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|---|---|---|---|
'''
undecorate_for_profiling.py
Explore all the python functions in the user-specified directory,
and remove decoration @profile from appropriate functions
'''
import os
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('rootdir')
args = parser.parse_args()
main(args.rootdir)
|
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] | 3.135135 | 111 |
## classification.py
__all__ = ["Lazy",]
from .lazy import LazyClassifier
from .utils import *
from sklearn.model_selection import train_test_split
import pandas
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# Python
import logging
from os import path
# Abstract
from genie.abstract import Lookup
# Parser
from genie.libs import parser
from genie.metaparser.util.exceptions import SchemaEmptyParserError
# unicon
from unicon.eal.dialogs import Statement, Dialog
log = logging.getLogger(__name__)
def save_device_information(device, **kwargs):
"""Install the commit packages. This is for IOSXR devices.
Args:
Mandatory:
device (`obj`) : Device object.
Returns:
True: Result is PASSED
False: Result is PASSX
Raises:
None
Example:
>>> save_device_information(device=Device())
"""
# Checking the config-register has 0x2
# if not configure 0x2
# RP/0/RSP1/CPU0:PE1#admin config-register 0x2
if device.is_ha:
conn = device.active
else:
conn = device
# Install commit ( when thre are package to bring up features)
# from admin prompt
conn.admin_execute('install commit')
def get_default_dir(device):
""" Get the default directory of this device
Args:
Mandatory:
device (`obj`) : Device object.
Returns:
default_dir (`str`): Default directory of the system
Raises:
Exception
Example:
>>> get_default_dir(device=device)
"""
try:
lookup = Lookup.from_device(device)
parsed_dict = lookup.parser.show_platform.Dir(device=device).parse()
if ":" in parsed_dict['dir']['dir_name']:
default_dir = parsed_dict['dir']['dir_name']
else:
default_dir = ''
except SchemaEmptyParserError as e:
raise Exception("No output when executing 'dir' command") from e
except Exception as e:
raise Exception("Unable to execute 'dir' command") from e
# Return default_dir to caller
log.info("Default directory on '{d}' is '{dir}'".format(d=device.name,
dir=default_dir))
return default_dir
def configure_replace(device, file_location, timeout=60, file_name=None):
"""Configure replace on device
Args:
device (`obj`): Device object
file_location (`str`): File location
timeout (`int`): Timeout value in seconds
file_name (`str`): File name
Returns:
None
Raises:
pyATS Results
"""
if file_name:
file_location = '{}{}'.format(
file_location,
file_name)
try:
# check if file exist
device.execute.error_pattern.append('.*Path does not exist.*')
device.execute("dir {}".format(file_location))
except Exception:
raise Exception("File {} does not exist".format(file_location))
dialog = Dialog([
Statement(pattern=r'\[no\]',
action='sendline(y)',
loop_continue=True,
continue_timer=False)])
device.configure("load {}\ncommit replace".format(file_location),
timeout=timeout, reply=dialog)
|
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# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
import django.core.validators
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import unittest
from unittest.mock import (
AsyncMock,
call,
)
from uuid import (
UUID,
)
from minos.saga import (
SagaExecution,
SagaExecutionRepository,
)
from tests.utils import (
ADD_ORDER,
SagaTestCase,
)
if __name__ == "__main__":
unittest.main()
|
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220,
869,
11,
198,
8,
198,
6738,
334,
27112,
1330,
357,
198,
220,
220,
220,
471,
27586,
11,
198,
8,
198,
198,
6738,
949,
418,
13,
82,
8126,
1330,
357,
198,
220,
220,
220,
19743,
23002,
1009,
11,
198,
220,
220,
220,
19743,
23002,
1009,
6207,
13264,
11,
198,
8,
198,
6738,
5254,
13,
26791,
1330,
357,
198,
220,
220,
220,
27841,
62,
12532,
1137,
11,
198,
220,
220,
220,
19743,
14402,
20448,
11,
198,
8,
628,
628,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
198
] | 2.301587 | 126 |
import numpy as np
from scipy.integrate import solve_ivp
import matplotlib.pyplot as plt
|
[
11748,
299,
32152,
355,
45941,
198,
6738,
629,
541,
88,
13,
18908,
4873,
1330,
8494,
62,
452,
79,
198,
11748,
2603,
29487,
8019,
13,
9078,
29487,
355,
458,
83,
198
] | 2.966667 | 30 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
"""
sample using "by" keyword
"""
import click
# import matplotlib
# matplotlib.use("Agg")
# import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
import pandas as pd
from windrose import (WindroseAxes, FIGSIZE_DEFAULT, DPI_DEFAULT)
class Layout(object):
"""
Inspired from PdfPages
https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/backends/backend_pdf.py - PdfPages
http://matplotlib.org/api/backend_pdf_api.html
http://matplotlib.org/examples/pylab_examples/multipage_pdf.html
Inspired also from FFMpegWriter
http://matplotlib.org/examples/animation/moviewriter.html
https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/animation.py
MovieWriter
"""
@property
S_FIGSIZE_DEFAULT = ",".join(map(str, FIGSIZE_DEFAULT))
@click.command()
@click.option("--filename", default="samples/sample_wind_poitiers.csv", help="Input filename")
@click.option("--filename_out", default="windrose_animation.mp4", help="Output filename")
@click.option("--dpi", default=DPI_DEFAULT, help="Dot per inch for plot generation")
@click.option("--figsize", default=S_FIGSIZE_DEFAULT, help="Figure size x,y - default=%s" % S_FIGSIZE_DEFAULT)
@click.option("--fps", default=7, help="Number of frame per seconds for video generation")
@click.option("--bins_min", default=0.01, help="Bins minimum value")
@click.option("--bins_max", default=20, help="Bins maximum value")
@click.option("--bins_step", default=2, help="Bins step value")
if __name__ == "__main__":
main()
|
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22,
11,
1037,
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583,
4201,
329,
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5270,
4943,
198,
31,
12976,
13,
18076,
7203,
438,
65,
1040,
62,
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1600,
4277,
28,
15,
13,
486,
11,
1037,
2625,
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1040,
5288,
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4943,
198,
31,
12976,
13,
18076,
7203,
438,
65,
1040,
62,
9806,
1600,
4277,
28,
1238,
11,
1037,
2625,
33,
1040,
5415,
1988,
4943,
198,
31,
12976,
13,
18076,
7203,
438,
65,
1040,
62,
9662,
1600,
4277,
28,
17,
11,
1037,
2625,
33,
1040,
2239,
1988,
4943,
628,
198,
361,
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3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
1388,
3419,
198
] | 2.692552 | 631 |
import numpy as np
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
import utility
from epipolar import EPIPOLAR
import cv2
if __name__ == '__main__':
from option import args
import pickle
with open('/home/sap/frcnn_keras/mv_train_two_reid.pickle', 'rb') as f:
reid_pickle = pickle.load(f)
pred_box, pred_box_emb, pred_box_prob, reid_box_gt = reid_pickle
reid = REID(args)
reid_box_pred, is_valid = reid.get_reid_box(pred_box, pred_box_emb, pred_box_prob)
print('reid_box_pred.shape', reid_box_pred.shape, 'is_valid', is_valid.shape)
pred_box_batch, pred_box_emb_batch, pred_box_prob_batch = list(map(lambda a : np.expand_dims(a, 0), [pred_box, pred_box_emb, pred_box_prob]))
reid_box_pred_batch, is_valid_batch = reid.get_batch(pred_box_batch, pred_box_emb_batch, pred_box_prob_batch)
print('reid_box_pred_batch.shape', reid_box_pred_batch.shape, 'is_valid_batch', is_valid_batch.shape)
print(np.array_equal(reid_box_pred_batch[0], reid_box_pred), np.array_equal(is_valid_batch[0], is_valid))
'''
is_valid = np.ones((self.num_nms, self.num_valid_cam))
with open('/home/sap/frcnn_keras/pred_box_is_valid.pickle', 'wb') as f:
pickle.dump(is_valid, f)
for i in range(10) :
print('gt', reid_box_gt[i])
print('pred', reid_box_pred[i])
print('valid', is_valid[i])
if(np.array_equal(reid_box_gt, reid_box_pred)) :
print('good')
else :
print('bad')
'''
|
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62,
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58,
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220,
220,
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7,
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62,
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62,
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11,
302,
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62,
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62,
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220,
220,
220,
220,
220,
220,
220,
3601,
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198,
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220,
220,
2073,
1058,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
10786,
14774,
11537,
198,
220,
220,
220,
705,
7061,
198
] | 2.167614 | 704 |
__author__ = 'godq'
import os
import sys
from dagflow.flow_operation import send_start_flow_msg as sdk_send_start_flow_msg, \
send_finish_step_msg as sdk_send_finish_step_msg
from dagflow.loader import get_DagRepo_Object
dag_repo = get_DagRepo_Object()
|
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13,
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1330,
651,
62,
35,
363,
6207,
78,
62,
10267,
198,
67,
363,
62,
260,
7501,
796,
651,
62,
35,
363,
6207,
78,
62,
10267,
3419,
628,
628,
628,
628,
628,
628,
198
] | 2.54717 | 106 |
from common import compare_array
from prepare_hash import run_binary_hasher
import psycopg2
import tempfile
from psycopg2 import sql
TABLE_NAME='runs'
TABLE_TEXT_MATCHES= 'text_matches'
COLUMNS=('id', 'content', 'ASTHash')
COLUMNS_MATCH=('first_runs_id', 'second_runs_id', 'match_AST_v1')
PROBLEM_ID=3
COUNT_LIMIT = 100
|
[
6738,
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8996,
62,
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8035,
62,
44,
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28,
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62,
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62,
312,
3256,
705,
12227,
62,
48381,
62,
312,
3256,
705,
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62,
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62,
85,
16,
11537,
198,
4805,
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2538,
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28,
18,
198,
34,
28270,
62,
43,
3955,
2043,
796,
1802,
198
] | 2.596774 | 124 |
"""Define a custom static storage class."""
from django.contrib.staticfiles.storage import ManifestStaticFilesStorage
class RyrManifestStaticFilesStorage(ManifestStaticFilesStorage):
"""Define a custom static storage class."""
manifest_strict = False
|
[
37811,
7469,
500,
257,
2183,
9037,
6143,
1398,
526,
15931,
198,
6738,
42625,
14208,
13,
3642,
822,
13,
12708,
16624,
13,
35350,
1330,
36757,
45442,
25876,
31425,
628,
198,
4871,
371,
2417,
5124,
8409,
45442,
25876,
31425,
7,
5124,
8409,
45442,
25876,
31425,
2599,
198,
220,
220,
220,
37227,
7469,
500,
257,
2183,
9037,
6143,
1398,
526,
15931,
628,
220,
220,
220,
10561,
62,
301,
2012,
796,
10352,
198
] | 3.797101 | 69 |
# Create your tests here.
import json
from django.contrib.auth.models import User
from django.test import TestCase, Client
from .models import Todo
from django.utils import timezone
import datetime
class TestTodosModel(TestCase):
"""测试数据库model"""
class TestTodosViews(TestCase):
"""测试视图函数"""
# TODO test
def test_todo_put(self):
"""测试更新土豆"""
data = {
'title': '抽烟',
}
uuid = self.user1.todos.all()[0].uuid
rsp = self.client.put('/api/v1/todos/{}/'.format(uuid),
data=data,
content_type='application/json')
self.assertEqual(rsp.status_code, 200)
todo = self.user1.todos.all()[0]
self.assertEqual(todo.title, data['title'])
def test_todo_delete(self):
"""测试删除土豆"""
uuid = self.user1.todos.all()[0].uuid
rsp = self.client.delete('/api/v1/todos/{}/'.format(uuid))
print(rsp.content)
self.assertEqual(rsp.status_code, 200)
with self.assertRaises(Todo.DoesNotExist) as e:
self.user1.todos.get(uuid=uuid)
def test_todo_field_error(self):
"""测试字段不正确情况下报错"""
data = {
'titl': '抽烟',
}
uuid = self.user1.todos.all()[0].uuid
rsp = self.client.put('/api/v1/todos/{}/'.format(uuid),
data=data,
content_type='application/json')
self.assertEqual(rsp.status_code, 400)
class TestAuth(TestCase):
"""测试jwt认证"""
|
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7,
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37227,
38184,
233,
46237,
243,
73,
46569,
164,
106,
97,
46237,
223,
37811,
628
] | 1.741282 | 889 |
import bisect
from scipy.spatial.distance import euclidean
from common import (NO_QUADRANT, NORTH_EAST, NORTH_WEST, SOUTH_EAST,
SOUTH_WEST, Boundary, Point, belongs, compute_knn,
intersects, quadrants)
from node import TreeNode
# Constants for tuple access optimization
BOUNDARY = 0
POINTS = 1
|
[
11748,
47457,
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198,
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629,
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15087,
8,
198,
6738,
10139,
1330,
12200,
19667,
198,
198,
2,
4757,
1187,
329,
46545,
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23989,
198,
33,
15919,
13153,
796,
657,
198,
16402,
1268,
4694,
796,
352,
628
] | 2.397163 | 141 |
# N, a, b, c, d = list(map(int, input().split()))
#
#
# def jc(x):
# r = 1
# for i in range(1, x + 1):
# r *= i
# return r
#
# res = int(jc(N * N) / (jc(a) * jc(b) * jc(c) * jc(d))) %
# print(res)
from collections import defaultdict
n = int(input())
edges = defaultdict(list)
for _ in range(n - 1):
u, v = list(map(int, input().split()))
edges[u].append(v)
print(subtree(1))
|
[
2,
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7,
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7,
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198,
220,
220,
220,
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58,
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7,
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8,
628,
198,
198,
4798,
7,
7266,
21048,
7,
16,
4008,
198
] | 2.046875 | 192 |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# Copyright (c) 2002-2015 "Neo Technology,"
# Network Engine for Objects in Lund AB [http://neotechnology.com]
#
# This file is part of Neo4j.
#
# 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.
from __future__ import unicode_literals
from argparse import ArgumentParser
from json import loads as json_loads
import logging
from sys import stdout, stderr
from neo4j.session import GraphDatabase, CypherError
class ColourFormatter(logging.Formatter):
""" Colour formatter for pretty log output.
"""
class Watcher(object):
""" Log watcher for debug output.
"""
handlers = {}
if __name__ == "__main__":
main()
|
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] | 3.395415 | 349 |
import tensorflow as tf
class Trainer(object):
"""
Object representing a TensorFlow trainer.
"""
|
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] | 3.083333 | 36 |
from dpconverge.data_set import DataSet
import numpy as np
import pandas as pd
from matplotlib import pyplot
from sklearn.datasets.samples_generator import make_blobs
n_features = 2
points_per_feature = 100
centers = [[2, 1.35], [2, 2], [2, 3], [2.5, 1.5], [2.5, 2], [2.5, 2.5]]
blob1, y1 = make_blobs(
n_samples=1000,
n_features=1,
centers=centers[0],
cluster_std=[0.1, 0.15],
random_state=1
)
blob2, y2 = make_blobs(
n_samples=6000,
n_features=1,
centers=centers[1],
cluster_std=[0.2, 0.3],
random_state=2
)
blob3, y3 = make_blobs(
n_samples=3000,
n_features=1,
centers=centers[2],
cluster_std=[0.2, 0.1],
random_state=2
)
blob4, y4 = make_blobs(
n_samples=250,
n_features=1,
centers=centers[3],
cluster_std=[0.1, 0.1],
random_state=2
)
blob5, y5 = make_blobs(
n_samples=250,
n_features=1,
centers=centers[4],
cluster_std=[0.1, 0.1],
random_state=3
)
ds = DataSet(parameter_count=2)
ds.add_blob(1, blob1)
ds.add_blob(2, blob2)
ds.add_blob(3, blob3)
ds.add_blob(4, blob4)
ds.add_blob(5, blob5)
# ds.plot_blobs(ds.classifications, x_lim=[0, 4], y_lim=[0, 4])
component_count = 128
iteration_count = 5000
# use multiple runs of BEM to estimate the number of components
# and get initial conditions
max_log_like = None # the highest value for all runs
converged = False
results = [] # will be a list of dicts to convert to a DataFrame
while not converged:
print component_count
new_comp_counts = []
for seed in range(1, 17):
ds.results = None # reset results
ds.cluster(
component_count=component_count,
burn_in=0,
iteration_count=iteration_count,
random_seed=seed,
model='bem'
)
log_like = ds.get_log_likelihood_trace()[0]
print log_like
if log_like > max_log_like:
max_log_like = log_like
# if the new log_like is close to the max (within 1%),
# see if there are any empty components (pi < 0.0001)
if abs(max_log_like - log_like) < abs(max_log_like * 0.01):
tmp_comp_count = np.sum(ds.raw_results.pis > 0.0001)
new_comp_counts.append(tmp_comp_count)
# save good run to our results
results.append(
{
'comp': component_count,
'true_comp': tmp_comp_count,
'seed': seed,
'log_like': log_like,
'pis': ds.raw_results.pis,
'mus': ds.raw_results.mus,
'sigmas': ds.raw_results.sigmas
}
)
# ds.plot_classifications(0)
if len(new_comp_counts) > 0:
if int(np.mean(new_comp_counts)) < component_count:
component_count = int(np.mean(new_comp_counts))
else:
converged = True
else:
converged = True
results_df = pd.DataFrame(
results,
columns=['comp', 'true_comp', 'seed', 'log_like']
)
min_comp_count = results_df.comp.min()
best_index = results_df[results_df.comp == min_comp_count].log_like.argmax()
best_run = results[best_index]
ds.results = None
ds.cluster(
component_count=best_run['comp'],
burn_in=0,
iteration_count=iteration_count,
random_seed=best_run['seed'],
model='bem'
)
log_like = ds.get_log_likelihood_trace()[0]
print log_like
ds.plot_classifications(0)
# Re-run a chain using the initial conditions from the last iteration
last_iter = ds.raw_results.get_iteration(0)
initial_conditions = {
'pis': last_iter.pis.flatten(),
'mus': last_iter.mus,
'sigmas': last_iter.sigmas
}
# reset DataSet results
ds.results = None
ds.cluster(
component_count=best_run['comp'],
burn_in=0,
iteration_count=iteration_count,
random_seed=1,
initial_conditions=initial_conditions
)
ds.plot_log_likelihood_trace()
pyplot.show()
valid_components = ds.get_valid_components()
for i in range(best_run['comp']):
ds.plot_iteration_traces(i)
ds.plot_animated_trace()
pass
|
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6603,
198
] | 2.083714 | 1,971 |
from vit_pytorch.vit import ViT
from vit_pytorch.vit3d import ViT3d
from vit_pytorch.dino import Dino
|
[
6738,
9090,
62,
9078,
13165,
354,
13,
85,
270,
1330,
16049,
51,
198,
6738,
9090,
62,
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13165,
354,
13,
85,
270,
18,
67,
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51,
18,
67,
198,
6738,
9090,
62,
9078,
13165,
354,
13,
67,
2879,
1330,
39430,
198
] | 2.428571 | 42 |
# Copyright Pincer 2021-Present
# Full MIT License can be found in `LICENSE` at the project root.
from __future__ import annotations
from dataclasses import dataclass
from typing import TYPE_CHECKING
from ...utils.api_object import APIObject
from ...utils.types import MISSING
if TYPE_CHECKING:
from typing import List
from ..app.select_menu import SelectOption
from ..message.button import ButtonStyle
from ..message.emoji import Emoji
from ...utils.types import APINullable
@dataclass(repr=False)
class MessageComponent(APIObject):
"""Represents a Discord Message Component object
Attributes
----------
type: :class:`int`
Component type
options: List[:class:`~pincer.objects.app.select_menu.SelectOption`]
The choices in the select, max 25
custom_id: APINullable[:class:`str`]
A developer-defined identifier for the component,
max 100 characters
disabled: APINullable[:class:`bool`]
Whether the component is disabled,
defaults to `False`
style: APINullable[:class:`~pincer.objects.message.button.ButtonStyle`]
One of button styles
label: APINullable[:class:`str`]
Text that appears on the button, max 80 characters
emoji: APINullable[:class:`~pincer.objects.message.emoji.Emoji`]
``name``, ``id``, and ``animated``
url: APINullable[:class:`str`]
A url for link-style buttons
placeholder: APINullable[:class:`str`]
Custom placeholder text if nothing is selected,
max 100 characters
min_values: APINullable[:class:`int`]
The minimum number of items that must be chosen;
|default| ``1``, min ``0``, max ``25``
max_values: APINullable[:class:`int`]
The maximum number of items that can be chosen;
|default| ``1``, max ``25``
components: APINullable[List[:class:`~pincer.objects.message.component.MessageComponent`]]
A list of child components
"""
# noqa: E501
type: int
options: List[SelectOption] = MISSING
custom_id: APINullable[str] = MISSING
disabled: APINullable[bool] = False
style: APINullable[ButtonStyle] = MISSING
label: APINullable[str] = MISSING
emoji: APINullable[Emoji] = MISSING
url: APINullable[str] = MISSING
placeholder: APINullable[str] = MISSING
min_values: APINullable[int] = 1
max_values: APINullable[int] = 1
components: APINullable[List[MessageComponent]] = MISSING
|
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] | 2.669189 | 925 |
#!/usr/bin/env python
# coding=utf-8
"""
"""
import click
import os
from collections import defaultdict
from hundo.fasta import read_fasta, format_fasta_record
@click.group()
@click.pass_context
def cli(obj):
"""
"""
@cli.command("tax-to-newick")
@click.argument("tax", type=click.File("r"))
@click.argument("fasta", type=click.File("r"))
@click.argument("outfasta", type=click.File("w"))
@click.argument("outmap", type=click.File("w"))
@click.argument("outtre", type=click.File("w"))
def tax_to_newick(tax, fasta, outfasta, outmap, outtre):
"""
Tax and FASTA input files represent clusters at 99%% identity via:
https://unite.ut.ee/sh_files/sh_mothur_release_10.10.2017.zip
"""
t = tree()
saved = set()
for line in tax:
toks = line.strip().split("\t")
taxonomies = toks[1].strip(";").split(";")
if not taxonomies[0] == "k__Fungi": continue
assert(len(taxonomies) == 7)
tree_add(t, taxonomies)
print(toks[0], taxonomies[6], sep="\t", file=outmap)
saved.add(toks[0])
tree_str = tree_to_newick(t)
print(tree_str, file=outtre)
for name, seq in read_fasta(fasta):
if name in saved:
print(format_fasta_record(name, seq), file=outfasta)
if __name__ == '__main__':
cli()
|
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# -*- coding: utf-8 -*-
"""Abstract base classes.
