Datasets:

PatrickHaller

/

the-stack-python-1M

like 0

8fe41e532494a01d940f2785a24f1f0333f1aa5e

py

Python

docs/source/conf.py

JeremieHornus/ufoLib2

084dac404c2e84d0945e26ebb93eb699b260e743

docs/source/conf.py

JeremieHornus/ufoLib2

084dac404c2e84d0945e26ebb93eb699b260e743

docs/source/conf.py

JeremieHornus/ufoLib2

084dac404c2e84d0945e26ebb93eb699b260e743

# Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # import os import sys sys.path.insert(0, os.path.abspath("../../src")) # -- Project information ----------------------------------------------------- project = "ufoLib2" copyright = "2020, The FontTools Authors" author = "The FontTools Authors" # -- General configuration --------------------------------------------------- master_doc = "index" # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "sphinx.ext.autodoc", "sphinx.ext.intersphinx", "sphinx.ext.napoleon", "sphinx.ext.viewcode", "sphinx_rtd_theme", ] # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. # exclude_patterns = [] intersphinx_mapping = { "fontTools": ("https://fonttools.readthedocs.io/en/latest/", None), } # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = "sphinx_rtd_theme" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". # html_static_path = ["_static"]

97a34141ddb6da281c5baca69b4da056e8c6bb95

py

Python

python/project_kine/temp.py

OthmanEmpire/misc_code

4d487db518167b62969ad65abaffbc9e01777d91

python/project_kine/temp.py

OthmanEmpire/misc_code

4d487db518167b62969ad65abaffbc9e01777d91

python/project_kine/temp.py

OthmanEmpire/misc_code

4d487db518167b62969ad65abaffbc9e01777d91

__author__ = 'Ozkh' def get_text(name): return "lorem ipsum, {0} dolor sit amet".format(name) def p_decorate(func): def func_wrapper(name): return "<p>{0}</p>".format(func(name)) return func_wrapper my_get_text = p_decorate(get_text) print(my_get_text("John")) # <p>Outputs lorem ipsum, John dolor sit amet</p> print(id(get_text)) get_text = p_decorate(get_text) print(id(get_text)) print(get_text("John"))

6967414250bd3a4e9e9317ecf1cdb87ee0801abf

py

Python

mortgage_calculator.py

IanMadlenya/investable

2e0101158c7331119f62a3e07cff8349ea52b0e0

2017-02-08T20:29:08.000Z

2021-08-07T20:14:35.000Z

mortgage_calculator.py

IanMadlenya/investable

2e0101158c7331119f62a3e07cff8349ea52b0e0

2021-08-11T19:12:18.000Z

2021-08-11T19:12:18.000Z

mortgage_calculator.py

jttyeung/investable

2e0101158c7331119f62a3e07cff8349ea52b0e0

2017-02-22T02:55:47.000Z

2022-03-14T06:58:42.000Z

import re def calculate_mortgage(mortgage_details): """ Calculates mortgage monthly payment rate. Tests: >>> calculate_mortgage({'price': '$1,000,000', 'rate': '5.25', 'downpayment': '200000', 'loan': '30'}) ('$4,418', '$1,590,347') >>> calculate_mortgage({'price': '$650,000', 'rate': '3.83', 'downpayment': '169000', 'loan': '20'}) ('$2,872', '$689,246') >>> calculate_mortgage({'price': '$240,000', 'rate': '1.12', 'downpayment': '240000', 'loan': '15'}) ('$0', '$0') """ MONTHS_IN_YEAR = 12 PERCENT_CONVERSION = 100 # Get price, mortgage rate, downpayment amount price = int(mortgage_details['price']) rate = ((float(mortgage_details['rate'])/PERCENT_CONVERSION)/MONTHS_IN_YEAR) downpayment = int(re.sub('[^\d.]+', '', mortgage_details['downpayment'])) hoa = mortgage_details.get('hoa') # If HOA exists, turn it into an integer, otherwise it is zero try: hoa = int(mortgage_details['hoa']) except ValueError: hoa = 0 # Translate loan term in years to months loan = mortgage_details['loan'] # Total loan payments loan_payments = int(loan[0:2]) * MONTHS_IN_YEAR # Calculate monthly payment if rate == 0: monthly_payment = float(price)/loan_payments else: monthly_payment = (price - downpayment) * (rate * (1 + rate) ** loan_payments) / ((1 + rate) ** loan_payments - 1) # Calculate total monthly payment with HOA fees if one exists monthly_plus_hoa_payment = monthly_payment + hoa formatted_monthly_plus_hoa_payment = '${:,}'.format(int(round(monthly_plus_hoa_payment))) # Calculate total interest paid in span of loan total_interest_paid = monthly_payment * loan_payments - price formatted_total_interest_paid = '${:,}'.format(int(round(monthly_payment * loan_payments - price))) # Calculate total HOA fees paid in span of loan total_hoa_paid = hoa * loan_payments # Calculate the total mortgage paid with interest total_mortgage_payment = '${:,}'.format(int(round(price + total_interest_paid + total_hoa_paid))) return (formatted_monthly_plus_hoa_payment, total_mortgage_payment)

c2b71509c1e46f082585b6cea0a0528f3c2f5b9b

py

Python

web3_multicall/_utils/__init__.py

BrunoMazorra/web3_multicall_blocknumber

2f12f6b6bb9853b10db90b968f5b0b75a9b1a7b4

2021-12-15T04:07:25.000Z

2021-12-15T04:07:25.000Z

web3_multicall/_utils/__init__.py

BrunoMazorra/web3_multicall_blocknumber

2f12f6b6bb9853b10db90b968f5b0b75a9b1a7b4

web3_multicall/_utils/__init__.py

BrunoMazorra/web3_multicall_blocknumber

2f12f6b6bb9853b10db90b968f5b0b75a9b1a7b4

2021-12-15T04:07:45.000Z

2022-03-04T03:35:28.000Z

from .function import Function

5cbd69f7d158fd9b9882b43ff96a7fa08ec90c95

py

Python

py_placeroute.py

kiba09/unicostfortraveling

ce49ea3fca82d2013f47a2735e1c317526b67195

py_placeroute.py

kiba09/unicostfortraveling

ce49ea3fca82d2013f47a2735e1c317526b67195

py_placeroute.py

kiba09/unicostfortraveling

ce49ea3fca82d2013f47a2735e1c317526b67195

import googlemaps as GoogleMaps from pygeocoder import Geocoder results = Geocoder.geocode("Tian'anmen,Beijing") print(results[0].coordinates)

9594d65ded58f5cb4edc3f262f7ba340cb6c2d6a

py

Python

BND-DDQN/GazeboWorld.py

KerryWu16/BND-DDQN

30bc2bf7a29415c453746fe472ac2d558c481197

2019-07-18T14:22:23.000Z

2022-03-06T09:42:18.000Z

BND-DDQN/GazeboWorld.py

KerryWu16/BND-DDQN

30bc2bf7a29415c453746fe472ac2d558c481197

2020-01-18T07:47:50.000Z

2020-02-11T02:33:51.000Z

BND-DDQN/GazeboWorld.py

KerryWu16/BND-DDQN

30bc2bf7a29415c453746fe472ac2d558c481197

2019-07-18T14:22:33.000Z

2022-01-18T07:41:22.000Z

import rospy import numpy as np import cv2 import copy import tf from geometry_msgs.msg import Twist from gazebo_msgs.msg import ModelStates from gazebo_msgs.msg import ModelState from gazebo_msgs.msg import ContactsState from sensor_msgs.msg import Image from cv_bridge import CvBridge from nav_msgs.msg import Odometry from preprocessor import HistoryPreprocessor class GazeboWorld(): def __init__(self, ns='', start_location=(0,0), max_episode=500, window_size=4, input_shape=(80,100)): rospy.init_node('GazeboWorld', anonymous=False) #-----------Parameters----------------------- self.set_self_state = ModelState() self.set_self_state.model_name = ns + 'mobile_base' self.set_self_state.pose.position.x = start_location[0] self.set_self_state.pose.position.y = start_location[1] self.set_self_state.pose.position.z = 0. self.set_self_state.pose.orientation.x = 0.0 self.set_self_state.pose.orientation.y = 0.0 self.set_self_state.pose.orientation.z = 0.0 self.set_self_state.pose.orientation.w = 1.0 self.set_self_state.twist.linear.x = 0. self.set_self_state.twist.linear.y = 0. self.set_self_state.twist.linear.z = 0. self.set_self_state.twist.angular.x = 0. self.set_self_state.twist.angular.y = 0. self.set_self_state.twist.angular.z = 0. self.set_self_state.reference_frame = 'world' self.input_shape = input_shape self.bridge = CvBridge() self.object_state = [0, 0, 0, 0] self.object_name = [] self.action1_table = [0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1] self.action2_table = [np.pi*45/180, np.pi*30/180, np.pi*15/180, 0., -np.pi*15/180, -np.pi*30/180, -np.pi*45/180] self.self_speed = [0.7, 0.0] self.default_states = None self.start_table = [(0, 0)] self.depth_image = None self.bump = False self.time = 0 self.max_episode = max_episode self.preprocessor = HistoryPreprocessor(self.input_shape, history_length=window_size) self.window_size = window_size self.state = { 'old_state': np.zeros(shape=(input_shape[0], input_shape[1], window_size)), 'action1': 0, 'action2': 0, 'reward': 0, 'new_state': np.zeros(shape=(input_shape[0], input_shape[1], window_size)), 'is_terminal': False } #-----------Publisher and Subscriber------------- self.cmd_vel = rospy.Publisher(ns + 'cmd_vel', Twist, queue_size = 1) self.set_state = rospy.Publisher('gazebo/set_model_state', ModelState, queue_size = 1) self.resized_depth_img = rospy.Publisher(ns + '/camera/depth/image_resized',Image, queue_size = 1) self.object_state_sub = rospy.Subscriber('gazebo/model_states', ModelStates, self.ModelStateCallBack) self.depth_image_sub = rospy.Subscriber(ns + '/camera/depth/image_raw', Image, self.DepthImageCallBack) self.odom_sub = rospy.Subscriber(ns + '/odom', Odometry, self.OdometryCallBack) self.bumper_sub = rospy.Subscriber('bumper', ContactsState, self.BumperCallBack, queue_size = 1) rospy.sleep(2.) rospy.on_shutdown(self.shutdown) def ModelStateCallBack(self, data): # self state idx = data.name.index(self.set_self_state.model_name) quaternion = (data.pose[idx].orientation.x, data.pose[idx].orientation.y, data.pose[idx].orientation.z, data.pose[idx].orientation.w) euler = tf.transformations.euler_from_quaternion(quaternion) yaw = euler[2] self.self_state = [data.pose[idx].position.x, data.pose[idx].position.y, yaw, data.twist[idx].linear.x, data.twist[idx].linear.y, data.twist[idx].angular.z] for lp in range(len(self.object_name)): idx = data.name.index(self.object_name[lp]) quaternion = (data.pose[idx].orientation.x, data.pose[idx].orientation.y, data.pose[idx].orientation.z, data.pose[idx].orientation.w) euler = tf.transformations.euler_from_quaternion(quaternion) yaw = euler[2] self.object_state[lp] = [data.pose[idx].position.x, data.pose[idx].position.y, yaw] if self.default_states is None: self.default_states = copy.deepcopy(data) def DepthImageCallBack(self, img): self.depth_image = img def OdometryCallBack(self, odometry): self.self_linear_x_speed = odometry.twist.twist.linear.x self.self_linear_y_speed = odometry.twist.twist.linear.y self.self_rotation_z_speed = odometry.twist.twist.angular.z def BumperCallBack(self, bumper_data): bump = False for state in bumper_data.states: if 'ground_plane' not in state.collision2_name: bump = True break self.bump = bump def GetDepthImageObservation(self): # ros image to cv2 image try: cv_img = self.bridge.imgmsg_to_cv2(self.depth_image) #32FC1 except Exception as e: raise e cv_img = np.array(cv_img, dtype=np.float32) # resize dim = (self.input_shape[1], self.input_shape[0]) cv_img = cv2.resize(cv_img, dim, interpolation = cv2.INTER_NEAREST) #INTER_AREA cv_img[np.isnan(cv_img)] = 0. # normalize return(cv_img/5.) def PublishDepthPrediction(self, depth_img): # cv2 image to ros image and publish cv_img = np.array(depth_img, dtype=np.float32) try: resized_img = self.bridge.cv2_to_imgmsg(cv_img, "passthrough") except Exception as e: raise e self.resized_depth_img.publish(resized_img) def GetSelfState(self): return self.self_state def GetSelfLinearXSpeed(self): return self.self_linear_x_speed def GetSelfOdomeSpeed(self): v = np.sqrt(self.self_linear_x_speed**2 + self.self_linear_y_speed**2) return [v, self.self_rotation_z_speed] def GetSelfSpeed(self): return np.array(self.self_speed) def GetBump(self): return self.bump def SetRobotPose(self): quaternion = tf.transformations.quaternion_from_euler(0., 0., np.random.uniform(-np.pi, np.pi)) start_location = self.start_table[np.random.randint(0, len(self.start_table))] object_state = copy.deepcopy(self.set_self_state) object_state.pose.orientation.x = quaternion[0] object_state.pose.orientation.y = quaternion[1] object_state.pose.orientation.z = quaternion[2] object_state.pose.orientation.w = quaternion[3] object_state.pose.position.x = start_location[0] object_state.pose.position.y = start_location[1] self.set_state.publish(object_state) rospy.sleep(0.1) def SetObjectPose(self): object_state = ModelState() state = copy.deepcopy(self.default_states) for i in range(len(self.default_states.name)): if 'mobile_base' not in state.name[i]: object_state.model_name = state.name[i] object_state.pose = state.pose[i] object_state.twist = state.twist[i] object_state.reference_frame = 'world' self.set_state.publish(object_state) rospy.sleep(0.1) def ResetWorld(self): self.SetRobotPose() # reset robot self.SetObjectPose() # reset environment rospy.sleep(0.1) def Control(self, action1, action2): self.self_speed[0] = self.action1_table[int(action1)] self.self_speed[1] = self.action2_table[int(action2)] move_cmd = Twist() move_cmd.linear.x = self.self_speed[0] move_cmd.linear.y = 0. move_cmd.linear.z = 0. move_cmd.angular.x = 0. move_cmd.angular.y = 0. move_cmd.angular.z = self.self_speed[1] self.cmd_vel.publish(move_cmd) def shutdown(self): rospy.loginfo("Stop Moving") self.cmd_vel.publish(Twist()) rospy.sleep(1) def GetRewardAndTerminate(self): terminate = False reset = False [v, theta] = self.GetSelfOdomeSpeed() reward = 2*v * v * np.cos( 2* v* theta) -0.1 if self.GetBump(): reward = -10. terminate = True reset = True if self.time > self.max_episode: reset = True return reward, terminate, reset def GetState(self): return np.copy(self.state['old_state']), self.state['action1'], self.state['action2'], self.state['reward'], \ np.copy(self.state['new_state']), self.state['is_terminal'] def TakeAction(self, action1, action2): old_state = self.preprocessor.get_state() self.time += 1 self.Control(action1, action2) rospy.sleep(0.1) state = self.GetDepthImageObservation() reward, is_terminal, reset = self.GetRewardAndTerminate() self.preprocessor.process_state_for_memory(state) new_state = self.preprocessor.get_state() self.state['old_state'] = old_state self.state['action1'] = action1 self.state['action2'] = action2 self.state['reward'] = reward self.state['new_state'] = new_state self.state['is_terminal'] = is_terminal if reset: self.Reset() def Reset(self): move_cmd = Twist() move_cmd.linear.x = 0. move_cmd.linear.y = 0. move_cmd.linear.z = 0. move_cmd.angular.x = 0. move_cmd.angular.y = 0. move_cmd.angular.z = 0. self.cmd_vel.publish(move_cmd) self.ResetWorld() self.preprocessor.reset() self.time = 0 state = self.GetDepthImageObservation() for _ in range(self.window_size): self.preprocessor.process_state_for_memory(state)

47c2995d0a65ef5dbf9948db78ab9709b678b10a

py

Python

main.py

Saeko22/Discord-Bot

bc2ddfbde978441383af09a6e5b06d3ef649b477

main.py

Saeko22/Discord-Bot

bc2ddfbde978441383af09a6e5b06d3ef649b477

main.py

Saeko22/Discord-Bot

bc2ddfbde978441383af09a6e5b06d3ef649b477

import discord from discord.ext import commands import pytz from datetime import datetime bot = commands.Bot(intents=discord.Intents.all(), command_prefix='&') bot.remove_command('help') @bot.event async def on_ready(): await bot.change_presence(status=discord.Status.online, activity=discord.Activity( type=discord.ActivityType.playing, name="&help" )) print(f'BotId: {bot.user.id} - Name: {bot.user.name}') @bot.command() async def ping(ctx): await ctx.send(f":ping_pong: Pong! `{round(bot.latency*1000)}ms`") @bot.command() @commands.has_permissions(ban_members=True) async def ban(ctx, member: discord.Member, *, reason=None): await member.ban(reason=reason) await ctx.send(f"{member} was banned!") @bot.command() @commands.has_permissions(kick_members=True) async def kick(ctx, member: discord.Member, *, reason=None): await member.kick(reason=reason) await ctx.send(f"{member} was kicked!") @bot.command(name='test') async def test(ctx, *args): await ctx.send(f'Eingabe: {" ".join(args)}') @commands.has_permissions(manage_messages=True) # Premission einstellen! Wer kann diesen Cmd benutzen? @bot.command() async def clear(ctx, count=1): # !clear löscht nur eine Nachricht! messages = await ctx.channel.purge(limit=count+1) await ctx.send(f'Es wurden {len(messages)-1} Nachrichten gelöscht.', delete_after=4) @bot.command() async def help(ctx): embed = discord.Embed( title='Bot Commands', description='Welcome to the help section.Here are all the commands fot this game!', color=discord.Colour.purple() ) embed.set_thumbnail(url='https://discord.gg/47yAdzbwcp') embed.add_field( name='&help', value='list of all commands', inline=False ) embed.set_thumbnail(url='https://avatars.githubusercontent.com/u/86261346?v=4') embed.add_field( name='&clear', value='clear messages', inline=False ) embed.set_thumbnail(url='https://avatars.githubusercontent.com/u/86261346?v=4') embed.add_field( name='&userinfo', value='is a userinfo from the user', inline=False ) embed.set_thumbnail(url='https://avatars.githubusercontent.com/u/86261346?v=4') embed.add_field( name='&Test', value='is a test', inline=False ) embed.set_thumbnail(url='https://avatars.githubusercontent.com/u/86261346?v=4') embed.add_field( name='&ping', value='The bot make a pong back', inline=False ) embed.set_thumbnail(url='https://avatars.githubusercontent.com/u/86261346?v=4') embed.add_field( name='&Kick', value='The bot kicks user', inline=False ) embed.set_thumbnail(url='https://avatars.githubusercontent.com/u/86261346?v=4') embed.add_field( name='&Ban', value='The bot ban user', inline=False ) embed.set_footer(text=f'Angefordert von {ctx.author.name} • {ctx.author.id}') await ctx.send(embed=embed) @bot.command(name='userinfo') async def userinfo(ctx, member: discord.Member): de = pytz.timezone('Europe/Berlin') embed = discord.Embed(title=f'> Userinfo für {member.display_name}', description='', color=0x4cd137, timestamp=datetime.now().astimezone(tz=de)) embed.add_field(name='Name', value=f'```{member.name}#{member.discriminator}```', inline=True) embed.add_field(name='Bot', value=f'```{("Ja" if member.bot else "Nein")}```', inline=True) embed.add_field(name='Nickname', value=f'```{(member.nick if member.nick else "Nicht gesetzt")}```', inline=True) embed.add_field(name='Server beigetreten', value=f'```{member.joined_at}```', inline=True) embed.add_field(name='Discord beigetreten', value=f'```{member.created_at}```', inline=True) embed.add_field(name='Rollen', value=f'```{len(member.roles)}```', inline=True) embed.add_field(name='Höchste Rolle', value=f'```{member.top_role.name}```', inline=True) embed.add_field(name='Farbe', value=f'```{member.color}```', inline=True) embed.add_field(name='Booster', value=f'```{("Ja" if member.premium_since else "Nein")}```', inline=True) embed.set_footer(text=f'Angefordert von {ctx.author.name} • {ctx.author.id}', icon_url=ctx.author.avatar_url) await ctx.send(embed=embed) bot.run('BOTTOKEN')

b18b781c389ea140233f507cf22012762ca15c5f

py

Python

PyWorkSpace/helloworld/venv/Scripts/easy_install-script.py

FTTL/GitWorkSpace

86c38f792ad8743179716cf9ef86e02f15143ab0

PyWorkSpace/helloworld/venv/Scripts/easy_install-script.py

FTTL/GitWorkSpace

86c38f792ad8743179716cf9ef86e02f15143ab0

2021-01-05T07:53:12.000Z

2021-01-05T07:53:12.000Z

PyWorkSpace/helloworld/venv/Scripts/easy_install-script.py

FTTL/GitWorkSpace

86c38f792ad8743179716cf9ef86e02f15143ab0

#!E:\PyWorkSpace\helloworld\venv\Scripts\python.exe # EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==39.1.0','console_scripts','easy_install' __requires__ = 'setuptools==39.1.0' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('setuptools==39.1.0', 'console_scripts', 'easy_install')() )

d9bce8f172fe787bb92d9d0b4ef9323ef0e0e4ef

py

Python

pylearn2/sandbox/rnn/utils/__init__.py

BouchardLab/pylearn2

4cab785b870d22cd9e85a5f536d4cac234b6bf60

2015-01-01T14:07:52.000Z

2022-03-08T08:56:41.000Z

pylearn2/sandbox/rnn/utils/__init__.py

BouchardLab/pylearn2

4cab785b870d22cd9e85a5f536d4cac234b6bf60

2015-01-02T13:18:24.000Z

2021-08-20T18:03:28.000Z

pylearn2/sandbox/rnn/utils/__init__.py

BouchardLab/pylearn2

4cab785b870d22cd9e85a5f536d4cac234b6bf60

2015-01-01T17:08:51.000Z

2022-03-25T19:53:17.000Z

""" Utilities for RNN framework """

5a14b48d67578053cb00ebd113feb48f1d93caed

py

Python

mtdnn/common/linear_pooler.py

microsoft/mt-dnn

e5c3e07f3a8e55067433714ce261a6d28ba73d22

2020-05-08T08:02:51.000Z

2022-03-27T06:43:56.000Z

mtdnn/common/linear_pooler.py

microsoft/mt-dnn

e5c3e07f3a8e55067433714ce261a6d28ba73d22

2020-06-03T12:00:10.000Z

2021-03-15T07:36:44.000Z

mtdnn/common/linear_pooler.py

microsoft/mt-dnn

e5c3e07f3a8e55067433714ce261a6d28ba73d22

2020-05-11T13:13:22.000Z

2022-03-25T05:49:51.000Z

# coding=utf-8 # Copyright (c) Microsoft. All rights reserved. from torch import nn class LinearPooler(nn.Module): def __init__(self, hidden_size): super(LinearPooler, self).__init__() self.dense = nn.Linear(hidden_size, hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output

ce3dabbb0207d9d87e2f4219c43d583ea2c294a6

py

Python

src/csnVisualStudio2008.py

xplanes/CSnake

3cd7a2f5c23787262c42fe3c2763201afa1bdef4

2016-02-16T06:45:24.000Z

2021-08-09T14:59:12.000Z

src/csnVisualStudio2008.py

xplanes/CSnake

3cd7a2f5c23787262c42fe3c2763201afa1bdef4

2015-08-04T14:42:35.000Z

2016-03-18T09:08:01.000Z

src/csnVisualStudio2008.py

xplanes/CSnake

3cd7a2f5c23787262c42fe3c2763201afa1bdef4

2015-10-15T10:12:52.000Z

2021-11-08T15:20:46.000Z

## @package csnVisualStudio2008 # Definition of the csnVisualStudio2008 compilers. # \ingroup compiler import csnCompiler import os class Compiler(csnCompiler.Compiler): """ Abstract Visual Studio 2008 compiler. """ def __init__(self): csnCompiler.Compiler.__init__(self) self.postProcessor = PostProcessor() def GetCompileFlags(self): return [""] def IsForPlatform(self, _WIN32, _NOT_WIN32): return _WIN32 or (not _WIN32 and not _NOT_WIN32) def GetOutputSubFolder(self, _configuration = "${CMAKE_CFG_INTDIR}"): """ Returns the folder where the compiler should place binaries for _configuration. The default value for _configuration returns the output folder for the current configuration. for storing binaries. """ if _configuration == "DebugAndRelease": return "bin" else: return "bin/%s" % (_configuration) def GetBuildSubFolder(self, _projectType, _projectName): return "%s/%s" % (_projectType, _projectName) def GetThirdPartySubFolder(self): return "" def GetThirdPartyCMakeParameters(self): return [] def GetProjectCMakeParameters(self): return [] def GetAllowedConfigurations(self): return ["DebugAndRelease"] def GetPostProcessor(self): return self.postProcessor def TargetIsMac(self): return False def TargetIsLinux(self): return False class Compiler32(Compiler): """ Visual Studio 2008 32bits compiler. """ def GetName(self): return "Visual Studio 9 2008" def TargetIs32Bits(self): return True def TargetIs64Bits(self): return False class Compiler64(Compiler): """ Visual Studio 2008 64bits compiler. """ def GetName(self): return "Visual Studio 9 2008 Win64" def TargetIs32Bits(self): return False def TargetIs64Bits(self): return True class PostProcessor: def Do(self, _project): """ Post processes the vcproj file generated for _project. """ # vc proj to patch if not _project.dependenciesManager.isTopLevel: slnFilename = "%s/%s.sln" % (_project.GetBuildFolder(), _project.name) if os.path.exists(slnFilename): os.remove(slnFilename)

59c63f58a2da87bb0b0b0d15e7addf7e1eb18c75

py

Python

shoptimizer_api/optimizers_builtin/condition_optimizer.py

alex-berish/shoptimizer

3d8837352c0ae52dee2ac804750866a2b93809f1

2020-08-21T05:59:29.000Z

2022-03-30T17:26:44.000Z

shoptimizer_api/optimizers_builtin/condition_optimizer.py

alex-berish/shoptimizer

3d8837352c0ae52dee2ac804750866a2b93809f1

shoptimizer_api/optimizers_builtin/condition_optimizer.py

alex-berish/shoptimizer

3d8837352c0ae52dee2ac804750866a2b93809f1

2020-09-14T08:38:11.000Z

2022-03-13T22:37:40.000Z

# coding=utf-8 # Copyright 2021 Google LLC. # # 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. """A module for Shoptimizer API that fixes invalid condition values. Reference: https://support.google.com/merchants/answer/6324469 If the condition field is specified as "new", but other fields in the product imply that the condition is otherwise, this optimizer will reset the condition value to "used". """ import logging from typing import Any, Dict, List, Optional, Set from flask import current_app from models import optimization_result_counts from optimizers_abstract import base_optimizer from util import gpc_id_to_string_converter from util import optimization_util _GPC_STRING_TO_ID_MAPPING_CONFIG_FILE_NAME: str = 'gpc_string_to_id_mapping_{}' _NEW = 'new' _USED = 'used' class ConditionOptimizer(base_optimizer.BaseOptimizer): """An optimizer that fixes invalidly-set condition fields.""" _OPTIMIZER_PARAMETER = 'condition-optimizer' _condition_config = None _gpc_id_to_string_converter: Optional[ gpc_id_to_string_converter.GPCConverter] = None def _optimize( self, product_batch: Dict[str, Any], language: str, country: str, currency: str) -> optimization_result_counts.OptimizationResultCounts: """Runs the optimization. Fixes invalid condition values. See above for the definition of an invalid condition value. Args: product_batch: A batch of product data. language: The language to use for this optimizer. country: The country to use for this optimizer. currency: The currency to use for this optimizer. Returns: The number of products affected by this optimization. """ num_of_products_optimized = 0 num_of_products_excluded = 0 self._condition_config = current_app.config.get('CONFIGS', {}).get( f'condition_optimizer_config_{language}', {}) self._gpc_id_to_string_converter = gpc_id_to_string_converter.GPCConverter( _GPC_STRING_TO_ID_MAPPING_CONFIG_FILE_NAME.format(language)) for entry in product_batch['entries']: if (optimization_util.optimization_exclusion_specified( entry, self._OPTIMIZER_PARAMETER)): num_of_products_excluded += 1 continue product = entry['product'] google_product_category = product.get('googleProductCategory', '') gpc_string = self._gpc_id_to_string_converter.convert_gpc_id_to_string( google_product_category) if self._is_google_product_category_excluded(gpc_string): logging.info( 'Product ID: %s With Category %s was flagged for exclusion ' ' of the condition check', product.get('offerId', ''), gpc_string) continue used_tokens = set( token.lower() for token in self._condition_config['used_tokens']) if product.get('condition', '') == _NEW: # Category format must follow the official spec to be converted a list. # Ref: https://support.google.com/merchants/answer/6324436?hl=en. product_categories = gpc_string.split(' > ') if isinstance(product_categories, list) and product_categories: lowest_level_category = product_categories[-1] category_specific_tokens = self._get_tokens_for_category( lowest_level_category) if category_specific_tokens: category_specific_tokens = set( token.lower() for token in category_specific_tokens) used_tokens.update(category_specific_tokens) # Search for used tokens in both title and description and reset the # condition to used if any were detected. product_title = product.get('title', '') product_description = product.get('description', '') if self._field_contains_used_tokens( product_title, used_tokens) or self._field_contains_used_tokens( product_description, used_tokens): product['condition'] = _USED logging.info('Modified item %s: Setting new product to used.', product.get('offerId', '')) num_of_products_optimized += 1 base_optimizer.set_optimization_tracking(product, base_optimizer.SANITIZED) return optimization_result_counts.OptimizationResultCounts( num_of_products_optimized, num_of_products_excluded) def _is_google_product_category_excluded( self, google_product_category: str) -> bool: """Checks if the provided category was found in the exclusions config dict. Args: google_product_category: A string representing the product category. Returns: True if the given category was found in the condition config's list of categories to exclude from being optimized for condition due to those categories being at higher risk of containing false-positives. """ excluded_categories = self._condition_config.get( 'excluded_product_categories', []) # Ensure that the exclude category from the config matches the product's # category from the beginning of the string in order to support an entire # category family being matched, as well as enforcing avoidance of unrelated # matches if only a sub-category was specified. return any( google_product_category.startswith(category_to_exclude) for category_to_exclude in excluded_categories) def _field_contains_used_tokens(self, field_text: str, used_tokens: Set[str]) -> bool: """Checks if the provided field contains any terms in the given set. Args: field_text: A string representing the value of a product field. used_tokens: A set representing used condition indicators. Returns: True if any term was found in the target product field, otherwise False. """ return any(token in field_text.lower() for token in used_tokens) def _get_tokens_for_category(self, product_category: str) -> List[str]: """Gets the values in a list of dictionaries if the provided category was found. Args: product_category: The product's lowest-level category. Returns: A list of the tokens of the matching category, or an empty list. """ category_mappings = self._condition_config['target_product_categories'] return category_mappings.get(product_category, [])

32201cb8ace9921ff839504714ede379b3dd9c20

py

Python

bika/lims/browser/analysisprofile.py

hocinebendou/bika.gsoc

85bc0c587de7f52073ae0e89bddbc77bf875f295

bika/lims/browser/analysisprofile.py

hocinebendou/bika.gsoc

85bc0c587de7f52073ae0e89bddbc77bf875f295

bika/lims/browser/analysisprofile.py

hocinebendou/bika.gsoc

85bc0c587de7f52073ae0e89bddbc77bf875f295

from bika.lims.jsonapi import load_field_values from bika.lims.interfaces import IJSONReadExtender, IAnalysisProfile from zope.component import adapts from zope.interface import implements class JSONReadExtender(object): """- Place additional information about profile services into the returned records. Used in AR Add to prevent extra requests """ implements(IJSONReadExtender) adapts(IAnalysisProfile) def __init__(self, context): self.context = context def __call__(self, request, data): service_data = [] for service in self.context.getService(): this_service = {'UID': service.UID(), 'Title': service.Title(), 'Keyword': service.getKeyword(), 'Price': service.getPrice(), 'VAT': service.getVAT(), 'PointOfCapture': service.getPointOfCapture(), 'CategoryTitle': service.getCategory().Title()} service_data.append(this_service) data['service_data'] = service_data

9058e8f2eaed6e030aaadb0d42703b92bcf39c83

py

Python

elevenclock/lang/lang_el.py

wanderleihuttel/ElevenClock

de4272a650111233acf36c909c7e269c8dc810d2

elevenclock/lang/lang_el.py

wanderleihuttel/ElevenClock

de4272a650111233acf36c909c7e269c8dc810d2

elevenclock/lang/lang_el.py

wanderleihuttel/ElevenClock

de4272a650111233acf36c909c7e269c8dc810d2

# INSTRUCTIONS # Translate the text and write it between the " # EXAMPLE: original -> "This text is in english: value {0}" # translation -> "Aquest text està en anglès: valor {0}" # If you see sth like {0}, {1}, maintain it on the translated sentence # Meke special attention to elements like ":", etc. lang_3_2_1 = { "Open online help to troubleshoot problems": "", "Reset ElevenClock preferences to defaults": "", "Specify a minimum width for the clock": "", "Search on the settings": "", "No results were found": "", } lang_3_2 = lang_3_2_1 | { "Use system accent color as background color": "", "Check only the focused window on the fullscreen check": "", "Clock on monitor {0}": "", "Move to the left": "", "Show this clock on the left": "", "Show this clock on the right": "", "Restore clock position": "", } lang_3_1 = lang_3_2 | { "W": "", # The initial of the word week in your language: W for week, S for setmana, etc. "Disable the notification badge": "", "Override clock default height": "", "Adjust horizontal clock position": "", "Adjust vertical clock position": "", "Export log as a file": "", "Copy log to clipboard": "", "Announcements:": "", "Fetching latest announcement, please wait...": "", "Couldn't load the announcements. Please try again later": "", "ElevenClock's log": "", "Pick a color": "" } lang_3 = lang_3_1 | { "Hide the clock during 10 seconds when clicked": "", "Enable low-cpu mode": "", "You might lose functionalities, like the notification counter or the dynamic background": "", "Clock position and size:": "", "Clock size preferences, position offset, clock at the left, etc.": "", "Reset monitor blacklisting status": "", "Reset": "", "Third party licenses": "", "View": "", "ElevenClock": "", "Monitor tools": "", "Blacklist this monitor": "", "Third Party Open-Source Software in Elevenclock {0} (And their licenses)": "", "ElevenClock is an Open-Source application made with the help of other libraries made by the community:": "", "Ok": "", "More Info": "", "About Qt": "", "Success": "", "The monitors were unblacklisted successfully.": "", "Now you should see the clock everywhere": "", "Ok": "", "Blacklist Monitor": "", "Blacklisting a monitor will hide the clock on this monitor permanently.": "", "This action can be reverted from the settings window. under <b>Clock position and size</b>": "", "Are you sure do you want to blacklist the monitor \"{0}\"?": "", "Yes": "", "No": "", } lang_2_9_2 = lang_3 | { "Reload log": "", "Do not show the clock on secondary monitors": "", "Disable clock taskbar background color (make clock transparent)": "", "Open the welcome wizard": "", " (ALPHA STAGE, MAY NOT WORK)": "", "Welcome to ElevenClock": "", "Skip": "", "Start": "", "Next": "", "Finish": "", } lang_2_9 = lang_2_9_2 | { "Task Manager": "", "Change date and time": "", "Notification settings": "", "Updates, icon tray, language": "", "Hide extended options from the clock right-click menu (needs a restart to be aplied)": "", "Fullscreen behaviour, clock position, 1st monitor clock, other miscellanious settings": "", 'Add the "Show Desktop" button on the left corner of every clock': '', 'You might need to set a custom background color for this to work.&nbsp;More info <a href="{0}" style="color:DodgerBlue">HERE</a>': '', "Clock's font, font size, font color and background, text alignment": "", "Date format, Time format, seconds,weekday, weeknumber, regional settings": "", "Testing features and error-fixing tools": "", "Language pack author(s), help translating ElevenClock": "", "Info, report a bug, submit a feature request, donate, about": "", "Log, debugging information": "", } lang_2_8 = lang_2_9 | { "Force the clock to be at the top of the screen": "", "Show the clock on the primary screen": "", "Use a custom font color": "", "Use a custom background color": "", "Align the clock text to the center": "", "Select custom color": "", "Hide the clock when a program occupies all screens": "", } lang2_7_bis = lang_2_8 | { "Use a custom font": "", "Use a custom font size": "", "Enable hide when multi-monitor fullscreen apps are running": "", "<b>{0}</b> needs to be enabled to change this setting": "", "<b>{0}</b> needs to be disabled to change this setting": "", } lang2_7 = lang2_7_bis | { " (This feature has been disabled because it should work by default. If it is not, please report a bug)": "", "ElevenClock's language": "" } lang2_6 = lang2_7 | { "About Qt6 (PySide6)": "", "About": "", "Alternative non-SSL update server (This might help with SSL errors)": "", "Fixes and other experimental features: (Use ONLY if something is not working)": "", "Show week number on the clock": "", } lang2_5 = lang2_6 | { "Hide the clock when RDP Client or Citrix Workspace are running": "", "Clock Appearance:": "", "Force the clock to have black text": "", " - It is required that the Dark Text checkbox is disabled": "", "Debbugging information:": "", "Open ElevenClock's log": "", } lang2_4 = lang2_5 | { # Added text in version 2.4 "Show the clock on the primary screen (Useful if clock is set on the left)": "", "Show weekday on the clock" :"Προβολή ημέρας της εβδομάδας στο ρολόι", } lang2_3 = lang2_4 | { #Context menu "ElevenClock Settings" :"Ρυθμίσεις ElevenClock", # Also settings title "Reload Clocks" :"Επαναφόρτωση Ρολογιών", "ElevenClock v{0}" :"Έκδοση ElevenClock: {0}", "Restart ElevenClock" :"Επανεκκίνηση ElevenClock", "Hide ElevenClock" :"Απόκρυψη ElevenClock", "Quit ElevenClock" :"Τερματισμός ElevenClock", #General settings section "General Settings:" :"Γενικές Ρυθμίσεις", "Automatically check for updates" :"Αυτόματος έλεγχος για ενημερώσεις", "Automatically install available updates" :"Αυτόματη εγκατάσταση διαθέισμων ενημερώσεων", "Enable really silent updates" :"Ενεργοποίηση πραγματικά σιωπηλών ενημερώσεων", "Bypass update provider authenticity check (NOT RECOMMENDED, AT YOUR OWN RISK)" :"Παράκαμψη ελέγχου πιστοποίησης παρόχου ενημερώσεων (ΔΕΝ ΠΡΟΤΕΊΝΕΤΑΙ, ΜΕ ΔΙΚΗ ΣΑΣ ΕΥΘΥΝΗ)", "Show ElevenClock on system tray" :"Προβολή του ElevenClock στη γραμμή εργασιών", "Alternative clock alignment (may not work)" :"Εναλλακτική ευθυγράμμιση ρολογιού (ίσως να μην λειτουργεί)", "Change startup behaviour" :"Αλλαγή συμπεριφοράς κατά την εκκίνηση", "Change" :"Αλλαγή", "<b>Update to the latest version!</b>" :"<b>Ενημέρωση στην τελευταία έκδοση!</b>", "Install update" :"Εγκατάστσαη ενημέρωσης", #Clock settings "Clock Settings:" :"Ρυθμίσεις Ρολογιού", "Hide the clock in fullscreen mode" :"Απόκρυψη ρολογιού σε κατάσταση πλήρους οθόνης", "Hide the clock when RDP client is active" :"Απόκρυψη ρολογιού όταν χρησιμοποιείται η Απομακρυσμένη Πρόσβαση", "Force the clock to be at the bottom of the screen" :"Εξαναγκασμός ρολογιού στο κάτω μέρος της οθόνης", "Show the clock when the taskbar is set to hide automatically" :"Προβολή ρολογιού όταν η γραμμή εργασιών είναι ορισμένη για αυτόματη απόκρυψη", "Fix the hyphen/dash showing over the month" :"Διόρθωση της καθέτου που προβάλεται πάνω από τον μήνα", "Force the clock to have white text" :"Εξαναγκασμός ρολογίου για χρήση κειμένου σε λευκό χρώμα", "Show the clock at the left of the screen" :"Προβολή ρολογιού στα αριστερά της οθόνης", #Date & time settings "Date & Time Settings:" :"Ρυθμίσεις Ημερομηνίας & Ώρας", "Show seconds on the clock" :"Προβολή δευτερολέπτων στο ρολόι", "Show date on the clock" :"Προβολή ημερομηνίας στο ρολόι", "Show time on the clock" :"Προβολή ώρας στο ρολόι", "Change date and time format (Regional settings)" :"Αλλαγή μορφής ημερομηνίας και ώρας (Τοπικές ρυθμίσεις)", "Regional settings" :"Τοπικές ρυθμίσεις", #About the language pack "About the language pack:" :"Σχετικά με το πακέτο γλώσσας", "Translated to English by martinet101" :"Μετάφραση ελληνικών από panos78", # Here, make sute to give you some credits: Translated to LANGUAGE by USER/NAME/PSEUDONYM/etc. "Translate ElevenClock to your language" :"Μεταφραση του ElevenClock στη γλώσσα σας", "Get started" :"Ξεκινήστε", #About ElevenClock "About ElevenClock version {0}:" :"Σχετικά με την έκδοση {0} του ElevenClock:", "View ElevenClock's homepage" :"Μετάβαση στην ιστοσελίδα του ElevenClock", "Open" :"Άνοιγμα", "Report an issue/request a feature" :"Αναφορά θέματος / Αίτημα χαρακτηριστικού", "Report" :"Αναφορά", "Support the dev: Give me a coffee☕" :"Υποστηρίξτε τον δημιουργό: Κεράστε τον ένα καφέ☕", "Open page" :"Άνοιγμα σελίδας", "Icons by Icons8" :"Εικονίδια από Icons8", # Here, the word "Icons8" should not be translated "Webpage" :"Ιστοσελίδα", "Close settings" :"Κλείσιμο ρυθμίσεων", "Close" :"Κλείσιμο", } lang = lang2_3

d1fd8b73c4920fae582426473920c41effe5c00f

py

Python

jupyterurlparams/__init__.py

manics/jupyter-urlparams

2e40927e6bf2e1b780e37d440cc7a463415da91d

2020-03-12T18:21:19.000Z

2020-03-13T22:27:39.000Z

jupyterurlparams/__init__.py

manics/jupyter-urlparams

2e40927e6bf2e1b780e37d440cc7a463415da91d

jupyterurlparams/__init__.py

manics/jupyter-urlparams

2e40927e6bf2e1b780e37d440cc7a463415da91d

from .version import __version__ # noqa from .handlers import ( UIHandler, ) from notebook.utils import url_path_join def _jupyter_server_extension_paths(): return [{ 'module': 'jupyterurlparams', }] def load_jupyter_server_extension(nbapp): web_app = nbapp.web_app base_url = url_path_join(web_app.settings['base_url'], 'urlparams') handlers = [ (base_url, UIHandler), ] web_app.settings['nbapp'] = nbapp web_app.add_handlers('.*', handlers)

59818180ada892159244b8e7ca3cf197cf849760

py

Python

server/database/models.py

FemiBlack/flask-vue-building-spa

a275da149ee60242170440fba0fd0dc0ecefe659

server/database/models.py

FemiBlack/flask-vue-building-spa

a275da149ee60242170440fba0fd0dc0ecefe659

server/database/models.py

FemiBlack/flask-vue-building-spa

a275da149ee60242170440fba0fd0dc0ecefe659

from .db import db from flask_bcrypt import generate_password_hash,check_password_hash # class RemCol(db.Document): # remark = db.StringField() # response = db.StringField() class BuildingExtEnv(db.EmbeddedDocument): drv_rain = db.DictField() drainage_issue = db.DictField() water_log = db.DictField() # read-docs unkempt = db.DictField() # read-docs pollution = db.DictField() # read-docs topography = db.DictField() # read-docs radiation = db.DictField() # read-docs extreme_temp = db.DictField() # read-docs flood = db.DictField() # read-docs fire_source = db.DictField() # read-docs traffic_issue = db.DictField() # read-docs building_threat = db.DictField() # read-docs wind = db.DictField() # read-docs moisture = db.DictField() # read-docs class BuildingIntCond(db.EmbeddedDocument): moisture = db.DictField() # read-docs excess_heat = db.DictField() # read-docs ventilation = db.DictField() # read-docs dry_air = db.DictField() # read-docs class BuildingGenCond(db.EmbeddedDocument): foundation_sett = db.DictField() # read-docs deformation = db.DictField() # read-docs defects = db.DictField() # read-docs cracks = db.DictField() # read-docs class BuildingQualityofComponent(db.EmbeddedDocument): physical_app = db.DictField() # read-docs texture = db.DictField() # read-docs strength = db.DictField() # read-docs crack = db.DictField() # read-docs dimension = db.DictField() # read-docs deflection = db.DictField() # read-docs spalling = db.DictField() # read-docs corrosion = db.DictField() # read-docs structural_defect = db.DictField() # read-docs distress = db.DictField() # read-docs deformation = db.DictField() # read-docs deterioration = db.DictField() # read-docs class BuildingDesignLevel(db.EmbeddedDocument): dimension = db.DictField() # read-docs spanning = db.DictField() # read-docs configuration = db.DictField() # read-docs redundant_element = db.DictField() # read-docs loading = db.DictField() # read-docs structural_defect = db.DictField() # read-docs deformation = db.DictField() # read-docs class BuildingWorkXPLevel(db.EmbeddedDocument): dimension = db.DictField() # read-docs misalignment = db.DictField() # read-docs deflection = db.DictField() # read-docs excess_waviness = db.DictField() # read-docs corossion = db.DictField() # read-docs bar_spacing = db.DictField() # read-docs deficient_cover = db.DictField() # read-docs reinforcement_spec = db.DictField() # read-docs seq_construction = db.DictField() # read-docs class BuildingIndoorEnv(db.EmbeddedDocument): moisture = db.DictField() # read-docs humidity = db.DictField() # read-docs vibration = db.DictField() # read-docs excess_heat = db.DictField() # read-docs ventilation = db.DictField() # read-docs lighting = db.DictField() # read-docs class BuildingOutdoorEnv(db.EmbeddedDocument): drainage_issue = db.DictField() # read-docs flood_issue = db.DictField() # read-docs heat = db.DictField() # read-docs traffic_issue = db.DictField() # read-docs drv_rain = db.DictField() # read-docs unkempt = db.DictField() # read-docs pollution = db.DictField() # read-docs extreme_temp = db.DictField() # read-docs building_threat = db.DictField() # read-docs class BuildingInUseCond(db.EmbeddedDocument): addition = db.DictField() # read-docs overloading = db.DictField() # read-docs not_kept = db.DictField() # read-docs vibration = db.DictField() # read-docs vandalism = db.DictField() # read-docs residential_only = db.DictField() # read-docs class BuildingMaintenance(db.EmbeddedDocument): int_env = db.DictField() # read-docs ext_env = db.DictField() # read-docs struct_elements = db.DictField() # read-docs maintenance_issue = db.DictField() # read-docs damage_maintenance = db.DictField() # read-docs care_takers = db.DictField() # read-docs planned_frequency = db.DictField() # read-docs class NDTestRes(db.EmbeddedDocument): code = db.StringField() grid = db.StringField() ultrasonic = db.DictField() # read-docs eq_strength = db.IntField() hammer_val = db.IntField() class BuildingWeatherTemp(db.EmbeddedDocument): temp_17 = db.DictField() temp_18 = db.DictField() temp_19 = db.DictField() temp_20 = db.DictField() class BuildingWeatherRain(db.EmbeddedDocument): rain_17 = db.DictField() rain_18 = db.DictField() rain_19 = db.DictField() rain_20 = db.DictField() class Building(db.Document): building_no = db.StringField(required=True, unique=True) address = db.StringField(required=True) date = db.DateTimeField(required=True) building_age = db.IntField() last_repair_date = db.DateTimeField() nature_of_repair = db.StringField() frequency_of_repair = db.StringField() geometry = db.StringField() characteristics = db.StringField() compliance = db.StringField() deviation = db.StringField() external_env = db.EmbeddedDocumentField(BuildingExtEnv) internal_cond = db.EmbeddedDocumentField(BuildingIntCond) general_being = db.EmbeddedDocumentField(BuildingGenCond) component_quality = db.EmbeddedDocumentField(BuildingQualityofComponent) design_lvl = db.EmbeddedDocumentField(BuildingDesignLevel) work_xp_lvl = db.EmbeddedDocumentField(BuildingWorkXPLevel) indoor_env = db.EmbeddedDocumentField(BuildingIndoorEnv) outdoor_env = db.EmbeddedDocumentField(BuildingOutdoorEnv) in_use_cond = db.EmbeddedDocumentField(BuildingInUseCond) maintenance = db.EmbeddedDocumentField(BuildingMaintenance) nd_test_res = db.EmbeddedDocumentField(NDTestRes) weather_info_temp = db.EmbeddedDocumentField(BuildingWeatherTemp) weather_info_rain = db.EmbeddedDocumentField(BuildingWeatherRain) is_completed = db.BooleanField(default=False) # set to true on FIELD4 SUBMISSION added_by = db.ReferenceField('User') class User(db.Document): email = db.EmailField(required=True, unique=True) username = db.StringField(required=True) password = db.StringField(required=True, min_length=6) houses = db.ListField(db.ReferenceField('Building', reverse_delete_rule=db.PULL)) def hash_password(self): self.password = generate_password_hash(self.password).decode('utf8') def check_password(self, password): return check_password_hash(self.password, password) User.register_delete_rule(Building, 'added_by', db.CASCADE)

be9979bb3af15007831026978a63090be003432a

py

Python

happy/fun/urls.py

0xRumple/happy

bc4be2ae6320281887125d9a19cfa62a58a83331

2018-06-05T13:50:10.000Z

2021-08-04T12:13:53.000Z

happy/fun/urls.py

0xRumple/happy

bc4be2ae6320281887125d9a19cfa62a58a83331

2018-06-13T15:49:35.000Z

2021-06-10T20:32:08.000Z

happy/fun/urls.py

0xRumple/happy

bc4be2ae6320281887125d9a19cfa62a58a83331

2018-06-05T13:50:25.000Z

2019-04-01T08:28:24.000Z

# from django.urls import path, include # from rest_framework.urlpatterns import format_suffix_patterns # from . import views urlpatterns = [ ]

672f707120b9c828f99f25790702b47d2efc0e95

py

Python

Yeps/Yeps/wsgi.py

hezuoguang/Yeps-Server

04c9bc9674fc93f583a46fb4b4197ea1855e5fb7

2017-06-08T03:15:53.000Z

2017-06-08T03:15:53.000Z

Yeps/Yeps/wsgi.py

hezuoguang/Yeps-Server

04c9bc9674fc93f583a46fb4b4197ea1855e5fb7

Yeps/Yeps/wsgi.py

hezuoguang/Yeps-Server

04c9bc9674fc93f583a46fb4b4197ea1855e5fb7

""" WSGI config for Yeps project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/1.6/howto/deployment/wsgi/ """ import os os.environ.setdefault("DJANGO_SETTINGS_MODULE", "Yeps.settings") from django.core.wsgi import get_wsgi_application application = get_wsgi_application()

