Reset23/the-stack-v2-blamed2 · Datasets at Hugging Face

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/ROS/rosserial/install/lib/python2.7/dist-packages/rosserial_msgs/msg/_myTest.py

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# This Python file uses the following encoding: utf-8 """autogenerated by genpy from rosserial_msgs/myTest.msg. Do not edit.""" import sys python3 = True if sys.hexversion > 0x03000000 else False import genpy import struct class myTest(genpy.Message): _md5sum = "19b7d3627de9a4555d2aaa19dbf70a1d" _type = "rosserial_msgs/myTest" _has_header = False #flag to mark the presence of a Header object _full_text = """string my_name string last_name uint8 age uint32 score """ __slots__ = ['my_name','last_name','age','score'] _slot_types = ['string','string','uint8','uint32'] def __init__(self, *args, **kwds): """ Constructor. Any message fields that are implicitly/explicitly set to None will be assigned a default value. The recommend use is keyword arguments as this is more robust to future message changes. You cannot mix in-order arguments and keyword arguments. The available fields are: my_name,last_name,age,score :param args: complete set of field values, in .msg order :param kwds: use keyword arguments corresponding to message field names to set specific fields. """ if args or kwds: super(myTest, self).__init__(*args, **kwds) #message fields cannot be None, assign default values for those that are if self.my_name is None: self.my_name = '' if self.last_name is None: self.last_name = '' if self.age is None: self.age = 0 if self.score is None: self.score = 0 else: self.my_name = '' self.last_name = '' self.age = 0 self.score = 0 def _get_types(self): """ internal API method """ return self._slot_types def serialize(self, buff): """ serialize message into buffer :param buff: buffer, ``StringIO`` """ try: _x = self.my_name length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self.last_name length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_BI().pack(_x.age, _x.score)) except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self))))) except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self))))) def deserialize(self, str): """ unpack serialized message in str into this message instance :param str: byte array of serialized message, ``str`` """ try: end = 0 start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.my_name = str[start:end].decode('utf-8') else: self.my_name = str[start:end] start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.last_name = str[start:end].decode('utf-8') else: self.last_name = str[start:end] _x = self start = end end += 5 (_x.age, _x.score,) = _get_struct_BI().unpack(str[start:end]) return self except struct.error as e: raise genpy.DeserializationError(e) #most likely buffer underfill def serialize_numpy(self, buff, numpy): """ serialize message with numpy array types into buffer :param buff: buffer, ``StringIO`` :param numpy: numpy python module """ try: _x = self.my_name length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self.last_name length = len(_x) if python3 or type(_x) == unicode: _x = _x.encode('utf-8') length = len(_x) buff.write(struct.pack('<I%ss'%length, length, _x)) _x = self buff.write(_get_struct_BI().pack(_x.age, _x.score)) except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self))))) except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self))))) def deserialize_numpy(self, str, numpy): """ unpack serialized message in str into this message instance using numpy for array types :param str: byte array of serialized message, ``str`` :param numpy: numpy python module """ try: end = 0 start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.my_name = str[start:end].decode('utf-8') else: self.my_name = str[start:end] start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) start = end end += length if python3: self.last_name = str[start:end].decode('utf-8') else: self.last_name = str[start:end] _x = self start = end end += 5 (_x.age, _x.score,) = _get_struct_BI().unpack(str[start:end]) return self except struct.error as e: raise genpy.DeserializationError(e) #most likely buffer underfill _struct_I = genpy.struct_I def _get_struct_I(): global _struct_I return _struct_I _struct_BI = None def _get_struct_BI(): global _struct_BI if _struct_BI is None: _struct_BI = struct.Struct("<BI") return _struct_BI

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cannellajs2@vcu.edu

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/dockermap/map/policy/__init__.py

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# -*- coding: utf-8 -*- from __future__ import unicode_literals from .actions import ACTION_ATTACHED_FLAG, ACTION_DEPENDENCY_FLAG from .simple import SimplePolicy from .resume import ResumeUpdatePolicy

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matthias@erll.de

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/capture_raw_images_Malaga_yaw90.py

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saiabinesh/opencv-addons

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refs/heads/master

2020-12-01T20:53:07.866719

2019-12-29T15:23:06

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# In settings.json first activate computer vision mode: # https://github.com/Microsoft/AirSim/blob/master/docs/image_apis.md#computer-vision-mode # Working on the newer environement with new houses, cars and river (extended by Sean). # changed image folder in line 39. Commenting out import setup_path import airsim import os import numpy as np client = airsim.VehicleClient() client.confirmConnection() # below code didn't work beacause currently in computerVision mode # client.moveToPositionAsync(273, -7167, 1036, 10, 0).join() # , DrivetrainType.ForwardOnly, YawMode(False,0), 20, 1) #Change the code below to move the camera to desired position i =1 # for z in np.linspace(-30,-100,10): # for x in np.linspace(2.93,-37.37, 10): # for y in np.linspace(-88.17, -192.27,10): ###############################Trying to change weather ############################################### # client.simEnableWeather(True) # airsim.wait_key('Press any key to enable rain at 25%') # client.simSetWeatherParameter(airsim.WeatherParameter.Rain, 0.25); # exit() #Trying different cameras with just 8 images # for camera_number in ["0","1","2","3","4"]: # for z in np.linspace(-30,-100,3): # for x in np.linspace(-150,-30, 3): # for y in np.linspace(-60,-240,3): # client.simSetVehiclePose(airsim.Pose(airsim.Vector3r(x,y,z), airsim.to_quaternion(0,0,0)), True) # #print(client.simGetCameraInfo("0")) # responses = client.simGetImages([ # # airsim.ImageRequest("0", airsim.ImageType.Segmentation, True), #depth in perspective projection # # airsim.ImageRequest("0", airsim.ImageType.Segmentation, False, False)]) #scene vision image in uncompressed RGBA array # airsim.ImageRequest(camera_number, airsim.ImageType.Scene, False, False)]) # # airsim.ImageRequest("3", airsim.ImageType.Segmentation, False, False)]) # #print('Retrieved images: %d', len(responses)) # #save segmentation images in various formats # for idx, response in enumerate(responses): # filename = 'D:/AirSim/New/Images/Images_master_v2/image_' +str(i)+'_raw' # if response.pixels_as_float: # #print("Type %d, size %d" % (response.image_type, len(response.image_data_float))) # airsim.write_pfm(os.path.normpath(filename + '.pfm'), airsim.get_pfm_array(response)) # elif response.compress: #png format # #print("Type %d, size %d" % (response.image_type, len(response.image_data_uint8))) # airsim.write_file(os.path.normpath(filename + '.png'), response.image_data_uint8) # else: #uncompressed array - numpy demo # #print("Type %d, size %d" % (response.image_type, len(response.image_data_uint8))) # img1d = np.fromstring(response.image_data_uint8, dtype=np.uint8) #get numpy array # img_rgba = img1d.reshape(response.height, response.width, 4) #reshape array to 4 channel image array H X W X 4 # img_rgba = np.flipud(img_rgba) #original image is flipped vertically # airsim.write_png(os.path.normpath(filename + '.png'), img_rgba) #write to png # if i%10 == 0: # print("Image count = ", i) # i = i +1 # # exit() import math ninety_degrees_in_radians = (math.pi)/2 forty_five_degrees_in_radians = (math.pi)/4 client.simSetCameraOrientation(0, airsim.to_quaternion(0, 0, forty_five_degrees_in_radians)); #radians #Changing coordinates for new environment images for z in np.linspace(-10,-50,4): for x in np.linspace(-150,0, 10): for y in np.linspace(-60,-240,10): client.simSetVehiclePose(airsim.Pose(airsim.Vector3r(x,y,z), airsim.to_quaternion(0, 0, forty_five_degrees_in_radians)), True) #print(client.simGetCameraInfo("0")) responses = client.simGetImages([ # airsim.ImageRequest("0", airsim.ImageType.Segmentation, True), #depth in perspective projection # airsim.ImageRequest("0", airsim.ImageType.Segmentation, False, False)]) #scene vision image in uncompressed RGBA array airsim.ImageRequest("3", airsim.ImageType.Scene, False, False)]) # airsim.ImageRequest("3", airsim.ImageType.Segmentation, False, False)]) #print('Retrieved images: %d', len(responses)) #save segmentation images in various formats for idx, response in enumerate(responses): filename = 'D:/AirSim/New/Images/Images_master_v4_yaw_45/image_' +str(i)+'_raw' if response.pixels_as_float: #print("Type %d, size %d" % (response.image_type, len(response.image_data_float))) airsim.write_pfm(os.path.normpath(filename + '.pfm'), airsim.get_pfm_array(response)) elif response.compress: #png format #print("Type %d, size %d" % (response.image_type, len(response.image_data_uint8))) airsim.write_file(os.path.normpath(filename + '.png'), response.image_data_uint8) else: #uncompressed array - numpy demo #print("Type %d, size %d" % (response.image_type, len(response.image_data_uint8))) img1d = np.fromstring(response.image_data_uint8, dtype=np.uint8) #get numpy array img_rgba = img1d.reshape(response.height, response.width, 4) #reshape array to 4 channel image array H X W X 4 img_rgba = np.flipud(img_rgba) #original image is flipped vertically airsim.write_png(os.path.normpath(filename + '.png'), img_rgba) #write to png if i%10 == 0: print("Image count = ", i) i = i +1 # # #find unique colors # # print(np.unique(img_rgba[:,:,0], return_counts=True)) #red # # print(np.unique(img_rgba[:,:,1], return_counts=True)) #green # # print(np.unique(img_rgba[:,:,2], return_counts=True)) #blue # # print(np.unique(img_rgba[:,:,3], return_counts=True)) #blue # print(x,y,z)

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import os.path from lxml import etree from rest_framework.exceptions import ParseError from ..settings import api_settings from .exceptions import NotImplementedError class Schema: def __init__(self, root): self.root = root @property def schema(self): return self.get_schema(self.root) def get_schema(self, root): """Returns xml schema to validata message Must redefine in subclasses""" def gen_structure_specific_schema(structures): pass def get_main_schema(self, version, schema_file): """Returns the schema to validate files""" path = os.path.join( api_settings.DEFAULT_SCHEMA_PATH, 'sdmx', 'ml', version, schema_file) try: tree = etree.parse(path) except (etree.ParseError, ValueError) as exc: raise ParseError('XML schema parse error - %s' % exc) return etree.XMLSchema(tree) class Schema21(Schema): def get_schema(self, root): tag = root.tag if etree.QName(tag).localname.endswith('StructureSpecific'): raise NotImplementedError( detail='Schema generation for a {tag.localname} not yet implemented') else: schema = self.get_main_schema('2_1', 'SDMXMessage.xsd') return schema

[ "al459@columbia.edu" ]

al459@columbia.edu

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/app.py

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import datetime as dt from flask import Flask, render_template, abort, request, redirect, url_for import lastfm import logging app = Flask(__name__) # Set up logging handler = logging.StreamHandler() log_fmt = logging.Formatter( '%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]') handler.setFormatter(log_fmt) app.logger.addHandler(handler) app.logger.setLevel(logging.INFO) @app.route('/', methods=['GET', 'POST']) def home(): if request.method == 'POST' and request.form['username']: url = url_for('user_page', username=request.form['username']) print url return redirect(url) return render_template('home.html') @app.route('/<username>/') def user_page(username): error_message = None try: charts = lastfm_snapshot(username) except lastfm.LastFMException as e: charts = None error_message = str(e) app.logger.error("%s - %s" % (username, error_message)) if 'User not found' in error_message: return abort(404) app.logger.info("Loaded data for %s" % username) return render_template('user.html', username=username, charts=charts, error=error_message) def lastfm_snapshot(username): charts = lastfm.chart_list(username) six_months_ago = dt.date.today() - dt.timedelta(days=6*30) six_months = lastfm.top_albums(username, six_months_ago, charts=charts) one_year_ago = dt.date.today() - dt.timedelta(days=365) one_year = lastfm.top_albums(username, one_year_ago, charts=charts) two_year_ago = dt.date.today() - dt.timedelta(days=2*365) two_year = lastfm.top_albums(username, two_year_ago, charts=charts) three_year_ago = dt.date.today() - dt.timedelta(days=3*365) three_year = lastfm.top_albums(username, three_year_ago, charts=charts) four_year_ago = dt.date.today() - dt.timedelta(days=4*365) four_year = lastfm.top_albums(username, four_year_ago, charts=charts) five_year_ago = dt.date.today() - dt.timedelta(days=5*365) five_year = lastfm.top_albums(username, five_year_ago, charts=charts) charts = { 'six_months': six_months, 'one_year': one_year, 'two_year': two_year, 'three_year': three_year, 'four_year': four_year, 'five_year': five_year, } return charts if __name__ == '__main__': app.run(debug=True) app.logger.setLevel(logging.DEBUG)

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jeff.roche@gmail.com

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lukasz009/pp1

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refs/heads/master

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# BMI def oblicz_bmi(waga,wzrost): bmi = waga/(wzrost/100)**2 if bmi < 18.5: return bmi,'niedowaga' elif bmi >= 18.5 and bmi < 25: return bmi,'waga prawidłowa' elif bmi >=25 and bmi < 30: return bmi,'nadwaga' else: return bmi,'otyłość' class LessThanOneError(Exception): """ Błąd gdy wartość <= 0 """ pass try: wzrost = float(input('Podaj wzrost w cm: ')) if wzrost <= 0: raise LessThanOneError waga = float(input('Podaj wagę w kg: ')) if waga <= 0: raise LessThanOneError print('Wskaźnik BMI: {0:.2f} ({1})'.format(oblicz_bmi(waga,wzrost)[0],oblicz_bmi(waga,wzrost)[1])) except ValueError: print('Podana wartość jest nieprawidłowa!') except LessThanOneError: print('Wartość nie może być mniejsza od 1!')

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JosephLevinthal/Research-projects

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g = 3 m = 50 conta = m // g i = m%g print(conta) print(i)

[ "jvlo@icomp.ufam.edu.br" ]

jvlo@icomp.ufam.edu.br

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/Day_9/Day_9.py

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[]

no_license

bray4168/AoC-2020

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refs/heads/master

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file_array = [] # Read file into lines with open('Day_9/input.txt', 'r') as file: for line in file: file_array.append(line.strip()) preamble_range = 25 invalid_number = 0 def form_sum_array(index): sum_array = [] for each in range(index - preamble_range, index): for number in range(index - preamble_range, index): if each == number: continue else: sum_array.append(int(file_array[each]) + int(file_array[number])) return sum_array def find_number_set(index): sum = 0 max = 0 min = int(file_array[index]) while sum < invalid_number: number = int(file_array[index]) sum += number if number <= min: min = number elif number >= max: max = number index += 1 if sum == invalid_number: return max + min else: return 0 # Puzzle 1 for index, number in enumerate(file_array): if index < preamble_range: continue else: sum_array = form_sum_array(index) if int(number) in sum_array: continue else: invalid_number = int(number) break # Puzzle 2 puzzle_2_value = 0 for index, number in enumerate(file_array): sum = find_number_set(index) if sum == 0: continue else: puzzle_2_value = sum break print("Puzzle 1 solution: " + str(invalid_number)) print("Puzzle 2 solution: " + str(puzzle_2_value))

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bray4168@yahoo.com

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4RG0S/2021-Winter-Jookgorithm

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# 터렛 import sys import math input = sys.stdin.readline N = int(input()) for n in range(N): (x1, y1, r1, x2, y2, r2) = list(map(int, input().split(' '))) r = math.sqrt((x1-x2)**2+(y1-y2)**2) # case 0 : find infinity locations if r==0 and r1==r2: print(-1) # case 1 : can't find location elif r>r1+r2: print(0) elif (r1 if r1>r2 else r2)>r+(r2 if r1>r2 else r1): print(0) # case 2 : find one location elif r==r1+r2: print(1) elif (r1 if r1>r2 else r2)==r+(r2 if r1>r2 else r1): print(1) # case 3 : find two location elif r<r1+r2: print(2) elif (r1 if r1>r2 else r2)<r+(r2 if r1>r2 else r1): print(2)

[ "ygo65312@naver.com" ]

ygo65312@naver.com

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from model import * from model2 import * from data import * from keras.callbacks import ModelCheckpoint from model import unet #os.environ["CUDA_VISIBLE_DEVICES"] = "0" from IntraoralArea_project.data import trainGenerator from IntraoralArea_project.model import get_model n_classes = 2 #设定输入图片的width，height。设定使用的模型类别，设定分类的个数。 model = get_model(width =256, height = 128, model_name = 'unet_mini', n_classes = n_classes) param = model.summary() # model = unet() def train(): data_gen_args = dict(rotation_range=0.2, width_shift_range=0.05, height_shift_range=0.05, shear_range=0.05, zoom_range=0.05, horizontal_flip=True, fill_mode='nearest') img_target_size = (model.input_height,model.input_width) mask_target_size = (model.output_height,model.output_width) myGene = trainGenerator(2,'data/intraoralArea/train_252_0.5','image','label', data_gen_args, n_classes=n_classes,target_size = img_target_size, mask_target_size = mask_target_size, save_to_dir = None) model_checkpoint = ModelCheckpoint('intraoralArea.hdf5', monitor='loss',verbose=1, save_best_only=True) model.fit_generator(myGene, steps_per_epoch=300, epochs=8, callbacks=[model_checkpoint]) if __name__=='__main__': train()

[ "471791040@qq.com" ]

471791040@qq.com

f714c1d17e7f59e7bbd574152622c59b0c6c4889

2a84e915ba499e97c6d087305aa928c7602aee76

/LineSketch/LineSketch.py

ffa4032bbac1d72dc701e09deefe78a2de4d4702

[ "MIT" ]

permissive

linny006/Amazing-Python-Scripts

05c5e9a5e694f92da2749be6139f1148404c6f98

d3369b9bba2cf7820513465ee05189573f13bb9d

refs/heads/master

2023-08-28T08:57:30.015410

2021-11-03T16:17:40

null

0

null

UTF-8

Python

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560

py

import turtle as t tim = t.Turtle() scr = t.Screen() def move_forward(): tim.forward(10) def move_backward(): tim.backward(10) def clockwise(): tim.setheading(tim.heading() - 10) def anticlockwise(): tim.setheading(tim.heading() + 10) def clear(): tim.clear() tim.penup() tim.home() tim.pendown() scr.listen() scr.onkey(key="f", fun=move_forward) scr.onkey(key="d", fun=move_backward) scr.onkeypress(key="c", fun=clockwise) scr.onkeypress(key="a", fun=anticlockwise) scr.onkey(key="x", fun=clear) scr.exitonclick()

[ "anushka.pathak17@gmail.com" ]

anushka.pathak17@gmail.com

71964f4f03a88c44231adb44e53196613a314941

9b6133ea731aaa6260f4babcc749ce4fa033b85c

/lesson03/hw_3_3.py

5ce8aa423d46a89efe377ed3b3d5f36b699515c2

[]

no_license

Alexkvintin/PyBase08

1585031c80d8430e0fbadf68bfb30ce715b7f72b

f29337065cd7e4e79911d46ab8e8bbd7e2bd2817

refs/heads/master

2020-07-29T20:40:43.076130

2019-10-22T18:23:48

209,952,032

1

0

null

UTF-8

Python

false

2,179

py

def circle(x): y = int(x ** 2) s = 3.14 * y print("площадь круга равна:", s) def triangle(x, y, z): import math p = float((x + y + z) / 2) s = (math.sqrt(p * (p - x) * (p - y) * (p - z))) print("площадь треугольника равна:", s) def rectangle(x, y): s = int(x * y) print("Площадь прямоугольника равна", s) f = input("для начала работы програмы нажмите ENTER ") while f is not None: try: a = int(input(""" Площадь какой фигуры вы хотите вычеслить ? Круг (1) Треугольник (2) Квадрат (3) Для выхода нажмите ENTER""")) if a == 1: b = float(input("Ввудите радиус круга:")) circle(b) k = int(input("для продолжения нажмите 1 для выхода 0: ")) if k == 1: continue elif k == 0: break elif a == 2: b = float(input("Введите размер первой стороны треугольника:")) c = float(input("Введите размер второй стороны треугольника:")) d = float(input("Введите размер третей стороны треугольника:")) triangle(b, c, d) k = int(input("для продолжения нажмите 1 для выхода 0: ")) if k == 1: continue elif k == 0: break elif a == 3: d = float(input("Введите длину прямоугольника:")) c = float(input("Введите ширину прямоугольника:")) rectangle(d, c) k = int(input("для продолжения нажмите 1 для выхода 0: ")) if k == 1: continue elif k == 0: break except: break print("работа программы завершена")

[ "noreply@github.com" ]

noreply@github.com

e2a9c106808551355d57ac6954bab4a7a5b9a666

440332ab0e7fee9ce1a27a6796629a1fbb0385cf

/Go/tests/test_get_neighbours.py

4f0b0fdbac27504c301e46d0d55f8b0c05f497ab

[]

no_license

SveinungOverland/ZeroGO

f7123419499599e6ff350006e5ad8e9c84809eea

e979026711407ecc480e7d20ee27077bf693c1ea

refs/heads/master

2020-08-15T06:32:34.749694

2019-11-21T18:02:15

215,293,642

2

0

null

UTF-8

Python

false

729

py

from ..go import get_neighbours from .test_find_group import group_in_set import numpy as np def test_get_neighbours(): board = np.array([ [0, 0, 0, 0], [0, 1, 0, 0], [0, 1, 1, 1], [0, 0, 1, 0], ]) group = get_neighbours(board, 2, 2) print(group) assert group_in_set(group, { (2, 1, 1), (2, 3, 1), (1, 2, 0), (3, 2, 1), }) def test_get_neighbours(): board = np.array([ [0, 0, 0, 0], [0, 1, 0, 0], [0, 1, 1, 1], [0, 0, 1, 0], ]) group = get_neighbours(board, 2, 2, point_type=1) print(group) assert group_in_set(group, { (2, 1, 1), (2, 3, 1), (3, 2, 1), })

[ "andershallemiversen@hotmail.com" ]

andershallemiversen@hotmail.com

c9cf7689a50286a5c1017bfd446fa36d67ab48be

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p02663/s577432279.py

fb0a161b678de985a05e44a3e1290554f0f0f831

[]

no_license

Aasthaengg/IBMdataset

7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901

f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8

refs/heads/main

2023-04-22T10:22:44.763102

2021-05-13T17:27:22

367,112,348

0

null

UTF-8

Python

false

121

py

import sys input=sys.stdin.readline h1,m1,h2,m2,k=map(int,input().split()) h1=h1*60 h2=h2*60 m1+=h1 m2+=h2 print(m2-m1-k)

[ "66529651+Aastha2104@users.noreply.github.com" ]

66529651+Aastha2104@users.noreply.github.com

5d9851eedf62c0ea69485d51ed48d96036158b5a

717d369ce117dc8938a4dee13a62364e13fcd40b

/vodafone_sample_api/vodafone_api/api/resources/user.py

734ec9f8ad43616e56f9d1bda776326827f51e69

[]

no_license

tentengr/sample_app

750b1e615d695c6a01733d1c6bdb3f6e385852fd

994b9787a6b9782d3122942eaf364ae2cffcf877

refs/heads/master

2022-04-02T03:17:28.373935

2020-02-06T01:45:34

237,822,053

0

null

2020-02-06T01:45:36

2020-02-02T19:11:37

Python

UTF-8

Python

false

3,942

py

from flask import request from flask_restful import Resource from flask_jwt_extended import jwt_required from vodafone_api.models import User from vodafone_api.extensions import ma, db from vodafone_api.commons.pagination import paginate class UserSchema(ma.ModelSchema): id = ma.Int(dump_only=True) password = ma.String(load_only=True, required=True) class Meta: model = User sqla_session = db.session class UserResource(Resource): """Single object resource --- get: tags: - api parameters: - in: path name: user_id schema: type: integer responses: 200: content: application/json: schema: type: object properties: user: UserSchema 404: description: user does not exists put: tags: - api parameters: - in: path name: user_id schema: type: integer requestBody: content: application/json: schema: UserSchema responses: 200: content: application/json: schema: type: object properties: msg: type: string example: user updated user: UserSchema 404: description: user does not exists delete: tags: - api parameters: - in: path name: user_id schema: type: integer responses: 200: content: application/json: schema: type: object properties: msg: type: string example: user deleted 404: description: user does not exists """ method_decorators = [jwt_required] def get(self, user_id): schema = UserSchema() user = User.query.get_or_404(user_id) return {"user": schema.dump(user)} def put(self, user_id): schema = UserSchema(partial=True) user = User.query.get_or_404(user_id) user = schema.load(request.json, instance=user) db.session.commit() return {"msg": "user updated", "user": schema.dump(user)} def delete(self, user_id): user = User.query.get_or_404(user_id) db.session.delete(user) db.session.commit() return {"msg": "user deleted"} class UserList(Resource): """Creation and get_all --- get: tags: - api responses: 200: content: application/json: schema: allOf: - $ref: '#/components/schemas/PaginatedResult' - type: object properties: results: type: array items: $ref: '#/components/schemas/UserSchema' post: tags: - api requestBody: content: application/json: schema: UserSchema responses: 201: content: application/json: schema: type: object properties: msg: type: string example: user created user: UserSchema """ method_decorators = [jwt_required] def get(self): schema = UserSchema(many=True) query = User.query return paginate(query, schema) def post(self): schema = UserSchema() user = schema.load(request.json) db.session.add(user) db.session.commit() return {"msg": "user created", "user": schema.dump(user)}, 201

[ "grigorios.markopoulos@underwriteme.co.uk" ]

grigorios.markopoulos@underwriteme.co.uk

a9d382ed0329400bd7906b0287707b561ddd30cc

c38e00c81aad18fb31707f864c1aabe79f6eff4e

/figures/energyCostGraph.py

ad7f35a3a82afa3507a26440381c6e4a807c241a

[]

no_license

SEL-Columbia/lab_measurements

3ef2a62ef45f44650a2de9e7d136b6a89c52c04a

af30ec0bf6b070e3e5ec72ee7a18da995287637a

refs/heads/master

2021-01-01T17:47:58.825081

2012-01-26T23:51:14

2,440,445

0

null

UTF-8

Python

false

1,183

py

from __future__ import division import matplotlib.pyplot as plt def addEntry(key, cost, energy): dict[key] = {} dict[key]['cost'] = cost dict[key]['energy'] = energy dict[key]['cpkWh'] = dict[key]['cost'] / dict[key]['energy'] dict = {} addEntry('Cell Phone Charge', cost = 0.25, energy = 0.005) #addEntry('Kerosene', cost = 1.20, energy = 45/3.6) addEntry('D Cell', cost = 0.30, energy = 0.0022) addEntry('Car Battery Charge', cost = 1.0, energy = 0.12) #addEntry('SharedSolar', cost = 1.0, energy = 0.2) addEntry('Grid Mali', cost = 16, energy = 16/0.2) fig = plt.figure() axes = fig.add_subplot(111) for key in dict.keys(): axes.loglog(dict[key]['cost'], dict[key]['cpkWh'], 'ko') axes.text(dict[key]['cost'], dict[key]['cpkWh'], key) from matplotlib.patches import Rectangle rect = Rectangle((1, 1), 4, 5, facecolor="#dddddd") axes.add_artist(rect) #rect.set_clip_box(axes.bbox) axes.text(1.3,2,'Shared Solar') plt.xlim((0.1,50)) plt.ylim((0.01,500)) plt.title('Unit Cost of Energy and Purchase Price') plt.xlabel('Purchase Cost (USD)') plt.ylabel('Cost Per kWh') plt.grid() plt.savefig('costVsEnergy.pdf') plt.show() plt.close()

