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import pytest
from app.routes import is_running_locally
@pytest.mark.parametrize('url,expected_res', [
('http://localhost:5000/test', True),
('http://127.0.0.1:5000/test', True),
('http://live.website/test', False),
])
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from kivy.app import App
from kivy.uix.button import Button
from kivy import utils
if __name__ == "__main__":
MainApp().run()
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from __future__ import absolute_import, unicode_literals
from django.apps import AppConfig
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import asyncio
import json
from typing import List
from websockets.legacy.client import WebSocketClientProtocol
from nbr.schemas.message import Content
from nbr.schemas.result import ExecutionStatus, RunResult
from nbr.schemas.session import Session
from nbr.utils.message import create_message
from nbr.utils.websocket import connect_websocket
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# env.unwrapped.get_action_meanings()
#======================================================
# Agent classes
#======================================================
'''
Info:
Version: 1.0
Author: Young Lee
Created: Friday, 16 August 2019
'''
# Import modules
import os
import re
import sys
try:
get_ipython().system('pip install gym')
get_ipython().system('pip install tqdm')
get_ipython().system('pip install dropbox')
get_ipython().system('pip install gym[atari]')
except NameError:
pass
# get_ipython().system('apt-get install -y cmake libopenmpi-dev python3-dev zlib1g-dev')
# get_ipython().system('apt-get install -y python-mpi4py')
# get_ipython().system('pip install stable-baselines')
# get_ipython().system('brew install cmake openmpi')
# !pip install pandas
# !pip install keras
# !pip install matplotlib
# !pip install gym[atari]
try:
from stable_baselines.common.atari_wrappers import WarpFrame
except ModuleNotFoundError:
try:
from stable_baselines.common.atari_wrappers import WarpFrame
except ModuleNotFoundError:
from baselines.common.atari_wrappers import WarpFrame
import gym
from gym import spaces
from gym.wrappers.atari_preprocessing import AtariPreprocessing
from gym import envs
from tqdm import tqdm
import numpy as np
import pandas as pd
import random
import dropbox
from datetime import datetime
from scipy.special import softmax
import matplotlib.pyplot as plt
from copy import deepcopy
# %matplotlib inline
# Import custom modules
try:
sys.path.append(os.path.dirname(os.path.abspath(os.path.join(__file__, '..')))) # 1 level upper dir
sys.path.append(os.path.dirname(os.path.abspath(os.path.join(__file__, '..', '..')))) # 2 levels upper dir
except NameError:
sys.path.append('.') # current dir
sys.path.append('..') # 1 level upper dir
sys.path.append(os.path.join(os.getcwd(), '..')) # 1 levels upper dir
sys.path.append(os.path.join(os.getcwd(), '..', '..')) # 2 levels upper dir
from config.paths import main_dir
import utility.util_general as gen
from collections import deque
from tensorflow.keras.models import Sequential
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import concatenate
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
from tensorflow.keras.layers import Conv3D, MaxPooling3D, AveragePooling3D
from tensorflow.keras.optimizers import Adam, Nadam, RMSprop, SGD
from tensorflow.keras.models import clone_model
from tensorflow.keras import backend as K
import tensorflow as tf
# dtype = 'float16'
# K.set_floatx(dtype)
# K.set_epsilon(1e-4)
# print(tf.__version__)
# config = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=12, device_count = {'CPU': 12 })
# session = tf.compat.v1.Session(config=config)
# K.set_session(session)
# Suppress warnings
import logging, os
logging.disable(logging.WARNING)
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
#------------------------------
# DQN Agent
#------------------------------
# Define agent
# Initialise
# Model predicts the action values (Q-values)
# Target network
# Update target model
# Story in memory
# Epsilon greedy or Boltzmann action
# Replay memory
# Replay memory
# Load
# Save | [
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] | 2.796926 | 1,236 |
from fastapi_integration.current_user_resolvers.role_checkers import RoleCheckerFactory
from galo_ioc import get_factory
__all__ = [
"load",
]
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# Copyright (c) 2013 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Class representing instrumentation test apk and jar."""
import os
from devil.android import apk_helper
from pylib.instrumentation import test_jar
from pylib.local.device import local_device_test_run
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#
# Copyright 2019 BrainPad Inc. All Rights Reserved.
#
# 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.
#
import os
from cliboa.util.cache import StorageIO
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from django import forms
from resources.models import Pet
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from utils import normdata, myrmse
from sklearn.metrics import (
accuracy_score,
roc_curve,
auc,
roc_auc_score,
mean_squared_error,
)
import numpy as np
import random
import matplotlib.pyplot as plt
def performance_vs_confidence(
original_data,
imp_data,
missing_data,
testY,
test_idx,
total_uncertainty,
coeff_variation,
clf=None,
):
"""
Computes the performance vs confidence (i.e exclusions)
Args:
analysis_scores (dict): dict of different analysis scores
"""
df_mis = missing_data
testX = original_data
percents = np.linspace(0.01, 0.9, 10)
amounts = percents * testX.shape[0]
# sort based on variance
uncert = np.argsort(total_uncertainty)
# sort based on CV
cv_uncert = np.argsort(coeff_variation)[::-1]
uncert_rmses_retention = []
cv_rmses_retention = []
random_rmses_retention = []
y_score_retention = []
auc_retention = []
gt_y = []
acc_scores = []
# apply mask
true = testX[~missing_data.astype(bool)]
preds = imp_data[~missing_data.astype(bool)]
# oracle error
errors = np.abs(preds - true)
# sort based on error - oracle
uncert_oracle = np.argsort(errors)
rmse_oracle = []
for count, amount in enumerate(amounts):
idx = int(amount)
# Calculations and exclusions based on variance
excl = uncert[:-idx]
ori_data = testX[excl, :]
imputed_data = imp_data[excl, :]
data_m = np.array(df_mis != df_mis)[excl, :]
rmse = myrmse(
actual=ori_data, predicted=imputed_data, mask=~data_m.astype(bool)
)
uncert_rmses_retention.append(rmse)
# Calculations for oracle
if count > 0:
excl_oracle = uncert_oracle[: -int(amount)]
rmseval = mean_squared_error(true[excl_oracle], preds[excl_oracle])
rmse_oracle.append(rmseval)
else:
rmse_oracle.append(rmse)
excl_oracle = uncert_oracle[: -int(amount)]
rmseval = mean_squared_error(true[excl_oracle], preds[excl_oracle])
rmse_oracle.append(rmseval)
# if a classifier is specified apply the sortings for diff acc and auc
if clf:
y_preds = clf.predict(imputed_data[:, 0:-1])
y_scores = clf.predict_proba(imputed_data[:, 0:-1])[:, 1]
if len(np.unique(testY)) == 2:
auc_retention.append(
roc_auc_score(testY[excl], y_scores, multi_class="ovr")
)
y_score_retention.append(y_scores)
gt_y.append(testY[excl])
acc_scores.append(accuracy_score(testY[excl], y_preds))
# Calculations and exclusions based on CV
excl = cv_uncert[:-idx]
ori_data = testX[excl, :]
imputed_data = imp_data[excl, :]
data_m = np.array(df_mis != df_mis)[excl, :]
rmse = myrmse(
actual=ori_data, predicted=imputed_data, mask=~data_m.astype(bool)
)
cv_rmses_retention.append(rmse)
# Calculations and exclusions based on random
rand_excl = random.sample(range(len(uncert)), idx)
ori_data = testX[rand_excl, :]
imputed_data = imp_data[rand_excl, :]
data_m = np.array(df_mis != df_mis)[rand_excl, :]
rmse = myrmse(
actual=ori_data, predicted=imputed_data, mask=~data_m.astype(bool)
)
random_rmses_retention.append(rmse)
return (
uncert_rmses_retention,
cv_rmses_retention,
random_rmses_retention,
y_score_retention,
auc_retention,
gt_y,
acc_scores,
rmse_oracle[:-1],
)
def plot_rmse_conf_curve(analysis_scores, dataset, filename):
"""
Plots the RMSE Confidence-Exclusion curve
"""
plt.style.reload_library()
plt.style.use(["science", "ieee", "no-latex", "notebook", "grid", "vibrant"])
mean_uncert = np.mean(analysis_scores["uncert_rmses_retention"], axis=0)
std_uncert = np.std(analysis_scores["uncert_rmses_retention"], axis=0)
plt.plot(np.linspace(0, 1, 10), mean_uncert, label="Variance", marker="o")
plt.fill_between(
np.linspace(0, 1, 10),
mean_uncert - std_uncert,
mean_uncert + std_uncert,
alpha=0.25,
)
mean_uncert = np.mean(analysis_scores["random_rmses_retention"], axis=0)
std_uncert = np.std(analysis_scores["random_rmses_retention"], axis=0)
plt.plot(np.linspace(0, 1, 10), mean_uncert, label="Random", marker="o")
plt.fill_between(
np.linspace(0, 1, 10),
mean_uncert - std_uncert,
mean_uncert + std_uncert,
alpha=0.25,
)
mean_uncert = np.mean(analysis_scores["cv_rmses_retention"], axis=0)
std_uncert = np.std(analysis_scores["cv_rmses_retention"], axis=0)
plt.plot(np.linspace(0, 1, 10), mean_uncert, label="CV", marker="o")
plt.fill_between(
np.linspace(0, 1, 10),
mean_uncert - std_uncert,
mean_uncert + std_uncert,
alpha=0.25,
)
mean_uncert = np.mean(analysis_scores["rmse_oracle"], axis=0)
std_uncert = np.std(analysis_scores["rmse_oracle"], axis=0)
plt.plot(np.linspace(0, 1, 11), mean_uncert, label="Oracle", marker="o")
plt.fill_between(
np.linspace(0, 1, 11),
mean_uncert - std_uncert,
mean_uncert + std_uncert,
alpha=0.25,
)
plt.xlabel("Proportion Data Excluded")
plt.ylabel("RMSE")
plt.legend()
plt.savefig(f"data/results/{dataset}/{filename}.png")
def plot_reliability_diagram(uncertainty_list, rmses, dataset, filename):
"""
Plots the Reliability diagram
"""
f, ax = plt.subplots(figsize=(6, 6))
ax.scatter(uncertainty_list, rmses, c=".3")
(diag_line,) = ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3")
plt.xlabel("Uncertainty")
plt.ylabel("RMSE")
plt.savefig(f"data/results/{dataset}/reliability_{filename}.png")
def plot_auc_sparsification(
aoc_uncert_lists,
aoc_uncert_rand_lists,
aoc_uncert_cv_lists,
auc_uncerts,
auc_rands,
auc_cvs,
dataset,
filename,
):
"""
Plots the Sparsification curve
"""
plt.figure()
xx = np.linspace(0, 1, 10)
mean_uncert = np.mean(aoc_uncert_lists, axis=0)
std_uncert = np.std(aoc_uncert_lists, axis=0)
auc_label = f"Variances Scores AUC: {round(np.mean(auc_uncerts),2)}+-{round(np.std(auc_uncerts),2)}"
plt.plot(np.linspace(0, 1, 10), mean_uncert, marker="o", label=auc_label)
plt.fill_between(
np.linspace(0, 1, 10),
mean_uncert - std_uncert,
mean_uncert + std_uncert,
alpha=0.25,
)
mean_uncert = np.mean(aoc_uncert_rand_lists, axis=0)
std_uncert = np.std(aoc_uncert_rand_lists, axis=0)
auc_label = f"Random Scores AUC: {round(np.mean(auc_rands),2)}+-{round(np.std(auc_rands),2)}"
plt.plot(np.linspace(0, 1, 10), mean_uncert, marker="o", label=auc_label)
plt.fill_between(
np.linspace(0, 1, 10),
mean_uncert - std_uncert,
mean_uncert + std_uncert,
alpha=0.25,
)
mean_uncert = np.mean(aoc_uncert_cv_lists, axis=0)
std_uncert = np.std(aoc_uncert_cv_lists, axis=0)
auc_label = (
f"Coeff Variation AUC: {round(np.mean(auc_cvs),2)}+-{round(np.std(auc_cvs),2)}"
)
plt.plot(np.linspace(0, 1, 10), mean_uncert, marker="o", label=auc_label)
plt.fill_between(
np.linspace(0, 1, 10),
mean_uncert - std_uncert,
mean_uncert + std_uncert,
alpha=0.25,
)
plt.legend()
plt.title("Area under the sparsification curve")
plt.savefig(f"data/results/{dataset}/{filename}.png")
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] | 2.038827 | 3,786 |
import pytest
@pytest.fixture
def passed_submission_py() -> str:
"""
Function which returns a passed execution output in python
"""
return """
..
----------------------------------------------------------------------
Ran 2 tests in 0.000s
OK
"""
@pytest.fixture
@pytest.fixture
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] | 3.326087 | 92 |
import logging
import csv
import json
from platform import system
from sys import stderr, stdout
# not needed in python >= 3.6? as default dict keeps order
from collections import OrderedDict, deque
try:
from google.protobuf.json_format import MessageToDict
except ImportError:
# not in debian stretch dpkg/apt version of the pb lib
from google.protobuf.json_format import MessageToJson
from google.protobuf.message import DecodeError
from bblogger import bb_log_entry_pb2
from bblogger.defs import BlueBerryLogEntryFields
from bblogger.outputwriter import mk_OutputWriter
logger = logging.getLogger(__name__)
TXT_COL_WIDTH = 10
_COLNAME_TO_FLD = {}
_COLNAME_TO_UNITS = {}
_COLNAME_TO_TXTFMT = {}
_PBNAME_TO_FLD = {}
for x in BlueBerryLogEntryFields:
fld = x.value
_PBNAME_TO_FLD[fld.pbname] = fld
for colname in fld.colnames:
_COLNAME_TO_FLD[colname] = fld
_COLNAME_TO_UNITS[colname] = fld.unit
_COLNAME_TO_TXTFMT[colname]= fld.txtfmt
class _PacketBuffer:
"""
FIFO buffer preserving BLE packets. can handle packets out of order and
drop induvidual packets
'pkt' - bluteooth package (chunk of bytes)
"""
def peek(self, size, pkt_order=None):
""" returns a bytearray of len size or less """
res = bytearray()
if not size:
return res
if pkt_order is None:
pkt_order = range(0, len(self._q))
for i in pkt_order:
remains = size - len(res)
if remains <= 0:
break
try:
pkt = self._q[i]
except IndexError:
break
# remains could be out of range (no error raised)
chunk = pkt[0 : remains]
res.extend(chunk)
return res
def getc(self):
""" read a single char/byte """
try:
c = self._q[0][0]
except IndexError:
raise EOFError()
self._q[0] = self._q[0][1:] # pop left
return int(c)
def seek_fwd(self, size, pkt_order=None):
""" Move "read cursor" forward N bytes """
if not size:
return
if pkt_order is None:
pkt_order = range(0, len(self._q))
remains = size
to_del = []
for i in pkt_order:
try:
pkt = self._q[i]
except IndexError:
break
if remains < len(pkt):
self._q[i] = pkt[remains:]
remains = 0
break
to_del.append(i)
remains -= len(pkt)
if remains <= 0:
break
if remains > 0:
raise EOFError()
# reverse sort to preserve index while deleting
for i in sorted(to_del, reverse=True):
del self._q[i]
class BlueBerryDeserializer:
"""
reads a stream of protobuf data with the format
<len><protobuf message of size len><len>,...
abbrevations and definitions used:
'msg' - bytes or pb object for a complete message
'pkg' - bluteooth package (chunk of bytes)
"""
@property
def _MessageToOrderedDict(self, pb, columnize=False):
"""
mimic name from protobuf lib.
assumption: all values can be converted to float or list of floats.
if the protobuf format change, the built in MessageToDict() function
can be used. requres python > 3.6 (?) where the default dict heaviour
rememebers insertion order.
"""
od = OrderedDict()
for descr in pb.DESCRIPTOR.fields:
fld = _PBNAME_TO_FLD[descr.name]
val = getattr(pb, descr.name)
if descr.label == descr.LABEL_REPEATED:
# HasField() do not work on repeated, use len instead. hack
if not len(val):
continue
if columnize:
for i in range(0, len(val)):
name = fld.colnames[i]
od[name] = val[i]
else:
name = fld.colnames[0]
od[name] = list(val) # [x for x in val]
else:
if not pb.HasField(descr.name):
continue
name = fld.colnames[0]
od[name] = val
return od
def _is_end_of_log_msg(self, odm):
""" end of log "EOF" is a empty messagge with only the required
timestamp field """
if len(odm) == 1:
if "TS" not in odm:
logger.warning("unexpected last msg keys {}".format(odm.keys()))
return True
else:
return False
def _parse_pkt_buf(self, pkt_order=None):
""" parse data previously added to pkt_buf """
if self._msg_size is None:
self._msg_size = self._pkt_buf.getc() # raises EOFError if no data
if self._msg_size == 0:
raise RuntimeError("msg_size is zero. Where to start?")
msg_bytes = self._pkt_buf.peek(self._msg_size, pkt_order)
if len(msg_bytes) < self._msg_size:
raise EOFError("Need more data")
if self._debug_dump:
self._print_msg_bytes(self._msg_count, self._msg_size, msg_bytes)
done = False
else:
done = self.parse_msg_bytes(msg_bytes)
entry = (self._msg_count, self._msg_size, msg_bytes, "")
self._msg_hist.append(entry)
self._msg_count += 1
# reset
self._pkt_buf.seek_fwd(self._msg_size, pkt_order)
self._msg_size = None
return done # might have more msg in pkt_buf
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423,
517,
31456,
287,
279,
21841,
62,
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628,
628
] | 2.041204 | 2,791 |
import contextlib as cl
from . import accessors
@cl.contextmanager
@cl.contextmanager
| [
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] | 3.37037 | 27 |
from error_handlers import captha
from error_handlers import rps
error_handlers_bp = (
rps.user,
captha.user,
)
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] | 2.520833 | 48 |
import pytest
from RNA_describe import RNA_describer
from RNA_describe import ORF_counter
from RNA_describe import ORF_RE
# The following unix command will run all tests.
# $ pytest
# The -v option will list each test and show progress.
# $ pytest -v
# By default, pytest captures stdout unless the tests fail.
# Use this option to see the output of print() statements.
# $ pytest --capture=tee-sys
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] | 3.389831 | 118 |
from .navbar import nav_bar
from .visuals import gdp_viz | [
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] | 2.947368 | 19 |
import argparse
import corenlp
import gzip
import inflect
from tqdm import tqdm
if __name__ == '__main__':
parser = argparse.ArgumentParser('Generate negative examples for LM agreement task.')
