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92f10be0db1bfd9a43f5256577f3d2fdfd1c920b | 13,185 | py | Python | marvel_world/views.py | xiaoranppp/si664-final | f5545c04452fd674ddf1d078444e79ea58385e7e | [
"MIT"
] | null | null | null | marvel_world/views.py | xiaoranppp/si664-final | f5545c04452fd674ddf1d078444e79ea58385e7e | [
"MIT"
] | 1 | 2018-11-25T21:07:37.000Z | 2018-11-25T21:07:37.000Z | marvel_world/views.py | xiaoranppp/si664-final | f5545c04452fd674ddf1d078444e79ea58385e7e | [
"MIT"
] | 1 | 2018-12-21T12:06:03.000Z | 2018-12-21T12:06:03.000Z | from django.shortcuts import render,redirect
from django.http import HttpResponse,HttpResponseRedirect
from django.views import generic
from django.contrib.auth.decorators import login_required
from django.utils.decorators import method_decorator
from .models import Character,Comic,Power,CharacterPower,CharacterComic
from django_filters.views import FilterView
from .filters import Marvel_worldFilter,Marvel_comicFilter
from .forms import CharacterForm,PowerForm,ComicForm
from django.urls import reverse,reverse_lazy
#class CharacterDetailView(generic.DetailView):model = Characters context_object_name= 'character'template_name='marvel_world/character_information.html' | 31.618705 | 154 | 0.754873 |
92f1aa0fa9769c9eeef09b7c084da22948285538 | 267 | py | Python | src/rpi/fwd.py | au-chrismor/selfdrive | 31325dd7a173bbb16a13e3de4c9598aab0a50632 | [
"BSD-3-Clause"
] | null | null | null | src/rpi/fwd.py | au-chrismor/selfdrive | 31325dd7a173bbb16a13e3de4c9598aab0a50632 | [
"BSD-3-Clause"
] | 6 | 2018-03-15T05:23:55.000Z | 2018-10-26T10:28:47.000Z | src/rpi/fwd.py | au-chrismor/selfdrive | 31325dd7a173bbb16a13e3de4c9598aab0a50632 | [
"BSD-3-Clause"
] | null | null | null | """Set-up and execute the main loop"""
import RPi.GPIO as GPIO
import time
GPIO.setmode(GPIO.BCM)
GPIO.setwarnings(False)
#Right motor input A
GPIO.setup(18,GPIO.OUT)
#Right motor input B
GPIO.setup(23,GPIO.OUT)
GPIO.output(18,GPIO.HIGH)
GPIO.output(23,GPIO.LOW)
| 16.6875 | 38 | 0.749064 |
92f3155f2bddf2f3a6336a7c75d33f3d299e7e91 | 931 | py | Python | util/get_from_db.py | Abel-Huang/simple-image-classifier | 89d2822c2b06cdec728f734d43d9638f4b601348 | [
"MIT"
] | 4 | 2017-05-17T08:01:38.000Z | 2018-07-22T11:13:55.000Z | util/get_from_db.py | Abel-Huang/ImageClassifier | 89d2822c2b06cdec728f734d43d9638f4b601348 | [
"MIT"
] | null | null | null | util/get_from_db.py | Abel-Huang/ImageClassifier | 89d2822c2b06cdec728f734d43d9638f4b601348 | [
"MIT"
] | null | null | null | import pymysql
#
config = {
'host': '127.0.0.1',
'port': 3306,
'user': 'root',
'password': '',
'db': 'classdata',
'charset': 'utf8',
'cursorclass': pymysql.cursors.DictCursor,
}
| 20.23913 | 54 | 0.541353 |
92f361f8ed017247a610edc6823b43b8f1e0fefd | 290 | py | Python | registerapp/api.py | RajapandiR/django-register | cf20829fe3515bdd3112a88a890d83d852f09bde | [
"MIT"
] | null | null | null | registerapp/api.py | RajapandiR/django-register | cf20829fe3515bdd3112a88a890d83d852f09bde | [
"MIT"
] | 6 | 2021-03-19T04:17:51.000Z | 2021-09-22T19:07:17.000Z | registerapp/api.py | RajapandiR/django-register | cf20829fe3515bdd3112a88a890d83d852f09bde | [
"MIT"
] | null | null | null | from rest_framework import viewsets
from rest_framework.views import APIView
from registerapp import serializers
from registerapp import models
| 26.363636 | 50 | 0.855172 |
92f638d897dda2bf328a3077b43f492f38f39bb7 | 1,412 | py | Python | jduck/robot.py | luutp/jduck | 3c60a79c926bb9452777cddbebe28982273068a6 | [
"Apache-2.0"
] | null | null | null | jduck/robot.py | luutp/jduck | 3c60a79c926bb9452777cddbebe28982273068a6 | [
"Apache-2.0"
] | null | null | null | jduck/robot.py | luutp/jduck | 3c60a79c926bb9452777cddbebe28982273068a6 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
jduck.py
Description:
Author: luutp
Contact: [email protected]
Created on: 2021/02/27
"""
# Utilities
# %%
# ================================IMPORT PACKAGES====================================
# Utilities
from traitlets.config.configurable import SingletonConfigurable
# Custom Packages
from jduck.DCMotor import DCMotor
# ================================================================================
| 25.214286 | 85 | 0.61119 |