[ "no140@columbia.edu" ]

no140@columbia.edu

9c1baafdaefea09ba51c9303e42aa722840cf9de

b2f58607ab7dc003781496d1222b8538de01816a

/opennmt-baseline/utils/loss.py

ce7239450b7fb20352a5d94b4228c81f1683fe03

[]

no_license

Amazing-J/structural-transformer

3ab99a79a52a26ef73b887ce9fcd01ba3aec0c66

daef1f28cce74ecf984603dbfe796c5f71b1b39c

refs/heads/master

2020-07-09T12:59:49.788084

2019-12-25T05:46:41

203,974,442

79

11

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UTF-8

Python

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11,249

py

""" This includes: LossComputeBase and the standard NMTLossCompute, and sharded loss compute stuff. """ from __future__ import division import torch import torch.nn as nn import torch.nn.functional as F import onmt import onmt.constants as Constants from utils.misc import use_gpu from utils.statistics import Statistics def build_loss_compute(model, tgt_vocab, opt, train=True): """ Returns a LossCompute subclass which wraps around an nn.Module subclass (such as nn.NLLLoss) which defines the loss criterion. The LossCompute object allows this loss to be computed in shards and passes the relevant data to a Statistics object which handles training/validation logging. Currently, the NMTLossCompute class handles all loss computation except for when using a copy mechanism. Despite their name, LossCompute objects do not merely compute the loss but also perform the backward pass inside their sharded_compute_loss method. """ device = torch.device("cuda" if use_gpu(opt) else "cpu") padding_idx = tgt_vocab.stoi[Constants.PAD_WORD] if opt.label_smoothing > 0 and train: criterion = LabelSmoothingLoss( opt.label_smoothing, len(tgt_vocab), ignore_index=padding_idx ) else: criterion = nn.NLLLoss(ignore_index=padding_idx, reduction='sum') # if the loss function operates on vectors of raw logits instead of # probabilities, only the first part of the generator needs to be # passed to the NMTLossCompute. At the moment, the only supported # loss function of this kind is the sparsemax loss. loss_gen = model.generator compute = NMTLossCompute(criterion, loss_gen) compute.to(device) return compute class LossComputeBase(nn.Module): """ Class for managing efficient loss computation. Handles sharding next step predictions and accumulating multiple loss computations Users can implement their own loss computation strategy by making subclass of this one. Users need to implement the _compute_loss() and make_shard_state() methods. Args: generator (:obj:`nn.Module`) : module that maps the output of the decoder to a distribution over the target vocabulary. tgt_vocab (:obj:`Vocab`) : torchtext vocab object representing the target output normalzation (str): normalize by "sents" or "tokens" """ def __init__(self, criterion, generator): super(LossComputeBase, self).__init__() self.criterion = criterion self.generator = generator @property def padding_idx(self): return self.criterion.ignore_index def _make_shard_state(self, batch, output, range_, attns=None): """ Make shard state dictionary for shards() to return iterable shards for efficient loss computation. Subclass must define this method to match its own _compute_loss() interface. Args: batch: the current batch. output: the predict output from the model. range_: the range of examples for computing, the whole batch or a trunc of it? attns: the attns dictionary returned from the model. """ return NotImplementedError def _compute_loss(self, batch, output, target, **kwargs): """ Compute the loss. Subclass must define this method. Args: batch: the current batch. output: the predict output from the model. target: the validate target to compare output with. **kwargs(optional): additional info for computing loss. """ return NotImplementedError def monolithic_compute_loss(self, batch, output, attns): """ Compute the forward loss for the batch. Args: batch (batch): batch of labeled examples output (:obj:`FloatTensor`): output of decoder model `[tgt_len x batch x hidden]` attns (dict of :obj:`FloatTensor`) : dictionary of attention distributions `[tgt_len x batch x src_len]` Returns: :obj:`onmt.utils.Statistics`: loss statistics """ range_ = (0, batch.tgt.size(0)) shard_state = self._make_shard_state(batch, output, range_, attns) _, batch_stats = self._compute_loss(batch, **shard_state) return batch_stats def sharded_compute_loss(self, batch, output, attns, cur_trunc, trunc_size, shard_size, normalization): """Compute the forward loss and backpropagate. Computation is done with shards and optionally truncation for memory efficiency. Also supports truncated BPTT for long sequences by taking a range in the decoder output sequence to back propagate in. Range is from `(cur_trunc, cur_trunc + trunc_size)`. Note sharding is an exact efficiency trick to relieve memory required for the generation buffers. Truncation is an approximate efficiency trick to relieve the memory required in the RNN buffers. Args: batch (batch) : batch of labeled examples output (:obj:`FloatTensor`) : output of decoder model `[tgt_len x batch x hidden]` attns (dict) : dictionary of attention distributions `[tgt_len x batch x src_len]` cur_trunc (int) : starting position of truncation window trunc_size (int) : length of truncation window shard_size (int) : maximum number of examples in a shard normalization (int) : Loss is divided by this number Returns: :obj:`onmt.utils.Statistics`: validation loss statistics """ batch_stats = Statistics() range_ = (cur_trunc, cur_trunc + trunc_size) shard_state = self._make_shard_state(batch, output, range_, attns) for shard in shards(shard_state, shard_size): loss, stats = self._compute_loss(batch, **shard) loss.div(float(normalization)).backward() batch_stats.update(stats) return batch_stats def _stats(self, loss, scores, target): """ Args: loss (:obj:`FloatTensor`): the loss computed by the loss criterion. scores (:obj:`FloatTensor`): a score for each possible output target (:obj:`FloatTensor`): true targets Returns: :obj:`onmt.utils.Statistics` : statistics for this batch. """ pred = scores.max(1)[1] non_padding = target.ne(self.padding_idx) num_correct = pred.eq(target).masked_select(non_padding).sum().item() num_non_padding = non_padding.sum().item() return Statistics(loss.item(), num_non_padding, num_correct) def _bottle(self, _v): return _v.view(-1, _v.size(2)) def _unbottle(self, _v, batch_size): return _v.view(-1, batch_size, _v.size(1)) class LabelSmoothingLoss(nn.Module): """ With label smoothing, KL-divergence between q_{smoothed ground truth prob.}(w) and p_{prob. computed by model}(w) is minimized. """ def __init__(self, label_smoothing, tgt_vocab_size, ignore_index=-100): assert 0.0 < label_smoothing <= 1.0 self.ignore_index = ignore_index super(LabelSmoothingLoss, self).__init__() smoothing_value = label_smoothing / (tgt_vocab_size - 2) one_hot = torch.full((tgt_vocab_size,), smoothing_value) one_hot[self.ignore_index] = 0 self.register_buffer('one_hot', one_hot.unsqueeze(0)) self.confidence = 1.0 - label_smoothing def forward(self, output, target): """ output (FloatTensor): batch_size x n_classes target (LongTensor): batch_size """ model_prob = self.one_hot.repeat(target.size(0), 1) model_prob.scatter_(1, target.unsqueeze(1), self.confidence) model_prob.masked_fill_((target == self.ignore_index).unsqueeze(1), 0) return F.kl_div(output, model_prob, reduction='sum') class NMTLossCompute(LossComputeBase): """ Standard NMT Loss Computation. """ def __init__(self, criterion, generator, normalization="sents"): super(NMTLossCompute, self).__init__(criterion, generator) def _make_shard_state(self, batch, output, range_, attns=None): return { "output": output, "target": batch.tgt[range_[0] + 1: range_[1]], } def _compute_loss(self, batch, output, target): bottled_output = self._bottle(output) scores = self.generator(bottled_output) gtruth = target.view(-1) loss = self.criterion(scores, gtruth) stats = self._stats(loss.clone(), scores, gtruth) return loss, stats def filter_shard_state(state, shard_size=None): for k, v in state.items(): if shard_size is None: yield k, v if v is not None: v_split = [] if isinstance(v, torch.Tensor): for v_chunk in torch.split(v, shard_size): v_chunk = v_chunk.data.clone() v_chunk.requires_grad = v.requires_grad v_split.append(v_chunk) yield k, (v, v_split) def shards(state, shard_size, eval_only=False): """ Args: state: A dictionary which corresponds to the output of *LossCompute._make_shard_state(). The values for those keys are Tensor-like or None. shard_size: The maximum size of the shards yielded by the model. eval_only: If True, only yield the state, nothing else. Otherwise, yield shards. Yields: Each yielded shard is a dict. Side effect: After the last shard, this function does back-propagation. """ if eval_only: yield filter_shard_state(state) else: # non_none: the subdict of the state dictionary where the values # are not None. non_none = dict(filter_shard_state(state, shard_size)) # Now, the iteration: # state is a dictionary of sequences of tensor-like but we # want a sequence of dictionaries of tensors. # First, unzip the dictionary into a sequence of keys and a # sequence of tensor-like sequences. keys, values = zip(*((k, [v_chunk for v_chunk in v_split]) for k, (_, v_split) in non_none.items())) # Now, yield a dictionary for each shard. The keys are always # the same. values is a sequence of length #keys where each # element is a sequence of length #shards. We want to iterate # over the shards, not over the keys: therefore, the values need # to be re-zipped by shard and then each shard can be paired # with the keys. for shard_tensors in zip(*values): yield dict(zip(keys, shard_tensors)) # Assumed backprop'd variables = [] for k, (v, v_split) in non_none.items(): if isinstance(v, torch.Tensor) and state[k].requires_grad: variables.extend(zip(torch.split(state[k], shard_size), [v_chunk.grad for v_chunk in v_split])) inputs, grads = zip(*variables) torch.autograd.backward(inputs, grads)

[ "zhujie951121@gmail.com" ]

zhujie951121@gmail.com

570d5c279ef4c76aad6ed5844ecc6bea270092c3

c7fb854c1e787adfb066d485a86cb384848c8a89

/app/models/comment.py

e9ac0428df2289dcdb6cb4db5c99cc2ffd2342a1

[]

no_license

longfe1yang/flask_blog

51218bec01bc706a088c6bf6d1000389936670ea

3b54e417878742e886d0c629230d72b4dbd24045

refs/heads/master

2021-06-08T09:15:52.040002

2016-09-04T09:39:22

null

0

null

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994

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from . import db from . import ReprMixin import time class Comment(db.Model, ReprMixin): __tablename__ = 'comments' id = db.Column(db.Integer, primary_key=True) content = db.Column(db.String()) reply_username = db.Column(db.Integer) tweet_id = db.Column(db.Integer, db.ForeignKey('tweets.id')) created_time = db.Column(db.Integer) def __init__(self, form): self.content = form.get('content', '') self.created_time = int(time.time()) self.tweet_id = form.get('tweet_id', '') def json(self): self.id d = {k: v for k, v in self.__dict__.items() if k not in self.blacklist()} return d def blacklist(self): b = [ '_sa_instance_state', ] return b def save(self): db.session.add(self) db.session.commit() def delete(self): db.session.delete(self) db.session.commit() """ 时间问题没有解决，依旧是只用了一个时间 """

[ "yanglongfei71937315@gmail.com" ]

yanglongfei71937315@gmail.com

8a68a48afac4164eb404a1c903aea21610418776

4133ecadccf991de5e2cb2aade0d61dc55cb1b57

/ProTwo/settings.py

b62f137cc9248c0323c4227159a01b214015972e

[]

no_license

EmonZaman/Web-Development

f357035772e41b8f65c9265d70236d3c6b24c173

8a51813469cdef39096e525d9b3b4933570c9bbe

refs/heads/main

2023-06-26T10:04:26.644356

2021-07-29T09:08:13

381,151,748

0

null

UTF-8

Python

false

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""" Django settings for ProTwo project. Generated by 'django-admin startproject' using Django 3.2.4. For more information on this file, see https://docs.djangoproject.com/en/3.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.2/ref/settings/ """ from pathlib import Path import os # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent STATIC_DIR = os.path.join(BASE_DIR,"static") # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'django-insecure-wv()pj@*7vjit7=-i-wy&uu5)_zsa)9g^bzlas!y5@*jwu%g4a' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'AppTwo' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'ProTwo.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR,'templates')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'ProTwo.wsgi.application' # Database # https://docs.djangoproject.com/en/3.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.2/howto/static-files/ STATIC_URL = '/static/' STATICFILES_DIRS = [ STATIC_DIR, ] # Default primary key field type # https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'

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''' Three characters { #, *, . } represents a constellation of stars and galaxies in space. Each galaxy is demarcated by # characters. There can be one or many stars in a given galaxy. Stars can only be in shape of vowels { A, E, I, O, U } . A collection of * in the shape of the vowels is a star. A star is contained in a 3x3 block. Stars cannot be overlapping. The dot(.) character denotes empty space. Given 3xN matrix comprising of { #, *, . } character, find the galaxy and stars within them. Note: Please pay attention to how vowel A is denoted in a 3x3 block in the examples section below. Constraints 3 <= N <= 10^5 Input Input consists of single integer N denoting number of columns. Output Output contains vowels (stars) in order of their occurrence within the given galaxy. Galaxy itself is represented by # character. Example 1 Input 18 * . * # * * * # * * * # * * * . * . * . * # * . * # . * . # * * * * * * * * * # * * * # * * * # * * * * . * Output U#O#I#EA Explanation As it can be seen that the stars make the image of the alphabets U, O, I, E and A respectively. Example 2 Input 12 * . * # . * * * # . * . * . * # . . * . # * * * * * * # . * * * # * . * Output U#I#A Explanation As it can be seen that the stars make the image of the alphabet U, I and A. Possible solution: Input: 12 * . * # . * * * # . * . * . * # . . * . # * * * * * * # . * * * # * . * ''' n = int(input()) galaxy = [list(map(int, input().split())) for _ in range(3)] for i in range(n): if galaxy[0][i] == '#' and galaxy[1][j] == '#' and galaxy[2][i] == '#': print('#', end='') elif galaxy[0][i] == '.' and galaxy[1][j] == '.' and galaxy[2][i] == '.': pass else: x = i a, b, c, a1, b1, c1, a2, b2, c2 = galaxy[0][x], galaxy[0][x+1], galaxy[0][x+2], galaxy[1][x], galaxy[1][x+1], galaxy[1][x+2], galaxy[2][x], galaxy[2][x+1], galaxy[2][x+2] if a == '.' and b == '*' and c == '.' and a1=='*' and b1 == '*' and c1 == '*' and a2=='*' and b2 == '.' and c2 == '*': print("A", end='') i = i + 2 if a == '*' and b == '*' and c == '*' and a1 == '*' and b1 == '*' and c1 == '*' and a2 == '*' and b2 == '*' and c2 == '*': print("E", end='') i = i + 2 if a == '*' and b == '*' and c == '*' and a1 == '.' and b1 == '*' and c1 == '.' and a2 == '*' and b2 == '*' and c2 == '*': print("I", end='') i = i + 2 if a == '*' and b == '*' and c == '*' and a1 == '*' and b1 == '.' and c1 == '*' and a2 == '*' and b2 == '*' and c2 == '*': print("O", end='') i = i + 2 if a == '*' and b == '.' and c == '*' and a1 == '*' and b1 == '.' and c1 == '*' and a2 == '*' and b2 == '*' and c2 =='*': print("U", end='') i = i + 2

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#From https://gist.github.com/EndingCredits/b5f35e84df10d46cfa716178d9c862a3 from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import state_ops from tensorflow.python.framework import ops from tensorflow.python.training import optimizer import tensorflow as tf import hyperchamber as hc import numpy as np import inspect from operator import itemgetter from hypergan.train_hooks.base_train_hook import BaseTrainHook class WeightPenaltyTrainHook(BaseTrainHook): def __init__(self, gan=None, config=None, trainer=None, name="WeightPenaltyTrainHook", memory_size=2, top_k=1): super().__init__(config=config, gan=gan, trainer=trainer, name=name) d_losses = [] weights = self.gan.weights() if config.only_d: weights = self.discriminator.weights() if config.l2nn_penalty: l2nn_penalties = [] if len(weights) > 0: for w in weights: w = tf.reshape(w, [-1, self.ops.shape(w)[-1]]) wt = tf.transpose(w) wtw = tf.matmul(wt,w) wwt = tf.matmul(w,wt) def _l(m): m = tf.abs(m) m = tf.reduce_sum(m, axis=0,keep_dims=True) m = tf.maximum(m-1, 0) m = tf.reduce_max(m, axis=1,keep_dims=True) return m l2nn_penalties.append(tf.minimum(_l(wtw), _l(wwt))) print('l2nn_penalty', self.config.l2nn_penalty, l2nn_penalties) l2nn_penalty = self.config.l2nn_penalty * tf.add_n(l2nn_penalties) self.add_metric('l2nn_penalty', self.gan.ops.squash(l2nn_penalty)) d_losses.append(l2nn_penalty) if config.ortho_penalty: penalties = [] for w in self.gan.weights(): print("PENALTY", w) w = tf.reshape(w, [-1, self.ops.shape(w)[-1]]) wt = tf.transpose(w) wtw = tf.matmul(wt,w) wwt = tf.matmul(w,wt) mwtw = tf.matmul(w, wtw) mwwt = tf.matmul(wt, wwt) def _l(w,m): l = tf.reduce_mean(tf.abs(w - m)) l = self.ops.squash(l) return l penalties.append(tf.minimum(_l(w, mwtw), _l(wt, mwwt))) penalty = self.config.ortho_penalty * tf.add_n(penalties) self.add_metric('ortho_penalty', self.gan.ops.squash(penalty)) print("PENALTY", penalty) penalty = tf.reshape(penalty, [1,1]) penalty = tf.tile(penalty, [self.gan.batch_size(), 1]) d_losses.append(penalty) self.loss = self.ops.squash(d_losses) def losses(self): return [self.loss, self.loss] def after_step(self, step, feed_dict): pass def before_step(self, step, feed_dict): pass

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from django.db import models # Create your models here. class Hero(models.Model): name = models.CharField(max_length=60) alias = models.CharField(max_length=60) def __str__(self): return self.name class Spell(models.Model): name = models.CharField(max_length=60) description = models.TextField() def __str__(self): return self.name

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# -*- coding:utf-8 -*- """ http uri 路由装饰器 """ from utils import log as logger class route(object): """ @route('/some/path') class SomeRequestHandler(RequestHandler): pass @route('/some/path', name='other') class SomeOtherRequestHandler(RequestHandler): pass my_routes = route.make_routes(['api']) """ _routes = [] def __init__(self, uri, name=None): """ 装饰器 @param uri 注册的uri名字，支持uri正则表达式 @param name 注册的uri别名 """ self.uri = uri if not name: name = '-'.join(uri.split('/')) self.name = name def __call__(self, _handler): """ gets called when we class decorate """ for item in self._routes: if item.get('uri') == self.uri: logger.error('uri aleady exists! uri:', self.uri, 'name:', self.name, 'handler:', _handler, caller=self) if item.get('name') == self.name: logger.warn('name aleady exists! uri:', self.uri, 'name:', self.name, 'handler:', _handler, caller=self) self._routes.append({'uri': self.uri, 'name': self.name, 'handler': _handler}) return _handler @classmethod def make_routes(cls, dirs): """ 注册并返回所有的handler @param dirs list，需要注册uri路由的处理方法路径 """ for dir in dirs: s = 'import %s' % dir exec(s) routes = [] for handler_dic in cls._routes: logger.info('register uri:', handler_dic['uri'], 'handler:', handler_dic.get('handler'), caller=cls) routes.append((handler_dic.get('uri'), handler_dic.get('handler'))) return routes

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Apr 12 14:51:46 2020 @author: shanyang """ # import library import numpy as np from sklearn.model_selection import train_test_split; from tensorflow import keras from keras.models import Sequential from keras.layers import Dense from keras import initializers from keras import optimizers import matplotlib.pyplot as plt # fit the total energy with AIMD go get potential energy surface at finite temperatures # preprocess data class load_data: def read_xdatcar(self,path): """ read the XDARCAR file from AIMD (VASP) """ try: with open(path,'r') as xdatcar: self._system=xdatcar.readline() self._scale_supercell=float(xdatcar.readline().rstrip('\n').rstrip()); self._a_supercell_vector=np.array([float(i)*self._scale_supercell for i in xdatcar.readline().rstrip('\n').split()]) self._b_supercell_vector=np.array([float(i)*self._scale_supercell for i in xdatcar.readline().rstrip('\n').split()]) self._c_supercell_vector=np.array([float(i)*self._scale_supercell for i in xdatcar.readline().rstrip('\n').split()]) self._latticevector_matrix_supercell=np.round(np.stack((self._a_supercell_vector,self._b_supercell_vector,self._c_supercell_vector)),6) self._element_names = [name for name in xdatcar.readline().rstrip('\n').split()] self._element_numbers = np.array([int(number) for number in xdatcar.readline().rstrip('\n').split()]) self._total_number = np.sum(self._element_numbers) self._xdatcar=[] self._count = 0 while True: line=xdatcar.readline().rstrip('\n').split(); if not line: break if (self._isfloat(*[items for items in line])): self._xdatcar.append(line) self._count +=1 #self._xdatcar_fract = np.asarray(self._xdatcar,dtype = float) self._steps = int(self._count/self._total_number) except FileNotFoundError as e: print('XDARCAR file does not exist:{}'.format(e)) raise e """ reshape the data from XDATCAR to 3D matrix steps * atoms * xyz(direction)""" self._xdatcar_fract = np.zeros((self._steps,self._total_number*3)); for t in range(self._steps): self._xdatcar_fract[t,:] = np.asarray(self._xdatcar,dtype = float)[t*self._total_number:(t+1)*self._total_number,:].flatten(); return self._xdatcar_fract; def _isfloat(self,*value): for it in value: try: float(it) except ValueError: return False return True; def read_energy(self,path): try: self._energy = np.loadtxt(path); except FileNotFoundError as e: print('Energy file does not exist:{}'.format(e)) raise e return self._energy; def get_total_steps(self): return self._steps def get_total_atoms(self): return self._total_number; x=np.loadtxt('x') y=np.loadtxt('y') # train_test split x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, shuffle = True ,random_state=42); # Create the deep neural network model model = Sequential(); #total_atoms = data_loader.get_total_atoms(); total_atoms = 96; init = initializers.RandomNormal(mean=0, stddev=1, seed=None) model.add(Dense(32, input_shape=(total_atoms*3,), activation='tanh',kernel_initializer= init)); model.add(Dense(32, activation='tanh',kernel_initializer= init)); model.add(Dense(32,activation='tanh',kernel_initializer= init)); model.add(Dense(1,activation=None)); optimizer_adam = optimizers.Adam(lr=0.005,decay = 0.01) model.compile(loss='mean_absolute_error', optimizer=optimizer_adam) model.summary() # Train the model # call back early_stopping_cb = keras.callbacks.EarlyStopping(monitor='val_loss', patience = 30,restore_best_weights = True) hist=model.fit(x_train,y_train,epochs =1000, batch_size =30, validation_split=0.2,callbacks=[early_stopping_cb]) mse_test = model.evaluate(x_test, y_test) # plot loss function plt.figure() plt.plot(hist.history['loss']) plt.plot(hist.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'validation'], loc='upper left') plt.show() # Test on the test set y_pred = model.predict(x_test) plt.figure() plt.plot(y_pred) plt.plot(y_test) plt.title('prediction and test') plt.ylabel('Y') plt.xlabel('configurations') plt.legend(['y_pred', 'y_test'], loc='upper left') plt.show() # save the model model.save("Potential_model.h5")

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import numpy as np from scipy.ndimage import affine_transform # Functions to convert points to homogeneous coordinates and back pad = lambda x: np.hstack([x, np.ones((x.shape[0], 1))]) unpad = lambda x: x[:,:-1] def plot_matches(ax, image1, image2, keypoints1, keypoints2, matches, keypoints_color='k', matches_color=None, only_matches=False): """Plot matched features. Parameters ---------- ax : matplotlib.axes.Axes Matches and image are drawn in this ax. image1 : (N, M [, 3]) array First grayscale or color image. image2 : (N, M [, 3]) array Second grayscale or color image. keypoints1 : (K1, 2) array First keypoint coordinates as ``(row, col)``. keypoints2 : (K2, 2) array Second keypoint coordinates as ``(row, col)``. matches : (Q, 2) array Indices of corresponding matches in first and second set of descriptors, where ``matches[:, 0]`` denote the indices in the first and ``matches[:, 1]`` the indices in the second set of descriptors. keypoints_color : matplotlib color, optional Color for keypoint locations. matches_color : matplotlib color, optional Color for lines which connect keypoint matches. By default the color is chosen randomly. only_matches : bool, optional Whether to only plot matches and not plot the keypoint locations. """ image1.astype(np.float32) image2.astype(np.float32) new_shape1 = list(image1.shape) new_shape2 = list(image2.shape) if image1.shape[0] < image2.shape[0]: new_shape1[0] = image2.shape[0] elif image1.shape[0] > image2.shape[0]: new_shape2[0] = image1.shape[0] if image1.shape[1] < image2.shape[1]: new_shape1[1] = image2.shape[1] elif image1.shape[1] > image2.shape[1]: new_shape2[1] = image1.shape[1] if new_shape1 != image1.shape: new_image1 = np.zeros(new_shape1, dtype=image1.dtype) new_image1[:image1.shape[0], :image1.shape[1]] = image1 image1 = new_image1 if new_shape2 != image2.shape: new_image2 = np.zeros(new_shape2, dtype=image2.dtype) new_image2[:image2.shape[0], :image2.shape[1]] = image2 image2 = new_image2 image = np.concatenate([image1, image2], axis=1) offset = image1.shape if not only_matches: ax.scatter(keypoints1[:, 1], keypoints1[:, 0], facecolors='none', edgecolors=keypoints_color) ax.scatter(keypoints2[:, 1] + offset[1], keypoints2[:, 0], facecolors='none', edgecolors=keypoints_color) ax.imshow(image, interpolation='nearest', cmap='gray') ax.axis((0, 2 * offset[1], offset[0], 0)) for i in range(len(matches)): idx1 = matches[i, 0] idx2 = matches[i, 1] if matches_color is None: color = np.random.rand(3) else: color = matches_color ax.plot((keypoints1[idx1, 1], keypoints2[idx2, 1] + offset[1]), (keypoints1[idx1, 0], keypoints2[idx2, 0]), '-', color=color) def get_output_space(img_ref, imgs, transforms): """ Args: img_ref: reference image imgs: images to be transformed transforms: list of affine transformation matrices. transforms[i] maps points in imgs[i] to the points in img_ref Returns: output_shape """ assert (len(imgs) == len(transforms)) r, c = img_ref.shape corners = np.array([[0, 0], [r, 0], [0, c], [r, c]]) all_corners = [corners] for i in range(len(imgs)): r, c = imgs[i].shape H = transforms[i] corners = np.array([[0, 0], [r, 0], [0, c], [r, c]]) warped_corners = corners.dot(H[:2,:2]) + H[2,:2] all_corners.append(warped_corners) # Find the extents of both the reference image and the warped # target image all_corners = np.vstack(all_corners) # The overall output shape will be max - min corner_min = np.min(all_corners, axis=0) corner_max = np.max(all_corners, axis=0) output_shape = (corner_max - corner_min) # Ensure integer shape with np.ceil and dtype conversion output_shape = np.ceil(output_shape).astype(int) offset = corner_min return output_shape, offset def warp_image(img, H, output_shape, offset): # Note about affine_transfomr function: # Given an output image pixel index vector o, # the pixel value is determined from the input image at position # np.dot(matrix,o) + offset. Hinv = np.linalg.inv(H) m = Hinv.T[:2,:2] b = Hinv.T[:2,2] img_warped = affine_transform(img.astype(np.float32), m, b+offset, output_shape, cval=-1) return img_warped

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from supergrass import app, health import json import time import os class SupergrassReporter(app.App): def report(self): healthchecks = health.HealthChecks() healthchecks.system_code = 393 healthchecks.name = 'Flex' healthchecks.description = 'FT video team workflow and publishing engine' healthchecks.checks = [] for m in [self.failure_proportion(), self.bytes_per_second_proportion('project-workflow'), self.bytes_per_second_proportion('ingest-workflow'), self.bytes_per_second_proportion('publish-workflow')]: healthchecks.checks.append( m ) return healthchecks.dump() def failure_proportion(self): h = health.HealthCheck() h.name = 'Workflow Failure Rates' h.severity = 3 h.impact = 'Videos will not be archived and/or published automatically' h.summary = 'Tests the proportion of failed workflows to successful workflows' h.panic_guide = 'N/a' totals = self.workflows_by_time(3600) if totals['failed'] == 0: h.ok = True if totals['success'] == 0: h.check_output = 'No workflows seem to have run.' else: h.check_output = 'All workflows succeeding.' else: if totals['success'] == 0: h.check_output = 'Every workflow is failing.' h.ok = False else: percentage = ( float(totals['failed']) / float(totals['success']) ) * 100 if percentage > 10: h.check_output = 'More than 10% of workflows have failed.' h.ok = False else: h.check_output = 'Less than 10% of workflows failed.' h.ok = True h.check_output += " Succesful Workflow Runs: {}, Failed Workflow Runs: {}".format(totals['success'], totals['failed']) return h def bytes_per_second_proportion(self, wf): h = health.HealthCheck() h.name = 'Processing time per kb for workflow: {}'.format(wf) h.severity = 3 h.impact = 'Waiting time for videos will be higher than expected' h.summary = 'Tests that workflows are being completed in a timely manner' h.panic_guide = 'N/a' h.ok = True all_workflows = self.workflows.by_time(wf) recent_workflows = self.workflows.time_period(all_workflows, length=3600) if len(all_workflows) == 0 or len(recent_workflows) == 0: h.check_output = 'Not enough data to determine whether workflows take too long.' else: all_bytes_per_second = self.bytes_per_second(all_workflows) recent_bytes_per_second = self.bytes_per_second(recent_workflows) diff = recent_bytes_per_second / all_bytes_per_second if diff < 0.85: h.ok = False h.check_output = 'Throughput has dropped to {}% of previous values'.format(diff*100) return h def workflows_by_time(self, check_period=3600): failed = self.workflows.failed() success = self.workflows.successful() return {'failed': len(self.workflows.time_period(failed, length=check_period)), 'success': len(self.workflows.time_period(success, length=check_period))} def bytes_per_second(self, workflows): time = 0 size = 0 for wf in workflows: if wf['Failed'] == False: next time += wf['ProcessTime'] size += wf['AssetSize'] return float(size) / float(time) def load(event, context): mio = event.get('mio', 'http://localhost:9898') username = event.get('username', 'masteruser') password = event.get('password', os.environ.get('MIO_PASSWORD')) sr = SupergrassReporter(mio=mio, username=username, password=password) return sr.report() if __name__ == '__main__': print json.dumps(load({},{}), ensure_ascii=False)

[ "james@zero-internet.org.uk" ]

james@zero-internet.org.uk

46f20c311de9662ae52e0d17c54e38adf2809320

163eae2f1e83140ba8f082909793de9a1212371b

/ileriseviyeprogramlama/karakterfrekansı.py

080518131d29fccfacadc8e79b7f57a04632fdfd

[]

no_license

MusabBayram/Python-Studies

b2ba99487c9509c05c85904cc77d3df35775ea38

e2b388c0d05c30852a80eaac966d5b4a12f6d85f

refs/heads/master

2021-05-17T11:57:36.777335

2020-03-28T10:23:20

250,763,552

0

null

UTF-8

Python

false

174

py

dize = "ProgramlamaÖdeviİleriSeviyeVeriYapılarıveObjeleripynb" say = dict() for i in dize: if (i in say): say[i] +=1 else: say[i] = 1 print(say)

[ "Musab.bayram@hotmail.com" ]

Musab.bayram@hotmail.com

bb7645b996dd70bb11bceb7fa31190757f205a92

141d1fb160fcfb4294d4b0572216033218da702d

/exec -l /bin/zsh/google-cloud-sdk/lib/surface/composer/environments/run.py

b81165e938f3ff95fea3676709e9be6e342bacc4

[ "LicenseRef-scancode-unknown-license-reference", "Apache-2.0" ]

permissive

sudocams/tech-club

1f2d74c4aedde18853c2b4b729ff3ca5908e76a5

c8540954b11a6fd838427e959e38965a084b2a4c

refs/heads/master

2021-07-15T03:04:40.397799

2020-12-01T20:05:55

245,985,795

0

1

null

2021-04-30T21:04:39

2020-03-09T08:51:41

Python

UTF-8

Python

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7,255

py

# -*- coding: utf-8 -*- # # Copyright 2017 Google LLC. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Command to run an Airflow CLI sub-command in an environment.""" from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals import argparse from googlecloudsdk.api_lib.composer import environments_util as environments_api_util from googlecloudsdk.api_lib.composer import util as api_util from googlecloudsdk.calliope import base from googlecloudsdk.command_lib.composer import resource_args from googlecloudsdk.command_lib.composer import util as command_util from googlecloudsdk.core import log from googlecloudsdk.core.console import console_io WORKER_POD_SUBSTR = 'worker' WORKER_CONTAINER = 'airflow-worker' DEPRECATION_WARNING = ('Because Cloud Composer manages the Airflow metadata ' 'database for your environment, support for the Airflow ' '`{}` subcommand is being deprecated. ' 'To avoid issues related to Airflow metadata, we ' 'recommend that you do not use this subcommand unless ' 'you understand the outcome.') @base.ReleaseTracks(base.ReleaseTrack.GA) class Run(base.Command): """Run an Airflow sub-command remotely in a Cloud Composer environment. Executes an Airflow CLI sub-command remotely in an environment. If the sub-command takes flags, separate the environment name from the sub-command and its flags with ``--''. This command waits for the sub-command to complete; its exit code will match the sub-command's exit code. ## EXAMPLES The following command: {command} myenv trigger_dag -- some_dag --run_id=foo is equivalent to running the following command from a shell inside the *my-environment* environment: airflow trigger_dag some_dag --run_id=foo """ @staticmethod def Args(parser): resource_args.AddEnvironmentResourceArg( parser, 'in which to run an Airflow command') parser.add_argument( 'subcommand', metavar='SUBCOMMAND', choices=command_util.SUBCOMMAND_WHITELIST, help=('The Airflow CLI subcommand to run. Available subcommands ' 'include: {} (see https://airflow.apache.org/cli.html for more ' 'info). Note that delete_dag is available from Airflow 1.10.1, ' 'and list_dag_runs, next_execution are available from Airflow ' '1.10.2.').format(', '.join(command_util.SUBCOMMAND_WHITELIST))) parser.add_argument( 'cmd_args', metavar='CMD_ARGS', nargs=argparse.REMAINDER, help='Command line arguments to the subcommand.', example='{command} myenv trigger_dag -- some_dag --run_id=foo') def BypassConfirmationPrompt(self, args): """Bypasses confirmations with "yes" responses. Prevents certain Airflow CLI subcommands from presenting a confirmation prompting (which can hang the gcloud CLI). When necessary, bypass confirmations with a "yes" response. Args: args: argparse.Namespace, An object that contains the values for the arguments specified in the .Args() method. """ prompting_subcommands = ['delete_dag'] if args.subcommand in prompting_subcommands and set( args.cmd_args).isdisjoint({'-y', '--yes'}): args.cmd_args.append('--yes') def DeprecationWarningPrompt(self, args): response = True if args.subcommand in command_util.SUBCOMMAND_DEPRECATION: response = console_io.PromptContinue( message=DEPRECATION_WARNING.format(args.subcommand), default=False, cancel_on_no=True) return response def ConvertKubectlError(self, error, env_obj): del env_obj # Unused argument. return error def Run(self, args): self.DeprecationWarningPrompt(args) running_state = ( api_util.GetMessagesModule(release_track=self.ReleaseTrack()) .Environment.StateValueValuesEnum.RUNNING) env_ref = args.CONCEPTS.environment.Parse() env_obj = environments_api_util.Get( env_ref, release_track=self.ReleaseTrack()) if env_obj.state != running_state: raise command_util.Error( 'Cannot execute subcommand for environment in state {}. ' 'Must be RUNNING.'.format(env_obj.state)) cluster_id = env_obj.config.gkeCluster cluster_location_id = command_util.ExtractGkeClusterLocationId(env_obj) with command_util.TemporaryKubeconfig(cluster_location_id, cluster_id): try: kubectl_ns = command_util.FetchKubectlNamespace( env_obj.config.softwareConfig.imageVersion) pod = command_util.GetGkePod( pod_substr=WORKER_POD_SUBSTR, kubectl_namespace=kubectl_ns) log.status.Print( 'Executing within the following kubectl namespace: {}'.format( kubectl_ns)) self.BypassConfirmationPrompt(args) kubectl_args = [ 'exec', pod, '-tic', WORKER_CONTAINER, 'airflow', args.subcommand ] if args.cmd_args: # Add '--' to the argument list so kubectl won't eat the command args. kubectl_args.extend(['--'] + args.cmd_args) command_util.RunKubectlCommand( command_util.AddKubectlNamespace(kubectl_ns, kubectl_args), out_func=log.status.Print) except command_util.KubectlError as e: raise self.ConvertKubectlError(e, env_obj) @base.ReleaseTracks(base.ReleaseTrack.BETA, base.ReleaseTrack.ALPHA) class RunBeta(Run): """Run an Airflow sub-command remotely in a Cloud Composer environment. Executes an Airflow CLI sub-command remotely in an environment. If the sub-command takes flags, separate the environment name from the sub-command and its flags with ``--''. This command waits for the sub-command to complete; its exit code will match the sub-command's exit code. ## EXAMPLES The following command: {command} myenv trigger_dag -- some_dag --run_id=foo is equivalent to running the following command from a shell inside the *my-environment* environment: airflow trigger_dag some_dag --run_id=foo """ def ConvertKubectlError(self, error, env_obj): is_private = ( env_obj.config.privateEnvironmentConfig and env_obj.config.privateEnvironmentConfig.enablePrivateEnvironment) if is_private: return command_util.Error( str(error) + ' Make sure you have followed https://cloud.google.com/composer/docs/how-to/accessing/airflow-cli#running_commands_on_a_private_ip_environment ' 'to enable access to your private Cloud Composer environment from ' 'your machine.') return error