parser.add_argument('--source', required=True, type=str)
parser.add_argument('--output', default='verb_negative_examples.txt')
args = parser.parse_args()
run(args)
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] | 3 | 127 |
# Valentin Macé
# [email protected]
# Developed for fun
# Feel free to use this code as you wish as long as you quote me as author
"""
snake.py
~~~~~~~~~~
This module is for building the snake itself in the snake game
The snake:
- Is on the form of a list, each element for a body block (containing its coordinates)
- Has a head pointing on the first block, a direction and also a neural network (brain)
- Has vision given by the map (Map.scan method)
- Is in charge of moving its blocks, aging, growing by adding a block to the right place
and makes decision with neural net
- Gives its fitness based on self age and length
"""
from neural_network import *
class Snake:
"""Snake Class"""
def __init__(self, neural_net=None, xMaxSize = 20, yMaxSize = 20):
"""
:param neural_net: NeuralNet given to the snake in charge of decisions (AI)
"""
self.body = [[10, 10], [9, 10], [9, 11], [9, 12]] # the snake is in fact a list of coordinates
self.head = self.body[0][:] # first body block
self.old_tail = self.head[:] # useful to grow
self.direction = RIGHT
self.age = 0
self.starve = 500 # useful to avoid looping AI snakes
self.alive = True
self.neural_net = neural_net
self.vision = [] # holds the map.scan() and is used by the neural net
self.xMaxSize = xMaxSize
self.yMaxSize = yMaxSize
def update(self):
"""
Actualize the snake through time, making it older and more hungryat each game iteration,
sorry snek
"""
self.age += 1
self.starve -= 1
if self.starve < 1:
self.alive = False
self.move()
def grow(self):
"""
Makes snake grow one block longer
Called by map.update() when snake's head is in collision with food
"""
self.starve = 500 # useful to avoid looping AI snakes (they die younger -> bad fitness)
self.body.append(self.old_tail) # that's why I keep old_tail
def move(self):
"""
Makes the snake move, head moves in current direction and each blocks replace its predecessor
"""
self.old_tail = self.body[-1][:] # save old position of last block
self.head[0] += self.direction[0] # moves head
self.head[1] += self.direction[1]
self.head[0] = (self.head[0] + self.xMaxSize) % self.xMaxSize
self.head[1] = (self.head[1] + self.yMaxSize) % self.yMaxSize
if self.head in self.body[1:]: # if snakes hits himself
self.alive = False
self.body.insert(0, self.body.pop()) # each block is replace by predecessor
self.body[0] = self.head[:] # first block is head
def turn_right(self):
"""
Makes the snake direction to the right of the current direction
Current direction = [x,y], turn_right gives [-y,x]
Example:
If [0,1] (down) is current direction, [-1,0] (right) is new direction
"""
temp = self.direction[0]
self.direction[0] = -self.direction[1]
self.direction[1] = temp
def turn_left(self):
"""
Makes the snake direction to the right of the current direction
Current direction = [x,y], turn_right gives [y,-x]
"""
temp = self.direction[0]
self.direction[0] = self.direction[1]
self.direction[1] = -temp
def AI(self):
"""
Makes decision for the snake direction according to its current vision
Vision is given to the NeuralNetwork and most activated output neuron is considered as decision
"""
decision = np.argmax(self.neural_net.feed_forward(self.vision))
if decision == 1:
self.turn_right()
elif decision == 2:
self.turn_left()
def fitness(self):
"""
Measures how well the snake is doing as a function of its length and age
Note:
- You can be creative with the formula and find a better solution
- It has a big impact on the genetic algorithm
:return: integer representing how good the snake is performing
"""
return (len(self.body)**2) * self.age
def render(self, window):
"""
Renders the map (background, walls and food) on the window surface and calls render() of snake
Very very very unoptimized since render does not affect the genetic algorithm
:param window: surface window
"""
body = pygame.image.load(IMAGE_SNAKE).convert_alpha() # loading image
for block in self.body:
window.blit(body, (block[0]*SPRITE_SIZE, block[1]*SPRITE_SIZE)) # painting a beautiful snek
if self.neural_net: # calls for neural net rendering
self.neural_net.render(window, self.vision)
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] | 2.354157 | 2,177 |
# project/test_basic.py
import os
import unittest
from app.app import Kanban_app
from app.models import db, User, Card
TEST_DB = 'test.db'
# execute before each test
# execute after each test
# methods to help pass data to views
# tests to run
# check home view works
# test user can register
# test invalid email during registration
# test invalid passwords during registration
# test log in works
# test incorrect password
# test bad email prevents login
# test app prevents duplicate emails
# test logout
if __name__ == "__main__":
unittest.main()
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] | 3.2 | 195 |
"""
Direct access to the Pwned Passwords API for checking whether a
password is compromised.
"""
import hashlib
import logging
import sys
import requests
from django.conf import settings
from . import __version__
log = logging.getLogger(__name__)
API_ENDPOINT = "https://api.pwnedpasswords.com/range/{}"
REQUEST_TIMEOUT = 1.0 # 1 second
USER_AGENT = "pwned-passwords-django/{} (Python/{} | requests/{})".format(
__version__, "{}.{}.{}".format(*sys.version_info[:3]), requests.__version__
)
def _get_pwned(prefix):
"""
Fetches a dict of all hash suffixes from Pwned Passwords for a
given SHA-1 prefix.
"""
try:
response = requests.get(
url=API_ENDPOINT.format(prefix),
headers={"User-Agent": USER_AGENT},
timeout=getattr(settings, "PWNED_PASSWORDS_API_TIMEOUT", REQUEST_TIMEOUT),
)
response.raise_for_status()
except requests.RequestException as e:
# Gracefully handle timeouts and HTTP error response codes.
log.warning("Skipped Pwned Passwords check due to error: %r", e)
return None
results = {}
for line in response.text.splitlines():
line_suffix, _, times = line.partition(":")
results[line_suffix] = int(times)
return results
def pwned_password(password):
"""
Checks a password against the Pwned Passwords database.
"""
if not isinstance(password, str):
raise TypeError("Password values to check must be Unicode strings.")
password_hash = hashlib.sha1(password.encode("utf-8")).hexdigest().upper()
prefix, suffix = password_hash[:5], password_hash[5:]
results = _get_pwned(prefix)
if results is None:
# Gracefully handle timeouts and HTTP error response codes.
return None
return results.get(suffix, 0)
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] | 2.626801 | 694 |
import numpy as np
from smartredis import Dataset
def test_add_get_tensor(mock_data):
"""Test adding and retrieving 1D tensors to
a dataset and with all datatypes
"""
dataset = Dataset("test-dataset")
# 1D tensors of all data types
data = mock_data.create_data(10)
add_get_arrays(dataset, data)
def test_add_get_tensor_2D(mock_data):
"""Test adding and retrieving 2D tensors to
a dataset and with all datatypes
"""
dataset = Dataset("test-dataset")
# 2D tensors of all data types
data_2D = mock_data.create_data((10, 10))
add_get_arrays(dataset, data_2D)
def test_add_get_tensor_3D(mock_data):
"""Test adding and retrieving 3D tensors to
a dataset and with all datatypes
"""
dataset = Dataset("test-dataset")
# 3D tensors of all datatypes
data_3D = mock_data.create_data((10, 10, 10))
add_get_arrays(dataset, data_3D)
def test_add_get_scalar(mock_data):
"""Test adding and retrieving scalars to
a dataset and with all datatypes
"""
dataset = Dataset("test-dataset")
# 1D tensors of all data types
data = mock_data.create_metadata_scalars(10)
add_get_scalars(dataset, data)
def test_add_get_strings(mock_data):
"""Test adding and retrieving strings to
a dataset
"""
dataset = Dataset("test-dataset")
# list of strings
data = mock_data.create_metadata_strings(10)
add_get_strings(dataset, data)
# ------- Helper Functions -----------------------------------------------
def add_get_arrays(dataset, data):
"""Helper for dataset tests"""
# add to dataset
for index, array in enumerate(data):
key = f"array_{str(index)}"
dataset.add_tensor(key, array)
# get from dataset
for index, array in enumerate(data):
key = f"array_{str(index)}"
rarray = dataset.get_tensor(key)
np.testing.assert_array_equal(
rarray,
array,
"Returned array from get_tensor not equal to tensor added to dataset",
)
def add_get_scalars(dataset, data):
"""Helper for metadata tests"""
# add to dataset
for index, scalars in enumerate(data):
key = f"meta_scalars_{index}"
for scalar in scalars:
dataset.add_meta_scalar(key, scalar)
# get from dataset
for index, scalars in enumerate(data):
key = f"meta_scalars_{index}"
rscalars = dataset.get_meta_scalars(key)
np.testing.assert_array_equal(
rscalars,
scalars,
"Returned scalars from get_meta_scalars not equal to scalars added to dataset",
)
def add_get_strings(dataset, data):
"""Helper for metadata tests"""
# add to dataset
key = "test_meta_strings"
for meta_string in data:
dataset.add_meta_string(key, meta_string)
# get from dataset
rdata = dataset.get_meta_strings(key)
assert len(data) == len(rdata)
assert all([a == b for a, b in zip(data, rdata)])
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275,
287,
19974,
7,
7890,
11,
374,
7890,
8,
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198
] | 2.426494 | 1,238 |
from rest_framework import generics
from .models import Item
from .serializers import ItemSerializer
| [
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] | 4.16 | 25 |
from distutils.core import setup, Extension
from Cython.Build import cythonize
# ext = Extension(name="wrap_fib", source=["cfibc.c", "wrap_fib.pyx"])
# ext = ["hermite_splines.pyx", "source_iteration.pyx", "splines.pyx"]
ext = ["multi_group.pyx", "x_sweeps.pyx"] #, "x_sweeps.pxd"]
setup(ext_modules=cythonize(ext, language_level="3"))
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] | 2.471014 | 138 |
import os
from django.core.wsgi import get_wsgi_application
from dj_static import Cling
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'bus_plan.settings')
application = Cling(get_wsgi_application())
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#!/usr/bin/env python3
# encoding: utf-8
import asyncio
asyncio.run(main())
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#
# Embed a photo data inside a Tk frame
#
import tkinter as tk
import smimgpng as smimg
AUTHOR = "Alexandre Gomiero de Oliveira"
REPO = "https://github.com/gomiero/bin2src"
# Entry point: create the root window...
root = tk.Tk()
# ...the App instance...
app = App(master = root)
# ...and run the main loop.
app.mainloop()
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] | 2.739496 | 119 |
import struct
| [
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] | 5 | 3 |
import numpy as np
from instance_occlsegm_lib.datasets.apc.apc2016 import JskAPC2016Dataset
| [
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] | 2.5 | 38 |
import random
from optparse import make_option
from django.conf import settings
from django.core.management.base import BaseCommand
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] | 4.060606 | 33 |
from lib.mlnet import MLNet
from data.data_loader import DataLoader
from data.utils import split_and_pack
import tensorflow as tf
import numpy as np
import time
def get_descs_and_labels(net: MLNet, sess: tf.Session, modal,
paths_with_labels, process_fn, batch_size):
"""
This function computes description vectors for image and text samples.
"""
if net.is_training: raise Exception("should not run this in training mode")
if net.is_retrieving: raise Exception("should not run this in retrieving mode")
descriptors = []
labels = []
loader = DataLoader(paths_with_labels, batch_size, shuffle=False, process_fn=process_fn)
for batch in range(loader.n_batches):
batch_data, batch_labels = loader.get_batch_by_index(batch)
batch_data = split_and_pack(batch_data)
if modal == 1:
feed_dict = {}
for ph, data in zip(net.ph1, batch_data):
feed_dict[ph] = data
batch_descs = net.descriptors_1.eval(session=sess, feed_dict=feed_dict)
elif modal == 2:
feed_dict = {}
for ph, data in zip(net.ph2, batch_data):
feed_dict[ph] = data
batch_descs = net.descriptors_2.eval(session=sess, feed_dict=feed_dict)
else:
raise Exception("modal should be either 1 or 2")
descriptors.append(batch_descs)
labels.append(batch_labels)
if loader.n_remain > 0:
batch_data, batch_labels = loader.get_remaining()
batch_data = split_and_pack(batch_data)
if modal == 1:
feed_dict = {}
for ph, data in zip(net.ph1, batch_data):
feed_dict[ph] = data
batch_descs = net.descriptors_1.eval(session=sess, feed_dict=feed_dict)
elif modal == 2:
feed_dict = {}
for ph, data in zip(net.ph2, batch_data):
feed_dict[ph] = data
batch_descs = net.descriptors_2.eval(session=sess, feed_dict=feed_dict)
else:
raise Exception("modal should be either 1 or 2")
descriptors.append(batch_descs[:loader.n_remain])
labels.append(batch_labels[:loader.n_remain])
descriptors = np.concatenate(descriptors, axis=0)
labels = np.concatenate(labels, axis=0)
return descriptors, labels
def average_precisions(net: MLNet, sess: tf.Session,
q_descs, q_labels, r_descs, r_labels,
at=100, batch_size=128):
"""
:param net: an MLNet model
:param sess: a tensorflow session=
:param q_descs: descriptors for querying data
:param q_labels: labels for querying data
:param r_descs: descriptors for retrieved data
:param r_labels: labels for retrieved data
:param at: if mAP@100 is desired, assign 'at' with 100, if mAP@ALL is desired, assign 'at' with 0
:param batch_size: batch size
:return: average procisions
"""
n_samples, n_entries = len(q_descs), len(r_descs)
APs = []
for query_idx in range(n_samples):
time1 = time.time()
_, average_precision = retrieve(net, sess, q_descs[query_idx], q_labels[query_idx], r_descs, r_labels, at=at, batch_size=batch_size)
APs.append(average_precision)
time2 = time.time()
ellapsed = time2 - time1
print("sample %4d/%4d, AP: %5.3f, time: %5.2fs" %
(query_idx + 1, n_samples, average_precision, ellapsed), end='\r')
return APs
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] | 2.212255 | 1,583 |
"""Testing invalid cookiecutter template repositories."""
import pytest
from tackle import exceptions, main
def test_should_raise_error_if_repo_does_not_exist(chdir):
"""Cookiecutter invocation with non-exist repository should raise error."""
chdir('/')
with pytest.raises(exceptions.UnknownSourceException):
main.tackle('definitely-not-a-valid-repo-dir')
| [
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] | 3.132231 | 121 |
import torch as ch
import pandas as pd
import numpy as np
from pathlib import Path
from pathos.multiprocessing import Pool
from argparse import ArgumentParser
from numpy.random import seed
import matplotlib as mpl
from matplotlib import rc
from matplotlib import pyplot as plt
import seaborn as sns
sns.set()
mpl.style.use('ggplot')
rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})
rc('text', usetex=True)
## Copied verbatim from Recht et al code release
# Selection frequency ops
# Bootstrap
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('--trials', type=int, default=10)
parser.add_argument('--workers', type=int, default=2)
parser.add_argument('--experiment', required=True,
choices=['heldout', 'naiveest', 'ezflickr'])
parser.add_argument('--out-dir', required=True)
parser.add_argument('--df-path', required=True)
args = parser.parse_args()
print("Loading data...")
MY_PATH = Path(args.out_dir)
df = ch.load(args.df_path)
print(f"Loaded data (currently {len(df)} annotations)")
CLA_KEYS = [k for k in df.columns if k.startswith('correct_')]
p = Pool(args.workers)
if args.experiment == 'ezflickr':
FORMAT_STR = "Accs (v1, v2, v2_EZ): ({0}, {1}, {2}) | " \
"SFs (v1, v2, v2_EZ): ({3}, {4}, {5}) | " \
"v2 heldout SF: {6}"
res = p.map(flickr_ez_exp, range(args.trials))
print(FORMAT_STR.format(*list(np.array(res).mean(0))))
elif args.experiment == 'heldout':
FORMAT_STR = "SFs (v1, v2): ({0:.3f}, {1:.3f}) | " \
"v2 heldout SF: {2:.3f}"
stats = p.map(heldout_sf_exp, range(args.trials))
print(FORMAT_STR.format(*list(np.array(stats).mean(0))))
elif args.experiment == 'naiveest':
fig, ax = plt.subplots(1, 1, figsize=(6,2))
xs = [5, 6, 7, 8, 9, 10]
res = np.array(p.map(naive_est_exp, [xs] * args.trials))
res_df = pd.DataFrame(columns=xs, data=res).melt(var_name='xs', value_name='adj_acc')
ch.save(res_df, str(MY_PATH / 'orig_data_naive_est_data.pt'))
print(f"X: {xs} | Y: {res.mean(0)}")
sns.lineplot(data=res_df, x='xs', y='adj_acc',
ax=ax, palette=sns.color_palette("tab10", 1))
ax.set(xlabel='Number of annotators per image',
ylabel='ImageNet v1/v2 accuracy gap')
plt.tight_layout()
fig.savefig(str(MY_PATH / 'orig_data_naive_est.png'))
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] | 2.198406 | 1,129 |
#!/usr/bin/python3
import argparse
from http import server
import json
import subprocess
if __name__ == '__main__':
main()
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import mysql
import configparser
def initialize(config_path):
'''
Import a config .ini file.
It should have the following definition:
'''
config = configparser.ConfigParser()
config.read(config_path)
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# write tests for parsers
from seqparser import (
FastaParser,
FastqParser)
def test_freebie_parser_1():
"""
This one is a freebie
DO NOT MODIFY THIS FUNCTION
"""
assert True
def test_freebie_parser_2():
"""
This too is a freebie
DO NOT MODIFY THIS FUNCTION
"""
assert 1 != 2
def test_FastaParser():
"""
Write your unit test for your FastaParser
class here. You should generate an instance of
your FastaParser class and assert that it properly
reads in the example Fasta File.
"""
fa = FastaParser("./data/test.fa")
records = [r for r in fa]
assert len(records) == 100, "did not read in correct number of records" # 100 records in total
for r in records:
assert len(r) == 2, "the record is the wrong length" # each record consists of header and sequence
def test_FastqParser():
"""
Write your unit test for your FastqParser
class here. You should generate an instance of
your FastqParser class and assert that it properly
reads in the example Fastq File.