These are necessary to avoid circular imports between core.py and fields.py.
"""
import copy
class FieldABC(object):
"""Abstract base class from which all Field classes inherit.
"""
parent = None
name = None
class SchemaABC(object):
"""Abstract base class from which all Schemas inherit."""
|
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] | 3.306306 | 111 |
import torch
from torchvision import transforms
import os
import cv2
import time
import numpy as np
# alphabetfrom .keys import alphabet
import params
from torch.autograd import Variable
from PIL import Image
from utils import strLabelConverter,resizeNormalize
converter = strLabelConverter(params.alphabet)
# converter = strLabelConverter(''.join(alphabet))
|
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] | 3.594059 | 101 |
peers = {
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] | 1.676198 | 3,777 |
import numpy as np
import torch
import torch.nn.functional as F
import torch.nn as nn
|
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] | 3.296296 | 27 |
"""RealNVP bijector."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
ConditionalBijector = tf.contrib.distributions.bijectors.ConditionalBijector
__all__ = [
"RealNVPBijector",
]
def checkerboard(shape, parity='even', dtype=tf.bool):
"""TODO: Implement for dimensions >1"""
if len(shape) > 1:
raise NotImplementedError(
"checkerboard not yet implemented for dimensions >1")
unit = (tf.constant((True, False))
if parity == 'even' else tf.constant((False, True)))
num_elements = np.prod(shape)
tiled = tf.tile(unit, ((num_elements // 2) + 1, ))[:num_elements]
return tf.cast(tf.reshape(tiled, shape), dtype)
class CouplingBijector(ConditionalBijector):
"""TODO"""
def __init__(self,
parity,
translation_fn,
scale_fn,
event_ndims=0,
validate_args=False,
name="coupling_bijector"):
"""Instantiates the `CouplingBijector` bijector.
Args:
TODO
event_ndims: Python scalar indicating the number of dimensions
associated with a particular draw from the distribution.
validate_args: Python `bool` indicating whether arguments should be
checked for correctness.
name: Python `str` name given to ops managed by this object.
Raises:
ValueError: if TODO happens
"""
self._graph_parents = []
self._name = name
self._validate_args = validate_args
self.parity = parity
self.translation_fn = translation_fn
self.scale_fn = scale_fn
super().__init__(event_ndims=event_ndims,
validate_args=validate_args,
name=name)
# TODO: Properties
def _maybe_assert_valid_x(self, x):
"""TODO"""
if not self.validate_args:
return x
raise NotImplementedError("_maybe_assert_valid_x")
def _maybe_assert_valid_y(self, y):
"""TODO"""
if not self.validate_args:
return y
raise NotImplementedError("_maybe_assert_valid_y")
class RealNVPBijector(ConditionalBijector):
"""TODO"""
def __init__(self,
num_coupling_layers=2,
translation_hidden_sizes=(25,),
scale_hidden_sizes=(25,),
event_ndims=0,
validate_args=False,
name="real_nvp"):
"""Instantiates the `RealNVPBijector` bijector.
Args:
TODO
event_ndims: Python scalar indicating the number of dimensions
associated with a particular draw from the distribution.
validate_args: Python `bool` indicating whether arguments should be
checked for correctness.
name: Python `str` name given to ops managed by this object.
Raises:
ValueError: if TODO happens
"""
self._graph_parents = []
self._name = name
self._validate_args = validate_args
self._num_coupling_layers = num_coupling_layers
self._translation_hidden_sizes = tuple(translation_hidden_sizes)
self._scale_hidden_sizes = tuple(scale_hidden_sizes)
self.build()
super().__init__(event_ndims=event_ndims,
validate_args=validate_args,
name=name)
# TODO: Properties
def _maybe_assert_valid_x(self, x):
"""TODO"""
if not self.validate_args:
return x
raise NotImplementedError("_maybe_assert_valid_x")
def _maybe_assert_valid_y(self, y):
"""TODO"""
if not self.validate_args:
return y
raise NotImplementedError("_maybe_assert_valid_y")
|
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] | 2.137894 | 1,842 |
# politician/views.py
# Brought to you by We Vote. Be good.
# -*- coding: UTF-8 -*-
from django.http import HttpResponseRedirect
from django.shortcuts import get_object_or_404, render
from django.contrib import messages
from django.contrib.messages import get_messages
from django.core.urlresolvers import reverse
from django.views import generic
from django.views.generic import TemplateView
from django.utils import timezone
from politician.forms import TagNewForm
from politician.models import Politician, PoliticianTagLink
from tag.models import Tag
# TODO Next step is to get Twitter vacuum working so we can pull in Tweets automatically based on tags/handles
def politician_tag_new_view(request, politician_id):
"""
Form to add a new link tying a politician to twitter tags
:param request:
:return:
"""
messages_on_stage = get_messages(request)
# for message in messages_on_stage:
# if message.level is ERROR:
politician_on_stage = get_object_or_404(Politician, id=politician_id)
try:
tag_link_list = politician_on_stage.tag_link.all()
except PoliticianTagLink.DoesNotExist:
tag_link_list = None
template_values = {
'politician_on_stage': politician_on_stage,
'tag_link_list': tag_link_list,
'messages_on_stage': messages_on_stage,
}
return render(request, 'politician/politician_tag_new.html', template_values)
def politician_tag_new_test_view(request, politician_id):
"""
Form to add a new link tying a politician to twitter tags
:param request:
:return:
"""
tag_new_form = TagNewForm()
politician_on_stage = get_object_or_404(Politician, id=politician_id)
# TODO Find the tags attached to this politician
try:
tag_list = PoliticianTagLink.objects.get(politician=politician_on_stage)
except PoliticianTagLink.DoesNotExist:
tag_list = None
template_values = {
'tag_new_form': tag_new_form,
'politician_on_stage': politician_on_stage,
'tag_list': tag_list,
}
return render(request, 'politician/politician_tag_new_test.html', template_values)
def politician_tag_new_process_view(request, politician_id):
"""
Process the form to add a new link tying a politician to twitter tags
"""
politician_on_stage = get_object_or_404(Politician, id=politician_id)
new_tag = request.POST['new_tag']
# If an invalid tag didn't come in, redirect back to tag_new
if not is_tag_valid(new_tag):
messages.add_message(request, messages.INFO, 'That is not a valid tag. Please enter a different tag.')
return HttpResponseRedirect(reverse('politician:politician_tag_new', args=(politician_id,)))
new_tag_temp, created = Tag.objects.get_or_create(hashtag_text=new_tag)
new_tag_link = PoliticianTagLink(tag=new_tag_temp, politician=politician_on_stage)
new_tag_link.save()
return HttpResponseRedirect(reverse('politician:politician_detail', args=(politician_id,)))
|
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628
] | 2.816822 | 1,070 |
import sys
sys.path.insert(0, '..')
import updates_msbm_vi_iter
import updates_msbm_vi
import updates_msbm2_vi_iter
import updates_msbm2_vi
import os
import util
import init_msbm_vi as im
import numpy as np
import numpy.random as npr
import pdb
# ###########################################################
# ###########################################################
# ###########################################################
if __name__ == '__main__':
file_url = os.path.join('..', 'experiments', 'two_prototype', 'data', 'twoprototype_105_250.pickle')
remove_self_loops = False
updater_einsum = updates_msbm_vi
updater_iter = updates_msbm_vi_iter
runner = TestUpdates(updater_einsum, updater_iter, file_url, remove_self_loops)
runner.test_all()
updater_einsum = updates_msbm2_vi
updater_iter = updates_msbm2_vi_iter
runner = TestUpdates(updater_einsum, updater_iter, file_url, remove_self_loops)
runner.test_all()
remove_self_loops = True
updater_einsum = updates_msbm_vi
updater_iter = updates_msbm_vi_iter
runner = TestUpdates(updater_einsum, updater_iter, file_url, remove_self_loops)
runner.test_all()
updater_einsum = updates_msbm2_vi
updater_iter = updates_msbm2_vi_iter
runner = TestUpdates(updater_einsum, updater_iter, file_url, remove_self_loops)
runner.test_all()
|
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2676,
628,
220,
220,
220,
17490,
796,
6208,
4933,
19581,
7,
929,
67,
729,
62,
68,
1040,
388,
11,
2325,
729,
62,
2676,
11,
2393,
62,
6371,
11,
4781,
62,
944,
62,
5439,
2840,
8,
198,
220,
220,
220,
17490,
13,
9288,
62,
439,
3419,
628,
220,
220,
220,
4781,
62,
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62,
5439,
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796,
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628,
220,
220,
220,
2325,
729,
62,
68,
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62,
907,
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62,
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220,
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2325,
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62,
68,
1040,
388,
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5992,
62,
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17,
62,
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198,
220,
220,
220,
2325,
729,
62,
2676,
796,
5992,
62,
907,
20475,
17,
62,
8903,
62,
2676,
628,
220,
220,
220,
17490,
796,
6208,
4933,
19581,
7,
929,
67,
729,
62,
68,
1040,
388,
11,
2325,
729,
62,
2676,
11,
2393,
62,
6371,
11,
4781,
62,
944,
62,
5439,
2840,
8,
198,
220,
220,
220,
17490,
13,
9288,
62,
439,
3419,
198
] | 2.724206 | 504 |
import networkx as nx
|
[
11748,
3127,
87,
355,
299,
87,
628
] | 3.285714 | 7 |
# -*- coding:utf8 -*-
r'''
TODO: remove all tensorflow graph construction in `build_op_info`
'''
import os
import numpy as np
import idx2numpy as idx2np
import tensorflow as tf
from utensor_cgen.ir import OperationInfo, TensorInfo
from utensor_cgen.ir.converter import (AttrValueConverter, DataTypeConverter,
GenericTensorConverterMixin)
from utensor_cgen.logger import logger
from utensor_cgen.matcher import OpEqualityDelegate, _morphism
from utensor_cgen.transformer.optimizer import RefCntOptimizer
from utensor_cgen.utils import NamescopedKWArgsParser
from .snippets import * # pylint: disable=W0401,W0614
__all__ = ['OperatorFactory', 'OpNotSupportedError']
@OperatorFactory.register
@OpEqualityDelegate.is_compatible_with("Inline", _morphism.Const2InlineMorphism)
@OperatorFactory.register
@OpEqualityDelegate.is_associative(
permutations=((0, 1), (1, 0))
)
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
#hard coding to uint8_t uint8_t int32_t for now
@OperatorFactory.register
@OperatorFactory.register
@OpEqualityDelegate.is_compatible_with("Const", _morphism.Inline2ConstMorphism)
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
@OperatorFactory.register
|
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198,
198,
31,
18843,
1352,
22810,
13,
30238,
198
] | 2.966412 | 655 |
#!/usr/bin/env python
"""Unit tests for WSGI HTTP Basic Auth handler
NERC DataGrid Project
"""
__author__ = "P J Kershaw"
__date__ = "13/10/09"
__copyright__ = "(C) 2009 Science and Technology Facilities Council"
__license__ = "BSD - see LICENSE file in top-level directory"
__contact__ = "[email protected]"
__revision__ = '$Id$'
import logging
logging.basicConfig(level=logging.DEBUG)
import unittest
import urllib.request, urllib.error, urllib.parse
import base64
import paste.fixture
from paste.httpexceptions import HTTPUnauthorized
from ndg.security.server.test.base import BaseTestCase
from ndg.security.server.wsgi.httpbasicauth import HttpBasicAuthMiddleware
class TestAuthnApp(object):
'''Test Application for the Authentication handler to protect'''
response = b"Test HTTP Basic Authentication application"
if __name__ == "__main__":
unittest.main()
|
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220,
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198,
198,
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6208,
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7,
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220,
220,
220,
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21360,
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6,
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220,
220,
220,
2882,
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275,
1,
14402,
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14392,
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3586,
1,
628,
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220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419
] | 2.941748 | 309 |
"""methods for liners and domes"""
import numpy as np
from tankoh2 import pychain
from tankoh2.service import log
from tankoh2.exception import Tankoh2Error
from tankoh2.utilities import updateName, copyAsJson
# #########################################################################################
# Create Liner
# #########################################################################################
def domeContourLength(dome):
"""Returns the contour length of a dome"""
contourCoords = np.array([dome.getXCoords(), dome.getRCoords()]).T
contourDiffs = contourCoords[1:,:] - contourCoords[:-1]
contourLength = np.sum(np.linalg.norm(contourDiffs, axis=1))
return contourLength
def getDome(cylinderRadius, polarOpening, domeType=None,
x=None, r=None):
"""
:param cylinderRadius: radius of the cylinder
:param polarOpening: polar opening radius
:param domeType: pychain.winding.DOME_TYPES.ISOTENSOID or pychain.winding.DOME_TYPES.CIRCLE
:param x: x-coordinates of a custom dome contour
:param r: radius-coordinates of a custom dome contour. r[0] starts at cylinderRadius
"""
if domeType is None:
domeType = pychain.winding.DOME_TYPES.ISOTENSOID
elif isinstance(domeType, str):
domeType = domeType.lower()
if domeType == 'isotensoid':
domeType = pychain.winding.DOME_TYPES.ISOTENSOID
elif domeType == 'circle':
domeType = pychain.winding.DOME_TYPES.CIRCLE
else:
raise Tankoh2Error(f'wrong dome type "{domeType}". Valid dome types: [isotensoid, circle]')
# build dome
dome = pychain.winding.Dome()
dome.buildDome(cylinderRadius, polarOpening, domeType)
if x is not None and r is not None:
if not np.allclose(r[0], cylinderRadius):
raise Tankoh2Error('cylinderRadius and r-vector do not fit')
if not np.allclose(r[-1], polarOpening):
raise Tankoh2Error('polarOpening and r-vector do not fit')
dome.setPoints(x, r)
return dome
def getLiner(dome, length, linerFilename=None, linerName=None, dome2 = None, nodeNumber = 500):
"""Creates a liner
:param dome: dome instance
:param length: zylindrical length of liner
:param linerFilename: if given, the liner is saved to this file for visualization in µChainWind
:param linerName: name of the liner written to the file
:return:
"""
# create a symmetric liner with dome information and cylinder length
liner = pychain.winding.Liner()
# spline for winding calculation is left on default of 1.0
if dome2:
contourLength = length + domeContourLength(dome) + domeContourLength(dome2)
else:
contourLength = length / 2 + domeContourLength(dome) # use half model (one dome, half cylinder)
deltaLengthSpline = contourLength / nodeNumber # just use half side
if dome2 is not None:
log.info("Creat unsymmetric vessel")
liner.buildFromDomes(dome, dome2, length, deltaLengthSpline)
else:
log.info("Create symmetric vessel")
liner.buildFromDome(dome, length, deltaLengthSpline)
if linerFilename:
liner.saveToFile(linerFilename)
updateName(linerFilename, linerName, ['liner'])
copyAsJson(linerFilename, 'liner')
liner.loadFromFile(linerFilename)
return liner
|
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] | 2.657299 | 1,281 |
import syft as sy
import torch as th
from grid.client import GridClient
|
[
11748,
827,
701,
355,
827,
198,
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28034,
355,
294,
198,
198,
6738,
10706,
13,
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1330,
24846,
11792,
628
] | 3.7 | 20 |
import unittest
import pytest
import tensorkit as tk
from tensorkit import tensor as T
from tensorkit.arg_check import *
from tests.helper import *
|
[
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555,
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198,
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525,
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1635,
628
] | 3.145833 | 48 |
#from stockfighter import Stockfighter
import os, time
# sf = Stockfighter()
#
# level = sf.levels['chock_a_block']
# info = level.start()
# print(info)
#
# sf = Stockfighter()
# print(sf.heartbeat())
#
# venue = sf.venues['PVIEX']
#
# stock = venue.stocks['SOF']
# for stock in venue.stocks:
# print(stock)
#
# ORDER_SIZE = 50
# remaining = 100000 - 42823
# goal = 9103
#
# def run():
# while(remaining > 0):
# quote = stock.quote()
# size = quote['askSize']
# if(size < 1):
# continue
# time.sleep(1)
# ask = quote['ask']
# if(ask > goal):
# continue
# time.sleep(1)
# order = min(remaining, size, ORDER_SIZE)
# if order > 0:
# print('Placing order for {} at {}. Remaining: {}'.format(order, ask, remaining))
# stock.buy(ACCOUNT, ask, order)
# remaining -= order
#(venue='CENOEX', account='SAS22786391')
|
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########################################################################
#
# License: BSD
# Created: October 11, 2013
# Author: Francesc Alted
#
########################################################################
"""
Implementation of an out of core matrix-matrix multiplication for PyTables.
"""
import sys, math
import numpy as np
import tables as tb
_MB = 2**20
OOC_BUFFER_SIZE = 32*_MB
"""The buffer size for out-of-core operations.
"""
def dot(a, b, out=None):
"""
Matrix multiplication of two 2-D arrays.
Parameters
----------
a : array_like
First argument.
b : array_like
Second argument.
out : array_like, optional
Output argument. This must have the exact kind that would be
returned if it was not used.
Returns
-------
output : CArray or scalar
Returns the dot product of `a` and `b`. If `a` and `b` are
both scalars or both 1-D arrays then a scalar is returned;
otherwise a new CArray (in file dot.h5:/out) is returned. If
`out` parameter is provided, then it is returned instead.
Raises
------
ValueError
If the last dimension of `a` is not the same size as the
second-to-last dimension of `b`.
"""
if len(a.shape) != 2 or len(b.shape) != 2:
raise (ValueError, "only 2-D matrices supported")
if a.shape[1] != b.shape[0]:
raise (ValueError,
"last dimension of `a` does not match first dimension of `b`")
l, m, n = a.shape[0], a.shape[1], b.shape[1]
if out is not None:
if out.shape != (l, n):
raise (ValueError, "`out` array does not have the correct shape")
else:
f = tb.openFile('dot.h5', 'w')
filters = tb.Filters(complevel=5, complib='blosc')
out = f.createCArray(f.root, 'out', tb.Atom.from_dtype(a.dtype),
shape=(l, n), filters=filters)
# Compute a good block size
buffersize = OOC_BUFFER_SIZE
bl = math.sqrt(buffersize / out.dtype.itemsize)
bl = 2**int(math.log(bl, 2))
for i in range(0, l, bl):
for j in range(0, n, bl):
for k in range(0, m, bl):
a0 = a[i:min(i+bl, l), k:min(k+bl, m)]
b0 = b[k:min(k+bl, m), j:min(j+bl, n)]
out[i:i+bl, j:j+bl] += np.dot(a0, b0)
return out
if __name__ == "__main__":
"""Small benchmark for comparison against numpy.dot() speed"""
from time import time
# Matrix dimensions
L, M, N = 1000, 100, 2000
print "Multiplying (%d, %d) x (%d, %d) matrices" % (L, M, M, N)
a = np.linspace(0, 1, L*M).reshape(L, M)
b = np.linspace(0, 1, M*N).reshape(M, N)
t0 = time()
cdot = np.dot(a,b)
print "Time for np.dot->", round(time()-t0, 3), cdot.shape
f = tb.openFile('matrix-pt.h5', 'w')
l, m, n = a.shape[0], a.shape[1], b.shape[1]
filters = tb.Filters(complevel=5, complib='blosc')
ad = f.createCArray(f.root, 'a', tb.Float64Atom(), (l,m),
filters=filters)
ad[:] = a
bd = f.createCArray(f.root, 'b', tb.Float64Atom(), (m,n),
filters=filters)
bd[:] = b
cd = f.createCArray(f.root, 'c', tb.Float64Atom(), (l,n),
filters=filters)
t0 = time()
dot(a, b, out=cd)
print "Time for ooc dot->", round(time()-t0, 3), cd.shape
np.testing.assert_almost_equal(cd, cdot)
f.close()
|
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] | 2.198227 | 1,579 |
## Helper functions to clean up Clubes de Ciencia notebooks
## 5 July 2019 EHU
import xarray as xr
import pandas as pd
import numpy as np
from oggm import utils
def ice_to_freshwater(icevol, rho_ice=900, rho_water=1000):
"""Cleanly convert volume of glacial ice (km3) to equivalent volume fresh water (liter).
Arguments:
icevol = volume of ice to convert, in km3
rho_ice = density of glacial ice (default 900 kg/m3)
rho_water = density of freshwater (default 1000 kg/m3)
"""
km3_to_ltr = 1E12
water_vol_km3 = icevol * rho_ice / rho_water
return water_vol_km3 * km3_to_ltr
def read_run_results(gdir, filesuffix=None):
"""Reads the output diagnostics of a simulation and puts the data in a pandas dataframe.