7d09ff9e14923067e653ca7b7a251c840fd6789c

py

Python

theano/tensor/tests/test_basic.py

gundun/theano

09d17fff10487dca7149e34601b8c6efdc572a19

theano/tensor/tests/test_basic.py

gundun/theano

09d17fff10487dca7149e34601b8c6efdc572a19

theano/tensor/tests/test_basic.py

gundun/theano

09d17fff10487dca7149e34601b8c6efdc572a19

from __future__ import absolute_import, print_function, division import itertools import logging import operator import os import sys from tempfile import mkstemp import unittest import warnings from copy import copy, deepcopy # Import builtin min to be able to use it after importing the tensor version. from theano.compat import izip from six import iteritems from six.moves import xrange from six.moves.builtins import min as builtin_min from nose.tools import assert_raises from nose.plugins.skip import SkipTest import numpy from numpy.testing import dec, assert_array_equal, assert_allclose from distutils.version import LooseVersion from functools import partial import theano from theano.compat import PY3, exc_message, operator_div from six.moves import StringIO, reduce from theano import compile, config, function, gof, tensor, shared from theano.compile import DeepCopyOp from theano.compile.mode import get_default_mode from theano.tensor import (_shared, wvector, bvector, autocast_float_as, argmin, max_and_argmax, cscalar, ctensor3, join, horizontal_stack, vertical_stack, argmax, get_vector_length, fscalar, zeros_like, sum, tensor3, vector, add, addbroadcast, alloc, as_tensor_variable, tensor_from_scalar, ARange, autocast_float, clip, constant, default, dot, batched_dot, dmatrix, dscalar, dvector, eq, eye, fill, flatten, inverse_permutation, tensor4, permute_row_elements, Flatten, fmatrix, fscalars, grad, inplace, iscalar, matrix, minimum, matrices, maximum, mul, neq, Reshape, row, scalar, scalars, second, smallest, stack, sub, Tensor, tensor_copy, tensordot, TensorType, Tri, tri, tril, triu, unbroadcast, var, Join, shape, MaxAndArgmax, lscalar, zvector, exp, get_scalar_constant_value, ivector, reshape, scalar_from_tensor, scal, iscalars, arange, dscalars, fvector, imatrix, numeric_grad, opt, lvector, lmatrix, true_div, max, min, Split, roll, tile, patternbroadcast, Eye, Shape, Dot, PermuteRowElements, ScalarFromTensor, TensorFromScalar, dtensor4, Rebroadcast, Alloc, dtensor3, SpecifyShape, Mean, itensor3, Tile, switch, Diagonal, Diag, nonzero, flatnonzero, nonzero_values, stacklists, DimShuffle, hessian, ptp, power, swapaxes, choose, Choose, NoneConst, AllocEmpty, isclose, allclose, mgrid, ogrid, extract_constant, ) from theano.tests import unittest_tools as utt from theano.tests.unittest_tools import attr imported_scipy_special = False mode_no_scipy = get_default_mode() try: import scipy.special import scipy.stats from scipy import __version__ as scipy_version imported_scipy_special = True except ImportError: if config.mode == "FAST_COMPILE": mode_no_scipy = "FAST_RUN" floatX = config.floatX if config.mode == "FAST_COMPILE": mode_opt = "FAST_RUN" else: mode_opt = get_default_mode() ### seed random number generator so that unittests are deterministic ### utt.seed_rng() if PY3: def L(i): return i else: def L(i): return long(i) def inplace_func(inputs, outputs, mode=None, allow_input_downcast=False, on_unused_input='raise', name=None): if mode is None: mode = get_default_mode() return function(inputs, outputs, mode=mode, allow_input_downcast=allow_input_downcast, accept_inplace=True, on_unused_input=on_unused_input, name=name) def eval_outputs(outputs): variables = inplace_func([], outputs)() if isinstance(variables, (tuple, list)) and len(variables) == 1: return variables[0] return variables def get_numeric_subclasses(cls=numpy.number, ignore=None): """ Return subclasses of `cls` in the numpy scalar hierarchy. We only return subclasses that correspond to unique data types. The hierarchy can be seen here: http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html """ if ignore is None: ignore = [] rval = [] dtype = numpy.dtype(cls) dtype_num = dtype.num if dtype_num not in ignore: # Safety check: we should be able to represent 0 with this data type. numpy.array(0, dtype=dtype) rval.append(cls) ignore.append(dtype_num) for sub in cls.__subclasses__(): rval += [c for c in get_numeric_subclasses(sub, ignore=ignore)] return rval def get_numeric_types(with_int=True, with_float=True, with_complex=False, only_theano_types=True): """ Return numpy numeric data types. :param with_int: Whether to include integer types. :param with_float: Whether to include floating point types. :param with_complex: Whether to include complex types. :param only_theano_types: If True, then numpy numeric data types that are not supported by Theano are ignored (i.e. those that are not declared in scalar/basic.py). :returns: A list of unique data type objects. Note that multiple data types may share the same string representation, but can be differentiated through their `num` attribute. Note that when `only_theano_types` is True we could simply return the list of types defined in the `scalar` module. However with this function we can test more unique dtype objects, and in the future we may use it to automatically detect new data types introduced in numpy. """ if only_theano_types: theano_types = [d.dtype for d in theano.scalar.all_types] rval = [] def is_within(cls1, cls2): # Return True if scalars defined from `cls1` are within the hierarchy # starting from `cls2`. # The third test below is to catch for instance the fact that # one can use ``dtype=numpy.number`` and obtain a float64 scalar, even # though `numpy.number` is not under `numpy.floating` in the class # hierarchy. return (cls1 is cls2 or issubclass(cls1, cls2) or isinstance(numpy.array([0], dtype=cls1)[0], cls2)) for cls in get_numeric_subclasses(): dtype = numpy.dtype(cls) if ((not with_complex and is_within(cls, numpy.complexfloating)) or (not with_int and is_within(cls, numpy.integer)) or (not with_float and is_within(cls, numpy.floating)) or (only_theano_types and dtype not in theano_types)): # Ignore this class. continue rval.append([str(dtype), dtype, dtype.num]) # We sort it to be deterministic, then remove the string and num elements. return [x[1] for x in sorted(rval, key=str)] def _numpy_checker(x, y): """ Checks if x.data and y.data have the same contents. Used in DualLinker to compare C version with Python version. """ x, y = x[0], y[0] if (x.dtype != y.dtype or x.shape != y.shape or numpy.any(numpy.abs(x - y) > 1e-10)): raise Exception("Output mismatch.", {'performlinker': x, 'clinker': y}) def safe_make_node(op, *inputs): """ Emulate the behaviour of make_node when op is a function. Normally op in an instead of the Op class. """ node = op(*inputs) if isinstance(node, list): return node[0].owner else: return node.owner def upcast_float16_ufunc(fn): """Decorator that enforces computation is not done in float16 by NumPy. Some ufuncs in NumPy will compute float values on int8 and uint8 in half-precision (float16), which is not enough, and not compatible with the C code. :param fn: numpy ufunc :returns: function similar to fn.__call__, computing the same value with a minimum floating-point precision of float32 """ def ret(*args, **kwargs): out_dtype = numpy.find_common_type( [a.dtype for a in args], [numpy.float16]) if out_dtype == 'float16': # Force everything to float32 sig = 'f' * fn.nin + '->' + 'f' * fn.nout kwargs.update(sig=sig) return fn(*args, **kwargs) return ret def upcast_int8_nfunc(fn): """Decorator that upcasts input of dtype int8 to float32. This is so that floating-point computation is not carried using half-precision (float16), as some NumPy functions do. :param fn: function computing a floating-point value from inputs :returns: function similar to fn, but upcasting its uint8 and int8 inputs before carrying out the computation. """ def ret(*args, **kwargs): args = list(args) for i, a in enumerate(args): if getattr(a, 'dtype', None) in ('int8', 'uint8'): args[i] = a.astype('float32') return fn(*args, **kwargs) return ret def makeTester(name, op, expected, checks=None, good=None, bad_build=None, bad_runtime=None, grad=None, mode=None, grad_rtol=None, eps=1e-10, skip=False, test_memmap=True, check_name=True, grad_eps=None): """ :param check_name: Use only for tester that aren't in Theano. """ if checks is None: checks = {} if good is None: good = {} if bad_build is None: bad_build = {} if bad_runtime is None: bad_runtime = {} if grad is None: grad = {} if grad is True: grad = good _op, _expected, _checks, _good = op, expected, checks, good _bad_build, _bad_runtime, _grad = bad_build, bad_runtime, grad _mode, _grad_rtol, _eps, skip_ = mode, grad_rtol, eps, skip _test_memmap = test_memmap _check_name = check_name _grad_eps = grad_eps class Checker(unittest.TestCase): op = staticmethod(_op) expected = staticmethod(_expected) checks = _checks check_name = _check_name good = _good bad_build = _bad_build bad_runtime = _bad_runtime grad = _grad mode = _mode skip = skip_ test_memmap = _test_memmap def setUp(self): # Verify that the test's name is correctly set. # Some tests reuse it outside this module. if self.check_name: eval(self.__class__.__module__ + '.' + self.__class__.__name__) # We keep a list of temporary files created in add_memmap_values, # to remove them at the end of the test. self.tmp_files = [] def add_memmap_values(self, val_dict): # If test_memmap is True, we create a temporary file # containing a copy of the data passed in the "val_dict" dict, # then open it as a memmapped array, and we can use the result as a # new test value. if not self.test_memmap: return val_dict # Copy dict before modifying them val_dict = val_dict.copy() # Note that we sort items in the dictionary to ensure tests are # deterministic (since the loop below will break on the first valid # item that can be memmapped). for k, v in sorted(val_dict.items()): new_k = '_'.join((k, 'memmap')) if new_k in val_dict: # A corresponding key was already provided break new_v = [] for inp in v: if type(inp) is numpy.ndarray and inp.size > 0: f, fname = mkstemp() self.tmp_files.append((f, fname)) new_inp = numpy.memmap(fname, dtype=inp.dtype, mode='w+', shape=inp.shape) new_inp[...] = inp[...] new_v.append(new_inp) else: new_v.append(inp) val_dict[new_k] = new_v # We only need one value, no need to copy all of them break return val_dict def tearDown(self): # This is to avoid a problem with deleting memmap files on windows. import gc gc.collect() for f, fname in self.tmp_files: os.close(f) os.remove(fname) def test_good(self): if skip: raise SkipTest(skip) good = self.add_memmap_values(self.good) for testname, inputs in iteritems(good): inputs = [copy(input) for input in inputs] inputrs = [TensorType( dtype=input.dtype, broadcastable=[shape_elem == 1 for shape_elem in input.shape] )() for input in inputs] try: node = safe_make_node(self.op, *inputrs) except Exception as exc: err_msg = ("Test %s::%s: Error occurred while" " making a node with inputs %s") % ( self.op, testname, inputs) exc.args += (err_msg,) raise try: f = inplace_func(inputrs, node.outputs, mode=mode, name='test_good') except Exception as exc: err_msg = ("Test %s::%s: Error occurred while" " trying to make a Function") % (self.op, testname) exc.args += (err_msg,) raise if (isinstance(self.expected, dict) and testname in self.expected): expecteds = self.expected[testname] # with numpy version, when we print a number and read it # back, we don't get exactly the same result, so we accept # rounding error in that case. eps = 5e-9 else: expecteds = self.expected(*inputs) eps = 1e-10 if any([i.dtype in ('float32', 'int8', 'uint8') for i in inputs]): eps = 1e-6 eps = numpy.max([eps, _eps]) try: variables = f(*inputs) except Exception as exc: err_msg = ("Test %s::%s: Error occurred while calling" " the Function on the inputs %s") % ( self.op, testname, inputs) exc.args += (err_msg,) raise if not isinstance(expecteds, (list, tuple)): expecteds = (expecteds, ) for i, (variable, expected) in enumerate( izip(variables, expecteds)): if (variable.dtype != expected.dtype or variable.shape != expected.shape or not numpy.allclose(variable, expected, atol=eps, rtol=eps)): self.fail(("Test %s::%s: Output %s gave the wrong" " value. With inputs %s, expected %s (dtype %s)," " got %s (dtype %s). eps=%f" " numpy.allclose returns %s %s") % ( self.op, testname, i, inputs, expected, expected.dtype, variable, variable.dtype, eps, numpy.allclose(variable, expected, atol=eps, rtol=eps), numpy.allclose(variable, expected))) for description, check in iteritems(self.checks): if not check(inputs, variables): self.fail(("Test %s::%s: Failed check: %s (inputs" " were %s, outputs were %s)") % ( self.op, testname, description, inputs, variables)) def test_bad_build(self): if skip: raise SkipTest(skip) for testname, inputs in iteritems(self.bad_build): inputs = [copy(input) for input in inputs] inputrs = [shared(input) for input in inputs] self.assertRaises(Exception, safe_make_node, self.op, *inputrs) # The old error string was ("Test %s::%s: %s was successfully # instantiated on the following bad inputs: %s" # % (self.op, testname, node, inputs)) def test_bad_runtime(self): if skip: raise SkipTest(skip) for testname, inputs in iteritems(self.bad_runtime): inputrs = [shared(input) for input in inputs] try: node = safe_make_node(self.op, *inputrs) except Exception as exc: err_msg = ("Test %s::%s: Error occurred while trying" " to make a node with inputs %s") % ( self.op, testname, inputs) exc.args += (err_msg,) raise try: f = inplace_func([], node.outputs, mode=mode, name="test_bad_runtime") except Exception as exc: err_msg = ("Test %s::%s: Error occurred while trying" " to make a Function") % (self.op, testname) exc.args += (err_msg,) raise # Add tester return a ValueError. Should we catch only this # one? # TODO: test that only this one is raised and catch only this # one or the subset that get raised. self.assertRaises(Exception, f, []) def test_grad(self): if skip: raise SkipTest(skip) # Disable old warning that may be triggered by this test. backup = config.warn.sum_div_dimshuffle_bug config.warn.sum_div_dimshuffle_bug = False try: for testname, inputs in iteritems(self.grad): inputs = [copy(input) for input in inputs] try: utt.verify_grad(self.op, inputs, mode=self.mode, rel_tol=_grad_rtol, eps=_grad_eps) except Exception as exc: err_msg = ("Test %s::%s: Error occurred while" " computing the gradient on the following" " inputs: %s") % (self.op, testname, inputs) exc.args += (err_msg,) raise finally: config.warn.sum_div_dimshuffle_bug = backup def test_grad_none(self): # Check that None is never returned as input gradient # when calling self.op.grad # We use all values in self.good because this has to be true # whether or not the values work for utt.verify_grad. if skip: raise SkipTest(skip) if not hasattr(self.op, 'grad'): # This is not actually an Op return for testname, inputs in iteritems(self.good): inputs = [copy(input) for input in inputs] inputrs = [TensorType( dtype=input.dtype, broadcastable=[shape_elem == 1 for shape_elem in input.shape] )() for input in inputs] if (isinstance(self.expected, dict) and testname in self.expected): expecteds = self.expected[testname] # with numpy version, when we print a number and read it # back, we don't get exactly the same result, so we accept # rounding error in that case. else: expecteds = self.expected(*inputs) if not isinstance(expecteds, (list, tuple)): expecteds = (expecteds, ) out_grad_vars = [] for out in expecteds: if str(out.dtype) in tensor.discrete_dtypes: dtype = floatX else: dtype = str(out.dtype) bcast = [shape_elem == 1 for shape_elem in out.shape] var = TensorType(dtype=dtype, broadcastable=bcast)() out_grad_vars.append(var) try: in_grad_vars = self.op.grad(inputrs, out_grad_vars) except (gof.utils.MethodNotDefined, NotImplementedError): pass else: assert None not in in_grad_vars Checker.__name__ = name if hasattr(Checker, '__qualname__'): Checker.__qualname__ = name return Checker def rand(*shape): r = numpy.random.rand(*shape) * 2 - 1 return numpy.asarray(r, dtype=config.floatX) def rand_nonzero(shape, eps=3e-4): """Like rand, but the absolute value has to be at least eps""" # covers [0, 1) r = numpy.asarray(numpy.random.rand(*shape), dtype=config.floatX) # covers [0, (1 - eps) / 2) U [(1 + eps) / 2, 1) r = r * (1 - eps) + eps * (r >= 0.5) # covers [-1, -eps) U [eps, 1) r = r * 2 - 1 return r def randint(*shape): return numpy.random.randint(-5, 6, shape) def randuint(*shape): return numpy.array(numpy.random.randint(5, size=shape), dtype=numpy.uint32) # XXX: this so-called complex random array as all-zero imaginary parts def randcomplex(*shape): r = numpy.asarray(numpy.random.rand(*shape), dtype=config.floatX) return numpy.complex128(2 * r - 1) def randcomplex_nonzero(shape, eps=1e-4): return numpy.complex128(rand_nonzero(shape, eps)) def randint_nonzero(*shape): r = numpy.random.randint(-5, 5, shape) return r + (r == 0) * 5 def rand_ranged(min, max, shape): return numpy.asarray(numpy.random.rand(*shape) * (max - min) + min, dtype=config.floatX) def randint_ranged(min, max, shape): return numpy.random.randint(min, max+1, shape) def randc128_ranged(min, max, shape): return numpy.asarray(numpy.random.rand(*shape) * (max - min) + min, dtype='complex128') def rand_of_dtype(shape, dtype): if 'int' in dtype: return randint(*shape).astype(dtype) elif 'float' in dtype: return rand(*shape).astype(dtype) elif 'complex' in dtype: return randcomplex(*shape).astype(dtype) else: raise TypeError() def makeBroadcastTester(op, expected, checks=None, name=None, **kwargs): if checks is None: checks = {} if name is None: name = str(op) # Here we ensure the test name matches the name of the variable defined in # this script. This is needed to properly identify the test e.g. with the # --with-id option of nosetests, or simply to rerun a specific test that # failed. capitalize = False if name.startswith('Elemwise{') and name.endswith(',no_inplace}'): # For instance: Elemwise{add,no_inplace} -> Add name = name[9:-12] capitalize = True elif name.endswith('_inplace'): # For instance: sub_inplace -> SubInplace capitalize = True if capitalize: name = ''.join([x.capitalize() for x in name.split('_')]) # Some tests specify a name that already ends with 'Tester', while in other # cases we need to add it manually. if not name.endswith('Tester'): name += "Tester" if 'inplace' in kwargs: if kwargs['inplace']: _expected = expected if not isinstance(_expected, dict): expected = lambda *inputs: numpy.array(_expected(*inputs), dtype=inputs[0].dtype) def inplace_check(inputs, outputs): # this used to be inputs[0] is output[0] # I changed it so that it was easier to satisfy by the # DebugMode return numpy.all(inputs[0] == outputs[0]) checks = dict(checks, inplace_check=inplace_check) del kwargs['inplace'] return makeTester(name, op, expected, checks, **kwargs) _good_broadcast_binary_normal = dict( same_shapes=(rand(2, 3), rand(2, 3)), not_same_dimensions=(rand(2, 2), rand(2)), scalar=(rand(2, 3), rand(1, 1)), row=(rand(2, 3), rand(1, 3)), column=(rand(2, 3), rand(2, 1)), integers=(randint(2, 3), randint(2, 3)), dtype_mixup_1=(rand(2, 3), randint(2, 3)), dtype_mixup_2=(randint(2, 3), rand(2, 3)), complex1=(randcomplex(2, 3), randcomplex(2, 3)), complex2=(randcomplex(2, 3), rand(2, 3)), # Disabled as we test the case where we reuse the same output as the # first inputs. # complex3=(rand(2,3),randcomplex(2,3)), empty=(numpy.asarray([], dtype=config.floatX), numpy.asarray([1], dtype=config.floatX)), ) _bad_build_broadcast_binary_normal = dict() _bad_runtime_broadcast_binary_normal = dict( bad_shapes=(rand(2, 3), rand(3, 2)), bad_row=(rand(2, 3), rand(1, 2))) _grad_broadcast_binary_normal = dict( same_shapes=(rand(2, 3), rand(2, 3)), scalar=(rand(2, 3), rand(1, 1)), row=(rand(2, 3), rand(1, 3)), column=(rand(2, 3), rand(2, 1)), # This don't work as verify grad don't support that #empty=(numpy.asarray([]), numpy.asarray([1])) # complex1=(randcomplex(2,3),randcomplex(2,3)), # complex2=(randcomplex(2,3),rand(2,3)), # Disabled as we test the case where we reuse the same output as the # first inputs. # complex3=(rand(2,3),randcomplex(2,3)), ) def check_floatX(inputs, rval): """ :param inputs: Inputs to a function that returned `rval` with these inputs. :param rval: Value returned by a function with inputs set to `inputs`. :returns: Either `rval` unchanged, or `rval` cast in float32. The idea is that when a numpy function would have returned a float64, Theano may prefer to return a float32 instead when `config.cast_policy` is set to 'numpy+floatX' and config.floatX to 'float32', and there was no float64 input. """ if (isinstance(rval, numpy.ndarray) and rval.dtype == 'float64' and config.cast_policy == 'numpy+floatX' and config.floatX == 'float32' and all(x.dtype != 'float64' for x in inputs)): # Then we expect float32 instead of float64. return rval.astype('float32') else: return rval AddTester = makeBroadcastTester( op=add, expected=lambda *inputs: check_floatX( inputs, reduce(lambda x, y: x + y, inputs)), good=dict( three_inputs_same_shapes=(rand(2, 3), rand(2, 3), rand(2, 3)), three_inputs_same_shapes_uint=(randuint(2,3), randuint(2,3), randuint(2,3)), four_inputs_broadcast=(rand(2, 3), rand(1, 3), rand(2, 1), rand(1, 1)), **_good_broadcast_binary_normal), bad_build=_bad_build_broadcast_binary_normal, bad_runtime=_bad_runtime_broadcast_binary_normal) AddInplaceTester = makeBroadcastTester( op=inplace.add_inplace, expected=lambda x, y: x + y, good=_good_broadcast_binary_normal, bad_build=_bad_build_broadcast_binary_normal, bad_runtime=_bad_runtime_broadcast_binary_normal, inplace=True) SubTester = makeBroadcastTester( op=sub, expected=lambda x, y: check_floatX((x, y), x - y), good=_good_broadcast_binary_normal, bad_build=_bad_build_broadcast_binary_normal, bad_runtime=_bad_runtime_broadcast_binary_normal, grad=_grad_broadcast_binary_normal) SubInplaceTester = makeBroadcastTester(op=inplace.sub_inplace, expected=lambda x, y: x - y, good=_good_broadcast_binary_normal, bad_build=_bad_build_broadcast_binary_normal, bad_runtime=_bad_runtime_broadcast_binary_normal, grad=_grad_broadcast_binary_normal, inplace=True) SwitchTester = makeBroadcastTester( op=switch, expected=numpy.where, good=dict(all_true=(numpy.asarray(1, dtype=config.floatX), rand(4, 5), rand(4, 5)), false_true=(numpy.asarray(0, dtype=config.floatX), rand(4, 5), rand(4, 5)), mixed=(randint_ranged(0, 1, (4, 5)), rand(4, 5), rand(4, 5)) ), bad_build=dict(all_true=(numpy.asarray(1, dtype=config.floatX), rand(4, 5))), bad_runtime=dict(all_true=(numpy.asarray(1, dtype=config.floatX), rand(3, 5), rand(4, 5)), false_true=(numpy.asarray(0, dtype=config.floatX), rand(4, 6), rand(4, 5)), ), # We suppose that cond+eps do not switch branch in switch.grad() # So we can't call verify_grad with cond 0. grad=dict(all_true=(numpy.asarray(1, dtype=config.floatX), rand(4, 5), rand(4, 5)), # false_true=(numpy.asarray(0, dtype=config.floatX), # rand(4, 5), rand(4, 5)), # mixed=(randint_ranged(0, 1, (4, 5)).astype(config.floatX), # rand(4, 5), rand(4, 5)) ), ) MaximumTester = makeBroadcastTester(op=maximum, expected=lambda *inputs: check_floatX(inputs, numpy.maximum(*inputs)), good=_good_broadcast_binary_normal, bad_build=_bad_build_broadcast_binary_normal, bad_runtime=_bad_runtime_broadcast_binary_normal, grad=_grad_broadcast_binary_normal) MaximumInplaceTester = makeBroadcastTester(op=inplace.maximum_inplace, expected=numpy.maximum, good=_good_broadcast_binary_normal, bad_build=_bad_build_broadcast_binary_normal, bad_runtime=_bad_runtime_broadcast_binary_normal, grad=_grad_broadcast_binary_normal, inplace=True) MinimumTester = makeBroadcastTester(op=minimum, expected=lambda *inputs: check_floatX(inputs, numpy.minimum(*inputs)), good=_good_broadcast_binary_normal, bad_build=_bad_build_broadcast_binary_normal, bad_runtime=_bad_runtime_broadcast_binary_normal, grad=_grad_broadcast_binary_normal) MinimumInplaceTester = makeBroadcastTester(op=inplace.minimum_inplace, expected=numpy.minimum, good=_good_broadcast_binary_normal, bad_build=_bad_build_broadcast_binary_normal, bad_runtime=_bad_runtime_broadcast_binary_normal, grad=_grad_broadcast_binary_normal, inplace=True) MulTester = makeBroadcastTester(op=mul, expected=lambda *inputs: check_floatX(inputs, reduce(lambda x, y: x * y, inputs)), good=dict(three_inputs_same_shapes=(rand(2, 3), rand(2, 3), rand(2, 3)), four_inputs_broadcast=(rand(2, 3), rand(1, 3), rand(2, 1), rand(1, 1)), **_good_broadcast_binary_normal), bad_build=_bad_build_broadcast_binary_normal, bad_runtime=_bad_runtime_broadcast_binary_normal, grad=dict(three_inputs_same_shapes=(rand(2, 3), rand(2, 3), rand(2, 3)), four_inputs_broadcast=(rand(2, 3), rand(1, 3), rand(2, 1), rand(1, 1)), **_grad_broadcast_binary_normal)) MulInplaceTester = makeBroadcastTester(op=inplace.mul_inplace, expected=lambda x, y: x * y, good=_good_broadcast_binary_normal, bad_build=_bad_build_broadcast_binary_normal, bad_runtime=_bad_runtime_broadcast_binary_normal, grad=_grad_broadcast_binary_normal, inplace=True) def copymod(dct, without=None, **kwargs): """Return dct but with the keys named by args removed, and with kwargs added. """ if without is None: without = [] rval = copy(dct) for a in without: if a in rval: del rval[a] for kw, val in iteritems(kwargs): rval[kw] = val return rval _good_broadcast_div_mod_normal_float_no_complex = dict( same_shapes=(rand(2, 3), rand_nonzero((2, 3))), scalar=(rand(2, 3), rand_nonzero((1, 1))), row=(rand(2, 3), rand_nonzero((1, 3))), column=(rand(2, 3), rand_nonzero((2, 1))), dtype_mixup_1=(rand(2, 3), randint_nonzero(2, 3)), dtype_mixup_2=(randint_nonzero(2, 3), rand_nonzero((2, 3))), integer=(randint(2, 3), randint_nonzero(2, 3)), uinteger=(randint(2, 3).astype("uint8"), randint_nonzero(2, 3).astype("uint8")), int8=[numpy.tile(numpy.arange(-127, 128, dtype='int8'), [254, 1]).T, numpy.tile(numpy.array(list(range(-127, 0)) + list(range(1, 128)), dtype='int8'), [255, 1])], # This empty2 doesn't work for some tests. I don't remember why #empty2=(numpy.asarray([0]), numpy.asarray([])), ) if PY3: _good_broadcast_div_mod_normal_float_inplace = copymod( _good_broadcast_div_mod_normal_float_no_complex, empty1=(numpy.asarray([]), numpy.asarray([1])), # No complex floor division in python 3.x ) else: _good_broadcast_div_mod_normal_float_inplace = copymod( _good_broadcast_div_mod_normal_float_no_complex, empty1=(numpy.asarray([], dtype=config.floatX), numpy.asarray([1], dtype=config.floatX)), complex1=(randcomplex(2, 3), randcomplex_nonzero((2, 3))), complex2=(randcomplex(2, 3), rand_nonzero((2, 3))), # Inplace on the first element. Must have the same type. #complex3=(rand(2, 3) ,randcomplex(2, 3)), ) _good_broadcast_div_mod_normal_float = copymod( _good_broadcast_div_mod_normal_float_inplace, empty2=(numpy.asarray([0], dtype=config.floatX), numpy.asarray([], dtype=config.floatX)) ) _grad_broadcast_div_mod_normal = dict( same_shapes=(rand(2, 3), rand_nonzero((2, 3))), scalar=(rand(2, 3), rand_nonzero((1, 1))), row=(rand(2, 3), rand_nonzero((1, 3))), column=(rand(2, 3), rand_nonzero((2, 1))), #complex1=(randcomplex(2, 3), randcomplex_nonzero((2, 3))), #complex2=(randcomplex(2, 3), rand_nonzero((2, 3))), #complex3=(rand(2, 3), randcomplex_nonzero((2, 3))), #dtype_mixup_1=(rand(2, 3), randint_nonzero(2, 3)), #dtype_mixup_2=(randint_nonzero(2, 3), rand_nonzero((2, 3))), #empty1=(numpy.asarray([]), numpy.asarray([1.])), #empty2=(numpy.asarray([0]), numpy.asarray([])), ) div_grad_rtol = None if config.floatX == 'float32': # We raise the relative tolerance for the grad as there can be errors in # float32. # This is probably caused by our way of computing the gradient error. div_grad_rtol = 0.025 def _numpy_true_div(x, y): """Performs true division, and cast the result in the type we expect. We define that function so we can use it in TrueDivTester.expected, because simply calling numpy.true_divide could cause a dtype mismatch. """ out = numpy.true_divide(x, y) # Use floatX as the result of int / int if x.dtype in tensor.discrete_dtypes and y.dtype in tensor.discrete_dtypes: out = theano._asarray(out, dtype=config.floatX) return out TrueDivTester = makeBroadcastTester( op=tensor.true_div, expected=_numpy_true_div, good=_good_broadcast_div_mod_normal_float_no_complex, grad=_grad_broadcast_div_mod_normal, grad_rtol=div_grad_rtol, ) TrueDivInplaceTester = makeBroadcastTester( op=inplace.true_div_inplace, expected=_numpy_true_div, good=copymod( _good_broadcast_div_mod_normal_float_inplace, # The output is now in float, we cannot work inplace on an int. without=['integer', 'uinteger', 'int8']), grad=_grad_broadcast_div_mod_normal, grad_rtol=div_grad_rtol, inplace=True) _good_inv = dict( normal=[5 * rand_nonzero((2, 3))], integers=[randint_nonzero(2, 3)], int8=[numpy.array(list(range(-127, 0)) + list(range(1, 127)), dtype='int8')], complex=[randcomplex_nonzero((2, 3))], empty=[numpy.asarray([], dtype=config.floatX)]) _good_inv_inplace = copymod(_good_inv, without=['integers', 'int8', 'complex']) _grad_inv = copymod(_good_inv, without=['integers', 'int8', 'complex', 'empty']) _bad_runtime_inv = dict( float=[numpy.zeros((2, 3))], integers=[numpy.zeros((2, 3), dtype='int64')], int8=[numpy.zeros((2, 3), dtype='int8')], complex=[numpy.zeros((2, 3), dtype='complex128')]) InvTester = makeBroadcastTester( op=tensor.inv, expected=lambda x: upcast_int8_nfunc(numpy.true_divide)(numpy.int8(1), x), good=_good_inv, bad_runtime=_bad_runtime_inv, grad=_grad_inv, grad_rtol=div_grad_rtol) InvInplaceTester = makeBroadcastTester( op=inplace.inv_inplace, expected=lambda x: _numpy_true_div(numpy.int8(1), x), good=_good_inv_inplace, bad_runtime=_bad_runtime_inv, grad=_grad_inv, grad_rtol=div_grad_rtol, inplace=True) CeilIntDivTester = makeBroadcastTester( op=tensor.ceil_intdiv, expected=lambda x, y: check_floatX((x, y), (x // y) + ((x % y) != 0)), good=_good_broadcast_div_mod_normal_float_no_complex, name='CeilIntDiv', # As we implement this function with neq, the gradient returned is always 0. # grad=_grad_broadcast_div_mod_normal, # grad_rtol=div_grad_rtol, ) ModTester = makeBroadcastTester( op=tensor.mod, expected=lambda x, y: numpy.asarray( x % y, dtype=theano.scalar.basic.upcast(x.dtype, y.dtype)), good=copymod(_good_broadcast_div_mod_normal_float, ['complex1', 'complex2']), grad=_grad_broadcast_div_mod_normal, grad_eps=1e-5, ) ModInplaceTester = makeBroadcastTester( op=inplace.mod_inplace, expected=lambda x, y: numpy.asarray( x % y, dtype=theano.scalar.basic.upcast(x.dtype, y.dtype)), good=copymod(_good_broadcast_div_mod_normal_float_inplace, ["complex1", "complex2"]), grad=_grad_broadcast_div_mod_normal, grad_eps=1e-5, inplace=True) _good_broadcast_pow_normal_float = dict(same_shapes=(rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 3))), scalar=(rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 1))), row=(rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 3))), column=(rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 1))), dtype_mixup=(rand_ranged(-3, 3, (2, 3)), randint_ranged(-3, 3, (2, 3))), complex1=(randcomplex(2, 3), randcomplex(2, 3)), complex2=(randcomplex(2, 3), rand(2, 3)), # complex3 = (rand(2,3),randcomplex(2,3)), # Inplace on the first element. empty1=(numpy.asarray([], dtype=config.floatX), numpy.asarray([1], dtype=config.floatX)), empty2=(numpy.asarray([0], dtype=config.floatX), numpy.asarray([], dtype=config.floatX)), empty3=(numpy.asarray([], dtype=config.floatX), numpy.asarray([], dtype=config.floatX)), ) _grad_broadcast_pow_normal = dict(same_shapes=(rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 3))), scalar=(rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 1))), row=( rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 3))), column=(rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 1))), #complex1 = (randcomplex(2,3),randcomplex(2,3)), #complex2 = (randcomplex(2,3),rand(2,3)), #complex3 = (rand(2,3),randcomplex(2,3)), #empty1 = (numpy.asarray([]), numpy.asarray([1])), #empty2 = (numpy.asarray([0]), numpy.asarray([])), x_eq_zero=( numpy.asarray([0.], dtype=config.floatX), numpy.asarray([2.], dtype=config.floatX) ), # Test for issue 1780 ) # empty2 case is not supported by numpy. _good_broadcast_pow_normal_float_pow = copy(_good_broadcast_pow_normal_float) del _good_broadcast_pow_normal_float_pow["empty2"] # Disable NAN checking for pow operator per issue #1780 m = copy(theano.compile.get_default_mode()) m.check_isfinite = False PowTester = makeBroadcastTester( op=pow, expected=lambda x, y: check_floatX((x, y), x ** y), good=_good_broadcast_pow_normal_float, grad=_grad_broadcast_pow_normal, name='Pow', mode=m ) PowInplaceTester = makeBroadcastTester( op=inplace.pow_inplace, expected=lambda x, y: x ** y, good=_good_broadcast_pow_normal_float_pow, grad=_grad_broadcast_pow_normal, inplace=True, mode=m ) # Those are corner case when rounding. Their is many rounding algo. # c round() fct and numpy round are not the same! corner_case = numpy.asarray( [-2.5, -2., -1.5, -1., -0.5, -.51, -.49, 0, 0.49, 0.5, 0.9, 1, 1.5, 2, 2.5], dtype=floatX) # we remove 0 here as the grad is not always computable numerically. corner_case_grad = numpy.asarray( [-2.5, -2., -1.5, -1., -0.5, -.51, -.49, 0.49, 0.5, 0.9, 1, 1.5, 2, 2.5], dtype=floatX) _good_broadcast_unary_normal_float = dict( normal=[rand_ranged(-5, 5, (2, 3))], corner_case=[corner_case], complex=[randcomplex(2, 3)], empty=[numpy.asarray([], dtype=config.floatX)]) _good_broadcast_unary_normal_float_no_empty = copymod( _good_broadcast_unary_normal_float, without=['empty']) _good_broadcast_unary_normal_float_no_empty_no_complex = copymod( _good_broadcast_unary_normal_float_no_empty, without=['complex']) _good_broadcast_unary_normal_float_no_complex = copymod( _good_broadcast_unary_normal_float, without=['complex']) _good_broadcast_unary_normal_float_no_complex_small_neg_range = dict( normal=[rand_ranged(-2, 5, (2, 3))], corner_case=[corner_case], empty=[numpy.asarray([], dtype=config.floatX)]) _good_broadcast_unary_normal = dict( normal=[numpy.asarray(rand_ranged(-5, 5, (2, 3)), dtype=config.floatX)], integers=[randint_ranged(-5, 5, (2, 3))], # not using -128 because numpy.allclose would return False int8=[numpy.arange(-127, 128, dtype='int8')], corner_case=[corner_case], complex=[randcomplex(2, 3)], empty=[numpy.asarray([], dtype=config.floatX)], ) _good_broadcast_unary_normal_no_complex = dict( normal=[numpy.asarray(rand_ranged(-5, 5, (2, 3)), dtype=floatX)], integers=[randint_ranged(-5, 5, (2, 3))], int8=[numpy.arange(-127, 128, dtype='int8')], corner_case=[corner_case], empty=[numpy.asarray([], dtype=config.floatX)], ) _grad_broadcast_unary_normal_no_complex = dict( normal=[numpy.asarray(rand_ranged(-5, 5, (2, 3)), dtype=floatX)], corner_case=[corner_case_grad]) _grad_broadcast_unary_normal = dict( normal=[numpy.asarray(rand_ranged(-5, 5, (2, 3)), dtype=floatX)], corner_case=[corner_case_grad], # empty = [numpy.asarray([])] # XXX: should this be included? ) _grad_broadcast_unary_normal_small_neg_range = dict( normal=[numpy.asarray(rand_ranged(-2, 5, (2, 3)), dtype=floatX)], corner_case=[corner_case_grad]) _grad_broadcast_unary_normal_no_complex_no_corner_case = copymod( _grad_broadcast_unary_normal_no_complex, without=['corner_case']) _grad_broadcast_unary_abs1_no_complex = dict( normal=[numpy.asarray(rand_ranged(-1, 1, (2, 3)), dtype=floatX)], ) _grad_broadcast_unary_0_2_no_complex = dict( # Don't go too close to 2 for tests in float32 normal=[numpy.asarray(rand_ranged(0, 1.9, (2, 3)), dtype=floatX)], ) # inplace ops when the input is integer and the output is float* # don't have a well defined behavior. We don't test that case. AbsTester = makeBroadcastTester(op=tensor.abs_, expected=lambda x: abs(x), good=_good_broadcast_unary_normal, grad=_grad_broadcast_unary_normal) _good_broadcast_unary_normal_abs = copy(_good_broadcast_unary_normal) # Can't do inplace on Abs as the input/output are not of the same type! del _good_broadcast_unary_normal_abs['complex'] AbsInplaceTester = makeBroadcastTester(op=inplace.abs__inplace, expected=lambda x: numpy.abs(x), good=_good_broadcast_unary_normal_abs, grad=_grad_broadcast_unary_normal, inplace=True) NegTester = makeBroadcastTester(op=tensor.neg, expected=lambda x: -x, good=_good_broadcast_unary_normal, grad=_grad_broadcast_unary_normal) NegInplaceTester = makeBroadcastTester(op=inplace.neg_inplace, expected=lambda x: -x, good=_good_broadcast_unary_normal, grad=_grad_broadcast_unary_normal, inplace=True) SgnTester = makeBroadcastTester(op=tensor.sgn, expected=numpy.sign, good=_good_broadcast_unary_normal_no_complex, grad=_grad_broadcast_unary_normal,) SgnInplaceTester = makeBroadcastTester(op=inplace.sgn_inplace, expected=numpy.sign, good=_good_broadcast_unary_normal_no_complex, grad=_grad_broadcast_unary_normal, inplace=True) IntDivTester = makeBroadcastTester( op=tensor.int_div, expected=lambda x, y: check_floatX((x, y), x // y), good=_good_broadcast_div_mod_normal_float, # I don't test the grad as the output is always an integer # (this is not a continuous output). # grad=_grad_broadcast_div_mod_normal, ) IntDivInplaceTester = makeBroadcastTester( op=inplace.int_div_inplace, expected=lambda x, y: check_floatX((x, y), x // y), good=_good_broadcast_div_mod_normal_float_inplace, # I don't test the grad as the output is always an integer # (this is not a continuous output). # grad=_grad_broadcast_div_mod_normal, inplace=True ) CeilTester = makeBroadcastTester(op=tensor.ceil, expected=lambda a: numpy.asarray( numpy.ceil(a), a.dtype), good=_good_broadcast_unary_normal_no_complex, grad=copymod(_grad_broadcast_unary_normal, without=['corner_case'], # corner_case includes ints where ceil is not differentiable extra=[numpy.asarray([-2.5, -1.5, -1.51, 0.49, .98, 1.02], dtype=floatX)])) CeilInplaceTester = makeBroadcastTester(op=inplace.ceil_inplace, expected=lambda a: numpy.asarray(numpy.ceil(a), a.dtype), good=_good_broadcast_unary_normal_no_complex, # corner cases includes a lot of integers: points where Ceil is not # continuous (not differentiable) grad=copymod(_grad_broadcast_unary_normal, without=['corner_case'], # corner_case includes ints where ceil is not differentiable extra=[numpy.asarray([-2.5, -1.5, -1.51, 0.49, .98, 1.02], dtype=floatX)]), inplace=True) FloorTester = makeBroadcastTester(op=tensor.floor, expected=lambda a: numpy.asarray(numpy.floor(a), a.dtype), good=_good_broadcast_unary_normal_no_complex, # XXX: why does grad of floor not give huge values at # the integer points in the 'corner_case' in # _grad_broadcast_unary_normal? It seems this test should fail, # yet it does not... grad=_grad_broadcast_unary_normal) FloorInplaceTester = makeBroadcastTester(op=inplace.floor_inplace, expected=lambda a: numpy.asarray(numpy.floor(a), a.dtype), good=_good_broadcast_unary_normal_no_complex, grad=_grad_broadcast_unary_normal, inplace=True) TruncInplaceTester = makeBroadcastTester( op=inplace.trunc_inplace, expected=lambda a: numpy.asarray(numpy.trunc(a), a.dtype), good=_good_broadcast_unary_normal_no_complex, inplace=True) TruncTester = makeBroadcastTester( op=tensor.trunc, expected=lambda a: numpy.asarray(numpy.trunc(a), a.dtype), good=_good_broadcast_unary_normal_no_complex) RoundHalfToEvenTester = makeBroadcastTester( op=tensor.round_half_to_even, expected=numpy.round, good=_good_broadcast_unary_normal_float_no_complex, grad=_grad_broadcast_unary_normal_no_complex_no_corner_case) RoundHalfToEvenInplaceTester = makeBroadcastTester( op=inplace.round_half_to_even_inplace, expected=numpy.round, good=_good_broadcast_unary_normal_float_no_complex, grad=_grad_broadcast_unary_normal_no_complex_no_corner_case, inplace=True) # numpy.vectorize don't handle correctly empty ndarray. # see in their file numpy/lib/function_base.py in class vectorize.__call__ # This happen in float32 mode. RoundHalfAwayFromZeroTester = makeBroadcastTester( op=tensor.round_half_away_from_zero, expected=lambda a: theano.scalar.basic.round_half_away_from_zero_vec(a), good=_good_broadcast_unary_normal_float_no_empty_no_complex, grad=_grad_broadcast_unary_normal_no_complex_no_corner_case) #_good_broadcast_unary_normal_float) RoundHalfAwayFromZeroInplaceTester = makeBroadcastTester( op=inplace.round_half_away_from_zero_inplace, expected=lambda a: theano.scalar.basic.round_half_away_from_zero_vec(a), good=_good_broadcast_unary_normal_float_no_empty_no_complex, grad=_grad_broadcast_unary_normal_no_complex_no_corner_case, inplace=True) SqrTester = makeBroadcastTester(op=tensor.sqr, expected=numpy.square, good=_good_broadcast_unary_normal, grad=_grad_broadcast_unary_normal) SqrInplaceTester = makeBroadcastTester(op=inplace.sqr_inplace, expected=numpy.square, good=_good_broadcast_unary_normal, grad=_grad_broadcast_unary_normal, inplace=True) ExpTester = makeBroadcastTester( op=tensor.exp, expected=upcast_float16_ufunc(numpy.exp), good=dict(_good_broadcast_unary_normal, int8=[numpy.arange(-127, 89, dtype='int8')]), grad=_grad_broadcast_unary_normal) ExpInplaceTester = makeBroadcastTester( op=inplace.exp_inplace, expected=numpy.exp, good=_good_broadcast_unary_normal_float, grad=_grad_broadcast_unary_normal, inplace=True) Exp2Tester = makeBroadcastTester(op=tensor.exp2, expected=upcast_float16_ufunc(numpy.exp2), good=_good_broadcast_unary_normal, grad=_grad_broadcast_unary_normal) Exp2InplaceTester = makeBroadcastTester( op=inplace.exp2_inplace, expected=numpy.exp2, good=_good_broadcast_unary_normal_float, grad=_grad_broadcast_unary_normal, inplace=True) Expm1Tester = makeBroadcastTester( op=tensor.expm1, expected=upcast_float16_ufunc(numpy.expm1), good=dict(_good_broadcast_unary_normal, int8=[numpy.arange(-127, 89, dtype='int8')]), grad=_grad_broadcast_unary_normal) Expm1InplaceTester = makeBroadcastTester( op=inplace.expm1_inplace, expected=numpy.expm1, good=_good_broadcast_unary_normal_float, grad=_grad_broadcast_unary_normal, inplace=True) _good_broadcast_unary_positive = dict( normal=(rand_ranged(0.001, 5, (2, 3)),), integers=(randint_ranged(1, 5, (2, 3)),), uint8=[numpy.arange(1, 256, dtype='uint8')], complex=(randc128_ranged(1, 5, (2, 3)),), empty=(numpy.asarray([], dtype=config.floatX),), ) _good_broadcast_unary_positive_float = copymod( _good_broadcast_unary_positive, without=['integers', 'uint8']) _grad_broadcast_unary_positive = dict(normal=(rand_ranged(0.001, 5, (2, 3)),),) LogTester = makeBroadcastTester(op=tensor.log, expected=upcast_float16_ufunc(numpy.log), good=_good_broadcast_unary_positive, grad=_grad_broadcast_unary_positive) LogInplaceTester = makeBroadcastTester( op=inplace.log_inplace, expected=numpy.log, good=_good_broadcast_unary_positive_float, grad=_grad_broadcast_unary_positive, inplace=True) Log2Tester = makeBroadcastTester(op=tensor.log2, expected=upcast_float16_ufunc(numpy.log2), good=_good_broadcast_unary_positive, grad=_grad_broadcast_unary_positive) Log2InplaceTester = makeBroadcastTester( op=inplace.log2_inplace, expected=numpy.log2, good=_good_broadcast_unary_positive_float, grad=_grad_broadcast_unary_positive, inplace=True) Log10Tester = makeBroadcastTester(op=tensor.log10, expected=upcast_float16_ufunc(numpy.log10), good=_good_broadcast_unary_positive, grad=_grad_broadcast_unary_positive) Log10InplaceTester = makeBroadcastTester( op=inplace.log10_inplace, expected=numpy.log10, good=_good_broadcast_unary_positive_float, grad=_grad_broadcast_unary_positive, inplace=True) Log1pTester = makeBroadcastTester(op=tensor.log1p, expected=upcast_float16_ufunc(numpy.log1p), good=_good_broadcast_unary_positive, grad=_grad_broadcast_unary_positive) Log1pInplaceTester = makeBroadcastTester( op=inplace.log1p_inplace, expected=numpy.log1p, good=_good_broadcast_unary_positive_float, grad=_grad_broadcast_unary_positive, inplace=True) SqrtTester = makeBroadcastTester(op=tensor.sqrt, expected=upcast_float16_ufunc(numpy.sqrt), good=_good_broadcast_unary_positive, grad=_grad_broadcast_unary_positive) SqrtInplaceTester = makeBroadcastTester( op=inplace.sqrt_inplace, expected=numpy.sqrt, good=_good_broadcast_unary_positive_float, grad=_grad_broadcast_unary_positive, inplace=True) _good_broadcast_unary_wide = dict( normal=(rand_ranged(-1000, 1000, (2, 3)),), integers=(randint_ranged(-1000, 1000, (2, 3)),), int8=[numpy.arange(-127, 128, dtype='int8')], complex=(randc128_ranged(-1000, 1000, (2, 3)),), empty=(numpy.asarray([], dtype=config.floatX),),) _good_broadcast_unary_wide_float = copymod( _good_broadcast_unary_wide, without=['integers', 'int8']) _grad_broadcast_unary_wide = dict(normal=(rand_ranged(-1000, 1000, (2, 3)),),) if theano.config.floatX == 'float32': angle_eps = 1e-4 else: angle_eps = 1e-10 Deg2radTester = makeBroadcastTester( op=tensor.deg2rad, expected=upcast_float16_ufunc(numpy.deg2rad), good=_good_broadcast_unary_normal_no_complex, grad=_grad_broadcast_unary_normal_no_complex, eps=angle_eps) Deg2radInplaceTester = makeBroadcastTester( op=inplace.deg2rad_inplace, expected=numpy.deg2rad, good=_good_broadcast_unary_normal_float_no_complex, grad=_grad_broadcast_unary_normal_no_complex, inplace=True, eps=angle_eps) Rad2degTester = makeBroadcastTester( op=tensor.rad2deg, expected=upcast_float16_ufunc(numpy.rad2deg), good=_good_broadcast_unary_normal_no_complex, grad=_grad_broadcast_unary_normal_no_complex, eps=angle_eps) Rad2degInplaceTester = makeBroadcastTester( op=inplace.rad2deg_inplace, expected=numpy.rad2deg, good=_good_broadcast_unary_normal_float_no_complex, grad=_grad_broadcast_unary_normal_no_complex, inplace=True, eps=angle_eps) SinTester = makeBroadcastTester(op=tensor.sin, expected=upcast_float16_ufunc(numpy.sin), good=_good_broadcast_unary_wide, grad=_grad_broadcast_unary_wide) SinInplaceTester = makeBroadcastTester( op=inplace.sin_inplace, expected=numpy.sin, good=_good_broadcast_unary_wide_float, grad=_grad_broadcast_unary_wide, inplace=True) _good_broadcast_unary_arcsin = dict( normal=(rand_ranged(-1, 1, (2, 3)),), integers=(randint_ranged(-1, 1, (2, 3)),), int8=[numpy.arange(-1, 2, dtype='int8')], complex=(randc128_ranged(-1, 1, (2, 3)),), empty=(numpy.asarray([], dtype=config.floatX),),) _good_broadcast_unary_arcsin_float = copymod( _good_broadcast_unary_arcsin, without=['integers', 'int8']) # The actual range is [-1, 1] but the numerical gradient is too # unstable near those values _grad_broadcast_unary_arcsin = dict(normal=(rand_ranged(-0.9, 0.9, (2, 3)),),) ArcsinTester = makeBroadcastTester(op=tensor.arcsin, expected=upcast_float16_ufunc(numpy.arcsin), good=_good_broadcast_unary_arcsin, grad=_grad_broadcast_unary_arcsin) ArcsinInplaceTester = makeBroadcastTester( op=inplace.arcsin_inplace, expected=numpy.arcsin, good=_good_broadcast_unary_arcsin_float, grad=_grad_broadcast_unary_arcsin, inplace=True) CosTester = makeBroadcastTester(op=tensor.cos, expected=upcast_float16_ufunc(numpy.cos), good=_good_broadcast_unary_wide, grad=_grad_broadcast_unary_wide) CosInplaceTester = makeBroadcastTester( op=inplace.cos_inplace, expected=numpy.cos, good=_good_broadcast_unary_wide_float, grad=_grad_broadcast_unary_wide, inplace=True) def test_py_c_match(): a = tensor.TensorType(dtype='int8', broadcastable=(False,))() f = theano.function([a], tensor.arccos(a), mode='DebugMode') # This can fail in DebugMode f(numpy.asarray([1, 0, -1], dtype='int8')) ArccosTester = makeBroadcastTester(op=tensor.arccos, expected=upcast_float16_ufunc(numpy.arccos), good=_good_broadcast_unary_arcsin, grad=_grad_broadcast_unary_arcsin) ArccosInplaceTester = makeBroadcastTester( op=inplace.arccos_inplace, expected=numpy.arccos, good=_good_broadcast_unary_arcsin_float, grad=_grad_broadcast_unary_arcsin, inplace=True) _good_broadcast_unary_tan = dict( normal=(rand_ranged(-3.14, 3.14, (2, 3)),), shifted=(rand_ranged(3.15, 6.28, (2, 3)),), integers=(randint_ranged(-3, 3, (2, 3)),), int8=[numpy.arange(-3, 4, dtype='int8')], complex=(randc128_ranged(-3.14, 3.14, (2, 3)),), empty=(numpy.asarray([], dtype=config.floatX),),) # We do not want to test around the discontinuity. _grad_broadcast_unary_tan = dict(normal=(rand_ranged(-1.5, 1.5, (2, 3)),), shifted=(rand_ranged(1.6, 4.6, (2, 3)),)) TanTester = makeBroadcastTester(op=tensor.tan, expected=upcast_float16_ufunc(numpy.tan), good=_good_broadcast_unary_tan, grad=_grad_broadcast_unary_tan) TanInplaceTester = makeBroadcastTester( op=inplace.tan_inplace, expected=numpy.tan, good=copymod(_good_broadcast_unary_tan, without=['integers', 'int8']), grad=_grad_broadcast_unary_tan, inplace=True) ArctanTester = makeBroadcastTester(op=tensor.arctan, expected=upcast_float16_ufunc(numpy.arctan), good=_good_broadcast_unary_wide, grad=_grad_broadcast_unary_wide) ArctanInplaceTester = makeBroadcastTester( op=inplace.arctan_inplace, expected=numpy.arctan, good=_good_broadcast_unary_wide_float, grad=_grad_broadcast_unary_wide, inplace=True) _good_broadcast_binary_arctan2 = dict( same_shapes=(rand(2, 3), rand(2, 3)), not_same_dimensions=(rand(2, 2), rand(2)), scalar=(rand(2, 3), rand(1, 1)), row=(rand(2, 3), rand(1, 3)), column=(rand(2, 3), rand(2, 1)), integers=(randint(2, 3), randint(2, 3)), int8=[numpy.arange(-127, 128, dtype='int8'), numpy.arange(-127, 128, dtype='int8')[:, numpy.newaxis]], dtype_mixup_1=(rand(2, 3), randint(2, 3)), dtype_mixup_2=(randint(2, 3), rand(2, 3)), empty=(numpy.asarray([], dtype=config.floatX), numpy.asarray([1], dtype=config.floatX)), ) _grad_broadcast_binary_arctan2 = dict( same_shapes=(rand(2, 3), rand(2, 3)), scalar=(rand(2, 3), rand(1, 1)), row=(rand(2, 3), rand(1, 3)), column=(rand(2, 3), rand(2, 1)), ) Arctan2Tester = makeBroadcastTester( op=tensor.arctan2, expected=upcast_float16_ufunc(numpy.arctan2), good=_good_broadcast_binary_arctan2, grad=_grad_broadcast_binary_arctan2) Arctan2InplaceTester = makeBroadcastTester( op=inplace.arctan2_inplace, expected=numpy.arctan2, good=copymod(_good_broadcast_binary_arctan2, without=['integers', 'int8']), grad=_grad_broadcast_binary_arctan2, inplace=True) CoshTester = makeBroadcastTester( op=tensor.cosh, expected=upcast_float16_ufunc(numpy.cosh), good=dict(_good_broadcast_unary_normal, int8=[numpy.arange(-89, 90, dtype='int8')]), grad=_grad_broadcast_unary_normal) CoshInplaceTester = makeBroadcastTester( op=inplace.cosh_inplace, expected=numpy.cosh, good=_good_broadcast_unary_normal_float, grad=_grad_broadcast_unary_normal, inplace=True) _good_broadcast_unary_arccosh = dict( normal=(rand_ranged(1, 1000, (2, 3)),), integers=(randint_ranged(1, 1000, (2, 3)),), uint8=[numpy.arange(1, 256, dtype='uint8')], complex=(randc128_ranged(1, 1000, (2, 3)),), empty=(numpy.asarray([], dtype=config.floatX),),) _grad_broadcast_unary_arccosh = dict(normal=(rand_ranged(1, 1000, (2, 3)),),) ArccoshTester = makeBroadcastTester( op=tensor.arccosh, expected=upcast_float16_ufunc(numpy.arccosh), good=_good_broadcast_unary_arccosh, grad=_grad_broadcast_unary_arccosh) ArccoshInplaceTester = makeBroadcastTester( op=inplace.arccosh_inplace, expected=numpy.arccosh, good=copymod(_good_broadcast_unary_arccosh, without=['integers', 'uint8']), grad=_grad_broadcast_unary_arccosh, inplace=True) SinhTester = makeBroadcastTester( op=tensor.sinh, expected=upcast_float16_ufunc(numpy.sinh), good=dict(_good_broadcast_unary_normal, int8=[numpy.arange(-89, 90, dtype='int8')]), grad=_grad_broadcast_unary_normal) SinhInplaceTester = makeBroadcastTester( op=inplace.sinh_inplace, expected=numpy.sinh, good=_good_broadcast_unary_normal_float, grad=_grad_broadcast_unary_normal, inplace=True) ArcsinhTester = makeBroadcastTester( op=tensor.arcsinh, expected=upcast_float16_ufunc(numpy.arcsinh), good=_good_broadcast_unary_normal, grad=_grad_broadcast_unary_normal) ArcsinhInplaceTester = makeBroadcastTester( op=inplace.arcsinh_inplace, expected=numpy.arcsinh, good=_good_broadcast_unary_normal_float, grad=_grad_broadcast_unary_normal, inplace=True) TanhTester = makeBroadcastTester(op=tensor.tanh, expected=upcast_float16_ufunc(numpy.tanh), good=_good_broadcast_unary_normal, grad=_grad_broadcast_unary_normal) TanhInplaceTester = makeBroadcastTester( op=inplace.tanh_inplace, expected=numpy.tanh, good=_good_broadcast_unary_normal_float, grad=_grad_broadcast_unary_normal, inplace=True) _eps = 1e-2 _good_broadcast_unary_arctanh = dict( normal=(rand_ranged(-1 + _eps, 1 - _eps, (2, 3)),), integers=(randint_ranged(-1 + _eps, 1 - _eps, (2, 3)),), int8=[numpy.arange(0, 1, dtype='int8')], complex=(randc128_ranged(-1 + _eps, 1 - _eps, (2, 3)),), empty=(numpy.asarray([], dtype=config.floatX),),) _grad_broadcast_unary_arctanh = dict( normal=(rand_ranged(-1 + _eps, 1 - _eps, (2, 3)),),) ArctanhTester = makeBroadcastTester( op=tensor.arctanh, expected=upcast_float16_ufunc(numpy.arctanh), good=_good_broadcast_unary_arctanh, grad=_grad_broadcast_unary_arctanh) ArctanhInplaceTester = makeBroadcastTester( op=inplace.arctanh_inplace, expected=numpy.arctanh, good=copymod(_good_broadcast_unary_arctanh, without=['integers', 'int8']), grad=_grad_broadcast_unary_arctanh, inplace=True) # We can't test it if scipy is not installed! # Precomputing the result is brittle(it have been broken!) # As if we do any modification to random number here, # The input random number will change and the output! if imported_scipy_special: expected_erf = scipy.special.erf expected_erfc = scipy.special.erfc expected_erfinv = scipy.special.erfinv expected_erfcinv = scipy.special.erfcinv expected_gamma = scipy.special.gamma expected_gammaln = scipy.special.gammaln expected_psi = scipy.special.psi expected_chi2sf = lambda x, df: scipy.stats.chi2.sf(x, df).astype(x.dtype) expected_j0 = scipy.special.j0 expected_j1 = scipy.special.j1 skip_scipy = False if LooseVersion(scipy_version) >= LooseVersion("0.12.0"): expected_erfcx = scipy.special.erfcx skip_scipy12 = False else: expected_erfcx = [] skip_scipy12 = "the erfcx op requires scipy version >= 0.12, installed version is " + scipy_version else: expected_erf = [] expected_erfc = [] expected_erfcx = [] expected_erfinv = [] expected_erfcinv = [] expected_gamma = [] expected_gammaln = [] expected_psi = [] expected_chi2sf = [] expected_j0 = [] expected_j1 = [] skip_scipy = "scipy is not present" skip_scipy12 = "scipy is not present" ErfTester = makeBroadcastTester( op=tensor.erf, expected=expected_erf, good=_good_broadcast_unary_normal, grad=_grad_broadcast_unary_normal, eps=2e-10, mode=mode_no_scipy, skip=skip_scipy) ErfInplaceTester = makeBroadcastTester( op=inplace.erf_inplace, expected=expected_erf, good=_good_broadcast_unary_normal_float, grad=_grad_broadcast_unary_normal, mode=mode_no_scipy, eps=2e-10, inplace=True, skip=skip_scipy) ErfcTester = makeBroadcastTester( op=tensor.erfc, expected=expected_erfc, good=_good_broadcast_unary_normal_float_no_complex, grad=_grad_broadcast_unary_normal, eps=2e-10, mode=mode_no_scipy, skip=skip_scipy) ErfcInplaceTester = makeBroadcastTester( op=inplace.erfc_inplace, expected=expected_erfc, good=_good_broadcast_unary_normal_float_no_complex, grad=_grad_broadcast_unary_normal, eps=2e-10, mode=mode_no_scipy, inplace=True, skip=skip_scipy) ErfcxTester = makeBroadcastTester( op=tensor.erfcx, expected=expected_erfcx, good=_good_broadcast_unary_normal_float_no_complex_small_neg_range, grad=_grad_broadcast_unary_normal_small_neg_range, eps=2e-10, mode=mode_no_scipy, skip=skip_scipy12) ErfcxInplaceTester = makeBroadcastTester( op=inplace.erfcx_inplace, expected=expected_erfcx, good=_good_broadcast_unary_normal_float_no_complex_small_neg_range, grad=_grad_broadcast_unary_normal_small_neg_range, eps=2e-10, mode=mode_no_scipy, inplace=True, skip=skip_scipy12) ErfinvTester = makeBroadcastTester( op=tensor.erfinv, expected=expected_erfinv, good={'normal': [rand_ranged(-.9, .9, (2, 3))], 'empty': [numpy.asarray([], dtype=config.floatX)]}, grad=_grad_broadcast_unary_abs1_no_complex, eps=2e-10, mode=mode_no_scipy, skip=skip_scipy) ErfcinvTester = makeBroadcastTester( op=tensor.erfcinv, expected=expected_erfcinv, good={'normal': [rand_ranged(0.001, 1.9, (2, 3))], 'empty': [numpy.asarray([], dtype=config.floatX)]}, grad=_grad_broadcast_unary_0_2_no_complex, eps=2e-10, mode=mode_no_scipy, skip=skip_scipy) _good_broadcast_unary_gammaln = dict( normal=(rand_ranged(-1 + 1e-2, 10, (2, 3)),), empty=(numpy.asarray([], dtype=config.floatX),),) _grad_broadcast_unary_gammaln = dict( # smaller range as our grad method does not estimate it well enough. normal=(rand_ranged(1e-1, 8, (2, 3)),),) GammaTester = makeBroadcastTester( op=tensor.gamma, expected=expected_gamma, good=_good_broadcast_unary_gammaln, grad=_grad_broadcast_unary_gammaln, mode=mode_no_scipy, eps=1e-5, skip=skip_scipy) GammaInplaceTester = makeBroadcastTester( op=inplace.gamma_inplace, expected=expected_gamma, good=_good_broadcast_unary_gammaln, grad=_grad_broadcast_unary_gammaln, mode=mode_no_scipy, eps=1e-5, inplace=True, skip=skip_scipy) GammalnTester = makeBroadcastTester( op=tensor.gammaln, expected=expected_gammaln, good=_good_broadcast_unary_gammaln, grad=_grad_broadcast_unary_gammaln, eps=2e-10, mode=mode_no_scipy, skip=skip_scipy) GammalnInplaceTester = makeBroadcastTester( op=inplace.gammaln_inplace, expected=expected_gammaln, good=_good_broadcast_unary_gammaln, grad=_grad_broadcast_unary_gammaln, eps=2e-10, mode=mode_no_scipy, inplace=True, skip=skip_scipy) _good_broadcast_unary_psi = dict( normal=(rand_ranged(1, 10, (2, 3)),), empty=(numpy.asarray([], dtype=config.floatX),),) PsiTester = makeBroadcastTester( op=tensor.psi, expected=expected_psi, good=_good_broadcast_unary_psi, eps=2e-10, mode=mode_no_scipy, skip=skip_scipy) PsiInplaceTester = makeBroadcastTester( op=inplace.psi_inplace, expected=expected_psi, good=_good_broadcast_unary_psi, eps=2e-10, mode=mode_no_scipy, inplace=True, skip=skip_scipy) # chi2sf takes two inputs, a value (x) and a degrees of freedom (k). # not sure how to deal with that here... _good_broadcast_unary_chi2sf = dict( normal=(rand_ranged(1, 10, (2, 3)), numpy.asarray(1, dtype=config.floatX)), empty=(numpy.asarray([], dtype=config.floatX), numpy.asarray(1, dtype=config.floatX))) Chi2SFTester = makeBroadcastTester( op=tensor.chi2sf, expected=expected_chi2sf, good=_good_broadcast_unary_chi2sf, eps=2e-10, mode=mode_no_scipy, skip=skip_scipy, name='Chi2SF') Chi2SFInplaceTester = makeBroadcastTester( op=inplace.chi2sf_inplace, expected=expected_chi2sf, good=_good_broadcast_unary_chi2sf, eps=2e-10, mode=mode_no_scipy, inplace=True, skip=skip_scipy, name='Chi2SF') _good_broadcast_unary_j = dict( normal=(rand_ranged(0.1, 8, (2, 3)),),) J0Tester = makeBroadcastTester( op=tensor.j0, expected=expected_j0, good=_good_broadcast_unary_j, grad=_good_broadcast_unary_j, eps=2e-10, mode=mode_no_scipy, skip=skip_scipy) J0InplaceTester = makeBroadcastTester( op=inplace.j0_inplace, expected=expected_j0, good=_good_broadcast_unary_j, grad=_good_broadcast_unary_j, eps=2e-10, mode=mode_no_scipy, inplace=True, skip=skip_scipy) J1Tester = makeBroadcastTester( op=tensor.j1, expected=expected_j1, good=_good_broadcast_unary_j, eps=2e-10, mode=mode_no_scipy, skip=skip_scipy) J1InplaceTester = makeBroadcastTester( op=inplace.j1_inplace, expected=expected_j1, good=_good_broadcast_unary_j, eps=2e-10, mode=mode_no_scipy, inplace=True, skip=skip_scipy) ZerosLikeTester = makeBroadcastTester( op=tensor.zeros_like, expected=numpy.zeros_like, good=_good_broadcast_unary_normal, grad=_grad_broadcast_unary_normal, name='ZerosLike') OnesLikeTester = makeBroadcastTester( op=tensor.ones_like, expected=numpy.ones_like, good=_good_broadcast_unary_normal, grad=_grad_broadcast_unary_normal, name='OnesLike') # Complex operations _good_complex_from_polar = dict( same_shapes=(abs(rand(2, 3)), rand(2, 3)), not_same_dimensions=(abs(rand(2, 2)), rand(2)), scalar=(abs(rand(2, 3)), rand(1, 1)), row=(abs(rand(2, 3)), rand(1, 3)), column=(abs(rand(2, 3)), rand(2, 1)), integers=(abs(randint(2, 3)), randint(2, 3)), empty=(numpy.asarray([], dtype=config.floatX), numpy.asarray([1], dtype=config.floatX)),) _grad_complex_from_polar = dict( same_shapes=(abs(rand(2, 3)), rand(2, 3)), scalar=(abs(rand(2, 3)), rand(1, 1)), row=(abs(rand(2, 3)), rand(1, 3)), column=(abs(rand(2, 3)), rand(2, 1))) ComplexFromPolarTester = makeBroadcastTester( op=tensor.complex_from_polar, expected=lambda r, theta: r * numpy.cos(theta) + 1j * r * numpy.sin(theta), good=_good_complex_from_polar) ConjTester = makeBroadcastTester( op=tensor.conj, expected=numpy.conj, good=_good_broadcast_unary_normal) ConjInplaceTester = makeBroadcastTester( op=inplace.conj_inplace, expected=numpy.conj, good=_good_broadcast_unary_normal, inplace=True) DotTester = makeTester(name='DotTester', op=dot, expected=lambda x, y: numpy.dot(x, y), checks={}, good=dict(correct1=(rand(5, 7), rand(7, 5)), correct2=(rand(5, 7), rand(7, 9)), correct3=(rand(5, 7), rand(7)), correct4=(rand(5), rand(5, 7)), mixed1=(rand(5).astype('float32'), rand(5, 7)), mixed2=(rand(5).astype('float64'), rand(5, 7)), complex1=(randcomplex(5, 7), randcomplex(7)), complex2=(rand(5, 7), randcomplex(7)), complex3=(randcomplex(5, 7), rand(7)), empty1=(numpy.asarray([], dtype=config.floatX), numpy.asarray([], dtype=config.floatX)), empty2=(rand(5, 0), rand(0, 2)), empty3=(rand(0, 5), rand(5, 0)), ), bad_build=dict(), bad_runtime=dict(bad1=(rand(5, 7), rand(5, 7)), bad2=(rand(5, 7), rand(8, 3)))) BatchedDotTester = makeTester( name='BatchedDotTester', op=batched_dot, expected=(lambda xs, ys: numpy.asarray( list(x * y if x.ndim == 0 or y.ndim == 0 else numpy.dot(x, y) for x, y in zip(xs, ys)), dtype=theano.scalar.upcast(xs.dtype, ys.dtype))), checks={}, grad=dict(correct1=(rand(3, 5, 7), rand(3, 7, 5)), correct2=(rand(3, 5, 7), rand(3, 7, 9)), correct3=(rand(3, 5, 7), rand(3, 7)), correct4=(rand(3, 5), rand(3, 5, 7)), correct5=(rand(3), rand(3, 5, 7)), correct6=(rand(3, 5), rand(3)), correct7=(rand(3, 5), rand(3, 5)), correct8=(rand(3), rand(3)), correct9=(rand(3, 5, 7, 11), rand(3)), correct10=(rand(3, 7, 11, 5), rand(3, 5)), correct11=(rand(3, 7, 11, 5), rand(3, 5, 13)), correct12=(rand(3, 7, 11, 5), rand(3, 13, 5, 17)), mixed1=(rand(3, 5).astype('float32'), rand(3, 5, 7)), mixed2=(rand(3, 5).astype('float64'), rand(3, 5, 7))), good=dict(correct1=(rand(3, 5, 7), rand(3, 7, 5)), correct2=(rand(3, 5, 7), rand(3, 7, 9)), correct3=(rand(3, 5, 7), rand(3, 7)), correct4=(rand(3, 5), rand(3, 5, 7)), correct5=(rand(3), rand(3, 5, 7)), correct6=(rand(3, 5), rand(3)), correct7=(rand(3, 5), rand(3, 5)), correct8=(rand(3), rand(3)), correct9=(rand(3, 5, 7, 11), rand(3)), correct10=(rand(3, 7, 11, 5), rand(3, 5)), correct11=(rand(3, 7, 11, 5), rand(3, 5, 13)), correct12=(rand(3, 7, 11, 5), rand(3, 13, 5, 17)), mixed1=(rand(3, 5).astype('float32'), rand(3, 5, 7)), mixed2=(rand(3, 5).astype('float64'), rand(3, 5, 7))), bad_build=dict(no_batch_axis2=(rand(), rand(3, 5)), no_batch_axis3=(rand(3, 5), rand())), bad_runtime=dict(batch_dim_mismatch1=(rand(2, 5, 7), rand(3, 7, 9)), batch_dim_mismatch2=(rand(3, 5, 7), rand(2, 7, 9)), batch_dim_mismatch3=(rand(3), rand(5)), bad_dim1=(rand(3, 5, 7), rand(3, 5, 7)), bad_dim2=(rand(3, 5, 7), rand(3, 8, 3)), bad_dim3=(rand(3, 5), rand(3, 7)), bad_dim4=(rand(3, 5, 7, 11), rand(3, 5)), bad_dim5=(rand(3, 5, 7, 11), rand(3, 5, 13)), bad_dim6=(rand(3, 5, 7, 11), rand(3, 13, 5, 17)))) def _numpy_second(x, y): return numpy.broadcast_arrays(x, y)[1] ALL_DTYPES = ('int8', 'int16', 'int32', 'int64', 'float32', 'float64', 'complex64', 'complex128') REAL_DTYPES = ALL_DTYPES[:-2] COMPLEX_DTYPES = ALL_DTYPES[-2:] def multi_dtype_checks(shape1, shape2, dtypes=ALL_DTYPES, nameprefix=''): for dtype1, dtype2 in itertools.combinations(dtypes, 2): name1 = '%s_%s_%s' % (nameprefix, dtype1, dtype2) name2 = '%s_%s_%s' % (nameprefix, dtype2, dtype1) obj1 = rand_of_dtype(shape1, dtype1) obj2 = rand_of_dtype(shape2, dtype2) yield (name1, (obj1, obj2)) yield (name2, (obj2, obj1)) def multi_dtype_cast_checks(shape, dtypes=ALL_DTYPES, nameprefix=''): for dtype1, dtype2 in itertools.combinations(dtypes, 2): name1 = '%s_%s_%s' % (nameprefix, dtype1, dtype2) name2 = '%s_%s_%s' % (nameprefix, dtype2, dtype1) obj1 = rand_of_dtype(shape, dtype1) obj2 = rand_of_dtype(shape, dtype2) yield (name1, (obj1, dtype2)) yield (name2, (obj2, dtype1)) SecondBroadcastTester = makeTester( name='SecondBroadcastTester', op=second, expected=_numpy_second, good=dict(itertools.chain( multi_dtype_checks((4, 5), (5,)), multi_dtype_checks((2, 3, 2), (3, 2)), multi_dtype_checks((2, 3, 2), (2,)), )), # I can't think of any way to make this fail at # build time # Just some simple smoke tests bad_runtime=dict( fail1=(rand(5, 4), rand(5)), fail2=(rand(3, 2, 3), rand(6, 9)), fail3=(randint(6, 2, 9), rand(3, 2)), ) ) # We exclude local_fill_to_alloc because it optimizes the "second" node # away from the graph. SecondSameRankTester = makeTester( name='SecondSameRankTester', op=second, expected=_numpy_second, good=dict(itertools.chain( multi_dtype_checks((4, 5), (4, 5)), multi_dtype_checks((1, 2), (3, 2)), multi_dtype_checks((3, 2), (1, 2)), )), # These sizes are not broadcastable to one another # and SHOULD raise an error, but currently don't. bad_runtime=dict(itertools.chain( multi_dtype_checks((4, 5), (5, 4)), multi_dtype_checks((1, 5), (5, 4)), )), mode=get_default_mode().excluding( 'local_fill_to_alloc', 'local_useless_fill') ) # Alloc AllocTester = makeBroadcastTester( name='AllocTester', op=alloc, expected=(lambda x, *shp: numpy.zeros(shp, dtype=x.dtype) + x), good=dict( correct01=(rand(), numpy.int32(7)), correct01_bcast=(rand(1), numpy.int32(7)), correct02=(rand(), numpy.int32(4), numpy.int32(7)), correct12=(rand(7), numpy.int32(4), numpy.int32(7)), correct13=(rand(7), numpy.int32(2), numpy.int32(4), numpy.int32(7)), correct23=(rand(4, 7), numpy.int32(2), numpy.int32(4), numpy.int32(7)), correctb1=(rand(1, 7), numpy.int32(4), numpy.int32(7)), correctb2=(rand(1, 7), numpy.int32(2), numpy.int32(4), numpy.int32(7)), correctb3=(rand(7, 1), numpy.int32(7), numpy.int32(4)), correctb4=(rand(7, 1), numpy.int32(2), numpy.int32(7), numpy.int32(4)), ), bad_runtime=dict( bad_shape12=(rand(7), numpy.int32(7), numpy.int32(5)), ), bad_build=dict( vec=(rand(1), [numpy.int32(2)]), too_big32=(rand(6, 2, 4), numpy. int32(6), numpy.int32(2)), too_big32b=(rand(6, 2, 4), numpy. int32(6), numpy.int32(4)), too_big32c=(rand(6, 2, 4), numpy. int32(2), numpy.int32(4)), too_big32d=(rand(6, 2, 4), numpy. int32(2), numpy.int32(6)), too_big32e=(rand(6, 2, 4), numpy. int32(4), numpy.int32(6)), too_big32f=(rand(6, 2, 4), numpy. int32(4), numpy.int32(2)), ), ) # Since not all inputs of Alloc are differentiable, we need different testers s1, s2, s3 = randint_ranged(1, 13, (3,)) # alloc a scalar into a vector Alloc01GradTester = makeBroadcastTester( name='Alloc01GradTester', #op = (lambda self, x: alloc(x, s1)), op=(lambda x: alloc(x, s1)), expected=(lambda x: numpy.zeros((s1,), dtype=x.dtype) + x), grad=dict( x1=(rand(),), x2=(rand(),), x3=(rand(),), ), ) # alloc a vector into a tensor3 Alloc13GradTester = makeBroadcastTester( name='Alloc13GradTester', #op = (lambda self, x: alloc(x, s1, s2, s3)), op=(lambda x: alloc(x, s1, s2, s3)), expected=(lambda x: numpy.zeros((s1, s2, s3), dtype=x.dtype) + x), grad=dict( x1=(rand(s3),), x2=(rand(s3),), x3=(rand(s3),), ), ) # unbroadcast a row to a matrix Allocb1GradTester = makeBroadcastTester( name='Allocb1GradTester', op=lambda x: alloc(x, s1, s2), expected=(lambda x: numpy.zeros((s1, s2), dtype=x.dtype) + x), grad=dict( x1=(rand(1, s2),), x2=(rand(1, s2),), x3=(rand(1, s2),), ), ) # unbroadcast a row to a tensor3 Allocb2GradTester = makeBroadcastTester( name='Allocb2GradTester', op=lambda x: alloc(x, s1, s2, s3), expected=(lambda x: numpy.zeros((s1, s2, s3), dtype=x.dtype) + x), grad=dict( x1=(rand(1, s3),), x2=(rand(1, s3),), x3=(rand(1, s3),), ), ) # unbroadcast a col to a matrix Allocb3GradTester = makeBroadcastTester( name='Allocb3GradTester', op=lambda x: alloc(x, s1, s2), expected=(lambda x: numpy.zeros((s1, s2), dtype=x.dtype) + x), grad=dict( x1=(rand(s1, 1),), x2=(rand(s1, 1),), x3=(rand(s1, 1),), ), ) # unbroadcast a col to a tensor3 Allocb4GradTester = makeBroadcastTester( name='Allocb4GradTester', op=lambda x: alloc(x, s1, s2, s3), expected=(lambda x: numpy.zeros((s1, s2, s3), dtype=x.dtype) + x), grad=dict( x1=(rand(s2, 1),), x2=(rand(s2, 1),), x3=(rand(s2, 1),), ), ) # Partial un broadcast of a dimshuffled input AllocDimshuffleGradTester = makeBroadcastTester( name='Allocb4GradTester', op=lambda x: alloc(x.dimshuffle('x', 'x', 0), 1, s2, s3), expected=(lambda x: numpy.zeros((1, s2, s3), dtype=x.dtype) + x), grad=dict( x1=(rand(s3),), x2=(rand(s3),), x3=(rand(s3),), ), ) AllocDimshuffleGradTester2 = makeBroadcastTester( name='Allocb4GradTester', op=lambda x: alloc(x.dimshuffle('x', 0), 1, s2, s3), expected=(lambda x: numpy.zeros((1, s2, s3), dtype=x.dtype) + x), grad=dict( x1=(rand(s3),), x2=(rand(s3),), x3=(rand(s3),), ), ) class ApplyDefaultTestOp(theano.Op): def __init__(self, id): self.default_output = id def make_node(self, x): x = theano.tensor.as_tensor_variable(x) return theano.Apply(self, [x], [x.type()]) class TestAsTensorVariable(unittest.TestCase): """ Unit test for ensuring that as_tensor_variable handles Apply objects correctly and removes leading broadcastable dimensions when possible. """ def setUp(self): self.x = tensor.scalar('x') def test_one_output(self): good_apply_var = ApplyDefaultTestOp(0).make_node(self.x) x = as_tensor_variable(good_apply_var) def test_below_zero_output(self): bad_apply_var = ApplyDefaultTestOp(-1).make_node(self.x) self.assertRaises(AttributeError, as_tensor_variable, bad_apply_var) def test_above_output_len(self): bad_apply_var = ApplyDefaultTestOp(2).make_node(self.x) self.assertRaises(AttributeError, as_tensor_variable, bad_apply_var) def test_list(self): bad_apply_var = ApplyDefaultTestOp([0, 1]).make_node(self.x) self.assertRaises(AttributeError, as_tensor_variable, bad_apply_var) def test_strip_leading_broadcastable(self): x = tensor.TensorType(config.floatX, (True, False))('x') x = as_tensor_variable(x, ndim=1) assert(x.ndim == 1) x = tensor.matrix('x', dtype=config.floatX) self.assertRaises(ValueError, as_tensor_variable, x, ndim=1) class TestAlloc(unittest.TestCase): dtype = config.floatX mode = mode_opt shared = staticmethod(theano.shared) allocs = [tensor.Alloc()] * 3 def setUp(self): self.rng = numpy.random.RandomState(seed=utt.fetch_seed()) def test_alloc_constant_folding(self): test_params = numpy.asarray(self.rng.randn(50 * 60), self.dtype) some_vector = vector('some_vector', dtype=self.dtype) some_matrix = some_vector.reshape((60, 50)) variables = self.shared(numpy.ones((50,), dtype=self.dtype)) idx = tensor.constant(numpy.arange(50)) for alloc, (subtensor, n_alloc) in zip(self.allocs, [ # IncSubtensor1 (some_matrix[:60], 2), # AdvancedIncSubtensor1 (some_matrix[arange(60)], 2), # AdvancedIncSubtensor (some_matrix[idx, idx], 1) ]): derp = sum(dot(subtensor, variables)) fobj = theano.function([some_vector], derp, mode=self.mode) grad_derp = theano.grad(derp, some_vector) fgrad = theano.function([some_vector], grad_derp, mode=self.mode) topo_obj = fobj.maker.fgraph.toposort() #<= is needed as the GPU currently don't implement # AdvancedIncSubtensor. When this is the case it can be # replaced with ==. assert numpy.sum([isinstance(node.op, type(alloc)) for node in topo_obj]) <= 1 topo_grad = fgrad.maker.fgraph.toposort() # print subtensor # theano.printing.debugprint(fgrad) assert numpy.sum([isinstance(node.op, type(alloc)) for node in topo_grad]) == n_alloc, ( alloc, subtensor, n_alloc, topo_grad) fobj(test_params) fgrad(test_params) def test_alloc_output(self): val = tensor.constant(self.rng.randn(1, 1), dtype=self.dtype) for alloc in self.allocs: # The output is the result of the alloc operation, # we do not want it to be constant-folded out = alloc(val, 50, 60) f = theano.function([], out, mode=self.mode) topo = f.maker.fgraph.toposort() assert numpy.sum([isinstance(node.op, type(alloc)) for node in topo]) == 1 assert not isinstance(topo[0].op, DeepCopyOp) def test_ones(self): for shp in [[], 1, [1], [1, 2], [1, 2, 3]]: ones = theano.function([], [tensor.ones(shp)], mode=self.mode) assert numpy.allclose(ones(), numpy.ones(shp)) # scalar doesn't have to be provided as input x = scalar() shp = [] ones_scalar = theano.function([], [tensor.ones(x.shape)], mode=self.mode) assert numpy.allclose(ones_scalar(), numpy.ones(shp)) for (typ, shp) in [(vector, [3]), (matrix, [3, 4])]: x = typ() ones_tensor = theano.function([x], [tensor.ones(x.shape)], mode=self.mode) inp = numpy.zeros(shp, dtype=config.floatX) assert numpy.allclose(ones_tensor(inp), numpy.ones(shp)) def test_zeros(self): for shp in [[], 1, [1], [1, 2], [1, 2, 3]]: zeros = theano.function([], [tensor.zeros(shp)], mode=self.mode) assert numpy.allclose(zeros(), numpy.zeros(shp)) # scalar doesn't have to be provided as input x = scalar() shp = [] zeros_scalar = theano.function([], [tensor.zeros(x.shape)], mode=self.mode) assert numpy.allclose(zeros_scalar(), numpy.zeros(shp)) for (typ, shp) in [(vector, [3]), (matrix, [3, 4])]: x = typ() zeros_tensor = theano.function([x], [tensor.zeros(x.shape)], mode=self.mode) inp = numpy.zeros(shp, dtype=config.floatX) assert numpy.allclose(zeros_tensor(inp), numpy.zeros(shp)) # This is slow for the ('int8', 3) version. def test_eye(): def check(dtype, N, M_=None, k=0): # Theano does not accept None as a tensor. # So we must use a real value. M = M_ # Currently DebugMode does not support None as inputs even if this is # allowed. if M is None and theano.config.mode in ['DebugMode', 'DEBUG_MODE']: M = N N_symb = tensor.iscalar() M_symb = tensor.iscalar() k_symb = tensor.iscalar() f = function([N_symb, M_symb, k_symb], eye(N_symb, M_symb, k_symb, dtype=dtype)) result = f(N, M, k) assert numpy.allclose(result, numpy.eye(N, M_, k, dtype=dtype)) assert result.dtype == numpy.dtype(dtype) for dtype in ALL_DTYPES: yield check, dtype, 3 # M != N, k = 0 yield check, dtype, 3, 5 yield check, dtype, 5, 3 # N == M, k != 0 yield check, dtype, 3, 3, 1 yield check, dtype, 3, 3, -1 # N < M, k != 0 yield check, dtype, 3, 5, 1 yield check, dtype, 3, 5, -1 # N > M, k != 0 yield check, dtype, 5, 3, 1 yield check, dtype, 5, 3, -1 class test_triangle(unittest.TestCase): def test_tri(self): def check(dtype, N, M_=None, k=0): # Theano does not accept None as a tensor. # So we must use a real value. M = M_ # Currently DebugMode does not support None as inputs even if this is # allowed. if M is None and theano.config.mode in ['DebugMode', 'DEBUG_MODE']: M = N N_symb = tensor.iscalar() M_symb = tensor.iscalar() k_symb = tensor.iscalar() f = function([N_symb, M_symb, k_symb], tri(N_symb, M_symb, k_symb, dtype=dtype)) result = f(N, M, k) self.assertTrue( numpy.allclose(result, numpy.tri(N, M_, k, dtype=dtype))) self.assertTrue(result.dtype == numpy.dtype(dtype)) for dtype in ALL_DTYPES: yield check, dtype, 3 # M != N, k = 0 yield check, dtype, 3, 5 yield check, dtype, 5, 3 # N == M, k != 0 yield check, dtype, 3, 3, 1 yield check, dtype, 3, 3, -1 # N < M, k != 0 yield check, dtype, 3, 5, 1 yield check, dtype, 3, 5, -1 # N > M, k != 0 yield check, dtype, 5, 3, 1 yield check, dtype, 5, 3, -1 def test_tril_triu(self): def check_l(m, k=0): m_symb = matrix(dtype=m.dtype) k_symb = iscalar() f = function([m_symb, k_symb], tril(m_symb, k_symb)) result = f(m, k) self.assertTrue(numpy.allclose(result, numpy.tril(m, k))) self.assertTrue(result.dtype == numpy.dtype(dtype)) def check_u(m, k=0): m_symb = matrix(dtype=m.dtype) k_symb = iscalar() f = function([m_symb, k_symb], triu(m_symb, k_symb)) result = f(m, k) self.assertTrue(numpy.allclose(result, numpy.triu(m, k))) self.assertTrue(result.dtype == numpy.dtype(dtype)) for dtype in ALL_DTYPES: m = rand_of_dtype((10, 10), dtype) yield check_l, m, 0 yield check_l, m, 1 yield check_l, m, -1 yield check_u, m, 0 yield check_u, m, 1 yield check_u, m, -1 m = rand_of_dtype((10, 5), dtype) yield check_l, m, 0 yield check_l, m, 1 yield check_l, m, -1 yield check_u, m, 0 yield check_u, m, 1 yield check_u, m, -1 class test_nonzero(unittest.TestCase): def test_nonzero(self): def check(m): m_symb = theano.tensor.tensor(dtype=m.dtype, broadcastable=(False,) * m.ndim) f_tuple = function([m_symb], nonzero(m_symb, return_matrix=False)) f_matrix = function([m_symb], nonzero(m_symb, return_matrix=True)) self.assertTrue(numpy.allclose(f_matrix(m), numpy.vstack(numpy.nonzero(m)))) for i, j in zip(f_tuple(m), numpy.nonzero(m)): self.assertTrue(numpy.allclose(i, j)) rand0d = numpy.array(rand()) self.assertRaises(ValueError, check, rand0d) rand1d = rand(8) rand1d[:4] = 0 check(rand1d) rand2d = rand(8, 9) rand2d[:4] = 0 check(rand2d) rand3d = rand(8, 9, 10) rand3d[:4] = 0 check(rand3d) rand4d = rand(8, 9, 10, 11) rand4d[:4] = 0 check(rand4d) def test_flatnonzero(self): def check(m): m_symb = theano.tensor.tensor(dtype=m.dtype, broadcastable=(False,) * m.ndim) f = function([m_symb], flatnonzero(m_symb)) result = f(m) assert numpy.allclose(result, numpy.flatnonzero(m)) rand0d = numpy.array(rand()) self.assertRaises(ValueError, check, rand0d) rand1d = rand(8) rand1d[:4] = 0 check(rand1d) rand2d = rand(8, 9) rand2d[:4] = 0 check(rand2d) rand3d = rand(8, 9, 10) rand3d[:4] = 0 check(rand3d) rand4d = rand(8, 9, 10, 11) rand4d[:4] = 0 check(rand4d) def test_nonzero_values(self): def check(m): m_symb = theano.tensor.tensor(dtype=m.dtype, broadcastable=(False,) * m.ndim) f = function([m_symb], nonzero_values(m_symb)) result = f(m) assert numpy.allclose(result, m[numpy.nonzero(m)]) rand0d = rand() self.assertRaises(ValueError, check, rand0d) rand1d = rand(8) rand1d[:4] = 0 check(rand1d) rand2d = rand(8, 9) rand2d[:4] = 0 check(rand2d) rand3d = rand(8, 9, 10) rand3d[:4] = 0 check(rand3d) rand4d = rand(8, 9, 10, 11) rand4d[:4] = 0 check(rand4d) def test_identity(): def check(dtype): obj = rand_of_dtype((2,), dtype) sym = tensor.vector(dtype=dtype) f = function([sym], tensor_copy(sym)) assert numpy.all(obj == f(obj)) assert obj.dtype == f(obj).dtype topo = f.maker.fgraph.toposort() assert len(topo) == 1 if theano.config.mode != 'FAST_COMPILE': assert isinstance(topo[0].op, DeepCopyOp) for dtype in ALL_DTYPES: yield check, dtype class CastTester(unittest.TestCase): def test_good_between_real_types(self): good = itertools.chain( multi_dtype_cast_checks((2,), dtypes=REAL_DTYPES), # Casts from foo to foo [('%s_%s' % (rand_of_dtype((2,), dtype), dtype), (rand_of_dtype((2,), dtype), dtype)) for dtype in ALL_DTYPES]) for testname, (obj, dtype) in good: inp = tensor.vector(dtype=obj.dtype) out = tensor.cast(inp, dtype=dtype) f = function([inp], out) assert f(obj).dtype == numpy.dtype(dtype) # Test astype too out2 = inp.astype(dtype=dtype) assert out2.type == out.type def test_cast_from_real_to_complex(self): for real_dtype in REAL_DTYPES: for complex_dtype in COMPLEX_DTYPES: inp = tensor.vector(dtype=real_dtype) out = tensor.cast(inp, dtype=complex_dtype) f = function([inp], out) obj = rand_of_dtype((2, ), real_dtype) assert f(obj).dtype == numpy.dtype(complex_dtype) def test_cast_from_complex_to_real_raises_error(self): for real_dtype in REAL_DTYPES: for complex_dtype in COMPLEX_DTYPES: inp = tensor.vector(dtype=real_dtype) self.assertRaises(TypeError, tensor.cast( inp, dtype=complex_dtype)) ClipTester = makeTester(name='ClipTester', op=clip, expected=lambda x, y, z: numpy.clip(x, y, z), good=dict(correct1=((5 * rand(5, 5)).astype('float32'), numpy.array(-1, dtype='float32'), numpy.array(1, dtype='float32')), correct2=((5 * rand(5, 5)).astype('float64'), numpy.array(-1, dtype='float64'), numpy.array(1, dtype='float64')), correct3=(randint(5, 5).astype('int8'), numpy.array(-1, dtype='int8'), numpy.array(1, dtype='int8')), correct4=(randint(5, 5).astype('int16'), numpy.array(-1, dtype='int16'), numpy.array(1, dtype='int16')), correct5=(randint(5, 5).astype('int32'), numpy.array(-1, dtype='int32'), numpy.array(1, dtype='int32')), correct6=(randint(5, 5).astype('int64'), numpy.array(-1, dtype='int64'), numpy.array(1, dtype='int64')), # min > max. messed up behaviour, but # should be same as NumPy's correct7=((5 * rand(5, 5)).astype('float64'), numpy.array(1, dtype='float64'), numpy.array(-1, dtype='float64'))) ) # I can't think of any way to make this fail at runtime class T_Clip(unittest.TestCase): def test_complex_value(self): for dtype in ['complex64', 'complex128']: a = tensor.vector(dtype=dtype) b = tensor.scalar() c = tensor.scalar() self.assertRaises(TypeError, clip, a, b, c) def test_clip_repeat_grad(self): # This is testing for the issue #633 x, y = tensor.vectors('xy') a = clip(x, y, x) g = theano.gradient.grad(a.sum(), x) fn = theano.function([x, y], [g]) # Test the other way around as well a2 = clip(x, x, y) g2 = theano.gradient.grad(a2.sum(), x) fn2 = theano.function([x, y], [g2]) # Test for the equal case too a3 = theano.tensor.clip(x, x, x) g3 = theano.gradient.grad(a3.sum(), x) fn3 = theano.function([x], [g3]) rng = numpy.random.RandomState(utt.fetch_seed()) nvals = 50 xval = rng.rand(nvals).astype(config.floatX) # To ensure that the min < x yval_mn = rng.rand(nvals).astype(config.floatX) - 1.0 # To ensure that the max > x yval_mx = rng.rand(nvals).astype(config.floatX) + 1.0 aval, = fn(xval, yval_mn) aval2, = fn2(xval, yval_mx) aval3, = fn3(xval) self.assertTrue(numpy.all(aval == 1.)) self.assertTrue(numpy.all(aval2 == 1.)) self.assertTrue(numpy.all(aval3 == 1.)) def test_clip_repeat_verify_grad(self): # Additional tests for issue gh-633 utt.verify_grad( op=lambda x: clip(x, 0, x), pt=[rand_nonzero((3, 7))]) utt.verify_grad( op=lambda x: clip(x, x, 0), pt=[rand_nonzero((3, 7))]) utt.verify_grad( op=lambda x: clip(0, x, x), pt=[rand_nonzero((3, 7))]) utt.verify_grad( op=lambda x: clip(x, x, x), pt=[rand_nonzero((3, 7))]) # TODO: consider moving this function / functionality to gradient.py # rationale: it's tricky, and necessary everytime you want to verify # gradient numerically # useful mostly for unit tests def _approx_eq(a, b, eps=1.0e-4): a = numpy.asarray(a) b = numpy.asarray(b) if a.shape != b.shape: if _approx_eq.debug: print(a.shape, b.shape) return False abs_rel_err = numeric_grad.abs_rel_err(a, b) # numpy.max don't like empty ndarray. if a.size == b.size == 0: return True if numpy.max(abs_rel_err) >= eps: if _approx_eq.debug: print(a, b) return False return True _approx_eq.debug = 0 def test_batched_dot(): first = theano.tensor.tensor3("first") second = theano.tensor.tensor3("second") output = theano.tensor.basic.batched_dot(first, second) first_val = numpy.random.rand(10, 10, 20).astype(config.floatX) second_val = numpy.random.rand(10, 20, 5).astype(config.floatX) result_fn = theano.function([first, second], output) result = result_fn(first_val, second_val) assert result.shape[0] == first_val.shape[0] assert result.shape[1] == first_val.shape[1] assert result.shape[2] == second_val.shape[2] first_mat = theano.tensor.dmatrix("first") second_mat = theano.tensor.dmatrix("second") output = theano.tensor.basic.batched_dot(first_mat, second_mat) first_mat_val = numpy.random.rand(10, 10).astype(config.floatX) second_mat_val = numpy.random.rand(10, 10).astype(config.floatX) result_fn = theano.function([first_mat, second_mat], output) result = result_fn(first_mat_val, second_mat_val) assert result.shape[0] == first_mat_val.shape[0] def test_batched_tensordot(): first = theano.tensor.tensor4("first") second = theano.tensor.tensor4("second") axes = [[1, 2], [3, 1]] output = theano.tensor.basic.batched_tensordot(first, second, axes) first_val = numpy.random.rand(8, 10, 20, 3).astype(config.floatX) second_val = numpy.random.rand(8, 20, 5, 10).astype(config.floatX) result_fn = theano.function([first, second], output) result = result_fn(first_val, second_val) assert result.shape[0] == first_val.shape[0] assert result.shape[1] == first_val.shape[3] assert result.shape[2] == second_val.shape[2] first_mat = theano.tensor.dmatrix("first") second_mat = theano.tensor.dmatrix("second") axes = 1 output = theano.tensor.basic.batched_tensordot(first_mat, second_mat, axes) first_mat_val = numpy.random.rand(10, 4).astype(config.floatX) second_mat_val = numpy.random.rand(10, 4).astype(config.floatX) result_fn = theano.function([first_mat, second_mat], output) result = result_fn(first_mat_val, second_mat_val) assert result.shape[0] == first_mat_val.shape[0] assert len(result.shape) == 1 def test_tensor_values_eq_approx(): # test, inf, -inf and nan equal themself a = numpy.asarray([-numpy.inf, -1, 0, 1, numpy.inf, numpy.nan]) assert TensorType.values_eq_approx(a, a) # test inf, -inf don't equal themself b = numpy.asarray([numpy.inf, -1, 0, 1, numpy.inf, numpy.nan]) assert not TensorType.values_eq_approx(a, b) b = numpy.asarray([-numpy.inf, -1, 0, 1, -numpy.inf, numpy.nan]) assert not TensorType.values_eq_approx(a, b) # test allow_remove_inf b = numpy.asarray([numpy.inf, -1, 0, 1, 5, numpy.nan]) assert TensorType.values_eq_approx(a, b, allow_remove_inf=True) b = numpy.asarray([numpy.inf, -1, 0, 1, 5, 6]) assert not TensorType.values_eq_approx(a, b, allow_remove_inf=True) # test allow_remove_nan b = numpy.asarray([numpy.inf, -1, 0, 1, 5, numpy.nan]) assert not TensorType.values_eq_approx(a, b, allow_remove_nan=False) b = numpy.asarray([-numpy.inf, -1, 0, 1, numpy.inf, 6]) assert not TensorType.values_eq_approx(a, b, allow_remove_nan=False) def test_nan_inf_constant_signature(): # Test that the signature of a constant tensor containing NaN and Inf # values is correct. test_constants = [ [numpy.nan, numpy.inf, 0, 1], [numpy.nan, numpy.inf, -numpy.inf, 1], [0, numpy.inf, -numpy.inf, 1], [0, 3, -numpy.inf, 1], [0, 3, numpy.inf, 1], [numpy.nan, 3, 4, 1], [0, 3, 4, 1], numpy.nan, numpy.inf, -numpy.inf, 0, 1, ] n = len(test_constants) # We verify that signatures of two rows i, j in the matrix above are # equal if and only if i == j. for i in xrange(n): for j in xrange(n): x = constant(test_constants[i]) y = constant(test_constants[j]) assert (x.signature() == y.signature()) == (i == j) # Also test that nan !