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07_Greedy/distribute_candy.py | Sheetal0601/InterviewBit | 61 | 54117 | # Distribute Candy
# https://www.interviewbit.com/problems/distribute-candy/
#
# There are N children standing in a line. Each child is assigned a rating value.
#
# You are giving candies to these children subjected to the following requirements:
# Each child must have at least one candy.
# Children with a higher rating get more candies than their neighbors.
# What is the minimum candies you must give?
#
# Sample Input :
#
# Ratings : [1 2]
# Sample Output :
#
# 3
# The candidate with 1 rating gets 1 candy and candidate with rating cannot get 1 candy as 1 is
# its neighbor. So rating 2 candidate gets 2 candies. In total, 2+1 = 3 candies need to be given out.
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
class Solution:
# @param A : list of integers
# @return an integer
def candy(self, A):
candies = [1]
for i in range(1, len(A)):
candies.append(candies[-1] + 1 if A[i] > A[i - 1] else 1)
result = candies[-1]
for i in range(len(A) - 2, -1, -1):
curr = candies[i + 1] + 1 if A[i] > A[i + 1] else 1
result += max(curr, candies[i])
candies[i] = curr
return result
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
if __name__ == "__main__":
s = Solution()
print(s.candy([1, 2]))
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foundation/backend/controller/__init__.py | tbone255/foundation | 0 | 121741 | <reponame>tbone255/foundation<gh_stars>0
from .base import *
from .mixins import *
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solutions/previous_solution_python/leetcode_1041.py | YuhanShi53/Leetcode_solutions | 0 | 46203 | """ Leetcode 1041 - Robot Bounded in Circle
https://leetcode.com/problems/robot-bounded-in-circle/
"""
class Solution1:
""" 1. MINE Straight-Forward """
def is_robot_bounded(self, instructions: str) -> bool:
nums = {'G': 0, 'R': 1, 'L': -1}
direction = [1, -1]
position = [0, 0]
idx = 0
summation = 0
factor = 1
for x in instructions:
summation += nums[x]
if x in ('R', 'L'):
idx = 1 - idx
factor *= nums[x] * direction[idx]
else:
position[idx] += factor * 1
if summation % 4 == 0 and position != [0, 0]:
return False
return True
if __name__ == '__main__':
instructions = "GGG"
res = Solution1().is_robot_bounded(instructions)
print(res)
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behave/reporter/summary.py | fluendo/behave | 0 | 18121 | # -*- coding: UTF-8 -*-
"""
Provides a summary after each test run.
"""
from __future__ import absolute_import, division, print_function
import sys
from time import time as time_now
from behave.model import Rule, ScenarioOutline # MAYBE: Scenario
from behave.model_core import Status
from behave.reporter.base import Reporter
from behave.formatter.base import StreamOpener
# ---------------------------------------------------------------------------
# CONSTANTS:
# ---------------------------------------------------------------------------
# -- DISABLED: OPTIONAL_STEPS = ('untested', 'undefined')
OPTIONAL_STEPS = (Status.untested,) # MAYBE: Status.undefined
STATUS_ORDER = (Status.passed, Status.failed, Status.skipped,
Status.undefined, Status.untested)
# ---------------------------------------------------------------------------
# UTILITY FUNCTIONS:
# ---------------------------------------------------------------------------
def pluralize(word, count=1, suffix="s"):
if count == 1:
return word
# -- OTHERWISE:
return "{0}{1}".format(word, suffix)
def compute_summary_sum(summary):
"""Compute sum of all summary counts (except: all)
:param summary: Summary counts (as dict).
:return: Sum of all counts (as integer).
"""
counts_sum = 0
for name, count in summary.items():
if name == "all":
continue # IGNORE IT.
counts_sum += count
return counts_sum
def format_summary0(statement_type, summary):
parts = []
for status in STATUS_ORDER:
if status.name not in summary:
continue
counts = summary[status.name]
if status in OPTIONAL_STEPS and counts == 0:
# -- SHOW-ONLY: For relevant counts, suppress: untested items, etc.
continue
if not parts:
# -- FIRST ITEM: Add statement_type to counter.
label = statement_type
if counts != 1:
label += 's'
part = u"%d %s %s" % (counts, label, status.name)
else:
part = u"%d %s" % (counts, status.name)
parts.append(part)
return ", ".join(parts) + "\n"
def format_summary(statement_type, summary):
parts = []
for status in STATUS_ORDER:
if status.name not in summary:
continue
counts = summary[status.name]
if status in OPTIONAL_STEPS and counts == 0:
# -- SHOW-ONLY: For relevant counts, suppress: untested items, etc.
continue
name = status.name
if status.name == "passed":
statement = pluralize(statement_type, counts)
name = u"%s passed" % statement
part = u"%d %s" % (counts, name)
parts.append(part)
return ", ".join(parts) + "\n"
# -- PREPARED:
def format_summary2(statement_type, summary, end="\n"):
"""Format the summary line for one statement type.
.. code-block::
6 scenarios (passed: 5, failed: 1, skipped: 0, untested: 0)
:param statement_type:
:param summary:
:return:
"""
parts = []
for status in STATUS_ORDER:
if status.name not in summary:
continue
counts = summary[status.name]
if status in OPTIONAL_STEPS and counts == 0:
# -- SHOW-ONLY: For relevant counts, suppress: untested items, etc.
continue
parts.append((status.name, counts))
counts_sum = summary["all"]
statement = pluralize(statement_type, sum)
parts_text = ", ".join(["{0}: {1}".format(name, value)
for name, value in parts])
return "{count:4} {statement:<9} ({parts}){end}".format(
count=counts_sum, statement=statement, parts=parts_text, end=end)
# ---------------------------------------------------------------------------
# REPORTERS:
# ---------------------------------------------------------------------------
class SummaryReporter(Reporter):
show_failed_scenarios = True
output_stream_name = "stdout"
def __init__(self, config):
super(SummaryReporter, self).__init__(config)
stream = getattr(sys, self.output_stream_name, sys.stderr)
self.stream = StreamOpener.ensure_stream_with_encoder(stream)
summary_zero_data = {
"all": 0,
Status.passed.name: 0,
Status.failed.name: 0,
Status.skipped.name: 0,
Status.untested.name: 0
}
self.feature_summary = summary_zero_data.copy()
self.rule_summary = summary_zero_data.copy()
self.scenario_summary = summary_zero_data.copy()
self.step_summary = {Status.undefined.name: 0}
self.step_summary.update(summary_zero_data)
self.duration = 0.0
self.run_starttime = 0
self.run_endtime = 0
self.failed_scenarios = []
self.show_rules = True
def testrun_started(self, timestamp=None):
if timestamp is None:
timestamp = time_now()
self.run_starttime = timestamp
def testrun_finished(self, timestamp=None):
if timestamp is None:
timestamp = time_now()
self.run_endtime = timestamp
def print_failing_scenarios(self, stream=None):
if stream is None:
stream = self.stream
stream.write("\nFailing scenarios:\n")
for scenario in self.failed_scenarios:
# add the list of tags matching ###-### so we will show the issue identifier with the error OPE-1234
stream.write(u" %s %s %s\n" % (', '.join(t for t in scenario.tags if len(t.split('-'))==2 ), scenario.location, scenario.name))
def compute_summary_sums(self):
"""(Re)Compute summary sum of all counts (except: all)."""
summaries = [
self.feature_summary,
self.rule_summary,
self.scenario_summary,
self.step_summary
]
for summary in summaries:
summary["all"] = compute_summary_sum(summary)
def print_summary(self, stream=None, with_duration=True):
if stream is None:
stream = self.stream
self.compute_summary_sums()
has_rules = (self.rule_summary["all"] > 0)
stream.write(format_summary("feature", self.feature_summary))
if self.show_rules and has_rules:
# -- HINT: Show only rules, if any exists.
self.stream.write(format_summary("rule", self.rule_summary))
stream.write(format_summary("scenario", self.scenario_summary))
stream.write(format_summary("step", self.step_summary))
# -- DURATION:
if with_duration:
timings = (int(self.duration / 60.0), self.duration % 60)
stream.write('Took %dm%02.3fs\n' % timings)
# -- REPORTER-API:
def feature(self, feature):
if self.run_starttime == 0:
# -- DISCOVER: TEST-RUN started.
self.testrun_started()
self.process_feature(feature)
def end(self):
self.testrun_finished()
# -- SHOW FAILED SCENARIOS (optional):
if self.show_failed_scenarios and self.failed_scenarios:
self.print_failing_scenarios()
self.stream.write("\n")
# -- SHOW SUMMARY COUNTS:
self.print_summary()
def process_run_items_for(self, parent):
for run_item in parent:
if isinstance(run_item, Rule):
self.process_rule(run_item)
elif isinstance(run_item, ScenarioOutline):
self.process_scenario_outline(run_item)
else:
# assert isinstance(run_item, Scenario)
self.process_scenario(run_item)
def process_feature(self, feature):
self.duration += feature.duration
self.feature_summary[feature.status.name] += 1
self.process_run_items_for(feature)
def process_rule(self, rule):
self.rule_summary[rule.status.name] += 1
self.process_run_items_for(rule)
def process_scenario(self, scenario):
if scenario.status == Status.failed:
self.failed_scenarios.append(scenario)
self.scenario_summary[scenario.status.name] += 1
for step in scenario:
self.step_summary[step.status.name] += 1
def process_scenario_outline(self, scenario_outline):
for scenario in scenario_outline.scenarios:
self.process_scenario(scenario)
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] |
utils/crop_image.py | Mhaiyang/iccv | 2 | 47300 | """
@Time : 203/21/19 17:11
@Author : TaylorMei
@Email : <EMAIL>
@Project : iccv
@File : crop_image.py
@Function:
"""
import os
import numpy as np
import skimage.io
input_path = '/media/iccd/TAYLORMEI/depth/image'
output_path = '/media/iccd/TAYLORMEI/depth/crop'
if not os.path.exists(output_path):
os.mkdir(output_path)
imglist = os.listdir(input_path)
for i, imgname in enumerate(imglist):
print(i, imgname)
image = skimage.io.imread(os.path.join(input_path, imgname))
print(np.sum(image[80, :, :]))
for j in range(640):
if np.sum(image[j, :, :]) !=0 and np.sum(image[j, :, :]) !=367200:
print(j)
break
# crop = image[80:560, :, :]
# skimage.io.imsave(os.path.join(output_path, imgname), crop)
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] |
entities/api_renderers.py | acdh-oeaw/daacda-go-digital | 0 | 1615613 | import json
from rest_framework import renderers
class GeoJsonRenderer(renderers.BaseRenderer):
media_type = 'text/json'
format = 'geojson'
def render(self, data, media_type=None, renderer_context=None):
geojson_start = """{"type": "FeatureCollection","features": """
geolist = []
for x in data:
if x:
geolist.append(x)
else:
pass
geojson_end = "}"
geolist = json.dumps(geolist)
geojson = "{}{}{}".format(geojson_start, geolist, geojson_end)
return geojson
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networkforgat.py | LouGalata/MAGAT | 3 | 71691 | import torch
import torch.nn as nn
import torch.nn.functional as f
import numpy as np
from layers import GraphAttentionLayer
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1. / np.sqrt(fan_in)
return (-lim, lim)
class GAT(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
"""Dense version of GAT."""
super(GAT, self).__init__()
self.dropout = dropout
self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout, alpha=alpha, concat=True) for _ in
range(nheads)]
for i, attention in enumerate(self.attentions):
self.add_module('attention_{}'.format(i), attention)
self.out_att = GraphAttentionLayer(nhid * nheads, nclass, dropout=dropout, alpha=alpha, concat=False)
def forward(self, x, adj):
x = f.dropout(x, self.dropout, training=self.training)
x = torch.cat([att(x, adj) for att in self.attentions], dim=-1)
x = f.dropout(x, self.dropout, training=self.training)
x = f.elu(self.out_att(x, adj))
return x
# return F.log_softmax(x, dim=1)
class CriticNetwork(nn.Module):
def __init__(self, input_dim, hidden_gat_dim, hidden_in_dim, hidden_out_dim, output_dim, actor=False):
super(CriticNetwork, self).__init__()
"""self.input_norm = nn.BatchNorm1d(input_dim)
self.input_norm.weight.data.fill_(1)
self.input_norm.bias.data.fill_(0)"""
self.gat = GAT(nfeat=input_dim,
nhid=hidden_gat_dim,
nclass=input_dim,
dropout=0.0,
nheads=4,
alpha=0.1)
self.gat2 = GAT(nfeat=input_dim,
nhid=hidden_gat_dim,
nclass=input_dim,
dropout=0.0,
nheads=4,
alpha=0.1)
# dense_input_dim = input_dim * 5 * 2 # num_agents * res connections
dense_input_dim = input_dim * 5 * 2
self.fc1 = nn.Linear(dense_input_dim, hidden_in_dim)
self.fc2 = nn.Linear(hidden_in_dim, hidden_out_dim)
self.fc3 = nn.Linear(hidden_out_dim, output_dim)
# self.fc3 = nn.Linear(hidden_in_dim, output_dim)
# self.nonlin = f.relu # leaky_relu
self.nonlin = f.leaky_relu
self.actor = actor
# self.reset_parameters()
def reset_parameters(self):
self.gat.weight.data.uniform_(*hidden_init(self.gat))
self.gat2.weight.data.uniform_(*hidden_init(self.gat2))
self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
self.fc3.weight.data.uniform_(-1e-3, 1e-3)
def forward(self, x, adj):
gat = self.gat(x, adj)
gat = self.gat2(gat, adj)
resgat = torch.cat((x, gat), dim=-1) # review
flatten = torch.flatten(gat, start_dim=1)
h1 = self.nonlin(self.fc1(flatten))
h2 = self.nonlin(self.fc2(h1))
h3 = (self.fc3(h2))
return h3
class ActorNetwork(nn.Module):
def __init__(self, input_dim, hidden_in_dim, hidden_out_dim, output_dim, actor=False):
super(ActorNetwork, self).__init__()
"""self.input_norm = nn.BatchNorm1d(input_dim)
self.input_norm.weight.data.fill_(1)
self.input_norm.bias.data.fill_(0)"""
self.fc1 = nn.Linear(input_dim, hidden_in_dim)
self.fc2 = nn.Linear(hidden_in_dim, hidden_out_dim)
self.fc3 = nn.Linear(hidden_out_dim, output_dim)
self.nonlin = f.relu # leaky_relu
self.actor = actor
# self.reset_parameters()
def reset_parameters(self):
self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
self.fc3.weight.data.uniform_(-1e-3, 1e-3)
def forward(self, x):
# return a vector of the force
h1 = self.nonlin(self.fc1(x))
h2 = self.nonlin(self.fc2(h1))
h3 = (self.fc3(h2))
norm = torch.norm(h3)
# h3 is a 2D vector (a force that is applied to the agent)
# we bound the norm of the vector to be between 0 and 10
# return 10.0*(torch.tanh(norm))*h3/norm if norm > 0 else 10*h3
return 1.0 * (torch.tanh(norm)) * h3 / norm if norm > 0 else 1 * h3
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lib/event.py | PaulMndn/Fenrir-Discord-Bot | 1 | 190444 | <reponame>PaulMndn/Fenrir-Discord-Bot
import discord
import datetime as dt
class Event:
def __init__(self, title: str, date_time: dt.datetime, event_channel_id: int, msg_id: int = None):
self.title = title
self.date_time = date_time
self.event_channel_id = event_channel_id
self.msg_id = msg_id
def __str__(self):
return f"**{self.title}** {self.date_time.strftime('%d.%m.%Y at %I:%M%p')}"
def __lt__(self, other):
if isinstance(other, Event):
return self.date_time < other.date_time
elif isinstance(other, dt.datetime):
return self.date_time < other
elif isinstance(other, dt.date):
return self.date_time.date() < other
else:
raise NotImplementedError(f"Can't compare type '{type(self)}'v to type '{type(other)}'.")
def __le__(self, other):
if isinstance(other, Event):
return self.date_time <= other.date_time
elif isinstance(other, dt.datetime):
return self.date_time <= other
elif isinstance(other, dt.date):
return self.date_time.date() <= other
else:
raise NotImplementedError(f"Can't compare type '{type(self)}' to type '{type(other)}'.")
def __ge__(self, other):
if isinstance(other, Event):
return self.date_time >= other.date_time
elif isinstance(other, dt.datetime):
return self.date_time >= other
elif isinstance(other, dt.date):
return self.date_time.date() >= other
else:
raise NotImplementedError(f"Can't compare type '{type(self)}' to type '{type(other)}'.")
def __eq__(self, other):
if isinstance(other, Event):
return self.date_time == other.date_time
elif isinstance(other, dt.datetime):
return self.date_time == other
elif isinstance(other, dt.date):
return self.date_time.date() == other
else:
raise NotImplementedError(f"Can't compare type '{type(self)}' to type '{type(other)}'.")
@property
def key(self):
'''Message ID if it exists. Otherwise the datetime in iso-format.'''
return str(self.msg_id) if self.msg_id is not None else self.date_time.isoformat()
if __name__ == "__main__":
pass | [
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exercises_and_metrics_types/MetricSingleInputWidget.py | noooway/exj | 1 | 59756 | from kivy.app import App
from kivy.uix.boxlayout import BoxLayout
from kivy.uix.gridlayout import GridLayout
from kivy.uix.button import Button
from kivy.uix.label import Label
from kivy.uix.textinput import TextInput
from MetricSingle import *
class MetricSingleInputWidget( GridLayout ):
def __init__( self, metric_single,
current_training_screen, **kwargs ):
super( MetricSingleInputWidget, self ).__init__( **kwargs )
self.cols = 1
self.spacing = 1
self.row_default_height = 40
self.row_force_default = True
self.size_hint_y = None
self.bind( minimum_height = self.setter('height') )
self.metric_name = metric_single.description['name']
input_layout = BoxLayout( orientation = 'horizontal',
spacing = 30 )
metric_label = Label( text = self.metric_name )
input_layout.add_widget( metric_label )
self.value_input = TextInput(
hint_text = str( metric_single.description['value'] ) )
self.value_input.bind(
text = current_training_screen.update_training_from_user_input)
input_layout.add_widget( self.value_input )
del_metric_btn = Button( text = "Del Metr", size_hint_x = 0.3 )
del_metric_btn.on_press = \
lambda: current_training_screen.remove_exercise( self )
input_layout.add_widget( del_metric_btn )
self.add_widget( input_layout )
comment_text = metric_single.description.get(
'comment', 'Comment Metric')
self.comment = TextInput( hint_text = comment_text )
self.comment.bind(
text = current_training_screen.update_training_from_user_input)
self.add_widget( self.comment )
def exercise_from_user_input( self ):
# todo: add input check
value_input = self.value_input.text
comment = self.comment.text
metric = MetricSingle( name = self.metric_name,
value = value_input,
description_dict = { 'comment': comment } )
return( metric )
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bounce2.py | Yokohama-Miyazawa/bounce_games | 4 | 27099 | from tkinter import *
import random
import time
class Widget(object): # 画面上で動く物の基本となるクラス
def __init__(self, window, size, color, pos, speed=[0, 0]):
self.window = window
self.size = size
self.color = color
self.pos = pos
self.speed = speed
def acty(self): # インスタンスを動かす
self.window.move(self.id, self.speed[0], self.speed[1])
def xturn(self): # 横軸の方向転換
self.speed[0] *= -1
def yturn(self): # 縦軸の方向転換
self.speed[1] *= -1
def current_speed(self): # 現在の速度
return self.speed
class Ball(Widget): # Widgetを継承する、ボールのためのクラス
def __init__(self, window, size, color, pos, speed):
super().__init__(window, size, color, pos, speed)
self.id = self.window.create_oval(self.pos[0], self.pos[1],
self.pos[0]+self.size,
self.pos[1]+self.size,
fill=self.color)
def current_place(self): # 今いる場所
return self.window.coords(self.id)
def hit_check(self, obj): # 当たったかどうかのチェック
own_pos = self.current_place()
obj_pos = obj.current_place()
own_center = (own_pos[0] + own_pos[2])/2
if (own_center > obj_pos[0] and own_center < obj_pos[2]) \
and (own_pos[1] <= obj_pos[3] and own_pos[3] >= obj_pos[1]):
return 1
else:
return 0
class Bar(Widget): # Widgetを継承する、長方形物体用のクラス
def __init__(self, window, size, color, pos):
super().__init__(window, size, color, pos)
self.point = 0
self.id = self.window.create_rectangle(self.pos[0], self.pos[1],
self.pos[0]+self.size[0],
self.pos[1]+self.size[1],
fill=self.color)
def current_place(self): # 今いる場所
return self.window.coords(self.id)
def current_point(self): # ☆現在の得点
return self.point
def add_point(self, add=1): # ☆得点加算
self.point += add
class Player_Racket(Bar): # Barを継承する、プレイヤーラケット用のクラス
def __init__(self, window, size, color, pos, step=10):
super().__init__(window, size, color, pos)
self.step = step
self.window.bind_all('<Key>', self.control)
def control(self, event): # 操作設定
if event.keysym == "Right":
self.speed = [self.step, 0]
elif event.keysym == "Left":
self.speed = [-self.step, 0]
else:
return
self.acty()
class COM_Racket(Bar): # ☆Barを継承する、COMラケット用のクラス
def __init__(self, window, size, color, pos, step=10, count=10,
distance=100):
super().__init__(window, size, color, pos)
self.step = step
self.count_range = count
self.counter = 0
self.distance = distance
def control(self, obj):
self.counter += 1
if self.counter == self.count_range:
self.counter = 0
self.speed[0] = random.randrange(-self.step, self.step)
if (obj.current_place()[0] - self.current_place()[0] >=
self.distance and self.speed[0] < 0) \
or (self.current_place()[2] - obj.current_place()[2] >=
self.distance and self.speed[0] > 0):
self.xturn()
self.acty()
# ウィンドウの設定
tk = Tk()
canvas_size = [500, 400]
canvas = Canvas(tk, width=canvas_size[0], height=canvas_size[1])
tk.title("熱くなれよ!!!")
canvas.pack()
# ☆画面表示の設定
canvas.create_text(50, 150, text='COM', fill='green', font=('メイリオ', 20))
canvas.create_text(50, 250, text='YOU', fill='red', font=('メイリオ', 20))
canvas.create_text(50, 200, text='TIME', fill='purple', font=('メイリオ', 20))
enemy_score = canvas.create_text(130, 150, fill='green', font=('メイリオ', 20))
my_score = canvas.create_text(130, 250, fill='red', font=('メイリオ', 20))
play_time = canvas.create_text(130, 200, fill='purple', font=('メイリオ', 20))
def show_score(player_score, score):
canvas.itemconfig(player_score, text=str(score))
def show_time(time_text, time_game):
canvas.itemconfig(time_text, text=str(time_game))
# ☆試合の設定
finish_point = 3
# ボールとラケットの設定
ball_radius = 50
ball_start = [random.randrange(50, 400), random.randrange(50, 100)]
ball_init_speed = [2.0, 2.0]
bar_size = [100, 10]
player_start = [200, 340]
# ☆COMの設定
com_start = [200, 50]
com_distance = 100
# ボールとラケットのインスタンス作成
ball = Ball(canvas, ball_radius, 'blue', ball_start, ball_init_speed)
player_racket = Player_Racket(canvas, bar_size, 'red', pos=player_start)
com_racket = COM_Racket(canvas, bar_size, 'green', pos=com_start,
distance=com_distance)
# ☆時刻設定
game_start = int(time.perf_counter())
game_time = game_start
while True:
ball.acty() # ボールを動かす
ball_pos = ball.current_place()
ball_speed = ball.current_speed()
com_racket.control(ball) # ☆COMを動かす
# ☆画面表示の更新
show_score(my_score, player_racket.current_point())
show_score(enemy_score, com_racket.current_point())
show_time(play_time, game_time-game_start)
now_time = int(time.perf_counter())
if now_time - game_time >= 1: # ☆一秒経過したら時刻表示切り替え
game_time = now_time
if player_racket.current_point() >= finish_point: # ☆プレイヤーの勝利
judge_text = 'YOU WIN'
judge_color = 'blue'
break
if com_racket.current_point() >= finish_point: # ☆COMの勝利
judge_text = 'YOU LOSE'
judge_color = 'red'
break
if ball_pos[2] >= canvas_size[0] or ball_pos[0] <= 0:
ball.xturn()
if ball_pos[3] >= canvas_size[1]: # ☆COMの得点
com_racket.add_point()
ball.yturn()
if ball_pos[1] <= 0: # ☆プレイヤーの得点
player_racket.add_point()
ball.yturn()
if (ball.hit_check(player_racket) == 1 and ball_speed[1] > 0) \
or (ball.hit_check(com_racket) == 1 and ball_speed[1] < 0):
ball.yturn() # ☆相手のラケットに当たった場合を追加
tk.update()
time.sleep(0.01)
# ☆結果発表
canvas.create_text(250, 200, text=judge_text,
fill=judge_color, font=('メイリオ', 30))
tk.update()
time.sleep(10)
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] |
microproxy/test/interceptor/test_msg_publisher.py | mike820324/microProxy | 20 | 50068 | <reponame>mike820324/microProxy<gh_stars>10-100
import json
import unittest
import mock
from microproxy.context import ViewerContext
from microproxy.interceptor.msg_publisher import MsgPublisher
from microproxy.version import VERSION
class TestMsgPublisher(unittest.TestCase):
def setUp(self):
self.zmq_socket = mock.Mock()
self.msg_publisher = MsgPublisher(None, self.zmq_socket)
def test_publish(self):
ctx_data = {
"scheme": "https",
"host": "example.com",
"path": "/index",
"port": 443,
"version": VERSION,
"client_tls": None,
"server_tls": None,
"response": None,
"request": None,
}
ctx = ViewerContext.deserialize(ctx_data)
self.msg_publisher.publish(ctx)
self.zmq_socket.send_multipart.assert_called_with([
"message",
JsonStrMatcher(ctx_data)])
class JsonStrMatcher(object):
def __init__(self, data):
self.data = data
def __eq__(self, other):
return json.loads(other) == self.data
def __str__(self):
return str(self.data)
| [
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] |
contests/2020_spring/1.py | clarkchen/leetcode_python | 0 | 113948 | <filename>contests/2020_spring/1.py<gh_stars>0
from typing import List
class Solution:
def minCount(self, coins: List[int]) -> int:
res = []
for x in coins:
res.append(x//2+x%2)
return sum(res)
if __name__ == '__main__':
s = Solution()
ans = s.minCount([4,2,1])
print(ans)
ans = s.minCount([2,3,10])
print(ans)
ans = s.minCount([1])
print(ans)
ans = s.minCount([2])
print(ans) | [
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2
] |
src/icemac/addressbook/tests/test_entities.py | icemac/icemac.addressbook | 1 | 101286 | from icemac.addressbook.address import phone_number_entity
from icemac.addressbook.address import postal_address_entity
from icemac.addressbook.addressbook import address_book_entity
from icemac.addressbook.entities import Entities, PersistentEntities, Field
from icemac.addressbook.entities import Entity, get_bound_schema_field
from icemac.addressbook.entities import EntityOrder
from icemac.addressbook.entities import FieldCustomization
from icemac.addressbook.entities import NoFieldCustomization
from icemac.addressbook.interfaces import IAddressBook, IHomePageAddress
from icemac.addressbook.interfaces import IEntities, IEntityOrder, IEntity
from icemac.addressbook.interfaces import IField, IOrderStorage
from icemac.addressbook.interfaces import IFieldCustomization
from icemac.addressbook.interfaces import IPhoneNumber, IPerson, IKeyword
from icemac.addressbook.interfaces import IUserFieldStorage
from icemac.addressbook.testing import pyTestStackDemoStorage
from icemac.addressbook.tests.conftest import IDog, IKwack, Kwack
import persistent
import pytest
import zope.component
import zope.component.hooks
import zope.interface
import zope.interface.verify
import zope.schema
import zope.schema.interfaces
# Constants
DEFAULT_ORDER = [u'person',
u'postal address',
u'phone number',
u'e-mail address',
u'home page address']
# Fixtures
@pytest.fixture(scope='function')
def entityOrder(address_book):
"""Get the entity order utility."""
return zope.component.getUtility(IEntityOrder)
@pytest.fixture(scope='module')
def unknownEntity():
"""Get an entity which is not known in the address book."""
return Entity(u'', IEntities, 'icemac.addressbook.entities.Entities')
@pytest.fixture(scope='function')
def minimalEntity(address_book):
"""Get an entity which is not registered in the address book."""
return IEntity(IEntities)
@pytest.fixture(scope='function')
def field():
"""Get a user defined field."""
field = Field()
field.type = 'TextLine'
return field
@pytest.fixture(scope='function')
def schemaized_field(field):
"""Get a user defined field adapted to a `zope.schema` field."""
return zope.schema.interfaces.IField(field)
@pytest.fixture(scope='function')
def entity():
"""Get a custom entity."""
return Entity(
u'Dummy', IDummy, 'icemac.addressbook.tests.test_entities.Dummy')
@pytest.fixture(scope='function')
def entity_with_field(address_book, entity, field):
"""Get a custom entity with a user defined field."""
entity.addField(field)
return entity
@pytest.fixture(scope='function')
def entities(address_book):
"""Get the entities utility."""
return zope.component.getUtility(IEntities)
@pytest.yield_fixture(scope='function')
def root_folder(zodbS):
"""Get the root folder of the ZODB."""
for connection in pyTestStackDemoStorage(zodbS, 'root_folder'):
yield connection.rootFolder
# Helper classes
class IDummy(zope.interface.Interface):
"""Interface for test entity."""
dummy = zope.schema.Text(title=u'dummy')
dummy2 = zope.schema.Text(title=u'dummy2')
@zope.interface.implementer(IDummy)
class Dummy(object):
"""Test entity."""
# Helper functions
def getMainEntities_titles(sorted):
"""Return the entities titles when calling `getMainEntities()`."""
entities = zope.component.getUtility(IEntities)
return [x.title for x in entities.getMainEntities(sorted=sorted)]
# Tests
def test_entities__Entities__1():
"""`Entities` fulfills the `IEntities` interface."""
zope.interface.verify.verifyObject(IEntities, Entities())
def test_entities__PersistentEntities__1():
"""`PersistentEntities` fulfills the `IEntities` interface."""
zope.interface.verify.verifyObject(IEntities, PersistentEntities())
def test_entities__EntityOrder__1():
"""`EntityOrder` fulfills the `IEntityOrder` interface."""
zope.interface.verify.verifyObject(IEntityOrder, EntityOrder())
def test_entities__Field__1():
"""`Field` fulfills the `IField` interface."""
zope.interface.verify.verifyObject(IField, Field())
def test_entities__Entity__1():
"""`Entity` fulfills the `IEntity` interface."""
entity = Entity(None, IEntity, 'Entity')
zope.interface.verify.verifyObject(IEntity, entity)
def test_entities__getEntities__1(stubSortOrder, stubEntities):
"""`(Persistent)Entities.getEntities()` returns entities sorted.
The sort order is defined in the sort order storage.
"""
e = stubEntities
assert [e.cat, e.kwack, e.duck] == e.entities.getEntities()
def test_entities__getEntities__2(stubEntities):
"""`(Persistent)Entities.getEntities()` might return entities unsorted."""
e = stubEntities
assert (set([e.kwack, e.duck, e.cat]) ==
set(e.entities.getEntities(sorted=False)))
def test_entities__getEntities__3(stubSortOrder, stubEntities):
"""`(Persistent)Entities.getEntities()` respects changes in sort order."""
e = stubEntities
stubSortOrder.up(e.duck)
assert [e.cat, e.duck, e.kwack] == e.entities.getEntities()
def test_entities__PersistentEntities__getMainEntities__1(address_book):
"""By default the result of `getMainEntities()` is sort independent."""
assert DEFAULT_ORDER == getMainEntities_titles(True)
assert DEFAULT_ORDER == getMainEntities_titles(False)
def test_entities__PersistentEntities__getMainEntities__2(entityOrder):
"""`getMainEntities()` respects a changed sort order."""
entityOrder.up(IEntity(IPhoneNumber))
assert ([u'person',
u'phone number',
u'postal address',
u'e-mail address',
u'home page address'] == getMainEntities_titles(True))
# The unordered variant still returns the default order:
assert DEFAULT_ORDER == getMainEntities_titles(False)
def test_entities__EntityOrder__get__1(entityOrder):
"""`get()` returns the position of the entity."""
assert 1 == entityOrder.get(IEntity(IPerson))
assert 8 == entityOrder.get(IEntity(IKeyword))
def test_entities__EntityOrder__get__2(entityOrder, unknownEntity):
"""`get()` raises a KeyError if entity is not known to the sort order."""
with pytest.raises(KeyError):
entityOrder.get(unknownEntity)
def test_entities__EntityOrder__get__3(entityOrder, minimalEntity):
"""`get()` raises a KeyError if the entity has no name."""
with pytest.raises(KeyError):
entityOrder.get(minimalEntity)
def test_entities__EntityOrder__get__4(entityOrder, AddressBookFactory):
"""It accesses the address book in `zope.component.hooks.site`."""
other_address_book = AddressBookFactory('other_address_book')
# Changes in an address book ...
person = IEntity(IPerson)
assert 1 == entityOrder.get(person)
entityOrder.down(person)
assert 2 == entityOrder.get(person)
# ... are not reflected in another address book:
with zope.component.hooks.site(other_address_book):
assert 1 == entityOrder.get(person)
def test_entities__EntityOrder__get__5(entityOrder):
"""`get()` raises a ComponentLookupError if no site is set."""
person = IEntity(IPerson)
with zope.component.hooks.site(None):
with pytest.raises(zope.component.ComponentLookupError):
entityOrder.get(person)
def test_entities__EntityOrder__isFirst__1(entityOrder):
"""`isFirst()` returns `True` for the first entity."""
assert entityOrder.isFirst(IEntity(IAddressBook))
def test_entities__EntityOrder__isFirst__2(entityOrder):
"""`isFirst()` returns `False` for an entity which is not the first."""
assert not entityOrder.isFirst(IEntity(IPhoneNumber))
def test_entities__EntityOrder__isFirst__3(entityOrder, unknownEntity):
"""`isFirst()` raises KeyError if entity is not known to the sort order."""
with pytest.raises(KeyError):
entityOrder.isFirst(unknownEntity)
def test_entities__EntityOrder__isFirst__4(entityOrder, minimalEntity):
"""`isFirst()` raises KeyError if the entity has no name."""
with pytest.raises(KeyError):
entityOrder.isFirst(minimalEntity)
def test_entities__EntityOrder__isLast__1(entityOrder):
"""`isLast()` returns `True` for the first entity."""
assert entityOrder.isLast(IEntity(IKeyword))
def test_entities__EntityOrder__isLast__2(entityOrder):
"""`isLast()` returns `False` for an entity which is not the first."""
assert not entityOrder.isLast(IEntity(IPhoneNumber))
def test_entities__EntityOrder__isLast__3(entityOrder, unknownEntity):
"""`isLast()` raises KeyError if entity is not known to the sort order."""
with pytest.raises(KeyError):
entityOrder.isLast(unknownEntity)
def test_entities__EntityOrder__isLast__4(entityOrder, minimalEntity):
"""`isLast()` raises KeyError if the entity has no name."""
with pytest.raises(KeyError):
entityOrder.isLast(minimalEntity)
def test_entities__EntityOrder____iter____1(entityOrder):
"""It is iterable, returning entity names."""
assert ([
'IcemacAddressbookAddressbookAddressbook',
'IcemacAddressbookPersonPerson',
'IcemacAddressbookPersonPersondefaults',
'IcemacAddressbookAddressPostaladdress',
'IcemacAddressbookAddressPhonenumber',
'IcemacAddressbookAddressEmailaddress',
'IcemacAddressbookAddressHomepageaddress',
'IcemacAddressbookFileFileFile',
'IcemacAddressbookKeywordKeyword',
] == list(iter(entityOrder)))
def test_entities__EntityOrder__up__1(entityOrder):
"""`up()` moved one position up."""
person = IEntity(IPerson)
assert 1 == entityOrder.get(person)
entityOrder.up(person)
assert 0 == entityOrder.get(person)
def test_entities__EntityOrder__up__2(entityOrder):
"""`up(n)` moves n positions up."""
hp = IEntity(IHomePageAddress)
assert 6 == entityOrder.get(hp)
entityOrder.up(hp, 3)
assert 3 == entityOrder.get(hp)
def test_entities__EntityOrder__up__3(entityOrder):
"""If the delta is too big when calling `up()` a ValueError is raised."""
person = IEntity(IPerson)
assert 1 == entityOrder.get(person)
with pytest.raises(ValueError):
entityOrder.up(person, 2)
def test_entities__EntityOrder__down__1(entityOrder):
"""`down()` moved one position down."""
person = IEntity(IPerson)
assert 1 == entityOrder.get(person)
entityOrder.down(person)
assert 2 == entityOrder.get(person)
def test_entities__EntityOrder__down__2(entityOrder):
"""`down(n)` moves n positions down."""
ab = IEntity(IAddressBook)
assert 0 == entityOrder.get(ab)
entityOrder.down(ab, 3)
assert 3 == entityOrder.get(ab)
def test_entities__EntityOrder__down__3(entityOrder):
"""If the delta is too big when calling `down()` a ValueError is raised."""
person = IEntity(IPerson)
assert 1 == entityOrder.get(person)
with pytest.raises(ValueError):
entityOrder.down(person, 8)
def test_entities__entity_by_name__1(stubEntities, entityAdapters):
"""`entity_by_name` raises a `ValueError` if string name is unknown."""
with pytest.raises(ValueError):
IEntity('asdf')
def test_entities__entity_by_name__2(stubEntities, entityAdapters):
"""`entity_by_name` raises a `ValueError` if unicode name is unknown."""
with pytest.raises(ValueError):
IEntity(u'asdf')
def test_entities__entity_by_name__3(stubEntities, entityAdapters):
"""`entity_by_name` raises a `ValueError` if class name is used."""
with pytest.raises(ValueError):
IEntity('icemac.addressbook.tests.stubs.Duck')
def test_entities__entity_by_name__4(stubEntities, entityAdapters):
"""`entity_by_name` raises a `ValueError` if class name is used."""
with pytest.raises(ValueError):
IEntity('icemac.addressbook.tests.conftest.Duck')
def test_entities__entity_by_name__5(stubEntities, entityAdapters):
"""`entity_by_name` adapts from string entity name."""
assert stubEntities.duck == IEntity('IcemacAddressbookTestsConftestDuck')
def test_entities__entity_by_name__6(stubEntities, entityAdapters):
"""`entity_by_name` adapts from unicode entity name."""
assert stubEntities.duck == IEntity(u'IcemacAddressbookTestsConftestDuck')
def test_entities__entity_by_interface__1(stubEntities, entityAdapters):
"""`entity_by_interface` returns a minimal entity for unknown interface."""
entity = IEntity(IDog)
assert None is entity.title
assert IDog is entity.interface
assert None is entity.class_name
def test_entities__entity_by_interface__2(stubEntities, entityAdapters):
"""`entity_by_interface` returns the entity for a known interface."""
assert stubEntities.kwack == IEntity(IKwack)
def test_entities__entity_by_obj__1(stubEntities, entityAdapters):
"""`entity_by_obj` raises a ValueError for an unknown instance."""
obj = persistent.Persistent()
with pytest.raises(ValueError):
IEntity(obj)
def test_entities__entity_by_obj__2(stubEntities, entityAdapters):
"""`entity_by_obj` returns the instance for a known instance."""
assert stubEntities.kwack == IEntity(Kwack())
def test_entities__Entity__addField__1(entities, entity, field):
"""It adds a field to an entity."""
entity.addField(field)
assert ['Field-1'] == list(entities.keys())
assert field is entities[u'Field-1']
def test_entities__Entity__addField__2(entities, entity, field):
"""It stores the interface of the entity on the field."""
entity.addField(field)
assert IDummy == entities[u'Field-1'].interface
def test_entities__Entity__addField__3(address_book, field):
"""It registers an adapter for the field."""
address_book_entity.addField(field)
adapted_entity = zope.component.getMultiAdapter(
(address_book_entity, address_book), IField, name=u'Field-1')
assert field is adapted_entity
def test_entities__entity_for_field__1(address_book, field):
"""It adapts a field to its entity."""
address_book_entity.addField(field)
adapted_field = IEntity(field)
assert address_book_entity == adapted_field
def test_entities__Entity__removeField__1(entities, entity_with_field, field):
"""It removes a field from an entity."""
assert field in entities.values()
entity_with_field.removeField(field)
assert field not in entities.values()
def test_entities__Entity__removeField__2(entity_with_field, field):
"""It removes the interface of the entity from the field."""
entity_with_field.removeField(field)
assert field.interface is None
def test_entities__Entity__removeField__3(entity_with_field, field):
"""It removes the adapter registrations for the field."""
entity_with_field.removeField(field)
assert None is zope.component.queryMultiAdapter(
(entity, Dummy()), IField, name=u'Field')
assert None is IEntity(field, None)
def test_entities__Entity__setFieldOrder__1(entity_with_field, field):
"""It changes the initial (empty) order."""
assert [] == entity_with_field.getFieldOrder()
entity_with_field.setFieldOrder(['dummy2', field.__name__, 'dummy'])
assert (['dummy2', field.__name__, 'dummy'] ==
entity_with_field.getFieldOrder())
def test_entities__Entity__setFieldOrder__2(entity_with_field):
"""It ignores unknown field names and does not store them."""
entity = entity_with_field
entity.setFieldOrder(['dummy2', 'I-do-not-exist', 'dummy'])
assert ['dummy2', 'dummy'] == entity.getFieldOrder()
# Unknown field names are not written into storage:
order_storage = zope.component.getUtility(IOrderStorage)
assert (['dummy2', 'dummy'] ==
order_storage.byNamespace(entity.order_storage_namespace))
def test_entities__Entity__getFieldOrder__1(entity):
"""It returns the initially emtpy field order."""
assert [] == entity.getFieldOrder()
def test_entities__Entity__getFieldOrder__2(entity_with_field, field):
"""It only contains the values set by `setFieldOrder`."""
entity = entity_with_field
entity.setFieldOrder([field.__name__, 'dummy'])
assert [field.__name__, 'dummy'] == entity.getFieldOrder()
def test_entities__Entity__getFieldOrder__3(address_book):
"""It returns `[]` if the namespace cannot be computed."""
# The namespace in the order utility depends on the name of the
# entity which itself depends on the class_name stored on the
# entity. But this class name is optional, so the name might not be
# computable:
entity = Entity(u'Dummy', IDummy, None)
assert [] == entity.getFieldOrder()
def test_entities__Entity__name__1(entity):
"""`name` is the name of the entity containing the module path."""
assert 'IcemacAddressbookTestsTestEntitiesDummy' == entity.name
def test_entities__Entity__name__2(address_book):
"""`name` raises a ValueError if no class name is set."""
entity = Entity(None, IDummy, None)
with pytest.raises(ValueError):
entity.name
def test_entities__Entity__getRawFields__1(entity_with_field, field):
"""`getRawFields` sorts fields missing in sort order to the end."""
entity = entity_with_field
entity.setFieldOrder([field.__name__, 'dummy'])
assert ([field.__name__, 'dummy', 'dummy2'] ==
[x[0] for x in entity.getRawFields()])
def test_entities__Entity__getRawFields__2(entity_with_field, field):
"""`getRawFields` returns the fields sorted by the field order."""
entity = entity_with_field
entity.setFieldOrder(['dummy2', field.__name__, 'dummy'])
assert ([('dummy2', IDummy['dummy2']),
(field.__name__, field),
('dummy', IDummy['dummy'])] == list(entity.getRawFields()))
def test_entities__Entity__getRawFields__3(entity_with_field, field):
"""`getRawFields` is able to return the fields unsorted (default order).
When `sorted` is `False` the `zope.schema` fields are returned first (the
order is defined by the order in the interface) and than the user defined
fields (order is undefined here.)
"""
entity = entity_with_field
entity.setFieldOrder(['dummy2', 'Field', 'dummy'])
assert ([('dummy', IDummy['dummy']),
('dummy2', IDummy['dummy2']),
(field.__name__, field)] ==
list(entity.getRawFields(sorted=False)))
def test_entities__Entity__getFields__1(entity_with_field, schemaized_field):
"""`getFields` returns all fields as `zope.schema` in order."""
entity = entity_with_field
entity.setFieldOrder(['dummy2', schemaized_field.__name__, 'dummy'])
assert ([('dummy2', IDummy['dummy2']),
(schemaized_field.__name__, schemaized_field),
('dummy', IDummy['dummy'])] == list(entity.getFields()))
def test_entities__Entity__getFields__2(entity_with_field, schemaized_field):
"""`geFields` is able to return the fields unsorted (default order).
When `sorted` is `False` the `zope.schema` fields are returned first (the
order is defined by the order in the interface) and then the user defined
fields (order is undefined here.)
"""
entity = entity_with_field
entity.setFieldOrder(['dummy2', schemaized_field.__name__, 'dummy'])
assert ([('dummy', IDummy['dummy']),
('dummy2', IDummy['dummy2']),
(schemaized_field.__name__, schemaized_field)] ==
list(entity.getFields(sorted=False)))
def test_entities__Entity__getFieldValues__1(
entity_with_field, schemaized_field):
"""`getFieldValues` returns all fields as `zope.schema` in order."""
entity = entity_with_field
entity.setFieldOrder(['dummy2', schemaized_field.__name__, 'dummy'])
assert ([IDummy['dummy2'], schemaized_field, IDummy['dummy']] ==
entity.getFieldValues())
def test_entities__Entity__getFieldValues__2(
entity_with_field, schemaized_field):
"""`getFieldValues` is able to return the fields unsorted (default order).
