activity_category_id, func.sum(ActivityStats.total_subsidy), func.min(ActivityStats.date), func.max(ActivityStats.date), func.sum(ActivityStats.quantity), ) .group_by(ActivityStats.restaurant_id, ActivityStats.activity_id, ActivityStats.activity_category_id) .filter(ActivityStats.pay_record_id == pay_record_id) .filter(ActivityStats.status == ActivityStats.STATUS_PAY_SUCCESS) .all() ) return results def get_success_record_ids_by_restaurant(restaurant_id, activity_id=None, activity_category_id=None): with zeus_session() as session: query = ( session.query(SubsidyPayRecord.id) .filter(SubsidyPayRecord.restaurant_id == restaurant_id) .filter(SubsidyPayRecord.status == SubsidyPayRecord.STATUS_SUCCESS) ) if activity_id is not None: query.filter(SubsidyPayRecord.activity_id == activity_id) if activity_category_id is not None: query.filter(SubsidyPayRecord.activity_category_id == activity_category_id) record_ids = query.all() return [r[0] for r in record_ids] PAYLOG_STATUS_LIST = { ActivityStats.STATUS_PAY_RECORD_GENERATED, ActivityStats.STATUS_PAY_SUCCESS, ActivityStats.STATUS_PAY_FAIL, } @zeus_db_handler def query_paylog_by_rst(restaurant_id, activity_id=None, activity_category_id=None, offset=None, limit=None): """Except ActivityStats.STATUS_PENDING （未审核状态）""" q = ( session.query( ActivityStats.pay_record_id, ActivityStats.activity_id, ActivityStats.activity_category_id, ActivityStats.status, func.min(ActivityStats.date), func.max(ActivityStats.date), func.sum(ActivityStats.quantity), func.sum(ActivityStats.total_subsidy), SubsidyPayRecord.created_at, func.max(SubsidyProcessRecord.id), ) .group_by(ActivityStats.pay_record_id, ActivityStats.activity_id, ActivityStats.activity_category_id) .outerjoin(SubsidyPayRecord, SubsidyPayRecord.id == ActivityStats.pay_record_id) .outerjoin(SubsidyProcessRecord, SubsidyProcessRecord.pay_record_id == SubsidyPayRecord.id) .filter(ActivityStats.restaurant_id == restaurant_id) .filter(ActivityStats.status.in_(PAYLOG_STATUS_LIST)) .order_by(SubsidyPayRecord.created_at.desc()) ) if activity_id is not None: q = q.filter(ActivityStats.activity_id == activity_id) if activity_category_id is not None: q = q.filter(ActivityStats.activity_category_id == activity_category_id) if limit is not None: q = q.limit(min(limit, MAX_LIST_SIZE)) else: q = q.limit(DEFAULT_LIST_SIZE) if offset is not None: q = q.offset(offset) return q @zeus_db_handler def query_pay_records(restaurant_id, offset=None, limit=None): q = ( session.query(SubsidyPayRecord) .filter(SubsidyPayRecord.restaurant_id == restaurant_id) .order_by(SubsidyPayRecord.created_at.desc()) ) if limit is not None: q = q.limit(min(limit, MAX_LIST_SIZE)) else: q = q.limit(DEFAULT_LIST_SIZE) if offset is not None: q = q.offset(offset) return q.all() @zeus_db_handler def query_paylog(pay_record_ids, activity_id=None, activity_category_id=None, offset=None, limit=None): q = ( session.query( ActivityStats.pay_record_id, ActivityStats.activity_id, ActivityStats.activity_category_id, ActivityStats.status, func.min(ActivityStats.date), func.max(ActivityStats.date), func.sum(ActivityStats.quantity), func.sum(ActivityStats.total_subsidy), ) .group_by(ActivityStats.pay_record_id, ActivityStats.activity_id, ActivityStats.activity_category_id) .filter(ActivityStats.pay_record_id.in_(pay_record_ids)) .filter(ActivityStats.status.in_(PAYLOG_STATUS_LIST)) .order_by(ActivityStats.created_at.desc()) ) if activity_id is not None: q = q.filter(ActivityStats.activity_id == activity_id) if activity_category_id is not None: q = q.filter(ActivityStats.activity_category_id == activity_category_id) if limit is not None: q = q.limit(min(limit, MAX_LIST_SIZE)) else: q = q.limit(DEFAULT_LIST_SIZE) if offset is not None: q = q.offset(offset) return q @zeus_db_handler def get_max_subsidy_process_record_ids(pay_record_ids): q = ( session.query(func.max(SubsidyProcessRecord.id)) .group_by(SubsidyProcessRecord.pay_record_id) .filter(SubsidyProcessRecord.pay_record_id.in_(pay_record_ids)) ) return q @zeus_db_handler def count_paylog_by_rst(restaurant_id, activity_id=None, activity_category_id=None): """Except ActivityStats.STATUS_PENDING （未审核状态）""" q = ( session.query(ActivityStats.id) .group_by(ActivityStats.pay_record_id, ActivityStats.activity_id, ActivityStats.activity_category_id) .filter(ActivityStats.restaurant_id == restaurant_id) .filter(ActivityStats.status.in_(PAYLOG_STATUS_LIST)) ) if activity_id is not None: q = q.filter(ActivityStats.activity_id == activity_id) if activity_category_id is not None: q = q.filter(ActivityStats.activity_category_id == activity_category_id) return len(q.all()) @zeus_db_handler def query_process_records_by_ids(process_ids): query = session.query(SubsidyProcessRecord).filter(SubsidyProcessRecord.id.in_(process_ids)) return query.all() @zeus_db_handler def get_subsidy_record_process_time(record_ids, status): return ( session.query(SubsidyProcessRecord.pay_record_id, SubsidyProcessRecord.processed_at) .filter(SubsidyProcessRecord.pay_record_id.in_(record_ids)) .filter(SubsidyProcessRecord.status == status) .all() ) def get_pay_activities_by_restaurant(rst_id): with zeus_session() as session: query = ( session.query( ActivityStats.activity_id, ActivityStats.activity_category_id, ) .group_by(ActivityStats.activity_id, ActivityStats.activity_category_id) .filter(ActivityStats.restaurant_id == rst_id) ) return query.all() # javis model begins def query_sms_send_info(start_time=None, end_time=None, phone=None, restaurant_id=None, card_num_tail=None, status=None): with walis_session() as session: query = session.query(NoticeRecord) if phone: query = query.filter(NoticeRecord.phone == phone) if restaurant_id: query = query.filter(NoticeRecord.restaurant_id == restaurant_id) if card_num_tail: query = query.filter(NoticeRecord.card_num_tail == card_num_tail) if status: query = query.filter(NoticeRecord.status == status) if not start_time: start_time = get_today_begin_time() if not end_time: end_time = get_today_end_time() query = query.filter(NoticeRecord.created_at > start_time).filter(NoticeRecord.created_at < end_time) return query.all() def query_sms_send_count(start_time=None, end_time=None, status=None): with walis_session() as session: if not start_time: start_time = get_today_begin_time() if not end_time: end_time = get_today_end_time() query = ( session.query(func.count(NoticeRecord.record_id)) .filter(NoticeRecord.created_at > start_time) .filter(NoticeRecord.created_at < end_time) ) if status is not None: query = query.filter(NoticeRecord.status == status) return query.scalar() @zeus_db_handler def query_auto_pay_activity_stats_result( city_ids=None, restaurant_ids=None, activity_id=None, activity_category_id=None, from_date=None, to_date=None, statuses=None, offset=None, limit=None, with_subsidy=None, ): q = ( session.query( ActivityStats.restaurant_id, ActivityStats.activity_id, ActivityStats.activity_category_id, func.sum(ActivityStats.quantity), func.sum(ActivityStats.total_subsidy), func.min(ActivityStats.date), func.max(ActivityStats.date), ) .group_by(ActivityStats.restaurant_id, ActivityStats.activity_id, ActivityStats.activity_category_id) .order_by(ActivityStats.restaurant_id.desc()) ) return _query_activity_stats( q, city_ids, restaurant_ids, activity_id, activity_category_id, from_date, to_date, statuses, with_subsidy, offset, limit, ) def _query_activity_stats( q, city_ids=None, restaurant_ids=None, activity_id=None, activity_category_id=None, from_date=None, to_date=None, statuses=None, with_subsidy=None, offset=None, limit=None, ): if activity_id is not None: q = q.filter(ActivityStats.activity_id == activity_id) if activity_category_id is not None: q = q.filter(ActivityStats.activity_category_id == activity_category_id) # noqa if city_ids is not None: q = q.filter(ActivityStats.city_id.in_(city_ids)) if restaurant_ids is not None: q = q.filter(ActivityStats.restaurant_id.in_(restaurant_ids)) if from_date is not None: q = q.filter(ActivityStats.date >= from_date) if to_date is not None: q = q.filter(ActivityStats.date <= to_date) if statuses is not None: q = q.filter(ActivityStats.status.in_(statuses)) if with_subsidy is not None: if with_subsidy: q = q.filter(ActivityStats.total_subsidy > 0) else: q = q.filter(ActivityStats.total_subsidy == 0) if offset is not None: q = q.offset(offset) q = q.limit(1000) return q

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56, 61, 62, 70, 71, 73, 74, 83, 88, 89, 97, 98, 106, 107, 109, 110, 8, 10, 16, 17, 25, 30, 31, 41, 42, 44, 47, 58, 59, 61, 62, 8, 10, 16, 17, 25, 30, 31, 37, 38, 40, 42, 45, 48, 49, 55, 56, 60, 62, 4, 6, 4, 7, 16, 17, 19, 22, 26, 31, 32, 8, 10, 12, 14, 15, 21, 22, 30, 35, 36, 44, 45, 47, 50, 59, 60, 62, 63, 4, 7, 16, 17, 25, 26, 28, 31, 35, 40, 41, 8, 10, 12, 14, 15, 21, 22, 30, 35, 36, 44, 45, 53, 54, 56, 59, 68, 69, 77, 78, 80, 81, 4, 11, 15, 16, 17, 18, 21, 24, 1, 1, 2, 4, 6, 7, 9, 10, 17, 1, 4, 10, 11, 18, 19, 26, 27, 29, 30, 33, 34, 41, 42, 45, 47, 4, 10, 12, 20, 21, 26, 27, 30, 33, 34, 41, 47, 49, 55, 56, 59, 62, 63, 70, 76, 77, 79, 80, 82, 84, 91, 92, 95, 97, 4, 11, 17, 18, 21, 23, 1, 1, 2, 4, 6, 7, 9, 10, 17, 1, 4, 8, 9, 12, 15, 16, 17, 23, 24, 31, 32, 36, 37, 43, 44, 47, 48, 55, 57, 4, 11, 13, 8, 16, 17, 22, 23, 26, 29, 30, 37, 43, 44, 47, 48, 54, 55, 57, 58, 62, 65, 66, 73, 79, 80, 89, 90, 92, 93, 8, 10, 16, 17, 20, 23, 24, 31, 37, 38, 41, 42, 48, 49, 51, 52, 54, 56, 62, 63, 66, 68, 8, 10, 16, 17, 20, 23, 24, 31, 37, 38, 44, 47, 54, 55, 8, 10, 13, 15, 4, 6, 1, 1, 4, 8, 9, 12, 13, 23, 24, 26, 27, 37, 38, 41, 42, 44, 46, 4, 9, 12, 14, 15, 22, 24, 27, 35, 36, 8, 14, 16, 18, 12, 20, 21, 26, 27, 16, 25, 30, 31, 39, 40, 42, 43, 16, 25, 30, 31, 39, 40, 48, 49, 51, 52, 12, 14, 12, 14, 19, 20, 22, 23, 31, 36, 37, 45, 46, 48, 49, 58, 63, 64, 72, 73, 81, 82, 84, 85, 12, 14, 20, 21, 29, 34, 35, 45, 46, 48, 51, 55, 56, 58, 59, 8, 10, 8, 15, 21, 22, 25, 27, 1, 1, 2, 4, 8, 14, 21, 1, 4, 10, 11, 14, 15, 19, 20, 24, 25, 30, 31, 35, 36, 40, 41, 45, 46, 50, 51, 55, 56, 62, 63, 67, 68, 79, 80, 82, 83, 87, 88, 93, 94, 97, 98, 102, 103, 107, 108, 115, 116, 120, 122, 4, 9, 11, 15, 16, 23, 25, 28, 36, 37, 8, 14, 16, 24, 25, 30, 31, 37, 43, 44, 8, 11, 17, 18, 12, 18, 20, 26, 27, 33, 34, 40, 46, 47, 52, 55, 61, 62, 8, 11, 22, 23, 25, 26, 12, 18, 20, 26, 27, 33, 34, 40, 46, 47, 57, 58, 60, 63, 74, 75, 77, 78, 8, 11, 16, 17, 20, 21, 25, 26, 12, 18, 20, 26, 27, 33, 34, 40, 46, 47, 51, 52, 55, 56, 60, 63, 68, 69, 72, 73, 77, 78, 8, 11, 18, 19, 12, 18, 20, 26, 27, 33, 34, 40, 46, 47, 53, 56, 63, 64, 8, 11, 15, 21, 22, 26, 27, 12, 18, 19, 23, 25, 29, 30, 35, 36, 41, 42, 46, 48, 8, 11, 15, 19, 20, 24, 25, 12, 16, 17, 21, 23, 27, 28, 33, 34, 37, 38, 42, 44, 8, 14, 16, 22, 23, 29, 30, 36, 42, 43, 50, 51, 53, 55, 61, 62, 66, 68, 74, 75, 81, 87, 88, 95, 96, 98, 100, 104, 105, 109, 110, 8, 15, 21, 22, 25, 27, 1, 1, 4, 10, 11, 14, 15, 19, 20, 25, 26, 31, 32, 36, 37, 41, 42, 46, 47, 