=0 and nan != nan. x = tensor.scalar() mode = get_default_mode() if isinstance(mode, theano.compile.debugmode.DebugMode): # Disable the check preventing usage of NaN / Inf values. # We first do a copy of the mode to avoid side effects on other tests. mode = copy(mode) mode.check_isfinite = False f = theano.function([x], eq(x, numpy.nan), mode=mode) assert f(0) == 0 assert f(numpy.nan) == 0 class T_Shape(unittest.TestCase): def test_basic0(self): s = shape(numpy.ones((5, 3))) self.assertTrue((eval_outputs([s]) == [5, 3]).all()) def test_basic1(self): s = shape(numpy.ones((2))) self.assertTrue((eval_outputs([s]) == [2]).all()) def test_basic2(self): s = shape(numpy.ones((5, 3, 10))) self.assertTrue((eval_outputs([s]) == [5, 3, 10]).all()) class T_max_and_argmax(unittest.TestCase): def setUp(self): utt.seed_rng() MaxAndArgmax.debug = 0 def test0(self): n = as_tensor_variable(5.0) v, i = eval_outputs(max_and_argmax(n)) self.assertTrue(v == 5.0) self.assertTrue(i == 0) assert i.dtype == 'int64' v = eval_outputs(max_and_argmax(n)[0].shape) assert len(v) == 0 v = eval_outputs(max_and_argmax(n)[1].shape) assert len(v) == 0 def test1(self): n = as_tensor_variable([1, 2, 3, 2, -6]) v, i = eval_outputs(max_and_argmax(n)) self.assertTrue(v == 3) self.assertTrue(i == 2) assert i.dtype == 'int64' v = eval_outputs(max_and_argmax(n)[0].shape) assert len(v) == 0 def test2(self): data = rand(2, 3) n = as_tensor_variable(data) for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (None, None), ([0, 1], None), ([1, 0], None), (NoneConst.clone(), None), (constant(0), 0)]: v, i = eval_outputs(max_and_argmax(n, axis)) assert i.dtype == 'int64' self.assertTrue(numpy.all(v == numpy.max(data, np_axis))) self.assertTrue(numpy.all(i == numpy.argmax(data, np_axis))) v_shape = eval_outputs(max_and_argmax(n, axis)[0].shape) assert tuple(v_shape) == numpy.max(data, np_axis).shape def test2_invalid(self): n = as_tensor_variable(rand(2, 3)) # Silence expected error messages _logger = logging.getLogger('theano.gof.opt') oldlevel = _logger.level _logger.setLevel(logging.CRITICAL) try: try: eval_outputs(max_and_argmax(n, 3)) assert False except ValueError as e: pass finally: _logger.setLevel(oldlevel) def test2_invalid_neg(self): n = as_tensor_variable(rand(2, 3)) old_stderr = sys.stderr sys.stderr = StringIO() try: try: eval_outputs(max_and_argmax(n, -3)) assert False except ValueError as e: pass finally: sys.stderr = old_stderr def test2_valid_neg(self): n = as_tensor_variable(rand(2, 3)) v, i = eval_outputs(max_and_argmax(n, -1)) assert i.dtype == 'int64' self.assertTrue(v.shape == (2,)) self.assertTrue(i.shape == (2,)) self.assertTrue(numpy.all(v == numpy.max(n.value, -1))) self.assertTrue(numpy.all(i == numpy.argmax(n.value, -1))) v, i = eval_outputs(max_and_argmax(n, -2)) assert i.dtype == 'int64' self.assertTrue(v.shape == (3,)) self.assertTrue(i.shape == (3,)) self.assertTrue(numpy.all(v == numpy.max(n.value, -2))) self.assertTrue(numpy.all(i == numpy.argmax(n.value, -2))) v = eval_outputs(max_and_argmax(n, -1)[0].shape) assert v == (2) v = eval_outputs(max_and_argmax(n, -2)[0].shape) assert v == (3) def test3(self): data = rand(2, 3, 4) n = as_tensor_variable(data) for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (None, None), ([0, 1, 2], None), ([1, 2, 0], None)]: v, i = eval_outputs(max_and_argmax(n, axis)) assert i.dtype == 'int64' self.assertTrue(numpy.all(v == numpy.max(data, np_axis))) self.assertTrue(numpy.all(i == numpy.argmax(data, np_axis))) v = eval_outputs(max_and_argmax(n, axis)[0].shape) assert tuple(v) == numpy.max(data, np_axis).shape def test_arg_grad(self): """ The test checks that the gradient of argmax(x).sum() is 0 """ x = matrix() cost = argmax(x, axis=0).sum() gx = grad(cost, x) val = tensor.get_scalar_constant_value(gx) assert val == 0.0 def test_grad(self): data = rand(2, 3) n = as_tensor_variable(data) def safe_verify_grad(func, data): """ Wrapper around 'verify_grad' that picks a proper value for epsilon. This is needed because 'verify_grad' may fail when its epsilon is too large, due to the fact the argmax is not continuous. We make sure epsilon is less than the minimum absolute value found in the matrix of pairwise differences between all elements in the data. This way, the argmax will not change when adding epsilon. """ # 'data' is a one-element list. data_tensor, = data # Flatten it into a 1D vector. data_vector = data_tensor.flatten() # Compute pairwise absolute differences. diff = numpy.abs(data_vector.reshape((-1, 1)) - data_vector) # Alter the diagonal to avoid a zero minimum. for i in xrange(len(diff)): diff[i, i] = 1 # Find an appropriate epsilon. eps = builtin_min(numeric_grad.type_eps[config.floatX], diff.min() / 2) # Run gradient verification. utt.verify_grad(func, data, eps=eps) def check_grad_max(data, max_grad_data, axis=None): """ Why this is needed? verify_grad is not enough? """ # This works only for axis in [0, None]. assert axis in [0, None] z = numpy.zeros_like(data) z = z.flatten() argmax = numpy.argmax(data, axis=axis) if argmax.ndim == 0: z[argmax] += 1 else: for id, v in enumerate(argmax): z[v * numpy.prod(data.shape[data.ndim - 1:axis:-1]) + id] += 1 z = z.reshape(data.shape) assert numpy.all(max_grad_data == z) for axis in (-1, 0, 1, None): for j in xrange(2): safe_verify_grad(lambda v: max_and_argmax(v, axis=axis)[j], [data]) if axis != 1: safe_verify_grad(lambda v: max_and_argmax(v.flatten(), axis=axis)[j], [data]) if axis in (0, None): check_grad_max(data, eval_outputs(grad( max_and_argmax(n, axis=axis)[0].sum(), n)), axis=axis) check_grad_max(data, eval_outputs(grad( max_and_argmax(n.flatten())[0], n))) # Test 3d inner dimensions data = rand(3, 4, 5) for i in [0, 1, 2]: safe_verify_grad(lambda v: max_and_argmax(v, axis=[i])[0], [data]) safe_verify_grad(lambda v: max_and_argmax(v, axis=[i])[1], [data]) # Test 4d inner dimensions data = rand(2, 3, 4, 5) for i in [0, 1, 2, 3]: safe_verify_grad(lambda v: max_and_argmax(v, axis=[i])[0], [data]) safe_verify_grad(lambda v: max_and_argmax(v, axis=[i])[1], [data]) # Test grad with multiple axes for i in [[0, 1], [0, 0]]: safe_verify_grad(lambda v: max_and_argmax(v, axis=i)[0], [data]) safe_verify_grad(lambda v: max_and_argmax(v, axis=i)[1], [data]) def test_preserve_broadcastable(self): """ Ensure the original broadcastable flags are preserved by Max/Argmax. """ x = tensor.matrix().dimshuffle('x', 0, 'x', 1, 'x') y = x.max(axis=1) assert y.type.broadcastable == (True, True, False, True) def test_multiple_axes(self): data = numpy.arange(24).reshape(3, 2, 4) x = as_tensor_variable(data) v, i = eval_outputs(max_and_argmax(x, [1, -1])) assert numpy.all(v == numpy.array([7, 15, 23])) assert numpy.all(i == numpy.array([7, 7, 7])) v = eval_outputs(max_and_argmax(x, [1, -1])[0].shape) assert tuple(v) == numpy.max(data, (1, -1)).shape def test_zero_shape(self): x = tensor.matrix() m, i = max_and_argmax(x, axis=1) f = theano.function([x], [m, i]) xv = numpy.zeros((0, 4), dtype=floatX) mv, iv = f(xv) assert mv.shape == (0,) assert iv.shape == (0,) class T_argmin_argmax(unittest.TestCase): def setUp(self): utt.seed_rng() MaxAndArgmax.debug = 0 def test_scalar(self): for fct in [argmin, argmax]: n = as_tensor_variable(5.0) i = eval_outputs(fct(n)) self.assertTrue(i == 0) v = eval_outputs(fct(n).shape) assert len(v) == 0 def test_list(self): n = as_tensor_variable([1, 2, 3, 2, -6]) i = eval_outputs(argmin(n)) self.assertTrue(i == 4) v = eval_outputs(argmin(n).shape) assert len(v) == 0 n = as_tensor_variable([1, 2, 3, 2, -6]) i = eval_outputs(argmax(n)) self.assertTrue(i == 2) v = eval_outputs(argmax(n).shape) assert len(v) == 0 def test2(self): data = rand(2, 3) n = as_tensor_variable(data) for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]: for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (None, None), ([0, 1], None), ([1, 0], None)]: v = eval_outputs(fct(n, axis)) self.assertTrue(numpy.all(v == nfct(data, np_axis))) v_shape = eval_outputs(fct(n, axis).shape) assert tuple(v_shape) == nfct(data, np_axis).shape def test2_invalid(self): for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]: n = as_tensor_variable(rand(2, 3)) # Silence expected error messages _logger = logging.getLogger('theano.gof.opt') oldlevel = _logger.level _logger.setLevel(logging.CRITICAL) try: try: eval_outputs(fct(n, 3)) assert False except ValueError as e: pass finally: _logger.setLevel(oldlevel) def test2_invalid_neg(self): for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]: n = as_tensor_variable(rand(2, 3)) old_stderr = sys.stderr sys.stderr = StringIO() try: try: eval_outputs(fct(n, -3)) assert False except ValueError as e: pass finally: sys.stderr = old_stderr def test2_valid_neg(self): for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]: n = as_tensor_variable(rand(2, 3)) i = eval_outputs(fct(n, -1)) self.assertTrue(i.shape == (2,)) self.assertTrue(numpy.all(i == nfct(n.value, -1))) i = eval_outputs(fct(n, -2)) self.assertTrue(i.shape == (3,)) self.assertTrue(numpy.all(i == nfct(n.value, -2))) v = eval_outputs(fct(n, -1).shape) assert v == (2) v = eval_outputs(fct(n, -2).shape) assert v == (3) def test3(self): data = rand(2, 3, 4) n = as_tensor_variable(data) for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]: for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (2, 2), (None, None), ([0, 1, 2], None), ([1, 0, 2], None)]: v = eval_outputs(fct(n, axis)) self.assertTrue(numpy.all(v == nfct(data, np_axis))) v_shape = eval_outputs(fct(n, axis).shape) assert tuple(v_shape) == nfct(data, np_axis).shape def test_grad_argmin(self): data = rand(2, 3) n = as_tensor_variable(data) n.name = 'n' # test grad of argmin utt.verify_grad(lambda v: argmin(v, axis=-1), [data]) utt.verify_grad(lambda v: argmin(v, axis=[0]), [data]) utt.verify_grad(lambda v: argmin(v, axis=[1]), [data]) utt.verify_grad(lambda v: argmin(v.flatten()), [data]) try: cost = argmin(n, axis=-1) cost.name = None g = grad(cost, n) raise Exception('Expected an error') except TypeError: pass def test_grad_argmax(self): data = rand(2, 3) n = as_tensor_variable(data) # test grad of argmax utt.verify_grad(lambda v: argmax(v, axis=-1), [data]) utt.verify_grad(lambda v: argmax(v, axis=[0]), [data]) utt.verify_grad(lambda v: argmax(v, axis=[1]), [data]) utt.verify_grad(lambda v: argmax(v.flatten()), [data]) try: grad(argmax(n, axis=-1), n) raise Exception('Expected an error') except TypeError: pass class T_min_max(unittest.TestCase): def setUp(self): utt.seed_rng() MaxAndArgmax.debug = 0 def test_scalar(self): for fct in [max, min]: n = as_tensor_variable(5.0) v = eval_outputs(fct(n)) self.assertTrue(v == 5.0) v = eval_outputs(fct(n).shape) assert len(v) == 0 def test_list(self): for fct, nfct in [(max, numpy.max), (min, numpy.min)]: n = as_tensor_variable([1, 2, 3, 2, -6]) v = eval_outputs([fct(n)]) self.assertTrue(v == nfct(n.value)) v = eval_outputs(fct(n).shape) assert len(v) == 0 def test2(self): data = rand(2, 3) n = as_tensor_variable(data) for fct, nfct in [(max, numpy.max), (min, numpy.min)]: for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (None, None), ([0, 1], None), ([1, 0], None)]: v = eval_outputs(fct(n, axis)) self.assertTrue(numpy.all(v == nfct(data, np_axis))) v_shape = eval_outputs(fct(n, axis).shape) assert tuple(v_shape) == nfct(data, np_axis).shape def test2_invalid(self): for fct in [max, min]: n = as_tensor_variable(rand(2, 3)) # Silence expected error messages _logger = logging.getLogger('theano.gof.opt') oldlevel = _logger.level _logger.setLevel(logging.CRITICAL) try: try: eval_outputs(fct(n, 3)) assert False except ValueError as e: pass finally: _logger.setLevel(oldlevel) def test2_invalid_neg(self): for fct in [max, min]: n = as_tensor_variable(rand(2, 3)) old_stderr = sys.stderr sys.stderr = StringIO() try: try: eval_outputs(fct(n, -3)) assert False except ValueError as e: pass finally: sys.stderr = old_stderr def test2_valid_neg(self): for fct, nfct in [(max, numpy.max), (min, numpy.min)]: n = as_tensor_variable(rand(2, 3)) v = eval_outputs(fct(n, -1)) self.assertTrue(v.shape == (2,)) self.assertTrue(numpy.all(v == nfct(n.value, -1))) v = eval_outputs(fct(n, -2)) self.assertTrue(v.shape == (3,)) self.assertTrue(numpy.all(v == nfct(n.value, -2))) v = eval_outputs(fct(n, -1).shape) assert v == (2) v = eval_outputs(fct(n, -2).shape) assert v == (3) def test3(self): # Test with 1 axis or all axis out of 3 dims data = rand(2, 3, 4) n = as_tensor_variable(data) for fct, nfct in [(max, numpy.max), (min, numpy.min)]: for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (2, 2), (None, None), ([0, 1, 2], None), ([1, 0, 2], None)]: v = eval_outputs(fct(n, axis)) self.assertTrue(numpy.all(v == nfct(data, np_axis))) v_shape = eval_outputs(fct(n, axis).shape) assert tuple(v_shape) == nfct(data, np_axis).shape def test3b(self): # Test with 2 axis out of 3 dims data = rand(2, 3, 4) n = as_tensor_variable(data) for fct, nfct in [(max, numpy.max), (min, numpy.min)]: for axis in [[0, 1], [1, 2], [0, 2]]: v = eval_outputs(fct(n, axis)) np_v = nfct(nfct(data, axis[1]), axis[0]) self.assertTrue(numpy.all(v == np_v)) v_shape = eval_outputs(fct(n, axis).shape) assert tuple(v_shape) == np_v.shape def test_grad_max(self): data = rand(2, 3) n = as_tensor_variable(data) def check_grad_max(data, max_grad_data, axis=None): # This work only for axis in [0,None] assert axis in [0, None] z = numpy.zeros_like(data) z = z.flatten() argmax = numpy.argmax(data, axis=axis) if argmax.ndim == 0: z[numpy.argmax(data, axis=axis)] += 1 else: for id, v in enumerate(argmax): z[v * numpy.prod(data.shape[data.ndim - 1:axis:-1]) + id] += 1 z = z.reshape(data.shape) assert numpy.all(max_grad_data == z) # test grad of max # axis is the last one utt.verify_grad(lambda v: max(v, axis=-1), [data]) utt.verify_grad(lambda v: max(v, axis=[0]), [data]) check_grad_max(data, eval_outputs(grad(max(n, axis=0).sum(), n)), axis=0) utt.verify_grad(lambda v: max(v, axis=[1]), [data]) # check_grad_max(data,eval_outputs(grad(max(n,axis=1),n)),axis=1) utt.verify_grad(lambda v: max(v.flatten()), [data]) check_grad_max(data, eval_outputs(grad(max(n.flatten()), n))) def test_grad_min(self): data = rand(2, 3) n = as_tensor_variable(data) def check_grad_min(data, min_grad_data, axis=None): # This work only for axis in [0, None] assert axis in [0, None] z = numpy.zeros_like(data) z = z.flatten() argmin = numpy.argmin(data, axis=axis) if argmin.ndim == 0: z[numpy.argmin(data, axis=axis)] += 1 else: for id, v in enumerate(argmin): z[v * numpy.prod(data.shape[data.ndim - 1:axis:-1]) + id] += 1 z = z.reshape(data.shape) assert numpy.all(min_grad_data == z) # test grad of min # axis is the last one utt.verify_grad(lambda v: min(v, axis=-1), [data]) utt.verify_grad(lambda v: min(v, axis=[0]), [data]) check_grad_min(data, eval_outputs(grad(min(n, axis=0).sum(), n)), axis=0) utt.verify_grad(lambda v: min(v, axis=[1]), [data]) # check_grad_min(data,eval_outputs(grad(min(n,axis=1),n)),axis=1) utt.verify_grad(lambda v: min(v.flatten()), [data]) check_grad_min(data, eval_outputs(grad(min(n.flatten()), n))) def _grad_list(self): """ Test the gradient when we have multiple axis at the same time. This not implemented, so we disable the test. See ticket: http://www.assembla.com/spaces/theano/tickets/511 """ data = rand(2, 3) n = as_tensor_variable(data) for fct in [max_and_argmax, max, min]: utt.verify_grad(lambda v: fct(v, axis=[0, 1]), [data]) # check_grad_max(data, eval_outputs(grad(max_and_argmax(n, # axis=1)[0], n)),axis=1) def test_basic_allclose(): # This was raised by a user in https://github.com/Theano/Theano/issues/2975 assert tensor.basic._allclose(-0.311023883434, -0.311022856884) class T_outer(unittest.TestCase): def test_outer(self): for m in range(4): for n in range(4): x = tensor.tensor(dtype='floatX', broadcastable=(False,) * m) y = tensor.tensor(dtype='floatX', broadcastable=(False,) * n) s1 = numpy.random.randint(1, 10, m) s2 = numpy.random.randint(1, 10, n) v1 = numpy.asarray(numpy.random.rand(*s1)).astype(floatX) v2 = numpy.asarray(numpy.random.rand(*s2)).astype(floatX) o = tensor.outer(x, y).eval({x: v1, y: v2}) assert_allclose(o, numpy.outer(v1, v2)) def test_grad(self): """ Test the combined graph of the graph of outer with broadcastable dimensions, just in case. """ for shp0, shp1 in [((1,), (2,)), ((3,), (1,)), ((1,), (1,)), ((3,), (2,)), ((3, 2), (1, 1)), ((3, 2), (1, 4)), ((3, 2), (4, 1)), ((3, 2), (4, 5)), ((1, 2), (4, 5)), ((3, 1), (4, 5)), ((1, 1), (4, 5)), ((1, 1), (1, 1)), ]: data0 = numpy.random.rand(*shp0).astype(floatX) data1 = numpy.random.rand(*shp1).astype(floatX) utt.verify_grad(tensor.outer, [data0, data1]) class T_GetVectorLength(unittest.TestCase): def test_get_vector_length(self): x = theano.shared(numpy.zeros((2, 3, 4, 5))) assert len(list(x.shape)) == 4 assert len(list(x.shape[2:4])) == 2 assert len(list(x.shape[2:])) == 2 assert len(list(x.shape[1:4])) == 3 assert len(list(x.shape[2:2])) == 0 assert len(list(x.shape[1:5])) == 3 assert len(list(x.shape[1:10])) == 3 # Test step assert len(list(x.shape[1:10:2])) == 2 # Test neg start assert len(list(x.shape[-1:4])) == 1 assert len(list(x.shape[-6:4])) == 4 # test neg stop assert len(list(x.shape[1:-2])) == 1 assert len(list(x.shape[1:-1])) == 2 class T_Join_and_Split(unittest.TestCase): """ Split is tested by each verify_grad method. """ def setUp(self): Join.debug = False utt.seed_rng() self.mode = theano.compile.get_default_mode().excluding( 'constant_folding' ) self.join_op = Join() self.split_op_class = Split self.make_vector_op = opt.MakeVector() self.floatX = config.floatX self.hide_error = theano.config.mode not in ['DebugMode', 'DEBUG_MODE', 'FAST_COMPILE'] self.shared = shared def eval_outputs_and_check_join(self, outputs): f = theano.function([], outputs, self.mode) topo = f.maker.fgraph.toposort() assert [True for node in topo if isinstance(node.op, type(self.join_op))] variables = f() if isinstance(variables, (tuple, list)) and len(variables) == 1: return variables[0] return variables def eval_outputs_and_check_vector(self, outputs, make_vector_op=None): if make_vector_op is None: make_vector_op = self.make_vector_op f = theano.function([], outputs, self.mode) topo = f.maker.fgraph.toposort() assert [True for node in topo if isinstance(node.op, type(make_vector_op))] variables = f() if isinstance(variables, (tuple, list)) and len(variables) == 1: return variables[0] return variables def test_join_scalar(self): a = as_tensor_variable(1) b = as_tensor_variable(2) try: s = join(0, a, b) except TypeError: return self.fail() def test_stack_mixed_type_constants(self): # tested only on cpu as gpu support only float32 a = as_tensor_variable(1) b = as_tensor_variable(2.0) c = tensor._shared(numpy.asarray(3.0, dtype=self.floatX)) s = stack([a, b, c]) want = numpy.array([1, 2, 3]) out = self.eval_outputs_and_check_vector([s], opt.MakeVector()) self.assertTrue((out == want).all()) def test_stack_scalar(self): a = self.shared(numpy.asarray(1., dtype=self.floatX)) b = as_tensor_variable(2.) c = as_tensor_variable(3.) s = stack([a, b, c]) want = numpy.array([1, 2, 3]) out = self.eval_outputs_and_check_vector([s]) self.assertTrue((out == want).all()) def test_stack_scalar_make_vector(self): """Test that calling stack() on scalars instantiates MakeVector, not Join. Test that the floatX dtype stay floatX, not downcasted to int64""" a = tensor.scalar('a', dtype=self.floatX) b = tensor.scalar('b', dtype=self.floatX) s = stack([a, b, a, b]) f = function([a, b], s, mode=self.mode) val = f(1, 2) # print val self.assertTrue(numpy.all(val == [1, 2, 1, 2])) topo = f.maker.fgraph.toposort() assert len([n for n in topo if isinstance(n.op, opt.MakeVector)]) > 0 assert len([n for n in topo if isinstance(n, type(self.join_op))]) == 0 assert f.maker.fgraph.outputs[0].dtype == self.floatX def test_stack_scalar_make_vector_dtype(self): '''Test that calling stack() on scalars instantiates MakeVector, event when the scalar don't have the same dtype.''' a = tensor.iscalar('a') b = tensor.lscalar('b') s = stack([a, b, a, b]) f = function([a, b], s, mode=self.mode) val = f(1, 2) self.assertTrue(numpy.all(val == [1, 2, 1, 2])) topo = f.maker.fgraph.toposort() assert len([n for n in topo if isinstance(n.op, opt.MakeVector)]) > 0 assert len([n for n in topo if isinstance(n, type(self.join_op))]) == 0 assert f.maker.fgraph.outputs[0].dtype == 'int64' def test_stack_scalar_make_vector_constant(self): '''Test that calling stack() on scalars instantiates MakeVector, event when the scalar are simple int type.''' a = tensor.iscalar('a') b = tensor.lscalar('b') # test when the constant is the first element. # The first element is used in a special way s = stack([10, a, b, numpy.int8(3)]) f = function([a, b], s, mode=self.mode) val = f(1, 2) self.assertTrue(numpy.all(val == [10, 1, 2, 3])) topo = f.maker.fgraph.toposort() assert len([n for n in topo if isinstance(n.op, opt.MakeVector)]) > 0 assert len([n for n in topo if isinstance(n, type(self.join_op))]) == 0 assert f.maker.fgraph.outputs[0].dtype == 'int64' def test_stack_new_interface(self): """Test the new numpy-like interface: stack(tensors, axis=0).""" # Testing against old interface warnings.simplefilter('always', DeprecationWarning) a = tensor.imatrix('a') b = tensor.imatrix('b') s1 = stack(a, b) s2 = stack([a, b]) f = function([a, b], [s1, s2], mode=self.mode) v1, v2 = f([[1, 2]], [[3, 4]]) self.assertTrue(v1.shape == v2.shape) self.assertTrue(numpy.all(v1 == v2)) # Testing axis parameter s3 = stack([a, b], 1) f = function([a, b], s3, mode=self.mode) v3 = f([[1, 2]], [[3, 4]]) v4 = numpy.array([[[1, 2], [3, 4]]]) self.assertTrue(v3.shape == v4.shape) self.assertTrue(numpy.all(v3 == v4)) # Testing negative axis v1 = [[1, 2, 3], [4, 5, 6]] v2 = [[7, 8, 9], [10, 11, 12]] s = stack([a, b], axis=-1) f = function([a, b], s, mode=self.mode) v = numpy.zeros((2, 3, 2)) v[:,:,0] = v1 v[:,:,1] = v2 out = f(v1, v2) self.assertTrue(v.shape == out.shape) self.assertTrue(numpy.all(v == out)) s = stack([a, b], axis=-2) f = function([a, b], s, mode=self.mode) v = numpy.zeros((2, 2, 3)) v[:,0,:] = v1 v[:,1,:] = v2 out = f(v1, v2) self.assertTrue(v.shape == out.shape) self.assertTrue(numpy.all(v == out)) # Testing out-of-bounds axis self.assertRaises(IndexError, stack, [a, b], 4) self.assertRaises(IndexError, stack, [a, b], -4) # Testing depreciation warning with warnings.catch_warnings(record=True) as w: s = stack(a, b) assert len(w) == 1 assert issubclass(w[-1].category, DeprecationWarning) with warnings.catch_warnings(record=True) as w: s = stack([a, b]) s = stack([a, b], 1) s = stack([a, b], axis=1) s = stack(tensors=[a, b]) s = stack(tensors=[a, b], axis=1) assert not w def test_stack_hessian(self): # Test the gradient of stack when used in hessian, see gh-1589 a = tensor.dvector('a') b = tensor.dvector('b') A = stack([a, b]) B = A.T.dot(A) Ha, Hb = hessian(B.sum(), [a, b]) # Try some values a_v = numpy.random.rand(4) b_v = numpy.random.rand(4) f = theano.function([a, b], [Ha, Hb]) Ha_v, Hb_v = f(a_v, b_v) # The Hessian is always a matrix full of 2 assert Ha_v.shape == (4, 4) assert Hb_v.shape == (4, 4) assert numpy.allclose(Ha_v, 2.) assert numpy.allclose(Hb_v, 2.) def test_stack_hessian2(self): # Test the hessian macro when the gradient itself does not depend # on the input (but the cost does) a = tensor.dvector('a') b = tensor.dvector('b') A = stack([a, b]) Ha, Hb = hessian(A.sum(), [a, b]) # Try some values a_v = numpy.random.rand(4) b_v = numpy.random.rand(4) f = theano.function([a, b], [Ha, Hb]) Ha_v, Hb_v = f(a_v, b_v) # The Hessian is always a matrix full of 0 assert Ha_v.shape == (4, 4) assert Hb_v.shape == (4, 4) assert numpy.allclose(Ha_v, 0.) assert numpy.allclose(Hb_v, 0.) def test_join_concatenate_one_element(self): ''' Fast test of concatenate as this is an alias for join. also test that we remove the Join op if there is only 1 input''' m = tensor.fmatrix() c = tensor.concatenate([m]) f = theano.function(inputs=[m], outputs=[c], mode=self.mode.including('local_join_1')) topo = f.maker.fgraph.toposort() assert len(topo) == 1 assert isinstance(topo[0].op, DeepCopyOp) def test_join_vector(self): a = self.shared(numpy.array([1, 2, 3], dtype=self.floatX)) b = as_tensor_variable(numpy.array([7, 8, 9], dtype=self.floatX)) s = join(0, a, b) want = numpy.array([1, 2, 3, 7, 8, 9]) out = self.eval_outputs_and_check_join([s]) self.assertTrue((out == want).all()) def test_roll(self): for get_shift in [lambda a:a, lambda x:theano.shared(x)]: # Test simple 1D example a = self.shared(numpy.array([1, 2, 3, 4, 5, 6], dtype=self.floatX)) b = roll(a, get_shift(2)) want = numpy.array([5, 6, 1, 2, 3, 4]) out = theano.function([], b)() assert (out == want).all() # Test simple 1D example with explicit 0 axis b = roll(a, get_shift(-1), 0) want = numpy.array([2, 3, 4, 5, 6, 1]) out = theano.function([], b)() assert (out == want).all() # Test 2D example - ensure that behavior matches numpy.roll behavior a = self.shared(numpy.arange(21).reshape((3, 7)).astype(self.floatX)) b = roll(a, get_shift(-2), 1) want = numpy.roll(a.get_value(borrow=True), -2, 1) out = theano.function([], b)() assert (out == want).all() # Test rolling on axis 0 want = numpy.roll(a.get_value(borrow=True), -2, 0) b = roll(a, get_shift(-2), 0) out = theano.function([], b)() assert (out == want).all() # Test rolling on default axis with ndim > 1 want = numpy.roll(a.get_value(borrow=True), 2) b = roll(a, get_shift(2)) out = theano.function([], b)() assert (out == want).all() # Test rolling on axis 0 with a positive shift that is # larger than axis size want = numpy.roll(a.get_value(borrow=True), 4, 0) b = roll(a, get_shift(4), 0) out = theano.function([], b)() assert (out == want).all() # Test rolling on axis 0 with a negative shift that is # larger than axis size want = numpy.roll(a.get_value(borrow=True), -4, 0) b = roll(a, get_shift(-4), 0) out = theano.function([], b)() assert (out == want).all() def test_stack_vector(self): a = self.shared(numpy.array([1, 2, 3], dtype=self.floatX)) b = as_tensor_variable(numpy.array([7, 8, 9], dtype=self.floatX)) s = stack([a, b]) want = numpy.array([[1, 2, 3], [7, 8, 9]]) out = self.eval_outputs_and_check_join([s]) self.assertTrue((out == want).all()) def test_join_matrix0(self): a = self.shared(numpy.array([[1, 2, 3], [4, 5, 6]], dtype=self.floatX)) b = as_tensor_variable(numpy.array([[7, 8, 9]], dtype=self.floatX)) s = join(0, a, b) want = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) out = self.eval_outputs_and_check_join([s]) self.assertTrue((out == want).all()) def test_join_matrix1(self): av = numpy.array([[.1, .2, .3], [.4, .5, .6]], dtype='float32') bv = numpy.array([[.7], [.8]], dtype='float32') a = self.shared(av) b = as_tensor_variable(bv) s = join(1, a, b) want = numpy.array([[.1, .2, .3, .7], [.4, .5, .6, .8]], dtype='float32') out = self.eval_outputs_and_check_join([s]) self.assertTrue((out == want).all()) utt.verify_grad(lambda a, b: join(1, a, b), [av, bv], mode=self.mode) def test_join_matrix_dtypes(self): if "float32" in self.shared.__name__: raise SkipTest( "The shared variable constructor" " need to support other dtype then float32") # Test mixed dtype. There was a bug that caused crash in the past. av = numpy.array([[1, 2, 3], [4, 5, 6]], dtype='int8') bv = numpy.array([[7], [8]], dtype='float32') a = self.shared(av) b = as_tensor_variable(bv) s = join(1, a, b) want = numpy.array([[1, 2, 3, 7], [4, 5, 6, 8]], dtype='float32') out = self.eval_outputs_and_check_join([s]) self.assertTrue((out == want).all()) grad(s.sum(), b) grad(s.sum(), a) utt.verify_grad(lambda b: join(1, a, b), [bv], eps=1.0e-2, mode=self.mode) def test_join_matrix_ints(self): if "float32" in self.shared.__name__: raise SkipTest( "The shared variable constructor" " need to support other dtype then float32") # Test mixed dtype. There was a bug that caused crash in the past. av = numpy.array([[1, 2, 3], [4, 5, 6]], dtype='int8') bv = numpy.array([[7], [8]], dtype='int32') a = self.shared(av) b = as_tensor_variable(bv) s = join(1, a, b) want = numpy.array([[1, 2, 3, 7], [4, 5, 6, 8]], dtype='float32') out = self.eval_outputs_and_check_join([s]) self.assertTrue((out == want).all()) assert (numpy.asarray(grad(s.sum(), b).eval()) == 0).all() assert (numpy.asarray(grad(s.sum(), a).eval()) == 0).all() def test_join_matrix1_using_vertical_stack(self): a = self.shared(numpy.array([[1, 2, 3], [4, 5, 6]], dtype=self.floatX)) b = as_tensor_variable(numpy.array([[7, 8, 9]], dtype=self.floatX)) c = as_tensor_variable(numpy.array([[9, 8, 7]], dtype=self.floatX)) s = vertical_stack(a, b, c) want = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [9, 8, 7]]) out = self.eval_outputs_and_check_join([s]) self.assertTrue((out == want).all()) def test_join_matrix1_using_horizontal_stack(self): av = numpy.array([[.1, .2, .3], [.4, .5, .6]], dtype='float32') bv = numpy.array([[.7], [.8]], dtype='float32') cv = numpy.array([[.3, .2, .1], [.6, .5, .4]], dtype='float32') a = self.shared(av) b = as_tensor_variable(bv) c = as_tensor_variable(cv) s = horizontal_stack(a, b, c) want = numpy.array([[.1, .2, .3, .7, .3, .2, .1], [.4, .5, .6, .8, .6, .5, .4]], dtype='float32') out = self.eval_outputs_and_check_join([s]) self.assertTrue((out == want).all()) utt.verify_grad(lambda a, b: join(1, a, b), [av, bv], mode=self.mode) def test_join_matrixV(self): """variable join axis""" v = numpy.array([[.1, .2, .3], [.4, .5, .6]], dtype=self.floatX) a = self.shared(v) b = as_tensor_variable(v) ax = lscalar() s = join(ax, a, b) f = inplace_func([ax], [s], mode=self.mode) topo = f.maker.fgraph.toposort() assert [True for node in topo if isinstance(node.op, type(self.join_op))] want = numpy.array([[.1, .2, .3], [.4, .5, .6], [.1, .2, .3], [.4, .5, .6]]) got = f(0) assert numpy.allclose(got, want) want = numpy.array([[.1, .2, .3, .1, .2, .3], [.4, .5, .6, .4, .5, .6]]) got = f(1) assert numpy.allclose(got, want) utt.verify_grad(lambda a, b: join(0, a, b), [v, 2 * v], mode=self.mode) utt.verify_grad(lambda a, b: join(1, a, b), [v, 2 * v], mode=self.mode) def test_join_matrixV_negative_axis(self): """variable join negative axis""" v = numpy.array([[.1, .2, .3], [.4, .5, .6]], dtype=self.floatX) a = self.shared(v) b = as_tensor_variable(v) ax = lscalar() s = join(ax, a, b) f = inplace_func([ax], [s], mode=self.mode) topo = f.maker.fgraph.toposort() assert [True for node in topo if isinstance(node.op, type(self.join_op))] want = numpy.array([[.1, .2, .3, .1, .2, .3], [.4, .5, .6, .4, .5, .6]]) got = f(-1) assert numpy.allclose(got, want) want = numpy.array([[.1, .2, .3], [.4, .5, .6], [.1, .2, .3], [.4, .5, .6]]) got = f(-2) assert numpy.allclose(got, want) self.assertRaises(IndexError, f, -3) def test_join_matrixC_negative_axis(self): """constant join negative axis""" v = numpy.array([[.1, .2, .3], [.4, .5, .6]], dtype=self.floatX) a = self.shared(v) b = as_tensor_variable(v) s = join(-1, a, b) f = theano.function([], [s], mode=self.mode) topo = f.maker.fgraph.toposort() assert [True for node in topo if isinstance(node.op, type(self.join_op))] want = numpy.array([[.1, .2, .3, .1, .2, .3], [.4, .5, .6, .4, .5, .6]]) got = f() assert numpy.allclose(got, want) s = join(-2, a, b) f = theano.function([], [s], mode=self.mode) topo = f.maker.fgraph.toposort() assert [True for node in topo if isinstance(node.op, type(self.join_op))] want = numpy.array([[.1, .2, .3], [.4, .5, .6], [.1, .2, .3], [.4, .5, .6]]) got = f() assert numpy.allclose(got, want) self.assertRaises(IndexError, join, -3, a, b) utt.verify_grad(lambda a, b: join(-1, a, b), [v, 2 * v], mode=self.mode) def test_vector_len(self): x = lscalar('x') y = dscalar('y') triple = as_tensor_variable((x, y, 9.0)) assert 3 == get_vector_length(triple) a, b, c = triple f = function([x, y], [b, c, a], mode=self.mode) topo = f.maker.fgraph.toposort() assert [True for node in topo if isinstance(node.op, opt.MakeVector)] assert numpy.allclose(f(4, 5), [5, 9, 4]) def test_broadcastable_flag_assignment_mixed_otheraxes(self): """ Test that the broadcastable flags for the output of a join operation on non-join axes are True if one or more inputs is broadcastable on that dimension. """ rng = numpy.random.RandomState(seed=utt.fetch_seed()) a_val = rng.rand(1, 4, 1).astype(self.floatX) b_val = rng.rand(1, 3, 1).astype(self.floatX) a = self.shared(a_val, broadcastable=(False, False, True)) b = self.shared(b_val, broadcastable=(True, False, True)) c = self.join_op(1, a, b) assert c.type.broadcastable[0] and c.type.broadcastable[2] assert not c.type.broadcastable[1] # Opt can remplace the int by a Theano constant c = self.join_op(theano.tensor.constant(1), a, b) assert c.type.broadcastable[0] and c.type.broadcastable[2] assert not c.type.broadcastable[1] # In case futur opt insert other useless stuff c = self.join_op(theano.tensor.cast(theano.tensor.constant(1), dtype="int32"), a, b) assert c.type.broadcastable[0] and c.type.broadcastable[2] assert not c.type.broadcastable[1] f = function([], c, mode=self.mode) topo = f.maker.fgraph.toposort() assert [True for node in topo if isinstance(node.op, type(self.join_op))] f() utt.verify_grad((lambda a, b: join(1, a, b)), [a_val, b_val], rng=rng, mode=self.mode) # Should raise an error if dimension 0 does not match a.set_value(rng.rand(2, 4, 1).astype(self.floatX)) self.assertRaises(ValueError, f) def test_broadcastable_flag_assignment_mixed_thisaxes(self): """ Test that the broadcastable flag of the join axis is False when some inputs are broadcastable on that dimension. """ rng = numpy.random.RandomState(seed=utt.fetch_seed()) a_val = rng.rand(2, 4, 1).astype(self.floatX) b_val = rng.rand(1, 4, 1).astype(self.floatX) a = self.shared(a_val, broadcastable=(False, False, True)) b = self.shared(b_val, broadcastable=(True, False, True)) c = self.join_op(0, a, b) assert not c.type.broadcastable[0] f = function([], c, mode=self.mode) topo = f.maker.fgraph.toposort() assert [True for node in topo if isinstance(node.op, type(self.join_op))] f() utt.verify_grad((lambda a, b: join(0, a, b)), [a_val, b_val], rng=rng, mode=self.mode) # Should raise an error if b_val.shape[0] is not 1 # We can't set the value| self.assertRaises(TypeError, b.set_value, rng.rand(3, 4, 1).astype(self.floatX)) a = TensorType(dtype=self.floatX, broadcastable=[0, 0, 1])() b = TensorType(dtype=self.floatX, broadcastable=[1, 0, 1])() c = self.join_op(0, a, b) f = function([a, b], c, mode=self.mode) bad_b_val = rng.rand(3, 4, 1).astype(self.floatX) self.assertRaises(TypeError, f, a_val, bad_b_val) def test_broadcastable_flags_all_broadcastable_on_joinaxis(self): """ Test that joining together several inputs which are all broadcastable on the join dimension results in the output being non-broadcastable on the join dimension. """ rng = numpy.random.RandomState(seed=utt.fetch_seed()) a_val = rng.rand(1, 4, 1).astype(self.floatX) b_val = rng.rand(1, 4, 1).astype(self.floatX) a = self.shared(a_val, broadcastable=(True, False, True)) b = self.shared(b_val, broadcastable=(True, False, True)) c = self.join_op(0, a, b) assert not c.type.broadcastable[0] f = function([], c, mode=self.mode) topo = f.maker.fgraph.toposort() assert [True for node in topo if isinstance(node.op, type(self.join_op))] f() utt.verify_grad((lambda a, b: join(0, a, b)), [a_val, b_val], rng=rng, mode=self.mode) def test_broadcastable_single_input_broadcastable_dimension(self): # Test that all broadcastable flags are preserved by a # single-input join. rng = numpy.random.RandomState(seed=utt.fetch_seed()) a_val = rng.rand(1, 4, 1).astype(self.floatX) a = self.shared(a_val, broadcastable=(True, False, True)) b = self.join_op(0, a) assert b.type.broadcastable[0] assert b.type.broadcastable[2] assert not b.type.broadcastable[1] f = function([], b, mode=self.mode) topo = f.maker.fgraph.toposort() if theano.config.mode != 'FAST_COMPILE': assert not [True for node in topo if isinstance(node.op, type(self.join_op))] f() utt.verify_grad((lambda a: join(0, a)), [a_val], rng=rng, mode=self.mode) # Should raise an error if length of dimension 0 is not 1 self.assertRaises(TypeError, a.set_value, rng.rand(2, 4, 1).astype(self.floatX)) #self.assertRaises(TypeError, f, bad_a_val) def test_broadcastable_flags_many_dims_and_inputs(self): # Test that the right broadcastable flags get set for a join # with many inputs and many input dimensions. a = TensorType(dtype=self.floatX, broadcastable=[1, 0, 1, 0, 0, 0])() b = TensorType(dtype=self.floatX, broadcastable=[1, 1, 1, 0, 0, 0])() c = TensorType(dtype=self.floatX, broadcastable=[1, 0, 0, 0, 0, 0])() d = TensorType(dtype=self.floatX, broadcastable=[1, 0, 1, 1, 0, 1])() e = TensorType(dtype=self.floatX, broadcastable=[1, 0, 1, 0, 0, 1])() f = self.join_op(0, a, b, c, d, e) fb = f.type.broadcastable assert not fb[0] and fb[1] and fb[2] and fb[3] and not fb[4] and fb[5] g = self.join_op(1, a, b, c, d, e) gb = g.type.broadcastable assert gb[0] and not gb[1] and gb[2] and gb[3] and not gb[4] and gb[5] h = self.join_op(4, a, b, c, d, e) hb = h.type.broadcastable assert hb[0] and hb[1] and hb[2] and hb[3] and not hb[4] and hb[5] f = function([a, b, c, d, e], f, mode=self.mode) topo = f.maker.fgraph.toposort() assert [True for node in topo if isinstance(node.op, type(self.join_op))] rng = numpy.random.RandomState(seed=utt.fetch_seed()) a_val = rng.rand(1, 1, 1, 1, 2, 1).astype(self.floatX) b_val = rng.rand(1, 1, 1, 1, 2, 1).astype(self.floatX) c_val = rng.rand(1, 1, 1, 1, 2, 1).astype(self.floatX) d_val = rng.rand(1, 1, 1, 1, 2, 1).astype(self.floatX) e_val = rng.rand(1, 1, 1, 1, 2, 1).astype(self.floatX) f(a_val, b_val, c_val, d_val, e_val) utt.verify_grad((lambda a, b, c, d, e: join(0, a, b, c, d, e)), [a_val, b_val, c_val, d_val, e_val], rng=rng, mode=self.mode) # Should raise an error if length of dimension 0 is not 1 bad_val = rng.rand(2, 1, 1, 1, 2, 1).astype(self.floatX) self.assertRaises(TypeError, f, bad_val, b_val, c_val, d_val, e_val) self.assertRaises(TypeError, f, a_val, bad_val, c_val, d_val, e_val) self.assertRaises(TypeError, f, a_val, b_val, bad_val, d_val, e_val) self.assertRaises(TypeError, f, a_val, b_val, c_val, bad_val, e_val) self.assertRaises(TypeError, f, a_val, b_val, c_val, d_val, bad_val) # Should raise an error if any dimension other than 4 has length != 1 bad_a_val = rng.rand(1, 2, 1, 1, 2, 1).astype(self.floatX) bad_b_val = rng.rand(1, 1, 1, 1, 2, 2).astype(self.floatX) bad_c_val = rng.rand(1, 1, 2, 1, 2, 1).astype(self.floatX) bad_d_val = rng.rand(1, 2, 1, 1, 2, 1).astype(self.floatX) bad_e_val = rng.rand(1, 1, 1, 2, 2, 1).astype(self.floatX) self.assertRaises(ValueError, f, bad_a_val, b_val, c_val, d_val, e_val) self.assertRaises(ValueError, f, a_val, bad_b_val, c_val, d_val, e_val) self.assertRaises(ValueError, f, a_val, b_val, bad_c_val, d_val, e_val) self.assertRaises(ValueError, f, a_val, b_val, c_val, bad_d_val, e_val) self.assertRaises(ValueError, f, a_val, b_val, c_val, d_val, bad_e_val) def test_infer_shape_join(self): def get_mat(s1, s2): return numpy.asarray(numpy.random.uniform(size=(s1, s2)), dtype=self.floatX) x1 = self.shared(get_mat(3, 4)) x2 = self.shared(get_mat(2, 4)) x3 = self.shared(get_mat(1, 4)) # Test dim 0 z = self.join_op(0, x1, x2, x3) f = theano.function([], z.shape, mode=self.mode) topo = f.maker.fgraph.toposort() out = f() assert (out == [6, 4]).all() if theano.config.mode != 'FAST_COMPILE': for node in f.maker.fgraph.toposort(): assert not isinstance(node.op, type(self.join_op)) # Test dim 1 z = self.join_op(1, x1, x2, x3) f = theano.function([], z.shape, mode=self.mode) topo = f.maker.fgraph.toposort() x1.set_value(get_mat(3, 4)) x2.set_value(get_mat(3, 4)) x3.set_value(get_mat(3, 5)) out = f() assert (out == [3, 13]).all() if theano.config.mode != 'FAST_COMPILE': for node in topo: assert not isinstance(node.op, type(self.join_op)) # Test hide error x1.set_value(get_mat(3, 4)) x2.set_value(get_mat(3, 4)) x3.set_value(get_mat(2, 5)) if not self.hide_error: self.assertRaises(ValueError, f) else: f() def test_rebroadcast(self): # Regression test for a crash that used to happen when rebroadcasting. x = tensor.TensorType(self.floatX, [False, False, True])() u = tensor.TensorType(self.floatX, [False, False, True])() # This line used to crash. z = tensor.concatenate([x, -u], axis=2) def test_concatenate_same(self): # Test that we can concatenate the same tensor multiple time. # In the past it was broken on the GPU. rng = numpy.random.RandomState(seed=utt.fetch_seed()) T_shared = self.shared(rng.rand(3, 4).astype(self.floatX)) Tout = tensor.concatenate([T_shared, T_shared]) f = function([], Tout, mode=self.mode) out = f() if theano.config.mode != 'FAST_COMPILE': assert [True for node in f.maker.fgraph.toposort() if isinstance(node.op, type(self.join_op))] assert numpy.allclose(out, numpy.concatenate([T_shared.get_value(), T_shared.get_value()])) def test_mixed_ndim_error(self): rng = numpy.random.RandomState(seed=utt.fetch_seed()) v = self.shared(rng.rand(4).astype(self.floatX)) m = self.shared(rng.rand(4, 4).astype(self.floatX)) self.assertRaises(TypeError, self.join_op, 0, v, m) def test_split_0elem(self): rng = numpy.random.RandomState(seed=utt.fetch_seed()) m = self.shared(rng.rand(4, 6).astype(self.floatX)) o = self.split_op_class(2)(m, 0, [4, 0]) f = function([], o, mode=self.mode) assert any([isinstance(node.op, self.split_op_class) for node in f.maker.fgraph.toposort()]) o1, o2 = f() assert numpy.allclose(o1, m.get_value(borrow=True)) assert numpy.allclose(o2, m.get_value(borrow=True)[4:]) def test_split_neg(self): rng = numpy.random.RandomState(seed=utt.fetch_seed()) m = self.shared(rng.rand(4, 6).astype(self.floatX)) o = self.split_op_class(2)(m, 0, [5, -1]) f = function([], o, mode=self.mode) assert any([isinstance(node.op, self.split_op_class) for node in f.maker.fgraph.toposort()]) self.assertRaises(ValueError, f) class test_comparison(unittest.TestCase): """Test <, >, <=, >=, == and != Test that we can do the comparison with different combination of tensor(shared and constant variable) with ndarray. ndarray cmp tensor was crashing. In a NumPy PR (should be in the release 1.8 of NumPy), it will work. So we assert it work(futur behavior) or raise an error(current NumPy release). """ def setUp(self): utt.seed_rng() self.mode = None self.shared = shared self.dtypes = ['float64', 'float32', 'complex64', 'complex128'] def inplace_func(self, inputs, outputs, check_isfinite=None): mode = self.mode if check_isfinite is False: if mode is None: mode = get_default_mode() mode.check_isfinite = False f = inplace_func(inputs, outputs, mode=mode) return f def test_gt(self): for dtype in self.dtypes: l = numpy.asarray([0., -1., 1.], dtype=dtype) r = numpy.asarray([0., 1., -1.], dtype=dtype) for x, y, err in [ (self.shared(l.astype(dtype)), self.shared(r.astype(dtype)), False), (l, self.shared(r.astype(dtype)), True), (tensor.constant(l), self.shared(r.astype(dtype)), False), (self.shared(l.astype(dtype)), r, False), (self.shared(l.astype(dtype)), tensor.constant(r), False), ]: try: fn = self.inplace_func([], x > y) v = fn() self.assertTrue(numpy.all(v == (l > r)), (v, (l > r))) except TypeError: assert err def test_lt(self): for dtype in self.dtypes: l = numpy.asarray([0., -1., 1.], dtype=dtype) r = numpy.asarray([0., 1., -1.], dtype=dtype) for x, y, err in [ (self.shared(l.astype(dtype)), self.shared(r.astype(dtype)), False), (l, self.shared(r.astype(dtype)), True), (tensor.constant(l), self.shared(r.astype(dtype)), False), (self.shared(l.astype(dtype)), r, False), (self.shared(l.astype(dtype)), tensor.constant(r), False), ]: try: fn = self.inplace_func([], x < y) v = fn() self.assertTrue(numpy.all(v == (l < r)), (v, (l < r))) except TypeError: assert err def test_le(self): for dtype in self.dtypes: l = numpy.asarray([0., -1., 1.], dtype=dtype) r = numpy.asarray([0., 1., -1.], dtype=dtype) for x, y, err in [ (self.shared(l.astype(dtype)), self.shared(r.astype(dtype)), False), (l, self.shared(r.astype(dtype)), True), (tensor.constant(l), self.shared(r.astype(dtype)), False), (self.shared(l.astype(dtype)), r, False), (self.shared(l.astype(dtype)), tensor.constant(r), False), ]: try: fn = self.inplace_func([], x <= y) v = fn() self.assertTrue(numpy.all(v == (l <= r)), (v, (l <= r))) except TypeError: assert err def test_ge(self): for dtype in self.dtypes: l = numpy.asarray([0., -1., 1.], dtype=dtype) r = numpy.asarray([0., 1., -1.], dtype=dtype) for x, y, err in [ (self.shared(l.astype(dtype)), self.shared(r.astype(dtype)), False), (l, self.shared(r.astype(dtype)), True), (tensor.constant(l), self.shared(r.astype(dtype)), False), (self.shared(l.astype(dtype)), r, False), (self.shared(l.astype(dtype)), tensor.constant(r), False), ]: try: fn = self.inplace_func([], x >= y) v = fn() self.assertTrue(numpy.all(v == (l >= r)), (v, (l >= r))) except TypeError: assert err def test_eq(self): for dtype in self.dtypes: l = numpy.asarray([0., -1., 1.], dtype=dtype) r = numpy.asarray([0., 1., -1.], dtype=dtype) for x, y, err in [ (self.shared(l.astype(dtype)), self.shared(r.astype(dtype)), False), (l, self.shared(r.astype(dtype)), True), (tensor.constant(l), self.shared(r.astype(dtype)), False), (self.shared(l.astype(dtype)), r, False), (self.shared(l.astype(dtype)), tensor.constant(r), False), ]: try: fn = self.inplace_func([], eq(x, y)) v = fn() self.assertTrue(numpy.all(v == (l == r)), (v, (l == r))) except TypeError: assert err def test_neq(self): for dtype in self.dtypes: l = numpy.asarray([0., -1., 1.], dtype=dtype) r = numpy.asarray([0., 1., -1.], dtype=dtype) for x, y, err in [ (self.shared(l.astype(dtype)), self.shared(r.astype(dtype)), False), (l, self.shared(r.astype(dtype)), True), (tensor.constant(l), self.shared(r.astype(dtype)), False), (self.shared(l.astype(dtype)), r, False), (self.shared(l.astype(dtype)), tensor.constant(r), False), ]: try: fn = self.inplace_func([], neq(x, y)) v = fn() self.assertTrue(numpy.all(v == (l != r)), (v, (l != r))) except TypeError: assert err def test_isclose(self): for dtype in self.dtypes: l = numpy.asarray( [0., 1., -1., 0., numpy.nan, numpy.inf, -numpy.inf, numpy.inf], dtype=dtype) r = numpy.asarray( [0., 1.0001, -1.000000000001, numpy.nan, numpy.nan, numpy.inf, numpy.inf, 0.], dtype=dtype) for x, y, err in [ (self.shared(l.astype(dtype)), self.shared(r.astype(dtype)), False), (l, self.shared(r.astype(dtype)), True), (constant(l), self.shared(r.astype(dtype)), False), (self.shared(l.astype(dtype)), r, False), (self.shared(l.astype(dtype)), constant(r), False), ]: try: o1 = isclose(x, y, equal_nan=False) fn1 = self.inplace_func([], o1, check_isfinite=False) o2 = isclose(x, y, equal_nan=True) fn2 = self.inplace_func([], o2, check_isfinite=False) v1 = fn1() v2 = fn2() self.assertTrue( numpy.all( v1 == numpy.asarray( [True, False, True, False, False, True, False, False], dtype="bool" ) ), numpy.all( v2 == numpy.asarray( [True, False, True, False, True, True, False, False], dtype="bool" ) ) ) except TypeError: if not dtype.startswith('complex'): raise assert err def test_allclose(self): # equal_nan argument not in current version of numpy allclose, # force it to False. for dtype in self.dtypes: l = numpy.asarray( [0., 1., -1., 0., numpy.nan, numpy.inf, -numpy.inf, numpy.inf], dtype=dtype) r = numpy.asarray( [0., 1.0001, -1.000000000001, numpy.nan, numpy.nan, numpy.inf, numpy.inf, 0.], dtype=dtype) for x, y, err in [ (self.shared(l.astype(dtype)), self.shared(r.astype(dtype)), False), (l, self.shared(r.astype(dtype)), True), (constant(l), self.shared(r.astype(dtype)), False), (self.shared(l.astype(dtype)), r, False), (self.shared(l.astype(dtype)), constant(r), False), ]: try: fn = self.inplace_func([], allclose(x, y, equal_nan=False), check_isfinite=False) v = fn() self.assertTrue(numpy.all(v == numpy.allclose(l, r))) except TypeError: if not dtype.startswith('complex'): assert err class test_bitwise(unittest.TestCase): dtype = ['int8', 'int16', 'int32', 'int64', ] def test_or(self): for dtype in self.dtype: x, y = vector(dtype=dtype), vector(dtype=dtype) fn = inplace_func([x, y], x | y) l = theano._asarray([0, 0, 1, 1], dtype=dtype) r = theano._asarray([0, 1, 0, 1], dtype=dtype) v = fn(l, r) self.assertTrue(numpy.all(v == (operator.or_(l, r))), (l, r, v)) def test_xor(self): for dtype in self.dtype: x, y = vector(dtype=dtype), vector(dtype=dtype) fn = inplace_func([x, y], x ^ y) ix = x ix = inplace.xor_inplace(ix, y) gn = inplace_func([x, y], ix) l = theano._asarray([0, 0, 1, 1], dtype=dtype) r = theano._asarray([0, 1, 0, 1], dtype=dtype) v = fn(l, r) self.assertTrue(numpy.all(v == (operator.xor(l, r))), (l, r, v)) v = gn(l, r) # test the in-place stuff self.assertTrue(numpy.all(l == numpy.asarray([0, 1, 1, 0])), l) def test_and(self): for dtype in self.dtype: x, y = vector(dtype=dtype), vector(dtype=dtype) fn = inplace_func([x, y], x & y) l = theano._asarray([0, 0, 1, 1], dtype=dtype) r = theano._asarray([0, 1, 0, 1], dtype=dtype) v = fn(l, r) self.assertTrue(numpy.all(v == (operator.and_(l, r))), (l, r, v)) def test_inv(self): for dtype in self.dtype: x = vector(dtype=dtype) fn = inplace_func([x], ~x) for l in [[0, 0, 1, 1], [0, 1, 0, 1], [0, 0, 1, 1], [0, 1, 0, 1], [-1, 2 ** 16, 2 ** 16 - 1] ]: l = theano._asarray([0, 0, 1, 1], dtype=dtype) v = fn(l) self.assertTrue(numpy.all(v == (~l)), (l, v)) def test_eye(self): n = iscalar() m = iscalar() k = iscalar() fn = theano.function([m, n, k], eye(m, n, k)) self.assertTrue(numpy.all(fn(5, 6, 1) == numpy.eye(5, 6, 1))) class T_add(unittest.TestCase): def setUp(self): utt.seed_rng() def test_complex_all_ops(self): for nbits in (64, 128): a = shared(numpy.ones(3, dtype='complex%i' % nbits) + 0.5j) b = shared(numpy.ones(3, dtype='complex%i' % nbits) + 1.5j) tests = (("+", lambda x, y: x + y), ("-", lambda x, y: x - y), ("*", lambda x, y: x * y), ("/", lambda x, y: x / y)) for s, fn in tests: f = inplace_func([], fn(a, b)) # print 'valid output:', fn(a.data, b.data) # print 'theano output:', f(a.data, b.data) self.assertTrue(a.type.values_eq_approx(fn( a.get_value(), b.get_value()), f())) def test_grad_scalar_l(self): utt.verify_grad(add, [numpy.asarray([3.0]), rand(3)]) def test_grad_scalar_r(self): utt.verify_grad(add, [rand(3), numpy.asarray([3.0])]) def test_grad_row(self): utt.verify_grad(add, [rand(3, 5), rand(1, 5)]) def test_grad_col(self): utt.verify_grad(add, [rand(3, 5), rand(3, 1)]) class T_ceil(unittest.TestCase): def test_complex(self): self.assertRaises(TypeError, tensor.ceil, tensor.zvector()) class T_exp(unittest.TestCase): def test_grad_0(self): utt.verify_grad(exp, [ numpy.asarray([[1.5089518, 1.48439076, -4.7820262], [2.04832468, 0.50791564, -1.58892269]])]) def test_grad_1(self): utt.verify_grad(inplace.exp_inplace, [ numpy.asarray([[1.5089518, 1.48439076, -4.7820262], [2.04832468, 0.50791564, -1.58892269]])]) def test_int(self): x = ivector() f = function([x], exp(x)) exp_3 = f([3]) assert exp_3.dtype == 'float64' def test_complex(self): x = zvector() assert exp(x).dtype == 'complex128' f = function([x], exp(x)) exp_3 = f([3 + 2j]) assert numpy.allclose(exp_3, numpy.exp(3 + 2j)) class T_divimpl(unittest.TestCase): def test_impls(self): i = iscalar() ii = lscalar() d = dscalar() f = fscalar() c = cscalar() assert numpy.allclose(function([i, d], i / d)(5, 7.0), (5.0 / 7.0)) assert numpy.allclose(function([i, d], d / i)(5, 7.0), (7.0 / 5.0)) assert numpy.allclose(function([i, f], i / f)(5, 11.0), (5.0 / 11.0)) assert numpy.allclose(function([i, f], f / i)(5, 11.0), (11.0 / 5.0)) assert numpy.allclose(function([i, ii], i // ii)(5, 3), (5 // 3)) assert numpy.allclose(function([i, ii], ii // i)(5, 3), (3 // 5)) assert numpy.allclose(function([i, ii], true_div(i, ii))(5, 3), (5. / 3.)) assert numpy.allclose(function([i, ii], true_div(ii, i))(5, 3), (3. / 5.)) assert numpy.allclose(function([i, c], i / c)(5, numpy.complex(5, 3)), (5. / (5 + 3j))) assert numpy.allclose(function([i, c], c / i)(5, numpy.complex(5, 3)), ((5 + 3j) / 5.)) class T_mean(unittest.TestCase): def test_regression_mean_of_ndarray_failure(self): try: tensor.mean(numpy.zeros(1)) except AttributeError: self.fail() def test0(self): # Simple test... x = tensor.vector() f = theano.function([x], tensor.mean(x)) data = rand(50) assert numpy.allclose(f(data), numpy.mean(data)) def test_list(self): ll = [theano.shared(0.), theano.shared(2.)] tensor.mean(ll).eval() == 1 class test_matinv(unittest.TestCase): def setUp(self): utt.seed_rng() def mat_reciprocal(self, dim): # symbolic program # broadcastable=[False,False] means that the shape of matrix is two dimensional, # and none of the dimensions are constrained to have length 1. # Note that TensorType's constructor does not actually allocate any memory. # TODO: Make TensorType syntax more explicit, and maybe give shape or number of dimensions. utt.seed_rng() a, b = matrices('ab') ab = a * b # Here, as_tensor_variable actually uses the data allocated by numpy. diff = ab - as_tensor_variable(numpy.ones((dim, dim), dtype=config.floatX)) # Sum of squared errors ssdiff = sum((diff ** 2.0)) g_b = grad(ssdiff, b) # compilation to function # [a,b] are the inputs, [ssdiff,g_b] are the outputs fn = inplace_func([a, b], [ssdiff, g_b]) # use the function x = rand(dim, dim) + 0.1 # Initialized s.t. x is not too tiny w = rand(dim, dim) x = numpy.asarray(x, dtype=config.floatX) w = numpy.asarray(w, dtype=config.floatX) for i in xrange(100): ssd, gw = fn(x, w) # print ssd, x*w, x, w if i == 0: ssd0 = ssd w -= 0.4 * gw return ssd0, ssd def test_reciprocal(self): """Matrix reciprocal by gradient descent""" ssd0, ssd = self.mat_reciprocal(3) utt.seed_rng() # hand-coded numpy implementation for verification x = rand(3, 3) + 0.1 w = rand(3, 3) x = numpy.asarray(x, dtype=config.floatX) w = numpy.asarray(w, dtype=config.floatX) ones = numpy.ones((3, 3), dtype=config.floatX) myssd0 = numpy.sum((x * w - ones) ** 2.0) # we want at least a test that is not too fast. So we make one here. for i in xrange(100): gw = 2 * (x * w - ones) * x # derivative of dMSE/dw myssd = numpy.sum((x * w - ones) ** 2) w -= 0.4 * gw self.assertAlmostEqual(ssd0, myssd0) self.assertAlmostEqual(ssd, myssd) class t_dot(unittest.TestCase): def setUp(self): utt.seed_rng() def cmp_dot(self, x, y): # x, y are matrices or numbers def spec(x): x = numpy.asarray(x) return type(x), x.dtype, x.shape nz = numpy.dot(x, y) tz = eval_outputs([dot(as_tensor_variable(x), as_tensor_variable(y))]) self.assertTrue(tz.dtype == nz.dtype, (tz.dtype, tz.dtype.num, nz.dtype, nz.dtype.num)) self.assertTrue(tz.shape == nz.shape, (tz.shape, nz.shape)) self.assertTrue(_approx_eq(nz, tz)) def test_Op_dims(self): # _dot is a Dot op instance _dot = theano.tensor.basic._dot d0 = scalar() d1 = vector() d2 = matrix() d3 = tensor3() self.assertRaises(TypeError, _dot, d0, d0) self.assertRaises(TypeError, _dot, d0, d1) self.assertRaises(TypeError, _dot, d0, d2) self.assertRaises(TypeError, _dot, d0, d3) self.assertRaises(TypeError, _dot, d1, d0) _dot(d1, d1) _dot(d1, d2) self.assertRaises(TypeError, _dot, d1, d3) self.assertRaises(TypeError, _dot, d2, d0) _dot(d2, d1) _dot(d2, d2) self.assertRaises(TypeError, _dot, d2, d3) self.assertRaises(TypeError, _dot, d3, d0) self.assertRaises(TypeError, _dot, d3, d1) self.assertRaises(TypeError, _dot, d3, d2) self.assertRaises(TypeError, _dot, d3, d3) def test_dot_0d_0d(self): self.cmp_dot(rand(), rand()) def test_dot_0d_1d(self): self.cmp_dot(rand(), rand(5)) def test_dot_0d_2d(self): self.cmp_dot(rand(), rand(6, 7)) def test_dot_0d_3d(self): self.cmp_dot(rand(), rand(8, 6, 7)) def test_dot_1d_0d(self): self.cmp_dot(rand(5), rand()) def test_dot_1d_1d(self): self.cmp_dot(rand(5), rand(5)) def test_dot_1d0_1d0(self): self.cmp_dot(rand(0), rand(0)) # numpy return matrix not aligned... def test_dot_1d_1d0(self): self.assertRaises(ValueError, self.cmp_dot, rand(5), rand(0)) # numpy return matrix not aligned... def test_dot_1d0_1d(self): self.assertRaises(ValueError, self.cmp_dot, rand(0), rand(5)) def test_dot_1d_2d(self): self.cmp_dot(rand(6), rand(6, 7)) def test_dot_1d0_2d(self): self.cmp_dot(rand(0), rand(0, 7)) def test_dot_1d_2d0(self): self.cmp_dot(rand(6), rand(6, 0)) def test_dot_1d0_2d0(self): self.cmp_dot(rand(0), rand(0, 0)) def test_dot_1d_3d(self): self.cmp_dot(rand(6), rand(8, 6, 7)) def test_dot_2d_0d(self): self.cmp_dot(rand(5, 6), rand()) def test_dot_2d_1d(self): self.cmp_dot(rand(5, 6), rand(6)) def test_dot_2d0_1d(self): self.cmp_dot(rand(0, 6), rand(6)) def test_dot_2d_1d0(self): self.cmp_dot(rand(5, 0), rand(0)) def test_dot_2d0_1d0(self): self.cmp_dot(rand(0, 0), rand(0)) def test_dot_2d_2d(self): self.cmp_dot(rand(5, 6), rand(6, 7)) def test_dot_2d0_2d(self): self.cmp_dot(rand(0, 6), rand(6, 7)) def test_dot_2d_2d0(self): self.cmp_dot(rand(5, 6), rand(6, 0)) def test_dot_2d0_2d0(self): self.cmp_dot(rand(0, 6), rand(6, 0)) def test_dot_2d_0_2d(self): self.cmp_dot(rand(5, 0), rand(0, 7)) def test_dot_2d0_0_2d0(self): self.cmp_dot(rand(0, 6), rand(6, 0)) def test_dot_2d_3d(self): self.cmp_dot(rand(5, 6), rand(8, 6, 7)) def test_dot_3d_0d(self): self.cmp_dot(rand(4, 5, 6), rand()) def test_dot_3d_1d(self): self.cmp_dot(rand(4, 5, 6), rand(6)) def test_dot_3d_2d(self): self.cmp_dot(rand(4, 5, 6), rand(6, 7)) def test_dot_3d_3d(self): self.cmp_dot(rand(4, 5, 6), rand(8, 6, 7)) def not_aligned(self, x, y): ctv_backup = config.compute_test_value config.compute_test_value = 'off' try: z = dot(x, y) finally: config.compute_test_value = ctv_backup # constant folding will complain to _logger that things are not aligned # this is normal, testers are not interested in seeing that output. _logger = logging.getLogger('theano.gof.opt') oldlevel = _logger.level _logger.setLevel(logging.CRITICAL) try: try: tz = eval_outputs([z]) assert False # should have raised exception except ValueError as e: e0 = exc_message(e) self.assertTrue( # Reported by numpy. e0.split()[1:4] == ['are', 'not', 'aligned'] or # Reported by blas or Theano. e0.split()[0:2] == ['Shape', 'mismatch:'] or # Reported by Theano perform e0.split()[0:4] == ['Incompatible', 'shapes', 'for', 'gemv'] or e) finally: _logger.setLevel(oldlevel) def test_align_1_1(self): self.not_aligned(rand(5), rand(6)) def test_align_1_2(self): self.not_aligned(rand(5), rand(6, 4)) def test_align_1_3(self): self.not_aligned(rand(5), rand(6, 4, 7)) def test_align_2_1(self): self.not_aligned(rand(5, 4), rand(6)) def test_align_2_2(self): self.not_aligned(rand(5, 4), rand(6, 7)) def test_align_2_3(self): self.not_aligned(rand(5, 4), rand(6, 7, 8)) def test_align_3_1(self): self.not_aligned(rand(5, 4, 3), rand(6)) def test_align_3_2(self): self.not_aligned(rand(5, 4, 3), rand(6, 7)) def test_align_3_3(self): self.not_aligned(rand(5, 4, 3), rand(6, 7, 8)) def test_grad(self): utt.verify_grad(dot, [rand(2, 3), rand(3, 2)]) utt.verify_grad(dot, [rand(2), rand(2, 3)]) utt.verify_grad(dot, [rand(3, 2), rand(2)]) utt.verify_grad(dot, [rand(2), rand(2)]) utt.verify_grad(dot, [rand(), rand(2)]) utt.verify_grad(dot, [rand(), rand(2, 5)]) utt.verify_grad(dot, [rand(2), rand()]) utt.verify_grad(dot, [rand(2, 5), rand()]) utt.verify_grad(dot, [rand(2, 3, 4), rand(4)]) utt.verify_grad(dot, [rand(3), rand(2, 3, 4)]) utt.verify_grad(dot, [rand(4, 3), rand(2, 3, 4)]) utt.verify_grad(dot, [rand(2, 3, 4), rand(4, 5)]) utt.verify_grad(dot, [rand(2, 3, 4), rand(3, 4, 5)]) @attr('slow') def test_broadcastable_patterns(self): # # These examples should all work because we broadcastable or # no, all dimensions of all results have size 1. # def val_for(r): if r.dtype.startswith('complex'): # We want to test complex at the same time, so we give a value # To the imaginary component. # This strange way of doing things is the only way that worked # on numpy 1.4.1 if r.ndim == 0: return numpy.asarray(numpy.complex(1.1, 2.1), dtype=r.dtype) if r.ndim == 1: if r.dtype == 'complex64': return numpy.complex64([numpy.complex(1.2, 2.2)]) elif r.dtype == 'complex128': return numpy.complex128([numpy.complex(1.2, 2.2)]) elif r.ndim == 2: if r.dtype == 'complex64': return numpy.complex64([[numpy.complex(1.3, 2.3)]]) elif r.dtype == 'complex128': return numpy.complex128([[numpy.complex(1.3, 2.3)]]) if r.ndim == 0: return numpy.asarray(1.1, dtype=r.dtype) if r.ndim == 1: return numpy.asarray([1.2], dtype=r.dtype) elif r.ndim == 2: return numpy.asarray([[1.3]], dtype=r.dtype) raise ValueError() for dtype0 in ('float32', 'float64', 'complex64'): for dtype1 in ('float32', 'complex64', 'complex128'): for bc0 in ((True,), (False,), (True, True), (True, False), (False, True), (False, False)): x = TensorType(dtype=dtype0, broadcastable=bc0)() for bc1 in ((True,), (False,), (True, True), (True, False), (False, True), (False, False)): y = TensorType(dtype=dtype1, broadcastable=bc1)() z = dot(x, y) t = TensorType(dtype=dtype0, broadcastable=z.broadcastable)() rval = z * 3 + 2 * t f = function([x, y, t], rval) xval = val_for(x) yval = val_for(y) tval = val_for(t) f(xval, yval, tval) # debugmode checks result if (dtype0.startswith('float') and dtype1.startswith('float')): g = grad(z.sum(), x) assert g.broadcastable == x.broadcastable g = grad(z.sum(), y) assert g.broadcastable == y.broadcastable class T_tensorfromscalar(unittest.TestCase): def test0(self): s = scal.constant(56) t = tensor_from_scalar(s) self.assertTrue(t.owner.op is tensor_from_scalar) self.assertTrue(t.type.broadcastable == (), t.type.broadcastable) self.assertTrue(t.type.ndim == 0, t.type.ndim) self.assertTrue(t.type.dtype == s.type.dtype) v = eval_outputs([t]) self.assertTrue(v == 56, v) self.assertTrue(isinstance(v, numpy.ndarray)) self.assertTrue(v.shape == (), v.shape) def test1(self): s = scal.constant(56) t = as_tensor_variable(s) self.assertTrue(t.owner.op is tensor_from_scalar) self.assertTrue(t.type.broadcastable == (), t.type.broadcastable) self.assertTrue(t.type.ndim == 0, t.type.ndim) self.assertTrue(t.type.dtype == s.type.dtype) v = eval_outputs([t]) self.assertTrue(v == 56, v) self.assertTrue(isinstance(v, numpy.ndarray)) self.assertTrue(v.shape == (), v.shape) g = grad(t, s) self.assertTrue(eval_outputs([g]) == 0.) def test2(self): s = scal.constant(56.) t = as_tensor_variable(s) self.assertTrue(t.owner.op is tensor_from_scalar) self.assertTrue(t.type.broadcastable == (), t.type.broadcastable) self.assertTrue(t.type.ndim == 0, t.type.ndim) self.assertTrue(t.type.dtype == s.type.dtype) v = eval_outputs([t]) self.assertTrue(v == 56., v) self.assertTrue(isinstance(v, numpy.ndarray)) self.assertTrue(v.shape == (), v.shape) g = grad(t, s) self.assertTrue(eval_outputs([g]) == 1.) class T_scalarfromtensor(unittest.TestCase): def test0(self): tt = constant(56) # scal.constant(56) ss = scalar_from_tensor(tt) self.assertTrue(ss.owner.op is scalar_from_tensor) self.assertTrue(ss.type.dtype == tt.type.dtype) v = eval_outputs([ss]) self.assertTrue(v == 56, v) if config.cast_policy == 'custom': self.assertTrue(isinstance(v, numpy.int16)) elif config.cast_policy in ('numpy', 'numpy+floatX'): self.assertTrue(isinstance( v, getattr(numpy, str(numpy.asarray(56).dtype)))) else: raise NotImplementedError(config.cast_policy) self.assertTrue(v.shape == (), v.shape) tt = lscalar() ss = scalar_from_tensor(tt) g = ss.owner.op.grad([tt], [ss]) fff = function([tt], ss) v = fff(numpy.asarray(5)) self.assertTrue(v == 5, v) self.assertTrue(isinstance(v, numpy.int64)) self.assertTrue(v.shape == (), v.shape) class test_grad(unittest.TestCase): class O(gof.op.Op): def __init__(self): self.gval0 = scalar('e') self.gval1 = scalar('f') def make_node(self): inputs = [scalar('a'), scalar('c')] outputs = [scalar('b'), scalar('d')] return gof.Apply(self, inputs, outputs) def grad(self, inp, grads): x0, x1 = inp gz0, gz1 = grads return self.gval0, self.gval1 def test_1param(self): """grad: Test passing a single variable param""" o = test_grad.O() a1 = o.make_node() self.assertTrue(o.gval0 is tensor.grad(a1.outputs[0], a1.inputs[0])) def test_Nparam(self): """grad: Test passing multiple variable params""" o = test_grad.O() a1 = o.make_node() g0, g1 = grad(a1.outputs[0], a1.inputs) g0.name = None self.assertTrue(o.gval0 is g0) self.assertTrue(o.gval1 is g1) def test_grad_keep_type(self): """Tests that the theano grad method returns a list if it is passed a list and a single variable if it is passed a single variable. pylearn2 depends on theano behaving this way. This functionality has been added three times and erroneously removed twice. If you do anything that requires changing this test or making it fail you are almost certainly making a common mistake, NOT fixing something. """ X = tensor.matrix() y = X.sum() G = tensor.grad(y, [X]) assert isinstance(G, list) G = tensor.grad(y, X) assert not isinstance(G, list) def test_1None_rval(self): """grad: Test returning a single zero value from grad""" o = test_grad.O() a1 = o.make_node() g = grad(a1.outputs[0], a1.outputs[1], disconnected_inputs='ignore') self.assertTrue(g.owner.op == fill) self.assertTrue(g.owner.inputs[1].data == 0) self.assertRaises(TypeError, grad, a1.outputs[0], 'wtf') def test_NNone_rval(self): """grad: Test returning some zero value from grad""" o = test_grad.O() a1 = o.make_node() g0, g1, g2 = grad(a1.outputs[0], a1.inputs + [scalar('z')], disconnected_inputs='ignore') self.assertTrue(o.gval0 is g0) self.assertTrue(o.gval1 is g1) self.assertTrue(g2.owner.op == fill) self.assertTrue(g2.owner.inputs[1].data == 0) def test_zero_gradient_shape(self): """Ensure that a zero gradient has the proper shape.""" x = dmatrix() f = theano.function([x], grad(dscalar(), x, disconnected_inputs='ignore')) a = numpy.ones((3, 7)) self.assertTrue((f(a) == 0).all()) # Zero gradient. self.assertTrue(a.shape == f(a).shape) # With proper shape. def test_cost_is_scalar(self): '''grad: Test that a non-scalar cost raises a TypeError''' s = scalar() v = vector() m = matrix() # grad(v,...) and grad(m,...) should fail self.assertRaises(TypeError, grad, v, v) self.assertRaises(TypeError, grad, m, m) class T_op_cache(unittest.TestCase): def setUp(self): utt.seed_rng() def test0(self): """trigger bug in ticket #162 """ lr = constant(0.011) v = matrix() v.name = 'v' gv = fill(v / v, 1.0) / v - (fill(v / v, 1.0) * v) / (v * v) fn_py = inplace_func([v], gv) fn_c_or_py = inplace_func([v], gv) a = rand(5, 2).astype(config.floatX) self.assertTrue(numpy.all(fn_py(a) == fn_c_or_py(a))) class T_reshape(utt.InferShapeTester, utt.TestOptimizationMixin): def __init__(self, name, shared=tensor._shared, op=Reshape, mode=None, ignore_topo=(DeepCopyOp, opt.MakeVector, opt.Shape_i, DimShuffle, theano.tensor.Elemwise)): self.shared = shared self.op = op # The tag canonicalize is needed for the shape test in FAST_COMPILE self.mode = mode self.ignore_topo = ignore_topo super(T_reshape, self).__init__(name) def function(self, inputs, outputs, ignore_empty=False): f = function(inputs, outputs, mode=self.mode) if self.mode is not None or theano.config.mode != "FAST_COMPILE": topo = f.maker.fgraph.toposort() topo_ = [node for node in topo if not isinstance(node.op, self.ignore_topo)] if ignore_empty: assert len(topo_) <= 1, topo_ else: assert len(topo_) == 1, topo_ if len(topo_) > 0: assert type(topo_[0].op) is self.op return f def test_reshape(self): a = dvector() b = dmatrix() d = dmatrix() # basic to 1 dim(without list) c = reshape(b, as_tensor_variable(6), ndim=1) f = self.function([b], c) b_val1 = numpy.asarray([[0, 1, 2], [3, 4, 5]]) c_val1 = numpy.asarray([0, 1, 2, 3, 4, 5]) b_val2 = b_val1.T c_val2 = numpy.asarray([0, 3, 1, 4, 2, 5]) f_out1 = f(b_val1) f_out2 = f(b_val2) assert numpy.all(f_out1 == c_val1), (f_out1, c_val1) assert numpy.all(f_out2 == c_val2), (f_out2, c_val2) # print f.maker.fgraph.toposort() # check that we remove the useless reshape # basic to 1 dim(with list) c = reshape(b, (as_tensor_variable(6),), ndim=1) f = self.function([b], c) assert numpy.all(f(numpy.asarray([[0, 1, 2], [3, 4, 5]])) == numpy.asarray([0, 1, 2, 3, 4, 5])) # print f.maker.fgraph.toposort() # check that we remove the useless reshape # basic to shape object of same ndim c = reshape(b, d.shape) f = self.function([b, d], c) assert numpy.all(f(numpy.asarray([[0, 1, 2], [3, 4, 5]]), [[0, 1], [2, 3], [4, 5]]) == numpy.asarray([[0, 1], [2, 3], [4, 5]])) # basic to 2 dims c = reshape(a, [2, 3]) f = self.function([a], c) assert numpy.all(f(numpy.asarray([0, 1, 2, 3, 4, 5])) == numpy.asarray([[0, 1, 2], [3, 4, 5]])) # test that it works without inplace operations a_val = numpy.asarray([0, 1, 2, 3, 4, 5]) a_val_copy = numpy.asarray([0, 1, 2, 3, 4, 5]) b_val = numpy.asarray([[0, 1, 2], [3, 4, 5]]) f_sub = self.function([a, b], c - b) assert numpy.all(f_sub(a_val, b_val) == 0.0) assert numpy.all(a_val == a_val_copy) # test that it works with inplace operations a_val = theano._asarray([0, 1, 2, 3, 4, 5], dtype='float64') a_val_copy = theano._asarray([0, 1, 2, 3, 4, 5], dtype='float64') b_val = theano._asarray([[0, 1, 2], [3, 4, 5]], dtype='float64') f_sub = self.function([a, b], c - b) assert numpy.all(f_sub(a_val, b_val) == 0.0) assert numpy.all(a_val == a_val_copy) # verify gradient def just_vals(v): return Reshape(2)(v, theano._asarray([2, 3], dtype='int32')) utt.verify_grad(just_vals, [a_val], mode=self.mode) # test infer_shape self._compile_and_check([a], [c], (a_val,), self.op) # test broadcast flag for constant value of 1 c = reshape(b, (b.shape[0], b.shape[1], 1)) # That reshape may get replaced with a dimshuffle, with is ignored, # so we pass "ignore_empty=True" f = self.function([b], c, ignore_empty=True) assert numpy.all(f(numpy.asarray([[0, 1, 2], [3, 4, 5]])) == numpy.asarray([[[0], [1], [2]], [[3], [4], [5]]])) assert (f.maker.fgraph.toposort()[-1].outputs[0].type.broadcastable == (False, False, True)) # test broadcast flag for constant value of 1 if it cannot be # replaced with dimshuffle c = reshape(b, (b.shape[1], b.shape[0], 1)) f = self.function([b], c, ignore_empty=True) assert numpy.all(f(numpy.asarray([[0, 1, 2], [3, 4, 5]])) == numpy.asarray([[[0], [1]], [[2], [3]], [[4], [5]]])) assert (f.maker.fgraph.toposort()[-1].outputs[0].type.broadcastable == (False, False, True)) def test_m1(self): t = tensor3() rng = numpy.random.RandomState(seed=utt.fetch_seed()) val = rng.uniform(size=(3, 4, 5)).astype(config.floatX) for out in [t.reshape([-1]), t.reshape([-1, 5]), t.reshape([5, -1]), t.reshape([5, -1, 3])]: self._compile_and_check([t], [out], [val], self.op) def test_reshape_long_in_shape(self): v = dvector('v') r = v.reshape((v.shape[0], L(1))) print(r.eval({v: numpy.arange(5.)})) assert numpy.allclose(r.eval({v: numpy.arange(5.)