When `sorted` is `False` the `zope.schema` fields are returned first (the
order is defined by the order in the interface) and then the user defined
fields (order is undefined here.)
"""
entity = entity_with_field
entity.setFieldOrder(['dummy2', schemaized_field.__name__, 'dummy'])
assert ([IDummy['dummy'], IDummy['dummy2'], schemaized_field] ==
entity.getFieldValues(sorted=False))
def test_entities__Entity__getField__1(entity):
"""`getField` raises a `KeyError` for an unknown field name."""
with pytest.raises(KeyError):
entity.getField('asdf')
def test_entities__Entity__getField__2(entity):
"""`getField` can return a `zope.schema` field."""
assert IDummy['dummy2'] == entity.getField('dummy2')
def test_entities__Entity__getField__3(entity_with_field, schemaized_field):
"""`getField` can return a user defined field as `zope.schema` field."""
assert (schemaized_field ==
entity_with_field.getField(schemaized_field.__name__))
def test_entities__Entity__getRawField__1(entity):
"""`getRawField` raises a `KeyError` for an unknown field name."""
with pytest.raises(KeyError):
entity.getRawField('asdf')
def test_entities__Entity__getRawField__2(entity):
"""`getRawField` can return a `zope.schema` field."""
assert IDummy['dummy2'] == entity.getRawField('dummy2')
def test_entities__Entity__getRawField__3(entity_with_field, field):
"""`getRawField` can return a user defined field."""
assert field == entity_with_field.getRawField(field.__name__)
def test_entities__Entity__getClass__1(entity):
"""`getClass` returns the class object associated with the entity."""
assert Dummy == entity.getClass()
def test_entities__Entity__getClass__2():
"""`getClass` raises a `ValueError` if no `class_name` is set."""
e = Entity(None, IDummy, None)
with pytest.raises(ValueError):
e.getClass()
def test_entities__Entity__tagged_values__1():
"""`tagged_values` is a dict of the kwargs used during entity __init__."""
e = Entity(u'Dummy', IDummy, 'Dummy', a=1, b='asdf')
assert dict(a=1, b='asdf') == e.tagged_values
def test_entities__Entity__tagged_values__2():
"""`tagged_values` is actually a copy of the kwargs dict.
Tagged values not modifiable by modifying the returned dict.
"""
e = Entity(u'Dummy', IDummy, 'Dummy', a=1, b='asdf')
e.tagged_values['a'] = 2
assert dict(a=1, b='asdf') == e.tagged_values
def test_entities__get_bound_schema_field__1(address_book):
"""It returns the schema field if the obj provides the entity interface."""
field = get_bound_schema_field(address_book, address_book_entity,
address_book_entity.getRawField('title'))
assert address_book == field.context
assert 'title' == field.__name__
assert isinstance(field, zope.schema.TextLine)
def test_entities__get_bound_schema_field__2(address_book, FullPersonFactory):
"""It looks up the default obj on obj if iface is not provided by obj."""
person = FullPersonFactory(address_book, u'Koch')
field = get_bound_schema_field(
person, postal_address_entity,
postal_address_entity.getRawField('country'))
assert person.default_postal_address == field.context
assert 'country' == field.__name__
assert isinstance(field, zope.schema.Choice)
def test_entities__get_bound_schema_field__3(address_book, FullPersonFactory):
"""It binds field to obj if `default_attrib_fallback` is `False`.
Additional condition: The interface is not provided by obj.
"""
person = FullPersonFactory(address_book, u'Koch')
field = get_bound_schema_field(
person, postal_address_entity,
postal_address_entity.getRawField('country'),
default_attrib_fallback=False)
assert person == field.context
def test_entities__get_bound_schema_field__4(
address_book, FullPersonFactory, FieldFactory):
"""It returns a user defined field as a `zope.schema` field."""
person = FullPersonFactory(address_book, u'Koch')
FieldFactory(address_book, IPhoneNumber, 'Datetime', u'last call',
notes=u'using a phone')
field = get_bound_schema_field(
person, phone_number_entity,
phone_number_entity.getRawField('Field-1'))
assert IUserFieldStorage(person.default_phone_number) == field.context
assert 'Field-1' == field.__name__
assert 'using a phone' == field.description
assert isinstance(field, zope.schema.Datetime)
def test_entities__NoFieldCustomization__1(root_folder):
"""It implements the IFieldCustomization interface."""
nfc = IFieldCustomization(root_folder)
assert isinstance(nfc, NoFieldCustomization)
assert zope.interface.verify.verifyObject(IFieldCustomization, nfc)
def test_entities__NoFieldCustomization__get_value__1(root_folder):
"""It raises a KeyError."""
nfc = IFieldCustomization(root_folder)
with pytest.raises(KeyError):
nfc.get_value(IAddressBook['time_zone'], 'label')
def test_entities__NoFieldCustomization__default_value__1(root_folder):
"""It returns the title of the field (default value)."""
nfc = IFieldCustomization(root_folder)
assert u'Time zone' == nfc.default_value(
IAddressBook['time_zone'], 'label')
@pytest.mark.parametrize(
'kind, result',
((u'label', u'Time zone'),
(u'description', u'Fallback in case a user has not set up his personal'
u' time zone in the preferences.')))
def test_entities__NoFieldCustomization__query_value__1(
root_folder, kind, result):
"""It returns the default value."""
nfc = IFieldCustomization(root_folder)
assert result == nfc.query_value(IAddressBook['time_zone'], kind)
def test_entities__NoFieldCustomization__set_value__1(root_folder):
"""It is not implemented."""
nfc = IFieldCustomization(root_folder)
with pytest.raises(NotImplementedError):
nfc.set_value(IAddressBook['time_zone'], u'label', u'foo')
def test_entities__FieldCustomization__1(address_book):
"""It implements the IFieldCustomization interface."""
fc = IFieldCustomization(address_book)
assert isinstance(fc, FieldCustomization)
assert zope.interface.verify.verifyObject(IFieldCustomization, fc)
def test_entities__FieldCustomization__get_value__1(address_book):
"""It raises a KeyError if no custom value is stored."""
fc = IFieldCustomization(address_book)
with pytest.raises(KeyError):
fc.get_value(IAddressBook['time_zone'], 'label')
def test_entities__FieldCustomization__get_value__2(address_book):
"""It raises a KeyError if the field does not belong to an interface."""
fc = IFieldCustomization(address_book)
field = zope.schema.Text()
with pytest.raises(KeyError):
fc.get_value(field, 'label')
def test_entities__FieldCustomization__default_value__1(address_book):
"""It returns the title of the field (default value)."""
fc = IFieldCustomization(address_book)
assert u'Time zone' == fc.default_value(IAddressBook['time_zone'], 'label')
@pytest.mark.parametrize(
'kind, result',
((u'label', u'Time zone'),
(u'description', u'Fallback in case a user has not set up his personal'
u' time zone in the preferences.')))
def test_entities__FieldCustomization__query_value__1(
address_book, kind, result):
"""It returns the default value if no custom label is stored."""
fc = IFieldCustomization(address_book)
assert result == fc.query_value(IAddressBook['time_zone'], kind)
def test_entities__FieldCustomization__set_value__1(address_book):
"""It stores the given custom value."""
fc = IFieldCustomization(address_book)
field = IAddressBook['time_zone']
fc.set_value(field, u'label', u'Default time zone value 123')
assert u'Default time zone value 123' == fc.get_value(field, 'label')
assert u'Default time zone value 123' == fc.query_value(field, 'label')
def test_entities__FieldCustomization__set_value__2(address_book):
"""Storing `None` removes the custom value."""
fc = IFieldCustomization(address_book)
field = IAddressBook['time_zone']
fc.set_value(field, u'label', u'Default time zone value 123')
fc.set_value(field, u'label', None)
with pytest.raises(KeyError):
fc.get_value(field, 'label')
def test_entities__FieldCustomization__set_value__3(address_book):
"""It does not break on storing `None` with no custom value stored."""
fc = IFieldCustomization(address_book)
field = IAddressBook['time_zone']
fc.set_value(field, u'label', None)
with pytest.raises(KeyError):
fc.get_value(field, 'label')
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resources/org/caf/normalize-build-matrix.py | actor-framework/continuous-integration | 0 | 97696 | #!/usr/bin/env python
import sys, json
from functools import reduce
inputFile = sys.argv[1]
outputFile = sys.argv[2]
with open(inputFile, 'r') as f:
data = f.read()
config = json.loads(data)
def copy_without(xs, key):
ys = xs.copy()
ys.pop(key)
return ys
def merge_filtered(result, xs, pred):
for key, val in xs.items():
if pred(key):
result[key] = val
return result
def make_pipeline(*functions):
return reduce(lambda f, g: lambda x: g(f(x)), functions)
def filter_step(f):
return lambda xs: filter(f, xs)
def map_step(f):
return lambda xs: map(f, xs)
def reduce_step(f):
return lambda xs: reduce(f, xs)
def lift_builds(os, settings):
res = map(lambda build: [os, build, copy_without(settings, 'builds')],
settings['builds'])
return list(res)
def normalize(what, settings, buildType, defaults):
key1 = 'extraBuild{}'.format(what.capitalize())
key2 = 'extra{}Build{}'.format(buildType.capitalize(), what.capitalize())
res1 = settings[key1] if key1 in settings else []
res2 = settings[key2] if key2 in settings else []
return merge_filtered({ what: defaults + res1 + res2 },
settings,
lambda key: not key.endswith(what.capitalize()))
def normalize_entry(os, build_type, settings, what):
key = 'build{}'.format(what.capitalize())
defaults = config[key] if key in config else []
return [os, build_type, normalize(what, settings, build_type, defaults)]
def add_tags_if_missing(os, build_type, settings):
res = settings
if not 'tags' in res:
res['tags'] = []
return [os, build_type, res]
def call(f, args):
return f(*args)
buildMatrix = config['buildMatrix']
pipeline = make_pipeline( \
map_step(lambda entry: lift_builds(*entry)),
reduce_step(lambda x, y: x + y),
map_step(lambda entry: call(normalize_entry, entry + ['flags'])),
map_step(lambda entry: call(normalize_entry, entry + ['env'])),
map_step(lambda entry: add_tags_if_missing(*entry)))
normalizedBuildMatrix = list(pipeline(buildMatrix))
with open(outputFile, 'w') as f:
json.dump(normalizedBuildMatrix, f, indent=2)
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] |
nine/c5.py | xiaolinzi-xl/python_imooc | 0 | 40575 | from c6 import People
# Student类继承People
class Student(People):
def __init__(self,school,name,age):
self.school = school
# People.__init__(self,name,age)
super(Student,self).__init__(name,age)
Student.sum += 1
def do_homework(self):
print('do homework')
student1 = Student('农村小学',"石敢当",19)
print(student1.sum)
print(Student.sum)
print(student1.name)
print(student1.age)
People.do_homework("","xiaolinzi")
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AUTOENCODERS/DataPreparing/CICIDSPreprocessor.py | pawelptak/AI-Anomaly-Detection | 1 | 20201 | from sklearn.preprocessing import StandardScaler, LabelEncoder, MinMaxScaler, OneHotEncoder
import numpy as np
import pandas as pd
import tqdm
"""
Class for Preprocessing CICIDS2017 Data represented as rows
"""
class CICIDSPreprocessor:
def __init__(self):
self.to_delete_columns = ['Flow ID', ' Timestamp']
self.label_column = ' Label'
def preprocess_train_data(self, df: pd.DataFrame, label="BENIGN"):
df = df.drop(self.to_delete_columns, axis=1)
df = df[df[self.label_column] == label]
df.reset_index(drop=True, inplace=True)
df.drop(self.label_column, axis=1, inplace=True)
return df.fillna(0)
def preprocess_test_data(self, df: pd.DataFrame, label="BENIGN"):
df = df.drop(self.to_delete_columns, axis=1)
df = df[df[self.label_column] == label]
df.reset_index(drop=True, inplace=True)
df.drop(self.label_column, axis=1, inplace=True)
return df.fillna(0)
def __get_windows(self, df, window_size=20, stride=10):
windows_arr = []
for i in tqdm.tqdm(range(0, len(df)-window_size+1, stride)):
windows_arr.append(df.iloc[i:i+window_size, :].to_numpy())
return np.array(windows_arr)
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Lib/filibuster/titlecase.py | LettError/filibuster | 10 | 130633 | <reponame>LettError/filibuster<filename>Lib/filibuster/titlecase.py
# -*- coding: UTF-8 -*-
"""
titlecase.py v0.2
Original Perl version by: <NAME> http://daringfireball.net/ 10 May 2008
Python version by <NAME> http://muffinresearch.co.uk
License: http://www.opensource.org/licenses/mit-license.php
"""
import unittest
import sys
import re
SMALL = 'a|an|and|as|at|but|by|en|for|if|in|of|on|or|the|to|v\.?|via|vs\.?'
PUNCT = "[!\"#$%&'‘()*+,-./:;?@[\\\\\\]_`{|}~]"
SMALL_WORDS = re.compile(r'^(%s)$' % SMALL, re.I)
INLINE_PERIOD = re.compile(r'[a-zA-Z][.][a-zA-Z]')
UC_ELSEWHERE = re.compile(r'%s*?[a-zA-Z]+[A-Z]+?' % PUNCT)
CAPFIRST = re.compile(r"^%s*?([A-Za-z])" % PUNCT)
SMALL_FIRST = re.compile(r'^(%s*)(%s)\b' % (PUNCT, SMALL), re.I)
SMALL_LAST = re.compile(r'\b(%s)%s?$' % (SMALL, PUNCT), re.I)
SUBPHRASE = re.compile(r'([:.;?!][ ])(%s)' % SMALL)
def titlecase(text):
"""
Titlecases input text
This filter changes all words to Title Caps, and attempts to be clever
about *un*capitalizing SMALL words like a/an/the in the input.
The list of "SMALL words" which are not capped comes from
the New York Times Manual of Style, plus 'vs' and 'v'.
"""
words = re.split('\s', text)
line = []
for word in words:
if INLINE_PERIOD.search(word) or UC_ELSEWHERE.match(word):
line.append(word)
continue
if SMALL_WORDS.match(word):
line.append(word.lower())
continue
line.append(CAPFIRST.sub(lambda m: m.group(0).upper(), word))
line = " ".join(line)
line = SMALL_FIRST.sub(lambda m: '%s%s' % (
m.group(1),
m.group(2).capitalize()
), line)
line = SMALL_LAST.sub(lambda m: m.group(0).capitalize(), line)
line = SUBPHRASE.sub(lambda m: '%s%s' % (
m.group(1),
m.group(2).capitalize()
), line)
return line
class TitlecaseTests(unittest.TestCase):
"""Tests to ensure titlecase follows all of the rules"""
def test_q_and_a(self):
u"""Testing: Q&A With <NAME>: 'That’s What Happens In Technology' """
text = titlecase(
u"Q&A with steve jobs: 'that’s what happens in technology'"
)
result = u"Q&A With Steve Jobs: 'That’s What Happens in Technology'"
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_at_and_t(self):
u"""Testing: What Is AT&T's Problem?"""
text = titlecase(u"What is AT&T’s problem?")
result = u"What Is AT&T’s Problem?"
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_apple_deal(self):
"""Testing: Apple Deal With AT&T Falls Through"""
text = titlecase("Apple deal with AT&T falls through")
result = "Apple Deal With AT&T Falls Through"
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_this_v_that(self):
"""Testing: this v that"""
text = titlecase("this v that")
result = "This v That"
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_this_v_that2(self):
"""Testing: this v. that"""
text = titlecase("this v. that")
result = "This v. That"
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_this_vs_that(self):
"""Testing: this vs that"""
text = titlecase("this vs that")
result = "This vs That"
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_this_vs_that2(self):
"""Testing: this vs. that"""
text = titlecase("this vs. that")
result = "This vs. That"
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_apple_sec(self):
u"""Testing: The SEC’s Apple Probe: What You Need to Know"""
text = titlecase("The SEC’s Apple Probe: What You Need to Know")
result = u"The SEC’s Apple Probe: What You Need to Know"
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_small_word_quoted(self):
"""Testing: 'by the Way, Small word at the start but within quotes.'"""
text = titlecase(
"'by the Way, small word at the start but within quotes.'"
)
result = "'By the Way, Small Word at the Start but Within Quotes.'"
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_small_word_end(self):
"""Testing: Small word at end is nothing to be afraid of"""
text = titlecase("Small word at end is nothing to be afraid of")
result = "Small Word at End Is Nothing to Be Afraid Of"
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_sub_phrase_small_word(self):
"""Testing: Starting Sub-Phrase With a Small Word: a Trick, Perhaps?"""
text = titlecase(
"Starting Sub-Phrase With a Small Word: a Trick, Perhaps?"
)
result = "Starting Sub-Phrase With a Small Word: A Trick, Perhaps?"
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_small_word_quotes(self):
"""Testing: Sub-Phrase With a Small Word in Quotes: 'a Trick..."""
text = titlecase(
"Sub-Phrase With a Small Word in Quotes: 'a Trick, Perhaps?'"
)
result = "Sub-Phrase With a Small Word in Quotes: 'A Trick, Perhaps?'"
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_small_word_double_quotes(self):
"""Testing: Sub-Phrase With a Small Word in Quotes: \"a Trick..."""
text = titlecase(
'Sub-Phrase With a Small Word in Quotes: "a Trick, Perhaps?"'
)
result = 'Sub-Phrase With a Small Word in Quotes: "A Trick, Perhaps?"'
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_nothing_to_be_afraid_of(self):
"""Testing: \"Nothing to Be Afraid of?\""""
text = titlecase('"Nothing to Be Afraid of?"')
result = '"Nothing to Be Afraid Of?"'
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_nothing_to_be_afraid_of2(self):
"""Testing: \"Nothing to Be Afraid Of?\""""
text = titlecase('"Nothing to be Afraid Of?"')
result = '"Nothing to Be Afraid Of?"'
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_a_thing(self):
"""Testing: a thing"""
text = titlecase('a thing')
result = 'A Thing'
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_vapourware(self):
"""Testing: 2lmc Spool: 'Gruber on OmniFocus and Vapo(u)rware'"""
text = titlecase(
"2lmc Spool: 'gruber on OmniFocus and vapo(u)rware'"
)
result = "2lmc Spool: 'Gruber on OmniFocus and Vapo(u)rware'"
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_domains(self):
"""Testing: this is just an example.com"""
text = titlecase('this is just an example.com')
result = 'This Is Just an example.com'
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_domains2(self):
"""Testing: this is something listed on an del.icio.us"""
text = titlecase('this is something listed on del.icio.us')
result = 'This Is Something Listed on del.icio.us'
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_itunes(self):
"""Testing: iTunes should be unmolested"""
text = titlecase('iTunes should be unmolested')
result = 'iTunes Should Be Unmolested'
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_thoughts_on_music(self):
u"""Testing: Reading Between the Lines of Steve Jobs’s..."""
text = titlecase(
u'Reading between the lines of steve jobs’s ‘thoughts on music’'
)
result = u'Reading Between the Lines of Steve Jobs’s ‘Thoughts on Music’'
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_repair_perms(self):
u"""Testing: Seriously, ‘Repair Permissions’ Is Voodoo"""
text = titlecase(u'seriously, ‘repair permissions’ is voodoo')
result = u'Seriously, ‘Repair Permissions’ Is Voodoo'
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
def test_generalissimo(self):
"""Testing: General<NAME>..."""
text = titlecase(
'generalissimo francisco franco: still dead; <NAME>: '\
'still a jackass'
)
result = u"""Generalissimo Francisco Franco: Still Dead; <NAME>: Still a Jackass."""
self.assertEqual(text, result, "%s should be: %s" % (text, result, ))
if __name__ == '__main__':
if not sys.stdin.isatty():
for line in sys.stdin:
print(titlecase(line))
else:
suite = unittest.TestLoader().loadTestsFromTestCase(TitlecaseTests)
unittest.TextTestRunner(verbosity=2).run(suite)
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tests/db_test.py | hall-lab/lah | 0 | 92369 | import filecmp, os, tempfile, unittest
from lah.db import LahDb
class LahDbTest(unittest.TestCase):
def setUp(self):
self.data_d = os.path.join(os.path.dirname(__file__), "data", "dataset")
self.dbfile = os.path.join(self.data_d, "test.db")
self.temp_d = tempfile.TemporaryDirectory()
self.new_dbfile = os.path.join(self.temp_d.name, "new.db")
def tearDown(self):
self.temp_d.cleanup()
def test0_db_init(self):
db = LahDb(dbfile=self.dbfile)
self.assertIsNotNone(db)
self.assertEqual(db, LahDb.current())
self.assertEqual(db.dbfile, self.dbfile)
self.assertEqual(db.dburl(), "sqlite:///"+self.dbfile)
def test1_lah_db_connect(self):
db = LahDb(dbfile=self.dbfile)
self.assertIsNotNone(db)
self.assertEqual(db, LahDb.current())
db.connect()
self.assertIsNotNone(db.sessionmaker)
session = LahDb.session()
self.assertIsNotNone(session)
def test2_lah_db_create(self):
new_dbfile = self.new_dbfile
db = LahDb(dbfile=new_dbfile)
db.create()
self.assertTrue(os.path.exists(new_dbfile))
self.assertTrue(os.path.getsize(new_dbfile), 61440)
db = LahDb(dbfile=new_dbfile)
db.connect()
session = db.session()
self.assertIsNotNone(session)
# -- LahDbTest
if __name__ == '__main__':
unittest.main(verbosity=2)
#-- __main__
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] |
src/domain/node.py | kovanostra/graph-to-graph | 1 | 101197 | <reponame>kovanostra/graph-to-graph
import numpy as np
from src.domain.graph import Graph
class Node:
def __init__(self, graph: Graph, node_id: int):
self.node_id = node_id
self.features = self._get_node_features(graph)
self.neighbors = self._get_neighbors(graph)
self.neighbors_count = len(self.neighbors)
def _get_node_features(self, graph: Graph) -> np.ndarray:
return graph.node_features[self.node_id]
def _get_neighbors(self, graph: Graph) -> np.ndarray:
return np.nonzero(graph.adjacency_matrix[self.node_id])[0]
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] |
securify/analyses/patterns/source/rtlo_pattern.py | AlexandreH/securify2 | 258 | 190816 | import bisect
import re
from typing import List, Dict
from securify.analyses.patterns.abstract_pattern import AbstractPattern, PatternMatch, Level, Severity, PatternMatchError, \
MatchComment, MatchSourceLocation
class RightToLeftOverridePattern(AbstractPattern):
regex_pattern = re.compile("\u202e".encode('utf-8'))
@property
def name(self) -> str:
return "Right-to-left-override pattern"
@property
def description(self) -> str:
return "Finds usages of the Right-To-Left-Override (U+202E) character in source code"
@property
def severity(self) -> Severity:
return Severity.CRITICAL
@property
def level(self) -> Level:
return Level.SOURCE
@property
def tags(self) -> Dict[str, str]:
return {}
def find_matches(self) -> List[PatternMatch]:
analysis_context = self.analysis_context
if analysis_context.source_code is None:
raise PatternMatchError("Source code is not available.")
encoding = analysis_context.config.encoding
source_code = analysis_context.source_code.encode(encoding)
rtlo_pattern = re.compile("\u202e".encode(encoding))
newl_pattern = re.compile("\n".encode(encoding))
newlines = sorted((m.start() for m in newl_pattern.finditer(source_code)))
def get_line(b):
return bisect.bisect(newlines, b) + 1
def get_contract(source_code, until_line):
source_code_lines = str(source_code).split("\n")[:until_line]
last_contract_line = [c for c in source_code_lines if "contract" in c][-1]
if not last_contract_line:
return "no contract"
m = re.search('contract (\2w+)', last_contract_line)
if m:
return m.group(1)
matches = [
self.match_violation().with_info(
MatchComment(
"Found right-to-left-override character"
),
MatchSourceLocation(
encoding,
m.start(),
m.end(),
get_line(m.start()),
get_contract(source_code, get_line(m.start()))
)
) for m in rtlo_pattern.finditer(source_code)]
if not matches:
return [self.match_compliant()]
return matches
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] |
bdsim/blocks/__init__.py | petercorke/bdsim | 64 | 44469 | from .functions import *
from .sources import *
from .sinks import *
from .transfers import *
from .discrete import *
from .linalg import *
from .displays import *
from .connections import *
url = "https://petercorke.github.io/bdsim/" + __package__
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PaddleCV/image_classification/infer.py | nepeplwu/models | 1 | 194064 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import time
import sys
import paddle
import paddle.fluid as fluid
import reader
import argparse
import functools
import models
import utils
from utils.utility import add_arguments,print_arguments
import math
parser = argparse.ArgumentParser(description=__doc__)
# yapf: disable
add_arg = functools.partial(add_arguments, argparser=parser)
add_arg('use_gpu', bool, True, "Whether to use GPU or not.")
add_arg('class_dim', int, 1000, "Class number.")
add_arg('image_shape', str, "3,224,224", "Input image size")
add_arg('with_mem_opt', bool, True, "Whether to use memory optimization or not.")
add_arg('pretrained_model', str, None, "Whether to use pretrained model.")
add_arg('model', str, "SE_ResNeXt50_32x4d", "Set the network to use.")
add_arg('save_inference', bool, False, "Whether to save inference model or not")
# yapf: enable
def infer(args):
# parameters from arguments
class_dim = args.class_dim
model_name = args.model
save_inference = args.save_inference
pretrained_model = args.pretrained_model
with_memory_optimization = args.with_mem_opt
image_shape = [int(m) for m in args.image_shape.split(",")]
model_list = [m for m in dir(models) if "__" not in m]
assert model_name in model_list, "{} is not in lists: {}".format(args.model,
model_list)
image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
# model definition
model = models.__dict__[model_name]()
if model_name == "GoogleNet":
out, _, _ = model.net(input=image, class_dim=class_dim)
else:
out = model.net(input=image, class_dim=class_dim)
out = fluid.layers.softmax(out)
test_program = fluid.default_main_program().clone(for_test=True)
fetch_list = [out.name]
if with_memory_optimization and not save_inference:
fluid.memory_optimize(
fluid.default_main_program(), skip_opt_set=set(fetch_list))
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if pretrained_model:
def if_exist(var):
return os.path.exists(os.path.join(pretrained_model, var.name))
fluid.io.load_vars(exe, pretrained_model, predicate=if_exist)
if save_inference:
fluid.io.save_inference_model(
dirname=model_name,
feeded_var_names=['image'],
main_program=test_program,
target_vars=out,
executor=exe,
model_filename='model',
params_filename='params')
print("model: ",model_name," is already saved")
exit(0)
test_batch_size = 1
test_reader = paddle.batch(reader.test(), batch_size=test_batch_size)
feeder = fluid.DataFeeder(place=place, feed_list=[image])
TOPK = 1
for batch_id, data in enumerate(test_reader()):
result = exe.run(test_program,
fetch_list=fetch_list,
feed=feeder.feed(data))
result = result[0][0]
pred_label = np.argsort(result)[::-1][:TOPK]
print("Test-{0}-score: {1}, class {2}"
.format(batch_id, result[pred_label], pred_label))
sys.stdout.flush()
def main():
args = parser.parse_args()
print_arguments(args)
infer(args)
if __name__ == '__main__':
main()
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test/msan/lit.cfg.py | QuarkTheAwesome/compiler-rt-be-aeabi | 118 | 9489 | # -*- Python -*-
import os
# Setup config name.
config.name = 'MemorySanitizer' + getattr(config, 'name_suffix', 'default')
# Setup source root.
config.test_source_root = os.path.dirname(__file__)
# Setup default compiler flags used with -fsanitize=memory option.
clang_msan_cflags = (["-fsanitize=memory",
"-mno-omit-leaf-frame-pointer",
"-fno-omit-frame-pointer",
"-fno-optimize-sibling-calls"] +
[config.target_cflags] +
config.debug_info_flags)
# Some Msan tests leverage backtrace() which requires libexecinfo on FreeBSD.
if config.host_os == 'FreeBSD':
clang_msan_cflags += ["-lexecinfo", "-fPIC"]
clang_msan_cxxflags = config.cxx_mode_flags + clang_msan_cflags
# Flags for KMSAN invocation. This is C-only, we're not interested in C++.
clang_kmsan_cflags = (["-fsanitize=kernel-memory"] +
[config.target_cflags] +
config.debug_info_flags)
def build_invocation(compile_flags):
return " " + " ".join([config.clang] + compile_flags) + " "
config.substitutions.append( ("%clang_msan ", build_invocation(clang_msan_cflags)) )
config.substitutions.append( ("%clangxx_msan ", build_invocation(clang_msan_cxxflags)) )
config.substitutions.append( ("%clang_kmsan ", build_invocation(clang_kmsan_cflags)) )
# Default test suffixes.
config.suffixes = ['.c', '.cc', '.cpp']
if config.host_os not in ['Linux', 'NetBSD', 'FreeBSD']:
config.unsupported = True
# For mips64, mips64el we have forced store_context_size to 1 because these
# archs use slow unwinder which is not async signal safe. Therefore we only
# check the first frame since store_context size is 1.
if config.host_arch in ['mips64', 'mips64el']:
config.substitutions.append( ('CHECK-%short-stack', 'CHECK-SHORT-STACK'))
else:
config.substitutions.append( ('CHECK-%short-stack', 'CHECK-FULL-STACK'))
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mac.py | cosven/feeluown-mac-hotkey-plugin | 2 | 169466 | <filename>mac.py
# -*- coding=utf8 -*-
import asyncio
import logging
import os
import socket
import threading
logger = logging.getLogger(__name__)
def send_cmd(cmd):
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.connect(('0.0.0.0', 23333))
sock.recv(1024)
sock.sendall(bytes(cmd, 'utf-8') + b'\n')
sock.close()
def keyboard_tap_callback(proxy, type_, event, refcon):
from AppKit import NSKeyUp, NSEvent, NSBundle
NSBundle.mainBundle().infoDictionary()['NSAppTransportSecurity'] =\
dict(NSAllowsArbitraryLoads=True)
if type_ < 0 or type_ > 0x7fffffff:
logger.error('Unkown mac event')
run_event_loop()
logger.error('restart mac key board event loop')
return event
try:
key_event = NSEvent.eventWithCGEvent_(event)
except:
logger.info("mac event cast error")
return event
if key_event.subtype() == 8:
key_code = (key_event.data1() & 0xFFFF0000) >> 16
key_state = (key_event.data1() & 0xFF00) >> 8
if key_code in (16, 19, 20):
# 16 for play-pause, 19 for next, 20 for previous
if key_state == NSKeyUp:
if key_code is 19:
logger.info('mac hotkey: play next')
send_cmd('next')
elif key_code is 20:
logger.info('mac hotkey: play last')
send_cmd('previous')
elif key_code is 16:
os.system('echo "play_pause" | nc -4u -w0 localhost 8000')
send_cmd('toggle')
return None
return event
def run_event_loop():
logger.info("try to load mac hotkey event loop")
import Quartz
from AppKit import NSSystemDefined
# Set up a tap, with type of tap, location, options and event mask
tap = Quartz.CGEventTapCreate(
Quartz.kCGSessionEventTap, # Session level is enough for our needs
Quartz.kCGHeadInsertEventTap, # Insert wherever, we do not filter
Quartz.kCGEventTapOptionDefault,
# NSSystemDefined for media keys
Quartz.CGEventMaskBit(NSSystemDefined),
keyboard_tap_callback,
None
)
run_loop_source = Quartz.CFMachPortCreateRunLoopSource(
None, tap, 0)
Quartz.CFRunLoopAddSource(
Quartz.CFRunLoopGetCurrent(),
run_loop_source,
Quartz.kCFRunLoopDefaultMode
)
# Enable the tap
Quartz.CGEventTapEnable(tap, True)
# and run! This won't return until we exit or are terminated.
Quartz.CFRunLoopRun()
logger.error('Mac hotkey event loop exit')
return []
def run():
t = threading.Thread(target=run_event_loop, name='MacHotkeyThread')
t.daemon = True
t.start()
logger.info('mac hotkey listener started.')
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] |
buildscripts/linter/filediff.py | benety/mongo | 0 | 77608 | """Modules for find which files should be linted."""
import os
import sys
from typing import Tuple, List, Dict, Callable
from git import Repo
import structlog
# Get relative imports to work when the package is not installed on the PYTHONPATH.
if __name__ == "__main__" and __package__ is None:
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(os.path.realpath(__file__)))))
# pylint: disable=wrong-import-position
from buildscripts.linter import git
from buildscripts.patch_builds.change_data import generate_revision_map, \
RevisionMap, find_changed_files_in_repos
# pylint: enable=wrong-import-position
LOGGER = structlog.get_logger(__name__)
MONGO_REVISION_ENV_VAR = "REVISION"
ENTERPRISE_REVISION_ENV_VAR = "ENTERPRISE_REV"
def _get_repos_and_revisions() -> Tuple[List[Repo], RevisionMap]:
"""Get the repo object and a map of revisions to compare against."""
modules = git.get_module_paths()
repos = [Repo(path) for path in modules]
revision_map = generate_revision_map(
repos, {
"mongo": os.environ.get(MONGO_REVISION_ENV_VAR),
"enterprise": os.environ.get(ENTERPRISE_REVISION_ENV_VAR)
})
return repos, revision_map
def _filter_file(filename: str, is_interesting_file: Callable) -> bool:
"""
Determine if file should be included based on existence and passed in method.
:param filename: Filename to check.
:param is_interesting_file: Function to determine if file is interesting.
:return: True if file exists and is interesting.
"""
return os.path.exists(filename) and is_interesting_file(filename)
def gather_changed_files_for_lint(is_interesting_file: Callable) -> List[str]:
"""
Get the files that have changes since the last git commit.
:param is_interesting_file: Filter for whether a file should be returned.
:return: List of files for linting.
"""
repos, revision_map = _get_repos_and_revisions()
LOGGER.info("revisions", revision=revision_map)
candidate_files = find_changed_files_in_repos(repos, revision_map)
files = [
filename for filename in candidate_files if _filter_file(filename, is_interesting_file)
]
LOGGER.info("Found files to lint", files=files)
return files
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gdata.py | Arslan223/QuestBot | 0 | 165953 | import json
def load():
with open("data.json", "r") as f:
return json.load(f)
def update(obj):
with open("data.json", "w") as f:
json.dump(obj, f, ensure_ascii=False)
def loadCorpus():
with open("corpus.json", "r", encoding="utf-8") as f:
return json.load(f)
def updateCorpus(obj):
with open("corpus.json", "w", encoding="utf-8") as f:
json.dump(obj, f) | [
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15070,
29898,
5415,
29892,
285,
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2
] |
Orest Lab 2/2d.py | PetroSidliar/Lab-2 | 0 | 51457 | grade = eval ( input ( ' Enter your credit: ' ))
if grade<=23:
print ( ' Freshman ' )
elif grade>=24 and grade<=53:
print ( ' Sophomore ' )
elif grade>=54 and grade<=83:
print ( ' Juniors ' )
elif grade>=84:
print ( ' Seniors ' )
| [
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18930,
525,
1723,
13,
2
] |
pol/res.py | MoguCloud/server | 0 | 91567 | from typing import Any, Dict, Type, Optional
import orjson
from starlette.responses import JSONResponse
class ORJSONResponse(JSONResponse):
media_type = "application/json"
def render(self, content: Any) -> bytes:
return orjson.dumps(content)
class HTTPException(Exception):
def __init__(
self,
status_code: int,
title: str,
description: str,
detail: Any = None,
headers: Optional[Dict[str, Any]] = None,
) -> None:
self.status_code = status_code
self.detail = detail
self.headers = headers
self.title = title
self.description = description
def response(model: Type = None, description: str = None) -> Dict[str, Any]:
d: Dict[str, Any] = {}
if model is not None:
d["model"] = model
if description:
d["description"] = description
return d
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13,
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565,
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13,
4706,
270,
3366,
8216,
3108,
353,
6139,
13,
1678,
736,
270,
13,
2
] |
nsde/nsde.py | daniel-de-vries/differential-evolution | 4 | 60113 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Definition of the NSDE algorithm"""
import numpy as np
try:
from openmdao.utils.concurrent import concurrent_eval
except ModuleNotFoundError:
import warnings
warnings.warn("OpenMDAO is not installed. Concurrent evaluation is not available.")
from . import sorting, hv
from .strategies import EvolutionStrategy
def mpi_fobj_wrapper(fobj):
"""
Wrapper for the objective function to keep track of individual indices when running under MPI.
Parameters
----------
fobj : callable
Original objective function
Returns
-------
callable
Wrapped function which, in addition to x, takes the individual's index and returns it along with f
"""
def wrapped(x, ii):
return fobj(x), ii
return wrapped
class NSDE:
"""
Non-dominated Sorting Differential Evolution (NSDE) Algorithm.
Attributes
----------
fobj : callable
Objective function.
Should have a single argument of type array_like which corresponds to the design vector.
Should have either a single float or 1D array output corresponding to the objective function value(s),
or two array_like outputs, the first of which corresponds to the objective function value(s) and the second
to the constraint violations.
Constraints are assumed to be satisfied if constraint violations <= constraint tolerance.
lb, ub : array_like
Lower and upper bounds
range : array_like
Distances between the lower and upper bounds
f, cr : float
Mutation rate and crossover probabilities
adaptivity : int
Method of self-adaptivity.
- 0: No self-adaptivity. Specified mutation rate and crossover probability are used.
- 1: Simple self-adaptability. Mutation rate and crossover probability are optimized Mote-Carlo style.
- 2: Complex self-adaptability. Mutation rate and crossover probability are mutated with specified strategy.
max_gen : int
Maximum number of generations
tolx, tolf : float
Tolerances on the design vectors' and objective function values' spreads
tolc : float
Constraint violation tolerance.
n_dim : int
Number of dimension of the problem
n_pop : int
Population size
rng : np.random.Generator
Random number generator
comm : MPI communicator or None
The MPI communicator that will be used objective evaluation for each generation
model_mpi : None or tuple
If the model in fobj is also parallel, then this will contain a tuple with the the
total number of population points to evaluate concurrently, and the color of the point
to evaluate on this rank
strategy : EvolutionStrategy
Evolution strategy to use for procreation
pop : np.array
List of the individuals' chromosomes making up the current population
fit : np.array
Fitness of the individuals in the population
con : np.array
Constraint violations of the individuals in the population
generation : int
Generation counter
"""
def __init__(
self,
strategy=None,
mut=0.85,
crossp=1.0,
adaptivity=0,
max_gen=1000,
tolx=1e-8,
tolf=1e-8,
tolc=1e-6,
n_pop=None,
seed=None,
comm=None,
model_mpi=None,
):
self.fobj = None
self.lb, self.ub = None, None
self.range = 0
self.f = mut
self.cr = crossp
self.max_gen = max_gen
self.tolx = tolx
self.tolf = tolf
self.tolc = tolc
self.n_dim = 0
self.n_obj = 0
self.n_con = 0
self.n_pop = n_pop
self.rng = np.random.default_rng(seed)
if adaptivity not in [0, 1, 2]:
raise ValueError("self_adaptivity must be one of (0, 1, 2).")
self.adaptivity = adaptivity
self.comm = comm
self.model_mpi = model_mpi
if strategy is None:
self.strategy = EvolutionStrategy("rand-to-best/1/bin/random")
elif isinstance(strategy, EvolutionStrategy):
self.strategy = strategy
elif isinstance(strategy, str):
self.strategy = EvolutionStrategy(strategy)
else:
raise ValueError(
"Argument `strategy` should be None, a str, or an instance of EvolutionStrategy."
)
self.pop = None
self.fit = None
self.con = None
self.fronts = None
self.dx, self.df, self.hv = np.inf, np.inf, np.inf
self.pareto_lb = +np.inf
self.pareto_ub = -np.inf
self.generation = 0
self._is_initialized = False
self._running_under_mpi = comm is not None and hasattr(comm, "bcast")
def init(self, fobj, bounds, pop=None):
"""
Initialize the algorithm.
Parameters
----------
fobj : callable
Objective function
bounds : list of 2-tuples
List of (lower, upper) bounds
pop : None or array_like, optional
Initial population. If None, it will be created at random.
"""
# Set default values for the mutation and crossover parameters
if self.f is None or 0.0 > self.f > 1.0:
self.f = 0.85
if self.cr is None or 0.0 > self.cr > 1.0:
self.cr = 1.0
# Prepare the objective function and compute the bounds and variable range
self.fobj = fobj if self.comm is None else mpi_fobj_wrapper(fobj)
self.lb, self.ub = np.asarray(bounds).T
self.range = self.ub - self.lb
# Compute the number of dimensions
self.n_dim = len(bounds)
def create_f_cr(adaptivity, f, cr, n, rng):
# Create random mutation/crossover parameters if self-adaptivity is used
if adaptivity == 0:
f = f * np.ones(n)
cr = cr * np.ones(n)
elif adaptivity == 1:
f = rng.uniform(size=n) * 0.9 + 0.1
cr = rng.uniform(size=n)
elif adaptivity == 2:
f = rng.uniform(size=n) * 0.15 + 0.5
cr = rng.uniform(size=n) * 0.15 + 0.5
return f, cr
adjust_pop = False
if pop is not None:
self.n_pop = pop.shape[0]
self.pop = pop
self.f, self.cr = create_f_cr(
self.adaptivity, self.f, self.cr, self.n_pop, self.rng
)
else:
if self.n_pop is None or self.n_pop <= 0:
self.pop = self.rng.uniform(self.lb, self.ub, size=(1, self.n_dim))
adjust_pop = True
self.n_pop = 1
else:
self.pop = self.rng.uniform(
self.lb, self.ub, size=(self.n_pop, self.n_dim)
)
self.f, self.cr = create_f_cr(
self.adaptivity, self.f, self.cr, self.n_pop, self.rng
)
# Ensure all processors have the same population and mutation/crossover parameters
if self._running_under_mpi:
self.pop, self.f, self.cr = self.comm.bcast(
(self.pop, self.f, self.cr), root=0
)
self.fit, self.con = self(self.pop)
self.n_obj = self.fit.shape[1]
if self.con is not None:
self.n_con = self.con.shape[1]
if adjust_pop:
self.n_pop = 5 * self.n_dim * self.n_obj
# If we are running under MPI, expand population to fully exploit all processors
if self._running_under_mpi:
self.n_pop = int(np.ceil(self.n_pop / self.comm.size) * self.comm.size)
self.pop = np.concatenate(
(
self.pop,
self.rng.uniform(
self.lb, self.ub, size=(self.n_pop - 1, self.n_dim)
),
)
)
self.f, self.cr = create_f_cr(
self.adaptivity, self.f, self.cr, self.n_pop, self.rng
)
if self._running_under_mpi:
self.pop, self.f, self.cr = self.comm.bcast(
(self.pop, self.f, self.cr), root=0
)
self.fit, self.con = self(self.pop)
self.update()
# Set generation counter to 0
self.generation = 0
# Mark class as initialized
self._is_initialized = True
@property
def is_initialized(self):
"""bool: True if the algorithm has been initialized, False if not."""
return self._is_initialized
def __iter__(self):
"""
This class is an iterator itself.
Raises
------
RuntimeError
If this class is being used as an iterator before it has been initialized.
"""
if not self._is_initialized:
raise RuntimeError("NSDE is not yet initialized.")
return self
def __next__(self):
"""
Main iteration.
Returns
-------
NSDE
The new state at the next generation.
"""
if (
self.generation < self.max_gen
and self.dx > self.tolx
and self.df > self.tolf
):
# Create a new population and mutation/crossover parameters
pop_new, f_new, cr_new = self.procreate()
# Ensure all processors have the same updated population and mutation/crossover parameters
if self._running_under_mpi:
pop_new, f_new, cr_new = self.comm.bcast(
(pop_new, f_new, cr_new), root=0
)
# Evaluate the fitness of the new population
fit_new, con_new = self(pop_new)
# Update the class with the new data
self.update(pop_new, fit_new, con_new, f_new, cr_new)
# Compute spreads and update generation counter
if self.n_obj == 1:
self.dx = np.linalg.norm(self.pop[0] - self.pop[-1])
self.df = np.abs(self.fit[0] - self.fit[-1])
else:
pareto = self.fit[self.fronts[0]]
self.pareto_lb = np.minimum(self.pareto_lb, np.min(pareto, axis=0, keepdims=True))
self.pareto_ub = np.maximum(self.pareto_ub, np.max(pareto, axis=0, keepdims=True))
pareto_norm = 1 + (pareto - self.pareto_lb) / (self.pareto_ub - self.pareto_lb)
self.hv = hv.hv(pareto_norm, 2.1 * np.ones(self.n_obj))
self.generation += 1
# Return the new state
return self
else:
raise StopIteration
def __call__(self, pop):
"""
Evaluate the fitness of the given population.
Parameters
----------
pop : array_like
List of chromosomes of the individuals in the population
Returns
-------
fit : np.array
Fitness of the individuals in the given population
con : np.array or None
Constraint violations of the individuals in the given population if present. None otherwise.
Notes
-----
If this class has an MPI communicator the individuals will be evaluated in parallel.
Otherwise function evaluation will be serial.