51, 52, 56, 57, 64, 65, 69, 71, 4, 9, 11, 15, 16, 23, 25, 28, 36, 37, 8, 11, 15, 21, 22, 26, 27, 12, 18, 19, 23, 25, 29, 30, 35, 36, 41, 42, 46, 48, 8, 11, 15, 19, 20, 24, 25, 12, 16, 17, 21, 23, 27, 28, 33, 34, 37, 38, 42, 44, 8, 14, 16, 18, 12, 20, 21, 26, 27, 31, 32, 37, 38, 44, 50, 51, 57, 58, 60, 62, 12, 14, 20, 21, 27, 33, 34, 41, 42, 44, 46, 52, 53, 57, 58, 12, 14, 20, 21, 27, 33, 34, 41, 42, 44, 46, 50, 51, 55, 56, 8, 10, 8, 11, 18, 21, 25, 30, 31, 12, 18, 20, 26, 27, 33, 34, 40, 46, 47, 53, 56, 63, 64, 8, 15, 21, 22, 28, 30, 1, 1, 2, 4, 6, 7, 9, 10, 17, 1, 4, 10, 11, 15, 16, 19, 20, 28, 29, 34, 35, 41, 42, 4, 9, 10, 13, 14, 18, 19, 4, 15, 16, 19, 20, 24, 25, 4, 13, 14, 16, 17, 21, 22, 4, 13, 14, 22, 23, 25, 26, 30, 31, 4, 9, 10, 14, 15, 19, 20, 4, 7, 8, 12, 13, 17, 18, 4, 13, 14, 18, 19, 4, 11, 12, 16, 17, 4, 10, 11, 15, 16, 4, 9, 10, 13, 16, 17, 18, 22, 23, 1, 3, 4, 6, 8, 10, 8, 16, 17, 22, 23, 12, 21, 26, 27, 37, 38, 40, 41, 12, 21, 26, 27, 35, 36, 38, 39, 12, 21, 26, 27, 35, 36, 44, 45, 47, 48, 12, 17, 18, 21, 22, 30, 35, 36, 44, 46, 12, 17, 18, 21, 22, 30, 35, 36, 41, 42, 45, 48, 49, 51, 12, 17, 18, 21, 22, 30, 35, 36, 40, 42, 12, 17, 18, 21, 22, 30, 35, 36, 40, 42, 8, 10, 8, 10, 15, 16, 18, 19, 27, 32, 33, 43, 44, 46, 47, 56, 61, 62, 70, 71, 73, 74, 83, 88, 89, 97, 98, 106, 107, 109, 110, 8, 10, 15, 16, 18, 19, 27, 32, 33, 43, 44, 46, 47, 51, 54, 4, 6, 4, 11, 13, 18, 19, 27, 28, 33, 34, 8, 10, 11, 8, 13, 14, 17, 18, 8, 19, 20, 23, 24, 8, 17, 18, 20, 21, 8, 17, 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a32fb683f8d46f901e8dcd2d075ace22ee81e076

import base64 import string def hexStringtoBytes(hexstring): byteArray = bytes.fromhex(hexstring) return byteArray def xorBytes(bytes1, bytes2): xored = bytes([x ^ bytes2[i] for i, x in enumerate(bytes1)]) return xored def xorAgainstCharacter(byteArray, character): str2 = [ord(character)] * len(byteArray) return xorBytes(byteArray, str2) def scoreString(input): arr = [(chr(x) in string.printable) for x in input] return arr.count(True) if __name__ == "__main__": hexstring = "1b37373331363f78151b7f2b783431333d78397828372d363c78373e783a393b3736" bytes1 = hexStringtoBytes(hexstring) scores = [] for x in string.printable: temp = xorAgainstCharacter(bytes1, x) print(str(x), str(temp)) scores.append(scoreString(temp))

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9b581df505765e895047584c5bb586faef95295f

import dash import dash_core_components as dcc import dash_html_components as html import dash_table as dt import plotly.express as px import pandas as pd import plotly.graph_objects as go import numpy as np from datetime import datetime as dat from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn import metrics from sklearn.feature_selection import f_regression from sklearn.feature_selection import SelectKBest # TODO: # The model doesn't really take the opponent into consideration when calculating the win percentage. As you would expect, this is not ideal and is something that needs to be fixed # # The bar charts only graph 2019 data. Allowing the user to choose the year would be an easy addition. Future implemenatations could also include a previous X games option instead # # The bar chart only graphs stats correctly if they are selected in order. For example, if the set of possible stats are ['assists', 'rebounds', 'blocks'], they must all be selected # in order to show all the values correctly. If the user selects only 'assists' and 'blocks' then 'assists' graphs correctly. 