}).T, numpy.arange(5.)) def test_bad_shape(self): a = matrix('a') shapes = ivector('shapes') rng = numpy.random.RandomState(seed=utt.fetch_seed()) a_val = rng.uniform(size=(3, 4)).astype(config.floatX) # Test reshape to 1 dim r = a.reshape(shapes, ndim=1) z = zeros_like(r) f = self.function([a, shapes], r) self.assertRaises(ValueError, f, a_val, [13]) # Test reshape to 2 dim r = a.reshape(shapes, ndim=2) z = zeros_like(r) f = self.function([a, shapes], r) self.assertRaises(ValueError, f, a_val, [-1, 5]) self.assertRaises(ValueError, f, a_val, [7, -1]) self.assertRaises(ValueError, f, a_val, [7, 5]) self.assertRaises(ValueError, f, a_val, [-1, -1]) def test_0(self): x = fvector('x') f = self.function([x], x.reshape((0, 100))) assert f(numpy.ndarray((0,), dtype='float32')).shape == (0, 100) def test_empty_shp(self): const = theano.tensor.constant([1]).reshape(()) f = function([], const) assert f().shape == () def test_make_column_matrix_broadcastable(): # The goal of the operation made by `b` is to ensure the second dimension # of the column matrix is broadcastable. a = tensor.dmatrix() b = a.reshape((a.shape[0], )).dimshuffle(0, 'x') f = function([a], b) assert (f(numpy.zeros((3, 1))) + numpy.ones(2) == numpy.ones((3, 2))).all() def test_flatten_outdimNone(): a = dmatrix() c = flatten(a) f = inplace_func([a], c) a_val = theano._asarray([[0, 1, 2], [3, 4, 5]], dtype='float64') c_val = theano._asarray([0, 1, 2, 3, 4, 5], dtype='float64') assert numpy.all(f(a_val) == c_val) f = inplace_func([a], c) assert numpy.all(f(a_val) == c_val) utt.verify_grad(flatten, [a_val]) def test_flatten_scalar(): a = dscalar() c = flatten(a) f = inplace_func([a], c) a_val = theano._asarray(3.0, dtype='float64') c_val = theano._asarray([3.0], dtype='float64') assert numpy.all(f(a_val) == c_val) f = inplace_func([a], c) assert numpy.all(f(a_val) == c_val) # utt.verify_grad(flatten, [a_val]) #TODO: fix verify_grd to work on scalars def test_flatten_outdim1(): a = dmatrix() c = flatten(a, 1) f = inplace_func([a], c) a_val = theano._asarray([[0, 1, 2], [3, 4, 5]], dtype='float64') c_val = theano._asarray([0, 1, 2, 3, 4, 5], dtype='float64') assert numpy.all(f(a_val) == c_val) f = inplace_func([a], c) assert numpy.all(f(a_val) == c_val) utt.verify_grad(flatten, [a_val]) def test_flatten_outdim2(): a = dmatrix() c = flatten(a, 2) f = inplace_func([a], c) a_val = theano._asarray([[0, 1, 2], [3, 4, 5]], dtype='float64') assert numpy.all(f(a_val) == a_val) f = inplace_func([a], c) assert numpy.all(f(a_val) == a_val) flatten_2 = partial(flatten, outdim=2) utt.verify_grad(flatten_2, [a_val]) def test_flatten_outdim2_of_3(): a = TensorType('float64', (False, False, False))() c = flatten(a, 2) f = inplace_func([a], c) a_val = theano._asarray([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype='float64') c_val = theano._asarray([[0, 1, 2, 3], [4, 5, 6, 7]], dtype='float64') assert numpy.all(f(a_val) == c_val) f = inplace_func([a], c) assert numpy.all(f(a_val) == c_val) flatten_2 = partial(flatten, outdim=2) utt.verify_grad(flatten_2, [a_val]) def test_flatten_broadcastable(): # Ensure that the broadcastable pattern of the output is coherent with # that of the input inp = TensorType('float64', (False, False, False, False))() out = flatten(inp, outdim=2) assert out.broadcastable == (False, False) inp = TensorType('float64', (False, False, False, True))() out = flatten(inp, outdim=2) assert out.broadcastable == (False, False) inp = TensorType('float64', (False, True, False, True))() out = flatten(inp, outdim=2) assert out.broadcastable == (False, False) inp = TensorType('float64', (False, True, True, True))() out = flatten(inp, outdim=2) assert out.broadcastable == (False, True) inp = TensorType('float64', (True, False, True, True))() out = flatten(inp, outdim=3) assert out.broadcastable == (True, False, True) def test_flatten_outdim_invalid(): a = dmatrix() try: c = flatten(a, 3) assert False except ValueError: pass try: c = flatten(a, 0) assert False except ValueError: pass def test_is_flat(): """ tests is_flat method for constant and symbolic variables, as well as reshaped constant and symbolic variables on the given outdim """ # Constant variable assert tensor.is_flat(tensor.as_tensor_variable(numpy.zeros((10)))) assert tensor.is_flat(tensor.as_tensor_variable(numpy.zeros((10, 10, 10))), outdim=3) assert not tensor.is_flat( tensor.as_tensor_variable(numpy.zeros((10, 10, 10)))) # Symbolic variable assert tensor.is_flat(tensor.vector()) assert tensor.is_flat(tensor.tensor3(), outdim=3) assert not tensor.is_flat(tensor.tensor3()) # Reshape with constant shape X = tensor.tensor4() assert tensor.is_flat(X.reshape((-1, ))) assert tensor.is_flat(X.reshape((10, 10, -1)), outdim=3) assert not tensor.is_flat(X.reshape((10, 10, -1))) # Reshape with symbolic shape X = tensor.tensor4() assert tensor.is_flat(X.reshape((tensor.iscalar(), ))) assert tensor.is_flat(X.reshape((tensor.iscalar(), ) * 3), outdim=3) assert not tensor.is_flat(X.reshape((tensor.iscalar(), ) * 3)) def test_tile(): def run_tile(x, x_, reps, use_symbolic_reps): if use_symbolic_reps: rep_symbols = [iscalar() for _ in range(len(reps))] f = function([x] + rep_symbols, tile(x, rep_symbols)) return f(*([x_] + list(reps))) else: f = function([x], tile(x, reps)) return f(x_) rng = numpy.random.RandomState(utt.fetch_seed()) for use_symbolic_reps in [False, True]: # Test the one-dimensional case. x = vector() x_ = rng.randn(5).astype(config.floatX) assert numpy.all(run_tile(x, x_, (2,), use_symbolic_reps) == numpy.tile(x_, (2,))) # Test the two-dimensional case. x = matrix() x_ = rng.randn(2, 4).astype(config.floatX) assert numpy.all(run_tile(x, x_, (2, 3), use_symbolic_reps) == numpy.tile(x_, (2, 3))) # Test the three-dimensional case. x = tensor3() x_ = rng.randn(2, 4, 3).astype(config.floatX) assert numpy.all(run_tile(x, x_, (2, 3, 4), use_symbolic_reps) == numpy.tile(x_, (2, 3, 4))) # Test the four-dimensional case. x = tensor4() x_ = rng.randn(2, 4, 3, 5).astype(config.floatX) assert numpy.all(run_tile(x, x_, (2, 3, 4, 6), use_symbolic_reps) == numpy.tile(x_, (2, 3, 4, 6))) # Test when reps is integer, tensor.scalar or tensor.vector. # Test 1,2,3,4-dimensional cases. # Test input x has the shape [2], [2, 4], [2, 4, 3], [2, 4, 3, 5]. test_shape = [2, 4, 3, 5] k = 0 for xtype in [vector(), matrix(), tensor3(), tensor4()]: x = xtype k = k+1 x_ = rng.randn(*test_shape[0:k]).astype(config.floatX) # integer: reps_ = 2 f = function([x], tile(x, reps_)) assert numpy.all( f(x_) == numpy.tile(x_, reps_)) # tensor.scalar: reps = iscalar() reps_ = 2 f = function([x, reps], tile(x, reps)) assert numpy.all( f(x_, reps_) == numpy.tile(x_, reps_)) # tensor.vector: reps = ivector() reps_ = [2] if k == 1 or k == 2 else [2, 3] ndim_ = k f = function([x, reps], tile(x, reps, ndim_)) assert numpy.all( f(x_, reps_) == numpy.tile(x_, reps_)) # list of integers: reps_ = [2, 3, 4] f = function([x], tile(x, reps_)) assert numpy.all( f(x_) == numpy.tile(x_, reps_)) # list of integers and tensor.scalars: d = iscalar() reps = [2, d, 4] f = function([x, d], tile(x, reps)) reps_ = [2, 3, 4] assert numpy.all( f(x_, 3) == numpy.tile(x_, reps_)) # reps is list, len(reps) > x.ndim, 3 cases below: r = [2, 3, 4, 5, 6] reps_ = r[:k+1] # len(reps_) = x.ndim+1 # (1) ndim = None. f = function([x], tile(x, reps_)) assert numpy.all( f(x_) == numpy.tile(x_, reps_)) # (2) ndim = len(reps). ndim_ = len(reps_) f = function([x], tile(x, reps_, ndim_)) assert numpy.all( f(x_) == numpy.tile(x_, reps_)) # (3) ndim > len(reps) ndim_ = len(reps_) + 1 f = function([x], tile(x, reps_, ndim_)) assert numpy.all( f(x_) == numpy.tile(x_, [1] + reps_)) # reps is list, ndim > x.ndim > len(reps): r = [2, 3, 4, 5] if k > 1: ndim_ = k+1 reps_ = r[:k-1] f = function([x], tile(x, reps_, ndim_)) assert numpy.all( f(x_) == numpy.tile(x_, [1, 1] + reps_)) # error raising test: ndim not specified when reps is vector reps = ivector() numpy.testing.assert_raises(ValueError, tile, x, reps) # error raising test: not a integer for reps in [2.5, fscalar(), fvector()]: numpy.testing.assert_raises(ValueError, tile, x, reps) # error raising test: the dimension of reps exceeds 1 reps = imatrix() numpy.testing.assert_raises(ValueError, tile, x, reps) # error raising test: ndim is not None, ndim < x.ndim # 3 cases below (reps is list/tensor.scalar/tensor.vector): for reps in [[2,3,4], iscalar(), ivector()]: if k > 1: ndim = k-1 numpy.testing.assert_raises(ValueError, tile, x, reps, ndim) # error raising test: reps is list, len(reps) > ndim r = [2, 3, 4, 5, 6] reps = r[:k+1] ndim = k numpy.testing.assert_raises(ValueError, tile, x, reps, ndim) # error raising test: # reps is tensor.vector and len(reps_value) > ndim, # reps_value is the real value when excuting the function. reps = ivector() r = [2, 3, 4, 5, 6, 7] reps_ = r[:k+2] ndim_ = k+1 f = function([x, reps], tile(x, reps, ndim_)) numpy.testing.assert_raises(AssertionError, f, x_, reps_) def test_tile_grad(): def grad_tile(x, reps, np_x): y = tile(x, reps) z = y.sum() g = theano.function([x], grad(z, x)) grad_res = g(np_x) # The gradient should be the product of the tiling dimensions # (since the gradients are additive through the tiling operation) assert numpy.all(grad_res == numpy.prod(reps)) rng = numpy.random.RandomState(utt.fetch_seed()) # test vector grad_tile(vector('x'), [3], rng.randn(5).astype(config.floatX)) # test matrix grad_tile(matrix('x'), [3, 4], rng.randn(2, 3).astype(config.floatX)) # test tensor3 grad_tile(tensor3('x'), [3, 4, 5], rng.randn(2, 4, 3).astype(config.floatX)) # test tensor4 grad_tile(tensor4('x'), [3, 4, 5, 6], rng.randn(2, 4, 3, 5).astype(config.floatX)) class TestARange(unittest.TestCase): def setUp(self): utt.seed_rng() def test_Op_integers(self): """Test behaviour of ARange Op on integer inputs""" start, stop, step = iscalars('start', 'stop', 'step') out = ARange(start.type.dtype)(start, stop, step) f = function([start, stop, step], out) assert numpy.all(f(0, 5, 1) == numpy.arange(0, 5, 1)) assert numpy.all(f(2, 11, 4) == numpy.arange(2, 11, 4)) assert numpy.all(f(-5, 1, 1) == numpy.arange(-5, 1, 1)) assert numpy.all(f(10, 2, -2) == numpy.arange(10, 2, -2)) assert numpy.all(f(10, 2, 2) == numpy.arange(10, 2, 2)) assert numpy.all(f(0, 0, 1) == numpy.arange(0, 0, 1)) def test_integers(self): """Test arange constructor, on integer outputs""" start, stop, step = iscalars('start', 'stop', 'step') out = arange(start, stop, step) f = function([start, stop, step], out) if config.cast_policy == 'custom': assert out.dtype == 'int64' elif config.cast_policy in ('numpy', 'numpy+floatX'): numpy_dtype = numpy.arange(numpy.array(1, dtype='int32')).dtype assert out.dtype == numpy_dtype else: raise NotImplementedError(config.cast_policy) assert numpy.all(f(0, 5, 1) == numpy.arange(0, 5, 1)) assert numpy.all(f(2, 11, 4) == numpy.arange(2, 11, 4)) assert numpy.all(f(-5, 1, 1) == numpy.arange(-5, 1, 1)) assert numpy.all(f(10, 2, -2) == numpy.arange(10, 2, -2)) assert numpy.all(f(10, 2, 2) == numpy.arange(10, 2, 2)) assert numpy.all(f(0, 0, 1) == numpy.arange(0, 0, 1)) def test_float32(self): """Test arange constructor, on float32 outputs""" start, stop, step = fscalars('start', 'stop', 'step') out = arange(start, stop, step) f = function([start, stop, step], out) if config.cast_policy == 'custom': assert out.dtype == start.type.dtype elif config.cast_policy == 'numpy': numpy_dtype = numpy.arange(numpy.array(0, dtype=start.dtype), numpy.array(1, dtype=stop.dtype), numpy.array(1, dtype=step.dtype)).dtype assert out.dtype == numpy_dtype elif config.cast_policy == 'numpy+floatX': assert out.dtype == config.floatX else: raise NotImplementedError(config.cast_policy) arg_vals = [(0, 5, 1), (2, 11, 4), (-5, 1.1, 1.2), (1.3, 2, -2.1), (10, 2, 2)] for arg_v in arg_vals: start_v, stop_v, step_v = arg_v start_v_, stop_v_, step_v_ = numpy.asarray(arg_v, dtype=start.type.dtype) f_val = f(start_v_, stop_v_, step_v_) if config.cast_policy == 'custom': expected_val = numpy.arange(start_v, stop_v, step_v, dtype=start.type.dtype) elif config.cast_policy in ('numpy', 'numpy+floatX'): expected_val = numpy.arange(start_v_, stop_v_, step_v_, dtype=out.dtype) else: raise NotImplementedError(config.cast_policy) assert numpy.all(f_val == expected_val) def test_float64(self): """Test arange constructor, on float64 outputs""" start, stop, step = dscalars('start', 'stop', 'step') out = arange(start, stop, step) f = function([start, stop, step], out) assert out.dtype == start.type.dtype arg_vals = [(0, 5, 1), (2, 11, 4), (-5, 1.1, 1.2), (1.3, 2, -2.1), (10, 2, 2)] for arg_v in arg_vals: start_v, stop_v, step_v = arg_v start_v_, stop_v_, step_v_ = numpy.asarray(arg_v, dtype=start.type.dtype) f_val = f(start_v_, stop_v_, step_v_) if config.cast_policy == 'custom': expected_val = numpy.arange(start_v, stop_v, step_v, dtype=start.type.dtype) elif config.cast_policy in ('numpy', 'numpy+floatX'): expected_val = numpy.arange(start_v_, stop_v_, step_v_) else: raise NotImplementedError(config.cast_policy) assert numpy.all(f_val == expected_val) def test_default_step(self): """Test that arange constructor uses the correct default step""" start, stop = iscalars('start', 'stop') out = arange(start, stop) f = function([start, stop], out) if config.cast_policy == 'custom': assert out.dtype == 'int64' elif config.cast_policy in ('numpy', 'numpy+floatX'): assert out.dtype == numpy.arange(numpy.int32(0), numpy.int32(1)).dtype else: raise NotImplementedError(config.cast_policy) assert numpy.all(f(0, 5) == numpy.arange(0, 5)) assert numpy.all(f(-5, 1) == numpy.arange(-5, 1)) assert numpy.all(f(0, 0) == numpy.arange(0, 0)) dstart, dstop = dscalars('start', 'stop') dout = arange(dstart, dstop) df = function([dstart, dstop], dout) assert dout.dtype == dstart.type.dtype # print df(0.2, 5.3) # print numpy.arange(0.2, 5.3) assert numpy.all(df(0.2, 5.3) == numpy.arange(0.2, 5.3)) assert numpy.all(df(0.8, 5.3) == numpy.arange(0.8, 5.3)) assert numpy.all(df(-0.7, 5.3) == numpy.arange(-0.7, 5.3)) def test_default_start(self): """Test that arange constructor uses the correct default start""" stop = iscalar('stop') out = arange(stop) f = function([stop], out) if config.cast_policy == 'custom': assert out.dtype == 'int64' elif config.cast_policy in ('numpy', 'numpy+floatX'): assert out.dtype == numpy.arange(numpy.int32(1)).dtype else: raise NotImplementedError(config.cast_policy) assert numpy.all(f(8) == numpy.arange(8)) assert numpy.all(f(-2) == numpy.arange(-2)) fstop = fscalar('stop') fout = arange(fstop) ff = function([fstop], fout) if config.cast_policy == 'custom': assert fout.dtype == fstop.type.dtype elif config.cast_policy == 'numpy': assert fout.dtype == numpy.arange(numpy.float32(1)).dtype elif config.cast_policy == 'numpy+floatX': if config.floatX == 'float32': assert fout.dtype == 'float32' else: assert fout.dtype == numpy.arange(numpy.float32(1)).dtype else: raise NotImplementedError(config.cast_policy) fstop_values = [0.2, -0.7, 8.5] for fstop_v in fstop_values: fstop_v32 = numpy.float32(fstop_v) assert numpy.all(ff(fstop_v32) == numpy.arange(fstop_v)) def test_upcast(self): """Test that arange computes output type adequately""" if config.cast_policy == 'custom': assert arange(iscalar()).dtype == 'int64' assert arange(fscalar()).dtype == fscalar().dtype assert arange(dscalar()).dtype == dscalar().dtype # int32 + float32 -> float64 assert arange(iscalar(), fscalar()).dtype == dscalar().dtype assert arange(iscalar(), dscalar()).dtype == dscalar().dtype assert arange(fscalar(), dscalar()).dtype == dscalar().dtype assert arange(iscalar(), fscalar(), dscalar()).dtype == \ dscalar().dtype elif config.cast_policy in ('numpy', 'numpy+floatX'): for dtype in get_numeric_types(): # Test with a single argument. arange_dtype = arange(scalar(dtype=str(dtype))).dtype numpy_dtype = numpy.arange(numpy.array(1, dtype=dtype)).dtype if (dtype != 'float64' and numpy_dtype == 'float64' and config.cast_policy == 'numpy+floatX' and config.floatX == 'float32'): # We want a float32 arange. assert arange_dtype == 'float32' else: # Follow numpy. assert arange_dtype == numpy_dtype # Test with two arguments. for stop_dtype in get_numeric_types(): arange_dtype = arange( start=scalar(dtype=str(dtype)), stop=scalar(dtype=str(stop_dtype))).dtype numpy_dtype = numpy.arange( start=numpy.array(0, dtype=dtype), stop=numpy.array(1, dtype=stop_dtype)).dtype if (dtype != 'float64' and stop_dtype != 'float64' and numpy_dtype == 'float64' and config.cast_policy == 'numpy+floatX' and config.floatX == 'float32'): # We want a float32 arange. assert arange_dtype == 'float32' else: # Follow numpy. assert arange_dtype == numpy_dtype # Test with three arguments. for step_dtype in get_numeric_types(): arange_dtype = arange( start=scalar(dtype=str(dtype)), stop=scalar(dtype=str(stop_dtype)), step=scalar(dtype=str(step_dtype))).dtype numpy_dtype = numpy.arange( start=numpy.array(0, dtype=dtype), stop=numpy.array(1, dtype=stop_dtype), step=numpy.array(1, dtype=step_dtype)).dtype if (dtype != 'float64' and stop_dtype != 'float64' and step_dtype != 'float64' and numpy_dtype == 'float64' and config.cast_policy == 'numpy+floatX' and config.floatX == 'float32'): # We want a float32 arange. assert arange_dtype == 'float32' else: # Follow numpy. assert arange_dtype == numpy_dtype else: raise NotImplementedError(config.cast_policy) def test_dtype_cache(self): """Checks that the same Op is returned on repeated calls to arange using the same dtype, but not for different dtypes.""" start, stop, step = iscalars('start', 'stop', 'step') out1 = arange(start, stop, step) out2 = arange(start, stop, step, dtype=out1.dtype) out3 = arange(start, stop, 2., dtype=out1.dtype) out4 = arange(start, stop, 2.) assert out1.owner.op is out2.owner.op assert out2.owner.op is out3.owner.op assert out3.owner.op is not out4.owner.op def test_infer_shape(self): start, stop, step = iscalars('start', 'stop', 'step') out = arange(start, stop, step) mode = theano.config.mode if mode == 'FAST_COMPILE': mode = 'FAST_RUN' mode = compile.mode.get_mode(mode).excluding('fusion') f = function([start, stop, step], out.shape, mode=mode) assert len(f.maker.fgraph.toposort()) == 9 if config.cast_policy == 'custom': assert out.dtype == 'int64' elif config.cast_policy in ('numpy', 'numpy+floatX'): numpy_dtype = numpy.arange(numpy.array(0, dtype=start.dtype), numpy.array(1, dtype=stop.dtype), numpy.array(1, dtype=step.dtype)).dtype assert out.dtype == numpy_dtype else: raise NotImplementedError(config.cast_policy) assert numpy.all(f(0, 5, 1) == len(numpy.arange(0, 5, 1))) assert numpy.all(f(2, 11, 4) == len(numpy.arange(2, 11, 4))) assert numpy.all(f(-5, 1, 1) == len(numpy.arange(-5, 1, 1))) assert numpy.all(f(10, 2, -2) == len(numpy.arange(10, 2, -2))) assert numpy.all(f(10, 2, 2) == len(numpy.arange(10, 2, 2))) assert numpy.all(f(0, 0, 1) == len(numpy.arange(0, 0, 1))) out = arange(start, stop, 1) f = function([start, stop], out.shape, mode=mode) assert len(f.maker.fgraph.toposort()) == 5 # 4 [Elemwise{sub,no_inplace}(stop, start), Elemwise{Cast{int64}}(Elemwise{sub,no_inplace}.0), Elemwise{Maximum{output_types_preference=transfer_type{0}}}[(0, 0)](Elemwise{Cast{int64}}.0, 0), MakeVector(Elemwise{Maximum{output_types_preference=transfer_type{0}}}[(0, 0)].0)] if config.cast_policy == 'custom': assert out.dtype == 'int64' elif config.cast_policy in ('numpy', 'numpy+floatX'): assert out.dtype == numpy.arange( numpy.int32(0), numpy.int32(1), numpy.int32(1)).dtype else: raise NotImplementedError(config.cast_policy) assert numpy.all(f(0, 5) == len(numpy.arange(0, 5))) assert numpy.all(f(2, 11) == len(numpy.arange(2, 11))) assert numpy.all(f(-5, 1) == len(numpy.arange(-5, 1))) assert numpy.all(f(10, 2) == len(numpy.arange(10, 2))) assert numpy.all(f(10, 2) == len(numpy.arange(10, 2))) assert numpy.all(f(0, 0) == len(numpy.arange(0, 0))) assert numpy.all(f(-64, 64) == len(numpy.arange(-64, 64))) assert arange(-64, 64).shape.eval() == [128] assert arange(-64, 64, 2).shape.eval() == [64] out = arange(0, stop, 1) f = function([stop], out.shape, mode=mode) assert len(f.maker.fgraph.toposort()) == 2 #[Elemwise{Cast{int64}}(stop), MakeVector(Elemwise{Cast{int64}}.0)] if config.cast_policy == 'custom': assert out.dtype == 'int64' elif config.cast_policy in ('numpy', 'numpy+floatX'): numpy_dtype = numpy.arange(0, numpy.array(1, dtype=stop.dtype), 1).dtype assert out.dtype == numpy_dtype else: raise NotImplementedError(config.cast_policy) assert numpy.all(f(5) == len(numpy.arange(0, 5))) assert numpy.all(f(11) == len(numpy.arange(0, 11))) assert numpy.all(f(1) == len(numpy.arange(0, 1))) assert numpy.all(f(2) == len(numpy.arange(0, 2))) assert numpy.all(f(2) == len(numpy.arange(0, 2))) assert numpy.all(f(0) == len(numpy.arange(0, 0))) class TestNdGrid(unittest.TestCase): def setUp(self): pass def test_mgrid_numpy_equiv(self): nmgrid = (numpy.mgrid[0:1:.1, 1:10:1., 10:100:10.], numpy.mgrid[0:2:1, 1:10:1, 10:100:10]) tmgrid = (mgrid[0:1:.1, 1:10:1., 10:100:10.], mgrid[0:2:1, 1:10:1, 10:100:10]) for n, t in zip(nmgrid, tmgrid): for ng, tg in zip(n, t): utt.assert_allclose(ng, tg.eval()) def test_ogrid_numpy_equiv(self): nogrid = (numpy.ogrid[0:1:.1, 1:10:1., 10:100:10.], numpy.ogrid[0:2:1, 1:10:1, 10:100:10]) togrid = (ogrid[0:1:.1, 1:10:1., 10:100:10.], ogrid[0:2:1, 1:10:1, 10:100:10]) for n, t in zip(nogrid, togrid): for ng, tg in zip(n, t): utt.assert_allclose(ng, tg.eval()) def test_mgrid_theano_variable_numpy_equiv(self): nfmgrid = numpy.mgrid[0:1:.1, 1:10:1., 10:100:10.] nimgrid = numpy.mgrid[0:2:1, 1:10:1, 10:100:10] i,j,k = dscalars('i','j','k') l,m,n = iscalars('l','m','n') tfmgrid = mgrid[i:1:.1, 1:j:1., 10:100:k] timgrid = mgrid[l:2:1, 1:m:1, 10:100:n] ff = theano.function([i, j, k], tfmgrid) fi = theano.function([l, m, n], timgrid) for n, t in zip((nfmgrid,nimgrid), (ff(0, 10, 10.),fi(0, 10, 10))): for ng, tg in zip(n, t): utt.assert_allclose(ng, tg) def test_ogrid_theano_variable_numpy_equiv(self): nfogrid = numpy.ogrid[0:1:.1, 1:10:1., 10:100:10.] niogrid = numpy.ogrid[0:2:1, 1:10:1, 10:100:10] i,j,k = dscalars('i','j','k') l,m,n = iscalars('l','m','n') tfogrid = ogrid[i:1:.1, 1:j:1., 10:100:k] tiogrid = ogrid[l:2:1, 1:m:1, 10:100:n] ff = theano.function([i, j, k], tfogrid) fi = theano.function([l, m, n], tiogrid) for n, t in zip((nfogrid,niogrid), (ff(0, 10, 10.),fi(0, 10, 10))): for ng, tg in zip(n, t): utt.assert_allclose(ng, tg) class TestInversePermutation(unittest.TestCase): def setUp(self): utt.seed_rng() def test_dim1(self): """Test the inversion of one permutation (int vector)""" p = ivector() inv = inverse_permutation(p) assert inv.dtype == p.dtype f_inverse = function([p], inv) # Generate a random permutation rng = numpy.random.RandomState(utt.fetch_seed()) p_val = rng.permutation(10).astype('int32') inv_val = f_inverse(p_val) # Check that the inverse of the inverse is the original permutation assert numpy.all(f_inverse(inv_val) == p_val) # Check that permutation(inverse) == inverse(permutation) = identity assert numpy.all(p_val[inv_val] == numpy.arange(10)) assert numpy.all(inv_val[p_val] == numpy.arange(10)) def test_dim2(self): """Test the inversion of several permutations at a time""" # Each row of p is a different permutation to inverse p = imatrix() inv = inverse_permutation(p) f_inverse = function([p], inv) rng = numpy.random.RandomState(utt.fetch_seed()) # Generate 10 random permutations p_val = numpy.asarray([rng.permutation(10) for i in range(7)], dtype='int32') inv_val = f_inverse(p_val) # Check that the inverse of the inverse is the original permutation list assert numpy.all(f_inverse(inv_val) == p_val) # Check that, for each permutation, # permutation(inverse) == inverse(permutation) = identity for p_row, i_row in zip(p_val, inv_val): assert numpy.all(p_row[i_row] == numpy.arange(10)) assert numpy.all(i_row[p_row] == numpy.arange(10)) class TestPermuteRowElements(unittest.TestCase): def setUp(self): utt.seed_rng() def test_1_1(self): """Test PermuteRowElements(vector, vector)""" input = dvector() p = ivector() out = permute_row_elements(input, p) permute = function([input, p], out) rng = numpy.random.RandomState(utt.fetch_seed()) input_val = rng.uniform(size=(5,)) p_val = rng.permutation(5).astype('int32') out_val = permute(input_val, p_val) # Should be equivalent to advanced indexing out_bis = input_val[p_val] assert numpy.all(out_val == out_bis) # Verify gradient def permute_fixed(s_input): """Auxiliary op defined to get rid of gradient wrt p_val""" return permute_row_elements(s_input, p_val) utt.verify_grad(permute_fixed, [input_val]) def test_2_1(self): """Test broadcasting in PermuteRowElements(matrix, vector)""" input = matrix() p = ivector() out = permute_row_elements(input, p) permute = function([input, p], out) rng = numpy.random.RandomState(utt.fetch_seed()) input_val = rng.uniform(size=(3, 5)).astype(config.floatX) p_val = rng.permutation(5).astype('int32') out_val = permute(input_val, p_val) # The same permutation should be applied to every row of the input matrix. out_bis = numpy.asarray([r[p_val] for r in input_val]) assert numpy.all(out_val == out_bis) # Verify gradient def permute_fixed(s_input): """Auxiliary op defined to get rid of gradient wrt p_val""" return permute_row_elements(s_input, p_val) utt.verify_grad(permute_fixed, [input_val]) def test_2_2(self): """Test PermuteRowElements(matrix, matrix)""" input = matrix() p = imatrix() out = permute_row_elements(input, p) permute = function([input, p], out) rng = numpy.random.RandomState(utt.fetch_seed()) input_val = rng.uniform(size=(3, 5)).astype(config.floatX) p_val = numpy.asarray([rng.permutation(5) for i in range(3)], dtype='int32') out_val = permute(input_val, p_val) # Each row of p contains a permutation to apply to the corresponding # row of input out_bis = numpy.asarray([i_row[p_row] for i_row, p_row in zip(input_val, p_val)]) assert numpy.all(out_val == out_bis) # Verify gradient def permute_fixed(s_input): """Auxiliary op defined to get rid of gradient wrt p_val""" return permute_row_elements(s_input, p_val) utt.verify_grad(permute_fixed, [input_val]) def test_1_2(self): """Test PermuteRowElements(vector, matrix) Different permutations will be applied to the same input vector""" input = vector() p = imatrix() out = permute_row_elements(input, p) permute = function([input, p], out) rng = numpy.random.RandomState(utt.fetch_seed()) input_val = rng.uniform(size=(5,)).astype(config.floatX) p_val = numpy.asarray([rng.permutation(5) for i in range(3) ], dtype='int32') out_val = permute(input_val, p_val) # Each row of p contains a permutation to apply to the input vector out_bis = numpy.asarray([input_val[p_row] for p_row in p_val]) assert numpy.all(out_val == out_bis) # Verify gradient def permute_fixed(s_input): """Auxiliary op defined to get rid of gradient wrt p_val""" return permute_row_elements(s_input, p_val) utt.verify_grad(permute_fixed, [input_val]) def test_3b_2(self): """Test permute_row_elements on a more complex broadcasting pattern: input.type.broadcastable = (False, True, False), p.type.broadcastable = (False, False).""" input = TensorType('floatX', (False, True, False))() p = imatrix() out = permute_row_elements(input, p) permute = function([input, p], out) rng = numpy.random.RandomState(utt.fetch_seed()) input_val = rng.uniform(size=(4, 1, 5)).astype(config.floatX) p_val = numpy.asarray([rng.permutation(5) for i in range(3)], dtype='int32') out_val = permute(input_val, p_val) # Each row of p contains a permutation to apply to each row # of the input tensor out_bis = numpy.asarray([[in_mat[0, p_row] for p_row in p_val] for in_mat in input_val]) assert numpy.all(out_val == out_bis) # Verify gradient def permute_fixed(s_input): """Auxiliary op defined to get rid of gradient wrt p_val""" return permute_row_elements(s_input, p_val) utt.verify_grad(permute_fixed, [input_val]) class test_tensordot(unittest.TestCase): def TensorDot(self, axes): """ Since tensordot is no longer an op, mimic the old op signature to allow easy use of verify_grad. """ return lambda a, b: tensordot(a, b, axes) def setUp(self): utt.seed_rng() def test0(self): # Test vector-vector avec = vector() bvec = vector() axes = ((0, ), (0, )) c = tensordot(avec, bvec, axes) f1 = inplace_func([avec, bvec], c) aval = rand(5) bval = rand(5) out0 = numpy.tensordot(aval, bval, axes) out1 = f1(aval, bval) utt.assert_allclose(out0, out1) utt.verify_grad(self.TensorDot(axes), [aval, bval]) # Test matrix-vector bmat = matrix() axes = ((0, ), (1, )) c = tensordot(avec, bmat, axes) f2 = inplace_func([avec, bmat], c) aval = rand(5) bval = rand(8, 5) utt.assert_allclose(numpy.tensordot(aval, bval, axes), f2(aval, bval)) utt.verify_grad(self.TensorDot(axes), [aval, bval]) # Test matrix-matrix amat = matrix() for axes, shps in [[((0,), (0,)), [(4, 7), (4, 9)]], [((0,), (1,)), [(4, 7), (9, 4)]], [((1,), (0,)), [(4, 7), (7, 9)]], [((1,), (1,)), [(4, 7), (9, 7)]], [((0, 1), (0, 1)), [(4, 7), (4, 7)]], # [((0, 1), (1, 0)), [(4, 7), (7, 4)]], # [((1, 0), (1, 0)), [(4, 7), (4, 7)]], # [((1, 0), (0, 1)), [(4, 7), (7, 4)]], ]: c = tensordot(amat, bmat, axes) f3 = inplace_func([amat, bmat], c) aval = rand(*shps[0]) bval = rand(*shps[1]) utt.assert_allclose(numpy.tensordot(aval, bval, axes), f3(aval, bval)) utt.verify_grad(self.TensorDot(axes), [aval, bval]) # Test ndarray-matrix, sum over one dim of matrix for axes, shps in [[((2,), (1,)), [(1, 2, 3, 4), (2, 3)]], [((0,), (1,)), [(1, 2, 3, 4), (3, 1)]], [((0,), (0,)), [(1, 2, 3, 4), (1, 3)]], [((3,), (0,)), [(1, 2, 3, 4), (4, 1)]], # [((3, 1), (0, 1)), [(1, 2, 3, 4), (4, 2)]], # [((0, 1), (1, 0)), [(1, 2, 3, 4), (2, 1)]], # [((3, 1), (1, 0)), [(1, 2, 3, 4), (2, 4)]], ]: atens = tensor4() c = tensordot(atens, bmat, axes) f4 = inplace_func([atens, bmat], c) aval = rand(*shps[0]) bval = rand(*shps[1]) utt.assert_allclose(numpy.tensordot(aval, bval, axes), f4(aval, bval)) utt.verify_grad(self.TensorDot(axes), [aval, bval]) # Test ndarray-ndarray atens = tensor4() btens = tensor3() axes = ((1, 3), (0, 2)) c = tensordot(atens, btens, axes) f5 = inplace_func([atens, btens], c) aval = rand(4, 3, 5, 2) bval = rand(3, 4, 2) utt.assert_allclose(numpy.tensordot(aval, bval, axes), f5(aval, bval)) utt.verify_grad(self.TensorDot(axes), [aval, bval]) axes = (axes[1], axes[0]) c = tensordot(btens, atens, axes) f6 = inplace_func([btens, atens], c) utt.assert_allclose(numpy.tensordot(bval, aval, axes), f6(bval, aval)) utt.verify_grad(self.TensorDot(axes), [bval, aval]) def test_raise_error(self): amat = matrix() bmat = matrix() bvec = vector() # Test invalid length for axes self.assertRaises(ValueError, tensordot, amat, bmat, (0, 1, 2)) # Test axes of uneven length self.assertRaises(ValueError, tensordot, amat, bmat, ((0, 1), (0))) # Test invalid len(axes) given inputs are matrices self.assertRaises(ValueError, tensordot, amat, bmat, ((0, 1, 2), (0, 1, 2))) # Test invalid axes[1] given that y is a vector self.assertRaises(ValueError, tensordot, amat, bvec, (0, 1)) # Test invalid scalar axes given inputs are matrices self.assertRaises(ValueError, tensordot, amat, bvec, 2) def test_weird_valid_axes(self): # Test matrix-matrix amat = matrix() bmat = matrix() for axes in [0, (1, 0), [1, 0], (1, (0, )), ((1, ), 0), ([1], [0]), ([], [])]: c = tensordot(amat, bmat, axes) f3 = inplace_func([amat, bmat], c) aval = rand(4, 7) bval = rand(7, 9) self.assertTrue(numpy.allclose(numpy.tensordot(aval, bval, axes), f3(aval, bval))) utt.verify_grad(self.TensorDot(axes), [aval, bval]) def test_scalar_axes(self): # Test matrix-matrix amat = fmatrix() bmat = dmatrix() # We let at float64 to test mix of float32 and float64. axes = 1 aval = rand(4, 5).astype('float32') bval = rand(5, 3) c = tensordot(amat, bmat, axes) f3 = inplace_func([amat, bmat], c) self.assertTrue(numpy.allclose(numpy.tensordot(aval, bval, axes), f3(aval, bval))) utt.verify_grad(self.TensorDot(axes), [aval, bval]) # Test tensor-tensor amat = tensor3() bmat = tensor3() axes = 2 aval = rand(3, 4, 5) bval = rand(4, 5, 3) c = tensordot(amat, bmat, axes) f3 = inplace_func([amat, bmat], c) self.assertTrue(numpy.allclose(numpy.tensordot(aval, bval, axes), f3(aval, bval))) utt.verify_grad(self.TensorDot(axes), [aval, bval]) def test_scalar0(self): # Test tensor-tensor amat = matrix() bmat = matrix() axes = 0 aval = rand(4, 5) bval = rand(5, 4) c = tensordot(amat, bmat, axes) f3 = inplace_func([amat, bmat], c) self.assertTrue(numpy.allclose(numpy.tensordot(aval, bval, axes), f3(aval, bval))) utt.verify_grad(self.TensorDot(axes), [aval, bval]) def test_broadcastable1(self): x = TensorType(dtype=floatX, broadcastable=(True, False, False))('x') y = tensor3('y') z = tensordot(x, y) assert z.broadcastable == (True, False) f = inplace_func([x, y], z) xv = rand(1, 3, 4) yv = rand(3, 4, 5) zv = f(xv, yv) self.assertTrue(numpy.allclose(numpy.tensordot(xv, yv), zv)) def test_broadcastable2(self): x = TensorType(dtype=floatX, broadcastable=(True, False, False))('x') y = tensor3('y') axes = [[2, 1], [0, 1]] z = tensordot(x, y, axes=axes) assert z.broadcastable == (True, False) f = inplace_func([x, y], z) xv = rand(1, 3, 4) yv = rand(4, 3, 5) zv = f(xv, yv) self.assertTrue(numpy.allclose(numpy.tensordot(xv, yv, axes=axes), zv)) def test_smallest_stack(): sx, sy = dscalar(), dscalar() rval = inplace_func([sx, sy], stack([sx, sy]))(-4.0, -2.0) assert type(rval) == numpy.ndarray assert [-4, -2] == list(rval) def test_smallest(): x = dvector() y = dvector() z = dvector() f1 = inplace_func([x], smallest(x)) assert numpy.all([1, 2, 3] == f1([1, 2, 3])) f3 = inplace_func([x, y, z], smallest(x, y, z)) assert numpy.all([1, 2, 3] == f3([1, 3, 9], [7, 7, 7], [8, 2, 3])) sx, sy = dscalar(), dscalar() assert -4 == inplace_func([sx, sy], smallest(sx, sy))(-4.0, -2.0) def test_reshape_member_fn(): x = dmatrix() y = x.reshape((4, 5, 6)) assert y.owner.op == Reshape(3) def test_var(): a = Tensor(dtype='float64', broadcastable=[False, False, False])() f = function([a], var(a)) a_val = numpy.arange(60).reshape(3, 4, 5) assert numpy.allclose(numpy.var(a_val), f(a_val)) f = function([a], var(a, axis=0)) assert numpy.allclose(numpy.var(a_val, axis=0), f(a_val)) f = function([a], var(a, axis=1)) assert numpy.allclose(numpy.var(a_val, axis=1), f(a_val)) f = function([a], var(a, axis=2)) assert numpy.allclose(numpy.var(a_val, axis=2), f(a_val)) f = function([a], var(a, axis=0, ddof=0)) assert numpy.allclose(numpy.var(a_val, axis=0, ddof=0), f(a_val)) f = function([a], var(a, axis=1, ddof=1)) assert numpy.allclose(numpy.var(a_val, axis=1, ddof=1), f(a_val)) f = function([a], var(a, axis=2, ddof=1)) assert numpy.allclose(numpy.var(a_val, axis=2, ddof=1), f(a_val)) f = function([a], var(a, ddof=0, corrected=True)) mean_a = numpy.mean(a_val) centered_a = a_val - mean_a v = numpy.mean(centered_a ** 2) error = (numpy.mean(centered_a)) ** 2 v = v - error assert numpy.allclose(v, f(a_val)) f = function([a], var(a, axis=2, ddof=1, corrected=True)) mean_a = numpy.mean(a_val, axis=2, keepdims=True) centered_a = a_val - mean_a v = numpy.var(a_val, axis=2, ddof=1) shp_inp = numpy.shape(a_val) shp = shp_inp - numpy.array(1) error = (numpy.sum(centered_a, axis=2)) ** 2 error = numpy.true_divide(error, shp[1] * shp_inp[1]) v = v - error assert numpy.allclose(v, f(a_val)) class T_sum(unittest.TestCase): def test_sum_overflow(self): """Ensure that overflow errors are a little bit harder to get""" a = Tensor(dtype='int8', broadcastable=[False])() f = function([a], sum(a)) assert f([1] * 300) == 300 def test_list(self): ll = [theano.shared(0.), theano.shared(2.)] tensor.sum(ll).eval() == 2 @dec.skipif( isinstance(get_default_mode(), theano.compile.debugmode.DebugMode), ("This test fails in DEBUG_MODE, but the generated code is OK. " "It is actually a problem of DEBUG_MODE, see #626.")) def test_default(): x, y = scalars('xy') z = default(x, y) f = function([x, y], z) assert f(1, 2) == 1 assert f(None, 2) == 2 assert f(1, None) == 1 @dec.skipif( isinstance(get_default_mode(), theano.compile.debugmode.DebugMode), ("This test fails in DEBUG_MODE, but the generated code is OK. " "It is actually a problem of DEBUG_MODE, see #626.")) def test_default_state(): x, y = scalars('xy') # print config.floatX # print x.type # print y.type z = default(x, 3.8) new_x = y + z f = function([y, compile.In(x, update=new_x, value=12.0)], new_x) assert f(3) == 15 f['x'] = None assert numpy.allclose(f(1), 4.8) assert numpy.allclose(f(numpy.asarray(2.2, dtype=config.floatX)), 7) def test_autocast(): backup_config = config.cast_policy # Call test functions for all possible values of `config.cast_policy`. for autocast_cfg in ( 'custom', #'numpy', # Commented out until it is implemented properly. 'numpy+floatX', ): config.cast_policy = autocast_cfg try: eval('_test_autocast_' + autocast_cfg.replace('+', '_'))() finally: config.cast_policy = backup_config def _test_autocast_custom(): """Called from `test_autocast`.""" assert config.cast_policy == 'custom' orig_autocast = autocast_float.dtypes # Test that autocast_float_as sets the autocast dtype correctly with autocast_float_as('float32'): assert autocast_float.dtypes == ('float32',) assert autocast_float.dtypes == orig_autocast with autocast_float_as('float64'): assert autocast_float.dtypes == ('float64',) assert autocast_float.dtypes == orig_autocast # Test that we can set it back to something, and nest it with autocast_float_as('float32'): assert autocast_float.dtypes == ('float32',) with autocast_float_as('float64'): assert autocast_float.dtypes == ('float64',) assert autocast_float.dtypes == ('float32',) assert autocast_float.dtypes == orig_autocast # Test that the autocasting dtype is used correctly in expression-building with autocast_float_as('float32'): assert (dvector() + 1.1).dtype == 'float64' assert (fvector() + 1.1).dtype == 'float32' assert (fvector() + theano._asarray(1.1, dtype='float64')).dtype == \ 'float64' assert (fvector() + theano._asarray(1.1, dtype='float32')).dtype == \ 'float32' assert (dvector() + 1).dtype == 'float64' assert (fvector() + 1).dtype == 'float32' # Test that the autocasting dtype is used correctly in expression-building with autocast_float_as('float64'): assert (dvector() + 1.1).dtype == 'float64' assert (fvector() + 1.1).dtype == 'float64' assert (fvector() + 1.0).dtype == 'float64' assert (fvector() + theano._asarray(1.1, dtype='float64')).dtype == \ 'float64' assert (fvector() + theano._asarray(1.1, dtype='float32')).dtype == \ 'float32' assert (dvector() + 1).dtype == 'float64' assert (fvector() + 1).dtype == 'float32' # Test that the autocasting dtype is used correctly in expression-building with autocast_float_as('float32', 'float64'): assert (dvector() + 1.1).dtype == 'float64' assert (fvector() + 1.1).dtype == theano.config.floatX assert (fvector() + 1.0).dtype == 'float32' assert (dvector() + numpy.float32(1.1)).dtype == 'float64' assert (dvector() + numpy.float64(1.1)).dtype == 'float64' assert (dvector() + numpy.float(1.1)).dtype == 'float64' assert (fvector() + numpy.float32(1.1)).dtype == 'float32' assert (fvector() + numpy.float64(1.1)).dtype == 'float64' assert (fvector() + numpy.float(1.1)).dtype == theano.config.floatX assert (lvector() + numpy.int64(1)).dtype == 'int64' assert (lvector() + numpy.int32(1)).dtype == 'int64' assert (lvector() + numpy.int16(1)).dtype == 'int64' assert (lvector() + numpy.int8(1)).dtype == 'int64' assert (ivector() + numpy.int8(1)).dtype == 'int32' assert (wvector() + numpy.int8(1)).dtype == 'int16' assert (bvector() + numpy.int8(1)).dtype == 'int8' with autocast_float_as('float64'): assert (fvector() + 1.0).dtype == 'float64' def _test_autocast_numpy(): """Called from `test_autocast`.""" assert config.cast_policy == 'numpy' # Go through some typical scalar values. def ok(z): assert tensor.constant(z).dtype == numpy.asarray(z).dtype for x in ([2 ** i for i in xrange(63)] + [0, L(0), L(1), L(2 ** 63 - 1)] + [0., 1., 1.1, 1.5]): n_x = numpy.asarray(x) # Make sure the data type is the same as the one found by numpy. ok(x) ok(-x) ok(x - 1) ok(-x + 1) ok(n_x) def _test_autocast_numpy_floatX(): """Called from `test_autocast`.""" assert config.cast_policy == 'numpy+floatX' backup_floatX = config.floatX def ok(z, floatX): if (isinstance(z, float) and floatX == 'float32' and not hasattr(z, 'dtype')): # Special case where we use 'float32' instead of 'float64'. assert tensor.constant(z).dtype == 'float32' else: assert tensor.constant(z).dtype == numpy.asarray(z).dtype try: # Test with various values of `config.floatX`. for floatX in ('float32', 'float64'): config.floatX = floatX # Go through some typical scalar values. # We only consider 'int' and 'long' Python values that can fit # into int64, as that is the maximal integer type that Theano # supports, and that is the maximal type in Python indexing. for x in ([2 ** i - 1 for i in xrange(64)] + [0, L(0), L(1), L(2 ** 63 - 1)] + [0., 1., 1.1, 1.5]): ok(x, floatX) ok(-x, floatX) ok(x - 1, floatX) ok(-x + 1, floatX) ok(numpy.asarray(x), floatX) ok(numpy.float64(x), floatX) finally: config.floatX = backup_floatX class test_arithmetic_cast(unittest.TestCase): """ Test output types of basic arithmeric operations (* / + - //). We only test the behavior for `config.cast_policy` set to either 'numpy' or 'numpy+floatX': the 'custom' behavior is (at least partially) tested in `_test_autocast_custom`. """ def test_arithmetic_cast(self): backup_config = config.cast_policy dtypes = get_numeric_types(with_complex=True) # Here: # scalar == scalar stored as a 0d array # array == 1d array # i_scalar == scalar type used internally by Theano theano_scalar = lambda dtype: tensor.scalar(dtype=str(dtype)) numpy_scalar = lambda dtype: numpy.array(1, dtype=dtype) theano_array = lambda dtype: tensor.vector(dtype=str(dtype)) numpy_array = lambda dtype: numpy.array([1], dtype=dtype) theano_i_scalar = lambda dtype: theano.scalar.Scalar(str(dtype))() numpy_i_scalar = numpy_scalar if config.int_division == 'int': # Avoid deprecation warning during tests. warnings.filterwarnings('ignore', message='Division of two integer', category=DeprecationWarning) try: for cfg in ('numpy+floatX', ): # Used to test 'numpy' as well. config.cast_policy = cfg for op in (operator.add, operator.sub, operator.mul, operator_div, operator.floordiv): for a_type in dtypes: for b_type in dtypes: # Note that we do not test division between # integers if it is forbidden. # Theano deals with integer division in its own # special way (depending on `config.int_division`). is_int_division = ( op is operator_div and a_type in tensor.discrete_dtypes and b_type in tensor.discrete_dtypes) # We will test all meaningful combinations of # scalar and array operations. for combo in ( ('scalar', 'scalar'), ('array', 'array'), ('scalar', 'array'), ('array', 'scalar'), ('i_scalar', 'i_scalar'), ): theano_args = list(map(eval, ['theano_%s' % c for c in combo])) numpy_args = list(map(eval, ['numpy_%s' % c for c in combo])) try: theano_dtype = op( theano_args[0](a_type), theano_args[1](b_type)).type.dtype # Should have crashed if it is an integer # division and `config.int_division` does # not allow it. assert not (is_int_division and config.int_division == 'raise') except theano.scalar.IntegerDivisionError: assert (is_int_division and config.int_division == 'raise') # This is the expected behavior. continue # For numpy we have a problem: # http://projects.scipy.org/numpy/ticket/1827 # As a result we only consider the highest data # type that numpy may return. numpy_dtypes = [ op(numpy_args[0](a_type), numpy_args[1](b_type)).dtype, op(numpy_args[1](b_type), numpy_args[0](a_type)).dtype] numpy_dtype = theano.scalar.upcast( *list(map(str, numpy_dtypes))) if numpy_dtype == theano_dtype: # Same data type found, all is good! continue if (cfg == 'numpy+floatX' and config.floatX == 'float32' and a_type != 'float64' and b_type != 'float64' and numpy_dtype == 'float64'): # We should keep float32. assert theano_dtype == 'float32' continue if 'array' in combo and 'scalar' in combo: # For mixed scalar / array operations, # Theano may differ from numpy as it does # not try to prevent the scalar from # upcasting the array. array_type, scalar_type = ( (a_type, b_type)[ list(combo).index(arg)] for arg in ('array', 'scalar')) up_type = theano.scalar.upcast(array_type, scalar_type) if ( # The two data types are different. scalar_type != array_type and # The array type is not enough to hold # the scalar type as well. array_type != up_type and # Theano upcasted the result array. theano_dtype == up_type and # But Numpy kept its original type. array_type == numpy_dtype): # Then we accept this difference in # behavior. continue if (is_int_division and config.int_division == 'floatX'): assert theano_dtype == config.floatX continue if (cfg == 'numpy+floatX' and a_type == 'complex128' and (b_type == 'float32' or b_type == 'float16') and combo == ('scalar', 'array') and theano_dtype == 'complex128' and numpy_dtype == 'complex64'): # In numpy 1.6.x adding a complex128 with # a float32 may result in a complex64. As # of 1.9.2. this is still the case so it is # probably by design raise SkipTest("Known issue with" "numpy see #761") # In any other situation: something wrong is # going on! assert False finally: config.cast_policy = backup_config if config.int_division == 'int': # Restore default deprecation warning behavior. warnings.filterwarnings( 'default', message='Division of two integer', category=DeprecationWarning) class T_long_tensor(unittest.TestCase): def test_fit_int64(self): for exp in xrange(theano.configdefaults.python_int_bitwidth()): val = L(2 ** exp - 1) scalar_ct = constant(val) assert scalar_ct.dtype.startswith('int'), (exp, val, scalar_ct.dtype) assert scalar_ct.value == val vector_ct = constant([val, val]) assert vector_ct.dtype == 'int64' assert numpy.all(vector_ct.value == val) matrix_ct = constant([[val, val]]) assert matrix_ct.dtype == 'int64' assert numpy.all(matrix_ct.value == val) def test_too_big(self): val = L(2 ** 63) # NumPy 1.7 this will raise an exception # NumPy 1.7.1 this will work try: cst = constant(val) assert cst.value == val assert cst.dtype == "uint64" except OverflowError: pass try: cst = constant([val, val]) assert cst.value[0] == val assert cst.value[1] == val assert cst.value.size == 2 assert cst.dtype == "uint64" except TypeError: pass try: cst = constant([[val, val]]) assert cst.value[0, 0] == val assert cst.value[0, 1] == val assert cst.value.size == 2 assert cst.dtype == "uint64" except TypeError: pass val = L(2 ** 64) # This fail for all NumPy version. self.assertRaises(Exception, constant, val) self.assertRaises(Exception, constant, [val, val]) self.assertRaises(Exception, constant, [[val, val]]) class test_broadcast(unittest.TestCase): def test_broadcast_bigdim(self): def f(): x = matrix() addbroadcast(x, 2) self.assertRaises(ValueError, f) def test_unbroadcast_addbroadcast(self): """ test that the unbroadcast fct don't insert not needed broadcast and fuse consecutive Rebroadcast op """ x = matrix() assert unbroadcast(x, 0) is x assert unbroadcast(x, 1) is x assert unbroadcast(x, 1, 0) is x assert unbroadcast(x, 0, 1) is x assert addbroadcast(x, 0) is not x assert addbroadcast(x, 1) is not x assert addbroadcast(x, 1, 0).owner.inputs[0] is x assert unbroadcast(addbroadcast(x, 0), 0) is x assert addbroadcast(unbroadcast(x, 0), 0) is not x x = row() assert unbroadcast(x, 0) is not x assert unbroadcast(x, 1) is x assert unbroadcast(x, 1, 0) is not x assert unbroadcast(x, 0, 1) is not x assert addbroadcast(x, 0) is x assert addbroadcast(x, 1).owner.inputs[0] is x assert addbroadcast(x, 1, 0).owner.inputs[0] is x assert addbroadcast(x, 0, 1).owner.inputs[0] is x assert unbroadcast(addbroadcast(x, 1), 1) is x assert addbroadcast(unbroadcast(x, 1), 1) is not x # The first broadcast is remove the broadcast, so the second # should not make one assert unbroadcast(unbroadcast(x, 0), 0).owner.inputs[0] is x # Test that consecutive Rebroadcast op are fused x = TensorType(dtype='float64', broadcastable=(True, True))() assert unbroadcast(unbroadcast(x, 1), 0).owner.inputs[0] is x assert addbroadcast(unbroadcast(x, 1), 0).owner.inputs[0] is x assert addbroadcast(unbroadcast(x, 0), 0) is x def test_patternbroadcast(self): # Test that patternbroadcast with an empty broadcasting pattern works x = scalar('x') m = tensor.matrix('m') s = patternbroadcast(m, x.broadcastable) assert s is m x2 = patternbroadcast(x, x.broadcastable) assert x2 is x def test_infer_shape(self): x = matrix() y = addbroadcast(x, 0) f = theano.function([x], y.shape) assert (f(numpy.zeros((1, 5), dtype=config.floatX)) == [1, 5]).all() topo = f.maker.fgraph.toposort() if theano.config.mode != 'FAST_COMPILE': assert len(topo) == 2 assert isinstance(topo[0].op, opt.Shape_i) assert isinstance(topo[1].op, opt.MakeVector) x = matrix() y = unbroadcast(x, 0) f = theano.function([x], y.shape) assert (f(numpy.zeros((2, 5), dtype=config.floatX)) == [2, 5]).all() topo = f.maker.fgraph.toposort() if theano.config.mode != 'FAST_COMPILE': assert len(topo) == 3 assert isinstance(topo[0].op, opt.Shape_i) assert isinstance(topo[1].op, opt.Shape_i) assert isinstance(topo[2].op, opt.MakeVector) x = row() y = unbroadcast(x, 0) f = theano.function([x], y.shape) assert (f(numpy.zeros((1, 5), dtype=config.floatX)) == [1, 5]).all() topo = f.maker.fgraph.toposort() if theano.config.mode != 'FAST_COMPILE': assert len(topo) == 2 assert isinstance(topo[0].op, opt.Shape_i) assert isinstance(topo[1].op, opt.MakeVector) def test_len(): for shape in [(5,), (3, 4), (7, 4, 6)]: x = tensor.tensor(dtype='floatX', broadcastable=(False,) * len(shape)) try: len(x) assert False, "Expected an error" except TypeError: pass def test_mod(): """ We add this test as not all language and C implementation give the same sign to the result. This check that the c_code of `Mod` is implemented as Python. That is what we want. """ x, y = fscalars('xy') fn = gof.DualLinker().accept( gof.FunctionGraph([x, y], [x % y])).make_function() for a, b in ((0, 1), (1, 1), (0, -1), (1, -1), (-1, -1), (1, 2), (-1, 2), (1, -2), (-1, -2), (5, 3), (-5, 3), (5, -3), (-5, -3) ): assert fn(a, b) == a % b, (a,) def test_divmod(): """ Confirm that divmod is equivalent to the python version. """ x, y = fscalars('xy') d, r = divmod(x, y) fn = gof.DualLinker().accept( gof.FunctionGraph([x, y], [d, r])).make_function() for a, b in ((0, 1), (1, 1), (0, -1), (1, -1), (-1, -1), (1, 2), (-1, 2), (1, -2), (-1, -2), (5, 3), (-5, 3), (5, -3), (-5, -3) ): d_v, r_v = fn(a, b) d_vp, r_vp = divmod(a, b) assert d_v == d_vp and r_v == r_vp, (a,) def test_mod_compile(): """ This test generate an Elemwise of Composite as: Elemwise{ Composite{ Composite{ Composite{ Composite{mod,EQ}, Switch}, mul}, add}} The c_code generated is not compiling as of 30 June 2010. I fix the compilation in the same commit. """ x = tensor.vector() y = tensor.vector() shape = x.shape out = tensor.switch(tensor.eq(3 % x.shape[0], 0), y, y[:-1]) f = theano.function([x, y], out) def test_unalign(): if config.floatX == 'float64': dtype = "b1,f8" else: dtype = "b1,f4" a = numpy.empty(10000, dtype=dtype)['f1'] b = numpy.empty(10000, dtype=dtype)['f1'] assert not a.flags.aligned assert not b.flags.aligned a[:] = rand(len(a)) b[:] = rand(len(b)) out_numpy = 2 * a + 3 * b av, bv = tensor.vectors('ab') f = theano.function([av, bv], 2 * av + 3 * bv) f.maker.fgraph.toposort() try: out_theano = f(a, b) assert not a.flags.aligned assert not b.flags.aligned assert numpy.allclose(out_numpy, out_theano) assert False except TypeError as e: pass a = numpy.empty((), dtype=dtype)['f1'] b = numpy.empty((), dtype=dtype)['f1'] assert not a.flags.aligned assert not b.flags.aligned out_numpy = 2 * a + 3 * b av, bv = tensor.scalars('ab') f = theano.function([av, bv], 2 * av + 3 * bv) f.maker.fgraph.toposort() try: out_theano = f(a, b) assert not a.flags.aligned assert not b.flags.aligned assert numpy.allclose(out_numpy, out_theano) assert False except TypeError as e: pass def test_dimshuffle_duplicate(): x = tensor.vector() success = False try: y = tensor.DimShuffle((False, ), (0, 0))(x) except ValueError as e: assert str(e).find("may not appear twice") != -1 success = True assert success class T_get_scalar_constant_value(unittest.TestCase): def test_get_scalar_constant_value(self): a = tensor.stack([1, 2, 3]) assert get_scalar_constant_value(a[0]) == 1 assert get_scalar_constant_value(a[1]) == 2 assert get_scalar_constant_value(a[2]) == 3 b = tensor.iscalar() a = tensor.stack([b, 2, 3]) self.assertRaises(tensor.basic.NotScalarConstantError, get_scalar_constant_value, a[0]) assert get_scalar_constant_value(a[1]) == 2 assert get_scalar_constant_value(a[2]) == 3 # For now get_scalar_constant_value goes through only MakeVector and Join of # scalars. v = tensor.ivector() a = tensor.stack([v, [2], [3]]) self.assertRaises(tensor.NotScalarConstantError, get_scalar_constant_value, a[0]) self.assertRaises(tensor.NotScalarConstantError, get_scalar_constant_value, a[1]) self.assertRaises(tensor.NotScalarConstantError, get_scalar_constant_value, a[2]) # Test the case SubTensor(Shape(v)) when the dimensions # is broadcastable. v = tensor.row() assert get_scalar_constant_value(v.shape[0]) == 1 def test_subtensor_of_constant(self): c = constant(rand(5)) for i in range(c.value.shape[0]): assert get_scalar_constant_value(c[i]) == c.value[i] c = constant(rand(5, 5)) for i in range(c.value.shape[0]): for j in range(c.value.shape[1]): assert get_scalar_constant_value(c[i, j]) == c.value[i, j] def test_numpy_array(self): # Regression test for crash when called on a numpy array. assert get_scalar_constant_value(numpy.array(3)) == 3 self.assertRaises( tensor.NotScalarConstantError, get_scalar_constant_value, numpy.array([0, 1])) self.assertRaises( tensor.EmptyConstantError, get_scalar_constant_value, numpy.array([])) def test_make_vector(self): mv = opt.make_vector(1, 2, 3) self.assertRaises( tensor.NotScalarConstantError, get_scalar_constant_value, mv) assert get_scalar_constant_value(mv[0]) == 1 assert get_scalar_constant_value(mv[1]) == 2 assert get_scalar_constant_value(mv[2]) == 3 assert get_scalar_constant_value(mv[numpy.int32(0)]) == 1 assert get_scalar_constant_value(mv[numpy.int64(1)]) == 2 assert get_scalar_constant_value(mv[numpy.uint(2)]) == 3 t = theano.scalar.Scalar('int64') self.assertRaises( tensor.NotScalarConstantError, get_scalar_constant_value, mv[t()]) def test_shape_i(self): c = theano.tensor.constant(numpy.random.rand(3, 4)) s = opt.Shape_i(0)(c) assert get_scalar_constant_value(s) == 3 s = opt.Shape_i(1)(c) assert get_scalar_constant_value(s) == 4 d = theano.shared(numpy.random.randn(1,1), broadcastable=(True, True)) f = theano.tensor.basic.ScalarFromTensor()(opt.Shape_i(0)(d)) assert get_scalar_constant_value(f) == 1 def test_elemwise(self): # We test only for a few elemwise, the list of all supported # elemwise are in the fct. c = theano.tensor.constant(numpy.random.rand()) s = c + 1 assert numpy.allclose(get_scalar_constant_value(s), c.data + 1) s = c - 1 assert numpy.allclose(get_scalar_constant_value(s), c.data - 1) s = c * 1.2 assert numpy.allclose(get_scalar_constant_value(s), c.data * 1.2) s = c < 0.5 assert numpy.allclose(get_scalar_constant_value(s), int(c.data < 0.5)) s = tensor.second(c, .4) assert numpy.allclose(get_scalar_constant_value(s), .4) def test_second(self): # Second should apply when the value is constant but not the shape c = theano.tensor.constant(numpy.random.rand()) shp = theano.tensor.vector() s = theano.tensor.second(shp, c) assert get_scalar_constant_value(s) == c.data def test_copy(self): # Make sure we do not return the internal storage of a constant, # so we cannot change the value of a constant by mistake. c = theano.tensor.constant(3) d = extract_constant(c) d += 1 e = extract_constant(c) self.assertTrue(e == 3, (c, d, e)) class T_as_tensor_variable(unittest.TestCase): """ We test that ticket #649 stay fixed. We should not allow as_tensor_variable to accept True or False But it should upcast an ndarray of bool to uint8 """ def test_bool(self): self.assertRaises(TypeError, as_tensor_variable, True) self.assertRaises(TypeError, as_tensor_variable, False) def test_ndarray_bool(self): ten = as_tensor_variable(numpy.array([True, False, False, True, True])) assert ten.type.dtype == 'uint8' def test_memmap(self): inp = numpy.random.rand(4, 3) f, fname = mkstemp() new_inp = numpy.memmap(fname, dtype=inp.dtype, mode='w+', shape=inp.shape) new_inp[...] = inp x = as_tensor_variable(new_inp) def test_empty_dtype(self): old = theano.config.floatX for dtype in ['float16', 'float32', 'float64']: try: theano.config.floatX = dtype assert theano.tensor.as_tensor_variable(()).dtype == dtype assert theano.tensor.as_tensor_variable([]).dtype == dtype finally: theano.config.floatX = old class test_complex_mod(unittest.TestCase): """Make sure % fails on complex numbers.""" def test_fail(self): x = vector(dtype='complex64') try: x % 5 assert False except theano.scalar.ComplexError: pass class test_size(unittest.TestCase): """ Ensure the `size` attribute of tensors behaves as in numpy. """ def test_matrix(self): x = tensor.matrix() y = numpy.zeros((5, 7), dtype=config.floatX) assert y.size == function([x], x.size)(y) def test_vector(self): x = tensor.vector() y = numpy.zeros(7, dtype=config.floatX) assert y.size == function([x], x.size)(y) def test_scalar(self): x = tensor.scalar() y = numpy.array(7, dtype=config.floatX) assert y.size == function([x], x.size)(y) def test_shared(self): # NB: we also test higher order tensors at the same time. y = numpy.zeros((1, 2, 3, 4), dtype=config.floatX) x = theano.shared(y) assert y.size == function([], x.size)() class test_numpy_assumptions(unittest.TestCase): """ Verify that some assumptions Theano makes on Numpy's behavior still hold. """ def test_ndarray_copy(self): """ A copy or deepcopy of the ndarray type should not create a new object. This is because Theano makes some comparisons of the form: if type(x) is numpy.ndarray """ assert copy(numpy.ndarray) is numpy.ndarray assert deepcopy(numpy.ndarray) is numpy.ndarray def test_dtype_equality(self): """ Ensure dtype string comparisons are consistent. Theano often uses string representations of dtypes (e.g. 'float32'). We need to make sure that comparing the string representations is the same as comparing the dtype objects themselves. """ dtypes = get_numeric_types(with_complex=True) # Perform all pairwise comparisons of dtypes, making sure comparing # their string representation yields the same result. for dtype1_idx, dtype1 in enumerate(dtypes): for dtype2 in dtypes[dtype1_idx + 1:]: assert (dtype1 == dtype2) == (str(dtype1) == str(dtype2)) def test_transpose(): x1 = tensor.dvector('x1') x2 = tensor.dmatrix('x2') x3 = tensor.dtensor3('x3') x1v = numpy.arange(24) x2v = numpy.arange(24).reshape(2, 12) x3v = numpy.arange(24).reshape(2, 3, 4) f = theano.function([x1, x2, x3], [ tensor.transpose(x1), tensor.transpose(x2), tensor.transpose(x3), x1.transpose(), x2.transpose(), x3.transpose(), x2.transpose(0, 1), x3.transpose((0, 2, 1)), tensor.transpose(x2, [0, 1]), tensor.transpose(x3, [0, 2, 1]), ]) t1, t2, t3, t1b, t2b, t3b, t2c, t3c, t2d, t3d = f(x1v, x2v, x3v) assert t1.shape == numpy.transpose(x1v).shape assert t2.shape == numpy.transpose(x2v).shape assert t3.shape == numpy.transpose(x3v).shape assert numpy.all(t1 == numpy.transpose(x1v)) assert numpy.all(t2 == numpy.transpose(x2v)) assert numpy.all(t3 == numpy.transpose(x3v)) assert numpy.all(t1b == x1v.transpose()) assert numpy.all(t2b == x2v.transpose()) assert numpy.all(t3b == x3v.transpose()) assert t2c.shape == (2, 12) assert t3c.shape == (2, 4, 3) assert numpy.all(t2c == x2v.transpose([0, 1])) assert numpy.all(t3c == x3v.transpose([0, 2, 1])) assert t2d.shape == (2, 12) assert t3d.shape == (2, 4, 3) assert numpy.all(t2d == numpy.transpose(x2v, [0, 1])) assert numpy.all(t3d == numpy.transpose(x3v, [0, 2, 1])) # Check that we create a name. assert tensor.transpose(x1).name == 'x1.T' assert tensor.transpose(x2).name == 'x2.T' assert tensor.transpose(x3).name == 'x3.T' assert tensor.transpose(tensor.dmatrix()).name is None def test_stacklists(): a, b, c, d = map(scalar, 'abcd') X = stacklists([[a, b], [c, d]]) f = function([a, b, c, d], X) result = f(1, 2, 3, 4) assert result.shape == (2, 2) assert numpy.allclose(f(1, 2, 3, 4), numpy.asarray([[1, 2], [3, 4]])) X = stacklists([a, b, c, d]) f = function([a, b, c, d], X) result = f(1, 2, 3, 4) assert result.shape == (4,) assert numpy.allclose(f(1, 2, 3, 4), numpy.asarray([[1, 2, 3, 4]])) X = stacklists([[[a], [b]], [[c], [d]]]) f = function([a, b, c, d], X) result = f(1, 2, 3, 4) assert result.shape == (2, 2, 1) a, b, c, d = [matrix(a) for a in 'abcd'] X = stacklists([[a, b], [c, d]]) f = function([a, b, c, d], X) x = numpy.ones((4, 4), 'float32') assert f(x, x, x, x).shape == (2, 2, 4, 4) class TestSpecifyShape(unittest.TestCase): mode = None input_type = TensorType def shortDescription(self): return None def test_bad_shape(self): """ Test that at run time we raise an exception when the shape is not the one specified""" specify_shape = SpecifyShape() x = vector() xval = numpy.random.rand(2).astype(floatX) f = theano.function([x], specify_shape(x, [2]), mode=self.mode) f(xval) xval = numpy.random.rand(3).astype(floatX) self.assertRaises(AssertionError, f, xval) theano.printing.debugprint(f) assert isinstance([n for n in f.maker.fgraph.toposort() if isinstance(n.op, SpecifyShape)][0].inputs[0].type, self.input_type) x = matrix() xval = numpy.random.rand(2, 3).astype(floatX) f = theano.function([x], specify_shape(x, [2, 3]), mode=self.mode) assert isinstance([n for n in f.maker.fgraph.toposort() if isinstance(n.op, SpecifyShape)][0].inputs[0].type, self.input_type) f(xval) for shape in [(1, 3), (2, 2), (5, 5)]: xval = numpy.random.rand(*shape).astype(floatX) self.assertRaises(AssertionError, f, xval) def test_bad_number_of_shape(self): """ Test that the number of dimensions provided is good""" specify_shape = SpecifyShape() x = vector() shape_vec = ivector() xval = numpy.random.rand(2).astype(floatX) self.assertRaises(AssertionError, specify_shape, x, []) self.assertRaises(AssertionError, specify_shape, x, [2, 2]) f = theano.function([x, shape_vec], specify_shape(x, shape_vec), mode=self.mode) assert isinstance([n for n in f.maker.fgraph.toposort() if isinstance(n.op, SpecifyShape)][0].inputs[0].type, self.input_type) self.assertRaises(AssertionError, f, xval, []) self.assertRaises(AssertionError, f, xval, [2, 2]) x = matrix() xval = numpy.random.rand(2, 3).astype(floatX) for shape in [(), (1,), (2, 3, 4)]: self.assertRaises(AssertionError, specify_shape, x, shape) f = theano.function([x, shape_vec], specify_shape(x, shape_vec), mode=self.mode) assert isinstance([n for n in f.maker.fgraph.toposort() if isinstance(n.op, SpecifyShape)][0].inputs[0].type, self.input_type) self.assertRaises(AssertionError, f, xval, shape) class TestInferShape(utt.InferShapeTester): def test_infer_shape(self): # Flatten atens3 = tensor3() atens3_val = rand(4, 5, 3) self._compile_and_check([atens3], [flatten(atens3, 1)], [atens3_val], Reshape) for outdim in (3, 2, 1): self._compile_and_check([atens3], [flatten(atens3, outdim)], [atens3_val], Reshape) amat = matrix() amat_val = rand(4, 5) for outdim in (2, 1): self._compile_and_check([amat], [flatten(amat, outdim)], [amat_val], Reshape) avec = vector() avec_val = rand(4) outdim = 1 self._compile_and_check([avec], [flatten(avec, outdim)], [avec_val], Reshape, excluding=['local_useless_reshape']) # Eye aiscal = iscalar() biscal = iscalar() ciscal = iscalar() self._compile_and_check([aiscal, biscal, ciscal], [Eye()(aiscal, biscal, ciscal)], [4, 4, 0], Eye) self._compile_and_check([aiscal, biscal, ciscal], [Eye()(aiscal, biscal, ciscal)], [4, 5, 0], Eye) self._compile_and_check([aiscal, biscal, ciscal], [Eye()(aiscal, biscal, ciscal)], [3, 5, 0], Eye) # Tri aiscal = iscalar() biscal = iscalar() ciscal = iscalar() self._compile_and_check([aiscal, biscal, ciscal], [Tri()(aiscal, biscal, ciscal)], [4, 4, 0], Tri) self._compile_and_check([aiscal, biscal, ciscal], [Tri()(aiscal, biscal, ciscal)], [4, 5, 0], Tri) self._compile_and_check([aiscal, biscal, ciscal], [Tri()(aiscal, biscal, ciscal)], [3, 5, 0], Tri) # Diagonal atens3 = tensor3() atens3_val = rand(4, 5, 3) atens3_diag = Diagonal()(atens3) self._compile_and_check([atens3], [atens3_diag], [atens3_val], Diagonal) atens3_diag = Diagonal(1)(atens3) self._compile_and_check([atens3], [atens3_diag], [atens3_val], Diagonal) atens3_diag = Diagonal(-1)(atens3) self._compile_and_check([atens3], [atens3_diag], [atens3_val], Diagonal) atens3_diag = Diagonal(1, 0, 2)(atens3) self._compile_and_check([atens3], [atens3_diag], [atens3_val], Diagonal) atens3_diag = Diagonal(1, 1, 2)(atens3) self._compile_and_check([atens3], [atens3_diag], [atens3_val], Diagonal) atens3_diag = Diagonal(1, 2, 0)(atens3) self._compile_and_check([atens3], [atens3_diag], [atens3_val], Diagonal) # Diag advec = dvector() advec_val = rand(4) self._compile_and_check([advec], [Diag()(advec)], [advec_val], Diag) # Shape # 'opt.Makevector' precludes optimizer from disentangling # elements of shape adtens = tensor3() adtens_val = rand(4, 5, 3) self._compile_and_check([adtens], [Shape()(adtens)], [adtens_val], (opt.MakeVector, Shape)) # Dot # vec/vec advec = dvector() bdvec = dvector() advec_val = rand(4) bdvec_val = rand(4) self._compile_and_check([advec, bdvec], [Dot()(advec, bdvec)], [advec_val, bdvec_val], (Dot, tensor.blas.Dot22, tensor.blas.Gemv, tensor.blas_c.CGemv)) # mat/mat admat = dmatrix() bdmat = dmatrix() admat_val = rand(4, 5) bdmat_val = rand(5, 3) self._compile_and_check([admat, bdmat], [Dot()(admat, bdmat)], [admat_val, bdmat_val], (Dot, tensor.blas.Dot22)) # vec/mat bdmat_val = rand(4, 5) self._compile_and_check([advec, bdmat], [Dot()(advec, bdmat)], [advec_val, bdmat_val], (Dot, tensor.blas.Dot22, tensor.blas.Gemv, tensor.blas_c.CGemv)) # mat/vec admat_val = rand(5, 4) self._compile_and_check([admat, bdvec], [Dot()(admat, bdvec)], [admat_val, bdvec_val], (Dot, tensor.blas.Dot22, tensor.blas.Gemv, tensor.blas_c.CGemv)) # Split aivec = ivector() adtens_val = rand(4, 10, 3) aivec_val = [2, 5, 3] for aiscal_val in [1, -2]: self._compile_and_check( [adtens, aiscal, aivec], [Split(3)(adtens, aiscal, aivec)[0]], [adtens_val, aiscal_val, aivec_val], (Split)) # Join cdmat = dmatrix() admat_val = rand(1, 3) bdmat_val = rand(2, 3) cdmat_val = rand(4, 3) for aiscal_val in [0, -2]: self._compile_and_check( [aiscal, admat, bdmat, cdmat], [Join()(aiscal, admat, bdmat, cdmat)], [aiscal_val, admat_val, bdmat_val, cdmat_val], Join) admat_val = rand(4, 1) bdmat_val = rand(4, 3) cdmat_val = rand(4, 2) for aiscal_val in [-1, 1]: self._compile_and_check( [aiscal, admat, bdmat, cdmat], [Join()(aiscal, admat, bdmat, cdmat)], [aiscal_val, admat_val, bdmat_val, cdmat_val], Join) # PermuteRowElements abool = True rng = numpy.random.RandomState(utt.fetch_seed()) advec_val = rand(5) aivec_val = rng.permutation(5).astype('int32') self._compile_and_check([advec, aivec], [PermuteRowElements()(advec, aivec, abool)], [advec_val, aivec_val], PermuteRowElements) admat_val = rand(3, 5) self._compile_and_check([admat, aivec], [PermuteRowElements()(admat, aivec, abool)], [admat_val, aivec_val], PermuteRowElements) adtens3 = dtensor3() adtens3_val = rand(3, 2, 5) self._compile_and_check([adtens3, aivec], [PermuteRowElements()(adtens3, aivec, abool)], [adtens3_val, aivec_val], PermuteRowElements) aimat = imatrix() perma = rng.permutation(5).astype('int32') permb = rng.permutation(5).astype('int32') permc = rng.permutation(5).astype('int32') aimat_val = numpy.vstack((perma, permb, permc)) admat_val = rand(3, 5) self._compile_and_check([admat, aimat], [PermuteRowElements()(admat, aimat, abool)], [admat_val, aimat_val], PermuteRowElements) aitens3 = itensor3() perma = rng.permutation(5).astype('int32') permb = rng.permutation(5).astype('int32') permc = rng.permutation(5).astype('int32') bimat_val = numpy.vstack((perma, permb, permc)) aitens3_val = numpy.empty((2, 3, 5), 'int32') aitens3_val[0, ::, ::] = aimat_val aitens3_val[1, ::, ::] = bimat_val self._compile_and_check([admat, aitens3], [PermuteRowElements()(admat, aitens3, abool)], [admat_val, aitens3_val], PermuteRowElements) # ScalarFromTensor aiscal = iscalar() self._compile_and_check([aiscal], [TensorFromScalar()(ScalarFromTensor()(aiscal))], [45], ScalarFromTensor, excluding=["local_tensor_scalar_tensor"]) # TensorFromScalar aiscal = scal.float64() self._compile_and_check([aiscal], [TensorFromScalar()(aiscal)], [4.], TensorFromScalar) # Rebroadcast adtens4 = dtensor4() adict = [(0, False), (1, True), (2, False), (3, True)] adtens4_val = rand(2, 1, 3, 1) self._compile_and_check([adtens4], [Rebroadcast(*adict)(adtens4)], [adtens4_val], Rebroadcast, warn=False) adtens4_bro = TensorType('float64', (True, True, True, False))() bdict = [(0, True), (1, False), (2, False), (3, False)] adtens4_bro_val = rand(1, 1, 1, 3) self._compile_and_check([adtens4_bro], [Rebroadcast(*bdict)(adtens4_bro)], [adtens4_bro_val], Rebroadcast) # Alloc randint = numpy.random.randint adscal = dscalar() aiscal = lscalar() biscal = lscalar() ciscal = lscalar() discal = lscalar() adscal_val = rand() aiscal_val = randint(3, 6, size=()) biscal_val = randint(3, 6, size=()) ciscal_val = randint(3, 6, size=()) discal_val = randint(3, 6, size=()) self._compile_and_check([adscal, aiscal, biscal, ciscal, discal], [Alloc()(adscal, aiscal, biscal, ciscal, discal)], [adscal_val, aiscal_val, biscal_val, ciscal_val, discal_val], Alloc) # MaxAndArgmax, adtens3_val = rand(4, 5, 3) self._compile_and_check([adtens3], MaxAndArgmax()(adtens3, None), [adtens3_val], MaxAndArgmax) self._compile_and_check([adtens3], MaxAndArgmax()(adtens3, 0), [adtens3_val], MaxAndArgmax) self._compile_and_check([adtens3], MaxAndArgmax()(adtens3, 1), [adtens3_val], MaxAndArgmax) self._compile_and_check([adtens3], MaxAndArgmax()(adtens3, 2), [adtens3_val], MaxAndArgmax) self._compile_and_check([adtens3], MaxAndArgmax()(adtens3, [0, 1, 2]), [adtens3_val], MaxAndArgmax) # ARange self._compile_and_check([aiscal, biscal, ciscal], [ARange('int64')(aiscal, biscal, ciscal)], [0, 5, 1], ARange) self._compile_and_check([aiscal, biscal, ciscal], [ARange('int64')(aiscal, biscal, ciscal)], [2, 11, 4], ARange) self._compile_and_check([aiscal, biscal, ciscal], [ARange('int64')(aiscal, biscal, ciscal)], [-5, 1, 1], ARange) self._compile_and_check([aiscal, biscal, ciscal], [ARange('int64')(aiscal, biscal, ciscal)], [10, 2, -2], ARange) self._compile_and_check([aiscal, biscal, ciscal], [ARange('int64')(aiscal, biscal, ciscal)], [10, 2, 2], ARange) self._compile_and_check([aiscal, biscal, ciscal], [ARange('int64')(aiscal, biscal, ciscal)], [0, 0, 1], ARange) # SpecifyShape aivec_val = [3, 4, 2, 5] adtens4_val = rand(*aivec_val) self._compile_and_check([adtens4, aivec], [SpecifyShape()(adtens4, aivec)], [adtens4_val, aivec_val], SpecifyShape) # Mean adtens3_val = rand(3, 4, 5) aiscal_val = 2 self._compile_and_check([adtens3], [Mean(None)(adtens3)], [adtens3_val], Mean) self._compile_and_check([adtens3], [Mean(aiscal_val)(adtens3)], [adtens3_val], Mean) # Reshape # TODO: generalize infer_shape to account for tensor variable # (non-constant) input shape admat = dmatrix() aivec = ivector() ndim = 1 admat_val = rand(3, 4) self._compile_and_check([admat], [Reshape(ndim)(admat, [12])], [admat_val], Reshape) self._compile_and_check([admat], [Reshape(ndim)(admat, [-1])], [admat_val], Reshape) ndim = 2 self._compile_and_check([admat], [Reshape(ndim)(admat, [4, 3])], [admat_val], Reshape) self._compile_and_check([admat], [Reshape(ndim)(admat, [4, -1])], [admat_val], Reshape) self._compile_and_check([admat], [Reshape(ndim)(admat, [3, -1])], [admat_val], Reshape) self._compile_and_check([admat], [Reshape(ndim)(admat, [-1, 3])], [admat_val], Reshape) self._compile_and_check([admat], [Reshape(ndim)(admat, [-1, 4])], [admat_val], Reshape) # enable when infer_shape is generalized: # self._compile_and_check([admat, aivec], # [Reshape(ndim)(admat, aivec)], # [admat_val, [4, 3]], Reshape) # # self._compile_and_check([admat, aivec], # [Reshape(ndim)(admat, aivec)], # [admat_val, [4, -1]], Reshape) adtens4 = dtensor4() ndim = 4 adtens4_val = rand(2, 4, 3, 5) self._compile_and_check([adtens4], [Reshape(ndim)(adtens4, [1, -1, 10, 4])], [adtens4_val], Reshape) self._compile_and_check([adtens4], [Reshape(ndim)(adtens4, [1, 3, 10, 4])], [adtens4_val], Reshape) # enable when infer_shape is generalized: # self._compile_and_check([adtens4, aivec], # [Reshape(ndim)(adtens4, aivec)], # [adtens4_val, [1, -1, 10, 4]], Reshape) # # self._compile_and_check([adtens4, aivec], # [Reshape(ndim)(adtens4, aivec)], # [adtens4_val, [1, 3, 10, 4]], Reshape) # Tile op is deprecated so the tile function doesn't use it # anymore, we'll test here the op directly advec = dvector() advec_val = rand(5) aivec_val = [3] ndim = 1 self._compile_and_check([advec], [Tile(ndim)(advec, aivec_val)], [advec_val], Tile) admat = dmatrix() admat_val = rand(2, 4) aivec_val = [2, 3] ndim = 2 self._compile_and_check([admat], [Tile(ndim)(admat, aivec_val)], [admat_val], Tile) adtens4 = dtensor4() adtens4_val = rand(2, 4, 3, 5) aivec_val = [2, 3, 1, 4] ndim = 4 self._compile_and_check([adtens4], [Tile(ndim)(adtens4, aivec_val)], [adtens4_val], Tile) class TestTensorInstanceMethods(unittest.TestCase): def setUp(self): self.vars = matrices('X', 'Y') self.vals = [m.astype(floatX) for m in [rand(2, 2), rand(2, 2)]] def test_argmin(self): X, _ = self.vars x, _ = self.vals assert_array_equal(X.argmin().eval({X: x}), x.argmin()) def test_argmax(self): X, _ = self.vars x, _ = self.vals assert_array_equal(X.argmax().eval({X: x}), x.argmax()) def test_argsort(self): X, _ = self.vars x, _ = self.vals assert_array_equal(X.argsort().eval({X: x}), x.argsort()) assert_array_equal(X.argsort(1).eval({X: x}), x.argsort(1)) def test_clip(self): X, Y = self.vars x, y = self.vals # numpy.clip gives unexpected values when min > max, # so we have to make sure that min <= max in that test, # otherwise it randomly fails. Z = X.clip(Y - 0.5, Y + 0.5) z = x.clip(y - 0.5, y + 0.5) assert_array_equal(Z.eval({X: x, Y: y}), z) def test_dot(self): X, Y = self.vars x, y = self.vals # Use allclose comparison as a user reported on the mailing # list failure otherwise with array that print exactly the same. assert_allclose(x.dot(y), X.dot(Y).eval({X: x, Y: y})) Z = X.dot(Y) z = x.dot(y) assert_allclose(x.dot(z), X.dot(Z).eval({X: x, Z: z})) def test_real_imag(self): X, Y = self.vars x, y = self.vals Z = X + Y * 1j z = x + y * 1j assert_array_equal(Z.real.eval({Z: z}), x) assert_array_equal(Z.imag.eval({Z: z}), y) def test_conj(self): X, Y = self.vars x, y = self.vals Z = X + Y * 1j z = x + y * 1j assert_array_equal(Z.conj().eval({Z: z}), z.conj()) assert_array_equal(Z.conjugate().eval({Z: z}), z.conj()) def test_round(self): X, _ = self.vars x, _ = self.vals assert_array_equal(X.round().eval({X: x}), x.round()) def test_std(self): X, _ = self.vars x, _ = self.vals # std() is implemented as theano tree and does not pass its # args directly to numpy. This sometimes results in small # difference, so we use allclose test. assert_allclose(X.std().eval({X: x}), x.std()) def test_repeat(self): X, _ = self.vars x, _ = self.vals assert_array_equal(X.repeat(2).eval({X: x}), x.repeat(2)) def test_trace(self): X, _ = self.vars x, _ = self.vals assert_array_equal(X.trace().eval({X: x}), x.trace()) def test_ravel(self): X, _ = self.vars x, _ = self.vals assert_array_equal(X.ravel().eval({X: x}), x.ravel()) def test_diagonal(self): X, _ = self.vars x, _ = self.vals assert_array_equal(X.diagonal().eval({X: x}), x.diagonal()) assert_array_equal(X.diagonal(1).eval({X: x}), x.diagonal(1)) assert_array_equal(X.diagonal(-1).eval({X: x}), x.diagonal(-1)) for offset, axis1, axis2 in [(1, 0, 1), (-1, 0, 1), (0, 1, 0), (-2, 1, 0)]: assert_array_equal(X.diagonal(offset, axis1, axis2).eval({X: x}), x.diagonal(offset, axis1, axis2)) def test_take(self): X, _ = self.vars x, _ = self.vals indices = [1, 0, 3] assert_array_equal(X.take(indices).eval({X: x}), x.take(indices)) indices = [1, 0, 1] assert_array_equal(X.take(indices, 1).eval({X: x}), x.take(indices, 1)) indices = numpy.array([-10, 5, 12], dtype='int32') assert_array_equal(X.take(indices, 1, mode='wrap').eval({X: x}), x.take(indices, 1, mode='wrap')) assert_array_equal(X.take(indices, -1, mode='wrap').eval({X: x}), x.take(indices, -1, mode='wrap')) assert_array_equal(X.take(indices, 1, mode='clip').eval({X: x}), x.take(indices, 1, mode='clip')) assert_array_equal(X.take(indices, -1, mode='clip').eval({X: x}), x.take(indices, -1, mode='clip')) # Test error handling self.assertRaises(IndexError, X.take(indices).eval, {X: x}) self.assertRaises(IndexError, (2 * X.take(indices)).eval, {X: x}) self.assertRaises(TypeError, X.take, [0.0]) indices = [[1, 0, 1], [0, 1, 1]] assert_array_equal(X.take(indices, 1).eval({X: x}), x.take(indices, 1)) # Test equivalent advanced indexing assert_array_equal(X[:, indices].eval({X: x}), x[:, indices]) def test_cumsum(self): X, _ = self.vars x, _ = self.vals assert_array_equal(X.cumsum().eval({X: x}), x.cumsum()) def test_cumprod(self): X, _ = self.vars x, _ = self.vals assert_array_equal(X.cumprod().eval({X: x}), x.cumprod()) def test_norm(): x = theano.tensor.vector('x') n = x.norm(2) f = theano.function([x], n) assert numpy.allclose(f([1, 1]), numpy.sqrt(2)) class test_ptp(unittest.TestCase): def test_scalar(self): """ Should return 0 for all scalar """ x = scalar('x') p = ptp(x) f = theano.function([x], p) y = numpy.asarray(rand() * 2000 - 1000, dtype=config.floatX) result = f(y) numpyResult = numpy.ptp(y) self.assertTrue(numpy.array_equal(result, numpyResult)) def test_vector(self): x = vector('x') p = ptp(x, 0) f = theano.function([x], p) y = rand_ranged(-1000, 1000, [100]) result = f(y) numpyResult = numpy.ptp(y, 0) self.assertTrue(numpy.array_equal(result, numpyResult)) def test_matrix_first_axis(self): x = matrix('x') p = ptp(x, 1) f = theano.function([x], p) y = rand_ranged(-1000, 1000, [100, 100]) result = f(y) numpyResult = numpy.ptp(y, 1) self.assertTrue(numpy.array_equal(result, numpyResult)) def test_matrix_second_axis(self): x = matrix('x') p = ptp(x, 0) f = theano.function([x], p) y = rand_ranged(-1000, 1000, [100, 100]) result = f(y) numpyResult = numpy.ptp(y, 0) self.assertTrue(numpy.array_equal(result, numpyResult)) def test_matrix_neg_axis(self): x = matrix('x') p = ptp(x, -1) f = theano.function([x], p) y = rand_ranged(-1000, 1000, [100, 100]) result = f(y) numpyResult = numpy.ptp(y, -1) self.assertTrue(numpy.array_equal(result, numpyResult)) def test_matrix_no_axis(self): x = matrix('x') p = ptp(x) f = theano.function([x], p) y = rand_ranged(-1000, 1000, [100, 100]) result = f(y) numpyResult = numpy.ptp(y) self.assertTrue(numpy.array_equal(result, numpyResult)) def test_interface(self): x = matrix('x') p = x.ptp(1) f = theano.function([x], p) y = rand_ranged(-1000, 1000, [100, 100]) result = f(y) numpyResult = numpy.ptp(y, 1) self.assertTrue(numpy.array_equal(result, numpyResult)) if __name__ == '__main__': t = TestInferShape('setUp') t.setUp() t.test_infer_shape() class T_swapaxes(unittest.TestCase): def test_no_dimensional_input(self): self.assertRaises(IndexError, swapaxes, 2, 0, 1) def test_unidimensional_input(self): self.assertRaises(IndexError, swapaxes, [2, 1], 0, 1) def test_not_enough_dimension(self): self.assertRaises(IndexError, swapaxes, [[2, 1], [3, 4]], 3, 4) def test_doubleswap(self): y = matrix() n = swapaxes(y, 0, 1) f = function([y], n) testMatrix = [[2, 1], [3, 4]] self.assertTrue(numpy.array_equal(testMatrix, f(f(testMatrix)))) def test_interface(self): x = theano.tensor.matrix() x.swapaxes(0, 1) def test_numpy_compare(self): rng = numpy.random.RandomState(utt.fetch_seed()) A = tensor.matrix("A", dtype=theano.config.floatX) Q = swapaxes(A, 0, 1) fn = function([A], [Q]) a = rng.rand(4, 4).astype(theano.config.floatX) n_s = numpy.swapaxes(a, 0, 1) t_s = fn(a) assert numpy.allclose(n_s, t_s) class T_Power(unittest.TestCase): def test_numpy_compare(self): rng = numpy.random.RandomState(utt.fetch_seed()) A = tensor.matrix("A", dtype=theano.config.floatX) Q = power(A, 3) fn = function([A], [Q]) a = rng.rand(4, 4).astype(theano.config.floatX) n_p = numpy.power(a, 3) t_p = fn(a) assert numpy.allclose(n_p, t_p) def test_multiple_power(self): x = tensor.vector() y = [1, 2, 3] z = power(x, y) f = function([x], z) assert numpy.allclose(f([1, 2, 3]), [1, 4, 27]) def test_wrong_shape(self): x = tensor.vector() y = [1, 2, 3] z = power(x, y) f = function([x], z) self.assertRaises(ValueError, f, [1, 2, 3, 4]) class T_Choose(utt.InferShapeTester): op = staticmethod(choose) op_class = Choose modes = ['raise', 'wrap', 'clip'] def test_numpy_compare(self): a = tensor.vector(dtype='int32') b = tensor.matrix(dtype='float32') A = numpy.random.randint(0, 4, 4).astype('int32') B = numpy.asarray(numpy.random.rand(4, 4), dtype='float32') for m in self.modes: f = function([a, b], choose(a, b, mode=m)) t_c = f(A, B) n_c = numpy.choose(A, B, mode=m) assert numpy.allclose(t_c, n_c) def test_broadcasted(self): a = tensor.scalar(dtype='int32') b = tensor.matrix(dtype='float32') # Test when a is broadcastable A = 3 B = numpy.asarray(numpy.random.rand(4, 4), dtype='float32') for m in self.modes: f = function([a, b], choose(a, b, mode=m)) t_c = f(A, B) n_c = numpy.choose(A, B, mode=m) assert numpy.allclose(t_c, n_c) # Test when the result should be broadcastable b = theano.tensor.col(dtype='float32') B = numpy.asarray(numpy.random.rand(4, 1), dtype='float32') for m in self.modes: f = function([a, b], choose(a, b, mode=m)) assert choose(a, b, mode=m).broadcastable[0] t_c = f(A, B) n_c = numpy.choose(A, B, mode=m) assert numpy.allclose(t_c, n_c) def test_dtype_error(self): a = tensor.scalar(dtype='float32') b = tensor.matrix(dtype='float32') A = 3 B = numpy.asarray(numpy.random.rand(4, 4), dtype='float32') self.assertRaises(TypeError, choose, a, b) def test_numpy_compare_tuple(self): a = tensor.tensor3(dtype='int32') b = tensor.tensor3(dtype='float32') c = tensor.tensor3(dtype='float32') A = numpy.random.randint(0, 2, (2, 1, 1)).astype('int32') B = numpy.asarray(numpy.random.rand(1, 6, 1), dtype='float32') C = numpy.asarray(numpy.random.rand(1, 1, 5), dtype='float32') for m in self.modes: f = function([a, b, c], choose(a, (b, c), mode=m)) t_c = f(A, B, C) n_c = numpy.choose(A, (B, C), mode=m) assert numpy.allclose(t_c, n_c) def test_infer_shape(self): for shp1, shp2 in [ ((5, 4), (7, 4)), ((1, 4), (7, 4)), ((5, 1), (7, 4)), ((5, 4), (1, 4)), ((5, 4), (7, 1)), ((5, 4), (4,)), ((1, 4), (4,)), ((5, 1), (4,)), ((5, 4), (1,)), ((4,), (5, 4)), ((1,), (5, 4)), ((4,), (1, 4)), ((4,), (3, 1)), ((4,), (4,)), ((1,), (4,)), ((4,), (1,)), ((1,), (1,)), ]: a = tensor.tensor(dtype='int32', broadcastable=[n == 1 for n in shp1]) c = tensor.tensor(dtype='float32', broadcastable=[n == 1 for n in shp2]) A = numpy.asarray(numpy.random.rand(*shp1) * shp2[0], dtype='int32') C = numpy.asarray(numpy.random.rand(*shp2) * shp2[0], dtype='float32') self._compile_and_check([a, c], # theano.function inputs [self.op(a, c)], # theano.function outputs # Always use not square matrix! # inputs data [A, C], # Op that should be removed from the graph. self.op_class) # Disabled as it isn't implemented. def ___test_infer_shape_tuple(self): a = tensor.tensor3(dtype='int32') b = tensor.tensor3(dtype='int32') c = tensor.tensor3(dtype='int32') A = numpy.asarray([1, 0], dtype='int32').reshape((2, 1, 1)) B = numpy.asarray(numpy.random.rand(1, 4, 1), dtype='int32') C = numpy.asarray(numpy.random.rand(1, 1, 7), dtype='int32') f = function([a, b, c], choose(a, (b, c))) shape = (2, 4, 7) assert numpy.allclose(f(A, B, C).shape, shape) self._compile_and_check([a, b, c], # theano.function inputs [self.op(a, (b, c))], # theano.function outputs # Always use not square matrix! # inputs data [A, B, C], # Op that should be removed from the graph. self.op_class) def test_allocempty(): # Test that we allocated correctly f = theano.function([], AllocEmpty("float32")(2, 3)) assert len(f.maker.fgraph.apply_nodes) == 1 out = f() assert out.shape == (2, 3) assert out.dtype == 'float32' def test_symbolic_slice(): x = theano.tensor.tensor4('x') a, b = x.shape[:2] output = a.eval({x: numpy.zeros((5, 4, 3, 2), dtype=theano.config.floatX)}) assert output == numpy.array(5) def test_composite_neg_bool(): # Check that taking the negation of a Boolean intermediate value # works correctly with Python code. It used to be an issue because # `-numpy.bool_(True)` is False and `-numpy.bool_(False)` is True. x = theano.tensor.vector() f = theano.function([x], - (x > 0), mode=theano.Mode(linker='py')) utt.assert_allclose(f([-1, 0, 1]), [0, 0, -1]) """ if __name__ == '__main__': if 0: unittest.main() else: testcase = FloorInplaceTester suite = unittest.TestLoader() suite = suite.loadTestsFromTestCase(testcase) unittest.TextTestRunner(verbosity=2).run(suite) """