[ "yogocamlus@gmail.com" ]

yogocamlus@gmail.com

efe6000e08a97a745405e6fd91c9d473d802d800

9b72185bacfeec4c44c85d4ef1b0a23295db2769

/TestInvoice.py

ed05480a51cf5ece2dabac58471bbc66b375650a

[]

no_license

A-ozmez/lab2Part4Group

a69e47747c100313f1d23afce9a9146a632e6ac2

9aae974119313217c40b2b1ea25f78104e7e4b3c

refs/heads/master

2021-04-03T23:50:20.618534

2020-03-19T04:01:30

null

0

null

UTF-8

Python

false

1,297

py

import pytest from Invoice import Invoice @pytest.fixture() def products(): products = {'Pen': {'qnt': 10, 'unit_price': 3.75, 'discount': 5}, 'Notebook': {'qnt': 5, 'unit_price': 7.5, 'discount': 10}} return products @pytest.fixture() def invoice(): invoice = Invoice() return invoice def test_CanCalculateTotalImpurePrice(invoice, products): invoice.totalImpurePrice(products) assert invoice.totalImpurePrice(products) == 75 def test_CanCalculateTotalDiscount(invoice, products): invoice.totalDiscount(products) assert invoice.totalDiscount(products) == 5.62 def test_CanCalculateTotalPurePrice(invoice, products): invoice.totalPurePrice(products) assert invoice.totalPurePrice(products) == 69.38 # tests that the inputs are not zero def test_PositiveQuantity(invoice, products): for k, v in products.items(): if (v['qnt'] < 0): print("qnt cannot be negative") # tests that the inputs are not zero def test_PositivePriceAndDiscount(invoice, products): for k, v in products.items(): if (v['discount'] < 0): print("discount cannot be negative") if (v['unit_price'] < 0): print("price cannot be negative")

[ "noreply@github.com" ]

noreply@github.com

fd5834d71ffff170665dd44f08b74372e8c33d67

85431b353749dd8f6ea308b439b9e5c42b2a7352

/UnicornLog/UnicornLog/asgi.py

1604c11dd14442cce813b200b702b615d37ef0d4

[]

no_license

evansimmons/DjangoUnicorn

d018b229f7468e371a939ffb8fbf6a4afc2f1903

5e5c5fb6401daeabeb12404ebc390248cb29324d

refs/heads/main

2023-06-19T19:36:57.036735

2021-07-14T19:52:38

381,471,923

0

null

UTF-8

Python

false

397

py

""" ASGI config for UnicornLog project. It exposes the ASGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ """ import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'UnicornLog.settings') application = get_asgi_application()

[ "etscodelancer@gmail.com" ]

etscodelancer@gmail.com

ae587eaf8f61b8ea33ef42bd8a1a5fb0c40cd54e

7866e1a7c043e5d1b539949f1d472c63003eb135

/stringToCamelCase.py

0ff3a534280f7232b2e04112eadf6a29aaae4b72

[]

no_license

sindhujacoder/guvi

ce7362db7d87ad708ae69fcf3316d9c1a43ac002

5500625ed748358da76201645aa2a0780163786a

refs/heads/master

2021-01-19T12:15:42.429802

2016-09-24T10:10:57

69,040,570

0

null

UTF-8

Python

false

135

py

testcases = input() while testcases: word = raw_input() word = word.title() print word testcases = testcases - 1

[ "noreply@github.com" ]

noreply@github.com

52d71685ffeb1d6312ac1a58a2bf9d34f0c1a4a3

a90bb6995f15a864708c493ae8539f9c6bb0a1ea

/Zip - V.4.3.1.py

e99f4d551773775b3cf3f25b8fbf5859df2bb3cb

[]

no_license

ComplexChris/VigenereArchive

aac7f9cf06a955f7cf1484db9ea5e7641f4b6edc

1e7b7288e3dff4293b568979396882b81d89f49b

refs/heads/master

2016-09-14T13:16:31.986337

2016-05-12T09:48:42

57,876,954

0

null

UTF-8

Python

false

21,605

py

# coding: utf-8 ## Container Archive Encryption Program ## Written and Devoloped by Chris J. Nguyen import os, sys, zipfile, StringIO, string, random, shutil, time __version__ = "4.3.1" __author__ = "Chris J. Nguyen" __date__ = "April 6th, 2016" __copyright__ = "(C) 2016-2018 Chris J. Nguyen. GNU GPL 3." ## freeNote.net ## irc ## filename encryption python tmp_s = 'There he was...' tmp_k = 'password' #TmpZip = zipfile.ZipFile('Test.zip', 'w') _Bricks = "~-"*16 _Fancy = "\n\n%s\n\n" % _Bricks COMP_TYPE = 8 BLOCK = 256 try: _ScriptLoc = os.path.dirname(os.path.abspath(__file__)) except NameError: _ScriptLoc = os.path.dirname(os.path.abspath(sys.argv[0])) _LogLoc = os.path.join(_ScriptLoc, 'Log.txt' ) _CacheLoc = os.path.join(_ScriptLoc, '_Cache') _UsersLoc = os.path.join(_ScriptLoc, '_Users') FAQ = """ Q: How do I add my own document to my Archive? \nA: Cut/Move a file from your computer and paste it into the temporary directory that is opened when you "Start" your archive in the Main Menu! \nQ: How do I Quit the program? \nA: Simpily input the corresponding option relating to "Exit" or "Logout" or just type "EXIT" when promted to input text! \nQ: How do I thank or ask the Devolopers? \nA: Email him at: ChrisNguyen180@gmail.com! \nQ: What type of files may I import to my archive? \nA: Any file less than a couple MBs. As the archive uses a high compression ratio which may take up to a minute per MB! \nQ: How long did it take to make this program? \nA: Apx <1 Month. \nQ: How many lines of code is this program? \nA: About 500 lines. \nQ: What are other acceptable inputs? \nA: "LS" or "DIR", "PWD", "FAQ" or "HELP", "START" or "OPEN", "CD" followed by a path """ DEFAULTS = { 'MyPasswords.txt': 'Facebook = "Bob1992" ', 'MyNotes.txt': 'To do: Decrypt Russian Nuclear Launch Codes "XMG-01 (WMD)" ', "FAQ.txt": FAQ} class SAK(): ExitCode = "STOP, EXIT, QUIT, FUCK" Sleep = lambda self, zzz=1: time.sleep(zzz) Exists = lambda self, path: os.path.exists(path) #GetSize = lambda self, path: os.path.getsize(path) CD = lambda self, path: os.chdir(path) if self.Exists(path) else 0 def GetSize(self, path): if os.path.isdir(path): total = 0 for root, dirs, files in os.walk(path): for f in files: total += os.path.getsize( os.path.join(root, f)) return total else: return os.path.getsize(path) def GetDetails(self): cwd = os.getcwd() sVer = sys.version items = [_Fancy, "\nScript Location: %s \nCache Location: %s \nArchive Location: %s " % (_ScriptLoc, _CacheLoc, _UsersLoc), "Current Directory: %s \n\nProgram Version: %s \nSystem Version %s " % (cwd, __version__, sVer), "\nRelease Date: %s \nCopyright: %s \nAuthor: %s " % (__date__, __copyright__, __author__) ] for line in items: print line def Clean(self, Path, verbose=True): try: if os.path.isdir(Path): shutil.rmtree(Path) else: os.remove(Path) except Exception as E: if verbose: print '\nUnable to remove file(s)!\nPlease close any open processes!\n', E return False def Log(self, Stat, LogName= _LogLoc ): self.WriteFile(LogName, '\n\n'+Stat, 'a') def ReadFile(self, FileName, mode='rb'): with file(FileName, mode) as f: return f.read() def WriteFile(self, FileName, Content, mode='wb'): with file(FileName, mode) as f: f.write(Content) f.close() def RandomString(self, length=10, CanExist=False ): """ Generates random string """ BaseChars = (string.ascii_letters + string.digits) * random.randint(4,8) while True: Out = ''.join( random.sample( BaseChars, length ) ) if self.Exists(Out) == CanExist: return Out def Smart_Input(self, msg='(Y/N)'): """ Extended raw_input with system functions""" while True: Out = raw_input(msg) OutUp = Out.upper() LOO = len(Out) path = Out[2:].strip(' ') ## Used for "CD" if path.upper() in ["HOME"]: path = _ScriptLoc if OutUp in self.ExitCode and len(Out)==4: raise SystemExit elif OutUp in ["LS", "DIR"]: print _Fancy, os.listdir('.') elif OutUp in ["GWD", "PWD"]: print _Fancy, os.getcwd() elif OutUp in ["FAQ", "HELP"]: print _Fancy, FAQ elif OutUp in ["START", "OPEN"]: os.startfile('.') elif OutUp[:2]=="CD" and self.Exists(path): os.chdir(path) print "\nChanging directories to: \n%s \n" % os.getcwd() else: return Out def Raw_Choice(self, Msg='(Y/N)', Options='YN', Length=1): """ User input specific answer choice """ while True: Answer = self.Smart_Input(Msg+'> ').upper() if Answer in Options and len(Answer)==Length: return Answer def MakeASCII(self, String): """ Filters out invalid characters """ Bad = '/\:*?<>"|' Bad = string.punctuation BaseChars = (string.ascii_letters + string.digits) LBS = len(BaseChars) out = '' for c in String: if c not in BaseChars: out += BaseChars[ord(c) % LBS ] else: out += c return out def GetLogin(self, Confirm=True): """ Gets login credentials form user input """ while True: print _Fancy UN = self.Smart_Input('Enter User Name >>> ').upper() PW = self.Smart_Input('Enter Password >>> ') if Confirm: PW2 = self.Smart_Input('Confirm Password >>> ') if PW == PW2: Confirm=False else: print '\nPasswords do not match!' raise EnvironmentError if Confirm==0: return UN, PW def GetFileName(self, UN, PW): """ Mix variables and Collatz algorithm to produce one-way string file name """ DefaultPhrase = self.Vigenere('_CoMpLeXiTy_314', UN) P1 = self.Vigenere(DefaultPhrase, UN, True) P2 = self.Vigenere(P1, PW, True) Name = '' for char in P1+P2: cInt = self.Collatz( ord(char) * 4 )%256 Name += chr( cInt ) if cInt>0 else '' return Name #(Name, P1, P2) def Collatz(self, n): """ Simple algorithm representation of Collatz Conjecture """ ## Basic algorithm n = abs(n); numb=[] if n<=2: return n while n!=1: numb.append(n) if n%2==0 and n!=0: n/=2 else: n=(n*3)+1 return max(numb) def Vigenere(self, string, key, encrypt=None): """ Primary method for just single poly-alphabetic encryption """ #return string LP = len(key) out = ''; x=0 los = len(string) if "" in (string, key): return string for x in range( los ): val1, val2 = ord( string[x] ), ord( key[x % LP ] ) if encrypt != None: val3 = val1+val2 if encrypt==False else val1-val2 ## if True else (False) else: a, b = min([val1, val2]), max([val1, val2]) val3 = val1+val2 if (val2<val1 and val1+val2<256) else b-a v3 = val3%256 out += chr(v3) return out def VigLarge(self, string, key, encrypt=None, Blocks=512): """ Encrypts string in small blocks """ if False: #len(string) < Blocks/2.5: print "\nLarge going to straight..." return self.Vigenere(string, key, encrypt) middle = len(string)/2 a,b = middle-Blocks, middle+Blocks modified = string[:Blocks] + string[-Blocks:] viged = self.Vigenere(modified, key, encrypt) out = viged[:Blocks] + string[Blocks:-Blocks] + viged[-Blocks:] #print middle, a,b return out def MakeBlocks(self, String, Size): """ Creates blocks for larger string encryption """ ## Creates blocks from string based on "Size" x=0; LOS=len(String) while x*Size < LOS: #for x in range(1, (len(String) / Size) ) : x += 1 a, b = ((x-1)*Size), (x*Size) #print "The X: ", x, " |\tA, B : ", a, b yield String[ a : b ] def VigBlocks(self, String, PW, mode=True, Size=2**15, Path=None): """ Used for high volumes of data. Highest block method available """ ## Encrypts blocks for large strings if False: #len(String) < 2**20 : print "\nBlocks going to large..." return self.VigLarge(String, PW, mode, Size) LOS = len(String) x=-1 mass = '' for part in self.MakeBlocks(String, Size): x+=1 #frag = String[ (x-1)*BlockSize : x*BlockSize ] if x % 2 == 0: chunk = Tools.VigLarge(part, PW, mode, Size/8) # Tools.VigLarge( part, PW, mode, cap ) else: chunk = part mass += chunk #Tools.WriteFile(Path, chunk, "ab") return mass def AutoVig(self, string, key, encrypt=None, Blocks=512): """ Execute string encryption based on size of text """ LOS = len(string) if LOS > 2**20: print "\nUsing Blocks Method..." product = self.VigBlocks(string, key, encrypt, 2048) elif LOS > 2**15: print "\nUsing Large Method..." product = self.VigLarge(string, key, encrypt, 512) else: print "\nUsing Primary Method..." product = self.Vigenere(string, key, encrypt) #product = self.Vig(string, key, encrypt, limit=2056, Blocks=512)[0] return product Tools = SAK() class ArcZip(): """ Class for handling String IO and zipfile instances """ def __init__(self, Active=True): pass def WalkDir(self, path): """ Generator function for directory files """ ## Generator object for directory walks for root, dirs, files in os.walk(path): for f in files: yield os.path.join(root, f) def CreateZip(self, ZipName, Password): """ Creates zipfile containing default information """ ## Creates archive file in local directory FileInst = StringIO.StringIO() with zipfile.ZipFile( FileInst, mode='w', compression=COMP_TYPE ) as ZipF: for item in DEFAULTS: if Tools.Exists(item): print "\nCopying default file..." ZipF.write(item, item) else: ZipF.writestr(item, DEFAULTS[item]) ZipF.close() Encrypted = Tools.AutoVig( FileInst.getvalue(), Password, True, BLOCK) Tools.WriteFile(ZipName, Encrypted ) def OpenZip(self, ZipName, Password): """ Decrypts a zipfile and returns zipfile IO instance """ ## Opens and decrypts zip file and returns decrypted zip file instance raw_zip = Tools.ReadFile(ZipName) Decrypted = Tools.AutoVig(raw_zip, Password, False, BLOCK) buff = StringIO.StringIO(Decrypted) if zipfile.is_zipfile( buff ): print "\nProcessing Archive IO Instance..." ZipInst = zipfile.ZipFile( buff, 'r', compression=COMP_TYPE ) return ZipInst else: return False def WriteZip(self, TmpDir, Password=None, Destination=None): """ Reads all files in temp directory in writes to zipfile """ ## Reads all content of temp directory ## and writes to zip IO instance FileInst = StringIO.StringIO() with zipfile.ZipFile( FileInst, mode='w', compression=COMP_TYPE ) as ZipInst: for raw_file in self.WalkDir( TmpDir ): ZipInst.write( raw_file, os.path.relpath(raw_file, TmpDir) ) # os.path.join(root, raw_file)) if (Destination and Password) != (None, None): Path = os.path.join(_ScriptLoc, Destination) Encrypt = Tools.AutoVig(FileInst.getvalue(), Password, True, BLOCK) Tools.WriteFile(Path, Encrypt) def Decompress(self, ZipInst, ToDir = None ): """ Extracts all files in a decrypted zipfile instance """ #Extracts all files in decrypted Zip IO instance if ToDir==None: ToDir = self.ToDir ZipInst.extractall( ToDir ) class User(ArcZip): """ Primary UI class for Zip Archive Usage """ def __init__(self, Active=True): if Active: os.chdir(_ScriptLoc) Tools.CD(_ScriptLoc) if Tools.Exists(_CacheLoc): Tools.Clean (_CacheLoc) if Tools.Exists(_CacheLoc)==False: os.mkdir(_CacheLoc) if Tools.Exists(_UsersLoc)==False: os.mkdir(_UsersLoc) if Tools.Exists("FAQ.txt"): Tools.WriteFile("FAQ.txt", FAQ, 'w') self.Username, self.Password = '', '' self.ZipName, self.ZipInst = '', None self.Start() def Depart(self, Cleanup=True, verbose=True): print _Fancy, '\nGoodBye!' if verbose else "" shutil.rmtree(_CacheLoc) sys.exit() ## Delete Tmp Dir... def UpdateUserVar(self, Switch): """ Gathers primary user variables """ self.Username, self.Password = Tools.GetLogin( Switch ) #'CHRIS', 'ares' Name = Tools.MakeASCII(Tools.GetFileName(self.Username, self.Password) )[:20]+'.ADF' self.ZipName = os.path.join( _ScriptLoc, "_Users", Name) self.ZipBackup = os.path.join( _ScriptLoc, "_Users", Name.replace(".ADF", ".ABDF")) RawDir = Name.rstrip('.ADF') self.ToDir = os.path.join(_ScriptLoc, '_Cache', ".$"+RawDir) ##'.$' def Start(self): """ First menu interface to start archive process """ Menu = "a. Log in \t\tb. Create Account \nc. Exit" while True: Tools.Sleep(1.5) print _Fancy, Menu Choice = Tools.Raw_Choice('(A/B/C)', 'ABC') if Choice=='C': self.Depart() else: ## Complex method of assigning boolean values (Encrypt, Decrypt) Either = False if Choice=='A' else True self.UpdateUserVar(Either) ArcStat = Tools.Exists(self.ZipName) ArcStatBak = Tools.Exists(self.ZipBackup) if Either==False: if ArcStat or ArcStatBak: if ArcStat: shutil.copy(self.ZipName, self.ZipBackup) self.ZipInst = self.OpenZip(self.ZipName, self.Password ) else: print "\nRestoring from backup file..." shutil.copy(self.ZipBackup, self.ZipName) self.ZipInst = self.OpenZip(self.ZipName+".ADF", self.Password ) if self.ZipInst != False: self.Run() else: print "\nUnable to proccess archive!" else: print "\nAccount not found!" else: if ArcStat==0: self.CreateZip(self.ZipName, self.Password) print "\nAccount created successfully! \nPlease re-login to continue!" else: print "\nArchive already exists. Please log in instead." def Run(self): """ UI for Archive instances once variables are established """ Menu1 = "MAIN MENU: \na. Start Archive \tb. Save Archive \nc. More \t\td. Logout" Menu2 = "EXTENDED MENU: \na. Clear Cache \t\tb. Delete Archive \nc. Info \t\td. Back" Msg, Options = "(A/B/C/D)", "ABCD" MainMenu = True print _Fancy, "\nWelcome %s!" % self.Username.title() while True: Tools.Sleep(2) print _Fancy, Menu1 if MainMenu else Menu2 Choice = Tools.Raw_Choice(Msg, Options) if MainMenu == True: if Choice=="C": MainMenu=False else: self.MainMenu(Choice) else: if Choice=="D": MainMenu=True self.ExtendedMenu(Choice) def ExtendedMenu(self, Choice): """ Extended menu for alternitive options """ if Choice == "A": if Tools.Exists(self.ToDir): print "\nWarning: Any unsaved changes to your archive will be lost!" c = Tools.Raw_Choice("Continue? (Y/N) > ") if c=="N": return else: print "\nProceeding..." for x in range(100): Tools.Clean( _CacheLoc, False ) if Tools.Exists(_CacheLoc)==0: print "\nCache has been cleared!" break elif Choice == "B": self.RemoveArchive() elif Choice=="C": Tools.GetDetails() def MainMenu(self, Choice): """ Main menu with basic archive control """ if Choice == 'A': self.StartArchive() elif Choice == 'B': self.SaveArchive() elif Choice=="D": if Tools.Exists(self.ToDir): print "\nWarning! Did you want to save your archive first?" Ans = Tools.Raw_Choice() if Ans == "Y": self.SaveArchive() raise EnvironmentError def RemoveArchive(self): """ Removes Archive and temp directory if one exists """ print "\nConfirm removale of entire archive \nby entering the Captcha between > and <:" code = Tools.RandomString(6, False) attempt = raw_input("@#!>%s<?*@ \n> " % code) OVW = "SUOVW" ; CONT=False if attempt==code or attempt==OVW: if attempt != OVW: print "\nNow re-login: " a, b = Tools.GetLogin(False) if (a,b) == (self.Username, self.Password): CONT = True else: print "\nIncorrect login!" else: CONT=True if CONT==True: stat = Tools.Clean(self.ZipName) if stat != False: print "\nArchive has been removed!" raise EnvironmentError else: print "\nUnable to remove archive at this time!" else: print "\nIncorrect Captcha!" def StartArchive(self): """ Opens zipfile and decrompresses content into temp directory """ if Tools.Exists(self.ToDir): print "\nAn archive is already started. Please Save or manually remove direcory: \n%s " % self.ToDir else: os.mkdir(self.ToDir) self.Decompress(self.ZipInst, self.ToDir) print "\nArchive has been opened at: \n%s " % (self.ToDir) try: os.startfile(self.ToDir) except: pass def SaveArchive(self): """ Reads, encrypts, and writes files back into zipfile container """ if Tools.Exists(self.ToDir): # and Update==False: a, b = Tools.GetSize(self.ToDir), Tools.GetSize(self.ZipName) if a < b and b>1: print "\nDetected changes are less than original archive file size!" if a<1: print "\nAll file(s) will be removed!\n" if Tools.Raw_Choice("Proceed with changes? (Y/N) >")=="N": return print '\nSaving...' ZipInstByte = self.WriteZip(self.ToDir, self.Password, self.ZipName ) RmStat = Tools.Clean( self.ToDir ) print "\nArchive has been saved!" print "...and the temporary directory %s removed" % ("WAS" if RmStat!=False else "WAS NOT") print "Location: ", os.path.relpath( self.ToDir ) self.ZipInst = self.OpenZip(self.ZipName, self.Password) ## Update shutil.copy(self.ZipName, self.ZipBackup) else: print "\nArchive not started! \nNo changes made to archive!" if __name__=="__main__": while True: try: c = User() except EnvironmentError: pass except SystemExit: if Tools.Exists(_CacheLoc): print "\nClearing...\n" Tools.Clean(_CacheLoc) print "\nExiting...\n" break

[ "ChrisNguyen180@gmail.com" ]

ChrisNguyen180@gmail.com

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/Learning/130_Fluent_Python/fp2-utf8/freeinteractive/freeinteractive 103.py

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[]

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FrenchBear/Python

58204d368e3e72071eef298ff00d06ff51bd7914

b41ab4b6a59ee9e145ef2cd887a5fe306973962b

refs/heads/master

2023-08-31T18:43:37.792427

2023-08-26T15:53:20

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0

null

UTF-8

Python

false

24

py

>>> b = 6 >>> f1(3) 3 6

[ "FrenchBear38@outlook.com" ]

FrenchBear38@outlook.com

3e67c476deabc53331ccd7582f0feff94455d632

31741f4807f857675f9304088b689af9b043e7b1

/chp10/django_ecommerce/contact/views.py

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[]

no_license

ujrc/Realpython_book3

c487ff0569f90b0e21c2c51cf951d6aad4755541

aaff8db074b8dd33d6c7305ac0a94c2ef161c847

refs/heads/master

2021-01-10T02:02:11.247279

2016-01-11T17:06:59

48,060,189

0

null

2015-12-31T16:48:52

2015-12-15T18:03:47

Python

UTF-8

Python

false

755

py

from django.http import HttpResponse, HttpResponseRedirect from django.template import RequestContext, loader from .forms import ContactView from django.contrib import messages def contact(request): if request.method == 'POST': form = ContactView(request.POST) if form.is_valid(): our_form = form.save(commit=False) our_form.save() messages.add_message( request, messages.INFO, 'Your message has been sent. Thank you.' ) return HttpResponseRedirect('/') else: form = ContactView() t = loader.get_template('contact/contact.html') c = RequestContext(request, {'form': form, }) return HttpResponse(t.render(c))# Create your views here.

[ "uwjearc@yahoo.com" ]

uwjearc@yahoo.com

d417b2b14d4f4e28483f2fafa8effd65731f1db4

e38b2cc76373c54a766485c9ca659e6c6e598620

/Tuple_Teerapat.py

3f061bb279ce68485442360e32b60226ba0457c0

[]

no_license

icelake10/-

d660a3cc5c93c880c815f80168dceda1d3f6995f

d089f8f5311232d3b6ac6d0c114ea1620395aa97

refs/heads/master

2020-07-30T08:52:13.986406

2019-09-22T14:41:23

210,162,628

0

null

UTF-8

Python

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1,479

py

#!/usr/bin/env python # coding: utf-8 # 1.กำหนดให้ brand_cars = ("Toyota", "Honda", "Benz", "BMW", "Tesla", "Ford", "KIA", "Volvo" ) # # 1.1 ให้เขียนคำสั่งโปรแกรมแสดงตำแหน่งของ Benz, Ford และ Volvo # 1.2 ให้เขียนคำสั่งโปรแกรมแสดงจำนวนข้อมูลทั้งหมดในทูเพิล # 1.3 ให้เขียนคำสั่งโปรแกรมตรวจสอบมียี่ห้อรถ Suzuki, Ferrari, Ford อยู่ใน cars หรือไม่ # In[3]: brand_cars = ("Toyota", "Honda", "Benz", "BMW", "Tesla", "Ford", "KIA", "Volvo" ) print("ตำแหน่งของ Benz คือ",brand_cars.index("Benz")) print("ตำแหน่งของ Ford คือ",brand_cars.index("Ford")) print("ตำแหน่งของ Volvo คือ",brand_cars.index("Volvo")) print("จำนวนข้อมูลทั้งหมดในทูเพิล คือ",len(brand_cars),"แบน") print("มีแบนรถยน Suzuki อยู่ใน brand_cars หรือไม่ =","Suzuki" in brand_cars) print("มีแบนรถยน Ferrari อยู่ใน brand_cars หรือไม่ =","Ferrari" in brand_cars) print("มีแบนรถยน Ford อยู่ใน brand_cars หรือไม่ =","Ford" in brand_cars) # In[ ]:

[ "noreply@github.com" ]

noreply@github.com

403e6416ef4d218912c9202b4988ddf498e0997d

594102aed31b51ddc54e429bcde76fd733faf18f

/test_request_teacher/api_page/address_page.py

fb94cb949687c65fb4e714479d7f200157eddc46

[]

no_license

niujiama/LilyGithub

995c81b0b3574e587008b0807ec92e8f3a1106c6

91881dd57a139198c459fc33a41e286cdeaad51f

refs/heads/master

2023-04-19T12:50:36.377748

2020-11-23T10:56:41

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from test_request_teacher.api_page.base_api import BaseApi from test_request_teacher.api_page.wework_utils import WeWorkUtils class AddressPage(BaseApi): """ 通讯录管理，包括增删改查 """ def __init__(self): _corpsecret = "6e-e9pUV0QZnJppDPwzRZA9PCtKb9urb9TWNf-6v5fA" utils = WeWorkUtils() self.token = utils.get_token(_corpsecret) def get_member_info(self): data = { "method": "get", "url": f"https://qyapi.weixin.qq.com/cgi-bin/user/get", "params": {"access_token": self.token, "userid": "labixiaoxin"} } return self.send_api(data) def add_member(self): data = { "method": "post", "url": f"https://qyapi.weixin.qq.com/cgi-bin/user/create", "params": {"access_token": self.token}, "json": {"userid": "labixiaoxin", "name": "蜡笔小新", "mobile": "10111111115", "department": [1]}} return self.send_api(data) def delete_member(self): data = { "url": f"https://qyapi.weixin.qq.com/cgi-bin/user/delete?access_token={self.token}&userid=labixiaoxin", "method": "get" } return self.send_api(data) def update_member(self): data = { "method": "post", "url": f"https://qyapi.weixin.qq.com/cgi-bin/user/update?access_token={self.token}", "json": { "userid": "labixiaoxin", "name": "wangwu"} } return self.send_api(data)

[ "13426251727@163.com" ]

13426251727@163.com

47f0356ebef2eca13e87788037bd242513af73be

cec3584cb40c3f762f8e550231c646efb8905004

/main.py

00436c722fa86f195e4f6332a2413482a410f514

[]

no_license

XXX-CODER/pythondiary-1

a40f22dc10d8ecf119430be39cba31c7e97a0d1e

60e3ca72f047aa78485e8be6e8d7c1db13943627

refs/heads/master

2020-07-06T09:13:37.343064

2019-08-18T06:20:23

null

0

null

UTF-8

Python

false

908

py

from flask import Flask, render_template import glob from datetime import datetime import os app = Flask("Elwing") @app.route("/category/<c>") def category(c): fs = glob.glob("articles/" + c + "/*.txt") fill = [] for i, f in enumerate(fs): a = open(f) article = a.read() a.close() fp = f.split("/")[-1].replace(".txt", "") utc = datetime.utcfromtimestamp(os.path.getmtime(f)) t = (i, fp, str(utc), article) fill.append(t) return render_template("category.html", cat=fill, title=c) @app.route("/") def home(): temp = glob.glob("articles/*") fill = [] for t in temp: length = len(glob.glob(t + "/*.txt")) category = t.replace("articles/", "") f = (category, length) fill.append(f) return render_template("index.html", cat=fill) if __name__ == "__main__": app.run(debug=True, host="0.0.0.0", port="3000")