Parameters
----------
gdir : the glacier directory
filesuffix : the file identifier
Returns
-------
a pandas Dataframe with monthly temp and precip
"""
with xr.open_dataset(gdir.get_filepath('model_diagnostics', filesuffix=filesuffix)) as ds:
ds = ds.load()
# Lemgth needs filtering
ts = ds.length_m.to_series()
ts = ts.rolling(12*3).min()
ts.iloc[0:12*3] = ts.iloc[12*3]
# Volume change
delta_vol = np.append(ds.volume_m3.data[1:] - ds.volume_m3.data[0:-1], [0])
if ds.calendar_month[0] == 10 and gdir.cenlat < 0:
# this is to cover up a bug in OGGM
_, m = utils.hydrodate_to_calendardate(ds.hydro_year.data, ds.hydro_month.data, start_month=4)
ds.calendar_month[:] = m
odf = pd.DataFrame()
odf['length_m'] = ts
odf['volume_m3'] = ds.volume_m3
odf['delta_water_m3'] = delta_vol * 0.9
odf['month'] = ds.calendar_month
return odf
def read_climate_statistics(gdir):
"""Reads the annual cycle of climate for [1985-2015] at the glacier terminus elevation.
Parameters
----------
gdir : the glacier directory
Returns
-------
a pandas Dataframe with monthly average temp and precip
"""
with xr.open_dataset(gdir.get_filepath('climate_monthly')) as ds:
ds = ds.load()
ds = ds.sel(time=slice('1985', '2015'))
dsm = ds.groupby('time.month').mean(dim='time')
odf = pd.DataFrame()
odf['temp_celcius'] = dsm.temp.to_series()
odf['prcp_mm_mth'] = dsm.prcp.to_series()
# We correct for altitude difference
d = utils.glacier_statistics(gdir)
odf['temp_celcius'] += (ds.ref_hgt - d['flowline_min_elev']) * 0.0065
return odf
def plot_xz_bed(x, bed, ax=None, ylim=None):
"""This function implements a glacier bed, prepared axes and a legend in
altitude vs. distance along a glacier plot. Based on function of the same
name in OGGM-Edu, but adds explicit axes argument.
Parameters
----------
x : ndarray
distance along glacier (all steps in km)
bed : ndarray
bed rock
Parameters (Optional)
----------
ax : matplotlib axes instance on which to plot
If None, calls plt.gca()
ylim : tuple, y-limits of plot
If None, calls ax.get_ylim()
"""
if ax is None:
ax = plt.gca()
if ylim is None:
ylim = ax.get_ylim()
ax.plot(x, bed, color='k', label='Bedrock', linestyle=':', linewidth=1.5)
ax.set_xlabel('Distance along glacier [km]')
ax.set_ylabel('Altitude [m]')
ax.set_ylim(ylim)
ax.legend(loc='best', frameon=False)
|
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] | 2.283563 | 1,527 |
# -*- coding: utf-8 -*-
import re
import config
import surllib
# Map of children => urlPrefix
# 'Andrea 0A' => '/parent/1234/Andrea/'
_children = None
def getChildren():
'''Returns of list of "available" children in the system'''
global _children
if not _children:
_children = dict()
seen = set()
config.log(u'Henter liste af børn')
data = surllib.skoleLogin()
# Name of "First child"
fst = data.find(id="sk-personal-menu-button").text.strip()
for a in data.findAll('a', href=re.compile('^(/[^/]*){3}/Index$')):
url = a['href'].rsplit('/', 1)[0].rstrip('/')
if url in seen:
continue
seen.add(url)
name = a.text.strip() or fst
if name not in _children:
config.log(u'Barn %s => %s' % (name, url), 2)
_children[name] = url
cns = sorted(_children.keys(), key=ckey)
config.log(u'Følgende børn blev fundet: ' + u', '.join(cns))
return sorted(_children.keys(), key=ckey)
|
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2555,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1775,
13,
2860,
7,
6371,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
796,
257,
13,
5239,
13,
36311,
3419,
393,
277,
301,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1438,
407,
287,
4808,
17197,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4566,
13,
6404,
7,
84,
6,
47359,
4064,
82,
5218,
4064,
82,
6,
4064,
357,
3672,
11,
19016,
828,
362,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
17197,
58,
3672,
60,
796,
19016,
198,
220,
220,
220,
220,
220,
220,
220,
269,
5907,
796,
23243,
28264,
17197,
13,
13083,
22784,
1994,
28,
66,
2539,
8,
198,
220,
220,
220,
220,
220,
220,
220,
4566,
13,
6404,
7,
84,
6,
37,
24172,
75,
70,
38396,
275,
24172,
35906,
275,
2768,
1814,
316,
25,
705,
1343,
334,
3256,
45302,
22179,
7,
66,
5907,
4008,
628,
220,
220,
220,
1441,
23243,
28264,
17197,
13,
13083,
22784,
1994,
28,
66,
2539,
8,
628,
198
] | 2.073501 | 517 |
"""
Decobrir se um numero é impar oou par
"""
print(25*"-")
while True:
numero = int(input("Digite um numero: "))
if (numero % 2) == 0:
print(f"Numero digitado, {numero} é PAR: ")
elif(numero % 2) != 0:
print(f"Numero digitado, {numero} é IMPAR: ")
print(25*"-")
|
[
37811,
198,
10707,
672,
29283,
384,
23781,
997,
3529,
38251,
848,
283,
267,
280,
1582,
198,
37811,
198,
198,
4798,
7,
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9,
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8,
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25,
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3601,
7,
69,
1,
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11,
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3529,
92,
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8,
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220,
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1288,
361,
7,
22510,
3529,
4064,
362,
8,
14512,
657,
25,
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3601,
7,
69,
1,
33111,
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11,
1391,
22510,
3529,
92,
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1503,
25,
366,
8,
198,
220,
220,
220,
3601,
7,
1495,
9,
26793,
4943,
198
] | 2.106383 | 141 |
import json
import numpy as np
import xgboost as xgb
import testing as tm
import pytest
try:
import matplotlib
matplotlib.use('Agg')
from matplotlib.axes import Axes
from graphviz import Source
except ImportError:
pass
pytestmark = pytest.mark.skipif(**tm.no_multiple(tm.no_matplotlib(),
tm.no_graphviz()))
dpath = 'demo/data/agaricus.txt.train'
|
[
11748,
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256,
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7,
1174,
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13,
3919,
62,
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7,
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13,
3919,
62,
6759,
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8019,
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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,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
256,
76,
13,
3919,
62,
34960,
85,
528,
3419,
4008,
198,
198,
67,
6978,
796,
705,
9536,
78,
14,
7890,
14,
32452,
24552,
13,
14116,
13,
27432,
6,
628
] | 2.1875 | 192 |
# -*- coding: utf-8 -*-
import unittest
import six
import tensorflow as tf
from tfsnippet.components import DictMapper, Linear, Dense
from tests.helper import TestCase
if __name__ == '__main__':
unittest.main()
|
[
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
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555,
715,
395,
198,
198,
11748,
2237,
198,
11748,
11192,
273,
11125,
355,
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198,
198,
6738,
256,
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77,
3974,
316,
13,
5589,
3906,
1330,
360,
713,
44,
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11,
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11,
360,
1072,
198,
6738,
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13,
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525,
1330,
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628,
198,
198,
361,
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3672,
834,
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705,
834,
12417,
834,
10354,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
198
] | 2.650602 | 83 |
from flask import Flask
from flask_restful import Resource
from flask_restful import Api
import numpy as np
import cv2
import werkzeug
from flask_restful import reqparse
parser = reqparse.RequestParser()
parser.add_argument("file", type = werkzeug.datastructures.FileStorage, location = "files")
app = Flask(__name__)
api = Api(app)
import base64
api.add_resource(ImageServer, "/")
if __name__ == "__main__":
app.run(debug = True, port = 5000)
|
[
6738,
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198,
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15042,
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366,
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220,
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598,
13,
5143,
7,
24442,
796,
6407,
11,
2493,
796,
23336,
8,
198
] | 2.987097 | 155 |
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
import boto3
#Evaluate Risk Level
#Return True to raise alert if risk level exceeds threshold
#Return False to Archive finding
|
[
11748,
18931,
198,
6404,
1362,
796,
18931,
13,
1136,
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1362,
3419,
198,
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2,
13615,
10352,
284,
20816,
4917,
198
] | 3.5 | 58 |
#Pluginname="GLS Tracking (Android)"
#Type=App
import os
import json
import tempfile
|
[
2,
37233,
3672,
2625,
38,
6561,
37169,
357,
25934,
16725,
201,
198,
2,
6030,
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11748,
33918,
201,
198,
11748,
20218,
7753,
201,
198,
201
] | 2.735294 | 34 |
# Module for UTF-7 encoding
from base64 import b64encode
from utf16 import utf16_encode, UTF16_MAXIMUM_CODEPOINT
DIRECT_CHARACTERS = '\'(),-./:?'
UTF7_MAXIMUM_CODEPOINT = UTF16_MAXIMUM_CODEPOINT
|
[
2,
19937,
329,
41002,
12,
22,
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22921,
3955,
5883,
62,
34,
3727,
8905,
46,
12394,
628
] | 2.313953 | 86 |
# -*- coding: utf-8 -*-
"""Command line execution listener module of ping-me"""
from __future__ import print_function
from dateutil import parser
import argparse
import datetime
import getpass
import hashlib
import os
import parsedatetime
import sys
import time
import ping_me.authenticate
import ping_me.engine
home = os.path.expanduser("~")
cal = parsedatetime.Calendar()
def main():
"""Parse the arguments using argparse package"""
argparser = argparse.ArgumentParser(description='ping-me')
argparser.add_argument("-e", action="store_true", default=False)
argparser.add_argument("-V", "--version", action="store_true",
default=False)
argparser.add_argument("-d", "--date", action="store", dest="DATE",
default=None, nargs="+")
argparser.add_argument("-t", "--time", action="store", dest="TIME",
default=None, nargs="+")
argparser.add_argument("message", action="store", help="Message",
default=None, nargs="*")
argparser.add_argument("-v", action="store_true", default=False)
args = argparser.parse_args()
process(args)
def process(args):
"""Process the arguments. Call engine if flags are used."""
if args.e:
detailed_usage()
sys.exit(2)
if args.version:
import release
print(release.__version__)
sys.exit(2)
if args.DATE is not None and args.TIME is not None:
message = ' '.join(args.message).lstrip('to ')
date_time = parser.parse(' '.join(args.DATE) +
' ' + ' '.join(args.TIME))
if len(message) == 0:
print("What is the message of your reminder?\n")
print("Use ping-me -h for help\n")
sys.exit(2)
ping_me.engine.engine(message, date_time.year, date_time.month,
date_time.day, date_time.hour, date_time.minute,
args.v)
elif args.TIME is not None:
m_time = parser.parse(' '.join(args.TIME))
c_time = datetime.datetime.now()
if (m_time - c_time).days == -1:
m_time += datetime.timedelta(1)
message = ' '.join(args.message).lstrip('to ')
if len(message) == 0:
print("What is the message of your reminder?\n")
print("Use ping-me -h for help\n")
sys.exit(2)
ping_me.engine.engine(message, m_time.year, m_time.month,
m_time.day, m_time.hour, m_time.minute, args.v)
elif args.DATE is not None:
c_time = repr(time.localtime().tm_hour) + ":" + \
repr(time.localtime().tm_min)
m_date = parser.parse(' '.join(args.DATE) + ' ' + c_time)
message = ' '.join(args.message).lstrip('to ')
if len(message) == 0:
print("What is the message of your reminder?\n")
print("Use ping-me -h for help\n")
sys.exit(2)
ping_me.engine.engine(message, m_date.year, m_date.month,
m_date.day, m_date.hour, m_date.minute, args.v)
else:
if len(args.message) == 0:
sys.stderr.write("Use ping-me -h for help\n")
sys.exit(2)
elif len(args.message) == 1 and args.message == ['config']:
ping_me.authenticate.newuser()
elif len(args.message) == 1 and args.message == ['reconfig']:
reconfig()
else:
nlp_process(args)
def nlp_process(args):
"""Process arguments using Natural Language Processing."""
# If there is something like "to do something in 2 mins"
try:
mins_index = args.message.index('mins')
args.message[mins_index] = 'minutes'
except ValueError:
pass
to_parse = ' '.join(args.message)
try:
m_date = cal.nlp(to_parse)[0][0]
except TypeError:
print("Sorry, couldn't understand your message. Try again.")
sys.exit(2)
# Remove the keywords
keywords = cal.nlp(to_parse)[0][-1].split()
for word in keywords:
args.message.remove(word)
# Remove redundant word 'this'
try:
args.message.remove('this')
except ValueError:
pass
if 'to' in args.message:
args.message.remove('to')
message = ' '.join(args.message)
ping_me.engine.engine(message, m_date.year, m_date.month,
m_date.day, m_date.hour, m_date.minute,
args.v)
def detailed_usage():
"""Detailed documentation of ping-me."""
print("Welcome to the detailed documentation of ping-me !")
# Inspired from 'import this'
s = " "; l = "_ "; r = " _"; f = "/"; b = "\\"; p = "|"; d = "— "
print(s*6 + l*5 + s + l*4 + r + s*12 + l + r*5 + s*2 + r + s*8 + l +
s*7 + l*4)
print(s*5 + f + s*8 + f + s*5 + f + s*4 + f + s + b + s*10 + f + s + f +
s*12 + f + s + b + s*6 + f + s + p + s*6 + f + s*7)
print(s*4 + f + s*8 + f + s*5 + f + s*4 + f + s*3 + b + s*8 + f + s + f +
s*12 + f + s*3 + b + s*4 + f + s*2 + p + s*5 + f + s*7)
print(s*3 + f + r*4 + f + s*5 + f + s*4 + f + s*5 + b + s*6 + f + s + f +
s*2 + r*4 + s*2 + f + 5*s + b + s*2 + f + s*3 + p + s*4 + f + l*4)
print(s*2 + f + s*14 + f + s*4 + f + s*7 + b + s*4 + f + s + f + s*9 + f +
s*2 + f + s*7 + b + f + s*4 + p + s*3 + f + s*7)
print(s + f + s*14 + f + s*4 + f + s*9 + b + s*2 + f + s + f + s*9 + f +
s*2 + f + s*14 + p + s*2 + f + s*7)
print(f + s*11 + d*4 + f + s*11 + b + f + s + f + (r*5)[1:] + f + s*2 +
f + s*15 + p + s + f + (r*4)[1:])
print("")
print("ping-me works well with time and date flags already. " +
"Use 'ping-me -h' for that option. " +
"However, ping-me is smart enough to work without flags.\n")
print("Examples : ")
print("\t\t1. ping-me to call mom tonight")
print("\t\t2. ping-me to buy milk early today")
print("\t\t3. ping-me to go home seven days from now")
print("\t\t4. ping-me to take a nap this afternoon")
print("\t\t5. ping-me to go workout next month")
print("")
print("Report (and track process on fixing) bugs on " +
"https://github.com/OrkoHunter/ping-me. Or simply write a mail " +
"to Himanshu Mishra at himanshumishra[at]iitkgp[dot]ac[dot]in")
def reconfig():
"""Reconfigure the user. Removes all the information of existing one."""
if not os.path.exists(home + "/.pingmeconfig"):
ping_me.authenticate.newuser()
else:
old_pass = hashlib.md5(getpass.getpass("Old Password : " +
"").rstrip()).hexdigest()
if old_pass == ping_me.authenticate.extract_password():
ping_me.authenticate.newuser()
else:
print("Authentication failed.")
sys.exit(2)
if __name__ == "__main__":
main()
|
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834,
12417,
834,
1298,
198,
220,
220,
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1388,
3419,
198
] | 2.091951 | 3,317 |
"""Test workflow of forecasting model."""
import sys
sys.path.append("../Forecasting")
import model
def test_forecast():
"""Optimize an ARIMA model and predict a few data points."""
START = 5
END = 10
print("Forecasting...")
f = model.Forecast("../Forecasting/blockchain.csv")
f.optimizeARIMA(range(5), range(5), range(5), f.endog, f.exog)
pred = f.predictARIMA(START, END)
assert len(pred) == (END - START)
|
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357,
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532,
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198
] | 2.652695 | 167 |
# prototxt_basic
# ----------------------------------------------------------------
|
[
2,
1237,
313,
742,
62,
35487,
628,
198,
198,
2,
16529,
628,
628,
198
] | 6.5 | 14 |
"""
Tests for checking gregorian calendar date.
Astronomical year contains 365,2425 days:
365 for usual year and 366 for leap
Leap years:
0.2425 is 97 / 400 or 1/4 - 1/100 + 1/400
It means:
- each 4th year is leap, except 3 of 4 round dates
- 2004, 2008, 2012 and etc are leap
- 2000, 2400, 2800 and etc. is leap
- 2100, 2200, 2300, 2500, 2600, 2700, 2900 and etc. are NOT leap
- for 400 years 97 are leap
"""
import pytest
from src.calendar import date_to_gregorian, GregorianDate
@pytest.mark.parametrize(
"day, month, year",
[
pytest.param(1, 1, 1, id="Minimum supported date"),
pytest.param(31, 12, 9999, id="Maximum supported date"),
pytest.param(29, 2, 4, id="First supported usual 4th leap year"),
pytest.param(29, 2, 2020, id="Usual 4th leap year"),
pytest.param(29, 2, 9996, id="Last supported usual 4th leap year"),
pytest.param(29, 2, 400, id="First supported round leap year"),
pytest.param(29, 2, 2000, id="Usual round leap year"),
pytest.param(29, 2, 9600, id="Last supported round leap year"),
],
)
def test_correct_date_format(day, month, year):
"""Check correct date"""
result: GregorianDate = date_to_gregorian(day=day, month=month, year=year)
assert (
result.correct
), f"Correct date '{day}-{month}-{year}'(day-month-year) is recognized as incorrect"
def test_400_years_contain_97_leap_years():
"""Check property of count leap years for 400 consecutive years"""
start_year = 2000
leap_years = [
year
for year in range(start_year, start_year + 400)
if date_to_gregorian(year=year, month=2, day=29).correct
]
actual_count = len(leap_years)
expected_count = 97
assert actual_count == expected_count, (
f"For 400 consecutive years '{expected_count}' should be leap. "
f"But actual count: '{actual_count}'. "
f"Years, recognized as leap:\n{leap_years}"
)
@pytest.mark.parametrize(
"day, month, year",
[
# 29th february for not leap years
pytest.param(29, 2, 1, id="First supported usual year"),
pytest.param(29, 2, 2021, id="Usual year"),
pytest.param(29, 2, 9999, id="Last supported usual year"),
pytest.param(29, 2, 100, id="First supported round usual year"),
pytest.param(29, 2, 2100, id="Usual round year"),
pytest.param(29, 2, 9900, id="Last supported round usual year"),
# day format
pytest.param(32, 1, 1900, id="Nonexistent 32th day"),
pytest.param(31, 4, 1900, id="Nonexistent 31th day"),
pytest.param(0, 1, 1900, id="Nonexistent 0th day"),
pytest.param(-1, 1, 1900, id="Negative day"),
# month format
pytest.param(1, 0, 1900, id="Nonexistent 0th day"),
pytest.param(1, 13, 1900, id="Nonexistent 13th month"),
pytest.param(1, -1, 1900, id="Negative month"),
],
)
def test_incorrect_date_format(day, month, year):
"""Check incorrect date"""
result: GregorianDate = date_to_gregorian(day=day, month=month, year=year)
assert (
not result.correct
), f"Incorrect date '{day}-{month}-{year}'(day-month-year) is recognized as correct"
@pytest.mark.parametrize(
"day, month, year",
[
pytest.param(31, 1, 0, id="Unsupported bottom boundary year"),
pytest.param(1, 1, 10_000, id="Unsupported top boundary year"),
pytest.param(31, 1, -1, id="Negative year"),
],
)
def test_unsupported_date_format(day, month, year):
"""Check unsupported date"""
result: GregorianDate = date_to_gregorian(day=day, month=month, year=year)
assert (
not result.supported
), f"Unsupported date '{day}-{month}-{year}'(day-month-year) is recognized as supported"
|
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220,
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8054,
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2625,
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2835,
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614,
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220,
220,
220,
16589,
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8,
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4299,
1332,
62,
30283,
62,
4475,
62,
18982,
7,
820,
11,
1227,
11,
614,
2599,
198,
220,
220,
220,
37227,
9787,
3376,
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37811,
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220,
220,
220,
1255,
25,
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22618,
10430,
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62,
1462,
62,
9903,
22618,
7,
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28,
820,
11,
1227,
28,
8424,
11,
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28,
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8,
198,
220,
220,
220,
6818,
357,
198,
220,
220,
220,
220,
220,
220,
220,
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13,
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220,
220,
10612,
277,
1,
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318,
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1,
628,
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4299,
1332,
62,
7029,
62,
19002,
62,
3642,
391,
62,
5607,
62,
293,
499,
62,
19002,
33529,
198,
220,
220,
220,
37227,
9787,
3119,
286,
954,
16470,
812,
329,
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12785,
812,
37811,
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220,
220,
923,
62,
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220,
220,
16470,
62,
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685,
198,
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220,
220,
220,
220,
220,
220,
614,
198,
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220,
220,
220,
220,
220,
220,
329,
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287,
2837,
7,
9688,
62,
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11,
923,
62,
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1343,
7337,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
3128,
62,
1462,
62,
9903,
22618,
7,
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28,
1941,
11,
1227,
28,
17,
11,
1110,
28,
1959,
737,
30283,
198,
220,
220,
220,
2361,
198,
220,
220,
220,
4036,
62,
9127,
796,
18896,
7,
293,
499,
62,
19002,
8,
198,
220,
220,
220,
2938,
62,
9127,
796,
10111,
198,
220,
220,
220,
6818,
4036,
62,
9127,
6624,
2938,
62,
9127,
11,
357,
198,
220,
220,
220,
220,
220,
220,
220,
277,
1,
1890,
7337,
12785,
812,
705,
90,
40319,
62,
9127,
92,
6,
815,
307,
16470,
13,
366,
198,
220,
220,
220,
220,
220,
220,
220,
277,
1,
1537,
4036,
954,
25,
705,
90,
50039,
62,
9127,
92,
4458,
366,
198,
220,
220,
220,
220,
220,
220,
220,
277,
1,
40630,
11,
8018,
355,
16470,
7479,
77,
90,
293,
499,
62,
19002,
36786,
198,
220,
220,
220,
1267,
628,
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31,
9078,
9288,
13,
4102,
13,
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316,
380,
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7,
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366,
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11,
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11,
614,
1600,
198,
220,
220,
220,
685,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
2808,
400,
730,
65,
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329,
407,
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812,
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220,
220,
220,
220,
220,
220,
220,
12972,
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13,
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7,
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11,
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11,
352,
11,
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2625,
5962,
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614,
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220,
220,
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220,
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33448,
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2625,
5842,
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220,
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220,
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11,
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2625,
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2835,
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614,
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220,
220,
220,
220,
220,
220,
220,
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11,
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2835,
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220,
220,
220,
220,
220,
220,
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405,
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2835,
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198,
220,
220,
220,
220,
220,
220,
220,
1303,
1110,
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220,
220,
220,
220,
220,
220,
220,
12972,
9288,
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17143,
7,
2624,
11,
352,
11,
21489,
11,
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14202,
87,
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3933,
400,
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220,
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400,
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220,
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15,
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400,
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220,
220,
220,
220,
220,
220,
12972,
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13,
17143,
32590,
16,
11,
352,
11,
21489,
11,
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876,
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220,
220,
220,
220,
220,
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1303,
1227,
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220,
220,
220,
220,
220,
220,
220,
12972,
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220,
220,
220,
220,
220,
220,
220,
12972,
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7,
16,
11,
532,
16,
11,
21489,
11,
4686,
2625,
32863,
876,
1227,
12340,
198,
220,
220,
220,
16589,
198,
8,
198,
4299,
1332,
62,
1939,
47315,
62,
4475,
62,
18982,
7,
820,
11,
1227,
11,
614,
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198,
220,
220,
220,
37227,
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11491,
3128,
37811,
198,
220,
220,
220,
1255,
25,
8547,
22618,
10430,
796,
3128,
62,
1462,
62,
9903,
22618,
7,
820,
28,
820,
11,
1227,
28,
8424,
11,
614,
28,
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8,
198,
220,
220,
220,
6818,
357,
198,
220,
220,
220,
220,
220,
220,
220,
407,
1255,
13,
30283,
198,
220,
220,
220,
10612,
277,
1,
818,
30283,
3128,
705,
90,
820,
92,
12,
90,
8424,
92,
12,
90,
1941,
92,
6,
7,
820,
12,
8424,
12,
1941,
8,
318,
8018,
355,
3376,
1,
628,
198,
31,
9078,
9288,
13,
4102,
13,
17143,
316,
380,
2736,
7,
198,
220,
220,
220,
366,
820,
11,
1227,
11,
614,
1600,
198,
220,
220,
220,
685,
198,
220,
220,
220,
220,
220,
220,
220,
12972,
9288,
13,
17143,
7,
3132,
11,
352,
11,
657,
11,
4686,
2625,
3118,
15999,
4220,
18645,
614,
12340,
198,
220,
220,
220,
220,
220,
220,
220,
12972,
9288,
13,
17143,
7,
16,
11,
352,
11,
838,
62,
830,
11,
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2625,
3118,
15999,
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220,
220,
220,
220,
220,
12972,
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352,
11,
532,
16,
11,
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2625,
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876,
614,
12340,
198,
220,
220,
220,
16589,
198,
8,
198,
4299,
1332,
62,
403,
15999,
62,
4475,
62,
18982,
7,
820,
11,
1227,
11,
614,
2599,
198,
220,
220,
220,
37227,
9787,
24222,
3128,
37811,
198,
220,
220,
220,
1255,
25,
8547,
22618,
10430,
796,
3128,
62,
1462,
62,
9903,
22618,
7,
820,
28,
820,
11,
1227,
28,
8424,
11,
614,
28,
1941,
8,
198,
220,
220,
220,
6818,
357,
198,
220,
220,
220,
220,
220,
220,
220,
407,
1255,
13,
15999,
198,
220,
220,
220,
10612,
277,
1,
3118,
15999,
3128,
705,
90,
820,
92,
12,
90,
8424,
92,
12,
90,
1941,
92,
6,
7,
820,
12,
8424,
12,
1941,
8,
318,
8018,
355,
4855,
1,
198
] | 2.484509 | 1,517 |
import math
a = int(input("a = "))
b = int(input("b = "))
c = a + b
d = a - b
e = a * b
f = a / b
g = a % b
h = math.log10(a)
i = a**b
print(c,d,e,f,g,h,i)
|
[
11748,
10688,
198,
198,
64,
796,
493,
7,
15414,
7203,
64,
796,
366,
4008,
198,
65,
796,
493,
7,
15414,
7203,
65,
796,
366,
4008,
198,
198,
66,
796,
257,
1343,
275,
198,
67,
796,
257,
532,
275,
198,
68,
796,
257,
1635,
275,
198,
69,
796,
257,
1220,
275,
198,
70,
796,
257,
4064,
275,
198,
71,
796,
10688,
13,
6404,
940,
7,
64,
8,
198,
72,
796,
257,
1174,
65,
198,
198,
4798,
7,
66,
11,
67,
11,
68,
11,
69,
11,
70,
11,
71,
11,
72,
8,
198
] | 1.747253 | 91 |
# -*- coding: utf-8 -*-
# !/usr/bin/env python3
|
[
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
5145,
14,
14629,
14,
8800,
14,
24330,
21015,
18
] | 1.958333 | 24 |
"""
This is a helper script meant to generate a
working config.py file from the config template.
