content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
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| ratio_char_token
float64 0.38
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| token_count
int64 1
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|
|---|---|---|---|
import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="choixpeau",
version="0.0.9",
author="Keurcien Luu",
author_email="[email protected]",
description="Efficiently assign users to buckets.",
long_description=long_description,
long_description_content_type="text/markdown",
packages=setuptools.find_packages(exclude=['tests']),
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires='>=3.6',
install_requires=[
"redis"
]
) | [
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366,
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] | 2.5 | 266 |
#================================
# RESEARCH GROUP PROJECT [RGP]
#================================
# This file is part of the COMP3096 Research Group Project.
# System
import logging
# Gym Imports
import gym
from gym.spaces import Box, Discrete, Tuple
# PySC2 Imports
from pysc2.lib.actions import FUNCTIONS, FunctionCall
from pysc2.lib.features import SCREEN_FEATURES
# Numpy
import numpy as np
# Typing
from typing import List
from sc2g.env.unit_tracking import UnitTrackingEnv
# Setup
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
| [
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"""
Initialize an authenticated instance of PRAW to interact with.
$ python -i initialize_session.py
"""
from ttrv.docs import AGENT
from ttrv.packages import praw
from ttrv.content import RequestHeaderRateLimiter
from ttrv.config import Config
config = Config()
config.load_refresh_token()
reddit = praw.Reddit(
user_agent=AGENT.format(version='test_session'),
decode_html_entities=False,
disable_update_check=True,
timeout=10, # 10 second request timeout
handler=RequestHeaderRateLimiter())
reddit.set_oauth_app_info(
config['oauth_client_id'],
config['oauth_client_secret'],
config['oauth_redirect_uri'])
reddit.refresh_access_information(config.refresh_token)
inbox = reddit.get_inbox()
items = [next(inbox) for _ in range(20)]
pass
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"""
Example on how to plot a Skew-T plot of a sounding
--------------------------------------------------
This example shows how to make a Skew-T plot from a sounding
and calculate stability indicies. METPy needs to be installed
in order to run this example
"""
import act
from matplotlib import pyplot as plt
try:
import metpy
METPY = True
except ImportError:
METPY = False
if METPY:
# Read data
sonde_ds = act.io.armfiles.read_netcdf(
act.tests.sample_files.EXAMPLE_SONDE1)
print(list(sonde_ds))
# Calculate stability indicies
sonde_ds = act.retrievals.calculate_stability_indicies(
sonde_ds, temp_name="tdry", td_name="dp", p_name="pres",
rh_name='rh')
print(sonde_ds["lifted_index"])
# Set up plot
skewt = act.plotting.SkewTDisplay(sonde_ds, figsize=(15, 10))
# Add data
skewt.plot_from_u_and_v('u_wind', 'v_wind', 'pres', 'tdry', 'dp')
sonde_ds.close()
plt.show()
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import sys
predotarOutput(sys.argv[1],sys.argv[2])
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"""
Author: Sarah Masud
Copyright (c): Sarah Masud
"""
import json
import numpy as np
from datetime import datetime, timedelta
import os
import sys
main_dir = os.path.join("data", "reddit_data")
with open(os.path.join(main_dir, "selected_discussion_nov.jsonlist"),
"r") as f:
data = f.readlines()
n = len(data)
print(n)
sys.stdout.flush()
subreddit_stats = {}
input_dir_path = os.path.join("data", "reddit_data", "NOV_INPUT")
output_dir_path = os.path.join("data", "reddit_data", "NOV_OUTPUT")
output_hour_dir_path = os.path.join("data", "reddit_data", "NOV_OUTPUT_HOUR")
for i in range(n):
print(i)
sys.stdout.flush()
each_reddit = json.loads(data[i])
key = list(each_reddit.keys())[0]
each_reddit = each_reddit[key]
subreddit_stats[i] = {}
subreddit_stats[i]['total'] = len(each_reddit)
atleast_1 = 0
atleast_10 = 0
sub_dir_path = os.path.join(input_dir_path, str(i))
os.mkdir(sub_dir_path)
out_sub_dir_path = os.path.join(output_dir_path, str(i))
os.mkdir(out_sub_dir_path)
out_hour_sub_dir_path = os.path.join(output_hour_dir_path, str(i))
os.mkdir(out_hour_sub_dir_path)
for index, each_post in enumerate(each_reddit):
event_list_temp = []
if len(each_post['comments']) < 10:
continue
d1 = datetime.fromtimestamp(each_post['created_utc'])
event_list_temp = []
for each_comment in each_post['comments']:
d2 = datetime.fromtimestamp(each_comment['created_utc'])
if d2 > d1 + timedelta(days=30):
break
td = d2 - d1
td = td.total_seconds() / 3600 # in hours
event_list_temp.append(str(td) + " " + "1")
l = len(event_list_temp)
if l < 10:
atleast_1 += 1
continue
event_list = []
event_list.append(str(l + 1) + " " + str(each_post['created_utc']))
event_list.append("0.0 1")
event_list.extend(event_list_temp)
atleast_1 += 1
atleast_10 += 1
file_path = os.path.join(sub_dir_path, str(index) + ".txt")
with open(file_path, "w") as f:
for each_line in event_list:
f.write(each_line + "\n")
if atleast_10 == 0:
print("10-0", i)
sys.stdout.flush()
os.rmdir(sub_dir_path)
os.rmdir(out_sub_dir_path)
os.rmdir(out_hour_sub_dir_path)
subreddit_stats[i]['atleast_1'] = atleast_1
subreddit_stats[i]['atleast_10'] = atleast_10
with open(os.path.join(main_dir, "subreddit_stats_nov_10.json"), "w") as f:
json.dump(subreddit_stats, f, indent=True)
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10748,
62,
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11,
277,
11,
33793,
28,
17821,
8,
198
] | 2.07565 | 1,269 |
# Generated by Django 2.2.4 on 2019-10-06 15:46
from django.db import migrations, models
| [
2,
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362,
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13,
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319,
13130,
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] | 2.84375 | 32 |
from ibslib.io import aims_extractor
__author__='Manny Bier'
def extract(struct_dir, extractor="aims", extractor_kwargs={}):
"""
Purpose is to extract information from a specific direcoty format.
For example, extract FHI-aims calculation directories to a Structure
json file.
Arguments
---------
struct_dir: path
Path to the directory that information will be extracted from
extractor: str
Extraction method to use
kwargs: dict
Dictionary of keyword arguments which will be passed to the extraction
process.
"""
if extractor == "aims":
result = aims_extractor.extract(struct_dir, extractor_kwargs)
return result
if __name__ == "__main__":
struct_dir = "/Users/ibier/Research/Results/Hab_Project/FUQJIK/2_mpc/Genarris/Relaxation"
result = extract(struct_dir, extractor="aims")
| [
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14,
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62,
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48,
41,
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14,
17,
62,
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14,
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341,
1,
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220,
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1255,
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7,
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62,
15908,
11,
7925,
273,
2625,
1385,
82,
4943,
198
] | 2.572222 | 360 |
from sqlalchemy.orm import scoped_session as ss
| [
6738,
44161,
282,
26599,
13,
579,
1330,
629,
19458,
62,
29891,
355,
37786,
628
] | 3.5 | 14 |
import re
typ='Extra stings Hello 2134567 World_This is a Regex Demo Extra stings'
result = re.search('(Extra) stings Hello 2134567 (.*) is a Regex Demo Extra (.*)',typ ,re.S)
print(result.group(1,2,3))
# type="submit" id="su" value="百度一下" class="bg s_btn"></span><span class="tools"><span id="mHolder"><div id="mCon"><span>输入法</span></div><ul id="mMenu"> | [
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] | 2.524823 | 141 |
import io
import sys
from typing import List
from hstest.dynamic.input.dynamic_input_func import DynamicTestFunction, DynamicInputFunction
from hstest.dynamic.input.input_mock import InputMock
| [
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] | 3.482143 | 56 |
import rx
from rx import operators as ops
import operator
def demo_starmap():
'''tuple unpacking'''
a = rx.of(1, 2, 3, 4)
b = rx.of(2, 2, 4, 4)
a.pipe(
ops.zip(b),
ops.starmap(operator.mul)
).subscribe(print)
if __name__ == '__main__':
#demo_zip()
demo_starmap() | [
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import unittest
from cloudwanderer import URN
from ..helpers import CloudWandererCalls, ExpectedCall, MultipleResourceScenario, NoMotoMock, SingleResourceScenario
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import os
from flask import Flask, json, Response, request, render_template, send_file, jsonify, send_from_directory
from werkzeug.utils import secure_filename
import requests
from flask_cors import CORS
from datetime import datetime
import torch
import torch.nn.functional as F
import numpy as np
import json
import torchvision.transforms as transforms
# import matplotlib.pyplot as plt
# import matplotlib.cm as cm
import skimage.transform
import argparse
from scipy.misc import imread, imresize
from PIL import Image
import shutil
from PIL import Image
torch.set_default_tensor_type('torch.cuda.FloatTensor')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# device = 'cpu'
print('-------------', device)
def caption_image_beam_search(encoder, decoder, image_path, word_map, beam_size):
"""
Reads an image and captions it with beam search.
:param encoder: encoder model
:param decoder: decoder model
:param image_path: path to image
:param word_map: word map
:param beam_size: number of sequences to consider at each decode-step
:return: caption, weights for visualization
"""
k = beam_size
vocab_size = len(word_map)
# Read image and process
img = imread(image_path)
if len(img.shape) == 2:
img = img[:, :, np.newaxis]
img = np.concatenate([img, img, img], axis=2)
# img = imresize(img, (256, 256))
img = img.transpose(2, 0, 1)
img = img / 255.
img = torch.FloatTensor(img).to(device)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform = transforms.Compose([normalize])
image = transform(img) # (3, 256, 256)
# Encode
image = image.unsqueeze(0) # (1, 3, 256, 256)
# (1, enc_image_size, enc_image_size, encoder_dim)
encoder_out = encoder(image)
enc_image_size = encoder_out.size(1)
encoder_dim = encoder_out.size(3)
# Flatten encoding
# (1, num_pixels, encoder_dim)
encoder_out = encoder_out.view(1, -1, encoder_dim)
num_pixels = encoder_out.size(1)
# We'll treat the problem as having a batch size of k
# (k, num_pixels, encoder_dim)
encoder_out = encoder_out.expand(k, num_pixels, encoder_dim)
# Tensor to store top k previous words at each step; now they're just <start>
k_prev_words = torch.LongTensor(
[[word_map['<start>']]] * k).to(device) # (k, 1)
# Tensor to store top k sequences; now they're just <start>
seqs = k_prev_words # (k, 1)
# Tensor to store top k sequences' scores; now they're just 0
top_k_scores = torch.zeros(k, 1).to(device) # (k, 1)
# Tensor to store top k sequences' alphas; now they're just 1s
seqs_alpha = torch.ones(k, 1, enc_image_size, enc_image_size).to(
device) # (k, 1, enc_image_size, enc_image_size)
# Lists to store completed sequences, their alphas and scores
complete_seqs = list()
complete_seqs_alpha = list()
complete_seqs_scores = list()
# Start decoding
step = 1
h, c = decoder.init_hidden_state(encoder_out)
# s is a number less than or equal to k, because sequences are removed from this process once they hit <end>
while True:
embeddings = decoder.embedding(
k_prev_words).squeeze(1) # (s, embed_dim)
# (s, encoder_dim), (s, num_pixels)
awe, alpha = decoder.attention(encoder_out, h)
# (s, enc_image_size, enc_image_size)
alpha = alpha.view(-1, enc_image_size, enc_image_size)
# gating scalar, (s, encoder_dim)
gate = decoder.sigmoid(decoder.f_beta(h))
awe = gate * awe
h, c = decoder.decode_step(
torch.cat([embeddings, awe], dim=1), (h, c)) # (s, decoder_dim)
scores = decoder.fc(h) # (s, vocab_size)
scores = F.log_softmax(scores, dim=1)
# Add
scores = top_k_scores.expand_as(scores) + scores # (s, vocab_size)
# For the first step, all k points will have the same scores (since same k previous words, h, c)
if step == 1:
top_k_scores, top_k_words = scores[0].topk(k, 0, True, True) # (s)
else:
# Unroll and find top scores, and their unrolled indices
# (s)
top_k_scores, top_k_words = scores.view(-1).topk(k, 0, True, True)
# Convert unrolled indices to actual indices of scores
prev_word_inds = top_k_words / vocab_size # (s)
next_word_inds = top_k_words % vocab_size # (s)
# Add new words to sequences, alphas
seqs = torch.cat(
[seqs[prev_word_inds], next_word_inds.unsqueeze(1)], dim=1) # (s, step+1)
seqs_alpha = torch.cat([seqs_alpha[prev_word_inds], alpha[prev_word_inds].unsqueeze(1)],
dim=1) # (s, step+1, enc_image_size, enc_image_size)
# Which sequences are incomplete (didn't reach <end>)?
incomplete_inds = [ind for ind, next_word in enumerate(next_word_inds) if
next_word != word_map['<end>']]
complete_inds = list(
set(range(len(next_word_inds))) - set(incomplete_inds))
# Set aside complete sequences
if len(complete_inds) > 0:
complete_seqs.extend(seqs[complete_inds].tolist())
complete_seqs_alpha.extend(seqs_alpha[complete_inds].tolist())
complete_seqs_scores.extend(top_k_scores[complete_inds])
k -= len(complete_inds) # reduce beam length accordingly
# Proceed with incomplete sequences
if k == 0:
break
seqs = seqs[incomplete_inds]
seqs_alpha = seqs_alpha[incomplete_inds]
h = h[prev_word_inds[incomplete_inds]]
c = c[prev_word_inds[incomplete_inds]]
encoder_out = encoder_out[prev_word_inds[incomplete_inds]]
top_k_scores = top_k_scores[incomplete_inds].unsqueeze(1)
k_prev_words = next_word_inds[incomplete_inds].unsqueeze(1)
# Break if things have been going on too long
if step > 50:
break
step += 1
i = complete_seqs_scores.index(max(complete_seqs_scores))
seq = complete_seqs[i]
alphas = complete_seqs_alpha[i]
# print('seq', seq)
return seq, alphas
app = Flask(__name__, static_folder='storage')
init_app()
path_model = 'BEST_checkpoint_coco_5_cap_per_img_5_min_word_freq.pth.tar'
path_word_map = 'WORDMAP_coco_5_cap_per_img_5_min_word_freq.json'
beam_size = 5
# Load model
checkpoint = torch.load(path_model, map_location=str(device))
decoder = checkpoint['decoder']
decoder = decoder.to(device)
decoder.eval()
encoder = checkpoint['encoder']
encoder = encoder.to(device)
encoder.eval()
# Load word map (word2ix)
with open(path_word_map, 'r') as j:
word_map = json.load(j)
rev_word_map = {v: k for k, v in word_map.items()} # ix2word
VALID_IMAGE_EXTENSIONS = [
".jpg",
".jpeg",
".png",
".gif",
]
@app.route('/api', methods=['GET'])
@app.route('/api/add_image', methods=['POST'])
@app.route('/api/add_url_image', methods=['GET'])
if __name__ == '__main__':
app.run(
host='0.0.0.0',
port=5000
#debug=False,
#threaded=False
)
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220,
220,
220,
2583,
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15,
13,
15,
13,
15,
13,
15,
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220,
220,
220,
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220,
220,
220,
2493,
28,
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220,
220,
220,
1303,
16663,
276,
28,
25101,
198,
220,
220,
220,
1267,
628
] | 2.287793 | 3,162 |
from aiogram import types
from aiogram.dispatcher.filters.builtin import CommandStart
from loader import dp
@dp.message_handler(CommandStart())
| [
6738,
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] | 3.340909 | 44 |
import unittest
from pytextgame.colors import *
from pytextgame.displays import Displays
from pytextgame.geometry import *
from pytextgame.window import Window
if __name__ == '__main__':
unittest.main()
| [
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] | 3.072464 | 69 |
# Copyright 2018 Google LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# https://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.
"""Test create and print a batch from the flying shapes dataset.
"""
from third_party import dataset
def test_flying_shapes():
"""Wrapper for flying_shapes.py data generator."""
config = {}
config['seq_length'] = 10
config['batch_size'] = 2
config['image_size'] = 600
config['num_digits'] = 3
config['step_length'] = 0.5
config['digit_size'] = 180
config['frame_size'] = (config['image_size']**2) * 3
config['file_path'] = 'flying_shapes.npy'
data_generator = dataset.FlyingShapesDataHandler(config)
x, bboxes = data_generator.GetUnlabelledBatch()
data_generator.DisplayData(x, bboxes)
x2, bboxes2 = data_generator.GetLabelledBatch()
data_generator.DisplayData(x2, bboxes2)
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] | 3.293059 | 389 |
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
red='\033[31m'
reset='\033[0m'
s = "[€€€éä] nice " + red + "colors" + reset + '!\n'
print(s)
#import tempfile
#fd, path = tempfile.mkstemp()
#print fd, path
"""
tmpf = os.fdopen(fd, 'w')
try:
with as tmp:
# do stuff with temp file
tmp.write('stuff')
finally:
os.remove(path)
f = tempfile.NamedTemporaryFile()
"""
# write string into file
f = open("tmp.txt", 'w')
for i in range(5):
f.write(s)
f.close()
# read string from file
f = open("tmp.txt", 'r')
for l in f.readlines():
print(l, end=' ')
#print l.encode('utf-8').decode('unicode_escape'),
#print l.decode('unicode_escape')
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] | 2.171975 | 314 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import logging
from gmprocess.subcommands.lazy_loader import LazyLoader
arg_dicts = LazyLoader(
'arg_dicts', globals(), 'gmprocess.subcommands.arg_dicts')
base = LazyLoader('base', globals(), 'gmprocess.subcommands.base')
distributed = LazyLoader('distributed', globals(), 'dask.distributed')
ws = LazyLoader('ws', globals(), 'gmprocess.io.asdf.stream_workspace')
station_summary = LazyLoader(
'station_summary', globals(), 'gmprocess.metrics.station_summary')
const = LazyLoader('const', globals(), 'gmprocess.utils.constants')
class ComputeWaveformMetricsModule(base.SubcommandModule):
"""Compute waveform metrics.
"""
command_name = 'compute_waveform_metrics'
aliases = ('wm', )
arguments = [
arg_dicts.ARG_DICTS['eventid'],
arg_dicts.ARG_DICTS['textfile'],
arg_dicts.ARG_DICTS['label'],
arg_dicts.ARG_DICTS['overwrite'],
arg_dicts.ARG_DICTS['num_processes']
]
def main(self, gmrecords):
"""Compute waveform metrics.
Args:
gmrecords:
GMrecordsApp instance.
"""
logging.info('Running subcommand \'%s\'' % self.command_name)
self.gmrecords = gmrecords
self._check_arguments()
self._get_events()
for event in self.events:
self._compute_event_waveform_metrics(event)
self._summarize_files_created()
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25,
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220,
220,
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220,
220,
220,
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220,
220,
220,
2116,
13557,
5589,
1133,
62,
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62,
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62,
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10466,
7,
15596,
8,
628,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
16345,
3876,
1096,
62,
16624,
62,
25598,
3419,
198
] | 2.334405 | 622 |
import pandas as pd
import math
import cv2
import numpy as np
import matplotlib
import random
from bestguess.bestguess import BestGuessModule, MODEL_CHANNELS
from stochastic_optimizer import BestChangeLayer
# matplotlib.use('agg')
import matplotlib.pyplot as plt
import time
import torch
import torch.nn as nn
import os
from torch import optim
from torch.utils.data import Dataset, DataLoader
from torch.utils.tensorboard import SummaryWriter
LR = 1e-4
BATCH_SIZE = 128
STEPS_PER_EPOCH = 1024
EPOCHS = 128
GOL_DELTA = 2
TEST_SAMPLES = 20
HALF_LR_AFTER_N_EPOCHS = 32
OUTLINE_SIZE = 5*2
RUN_NAME = time.strftime("%Y_%m_%d_%H_%M_%S") + '_GoL_delta_' + str(GOL_DELTA)
SNAPSHOTS_DIR = '../out/training/snapshots/{}'.format(RUN_NAME)
TENSORBOARD_LOGS_DIR = '../out/training/logs'
VIDEO_DIR = '../out/training/videos/{}'.format(RUN_NAME)
SUBMISSION_DIR = '../out/submissions'
SUBMISSION_FILE_FORMAT = SUBMISSION_DIR + '/submission_{}.csv'
SCORE_FILE_FORMAT = SUBMISSION_DIR + '/score_{}.csv'
if __name__ == "__main__":
improve_submission()
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] | 2.516667 | 420 |
import copy
import numpy as np
from sparse_dot_mkl import dot_product_mkl
from utils import MIN_FLOAT
from IPython import embed
#def raw_overlap(node, constraint, num_points):
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"""
Name: InsanityNet
Last Date Edited: 18-Feb-2022
Description:
Imperial and Metric conversion.
Feet and Inches to Meters OR Meters to Feet and Inches
Requirements:
fractions.Fraction function built into Python3
Known Issues:
1. Negative Feet and Positive Inches. I can't be bothered to fix it at 11 PM.
"""
# Necessary imports for this script to function.
from fractions import Fraction as Fracs
# Imperial to Metric Conversion
def imperial(x):
"""
This function will convert Imperial Feet (and Inches) to Metric Meters (or Centimeters if less than 1 whole Meter
:return: Converted value from Imperial to Metric
"""
# FEET SECTION
# Take the feet from Array and set to variable frac_ft
frac_ft = float(x[0])
# Convert from Feet to Meters
result_1 = frac_ft * 0.3048
# Format the resulting converted float to have 4 decimal places
result_1 = float("{:.4f}".format(result_1))
# INCHES SECTION
# INCH TO METERS
# Connvert the inch (and fraction inch) to decimal inch
frac = float(sum(Fracs(s) for s in x[1].split()))
# Calculate Inch section of results
result_2 = frac / 39.37
# RESULTS
# Calculate the results
result = result_1 + result_2
# Format to 4 decimal places
result = float("{:.4f}".format(result))
# RETURN SECTION
# Return the converted result to be displayed
return result
# Metric to Imperial Conversion
def metric(x):
"""
This function will convert Metric meters to Imperial Feet (and Inches).
:return: Converted value from Metric to Imperial
"""
# Initial conversion
# Meters to Feet
meters_in_ft = float("{:.4f}".format(x * 3.280839895))
# Inches portion of conversion
meters_in_in = meters_in_ft % 1 * 12
# For the weird rounding issues where it assumes .999 is 12 inches (1ft) just convert it over to prevent
# 5 ft 12 inch issues
if meters_in_in >= 11.992:
meters_in_ft = meters_in_ft + 1
meters_in_in = meters_in_in - 11.992
# LIMIT/FORMAT OUTPUTS
# Limit Feet to 0 decimal places
meters_in_ft = int(meters_in_ft)
# Limit Inches to 2 decimal places
meters_in_in = float("{:.2f}".format(meters_in_in))
# Return the
return meters_in_ft, meters_in_in
# Main function
# If not called as library, run the specified function automatically.
if __name__ == "__main__":
# MEME CONTROL! 'Cause it's 11 PM and I have no impulse control.
coconut = 1
# If coconut != NULL run the program
if coconut != '':
main()
# Otherwise, find the coconut so the program can run!
else:
print(f"Coconut is NULL. Find the Coconut.")
# My friend Kolock can go <explitive> himself for recommending complexity be added to this program.
# If negative feet and positive inches are input, wrong answer. I do not care enough to fix it at this time
# This is essentially python 101 so this is already overcomplicated based on your teachings. -_-
# My friend Kolock also specified I needed to add this: Coconut.jpg
# The above Something about good luck. Oh well, it's 11 PM, and I am tired of programming for the day.
# My Australian friend is a bad influence on me when I do not have impulse control this late at night.
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379,
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] | 3.052045 | 1,076 |
# Authors:
# Trevor Perrin
# Moxie Marlinspike
#
# See the LICENSE file for legal information regarding use of this file.
import sys
from tack.version import __version__
from tack.commands.Command import Command
from tack.commands.GenerateKeyCommand import GenerateKeyCommand
from tack.commands.SignCommand import SignCommand
from tack.commands.ViewCommand import ViewCommand
from tack.commands.PackCommand import PackCommand
from tack.commands.UnpackCommand import UnpackCommand
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import serial
from .serial_mock.serial import Serial as SerialMock
from time import sleep
DELAY_BETWEEN_MESSAGES = 0.05
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from collections import Collection
import altair as alt
import pandas as pd
def histogram(x, data, opacity=1., maxbins=30, color=None, padding=0,):
"""Display a histogram.
Parameters
----------
x : str
value to be binned
data : pandas.DataFrame
dataframe containing x
opacity : float
opacity of the histogram layer
maxbins : int
max bins allowable in the histogram
color : str, None
Color of histogram layer
padding : int
Amount of padding on ends of x-axis
Example
-------
>>> import cosilico.base as base
>>> import seaborn as sns
>>>
>>> iris = sns.load_dataset('iris')
>>>
>>> base.histogram('sepal_length', iris)
Returns
-------
altair.Chart
"""
mark_kwargs = {
'opacity': opacity,
}
if color is not None: mark_kwargs['color'] = color
chart = alt.Chart(data).mark_bar(**mark_kwargs).encode(
x=alt.X(f'{x}:Q',
bin=alt.Bin(maxbins=maxbins),
title=x,
scale=alt.Scale(padding=padding)
),
y=alt.Y('count():Q',
title='Count',
)
)
return chart
def layered_histogram(x, hue, data, opacity=.6, maxbins=100,
stack=None, padding=0):
"""Display a layered histogram.
Parameters
----------
x : str
value to be binned
hue : str
value defining layers of the histogram
data : pandas.DataFrame
dataframe containing x and hue columns
opacity : float
opacity of the histogram layers
maxbins : int
max bins allowable in the histogram
stack : str, None, bool
argument for stack parameter in altair. If None,
then the areas of the layers that overlap will be
different colors. If 'zero', then the layers will
completly occlude one another.
padding : int
Amount of padding on ends of x-axis
Example
-------
>>> import cosilico.base as base
>>> import seaborn as sns
>>>
>>> iris = sns.load_dataset('iris')
>>>
>>> base.layered_histogram('sepal_length', 'species', iris)
Returns
-------
altair.Chart
"""
chart = alt.Chart(data).mark_area(
opacity=opacity,
interpolate='step'
).encode(
alt.X(f'{x}:Q', bin=alt.Bin(maxbins=100), title=x,
scale=alt.Scale(padding=padding)),
alt.Y('count()', stack=stack, title='Count'),
alt.Color(f'{hue}:N')
)
return chart
def distribution_plot(x, data, color=None, opacity=.6, bandwidth=.3,
filled=True, steps=200, x_pad_scaler=.2, line_only=False,
orientation='vertical'):
"""Display a simple distribution plot.