[ "noreply@github.com" ]

noreply@github.com

0bc10e89e9c0f58d8de8e45fd99cba81ea6fcae8

94583800a07384bebcde02e147066b0ed8636234

/spider/内蒙贷_完全版1.0.py

08066c0e748411fcfb5e5c5465e249f422a413d7

[]

no_license

jk123415/python_some_js

2f3323143cf932e67411e29aab28cdb2fffd1014

b5bcdda35bf8bf9e11680d55c5efc91bfe802fa4

refs/heads/master

2020-04-24T14:09:07.977279

2019-02-22T06:51:16

172,010,361

0

null

UTF-8

Python

false

17,488

py

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date : 2018-07-10 11:14:08 # @Author : leopython (hailson@sina.com) # @Link : http://www.cnblogs.com/leopython/ # @Version : $Id$ import os import requests import pprint import re import sqlite3 from bs4 import BeautifulSoup import lxml.html import jsonpath # 处理头消息 def header(stri): if stri == '': return dict() elif isinstance(stri, str): a = stri.split('\n') data = {} for i in a: lst = i.split(": ") data[lst[0]] = lst[1] return data else: pass # 初始化数据库生成Content表 def initialize_db(db_file): post = {'title': "标题", 'borrowid': "编号", 'siteid': '网站编号', 'lastpostdate': '时间', 'daystr': '借款期限', 'typeimg': '类型图片', 'posttype': '回复类型', 'postdata': '回复内容', 'money': '借款金额', 'rate': '年利率', 'senddate': '发标时间', 'username': '作者', 'jiangli': '投标奖励', 'jianglimoney': '奖励金额', 'ratetype': '利率类型', 'repayment_type': '还款方式', 'borrow_url': '网址', 'sex': '性别', 'age': '年龄', 'industry': '所属行业', 'df': '所在地', 'organization': '发布机构', 'borrow_use': '借款用途', 'borrower_type': '借款类别', 'borrow_info': '借款详情', } print('''start initialize database: ''', db_file) conn = sqlite3.connect(db_file) db = conn.cursor() db.execute("SELECT name FROM sqlite_master WHERE type='table'") table_name = db.fetchall() if ('Content',) not in table_name: print('create table: Content in ', db_file) s = post.values() v = '' for i in s: c = ', ' + i + ' TEXT' v += c db.execute( '''CREATE TABLE Content(ID INTEGER PRIMARY KEY AUTOINCREMENT,已采 TINYINT(1),已发 TINYINT(1){})'''.format(v)) else: print('already exist table: Content in ', db_file) conn.commit() conn.close() # 更新数据库Content表列名，根据传入的lst def update_db_field(db_file, lst=[]): print('start update table Content fields') # 链接数据库 conn = sqlite3.connect(db_file) # 创建游标 db = conn.cursor() # 查询Content信息 db.execute('''PRAGMA table_info([Content])''') # 获取查询结果 colculmns = db.fetchall() # print(colculmns) # 生成空列表lt lt = [] # 对查询结果进行迭代，生成lt 列名数据 for col in colculmns: lt.append(col[1]) # 对传入数据lst进行迭代 for val in lst: # 判断lst中的字段是否在表content中存在 if val not in lt: # 如果不存在，生成字段 sta = '''ALTER TABLE Content ADD {} TEXT'''.format(val) # print(sta) db.execute(sta) print(val, ' is yes') else: # 如果存在则，返回yes # print('yes') pass print('Table Content fields already update') conn.commit() conn.close() # 根据传入的字典更新数据库Content表信息 def update_db(db_file, dic={}, conditions=''): conn = sqlite3.connect(db_file) db = conn.cursor() if conditions == '': columns = ', '.join(dic.keys()) value = (', ?' * len(dic.keys())).strip(', ') s = 'select * from Content where 编号="%s"' % dic['编号'] # print(s) db.execute(s) lst = db.fetchall() if lst == []: statement = 'insert into Content({}) values ({})'.format( columns, value) db.execute(statement, tuple(dic.values())) else: print(dic['标题'], ' 已经采集过') return else: for item in dic.items(): # print(item) str = "UPDATE Content set {0}='{1}' where {2}".format( item[0], item[1], conditions) # print(str) db.execute(str) conn.commit() db.close() conn.close() print(dic['标题'], ' is done') # 发布数据方法 def publish34(db_file): post = {'title': "标题", 'borrowid': "编号", 'siteid': '网站编号', 'lastpostdate': '时间', 'daystr': '借款期限', 'typeimg': '类型图片', 'posttype': '回复类型', 'postdata': '回复内容', 'money': '借款金额', 'rate': '年利率', 'senddate': '发标时间', 'username': '作者', 'jiangli': '投标奖励', 'jianglimoney': '奖励金额', 'ratetype': '利率类型', 'repayment_type': '还款方式', 'borrow_url': '网址', 'sex': '性别', 'age': '年龄', 'industry': '所属行业', 'df': '所在地', 'organization': '发布机构', 'borrow_use': '借款用途', 'borrower_type': '借款类别', 'borrow_info': '借款详情', } reg = re.compile('ok') post_uri = 'http://101.201.75.34/curl/insert.php' colculmus = ','.join(post.values()) conn = sqlite3.connect(db_file) db = conn.cursor() db.execute('''SELECT {} FROM Content WHERE 已采=1 AND 已发=0'''.format(colculmus,)) # print(db.fetchall()) lst = db.fetchall() if lst == []: print('Need post data is 0') conn.close() return else: for postval in lst: publish_data = dict(zip(post.keys(), postval)) # print(publish_data) rr = requests.post(post_uri, data=publish_data) if re.search(reg, rr.text): print(publish_data['title'], ' issued successfull') db.execute('''UPDATE Content SET 已发=1 WHERE 编号="{}"'''.format( publish_data['borrowid'])) else: print(publish_data['title'], ' issued failed') db.execute('''UPDATE Content SET 已发=2 WHERE 编号="{}"'''.format( publish_data['borrowid'])) conn.commit() conn.close() # 标签类 class Tag(): def __init__(self, name, value, entry, *body): self.name = name self.alias = value['name'] self.value = value['value'] if value['extend'] != {}: # print(value['extend']) for extend_name, extend_value in value['extend'].items(): if extend_name == 'xpath' and extend_value[0] == 'text_content()': html = lxml.html.fromstring(str(entry)) s = html.xpath(extend_value[1]) try: f = s[0].text_content() self.value = f.strip(' ') except: print(value['name'], ' 获取异常') elif extend_name == 'css': # print(str(entry)) var = entry.select(extend_value[1]) if extend_value[0] == 'string': # print(var) self.value = str(var[0].string) elif extend_value[0] == 'href': self.value = str(var[0].get('href')) elif extend_value[0] == 'remove_html_tag': rr = re.compile('<.*?>') # print(type(regx)) s = rr.subn('', str(var[0])) self.value = s[0] # print(s[0]) elif extend_name == 'calculate': if extend_value[0] == '+' and extend_value[2] == 'value': self.value = extend_value[1] + self.value elif extend_name == 'substitute': try: rr = re.compile(extend_value[0]) s = re.search(rr, self.value) self.value = s.group(extend_value[1]) except: print(value['name'], ' 获取异常') # print(s.group(1)) elif extend_name == 'to_10000': if re.search('万', self.value): s = self.value.replace('万', '') self.value = str(float(s) * 10000) # print(s) elif extend_name == 'replace' and extend_value[0] == 'pay_method': # print(extend_value) try: rr = re.compile('到期还本') if re.search(rr, self.value): self.value = 4 except: print(value['name'], ' 获取异常') elif extend_name == 'loop_match': # print(str(entry)) rr = re.compile(extend_value[0]) # print(extend_value[0]) #rr = re.compile(r'''<tr class="list_record tab1" >(?:.|\n)*?<td width="35%" style="text-align:left;">(.*?)</td>(?:.|\n)*?<td width="30%" style="">(.*?)</td>(?:.|\n)*?<td width="35%" style="text-align:right;">(.*?)</td>(?:.|\n)*?</tr>''') # print(body[0]) n = rr.findall(body[0]) # print(n) records = '' for investrecord in n: # print(investrecord) # print(extend_value[1]) s = extend_value[1].format(t=investrecord) records += s # print(records) self.value = records # elif extend_name == 'time_first': # elif extend_name == 'website_match': class Entry(): def __init__(self, entry, variable): #uri_1 = '' dic_0 = {} dic_1 = {} for tag_name, tag_value in variable['page_0']['tag'].items(): tag = Tag(tag_name, tag_value, entry) ety = getattr(tag, '__dict__') # print(ety) dic_0[ety['alias']] = ety['value'] dic_1[ety['name']] = ety['value'] # print(dic_0) uri_1 = dic_0['网址'] body = requests.get(uri_1).text content = BeautifulSoup(body, 'lxml') for tag_name, tag_value in variable['page_1']['tag'].items(): tag = Tag(tag_name, tag_value, content, body) ety = getattr(tag, '__dict__') # print(ety) dic_0[ety['alias']] = ety['value'] dic_1[ety['name']] = ety['value'] # 编号从网址中采集 # print(dic_0['编号']=='website_match') if dic_0['编号'] == 'website_match': # print(variable['page_1']['tag']['borrowid']['extend']['website_match'][0]) rr = re.compile(variable['page_1']['tag'] ['borrowid']['extend']['website_match'][0]) s = re.search(rr, dic_0['网址']) # print(s.group(1)) # print(type(dic_0)) dic_0['编号'] = variable['page_1']['tag']['borrowid']['extend']['website_match'][1] + \ '-' + str(s.group(1)) # 从回复内容中采时间 if dic_0['时间'] == 'time_first': rr = re.compile(r'{.*?postdate=(.*?)\|status=全部通过}.*') s = re.search(rr, dic_0['回复内容']) dic_0['时间'] = s.group(1) # print(dic_0['完成度']) # sqlite3 入库=========================================================== # #conn = sqlite3.connect(variable['base']['db_file']) if dic_0['完成度'] != '1': print(dic_0['标题'], '--未完成') dic_0['已采'] = 0 elif 'noNone' in dic_0.values(): dic_0['已采'] = 0 else: dic_0['已采'] = 1 dic_0['已发'] = 0 # if dic_0['完成度'] ！='1': # print(dic_0['标题'],'未完成不采集') # pprint.pprint(dic_0) # 更新数据表Content数据 if dic_0['已采'] == 1: update_db(variable['base']['db_file'], dic_0) class Spyder(): def __init__(self): self.variable = {} # 采集数据 def spider(self): # 生成标签字段列表 scope = {} scope['b'] = [] scope['a'] = jsonpath.jsonpath(self.variable, expr='$..name') for i in scope['a']: scope['b'].append(i) # print(scope['b']) lts = scope['b'] # 初始化数据库，生成Content表 initialize_db(self.variable['base']['db_file']) # 根据标签更新数据库表Content字段 update_db_field(self.variable['base']['db_file'], lts) for var in range(self.variable['page_0']['uri'][1][0], self.variable['page_0']['uri'][1][1]): # print(var) # print(self.variable['page_0']['uri'][0]) url = self.variable['page_0']['uri'][0].format(var) # print(url) req_body = (requests.get(url)).text # print(req_body) soup = BeautifulSoup(req_body, 'lxml').find( 'div', 'content3').contents soup.remove('\n') soup.remove(' ') #index = 0 #s = (soup[0]) # pprint.pprint(s) for ety in soup: # print(type(ety)) # try: Entry(ety, self.variable) # except: # print('数据获取异常，联系技术人员') # 发布数据 spyder = Spyder() spyder.variable = { 'base': {'db_file': r'C:\Users\Administrator\Desktop\spider\db_file\Nmd.db3', # 内蒙贷 'headers': ''''''}, 'page_0': {'uri': ('https://www.nmgdai.com/Invest/investList/p/{}', (1, 7)), 'tag': {'borrow_url': {'name': '网址', 'value': 'noNone', 'extend': {'css': ('href', '.block_r_txt1 > a'), 'calculate': ('+', 'https://www.nmgdai.com', 'value'), }}, 'title': {'name': '标题', 'value': 'noNone', 'extend': {'css': ('string', '.block_r_txt1 > a'), }}, 'progress': {'name': '完成度', 'value': 'noNone', 'extend': {'css': ('string', '.pertxt'), }}, }}, 'page_1': {'tag': {'money': {'name': '借款金额', 'value': 'noNone', 'extend': {'css': ('remove_html_tag', '.investDetail_span_money'), 'substitute': (r'借款金额(.*?)元', 1), 'to_10000': (), }}, 'rate': {'name': '年利率', 'value': 'noNone', 'extend': {'xpath': ('text_content()', '/html/body/div[5]/div[1]/div[2]/span[2]/font/b'), }}, 'daystr': {'name': '借款期限', 'value': 'noNone', 'extend': {'xpath': ('text_content()', '/html/body/div[5]/div[1]/div[2]/span[3]'), 'substitute': (r'期限(.*)', 1)}}, 'username': {'name': '作者', 'value': 'noNone', 'extend': {'xpath': ('text_content()', '/html/body/div[5]/div[1]/div[2]/span[4]'), }}, 'senddate': {'name': '发标时间', 'value': 'noNone', 'extend': {'xpath': ('text_content()', '/html/body/div[5]/div[1]/div[3]/div[6]'), 'substitute': (r'上线时间：(.*)', 1)}}, 'repayment_type': {'name': '还款方式', 'value': 'noNone', 'extend': {'xpath': ('text_content()', '/html/body/div[5]/div[1]/div[3]/div[2]'), 'substitute': (r'还款方式：(.*)', 1), 'replace': ('pay_method',)}}, 'posttype': {'name': '回复类型', 'value': 1, 'extend': {}}, 'postdata': {'name': '回复内容', 'value': 'noNone', 'extend': {'loop_match': (r'''<tr class="list_record tab1" >(?:.|\n)*?<td width="35%" style="text-align:left;">(.*?)</td>(?:.|\n)*?<td width="30%" style="">(.*?)</td>(?:.|\n)*?<td width="35%" style="text-align:right;">(.*?)</td>(?:.|\n)*?</tr>''', '{{username={t[1]}|rate=-1|postmoney={t[2]}|money={t[2]}|postdate={t[0]}|status=全部通过}}'), }}, 'lastpostdate': {'name': '时间', 'value': 'time_first', 'extend': {'time_first': ()}}, 'siteid': {'name': '网站编号', 'value': '5730', 'extend': {'time_first': ()}}, 'borrowid': {'name': '编号', 'value': 'website_match', 'extend': {'website_match': (r'https://www.nmgdai.com/Invest/investDetail/borrow_id/(\w+)', '内蒙贷')}}, }}, 'page_2': {}, } spyder.spider() print(spyder.variable['base']['db_file']) publish34(spyder.variable['base']['db_file']) #input('请按回车键结束脚本： ')

[ "289891598@qq.com" ]

289891598@qq.com

9d9c75dc71a08948292a19969a209d9e9e35aaba

04f194dfd80367756cc3971b57b48065b2edbfb3

/topics/number_line.py

37386cf3172b78370d0618372e8485ba120c6294

[]

no_license

arthurtcl/manim

de2bfcf495981fb332e036b63e7c074e0db50624

ad05030641483b7f99b382cf6492bebcd4aa6d18

refs/heads/master

2021-01-17T11:44:48.968626

2017-03-04T01:34:05

null

0

null

UTF-8

Python

false

8,977

py

from helpers import * from mobject import Mobject1D from mobject.vectorized_mobject import VMobject, VGroup from mobject.tex_mobject import TexMobject from topics.geometry import Line, Arrow from scene import Scene class NumberLine(VMobject): CONFIG = { "color" : BLUE, "x_min" : -SPACE_WIDTH, "x_max" : SPACE_WIDTH, "space_unit_to_num" : 1, "tick_size" : 0.1, "tick_frequency" : 1, "leftmost_tick" : None, #Defaults to ceil(x_min) "numbers_with_elongated_ticks" : [0], "numbers_to_show" : None, "longer_tick_multiple" : 2, "number_at_center" : 0, "propogate_style_to_family" : True } def __init__(self, **kwargs): digest_config(self, kwargs) if self.leftmost_tick is None: self.leftmost_tick = np.ceil(self.x_min) VMobject.__init__(self, **kwargs) def generate_points(self): self.main_line = Line(self.x_min*RIGHT, self.x_max*RIGHT) self.tick_marks = VMobject() self.add(self.main_line, self.tick_marks) for x in self.get_tick_numbers(): self.add_tick(x, self.tick_size) for x in self.numbers_with_elongated_ticks: self.add_tick(x, self.longer_tick_multiple*self.tick_size) self.stretch(self.space_unit_to_num, 0) self.shift(-self.number_to_point(self.number_at_center)) def add_tick(self, x, size): self.tick_marks.add(Line( x*RIGHT+size*DOWN, x*RIGHT+size*UP, )) return self def get_tick_marks(self): return self.tick_marks def get_tick_numbers(self): return np.arange( self.leftmost_tick, self.x_max + self.tick_frequency, self.tick_frequency ) def number_to_point(self, number): alpha = float(number-self.x_min)/(self.x_max - self.x_min) return interpolate( self.main_line.get_start(), self.main_line.get_end(), alpha ) def point_to_number(self, point): left_point, right_point = self.main_line.get_start_and_end() full_vect = right_point-left_point def distance_from_left(p): return np.dot(p-left_point, full_vect)/np.linalg.norm(full_vect) return interpolate( self.x_min, self.x_max, distance_from_left(point)/distance_from_left(right_point) ) def default_numbers_to_display(self): if self.numbers_to_show is not None: return self.numbers_to_show return np.arange(self.leftmost_tick, self.x_max, 1) def get_vertical_number_offset(self, direction = DOWN): return 4*direction*self.tick_size def get_number_mobjects(self, *numbers, **kwargs): #TODO, handle decimals if len(numbers) == 0: numbers = self.default_numbers_to_display() result = VGroup() for number in numbers: mob = TexMobject(str(int(number))) mob.scale_to_fit_height(3*self.tick_size) mob.shift( self.number_to_point(number), self.get_vertical_number_offset(**kwargs) ) result.add(mob) return result def add_numbers(self, *numbers, **kwargs): self.numbers = self.get_number_mobjects( *numbers, **kwargs ) self.add(*self.numbers) return self class UnitInterval(NumberLine): CONFIG = { "x_min" : 0, "x_max" : 1, "space_unit_to_num" : 6, "tick_frequency" : 0.1, "numbers_with_elongated_ticks" : [0, 1], "number_at_center" : 0.5, } class Axes(VGroup): CONFIG = { "propogate_style_to_family" : True } def __init__(self, **kwargs): VGroup.__init__(self) self.x_axis = NumberLine(**kwargs) self.y_axis = NumberLine(**kwargs).rotate(np.pi/2) self.add(self.x_axis, self.y_axis) class NumberPlane(VMobject): CONFIG = { "color" : BLUE_D, "secondary_color" : BLUE_E, "axes_color" : WHITE, "secondary_stroke_width" : 1, "x_radius": SPACE_WIDTH, "y_radius": SPACE_HEIGHT, "space_unit_to_x_unit" : 1, "space_unit_to_y_unit" : 1, "x_line_frequency" : 1, "y_line_frequency" : 1, "secondary_line_ratio" : 1, "written_coordinate_height" : 0.2, "written_coordinate_nudge" : 0.1*(DOWN+RIGHT), "num_pair_at_center" : (0, 0), "propogate_style_to_family" : False, } def generate_points(self): self.axes = VMobject() self.main_lines = VMobject() self.secondary_lines = VMobject() tuples = [ ( self.x_radius, self.x_line_frequency, self.y_radius*DOWN, self.y_radius*UP, RIGHT ), ( self.y_radius, self.y_line_frequency, self.x_radius*LEFT, self.x_radius*RIGHT, UP, ), ] for radius, freq, start, end, unit in tuples: main_range = np.arange(0, radius, freq) step = freq/float(freq + self.secondary_line_ratio) for v in np.arange(0, radius, step): line1 = Line(start+v*unit, end+v*unit) line2 = Line(start-v*unit, end-v*unit) if v == 0: self.axes.add(line1) elif v in main_range: self.main_lines.add(line1, line2) else: self.secondary_lines.add(line1, line2) self.add(self.secondary_lines, self.main_lines, self.axes) self.stretch(self.space_unit_to_x_unit, 0) self.stretch(self.space_unit_to_y_unit, 1) #Put x_axis before y_axis y_axis, x_axis = self.axes.split() self.axes = VMobject(x_axis, y_axis) def init_colors(self): VMobject.init_colors(self) self.axes.set_stroke(self.axes_color, self.stroke_width) self.main_lines.set_stroke(self.color, self.stroke_width) self.secondary_lines.set_stroke( self.secondary_color, self.secondary_stroke_width ) return self def get_center_point(self): return self.num_pair_to_point(self.num_pair_at_center) def num_pair_to_point(self, pair): pair = np.array(pair) + self.num_pair_at_center result = self.axes.get_center() result[0] += pair[0]*self.space_unit_to_x_unit result[1] += pair[1]*self.space_unit_to_y_unit return result def point_to_num_pair(self, point): new_point = point-self.get_center() center_x, center_y = self.num_pair_at_center x = center_x + point[0]/self.space_unit_to_x_unit y = center_y + point[1]/self.space_unit_to_y_unit return x, y def get_coordinate_labels(self, x_vals = None, y_vals = None): result = [] if x_vals == None and y_vals == None: x_vals = range(-int(self.x_radius), int(self.x_radius)) y_vals = range(-int(self.y_radius), int(self.y_radius)) for index, vals in enumerate([x_vals, y_vals]): num_pair = [0, 0] for val in vals: num_pair[index] = val point = self.num_pair_to_point(num_pair) num = TexMobject(str(val)) num.scale_to_fit_height( self.written_coordinate_height ) num.shift( point-num.get_corner(UP+LEFT), self.written_coordinate_nudge ) result.append(num) return result def get_axes(self): return self.axes def get_axis_labels(self, x_label = "x", y_label = "y"): x_axis, y_axis = self.get_axes().split() x_label_mob = TexMobject(x_label) y_label_mob = TexMobject(y_label) x_label_mob.next_to(x_axis, DOWN) x_label_mob.to_edge(RIGHT) y_label_mob.next_to(y_axis, RIGHT) y_label_mob.to_edge(UP) return VMobject(x_label_mob, y_label_mob) def add_coordinates(self, x_vals = None, y_vals = None): self.add(*self.get_coordinate_labels(x_vals, y_vals)) return self def get_vector(self, coords, **kwargs): point = coords[0]*RIGHT + coords[1]*UP arrow = Arrow(ORIGIN, coords, **kwargs) return arrow def prepare_for_nonlinear_transform(self, num_inserted_anchor_points = 50): for mob in self.family_members_with_points(): num_anchors = mob.get_num_anchor_points() if num_inserted_anchor_points > num_anchors: mob.insert_n_anchor_points(num_inserted_anchor_points-num_anchors) mob.make_smooth() return self

[ "grantsanderson7@gmail.com" ]

grantsanderson7@gmail.com

07d6dbce7c8ee75f85135f932a4b99236ba69e9e

52d6cacfc6df5d0f5ed6724bd92ff0239dea682e

/manage.py

6f8028621eaa81b9b5e8bb643876da1cf59929dc

[]

no_license

scyilsamrat/GST-Printing-Billing-webapp

eff26f06169853a48427657d89321b8e691c37cd

8915c41d48996919523edc62da7c9bf1461682dc

refs/heads/master

2022-04-27T22:25:32.167991

2020-04-28T18:17:24

257,631,308

0

null

UTF-8

Python

false

806

py

#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): print("Please Wait Scyil pvt Ltd server is starting on /Dettol Software /user 09253 ") print("Please Wait Scyil pvt Ltd server is starting on /Dettol Software /user 09255 ") os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'dettol.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

[ "samratsahil180@gmail.com" ]

samratsahil180@gmail.com

6755be04d36dd75036f3b787a0ebebb8b8d709d7

e41d8daac285e37551e17778fa1d31698d707311

/Project4/etl.py

8eb9b3a90698232a2aed3aa054f58558f6190cdd

[]

no_license

Johannes-Handloser/Data-Engineer-Nanodegree

d0224011b28ce1fdd9d9cc9b037032216cffc39a

5da2123c5b0eff436ff570281be6ed3161d95c2a

refs/heads/master

2022-11-26T19:47:31.795613

2020-08-08T10:34:53

271,854,680

0

null

UTF-8

Python

false

4,904

py

import configparser from datetime import datetime import os from pyspark.sql import SparkSession from pyspark.sql.functions import year, month, dayofmonth, hour, weekofyear, dayofweek, date_format, monotonically_increasing_id, udf, col, to_date from pyspark.sql.types import TimestampType config = configparser.ConfigParser() config.read('dl.cfg') os.environ['AWS_ACCESS_KEY_ID'] = config['AWS']['AWS_ACCESS_KEY_ID'] os.environ['AWS_SECRET_ACCESS_KEY'] = config['AWS']['AWS_SECRET_ACCESS_KEY'] def create_spark_session(): """ Create a Spark Session """ spark = SparkSession \ .builder \ .config("spark.jars.packages", "org.apache.hadoop:hadoop-aws:2.7.0") \ .getOrCreate() return spark def process_song_data(spark, input_data, output_data): """ Function to transform song_data on S3 into song and artist tables """ # get filepath to song data file song_data = input_data + "song_data/*/*/*/*.json" # read song data file df = spark.read.json(song_data) # extract columns to create songs table songs_table = df.select("song_id", "title", "artist_id", "year", "duration").dropDuplicates() songs_table.createOrReplaceTempView("songs_table") # write songs table to parquet files partitioned by year and artist songs_table.write.mode("overwrite").partitionBy("year", "artist_id").parquet(output_data+"songs_table/songs.parquet") # extract columns to create artists table artists_table = df.select("artist_id", "artist_latitude", "artist_location", "artist_longitude", "artist_name").withColumnRenamed("artist_name", "name") \ .withColumnRenamed("artist_latitude", "latitude") \ .withColumnRenamed("artist_longitude", "longitude") \ .withColumnRenamed("artist_location", "location") \ .dropDuplicates() artists_table.createOrReplaceTempView("artists_table") # write artists table to parquet files artists_table.write.mode("overwrite").parquet(output_data + "artists_table/artists.parquet") def process_log_data(spark, input_data, output_data): """ Load data from log_data dataset and extract columns for user and time tables. Data is written to parquet files and stored on S3 """ # get filepath to log data file log_data = input_data + "log_data/*/*/*.json" # read log data file df = spark.read.json(log_data) # filter by actions for song plays df = df.filter(df.page == "NextSong") # extract columns for users table users_table = df.select("userId", "firstName", "lastName", "gender", "level").dropDuplicates() # write users table to parquet files users_table.write.mode("overwrite").parquet(output_data + "users_table/users.parquet") # create timestamp column from original timestamp column get_timestamp = udf(lambda x: datetime.fromtimestamp(x / 1000), TimestampType()) df = df.withColumn("start_time", get_timestamp(df.ts)) # extract columns to create time table time_table = df.select("start_time") \ .withColumn("hour", hour("start_time")) \ .withColumn("day", dayofmonth("start_time")) \ .withColumn("week", weekofyear("start_time")) \ .withColumn("month", month("start_time")) \ .withColumn("year", year("start_time")) \ .withColumn("weekday", dayofweek("start_time")) \ .dropDuplicates() # write time table to parquet files partitioned by year and month time_table.write.mode("overwrite").partitionBy("year", "month").parquet(output_data+"time_table/time.parquet") # read in song data to use for songplays table song_df = spark.read.json(input_data + "song_data/*/*/*/*.json") # extract columns from joined song and log datasets to create songplays table joined_df = df.join(song_df, song_df.artist_name == df.artist, "inner") songplays_table = joined_df.select( col("start_time"), col("userId").alias("user_id"), col("level"), col("song_id"), col("artist_id"), col("sessionId").alias("session_id"), col("location"), year("start_time").alias("year"), month("start_time").alias("month"), col("userAgent").alias("user_agent"))\ .withColumn("songplay_id", monotonically_increasing_id()) # write songplays table to parquet files partitioned by year and month songplays_table.write.mode("overwrite").partitionBy("year", "month").parquet(output_data+"songplays/songplays.parquet") def main(): spark = create_spark_session() input_data = "s3a://udacity-dend/" output_data = "s3a://udacity-jh-dend/" process_song_data(spark, input_data, output_data) process_log_data(spark, input_data, output_data) if __name__ == "__main__": main()

[ "johannes.handloser@bmw.de" ]

johannes.handloser@bmw.de

7acc3c35279f863cec0ecbea75665131958ceafb

6007b761a47ca3f437f8bb038714fccb6d3592db

/api/migrations/0003_alter_estudiante_id.py

049b165d2feeefab8d1c100865396b48f0e56aa4

[]

no_license

Mauville/DjangoBoilerplate

165c3ef0386b33ce47b9249a4d8df0059c486e31

89ba241dffd6f8255b7c7806e215de45d1382958

refs/heads/main

2023-05-15T03:19:56.217191

2021-05-31T03:41:14

368,931,021

0

null

UTF-8

Python

false

442

py

# Generated by Django 3.2 on 2021-04-30 02:20 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('api', '0002_alter_estudiante_id'), ] operations = [ migrations.AlterField( model_name='estudiante', name='id', field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'), ), ]

[ "daniel@semantyk.com" ]

daniel@semantyk.com

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import addressfind import addresstotext import converttoaudio # This is my main method def main(): # here we ask for postalCode from user cepcodevalue = input('Write here your postal code: ') # the function above send the postalcode writed by User # addressfind use a free api for serach the address by postalcode completeaddress = addressfind.find(cepcodevalue) # function above convert ( parse ) the Json Address to text # this text returned will used to converted in audio text = addresstotext.totext(completeaddress) # at last, the text is converted in audio converttoaudio.convert(text) if __name__ == "__main__": main()

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saleed/LeetCode

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class Solution(object): def canFinish(self, numCourses, prerequisites): """ :type numCourses: int :type prerequisites: List[List[int]] :rtype: bool """ graph={} indegree=[0]*numCourses for i in range(numCourses): graph[i]=[] for pair in prerequisites: graph[pair[1]].append(pair[0]) indegree[pair[0]]+=1 res=[] while True: flag=0 for node in range(len(indegree)): if indegree[node]==0: indegree[node]=float("inf") res.append(node) for n in graph[node]: indegree[n]-=1 del graph[node] flag=1 break if flag==0: break return len(res)==numCourses a=Solution() presp=[[1,0]] num=2 print(a.canFinish(num,presp)) nums=2 psp=[[1,0],[0,1]] print(a.canFinish(num,psp))

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1533441387@qq.com

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/old/temperatura_precipitacion_acidez_example.py

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[]