"""
from getpass import getpass
import json
import os.path
from random import choice
import string
import sys
from urllib2 import urlopen
import argparse
el = string.ascii_letters + string.digits
rand_str = lambda n: ''.join(choice(el) for _ in range(n))
if __name__ == '__main__':
generate_config()
|
[
37811,
198,
1212,
318,
257,
31904,
4226,
4001,
284,
7716,
257,
198,
16090,
4566,
13,
9078,
2393,
422,
262,
4566,
11055,
13,
198,
37811,
198,
198,
6738,
651,
6603,
1330,
651,
6603,
198,
11748,
33918,
198,
11748,
28686,
13,
6978,
198,
6738,
4738,
1330,
3572,
198,
11748,
4731,
198,
11748,
25064,
198,
6738,
2956,
297,
571,
17,
1330,
19016,
9654,
198,
198,
11748,
1822,
29572,
628,
198,
417,
796,
4731,
13,
292,
979,
72,
62,
15653,
1343,
4731,
13,
12894,
896,
198,
25192,
62,
2536,
796,
37456,
299,
25,
705,
4458,
22179,
7,
25541,
7,
417,
8,
329,
4808,
287,
2837,
7,
77,
4008,
628,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
7716,
62,
11250,
3419,
198
] | 3.256 | 125 |
import time
import aiohttp
import traceback
import discord
import textwrap
import io
import json
from dhooks import Webhook
from utils.chat_formatting import pagify
from contextlib import redirect_stdout
from copy import copy
from typing import Union
from utils import repo, default, http, dataIO
from discord.ext import commands
|
[
11748,
640,
198,
11748,
257,
952,
4023,
198,
11748,
12854,
1891,
198,
11748,
36446,
198,
11748,
2420,
37150,
198,
11748,
33245,
198,
11748,
33918,
198,
198,
6738,
288,
25480,
82,
1330,
5313,
25480,
198,
6738,
3384,
4487,
13,
17006,
62,
18982,
889,
1330,
42208,
1958,
198,
6738,
4732,
8019,
1330,
18941,
62,
19282,
448,
198,
6738,
4866,
1330,
4866,
198,
6738,
19720,
1330,
4479,
198,
6738,
3384,
4487,
1330,
29924,
11,
4277,
11,
2638,
11,
1366,
9399,
198,
6738,
36446,
13,
2302,
1330,
9729,
628,
198
] | 3.872093 | 86 |
'''
@Authors: Harrison Leece, James Hribal, Max Fung, Nils Heidenreich
@Purpose: Explore 6DOF rocket trajectory, esspecially quaternion rotation
Learning resources: https://eater.net/quaternions
'''
import numpy as np
import oyaml as yaml
import math
class Rotator:
'''
https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation#Using_quaternions_as_rotations
This function should take inputs: 'Cartesian, unit rotation-axis (Vector),
Rotation Angle in radians (Theta) and form a quaternion vector
'''
'''
https://math.stackexchange.com/questions/40164/how-do-you-rotate-a-vector-by-a-unit-quaternion
'''
'''
https://en.wikipedia.org/wiki/Quaternion#Hamilton_product
https://math.stackexchange.com/questions/40164/how-do-you-rotate-a-vector-by-a-unit-quaternion
'''
'''
Convert some arbitrary vector to a unit vector (divide components by the magnitude)
'''
'''
Checker function to verify a vector of arbitrary length is a unit vector
Tolerance variable to allow 'close enough' cases to succeed
'''
'''
https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation#Using_quaternions_as_rotations
q' = q2q1
q1 first, then q2
USE QUATERNION MULTIPLICATION RULE:
v*w where v and w are both quaternions with no real part
v*w = v x w - v * w
v*w where v and w are both quaternions with real part s and t (see wikipedia)
v*w = (s + v)*(t + w) = (st - v * w)+(sw + tv + v x w)
'''
class Rocket(Rotator):
'''
Calculate the angle of attack (alpha) in radians using the rocket's velocity direction
and rotation state.
Return alpha
'''
'''
Use environmental data regarding gust velocity and rocket geometry to estimate
the rotation axis and rotation magnitude (radians) of a rocket
Return a rotation quaternion for this axis and magnitude
'''
'''
Use the angle of attack, drag+lift coefficients and rocket geometry to
estimate the rotation axis and rotation magnitude (radians) of a rocket
Return a rotation quaternion for this axis and magnitude
'''
'''
Place holder - calcultes tvc rotation
Not needed for final version
Return a rotation quaternion for this axis and magnitude
'''
'''
lock rotation of the craft despite forces acting on the body
useful for constraining rocket to a rail at launch for example
(Prevents integration of accelerations to velocities)
'''
'''
Unlocks rotation
'''
class Environment():
'''
The environment object helps compartmentalize environmental data (atmospheric
temperature, pressure, gusts etc...). The object can then be accessed
to fetch atmospheric or environmental data for the rotator object desired
'''
'''
For these be sure to check which altitude you are working with. For now I have
it as altitude relative to center of the earth
'''
'''
The Reference object is a Fixed Earth, centered at 0,0,0 with no rotation
'''
'''
Inherits the Reference (Non-rotating earth) and creates a rotating earth
'''
class Launchpad(RotatingEarth):
'''
Turn the coordinates of the launch site into spherical coordinates and
set as the position of the object
RRS coordinates:
fmt=dms
35 degrees, 21 minutes, 2 seconds North
117 degrees, 48 minutes, 30 seconds West
fmts:>> 'dd' << decimal degree, >> 'dmm' << degree + decimal minutes
>> dms << degrees, minutes, and seconds
Format input as nested lists, North first, then west
list = [[35,21,2],[117,48,39]]
'''
if __name__ == '__main__':
with open('rocket_info.yaml') as rocket_info:
rocket_data = yaml.load(rocket_info, Loader=yaml.FullLoader)
rot_tester = Rotator()
rot_tester.report_body_vector()
rot_quaternion = np.array([[-.707],[0], [.707],[0]])
rot_tester.rotate_body(rot_quaternion)
rot_tester.report_body_vector()
rocenv = Environment(None, None)
rocket = Rocket(rocket_data, rocenv)
|
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] | 2.90634 | 1,388 |
import Framework.Utils as Utils
from Framework.Utils import data_utils
from Fremework.Utils.testcase_Utils import pNote
class MyActions(object):
"""" Default __init__ field must be used when using classes for keywords """
def full_name(self, student, first_name= 'first', last_name= 'last', full_name= 'first last'):
"""
combine first and last name
"""
# status will be used to save the status of the test that wheather it is failed or pass
status = True
# we will return the dictionary of keys and value to maintain the logs
log_dic={}
wdesc= 'combine first and last name'
full_name = None
data = data_Utils.get_credentials(self.datafile, student, [first_name, last_name, full_name])
if first_name and last_name:
pNote("first name is {0}".format(first_name))
pNote("last name is {0}".format(last_name))
temp_full_name = first_name + ' ' + last_name
if temp_full_name != full_name:
status= False
pNote('full name is {0}'.format(full_name))
else:
pNote("names are not provided")
status = False
log_dic["student"]= student
log_dic["first_names"]= first_name
log_dic["second_name"]= second_name
log_dic["full_name"]= full_name
return status, log_dic
|
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'''显示结果'''
import numpy
from helpers.drawer import draw_heatmap
def main():
'''入口'''
lval = 5.20
rpath = 'heatmap8/avsb'
uval = numpy.load('{0}/{1:.2f}U.npy'.format(rpath, lval))
draw_heatmap(uval[0, 0, 0, 0, :, :, 0])
draw_heatmap(uval[1, 1, 1, 1, :, :, 0])
draw_heatmap(uval[1, 0, 0, 1, :, :, 0])
draw_heatmap(uval[0, 1, 1, 0, :, :, 0])
if __name__ == '__main__':
main()
|
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import unittest
import pytest
import sys
import os
from util4tests import enable_test_logging, run_single_test, log
from pykg2tbl import KG2TblService, KGFileSource, KG2EndpointSource, J2SparqlBuilder
ALL_TRIPLES_SPARQL = "SELECT * WHERE { ?s ?p ?o. } LIMIT 10"
BODC_ENDPOINT = "http://vocab.nerc.ac.uk/sparql/sparql"
if __name__ == "__main__":
run_single_test(__file__)
|
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"""
.. _tut-erp:
EEG processing and Event Related Potentials (ERPs)
==================================================
This tutorial shows how to perform standard ERP analyses in MNE-Python. Most of
the material here is covered in other tutorials too, but for convenience the
functions and methods most useful for ERP analyses are collected here, with
links to other tutorials where more detailed information is given.
As usual we'll start by importing the modules we need and loading some example
data. Instead of parsing the events from the raw data's :term:`stim channel`
(like we do in :ref:`this tutorial <tut-events-vs-annotations>`), we'll load
the events from an external events file. Finally, to speed up computations so
our documentation server can handle them, we'll crop the raw data from ~4.5
minutes down to 90 seconds.
"""
import os
import numpy as np
import matplotlib.pyplot as plt
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, preload=False)
sample_data_events_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_filt-0-40_raw-eve.fif')
events = mne.read_events(sample_data_events_file)
raw.crop(tmax=90) # in seconds; happens in-place
# discard events >90 seconds (not strictly necessary: avoids some warnings)
events = events[events[:, 0] <= raw.last_samp]
###############################################################################
# The file that we loaded has already been partially processed: 3D sensor
# locations have been saved as part of the ``.fif`` file, the data have been
# low-pass filtered at 40 Hz, and a common average reference is set for the
# EEG channels, stored as a projector (see :ref:`section-avg-ref-proj` in the
# :ref:`tut-set-eeg-ref` tutorial for more info about when you may want to do
# this). We'll discuss how to do each of these below.
#
# Since this is a combined EEG+MEG dataset, let's start by restricting the data
# to just the EEG and EOG channels. This will cause the other projectors saved
# in the file (which apply only to magnetometer channels) to be removed. By
# looking at the measurement info we can see that we now have 59 EEG channels
# and 1 EOG channel.
raw.pick(['eeg', 'eog']).load_data()
raw.info
###############################################################################
# Channel names and types
# ^^^^^^^^^^^^^^^^^^^^^^^
#
# In practice it's quite common to have some channels labelled as EEG that are
# actually EOG channels. `~mne.io.Raw` objects have a
# `~mne.io.Raw.set_channel_types` method that you can use to change a channel
# that is labeled as ``eeg`` into an ``eog`` type. You can also rename channels
# using the `~mne.io.Raw.rename_channels` method. Detailed examples of both of
# these methods can be found in the tutorial :ref:`tut-raw-class`. In this data
# the channel types are all correct already, so for now we'll just rename the
# channels to remove a space and a leading zero in the channel names, and
# convert to lowercase:
channel_renaming_dict = {name: name.replace(' 0', '').lower()
for name in raw.ch_names}
_ = raw.rename_channels(channel_renaming_dict) # happens in-place
###############################################################################
# Channel locations
# ^^^^^^^^^^^^^^^^^
#
# The tutorial :ref:`tut-sensor-locations` describes MNE-Python's handling of
# sensor positions in great detail. To briefly summarize: MNE-Python
# distinguishes :term:`montages <montage>` (which contain sensor positions in
# 3D: ``x``, ``y``, ``z``, in meters) from :term:`layouts <layout>` (which
# define 2D arrangements of sensors for plotting approximate overhead diagrams
# of sensor positions). Additionally, montages may specify *idealized* sensor
# positions (based on, e.g., an idealized spherical headshape model) or they
# may contain *realistic* sensor positions obtained by digitizing the 3D
# locations of the sensors when placed on the actual subject's head.
#
# This dataset has realistic digitized 3D sensor locations saved as part of the
# ``.fif`` file, so we can view the sensor locations in 2D or 3D using the
# `~mne.io.Raw.plot_sensors` method:
raw.plot_sensors(show_names=True)
fig = raw.plot_sensors('3d')
###############################################################################
# If you're working with a standard montage like the `10-20 <ten_twenty_>`_
# system, you can add sensor locations to the data like this:
# ``raw.set_montage('standard_1020')``. See :ref:`tut-sensor-locations` for
# info on what other standard montages are built-in to MNE-Python.
#
# If you have digitized realistic sensor locations, there are dedicated
# functions for loading those digitization files into MNE-Python; see
# :ref:`reading-dig-montages` for discussion and :ref:`dig-formats` for a list
# of supported formats. Once loaded, the digitized sensor locations can be
# added to the data by passing the loaded montage object to
# ``raw.set_montage()``.
#
#
# Setting the EEG reference
# ^^^^^^^^^^^^^^^^^^^^^^^^^
#
# As mentioned above, this data already has an EEG common average reference
# added as a :term:`projector`. We can view the effect of this on the raw data
# by plotting with and without the projector applied:
for proj in (False, True):
fig = raw.plot(n_channels=5, proj=proj, scalings=dict(eeg=50e-6))
fig.subplots_adjust(top=0.9) # make room for title
ref = 'Average' if proj else 'No'
fig.suptitle(f'{ref} reference', size='xx-large', weight='bold')
###############################################################################
# The referencing scheme can be changed with the function
# `mne.set_eeg_reference` (which by default operates on a *copy* of the data)
# or the `raw.set_eeg_reference() <mne.io.Raw.set_eeg_reference>` method (which
# always modifies the data in-place). The tutorial :ref:`tut-set-eeg-ref` shows
# several examples of this.
#
#
# Filtering
# ^^^^^^^^^
#
# MNE-Python has extensive support for different ways of filtering data. For a
# general discussion of filter characteristics and MNE-Python defaults, see
# :ref:`disc-filtering`. For practical examples of how to apply filters to your
# data, see :ref:`tut-filter-resample`. Here, we'll apply a simple high-pass
# filter for illustration:
raw.filter(l_freq=0.1, h_freq=None)
###############################################################################
# Evoked responses: epoching and averaging
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# The general process for extracting evoked responses from continuous data is
# to use the `~mne.Epochs` constructor, and then average the resulting epochs
# to create an `~mne.Evoked` object. In MNE-Python, events are represented as
# a :class:`NumPy array <numpy.ndarray>` of sample numbers and integer event
# codes. The event codes are stored in the last column of the events array:
np.unique(events[:, -1])
###############################################################################
# The :ref:`tut-event-arrays` tutorial discusses event arrays in more detail.
# Integer event codes are mapped to more descriptive text using a Python
# :class:`dictionary <dict>` usually called ``event_id``. This mapping is
# determined by your experiment code (i.e., it reflects which event codes you
# chose to use to represent different experimental events or conditions). For
# the :ref:`sample-dataset` data has the following mapping:
event_dict = {'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3,
'visual/right': 4, 'face': 5, 'buttonpress': 32}
###############################################################################
# Now we can extract epochs from the continuous data. An interactive plot
# allows you to click on epochs to mark them as "bad" and drop them from the
# analysis (it is not interactive on the documentation website, but will be
# when you run `epochs.plot() <mne.Epochs.plot>` in a Python console).
epochs = mne.Epochs(raw, events, event_id=event_dict, tmin=-0.3, tmax=0.7,
preload=True)
fig = epochs.plot()
###############################################################################
# It is also possible to automatically drop epochs, when first creating them or
# later on, by providing maximum peak-to-peak signal value thresholds (pass to
# the `~mne.Epochs` constructor as the ``reject`` parameter; see
# :ref:`tut-reject-epochs-section` for details). You can also do this after
# the epochs are already created, using the `~mne.Epochs.drop_bad` method:
reject_criteria = dict(eeg=100e-6, # 100 µV
eog=200e-6) # 200 µV
_ = epochs.drop_bad(reject=reject_criteria)
###############################################################################
# Next we generate a barplot of which channels contributed most to epochs
# getting rejected. If one channel is responsible for lots of epoch rejections,
# it may be worthwhile to mark that channel as "bad" in the `~mne.io.Raw`
# object and then re-run epoching (fewer channels w/ more good epochs may be
# preferable to keeping all channels but losing many epochs). See
# :ref:`tut-bad-channels` for more info.
epochs.plot_drop_log()