"""
if self.is_initialized:
fit = np.empty((self.n_pop, self.n_obj))
con = None if self.n_con is None else np.empty((self.n_pop, self.n_con))
else:
fit = pop.shape[0] * [None]
con = None
def handle_result(_v, _i, _fit, _con):
if isinstance(_v, tuple):
_fit[_i] = np.asarray(_v[0])
c = np.asarray(_v[1])
if _con is None:
_con = np.empty((pop.shape[0], c.size))
_con[_i] = c
else:
_fit[_i] = _v
return _fit, _con
# Evaluate generation
if self._running_under_mpi:
# Construct run cases
cases = [((item, ii), None) for ii, item in enumerate(pop)]
# Pad the cases with some dummy cases to make the cases divisible amongst the procs.
extra = len(cases) % self.comm.size
if extra > 0:
for j in range(self.comm.size - extra):
cases.append(cases[-1])
# Compute the fitness of all individuals in parallel using MPI
results = concurrent_eval(
self.fobj, cases, self.comm, allgather=True, model_mpi=self.model_mpi
)
# Gather the results
for result in results:
retval, err = result
if err is not None or retval is None:
raise Exception(err)
else:
fit, con = handle_result(*retval, fit, con)
else:
# Evaluate the population in serial
for idx, ind in enumerate(pop):
val = self.fobj(ind)
fit, con = handle_result(val, idx, fit, con)
# Turn all NaNs in the fitnesses into infs
fit = np.reshape(np.where(np.isnan(fit), np.inf, fit), (pop.shape[0], -1))
if con is not None:
con = np.reshape(np.where(np.isnan(con), np.inf, con), (pop.shape[0], -1))
return fit, con
def run(self):
for _ in self:
pass
def procreate(self):
"""
Generate a new population using the selected evolution strategy.
Returns
-------
pop_new : np.array
Chromosomes of the individuals in the next generation
f_new : np.array
New set of mutation rates
cr_new : np.array
New set of crossover probabilities
"""
pop_old_norm = (np.copy(self.pop) - self.lb) / self.range
pop_new_norm = np.empty_like(pop_old_norm)
# If there are constraints, augment the fitness to penalize infeasible individuals while procreating.
# This stops the best and rand-to-best strategies to keep the best infeasible individual alive indefinitely.
if self.n_con and False:
fit = np.where(
np.any(self.con >= 1e-6, axis=1, keepdims=True),
np.linalg.norm(self.con, axis=1, keepdims=True) + np.max(self.fit),
self.fit,
)
else:
fit = self.fit
if self.adaptivity == 0 or self.adaptivity == 1:
if self.adaptivity == 0:
# No adaptivity. Use static f and cr.
f_new = self.f
cr_new = self.cr
else:
# Simple adaptivity. Use new f and cr.
f_new = np.where(
self.rng.uniform(size=self.n_pop) < 0.9,
self.f,
self.rng.uniform(size=self.n_pop) * 0.9 + 0.1,
)
cr_new = np.where(
self.rng.uniform(size=self.n_pop) < 0.9,
self.cr,
self.rng.uniform(size=self.n_pop),
)
for idx in range(self.n_pop):
pop_new_norm[idx], _, _ = self.strategy(
idx, pop_old_norm, fit, self.fronts, f_new, cr_new, self.rng, False
)
else:
# Complex adaptivity. Mutate f and cr.
f_new = np.copy(self.f)
cr_new = np.copy(self.cr)
for idx in range(self.n_pop):
pop_new_norm[idx], f_new[idx], cr_new[idx] = self.strategy(
idx, pop_old_norm, fit, self.fronts, self.f, self.cr, self.rng, True
)
pop_new = self.lb + self.range * np.asarray(pop_new_norm)
return pop_new, f_new, cr_new
def update(self, pop_new=None, fit_new=None, con_new=None, f_new=None, cr_new=None):
"""
Update the population (and f/cr if self-adaptive).
Parameters
----------
pop_new : np.array or None, optional
Proposed new population resulting from procreation
fit_new : np.array or None, optional
Fitness of the individuals in the new population
con_new : np.array or None, optional
Constraint violations of the individuals in the new population
f_new : np.array or None, optional
New set of mutation rates
cr_new : np.array or None, optional
New set of crossover probabilities
Notes
-----
Individuals in the old population will only be replaced by the new ones if they have improved fitness.
Mutation rate and crossover probabilities will only be replaced if self-adaptivity is turned on and if their
corresponding individuals have improved fitness.
"""
if self.n_obj == 1:
self._update_single(pop_new, fit_new, con_new, f_new, cr_new)
else:
self._update_multi(pop_new, fit_new, con_new, f_new, cr_new)
def _update_single(
self, pop_new=None, fit_new=None, con_new=None, f_new=None, cr_new=None
):
if self.n_con:
cs = np.sum(
np.where(np.greater(self.con, self.tolc), self.con, 0.0),
axis=1
)
else:
cs = 0
if (
pop_new is not None
and fit_new is not None
and f_new is not None
and cr_new is not None
):
if self.n_con:
c_new = np.all(con_new <= self.tolc, axis=1)
c_old = np.all(self.con <= self.tolc, axis=1)
cs_new = np.sum(
np.where(np.greater(con_new, self.tolc), con_new, 0.0), axis=1
)
improved_indices = np.argwhere(
((c_new & c_old) & (fit_new <= self.fit).flatten())
+ (c_new & ~c_old)
+ ((~c_new & ~c_old) & (cs_new <= cs))
)
self.con[improved_indices] = con_new[improved_indices]
cs[improved_indices] = cs_new[improved_indices]
else:
improved_indices = np.argwhere((fit_new <= self.fit).flatten())
self.pop[improved_indices] = pop_new[improved_indices]
self.fit[improved_indices] = fit_new[improved_indices]
if self.adaptivity != 0:
self.f[improved_indices] = f_new[improved_indices]
self.cr[improved_indices] = cr_new[improved_indices]
# Sort population so the best individual is always the first
idx_sort = np.argsort(
self.fit.flatten() + np.where(cs != 0.0, cs * np.max(self.fit), 0.0)
)
self.pop = self.pop[idx_sort]
self.fit = self.fit[idx_sort]
if self.n_con:
self.con = self.con[idx_sort]
if self.adaptivity != 0:
self.f = self.f[idx_sort]
self.cr = self.cr[idx_sort]
def _update_multi(
self, pop_new=None, fit_new=None, con_new=None, f_new=None, cr_new=None
):
if (
pop_new is not None
and fit_new is not None
and f_new is not None
and cr_new is not None
):
self.pop = np.concatenate((self.pop, pop_new))
self.fit = np.concatenate((self.fit, fit_new))
if self.n_con:
self.con = np.concatenate((self.con, con_new))
if self.adaptivity != 0:
self.f = np.concatenate((self.f, f_new))
self.cr = np.concatenate((self.cr, cr_new))
if self.n_con:
fronts = sorting.nonDominatedSorting(self.fit, self.con, self.n_pop)
else:
fronts = sorting.nonDominatedSorting(self.fit, self.n_pop)
fronts[-1] = np.asarray(fronts[-1])[
sorting.crowdingDistanceSorting(self.fit[fronts[-1]])[
: (self.n_pop - sum(len(f) for f in fronts[:-1]))
]
].tolist()
new_idxs = []
counter = 0
self.fronts = []
for front in fronts:
new_idxs += front
self.fronts += [list(range(counter, counter + len(front)))]
counter += len(front)
self.pop = self.pop[new_idxs]
self.fit = self.fit[new_idxs]
if self.n_con:
self.con = self.con[new_idxs]
if self.adaptivity != 0:
self.f = self.f[new_idxs]
self.cr = self.cr[new_idxs]
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jobs/migrations/0002_auto_20201219_0911.py | Platz-Work/platzi-work-backend | 0 | 43278 | <filename>jobs/migrations/0002_auto_20201219_0911.py
# Generated by Django 3.1.4 on 2020-12-19 14:11
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('jobs', '0001_initial'),
]
operations = [
migrations.AlterModelOptions(
name='category',
options={'ordering': ['-created_at'], 'verbose_name_plural': 'Categories'},
),
migrations.AlterField(
model_name='profile',
name='category',
field=models.ForeignKey(blank=True, error_messages={'blank': "The category_id field can't be blank.", 'invalid': 'The category_id field is invalid.', 'null': "The category_id field can't be null."}, null=True, on_delete=django.db.models.deletion.PROTECT, to='jobs.category'),
),
migrations.AlterField(
model_name='profile',
name='salary_end',
field=models.PositiveIntegerField(blank=True, error_messages={'blank': "The salary_end field can't be blank.", 'invalid': 'The salary_end field is invalid.', 'null': "The salary_end field can't be null."}, null=True, verbose_name='Salary up'),
),
migrations.AlterField(
model_name='profile',
name='salary_start',
field=models.PositiveIntegerField(blank=True, error_messages={'blank': "The salary_start field can't be blank.", 'invalid': 'The salary_start field is invalid.', 'null': "The salary_start field can't be null."}, null=True, verbose_name='Salary from'),
),
]
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teraserver/python/modules/FlaskModule/API/user/UserLogin.py | introlab/opentera | 10 | 30410 | <filename>teraserver/python/modules/FlaskModule/API/user/UserLogin.py
from flask import session, request
from flask_restx import Resource, reqparse
from flask_babel import gettext
from modules.LoginModule.LoginModule import user_http_auth
from modules.FlaskModule.FlaskModule import user_api_ns as api
from opentera.redis.RedisRPCClient import RedisRPCClient
from opentera.modules.BaseModule import ModuleNames
# model = api.model('Login', {
# 'websocket_url': fields.String,
# 'user_uuid': fields.String,
# 'user_token': fields.String
# })
# Parser definition(s)
get_parser = api.parser()
class UserLogin(Resource):
def __init__(self, _api, *args, **kwargs):
Resource.__init__(self, _api, *args, **kwargs)
self.module = kwargs.get('flaskModule', None)
self.parser = reqparse.RequestParser()
@user_http_auth.login_required
@api.expect(get_parser)
@api.doc(description='Login to the server using HTTP Basic Authentification (HTTPAuth)')
def get(self):
session.permanent = True
# Redis key is handled in LoginModule
servername = self.module.config.server_config['hostname']
port = self.module.config.server_config['port']
if 'X_EXTERNALHOST' in request.headers:
if ':' in request.headers['X_EXTERNALHOST']:
servername, port = request.headers['X_EXTERNALHOST'].split(':', 1)
else:
servername = request.headers['X_EXTERNALHOST']
if 'X_EXTERNALPORT' in request.headers:
port = request.headers['X_EXTERNALPORT']
# Get user token key from redis
from opentera.redis.RedisVars import RedisVars
token_key = self.module.redisGet(RedisVars.RedisVar_UserTokenAPIKey)
# Get token for user
from opentera.db.models.TeraUser import TeraUser
current_user = TeraUser.get_user_by_uuid(session['_user_id'])
# Verify if user already logged in
rpc = RedisRPCClient(self.module.config.redis_config)
online_users = rpc.call(ModuleNames.USER_MANAGER_MODULE_NAME.value, 'online_users')
if current_user.user_uuid in online_users:
self.module.logger.log_warning(self.module.module_name,
UserLogin.__name__,
'get', 403,
'User already logged in', current_user.to_json(minimal=True))
return gettext('User already logged in.'), 403
current_user.update_last_online()
user_token = current_user.get_token(token_key)
print('Login - setting key with expiration in 60s', session['_id'], session['_user_id'])
self.module.redisSet(session['_id'], session['_user_id'], ex=60)
# Return reply as json object
reply = {"websocket_url": "wss://" + servername + ":" + str(port) + "/wss/user?id=" + session['_id'],
"user_uuid": session['_user_id'],
"user_token": user_token}
# Verify client version (optional for now)
# And add info to reply
if 'X-Client-Name' in request.headers and 'X-Client-Version' in request.headers:
try:
# Extract information
client_name = request.headers['X-Client-Name']
client_version = request.headers['X-Client-Version']
client_version_parts = client_version.split('.')
# Load known version from database.
from opentera.utils.TeraVersions import TeraVersions
versions = TeraVersions()
versions.load_from_db()
# Verify if we have client information in DB
client_info = versions.get_client_version_with_name(client_name)
if client_info:
# We have something stored for this client, let's verify version numbers
# For now, we still allow login even when version mismatch
# Reply full version information
reply['version_latest'] = client_info.to_dict()
if client_info.version != client_version:
reply['version_error'] = gettext('Client version mismatch')
# If major version mismatch, kill client, first part of the version
stored_client_version_parts = client_info.version.split('.')
if len(stored_client_version_parts) and len(client_version_parts):
if stored_client_version_parts[0] != client_version_parts[0]:
# return 426 = upgrade required
self.module.logger.log_warning(self.module.module_name,
UserLogin.__name__,
'get', 426,
'Client major version too old, not accepting login',
stored_client_version_parts[0],
client_version_parts[0])
return gettext('Client major version too old, not accepting login'), 426
else:
return gettext('Invalid client name :') + client_name, 403
except BaseException as e:
self.module.logger.log_error(self.module.module_name,
UserLogin.__name__,
'get', 500, 'Invalid client version handler', str(e))
return gettext('Invalid client version handler') + str(e), 500
return reply
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] |
users.py | ArVID220u/SnallisBot | 0 | 139966 | # import twythonaccess to be able to make twitter requests
from . import twythonaccess
# import apikeys
from . import apikeys
# import twythonstreamer for the SwedishMiner down below
from twython import TwythonStreamer
# import threading
from threading import Thread
# import time
import time
# this class provides Swedish usernames
# all unique, and as long as there are users left in Swedish Twitter, this class will keep churning them out
class Users:
# the added users is a set of the user ids of all that have been added
added_users = set()
# the next users is a queue for the users to be returned
next_users = []
# the mine followers is also a queue for the users whose follower list hasn't yet been mined
mine_followers = []
# initializer
def __init__(self):
# initialize added_users from the data file
self.init_added_users()
# create a swedish_miner
self.swedish_miner = SwedishMiner(apikeys.MINE_CONSUMER_KEY, apikeys.MINE_CONSUMER_SECRET, apikeys.MINE_ACCESS_TOKEN, apikeys.MINE_ACCESS_TOKEN_SECRET)
# set its users property to self
# warning: this will create a memory leak, as both references are strong
self.swedish_miner.users = self
# start a thread for mining the swedish users
swedish_miner_thread = Thread(target = self.swedish_miner_streamer)
swedish_miner_thread.start()
print("has initialized users by starting miner thread")
# initialize added users from the file added_users.data
def init_added_users(self):
with open("/home/arvid220u/twitterbots/nicebot/added_users.data", 'r') as datafile:
# at every line, there is exactly one user id, that has already beeen processed
for line in datafile:
userid = int(line.strip())
self.added_users.add(userid)
# add a user to the lists, checking first if it's unique
def add_user(self, userid):
if userid in self.added_users:
# already added this user, abort
return
print("adding user with id: " + str(userid))
# if there already are too many users in the mine_followers array, disconnect the swedish_miner
if len(self.mine_followers) > 100:
if self.swedish_miner.alive:
print("disconnect swedish miner due to many already in queue")
self.swedish_miner.disconnect()
self.swedish_miner.alive = False
# add user to added_users
self.added_users.add(userid)
# also add to the data file called added_users.data
# append user to both the minefollowers queue and the nextusers queue
self.next_users.append(userid)
self.mine_followers.append(userid)
# return a screen name of a user
def get_user(self):
# if the number of users in next_users is less than 10, mine some followers
if len(self.next_users) < 10 and len(self.mine_followers) > 0:
# do it in a separate thread
print("need to mine some users since next_users is of length: " + str(len(self.next_users)))
mine_followers_thread = Thread(target = self.mine_some_followers)
mine_followers_thread.start()
# if next users length is zero, which just should never happen, wait
while len(self.next_users) == 0:
print("next users is 0, will sleep for 6 minutes")
time.sleep(6*60)
# do not respond to protected users
screenname = ""
userid = 0
while True:
# get the frontmost user in the next_users queue
firstid = self.next_users.pop(0)
# get the screen name of this particular user
twythonaccess.sleep_if_requests_are_maximum(170, main=False)
user = twythonaccess.authorize(main=False).show_user(user_id=firstid)
if not user["protected"]:
screenname = user["screen_name"]
userid = user["id"]
break
elif len(self.next_users) == 0:
return self.get_user()
print("found user with screenname: " + screenname)
# add this user to the added users data file, so as to not tweet to the same person twice
with open("/home/arvid220u/twitterbots/nicebot/added_users.data", 'a') as datafile:
datafile.write(str(userid) + '\n')
# return it
return screenname
# mine some followers
def mine_some_followers(self):
# if the mine_followers queue contains less than 200 items, start the swedish streamer
if len(self.mine_followers) < 20 and not self.swedish_miner.alive:
print("starting swedish miner anew since mine followers length is " + str(len(self.mine_followers)))
swedish_miner_thread = Thread(target = self.swedish_miner_streamer)
swedish_miner_thread.start()
while len(self.mine_followers) == 0:
print("mine followers is 0, will sleep for 6 minutes")
time.sleep(6*60)
counter = 0
while True:
# get the first user
userid = self.mine_followers.pop(0)
# get user
twythonaccess.sleep_if_requests_are_maximum(170, main=False)
user = twythonaccess.authorize(main=False).show_user(user_id=userid)
# if user is protected, continue
if user["protected"]:
if len(self.mine_followers) == 0:
break
continue
# get a list of the followers
twythonaccess.sleep_if_requests_are_maximum(13, main=False)
followers = twythonaccess.authorize(main=False).get_followers_list(user_id=userid, count=100)["users"]
for follower in followers:
if follower["lang"] == "sv":
self.add_user(follower["id"])
count += 1
if count > 100 or len(mine_followers) == 0:
break
print("finished mining followers")
# start the swedish miner streamer
def swedish_miner_streamer(self):
# get most of all swedish tweets
# it performs an or search, with language sv
print("starting swedish miner streamer")
self.swedish_miner.alive = True
self.swedish_miner.statuses.filter(track="i,och,att,det,som,en,på,är,av,för", language="sv")
# this class finds users based on them sending tweets marked as swedish
class SwedishMiner(TwythonStreamer):
# check if alive
alive = False
# this function is called when a tweet is received
def on_success(self, tweet):
# add the user
#print("found new user from swedish streamer miner")
self.users.add_user(tweet["user"]["id"])
def on_error(self, status_code, data):
print("STREAMING API ERROR in SwedishStreamer")
print("Status code:")
print(status_code)
print("other data:")
print(data)
print("END OF ERROR MESSAGE")
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] |
venv/Lib/site-packages/comet_ml/loggers/shap_logger.py | jinga-lala/stupidNMT | 0 | 107579 | <filename>venv/Lib/site-packages/comet_ml/loggers/shap_logger.py
# -*- coding: utf-8 -*-
# *******************************************************
# ____ _ _
# / ___|___ _ __ ___ ___| |_ _ __ ___ | |
# | | / _ \| '_ ` _ \ / _ \ __| | '_ ` _ \| |
# | |__| (_) | | | | | | __/ |_ _| | | | | | |
# \____\___/|_| |_| |_|\___|\__(_)_| |_| |_|_|
#
# Sign up for free at http://www.comet.ml
# Copyright (C) 2015-2020 Comet ML INC
# This file can not be copied and/or distributed without
# the express permission of Comet ML Inc.
# *******************************************************
import logging
from .._logging import check_module
from .._typing import Any
from ..experiment import BaseExperiment
LOGGER = logging.getLogger(__name__)
class SHAPLogger(object):
def __init__(self, title):
self.show = True
self.title = title
def before(self, experiment, original, *args, **kwargs):
"""
Little wrapper to make sure show is False
"""
self.show = kwargs.get("show", True)
kwargs["show"] = False
return (args, kwargs)
def after(self, experiment, original, results, *args, **kwargs):
# type: (BaseExperiment, Any, Any, Any, Any) -> None
"""
Little wrapper to log figure, and show, if needed.
"""
# The post callback shouldn't execute in case of exception in original, so we should have
# matplotlib except if some shap analysis do not need matplotlib in the future
try:
import matplotlib.pyplot as plt
except ImportError:
LOGGER.warning("matplotlib not installed; shap logging disabled")
return
if experiment.config["comet.auto_log.figures"]:
experiment._storage["shap"]["counter"] += 1
experiment._log_figure(
figure_name="shap-%s-%s"
% (self.title, experiment._storage["shap"]["counter"]),
figure_type="shap",
framework="shap",
)
if self.show:
plt.show()
def patch(module_finder):
check_module("shap")
modules = [
("shap.plots._bar", "bar", "bar"),
("shap.plots._bar", "bar_legacy", "bar_plot"),
("shap.plots._image", "image", "image_plot"),
("shap.plots._beeswarm", "beeswarm", "beeswarm"),
("shap.plots._beeswarm", "summary_legacy", "summary_plot"),
("shap.plots._decision", "decision", "decision_plot"),
("shap.plots._decision", "multioutput_decision", "multioutput_decision_plot"),
("shap.plots._embedding", "embedding", "embedding_plot"),
("shap.plots._force", "force", "force_plot"),
("shap.plots._group_difference", "group_difference", "group_difference_plot"),
("shap.plots._heatmap", "heatmap", "heatmap"),
("shap.plots._scatter", "scatter", "scatter"),
("shap.plots._scatter", "dependence_legacy", "dependence_plot"),
("shap.plots._monitoring", "monitoring", "monitoring_plot"),
(
"shap.plots._partial_dependence",
"partial_dependence",
"partial_dependence_plot",
),
("shap.plots._violin", "violin", "violin"),
("shap.plots._waterfall", "waterfall", "waterfall_plot"),
]
for module, function, title in modules:
shap_logger = SHAPLogger(title)
module_finder.register_before(module, function, shap_logger.before)
module_finder.register_after(module, function, shap_logger.after)
check_module("shap")
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tests/integration/integration_fixture_builders.py | dendisuhubdy/trinity | 3 | 163879 | <reponame>dendisuhubdy/trinity
from eth_keys import keys
from eth_utils import decode_hex
from tests.core.integration_test_helpers import FUNDED_ACCT, load_mining_chain
from .churn_state.builder import (
delete_churn,
deploy_storage_churn_contract,
update_churn,
)
RECEIVER = keys.PrivateKey(
decode_hex("b71c71a67e1177ad4e901695e1b4b9ee17ae16c6668d313eac2f96dbcda3f291"))
def build_pow_fixture(write_db, num_blocks=20):
chain = load_mining_chain(write_db)
recipient_address = RECEIVER.public_key.to_canonical_address()
for i in range(num_blocks):
tx = chain.create_unsigned_transaction(
nonce=i,
gas_price=1234,
gas=123400,
to=recipient_address,
value=i,
data=b'',
)
chain.apply_transaction(tx.as_signed_transaction(FUNDED_ACCT))
chain.mine_block()
return chain.chaindb
def build_pow_churning_fixture(write_db, num_blocks=40):
chain = load_mining_chain(write_db)
contract_addr = deploy_storage_churn_contract(chain)
half_blocks = num_blocks // 2
nymph_contracts, nonce = update_churn(chain, contract_addr, num_blocks=half_blocks)
delete_churn(chain, nymph_contracts, contract_addr, start_nonce=nonce, num_blocks=half_blocks)
return chain, contract_addr
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backend/db/entities/mixin/pengaturan.py | R-N/sistem_gaji_vue_thrift | 0 | 39729 | from sqlalchemy import Column, Integer, Numeric
from sqlalchemy.ext.declarative import declared_attr
from .pengaturan_base import MxPengaturanBase
class MxPengaturan(MxPengaturanBase):
# TODO: Set precision & scale for Numerics
@declared_attr
def bpjs_ketenagakerjaan_perusahaan(cls):
return Column(Numeric, nullable=False)
@declared_attr
def bpjs_ketenagakerjaan_karyawan(cls):
return Column(Numeric, nullable=False)
@declared_attr
def bpjs_kesehatan_perusahaan(cls):
return Column(Numeric, nullable=False)
@declared_attr
def bpjs_kesehatan_karyawan(cls):
return Column(Numeric, nullable=False)
@declared_attr
def upah_minimum(cls):
return Column(Integer, nullable=False)
@declared_attr
def iuran_rumah(cls):
return Column(Integer, nullable=False)
@declared_attr
def iuran_koperasi(cls):
return Column(Integer, nullable=False)
@declared_attr
def pendaftaran_koperasi(cls):
return Column(Integer, nullable=False)
@declared_attr
def uang_makan(cls):
return Column(Integer, nullable=False)
@declared_attr
def uang_transport(cls):
return Column(Integer, nullable=False)
@declared_attr
def koef_absen(cls):
return Column(Numeric, nullable=False)
'''
def mx_init(
self,
*args,
bpjs_ketenagakerjaan_perusahaan,
bpjs_ketenagakerjaan_karyawan,
bpjs_kesehatan_perusahaan,
bpjs_kesehatan_karyawan,
upah_minimum,
iuran_rumah,
iuran_koperasi,
pendaftaran_koperasi,
uang_makan,
uang_transport,
koef_absen,
**kwargs
):
MxPengaturanBase.mx_init(*args, **kwargs)
self.bpjs_ketenagakerjaan_perusahaan = bpjs_ketenagakerjaan_perusahaan
self.bpjs_ketenagakerjaan_karyawan = bpjs_ketenagakerjaan_karyawan
self.bpjs_kesehatan_perusahaan = bpjs_kesehatan_perusahaan
self.bpjs_ketenagakerjaan_karyawan = bpjs_ketenagakerjaan_karyawan
self.upah_minimum = upah_minimum
self.iuran_rumah = iuran_rumah
self.iuran_koperasi = iuran_koperasi
self.pendaftaran_koperasi = pendaftaran_koperasi
self.uang_makan = uang_makan
self.uang_transport = uang_transport
self.koef_absen = koef_absen
'''
def mx_reconstruct(self):
MxPengaturanBase.mx_reconstruct(self)
def mx_repr(self):
return '%s' % (MxPengaturanBase.mx_repr(self),)
'''
def mx_repr(self):
return "TODO" % (
self.id, self.nama,
)
'''
def mx_init_repr(self):
ret = MxPengaturanBase.mx_init_repr(self)
ret.update({
'bpjs_ketenagakerjaan_perusahaan': self.bpjs_ketenagakerjaan_perusahaan,
'bpjs_ketenagakerjaan_karyawan': self.bpjs_ketenagakerjaan_karyawan,
'bpjs_kesehatan_perusahaan': self.bpjs_kesehatan_perusahaan,
'bpjs_kesehatan_karyawan': self.bpjs_kesehatan_karyawan,
'upah_minimum': self.upah_minimum,
'iuran_rumah': self.iuran_rumah,
'iuran_koperasi': self.iuran_koperasi,
'pendaftaran_koperasi': self.pendaftaran_koperasi,
'uang_makan': self.uang_makan,
'uang_transport': self.uang_transport,
'koef_absen': self.koef_absen
})
return ret
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algorithm/codility.com/20160330/longest_0_binary_gap.py | leonard-sxy/algorithm-practice | 1 | 40346 | def solution(N):
str = '{0:b}'.format(N)
counter = prev_counter = 0
for c in str:
if c is '0':
counter += 1
else:
if prev_counter == 0 or counter > prev_counter:
prev_counter = counter
counter = 0
return prev_counter if prev_counter > counter else counter
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tools/migrate_chart/migrate_chart.py | kschu91/harbor | 12,706 | 63034 | #!/usr/local/bin/python3
import subprocess
import signal
import sys
from pathlib import Path
import click
import requests
MIGRATE_CHART_SCRIPT = '/migrate_chart.sh'
HELM_CMD = '/linux-amd64/helm'
CA_UPDATE_CMD = 'update-ca-certificates'
CHART_URL_PATTERN = "https://{host}/api/v2.0/projects/{project}/repositories/{name}/artifacts/{version}"
CHART_SOURCE_DIR = Path('/chart_storage')
errs = []
def print_exist_errs():
if errs:
click.echo("Following errors exist", err=True)
for e in errs:
click.echo(e, err=True)
def graceful_exit(signum, frame):
print_exist_errs()
sys.exit()
signal.signal(signal.SIGINT, graceful_exit)
signal.signal(signal.SIGTERM, graceful_exit)
class ChartV2:
def __init__(self, filepath:Path):
self.filepath = filepath
self.project = self.filepath.parts[-2]
parts = self.filepath.stem.split('-')
flag = False
for i in range(len(parts)-1, -1, -1):
if parts[i][0].isnumeric():
self.name, self.version = '-'.join(parts[:i]), '-'.join(parts[i:])
flag = True
break
if not flag:
raise Exception('chart name: {} is illegal'.format('-'.join(parts)))
def __check_exist(self, hostname, username, password):
return requests.get(CHART_URL_PATTERN.format(
host=hostname,
project=self.project,
name=self.name,
version=self.version),
auth=requests.auth.HTTPBasicAuth(username, password))
def migrate(self, hostname, username, password):
res = self.__check_exist(hostname, username, password)
if res.status_code == 200:
raise Exception("Artifact already exist in harbor")
if res.status_code == 401:
raise Exception(res.reason)
oci_ref = "{host}/{project}/{name}:{version}".format(
host=hostname,
project=self.project,
name=self.name,
version=self.version)
return subprocess.run([MIGRATE_CHART_SCRIPT, HELM_CMD, self.filepath, oci_ref],
text=True, stdout=subprocess.DEVNULL, stderr=subprocess.PIPE)
@click.command()
@click.option('--hostname', default='127.0.0.1', help='the password to login harbor')
@click.option('--username', default='admin', help='The username to login harbor')
@click.option('--password', default='<PASSWORD>', help='the password to login harbor')
def migrate(hostname, username, password):
"""
Migrate chart v2 to harbor oci registry
"""
if username != 'admin':
raise Exception('This operation only allowed for admin')
subprocess.run([CA_UPDATE_CMD])
subprocess.run([HELM_CMD, 'registry', 'login', hostname, '--username', username, '--password', password])
charts = [ChartV2(c) for p in CHART_SOURCE_DIR.iterdir() if p.is_dir() for c in p.iterdir() if c.is_file() and c.name != "index-cache.yaml"]
with click.progressbar(charts, label="Migrating chart ...", length=len(charts),
item_show_func=lambda x: "{}/{}:{} total errors: {}".format(x.project, x.name, x.version, len(errs)) if x else '') as bar:
for chart in bar:
try:
result = chart.migrate(hostname, username, password)
if result.stderr:
errs.append("chart: {name}:{version} in {project} has err: {err}".format(
name=chart.name,
version=chart.version,
project=chart.project,
err=result.stderr
))
except Exception as e:
errs.append("chart: {name}:{version} in {project} has err: {err}".format(
name=chart.name,
version=chart.version,
project=chart.project,
err=e))
click.echo("Migration is Done.")
print_exist_errs()
if __name__ == '__main__':
migrate()
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src/survivor_scraping/reddit/reddit_transform.py | SeanAmmirati/survivor_processing | 0 | 160683 | <gh_stars>0
from ..helpers.db_funcs import create_season_times, create_episode_times
from ..helpers.transform_helpers import add_to_df, coerce_col, sync_with_remote
import pandas as pd
from collections import OrderedDict
def process_utc(df, *args, **kwargs):
return pd.DataFrame(pd.to_datetime(df['created_utc'], unit='s'))
def process_nearest_event(df, event_times, match_time_col,
event_time_col, subset_cols=None,
direction='forward'):
event_times = event_times[event_times[event_time_col].notnull()]
sorted_times = event_times.sort_values(by=event_time_col)
merged_asof = pd.merge_asof(df[df[match_time_col].notnull()],
sorted_times,
left_on=[match_time_col],
right_on=[event_time_col])
if not subset_cols:
subset_cols = merged_asof.columns
return merged_asof[subset_cols]
def process_within_season(df, season_times, *args, **kwargs):
merged = df.merge(season_times, left_on='most_recent_season',
right_on='season_id')
within = merged['most_recent_season']
within.loc[merged['created_dt'] > merged['showing_ended']] = None
return pd.DataFrame(within)
def process_nearest_season_started(df, season_times, *args, **kwargs):
return process_nearest_event(df, season_times, 'created_dt',
'showing_started', ['season_id'])
def process_nearest_episode_aired(df, episode_times, *args, **kwargs):
return process_nearest_event(df, episode_times, 'created_dt',
'firstbroadcast', ['episode_id'])
# def stringize_dict_and_list_cols(df):
def transform_reddit(reddit_dfs, eng):
season_times = create_season_times(eng)
episode_times = create_episode_times(eng)
ret_list = []
table_names = ['submissions', 'comments']
table_names = ['reddit_' + t for t in table_names]
for i, df in enumerate(reddit_dfs):
if df.empty or ('created_utc' not in df):
ret_list.append(df)
continue
df[['created_dt']] = process_utc(df)
processing_columns = OrderedDict()
processing_columns[('created_dt',)] = process_utc
processing_columns[('most_recent_season', )
] = process_nearest_season_started
processing_columns[('most_recent_episode',)
] = process_nearest_episode_aired
processing_columns[('within_season', )] = process_within_season
added = add_to_df(df, processing_columns,
inplace=False, season_times=season_times,
episode_times=episode_times)
dict_cols = added.columns[added.applymap(
lambda x: isinstance(x, dict) or isinstance(x, list)).any()]
for col in dict_cols:
added.loc[df[col].notnull(), col] = \
added.loc[df[col].notnull(), col].astype(str)
added = sync_with_remote(added, eng, table_names[i])
ret_list.append(added)
# TODO: IT would be cool to transform the flairs, or mentions, to the contestant IDs
return ret_list
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memorytestgame/example.config.py | bradcornford/Memory-Test-Game | 0 | 102151 | game = {
'leds': (
# GPIO05 - Pin 29
5,
# GPIO12 - Pin 32
12,
# GPIO17 - Pin 11
17,
# GPIO22 - Pin 15
22,
# GPIO25 - Pin 22
25
),
'switches': (
# GPIO06 - Pin 31
6,
# GPIO13 - Pin 33
13,
# GPIO19 - Pin 35
19,
# GPIO23 - Pin 16
23,
# GPIO24 - Pin 18
24
),
'countdown': 5,
'game_time': 60,
'score_increment': 1
}
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] |
custom/icds_reports/dashboard_utils.py | tstalka/commcare-hq | 0 | 12765 | <filename>custom/icds_reports/dashboard_utils.py<gh_stars>0
from corehq.apps.locations.util import location_hierarchy_config
from custom.icds_reports.utils import icds_pre_release_features
def get_dashboard_template_context(domain, couch_user):
context = {}
context['location_hierarchy'] = location_hierarchy_config(domain)
context['user_location_id'] = couch_user.get_location_id(domain)
context['all_user_location_id'] = list(couch_user.get_sql_locations(
domain
).location_ids())
context['state_level_access'] = 'state' in set(
[loc.location_type.code for loc in couch_user.get_sql_locations(
domain
)]
)
context['have_access_to_features'] = icds_pre_release_features(couch_user)
context['have_access_to_all_locations'] = couch_user.has_permission(
domain, 'access_all_locations'
)
if context['have_access_to_all_locations']:
context['user_location_id'] = None
if couch_user.is_web_user():
context['is_web_user'] = True
return context
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slbo/policies/__init__.py | LinZichuan/AdMRL | 27 | 119978 | import abc
from typing import Union
import lunzi.nn as nn
class BasePolicy(abc.ABC):
@abc.abstractmethod
def get_actions(self, states):
pass
BaseNNPolicy = Union[BasePolicy, nn.Module] # should be Intersection, see PEP544
| [
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] |
codestepbystep/convert_to_alt_caps.py | aleeper/python_sandbox | 0 | 79272 |
def convert_to_alt_caps(message):
lower = message.lower()
upper = message.upper()
data = []
space_offset = 0
for i in range(len(lower)):
if not lower[i].isalpha():
space_offset += 1
if (i + space_offset) % 2 == 0:
data.append(lower[i])
else:
data.append(upper[i])
return ''.join(data)
def main():
input_str = "Pikachu"
print(input_str)
print(convert_to_alt_caps(input_str))
input_str = "section is AWESOME"
print(input_str)
print(convert_to_alt_caps(input_str))
if __name__ == '__main__':
main() | [
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toy_gradlogp/utils.py | Ending2015a/toy_gradlogp | 13 | 141644 | # --- built in ---
import os
# --- 3rd party ---
import numpy as np
import torch
from torch import nn
# --- my module ---
__all__ = [
'langevin_dynamics',
'anneal_langevin_dynamics',
'sample_score_field',
'sample_energy_field'
]
# --- dynamics ---
def langevin_dynamics(
score_fn,
x,
eps=0.1,
n_steps=1000
):
"""Langevin dynamics
Args:
score_fn (callable): a score function with the following sign
func(x: torch.Tensor) -> torch.Tensor
x (torch.Tensor): input samples
eps (float, optional): noise scale. Defaults to 0.1.
n_steps (int, optional): number of steps. Defaults to 1000.
"""
for i in range(n_steps):
x = x + eps/2. * score_fn(x).detach()
x = x + torch.randn_like(x) * np.sqrt(eps)
return x
def anneal_langevin_dynamics(
score_fn,
x,
sigmas=None,
eps=0.1,
n_steps_each=100
):
"""Annealed Langevin dynamics
Args:
score_fn (callable): a score function with the following sign
func(x: torch.Tensor, sigma: float) -> torch.Tensor
x (torch.Tensor): input samples
sigmas (torch.Tensor, optional): noise schedule. Defualts to None.
eps (float, optional): noise scale. Defaults to 0.1.
n_steps (int, optional): number of steps. Defaults to 1000.
"""
# default sigma schedule
if sigmas is None:
sigmas = np.exp(np.linspace(np.log(20), 0., 10))
for sigma in sigmas:
for i in range(n_steps_each):
cur_eps = eps * (sigma / sigmas[-1]) ** 2
x = x + cur_eps/2. * score_fn(x, sigma).detach()
x = x + torch.randn_like(x) * np.sqrt(eps)
return x
# --- sampling utils ---
def sample_score_field(
score_fn,
range_lim=4,
grid_size=50,
device='cpu'
):
"""Sampling score field from an energy model
Args:
score_fn (callable): a score function with the following sign
func(x: torch.Tensor) -> torch.Tensor
range_lim (int, optional): Range of x, y coordimates. Defaults to 4.
grid_size (int, optional): Grid size. Defaults to 50.
device (str, optional): torch device. Defaults to 'cpu'.
"""
mesh = []
x = np.linspace(-range_lim, range_lim, grid_size)
y = np.linspace(-range_lim, range_lim, grid_size)
for i in x:
for j in y:
mesh.append(np.asarray([i, j]))
mesh = np.stack(mesh, axis=0)
x = torch.from_numpy(mesh).float()
x = x.to(device=device)
scores = score_fn(x.detach()).detach()
scores = scores.cpu().numpy()
return mesh, scores
def sample_energy_field(
energy_fn,
range_lim=4,
grid_size=1000,
device='cpu'
):
"""Sampling energy field from an energy model
Args:
energy_fn (callable): an energy function with the following sign
func(x: torch.Tensor) -> torch.Tensor
range_lim (int, optional): range of x, y coordinates. Defaults to 4.
grid_size (int, optional): grid size. Defaults to 1000.
device (str, optional): torch device. Defaults to 'cpu'.
"""
energy = []
x = np.linspace(-range_lim, range_lim, grid_size)
y = np.linspace(-range_lim, range_lim, grid_size)
for i in y:
mesh = []
for j in x:
mesh.append(np.asarray([j, i]))
mesh = np.stack(mesh, axis=0)
inputs = torch.from_numpy(mesh).float()
inputs = inputs.to(device=device)
e = energy_fn(inputs.detach()).detach()
e = e.view(grid_size).cpu().numpy()
energy.append(e)
energy = np.stack(energy, axis=0) # (grid_size, grid_size)
return energy | [
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standards/tests/test_jurisdiction.py | GROCCAD/groccad | 1 | 170802 | <reponame>GROCCAD/groccad
import django
import pytest
from standards.models import Jurisdiction, UserProfile
@pytest.mark.django_db
def test_juri():
juri = Jurisdiction(
name="Ghana",
display_name="Ghana NaCCA",
country='GH'
)
juri.save()
assert juri.id
# print(juri.__dict__)
@pytest.mark.django_db
def test_no_duplicate_names():
juri = Jurisdiction(name="Ghana", display_name="Ghana NaCCA", country='GH')
juri.save()
assert juri.id
juri2 = Jurisdiction(name="Ghana", display_name="Ghana NaCCA", country='GH')
with pytest.raises(django.db.utils.IntegrityError):
juri2.save()
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rlpyt/ul/algos/ul_for_rl/stc.py | Slienteagle-wyb/rlpyt | 0 | 126359 | import torch
from collections import namedtuple
import copy
from rlpyt.utils.tensor import valid_mean
from rlpyt.ul.algos.ul_for_rl.base import BaseUlAlgorithm
from rlpyt.utils.quick_args import save__init__args
from rlpyt.utils.logging import logger
from rlpyt.ul.replays.offline_ul_replay import OfflineUlReplayBuffer
from rlpyt.utils.buffer import buffer_to
from rlpyt.models.utils import update_state_dict
from rlpyt.ul.models.ul.encoders import DmlabEncoderModel
from rlpyt.ul.algos.utils.data_augs import random_shift
from rlpyt.ul.replays.offline_dataset import OfflineDatasets
IGNORE_INDEX = -100 # Mask TC samples across episode boundary.