Parameters
----------
x : str
value to calculate distribution for.
data : pandas.DataFrame
dataframe containing x column
color : str, None
color of the distribution mark
opacity : float
opacity of the distribution plot layers
bandwidth : float
bandwidth used for density calculations
steps : int
number of steps used for smoothing distribution lines
x_pad_scaler : float
Used to extend x-axis range if needed. Adds
x_pad_scaler * (x_max_value - x_min_value) to each
side of the x-axis.
filled : bool
Whether the curve is filled or not.
line_only : bool
Whether to include only the distribution plot kernel line
orientation : str
Can either be 'vertical' or 'horizontal'
Example
-------
>>> import cosilico.base as base
>>> import seaborn as sns
>>>
>>> iris = sns.load_dataset('iris')
>>> base.distribution_plot('sepal_length', iris)
Returns
-------
altair.Chart
"""
chart = alt.Chart(data)
value_range = max(data[x]) - min(data[x])
chart = chart.transform_density(
density=x,
bandwidth=bandwidth,
counts=True,
extent=[min(data[x]) - float(x_pad_scaler * value_range),
max(data[x]) + float(x_pad_scaler * value_range)],
steps=steps,
)
axis_kwargs, mark_kwargs = {}, {}
if orientation == 'vertical':
mark_kwargs['orient'] = alt.Orientation('vertical')
if line_only:
chart = chart.mark_line(opacity=opacity, **mark_kwargs)
# axis_kwargs['axis'] = None
# if orientation == 'vertical':
# encode_kwargs['order'] = 'value:Q'
else:
chart = chart.mark_area(opacity=opacity, filled=filled,
**mark_kwargs)
# chart = chart.encode(
# x=alt.X(f'value:Q',
# title=x,
# **axis_kwargs
# ),
# y=alt.Y('density:Q',
# **axis_kwargs
# ),
# )
if orientation == 'horizontal':
chart = chart.encode(
x=alt.X(f'value:Q',
title=x,
**axis_kwargs
),
y=alt.Y('density:Q',
**axis_kwargs
),
)
else:
chart = chart.encode(
y=alt.X(f'value:Q',
title=x,
**axis_kwargs
),
x=alt.Y('density:Q',
**axis_kwargs
),
order='value:Q'
)
return chart
def layered_distribution_plot(x, data, hue=None, opacity=.6, bandwidth=.3,
steps=200, stack=None, x_pad_scaler=.2, filled=True):
"""Display a layered distribution plot.
Parameters
----------
x : Collection, str
value to calculate distribution for.
If x is an iterable, then x will be treated as a list values
to use for a fold transform. If x is a str, data will not be
fold transformed
data : pandas.DataFrame
dataframe containing values
hue : str, None
value defining layers of the distribution plot. If x is a
a string, then hue must be specified. Otherwise legend will
be named by the hue value.
opacity : float
opacity of the distribution plot layers
bandwidth : float
bandwidth used for density calculations
steps : int
number of steps used for smoothing distribution lines
stack : str, None, bool
argument for stack parameter in altair. If None,
then the areas of the layers that overlap will be
different colors. If 'zero', then the layers will
completly occlude one another.
x_pad_scaler : float
Used to extend x-axis range if needed. Adds
x_pad_scaler * (x_max_value - x_min_value) to each
side of the x-axis.
filled : bool
Whether the layers are filled or not.
Example
-------
>>> import cosilico.base as base
>>> import seaborn as sns
>>>
>>> iris = sns.load_dataset('iris')
>>> variables = ['sepal_length', 'sepal_width',
... 'petal_length', 'petal_width']
>>> base.layered_distribution_plot(variables, iris)
Returns
-------
altair.Chart
"""
transformed = data.copy()
if isinstance(x, Collection) and not isinstance(x, str):
transformed = data.melt(value_vars=x)
x = 'value'
if hue is not None:
transformed.columns = [hue if c == 'variable' else c
for c in transformed.columns]
else:
hue = 'variable'
value_range = max(transformed[x]) - min(transformed[x])
chart = alt.Chart(transformed).transform_density(
density=x,
bandwidth=bandwidth,
groupby=[hue],
counts=True,
extent=[min(transformed[x]) - float(x_pad_scaler * value_range),
max(transformed[x]) + float(x_pad_scaler * value_range)],
steps=steps,
).mark_area(
opacity=opacity,
filled=filled,
).encode(
x=alt.X(f'value:Q',
title=x
),
y=alt.Y('density:Q', stack=stack),
color=alt.Color(f'{hue}:N')
)
return chart
def boxplot(x, y, data, color=None):
"""Display a boxplot.
Arguments
---------
x : str
column in data holding x-axis categories
y : str
column in data holding y-axis values
data : pandas.DataFrame
dataframe holding x and y
color : str, None
If color is None, boxes will be colored by x.
Otherwise all boxes will be set to color.
Example
-------
>>> import seaborn as sns
>>> import cosilico.base as base
>>>
>>> iris = sns.load_dataset('iris')
>>>
>>> base.boxplot('species', 'sepal_width', iris)
Output
------
altair.Chart
"""
mark_kwargs, encode_kwargs = {}, {}
if color is not None:
mark_kwargs['color'] = color
else:
encode_kwargs['color'] = color=alt.Color(f'{x}:N')
chart = alt.Chart(data).mark_boxplot(**mark_kwargs).encode(
x=alt.X(f'{x}:N'),
y=alt.Y(f'{y}:Q'),
**encode_kwargs
)
return chart
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518,
38362,
45,
11537,
198,
220,
220,
220,
1267,
628,
220,
220,
220,
1441,
8262,
198,
198,
4299,
6082,
62,
29487,
7,
87,
11,
1366,
11,
3124,
28,
14202,
11,
45912,
28,
13,
21,
11,
19484,
28,
13,
18,
11,
198,
220,
220,
220,
220,
220,
220,
220,
5901,
28,
17821,
11,
4831,
28,
2167,
11,
2124,
62,
15636,
62,
1416,
36213,
28,
13,
17,
11,
1627,
62,
8807,
28,
25101,
11,
198,
220,
220,
220,
220,
220,
220,
220,
12852,
11639,
1851,
605,
6,
2599,
198,
220,
220,
220,
37227,
23114,
257,
2829,
6082,
7110,
13,
628,
220,
220,
220,
40117,
198,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
2124,
1058,
965,
198,
220,
220,
220,
220,
220,
220,
220,
1988,
284,
15284,
6082,
329,
13,
198,
220,
220,
220,
1366,
1058,
19798,
292,
13,
6601,
19778,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
14535,
7268,
2124,
5721,
198,
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220,
220,
3124,
1058,
965,
11,
6045,
198,
220,
220,
220,
220,
220,
220,
220,
3124,
286,
262,
6082,
1317,
198,
220,
220,
220,
45912,
1058,
12178,
198,
220,
220,
220,
220,
220,
220,
220,
45912,
286,
262,
6082,
7110,
11685,
198,
220,
220,
220,
19484,
1058,
12178,
198,
220,
220,
220,
220,
220,
220,
220,
19484,
973,
329,
12109,
16765,
198,
220,
220,
220,
4831,
1058,
493,
198,
220,
220,
220,
220,
220,
220,
220,
1271,
286,
220,
4831,
973,
329,
32746,
722,
6082,
3951,
198,
220,
220,
220,
2124,
62,
15636,
62,
1416,
36213,
1058,
12178,
198,
220,
220,
220,
220,
220,
220,
220,
16718,
284,
9117,
2124,
12,
22704,
2837,
611,
2622,
13,
34333,
198,
220,
220,
220,
220,
220,
220,
220,
2124,
62,
15636,
62,
1416,
36213,
1635,
357,
87,
62,
9806,
62,
8367,
532,
2124,
62,
1084,
62,
8367,
8,
284,
1123,
198,
220,
220,
220,
220,
220,
220,
220,
1735,
286,
262,
2124,
12,
22704,
13,
198,
220,
220,
220,
5901,
1058,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
10127,
262,
12133,
318,
5901,
393,
407,
13,
198,
220,
220,
220,
1627,
62,
8807,
1058,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
10127,
284,
2291,
691,
262,
6082,
7110,
9720,
1627,
198,
220,
220,
220,
12852,
1058,
965,
198,
220,
220,
220,
220,
220,
220,
220,
1680,
2035,
307,
705,
1851,
605,
6,
393,
705,
17899,
38342,
6,
628,
220,
220,
220,
17934,
198,
220,
220,
220,
35656,
198,
220,
220,
220,
13163,
1330,
8615,
346,
3713,
13,
8692,
355,
2779,
198,
220,
220,
220,
13163,
1330,
384,
397,
1211,
355,
3013,
82,
198,
220,
220,
220,
13163,
198,
220,
220,
220,
13163,
4173,
271,
796,
3013,
82,
13,
2220,
62,
19608,
292,
316,
10786,
29616,
11537,
198,
220,
220,
220,
13163,
2779,
13,
17080,
3890,
62,
29487,
10786,
325,
18596,
62,
13664,
3256,
4173,
271,
8,
628,
220,
220,
220,
16409,
198,
220,
220,
220,
35656,
198,
220,
220,
220,
5988,
958,
13,
45488,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
8262,
796,
5988,
13,
45488,
7,
7890,
8,
628,
220,
220,
220,
1988,
62,
9521,
796,
3509,
7,
7890,
58,
87,
12962,
532,
949,
7,
7890,
58,
87,
12962,
198,
220,
220,
220,
8262,
796,
8262,
13,
35636,
62,
43337,
7,
198,
220,
220,
220,
220,
220,
220,
220,
12109,
28,
87,
11,
198,
220,
220,
220,
220,
220,
220,
220,
19484,
28,
3903,
10394,
11,
198,
220,
220,
220,
220,
220,
220,
220,
9853,
28,
17821,
11,
198,
220,
220,
220,
220,
220,
220,
220,
6287,
41888,
1084,
7,
7890,
58,
87,
12962,
532,
12178,
7,
87,
62,
15636,
62,
1416,
36213,
1635,
1988,
62,
9521,
828,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3509,
7,
7890,
58,
87,
12962,
1343,
12178,
7,
87,
62,
15636,
62,
1416,
36213,
1635,
1988,
62,
9521,
8,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
4831,
28,
20214,
11,
198,
220,
220,
220,
1267,
628,
220,
220,
220,
16488,
62,
46265,
22046,
11,
1317,
62,
46265,
22046,
796,
1391,
5512,
23884,
198,
220,
220,
220,
611,
12852,
6624,
705,
1851,
605,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
1317,
62,
46265,
22046,
17816,
13989,
20520,
796,
5988,
13,
46,
8289,
341,
10786,
1851,
605,
11537,
198,
220,
220,
220,
611,
1627,
62,
8807,
25,
198,
220,
220,
220,
220,
220,
220,
220,
8262,
796,
8262,
13,
4102,
62,
1370,
7,
404,
4355,
28,
404,
4355,
11,
12429,
4102,
62,
46265,
22046,
8,
198,
2,
220,
220,
220,
220,
220,
220,
220,
16488,
62,
46265,
22046,
17816,
22704,
20520,
796,
6045,
198,
2,
220,
220,
220,
220,
220,
220,
220,
611,
12852,
6624,
705,
1851,
605,
10354,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
37773,
62,
46265,
22046,
17816,
2875,
20520,
796,
705,
8367,
25,
48,
6,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
8262,
796,
8262,
13,
4102,
62,
20337,
7,
404,
4355,
28,
404,
4355,
11,
5901,
28,
20286,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12429,
4102,
62,
46265,
22046,
8,
198,
1303,
220,
220,
8262,
796,
8262,
13,
268,
8189,
7,
198,
1303,
220,
220,
220,
220,
220,
220,
2124,
28,
2501,
13,
55,
7,
69,
6,
8367,
25,
48,
3256,
198,
1303,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3670,
28,
87,
11,
198,
1303,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12429,
22704,
62,
46265,
22046,
198,
1303,
220,
220,
220,
220,
220,
220,
10612,
198,
1303,
220,
220,
220,
220,
220,
220,
331,
28,
2501,
13,
56,
10786,
43337,
25,
48,
3256,
198,
1303,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12429,
22704,
62,
46265,
22046,
198,
1303,
220,
220,
220,
220,
220,
220,
10612,
198,
1303,
220,
220,
1267,
628,
220,
220,
220,
220,
198,
220,
220,
220,
611,
12852,
6624,
705,
17899,
38342,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
8262,
796,
8262,
13,
268,
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7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
28,
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55,
7,
69,
6,
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25,
48,
3256,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3670,
28,
87,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12429,
22704,
62,
46265,
22046,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10612,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
28,
2501,
13,
56,
10786,
43337,
25,
48,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12429,
22704,
62,
46265,
22046,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10612,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
8262,
796,
8262,
13,
268,
8189,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
28,
2501,
13,
55,
7,
69,
6,
8367,
25,
48,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3670,
28,
87,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12429,
22704,
62,
46265,
22046,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10612,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
28,
2501,
13,
56,
10786,
43337,
25,
48,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12429,
22704,
62,
46265,
22046,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10612,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1502,
11639,
8367,
25,
48,
6,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
1441,
8262,
628,
198,
4299,
37748,
62,
17080,
3890,
62,
29487,
7,
87,
11,
1366,
11,
37409,
28,
14202,
11,
45912,
28,
13,
21,
11,
19484,
28,
13,
18,
11,
198,
220,
220,
220,
220,
220,
220,
220,
4831,
28,
2167,
11,
8931,
28,
14202,
11,
2124,
62,
15636,
62,
1416,
36213,
28,
13,
17,
11,
5901,
28,
17821,
2599,
198,
220,
220,
220,
37227,
23114,
257,
37748,
6082,
7110,
13,
628,
220,
220,
220,
40117,
198,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
2124,
1058,
12251,
11,
965,
198,
220,
220,
220,
220,
220,
220,
220,
1988,
284,
15284,
6082,
329,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1002,
2124,
318,
281,
11629,
540,
11,
788,
2124,
481,
307,
5716,
355,
257,
1351,
3815,
198,
220,
220,
220,
220,
220,
220,
220,
284,
779,
329,
257,
5591,
6121,
13,
1002,
2124,
318,
257,
965,
11,
1366,
481,
407,
307,
198,
220,
220,
220,
220,
220,
220,
220,
5591,
14434,
198,
220,
220,
220,
1366,
1058,
19798,
292,
13,
6601,
19778,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
14535,
7268,
3815,
198,
220,
220,
220,
37409,
1058,
965,
11,
6045,
198,
220,
220,
220,
220,
220,
220,
220,
1988,
16215,
11685,
286,
262,
6082,
7110,
13,
1002,
2124,
318,
257,
220,
198,
220,
220,
220,
220,
220,
220,
220,
257,
4731,
11,
788,
37409,
1276,
307,
7368,
13,
15323,
8177,
481,
198,
220,
220,
220,
220,
220,
220,
220,
307,
3706,
416,
262,
37409,
1988,
13,
198,
220,
220,
220,
45912,
1058,
12178,
198,
220,
220,
220,
220,
220,
220,
220,
45912,
286,
262,
6082,
7110,
11685,
198,
220,
220,
220,
19484,
1058,
12178,
198,
220,
220,
220,
220,
220,
220,
220,
19484,
973,
329,
12109,
16765,
198,
220,
220,
220,
4831,
1058,
493,
198,
220,
220,
220,
220,
220,
220,
220,
1271,
286,
220,
4831,
973,
329,
32746,
722,
6082,
3951,
198,
220,
220,
220,
8931,
1058,
965,
11,
6045,
11,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
4578,
329,
8931,
11507,
287,
5988,
958,
13,
1002,
6045,
11,
198,
220,
220,
220,
220,
220,
220,
220,
788,
262,
3006,
286,
262,
11685,
326,
21721,
481,
307,
198,
220,
220,
220,
220,
220,
220,
220,
1180,
7577,
13,
1002,
705,
22570,
3256,
788,
262,
11685,
481,
198,
220,
220,
220,
220,
220,
220,
220,
1224,
83,
306,
1609,
38792,
530,
1194,
13,
198,
220,
220,
220,
2124,
62,
15636,
62,
1416,
36213,
1058,
12178,
198,
220,
220,
220,
220,
220,
220,
220,
16718,
284,
9117,
2124,
12,
22704,
2837,
611,
2622,
13,
34333,
198,
220,
220,
220,
220,
220,
220,
220,
2124,
62,
15636,
62,
1416,
36213,
1635,
357,
87,
62,
9806,
62,
8367,
532,
2124,
62,
1084,
62,
8367,
8,
284,
1123,
198,
220,
220,
220,
220,
220,
220,
220,
1735,
286,
262,
2124,
12,
22704,
13,
198,
220,
220,
220,
5901,
1058,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
10127,
262,
11685,
389,
5901,
393,
407,
13,
628,
220,
220,
220,
17934,
198,
220,
220,
220,
35656,
198,
220,
220,
220,
13163,
1330,
8615,
346,
3713,
13,
8692,
355,
2779,
198,
220,
220,
220,
13163,
1330,
384,
397,
1211,
355,
3013,
82,
198,
220,
220,
220,
13163,
198,
220,
220,
220,
13163,
4173,
271,
796,
3013,
82,
13,
2220,
62,
19608,
292,
316,
10786,
29616,
11537,
198,
220,
220,
220,
13163,
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] | 2.265424 | 3,971 |
"""A client to OWL."""
from typing import Optional
from indra.databases.owl_client import OwlClient
_client = OwlClient('ido')
def get_ido_name_from_ido_id(ido_id: str) -> Optional[str]:
"""Return the HP name corresponding to the given HP ID.
Parameters
----------
ido_id :
The IDO identifier to be converted. Example: "0000403"
Returns
-------
:
The IDO name corresponding to the given IDO identifier.
"""
return _client.get_name_from_id(ido_id)
def get_ido_id_from_ido_name(ido_name: str) -> Optional[str]:
"""Return the HP identifier corresponding to the given IDO name.
Parameters
----------
ido_name :
The IDO name to be converted. Example: "parasite role"
Returns
-------
:
The IDO identifier corresponding to the given IDO name.
"""
return _client.get_id_from_name(ido_name)
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] | 2.70997 | 331 |
# Python solution for 'max diff - easy' codewars question.
# Level: 7 kyu
# Tags: FUNDAMENTALS, MATHEMATICS, ALGORITHMS, NUMBERS, COLLECTIONS, LISTS, DATA STRUCTURES, AND ARRAYS.
# Author: Jack Brokenshire
# Date: 16/07/2020
import unittest
def max_diff(lst):
"""
Finds the difference beteween the largest and smallest items in a list.
:param lst: a list of integers.
:return: the difference between the biggest and the smallest value in a list received as parameter, otherwise, 0.
"""
if lst:
return max(lst) - min(lst)
return 0
class TestMaxDiff(unittest.TestCase):
"""Class to test 'max_diff' function"""
if __name__ == '__main__':
unittest.main()
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] | 2.742188 | 256 |
from PyQt5.QtCore import QDataStream, QIODevice, QObject, QByteArray, pyqtSignal
from PyQt5.QtCore import *
from PyQt5.QtWidgets import QApplication, QDialog, QMainWindow, QLineEdit, QPushButton, QVBoxLayout
from PyQt5.QtWidgets import *
from PyQt5.QtNetwork import QTcpSocket, QAbstractSocket
from PyQt5.QtNetwork import *
from PyQt5.QtGui import QPixmap, QImage
from PyQt5 import QtWidgets, uic
from functools import partial
from libs.clientAbstract import ClientAbstract
# PORTS = (9998, 8000)
# class Client(QMainWindow, Ui_MainWindow):
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] | 2.443038 | 237 |
# from __future__ import absolute_import
# from __future__ import division
from __future__ import print_function
import os
import glob
import numpy as np
from PIL import Image
import cv2
if __name__ == '__main__':
main() | [
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import csv, os
os.chdir('C:\\Users\\tomas\\Desktop\\Multi-Side Flashcard Project\\Sentences from Tatoeba\\')
csv_handle = open("jpn_sentences.tsv",'r')
csv_read = csv.reader(csv_handle, delimiter="\t")
csvList = list(csv_read)
#print(len(csvList))
#print(f"Row 1, column 3: {csvList[0][2]}",f"Row 1, column 1: {csvList[0][0]}")
tatoebaDict = {}
for row in csvList:
tatoebaDict[row[0]] = row[2]
import shelve
os.chdir('C:\\Users\\tomas\\Desktop\\Multi-Side Flashcard Project\\')
d = shelve.open("N1 vocab data")
vocab = d['Vocab 1']
d.close()
#print(len(vocab))
#sentence matching.
#create a list matching sentences for each word
#first match my kanji, then match by kana, then match by kanji minus a ru at the end(or similar)
#Else append 'Sentence not found'
#NOTE -> you want to get some sort of autoconjugator for words
#refine this list to one sentence if 1) that sentence that has other words from vocab in it. (the most words)
#2) if length is 1 then no need to refine.
listOfLists = []
from progress.bar import ChargingBar
endings = {'しい':['しく','しな'],
"う":["って","った","わない",'い','え'],
"つ":["って","った","た",'ち','て'],
'いる':['いた','いて','い','いま','いない','いろ'],
'える':['えた','えて','え','えま','えない','えろ'],
"る":["って","て","った","た",'り','れ','ま','ない','ろ'],
"く":["いて","いた","か","き","け"],
"ぐ":["いで","いだ","が","ぎ","げ"],
"ぬ":["んで","んだ","な","に","ね"],
"ぶ":["んで","んだ","ば","び","べ"],
"む":["んで","んだ","ま","み","め"],
"す":["して","した","さ","し","せ"]}
with ChargingBar(max=len(vocab)) as bar:
for word in vocab:
nestedList = []
for code in tatoebaDict:
#print(f"DEBUGGING\nWord index zero = {word[0]}\nType of word index zero = {type(word[0])}")
#print(f"DEBUGGING\nContents of current key = {tatoebaDict[code]}\nType of content at current key = {type(tatoebaDict[code])}")
if word[0] in tatoebaDict[code]:
#print("DEBUGGING: IF")
nestedList.append(tatoebaDict[code])
elif word[0][-1] in list(endings.keys()):
for subend in endings[word[0][-1]]:
tempWord = word[0][:-1] + subend
if tempWord in tatoebaDict[code]:
#print("DEBUGGING: ELIF2")
if tatoebaDict[code] not in nestedList:
nestedList.append(tatoebaDict[code])
elif word[1] in tatoebaDict[code]:
#print("DEBUGGING: ELIF1")
nestedList.append(tatoebaDict[code])
if len(nestedList) == 0:
nestedList.append('Sentence not found.')
#print('Sentence not found.')
listOfLists.append(nestedList)
#print(f"LENGTH of listOfLists:{len(listOfLists)} ")
bar.next()
#print('LENGTH:\n',len(listOfLists),'CONTENTS:\n',listOfLists)
newData = shelve.open('Raw Sentences')
newData['Raw Sentences 2'] = listOfLists
newData.close() | [
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] | 2.178427 | 1,233 |
import numpy as np
import chainer
import chainer.functions as F
from chainer import initializers
from chainer import variable
class Normalize(chainer.Link):
"""Learnable L2 normalization [#]_.
This link normalizes input along the channel axis and scales it.
The scale factors are trained channel-wise.
.. [#] Wei Liu, Andrew Rabinovich, Alexander C. Berg.
ParseNet: Looking Wider to See Better. ICLR 2016.
Args:
n_channel (int): The number of channels.
initial: A value to initialize the scale factors. It is pased to
:meth:`chainer.initializers._get_initializer`. The default value
is 0.
eps (float): A small value to avoid zero-division. The default value
is :math:`1e-5`.
"""
def forward(self, x):
"""Normalize input and scale it.
Args:
x (chainer.Variable): A variable holding 4-dimensional array.
Its :obj:`dtype` is :obj:`numpy.float32`.
Returns:
chainer.Variable:
The shape and :obj:`dtype` are same as those of input.
"""
x = F.normalize(x, eps=self.eps, axis=1)
scale = F.broadcast_to(self.scale[:, np.newaxis, np.newaxis], x.shape)
return F.cast(x * scale, chainer.get_dtype())
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] | 2.43633 | 534 |
#!/usr/bin/python2.4
# Small script to show PostgreSQL and Pyscopg together
#
import json
import psycopg2
import sys
match_id = sys.argv[1]
match_id = 2125002095
conn = psycopg2.connect("dbname='postgres' user='petergleixner' host='localhost' password=''")
cursor = conn.cursor()
cursor.execute('''SELECT replay_file FROM replay_data WHERE match_id = %s''' %(match_id))
db_file = cursor.fetchone()
replay_file = json.dumps(db_file)[1:-1]
meta_info = json.loads(replay_file)["meta_info"]
game_info = meta_info["game_info"]
player_info = meta_info["player_info"]
replay = json.loads(replay_file)["replay"]
#Creating dict with heroname:list
hero_dict = dict()
for g in player_info:
hero_dict[g["hero_name"]] = []
count = 0
for r in replay:
if "gold_total" in r["data"]:
count = count + 1
hero_name = r["data"]["hero_name"]
tick = r["tick"]
gold = r["data"]["gold_total"]
tmp_dict = {"x":tick, "y":gold}
hero_dict[hero_name].append(tmp_dict)
print count
print hero_name
print "------------------"
#if hero_name == "lion":
for hero_name in hero_dict:
tmp_hero_gold = json.dumps(hero_dict[hero_name])
#cursor.execute('''INSERT INTO gold_xp (match_id, hero_name, team_id, gold_data) VALUES (%s,%s,%s,%s)''', (match_id, hero_name, 4, tmp_hero_gold))
#conn.commit()
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] | 2.371528 | 576 |
import pandas as pd
import numpy as np
import operator
import warnings
from ml.analysis.hypothesis_testing import HypothesisTester
class HTestAutoPilot:
"""
Chooses what is the most adequate hypothesis test for a given dataset,
based on its datatypes and the assumptions of each test
"""
@staticmethod
def check_binary(col):
"""
Check if data is binary
Parameters
----------
col : array_like
Array of sample data, must be quantitative data.