###############################################################################
# Another way in which epochs can be automatically dropped is if the
# `~mne.io.Raw` object they're extracted from contains :term:`annotations` that
# begin with either ``bad`` or ``edge`` ("edge" annotations are automatically
# inserted when concatenating two separate `~mne.io.Raw` objects together). See
# :ref:`tut-reject-data-spans` for more information about annotation-based
# epoch rejection.
#
# Now that we've dropped the bad epochs, let's look at our evoked responses for
# some conditions we care about. Here the `~mne.Epochs.average` method will
# create and `~mne.Evoked` object, which we can then plot. Notice that we\
# select which condition we want to average using the square-bracket indexing
# (like a :class:`dictionary <dict>`); that returns a smaller epochs object
# containing just the epochs from that condition, to which we then apply the
# `~mne.Epochs.average` method:
l_aud = epochs['auditory/left'].average()
l_vis = epochs['visual/left'].average()
###############################################################################
# These `~mne.Evoked` objects have their own interactive plotting method
# (though again, it won't be interactive on the documentation website):
# click-dragging a span of time will generate a scalp field topography for that
# time span. Here we also demonstrate built-in color-coding the channel traces
# by location:
fig1 = l_aud.plot()
fig2 = l_vis.plot(spatial_colors=True)
###############################################################################
# Scalp topographies can also be obtained non-interactively with the
# `~mne.Evoked.plot_topomap` method. Here we display topomaps of the average
# field in 50 ms time windows centered at -200 ms, 100 ms, and 400 ms.
l_aud.plot_topomap(times=[-0.2, 0.1, 0.4], average=0.05)
###############################################################################
# Considerable customization of these plots is possible, see the docstring of
# `~mne.Evoked.plot_topomap` for details.
#
# There is also a built-in method for combining "butterfly" plots of the
# signals with scalp topographies, called `~mne.Evoked.plot_joint`. Like
# `~mne.Evoked.plot_topomap` you can specify times for the scalp topographies
# or you can let the method choose times automatically, as is done here:
l_aud.plot_joint()
###############################################################################
# Global field power (GFP)
# ^^^^^^^^^^^^^^^^^^^^^^^^
#
# Global field power :footcite:`Lehmann1980,Lehmann1984,Murray2008` is,
# generally speaking, a measure of agreement of the signals picked up by all
# sensors across the entire scalp: if all sensors have the same value at a
# given time point, the GFP will be zero at that time point; if the signals
# differ, the GFP will be non-zero at that time point. GFP
# peaks may reflect "interesting" brain activity, warranting further
# investigation. Mathematically, the GFP is the population standard
# deviation across all sensors, calculated separately for every time point.
#
# You can plot the GFP using `evoked.plot(gfp=True) <mne.Evoked.plot>`. The GFP
# trace will be black if ``spatial_colors=True`` and green otherwise. The EEG
# reference does not affect the GFP:
# sphinx_gallery_thumbnail_number=11
for evk in (l_aud, l_vis):
evk.plot(gfp=True, spatial_colors=True, ylim=dict(eeg=[-12, 12]))
###############################################################################
# To plot the GFP by itself you can pass ``gfp='only'`` (this makes it easier
# to read off the GFP data values, because the scale is aligned):
l_aud.plot(gfp='only')
###############################################################################
# As stated above, the GFP is the population standard deviation of the signal
# across channels. To compute it manually, we can leverage the fact that
# `evoked.data <mne.Evoked.data>` is a :class:`NumPy array <numpy.ndarray>`,
# and verify by plotting it using matplotlib commands:
gfp = l_aud.data.std(axis=0, ddof=0)
# Reproducing the MNE-Python plot style seen above
fig, ax = plt.subplots()
ax.plot(l_aud.times, gfp * 1e6, color='lime')
ax.fill_between(l_aud.times, gfp * 1e6, color='lime', alpha=0.2)
ax.set(xlabel='Time (s)', ylabel='GFP (µV)', title='EEG')
###############################################################################
# Analyzing regions of interest (ROIs): averaging across channels
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# Since our sample data is responses to left and right auditory and visual
# stimuli, we may want to compare left versus right ROIs. To average across
# channels in a region of interest, we first find the channel indices we want.
# Looking back at the 2D sensor plot above, we might choose the following for
# left and right ROIs:
left = ['eeg17', 'eeg18', 'eeg25', 'eeg26']
right = ['eeg23', 'eeg24', 'eeg34', 'eeg35']
left_ix = mne.pick_channels(l_aud.info['ch_names'], include=left)
right_ix = mne.pick_channels(l_aud.info['ch_names'], include=right)
###############################################################################
# Now we can create a new Evoked with 2 virtual channels (one for each ROI):
roi_dict = dict(left_ROI=left_ix, right_ROI=right_ix)
roi_evoked = mne.channels.combine_channels(l_aud, roi_dict, method='mean')
print(roi_evoked.info['ch_names'])
roi_evoked.plot()
###############################################################################
# Comparing conditions
# ^^^^^^^^^^^^^^^^^^^^
#
# If we wanted to compare our auditory and visual stimuli, a useful function is
# `mne.viz.plot_compare_evokeds`. By default this will combine all channels in
# each evoked object using global field power (or RMS for MEG channels); here
# instead we specify to combine by averaging, and restrict it to a subset of
# channels by passing ``picks``:
evokeds = dict(auditory=l_aud, visual=l_vis)
picks = [f'eeg{n}' for n in range(10, 15)]
mne.viz.plot_compare_evokeds(evokeds, picks=picks, combine='mean')
###############################################################################
# We can also easily get confidence intervals by treating each epoch as a
# separate observation using the `~mne.Epochs.iter_evoked` method. A confidence
# interval across subjects could also be obtained, by passing a list of
# `~mne.Evoked` objects (one per subject) to the
# `~mne.viz.plot_compare_evokeds` function.
evokeds = dict(auditory=list(epochs['auditory/left'].iter_evoked()),
visual=list(epochs['visual/left'].iter_evoked()))
mne.viz.plot_compare_evokeds(evokeds, combine='mean', picks=picks)
###############################################################################
# We can also compare conditions by subtracting one `~mne.Evoked` object from
# another using the `mne.combine_evoked` function (this function also allows
# pooling of epochs without subtraction).
aud_minus_vis = mne.combine_evoked([l_aud, l_vis], weights=[1, -1])
aud_minus_vis.plot_joint()
###############################################################################
# .. warning::
#
# The code above yields an **equal-weighted difference**. If you have
# imbalanced trial numbers, you might want to equalize the number of events
# per condition first by using `epochs.equalize_event_counts()
# <mne.Epochs.equalize_event_counts>` before averaging.
#
#
# Grand averages
# ^^^^^^^^^^^^^^
#
# To compute grand averages across conditions (or subjects), you can pass a
# list of `~mne.Evoked` objects to `mne.grand_average`. The result is another
# `~mne.Evoked` object.
grand_average = mne.grand_average([l_aud, l_vis])
print(grand_average)
###############################################################################
# For combining *conditions* it is also possible to make use of :term:`HED`
# tags in the condition names when selecting which epochs to average. For
# example, we have the condition names:
list(event_dict)
###############################################################################
# We can select the auditory conditions (left and right together) by passing:
epochs['auditory'].average()
###############################################################################
# see :ref:`tut-section-subselect-epochs` for details.
#
# The tutorials :ref:`tut-epochs-class` and :ref:`tut-evoked-class` have many
# more details about working with the `~mne.Epochs` and `~mne.Evoked` classes.
#
# .. _ten_twenty: https://en.wikipedia.org/wiki/10%E2%80%9320_system_(EEG)
#
#
# References
# ----------
# .. footbibliography::
|
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318,
262,
3265,
3210,
198,
2,
28833,
1973,
477,
15736,
11,
10488,
13869,
329,
790,
640,
966,
13,
198,
2,
198,
2,
921,
460,
7110,
262,
402,
5837,
1262,
4600,
1990,
6545,
13,
29487,
7,
70,
46428,
28,
17821,
8,
1279,
76,
710,
13,
15200,
6545,
13,
29487,
29,
44646,
383,
402,
5837,
198,
2,
12854,
481,
307,
2042,
611,
7559,
2777,
34961,
62,
4033,
669,
28,
17821,
15506,
290,
4077,
4306,
13,
383,
48749,
198,
2,
4941,
857,
407,
2689,
262,
402,
5837,
25,
198,
198,
2,
599,
20079,
87,
62,
24460,
62,
400,
20566,
62,
17618,
28,
1157,
198,
1640,
819,
74,
287,
357,
75,
62,
3885,
11,
300,
62,
4703,
2599,
198,
220,
220,
220,
819,
74,
13,
29487,
7,
70,
46428,
28,
17821,
11,
21739,
62,
4033,
669,
28,
17821,
11,
331,
2475,
28,
11600,
7,
1453,
70,
41888,
12,
1065,
11,
1105,
60,
4008,
198,
198,
29113,
29113,
7804,
4242,
21017,
198,
2,
1675,
7110,
262,
402,
5837,
416,
2346,
345,
460,
1208,
7559,
70,
46428,
11639,
8807,
6,
15506,
357,
5661,
1838,
340,
4577,
198,
2,
284,
1100,
572,
262,
402,
5837,
1366,
3815,
11,
780,
262,
5046,
318,
19874,
2599,
198,
198,
75,
62,
3885,
13,
29487,
7,
70,
46428,
11639,
8807,
11537,
198,
198,
29113,
29113,
7804,
4242,
21017,
198,
2,
1081,
5081,
2029,
11,
262,
402,
5837,
318,
262,
3265,
3210,
28833,
286,
262,
6737,
198,
2,
1973,
9619,
13,
1675,
24061,
340,
14500,
11,
356,
460,
16094,
262,
1109,
326,
198,
2,
4600,
1990,
6545,
13,
7890,
1279,
76,
710,
13,
15200,
6545,
13,
7890,
29,
63,
318,
257,
1058,
4871,
25,
63,
33111,
20519,
7177,
1279,
77,
32152,
13,
358,
18747,
29,
47671,
198,
2,
290,
11767,
416,
29353,
340,
1262,
2603,
29487,
8019,
9729,
25,
198,
198,
70,
46428,
796,
300,
62,
3885,
13,
7890,
13,
19282,
7,
22704,
28,
15,
11,
49427,
1659,
28,
15,
8,
198,
198,
2,
36551,
2259,
262,
337,
12161,
12,
37906,
7110,
3918,
1775,
2029,
198,
5647,
11,
7877,
796,
458,
83,
13,
7266,
489,
1747,
3419,
198,
897,
13,
29487,
7,
75,
62,
3885,
13,
22355,
11,
308,
46428,
1635,
352,
68,
21,
11,
3124,
11639,
27299,
11537,
198,
897,
13,
20797,
62,
23395,
7,
75,
62,
3885,
13,
22355,
11,
308,
46428,
1635,
352,
68,
21,
11,
3124,
11639,
27299,
3256,
17130,
28,
15,
13,
17,
8,
198,
897,
13,
2617,
7,
87,
18242,
11639,
7575,
357,
82,
8,
3256,
331,
18242,
11639,
38,
5837,
357,
126,
113,
53,
8,
3256,
3670,
11639,
6500,
38,
11537,
198,
198,
29113,
29113,
7804,
4242,
21017,
198,
2,
16213,
9510,
7652,
286,
1393,
357,
13252,
3792,
2599,
20430,
1973,
9619,
198,
2,
10563,
39397,
39397,
39397,
39397,
39397,
39397,
39397,
39397,
39397,
39397,
39397,
39397,
39397,
39397,
39397,
18237,
198,
2,
198,
2,
4619,
674,
6291,
1366,
318,
9109,
284,
1364,
290,
826,
38949,
290,
5874,
198,
2,
25973,
11,
356,
743,
765,
284,
8996,
1364,
9051,
826,
15107,
3792,
13,
1675,
2811,
1973,
198,
2,
9619,
287,
257,
3814,
286,
1393,
11,
356,
717,
1064,
262,
6518,
36525,
356,
765,
13,
198,
2,
15616,
736,
379,
262,
362,
35,
12694,
7110,
2029,
11,
356,
1244,
3853,
262,
1708,
329,
198,
2,
1364,
290,
826,
15107,
3792,
25,
198,
198,
9464,
796,
37250,
1453,
70,
1558,
3256,
705,
1453,
70,
1507,
3256,
705,
1453,
70,
1495,
3256,
705,
1453,
70,
2075,
20520,
198,
3506,
796,
37250,
1453,
70,
1954,
3256,
705,
1453,
70,
1731,
3256,
705,
1453,
70,
2682,
3256,
705,
1453,
70,
2327,
20520,
198,
198,
9464,
62,
844,
796,
285,
710,
13,
27729,
62,
354,
8961,
7,
75,
62,
3885,
13,
10951,
17816,
354,
62,
14933,
6,
4357,
2291,
28,
9464,
8,
198,
3506,
62,
844,
796,
285,
710,
13,
27729,
62,
354,
8961,
7,
75,
62,
3885,
13,
10951,
17816,
354,
62,
14933,
6,
4357,
2291,
28,
3506,
8,
198,
198,
29113,
29113,
7804,
4242,
21017,
198,
2,
2735,
356,
460,
2251,
257,
649,
4319,
6545,
351,
362,
7166,
9619,
357,
505,
329,
1123,
15107,
40,
2599,
198,
305,
72,
62,
11600,
796,
8633,
7,
9464,
62,
13252,
40,
28,
9464,
62,
844,
11,
826,
62,
13252,
40,
28,
3506,
62,
844,
8,
198,
305,
72,
62,
1990,
6545,
796,
285,
710,
13,
354,
8961,
13,
24011,
500,
62,
354,
8961,
7,
75,
62,
3885,
11,
686,
72,
62,
11600,
11,
2446,
11639,
32604,
11537,
198,
4798,
7,
305,
72,
62,
1990,
6545,
13,
10951,
17816,
354,
62,
14933,
6,
12962,
198,
305,
72,
62,
1990,
6545,
13,
29487,
3419,
198,
198,
29113,
29113,
7804,
4242,
21017,
198,
2,
3082,
1723,
3403,
198,
2,
10563,
39397,
39397,
39397,
39397,
18237,
61,
198,
2,
198,
2,
1002,
356,
2227,
284,
8996,
674,
38949,
290,
5874,
25973,
11,
257,
4465,
2163,
318,
198,
2,
4600,
76,
710,
13,
85,
528,
13,
29487,
62,
5589,
533,
62,
1990,
482,
5379,
44646,
2750,
4277,
428,
481,
12082,
477,
9619,
287,
198,
2,
1123,
819,
6545,
2134,
1262,
3298,
2214,
1176,
357,
273,
371,
5653,
329,
337,
7156,
9619,
1776,
994,
198,
2,
2427,
356,
11986,
284,
12082,
416,
20430,
11,
290,
4239,
340,
284,
257,
24637,
286,
198,
2,
9619,
416,
6427,
7559,
79,
3378,
15506,
25,
198,
198,
1990,
482,
5379,
796,
8633,
7,
3885,
37765,
28,
75,
62,
3885,
11,
5874,
28,
75,
62,
4703,
8,
198,
79,
3378,
796,
685,
69,
6,
1453,
70,
90,
77,
92,
6,
329,
299,
287,
2837,
7,
940,
11,
1315,
15437,
198,
76,
710,
13,
85,
528,
13,
29487,
62,
5589,
533,
62,
1990,
482,
5379,
7,
1990,
482,
5379,
11,
11103,
28,
79,
3378,
11,
12082,
11639,
32604,
11537,
198,
198,
29113,
29113,
7804,
4242,
21017,
198,
2,
775,
460,
635,
3538,
651,
6628,
20016,
416,
13622,
1123,
36835,
355,
257,
198,
2,
4553,
13432,
1262,
262,
4600,
93,
76,
710,
13,
13807,
5374,
82,
13,
2676,
62,
1990,
6545,
63,
2446,
13,
317,
6628,
198,
2,
16654,
1973,
7481,
714,
635,
307,
6492,
11,
416,
6427,
257,
1351,
286,
198,
2,
4600,
93,
76,
710,
13,
15200,
6545,
63,
5563,
357,
505,
583,
2426,
8,
284,
262,
198,
2,
4600,
93,
76,
710,
13,
85,
528,
13,
29487,
62,
5589,
533,
62,
1990,
482,
5379,
63,
2163,
13,
198,
198,
1990,
482,
5379,
796,
8633,
7,
3885,
37765,
28,
4868,
7,
538,
5374,
82,
17816,
3885,
37765,
14,
9464,
6,
4083,
2676,
62,
1990,
6545,
3419,
828,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5874,
28,
4868,
7,
538,
5374,
82,
17816,
41464,
14,
9464,
6,
4083,
2676,
62,
1990,
6545,
3419,
4008,
198,
76,
710,
13,
85,
528,
13,
29487,
62,
5589,
533,
62,
1990,
482,
5379,
7,
1990,
482,
5379,
11,
12082,
11639,
32604,
3256,
11103,
28,
79,
3378,
8,
198,
198,
29113,
29113,
7804,
4242,
21017,
198,
2,
775,
460,
635,
8996,
3403,
416,
34128,
278,
530,
4600,
93,
76,
710,
13,
15200,
6545,
63,
2134,
422,
198,
2,
1194,
1262,
262,
4600,
76,
710,
13,
24011,
500,
62,
1990,
6545,
63,
2163,
357,
5661,
2163,
635,
3578,
198,
2,
5933,
278,
286,
36835,
82,
1231,
13284,
7861,
737,
198,
198,
3885,
62,
40191,
62,
4703,
796,
285,
710,
13,
24011,
500,
62,
1990,
6545,
26933,
75,
62,
3885,
11,
300,
62,
4703,
4357,
19590,
41888,
16,
11,
532,
16,
12962,
198,
3885,
62,
40191,
62,
4703,
13,
29487,
62,
73,
1563,
3419,
198,
198,
29113,
29113,
7804,
4242,
21017,
198,
2,
11485,
6509,
3712,
198,
2,
198,
2,
220,
220,
220,
220,
383,
2438,
2029,
19299,
281,
12429,
40496,
12,
6551,
276,
3580,
1174,
13,
1002,
345,
423,
198,
2,
220,
220,
220,
220,
545,
27753,
4473,
3146,
11,
345,
1244,
765,
284,
4961,
1096,
262,
1271,
286,
2995,
198,
2,
220,
220,
220,
220,
583,
4006,
717,
416,
1262,
4600,
538,
5374,
82,
13,
40496,
1096,
62,
15596,
62,
9127,
82,
3419,
198,
2,
220,
220,
220,
220,
1279,
76,
710,
13,
13807,
5374,
82,
13,
40496,
1096,
62,
15596,
62,
9127,
82,
29,
63,
878,
20430,
13,
198,
2,
198,
2,
198,
2,
5675,
25694,
198,
2,
10563,
39397,
39397,
39397,
61,
198,
2,
198,
2,
1675,
24061,
4490,
25694,
1973,
3403,
357,
273,
7481,
828,
345,
460,
1208,
257,
198,
2,
1351,
286,
4600,
93,
76,
710,
13,
15200,
6545,
63,
5563,
284,
4600,
76,
710,
13,
23936,
62,
23913,
44646,
383,
1255,
318,
1194,
198,
2,
4600,
93,
76,
710,
13,
15200,
6545,
63,
2134,
13,
198,
198,
23936,
62,
23913,
796,
285,
710,
13,
23936,
62,
23913,
26933,
75,
62,
3885,
11,
300,
62,
4703,
12962,
198,
4798,
7,
23936,
62,
23913,
8,
198,
198,
29113,
29113,
7804,
4242,
21017,
198,
2,
1114,
19771,
1635,
17561,
1756,
9,
340,
318,
635,
1744,
284,
787,
779,
286,
1058,
4354,
25,
63,
39,
1961,
63,
198,
2,
15940,
287,
262,
4006,
3891,
618,
17246,
543,
36835,
82,
284,
2811,
13,
1114,
198,
2,
1672,
11,
356,
423,
262,
4006,
3891,
25,
198,
198,
4868,
7,
15596,
62,
11600,
8,
198,
198,
29113,
29113,
7804,
4242,
21017,
198,
2,
775,
460,
2922,
262,
38949,
3403,
357,
9464,
290,
826,
1978,
8,
416,
6427,
25,
198,
198,
538,
5374,
82,
17816,
3885,
37765,
6,
4083,
23913,
3419,
198,
198,
29113,
29113,
7804,
4242,
21017,
198,
2,
766,
1058,
5420,
25,
63,
83,
315,
12,
5458,
12,
7266,
19738,
12,
538,
5374,
82,
63,
329,
3307,
13,
198,
2,
198,
2,
383,
27992,
1058,
5420,
25,
63,
83,
315,
12,
538,
5374,
82,
12,
4871,
63,
290,
1058,
5420,
25,
63,
83,
315,
12,
1990,
6545,
12,
4871,
63,
423,
867,
198,
2,
517,
3307,
546,
1762,
351,
262,
4600,
93,
76,
710,
13,
13807,
5374,
82,
63,
290,
4600,
93,
76,
710,
13,
15200,
6545,
63,
6097,
13,
198,
2,
198,
2,
11485,
4808,
1452,
62,
4246,
3787,
25,
3740,
1378,
268,
13,
31266,
13,
2398,
14,
15466,
14,
940,
4,
36,
17,
4,
1795,
4,
6052,
1238,
62,
10057,
41052,
6500,
38,
8,
198,
2,
198,
2,
198,
2,
31458,
198,
2,
24200,
438,
198,
2,
11485,
2366,
65,
45689,
3712,
198
] | 3.431481 | 5,254 |
"""
*
* Author: Juarez Paulino(coderemite)
* Email: [email protected]
*
"""
n,k=int(input()),0
s,x,t='',1,input()
while k<n: s+=t[k];k+=x;x+=1
print(s)
|
[
37811,
198,
1635,
198,
1635,
6434,
25,
220,
12585,
19655,
3362,
2879,
7,
19815,
567,
32937,
8,
198,
1635,
9570,
25,
7544,
19655,
13,
79,
2518,
2879,
31,
14816,
13,
785,
198,
1635,
198,
37227,
198,
77,
11,
74,
28,
600,
7,
15414,
3419,
828,
15,
198,
82,
11,
87,
11,
83,
11639,
3256,
16,
11,
15414,
3419,
198,
4514,
479,
27,
77,
25,
264,
47932,
83,
58,
74,
11208,
74,
47932,
87,
26,
87,
47932,
16,
198,
4798,
7,
82,
8
] | 2 | 82 |
# -*- coding: utf-8 -*-
"""Command line interface for Axonius API Client."""