fa447e2b35fde4753e31b63c558c07e8914c853f

py

Python

oop 1-1.py

johndaguio/OOP---1-1

ea94f38412aac5f8d0ee99a16bf252af97546c8b

oop 1-1.py

johndaguio/OOP---1-1

ea94f38412aac5f8d0ee99a16bf252af97546c8b

oop 1-1.py

johndaguio/OOP---1-1

ea94f38412aac5f8d0ee99a16bf252af97546c8b

from tkinter import * window = Tk() window.geometry("600x500+30+20") window.title("Welcome to Python Programming") #add Button widget btn = Button(window, text = "Click to add name", fg="blue") btn.place(x= 80, y = 100) #Add label widget lbl = Label(window, text = "Student Personal Information", fg = "Blue", bg = "orange") lbl.place(relx=.5, y=50,anchor='center') lbl2 = Label(window, text = "Gender", fg = "red") lbl2.place(x =80, y=150) #Add text field widget txtfld = Entry(window, bd = 3, font = ("verdana",16)) txtfld.place(x=150, y=100) #add radio button v1 = StringVar() v2 = StringVar v1.set(1) r1 = Radiobutton(window,text="Male",value=v1) r1.place(x=80, y = 200) r2 = Radiobutton(window,text = "Female", value = v2) r2.place(x=200,y = 200) v3 = IntVar() v4 = IntVar() v5 = IntVar() chkbox = Checkbutton(window,text = "Basketball", variable = v3) chkbox2 = Checkbutton(window, text = "Tennis", variable = v4) chkbox3 = Checkbutton(window, text = "Swimming", variable = v5) chkbox.place(x=80, y=300) chkbox2.place(x=250, y=300) chkbox3.place(x=350, y = 300) lbl3 = Label(window, text = "Sports") lbl3.place(x=80, y=250) lbl4 = Label(window, text = "Subjects") lbl4.place(x = 80, y=350) var = StringVar() var.set("Arithmetic") data1 = "Arithmetic" data2 = "Reading" data3 = "Writing" lstbox = Listbox(window,height = 5, selectmode = 'multiple' ) lstbox.insert(END, data1, data2, data3) lstbox.place(x = 80,y = 400) window.mainloop()

f33d08632137524aa64828c5ac39b2885b92195f

py

Python

bmstocker/apps.py

hchockarprasad/bmdjango

a978e4bca264eaa5a1f21df332f7da06f9f69ee5

2017-10-29T13:37:58.000Z

2017-11-06T15:31:35.000Z

bmstocker/apps.py

hchockarprasad/bmdjango

a978e4bca264eaa5a1f21df332f7da06f9f69ee5

bmstocker/apps.py

hchockarprasad/bmdjango

a978e4bca264eaa5a1f21df332f7da06f9f69ee5

from django.apps import AppConfig class BmstockerConfig(AppConfig): name = 'bmstocker'

ade4dc093d58dece6985550d71244e96ecc0c484

py

Python

misc/python/setup.py

rsignavong/materialize

1a3be2b7b73919d59274e45d100592813c186d44

2020-02-13T18:28:21.000Z

2022-03-31T17:25:04.000Z

misc/python/setup.py

rsignavong/materialize

1a3be2b7b73919d59274e45d100592813c186d44

2020-02-13T18:59:27.000Z

2022-03-31T21:50:24.000Z

misc/python/setup.py

morsapaes/materialize

9f70c024869d681dbd8a2644b6d368b5f7e9707e

2020-02-13T18:49:32.000Z

2022-03-30T10:55:12.000Z

# Copyright Materialize, Inc. and contributors. All rights reserved. # # Use of this software is governed by the Business Source License # included in the LICENSE file at the root of this repository. # # As of the Change Date specified in that file, in accordance with # the Business Source License, use of this software will be governed # by the Apache License, Version 2.0. from pathlib import Path from typing import List from setuptools import find_packages, setup # type: ignore # stub setup.py that allows running `pip install -e .` to install into a virtualenv HERE = Path(__file__).parent def requires(fname: str) -> List[str]: return [l for l in HERE.joinpath(fname).open().read().splitlines() if l] setup( name="materialize", packages=find_packages(), install_requires=requires("requirements.txt"), extras_require={ "dev": requires("requirements-dev.txt"), }, package_data={ "materialize": ["py.typed"], "materialize.optbench": ["schema/*.sql", "workload/*.sql"], }, include_package_data=True, )

c15d972c2dec4b9d93aefecc31bb25dda99bca9f

py

Python

plotly/validators/layout/scene/xaxis/tickformatstop/_enabled.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

2020-04-18T18:10:22.000Z

2021-12-06T10:11:15.000Z

plotly/validators/layout/scene/xaxis/tickformatstop/_enabled.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

2020-04-28T21:23:12.000Z

2021-06-25T15:36:38.000Z

plotly/validators/layout/scene/xaxis/tickformatstop/_enabled.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

2020-04-18T23:07:08.000Z

2021-11-18T07:53:06.000Z

import _plotly_utils.basevalidators class EnabledValidator(_plotly_utils.basevalidators.BooleanValidator): def __init__( self, plotly_name='enabled', parent_name='layout.scene.xaxis.tickformatstop', **kwargs ): super(EnabledValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, edit_type=kwargs.pop('edit_type', 'plot'), role=kwargs.pop('role', 'info'), **kwargs )

0692f27bed5be0b33f5b0d2ac143fadf6063eaac

py

Python

metaci/repository/models.py

sfdc-qbranch/MetaCI

78ac0d2bccd2db381998321ebd71029dd5d9ab39

2018-10-24T14:52:06.000Z

2022-03-25T21:14:50.000Z

metaci/repository/models.py

sfdc-qbranch/MetaCI

78ac0d2bccd2db381998321ebd71029dd5d9ab39

2018-10-31T20:59:16.000Z

2022-03-22T21:38:03.000Z

metaci/repository/models.py

sfdc-qbranch/MetaCI

78ac0d2bccd2db381998321ebd71029dd5d9ab39

2018-12-24T18:16:23.000Z

2021-12-15T17:57:27.000Z

import github3.exceptions from cumulusci.core.exceptions import GithubException from cumulusci.core.github import get_github_api_for_repo from django.apps import apps from django.db import models from django.http import Http404 from django.urls import reverse from model_utils.managers import SoftDeletableManager from model_utils.models import SoftDeletableModel from metaci.cumulusci.keychain import GitHubSettingsKeychain class RepositoryQuerySet(models.QuerySet): def for_user(self, user, perms=None): if user.is_superuser: return self if perms is None: perms = "plan.view_builds" PlanRepository = apps.get_model("plan.PlanRepository") return self.filter( planrepository__in=PlanRepository.objects.for_user(user, perms) ).distinct() def get_for_user_or_404(self, user, query, perms=None): try: return self.for_user(user, perms).get(**query) except Repository.DoesNotExist: raise Http404 class Repository(models.Model): name = models.CharField(max_length=255) owner = models.CharField(max_length=255) github_id = models.IntegerField(null=True, blank=True) url = models.URLField(max_length=255) release_tag_regex = models.CharField(max_length=255, blank=True, null=True) default_implementation_steps = models.JSONField(null=True, blank=True, default=list) metadata = models.JSONField(null=True, blank=True, default=dict) objects = RepositoryQuerySet.as_manager() class Meta: ordering = ["name", "owner"] verbose_name_plural = "repositories" def get_absolute_url(self): return reverse("repo_detail", kwargs={"owner": self.owner, "name": self.name}) def __str__(self): return f"{self.owner}/{self.name}" def get_github_api(self): gh = get_github_api_for_repo(GitHubSettingsKeychain(), self.owner, self.name) repo = gh.repository(self.owner, self.name) return repo @property def latest_release(self): try: return self.releases.latest() except Repository.DoesNotExist: return None class BranchManager(SoftDeletableManager): def get_queryset(self): return super().get_queryset().select_related("repo") class Branch(SoftDeletableModel): name = models.CharField(max_length=255) repo = models.ForeignKey( Repository, related_name="branches", on_delete=models.CASCADE ) objects = BranchManager() include_deleted = models.QuerySet.as_manager() class Meta: ordering = ["repo__name", "repo__owner", "name"] verbose_name_plural = "branches" def get_absolute_url(self): return reverse( "branch_detail", kwargs={ "owner": self.repo.owner, "name": self.repo.name, "branch": self.name, }, ) def __str__(self): return f"{self.repo.name}: {self.name}" def is_tag(self): """Returns True if this branch is related to a tag in GitHub""" return self.name.startswith("tag: ") def get_github_api(self): try: branch = self.repo.get_github_api().branch(self.name) except (github3.exceptions.NotFoundError, GithubException): branch = None return branch

77f574bc3dc735abe684c9910e4b6ccda9523230

py

Python

mvp/mvp/views/main_view.py

2110521-2563-1-Software-Architecture/TBD-Assignment-3

d78e849a50c6367e1e01c1271753301d3d8e4dd8

mvp/mvp/views/main_view.py

2110521-2563-1-Software-Architecture/TBD-Assignment-3

d78e849a50c6367e1e01c1271753301d3d8e4dd8

mvp/mvp/views/main_view.py

2110521-2563-1-Software-Architecture/TBD-Assignment-3

d78e849a50c6367e1e01c1271753301d3d8e4dd8

import wx from typing import List from mvp.contracts.main_contract import MainContract from mvp.models.entities.note import Note class MainView(MainContract.View): def __init__(self): MainContract.View.__init__(self, "MVP Note Application") def init_ui(self): panel = wx.Panel(self) vbox = wx.BoxSizer(wx.VERTICAL) new_note_label = wx.StaticText(panel, label="New Note:") note_input = wx.TextCtrl(panel) add_note_button = wx.Button(panel, label="Add Note") clear_all_button = wx.Button(panel, label="Clear All") note_list_label = wx.StaticText(panel, label="Note List:") vbox.Add(new_note_label) vbox.AddSpacer(8) vbox.Add(note_input, 0, wx.EXPAND) vbox.AddSpacer(8) vbox.Add(add_note_button, 0, wx.EXPAND) vbox.AddSpacer(8) vbox.Add(clear_all_button, 0, wx.EXPAND) vbox.AddSpacer(8) vbox.Add(note_list_label) add_note_button.Bind(wx.EVT_BUTTON, self.on_add_note_button_clicked) clear_all_button.Bind(wx.EVT_BUTTON, self.on_clear_all_button_clicked) panel.SetSizer(vbox) self.note_list_label = note_list_label self.note_input = note_input if self.presenter: self.presenter.get_all_notes() def update_view(self, items: List[Note]): self.note_list_label.SetLabel( "Note List:\n" + "\n".join([f"{i + 1}. {note.content}" for i, note in enumerate(items)])) def on_clear_all_button_clicked(self, e): # Clear all notes # Your code here self.presenter.clear_all() pass def on_add_note_button_clicked(self, e): content = self.note_input.GetValue() self.note_input.SetValue("") # Add note # Your code here self.presenter.add_note(content)

356b0230101a01dba88c99eaee5135d4b3aaa1e1

py

Python

samples/justcount.py

rodrigoacastro/seacow

17b89951bbb8d7f765d9cdbd330ef70e4bfcc2fa

samples/justcount.py

rodrigoacastro/seacow

17b89951bbb8d7f765d9cdbd330ef70e4bfcc2fa

samples/justcount.py

rodrigoacastro/seacow

17b89951bbb8d7f765d9cdbd330ef70e4bfcc2fa

# -*- coding: utf-8 -*- from SeaCOW import Query, Nonprocessor # Create a Query object and set whatever needs to be set. q = Query() q.corpus = 'decow16a-nano' # Lower-case name of the corpusto use. q.string = '[word="Chuzpe"]' # A normal CQL string as used in NoSketchEngine. q.max_hits = -1 # Maximal number of hits to return. Ignored for Nonprocessor. q.attributes = [] # For counting, you don't need word attributes. q.structures = [] # ... you don't need structural attributes. q.references = [] # ... you don't need reference attrs. q.container = None # Which container strutcure should be used? None is OK # only if class is Nonprocessor. # Using the deduplicator would NOT change the outcome. Switch off. q.set_deduplication(off = True) # Create a Processor object and attach it to the Query object. # The Nonprocessor processor does nothing. You can work with the results # yourself in the finalise method or just get the hits value from the # query object. It is the concordance as seported by Manatee. p = Nonprocessor() # Create a processor object of apporpriate type. q.processor = p # Attach the processor to the query. q.run() # Run the query. print('Query was: %s' % (q.string)) print('Corpus used: %s' % (q.corpus)) print('Query returned %d hits.' % (q.hits))