'blocks' is graphed but is given the value assigned # to 'rebounds' because it assumes the second position in the array of stats to be graphed. # # The model doesn't run well (and generally fails) for small schools due to a lack of data for those teams. Error checking needs to be implemented to eliminate this problem. def getStatsByYear(teamID, year, data): """Returns the stats for a chosen team for a specific year. Choices are 2016 - 2019""" teamStats = data[data["team_id"] == teamID] for index, row in teamStats.iterrows(): if row["season"] == year: teamStatsForGivenYear = teamStats[data["season"] == row["season"]] return teamStatsForGivenYear def generate_bar_chart(team, opponent, stats, stat_names, data): """Generates a bar chart for a the user selected team, opponent and stats""" teamStats = getStatsByYear(team, 2019, data) opponentStats = getStatsByYear(opponent, 2019, data) teamStatValues = teamStats[ [ "assists", "assists_turnover_ratio", "blocked_att", "blocks", "defensive_rebounds", "fast_break_pts", "field_goals_att", "field_goals_pct", "field_goals_made", "free_throws_att", "free_throws_pct", "free_throws_made", "offensive_rebounds", "personal_fouls", "points", "points_against", "points_in_paint", "points_off_turnovers", "rebounds", "second_chance_pts", "steals", "team_rebounds", "three_points_att", "three_points_pct", "three_points_made", "turnovers", "two_points_att", "two_points_pct", "two_points_made", ] ] opponentStatValues = opponentStats[ [ "assists", "assists_turnover_ratio", "blocked_att", "blocks", "defensive_rebounds", "fast_break_pts", "field_goals_att", "field_goals_pct", "field_goals_made", "free_throws_att", "free_throws_pct", "free_throws_made", "offensive_rebounds", "personal_fouls", "points", "points_against", "points_in_paint", "points_off_turnovers", "rebounds", "second_chance_pts", "steals", "team_rebounds", "three_points_att", "three_points_pct", "three_points_made", "turnovers", "two_points_att", "two_points_pct", "two_points_made", ] ] stats_to_be_graphed = [] for i in range(len(stat_names)): if i in stats: stats_to_be_graphed.append(stat_names[i]) # Graphs average stat values for the user's chosen team teamVals = go.Bar(x=stats_to_be_graphed, y=teamStatValues.mean(), name=data[(data.team_id == team)]["market"].iloc[0]) # Graphs average stat values for the opponent's team opponentVals = go.Bar( x=stats_to_be_graphed, y=opponentStatValues.mean(), name=data[(data.team_id == opponent)]["market"].iloc[0] ) data = [teamVals, opponentVals] layout = go.Layout(barmode="group") fig = go.Figure(data=data, layout=layout) return fig def getAllTeamMatchRecords(teamID, df): """Returns all game records for a given teamID""" return df[df["team_id"] == teamID] def select_features(X_train, y_train, X_test): """Selects features""" # configure to select all features fs = SelectKBest(score_func=f_regression, k="all") # learn relationship from training data fs.fit(X_train, y_train) # transform train input data X_train_fs = fs.transform(X_train) # transform test input data X_test_fs = fs.transform(X_test) return X_train_fs, X_test_fs, fs def overallFeatures(df): """Return list of top four features""" datasetForFS = df datasetForFS.fillna(0) X1 = datasetForFS[ [ "assists", "blocks", "defensive_rebounds", "opponent_drb", "fast_break_pts", "points_in_paint", "points_off_turnovers", "rebounds", "steals", "turnovers", "efg", "tov_pct", "orb_pct", "ftr", ] ] y1 = datasetForFS["win"] X_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size=0.2, random_state=0) X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test) colList = X1.columns.values.tolist() statScoreDF = pd.DataFrame(data={"Stat": pd.Series(colList), "Score": pd.Series(fs.scores_.tolist())}) statScoreDF = statScoreDF.sort_values(by=["Score"], ascending=False) return statScoreDF.head(n=4)["Stat"].tolist() def avgDataRow(df): """Returns the average values of a dataFrame""" df1 = dict() for columnName, columnData in df.iteritems(): df1[columnName] = [df[columnName].mean()] return pd.DataFrame(df1) def updateWinPct(dfMain, dfWin, reg): """Return new win percentage""" dfPred = reg.predict(dfMain) return pd.DataFrame({"Actual": dfWin.mean(), "Predicted (int)": np.around(dfPred), "Predicted (float)": dfPred}) def filterRowsFS(df): """Return dataframe with selected features""" return df[ [ "assists", "blocks", "defensive_rebounds", "opponent_drb", "fast_break_pts", "points_in_paint", "points_off_turnovers", "rebounds", "steals", "turnovers", "efg", "tov_pct", "orb_pct", "ftr", ] ] def learn(dataset): """Trains the model""" dataset = pd.read_csv("team_boxscores_v3.csv") dataset = dataset.fillna(0) # Shuffle dataset = dataset.sample(frac=1) X1 = dataset[ [ "assists", "blocks", "defensive_rebounds", "opponent_drb", "fast_break_pts", "points_in_paint", "points_off_turnovers", "rebounds", "steals", "turnovers", "efg", "tov_pct", "orb_pct", "ftr", ] ] y1 = dataset["win"] # No shuffle # X_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size=0.2, random_state=0) # W/ shuffle X_train = X1[int(len(X1) / 5) :] X_test = X1[: int(len(X1) / 5)] y_train = y1[int(len(y1) / 5) :] y_test = y1[: int(len(y1) / 5)] regressor = LinearRegression() regressor.fit(X_train, y_train) coeff_df = pd.DataFrame(regressor.coef_, X1.columns, columns=["Coefficient"]) y_pred = regressor.predict(X_test) y_pred_round = np.around(regressor.predict(X_test)) return regressor, pd.DataFrame({"Actual": y_test, "Predicted (int)": y_pred_round, "Predicted (float)": y_pred}) def calculate_win_percentage(team, stat1, stat2, stat3, stat4, regressor, data): """Calculates the win percentage for a team and the 4 selected stat values""" temp = getAllTeamMatchRecords(team, data) changed_stat1 = overallFeatures(temp)[0] changed_stat2 = overallFeatures(temp)[1] changed_stat3 = overallFeatures(temp)[2] changed_stat4 = overallFeatures(temp)[3] average_team_stats = avgDataRow(filterRowsFS(temp)) dfWin = temp["win"] dfFinal = pd.DataFrame( { "Actual": dfWin.mean(), "Predicted (int)": np.around(regressor.predict(average_team_stats)), "Predicted (float)": regressor.predict(average_team_stats), } ) origWinPct = dfFinal.at[0, "Predicted (float)"] average_team_stats.at[0, changed_stat1] = stat1 average_team_stats.at[0, changed_stat2] = stat2 average_team_stats.at[0, changed_stat3] = stat3 average_team_stats.at[0, changed_stat4] = stat4 win_percentage = updateWinPct(average_team_stats, dfWin, regressor).at[0, "Predicted (float)"] # Makes sure you can't have a win percentage of > 100% or < 0% if win_percentage > 1: win_percentage = 1 elif win_percentage < 0: win_percentage = 0 win_percentage = win_percentage * 100 win_percentage = round(win_percentage, 2) win_percentage_text = "Projected Win Percentage: " + str(win_percentage) + "%" return win_percentage_text def get_default_slider_values(team, data): """Gets the values the each of the 4 sliders should display. These values are what the model estimates the team will get in the matchup""" numSliders = 4 stat_column_names = [] stat_column_values = [] estimated_stat_values = avgDataRow(filterRowsFS(getAllTeamMatchRecords(team, data))) for i in range(numSliders): stat_column_names.append(overallFeatures(getAllTeamMatchRecords(team, data))[i]) stat_column_values.append(estimated_stat_values.at[0, stat_column_names[i]]) return stat_column_names, stat_column_values

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