OptInfo = namedtuple("OptInfo", ["stcLoss", "sprLoss", "contrastLoss",
"cpcAccuracy1", "cpcAccuracy2", "cpcAccuracyTm1", "cpcAccuracyTm2",
'contrast_accuracy', "gradNorm", 'current_lr'])
ValInfo = namedtuple("ValInfo", ["atcLoss", "accuracy", "convActivation"])
class DroneSTC(BaseUlAlgorithm):
"""
Spatial and Temporal Contrastive Pretraining with ConvGru features
"""
opt_info_fields = tuple(f for f in OptInfo._fields) # copy
def __init__(
self,
batch_T=32, # the default length of extracted batch
batch_B=16, # batch B is the sampled batch size for extraction
warmup_T=0,
clip_grad_norm=10.,
target_update_tau=0.01, # 1 for hard update
target_update_interval=1,
latent_size=256,
hidden_sizes=512,
rnn_size=256,
random_shift_prob=1.,
random_shift_pad=4,
spr_loss_coefficient=1.0,
contrast_loss_coefficient=1.0,
validation_split=0.0,
n_validation_batches=0,
ReplayCls=OfflineUlReplayBuffer,
EncoderCls=DmlabEncoderModel,
initial_state_dict=None,
optim_kwargs=None,
sched_kwargs=None,
encoder_kwargs=None,
replay_kwargs=None,
):
encoder_kwargs = dict() if encoder_kwargs is None else encoder_kwargs
save__init__args(locals())
self.c_e_loss = torch.nn.CrossEntropyLoss(ignore_index=IGNORE_INDEX)
self.batch_size = batch_B * batch_T # batch_size for calculating update params
self._replay_T = warmup_T + batch_T # self.replay_T == self._replay_T is the len of every sampled trajectory
def initialize(self, epochs, cuda_idx=None):
self.device = torch.device("cpu") if cuda_idx is None else torch.device("cuda", index=cuda_idx)
examples = self.load_replay()
self.itrs_per_epoch = self.replay_buffer.size // self.batch_size
self.n_updates = epochs * self.itrs_per_epoch
self.image_shape = image_shape = examples.observation.shape # [c, h, w]
self.encoder = self.EncoderCls(
image_shape=image_shape,
latent_size=self.latent_size,
hidden_sizes=self.hidden_sizes,
**self.encoder_kwargs
)
self.target_encoder = copy.deepcopy(self.encoder) # the target encoder is not tied with online encoder
trans_dim = self.replay_buffer.translation_dim
rotate_dim = self.replay_buffer.rotation_dim
ar_input_size = rotate_dim + trans_dim # no reward
self.aggregator_rnn = torch.nn.LSTM(
input_size=int(self.latent_size+ar_input_size),
hidden_size=self.rnn_size,
)
# linear transforms from one step prediction to T-1 forward steps
transforms = [None]
for _ in range(self.batch_T - 1):
transforms.append(
torch.nn.Linear(in_features=self.latent_size, out_features=self.latent_size, bias=False)
)
self.transforms = torch.nn.ModuleList(transforms)
self.byol_linear_trans = torch.nn.Linear(self.latent_size, self.latent_size, bias=False)
self.encoder.to(self.device)
self.target_encoder.to(self.device)
self.aggregator_rnn.to(self.device)
self.transforms.to(self.device)
self.byol_linear_trans.to(self.device)
self.optim_initialize(epochs)
# load the pretrained models
if self.initial_state_dict is not None:
self.load_state_dict(self.initial_state_dict)
def optimize(self, itr):
opt_info = OptInfo(*([] for _ in range(len(OptInfo._fields))))
samples = self.replay_buffer.sample_batch(self.batch_B) # batch b is the batch_size of every single trajectory
current_epoch = itr // self.itrs_per_epoch
if self.lr_scheduler is not None and itr % self.itrs_per_epoch == 0:
self.lr_scheduler.step(current_epoch)
current_lr = self.lr_scheduler.get_epoch_values(current_epoch)[0]
self.optimizer.zero_grad()
# calculate the loss func
loss, spr_loss, contrast_loss, pred_accuracies, contrast_accuracy = self.stc_loss(samples)
optimize_loss = spr_loss * self.spr_loss_coefficient + contrast_loss * self.contrast_loss_coefficient
optimize_loss.backward()
if self.clip_grad_norm is None:
grad_norm = 0.
else:
grad_norm = torch.nn.utils.clip_grad_norm_(
self.parameters(), self.clip_grad_norm)
self.optimizer.step()
# log the optimize info/result
opt_info.stcLoss.append(loss.item())
opt_info.sprLoss.append(spr_loss.item())
opt_info.contrastLoss.append(contrast_loss.item())
opt_info.cpcAccuracy1.append(pred_accuracies[0].item())
opt_info.cpcAccuracy2.append(pred_accuracies[1].item())
opt_info.cpcAccuracyTm1.append(pred_accuracies[2].item())
opt_info.cpcAccuracyTm2.append(pred_accuracies[3].item())
opt_info.contrast_accuracy.append(contrast_accuracy.item())
opt_info.current_lr.append(current_lr)
opt_info.gradNorm.append(grad_norm.item())
# opt_info.convActivation.append(
# conv_output[0].detach().cpu().view(-1).numpy()) # Keep 1 full one.
# the update interval for the momentun encoder
if itr % self.target_update_interval == 0:
update_state_dict(self.target_encoder,
self.encoder.state_dict(),
self.target_update_tau)
return opt_info
def stc_loss(self, samples):
# the dim of T for samples.observation is 1+delta_T
anchor = samples.observations
length, b, f, c, h, w = anchor.shape
anchor = anchor.view(length, b * f, c, h, w) # Treat all T,B as separate.(reshape the sample)
positive = copy.deepcopy(anchor)
translation = samples.translations
rotation = samples.rotations
action = torch.cat((translation, rotation), dim=-1)
if self.random_shift_prob > 0.:
anchor = random_shift(
imgs=anchor,
pad=self.random_shift_pad,
prob=self.random_shift_prob,
)
positive = random_shift(
imgs=positive,
pad=self.random_shift_pad,
prob=self.random_shift_prob,
)
# sned a tuple of tensor (anchor, postive) to device and return a tuple
anchor, positive, action = buffer_to((anchor, positive, action), device=self.device)
with torch.no_grad():
z_positive, z_conv_positive = self.target_encoder(positive)
z_anchor, z_conv_anchor = self.encoder(anchor)
spr_loss, pred_accuracies = self.spr_loss(z_anchor, z_positive, action)
contrast_loss, contrast_accuracy = self.contrast_loss(z_anchor, z_positive)
loss = spr_loss + contrast_loss
return loss, spr_loss, contrast_loss, pred_accuracies, contrast_accuracy
def spr_loss(self, z_anchor, z_positive, action):
rnn_input = torch.cat((z_anchor, action), dim=-1)
c_anchor, _ = self.aggregator_rnn(rnn_input)
# Extract only the ones to train (all were needed to compute).
c_anchor = c_anchor[self.warmup_T:]
z_positive = z_positive.detach()[self.warmup_T:]
T, B, Z = z_positive.shape
target_trans = z_positive.view(-1, Z).transpose(1, 0)
base_labels = torch.arange(T * B, dtype=torch.long, device=self.device).view(T, B)
prediction_list = list()
label_list = list()
# from 1 forward step to T-1 forward step
for delta_t in range(1, T):
prediction_list.append(self.transforms[delta_t](c_anchor[:-delta_t].view(-1, Z)))
label_list.append(base_labels[delta_t:].view(-1))
forward_steps = [0] + [len(label) for label in label_list]
cumulative_forward_steps = torch.cumsum(torch.tensor(forward_steps), dim=0)
# total predictions pairs: P=T*(T-1)/2*B
prdictions = torch.cat(prediction_list) # [P, z_dim]
labels = torch.cat(label_list) # [P]
logits = torch.matmul(prdictions, target_trans) # [P, z_dim] * [z_dim, T*B]
logits = logits - torch.max(logits, dim=1, keepdim=True)[0]
spr_loss = self.c_e_loss(logits, labels)
################################################
# calculate the prediction metrix
logits_d = logits.detach()
# begin, end, step (downsample==sample from one step forward predictions every 4 steps):
b, e, s = cumulative_forward_steps[0], cumulative_forward_steps[1], 4 # delta_t = 1
logits1, labels1 = logits_d[b:e:s], labels[b:e:s]
correct1 = torch.argmax(logits1, dim=1) == labels1
accuracy1 = valid_mean(correct1.float(), valid=labels1 >= 0) # IGNORE=-100
b, e, s = cumulative_forward_steps[1], cumulative_forward_steps[2], 4 # delta_t = 2
logits2, labels2 = logits_d[b:e:s], labels[b:e:s]
correct2 = torch.argmax(logits2, dim=1) == labels2
accuracy2 = valid_mean(correct2.float(), valid=labels2 >= 0)
b, e, s = cumulative_forward_steps[-2], cumulative_forward_steps[-1], 1 # delta_t = T - 1
logitsT1, labelsT1 = logits_d[b:e:s], labels[b:e:s]
correctT1 = torch.argmax(logitsT1, dim=1) == labelsT1
accuracyT1 = valid_mean(correctT1.float(), valid=labelsT1 >= 0)
b, e, s = cumulative_forward_steps[-3], cumulative_forward_steps[-2], 1 # delta_t = T - 2
logitsT2, labelsT2 = logits_d[b:e:s], labels[b:e:s]
correctT2 = torch.argmax(logitsT2, dim=1) == labelsT2
accuracyT2 = valid_mean(correctT2.float(), valid=labelsT2 >= 0)
accuracies = (accuracy1, accuracy2, accuracyT1, accuracyT2)
return spr_loss, accuracies
def contrast_loss(self, z_anchor, z_positive):
T, B, latent_dim = z_anchor.shape
z_positive_trans = z_positive.detach().view(-1, latent_dim).transpose(1, 0) # [latent_dim, T*B]
labels = torch.arange(T * B, dtype=torch.long, device=self.device)
pred = self.byol_linear_trans(z_anchor).view(-1, latent_dim)
logits = torch.matmul(pred, z_positive_trans) # [T*B, T*B]
logits = logits - torch.max(logits, dim=1, keepdim=True)[0]
contrast_loss = self.c_e_loss(logits, labels)
correct = torch.argmax(logits.detach(), dim=1) == labels
contrast_accuracy = torch.mean(correct.float())
return contrast_loss, contrast_accuracy
def validation(self, itr):
pass
def state_dict(self):
return dict(
encoder=self.encoder.state_dict(),
target_encoder=self.target_encoder.state_dict(),
aggregator_rnn=self.aggregator_rnn.state_dict(),
optimizer=self.optimizer.state_dict(),
)
def load_state_dict(self, state_dict):
self.encoder.load_state_dict(state_dict["encoder"])
self.target_encoder.load_state_dict(state_dict["target_encoder"])
self.aggregator_rnn.load_state_dict(state_dict['aggregator_rnn'])
self.optimizer.load_state_dict(state_dict["optimizer"])
def parameters(self):
yield from self.encoder.parameters()
yield from self.aggregator_rnn.parameters()
yield from self.transforms.parameters()
yield from self.byol_linear_trans.parameters()
def named_parameters(self):
"""To allow filtering by name in weight decay."""
yield from self.encoder.named_parameters()
yield from self.aggregator_rnn.named_parameters()
yield from self.transforms.named_parameters()
yield from self.byol_linear_trans.named_parameters()
def eval(self):
self.encoder.eval() # in case of batch norm
self.aggregator_rnn.eval()
self.transforms.eval()
self.byol_linear_trans.eval()
def train(self):
self.encoder.train()
self.aggregator_rnn.train()
self.transforms.train()
self.byol_linear_trans.train()
def load_replay(self, pixel_control_buffer=None):
logger.log('Loading replay buffer ...')
self.replay_buffer = self.ReplayCls(OfflineDatasets, **self.replay_kwargs)
logger.log("Replay buffer loaded")
example = self.replay_buffer.get_example()
return example
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] |
train-xception.py | jGsch/kaggle-dfdc | 124 | 38679 | <filename>train-xception.py<gh_stars>100-1000
import os
import csv
import shutil
import random
from PIL import Image
import numpy as np
import torch
from torch import nn, optim
from torch.utils.data import Dataset, DataLoader
import xception_conf as config
from model_def import xception
from augmentation_utils import train_transform, val_transform
def save_checkpoint(path, state_dict, epoch=0, arch="", acc1=0):
new_state_dict = {}
for k, v in state_dict.items():
if k.startswith("module."):
k = k[7:]
if torch.is_tensor(v):
v = v.cpu()
new_state_dict[k] = v
torch.save({
"epoch": epoch,
"arch": arch,
"acc1": acc1,
"state_dict": new_state_dict,
}, path)
class DFDCDataset(Dataset):
def __init__(self, data_csv, required_set, data_root="",
ratio=(0.25, 0.05), stable=False, transform=None):
video_info = []
data_list = []
with open(data_csv) as fin:
reader = csv.DictReader(fin)
for row in reader:
if row["set_name"] == required_set:
label = int(row["is_fake"])
n_frame = int(row["n_frame"])
select_frame = round(n_frame * ratio[label])
for sample_idx in range(select_frame):
data_list.append((len(video_info), sample_idx))
video_info.append({
"name": row["name"],
"label": label,
"n_frame": n_frame,
"select_frame": select_frame,
})
self.stable = stable
self.data_root = data_root
self.video_info = video_info
self.data_list = data_list
self.transform = transform
def __getitem__(self, index):
video_idx, sample_idx = self.data_list[index]
info = self.video_info[video_idx]
if self.stable:
frame_idx = info["n_frame"] * sample_idx // info["select_frame"]
else:
frame_idx = random.randint(0, info["n_frame"] - 1)
image_path = os.path.join(self.data_root, info["name"],
"%03d.png" % frame_idx)
try:
img = Image.open(image_path).convert("RGB")
except OSError:
img = np.random.randint(0, 255, (320, 320, 3), dtype=np.uint8)
if self.transform is not None:
# img = self.transform(img)
result = self.transform(image=np.array(img))
img = result["image"]
return img, info["label"]
def __len__(self):
return len(self.data_list)
def main():
torch.backends.cudnn.benchmark = True
train_dataset = DFDCDataset(config.data_list, "train", config.data_root,
transform=train_transform)
val_dataset = DFDCDataset(config.data_list, "val", config.data_root,
transform=val_transform, stable=True)
kwargs = dict(batch_size=config.batch_size, num_workers=config.num_workers,
shuffle=True, pin_memory=True)
train_loader = DataLoader(train_dataset, **kwargs)
val_loader = DataLoader(val_dataset, **kwargs)
# Model initialization
model = xception(num_classes=2, pretrained=None)
if hasattr(config, "resume") and os.path.isfile(config.resume):
ckpt = torch.load(config.resume, map_location="cpu")
start_epoch = ckpt.get("epoch", 0)
best_acc = ckpt.get("acc1", 0.0)
model.load_state_dict(ckpt["state_dict"])
else:
start_epoch = 0
best_acc = 0.0
model = model.cuda()
model = nn.DataParallel(model)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),
0.01, momentum=0.9, weight_decay=1e-4)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=2, gamma=0.2)
os.makedirs(config.save_dir, exist_ok=True)
for epoch in range(config.n_epoches):
if epoch < start_epoch:
scheduler.step()
continue
print("Epoch {}".format(epoch + 1))
model.train()
loss_record = []
acc_record = []
for count, (inputs, labels) in enumerate(train_loader):
inputs = inputs.cuda(non_blocking=True)
labels = labels.cuda(non_blocking=True)
outputs = model(inputs)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
iter_loss = loss.item()
loss_record.append(iter_loss)
preds = torch.argmax(outputs.data, 1)
iter_acc = torch.sum(preds == labels).item() / len(preds)
acc_record.append(iter_acc)
if count and count % 100 == 0:
print("T-Iter %d: loss=%.4f, acc=%.4f"
% (count, iter_loss, iter_acc))
epoch_loss = np.mean(loss_record)
epoch_acc = np.mean(acc_record)
print("Training: loss=%.4f, acc=%.4f" % (epoch_loss, epoch_acc))
model.eval()
loss_record = []
acc_record = []
with torch.no_grad():
for count, (inputs, labels) in enumerate(val_loader):
inputs = inputs.cuda(non_blocking=True)
labels = labels.cuda(non_blocking=True)
outputs = model(inputs)
preds = torch.argmax(outputs, 1)
loss = criterion(outputs, labels)
iter_loss = loss.item()
loss_record.append(iter_loss)
preds = torch.argmax(outputs.data, 1)
iter_acc = torch.sum(preds == labels).item() / len(preds)
acc_record.append(iter_acc)
if count and count % 100 == 0:
print("V-Iter %d: loss=%.4f, acc=%.4f"
% (count, iter_loss, iter_acc))
epoch_loss = np.mean(loss_record)
epoch_acc = np.mean(acc_record)
print("Validation: loss=%.4f, acc=%.4f" % (epoch_loss, epoch_acc))
scheduler.step()
ckpt_path = os.path.join(config.save_dir, "ckpt-%d.pth" % epoch)
save_checkpoint(
ckpt_path,
model.state_dict(),
epoch=epoch + 1,
acc1=epoch_acc)
if epoch_acc > best_acc:
print("Best accuracy!")
shutil.copy(ckpt_path,
os.path.join(config.save_dir, "best.pth"))
best_acc = epoch_acc
print()
if __name__ == "__main__":
main()
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] |
tests/test_jq_fetcher.py | zillionare/z-quotes | 0 | 141264 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import logging
import os
import unittest
from unittest import mock
import numpy as np
import datetime
import arrow
import jqadaptor
from jqadaptor.fetcher import FetcherQuotaError, AccountExpiredError
logger = logging.getLogger(__file__)
logging.basicConfig(level=logging.INFO)
class TestJQ(unittest.IsolatedAsyncioTestCase):
fetcher: "QuotesFetcher" # type: ignore # noqa
async def asyncSetUp(self) -> None: # pylint: disable=invalid-overridden-method
try:
account = os.environ["JQ_ACCOUNT"]
password = os.environ["JQ_PASSWORD"]
blablah = "blahblah"
self.fetcher = await jqadaptor.create_instance(
account=account, password=password, blablah=<PASSWORD>
)
except Exception as e:
logger.exception(e)
async def test_failed_login(self):
fetcher = await jqadaptor.create_instance(account="wrong", password="<PASSWORD>")
log = logging.getLogger("jqadaptor.fetcher")
with mock.patch.object(log, "warning") as m:
self.assertIsNone(await fetcher.get_security_list())
self.assertTupleEqual(("not connected",), m.call_args.args)
async def test_get_security_list(self):
sec_list = await self.fetcher.get_security_list()
print(sec_list[0])
self.assertTrue(len(sec_list) > 0)
async def test_get_bars(self):
sec = "000001.XSHE"
end = arrow.get("2020-04-04").date()
frame_type = "1d"
bars = await self.fetcher.get_bars(sec, end, 10, frame_type)
self.assertEqual(bars[0]["frame"], arrow.get("2020-03-23").date())
self.assertEqual(bars[-1]["frame"], arrow.get("2020-04-03").date())
end = arrow.get("2020-04-03").date()
frame_type = "1d"
bars = await self.fetcher.get_bars(sec, end, 3, frame_type)
self.assertEqual(bars[0]["frame"], arrow.get("2020-4-1").date())
self.assertEqual(bars[-1]["frame"], end)
end = arrow.get("2020-04-03 10:30:00").naive
frame_type = "30m"
bars = await self.fetcher.get_bars(sec, end, 3, frame_type)
self.assertEqual(
bars[0]["frame"],
arrow.get("2020-04-02 15:00:00").naive,
)
self.assertEqual(bars[-1]["frame"], end)
# 测试include_unclosed为False的情况
import datetime
end = datetime.date(2021, 2, 8)
bars = await self.fetcher.get_bars(sec, end, 1, "1w", False)
self.assertEqual(datetime.date(2021, 2, 5), bars["frame"][0])
# 测试include_unclosed为True的情况
sec = "000001.XSHE"
end = datetime.date(2021, 2, 8)
frame_type = "1w"
bars = await self.fetcher.get_bars(sec, end, 1, frame_type, True)
self.assertEqual(datetime.date(2021, 2, 8), bars["frame"][0])
async def test_get_bars_with_exceptions(self):
sec = "000001.XSHE"
end = arrow.get("2020-04-04").date()
frame_type = "1d"
with mock.patch("jqdatasdk.get_bars", side_effect=Exception("最大查询限制")):
with self.assertRaises(FetcherQuotaError):
await self.fetcher.get_bars(sec, end, 3, frame_type)
with mock.patch("jqdatasdk.get_bars", side_effect=Exception("账号过期")):
with self.assertRaises(AccountExpiredError):
await self.fetcher.get_bars(sec, end, 3, frame_type)
async def test_get_bars_not_in_trade(self):
sec = "600891.XSHG"
end = arrow.get("2020-03-05").date()
bars = await self.fetcher.get_bars(sec, end, 7, "1d")
print(bars)
self.assertEqual(arrow.get("2020-2-21").date(), bars["frame"][0])
self.assertAlmostEqual(1.25, bars[0]["open"], places=2)
self.assertEqual(arrow.get("2020-02-26").date(), bars["frame"][3])
self.assertAlmostEqual(1.18, bars["open"][3], places=2)
self.assertEqual(arrow.get("2020-03-02").date(), bars["frame"][-1])
self.assertAlmostEqual(1.13, bars["open"][-1], places=2)
# 600721, ST百花， 2020-4-29停牌一天
sec = "600721.XSHG"
end = arrow.get("2020-04-30 10:30").naive
frame_type = "60m"
bars = await self.fetcher.get_bars(sec, end, 6, frame_type)
print(bars)
self.assertEqual(6, len(bars))
self.assertEqual(
arrow.get("2020-04-27 15:00").naive,
bars["frame"][0],
)
# 检查是否停牌日被跳过
self.assertEqual(arrow.get("2020-4-28 15:00").naive, bars["frame"][-2])
self.assertEqual(arrow.get("2020-04-30 10:30").naive, bars["frame"][-1])
self.assertAlmostEqual(5.47, bars["open"][0], places=2)
self.assertAlmostEqual(5.26, bars["open"][-1], places=2)
# 测试分钟级别未结束的frame能否获取
end = arrow.get("2020-04-30 10:32").naive
bars = await self.fetcher.get_bars(sec, end, 7, "60m")
print(bars)
self.assertEqual(7, len(bars))
self.assertEqual(arrow.get("2020-04-27 15:00").naive, bars["frame"][0])
self.assertEqual(arrow.get("2020-4-30 10:32").naive, bars["frame"][-1])
self.assertEqual(arrow.get("2020-04-30 10:30").naive, bars["frame"][-2])
self.assertAlmostEqual(5.47, bars["open"][0], places=2)
self.assertAlmostEqual(5.26, bars["open"][-2], places=2)
self.assertAlmostEqual(5.33, bars["open"][-1], places=2)
async def test_get_valuation(self):
sec = "000001.XSHE"
day = arrow.get("2020-10-20").date()
valuation = await self.fetcher.get_valuation(sec, day)
self.assertSetEqual(set(valuation["code"].tolist()), set([sec]))
sec = ["600000.XSHG", "000001.XSHE"]
valuation = await self.fetcher.get_valuation(sec, day)
self.assertSetEqual(set(valuation["code"].tolist()), set(sec))
day = arrow.get("2020-11-2").date()
vals = await self.fetcher.get_valuation(sec, day, 3)
self.assertEqual(vals["frame"][0], arrow.get("2020-10-29").date())
self.assertEqual(vals["frame"][-1], day)
vals = await self.fetcher.get_valuation(None, day, 1)
self.assertTrue(len(vals) > 1000)
async def test_get_bars_batch(self):
secs = ["000001.XSHE", "600000.XSHG"]
end_at = arrow.get("2020-10-23").date()
n_bars = 5
frame_type = "1d"
bars = await self.fetcher.get_bars_batch(secs, end_at, n_bars, frame_type)
self.assertEqual(
bars["000001.XSHE"]["frame"][0], arrow.get("2020-10-19").date()
)
self.assertEqual(
bars["600000.XSHG"]["frame"][0], arrow.get("2020-10-19").date()
)
async def test_get_trade_price_limits(self):
secs = ["000001.XSHE"]
end_at = arrow.get("2020-10-23").date()
bars = await self.fetcher.get_trade_price_limits(
secs,
dt=end_at,
)
self.assertIsNotNone(bars)
self.assertEqual(datetime.date(2020, 10, 23), bars["frame"][0])
self.assertAlmostEqual(19.32, bars["high_limit"][0], places=2)
self.assertAlmostEqual(15.8, bars["low_limit"][0], places=2)
try:
await self.fetcher.get_trade_price_limits(123, dt=end_at)
except Exception as e:
self.assertIsInstance(e, TypeError)
try:
await self.fetcher.get_trade_price_limits(secs, dt=123)
except Exception as e:
self.assertIsInstance(e, TypeError)
try:
await self.fetcher.get_trade_price_limits(secs, dt=end_at)
except Exception as e:
self.assertIsInstance(e, TypeError)
try:
await self.fetcher.get_trade_price_limits(secs, dt=end_at)
except Exception as e:
self.assertIsInstance(e, ValueError)
try:
await self.fetcher.get_trade_price_limits(secs, dt=end_at)
except Exception as e:
self.assertIsInstance(e, TypeError)
async def test_get_fund_list(self):
fund_list = await self.fetcher.get_fund_list()
self.assertTrue(len(fund_list) > 0)
code = ["233333"]
vals = await self.fetcher.get_fund_list(codes=code)
self.assertFalse(len(vals))
async def test_get_fund_portfolio_stock(self):
await self.fetcher.get_fund_portfolio_stock(None, pub_date="2021-12-21")
codes = ["000001"]
portfolio_stocks = await self.fetcher.get_fund_portfolio_stock(codes)
self.assertTrue(len(portfolio_stocks))
async def test_get_fund_share_daily(self):
code = ["512690"]
day = arrow.get("2021-10-26").date()
fund_share_daily = await self.fetcher.get_fund_share_daily(code, day=day)
self.assertTrue(len(fund_share_daily))
code = None
day = arrow.get("2021-10-26").date()
fund_share_daily = await self.fetcher.get_fund_share_daily(code, day=day)
self.assertTrue(len(fund_share_daily))
async def test_get_fund_net_value(self):
codes = ["000029"]
day = arrow.get("2021-10-26").date()
fund_net_value = await self.fetcher.get_fund_net_value(codes, day=day)
self.assertTrue(len(fund_net_value))
async def test_get_quota(self):
quota = await self.fetcher.get_quota()
self.assertIsInstance(quota, dict)
self.assertIn("total", quota)
self.assertIn("spare", quota)
def test_result_size_limit(self):
self.assertEqual(self.fetcher.result_size_limit("bars"), 3000)
async def test_get_price(self):
price_bars = await self.fetcher.get_price(
["605366.XSHG"],
end_date=datetime.datetime(2022, 3, 1, 13, 16),
n_bars=2,
frame_type="1m",
)
bars = await self.fetcher.get_bars_batch(
["605366.XSHG"],
end_at=datetime.datetime(2022, 3, 1, 13, 16),
n_bars=2,
frame_type="1m",
)
for i in bars:
if not len(bars[i]):
print(f"code:{i}为空")
print(bars)
for code in price_bars:
bar1 = price_bars[code]
bar2 = bars[code]
self.assertEqual(len(bar1), len(bar2))
if len(bar1) != len(bar2):
print("长度不相等，错误")
break
for item1, item2 in zip(bar1, bar2):
# 判断字段是否相等
for field in [
"frame",
"open",
"high",
"low",
"close",
"volume",
"amount",
]:
self.assertEqual(item1[field], item2[field])
if field == "frame":
if item1[field].strftime("%Y-%m-%d") != item2[field].strftime(
"%Y-%m-%d"
):
print(f"不相等 item1:{item1}, item2:{item2}, field:{field}")
break
elif item1[field] != item2[field]:
print(f"不相等 item1:{item1}, item2:{item2}, field:{field}")
break
else:
continue
break
else:
continue
break
# fixme: 这里应该是判断结果是否符合预期，不能用打印来替代。如果actual != expected，需要让测试失败
print(bars)
print(price_bars)
| [
1,
18787,
4855,
29914,
2109,
29914,
6272,
3017,
13,
29937,
448,
29930,
29899,
14137,
29901,
23616,
29899,
29947,
448,
29930,
29899,
13,
5215,
12183,
13,
5215,
2897,
13,
5215,
443,
27958,
13,
3166,
443,
27958,
1053,
11187,
13,
5215,
12655,
408,
7442,
13,
5215,
12865,
13,
13,
5215,
16578,
13,
13,
5215,
432,
29939,
1114,
415,
272,
13,
3166,
432,
29939,
1114,
415,
272,
29889,
9155,
261,
1053,
383,
3486,
261,
2182,
4616,
2392,
29892,
16535,
9544,
2859,
2392,
13,
13,
21707,
353,
12183,
29889,
657,
16363,
22168,
1445,
1649,
29897,
13,
21027,
29889,
16121,
3991,
29898,
5563,
29922,
21027,
29889,
11690,
29897,
13,
13,
13,
1990,
4321,
29967,
29984,
29898,
348,
27958,
29889,
29902,
2929,
630,
2887,
948,
3934,
3057,
8259,
1125,
13,
1678,
6699,
261,
29901,
376,
2182,
4769,
20927,
261,
29908,
29871,
396,
1134,
29901,
11455,
396,
694,
25621,
13,
13,
1678,
7465,
822,
7465,
2697,
3373,
29898,
1311,
29897,
1599,
6213,
29901,
29871,
396,
282,
2904,
524,
29901,
11262,
29922,
20965,
29899,
957,
2429,
1145,
29899,
5696,
13,
4706,
1018,
29901,
13,
9651,
3633,
353,
2897,
29889,
21813,
3366,
29967,
29984,
29918,
2477,
18736,
3108,
13,
9651,
4800,
353,
2897,
29889,
21813,
3366,
29967,
29984,
29918,
25711,
17013,
3108,
13,
9651,
1999,
370,
8083,
353,
376,
29844,
29844,
29908,
13,
13,
9651,
1583,
29889,
9155,
261,
353,
7272,
432,
29939,
1114,
415,
272,
29889,
3258,
29918,
8758,
29898,
13,
18884,
3633,
29922,
10149,
29892,
4800,
29922,
5630,
29892,
1999,
370,
8083,
29922,
29966,
25711,
17013,
29958,
13,
9651,
1723,
13,
13,
4706,
5174,
8960,
408,
321,
29901,
13,
9651,
17927,
29889,
11739,
29898,
29872,
29897,
13,
13,
1678,
7465,
822,
1243,
29918,
26061,
29918,
7507,
29898,
1311,
1125,
13,
4706,
6699,
261,
353,
7272,
432,
29939,
1114,
415,
272,
29889,
3258,
29918,
8758,
29898,
10149,
543,
15866,
549,
613,
4800,
543,
29966,
25711,
17013,
29958,
1159,
13,
13,
4706,
1480,
353,
12183,
29889,
657,
16363,
703,
28695,
1114,
415,
272,
29889,
9155,
261,
1159,
13,
4706,
411,
11187,
29889,
5041,
29889,
3318,
29898,
1188,
29892,
376,
27392,
1159,
408,
286,
29901,
13,
9651,
1583,
29889,
9294,
3624,
8516,
29898,
20675,
6699,
261,
29889,
657,
29918,
8926,
29918,
1761,
3101,
13,
9651,
1583,
29889,
9294,
23215,
552,
9843,
29898,
703,
1333,
6631,
613,
511,
286,
29889,
4804,
29918,
5085,
29889,
5085,
29897,
13,
13,
1678,
7465,
822,
1243,
29918,
657,
29918,
8926,
29918,
1761,
29898,
1311,
1125,
13,
4706,
5226,
29918,
1761,
353,
7272,
1583,
29889,
9155,
261,
29889,
657,
29918,
8926,
29918,
1761,
580,
13,
4706,
1596,
29898,
3471,
29918,
1761,
29961,
29900,
2314,
13,
4706,
1583,
29889,
9294,
5574,
29898,
2435,
29898,
3471,
29918,
1761,
29897,
1405,
29871,
29900,
29897,
13,
13,
1678,
7465,
822,
1243,
29918,
657,
29918,
28408,
29898,
1311,
1125,
13,
4706,
5226,
353,
376,
29900,
29900,
29900,
29900,
29900,
29896,
29889,
29990,
7068,
29923,
29908,
13,
4706,
1095,
353,
16578,
29889,
657,
703,
29906,
29900,
29906,
29900,
29899,
29900,
29946,
29899,
29900,
29946,
2564,
1256,
580,
13,
4706,
3515,
29918,
1853,
353,
376,
29896,
29881,
29908,
13,
4706,
22306,
353,
7272,
1583,
29889,
9155,
261,
29889,
657,
29918,
28408,
29898,
3471,
29892,
1095,
29892,
29871,
29896,
29900,
29892,
3515,
29918,
1853,
29897,
13,
4706,
1583,
29889,
9294,
9843,
29898,
28408,
29961,
29900,
29962,
3366,
2557,
12436,
16578,
29889,
657,
703,
29906,
29900,
29906,
29900,
29899,
29900,
29941,
29899,
29906,
29941,
2564,
1256,
3101,
13,
4706,
1583,
29889,
9294,
9843,
29898,
28408,
14352,
29896,
29962,
3366,
2557,
12436,
16578,
29889,
657,
703,
29906,
29900,
29906,
29900,
29899,
29900,
29946,
29899,
29900,
29941,
2564,
1256,
3101,
13,
13,
4706,
1095,
353,
16578,
29889,
657,
703,
29906,
29900,
29906,
29900,
29899,
29900,
29946,
29899,
29900,
29941,
2564,
1256,
580,
13,
4706,
3515,
29918,
1853,
353,
376,
29896,
29881,
29908,
13,
4706,
22306,
353,
7272,
1583,
29889,
9155,
261,
29889,
657,
29918,
28408,
29898,
3471,
29892,
1095,
29892,
29871,
29941,
29892,
3515,
29918,
1853,
29897,
13,
4706,
1583,
29889,
9294,
9843,
29898,
28408,
29961,
29900,
29962,
3366,
2557,
12436,
16578,
29889,
657,
703,
29906,
29900,
29906,
29900,
29899,
29946,
29899,
29896,
2564,
1256,
3101,
13,
4706,
1583,
29889,
9294,
9843,
29898,
28408,
14352,
29896,
29962,
3366,
2557,
12436,
1095,
29897,
13,
13,
4706,
1095,
353,
16578,
29889,
657,
703,
29906,
29900,
29906,
29900,
29899,
29900,
29946,
29899,
29900,
29941,
29871,
29896,
29900,
29901,
29941,
29900,
29901,
29900,
29900,
2564,
1056,
573,
13,
4706,
3515,
29918,
1853,
353,
376,
29941,
29900,
29885,
29908,
13,
4706,
22306,
353,
7272,
1583,
29889,
9155,
261,
29889,
657,
29918,
28408,
29898,
3471,
29892,
1095,
29892,
29871,
29941,
29892,
3515,
29918,
1853,
29897,
13,
13,
4706,
1583,
29889,
9294,
9843,
29898,
13,
9651,
22306,
29961,
29900,
29962,
3366,
2557,
12436,
13,
9651,
16578,
29889,
657,
703,
29906,
29900,
29906,
29900,
29899,
29900,
29946,
29899,
29900,
29906,
29871,
29896,
29945,
29901,
29900,
29900,
29901,
29900,
29900,
2564,
1056,
573,
29892,
13,
4706,
1723,
13,
4706,
1583,
29889,
9294,
9843,
29898,
28408,
14352,
29896,
29962,
3366,
2557,
12436,
1095,
29897,
13,
13,
4706,
396,
29871,
31851,
31787,
2856,
29918,
348,
15603,
30573,
8824,
30210,
30993,
232,
137,
184,
13,
4706,
1053,
12865,
13,
13,
4706,
1095,
353,
12865,
29889,
1256,
29898,
29906,
29900,
29906,
29896,
29892,
29871,
29906,
29892,
29871,
29947,
29897,
13,
4706,
22306,
353,
7272,
1583,
29889,
9155,
261,
29889,
657,
29918,
28408,
29898,
3471,
29892,
1095,
29892,
29871,
29896,
29892,
376,
29896,
29893,
613,
7700,
29897,
13,
4706,
1583,
29889,
9294,
9843,
29898,
12673,
29889,
1256,
29898,
29906,
29900,
29906,
29896,
29892,
29871,
29906,
29892,
29871,
29945,
511,
22306,
3366,
2557,
3108,
29961,
29900,
2314,
13,
13,
4706,
396,
29871,
31851,
31787,
2856,
29918,
348,
15603,
30573,
5574,
30210,
30993,
232,
137,
184,
13,
4706,
5226,
353,
376,
29900,
29900,
29900,
29900,
29900,
29896,
29889,
29990,
7068,
29923,
29908,
13,
4706,
1095,
353,
12865,
29889,
1256,
29898,
29906,
29900,
29906,
29896,
29892,
29871,
29906,
29892,
29871,
29947,
29897,
13,
4706,
3515,
29918,
1853,
353,
376,
29896,
29893,
29908,
13,
4706,
22306,
353,
7272,
1583,
29889,
9155,
261,
29889,
657,
29918,
28408,
29898,
3471,
29892,
1095,
29892,
29871,
29896,
29892,
3515,
29918,
1853,
29892,
5852,
29897,
13,
4706,
1583,
29889,
9294,
9843,
29898,
12673,
29889,
1256,
29898,
29906,
29900,
29906,
29896,
29892,
29871,
29906,
29892,
29871,
29947,
511,
22306,
3366,
2557,
3108,
29961,
29900,
2314,
13,
13,
1678,
7465,
822,
1243,
29918,
657,
29918,
28408,
29918,
2541,
29918,
11739,
29879,
29898,
1311,
1125,
13,
4706,
5226,
353,
376,
29900,
29900,
29900,
29900,
29900,
29896,
29889,
29990,
7068,
29923,
29908,
13,
4706,
1095,
353,
16578,
29889,
657,
703,
29906,
29900,
29906,
29900,
29899,
29900,
29946,
29899,
29900,
29946,
2564,
1256,
580,
13,
4706,
3515,
29918,
1853,
353,
376,
29896,
29881,
29908,
13,
13,
4706,
411,
11187,
29889,
5041,
703,
28695,
14538,
8181,
29889,
657,
29918,
28408,
613,
2625,
29918,
15987,
29922,
2451,
703,
30878,
30257,
31213,
235,
178,
165,
31175,
31072,
5783,
29901,
13,
9651,
411,
1583,
29889,
9294,
29934,
1759,
267,
29898,
20927,
261,
2182,
4616,
2392,
1125,
13,
18884,
7272,
1583,
29889,
9155,
261,
29889,
657,
29918,
28408,
29898,
3471,
29892,
1095,
29892,
29871,
29941,
29892,
3515,
29918,
1853,
29897,
13,
13,
4706,
411,
11187,
29889,
5041,
703,
28695,
14538,
8181,
29889,
657,
29918,
28408,
613,
2625,
29918,
15987,
29922,
2451,
703,
235,
183,
169,
30850,
31138,
31117,
5783,
29901,
13,
9651,
411,
1583,
29889,
9294,
29934,
1759,
267,
29898,
10601,
9544,
2859,
2392,
1125,
13,
18884,
7272,
1583,
29889,
9155,
261,
29889,
657,
29918,
28408,
29898,
3471,
29892,
1095,
29892,
29871,
29941,
29892,
3515,
29918,
1853,
29897,
13,
13,
1678,
7465,
822,
1243,
29918,
657,
29918,
28408,
29918,
1333,
29918,
262,
29918,
3018,
311,
29898,
1311,
1125,
13,
4706,
5226,
353,
376,
29953,
29900,
29900,
29947,
29929,
29896,
29889,
29990,
7068,
29954,
29908,
13,
4706,
1095,
353,
16578,
29889,
657,
703,
29906,
29900,
29906,
29900,
29899,
29900,
29941,
29899,
29900,
29945,
2564,
1256,
580,
13,
4706,
22306,
353,
7272,
1583,
29889,
9155,
261,
29889,
657,
29918,
28408,
29898,
3471,
29892,
1095,
29892,
29871,
29955,
29892,
376,
29896,
29881,
1159,
13,
4706,
1596,
29898,
28408,
29897,
13,
4706,
1583,
29889,
9294,
9843,
29898,
2936,
29889,
657,
703,
29906,
29900,
29906,
29900,
29899,
29906,
29899,
29906,
29896,
2564,
1256,
3285,
22306,
3366,
2557,
3108,
29961,
29900,
2314,
13,
4706,
1583,
29889,
9294,
2499,
3242,
9843,
29898,
29896,
29889,
29906,
29945,
29892,
22306,
29961,
29900,
29962,
3366,
3150,
12436,
7600,
29922,
29906,
29897,
13,
13,
4706,
1583,
29889,
9294,
9843,
29898,
2936,
29889,
657,
703,
29906,
29900,
29906,
29900,
29899,
29900,
29906,
29899,
29906,
29953,
2564,
1256,
3285,
22306,
3366,
2557,
3108,
29961,
29941,
2314,
13,
4706,
1583,
29889,
9294,
2499,
3242,
9843,
29898,
29896,
29889,
29896,
29947,
29892,
22306,
3366,
3150,
3108,
29961,
29941,
1402,
7600,
29922,
29906,
29897,
13,
13,
4706,
1583,
29889,
9294,
9843,
29898,
2936,
29889,
657,
703,
29906,
29900,
29906,
29900,
29899,
29900,
29941,
29899,
29900,
29906,
2564,
1256,
3285,
22306,
3366,
2557,
3108,
14352,
29896,
2314,
13,
4706,
1583,
29889,
9294,
2499,
3242,
9843,
29898,
29896,
29889,
29896,
29941,
29892,
22306,
3366,
3150,
3108,
14352,
29896,
1402,
7600,
29922,
29906,
29897,
13,
13,
4706,
396,
29871,
29953,
29900,
29900,
29955,
29906,
29896,
29892,
6850,
31047,
30830,
30214,
29871,
29906,
29900,
29906,
29900,
29899,
29946,
29899,
29906,
29929,
232,
132,
159,
234,
140,
143,
30287,
30408,
13,
4706,
5226,
353,
376,
29953,
29900,
29900,
29955,
29906,
29896,
29889,
29990,
7068,
29954,
29908,
13,
4706,
1095,
353,
16578,
29889,
657,
703,
29906,
29900,
29906,
29900,
29899,
29900,
29946,
29899,
29941,
29900,
29871,
29896,
29900,
29901,
29941,
29900,
2564,
1056,
573,
13,
4706,
3515,
29918,
1853,
353,
376,
29953,
29900,
29885,
29908,
13,
13,
4706,
22306,
353,
7272,
1583,
29889,
9155,
261,
29889,
657,
29918,
28408,
29898,
3471,
29892,
1095,
29892,
29871,
29953,
29892,
3515,
29918,
1853,
29897,
13,
4706,
1596,
29898,
28408,
29897,
13,
4706,
1583,
29889,
9294,
9843,
29898,
29953,
29892,
7431,
29898,
28408,
876,
13,
4706,
1583,
29889,
9294,
9843,
29898,
13,
9651,
16578,
29889,
657,
703,
29906,
29900,
29906,
29900,
29899,
29900,
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tests/cases/run/match2.py | 3e45/minpiler | 0 | 120979 | <reponame>3e45/minpiler
from minpiler.std import M, emulator
match None:
case 0:
M.print(0)
case None:
M.print("None")
case 1:
M.print(10)
case 2:
M.print(20)
# > None
emulator.kill()
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scripts/python/merge_read_counts.py | BradhamLab/scPipe | 0 | 1609458 | <reponame>BradhamLab/scPipe
"""
Merge sample count files into a count matrix with genes as rows and samples as
columns.
Author: <NAME>
Date: August 16, 2018
"""
import os
import pandas as pd
import numpy as np
def simple_merge(count_dir):
data_frames = []
for x in os.listdir(count_dir):
sample = os.path.basename(os.path.splitext(x)[0])
sample_df = pd.read_csv(os.path.join(count_dir, x),
index_col=0, sep='\t',
header=0,
names=['Gene', sample],
skiprows=[0])
data_frames.append(sample_df)
return pd.concat(data_frames, axis=1)
def merge_counts(count_dir):
"""
Merge featureCounts output from several sample files into a
single dataframe.
Merge featureCounts quantification across samples. Returns raw feature
counts across datasets, as well as transcripts per million (TPM) counts.
Args:
count_dir (string): path to directory containing HTSeq output. Files
are expected to follow a {sample.name}.txt naming scheme.
Returns:
(dict, pd.DataFrame): dictionary of pandas dataframes containing raw read
counts and TPM normalized values. Keyed by 'count' and 'tpm'.
"""
data_frames = []
for x in os.listdir(count_dir):
sample = os.path.basename(os.path.splitext(x)[0])
sample_df = pd.read_csv(os.path.join(count_dir, x),
index_col=0, sep='\t',
header=0,
names=['Length', sample],
skiprows=[0])
# calculate count to feature length ratio
counts_per_base = sample_df.apply(lambda x: x[1] / x[0], axis=1)
# calculate sum of ratios / rates for normalization
total_cpb = counts_per_base.sum()
if total_cpb != 0:
tpm = counts_per_base.apply(lambda x: x * 1 / total_cpb * 10**6)
tpm.rename(sample, inplace=True)
# total rates is zero -> no transcriptcs, tpm = 0 for all
else:
tpm = sample_df[sample]
data_frames.append({'count': sample_df[sample], 'tpm': tpm})
count_matrix = pd.concat([each['count'] for each in data_frames], axis=1)
tpm_matrix = pd.concat([each['tpm'] for each in data_frames], axis=1)
return {'count':count_matrix, 'tpm': tpm_matrix}
def remove_zero_genes(count_df):
"""
Remove genes with no reads mapping to them across samples.
"""
count_df[count_df == 0] = np.NaN
filtered = count_df.dropna(how='all')
return filtered.fillna(value=0)
if __name__ == "__main__":
snakemake_exists = True
try:
snakemake
except NameError:
snakemake_exists = False
if snakemake_exists:
dataframes = merge_counts(snakemake.params['dir'])
filtered_counts = remove_zero_genes(dataframes['count'])
filtered_tpm = dataframes['tpm'].loc[filtered_counts.index,
filtered_counts.columns]
filtered_counts.to_csv(snakemake.output['count'])
filtered_tpm.to_csv(snakemake.output['tpm'])
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] |
genestack_client/scripts/genestack_user_setup.py | genestack/python-client | 2 | 45187 | <reponame>genestack/python-client
#!python
# -*- coding: utf-8 -*-
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from future import standard_library
standard_library.install_aliases()
from builtins import input
from builtins import *
import os
import re
import sys
from argparse import ArgumentParser
from getpass import getpass
from operator import attrgetter
from genestack_client import GenestackAuthenticationException
from genestack_client.genestack_shell import Command, GenestackShell
from genestack_client.settings import DEFAULT_HOST, User, config
from genestack_client.utils import interactive_select
def input_host():
host = input('host [%s]: ' % DEFAULT_HOST).strip()
return host or DEFAULT_HOST
def validate_alias(alias):
expression = re.compile('[a-zA-Z0-9_@\-]+$')
return bool(alias and expression.match(alias))
def input_alias(existing):
print('Please input alias. (Alias can contain: letters (a-z, A-Z), '
'digits (0-9), at-sign (@), underscore (_), hyphen (-))')
while True:
alias = input('alias: ').strip()
if not alias:
print('Alias cannot be empty')
continue
if not validate_alias(alias):
print('Restricted symbols message')
continue
if alias in existing:
print('Alias must be unique')
continue
return alias
def create_user_from_input(host, alias):
"""
Ask credentials interactively and return user that can login to platform.
:param host: server host
:type host: basestring
:param alias: user alias
:type alias: basestring
:return: user
:rtype: User
"""
by_token = 'by token'
items = [by_token, 'by email and password']
use_token = interactive_select(items, 'Select authentication') == by_token
if use_token:
return create_user_from_token(host, alias=alias)
else:
return create_user_from_input_email_and_password(host, alias=alias)
def create_user_from_input_email_and_password(host, alias=None):
"""
Ask email and password, check that it is possible to login with this credentials
and return user.
:param host: server host
:type host: basestring
:param alias: user alias
:type alias: basestring
:return: user
:rtype: User
"""
print('Specify email and password for host: "%s"' % host, end=' ')
if alias:
print('and alias: "%s"' % alias)
else:
print()
user_login = None
while True:
if user_login:
res = input('Please specify your user login (email) [%s]: ' % user_login).strip()
if res:
user_login = res
else:
user_login = input('Please specify your user login (email): ').strip()
if not user_login:
print('Login cannot be empty')
continue
user_password = getpass('Please specify your password for %s: ' % user_login)
if not user_password:
print('Password cannot be empty')
continue
if not user_login or not user_password:
print()
continue
user = User(user_login, host=host, password=<PASSWORD>, alias=alias)
try:
user.get_connection()
break
except GenestackAuthenticationException:
print('Your username or password was incorrect, please try again')
return user
def create_user_from_token(host, alias=None):
print('Host: %s' % host)
msg = 'Please specify Genestack API token%s: '
with_alias = '' if not alias else ' for "%s"' % alias
msg = msg % with_alias
while True:
token = getpass(msg)
if not token:
print('Token cannot be empty')
continue
user = User(email=None, host=host, password=None, alias=alias, token=token)
try:
user.get_connection()
break
except GenestackAuthenticationException:
print('Could not login with given token, please try again')
return user
def check_config():
config_path = config.get_settings_file()
if not os.path.exists(config_path):
print('You do not seem to have a config file yet. '
'Please run `genestack-user-setup init`. Exiting')
exit(1)
class AddUser(Command):
COMMAND = 'add'
DESCRIPTION = 'Add new user.'