"""
fq = FastqParser("./data/test.fq")
records = [r for r in fq]
assert len(records) == 100, "did not read in correct number of records" # 100 records in total
for r in records:
assert len(r) == 3, "the record is the wrong length" # each record is header, sequence, and quality
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11,
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] | 2.828974 | 497 |
# Generated by Django 3.1.2 on 2022-03-26 11:11
from django.db import migrations, models
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] | 2.84375 | 32 |
import pathlib
import ctypes
import numpy as np
from objc_util import c, create_objc_class, ObjCClass, ObjCInstance
import ui
#import pdbg
shader_path = pathlib.Path('./Shaders.metal')
# --- load objc classes
MTKView = ObjCClass('MTKView')
MTLCompileOptions = ObjCClass('MTLCompileOptions')
MTLRenderPipelineDescriptor = ObjCClass('MTLRenderPipelineDescriptor')
# --- initialize MetalDevice
MTLCreateSystemDefaultDevice = c.MTLCreateSystemDefaultDevice
MTLCreateSystemDefaultDevice.argtypes = []
MTLCreateSystemDefaultDevice.restype = ctypes.c_void_p
memcpy = c.memcpy
memcpy.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_size_t]
memcpy.restype = ctypes.c_void_p
err_ptr = ctypes.c_void_p()
nd_type = np.float32
# --- set Vertex
vertex_array = [
[[-1.0, -1.0, 1.0, 1.0], [1.0, 0.0, 0.0, 1.0]],
[[ 1.0, -1.0, 1.0, 1.0], [0.0, 1.0, 0.0, 1.0]],
[[ 1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]],
[[-1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]],
[[-1.0, -1.0, -1.0, 1.0], [0.0, 0.0, 1.0, 1.0]],
[[ 1.0, -1.0, -1.0, 1.0], [1.0, 1.0, 1.0, 1.0]],
[[ 1.0, 1.0, -1.0, 1.0], [1.0, 0.0, 0.0, 1.0]],
[[-1.0, 1.0, -1.0, 1.0], [0.0, 1.0, 0.0, 1.0]],
]
Vertex = (((ctypes.c_float * 4) * 2) * 8)
np_vertex = np.array(vertex_array, dtype=nd_type)
index_array = [
0, 1, 2, 2, 3, 0, # front
1, 5, 6, 6, 2, 1, # right
3, 2, 6, 6, 7, 3, # top
4, 5, 1, 1, 0, 4, # bottom
4, 0, 3, 3, 7, 4, # left
7, 6, 5, 5, 4, 7, # back
]
Index = (ctypes.c_uint16 * 36)
np_index = np.array(index_array, dtype=np.uint16)
#MatrixFloat4x4 = ((ctypes.c_float * 4) *4)
MatrixFloat4x4 = (ctypes.c_float *16)
# --- Matrix func
# todo: 無駄にキャストするテスト
__vertexData = np_vertex.ctypes.data_as(ctypes.POINTER(Vertex)).contents
_vertexData = np.ctypeslib.as_array(__vertexData)
vertexData = _vertexData.ctypes.data_as(ctypes.POINTER(Vertex)).contents
indexData = np_index.ctypes.data_as(ctypes.POINTER(Index)).contents
# --- MTKViewDelegate
PyRenderer = create_objc_class(
name='PyRenderer',
methods=[drawInMTKView_, mtkView_drawableSizeWillChange_],
protocols=['MTKViewDelegate'])
if __name__ == '__main__':
view = MetalView()
view.present(style='fullscreen', orientations=['portrait'])
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'''NeteaseCloudMusicApiPy
NeteaseCloudMusicApi 的 Python 绑定
https://github.com/NKID00/NeteaseCloudMusicApiPy
MIT License
Copyright (c) 2020 NKID00
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''
from typing import Iterable, Dict, Union, Optional
from subprocess import Popen, DEVNULL
from os import environ, kill
from signal import SIGTERM
from contextlib import contextmanager
from time import time
from requests import Session
from hashlib import md5
from base64 import b64decode
from io import BytesIO
__all__ = ['VERSION', 'start_ncmapi_server', 'stop_ncmapi_server',
'ncmapi', 'NeteaseCloudMusicApi']
VERSION = 'NeteaseCloudMusicApiPy 0.1.0'
def start_ncmapi_server(ncmapi_server_command: Iterable[str],
port: int = 3000, host: str = 'localhost') -> int:
'''启动指定的 NeteaseCloudMusicApi 服务进程并返回进程 pid'''
env = dict(environ)
env['HOST'] = str(host)
env['PORT'] = str(port)
p = Popen(tuple(ncmapi_server_command), stdin=DEVNULL,
stdout=DEVNULL, stderr=DEVNULL, env=env)
return p.pid
def stop_ncmapi_server(ncmapi_server_pid: int) -> None:
'''停止指定 pid 的 NeteaseCloudMusicApi 服务进程'''
kill(ncmapi_server_pid, SIGTERM)
@contextmanager
def ncmapi(ncmapi_server_command: Iterable[str],
port: int = 3000, host: str = 'localhost'):
'''启动指定的 NeteaseCloudMusicApi 服务进程
并返回 NeteaseCloudMusicApi 对象
退出运行时上下文时自动退出登录并停止 NeteaseCloudMusicApi 服务进程'''
pid = None
try:
pid = start_ncmapi_server(ncmapi_server_command, port, host)
with NeteaseCloudMusicApi(port, host) as api:
yield api
finally:
if pid is not None:
try:
stop_ncmapi_server(pid)
except OSError:
pass
class NeteaseCloudMusicApi:
'''保存有 API 地址、相关设置和登录状态的 NeteaseCloudMusicApi 对象
退出运行时上下文时自动退出登录'''
def call_api(self, api: str, args: Dict[str, Union[int, bool, str]],
add_timestamp: bool = False) -> dict:
'''调用 API'''
if self.add_timestamp or add_timestamp: # 添加时间戳
args['timestamp'] = int(time() * 1000)
r = self.api_session.get(self.api_url_base + api, params=args)
if self.raise_for_status: # 如果返回错误代码则抛出异常
r.raise_for_status()
return r.json()
def login(self, email: str, password: str = '',
md5_password: Optional[str] = None,
**args: Union[int, bool, str]) -> dict:
'''/login
邮箱登录
email: 邮箱
password: 密码
md5_password: md5 加密后的密码，传入后 password 将失效'''
if md5_password is None:
h = md5()
h.update(password.encode('utf8'))
md5_password = h.hexdigest()
args['email'] = email
args['md5_password'] = md5_password
return self.call_api('/login', args, add_timestamp=True)
def login_cellphone(self, phone: int, password: str = '',
countrycode: Optional[int] = None,
md5_password: Optional[str] = None,
**args: Union[int, bool, str]) -> dict:
'''/login/cellphone
手机登录
phone: 手机号码
password: 密码
countrycode: 国家码，用于国外手机号登录，例如美国传入1
md5_password: md5加密后的密码，传入后 password 将失效'''
if md5_password is None:
h = md5()
h.update(password.encode('utf8'))
md5_password = h.hexdigest()
args['phone'] = phone
if countrycode is not None:
args['countrycode'] = countrycode
args['md5_password'] = md5_password
return self.call_api('/login/cellphone', args, add_timestamp=True)
def login_qr_check(self, key: str, **args: Union[int, bool, str]) -> dict:
'''/login/qr/check
验证二维码登录
key: 二维码标识符'''
args['key'] = key
return self.call_api('/login/qr/check', args, add_timestamp=True)
def login_qr_create(self, key: str, qrimg: bool = True,
qrimg_str: bool = True,
**args: Union[int, bool, str]) -> str:
'''/login/qr/create
获取二维码链接
key: 二维码标识符
qrimg: 获取二维码图片
qrimg_str: 获取二维码图片字符画'''
args['key'] = key
args['qrimg'] = qrimg or qrimg_str
data = self.call_api('/login/qr/create', args, add_timestamp=True)
if qrimg_str:
from PIL import Image
img_base64 = data['data']['qrimg'].split(',')[1]
img = Image.open(BytesIO(b64decode(img_base64)))
img = img.resize((40, 40), Image.NEAREST).crop((1, 1, 39, 39))
img_str = ''
for y in range(38): # 遍历行
for x in range(38): # 遍历列
black = sum(img.getpixel((x, y))[:3]) < 384
img_str += '██' if black else ' '
img_str += '\n'
return img_str
if qrimg:
return data['data']['qrimg']
else:
return data['data']['qrurl']
def login_qr_key(self, **args: Union[int, bool, str]) -> str:
'''/login/qr/key
获取二维码标识符'''
data = self.call_api('/login/qr/check', args,
add_timestamp=True)
return data['data']['unikey']
def login_refresh(self, **args: Union[int, bool, str]) -> dict:
'''/login/refresh
刷新登录'''
return self.call_api('/login/refresh', args, add_timestamp=True)
def login_status(self, **args: Union[int, bool, str]) -> dict:
'''/login/status
获取登录状态
注意: 需要登录'''
return self.call_api('/login/status', args, add_timestamp=True)
def logout(self, **args: Union[int, bool, str]) -> dict:
'''/logout
退出登录
注意: 需要登录'''
return self.call_api('/logout', args, add_timestamp=True)
def playlist_detail(self, id: int, s: Optional[int] = None,
**args: Union[int, bool, str]) -> dict:
'''/playlist/detail
获取歌单详情
id: 歌单 id
s: 歌单最近的 s 个收藏者[默认8]
注意: 需要登录'''
args['id'] = id
if s:
args['s'] = s
return self.call_api('/playlist/detail', args)
def song_detail(self, ids: Union[int, Iterable[int]],
**args: Union[int, bool, str]) -> dict:
'''/song/detail
获取歌曲详情
ids: 音乐 id'''
if isinstance(ids, int):
args['ids'] = ids
else:
args['ids'] = ','.join(map(str, ids))
return self.call_api('/song/detail', args)
def user_playlist(self, uid: int, limit: Optional[int] = None,
offset: Optional[int] = None,
**args: Union[int, bool, str]) -> dict:
'''/user/playlist
获取用户歌单
uid: 用户 id
limit: 返回数量
offset: 偏移数量[默认0]
注意: 需要登录'''
args['uid'] = uid
if limit is not None:
args['limit'] = limit
if offset is not None:
args['offset'] = offset
return self.call_api('/user/playlist', args)
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1949,
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220,
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2845,
440,
5188,
81,
1472,
25,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
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1208,
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4871,
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68,
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32,
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25,
198,
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220,
220,
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46479,
251,
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246,
17312,
231,
7824,
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250,
108,
161,
251,
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23513,
33566,
116,
17739,
111,
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106,
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121,
106,
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240,
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119,
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35050,
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21410,
3433,
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32,
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107,
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164,
109,
94,
198,
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220,
16268,
222,
222,
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118,
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234,
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114,
41468,
10310,
233,
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229,
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114,
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229,
103,
27950,
101,
34460,
222,
49035,
118,
163,
247,
119,
37605,
243,
7061,
6,
628,
220,
220,
220,
825,
869,
62,
15042,
7,
944,
11,
40391,
25,
965,
11,
26498,
25,
360,
713,
58,
2536,
11,
4479,
58,
600,
11,
20512,
11,
965,
60,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
751,
62,
16514,
27823,
25,
20512,
796,
10352,
8,
4613,
8633,
25,
198,
220,
220,
220,
220,
220,
220,
220,
705,
7061,
164,
108,
225,
18796,
101,
7824,
7061,
6,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
2860,
62,
16514,
27823,
393,
751,
62,
16514,
27823,
25,
220,
1303,
10545,
115,
119,
27950,
254,
33768,
114,
29785,
112,
22755,
111,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26498,
17816,
16514,
27823,
20520,
796,
493,
7,
2435,
3419,
1635,
8576,
8,
198,
220,
220,
220,
220,
220,
220,
220,
374,
796,
2116,
13,
15042,
62,
29891,
13,
1136,
7,
944,
13,
15042,
62,
6371,
62,
8692,
1343,
40391,
11,
42287,
28,
22046,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
40225,
62,
1640,
62,
13376,
25,
220,
1303,
10263,
99,
224,
162,
252,
250,
32573,
242,
32368,
252,
165,
242,
247,
46237,
107,
47987,
163,
254,
223,
26344,
247,
162,
232,
249,
49035,
118,
28156,
224,
30585,
116,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
374,
13,
40225,
62,
1640,
62,
13376,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
374,
13,
17752,
3419,
628,
220,
220,
220,
825,
17594,
7,
944,
11,
3053,
25,
965,
11,
9206,
25,
965,
796,
705,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
45243,
20,
62,
28712,
25,
32233,
58,
2536,
60,
796,
6045,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12429,
22046,
25,
4479,
58,
600,
11,
20512,
11,
965,
12962,
4613,
8633,
25,
198,
220,
220,
220,
220,
220,
220,
220,
705,
7061,
14,
38235,
198,
220,
220,
220,
220,
220,
220,
220,
16268,
224,
106,
163,
106,
109,
163,
247,
119,
37605,
243,
198,
220,
220,
220,
220,
220,
220,
220,
3053,
25,
16268,
224,
106,
163,
106,
109,
198,
220,
220,
220,
220,
220,
220,
220,
9206,
25,
10263,
107,
228,
163,
254,
223,
198,
220,
220,
220,
220,
220,
220,
220,
45243,
20,
62,
28712,
25,
45243,
20,
10263,
232,
254,
43380,
228,
28938,
236,
21410,
43380,
228,
163,
254,
223,
171,
120,
234,
27670,
254,
17739,
98,
28938,
236,
9206,
10263,
108,
228,
13783,
109,
46763,
230,
7061,
6,
198,
220,
220,
220,
220,
220,
220,
220,
611,
45243,
20,
62,
28712,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
289,
796,
45243,
20,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
289,
13,
19119,
7,
28712,
13,
268,
8189,
10786,
40477,
23,
6,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
45243,
20,
62,
28712,
796,
289,
13,
33095,
12894,
395,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
26498,
17816,
12888,
20520,
796,
3053,
198,
220,
220,
220,
220,
220,
220,
220,
26498,
17816,
9132,
20,
62,
28712,
20520,
796,
45243,
20,
62,
28712,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
13345,
62,
15042,
10786,
14,
38235,
3256,
26498,
11,
751,
62,
16514,
27823,
28,
17821,
8,
628,
220,
220,
220,
825,
17594,
62,
3846,
4862,
7,
944,
11,
3072,
25,
493,
11,
9206,
25,
965,
796,
705,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1499,
8189,
25,
32233,
58,
600,
60,
796,
6045,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
45243,
20,
62,
28712,
25,
32233,
58,
2536,
60,
796,
6045,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12429,
22046,
25,
4479,
58,
600,
11,
20512,
11,
965,
12962,
4613,
8633,
25,
198,
220,
220,
220,
220,
220,
220,
220,
705,
7061,
14,
38235,
14,
3846,
4862,
198,
220,
220,
220,
220,
220,
220,
220,
10545,
231,
233,
17312,
118,
163,
247,
119,
37605,
243,
198,
220,
220,
220,
220,
220,
220,
220,
3072,
25,
10545,
231,
233,
17312,
118,
20998,
115,
163,
254,
223,
198,
220,
220,
220,
220,
220,
220,
220,
9206,
25,
10263,
107,
228,
163,
254,
223,
198,
220,
220,
220,
220,
220,
220,
220,
1499,
8189,
25,
10263,
249,
121,
22522,
114,
163,
254,
223,
171,
120,
234,
18796,
101,
12859,
236,
32368,
121,
13783,
244,
33699,
233,
17312,
118,
20998,
115,
163,
247,
119,
37605,
243,
171,
120,
234,
160,
122,
233,
36685,
224,
163,
122,
236,
32368,
121,
27670,
254,
17739,
98,
16,
198,
220,
220,
220,
220,
220,
220,
220,
45243,
20,
62,
28712,
25,
45243,
20,
27950,
254,
43380,
228,
28938,
236,
21410,
43380,
228,
163,
254,
223,
171,
120,
234,
27670,
254,
17739,
98,
28938,
236,
9206,
10263,
108,
228,
13783,
109,
46763,
230,
7061,
6,
198,
220,
220,
220,
220,
220,
220,
220,
611,
45243,
20,
62,
28712,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
289,
796,
45243,
20,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
289,
13,
19119,
7,
28712,
13,
268,
8189,
10786,
40477,
23,
6,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
45243,
20,
62,
28712,
796,
289,
13,
33095,
12894,
395,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
26498,
17816,
4862,
20520,
796,
3072,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1499,
8189,
318,
407,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26498,
17816,
19315,
8189,
20520,
796,
1499,
8189,
198,
220,
220,
220,
220,
220,
220,
220,
26498,
17816,
9132,
20,
62,
28712,
20520,
796,
45243,
20,
62,
28712,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
13345,
62,
15042,
10786,
14,
38235,
14,
3846,
4862,
3256,
26498,
11,
751,
62,
16514,
27823,
28,
17821,
8,
628,
220,
220,
220,
825,
17594,
62,
80,
81,
62,
9122,
7,
944,
11,
1994,
25,
965,
11,
12429,
22046,
25,
4479,
58,
600,
11,
20512,
11,
965,
12962,
4613,
8633,
25,
198,
220,
220,
220,
220,
220,
220,
220,
705,
7061,
14,
38235,
14,
80,
81,
14,
9122,
198,
220,
220,
220,
220,
220,
220,
220,
16268,
103,
234,
46237,
223,
12859,
234,
163,
119,
112,
163,
254,
223,
163,
247,
119,
37605,
243,
198,
220,
220,
220,
220,
220,
220,
220,
1994,
25,
220,
12859,
234,
163,
119,
112,
163,
254,
223,
43718,
229,
46237,
228,
163,
105,
99,
7061,
6,
198,
220,
220,
220,
220,
220,
220,
220,
26498,
17816,
2539,
20520,
796,
1994,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
13345,
62,
15042,
10786,
14,
38235,
14,
80,
81,
14,
9122,
3256,
26498,
11,
751,
62,
16514,
27823,
28,
17821,
8,
628,
220,
220,
220,
825,
17594,
62,
80,
81,
62,
17953,
7,
944,
11,
1994,
25,
965,
11,
10662,
3036,
70,
25,
20512,
796,
6407,
11,
198,
220,
220,
220,
220,
220,
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] | 1.833562 | 4,380 |
# -*- coding: utf-8 -*-
import unittest
import unittest.mock as mock
from fastapi.testclient import TestClient
from projects.api.main import app
from projects.database import session_scope
import tests.util as util
app.dependency_overrides[session_scope] = util.override_session_scope
TEST_CLIENT = TestClient(app)
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from django.views.generic.base import TemplateView | [
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13,
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37350,
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] | 4.166667 | 12 |
from __future__ import absolute_import, print_function, unicode_literals
import _test_utilities
# NOTHING FOR NOW
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# -*- coding: utf-8 -*-
"""
Created on Thu Dec 3 21:40:05 2020
@author: Amir Moradi
"""
import cv2
from Utils.undistortion import undistortion
from Utils.angle_calculation import angle_calculation
import numpy as np
import serial
video_StreamL = cv2.VideoCapture(2) # index of left camera
video_StreamR = cv2.VideoCapture(1) # index of right camera
face_cascade = cv2.CascadeClassifier('SmartCar/Cascades/haarcascade_frontalface_alt.xml')
eye_cascade = cv2.CascadeClassifier('SmartCar/Cascades/haarcascade_eye_tree_eyeglasses.xml')
cen_eyesL = []
cen_eyesR = []
Proj_R = np.load("SmartCar/Calibration/matrices/Proj_R.npy")
Proj_L = np.load("SmartCar/Calibration/matrices/Proj_L.npy")
ser = serial.Serial("COM5", 9600)
# Set this value according to your project.
mirror_pt = [-10, 10, 150]
while(True):
retL, imgL = vidStreamL.read()
retR, imgR = vidStreamR.read()
imgL, imgR = undistortion(imgL, imgR)
grayL = cv2.cvtColor(imgL, cv2.COLOR_BGR2GRAY)
grayR = cv2.cvtColor(imgR, cv2.COLOR_BGR2GRAY)
try:
facesL = face_cascade.detectMultiScale(grayL, 1.3, 5)
facesR = face_cascade.detectMultiScale(grayR, 1.3, 5)
for (x_l, y_l, w_l, h_l), (x_r, y_r, w_r, h_r) in zip(facesL, facesR):
roi_grayL = grayL[y_l:y_l+h_l, x_l:x_l+w_l]
roi_grayR = grayR[y_r:y_r + h_r, x_r:x_r + w_r]
eyesL = eye_cascade.detectMultiScale(roi_grayL)
eyesR = eye_cascade.detectMultiScale(roi_grayR)
inter_l = []
inter_r = []
for (ex_l,ey_l,ew_l,eh_l), (ex_r,ey_r,ew_r,eh_r) in zip(eyesL, eyesR):
cv2.rectangle(imgL, (ex_l + x_l, ey_l + y_l), (ex_l + ew_l + x_l, ey_l + eh_l + y_l), (0,255,0), 2)
cv2.rectangle(imgR, (ex_r + x_r, ey_r + y_r), (ex_r + ew_r + x_r, ey_r + eh_r + y_r), (0,255,0), 2)
inter_l.append(((2 * ex_l + ew_l)/2, (2 * ey_l + eh_l)/2))
inter_r.append(((2 * ex_r + ew_r)/2, (2 * ey_r + eh_r)/2))
eyeL_lو eyeR_l = inter_l[0], inter_l[1]
eyeLx_l = eyeL_l[0]
eyeLy_l = eyeL_l[1]
eyeRx_l = eyeR_l[0]
eyeRy_l = eyeR_l[1]
eyeL_r = inter_r[0]
eyeR_r = inter_r[1]
eyeLx_r = eyeL_r[0]
eyeLy_r = eyeL_r[1]
eyeRx_r = eyeR_r[0]
eyeRy_r = eyeR_r[1]
cen_pos_l = (int((eyeLx_l + eyeRx_l)/2 + x_l), int((eyeLy_l + eyeRy_l)/2 + y_l))
cen_pos_r = (int((eyeLx_r + eyeRx_r)/2 + x_r), int((eyeLy_r + eyeRy_r)/2 + y_r))
cen_eyesL.append(cen_pos_l)
cen_eyesR.append(cen_pos_r)
ptL = np.array([[cen_pos_l[0]], [cen_pos_l[1]]], dtype=np.float)
ptR = np.array([[cen_pos_r[0]], [cen_pos_r[1]]], dtype=np.float)
cv2.circle(imgL, cen_pos_l, radius=1, color=(0, 0, 255), thickness=10)
cv2.circle(imgR, cen_pos_r, radius=1, color=(0, 0, 255), thickness=10)
xyz_points = cv2.triangulatePoints(Proj_L, Proj_R, ptL, ptR)
xyz_points /= xyz_points[3]
driver_pt = [int(xyz_points[0][0]), int(xyz_points[1][0]), int(xyz_points[2][0])]
yaw, pitch = angle_calculation(driver_pt, mirror_pt)
pitch_angle = f"S2={pitch}"
yaw_angle = f"S1={yaw}"
ser.write(pitch_angle.encode())
ser.write(yaw_angle.encode())
"""
text_z = "Z is: {} cm".format(int(xyz_points[2][0]))
text_y = "Y is: {} cm".format(int(xyz_points[1][0]))
text_x = "X is: {} cm".format(int(xyz_points[0][0]))
cv2.putText(imgL, text_z, (int(w_l/2) + 20, int(h_l/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
cv2.putText(imgL, text_y, (int(w_l/2) + 20, int(h_l/2)+35), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
cv2.putText(imgL, text_x, (int(w_l/2) + 20, int(h_l/2)+70), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
"""
origin_R = np.dot(Proj_R[:3], xyz_points)
origin_L = np.dot(Proj_L[:3], xyz_points)
# Again, put in homogeneous form before using them
origin_R /= origin_R[2]
origin_L /= origin_L[2]
# Press "q" to break the loop
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.imshow('imgL', imgL)
cv2.imshow('imgR', imgR)
except:
pass
ser.close()
cv2.destroyAllWindows()
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] | 1.665963 | 2,844 |
from dataclasses import dataclass
# from pprint import pprint
import aiohttp
import discord
from discord.ext import commands
from bot import constants
API_URL = "https://livescore6.p.rapidapi.com/matches/v2/"
LIVE_MATCHES_URL = API_URL + "list-live"
HEADERS = {
"x-rapidapi-key": constants.RAPIDAPI_KEY,
"x-rapidapi-host": constants.RAPIDAPI_LIVESCORE6_HOST,
}
@dataclass
def setup(bot: commands.Bot):
"""Add Cricket Cog."""