Returns
-------
Bool
"""
for data in col:
if data not in [0, 1]:
return False
return True
@staticmethod
def check_norm(sample1, sample2, alpha=0.05, normality_method='shapiro'):
"""
Check normality
Parameters
----------
sample1 : array_like
Array of sample data, must be quantitative data.
sample2 : array_like
Array of sample data, must be quantitative data.
alpha : float
level of significance (default = 0.05)
normality_method : string
normality test to be applied
Returns
-------
Array
"""
return [HypothesisTester.normality_test(
s,
alpha=alpha,
method=normality_method,
show_graph=False
).loc['normal'][0] for s in [sample1, sample2]]
@staticmethod
def correlation(sample1, sample2, alpha=0.05,
alternative='two-sided', normality_method='shapiro',
show_graph=True, **kwargs):
"""
Autopilot for correlation tests
Parameters
----------
sample1 : array_like
Array of sample data, must be quantitative data.
sample2 : array_like
Array of sample data, must be quantitative data.
alpha : float
level of significance (default = 0.05)
alternative : string
Specify whether the alternative hypothesis is `'two-sided'`,
`'greater'` or `'less'` to specify the direction of the
test.
normality_method : string
normality test to be applied
Returns
-------
pd.DataFrame
"""
sample1, sample2 = np.array(sample1), np.array(sample2)
np_types = [np.dtype(i) for i in [np.int32, np.int64, np.float32,
np.float64]]
if any([t not in np_types for t in [sample1.dtype, sample2.dtype]]):
raise Exception('Samples are not numerical. ',
'Try using categorical_test method instead.')
check_bin1 = HTestAutoPilot.check_binary(sample1)
check_bin2 = HTestAutoPilot.check_binary(sample2)
if check_bin1 and check_bin2:
raise Exception('Both samples are binary, ',
'unable to calculate correlation.')
elif sum([check_bin1, check_bin2]) == 1:
print('One binary sample and one real sample.',
'Point-biserial correlation is going to be applied.')
corr_method = 'pointbiserial'
binary_sample = sample2 if not check_bin1 else sample1
num_sample = sample1 if not check_bin1 else sample2
sample1, sample2 = [binary_sample, num_sample]
else:
check_norm1, check_norm2 = HTestAutoPilot.check_norm(
sample1, sample2,
alpha, normality_method
)
if check_norm1 and check_norm2:
print('Samples are normally distributed.',
'Using Pearson correlation.')
corr_method = 'pearson'
else:
print('Samples are not normally distributed.',
'Using Spearman correlation.')
corr_method = 'spearman'
df_result = HypothesisTester.correlation_test(
sample1,
sample2,
method=corr_method,
alpha=alpha,
alternative=alternative,
show_graph=show_graph,
**kwargs
)
return df_result
@staticmethod
def categorical(df, sample1, sample2, alpha=0.05,
alternative='two-sided', correction=True,
show_graph=True, **kwargs):
"""
Autopilot for tests with categorical variables
Parameters
----------
df : pandas.DataFrame
The dataframe containing the ocurrences for the test.
sample1 : string
The variable name for the test. Must be names of columns in
``data``.
sample2 : string
The variable name for the test. Must be names of columns in
``data``.
alpha : float
level of significance (default = 0.05)
alternative : string
Specify whether to return `'two-sided'`, `'greater'` or
`'less'` p-value to specify the direction of the test.
correction : bool
Whether to apply Yates' correction when the degree of freedom
of the observed contingency table is 1 (Yates 1934). In case
of Chi-squared test.
show_graph: boolean
display the graph.
Returns
-------
pd.DataFrame
"""
df_chi2 = HypothesisTester.chi2_test(
df,
sample1, sample2,
correction,
alpha,
show_graph,
**kwargs
)
table = (df.groupby([sample1, sample2]).size() > 5)
if table.sum() == len(table):
df_result = df_chi2
else:
if len(df[sample1].unique()) == 2 and len(df[sample2].unique()) == 2:
warnings.warn("The number of observations is not indicated " +
"for the chi-squared test, cannot garantee a " +
"correct inference. Also using Fisher's exact" +
" test.")
df_fisher = HypothesisTester.fisher_exact_test(
df,
sample1,
sample2,
alpha,
show_graph=False
)
df_result = pd.concat([df_chi2, df_fisher], axis=1).fillna('-')
else:
warnings.warn("The number of observations is not indicated " +
"for the chi-squared test, cannot garantee a " +
"correct inference.")
df_result = df_chi2
return df_result
@staticmethod
def independent_difference(sample1, sample2, alpha=0.05,
alternative='two-sided', correction='auto',
r=0.707, normality_method='shapiro',
show_graph=True, **kwargs):
"""
Autopilot for testing the difference in means for independent samples
Parameters
----------
sample1 : array_like
Array of sample data, must be quantitative data.
sample2 : array_like
Array of sample data, must be quantitative data.
alpha : float
level of significance (default = 0.05)
alternative : string
Specify whether the alternative hypothesis is
`'two-sided'`, `'greater'` or `'less'` to specify
the direction of the test.
correction : string or boolean
For unpaired two sample T-tests, specify whether
or not to correct for unequal variances using Welch
separate variances T-test. If 'auto', it will automatically
uses Welch T-test when the sample sizes are unequal, as
recommended by Zimmerman 2004.
r : float
Cauchy scale factor for computing the Bayes Factor.
Smaller values of r (e.g. 0.5), may be appropriate when small
effect sizes are expected a priori; larger values of r are
appropriate when large effect sizes are expected
(Rouder et al 2009). The default is 0.707 (= :math:`\sqrt{2} / 2`).
normality_method : string
normality test to be applied
show_graph: boolean
display the graph.
Returns
-------
pd.DataFrame
"""
check_norm1, check_norm2 = HTestAutoPilot.check_norm(
sample1, sample2,
alpha, normality_method
)
if check_norm1 and check_norm2:
print('Samples are normally distributed, an ideal condition',
'for the application of t-test')
df_result = HypothesisTester.t_test(
sample1,
sample2,
paired=False,
alpha=alpha,
alternative=alternative,
correction=correction,
r=r,
show_graph=show_graph,
**kwargs
)
elif (check_norm1 is False and len(sample1) < 30) or \
(check_norm2 is False and len(sample2) < 30):
print('At least one of the samples is not normally distributed.',
'However, the t-test can be applied due to central limit',
'theorem (n>30). The Mann-Whitney test is also an option',
'as it does not make any assumptions about data ditribution',
'(non-parametric alternative)')
df_result = HypothesisTester.mann_whitney_2indep(
sample1,
sample2,
alpha,
alternative,
show_graph,
**kwargs
)
else:
print('At least one of the samples is not normally distributed',
'and due to the number of observations the central limit',
'theorem does not apply. In this case, the Mann-Whitney',
'test is used as it does not make any assumptions about',
'data ditribution (non-parametric alternative)')
df_result = HypothesisTester.t_test(
sample1,
sample2,
paired=False,
alpha=alpha,
alternative=alternative,
correction=correction,
r=r,
show_graph=show_graph,
**kwargs
)
df_result_non_param = HypothesisTester.mann_whitney_2indep(
sample1,
sample2,
alpha,
alternative,
show_graph=False
)
df_result = (
pd.concat([df_result, df_result_non_param], axis=1)
.reindex(['T', 'dof', 'cohen-d', 'BF10', 'power',
'U-val', 'RBC', 'CLES', 'p-val', 'CI95%',
'H0', 'H1', 'Result'])
.fillna('-')
)
return df_result
@staticmethod
def dependent_difference(sample1, sample2, alpha=0.05, alternative='two-sided',
correction='auto', r=0.707, normality_method='shapiro',
show_graph=True, **kwargs):
"""
Autopilot for testing the difference in means for dependent samples
Parameters
----------
sample1 : array_like
Array of sample data, must be quantitative data.
sample2 : array_like
Array of sample data, must be quantitative data.
alpha : float
level of significance (default = 0.05)
alternative : string
Specify whether the alternative hypothesis is
`'two-sided'`, `'greater'` or `'less'` to
specify the direction of the test.
correction : string or boolean
For unpaired two sample T-tests, specify whether
or not to correct for unequal variances using
Welch separate variances T-test. If 'auto', it
will automatically uses Welch T-test when the
sample sizes are unequal, as recommended by Zimmerman
2004.
r : float
Cauchy scale factor for computing the Bayes Factor.
Smaller values of r (e.g. 0.5), may be appropriate
when small effect sizes are expected a priori; larger
values of r are appropriate when large effect sizes are
expected (Rouder et al 2009).
The default is 0.707 (= :math:`\sqrt{2} / 2`).
normality_method : string
normality test to be applied
show_graph: boolean
display the graph.
Returns
-------
pd.DataFrame
"""
diff_sample = sorted(list(map(operator.sub, sample1, sample2)))
check_norm_diff = (
HypothesisTester
.normality_test(
diff_sample,
alpha,
normality_method,
show_graph=False
)
.loc['normal'][0]
)
if check_norm_diff:
print('The distribution of differences is normally distributed',
'an ideal condition for the application of t-test.')
df_result = HypothesisTester.t_test(
sample1,
sample2,
paired=True,
alpha=alpha,
alternative=alternative,
correction=correction,
r=r,
show_graph=show_graph,
**kwargs
)
elif len(sample1) > 30 and len(sample2) > 30:
print('The distribution of differences is not normally',
'distributed. However, the t-test can be applied',
'due to central limit theorem (n>30). The Wilcoxon',
'test is also an option as it does not make any assumptions',
'about data ditribution (non-parametric alternative).')
df_result = HypothesisTester.t_test(
sample1,
sample2,
paired=True,
alpha=alpha,
alternative=alternative,
correction=correction,
r=r,
show_graph=show_graph,
**kwargs
)
df_result_non_param = HypothesisTester.wilcoxon_test(
sample1,
sample2,
alpha,
alternative,
show_graph=False,
**kwargs
)
df_result = (
pd.concat([df_result, df_result_non_param], axis=1)
.reindex([
'T', 'dof', 'cohen-d', 'BF10',
'power', 'W-val', 'RBC', 'CLES',
'p-val', 'CI95%', 'H0', 'H1', 'Result'
])
.fillna('-')
)
else:
print('The distribution of differences is not normally',
'distributed and due to the number of observations the',
'central limit theorem does not apply. In this case,',
'the Wilcoxon test is indicated as it does not make',
'any assumptions about data distribution',
'(non-parametric alternative).')
df_result = HypothesisTester.wilcoxon_test(
sample1,
sample2,
alpha,
alternative,
show_graph,
**kwargs
)
return df_result
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] | 1.939811 | 8,274 |
"""
Initial migration
Revision ID: 4defdf508e78
Revises: (none)
Create Date: 2021-10-19 20:14:59.350979
"""
from alembic import op
import sqlalchemy as sa
from url_shortener.database.functions import utc_now
# revision identifiers, used by Alembic.
revision = '4defdf508e78'
down_revision = None
branch_labels = None
depends_on = None
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] | 2.780488 | 123 |
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 17 16:25:07 2018
@author: Albert
"""
import pandas as pd
import numpy as np
df = pd.read_csv("./PreprocessedDataFiles/MergedPreprocessedDataFiles/MergedPreprocessedDrinkingDataLabels v2.csv")
# originalDF = df
df['index'] = range(0, len(df))
splitRatio = [0.6,0.2,0.2]
if __name__=="__main__":
train, val, test = indicesSplit(splitRatio)
np.savez("./Splits/indicesSplits.npz", train = train, val = val, test = test)
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] | 2.431472 | 197 |
import functools
import inspect
import six
from doubles.call_count_accumulator import CallCountAccumulator
from doubles.exceptions import MockExpectationError, VerifyingBuiltinDoubleArgumentError
import doubles.lifecycle
from doubles.verification import verify_arguments
_any = object()
class Allowance(object):
"""An individual method allowance (stub)."""
def __init__(self, target, method_name, caller):
"""
:param Target target: The object owning the method to stub.
:param str method_name: The name of the method to stub.
"""
self._target = target
self._method_name = method_name
self._caller = caller
self.args = _any
self.kwargs = _any
self._custom_matcher = None
self._is_satisfied = True
self._call_counter = CallCountAccumulator()
self._return_value = lambda *args, **kwargs: None
def and_raise(self, exception, *args, **kwargs):
"""Causes the double to raise the provided exception when called.
If provided, additional arguments (positional and keyword) passed to
`and_raise` are used in the exception instantiation.
:param Exception exception: The exception to raise.
"""
self._return_value = proxy_exception
return self
def and_raise_future(self, exception):
"""Similar to `and_raise` but the doubled method returns a future.
:param Exception exception: The exception to raise.
"""
future = _get_future()
future.set_exception(exception)
return self.and_return(future)
def and_return_future(self, *return_values):
"""Similar to `and_return` but the doubled method returns a future.
:param object return_values: The values the double will return when called,
"""
futures = []
for value in return_values:
future = _get_future()
future.set_result(value)
futures.append(future)
return self.and_return(*futures)
def and_return(self, *return_values):
"""Set a return value for an allowance
Causes the double to return the provided values in order. If multiple
values are provided, they are returned one at a time in sequence as the double is called.
If the double is called more times than there are return values, it should continue to
return the last value in the list.
:param object return_values: The values the double will return when called,
"""
if not return_values:
raise TypeError('and_return() expected at least 1 return value')
return_values = list(return_values)
final_value = return_values.pop()
self.and_return_result_of(
lambda: return_values.pop(0) if return_values else final_value
)
return self
def and_return_result_of(self, return_value):
""" Causes the double to return the result of calling the provided value.
:param return_value: A callable that will be invoked to determine the double's return value.
:type return_value: any callable object
"""
if not check_func_takes_args(return_value):
self._return_value = lambda *args, **kwargs: return_value()
else:
self._return_value = return_value
return self
def is_satisfied(self):
"""Returns a boolean indicating whether or not the double has been satisfied.
Stubs are always satisfied, but mocks are only satisfied if they've been
called as was declared.
:return: Whether or not the double is satisfied.
:rtype: bool
"""
return self._is_satisfied
def with_args(self, *args, **kwargs):
"""Declares that the double can only be called with the provided arguments.
:param args: Any positional arguments required for invocation.
:param kwargs: Any keyword arguments required for invocation.
"""
self.args = args
self.kwargs = kwargs
self.verify_arguments()
return self
def with_args_validator(self, matching_function):
"""Define a custom function for testing arguments
:param func matching_function: The function used to test arguments passed to the stub.
"""
self.args = None
self.kwargs = None
self._custom_matcher = matching_function
return self
def __call__(self, *args, **kwargs):
"""A short hand syntax for with_args
Allows callers to do:
allow(module).foo.with_args(1, 2)
With:
allow(module).foo(1, 2)
:param args: Any positional arguments required for invocation.
:param kwargs: Any keyword arguments required for invocation.
"""
return self.with_args(*args, **kwargs)
def with_no_args(self):
"""Declares that the double can only be called with no arguments."""
self.args = ()
self.kwargs = {}
self.verify_arguments()
return self
def satisfy_any_args_match(self):
"""Returns a boolean indicating whether or not the stub will accept arbitrary arguments.
This will be true unless the user has specified otherwise using ``with_args`` or
``with_no_args``.
:return: Whether or not the stub accepts arbitrary arguments.
:rtype: bool
"""
return self.args is _any and self.kwargs is _any
def satisfy_exact_match(self, args, kwargs):
"""Returns a boolean indicating whether or not the stub will accept the provided arguments.
:return: Whether or not the stub accepts the provided arguments.
:rtype: bool
"""
if self.args is None and self.kwargs is None:
return False
elif self.args is _any and self.kwargs is _any:
return True
elif args == self.args and kwargs == self.kwargs:
return True
elif len(args) != len(self.args) or len(kwargs) != len(self.kwargs):
return False
if not all(x == y or y == x for x, y in zip(args, self.args)):
return False
for key, value in self.kwargs.items():
if key not in kwargs:
return False
elif not (kwargs[key] == value or value == kwargs[key]):
return False
return True
def satisfy_custom_matcher(self, args, kwargs):
"""Return a boolean indicating if the args satisfy the stub
:return: Whether or not the stub accepts the provided arguments.
:rtype: bool
"""
if not self._custom_matcher:
return False
try:
return self._custom_matcher(*args, **kwargs)
except Exception:
return False
def return_value(self, *args, **kwargs):
"""Extracts the real value to be returned from the wrapping callable.
:return: The value the double should return when called.
"""
self._called()
return self._return_value(*args, **kwargs)
def verify_arguments(self, args=None, kwargs=None):
"""Ensures that the arguments specified match the signature of the real method.
:raise: ``VerifyingDoubleError`` if the arguments do not match.
"""
args = self.args if args is None else args
kwargs = self.kwargs if kwargs is None else kwargs
try:
verify_arguments(self._target, self._method_name, args, kwargs)
except VerifyingBuiltinDoubleArgumentError:
if doubles.lifecycle.ignore_builtin_verification():
raise
@verify_count_is_non_negative
def exactly(self, n):
"""Set an exact call count allowance
:param integer n:
"""
self._call_counter.set_exact(n)
return self
@verify_count_is_non_negative
def at_least(self, n):
"""Set a minimum call count allowance
:param integer n:
"""
self._call_counter.set_minimum(n)
return self
@verify_count_is_non_negative
def at_most(self, n):
"""Set a maximum call count allowance
:param integer n:
"""
self._call_counter.set_maximum(n)
return self
def never(self):
"""Set an expected call count allowance of 0"""
self.exactly(0)
return self
def once(self):
"""Set an expected call count allowance of 1"""
self.exactly(1)
return self
def twice(self):
"""Set an expected call count allowance of 2"""
self.exactly(2)
return self
@property
time = times
def _called(self):
"""Indicate that the allowance was called
:raise MockExpectationError if the allowance has been called too many times
"""
if self._call_counter.called().has_too_many_calls():
self.raise_failure_exception()
def raise_failure_exception(self, expect_or_allow='Allowed'):
"""Raises a ``MockExpectationError`` with a useful message.
:raise: ``MockExpectationError``
"""
raise MockExpectationError(
"{} '{}' to be called {}on {!r} with {}, but was not. ({}:{})".format(
expect_or_allow,
self._method_name,
self._call_counter.error_string(),
self._target.obj,
self._expected_argument_string(),
self._caller.filename,
self._caller.lineno,
)
)
def _expected_argument_string(self):
"""Generates a string describing what arguments the double expected.
:return: A string describing expected arguments.
:rtype: str
"""
if self.args is _any and self.kwargs is _any:
return 'any args'
elif self._custom_matcher:
return "custom matcher: '{}'".format(self._custom_matcher.__name__)
else:
return build_argument_repr_string(self.args, self.kwargs)
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13557,
7783,
62,
8367,
796,
37456,
1635,
22046,
11,
12429,
46265,
22046,
25,
6045,
628,
220,
220,
220,
825,
290,
62,
40225,
7,
944,
11,
6631,
11,
1635,
22046,
11,
12429,
46265,
22046,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
24334,
2664,
262,
4274,
284,
5298,
262,
2810,
6631,
618,
1444,
13,
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220,
220,
220,
220,
220,
220,
1002,
2810,
11,
3224,
7159,
357,
1930,
1859,
290,
21179,
8,
3804,
284,
198,
220,
220,
220,
220,
220,
220,
220,
4600,
392,
62,
40225,
63,
389,
973,
287,
262,
6631,
9113,
3920,
13,
628,
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220,
220,
220,
220,
220,
220,
1058,
17143,
35528,
6631,
25,
383,
6631,
284,
5298,
13,
198,
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220,
220,
220,
220,
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37227,
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220,
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2116,
13557,
7783,
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15741,
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4516,
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1441,
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37227,
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4600,
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63,
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262,
15229,
2446,
5860,
257,
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1058,
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35528,
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63,
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2446,
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257,
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25,
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220,
2003,
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3419,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2003,
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2617,
62,
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7,
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8,
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220,
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220,
220,
220,
220,
220,
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69,
315,
942,
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220,
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37227,
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257,
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281,
24930,
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220,
220,
220,
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2810,
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13,
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220,
220,
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3815,
389,
2810,
11,
484,
389,
4504,
530,
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257,
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355,
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318,
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1002,
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1444,
517,
1661,
621,
612,
389,
1441,
3815,
11,
340,
815,
2555,
284,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
262,
938,
1988,
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262,
1351,
13,
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1058,
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25,
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220,
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220,
220,
220,
220,
220,
220,
5298,
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12331,
10786,
392,
62,
7783,
3419,
2938,
379,
1551,
352,
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1988,
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62,
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2116,
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62,
7783,
62,
20274,
62,
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220,
220,
220,
220,
220,
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2810,
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26498,
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220,
220,
220,
220,
220,
37227,
7469,
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257,
2183,
2163,
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1058,
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25439,
12336,
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2163,
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] | 2.481454 | 4,071 |
import tensorflow as tf
from network.ConvBlock import conv2d_bn
| [
11748,
11192,
273,
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355,
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198,
6738,
3127,
13,
3103,
85,
12235,
1330,
3063,
17,
67,
62,
9374,
628,
198
] | 3.142857 | 21 |
# myapp/models.py
from myapp import db
# define model here
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198
] | 3 | 20 |
#!/usr/bin/env python
import argparse
import logging
import os
import sys
from distutils.spawn import find_executable
from subprocess import call
if __name__ == '__main__':
logging.basicConfig(format='%(message)s', level=logging.INFO, stream=sys.stdout)
sys.exit(run_tests(parse_args()))
| [
2,
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37023,
7,
5143,
62,
41989,
7,
29572,
62,
22046,
3419,
4008,
198
] | 2.932692 | 104 |
# encoding: UTF-8
from vnpy.trader import vtConstant
from okcoinGateway import OkcoinGateway
gatewayClass = OkcoinGateway
gatewayName = 'OKEX'
gatewayDisplayName = u'OkEx'
gatewayType = vtConstant.GATEWAYTYPE_BTC
gatewayQryEnabled = True
| [
2,
21004,
25,
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13,
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25216,
62,
35964,
198,
10494,
1014,
48,
563,
20491,
796,
6407,
628
] | 2.802326 | 86 |
# -*- coding: utf-8 -*-
import collections
| [
2,
532,
9,
12,
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25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
11748,
17268,
628,
628,
198
] | 2.4 | 20 |
from lolviz import *
import time
import os
import psutil
def search(root:TrieNode, s:str, i=0) -> bool:
"Return true if s is prefix of word in Trie or full word in Trie"
p = root
while p is not None:
if i>=len(s): return True
e = ord(s[i]) - ord('a')
if p.edges[e] is None: return False
p = p.edges[e]
i += 1
return True
if __name__ == '__main__':
words = load()
#words = words[:12000] # reduce size of word list during development
print(f"{len(words)} words in dictionary")
process = psutil.Process(os.getpid())
print(f"{process.memory_info().rss/1024**2:,.3f} MB in use before creating TRIE")
root = create_trie(words)
process = psutil.Process(os.getpid())
print(f"{process.memory_info().rss/1024**2:,.3f} MB in use after creating TRIE")
trie_search(words)
#objviz(root).view() | [
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7,
69,
1,
90,
14681,
13,
31673,
62,
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22446,
42216,
14,
35500,
1174,
17,
25,
38508,
18,
69,
92,
10771,
287,
779,
706,
4441,
37679,
36,
4943,
628,
220,
220,
220,
1333,
68,
62,
12947,
7,
10879,
8,
628,
220,
220,
220,
1303,
26801,
85,
528,
7,
15763,
737,
1177,
3419
] | 2.415761 | 368 |
# Copyright 2019 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.
from collections import defaultdict
def _validate_runtime_features_graph(features):
"""
Raises AssertionError when sanity check failed.
@param features: a List[Dict]. See origin_trials().
@returns None
"""
feature_pool = {str(f['name']) for f in features}
origin_trial_pool = {
str(f['name'])
for f in features if f['origin_trial_feature_name']
}
for f in features:
assert not f['implied_by'] or not f['depends_on'], _error_message(
'Only one of implied_by and depends_on is allowed', f['name'])
for d in f['depends_on']:
assert d in feature_pool, _error_message(
'Depends on non-existent-feature', f['name'], d)
for i in f['implied_by']:
assert i in feature_pool, _error_message(
'Implied by non-existent-feature', f['name'], i)
assert f['origin_trial_feature_name'] or i not in origin_trial_pool, \
_error_message(
'A feature must be in origin trial if implied by an origin trial feature',
f['name'], i)
graph = {
str(feature['name']): feature['depends_on'] + feature['implied_by']
for feature in features
}
path = set()
for f in features:
assert not has_cycle(str(f['name'])), _error_message(
'Cycle found in depends_on/implied_by graph', f['name'])
def origin_trials(features):
"""
This function returns all features that are in origin trial.
The dependency is considered in origin trial if itself is in origin trial
or any of its dependencies are in origin trial. Propagate dependency
tag use DFS can find all features that are in origin trial.
@param features: a List[Dict]. Each Dict must have keys 'name',
'depends_on', 'implied_by' and 'origin_trial_feature_name'
(see runtime_enabled_features.json5).