OFFLINE = True
def run(self):
alias = input_alias(config.users.keys())
host = input_host()
user = create_user_from_input(host, alias)
config.add_user(user)
print('User "%s" has been created' % user.alias)
def select_user(users, selected=None):
"""
Choose user from users stored in config.
:param users:
:param selected:
:return:
:rtype: User
"""
user_list = sorted(users.values(), key=lambda x: x.alias)
return interactive_select(user_list, 'Select user', to_string=attrgetter('alias'), selected=selected)
class ChangePassword(Command):
COMMAND = 'change-password'
DESCRIPTION = 'Change password for user.'
OFFLINE = True
def update_parser(self, parent):
parent.add_argument('alias', metavar='<alias>', help='Alias for user to change password', nargs='?')
def run(self):
check_config()
users = config.users
user = users.get(self.args.alias)
if not user:
user = select_user(users)
if not user.email:
print('User without email could be authorized only by token')
return
while True:
user.password = getpass('Input password for %s: ' % user.alias.encode('utf-8'))
try:
user.get_connection()
break
except GenestackAuthenticationException:
continue
config.change_password(user.alias, user.password)
print('Password has been changed successfully')
class ChangeToken(Command):
COMMAND = 'change-token'
DESCRIPTION = 'Change token for user.'
OFFLINE = True
def update_parser(self, parent):
parent.add_argument('alias', metavar='<alias>',
help='Alias for user to change token for', nargs='?')
def run(self):
check_config()
users = config.users
user = users.get(self.args.alias)
if not user:
user = select_user(users)
new_user = create_user_from_token(user.host, alias=user.alias)
user.token = new_user.token
config.change_token(user.alias, user.token)
print('Token has been changed successfully')
class SetDefault(Command):
COMMAND = 'default'
DESCRIPTION = 'Set default user.'
OFFLINE = True
def update_parser(self, parent):
parent.add_argument('alias', metavar='<alias>', help='Alias for user to change password', nargs='?')
def run(self):
check_config()
users = config.users
user = users.get(self.args.alias)
if not user:
user = select_user(users, selected=config.default_user)
if user.alias != config.default_user.alias:
config.set_default_user(user)
print('Default user has been set to "%s"' % user.alias)
else:
print('Default user has not been changed')
class Remove(Command):
COMMAND = 'remove'
DESCRIPTION = 'Remove user.'
OFFLINE = True
def update_parser(self, parent):
parent.add_argument('alias', metavar='<alias>', help='Alias for user to change password', nargs='?')
def run(self):
check_config()
users = config.users
user = users.get(self.args.alias)
if not user:
user = select_user(users)
if user.alias == config.default_user.alias:
print('Cannot delete default user')
return
config.remove_user(user)
print('"%s" has been removed from config' % user.alias)
class RenameUser(Command):
COMMAND = 'rename'
DESCRIPTION = 'Rename user.'
OFFLINE = True
def update_parser(self, parent):
parent.add_argument('alias', metavar='<alias>', help='Alias to be renamed', nargs='?')
parent.add_argument('new_alias', metavar='<new_alias>', help='New alias', nargs='?')
def run(self):
check_config()
users = config.users
user = users.get(self.args.alias)
if not user:
print('Select user to rename')
user = select_user(users)
if not self.args.new_alias or not validate_alias(self.args.new_alias):
print('Enter new alias')
new_alias = input_alias(users.keys())
else:
new_alias = self.args.new_alias
new_user = User(email=user.email, alias=new_alias, host=user.host, password=<PASSWORD>,
token=user.token)
config.add_user(new_user, save=False)
if user.alias == config.default_user.alias:
config.set_default_user(new_user, save=False)
config.remove_user(user)
print('"%s" alias changed to "%s"' % (user.alias, new_user.alias))
class List(Command):
COMMAND = 'list'
DESCRIPTION = 'List all users.'
OFFLINE = True
def run(self):
check_config()
users = sorted(config.users.items())
default_user_alias = config.default_user and config.default_user.alias
for key, user in users:
print()
print('%s%s:' % (key, ' (default)' if default_user_alias == key else ''))
print(' %-10s%s' % ('email', user.email))
print(' %-10s%s' % ('host', user.host))
class Path(Command):
COMMAND = 'path'
DESCRIPTION = 'Show path to configuration file.'
OFFLINE = True
def run(self):
print(config.get_settings_file())
class Init(Command):
COMMAND = 'init'
DESCRIPTION = 'Create default settings.'
OFFLINE = True
def get_command_parser(self, parser=None):
parser = parser or ArgumentParser(description=self.DESCRIPTION)
parser.description = self.DESCRIPTION
group = parser.add_argument_group('command arguments')
self.update_parser(group)
group.add_argument('-H', '--host', default=DEFAULT_HOST,
help='Genestack host, '
'change it to connect somewhere else than %s' % DEFAULT_HOST,
metavar='<host>')
return parser
def run(self):
"""
Create config file if it is not present.
Catch ``KeyboardInterrupt`` and ``EOFError`` is required here for case
when this command is run for first time and in shell mode.
If we don't quit here, shell will continue execution and ask credentials once more.
"""
# Hardcoded alias that created for the first user only.
# Normal usecase is when user have single account and don't care about alias name.
# Advanced users can rename alias.
default_alias = 'Default'
try:
config_path = config.get_settings_file()
if os.path.exists(config_path):
print('A config file already exists at %s' % config_path)
return
print('If you do not have a Genestack account, you need to create one first')
user = create_user_from_input(self.args.host, default_alias)
config.add_user(user) # adding first user make him default.
print('Config file at "%s" has been created successfully' % config_path)
except (KeyboardInterrupt, EOFError):
sys.stdout.flush()
sys.stderr.write('\nError: Init is not finished\n')
exit(1)
class UserManagement(GenestackShell):
DESCRIPTION = 'Genestack user management application.'
COMMAND_LIST = [
Init,
List,
AddUser,
SetDefault,
ChangePassword,
ChangeToken,
Path,
Remove,
RenameUser
]
intro = "User setup shell.\nType 'help' for list of available commands.\n\n"
prompt = 'user_setup> '
def set_shell_user(self, args):
config_path = config.get_settings_file()
if not os.path.exists(config_path):
print('No config file was found, creating one interactively')
self.process_command(Init(), ['--host', args.host or DEFAULT_HOST], False)
args.host = None # do not provide host for future use of arguments
def main():
shell = UserManagement()
shell.cmdloop()
if __name__ == '__main__':
main()
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froide_crowdfunding/migrations/0005_crowdfunding_public_interest.py | okfde/froide-crowdfunding | 1 | 183047 | # Generated by Django 2.1.8 on 2019-04-15 09:32
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('froide_crowdfunding', '0004_contribution_public'),
]
operations = [
migrations.AddField(
model_name='crowdfunding',
name='public_interest',
field=models.TextField(blank=True),
),
]
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.github/Translator-Bot-V2/main.py | DarkSkull777/Translator-Bot-V2 | 1 | 1608706 | # Made with python3
# (C) @FayasNoushad
# Copyright permission under MIT License
# All rights reserved by FayasNoushad
# License -> https://github.com/FayasNoushad/Translator-Bot-V2/blob/main/LICENSE
import os
from pyrogram import Client, filters
from pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton
from googletrans import Translator
FayasNoushad = Client(
"Translator Bot",
bot_token = os.environ["BOT_TOKEN"],
api_id = int(os.environ["API_ID"]),
api_hash = os.environ["API_HASH"]
)
START_TEXT = """
Hello {}, I am a google translator telegram bot.
Made by @xskull7
"""
HELP_TEXT = """
- Just send a text with language code
- And select a language for translating
Made by @xskull7
"""
ABOUT_TEXT = """
- **Bot :** `Translator Bot V2`
- **Creator :** [Dimassrmdani](https://telegram.me/xskull7)
- **Channel :** [Dimassrmdani](https://telegram.me/xskull7)
- **Source :** [Click here](https://github.com/xskull7/Translator-Bot-V2)
- **Language :** [Python3](https://python.org)
- **Library :** [Pyrogram](https://pyrogram.org)
- **Server :** [Heroku](https://heroku.com)
"""
START_BUTTONS = InlineKeyboardMarkup(
[[
InlineKeyboardButton('Help', callback_data='help'),
InlineKeyboardButton('About', callback_data='about'),
InlineKeyboardButton('Close', callback_data='close')
]]
)
HELP_BUTTONS = InlineKeyboardMarkup(
[[
InlineKeyboardButton('Home', callback_data='home'),
InlineKeyboardButton('About', callback_data='about'),
InlineKeyboardButton('Close', callback_data='close')
]]
)
ABOUT_BUTTONS = InlineKeyboardMarkup(
[[
InlineKeyboardButton('Channel', url='https://telegram.me/xskull7'),
InlineKeyboardButton('Feedback', url='https://telegram.me/xskull7')
],[
InlineKeyboardButton('Home', callback_data='home'),
InlineKeyboardButton('Help', callback_data='help'),
InlineKeyboardButton('Close', callback_data='close')
]]
)
CLOSE_BUTTON = InlineKeyboardMarkup(
[[
InlineKeyboardButton('Close', callback_data='close')
]]
)
TRANSLATE_BUTTON = InlineKeyboardMarkup(
[[
InlineKeyboardButton('⚙ Join Updates Channel ⚙', url='https://telegram.me/xskull7')
]]
)
LANGUAGE_BUTTONS = InlineKeyboardMarkup(
[[
InlineKeyboardButton("മലയാളം", callback_data="Malayalam"),
InlineKeyboardButton("தமிழ்", callback_data="Tamil"),
InlineKeyboardButton("हिन्दी", callback_data="Hindi")
],[
InlineKeyboardButton("ಕನ್ನಡ", callback_data="Kannada"),
InlineKeyboardButton("తెలుగు", callback_data="Telugu"),
InlineKeyboardButton("मराठी", callback_data="Marathi")
],[
InlineKeyboardButton("ગુજરાતી", callback_data="Gujarati"),
InlineKeyboardButton("ଓଡ଼ିଆ", callback_data="Odia"),
InlineKeyboardButton("বাংলা", callback_data="bn")
],[
InlineKeyboardButton("ਪੰਜਾਬੀ", callback_data="Punjabi"),
InlineKeyboardButton("فارسی", callback_data="Persian"),
InlineKeyboardButton("English", callback_data="English")
],[
InlineKeyboardButton("español", callback_data="Spanish"),
InlineKeyboardButton("français", callback_data="French"),
InlineKeyboardButton("русский", callback_data="Russian")
],[
InlineKeyboardButton("עִברִית", callback_data="hebrew"),
InlineKeyboardButton("العربية", callback_data="arabic")
]]
)
@FayasNoushad.on_callback_query()
async def cb_data(bot, update):
if update.data == "home":
await update.message.edit_text(
text=START_TEXT.format(update.from_user.mention),
disable_web_page_preview=True,
reply_markup=START_BUTTONS
)
elif update.data == "help":
await update.message.edit_text(
text=HELP_TEXT,
disable_web_page_preview=True,
reply_markup=HELP_BUTTONS
)
elif update.data == "about":
await update.message.edit_text(
text=ABOUT_TEXT,
disable_web_page_preview=True,
reply_markup=ABOUT_BUTTONS
)
elif update.data == "close":
await update.message.delete()
else:
message = await update.message.edit_text("`Translating...`")
text = update.message.reply_to_message.text
language = update.data
translator = Translator()
try:
translate = translator.translate(text, dest=language)
translate_text = f"**Translated to {language}**"
translate_text += f"\n\n{translate.text}"
if len(translate_text) < 4096:
translate_text += "\n\nMade by @xskull7"
await message.edit_text(
text=translate_text,
disable_web_page_preview=True,
reply_markup=TRANSLATE_BUTTON
)
else:
with BytesIO(str.encode(str(translate_text))) as translate_file:
translate_file.name = language + ".txt"
await update.reply_document(
document=translate_file,
caption="Made by @xskull7",
reply_markup=TRANSLATE_BUTTON
)
await message.delete()
except Exception as error:
print(error)
await message.edit_text("Something wrong. Contact @xskull7.")
@FayasNoushad.on_message(filters.command(["start"]))
async def start(bot, update):
text = START_TEXT.format(update.from_user.mention)
reply_markup = START_BUTTONS
await update.reply_text(
text=text,
disable_web_page_preview=True,
reply_markup=reply_markup
)
@FayasNoushad.on_message(filters.private & filters.text)
async def translate(bot, update):
await update.reply_text(
text="Select a language below for translating",
disable_web_page_preview=True,
reply_markup=LANGUAGE_BUTTONS,
quote=True
)
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tests/BaseCase.py | YaroslavChyhryn/SchoolAPI | 0 | 28706 | <gh_stars>0
import unittest
from school_api.app import create_app
from school_api.db import create_tables, drop_tables
from school_api.data_generator import test_db
class BaseCase(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.app = create_app('test')
def setUp(self):
drop_tables(self.app)
create_tables(self.app)
test_db(self.app)
with self.app.app_context():
self.client = self.app.test_client()
def tearDown(self):
drop_tables(self.app)
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mkdocs/relative_path_ext.py | davidhrbac/mkdocs | 57 | 89925 | """
# Relative Path Markdown Extension
During the MkDocs build we rewrite URLs that link to local
Markdown or media files. Using the following pages configuration
we can look at how the output is changed.
pages:
- ['index.md']
- ['tutorial/install.md']
- ['tutorial/intro.md']
## Markdown URLs
When linking from `install.md` to `intro.md` the link would
simply be `[intro](intro.md)`. However, when we build
`install.md` we place it in a directory to create nicer URLs.
This means that the path to `intro.md` becomes `../intro/`
## Media URLs
To make it easier to work with media files and store them all
under one directory we re-write those to all be based on the
root. So, with the following markdown to add an image.
The output would depend on the location of the Markdown file it
was added too.
Source file | Generated Path | Image Path |
------------------- | ----------------- | ---------------------------- |
index.md | / | ./img/initial-layout.png |
tutorial/install.md | tutorial/install/ | ../img/initial-layout.png |
tutorial/intro.md | tutorial/intro/ | ../../img/initial-layout.png |
"""
from __future__ import unicode_literals
import logging
import os
from markdown.extensions import Extension
from markdown.treeprocessors import Treeprocessor
from markdown.util import AMP_SUBSTITUTE
from mkdocs import utils
from mkdocs.exceptions import MarkdownNotFound
log = logging.getLogger(__name__)
def _iter(node):
# TODO: Remove when dropping Python 2.6. Replace this
# function call with note.iter()
return [node] + node.findall('.//*')
def path_to_url(url, nav, strict):
scheme, netloc, path, params, query, fragment = (
utils.urlparse(url))
if scheme or netloc or not path or AMP_SUBSTITUTE in url:
# Ignore URLs unless they are a relative link to a markdown file.
# AMP_SUBSTITUTE is used internally by Markdown only for email,which is
# not a relative link. As urlparse errors on them, skip explicitly
return url
if nav and not utils.is_markdown_file(path):
path = utils.create_relative_media_url(nav, path)
elif nav:
# If the site navigation has been provided, then validate
# the internal hyperlink, making sure the target actually exists.
target_file = nav.file_context.make_absolute(path)
if target_file.startswith(os.path.sep):
target_file = target_file[1:]
if target_file not in nav.source_files:
source_file = nav.file_context.current_file
msg = (
'The page "%s" contained a hyperlink to "%s" which '
'is not listed in the "pages" configuration.'
) % (source_file, target_file)
# In strict mode raise an error at this point.
if strict:
raise MarkdownNotFound(msg)
# Otherwise, when strict mode isn't enabled, log a warning
# to the user and leave the URL as it is.
log.warning(msg)
return url
path = utils.get_url_path(target_file, nav.use_directory_urls)
path = nav.url_context.make_relative(path)
else:
path = utils.get_url_path(path).lstrip('/')
# Convert the .md hyperlink to a relative hyperlink to the HTML page.
fragments = (scheme, netloc, path, params, query, fragment)
url = utils.urlunparse(fragments)
return url
class RelativePathTreeprocessor(Treeprocessor):
def __init__(self, site_navigation, strict):
self.site_navigation = site_navigation
self.strict = strict
def run(self, root):
"""Update urls on anchors and images to make them relative
Iterates through the full document tree looking for specific
tags and then makes them relative based on the site navigation
"""
for element in _iter(root):
if element.tag == 'a':
key = 'href'
elif element.tag == 'img':
key = 'src'
else:
continue
url = element.get(key)
new_url = path_to_url(url, self.site_navigation, self.strict)
element.set(key, new_url)
return root
class RelativePathExtension(Extension):
"""
The Extension class is what we pass to markdown, it then
registers the Treeprocessor.
"""
def __init__(self, site_navigation, strict):
self.site_navigation = site_navigation
self.strict = strict
def extendMarkdown(self, md, md_globals):
relpath = RelativePathTreeprocessor(self.site_navigation, self.strict)
md.treeprocessors.add("relpath", relpath, "_end")
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services/worker/main.py | mrgrassho/geo-diff-2 | 0 | 25404 | <reponame>mrgrassho/geo-diff-2
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from geodiff_worker import GeoDiffWorker
from os import environ
from os.path import join, dirname
from dotenv import load_dotenv
dotenv_path = join(dirname(__file__), '.env')
load_dotenv(dotenv_path)
def main():
"""Main entry point to the program."""
# Get the location of the AMQP broker (RabbitMQ server) from
# an environment variable
amqp_url = environ['AMQP_URL']
task_queue = environ['TASK_QUEUE']
result_xchg = environ['RES_XCHG']
keep_alive_queue = environ['KEEP_ALIVE_QUEUE']
worker = GeoDiffWorker(amqp_url, task_queue, result_xchg, keep_alive_queue, debug=True)
worker.start()
if __name__ == '__main__':
main()
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usuarios/views.py | alvarocneto/alura_django | 1 | 8818 | <filename>usuarios/views.py
from django.shortcuts import redirect
from django.shortcuts import render
from django.contrib.auth.models import User
from django.views.generic.base import View
from perfis.models import Perfil
from usuarios.forms import RegistrarUsuarioForm
class RegistrarUsuarioView(View):
template_name = 'registrar.html'
def get(self, request):
return render(request, self.template_name)
def post(self, request):
# preenche o from
form = RegistrarUsuarioForm(request.POST)
# verifica se eh valido
if form.is_valid():
dados_form = form.data
# cria o usuario
usuario = User.objects.create_user(dados_form['nome'],
dados_form['email'],
dados_form['senha'])
# cria o perfil
perfil = Perfil(nome=dados_form['nome'],
telefone=dados_form['telefone'],
nome_empresa=dados_form['nome_empresa'],
usuario=usuario)
# grava no banco
perfil.save()
# redireciona para index
return redirect('index')
# so chega aqui se nao for valido
# vamos devolver o form para mostrar o formulario preenchido
return render(request, self.template_name, {'form': form})
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python/tests/test_stats.py | winni2k/tskit | 0 | 130817 | <filename>python/tests/test_stats.py
# MIT License
#
# Copyright (c) 2018-2019 Tskit Developers
# Copyright (C) 2016 University of Oxford
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
Test cases for stats calculations in tskit.
"""
import unittest
import io
import numpy as np
import msprime
import tskit
import _tskit
import tests.tsutil as tsutil
import tests.test_wright_fisher as wf
def get_r2_matrix(ts):
"""
Returns the matrix for the specified tree sequence. This is computed
via a straightforward Python algorithm.
"""
n = ts.get_sample_size()
m = ts.get_num_mutations()
A = np.zeros((m, m), dtype=float)
for t1 in ts.trees():
for sA in t1.sites():
assert len(sA.mutations) == 1
mA = sA.mutations[0]
A[sA.id, sA.id] = 1
fA = t1.get_num_samples(mA.node) / n
samples = list(t1.samples(mA.node))
for t2 in ts.trees(tracked_samples=samples):
for sB in t2.sites():
assert len(sB.mutations) == 1
mB = sB.mutations[0]
if sB.position > sA.position:
fB = t2.get_num_samples(mB.node) / n
fAB = t2.get_num_tracked_samples(mB.node) / n
D = fAB - fA * fB
r2 = D * D / (fA * fB * (1 - fA) * (1 - fB))
A[sA.id, sB.id] = r2
A[sB.id, sA.id] = r2
return A
class TestLdCalculator(unittest.TestCase):
"""
Tests for the LdCalculator class.
"""
num_test_sites = 50
def verify_matrix(self, ts):
m = ts.get_num_sites()
ldc = tskit.LdCalculator(ts)
A = ldc.get_r2_matrix()
self.assertEqual(A.shape, (m, m))
B = get_r2_matrix(ts)
self.assertTrue(np.allclose(A, B))
# Now look at each row in turn, and verify it's the same
# when we use get_r2 directly.
for j in range(m):
a = ldc.get_r2_array(j, direction=tskit.FORWARD)
b = A[j, j + 1:]
self.assertEqual(a.shape[0], m - j - 1)
self.assertEqual(b.shape[0], m - j - 1)
self.assertTrue(np.allclose(a, b))
a = ldc.get_r2_array(j, direction=tskit.REVERSE)
b = A[j, :j]
self.assertEqual(a.shape[0], j)
self.assertEqual(b.shape[0], j)
self.assertTrue(np.allclose(a[::-1], b))
# Now check every cell in the matrix in turn.
for j in range(m):
for k in range(m):
self.assertAlmostEqual(ldc.get_r2(j, k), A[j, k])
def verify_max_distance(self, ts):
"""
Verifies that the max_distance parameter works as expected.
"""
mutations = list(ts.mutations())
ldc = tskit.LdCalculator(ts)
A = ldc.get_r2_matrix()
j = len(mutations) // 2
for k in range(j):
x = mutations[j + k].position - mutations[j].position
a = ldc.get_r2_array(j, max_distance=x)
self.assertEqual(a.shape[0], k)
self.assertTrue(np.allclose(A[j, j + 1: j + 1 + k], a))
x = mutations[j].position - mutations[j - k].position
a = ldc.get_r2_array(j, max_distance=x, direction=tskit.REVERSE)
self.assertEqual(a.shape[0], k)
self.assertTrue(np.allclose(A[j, j - k: j], a[::-1]))
L = ts.get_sequence_length()
m = len(mutations)
a = ldc.get_r2_array(0, max_distance=L)
self.assertEqual(a.shape[0], m - 1)
self.assertTrue(np.allclose(A[0, 1:], a))
a = ldc.get_r2_array(m - 1, max_distance=L, direction=tskit.REVERSE)
self.assertEqual(a.shape[0], m - 1)
self.assertTrue(np.allclose(A[m - 1, :-1], a[::-1]))
def verify_max_mutations(self, ts):
"""
Verifies that the max mutations parameter works as expected.
"""
mutations = list(ts.mutations())
ldc = tskit.LdCalculator(ts)
A = ldc.get_r2_matrix()
j = len(mutations) // 2
for k in range(j):
a = ldc.get_r2_array(j, max_mutations=k)
self.assertEqual(a.shape[0], k)
self.assertTrue(np.allclose(A[j, j + 1: j + 1 + k], a))
a = ldc.get_r2_array(j, max_mutations=k, direction=tskit.REVERSE)
self.assertEqual(a.shape[0], k)
self.assertTrue(np.allclose(A[j, j - k: j], a[::-1]))
def test_single_tree_simulated_mutations(self):
ts = msprime.simulate(20, mutation_rate=10, random_seed=15)
ts = tsutil.subsample_sites(ts, self.num_test_sites)
self.verify_matrix(ts)
self.verify_max_distance(ts)
def test_deprecated_aliases(self):
ts = msprime.simulate(20, mutation_rate=10, random_seed=15)
ts = tsutil.subsample_sites(ts, self.num_test_sites)
ldc = tskit.LdCalculator(ts)
A = ldc.get_r2_matrix()
B = ldc.r2_matrix()
self.assertTrue(np.array_equal(A, B))
a = ldc.get_r2_array(0)
b = ldc.r2_array(0)
self.assertTrue(np.array_equal(a, b))
self.assertEqual(ldc.get_r2(0, 1), ldc.r2(0, 1))
def test_single_tree_regular_mutations(self):
ts = msprime.simulate(self.num_test_sites, length=self.num_test_sites)
ts = tsutil.insert_branch_mutations(ts)
# We don't support back mutations, so this should fail.
self.assertRaises(_tskit.LibraryError, self.verify_matrix, ts)
self.assertRaises(_tskit.LibraryError, self.verify_max_distance, ts)
def test_tree_sequence_regular_mutations(self):
ts = msprime.simulate(
self.num_test_sites, recombination_rate=1,
length=self.num_test_sites)
self.assertGreater(ts.get_num_trees(), 10)
t = ts.dump_tables()
t.sites.reset()
t.mutations.reset()
for j in range(self.num_test_sites):
site_id = len(t.sites)
t.sites.add_row(position=j, ancestral_state="0")
t.mutations.add_row(site=site_id, derived_state="1", node=j)
ts = t.tree_sequence()
self.verify_matrix(ts)
self.verify_max_distance(ts)
def test_tree_sequence_simulated_mutations(self):
ts = msprime.simulate(20, mutation_rate=10, recombination_rate=10)
self.assertGreater(ts.get_num_trees(), 10)
ts = tsutil.subsample_sites(ts, self.num_test_sites)
self.verify_matrix(ts)
self.verify_max_distance(ts)
self.verify_max_mutations(ts)
def set_partitions(collection):
"""
Returns an ierator over all partitions of the specified set.
From https://stackoverflow.com/questions/19368375/set-partitions-in-python
"""
if len(collection) == 1:
yield [collection]
else:
first = collection[0]
for smaller in set_partitions(collection[1:]):
for n, subset in enumerate(smaller):
yield smaller[:n] + [[first] + subset] + smaller[n + 1:]
yield [[first]] + smaller
def naive_mean_descendants(ts, reference_sets):
"""
Straightforward implementation of mean sample ancestry by iterating
over the trees and nodes in each tree.
"""
# TODO generalise this to allow arbitrary nodes, not just samples.
C = np.zeros((ts.num_nodes, len(reference_sets)))
T = np.zeros(ts.num_nodes)
tree_iters = [ts.trees(tracked_samples=sample_set) for sample_set in reference_sets]
for _ in range(ts.num_trees):
trees = [next(tree_iter) for tree_iter in tree_iters]
span = trees[0].span
for node in trees[0].nodes():
num_samples = trees[0].num_samples(node)
if num_samples > 0:
for j, tree in enumerate(trees):
C[node, j] += span * tree.num_tracked_samples(node)
T[node] += span
for node in range(ts.num_nodes):
if T[node] > 0:
C[node] /= T[node]
return C
class TestMeanDescendants(unittest.TestCase):
"""
Tests the TreeSequence.mean_descendants method.
"""
def verify(self, ts, reference_sets):
C1 = naive_mean_descendants(ts, reference_sets)
C2 = tsutil.mean_descendants(ts, reference_sets)
C3 = ts.mean_descendants(reference_sets)
self.assertEqual(C1.shape, C2.shape)
self.assertTrue(np.allclose(C1, C2))
self.assertTrue(np.allclose(C1, C3))
return C1
def test_two_populations_high_migration(self):
ts = msprime.simulate(
population_configurations=[
msprime.PopulationConfiguration(8),
msprime.PopulationConfiguration(8)],
migration_matrix=[[0, 1], [1, 0]],
recombination_rate=3,
random_seed=5)
self.assertGreater(ts.num_trees, 1)
self.verify(ts, [ts.samples(0), ts.samples(1)])
def test_single_tree(self):
ts = msprime.simulate(6, random_seed=1)
S = [range(3), range(3, 6)]
C = self.verify(ts, S)
for j, samples in enumerate(S):
tree = next(ts.trees(tracked_samples=samples))
for u in tree.nodes():
self.assertEqual(tree.num_tracked_samples(u), C[u, j])
def test_single_tree_partial_samples(self):
ts = msprime.simulate(6, random_seed=1)
S = [range(3), range(3, 4)]
C = self.verify(ts, S)
for j, samples in enumerate(S):
tree = next(ts.trees(tracked_samples=samples))
for u in tree.nodes():
self.assertEqual(tree.num_tracked_samples(u), C[u, j])
def test_single_tree_all_sample_sets(self):
ts = msprime.simulate(6, random_seed=1)
for S in set_partitions(list(range(ts.num_samples))):
C = self.verify(ts, S)
for j, samples in enumerate(S):
tree = next(ts.trees(tracked_samples=samples))
for u in tree.nodes():
self.assertEqual(tree.num_tracked_samples(u), C[u, j])
def test_many_trees_all_sample_sets(self):
ts = msprime.simulate(6, recombination_rate=2, random_seed=1)
self.assertGreater(ts.num_trees, 2)
for S in set_partitions(list(range(ts.num_samples))):
self.verify(ts, S)
def test_wright_fisher_unsimplified_all_sample_sets(self):
tables = wf.wf_sim(
4, 5, seed=1, deep_history=False, initial_generation_samples=False,
num_loci=10)
tables.sort()
ts = tables.tree_sequence()
for S in set_partitions(list(ts.samples())):
self.verify(ts, S)
def test_wright_fisher_unsimplified(self):
tables = wf.wf_sim(
20, 15, seed=1, deep_history=False, initial_generation_samples=False,
num_loci=20)
tables.sort()
ts = tables.tree_sequence()
samples = ts.samples()
self.verify(ts, [samples[:10], samples[10:]])
def test_wright_fisher_simplified(self):
tables = wf.wf_sim(
30, 10, seed=1, deep_history=False, initial_generation_samples=False,
num_loci=5)
tables.sort()
ts = tables.tree_sequence()
samples = ts.samples()
self.verify(ts, [samples[:10], samples[10:]])
def naive_genealogical_nearest_neighbours(ts, focal, reference_sets):
# Make sure everything is a sample so we can use the tracked_samples option.
# This is a limitation of the current API.
tables = ts.dump_tables()
tables.nodes.set_columns(
flags=np.ones_like(tables.nodes.flags),
time=tables.nodes.time)
ts = tables.tree_sequence()
A = np.zeros((len(focal), len(reference_sets)))
L = np.zeros(len(focal))
reference_set_map = np.zeros(ts.num_nodes, dtype=int) - 1
for k, ref_set in enumerate(reference_sets):
for u in ref_set:
reference_set_map[u] = k
tree_iters = [
ts.trees(tracked_samples=reference_nodes) for reference_nodes in reference_sets]
for _ in range(ts.num_trees):
trees = list(map(next, tree_iters))
length = trees[0].interval[1] - trees[0].interval[0]
for j, u in enumerate(focal):
focal_node_set = reference_set_map[u]
# delta(u) = 1 if u exists in any of the reference sets; 0 otherwise
delta = int(focal_node_set != -1)
v = u
while v != tskit.NULL:
total = sum(tree.num_tracked_samples(v) for tree in trees)
if total > delta:
break
v = trees[0].parent(v)
if v != tskit.NULL:
for k, tree in enumerate(trees):
# If the focal node is in the current set, we subtract its
# contribution from the numerator
n = tree.num_tracked_samples(v) - (k == focal_node_set)
# If the focal node is in *any* reference set, we subtract its
# contribution from the demoninator.
A[j, k] += length * n / (total - delta)
L[j] += length
# Normalise by the accumulated value for each focal node.
index = L > 0
L = L[index]
L = L.reshape((L.shape[0], 1))
A[index, :] /= L
return A
class TestGenealogicalNearestNeighbours(unittest.TestCase):
"""
Tests the TreeSequence.genealogical_nearest_neighbours method.
"""
#
# 8
# / \
# / \
# / \
# 7 \
# / \ 6
# / 5 / \
# / / \ / \
# 4 0 1 2 3
small_tree_ex_nodes = """\
id is_sample population time
0 1 0 0.00000000000000
1 1 0 0.00000000000000
2 1 0 0.00000000000000
3 1 0 0.00000000000000
4 1 0 0.00000000000000
5 0 0 0.14567111023387
6 0 0 0.21385545626353
7 0 0 0.43508024345063
8 0 0 1.60156352971203
"""
small_tree_ex_edges = """\
id left right parent child
0 0.00000000 1.00000000 5 0,1
1 0.00000000 1.00000000 6 2,3
2 0.00000000 1.00000000 7 4,5
3 0.00000000 1.00000000 8 6,7
"""
def verify(self, ts, reference_sets, focal=None):
if focal is None:
focal = [u for refset in reference_sets for u in refset]
A1 = naive_genealogical_nearest_neighbours(ts, focal, reference_sets)
A2 = tsutil.genealogical_nearest_neighbours(ts, focal, reference_sets)
A3 = ts.genealogical_nearest_neighbours(focal, reference_sets)
A4 = ts.genealogical_nearest_neighbours(focal, reference_sets, num_threads=3)
self.assertTrue(np.array_equal(A3, A4))
self.assertEqual(A1.shape, A2.shape)
self.assertEqual(A1.shape, A3.shape)
self.assertTrue(np.allclose(A1, A2))
self.assertTrue(np.allclose(A1, A3))
if ts.num_edges > 0 and all(ts.node(u).is_sample() for u in focal):
# When the focal nodes are samples, we can assert some stronger properties.
self.assertTrue(np.allclose(np.sum(A1, axis=1), 1))
return A1
def test_simple_example_all_samples(self):
ts = tskit.load_text(
nodes=io.StringIO(self.small_tree_ex_nodes),
edges=io.StringIO(self.small_tree_ex_edges), strict=False)
A = self.verify(ts, [[0, 1], [2, 3, 4]], [0])
self.assertEqual(list(A[0]), [1, 0])
A = self.verify(ts, [[0, 1], [2, 3, 4]], [4])
self.assertEqual(list(A[0]), [1, 0])
A = self.verify(ts, [[0, 1], [2, 3, 4]], [2])
self.assertEqual(list(A[0]), [0, 1])
A = self.verify(ts, [[0, 2], [1, 3, 4]], [0])
self.assertEqual(list(A[0]), [0, 1])
A = self.verify(ts, [[0, 2], [1, 3, 4]], [4])
self.assertEqual(list(A[0]), [0.5, 0.5])
def test_simple_example_missing_samples(self):
ts = tskit.load_text(
nodes=io.StringIO(self.small_tree_ex_nodes),
edges=io.StringIO(self.small_tree_ex_edges), strict=False)
A = self.verify(ts, [[0, 1], [2, 4]], [3])
self.assertEqual(list(A[0]), [0, 1])
A = self.verify(ts, [[0, 1], [2, 4]], [2])
self.assertTrue(np.allclose(A[0], [2 / 3, 1 / 3]))
def test_simple_example_internal_focal_node(self):
ts = tskit.load_text(
nodes=io.StringIO(self.small_tree_ex_nodes),
edges=io.StringIO(self.small_tree_ex_edges), strict=False)
focal = [7] # An internal node
reference_sets = [[4, 0, 1], [2, 3]]
GNN = naive_genealogical_nearest_neighbours(ts, focal, reference_sets)
self.assertTrue(np.allclose(GNN[0], np.array([1.0, 0.0])))
GNN = tsutil.genealogical_nearest_neighbours(ts, focal, reference_sets)
self.assertTrue(np.allclose(GNN[0], np.array([1.0, 0.0])))
GNN = ts.genealogical_nearest_neighbours(focal, reference_sets)
self.assertTrue(np.allclose(GNN[0], np.array([1.0, 0.0])))
focal = [8] # The root
GNN = naive_genealogical_nearest_neighbours(ts, focal, reference_sets)
self.assertTrue(np.allclose(GNN[0], np.array([0.6, 0.4])))
GNN = tsutil.genealogical_nearest_neighbours(ts, focal, reference_sets)
self.assertTrue(np.allclose(GNN[0], np.array([0.6, 0.4])))
GNN = ts.genealogical_nearest_neighbours(focal, reference_sets)
self.assertTrue(np.allclose(GNN[0], np.array([0.6, 0.4])))
def test_two_populations_high_migration(self):
ts = msprime.simulate(
population_configurations=[
msprime.PopulationConfiguration(18),
msprime.PopulationConfiguration(18)],
migration_matrix=[[0, 1], [1, 0]],
recombination_rate=8,
random_seed=5)
self.assertGreater(ts.num_trees, 1)
self.verify(ts, [ts.samples(0), ts.samples(1)])
def test_single_tree(self):
ts = msprime.simulate(6, random_seed=1)
S = [range(3), range(3, 6)]
self.verify(ts, S)
def test_single_tree_internal_reference_sets(self):
ts = msprime.simulate(10, random_seed=1)
tree = ts.first()
S = [[u] for u in tree.children(tree.root)]
self.verify(ts, S, ts.samples())
def test_single_tree_all_nodes(self):
ts = msprime.simulate(10, random_seed=1)
S = [np.arange(ts.num_nodes, dtype=np.int32)]
self.verify(ts, S, np.arange(ts.num_nodes, dtype=np.int32))
def test_single_tree_partial_samples(self):
ts = msprime.simulate(6, random_seed=1)
S = [range(3), range(3, 4)]
self.verify(ts, S)
def test_single_tree_all_sample_sets(self):
ts = msprime.simulate(6, random_seed=1)
for S in set_partitions(list(range(ts.num_samples))):
self.verify(ts, S)
def test_many_trees_all_sample_sets(self):
ts = msprime.simulate(6, recombination_rate=2, random_seed=1)
self.assertGreater(ts.num_trees, 2)
for S in set_partitions(list(range(ts.num_samples))):
self.verify(ts, S)
def test_many_trees_sequence_length(self):
for L in [0.5, 1.5, 3.3333]:
ts = msprime.simulate(6, length=L, recombination_rate=2, random_seed=1)
self.verify(ts, [range(3), range(3, 6)])
def test_many_trees_all_nodes(self):
ts = msprime.simulate(6, length=4, recombination_rate=2, random_seed=1)
S = [np.arange(ts.num_nodes, dtype=np.int32)]
self.verify(ts, S, np.arange(ts.num_nodes, dtype=np.int32))
def test_wright_fisher_unsimplified_all_sample_sets(self):
tables = wf.wf_sim(
4, 5, seed=1, deep_history=True, initial_generation_samples=False,
num_loci=10)
tables.sort()
ts = tables.tree_sequence()
for S in set_partitions(list(ts.samples())):
self.verify(ts, S)
def test_wright_fisher_unsimplified(self):
tables = wf.wf_sim(
20, 15, seed=1, deep_history=True, initial_generation_samples=False,
num_loci=20)
tables.sort()
ts = tables.tree_sequence()
samples = ts.samples()
self.verify(ts, [samples[:10], samples[10:]])
def test_wright_fisher_initial_generation(self):
tables = wf.wf_sim(
20, 15, seed=1, deep_history=True, initial_generation_samples=True,
num_loci=20)
tables.sort()
tables.simplify()
ts = tables.tree_sequence()
samples = ts.samples()
founders = [u for u in samples if ts.node(u).time > 0]
samples = [u for u in samples if ts.node(u).time == 0]
self.verify(ts, [founders[:10], founders[10:]], samples)
def test_wright_fisher_initial_generation_no_deep_history(self):
tables = wf.wf_sim(
20, 15, seed=2, deep_history=False, initial_generation_samples=True,
num_loci=20)
tables.sort()
tables.simplify()
ts = tables.tree_sequence()
samples = ts.samples()
founders = [u for u in samples if ts.node(u).time > 0]
samples = [u for u in samples if ts.node(u).time == 0]
self.verify(ts, [founders[:10], founders[10:]], samples)
def test_wright_fisher_unsimplified_multiple_roots(self):
tables = wf.wf_sim(
20, 15, seed=1, deep_history=False, initial_generation_samples=False,
num_loci=20)
tables.sort()
ts = tables.tree_sequence()
samples = ts.samples()
self.verify(ts, [samples[:10], samples[10:]])
def test_wright_fisher_simplified(self):
tables = wf.wf_sim(
31, 10, seed=1, deep_history=True, initial_generation_samples=False,
num_loci=5)
tables.sort()
ts = tables.tree_sequence().simplify()
samples = ts.samples()
self.verify(ts, [samples[:10], samples[10:]])
def test_wright_fisher_simplified_multiple_roots(self):
tables = wf.wf_sim(
31, 10, seed=1, deep_history=False, initial_generation_samples=False,
num_loci=5)
tables.sort()
ts = tables.tree_sequence()
samples = ts.samples()
self.verify(ts, [samples[:10], samples[10:]])
def test_empty_ts(self):
tables = tskit.TableCollection(1.0)
tables.nodes.add_row(1, 0)
tables.nodes.add_row(1, 0)
ts = tables.tree_sequence()
self.verify(ts, [[0], [1]])
def exact_genealogical_nearest_neighbours(ts, focal, reference_sets):
# Same as above, except we return the per-tree value for a single node.
# Make sure everyhing is a sample so we can use the tracked_samples option.
# This is a limitation of the current API.
tables = ts.dump_tables()
tables.nodes.set_columns(
flags=np.ones_like(tables.nodes.flags),
time=tables.nodes.time)
ts = tables.tree_sequence()
A = np.zeros((len(reference_sets), ts.num_trees))
L = np.zeros(ts.num_trees)
reference_set_map = np.zeros(ts.num_nodes, dtype=int) - 1
for k, ref_set in enumerate(reference_sets):
for u in ref_set:
reference_set_map[u] = k
tree_iters = [
ts.trees(tracked_samples=reference_nodes) for reference_nodes in reference_sets]
u = focal
focal_node_set = reference_set_map[u]
# delta(u) = 1 if u exists in any of the reference sets; 0 otherwise
delta = int(focal_node_set != -1)
for _ in range(ts.num_trees):
trees = list(map(next, tree_iters))
v = trees[0].parent(u)
while v != tskit.NULL:
total = sum(tree.num_tracked_samples(v) for tree in trees)
if total > delta:
break
v = trees[0].parent(v)
if v != tskit.NULL:
# The length is only reported where the statistic is defined.
L[trees[0].index] = trees[0].interval[1] - trees[0].interval[0]
for k, tree in enumerate(trees):
# If the focal node is in the current set, we subtract its
# contribution from the numerator
n = tree.num_tracked_samples(v) - (k == focal_node_set)
# If the focal node is in *any* reference set, we subtract its
# contribution from the demoninator.
A[k, tree.index] = n / (total - delta)
return A, L
def local_gnn(ts, focal, reference_sets):
# Temporary implementation of the treewise GNN.
reference_set_map = np.zeros(ts.num_nodes, dtype=int) - 1
for k, reference_set in enumerate(reference_sets):
for u in reference_set:
if reference_set_map[u] != -1:
raise ValueError("Duplicate value in reference sets")
reference_set_map[u] = k
K = len(reference_sets)
A = np.zeros((len(focal), ts.num_trees, K))
lefts = np.zeros(ts.num_trees, dtype=float)
rights = np.zeros(ts.num_trees, dtype=float)
parent = np.zeros(ts.num_nodes, dtype=int) - 1
sample_count = np.zeros((ts.num_nodes, K), dtype=int)
# Set the intitial conditions.
for j in range(K):
sample_count[reference_sets[j], j] = 1
for t, ((left, right), edges_out, edges_in) in enumerate(ts.edge_diffs()):
for edge in edges_out:
parent[edge.child] = -1
v = edge.parent
while v != -1:
sample_count[v] -= sample_count[edge.child]
v = parent[v]
for edge in edges_in:
parent[edge.child] = edge.parent
v = edge.parent
while v != -1:
sample_count[v] += sample_count[edge.child]
v = parent[v]
# Process this tree.
for j, u in enumerate(focal):
focal_reference_set = reference_set_map[u]
delta = int(focal_reference_set != -1)
p = parent[u]
lefts[t] = left
rights[t] = right
while p != tskit.NULL:
total = np.sum(sample_count[p])
if total > delta:
break
p = parent[p]
if p != tskit.NULL:
scale = 1 / (total - delta)
for k, reference_set in enumerate(reference_sets):
n = sample_count[p, k] - int(focal_reference_set == k)
A[j, t, k] = n * scale
return (A, lefts, rights)
class TestExactGenealogicalNearestNeighbours(TestGenealogicalNearestNeighbours):
# This is a work in progress - these tests will be adapted to use the
# treewise GNN when it's implemented.
def verify(self, ts, reference_sets, focal=None):
if focal is None:
focal = [u for refset in reference_sets for u in refset]
A = ts.genealogical_nearest_neighbours(focal, reference_sets)
G, lefts, rights = local_gnn(ts, focal, reference_sets)
for tree in ts.trees():
self.assertEqual(lefts[tree.index], tree.interval[0])
self.assertEqual(rights[tree.index], tree.interval[1])
for j, u in enumerate(focal):
T, L = exact_genealogical_nearest_neighbours(ts, u, reference_sets)
self.assertTrue(np.allclose(G[j], T.T))
# Ignore the cases where the node has no GNNs
if np.sum(L) > 0:
mean = np.sum(T * L, axis=1) / np.sum(L)
self.assertTrue(np.allclose(mean, A[j]))
return A
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typogrify/templatetags/typogrify_tags.py | tylerbutler/typogrify | 0 | 2494 | from typogrify.filters import amp, caps, initial_quotes, smartypants, titlecase, typogrify, widont, TypogrifyError
from functools import wraps
from django.conf import settings
from django import template
from django.utils.safestring import mark_safe
from django.utils.encoding import force_unicode
register = template.Library()
def make_safe(f):
"""
A function wrapper to make typogrify play nice with django's
unicode support.