from ....tools import json_dump
from ...context import CONTEXT_SETTINGS, click
from ...options import AUTH, add_options
KEY_NAME = click.option(
"--key-name",
"-kn",
"key_name",
help="Key name of file object in [bucket_name] to restore",
required=True,
show_envvar=True,
show_default=True,
)
BUCKET_NAME = click.option(
"--bucket-name",
"-bn",
"bucket_name",
default=None,
help="Name of bucket in S3 to get [key_name] from",
show_envvar=True,
show_default=True,
)
ACCESS_KEY_ID = click.option(
"--access-key-id",
"-aki",
"access_key_id",
default=None,
help="AWS Access Key Id to use to access [bucket_name]",
show_envvar=True,
show_default=True,
)
SECRET_ACCESS_KEY = click.option(
"--secret-access-key",
"-sak",
"secret_access_key",
default=None,
help="AWS Secret Access Key to use to access [bucket_name]",
show_envvar=True,
show_default=True,
)
PRESHARED_KEY = click.option(
"--preshared-key",
"-pk",
"preshared_key",
default=None,
help="Password to use to decrypt [key_name]",
show_envvar=True,
show_default=True,
)
ALLOW_RE_RESTORE = click.option(
"--allow-re-restore/--no-allow-re-restore",
"-arr/-narr",
"allow_re_restore",
help="Restore [key_name] even if it has already been restored",
is_flag=True,
default=False,
show_envvar=True,
show_default=True,
)
DELETE_BACKUPS = click.option(
"--delete-backups/--no-delete-backups",
"-db/-ndb",
"delete_backups",
help="Delete [key_name] from [bucket_name] after restore has finished",
is_flag=True,
default=None,
show_envvar=True,
show_default=True,
)
OPTIONS = [
*AUTH,
ACCESS_KEY_ID,
SECRET_ACCESS_KEY,
PRESHARED_KEY,
ALLOW_RE_RESTORE,
DELETE_BACKUPS,
BUCKET_NAME,
KEY_NAME,
]
EPILOG = """
If values for these options are not provided, they will default to
the settings under Global Settings > Amazon S3 Settings:
\b
* bucket-name: Amazon S3 bucket name
* access-key-id: AWS Access Key Id
* secret-access-key: AWS Secret Access Key
* preshared-key: Backup encryption passphrase
"""
@click.command(
name="restore-from-aws-s3",
context_settings=CONTEXT_SETTINGS,
epilog=EPILOG,
)
@add_options(OPTIONS)
@click.pass_context
def cmd(ctx, url, key, secret, **kwargs):
"""Perform a manual restore of a backup in AWS S3."""
client = ctx.obj.start_client(url=url, key=key, secret=secret)
with ctx.obj.exc_wrap(wraperror=ctx.obj.wraperror):
data = client.system.central_core.restore_from_aws_s3(**kwargs)
click.secho(json_dump(data))
|
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2,
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11,
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60,
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311,
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59,
65,
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12,
2539,
25,
30865,
3943,
8798,
7383,
198,
220,
1635,
906,
71,
1144,
12,
2539,
25,
35071,
15835,
1208,
34675,
198,
198,
37811,
628,
198,
31,
12976,
13,
21812,
7,
198,
220,
220,
220,
1438,
2625,
2118,
382,
12,
6738,
12,
8356,
12,
82,
18,
1600,
198,
220,
220,
220,
4732,
62,
33692,
28,
10943,
32541,
62,
28480,
51,
20754,
11,
198,
220,
220,
220,
2462,
346,
519,
28,
8905,
4146,
7730,
11,
198,
8,
198,
31,
2860,
62,
25811,
7,
3185,
51,
11053,
8,
198,
31,
12976,
13,
6603,
62,
22866,
198,
4299,
23991,
7,
49464,
11,
19016,
11,
1994,
11,
3200,
11,
12429,
46265,
22046,
2599,
198,
220,
220,
220,
37227,
5990,
687,
257,
10107,
11169,
286,
257,
11559,
287,
30865,
311,
18,
526,
15931,
198,
220,
220,
220,
5456,
796,
269,
17602,
13,
26801,
13,
9688,
62,
16366,
7,
6371,
28,
6371,
11,
1994,
28,
2539,
11,
3200,
28,
21078,
8,
628,
220,
220,
220,
351,
269,
17602,
13,
26801,
13,
41194,
62,
37150,
7,
29988,
525,
1472,
28,
49464,
13,
26801,
13,
29988,
525,
1472,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
796,
5456,
13,
10057,
13,
31463,
62,
7295,
13,
2118,
382,
62,
6738,
62,
8356,
62,
82,
18,
7,
1174,
46265,
22046,
8,
628,
220,
220,
220,
3904,
13,
325,
6679,
7,
17752,
62,
39455,
7,
7890,
4008,
198
] | 2.387826 | 1,150 |
from algoanim.array import Array
from algoanim.sort import Sort
SORT_CLASS = YSlowSort
|
[
6738,
435,
2188,
11227,
13,
18747,
1330,
15690,
198,
6738,
435,
2188,
11227,
13,
30619,
1330,
33947,
628,
628,
198,
50,
9863,
62,
31631,
796,
575,
36423,
42758,
198
] | 3.137931 | 29 |
from contextlib import contextmanager
from io import BytesIO
import hashlib
from numpy import random
import pytest
from requests_toolbelt import MultipartEncoder
from streaming_form_data import ParseFailedException, StreamingFormDataParser
from streaming_form_data.targets import (
BaseTarget,
FileTarget,
SHA256Target,
ValueTarget,
)
from streaming_form_data.validators import MaxSizeValidator, ValidationError
@contextmanager
# The following tests have been added from tornado's
# MultipartFormDataTestCase
# https://github.com/tornadoweb/tornado/blob/master/tornado/test/httputil_test.py
|
[
6738,
4732,
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220,
220,
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8,
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62,
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62,
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13,
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2024,
1330,
5436,
10699,
47139,
1352,
11,
3254,
24765,
12331,
628,
198,
31,
22866,
37153,
628,
628,
628,
628,
628,
628,
628,
628,
628,
628,
628,
628,
198,
2,
383,
1708,
5254,
423,
587,
2087,
422,
33718,
338,
198,
2,
7854,
541,
433,
8479,
6601,
14402,
20448,
198,
2,
3740,
1378,
12567,
13,
785,
14,
45910,
4584,
1765,
14,
45910,
4533,
14,
2436,
672,
14,
9866,
14,
45910,
4533,
14,
9288,
14,
2804,
1996,
346,
62,
9288,
13,
9078,
628,
628,
628,
628,
628,
628,
628,
628,
628,
198
] | 3.248756 | 201 |
#!/usr/bin/python
import re
import sys
import os
from subprocess import Popen,PIPE
if __name__ == '__main__':
push_rules()
|
[
2,
48443,
14629,
14,
8800,
14,
29412,
198,
198,
11748,
302,
198,
11748,
25064,
198,
11748,
28686,
198,
6738,
850,
14681,
1330,
8099,
268,
11,
47,
4061,
36,
628,
197,
197,
197,
198,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
197,
14689,
62,
38785,
3419,
197,
628,
197,
628,
628,
628,
628
] | 2.491228 | 57 |
import operator
try:
from collections import Counter
except ImportError:
from ._counter import Counter
from .backend import Backend
from ..query import Query
|
[
11748,
10088,
198,
28311,
25,
198,
220,
220,
220,
422,
17268,
1330,
15034,
198,
16341,
17267,
12331,
25,
198,
220,
220,
220,
422,
47540,
24588,
1330,
15034,
198,
198,
6738,
764,
1891,
437,
1330,
5157,
437,
198,
6738,
11485,
22766,
1330,
43301,
628
] | 3.906977 | 43 |
from mayavi import mlab
n_mer, n_long = 6, 11
dphi = np.pi / 1000.0
phi = np.arange(0.0, 2 * pi + 0.5 * dphi, dphi)
mu = phi * n_mer
x = np.cos(mu) * (1 + np.cos(n_long * mu / n_mer) * 0.5)
y = np.sin(mu) * (1 + np.cos(n_long * mu / n_mer) * 0.5)
z = np.sin(n_long * mu / n_mer) * 0.5
t = np.sin(mu)
mlab.plot3d(x, y, z, t, tube_radius=0.025, colormap='Spectral')
|
[
6738,
743,
15820,
1330,
285,
23912,
198,
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62,
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11,
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951,
579,
499,
11639,
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] | 1.915789 | 190 |
#-*- coding: utf-8 -*-
from __future__ import unicode_literals
try:
from urlparse import urljoin
except ImportError:
from urllib.parse import urljoin
from django.contrib.sites.models import get_current_site
|
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# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, with_statement
from cuisine import group_check as get
from cuisine import group_create as create
from cuisine import group_ensure as ensure
from cuisine import group_user_add as user_add
from cuisine import group_user_check as user_check
from cuisine import group_user_ensure as user_ensure
|
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62,
7220,
62,
641,
495,
355,
2836,
62,
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495,
198
] | 3.67 | 100 |
'''
Definitions
1. consuming service: is a network service that requires the consumption of additional VNF(s) that pertain to a different service, i.e., providing services.
2. CSC engaged VNFs (w.r.t. the consuming service): are exactly two distinct VNFs. One VNF forwards the traffic to the receiver CSC engaged VNF of the providing service, whereas the other VNF receives the traffic, which is now processed by (a subset of) the providing service.
General assumptions
1. Every network service is modelled as a directed graph. Nodes represent VNFs, and edges represent virtual links. VNFs require CPU, and edges require bandwidth.
2. We consider sequential services, e.g., 'A->B->C' is OK, whereas 'A->B,A->C,B->C' is NOT OK
Environment
1. CPU in [0.72, 1.44, 2.16, 2.88, 3.6GHz] (randomly)
2. bandwidth in [10Mbps, 200Mbps] (randomly)
3. length (i.e., number of VNFs per service) in [3,8] (randomly)
4. The two CSC engaged VNFs are randomly sampled (without replacement)
5. Duration in [3,10] time intervals
'''
# dependencies
import networkx as nx
import random
def initializeConsumingService(providing_services, service_index, time):
'''
Function that generates a consuming service, in the form of a graph. The consuming service pairs with a providing service, and the corresponding consuming service graph adds VNFs and edges for the CSC, also.
'''
# the providing service that the consuming service will pair with
providing_service = random.choice(providing_services)
# list of possible VNF CPU requirements
CPUs = [0.72, 1.44, 2.16, 2.88, 3.6]
CPUs = [round(cpu/7.2,2) for cpu in CPUs]
# the length of the network service
service_length = random.randint(3,8)
# list of CSC VNF indices
first_CSC_engaged_VNF = random.choice(range(service_length-1))
CSC_engaged_VNFs = [first_CSC_engaged_VNF, first_CSC_engaged_VNF+1]
# create empty directional graph
G = nx.DiGraph(id = service_index, type = 'consuming', provider_pair = providing_service.graph['id'], expires_in = time + random.randint(3,10))
# populate the consuming service graph with VNF nodes
for j in range(service_length):
if j not in CSC_engaged_VNFs:
VNF_type = 'VNF'
else:
VNF_type = 'C_CSC_VNF'
G.add_node("C{0}VNF{1}".format(service_index,j), type = VNF_type, cpu = random.choice(CPUs), serid = service_index, sertype = 'consumer')
nodes = list(G.nodes())
# add edges between VNFs sequentially
for j in range(service_length-1):
G.add_edge(nodes[j],nodes[j+1], source = nodes[j], dest= nodes[j+1], bandwidth = random.randrange(10,100), sertype = 'consuming')
# the corresponding CSC VNF indices of the providing service
CSC_engaged_VNFs_provider = [n for n in providing_service.nodes if providing_service.nodes[n]['type'] == 'CSC_VNF']
# add the CSC nodes of the providing service to the consuming service
for j in CSC_engaged_VNFs_provider:
G.add_node(j, type = 'P_CSC_VNF', sertype = 'provider')
# add the 2 CSC-engaged edges
# from consuming to providing
G.add_edge(nodes[CSC_engaged_VNFs[0]], CSC_engaged_VNFs_provider[0], source = nodes[CSC_engaged_VNFs[0]], dest = CSC_engaged_VNFs_provider[0], bandwidth = random.randrange(10,100), sertype = 'providing')
# from providing to consuming
if len(CSC_engaged_VNFs_provider) == 2:
G.add_edge(CSC_engaged_VNFs_provider[1], nodes[CSC_engaged_VNFs[1]], source = CSC_engaged_VNFs_provider[1], dest = nodes[CSC_engaged_VNFs[1]], bandwidth = random.randrange(10,100), sertype = 'providing')
else:
G.add_edge(CSC_engaged_VNFs_provider[0], nodes[CSC_engaged_VNFs[1]], source = CSC_engaged_VNFs_provider[0], dest = nodes[CSC_engaged_VNFs[1]], bandwidth = random.randrange(10,100), sertype = 'providing')
return G
|
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] | 2.701717 | 1,398 |
from django.db import models
from django.contrib.auth.models import User
# Create your models here.
|
[
6738,
42625,
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13,
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1330,
4981,
198,
6738,
42625,
14208,
13,
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13,
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2,
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] | 3.466667 | 30 |
#!/usr/bin/env python3
import sys
import os
import argparse
import re
import datetime
import subprocess
class EXIT():
"""
Exit codes from:
https://docs.icinga.com/latest/en/pluginapi.html
"""
OK = 0
WARN = 1
CRIT = 2
UNKOWN = 3
def commandline(args):
"""
Settings for the commandline arguments.
Returns the parsed arguments.
"""
parser = argparse.ArgumentParser(description='Checks the timestamps for files in a directory.')
parser.add_argument("-p", "--path", required=True,
help="Path to offline backup list file or directory")
parser.add_argument("-w", "--warning",
help="Threshold for warnings in days. Default: 2 Days")
parser.add_argument("-c", "--critical",
help="Threshold for criticals in days. Default: 5 Days")
parser.add_argument("-f", "--format",
help="Format of the date in the file. Default: Y-m-d")
parser.add_argument("-r", "--regex",
help="Regular Expression to extract date from file. Default: [0-9]{4}-[0-9]{2}-[0-9]{2}")
parser.add_argument("-v", "--verbose",
help="Increase output verbosity",
action="store_true")
parser.set_defaults(verbose=False,
critical=5,
warning=2)
return parser.parse_args(args)
def readdata(path):
"""
Checks if the path exists, then reads the file or directory and returns the data.
"""
if not os.path.exists(path):
print('No such path {0}'.format(path))
sys.exit(EXIT.WARN)
if os.path.isfile(path):
with open(path) as f:
data = f.read()
elif os.path.isdir(path):
data = subprocess.check_output(['ls', '--full-time', path])
data = data.decode("utf-8").rstrip('\n')
return data
def extract_dates(data, date_format='%Y-%m-%d', date_regex='[0-9]{4}-[0-9]{2}-[0-9]{2}'):
"""
Extracts dates from a string using regular expressions, then converts the dates to datetime objects and returns a list.
"""
dates = []
regex = re.compile(date_regex)
date_strings = regex.findall(data)
for date_string in date_strings:
dates.append(datetime.datetime.strptime(date_string, date_format).date())
return sorted(dates)
def check_delta(delta, warn, crit):
"""
Checks the category of the calculated delta (OK, WARN, FAIL) and exits the program accordingly.
"""
last_backup = 'Last backup was {0} days ago'.format(delta.days)
isokay = delta.days < warn
iswarn = delta.days >= warn and delta.days < crit
iscrit = delta.days >= crit
if isokay:
print('OK - ' + last_backup)
sys.exit(EXIT.OK)
elif iswarn:
print('WARN - ' + last_backup)
sys.exit(EXIT.WARN)
elif iscrit:
print('CRIT - ' + last_backup)
sys.exit(EXIT.CRIT)
else:
print('UNKNOWN - Not really sure what is happening')
sys.exit(EXIT.UNKOWN)
def calculate_delta(dates):
"""
Calculates how far the gives dates deviate from today's date. Returns a datetime.timedelta object.
"""
today = datetime.datetime.today().date()
delta = 0
for i in range(0, len(dates)):
delta = -(dates[i] - today)
# If there are to dates in the file for example
if not isinstance(delta, datetime.timedelta):
print('UNKNOWN - Probably error while reading the file')
sys.exit(EXIT.UNKOWN)
return delta
if __name__ == "__main__":
args = commandline(sys.argv[1:])
main(args)
|
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220,
220,
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7,
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13,
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58,
16,
25,
12962,
198,
220,
220,
220,
1388,
7,
22046,
8,
198
] | 2.34421 | 1,563 |
# coding: utf-8
#
# Copyright 2022 :Barry-Thomas-Paul: Moss
#
# Licensed under the Apache License, Version 2.0 (the "License")
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http: // www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Interface Class
# this is a auto generated file generated by Cheetah
# Libre Office Version: 7.3
# Namespace: com.sun.star.xml.sax
import typing
from abc import abstractmethod
from ...uno.x_interface import XInterface as XInterface_8f010a43
class XFastTokenHandler(XInterface_8f010a43):
"""
interface to translate XML strings to integer tokens.
An instance of this interface can be registered at a XFastParser. It should be able to translate all XML names (element local names, attribute local names and constant attribute values) to integer tokens.
A token value must be greater or equal to zero and less than FastToken.NAMESPACE. If a string identifier is not known to this instance, FastToken.DONTKNOW is returned.
See Also:
`API XFastTokenHandler <https://api.libreoffice.org/docs/idl/ref/interfacecom_1_1sun_1_1star_1_1xml_1_1sax_1_1XFastTokenHandler.html>`_
"""
__ooo_ns__: str = 'com.sun.star.xml.sax'
__ooo_full_ns__: str = 'com.sun.star.xml.sax.XFastTokenHandler'
__ooo_type_name__: str = 'interface'
__pyunointerface__: str = 'com.sun.star.xml.sax.XFastTokenHandler'
@abstractmethod
def getTokenFromUTF8(self, Identifier: 'typing.Tuple[int, ...]') -> int:
"""
returns an integer token for the given string
"""
@abstractmethod
def getUTF8Identifier(self, Token: int) -> 'typing.Tuple[int, ...]':
"""
returns an identifier for the given integer token as a byte sequence encoded in UTF-8.