05ee83a43ca22e64bab376d5cc1e218397af96b7

py

Python

ion_networks/ms_utils.py

swillems/ion_networks

5304a92248ec007ac2253f246a3d44bdb58ae110

2020-10-28T16:11:56.000Z

2020-12-03T13:19:18.000Z

ion_networks/ms_utils.py

swillems/ion_networks

5304a92248ec007ac2253f246a3d44bdb58ae110

ion_networks/ms_utils.py

swillems/ion_networks

5304a92248ec007ac2253f246a3d44bdb58ae110

#!python # builtin import os import sys import logging import json import time import contextlib import multiprocessing import urllib import csv # external import numpy as np import pandas as pd import h5py import pyteomics.mgf # local from ion_networks._version import __version__ as VERSION from ion_networks import numba_functions GITHUB_VERSION_FILE = "https://raw.githubusercontent.com/swillems/ion_networks/master/ion_networks/_version.py" BASE_PATH = os.path.dirname(__file__) UPDATE_COMMAND = os.path.join(os.path.dirname(BASE_PATH), "install", "update.sh") LIB_PATH = os.path.join(BASE_PATH, "lib") DEFAULT_PARAMETER_PATH = os.path.join(LIB_PATH, "default_parameters") DEFAULT_PARAMETER_FILES = { "convert": "convert_parameters.json", "create": "create_parameters.json", "evidence": "evidence_parameters.json", "interface": "interface_parameters.json", "database": "database_parameters.json", "annotation": "annotation_parameters.json", "mgf": "mgf_parameters.json", } DATA_TYPE_FILE_EXTENSIONS = { "DDA": ".mgf", "SONAR": "_Apex3DIons.csv", "HDMSE": "_Apex3DIons.csv", "SWIMDIA": "_Apex3DIons.csv", "DIAPASEF": "_centroids.hdf", } LOGGER = logging.getLogger("Ion-networks") MAX_THREADS = 1 @contextlib.contextmanager def open_logger(log_file_name, log_level=logging.INFO): # TODO: Docstring start_time = time.time() formatter = logging.Formatter('%(asctime)s > %(message)s') LOGGER.setLevel(log_level) if not LOGGER.hasHandlers(): console_handler = logging.StreamHandler(stream=sys.stdout) console_handler.setLevel(log_level) console_handler.setFormatter(formatter) LOGGER.addHandler(console_handler) if log_file_name is not None: if log_file_name == "": log_file_name = BASE_PATH else: log_file_name = os.path.abspath(log_file_name) if os.path.isdir(log_file_name): log_file_name = os.path.join(log_file_name, "log.txt") directory = os.path.dirname(log_file_name) if not os.path.exists(directory): os.makedirs(directory) file_handler = logging.FileHandler(log_file_name, mode="a") file_handler.setLevel(log_level) file_handler.setFormatter(formatter) LOGGER.addHandler(file_handler) LOGGER.info("=" * 50) LOGGER.info(f"COMMAND: ion_networks {' '.join(sys.argv[1:])}") LOGGER.info(f"VERSION: {VERSION}") LOGGER.info(f"LOGFILE: {log_file_name}") LOGGER.info("") try: yield LOGGER LOGGER.info("") LOGGER.info("Successfully finished execution") except: LOGGER.info("") LOGGER.exception("Something went wrong, execution incomplete!") finally: LOGGER.info(f"Time taken: {time.time() - start_time}") LOGGER.info("=" * 50) if log_file_name is not None: LOGGER.removeHandler(file_handler) def read_parameters_from_json_file(file_name="", default=""): """ Read a custom or default parameter file. Parameters ---------- default : str The default parameters that should be loaded. Options are: "create" "evidence" "interface" "" file_name : str The name of a .json file that contains parameters defined by the user. These will override the default parameters. Returns ------- dict A dictionary with parameters. """ if default == "": parameters = {"log_file_name": ""} else: default_parameter_file_name = os.path.join( DEFAULT_PARAMETER_PATH, DEFAULT_PARAMETER_FILES[default] ) with open(default_parameter_file_name, "r") as in_file: parameters = json.load(in_file) if file_name != "": with open(file_name, "r") as in_file: user_defined_parameters = json.load(in_file) parameters.update(user_defined_parameters) # TODO: Numba expects proper floats or integers, not a mixture # TODO: e.g. DT_error = 2.0, instead of DT_error = 2 if "threads" in parameters: set_threads(parameters["threads"]) return parameters def set_threads(threads): global MAX_THREADS max_cpu_count = multiprocessing.cpu_count() if threads > max_cpu_count: MAX_THREADS = max_cpu_count else: while threads <= 0: threads += max_cpu_count MAX_THREADS = threads def get_file_names_with_extension(input_path, extension=""): """ Get all file names with a specific extension from a list of files and folders. Parameters ---------- input_path : iterable[str] An iterable with files or folders from which all files with a specific extension need to be selected. extension : str The extension of the files of interest. Returns ------- list A sorted list with unique file names with the specific extension. """ input_files = set() if not isinstance(extension, str): for tmp_extension in extension: for file_name in get_file_names_with_extension( input_path, tmp_extension ): input_files.add(file_name) else: for current_path in input_path: if os.path.isfile(current_path): if current_path.endswith(extension): input_files.add(current_path) elif os.path.isdir(current_path): for current_file_name in os.listdir(current_path): if current_file_name.endswith(extension): file_name = os.path.join( current_path, current_file_name ) input_files.add(file_name) return sorted([os.path.abspath(file_name) for file_name in input_files]) def read_data_from_file( data_type, file_name, log_transform_intensity=True, ): """ Convert an [input_file.*] file to a pd.DataFrame with as columns the dimensions associated with the data type. Parameters ---------- data_type : str The data type of the [input_file.*] file. Options are: 'DDA' 'SONAR' 'HDMSE' 'SWIMDIA' 'DIAPASEF' file_name : str The file name containing centroided ions. log_transform_intensity : bool Transform the intensities to logarithmic values. Returns ------- pd.DataFrame A pd.DataFrame with as columns the PRECURSOR_RT, PRECURSOR_MZ, FRAGMENT_MZ and FRAGMENT_LOGINT dimensions. """ if data_type == "DDA": read_function = read_data_from_mgf_file elif data_type == "SONAR": read_function = read_data_from_sonar_file elif data_type == "HDMSE": read_function = read_data_from_hdmse_file elif data_type == "SWIMDIA": read_function = read_data_from_swimdia_file elif data_type == "DIAPASEF": read_function = read_data_from_diapasef_file data = read_function( file_name, log_transform_intensity=log_transform_intensity, ) return data def read_data_from_mgf_file( file_name, log_transform_intensity=True, ): """ Convert an [mgf_input.mgf] file to a pd.DataFrame with as columns the PRECURSOR_RT, PRECURSOR_MZ, FRAGMENT_MZ and FRAGMENT_LOGINT dimensions. Parameters ---------- file_name : str The file name of the DDA .mgf file (generated with ms-convert). log_transform_intensity : bool Transform the intensities to logarithmic values. Returns ------- pd.DataFrame A pd.DataFrame with as columns the PRECURSOR_RT, PRECURSOR_MZ, FRAGMENT_MZ and FRAGMENT_LOGINT dimensions. """ LOGGER.info(f"Reading mgf file {file_name}") mz1s = [] mz2s = [] rts = [] ints = [] for spectrum in pyteomics.mgf.read(file_name): peak_count = len(spectrum["intensity array"]) ints.append(spectrum["intensity array"]) mz2s.append(spectrum["m/z array"]) rts.append( np.repeat(spectrum["params"]["rtinseconds"] / 60, peak_count) ) mz1s.append(np.repeat(spectrum["params"]["pepmass"][0], peak_count)) mz1s = np.concatenate(mz1s) mz2s = np.concatenate(mz2s) rts = np.concatenate(rts) ints = np.concatenate(ints) if log_transform_intensity: ints = np.log2(ints) dimensions = [ "FRAGMENT_MZ", "PRECURSOR_RT", "FRAGMENT_LOGINT", "PRECURSOR_MZ" ] data = np.stack([mz2s, rts, ints, mz1s]).T return pd.DataFrame(data, columns=dimensions) def read_data_from_sonar_file( file_name, log_transform_intensity=True, ): """ Convert a [sonar_input.csv] file to a pd.DataFrame with as columns the PRECURSOR_RT, PRECURSOR_MZ, FRAGMENT_MZ and FRAGMENT_LOGINT dimensions. Parameters ---------- file_name : str The file name of the SONAR .csv file (generated with Waters' Apex3d). log_transform_intensity : bool Transform the intensities to logarithmic values. Returns ------- pd.DataFrame A pd.DataFrame with as columns the PRECURSOR_RT, PRECURSOR_MZ, FRAGMENT_MZ and FRAGMENT_LOGINT dimensions. """ LOGGER.info(f"Reading sonar file {file_name}") data = pd.read_csv( file_name, engine="c", dtype=np.float, usecols=["Function", "m_z", "rt", "mobility", "area"] ).values data = data[np.searchsorted(data[:, 0], 2):, 1:] if log_transform_intensity: data[:, 2] = np.log2(data[:, 2]) data[:, 3] = 400 + data[:, 3] * (900 - 400) / 200 dimensions = [ "FRAGMENT_MZ", "PRECURSOR_RT", "FRAGMENT_LOGINT", "PRECURSOR_MZ" ] return pd.DataFrame(data, columns=dimensions) def read_data_from_hdmse_file( file_name, log_transform_intensity=True, ): """ Convert a [hdmse_input.csv] file to a pd.DataFrame with as columns the PRECURSOR_RT, PRECURSOR_DT, FRAGMENT_MZ and FRAGMENT_LOGINT dimensions. Parameters ---------- file_name : str The file name of the HDMSE .csv file (generated with Waters' Apex3d). log_transform_intensity : bool Transform the intensities to logarithmic values. Returns ------- pd.DataFrame A pd.DataFrame with as columns the PRECURSOR_RT, PRECURSOR_DT, FRAGMENT_MZ and FRAGMENT_LOGINT dimensions. """ LOGGER.info(f"Reading hdmse file {file_name}") data = pd.read_csv( file_name, engine="c", dtype=np.float, usecols=["Function", "m_z", "rt", "mobility", "area"] ).values data = data[np.searchsorted(data[:, 0], 2):, 1:] if log_transform_intensity: data[:, 2] = np.log2(data[:, 2]) dimensions = [ "FRAGMENT_MZ", "PRECURSOR_RT", "FRAGMENT_LOGINT", "PRECURSOR_DT" ] return pd.DataFrame(data, columns=dimensions) def read_data_from_swimdia_file( file_name, log_transform_intensity=True, ): """ Convert a [swimdia_input.csv] file to a pd.DataFrame with as columns the PRECURSOR_RT, PRECURSOR_DT, FRAGMENT_MZ and FRAGMENT_LOGINT dimensions. Parameters ---------- file_name : str The file name of the SWIM-DIA .csv file (generated with Waters' Apex3d). log_transform_intensity : bool Transform the intensities to logarithmic values. Returns ------- pd.DataFrame A pd.DataFrame with as columns the PRECURSOR_RT, PRECURSOR_DT, FRAGMENT_MZ and FRAGMENT_LOGINT dimensions. """ LOGGER.info(f"Reading swimdia dile {file_name}") data = pd.read_csv( file_name, engine="c", dtype=np.float, usecols=["m_z", "rt", "mobility", "area"] ).values if log_transform_intensity: data[:, 2] = np.log2(data[:, 2]) dimensions = [ "FRAGMENT_MZ", "PRECURSOR_RT", "FRAGMENT_LOGINT", "PRECURSOR_DT" ] return pd.DataFrame(data, columns=dimensions) def read_data_from_diapasef_file( file_name, min_intensity=1000, min_cluster_size=10, log_transform_intensity=True, ): """ Convert a [diapasef_input_centroids.hdf] file to a pd.DataFrame with as columns the PRECURSOR_RT, PRECURSOR_DT, FRAGMENT_MZ and FRAGMENT_LOGINT dimensions. Parameters ---------- file_name : str The file name of the DIAPASEF _centroids.hdf file (generated with diapasef.py). min_intensity : float The minimimum intensity of an ion to retain it. min_cluster_size : int The minimimum cluster size of an ion to retain it. log_transform_intensity : bool Transform the intensities to logarithmic values. Returns ------- pd.DataFrame A pd.DataFrame with as columns the PRECURSOR_RT, PRECURSOR_DT, PRECURSOR_MZ, FRAGMENT_MZ and FRAGMENT_LOGINT dimensions. """ LOGGER.info(f"Reading diapasef file {file_name}") with h5py.File(file_name, "r") as hdf_file: centroided_fragment_mzs = hdf_file["fragment_mz_values"][...] centroided_fragment_intensities = hdf_file[ "fragment_intensity_values" ][...] centroided_precursor_mzs = hdf_file["precursor_mz_values"][...] centroided_precursor_dts = hdf_file["precursor_dt_values"][...] centroided_precursor_rts = hdf_file["precursor_rt_values"][...] cluster_sizes = hdf_file["cluster_sizes"][...] selection = (cluster_sizes > min_cluster_size) if min_intensity > 0: selection &= (centroided_fragment_intensities > min_intensity) selection = np.flatnonzero(selection) if log_transform_intensity: centroided_fragment_intensities = np.log2( centroided_fragment_intensities ) return pd.DataFrame( np.stack( [ centroided_fragment_mzs[selection], centroided_fragment_intensities[selection], centroided_precursor_mzs[selection], centroided_precursor_dts[selection], centroided_precursor_rts[selection] / 60, ] ).T, columns=[ "FRAGMENT_MZ", "FRAGMENT_LOGINT", "PRECURSOR_MZ", "PRECURSOR_DT", "PRECURSOR_RT", ] ) def read_centroided_csv_file( centroided_csv_file_name, parameters, ): """ Read a centroided .csv file and return this as a pd.DataFrame. Parameters ---------- centroided_csv_file_name : str The name of a .csv file with centroided ion peaks. parameters : dict A dictionary with optional parameters for the creation of an ion-network. Returns ------- pd.Dataframe A pd.Dataframe with centroided ion peaks. Raises ------- KeyError If the PRECURSOR_RT, FRAGMENT_MZ or FRAGMENT_LOGINT column is missing. """ LOGGER.info(f"Reading centroided csv file {centroided_csv_file_name}") data = pd.read_csv( centroided_csv_file_name, engine="c", ) if "PRECURSOR_RT" not in data: raise KeyError("No PRECURSOR_RT column present") if "FRAGMENT_MZ" not in data: raise KeyError("No FRAGMENT_MZ column present") if "FRAGMENT_LOGINT" not in data: raise KeyError("No FRAGMENT_LOGINT column present") data.sort_values( by=["PRECURSOR_RT", "FRAGMENT_MZ"], inplace=True ) return data def write_data_to_csv_file( data, out_file_name, ): """ Save a pandas dataframe with ion coordinates to a file. Parameters ---------- data : pd.DataFrame A pd.DataFrame with as columns the selection / separation dimensions. out_file_name : str The file name of the .csv file in which to save the data. """ LOGGER.info(f"Writing to centroided csv file {out_file_name}") data.to_csv(out_file_name, index=False) def get_github_version(): # TODO: Docstring try: with urllib.request.urlopen(GITHUB_VERSION_FILE) as version_file: for line in version_file.read().decode('utf-8').split("\n"): if line.startswith("__version__"): github_version = line.split("\"")[1] return github_version else: return None except IndexError: return None except urllib.error.URLError: return None def verify_version(): github_version = get_github_version() if github_version is None: return ( f'{"*" * 50}\n' f'{"Failed to check if version update is possible"}\n' f'{"*" * 50}\n' ) elif github_version != VERSION: return ( f'{("*" * 50)}\n' f"Github is at version {github_version}, " f"while local version is {VERSION}\n" f'{("Update by reinstalling or running the following command:")}\n' f"bash '{UPDATE_COMMAND}'\n" f'{("*" * 50)}\n' ) else: return "" def annotate_mgf( mgf_file_name, database, out_file_name, parameters, ): threads = MAX_THREADS LOGGER.info(f"Reading spectra of {mgf_file_name}") spectra = [spectrum for spectrum in pyteomics.mgf.read(mgf_file_name)] spectra_indptr = np.empty(len(spectra) + 1, np.int64) spectra_indptr[0] = 0 spectra_indptr[1:] = np.cumsum( [len(spectrum["m/z array"]) for spectrum in spectra] ) spectra_mzs = np.concatenate( [spectrum["m/z array"] for spectrum in spectra] ) if parameters["align_to_database"]: LOGGER.info(f"Aligning {mgf_file_name} to {database.file_name}") spectra_mzs_ = database.align_mz_values( spectra_mzs, np.repeat( [ spectrum['params']['rtinseconds'] for spectrum in spectra ], np.diff(spectra_indptr) ) / 60 ) else: spectra_mzs_ = spectra_mzs mz_order = np.argsort(spectra_mzs_) spectra_log_mzs = np.log(spectra_mzs_[mz_order]) * 10**6 LOGGER.info(f"Reading database {database.file_name}") peptide_pointers = database.get_fragment_coordinates("peptide_index") database_log_mzs = np.log(database.get_fragment_coordinates("mz")) * 10**6 LOGGER.info( f"Matching fragments of {mgf_file_name} with {database.file_name}" ) low_limits = np.searchsorted( database_log_mzs, spectra_log_mzs - parameters["annotation_ppm"], "left" ) high_limits = np.searchsorted( database_log_mzs, spectra_log_mzs + parameters["annotation_ppm"], "right" ) inv_order = np.argsort(mz_order) low_limits = low_limits[inv_order] high_limits = high_limits[inv_order] LOGGER.info( f"Annotating fragments of {mgf_file_name} with {database.file_name}" ) with multiprocessing.pool.ThreadPool(threads) as p: results = p.starmap( numba_functions.annotate_mgf, [ ( np.arange(i, spectra_indptr.shape[0] - 1, threads), spectra_indptr, low_limits, high_limits, peptide_pointers, ) for i in range(threads) ] ) scores = np.concatenate([r[0] for r in results]) fragments = np.concatenate([r[1] for r in results]) ion_indices = np.concatenate([r[2] for r in results]) count_results = np.concatenate([r[3] for r in results]) candidate_counts = np.concatenate([r[4] for r in results]) spectrum_sizes = np.concatenate([r[5] for r in results]) del results LOGGER.info("Calculating scores") modified_scores, fdr_values = calculate_modified_score( scores, count_results, spectrum_sizes, database, peptide_pointers[fragments] ) export_annotated_csv( scores=scores, fragments=fragments, ion_indices=ion_indices, count_results=count_results, candidate_counts=candidate_counts, spectrum_sizes=spectrum_sizes, spectra=spectra, spectra_indptr=spectra_indptr, spectra_mzs=spectra_mzs, database=database, peptide_pointers=peptide_pointers, out_file_name=out_file_name, export_decoys=parameters['export_decoys'], fdr_filter=parameters['fdr_filter'], fdr_values=fdr_values, modified_scores=modified_scores, calibrated_mzs=spectra_mzs_ ) def calculate_modified_score( likelihoods, hit_counts, neighbors, database, peptide_indices ): modified_scores = hit_counts.copy() modified_scores = hit_counts ** likelihoods modified_scores /= np.log2(1 + neighbors) sequence_lengths = np.array( [ len(s) for s in database.read_dataset( "sequence", "peptides" ) ] )[peptide_indices] modified_scores /= np.log2(sequence_lengths * 2 - 2) decoys = database.read_dataset( "decoy", "peptides" )[peptide_indices] order = np.argsort(modified_scores)[::-1] decoy_count = np.cumsum(decoys[order]) fdr_values = decoy_count / np.arange(1, decoy_count.shape[0] + 1) inv_order = np.argsort(order) fdr_values = fdr_values[inv_order] return modified_scores, fdr_values def export_annotated_csv( scores, fragments, ion_indices, count_results, candidate_counts, spectrum_sizes, spectra, spectra_indptr, spectra_mzs, database, peptide_pointers, # score_cutoff, out_file_name, export_decoys, fdr_filter, fdr_values, modified_scores, calibrated_mzs, ): LOGGER.info(f"Exporting {out_file_name}") peptides = peptide_pointers[fragments] decoys = database.read_dataset("decoy", "peptides") peptide_modifications = database.read_dataset("modifications", "peptides") peptide_sequences = database.read_dataset("sequence", "peptides") peptide_masses = database.read_dataset("mass", "peptides") # selection = np.flatnonzero((scores < score_cutoff) & (~decoys[peptides])) fragment_ion_numbers = database.get_fragment_coordinates("ionnumber") fragment_ion_mz_database = database.get_fragment_coordinates("mz") fragment_is_y_ion = database.get_fragment_coordinates("y_ion") self_ints = np.concatenate( [spectrum["intensity array"] for spectrum in spectra] ) spectrum_indices1 = np.searchsorted( spectra_indptr, ion_indices, "right" ) - 1 with open(out_file_name, "w", newline='') as raw_outfile: outfile = csv.writer(raw_outfile) header = [ "Fragment_index", "Fragment_mz", "Fragment_mz_calibrated", "Fragment_int", "Spectrum_title", "Spectrum_pepmass", "Spectrum_rtinseconds", "Database_index", "Database_mz", "Ion_type", "Ion_number", "Peptide_sequence", "Peptide_mods", "Peptide_length", "Peptide_mass", "Likelihood", "Count", "Candidates", "Spectrum_size", "Modified_score", "Decoy", "FDR", ] outfile.writerow(header) for i, ion_index in enumerate(ion_indices): fdr = fdr_values[i] if fdr > fdr_filter: continue fragment_index = fragments[i] peptide_index = peptides[i] if (not export_decoys): if decoys[peptide_index]: continue spectrum_index = spectrum_indices1[i] peptide_sequence = peptide_sequences[peptide_index] peptide_mass = peptide_masses[peptide_index] row = [ ion_index, spectra_mzs[ion_index], calibrated_mzs[ion_index], self_ints[ion_index], spectra[spectrum_index]['params']['title'], spectra[spectrum_index]['params']['pepmass'][0], spectra[spectrum_index]['params']['rtinseconds'], fragment_index, fragment_ion_mz_database[fragment_index], "Y" if fragment_is_y_ion[fragment_index] else "B", fragment_ion_numbers[fragment_index], peptide_sequence, peptide_modifications[peptide_index], len(peptide_sequence), peptide_mass, scores[i], count_results[i], candidate_counts[i], spectrum_sizes[i], modified_scores[i], decoys[peptide_index], fdr, ] outfile.writerow(row) class HDF_File(object): # TODO: Docstring @property def directory(self): return os.path.dirname(self.file_name) @property def file_name(self): return self.__file_name @property def original_file_name(self): return self.read_attr("original_file_name") @property def creation_time(self): return self.read_attr("creation_time") @property def last_updated(self): return self.read_attr("last_updated") @property def version(self): try: return self.read_attr("version") except KeyError: return "0.0.0" @property def is_read_only(self): return self.__is_read_only def __init__( self, file_name, is_read_only=True, new_file=False, ): # TODO: Docstring self.__file_name = os.path.abspath(file_name) if not isinstance(new_file, bool): raise ValueError( f"Could not determine if HDF_file {self.file_name} is read, " f"write or truncate." ) if new_file: is_read_only = False if not os.path.exists(self.directory): os.makedirs(self.directory) with h5py.File(self.file_name, "w") as hdf_file: hdf_file.attrs["creation_time"] = time.asctime() hdf_file.attrs["version"] = VERSION hdf_file.attrs["original_file_name"] = self.__file_name self.__update_timestamp(hdf_file) else: with h5py.File(self.file_name, "r") as hdf_file: self.check_version() self.__is_read_only = is_read_only def check_version(self): if self.version != VERSION: LOGGER.warning( f"WARNING: {self.file_name} was created with version " f"{self.version} instead of {VERSION}." ) def __eq__(self, other): return self.file_name == other.file_name def __hash__(self): return hash(self.file_name) def __str__(self): return f"<{self.file_name}>" def __repr__(self): return str(self) def __update_timestamp(self, hdf_file): hdf_file.attrs["last_updated"] = time.asctime() def __get_parent_group(self, hdf_file, parent_group_name): if parent_group_name == "": parent_group = hdf_file else: parent_group = hdf_file[parent_group_name] return parent_group def read_group(self, parent_group_name=""): # TODO: Docstring with h5py.File(self.file_name, "r") as hdf_file: parent_group = self.__get_parent_group(hdf_file, parent_group_name) group = sorted(parent_group) return group def read_attr(self, attr_key=None, parent_group_name=""): # TODO: Docstring with h5py.File(self.file_name, "r") as hdf_file: parent_group = self.__get_parent_group(hdf_file, parent_group_name) if attr_key is not None: attr = parent_group.attrs[attr_key] else: attr = sorted(parent_group.attrs) return attr def read_dataset( self, dataset_name, parent_group_name="", indices=Ellipsis, return_length=False, return_dtype=False, ): # TODO: Docstring try: iter(indices) except TypeError: fancy_indices = False else: fancy_indices = True with h5py.File(self.file_name, "r") as hdf_file: parent_group = self.__get_parent_group(hdf_file, parent_group_name) array = parent_group[dataset_name] if return_length: return len(parent_group[dataset_name]) if return_dtype: return len(parent_group[dataset_name].dtype) if fancy_indices: array = array[...] return array[indices] def write_group(self, group_name, parent_group_name="", overwrite=False): # TODO: Docstring if self.is_read_only: raise IOError(f"HDF {self.file_name} file is opened as read only") with h5py.File(self.file_name, "a") as hdf_file: parent_group = self.__get_parent_group(hdf_file, parent_group_name) if group_name not in parent_group: hdf_group = parent_group.create_group(group_name) elif overwrite: del parent_group[group_name] hdf_group = parent_group.create_group(group_name) else: return hdf_group.attrs["creation_time"] = time.asctime() self.__update_timestamp(hdf_file) def write_attr(self, attr_key, attr_value, parent_group_name=""): # TODO: Docstring if self.is_read_only: raise IOError(f"HDF {self.file_name} file is opened as read only") with h5py.File(self.file_name, "a") as hdf_file: parent_group = self.__get_parent_group(hdf_file, parent_group_name) if isinstance(attr_value, str): parent_group.attrs[attr_key] = attr_value else: try: iter(attr_value) except TypeError: parent_group.attrs[attr_key] = attr_value else: parent_group.attrs[attr_key] = str(attr_value) self.__update_timestamp(hdf_file) def write_dataset( self, dataset_name, dataset, parent_group_name="", overwrite=True, # compression="lzf" # Fails for windows with pyinstaller for some reason compression=None, ): # TODO: Docstring if self.is_read_only: raise IOError(f"HDF {self.file_name} file is opened as read only") if isinstance(dataset, pd.core.frame.DataFrame): self.write_group(dataset_name, parent_group_name, overwrite) for column in dataset.columns: self.write_dataset( column, dataset[column].values, dataset_name, overwrite, compression ) else: with h5py.File(self.file_name, "a") as hdf_file: parent_group = self.__get_parent_group( hdf_file, parent_group_name ) if overwrite and (dataset_name in parent_group): del parent_group[dataset_name] if dataset_name not in parent_group: if dataset.dtype.type == np.str_: dataset = dataset.astype(np.dtype('O')) if dataset.dtype == np.dtype('O'): hdf_dataset = parent_group.create_dataset( dataset_name, data=dataset, compression=compression, dtype=h5py.string_dtype() ) else: hdf_dataset = parent_group.create_dataset( dataset_name, data=dataset, compression=compression, ) hdf_dataset.attrs["creation_time"] = time.asctime() self.__update_timestamp(hdf_file)

30450f2ad0e5ebd695a8166de46746c3932be80c

py

Python

python/simple-linked-list/simple_linked_list_test.py

tamireinhorn/exercism

3ca78b262ad590b67c75c5d1cd83db02bc2d1e6e

python/simple-linked-list/simple_linked_list_test.py

tamireinhorn/exercism

3ca78b262ad590b67c75c5d1cd83db02bc2d1e6e

2021-12-18T16:31:51.000Z

2021-12-18T16:33:33.000Z

python/simple-linked-list/simple_linked_list_test.py

tamireinhorn/Exercism

3a3d5744e88ab4457df4e6ac20d772d8c50c43da

import unittest from simple_linked_list import LinkedList, EmptyListException # No canonical data available for this exercise class SimpleLinkedListTest(unittest.TestCase): def test_empty_list_has_len_zero(self): sut = LinkedList() self.assertEqual(len(sut), 0) def test_singleton_list_has_len_one(self): sut = LinkedList([1]) self.assertEqual(len(sut), 1) def test_non_empty_list_has_correct_len(self): sut = LinkedList([1, 2, 3]) self.assertEqual(len(sut), 3) def test_error_on_empty_list_head(self): sut = LinkedList() with self.assertRaises(EmptyListException) as err: sut.head() self.assertEqual(type(err.exception), EmptyListException) self.assertEqual(err.exception.args[0], "The list is empty.") def test_singleton_list_has_head(self): sut = LinkedList([1]) self.assertEqual(sut.head().value(), 1) def test_non_empty_list_has_correct_head(self): sut = LinkedList([1, 2]) self.assertEqual(sut.head().value(), 2) def test_can_push_to_non_empty_list(self): sut = LinkedList([1, 2, 3]) sut.push(4) self.assertEqual(len(sut), 4) def test_pushing_to_empty_list_changes_head(self): sut = LinkedList() sut.push(5) self.assertEqual(len(sut), 1) self.assertEqual(sut.head().value(), 5) def test_can_pop_from_non_empty_list(self): sut = LinkedList([3, 4, 5]) self.assertEqual(sut.pop(), 5) self.assertEqual(len(sut), 2) self.assertEqual(sut.head().value(), 4) def test_pop_from_singleton_list_removes_head(self): sut = LinkedList([1]) self.assertEqual(sut.pop(), 1) with self.assertRaises(EmptyListException) as err: sut.head() self.assertEqual(type(err.exception), EmptyListException) self.assertEqual(err.exception.args[0], "The list is empty.") def test_error_on_empty_list_pop(self): sut = LinkedList() with self.assertRaises(EmptyListException) as err: sut.pop() self.assertEqual(type(err.exception), EmptyListException) self.assertEqual(err.exception.args[0], "The list is empty.") def test_push_and_pop(self): sut = LinkedList([1, 2]) sut.push(3) self.assertEqual(len(sut), 3) self.assertEqual(sut.pop(), 3) self.assertEqual(sut.pop(), 2) self.assertEqual(sut.pop(), 1) self.assertEqual(len(sut), 0) sut.push(4) self.assertEqual(len(sut), 1) self.assertEqual(sut.head().value(), 4) def test_singleton_list_head_has_no_next(self): sut = LinkedList([1]) self.assertIsNone(sut.head().next()) def test_non_empty_list_traverse(self): sut = LinkedList(range(10)) current = sut.head() for i in range(10): self.assertEqual(current.value(), 9 - i) current = current.next() self.assertIsNone(current) def test_empty_linked_list_to_list_is_empty(self): sut = LinkedList() self.assertEqual(list(sut), []) def test_singleton_linked_list_to_list_list_with_singular_element(self): # For some reason, this is calling next TWICE, and therefore runs into an error. Why? sut = LinkedList([1]) self.assertEqual(list(sut), [1]) def test_non_empty_linked_list_to_list_is_list_with_all_elements(self): sut = LinkedList([1, 2, 3]) self.assertEqual(list(sut), [3, 2, 1]) def test_reversed_empty_list_is_empty_list(self): sut = LinkedList([]) self.assertEqual(list(sut.reversed()), []) def test_reversed_singleton_list_is_same_list(self): sut = LinkedList([1]) self.assertEqual(list(sut.reversed()), [1]) def test_reverse_non_empty_list(self): sut = LinkedList([1, 2, 3]) self.assertEqual(list(sut.reversed()), [1, 2, 3]) def test_even_bigger_guy(self): sut = LinkedList([1,2,3,4]) self.assertEqual(list(sut.reversed()), [1,2,3,4])

5811021a3bf72605de259a0722fc305d8c0e5a8f

py

Python

retirement/migrations/0010_auto_20190515_1732.py

Jerome-Celle/Blitz-API

7dfb7b837ed47b11afcfaa5f5aee831c1aa4e5e0

2019-10-22T00:16:49.000Z

2021-07-15T07:44:43.000Z

retirement/migrations/0010_auto_20190515_1732.py

Jerome-Celle/Blitz-API

7dfb7b837ed47b11afcfaa5f5aee831c1aa4e5e0

2018-04-19T18:40:30.000Z

2022-03-31T21:05:05.000Z

retirement/migrations/0010_auto_20190515_1732.py

Jerome-Celle/Blitz-API

7dfb7b837ed47b11afcfaa5f5aee831c1aa4e5e0

2018-04-17T19:16:42.000Z

2022-01-27T00:19:59.000Z

# Generated by Django 2.0.8 on 2019-05-15 21:32 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('retirement', '0009_reservation_orderline_allow_null'), ] operations = [ migrations.AddField( model_name='historicalretirement', name='has_shared_rooms', field=models.BooleanField(default=False), preserve_default=False, ), migrations.AddField( model_name='retirement', name='has_shared_rooms', field=models.BooleanField(default=False), preserve_default=False, ), ]

efbb1d5ef2574c6c57d58e5ebcebeab836c610eb

py

Python

Automate the boring stuff/Chapter_6/Regular_Expression.py

maainul/Paython

c72d7fff3b00bc4f379ca6f9dbef0678f01b55f9

Automate the boring stuff/Chapter_6/Regular_Expression.py

maainul/Paython

c72d7fff3b00bc4f379ca6f9dbef0678f01b55f9

Automate the boring stuff/Chapter_6/Regular_Expression.py

maainul/Paython

c72d7fff3b00bc4f379ca6f9dbef0678f01b55f9

import re phone=re.compile(r'\d\d\d-\d\d\d-\d\d\d\d') mo=phone.search('My phone number is 412-444-9870') print(mo.group()) """ OUTPUT: 412-444-9870 """

aee928c1a0bf28567fb0747743e8556660fa9507

py

Python

abstractBaseUser/abstract_base_user/admin.py

amateur-dev/Django_CustomUser

6bb7a8676c48d80c0817a164ca801a1008e874dc

abstractBaseUser/abstract_base_user/admin.py

amateur-dev/Django_CustomUser

6bb7a8676c48d80c0817a164ca801a1008e874dc

abstractBaseUser/abstract_base_user/admin.py

amateur-dev/Django_CustomUser

6bb7a8676c48d80c0817a164ca801a1008e874dc

# from django.contrib import admin # Register your models here. from django.contrib import admin from django.contrib.auth.admin import UserAdmin from .forms import CustomUserCreationForm, CustomUserChangeForm from .models import CustomUser class CustomUserAdmin(UserAdmin): add_form = CustomUserCreationForm form = CustomUserChangeForm model = CustomUser list_display = ('email', 'is_staff', 'is_active',) list_filter = ('email', 'is_staff', 'is_active',) fieldsets = ( (None, {'fields': ('email', 'password', 'f_name', 'l_name', 'condo_name', 'unit_floor', 'unit_unit', 'has_access_to_facility')}), ('Permissions', {'fields': ('is_staff', 'is_active')}), ) add_fieldsets = ( (None, { 'classes': ('wide',), 'fields': ('email', 'password1', 'password2', 'is_staff', 'is_active')} ), ) search_fields = ('email', 'condo_name') ordering = ('email', 'condo_name') admin.site.register(CustomUser, CustomUserAdmin)

fa916a8d925503a3882ae31069e8c47191115590

py

Python

tools/gen.py

ikrivosheev/aiohttp

42ffdfc7bd70aedec57c5f44a6b1fc84ae565625

2021-11-11T04:05:06.000Z

2021-11-11T04:05:06.000Z

tools/gen.py

ikrivosheev/aiohttp

42ffdfc7bd70aedec57c5f44a6b1fc84ae565625

2020-11-01T08:02:46.000Z

2022-03-31T07:05:31.000Z

tools/gen.py

ikrivosheev/aiohttp

42ffdfc7bd70aedec57c5f44a6b1fc84ae565625

2021-11-11T04:04:59.000Z

2021-11-11T04:04:59.000Z

#!/usr/bin/env python import io import pathlib from collections import defaultdict import multidict ROOT = pathlib.Path.cwd() while ROOT.parent != ROOT and not (ROOT / ".git").exists(): ROOT = ROOT.parent def calc_headers(root): hdrs_file = root / "aiohttp/hdrs.py" code = compile(hdrs_file.read_text(), str(hdrs_file), "exec") globs = {} exec(code, globs) headers = [val for val in globs.values() if isinstance(val, multidict.istr)] return sorted(headers) headers = calc_headers(ROOT) def factory(): return defaultdict(factory) TERMINAL = object() def build(headers): dct = defaultdict(factory) for hdr in headers: d = dct for ch in hdr: d = d[ch] d[TERMINAL] = hdr return dct dct = build(headers) HEADER = """\ /* The file is autogenerated from aiohttp/hdrs.py Run ./tools/gen.py to update it after the origin changing. */ #include "_find_header.h" #define NEXT_CHAR() \\ { \\ count++; \\ if (count == size) { \\ /* end of search */ \\ return -1; \\ } \\ pchar++; \\ ch = *pchar; \\ last = (count == size -1); \\ } while(0); int find_header(const char *str, int size) { char *pchar = str; int last; char ch; int count = -1; pchar--; """ BLOCK = """ {label} NEXT_CHAR(); switch (ch) {{ {cases} default: return -1; }} """ CASE = """\ case '{char}': if (last) {{ return {index}; }} goto {next};""" FOOTER = """ {missing} missing: /* nothing found */ return -1; }} """ def gen_prefix(prefix, k): if k == "-": return prefix + "_" else: return prefix + k.upper() def gen_block(dct, prefix, used_blocks, missing, out): cases = {} for k, v in dct.items(): if k is TERMINAL: continue next_prefix = gen_prefix(prefix, k) term = v.get(TERMINAL) if term is not None: index = headers.index(term) else: index = -1 hi = k.upper() lo = k.lower() case = CASE.format(char=hi, index=index, next=next_prefix) cases[hi] = case if lo != hi: case = CASE.format(char=lo, index=index, next=next_prefix) cases[lo] = case label = prefix + ":" if prefix else "" if cases: block = BLOCK.format(label=label, cases="\n".join(cases.values())) out.write(block) else: missing.add(label) for k, v in dct.items(): if not isinstance(v, defaultdict): continue block_name = gen_prefix(prefix, k) if block_name in used_blocks: continue used_blocks.add(block_name) gen_block(v, block_name, used_blocks, missing, out) def gen(dct): out = io.StringIO() out.write(HEADER) missing = set() gen_block(dct, "", set(), missing, out) missing_labels = "\n".join(m for m in sorted(missing)) out.write(FOOTER.format(missing=missing_labels)) return out def gen_headers(headers): out = io.StringIO() out.write("# The file is autogenerated from aiohttp/hdrs.py\n") out.write("# Run ./tools/gen.py to update it after the origin changing.") out.write("\n\n") out.write("from . import hdrs\n") out.write("cdef tuple headers = (\n") for hdr in headers: out.write(" hdrs.{},\n".format(hdr.upper().replace("-", "_"))) out.write(")\n") return out # print(gen(dct).getvalue()) # print(gen_headers(headers).getvalue()) folder = ROOT / "aiohttp" with (folder / "_find_header.c").open("w") as f: f.write(gen(dct).getvalue()) with (folder / "_headers.pxi").open("w") as f: f.write(gen_headers(headers).getvalue())

cef06c01bf66f3405274c3dec0e9c75b0cbe287a

py

Python

task-library/veeam/VeeamGetHierarchyRoots.py

vNugget/blueprints

17183beebf8bc3da1d9d3ed4b8260dd18fdc1516

task-library/veeam/VeeamGetHierarchyRoots.py

vNugget/blueprints

17183beebf8bc3da1d9d3ed4b8260dd18fdc1516

task-library/veeam/VeeamGetHierarchyRoots.py

vNugget/blueprints

17183beebf8bc3da1d9d3ed4b8260dd18fdc1516

# region headers # * author: [email protected] # * version: v1.0 - initial version # * date: 11/03/2020 # task_name: VeeamGetHierarchyRoots # description: Get the hierarchyRoot UID # The script retreives the hierarchyRoots UID # input vars: veeam_session_cookie, vc_server, api_server # output vars: veeam_hierarchyRoot_uid # endregion # region capture Calm variables veeam_session_cookie = "@@{veeam_session_cookie}@@" api_server = "@@{veeam_endpoint}@@" vc_server = "@@{vc_endpoint}@@" # endregion # region prepare api call api_server_port = "9398" api_server_endpoint = "/api/hierarchyRoots" method = "GET" url = "https://{}:{}{}".format(api_server, api_server_port, api_server_endpoint) headers = {'Content-Type': 'application/json', 'Accept': 'application/json', 'X-RestSvcSessionId': veeam_session_cookie} # endregion # region API call function def process_request(url, method, headers, payload=None): if (payload is not None): payload = json.dumps(payload) r = urlreq(url, verb=method, params=payload, verify=False, headers=headers) if r.ok: print("Request was successful") print("Status code: {}".format(r.status_code)) else: print("Request failed") print('Status code: {}'.format(r.status_code)) print("Headers: {}".format(headers)) print("Payload: {}".format(json.dumps(payload))) print('Response: {}'.format(json.dumps(json.loads(r.content), indent=4))) exit(1) return r # endregion # region login print("Making a {} API call to {}".format(method, url)) resp = process_request(url, method, headers) # endregion # pass the repo_uid so that it may be captured by Calm. obj_uid = "" resp_parse = json.loads(resp.content) for obj in resp_parse['Refs']: if obj['Name'] == vc_server: obj_uid = obj['UID'] if obj_uid: print ("veeam_hierarchyroot_uid={}".format(obj_uid.rsplit(':', 1)[1])) exit(0) else: print("Error: Managed Server "+vc_server+" doesn't is not present ..") exit(1)

ddeff27a1f0c4531bd84ecb842b4950d10175b55

py

Python

watchman/test/async/test_dead_socket.py

47-studio-org/watchman

c50631dcf9a9e7d27b2bc05cd32649546add836e

2022-02-10T10:48:36.000Z

2022-02-21T23:18:10.000Z

watchman/test/async/test_dead_socket.py

47-studio-org/watchman

c50631dcf9a9e7d27b2bc05cd32649546add836e

watchman/test/async/test_dead_socket.py

47-studio-org/watchman

c50631dcf9a9e7d27b2bc05cd32649546add836e

2022-02-06T10:29:46.000Z

2022-02-06T10:29:46.000Z

#!/usr/bin/env python3 # vim:ts=4:sw=4:et: # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import asyncio import os import unittest import pywatchman_aio import WatchmanInstance # Note this does not extend AsyncWatchmanTestCase as it wants to start its # own Watchman server instances per test. class TestDeadSocket(unittest.TestCase): @unittest.skipIf(os.name == "nt", "not supported on windows") def test_query_dead_socket(self): async def test_core(wminst): with await pywatchman_aio.AIOClient.from_socket( sockname=wminst.getSockPath() ) as client: wminst.stop() with self.assertRaises(ConnectionResetError): await client.query("version") self._async_runner(test_core) @unittest.skipIf(os.name == "nt", "not supported on windows") def test_subscription_dead_socket(self): async def test_core(wminst): with await pywatchman_aio.AIOClient.from_socket( sockname=wminst.getSockPath() ) as client: root = f"{wminst.base_dir}/work" os.makedirs(root) await client.query("watch", root) await client.query("subscribe", root, "sub", {"expression": ["exists"]}) wminst.stop() with self.assertRaises(ConnectionResetError): await client.get_subscription("sub", root) self._async_runner(test_core) def _async_runner(self, test_core): wminst = WatchmanInstance.Instance() wminst.start() try: return asyncio.new_event_loop().run_until_complete(test_core(wminst)) finally: wminst.stop()

4f2601b4cc671dc9d5e402d735f8cd71a8ec14ff

py

Python

libs/models/detectors/scrdet/build_whole_network.py

PauliKarl/RotationDetection

84bbfe5b1a3ee36e8ad66fd0f36a5ef7b9b0019e

2020-10-27T08:51:54.000Z

2022-03-30T15:12:06.000Z

libs/models/detectors/scrdet/build_whole_network.py

PauliKarl/RotationDetection

84bbfe5b1a3ee36e8ad66fd0f36a5ef7b9b0019e

2020-12-01T02:18:47.000Z

2022-03-30T08:14:27.000Z

libs/models/detectors/scrdet/build_whole_network.py

PauliKarl/RotationDetection

84bbfe5b1a3ee36e8ad66fd0f36a5ef7b9b0019e

2020-10-29T03:30:32.000Z

2022-03-29T09:53:23.000Z

# -*-coding: utf-8 -*- from __future__ import absolute_import, division, print_function import numpy as np import tensorflow as tf import tensorflow.contrib.slim as slim from libs.models.detectors.two_stage_base_network import DetectionNetworkBase from libs.models.losses.losses import Loss from libs.utils import bbox_transform, nms_rotate from libs.models.anchor_heads import generate_h_anchors, anchor_utils from libs.models.samplers.r2cnn.anchor_sampler_r2cnn import AnchorSamplerR2CNN from libs.models.samplers.r2cnn.proposal_sampler_r2cnn import ProposalSamplerR2CNN from libs.models.roi_extractors.roi_extractors import RoIExtractor from libs.models.box_heads.box_head_base import BoxHead from utils.box_ops import clip_boxes_to_img_boundaries class DetectionNetworkSCRDet(DetectionNetworkBase): def __init__(self, cfgs, is_training): super(DetectionNetworkSCRDet, self).__init__(cfgs, is_training) self.proposal_sampler_r2cnn = ProposalSamplerR2CNN(cfgs) self.anchor_sampler_r2cnn = AnchorSamplerR2CNN(cfgs) self.losses = Loss(cfgs) self.roi_extractor = RoIExtractor(cfgs) self.box_head = BoxHead(cfgs) def rpn(self, inputs): rpn_conv3x3 = slim.conv2d(inputs, 512, [3, 3], trainable=self.is_training, weights_initializer=self.cfgs.INITIALIZER, activation_fn=tf.nn.relu, scope='rpn_conv/3x3') rpn_cls_score = slim.conv2d(rpn_conv3x3, self.num_anchors_per_location * 2, [1, 1], stride=1, trainable=self.is_training, weights_initializer=self.cfgs.INITIALIZER, activation_fn=None, scope='rpn_cls_score') rpn_box_pred = slim.conv2d(rpn_conv3x3, self.num_anchors_per_location * 4, [1, 1], stride=1, trainable=self.is_training, weights_initializer=self.cfgs.BBOX_INITIALIZER, activation_fn=None, scope='rpn_bbox_pred') rpn_cls_prob = slim.softmax(rpn_cls_score, scope='rpn_cls_prob') return rpn_box_pred, rpn_cls_score, rpn_cls_prob def make_anchors(self, feature_to_cropped): featuremap_height, featuremap_width = tf.shape(feature_to_cropped)[1], tf.shape(feature_to_cropped)[2] featuremap_height = tf.cast(featuremap_height, tf.float32) featuremap_width = tf.cast(featuremap_width, tf.float32) anchors = anchor_utils.make_anchors(base_anchor_size=self.cfgs.BASE_ANCHOR_SIZE_LIST, anchor_scales=self.cfgs.ANCHOR_SCALES, anchor_ratios=self.cfgs.ANCHOR_RATIOS, featuremap_height=featuremap_height, featuremap_width=featuremap_width, stride=self.cfgs.ANCHOR_STRIDE, name="make_anchors_forRPN") return anchors def build_loss(self, rpn_box_pred, rpn_bbox_targets, rpn_cls_score, rpn_labels, bbox_pred_h, bbox_targets_h, cls_score_h, bbox_pred_r, bbox_targets_r, rois, target_gt_r, cls_score_r, labels, mask_gt, pa_mask_pred): ''' :param rpn_box_pred: [-1, 4] :param rpn_bbox_targets: [-1, 4] :param rpn_cls_score: [-1] :param rpn_labels: [-1] :param bbox_pred_h: [-1, 4*(cls_num+1)] :param bbox_targets_h: [-1, 4*(cls_num+1)] :param cls_score_h: [-1, cls_num+1] :param bbox_pred_r: [-1, 5*(cls_num+1)] :param bbox_targets_r: [-1, 5*(cls_num+1)] :param cls_score_r: [-1, cls_num+1] :param labels: [-1] :return: ''' with tf.variable_scope('build_loss'): with tf.variable_scope('rpn_loss'): rpn_reg_loss = self.losses.smooth_l1_loss_rpn(bbox_pred=rpn_box_pred, bbox_targets=rpn_bbox_targets, label=rpn_labels, sigma=self.cfgs.RPN_SIGMA) rpn_select = tf.reshape(tf.where(tf.not_equal(rpn_labels, -1)), [-1]) rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, rpn_select), [-1, 2]) rpn_labels = tf.reshape(tf.gather(rpn_labels, rpn_select), [-1]) rpn_cls_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score, labels=rpn_labels)) self.losses_dict['rpn_cls_loss'] = rpn_cls_loss * self.cfgs.RPN_CLASSIFICATION_LOSS_WEIGHT self.losses_dict['rpn_reg_loss'] = rpn_reg_loss * self.cfgs.RPN_LOCATION_LOSS_WEIGHT with tf.variable_scope('FastRCNN_loss'): reg_loss_h = self.losses.smooth_l1_loss_rcnn_h(bbox_pred=bbox_pred_h, bbox_targets=bbox_targets_h, label=labels, num_classes=self.cfgs.CLASS_NUM + 1, sigma=self.cfgs.FASTRCNN_SIGMA) if self.cfgs.USE_IOU_FACTOR: reg_loss_r = self.losses.iou_smooth_l1_loss_rcnn_r(bbox_pred=bbox_pred_r, bbox_targets=bbox_targets_r, label=labels, rois=rois, target_gt_r=target_gt_r, num_classes=self.cfgs.CLASS_NUM + 1, sigma=self.cfgs.FASTRCNN_SIGMA) else: reg_loss_r = self.losses.smooth_l1_loss_rcnn_r(bbox_pred=bbox_pred_r, bbox_targets=bbox_targets_r, label=labels, num_classes=self.cfgs.CLASS_NUM + 1, sigma=self.cfgs.FASTRCNN_SIGMA) cls_loss_h = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( logits=cls_score_h, labels=labels)) # beacause already sample before cls_loss_r = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( logits=cls_score_r, labels=labels)) self.losses_dict['fast_cls_loss_h'] = cls_loss_h * self.cfgs.FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT self.losses_dict['fast_reg_loss_h'] = reg_loss_h * self.cfgs.FAST_RCNN_LOCATION_LOSS_WEIGHT self.losses_dict['fast_cls_loss_r'] = cls_loss_r * self.cfgs.FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT self.losses_dict['fast_reg_loss_r'] = reg_loss_r * self.cfgs.FAST_RCNN_LOCATION_LOSS_WEIGHT with tf.variable_scope('build_attention_loss', regularizer=slim.l2_regularizer(self.cfgs.WEIGHT_DECAY)): attention_loss = self.losses.build_attention_loss(mask_gt, pa_mask_pred) self.losses_dict['attention_loss'] = attention_loss def build_whole_detection_network(self, input_img_batch, gtboxes_batch_h=None, gtboxes_batch_r=None, mask_batch=None, gpu_id=0): if self.is_training: gtboxes_batch_h = tf.reshape(gtboxes_batch_h, [-1, 5]) gtboxes_batch_h = tf.cast(gtboxes_batch_h, tf.float32) gtboxes_batch_r = tf.reshape(gtboxes_batch_r, [-1, 6]) gtboxes_batch_r = tf.cast(gtboxes_batch_r, tf.float32) img_shape = tf.shape(input_img_batch) # 1. build backbone feature, pa_mask = self.build_backbone(input_img_batch) # 2. build rpn rpn_box_pred, rpn_cls_score, rpn_cls_prob = self.rpn(feature) rpn_box_pred = tf.reshape(rpn_box_pred, [-1, 4]) rpn_cls_score = tf.reshape(rpn_cls_score, [-1, 2]) rpn_cls_prob = slim.softmax(rpn_cls_score, scope='rpn_cls_prob') # 3. generate anchors anchors = self.make_anchors(feature) # 4. postprocess rpn proposals. such as: decode, clip, NMS with tf.variable_scope('postprocess_RPN'): rois, roi_scores = self.postprocess_rpn_proposals(rpn_bbox_pred=rpn_box_pred, rpn_cls_prob=rpn_cls_prob, img_shape=img_shape, anchors=anchors, is_training=self.is_training) # 5. sample minibatch if self.is_training: with tf.variable_scope('sample_anchors_minibatch'): rpn_labels, rpn_bbox_targets = \ tf.py_func( self.anchor_sampler_r2cnn.anchor_target_layer, [gtboxes_batch_h, img_shape, anchors], [tf.float32, tf.float32]) rpn_bbox_targets = tf.reshape(rpn_bbox_targets, [-1, 4]) rpn_labels = tf.to_int32(rpn_labels, name="to_int32") rpn_labels = tf.reshape(rpn_labels, [-1]) self.add_anchor_img_smry(input_img_batch, anchors, rpn_labels, method=0) rpn_cls_category = tf.argmax(rpn_cls_prob, axis=1) kept_rpppn = tf.reshape(tf.where(tf.not_equal(rpn_labels, -1)), [-1]) rpn_cls_category = tf.gather(rpn_cls_category, kept_rpppn) acc = tf.reduce_mean(tf.to_float(tf.equal(rpn_cls_category, tf.to_int64(tf.gather(rpn_labels, kept_rpppn))))) tf.summary.scalar('ACC/fpn_accuracy', acc) with tf.control_dependencies([rpn_labels]): with tf.variable_scope('sample_RCNN_minibatch'): rois, labels, bbox_targets_h, bbox_targets_r, target_gt_h, target_gt_r = \ tf.py_func(self.proposal_sampler_r2cnn.proposal_target_layer, [rois, gtboxes_batch_h, gtboxes_batch_r], [tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32]) rois = tf.reshape(rois, [-1, 4]) labels = tf.to_int32(labels) labels = tf.reshape(labels, [-1]) bbox_targets_h = tf.reshape(bbox_targets_h, [-1, 4 * (self.cfgs.CLASS_NUM + 1)]) bbox_targets_r = tf.reshape(bbox_targets_r, [-1, 5 * (self.cfgs.CLASS_NUM + 1)]) self.add_roi_batch_img_smry(input_img_batch, rois, labels, method=0) # 6. build Fast-RCNN, include roi align/pooling, box head bbox_pred_h, cls_score_h, bbox_pred_r, cls_score_r = self.box_head.fc_head(self.roi_extractor, rois, feature, img_shape, self.is_training, mode=0) cls_prob_h = slim.softmax(cls_score_h, 'cls_prob_h') cls_prob_r = slim.softmax(cls_score_r, 'cls_prob_r') if self.is_training: cls_category_h = tf.argmax(cls_prob_h, axis=1) fast_acc_h = tf.reduce_mean(tf.to_float(tf.equal(cls_category_h, tf.to_int64(labels)))) tf.summary.scalar('ACC/fast_acc_h', fast_acc_h) cls_category_r = tf.argmax(cls_prob_r, axis=1) fast_acc_r = tf.reduce_mean(tf.to_float(tf.equal(cls_category_r, tf.to_int64(labels)))) tf.summary.scalar('ACC/fast_acc_r', fast_acc_r) # 8. build loss if self.is_training: self.build_loss(rpn_box_pred=rpn_box_pred, rpn_bbox_targets=rpn_bbox_targets, rpn_cls_score=rpn_cls_score, rpn_labels=rpn_labels, bbox_pred_h=bbox_pred_h, bbox_targets_h=bbox_targets_h, cls_score_h=cls_score_h, bbox_pred_r=bbox_pred_r, bbox_targets_r=bbox_targets_r, rois=rois, target_gt_r=target_gt_r, cls_score_r=cls_score_r, labels=labels, mask_gt=mask_batch, pa_mask_pred=pa_mask) # 9. postprocess_fastrcnn final_boxes_h, final_scores_h, final_category_h = self.postprocess_fastrcnn_h(rois=rois, bbox_ppred=bbox_pred_h, scores=cls_prob_h, img_shape=img_shape) final_boxes_r, final_scores_r, final_category_r = self.postprocess_fastrcnn_r(rois=rois, bbox_ppred=bbox_pred_r, scores=cls_prob_r, gpu_id=gpu_id) if self.is_training: return final_boxes_h, final_scores_h, final_category_h, \ final_boxes_r, final_scores_r, final_category_r, self.losses_dict else: return final_boxes_h, final_scores_h, final_category_h, \ final_boxes_r, final_scores_r, final_category_r, def postprocess_fastrcnn_r(self, rois, bbox_ppred, scores, gpu_id): ''' :param rois:[-1, 4] :param bbox_ppred: [-1, (cfgs.Class_num+1) * 5] :param scores: [-1, cfgs.Class_num + 1] :return: ''' with tf.name_scope('postprocess_fastrcnn'): rois = tf.stop_gradient(rois) scores = tf.stop_gradient(scores) bbox_ppred = tf.reshape(bbox_ppred, [-1, self.cfgs.CLASS_NUM + 1, 5]) bbox_ppred = tf.stop_gradient(bbox_ppred) bbox_pred_list = tf.unstack(bbox_ppred, axis=1) score_list = tf.unstack(scores, axis=1) allclasses_boxes = [] allclasses_scores = [] categories = [] x_c = (rois[:, 2] + rois[:, 0]) / 2 y_c = (rois[:, 3] + rois[:, 1]) / 2 h = rois[:, 2] - rois[:, 0] + 1 w = rois[:, 3] - rois[:, 1] + 1 theta = -90 * tf.ones_like(x_c) rois = tf.transpose(tf.stack([x_c, y_c, w, h, theta])) for i in range(1, self.cfgs.CLASS_NUM + 1): # 1. decode boxes in each class tmp_encoded_box = bbox_pred_list[i] tmp_score = score_list[i] tmp_decoded_boxes = bbox_transform.rbbox_transform_inv(boxes=rois, deltas=tmp_encoded_box, scale_factors=self.cfgs.ROI_SCALE_FACTORS) # 2. clip to img boundaries # tmp_decoded_boxes = boxes_utils.clip_boxes_to_img_boundaries(decode_boxes=tmp_decoded_boxes, # img_shape=img_shape) # 3. NMS if self.cfgs.SOFT_NMS: print("Using Soft NMS.......") raise NotImplementedError("soft NMS for rotate has not implemented") else: keep = nms_rotate.nms_rotate(decode_boxes=tmp_decoded_boxes, scores=tmp_score, iou_threshold=self.cfgs.FAST_RCNN_R_NMS_IOU_THRESHOLD, max_output_size=self.cfgs.FAST_RCNN_NMS_MAX_BOXES_PER_CLASS, use_gpu=self.cfgs.ROTATE_NMS_USE_GPU, gpu_id=gpu_id) perclass_boxes = tf.gather(tmp_decoded_boxes, keep) perclass_scores = tf.gather(tmp_score, keep) allclasses_boxes.append(perclass_boxes) allclasses_scores.append(perclass_scores) categories.append(tf.ones_like(perclass_scores) * i) final_boxes = tf.concat(allclasses_boxes, axis=0) final_scores = tf.concat(allclasses_scores, axis=0) final_category = tf.concat(categories, axis=0) if self.is_training: ''' in training. We should show the detecitons in the tensorboard. So we add this. ''' kept_indices = tf.reshape(tf.where(tf.greater_equal(final_scores, self.cfgs.VIS_SCORE)), [-1]) else: kept_indices = tf.reshape(tf.where(tf.greater_equal(final_scores, self.cfgs.FILTERED_SCORE)), [-1]) final_boxes = tf.gather(final_boxes, kept_indices) final_scores = tf.gather(final_scores, kept_indices) final_category = tf.gather(final_category, kept_indices) return final_boxes, final_scores, final_category def postprocess_fastrcnn_h(self, rois, bbox_ppred, scores, img_shape): ''' :param rois:[-1, 4] :param bbox_ppred: [-1, (cfgs.Class_num+1) * 4] :param scores: [-1, cfgs.Class_num + 1] :return: ''' with tf.name_scope('postprocess_fastrcnn_h'): rois = tf.stop_gradient(rois) scores = tf.stop_gradient(scores) bbox_ppred = tf.reshape(bbox_ppred, [-1, self.cfgs.CLASS_NUM + 1, 4]) bbox_ppred = tf.stop_gradient(bbox_ppred) bbox_pred_list = tf.unstack(bbox_ppred, axis=1) score_list = tf.unstack(scores, axis=1) allclasses_boxes = [] allclasses_scores = [] categories = [] for i in range(1, self.cfgs.CLASS_NUM + 1): # 1. decode boxes in each class tmp_encoded_box = bbox_pred_list[i] tmp_score = score_list[i] tmp_decoded_boxes = bbox_transform.bbox_transform_inv(boxes=rois, deltas=tmp_encoded_box, scale_factors=self.cfgs.ROI_SCALE_FACTORS) # 2. clip to img boundaries tmp_decoded_boxes = clip_boxes_to_img_boundaries(decode_boxes=tmp_decoded_boxes, img_shape=img_shape) # 3. NMS max_output_size = 4000 if 'DOTA' in self.cfgs.NET_NAME else 200 keep = tf.image.non_max_suppression( boxes=tmp_decoded_boxes, scores=tmp_score, max_output_size=100 if self.is_training else max_output_size, iou_threshold=self.cfgs.FAST_RCNN_H_NMS_IOU_THRESHOLD) perclass_boxes = tf.gather(tmp_decoded_boxes, keep) perclass_scores = tf.gather(tmp_score, keep) allclasses_boxes.append(perclass_boxes) allclasses_scores.append(perclass_scores) categories.append(tf.ones_like(perclass_scores) * i) final_boxes = tf.concat(allclasses_boxes, axis=0) final_scores = tf.concat(allclasses_scores, axis=0) final_category = tf.concat(categories, axis=0) # if self.is_training: ''' in training. We should show the detecitons in the tensorboard. So we add this. ''' if self.is_training: ''' in training. We should show the detecitons in the tensorboard. So we add this. ''' kept_indices = tf.reshape(tf.where(tf.greater_equal(final_scores, self.cfgs.VIS_SCORE)), [-1]) else: kept_indices = tf.reshape(tf.where(tf.greater_equal(final_scores, self.cfgs.FILTERED_SCORE)), [-1]) final_boxes = tf.gather(final_boxes, kept_indices) final_scores = tf.gather(final_scores, kept_indices) final_category = tf.gather(final_category, kept_indices) return final_boxes, final_scores, final_category

c29a2b2b3f3cce6d71365a0fa58861d1769fd702

py

Python

setup.py

majkrzak/kot

1ef7ee448d460bb46613c8400743b7c4185a2ed2

2019-10-06T12:00:41.000Z

2019-10-06T12:00:41.000Z

setup.py

majkrzak/kot

1ef7ee448d460bb46613c8400743b7c4185a2ed2

2019-10-06T12:31:11.000Z

2019-10-16T08:05:33.000Z

setup.py

majkrzak/kot

1ef7ee448d460bb46613c8400743b7c4185a2ed2

2019-10-06T12:41:18.000Z

2019-10-08T01:57:21.000Z

import setuptools setuptools.setup( name='kot', author='Piotr Majrzak', author_email='[email protected]', license='MIT', data_files = [("", ["LICENSE"])], classifiers=[ 'License :: OSI Approved :: MIT License', ], packages=[ 'kot', ], package_dir={'kot': './src'}, install_requires=[ 'requests', 'lxml' ], setup_requires=[ 'wheel' ] )

b4c2cbdf4813415960364a715dba4fb618d7ce93

py

Python

aim/web/api/runs/utils.py

gorarakelyan/aim

eed0ac76f4bdcc81277cef4a4de1dfc3dd690644

aim/web/api/runs/utils.py

gorarakelyan/aim

eed0ac76f4bdcc81277cef4a4de1dfc3dd690644

aim/web/api/runs/utils.py

gorarakelyan/aim

eed0ac76f4bdcc81277cef4a4de1dfc3dd690644

import numpy as np import struct from typing import Iterator, Tuple, Optional, List from aim.storage.context import Context from aim.sdk.run import Run from aim.sdk.metric import Metric from aim.sdk.metric import MetricCollection from aim.web.api.runs.pydantic_models import AlignedRunIn, TraceBase from aim.storage.treeutils import encode_tree def get_run_props(run: Run): return { 'name': run.name if run.name else None, 'experiment': run.experiment.name if run.experiment else None, 'tags': [{'id': tag.uuid, 'name': tag.name, 'color': tag.color, 'description': tag.description} for tag in run.props.tags], 'archived': run.archived if run.archived else False, 'creation_time': run.creation_time, 'end_time': run.end_time } def numpy_to_encodable(array: np.ndarray) -> Optional[dict]: encoded_numpy = { 'type': 'numpy', 'shape': array.shape[0], 'dtype': 'float64', # hardcoded for now } if array.dtype == 'float64': encoded_numpy['blob'] = array.tobytes() elif array.dtype == 'object': return None else: encoded_numpy['blob'] = array.astype('float64').tobytes() return encoded_numpy def sliced_np_array(array: np.ndarray, _slice: slice) -> np.ndarray: last_step_needed = (_slice.stop - 1) % _slice.step != 0 if last_step_needed: return np.append(array[_slice], array[-1]) else: return array[_slice] def sliced_array(array: list, _slice: slice) -> list: last_step_needed = (_slice.stop - 1) % _slice.step != 0 if last_step_needed: last_value = array[-1] return array[_slice] + [last_value] else: return array[_slice] def collect_x_axis_data(x_trace: Metric, iters: np.ndarray) -> Tuple[Optional[dict], Optional[dict]]: if not x_trace: return None, None x_axis_values = [] x_axis_iters = [] for idx in iters: x_val = x_trace.values[idx.item()] if x_val: x_axis_iters.append(idx.item()) x_axis_values.append(x_val) if not x_axis_iters: return None, None return numpy_to_encodable(np.array(x_axis_iters, dtype='float64')),\ numpy_to_encodable(np.array(x_axis_values, dtype='float64')) def collect_run_streamable_data(encoded_tree: Iterator[Tuple[bytes, bytes]]) -> bytes: result = bytes() for key, val in encoded_tree: result += struct.pack('I', len(key)) + key + struct.pack('I', len(val)) + val return result def custom_aligned_metrics_streamer(requested_runs: List[AlignedRunIn], x_axis: str) -> bytes: for run_data in requested_runs: run_hashname = run_data.run_id requested_traces = run_data.traces run = Run(hashname=run_hashname, read_only=True) traces_list = [] for trace_data in requested_traces: context = Context(trace_data.context) trace = run.get_metric(metric_name=trace_data.metric_name, context=context) x_axis_trace = run.get_metric(metric_name=x_axis, context=context) if not (trace and x_axis_trace): continue _slice = slice(*trace_data.slice) iters = trace.values.sparse_numpy()[0] sliced_iters = sliced_np_array(iters, _slice) x_axis_iters, x_axis_values = collect_x_axis_data(x_axis_trace, sliced_iters) traces_list.append({ 'metric_name': trace.name, 'context': trace.context.to_dict(), 'x_axis_values': x_axis_values, 'x_axis_iters': x_axis_iters, }) run_dict = { run_hashname: traces_list } encoded_tree = encode_tree(run_dict) yield collect_run_streamable_data(encoded_tree) def metric_search_result_streamer(traces: MetricCollection, steps_num: int, x_axis: Optional[str]) -> bytes: for run_trace_collection in traces.iter_runs(): run = None traces_list = [] for trace in run_trace_collection.iter(): if not run: run = run_trace_collection.run iters, values = trace.values.sparse_numpy() num_records = len(values) step = (num_records // steps_num) or 1 _slice = slice(0, num_records, step) sliced_iters = sliced_np_array(iters, _slice) x_axis_trace = run.get_metric(x_axis, trace.context) if x_axis else None x_axis_iters, x_axis_values = collect_x_axis_data(x_axis_trace, sliced_iters) traces_list.append({ 'metric_name': trace.name, 'context': trace.context.to_dict(), 'slice': [0, num_records, step], 'values': numpy_to_encodable(sliced_np_array(values, _slice)), 'iters': numpy_to_encodable(sliced_iters), 'epochs': numpy_to_encodable(sliced_np_array(trace.epochs.values_numpy(), _slice)), 'timestamps': numpy_to_encodable(sliced_np_array(trace.timestamps.values_numpy(), _slice)), 'x_axis_values': x_axis_values, 'x_axis_iters': x_axis_iters, }) if run: run_dict = { run.hashname: { 'params': run.get(...), 'traces': traces_list, 'props': get_run_props(run) } } encoded_tree = encode_tree(run_dict) yield collect_run_streamable_data(encoded_tree) def run_search_result_streamer(runs: MetricCollection, limit: int) -> bytes: run_count = 0 for run_trace_collection in runs.iter_runs(): run = run_trace_collection.run run_dict = { run.hashname: { 'params': run.get(...), 'traces': run.collect_metrics_info(), 'props': get_run_props(run) } } encoded_tree = encode_tree(run_dict) yield collect_run_streamable_data(encoded_tree) run_count += 1 if limit and run_count >= limit: break def collect_requested_traces(run: Run, requested_traces: List[TraceBase], steps_num: int = 200) -> List[dict]: processed_traces_list = [] for requested_trace in requested_traces: metric_name = requested_trace.metric_name context = Context(requested_trace.context) trace = run.get_metric(metric_name=metric_name, context=context) if not trace: continue iters, values = trace.values.sparse_list() num_records = len(values) step = (num_records // steps_num) or 1 _slice = slice(0, num_records, step) processed_traces_list.append({ 'metric_name': trace.name, 'context': trace.context.to_dict(), 'values': sliced_array(values, _slice), 'iters': sliced_array(iters, _slice), }) return processed_traces_list

7c7d26701cee124b0f80e46d6a66642c9a44b347

py

Python

discoursesimplification/utils/ID_generator.py

kkatsamaktsis/PyDiscourseSimplification

18d247894355b4b51f5abcced86e7a7292b17ac0

discoursesimplification/utils/ID_generator.py

kkatsamaktsis/PyDiscourseSimplification

18d247894355b4b51f5abcced86e7a7292b17ac0

discoursesimplification/utils/ID_generator.py

kkatsamaktsis/PyDiscourseSimplification

18d247894355b4b51f5abcced86e7a7292b17ac0

import uuid class IDGenerator: @staticmethod def generate_uuid(): return str(uuid.uuid4()).replace("-", "")

795a60daa3fea4af0f505d9ce73f9436f32951c8

py

Python

tests/rules/test_git_push_pull.py

WorkInProgress-Development/theplease

9b9a2dcee3efa0e1b4f197fc55904c9327dc13ba

tests/rules/test_git_push_pull.py

WorkInProgress-Development/theplease

9b9a2dcee3efa0e1b4f197fc55904c9327dc13ba

tests/rules/test_git_push_pull.py

WorkInProgress-Development/theplease

9b9a2dcee3efa0e1b4f197fc55904c9327dc13ba

import pytest from theplease.rules.git_push_pull import match, get_new_command from theplease.types import Command git_err = ''' To /tmp/foo ! [rejected] master -> master (non-fast-forward) error: failed to push some refs to '/tmp/bar' hint: Updates were rejected because the tip of your current branch is behind hint: its remote counterpart. Integrate the remote changes (e.g. hint: 'git pull ...') before pushing again. hint: See the 'Note about fast-forwards' in 'git push --help' for details. ''' git_err2 = ''' To /tmp/foo ! [rejected] master -> master (non-fast-forward) error: failed to push some refs to '/tmp/bar' hint: Updates were rejected because the remote contains work that you do hint: not have locally. This is usually caused by another repository pushing hint: to the same ref. You may want to first integrate the remote changes hint: (e.g., 'git pull ...') before pushing again. hint: See the 'Note about fast-forwards' in 'git push --help' for details. ''' git_uptodate = 'Everything up-to-date' git_ok = ''' Counting objects: 3, done. Delta compression using up to 4 threads. Compressing objects: 100% (2/2), done. Writing objects: 100% (3/3), 282 bytes | 0 bytes/s, done. Total 3 (delta 0), reused 0 (delta 0) To /tmp/bar 514eed3..f269c79 master -> master ''' @pytest.mark.parametrize('command', [ Command('git push', git_err), Command('git push nvbn', git_err), Command('git push nvbn master', git_err), Command('git push', git_err2), Command('git push nvbn', git_err2), Command('git push nvbn master', git_err2)]) def test_match(command): assert match(command) @pytest.mark.parametrize('command', [ Command('git push', git_ok), Command('git push', git_uptodate), Command('git push nvbn', git_ok), Command('git push nvbn master', git_uptodate), Command('git push nvbn', git_ok), Command('git push nvbn master', git_uptodate)]) def test_not_match(command): assert not match(command) @pytest.mark.parametrize('command, output', [ (Command('git push', git_err), 'git pull && git push'), (Command('git push nvbn', git_err), 'git pull nvbn && git push nvbn'), (Command('git push nvbn master', git_err), 'git pull nvbn master && git push nvbn master'), (Command('git push', git_err2), 'git pull && git push'), (Command('git push nvbn', git_err2), 'git pull nvbn && git push nvbn'), (Command('git push nvbn master', git_err2), 'git pull nvbn master && git push nvbn master')]) def test_get_new_command(command, output): assert get_new_command(command) == output

0830e8d5aeea10e0c2437007c6d9fbaf7b16ac1c

py

Python

tests/test_deprecations.py

MattToast/SmartSim

4bd5e231445abd9b888561930db859062708678a

tests/test_deprecations.py

MattToast/SmartSim

4bd5e231445abd9b888561930db859062708678a

tests/test_deprecations.py

MattToast/SmartSim

4bd5e231445abd9b888561930db859062708678a

import pytest from smartsim.database import ( CobaltOrchestrator, LSFOrchestrator, PBSOrchestrator, SlurmOrchestrator, ) tf_available = True try: import tensorflow except ImportError: tf_available = False def test_deprecated_orchestrators(wlmutils): with pytest.deprecated_call(): _ = SlurmOrchestrator(interface=wlmutils.get_test_interface()) with pytest.deprecated_call(): _ = LSFOrchestrator(interface=wlmutils.get_test_interface()) with pytest.deprecated_call(): _ = CobaltOrchestrator(interface=wlmutils.get_test_interface()) with pytest.deprecated_call(): _ = PBSOrchestrator(interface=wlmutils.get_test_interface()) @pytest.mark.skipif(not tf_available, reason="Requires TF to run") def test_deprecated_tf(): with pytest.deprecated_call(): from smartsim.tf import freeze_model def test_deprecated_constants(): with pytest.deprecated_call(): from smartsim import constants

32ee58937d90dd04aec0da61c72555dc6b062f04

py

Python

sources_oop.py

Joefdez/gravitaionalLensing1

213e1e62b4f2d3881f3e2df64ea8d09998eb7189

sources_oop.py

Joefdez/gravitaionalLensing1

213e1e62b4f2d3881f3e2df64ea8d09998eb7189

sources_oop.py

Joefdez/gravitaionalLensing1

213e1e62b4f2d3881f3e2df64ea8d09998eb7189

from source_generators import * # includes numpy import, np import matplotlib.pylab as plt class modelSource (): 'Source class to represent objects to be lensed' def __init__(self, name, stype, side, radius=0.0, aspectRatio = 1.0, maxLum = 1.0): """ Constructor method """ self.name = name self.type = stype self.aspectRatio = aspectRatio self.maxLum = maxLum if aspectRatio == 1.0: self.xsize, self.ysize = side, side else: self.xsize, self.ysize = side, side*aspectRatio self.radius = radius if stype == "square": self.view = square_source( radius, self.xsize, self.ysize, maxLum ) elif stype == "circular": self.view = circular_source( radius, self.xsize, self.ysize) elif stype == "discs": self.view = discs_source( radius, self.xsize, self.ysize) self.lensedView = None print "Source array " + self.name + " generated." def plotSource(self): """ Plot the source """ plt.figure('lens') #Declare figure ax1=plt.axes() #Declare axis ax1.xaxis.set_ticklabels([]) #Remove ticks ax1.yaxis.set_ticklabels([]) #plt.figtext(-2.5, -2.5, pn) #plt.title(pn,loc='center') plt.imshow(self.view) class imageSource(): Class for handling actual images as sources def __init__(self, file ): #Remember to open and close properly

6ec6852d793e6bb34994bd1cceab2494ed84c024

py

Python

src/condor_tests/test_htcondor_dags/writer/test_subdag_edges.py

shamoya/htcondor

c3bbc0eb8f72b863eda2d6d0a2e92594f7346b02

src/condor_tests/test_htcondor_dags/writer/test_subdag_edges.py

shamoya/htcondor

c3bbc0eb8f72b863eda2d6d0a2e92594f7346b02

src/condor_tests/test_htcondor_dags/writer/test_subdag_edges.py

shamoya/htcondor

c3bbc0eb8f72b863eda2d6d0a2e92594f7346b02

# Copyright 2020 HTCondor Team, Computer Sciences Department, # University of Wisconsin-Madison, WI. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest from .conftest import s, dagfile_lines def test_one_parent_one_child(dag, writer): parent = dag.subdag(name="parent", dag_file="parent.dag") child = parent.child_subdag(name="child", dag_file="child.dag") assert "PARENT parent CHILD child" in dagfile_lines(writer) def test_two_parents_one_child(dag, writer): parent1 = dag.subdag(name="parent1", dag_file="parent.dag") parent2 = dag.subdag(name="parent2", dag_file="parent.dag") child = parent1.child_subdag(name="child", dag_file="child.dag") child.add_parents(parent2) lines = dagfile_lines(writer) assert "PARENT parent1 CHILD child" in lines assert "PARENT parent2 CHILD child" in lines def test_one_parent_two_children(dag, writer): parent1 = dag.subdag(name="parent", dag_file="parent.dag") child1 = parent1.child_subdag(name="child1", dag_file="child.dag") child2 = parent1.child_subdag(name="child2", dag_file="child.dag") lines = dagfile_lines(writer) assert "PARENT parent CHILD child1" in lines assert "PARENT parent CHILD child2" in lines