"""
@wraps(f)
def wrapper(text):
text = force_unicode(text)
f.is_safe = True
out = text
try:
out = f(text)
except TypogrifyError, e:
if settings.DEBUG:
raise e
return text
return mark_safe(out)
wrapper.is_safe = True
return wrapper
register.filter('amp', make_safe(amp))
register.filter('caps', make_safe(caps))
register.filter('initial_quotes', make_safe(initial_quotes))
register.filter('smartypants', make_safe(smartypants))
register.filter('titlecase', make_safe(titlecase))
register.filter('typogrify', make_safe(typogrify))
register.filter('widont', make_safe(widont))
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] |
Data/GalZoo2_TFrecords.py | AshleySpindler/AstroSense | 0 | 137071 | <filename>Data/GalZoo2_TFrecords.py
import numpy as np
import tensorflow as tf
from astropy.io import fits
import matplotlib.image as mpimg
from skimage.transform import rescale
from skimage.color import rgb2gray
from numpy.random import choice
from skimage.exposure import rescale_intensity
import sys
from multiprocessing import Pool
import tqdm
import warnings
warnings.filterwarnings("ignore")
#TODO tidy this shit up
def load_image(ID):
img = mpimg.imread('/data/astroml/aspindler/GalaxyZoo/GalaxyZooImages/galaxy_'+str(ID)+'.png')
img_crop = img[84:340,84:340]
img_grey = rgb2gray(img_crop)
img_64 = rescale(img_grey, scale=0.25, preserve_range=True)
img_uint = rescale_intensity(img_64, out_range=(0,255)).astype('uint8')
return img_uint
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def grab_data(i):
ID = IDs[0][i]
condition = (IDs[1][i]).astype('uint8')
img = load_image(ID)
feature = {'image': _bytes_feature(tf.compat.as_bytes(img.tostring())),
'condition': _bytes_feature(tf.compat.as_bytes(condition.tostring()))
}
example = tf.train.Example(features=tf.train.Features(feature=feature))
return example#writer.write(example.SerializeToString())
#load IDs from fits
data = fits.open('Data/Scratch/GalZoo2/gz2_hart16.fits.gz')[1].data
spiral = (data['t02_edgeon_a05_no_flag'] & (data['t02_edgeon_a05_no_count'] > 20)).astype(bool)
data = data[spiral]
global IDs
IDs = [data['dr7objid'],
np.array([data['t03_bar_a06_bar_flag'],
data['t03_bar_a07_no_bar_flag'],
data['t04_spiral_a08_spiral_flag'],
data['t04_spiral_a09_no_spiral_flag'],
data['t05_bulge_prominence_a10_no_bulge_flag'],
data['t05_bulge_prominence_a11_just_noticeable_flag'],
data['t05_bulge_prominence_a12_obvious_flag'],
data['t05_bulge_prominence_a13_dominant_flag']
])]
IDs[1] = np.swapaxes(IDs[1],0,1)
train, valid, test = 10000, 10000, 25000
gals = choice(len(IDs[0]), train+valid+test)
global gals_train, gals_valid, gals_test
gals_train = gals[0:train]
gals_valid = gals[train:train+valid]
gals_test = gals[train+valid:train+valid+test]
train_filename = 'Data/Scratch/GalZoo2/galzoo_spiral_flags_train.tfrecords' # address to save the TFRecords file
# open the TFRecords file
writer = tf.python_io.TFRecordWriter(train_filename)
pool = Pool(processes=20) #Set pool of processors
for result in tqdm.tqdm(pool.imap_unordered(grab_data, gals_train), total=len(gals_train)):
writer.write(result.SerializeToString())
writer.close()
sys.stdout.flush()
valid_filename = 'Data/Scratch/GalZoo2/galzoo_spiral_flags_valid.tfrecords' # address to save the TFRecords file
# open the TFRecords file
writer = tf.python_io.TFRecordWriter(valid_filename)
pool = Pool(processes=20) #Set pool of processors
for result in tqdm.tqdm(pool.imap_unordered(grab_data, gals_valid), total=len(gals_valid)):
writer.write(result.SerializeToString())
writer.close()
sys.stdout.flush()
test_filename = 'Data/Scratch/GalZoo2/galzoo_spiral_flags_test.tfrecords' # address to save the TFRecords file
# open the TFRecords file
writer = tf.python_io.TFRecordWriter(test_filename)
pool = Pool(processes=20) #Set pool of processors
for result in tqdm.tqdm(pool.imap_unordered(grab_data, gals_test), total=len(gals_test)):
writer.write(result.SerializeToString())
writer.close()
sys.stdout.flush()
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23126,
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13,
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2
] |
Interview-Preparation/Facebook/BinarySearch-find-smallest-divisor-given-a-threshold.py | shoaibur/SWE | 1 | 91002 | class Solution:
def smallestDivisor(self, nums: List[int], threshold: int) -> int:
def condition(divisor) -> bool:
return sum((num - 1) // divisor + 1 for num in nums) <= threshold
lo, hi = 1, max(nums)
while lo < hi:
mid = lo + (hi - lo) // 2
if condition(mid):
hi = mid
else:
lo = mid + 1
return lo
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pipeline/reach-es-extractor/refparse/utils/__init__.py | wellcometrust/reach | 11 | 36198 | <filename>pipeline/reach-es-extractor/refparse/utils/__init__.py
from .parse import structure_reference
from .fuzzy_match import FuzzyMatcher
from .file_manager import FileManager
from .serialiser import serialise_matched_reference, serialise_reference
from .exact_match import ExactMatcher
__all__ = [
structure_reference,
FuzzyMatcher,
FileManager,
serialise_matched_reference,
serialise_reference,
ExactMatcher
]
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tests/src/Diksha_Reports/usage_by_textbook/download_all_collection_records.py | JalajaTR/cQube | 0 | 267 | <gh_stars>0
import os
import time
from selenium.webdriver.support.select import Select
from Data.parameters import Data
from get_dir import pwd
from reuse_func import GetData
class All_records_download():
def __init__(self,driver):
self.driver = driver
self.filename =''
def test_download_csv(self):
self.data = GetData()
self.p = pwd()
self.driver.find_element_by_xpath(Data.hyper_link).click()
self.data.page_loading(self.driver)
colltype = Select(self.driver.find_element_by_name('collection_type'))
colltype.select_by_visible_text(' Overall ')
self.data.page_loading(self.driver)
self.driver.find_element_by_id(Data.Download).click()
time.sleep(4)
self.filename = self.p.get_download_dir() + '/collectionType_all_data.csv'
time.sleep(2)
file = os.path.isfile(self.filename)
os.remove(self.filename)
return file
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pyrsa/io/meadows.py | PeerHerholz/pyrsa | 4 | 28506 | """Covers import of data downloaded from the
`Meadows online behavior platform <https://meadows-research.com/>`_.
For information on available file types see the meadows
`documentation on downloads <https://meadows-research.com/documentation\
/researcher/downloads/>`_.
"""
from os.path import basename
import numpy
from scipy.io import loadmat
from pyrsa.rdm.rdms import RDMs
def load_rdms(fpath, sort=True):
"""Read a Meadows results file and return any RDMs as a pyrsa object
Args:
fpath (str): path to .mat Meadows results file
sort (bool): whether to sort the RDM based on the stimulus names
Raises:
ValueError: Will raise an error if the file is missing an expected
variable. This can happen if the file does not contain MA task
data.
Returns:
RDMs: All rdms found in the data file as an RDMs object
"""
info = extract_filename_segments(fpath)
data = loadmat(fpath)
if info['participant_scope'] == 'single':
for var in ('stimuli', 'rdmutv'):
if var not in data:
raise ValueError(f'File missing variable: {var}')
utvs = data['rdmutv']
stimuli_fnames = data['stimuli']
pnames = [info['participant']]
else:
stim_vars = [v for v in data.keys() if v[:7] == 'stimuli']
stimuli_fnames = data[stim_vars[0]]
pnames = ['-'.join(v.split('_')[1:]) for v in stim_vars]
utv_vars = ['rdmutv_' + p.replace('-', '_') for p in pnames]
utvs = numpy.squeeze(numpy.stack([data[v] for v in utv_vars]))
desc_info_keys = (
'participant',
'task_index',
'task_name',
'experiment_name'
)
conds = [f.split('.')[0] for f in stimuli_fnames]
rdms = RDMs(
utvs,
dissimilarity_measure='euclidean',
descriptors={k: info[k] for k in desc_info_keys if k in info},
rdm_descriptors=dict(participants=pnames),
pattern_descriptors=dict(conds=conds),
)
if sort:
rdms.sort_by(conds='alpha')
return rdms
def extract_filename_segments(fpath):
"""Get information from the name of a downloaded results file
Will determine:
* participant_scope: 'single' or 'multiple', how many participant
sessions this file covers.
* task_scope: 'single' or 'multiple', how many experiment tasks this
file covers.
* participant: the Meadows nickname of the participant, if this is a
single participation file.
* task_index: the 1-based index of the task in the experiment, if
this is a single participant file.
* task_name: the name of the task in the experiment, if
this is not a single participant file.
* version: the experiment version as a string.
* experiment_name: name of the experiment on Meadows.
* structure: the structure of the data contained, one of 'tree',
'events', '1D', '2D', etc.
* filetype: the file extension and file format used to serialize the
data.
Args:
fpath (str): File system path to downloaded file
Returns:
dict: Dictionary with the fields described above.
"""
fname, ext = basename(fpath).split('.')
segments = fname.split('_')
info = dict(
task_scope='single',
version=segments[3].replace('v', ''),
experiment_name=segments[1],
structure=segments[-1],
filetype=ext
)
if segments[-2].isdigit():
info['participant_scope'] = 'single'
info['participant'] = segments[-3]
info['task_index'] = int(segments[-2])
else:
info['participant_scope'] = 'multiple'
info['task_name'] = segments[-2]
return info
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src/skiptracer/plugins/who_call_id/__init__.py | EdwardDantes/skiptracer | 912 | 50278 | """Whocallid.com search module"""
from __future__ import print_function
from __future__ import absolute_import
from ..base import PageGrabber
from ...colors.default_colors import DefaultBodyColors as bc
import re
import logging
try:
import __builtin__ as bi
except BaseException:
import builtins as bi
class WhoCallIdGrabber(PageGrabber):
"""
WhoCallID sales scraper for reverse telephone lookups
"""
def get_name(self):
"""
Grab the users name
"""
name = "Unknown"
try:
name = self.soup.find('h2', attrs={'class': 'name'})
if name:
name = name.text.strip()
print(" [" + bc.CGRN + "+" + bc.CEND + "] " +
bc.CRED + "Name: " + bc.CEND + str(name))
except BaseException:
pass
finally:
return name
def get_location(self):
"""
Get the location
"""
location = "Unknown"
try:
location = self.soup.find('h3', attrs={'class': 'location'})
if location:
location = location.text.strip()
print(" [" + bc.CGRN + "+" + bc.CEND + "] " +
bc.CRED + "Location: " + bc.CEND + str(location))
except BaseException:
pass
finally:
return location
def get_phone_type(self):
"""
Get the phone type
"""
phone_type = "Unknown"
try:
phone_type = self.soup.find("img").attrs['alt']
if phone_type:
phone_type = phone_type.strip()
print(" [" + bc.CGRN + "+" + bc.CEND + "] " +
bc.CRED + "Phone Type: " + bc.CEND + str(phone_type))
except BaseException:
pass
finally:
return phone_type
def get_carrier(self, phone_number):
"""
Get the phone carrier info
"""
carrier = ""
try:
self.url = "https://whocalld.com/+1{}?carrier".format(phone_number)
self.source = self.get_source(self.url)
self.soup = self.get_dom(self.source)
carrier = soup.find('span', attrs={'class': 'carrier'})
except BaseException:
pass
finally:
return carrier
def process_carrier(self, carrier):
"""
Take the carrier info and process it
"""
try:
if carrier:
carrier = carrier.text
print(" [" + bc.CGRN + "+" + bc.CEND + "] " +
bc.CRED + "Carrier: " + bc.CEND + str(carrier))
else:
carrier = ""
except BaseException:
carrier = ""
finally:
return carrier
def get_city(self):
"""
Grab the city info
"""
city = ""
try:
city = self.soup.find('span', attrs={'class': 'city'})
if city:
city = city.text
print(" [" + bc.CGRN + "+" + bc.CEND + "] " +
bc.CRED + "City: " + bc.CEND + str(city))
except BaseException:
pass
finally:
return city
def get_state(self):
"""
Grab the state info
"""
state = ""
try:
state = self.soup.find('span', attrs={'class': 'state'})
if state:
state = state.text
print(" [" + bc.CGRN + "+" + bc.CEND + "] " +
bc.CRED + "State: " + bc.CEND + str(state))
except BaseException:
pass
finally:
return state
def get_time(self):
"""
Grab time info
"""
time = ""
try:
time = self.soup.find('span', attrs={'class': 'time'})
if time:
time = time.text
print(" [" + bc.CGRN + "+" + bc.CEND + "] " +
bc.CRED + "Time: " + bc.CEND + str(time))
except BaseException:
pass
finally:
return time
def get_info(self, phone_number, lookup):
"""
Request, scrape and return values found
"""
print("[" + bc.CPRP + "?" + bc.CEND + "] " +
bc.CCYN + "WhoCalld" + bc.CEND)
# Get phone info
self.url = 'https://whocalld.com/+1{}'.format(phone_number)
self.source = self.get_source(self.url)
self.soup = self.get_dom(self.source)
try:
if self.soup.body.find_all(string=re.compile(
'.*{0}.*'.format('country')), recursive=True):
print(" [" + bc.CRED + "X" + bc.CEND + "] " +
bc.CYLW + "No WhoCallID data returned\n" + bc.CEND)
return
except:
print(" [" + bc.CRED + "X" + bc.CEND + "] " +
bc.CYLW + "Unable to extract data. Is the site online?\n" + bc.CEND)
name = self.get_name()
location = self.get_location()
phone_type = self.get_phone_type()
carrier = self.get_carrier(phone_number)
carrier = self.process_carrier(carrier)
city = self.get_city()
state = self.get_state()
time = self.get_time()
self.info_dict.update({
"carrier": carrier,
"city": city,
"location": location,
"name": name,
"phone_type": phone_type,
"state": state,
"time": time
})
print()
return self.info_dict
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ooobuild/lo/sheet/dde_item_info.py | Amourspirit/ooo_uno_tmpl | 0 | 1613870 | <gh_stars>0
# coding: utf-8
#
# Copyright 2022 :Barry-Thomas-Paul: Moss
#
# 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.
#
# Struct Class
# this is a auto generated file generated by Cheetah
# Namespace: com.sun.star.sheet
# Libre Office Version: 7.3
from ooo.oenv.env_const import UNO_NONE
import typing
class DDEItemInfo(object):
"""
Struct Class
describes an item of a DDE connection.
A DDE connection consists of the DDE service name, the DDE topic and a list of DDE items which may contain cached result sets.
**since**
OOo 3.1
See Also:
`API DDEItemInfo <https://api.libreoffice.org/docs/idl/ref/structcom_1_1sun_1_1star_1_1sheet_1_1DDEItemInfo.html>`_
"""
__ooo_ns__: str = 'com.sun.star.sheet'
__ooo_full_ns__: str = 'com.sun.star.sheet.DDEItemInfo'
__ooo_type_name__: str = 'struct'
typeName: str = 'com.sun.star.sheet.DDEItemInfo'
"""Literal Constant ``com.sun.star.sheet.DDEItemInfo``"""
def __init__(self, Results: typing.Optional[typing.Tuple[typing.Tuple[object, ...], ...]] = UNO_NONE, Item: typing.Optional[str] = '') -> None:
"""
Constructor
Arguments:
Results (typing.Tuple[typing.Tuple[object, ...], ...], optional): Results value.
Item (str, optional): Item value.
"""
super().__init__()
if isinstance(Results, DDEItemInfo):
oth: DDEItemInfo = Results
self.Results = oth.Results
self.Item = oth.Item
return
kargs = {
"Results": Results,
"Item": Item,
}
if kargs["Results"] is UNO_NONE:
kargs["Results"] = None
self._init(**kargs)
def _init(self, **kwargs) -> None:
self._results = kwargs["Results"]
self._item = kwargs["Item"]
@property
def Results(self) -> typing.Tuple[typing.Tuple[object, ...], ...]:
"""
The results of the item cached from the last update of the DDE link if available.
This sequence may be empty.
"""
return self._results
@Results.setter
def Results(self, value: typing.Tuple[typing.Tuple[object, ...], ...]) -> None:
self._results = value
@property
def Item(self) -> str:
"""
The name of the DDE item.
"""
return self._item
@Item.setter
def Item(self, value: str) -> None:
self._item = value
__all__ = ['DDEItemInfo']
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minato_namikaze/bot_files/lib/functions/user.py | ooliver1/yondaime-hokage | 1 | 109646 | from typing import Optional, Union
import discord
async def get_dm(user: Optional[Union[int, discord.Member]], ctx=None):
if isinstance(user, int):
user = ctx.bot.get_user(user)
return user.dm_channel if user.dm_channel else await user.create_dm()
def get_user(user: Union[int, discord.Member], ctx=None):
if isinstance(user, int):
user = ctx.bot.get_user(user)
return user
def get_roles(role: Union[int, discord.Role], ctx=None):
if isinstance(role, int):
role = discord.utils.get(ctx.guild.roles, id=role)
return role
async def get_bot_inviter(guild: discord.Guild, bot: discord.Client):
try:
async for i in guild.audit_logs(limit=1):
return i.user
except:
return guild.owner
async def get_or_fetch_member(guild, member_id):
"""Looks up a member in cache or fetches if not found.
Parameters
-----------
guild: Guild
The guild to look in.
member_id: int
The member ID to search for.
Returns
---------
Optional[Member]
The member or None if not found.
"""
member = guild.get_member(member_id)
if member is not None:
return member
shard = self.get_shard(guild.shard_id)
if shard.is_ws_ratelimited():
try:
member = await guild.fetch_member(member_id)
except discord.HTTPException:
return None
else:
return member
members = await guild.query_members(limit=1, user_ids=[member_id], cache=True)
if not members:
return None
return members[0]
async def resolve_member_ids(guild, member_ids):
"""Bulk resolves member IDs to member instances, if possible.
Members that can't be resolved are discarded from the list.
This is done lazily using an asynchronous iterator.
Note that the order of the resolved members is not the same as the input.
Parameters
-----------
guild: Guild
The guild to resolve from.
member_ids: Iterable[int]
An iterable of member IDs.
Yields
--------
Member
The resolved members.
"""
needs_resolution = []
for member_id in member_ids:
member = guild.get_member(member_id)
if member is not None:
yield member
else:
needs_resolution.append(member_id)
total_need_resolution = len(needs_resolution)
if total_need_resolution == 1:
shard = self.get_shard(guild.shard_id)
if shard.is_ws_ratelimited():
try:
member = await guild.fetch_member(needs_resolution[0])
except discord.HTTPException:
pass
else:
yield member
else:
members = await guild.query_members(limit=1, user_ids=needs_resolution, cache=True)
if members:
yield members[0]
elif total_need_resolution <= 100:
# Only a single resolution call needed here
resolved = await guild.query_members(limit=100, user_ids=needs_resolution, cache=True)
for member in resolved:
yield member
else:
# We need to chunk these in bits of 100...
for index in range(0, total_need_resolution, 100):
to_resolve = needs_resolution[index : index + 100]
members = await guild.query_members(limit=100, user_ids=to_resolve, cache=True)
for member in members:
yield member
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] |
shinrl/__init__.py | qqhann/ShinRL | 0 | 1608296 | from gym.envs.registration import register
# Common mathmetical (jitted) functions & classes.
from ._calc.backup_dp import (
calc_optimal_q,
calc_q,
calc_return,
calc_visit,
double_backup_dp,
expected_backup_dp,
munchausen_backup_dp,
optimal_backup_dp,
soft_expected_backup_dp,
)
from ._calc.backup_rl import (
double_backup_rl,
expected_backup_rl,
munchausen_backup_rl,
optimal_backup_rl,
soft_expected_backup_rl,
)
from ._calc.build_net import build_forward_conv, build_forward_fc, build_net_act
from ._calc.collect_samples import ACT_FN, Sample, collect_samples, make_replay_buffer
from ._calc.draw import draw_circle, line_aa
from ._calc.epsilon_greedy import calc_eps
from ._calc.loss import cross_entropy_loss, huber_loss, kl_loss, l2_loss
from ._calc.mdp import MDP
from ._calc.moving_average import calc_ma
from ._calc.sparse import SparseMat, sp_mul, sp_mul_t
# Common useful functions & classes.
from ._utils.config import Config
from ._utils.jittable import DictJittable
from ._utils.log import add_logfile_handler, initialize_log_style
from ._utils.minatar import make_minatar
from ._utils.params import ParamsDict
from ._utils.prepare_history_dir import prepare_history_dir
from ._utils.scalars import Scalars
from ._utils.tables import TbDict
# Implemented environments with access to the *oracle* quantities.
from .envs.base.config import EnvConfig
from .envs.base.env import OBS_FN, REW_FN, TRAN_FN, ShinEnv
from .envs.cartpole.env import CartPole
from .envs.maze.env import Maze
from .envs.mountaincar.env import MountainCar
from .envs.pendulum.env import Pendulum
# Implemented environments with access to the *oracle* quantities.
from .solvers.base.core.config import SolverConfig
from .solvers.base.core.history import History
from .solvers.base.core.mixin import (
GymEvalMixIn,
GymExploreMixIn,
ShinEvalMixIn,
ShinExploreMixIn,
)
from .solvers.base.solver import Solver
from .solvers.pi.discrete.solver import DiscretePiSolver
from .solvers.vi.discrete.solver import DiscreteViSolver
initialize_log_style()
# ----- register envs -----
register(
id="ShinMaze-v0",
entry_point="shinrl:Maze",
kwargs={"config": Maze.DefaultConfig()},
)
register(
id="ShinPendulum-v0",
entry_point="shinrl:Pendulum",
kwargs={"config": Pendulum.DefaultConfig()},
)
register(
id="ShinPendulumContinuous-v0",
entry_point="shinrl:Pendulum",
kwargs={"config": Pendulum.DefaultConfig(act_mode="continuous", dA=50)},
)
register(
id="ShinPendulumImage-v0",
entry_point="shinrl:Pendulum",
kwargs={"config": Pendulum.DefaultConfig(obs_mode="image")},
)
register(
id="ShinPendulumImageContinuous-v0",
entry_point="shinrl:Pendulum",
kwargs={
"config": Pendulum.DefaultConfig(act_mode="continuous", dA=50, obs_mode="image")
},
)
register(
id="ShinMountainCar-v0",
entry_point="shinrl:MountainCar",
kwargs={"config": MountainCar.DefaultConfig()},
)
register(
id="ShinMountainCarContinuous-v0",
entry_point="shinrl:MountainCar",
kwargs={"config": MountainCar.DefaultConfig(act_mode="continuous", dA=50)},
)
register(
id="ShinMountainCarImage-v0",
entry_point="shinrl:MountainCar",
kwargs={"config": MountainCar.DefaultConfig(obs_mode="image")},
)
register(
id="ShinMountainCarImageContinuous-v0",
entry_point="shinrl:MountainCar",
kwargs={
"config": MountainCar.DefaultConfig(
act_mode="continuous", dA=50, obs_mode="image"
)
},
)
register(
id="ShinCartPole-v0",
entry_point="shinrl:CartPole",
kwargs={"config": CartPole.DefaultConfig()},
)
register(
id="ShinCartPoleContinuous-v0",
entry_point="shinrl:CartPole",
kwargs={"config": CartPole.DefaultConfig(act_mode="continuous", dA=50)},
)
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] |
fhirclient/models/goal.py | carolinarsm/client-py | 418 | 1616039 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Generated from FHIR 4.0.0-a53ec6ee1b (http://hl7.org/fhir/StructureDefinition/Goal) on 2019-05-07.
# 2019, SMART Health IT.
from . import domainresource
class Goal(domainresource.DomainResource):
""" Describes the intended objective(s) for a patient, group or organization.
Describes the intended objective(s) for a patient, group or organization
care, for example, weight loss, restoring an activity of daily living,
obtaining herd immunity via immunization, meeting a process improvement
objective, etc.
"""
resource_type = "Goal"
def __init__(self, jsondict=None, strict=True):
""" Initialize all valid properties.
:raises: FHIRValidationError on validation errors, unless strict is False
:param dict jsondict: A JSON dictionary to use for initialization
:param bool strict: If True (the default), invalid variables will raise a TypeError
"""
self.achievementStatus = None
""" in-progress | improving | worsening | no-change | achieved |
sustaining | not-achieved | no-progress | not-attainable.
Type `CodeableConcept` (represented as `dict` in JSON). """
self.addresses = None
""" Issues addressed by this goal.
List of `FHIRReference` items (represented as `dict` in JSON). """
self.category = None
""" E.g. Treatment, dietary, behavioral, etc..
List of `CodeableConcept` items (represented as `dict` in JSON). """
self.description = None
""" Code or text describing goal.
Type `CodeableConcept` (represented as `dict` in JSON). """
self.expressedBy = None
""" Who's responsible for creating Goal?.
Type `FHIRReference` (represented as `dict` in JSON). """
self.identifier = None
""" External Ids for this goal.
List of `Identifier` items (represented as `dict` in JSON). """
self.lifecycleStatus = None
""" proposed | planned | accepted | active | on-hold | completed |
cancelled | entered-in-error | rejected.
Type `str`. """
self.note = None
""" Comments about the goal.
List of `Annotation` items (represented as `dict` in JSON). """
self.outcomeCode = None
""" What result was achieved regarding the goal?.
List of `CodeableConcept` items (represented as `dict` in JSON). """
self.outcomeReference = None
""" Observation that resulted from goal.
List of `FHIRReference` items (represented as `dict` in JSON). """
self.priority = None
""" high-priority | medium-priority | low-priority.
Type `CodeableConcept` (represented as `dict` in JSON). """
self.startCodeableConcept = None
""" When goal pursuit begins.
Type `CodeableConcept` (represented as `dict` in JSON). """
self.startDate = None
""" When goal pursuit begins.
Type `FHIRDate` (represented as `str` in JSON). """
self.statusDate = None
""" When goal status took effect.
Type `FHIRDate` (represented as `str` in JSON). """
self.statusReason = None
""" Reason for current status.
Type `str`. """
self.subject = None
""" Who this goal is intended for.
Type `FHIRReference` (represented as `dict` in JSON). """
self.target = None
""" Target outcome for the goal.
List of `GoalTarget` items (represented as `dict` in JSON). """
super(Goal, self).__init__(jsondict=jsondict, strict=strict)
def elementProperties(self):
js = super(Goal, self).elementProperties()
js.extend([
("achievementStatus", "achievementStatus", codeableconcept.CodeableConcept, False, None, False),
("addresses", "addresses", fhirreference.FHIRReference, True, None, False),
("category", "category", codeableconcept.CodeableConcept, True, None, False),
("description", "description", codeableconcept.CodeableConcept, False, None, True),
("expressedBy", "expressedBy", fhirreference.FHIRReference, False, None, False),
("identifier", "identifier", identifier.Identifier, True, None, False),
("lifecycleStatus", "lifecycleStatus", str, False, None, True),
("note", "note", annotation.Annotation, True, None, False),
("outcomeCode", "outcomeCode", codeableconcept.CodeableConcept, True, None, False),
("outcomeReference", "outcomeReference", fhirreference.FHIRReference, True, None, False),
("priority", "priority", codeableconcept.CodeableConcept, False, None, False),
("startCodeableConcept", "startCodeableConcept", codeableconcept.CodeableConcept, False, "start", False),
("startDate", "startDate", fhirdate.FHIRDate, False, "start", False),
("statusDate", "statusDate", fhirdate.FHIRDate, False, None, False),
("statusReason", "statusReason", str, False, None, False),
("subject", "subject", fhirreference.FHIRReference, False, None, True),
("target", "target", GoalTarget, True, None, False),
])
return js
from . import backboneelement
class GoalTarget(backboneelement.BackboneElement):
""" Target outcome for the goal.
Indicates what should be done by when.
"""
resource_type = "GoalTarget"
def __init__(self, jsondict=None, strict=True):
""" Initialize all valid properties.
:raises: FHIRValidationError on validation errors, unless strict is False
:param dict jsondict: A JSON dictionary to use for initialization
:param bool strict: If True (the default), invalid variables will raise a TypeError
"""
self.detailBoolean = None
""" The target value to be achieved.
Type `bool`. """
self.detailCodeableConcept = None
""" The target value to be achieved.
Type `CodeableConcept` (represented as `dict` in JSON). """
self.detailInteger = None
""" The target value to be achieved.
Type `int`. """
self.detailQuantity = None
""" The target value to be achieved.
Type `Quantity` (represented as `dict` in JSON). """
self.detailRange = None
""" The target value to be achieved.
Type `Range` (represented as `dict` in JSON). """
self.detailRatio = None
""" The target value to be achieved.
Type `Ratio` (represented as `dict` in JSON). """
self.detailString = None
""" The target value to be achieved.
Type `str`. """
self.dueDate = None
""" Reach goal on or before.
Type `FHIRDate` (represented as `str` in JSON). """
self.dueDuration = None
""" Reach goal on or before.
Type `Duration` (represented as `dict` in JSON). """
self.measure = None
""" The parameter whose value is being tracked.
Type `CodeableConcept` (represented as `dict` in JSON). """
super(GoalTarget, self).__init__(jsondict=jsondict, strict=strict)
def elementProperties(self):
js = super(GoalTarget, self).elementProperties()
js.extend([
("detailBoolean", "detailBoolean", bool, False, "detail", False),
("detailCodeableConcept", "detailCodeableConcept", codeableconcept.CodeableConcept, False, "detail", False),
("detailInteger", "detailInteger", int, False, "detail", False),
("detailQuantity", "detailQuantity", quantity.Quantity, False, "detail", False),
("detailRange", "detailRange", range.Range, False, "detail", False),
("detailRatio", "detailRatio", ratio.Ratio, False, "detail", False),
("detailString", "detailString", str, False, "detail", False),
("dueDate", "dueDate", fhirdate.FHIRDate, False, "due", False),
("dueDuration", "dueDuration", duration.Duration, False, "due", False),
("measure", "measure", codeableconcept.CodeableConcept, False, None, False),
])
return js
import sys
try:
from . import annotation
except ImportError:
annotation = sys.modules[__package__ + '.annotation']
try:
from . import codeableconcept
except ImportError:
codeableconcept = sys.modules[__package__ + '.codeableconcept']
try:
from . import duration
except ImportError:
duration = sys.modules[__package__ + '.duration']
try:
from . import fhirdate
except ImportError:
fhirdate = sys.modules[__package__ + '.fhirdate']
try:
from . import fhirreference
except ImportError:
fhirreference = sys.modules[__package__ + '.fhirreference']
try:
from . import identifier
except ImportError:
identifier = sys.modules[__package__ + '.identifier']
try:
from . import quantity
except ImportError:
quantity = sys.modules[__package__ + '.quantity']
try:
from . import range
except ImportError:
range = sys.modules[__package__ + '.range']
try:
from . import ratio
except ImportError:
ratio = sys.modules[__package__ + '.ratio']
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pyplots/numpy_intro_3.py | junghun73/Learning | 419 | 1615113 | import numpy as np
import matplotlib.pyplot as plt
img = plt.imread('../data/elephant.png')
print img.shape, img.dtype
# (200, 300, 3) dtype('float32')
plt.imshow(img)
plt.savefig('plot.png')
plt.show()
plt.imsave('red_elephant', img[:,:,0], cmap=plt.cm.gray)
# This saved only one channel (of RGB)
plt.imshow(plt.imread('red_elephant.png'))
plt.show()
# Other libraries:
from scipy.misc import imsave
imsave('tiny_elephant.png', img[::6,::6])
plt.imshow(plt.imread('tiny_elephant.png'), interpolation='nearest')
plt.show()
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] |
trading_alert/crypto/tasks.py | Mohammadrezafah/trading-alert | 1 | 124587 | import logging
import cryptocompare
from django.conf import settings
from django.contrib.auth import get_user_model
from django.core.exceptions import ImproperlyConfigured
from django.core.mail import EmailMessage
from config import celery_app
from trading_alert.crypto.models import CriptoAlert
User = get_user_model()
# Get an instance of a logger
logger = logging.getLogger(__name__)
def send_email_crypto(cripto_alert):
subject = f"Segera check {cripto_alert.code}"
body = f"{cripto_alert.code} sudah mencapai target {cripto_alert.operator} {cripto_alert.price}"
email = EmailMessage(
subject, body, settings.DEFAULT_FROM_EMAIL, [user.email for user in cripto_alert.notif_to.all()], [],
)
email.send()
def get_current_price(cripto_alert):
price = 0
try:
price = float(
cryptocompare.get_price(
cripto_alert.code,
currency=cripto_alert.currency
)[cripto_alert.code][cripto_alert.currency])
except Exception as e:
logger.error(str(e))
logger.info(f"Price is : {price}")
return price
@celery_app.task()
def send_alert_crypto(crpyto_id):
cripto_alert = CriptoAlert.objects.filter(pk=crpyto_id).first()
if not cripto_alert:
logger.error("CriptoAlert objects not found")
return "CriptoAlert objects not found"
if cripto_alert.limit_notif >= cripto_alert.success_notif:
cripto_alert.currency_price = get_current_price(cripto_alert)
is_email = False
if cripto_alert.currency_price != 0 and eval(
f"{cripto_alert.currency_price} {cripto_alert.operator} {cripto_alert.price}"
):
send_email_crypto(cripto_alert)
cripto_alert.success_notif = cripto_alert.success_notif + 1
cripto_alert.save()
return True
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quantitative_finance/L3/python/hjm_test.py | Aperture-Electronic/Vitis_Libraries | 1 | 82479 | #!/usr/bin/env python3
# Ensure environmental variable i.e. paths are set to used the modules
from xf_fintech_python import DeviceManager, HJM
import numpy as np
import argparse
def zcbAnalytical(rawData, maturity, tau = 0.5):
# Take last row
fc = np.copy(rawData[rawData.shape[0] - 1])
fc *= 0.01
accum = 0.0
for i in range(int(maturity / tau)):
accum += fc[i]
return np.exp(-tau * accum)
def zcbExample(hjm, hist_data, maturity, paths):
seeds = (np.random.rand(N_FACTORS * MC_UN) * 1000).astype(int)
print("Using seeds " + str(seeds))
outPrice = []
hjm.run(list(hist_data.flat), list(seeds.flat), outPrice, tenors, curves, paths, maturity, maturity)
runtime = hjm.lastruntime()
analyticalPrice = zcbAnalytical(hist_data, maturity)
print("[CPU] ZCB calculated analytically: %10.6f" % analyticalPrice)
print("[FPGA] ZCB calculated with HJM framework: %10.6f" % outPrice[0])
print("[FPGA] Runtime = %d" %runtime, "us")
diff = (outPrice[0] - analyticalPrice) / analyticalPrice * 100
print(" Diff = %.4f" % diff, "%")
parser = argparse.ArgumentParser(description='Example of Heath-Jarrow-Morton framework running on a FPGA')
parser.add_argument('data_in', type=str, help='Path to csv with historical rates data')
parser.add_argument('load', type=str, help='filename of xlcbin load, e.g. hjm.xclbin')
args = parser.parse_args()
N_FACTORS = 3
MC_UN = 4
hist_data = np.loadtxt(args.data_in, delimiter=',')
tenors = hist_data.shape[1]
curves = hist_data.shape[0]
xclbin_load = (args.load)
print("\nThe Heath-Jarrow-Morton model\n=================================\n")
# Program variables
deviceList = DeviceManager.getDeviceList("u200")
lastruntime = 0
runtime = 0
# Identify which cards installed and choose the first available U200 card
print("Found these {0} device(s):".format(len(deviceList)))
for x in deviceList:
print(x.getName())
chosenDevice = deviceList[0]
print("Choosing the first, ", str(chosenDevice), "\n")
# Selecting and loadings into FPGA of chosen card the financial model to be used
hjm = HJM(xclbin_load)
hjm.claimDevice(chosenDevice)
# Examples of possible operations, showing MC convergence of prices
print("Example 1) Pricing ZCB of maturity 10Y with 50 MonteCarlo paths")
zcbExample(hjm, hist_data, 10.0, 50)
print("Example 2) Pricing ZCB of maturity 10Y with 100 MonteCarlo paths")
zcbExample(hjm, hist_data, 10.0, 100)
print("Example 3) Pricing ZCB of maturity 10Y with 200 MonteCarlo paths")
zcbExample(hjm, hist_data, 10.0, 200)
print("Example 4) Pricing ZCB of maturity 10Y with 400 MonteCarlo paths")
zcbExample(hjm, hist_data, 10.0, 400)
hjm.releaseDevice()
print("End of example/test.\n")
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ramda/memoize_with_test.py | jakobkolb/ramda.py | 56 | 22619 | <gh_stars>10-100
from ramda.memoize_with import memoize_with
from ramda.product import product
from ramda.private.asserts import assert_equal as e
count = 0
def memoize_with_test():
@memoize_with(lambda x: -x)
def factorial(n):
global count
count += 1
return product(range(1, n + 1))
e(factorial(5), 120)
e(factorial(5), 120)
e(factorial(5), 120)
e(factorial(4), 24)
e(factorial(4), 24)
e(factorial(4), 24)
e(factorial(4), 24)
e(count, 2)
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instagram/views.py | Julia-Agasaro/Insta-clone | 0 | 107940 | from django.http import Http404
from django.http import HttpResponseRedirect
from django.shortcuts import render,redirect
from django.contrib.auth.decorators import login_required
from django.contrib import messages
from django.contrib.auth.models import User
from .models import Image, Profile,Comment,Likes,Follow
from .forms import ProfileForm,ImageForm,CommentForm,ProfileEditForm
# Create your views here.
@login_required(login_url='/accounts/login/')
def home(request):
current_user = request.user
all_images = Image.objects.all()
print(all_images)
profile = Profile.objects.all()
print(profile)
comments = Comment.objects.all()
likes = Likes.objects.all()
return render(request,'home.html',{'all_images':all_images,'profile':profile})
@login_required(login_url='/accounts/login/')
def profile(request,prof_id):
'''
Method that fetches a users profile page
'''
user=User.objects.get(pk=prof_id)
images = Image.objects.filter(profile = prof_id)
title = User.objects.get(pk = prof_id).username
profile = Profile.objects.filter(user = prof_id)
if Follow.objects.filter(followings=request.user,followers = user).exists():
is_follow = True
else:
is_follow = False
followers = Follow.objects.filter( followers = user).count()
followings = Follow.objects.filter(followings = user).count()
return render(request,'profile/profile.html',{"images":images,"profile":profile,"title":title,"is_follow":is_follow,"followers":followers,"followings":followings})
@login_required(login_url='accounts/login/')
def edit(request):
current_user = request.user
if request.method == 'POST':
if Profile.objects.filter(user_id= current_user):
profile_form = ProfileEditForm(request.POST,request.FILES,instance = Profile.objects.get(user_id=current_user))
else:
profile_form = ProfileEditForm(request.POST,request.FILES)
if profile_form.is_valid():
userProfile=profile_form.save(commit = False)
userProfile.user = current_user
userProfile.save()
return redirect('home')
else:
profile_form = ProfileEditForm()
return render(request, 'profile/profileForm.html', locals())
@login_required(login_url='accounts/login/')
def add_image(request):
current_user = request.user
if request.method == 'POST':
form = ImageForm(request.POST, request.FILES)
if form.is_valid():
add=form.save(commit=False)
add.profile = current_user
# add.profile_details= current_user.profile
add.save()
return redirect('home')
else:
form = ImageForm()
return render(request,'upload.html',locals())
@login_required(login_url='/accounts/login/')
def like(request, image_id):
current_user = request.user
image=Image.objects.get(id=image_id)
new_like,created= Likes.objects.get_or_create(liker=current_user, image=image)
new_like.save()
print(new_like)
return redirect('home')
def comment(request,image_id):
current_user=request.user
image = Image.objects.get(id=image_id)
profile_owner = User.objects.get(username=current_user)
comments = Comment.objects.all()
print(comments)
if request.method == 'POST':
form = CommentForm(request.POST)
if form.is_valid():
comment = form.save(commit=False)
comment.image = image
comment.comment_owner = current_user
comment.save()
print(comments)
return redirect(home)
else:
form = CommentForm()
return render(request, 'comment.html', locals())
@login_required(login_url='/accounts/login/')
def search(request):
if 'user' in request.GET and request.GET["user"]:
search_term = request.GET.get("user")
searched_user = Profile.search_username(search_term)
message = f"{search_term}"
print(searched_user)
return render(request, 'search.html',{"message":message,"users":searched_user})
else:
message = "You haven't searched for any term"
return render(request, 'search.html',{"message":message})
@login_required(login_url='/accounts/login/')
def follow(request,followers):
'''
Method that enables a user to follow another user.