@returns Set[str(runtime feature name)]
"""
_validate_runtime_features_graph(features)
origin_trials_set = set()
graph = defaultdict(list)
for feature in features:
for dependency in feature['depends_on']:
graph[dependency].append(str(feature['name']))
for feature in features:
if feature['origin_trial_feature_name']:
dfs(str(feature['name']))
return origin_trials_set
| [
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13,
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220,
220,
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3033,
25,
257,
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58,
35,
713,
4083,
5501,
360,
713,
1276,
423,
8251,
705,
3672,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
10378,
2412,
62,
261,
3256,
705,
23928,
798,
62,
1525,
6,
290,
705,
47103,
62,
45994,
62,
30053,
62,
3672,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
3826,
19124,
62,
25616,
62,
40890,
13,
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20,
737,
198,
220,
220,
220,
2488,
7783,
82,
5345,
58,
2536,
7,
43282,
3895,
1438,
15437,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
4808,
12102,
378,
62,
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62,
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62,
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7,
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8,
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220,
220,
220,
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62,
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874,
62,
2617,
796,
900,
3419,
628,
220,
220,
220,
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11600,
7,
4868,
8,
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220,
220,
220,
329,
3895,
287,
3033,
25,
198,
220,
220,
220,
220,
220,
220,
220,
329,
20203,
287,
3895,
17816,
10378,
2412,
62,
261,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4823,
58,
45841,
1387,
4083,
33295,
7,
2536,
7,
30053,
17816,
3672,
20520,
4008,
628,
220,
220,
220,
329,
3895,
287,
3033,
25,
198,
220,
220,
220,
220,
220,
220,
220,
611,
3895,
17816,
47103,
62,
45994,
62,
30053,
62,
3672,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
288,
9501,
7,
2536,
7,
30053,
17816,
3672,
20520,
4008,
628,
220,
220,
220,
1441,
8159,
62,
28461,
874,
62,
2617,
198
] | 2.537 | 1,000 |
Birds_year0 = 100
Birth_rate = 0.5
Death_rate = 0.2
# We repeat the "for" loop with an additional step: every time bird population reaches 1000, an epidemic kills half of them
Time=[0]
Birds=[100]
for year in range (1,51) :
Birds = Birds + [Birds[-1] + Birds[-1] * Birth_rate - Birds[-1] * Death_rate ]
Time = Time + [year]
if Birds[-1]>1000:
Birds[-1]=Birds[-1]/2
print(Birds[-1])
import matplotlib.pyplot as plt
plt.plot(Time,Birds)
plt.xlabel("Time (years)")
plt.ylabel("# of birds")
plt.title('Birds population growth')
plt.show()
| [
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489,
83,
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12860,
3419,
201,
198,
201,
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] | 2.292969 | 256 |
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 6 00:06:55 2019
@author: -
"""
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import os
from datetime import datetime
from gensim import corpora, models, similarities
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
os.getcwd()
os.chdir('C:\\Users\\abhishekpandey\\Desktop')
articles = pd.read_excel('speech_input.xlsx', sheet_name = 'Sheet1')
articles.head()
#Concatenating the articles titles and bodies
english_articles_content = (articles['Text']).tolist()
english_stopset = set(stopwords.words('english')).union(
{"things", "that's", "something", "take", "don't", "may", "want", "you're",
"set", "might", "says", "including", "lot", "much", "said", "know",
"good", "step", "often", "going", "thing", "things", "think",
"back", "actually", "better", "look", "find", "right", "example",
"verb", "verbs"})
#Tokenizing words of articles
tokenizer = RegexpTokenizer(r"(?u)[\b\#a-zA-Z][\w&-_]+\b")
english_articles_tokens = list(map(lambda d: [token for token in tokenizer.tokenize(d.lower()) if token not in english_stopset], english_articles_content))
bigram_transformer = models.Phrases(english_articles_tokens)
english_articles_unigrams_bigrams_tokens = list(bigram_transformer[english_articles_tokens])
#Creating a dictionary and filtering out too rare and too common tokens
english_dictionary = corpora.Dictionary(english_articles_unigrams_bigrams_tokens)
english_dictionary.compactify()
print(english_dictionary)
#Processing Bag-of-Words (BoW) for each article
english_articles_bow = [english_dictionary.doc2bow(doc) for doc in english_articles_unigrams_bigrams_tokens]
#Training the LDA topic model on English articles
lda_model = models.LdaModel(english_articles_bow, id2word=english_dictionary, num_topics=30, passes=10, iterations=500)
#Processing the topics for each article
english_articles_lda = lda_model[english_articles_bow]
#Computing the main topic of each article
topics_top_words = get_topics_top_words(lda_model, 5)
#Return the discovered topics, sorted by popularity
corpus_main_topics = get_main_topics(english_articles_lda, topics_top_words)
main_topics_df = pd.DataFrame(corpus_main_topics, columns=['topic']).groupby('topic').size().sort_values(ascending=True).reset_index()
main_topics_df.columns = ['topic','count']
main_topics_df.sort_values('count', ascending=False)
main_topics_df.plot(kind='barh', x='topic', y='count', figsize=(7,20), title='Main topics on shared English articles')
articles_full = articles
articles_full['tagged_keywords'] = corpus_main_topics
articles_full.drop('tagged_keywords', axis=1, inplace =True)
| [
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] | 2.651445 | 1,073 |
from getpass import getpass
from django.contrib.auth import get_user_model
from django.core.management.base import BaseCommand
from django.db.utils import IntegrityError
UserModel = get_user_model()
USERNAME_DEFAULT = "player"
class Command(BaseCommand):
"""Create the player special user."""
help = "Create player account."
def add_arguments(self, parser):
"""Add arguments for the command.
Args:
parser (argparse.ArgumentParser): Parser.
"""
parser.add_argument(
"--username",
help="Specifies the loging for the player. Default to '{}'.".format(
USERNAME_DEFAULT
),
)
parser.add_argument("--password", help="Specifies the password for the player.")
parser.add_argument(
"--noinput",
help="Tells Django to NOT prompt the user for input of any kind. "
"Use command line arguments only.",
action="store_true",
)
@staticmethod
def get_username():
"""Get username from user.
Returns:
str: Username.
"""
username = input("Username (default: '{}'): ".format(USERNAME_DEFAULT))
return username or USERNAME_DEFAULT
def get_password(self):
"""Get password from user.
Returns:
str: Password.
"""
while True:
password = getpass()
password_confirm = getpass("Password (again): ")
if not password == password_confirm:
self.stderr.write("Error: Your passwords didn't match.")
continue
return password
def create_player(self, username, password):
"""Create player from provided credentials.
Args:
username (str): Username for the player.
password (str): Password for the player.
"""
# check password
if not password:
self.stderr.write("Error: Blank passwords aren't allowed.")
return
try:
UserModel.objects.create_user(
username,
password=password,
email="{}@player".format(username),
validated_by_email=True,
validated_by_manager=True,
playlist_permission_level=UserModel.PLAYER,
)
except (IntegrityError, ValueError) as e:
self.stderr.write("Error: {}".format(e))
return
self.stdout.write("Player created successfully.")
def handle(self, *args, **options):
"""Handle the command."""
# in non interactive mode
if options["noinput"]:
self.create_player(
(options["username"] or USERNAME_DEFAULT), options["password"]
)
return
# interactive mode
# username
if options["username"]:
username = options["username"]
else:
username = self.get_username()
# password
if options["password"]:
password = options["password"]
else:
password = self.get_password()
self.create_player(username, password)
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# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: ga4gh/schemas/ga4gh/genotype_phenotype_service.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from ga4gh.schemas.ga4gh import common_pb2 as ga4gh_dot_schemas_dot_ga4gh_dot_common__pb2
from ga4gh.schemas.ga4gh import genotype_phenotype_pb2 as ga4gh_dot_schemas_dot_ga4gh_dot_genotype__phenotype__pb2
from ga4gh.schemas.google.api import annotations_pb2 as ga4gh_dot_schemas_dot_google_dot_api_dot_annotations__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='ga4gh/schemas/ga4gh/genotype_phenotype_service.proto',
package='ga4gh.schemas.ga4gh',
syntax='proto3',
serialized_pb=_b('\n4ga4gh/schemas/ga4gh/genotype_phenotype_service.proto\x12\x13ga4gh.schemas.ga4gh\x1a ga4gh/schemas/ga4gh/common.proto\x1a,ga4gh/schemas/ga4gh/genotype_phenotype.proto\x1a*ga4gh/schemas/google/api/annotations.proto\"b\n%SearchPhenotypeAssociationSetsRequest\x12\x12\n\ndataset_id\x18\x01 \x01(\t\x12\x11\n\tpage_size\x18\x02 \x01(\x03\x12\x12\n\npage_token\x18\x03 \x01(\t\"\x93\x01\n&SearchPhenotypeAssociationSetsResponse\x12P\n\x1aphenotype_association_sets\x18\x01 \x03(\x0b\x32,.ga4gh.schemas.ga4gh.PhenotypeAssociationSet\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t\"E\n\x11OntologyTermQuery\x12\x30\n\x05terms\x18\x01 \x03(\x0b\x32!.ga4gh.schemas.ga4gh.OntologyTerm\"O\n\x17\x45xternalIdentifierQuery\x12\x34\n\x03ids\x18\x01 \x03(\x0b\x32\'.ga4gh.schemas.ga4gh.ExternalIdentifier\"\xa4\x01\n\rEvidenceQuery\x12\x37\n\x0c\x65videnceType\x18\x01 \x01(\x0b\x32!.ga4gh.schemas.ga4gh.OntologyTerm\x12\x13\n\x0b\x64\x65scription\x18\x02 \x01(\t\x12\x45\n\x14\x65xternal_identifiers\x18\x03 \x03(\x0b\x32\'.ga4gh.schemas.ga4gh.ExternalIdentifier\"\xa8\x02\n\x17SearchPhenotypesRequest\x12$\n\x1cphenotype_association_set_id\x18\x01 \x01(\t\x12\n\n\x02id\x18\x02 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x03 \x01(\t\x12/\n\x04type\x18\x04 \x01(\x0b\x32!.ga4gh.schemas.ga4gh.OntologyTerm\x12\x35\n\nqualifiers\x18\x05 \x03(\x0b\x32!.ga4gh.schemas.ga4gh.OntologyTerm\x12\x37\n\x0c\x61ge_of_onset\x18\x06 \x01(\x0b\x32!.ga4gh.schemas.ga4gh.OntologyTerm\x12\x11\n\tpage_size\x18\x07 \x01(\x03\x12\x12\n\npage_token\x18\x08 \x01(\t\"o\n\x18SearchPhenotypesResponse\x12:\n\nphenotypes\x18\x01 \x03(\x0b\x32&.ga4gh.schemas.ga4gh.PhenotypeInstance\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t\"\xcf\x01\n\x1eSearchGenotypePhenotypeRequest\x12$\n\x1cphenotype_association_set_id\x18\x01 \x01(\t\x12\x13\n\x0b\x66\x65\x61ture_ids\x18\x02 \x03(\t\x12\x15\n\rphenotype_ids\x18\x03 \x03(\t\x12\x34\n\x08\x65vidence\x18\x04 \x03(\x0b\x32\".ga4gh.schemas.ga4gh.EvidenceQuery\x12\x11\n\tpage_size\x18\x05 \x01(\x03\x12\x12\n\npage_token\x18\x06 \x01(\t\"\x82\x01\n\x1fSearchGenotypePhenotypeResponse\x12\x46\n\x0c\x61ssociations\x18\x01 \x03(\x0b\x32\x30.ga4gh.schemas.ga4gh.FeaturePhenotypeAssociation\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t2\xcd\x04\n\x18GenotypePhenotypeService\x12\xd0\x01\n\x1eSearchPhenotypeAssociationSets\x12:.ga4gh.schemas.ga4gh.SearchPhenotypeAssociationSetsRequest\x1a;.ga4gh.schemas.ga4gh.SearchPhenotypeAssociationSetsResponse\"5\x82\xd3\xe4\x93\x02/\"*/v0.6.0a10/phenotypeassociationsets/search:\x01*\x12\x97\x01\n\x0fSearchPhenotype\x12,.ga4gh.schemas.ga4gh.SearchPhenotypesRequest\x1a-.ga4gh.schemas.ga4gh.SearchPhenotypesResponse\"\'\x82\xd3\xe4\x93\x02!\"\x1c/v0.6.0a10/phenotypes/search:\x01*\x12\xc3\x01\n\x1bSearchPhenotypeAssociations\x12\x33.ga4gh.schemas.ga4gh.SearchGenotypePhenotypeRequest\x1a\x34.ga4gh.schemas.ga4gh.SearchGenotypePhenotypeResponse\"9\x82\xd3\xe4\x93\x02\x33\"./v0.6.0a10/featurephenotypeassociations/search:\x01*b\x06proto3')
,
dependencies=[ga4gh_dot_schemas_dot_ga4gh_dot_common__pb2.DESCRIPTOR,ga4gh_dot_schemas_dot_ga4gh_dot_genotype__phenotype__pb2.DESCRIPTOR,ga4gh_dot_schemas_dot_google_dot_api_dot_annotations__pb2.DESCRIPTOR,])
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
_SEARCHPHENOTYPEASSOCIATIONSETSREQUEST = _descriptor.Descriptor(
name='SearchPhenotypeAssociationSetsRequest',
full_name='ga4gh.schemas.ga4gh.SearchPhenotypeAssociationSetsRequest',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='dataset_id', full_name='ga4gh.schemas.ga4gh.SearchPhenotypeAssociationSetsRequest.dataset_id', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='page_size', full_name='ga4gh.schemas.ga4gh.SearchPhenotypeAssociationSetsRequest.page_size', index=1,
number=2, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='page_token', full_name='ga4gh.schemas.ga4gh.SearchPhenotypeAssociationSetsRequest.page_token', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=201,
serialized_end=299,
)
_SEARCHPHENOTYPEASSOCIATIONSETSRESPONSE = _descriptor.Descriptor(
name='SearchPhenotypeAssociationSetsResponse',
full_name='ga4gh.schemas.ga4gh.SearchPhenotypeAssociationSetsResponse',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='phenotype_association_sets', full_name='ga4gh.schemas.ga4gh.SearchPhenotypeAssociationSetsResponse.phenotype_association_sets', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='next_page_token', full_name='ga4gh.schemas.ga4gh.SearchPhenotypeAssociationSetsResponse.next_page_token', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=302,
serialized_end=449,
)
_ONTOLOGYTERMQUERY = _descriptor.Descriptor(
name='OntologyTermQuery',
full_name='ga4gh.schemas.ga4gh.OntologyTermQuery',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='terms', full_name='ga4gh.schemas.ga4gh.OntologyTermQuery.terms', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=451,
serialized_end=520,
)
_EXTERNALIDENTIFIERQUERY = _descriptor.Descriptor(
name='ExternalIdentifierQuery',
full_name='ga4gh.schemas.ga4gh.ExternalIdentifierQuery',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='ids', full_name='ga4gh.schemas.ga4gh.ExternalIdentifierQuery.ids', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=522,
serialized_end=601,
)
_EVIDENCEQUERY = _descriptor.Descriptor(
name='EvidenceQuery',
full_name='ga4gh.schemas.ga4gh.EvidenceQuery',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='evidenceType', full_name='ga4gh.schemas.ga4gh.EvidenceQuery.evidenceType', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='description', full_name='ga4gh.schemas.ga4gh.EvidenceQuery.description', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='external_identifiers', full_name='ga4gh.schemas.ga4gh.EvidenceQuery.external_identifiers', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=604,
serialized_end=768,
)
_SEARCHPHENOTYPESREQUEST = _descriptor.Descriptor(
name='SearchPhenotypesRequest',
full_name='ga4gh.schemas.ga4gh.SearchPhenotypesRequest',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='phenotype_association_set_id', full_name='ga4gh.schemas.ga4gh.SearchPhenotypesRequest.phenotype_association_set_id', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='id', full_name='ga4gh.schemas.ga4gh.SearchPhenotypesRequest.id', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='description', full_name='ga4gh.schemas.ga4gh.SearchPhenotypesRequest.description', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='type', full_name='ga4gh.schemas.ga4gh.SearchPhenotypesRequest.type', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='qualifiers', full_name='ga4gh.schemas.ga4gh.SearchPhenotypesRequest.qualifiers', index=4,
number=5, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='age_of_onset', full_name='ga4gh.schemas.ga4gh.SearchPhenotypesRequest.age_of_onset', index=5,
number=6, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='page_size', full_name='ga4gh.schemas.ga4gh.SearchPhenotypesRequest.page_size', index=6,
number=7, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='page_token', full_name='ga4gh.schemas.ga4gh.SearchPhenotypesRequest.page_token', index=7,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=771,
serialized_end=1067,
)
_SEARCHPHENOTYPESRESPONSE = _descriptor.Descriptor(
name='SearchPhenotypesResponse',
full_name='ga4gh.schemas.ga4gh.SearchPhenotypesResponse',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='phenotypes', full_name='ga4gh.schemas.ga4gh.SearchPhenotypesResponse.phenotypes', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='next_page_token', full_name='ga4gh.schemas.ga4gh.SearchPhenotypesResponse.next_page_token', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1069,
serialized_end=1180,
)
_SEARCHGENOTYPEPHENOTYPEREQUEST = _descriptor.Descriptor(
name='SearchGenotypePhenotypeRequest',
full_name='ga4gh.schemas.ga4gh.SearchGenotypePhenotypeRequest',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='phenotype_association_set_id', full_name='ga4gh.schemas.ga4gh.SearchGenotypePhenotypeRequest.phenotype_association_set_id', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='feature_ids', full_name='ga4gh.schemas.ga4gh.SearchGenotypePhenotypeRequest.feature_ids', index=1,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='phenotype_ids', full_name='ga4gh.schemas.ga4gh.SearchGenotypePhenotypeRequest.phenotype_ids', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='evidence', full_name='ga4gh.schemas.ga4gh.SearchGenotypePhenotypeRequest.evidence', index=3,
number=4, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='page_size', full_name='ga4gh.schemas.ga4gh.SearchGenotypePhenotypeRequest.page_size', index=4,
number=5, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='page_token', full_name='ga4gh.schemas.ga4gh.SearchGenotypePhenotypeRequest.page_token', index=5,
number=6, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1183,
serialized_end=1390,
)
_SEARCHGENOTYPEPHENOTYPERESPONSE = _descriptor.Descriptor(
name='SearchGenotypePhenotypeResponse',
full_name='ga4gh.schemas.ga4gh.SearchGenotypePhenotypeResponse',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='associations', full_name='ga4gh.schemas.ga4gh.SearchGenotypePhenotypeResponse.associations', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='next_page_token', full_name='ga4gh.schemas.ga4gh.SearchGenotypePhenotypeResponse.next_page_token', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1393,
serialized_end=1523,
)
_SEARCHPHENOTYPEASSOCIATIONSETSRESPONSE.fields_by_name['phenotype_association_sets'].message_type = ga4gh_dot_schemas_dot_ga4gh_dot_genotype__phenotype__pb2._PHENOTYPEASSOCIATIONSET
_ONTOLOGYTERMQUERY.fields_by_name['terms'].message_type = ga4gh_dot_schemas_dot_ga4gh_dot_common__pb2._ONTOLOGYTERM
_EXTERNALIDENTIFIERQUERY.fields_by_name['ids'].message_type = ga4gh_dot_schemas_dot_ga4gh_dot_common__pb2._EXTERNALIDENTIFIER
_EVIDENCEQUERY.fields_by_name['evidenceType'].message_type = ga4gh_dot_schemas_dot_ga4gh_dot_common__pb2._ONTOLOGYTERM
_EVIDENCEQUERY.fields_by_name['external_identifiers'].message_type = ga4gh_dot_schemas_dot_ga4gh_dot_common__pb2._EXTERNALIDENTIFIER
_SEARCHPHENOTYPESREQUEST.fields_by_name['type'].message_type = ga4gh_dot_schemas_dot_ga4gh_dot_common__pb2._ONTOLOGYTERM
_SEARCHPHENOTYPESREQUEST.fields_by_name['qualifiers'].message_type = ga4gh_dot_schemas_dot_ga4gh_dot_common__pb2._ONTOLOGYTERM
_SEARCHPHENOTYPESREQUEST.fields_by_name['age_of_onset'].message_type = ga4gh_dot_schemas_dot_ga4gh_dot_common__pb2._ONTOLOGYTERM
_SEARCHPHENOTYPESRESPONSE.fields_by_name['phenotypes'].message_type = ga4gh_dot_schemas_dot_ga4gh_dot_genotype__phenotype__pb2._PHENOTYPEINSTANCE
_SEARCHGENOTYPEPHENOTYPEREQUEST.fields_by_name['evidence'].message_type = _EVIDENCEQUERY
_SEARCHGENOTYPEPHENOTYPERESPONSE.fields_by_name['associations'].message_type = ga4gh_dot_schemas_dot_ga4gh_dot_genotype__phenotype__pb2._FEATUREPHENOTYPEASSOCIATION
DESCRIPTOR.message_types_by_name['SearchPhenotypeAssociationSetsRequest'] = _SEARCHPHENOTYPEASSOCIATIONSETSREQUEST
DESCRIPTOR.message_types_by_name['SearchPhenotypeAssociationSetsResponse'] = _SEARCHPHENOTYPEASSOCIATIONSETSRESPONSE
DESCRIPTOR.message_types_by_name['OntologyTermQuery'] = _ONTOLOGYTERMQUERY
DESCRIPTOR.message_types_by_name['ExternalIdentifierQuery'] = _EXTERNALIDENTIFIERQUERY
DESCRIPTOR.message_types_by_name['EvidenceQuery'] = _EVIDENCEQUERY
DESCRIPTOR.message_types_by_name['SearchPhenotypesRequest'] = _SEARCHPHENOTYPESREQUEST
DESCRIPTOR.message_types_by_name['SearchPhenotypesResponse'] = _SEARCHPHENOTYPESRESPONSE
DESCRIPTOR.message_types_by_name['SearchGenotypePhenotypeRequest'] = _SEARCHGENOTYPEPHENOTYPEREQUEST
DESCRIPTOR.message_types_by_name['SearchGenotypePhenotypeResponse'] = _SEARCHGENOTYPEPHENOTYPERESPONSE
SearchPhenotypeAssociationSetsRequest = _reflection.GeneratedProtocolMessageType('SearchPhenotypeAssociationSetsRequest', (_message.Message,), dict(
DESCRIPTOR = _SEARCHPHENOTYPEASSOCIATIONSETSREQUEST,
__module__ = 'ga4gh.schemas.ga4gh.genotype_phenotype_service_pb2'
# @@protoc_insertion_point(class_scope:ga4gh.schemas.ga4gh.SearchPhenotypeAssociationSetsRequest)
))
_sym_db.RegisterMessage(SearchPhenotypeAssociationSetsRequest)
SearchPhenotypeAssociationSetsResponse = _reflection.GeneratedProtocolMessageType('SearchPhenotypeAssociationSetsResponse', (_message.Message,), dict(
DESCRIPTOR = _SEARCHPHENOTYPEASSOCIATIONSETSRESPONSE,
__module__ = 'ga4gh.schemas.ga4gh.genotype_phenotype_service_pb2'
# @@protoc_insertion_point(class_scope:ga4gh.schemas.ga4gh.SearchPhenotypeAssociationSetsResponse)
))
_sym_db.RegisterMessage(SearchPhenotypeAssociationSetsResponse)
OntologyTermQuery = _reflection.GeneratedProtocolMessageType('OntologyTermQuery', (_message.Message,), dict(
DESCRIPTOR = _ONTOLOGYTERMQUERY,
__module__ = 'ga4gh.schemas.ga4gh.genotype_phenotype_service_pb2'
# @@protoc_insertion_point(class_scope:ga4gh.schemas.ga4gh.OntologyTermQuery)
))
_sym_db.RegisterMessage(OntologyTermQuery)
ExternalIdentifierQuery = _reflection.GeneratedProtocolMessageType('ExternalIdentifierQuery', (_message.Message,), dict(
DESCRIPTOR = _EXTERNALIDENTIFIERQUERY,
__module__ = 'ga4gh.schemas.ga4gh.genotype_phenotype_service_pb2'
# @@protoc_insertion_point(class_scope:ga4gh.schemas.ga4gh.ExternalIdentifierQuery)
))
_sym_db.RegisterMessage(ExternalIdentifierQuery)
EvidenceQuery = _reflection.GeneratedProtocolMessageType('EvidenceQuery', (_message.Message,), dict(
DESCRIPTOR = _EVIDENCEQUERY,
__module__ = 'ga4gh.schemas.ga4gh.genotype_phenotype_service_pb2'
# @@protoc_insertion_point(class_scope:ga4gh.schemas.ga4gh.EvidenceQuery)
))
_sym_db.RegisterMessage(EvidenceQuery)
SearchPhenotypesRequest = _reflection.GeneratedProtocolMessageType('SearchPhenotypesRequest', (_message.Message,), dict(
DESCRIPTOR = _SEARCHPHENOTYPESREQUEST,
__module__ = 'ga4gh.schemas.ga4gh.genotype_phenotype_service_pb2'
# @@protoc_insertion_point(class_scope:ga4gh.schemas.ga4gh.SearchPhenotypesRequest)
))
_sym_db.RegisterMessage(SearchPhenotypesRequest)
SearchPhenotypesResponse = _reflection.GeneratedProtocolMessageType('SearchPhenotypesResponse', (_message.Message,), dict(
DESCRIPTOR = _SEARCHPHENOTYPESRESPONSE,
__module__ = 'ga4gh.schemas.ga4gh.genotype_phenotype_service_pb2'
# @@protoc_insertion_point(class_scope:ga4gh.schemas.ga4gh.SearchPhenotypesResponse)
))
_sym_db.RegisterMessage(SearchPhenotypesResponse)
SearchGenotypePhenotypeRequest = _reflection.GeneratedProtocolMessageType('SearchGenotypePhenotypeRequest', (_message.Message,), dict(
DESCRIPTOR = _SEARCHGENOTYPEPHENOTYPEREQUEST,
__module__ = 'ga4gh.schemas.ga4gh.genotype_phenotype_service_pb2'
# @@protoc_insertion_point(class_scope:ga4gh.schemas.ga4gh.SearchGenotypePhenotypeRequest)
))
_sym_db.RegisterMessage(SearchGenotypePhenotypeRequest)
SearchGenotypePhenotypeResponse = _reflection.GeneratedProtocolMessageType('SearchGenotypePhenotypeResponse', (_message.Message,), dict(
DESCRIPTOR = _SEARCHGENOTYPEPHENOTYPERESPONSE,
__module__ = 'ga4gh.schemas.ga4gh.genotype_phenotype_service_pb2'
# @@protoc_insertion_point(class_scope:ga4gh.schemas.ga4gh.SearchGenotypePhenotypeResponse)
))
_sym_db.RegisterMessage(SearchGenotypePhenotypeResponse)
# @@protoc_insertion_point(module_scope)
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] | 2.396204 | 10,275 |
import os, logging
from typing import Any
from dotenv import load_dotenv
from supabase import create_client, Client
from headlinenews import RSSParser
from postgrest_py import exceptions
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import asyncio
import functools
def futurized(o):
''' Makes the given object to be awaitable.
:param any o: Object to wrap
:return: awaitable that resolves to provided object
:rtype: asyncio.Future
Anything passed to :code:`futurized` is wrapped in :code:`asyncio.Future`.