"""
__all__ = ['XFastTokenHandler']
|
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220,
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220,
220,
5860,
281,
27421,
329,
262,
1813,
18253,
11241,
355,
257,
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8379,
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12,
23,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
198,
834,
439,
834,
796,
37250,
55,
22968,
30642,
25060,
20520,
628
] | 3.00554 | 722 |
import luigi
import subprocess
from os.path import join, dirname, basename
from ..utils.cap_task import CapTask
from ..config import PipelineConfig
from ..utils.conda import CondaPackage
from ..preprocessing.clean_reads import CleanReads
from ..databases.amr_db import GrootDB, MegaResDB, CardDB
|
[
198,
11748,
300,
84,
25754,
198,
11748,
850,
14681,
198,
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13,
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313,
11012,
11,
13421,
4965,
11012,
11,
5172,
11012,
628,
628
] | 3.420455 | 88 |
#coding=utf-8
#
# Created on Mar 21, 2014, by Junn
#
#
from django.contrib.auth.models import BaseUserManager
from django.utils import timezone
from utils import eggs, logs, http
from django.core.cache import cache
VALID_ATTRS = ('nickname', 'email', 'phone', 'gender', 'avatar')
|
[
2,
66,
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28,
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12,
23,
198,
2,
198,
2,
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319,
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11,
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11,
416,
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615,
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11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
198,
220,
220,
220,
220,
198,
220,
220,
220,
220
] | 2.528926 | 121 |
import cv2
import numpy as np
import mediapipe as mp
cap = cv2.VideoCapture(0)
ret, frame = cap. read ()
while (True):
ret, frame = cap. read ()
frame = cv2.flip(frame,1)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imshow('Gray Filter', gray)
if cv2.waitKey(10) & 0xFF==ord('q'):
break
if cv2.waitKey(10) & 0xFF==ord('s'):
import email_sender
cap. release ()
cv2.destroyAllWindows()
|
[
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11128,
13,
2650,
7499,
198,
33967,
17,
13,
41659,
3237,
11209,
3419
] | 2.15 | 200 |
import datetime
import uuid
import pytest
from docstore.models import Dimensions, Document, File, Thumbnail, from_json, to_json
@pytest.mark.parametrize("documents", [[1, 2, 3], {"a", "b", "c"}])
|
[
11748,
4818,
8079,
198,
11748,
334,
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198,
198,
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198,
198,
6738,
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13,
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1330,
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11,
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628,
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19779,
64,
1600,
366,
65,
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366,
66,
20662,
12962,
198
] | 2.833333 | 72 |
import setuptools
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setuptools.setup(
name="metalabs_sdk", # Replace with your own username
version="0.1.1",
author="Jeffrey Annaraj",
author_email="[email protected]",
description="SDK for MetaLabs API ",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/JAnnaraj/meta-labs_sdk",
packages=setuptools.find_packages(),
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires='>=3.6',
keywords='metalabs',
install_requires=['urllib3']
)
|
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366,
81,
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# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft and contributors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from msrest.serialization import Model
class FileSystemApplicationLogsConfig(Model):
"""
Application logs to file system configuration
:param level: Log level. Possible values include: 'Off', 'Verbose',
'Information', 'Warning', 'Error'
:type level: str or :class:`LogLevel <azure.mgmt.web.models.LogLevel>`
"""
_attribute_map = {
'level': {'key': 'level', 'type': 'LogLevel'},
}
|
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from __future__ import absolute_import
from . import teardrops
|
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import attr
from ndk.construct import Construct
from ndk.directives import *
from ndk.options import contact as options
@attr.s
|
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#! -*- coding: utf-8 -*-
# bert做Seq2Seq任务，采用UNILM方案
# 介绍链接：https://kexue.fm/archives/6933
from __future__ import print_function
import glob
import os
import numpy as np
import sys
from bert4keras.backend import keras, K
from bert4keras.layers import Loss
from bert4keras.models import build_transformer_model, tf
from bert4keras.tokenizers import Tokenizer, load_vocab
from bert4keras.optimizers import Adam
from bert4keras.snippets import sequence_padding, open
from bert4keras.snippets import DataGenerator, AutoRegressiveDecoder
from keras.models import Model
from examples import modeling
from examples.my_args import arg_dic
from tensorflow.python.framework.graph_util import convert_variables_to_constants
from keras import backend as K
from tensorflow.python.platform import gfile
# parameter ==========================
wkdir = '/Users/xusijun/Documents/NLP009/bert4keras-master001/keras_to_tensorflow-master'
pb_filename = 'model070.pb'
# 基本参数
maxlen = 256
batch_size = 16
# steps_per_epoch = 1000
steps_per_epoch = 1000
# epochs = 10000
epochs = 10
# bert配置
# config_path = '/root/kg/bert/chinese_wwm_L-12_H-768_A-12/bert_config.json'
# checkpoint_path = '/root/kg/bert/chinese_wwm_L-12_H-768_A-12/bert_model.ckpt'
# dict_path = '/root/kg/bert/chinese_wwm_L-12_H-768_A-12/vocab.txt'
# config_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/chinese_wwm_L-12_H-768_A-12/bert_config.json'
# checkpoint_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/chinese_wwm_L-12_H-768_A-12/bert_model.ckpt'
# dict_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/chinese_wwm_L-12_H-768_A-12/vocab.txt'
config_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/albert_tiny_google_zh_489k/albert_config.json'
checkpoint_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/albert_tiny_google_zh_489k/albert_model.ckpt'
dict_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/albert_tiny_google_zh_489k/vocab.txt'
# 训练样本。THUCNews数据集，每个样本保存为一个txt。
# txts = glob.glob('/root/thuctc/THUCNews/*/*.txt')
# txts = glob.glob('/Users/xusijun/Documents/NLP009/bert4keras-master001/MyNews/*/*.txt')
txts = glob.glob('/Users/xusijun/Documents/NLP009/bert4keras-master001/THUCNews/*/*.txt')
# 加载并精简词表，建立分词器
# token_dict, keep_tokens = load_vocab(
# dict_path=dict_path,
# simplified=True,
# startswith=['[PAD]', '[UNK]', '[CLS]', '[SEP]'],
# )
token_dict = load_vocab(
dict_path=dict_path,
# startswith=['[PAD]', '[UNK]', '[CLS]', '[SEP]'],
)
tokenizer = Tokenizer(token_dict, do_lower_case=True)
class data_generator(DataGenerator):
"""数据生成器
"""
class CrossEntropy(Loss):
"""交叉熵作为loss，并mask掉输入部分
"""
model = build_transformer_model(
config_path,
checkpoint_path,
application='unilm',
# keep_tokens=keep_tokens, # 只保留keep_tokens中的字，精简原字表
keep_tokens=None, # 只保留keep_tokens中的字，精简原字表
)
output = CrossEntropy(2)(model.inputs + model.outputs)
model = Model(model.inputs, output)
model.compile(optimizer=Adam(1e-5))
model.summary()
class AutoTitle(AutoRegressiveDecoder):
"""seq2seq解码器
"""
@AutoRegressiveDecoder.wraps(default_rtype='probas')
autotitle = AutoTitle(start_id=None, end_id=tokenizer._token_end_id, maxlen=32)
# save model to pb ====================
def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
"""
Freezes the state of a session into a pruned computation graph.
Creates a new computation graph where variable nodes are replaced by
constants taking their current value in the session. The new graph will be
pruned so subgraphs that are not necessary to compute the requested
outputs are removed.
@param session The TensorFlow session to be frozen.
@param keep_var_names A list of variable names that should not be frozen,
or None to freeze all the variables in the graph.
@param output_names Names of the relevant graph outputs.
@param clear_devices Remove the device directives from the graph for better portability.
@return The frozen graph definition.
"""
graph = session.graph
with graph.as_default():
freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
output_names = output_names or []
output_names += [v.op.name for v in tf.global_variables()]
input_graph_def = graph.as_graph_def()
if clear_devices:
for node in input_graph_def.node:
node.device = ""
frozen_graph = convert_variables_to_constants(session, input_graph_def,
output_names, freeze_var_names)
return frozen_graph
class Evaluator(keras.callbacks.Callback):
"""评估与保存
"""
if __name__ == '__main__':
model.load_weights('./myFile70.h5')
just_show()
evaluator = Evaluator()
train_generator = data_generator(txts, batch_size)
model.fit(
train_generator.forfit(),
steps_per_epoch=steps_per_epoch,
epochs=epochs,
callbacks=[evaluator]
)
else:
model.load_weights('./best_model003.weights')
|
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11537,
198
] | 2.305506 | 2,252 |
# check
# lowercase letters
# count all the letters of T which S contains
# save indices of letters in T
|
[
2,
2198,
198,
2,
2793,
7442,
7475,
198,
198,
2,
954,
477,
262,
7475,
286,
309,
543,
311,
4909,
198,
2,
3613,
36525,
286,
7475,
287,
309
] | 3.888889 | 27 |
__all__ = ()
from sys import platform as PLATFORM
from os.path import join as join_paths
from os import listdir as list_directory, environ as ENVIRONMENTAL_VARIABLES
from tempfile import gettempdir as get_temporary_directory
from scarletio import set_docs
from .constants import PAYLOAD_KEY_EVENT, EVENT_ERROR, PAYLOAD_KEY_DATA
from. exceptions import DiscordRPCError
if PLATFORM in ('linux', 'darwin'):
TEMPORARY_DIRECTORY = ENVIRONMENTAL_VARIABLES.get('XDG_RUNTIME_DIR', None)
if (TEMPORARY_DIRECTORY is None):
TEMPORARY_DIRECTORY = ENVIRONMENTAL_VARIABLES.get('TMPDIR', None)
if (TEMPORARY_DIRECTORY is None):
TEMPORARY_DIRECTORY = ENVIRONMENTAL_VARIABLES.get('TMP', None)
if (TEMPORARY_DIRECTORY is None):
TEMPORARY_DIRECTORY = ENVIRONMENTAL_VARIABLES.get('TEMP', None)
if (TEMPORARY_DIRECTORY is None):
TEMPORARY_DIRECTORY = get_temporary_directory()
elif PLATFORM == 'win32':
TEMPORARY_DIRECTORY = '\\\\?\\pipe'
else:
set_docs(get_ipc_path,
"""
Gets Discord inter process communication path.
Parameters
----------
pipe : `None` or `str`
# TODO
Returns
-------
path : `None` or `str`
""")
def check_for_error(data):
"""
Checks whether the given data contains an errors.
Parameters
----------
data : `dict` of (`str`, `Any`) items
Data received from Discord.
Raises
------
DiscordRPCError
"""
try:
event = data[PAYLOAD_KEY_EVENT]
except KeyError:
pass
else:
if event == EVENT_ERROR:
error_data = data[PAYLOAD_KEY_DATA]
error_code = error_data['code']
error_message = error_data['message']
raise DiscordRPCError(error_code, error_message)
|
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] | 2.212589 | 842 |
#!/usr/bin/env python
# encoding=utf8
import os
import re
import sys
import struct
import pprint
import random
import argparse
import datetime
import tiddlywiki as tiddly
import cdam_gen_files as gen
import importlib
import bitarray
importlib.reload(sys)
# sys.setdefaultencoding('utf8')
VERSION = "1.0"
BINARY_VER = "1.0.5"
# For holding binary variable keys and values.
VARIABLES = {}
FLAGS = {}
TITLE_MAP = {}
STORY_MAP = {}
PASSAGES = {}
STORY_TITLE = ""
STORY_AUTHOR = ""
STORY_SUBTITLE = ""
STORY_CREDITS = ""
STORY_VERSION = ""
STORY_CONTACT = ""
STORY_LANGUAGE = ""
REPORT = ""
OPERATION_TEST = bytearray()
TOTAL_OPS = 0
VERBOSE = False
LINEAR = False
HTML = False
SEED = None
PP = pprint.PrettyPrinter(indent = 4)
kAppend = "<append>"
kContinue = "<continue>"
kContinueCopy = '<continue>'
kGotoTempTag = "-GOTO-"
if __name__ == '__main__':
#global _UPDATE
#global _FORCE
main()
|
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"""
Common geometric primitives.
Author: Jeff Mahler
"""
import numpy as np
class Box(object):
"""A 2D box or 3D rectangular prism.
Attributes
----------
dims : :obj:`numpy.ndarray` of float
Maximal extent in x, y, and (optionally) z.
width : float
Maximal extent in x.
height : float
Maximal extent in y.
area : float
Area of projection onto xy plane.
min_pt : :obj:`numpy.ndarray` of float
The minimum x, y, and (optionally) z points.
max_pt : :obj:`numpy.ndarray` of float
The maximum x, y, and (optionally) z points.
center : :obj:`numpy.ndarray` of float
The center of the box in 2 or 3D coords.
frame : :obj:`str`
The frame in which this box is placed.
"""
def __init__(self, min_pt, max_pt, frame='unspecified'):
"""Initialize a box.
Parameters
----------
min_pt : :obj:`numpy.ndarray` of float
The minimum x, y, and (optionally) z points.
max_pt : :obj:`numpy.ndarray` of float
The maximum x, y, and (optionally) z points.
frame : :obj:`str`
The frame in which this box is placed.
Raises
------
ValueError
If max_pt is not strictly larger than min_pt in all dims.
"""
if np.any((max_pt - min_pt) < 0):
raise ValueError('Min point must be smaller than max point')
self._min_pt = min_pt
self._max_pt = max_pt
self._frame = frame
@property
def dims(self):
""":obj:`numpy.ndarray` of float: Maximal extent in x, y, and (optionally) z
"""
return self._max_pt - self._min_pt
@property
def width(self):
"""float: Maximal extent in x.
"""
return int(np.round(self.dims[1]))
@property
def height(self):
"""float: Maximal extent in y.
"""
return int(np.round(self.dims[0]))
@property
def area(self):
"""float: Area of projection onto xy plane.
"""
return self.width * self.height
@property
def min_pt(self):
""":obj:`numpy.ndarray` of float: The minimum x, y, and (optionally) z points.
"""
return self._min_pt
@property
def max_pt(self):
""":obj:`numpy.ndarray` of float: The maximum x, y, and (optionally) z points.
"""
return self._max_pt
@property
def center(self):
""":obj:`numpy.ndarray` of float: The center of the box in 2 or 3D coords.
"""
return self.min_pt + self.dims / 2.0
@property
def ci(self):
"""float value of center i coordinate"""
return self.center[0]
@property
def cj(self):
"""float value of center j coordinate"""
return self.center[1]
@property
def frame(self):
""":obj:`str`: The frame in which this box is placed.
"""
return self._frame
class Contour(object):
""" A set of pixels forming the boundary of an object of interest in an image.
Attributes
----------
boundary_pixels : :obj:`numpy.ndarray`
Nx2 array of pixel coordinates on the boundary of a contour
bounding_box : :obj:`Box`
smallest box containing the contour
area : float
area of the contour
num_pixels : int
number of pixels along the boundary
"""
@property
|
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220,
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2488,
26745,
628
] | 2.283156 | 1,508 |
import pytest
import responses
from sparkpost import SparkPost
from sparkpost.exceptions import SparkPostAPIException
@responses.activate
@responses.activate
@responses.activate
@responses.activate
|
[
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import sys
|
[
11748,
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198
] | 3.666667 | 3 |
"""
Test that plugins that load commands work correctly.
"""
import os, time
import re
import unittest2
import lldb
from lldbtest import *
import lldbutil
if __name__ == '__main__':
import atexit
lldb.SBDebugger.Initialize()
atexit.register(lambda: lldb.SBDebugger.Terminate())
unittest2.main()
|
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] | 2.730435 | 115 |
import rclpy
from rclpy.node import Node
from geometry_msgs.msg import PoseStamped
from code_map_localization_msgs.msg import Localization
from .convert_message import convert_to_ros_msgs
from codemap.webcam import WebCamLocalization
import ctypes
import time
libcodemap = ctypes.cdll.LoadLibrary('libcodemap.so')
if __name__ == '__main__':
main()
|
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] | 2.975 | 120 |
from contextlib import suppress
from os import remove
from os.path import islink, join
from sys import exit
from snakypy.helpers import FG, printer
from snakypy.dotctrl.config.base import Base
from snakypy.dotctrl.utils import check_init, join_two, listing_files, rm_garbage_config
|
[
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] | 3.313953 | 86 |
# Copyright (c) 2019 Andres Gomez Ramirez.
# All Rights Reserved.
import sys
import time
import subprocess
import logging
import os.path
import time
from arhuaco.sensors.source.source import Source
|
[
2,
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r"""
http://github.com/in4lio/yupp/
__ __ _____ _____
/\ \ /\ \ /\ _ \ _ \
\ \ \_\/ \_\/ \_\ \ \_\ \
\ \__ /\____/\ __/\ __/
\/_/\_\/___/\ \_\/\ \_\/
\/_/ \/_/ \/_/
Python 'yupp' Codec Support
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from builtins import str
from future import standard_library
standard_library.install_aliases()
import codecs
from encodings import utf_8, search_function
from .pp.yulic import VERSION, DESCRIPTION, HOLDER, EMAIL
from .pp.yup import cli
from .pp.yup import proc_file as translate
# ---------------------------------------------------------------------------
__pp_name__ = 'yupp'
__version__ = VERSION
__description__ = DESCRIPTION
__author__ = HOLDER
__author_email__ = EMAIL
__url__ = 'http://github.com/in4lio/yupp/'
# ---------------------------------------------------------------------------
def read_header( fn ):
'''
Read shebang and magic comment from the source file.
'''
header = ''
try:
with open( fn, 'r' ) as f:
header = f.readline()
if 'coding:' not in header:
header += f.readline()
except:
pass
return header
# ---------------------------------------------------------------------------
# ---------------------------------------------------------------------------
# ---------------------------------------------------------------------------
# ---------------------------------------------------------------------------
# ---------------------------------------------------------------------------
# ---------------------------------------------------------------------------
codecs.register( yupp_search_function )
|
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834,
220,
220,
220,
220,
220,
796,
44156,
2849,
198,
834,
11213,
834,
220,
796,
22196,
40165,
198,
834,
9800,
834,
220,
220,
220,
220,
220,
220,
796,
49707,
14418,
198,
834,
9800,
62,
12888,
834,
796,
412,
5673,
4146,
198,
834,
6371,
834,
220,
220,
220,
220,
220,
220,
220,
220,
220,
796,
705,
4023,
1378,
12567,
13,
785,
14,
259,
19,
48590,
14,
88,
7211,
14,
6,
198,
198,
2,
220,
220,
16529,
32284,
198,
4299,
1100,
62,
25677,
7,
24714,
15179,
198,
220,
220,
220,
705,
7061,
198,
220,
220,
220,
4149,
673,
36668,
290,
5536,
2912,
422,
262,
2723,
2393,
13,
198,
220,
220,
220,
705,
7061,
198,
220,
220,
220,
13639,
796,
10148,
198,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
351,
1280,
7,
24714,
11,
705,
81,
6,
1267,
355,
277,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13639,
796,
277,
13,
961,
1370,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
705,
66,
7656,
32105,
407,
287,
13639,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13639,
15853,
277,
13,
961,
1370,
3419,
198,
220,
220,
220,
2845,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1208,
198,
220,
220,
220,
1441,
13639,
198,
198,
2,
220,
220,
16529,
32284,
198,
198,
2,
220,
220,
16529,
32284,
198,
198,
2,
220,
220,
16529,
32284,
198,
198,
2,
220,
220,
16529,
32284,
198,
198,
2,
220,
220,
16529,
32284,
198,
198,
2,
220,
220,
16529,
32284,
198,
19815,
721,
82,
13,
30238,
7,
331,
7211,
62,
12947,
62,
8818,
1267,
198
] | 3.253076 | 569 |
import time
HANDSHAKE_LENGTH = 1536
class Handshake(object):
"""
A handshake packet.
@ivar first: The first 4 bytes of the packet, represented as an unsigned
long.
@type first: 32bit unsigned int.
@ivar second: The second 4 bytes of the packet, represented as an unsigned
long.
@type second: 32bit unsigned int.
@ivar payload: A blob of data which makes up the rest of the packet. This
must be C{HANDSHAKE_LENGTH} - 8 bytes in length.
@type payload: C{str}
@ivar timestamp: Timestamp that this packet was created (in milliseconds).
@type timestamp: C{int}
"""
first = None
second = None
payload = None
timestamp = None
def encode(self, stream_buffer):
"""
Encodes this packet to a stream.
"""
stream_buffer.write_ulong(self.first or 0)
stream_buffer.write_ulong(self.second or 0)
stream_buffer.write(self.payload)
def decode(self, stream_buffer):
"""
Decodes this packet from a stream.
"""
self.first = stream_buffer.read_ulong()
self.second = stream_buffer.read_ulong()
self.payload = stream_buffer.read(HANDSHAKE_LENGTH - 8)
|
[
11748,
640,
628,
198,
39,
1565,
5258,
7801,
7336,
62,
43,
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796,
1315,
2623,
628,
198,
4871,
7157,
32431,
7,
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2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
317,
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13,
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220,
220,
220,
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452,
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25,
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604,
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262,
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220,
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220,
220,
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327,
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220,
220,
220,
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283,
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25,
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326,
428,
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373,
2727,
357,
259,
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737,
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220,
220,
220,
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25,
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90,
600,
92,
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220,
220,
220,
37227,
628,
220,
220,
220,
717,
796,
6045,
198,
220,
220,
220,
1218,
796,
6045,
198,
220,
220,
220,
21437,
796,
6045,
198,
220,
220,
220,
41033,
796,
6045,
628,
220,
220,
220,
825,
37773,
7,
944,
11,
4269,
62,
22252,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
14711,
4147,
428,
19638,
284,
257,
4269,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
4269,
62,
22252,
13,
13564,
62,
377,
506,
7,
944,
13,
11085,
393,
657,
8,
198,
220,
220,
220,
220,
220,
220,
220,
4269,
62,
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13,
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62,
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7,
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657,
8,
628,
220,
220,
220,
220,
220,
220,
220,
4269,
62,
22252,
13,
13564,
7,
944,
13,
15577,
2220,
8,
628,
220,
220,
220,
825,
36899,
7,
944,
11,
4269,
62,
22252,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
4280,
4147,
428,
19638,
422,
257,
4269,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
11085,
796,
4269,
62,
22252,
13,
961,
62,
377,
506,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
12227,
796,
4269,
62,
22252,
13,
961,
62,
377,
506,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
15577,
2220,
796,
4269,
62,
22252,
13,
961,
7,
39,
1565,
5258,
7801,
7336,
62,
43,
49494,
532,
807,
8,
198
] | 2.536232 | 483 |
from typing import Dict
import sys
from mediocre_agent import agent
if __name__ == "__main__":
def read_input():
"""
Reads input from stdin
"""
try:
return input()
except EOFError as eof:
raise SystemExit(eof)
step = 0
observation = Observation()
observation["updates"] = []
observation["step"] = 0
player_id = 0
while True:
inputs = read_input()
observation["updates"].append(inputs)
if step == 0:
player_id = int(observation["updates"][0])
observation.player = player_id
if inputs == "D_DONE":
actions = agent(observation, None)
observation["updates"] = []
step += 1
observation["step"] = step
print(",".join(actions))
print("D_FINISH")
|
[
6738,
19720,
1330,
360,
713,
198,
11748,
25064,
198,
6738,
29956,
62,
25781,
1330,
5797,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
220,
198,
220,
220,
220,
825,
1100,
62,
15414,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
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82,
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422,
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259,
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220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
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220,
220,
220,
220,
220,
220,
220,
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412,
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355,
304,
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25,
198,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
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7,
68,
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8,
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220,
220,
220,
2239,
796,
657,
198,
220,
220,
220,
13432,
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3419,
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220,
220,
220,
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929,
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8973,
796,
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220,
220,
220,
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796,
657,
198,
220,
220,
220,
2137,
62,
312,
796,
657,
198,
220,
220,
220,
981,
6407,
25,
198,
220,
220,
220,
220,
220,
220,
220,
17311,
796,
1100,
62,
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198,
220,
220,
220,
220,
220,
220,
220,
13432,
14692,
929,
19581,
1,
4083,
33295,
7,
15414,
82,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2239,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2137,
62,
312,
796,
493,
7,
672,
3168,
341,
14692,
929,
19581,
1,
7131,
15,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13432,
13,
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220,
220,
220,
220,
220,
611,
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6624,
366,
35,
62,
35,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4028,
796,
5797,
7,
672,
3168,
341,
11,
6045,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13432,
14692,
929,
19581,
8973,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2239,
15853,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13432,
14692,
9662,
8973,
796,
2239,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
2430,
13,
22179,
7,
4658,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
35,
62,
20032,
18422,
4943,
198
] | 2.088517 | 418 |
class Agent(object):
"""
represents the agent who takes the calls from the queue
"""
def __init__(self, id, free, minutes_till_ready=0):
"""
constructor just sets the id
:param name: string
"""
self.id = id
self.free = free
self.minutes_till_ready = minutes_till_ready
@staticmethod
def consume(caller_list):
"""
consumes callers from the queue and chats with the
caller.