bot.add_cog(Cricket(bot))
| [
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] | 2.492147 | 191 |
# -*- coding: utf-8 -*-
import family
| [
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] | 2.105263 | 19 |
#!/usr/bin/python2
# coding: utf-8
# Daniel Elsner
# Get the amino acid sequence from the correct url for a kegg entry...
# Use best with GNU parallel (Tange 2011a) and an input list containing all the gene IDs from a kegg pathway.
import sys
from bs4 import BeautifulSoup
import requests
url = sys.argv[1]
r = requests.get(url)
data = r.text
soup = BeautifulSoup(data, 'html.parser')
print soup.pre.get_text() | [
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from .axle import * # noqa
from .cycle import * # noqa
| [
6738,
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] | 2.714286 | 21 |
from itertools import chain
from django.template.defaultfilters import linebreaksbr, urlize
from django.utils.html import format_html, mark_safe
from feincms3_forms.models import FormFieldBase
| [
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# Copyright (C) 2017-2020 Pascal Pepe <[email protected]>
#
# 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.
"""Core models."""
import uuid
from django.conf import settings
from django.db import models
from django.utils.translation import gettext_lazy as _
class ArchivableModel(models.Model):
"""Abstract model that can be archived."""
is_archived = models.BooleanField(
default=False,
verbose_name=_('archived?'),
)
class OrderableModel(models.Model):
"""Abstract model that can be ordered."""
order = models.PositiveSmallIntegerField(
blank=True,
null=True,
verbose_name=_('order'),
)
class OwnableModel(models.Model):
"""Abstract model with an optional owner."""
owner = models.ForeignKey(
settings.AUTH_USER_MODEL,
on_delete=models.SET_NULL,
blank=True,
null=True,
verbose_name=_('owner'),
)
class PublishableModel(models.Model):
"""Abstract model with publication features."""
PUB_STATUS_CHOICES = [
('DRAFT', _('draft')),
('PENDING', _('pending')),
('PUBLISHED', _('published')),
]
pub_date = models.DateTimeField(
blank=True,
null=True,
verbose_name=_('publication date'),
)
pub_status = models.CharField(
max_length=32,
choices=PUB_STATUS_CHOICES,
default='DRAFT',
verbose_name=_('publication status'),
)
class SEOModel(models.Model):
"""Abstract model with SEO-specific fields."""
search_title = models.CharField(
max_length=255,
blank=True,
verbose_name=_('search title'),
)
search_description = models.CharField(
max_length=255,
blank=True,
verbose_name=_('search description'),
)
class UUIDModel(models.Model):
"""Abstract model with a UUID as primary key."""
id = models.UUIDField(
primary_key=True,
default=uuid.uuid4,
editable=False,
verbose_name=_('ID'),
)
class VisibilityStatusModel(models.Model):
"""Abstract model with a visibility status."""
VISIBILITY_STATUS_CHOICES = [
('PRIVATE', _('private')),
('PUBLIC', _('public')),
]
visibility_status = models.CharField(
max_length=32,
choices=VISIBILITY_STATUS_CHOICES,
default='PUBLIC',
verbose_name=_('visibility status'),
)
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"""
=====
About
=====
Acquire measurement information from DWD and filter using SQL.
=====
Setup
=====
::
pip install wetterdienst[sql]
"""
import logging
from wetterdienst import DWDStationRequest
from wetterdienst import TimeResolution, Parameter, PeriodType
log = logging.getLogger()
if __name__ == "__main__":
main()
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import sys
from enum import Enum
from PySide2.QtUiTools import QUiLoader #allows us to load .ui files
#be sure to import any widget that you want to manipulate
from PySide2.QtWidgets import QApplication, QPushButton, QGridLayout, QSizePolicy
from PySide2.QtCore import QFile, QObject
import random
#class constructor
if __name__ == '__main__':
app = QApplication(sys.argv)
main_window = MainWindow()
sys.exit(app.exec_()) | [
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# List GCP Regional project quotas
# Official GCP SDK (Python) Documentation: https://googleapis.github.io/google-api-python-client/docs/dyn/
import json
import ipcalc
import sys
import argparse
from googleapiclient import discovery
from oauth2client.client import GoogleCredentials
from google.cloud import resource_manager
client = resource_manager.Client()
credentials = GoogleCredentials.get_application_default()
compute = discovery.build('compute', 'v1', credentials=credentials)
# Filter of Projects that will be scanned
parser_args = argparse.ArgumentParser(description='Define the projetc_id filter.'
'if empity will looking for all the active project_id that the credential have access')
parser_args.add_argument('--project')
project_Filter = parser_args.parse_args()
if project_Filter.project is None:
env_filter = {'lifecycleState': 'ACTIVE' }
else:
env_filter = {'projectId': project_Filter.project ,'lifecycleState': 'ACTIVE' }
# print csv header
print ('project_id;project_name;region;metric;limit;usage')
for project in client.list_projects(env_filter):
region_request = compute.regions().list(project=project.project_id)
regions = region_request.execute()
for region in regions['items']:
for quota in region['quotas']:
print(
project.project_id, ';',
project.name, ';',
region.get('name'),';',
quota.get('metric'),';',
quota.get('limit'),';',
quota.get('usage'),';'
) | [
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220,
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220,
220,
220,
220,
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7,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
32539,
13,
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10786,
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6,
628,
220,
220,
220,
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220,
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220,
220,
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220,
220,
1267,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220
] | 2.670628 | 589 |
from django import forms
from django.contrib import admin
from osmaxx.profile.models import Profile
admin.site.register(Profile, ProfileAdmin)
| [
6738,
42625,
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1330,
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15654,
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11,
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46787,
8,
198
] | 3.560976 | 41 |
#
# Copyright (C) 2002-2008 greg Landrum and Rational Discovery LLC
#
""" unit tests for the model and descriptor packager """
import os
import random
import unittest
from xml.dom import minidom
from xml.etree import ElementTree as ET
from rdkit import Chem
from rdkit import RDConfig
from rdkit.Chem import Descriptors
from rdkit.ML.Composite import Composite
from rdkit.ML.Data import DataUtils
from rdkit.ML.Descriptors.MoleculeDescriptors import MolecularDescriptorCalculator
from rdkit.ML.ModelPackage import Packager, PackageUtils
from rdkit.ML.ModelPackage.Packager import ModelPackage
from io import BytesIO
import pickle
if __name__ == '__main__': # pragma: nocover
unittest.main()
| [
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25,
299,
420,
2502,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
198
] | 3.18552 | 221 |
from fractions import Fraction
import pytest
from mugen.utilities import conversion, general
@pytest.mark.parametrize(
"a_start, a_end, b_start, b_end, do_overlap",
[
(5, 10, 11, 12, False), # disjoint
(5, 10, 10, 12, False), # contiguous
(5, 10, 9, 12, True), # overlaps right
(5, 10, 6, 8, True), # contained
(5, 10, 4, 11, True), # contains
(5, 10, 5, 10, True), # equal
(5, 10, 4, 5, False), # contiguous
(5, 10, 4, 6, True), # overlaps left
],
)
@pytest.mark.parametrize(
"float_var, expected_fraction",
[
(0.5, Fraction(numerator=1, denominator=2)),
(1 / 3, Fraction(numerator=1, denominator=3)),
(5, Fraction(numerator=5, denominator=1)),
],
)
@pytest.mark.parametrize(
"slices, length, expected_slices",
[
([slice(1, 2)], 0, [slice(0, 1), slice(1, 2)]),
([slice(2, 3)], 2, [slice(0, 2), slice(2, 3)]),
([slice(0, 8)], 2, [slice(0, 8)]),
([slice(1, 3)], 5, [slice(0, 1), slice(1, 3), slice(3, 5)]),
([slice(0, 3), slice(3, 4)], 5, [slice(0, 3), slice(3, 4), slice(4, 5)]),
(
[slice(1, 3), slice(5, 7)],
8,
[slice(0, 1), slice(1, 3), slice(3, 5), slice(5, 7), slice(7, 8)],
),
],
)
@pytest.mark.parametrize(
"time, expected_seconds",
[
(15.4, 15.4),
((1, 21.5), 81.5),
((1, 1, 2), 3662),
(".5", 0.5),
("33", 33),
("33.045", 33.045),
("1:21.5", 81.5),
("01:33.045", 93.045),
("1:33.045", 93.045),
("00:00:33.045", 33.045),
("01:01:33.045", 3693.045),
("01:01:33.5", 3693.5),
("01:01:33,5", 3693.5),
],
)
@pytest.mark.parametrize(
"seconds, expected_time_code",
[(25, "00:00:25.000"), (500.45, "00:08:20.450"), (50000.085, "13:53:20.085")],
)
@pytest.mark.parametrize(
"hex_value, expected_rgb",
[
("#000000", [0, 0, 0]),
("#ffffff", [255, 255, 255]),
("#3563df", [53, 99, 223]),
("#FF4500", [255, 69, 0]),
],
)
@pytest.mark.parametrize(
"color, expected_hex_code",
[("#123456", "#123456"), ("black", "#000000"), ("white", "#ffffff")],
)
| [
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487,
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8,
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] | 1.847097 | 1,223 |
import unittest
import json
import os
import datetime
from prometheus_api_client import MetricsList
if __name__ == "__main__":
unittest.main()
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] | 3 | 50 |
from random import uniform
import numpy as np
from collections import OrderedDict, defaultdict
from itertools import tee
import time
# ----------------------------------------------- | [
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import pytest
from jamesbond import bonddata
def test_load_data():
"""
Test the row & column count (shape)
Test the first column from the last row of the dataset 'Spectre'.
"""
df = bonddata.load_data()
shape = df.shape
last_row_first_col = df.iloc[-1, 1]
assert shape == (24, 27)
assert last_row_first_col == 'Spectre'
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] | 2.613139 | 137 |
"""Unit test package for helplotlib."""
| [
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29487,
8019,
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198
] | 3.636364 | 11 |
a=1,b=2
print(assertEqual(a,b)) #验证是否一致
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import numpy as np
import scipy
import matcompat
# if available import pylab (from matlibplot)
try:
import matplotlib.pylab as plt
except ImportError:
pass | [
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import copy
import datetime
import json
import logging
from django.http import HttpResponse
from django.utils.decorators import classonlymethod, method_decorator
from django.views.generic import View
from corehq.util.es.elasticsearch import ElasticsearchException, NotFoundError
from casexml.apps.case.models import CommCareCase
from corehq.util.es.interface import ElasticsearchInterface
from dimagi.utils.logging import notify_exception
from dimagi.utils.parsing import ISO_DATE_FORMAT
from corehq.apps.api.models import ESCase, ESXFormInstance
from corehq.apps.api.resources.v0_1 import TASTYPIE_RESERVED_GET_PARAMS
from corehq.apps.api.util import object_does_not_exist
from corehq.apps.domain.decorators import login_and_domain_required
from corehq.apps.es import filters
from corehq.apps.es.forms import FormES
from corehq.apps.es.cases import CaseES
from corehq.apps.es.utils import flatten_field_dict
from corehq.apps.reports.filters.forms import FormsByApplicationFilter
from corehq.elastic import (
ESError,
get_es_new,
report_and_fail_on_shard_failures,
)
from corehq.pillows.base import VALUE_TAG, restore_property_dict
from corehq.pillows.mappings.case_mapping import CASE_ES_ALIAS
from corehq.pillows.mappings.reportcase_mapping import REPORT_CASE_ES_ALIAS
from corehq.pillows.mappings.reportxform_mapping import REPORT_XFORM_ALIAS
from corehq.pillows.mappings.xform_mapping import XFORM_ALIAS
from no_exceptions.exceptions import Http400
logger = logging.getLogger('es')
class ESView(View):
"""
Generic CBV for interfacing with the Elasticsearch REST api.
This is necessary because tastypie's built in REST assumptions don't like
ES's POST for querying, which we can set explicitly here.
For security purposes, queries ought to be domain'ed by the requesting user, so a base_query
is encouraged to be added.
Access to the APIs can be done via url endpoints which are attached to the corehq.api.urls
or programmatically via the self.run_query() method.
This current iteration of the ESView must require a domain for its usage for security purposes.
"""
#note - for security purposes, csrf protection is ENABLED
#search POST queries must take the following format:
#query={query_json}
#csrfmiddlewaretoken=token
#in curl, this is:
#curl -b "csrftoken=<csrftoken>;sessionid=<session_id>" -H "Content-Type: application/json" -XPOST http://server/a/domain/api/v0.1/xform_es/
# -d"[email protected]&csrfmiddlewaretoken=<csrftoken>"
#or, call this programmatically to avoid CSRF issues.
es_alias = ""
domain = ""
es = None
doc_type = None
model = None
http_method_names = ['get', 'post', 'head', ]
@method_decorator(login_and_domain_required)
#@method_decorator(csrf_protect)
# todo: csrf_protect temporarily removed and left to implementor's prerogative
# getting ajax'ed csrf token method needs revisit.
@classonlymethod
def as_view(cls, **initkwargs):
"""
Django as_view cannot be used since the constructor requires information only present in the request.
"""
raise Exception('as_view not supported for domain-specific ESView')
@classonlymethod
def as_domain_specific_view(cls, **initkwargs):
"""
Creates a simple domain-specific class-based view for passing through ES requests.
"""
return view
def run_query(self, es_query, es_type=None):
"""
Run a more advanced POST based ES query
Returns the raw query json back, or None if there's an error
"""
logger.info("ESlog: [%s.%s] ESquery: %s" % (self.__class__.__name__, self.domain, json.dumps(es_query)))
if 'fields' in es_query or 'script_fields' in es_query:
#nasty hack to add domain field to query that does specific fields.
#do nothing if there's no field query because we get everything
fields = es_query.get('fields', [])
fields.append('domain')
es_query['fields'] = fields
try:
es_results = self.es_interface.search(self.es_alias, es_type, body=es_query)
report_and_fail_on_shard_failures(es_results)
except ElasticsearchException as e:
if 'query_string' in es_query.get('query', {}).get('filtered', {}).get('query', {}):
# the error may have been caused by a bad query string
# re-run with no query string to check
querystring = es_query['query']['filtered']['query']['query_string']['query']
new_query = es_query
new_query['query']['filtered']['query'] = {"match_all": {}}
new_results = self.run_query(new_query)
if new_results:
# the request succeeded without that query string
# an error with a blank query will return None
raise ESUserError("Error with elasticsearch query: %s" %
querystring)
msg = "Error in elasticsearch query [%s]: %s\nquery: %s" % (self.es_alias, str(e), es_query)
raise ESError(msg)
hits = []
for res in es_results['hits']['hits']:
if '_source' in res:
res_domain = res['_source'].get('domain', None)
elif 'fields' in res:
res['fields'] = flatten_field_dict(res)
res_domain = res['fields'].get('domain', None)
# security check
if res_domain == self.domain:
hits.append(res)
else:
logger.info("Requester domain %s does not match result domain %s" % (
self.domain, res_domain))
es_results['hits']['hits'] = hits
return es_results
class CaseESView(ESView):
"""
Expressive CaseES interface. Yes, this is redundant with pieces of the v0_1.py CaseAPI - todo to merge these applications
Which this should be the final say on ES access for Casedocs
"""
es_alias = CASE_ES_ALIAS
doc_type = "CommCareCase"
model = ESCase
def report_term_filter(terms, mapping):
"""convert terms to correct #value term queries based upon the mapping
does it match up with pre-defined stuff in the mapping?
"""
ret_terms = []
for orig_term in terms:
curr_mapping = mapping.get('properties')
split_term = orig_term.split('.')
for ix, sub_term in enumerate(split_term, start=1):
is_property = sub_term in curr_mapping
if ix == len(split_term):
#it's the last one, and if it's still not in it, then append a value
if is_property:
ret_term = orig_term
else:
ret_term = '%s.%s' % (orig_term, VALUE_TAG)
ret_terms.append(ret_term)
if is_property and 'properties' in curr_mapping[sub_term]:
curr_mapping = curr_mapping[sub_term]['properties']
return ret_terms
class ElasticAPIQuerySet(object):
"""
An abstract representation of an elastic search query,
modeled somewhat after Django's QuerySet but with
the only important goal being compatibility
with Tastypie's classes. Key capabilities, by piece of
Tastypie:
Pagination:
- `__getitem__([start:stop])` which should efficiently pass the bounds on to ES
- `count()` which should efficiently ask ES for the total matching (regardless of slice)
Sorting:
- order_by('field') or order_by('-field') both become ES service-side sort directives
Serialization:
- `__iter__()`
"""
# Also note https://github.com/llonchj/django-tastypie-elasticsearch/ which is
# not very mature, plus this code below may involve Dimagic-specific assumptions
def __init__(self, es_client, payload=None, model=None):
"""
Instantiate with an entire ElasticSearch payload,
since "query", "filter", etc, all exist alongside
each other.