This makes it awaitable (can be run with :code:`await` or :code:`yield from`) as
a result of await it returns the original object.
If provided object is a Exception (or its sublcass) then the `Future` will raise it on await.
.. code-block:: python
fut = aiounittest.futurized('SOME TEXT')
ret = await fut
print(ret) # prints SOME TEXT
fut = aiounittest.futurized(Exception('Dummy error'))
ret = await fut # will raise the exception "dummy error"
The main goal is to use it with :code:`unittest.mock.Mock` (or :code:`MagicMock`) to
be able to mock awaitable functions (coroutines).
Consider the below code
.. code-block:: python
from asyncio import sleep
async def add(x, y):
await sleep(666)
return x + y
You rather don't want to wait 666 seconds, you've gotta mock that.
.. code-block:: python
from aiounittest import futurized, AsyncTestCase
from unittest.mock import Mock, patch
import dummy_math
class MyAddTest(AsyncTestCase):
async def test_add(self):
mock_sleep = Mock(return_value=futurized('whatever'))
patch('dummy_math.sleep', mock_sleep).start()
ret = await dummy_math.add(5, 6)
self.assertEqual(ret, 11)
mock_sleep.assert_called_once_with(666)
async def test_fail(self):
mock_sleep = Mock(return_value=futurized(Exception('whatever')))
patch('dummy_math.sleep', mock_sleep).start()
with self.assertRaises(Exception) as e:
await dummy_math.add(5, 6)
mock_sleep.assert_called_once_with(666)
'''
f = asyncio.Future()
if isinstance(o, Exception):
f.set_exception(o)
else:
f.set_result(o)
return f
def run_sync(func=None, loop=None):
''' Runs synchonously given function (coroutine)
:param callable func: function to run (mostly coroutine)
:param ioloop loop: event loop to use to run `func`
:type loop: event loop of None
By default the brand new event loop will be created (old closed). After completion, the loop will be closed and then recreated, set as default,
leaving asyncio clean.
**Note**: :code:`aiounittest.async_test` is an alias of :code:`aiounittest.helpers.run_sync`
Function can be used like a `pytest.mark.asyncio` (implemetation differs),
but it's compatible with :code:`unittest.TestCase` class.
.. code-block:: python
import asyncio
import unittest
from aiounittest import async_test
async def add(x, y):
await asyncio.sleep(0.1)
return x + y
class MyAsyncTestDecorator(unittest.TestCase):
@async_test
async def test_async_add(self):
ret = await add(5, 6)
self.assertEqual(ret, 11)
.. note::
If the loop is provided, it won't be closed. It's up to you.
This function is also used internally by :code:`aiounittest.AsyncTestCase` to run coroutines.
'''
if func is None:
return decorator
else:
return decorator(func)
async_test = run_sync
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] | 2.289067 | 1,619 |
"""
check engine light
"""
import tkinter as tk
from tkinter import ttk
from typing import TYPE_CHECKING, Dict, Optional
from core.gui.dialogs.dialog import Dialog
from core.gui.themes import PADX, PADY
from core.gui.widgets import CodeText
from core.gui.wrappers import ExceptionEvent, ExceptionLevel
if TYPE_CHECKING:
from core.gui.app import Application
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""" Game fix for STAR WARS Galactic Battlegrounds Saga
"""
#pylint: disable=C0103
from protonfixes import util
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a=1
b=2
c=3
d=4
你是不是个沙雕
f=6
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import os
import uuid
from viadot.sources import AzureDataLake
from viadot.tasks import (
AzureDataLakeDownload,
AzureDataLakeToDF,
AzureDataLakeUpload,
AzureDataLakeCopy,
AzureDataLakeList,
)
uuid_4 = uuid.uuid4()
uuid_4_2 = uuid.uuid4()
file_name = f"test_file_{uuid_4}.csv"
file_name_2 = f"test_file_{uuid_4}.csv"
adls_path = f"raw/supermetrics/{file_name}"
adls_path_2 = f"raw/supermetrics/{file_name_2}"
file_name_parquet = f"test_file_{uuid_4}.parquet"
adls_path_parquet = f"raw/supermetrics/{file_name_parquet}"
# TODO: add pytest-depends as download tests depend on the upload
# and can't be ran separately
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319,
262,
9516,
198,
2,
290,
460,
470,
307,
4966,
13869,
628,
628,
628,
198
] | 2.428571 | 266 |
#Author: Satwik Bhattamishra
import tensorflow as tf
import numpy as np
import tensorflow.examples.tutorials.mnist.input_data as input_data
if __name__ == '__main__':
denoising_autoencoder()
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# -*- coding: utf-8 -*-
"""
@Time : 2020/10/25 17:14
@Auth : Qi
@IDE : PyCharm
@Title: 845. 数组中的最长山脉
@Link : https://leetcode-cn.com/problems/longest-mountain-in-array/
"""
if __name__ == '__main__':
# 测试用例
s = Solution()
print(s.longestMountain([2, 1, 4, 7, 3, 2, 5]))
print(s.longestMountain([0, 1, 0, 0, 1, 1, 1, 1]))
print(s.longestMountain([2, 2, 2]))
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] | 1.814286 | 210 |
import os
import time
import cv2
from inference_new import *
import argparse
from glob import glob
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Command for running the Inpainting Pipeline on RGBD satellite imagery")
parser.add_argument('--gpu', type=str, default='0', help='the gpu that will be used, e.g "0"')
parser.add_argument('--nrep', type=int, default=9, help='repeated depth-inpainting iterations (def: 9)')
parser.add_argument('--input-rgb', type=str, default='./example_input_RGB.png', help='path to the 3-channel RGB input file.')
parser.add_argument('--input-dsm', type=str, default='./example_input_DSM.tif', help='path to the DSM input file.')
parser.add_argument('--input-dtm', type=str, default='./example_input_DTM.tif', help='path to the DTM input file.')
parser.add_argument('--outputdir', type=str, default='./results', help='path to write output prediction')
parser.add_argument('--outputfile', type=str, default='example_output', help='Inpainted output')
parser.add_argument('--fp16', action='store_true', default=False, help='whether to use FP16 inference.')
parser.add_argument('--trn-dir', type=str, default='./models', help='directory which contains caffe model for inference')
parser.add_argument('--iter', type=int, default=0, help='which iteration model to choose (def: 0 [choose latest])')
parser.add_argument('--model-type', type=str, default='rgbd', help='Model Type')
parser.add_argument('--extra-pad', type=int, default=0, help='add extra mirror padding to input '
'[sometimes improves results at border pixels] (def: 0)')
args = parser.parse_args()
log.info(args)
main() | [
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44438,
29810,
19575,
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60,
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2604,
13,
10951,
7,
22046,
8,
198,
220,
220,
220,
1388,
3419
] | 2.877651 | 613 |
from __future__ import print_function
from __future__ import division
from builtins import zip
from builtins import range
from builtins import object
from past.utils import old_div
__author__ = 'grburgess'
import collections
import os
import numpy as np
import pandas as pd
from pandas import HDFStore
from threeML.exceptions.custom_exceptions import custom_warnings
from threeML.io.file_utils import sanitize_filename
from threeML.utils.spectrum.binned_spectrum import Quality
from threeML.utils.time_interval import TimeIntervalSet
from threeML.utils.time_series.polynomial import polyfit, unbinned_polyfit, Polynomial
# find out how many splits we need to make
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import requests
import json
from flask.cli import FlaskGroup
from flask import jsonify
from serivce.app import create_app
app = create_app()
data = json.dumps(dict(name='service', port='8000'))
headers = {'Content-type': 'application/json'}
requests.post('http://localhost:5000/instance', headers=headers, data=data)
cli = FlaskGroup(app)
if __name__ == '__main__':
cli()
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"""Canvas that displays the full game board."""
# Standard Python Libraries
import tkinter as tk
# Third-Party Libraries
import numpy as np
from . import constants
from .board import DorfBoard
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import requests
from bs4 import BeautifulSoup
from tabulate import tabulate
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Parser test for MacOS Cups IPP Log files."""
import unittest
from dfvfs.helpers import fake_file_system_builder
from dfvfs.path import fake_path_spec
from plaso.lib import definitions
from plaso.lib import errors
from plaso.parsers import cups_ipp
from tests.parsers import test_lib
class CupsIppParserTest(test_lib.ParserTestCase):
"""Tests for MacOS Cups IPP parser."""
# pylint: disable=protected-access
_ATTRIBUTES_GROUP_DATA = bytes(bytearray([
0x01, 0x47, 0x00, 0x12, 0x61, 0x74, 0x74, 0x72, 0x69, 0x62, 0x75, 0x74,
0x65, 0x73, 0x2d, 0x63, 0x68, 0x61, 0x72, 0x73, 0x65, 0x74, 0x00, 0x05,
0x75, 0x74, 0x66, 0x2d, 0x38, 0x03]))
def _CreateAttributeTestData(self, parser, tag_value, name, value_data):
"""Creates attribute test data.
Args:
parser (CupsIppParser): CUPS IPP parser.
tag_value (int): value of the attribute tag.
name (str): name of the attribute.
value_data (bytes): data of the attribute value.
Returns:
bytes: attribute test data.
"""
attribute_map = parser._GetDataTypeMap('cups_ipp_attribute')
attribute = attribute_map.CreateStructureValues(
tag_value=tag_value, name_size=len(name), name=name,
value_data_size=len(value_data), value_data=value_data)
return attribute_map.FoldByteStream(attribute)
def _CreateDateTimeValueData(self, parser):
"""Creates date time value test data.
Args:
parser (CupsIppParser): CUPS IPP parser.
Returns:
bytes: date time value test data.
"""
datetime_map = parser._GetDataTypeMap('cups_ipp_datetime_value')
datetime = datetime_map.CreateStructureValues(
year=2018, month=11, day_of_month=27, hours=16, minutes=41, seconds=51,
deciseconds=5, direction_from_utc=ord('+'), hours_from_utc=1,
minutes_from_utc=0)
return datetime_map.FoldByteStream(datetime)
def _CreateHeaderData(self, parser):
"""Creates header test data.
Args:
parser (CupsIppParser): CUPS IPP parser.
Returns:
bytes: header test data.
"""
header_map = parser._GetDataTypeMap('cups_ipp_header')
header = header_map.CreateStructureValues(
major_version=1, minor_version=1, operation_identifier=5,
request_identifier=0)
return header_map.FoldByteStream(header)
def testGetStringValue(self):
"""Tests the _GetStringValue function."""
parser = cups_ipp.CupsIppParser()
string_value = parser._GetStringValue({}, 'test')
self.assertIsNone(string_value)
string_value = parser._GetStringValue({'test': ['1', '2,3', '4']}, 'test')
self.assertEqual(string_value, '1, "2,3", 4')
def testParseAttribute(self):
"""Tests the _ParseAttribute function."""
parser = cups_ipp.CupsIppParser()
attribute_data = self._CreateAttributeTestData(
parser, 0x00, 'test', b'\x12')
file_object = self._CreateFileObject('cups_ipp', attribute_data)
name, value = parser._ParseAttribute(file_object)
self.assertEqual(name, 'test')
self.assertEqual(value, b'\x12')
# Test with attribute data too small.
file_object = self._CreateFileObject('cups_ipp', attribute_data[:-1])
with self.assertRaises(errors.ParseError):
parser._ParseAttribute(file_object)
# Test attribute with integer value.
attribute_data = self._CreateAttributeTestData(
parser, 0x21, 'int', b'\x12\x34\x56\x78')
file_object = self._CreateFileObject('cups_ipp', attribute_data)
name, value = parser._ParseAttribute(file_object)
self.assertEqual(name, 'int')
self.assertEqual(value, 0x12345678)
# Test attribute with boolean value.
attribute_data = self._CreateAttributeTestData(
parser, 0x22, 'bool', b'\x01')
file_object = self._CreateFileObject('cups_ipp', attribute_data)
name, value = parser._ParseAttribute(file_object)
self.assertEqual(name, 'bool')
self.assertEqual(value, True)
# Test attribute with date time value.
datetime_data = self._CreateDateTimeValueData(parser)
attribute_data = self._CreateAttributeTestData(
parser, 0x31, 'datetime', datetime_data)
file_object = self._CreateFileObject('cups_ipp', attribute_data)
name, value = parser._ParseAttribute(file_object)
self.assertEqual(name, 'datetime')
self.assertIsNotNone(value)
self.assertEqual(value.year, 2018)
# Test attribute with string without language.
attribute_data = self._CreateAttributeTestData(
parser, 0x42, 'string', b'NOLANG')
file_object = self._CreateFileObject('cups_ipp', attribute_data)
name, value = parser._ParseAttribute(file_object)
self.assertEqual(name, 'string')
self.assertEqual(value, 'NOLANG')
# Test attribute with ASCII string and tag value charset.
attribute_data = self._CreateAttributeTestData(
parser, 0x47, 'charset', b'utf8')
file_object = self._CreateFileObject('cups_ipp', attribute_data)
name, value = parser._ParseAttribute(file_object)
self.assertEqual(name, 'charset')
self.assertEqual(value, 'utf8')
def testParseAttributesGroup(self):
"""Tests the _ParseAttributesGroup function."""
parser = cups_ipp.CupsIppParser()
file_object = self._CreateFileObject(
'cups_ipp', self._ATTRIBUTES_GROUP_DATA)
name_value_pairs = list(parser._ParseAttributesGroup(file_object))
self.assertEqual(name_value_pairs, [('attributes-charset', 'utf-8')])
# Test with unsupported attributes groups start tag value.
file_object = self._CreateFileObject('cups_ipp', b''.join([
b'\xff', self._ATTRIBUTES_GROUP_DATA[1:]]))
with self.assertRaises(errors.ParseError):
list(parser._ParseAttributesGroup(file_object))
def testParseBooleanValue(self):
"""Tests the _ParseBooleanValue function."""
parser = cups_ipp.CupsIppParser()
boolean_value = parser._ParseBooleanValue(b'\x00')
self.assertFalse(boolean_value)
boolean_value = parser._ParseBooleanValue(b'\x01')
self.assertTrue(boolean_value)
# Test with unsupported data.
with self.assertRaises(errors.ParseError):
parser._ParseBooleanValue(b'\x02')
def testParseDateTimeValue(self):
"""Tests the _ParseDateTimeValue function."""
parser = cups_ipp.CupsIppParser()
datetime_data = self._CreateDateTimeValueData(parser)
datetime_value = parser._ParseDateTimeValue(datetime_data, 0)
self.assertIsNotNone(datetime_value)
self.assertEqual(datetime_value.year, 2018)
# Test with data too small.
with self.assertRaises(errors.ParseError):
parser._ParseDateTimeValue(datetime_data[:-1], 0)
def testParseIntegerValue(self):
"""Tests the _ParseIntegerValue function."""
parser = cups_ipp.CupsIppParser()
integer_value = parser._ParseIntegerValue(b'\x00\x00\x00\x01', 0)
self.assertEqual(integer_value, 1)
# Test with data too small.
with self.assertRaises(errors.ParseError):
parser._ParseIntegerValue(b'\x01\x00\x00', 0)
def testParseHeader(self):
"""Tests the _ParseHeader function."""
file_system_builder = fake_file_system_builder.FakeFileSystemBuilder()
file_system_builder.AddFile('/cups_ipp', b'')
test_path_spec = fake_path_spec.FakePathSpec(location='/cups_ipp')
test_file_entry = file_system_builder.file_system.GetFileEntryByPathSpec(
test_path_spec)
storage_writer = self._CreateStorageWriter()
parser_mediator = self._CreateParserMediator(
storage_writer, file_entry=test_file_entry)
parser = cups_ipp.CupsIppParser()
header_data = self._CreateHeaderData(parser)
file_object = self._CreateFileObject('cups_ipp', header_data)
parser._ParseHeader(parser_mediator, file_object)
# Test with header data too small.
file_object = self._CreateFileObject('cups_ipp', header_data[:-1])
with self.assertRaises(errors.UnableToParseFile):
parser._ParseHeader(parser_mediator, file_object)
# Test with unsupported format version.
header_map = parser._GetDataTypeMap('cups_ipp_header')
header = header_map.CreateStructureValues(
major_version=99, minor_version=1, operation_identifier=5,
request_identifier=0)
header_data = header_map.FoldByteStream(header)
file_object = self._CreateFileObject('cups_ipp', header_data)
with self.assertRaises(errors.UnableToParseFile):
parser._ParseHeader(parser_mediator, file_object)
# Test with unsupported operation identifier.
header = header_map.CreateStructureValues(
major_version=1, minor_version=1, operation_identifier=99,
request_identifier=0)
header_data = header_map.FoldByteStream(header)
file_object = self._CreateFileObject('cups_ipp', header_data)
parser._ParseHeader(parser_mediator, file_object)
def testParseFileObject(self):
"""Tests the ParseFileObject function."""
parser = cups_ipp.CupsIppParser()
header_data = self._CreateHeaderData(parser)
storage_writer = self._CreateStorageWriter()
parser_mediator = self._CreateParserMediator(storage_writer)
file_object = self._CreateFileObject('cups_ipp', b''.join([
header_data, self._ATTRIBUTES_GROUP_DATA]))
parser.ParseFileObject(parser_mediator, file_object)
number_of_events = storage_writer.GetNumberOfAttributeContainers('event')
self.assertEqual(number_of_events, 0)
number_of_warnings = storage_writer.GetNumberOfAttributeContainers(
'extraction_warning')
self.assertEqual(number_of_warnings, 0)
number_of_warnings = storage_writer.GetNumberOfAttributeContainers(
'recovery_warning')
self.assertEqual(number_of_warnings, 0)
# Test with attribute group data too small.
storage_writer = self._CreateStorageWriter()
parser_mediator = self._CreateParserMediator(storage_writer)
file_object = self._CreateFileObject('cups_ipp', b''.join([
header_data, self._ATTRIBUTES_GROUP_DATA[:-1]]))
parser.ParseFileObject(parser_mediator, file_object)
number_of_events = storage_writer.GetNumberOfAttributeContainers('event')
self.assertEqual(number_of_events, 0)
number_of_warnings = storage_writer.GetNumberOfAttributeContainers(
'extraction_warning')
self.assertEqual(number_of_warnings, 1)
number_of_warnings = storage_writer.GetNumberOfAttributeContainers(
'recovery_warning')
self.assertEqual(number_of_warnings, 0)
# Test attribute with date time value.
datetime_data = self._CreateDateTimeValueData(parser)
attribute_data = self._CreateAttributeTestData(
parser, 0x31, 'date-time-at-creation', datetime_data)
storage_writer = self._CreateStorageWriter()
parser_mediator = self._CreateParserMediator(storage_writer)
file_object = self._CreateFileObject('cups_ipp', b''.join([
header_data, b'\x01', attribute_data, b'\x03']))
parser.ParseFileObject(parser_mediator, file_object)
number_of_events = storage_writer.GetNumberOfAttributeContainers('event')
self.assertEqual(number_of_events, 1)
number_of_warnings = storage_writer.GetNumberOfAttributeContainers(
'extraction_warning')
self.assertEqual(number_of_warnings, 0)
number_of_warnings = storage_writer.GetNumberOfAttributeContainers(
'recovery_warning')
self.assertEqual(number_of_warnings, 0)
def testParse(self):
"""Tests the Parse function."""
# TODO: only tested against MacOS Cups IPP (Version 2.0)
parser = cups_ipp.CupsIppParser()
storage_writer = self._ParseFile(['mac_cups_ipp'], parser)
number_of_events = storage_writer.GetNumberOfAttributeContainers('event')
self.assertEqual(number_of_events, 3)
number_of_warnings = storage_writer.GetNumberOfAttributeContainers(
'extraction_warning')
self.assertEqual(number_of_warnings, 0)
number_of_warnings = storage_writer.GetNumberOfAttributeContainers(
'recovery_warning')
self.assertEqual(number_of_warnings, 0)
events = list(storage_writer.GetSortedEvents())
expected_event_values = {
'application': 'LibreOffice',
'computer_name': 'localhost',
'copies': 1,
'data_type': 'cups:ipp:event',
'date_time': '2013-11-03 18:07:21',
'doc_type': 'application/pdf',
'job_id': 'urn:uuid:d51116d9-143c-3863-62aa-6ef0202de49a',
'job_name': 'Assignament 1',
'owner': 'Joaquin Moreno Garijo',
'printer_id': 'RHULBW',
'timestamp_desc': definitions.TIME_DESCRIPTION_CREATION,
'uri': 'ipp://localhost:631/printers/RHULBW',
'user': 'moxilo'}
self.CheckEventValues(storage_writer, events[0], expected_event_values)
expected_event_values = {
'data_type': 'cups:ipp:event',
'date_time': '2013-11-03 18:07:21',
'timestamp_desc': definitions.TIME_DESCRIPTION_START}
self.CheckEventValues(storage_writer, events[1], expected_event_values)
expected_event_values = {
'data_type': 'cups:ipp:event',
'date_time': '2013-11-03 18:07:32',
'timestamp_desc': definitions.TIME_DESCRIPTION_END}
self.CheckEventValues(storage_writer, events[2], expected_event_values)
if __name__ == '__main__':
unittest.main()
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] | 2.630245 | 5,052 |
from colorama import Fore
from oeda.log import *
from oeda.rtxlib.preprocessors.SparkPreProcessor import SparkPreProcessor
def init_pre_processors(wf):
""" we look into the workflows definition and run the required preprocessors """
if hasattr(wf, "pre_processors"):
pp = wf.pre_processors
for p in pp:
if p["type"] == "spark":
p["instance"] = SparkPreProcessor(wf, p)
else:
info("> Preprocessor | None", Fore.CYAN)
def kill_pre_processors(wf):
""" after the experiment, we stop all preprocessors """
try:
for p in wf.pre_processors:
p["instance"].shutdown()
info("> Shutting down Spark preprocessor")
except AttributeError:
pass
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] | 2.316265 | 332 |
# -*- coding: utf-8 -*-
"""
Language translation.
"""
__all__ = [
"ThZhTranslator",
"ZhThTranslator",
"Translate"
]
from pythainlp.translate.core import Translate
from pythainlp.translate.zh_th import (
ThZhTranslator,
ZhThTranslator,
)
| [
2,
532,
9,
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57,
71,
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41880,
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1,
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10511,
817,
8291,
41880,
11,
198,
8,
198
] | 2.300885 | 113 |
# Copyright (C) 2019 Google Inc.
# Licensed under http://www.apache.org/licenses/LICENSE-2.0 <see LICENSE file>
"""Test Custom Attribute Definition validation"""
import unittest
from mock import MagicMock
from ggrc.models import all_models
from ggrc.access_control import role as acr
class TestCustomAttributeDefinition(unittest.TestCase):
"""Test Custom Attribute Definition validation"""
def test_title_with_asterisk_throws(self):
"""Test if raises if title contains * symbol"""
with self.assertRaises(ValueError):
title = "Title with asterisk *"
self.cad.definition_type = "assessment_template"
self.cad.validate_title("title", title)
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378,
62,
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7203,
7839,
1600,
3670,
8,
198
] | 3.164319 | 213 |
file = open("13")
sum = 0
for numbers in file:
#print(numbers.rstrip())
numbers = int(numbers)
sum += numbers;
print(sum)
sum = str(sum)
print(sum[:10])
| [
7753,
796,
1280,
7203,
1485,
4943,
198,
198,
16345,
796,
657,
198,
1640,
3146,
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198
] | 2.342466 | 73 |
"""
MIT License
Copyright (c) 2016 deeplearningathome. http://deeplearningathome.com/
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.
"""
import tensorflow as tf
from autoencoder import AutoEncoder
import numpy
import time
#these are helper functions to read mnist data. They are part of Tensorflow models
from utils import maybe_download, extract_data, extract_labels, variable_summaries
from tensorflow.contrib.tensorboard.plugins import projector
flags = tf.flags
flags.DEFINE_string("encoder_network", "784,128,10", "specifies encoder network")
flags.DEFINE_float("noise_level", 0.0, "noise level for denoising autoencoder")
flags.DEFINE_integer("batch_size", 128, "batch size")
flags.DEFINE_integer("num_epochs", 60, "number of epochs")
flags.DEFINE_integer("eval_every_step", 2000, "evaluate every x steps")
flags.DEFINE_string("acitivation_kind", "sigmoid", "type of neuron activations")
flags.DEFINE_float("learning_rate", 0.1, "learning rate")
flags.DEFINE_string("optimizer_kind", "rmsprop", "type of oprtimizer")
flags.DEFINE_string("logdir", "tblogs", "tensorboard logs")
FLAGS = flags.FLAGS
if __name__ == "__main__":
tf.app.run(main=main)
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198
] | 3.408293 | 627 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = 'Jiayi Li'
import time, os, re
from bson.objectid import ObjectId
from werkzeug.utils import secure_filename
from flask import request, redirect, url_for, jsonify, abort, Blueprint, make_response, g, flash, current_app
from app import db
from app.models import User, Blog, Comment
from app.filters import markdown_filter
from app.utilities import allowed_file, cookie_to_user
api = Blueprint('api', __name__, url_prefix='/api')
APIS = ('blogs', 'users', 'comments')
#************************************
#----------------APIs----------------
#************************************
@api.route('/<collection>')
def api_get_all(collection):
'''
get all documents from a collection
'''
if collection not in APIS:
abort(400)
cursor = db[collection]
a = []
for document in cursor.find().sort("created", -1):
if collection == 'users':
document.update(password='******')
document.update(_id=str(document['_id']))
a.append(document)
return jsonify({collection:a})
@api.route('/<collection>/<item_id>')
def api_get_one(collection, item_id):
'''
get a single document from a collection
'''
document = db[collection].find_one({'_id': ObjectId(item_id)})
if not document:
abort(400)
if collection == 'users':
document.update(password='******')
document.update(_id=str(document['_id']))
return jsonify(document)
@api.route('/blogs/<blog_id>/comments')
def api_get_blog_comments(blog_id):
'''
get all comments from a blog
'''
comments = []
for comment in db.comments.find({'blog_id':blog_id}).sort("created", -1):
comment.update(_id=str(comment['_id']))
comment.update(content=markdown_filter(comment['content']))
if comment.get('subcomment'):
for subcomment in comment.get('subcontent'):
subcomment.update(content=markdown_filter(subcomment['content']))
comments.append(comment)
return jsonify(comments=comments)
@api.route('/blogs', methods=['POST'])
def api_post_blog():
'''
post a new blog
'''
if not g.__user__.get('admin'):
return make_response('Permission denied.', 403)
title = request.form.get('title')
tag = request.form.get('tag').lstrip(r'/\;,. ').rstrip(r'/\;,. ')
content = request.form.get('content')
# create a new Blog and save it to mongodb
blog = Blog(
user_id = g.__user__.get('_id'),
user_name = g.__user__.get('name'),
user_image = g.__user__.get('image'),
title = title.strip(),
tag = re.split(r'[\s\;\,\.\\\/]+', tag),
content = content.lstrip('\n').rstrip()
)
blog_resp = blog.__dict__
return jsonify(blog_id=str(blog_resp['_id']))
@api.route('/blogs/<blog_id>', methods=['POST'])
def api_edit_blog(blog_id):
'''
edit a blog and post it
'''
if not g.__user__.get('admin'):
return make_response('Permission denied.', 403)
title = request.form.get('title')
tag = request.form.get('tag').lstrip(r'/\;,. ').rstrip(r'/\;,. ')
content = request.form.get('content')
content = content.lstrip('\n').rstrip()
db.blogs.update_one(
{'_id': ObjectId(blog_id)},
{
'$set': {
'title': title.strip(),
'tag': re.split(r'[\s\;\,\.\\\/]+', tag),
'content': content,
'summary': '%s%s' % (content[:140], '...'),
'last_modified': True,
'modified': int(time.time())
}
})
return jsonify(blog_id=blog_id)
@api.route('/blogs/<blog_id>/comments', methods=['POST'])
def api_post_and_get_comment(blog_id):
'''
post a new comment
'''
if not g.__user__:
return make_response('Please login', 403)
content = request.form.get('content').lstrip('\n').rstrip()
if not content:
return make_response('Content cannot be empty.')