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josprimen/TFG

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refs/heads/master

2020-04-23T21:32:48.166183

2020-02-08T14:34:08

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import numpy as np import math from pandas import read_csv from datetime import datetime from math import sqrt from numpy import concatenate from matplotlib import pyplot from pandas import DataFrame from pandas import concat from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import LabelEncoder from sklearn.metrics import mean_squared_error from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM datos_clima_2015 = read_csv('files/datos_clima_abril_mayo_2015.csv', usecols=[1,2], engine='python') datos_clima_2015 = datos_clima_2015.values datos_clima_2015df = DataFrame(datos_clima_2015) datos_clima_2015c1 = datos_clima_2015df[0].values datos_clima_2015c2 = datos_clima_2015df[1].values datos_clima_2016 = read_csv('files/datos_clima_abril_mayo_2016.csv', usecols=[1,2], engine='python') datos_clima_2016 = datos_clima_2016.values datos_clima_2016df = DataFrame(datos_clima_2016) datos_clima_2016c1 = datos_clima_2016df[0].values datos_clima_2016c2 = datos_clima_2016df[1].values datos_clima_2017 = read_csv('files/datos_clima_abril_mayo_2017.csv', usecols=[1,2], engine='python') datos_clima_2017 = datos_clima_2017.values datos_clima_2017df = DataFrame(datos_clima_2017) datos_clima_2017c1 = datos_clima_2017df[0].values datos_clima_2017c2 = datos_clima_2017df[1].values datos_clima_2018 = read_csv('files/datos_clima_abril_mayo_2018.csv', usecols=[1,2], engine='python') datos_clima_2018 = datos_clima_2018.values datos_clima_2018df = DataFrame(datos_clima_2018) datos_clima_2018c1 = datos_clima_2018df[0].values datos_clima_2018c2 = datos_clima_2018df[1].values datos_precipitacion = np.concatenate((datos_clima_2015c1, datos_clima_2016c1, datos_clima_2017c1, datos_clima_2018c1)) datos_precipitacion = DataFrame(datos_precipitacion) datos_precipitacion = datos_precipitacion.values datos_temperatura = np.concatenate((datos_clima_2015c2, datos_clima_2016c2, datos_clima_2017c2, datos_clima_2018c2)) datos_temperatura = DataFrame(datos_temperatura) datos_temperatura = datos_temperatura.values datos_acidez_2015 = read_csv('files/media_acidez_dias_2015.csv', usecols=[1], engine='python') datos_acidez_2015 = datos_acidez_2015.values[0:61] ''' Salen los datos de manera distinta por eso aqui arriba pasamos a dataframe y de nuevo a values datos_acidez_2015 = read_csv('files/media_acidez_dias_2015.csv', usecols=[1], engine='python') datos_acidez_2015 = datos_acidez_2015.values datos_acidez_2015df = DataFrame(datos_acidez_2015) datos_acidez_2015 = datos_acidez_2015df[0].values Con esto tenemos un array normal [] y con lo que hay tenemos un [[]] ''' datos_acidez_2016 = read_csv('files/media_acidez_dias_2016.csv', usecols=[1], engine='python') datos_acidez_2016 = datos_acidez_2016.values[0:61] datos_acidez_2017 = read_csv('files/media_acidez_dias_2017.csv', usecols=[1], engine='python') datos_acidez_2017 = datos_acidez_2017.values[0:61] datos_acidez_2018 = read_csv('files/media_acidez_dias_2018.csv', usecols=[1], engine='python') datos_acidez_2018 = datos_acidez_2018.values[0:61] datos_acidez = np.concatenate((datos_acidez_2015, datos_acidez_2016, datos_acidez_2017, datos_acidez_2018)) groups = [datos_temperatura, datos_precipitacion, datos_acidez] aux = 1 pyplot.figure() for group in groups: pyplot.subplot(3, 1, aux) pyplot.plot(group) if group[0]==datos_temperatura[0]: pyplot.title('Temperatura abril-mayo') if group[0] == datos_precipitacion[0]: pyplot.title('Precipitacion abril-mayo') else: pyplot.title('acidez') aux = aux+1 pyplot.show() conjunto = concatenate((datos_acidez, datos_temperatura, datos_precipitacion), axis=1) scaler = MinMaxScaler(feature_range=(0, 1)) conjunto_normalizado = scaler.fit_transform(conjunto) def datosX (conjunto): df = DataFrame(conjunto) aux = [] for i in range (len(conjunto)-1): a = [[df[0][i], df[1][i], df[2][i]]] aux.append(a) return np.array(aux) def datosY (conjunto): df = DataFrame(conjunto) aux = [] for i in range (len(conjunto)-1): aux.append(df[0][i+1]) return np.array(aux) tamaño_entrenamiento = int(len(conjunto_normalizado) * 0.75) tamaño_test = len(conjunto_normalizado) - tamaño_entrenamiento entrenamiento = conjunto_normalizado[0:tamaño_entrenamiento] test = conjunto_normalizado[tamaño_entrenamiento:len(conjunto_normalizado)] entrenamientoX, entrenamientoY = datosX(entrenamiento), datosY(entrenamiento) testX, testY = datosX(test), datosY(test) print('DATOS entrenamientoX') print(entrenamientoX) print('DATOS entrenamientoY') print(entrenamientoY) print('DATOS testX') print(testX) print('DATOS testY') print(testY) """ ################################################################ da1 = read_csv('files/datos_aceituna_tratados_2015_2016.csv', usecols=[1], engine='python') da2 = read_csv('files/datos_aceituna_tratados_2016_2017.csv', usecols=[1], engine='python') da3 = read_csv('files/datos_aceituna_tratados_2017_2018.csv', usecols=[1], engine='python') da4 = read_csv('files/datos_aceituna_tratados_2018_2019.csv', usecols=[1], engine='python') da1 = da1.values da2 = da2.values da3 = da3.values da4 = da4.values da_prueba = np.concatenate((da1,da2,da3,da4)) da_prueba_df = DataFrame(da_prueba) da_prueba_df.to_csv('files/datos_aceituna_tratados.csv') ################################################################ """ model = Sequential() model.add(LSTM(50, input_shape=(1, 3))) model.add(Dense(1)) model.compile(loss='mae', optimizer='adam') # fit network history = model.fit(entrenamientoX, entrenamientoY, epochs=25, validation_data=(testX, testY), verbose=2) # plot history pyplot.plot(history.history['loss'], label='train') pyplot.plot(history.history['val_loss'], label='test') pyplot.legend() pyplot.show() #Hacer las predicciones sobre la acidez prediccion_test = model.predict(testX) print('DATO prediccion_test') print(prediccion_test) #Invertir el normalizado para tener los datos en la escala original #1 Hacer el array del mismo tamaño que el de la salida para poder concatenar testX = testX.reshape((testX.shape[0], testX.shape[2])) testY = testY.reshape((len(testY), 1)) #2 Concatenar concatenado_test_real = concatenate((testY, testX[:, 1:]), axis=1) concatenado_test_prediccion = concatenate((prediccion_test, testX[:, 1:]), axis=1) #3 Invertir el normalizado inversion_test_real = scaler.inverse_transform(concatenado_test_real) inversion_test_prediccion = scaler.inverse_transform(concatenado_test_prediccion) #4 Obtener las predicciones invertidas datos_real_test = inversion_test_real[:, 0] print('Datos acidez test') print(datos_real_test) datos_prediccion_test = inversion_test_prediccion[:, 0] print('datos prediccion test') print(datos_prediccion_test) #Calcular el error cuadrático medio testScore = sqrt(mean_squared_error(datos_real_test, datos_prediccion_test)) print('Test Score: %.2f RMSE' % (testScore)) #Comparamos graficamente lo real y lo predicho results = [[datos_real_test, datos_prediccion_test]] aux = 1 pyplot.figure() for result in results: pyplot.subplot(2, 1, aux) pyplot.plot(result[0]) pyplot.plot(result[1]) aux = aux+1 pyplot.show()

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#pegue uma temperatura em °C e converta em °F ce = float(input('temperatura em °C ')) fa = (ce * 9/5)+ 32 print('{}°C equivale a {}°F'.format(ce, fa))

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noreply@github.com

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audy/cd-hit-that

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#!/usr/bin/env python # outputs a FASTQ file but with its filename in the header (sorta) # Also puts paired reads together with their 5' ends touching # This is for clustering # Takes input from STDIN import sys import os from itertools import cycle import string _complement = string.maketrans('GATCRYgatcry','CTAGYRctagyr') c = cycle([0, 1]) seq = { 0: '', 1: ''} i = 0 infile = sys.argv[1] minimum_read_length = int(sys.argv[2]) f_num = int(infile.split('_')[-1].split('.')[0]) kept, skipped = 0, 0 with open(infile) as handle: for line in handle: if line.startswith('>'): n = c.next() i += 1 if n == 1: header = '>%s:%s' % (f_num, hex(i)[2:]) else: seq[n] += line.strip() if n == 1: # Reverse-complement 3' pair seq[1] = seq[1].translate(_complement)[::-1] # Make sure reads are minimum length if (len(seq[0]) >= minimum_read_length) \ and (len(seq[1]) >= minimum_read_length): print header print '%s%s' % (seq[1], seq[0]) kept +=1 else: skipped +=1 seq = { 0: '', 1: ''} print >> sys.stderr, "kept: %.2f percent of pairs (%s : %s)" % (float(kept)/(skipped + kept), skipped, kept)

[ "harekrishna@gmail.com" ]

harekrishna@gmail.com

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#calss header class _BANKED(): def __init__(self,): self.name = "BANKED" self.definitions = bank self.parents = [] self.childen = [] self.properties = [] self.jsondata = {} self.basic = ['bank']

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kazetof/dsci-utils

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import os def _make_savedir_from_savename(savename: str) -> None: savedir = os.path.dirname(savename) os.makedirs(savedir, exist_ok=True) if __name__ == '__main__': pass

[ "fukasawakaze@gmail.com" ]

fukasawakaze@gmail.com

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mksime/my-first-blog

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# -*- coding: utf-8 -*- # Generated by Django 1.9.13 on 2018-06-02 02:34 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion import django.utils.timezone class Migration(migrations.Migration): dependencies = [ ('blog', '0003_auto_20180525_1431'), ] operations = [ migrations.CreateModel( name='Comment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('author', models.CharField(max_length=200)), ('text', models.TextField()), ('created_date', models.DateTimeField(default=django.utils.timezone.now)), ('approved_comment', models.BooleanField(default=False)), ('post', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='comments', to='blog.Post')), ], ), ]

[ "marcelo.sime@gmail.com" ]

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from flask import render_template, Blueprint, jsonify, request from flask_cors import CORS from app.models import ResultList from app.core import db from app.schemas import ValidationSchema, ResultListSchema, AllResultsListSchema from app.toolbox.results import AllResults api = Blueprint('api', __name__, static_folder='../static', template_folder='../templates') CORS(api) @api.route('/', defaults={'path': ''}) @api.route('/<path:path>') def catch_all(path): return render_template('error.html', message='404 not found'), 404 @api.route('/calculate', methods=['POST', 'GET']) def calculate(): if request.method == 'POST': req_data = request.get_json() data, errors_validation = ValidationSchema().load(req_data) if errors_validation: return jsonify(errors_validation), 400 result = ResultList(req_data['vector1'], req_data['vector2']) db.session.add(result) try: db.session.commit() except Exception as e: # this shouldn't happen return jsonify({'error': e}), 500 final_result, errors_results = ResultListSchema().dump(result) if errors_results: jsonify(errors_results), 400 return jsonify(final_result) else: results = ResultList.query.all() results_obj = AllResults(results) all_results, errors_all_results = AllResultsListSchema().dump(results_obj) if errors_all_results: jsonify(errors_all_results), 400 return jsonify(all_results) @api.route('/health', methods=['GET']) def health(): return jsonify({'message': 'okay'})

[ "Kylebowman99@gmail.com" ]

Kylebowman99@gmail.com

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def dispatch(*args, table, default=lambda k, e: k): ''' Dispatch behavior in *even* positions within `args` against mapping `table`. It accepts a variable list of arguments, however of even length, where *hashable* objects in *even* positions are used in the key for dispatching against logic container `table`, namely a mapping of functions; in parallel, objects in *odd* positions within `args` are used as values, respectively. Keyword argument `default` is a function that consumes two arguments: the former is the key not found in the dispatch `table`; the latter one is the caught exception, if re-raising would be performed. Its default behavior is to return the key as it is. ''' key = tuple([args[e] for e in range(0, len(args), 2)]) values = [args[o] for o in range(1, len(args), 2)] try: method = table[key] return method(*values) except KeyError as e: return default(key, e)

[ "massimo.nocentini@gmail.com" ]

massimo.nocentini@gmail.com

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/day_3/day_3_part_2_test.py

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[]

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nqeron/advent_of_code_2019

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def produce_points(directions) -> set: points = set() pos = (0, 0) for d in directions: bearing = d[0] dist = int(d[1:]) x_bear = () y_bear = () if bearing == "L": x_bear = range(pos[0]-dist, pos[0]+1) y_bear = (pos[1] for _ in range(dist)) pos = (pos[0] - dist, pos[1]) elif bearing == "R": x_bear = range(pos[0], pos[0] + dist + 1) y_bear = (pos[1] for _ in range(dist)) pos = (pos[0] + dist, pos[1]) elif bearing == "U": y_bear = range(pos[1], pos[1] + dist + 1) x_bear = (pos[0] for _ in range(dist)) pos = (pos[0], pos[1] + dist) elif bearing == "D": y_bear = range(pos[1] - dist, pos[1] + 1) x_bear = (pos[0] for _ in range(dist)) pos = (pos[0], pos[1] - dist) for x, y in zip(x_bear, y_bear): points.add((x, y)) return points def gen_points(directions): pos = (0, 0) for d in directions: bearing = d[0] dist = int(d[1:]) x_bear = () y_bear = () if bearing == "L": x_bear = range(pos[0], pos[0] - dist - 1, -1) y_bear = (pos[1] for _ in range(dist)) pos = (pos[0] - dist, pos[1]) elif bearing == "R": x_bear = range(pos[0], pos[0] + dist + 1) y_bear = (pos[1] for _ in range(dist)) pos = (pos[0] + dist, pos[1]) elif bearing == "U": y_bear = range(pos[1], pos[1] + dist + 1) x_bear = (pos[0] for _ in range(dist)) pos = (pos[0], pos[1] + dist) elif bearing == "D": y_bear = range( pos[1], pos[1] - dist - 1, -1) x_bear = (pos[0] for _ in range(dist)) pos = (pos[0], pos[1] - dist) for x, y in zip(x_bear, y_bear): yield (x, y) yield pos def m_dist(point_1: tuple, point_2: tuple) -> int: return abs(point_1[0] - point_2[0]) + abs(point_1[1] - point_2[1]) def analyze(file): with open(file) as f: directions_1 = f.readline().split(",") directions_2 = f.readline().split(",") points_1 = produce_points("R75,D30,R83,U83,L12,D49,R71,U7,L72".split(",")) points_temp = gen_points("R75,D30,R83,U83,L12,D49,R71,U7,L72".split(",")) #print(set(points_temp)) #print(points_1) #print(set(points_temp) - points_1) points_temp = gen_points("R75,D30,R83,U83,L12,D49,R71,U7,L72".split(",")) #print([i for i in points_temp]) points_2 = produce_points("U62,R66,U55,R34,D71,R55,D58,R83".split(",")) points_2_temp = gen_points("U62,R66,U55,R34,D71,R55,D58,R83".split(",")) #print(points_2 - set(points_2_temp)) intersections = points_1.intersection(points_2) int_temp = set(points_temp).intersection(set(points_2_temp)) print(intersections) print(int_temp) #print(min( (m_dist(intersection, (0, 0)) for intersection in intersections))) if __name__ == '__main__': analyze("../inputs/day_3.txt")

[ "nqeron@gmail.com" ]

nqeron@gmail.com

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99259216f11b15ec60446b4a141b3592a35560ce

/wex-python-api/test/test_json_node.py

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no_license

adam725417/Walsin

296ba868f0837077abff93e4f236c6ee50917c06

7fbefb9bb5064dabccf4a7e2bf49d2a43e0f66e9

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2020-04-12T14:14:07.607675

2019-03-05T01:54:03

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# coding: utf-8 """ WEX REST APIs Authentication methods - Basic Auth - JSON Web Token - [POST /api/v1/usermgmt/login](#!/User/signinUser) - [POST /api/v1/usermgmt/logout](#!/User/doLogout) - Python client sample [Download](/docs/wex-python-api.zip) OpenAPI spec version: 12.0.2.417 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import os import sys import unittest import ibmwex from ibmwex.rest import ApiException from ibmwex.models.json_node import JsonNode class TestJsonNode(unittest.TestCase): """ JsonNode unit test stubs """ def setUp(self): pass def tearDown(self): pass def testJsonNode(self): """ Test JsonNode """ # FIXME: construct object with mandatory attributes with example values #model = ibmwex.models.json_node.JsonNode() pass if __name__ == '__main__': unittest.main()

[ "adamtp_chen@walsin.com" ]

adamtp_chen@walsin.com

dd292841168cd88e929dbb617056733aa1f5364b

4a934b1d646cc6660b1b38ac09fc0cb7b343445c

/Palta/generateTextCollection.py

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[]

no_license

chobeat/mapredush

85aece9d4b4e8ebfcba610b1f13ce95358dee9ef

c827e7ecd1076a30ab3cd5395d79bcd155ca2f6b

refs/heads/master

2021-01-19T03:23:21.116562

2014-06-12T18:05:19

null

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py

from faker import * from pymongo import * client=MongoClient() db=client.db tc=db['textCollection3'] f=Faker() l=[f.text(140) for i in range(1000)] for i in range(len(l)): tc.insert({"_id":i,"text":l[i]})

[ "simone.robutti@gmail.com" ]

simone.robutti@gmail.com

9eff05ce12331a82a2df274f4ab930c8296168b5

7ddffbc0b183e880e6f440bad44e49aceddf3b6a

/Alexa/alexaDomain/apps.py

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[]

no_license

mabraca/Alexa

bd15e0d044609b80abf43232e2c38b19b956cac3

cc666b729f5e1feb39744b642a35115f4f7d905e

refs/heads/master

2020-04-01T18:31:20.767238

2018-10-22T06:29:40

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py

from django.apps import AppConfig class AlexadomainConfig(AppConfig): name = 'alexaDomain'

[ "mabraca18@gmail.com" ]

mabraca18@gmail.com

6e887cf7d59d6082baba621dd168862f85c5d1b5

d3bee7fead4c206116e855b91c7024222a01208d

/dpAPI/MPGW.py

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no_license

itayb1/dp-python

d5cbe5bae44f411dc4ee2b86458b1e5c75aab029

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refs/heads/master

2020-05-15T16:52:15.975891

2020-05-04T13:19:24

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2020-04-27T12:32:49

2019-04-20T11:16:47

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from .const import API_PATH, MPGW_request_body, policy_attachment_request_body from .base import api_call from copy import deepcopy from .DPEndpoint import DPEndpoint class MPGW(DPEndpoint): def __init__(self, auth, base_url, domain): DPEndpoint.__init__(self, auth=auth, base_url=base_url, domain=domain) self.parent_key = "MultiProtocolGateway" self.api_path = API_PATH["mpgw"] def create(self, name, front_handlers, xml_manager, style_policy, state="enabled", **kwargs): """Creates a new ``Multi Protocol Gateway`` Parameters: name (str): The name of the mpgw front_handlers (list): A list of strings representing front handlers to be attached to the mpgw xml_manager (str): The name of the xml manager to be attached to the mpgw style_policy (str): The name of the style policy to be attached to the mpgw state (str): The state of the mpgw (default is enabled) Returns: dict: a dict/json object of the new mpgw """ request_body = deepcopy(MPGW_request_body) self.__create_mpgw_policy_attachment(name) request_body[self.parent_key]["name"] = name request_body[self.parent_key]["FrontProtocol"] = [ { "value": handler } for handler in front_handlers ] request_body[self.parent_key]["mAdminState"] = state request_body[self.parent_key]["XMLManager"] = { "value": xml_manager } request_body[self.parent_key]["StylePolicy"] = { "value": style_policy } request_body[self.parent_key]["PolicyAttachments"] = { "value": name } self._append_kwargs(request_body, **kwargs) response = api_call.post(self.base_url + (self.api_path).format(domain=self.domain), auth=self.auth, data=request_body) return request_body[self.parent_key] def __create_mpgw_policy_attachment(self, name): request_body = deepcopy(policy_attachment_request_body) request_body["PolicyAttachments"]["name"] = name return api_call.post(self.base_url + (API_PATH["policy_attachments"]).format(domain=self.domain), auth=self.auth, data=request_body)

[ "itay4445@gmail.com" ]

itay4445@gmail.com

45789a6830e3cc51c628742349487ac491954b52

df82b5aff1985cf89c8a4e08cd9befe28c946b25

/06-Segmentation/Answers/segmentByClustering.py

f39c879499bd3f98741988d498f07c1d78241e23

[]

no_license

steff456/IBIO4680

ca2cee74277fe17321c2229d69cfe99cd317738a

23125aa14d7213231ab9b1b6dcc5fddc0c7b5a1c

refs/heads/master

2021-09-14T07:44:04.872265

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#!/usr/local/bin/python3 #main function performing segmentation def segmentByClustering(rgbImage, featureSpace, clusteringMethod, numberOfClusters): #importing necessary packages and libraries import numpy as np from skimage import io, color import cv2 import matplotlib.pyplot as plt import numpy.matlib from sklearn.cluster import KMeans import scipy.io as sio from PIL import Image import PIL from sklearn import mixture from sklearn import cluster import time from scipy import ndimage as ndi from skimage.morphology import watershed from skimage.feature import peak_local_max import glob import math import scipy #verify the image is in np array, if not, it is casted rgbImage=np.array(rgbImage) #color and space maximum normalization values. It will change the representativeness of each channel colmax=5 spacemax=5 #Image in RGB if featureSpace == 'rgb': image=rgbImage image=cv2.normalize(image,np.zeros((image.shape),dtype=np.uint8),alpha=0, beta=colmax,norm_type=cv2.NORM_MINMAX,dtype=cv2.CV_32F) #convert image to lab and resizing the channels to 0-255 elif featureSpace=='lab': image= color.rgb2lab(rgbImage) image=cv2.normalize(image,np.zeros((image.shape),dtype=np.uint8),alpha=0, beta=colmax,norm_type=cv2.NORM_MINMAX,dtype=cv2.CV_32F) #convert image to hsv and resizing the channels to 0-255 elif featureSpace=='hsv': image= color.rgb2hsv(rgbImage) image=cv2.normalize(image,np.zeros((image.shape),dtype=np.uint8),alpha=0, beta=colmax,norm_type=cv2.NORM_MINMAX,dtype=cv2.CV_32F) #convert image to rgb+xy and resizing all channels to 0-255 elif featureSpace=='rgb+xy': image=rgbImage #generating the x position and y position matrices to stack to the image xcoord=np.matlib.repmat(np.array(range(image.shape[1])),image.shape[0],1) ycoord=np.matlib.repmat(np.transpose([np.array(range(image.shape[0]))]),1,image.shape[1]) #set channels to 0-255 range image=cv2.normalize(image,np.zeros((image.shape),dtype=np.uint8),alpha=0, beta=colmax,norm_type=cv2.NORM_MINMAX,dtype=cv2.CV_32F) xcoord=cv2.normalize(ycoord,np.zeros((xcoord.shape),dtype=np.uint8),alpha=0, beta=spacemax,norm_type=cv2.NORM_MINMAX,dtype=cv2.CV_32F) ycoord=cv2.normalize(ycoord,np.zeros((ycoord.shape),dtype=np.uint8),alpha=0, beta=spacemax,norm_type=cv2.NORM_MINMAX,dtype=cv2.CV_32F) #concatenating image and x,y position matrices image=np.stack((image[:,:,0],image[:,:,1],image[:,:,2],xcoord,ycoord),axis=2) #convert image to lab+xy and resizing all channels to 0-255 elif featureSpace=='lab+xy': #convert image to lab colorspace image= color.rgb2lab(rgbImage) #generating the x position and y position matrices to stack to the image xcoord=np.matlib.repmat(np.array(range(image.shape[1])),image.shape[0],1) ycoord=np.matlib.repmat(np.transpose([np.array(range(image.shape[0]))]),1,image.shape[1]) #set channels to 0-255 range image=cv2.normalize(image,np.zeros((image.shape),dtype=np.uint8),alpha=0, beta=colmax,norm_type=cv2.NORM_MINMAX,dtype=cv2.CV_32F) xcoord=cv2.normalize(xcoord,np.zeros((xcoord.shape),dtype=np.uint8),alpha=0, beta=spacemax,norm_type=cv2.NORM_MINMAX,dtype=cv2.CV_32F) ycoord=cv2.normalize(ycoord,np.zeros((ycoord.shape),dtype=np.uint8),alpha=0, beta=spacemax,norm_type=cv2.NORM_MINMAX,dtype=cv2.CV_32F) #concatenating image and x,y position matrices image=np.stack((image[:,:,0],image[:,:,1],image[:,:,2],xcoord,ycoord),axis=2) elif featureSpace=='hsv+xy': #convert image to hsv colorspace image= color.rgb2hsv(rgbImage) #generating the x position and y position matrices to stack to the image xcoord=np.matlib.repmat(np.array(range(image.shape[1])),image.shape[0],1) ycoord=np.matlib.repmat(np.transpose([np.array(range(image.shape[0]))]),1,image.shape[1]) #set channels to 0-255 range image=cv2.normalize(image,np.zeros((image.shape),dtype=np.uint8),alpha=0, beta=colmax,norm_type=cv2.NORM_MINMAX,dtype=cv2.CV_32F) xcoord=cv2.normalize(xcoord,np.zeros((xcoord.shape),dtype=np.uint8),alpha=0, beta=spacemax,norm_type=cv2.NORM_MINMAX,dtype=cv2.CV_32F) ycoord=cv2.normalize(ycoord,np.zeros((ycoord.shape),dtype=np.uint8),alpha=0, beta=spacemax,norm_type=cv2.NORM_MINMAX,dtype=cv2.CV_32F) #concatenating image and x,y position matrices image=np.stack((image[:,:,0],image[:,:,1],image[:,:,2],xcoord,ycoord),axis=2) if clusteringMethod=='kmeans': imager=np.reshape(image,(1,image.shape[0]*image.shape[1],image.shape[2]))[0] kmeans=KMeans(n_clusters=numberOfClusters,random_state=0).fit(imager) assig=kmeans.labels_ seg=np.reshape(assig,(image.shape[0],image.shape[1])) return seg elif clusteringMethod=='gmm': imager=np.reshape(image,(1,image.shape[0]*image.shape[1],image.shape[2]))[0] gmm=mixture.GaussianMixture(n_components=numberOfClusters,covariance_type='full').fit(imager) assig=gmm.predict(imager) seg=np.reshape(assig,(image.shape[0],image.shape[1])) return seg elif clusteringMethod=='hierarchical': a=image.shape image=cv2.resize(image,(100,100)) image=np.array(image) imager=np.reshape(image,(1,image.shape[0]*image.shape[1],image.shape[2]))[0] hierClus=cluster.AgglomerativeClustering(n_clusters=numberOfClusters,affinity='euclidean') assig=hierClus.fit_predict(imager) seg=np.reshape(assig,(100,100)) seg=scipy.misc.imresize(seg,(a[0],a[1]),interp='nearest') return seg elif clusteringMethod=='watershed': if featureSpace=='rgb+xy' or featureSpace=='rgb': image=image[:,:,0:3] image=np.mean(image,axis=2) else: image=image[:,:,0] local_max = peak_local_max(-1*image, indices=False,num_peaks=numberOfClusters,num_peaks_per_label=1) markers=ndi.label(local_max)[0] seg=watershed(image,markers) seg=seg-1 return seg

[ "sergioalgl2@gmail.com" ]

sergioalgl2@gmail.com

67ceb865d11bf7d82086694f8879b057f68bf848

864285315c3a154639355f14ab1ff14633576405

/mapclientplugins/segmentationstep/tools/handlers/abstractselection.py

4315d4f55f509d3c4b410e9a7a07ad7b29f48cb1

[]

no_license

hsorby/segmentationstep

774dc537967c9643bd0094dc4e64eefa472588b0

321505374f9434ac0ae832b0b00398c2d4ac1fbe

refs/heads/main

2021-09-28T09:06:07.197158

2015-08-14T07:59:55

21,375,254

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''' MAP Client, a program to generate detailed musculoskeletal models for OpenSim. Copyright (C) 2012 University of Auckland This file is part of MAP Client. (http://launchpad.net/mapclient) MAP Client is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. MAP Client is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with MAP Client. If not, see <http://www.gnu.org/licenses/>.. ''' from PySide import QtCore from mapclientplugins.segmentationstep.tools.handlers.abstracthandler import AbstractHandler from mapclientplugins.segmentationstep.zincutils import setGlyphSize, setGlyphOffset, COORDINATE_SYSTEM_LOCAL, \ createSelectionBox from mapclientplugins.segmentationstep.undoredo import CommandSelection from mapclientplugins.segmentationstep.definitions import SELECTION_BOX_3D_GRAPHIC_NAME class SelectionMode(object): NONE = -1 EXCULSIVE = 0 ADDITIVE = 1 class AbstractSelection(AbstractHandler): def __init__(self, plane, undo_redo_stack): super(AbstractSelection, self).__init__(plane, undo_redo_stack) self._selection_box = createSelectionBox(plane.getRegion(), SELECTION_BOX_3D_GRAPHIC_NAME) self._selection_mode = SelectionMode.NONE self._selection_position_start = None def mousePressEvent(self, event): self._selection_mode = SelectionMode.NONE if event.modifiers() & QtCore.Qt.SHIFT and event.button() == QtCore.Qt.LeftButton: self._selection_position_start = [event.x(), event.y()] self._selection_mode = SelectionMode.EXCULSIVE if event.modifiers() & QtCore.Qt.ALT: self._selection_mode = SelectionMode.ADDITIVE self._start_selection = self._model.getCurrentSelection() else: super(AbstractSelection, self).mousePressEvent(event) def mouseMoveEvent(self, event): if self._selection_mode != SelectionMode.NONE: x = event.x() y = event.y() xdiff = float(x - self._selection_position_start[0]) ydiff = float(y - self._selection_position_start[1]) if abs(xdiff) < 0.0001: xdiff = 1 if abs(ydiff) < 0.0001: ydiff = 1 xoff = float(self._selection_position_start[0]) / xdiff + 0.5 yoff = float(self._selection_position_start[1]) / ydiff + 0.5 scene = self._selection_box.getScene() scene.beginChange() setGlyphSize(self._selection_box, [xdiff, -ydiff, 0.999]) setGlyphOffset(self._selection_box, [xoff, yoff, 0]) self._selection_box.setVisibilityFlag(True) scene.endChange() else: super(AbstractSelection, self).mouseMoveEvent(event) def mouseReleaseEvent(self, event): if self._selection_mode != SelectionMode.NONE: x = event.x() y = event.y() # Construct a small frustrum to look for nodes in. region = self._model.getRegion() region.beginHierarchicalChange() self._selection_box.setVisibilityFlag(False) selection_group = self._model.getSelectionGroupField() if (x != self._selection_position_start[0] and y != self._selection_position_start[1]): left = min(x, self._selection_position_start[0]) right = max(x, self._selection_position_start[0]) bottom = min(y, self._selection_position_start[1]) top = max(y, self._selection_position_start[1]) self._zinc_view.setPickingRectangle(COORDINATE_SYSTEM_LOCAL, left, bottom, right, top) if self._selection_mode == SelectionMode.EXCULSIVE: selection_group.clear() self._zinc_view.addPickedNodesToFieldGroup(selection_group) else: node = self._zinc_view.getNearestNode(x, y) if self._selection_mode == SelectionMode.EXCULSIVE and not node.isValid(): selection_group.clear() if node.isValid(): group = self._model.getSelectionGroup() if self._selection_mode == SelectionMode.EXCULSIVE: remove_current = group.getSize() == 1 and group.containsNode(node) selection_group.clear() if not remove_current: group.addNode(node) elif self._selection_mode == SelectionMode.ADDITIVE: if group.containsNode(node): group.removeNode(node) else: group.addNode(node) end_selection = self._model.getCurrentSelection() c = CommandSelection(self._model, self._start_selection, end_selection) self._undo_redo_stack.push(c) region.endHierarchicalChange() self._selection_mode = SelectionMode.NONE else: super(AbstractSelection, self).mouseReleaseEvent(event)

[ "h.sorby@auckland.ac.nz" ]

h.sorby@auckland.ac.nz

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8ad05f071487a25649ed51c42f5eb76ecccffa79