"""
self.es_client = es_client
self.payload = payload
self.model = model
self.__results = None
def with_fields(self, es_client=None, payload=None, model=None):
"Clones this queryset, optionally changing some fields"
return ElasticAPIQuerySet(es_client=es_client or self.es_client,
payload=payload or self.payload,
model=model or self.model)
@property
SUPPORTED_DATE_FORMATS = [
ISO_DATE_FORMAT,
'%Y-%m-%dT%H:%M:%S',
'%Y-%m-%dT%H:%M:%S.%f',
'%Y-%m-%dT%H:%MZ', # legacy Case API date format
]
RESERVED_QUERY_PARAMS = set(['limit', 'offset', 'order_by', 'q', '_search'] + TASTYPIE_RESERVED_GET_PARAMS)
query_param_consumers = [
TermParam('xmlns', 'xmlns.exact'),
TermParam('xmlns.exact'),
TermParam('case_name', 'name', analyzed=True),
TermParam('case_type', 'type', analyzed=True),
# terms listed here to prevent conversion of their values to lower case since
# since they are indexed as `not_analyzed` in ES
TermParam('type.exact'),
TermParam('name.exact'),
TermParam('external_id.exact'),
TermParam('contact_phone_number'),
DateRangeParams('received_on'),
DateRangeParams('server_modified_on'),
DateRangeParams('date_modified', 'modified_on'),
DateRangeParams('server_date_modified', 'server_modified_on'),
DateRangeParams('indexed_on', 'inserted_at'),
]
| [
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28463,
276,
62,
265,
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60,
628,
198
] | 2.495305 | 3,834 |
from django.db import models
| [
6738,
42625,
14208,
13,
9945,
1330,
4981,
628
] | 3.75 | 8 |
#!/usr/bin/env python
# coding: utf-8
# In[69]:
# In[70]:
isPrime(7)
# In[71]:
isPrime(8)
# In[72]:
pno = []
# In[73]:
lst = list(range(0,2500))
# In[74]:
for item in lst:
if isPrime(item):
pno.append(item)
# In[75]:
print(pno)
# In[76]:
lst_prime_no = filter(isPrime,lst)
# In[77]:
print(list(lst_prime_no))
# In[ ]:
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62,
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7,
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11,
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8,
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2,
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2,
554,
58,
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25,
628,
628,
198
] | 1.917098 | 193 |
# -*- coding: utf-8 -*-
from deliver.tests.test_base import BaseTest, load_msg, load_all_msg
class ConverterTest(BaseTest):
'''Tests for the UnicodeMessage class'''
| [
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] | 2.786885 | 61 |
import requests
import random
import os
DEVELOPMENT_MODE = os.getenv("DEVELOPMENT_MODE", True)
if DEVELOPMENT_MODE:
# Import package multirunnable
import pathlib
import sys
package_path = str(pathlib.Path(__file__).absolute().parent.parent.parent)
sys.path.append(package_path)
# multirunnable package
from multirunnable import RunningMode, SimplePool, sleep, async_sleep
if __name__ == '__main__':
print("This is system client: ")
o_pool = ExamplePool(pool_size=3, task_size=10)
o_pool.main_run()
| [
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62,
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62,
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] | 2.765306 | 196 |
# %%
import torch
import torchvision
import threading
import time
from utils import preprocess
import torch.nn.functional as F
import traitlets
import torchvision.transforms as transforms
from dataset import ImageClassificationDataset
device = torch.device('cuda')
TASK = 'balls'
CATEGORIES = ['red_ball',' blue_ball']
DATASETS = ['A']
TRANSFORMS = transforms.Compose([
transforms.ColorJitter(0.2, 0.2, 0.2, 0.2),
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
datasets = {}
dataset = datasets[DATASETS[0]]
path = ""
# ALEXNET
# model = torchvision.models.alexnet(pretrained=True)
# model.classifier[-1] = torch.nn.Linear(4096, len(dataset.categories))
# SQUEEZENET
# model = torchvision.models.squeezenet1_1(pretrained=True)
# model.classifier[1] = torch.nn.Conv2d(512, len(dataset.categories), kernel_size=1)
# model.num_classes = len(dataset.categories)
# RESNET 18
model = torchvision.models.resnet18(pretrained=True)
model.fc = torch.nn.Linear(512, len(dataset.categories))
# RESNET 34
# model = torchvision.models.resnet34(pretrained=True)
# model.fc = torch.nn.Linear(512, len(dataset.categories))
model = model.to(device)
# display(model_widget)
print("model configured and model_widget created")
BATCH_SIZE = 8
optimizer = torch.optim.Adam(model.parameters())
# optimizer = torch.optim.SGD(model.parameters(), lr=1e-3, momentum=0.9)
epochs_widget = 1
# display(train_eval_widget)
print("trainer configured and train_eval_widget created") | [
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7,
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7307,
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300,
81,
28,
16,
68,
12,
18,
11,
12858,
28,
15,
13,
24,
8,
201,
198,
201,
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538,
5374,
82,
62,
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201,
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2,
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7,
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62,
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62,
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8,
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4798,
7203,
2213,
10613,
17839,
290,
4512,
62,
18206,
62,
42655,
2727,
4943
] | 2.53125 | 640 |
import unittest
try:
from mock import MagicMock
except ImportError:
from unittest.mock import MagicMock
from pubnub.pubnub import PubNub
import pubnub.enums
from pubnub.endpoints.push.remove_channels_from_push import RemoveChannelsFromPush
from tests.helper import pnconf, sdk_name
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] | 2.864078 | 103 |
rhombusD1 = float(input("Enter Rhombus First Diagonal = "))
rhombusD2 = float(input("Enter Rhombus Second Diagonal = "))
rhombusArea = calRhombusArea(rhombusD1, rhombusD2)
print("The Area of a Rhombus = %.3f" %rhombusArea)
| [
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] | 2.43617 | 94 |
import sys, subprocess
import os
import os.path
import shutil
import re
import glob
from optparse import OptionParser
#-------------------------------------------------------------------------------
# the main function
# Below we deform the moving image segmentation by the current result as well as
# by a previous stored result. This makes this test a regression test.
#
# We could instead compare with a fixed image segmentation, but that would require
# the tested registrations to be relatively good, which they are not to save time.
#-------------------------------------------------------------------------------
if __name__ == '__main__':
sys.exit(main())
| [
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220,
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13,
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7,
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198
] | 4.890511 | 137 |
# Copyright (c) 2017 Sony Corporation. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import generator_common.common as common
import utils.type_conv
| [
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262,
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13,
11321,
355,
2219,
198,
11748,
3384,
4487,
13,
4906,
62,
42946,
628
] | 3.813559 | 177 |
import re
| [
11748,
302,
628,
198
] | 3 | 4 |
import torch
import torch
import torch.nn as nn
import torch.nn.functional as F
def batch_cosine_fully_connected_layer(x_in, weight, scale=None, bias=None):
"""
Args:
x_in: a 3D tensor with shape
[meta_batch_size x num_examples x num_features_in]
weight: a 3D tensor with shape
[meta_batch_size x num_features_in x num_features_out]
scale: (optional) a scalar value
bias: (optional) a 1D tensor with shape [num_features_out]
Returns:
x_out: a 3D tensor with shape
[meta_batch_size x num_examples x num_features_out]
"""
assert(x_in.dim() == 3)
assert(weight.dim() == 3)
assert(x_in.size(0) == weight.size(0))
assert(x_in.size(2) == weight.size(1))
x_in = F.normalize(x_in, p=2, dim=2, eps=1e-12)
weight = F.normalize(weight, p=2, dim=1, eps=1e-12)
x_out = torch.bmm(x_in, weight)
if scale is not None:
x_out = x_out * scale
if bias is not None:
x_out = x_out + bias
return x_out
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# !/usr/bin/env python
# -*- coding:utf-8 -*-
import ctypes
from ctypes import *
import os
import numpy as np
import time
JPGENC_FORMAT_NV12 = 0x10
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# 보석 쇼핑
if __name__ == "__main__":
gems = ["DIA", "RUBY", "RUBY", "DIA", "DIA", "EMERALD", "SAPPHIRE", "DIA"]
print(solution(gems))
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import pygame
import numpy as np
from Source import UI_functions as UI
from Source import battleships_functions_bot as bfb
from Source import battleships_functions_check as bfc | [
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# -*- coding: utf-8 -*-
"""
Created on Sat Jul 30 15:20:09 2016
@author: hjalmar
"""
import tensorflow as tf
from ht_helper import HeSD, angle2class, FrameStepper, class2angle, whiten
from ht_helper import anglediff, get_max_gaze_line, CountdownPrinter
from ht_helper import angles2complex, complex2angles, softmax, get_error
from data_preparation import read_log_data
import numpy as np
import re
import os
from scipy.misc import imresize
from glob import glob
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.animation as manimation
class TrainModel:
"""
"""
def get_inputs(self, fname, Nepoch, Nex_per_epoch, train=False, batch_sz=None):
"""
Nex_per_epoch - Ntrain or Nvalid: number_of_examples_per_epoch
"""
if not os.path.isfile(fname):
raise FileNotFoundError('Failed to find file: %s' % fname)
if batch_sz is None:
batch_sz = self.batch_sz
with tf.name_scope('input'):
fname_queue = tf.train.string_input_producer(
[fname], num_epochs=Nepoch)
# Even when reading in multiple threads, share the filename
# queue.
im, angle, angle_ok, pos_x, pos_y = self._read_and_decode(fname_queue)
if train:
# Distort im
im = self._distort_inputs(im)
n_threads = 8
else:
n_threads = 4
# Subtract off the mean and divide by the variance of the pixels.
im = tf.image.per_image_whitening(im)
# Shuffle the examples and collect them into batch_sz batches.
# (Internally uses a RandomShuffleQueue.)
# We run this in two threads to avoid being a bottleneck.
# Ensures a minimum amount of shuffling of examples.
min_queue_examples = int(Nex_per_epoch * 0.4)
capacity = min_queue_examples + 3 * batch_sz
im, angle, angle_ok, pos_x, pos_y = tf.train.shuffle_batch([im,
angle,
angle_ok,
pos_x,
pos_y],
batch_size=batch_sz,
num_threads=n_threads,
capacity=capacity,
min_after_dequeue=min_queue_examples)
return im, angle, angle_ok, pos_x, pos_y
def _distort_inputs(self, im):
"""
Don't flip orientation images
"""
im = tf.image.random_brightness(im, max_delta=63)
im = tf.image.random_contrast(im, lower=0.2, upper=1.8)
return im
def train(self, Nepoch, lmbda=5e-4):
"""
"""
model_fname = os.path.join(self.model_dir, 'CAM')
train_fname = os.path.join(self.data_dir, 'train_CAM_N*.tfrecords')
valid_fname = os.path.join(self.data_dir, 'dev_CAM_N*.tfrecords')
train_fname = glob(train_fname)
if not len(train_fname) == 1:
raise ValueError('Something wrong with the file name of the training data.')
else:
train_fname = train_fname[0]
valid_fname = glob(valid_fname)
if not len(valid_fname) == 1:
raise ValueError('Something wrong with the file name of the validation data.')
else:
valid_fname = valid_fname[0]
batch_sz = self.batch_sz
Nvalid = int(re.search(r'[\d]{4,6}', valid_fname.split('/')[-1]).group())
Ntrain = int(re.search(r'[\d]{4,6}', train_fname.split('/')[-1]).group())
Nbatch_per_epoch = Ntrain // batch_sz
#Nbatch = Nbatch_per_epoch * Nepoch
valid_batch_sz = 50
learning_rate = 1e-4
valid_X, valid_y = [], []
model = Model(Nclass=self.Nclass, im_w=self.im_w, im_h=self.im_h, lmbda=lmbda)
print('Starting training for %d epochs.' % Nepoch)
with model.graph.as_default():
# Input images and labels.
images, angles, angles_ok, _, _ = self.get_inputs(train_fname,
Nepoch,
Ntrain,
train=True)
valid_images, valid_angles, valid_angles_ok, _, _ = self.get_inputs(valid_fname, 1, Nvalid, train=False, batch_sz=valid_batch_sz)
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(model.loss)
with tf.Session(graph=model.graph) as session:
session.run(tf.initialize_all_variables())
session.run(tf.initialize_local_variables())
# Start input enqueue threads
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=session, coord=coord)
validation_accuracy = []
train_accuracy = []
print('%s\n step | loss | acc | epoch \n%s' % ('='*30, '='*30))
step, epoch = 0, 0
while (epoch < Nepoch) and not coord.should_stop():
step += 1
# Train
X, theta, theta_ok = session.run([images, angles,
angles_ok])
y = angle2class(theta, self.Nclass,
angles_ok=theta_ok, units='deg')
optimizer.run(feed_dict={model.X: X, model.y_: y})
if (step % Nbatch_per_epoch == 0):
l, acc = session.run([model.loss, model.accuracy],
feed_dict={model.X: X,
model.y_: y})
epoch += 1
print(' %-5d| %-6.3f| %-6.2f| %-5d' % (step, l,
acc, epoch))
if (epoch % 10 == 0) or (epoch == Nepoch):
v_acc, i = 0.0, 0
if len(valid_y) < 1:
load_valid = True
else:
load_valid = False
while i < (Nvalid // valid_batch_sz):
if load_valid:
X, theta, theta_ok = session.run([valid_images,
valid_angles,
valid_angles_ok])
y = angle2class(theta, self.Nclass,
angles_ok=theta_ok,
units='deg')
valid_X.append(X)
valid_y.append(y)
feed_dict = {model.X: valid_X[i],
model.y_: valid_y[i]}
v_acc += model.accuracy.eval(feed_dict=feed_dict)
i += 1
validation_accuracy.append(v_acc/i)
train_accuracy.append(np.mean(acc))
model.saver.save(session, ('%s_Nclass%d_acc%1.1f_%d.ckpt' %
(model_fname,
self.Nclass,
validation_accuracy[-1],
epoch)))
print('Done training for %d epochs, %d steps.' % (epoch, step-1))
# Ask threads to stop
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)
session.close()
print('Training accuracy:', train_accuracy)
print('Validation accuracy:', validation_accuracy)
return validation_accuracy, train_accuracy
class HeadTracker:
"""
"""
def track2video(self, in_fname, out_fname, log_fname=None,
t_start=0.0, t_end=-1, dur=None, verbose=True):
"""
t_start : only used if no log_fname is provided
t_end : only used if no log_fname is provided
dur : only used if no log_fname is provided
"""
if not tf.gfile.Exists(in_fname):
raise ValueError('Failed to find file: %s' % in_fname)
fst = FrameStepper(in_fname)
fps = int(round(1/fst.dt))
FFMpegWriter = manimation.writers['ffmpeg']
ttl = 'Head position tracking from video %s.' % in_fname.split('/')[-1]
metadata = dict(title=ttl, artist='Matplotlib',
comment='more info...') # TODO!
writer = FFMpegWriter(fps=fps, metadata=metadata, bitrate=20000, codec=None) # TODO: set a good codec
dpi = 96
figsize = (fst.frame.shape[1]/dpi, fst.frame.shape[0]/dpi)
fig = plt.figure(figsize=figsize, dpi=dpi) # TODO dpi depends on the monitor used, remove this dependence
# see: http://stackoverflow.com/questions/13714454/specifying-and-saving-a-figure-with-exact-size-in-pixels
if t_start < 0:
raise ValueError('t_start cannot be less than 0.0 (beginning of the video).')
if t_end < 0:
t_end = fst.duration
if not dur is None:
t_end = min(t_end, t_start + dur)
if t_end > fst.duration:
raise ValueError('t_end cannot be later %1.3f (time of the last frame)' %
fst.duration)
if not log_fname is None:
if not tf.gfile.Exists(log_fname):
raise ValueError('Failed to find file: %s' % log_fname)
else:
log_data, log_header = read_log_data(log_fname)
Nframe = len(log_data)
if verbose:
# Counter printed on command line
cdp = CountdownPrinter(Nframe)
with writer.saving(fig, out_fname, dpi):
for i, dat in enumerate(log_data):
if verbose:
cdp.print(i)
fst.read_t(dat['frame_time'])
true_pos = {'x': dat['center_x'], 'y': dat['center_y']}
if dat['angle_ok']:
true_angle = (180 * (dat['angle'] / np.pi)).round()
else:
true_angle = None
self.plot(fst.frame, true_pos=true_pos,
true_angle=true_angle, fig=fig, verbose=False)
writer.grab_frame()
fig.clf()
else:
Nframe = int(np.ceil((t_end - t_start) / fst.dt))
if verbose:
# Counter printed on command line
cdp = CountdownPrinter(Nframe)
with writer.saving(fig, out_fname, dpi):
ok = fst.read_t(t_start)
i = 0
while (fst.t < t_end) and ok:
if verbose:
cdp.print(i)
self.plot(fst.frame, true_pos=None, fig=fig, verbose=False)
writer.grab_frame()
fig.clf()
ok = fst.next()
i += ok
fst.close()
def track2fig(self, in_fname, out_fname, log_data, verbose=True):
"""
"""
if not tf.gfile.Exists(in_fname):
raise ValueError('Failed to find file: %s' % in_fname)
fst = FrameStepper(in_fname)
#figsize=figsize, dpi=dpi
fig = plt.figure()
Nframe = len(log_data)
if verbose:
# Counter printed on command line
cdp = CountdownPrinter(Nframe)
for i, dat in enumerate(log_data):
if verbose:
cdp.print(i)
print(i, dat['frame_time'])
fst.read_t(dat['frame_time'])
true_pos = {'x': dat['center_x'], 'y': dat['center_y']}
if dat['angle_ok']:
true_angle = (180 * (dat['angle'] / np.pi)).round()
else:
true_angle = None
self.plot(fst.frame, true_pos=true_pos,
true_angle=true_angle, fig=fig, verbose=False)
fig.savefig('%s_%03d.svg' % (out_fname, i))
fig.savefig('%s_%03d.png' % (out_fname, i))
fig.clf()
fst.close()
def track(self, video_fname, t_start=0.0, t_end=-1, dur=None, verbose=True):
"""
"""
if not tf.gfile.Exists(video_fname):
raise ValueError('Failed to find file: %s' % video_fname)
fst = FrameStepper(video_fname)
if t_start < 0:
raise ValueError('t_start cannot be less than 0.0 (beginning of the video).')
if t_end < 0:
t_end = fst.duration
if not dur is None:
t_end = min(t_end, t_start + dur)
if t_end > fst.duration:
raise ValueError('t_end cannot be later %1.3f (time of the last frame)' %
fst.duration)
Nframe = int(np.ceil((t_end - t_start) / fst.dt))
if verbose:
cdp = CountdownPrinter(Nframe)
est_track = np.recarray(shape=Nframe+1,
dtype=[('t', float), ('x', float),
('y', float), ('angle', float),
('angle_w', float)])
i = 0
ok = fst.read_t(t_start)
while (fst.t < t_end) and ok:
if verbose:
cdp.print(i)
x, y, angle, angle_w, _ = self.predict(fst.frame, verbose=False)
est_track[i].x = x
est_track[i].y = y
est_track[i].angle = angle
est_track[i].angle_w = angle_w
est_track[i].t = fst.t
ok = fst.next()
i += ok
est_track = est_track[:i]
fst.close()
return est_track
def test_track(self, log_fname, video_dir, Nframe=None):
"""
Nframe : number of frames to predict.
Default all frames in the log file.
"""
verbose=False
log_data, log_header = read_log_data(log_fname)
if Nframe is None:
Nframe = len(log_data) - 1
if Nframe >= len(log_data):
raise ValueError('Nframes cannot be greater than the number of frames in the log file.')
#video_fname = '%s/%s' % (video_dir.rstrip('/'), log_header['video_fname'])
video_fname = os.path.join(video_dir.rstrip('/'),
log_header['video_fname'])
video_fname = glob(video_fname)[0]
fst = FrameStepper(video_fname)
est_track = np.recarray(shape=Nframe,
dtype=[('t', float), ('x', float),
('y', float), ('angle', float),
('angle_w', float)])
true_track = np.recarray(shape=Nframe,
dtype=[('t', float), ('x', float),
('y', float), ('angle', float)])
if verbose:
cdp = CountdownPrinter(Nframe)
for i, dat in enumerate(log_data[:Nframe]):
if verbose:
cdp.print(i)
# Read the frame
fst.read_t(dat['frame_time'])
# Time of frame
true_track[i].t = fst.t
est_track[i].t = fst.t
# True head position
true_track[i].x = dat['center_x']
true_track[i].y = dat['center_y']
# True head orientation
if not dat['angle_ok']:
true_track[i].angle = np.nan
else:
true_track[i].angle = 180. * (dat['angle'] / np.pi)
# Estimated head position and orientation
x, y, angle, angle_w, _ = self.predict(fst.frame, verbose=verbose)
est_track[i].x = x
est_track[i].y = y
est_track[i].angle = angle
est_track[i].angle_w = angle_w
fst.close()
error, error_desrc = get_error(est_track, true_track)
return est_track, true_track, error, error_desrc
def predict(self, frame, verbose=True):
"""
Frame by frame
x, y -- in frame coordinates
"""
self.restore_model(verbose=verbose)
if frame.ndim == 3:
frame = frame.mean(axis=2)
rescale = False
if frame.shape[0] == 480 and frame.shape[1] == 640:
im = imresize(frame, self.im_scale)
rescale = True
elif frame.shape[0] == self.im_h and frame.shape[1] == self.im_w:
im = frame
else:
raise ValueError('Some odd differences btw frame.shape and'
' self.im_w/im_w. FIX this.')