# create a new Comment and save it to mongodb
blog = db.blogs.find_one({'_id': ObjectId(blog_id)})
comment = Comment(
blog_id = blog_id,
blog_author = blog.get('user_name'),
blog_title = blog.get('title'),
user_id = g.__user__.get('_id'),
user_name = g.__user__.get('name'),
user_image = g.__user__.get('image'),
content = content
)
comments = []
for document in db.comments.find({'blog_id':blog_id}).sort("created", -1):
document.update(_id=str(document['_id']))
document.update(content=markdown_filter(document['content']))
if document.get('subcomment'):
for subcomment in document.get('subcontent'):
subcomment.update(content=markdown_filter(subcomment['content']))
comments.append(document)
return jsonify(comments=comments)
@api.route('/blogs/<blog_id>/comments/<comment_id>', methods=['POST'])
def api_pose_subcomment(blog_id, comment_id):
'''
post a subcomment
'''
if not g.__user__:
return make_response('Please login', 403)
content = request.form.get('content').lstrip('\n').rstrip()
if not content:
return make_response('Content cannot be empty', 403)
comment = db.comments.find_one({'_id': ObjectId(comment_id)})
db.comments.update_one(
{'_id': ObjectId(comment_id)},
{
'$set': {'subcomment': True},
'$push': {
'subcontent': {
'_id': str(ObjectId()),
'user_id': g.__user__.get('_id'),
'user_name': g.__user__.get('name'),
'user_image': g.__user__.get('image'),
'content': content,
'created': int(time.time())
}
}
})
comments = []
for document in db.comments.find({'blog_id': blog_id}).sort("created", -1):
document.update(_id=str(document['_id']))
document.update(content=markdown_filter(document['content']))
if document.get('subcomment'):
for subcomment in document.get('subcontent'):
subcomment.update(content=markdown_filter(subcomment['content']))
comments.append(document)
return jsonify(comments=comments)
@api.route('/<collection>/<item_id>/delete', methods=['POST'])
def api_delete_one(collection, item_id):
'''
delete one document from a certain collection
'''
if not g.__user__.get('admin'):
return make_response('Permission denied.', 403)
if collection == 'comments':
blog_id = db.comments.find_one({'_id': ObjectId(item_id)}).get('blog_id')
db[collection].delete_one({'_id': ObjectId(item_id)})
if collection == 'blogs':
db.comments.delete_many({'blog_id': ObjectId(item_id)})
if collection == 'comments':
return redirect(url_for('api.api_get_blog_comments', blog_id=blog_id))
return jsonify(item_id=item_id)
@api.route('/comments/<comment_id>/delete/<own_id>', methods=['POST'])
def api_delete_subcomment(comment_id, own_id):
'''
delete a subcomment from a certain comment
'''
if not g.__user__.get('admin'):
return make_response('Permission denied.', 403)
db.comments.update_one(
{'_id': ObjectId(comment_id)},
{
'$pull': {'subcontent': {'_id': own_id}}
})
if not db.comments.find_one({'_id': ObjectId(comment_id)}).get('subcontent'):
db.comments.update_one(
{'_id': ObjectId(comment_id)},
{
'$set': {'subcomment': False}
})
blog_id = db.comments.find_one({'_id': ObjectId(comment_id)}).get('blog_id')
return redirect(url_for('api.api_get_blog_comments', blog_id=blog_id))
@api.route('/image/<user_id>', methods=['POST'])
def api_upload(user_id):
'''
upload image files for user avatar
'''
if 'file' not in request.files:
flash('No file part')
return redirect(request.referrer)
file = request.files['file']
if file.filename == '':
flash('No selected file')
return redirect(request.referrer)
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(current_app.config['UPLOAD_FOLDER'], filename))
# update users
db.users.update_one(
{'_id': ObjectId(user_id)},
{
'$set': {'image': '/static/img/' + filename}
})
# update blogs
db.blogs.update_many(
{'user_id': user_id},
{
'$set': {'user_image': '/static/img/' + filename}
})
# update comments
db.comments.update_many(
{'user_id': user_id},
{
'$set': {'user_image': '/static/img/' + filename}
})
# update subcomments in comments
for comment in db.comments.find():
if comment.get('subcomment'):
for subcomment in comment['subcontent']:
# find one match and update one
if user_id in subcomment.values():
db.comments.update_one(
{
'_id': comment['_id'],
'subcontent': {'$elemMatch': {'_id': subcomment['_id']}}
},
{
'$set': {
'subcontent.$.user_image': '/static/img/' + filename
}
})
else:
flash('File not allowed')
return redirect(request.referrer) | [
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] | 2.134409 | 4,650 |
import cv
# TODO: This class doesn't seem to be used and is based on old OpenCV bindings.
# Either finish the class or remove it.
def convert_np_to_cvmat(img_np):
"""
This gives a: AttributeError: 'numpy.ndarray' object has no attribute
'from_array'
ImageAlignment.template_image = ImageAlignment.template_image.from_array()
"""
# Inspired from https://stackoverflow.com/questions/5575108/how-to-convert-a-numpy-array-view-to-opencv-matrix :
h_np, w_np = img_np.shape[:2]
tmp_cv = cv.CreateMat(h_np, w_np, cv.CV_8UC3)
cv.SetData(tmp_cv, img_np.data, img_np.strides[0])
return tmp_cv
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] | 2.468504 | 254 |
#!/usr/bin/env python
"""
Script that uses output from cutadapt to quickly detect fully overlapping pairs.
It is based on the fact that if sequencing adapters are trimmed from both paired-ends, the resulting
fragment needs to be shorter than the pair-end length
Depends of cutadapt seqio and xopen modules from version 1.6
Author: Mauricio Barrientos-Somarribas
Email: [email protected]
Copyright 2014 Mauricio Barrientos-Somarribas
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 sys
import argparse
import os.path
#Time of script execution and logging module
import time
import logging
import re
import math
import itertools
from collections import *
from cutadapt import seqio,xopen
from distance import hamming
import ctypes
#Data Analysis libs
import numpy as np
#****************Begin of Main ***************
#*****************End of Main**********************
#Assumes sequences are the same length, with a hamming distance of less than 0.05% of the length
#Assumes sequences are aligned and the same length
if __name__ == '__main__':
#Process command line arguments
parser = argparse.ArgumentParser(description="Script to process fastq files after adapter removal and extracts sequences fragments smaller than read length")
parser.add_argument("R1",help="Fastq with forward paired-end")
parser.add_argument("R2",help="Fastq with reverse paired-end")
parser.add_argument("-o","--output-prefix", default=None, help="Prefix of the output files" )
parser.add_argument("--raw_read_length", default=301,type=int, help="Length of raw reads (before adapter trimming). Default: 301" )
parser.add_argument("--min-trim", default=10,type=int, help="Minimum number of bases trimmed to consider the adapter removed was not spurious. Default: 10" )
parser.add_argument("-l","--log-file", default=None, help="Name of the log file")
args = parser.parse_args()
if validate_args(args):
#Initialize log
log_level = logging.INFO
if args.log_file:
logging.basicConfig(filename=args.log_file,level=log_level)
else:
logging.basicConfig(stream=sys.stderr,level=log_level)
time_start = time.time()
main( args )
logging.info("Time elapsed: "+str(time.time() - time_start)+"\n")
else:
logging.error("Invalid arguments. Exiting script\n")
sys.exit(1)
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198,
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] | 3.455901 | 805 |
import torch
import torch.nn as nn
import torch.nn.functional as F
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] | 3.285714 | 21 |
from battleship.grid import Grid, Outcome
from battleship.ship import Ship
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] | 3.809524 | 21 |
import copy
import pytest
from typing import Tuple, Optional
import torch
from torch_sparse import SparseTensor
from torch_sparse.matmul import spmm
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import softmax
edge_index = torch.tensor([
[0, 0, 0, 1, 1],
[0, 1, 2, 0, 2],
])
adj_t = SparseTensor(row=edge_index[1], col=edge_index[0])
x = (
torch.arange(1, 3, dtype=torch.float),
torch.arange(1, 4, dtype=torch.float),
)
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] | 2.526316 | 190 |
# Author : Ali Snedden
# Date : 3/21/20
# License: MIT
# Purpose:
# This code plots the Johns Hoptins Covid-19 Data
#
#
#
# Notes :
#
# References :
# 1. https://github.com/CSSEGISandData/COVID-19
#
#
# Future:
#
#
#
import sys
import numpy as np
import time
import pandas as pd
from matplotlib import pyplot as plt
from error import exit_with_error
from classes import ITALY_DATA
from scipy import optimize
def print_help(ExitCode):
"""
ARGS:
RETURN:
DESCRIPTION:
DEBUG:
FUTURE:
"""
sys.stderr.write(
"python3 ./src/plot_jhu_data.py country log-lin slice_index\n"
" country : See time_series_covid19_confirmed_global.csv\n"
" for coutries to plot options\n"
" log-lin : required, plot y axis in natural log, if fit is \n"
" straight line then experiencing exponential growth.\n"
" My hope is to someday implement other to be fit types \n"
" (e.g. lin-lin)\n"
" slice_index : required, for fitting, e.g. \n"
" if = -10, it will fit the last 10 points\n"
" if = 10, it will fit the first 10 points\n"
" \n"
" To Run: \n"
" source ~/.local/virtualenvs/python3.7/bin/activate\n")
sys.exit(ExitCode)
def main():
"""
ARGS:
RETURN:
DESCRIPTION:
DEBUG:
FUTURE:
1. Add option to fit only a specific section of data.
"""
# Check Python version
nArg = len(sys.argv)
# Use python 3
if(sys.version_info[0] != 3):
exit_with_error("ERROR!!! Use Python 3\n")
# Get options
if("-h" in sys.argv[1]):
print_help(0)
elif(nArg != 4 and nArg != 3):
print_help(1)
if(nArg == 4):
slcIdx = int(sys.argv[3])
startTime = time.time()
print("{} \n".format(sys.argv),flush=True)
print(" Start Time : {}".format(time.strftime("%a, %d %b %Y %H:%M:%S ",
time.localtime())),flush=True)
# Get args
country = sys.argv[1]
plotType = sys.argv[2] # Straight line equals linear growth
dataPath = "data/jhu/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
countryFound = False
df = pd.read_csv(dataPath)
lastDate = df.columns[-1]
for index, row in df.iterrows():
# Select country specified
if(row.values[1].lower() == country.lower()):
if(countryFound == True):
exit_with_error("ERROR!! {} should only occur "
"once".format(country.lower()))
yV = np.asarray(row.values[4:],dtype=np.float32) # y vector -cases
xV = np.asarray(range(len(yV))) # x vector - days
n = len(xV) # Number of days
countryFound = True
fig, ax = plt.subplots(1,1)
# Generate Plot
if(plotType == "log-lin"):
ylabel = "ln(cases + 1)"
print(yV)
yV = yV + 1
yV = np.log(yV)
# Slice and only keep what
if(nArg == 4):
if(slcIdx < 0):
xfit = xV[slcIdx:]
yfit = yV[slcIdx:]
elif(slcIdx > 0):
xfit = xV[:slcIdx]
yfit = yV[:slcIdx]
fit = np.polyfit(xfit,yfit,deg=1)
# Reuse xfit, and yfit
xfit= np.asarray([x for x in np.arange(0,n,n/100.0)])
yfit= fit[0]*xfit + fit[1]
ax.plot(xfit, yfit, label="Fit - y={:.3f}x+{:.3f}".format(fit[0],fit[1]))
ax.set_title("Covid-19 in {} (ending {})".format(country, lastDate))
elif(plotType == "lin-lin"):
ylabel = "Covid-19_Cases"
exit_with_error("ERROR!! I haven't handled this option yet\n")
else:
exit_with_error("ERROR!! Invalid plotType option\n")
ax.plot(xV, yV, label=ylabel)
ax.set_xlabel("Time spanning 0-{} days".format(n-1))
ax.set_ylabel("{}".format(ylabel))
ax.legend()
plt.show()
print("Ended : %s"%(time.strftime("%D:%H:%M:%S")))
print("Run Time : {:.4f} h".format((time.time() - startTime)/3600.0))
sys.exit(0)
if __name__ == "__main__":
main()
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1298,
198,
220,
220,
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1388,
3419,
198
] | 1.943713 | 2,203 |
#! /usr/bin/env python3
# -*- coding:utf-8 -*-
###############################################################
# © kenwaldek MIT-license
#
# Title: tkinter_image Version: 1.0
# Date: 26-12-16 Language: python3
# Description: tkinter inladen van image en text via menubar
#
###############################################################
from PIL import Image, ImageTk
from tkinter import *
root = Tk()
root.geometry('400x300')
app = Window(root)
root.mainloop()
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1324,
796,
26580,
7,
15763,
8,
198,
15763,
13,
12417,
26268,
3419,
198
] | 2.588235 | 204 |
from django.apps import AppConfig
| [
6738,
42625,
14208,
13,
18211,
1330,
2034,
16934,
628
] | 3.888889 | 9 |
#!/usr/bin/env python
import logging
import time
from urllib.parse import urljoin
import requests
from relay_sdk import Interface, Dynamic as D
relay = Interface()
relay_api_url = relay.get(D.connection.relayAPIURL)
relay_api_token = relay.get(D.connection.token)
run_id = relay.get(D.id)
headers = {'Authorization': f'Bearer {relay_api_token}'}
while True:
r = requests.get(urljoin(relay_api_url, f'_puppet/runs/{run_id}'), headers=headers)
r.raise_for_status()
run = r.json()
if run['state']['status'] != 'complete':
# XXX: FIXME: We need to take into account next_update_before to handle
# this properly.
logging.info('Run is not yet complete (currently {}), waiting...'.format(run['state']['status']))
time.sleep(5)
continue
if run['state'].get('job_id'):
relay.outputs.set('jobID', run['state']['job_id'])
if run['state'].get('outcome'):
relay.outputs.set('outcome', run['state']['outcome'])
logging.info('Run complete with outcome {}'.format(run['state'].get('outcome', '(unknown)')))
break
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5219,
6,
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6,
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2958,
6,
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628,
220,
220,
220,
18931,
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29513,
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22305,
628,
220,
220,
220,
2270,
198
] | 2.553488 | 430 |
#!/usr/bin/python
# you need to config this!
# set the model file, and if the model supports big-grams: set seed with bigrams..
## the conf dict stores all relevant config parameters
conf={}
conf['model'] = "climate2_2015_7.txt.2gram.small.model" # default dummy model
#conf['model'] = "climate2_2015_7.txt.2gram.model"
# if using a bigram model
conf['seedfn'] = "../data/climate.seed" # bigram seed for climate change models
# config for hypernym extraction
conf['num_taxomy_best'] = 1 # number of most similar terms to consider when building a taxonomy
conf['sim_threshold'] = 0.40
# if using a unigram model
#conf['seedfn'] = "../data/climate-single-word.seed"
# config for hypernym extraction
#conf['num_taxomy_best'] = 3 # number of most similar terms to consider when building a taxonomy
#conf['sim_threshold'] = 0.23
conf['binary_model'] = True # default: using a binary word2vec model (like created by Mikolov's C implementation)
conf['domain'] = "climate change" # your domain of knowledge -- not important for the algorithms ..
########################################################################################################################
# no need to change below this
DB_PATH= "../data/our.db"
#DB_PATH= "/home/wohlg/workspace/dl4j-0.4-examples/src/main/java/MinicBac/python/data/our.db"
print "db-path", DB_PATH
import sqlite3
def get_db():
""" just connect to the sqlite3 database """
conf['db'] = sqlite3.connect(DB_PATH)
# model file name
conf['MFN'] = "../data/models/" + conf['model']
# setup logging
import logging, os
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
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2,
9058,
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13,
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7,
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4,
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310,
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8,
82,
1058,
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7,
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3672,
8,
82,
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4064,
7,
20500,
8,
82,
3256,
1241,
28,
6404,
2667,
13,
10778,
8,
198
] | 3.213462 | 520 |
from .neumiss_layer import NeuMiss
from .neumiss_mlp import NeuMissMLP
| [
6738,
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747,
62,
29289,
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3169,
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6738,
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62,
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] | 2.84 | 25 |
"""
REST API Resource Routing
http://flask-restplus.readthedocs.io
"""
from datetime import datetime
from flask import request
from flask_restplus import Resource
from .security import require_auth
from . import api_rest
from .func import *
from .engine import * # wildcard import the TTDS lib
class SecureResource(Resource):
""" Calls require_auth decorator on all requests """
method_decorators = [require_auth]
@api_rest.route('/resource/<string:resource_id>')
class ResourceOne(Resource):
""" Unsecure Resource Class: Inherit from Resource """
@api_rest.route('/secure-resource/<string:resource_id>')
class SecureResourceOne(SecureResource):
""" Unsecure Resource Class: Inherit from Resource """
# this is the example hardcode test
@api_rest.route('/hello')
@api_rest.route('/demo10')
@api_rest.route('/search')
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] | 3.231061 | 264 |
#sachin_katageri
#SKATCODE
from tkinter import*
me=Tk()
me.geometry("354x460")
me.title("CALCULATOR")
melabel = Label(me,text="CALCULATOR",bg='White',font=("Times",30,'bold'))
melabel.pack(side=TOP)
me.config(background='Dark gray')
textin=StringVar()
operator=""
metext=Entry(me,font=("Courier New",12,'bold'),textvar=textin,width=25,bd=5,bg='powder blue')
metext.pack()
but1=Button(me,padx=14,pady=14,bd=4,bg='white',command=lambda:clickbut(1),text="1",font=("Courier New",16,'bold'))
but1.place(x=10,y=100)
but2=Button(me,padx=14,pady=14,bd=4,bg='white',command=lambda:clickbut(2),text="2",font=("Courier New",16,'bold'))
but2.place(x=10,y=170)
but3=Button(me,padx=14,pady=14,bd=4,bg='white',command=lambda:clickbut(3),text="3",font=("Courier New",16,'bold'))
but3.place(x=10,y=240)
but4=Button(me,padx=14,pady=14,bd=4,bg='white',command=lambda:clickbut(4),text="4",font=("Courier New",16,'bold'))
but4.place(x=75,y=100)
but5=Button(me,padx=14,pady=14,bd=4,bg='white',command=lambda:clickbut(5),text="5",font=("Courier New",16,'bold'))
but5.place(x=75,y=170)
but6=Button(me,padx=14,pady=14,bd=4,bg='white',command=lambda:clickbut(6),text="6",font=("Courier New",16,'bold'))
but6.place(x=75,y=240)
but7=Button(me,padx=14,pady=14,bd=4,bg='white',command=lambda:clickbut(7),text="7",font=("Courier New",16,'bold'))
but7.place(x=140,y=100)
but8=Button(me,padx=14,pady=14,bd=4,bg='white',command=lambda:clickbut(8),text="8",font=("Courier New",16,'bold'))
but8.place(x=140,y=170)
but9=Button(me,padx=14,pady=14,bd=4,bg='white',command=lambda:clickbut(9),text="9",font=("Courier New",16,'bold'))
but9.place(x=140,y=240)
but0=Button(me,padx=14,pady=14,bd=4,bg='white',command=lambda:clickbut(0),text="0",font=("Courier New",16,'bold'))
but0.place(x=10,y=310)
butdot=Button(me,padx=47,pady=14,bd=4,bg='white',command=lambda:clickbut("."),text=".",font=("Courier New",16,'bold'))
butdot.place(x=75,y=310)
butpl=Button(me,padx=14,pady=14,bd=4,bg='white',text="+",command=lambda:clickbut("+"),font=("Courier New",16,'bold'))
butpl.place(x=205,y=100)
butsub=Button(me,padx=14,pady=14,bd=4,bg='white',text="-",command=lambda:clickbut("-"),font=("Courier New",16,'bold'))
butsub.place(x=205,y=170)
butml=Button(me,padx=14,pady=14,bd=4,bg='white',text="*",command=lambda:clickbut("*"),font=("Courier New",16,'bold'))
butml.place(x=205,y=240)
butdiv=Button(me,padx=14,pady=14,bd=4,bg='white',text="/",command=lambda:clickbut("/"),font=("Courier New",16,'bold'))
butdiv.place(x=205,y=310)
butclear=Button(me,padx=14,pady=119,bd=4,bg='white',text="CE",command=clrbut,font=("Courier New",16,'bold'))
butclear.place(x=270,y=100)
butequal=Button(me,padx=151,pady=14,bd=4,bg='white',command=equlbut,text="=",font=("Courier New",16,'bold'))
butequal.place(x=10,y=380)
me.mainloop()
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] | 2.08388 | 1,371 |
def SortArrayByArgMinIndex(array,index):
''' MAKE SURE TO SORT BY MOST IMPORTANT INDEX LAST!!! '''
a = array
L = len(a)
for i in range(L):
temp = a[i]
flag = 0
j = 0
while j < i and flag == 0:
if temp[index] < a[j][index]:
a[j+1] = a[j]
a[j] = temp
j += 1
else:
flag = 1
return(a)
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# This file lists files which should be ignored by pytest
collect_ignore = ["setup.py", "connery.py", "connery/modules/ipython.py"]
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"""
Tests for `nas` module.
"""
import pytest
from nas import nas
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#%%
import os
# IN_FILE = 'test.ncm'
# OUT_FILE = IN_FILE.split('.')[0]
fileToC('up5.html','htmlData')
# %%
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"""
This module is the concrete implementation of S2FGAN.
This module structure is following:
make_kernel is used to intialise the kernel for blurring image
Blur, a layer used to apply blur kerbel to input
PixelNorm, a layer used to apply pixel normalization
EqualConv1d, convolution 1d with equalized learning trick
EqualConv2d, convolution 2d with equalized learning trick
Equallinear, linear layerwith equalized learning trick
Embedding, attribute mapping networks.
Encoder, the encoder of S2FGAN.
StyledConv, the upblock for the decoder of S2FGAN.
Discriminator, the discrimantor of S2FGAN.
VGGPerceptualLoss, the perceptual loss based on VGG19.
"""
import math
import torch
import torchvision
from torch import nn
from torch.nn import functional as F
from torch.autograd import Function
from torch.nn.init import normal_
from torch import autograd, optim
from op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d, conv2d_gradfix
#Pixel Normalization
#create blur kernel
#Blur Layer
#Equlized convlution 2d
#trainable input layer for decoder
#Block for Attribute Mapping Network
#Attribute Mapping Network
#encoder
#decoder
#convolution layer with dowmsample and activation function
#residual block
#domain discriminator
#model discriminator
def requires_grad(model, flag=True):
"""
Return None
Parameters
----------
model : pytorch model
flag : bool, default true
Returns
-------
None
set requires_grad flag for model
"""
for p in model.parameters():
p.requires_grad = flag
#calculate generator loss
#VGG Perceptual loss
#The function is used downsample and binarize the input
#calculte r1 loss
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] | 2.604938 | 729 |
#\input texinfo
"""
Searching for names with given scope and name. This is very central in Jedi and
Python. The name resolution is quite complicated with descripter,
``__getattribute__``, ``__getattr__``, ``global``, etc.
Flow checks
+++++++++++
Flow checks are not really mature. There's only a check for ``isinstance``. It
would check whether a flow has the form of ``if isinstance(a, type_or_tuple)``.
Unfortunately every other thing is being ignored (e.g. a == '' would be easy to
check for -> a is a string). There's big potential in these checks.
"""
from itertools import chain
from jedi._compatibility import unicode, u
from jedi.parser import tree as pr
from jedi import debug
from jedi import common
from jedi import settings
from jedi.evaluate import representation as er
from jedi.evaluate import dynamic
from jedi.evaluate import compiled
from jedi.evaluate import docstrings
from jedi.evaluate import iterable
from jedi.evaluate import imports
from jedi.evaluate import analysis
from jedi.evaluate import flow_analysis
from jedi.evaluate import param
from jedi.evaluate import helpers
from jedi.evaluate.cache import memoize_default
def filter_after_position(names, position):
"""
Removes all names after a certain position. If position is None, just
returns the names list.
"""
if position is None:
return names
names_new = []
for n in names:
# Filter positions and also allow list comprehensions and lambdas.
if n.start_pos[0] is not None and n.start_pos < position \
or isinstance(n.get_definition(), (pr.CompFor, pr.Lambda)):
names_new.append(n)
return names_new
def filter_definition_names(names, origin, position=None):
"""
Filter names that are actual definitions in a scope. Names that are just
used will be ignored.