41ded2cad2689c283588b8cc60a59360703cfd05

py

Python

goldcoin/pools/pool_wallet_info.py

DevMau5x/goldcoin-blockchain-2

ed223dd16fa290ea710db7202d6c52a056242cfa

2021-09-08T17:07:54.000Z

2022-03-30T04:11:58.000Z

goldcoin/pools/pool_wallet_info.py

DevMau5x/goldcoin-blockchain-2

ed223dd16fa290ea710db7202d6c52a056242cfa

2021-09-28T21:09:49.000Z

2022-03-22T21:13:23.000Z

goldcoin/pools/pool_wallet_info.py

Pierre21dd/gold2

4a35f207ed4c8a7745bfbc73fd3c190bd8b60a3f

2021-09-12T10:03:23.000Z

2022-03-15T08:35:11.000Z

from dataclasses import dataclass from enum import IntEnum from typing import Optional, Dict from blspy import G1Element from goldcoin.protocols.pool_protocol import POOL_PROTOCOL_VERSION from goldcoin.types.blockchain_format.coin import Coin from goldcoin.types.blockchain_format.program import Program from goldcoin.types.blockchain_format.sized_bytes import bytes32 from goldcoin.util.byte_types import hexstr_to_bytes from goldcoin.util.ints import uint32, uint8 from goldcoin.util.streamable import streamable, Streamable class PoolSingletonState(IntEnum): """ From the user's point of view, a pool group can be in these states: `SELF_POOLING`: The singleton exists on the blockchain, and we are farming block rewards to a wallet address controlled by the user `LEAVING_POOL`: The singleton exists, and we have entered the "escaping" state, which means we are waiting for a number of blocks = `relative_lock_height` to pass, so we can leave. `FARMING_TO_POOL`: The singleton exists, and it is assigned to a pool. `CLAIMING_SELF_POOLED_REWARDS`: We have submitted a transaction to sweep our self-pooled funds. """ SELF_POOLING = 1 LEAVING_POOL = 2 FARMING_TO_POOL = 3 SELF_POOLING = PoolSingletonState.SELF_POOLING LEAVING_POOL = PoolSingletonState.LEAVING_POOL FARMING_TO_POOL = PoolSingletonState.FARMING_TO_POOL @dataclass(frozen=True) @streamable class PoolState(Streamable): """ `PoolState` is a type that is serialized to the blockchain to track the state of the user's pool singleton `target_puzzle_hash` is either the pool address, or the self-pooling address that pool rewards will be paid to. `target_puzzle_hash` is NOT the p2_singleton puzzle that block rewards are sent to. The `p2_singleton` address is the initial address, and the `target_puzzle_hash` is the final destination. `relative_lock_height` is zero when in SELF_POOLING state """ version: uint8 state: uint8 # PoolSingletonState # `target_puzzle_hash`: A puzzle_hash we pay to # When self-farming, this is a main wallet address # When farming-to-pool, the pool sends this to the farmer during pool protocol setup target_puzzle_hash: bytes32 # TODO: rename target_puzzle_hash -> pay_to_address # owner_pubkey is set by the wallet, once owner_pubkey: G1Element pool_url: Optional[str] relative_lock_height: uint32 def initial_pool_state_from_dict(state_dict: Dict, owner_pubkey: G1Element, owner_puzzle_hash: bytes32) -> PoolState: state_str = state_dict["state"] singleton_state: PoolSingletonState = PoolSingletonState[state_str] if singleton_state == SELF_POOLING: target_puzzle_hash = owner_puzzle_hash pool_url: str = "" relative_lock_height = uint32(0) elif singleton_state == FARMING_TO_POOL: target_puzzle_hash = bytes32(hexstr_to_bytes(state_dict["target_puzzle_hash"])) pool_url = state_dict["pool_url"] relative_lock_height = uint32(state_dict["relative_lock_height"]) else: raise ValueError("Initial state must be SELF_POOLING or FARMING_TO_POOL") # TODO: change create_pool_state to return error messages, as well assert relative_lock_height is not None return create_pool_state(singleton_state, target_puzzle_hash, owner_pubkey, pool_url, relative_lock_height) def create_pool_state( state: PoolSingletonState, target_puzzle_hash: bytes32, owner_pubkey: G1Element, pool_url: Optional[str], relative_lock_height: uint32, ) -> PoolState: if state not in set(s.value for s in PoolSingletonState): raise AssertionError("state {state} is not a valid PoolSingletonState,") ps = PoolState( POOL_PROTOCOL_VERSION, uint8(state), target_puzzle_hash, owner_pubkey, pool_url, relative_lock_height ) # TODO Move verify here return ps @dataclass(frozen=True) @streamable class PoolWalletInfo(Streamable): """ Internal Pool Wallet state, not destined for the blockchain. This can be completely derived with the Singleton's CoinSpends list, or with the information from the WalletPoolStore. """ current: PoolState target: Optional[PoolState] launcher_coin: Coin launcher_id: bytes32 p2_singleton_puzzle_hash: bytes32 current_inner: Program # Inner puzzle in current singleton, not revealed yet tip_singleton_coin_id: bytes32 singleton_block_height: uint32 # Block height that current PoolState is from

cb34afdbeb2d437767063fef7996f3093f16732c

py

Python

sdk/python/pulumi_azure_native/securityandcompliance/latest/private_endpoint_connections_adt_api.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

sdk/python/pulumi_azure_native/securityandcompliance/latest/private_endpoint_connections_adt_api.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

sdk/python/pulumi_azure_native/securityandcompliance/latest/private_endpoint_connections_adt_api.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables from . import outputs from ._enums import * from ._inputs import * __all__ = ['PrivateEndpointConnectionsAdtAPI'] warnings.warn("""The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:securityandcompliance:PrivateEndpointConnectionsAdtAPI'.""", DeprecationWarning) class PrivateEndpointConnectionsAdtAPI(pulumi.CustomResource): warnings.warn("""The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:securityandcompliance:PrivateEndpointConnectionsAdtAPI'.""", DeprecationWarning) def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, private_endpoint_connection_name: Optional[pulumi.Input[str]] = None, private_link_service_connection_state: Optional[pulumi.Input[pulumi.InputType['PrivateLinkServiceConnectionStateArgs']]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, resource_name_: Optional[pulumi.Input[str]] = None, __props__=None, __name__=None, __opts__=None): """ The Private Endpoint Connection resource. Latest API Version: 2021-01-11. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] private_endpoint_connection_name: The name of the private endpoint connection associated with the Azure resource :param pulumi.Input[pulumi.InputType['PrivateLinkServiceConnectionStateArgs']] private_link_service_connection_state: A collection of information about the state of the connection between service consumer and provider. :param pulumi.Input[str] resource_group_name: The name of the resource group that contains the service instance. :param pulumi.Input[str] resource_name_: The name of the service instance. """ pulumi.log.warn("""PrivateEndpointConnectionsAdtAPI is deprecated: The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:securityandcompliance:PrivateEndpointConnectionsAdtAPI'.""") if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['private_endpoint_connection_name'] = private_endpoint_connection_name if private_link_service_connection_state is None and not opts.urn: raise TypeError("Missing required property 'private_link_service_connection_state'") __props__['private_link_service_connection_state'] = private_link_service_connection_state if resource_group_name is None and not opts.urn: raise TypeError("Missing required property 'resource_group_name'") __props__['resource_group_name'] = resource_group_name if resource_name_ is None and not opts.urn: raise TypeError("Missing required property 'resource_name_'") __props__['resource_name'] = resource_name_ __props__['name'] = None __props__['private_endpoint'] = None __props__['provisioning_state'] = None __props__['system_data'] = None __props__['type'] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:securityandcompliance/latest:PrivateEndpointConnectionsAdtAPI"), pulumi.Alias(type_="azure-native:securityandcompliance:PrivateEndpointConnectionsAdtAPI"), pulumi.Alias(type_="azure-nextgen:securityandcompliance:PrivateEndpointConnectionsAdtAPI"), pulumi.Alias(type_="azure-native:securityandcompliance/v20210111:PrivateEndpointConnectionsAdtAPI"), pulumi.Alias(type_="azure-nextgen:securityandcompliance/v20210111:PrivateEndpointConnectionsAdtAPI")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(PrivateEndpointConnectionsAdtAPI, __self__).__init__( 'azure-native:securityandcompliance/latest:PrivateEndpointConnectionsAdtAPI', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'PrivateEndpointConnectionsAdtAPI': """ Get an existing PrivateEndpointConnectionsAdtAPI resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() __props__["name"] = None __props__["private_endpoint"] = None __props__["private_link_service_connection_state"] = None __props__["provisioning_state"] = None __props__["system_data"] = None __props__["type"] = None return PrivateEndpointConnectionsAdtAPI(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ The name of the resource """ return pulumi.get(self, "name") @property @pulumi.getter(name="privateEndpoint") def private_endpoint(self) -> pulumi.Output[Optional['outputs.PrivateEndpointResponse']]: """ The resource of private end point. """ return pulumi.get(self, "private_endpoint") @property @pulumi.getter(name="privateLinkServiceConnectionState") def private_link_service_connection_state(self) -> pulumi.Output['outputs.PrivateLinkServiceConnectionStateResponse']: """ A collection of information about the state of the connection between service consumer and provider. """ return pulumi.get(self, "private_link_service_connection_state") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> pulumi.Output[str]: """ The provisioning state of the private endpoint connection resource. """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter(name="systemData") def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']: """ Required property for system data """ return pulumi.get(self, "system_data") @property @pulumi.getter def type(self) -> pulumi.Output[str]: """ The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts" """ return pulumi.get(self, "type") def translate_output_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

b2ed2c41e875080089f3e3aa514f5454512e494a

py

Python

setup.py

Neradoc/discotool

b242be28f490eabcbd115b6ca90f4c14e3b9184b

2021-07-15T20:09:51.000Z

2022-03-20T04:32:38.000Z

setup.py

Neradoc/discotool

b242be28f490eabcbd115b6ca90f4c14e3b9184b

2021-04-03T06:34:48.000Z

2022-02-16T18:01:28.000Z

setup.py

Neradoc/discotool

b242be28f490eabcbd115b6ca90f4c14e3b9184b

2022-01-25T07:15:18.000Z

2022-01-25T07:15:18.000Z

import os import re import setuptools import subprocess import sys here = os.path.abspath(os.path.dirname(__file__)) repository_name = "Neradoc/discotool" current_tag = subprocess.run("git describe --tags --abbrev=0", capture_output = True, encoding = "utf-8", shell = True, ).stdout.strip() with open(os.path.join(here,"README.md"), "r", encoding="utf-8") as fh: long_description = fh.read() # long_description = long_description.split("## Screenshots")[0].strip() long_description = re.sub(r'\(docs/(.*.png)\)', r'(https://raw.githubusercontent.com/' + repository_name + '/' + current_tag + r'/docs/\1)', long_description) long_description = re.sub(r'\(docs/(.*.md)\)', r'(https://github.com/' + repository_name + '/blob/' + current_tag+r'/docs/\1)', long_description) # with open(os.path.join(here,"requirements.txt"), "r", encoding="utf-8") as fp: # required_modules = fp.read().split("\n") # # platform_req = os.path.join(here,f"requirements-{sys.platform}.txt") # if os.path.exists(platform_req): # with open(platform_req, "r", encoding="utf-8") as fp: # required_modules += fp.read().split("\n") # required_modules = [mod for mod in required_modules if mod] required_modules = [ "click >= 7.1.2", "click-aliases == 1.0.1", "psutil >= 5.8.0", "pyserial >= 3.4", "wmi;platform_system=='Windows'", "pywin32;platform_system=='Windows'", "pyudev;platform_system=='Linux'", ] setuptools.setup( name="discotool", author="Neradoc", author_email="[email protected]", description="Discover, list, and use USB microcontoller boards.", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/Neradoc/discotool", license="MIT", project_urls={ "Bug Tracker": "https://github.com/Neradoc/discotool/issues", }, classifiers=[ "Programming Language :: Python :: 3", "Environment :: Console", "Intended Audience :: Developers", "Intended Audience :: Education", "License :: OSI Approved :: MIT License", "Operating System :: POSIX", "Operating System :: MacOS :: MacOS X", "Operating System :: Microsoft :: Windows", ], packages=setuptools.find_packages(where="."), python_requires=">=3.6", use_scm_version={ 'write_to': 'discotool/_version.py' }, setup_requires=["setuptools_scm"], install_requires=required_modules, entry_points={"console_scripts": ["discotool=discotool.discotool:main"]}, keywords="circuitpython, micropython", )

110d4d3ef5a6d86a9c343a64fedadc4f1c685d0c

py

Python

kruiser_palace/users/tests/test_forms.py

nickblitz/kruisers_palace

bee4a14d3cdbc9501ec02d371199d648776065ee

kruiser_palace/users/tests/test_forms.py

nickblitz/kruisers_palace

bee4a14d3cdbc9501ec02d371199d648776065ee

kruiser_palace/users/tests/test_forms.py

nickblitz/kruisers_palace

bee4a14d3cdbc9501ec02d371199d648776065ee

import pytest from kruiser_palace.users.forms import UserCreationForm from kruiser_palace.users.tests.factories import UserFactory pytestmark = pytest.mark.django_db class TestUserCreationForm: def test_clean_username(self): # A user with proto_user params does not exist yet. proto_user = UserFactory.build() form = UserCreationForm( { "username": proto_user.username, "password1": proto_user._password, "password2": proto_user._password, } ) assert form.is_valid() assert form.clean_username() == proto_user.username # Creating a user. form.save() # The user with proto_user params already exists, # hence cannot be created. form = UserCreationForm( { "username": proto_user.username, "password1": proto_user._password, "password2": proto_user._password, } ) assert not form.is_valid() assert len(form.errors) == 1 assert "username" in form.errors

4af08ca9147157b907bffd27adc2bdb19c5cdfe1

py

Python

src/prefect/engine/__init__.py

skyline-ai/prefect

92430f2f91215d6c27d92ad67df67ccd639e587c

src/prefect/engine/__init__.py

skyline-ai/prefect

92430f2f91215d6c27d92ad67df67ccd639e587c

src/prefect/engine/__init__.py

skyline-ai/prefect

92430f2f91215d6c27d92ad67df67ccd639e587c

from warnings import warn from prefect import config import prefect.engine.executors import prefect.engine.state import prefect.engine.signals import prefect.engine.result import prefect.engine.result_handlers from prefect.engine.flow_runner import FlowRunner from prefect.engine.task_runner import TaskRunner import prefect.engine.cloud def get_default_executor_class() -> type: """ Returns the `Executor` class specified in `prefect.config.engine.executor.default_class`. If the value is a string, it will attempt to load the already-imported object. Otherwise, the value is returned. Defaults to `SynchronousExecutor` if the string config value can not be loaded """ config_value = config.get_nested("engine.executor.default_class") if isinstance(config_value, str): try: return prefect.utilities.serialization.from_qualified_name(config_value) except ValueError: warn( "Could not import {}; using " "prefect.engine.executors.SynchronousExecutor instead.".format( config_value ) ) return prefect.engine.executors.SynchronousExecutor else: return config_value def get_default_flow_runner_class() -> type: """ Returns the `FlowRunner` class specified in `prefect.config.engine.flow_runner.default_class` If the value is a string, it will attempt to load the already-imported object. Otherwise, the value is returned. Defaults to `FlowRunner` if the string config value can not be loaded """ config_value = config.get_nested("engine.flow_runner.default_class") if isinstance(config_value, str): try: return prefect.utilities.serialization.from_qualified_name(config_value) except ValueError: warn( "Could not import {}; using " "prefect.engine.flow_runner.FlowRunner instead.".format(config_value) ) return prefect.engine.flow_runner.FlowRunner else: return config_value def get_default_task_runner_class() -> type: """ Returns the `TaskRunner` class specified in `prefect.config.engine.task_runner.default_class` If the value is a string, it will attempt to load the already-imported object. Otherwise, the value is returned. Defaults to `TaskRunner` if the string config value can not be loaded """ config_value = config.get_nested("engine.task_runner.default_class") if isinstance(config_value, str): try: return prefect.utilities.serialization.from_qualified_name(config_value) except ValueError: warn( "Could not import {}; using " "prefect.engine.task_runner.TaskRunner instead.".format(config_value) ) return prefect.engine.task_runner.TaskRunner else: return config_value def get_default_result_handler_class() -> type: """ Returns the `ResultHandler` class specified in `prefect.config.engine.result_handler.default_class` If the value is a string, it will attempt to load the already-imported object. Otherwise, the value is returned. Defaults to `None` if the string config value can not be loaded """ config_value = config.get_nested("engine.result_handler.default_class") if isinstance(config_value, str): if not config_value: return lambda *args, **kwargs: None # type: ignore try: return prefect.utilities.serialization.from_qualified_name(config_value) except ValueError: warn("Could not import {}; using " "None instead.".format(config_value)) return lambda *args, **kwargs: None # type: ignore else: return config_value

dd092c5dc8b1449a7fbac1f6ed5820fefb4bab83

py

Python

email_split/test/test.py

underdogio/python-email-split

a5fa8a657ee90db68740cfe5d028b8c92afae00d

2016-02-04T01:37:51.000Z

2019-01-28T12:11:47.000Z

email_split/test/test.py

underdogio/python-email-split

a5fa8a657ee90db68740cfe5d028b8c92afae00d

2016-02-04T13:00:29.000Z

2016-12-30T20:45:15.000Z

email_split/test/test.py

underdogio/python-email-split

a5fa8a657ee90db68740cfe5d028b8c92afae00d

2019-12-12T16:30:27.000Z

2022-01-19T08:36:19.000Z

# Load in our dependencies from unittest import TestCase from email_split import email_split # Define our tests class TestEmailSplitFunction(TestCase): def test_top_level_domain(self): """ email-split splitting an email with a top-level domain returns the local part returns the domain part """ email = email_split('[email protected]') self.assertEqual(email.local, 'todd') self.assertEqual(email.domain, 'underdog.io') def test_subdomain(self): """ email-split splitting an email on a subdomain returns the local part returns the domain part (including subdomain) """ email = email_split('[email protected]') self.assertEqual(email.local, 'you') self.assertEqual(email.domain, 'are.super.cool')

16fb78c0ef273b23bd6665eb29fe0e213baa47af

py

Python

ppdet/modeling/backbones/ghostnet.py

cristicmf/PaddleDetection

818533bb299d49f114d36b60b1bff199d0231055

2021-07-06T09:09:12.000Z

2021-07-08T08:06:40.000Z

ppdet/modeling/backbones/ghostnet.py

cristicmf/PaddleDetection

818533bb299d49f114d36b60b1bff199d0231055

ppdet/modeling/backbones/ghostnet.py

cristicmf/PaddleDetection

818533bb299d49f114d36b60b1bff199d0231055

2021-09-30T02:50:21.000Z

2021-11-16T12:38:15.000Z

# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import paddle from paddle import ParamAttr import paddle.nn as nn import paddle.nn.functional as F from paddle.nn import Conv2D, BatchNorm, AdaptiveAvgPool2D, Linear from paddle.regularizer import L2Decay from paddle.nn.initializer import Uniform, KaimingNormal from ppdet.core.workspace import register, serializable from numbers import Integral from ..shape_spec import ShapeSpec from .mobilenet_v3 import make_divisible, ConvBNLayer __all__ = ['GhostNet'] class ExtraBlockDW(nn.Layer): def __init__(self, in_c, ch_1, ch_2, stride, lr_mult, conv_decay=0., norm_type='bn', norm_decay=0., freeze_norm=False, name=None): super(ExtraBlockDW, self).__init__() self.pointwise_conv = ConvBNLayer( in_c=in_c, out_c=ch_1, filter_size=1, stride=1, padding=0, act='relu6', lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name=name + "_extra1") self.depthwise_conv = ConvBNLayer( in_c=ch_1, out_c=ch_2, filter_size=3, stride=stride, padding=1, # num_groups=int(ch_1), act='relu6', lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name=name + "_extra2_dw") self.normal_conv = ConvBNLayer( in_c=ch_2, out_c=ch_2, filter_size=1, stride=1, padding=0, act='relu6', lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name=name + "_extra2_sep") def forward(self, inputs): x = self.pointwise_conv(inputs) x = self.depthwise_conv(x) x = self.normal_conv(x) return x class SEBlock(nn.Layer): def __init__(self, num_channels, lr_mult, reduction_ratio=4, name=None): super(SEBlock, self).__init__() self.pool2d_gap = AdaptiveAvgPool2D(1) self._num_channels = num_channels stdv = 1.0 / math.sqrt(num_channels * 1.0) med_ch = num_channels // reduction_ratio self.squeeze = Linear( num_channels, med_ch, weight_attr=ParamAttr( learning_rate=lr_mult, initializer=Uniform(-stdv, stdv), name=name + "_1_weights"), bias_attr=ParamAttr( learning_rate=lr_mult, name=name + "_1_offset")) stdv = 1.0 / math.sqrt(med_ch * 1.0) self.excitation = Linear( med_ch, num_channels, weight_attr=ParamAttr( learning_rate=lr_mult, initializer=Uniform(-stdv, stdv), name=name + "_2_weights"), bias_attr=ParamAttr( learning_rate=lr_mult, name=name + "_2_offset")) def forward(self, inputs): pool = self.pool2d_gap(inputs) pool = paddle.squeeze(pool, axis=[2, 3]) squeeze = self.squeeze(pool) squeeze = F.relu(squeeze) excitation = self.excitation(squeeze) excitation = paddle.clip(x=excitation, min=0, max=1) excitation = paddle.unsqueeze(excitation, axis=[2, 3]) out = paddle.multiply(inputs, excitation) return out class GhostModule(nn.Layer): def __init__(self, in_channels, output_channels, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True, lr_mult=1., conv_decay=0., norm_type='bn', norm_decay=0., freeze_norm=False, name=None): super(GhostModule, self).__init__() init_channels = int(math.ceil(output_channels / ratio)) new_channels = int(init_channels * (ratio - 1)) self.primary_conv = ConvBNLayer( in_c=in_channels, out_c=init_channels, filter_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2), num_groups=1, act="relu" if relu else None, lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name=name + "_primary_conv") self.cheap_operation = ConvBNLayer( in_c=init_channels, out_c=new_channels, filter_size=dw_size, stride=1, padding=int((dw_size - 1) // 2), num_groups=init_channels, act="relu" if relu else None, lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name=name + "_cheap_operation") def forward(self, inputs): x = self.primary_conv(inputs) y = self.cheap_operation(x) out = paddle.concat([x, y], axis=1) return out class GhostBottleneck(nn.Layer): def __init__(self, in_channels, hidden_dim, output_channels, kernel_size, stride, use_se, lr_mult, conv_decay=0., norm_type='bn', norm_decay=0., freeze_norm=False, return_list=False, name=None): super(GhostBottleneck, self).__init__() self._stride = stride self._use_se = use_se self._num_channels = in_channels self._output_channels = output_channels self.return_list = return_list self.ghost_module_1 = GhostModule( in_channels=in_channels, output_channels=hidden_dim, kernel_size=1, stride=1, relu=True, lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name=name + "_ghost_module_1") if stride == 2: self.depthwise_conv = ConvBNLayer( in_c=hidden_dim, out_c=hidden_dim, filter_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2), num_groups=hidden_dim, act=None, lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name=name + "_depthwise_depthwise" # looks strange due to an old typo, will be fixed later. ) if use_se: self.se_block = SEBlock(hidden_dim, lr_mult, name=name + "_se") self.ghost_module_2 = GhostModule( in_channels=hidden_dim, output_channels=output_channels, kernel_size=1, relu=False, lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name=name + "_ghost_module_2") if stride != 1 or in_channels != output_channels: self.shortcut_depthwise = ConvBNLayer( in_c=in_channels, out_c=in_channels, filter_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2), num_groups=in_channels, act=None, lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name=name + "_shortcut_depthwise_depthwise" # looks strange due to an old typo, will be fixed later. ) self.shortcut_conv = ConvBNLayer( in_c=in_channels, out_c=output_channels, filter_size=1, stride=1, padding=0, num_groups=1, act=None, lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name=name + "_shortcut_conv") def forward(self, inputs): y = self.ghost_module_1(inputs) x = y if self._stride == 2: x = self.depthwise_conv(x) if self._use_se: x = self.se_block(x) x = self.ghost_module_2(x) if self._stride == 1 and self._num_channels == self._output_channels: shortcut = inputs else: shortcut = self.shortcut_depthwise(inputs) shortcut = self.shortcut_conv(shortcut) x = paddle.add(x=x, y=shortcut) if self.return_list: return [y, x] else: return x @register @serializable class GhostNet(nn.Layer): __shared__ = ['norm_type'] def __init__( self, scale=1.3, feature_maps=[6, 12, 15], with_extra_blocks=False, extra_block_filters=[[256, 512], [128, 256], [128, 256], [64, 128]], lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0], conv_decay=0., norm_type='bn', norm_decay=0.0, freeze_norm=False): super(GhostNet, self).__init__() if isinstance(feature_maps, Integral): feature_maps = [feature_maps] if norm_type == 'sync_bn' and freeze_norm: raise ValueError( "The norm_type should not be sync_bn when freeze_norm is True") self.feature_maps = feature_maps self.with_extra_blocks = with_extra_blocks self.extra_block_filters = extra_block_filters inplanes = 16 self.cfgs = [ # k, t, c, SE, s [3, 16, 16, 0, 1], [3, 48, 24, 0, 2], [3, 72, 24, 0, 1], [5, 72, 40, 1, 2], [5, 120, 40, 1, 1], [3, 240, 80, 0, 2], [3, 200, 80, 0, 1], [3, 184, 80, 0, 1], [3, 184, 80, 0, 1], [3, 480, 112, 1, 1], [3, 672, 112, 1, 1], [5, 672, 160, 1, 2], # SSDLite output [5, 960, 160, 0, 1], [5, 960, 160, 1, 1], [5, 960, 160, 0, 1], [5, 960, 160, 1, 1] ] self.scale = scale conv1_out_ch = int(make_divisible(inplanes * self.scale, 4)) self.conv1 = ConvBNLayer( in_c=3, out_c=conv1_out_ch, filter_size=3, stride=2, padding=1, num_groups=1, act="relu", lr_mult=1., conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name="conv1") # build inverted residual blocks self._out_channels = [] self.ghost_bottleneck_list = [] idx = 0 inplanes = conv1_out_ch for k, exp_size, c, use_se, s in self.cfgs: lr_idx = min(idx // 3, len(lr_mult_list) - 1) lr_mult = lr_mult_list[lr_idx] # for SSD/SSDLite, first head input is after ResidualUnit expand_conv return_list = self.with_extra_blocks and idx + 2 in self.feature_maps ghost_bottleneck = self.add_sublayer( "_ghostbottleneck_" + str(idx), sublayer=GhostBottleneck( in_channels=inplanes, hidden_dim=int(make_divisible(exp_size * self.scale, 4)), output_channels=int(make_divisible(c * self.scale, 4)), kernel_size=k, stride=s, use_se=use_se, lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, return_list=return_list, name="_ghostbottleneck_" + str(idx))) self.ghost_bottleneck_list.append(ghost_bottleneck) inplanes = int(make_divisible(c * self.scale, 4)) idx += 1 self._update_out_channels( int(make_divisible(exp_size * self.scale, 4)) if return_list else inplanes, idx + 1, feature_maps) if self.with_extra_blocks: self.extra_block_list = [] extra_out_c = int(make_divisible(self.scale * self.cfgs[-1][1], 4)) lr_idx = min(idx // 3, len(lr_mult_list) - 1) lr_mult = lr_mult_list[lr_idx] conv_extra = self.add_sublayer( "conv" + str(idx + 2), sublayer=ConvBNLayer( in_c=inplanes, out_c=extra_out_c, filter_size=1, stride=1, padding=0, num_groups=1, act="relu6", lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name="conv" + str(idx + 2))) self.extra_block_list.append(conv_extra) idx += 1 self._update_out_channels(extra_out_c, idx + 1, feature_maps) for j, block_filter in enumerate(self.extra_block_filters): in_c = extra_out_c if j == 0 else self.extra_block_filters[j - 1][1] conv_extra = self.add_sublayer( "conv" + str(idx + 2), sublayer=ExtraBlockDW( in_c, block_filter[0], block_filter[1], stride=2, lr_mult=lr_mult, conv_decay=conv_decay, norm_type=norm_type, norm_decay=norm_decay, freeze_norm=freeze_norm, name='conv' + str(idx + 2))) self.extra_block_list.append(conv_extra) idx += 1 self._update_out_channels(block_filter[1], idx + 1, feature_maps) def _update_out_channels(self, channel, feature_idx, feature_maps): if feature_idx in feature_maps: self._out_channels.append(channel) def forward(self, inputs): x = self.conv1(inputs['image']) outs = [] for idx, ghost_bottleneck in enumerate(self.ghost_bottleneck_list): x = ghost_bottleneck(x) if idx + 2 in self.feature_maps: if isinstance(x, list): outs.append(x[0]) x = x[1] else: outs.append(x) if not self.with_extra_blocks: return outs for i, block in enumerate(self.extra_block_list): idx = i + len(self.ghost_bottleneck_list) x = block(x) if idx + 2 in self.feature_maps: outs.append(x) return outs @property def out_shape(self): return [ShapeSpec(channels=c) for c in self._out_channels]

dedd5492d697c0bfb2a1d4e8a1d0cc2649cea10a

py

Python

setup.py

wazizian/torch_spspmm_out

b97297cec7263ec9a34e4230d867a59bf01b3a4b

setup.py

wazizian/torch_spspmm_out

b97297cec7263ec9a34e4230d867a59bf01b3a4b

setup.py

wazizian/torch_spspmm_out

b97297cec7263ec9a34e4230d867a59bf01b3a4b

from setuptools import setup import os import torch from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension cuda_support = torch.cuda.is_available() def get_extensions(): extra_link_args = [] extension_dir = os.path.join(os.path.dirname(__file__), "csrc") if cuda_support: Extension = CUDAExtension extra_link_args += ["-lcusparse"] else: Extension = CppExtension extension = Extension(name="torch_spspmm_out._spspmm_out", sources=[ "csrc/spspmm_out.cpp", ], include_dirs=[extension_dir], extra_link_args=extra_link_args, ) return [extension] setup( name="torch_spspmm_out", install_requires=["torch"], ext_modules=get_extensions(), cmdclass={"build_ext": BuildExtension.with_options(use_ninja=False, no_python_abi_suffix=True)} )

d262781e2f1ae6d216bcc38a963b29a8b3531299

py

Python

ArubaOS-Sw_API_Scripts/python_and_rest_api_vid_scripts/get_cookie_create_vlan.py

smallfount/scriptsonly

cabdfc301da4e1653705d713b306f3fbf7f6934d

2016-05-24T23:32:02.000Z

2021-11-17T07:53:50.000Z

ArubaOS-Sw_API_Scripts/python_and_rest_api_vid_scripts/get_cookie_create_vlan.py

posai8701/scriptsonly

cabdfc301da4e1653705d713b306f3fbf7f6934d

2016-09-25T15:55:02.000Z

2018-09-06T10:54:45.000Z

ArubaOS-Sw_API_Scripts/python_and_rest_api_vid_scripts/get_cookie_create_vlan.py

posai8701/scriptsonly

cabdfc301da4e1653705d713b306f3fbf7f6934d

2016-03-02T17:37:07.000Z

2021-11-17T07:54:04.000Z

import requests vlan_number = input('Enter VLAN number:') vlan_name = input('Enter VLAN name:') url_login = "http://192.168.1.29/rest/v1/login-sessions" url_vlans = "http://192.168.1.29/rest/v1/vlans" payload_login = "{\"userName\": \"joe\", \"password\": \"x\"}" get_cookie = requests.request("POST", url_login, data=payload_login) r_cookie = get_cookie.json()['cookie'] print(r_cookie) payload_vlan = "{\"vlan_id\":"+vlan_number+",\"name\":\""+vlan_name+"\"}" print(payload_vlan) headers = {'cookie': r_cookie } config_vlan = requests.request("POST", url_vlans, data=payload_vlan, headers=headers) print(config_vlan)

21a75d48ee5d30d0bbc8bd951cda3ac93a14af4b

py

Python

tests/bhive/test_block.py

TheCrazyGM/bhive

1494e90a99123ecfc5efbd927258f9ba59443e2e

2020-03-21T23:50:22.000Z

2020-03-25T19:10:48.000Z

tests/bhive/test_block.py

TheCrazyGM/bhive

1494e90a99123ecfc5efbd927258f9ba59443e2e

tests/bhive/test_block.py

TheCrazyGM/bhive

1494e90a99123ecfc5efbd927258f9ba59443e2e

2020-03-21T23:50:25.000Z

2020-03-21T23:50:25.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from builtins import super import unittest from parameterized import parameterized from pprint import pprint from bhive import Hive, exceptions from bhive.block import Block, BlockHeader from datetime import datetime from bhive.instance import set_shared_hive_instance from bhive.nodelist import NodeList wif = "5KQwrPbwdL6PhXujxW37FSSQZ1JiwsST4cqQzDeyXtP79zkvFD3" class Testcases(unittest.TestCase): @classmethod def setUpClass(cls): nodelist = NodeList() nodelist.update_nodes(hive_instance=Hive(node=nodelist.get_nodes(exclude_limited=False), num_retries=10)) cls.bts = Hive( node=nodelist.get_nodes(exclude_limited=True), nobroadcast=True, keys={"active": wif}, num_retries=10 ) cls.test_block_id = 19273700 # from getpass import getpass # self.bts.wallet.unlock(getpass()) set_shared_hive_instance(cls.bts) cls.bts.set_default_account("test") def test_block(self): bts = self.bts test_block_id = self.test_block_id block = Block(test_block_id, hive_instance=bts) self.assertEqual(block.identifier, test_block_id) self.assertTrue(isinstance(block.time(), datetime)) self.assertTrue(isinstance(block, dict)) self.assertTrue(len(block.operations)) self.assertTrue(isinstance(block.ops_statistics(), dict)) block2 = Block(test_block_id + 1, hive_instance=bts) self.assertTrue(block2.time() > block.time()) with self.assertRaises( exceptions.BlockDoesNotExistsException ): Block(0, hive_instance=bts) def test_block_only_ops(self): bts = self.bts test_block_id = self.test_block_id block = Block(test_block_id, only_ops=True, hive_instance=bts) self.assertEqual(block.identifier, test_block_id) self.assertTrue(isinstance(block.time(), datetime)) self.assertTrue(isinstance(block, dict)) self.assertTrue(len(block.operations)) self.assertTrue(isinstance(block.ops_statistics(), dict)) block2 = Block(test_block_id + 1, hive_instance=bts) self.assertTrue(block2.time() > block.time()) with self.assertRaises( exceptions.BlockDoesNotExistsException ): Block(0, hive_instance=bts) def test_block_header(self): bts = self.bts test_block_id = self.test_block_id block = BlockHeader(test_block_id, hive_instance=bts) self.assertEqual(block.identifier, test_block_id) self.assertTrue(isinstance(block.time(), datetime)) self.assertTrue(isinstance(block, dict)) block2 = BlockHeader(test_block_id + 1, hive_instance=bts) self.assertTrue(block2.time() > block.time()) with self.assertRaises( exceptions.BlockDoesNotExistsException ): BlockHeader(0, hive_instance=bts) def test_export(self): bts = self.bts block_num = 2000000 if bts.rpc.get_use_appbase(): block = bts.rpc.get_block({"block_num": block_num}, api="block") if block and "block" in block: block = block["block"] else: block = bts.rpc.get_block(block_num) b = Block(block_num, hive_instance=bts) keys = list(block.keys()) json_content = b.json() for k in keys: if k not in "json_metadata": if isinstance(block[k], dict) and isinstance(json_content[k], list): self.assertEqual(list(block[k].values()), json_content[k]) else: self.assertEqual(block[k], json_content[k]) if bts.rpc.get_use_appbase(): block = bts.rpc.get_block_header({"block_num": block_num}, api="block") if "header" in block: block = block["header"] else: block = bts.rpc.get_block_header(block_num) b = BlockHeader(block_num, hive_instance=bts) keys = list(block.keys()) json_content = b.json() for k in keys: if k not in "json_metadata": if isinstance(block[k], dict) and isinstance(json_content[k], list): self.assertEqual(list(block[k].values()), json_content[k]) else: self.assertEqual(block[k], json_content[k])

6e1fffc0dc7165bf31e251806efb26ae83f9d194

py

Python

UMLRT2Kiltera_MM/graph_MT_post__Trigger_S.py

levilucio/SyVOLT

7526ec794d21565e3efcc925a7b08ae8db27d46a

2017-06-02T19:26:27.000Z

2021-06-14T04:25:45.000Z

UMLRT2Kiltera_MM/graph_MT_post__Trigger_S.py

levilucio/SyVOLT

7526ec794d21565e3efcc925a7b08ae8db27d46a

2016-08-24T07:04:07.000Z

2017-05-26T16:22:47.000Z

UMLRT2Kiltera_MM/graph_MT_post__Trigger_S.py

levilucio/SyVOLT

7526ec794d21565e3efcc925a7b08ae8db27d46a

2019-10-31T06:00:23.000Z

2019-10-31T06:00:23.000Z

""" __graph_MT_post__Trigger_S.py___________________________________________________________ Automatically generated graphical appearance ---> MODIFY DIRECTLY WITH CAUTION _____________________________________________________________________________ """ import tkFont from graphEntity import * from GraphicalForm import * from ATOM3Constraint import * class graph_MT_post__Trigger_S(graphEntity): def __init__(self, x, y, semObject = None): self.semanticObject = semObject self.sizeX, self.sizeY = 172, 82 graphEntity.__init__(self, x, y) self.ChangesAtRunTime = 0 self.constraintList = [] if self.semanticObject: atribs = self.semanticObject.attributesToDraw() else: atribs = None self.graphForms = [] self.imageDict = self.getImageDict() def DrawObject(self, drawing, showGG = 0): self.dc = drawing if showGG and self.semanticObject: self.drawGGLabel(drawing) h = drawing.create_oval(self.translate([189.0, 62.0, 189.0, 62.0]), tags = (self.tag, 'connector'), outline = '', fill = '' ) self.connectors.append( h ) h = drawing.create_rectangle(self.translate([20.0, 20.0, 190.0, 100.0]), tags = self.tag, stipple = '', width = 1, outline = 'black', fill = 'moccasin') self.gf4 = GraphicalForm(drawing, h, "gf4") self.graphForms.append(self.gf4) font = tkFont.Font( family='Arial', size=12, weight='normal', slant='roman', underline=0) h = drawing.create_text(self.translate([30.0, 31.0, 30.0, 12.0])[:2], tags = self.tag, font=font, fill = 'grey45', anchor = 'center', text = '', width = '0', justify= 'left', stipple='' ) self.gf7 = GraphicalForm(drawing, h, 'gf7', fontObject=font) self.graphForms.append(self.gf7) font = tkFont.Font( family='Arial', size=12, weight='normal', slant='roman', underline=0) h = drawing.create_text(self.translate([100.0, 40.0, 100.0, 12.0])[:2], tags = self.tag, font=font, fill = 'black', anchor = 'center', text = 'Element', width = '0', justify= 'left', stipple='' ) self.gf8 = GraphicalForm(drawing, h, 'gf8', fontObject=font) self.graphForms.append(self.gf8) helv12 = tkFont.Font ( family="Helvetica", size=12, weight="bold" ) h = drawing.create_text(self.translate([-3, -3]), font=helv12, tags = (self.tag, self.semanticObject.getClass()), fill = "black", text=self.semanticObject.MT_label__.toString()) self.attr_display["MT_label__"] = h self.gf_label = GraphicalForm(drawing, h, 'gf_label', fontObject=helv12) self.graphForms.append(self.gf_label) def postCondition( self, actionID, * params): return None def preCondition( self, actionID, * params): return None def getImageDict( self ): imageDict = dict() return imageDict new_class = graph_MT_post__Trigger_S

cb896d37c24a13ca79091ec139a8c83b9a297798

py

Python

test/rpc_spentindex.py

odavila466/Kron-Project

8a915e6287ac6d21ac0a32ff69f6f04e260bd1f5

2021-05-18T05:11:56.000Z

2021-12-05T11:25:38.000Z

test/rpc_spentindex.py

BaymaxValero/Kron-Project

e56e596ee36e4b6949ebb75a01867c08481139e2

2021-05-13T19:01:05.000Z

2021-05-13T19:01:57.000Z

test/rpc_spentindex.py

BaymaxValero/Kron-Project

e56e596ee36e4b6949ebb75a01867c08481139e2

2021-05-18T05:11:58.000Z

2021-05-18T05:11:58.000Z

#!/usr/bin/env python3 # Copyright (c) 2014-2015 The Bitcoin Core developers # Copyright (c) 2017-2020 The Kron Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test RPC addressindex generation and fetching""" import binascii from test_framework.test_framework import KronTestFramework from test_framework.util import connect_nodes_bi, assert_equal from test_framework.script import CScript, OP_DUP, OP_HASH160, OP_EQUALVERIFY, OP_CHECKSIG from test_framework.mininode import CTransaction, CTxIn, COutPoint, CTxOut class SpentIndexTest(KronTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 4 def setup_network(self): self.add_nodes(4, [ # Nodes 0/1 are "wallet" nodes [], ["-spentindex"], # Nodes 2/3 are used for testing ["-spentindex"], ["-spentindex", "-txindex"]]) self.start_nodes() connect_nodes_bi(self.nodes, 0, 1) connect_nodes_bi(self.nodes, 0, 2) connect_nodes_bi(self.nodes, 0, 3) self.sync_all() def run_test(self): self.log.info("Mining blocks...") self.nodes[0].generate(105) self.sync_all() chain_height = self.nodes[1].getblockcount() assert_equal(chain_height, 105) # Check that self.log.info("Testing spent index...") fee_satoshis = 192000 privkey = "cSdkPxkAjA4HDr5VHgsebAPDEh9Gyub4HK8UJr2DFGGqKKy4K5sG" #address = "mgY65WSfEmsyYaYPQaXhmXMeBhwp4EcsQW" address_hash = bytes([11,47,10,12,49,191,224,64,107,12,204,19,129,253,190,49,25,70,218,220]) script_pub_key = CScript([OP_DUP, OP_HASH160, address_hash, OP_EQUALVERIFY, OP_CHECKSIG]) unspent = self.nodes[0].listunspent() tx = CTransaction() amount = int(unspent[0]["amount"] * 100000000 - fee_satoshis) tx.vin = [CTxIn(COutPoint(int(unspent[0]["txid"], 16), unspent[0]["vout"]))] tx.vout = [CTxOut(amount, script_pub_key)] tx.rehash() signed_tx = self.nodes[0].signrawtransaction(binascii.hexlify(tx.serialize()).decode("utf-8")) txid = self.nodes[0].sendrawtransaction(signed_tx["hex"], True) self.nodes[0].generate(1) self.sync_all() self.log.info("Testing getspentinfo method...") # Check that the spentinfo works standalone info = self.nodes[1].getspentinfo({"txid": unspent[0]["txid"], "index": unspent[0]["vout"]}) assert_equal(info["txid"], txid) assert_equal(info["index"], 0) assert_equal(info["height"], 106) self.log.info("Testing getrawtransaction method...") # Check that verbose raw transaction includes spent info tx_verbose = self.nodes[3].getrawtransaction(unspent[0]["txid"], 1) assert_equal(tx_verbose["vout"][unspent[0]["vout"]]["spentTxId"], txid) assert_equal(tx_verbose["vout"][unspent[0]["vout"]]["spentIndex"], 0) assert_equal(tx_verbose["vout"][unspent[0]["vout"]]["spentHeight"], 106) # Check that verbose raw transaction includes input values tx_verbose2 = self.nodes[3].getrawtransaction(txid, 1) assert_equal(float(tx_verbose2["vin"][0]["value"]), (amount + fee_satoshis) / 100000000) assert_equal(tx_verbose2["vin"][0]["valueSat"], amount + fee_satoshis) # Check that verbose raw transaction includes address values and input values #privkey2 = "cSdkPxkAjA4HDr5VHgsebAPDEh9Gyub4HK8UJr2DFGGqKKy4K5sG" address2 = "mgY65WSfEmsyYaYPQaXhmXMeBhwp4EcsQW" address_hash2 = bytes([11, 47, 10, 12, 49, 191, 224, 64, 107, 12, 204, 19, 129, 253, 190, 49, 25, 70, 218, 220]) script_pub_key2 = CScript([OP_DUP, OP_HASH160, address_hash2, OP_EQUALVERIFY, OP_CHECKSIG]) tx2 = CTransaction() tx2.vin = [CTxIn(COutPoint(int(txid, 16), 0))] amount = int(amount - fee_satoshis) tx2.vout = [CTxOut(amount, script_pub_key2)] tx.rehash() self.nodes[0].importprivkey(privkey) signed_tx2 = self.nodes[0].signrawtransaction(binascii.hexlify(tx2.serialize()).decode("utf-8")) txid2 = self.nodes[0].sendrawtransaction(signed_tx2["hex"], True) # Check the mempool index self.sync_all() tx_verbose3 = self.nodes[1].getrawtransaction(txid2, 1) assert_equal(tx_verbose3["vin"][0]["address"], address2) assert_equal(tx_verbose3["vin"][0]["valueSat"], amount + fee_satoshis) assert_equal(float(tx_verbose3["vin"][0]["value"]), (amount + fee_satoshis) / 100000000) # Check the database index block_hash = self.nodes[0].generate(1) self.sync_all() tx_verbose4 = self.nodes[3].getrawtransaction(txid2, 1) assert_equal(tx_verbose4["vin"][0]["address"], address2) assert_equal(tx_verbose4["vin"][0]["valueSat"], amount + fee_satoshis) assert_equal(float(tx_verbose4["vin"][0]["value"]), (amount + fee_satoshis) / 100000000) # Check block deltas self.log.info("Testing getblockdeltas...") block = self.nodes[3].getblockdeltas(block_hash[0]) assert_equal(len(block["deltas"]), 2) assert_equal(block["deltas"][0]["index"], 0) assert_equal(len(block["deltas"][0]["inputs"]), 0) assert_equal(len(block["deltas"][0]["outputs"]), 0) assert_equal(block["deltas"][1]["index"], 1) assert_equal(block["deltas"][1]["txid"], txid2) assert_equal(block["deltas"][1]["inputs"][0]["index"], 0) assert_equal(block["deltas"][1]["inputs"][0]["address"], "mgY65WSfEmsyYaYPQaXhmXMeBhwp4EcsQW") assert_equal(block["deltas"][1]["inputs"][0]["satoshis"], (amount + fee_satoshis) * -1) assert_equal(block["deltas"][1]["inputs"][0]["prevtxid"], txid) assert_equal(block["deltas"][1]["inputs"][0]["prevout"], 0) assert_equal(block["deltas"][1]["outputs"][0]["index"], 0) assert_equal(block["deltas"][1]["outputs"][0]["address"], "mgY65WSfEmsyYaYPQaXhmXMeBhwp4EcsQW") assert_equal(block["deltas"][1]["outputs"][0]["satoshis"], amount) self.log.info("All Tests Passed") if __name__ == '__main__': SpentIndexTest().main()

7d4b1b7ef34660509d17409582e068d17d33c3a0

py

Python

accounts/migrations/0002_auto_20211112_0737.py

shakori999/Django_CRM

82789878b679e68e993295fde0040b16a1c56767

accounts/migrations/0002_auto_20211112_0737.py

shakori999/Django_CRM

82789878b679e68e993295fde0040b16a1c56767

2022-03-21T08:48:46.000Z

2022-03-21T08:49:57.000Z

accounts/migrations/0002_auto_20211112_0737.py

shakori999/Django_CRM

82789878b679e68e993295fde0040b16a1c56767

# Generated by Django 3.2.9 on 2021-11-12 04:37 from django.db import migrations, models import djmoney.models.fields class Migration(migrations.Migration): dependencies = [ ('accounts', '0001_initial'), ] operations = [ migrations.AddField( model_name='customer', name='wallet_currency', field=djmoney.models.fields.CurrencyField(choices=[('XUA', 'ADB Unit of Account'), ('AFN', 'Afghan Afghani'), ('AFA', 'Afghan Afghani (1927–2002)'), ('ALL', 'Albanian Lek'), ('ALK', 'Albanian Lek (1946–1965)'), ('DZD', 'Algerian Dinar'), ('ADP', 'Andorran Peseta'), ('AOA', 'Angolan Kwanza'), ('AOK', 'Angolan Kwanza (1977–1991)'), ('AON', 'Angolan New Kwanza (1990–2000)'), ('AOR', 'Angolan Readjusted Kwanza (1995–1999)'), ('ARA', 'Argentine Austral'), ('ARS', 'Argentine Peso'), ('ARM', 'Argentine Peso (1881–1970)'), ('ARP', 'Argentine Peso (1983–1985)'), ('ARL', 'Argentine Peso Ley (1970–1983)'), ('AMD', 'Armenian Dram'), ('AWG', 'Aruban Florin'), ('AUD', 'Australian Dollar'), ('ATS', 'Austrian Schilling'), ('AZN', 'Azerbaijani Manat'), ('AZM', 'Azerbaijani Manat (1993–2006)'), ('BSD', 'Bahamian Dollar'), ('BHD', 'Bahraini Dinar'), ('BDT', 'Bangladeshi Taka'), ('BBD', 'Barbadian Dollar'), ('BYN', 'Belarusian Ruble'), ('BYB', 'Belarusian Ruble (1994–1999)'), ('BYR', 'Belarusian Ruble (2000–2016)'), ('BEF', 'Belgian Franc'), ('BEC', 'Belgian Franc (convertible)'), ('BEL', 'Belgian Franc (financial)'), ('BZD', 'Belize Dollar'), ('BMD', 'Bermudan Dollar'), ('BTN', 'Bhutanese Ngultrum'), ('BOB', 'Bolivian Boliviano'), ('BOL', 'Bolivian Boliviano (1863–1963)'), ('BOV', 'Bolivian Mvdol'), ('BOP', 'Bolivian Peso'), ('BAM', 'Bosnia-Herzegovina Convertible Mark'), ('BAD', 'Bosnia-Herzegovina Dinar (1992–1994)'), ('BAN', 'Bosnia-Herzegovina New Dinar (1994–1997)'), ('BWP', 'Botswanan Pula'), ('BRC', 'Brazilian Cruzado (1986–1989)'), ('BRZ', 'Brazilian Cruzeiro (1942–1967)'), ('BRE', 'Brazilian Cruzeiro (1990–1993)'), ('BRR', 'Brazilian Cruzeiro (1993–1994)'), ('BRN', 'Brazilian New Cruzado (1989–1990)'), ('BRB', 'Brazilian New Cruzeiro (1967–1986)'), ('BRL', 'Brazilian Real'), ('GBP', 'British Pound'), ('BND', 'Brunei Dollar'), ('BGL', 'Bulgarian Hard Lev'), ('BGN', 'Bulgarian Lev'), ('BGO', 'Bulgarian Lev (1879–1952)'), ('BGM', 'Bulgarian Socialist Lev'), ('BUK', 'Burmese Kyat'), ('BIF', 'Burundian Franc'), ('XPF', 'CFP Franc'), ('KHR', 'Cambodian Riel'), ('CAD', 'Canadian Dollar'), ('CVE', 'Cape Verdean Escudo'), ('KYD', 'Cayman Islands Dollar'), ('XAF', 'Central African CFA Franc'), ('CLE', 'Chilean Escudo'), ('CLP', 'Chilean Peso'), ('CLF', 'Chilean Unit of Account (UF)'), ('CNX', 'Chinese People’s Bank Dollar'), ('CNY', 'Chinese Yuan'), ('CNH', 'Chinese Yuan (offshore)'), ('COP', 'Colombian Peso'), ('COU', 'Colombian Real Value Unit'), ('KMF', 'Comorian Franc'), ('CDF', 'Congolese Franc'), ('CRC', 'Costa Rican Colón'), ('HRD', 'Croatian Dinar'), ('HRK', 'Croatian Kuna'), ('CUC', 'Cuban Convertible Peso'), ('CUP', 'Cuban Peso'), ('CYP', 'Cypriot Pound'), ('CZK', 'Czech Koruna'), ('CSK', 'Czechoslovak Hard Koruna'), ('DKK', 'Danish Krone'), ('DJF', 'Djiboutian Franc'), ('DOP', 'Dominican Peso'), ('NLG', 'Dutch Guilder'), ('XCD', 'East Caribbean Dollar'), ('DDM', 'East German Mark'), ('ECS', 'Ecuadorian Sucre'), ('ECV', 'Ecuadorian Unit of Constant Value'), ('EGP', 'Egyptian Pound'), ('GQE', 'Equatorial Guinean Ekwele'), ('ERN', 'Eritrean Nakfa'), ('EEK', 'Estonian Kroon'), ('ETB', 'Ethiopian Birr'), ('EUR', 'Euro'), ('XBA', 'European Composite Unit'), ('XEU', 'European Currency Unit'), ('XBB', 'European Monetary Unit'), ('XBC', 'European Unit of Account (XBC)'), ('XBD', 'European Unit of Account (XBD)'), ('FKP', 'Falkland Islands Pound'), ('FJD', 'Fijian Dollar'), ('FIM', 'Finnish Markka'), ('FRF', 'French Franc'), ('XFO', 'French Gold Franc'), ('XFU', 'French UIC-Franc'), ('GMD', 'Gambian Dalasi'), ('GEK', 'Georgian Kupon Larit'), ('GEL', 'Georgian Lari'), ('DEM', 'German Mark'), ('GHS', 'Ghanaian Cedi'), ('GHC', 'Ghanaian Cedi (1979–2007)'), ('GIP', 'Gibraltar Pound'), ('XAU', 'Gold'), ('GRD', 'Greek Drachma'), ('GTQ', 'Guatemalan Quetzal'), ('GWP', 'Guinea-Bissau Peso'), ('GNF', 'Guinean Franc'), ('GNS', 'Guinean Syli'), ('GYD', 'Guyanaese Dollar'), ('HTG', 'Haitian Gourde'), ('HNL', 'Honduran Lempira'), ('HKD', 'Hong Kong Dollar'), ('HUF', 'Hungarian Forint'), ('IMP', 'IMP'), ('ISK', 'Icelandic Króna'), ('ISJ', 'Icelandic Króna (1918–1981)'), ('INR', 'Indian Rupee'), ('IDR', 'Indonesian Rupiah'), ('IRR', 'Iranian Rial'), ('IQD', 'Iraqi Dinar'), ('IEP', 'Irish Pound'), ('ILS', 'Israeli New Shekel'), ('ILP', 'Israeli Pound'), ('ILR', 'Israeli Shekel (1980–1985)'), ('ITL', 'Italian Lira'), ('JMD', 'Jamaican Dollar'), ('JPY', 'Japanese Yen'), ('JOD', 'Jordanian Dinar'), ('KZT', 'Kazakhstani Tenge'), ('KES', 'Kenyan Shilling'), ('KWD', 'Kuwaiti Dinar'), ('KGS', 'Kyrgystani Som'), ('LAK', 'Laotian Kip'), ('LVL', 'Latvian Lats'), ('LVR', 'Latvian Ruble'), ('LBP', 'Lebanese Pound'), ('LSL', 'Lesotho Loti'), ('LRD', 'Liberian Dollar'), ('LYD', 'Libyan Dinar'), ('LTL', 'Lithuanian Litas'), ('LTT', 'Lithuanian Talonas'), ('LUL', 'Luxembourg Financial Franc'), ('LUC', 'Luxembourgian Convertible Franc'), ('LUF', 'Luxembourgian Franc'), ('MOP', 'Macanese Pataca'), ('MKD', 'Macedonian Denar'), ('MKN', 'Macedonian Denar (1992–1993)'), ('MGA', 'Malagasy Ariary'), ('MGF', 'Malagasy Franc'), ('MWK', 'Malawian Kwacha'), ('MYR', 'Malaysian Ringgit'), ('MVR', 'Maldivian Rufiyaa'), ('MVP', 'Maldivian Rupee (1947–1981)'), ('MLF', 'Malian Franc'), ('MTL', 'Maltese Lira'), ('MTP', 'Maltese Pound'), ('MRU', 'Mauritanian Ouguiya'), ('MRO', 'Mauritanian Ouguiya (1973–2017)'), ('MUR', 'Mauritian Rupee'), ('MXV', 'Mexican Investment Unit'), ('MXN', 'Mexican Peso'), ('MXP', 'Mexican Silver Peso (1861–1992)'), ('MDC', 'Moldovan Cupon'), ('MDL', 'Moldovan Leu'), ('MCF', 'Monegasque Franc'), ('MNT', 'Mongolian Tugrik'), ('MAD', 'Moroccan Dirham'), ('MAF', 'Moroccan Franc'), ('MZE', 'Mozambican Escudo'), ('MZN', 'Mozambican Metical'), ('MZM', 'Mozambican Metical (1980–2006)'), ('MMK', 'Myanmar Kyat'), ('NAD', 'Namibian Dollar'), ('NPR', 'Nepalese Rupee'), ('ANG', 'Netherlands Antillean Guilder'), ('TWD', 'New Taiwan Dollar'), ('NZD', 'New Zealand Dollar'), ('NIO', 'Nicaraguan Córdoba'), ('NIC', 'Nicaraguan Córdoba (1988–1991)'), ('NGN', 'Nigerian Naira'), ('KPW', 'North Korean Won'), ('NOK', 'Norwegian Krone'), ('OMR', 'Omani Rial'), ('PKR', 'Pakistani Rupee'), ('XPD', 'Palladium'), ('PAB', 'Panamanian Balboa'), ('PGK', 'Papua New Guinean Kina'), ('PYG', 'Paraguayan Guarani'), ('PEI', 'Peruvian Inti'), ('PEN', 'Peruvian Sol'), ('PES', 'Peruvian Sol (1863–1965)'), ('PHP', 'Philippine Piso'), ('XPT', 'Platinum'), ('PLN', 'Polish Zloty'), ('PLZ', 'Polish Zloty (1950–1995)'), ('PTE', 'Portuguese Escudo'), ('GWE', 'Portuguese Guinea Escudo'), ('QAR', 'Qatari Rial'), ('XRE', 'RINET Funds'), ('RHD', 'Rhodesian Dollar'), ('RON', 'Romanian Leu'), ('ROL', 'Romanian Leu (1952–2006)'), ('RUB', 'Russian Ruble'), ('RUR', 'Russian Ruble (1991–1998)'), ('RWF', 'Rwandan Franc'), ('SVC', 'Salvadoran Colón'), ('WST', 'Samoan Tala'), ('SAR', 'Saudi Riyal'), ('RSD', 'Serbian Dinar'), ('CSD', 'Serbian Dinar (2002–2006)'), ('SCR', 'Seychellois Rupee'), ('SLL', 'Sierra Leonean Leone'), ('XAG', 'Silver'), ('SGD', 'Singapore Dollar'), ('SKK', 'Slovak Koruna'), ('SIT', 'Slovenian Tolar'), ('SBD', 'Solomon Islands Dollar'), ('SOS', 'Somali Shilling'), ('ZAR', 'South African Rand'), ('ZAL', 'South African Rand (financial)'), ('KRH', 'South Korean Hwan (1953–1962)'), ('KRW', 'South Korean Won'), ('KRO', 'South Korean Won (1945–1953)'), ('SSP', 'South Sudanese Pound'), ('SUR', 'Soviet Rouble'), ('ESP', 'Spanish Peseta'), ('ESA', 'Spanish Peseta (A account)'), ('ESB', 'Spanish Peseta (convertible account)'), ('XDR', 'Special Drawing Rights'), ('LKR', 'Sri Lankan Rupee'), ('SHP', 'St. Helena Pound'), ('XSU', 'Sucre'), ('SDD', 'Sudanese Dinar (1992–2007)'), ('SDG', 'Sudanese Pound'), ('SDP', 'Sudanese Pound (1957–1998)'), ('SRD', 'Surinamese Dollar'), ('SRG', 'Surinamese Guilder'), ('SZL', 'Swazi Lilangeni'), ('SEK', 'Swedish Krona'), ('CHF', 'Swiss Franc'), ('SYP', 'Syrian Pound'), ('STN', 'São Tomé & Príncipe Dobra'), ('STD', 'São Tomé & Príncipe Dobra (1977–2017)'), ('TVD', 'TVD'), ('TJR', 'Tajikistani Ruble'), ('TJS', 'Tajikistani Somoni'), ('TZS', 'Tanzanian Shilling'), ('XTS', 'Testing Currency Code'), ('THB', 'Thai Baht'), ('XXX', 'The codes assigned for transactions where no currency is involved'), ('TPE', 'Timorese Escudo'), ('TOP', 'Tongan Paʻanga'), ('TTD', 'Trinidad & Tobago Dollar'), ('TND', 'Tunisian Dinar'), ('TRY', 'Turkish Lira'), ('TRL', 'Turkish Lira (1922–2005)'), ('TMT', 'Turkmenistani Manat'), ('TMM', 'Turkmenistani Manat (1993–2009)'), ('USD', 'US Dollar'), ('USN', 'US Dollar (Next day)'), ('USS', 'US Dollar (Same day)'), ('UGX', 'Ugandan Shilling'), ('UGS', 'Ugandan Shilling (1966–1987)'), ('UAH', 'Ukrainian Hryvnia'), ('UAK', 'Ukrainian Karbovanets'), ('AED', 'United Arab Emirates Dirham'), ('UYW', 'Uruguayan Nominal Wage Index Unit'), ('UYU', 'Uruguayan Peso'), ('UYP', 'Uruguayan Peso (1975–1993)'), ('UYI', 'Uruguayan Peso (Indexed Units)'), ('UZS', 'Uzbekistani Som'), ('VUV', 'Vanuatu Vatu'), ('VES', 'Venezuelan Bolívar'), ('VEB', 'Venezuelan Bolívar (1871–2008)'), ('VEF', 'Venezuelan Bolívar (2008–2018)'), ('VND', 'Vietnamese Dong'), ('VNN', 'Vietnamese Dong (1978–1985)'), ('CHE', 'WIR Euro'), ('CHW', 'WIR Franc'), ('XOF', 'West African CFA Franc'), ('YDD', 'Yemeni Dinar'), ('YER', 'Yemeni Rial'), ('YUN', 'Yugoslavian Convertible Dinar (1990–1992)'), ('YUD', 'Yugoslavian Hard Dinar (1966–1990)'), ('YUM', 'Yugoslavian New Dinar (1994–2002)'), ('YUR', 'Yugoslavian Reformed Dinar (1992–1993)'), ('ZWN', 'ZWN'), ('ZRN', 'Zairean New Zaire (1993–1998)'), ('ZRZ', 'Zairean Zaire (1971–1993)'), ('ZMW', 'Zambian Kwacha'), ('ZMK', 'Zambian Kwacha (1968–2012)'), ('ZWD', 'Zimbabwean Dollar (1980–2008)'), ('ZWR', 'Zimbabwean Dollar (2008)'), ('ZWL', 'Zimbabwean Dollar (2009)')], default='IQD', editable=False, max_length=3), ), migrations.AddField( model_name='order', name='price_currency', field=djmoney.models.fields.CurrencyField(choices=[('XUA', 'ADB Unit of Account'), ('AFN', 'Afghan Afghani'), ('AFA', 'Afghan Afghani (1927–2002)'), ('ALL', 'Albanian Lek'), ('ALK', 'Albanian Lek (1946–1965)'), ('DZD', 'Algerian Dinar'), ('ADP', 'Andorran Peseta'), ('AOA', 'Angolan Kwanza'), ('AOK', 'Angolan Kwanza (1977–1991)'), ('AON', 'Angolan New Kwanza (1990–2000)'), ('AOR', 'Angolan Readjusted Kwanza (1995–1999)'), ('ARA', 'Argentine Austral'), ('ARS', 'Argentine Peso'), ('ARM', 'Argentine Peso (1881–1970)'), ('ARP', 'Argentine Peso (1983–1985)'), ('ARL', 'Argentine Peso Ley (1970–1983)'), ('AMD', 'Armenian Dram'), ('AWG', 'Aruban Florin'), ('AUD', 'Australian Dollar'), ('ATS', 'Austrian Schilling'), ('AZN', 'Azerbaijani Manat'), ('AZM', 'Azerbaijani Manat (1993–2006)'), ('BSD', 'Bahamian Dollar'), ('BHD', 'Bahraini Dinar'), ('BDT', 'Bangladeshi Taka'), ('BBD', 'Barbadian Dollar'), ('BYN', 'Belarusian Ruble'), ('BYB', 'Belarusian Ruble (1994–1999)'), ('BYR', 'Belarusian Ruble (2000–2016)'), ('BEF', 'Belgian Franc'), ('BEC', 'Belgian Franc (convertible)'), ('BEL', 'Belgian Franc (financial)'), ('BZD', 'Belize Dollar'), ('BMD', 'Bermudan Dollar'), ('BTN', 'Bhutanese Ngultrum'), ('BOB', 'Bolivian Boliviano'), ('BOL', 'Bolivian Boliviano (1863–1963)'), ('BOV', 'Bolivian Mvdol'), ('BOP', 'Bolivian Peso'), ('BAM', 'Bosnia-Herzegovina Convertible Mark'), ('BAD', 'Bosnia-Herzegovina Dinar (1992–1994)'), ('BAN', 'Bosnia-Herzegovina New Dinar (1994–1997)'), ('BWP', 'Botswanan Pula'), ('BRC', 'Brazilian Cruzado (1986–1989)'), ('BRZ', 'Brazilian Cruzeiro (1942–1967)'), ('BRE', 'Brazilian Cruzeiro (1990–1993)'), ('BRR', 'Brazilian Cruzeiro (1993–1994)'), ('BRN', 'Brazilian New Cruzado (1989–1990)'), ('BRB', 'Brazilian New Cruzeiro (1967–1986)'), ('BRL', 'Brazilian Real'), ('GBP', 'British Pound'), ('BND', 'Brunei Dollar'), ('BGL', 'Bulgarian Hard Lev'), ('BGN', 'Bulgarian Lev'), ('BGO', 'Bulgarian Lev (1879–1952)'), ('BGM', 'Bulgarian Socialist Lev'), ('BUK', 'Burmese Kyat'), ('BIF', 'Burundian Franc'), ('XPF', 'CFP Franc'), ('KHR', 'Cambodian Riel'), ('CAD', 'Canadian Dollar'), ('CVE', 'Cape Verdean Escudo'), ('KYD', 'Cayman Islands Dollar'), ('XAF', 'Central African CFA Franc'), ('CLE', 'Chilean Escudo'), ('CLP', 'Chilean Peso'), ('CLF', 'Chilean Unit of Account (UF)'), ('CNX', 'Chinese People’s Bank Dollar'), ('CNY', 'Chinese Yuan'), ('CNH', 'Chinese Yuan (offshore)'), ('COP', 'Colombian Peso'), ('COU', 'Colombian Real Value Unit'), ('KMF', 'Comorian Franc'), ('CDF', 'Congolese Franc'), ('CRC', 'Costa Rican Colón'), ('HRD', 'Croatian Dinar'), ('HRK', 'Croatian Kuna'), ('CUC', 'Cuban Convertible Peso'), ('CUP', 'Cuban Peso'), ('CYP', 'Cypriot Pound'), ('CZK', 'Czech Koruna'), ('CSK', 'Czechoslovak Hard Koruna'), ('DKK', 'Danish Krone'), ('DJF', 'Djiboutian Franc'), ('DOP', 'Dominican Peso'), ('NLG', 'Dutch Guilder'), ('XCD', 'East Caribbean Dollar'), ('DDM', 'East German Mark'), ('ECS', 'Ecuadorian Sucre'), ('ECV', 'Ecuadorian Unit of Constant Value'), ('EGP', 'Egyptian Pound'), ('GQE', 'Equatorial Guinean Ekwele'), ('ERN', 'Eritrean Nakfa'), ('EEK', 'Estonian Kroon'), ('ETB', 'Ethiopian Birr'), ('EUR', 'Euro'), ('XBA', 'European Composite Unit'), ('XEU', 'European Currency Unit'), ('XBB', 'European Monetary Unit'), ('XBC', 'European Unit of Account (XBC)'), ('XBD', 'European Unit of Account (XBD)'), ('FKP', 'Falkland Islands Pound'), ('FJD', 'Fijian Dollar'), ('FIM', 'Finnish Markka'), ('FRF', 'French Franc'), ('XFO', 'French Gold Franc'), ('XFU', 'French UIC-Franc'), ('GMD', 'Gambian Dalasi'), ('GEK', 'Georgian Kupon Larit'), ('GEL', 'Georgian Lari'), ('DEM', 'German Mark'), ('GHS', 'Ghanaian Cedi'), ('GHC', 'Ghanaian Cedi (1979–2007)'), ('GIP', 'Gibraltar Pound'), ('XAU', 'Gold'), ('GRD', 'Greek Drachma'), ('GTQ', 'Guatemalan Quetzal'), ('GWP', 'Guinea-Bissau Peso'), ('GNF', 'Guinean Franc'), ('GNS', 'Guinean Syli'), ('GYD', 'Guyanaese Dollar'), ('HTG', 'Haitian Gourde'), ('HNL', 'Honduran Lempira'), ('HKD', 'Hong Kong Dollar'), ('HUF', 'Hungarian Forint'), ('IMP', 'IMP'), ('ISK', 'Icelandic Króna'), ('ISJ', 'Icelandic Króna (1918–1981)'), ('INR', 'Indian Rupee'), ('IDR', 'Indonesian Rupiah'), ('IRR', 'Iranian Rial'), ('IQD', 'Iraqi Dinar'), ('IEP', 'Irish Pound'), ('ILS', 'Israeli New Shekel'), ('ILP', 'Israeli Pound'), ('ILR', 'Israeli Shekel (1980–1985)'), ('ITL', 'Italian Lira'), ('JMD', 'Jamaican Dollar'), ('JPY', 'Japanese Yen'), ('JOD', 'Jordanian Dinar'), ('KZT', 'Kazakhstani Tenge'), ('KES', 'Kenyan Shilling'), ('KWD', 'Kuwaiti Dinar'), ('KGS', 'Kyrgystani Som'), ('LAK', 'Laotian Kip'), ('LVL', 'Latvian Lats'), ('LVR', 'Latvian Ruble'), ('LBP', 'Lebanese Pound'), ('LSL', 'Lesotho Loti'), ('LRD', 'Liberian Dollar'), ('LYD', 'Libyan Dinar'), ('LTL', 'Lithuanian Litas'), ('LTT', 'Lithuanian Talonas'), ('LUL', 'Luxembourg Financial Franc'), ('LUC', 'Luxembourgian Convertible Franc'), ('LUF', 'Luxembourgian Franc'), ('MOP', 'Macanese Pataca'), ('MKD', 'Macedonian Denar'), ('MKN', 'Macedonian Denar (1992–1993)'), ('MGA', 'Malagasy Ariary'), ('MGF', 'Malagasy Franc'), ('MWK', 'Malawian Kwacha'), ('MYR', 'Malaysian Ringgit'), ('MVR', 'Maldivian Rufiyaa'), ('MVP', 'Maldivian Rupee (1947–1981)'), ('MLF', 'Malian Franc'), ('MTL', 'Maltese Lira'), ('MTP', 'Maltese Pound'), ('MRU', 'Mauritanian Ouguiya'), ('MRO', 'Mauritanian Ouguiya (1973–2017)'), ('MUR', 'Mauritian Rupee'), ('MXV', 'Mexican Investment Unit'), ('MXN', 'Mexican Peso'), ('MXP', 'Mexican Silver Peso (1861–1992)'), ('MDC', 'Moldovan Cupon'), ('MDL', 'Moldovan Leu'), ('MCF', 'Monegasque Franc'), ('MNT', 'Mongolian Tugrik'), ('MAD', 'Moroccan Dirham'), ('MAF', 'Moroccan Franc'), ('MZE', 'Mozambican Escudo'), ('MZN', 'Mozambican Metical'), ('MZM', 'Mozambican Metical (1980–2006)'), ('MMK', 'Myanmar Kyat'), ('NAD', 'Namibian Dollar'), ('NPR', 'Nepalese Rupee'), ('ANG', 'Netherlands Antillean Guilder'), ('TWD', 'New Taiwan Dollar'), ('NZD', 'New Zealand Dollar'), ('NIO', 'Nicaraguan Córdoba'), ('NIC', 'Nicaraguan Córdoba (1988–1991)'), ('NGN', 'Nigerian Naira'), ('KPW', 'North Korean Won'), ('NOK', 'Norwegian Krone'), ('OMR', 'Omani Rial'), ('PKR', 'Pakistani Rupee'), ('XPD', 'Palladium'), ('PAB', 'Panamanian Balboa'), ('PGK', 'Papua New Guinean Kina'), ('PYG', 'Paraguayan Guarani'), ('PEI', 'Peruvian Inti'), ('PEN', 'Peruvian Sol'), ('PES', 'Peruvian Sol (1863–1965)'), ('PHP', 'Philippine Piso'), ('XPT', 'Platinum'), ('PLN', 'Polish Zloty'), ('PLZ', 'Polish Zloty (1950–1995)'), ('PTE', 'Portuguese Escudo'), ('GWE', 'Portuguese Guinea Escudo'), ('QAR', 'Qatari Rial'), ('XRE', 'RINET Funds'), ('RHD', 'Rhodesian Dollar'), ('RON', 'Romanian Leu'), ('ROL', 'Romanian Leu (1952–2006)'), ('RUB', 'Russian Ruble'), ('RUR', 'Russian Ruble (1991–1998)'), ('RWF', 'Rwandan Franc'), ('SVC', 'Salvadoran Colón'), ('WST', 'Samoan Tala'), ('SAR', 'Saudi Riyal'), ('RSD', 'Serbian Dinar'), ('CSD', 'Serbian Dinar (2002–2006)'), ('SCR', 'Seychellois Rupee'), ('SLL', 'Sierra Leonean Leone'), ('XAG', 'Silver'), ('SGD', 'Singapore Dollar'), ('SKK', 'Slovak Koruna'), ('SIT', 'Slovenian Tolar'), ('SBD', 'Solomon Islands Dollar'), ('SOS', 'Somali Shilling'), ('ZAR', 'South African Rand'), ('ZAL', 'South African Rand (financial)'), ('KRH', 'South Korean Hwan (1953–1962)'), ('KRW', 'South Korean Won'), ('KRO', 'South Korean Won (1945–1953)'), ('SSP', 'South Sudanese Pound'), ('SUR', 'Soviet Rouble'), ('ESP', 'Spanish Peseta'), ('ESA', 'Spanish Peseta (A account)'), ('ESB', 'Spanish Peseta (convertible account)'), ('XDR', 'Special Drawing Rights'), ('LKR', 'Sri Lankan Rupee'), ('SHP', 'St. Helena Pound'), ('XSU', 'Sucre'), ('SDD', 'Sudanese Dinar (1992–2007)'), ('SDG', 'Sudanese Pound'), ('SDP', 'Sudanese Pound (1957–1998)'), ('SRD', 'Surinamese Dollar'), ('SRG', 'Surinamese Guilder'), ('SZL', 'Swazi Lilangeni'), ('SEK', 'Swedish Krona'), ('CHF', 'Swiss Franc'), ('SYP', 'Syrian Pound'), ('STN', 'São Tomé & Príncipe Dobra'), ('STD', 'São Tomé & Príncipe Dobra (1977–2017)'), ('TVD', 'TVD'), ('TJR', 'Tajikistani Ruble'), ('TJS', 'Tajikistani Somoni'), ('TZS', 'Tanzanian Shilling'), ('XTS', 'Testing Currency Code'), ('THB', 'Thai Baht'), ('XXX', 'The codes assigned for transactions where no currency is involved'), ('TPE', 'Timorese Escudo'), ('TOP', 'Tongan Paʻanga'), ('TTD', 'Trinidad & Tobago Dollar'), ('TND', 'Tunisian Dinar'), ('TRY', 'Turkish Lira'), ('TRL', 'Turkish Lira (1922–2005)'), ('TMT', 'Turkmenistani Manat'), ('TMM', 'Turkmenistani Manat (1993–2009)'), ('USD', 'US Dollar'), ('USN', 'US Dollar (Next day)'), ('USS', 'US Dollar (Same day)'), ('UGX', 'Ugandan Shilling'), ('UGS', 'Ugandan Shilling (1966–1987)'), ('UAH', 'Ukrainian Hryvnia'), ('UAK', 'Ukrainian Karbovanets'), ('AED', 'United Arab Emirates Dirham'), ('UYW', 'Uruguayan Nominal Wage Index Unit'), ('UYU', 'Uruguayan Peso'), ('UYP', 'Uruguayan Peso (1975–1993)'), ('UYI', 'Uruguayan Peso (Indexed Units)'), ('UZS', 'Uzbekistani Som'), ('VUV', 'Vanuatu Vatu'), ('VES', 'Venezuelan Bolívar'), ('VEB', 'Venezuelan Bolívar (1871–2008)'), ('VEF', 'Venezuelan Bolívar (2008–2018)'), ('VND', 'Vietnamese Dong'), ('VNN', 'Vietnamese Dong (1978–1985)'), ('CHE', 'WIR Euro'), ('CHW', 'WIR Franc'), ('XOF', 'West African CFA Franc'), ('YDD', 'Yemeni Dinar'), ('YER', 'Yemeni Rial'), ('YUN', 'Yugoslavian Convertible Dinar (1990–1992)'), ('YUD', 'Yugoslavian Hard Dinar (1966–1990)'), ('YUM', 'Yugoslavian New Dinar (1994–2002)'), ('YUR', 'Yugoslavian Reformed Dinar (1992–1993)'), ('ZWN', 'ZWN'), ('ZRN', 'Zairean New Zaire (1993–1998)'), ('ZRZ', 'Zairean Zaire (1971–1993)'), ('ZMW', 'Zambian Kwacha'), ('ZMK', 'Zambian Kwacha (1968–2012)'), ('ZWD', 'Zimbabwean Dollar (1980–2008)'), ('ZWR', 'Zimbabwean Dollar (2008)'), ('ZWL', 'Zimbabwean Dollar (2009)')], default='IQD', editable=False, max_length=3), ), migrations.AlterField( model_name='customer', name='gifts', field=models.IntegerField(editable=False, null=True), ), migrations.AlterField( model_name='customer', name='wallet', field=djmoney.models.fields.MoneyField(decimal_places=0, default_currency='IQD', max_digits=14, null=True), ), migrations.AlterField( model_name='order', name='price', field=djmoney.models.fields.MoneyField(decimal_places=0, default_currency='IQD', max_digits=14), ), ]

aab312bbb68dba1597d1ea79afa0ecdbd4110f03

py

Python

wsme/tests/test_restxml.py

Kjir/wsme

0135b7dac67668815bf34f15894f05beb0c94faa

wsme/tests/test_restxml.py

Kjir/wsme

0135b7dac67668815bf34f15894f05beb0c94faa

wsme/tests/test_restxml.py

Kjir/wsme

0135b7dac67668815bf34f15894f05beb0c94faa

import decimal import datetime import base64 from six import u, b import six import wsme.tests.protocol from wsme.utils import parse_isodatetime, parse_isodate, parse_isotime from wsme.types import isarray, isdict, isusertype, register_type from wsme.rest.xml import fromxml, toxml try: import xml.etree.ElementTree as et except: import cElementTree as et # noqa def dumpxml(key, obj, datatype=None): el = et.Element(key) if isinstance(obj, tuple): obj, datatype = obj if isinstance(datatype, list): for item in obj: el.append(dumpxml('item', item, datatype[0])) elif isinstance(datatype, dict): key_type, value_type = list(datatype.items())[0] for item in obj.items(): node = et.SubElement(el, 'item') node.append(dumpxml('key', item[0], key_type)) node.append(dumpxml('value', item[1], value_type)) elif datatype == wsme.types.binary: el.text = base64.encodestring(obj).decode('ascii') elif isinstance(obj, wsme.types.bytes): el.text = obj.decode('ascii') elif isinstance(obj, wsme.types.text): el.text = obj elif type(obj) in (int, float, bool, decimal.Decimal): el.text = six.text_type(obj) elif type(obj) in (datetime.date, datetime.time, datetime.datetime): el.text = obj.isoformat() elif isinstance(obj, type(None)): el.set('nil', 'true') elif hasattr(datatype, '_wsme_attributes'): for attr in datatype._wsme_attributes: name = attr.name if name not in obj: continue o = obj[name] el.append(dumpxml(name, o, attr.datatype)) elif type(obj) == dict: for name, value in obj.items(): el.append(dumpxml(name, value)) print(obj, datatype, et.tostring(el)) return el def loadxml(el, datatype): print (el, datatype, len(el)) if el.get('nil') == 'true': return None if isinstance(datatype, list): return [loadxml(item, datatype[0]) for item in el.findall('item')] elif isarray(datatype): return [ loadxml(item, datatype.item_type) for item in el.findall('item') ] elif isinstance(datatype, dict): key_type, value_type = list(datatype.items())[0] return dict(( (loadxml(item.find('key'), key_type), loadxml(item.find('value'), value_type)) for item in el.findall('item') )) elif isdict(datatype): return dict(( (loadxml(item.find('key'), datatype.key_type), loadxml(item.find('value'), datatype.value_type)) for item in el.findall('item') )) elif isdict(datatype): return dict(( (loadxml(item.find('key'), datatype.key_type), loadxml(item.find('value'), datatype.value_type)) for item in el.findall('item') )) elif len(el): d = {} for attr in datatype._wsme_attributes: name = attr.name child = el.find(name) print (name, attr, child) if child is not None: d[name] = loadxml(child, attr.datatype) print (d) return d else: if datatype == wsme.types.binary: return base64.decodestring(el.text.encode('ascii')) if isusertype(datatype): datatype = datatype.basetype if datatype == datetime.date: return parse_isodate(el.text) if datatype == datetime.time: return parse_isotime(el.text) if datatype == datetime.datetime: return parse_isodatetime(el.text) if datatype == wsme.types.text: return datatype(el.text if el.text else u('')) if datatype == bool: return el.text.lower() != 'false' if datatype is None: return el.text if datatype is wsme.types.bytes: return el.text.encode('ascii') return datatype(el.text) class TestRestXML(wsme.tests.protocol.RestOnlyProtocolTestCase): protocol = 'restxml' def call(self, fpath, _rt=None, _accept=None, _no_result_decode=False, body=None, **kw): if body: el = dumpxml('body', body) else: el = dumpxml('parameters', kw) content = et.tostring(el) headers = { 'Content-Type': 'text/xml', } if _accept is not None: headers['Accept'] = _accept res = self.app.post( '/' + fpath, content, headers=headers, expect_errors=True) print ("Received:", res.body) if _no_result_decode: return res el = et.fromstring(res.body) if el.tag == 'error': raise wsme.tests.protocol.CallException( el.find('faultcode').text, el.find('faultstring').text, el.find('debuginfo') is not None and el.find('debuginfo').text or None ) else: return loadxml(et.fromstring(res.body), _rt) def test_encode_sample_value(self): class MyType(object): aint = int atext = wsme.types.text register_type(MyType) value = MyType() value.aint = 5 value.atext = u('test') language, sample = wsme.rest.xml.encode_sample_value( MyType, value, True) print (language, sample) assert language == 'xml' assert sample == b("""<value> <aint>5</aint> <atext>test</atext> </value>""") def test_encode_sample_params(self): lang, content = wsme.rest.xml.encode_sample_params( [('a', int, 2)], True) assert lang == 'xml', lang assert content == b('<parameters>\n <a>2</a>\n</parameters>'), content def test_encode_sample_result(self): lang, content = wsme.rest.xml.encode_sample_result(int, 2, True) assert lang == 'xml', lang assert content == b('<result>2</result>'), content def test_nil_fromxml(self): for dt in ( str, [int], {int: str}, bool, datetime.date, datetime.time, datetime.datetime): e = et.Element('value', nil='true') assert fromxml(dt, e) is None def test_nil_toxml(self): for dt in ( wsme.types.bytes, [int], {int: str}, bool, datetime.date, datetime.time, datetime.datetime): x = et.tostring(toxml(dt, 'value', None)) assert x == b('<value nil="true" />'), x def test_unset_attrs(self): class AType(object): someattr = wsme.types.bytes wsme.types.register_type(AType) x = et.tostring(toxml(AType, 'value', AType())) assert x == b('<value />'), x