# Reshape and whiten the image
im = whiten(im.astype(float)).reshape((1, self.im_h, self.im_w, 1))
p = softmax(self.model.logits.eval(session=self.model.session,
feed_dict={self.model.X: im}))
label = p.argmax()
angles = class2angle(np.arange(self.Nclass-1), self.Nclass-1)
# Use the Softmax output, p, as weights for a weighted average.
p = (p[0, :-1] / p[0, :-1].sum()).flatten()
z_w = (angles2complex(angles) * p).sum()
angle_w = complex2angles(z_w)
if (label == (self.Nclass - 1)): # head orientation is the horiz plane not visible.
angle = np.nan
angle_w = np.nan
else:
angle = angles[label]
cam = self.model.cam.eval(session=self.model.session,
feed_dict={self.model.X: im,
self.model.y_: label})
# rescale cam to the same size as frame
if rescale:
cam = imresize(cam.reshape((self.im_h, self.im_w)), 1/self.im_scale)
else:
cam = cam.reshape((self.im_h, self.im_w))
y, x = np.unravel_index(cam.argmax(), cam.shape)
return x, y, angle, angle_w, cam
def restore_model(self, verbose=True):
"""
"""
if hasattr(self, 'model'):
msg = ('Model %s already restored.' %
self.model.fname.split('/')[-1])
else:
model = Model(Nclass=self.Nclass, im_w=self.im_w, im_h=self.im_h)
model_fn = os.path.join(self.model_dir,
'CAM_Nclass%d_acc*.ckpt' % self.Nclass)
#model_fn = '%s/CAM_Nclass%d_acc*.ckpt' % (self.model_dir, self.Nclass)
model_fn = glob(model_fn)
model_fn.sort()
if model_fn[-1].endswith('meta'):
model.fname = model_fn[-1].rstrip('.meta')
else:
model.fname = model_fn[-1]
# Following rlrs's comment on:
# https://github.com/tensorflow/tensorflow/issues/1325
# seems to be neccesary for getting access to the GAP weights
model_fn_meta = glob('%s.meta' % model.fname)[0]
saved = tf.train.import_meta_graph(model_fn_meta)
model.session = tf.Session(graph=model.graph)
saved.restore(model.session, model.fname)
# Restore variables from disk.
#model.saver.restore(model.session, model.fname)
self.model = model
msg = ('Model %s restored.' % model.fname.split('/')[-1])
if verbose:
print(msg)
def plot(self, frame, true_pos=None, true_angle=None,
fname=None, fig=None, verbose=False):
"""
"""
x, y, angle, angle_w, cam = self.predict(frame, verbose=verbose)
if fig is None:
fig = plt.figure(frameon=False)
ax = fig.add_axes([0, 0, 1, 1])
ax.imshow(frame)
im_h, im_w = frame.shape[:2]
plt.hold(True)
ax.imshow(cam, cmap=plt.cm.jet, alpha=0.3, interpolation='bilinear')
if not np.isnan(angle):
ax.plot(x, y, 'o', ms=5, mec=[1, 0.6, 0.3], mfc='none', mew=1)
ax.plot(x, y, 'o', ms=20, mec=[1, 0.6, 0.3], mfc='none', mew=1)
x1, y1 = get_max_gaze_line(angle, x, y, im_w, im_h, units='deg')
ax.plot([x, x1], [y, y1], '-', color=[1, 0.6, 0.2], lw=2, label='argmax')
x1, y1 = get_max_gaze_line(angle_w, x, y, im_w, im_h, units='deg')
ax.plot([x, x1], [y, y1], '-', color=[1, 0.3, 0.0], lw=2, label='weighted')
else:
ax.plot(x, y, 'o', ms=20, mfc='w', mec='w', lw=2)
if not true_pos is None:
# Maximum possible error given x, y
max_xerr, max_yerr = max(x, im_w-x), max(y, im_h-y)
max_err = np.sqrt(max_xerr**2 + max_yerr**2)
error = im_h * np.sqrt((x - true_pos['x'])**2 + (y - true_pos['y'])**2) / max_err
# Note that x,y gets replaced so that true_angle will be drawn
# starting at true_pos instead of predicted pos.
x, y = true_pos['x'], true_pos['y']
ax.plot(x, y, 'o', ms=5, mec='g', mfc='none', mew=1)
ax.plot(x, y, 'o', ms=20, mec='g', mfc='none', mew=1)
# draw position error as a bar to the right
ax.plot([im_w-4, im_w-4], [0, error], '-', c='r', lw=4)
if not true_angle is None:
x1, y1 = get_max_gaze_line(true_angle, x, y, im_w, im_h, units='deg')
ax.plot([x, x1], [y, y1], '-', color=[.3, 1., 0.], lw=2, label='True')
error_w = im_h * np.abs(anglediff(true_angle, angle_w, 'deg')) / 180
error = im_h * np.abs(anglediff(true_angle, angle, 'deg')) / 180
# Draw orientation error as a bar to the left
ax.plot([4, 4], [0, error], '-', c=[1, .6, .2], lw=4)
ax.plot([11, 11], [0, error_w], '-', c=[1, .3, 0.], lw=4)
ax.set_xlim([0, im_w])
ax.set_ylim([0, im_h])
ax.set_xticks([])
ax.set_yticks([])
#ax.legend()
if not fname is None:
fig.savefig(fname)
plt.close(fig)
def close(self):
"""
"""
if hasattr(self, 'model'):
if hasattr(self.model, 'session'):
self.model.session.close()
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] | 1.662766 | 14,785 |
import marshal
import imp
import struct
import os
import sys
import base64
import new
import dis
from dis import opmap, opname
class Bytecode():
'''
Class to store individual instruction as a node in the graph
'''
def len(self):
'''
Returns the length of the bytecode
1 for no argument
3 for argument
'''
if self.opcode < dis.HAVE_ARGUMENT:
return 1
else:
return 3
def disassemble(self):
'''
Return disassembly of bytecode
'''
rvalue = opname[self.opcode].ljust(20)
if self.opcode >= dis.HAVE_ARGUMENT:
rvalue += " %04x" % (self.oparg)
return rvalue
def hex(self):
'''
Return ASCII hex representation of bytecode
'''
rvalue = "%02x" % self.opcode
if self.opcode >= dis.HAVE_ARGUMENT:
rvalue += "%02x%02x" % \
(self.oparg & 0xff, (self.oparg >> 8) & 0xff)
return rvalue
def bin(self):
'''
Return bytecode string
'''
if self.opcode >= dis.HAVE_ARGUMENT:
return struct.pack("<BH", self.opcode, self.oparg)
else:
return struct.pack("<B", self.opcode)
def get_target_addr(self):
'''
Returns the target address for the current instruction based on the
current address.
'''
rvalue = None
if self.opcode in dis.hasjrel:
rvalue = self.addr + self.oparg + self.len()
if self.opcode in dis.hasjabs:
rvalue = self.oparg
return rvalue
def clean_ROT_TWO(bcg, skip_xrefs=True):
'''
Replace two sequential ROT_TWO sequences with NOPS
'''
count = 0
for current in bcg.nodes():
if current.next is None:
break
if current.opcode == opmap['ROT_TWO'] and \
current.next.opcode == opmap['ROT_TWO']:
if current.next.xrefs != [] and skip_xrefs:
continue
else:
current.opcode = opmap['NOP']
current.next.opcode = opmap['NOP']
count += 1
return count
def clean_ROT_THREE(bcg, skip_xrefs=True):
'''
Replace three sequential ROT_THREE sequences with NOPS
'''
count = 0
for current in bcg.nodes():
if current.next is None or current.next.next is None:
break
if current.opcode == opmap['ROT_THREE'] and \
current.next.opcode == opmap['ROT_THREE'] and \
current.next.next.opcode == opmap['ROT_THREE']:
if (current.next.xrefs != [] or current.next.next.xrefs != []) \
and skip_xrefs:
continue
else:
current.opcode = opmap['NOP']
current.next.opcode = opmap['NOP']
current.next.next.opcode = opmap['NOP']
count += 1
return count
def clean_LOAD_POP(bcg, skip_xrefs=True):
'''
Replace LOAD_CONST/POP_TOP sequences with NOPS
'''
count = 0
for current in bcg.nodes():
if current.next is None:
break
if current.opcode == opmap['LOAD_CONST'] and \
current.next.opcode == opmap['POP_TOP']:
if current.next.xrefs != [] and skip_xrefs:
continue
else:
current.opcode = opmap['NOP']
current.next.opcode = opmap['NOP']
count += 1
return count
def clean_NOPS(bcg):
'''
Remove NOP instrustions from bytecode
'''
count = 0
for current in bcg.nodes():
if current.opcode == opmap['NOP']:
bcg.delete_node(current)
count += 1
return count
if __name__ == "__main__":
main(sys.argv)
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] | 1.970918 | 1,960 |
import logging
from collections import defaultdict
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Dict, List, Literal, NamedTuple, Optional
from eth_utils import to_checksum_address
from gevent.lock import Semaphore
from rotkehlchen.accounting.structures.balance import AssetBalance, Balance
from rotkehlchen.chain.ethereum.contracts import EthereumContract
from rotkehlchen.chain.ethereum.defi.defisaver_proxy import HasDSProxy
from rotkehlchen.chain.ethereum.graph import (
SUBGRAPH_REMOTE_ERROR_MSG,
Graph,
format_query_indentation,
)
from rotkehlchen.chain.ethereum.utils import multicall_2, token_normalized_value_decimals
from rotkehlchen.constants.assets import A_ETH, A_LQTY, A_LUSD, A_USD
from rotkehlchen.constants.ethereum import LIQUITY_TROVE_MANAGER
from rotkehlchen.errors.misc import ModuleInitializationFailure, RemoteError
from rotkehlchen.errors.serialization import DeserializationError
from rotkehlchen.fval import FVal
from rotkehlchen.history.price import PriceHistorian
from rotkehlchen.inquirer import Inquirer
from rotkehlchen.logging import RotkehlchenLogsAdapter
from rotkehlchen.premium.premium import Premium
from rotkehlchen.serialization.deserialize import (
deserialize_asset_amount,
deserialize_optional_to_fval,
)
from rotkehlchen.types import ChecksumEthAddress, Timestamp
from rotkehlchen.user_messages import MessagesAggregator
from rotkehlchen.utils.mixins.serializableenum import SerializableEnumMixin
from .graph import QUERY_STAKE, QUERY_TROVE
if TYPE_CHECKING:
from rotkehlchen.chain.ethereum.manager import EthereumManager
from rotkehlchen.db.dbhandler import DBHandler
MIN_COLL_RATE = '1.1'
logger = logging.getLogger(__name__)
log = RotkehlchenLogsAdapter(logger)
@dataclass(frozen=True)
@dataclass(frozen=True)
@dataclass(frozen=True)
# -- Methods following the EthereumModule interface -- #
| [
11748,
18931,
198,
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] | 3.038095 | 630 |
from rest_framework import decorators, permissions, response, status
from rest_framework.request import Request
from django.contrib.auth import get_user_model
from django.utils.translation import ugettext as _
from .serializers import UserCreateSerializer
User = get_user_model()
@decorators.api_view(["POST"])
@decorators.permission_classes([permissions.AllowAny])
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] | 3.5 | 106 |
# Generated by Django 2.1.7 on 2019-03-31 16:43
from django.db import migrations, models
import django.db.models.deletion
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# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2019.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
# pylint: disable=cell-var-from-loop
"""Replace each block of consecutive gates by a single Unitary node."""
from qiskit.circuit import QuantumRegister, ClassicalRegister, QuantumCircuit, Gate
from qiskit.quantum_info.operators import Operator
from qiskit.quantum_info.synthesis import TwoQubitBasisDecomposer
from qiskit.extensions import UnitaryGate
from qiskit.circuit.library.standard_gates import CXGate
from qiskit.transpiler.basepasses import TransformationPass
from qiskit.transpiler.exceptions import TranspilerError
from qiskit.transpiler.passes.synthesis import unitary_synthesis
class ConsolidateBlocks(TransformationPass):
"""Replace each block of consecutive gates by a single Unitary node.
Pass to consolidate sequences of uninterrupted gates acting on
the same qubits into a Unitary node, to be resynthesized later,
to a potentially more optimal subcircuit.
Notes:
This pass assumes that the 'blocks_list' property that it reads is
given such that blocks are in topological order. The blocks are
collected by a previous pass, such as `Collect2qBlocks`.
"""
def __init__(self,
kak_basis_gate=None,
force_consolidate=False,
basis_gates=None):
"""ConsolidateBlocks initializer.
Args:
kak_basis_gate (Gate): Basis gate for KAK decomposition.
force_consolidate (bool): Force block consolidation
basis_gates (List(str)): Basis gates from which to choose a KAK gate.
"""
super().__init__()
self.basis_gates = basis_gates
self.force_consolidate = force_consolidate
if kak_basis_gate is not None:
self.decomposer = TwoQubitBasisDecomposer(kak_basis_gate)
elif basis_gates is not None:
kak_basis_gate = unitary_synthesis._choose_kak_gate(basis_gates)
if kak_basis_gate is not None:
self.decomposer = TwoQubitBasisDecomposer(kak_basis_gate)
else:
self.decomposer = None
else:
self.decomposer = TwoQubitBasisDecomposer(CXGate())
def run(self, dag):
"""Run the ConsolidateBlocks pass on `dag`.
Iterate over each block and replace it with an equivalent Unitary
on the same wires.
"""
if self.decomposer is None:
return dag
new_dag = dag._copy_circuit_metadata()
# compute ordered indices for the global circuit wires
global_index_map = {wire: idx for idx, wire in enumerate(dag.qubits)}
blocks = self.property_set['block_list']
# just to make checking if a node is in any block easier
all_block_nodes = {nd for bl in blocks for nd in bl}
for node in dag.topological_op_nodes():
if node not in all_block_nodes:
# need to add this node to find out where in the list it goes
preds = [nd for nd in dag.predecessors(node) if nd.type == 'op']
block_count = 0
while preds:
if block_count < len(blocks):
block = blocks[block_count]
# if any of the predecessors are in the block, remove them
preds = [p for p in preds if p not in block]
else:
# should never occur as this would mean not all
# nodes before this one topologically had been added
# so not all predecessors were removed
raise TranspilerError("Not all predecessors removed due to error"
" in topological order")
block_count += 1
# we have now seen all predecessors
# so update the blocks list to include this block
blocks = blocks[:block_count] + [[node]] + blocks[block_count:]
# create the dag from the updated list of blocks
basis_gate_name = self.decomposer.gate.name
for block in blocks:
if len(block) == 1 and block[0].name != basis_gate_name:
# pylint: disable=too-many-boolean-expressions
if block[0].type == 'op' \
and self.basis_gates \
and block[0].name not in self.basis_gates \
and len(block[0].cargs) == 0 and block[0].condition is None \
and isinstance(block[0].op, Gate) \
and hasattr(block[0].op, '__array__') \
and not block[0].op.is_parameterized():
new_dag.apply_operation_back(UnitaryGate(block[0].op.to_matrix()),
block[0].qargs, block[0].cargs)
else:
# an intermediate node that was added into the overall list
new_dag.apply_operation_back(block[0].op, block[0].qargs,
block[0].cargs)
else:
# find the qubits involved in this block
block_qargs = set()
block_cargs = set()
for nd in block:
block_qargs |= set(nd.qargs)
if nd.condition:
block_cargs |= set(nd.condition[0])
# convert block to a sub-circuit, then simulate unitary and add
q = QuantumRegister(len(block_qargs))
# if condition in node, add clbits to circuit
if len(block_cargs) > 0:
c = ClassicalRegister(len(block_cargs))
subcirc = QuantumCircuit(q, c)
else:
subcirc = QuantumCircuit(q)
block_index_map = self._block_qargs_to_indices(block_qargs,
global_index_map)
basis_count = 0
for nd in block:
if nd.op.name == basis_gate_name:
basis_count += 1
subcirc.append(nd.op, [q[block_index_map[i]] for i in nd.qargs])
unitary = UnitaryGate(Operator(subcirc)) # simulates the circuit
max_2q_depth = 20 # If depth > 20, there will be 1q gates to consolidate.
if ( # pylint: disable=too-many-boolean-expressions
self.force_consolidate
or unitary.num_qubits > 2
or self.decomposer.num_basis_gates(unitary) < basis_count
or len(subcirc) > max_2q_depth
or (self.basis_gates is not None
and not set(subcirc.count_ops()).issubset(self.basis_gates))
):
new_dag.apply_operation_back(
UnitaryGate(unitary),
sorted(block_qargs, key=lambda x: block_index_map[x]))
else:
for nd in block:
new_dag.apply_operation_back(nd.op, nd.qargs, nd.cargs)
return new_dag
def _block_qargs_to_indices(self, block_qargs, global_index_map):
"""Map each qubit in block_qargs to its wire position among the block's wires.
Args:
block_qargs (list): list of qubits that a block acts on
global_index_map (dict): mapping from each qubit in the
circuit to its wire position within that circuit
Returns:
dict: mapping from qarg to position in block
"""
block_indices = [global_index_map[q] for q in block_qargs]
ordered_block_indices = sorted(block_indices)
block_positions = {q: ordered_block_indices.index(global_index_map[q])
for q in block_qargs}
return block_positions
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] | 2.061843 | 4,091 |
# Generated by Django 3.2.10 on 2021-12-24 07:16
from django.db import migrations, models
import django.db.models.deletion
| [
2,
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416,
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13,
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] | 2.840909 | 44 |
import requests
from bs4 import BeautifulSoup
from time import sleep
from retrying import retry
import json
import re
import pymongo
import datetime
from feichangzun import config
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 '
'(KHTML, like Gecko) Chrome/49.0.2623.221 Safari/537.36 SE 2.X MetaSr 1.0'}
feichangzun = 'http://www.variflight.com'
allUrl = "http://www.variflight.com/sitemap.html?AE71649A58c77="
pausetime = 1000
if __name__ == '__main__':
fp = FCZPAC()
fp.start()
# flightdata = fp.getchuanghanglist()
# flightlink = flightdata.flightlink
# fp.getListData(flightlink)
# fp.getaflightinfo(['/schedule/SZX-CTU-3U3033.html?AE71649A58c77=']) | [
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277,
79,
796,
10029,
57,
44938,
3419,
198,
220,
220,
220,
277,
79,
13,
9688,
3419,
198,
220,
220,
220,
1303,
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7890,
796,
277,
79,
13,
1136,
354,
7258,
456,
648,
4868,
3419,
198,
220,
220,
220,
1303,
5474,
8726,
796,
5474,
7890,
13,
22560,
8726,
198,
220,
220,
220,
1303,
277,
79,
13,
1136,
8053,
6601,
7,
22560,
8726,
8,
198,
220,
220,
220,
1303,
277,
79,
13,
1136,
1878,
2971,
10951,
7,
17816,
14,
15952,
5950,
14,
50,
40692,
12,
4177,
52,
12,
18,
52,
1270,
2091,
13,
6494,
30,
14242,
22,
1433,
2920,
32,
3365,
66,
3324,
11639,
12962
] | 2.332288 | 319 |
from numpy import zeros
import time
n=6 # number of equations
A=[[10.0, -1.0, 4.0, 0.0, 2.0, 9.0, 19.0], [0.0, 25.0, -2.0, 7.0, 8.0, 4.0, 2.0], [1.0, 0.0, 15.0, 7.0, 3.0, -2.0, 13.0], [6.0, -1.0, 2.0, 23.0, 0.0, 8.0, -7.0], [-4.0, 2.0, 0.0, 5.0, -25.0, 3.0, -9.0], [0.0, 7.0, -1.0, 5.0, 4.0, -22.0, 2.0]] #the augmented matrix
x = zeros(n) # solution matrix
x=GE(A)
print(x)
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767,
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16,
13,
15,
11,
642,
13,
15,
11,
604,
13,
15,
11,
532,
1828,
13,
15,
11,
362,
13,
15,
11907,
1303,
1169,
30259,
17593,
198,
87,
796,
1976,
27498,
7,
77,
8,
1303,
4610,
17593,
198,
87,
28,
8264,
7,
32,
8,
198,
4798,
7,
87,
8,
198
] | 1.62069 | 232 |
"""
Code up the game tic tac toe
1 class solution
"""
if __name__ == "__main__":
t = TicTacToe()
t.print_board()
t.play_game()
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] | 2.274194 | 62 |
from model.contact import Contact
testdata = [
Contact(firstname="name1", lastname="lastname1", middlename="middlename1",
nickname="nickname1", title="title1", company="company1",
address="address1", homephone="homephone1", mobilephone="mobphone1",
workphone="workphone1", fax="fax1", email="email1", secondaryphone="secphone1")
]
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] | 2.657143 | 140 |
# --------------------------------------------------------------------------
# Source file provided under Apache License, Version 2.0, January 2004,
# http://www.apache.org/licenses/
# (c) Copyright IBM Corp. 2015, 2016
# --------------------------------------------------------------------------
"""
Configuration of the CP Optimizer Python API
This module is the top-level handler of the configuration parameters for
the CP Optimizer Python API. It contains the default values of the different
configuration parameters.