"""
# Just calculate the scope from the first
stmt = names[0].get_definition()
scope = stmt.get_parent_scope()
if not (isinstance(scope, er.FunctionExecution)
and isinstance(scope.base, er.LambdaWrapper)):
names = filter_after_position(names, position)
names = [name for name in names if name.is_definition()]
# Private name mangling (compile.c) disallows access on names
# preceeded by two underscores `__` if used outside of the class. Names
# that also end with two underscores (e.g. __id__) are not affected.
for name in list(names):
if name.value.startswith('__') and not name.value.endswith('__'):
if filter_private_variable(scope, origin):
names.remove(name)
return names
@memoize_default([], evaluator_is_first_arg=True)
def _remove_statements(evaluator, stmt, name):
"""
This is the part where statements are being stripped.
Due to lazy evaluation, statements like a = func; b = a; b() have to be
evaluated.
"""
types = []
# Remove the statement docstr stuff for now, that has to be
# implemented with the evaluator class.
#if stmt.docstr:
#res_new.append(stmt)
check_instance = None
if isinstance(stmt, er.InstanceElement) and stmt.is_class_var:
check_instance = stmt.instance
stmt = stmt.var
types += evaluator.eval_statement(stmt, seek_name=name)
if check_instance is not None:
# class renames
types = [er.get_instance_el(evaluator, check_instance, a, True)
if isinstance(a, (er.Function, pr.Function))
else a for a in types]
return types
def check_flow_information(evaluator, flow, search_name, pos):
""" Try to find out the type of a variable just with the information that
is given by the flows: e.g. It is also responsible for assert checks.::
if isinstance(k, str):
k. # <- completion here
ensures that `k` is a string.
"""
if not settings.dynamic_flow_information:
return None
result = []
if flow.is_scope():
# Check for asserts.
try:
names = reversed(flow.names_dict[search_name.value])
except (KeyError, AttributeError):
names = []
for name in names:
ass = name.get_parent_until(pr.AssertStmt)
if isinstance(ass, pr.AssertStmt) and pos is not None and ass.start_pos < pos:
result = _check_isinstance_type(evaluator, ass.assertion(), search_name)
if result:
break
if isinstance(flow, (pr.IfStmt, pr.WhileStmt)):
element = flow.children[1]
result = _check_isinstance_type(evaluator, element, search_name)
return result
def global_names_dict_generator(evaluator, scope, position):
"""
For global name lookups. Yields tuples of (names_dict, position). If the
position is None, the position does not matter anymore in that scope.
This function is used to include names from outer scopes. For example, when
the current scope is function:
>>> from jedi._compatibility import u, no_unicode_pprint
>>> from jedi.parser import Parser, load_grammar
>>> parser = Parser(load_grammar(), u('''
... x = ['a', 'b', 'c']
... def func():
... y = None
... '''))
>>> scope = parser.module.subscopes[0]
>>> scope
<Function: func@3-5>
`global_names_dict_generator` is a generator. First it yields names from
most inner scope.
>>> from jedi.evaluate import Evaluator
>>> evaluator = Evaluator(load_grammar())
>>> scope = er.wrap(evaluator, scope)
>>> pairs = list(global_names_dict_generator(evaluator, scope, (4, 0)))
>>> no_unicode_pprint(pairs[0])
({'func': [], 'y': [<Name: y@4,4>]}, (4, 0))
Then it yields the names from one level "lower". In this example, this
is the most outer scope. As you can see, the position in the tuple is now
None, because typically the whole module is loaded before the function is
called.
>>> no_unicode_pprint(pairs[1])
({'func': [<Name: func@3,4>], 'x': [<Name: x@2,0>]}, None)
After that we have a few underscore names that are part of the module.
>>> sorted(pairs[2][0].keys())
['__doc__', '__file__', '__name__', '__package__']
>>> pairs[3] # global names -> there are none in our example.
({}, None)
>>> pairs[4] # package modules -> Also none.
({}, None)
Finally, it yields names from builtin, if `include_builtin` is
true (default).
>>> pairs[5][0].values() #doctest: +ELLIPSIS
[[<CompiledName: ...>], ...]
"""
in_func = False
while scope is not None:
if not (scope.type == 'classdef' and in_func):
# Names in methods cannot be resolved within the class.
for names_dict in scope.names_dicts(True):
yield names_dict, position
if scope.type == 'funcdef':
# The position should be reset if the current scope is a function.
in_func = True
position = None
scope = er.wrap(evaluator, scope.get_parent_scope())
# Add builtins to the global scope.
for names_dict in compiled.builtin.names_dicts(True):
yield names_dict, None
def check_tuple_assignments(types, name):
"""
Checks if tuples are assigned.
"""
for index in name.assignment_indexes():
new_types = []
for r in types:
try:
func = r.get_exact_index_types
except AttributeError:
debug.warning("Invalid tuple lookup #%s of result %s in %s",
index, types, name)
else:
try:
new_types += func(index)
except IndexError:
pass
types = new_types
return types
def filter_private_variable(scope, origin_node):
"""Check if a variable is defined inside the same class or outside."""
instance = scope.get_parent_scope()
coming_from = origin_node
while coming_from is not None \
and not isinstance(coming_from, (pr.Class, compiled.CompiledObject)):
coming_from = coming_from.get_parent_scope()
# CompiledObjects don't have double underscore attributes, but Jedi abuses
# those for fakes (builtins.pym -> list).
if isinstance(instance, compiled.CompiledObject):
return instance != coming_from
else:
return isinstance(instance, er.Instance) and instance.base.base != coming_from
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] | 2.610565 | 3,256 |
#!/usr/bin/env python
"""
A Python implementation of ANSI X9.31 using AES 128, following:
http://csrc.nist.gov/groups/STM/cavp/documents/rng/931rngext.pdf
Copyright (C) 2015 - Brian Caswell <[email protected]>
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.
"""
#import random
#import unittest
from Crypto.Cipher import AES
class PRNG(object):
"""
A python implementation of ANSI X9.31 using AES 128
Attributes:
random_data: Currently available block of generated random data
V: "seed value which is also kept secret"
DT: "date/time vector updated upon each iteration"
I: Intermediate value
aes_ctx: AES state machine context
"""
BLOCK_SIZE = 16
def __init__(self, seed=None):
"""
Seed is V + Key + DT as a string
"""
if seed is not None:
assert len(seed) == 48
else:
seed = "zaybxcwdveuftgsh" + "0123456789abcdef" + "\x00" * 16
self.V, key, self.DT = [seed[i:i+PRNG.BLOCK_SIZE] for i in range(0, len(seed), PRNG.BLOCK_SIZE)]
self.random_data = ''
self.I = "\x00" * PRNG.BLOCK_SIZE
self.aes_ctx = AES.new(key, mode=AES.MODE_ECB)
@staticmethod
def _xor_string(value_1, value_2):
"""
value_1 ^ value_2
Exceptions:
AssertionError if value_1 and value_2 are not the same length
"""
assert len(value_1) == len(value_2)
return ''.join(chr(ord(a) ^ ord(b)) for a, b in zip(value_1, value_2))
def _get_block(self):
"""
Get the next block from the PRNG, saving it to self.random_data
Arguments:
None
Returns:
None
Exceptions:
None
"""
# encrypt the counter value, giving intermediate value I
self.I = self.aes_ctx.encrypt(self.DT)
# XOR I with secret vector V, encrypt the result to obtain pseudo
# random data
tmp = self._xor_string(self.I, self.V)
self.random_data = self.aes_ctx.encrypt(tmp)
# XOR random data with I, and encrypt to get new secret vector V
tmp = self._xor_string(self.random_data, self.I)
self.V = self.aes_ctx.encrypt(tmp)
# update DT value
i = PRNG.BLOCK_SIZE - 1
while i >= 0:
out = (ord(self.DT[i]) + 1) % 256
self.DT = self.DT[:i] + chr(out) + self.DT[i+1:]
if out != 0:
break
i -= 1
def get(self, size):
"""
Get 'size' bytes of random data
Arguments:
size: Amount of random data to return
Returns:
str of length 'size' of random data
Exceptions:
AssertionError if size is not a positive integer
"""
assert isinstance(size, int)
assert size > 0
result = ''
while len(result) < size:
need = size - len(result)
if not len(self.random_data):
self._get_block()
result += self.random_data[:need]
self.random_data = self.random_data[need:]
return result
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4810,
10503,
13,
9148,
11290,
62,
33489,
532,
352,
198,
220,
220,
220,
220,
220,
220,
220,
981,
1312,
18189,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
503,
796,
357,
585,
7,
944,
13,
24544,
58,
72,
12962,
1343,
352,
8,
4064,
17759,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
24544,
796,
2116,
13,
24544,
58,
25,
72,
60,
1343,
442,
81,
7,
448,
8,
1343,
2116,
13,
24544,
58,
72,
10,
16,
47715,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
503,
14512,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1312,
48185,
352,
628,
220,
220,
220,
825,
651,
7,
944,
11,
2546,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3497,
705,
7857,
6,
9881,
286,
4738,
1366,
628,
220,
220,
220,
220,
220,
220,
220,
20559,
2886,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2546,
25,
26308,
286,
4738,
1366,
284,
1441,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
965,
286,
4129,
705,
7857,
6,
286,
4738,
1366,
628,
220,
220,
220,
220,
220,
220,
220,
1475,
11755,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2195,
861,
295,
12331,
611,
2546,
318,
407,
257,
3967,
18253,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
6818,
318,
39098,
7,
7857,
11,
493,
8,
198,
220,
220,
220,
220,
220,
220,
220,
6818,
2546,
1875,
657,
628,
220,
220,
220,
220,
220,
220,
220,
1255,
796,
10148,
198,
220,
220,
220,
220,
220,
220,
220,
981,
18896,
7,
20274,
8,
1279,
2546,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
761,
796,
2546,
532,
18896,
7,
20274,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
407,
18896,
7,
944,
13,
25120,
62,
7890,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
1136,
62,
9967,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1255,
15853,
2116,
13,
25120,
62,
7890,
58,
25,
31227,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
25120,
62,
7890,
796,
2116,
13,
25120,
62,
7890,
58,
31227,
47715,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
1255,
198
] | 2.387413 | 1,732 |
import typing
# Segment Tree | [
11748,
19720,
198,
198,
2,
1001,
5154,
12200
] | 3.625 | 8 |
import pandas as pd
from unittest2 import TestCase # or `from unittest import ...` if on Python 3.4+
import category_encoders as encoders
| [
11748,
19798,
292,
355,
279,
67,
198,
6738,
555,
715,
395,
17,
1330,
6208,
20448,
220,
1303,
393,
4600,
6738,
555,
715,
395,
1330,
2644,
63,
611,
319,
11361,
513,
13,
19,
10,
198,
198,
11748,
6536,
62,
12685,
375,
364,
355,
2207,
375,
364,
628
] | 3.065217 | 46 |
import unittest
import pythonioc
class TestDepCycle(unittest.TestCase):
"""
Regression test for issue #3 dependency cycle on error.
"""
| [
11748,
555,
715,
395,
198,
11748,
21015,
72,
420,
198,
198,
4871,
6208,
12156,
20418,
2375,
7,
403,
715,
395,
13,
14402,
20448,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
3310,
2234,
1332,
329,
2071,
1303,
18,
20203,
6772,
319,
4049,
13,
198,
220,
220,
220,
37227,
628
] | 2.941176 | 51 |
import os
import fabtools
from fabtools import require
from fabric.api import task, sudo, cd
from fabric.colors import cyan
from dploy.context import ctx
from dploy.utils import git_dirname
@task
def checkout():
"""
Checkouts the code on the remote location using git
"""
branch = ctx('git.branch')
git_root = ctx('git.dirs.root')
git_dir = git_dirname(ctx('git.repository'))
git_path = os.path.join(git_root, git_dir)
if not fabtools.deb.is_installed('git'):
fabtools.deb.install('git')
print(cyan('Checking out {} @ {} -> {}'.format(
branch, ctx('git.repository'), git_path)))
# Experimental
require.git.working_copy(ctx('git.repository'),
path=git_path, branch=branch, update=True,
use_sudo=True)
with cd(git_path):
sudo('git submodule update --init --recursive')
sudo("find . -iname '*.pyc' | xargs rm -f")
# /Experimental
# if files.exists(os.path.join(git_path, '.git'), use_sudo=True):
# print(cyan('Updating {} on {}'.format(branch, env.stage)))
# with cd(git_path):
# sudo('git reset --hard')
# sudo('git pull')
# sudo('git submodule update --init --recursive')
# sudo('git checkout {}'.format(branch))
# sudo("find . -iname '*.pyc' | xargs rm -f")
# else:
# print(cyan('Cloning {} on {}'.format(branch, env.stage)))
# with cd(git_root):
# sudo('git clone --recursive -b {} {} {}'.format(
# ctx('git.branch'), ctx('git.repository'), git_dir))
| [
11748,
28686,
198,
11748,
7843,
31391,
198,
198,
6738,
7843,
31391,
1330,
2421,
198,
6738,
9664,
13,
15042,
1330,
4876,
11,
21061,
11,
22927,
198,
6738,
9664,
13,
4033,
669,
1330,
36818,
198,
6738,
288,
1420,
13,
22866,
1330,
269,
17602,
198,
6738,
288,
1420,
13,
26791,
1330,
17606,
62,
15908,
3672,
628,
198,
31,
35943,
198,
4299,
28006,
33529,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
6822,
5269,
262,
2438,
319,
262,
6569,
4067,
1262,
17606,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
8478,
796,
269,
17602,
10786,
18300,
13,
1671,
3702,
11537,
198,
220,
220,
220,
17606,
62,
15763,
796,
269,
17602,
10786,
18300,
13,
15908,
82,
13,
15763,
11537,
198,
220,
220,
220,
17606,
62,
15908,
796,
17606,
62,
15908,
3672,
7,
49464,
10786,
18300,
13,
260,
1930,
37765,
6,
4008,
198,
220,
220,
220,
17606,
62,
6978,
796,
28686,
13,
6978,
13,
22179,
7,
18300,
62,
15763,
11,
17606,
62,
15908,
8,
198,
220,
220,
220,
611,
407,
7843,
31391,
13,
11275,
13,
271,
62,
37050,
10786,
18300,
6,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
7843,
31391,
13,
11275,
13,
17350,
10786,
18300,
11537,
628,
220,
220,
220,
3601,
7,
948,
272,
10786,
9787,
278,
503,
23884,
2488,
23884,
4613,
23884,
4458,
18982,
7,
198,
220,
220,
220,
220,
220,
220,
220,
8478,
11,
269,
17602,
10786,
18300,
13,
260,
1930,
37765,
33809,
17606,
62,
6978,
22305,
198,
220,
220,
220,
1303,
32286,
198,
220,
220,
220,
2421,
13,
18300,
13,
16090,
62,
30073,
7,
49464,
10786,
18300,
13,
260,
1930,
37765,
33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3108,
28,
18300,
62,
6978,
11,
8478,
28,
1671,
3702,
11,
4296,
28,
17821,
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,
220,
220,
220,
220,
220,
779,
62,
24032,
28,
17821,
8,
198,
220,
220,
220,
351,
22927,
7,
18300,
62,
6978,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
21061,
10786,
18300,
850,
21412,
4296,
1377,
15003,
1377,
8344,
30753,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
21061,
7203,
19796,
764,
532,
259,
480,
705,
24620,
9078,
66,
6,
930,
2124,
22046,
42721,
532,
69,
4943,
198,
220,
220,
220,
1303,
1220,
20468,
9134,
628,
220,
220,
220,
1303,
611,
3696,
13,
1069,
1023,
7,
418,
13,
6978,
13,
22179,
7,
18300,
62,
6978,
11,
45302,
18300,
33809,
779,
62,
24032,
28,
17821,
2599,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
3601,
7,
948,
272,
10786,
4933,
38734,
23884,
319,
23884,
4458,
18982,
7,
1671,
3702,
11,
17365,
13,
14247,
22305,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
351,
22927,
7,
18300,
62,
6978,
2599,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
220,
220,
220,
220,
21061,
10786,
18300,
13259,
1377,
10424,
11537,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
220,
220,
220,
220,
21061,
10786,
18300,
2834,
11537,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
220,
220,
220,
220,
21061,
10786,
18300,
850,
21412,
4296,
1377,
15003,
1377,
8344,
30753,
11537,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
220,
220,
220,
220,
21061,
10786,
18300,
28006,
23884,
4458,
18982,
7,
1671,
3702,
4008,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
220,
220,
220,
220,
21061,
7203,
19796,
764,
532,
259,
480,
705,
24620,
9078,
66,
6,
930,
2124,
22046,
42721,
532,
69,
4943,
198,
220,
220,
220,
1303,
2073,
25,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
3601,
7,
948,
272,
10786,
2601,
12484,
23884,
319,
23884,
4458,
18982,
7,
1671,
3702,
11,
17365,
13,
14247,
22305,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
351,
22927,
7,
18300,
62,
15763,
2599,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
220,
220,
220,
220,
21061,
10786,
18300,
17271,
1377,
8344,
30753,
532,
65,
23884,
23884,
23884,
4458,
18982,
7,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
17602,
10786,
18300,
13,
1671,
3702,
33809,
269,
17602,
10786,
18300,
13,
260,
1930,
37765,
33809,
17606,
62,
15908,
4008,
198
] | 2.253463 | 722 |
number = 100
factorialnumber = factorial(number)
print(f"Factorial({number}) = {factorialnumber}")
sumfactorialnumber = sum_of_digits(factorialnumber)
print(
f"Sum of digits in the factorial number({number}) = {sumfactorialnumber}")
| [
201,
198,
201,
198,
201,
198,
17618,
796,
1802,
201,
198,
22584,
5132,
17618,
796,
1109,
5132,
7,
17618,
8,
201,
198,
4798,
7,
69,
1,
29054,
5132,
15090,
17618,
30072,
796,
1391,
22584,
5132,
17618,
92,
4943,
201,
198,
16345,
22584,
5132,
17618,
796,
2160,
62,
1659,
62,
12894,
896,
7,
22584,
5132,
17618,
8,
201,
198,
4798,
7,
201,
198,
220,
220,
220,
277,
1,
13065,
286,
19561,
287,
262,
1109,
5132,
1271,
15090,
17618,
30072,
796,
1391,
16345,
22584,
5132,
17618,
92,
4943,
201,
198
] | 2.829545 | 88 |
#
# PySNMP MIB module HPN-ICF-STACK-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/HPN-ICF-STACK-MIB
# Produced by pysmi-0.3.4 at Wed May 1 13:41:29 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
OctetString, Integer, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "OctetString", "Integer", "ObjectIdentifier")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsUnion, SingleValueConstraint, ValueSizeConstraint, ValueRangeConstraint, ConstraintsIntersection = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsUnion", "SingleValueConstraint", "ValueSizeConstraint", "ValueRangeConstraint", "ConstraintsIntersection")
entPhysicalIndex, = mibBuilder.importSymbols("ENTITY-MIB", "entPhysicalIndex")
hpnicfCommon, = mibBuilder.importSymbols("HPN-ICF-OID-MIB", "hpnicfCommon")
ifDescr, ifIndex = mibBuilder.importSymbols("IF-MIB", "ifDescr", "ifIndex")
ModuleCompliance, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup")
MibIdentifier, Gauge32, Unsigned32, iso, Bits, Integer32, Counter64, ObjectIdentity, Counter32, TimeTicks, IpAddress, MibScalar, MibTable, MibTableRow, MibTableColumn, NotificationType, ModuleIdentity = mibBuilder.importSymbols("SNMPv2-SMI", "MibIdentifier", "Gauge32", "Unsigned32", "iso", "Bits", "Integer32", "Counter64", "ObjectIdentity", "Counter32", "TimeTicks", "IpAddress", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "NotificationType", "ModuleIdentity")
DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention")
hpnicfStack = ModuleIdentity((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91))
hpnicfStack.setRevisions(('2008-04-30 16:50',))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
if mibBuilder.loadTexts: hpnicfStack.setRevisionsDescriptions(('The initial revision of this MIB module.',))
if mibBuilder.loadTexts: hpnicfStack.setLastUpdated('200804301650Z')
if mibBuilder.loadTexts: hpnicfStack.setOrganization('')
if mibBuilder.loadTexts: hpnicfStack.setContactInfo('')
if mibBuilder.loadTexts: hpnicfStack.setDescription('This MIB is used to manage STM (Stack Topology Management) information for IRF (Intelligent Resilient Framework) device. This MIB is applicable to IRF-capable products. Some objects in this MIB may be used only for some specific products, so users should refer to the related documents to acquire more detailed information.')
hpnicfStackGlobalConfig = MibIdentifier((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 1))
hpnicfStackMaxMember = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 1, 1), Integer32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpnicfStackMaxMember.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackMaxMember.setDescription('The maximum number of members in a stack.')
hpnicfStackMemberNum = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 1, 2), Integer32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpnicfStackMemberNum.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackMemberNum.setDescription('The number of members currently in a stack.')
hpnicfStackMaxConfigPriority = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 1, 3), Integer32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpnicfStackMaxConfigPriority.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackMaxConfigPriority.setDescription('The highest priority that can be configured for a member in a stack.')
hpnicfStackAutoUpdate = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 1, 4), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("disabled", 1), ("enabled", 2)))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: hpnicfStackAutoUpdate.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackAutoUpdate.setDescription('The function for automatically updating the image from the master to a device that is attempting to join the stack. When a new device tries to join a stack, STM verifies the image consistency between the joining device and the master. If the joining device uses a different image version than the master, the function updates the joining device with the image of the master. When this function is disabled, the new device can not join the stack if it uses a different software version than the master. disabled: disable auto update function enabled: enable auto update function')
hpnicfStackMacPersistence = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 1, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("notPersist", 1), ("persistForSixMin", 2), ("persistForever", 3)))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: hpnicfStackMacPersistence.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackMacPersistence.setDescription('The mode of bridge MAC address persistence. When a stack starts, the bridge MAC address of the master is used as that of the stack. When the master leaves the stack, the bridge MAC address of the stack changes depending on the mode of bridge MAC address persistence. notPersist: The bridge MAC address of the new master is used as that of the stack immediately. persistForSixMin: The original bridge MAC address will be reserved for six minutes. In this period, if the master that has left rejoins the stack, the bridge MAC address of the stack will not change. If the old master does not rejoin the stack within this period, the bridge MAC address of the new master will be used as that of the stack. persistForever: Whether the master leaves or not, the bridge MAC address of the stack will never change.')
hpnicfStackLinkDelayInterval = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 1, 6), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 30000))).setUnits('millisecond').setMaxAccess("readwrite")
if mibBuilder.loadTexts: hpnicfStackLinkDelayInterval.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackLinkDelayInterval.setDescription('Delay for stack ports to report a link down event. If the link comes up before the delay timer expires, the stack port will not report the link down event. If the link is not recovered before the delay timer expires, the stack port will report the change. If the delay is set to 0, the stack ports will report a link down event without delay. 0: no delay 1-30000(ms): delay time')
hpnicfStackTopology = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 1, 7), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("chainConn", 1), ("ringConn", 2)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpnicfStackTopology.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackTopology.setDescription('Stack topology. chainConn: daisy-chain connection ringConn: ring connection')
hpnicfStackDeviceConfigTable = MibTable((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 2), )
if mibBuilder.loadTexts: hpnicfStackDeviceConfigTable.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackDeviceConfigTable.setDescription('This table contains objects to manage device information in a stack.')
hpnicfStackDeviceConfigEntry = MibTableRow((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 2, 1), ).setIndexNames((0, "ENTITY-MIB", "entPhysicalIndex"))
if mibBuilder.loadTexts: hpnicfStackDeviceConfigEntry.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackDeviceConfigEntry.setDescription('This table contains objects to manage device information in a stack.')
hpnicfStackMemberID = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 2, 1, 1), Integer32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpnicfStackMemberID.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackMemberID.setDescription('The member ID of the device in a stack.')
hpnicfStackConfigMemberID = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 2, 1, 2), Integer32()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: hpnicfStackConfigMemberID.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackConfigMemberID.setDescription('The configured member ID of the device. The valid value ranges from 1 to the value in hpnicfStackMaxMember. The configured member ID will take effect at a reboot if it is unique within the stack.')
hpnicfStackPriority = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 2, 1, 3), Integer32()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: hpnicfStackPriority.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackPriority.setDescription('The priority of a device in the stack. The valid value ranges from 1 to the value in hpnicfStackMaxConfigPriority.')
hpnicfStackPortNum = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 2, 1, 4), Integer32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpnicfStackPortNum.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackPortNum.setDescription('The number of stack ports enabled in a device.')
hpnicfStackPortMaxNum = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 2, 1, 5), Integer32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpnicfStackPortMaxNum.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackPortMaxNum.setDescription('The maximum number of stack ports in a device.')
hpnicfStackBoardConfigTable = MibTable((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 3), )
if mibBuilder.loadTexts: hpnicfStackBoardConfigTable.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackBoardConfigTable.setDescription('This table contains objects to manage MPU information for a stack.')
hpnicfStackBoardConfigEntry = MibTableRow((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 3, 1), ).setIndexNames((0, "ENTITY-MIB", "entPhysicalIndex"))
if mibBuilder.loadTexts: hpnicfStackBoardConfigEntry.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackBoardConfigEntry.setDescription('This table contains objects to manage MPU information for a stack.')
hpnicfStackBoardRole = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 3, 1, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4))).clone(namedValues=NamedValues(("slave", 1), ("master", 2), ("loading", 3), ("other", 4)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpnicfStackBoardRole.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackBoardRole.setDescription('The role of the MPU in a stack. slave: Standby MPU master: Master MPU loading: Standby MPU is loading the software image from the master. other: other')
hpnicfStackBoardBelongtoMember = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 3, 1, 2), Integer32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpnicfStackBoardBelongtoMember.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackBoardBelongtoMember.setDescription('Member ID of the device that holds the current board.')
hpnicfStackPortInfoTable = MibTable((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 4), )
if mibBuilder.loadTexts: hpnicfStackPortInfoTable.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackPortInfoTable.setDescription('This table contains objects to manage stack port information for IRF stacked devices.')
hpnicfStackPortInfoEntry = MibTableRow((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 4, 1), ).setIndexNames((0, "HPN-ICF-STACK-MIB", "hpnicfStackMemberID"), (0, "HPN-ICF-STACK-MIB", "hpnicfStackPortIndex"))
if mibBuilder.loadTexts: hpnicfStackPortInfoEntry.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackPortInfoEntry.setDescription('This table contains objects to manage stack port information for IRF stacked devices.')
hpnicfStackPortIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 4, 1, 1), Integer32()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: hpnicfStackPortIndex.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackPortIndex.setDescription('The index of a stack port of the device.')