/main.py

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[]

no_license

combjellly/WaveDash

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e388469e1beebb8f1c8608ea85c1b6ecb7bb4ae2

refs/heads/main

2023-02-16T08:49:36.545841

2021-01-16T02:47:54

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'''wavenum = input("how many voices? ") lenght = input("how long for cycle:seconds ") slowest = input("voice with least ammount of cycles? # of cycles " ) fastest = input("voice with most ammount of cycles? # of cycles " ) ''' import tkinter as tk import pdfileformat as pdff wavenum = 20 length = 20 slowest = 2 fastest = 100 file = open("pendulum.txt","w+") d={} root=tk.Tk() root.title('PENDULUM CALCULATOR') root.geometry("400x400") root.configure(bg="brown") output = tk.StringVar() output.set("Generate list of voices in ms\n\n\n\n\n\n\n\n\n\n") mytext = tk.StringVar(value='test ' * 30) myentry = tk.Entry(root, textvariable=output, state='readonly') myscroll = tk.Scrollbar(root, orient='horizontal', command=myentry.xview) myentry.config(xscrollcommand=myscroll.set) #######GUI############################# # LABELS lvoices = tk.Label(root, text="Number of voices") llength = tk.Label(root, text="Length of cycle (seconds)") lslowest = tk.Label(root, text="Slowest voice (# of cycles) ") lfastest = tk.Label(root, text="Fastest voice (# of cycles) ") loutput = tk.Label(root, textvariable= output) # ENTRY evoices = tk.Entry(root, text="Number of voices") elength = tk.Entry(root, text="Length of cycle") eslowest = tk.Entry(root, text="Slowest voices") efastest = tk.Entry(root, text="Fastest voices") #PACKING lvoices.pack() evoices.pack() llength.pack() elength.pack() lslowest.pack() eslowest.pack() lfastest.pack() efastest.pack() loutput.pack() myscroll.pack ###FUNCTIONS################## #gets values from entry widgets def getvalues(event): global length wavenum = int(evoices.get()) length = int(elength.get()) slowest = int(eslowest.get()) fastest = int(efastest.get()) incrementcalc(wavenum,length,slowest,fastest) root.bind("<Return>", getvalues) # calculates how each voice should increment in (?) def incrementcalc(wavenum,length,slowest,fastest): increment= (int(fastest)-int(slowest)) / (int(wavenum)-1) changeifstatements(wavenum,length,slowest,fastest,increment) print(increment) # tbh idr def changeifstatements(wavenum,length,slowest,fastest,increment): if (increment*wavenum)>fastest: change=-1 elif (increment*wavenum)<fastest: change=1 else: change = 0 wavenumdesignation(wavenum,length,slowest,fastest,increment) # calculates n writes voice BPM def wavenumdesignation(wavenum,length,slowest,fastest,increment): d={} bpmlist = "" d['1'] = slowest for x in range(1,wavenum): d[x+1]=((x*increment)+slowest) for x in d: y = (d[x]) seconds = length/y seconds = seconds*1000 bpm = 60/seconds # bpmlist not actually bpm, making list with seconds for metro bpmlist+=str(seconds)+"\n" output.set(bpmlist) file = open("pendulum.txt","w") file.write(bpmlist) file.close() pdff.format() root.mainloop()

[ "noreply@github.com" ]

noreply@github.com

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/ex_dictionary.py

7da8fa0e9b314983af398f667b9c16942e134fec

[]

no_license

ask4git/BUFS-Post-Processing-Module

12282a088296c9014dbffed2dd413d5b6c54fd3d

5e811b3b7a1e129dc16567d6b1751b9aecaef519

refs/heads/master

2020-05-13T16:33:28.298874

2019-04-22T11:35:53

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# -*- coding: utf-8 -*- import pickle class ExDictionary: @staticmethod def make_ex_dictionary(): dictionary = list() with open('.\\res\\exception_expression.txt', 'r', encoding='utf-8-sig') as ex_data_file: for each_line in ex_data_file: try: expression = each_line.strip() morpheme = expression.split('+') expression = list() for i in range(len(morpheme)): expression.append(morpheme[i].split('/')) eojeol = list() eojeol_type = list() for i in range(len(expression)): eojeol.append(expression[i][0]) eojeol_type.append(expression[i][1]) expression = list() expression.append(eojeol) expression.append(eojeol_type) dictionary.append(expression) except IndexError as error: print(error) return None dictionary = dictionary[1:] return dictionary @staticmethod def save_dictionary(data, path): with open(path, 'wb') as dictionary: pickle.dump(data, dictionary) @staticmethod def load_dictionary(path): with open(path, 'rb') as dictionary: return pickle.load(dictionary, encoding='utf-8')

[ "ask4git@gmail.com" ]

ask4git@gmail.com

f0284d22965f628a9a0b899b316fe6e649b59ee5

ce76b3ef70b885d7c354b6ddb8447d111548e0f1

/same_place/own_number/bad_way_or_year/different_week_or_able_work.py

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[]

no_license

JingkaiTang/github-play

9bdca4115eee94a7b5e4ae9d3d6052514729ff21

51b550425a91a97480714fe9bc63cb5112f6f729

refs/heads/master

2021-01-20T20:18:21.249162

2016-08-19T07:20:12

60,834,519

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UTF-8

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#! /usr/bin/env python def place(str_arg): find_world_by_case(str_arg) print('point_and_little_problem') def find_world_by_case(str_arg): print(str_arg) if __name__ == '__main__': place('last_day')

[ "jingkaitang@gmail.com" ]

jingkaitang@gmail.com

ac4d6e76ee26b19ee2ff04a77b386ed4cf0059c9

f7c1282dd377b95621436587fd2a6cb28a455d74

/om_hr_payroll/__manifest__.py

2c39fcc961672ef5c2c5369414d6b86b5a869f74

[]

no_license

odoomates/odooapps

a22fa15346694563733008c42549ebc0da7fc9f6

459f3b25d31da24043523e72f8be09af9a1e67e9

refs/heads/master

2023-08-11T15:25:28.508718

2022-10-14T07:58:36

173,598,986

182

306

null

2023-08-10T17:58:46

2019-03-03T16:20:23

Python

UTF-8

Python

false

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py

# -*- coding:utf-8 -*- { 'name': 'Odoo 16 HR Payroll', 'category': 'Generic Modules/Human Resources', 'version': '16.0.1.0.0', 'sequence': 1, 'author': 'Odoo Mates, Odoo SA', 'summary': 'Payroll For Odoo 16 Community Edition', 'live_test_url': 'https://www.youtube.com/watch?v=0kaHMTtn7oY', 'description': "Odoo 16 Payroll, Payroll Odoo 16, Odoo Community Payroll", 'website': 'https://www.odoomates.tech', 'license': 'LGPL-3', 'depends': [ 'mail', 'hr_contract', 'hr_holidays', ], 'data': [ 'security/hr_payroll_security.xml', 'security/ir.model.access.csv', 'data/hr_payroll_sequence.xml', 'data/hr_payroll_category.xml', 'data/hr_payroll_data.xml', 'wizard/hr_payroll_payslips_by_employees_views.xml', 'views/hr_contract_type_views.xml', 'views/hr_contract_views.xml', 'views/hr_salary_rule_views.xml', 'views/hr_payslip_views.xml', 'views/hr_employee_views.xml', 'views/hr_payroll_report.xml', 'wizard/hr_payroll_contribution_register_report_views.xml', 'views/res_config_settings_views.xml', 'views/report_contribution_register_templates.xml', 'views/report_payslip_templates.xml', 'views/report_payslip_details_templates.xml', 'views/hr_contract_history_views.xml', 'views/hr_leave_type_view.xml', 'data/mail_template.xml', ], 'images': ['static/description/banner.png'], 'application': True, }

[ "odoomates@gmail.com" ]

odoomates@gmail.com

30f8760515740badc64e653bb429b07937a5b028

00020e9785085a14a3ced97301ec8638e770eca3

/embark/uploader/test_models.py

2ccfd581526c64b8f41b8b317bb67f9214ca2418

[ "MIT" ]

permissive

MaximilianWagner/amos-ss2021-emba-service

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afda9ec5a180c8462a1fed2422df2c0e2b7c2c7a

refs/heads/main

2023-05-28T19:33:06.508271

2021-06-16T10:58:54

null

0

null

UTF-8

Python

false

2,049

py

from django.test import TestCase from .models import Firmware class test_models(TestCase): def setUp(self): self.fw_file = None # TODO: add timeout def test_get_flags_all_true(self): """ test get_flags() if all flags set to String/True """ firmware = Firmware(firmware=self.fw_file) firmware.version = "version" firmware.vendor = "vendor" firmware.device = "device" firmware.notes = "notes" firmware.firmware_Architecture = "x64" firmware.cwe_checker = True firmware.docker_container = True firmware.deep_extraction = True firmware.log_path = True firmware.grep_able_log = True firmware.relative_paths = True firmware.ANSI_color = True firmware.web_reporter = True firmware.emulation_test = True firmware.dependency_check = True firmware.multi_threaded = True expected_string = " -X version -Y vendor -Z device -N notes -a x64 -c -x -i -g -s -z -W -E -F -t" self.assertEqual(firmware.get_flags(), expected_string) def test_get_flags_all_false(self): """ test get_flags() if all flags set to None/False """ firmware = Firmware(firmware=self.fw_file) firmware.version = None firmware.vendor = None firmware.device = None firmware.notes = None firmware.firmware_Architecture = None firmware.cwe_checker = False firmware.docker_container = False firmware.deep_extraction = False firmware.log_path = False firmware.grep_able_log = False firmware.relative_paths = False firmware.ANSI_color = False firmware.web_reporter = False firmware.emulation_test = False firmware.dependency_check = False firmware.multi_threaded = False expected_string = "" self.assertEqual(firmware.get_flags(), expected_string)

[ "wagnermaximilian@aol.com" ]

wagnermaximilian@aol.com

178960390eb252c06cada38f3ada6b9792f26a8f

c12171e8838f70a02d8ed52bcdb620ffd1864a8d

/uh_paper_analysis.py

1aeb6e9e4ed893d89c15cae3498f6d0870a10273

[]

no_license

berdakh/source-Imaging

02b6ecb226685610b8b4ea924a46f3bef8bee135

9d1ccbec64fc29da587fe126e3af25a885145c56

refs/heads/master

2021-06-12T22:12:03.677295

2021-04-09T06:40:53

184,751,119

7

0

null

UTF-8

Python

false

9,219

py

import numpy as np from numpy.random import randn from scipy import stats as stats import os import mne import matplotlib.pyplot as plt from mne.minimum_norm import (apply_inverse,apply_inverse_epochs,make_inverse_operator) from mne.connectivity import seed_target_indices, spectral_connectivity import glob """ The goal of the analysis is to make a statistical appraisal of the neural activation evoked from the BMI triggered exoskeleton at movement onset detected by Robot event. 1) The question is whether evidence can be found against the hull hypothesis that neural activations (plasticity) on (the somatosensory and motor) cortex a) does not depend on whether or not the when BMI is inactive (subject is idle) b) does not change across sessions 2) Can we find any evidence against hull hypothesis that neural activations patterns on the cortex does remain the same across sessions ? * In other words, there is no evidence of cortical reorganization. """ #%%############################################################################## # Read the EEG data from uhClass import MRCP mainDir = 'C:\\uhdata\\freesurfer' subject = 'S9011' os.chdir(os.path.join(mainDir, subject)) eegfiles = glob.glob('*-epoall.pickle')[0] #%% import pickle with open(eegfiles, 'rb') as pickle_file: eeg = pickle.load(pickle_file) #%% Plot EEG #from uhClass import MRCP #filename = eegfilename.split('_s')[0] ev =[] for ep in eeg: print(ep) evoked = ep.average() ev.append(evoked) #%% STEP 1: COMPUTER THE SOURCE SPACE (SOURCE GRID ON MRI) """ The source space defines the position of the candidate source locations. The following code compute such a cortical source space with an OCT-5 resolution. """ # this is a freesurfer folder name #src = mne.setup_source_space(subject, spacing='oct5', subjects_dir = mainDir) #%% #src.plot(head=True, brain=True, skull = True, subjects_dir = mainDir) #%% READ FORWARD SOLUTION fname_fwd = os.path.join(mainDir, subject) + '\\' + subject + '-fwd.fif' fwd = mne.read_forward_solution(fname_fwd) #%% noise_cov = mne.compute_covariance(eeg[1], keep_sample_mean=True, tmin=-0.5, tmax=-0.0) stcs = [] for ii, epoch in enumerate(ev): # tmin, tmax = epoch.time_as_index([-1, -0.5]) # calculate noise covariance matrix # make inverse operator info = epoch.info inverse_operator = make_inverse_operator(info, fwd, noise_cov, loose=0.2, depth=None) epoch.set_eeg_reference(ref_channels = "average", projection=True) # apply inverse solution method = 'MNE' snr = 3. lambda2 = 1. / snr ** 2 stc = apply_inverse(epoch, inverse_operator, lambda2,method= method, pick_ori="normal",) stcs.append(stc) #%% #e1 = copy.deepcopy(evoked) #e2 = copy.deepcopy(evoked) # #e1.crop(-0.5, 0) #e2.crop(0, 0.5) #%% import copy # condition 1 -- > baseline cond1 = [] # condition 2 ---> Movement detected by Robot cond2 = [] for source in stcs: cond1.append(copy.deepcopy(source).crop(-0.5, 0)) cond2.append(copy.deepcopy(source).crop(0, 0.5)) #%% n_vertices_sample, n_times = cond1[2].data.shape n_subjects = len(cond2) #%% try: del X except Exception: pass X = np.zeros([n_vertices_sample, n_times, n_subjects, 2]) for ii, c1 in enumerate(cond1): X[:,:,ii,0] = c1.data for ii, c2 in enumerate(cond2): X[:,:,ii,1] = c2.data """" X = np.zeros([n_vertices_sample, n_times, n_subjects]) for ii, c in enumerate(cond2): X[:,:, ii] = c.data """ #%%############################################################################ # Finally, we want to compare the overall activity levels in each condition, # the diff is taken along the last axis (condition). The negative sign makes # it so condition1 > condition2 shows up as "red blobs" (instead of blue). X = np.abs(X) # only magnitude X = X[:, :, :, 0] - X[:, :, :, 1] # make paired contrast #%%############################################################################ src_fname = os.path.join(mainDir, subject) + '\\'+ subject + '-src.fif' # Read the source space we are morphing to src = mne.read_source_spaces(src_fname) fsave_vertices = [s['vertno'] for s in src] #%%############################################################################ # Compute statistic # ----------------- # To use an algorithm optimized for spatio-temporal clustering, we # just pass the spatial connectivity matrix (instead of spatio-temporal) print('Computing connectivity.') connectivity = mne.spatial_src_connectivity(src) #X1 = X[:,:,:,1] # Note that X needs to be a multi-dimensional array of shape # samples (subjects) x time x space, so we permute dimensions X1 = np.transpose(X, [2, 1, 0]) # Now let's actually do the clustering. This can take a long time... #%% Here we set the threshold quite high to reduce computation. p_threshold = 0.05 #t_threshold = -stats.distributions.t.ppf(p_threshold / 2., n_subjects - 1) t_threshold = -stats.distributions.t.ppf(p_threshold / 2., n_subjects - 1) #%% tstep = cond2[0].tstep from mne.stats import (spatio_temporal_cluster_1samp_test, summarize_clusters_stc) print('Clustering.') T_obs, clusters, cluster_p_values, H0 = clu = \ spatio_temporal_cluster_1samp_test(X1, connectivity=None, n_jobs=1, threshold=t_threshold) # Now select the clusters that are sig. at p < 0.05 (note that this value # is multiple-comparisons corrected). good_cluster_inds = np.where(cluster_p_values < 0.05)[0] #%% #from mne.stats import spatio_temporal_cluster_test # #a = spatio_temporal_cluster_test( # X, threshold=None, n_permutations=1024, tail=0, stat_fun=None, # connectivity=None, verbose=None, n_jobs=1, seed=None, max_step=1, # spatial_exclude=None, step_down_p=0, t_power=1, out_type='indices', # check_disjoint=False, buffer_size=1000) #%%############################################################################ # Visualize the clusters # ---------------------- print('Visualizing clusters.') # Now let's build a convenient representation of each cluster, where each # cluster becomes a "time point" in the SourceEstimate #fsave_vertices = [np.arange(X.shape[0]), np.arange(X.shape[0])] stc_all_cluster_vis = summarize_clusters_stc(clu, p_thresh=0.05, tstep=tstep, vertices=fsave_vertices, subject=subject) #%% Let's actually plot the first "time point" in the SourceEstimate, which # shows all the clusters, weighted by duration subjects_dir = os.path.join(mainDir) # blue blobs are for condition A < condition B, red for A > B brain = stc_all_cluster_vis.plot(surface='inflated', hemi='both', views='lateral', subjects_dir=subjects_dir, time_label='Duration significant (ms)', size=(800, 800), smoothing_steps=10) # brain.save_image('clusters.png') #%% Statistics """ 1) Parametric Hypothesis Testing assumes Normal distribution or iid 2) Non-parametric Hypothesis Testing does not rely on any assumption and is usually done by methods such as Bootstrap analysis & Permutation Test a) Paired data has dependent samples since the data is acquired from the same subject (multiple data) Paired data: there are two measurements from each patient, one before treatment and one after treatment. These two measurements relate to one another, we are interested in the difference between the two measurements (the log ratio) to determine whether a gene has been up-regulated or down-regulated in breast cancer following that treatment. b) Unpaired data has independent samples as the data is acqured from two different/distinct subjects c) Complex data has more than two Groups (ANOVA) ########################### The significance level (alpha) is related to the degree of certainty you require in order to reject the null hypothesis in favor of the alternative e.g. alpha = 0.05 The p-value is the probability of observing the given sample result under the assumption that the null hypothesis is true. If the p-value is less than alpha, then you reject the null hypothesis. For example, if alpha = 0.05 and the p-value is 0.03, then you reject the null hypothesis ########################## Confidence intervals: a range of values that have a chosen probability of containing the true hypothesized quantity. ########################### Steps of Hypthesis testing: 1. Determine the null and alternative hypothesis, using mathematical expressions if applicable. 2. Select a significance level (alpha). 3. Take a random sample from the population of interest. 4. Calculate a test statistic from the sample that provides information about the null hypothesis. 5. Decision >>> If the value of the statistic is consistent with the null hypothesis then do not reject H0. >>> If the value of the statistic is not consistent with the null hypothesis, then reject H0 and accept the alternative hypothesis. ########################## """ #%% #fname = 'S9017_ses1_cond1_block0001-rh.stc' #stc = mne.read_source_estimate(fname)

[ "noreply@github.com" ]

noreply@github.com

1fe26cb3b013a3d92b2e14e647855da39908047c

cf5d54f3808036c2d68508fe7b5f0bbf336ef6a9

/regression/compactiv/src/play.py

944675fe6086b08be7b81484ae666a55f6ad69b5

[]

no_license

pedroceles/maters_db

96eeb05172b4a10ca3f5355d55c835e9b4cb2f13

f33d731685ab194c661088cfb1fb37d69ef084cf

refs/heads/master

2021-01-19T00:28:22.560260

2016-06-21T14:57:49

61,642,759

0

null

UTF-8

Python

false

534

py

import os import pandas as pd from imputation.data_treatment import BaseTreatment class CustomTreatment(BaseTreatment): def __init__(self, source_path=None, *args, **kwargs): dir_ = os.path.dirname(__file__) source_path = os.path.join(dir_, '../original/compactiv.dat') super(CustomTreatment, self).__init__(source_path, *args, **kwargs) def read_file(self): self._df = pd.read_csv(self._source_path, header=None, index_col=None) self._df.iloc[:, -1] += 1 return self._df

[ "pedro.celes123@gmail.com" ]

pedro.celes123@gmail.com

820340e47e5849f5a80c36ab04b86e1b9e1ccccb

1042cf47b885d08075e9f1bafba202726745d0e4

/pysnips/audio/utils.py

19ae2a2722f3e639c8cdf78d4ea0614ac9c33713

[ "MIT" ]

permissive

phenyque/python-snippets

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b8feecfd87663af13db54784c245b3fba0c93202

refs/heads/master

2022-03-17T19:38:12.421745

2019-12-08T11:02:40

114,359,224

0

null

UTF-8

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false

2,706

py

""" Helper functions to solve common problems/tasks when messing with audio files """ import numpy as np import soundfile as sf import itertools from collections import OrderedDict __all__ = ['ambisonics_reorder_channels', 'extract_channels_from_wav', 'monofiles_to_multitrack'] FUMA = OrderedDict(enumerate(['w', 'x', 'y', 'z', 'r', 's', 't', 'u', 'v', 'k', 'l', 'm', 'n', 'o', 'p', 'q'])) ACN = OrderedDict(enumerate(['w', 'y', 'z', 'x', 'v', 't', 'r', 's', 'u', 'q', 'o', 'm', 'k', 'l', 'n', 'p'])) AMBISONICS_ORDERINGS = {'fuma': FUMA, 'acn': ACN} def _ambisonics_channel_count_from_order(order, three_dim=True): """ Helper function that computes the number of channels for a given ambisonics order. """ return (order + 1)**2 if three_dim else (2 * order + 1) def ambisonics_reorder_channels(signal_array, order, input_ordering, output_ordering): """ Reorder ambisonics signals from one channel ordering to another. signal_array - Array with the signals as given by soundfile.read from a wav file order - order of the ambisonics signals, full sphere representation is assumed input_ordering - name of channel ordering of the array ['fuma', 'acn'] output_ordering - desired output ordering ['fuma', 'acn'] """ channel_count = _ambisonics_channel_count_from_order(order) assert(signal_array.shape[1] == channel_count) input_ordering = OrderedDict(itertools.islice(AMBISONICS_ORDERINGS[input_ordering].items(), channel_count)) output_ordering = OrderedDict(itertools.islice(AMBISONICS_ORDERINGS[output_ordering].items(), channel_count)) input_ordering = {v: k for k, v in input_ordering.items()} new_order = [input_ordering[output_ordering[i]] for i in output_ordering.keys()] return signal_array[:, new_order] def extract_channels_from_wav(filename, channels, write_file=None): """Read wav file and extract only the specified channel numbers""" s, fs = sf.read(filename) if type(channels) == int: channels = [channels] s = s[:, channels] if write_file is not None: sf.write(write_file, s, fs) return s def monofiles_to_multitrack(monofiles, new_filename): """Read mono wav files and combine them into a multitrack wavfile""" # TODO: this causes MemoryError when signals are very long signals = [] for f in monofiles: s, fs = sf.read(f) signals.append(s) multitrack_array = np.asarray(signals).T sf.write(new_filename, multitrack_array, fs)

[ "jankiene@onlinehome.de" ]

jankiene@onlinehome.de

42a1f97987615325f30edc75f358e38ff7f7ba56

450916eee7580beb928ed8f387db4f0a8c1aa508

/src/amuse/community/petar/__init__.py

4b06f767ad173c110590200c195514cd334c5292

[ "Apache-2.0", "LicenseRef-scancode-warranty-disclaimer" ]

permissive

amusecode/amuse

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b57c1e2fda1457d5025307be105c2aa59b19b574

refs/heads/main

2023-08-31T04:50:48.880044

2023-08-30T12:00:20

18,516,331

158

118

Apache-2.0

2023-08-30T12:00:22

2014-04-07T12:35:07

AMPL

UTF-8

Python

false

29

py

from .interface import Petar

[ "steven@rieder.nl" ]

steven@rieder.nl

4ffe1c4e6d09d5cf2a2e002c6ba1082879a802a5

84f428469eb718e4c8d3e75f9f61a46662e24a7a

/seahub/api2/endpoints/group_discussions.py

81c5960b9f27f6b761469db7303317e01de4cca0

[ "Apache-2.0" ]

permissive

peckjerry/seahub

52a324a9898dc70082b971ca510c56c8d80ec0fc

49ac5b9ec26844ec7cee26285c7438f894853182

refs/heads/master

2020-12-31T03:17:07.186293

2016-03-15T10:19:12

null

0

null

UTF-8

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false

3,220

py

import json from django.core.paginator import EmptyPage, InvalidPage from django.http import HttpResponse from django.utils.dateformat import DateFormat from rest_framework import status from rest_framework.authentication import SessionAuthentication from rest_framework.permissions import IsAuthenticated from rest_framework.response import Response from rest_framework.views import APIView from seahub.api2.authentication import TokenAuthentication from seahub.api2.permissions import IsGroupMember from seahub.api2.throttling import UserRateThrottle from seahub.api2.utils import api_error from seahub.group.models import GroupMessage from seahub.utils.paginator import Paginator from .utils import api_check_group json_content_type = 'application/json; charset=utf-8' class GroupDiscussions(APIView): authentication_classes = (TokenAuthentication, SessionAuthentication) permission_classes = (IsAuthenticated, IsGroupMember) throttle_classes = (UserRateThrottle, ) @api_check_group def get(self, request, group_id, format=None): """List all group discussions. Only group members can perform this op. """ # 1 <= page, defaults to 1 try: page = int(request.GET.get('page', '1')) except ValueError: page = 1 if page < 0: page = 1 # 1 <= per_page <= 100, defaults to 20 try: per_page = int(request.GET.get('per_page', '20')) except ValueError: per_page = 20 if per_page < 1 or per_page > 100: per_page = 20 paginator = Paginator(GroupMessage.objects.filter( group_id=group_id).order_by('-timestamp'), per_page) try: group_msgs = paginator.page(page) except (EmptyPage, InvalidPage): group_msgs = paginator.page(paginator.num_pages) msgs = [] for e in group_msgs: msgs.append({ "group_id": group_id, "discussion_id": e.pk, "user": e.from_email, "content": e.message, "created_at": e.timestamp.strftime("%Y-%m-%dT%H:%M:%S") + DateFormat(e.timestamp).format('O'), }) return HttpResponse(json.dumps(msgs), status=200, content_type=json_content_type) @api_check_group def post(self, request, group_id, format=None): """Post a group discussions. Only group members can perform this op. """ content = request.data.get('content', '') if not content: return api_error(status.HTTP_400_BAD_REQUEST, 'Content can not be empty.') username = request.user.username discuss = GroupMessage.objects.create(group_id=group_id, from_email=username, message=content) return Response({ "group_id": group_id, "discussion_id": discuss.pk, "user": username, "content": discuss.message, "created_at": discuss.timestamp.strftime("%Y-%m-%dT%H:%M:%S") + DateFormat(discuss.timestamp).format('O'), }, status=201)

[ "xiez1989@gmail.com" ]

xiez1989@gmail.com

79663e1e776c4e22aac164836fee33a006dcd6c3

67c18922f98936713be1b2d173797eaf70570630

/models/library_book.py

39cb0a63f4b49744596fdf16abde087477d0dd6b

[]

no_license

edinsonlenin/my_library

4fd301f83b2bd5d0afc6b3be5d1aca405218c7dd

a6f36baa861ffcfaeab0395db1a059d478ed3f49

refs/heads/main

2023-08-15T23:41:06.310649

2021-10-15T14:31:32

null

0

null

UTF-8

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py

from odoo import models, fields class LibraryBook(models.Model): _name = 'library.book' name = fields.Char('Title', required=True) date_release = fields.Date('Release Date') author_ids = fields.Many2many( 'res.partner', string='Authors' )

[ "edinsonlen@hotmail.com" ]

edinsonlen@hotmail.com

1ae237d50f3b39abb4962276db742147c966c2c6

3c000380cbb7e8deb6abf9c6f3e29e8e89784830

/venv/Lib/site-packages/cobra/modelimpl/l3ext/domdef.py

d549d045a6b6b4cb4b650cffb5b307b2cc6edf07

[]