It should NOT be changed directly.
The preferable way is to add at least one of the following files that contain the changes
to be performed:
* *cpo_config.py*, a local set of changes on these parameters,
* *cpo_config_<hostname>.py*, a hostname dependent set of changes.
* *docloud_config.py* (for DOcloud url and key, file shared with docplex.mp package).
Final set of parameters is obtained by reading first this module, and then those
listed above.
These modules should be visible from the *PYTHONPATH* and are loaded in
this order to overwrite default values.
This module also defines two global variables:
* *DOCLOUD_CONTEXT*, that contains the configuration necessary to solve a model on DOcloud.
This context is the context by default, referenced by the global variable 'context'.
* *LOCAL_CONTEXT*, that contains the configuration appropriate to solve a model with a local
installation of the CPO solver.
This configuration is available for solver with version number greater or equal to 12.7.0.
The method :meth:`set_default` allows to set the default configuration to one that is predefined,
or another that has been totally customized.
If called as main, this module prints the actual configuration on standard output, including
all customizations made using the mechanism described above.
Following sections describe the most important parameters that can be easily modified to customize
the behavior of the Python API.
All available parameters are available by consulting the source code of this module.
General parameters
------------------
*context.log_output = sys.stdout*
This parameter contains the default log stream.
By default it is set to the standard output.
A value of *None* can be used to disable all logs.
*context.verbose = 0*
This parameter controls the verbosity level of the log, between 0 and 9, if *log_output* is not None.
The default value of 0 means no log.
*context.model.add_source_location = True*
This parameter indicates that when the model is transformed into CPO format, additional information is added
to correlate expressions with the Python file and line where it has been generated.
If any error is raised by the solver during the solve, this information is provided in the
error description, which allows for easier debugging.
*context.model.length_for_alias = 15*
This parameter allows to associate a shorter alias to variables whose name is longer than the given length.
In the CPO representation of the model, variable is declared with its original name and an alias is created
to use it with a shorter name in model expressions, allowing to reduce the size of the generated CPO format.
In the returned solution, variable can be still retrieved with their original names.
By default, the value is 15. A value of None would indicate to always keep original variable names.
*context.model.length_for_rename = None*
This parameter allows to replace the names of the variables when it is longer than the given length.
A shorter name is generated and is used everywhere in the generated model CPO format in place of the original name.
This allows to drastically reduce the size of the model generated in the CPO format.
In the returned solution, the value of such variables can be retrieved thanks to a mapping between previous and
new names, that is maintained in the client Python program.
By default, the value is None, indicating to keep original variable names.
*context.model.name_all_constraints = False*
This parameter enables the naming of all constraints when the model is generated in CPO format.
It is mandatory only if the *refine conflict* function is called.
Anyway, if the *refine conflict* function is called, and if the CPO format of the model has already been generated,
it is generated again with this option set in order to allow proper completion of the request.
Setting it to *True* is preferable only if *refine conflict* function is called on a big model.
*context.model.dump_directory = None*
This parameter gives the name of a directory where the CPO files that are generated for solving models are stored
for logging purpose.
If not None, the directory is created and generated models are stored in files named `<model_name>.cpo`.
*context.model.cache.size = 10000*
This parameter gives the maximum capacity of the internal cache used to speed-up conversion of Python expressions
into CPO expressions.
*context.model.cache.active = True*
This parameter allows to enable or disable the expression cache mechanism.
Value os a boolean (True or False). Default value is True.
*context.params.\**
The parameter `context.params` is an instance of the class
:class:`~docplex.cp.parameters.CpoParameters` (in :doc:`parameters.py</docplex.cp.parameters.py>`)
which describes all of the public solver parameters as properties.
The default configuration limits the solving time to 100 seconds by using following settings:
::
context.params.TimeMode = "ElapsedTime"
context.params.TimeLimit = 100
These parameters may have a different default setting if the solver is not *DOcplexcloud*.
Configuration of the model solving
----------------------------------
*context.solver.trace_cpo = False*
This parameter indicates to trace the CPO model that is generated before submitting it for solving.
The model is printed on the `context.log_output stream`, if given.
*context.solver.trace_log = False*
This parameter indicates to trace the log generated by the solver when solving the CPO model.
The log is printed on the `context.log_output stream`, if given.
The default value of this parameter is False for a solve on the cloud, and True for a local solve.
*context.solver.enable_undocumented_params = False*
This parameter allows to enable the possibility to set solving parameters that are not in the public parameters
detailed in the class
:class:`~docplex.cp.parameters.CpoParameters` (in :doc:`parameters.py</docplex.cp.parameters.py>`).
*context.solver.add_log_to_solution = True*
This parameter indicates to add the solver log content to the solution object.
By default, this parameter is True but it can be set to False if the log is very big or of no interest.
*context.solver.agent = 'docloud'*
This parameter specifies the name of the solver agent that is used to solve the model.
The value of this parameter is the name of a child context of `context.solver`, which contains necessary attributes
that allow to create and run the required agent.
There are two different agents described in the default configuration file:
* `docloud`, the default agent, for solving a CPO model using the DOcplexcloud service.
* `local`, the agent allowing to solve models locally using the CP Optimizer Interactive coming with
versions of COS greater or equal to 12.7.0.
If the CP Optimizer Interactive program *cpoptimizer(.exe)* is detected in the system path, the default solver
agent is automatically set to *local* instead of *docloud*.
*context.solver.log_prefix = "[Solver] "*
Prefix that is added to every message that is logged by the solver component.
Configuration of the `docloud` solving agent
--------------------------------------------
*context.solver.docloud.url = "https://api-oaas.docloud.ibmcloud.com/job_manager/rest/v1/"*
This parameter is used to specify the URL of the *DOcplexcloud* service.
*context.solver.docloud.key = "'Set your key in docloud_config.py'"*
This parameter contains the personal key for authorizing access to the *DOcplexcloud* service.
Access credentials (base URL and access key) can be retrieved after registration from `<http://developer.ibm.com/docloud/docs/api-key/>`_.
*context.solver.docloud.verify_ssl = True*
This parameter allows to enable/disable the verification of SSL certificates.
*context.solver.docloud.proxies = None*
This parameter allows to optionally define proxies to be used in the connection with *DOcplexcloud*.
It is a Python dictionary protocol_name / endpoint, as described in http://docs.python-requests.org/en/master/user/advanced/#proxies.
*context.solver.docloud.request_timeout = 30*
This parameter contains the maximum time, in seconds, that a response is waited for after a unitary request to *DOcplexcloud* server.
*context.solver.docloud.result_wait_extra_time = 60*
This parameter is a time in seconds added to the expected solve time to compute the total result waiting timeout.
*context.solver.docloud.clean_job_after_solve = True*
This parameter indicates whether the job is automatically cleaned after the model is solved.
If not set to True, the model stays on the *DOcplexcloud* server and is visible from its *DropSolve* interface.
Note that the server may block future solving requests if there are too many jobs waiting.
*context.solver.docloud.polling = Context(min=1, max=3, incr=0.2)*
This parameter describes how the Python client polls the result of the solve on *DOcplexcloud*.
Polling delay is inside an interval [min, max], starting by min, growing to max with the given increment.
Configuration of the `local` solving agent
------------------------------------------
*context.solver.local.execfile*
Name or full path of the CP Optimizer Interactive executable file.
By default, it is set to *cpoptimizer(.exe)*, which supposes that the program is visible from the system path.
Configuration for best performances
-----------------------------------
To configure the CP Python API for best performances, the following configuration settings may be used.
Obviously, this performance is won at the cost of the loss of some features that may be useful in other cases.
::
context.verbose = 0
context.model.add_source_location = False
context.model.length_for_rename = 10
context.model.name_all_constraints = False
context.model.dump_directory = None
context.solver.trace_cpo = False
context.solver.trace_log = False
context.solver.add_log_to_solution = False
Detailed description
--------------------
"""
from docplex.cp.utils import Context, CpoException, search_file_in_path, IS_IN_NOTEBOOK, is_string
from docplex.cp.parameters import CpoParameters, ALL_PARAMETER_NAMES
import sys, socket, os, platform, traceback
try:
import docplex.util.environment as runenv
ENVIRONMENT_PRESENT = True
except:
ENVIRONMENT_PRESENT = False
EXE_EXTENSION = ".exe" if platform.system() == 'Windows' else ""
##############################################################################
## Define default context for DOcloud solving
##############################################################################
#-----------------------------------------------------------------------------
# Global context
# Create default context infrastructure
DOCLOUD_CONTEXT = Context(model=Context(),
params=CpoParameters(),
solver=Context())
context = DOCLOUD_CONTEXT
# Default log output
context.log_output = sys.stdout
# Default log verbosity
context.verbose = 0
# Visu enable indicator (internal, can be disabled for testing purpose)
context.visu_enabled = True
#-----------------------------------------------------------------------------
# Modeling context
# Indicate to add source location in model
context.model.add_source_location = True
# Minimal variable name length that trigger use of shorter alias. None for no alias.
context.model.length_for_alias = 15
# Minimal variable name length that trigger renaming variable with a shorter name. None for no rename.
context.model.length_for_rename = None
# Automatically add a name to every top-level constraint
context.model.name_all_constraints = False
# Name of the directory where store copy of the generated CPO files. None for no dump.
context.model.dump_directory = None
# Expression cache
context.model.cache = Context()
context.model.cache.size = 10000
context.model.cache.active = True
#-----------------------------------------------------------------------------
# Solving parameters
# Default time limit
context.params.TimeLimit = 100
# Workers count
context.params.Workers = 4
#-----------------------------------------------------------------------------
# Solving context
# Indicate to trace CPO model before solving
context.solver.trace_cpo = False
# Indicate to trace solver log on log_output.
context.solver.trace_log = False
# Enable undocumented parameters
context.solver.enable_undocumented_params = False
# Max number of threads allowed for model solving
context.solver.max_threads = None
if ENVIRONMENT_PRESENT:
context.solver.max_threads = runenv.get_environment().get_available_core_count()
# Indicate to add solver log to the solution
context.solver.add_log_to_solution = True
# Indicate to auto-publish solve details and results in environment
context.solver.auto_publish = True
# Indicate to replace simple solve by a start/next loop
context.solver.solve_with_start_next = False
# Log prefix
context.solver.log_prefix = "[Solver] "
# Name of the agent to be used for solving. Value is name of one of this context child context (i.e. 'docloud').
context.solver.agent = 'docloud'
#-----------------------------------------------------------------------------
# DoCloud solving agent context
context.solver.docloud = Context()
# Agent class name
context.solver.docloud.class_name = "docplex.cp.solver.solver_docloud.CpoSolverDocloud"
# Url of the DOCloud service
context.solver.docloud.url = "https://api-oaas.docloud.ibmcloud.com/job_manager/rest/v1/"
# Authentication key.
context.solver.docloud.key = "'Set your key in docloud_config.py''"
# Secret key.
context.solver.docloud.secret = None
# Indicate to verify SSL certificates
context.solver.docloud.verify_ssl = True
# Proxies (map protocol_name/endpoint, as described in http://docs.python-requests.org/en/master/user/advanced/#proxies)
context.solver.docloud.proxies = None
# Default unitary request timeout in seconds
context.solver.docloud.request_timeout = 30
# Time added to expected solve time to compute the total result waiting timeout
context.solver.docloud.result_wait_extra_time = 60
# Clean job after solve indicator
context.solver.docloud.clean_job_after_solve = True
# Add 'Connection close' in all headers
context.solver.docloud.always_close_connection = False
# Log prefix
context.solver.docloud.log_prefix = "[DOcloud] "
# Polling delay (min, max and increment)
context.solver.docloud.polling = Context(min=1, max=3, incr=0.2)
#-----------------------------------------------------------------------------
# Local solving agent context
context.solver.local = Context(class_name = "docplex.cp.solver.solver_local.CpoSolverLocal",
execfile = "cpoptimizer" + EXE_EXTENSION,
parameters = ['-angel'],
log_prefix = "[Local] ")
LOCAL_CONTEXT = context.clone()
LOCAL_CONTEXT.params.pop('TimeLimit')
LOCAL_CONTEXT.params.pop('Workers')
LOCAL_CONTEXT.solver.trace_log = not IS_IN_NOTEBOOK
LOCAL_CONTEXT.solver.agent = 'local'
LOCAL_CONTEXT.solver.max_threads = None
# Select local context if exec file is visible in the path
cpfile = search_file_in_path(LOCAL_CONTEXT.solver.local.execfile)
if cpfile:
LOCAL_CONTEXT.solver.local.execpath = cpfile
context = LOCAL_CONTEXT
##############################################################################
## Public functions
##############################################################################
def get_default():
""" Get the default context
Default context is also accessible with the global variable 'context' in this module.
Returns:
Current default context
"""
return context
def set_default(ctx):
""" Set the default context.
Default context becomes accessible in the global variable 'context' in this module.
Args:
ctx: New default context
"""
if ctx is None:
ctx = Context()
else:
assert isinstance(ctx, Context), "Context object must be of class Context"
sys.modules[__name__].context = ctx
# Attribute values denoting a default value
DEFAULT_VALUES = ("ENTER YOUR KEY HERE", "ENTER YOUR URL HERE", "default")
def _get_effective_context(**kwargs):
""" Build a effective context from a variable list of arguments that may specify changes to default.
Args:
context (optional): Source context, if not default.
params (optional): Solving parameters (CpoParameters) that overwrite those in the solving context
(others) (optional): All other context parameters that can be changed.
Returns:
Updated (cloned) context
"""
# If 'url' and 'key' are defined, force agent to be docloud
if ('agent' not in kwargs) and not ENVIRONMENT_PRESENT:
url = kwargs.get('url')
key = kwargs.get('key')
if url and key and is_string(url) and is_string(key) and url.startswith('http'):
kwargs['agent'] = 'docloud'
# Determine source context
ctx = kwargs.get('context')
if (ctx is None) or (ctx in DEFAULT_VALUES):
ctx = context
ctx = ctx.clone()
# print("\n*** Source context");
# ctx.print_context()
# First set parameters if given
prms = kwargs.get('params')
if prms is not None:
ctx.params.add(prms)
# Process other changes
rplist = [] # List of replacements to be done in solving parameters
for k, v in kwargs.items():
if (k != 'context') and (k != 'params') and (v not in DEFAULT_VALUES):
rp = ctx.search_and_replace_attribute(k, v)
# If not found, set in solving parameters
if (rp is None):
rplist.append((k, v))
# Replace or set remaining fields in parameters
if rplist:
params = ctx.params
chkparams = not ctx.solver.enable_undocumented_params
if isinstance(params, CpoParameters):
for k, v in rplist:
if chkparams and not k in ALL_PARAMETER_NAMES:
raise CpoException("CPO solver does not accept a parameter named '{}'".format(k))
setattr(params, k, v)
# Return
# print("\n*** Result context");
# ctx.print_context()
return ctx
##############################################################################
## Overload this configuration with other customized configuraton python files
##############################################################################
def _eval_file(file):
""" If exists, evaluate the content of a python module in this module.
Args:
file: Python file to evaluate
"""
for f in filter(os.path.isfile, [dir + "/" + file for dir in sys.path]):
try:
exec(open(f).read())
except Exception as e:
traceback.print_exc()
raise Exception("Error while loading config file {}: {}".format(f, str(e)))
# Initialize default list of files to load
FILE_LIST = ("cpo_config.py",
"cpo_config_" + socket.gethostname() + ".py",
"docloud_config.py")
# Load all config changes
for f in FILE_LIST:
_eval_file(f)
##############################################################################
## Print configuration when called as main
##############################################################################
if __name__ == "__main__":
context.print_context()
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] | 3.190871 | 6,507 |
from peewee import DoesNotExist
from datetime import datetime
from ServiceManager.utilities import generate_module_name
from shared.const import DBTypeIDs, DBClassIDs
from shared.utils import need_connection
from shared.db_models import *
from ServiceManager.logger import manager_logger as logger
@need_connection
def get_object(object_id):
"""
Gets the record of the object with the given ID.
Returns:
(GamObject/None): The object with the given ID, None if not found.
"""
return GamObject.get_or_none(GamObject.ob_id == object_id)
@need_connection
def get_object_id(object_name):
"""
Get the ID of the object with the given name.
Returns:
(int/None): The object ID, None if object with given name not found.
"""
obj = GamObject.get_or_none(GamObject.ob_name == object_name)
return obj.ob_id if obj else None
@need_connection
def get_max_object_id():
"""
Get the ID highest ID.
Returns:
(int): The highest object ID in the database or zero if there isn't any present.
"""
query = GamObject.select(GamObject.ob_id).order_by(GamObject.ob_id)
if not query:
return 0
else:
return query[-1].ob_id
@need_connection
def get_object_name(object_id):
"""
Gets the name of the object with the given ID.
Args:
object_id (int): The object ID.
Returns:
(str/None): The object name, None if object with given ID not found.
"""
obj = GamObject.get_or_none(GamObject.ob_id == object_id)
return obj.ob_name if obj else None
@need_connection
def get_all_object_names():
"""
Get a list of all object names.
Returns:
(list): The object names.
"""
query = GamObject.select(GamObject.ob_name)
return [x.ob_name for x in query if x]
@need_connection
def get_all_type_names():
"""
Get a list of all object type names.
Returns:
(list): The object type names.
"""
query = GamObjecttype.select(GamObjecttype.ot_name)
return [x.ot_name for x in query if x]
@need_connection
def get_all_display_names():
"""
Get a list of all display group names.
Returns:
(list): The display group names.
"""
query = GamDisplaygroup.select(GamDisplaygroup.dg_name)
return [x.dg_name for x in query if x]
@need_connection
def get_type_id(type_name: str):
"""
Get the ID of the object type with the given name.
Args:
type_name (str): The object type name.
Returns:
type_id (int/None): The object type ID, None if not found.
"""
obj_type = GamObjecttype.get_or_none(GamObjecttype.ot_name == type_name)
return obj_type.ot_id if obj_type else None
@need_connection
def get_display_group_id(display_group: str):
"""
Get the ID of the object type with the given name.
Args:
display_group (str): The display group name.
Returns:
type_id (int/None): The object type ID, None if not found.
"""
group = GamDisplaygroup.get_or_none(GamDisplaygroup.dg_name == display_group)
return group.dg_id if group else None
@need_connection
def get_object_type(object_id: int):
"""
Returns the type name of the object with the specified ID.
Args:
object_id (int): The object ID.
Returns:
(str/None): The object type name, None if not found.
"""
obj = GamObject.get_or_none(GamObject.ob_id == object_id)
return obj.ob_objecttype.ot_name if obj else None
@need_connection
def get_object_class(object_id: int):
"""
Returns the class name of the object with the specified ID.
Args:
object_id (int): The object ID.
Returns:
(str/None): The object class name, None if not found.