'''
user=User.objects.get(id=followers )
is_follow=False
if Follow.objects.filter( followings =request.user,followers = user).exists():
Follow.objects.filter( followings =request.user,followers = user).delete()
is_follow=False
else:
Follow( followings =request.user,followers = user).save()
is_follow=True
return HttpResponseRedirect(request.META.get('HTTP_REFERER')) | [
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graphene/graphene_core/relation_extraction/model/extraction.py | kkatsamaktsis/PyGraphene | 0 | 131259 | <gh_stars>0
class Extraction:
pass
| [
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13,
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13857,
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] |
tests/unit/test_nest.py | hkak03key/python-lib-sample | 0 | 112029 | <gh_stars>0
import sys
def test_good_bye_nest(capfd):
import lib_sample.nest as nest
nest.good_bye()
out, err = capfd.readouterr()
assert out == "good_bye, this is nest/nest.py.\n"
assert err is ""
def test_good_bye_lib_sample(capfd):
import lib_sample
lib_sample.good_bye()
out, err = capfd.readouterr()
assert out == "good_bye, this is nest/nest.py.\n"
assert err is ""
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] |
pythonql/Executor.py | hi117/pythonql | 0 | 43097 | <reponame>hi117/pythonql
from pythonql.algebra.operator import plan_from_list
from pythonql.algebra.operators import *
from pythonql.PQTuple import PQTuple
from pythonql.helpers import flatten
from pythonql.Rewriter import rewrite
from pythonql.debug import Debug
import json
import types
def make_pql_tuple(vals,lcs):
t = []
als = []
for v in vals:
t.append(eval(v[0],lcs,globals()))
alias = v[1] if v[1] else v[0]
als.append(alias)
schema = {n:i for (i,n) in enumerate(als)}
return PQTuple(t,schema)
def str_dec(string):
res = ""
prev_slash = False
for ch in string:
if ch == chr(92):
if not prev_slash:
prev_slash = True
else:
res += ch
prev_slash = False
else:
prev_slash = False
res += ch
return res
# isList predicate for path expressions
def isList(x):
return (hasattr(x,'__iter__') and not
hasattr(x,'keys') and not
isinstance(x,str))
# isMap predicate for path expression
def isMap(x):
return hasattr(x,'keys')
# Implement a child step on some collection or map
def PQChildPath (coll,f,lcs):
f = eval(str_dec(f), globals(), lcs) if f!='_' else None
if isList(coll):
for i in flatten(coll):
if isMap(i):
for j in i.keys():
if f is None:
yield i[j]
elif f and j==f:
yield i[j]
if isMap(coll):
for i in coll.keys():
if f is None:
yield coll[i]
elif f and i==f:
yield coll[i]
class map_tuple:
def __init__(self,key,value):
self.key = key
self.value = value
def __repr__(self):
return ("<" + repr(self.key) + ":" + repr(self.value) + ">")
# Implement a descendents path on some collection or map
def PQDescPath(coll,f,lcs):
f = eval(f,globals(),lcs) if f!='_' else None
stack = []
if isList(coll):
stack = [i for i in flatten(coll)]
elif isMap(coll):
stack = [map_tuple(k,v) for (k,v) in coll.items()]
while stack:
i = stack.pop()
if isinstance(i,map_tuple):
if f is None:
yield i.value
elif f and i.key==f:
yield i.value
i = i.value
if isList(i):
it = iter(i)
frst = next(it)
[stack.append(j) for j in it]
if isList(frst):
stack.extend([ci for ci in frst])
elif isMap(frst):
stack.extend([map_tuple(k,v) for (k,v) in frst.items()])
elif isMap(i):
keys = list(i.keys())
[stack.append(map_tuple(j,i[j])) for j in keys]
def PQTry( try_expr, except_expr, lcs):
try_expr = str_dec(try_expr)
except_expr = str_dec(except_expr)
try:
return eval(try_expr,lcs,globals())
except:
return eval(except_expr,lcs,globals())
# create a table with an empty tuple
def emptyTuple(schema):
return PQTuple([None] * len(schema), schema)
# Execute the query
def PyQuery( clauses, prior_locs, prior_globs, returnType ):
data = []
data.append( emptyTuple([]) )
clauses = list(clauses)
clauses.reverse()
plan = plan_from_list(clauses)
plan = rewrite(plan, prior_locs)
if Debug().print_optimized:
print("Rewritten query:",plan)
data = plan.execute(data, prior_locs, prior_globs)
if returnType == "gen":
return data
elif returnType == "list":
return list(data)
elif returnType == "set":
return set(data)
else:
return dict(data)
# Process Select clause
# We still keep that feature of generating tuples for now
def processSelectClause(c, table, prior_lcs, prior_globs):
# If this is a list/set comprehension:
if c.expr:
# Compile the expression:
e = compile(c.expr.lstrip(), '<string>','eval')
for t in table:
lcs = dict(prior_lcs)
lcs.update(t.getDict())
yield eval(e,prior_globs,lcs)
else:
k_expr = compile(c.key_expr.lstrip(),'<string>','eval')
v_expr = compile(c.value_expr.lstrip(),'<string>','eval')
for t in table:
lcs = prior_lcs
lcs.update(t.getDict())
k = eval(k_expr,prior_globs,lcs)
v = eval(v_expr,prior_globs,lcs)
yield (k,v)
# Process the for clause. This clause creates a cartesian
# product of the input table with new sequence
def processForClause(c, table, prior_lcs, prior_globs):
new_schema = None
print(c.expr)
comp_expr = compile(c.expr.lstrip(), "<string>", "eval")
for t in table:
if not new_schema:
new_schema = dict(t.schema)
for (i,v) in enumerate(c.vars):
new_schema[v] = len(t.schema) + i
lcs = dict(prior_lcs)
lcs.update(t.getDict())
vals = eval(comp_expr, prior_globs, lcs)
if len(c.vars) == 1:
for v in vals:
new_t_data = list(t.tuple)
new_t_data.append(v)
new_t = PQTuple(new_t_data, new_schema)
yield new_t
else:
for v in vals:
unpack_expr = "[ %s for %s in [ __v ]]" % (
'(' + ",".join(c.vars) + ')', c.unpack)
unpacked_vals = eval(unpack_expr, prior_globs, {'__v':v})
new_t_data = list(t.tuple)
for tv in unpacked_vals[0]:
new_t_data.append(tv)
new_t = PQTuple(new_t_data, new_schema)
yield new_t
# Process the let clause. Here we just add a variable to each
# input tuple
def processLetClause(c, table, prior_lcs, prior_globs):
comp_expr = compile(c.expr.lstrip(), "<string>", "eval")
new_schema = None
for t in table:
if not new_schema:
new_schema = dict(t.schema)
for (i,v) in enumerate(c.vars):
new_schema[v] = len(t.schema) + i
lcs = dict(prior_lcs)
lcs.update(t.getDict())
v = eval(comp_expr, prior_globs, lcs)
if len(c.vars) == 1:
t.tuple.append(v)
new_t = PQTuple( t.tuple, new_schema )
yield new_t
else:
unpack_expr = "[ %s for %s in [ __v ]]" % (
'(' + ",".join(c.vars) + ')', c.unpack)
unpacked_vals = eval(unpack_expr, prior_globs, {'__v':v})
new_t_data = list(t.tuple)
for tv in unpacked_vals[0]:
new_t_data.append(tv)
new_t = PQTuple(new_t_data, new_schema)
yield new_t
# Process a join
def processJoin(c, table, prior_lcs, prior_globs, left_arg, right_arg):
new_schema = None
left_conds = c.left_conds
right_conds = c.right_conds
join_type = 'nl'
dir = 'right'
if c.hint:
join_type = c.hint['join_type']
dir = c.hint['dir']
if dir == 'left':
left_arg,right_arg = right_arg,left_arg
r_init_data = []
r_init_data.append( emptyTuple([]) )
# Build an index on the right relation, if we're doing
# an index join.
index = None
if join_type == 'index':
index = {}
r_data = r_init_data
r_data = right_arg.execute(r_data, prior_lcs)
for t in r_data:
index_tuple = []
for rcond in right_conds:
lcs = dict(prior_lcs)
lcs.update(t.getDict())
rcond_val = eval(rcond, prior_globs, lcs)
index_tuple.append( rcond_val )
index_tuple = tuple(index_tuple)
if not index_tuple in index:
index[ index_tuple ] = []
index[ index_tuple ].append( t )
# Iterate over the tuples of the left relation and
# compute the tuple of condition vars
table = left_arg.execute(table, prior_lcs, prior_globs)
for t in table:
cond_tuple = []
for lcond in left_conds:
lcs = dict(prior_lcs)
lcs.update(t.getDict())
lcond_val = eval(lcond, prior_globs, lcs)
cond_tuple.append( lcond_val )
cond_tuple = tuple(cond_tuple)
if index:
if cond_tuple in index:
for t2 in index[cond_tuple]:
if not new_schema:
new_schema = dict(t.schema)
for i,_ in enumerate(t2):
v = [x for x in t2.schema.items() if x[1]==i][0][0]
new_schema[v] = len(new_schema) + i
new_t_data = list(t.tuple)
new_t_data += list(t2.tuple)
new_t = PQTuple(new_t_data, new_schema)
yield new_t
else:
continue
else:
r_data = r_init_data
r_data = right_arg.execute(r_data, prior_lcs, prior_globs)
for t2 in r_data:
rcond_tuple = []
for rcond in right_conds:
lcs = dict(prior_lcs)
lcs.update(t2.getDict())
rcond_val = eval(rcond, prior_globs, lcs)
rcond_tuple.append( rcond_val )
rcond_tuple = tuple(rcond_tuple)
if cond_tuple == rcond_tuple:
if not new_schema:
new_schema = dict(t.schema)
for i,_ in enumerate(t2):
v = [x for x in t2.schema.items() if x[1]==i][0][0]
new_schema[v] = len(new_schema) + i
new_t_data = list(t.tuple)
new_t_data += list(t2.tuple)
new_t = PQTuple(new_t_data, new_schema)
yield new_t
# Process the match claise
def processMatchClause(c, table, prior_lcs, prior_globs):
clause_expr = compile(c.expr, "<string>", "eval")
# Fetch and compile all expressions in the
# pattern match clause
e_patterns = []
patterns = list(c.pattern)
while patterns:
p = patterns.pop()
if 'expr_cond' in p:
e_patterns.append(p)
if 'pattern' in p:
patterns.append(p['pattern'])
for ep in e_patterns:
ep['expr_cond'] = compile(ep["expr_cond"], "<string>", "eval")
new_schema = None
for t in table:
if not new_schema:
new_schema = dict(t.schema)
for (i,v) in enumerate(c.vars):
new_schema[v] = len(t.schema) + i
lcs = dict(prior_lcs)
lcs.update(t.getDict())
vals = eval(clause_expr, prior_globs, lcs)
for v in vals:
if not hasattr(v, '__contains__'):
continue
new_t_data = list(t.tuple) + [None]*len(c.vars)
new_t = PQTuple(new_t_data, new_schema)
if match_pattern(c.pattern, c.exact, v, new_t, lcs, prior_globs):
yield new_t
def match_pattern(ps, isExact, v, new_t, lcs, prior_globs):
all_heads = []
for p in [x for x in ps if 'match' in x]:
match = p['match'][1:-1]
all_heads.append(match)
if match not in v:
return False
if 'const_cond' in p:
if v[match] != p['const_cond'][1:-1]:
return False
if 'bind_to' in p:
new_t[p['bind_to']] = v[match]
lcs.update({p['bind_to']:v[match]})
if 'expr_cond' in p:
val = eval(p['expr_cond'], prior_globs, lcs)
if not val:
return False
if 'pattern' in p:
if not match_pattern(p['pattern'], isExact, v[match], new_t, lcs, prior_globs):
return False
if isExact and any([x for x in v if x not in all_heads]):
return False
bind_parent = next((x for x in ps if 'bind_parent_to' in x), None)
if bind_parent:
new_t[bind_parent['bind_parent_to']] = v
lcs.update({bind_parent['bind_parent_to']:v})
return True
# Process the count clause. Similar to let, but simpler
def processCountClause(c, table, prior_lcs, prior_globs):
new_schema = None
for (i,t) in enumerate(table):
if not new_schema:
new_schema = dict(t.schema)
new_schema[c.var] = len(t.schema)
new_t = PQTuple( t.tuple + [i], new_schema )
yield new_t
# Process the group-by
def processGroupByClause(c, table, prior_lcs, prior_globs):
gby_aliases = [g if isinstance(g,str) else g[1]
for g in c.groupby_list]
gby_exprs = [g if isinstance(g,str) else g[0]
for g in c.groupby_list]
comp_exprs = [compile(e,'<string>','eval') for e in gby_exprs]
grp_table = {}
schema = None
# Group tuples in a hashtable
for t in table:
if not schema:
schema = t.schema
lcs = dict(prior_lcs)
lcs.update(t.getDict())
# Compute the key
k = tuple( [eval(e,prior_globs,lcs) for e in comp_exprs] )
if not k in grp_table:
grp_table[k] = []
grp_table[k].append(t)
if not grp_table:
return
yield
# Construct the new table
# Non-key variables
non_key_vars = [v for v in schema if not v in gby_aliases ]
new_schema = {v:i for (i,v) in enumerate( gby_aliases + non_key_vars )}
for k in grp_table:
t = PQTuple([None]*len(new_schema), new_schema)
#Copy over the key
for (i,v) in enumerate(gby_aliases):
t[v] = k[i]
#Every other variable (not in group by list) is turned into a lists
#First create empty lists
for v in non_key_vars:
t[v] = []
# Now fill in the lists:
for part_t in grp_table[k]:
for v in non_key_vars:
t[v].append( part_t[v] )
yield t
# Process where clause
def processWhereClause(c, table, prior_lcs, prior_globs):
comp_expr = compile(c.expr.lstrip(),"<string>","eval")
for t in table:
lcs = dict(prior_lcs)
lcs.update(t.getDict())
val = eval(comp_expr, prior_globs, lcs)
if val:
yield t
# Process the orderby clause
def processOrderByClause(c, table, prior_lcs, prior_globs):
# Here we do n sorts, n is the number of sort specifications
# For each sort we first need to compute a sort value (could
# be some expression)
sort_exprs = [ compile(os[0].lstrip(),"<string>","eval") for os in c.orderby_list]
sort_rev = [ o[1]=='desc' for o in c.orderby_list]
def computeSortSpec(tup,sort_spec):
lcs = dict(prior_lcs)
lcs.update(tup.getDict())
return eval(sort_spec, prior_globs, lcs)
sort_exprs.reverse()
sort_rev.reverse()
if isinstance(table,types.GeneratorType):
table = list(table)
for (i,e) in enumerate(sort_exprs):
table.sort( key = lambda x: computeSortSpec(x,e),
reverse= sort_rev[i])
for t in table:
yield t
# Create the set of variables for a new window
# This is the full set just for convienience, the
# query might not use all of these vars.
# The names of the variables coincide with the
# names in the specification of window clause
def make_window_vars():
return {"s_curr":None, "s_at":None, "s_prev":None, "s_next":None,
"e_curr":None, "e_at":None, "e_prev":None, "e_next":None}
# Start variables from a list of variables
all_start_vars = ["s_curr","s_at","s_prev","s_next"]
# Fill in the start vars of the window, given the value list and current index
def fill_in_start_vars(vars, binding_seq, i ):
vars["s_curr"] = binding_seq[i]
vars["s_at"] = i
vars["s_prev"] = binding_seq[i-1] if i>0 else None
vars["s_next"] = binding_seq[i+1] if i+1<len(binding_seq) else None
# Fill in the end vars of the window, given the values list and current index
def fill_in_end_vars(vars, binding_seq, i ):
vars["e_curr"] = binding_seq[i]
vars["e_at"] = i
vars["e_prev"] = binding_seq[i-1] if i>0 else None
vars["e_next"] = binding_seq[i+1] if i+1<len(binding_seq) else None
# Check the start condition of the window, i.e. whether we should
# start a new window at this location (without considering tumbling
# windows, that check is done elsewhere).
def check_start_condition(all_vars,clause,locals,prior_globs,var_mapping):
# we just need to evaluate the when expression
# but we need to set up the vars correctly, respecting the visibility
# conditions
start_vars = set(all_start_vars).intersection(
set(var_mapping.keys()) )
start_bindings = { var_mapping[v] : all_vars[v] for v in start_vars }
# add the binding to the locals
locals.update( start_bindings )
#evaluate the when condition
return eval( clause.s_when, prior_globs, locals )
# Check the end condition of the window.
def check_end_condition(vars,clause,locals,prior_globs,var_mapping):
# If there is no 'when' clause, return False
if not clause.e_when:
return False
end_vars = set(vars.keys()).intersection( set(var_mapping.keys()))
end_binding = { var_mapping[v] : vars[v] for v in end_vars }
locals.update( end_binding )
res = eval( clause.e_when, prior_globs, locals)
return res
# Process window clause
def processWindowClause(c, table, prior_lcs, prior_globs):
schema = None
new_schema = None
# Create window variable name mapping
var_mapping = {}
for v in c.vars:
var_mapping[v] = c.vars[v]
for t in table:
if not schema:
schema = t.schema
# Create a new schema with window variables added
new_schema = dict(t.schema)
for v in c.vars:
new_schema[c.vars[v]] = len(new_schema)
lcs = dict(prior_lcs)
lcs.update(t.getDict())
# Evaluate the binding sequence
binding_seq = list(eval(c.binding_seq, prior_globs, lcs))
# Create initial window variables
# Initialize the windows
open_windows = []
closed_windows = []
# Iterate over the binding sequence
for (i,v) in enumerate(binding_seq):
# Try to open a new window
# in case of tumbling windows, only open a
# window if there are no open windows
if not c.tumbling or (c.tumbling and not open_windows):
vars = make_window_vars()
fill_in_start_vars(vars,binding_seq,i)
if check_start_condition(vars,c,dict(lcs),prior_globs,var_mapping):
open_windows.append( {"window":[], "vars":vars} )
new_open_windows = []
#update all open windows, close those that are finished
for w in open_windows:
# Add currnt value to the window
w["window"].append(v)
fill_in_end_vars(w["vars"],binding_seq,i)
if check_end_condition(w["vars"],c,dict(lcs),prior_globs,var_mapping):
closed_windows.append(w)
else:
new_open_windows.append(w)
open_windows = new_open_windows
#close or remove all remaining open windows
#if only is specified, we ignore non-closed windows
if not c.only:
closed_windows.extend(open_windows)
# create a new tuple by extending the tuple from previous clauses
# with the window variables, for each closed window
for w in closed_windows:
new_t = PQTuple( t.tuple + [None]*(len(new_schema)-len(schema)), new_schema)
new_t[ var_mapping["var"] ] = w["window"]
for v in [v for v in w["vars"].keys() if v in var_mapping]:
new_t[ var_mapping[v] ] = w["vars"][v]
yield new_t
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cputils/dependencyManager.py | computePods/pythonUtils | 0 | 112814 | <gh_stars>0
import yaml
from cputils.settlingTimerMixin import mixinSettlingTimer
class DependencyManager :
""" The ComputePods Dependency Manager component """
def __init__(self, natsClient) :
self.nc = natsClient
mixinSettlingTimer(self)
async def listenForDependencyMessages(self) :
async def registeredTypesCallback(aSubject, theSubject, theMessage) :
self.unSettle()
for aType in theMessage :
await self.nc.sendMessage('build.howTo.{}'.format(aType))
await self.nc.listenToSubject(
'artefact.registered.types',
registeredTypesCallback
)
async def canBuildFromCallback(aSubject, theSubject, theMessage) :
self.unSettle()
await self.nc.listenToSubject(
"build.from.>",
canBuildFromCallback
) | [
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venv/Lib/site-packages/coincurve/utils.py | CompeteLeak/crankycoin | 0 | 84293 | <filename>venv/Lib/site-packages/coincurve/utils.py<gh_stars>0
from base64 import b64decode, b64encode
from binascii import hexlify, unhexlify
from hashlib import sha256 as _sha256
from os import urandom
from coincurve.context import GLOBAL_CONTEXT
from ._libsecp256k1 import ffi, lib
GROUP_ORDER = (b'\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff'
b'\xfe\xba\xae\xdc\xe6\xafH\xa0;\xbf\xd2^\x8c\xd06AA')
GROUP_ORDER_INT = 0xfffffffffffffffffffffffffffffffebaaedce6af48a03bbfd25e8cd0364141
KEY_SIZE = 32
ZERO = b'\x00'
PEM_HEADER = b'-----BEGIN PRIVATE KEY-----\n'
PEM_FOOTER = b'-----END PRIVATE KEY-----\n'
def ensure_unicode(s):
if isinstance(s, bytes):
s = s.decode('utf-8')
return s
def pad_hex(hexed):
# Pad odd-length hex strings.
return hexed if not len(hexed) & 1 else '0' + hexed
if hasattr(int, "from_bytes"):
def bytes_to_int(bytestr):
return int.from_bytes(bytestr, 'big')
else:
def bytes_to_int(bytestr):
return int(bytestr.encode('hex'), 16)
if hasattr(int, "to_bytes"):
def int_to_bytes(num):
return num.to_bytes((num.bit_length() + 7) // 8 or 1, 'big')
def int_to_bytes_padded(num):
return pad_scalar(
num.to_bytes((num.bit_length() + 7) // 8 or 1, 'big')
)
else:
def int_to_bytes(num):
return unhexlify(pad_hex('%x' % num))
def int_to_bytes_padded(num):
return pad_scalar(unhexlify(pad_hex('%x' % num)))
if hasattr(bytes, "hex"):
def bytes_to_hex(bytestr):
return bytestr.hex()
else:
def bytes_to_hex(bytestr):
return ensure_unicode(hexlify(bytestr))
if hasattr(bytes, "fromhex"):
def hex_to_bytes(hexed):
return pad_scalar(bytes.fromhex(pad_hex(hexed)))
else:
def hex_to_bytes(hexed):
print(hexed)
return pad_scalar(unhexlify(pad_hex(hexed)))
def sha256(bytestr):
return _sha256(bytestr).digest()
def chunk_data(data, size):
return (data[i:i + size] for i in range(0, len(data), size))
def der_to_pem(der):
return b''.join([
PEM_HEADER,
b'\n'.join(chunk_data(b64encode(der), 64)), b'\n',
PEM_FOOTER
])
def pem_to_der(pem):
return b64decode(
pem.strip()[28:-25].replace(b'\n', b'')
)
def get_valid_secret():
while True:
secret = urandom(KEY_SIZE)
if 0 < bytes_to_int(secret) < GROUP_ORDER_INT:
print bytes_to_int(secret)
return secret
def pad_scalar(scalar):
return (int_to_bytes(1) * (KEY_SIZE - len(scalar))) + scalar
def validate_secret(secret):
print("Validation")
print(format(bytes_to_int(secret)))
if not 0 < bytes_to_int(secret) < GROUP_ORDER_INT:
raise ValueError('Secret scalar must be greater than 0 and less than '
'{}.'.format(GROUP_ORDER_INT))
return pad_scalar(secret)
def verify_signature(signature, message, public_key, hasher=sha256, context=GLOBAL_CONTEXT):
pubkey = ffi.new('secp256k1_pubkey *')
pubkey_parsed = lib.secp256k1_ec_pubkey_parse(
context.ctx, pubkey, public_key, len(public_key)
)
if not pubkey_parsed:
raise ValueError('The public key could not be parsed or is invalid.')
msg_hash = hasher(message) if hasher is not None else message
if len(msg_hash) != 32:
raise ValueError('Message hash must be 32 bytes long.')
sig = ffi.new('secp256k1_ecdsa_signature *')
sig_parsed = lib.secp256k1_ecdsa_signature_parse_der(
context.ctx, sig, signature, len(signature)
)
if not sig_parsed:
raise ValueError('The DER-encoded signature could not be parsed.')
verified = lib.secp256k1_ecdsa_verify(
context.ctx, sig, msg_hash, pubkey
)
# A performance hack to avoid global bool() lookup.
return not not verified
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invenio-rdm/setup/provstore-push.py | ludwig-burtscher/invenio-with-provenance | 0 | 155442 | <gh_stars>0
#!/usr/bin/env python3
# Provstore credentials over ENV variables:
# Provstore username: PROVSTORE_USERNAME
# Provstore apikey: PROVSTORE_APIKEY
# exit codes:
# 0: All good - provstore document created successfully
# 1: No provstore credentials
# 2: Provstore document upload failed
# 3: Invalid command line arguments
import requests
import uuid
import json
import os
import sys
import time
import uuid
import datetime
from prov.model import ProvDocument
def push_json(prov_dict, username, apikey):
url = "https://openprovenance.org/store/api/v0/documents/"
authorization = "ApiKey {}:{}".format(username, apikey)
body = {"rec_id": str(uuid.uuid4()), "public": False, "content": prov_dict}
r = requests.post(url, headers={"Authorization": authorization}, json=body)
if r.status_code is not 201:
eprint("Statuscode: {}".format(r.status_code))
eprint("Response:{}".format(r.text))
exit(2)
def build_prov_json(s_user, s_activity, o_record_before, o_record_after, args, kwargs):
#returns single dict representing the prov_json
activity = parse_activity(s_activity)
activity_id = str(uuid.uuid4())
timestamp = datetime.datetime.now()
if not activity:
return {}
d = get_base_prov_document()
u = d.agent("invenio:user_{}".format(s_user), {"invenio:email": s_user})
a = d.activity("invenio:activity_{}_{}".format(activity, activity_id), timestamp, None, {"invenio:activityType": activity})
if activity == "read":
record_id = parse_record_id_before(o_record_before)
revision = parse_revision_after(o_record_after)
e = d.entity("invenio:record_{}_{}".format(record_id, revision), {"invenio:recordId": record_id, "invenio:revision": revision})
d.wasAssociatedWith(a, u)
d.used(a, e)
elif activity == "create":
record_id = parse_record_id_after(o_record_after)
revision = "0"
e = d.entity("invenio:record_{}_{}".format(record_id, revision), {"invenio:recordId": record_id, "invenio:revision": revision})
d.wasAssociatedWith(a, u)
d.wasGeneratedBy(e, a)
elif activity == "update":
new_record_id = parse_record_id_after(o_record_after)
old_record_id = parse_record_id_before(o_record_before)
new_revision = parse_revision_after(o_record_after)
old_revision = str(int(new_revision) - 1)
new_e = d.entity("invenio:record_{}_{}".format(new_record_id, new_revision), {"invenio:recordId": new_record_id, "invenio:revision": new_revision})
old_e = d.entity("invenio:record_{}_{}".format(old_record_id, old_revision), {"invenio:recordId": old_record_id, "invenio:revision": old_revision})
d.wasAssociatedWith(a, u)
d.used(a, old_e)
d.used(a, new_e)
d.wasDerivedFrom(new_e, old_e)
elif activity == "list":
hits = json.loads(o_record_after["response"][0].replace("'", "").replace("\\", "")[1:])["hits"]["hits"]
for hit in hits:
record_id = hit["id"]
revision = hit["revision"]
e = d.entity("invenio:record_{}_{}".format(record_id, revision), {"invenio:recordId": record_id, "invenio:revision": revision})
d.used(a, e)
d.wasAssociatedWith(a, u)
elif activity == "delete":
record_id = kwargs["pid"].split("recid:")[1].split(" ")[0]
e = d.entity("invenio:record_{}".format(record_id), {"invenio:recordId": record_id})
d.wasAssociatedWith(a, u)
d.used(a, e)
else:
return {}
return d.serialize(indent=2)
def parse_activity(activity):
no_activity = "noop"
if not activity:
return no_activity
return activity[:-19] #trim _permission_factory
def parse_record_id_before(o_record_before):
no_record_id = "unidentified-record"
if not o_record_before:
return no_record_id
return o_record_before.get("recid", no_record_id)
def parse_record_id_after(o_record_after):
no_record_id = "unidentified-record"
if not o_record_after:
return no_record_id
return o_record_after["response"][0].split("recid")[1][3:].split("\",\"")[0]
def parse_revision_after(o_record_after):
# only takes first occurence of "revision" into account
no_revision = get_timestamp_now()
if not o_record_after:
return no_revision
return o_record_after["response"][0].split("revision")[1][2:].split(",")[0]
def get_timestamp_now():
return str(int(time.time()))
def get_base_prov_document():
d = ProvDocument()
d.add_namespace("invenio", "http://example.org/invenio/")
return d
def eprint(*args, **kwargs):
print(*args, file=open("/tmp/error.txt", "a+"), **kwargs)
if __name__ == "__main__":
arglen = len(sys.argv)
if arglen is not 7:
eprint("Exactly 7 command line arguments are required")
exit(3)
try:
s_user = sys.argv[1]
s_activity = sys.argv[2]
o_record_after = json.loads(sys.argv[3]) if sys.argv[3] != "null" else None
o_record_before = json.loads(sys.argv[4]) if sys.argv[4] != "null" else None
args = json.loads(sys.argv[5])
kwargs = json.loads(sys.argv[6])
except:
eprint("Command line arguments are not valid")
exit(3)
provstore_username = os.getenv("PROVSTORE_USERNAME")
provstore_apikey = os.getenv("PROVSTORE_APIKEY")
if not provstore_username or not provstore_apikey:
eprint("No PROVSTORE credentials found in env variables")
exit(1)
push_json(build_prov_json(s_user, s_activity, o_record_before, o_record_after, args, kwargs), provstore_username, provstore_apikey)
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] |
main.py | mywrong/walabot_heatmap_camera | 1 | 1609055 | <reponame>mywrong/walabot_heatmap_camera
from walabot import Walabot
import time
import json
import collections
from multiprocessing import Process,Value
import cv2
class Getmap:
def __init__(self):
self.w=Walabot()
self.w.ARENA=[(100, 500, 10), (-45, 45, 3), (-45, 45, 3)]
self.RF_array=collections.OrderedDict()
self.cap = cv2.VideoCapture(1)
def work(self,map_num,mapfile,photofile):
self.w.isConnectedAny()
self.w.simpleInit()
for i in range(map_num):
rawImage=self.w.triggerAndGetRawImage()
self.RF_array['{:.2f}'.format(time.time())]=rawImage
ret, frame = self.cap.read()
cv2.imshow('frame', frame)
cv2.imwrite(photofile+'/{:.2f}.jpg'.format(time.time()), frame)
cv2.waitKey(1)
self.w.disConnected()
self.cap.release()
cv2.destroyAllWindows()
with open(mapfile+'\rawImage.txt', 'w') as f:
json.dump(self.RF_array, f)
if __name__=='__main__':
w=Getmap()
w.work()
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bokeh/plot_object.py | csaid/bokeh | 1 | 79321 | from __future__ import absolute_import, print_function
import os.path
from uuid import uuid4
from functools import wraps
import warnings
import logging
logger = logging.getLogger(__file__)
from six import add_metaclass, iteritems
from six.moves.urllib.parse import urlsplit
from .embed import autoload_static, autoload_server
from .properties import HasProps, MetaHasProps, Instance, String
from .protocol import serialize_json
from .utils import get_ref, convert_references, dump
class Viewable(MetaHasProps):
""" Any plot object (Data Model) which has its own View Model in the
persistence layer.
Adds handling of a __view_model__ attribute to the class (which is
provided by default) which tells the View layer what View class to
create.
One thing to keep in mind is that a Viewable should have a single
unique representation in the persistence layer, but it might have
multiple concurrent client-side Views looking at it. Those may
be from different machines altogether.
"""
# Stores a mapping from subclass __view_model__ names to classes
model_class_reverse_map = {}
# Mmmm.. metaclass inheritance. On the one hand, it seems a little
# overkill. On the other hand, this is exactly the sort of thing
# it's meant for.
def __new__(cls, class_name, bases, class_dict):
if "__view_model__" not in class_dict:
class_dict["__view_model__"] = class_name
class_dict["get_class"] = Viewable.get_class
# Create the new class
newcls = super(Viewable,cls).__new__(cls, class_name, bases, class_dict)
entry = class_dict["__view_model__"]
# Add it to the reverse map, but check for duplicates first
if entry in Viewable.model_class_reverse_map:
raise Warning("Duplicate __view_model__ declaration of '%s' for " \
"class %s. Previous definition: %s" % \
(entry, class_name,
Viewable.model_class_reverse_map[entry]))
Viewable.model_class_reverse_map[entry] = newcls
return newcls
@classmethod
def _preload_models(cls):
from . import objects, widgetobjects
from .crossfilter import objects
@classmethod
def get_class(cls, view_model_name):
""" Given a __view_model__ name, returns the corresponding class
object
"""
cls._preload_models()
d = Viewable.model_class_reverse_map
if view_model_name in d:
return d[view_model_name]
else:
raise KeyError("View model name '%s' not found" % view_model_name)
def usesession(meth):
""" Checks for 'session' in kwargs and in **self**, and guarantees
that **kw** always has a valid 'session' parameter. Wrapped methods
should define 'session' as an optional argument, and in the body of
the method, should expect an
"""
@wraps(meth)
def wrapper(self, *args, **kw):
session = kw.get("session", None)
if session is None:
session = getattr(self, "session")
if session is None:
raise RuntimeError("Call to %s needs a session" % meth.__name__)
kw["session"] = session
return meth(self, *args, **kw)
return wrapper
def is_ref(frag):
return isinstance(frag, dict) and \
frag.get('type') and \
frag.get('id')
def json_apply(fragment, check_func, func):
"""recursively searches through a nested dict/lists
if check_func(fragment) is True, then we return
func(fragment)
"""
if check_func(fragment):
return func(fragment)
elif isinstance(fragment, list):
output = []
for val in fragment:
output.append(json_apply(val, check_func, func))
return output
elif isinstance(fragment, dict):
output = {}
for k, val in fragment.items():
output[k] = json_apply(val, check_func, func)
return output
else:
return fragment
def resolve_json(fragment, models):
check_func = is_ref
def func(fragment):
if fragment['id'] in models:
return models[fragment['id']]
else:
logging.error("model not found for %s", fragment)
return None
return json_apply(fragment, check_func, func)
@add_metaclass(Viewable)
class PlotObject(HasProps):
""" Base class for all plot-related objects """
session = Instance(".session.Session")
name = String()
def __init__(self, **kwargs):
# Eventually should use our own memo instead of storing
# an attribute on the class
if "id" in kwargs:
self._id = kwargs.pop("id")
else:
self._id = str(uuid4())
self._dirty = True
self._callbacks_dirty = False
self._callbacks = {}
self._callback_queue = []
self._block_callbacks = False
block_events = kwargs.pop('_block_events', False)
if not block_events:
super(PlotObject, self).__init__(**kwargs)
self.setup_events()
else:
self._block_callbacks = True
super(PlotObject, self).__init__(**kwargs)
def get_ref(self):
return {
'type': self.__view_model__,
'id': self._id,
}
def setup_events(self):
pass
@classmethod
def load_json(cls, attrs, instance=None):
"""Loads all json into a instance of cls, EXCEPT any references
which are handled in finalize
"""
if 'id' not in attrs:
raise RuntimeError("Unable to find 'id' attribute in JSON: %r" % attrs)
_id = attrs.pop('id')
if not instance:
instance = cls(id=_id, _block_events=True)
_doc = attrs.pop("doc", None)
ref_props = {}
for p in instance.properties_with_refs():
if p in attrs:
ref_props[p] = attrs.pop(p)
special_props = {}
for p in dict(attrs):
if p not in instance.properties():
special_props[p] = attrs.pop(p)
instance._ref_props = ref_props
instance._special_props = special_props
instance.update(**attrs)
return instance
def finalize(self, models):
"""Convert any references into instances
models is a dict of id->model mappings
"""
if hasattr(self, "_ref_props"):
return resolve_json(self._ref_props, models)
else:
return {}
@classmethod
def collect_plot_objects(cls, *input_objs):
""" Iterate over ``input_objs`` and descend through their structure
collecting all nested ``PlotObjects`` on the go. The resulting list
is duplicate-free based on objects' identifiers.
"""
ids = set([])
objs = []
def descend_props(obj):
for attr in obj.properties_with_refs():
descend(getattr(obj, attr))
def descend(obj):
if isinstance(obj, PlotObject):
if obj._id not in ids:
ids.add(obj._id)
descend_props(obj)
objs.append(obj)
elif isinstance(obj, HasProps):
descend_props(obj)
elif isinstance(obj, (list, tuple)):
for item in obj:
descend(item)
elif isinstance(obj, dict):
for key, value in iteritems(obj):
descend(key); descend(value)
descend(input_objs)
return objs
def references(self):
"""Returns all ``PlotObjects`` that this object has references to. """
return set(self.collect_plot_objects(self))
#---------------------------------------------------------------------
# View Model connection methods
#
# Whereas a rich client rendering framework can maintain view state
# alongside model state, we need an explicit send/receive protocol for
# communicating with a set of view models that reside on the front end.
# Many of the calls one would expect in a rich client map instead to
# batched updates on the M-VM-V approach.
#---------------------------------------------------------------------
def vm_props(self):
""" Returns the ViewModel-related properties of this object. """
props = self.changed_properties_with_values()
props.pop("session", None)
return props
def vm_serialize(self):
""" Returns a dictionary of the attributes of this object, in
a layout corresponding to what BokehJS expects at unmarshalling time.
"""
attrs = self.vm_props()
attrs['id'] = self._id
return attrs
def dump(self, docid=None):
"""convert all references to json
"""
models = self.references()
return dump(models, docid=docid)
def update(self, **kwargs):
for k,v in kwargs.items():
setattr(self, k, v)
def __str__(self):
return "%s, ViewModel:%s, ref _id: %s" % (self.__class__.__name__,
self.__view_model__, getattr(self, "_id", None))
def on_change(self, attrname, obj, callbackname=None):
"""when attrname of self changes, call callbackname
on obj
"""
callbacks = self._callbacks.setdefault(attrname, [])
callback = dict(obj=obj, callbackname=callbackname)
if callback not in callbacks:
callbacks.append(callback)
self._callbacks_dirty = True
def _trigger(self, attrname, old, new):
"""attrname of self changed. So call all callbacks
"""
callbacks = self._callbacks.get(attrname)
if callbacks:
for callback in callbacks:
obj = callback.get('obj')
callbackname = callback.get('callbackname')
fn = obj if callbackname is None else getattr(obj, callbackname)
fn(self, attrname, old, new)
# TODO: deprecation warnign about args change (static_path)
def create_html_snippet(
self, server=False, embed_base_url="", embed_save_loc=".",
static_path="http://localhost:5006/bokehjs/static"):
"""create_html_snippet is used to embed a plot in an html page.
create_html_snippet returns the embed string to be put in html.
This will be a <script> tag.
To embed a plot dependent on the Bokeh Plot Server, set server=True,
otherwise a file with the data for the plot will be built.
embed_base_url is used for non-server embedding. This is used
as the root of the url where the embed.js file will be saved.
embed_save_loc controls where the embed.js will be actually written to.
static_path controls where the embed snippet looks to find
bokeh.js and the other resources it needs for bokeh.
"""
if server:
from .session import Session
if embed_base_url:
session = Session(root_url=server_url)
else:
session = Session()
return autoload_server(self, session)
from .templates import AUTOLOAD, AUTOLOAD_STATIC
import uuid
js_filename = "%s.embed.js" % self._id
script_path = embed_base_url + js_filename
elementid = str(uuid.uuid4())
js = AUTOLOAD.render(
all_models = serialize_json(self.dump()),
js_url = static_path + "js/bokeh.min.js",
css_files = [static_path + "css/bokeh.min.css"],
elementid = elementid,
)
tag = AUTOLOAD_STATIC.render(
src_path = script_path,
elementid = elementid,
modelid = self._id,
modeltype = self.__view_model__,
)
save_path = os.path.join(embed_save_loc, js_filename)
with open(save_path,"w") as f:
f.write(js)
return tag
def inject_snippet(
self, server=False, embed_base_url="", embed_save_loc=".",
static_path="http://localhost:5006/bokeh/static/"):
warnings.warn("inject_snippet is deprecated, please use create_html_snippet")
return self.create_html_snippet(
server, embed_base_url, embed_save_loc, static_path)
def _build_server_snippet(self, base_url=False):
sess = self._session
modelid = self._id
typename = self.__view_model__
if not base_url:
base_url = sess.root_url
split = urlsplit(base_url)
if split.scheme == 'http':
ws_conn_string = "ws://%s/bokeh/sub" % split.netloc
else:
ws_conn_string = "wss://%s/bokeh/sub" % split.netloc
f_dict = dict(
docid = sess.docid,
ws_conn_string = ws_conn_string,
docapikey = sess.apikey,
root_url = base_url,
modelid = modelid,
modeltype = typename,
script_url = base_url + "bokeh/embed.js")
e_str = '''<script src="%(script_url)s" bokeh_plottype="serverconn"
bokeh_docid="%(docid)s" bokeh_ws_conn_string="%(ws_conn_string)s"
bokeh_docapikey="%(docapikey)s" bokeh_root_url="%(root_url)s"
bokeh_modelid="%(modelid)s" bokeh_modeltype="%(modeltype)s" async="true"></script>
'''
return "", e_str % f_dict
def _build_static_embed_snippet(self, static_path, embed_base_url):
embed_filename = "%s.embed.js" % self._id
full_embed_path = embed_base_url + embed_filename
js_str = self._session.embed_js(self._id, static_path)
sess = self._session
modelid = self._id
typename = self.__view_model__
embed_filename = full_embed_path
f_dict = dict(modelid = modelid, modeltype = typename,
embed_filename=embed_filename)
e_str = '''<script src="%(embed_filename)s" bokeh_plottype="embeddata"
bokeh_modelid="%(modelid)s" bokeh_modeltype="%(modeltype)s" async="true"></script>
'''
return js_str, e_str % f_dict
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data_preprocess/drone_images/rgb_downsampling.py | Shanci-Li/TOPO-DataGen | 5 | 175935 | <reponame>Shanci-Li/TOPO-DataGen<gh_stars>1-10
import os
import glob
import numpy as np
from skimage import io, transform
import argparse
import multiprocessing as mp
def _func_downsampling(in_img, out_img, ttl_iter, mp_progress, mp_lock):
img = io.imread(in_img)
out = transform.resize(img, [480, 720], preserve_range=True, anti_aliasing=True)
out = out.astype(np.uint8)
io.imsave(out_img, out)
# release memory
img = None
out = None
with mp_lock:
print("\rDownsampling {:s} ---> {:s}, progress: {:d}/{:d}".format(os.path.basename(in_img),
os.path.basename(out_img),
mp_progress.value, ttl_iter),
end="", flush=True)
mp_progress.value += 1
def main():
parser = argparse.ArgumentParser()
parser.add_argument("path", nargs='+', type=str, help="Path of the dataset to correct images.")
args = parser.parse_args()
dataset_dir_ls = args.path
save_dir_ls = [os.path.abspath(os.path.join(data_dir, '../downsampled')) for data_dir in dataset_dir_ls]
for dataset_dir, save_dir in zip(dataset_dir_ls, save_dir_ls):
dataset_dir = os.path.abspath(dataset_dir)
save_dir = os.path.abspath(save_dir)
os.makedirs(save_dir, exist_ok=True)
files = list(filter(os.path.isfile, glob.glob(dataset_dir + "/*")))
files.sort(key=lambda x: os.path.getmtime(x))
# multiprocessing preparation
mp_args_ls = []
mp_manager = mp.Manager()
mp_lock = mp_manager.Lock()
mp_progress = mp_manager.Value('i', 1)
for idx, file in enumerate(files):
old_name = os.path.abspath(file)
new_name = os.path.join(save_dir, os.path.basename(old_name))
mp_args_ls.append((old_name, new_name, len(files), mp_progress, mp_lock))
# mp pools
with mp.Pool(processes=os.cpu_count()) as p:
p.starmap(_func_downsampling, mp_args_ls, chunksize=1)
print("\nTask is finished! {:s} ---> {:s}".format(dataset_dir, save_dir))
if __name__ == '__main__':
main()
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src/Honeybee_EnergyPlus NoMass Opaque Material.py | rdzeldenrust/Honeybee | 1 | 1612284 | <reponame>rdzeldenrust/Honeybee<filename>src/Honeybee_EnergyPlus NoMass Opaque Material.py
# By <NAME>
# <EMAIL>
# Honeybee started by <NAME> is licensed
# under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
"""
Use this component to create a custom opaque material that has no mass, which can be plugged into the "Honeybee_EnergyPlus Construction" component.
_
It is important to note that this component creates a material with no mass and, because of this, the accuracy of the component is not as great as a material that has mass. However, this component is very useful if you only have an R-value for a material (or a construction) and you know that the mass is relatively small.
_
If you want to create a material that accounts for mass, you should use the "Honeybee_EnergyPlus Window Material" component.
-
Provided by Honeybee 0.0.55
Args:
_name: A text name for your NoMass Opaque Material.
_roughness_: A text value that indicated the roughness of your material. This can be either "VeryRough", "Rough", "MediumRough", "MediumSmooth", "Smooth", and "VerySmooth". The default is set to "Rough".
_R-Value: A number representing the R-Value of the material in m2-K/W.
_thermAbsp_: An number between 0 and 1 that represents the thermal abstorptance of the material. The default is set to 0.9, which is common for most non-metallic materials.
_solAbsp_: An number between 0 and 1 that represents the abstorptance of solar radiation by the material. The default is set to 0.7, which is common for most non-metallic materials.
_visAbsp_: An number between 0 and 1 that represents the abstorptance of visible light by the material. The default is set to 0.7, which is common for most non-metallic materials.
Returns:
EPMaterial: A no-mass opaque material that can be plugged into the "Honeybee_EnergyPlus Construction" component.
"""
ghenv.Component.Name = "Honeybee_EnergyPlus NoMass Opaque Material"
ghenv.Component.NickName = 'EPNoMassMat'
ghenv.Component.Message = 'VER 0.0.55\nOCT_24_2014'
ghenv.Component.Category = "Honeybee"
ghenv.Component.SubCategory = "06 | Energy | Material | Construction"
#compatibleHBVersion = VER 0.0.55\nAUG_25_2014
#compatibleLBVersion = VER 0.0.58\nAUG_20_2014
try: ghenv.Component.AdditionalHelpFromDocStrings = "1"
except: pass
import Grasshopper.Kernel as gh
w = gh.GH_RuntimeMessageLevel.Warning
def checkInputs():
#Check to be sure that SHGC and VT are between 0 and 1.
checkData = True
def checkBtwZeroAndOne(variable, default, variableName):
if variable == None: newVariable = default
else:
if variable <= 1 and variable >= 0: newVariable = variable
else:
newVariable = 0
checkData = False
warning = variableName + " must be between 0 and 1."
print warning
ghenv.Component.AddRuntimeMessage(w, warning)
return newVariable
thermAbs = checkBtwZeroAndOne(_thermAbsp_, None, "_thermAbsp_")
solAbsp = checkBtwZeroAndOne(_solAbsp_, None, "_solAbsp_")
visAbsp = checkBtwZeroAndOne(_visAbsp_, None, "_visAbsp_")
#Check the Roughness value.
if _roughness_ != None: _roughness = _roughness_.upper()
else: _roughness = None
if _roughness == None or _roughness == "VERYROUGH" or _roughness == "ROUGH" or _roughness == "MEDIUMROUGH" or _roughness == "MEDIUMSMOOTH" or _roughness == "SMOOTH" or _roughness == "VERYSMOOTH": pass
else:
checkData = False
warning = "_roughness_ is not valid."
print warning
ghenv.Component.AddRuntimeMessage(w, warning)
return checkData
def main(name, roughness, R_Value, thermAbsp, solAbsp, visAbsp):
if roughness == None: roughness = "Rough"
if thermAbsp == None: thermAbsp = 0.9
if solAbsp == None: solAbsp = 0.7
if visAbsp == None: visAbsp = 0.7
values = [name.upper(), roughness, R_Value, thermAbsp, solAbsp, visAbsp]
comments = ["Name", "Roughness", "Thermal Resistance {m2-K/W}", "Thermal Absorptance", "Solar Absorptance", "Visible Absorptance"]
materialStr = "Material:NoMass,\n"
for count, (value, comment) in enumerate(zip(values, comments)):
if count!= len(values) - 1:
materialStr += str(value) + ", !" + str(comment) + "\n"
else:
materialStr += str(value) + "; !" + str(comment)
return materialStr
if _name and _R_Value:
checkData = checkInputs()
if checkData == True:
EPMaterial = main(_name, _roughness_, _R_Value, _thermAbsp_, _solAbsp_, _visAbsp_) | [
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igraph-test/local_graph.py | firetto/JanusGraphTest | 0 | 180205 | <gh_stars>0
"""
local_graph.py
Contains the LocalGraph class, which contains an igraph.Graph instance and some methods to access and modify it.
Used for subgraphs in a GraphInterface.
<NAME>, 2021
"""
from log_to_file import log_to_file
from datetime import datetime
import igraph
class LocalGraph():
"""
A wrapper class for an igraph.Graph instance.
:ivar graph: Contains the igraph Graph.
"""
graph: igraph.Graph
def __init__(self) -> None:
"""
Instantiate an empty igraph.Graph instance.
"""
self.graph = igraph.Graph()
def create_from_connections_undirected(self, vertex_connections: list) -> None:
"""
Instantiate a simple undirected igraph.Graph given pairs of vertices to connect.
:param vertex_connections: A list of the format [(name1, name2), (name3, name4), ...] where name# represents the names of the vertices.