:param caller_list:
:return:
"""
temp_caller = caller_list.consume_caller()
print("agent consumes - " + str(temp_caller.chat()))
|
[
4871,
15906,
7,
15252,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
6870,
262,
5797,
508,
2753,
262,
3848,
422,
262,
16834,
628,
220,
220,
220,
37227,
628,
220,
220,
220,
825,
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834,
7,
944,
11,
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11,
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11,
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62,
83,
359,
62,
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28,
15,
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198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
23772,
655,
5621,
262,
4686,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
1438,
25,
4731,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
312,
796,
4686,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
5787,
796,
1479,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
1084,
1769,
62,
83,
359,
62,
1493,
796,
2431,
62,
83,
359,
62,
1493,
628,
220,
220,
220,
2488,
12708,
24396,
198,
220,
220,
220,
825,
15000,
7,
13345,
263,
62,
4868,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
37225,
869,
364,
422,
262,
16834,
290,
40815,
351,
262,
198,
220,
220,
220,
220,
220,
220,
220,
24955,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
24955,
62,
4868,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
20218,
62,
13345,
263,
796,
24955,
62,
4868,
13,
5936,
2454,
62,
13345,
263,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
25781,
37225,
532,
366,
1343,
965,
7,
29510,
62,
13345,
263,
13,
17006,
3419,
4008,
198
] | 2.272727 | 286 |
from os import environ
from pathlib import Path
from django.core.wsgi import get_wsgi_application
from config import get_project_root_path, import_env_vars
import_env_vars(Path(get_project_root_path(), "envdir"))
environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.base")
application = get_wsgi_application()
|
[
6738,
28686,
1330,
551,
2268,
198,
6738,
3108,
8019,
1330,
10644,
198,
198,
6738,
42625,
14208,
13,
7295,
13,
18504,
12397,
1330,
651,
62,
18504,
12397,
62,
31438,
198,
198,
6738,
4566,
1330,
651,
62,
16302,
62,
15763,
62,
6978,
11,
1330,
62,
24330,
62,
85,
945,
198,
198,
11748,
62,
24330,
62,
85,
945,
7,
15235,
7,
1136,
62,
16302,
62,
15763,
62,
6978,
22784,
366,
24330,
15908,
48774,
198,
198,
268,
2268,
13,
2617,
12286,
7203,
35028,
1565,
11230,
62,
28480,
51,
20754,
62,
33365,
24212,
1600,
366,
11250,
13,
33692,
13,
8692,
4943,
198,
198,
31438,
796,
651,
62,
18504,
12397,
62,
31438,
3419,
198
] | 3 | 108 |
print("I'm Sexy")
|
[
4798,
7203,
40,
1101,
49131,
4943,
198
] | 2.571429 | 7 |
import sys
import os, os.path
# May need this for the path issue for gpaw-python
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from src.structure import get_structure
from src.supercell import make_super, add_adatom
from src.neb import neb, calc_img
import shutil
from ase.parallel import paropen, parprint, world, rank, broadcast
from ase.visualize import view
# Name=Zn, Co
if __name__ == "__main__":
assert len(sys.argv) == 3
mater = sys.argv[1]
imag = sys.argv[2]
main(name=mater, imag=imag)
|
[
11748,
25064,
198,
11748,
28686,
11,
28686,
13,
6978,
198,
2,
1737,
761,
428,
329,
262,
3108,
2071,
329,
27809,
707,
12,
29412,
198,
17597,
13,
6978,
13,
33295,
7,
418,
13,
6978,
13,
15908,
3672,
7,
418,
13,
6978,
13,
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2777,
776,
7,
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7,
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13,
853,
85,
8,
6624,
513,
198,
220,
220,
220,
26910,
796,
25064,
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853,
85,
58,
16,
60,
198,
220,
220,
220,
3590,
796,
25064,
13,
853,
85,
58,
17,
60,
198,
220,
220,
220,
1388,
7,
3672,
28,
76,
729,
11,
3590,
28,
48466,
8,
198
] | 2.690355 | 197 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import plugins
import sys
if __name__ == '__main__':
Plugin().execute()
|
[
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
11748,
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705,
834,
12417,
834,
10354,
198,
220,
220,
220,
42636,
22446,
41049,
3419,
198
] | 2.530612 | 49 |
import tensorflow as tf
import sonnet as snt
from .build_utils import residual_stack, maybe_set_l2_conv_contractive_regularizer
from .AbstractResNetLayer import AbstractResNetLayer
class ResEnc(AbstractResNetLayer):
"""
res enc used in VQ
"""
#TODO remove biases before batch norm, see if it makes any difference. Remove dropouts?
|
[
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611,
340,
1838,
597,
3580,
13,
17220,
4268,
5269,
30,
628,
198
] | 3.295238 | 105 |
"""
Represents a PaymentChannelFund transaction on the XRP Ledger.
A PaymentChannelFund transaction adds additional XRP to an open payment channel,
and optionally updates the expiration time of the channel. Only the source address
of the channel can use this transaction.
`See PaymentChannelFund <https://xrpl.org/paymentchannelfund.html>`_
"""
from dataclasses import dataclass, field
from typing import Optional
from xrpl.models.required import REQUIRED
from xrpl.models.transactions.transaction import Transaction, TransactionType
from xrpl.models.utils import require_kwargs_on_init
@require_kwargs_on_init
@dataclass(frozen=True)
class PaymentChannelFund(Transaction):
"""
Represents a PaymentChannelFund transaction on the XRP Ledger.
A PaymentChannelFund transaction adds additional XRP to an open payment channel,
and optionally updates the expiration time of the channel. Only the source address
of the channel can use this transaction.
`See PaymentChannelFund <https://xrpl.org/paymentchannelfund.html>`_
"""
#: This field is required.
channel: str = REQUIRED # type: ignore
#: This field is required.
amount: str = REQUIRED # type: ignore
expiration: Optional[int] = None
transaction_type: TransactionType = field(
default=TransactionType.PAYMENT_CHANNEL_FUND,
init=False,
)
|
[
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] | 3.39801 | 402 |
#
# * The source code in this file is developed independently by NEC Corporation.
#
# # NLCPy License #
#
# Copyright (c) 2020-2021 NEC Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# * Neither NEC Corporation nor the names of its contributors may be
# used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
import numpy
from nlcpy.ufuncs import ufuncs
from nlcpy.ufuncs import casting
from nlcpy.ufuncs import err
from nlcpy.ufuncs import ufunc_docs
# ----------------------------------------------------------------------------
# ufunc operations
# see: https://docs.scipy.org/doc/numpy/reference/ufuncs.html
# ----------------------------------------------------------------------------
# math_operations
add = ufuncs.create_ufunc(
'nlcpy_add',
numpy.add.types,
err._add_error_check,
doc=ufunc_docs._add_doc
)
subtract = ufuncs.create_ufunc(
'nlcpy_subtract',
casting._subtract_types,
err._subtract_error_check,
doc=ufunc_docs._subtract_doc
)
multiply = ufuncs.create_ufunc(
'nlcpy_multiply',
numpy.multiply.types,
err._multiply_error_check,
doc=ufunc_docs._multiply_doc
)
true_divide = ufuncs.create_ufunc(
'nlcpy_true_divide',
casting._true_divide_types,
err._true_divide_error_check,
doc=ufunc_docs._true_divide_doc
)
# ufunc_operation(divide,orig_types,valid_error_check)dnl
divide = true_divide
logaddexp = ufuncs.create_ufunc(
'nlcpy_logaddexp',
numpy.logaddexp.types,
err._logaddexp_error_check,
doc=ufunc_docs._logaddexp_doc
)
logaddexp2 = ufuncs.create_ufunc(
'nlcpy_logaddexp2',
numpy.logaddexp2.types,
err._logaddexp2_error_check,
doc=ufunc_docs._logaddexp2_doc
)
floor_divide = ufuncs.create_ufunc(
'nlcpy_floor_divide',
numpy.floor_divide.types,
err._floor_divide_error_check,
doc=ufunc_docs._floor_divide_doc
)
negative = ufuncs.create_ufunc(
'nlcpy_negative',
casting._negative_types,
err._negative_error_check,
doc=ufunc_docs._negative_doc
)
positive = ufuncs.create_ufunc(
'nlcpy_positive',
casting._positive_types,
err._positive_error_check,
doc=ufunc_docs._positive_doc
)
power = ufuncs.create_ufunc(
'nlcpy_power',
numpy.power.types,
err._power_error_check,
doc=ufunc_docs._power_doc
)
remainder = ufuncs.create_ufunc(
'nlcpy_remainder',
casting._remainder_types,
err._remainder_error_check,
doc=ufunc_docs._remainder_doc
)
# ufunc_operation(mod,orig_types,valid_error_check)dnl
mod = remainder
fmod = ufuncs.create_ufunc(
'nlcpy_fmod',
casting._fmod_types,
err._fmod_error_check,
doc=ufunc_docs._fmod_doc
)
# ufunc_operation(divmod,numpy_types,valid_error_check)dnl
absolute = ufuncs.create_ufunc(
'nlcpy_absolute',
numpy.absolute.types,
err._absolute_error_check,
doc=ufunc_docs._absolute_doc
)
fabs = ufuncs.create_ufunc(
'nlcpy_fabs',
casting._fabs_types,
err._fabs_error_check,
doc=ufunc_docs._fabs_doc
)
rint = ufuncs.create_ufunc(
'nlcpy_rint',
numpy.rint.types,
err._rint_error_check,
doc=ufunc_docs._rint_doc
)
sign = ufuncs.create_ufunc(
'nlcpy_sign',
casting._sign_types,
err._sign_error_check,
doc=ufunc_docs._sign_doc
)
heaviside = ufuncs.create_ufunc(
'nlcpy_heaviside',
numpy.heaviside.types,
err._heaviside_error_check,
doc=ufunc_docs._heaviside_doc
)
conjugate = ufuncs.create_ufunc(
'nlcpy_conjugate',
numpy.conjugate.types,
err._conjugate_error_check,
doc=ufunc_docs._conjugate_doc
)
# ufunc_operation(conj,numpy_types,valid_error_check)dnl
conj = conjugate
exp = ufuncs.create_ufunc(
'nlcpy_exp',
numpy.exp.types,
err._exp_error_check,
doc=ufunc_docs._exp_doc
)
exp2 = ufuncs.create_ufunc(
'nlcpy_exp2',
numpy.exp2.types,
err._exp2_error_check,
doc=ufunc_docs._exp2_doc
)
log = ufuncs.create_ufunc(
'nlcpy_log',
numpy.log.types,
err._log_error_check,
doc=ufunc_docs._log_doc
)
log2 = ufuncs.create_ufunc(
'nlcpy_log2',
numpy.log2.types,
err._log2_error_check,
doc=ufunc_docs._log2_doc
)
log10 = ufuncs.create_ufunc(
'nlcpy_log10',
numpy.log10.types,
err._log10_error_check,
doc=ufunc_docs._log10_doc
)
expm1 = ufuncs.create_ufunc(
'nlcpy_expm1',
numpy.expm1.types,
err._expm1_error_check,
doc=ufunc_docs._expm1_doc
)
log1p = ufuncs.create_ufunc(
'nlcpy_log1p',
numpy.log1p.types,
err._log1p_error_check,
doc=ufunc_docs._log1p_doc
)
sqrt = ufuncs.create_ufunc(
'nlcpy_sqrt',
numpy.sqrt.types,
err._sqrt_error_check,
doc=ufunc_docs._sqrt_doc
)
square = ufuncs.create_ufunc(
'nlcpy_square',
numpy.square.types,
err._square_error_check,
doc=ufunc_docs._square_doc
)
cbrt = ufuncs.create_ufunc(
'nlcpy_cbrt',
casting._cbrt_types,
err._cbrt_error_check,
doc=ufunc_docs._cbrt_doc
)
reciprocal = ufuncs.create_ufunc(
'nlcpy_reciprocal',
numpy.reciprocal.types,
err._reciprocal_error_check,
doc=ufunc_docs._reciprocal_doc
)
# ufunc_operation(gcd)dnl
# ufunc_operation(lcm)dnl
# bit-twiddling functions
bitwise_and = ufuncs.create_ufunc(
'nlcpy_bitwise_and',
casting._bitwise_and_types,
err._bitwise_and_error_check,
doc=ufunc_docs._bitwise_and_doc
)
bitwise_or = ufuncs.create_ufunc(
'nlcpy_bitwise_or',
casting._bitwise_or_types,
err._bitwise_or_error_check,
doc=ufunc_docs._bitwise_or_doc
)
bitwise_xor = ufuncs.create_ufunc(
'nlcpy_bitwise_xor',
casting._bitwise_xor_types,
err._bitwise_xor_error_check,
doc=ufunc_docs._bitwise_xor_doc
)
invert = ufuncs.create_ufunc(
'nlcpy_invert',
casting._invert_types,
err._invert_error_check,
doc=ufunc_docs._invert_doc
)
left_shift = ufuncs.create_ufunc(
'nlcpy_left_shift',
casting._left_shift_types,
err._left_shift_error_check,
doc=ufunc_docs._left_shift_doc
)
right_shift = ufuncs.create_ufunc(
'nlcpy_right_shift',
casting._right_shift_types,
err._right_shift_error_check,
doc=ufunc_docs._right_shift_doc
)
# comparison functions
greater = ufuncs.create_ufunc(
'nlcpy_greater',
numpy.greater.types,
err._greater_error_check,
doc=ufunc_docs._greater_doc
)
greater_equal = ufuncs.create_ufunc(
'nlcpy_greater_equal',
numpy.greater_equal.types,
err._greater_equal_error_check,
doc=ufunc_docs._greater_equal_doc
)
less = ufuncs.create_ufunc(
'nlcpy_less',
numpy.less.types,
err._less_error_check,
doc=ufunc_docs._less_doc
)
less_equal = ufuncs.create_ufunc(
'nlcpy_less_equal',
numpy.less_equal.types,
err._less_equal_error_check,
doc=ufunc_docs._less_equal_doc
)
not_equal = ufuncs.create_ufunc(
'nlcpy_not_equal',
numpy.not_equal.types,
err._not_equal_error_check,
doc=ufunc_docs._not_equal_doc
)
equal = ufuncs.create_ufunc(
'nlcpy_equal',
numpy.equal.types,
err._equal_error_check,
doc=ufunc_docs._equal_doc
)
logical_and = ufuncs.create_ufunc(
'nlcpy_logical_and',
numpy.logical_and.types,
err._logical_and_error_check,
doc=ufunc_docs._logical_and_doc
)
logical_or = ufuncs.create_ufunc(
'nlcpy_logical_or',
numpy.logical_or.types,
err._logical_or_error_check,
doc=ufunc_docs._logical_or_doc
)
logical_xor = ufuncs.create_ufunc(
'nlcpy_logical_xor',
numpy.logical_xor.types,
err._logical_xor_error_check,
doc=ufunc_docs._logical_xor_doc
)
logical_not = ufuncs.create_ufunc(
'nlcpy_logical_not',
numpy.logical_not.types,
err._logical_not_error_check,
doc=ufunc_docs._logical_not_doc
)
minimum = ufuncs.create_ufunc(
'nlcpy_minimum',
numpy.minimum.types,
err._minimum_error_check,
doc=ufunc_docs._minimum_doc
)
maximum = ufuncs.create_ufunc(
'nlcpy_maximum',
numpy.maximum.types,
err._maximum_error_check,
doc=ufunc_docs._maximum_doc
)
fmax = ufuncs.create_ufunc(
'nlcpy_fmax',
numpy.fmax.types,
err._fmax_error_check,
doc=ufunc_docs._fmax_doc
)
fmin = ufuncs.create_ufunc(
'nlcpy_fmin',
numpy.fmin.types,
err._fmin_error_check,
doc=ufunc_docs._fmin_doc
)
# trigonometric functions
sin = ufuncs.create_ufunc(
'nlcpy_sin',
numpy.sin.types,
err._sin_error_check,
doc=ufunc_docs._sin_doc
)
cos = ufuncs.create_ufunc(
'nlcpy_cos',
numpy.cos.types,
err._cos_error_check,
doc=ufunc_docs._cos_doc
)
tan = ufuncs.create_ufunc(
'nlcpy_tan',
numpy.tan.types,
err._tan_error_check,
doc=ufunc_docs._tan_doc
)
arcsin = ufuncs.create_ufunc(
'nlcpy_arcsin',
numpy.arcsin.types,
err._arcsin_error_check,
doc=ufunc_docs._arcsin_doc
)
arccos = ufuncs.create_ufunc(
'nlcpy_arccos',
numpy.arccos.types,
err._arccos_error_check,
doc=ufunc_docs._arccos_doc
)
arctan = ufuncs.create_ufunc(
'nlcpy_arctan',
numpy.arctan.types,
err._arctan_error_check,
doc=ufunc_docs._arctan_doc
)
arctan2 = ufuncs.create_ufunc(
'nlcpy_arctan2',
casting._arctan2_types,
err._arctan2_error_check,
doc=ufunc_docs._arctan2_doc
)
hypot = ufuncs.create_ufunc(
'nlcpy_hypot',
casting._hypot_types,
err._hypot_error_check,
doc=ufunc_docs._hypot_doc
)
sinh = ufuncs.create_ufunc(
'nlcpy_sinh',
numpy.sinh.types,
err._sinh_error_check,
doc=ufunc_docs._sinh_doc
)
cosh = ufuncs.create_ufunc(
'nlcpy_cosh',
numpy.cosh.types,
err._cosh_error_check,
doc=ufunc_docs._cosh_doc
)
tanh = ufuncs.create_ufunc(
'nlcpy_tanh',
numpy.tanh.types,
err._tanh_error_check,
doc=ufunc_docs._tanh_doc
)
arcsinh = ufuncs.create_ufunc(
'nlcpy_arcsinh',
numpy.arcsinh.types,
err._arcsinh_error_check,
doc=ufunc_docs._arcsinh_doc
)
arccosh = ufuncs.create_ufunc(
'nlcpy_arccosh',
numpy.arccosh.types,
err._arccosh_error_check,
doc=ufunc_docs._arccosh_doc
)
arctanh = ufuncs.create_ufunc(
'nlcpy_arctanh',
numpy.arctanh.types,
err._arctanh_error_check,
doc=ufunc_docs._arctanh_doc
)
deg2rad = ufuncs.create_ufunc(
'nlcpy_deg2rad',
casting._deg2rad_types,
err._deg2rad_error_check,
doc=ufunc_docs._deg2rad_doc
)
rad2deg = ufuncs.create_ufunc(
'nlcpy_rad2deg',
casting._rad2deg_types,
err._rad2deg_error_check,
doc=ufunc_docs._rad2deg_doc
)
degrees = ufuncs.create_ufunc(
'nlcpy_degrees',
casting._degrees_types,
err._degrees_error_check,
doc=ufunc_docs._degrees_doc
)
radians = ufuncs.create_ufunc(
'nlcpy_radians',
casting._radians_types,
err._radians_error_check,
doc=ufunc_docs._radians_doc
)
# floating functions
isfinite = ufuncs.create_ufunc(
'nlcpy_isfinite',
numpy.isfinite.types,
err._isfinite_error_check,
doc=ufunc_docs._isfinite_doc
)
isinf = ufuncs.create_ufunc(
'nlcpy_isinf',
numpy.isinf.types,
err._isinf_error_check,
doc=ufunc_docs._isinf_doc
)
isnan = ufuncs.create_ufunc(
'nlcpy_isnan',
numpy.isnan.types,
err._isnan_error_check,
doc=ufunc_docs._isnan_doc
)
# ufunc_operation(isnat,numpy_types,valid_error_check)dnl
signbit = ufuncs.create_ufunc(
'nlcpy_signbit',
numpy.signbit.types,
err._signbit_error_check,
doc=ufunc_docs._signbit_doc
)
copysign = ufuncs.create_ufunc(
'nlcpy_copysign',
numpy.copysign.types,
err._copysign_error_check,
doc=ufunc_docs._copysign_doc
)
nextafter = ufuncs.create_ufunc(
'nlcpy_nextafter',
numpy.nextafter.types,
err._nextafter_error_check,
doc=ufunc_docs._nextafter_doc
)
spacing = ufuncs.create_ufunc(
'nlcpy_spacing',
numpy.spacing.types,
err._spacing_error_check,
doc=ufunc_docs._spacing_doc
)
# ufunc_operation(modf,numpy_types,valid_error_check)dnl
ldexp = ufuncs.create_ufunc(
'nlcpy_ldexp',
numpy.ldexp.types,
err._ldexp_error_check,
doc=ufunc_docs._ldexp_doc
)
# ufunc_operation(frexp)dnl
floor = ufuncs.create_ufunc(
'nlcpy_floor',
casting._floor_types,
err._floor_error_check,
doc=ufunc_docs._floor_doc
)
ceil = ufuncs.create_ufunc(
'nlcpy_ceil',
casting._ceil_types,
err._ceil_error_check,
doc=ufunc_docs._ceil_doc
)
trunc = ufuncs.create_ufunc(
'nlcpy_trunc',
numpy.trunc.types,
err._trunc_error_check,
doc=ufunc_docs._trunc_doc
)
# matmul
matmul = ufuncs.create_ufunc(
'nlcpy_matmul',
numpy.matmul.types,
None,
doc=ufunc_docs._matmul_doc
)
# end of operator functions
|
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] | 2.197422 | 6,362 |
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