"""
obj = GamObject.get_or_none(GamObject.ob_id == object_id)
return obj.ob_objecttype.ot_objectclass.oc_name if obj else None
@need_connection
def get_class_id(type_id: int):
"""
Returns the object class ID of the object type with the specified ID.
Args:
type_id (int): The object type ID.
Returns:
(int/None): The object class ID, None if not found.
"""
obj_type = GamObjecttype.get_or_none(GamObjecttype.ot_id == type_id)
return obj_type.ot_objectclass.oc_id if obj_type else None
@need_connection
def get_object_function(object_id: int):
"""
Returns the object function name of the object with the specified ID.
Args:
object_id (int): The DB ID of the object.
Returns:
(str/None): The object function name, None if not found.
"""
obj = GamObject.get_or_none(GamObject.ob_id == object_id)
return obj.ob_objecttype.ot_objectclass.oc_function.of_name if obj else None
@need_connection
def get_measurement_types(object_class_id: int):
"""
Returns the measurement types of the class with the specified ID.
Args:
object_class_id (int): The class ID.
Returns:
(list/None): The measurement types, None if class was not found.
"""
try:
obj_class = GamObjectclass.get(GamObjectclass.oc_id == object_class_id)
return [obj_class.oc_measuretype1, obj_class.oc_measuretype2, obj_class.oc_measuretype3,
obj_class.oc_measuretype4, obj_class.oc_measuretype5]
except DoesNotExist:
return None
@need_connection
def get_object_display_group(object_id: int):
"""
Get the name of the object's display group if it has one.
Args:
object_id (int): The object ID.
Returns:
(str/None): The object's display group, None if not found.
"""
obj = GamObject.get_or_none(GamObject.ob_id == object_id)
try:
display_id = obj.ob_displaygroup
return display_id.dg_name
except (DoesNotExist, AttributeError):
return
@need_connection
@need_connection
def add_object(name: str, type_id: int, display_group_id: int = None, comment: str = None):
"""
Create a new object with the given name, type and comment.
Args:
name (str): The name of the object.
type_id (int): The type ID of the object.
display_group_id (int): The ID of the display group this object is part of.
comment (str): Object comment.
Returns:
(int): ID of the added object.
Raises:
DBObjectNameAlreadyExists: If an object with the given name already exists in the database.
"""
if get_object_id(object_name=name) is not None:
raise DBObjectNameAlreadyExists(f'Could not create object - '
f'Object with name "{name}" already exists in the database.')
record_id = GamObject.insert(ob_name=name, ob_objecttype=type_id, ob_displaygroup=display_group_id,
ob_comment=comment).execute()
logger.info(f'Created object no. {record_id} ("{name}") of type {type_id}.')
return record_id
@need_connection
def add_relation(or_object_id: int, or_object_id_assigned: int):
"""
Creates a relation between two objects. Note: Dates must be in '%Y-%m-%d %H:%M:%S' format.
Args:
or_object_id (int): The object ID (the object itself).
or_object_id_assigned (int): The assigned object ID (e.g. the SLD/ILM Module etc.).
"""
record_id = GamObjectrelation.insert(
or_primary=0,
or_object=or_object_id,
or_object_id_assigned=or_object_id_assigned,
or_date_assignment=datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
or_date_removal=None,
or_outflow=None,
or_bookingrequest=None
).execute()
logger.info(f'Added relation {record_id} for objects {or_object_id} - {or_object_id_assigned}.')
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] | 2.560651 | 3,009 |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2019 Shlomi Fish <[email protected]>
#
# Distributed under terms of the MIT license.
from pysol_cards.errors import SubclassResponsibility
| [
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] | 2.588235 | 85 |
from collections import defaultdict
from conceptnet5.edges import make_edge
from conceptnet5.formats.msgpack_stream import MsgpackStreamWriter
from conceptnet5.languages import ATOMIC_SPACE_LANGUAGES
from conceptnet5.nodes import split_uri
from conceptnet5.uri import get_uri_language, join_uri, Licenses
def prepare_vocab_for_morphology(language, input, output):
"""
Morfessor's input is a list of terms with their counts. Here, we
read a ConceptNet vocabulary file with counts (core_concept_counts.txt),
filter it for a single language, and convert it into the input form that
Morfessor expects.
We're stripping out the word sense information here, which would cause
the same term to appear multiple times. Because of that, we build up
a new dictionary of counts, summing all occurrences of a term.
We use _ to represent all spaces. In languages where the space-separated
segments are atomic (Vietnamese), we use _ to represent the locations where
subwords are allowed to end, and thus add _ to the end of the term as well.
"""
vocab_counts = defaultdict(int)
for line in input:
countstr, uri = line.strip().split(' ', 1)
if get_uri_language(uri) == language:
term = split_uri(uri)[2]
if language in ATOMIC_SPACE_LANGUAGES:
term += '_'
vocab_counts[term] += int(countstr)
for term, count in sorted(list(vocab_counts.items())):
print(count, term, file=output)
MORPH_SOURCES = [{'process': '/s/rule/morfessor'}]
def subwords_to_edges(language, input, output):
"""
Morfessor hypothesizes ways to break words into sub-word chunks. Produce
edges from these sub-words that can be used in retrofitting.
"""
writer = MsgpackStreamWriter(output)
for line in input:
line = line.rstrip()
if not line or line.startswith('#'):
continue
# Remove the unnecessary count ("1 ") from the start of each line
line = line.split(' ', 1)[1]
chunks = line.split(' + ')
# Strip a possible trailing underscore, which would particularly show
# up in the way we segment ATOMIC_SPACE_LANGUAGES (Vietnamese)
full_text = ''.join(chunks).strip('_')
end = join_uri('c', language, full_text)
for chunk in chunks:
if chunk != '_':
start = join_uri('x', language, chunk.strip('_'))
edge = make_edge(
'/r/SubwordOf', start, end,
dataset='/d/morphology',
license=Licenses.cc_attribution,
sources=MORPH_SOURCES,
weight=0.01
)
writer.write(edge)
writer.close()
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] | 2.497292 | 1,108 |
from __future__ import annotations
from calendar import monthrange
from datetime import timedelta, date
from .timerange import TimeRange
from .date_range import daterange
class CalendarMonth(TimeRange):
"""
Represent a calendar month.
"""
@property
def next(self) -> CalendarMonth:
"""Return an instance of next month."""
first_day_in_next_month = self.end.date() + timedelta(days=1)
return CalendarMonth(first_day_in_next_month.year, first_day_in_next_month.month)
@property
def prev(self) -> CalendarMonth:
"""Return an instance of previous month."""
last_day_in_previous_month = self.start.date() - timedelta(days=1)
return CalendarMonth(last_day_in_previous_month.year, last_day_in_previous_month.month)
@staticmethod
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] | 2.809028 | 288 |
# Generated by Django 2.0.2 on 2018-02-06 22:35
from django.db import migrations, models
import markupfield.fields
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] | 3.078947 | 38 |
from django.contrib import admin
from .models import blogPost
admin.site.register(blogPost) | [
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red = 0xff3d3d
green = 0xb8ff3d
blue = 0x2e66ff
yellow = 0xfff94d | [
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#!/usr/bin/env python
# Copyright 2018-2020 The PySCF Developers. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Author: Qiming Sun <[email protected]>
#
'''
Pseudo-spectral methods (COSX, PS, SN-K)
'''
import copy
import numpy
from pyscf import lib
from pyscf import gto
from pyscf import scf
from pyscf import mcscf
from pyscf.scf import _vhf
from pyscf.lib import logger
from pyscf.sgx import sgx_jk
from pyscf.df import df_jk
from pyscf import __config__
def sgx_fit(mf, auxbasis=None, with_df=None):
'''For the given SCF object, update the J, K matrix constructor with
corresponding SGX or density fitting integrals.
Args:
mf : an SCF object
Kwargs:
auxbasis : str or basis dict
Same format to the input attribute mol.basis. If auxbasis is
None, optimal auxiliary basis based on AO basis (if possible) or
even-tempered Gaussian basis will be used.
Returns:
An SCF object with a modified J, K matrix constructor which uses density
fitting integrals to compute J and K
Examples:
>>> mol = gto.M(atom='H 0 0 0; F 0 0 1', basis='ccpvdz', verbose=0)
>>> mf = sgx_fit(scf.RHF(mol))
>>> mf.scf()
-100.00978770917165
>>> mol.symmetry = 1
>>> mol.build(0, 0)
>>> mf = sgx_fit(scf.UHF(mol))
>>> mf.scf()
-100.00978770951018
'''
assert(isinstance(mf, scf.hf.SCF))
if with_df is None:
with_df = SGX(mf.mol)
with_df.max_memory = mf.max_memory
with_df.stdout = mf.stdout
with_df.verbose = mf.verbose
with_df.auxbasis = auxbasis
mf_class = mf.__class__
if isinstance(mf, _SGXHF):
if mf.with_df is None:
mf = mf_class(mf, with_df, auxbasis)
elif mf.with_df.auxbasis != auxbasis:
#logger.warn(mf, 'DF might have been initialized twice.')
mf = copy.copy(mf)
mf.with_df = with_df
return mf
return SGXHF(mf, with_df, auxbasis)
# A tag to label the derived SCF class
scf.hf.SCF.COSX = sgx_fit
mcscf.casci.CASCI.COSX = sgx_fit
def _make_opt(mol):
'''Optimizer to genrate 3-center 2-electron integrals'''
intor = mol._add_suffix('int3c2e')
cintopt = gto.moleintor.make_cintopt(mol._atm, mol._bas, mol._env, intor)
# intor 'int1e_ovlp' is used by the prescreen method
# 'SGXnr_ovlp_prescreen' only. Not used again in other places.
# It can be released early
vhfopt = _vhf.VHFOpt(mol, 'int1e_ovlp', 'SGXnr_ovlp_prescreen',
'SGXsetnr_direct_scf')
vhfopt._intor = intor
vhfopt._cintopt = cintopt
return vhfopt
if __name__ == '__main__':
from pyscf import scf
mol = gto.Mole()
mol.build(
atom = [["O" , (0. , 0. , 0.)],
[1 , (0. , -0.757 , 0.587)],
[1 , (0. , 0.757 , 0.587)] ],
basis = 'ccpvdz',
)
method = sgx_fit(scf.RHF(mol), 'weigend')
energy = method.scf()
print(energy - -76.02673747045691)
method.with_df.dfj = True
energy = method.scf()
print(energy - -76.02686422219752)
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] | 2.235692 | 1,625 |
import logging
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from timeit import default_timer as timer
from typing import Any, Dict, Optional
import docker
from gobbli.model.context import ContainerTaskContext
from gobbli.util import (
format_duration,
generate_uuid,
gobbli_version,
is_dir_empty,
model_dir,
read_metadata,
write_metadata,
)
LOGGER = logging.getLogger(__name__)
_WEIGHTS_DIR_NAME = "weights"
class BaseModel(ABC):
"""
Abstract base class for all models.
Derived classes should be careful to call super().__init__(...) with the appropriate
arguments if they override __init__() to preserve all the functionality.
Functionality to facilitate making GPU(s) available to derived classes is available.
"""
# File containing information about the model, including type of model and gobbli version
# the model was created under
_INFO_FILENAME = "gobbli-model-info.json"
# File containing model parameters (i.e. arguments to init())
_METADATA_FILENAME = "gobbli-model-meta.json"
_WEIGHTS_DIR_NAME = _WEIGHTS_DIR_NAME
_CONTAINER_WEIGHTS_PATH = Path("/model") / _WEIGHTS_DIR_NAME
def __init__(
self,
data_dir: Optional[Path] = None,
load_existing: bool = False,
use_gpu: bool = False,
nvidia_visible_devices: str = "all",
logger: Optional[logging.Logger] = None,
**kwargs,
):
"""
Create a model.
Args:
data_dir: Optional path to a directory used to store model data. If not given,
a unique directory under GOBBLI_DIR will be created and used.
load_existing: If True, ``data_dir`` should be a directory that was previously used
to create a model. Parameters will be loaded to match the original model, and
user-specified model parameters will be ignored. If False, the data_dir must
be empty if it already exists.
use_gpu: If True, use the
nvidia-docker runtime (https://github.com/NVIDIA/nvidia-docker) to expose
NVIDIA GPU(s) to the container. Will cause an error if the computer you're running
on doesn't have an NVIDIA GPU and/or doesn't have the nvidia-docker runtime installed.
nvidia_visible_devices: Which GPUs to make available to the container; ignored if
``use_gpu`` is False. If not 'all', should be a comma-separated string: ex. ``1,2``.
logger: If passed, use this logger for logging instead of the default module-level logger.
**kwargs: Additional model-specific parameters to be passed to the model's :meth:`init` method.
"""
self._logger = LOGGER
if logger is not None:
self._logger = logger
if data_dir is None:
self._data_dir = self.model_class_dir() / generate_uuid()
else:
self._data_dir = data_dir
# Ensure we have an absolute data dir so any derived paths used in metadata files, etc
# aren't ambiguous
self._data_dir = self._data_dir.resolve()
self._data_dir.mkdir(parents=True, exist_ok=True)
class_name = self.__class__.__name__
cur_gobbli_version = gobbli_version()
if self.info_path.exists():
info = read_metadata(self.info_path)
if not info["class"] == class_name:
raise ValueError(
f"Model class mismatch: the model stored in {data_dir} is of "
f"class '{info['class']}'. Expected '{class_name}'."
)
if not info["gobbli_version"] == cur_gobbli_version:
warnings.warn(
f"The model stored in {data_dir} was created with gobbli version "
f"{info['gobbli_version']}, but you're running version {cur_gobbli_version}. "
"You may encounter compatibility issues."
)
if load_existing and self.metadata_path.exists():
params = read_metadata(self.metadata_path)
if len(kwargs) > 0:
warnings.warn(
"User-passed params ignored due to existing model being "
f"loaded: {kwargs}"
)
else:
if not is_dir_empty(self._data_dir):
raise ValueError(
f"data_dir '{self._data_dir}' is non-empty;"
" it must be empty to avoid overwriting data."
)
params = kwargs
write_metadata(params, self.metadata_path)
write_metadata(
{"class": class_name, "gobbli_version": cur_gobbli_version},
self.info_path,
)
self.use_gpu = use_gpu
self.nvidia_visible_devices = nvidia_visible_devices
self.docker_client = docker.from_env()
self.init(params)
self._logger.info(
f"{class_name} initialized with data directory '{self._data_dir}'"
)
@property
def logger(self) -> logging.Logger:
"""
Returns:
A logger for derived models to use.
"""
return self._logger
@property
def info_path(self) -> Path:
"""
Returns:
The path to the model's info file, containing information about the model including
the type of model, gobbli version it was trained using, etc.
"""
return self.data_dir() / BaseModel._INFO_FILENAME
@property
def metadata_path(self) -> Path:
"""
Returns:
The path to the model's metadata file containing model-specific parameters.
"""
return self.data_dir() / BaseModel._METADATA_FILENAME
@abstractmethod
def init(self, params: Dict[str, Any]):
"""
Initialize a derived model using parameters specific to that model.
Args:
params: A dictionary where keys are parameter names and values are
parameter values.
"""
raise NotImplementedError
def _base_docker_run_kwargs(self, context: ContainerTaskContext) -> Dict[str, Any]:
"""
Establish a base set of docker run kwargs to handle GPU support, etc.
Map directories as specified by the context.
Returns:
Base kwargs for any model that will be run using Docker.
"""
kwargs = {
"environment": {
# Minimize the probability of containers exiting without dumping
# buffered output
"PYTHONUNBUFFERED": "1"
},
"detach": True,
"volumes": {
str(context.task_root_dir): {
"bind": str(context.container_root_dir),
"mode": "rw",
},
# Ideally we'd mount this as read-only, but some models (e.g. fastText)
# need to write to their weights
str(self.weights_dir): {
"bind": str(BaseModel._CONTAINER_WEIGHTS_PATH),
"mode": "rw",
},
},
} # type: Dict[str, Any]
if self.use_gpu:
kwargs["environment"][
"NVIDIA_VISIBLE_DEVICES"
] = self.nvidia_visible_devices
kwargs["runtime"] = "nvidia"
return kwargs
@property
def _base_docker_build_kwargs(self) -> Dict[str, Any]:
"""
Handle GPU support, etc via common args for any model Docker container.
Returns:
Base kwargs for any model that will be built using Docker.
"""
kwargs = {"buildargs": {}} # type: Dict[str, Any]
if self.use_gpu:
kwargs["buildargs"]["GPU"] = "1"
return kwargs
def data_dir(self) -> Path:
"""
Returns:
The main data directory unique to this instance of the model.
"""
return self._data_dir
@classmethod
def model_class_dir(cls) -> Path:
"""
Returns:
A directory shared among all classes of the model.
"""
return model_dir() / cls.__name__
@property
def class_weights_dir(self) -> Path:
"""
The root directory used to store initial model weights (before fine-tuning).
These should generally be some pretrained weights made available by model
developers. This directory will NOT be created by default; models should
download their weights and remove the weights directory if the download doesn't
finish properly.
Most models making use of this directory will have multiple sets of weights and
will need to store those in subdirectories under this directory.
Returns:
The path to the class-wide weights directory.
"""
return self.model_class_dir() / BaseModel._WEIGHTS_DIR_NAME
@property
def weights_dir(self) -> Path:
"""
The directory containing weights for a specific instance of the model.
This is the class weights directory by default, but subclasses might
define this property to return a subdirectory based on a set of pretrained
model weights.
Returns:
The instance-specific weights directory.
"""
return self.class_weights_dir
def build(self):
"""
Perform any pre-setup that needs to be done before running the model
(building Docker images, etc).
"""
self.logger.info("Starting build.")
start = timer()
self._build()
end = timer()
self.logger.info(f"Build finished in {format_duration(end - start)}.")
@abstractmethod
def _build(self):
"""
Used for derived classes to define their implementation of the build method.
"""
raise NotImplementedError
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62,
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7,
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220,
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16718,
329,
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286,
262,
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2446,
13,
198,
220,
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37227,
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220,
220,
5298,
1892,
3546,
1154,
12061,
12331,
198
] | 2.339353 | 4,264 |
from .tomorrow import threads
| [
6738,
764,
39532,
6254,
1330,
14390,
198
] | 4.285714 | 7 |
# -*- encoding: utf-8 -*-
import csv
import io
import itertools
import json
from aws_gate.constants import (
AWS_DEFAULT_PROFILE,
AWS_DEFAULT_REGION,
DEFAULT_LIST_OUTPUT_FIELDS,
DEFAULT_LIST_HUMAN_FIELDS,
DEFAULT_LIST_OUTPUT,
)
from aws_gate.query import get_multiple_instance_details
from aws_gate.utils import (
get_aws_client,
get_aws_resource,
)
# pylint: disable=unused-argument
| [
2,
532,
9,
12,
21004,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
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269,
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198,
11748,
33245,
198,
11748,
340,
861,
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198,
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62,
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62,
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62,
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198,
6738,
3253,
82,
62,
10494,
13,
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1330,
357,
198,
220,
220,
220,
651,
62,
8356,
62,
16366,
11,
198,
220,
220,
220,
651,
62,
8356,
62,
31092,
11,
198,
8,
628,
198,
2,
279,
2645,
600,
25,
15560,
28,
403,
1484,
12,
49140,
628,
628,
628
] | 2.372881 | 177 |
#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import logging
import random
from typing import Any, Dict, List, Set
from terragraph_thrift.Controller.ttypes import IperfTransportProtocol
from terragraph_thrift.Topology.ttypes import NodeStatusType
from tglib.clients import APIServiceClient
from tglib.exceptions import ClientRuntimeError
from ..models import NetworkTestDirection, NetworkTestType
from .base import BaseTest, TestAsset
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# ------------------------------
# 228. Summary Ranges
#
# Description:
#
# Version: 2.0
# 09/25/18 by Jianfa
# ------------------------------
# Used for testing
if __name__ == "__main__":
test = Solution()
# ------------------------------
# Summary:
# | [
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