:type vertex_connections: list[tuple[str, str]]
"""
# See https://igraph.org/python/doc/tutorial/tutorial.html#setting-and-retrieving-attributes for help regarding igraph vertex/edge attributes.
now = datetime.now()
# vertex_names[i] denotes the name of the i'th vertex in the local graph.
vertex_name_arr = []
# Dictionary of style {..., vertex_names[i]: i, ...}.
vertex_name_to_ind = {}
# List of 2-tuples containing the indices of the pairs of vertices to be added (instead of the names).
vertex_index_connections = []
for pair in vertex_connections:
indices = []
for i in range(len(pair)):
if pair[i] not in vertex_name_to_ind:
vertex_name_to_ind[pair[i]] = len(vertex_name_arr)
vertex_name_arr.append(pair[i])
indices.append(vertex_name_to_ind[pair[i]])
vertex_index_connections.append(tuple(indices))
log_to_file(message="Making local graph.")
self.graph = igraph.Graph(vertex_index_connections)
self.graph.vs['name'] = vertex_name_arr
self.graph.vs['label'] = self.graph.vs['name']
log_to_file(message=f"Done making local graph, that took {(datetime.now() - now).total_seconds()} seconds.")
def find_shortest_paths(self, name1: str, name2: str):
"""
Given two vertices labelled with <name1> and <name2>, return the SHORTEST paths between the vertices.
See https://igraph.org/python/doc/api/igraph._igraph.GraphBase.html#get_all_shortest_paths for documentation.
:param name1: name property of the vertex to start the traversal at
:type name1: str
:param name2: name property of the vertex to end the traversal at
:type name2: str
"""
# TODO: maybe perform a check to see if the vertices exist?
return self.graph.get_all_shortest_paths(name1, to=name2, weights=None, mode='all')
def visualize_graph(self, target: str) -> None:
"""
Export the graph as an image to :param target:.
:param target: File location to export the file to. Should be in PNG, PDF, SVG or PostScript format.
:type target: str
"""
igraph.plot(self.graph, target=target)
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AtCoder/ABC/000-159/ABC147_D.py | sireline/PyCode | 0 | 101577 | <reponame>sireline/PyCode
N = int(input())
A = [int(n) for n in input().split()]
Aset = set(A)
m = (10**9+7)
o = {}
ans = []
for a in A:
o.setdefault(a, 0)
o[a] += 1
for i in range(len(Aset)-1):
for j in range(i+1, len(Aset)):
ans.append((A[i]^A[j])*o[A[i]]*o[A[j]])
print(sum(ans)/m)
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transformers/median/median.py | affresco/demo_features | 0 | 95531 | <filename>transformers/median/median.py
import numpy as np
import logging
# Scientific
import pandas as pd
# Base class for all features
from transformers.abstract.mixin import AbstractFeature
# ##################################################################
# AVERAGE TRUE RANGE
# ##################################################################
class MedianFeature(AbstractFeature):
#
# Columns created will be prefixed with this keyword
PREFIX = "median"
# ##################################################################
# INIT
# ##################################################################
def __init__(self,
columns: list = None,
prefix: str = None):
"""
Feature a computation of the median of provided columns.
:param columns: columns on which computation will be performed
:param prefix: Corresponding to the name of the column output
"""
# Init super feature class
if prefix is None:
prefix = self.PREFIX
super(MedianFeature, self).__init__(prefix=prefix)
# Target columns on which we operate
self.columns = self._sanitize_columns(columns=columns)
# Initial span of data to be discarded
self.warmup = 0
# ##################################################################
# SKLEARN TRANSFORMER MIXIN IMPLEMENTATION
# ##################################################################
def fit(self, *args, **kwargs):
return self # nothing to do here
def transform(self, X, y=None, **fit_params):
assert isinstance(X, pd.DataFrame), "Transform must be a DataFrame"
if y is not None:
logging.warning(f"Argument 'y' of median transformation will be ignored.")
return self(df=X)
def fit_transform(self, X, y=None, **fit_params):
return self.transform(X, y, **fit_params)
def get_params(self, *args, **kwargs):
params = {
"prefix": self.prefix,
"columns": self.columns,
}
return params
# ##################################################################
# CORE METHODS
# ##################################################################
def __call__(self, df: pd.DataFrame):
return self.apply(df=df, prefix=self.PREFIX, columns=self.columns)
@classmethod
def apply(cls, df: pd.DataFrame, prefix: str = None, columns: list = None):
# Ensure that parameters are present
prefix = prefix or cls.PREFIX
# Make some assertions
cls._assert_before_applying(df=df, columns=columns, spans=None)
return cls.__compute_median(df=df,
prefix=prefix,
columns=columns)
@classmethod
def __compute_median(cls, df: pd.DataFrame, columns: list, prefix: str):
df[prefix] = np.median(df.loc[:, columns], axis=1)
return df
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] |
controler/userManage.py | sanxiadaba/pythonBlog | 2 | 155352 | """
File description.
Background management page for regular users and editors to view their articles, comments, favorites and other functions
encoding: utf-8
@author: <NAME>
@contact: <EMAIL>
@software: Pycharm
@time: 2022/1/12
@gituhb: sanxiadaba/pythonBlog
"""
import base64
import os
import time
import traceback
from math import ceil
from flask import Blueprint, session, jsonify, render_template, request
from common.myLog import logDanger, dirInDir, avatarPath, listLogger, allLogger
from common.utility import compress_image
from constant import everyPageInHou, emailAccount
from database.article import Article
from database.comment import Comment
from database.credit import Credit
from database.favorite import Favorite
from database.logs import Log
from database.users import Users
instanceArticle = Article()
instanceComment = Comment()
instanceCredit = Credit()
instanceFavorite = Favorite()
instanceLog = Log()
instanceUser = Users()
userManage = Blueprint("userManage", __name__)
@userManage.route("/userManage", methods=["GET"])
@logDanger
def baseUserManage():
userid = session.get("userid")
# My Collection
myFavo, lenMyFavo = instanceFavorite.myFavoriteArticle(userid)
# Number of my published articles
numOfAllMyArticle = len(instanceArticle.searchAllMyArticle())
# Number of visits to published articles
allNumOfAllArticleRead = instanceArticle.allNumOfAllArticleRead()
# Number of my favorite articles
numOfMyFavoriteArticle = lenMyFavo
# Number of my comments
numOfALLMyComment = instanceComment.numOfALLMyComment()
# My nickname
myNickname = instanceUser.searchNicknameByUserid(userid)
# My registered email
myEmail = instanceUser.searchMyEmail(userid)
# User Roles
myRole = session.get("role")
# My qq
myQQ = instanceUser.searchMyQQ(userid)
# Total remaining points
restOfMyCredit = instanceUser.findRestCredit()
# My avatar
myAvatar = instanceUser.searchMyAvatar(userid)
# Whether to apply to become an editor
whetherApplyForEditor = instanceUser.whetherApplyForEditor(userid)
# Information for managing articles in the user background
articleInfo = instanceArticle.articleInfo(userid)
# Check the number of all my posts (except deleted)
myArticleNum = instanceArticle.exceptDeleteNum()
# The article page of the administration page is divided into several pages
howManyPage = ceil(myArticleNum / everyPageInHou)
howManyPage_1 = howManyPage
howManyPage = list(range(2, howManyPage + 1))
# Determine which page to jump to
controlBiaoNum = session.get("controlBiaoNum")
controlBiaoNum = 0 if controlBiaoNum is None else int(controlBiaoNum)
MyInfo = {}
MyInfo["numOfAllMyArticle"] = numOfAllMyArticle
MyInfo["allNumOfAllArticleRead"] = allNumOfAllArticleRead
MyInfo["numOfMyFavoriteArticle"] = numOfMyFavoriteArticle
MyInfo["numOfALLMyComment"] = numOfALLMyComment
MyInfo["myNickname"] = myNickname
MyInfo["myEmail"] = myEmail
MyInfo["myRole"] = myRole
MyInfo["myQQ"] = myQQ
MyInfo["restOfMyCredit"] = restOfMyCredit
MyInfo["myAvatar"] = myAvatar
MyInfo["whetherApplyForEditor"] = whetherApplyForEditor
# My comments, number of comment likes, number of comment approvals, target posts, hidden comments
myComment, allMyCommentNum = instanceComment.searchMyComment(userid)
# with snoop:
guo = []
for i in myComment:
lin = []
for j in i:
lin.append(j)
lin.append(instanceArticle.searchHeadlineByArticleid(i[3]))
guo.append(lin)
myComment = guo
myLoginLog = instanceLog.searchLoginLog(userid)
# Data of point changes
allCreditChangeLog = instanceCredit.creditChangeLog(userid)
return render_template("userManage.html", myInfo=MyInfo, articleInfo=articleInfo, everyPageInHou=everyPageInHou,
myArticleNum=myArticleNum, howManyPage=howManyPage, howManyPage_1=howManyPage_1,
controlBiaoNum=controlBiaoNum, myComment=myComment, allMyCommentNum=allMyCommentNum,
myFavo=myFavo, lenMyFavo=lenMyFavo, myLoginLog=myLoginLog,
allCreditChangeLog=allCreditChangeLog, emailAccount=emailAccount)
# Back to my profile
# Number of articles, number of comments, total number of views, total points, my nickname, my avatar, my qq number, my role
@userManage.route("/userInfo", methods=["GET"])
@logDanger
def userInfo():
last, most, recommended = instanceArticle.searchLastMostRecommended()
return jsonify(last, most, recommended)
# 修改昵称
@userManage.route("/modifyNickname", methods=["POST"])
@logDanger
def modifyNickname():
userid = session.get('userid')
newNickname = request.form.get("newNickname")
try:
instanceUser.modifyUserNickname(newNickname)
info = f"The user with userid {userid} modifies the nickname to {newNickname}"
listLogger(userid, info, [0])
instanceLog.insertDetail(type="Change nickname", target=userid, credit=0, info=info)
return "1"
except:
e = traceback.format_exc()
allLogger(0, e)
return "0"
# Modify qq number
@userManage.route("/modifyQQ", methods=["POST"])
@logDanger
def modifyQQ():
newQQ = request.form.get("newQQ")
userid = session.get("userid")
try:
instanceUser.modifyUserQQnum(newQQ)
info = f"The userid of {userid} modifies the qq number to {newQQ}"
listLogger(userid, info, [0])
instanceLog.insertDetail(type="Modify QQ", target=userid, credit=0, info=info)
return "1"
except:
e = traceback.format_exc()
allLogger(0, e)
return "0"
# Apply for a role
@userManage.route("/applyEditor", methods=["POST"])
@logDanger
def applyEditor():
userid = session.get('userid')
try:
instanceUser.applyForBecomeEditor()
info = f"Users with userid {userid} apply to become editors"
listLogger(userid, info, [0])
instanceLog.insertDetail(type="Apply to become an editor", target=userid, credit=0, info=info)
return "1"
except:
e = traceback.format_exc()
allLogger(0, e)
return "0"
# cancle the Apply for a role
@userManage.route("/cancleApplyEditor", methods=["POST"])
@logDanger
def cancleApplyEditor():
userid = session.get('userid')
try:
instanceUser.cancleApplyForBecomeEditor()
info = f"Users with userid {userid} cancle the apply to become editors"
listLogger(userid, info, [0])
instanceLog.insertDetail(type="cancle Apply to become an editor", target=userid, credit=0, info=info)
return "1"
except:
e = traceback.format_exc()
allLogger(0, e)
return "0"
# Delete comments (note that the number of comments on the article will be set to subtract 1 when the time comes)
# Note that all comments under the article will also be set to hidden
@userManage.route("/deleteArticle", methods=["POST"])
@logDanger
def deleteArticle():
userid = session.get("userid")
articleid = request.form.get('articleid')
try:
instanceArticle.deleteArticle(articleid)
info = f"The user with userid {userid} has deleted the article with articleid {articleid}"
listLogger(userid, info, [0])
instanceLog.insertDetail(type="Delete article", target=userid, credit=0, info=info)
# Set the comments under the corresponding article to be hidden when deleting the article
instanceComment.hideCommnetWhenHideArticle(articleid)
# Set the favorite articles to un-favorite as well
instanceFavorite.hideFavoByArticleid(articleid)
return "1"
except:
e = traceback.format_exc()
allLogger(0, e)
return "0"
# Modify avatar
@userManage.route("/uploadUserAvatar", methods=["POST", "GET"])
@logDanger
def uploadUserAvatar():
if request.method == "GET":
return render_template("uploadUserAvatar.html")
# 对头像图片上传进行响应
@userManage.route('/uploadImg', methods=['POST', 'OPTIONS'])
@logDanger
def upload():
userid = session.get("userid")
myAvatarPath = avatarPath + "\\" + f"myPic_{userid}"
dirInDir(f"myPic_{userid}", avatarPath)
if request.form.get("action") == "add":
data = request.form.get("picStr")
imgdata = base64.b64decode(data)
thumbname = time.strftime("%Y%m%d_%H%M%S." + "jpg")
now = thumbname.split(".")
imgfile = os.path.join(myAvatarPath, "test.jpg")
file = open(imgfile, 'wb')
file.write(imgdata)
file.close()
oldpicPath = myAvatarPath + "\\" + "test.jpg"
newPicPath = myAvatarPath + "\\" + thumbname
compress_image(oldpicPath, newPicPath, 400)
newPicPath = f"myPic_{userid}" + "/" + thumbname
instanceUser.modifyUserThumbnail(newPicPath)
os.remove(oldpicPath)
allPathPic = [lists for lists in os.listdir(myAvatarPath) if os.path.isfile(os.path.join(myAvatarPath, lists))]
for i in allPathPic:
biJiao = i.split(".")
if biJiao < now:
os.remove(myAvatarPath + "\\" + i)
info = f"Users whose userid is {userid} change their avatar to {newPicPath}"
listLogger(userid, info, [0])
instanceLog.insertDetail(type="Change avatar", target=userid, credit=0, info=info)
return "1"
# Control which tab appears when the page is refreshed again
@userManage.route("/controlBiaoNum", methods=["POST", "GET"])
@logDanger
def controlBiaoNum():
if request.method == 'GET':
controlBiaoNum = session.get("controlBiaoNum")
session["controlBiaoNum"] = 0
if controlBiaoNum is not None and controlBiaoNum != "0":
return str(controlBiaoNum)
else:
return "0"
elif request.method == 'POST':
session["controlBiaoNum"] = str(request.form.get("controlBiaoNum"))
return "1"
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Day_19/etch_A_scketch.py | ecanro/100DaysOfCode_Python | 0 | 56405 | <reponame>ecanro/100DaysOfCode_Python
from turtle import Turtle, Screen
tim = Turtle() #->separete instance of tommy and any other turtle
screen = Screen()
tim.shape("turtle")#->states of tim
tim.color("red")
# tommy = Turtle()#->separate instance of tim and any other turtle
# tommy.color("blue")#->states of tommy
# tommy.goto(-400,0)
def tim_forwards():
tim.forward(10)
def tim_right():
new_heading = tim.heading() -10
tim.setheading(new_heading)
def tim_left():
new_heading = tim.heading() + 10
tim.setheading(new_heading)
def tim_backwards():
tim.back(10)
def tim_clear_screen():
tim.clear()
tim.penup()
#tim.goto(0,0)
tim.home()
tim.pendown()
#tim will move every time a key is push
screen.onkey(fun=tim_forwards, key="w")
screen.onkey(fun=tim_backwards, key="s")
screen.onkey(fun=tim_right, key="d")
screen.onkey(fun=tim_left, key="a")
screen.onkey(fun=tim_clear_screen, key="c")
screen.listen()
screen.exitonclick() | [
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] |
rolldecayestimators/tests/test_polynom_estimator.py | martinlarsalbert/rolldecay-estimators | 1 | 27717 | <reponame>martinlarsalbert/rolldecay-estimators<filename>rolldecayestimators/tests/test_polynom_estimator.py<gh_stars>1-10
import pytest
import pandas as pd
import os.path
from sklearn.datasets import make_regression
from sklearn.pipeline import Pipeline
from sklearn.feature_selection import SelectKBest
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.feature_selection import f_regression
from sklearn.feature_selection import VarianceThreshold
import sympy as sp
from rolldecayestimators.polynom_estimator import Polynom
import matplotlib.pyplot as plt
@pytest.fixture
def data():
# Generate some random regression data:
X, y = make_regression(n_samples=1000, n_features=1, n_informative=10, noise=10, random_state=1)
yield X, y
@pytest.fixture
def model(data):
X=data[0]
y=data[1]
polynomial_features = PolynomialFeatures(degree=1)
variance_treshold = VarianceThreshold()
linear_regression = LinearRegression()
select_k_best = SelectKBest(k=1, score_func=f_regression)
steps = [
('polynomial_feature', polynomial_features),
# ('standard_scaler', standard_scaler),
('variance_treshold', variance_treshold),
('select_k_best', select_k_best),
('linear_regression', linear_regression)
]
model = Pipeline(steps=steps)
model.fit(X=X, y=y)
yield model
def test_fit(data, model):
X=data[0]
x=X[:,0]
y=data[1]
y_symbol = sp.symbols('y')
polynom = Polynom(model=model, columns=['B_e_hat'], y_symbol=y_symbol)
polynom.fit(X=X, y=y)
fig,ax=plt.subplots()
ax.plot(x,y, '.')
ax.plot(x, model.predict(X=X), label='model')
ax.plot(x, polynom.predict(X=X), '--', label='polynom')
ax.legend()
plt.show()
@pytest.fixture
def polynom(data, model):
X = data[0]
y = data[1]
y_symbol = sp.symbols('y')
polynom = Polynom(model=model, columns=['B_e_hat'], y_symbol=y_symbol)
polynom.fit(X=X, y=y)
yield polynom
def test_predict_dict(polynom):
data = {
'B_e_hat':1,
}
polynom.predict(X=data)
def test_predict_series(polynom):
data = {
'B_e_hat':1,
}
data = pd.Series(data)
polynom.predict(X=data)
def test_save_load(tmpdir, polynom):
file_path=os.path.join(str(tmpdir), 'test.sym')
polynom.save(file_path=file_path)
polynom2 = Polynom.load(file_path=file_path)
data = {
'B_e_hat': 1,
}
polynom2.predict(X=data)
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2
] |
cnswd/scripts/wy_fhpg.py | NeoBert/liudengfeng-cnswd | 7 | 150496 | import re
import time
from multiprocessing import Pool
import pandas as pd
from cnswd.mongodb import get_db
from cnswd.websource.wy import fetch_fhpg
from ..setting.constants import MARKET_START, MAX_WORKER
from ..utils import make_logger
from .base import get_stock_status
logger = make_logger('网易分红配股')
NAMES = ['分红配股', '配股一览', '增发一览', '历年融资计划']
D_PAT = re.compile('日期$|日$')
START = MARKET_START.tz_localize(None)
def create_index_for(collection):
# 不存在索性信息时，创建索引
if not collection.index_information():
collection.create_index([("公告日期", 1)])
collection.create_index([("股票代码", 1)])
collection.create_index([("公告日期", 1), ("股票代码", 1)])
def get_max_dt(collection, code):
"""最后日期"""
pipe = [
{
'$match': {'股票代码': code}
},
{
'$sort': {
'公告日期': -1
}
},
{
'$project': {
'_id': 0,
'公告日期': 1
}
},
{
'$limit': 1
},
]
try:
res = list(collection.aggregate(pipe))[0]
dt = pd.Timestamp(res['公告日期'])
return dt
except (IndexError, ):
return START
def _fix_data(df):
if df.empty:
return df
for col in df.columns:
if D_PAT.findall(col):
df[col] = pd.to_datetime(df[col], errors='ignore')
return df
def _droped_null(doc):
res = {}
for k, v in doc.items():
if not pd.isnull(doc[k]):
res[k] = v
return res
def _refresh(code):
db = get_db('wy')
try:
dfs = fetch_fhpg(code)
except ValueError:
return
for name, df in zip(NAMES, dfs):
if df.empty:
continue
df['股票代码'] = code
df = _fix_data(df)
collection = db[name]
create_index_for(collection)
last_dt = get_max_dt(collection, code)
df = df[df['公告日期'] > last_dt]
if not df.empty:
for doc in df.to_dict('records'):
collection.insert_one(_droped_null(doc))
def refresh():
t = time.time()
codes = get_stock_status().keys()
with Pool(MAX_WORKER) as pool:
list(pool.imap_unordered(_refresh, codes))
logger.info(f"股票数量 {len(codes)}, 用时 {time.time() - t:.2f}秒")
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] |
redata/settings.py | mociarain/redata | 0 | 133395 | import os
GF_SECURITY_ADMIN_USER = os.environ["GF_SECURITY_ADMIN_USER"]
GF_SECURITY_ADMIN_PASSWORD = os.environ["GF_SECURITY_ADMIN_PASSWORD"]
METRICS_DB_URL = os.environ["REDATA_METRICS_DB_URL"]
REDATA_METRICS_DATABASE_HOST = os.environ["REDATA_METRICS_DATABASE_HOST"]
REDATA_METRICS_DATABASE_USER = os.environ["REDATA_METRICS_DATABASE_USER"]
REDATA_METRICS_DATABASE_PASSWORD = os.environ["REDATA_METRICS_DATABASE_PASSWORD"]
REDATA_METRICS_DATABASE_NAME = os.environ["REDATA_METRICS_DATABASE_NAME"]
GRAFANA_WEB_HOST = os.environ["GRAFANA_WEB_HOST"]
GRAFANA_WEB_PORT = os.environ["GRAFANA_WEB_PORT"]
REDATA_GRAFANA_SOURCE = "redata_metrics_db"
TEMPLATES_DIR_LOCATION = "redata/grafana/templates/"
HOME_DASHBOARD_LOCATION = TEMPLATES_DIR_LOCATION + "home.json"
HOME_OVERRIDES_LOCATION = TEMPLATES_DIR_LOCATION + "overrides.json"
TABLE_DASHBOARD_LOCATION = TEMPLATES_DIR_LOCATION + "table.json"
TARGETS_DASHBOARD_LOCATION = TEMPLATES_DIR_LOCATION + "targets.json"
ALERT_DASHBOARD_LOCATION = TEMPLATES_DIR_LOCATION + "alert.json"
CUSTOM_PANEL_LOCATION = TEMPLATES_DIR_LOCATION + "panel.json"
CUSTOM_ROW_LOCATION = TEMPLATES_DIR_LOCATION + "row.json"
REDATA_SOURCE_DBS = [
{
"name": el.replace("REDATA_SOURCE_DB_URL_", ""),
"db_url": os.environ[el],
"schema": os.environ.get(el.replace("_URL_", "_SCHEMA_", 1), None),
}
for el in os.environ
if el.startswith("REDATA_SOURCE_DB_URL_")
]
REDATA_AIRFLOW_SCHEDULE_INTERVAL = os.environ["REDATA_AIRFLOW_SCHEDULE_INTERVAL"]
REDATA_TIME_COL_BLACKLIST_REGEX = os.environ["REDATA_TIME_COL_BLACKLIST_REGEX"]
REDATA_BIGQUERY_DOCKER_CREDS_FILE_PATH = (
os.environ["REDATA_DEFAULT_BIGQUERY_DOCKER_CREDS_FILE_PATH"]
if os.environ.get("REDATA_BIGQUERY_KEY_FILE")
else None
)
ACCEPTABLE_Z_SCORE_DIFF = float(os.environ.get("REDATA_ACCEPTABLE_Z_SCORE_DIFF"))
SKIP_COLUMNS = ["id"]
REDATA_SLACK_NOTIFICATION_URL = os.environ.get("REDATA_SLACK_NOTIFICATION_URL")
FLASK_UI_SECRET_KEY = os.environ.get("REDATA_FLASK_SECRET_KEY")
GRAFNA_URL = (
os.environ.get("GRAFANA_REACHABLE_HOST") + ":" + os.environ.get("GRAFANA_WEB_PORT")
)
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] |
server/app/tests/test_views.py | atom-space/atomspace.od.ua | 3 | 145326 | <filename>server/app/tests/test_views.py
from django.test import TestCase, Client
from django.urls import reverse
from app.models import Resident, Mentor, Order, News, Merch
import json
class TestViews(TestCase):
def setUp(self):
self.client = Client()
self.mentors_url = reverse('mentors')
self.residents_url = reverse('residents')
self.api_orders_url = reverse('api_orders')
self.get_merches_url = reverse('get_merches')
self.get_news_url = reverse('get_news')
self.merch_url = reverse('merch')
self.news_url = reverse('news')
self.orders_url = reverse('orders')
self.people_url = reverse('people')
def test_mentors_GET(self):
res = self.client.get(self.mentors_url)
self.assertEquals(res.status_code, 200)
def test_residents_GET(self):
res = self.client.get(self.residents_url)
self.assertEquals(res.status_code, 200)
def test_api_orders_GET(self):
res = self.client.get(self.api_orders_url)
self.assertEquals(res.status_code, 200)
def test_get_merches_GET(self):
res = self.client.get(self.get_merches_url)
self.assertEquals(res.status_code, 200)
def test_get_news_GET(self):
res = self.client.get(self.get_news_url)
self.assertEquals(res.status_code, 200)
# Next views will return 302 status code because we are not logged in
def test_merch_GET(self):
res = self.client.get(self.merch_url)
self.assertEquals(res.status_code, 302)
def test_news_GET(self):
res = self.client.get(self.news_url)
self.assertEquals(res.status_code, 302)
def test_orders_GET(self):
res = self.client.get(self.orders_url)
self.assertEquals(res.status_code, 302)
def test_people_GET(self):
res = self.client.get(self.people_url)
self.assertEquals(res.status_code, 302) | [
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] |
pancake_problem.py | thyrdmc/Pancake-Problem-with-Artificial-Intelligence | 0 | 150816 | <gh_stars>0
# coding=utf-8
from __future__ import print_function
from simpleai.search import SearchProblem, breadth_first, depth_first, limited_depth_first, iterative_limited_depth_first, uniform_cost, greedy, astar
from simpleai.search.viewers import WebViewer, ConsoleViewer, BaseViewer
import random
import time
GOAL = (0,1,2,3,4)
my_viewer = WebViewer()
InitialState = [ ]
GOAL = []
ActionList = [ ]
class PancakeProblem(SearchProblem):
def actions(self, state):
ListOfNode = []
ListOfState = []
for x in state:
ListOfState.append(x)
for x in range (len(ListOfState)-1):
List = []
for y in range(len(ListOfState)):
List.append(ListOfState[y])
reversedList = []
for y in range (x, len(List)):
reversedList.append(List[y])
reversedList.reverse()
for y in range(x, len(List)):
List.pop()
for y in range(0, len(reversedList)):
List.append(reversedList[y])
ListOfNode.append(''.join(str(e) for e in List))
return ListOfNode
def result(self, state, action):
ListOfNode = action.split(", ")
randomSelection = random.randint(0, len(ListOfNode)-1)
return ListOfNode[randomSelection]
def is_goal(self, state):
return state == GOAL
def heuristic(self,node):
heuristicDefault = 0
status = []
for x in state:
status.append(x)
for x in range(len(status)-1):
List = []
for y in range(len(status)):
List.append(status[y])
List.sort()
bigNode = List[len(List)-1]
if(status[0] == bigNode):
status.reverse()
status.pop()
status.reverse()
else:
heuristicDefault = status.index(bigNode)
break
return heuristicDefault
def PancakeMixer(number_of_pancakes):
pancakes = [ ]
for x in range (number_of_pancakes):
randomPancake =random.randint(0,9)
pancakes.append(randomPancake)
return pancakes
number_of_pancakes =int(input ("Please, enter number of pancakes :"))
ordering_status = input("Do you want to enter ordering? :")
while True:
if ordering_status == "no" or ordering_status == "No":
InitialState = PancakeMixer(number_of_pancakes)
print("Initial state:"+ str(InitialState))
break
elif ordering_status == "yes" or ordering_status == "Yes":
while True:
InitialState = []
pancakesOrderState = input("Enter top to bottom ordering between [0 - n],seperated by spaces (Ex: 0,2,1..n):")
for x in range(0,len(pancakesOrderState),2):
InitialState.append(int(pancakesOrderState[x]))
if(len(InitialState) > number_of_pancakes):
print()
print("The number of pancakes we will list should be equal to the" + str(number_of_pancakes))
print()
elif (len(InitialState) == number_of_pancakes):
print("Search Algorithm is starting..\n")
break
else:
print("!! Incorrect Entry !!\n"+ str(InitialState))
break
break
else:
print("Incorrect Entry, Please try again")
ordering_status = input("Do you want to enter ordering? :")
problem = PancakeProblem(initial_state = ''.join(str(e) for e in InitialState))
for x in range(0,len(InitialState)):
GOAL.append(str(InitialState[x]))
GOAL.sort(reverse = True)
GOAL = ''.join(str(e) for e in GOAL)
print("\n1) Breadth First Search\n2) Depth First Search")
print("3) Depth Limited Search\n4) Iterative Deepening Search")
print("5) Uniform Cost Search\n6) Greedy Best First Search")
print("7) A* Search")
while True:
ordering_status = input("Please, enter the number of the algorithm you want to use: ")
if ordering_status == "1":
startTime = time.time()
result = breadth_first(problem,graph_search =True,viewer = my_viewer)
print("\n-----------\n")
print("Result: "+str(result.state))
print("Steps: "+str(result.path()))
break
elif ordering_status == "2":
startTime = time.time()
result = depth_first(problem,graph_search =True,viewer = my_viewer)
print("\n-----------\n")
print("Result: "+str(result.state))
print("Steps: "+str(result.path()))
break
elif ordering_status == "3":
startTime = time.time()
print()
depth_first = int(input("Please enter the maximum depth alloved: "))
startTime = time.time()
result = limited_depth_first(problem,depth_limit,graph_search =True,viewer = my_viewer)
print("\n-----------\n")
print("Result: "+str(result.state))
print("Steps: "+str(result.path()))
break
elif ordering_status == "4":
startTime = time.time()
result = iterative_limited_depth_first(problem,graph_search =True,viewer = my_viewer)
print("\n-----------\n")
print("Result: "+str(result.state))
print("Steps: "+str(result.path()))
break
elif ordering_status == "5":
startTime = time.time()
result = uniform_cost(problem,graph_search =True,viewer = my_viewer)
print("\n-----------\n")
print("Result: "+str(result.state))
print("Steps: "+str(result.path()))
break
elif ordering_status == "6":
startTime = time.time()
result = greedy(problem,graph_search =True,viewer = my_viewer)
print("\n-----------\n")
print("Result: "+str(result.state))
print("Steps: "+str(result.path()))
break
elif ordering_status == "7":
startTime = time.time()
result = astar(problem,graph_search =True,viewer = my_viewer)
print("\n-----------\n")
print("Result: "+str(result.state))
print("Steps: "+str(result.path()))
break
else:
print("Please, enter a correct number")
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assign_2.3.py | tramontana-software/Python_Coding_Carlos | 0 | 93316 | # My Script:
hrs=input('Enter Hours: ')
hrs=float(hrs)
rph=input('Enter your rate per hour: ')
rph=float(rph)
pay=hrs*rph
print('Pay:', pay)
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learn-kana/__main__.py | Ewpratten/learn-kana | 0 | 150617 | from luts import kana_to_romaji, romaji_to_kana, simple_kana_to_romaji
import random
import os
import time
## Term info ##
term = os.popen('stty size', 'r').read().split()
term_height, term_width = int(term[0]), int(term[1]) - 5
# term_width = 30 # i have issues
gap = 3
count = 50
max_count = 50
kana_set = simple_kana_to_romaji
## Functions ##
def acceptchar(chars):
"""Convert romaji to hiragana"""
# If this is a kana, just return it
if ord(chars[0]) > 122:
return chars
# Deal with conversion
if chars in kana_set:
return kana_set[chars]
else:
return ""
def setTicker(line):
"""Sets the ticker line with data. Truncates if screen width < data len"""
# Move to saved position
print("\u001b[u", end="", flush=True)
# Move up 1 line
print("\u001b[1A", end="", flush=True)
# Truncate the line to the terminal width
trunc_line = "".join(line)[: int(min(term_width, max_count) / 2)][1:]
first_char = line[0]
# Clear line
print(">" + " " * (term_width - 1), end="\r", flush=True)
# Display the first char in green
print("\u001b[37m\u001b[42;1m" + first_char, end="", flush=True)
# Display the rest of the ticker normally
print("\u001b[0m" + trunc_line + "\r", end="", flush=True)
# Move down 1
print("\u001b[1B", end="", flush=True)
def handleInput():
"""
Accept input,
and clean up terminal on \\n
"""
# Clear line
print(">" + " " * (term_width - 1), end="\r", flush=True)
# Read from user
data = input(">").strip()
if len(data) == 0:
data = " "
# Move up 1 line
print("\u001b[1A", end="", flush=True)
return data
def pickKana(n):
"""Select n random kana"""
keys = list(kana_set.keys())
return [random.choice(keys) for _ in range(n)]
## App ##
if __name__ == "__main__":
successes = 0
# Pick {count} random hiragana
challenge_set = pickKana(count)
# newlines for ticker and input
print("\n" * (3 + gap), end="")
# Move up
print(f"\u001b[{gap + 1}A", end="", flush=True)
# Print cheat line
print("AKS TNH MYR W\r", end="", flush=True)
# Move up 1
print(f"\u001b[1A", end="", flush=True)
# Save the cursor position
print("\u001b[s", end="", flush=True)
start_time = time.time()
problems = []
completed = []
# Run until we are out of kana
while len(challenge_set) > 0:
# Display the ticker
setTicker(challenge_set)
# read input
data = handleInput()
current_kana = challenge_set[0]
if data == " ":
continue
# Check if we should skip
if data == "ー":
challenge_set = challenge_set[1:]
completed.append((current_kana, False))
continue
# Check if we should stop the game
if data == "＝":
break
# Check if we should explain
if data == "＋":
# Move to saved position
print("\u001b[u", end="", flush=True)
# Move down 1
print("\u001b[1B", end="", flush=True)
# Print the options
print(" \r", end="", flush=True)
print(kana_set[current_kana], end="", flush=True)
successes -= 1
problems.append(current_kana)
continue
# Get input as hiragana
kana = acceptchar(data)
# Check if we should moce on
if kana == current_kana:
successes += 1
completed.append((current_kana, True))
else:
problems.append(current_kana)
completed.append((current_kana, False))
challenge_set = challenge_set[1:]
# Game end
# Move to saved position
print("\u001b[u", end="", flush=True)
# Move up 1 lines
print("\u001b[1A", end="", flush=True)
if successes != 0:
kps = round(round(time.time() - start_time) / successes)
else:
kps = 0
# Print kana list with problems highlighted
print(" "* max_count + "\r", end="", flush=True)
for kana in completed:
# Should this be Red BG?
if not kana[1]:
print("\u001b[37m\u001b[41;1m" + kana[0], end="", flush=True)
else:
print("\u001b[0m" + kana[0], end="", flush=True)
print("\u001b[0m")
print(
f"Completed {successes}/{count} in ~{round(time.time() - start_time)} seconds ({kps} seconds per kana)")
# print(f"You had issues with:\n{problems}")
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helloworld.py | justintdavis99/hello_UTC | 0 | 138042 | print{"Hello World! Written by Justin"}
print{"This is my first python code"}
x=int(input('Enter an integer: '))
if x%2 ==0;
print('')
print('Even')
else:
print('')
print('Odd')
print('Done with conditional')
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transition_amr_parser/action_pointer/amr_parser.py | IBM/transition-amr-parser | 76 | 118568 | # Standalone AMR parser
import os
import json
import torch
from transition_amr_parser.model import AMRModel
import transition_amr_parser.utils as utils
from fairseq.models.roberta import RobertaModel
from transition_amr_parser.roberta_utils import extract_features_aligned_to_words
class AMRParser():
def __init__(self, model_path, roberta_cache_path=None, oracle_stats_path=None, config_path=None, model_use_gpu=False, roberta_use_gpu=False, verbose=False, logger=None):
if not oracle_stats_path:
model_folder = os.path.dirname(model_path)
oracle_stats_path = os.path.join(model_folder, "train.rules.json")
assert os.path.isfile(oracle_stats_path), \
f'Expected train.rules.json in {model_folder}'
if not config_path:
model_folder = os.path.dirname(model_path)
config_path = os.path.join(model_folder, "config.json")
assert os.path.isfile(config_path), \
f'Expected config.json in {model_folder}'
self.model = self.load_model(model_path, oracle_stats_path, config_path, model_use_gpu)
self.roberta = self.load_roberta(roberta_use_gpu, roberta_cache_path)
self.logger = logger
def load_roberta(self, roberta_use_gpu, roberta_cache_path=None):
if not roberta_cache_path:
# Load the Roberta Model from torch hub
roberta = torch.hub.load('pytorch/fairseq', 'roberta.large')
else:
roberta = RobertaModel.from_pretrained(roberta_cache_path, checkpoint_file='model.pt')
roberta.eval()
if roberta_use_gpu:
roberta.cuda()
return roberta
def load_model(self, model_path, oracle_stats_path, config_path, model_use_gpu):
oracle_stats = json.load(open(oracle_stats_path))
config = json.load(open(config_path))
model = AMRModel(
oracle_stats=oracle_stats,
embedding_dim=config["embedding_dim"],
action_embedding_dim=config["action_embedding_dim"],
char_embedding_dim=config["char_embedding_dim"],
hidden_dim=config["hidden_dim"],
char_hidden_dim=config["char_hidden_dim"],
rnn_layers=config["rnn_layers"],
dropout_ratio=config["dropout_ratio"],
pretrained_dim=config["pretrained_dim"],
use_bert=config["use_bert"],
use_gpu=model_use_gpu,
use_chars=config["use_chars"],
use_attention=config["use_attention"],
use_function_words=config["use_function_words"],
use_function_words_rels=config["use_function_words_rels"],
parse_unaligned=config["parse_unaligned"],
weight_inputs=config["weight_inputs"],
attend_inputs=config["attend_inputs"]
)
model.load_state_dict(torch.load(model_path))
model.eval()
return model
def get_embeddings(self, tokens):
features = extract_features_aligned_to_words(self.roberta, tokens=tokens, use_all_layers=True, return_all_hiddens=True)
embeddings = []
for tok in features:
if str(tok) not in ['<s>', '</s>']:
embeddings.append(tok.vector)
embeddings = torch.stack(embeddings).detach().cpu().numpy()
return embeddings
def parse_sentence(self, tokens):
# The model expects <ROOT> token at the end of the input sentence
if tokens[-1] != "<ROOT>":
tokens.append("<ROOT>")
sent_rep = utils.vectorize_words(self.model, tokens, training=False, gpu=self.model.use_gpu)
bert_emb = self.get_embeddings(tokens)
amr = self.model.parse_sentence(tokens, sent_rep, bert_emb)
return amr
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] |
tests/test_draw.py | mrtrkmn/yellowbrick | 3,662 | 196841 | <gh_stars>1000+
# tests.test_draw
# Tests for the high-level drawing utility functions
#
# Author: <NAME> <<EMAIL>>
# Created: Sun Aug 19 11:21:04 2018 -0400
#
# ID: test_draw.py [dd915ad] <EMAIL> $
"""
Tests for the high-level drawing utility functions
"""
##########################################################################
## Imports
##########################################################################
import pytest
import numpy as np
import matplotlib.pyplot as plt
from yellowbrick.draw import *
from .base import VisualTestCase
##########################################################################
## Simple tests for high-level drawing utilities
##########################################################################
def test_manual_legend_uneven_colors():
"""
Raise exception when colors and labels are mismatched in manual_legend
"""
with pytest.raises(YellowbrickValueError, match="same number of colors as labels"):
manual_legend(None, ("a", "b", "c"), ("r", "g"))
@pytest.fixture(scope="class")
def data(request):
data = np.array(
[
[4, 8, 7, 6, 5, 2, 1],
[6, 7, 9, 6, 9, 3, 6],
[5, 1, 6, 8, 4, 7, 8],
[6, 8, 1, 5, 6, 7, 4],
]
)
request.cls.data = data
##########################################################################
## Visual test cases for high-level drawing utilities
##########################################################################
@pytest.mark.usefixtures("data")
class TestDraw(VisualTestCase):
"""
Visual tests for the high-level drawing utilities
"""
def test_manual_legend(self):
"""
Check that the manual legend is drawn without axes artists
"""
# Draw a random scatter plot
random = np.random.RandomState(42)
Ax, Ay = random.normal(50, 2, 100), random.normal(50, 3, 100)
Bx, By = random.normal(42, 3, 100), random.normal(44, 1, 100)
Cx, Cy = random.normal(20, 10, 100), random.normal(30, 1, 100)
_, ax = plt.subplots()
ax.scatter(Ax, Ay, c="r", alpha=0.35, label="a")
ax.scatter(Bx, By, c="g", alpha=0.35, label="b")
ax.scatter(Cx, Cy, c="b", alpha=0.35, label="c")
# Add the manual legend
manual_legend(
ax, ("a", "b", "c"), ("r", "g", "b"), frameon=True, loc="upper left"
)
# Assert image similarity
self.assert_images_similar(ax=ax, tol=0.5)
def test_vertical_bar_stack(self):
"""
Test bar_stack for vertical orientation
"""
_, ax = plt.subplots()
# Plots stacked bar charts
bar_stack(self.data, ax=ax, orientation="v")
# Assert image similarity
self.assert_images_similar(ax=ax, tol=0.1)
def test_horizontal_bar_stack(self):
"""
Test bar_stack for horizontal orientation
"""
_, ax = plt.subplots()
# Plots stacked bar charts
bar_stack(self.data, ax=ax, orientation="h")
# Assert image similarity
self.assert_images_similar(ax=ax, tol=0.1)
def test_single_row_bar_stack(self):
"""
Test bar_stack for single row
"""
data = np.array([[4, 8, 7, 6, 5, 2, 1]])
_, ax = plt.subplots()
# Plots stacked bar charts
bar_stack(data, ax=ax)
# Assert image similarity
self.assert_images_similar(ax=ax, tol=0.1)
def test_labels_vertical(self):
"""
Test labels and ticks for vertical barcharts
"""
labels = ["books", "cinema", "cooking", "gaming"]
ticks = ["noun", "verb", "adverb", "pronoun", "preposition", "digit", "other"]
_, ax = plt.subplots()
# Plots stacked bar charts
bar_stack(self.data, labels=labels, ticks=ticks, colors=["r", "b", "g", "y"])
# Extract tick labels from the plot
ticks_ax = [tick.get_text() for tick in ax.xaxis.get_ticklabels()]
# Assert that ticks are set properly
assert ticks_ax == ticks
# Assert image similarity
self.assert_images_similar(ax=ax, tol=0.05)
def test_labels_horizontal(self):
"""
Test labels and ticks with horizontal barcharts
"""
labels = ["books", "cinema", "cooking", "gaming"]
ticks = ["noun", "verb", "adverb", "pronoun", "preposition", "digit", "other"]
_, ax = plt.subplots()
# Plots stacked bar charts
bar_stack(
self.data, labels=labels, ticks=ticks, orientation="h", colormap="cool"
)
# Extract tick labels from the plot
ticks_ax = [tick.get_text() for tick in ax.yaxis.get_ticklabels()]
# Assert that ticks are set properly
assert ticks_ax == ticks
# Assert image similarity
self.assert_images_similar(ax=ax, tol=0.05)
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] |
code/test_trend.py | baolintian/Timeseries_feature_test | 0 | 62872 | import pytest
from feature_judge import *
from util import *
def test_monotone_increase():
timeseries_name = "root.CNNP.QF.1#.QF1RCP604MP"
config_path = "../config/" + timeseries_name
image_path = "../images/" + timeseries_name
timeseries_path = "../data/" + timeseries_name + ".csv"
trend_config, threshold_config, resample_frequency = read_config(config_path)
timeseries = read_timeseries(timeseries_path, str(resample_frequency) + "min")
Dplot = 'yes'
s_tf = trend_features(timeseries, timeseries_name + ".numvalue", trend_config, image_path, Dplot)
assert s_tf == [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
def test_wave():
timeseries_name = "wave_test"
config_path = "../config/" + timeseries_name
image_path = "../images/" + timeseries_name
timeseries_path = "../data/" + timeseries_name + ".csv"
trend_config, threshold_config, resample_frequency = read_config(config_path)
timeseries = read_timeseries(timeseries_path, str(resample_frequency) + "min")
Dplot = 'yes'
s_tf = trend_features(timeseries, timeseries_name, trend_config, image_path, Dplot)
assert s_tf == [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
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] |
ros2_batch_job/vendor/osrf_pycommon/tests/unit/test_process_utils/impl_aep_trollius.py | taewoong0627/ci | 31 | 187293 | from osrf_pycommon.process_utils import asyncio
from osrf_pycommon.process_utils.async_execute_process import async_execute_process
from osrf_pycommon.process_utils import get_loop
# allow module to be importable for --cover-inclusive
try:
from osrf_pycommon.process_utils.async_execute_process_trollius import From
except ImportError:
TROLLIUS_FOUND = False
else:
TROLLIUS_FOUND = True
from osrf_pycommon.process_utils.async_execute_process_trollius import Return
from .impl_aep_protocol import create_protocol
loop = get_loop()
@asyncio.coroutine
def run(cmd, **kwargs):
transport, protocol = yield From(async_execute_process(
create_protocol(), cmd, **kwargs))
retcode = yield asyncio.From(protocol.complete)
raise Return(protocol.stdout_buffer, protocol.stderr_buffer,
retcode)
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