4ca1af9c0083b4402e666ef57d901e82af7e7de1

py

Python

main_app/migrations/0001_initial.py

SUMEKAGARWAL/Rescue

98972f782846a2e82804dd35d371fb9799b8471d

main_app/migrations/0001_initial.py

SUMEKAGARWAL/Rescue

98972f782846a2e82804dd35d371fb9799b8471d

main_app/migrations/0001_initial.py

SUMEKAGARWAL/Rescue

98972f782846a2e82804dd35d371fb9799b8471d

# Generated by Django 3.0.8 on 2020-11-07 06:21 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name="contact", fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100)), ('email', models.EmailField(blank= True,max_length=254)), ('relation', models.CharField(choices=[('Father', 'Father'), ('Mother', 'Mother'), ('Brother', 'Brother'), ('Sister', 'Sister'), ('Husband', 'Husband'), ('Friend', 'Friend'), ('Relative', 'Relative'), ('Other', 'Other')], default='Other', max_length=10)), ('user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='contact', to=settings.AUTH_USER_MODEL)), ( "id", models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name="ID", ), ), ("name", models.CharField(max_length=100)), ("email", models.EmailField(max_length=254)), ( "relation", models.CharField( choices=[ ("Father", "Father"), ("Mother", "Mother"), ("Brother", "Brother"), ("Sister", "Sister"), ("Husband", "Husband"), ("Friend", "Friend"), ("Relative", "Relative"), ("Other", "Other"), ], default="Other", max_length=10, ), ), ( "user", models.ForeignKey( null=True, on_delete=django.db.models.deletion.CASCADE, related_name="contact", to=settings.AUTH_USER_MODEL, ), ), ], ), ]

151b6df07659af4118256ba996e4e21df3b97605

py

Python

100-199/180-189/189.py

dcragusa/LeetCode

01c30de0832b378a1b054d80d1ea1d3f09a2abd3

100-199/180-189/189.py

dcragusa/LeetCode

01c30de0832b378a1b054d80d1ea1d3f09a2abd3

100-199/180-189/189.py

dcragusa/LeetCode

01c30de0832b378a1b054d80d1ea1d3f09a2abd3

""" Given an array, rotate the array to the right by k steps, where k is non-negative. Example 1: Input: nums = [1, 2, 3, 4, 5, 6, 7], k = 3, Output: [5, 6, 7, 1, 2, 3, 4] Explanation: rotate 1 steps to the right: [7, 1, 2, 3, 4, 5, 6] rotate 2 steps to the right: [6, 7, 1, 2, 3, 4, 5] rotate 3 steps to the right: [5, 6, 7, 1, 2, 3, 4] Example 2: Input: nums = [-1, -100, 3, 99], k = 2, Output: [3, 99, -1, -100] Explanation: rotate 1 steps to the right: [99, -1, -100, 3] rotate 2 steps to the right: [3, 99, -1, -100] """ """ Our first instinct might be to repeatedly pop items from the end of the list and insert them at the front, but this is horrendously inefficient as inserts to the front are O(n) due to having to move the rest of the list in memory. Much better is identifying the front part of the list, extending the list with a copy of that part, then deleting the entire front part in one operation. """ # def rotate(nums, k): # k %= len(nums) # for _ in range(k): # nums.insert(0, nums.pop()) def rotate(nums, k): k = (len(nums) - k) % len(nums) nums.extend(nums[:k]) del nums[:k] nums = [1, 2, 3, 4, 5, 6, 7] rotate(nums, 3) assert nums == [5, 6, 7, 1, 2, 3, 4] nums = [-1, -100, 3, 99] rotate(nums, 2) assert nums == [3, 99, -1, -100] nums = [1, 2] rotate(nums, 3) assert nums == [2, 1]

b5061e6a58c17ca56e2671e8968df93b49ebdc93

py

Python

site/flask/lib/python2.7/site-packages/whoosh/lang/isri.py

theholyhades1/tartanHacks2015

a801b473f21cfbd136e2a5a74423e8c72d14f900

2016-09-22T15:54:48.000Z

2022-03-18T02:36:58.000Z

site/flask/lib/python2.7/site-packages/whoosh/lang/isri.py

theholyhades1/tartanHacks2015

a801b473f21cfbd136e2a5a74423e8c72d14f900

2017-04-01T15:06:36.000Z

2021-02-08T20:19:58.000Z

site/flask/lib/python2.7/site-packages/whoosh/lang/isri.py

theholyhades1/tartanHacks2015

a801b473f21cfbd136e2a5a74423e8c72d14f900

2016-10-06T16:05:32.000Z

2022-03-18T02:37:00.000Z

# -*- coding: utf-8 -*- # # Natural Language Toolkit: The ISRI Arabic Stemmer # # Copyright (C) 2001-2012 NLTK Proejct # Algorithm: Kazem Taghva, Rania Elkhoury, and Jeffrey Coombs (2005) # Author: Hosam Algasaier <[email protected]> # URL: <http://www.nltk.org/> # For license information, see LICENSE.TXT """ ISRI Arabic Stemmer The algorithm for this stemmer is described in: Taghva, K., Elkoury, R., and Coombs, J. 2005. Arabic Stemming without a root dictionary. Information Science Research Institute. University of Nevada, Las Vegas, USA. The Information Science Research Institute’s (ISRI) Arabic stemmer shares many features with the Khoja stemmer. However, the main difference is that ISRI stemmer does not use root dictionary. Also, if a root is not found, ISRI stemmer returned normalized form, rather than returning the original unmodified word. Additional adjustments were made to improve the algorithm: 1- Adding 60 stop words. 2- Adding the pattern (تفاعيل) to ISRI pattern set. 3- The step 2 in the original algorithm was normalizing all hamza. This step is discarded because it increases the word ambiguities and changes the original root. """ from __future__ import unicode_literals import re class ISRIStemmer(object): ''' ISRI Arabic stemmer based on algorithm: Arabic Stemming without a root dictionary. Information Science Research Institute. University of Nevada, Las Vegas, USA. A few minor modifications have been made to ISRI basic algorithm. See the source code of this module for more information. isri.stem(token) returns Arabic root for the given token. The ISRI Stemmer requires that all tokens have Unicode string types. If you use Python IDLE on Arabic Windows you have to decode text first using Arabic '1256' coding. ''' def __init__(self): self.stm = 'defult none' self.p3 = ['\u0643\u0627\u0644', '\u0628\u0627\u0644', '\u0648\u0644\u0644', '\u0648\u0627\u0644'] # length three prefixes self.p2 = ['\u0627\u0644', '\u0644\u0644'] # length two prefixes self.p1 = ['\u0644', '\u0628', '\u0641', '\u0633', '\u0648', '\u064a', '\u062a', '\u0646', '\u0627'] # length one prefixes self.s3 = ['\u062a\u0645\u0644', '\u0647\u0645\u0644', '\u062a\u0627\u0646', '\u062a\u064a\u0646', '\u0643\u0645\u0644'] # length three suffixes self.s2 = ['\u0648\u0646', '\u0627\u062a', '\u0627\u0646', '\u064a\u0646', '\u062a\u0646', '\u0643\u0645', '\u0647\u0646', '\u0646\u0627', '\u064a\u0627', '\u0647\u0627', '\u062a\u0645', '\u0643\u0646', '\u0646\u064a', '\u0648\u0627', '\u0645\u0627', '\u0647\u0645'] # length two suffixes self.s1 = ['\u0629', '\u0647', '\u064a', '\u0643', '\u062a', '\u0627', '\u0646'] # length one suffixes self.pr4 = {0: ['\u0645'], 1:['\u0627'], 2: ['\u0627', '\u0648', '\u064A'], 3:['\u0629']} # groups of length four patterns self.pr53 = {0: ['\u0627', '\u062a'], 1: ['\u0627', '\u064a', '\u0648'], 2: ['\u0627', '\u062a', '\u0645'], 3: ['\u0645', '\u064a', '\u062a'], 4: ['\u0645', '\u062a'], 5: ['\u0627', '\u0648'], 6: ['\u0627', '\u0645']} # Groups of length five patterns and length three roots self.re_short_vowels = re.compile(r'[\u064B-\u0652]') self.re_hamza = re.compile(r'[\u0621\u0624\u0626]') self.re_intial_hamza = re.compile(r'^[\u0622\u0623\u0625]') self.stop_words = ['\u064a\u0643\u0648\u0646', '\u0648\u0644\u064a\u0633', '\u0648\u0643\u0627\u0646', '\u0643\u0630\u0644\u0643', '\u0627\u0644\u062a\u064a', '\u0648\u0628\u064a\u0646', '\u0639\u0644\u064a\u0647\u0627', '\u0645\u0633\u0627\u0621', '\u0627\u0644\u0630\u064a', '\u0648\u0643\u0627\u0646\u062a', '\u0648\u0644\u0643\u0646', '\u0648\u0627\u0644\u062a\u064a', '\u062a\u0643\u0648\u0646', '\u0627\u0644\u064a\u0648\u0645', '\u0627\u0644\u0644\u0630\u064a\u0646', '\u0639\u0644\u064a\u0647', '\u0643\u0627\u0646\u062a', '\u0644\u0630\u0644\u0643', '\u0623\u0645\u0627\u0645', '\u0647\u0646\u0627\u0643', '\u0645\u0646\u0647\u0627', '\u0645\u0627\u0632\u0627\u0644', '\u0644\u0627\u0632\u0627\u0644', '\u0644\u0627\u064a\u0632\u0627\u0644', '\u0645\u0627\u064a\u0632\u0627\u0644', '\u0627\u0635\u0628\u062d', '\u0623\u0635\u0628\u062d', '\u0623\u0645\u0633\u0649', '\u0627\u0645\u0633\u0649', '\u0623\u0636\u062d\u0649', '\u0627\u0636\u062d\u0649', '\u0645\u0627\u0628\u0631\u062d', '\u0645\u0627\u0641\u062a\u0626', '\u0645\u0627\u0627\u0646\u0641\u0643', '\u0644\u0627\u0633\u064a\u0645\u0627', '\u0648\u0644\u0627\u064a\u0632\u0627\u0644', '\u0627\u0644\u062d\u0627\u0644\u064a', '\u0627\u0644\u064a\u0647\u0627', '\u0627\u0644\u0630\u064a\u0646', '\u0641\u0627\u0646\u0647', '\u0648\u0627\u0644\u0630\u064a', '\u0648\u0647\u0630\u0627', '\u0644\u0647\u0630\u0627', '\u0641\u0643\u0627\u0646', '\u0633\u062a\u0643\u0648\u0646', '\u0627\u0644\u064a\u0647', '\u064a\u0645\u0643\u0646', '\u0628\u0647\u0630\u0627', '\u0627\u0644\u0630\u0649'] def stem(self, token): """ Stemming a word token using the ISRI stemmer. """ self.stm = token self.norm(1) # remove diacritics which representing Arabic short vowels if self.stm in self.stop_words: return self.stm # exclude stop words from being processed self.pre32() # remove length three and length two prefixes in this order self.suf32() # remove length three and length two suffixes in this order self.waw() # remove connective ‘و’ if it precedes a word beginning with ‘و’ self.norm(2) # normalize initial hamza to bare alif if len(self.stm) <= 3: return self.stm # return stem if less than or equal to three if len(self.stm) == 4: # length 4 word self.pro_w4() return self.stm elif len(self.stm) == 5: # length 5 word self.pro_w53() self.end_w5() return self.stm elif len(self.stm) == 6: # length 6 word self.pro_w6() self.end_w6() return self.stm elif len(self.stm) == 7: # length 7 word self.suf1() if len(self.stm) == 7: self.pre1() if len(self.stm) == 6: self.pro_w6() self.end_w6() return self.stm return self.stm # if word length >7 , then no stemming def norm(self, num): """ normalization: num=1 normalize diacritics num=2 normalize initial hamza num=3 both 1&2 """ self.k = num if self.k == 1: self.stm = self.re_short_vowels.sub('', self.stm) return self.stm elif self.k == 2: self.stm = self.re_intial_hamza.sub(r'\u0627', self.stm) return self.stm elif self.k == 3: self.stm = self.re_short_vowels.sub('', self.stm) self.stm = self.re_intial_hamza.sub(r'\u0627', self.stm) return self.stm def pre32(self): """remove length three and length two prefixes in this order""" if len(self.stm) >= 6: for pre3 in self.p3: if self.stm.startswith(pre3): self.stm = self.stm[3:] return self.stm elif len(self.stm) >= 5: for pre2 in self.p2: if self.stm.startswith(pre2): self.stm = self.stm[2:] return self.stm def suf32(self): """remove length three and length two suffixes in this order""" if len(self.stm) >= 6: for suf3 in self.s3: if self.stm.endswith(suf3): self.stm = self.stm[:-3] return self.stm elif len(self.stm) >= 5: for suf2 in self.s2: if self.stm.endswith(suf2): self.stm = self.stm[:-2] return self.stm def waw(self): """remove connective ‘و’ if it precedes a word beginning with ‘و’ """ if (len(self.stm) >= 4) & (self.stm[:2] == '\u0648\u0648'): self.stm = self.stm[1:] return self.stm def pro_w4(self): """process length four patterns and extract length three roots""" if self.stm[0] in self.pr4[0]: # مفعل self.stm = self.stm[1:] return self.stm elif self.stm[1] in self.pr4[1]: # فاعل self.stm = self.stm[0] + self.stm[2:] return self.stm elif self.stm[2] in self.pr4[2]: # فعال - فعول - فعيل self.stm = self.stm[:2] + self.stm[3] return self.stm elif self.stm[3] in self.pr4[3]: # فعلة self.stm = self.stm[:-1] return self.stm else: self.suf1() # do - normalize short sufix if len(self.stm) == 4: self.pre1() # do - normalize short prefix return self.stm def pro_w53(self): """process length five patterns and extract length three roots""" if ((self.stm[2] in self.pr53[0]) & (self.stm[0] == '\u0627')): # افتعل - افاعل self.stm = self.stm[1] + self.stm[3:] return self.stm elif ((self.stm[3] in self.pr53[1]) & (self.stm[0] == '\u0645')): # مفعول - مفعال - مفعيل self.stm = self.stm[1:3] + self.stm[4] return self.stm elif ((self.stm[0] in self.pr53[2]) & (self.stm[4] == '\u0629')): # مفعلة - تفعلة - افعلة self.stm = self.stm[1:4] return self.stm elif ((self.stm[0] in self.pr53[3]) & (self.stm[2] == '\u062a')): # مفتعل - يفتعل - تفتعل self.stm = self.stm[1] + self.stm[3:] return self.stm elif ((self.stm[0] in self.pr53[4]) & (self.stm[2] == '\u0627')): #مفاعل - تفاعل self.stm = self.stm[1] + self.stm[3:] return self.stm elif ((self.stm[2] in self.pr53[5]) & (self.stm[4] == '\u0629')): # فعولة - فعالة self.stm = self.stm[:2] + self.stm[3] return self.stm elif ((self.stm[0] in self.pr53[6]) & (self.stm[1] == '\u0646')): # انفعل - منفعل self.stm = self.stm[2:] return self.stm elif ((self.stm[3] == '\u0627') & (self.stm[0] == '\u0627')): # افعال self.stm = self.stm[1:3] + self.stm[4] return self.stm elif ((self.stm[4] == '\u0646') & (self.stm[3] == '\u0627')): # فعلان self.stm = self.stm[:3] return self.stm elif ((self.stm[3] == '\u064a') & (self.stm[0] == '\u062a')): # تفعيل self.stm = self.stm[1:3] + self.stm[4] return self.stm elif ((self.stm[3] == '\u0648') & (self.stm[1] == '\u0627')): # فاعول self.stm = self.stm[0] + self.stm[2] + self.stm[4] return self.stm elif ((self.stm[2] == '\u0627') & (self.stm[1] == '\u0648')): # فواعل self.stm = self.stm[0] + self.stm[3:] return self.stm elif ((self.stm[3] == '\u0626') & (self.stm[2] == '\u0627')): # فعائل self.stm = self.stm[:2] + self.stm[4] return self.stm elif ((self.stm[4] == '\u0629') & (self.stm[1] == '\u0627')): # فاعلة self.stm = self.stm[0] + self.stm[2:4] return self.stm elif ((self.stm[4] == '\u064a') & (self.stm[2] == '\u0627')): # فعالي self.stm = self.stm[:2] + self.stm[3] return self.stm else: self.suf1() # do - normalize short sufix if len(self.stm) == 5: self.pre1() # do - normalize short prefix return self.stm def pro_w54(self): """process length five patterns and extract length four roots""" if (self.stm[0] in self.pr53[2]): #تفعلل - افعلل - مفعلل self.stm = self.stm[1:] return self.stm elif (self.stm[4] == '\u0629'): # فعللة self.stm = self.stm[:4] return self.stm elif (self.stm[2] == '\u0627'): # فعالل self.stm = self.stm[:2] + self.stm[3:] return self.stm def end_w5(self): """ending step (word of length five)""" if len(self.stm) == 3: return self.stm elif len(self.stm) == 4: self.pro_w4() return self.stm elif len(self.stm) == 5: self.pro_w54() return self.stm def pro_w6(self): """process length six patterns and extract length three roots""" if ((self.stm.startswith('\u0627\u0633\u062a')) or (self.stm.startswith('\u0645\u0633\u062a'))): # مستفعل - استفعل self.stm = self.stm[3:] return self.stm elif (self.stm[0] == '\u0645' and self.stm[3] == '\u0627' and self.stm[5] == '\u0629'): # مفعالة self.stm = self.stm[1:3] + self.stm[4] return self.stm elif (self.stm[0] == '\u0627' and self.stm[2] == '\u062a' and self.stm[4] == '\u0627'): # افتعال self.stm = self.stm[1] + self.stm[3] + self.stm[5] return self.stm elif (self.stm[0] == '\u0627' and self.stm[3] == '\u0648' and self.stm[2] == self.stm[4]): # افعوعل self.stm = self.stm[1] + self.stm[4:] return self.stm elif (self.stm[0] == '\u062a' and self.stm[2] == '\u0627' and self.stm[4] == '\u064a'): # تفاعيل new pattern self.stm = self.stm[1] + self.stm[3] + self.stm[5] return self.stm else: self.suf1() # do - normalize short sufix if len(self.stm) == 6: self.pre1() # do - normalize short prefix return self.stm def pro_w64(self): """process length six patterns and extract length four roots""" if (self.stm[0] and self.stm[4]) == '\u0627': # افعلال self.stm = self.stm[1:4] + self.stm[5] return self.stm elif (self.stm.startswith('\u0645\u062a')): # متفعلل self.stm = self.stm[2:] return self.stm def end_w6(self): """ending step (word of length six)""" if len(self.stm) == 3: return self.stm elif len(self.stm) == 5: self.pro_w53() self.end_w5() return self.stm elif len (self.stm) == 6: self.pro_w64() return self.stm def suf1(self): """normalize short sufix""" for sf1 in self.s1: if self.stm.endswith(sf1): self.stm = self.stm[:-1] return self.stm def pre1(self): """normalize short prefix""" for sp1 in self.p1: if self.stm.startswith(sp1): self.stm = self.stm[1:] return self.stm

30decdb588b901c970be0003cf8e42d9c9212676

py

Python

sensu_plugin/compat.py

sufiyanghori/sensu-plugin-python

6682163a2a2219e8132b4c9e1dd53663fa477ae5

2015-01-11T13:34:32.000Z

2017-04-28T11:20:02.000Z

sensu_plugin/compat.py

sufiyanghori/sensu-plugin-python

6682163a2a2219e8132b4c9e1dd53663fa477ae5

2017-10-02T12:05:15.000Z

2021-03-22T21:20:54.000Z

sensu_plugin/compat.py

sufiyanghori/sensu-plugin-python

6682163a2a2219e8132b4c9e1dd53663fa477ae5

2017-10-02T08:51:44.000Z

2022-02-12T16:36:55.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # pylint: disable=undefined-variable """ Python 2/3 compatibility code. """ try: compat_basestring = basestring except NameError: # Python 3 compat_basestring = (bytes, str)

041a341237c0f511dd15454bf4c1ef6c4e98d05c

py

Python

one_crm/users/tests/test_views.py

lianzhanshu/one_crm

7320c38416dd05fa95400ef4f5d50b0a35125c33

2020-08-09T08:47:35.000Z

2021-12-16T01:27:56.000Z

one_crm/users/tests/test_views.py

flingjie/one_crm

7320c38416dd05fa95400ef4f5d50b0a35125c33

one_crm/users/tests/test_views.py

flingjie/one_crm

7320c38416dd05fa95400ef4f5d50b0a35125c33

2020-08-09T08:43:01.000Z

2021-09-29T11:45:33.000Z

import pytest from django.contrib.auth.models import AnonymousUser from django.http.response import Http404 from django.test import RequestFactory from one_crm.users.models import User from one_crm.users.tests.factories import UserFactory from one_crm.users.views import ( # isort:skip UserRedirectView, UserUpdateView, user_detail_view, ) pytestmark = pytest.mark.django_db class TestUserUpdateView: """ TODO: extracting view initialization code as class-scoped fixture would be great if only pytest-django supported non-function-scoped fixture db access -- this is a work-in-progress for now: https://github.com/pytest-dev/pytest-django/pull/258 """ def test_get_success_url(self, user: User, rf: RequestFactory): view = UserUpdateView() request = rf.get("/fake-url/") request.user = user view.request = request assert view.get_success_url() == f"/users/{user.username}/" def test_get_object(self, user: User, rf: RequestFactory): view = UserUpdateView() request = rf.get("/fake-url/") request.user = user view.request = request assert view.get_object() == user class TestUserRedirectView: def test_get_redirect_url(self, user: User, rf: RequestFactory): view = UserRedirectView() request = rf.get("/fake-url") request.user = user view.request = request assert view.get_redirect_url() == f"/users/{user.username}/" class TestUserDetailView: def test_authenticated(self, user: User, rf: RequestFactory): request = rf.get("/fake-url/") request.user = UserFactory() response = user_detail_view(request, username=user.username) assert response.status_code == 200 def test_not_authenticated(self, user: User, rf: RequestFactory): request = rf.get("/fake-url/") request.user = AnonymousUser() # type: ignore response = user_detail_view(request, username=user.username) assert response.status_code == 302 assert response.url == "/accounts/login/?next=/fake-url/" def test_case_sensitivity(self, rf: RequestFactory): request = rf.get("/fake-url/") request.user = UserFactory(username="UserName") with pytest.raises(Http404): user_detail_view(request, username="username")

0d1c9e41c2abba66cecc17ba27fb2a124ac892cf

py

Python

app.py

striker43/storjWidget-exporter

e4bb98580b3d547ed4e0f7c6523e20ee584c7b41

2020-11-25T15:14:56.000Z

2021-09-20T07:20:41.000Z

app.py

striker43/storjWidget-exporter

e4bb98580b3d547ed4e0f7c6523e20ee584c7b41

app.py

striker43/storjWidget-exporter

e4bb98580b3d547ed4e0f7c6523e20ee584c7b41

2020-11-25T03:43:21.000Z

2020-11-25T03:43:21.000Z

from flask import Flask import requests import os import time import datetime import json from datetime import date from requests.adapters import HTTPAdapter app = Flask(__name__) persistencePath = '/var/www/storjWidgetVolume/payoutData.txt' nodes = os.environ.get('NODES_LIST', '').split(',') payoutData = {} payoutData['day'] = None try: with open(persistencePath) as json_file: payoutData = json.load(json_file) except OSError: print("ERROR: Could not read " + persistencePath) def getStringWithUnit(value): if(value < 1000): return str("{:.2f}".format(value)) + ' MB' else: return str("{:.2f}".format(value/1000)) + ' GB' def addUnits(data): data['ingress'] = getStringWithUnit(data['ingress']) data['egress'] = getStringWithUnit(data['egress']) return data def getRelevantDay(satellitesResponse): numberOfDays = len(satellitesResponse['bandwidthDaily']) relevantDay = None for i in range(0, numberOfDays): if(satellitesResponse['bandwidthDaily'][i]['intervalStart'].split('T')[0] == str(date.today())): relevantDay = i return relevantDay def getBandwidthData(satellitesResponse, data): relevantDay = getRelevantDay(satellitesResponse) ingress = (satellitesResponse['bandwidthDaily'][relevantDay]['ingress']['usage'] + satellitesResponse['bandwidthDaily'][relevantDay]['ingress']['repair'])/1000000 egress = (satellitesResponse['bandwidthDaily'][relevantDay]['egress']['usage'] + satellitesResponse['bandwidthDaily'][relevantDay]['egress']['repair'] + satellitesResponse['bandwidthDaily'][relevantDay]['egress']['audit'])/1000000 data['ingress'] += ingress data['egress'] += egress return data def getPayoutEstimationMonth(payoutResponse, data): data['estimatedPayoutTotal'] += payoutResponse['currentMonth']['egressBandwidthPayout'] + payoutResponse['currentMonth']['egressRepairAuditPayout'] + payoutResponse['currentMonth']['diskSpacePayout'] return data def getPayoutEstimationToday(data): actualDay = str(date.today()) if(payoutData['day'] != actualDay): payoutData[actualDay] = data['estimatedPayoutTotal'] payoutData['day'] = actualDay with open(persistencePath, 'w') as outfile: json.dump(payoutData, outfile) print(payoutData) print(data) data['estimatedPayoutToday'] = (data['estimatedPayoutTotal'] - payoutData[actualDay]) return data def getSpaceInfo(snoResponse, data): data['spaceUsed'] += snoResponse['diskSpace']['used']/1000000000000 data['spaceAvailable'] += snoResponse['diskSpace']['available']/1000000000000 return data def httpRequest(ipWithPort, path): try: response = requests.get('http://' + ipWithPort + '/api/' + path, timeout=5) return response.json() except requests.exceptions.Timeout: return None except requests.exceptions.ConnectionError: return None @app.route('/bandwidth') def get_data(): data = {} data['ingress'] = 0 data['egress'] = 0 data['estimatedPayoutTotal'] = 0.0 data['estimatedPayoutToday'] = 0.0 data['spaceUsed'] = 0.0 data['spaceAvailable'] = 0.0 data['totalNodesCount'] = len(nodes) data['nodesOnline'] = len(nodes) for ip in nodes: snoResponse = httpRequest(ip, 'sno') if(snoResponse != None): satellitesResponse = httpRequest(ip, 'sno/satellites') payoutResponse = httpRequest(ip, 'sno/estimated-payout') getBandwidthData(satellitesResponse, data) getPayoutEstimationMonth(payoutResponse, data) getSpaceInfo(snoResponse, data) else: data['nodesOnline'] -= 1 getPayoutEstimationToday(data) data['estimatedPayoutTotal'] = float("{:.2f}".format(data['estimatedPayoutTotal']/100)) data['estimatedPayoutToday'] = float("{:.2f}".format(data['estimatedPayoutToday']/100)) data['spaceUsed'] = float("{:.2f}".format(data['spaceUsed'])) data['spaceAvailable'] = float("{:.2f}".format(data['spaceAvailable'])) return json.dumps(addUnits(data))

d4aafb7f5cb6a5981703c8eabc8dde0dceba0586

py

Python

cwl_airflow/wes/server.py

silviu001/cwl-airflow

df45fc173ada83d94df94bc861777d9f6687b99a

cwl_airflow/wes/server.py

silviu001/cwl-airflow

df45fc173ada83d94df94bc861777d9f6687b99a

cwl_airflow/wes/server.py

silviu001/cwl-airflow

df45fc173ada83d94df94bc861777d9f6687b99a

#!/usr/bin/env python import connexion from connexion.resolver import Resolver from cwl_airflow.wes.backend import CWLAirflowBackend def run_wes_server(args): app = connexion.App(__name__) backend = CWLAirflowBackend() def rs(x): return getattr(backend, x.split('.')[-1]) app.add_api('openapi/swagger_configuration.yaml', resolver=Resolver(rs)) app.run(port=args.port, host=args.host)

cb153eb4c99e876cf184d972e1033f9a7f098956

py

Python

Encryption/Encryption.py

saurav0001kumar/HackerRank

38daaaf1e3f7b230ca70005480fa2f3e2c7a12be

2020-07-03T02:07:30.000Z

2020-07-03T02:07:30.000Z

Encryption/Encryption.py

saurav0001kumar/HackerRank

38daaaf1e3f7b230ca70005480fa2f3e2c7a12be

Encryption/Encryption.py

saurav0001kumar/HackerRank

38daaaf1e3f7b230ca70005480fa2f3e2c7a12be

#!/bin/python3 import math import os import random import re import sys # Complete the encryption function below. def encryption(s): res=[] l=list(s) l=list(filter(lambda x: x!=" " ,l)) s=''.join(l) le=len(s) row=math.sqrt(le) row=int(row) if row**2 == le: row=int(row) col=row else: col=row+1 row=int(row) for i in range(col): t="" for j in range(i,le,col): t=t+s[j] res.append(t) res1=" ".join(res) return(res1) if __name__ == '__main__': fptr = open(os.environ['OUTPUT_PATH'], 'w') s = input() result = encryption(s) fptr.write(result + '\n') fptr.close()

fe6e235a19e18ce11287af6f2b7e4213dd073f8e

py

Python

schemas/parquet_output_cluster_info_test.py

epapbak/insights-data-schemas

00eb5eba786a21fa82693633d3c9d1eee32130d8

schemas/parquet_output_cluster_info_test.py

epapbak/insights-data-schemas

00eb5eba786a21fa82693633d3c9d1eee32130d8

schemas/parquet_output_cluster_info_test.py

epapbak/insights-data-schemas

00eb5eba786a21fa82693633d3c9d1eee32130d8

#!/usr/bin/env python3 # vim: set fileencoding=utf-8 # Copyright © 2021 Pavel Tisnovsky # # 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. """Unit tests for parquet_output_cluster_info module.""" import pytest import sys from datetime import datetime from voluptuous import Invalid from parquet_output_cluster_info import schema, main from common import validate @pytest.fixture def validation_schema(): """Provide standard schema to check messages.""" return schema # verbosity level verbose = (True, False) # attributes attribute = ( "cluster_id", "cluster_version", "platform", "collected_at", "desired_version", "archive_path", "initial_version" ) @pytest.fixture def correct_message(): """Provide correct message to be tested.""" return { "cluster_id": b"123e4567-e89b-12d3-a456-426614174000", "cluster_version": b"1.2.3", "platform": b"AWS", "collected_at": datetime.now(), "desired_version": b"2.3.4", "archive_path": b"archives/compressed/00/00000000-0000-0000-0000-000000000000/202102/08/002219.tar.gz", "initial_version": b"1.2.3" } def test_main_help(): """Test the main function when -h parameter is given.""" sys.argv.append("-h") with pytest.raises(SystemExit) as excinfo: main() assert excinfo.value.code == 0 def test_main_input(): """Test the main function when -i parameter is given.""" sys.argv.append("-i test") with pytest.raises(SystemExit) as excinfo: main() assert excinfo.value.code == 0 @pytest.mark.parametrize("verbose", verbose) def test_validate_no_payload(validation_schema, verbose): """Test the validation for improper (no) payload.""" # it should fail with pytest.raises(ValueError) as excinfo: validate(schema, None, verbose) @pytest.mark.parametrize("verbose", verbose) def test_validate_correct_message(validation_schema, verbose, correct_message): """Test the validation for correct payload.""" # it should not fail validate(schema, correct_message, verbose) @pytest.mark.parametrize("verbose", verbose) def test_validate_message_without_cluster_id_attribute(validation_schema, verbose, correct_message): """Test the validation for improper payload.""" del correct_message["cluster_id"] # it should fail with pytest.raises(Invalid) as excinfo: validate(schema, correct_message, verbose) @pytest.mark.parametrize("attribute", attribute) @pytest.mark.parametrize("verbose", verbose) def test_validate_message_without_attributes(validation_schema, verbose, correct_message, attribute): """Test the validation for improper payload.""" del correct_message[attribute] # it should fail with pytest.raises(Invalid) as excinfo: validate(schema, correct_message, verbose) @pytest.mark.parametrize("attribute", attribute) @pytest.mark.parametrize("verbose", verbose) def test_validate_message_wrong_attributes(validation_schema, verbose, correct_message, attribute): """Test the validation for improper payload.""" # check with string not representing number correct_message[attribute] = b"foobar" # it should fail with pytest.raises(Invalid) as excinfo: validate(schema, correct_message, verbose) # check with number correct_message[attribute] = 123456 # it should fail with pytest.raises(Invalid) as excinfo: validate(schema, correct_message, verbose) # check with different data type correct_message[attribute] = [] # it should fail with pytest.raises(Invalid) as excinfo: validate(schema, correct_message, verbose)

5eb7d024eaca9e157b0e84b7d1b36c50c36ad9af

py

Python

tests/terminal.py

randy3k/ride

8a052daebaa8f03a9fff95eb38d45a32ac43bed9

2018-12-20T19:35:43.000Z

2022-03-31T19:44:35.000Z

tests/terminal.py

randy3k/ride

8a052daebaa8f03a9fff95eb38d45a32ac43bed9

2018-12-19T13:51:01.000Z

2022-03-31T09:33:43.000Z

tests/terminal.py

randy3k/ride

8a052daebaa8f03a9fff95eb38d45a32ac43bed9

2019-03-13T02:25:31.000Z

2022-03-15T16:21:50.000Z

import sys import pyte import operator import threading from contextlib import contextmanager import time import os if sys.platform.startswith("win"): import winpty else: import ptyprocess __all__ = ["PtyProcess", "Screen", "ByteStream", "Terminal"] if sys.platform.startswith("win"): ParentPtyProcess = winpty.PtyProcess else: ParentPtyProcess = ptyprocess.PtyProcess class PtyProcess(ParentPtyProcess): def read(self, nbytes): if sys.platform.startswith("win"): return super(PtyProcess, self).read(nbytes).encode("utf-8") else: return super(PtyProcess, self).read(nbytes) def write(self, data): if sys.platform.startswith("win"): super(PtyProcess, self).write(data.decode("utf-8")) else: super(PtyProcess, self).write(data) class Screen(pyte.Screen): def __init__(self, process, *args, **kwargs): self._process = process super(Screen, self).__init__(*args, **kwargs) def write_process_input(self, data): self._process.write(data.encode("utf-8")) class ByteStream(pyte.ByteStream): def start_feeding(self): screen = self.listener process = screen._process def reader(): while True: try: data = process.read(1024) except EOFError: break if data: self.feed(data) t = threading.Thread(target=reader) t.start() class Var(object): def __init__(self, getter): self.getter = getter def __getattr__(self, name): # fallback methods def _(*args, **kwargs): return Var(lambda: getattr(self.getter(), name)(*args, **kwargs)) return _ def observe(self, *args, **kwargs): return self.getter(*args, **kwargs) def _assert(self, op, operand, timeout=5): t = time.time() while time.time() - t < timeout: value = self.getter() if op(value, operand): break time.sleep(0.05) else: raise Exception("value is {}".format(value)) def assert_startswith(self, operand, timeout=5): self._assert(str.startswith, operand, timeout) def assert_endswith(self, operand, timeout=5): self._assert(str.endswith, operand, timeout) def assert_equal(self, operand, timeout=5): self._assert(operator.eq, operand, timeout) def assert_contains(self, operand, timeout=5): self._assert(operator.contains, operand, timeout) class Terminal(object): def __init__(self, process, screen, stream): self.process = process self.screen = screen self.stream = stream @classmethod @contextmanager def open(cls, cmd): # github actions windows-2019 doesn't like (24, 80) env = os.environ.copy() env["RETICULATE_PYTHON"] = sys.executable # don't not prompt to install miniconda env["RETICULATE_MINICONDA_ENABLED"] = "0" process = PtyProcess.spawn(cmd, dimensions=(40, 120), env=env) screen = Screen(process, 120, 40) stream = ByteStream(screen) stream.start_feeding() try: yield cls(process, screen, stream) finally: process.terminate(force=True) def sendintr(self): self.process.sendintr() def isalive(self): return self.process.isalive() def write(self, x): self.process.write(x.encode('utf-8')) def _line(self, num=0): # parent's `line` method return self.screen.display[num] def line(self, num=0): return Var(lambda: self._line(num)) def cursor(self): return Var(lambda: (self.screen.cursor.x, self.screen.cursor.y)) def current_line(self): return Var(lambda: self._line(self.screen.cursor.y)) def previous_line(self, num=1): return Var(lambda: self._line(self.screen.cursor.y - num))

e699c97c0b37da3099acfdc7d66113cacbc9976c

py

Python

dependencies.py

Plinius-Audio/ohdevtools

1a094d5ae918394f1307617fa6594d0bcc3005c2

2015-12-30T10:53:51.000Z

2021-11-11T00:10:02.000Z

dependencies.py

Plinius-Audio/ohdevtools

1a094d5ae918394f1307617fa6594d0bcc3005c2

2018-05-31T10:46:58.000Z

2022-02-13T22:43:28.000Z

dependencies.py

Plinius-Audio/ohdevtools

1a094d5ae918394f1307617fa6594d0bcc3005c2

2020-09-25T22:39:22.000Z

2021-11-08T21:11:16.000Z

import os import tarfile import zipfile import re import requests import platform import subprocess import json import shutil import io import tempfile from default_platform import default_platform import deps_cross_checker import aws # Master table of dependency types. # A dependency definition can specify 'type' to inherit definitions from one of these. # String values can depend on other string values from the dependency. For example, # if 'name' is defined as 'Example' then '${name}.exe' will expand to 'Example.exe'. # It does not matter which order the values are defined. # String values can also depend on boolean values. For example, the string # '${test-value?yes-result:no-result}' will get the value of the string named # 'yes-result' if 'test-value' is a true boolean value, and the string named # 'no-result' if 'test-value' is a false boolean value. # Finally, string values can also depend on a lookup table defined as a JSON object. # For example, given these definitions: # { # "servertable":{ # "Windows":"windows.openhome.org", # "Linux":"linux.openhome.org", # "*":"openhome.org" # }, # "server":"${servertable[$system]}" # } # If 'system' is defined as 'Windows', then 'server' will be defined as # 'windows.openhome.org'. The '*' entry is the default: if a lookup fails the default # will be used instead. # The principle string values that must be defined are 'archive-path' to point to the # .tar.gz file with the dependency's binaries, 'dest' to specify where to untar it, # and 'configure-args' to specify the list of arguments to pass to waf. # In order for source control fetching to work, the string 'source-git' should point # to the git repo and 'tag' should identify the git tag that corresponds to the # fetched binaries. DEPENDENCY_TYPES = { # Ignore dependencies # - ignored - effectively 'comments' out entire dependency 'ignore': { 'ignore': True }, # Openhome dependencies # - (legacy name - basically means that they are publicly visible and available) # - generally have an associated git repo to allow us to fetch source code. # - stored on AWS in the linn-artifacts-public bucket # # At a minimum must define: # name # version 'openhome': { 'archive-extension': '.tar.gz', 'archive-prefix': '', 'archive-suffix': '', 'binary-repo': 's3://linn-artifacts-public/artifacts', 'archive-directory': '${binary-repo}/${name}/', 'archive-filename': '${archive-prefix}${name}-${version}-${archive-platform}${archive-suffix}${archive-extension}', 'remote-archive-path': '${archive-directory}${archive-filename}', 'use-local-archive': False, 'archive-path': '${use-local-archive?local-archive-path:remote-archive-path}', 'source-path': '${linn-git-user}@core.linn.co.uk:/home/git', 'repo-name': '${name}', 'source-git': '${source-path}/${repo-name}.git', 'tag': '${repo-name}_${version}', 'any-platform': 'AnyPlatform', 'platform-specific': True, 'host-platform': default_platform(), 'archive-platform': '${platform-specific?platform:any-platform}', 'dest': 'dependencies/${archive-platform}/', 'configure-args': [] }, # Internal dependencies # - ony visible and available inside Linn # - stored on AWS in the linn-artifacts-private bucket # # At a minimum must define: # name # version 'internal': { 'binary-repo': 's3://linn-artifacts-private', 'source-git': None, 'any-platform': 'AnyPlatform', 'platform-specific': True, 'archive-suffix': '', 'archive-filename': '${name}-${version}-${platform}${archive-suffix}.tar.gz', 'archive-platform': '${platform-specific?platform:any-platform}', 'archive-path': '${binary-repo}/${name}/${archive-filename}', 'host-platform': default_platform(), 'dest': 'dependencies/${archive-platform}/', 'configure-args': [] }, # External dependencies # # - publicly visible and available # - no git repo that conforms to 'openhome standard' # - stored on AWS in the linn-artifacts-public bucket # # At a minimum must define: # name # archive-filename 'external': { 'binary-repo': 's3://linn-artifacts-public/artifacts', 'source-git': None, 'any-platform': 'AnyPlatform', 'platform-specific': True, 'archive-platform': '${platform-specific?platform:any-platform}', 'archive-path': '${binary-repo}/${archive-platform}/${archive-filename}', 'host-platform': default_platform(), 'dest': 'dependencies/${archive-platform}/', 'configure-args': [] }, } class FileFetcher(object): def __init__(self): pass def fetch(self, path): if path.startswith("file:") or path.startswith("smb:"): raise Exception("FETCH: File URLs deprecated") elif path.startswith("s3:"): return self.fetch_aws(path) elif re.match(r"[^\W\d]{2,8}:", path): raise Exception("FETCH: Legacy URLs no longer re-routed") return self.fetch_local(path) @staticmethod def fetch_aws(awspath): print(' from AWS %s' % awspath) temppath = tempfile.mktemp( suffix='.tmp' ) try: aws.copy(awspath, temppath) return temppath except: raise Exception("FETCH: Unable to retrieve %s from AWS" % awspath) return None @staticmethod def fetch_local(path): print( ' from LOCAL PATH %s' % path) return path class EnvironmentExpander(object): # template_regex matches template_regex = re.compile(r""" (?x) # Enable whitespace and comments (?P<dollar>\$\$)| # Match $$ (?P<word>\$[a-zA-Z_][a-zA-Z_0-9]*)| # Match $word (?P<parens>\$\{[^}]*\}) # Match ${any-thing} """) # Matches foo[bar] index_regex = re.compile(r""" (?x) # Enable whitespace and comments ^ # Match only at start of string ([^][]*) # Match table name (no brackets allowed) \[ # Match one open bracket: [ ([^][]*) # Match key (no brackets allowed) \] # Match one close bracket: ] $ """) def __init__(self, env_dict): self.env_dict = env_dict self.cache = {} self.expandset = set() def __getitem__(self, key): return self.expand(key) def getraw(self, key): return self.env_dict[key] def __contains__(self, key): return key in self.env_dict def keys(self): return self.env_dict.keys() def values(self): return [self.expand(key) for key in self.keys()] def items(self): return [(key, self.expand(key)) for key in self.keys()] def expand(self, key): if key in self.cache: return self.cache[key] if key in self.expandset: raise ValueError("Recursive expansion for key:", key) self.expandset.add(key) result = self._expand(key) self.cache[key] = result self.expandset.remove(key) return result def _expand(self, key): if key not in self.env_dict: raise KeyError("Key undefined:", key) value = self.env_dict[key] return self._expandvalue(value) def _expandvalue(self, value): if isinstance(value, ("".__class__, u"".__class__)): return self.expandstring(value) # return self.template_regex.sub(self.replacematch, value) elif isinstance(value, (list, tuple)): return [self._expandvalue(x) for x in value] elif isinstance(value, dict): return dict((k, self._expandvalue(v)) for (k, v) in value.items()) return value def expandstring(self, value): firstmatch = self.template_regex.match(value) if firstmatch is not None and firstmatch.group(0) == value and value != "$$": # Special case: The entire string is a single expansion. In this case, # we allow the expansion to be *anything* (bool, int, list...), # not just a string. return self.replacematch(firstmatch) return self.template_regex.sub(self.replacematch, value) def replacematch(self, match): if match.group('dollar'): return '$' key = None if match.group('word'): key = match.group('word')[1:] if match.group('parens'): key = match.group('parens')[2:-1] assert key is not None key = key.strip() if '[' in key: return self.expandlookup(key) if '?' in key: return self.expandconditional(key) return self.expand(key) def expandlookup(self, key): match = self.index_regex.match(key) if match is None: raise ValueError('lookup must be of form ${table[key]}') tablename = match.group(1).strip() keyname = match.group(2).strip() table = self.expand(tablename) if keyname.startswith('$'): key = self.expand(keyname[1:]) else: key = keyname if not isinstance(table, dict): raise ValueError("lookup table must expand to a JSON object (got {0!r} instead)".format(table)) if not isinstance(key, ("".__class__, u"".__class__)): raise ValueError("lookup index must expand to a JSON string (got {0!r} instead)".format(key)) if key not in table: if '*' in table: return table['*'] raise KeyError("Key not in table, and no default '*' entry found: key={0!r}\ntable={1!r}".format(key, table)) return table[key] def expandconditional(self, key): if '?' not in key: raise ValueError('conditional must be of form ${condition?result:alternative}') condition, rest = key.split('?', 1) if ':' not in rest: raise ValueError('conditional must be of form ${condition?result:alternative}') primary, alternative = rest.split(':', 1) condition, primary, alternative = [x.strip() for x in [condition, primary, alternative]] try: conditionvalue = self.expand(condition) except KeyError: conditionvalue = False if self.is_trueish(conditionvalue): return self.expand(primary) return self.expand(alternative) @staticmethod def is_trueish(value): if hasattr(value, "upper"): value = value.upper() return value in [1, "1", "YES", "Y", "TRUE", "ON", True] class Dependency(object): def __init__(self, name, environment, fetcher, has_overrides=False): self.expander = EnvironmentExpander(environment) self.has_overrides = has_overrides self.fetcher = fetcher def fetch(self): remote_path = self.expander.expand('archive-path') local_path = os.path.abspath(self.expander.expand('dest')) fetched_path = None print("\nFetching '%s'" % self.name) try: fetched_path = self.fetcher.fetch(remote_path) statinfo = os.stat(fetched_path) if not statinfo.st_size: os.unlink(fetched_path) print(" **** WARNING - failed to fetch %s ****" % os.path.basename(remote_path)) return False except IOError: print(" **** FAILED ****") return False try: os.makedirs(local_path) except OSError: # We get an error if the directory exists, which we are happy to # ignore. If something worse went wrong, we will find out very # soon when we try to extract the files. pass print(" unpacking to '%s'" % (local_path,)) if os.path.splitext(remote_path)[1].upper() in ['.ZIP', '.NUPKG', '.JAR']: self.unzip(fetched_path, local_path) else: self.untar(fetched_path, local_path) if fetched_path: if fetched_path != remote_path: os.unlink(fetched_path) print("OK") return True @property def name(self): return self['name'] def __getitem__(self, key): return self.expander.expand(key) def __contains__(self, key): return key in self.expander def items(self): return self.expander.items() def checkout(self): name = self['name'] sourcegit = self['source-git'] if sourcegit is None: print('No git repo defined for {0}'.format(name)) return False print("Fetching source for '%s'\n into '%s'" % (name, os.path.abspath('../' + name))) tag = self['tag'] try: if not os.path.exists('../' + name): print(' git clone {0} {1}'.format(sourcegit, name)) subprocess.check_call(['git', 'clone', sourcegit, name], cwd='..', shell=False) elif not os.path.isdir('../' + name): print('Cannot checkout {0}, because directory ../{0} already exists'.format(name)) return False else: print(' git fetch origin') subprocess.check_call(['git', 'fetch', 'origin'], cwd='../' + name, shell=False) print(" git checkout {0}".format(tag)) subprocess.check_call(['git', 'checkout', tag], cwd='../' + name, shell=False) except subprocess.CalledProcessError as cpe: print(str(cpe)) return False return True @staticmethod def untar(source, dest): tf = tarfile.open(source, 'r') for f in tf: try: tf.extract(f.name, path=dest) except IOError: os.unlink( os.path.join(dest, f.name )) tf.extract(f.name, path=dest) tf.close() @staticmethod def unzip(source, dest): zf = zipfile.ZipFile(source, mode='r') zf.extractall(path=dest) zf.close() def expand_remote_path(self): return self.expander.expand('archive-path') def expand_local_path(self): return self.expander.expand('dest') def expand_configure_args(self): return self.expander.expand('configure-args') class DependencyCollection(object): def __init__(self, env): fetcher = FileFetcher() self.base_env = env self.dependency_types = DEPENDENCY_TYPES self.dependencies = {} self.fetcher = fetcher def create_dependency(self, dependency_definition, overrides={}): defn = dependency_definition env = {} env.update(self.base_env) if 'type' in defn: dep_type = defn['type'] env.update(self.dependency_types[dep_type]) else: # default to an 'external' dependency type if none specified dep_type = 'external' env.update(self.dependency_types[dep_type]) env.update(defn) env.update(overrides) if 'name' not in env: raise ValueError('Dependency definition contains no name') name = env['name'] new_dependency = Dependency(name, env, self.fetcher, has_overrides=len(overrides) > 0) if 'ignore' in new_dependency and new_dependency['ignore']: return self.dependencies[name] = new_dependency def __contains__(self, key): return key in self.dependencies def __getitem__(self, key): return self.dependencies[key] def items(self): return self.dependencies.items() def _filter(self, subset=None): if subset is None: return self.dependencies.values() missing_dependencies = [name for name in subset if name not in self.dependencies] if len(missing_dependencies) > 0: raise Exception("No entries in dependency file named: " + ", ".join(missing_dependencies) + ".") return [self.dependencies[name] for name in subset] def get_args(self, subset=None): dependencies = self._filter(subset) configure_args = sum((d.expand_configure_args() for d in dependencies), []) return configure_args def fetch(self, subset=None): dependencies = self._filter(subset) failed_dependencies = [] filename = self.fetched_deps_filename(dependencies) fetched_deps = self.load_fetched_deps(filename) for d in dependencies: do_fetch = True name = '' path = '' dest = '' if 'name' in d.expander: name = d.expander.expand('name') if 'archive-path' in d.expander: path = d.expander.expand('archive-path') if 'dest' in d.expander: dest = d.expander.expand('dest') lookup = dest.rstrip( '/' ) + '/' + name version = os.path.basename(path) if lookup in fetched_deps: if fetched_deps[lookup] == version: print("Skipping fetch of %s as unchanged (%s)" % (name, version)) do_fetch = False if do_fetch: if not d.fetch(): failed_dependencies.append(d.name) else: fetched_deps[lookup] = version if filename: self.save_fetched_deps(filename, fetched_deps) if failed_dependencies: print("Failed to fetch some dependencies: " + ' '.join(failed_dependencies)) return False return True @staticmethod def fetched_deps_filename(deps): filename = None for d in deps: if 'dest' in d.expander: filename = os.path.join(d.expander.expand('dest').split('/')[0], 'loadedDeps.json') break return filename def load_fetched_deps(self, filename): loaded_deps = {} if filename and os.path.isfile(filename): try: f = open(filename, 'rt') loaded_deps = json.load(f) f.close() except: print("Error with current fetched dependency file: %s" % filename) return loaded_deps @staticmethod def save_fetched_deps(filename, deps): f = open(filename, 'wt') json.dump(deps, f) f.close() def checkout(self, subset=None): dependencies = self._filter(subset) failed_dependencies = [] for d in dependencies: if not d.checkout(): failed_dependencies.append(d.name) if failed_dependencies: print("Failed to check out some dependencies: " + ' '.join(failed_dependencies)) return False return True def read_json_dependencies(dependencyfile, overridefile, env): collection = DependencyCollection(env) dependencies = json.load(dependencyfile) overrides = json.load(overridefile) overrides_by_name = dict((dep['name'], dep) for dep in overrides) for d in dependencies: name = d['name'] override = overrides_by_name.get(name, {}) collection.create_dependency(d, override) return collection def read_json_dependencies_from_filename(dependencies_filename, overrides_filename, env): try: dependencyfile = open(dependencies_filename, "r") with open(dependencies_filename) as dependencyfile: if overrides_filename is not None and os.path.isfile(overrides_filename): with open(overrides_filename) as overridesfile: return read_json_dependencies(dependencyfile, overridesfile, env) else: return read_json_dependencies(dependencyfile, io.StringIO(u'[]'), env) except (OSError, IOError) as e: if e.errno != 2: raise return DependencyCollection(env) def clean_dirs(dir): """Remove the specified directory tree - don't remove anything if it would fail""" if os.path.isdir( dir ): locked = [] for dirName, _subdirList, fileList in os.walk(dir): for fileName in fileList: filePath = os.path.join(dirName, fileName) try: if not os.path.islink( filePath ): openAtt = 'r' if platform.system().lower() == 'windows': openAtt = 'a' f = open(filePath, openAtt) f.close() except: locked.append(filePath) if locked: for f in locked: print('Locked file:- ', f) raise Exception('Failed to clean dependencies\n') else: shutil.rmtree(dir) def fetch_dependencies(dependency_names=None, platform=None, env=None, fetch=True, clean=True, source=False, list_details=False, local_overrides=True, verbose=False): ''' Fetch all the dependencies defined in projectdata/dependencies.json and in projectdata/packages.config. platform: Name of target platform. E.g. 'Windows-x86', 'Linux-x64', 'Mac-x64'... env: Extra variables referenced by the dependencies file. fetch: True to fetch the listed dependencies, False to skip. clean: True to clean out directories before fetching, False to skip. source: True to fetch source for the listed dependencies, False to skip. ''' if env is None: env = {} if platform is not None: env['platform'] = None fName = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'platforms.txt') f = open(fName, 'rt') supported = f.readlines() f.close() for entry in supported: if platform in entry: env['platform'] = platform if not env['platform']: raise Exception('Platform not supported (%s) - see %s for list of supported platforms' % (platform, fName)) if 'platform' not in env: platform = env['platform'] = default_platform() if '-' in platform: env['system'], env['architecture'] = platform.split('-', 2) if platform is None: raise Exception('Platform not specified and unable to guess.') if clean and not list_details: try: os.unlink('dependencies/loadedDeps.json') except: pass clean_dirs('dependencies') overrides_filename = '../dependency_overrides.json' if local_overrides else None dependencies = read_json_dependencies_from_filename('projectdata/dependencies.json', overrides_filename, env=env) if list_details: for name, dependency in dependencies.items(): print("Dependency '{0}':".format(name)) print(" fetches from: {0!r}".format(dependency['archive-path'])) print(" unpacks to: {0!r}".format(dependency['dest'])) print(" local override: {0}".format("YES (see '../dependency_overrides.json')" if dependency.has_overrides else 'no')) if verbose: print(" all keys:") for key, value in sorted(dependency.items()): print(" {0} = {1!r}".format(key, value)) print("") else: if fetch: if not dependencies.fetch(dependency_names): raise Exception("Failed to load requested dependencies") if source: dependencies.checkout(dependency_names) # Finally perform cross-check of (major.minor) dependency versions to ensure that these are in sync # across this (current) repo and all its pulled-in dependencies. Done as totally seperate operation # to isolate from the main fetcher code to assist with any future maintenance if not clean: xcheck = deps_cross_checker.DepsCrossChecker( platform ) result = xcheck.execute() if result != 0: raise Exception( 'Failed: dependency cross-checker detected problem(s)' ) return dependencies

5b5f4d0da457327f84ba17ab73ca81baf0b9d8ed

py

Python

2a.py

znuxor/adventofcode2017

79d0df07f24ea8d2793df3b1c853a85b760791c1

2a.py

znuxor/adventofcode2017

79d0df07f24ea8d2793df3b1c853a85b760791c1

2a.py

znuxor/adventofcode2017

79d0df07f24ea8d2793df3b1c853a85b760791c1

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import numpy as np data_input = np.loadtxt('2a_data.txt') mysum = 0 minimums = np.min(data_input, axis=1) maximums = np.max(data_input, axis=1) diffs = maximums - minimums print(np.sum(diffs))

6ebb8f194bd9625cf0e55656f6b226c6c6453097

py

Python

python_stock/4/Python4-4.py

hollo08/stockstrategy

09ece2457d653439a8ace80a6ac7dd4da9813846

2020-09-18T15:08:46.000Z

2020-09-18T15:08:46.000Z

python_stock/4/Python4-4.py

hollo08/stockstrategy

09ece2457d653439a8ace80a6ac7dd4da9813846

python_stock/4/Python4-4.py

hollo08/stockstrategy

09ece2457d653439a8ace80a6ac7dd4da9813846

2022-01-23T03:26:22.000Z

2022-03-28T16:21:01.000Z

import random #导入random标准库 mymin =200 #定义变量，存放随机数中的最小数 i = 1 #定义变量，用于统计循环次数 while i <= 15 : r = random.randint(50,150) #在50~150之间随机产生一个数 i += 1 #循环次数加1 print("第 %d 随机数是: %s "%(i-1,r)) #显示第几个随机数是几 if r < mymin: mymin = r #把随机数中的最小数放到mymin中 else : print("\n\n这15个数中，最小的数是：",mymin)

53e3ffd4ac72b5ca13c1799d35991a28a5e0e78c

py

Python

plugins/trendmicro_deepsecurity/icon_trendmicro_deepsecurity/actions/list_rules/action.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

2019-06-05T20:47:58.000Z

2022-03-29T10:18:01.000Z

plugins/trendmicro_deepsecurity/icon_trendmicro_deepsecurity/actions/list_rules/action.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

2019-06-07T20:20:39.000Z

2022-03-30T17:35:01.000Z

plugins/trendmicro_deepsecurity/icon_trendmicro_deepsecurity/actions/list_rules/action.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

2019-07-09T14:13:58.000Z

2022-03-28T12:04:46.000Z

import komand from .schema import ListRulesInput, ListRulesOutput, Input, Output, Component # Custom imports below from icon_trendmicro_deepsecurity.util.shared import tryJSON from icon_trendmicro_deepsecurity.util.shared import checkResponse import requests class ListRules(komand.Action): def __init__(self): super(self.__class__, self).__init__( name="list_rules", description=Component.DESCRIPTION, input=ListRulesInput(), output=ListRulesOutput(), ) def run(self, params={}): """ List IPS rules """ # Get parameters self.scope = params.get(Input.SCOPE) self.id = params.get(Input.ID) ips_rules = set() covered_cves = set() hits = 0 self.logger.info(f"Getting rules from {self.scope} {self.id}") # Prepare request # Check if the rules should be assigned to a computer or policy if self.scope == "computer": url = f"{self.connection.dsm_url}/api/computers/{self.id}/intrusionprevention/rules" else: url = f"{self.connection.dsm_url}/api/policies/{self.id}/intrusionprevention/rules" # Send request response = requests.get(url, verify=self.connection.dsm_verify_ssl, headers=self.connection.headers) self.logger.info(f"url: {response.url}") self.logger.info(f"status: {response.status_code}") self.logger.info(f"reason: {response.reason}") # Check response errors checkResponse(response) # Try to convert the response data to JSON response_data = tryJSON(response) # Extract rules if response_data["intrusionPreventionRules"]: for rule in response_data["intrusionPreventionRules"]: ips_rules.add(rule["ID"]) if "CVE" in rule.keys(): self.logger.info(f"{rule['ID']}:\t{rule['name']} - " + ", ".join(rule["CVE"])) covered_cves.update(rule["CVE"]) else: self.logger.info(f"{rule['ID']}:\t{rule['name']}") else: self.logger.info("No rules found!") hits = len(response_data["intrusionPreventionRules"]) self.logger.info(f"Found {hits} rules covering the following CVEs: \n" + ", ".join(covered_cves)) # Return assigned rules and covered CVEs return { Output.RULES_ASSIGNED: list(ips_rules), Output.COVERED_CVES: list(covered_cves), Output.RESPONSE_JSON: response_data, }

04a6bf57031b07c8ce399db8dfd5c10266bcf5ac

py

Python

zdata.py

manimaul/mxmcc

923458b759c8daa74dd969e968bc72b17fdffe02

2016-08-24T21:30:45.000Z

2016-08-24T21:30:45.000Z

zdata.py

manimaul/mxmcc

923458b759c8daa74dd969e968bc72b17fdffe02

2021-03-18T23:25:15.000Z

2022-03-11T23:44:20.000Z

zdata.py

manimaul/mxmcc

923458b759c8daa74dd969e968bc72b17fdffe02

#!/usr/bin/env python __author__ = 'Will Kamp' __copyright__ = 'Copyright 2013, Matrix Mariner Inc.' __license__ = 'BSD' __email__ = '[email protected]' __status__ = 'Development' # 'Prototype', 'Development', or 'Production' '''MX Mariner zdata generator for regions / catalogs''' import codecs import os.path import zipfile from . import config from . import catalog from . import regions upd_fmt = U'UPDATE regions SET installeddate=\'%s\' WHERE name=\'%s\';\n' custom_fmt0 = u'DELETE from regions WHERE name=\'%s\';\n' custom_fmt1 = u'INSERT into [regions] ([name], [description], [image], [size], [installeddate] ) ' \ u'VALUES (\'%s\', \'%s\', \'%s\', \'%s\', \'%s\');\n' fmt0 = u'DELETE from charts where region=\'%s\';\n' fmt1 = u'INSERT INTO [charts] ([region], [file], [name], [updated], [scale], [outline], [depths], [zoom]) ' \ u'VALUES (\'%s\', \'%s\', \'%s\', \'%s\', %s, \'%s\', \'%s\', \'%s\');\n' def get_zdat_epoch(zdat_path): """ :param zdat_path: path to the <region>.zdat file :return: the installeddate value to be set """ zdat_file = zipfile.ZipFile(zdat_path, 'r', zipfile.ZIP_DEFLATED) line = str(zdat_file.open(zdat_file.namelist()[0], 'r').readlines()[1]) l = line.find('\'') + 1 r = line.find('\'', l) return line[l:r] def generate_update(): """generates and UPDATE.zdat file for all of the new (s)gemf regions rendered """ sql_fname = 'UPDATE.sql' sql_path = os.path.join(config.compiled_dir, sql_fname) zdat_path = os.path.join(config.compiled_dir, 'UPDATE.zdat') print(zdat_path) zdat = zipfile.ZipFile(zdat_path, 'w', zipfile.ZIP_DEFLATED) sqlf = open(sql_path, 'w') gemf_lst = [] for ea in os.listdir(config.compiled_dir): if ea.endswith('gemf'): gemf_lst.append(os.path.join(config.compiled_dir, ea)) gemf_lst.sort() if len(gemf_lst) is 0: return sqlstr = u'update regions set latestdate=\'%s\', size=\'%s\' where name=\'%s\';' sqlf.write(u'--MXMARINER-DBVERSION:1\n') for p in gemf_lst: size = str(os.path.getsize(p)) region = os.path.basename(p) region = region[:region.rfind('.')] z_path = os.path.join(config.compiled_dir, region + '.zdat') sqlf.write(sqlstr % (get_zdat_epoch(z_path), size, region) + '\n') sqlf.close() zdat.write(sql_path, sql_fname) os.remove(sql_path) zdat.close() print('update written to: ' + zdat_path) def format_entry(region: str, entry: dict): def san(thing): return str(thing).strip() return fmt1 % (region, os.path.basename(san(entry['path'])), san(entry['name']), san(entry['date']), san(entry['scale']), san(entry['outline']), san(entry['depths']), san(entry['max_zoom'])) def generate_zdat_for_catalog(catalog_name, description=None): """generates a zdat file for a region catalog_name - the name of the catalog / region to generate data for description - if this is a custom catalog / region... set the description here """ region = catalog_name.upper().strip() reader = catalog.get_reader_for_region(catalog_name) sql_fname = region + '.sql' sql_path = os.path.join(config.compiled_dir, sql_fname) zdat_path = os.path.join(config.compiled_dir, region + '.zdat') sql_file = codecs.open(sql_path, 'w', 'utf-8') zdat_file = zipfile.ZipFile(zdat_path, 'w', zipfile.ZIP_DEFLATED) sql_file.write('--MXMARINER-DBVERSION:3\n') if regions.is_valid_region(region): sql_file.write(upd_fmt % (config.epoch, region)) sql_file.write(fmt0 % region) else: num_bytes = os.path.getsize(os.path.join(config.compiled_dir, region + '.gemf')) sql_file.write(custom_fmt0 % region) sql_file.write(custom_fmt1 % (region, description, region.lower().replace('_', ''), num_bytes, config.epoch)) for entry in reader: sql_file.write(format_entry(region, entry)) sql_file.close() zdat_file.write(sql_path, sql_fname) os.remove(sql_path) zdat_file.close() if __name__ == '__main__': generate_update()

013df6455e514ab341995e60c2b29691d795cfc3

py

Python

apps/run_yolo_video.py

wenxingliu/smoke_detector_yolo3

2e67a4347256ad8378eddf5b4efdc3782b3fb8e2

apps/run_yolo_video.py

wenxingliu/smoke_detector_yolo3

2e67a4347256ad8378eddf5b4efdc3782b3fb8e2

apps/run_yolo_video.py

wenxingliu/smoke_detector_yolo3

2e67a4347256ad8378eddf5b4efdc3782b3fb8e2

2020-10-10T04:03:30.000Z

2020-10-10T04:03:30.000Z

import os current_path = os.path.dirname(os.path.abspath(__file__)) path_suffix = 'apps' if current_path.endswith(path_suffix): parent_path = current_path.rsplit(path_suffix, 1)[0] os.chdir(parent_path) from yolo_detect.yolo import YOLO from yolo_detect.detect_video import detect_video __author__ = 'sliu' if __name__ == '__main__': video_file_name = '41琉璃河ch0_CHANNEL0_20180108_11_56_50' video_path = 'input_data/videos/' + '%s.avi' % video_file_name out_path = 'output_data/41_boxed/' detect_video(YOLO(), video_path, out_path)

29cc46030fd01b7fda5165b4f9e50553d78e9b7f

py

Python

share/qt/clean_mac_info_plist.py

aptcoin/aptcoin

bcdea0990837ea8c22017fe2e34548c5375cd476

share/qt/clean_mac_info_plist.py

aptcoin/aptcoin

bcdea0990837ea8c22017fe2e34548c5375cd476