no_license

bkhoward/aciDOM

91b0406f00da7aac413a81c8db2129b4bfc5497b

f2674456ecb19cf7299ef0c5a0887560b8b315d0

refs/heads/master

2023-03-27T23:37:02.836904

2021-03-26T22:07:54

351,855,399

0

null

UTF-8

Python

false

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# coding=UTF-8 # ********************************************************************** # Copyright (c) 2013-2020 Cisco Systems, Inc. All rights reserved # written by zen warriors, do not modify! # ********************************************************************** from cobra.mit.meta import ClassMeta from cobra.mit.meta import StatsClassMeta from cobra.mit.meta import CounterMeta from cobra.mit.meta import PropMeta from cobra.mit.meta import Category from cobra.mit.meta import SourceRelationMeta from cobra.mit.meta import NamedSourceRelationMeta from cobra.mit.meta import TargetRelationMeta from cobra.mit.meta import DeploymentPathMeta, DeploymentCategory from cobra.model.category import MoCategory, PropCategory, CounterCategory from cobra.mit.mo import Mo # ################################################## class DomDef(Mo): """ This is generated and used only by internal processes. """ meta = ClassMeta("cobra.model.l3ext.DomDef") meta.moClassName = "l3extDomDef" meta.rnFormat = "l3dom-%(name)s" meta.category = MoCategory.REGULAR meta.label = "Outside L3 Domain" meta.writeAccessMask = 0x1 meta.readAccessMask = 0x80384001000601 meta.isDomainable = False meta.isReadOnly = True meta.isConfigurable = False meta.isDeletable = False meta.isContextRoot = False meta.childClasses.add("cobra.model.infra.RtDomAtt") meta.childClasses.add("cobra.model.fault.Counts") meta.childClasses.add("cobra.model.extnw.RtL3InstPToDomP") meta.childClasses.add("cobra.model.infra.RsVlanNs") meta.childClasses.add("cobra.model.extnw.RtL3DomAtt") meta.childClasses.add("cobra.model.infra.RtDomRef") meta.childClasses.add("cobra.model.fault.Inst") meta.childClasses.add("cobra.model.extnw.LblCont") meta.childClasses.add("cobra.model.infra.RtLDevDomP") meta.childClasses.add("cobra.model.infra.RtDomP") meta.childClasses.add("cobra.model.infra.RsVipAddrNs") meta.childClasses.add("cobra.model.infra.RtDynPathAtt") meta.childClasses.add("cobra.model.extnw.RsOut") meta.childClasses.add("cobra.model.health.Inst") meta.childClasses.add("cobra.model.infra.RsDomVxlanNsDef") meta.childClasses.add("cobra.model.infra.RsVlanNsDef") meta.childClasses.add("cobra.model.infra.RtExtDevDomP") meta.childClasses.add("cobra.model.infra.RtNicProfToDomP") meta.childClasses.add("cobra.model.fault.Delegate") meta.childClasses.add("cobra.model.infra.RtDomDef") meta.childNamesAndRnPrefix.append(("cobra.model.infra.RtNicProfToDomP", "rtextdevNicProfToDomP-")) meta.childNamesAndRnPrefix.append(("cobra.model.extnw.RtL3InstPToDomP", "rtl3extL3InstPToDomP-")) meta.childNamesAndRnPrefix.append(("cobra.model.infra.RtDynPathAtt", "rtl3extDynPathAtt-")) meta.childNamesAndRnPrefix.append(("cobra.model.extnw.RtL3DomAtt", "rtl3extL3DomAtt-")) meta.childNamesAndRnPrefix.append(("cobra.model.infra.RtExtDevDomP", "rtedmExtDevDomP-")) meta.childNamesAndRnPrefix.append(("cobra.model.infra.RsDomVxlanNsDef", "rsdomVxlanNsDef")) meta.childNamesAndRnPrefix.append(("cobra.model.infra.RtDomDef", "rtextdevDomDef-")) meta.childNamesAndRnPrefix.append(("cobra.model.infra.RtLDevDomP", "rtvnsLDevDomP-")) meta.childNamesAndRnPrefix.append(("cobra.model.infra.RtDomRef", "rtedmDomRef-")) meta.childNamesAndRnPrefix.append(("cobra.model.infra.RtDomAtt", "rtfvDomAtt-")) meta.childNamesAndRnPrefix.append(("cobra.model.infra.RsVipAddrNs", "rsvipAddrNs")) meta.childNamesAndRnPrefix.append(("cobra.model.infra.RsVlanNsDef", "rsvlanNsDef")) meta.childNamesAndRnPrefix.append(("cobra.model.infra.RsVlanNs", "rsvlanNs")) meta.childNamesAndRnPrefix.append(("cobra.model.fault.Counts", "fltCnts")) meta.childNamesAndRnPrefix.append(("cobra.model.extnw.LblCont", "lblcont")) meta.childNamesAndRnPrefix.append(("cobra.model.infra.RtDomP", "rtdomP-")) meta.childNamesAndRnPrefix.append(("cobra.model.fault.Inst", "fault-")) meta.childNamesAndRnPrefix.append(("cobra.model.extnw.RsOut", "rsout-")) meta.childNamesAndRnPrefix.append(("cobra.model.health.Inst", "health")) meta.childNamesAndRnPrefix.append(("cobra.model.fault.Delegate", "fd-")) meta.parentClasses.add("cobra.model.fv.RtdEpP") meta.superClasses.add("cobra.model.infra.ADomP") meta.superClasses.add("cobra.model.infra.DomP") meta.superClasses.add("cobra.model.l3ext.ADomP") meta.superClasses.add("cobra.model.pol.Obj") meta.superClasses.add("cobra.model.pol.Dom") meta.superClasses.add("cobra.model.naming.NamedObject") meta.superClasses.add("cobra.model.fv.ADomP") meta.superClasses.add("cobra.model.pol.Cont") meta.superClasses.add("cobra.model.extnw.DomP") meta.rnPrefixes = [ ('l3dom-', True), ] prop = PropMeta("str", "childAction", "childAction", 4, PropCategory.CHILD_ACTION) prop.label = "None" prop.isImplicit = True prop.isAdmin = True prop._addConstant("deleteAll", "deleteall", 16384) prop._addConstant("deleteNonPresent", "deletenonpresent", 8192) prop._addConstant("ignore", "ignore", 4096) meta.props.add("childAction", prop) prop = PropMeta("str", "configIssues", "configIssues", 4941, PropCategory.REGULAR) prop.label = "Configuration Issues" prop.isImplicit = True prop.isAdmin = True prop.defaultValue = 0 prop.defaultValueStr = "none" prop._addConstant("cdp-lldp-collision", "both-cdp-policy-and-lldp-policy-are-configured-for-attach-entity-profile", 16) prop._addConstant("enhanced-lacp-lag-creation-skipped", "enhanced-lacp-lag-policy-creation-skipped,-dvs-has-lacp-v1-enabled", 4096) prop._addConstant("invalid-mcast-addr", "missing-multicast-address-for-vxlan-mode", 512) prop._addConstant("invalid-port", "invalid-port-for-fabric-interface", 1024) prop._addConstant("invalid-vxlan-ns-range", "vxlan-range-below-0x800000-is-not-valid-for-n1kv-ns-mode", 128) prop._addConstant("missing-assoc-attEntP", "domain-is-missing-association-from-attach-entity-profile", 8) prop._addConstant("missing-encap", "invalid-or-missing-encapsulation", 1) prop._addConstant("missing-encapblk", "invalid-or-missing-encapsulation-blocks", 4) prop._addConstant("missing-epg", "association-to-end-point-group-not-specified", 2) prop._addConstant("missing-internal-vlan-blk", "missing-internal-vlan-encapsulation-blocks", 2048) prop._addConstant("missing-ns-assoc", "invalid-or-missing-association-to-vlan-or-vxlan-namespace", 256) prop._addConstant("multiple-cdp", "more-than-one-cdp-policy-found-for-attach-entity-profile", 64) prop._addConstant("multiple-lldp", "more-than-one-lldp-policy-found-for-attach-entity-profile", 32) prop._addConstant("none", "n/a", 0) meta.props.add("configIssues", prop) prop = PropMeta("str", "dn", "dn", 1, PropCategory.DN) prop.label = "None" prop.isDn = True prop.isImplicit = True prop.isAdmin = True prop.isCreateOnly = True meta.props.add("dn", prop) prop = PropMeta("str", "lcOwn", "lcOwn", 9, PropCategory.REGULAR) prop.label = "None" prop.isImplicit = True prop.isAdmin = True prop.defaultValue = 0 prop.defaultValueStr = "local" prop._addConstant("implicit", "implicit", 4) prop._addConstant("local", "local", 0) prop._addConstant("policy", "policy", 1) prop._addConstant("replica", "replica", 2) prop._addConstant("resolveOnBehalf", "resolvedonbehalf", 3) meta.props.add("lcOwn", prop) prop = PropMeta("str", "modTs", "modTs", 7, PropCategory.REGULAR) prop.label = "None" prop.isImplicit = True prop.isAdmin = True prop.defaultValue = 0 prop.defaultValueStr = "never" prop._addConstant("never", "never", 0) meta.props.add("modTs", prop) prop = PropMeta("str", "monPolDn", "monPolDn", 14212, PropCategory.REGULAR) prop.label = "Monitoring policy attached to this observable object" prop.isImplicit = True prop.isAdmin = True meta.props.add("monPolDn", prop) prop = PropMeta("str", "name", "name", 6853, PropCategory.REGULAR) prop.label = "Name" prop.isConfig = True prop.isAdmin = True prop.isCreateOnly = True prop.isNaming = True prop.range = [(1, 64)] prop.regex = ['[a-zA-Z0-9_.:-]+'] meta.props.add("name", prop) prop = PropMeta("str", "nameAlias", "nameAlias", 28417, PropCategory.REGULAR) prop.label = "Name alias" prop.isConfig = True prop.isAdmin = True prop.range = [(0, 63)] prop.regex = ['[a-zA-Z0-9_.-]+'] meta.props.add("nameAlias", prop) prop = PropMeta("str", "ownerKey", "ownerKey", 15232, PropCategory.REGULAR) prop.label = "None" prop.isConfig = True prop.isAdmin = True prop.range = [(0, 128)] prop.regex = ['[a-zA-Z0-9\\!#$%()*,-./:;@ _{|}~?&+]+'] meta.props.add("ownerKey", prop) prop = PropMeta("str", "ownerTag", "ownerTag", 15233, PropCategory.REGULAR) prop.label = "None" prop.isConfig = True prop.isAdmin = True prop.range = [(0, 64)] prop.regex = ['[a-zA-Z0-9\\!#$%()*,-./:;@ _{|}~?&+]+'] meta.props.add("ownerTag", prop) prop = PropMeta("str", "rn", "rn", 2, PropCategory.RN) prop.label = "None" prop.isRn = True prop.isImplicit = True prop.isAdmin = True prop.isCreateOnly = True meta.props.add("rn", prop) prop = PropMeta("str", "status", "status", 3, PropCategory.STATUS) prop.label = "None" prop.isImplicit = True prop.isAdmin = True prop._addConstant("created", "created", 2) prop._addConstant("deleted", "deleted", 8) prop._addConstant("modified", "modified", 4) meta.props.add("status", prop) prop = PropMeta("str", "targetDscp", "targetDscp", 1625, PropCategory.REGULAR) prop.label = "None" prop.isImplicit = True prop.isAdmin = True prop.range = [(0, 64)] prop.defaultValue = 64 prop.defaultValueStr = "unspecified" prop._addConstant("AF11", "af11-low-drop", 10) prop._addConstant("AF12", "af12-medium-drop", 12) prop._addConstant("AF13", "af13-high-drop", 14) prop._addConstant("AF21", "af21-low-drop", 18) prop._addConstant("AF22", "af22-medium-drop", 20) prop._addConstant("AF23", "af23-high-drop", 22) prop._addConstant("AF31", "af31-low-drop", 26) prop._addConstant("AF32", "af32-medium-drop", 28) prop._addConstant("AF33", "af33-high-drop", 30) prop._addConstant("AF41", "af41-low-drop", 34) prop._addConstant("AF42", "af42-medium-drop", 36) prop._addConstant("AF43", "af43-high-drop", 38) prop._addConstant("CS0", "cs0", 0) prop._addConstant("CS1", "cs1", 8) prop._addConstant("CS2", "cs2", 16) prop._addConstant("CS3", "cs3", 24) prop._addConstant("CS4", "cs4", 32) prop._addConstant("CS5", "cs5", 40) prop._addConstant("CS6", "cs6", 48) prop._addConstant("CS7", "cs7", 56) prop._addConstant("EF", "expedited-forwarding", 46) prop._addConstant("VA", "voice-admit", 44) prop._addConstant("unspecified", "unspecified", 64) meta.props.add("targetDscp", prop) meta.namingProps.append(getattr(meta.props, "name")) # Deployment Meta meta.deploymentQuery = True meta.deploymentType = "Path" meta.deploymentQueryPaths.append(DeploymentPathMeta("ADomPToEthIf", "Interface", "cobra.model.l1.EthIf")) def __init__(self, parentMoOrDn, name, markDirty=True, **creationProps): namingVals = [name] Mo.__init__(self, parentMoOrDn, markDirty, *namingVals, **creationProps) # End of package file # ##################################################

[ "bkhoward@live.com" ]

bkhoward@live.com

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/tests/dice/operators/test_advantage_operator.py

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extesla/dice-python

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2022-06-03T03:12:59.254841

2022-05-30T17:55:27

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# The MIT License (MIT) # # Copyright (c) 2016 Sean Quinn # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. from dice.operators import Advantage from dice.tokens import Dice import pytest def test_instantiate_advantage_operator(): operator = Advantage([5, 17]) assert operator.original_operands == ([5, 17],) assert operator.operands == ([5, 17],) def test_repr(): """ Test that the string representation of the operator is what is expected. Given an instance of the Advantage operator on operands When the method __repr__ is called Then the result should be "Advantage" """ operator = Advantage([5, 17]) assert repr(operator) == "Advantage([5, 17])" def test_advantage_function_when_choosing_from_empty_array(): operator = Advantage() with pytest.raises(IndexError): operator.function([]) def test_advantage_function_with_invalid_iterable(): operator = Advantage() with pytest.raises(TypeError): operator.function(1) def test_advantage_function_with_no_iterable(): operator = Advantage() with pytest.raises(TypeError): operator.function(None) def test_evaluate_advantage_with_single_value_in_scalar_array(): operator = Advantage([5, 17]) actual = operator.evaluate() assert actual == 17 assert operator.result == 17 assert actual == operator.result def test_evaluate_advantage_with_multiple_values_in_scalar_array(): operator = Advantage([13, 5, 17]) actual = operator.evaluate() assert actual == 17 assert operator.result == 17 assert actual == operator.result def test_evaluate_advantage_with_dice_token_value(mocker): mock_random = mocker.patch("dice.tokens.mt_rand") mock_random.side_effect = [5, 17] dice_token = Dice(sides=20, rolls=2) operator = Advantage(dice_token) actual = operator.evaluate() assert actual == 17 assert operator.result == 17 assert actual == operator.result

[ "swquinn@gmail.com" ]

swquinn@gmail.com

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/03_Implementacao/DataBase/true_or_false_question_while_and_for_cicles/question/version_2/answers_program.py

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projeto-exercicios/Exercicios-Python-de-correccao-automatica

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answer_1_true = while_cicle(48) answer_2_true = p answer_3_true = print_indexes(69) print(answer_1_true) print(answer_2_true) print(answer_3_true)

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ruski@milo.com

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/05. The FOR loop.py

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martaksx/UdemyPythonForDataScience

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2020-03-27T09:30:17.803314

2018-08-27T21:25:26

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# coding: utf-8 # In[1]: counter = 1 while counter < 12: print (counter) counter = counter + 1 # --- # In[2]: for i in range(5): print("Hello Python") # --- # In[3]: range(5) # In[4]: list(range(5)) # In[5]: for i in range(7): print("Hello Python: ", i) # In[6]: #Another way mylist = [10,100,1000] # In[7]: print (mylist) # In[8]: for jj in mylist: print("jj is equal to: ", jj)

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noreply@github.com

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/huffman_compressor.py

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haoccc/JPGCompression

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2020-07-28T03:32:54

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""" Implementation of archivator using Huffman coding works only with alphanumeric symbols, ASCII 0-255 """ from collections import Counter from queue import PriorityQueue import os class HuffmanNode: def __init__(self, char, freq=0, left=None, right=None): self.char = char self.freq = freq self.left = left self.right = right def __lt__(self, other): return self.freq < other.freq def encode(text): """Returns encoded string code with format [encoded_huffman_tree][extra_zeros_num][encoded_text]""" frequencies = Counter(text) queue = PriorityQueue() code_table = {} for char, f in frequencies.items(): queue.put(HuffmanNode(char, f)) # merge nodes while queue.qsize() > 1: l, r = queue.get(), queue.get() queue.put(HuffmanNode(None, l.freq + r.freq, l, r)) huffman_tree = queue.get() _fill_code_table(huffman_tree, "", code_table) encoded_text_code = "" for c in text: encoded_text_code += code_table[c] encoded_tree_code = _encode_huffman_tree(huffman_tree, "") # add extra zeros, as in python it is not possible read # file bit by bit (min byte) so extra zeros will be # added automatically which cause a loss of information num = 8 - (len(encoded_text_code) + len(encoded_tree_code)) % 8 if num != 0: encoded_text_code = num * "0" + encoded_text_code print(f"frequencies: {frequencies}") print(f"encoded huffman tree code: {encoded_tree_code}") print(f"encoded text code: {encoded_text_code}") return f"{encoded_tree_code}{num:08b}{encoded_text_code}" def decode(encoded_text): """Returns decoded string""" encoded_text_ar = list(encoded_text) encoded_tree = _decode_huffman_tree(encoded_text_ar) # remove extra zeros number_of_extra_0_bin = encoded_text_ar[:8] encoded_text_ar = encoded_text_ar[8:] number_of_extra_0 = int("".join(number_of_extra_0_bin), 2) encoded_text_ar = encoded_text_ar[number_of_extra_0:] # decode text text = "" current_node = encoded_tree for char in encoded_text_ar: current_node = current_node.left if char == '0' else current_node.right if current_node.char is not None: text += current_node.char current_node = encoded_tree return text def decompress(input_path, output_path): """Save decoded text to output file""" with open(input_path, "rb") as in_file, open(output_path, "w") as out_file: encoded_text = "" byte = in_file.read(1) while len(byte) > 0: encoded_text += f"{bin(ord(byte))[2:]:0>8}" byte = in_file.read(1) decoded_text = decode(encoded_text) out_file.write(decoded_text) def compress(input_path, output_path): """Save encoded text to output file""" with open(input_path) as in_file, open(output_path, "wb") as out_file: text = in_file.read() encoded_text = encode(text) b_arr = bytearray() for i in range(0, len(encoded_text), 8): b_arr.append(int(encoded_text[i:i+8], 2)) out_file.write(b_arr) def _fill_code_table(node, code, code_table): """Fill code table, which has chars and corresponded codes""" if node.char is not None: code_table[node.char] = code else: _fill_code_table(node.left, code + "0", code_table) _fill_code_table(node.right, code + "1", code_table) def _encode_huffman_tree(node, tree_text): """Encode huffman tree to save it in the file""" if node.char is not None: tree_text += "1" tree_text += f"{ord(node.char):08b}" else: tree_text += "0" tree_text = _encode_huffman_tree(node.left, tree_text) tree_text = _encode_huffman_tree(node.right, tree_text) return tree_text def _decode_huffman_tree(tree_code_ar): """Decoding huffman tree to be able to decode the encoded text""" # need to delete each use bit as we don't know the length of it and # can't separate it from the text code code_bit = tree_code_ar[0] del tree_code_ar[0] if code_bit == "1": char = "" for _ in range(8): char += tree_code_ar[0] del tree_code_ar[0] return HuffmanNode(chr(int(char, 2))) return HuffmanNode(None, left=_decode_huffman_tree(tree_code_ar), right=_decode_huffman_tree(tree_code_ar)) def _print_ratio(input_path, output_path): before_size = os.path.getsize(input_path) after_size = os.path.getsize(output_path) compression_percent = round(100 - after_size / before_size * 100, 1) print(f"before: {before_size}bytes, after: {after_size}bytes, " f"compression {compression_percent}%") '''file_to_compress, decompressed, compressed = "image.txt", "decompressed.txt", "compressed.bin" compress(file_to_compress, compressed)'''

[ "noreply@github.com" ]

noreply@github.com

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/LeetCode/pythonSols/Tree/binarySearchTreeIterator.py

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no_license

abhitrip/scratchpad

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2021-01-17T19:11:40.366951

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# Definition for a binary tree node # class TreeNode(object): # def __init__(self, x): # self.val = x # self.left = None # self.right = None class BSTIterator(object): def __init__(self, root): """ :type root: TreeNode """ self.stk = [] while root!=None: self.stk.append(root) root = root.left def hasNext(self): """ :rtype: bool """ return len(self.stk)!=0 def next(self): """ :rtype: int """ node = self.stk.pop() if node.right!=None: self.stk.append(node.right) left = node.right.left while left!=None: self.stk.append(left) left = left.left return node.val # Your BSTIterator will be called like this: # i, v = BSTIterator(root), [] # while i.hasNext(): v.append(i.next())

[ "atripath@eng.ucsd.edu" ]

atripath@eng.ucsd.edu

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/project8.py

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ArthurMelo9/100daysofCode

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alphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'] direction = input("Type 'encode' to encrypt, type 'decode' to decrypt:\n") text = input("Type your message:\n").lower() shift = int(input("Type the shift number:\n")) def encrypt(plain_text, shift_amount): cipher_text = "" for letter in plain_text: position = alphabet.index(letter) new_position = position + shift_amount cipher_text += alphabet[new_position] print(f"The encoded text is {cipher_text}") #TODO-1: Create a different function called 'decrypt' that takes the 'text' and 'shift' as inputs. def decrypt (plain_text, shift_amount): decrypt_text="" for letter in plain_text: position= alphabet.index(letter) new_position= position - shift_amount decrypt_text += alphabet[new_position] print(f"The decrypted code is {decrypt_text}") #TODO-2: Inside the 'decrypt' function, shift each letter of the 'text' *backwards* in the alphabet by the shift amount and print the decrypted text. #e.g. #cipher_text = "mjqqt" #shift = 5 #plain_text = "hello" #print output: "The decoded text is hello" #TODO-3: Check if the user wanted to encrypt or decrypt the message by checking the 'direction' variable. Then call the correct function based on that 'drection' variable. You should be able to test the code to encrypt *AND* decrypt a message. if direction =="encode": encrypt(plain_text=text, shift_amount=shift) elif direction == "decode": decrypt(plain_text =text, shift_amount=shift) else: print("Enter 'encode' or 'decode' to encrypt or decrypt your message.")

[ "arthurneuro7@gmail.com" ]

arthurneuro7@gmail.com

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/app/migrations/0002_imagen_imagencolor.py

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ericksulca/b-b2RGB

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refs/heads/main

2023-07-30T16:28:50.165068

2021-09-24T23:22:45

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# Generated by Django 3.2.7 on 2021-09-24 17:16 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('app', '0001_initial'), ] operations = [ migrations.AddField( model_name='imagen', name='imagencolor', field=models.ImageField(default='no-imagen.png', null=True, upload_to='', verbose_name='Fotografía a color'), ), ]

[ "ejyp259@hotmail.com" ]

ejyp259@hotmail.com

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/code.py

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PECNAS/Tokanomir

ba8b06c4ba25c2edafcabbe449ab5b24e021d2a4

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refs/heads/master

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2020-04-28T21:36:15

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''' Данная программа анализирует ТЕКСТОВУЮ базу данных с больными Больные имеют пять параметров: имя, класс, симптом, наличие прививки и дата поступления Просто гусь +-------------------------------------------------+ | ░░░░░▄▀▀▀▄░░░░░░░░░ | | ▄███▀░◐░░░▌░░░░░░░ | | ░░░░▌░░░░░▐░░░░░░░ | | ░░░░▐░░░░░▐░░░░░░░ | | ░░░░▌░░░░░▐▄▄░░░░░ | | ░░░░▌░░░░▄▀▒▒▀▀▀▀▄ | | ░░░▐░░░░▐▒▒▒▒▒▒▒▒▀▀▄ | | ░░░▐░░░░▐▄▒▒▒▒▒▒▒▒▒▒▀▄ | | ░░░░▀▄░░░░▀▄▒▒▒▒▒▒▒▒▒▒▀▄ | | ░░░░░░▀▄▄▄▄▄█▄▄▄▄▄▄▄▄▄▄▄▀ | | ░░░░░░░░░░░▌▌░▌▌░░░░░ | | ░░░░░░░░░░░▌▌░▌▌░░░░░ | | ░░░░░░░░░▄▄▌▌▄▌▌░░░░░ | +-------------------------------------------------+ Выполненные бонусы: —При запуске с аргументом -color заболевшие с прививкой выводятся зелёным цветом, без прививки — красным —В самом начале программы создаётся новая база данных, с только ДЕЙСТВИТЕЛЬНЫМИ датами. Программа работает с новой базой данных Добавлено: —Отключение и включение расцветки в меню —Поиск больных по двум параметрам(третий метод поиска) Исправить: —Коментарии <<<ИСПРАВЛЕНО>>> —Добавить help. ОБЯЗАТЕЛЬНО! <<<ИСПРАВЛЕНО>>> —Исправить olor <<<ИСПРАВЛЕНО>>> —Убрать тестовый файл при заливе... —Переделать под словарь(картинка dictionary_for_animals.png) —Сделать метод split(";") для строк 80-85 —Исправить действительность дат —Поменять termcolor на Escape выражение '/033[32mТЕКСТЕКСТЕКСТ[0m' для цвета <<<ИСПРАВЛЕНО>>> — ''' import sys import argparse from information.help import get_help from datetime import datetime from termcolor import colored time_now = datetime.now() # записываем, сколько сейчас времени color = False parser = argparse.ArgumentParser() #создаём образ парсера parser.add_argument('-c', '--color', action="store_true") # добавляем необязательный аргумент parser.add_argument('-gh', '--get_help', action="store_true") # добавляем необязательный аргумент args = parser.parse_args() if args.color == "--color": # проверяем аргумент color = True elif args.get_help == True: get_help() print("Идёт сканирование базы данных, это может занять некоторое время") database = open("database.txt", "r") # здесь мы открываем файл с параметром чтения array = [i for i in database.readlines()] # записываю генератор для записи всего из файла базы данных в массив names = [] # здесь я создаю массив для имен types = [] # здесь я создаю массив для типа diseases = [] # здесь я создаю массив для симптома vaccination = [] # здесь я создаю массив для вакцинирования arrival_date = [] # здесь я создаю массив для даты поступления exceptions = [] # здесь я создаю массив для исключений. Тут будут находится индексы тех больных, дата которых недействительная positive_vaccination = ["имеют прививку", "с прививкой", # здесь я создаю массив для слов, вариаций которых может быть множество "вакцинированы", "наличие прививки", "прививка"] negative_vaccination = ["без прививки", "не имеют прививки", # здесь я создаю массив для слов, вариаций которых может быть множество "не вакцинированы"] variations_diseases = ["симптом", "болезнь", "диагноз"] def insert(): for db in range(len(array)): # с помощью цикла, который повториться ровно столько раз, какова длина массива find_name = array[db].find(";") # записываю в переменную индекс того места, до которого нам нужно брать информацию find_type = array[db].find(";", find_name + 1) # записываю в переменную индекс того места, до которого нам нужно брать информацию и прибавляю один для того, что бы не брать точку с запятой find_diseases = array[db].find(";", find_type + 1) # записываю в переменную индекс того места, до которого нам нужно брать информацию и прибавляю один для того, что бы не брать точку с запятой find_vaccination = array[db].find(";", find_diseases + 1)#записываю в переменную индекс того места,до которого нам нужно брать информацию и прибавляю один для того, что бы не брать точку с запятой find_day = array[db].find(".") # записываю в переменную индекс точки, которая разделяет дату поступления на дни, месяца и года. Берём первую точку find_month = array[db].rfind(".") # записываю в переменную индекс точки, которая разделяет дату поступления на дни, месяца и года. Берём вторую точку names.append(array[db][:find_name]) # добавляю в массив имен имена types.append(array[db][find_name + 1:find_type]) # добляю в массив типа тип diseases.append(array[db][find_type + 1:find_diseases]) # добавляю в массив симптома симптом vaccination.append(array[db][find_diseases + 1:find_vaccination]) # добавляю в массив вакцинирования вакцинирован ли try: # ищем февральские дни data = datetime(int(array[db][find_month + 1:-1]), int(array[db][find_day + 1:find_month]), int(array[db][find_vaccination + 1:find_day])) # в переменную дата записываем дату больного if data <= time_now: # если дата действительная arrival_date.append(array[db][find_vaccination + 1:-1]) # добавляю в массив даты поступления дату поступления elif data > time_now: # если дата недействительная exceptions.append(db) # добавляем индекс в массив с исключениями except ValueError: # Если нашли неверный дни в феврале exceptions.append(db) # добавляем индекс в массив с исключениями def color_set(index): # функция определения цвета if args.color == True: if vaccination[index].lower() == "да": # если в массиве в срезе содержится да return 33 # то возвращаем зелёный цвет else: # если в массиве в срезе содержится нет return 31 # то возвращаем красный цвет else: # если аргумент -color не указан return 0 # то ставим белый цвет def first(): # первый метод поиска def print_value(conclusion, val): if conclusion.lower() == "имя": print(f"\033[{color_set(val)}m{names[val].capitalize()}\033[0m") # вывожу имена и сортирую их атрибутом end="\t" elif conclusion.lower() == "класс": print(f"\033[{color_set(val)}m{types[val].capitalize()}\033[0m") # то же самое, что и в прошлый раз elif conclusion.lower() in variations_diseases: print(f"\033[{color_set(val)}m{diseases[val].capitalize()}\033[0m") # то же самое, что и в прошлый раз elif conclusion.lower() in positive_vaccination: print(f"\033[{color_set(val)}m{vaccination[val].capitalize()}\033[0m") # то же самое, что и в прошлый раз elif conclusion.lower() == "дата": print(f"\033[{color_set(val)}m{arrival_date[val].capitalize()}\033[0m") # то же самое, что и в прошлый раз elif conclusion.lower() == "другой": # проверка для выбора другого номера ask_first() # вызываем функцию elif conclusion.lower() == "стоп": # проверяем на остановку sys.exit() # заканчиваем исполнение прогрммы elif conclusion.lower() == "всё": # проверяем на вывод всех показателей print(f"\033[{color_set(val)}m{array[val].replace(';', ', ').capitalize()}\033[0m") # выводим все показатели с заменой точки с зпаятой на запятую elif conclusion.lower() == "методы": # вызываем стартовое меню для смены методы поиска choice() # вызываем функцию else: print("Введён неверный параметр!\nПожалуйста введите параметр из существующих.\n'Имя', 'класс', 'симптом', 'наличие прививки', 'дата'\nВв" + "едите слово 'стоп', для остановки\nДля смены больного введите 'Другой'\nТак же можете использовать комманду 'всё' для вывода " + "всей информации о больном\nКоммманда 'методы' позволит сменить метод поиска больных ") # это сообщение об ошибке def ask_first(): val = input("Введите номер больного: ") if val != "": if val[0] in "0123456789": #если номер больного это цифра try: # начинаем ловить исключения val = int(val) # переменной val ставим тип integer while True: # бесконечный цикл if 0 <= val <= (len(names) - 1): # если номер больного введён правильно conclusion = input("Введите параметр: ") # то спрашиваем желаемый параметр у пользователя print_value(conclusion, val) # вызываем функцию else: # если значение введено неверно print("В нашей базе данных нет больного с таким номером!\nВсего заболевших " + str(len(names) - 1)) # выводим максимум больных и сообщение об ошибке ask_first() # вызываем функцию заново except ValueError: # если поймали исключение о вводе строки print("Введён неверный тип данных") # то выводим сообщение об ошибке ask_first() # и вызываем функцию заново elif val == "стоп": # однако если val равен стоп sys.exit() # останавливаем программу elif val.lower() == "методы": choice() else: print("Вы ввели неверный тип данных, нужно ввести нумерацию больного!\n") # Выдаём сообщение об ошибке ask_first() # заново вызываем функцию else: print("Это поле является обязательным для ввода!") ask_first() ask_first() # запускаем функцию def second(): # второй метод поиска def find_all_with_parametr(val): # функция вывода всех совпадающих значение count = 0 # создаём переменную счёта for i in range(len(array)): # циклом пробегаемся по массиву столько раз, какова длина массива if val in array[i]: # если введенное значение имеется в строке массива print(f"\033[{color_set(i)}m{str(i) + ') ' + array[i].replace(';', ', ').capitalize()}\033[0m") # вывести строку и заменить все точки с запятой на запятый count += 1 print("Всего насчитано " + str(count) + " больных с такими показателями") ask_second() # вызываем функцию снова def ask_second(): val = str(input("Введите значение, по которому хотите найти больных: ")) # принимаем значение от пользователя if (val in names) or (val in types) or (val in diseases) or (val in vaccination) or (val in arrival_date): # проверка на то, есть ли данное значение в массивах if val.lower()[0] in "абвгдеёжзийклмнопрстуфхцчшщъыьэюя1234567890.": # если запрос не содержит лишние символы find_all_with_parametr(val) # тогда вызываем функцию else: print("Введённые вами данные не существуют!\n") # Выдаём сообщение об ошибке find_all_with_parametr(val) # заново вызываем функцию elif val.lower() in positive_vaccination: find_all_with_parametr("Да") elif val.lower() in negative_vaccination: find_all_with_parametr("Нет") elif val.lower() == "стоп": sys.exit() elif val.lower() == "методы": choice() else: # если значения в массиве нет print("Извините, введённое вами значение не найдено ни в одном списке\nВыберите другое значение") # выдаём сообщение об ошибке ask_second() # заново вызываем функцию ask_second() # запуск def third(): def check_with_two_parametrs(first_arg, second_arg): count = 0 for c in array: if first_arg in c and second_arg in c: print(f'\033[{color_set(array.index(c))}m{c.replace(";", ", ")}\033[0m') count += 1 print("Всего насчитано " + str(count) + " больных с такими показателями") ask_third() def ask_third(): val = str(input("Введите значение, по которому хотите найти больных: ")) # принимаем значение от пользователя separator = val.find(",") # задаём переменной разделитель space = separator + 1 # задаём переменной значение разделителя плюс один, что бы не брать в учёт запятую if val[separator + 1] == " ": # если после запятой стоит пробел space = separator + 2 # мы задаём перменной значение разделителя плюс два, что бы не брать запятую с пробелом first_arg, second_arg = val[:separator], val[space:] # тут в действие идёт магия питона if (first_arg in names) or (first_arg in types) or (first_arg in diseases) or (first_arg in vaccination) or (first_arg in arrival_date): # проверка на то, есть ли первое значение в массивах if (second_arg in names) or (second_arg in types) or (second_arg in diseases) or (second_arg in vaccination) or (second_arg in arrival_date): # проверка на то, есть ли первое значение в массивах if val.lower()[0] in "абвгдеёжзийклмнопрстуфхцчшщъыьэюя1234567890.": # если запрос не содержит лишние символы check_with_two_parametrs(first_arg, second_arg) # тогда вызываем функцию elif first_arg == "" or second_arg == "": # если один из один из параметров пойска был пустой print("Вы обязательно должны ввести оба значения") # выводим сообщение об ошибке ask_third() # заново вызываем функцию вопросы else: print("Введённые вами данные не существуют!\n") # Выдаём сообщение об ошибке ask_third() # заново вызываем функцию elif second_arg.lower() in positive_vaccination: # тут мы облегчаем синтаксис check_with_two_parametrs(first_arg, "Да") # тут мы облегчаем синтаксис elif second_arg.lower() in negative_vaccination:# тут мы облегчаем синтаксис check_with_two_parametrs(first_arg, "Нет")# тут мы облегчаем синтаксис else: print("Второе введённое значение не найдено!\nВыберите другое значение") # выводим сообщение об ошибке ask_third() # вызываем функцию выбора снова elif first_arg == "" or second_arg == "": # если один из аргументов пустой print("Вы обязательно должны ввести оба значения") # выводим сообщение об ошибке ask_third() # вызываем функцию выбора снова elif first_arg.lower() in positive_vaccination: # тут мы облегчаем синтаксис check_with_two_parametrs("Да", second_arg) # тут мы облегчаем синтаксис elif first_arg.lower() in negative_vaccination: # тут мы облегчаем синтаксис check_with_two_parametrs("Нет", second_arg) # тут мы облегчаем синтаксис elif val.lower() == "стоп": # проверяем на остановку программы sys.exit() # завершаем программу elif val.lower() == "методы": # проверяем на смену метода поиска choice() # вызываем функцию поиска else: # если значения в массиве нет print("Первое введённое значение не найдено\nВыберите другое значение") # выдаём сообщение об ошибке ask_third() # заново вызываем функцию ask_third() def choice(): # функция выбора методов поиска select = input("Какую функцию поиска запустить(первая, вторая, третья)?\n") # спрашиваем пользователя if select.lower() == "первая": # если первый метод, то вызываем функцию первого метода first() elif select.lower() == "вторая": # то же самое second() elif select.lower() == "третья": print("Введите два значения через запятую") third() elif select.lower() == "стоп": # то же самое sys.exit() elif select.lower() == "отключить расцветку": global color color = False print("Расцветка выключена") choice() elif select.lower() == "включить расцветку": color = True print("Расцветка включена") choice() else: print("Функции с такиим номером не существует!\nВведите 'первая', 'вторая' или 'стоп'.\nДля отключения расцветки пропишите 'отключить расцветку'\nДля включения подсветки" + " пропишите 'включить расцветку'") # выввести сообщение об ошибке choice() # заново вызываем функцию insert()# запуск программы, входная точка new_database = open("new_database.txt", "w") # открываем открываем файл новой базы данных на ЗАПИСЬ counter = len(array) names = [] # здесь я обнуляю массив для имен, для того, что бы заполнить файл новыми значениями types = [] # здесь я обнуляю массив для типа, для того, что бы заполнить файл новыми значениями diseases = [] # здесь я обнуляю массив для симптома, для того, что бы заполнить файл новыми значениями vaccination = [] # здесь я обнуляю массив для вакцинирования, для того, что бы заполнить файл новыми значениями arrival_date = [] # здесь я обнуляю массив для даты поступления, для того, что бы заполнить файл новыми значениями for new in range(counter): if new in exceptions: # проверяем нет ли индекса в списке с исключениями pass # если есть, то просто игнорируем его else: # если его нет в списке с исключениями base_data = array[new] # создаём переменную new_database.write(base_data) # записываем в файл new_database.close() # закрываем новую базу данных new_database = open("new_database.txt", "r") # открываем новую базу данных на ЧТЕНИЕ array = [n for n in new_database.readlines()] # переназначаем список на новую базу данных new_database.close() # закрываем новую базу данных database.close() # закрываем базу данных insert() # функция заполнения списков choice() # функция выбора методов поиска