hpnicfStackPortEnable = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 4, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("disabled", 1), ("enabled", 2)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpnicfStackPortEnable.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackPortEnable.setDescription("The status of a stack port of the device. If no physical ports are added to the stack port, its status is 'disabled'. If the stack port has physical ports, its status is 'enabled'. disabled: The stack port is disabled. enabled: The stack port is enabled.")
hpnicfStackPortStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 4, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4))).clone(namedValues=NamedValues(("up", 1), ("down", 2), ("silent", 3), ("disabled", 4)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpnicfStackPortStatus.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackPortStatus.setDescription('The link status of a stack port on the device. up: A physical link is present on the stack port. down: No physical link is present on the stack port. silent: The link state of the stack port is up, but the port cannot transmit or receive traffic. disabled: The stack port does not contain physical links.')
hpnicfStackNeighbor = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 4, 1, 4), Integer32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpnicfStackNeighbor.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackNeighbor.setDescription("The member ID of the stack port's neighbor. 0 means no neighbor exists.")
hpnicfStackPortForwardingPath = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 4, 1, 5), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 511))).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpnicfStackPortForwardingPath.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackPortForwardingPath.setDescription("List of egress member IDs on a stack port. Each member device uses the egress member ID lists to choose the outgoing stack port for known unicast frames to be sent out of other member devices. The egress member ID lists are comma separated. A zero-length string means no egress members exist. For example: In a ring stack of 1-2-3-4-5-7-1, if hpnicfStackPortForwardingPath.1.1 returns '7,5,4', IRF-port 1/1 will be the outgoing port for frames to reach members 7, 5, and 4 from member 1.")
hpnicfStackPhyPortInfoTable = MibTable((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 5), )
if mibBuilder.loadTexts: hpnicfStackPhyPortInfoTable.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackPhyPortInfoTable.setDescription('This table contains objects to manage information about physical ports that can be used for IRF stacking.')
hpnicfStackPhyPortInfoEntry = MibTableRow((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 5, 1), ).setIndexNames((0, "ENTITY-MIB", "entPhysicalIndex"))
if mibBuilder.loadTexts: hpnicfStackPhyPortInfoEntry.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackPhyPortInfoEntry.setDescription('This table contains objects to manage information about physical ports that can be used for IRF stacking.')
hpnicfStackBelongtoPort = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 5, 1, 1), Integer32()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: hpnicfStackBelongtoPort.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackBelongtoPort.setDescription('The index of the stack port to which the physical port is added. 0 means the physical port is not added to any stack port. The value will take effect when IRF is enabled on the device.')
hpnicfStackTrap = MibIdentifier((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 6))
hpnicfStackTrapOjbects = MibIdentifier((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 6, 0))
hpnicfStackPortLinkStatusChange = NotificationType((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 6, 0, 1)).setObjects(("HPN-ICF-STACK-MIB", "hpnicfStackMemberID"), ("HPN-ICF-STACK-MIB", "hpnicfStackPortIndex"), ("HPN-ICF-STACK-MIB", "hpnicfStackPortStatus"))
if mibBuilder.loadTexts: hpnicfStackPortLinkStatusChange.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackPortLinkStatusChange.setDescription('The hpnicfStackPortLinkStatusChange trap indicates that the link status of the stack port has changed.')
hpnicfStackTopologyChange = NotificationType((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 6, 0, 2)).setObjects(("HPN-ICF-STACK-MIB", "hpnicfStackTopology"))
if mibBuilder.loadTexts: hpnicfStackTopologyChange.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackTopologyChange.setDescription('The hpnicfStackTopologyChange trap indicates that the topology type of the IRF stack has changed.')
hpnicfStackMadBfdChangeNormal = NotificationType((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 6, 0, 3)).setObjects(("IF-MIB", "ifIndex"), ("IF-MIB", "ifDescr"))
if mibBuilder.loadTexts: hpnicfStackMadBfdChangeNormal.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackMadBfdChangeNormal.setDescription('The hpnicfStackMadBfdChangeNormal trap indicates that the BFD MAD function changed to the normal state.')
hpnicfStackMadBfdChangeFailure = NotificationType((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 6, 0, 4)).setObjects(("IF-MIB", "ifIndex"), ("IF-MIB", "ifDescr"))
if mibBuilder.loadTexts: hpnicfStackMadBfdChangeFailure.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackMadBfdChangeFailure.setDescription('The hpnicfStackMadBfdChangeFailure trap indicates that the BFD MAD function changed to the failure state.')
hpnicfStackMadLacpChangeNormal = NotificationType((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 6, 0, 5)).setObjects(("IF-MIB", "ifIndex"), ("IF-MIB", "ifDescr"))
if mibBuilder.loadTexts: hpnicfStackMadLacpChangeNormal.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackMadLacpChangeNormal.setDescription('The hpnicfStackMadLacpChangeNormal trap indicates that the LACP MAD function changed to the normal state.')
hpnicfStackMadLacpChangeFailure = NotificationType((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 15, 2, 91, 6, 0, 6)).setObjects(("IF-MIB", "ifIndex"), ("IF-MIB", "ifDescr"))
if mibBuilder.loadTexts: hpnicfStackMadLacpChangeFailure.setStatus('current')
if mibBuilder.loadTexts: hpnicfStackMadLacpChangeFailure.setDescription('The hpnicfStackMadLacpChangeFailure trap indicates that the LACP MAD function changed to the failure state.')
mibBuilder.exportSymbols("HPN-ICF-STACK-MIB", hpnicfStackPortInfoEntry=hpnicfStackPortInfoEntry, hpnicfStackBelongtoPort=hpnicfStackBelongtoPort, PYSNMP_MODULE_ID=hpnicfStack, hpnicfStackMadBfdChangeNormal=hpnicfStackMadBfdChangeNormal, hpnicfStackTrapOjbects=hpnicfStackTrapOjbects, hpnicfStackMaxMember=hpnicfStackMaxMember, hpnicfStackPortForwardingPath=hpnicfStackPortForwardingPath, hpnicfStackTopologyChange=hpnicfStackTopologyChange, hpnicfStackPortLinkStatusChange=hpnicfStackPortLinkStatusChange, hpnicfStackPortMaxNum=hpnicfStackPortMaxNum, hpnicfStackNeighbor=hpnicfStackNeighbor, hpnicfStack=hpnicfStack, hpnicfStackPortStatus=hpnicfStackPortStatus, hpnicfStackTrap=hpnicfStackTrap, hpnicfStackMaxConfigPriority=hpnicfStackMaxConfigPriority, hpnicfStackMadLacpChangeNormal=hpnicfStackMadLacpChangeNormal, hpnicfStackTopology=hpnicfStackTopology, hpnicfStackBoardBelongtoMember=hpnicfStackBoardBelongtoMember, hpnicfStackConfigMemberID=hpnicfStackConfigMemberID, hpnicfStackMacPersistence=hpnicfStackMacPersistence, hpnicfStackPhyPortInfoEntry=hpnicfStackPhyPortInfoEntry, hpnicfStackMadBfdChangeFailure=hpnicfStackMadBfdChangeFailure, hpnicfStackPriority=hpnicfStackPriority, hpnicfStackPortNum=hpnicfStackPortNum, hpnicfStackPortIndex=hpnicfStackPortIndex, hpnicfStackMadLacpChangeFailure=hpnicfStackMadLacpChangeFailure, hpnicfStackPortEnable=hpnicfStackPortEnable, hpnicfStackMemberNum=hpnicfStackMemberNum, hpnicfStackBoardConfigTable=hpnicfStackBoardConfigTable, hpnicfStackBoardConfigEntry=hpnicfStackBoardConfigEntry, hpnicfStackDeviceConfigTable=hpnicfStackDeviceConfigTable, hpnicfStackLinkDelayInterval=hpnicfStackLinkDelayInterval, hpnicfStackMemberID=hpnicfStackMemberID, hpnicfStackAutoUpdate=hpnicfStackAutoUpdate, hpnicfStackBoardRole=hpnicfStackBoardRole, hpnicfStackPhyPortInfoTable=hpnicfStackPhyPortInfoTable, hpnicfStackDeviceConfigEntry=hpnicfStackDeviceConfigEntry, hpnicfStackGlobalConfig=hpnicfStackGlobalConfig, hpnicfStackPortInfoTable=hpnicfStackPortInfoTable)
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"""
Feature extraction algorithms.
Each algorithm works on the HandwrittenData class. They have to be applied like
this:
>>> import hwrt.features
>>> from hwrt.handwritten_data import HandwrittenData
>>> data_json = '[[{"time": 123, "x": 45, "y": 67}]]'
>>> a = HandwrittenData(raw_data_id=2953, raw_data_json=data_json)
>>> feature_list = [StrokeCount(),
... ConstantPointCoordinates(strokes=4,
... points_per_stroke=20,
... fill_empty_with=0)]
>>> x = a.feature_extraction(feature_list)
"""
# Core Library modules
import abc
import logging
import sys
from itertools import combinations_with_replacement as combinations_wr
from typing import Any, Dict, List
# Third party modules
import numpy
from PIL import Image, ImageDraw
# Local modules
from . import geometry, handwritten_data, preprocessing, utils
logger = logging.getLogger(__name__)
def get_features(model_description_features: List[Dict[str, Any]]):
"""Get features from a list of dictionaries
Parameters
----------
model_description_features : List[Dict[str, Any]]
Examples
--------
>>> l = [{'StrokeCount': None}, \
{'ConstantPointCoordinates': \
[{'strokes': 4}, \
{'points_per_stroke': 81}, \
{'fill_empty_with': 0}, \
{'pen_down': False}] \
} \
]
>>> get_features(l)
[StrokeCount, ConstantPointCoordinates
- strokes: 4
- points per stroke: 81
- fill empty with: 0
- pen down feature: False
- pixel_env: 0
]
"""
return utils.get_objectlist(
model_description_features, config_key="features", module=sys.modules[__name__]
)
def print_featurelist(feature_list: List):
"""
Print the feature_list in a human-readable form.
Parameters
----------
feature_list : List
feature objects
"""
input_features = sum(n.get_dimension() for n in feature_list)
print("## Features (%i)" % input_features)
print("```")
for algorithm in feature_list:
print("* %s" % str(algorithm))
print("```")
class Feature(metaclass=abc.ABCMeta):
"""Abstract class which defines which methods to implement for features."""
@abc.abstractmethod
def __call__(self, hwr_obj):
"""Get the features value for a given recording ``hwr_obj``."""
assert isinstance(
hwr_obj, handwritten_data.HandwrittenData
), "handwritten data is not of type HandwrittenData, but of %r" % type(hwr_obj)
@abc.abstractmethod
def get_dimension(self):
"""Return the length of the list which __call__ will return."""
# Only feature calculation classes follow
# Every feature class must have a __str__, __repr__ function so that error
# messages can help you to find and fix bugs in features.
# Every feature class must have a __call__ function which is used to get the
# features value(s) for a given recording.
# Every feature class must have a get_dimension function so that the total
# number of features can be calculated and checked for consistency.
#
# * __call__ must take exactly one argument of type HandwrittenData
# * __call__ must return a list of length get_dimension()
# * get_dimension must return a positive number
# * have a 'normalize' attribute that is either True or False
# Local features
class ConstantPointCoordinates(Feature):
"""Take the first ``points_per_stroke=20`` points coordinates of the first
``strokes=4`` strokes as features. This leads to
:math:`2 \\cdot \\text{points_per_stroke} \\cdot \\text{strokes}`
features.
If ``points`` is set to 0, the first ``points_per_stroke`` point
coordinates and the ``pen_down`` feature is used. This leads to
:math:`3 \\cdot \\text{points_per_stroke}` features.
Parameters
----------
strokes : int
points_per_stroke : int
fill_empty_with : float
pen_down : boolean
pixel_env : int
How big should the pixel map around the given point be?
"""
normalize = False
def get_dimension(self):
"""Get the dimension of the returned feature. This equals the number
of elements in the returned list of numbers."""
if self.strokes > 0:
if self.pixel_env > 0:
return (
(2 + (1 + 2 * self.pixel_env) ** 2)
* self.strokes
* self.points_per_stroke
)
else:
return 2 * self.strokes * self.points_per_stroke
else:
if self.pen_down:
return 3 * self.points_per_stroke
else:
return 2 * self.points_per_stroke
def _features_with_strokes(self, hwr_obj):
"""Calculate the ConstantPointCoordinates features for the case of
a fixed number of strokes."""
x = []
img = Image.new(
"L",
(
(int(hwr_obj.get_width() * self.scaling_factor) + 2),
(int(hwr_obj.get_height() * self.scaling_factor) + 2),
),
"black",
)
draw = ImageDraw.Draw(img, "L")
pointlist = hwr_obj.get_pointlist()
bb = hwr_obj.get_bounding_box()
for stroke_nr in range(self.strokes):
last_point = None
# make sure that the current symbol actually has that many
# strokes
if stroke_nr < len(pointlist):
for point_nr in range(self.points_per_stroke):
if point_nr < len(pointlist[stroke_nr]):
point = pointlist[stroke_nr][point_nr]
x.append(pointlist[stroke_nr][point_nr]["x"])
x.append(pointlist[stroke_nr][point_nr]["y"])
if last_point is None:
last_point = point
y_from = int(
(-bb["miny"] + last_point["y"]) * self.scaling_factor
)
x_from = int(
(-bb["minx"] + last_point["x"]) * self.scaling_factor
)
y_to = int((-bb["miny"] + point["y"]) * self.scaling_factor)
x_to = int((-bb["minx"] + point["x"]) * self.scaling_factor)
draw.line([x_from, y_from, x_to, y_to], fill="#ffffff", width=1)
if self.pixel_env > 0:
pix = img.load()
for x_offset in range(-self.pixel_env, self.pixel_env + 1):
for y_offset in range(
-self.pixel_env, self.pixel_env + 1
):
xp = (
int(
(-bb["minx"] + point["x"])
* self.scaling_factor
)
+ x_offset
)
yp = (
int(
(-bb["miny"] + point["y"])
* self.scaling_factor
)
+ y_offset
)
xp = max(0, xp)
yp = max(0, yp)
x.append(pix[xp, yp])
last_point = point
else:
x.append(self.fill_empty_with)
x.append(self.fill_empty_with)
if self.pixel_env > 0:
for _ in range((1 + 2 * self.pixel_env) ** 2):
x.append(self.fill_empty_with)
else:
for _ in range(self.points_per_stroke):
x.append(self.fill_empty_with)
x.append(self.fill_empty_with)
if self.pixel_env > 0:
for _ in range((1 + 2 * self.pixel_env) ** 2):
x.append(self.fill_empty_with)
del draw
return x
def _features_without_strokes(self, hwr_obj):
"""Calculate the ConstantPointCoordinates features for the case of
a single (callapesed) stroke with pen_down features."""
x = []
for point in hwr_obj.get_pointlist()[0]:
if len(x) >= 3 * self.points_per_stroke or (
len(x) >= 2 * self.points_per_stroke and not self.pen_down
):
break
x.append(point["x"])
x.append(point["y"])
if self.pen_down:
if "pen_down" not in point:
logger.error(
"The "
"ConstantPointCoordinates(strokes=0) "
"feature should only be used after "
"SpaceEvenly preprocessing step."
)
else:
x.append(int(point["pen_down"]))
if self.pen_down:
while len(x) != 3 * self.points_per_stroke:
x.append(self.fill_empty_with)
else:
while len(x) != 2 * self.points_per_stroke:
x.append(self.fill_empty_with)
return x
class FirstNPoints(Feature):
"""Similar to the ``ConstantPointCoordinates`` feature, this feature takes
the first ``n=81`` point coordinates. It also has the
``fill_empty_with=0`` to make sure that the dimension of this feature is
always the same."""
normalize = False
def get_dimension(self):
"""Get the dimension of the returned feature. This equals the number
of elements in the returned list of numbers."""
return 2 * self.n
# Global features
class Bitmap(Feature):
"""Get a fixed-size bitmap of the recording."""
normalize = True
def get_dimension(self):
"""Get the dimension of the returned feature. This equals the number
of elements in the returned list of numbers."""
return self.size ** 2
class StrokeCount(Feature):
"""Stroke count as a 1 dimensional recording."""
normalize = True
def get_dimension(self): # pylint: disable=R0201
"""Get the dimension of the returned feature. This equals the number
of elements in the returned list of numbers."""
return 1
class Ink(Feature):
"""Ink as a 1 dimensional feature. It gives a numeric value for the amount
of ink this would eventually have consumed.
"""
normalize = True
def get_dimension(self): # pylint: disable=R0201
"""Get the dimension of the returned feature. This equals the number
of elements in the returned list of numbers."""
return 1
class AspectRatio(Feature):
"""Aspect ratio of a recording as a 1 dimensional feature."""
normalize = True
def get_dimension(self): # pylint: disable=R0201
"""Get the dimension of the returned feature. This equals the number
of elements in the returned list of numbers."""
return 1
class Width(Feature):
"""Width of a recording as a 1 dimensional feature.
.. note::
This is the current width. So if the recording was scaled, this will
not be the original width.
"""
normalize = True
def get_dimension(self): # pylint: disable=R0201
"""Get the dimension of the returned feature. This equals the number
of elements in the returned list of numbers."""
return 1
class Height(Feature):
"""Height of a recording as a a 1 dimensional feature.
.. note::
This is the current hight. So if the recording was scaled, this will
not be the original height.
"""
normalize = True
def get_dimension(self): # pylint: disable=R0201
"""Get the dimension of the returned feature. This equals the number
of elements in the returned list of numbers."""
return 1
class Time(Feature):
"""The time in milliseconds it took to create the recording. This is a 1
dimensional feature."""
normalize = True
def get_dimension(self): # pylint: disable=R0201
"""Get the dimension of the returned feature. This equals the number
of elements in the returned list of numbers."""
return 1
class CenterOfMass(Feature):
"""Center of mass of a recording as a 2 dimensional feature."""
normalize = True
def get_dimension(self): # pylint: disable=R0201
"""Get the dimension of the returned feature. This equals the number
of elements in the returned list of numbers."""
return 2
class StrokeCenter(Feature):
"""Get the stroke center of mass coordinates as a 2 dimensional feature."""
normalize = True
def get_dimension(self):
"""Get the dimension of the returned feature. This equals the number
of elements in the returned list of numbers."""
return self.strokes * 2
class DouglasPeuckerPoints(Feature):
"""Get the number of points which are left after applying the Douglas
Peucker line simplification algorithm.
"""
normalize = True
def get_dimension(self):
"""Get the dimension of the returned feature. This equals the number
of elements in the returned list of numbers."""
return 1
def _stroke_simplification(self, pointlist):
"""The Douglas-Peucker line simplification takes a list of points as an
argument. It tries to simplifiy this list by removing as many points
as possible while still maintaining the overall shape of the stroke.
It does so by taking the first and the last point, connecting them
by a straight line and searchin for the point with the highest
distance. If that distance is bigger than 'epsilon', the point is
important and the algorithm continues recursively."""
# Find the point with the biggest distance
dmax = 0
index = 0
for i in range(1, len(pointlist)):
d = geometry.perpendicular_distance(
pointlist[i], pointlist[0], pointlist[-1]
)
if d > dmax:
index = i
dmax = d
# If the maximum distance is bigger than the threshold 'epsilon', then
# simplify the pointlist recursively
if dmax >= self.epsilon:
# Recursive call
rec_results1 = self._stroke_simplification(pointlist[0:index])
rec_results2 = self._stroke_simplification(pointlist[index:])
result_list = rec_results1[:-1] + rec_results2
else:
result_list = [pointlist[0], pointlist[-1]]
return result_list
class StrokeIntersections(Feature):
"""Count the number of intersections which strokes in the recording have
with each other in form of a symmetrical matrix for the first
``stroke=4`` strokes. The feature dimension is
:math:`round(\\frac{\\text{strokes}^2}{2} + \\frac{\\text{strokes}}{2})`
because the symmetrical part is discarded.
======= ======= ======= ======= ===
- stroke1 stroke2 stroke3
------- ------- ------- ------- ---
stroke1 0 1 0 ...
stroke2 1 2 0 ...
stroke3 0 0 0 ...
... ... ... ... ...
======= ======= ======= ======= ===
Returns values of upper triangular matrix (including diagonal)
from left to right, top to bottom.
..warning
This method has an error. It should probably not be used.
"""
normalize = True
def get_dimension(self):
"""Get the dimension of the returned feature. This equals the number
of elements in the returned list of numbers."""
return int(round(float(self.strokes ** 2) / 2 + float(self.strokes) / 2))
class ReCurvature(Feature):
"""Re-curvature is a 1 dimensional, stroke-global feature for a recording.
It is the ratio
:math:`\\frac{\\text{height}(s)}{\\text{length}(s)}`.
If ``length(s) == 0``, then the re-curvature is defined to be 1.
"""
normalize = True
def get_dimension(self):
"""Get the dimension of the returned feature. This equals the number
of elements in the returned list of numbers."""
return self.strokes
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] | 2.228287 | 7,530 |
from rest_framework import permissions, viewsets, generics, filters
from .serializers import JobsSerializer, HousingSerializer, ApplicantSerializer, HeatmapSerializer
from .models import Jobs, Housing, Applicant, Heatmap
from .data_collection.collect_data import CollectData
from django.shortcuts import render
debug = False
if(debug):
apa = CollectData() | [
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] | 3.831579 | 95 |
from django.views.generic import DetailView
from django.contrib.auth.mixins import LoginRequiredMixin, UserPassesTestMixin
from django.views.generic.edit import CreateView, UpdateView, DeleteView
from django.urls import reverse_lazy
from core.views import PaginatedListView
from .models import Tag, Ingredient, Recipe
from .forms.tag_forms import TagModelForm
from .forms.ingredient_forms import IngredientModelForm
from .forms.recipe_forms import RecipeModelForm
##############
# Tag Mixins #
##############
#############
# Tag Views #
#############
#####################
# Ingredient Mixins #
#####################
####################
# Ingredient Views #
####################
#################
# Recipe Mixins #
#################
################
# Recipe Views #
################
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2,
26694,
29978,
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198,
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628,
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198
] | 3.669683 | 221 |
import unittest
from libcet import cet
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] | 2.928571 | 14 |
"""Companies app"""
# Django
from django.apps import AppConfig
class CompaniesAppConfig(AppConfig):
"""Companies app config"""
name = "paranuara.companies"
verbose_name = 'Companies'
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] | 3.15873 | 63 |
from quasargui import *
layout = QInput(
classes='q-ma-lg',
label='Your city',
children=[
Slot('prepend', [
QIcon('place')
])
])
run(layout, title='slots example')
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] | 2.1 | 100 |
import io
import pathlib
from collections import namedtuple
from typing import Any, Dict, List, Optional, Tuple, Iterator, Union
from torchdata.datapipes.iter import IterDataPipe, Mapper, Zipper
from torchvision.prototype import features
from torchvision.prototype.datasets.utils import (
Dataset,
OnlineResource,
GDriveResource,
)
from torchvision.prototype.datasets.utils._internal import (
hint_sharding,
hint_shuffling,
)
from torchvision.prototype.features import Label
from .._api import register_dataset, register_info
NAME = "pcam"
_Resource = namedtuple("_Resource", ("file_name", "gdrive_id", "sha256"))
@register_info(NAME)
@register_dataset(NAME)
class PCAM(Dataset):
# TODO write proper docstring
"""PCAM Dataset
homepage="https://github.com/basveeling/pcam"
"""
_RESOURCES = {
"train": (
_Resource( # Images
file_name="camelyonpatch_level_2_split_train_x.h5.gz",
gdrive_id="1Ka0XfEMiwgCYPdTI-vv6eUElOBnKFKQ2",
sha256="d619e741468a7ab35c7e4a75e6821b7e7e6c9411705d45708f2a0efc8960656c",
),
_Resource( # Targets
file_name="camelyonpatch_level_2_split_train_y.h5.gz",
gdrive_id="1269yhu3pZDP8UYFQs-NYs3FPwuK-nGSG",
sha256="b74126d2c01b20d3661f9b46765d29cf4e4fba6faba29c8e0d09d406331ab75a",
),
),
"test": (
_Resource( # Images
file_name="camelyonpatch_level_2_split_test_x.h5.gz",
gdrive_id="1qV65ZqZvWzuIVthK8eVDhIwrbnsJdbg_",
sha256="79174c2201ad521602a5888be8f36ee10875f37403dd3f2086caf2182ef87245",
),
_Resource( # Targets
file_name="camelyonpatch_level_2_split_test_y.h5.gz",
gdrive_id="17BHrSrwWKjYsOgTMmoqrIjDy6Fa2o_gP",
sha256="0a522005fccc8bbd04c5a117bfaf81d8da2676f03a29d7499f71d0a0bd6068ef",
),
),
"val": (
_Resource( # Images
file_name="camelyonpatch_level_2_split_valid_x.h5.gz",
gdrive_id="1hgshYGWK8V-eGRy8LToWJJgDU_rXWVJ3",
sha256="f82ee1670d027b4ec388048d9eabc2186b77c009655dae76d624c0ecb053ccb2",
),
_Resource( # Targets
file_name="camelyonpatch_level_2_split_valid_y.h5.gz",
gdrive_id="1bH8ZRbhSVAhScTS0p9-ZzGnX91cHT3uO",
sha256="ce1ae30f08feb468447971cfd0472e7becd0ad96d877c64120c72571439ae48c",
),
),
}
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261,
17147,
62,
5715,
62,
17,
62,
35312,
62,
12102,
62,
88,
13,
71,
20,
13,
34586,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
308,
19472,
62,
312,
2625,
16,
65,
39,
23,
57,
49,
34369,
50,
53,
10910,
3351,
4694,
15,
79,
24,
12,
57,
89,
38,
77,
55,
6420,
66,
6535,
18,
84,
46,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
427,
64,
11645,
2625,
344,
16,
3609,
1270,
69,
2919,
69,
1765,
38472,
2598,
3720,
4869,
12993,
67,
15,
37856,
68,
22,
9423,
67,
15,
324,
4846,
67,
42802,
66,
2414,
10232,
66,
22,
28676,
1415,
2670,
3609,
2780,
66,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10612,
198,
220,
220,
220,
220,
220,
220,
220,
10612,
198,
220,
220,
220,
1782,
198
] | 1.790132 | 1,439 |
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