[ "busovrm4@gmail.com" ]

busovrm4@gmail.com

daa468830747de562fd243d8d431e43330be2146

823538725626b8b3da48d9407f703f72f8ee591f

/t628nn/implementation/cl_layers/__init__.py

663d55c131bb95b51c6fcb765842323a23edc2a6

[]

no_license

Denise-Li/t628nn

63cd2aac869e48f0fddbbab6ff7f9ce74593d6f4

d8ecd82bc6a42022974f7d5069ab96c8cf00c67a

refs/heads/master

2020-09-05T05:03:11.147912

2019-11-09T22:07:10

219,990,357

0

null

2019-11-07T14:11:56

2019-11-06T12:21:22

null

UTF-8

Python

false

143

py

from .MaxPoolLayer import MaxPoolLayer from .ConvolutionalLayer import ConvolutionalLayer from .FullyConnectedLayer import FullyConnectedLayer

[ "2387729l@student.gla.ac.uk" ]

2387729l@student.gla.ac.uk

651744644f01fcbc9afa85b5da4940fa6350128a

de46832c4fdafae716906e6b25bc1121646b7d03

/Python_Code/helloworld.py

7a5e6e25bd55d8de86eca7c087667da1ed7743f0

[]

no_license

Tessu/HarjoitusRep

da63a1db07277eb85054870b5075c7d932d4a133

974427f68cd0ab338741d9edf4bc7d563c81b5b3

refs/heads/master

2020-08-03T16:55:04.150888

2017-04-28T12:25:32

73,542,162

0

null

2016-11-17T10:49:43

2016-11-12T08:54:47

Python

UTF-8

Python

false

62

py

print "Hello World!" hello_world = "Hello" print hello_world

[ "henrithessler@gmail.com" ]

henrithessler@gmail.com

8aa4fdca170152b6545b4c99a900518c6cd068a9

b892b3141219973088a9642f830db5284c20471a

/vitaa/solver/__init__.py

613306339f9cf287c9ff2b469fc0f8e0f7dd719c

[]

no_license

Galaxfy/ViTAA

60f43c120db48d2824e7d8dbc3614e2e1c8f0cf2

0bd7638f07131035ba88c7fb7e115feebc57fd2f

refs/heads/master

2023-03-24T08:39:41.482237

2020-08-27T15:18:14

null

0

null

UTF-8

Python

false

191

py

[ "wangzhewz@buaa.edu.cn" ]

wangzhewz@buaa.edu.cn

33ab37d36bf060e9503eccc0a0f4e85f69338e95

4b27d461266c52e8c8855728b572afa9b6db4be3

/mysite/settings.py

77398e8ce7a9a5829481a4faa4dde3b3e02c2d71

[]

no_license

Netekss/Django-shop-manager

88bfb7e5507b9ca2077abc12c216f2f337966806

b35e6f37d7f9bd699ccb7a27af34e90f6d80860b

refs/heads/master

2023-02-28T01:37:38.754717

2021-01-26T09:45:04

315,080,285

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2021-01-26T09:45:05

2020-11-22T16:22:32

HTML

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py

""" Django settings for mysite project. Generated by 'django-admin startproject' using Django 3.1.3. For more information on this file, see https://docs.djangoproject.com/en/3.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.1/ref/settings/ """ from pathlib import Path # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'i*vwr^pss#n06ljg8^&2ibetq)78+4@xz9^w$7qd-i7y)=x&oy' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'warehouse', 'order', 'owner', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'mysite.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'mysite.wsgi.application' # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'Europe/Warsaw' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/'

[ "Jakubnetkowski29@gmail.com" ]

Jakubnetkowski29@gmail.com

58bf69943a02a438a32fc8d8965ddc47fcd1eb4e

ad6ea02c93cea570d9487f1f8b99b00acabe6abb

/docs/architecture/build/architecture_one_shard.py

dc785906ba7cf0367b72e796c5d5494c753bbe98

[]

permissive

talview/jitsi-deployment

47942ca3910cfda236ea0eface49b24496057ade

a2ddd8639bca3e4c812ce283e82551d781952cbe

refs/heads/develop

2023-04-29T03:23:19.251191

2020-06-15T12:29:23

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MIT

2021-05-14T01:18:38

2021-05-14T01:18:37

null

UTF-8

Python

false

2,349

py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- from diagrams import Diagram, Cluster from diagrams.custom import Custom from diagrams.k8s.clusterconfig import HPA from diagrams.k8s.compute import Deployment, Pod, StatefulSet from diagrams.k8s.network import Ingress, Service globe_img = "resources/globe.png" graph_attr = { "pad": "0.5" } with Diagram(filename="jitsi_meet_one_shard", direction='TB', show=False, outformat='png', graph_attr=graph_attr): with Cluster("Conference 1"): users_1 = [Custom("user", globe_img) for _ in range(3)] with Cluster("Conference 2"): users_2 = [Custom("user", globe_img) for _ in range(2)] all_users = Custom("all users", globe_img) with Cluster("Namespace 'jitsi'"): n_shards = 1 n_haproxy = 2 haproxy_sts = StatefulSet("haproxy") haproxy_pods = [Pod(f"haproxy-{j}") for j in range(n_haproxy)] haproxy_sts >> haproxy_pods web_service = Service("web") ingress = Ingress("jitsi.messenger.schule") ingress >> Service("haproxy") >> haproxy_pods >> web_service for k in range(n_shards): with Cluster(f"Shard-{k}"): web_pod = Pod(f"shard-{k}-web") prosody_pod = Pod(f"shard-{k}-prosody") jicofo_pod = Pod(f"shard-{k}-jicofo") Deployment(f"shard-{k}-prosody") >> prosody_pod Deployment(f"shard-{k}-jicofo") >> jicofo_pod web_service >> web_pod prosody_service = Service(f"shard-{k}-prosody") prosody_service >> prosody_pod prosody_service << web_pod prosody_service << jicofo_pod n_jvbs = 3 with Cluster(f"Jitsi Videobridge Shard-{k}"): jvb_pods = [Pod(f"shard-{k}-jvb-{i}") for i in range(n_jvbs)] jvb_services = [Service(f"shard-{k}-jvb-{i}") for i in range(n_jvbs)] [jvb_services[i] >> jvb_pods[i] >> prosody_service for i in range(n_jvbs)] jvb_pods << StatefulSet(f"shard-{k}-jvb") << HPA(f"shard-{k}-hpa") if k == 0: users_1 >> jvb_services[0] users_2 >> jvb_services[1] all_users >> ingress

[ "maximilian.kertel@woodmark.de" ]

maximilian.kertel@woodmark.de

559142186ed45fbbaffffb507ad1b88430ecdd8f

74b1cc170d107dc5fd48a8f01b90931f61c0c58a

/attrib.py

444c1be006c0bb7dd53ef3bb75391d126d6648c6

[]

no_license

narenaryan/Python-GIS

b980a7de044d9c34a2c2946576070ebc073969ac

b45be225a5e25b6fd2a4bb25f7527b3dab8e28c1

refs/heads/master

2020-08-27T05:57:32.362519

2014-12-07T13:28:28

null

0

null

UTF-8

Python

false

602

py

#Get attributes of a state according to Feature no from proj1 import layer_feature_name,shapefile layer = shapefile.GetLayer(0) maps = layer_feature_name(layer) for i in maps.items(): print 'Feature ',i[0],': ',i[1] choice = int(raw_input("Enter Feature No: ")) print '\nFeature %d has following attributes'%choice for k,v in layer.GetFeature(choice).items().items(): s = "||||%s||||%s||||"%(k,v) print '\n',s print len(s)*'-' feature = layer.GetFeature(choice) geometry = feature.GetGeometryRef() name = geometry.GetGeometryName() print "\nFeature's geometry data consists of a %s" % name

[ "narenarya@live.com" ]

narenarya@live.com

1171adb7fde817ce921d1b7439dc217dbbc905c3

74992d8406107d0ebae68813660f8082dd50bf21

/users/urls.py

5426c0a8f31b2461d8628e86b8513ee97458fc10

[]

no_license

Ethan009/python-Learning-Log

a36896c9ac66bcbe43e9f1e8d2c88c16853216e4

36e16f75c4990d6df2282d41daa333dd02dc979d

refs/heads/master

2020-04-08T11:18:41.883959

2018-11-27T08:33:20

159,301,023

1

null

2019-01-19T06:10:58

2018-11-27T08:26:09

Python

UTF-8

Python

false

309

py

from django.conf.urls import url from django.contrib.auth.views import login from . import views urlpatterns = [ url(r'^login/$',login,{'template_name' : 'users/login.html'} , name='login'), url(r'^logout/$',views.logout_view,name='logout'), url(r'^register/$',views.register,name='register'), ]

[ "ethan.lv@feixitek.com" ]

ethan.lv@feixitek.com

3a327dde0a2902cdb8b0486ca2a1b70db57f920e

05e65c5057cc07a3abbbb4c5fe3bb1b9ee70ea0a

/main.py

f147d00de6cd6a15ed242d659ed9209e624a9ceb

[]

no_license

VProgramMist/sshunter

a216edbbbb7ee957fd580c2f91329b1c933f6879

89a699d4023817f9c2285ff9c5a2717d22a54524

refs/heads/main

2023-05-09T02:24:29.870130

2021-05-22T16:12:39

369,852,090

0

null

UTF-8

Python

false

350

py

from flask_login import login_required, current_user from flask import Blueprint, render_template, redirect main = Blueprint('main', __name__) @main.route('/') def index(): return redirect('signup') @main.route('/profile') @login_required def profile(): return render_template('auth/profile.html', name=current_user.name)

[ "noreply@github.com" ]

noreply@github.com

c4c3379cb706b2d7acd0e06406bc415238457867

8b08da919a615acf07f19ff835952aed6ad3b897

/ml_hw2/bin/f2py

64b827dd5ed4221a4ba4b2ff63e3e678696cd2d8

[]

no_license

shailchokshi1992/NaiveBayes

ade7722050d8b2abb65f87fdcd6e4a174a9708a9

06bf338c64a8d0623a738c9e519f1cce970af31b

refs/heads/master

2022-12-12T07:10:09.915762

2018-02-02T17:49:59

108,792,943

0

1

null

2022-11-28T07:29:59

2017-10-30T02:39:17

Python

UTF-8

Python

false

786

#!/home/shail/Desktop/ML/HW2/ml_hw2/bin/python # See http://cens.ioc.ee/projects/f2py2e/ from __future__ import division, print_function import os import sys for mode in ["g3-numpy", "2e-numeric", "2e-numarray", "2e-numpy"]: try: i = sys.argv.index("--" + mode) del sys.argv[i] break except ValueError: pass os.environ["NO_SCIPY_IMPORT"] = "f2py" if mode == "g3-numpy": sys.stderr.write("G3 f2py support is not implemented, yet.\\n") sys.exit(1) elif mode == "2e-numeric": from f2py2e import main elif mode == "2e-numarray": sys.argv.append("-DNUMARRAY") from f2py2e import main elif mode == "2e-numpy": from numpy.f2py import main else: sys.stderr.write("Unknown mode: " + repr(mode) + "\\n") sys.exit(1) main()

[ "chokshishail@gmail.com" ]

chokshishail@gmail.com

9bf8c88b2d724bdddded87831f0850574c0aa119

81f777cd72ce7753d1a292a147c3fce694560524

/fib.py

0fe888e3a6ba8d916b3a177ae96c2c189ce4915c

[]

no_license

ajuse/python3_learn_note

1a9d2e0aaa2b6a9eb4dcc2927bf7b5ee59f50e99

cacc9500699efd45cedf8148f37a5e9d670d8c96

refs/heads/master

2020-04-15T12:33:44.078132

2019-06-10T11:53:33

164,679,925

0

null

UTF-8

Python

false

213

py

# coding=utf-8 def fib(num): result = [] a, b = 0, 1 while num > 0: result.append(a) a, b = b, a + b num = num - 1 print(result) if __name__ == "__main__": fib(15)

[ "luoyunfu.1218@gmail.com" ]

luoyunfu.1218@gmail.com

5c7e955a24c163cb331b64dfbc66e5db51d8b25a

3b334e9ac96ba3b975f427e84d2f981cacd884ff

/common/menu.py

9fd040c06646885131581851131788ed0df8dec3

[]

no_license

HminiL/flask-madird-titanic-docker

38a4d797501a252dbe1579e9e53e6f70f3ffc0a9

3670792f41427d22f5f2e10e4f7acbe8fa86b845

refs/heads/main

2023-08-11T03:41:33.002213

2021-09-24T03:25:04

null

0

null

UTF-8

Python

false

123

py

def print_menu(ls): t = '' for i, j in enumerate(ls): t += str(i) + '-' + j + '\t' return int(input(t))

[ "hmin10580@gmail.com" ]

hmin10580@gmail.com

fd0eeacd7c197199c2a694868d6d5ea34ded1d6a

6b5709ef2b69047c4a2e3c58a1fed6845bd67111

/leet_code/10_regular_expression_matching/solutions.py

ba77b7802836c7a4052afbaa4df50df0ac680e23

[]

no_license

aasawaree2/leetcode_python

4fd0184a7469ff598578431a82a493b2aeaa5ab2

28c2b0c824ac1c61c115544c12e796574a9b5c48

refs/heads/master

2020-04-19T17:59:47.281745

2019-07-22T21:15:18

168,350,517

0

null

UTF-8

Python

false

98

py

class Solution: pass def main(): s = Solution() if __name__ == '__main__': main()

[ "aasawaree.21090@gmail.com" ]

aasawaree.21090@gmail.com

a5aede7a61d80ba0b1cb090a5132fab906576547

713a2579e8474d181e3c86c79e618accbd8fe75b

/UdemyPythonCertCourse/Machines/vehicle_stuff.py

a2889010325b802adb430f8546bba003bf09d8f4

[]

no_license

shulme801/Python101

a3efd4c8577a35697acd5328fa55528227351bcf

3480773f2ab2d4f944e48bd669be2156890b5e28

refs/heads/master

2023-03-11T20:51:10.745992

2021-02-13T17:08:29

198,089,908

0

null

UTF-8

Python

false

613

py

class Vehicle: # this variable should not be changed outside of the class specification. # this is a class attribut vehicle_counter = 0 def __init__ (self, body_type, make): self.vehicle_body = body_type self.vehicle_make = make Vehicle.vehicle_counter += 1 def get_vehicle_count(self): return Vehicle.vehicle_counter def drive(self): print("Vehicle driving...") class Truck(Vehicle): def drive(self): print("truck driving...") class Motorcycle(Vehicle): def drive(self): print("Motorcycle driving very fast...")

[ "shulme801@gmail.com" ]

shulme801@gmail.com

fd8fd1f9cb3b2413c38a13369c6f285a47e1db9c

e63b09d912f15d753386426ef1467a665fb84276

/Lab_Asmt_10/Source Code/RetrainInceptionFinalLayer/label_image.py

84ebefc43ceae37ffcb09802c44b0e0a3c0debe5

[]

no_license

Lavakumar90/BigDataApplications

8d7e12c76798d071fbe8ffe56b4530fad522c0de

a884418c223ecfb1940a47d13c66a21aeba11b7b

refs/heads/master

2020-04-05T12:12:37.266566

2017-09-26T01:57:38

81,053,633

0

null

2017-07-28T15:01:49

2017-02-06T06:07:10

null

UTF-8

Python

false

1,175

py

import tensorflow as tf, sys #image_path = sys.argv[1] image_path = 'data/car_photos/sports/images (1).jpeg' # image_path = '676728-bigthumbnail.jpg' # Read in the image_data image_data = tf.gfile.FastGFile(image_path, 'rb').read() # Loads label file, strips off carriage return label_lines = [line.rstrip() for line in tf.gfile.GFile("data/output_labels.txt")] # Unpersists graph from file with tf.gfile.FastGFile("data/output_graph.pb", 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) _ = tf.import_graph_def(graph_def, name='') with tf.Session() as sess: # Feed the image_data as input to the graph and get first prediction softmax_tensor = sess.graph.get_tensor_by_name('final_result:0') predictions = sess.run(softmax_tensor, \ {'DecodeJpeg/contents:0': image_data}) # Sort to show labels of first prediction in order of confidence top_k = predictions[0].argsort()[-len(predictions[0]):][::-1] for node_id in top_k: human_string = label_lines[node_id] score = predictions[0][node_id] print('%s (score = %.5f)' % (human_string, score))

[ "lavasurparaju@gmail.com" ]

lavasurparaju@gmail.com

d22510d282ed3e0b33f8d3e501117b4b8527cca0

91438802ee114b2fb945aae4105a17993dd6953d

/build/learning_ros_noetic/Part_5/ur10_robot/ur_traj_client/catkin_generated/pkg.installspace.context.pc.py

4807c4137df7658f74a42609d61315e95299f603

[]

no_license

AlexLam616/Baxter-robot

3a4cef31fe46da0fdb23c0e3b5808d84b412d037

d10fdcd35f29427ca14bb75f14fa9c64af3b028c

refs/heads/master

2023-05-12T01:25:56.454549

2021-05-25T02:02:09

367,070,028

0

null

UTF-8

Python

false

421

py

# generated from catkin/cmake/template/pkg.context.pc.in CATKIN_PACKAGE_PREFIX = "" PROJECT_PKG_CONFIG_INCLUDE_DIRS = "".split(';') if "" != "" else [] PROJECT_CATKIN_DEPENDS = "roscpp;actionlib;trajectory_msgs;control_msgs".replace(';', ' ') PKG_CONFIG_LIBRARIES_WITH_PREFIX = "".split(';') if "" != "" else [] PROJECT_NAME = "ur_traj_client" PROJECT_SPACE_DIR = "/home/alex/workspace/install" PROJECT_VERSION = "0.0.0"

[ "1155135145@link.cuhk.edu.hk" ]

1155135145@link.cuhk.edu.hk

7e11aa95bc1e4a542925e169fb76e98a6c626988

35252c5ccf86022e4b08ea40dee1af2d68149687

/inventory_management/urls.py

776ebbfaf7b08bae038f93eae2074e2d2d44a4dd

[]

no_license

Coderknight439/inventory_management

e44ea1f091c9900c032c0a0df2ede6bf86759893

f990c0af22b213b6517e34ea051c7db408f1c2be

refs/heads/master

2023-07-29T01:49:49.112962

2021-09-17T22:29:41

406,515,565

0

null

UTF-8

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false

1,368

py

"""inventory_managemnt URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path, include from django.conf import settings from django.conf.urls.static import static urlpatterns = [ # path('admin/', admin.site.urls), path('', include('home.urls')), path('inventories/', include('inventories.urls')), path('products/', include('products.urls')), path('vendors/', include('vendor.urls')), path('accounts/', include('django.contrib.auth.urls')), path('purchase_order/', include('purchase_orders.urls')), ] + static(settings.STATIC_URL, document_root=settings.STATIC_ROOT) admin.site.site_header = "Cosmetic Shop Admin" admin.site.site_title = "Cosmetic Shop Admin Site" admin.site.index_title = "Cosmetic Shop Admin"

[ "mahadi.hasan@divine-it.net" ]

mahadi.hasan@divine-it.net

e92860ed96f4a2b3dea5c060bfdeb1f66ed08a37

5f1083c23f5163ad274d7690a597cb995ca88ec3

/application/api/stage_regularity_paint_api.py

bb85876bcc779fc2336eced26ebf55b05957e460

[]

no_license

lvwanyou/Wisdom_Mattress

f7af4236a4afe4d42651ae4dc840779a8732fb62

abf92d6ba2791e768d6b59d594c4af652d76fe10

refs/heads/master

2021-09-10T08:35:58.000379

2018-03-23T02:03:42

116,142,977

1

0

null

UTF-8

Python

false

1,542

py

# -*- coding: utf-8 -*- import falcon import json from application.analyser import Analyser from application.analysis.stage_regularity_analyser import StageRegularityAnalyser class StageRegularityPaintApi: def __init__(self, db): self.db = db self.analyser = Analyser(self.db) self.stage_regularity_analyser = StageRegularityAnalyser(self.db) def on_get(self, req, resp, user_id, date): """ 暂时不将作息规律性写入数据库中 # data_from_db = self.db.sleep_phase[user_id].find_one({'_id': date}) # if data_from_db is None or data_from_db['ver'] != application.arith.SLEEP_STAGE_ALGORI_VERSION: # 判断是否是读取数据库缓存还是直接进行计算。 # self.analyser.analyse(user_id, date) # analyser 进行分析，然后就分析的数据写入到数据库中去 # data_from_db = self.db.sleep_phase[user_id].find_one({'_id': date}) # sleep_stages = data_from_db.get('data', []) """ # 得到6:00-6:00 of the next day sleep_stages = self.stage_regularity_analyser.calc_sleep_regularity(user_id, date) sleep_stages_result = self.stage_regularity_analyser.translate_sleep_stages(sleep_stages) for item in sleep_stages_result: item['time'] = item['time'].isoformat() # print(item['time'] + " " + item['state']) result = {'result': sleep_stages_result} resp.body = json.dumps(result) resp.status = falcon.HTTP_200

[ "lvwanyou@163.com" ]

lvwanyou@163.com

e7d94e462e7b2e3fde8bb0883b5a7eeb7330bbdd

8ad9c8b4c7c888482c743bf05007a5611c49ad75

/utils/augmentation.py

c2c9d36193dc5626133645f08b1dd064389be03e

[ "MIT" ]

permissive

Papyrus-Analysis/mcrnn-pytorch

9083f13cc94466daadbfbf6bba5ca03a4a059f4b

6624eebe128770df5fa71235fbf0c6677f2d3b51

refs/heads/main

2023-05-24T08:54:38.338493

2021-05-20T20:58:29

null

0

null

UTF-8

Python

false

2,884

py

import cv2 import numpy as np from imgaug import augmenters as iaa from scipy.interpolate import griddata import sys INTERPOLATION = { "linear": cv2.INTER_LINEAR, "cubic": cv2.INTER_CUBIC } class GridDistortion: def __init__(self, prob=0.3): self.prob = prob def __call__(self, img): should_transform = np.random.choice(np.arange(0, 2), p=[1 - self.prob, self.prob]) img = np.array(img) if should_transform: return warp_image(img) return img def warp_image(img, random_state=None, **kwargs): if random_state is None: random_state = np.random.RandomState() w_mesh_interval = kwargs.get('w_mesh_interval', 12) w_mesh_std = kwargs.get('w_mesh_std', 1.5) h_mesh_interval = kwargs.get('h_mesh_interval', 12) h_mesh_std = kwargs.get('h_mesh_std', 1.5) interpolation_method = kwargs.get('interpolation', 'linear') h, w = img.shape[:2] if kwargs.get("fit_interval_to_image", True): # Change interval so it fits the image size w_ratio = w / float(w_mesh_interval) h_ratio = h / float(h_mesh_interval) w_ratio = max(1, round(w_ratio)) h_ratio = max(1, round(h_ratio)) w_mesh_interval = w / w_ratio h_mesh_interval = h / h_ratio ############################################ # Get control points source = np.mgrid[0:h+h_mesh_interval:h_mesh_interval, 0:w+w_mesh_interval:w_mesh_interval] source = source.transpose(1,2,0).reshape(-1,2) if kwargs.get("draw_grid_lines", False): if len(img.shape) == 2: color = 0 else: color = np.array([0,0,255]) for s in source: img[int(s[0]):int(s[0])+1,:] = color img[:,int(s[1]):int(s[1])+1] = color # Perturb source control points destination = source.copy() source_shape = source.shape[:1] destination[:,0] = destination[:,0] + random_state.normal(0.0, h_mesh_std, size=source_shape) destination[:,1] = destination[:,1] + random_state.normal(0.0, w_mesh_std, size=source_shape) # Warp image grid_x, grid_y = np.mgrid[0:h, 0:w] grid_z = griddata(destination, source, (grid_x, grid_y), method=interpolation_method).astype(np.float32) map_x = grid_z[:,:,1] map_y = grid_z[:,:,0] warped = cv2.remap(img, map_x, map_y, INTERPOLATION[interpolation_method], borderValue=(255,255,255)) return warped class ImgAugTransform: def __init__(self): self.aug = iaa.Sequential([ iaa.Sometimes(0.35, iaa.GaussianBlur(sigma=(0, 1.5))), iaa.Sometimes(0.35, iaa.OneOf([iaa.Dropout(p=(0, 0.05)), iaa.CoarseDropout(0, size_percent=0.05)])), ]) def __call__(self, img): img = np.array(img) return self.aug.augment_image(img)

[ "glmanhtu@gmail.com" ]

glmanhtu@gmail.com

2f819d9b7131ebb5ab3ba5de2b16433c41ef6657

da7a893f0dc9c130b5f8c29d4875e7c5d98ac64f

/code-slides/0019-fib-more-fast-examples.py

8dbdcdff07628e4544477b3860838a7d9f952cf8

[]

no_license

py-yyc/decorators

a489d89869582a9127a5272e9342b8131ad91fe3

bd7c65b78b3f00cf8da216eab945f3ef26c1b2a8

refs/heads/master

2020-06-20T18:29:59.884497

2016-02-23T21:48:09

52,392,195

1

0

null

UTF-8

Python

false

906

py

from __future__ import print_function # noslide ## <h1>how decorators work</h1> from time import time # noslide from contextlib import contextmanager # noslide @contextmanager # noslide def timer(): # noslide s = time() # noslide yield # noslide print("took {:.6f}s".format(time() - s)) # noslide def memoize(fn): # noslide cache = {} # noslide def wrapper(*args): # noslide try: # noslide return cache[args] # noslide except KeyError: # noslide r = fn(*args) # noslide cache[args] = r # noslide return r # noslide return wrapper # noslide @memoize # noslide def fib(x): # noslide if x in [1, 2]: # noslide return 1 # noslide return fib(x - 1) + fib(x - 2) # noslide with timer(): print("fib(100) =", fib(100)) with timer(): print("fib(200) =", fib(200)) ## show-output

[ "meejah@meejah.ca" ]

meejah@meejah.ca

fef8619855d686a10de3b4cc6d72b631190df666

15f321878face2af9317363c5f6de1e5ddd9b749

/solutions_python/Problem_201/2282.py

f467a7480d13917624dc75ae91326fb1c6115b5b

[]

no_license

dr-dos-ok/Code_Jam_Webscraper

c06fd59870842664cd79c41eb460a09553e1c80a

26a35bf114a3aa30fc4c677ef069d95f41665cc0

refs/heads/master

2020-04-06T08:17:40.938460

2018-10-14T10:12:47

null

0

null

UTF-8

Python

false

1,362

py

def rec_stall(n): res = [] if n == 1: return stalls[1] if n == 2: return stalls[2] if n == 3: return stalls[3] if n%2 == 0: a = rec_stall(n/2) b = rec_stall(n/2-1) res.extend([[n/2-1,n/2]]) c = [list(x) for x in zip(a,b)] c = [val for sublist in c for val in sublist] res.extend(c) res.extend([[0,0]]) return res else: a = rec_stall(n/2) res.extend([[n/2,n/2]]) c = [list(x) for x in zip(a,a)] c = [val for sublist in c for val in sublist] res.extend(c) res.extend([[0,0]]) return res stalls = [0,0,0,0] stalls[1] = [[0,0]] stalls[2] = [[0,1],[0,0]] stalls[3] = [[1,1],[0,0],[0,0]] #stalls[4] = [[1,2],[0,1],[0,0],[0,0]] #stalls[5] = [[2,2],[0,1],[0,1],[0,0],[0,0]] #stalls[6] = [[2,3],[1,1],[0,1],[0,0],[0,0],[0,0]] #print 1,rec_stall(1) #print 2,rec_stall(2) #print 3,rec_stall(3) #print 4,rec_stall(4) #print 5,rec_stall(5) #print 6,rec_stall(6) #print 7,rec_stall(7) #print 8,rec_stall(8) t = int(raw_input()) # read a line with a single integer for i in xrange(1, t + 1): n, m = [int(s) for s in raw_input().split(" ")] # read a list of integers, 2 in this case if n == m: print "Case #{}: {} {}".format(i, 0, 0) continue s = rec_stall(n) #print "Case #{}: {} {}", i, s, n, m, max(s[m-1]), min(s[m-1]) print "Case #{}: {} {}".format(i, max(s[m-1]), min(s[m-1]))

[ "miliar1732@gmail.com" ]

miliar1732@gmail.com

4cc34e361b07ef53d5e150374d1db0d50e01b2b9

553eadc801cfc8b3aacd0004be274f871cfd6c2b

/rango/admin.py

b3f1528fbe6ec5db485f83a1b26263ea6f7eace5

[]

no_license

subhro101/tango_with_django_project

1b16551c23e58e6a217a06f0e4b72da568afa1cb

e9c20e3e39d45ea20c47003b7827512a48f55974

refs/heads/master

2023-06-24T11:38:05.351457

2021-07-30T13:06:42

389,550,619

1

0

null

UTF-8

Python

false

397

py

from django.contrib import admin from rango.models import Category, Page from rango.models import UserProfile class PageAdmin(admin.ModelAdmin): list_display = ('title', 'category', 'url') class CategoryAdmin(admin.ModelAdmin): prepopulated_fields = {'slug':('name',)} admin.site.register(Page, PageAdmin) admin.site.register(Category, CategoryAdmin) admin.site.register(UserProfile)

[ "2601733P@student.gla.ac.uk" ]

2601733P@student.gla.ac.uk