92f8d1944416ddff1cb95f31ed4c8d93f364c956 | 5,193 | py | Python | src/nebulo/gql/alias.py | olirice/nebulo | de9b043fe66d0cb872c5c0f2aca3c5c6f20918a7 | [
"MIT"
] | 76 | 2020-04-03T01:21:47.000Z | 2021-12-06T02:54:53.000Z | src/nebulo/gql/alias.py | olirice/nebulo | de9b043fe66d0cb872c5c0f2aca3c5c6f20918a7 | [
"MIT"
] | 7 | 2020-04-06T04:44:10.000Z | 2021-05-17T12:38:15.000Z | src/nebulo/gql/alias.py | olirice/nebulo | de9b043fe66d0cb872c5c0f2aca3c5c6f20918a7 | [
"MIT"
] | 2 | 2020-10-23T10:25:16.000Z | 2020-10-28T14:16:57.000Z | # pylint: disable=missing-class-docstring,invalid-name
import typing
from graphql.language import (
InputObjectTypeDefinitionNode,
InputObjectTypeExtensionNode,
ObjectTypeDefinitionNode,
ObjectTypeExtensionNode,
)
from graphql.type import (
GraphQLArgument,
GraphQLBoolean,
GraphQLEnumType,
GraphQLEnumValue,
GraphQLField,
GraphQLFieldMap,
GraphQLFloat,
GraphQLID,
GraphQLInputFieldMap,
GraphQLInputObjectType,
GraphQLInt,
GraphQLInterfaceType,
GraphQLIsTypeOfFn,
GraphQLList,
GraphQLNonNull,
GraphQLObjectType,
GraphQLResolveInfo,
GraphQLScalarType,
GraphQLSchema,
GraphQLString,
GraphQLType,
Thunk,
)
from graphql.type.definition import GraphQLInputFieldOutType
from nebulo.sql.composite import CompositeType as SQLACompositeType
# Handle name changes from graphql-core and graphql-core-next
try:
from graphql.type import GraphQLInputObjectField as GraphQLInputField
except ImportError:
from graphql.type import GraphQLInputField
Type = GraphQLType
List = GraphQLList
NonNull = GraphQLNonNull
Argument = GraphQLArgument
Boolean = GraphQLBoolean
String = GraphQLString
ScalarType = GraphQLScalarType
ID = GraphQLID
InterfaceType = GraphQLInterfaceType
Int = GraphQLInt
InputField = GraphQLInputField
ResolveInfo = GraphQLResolveInfo
EnumType = GraphQLEnumType
EnumValue = GraphQLEnumValue
Schema = GraphQLSchema
Field = GraphQLField
Float = GraphQLFloat
EnumType = GraphQLEnumType
| 25.965 | 101 | 0.706913 |
92f9c4373d43c67eefcb0f04052b7d238d59ad11 | 2,297 | py | Python | integrations/tensorflow/bindings/python/pyiree/tf/compiler/saved_model_test.py | rise-lang/iree | 46ad3fe392d38ce3df6eff7826cc1ab331a40b72 | [
"Apache-2.0"
] | 1 | 2020-08-13T09:25:59.000Z | 2020-08-13T09:25:59.000Z | integrations/tensorflow/bindings/python/pyiree/tf/compiler/saved_model_test.py | rise-lang/iree | 46ad3fe392d38ce3df6eff7826cc1ab331a40b72 | [
"Apache-2.0"
] | null | null | null | integrations/tensorflow/bindings/python/pyiree/tf/compiler/saved_model_test.py | rise-lang/iree | 46ad3fe392d38ce3df6eff7826cc1ab331a40b72 | [
"Apache-2.0"
] | null | null | null | # Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import importlib
import os
import sys
import tempfile
from pyiree.tf import compiler
# Dynamically import tensorflow.
try:
# Use a dynamic import so as to avoid hermetic dependency analysis
# (i.e. we only want the tensorflow from the environment).
tf = importlib.import_module("tensorflow")
# Just in case if linked against a pre-V2 defaulted version.
if hasattr(tf, "enable_v2_behavior"):
tf.enable_v2_behavior()
tf = tf.compat.v2
except ImportError:
print("Not running tests because tensorflow is not available")
sys.exit(0)
if __name__ == "__main__":
tf.test.main()
| 29.831169 | 80 | 0.727035 |
92f9e01e28f69fd9e75ceb00e00af2fc86419292 | 1,618 | py | Python | api/models/indicator/child_objects/properties.py | taco-chainalysis/pypulsedive | e89a2651e1ef41a1a51ddbeabc1f914a0d4e467d | [
"MIT"
] | null | null | null | api/models/indicator/child_objects/properties.py | taco-chainalysis/pypulsedive | e89a2651e1ef41a1a51ddbeabc1f914a0d4e467d | [
"MIT"
] | null | null | null | api/models/indicator/child_objects/properties.py | taco-chainalysis/pypulsedive | e89a2651e1ef41a1a51ddbeabc1f914a0d4e467d | [
"MIT"
] | 1 | 2021-11-10T16:50:32.000Z | 2021-11-10T16:50:32.000Z | from .grandchild_objects import Cookies
from .grandchild_objects import Dns
from .grandchild_objects import Dom
from .grandchild_objects import Geo
#from .grandchild_objects import Http
#from .grandchild_objects import Meta
from .grandchild_objects import Ssl
#from .grandchild_objects import WhoIs | 33.708333 | 83 | 0.642769 |
92fa506f1dc831d005f72a65f033e46f94fe54e8 | 9,084 | py | Python | iRep/gc_skew.py | scottdaniel/iRep | 5d31688eeeab057ce54f39698e3f9cc5738e05ad | [
"MIT"
] | 55 | 2016-06-17T17:31:48.000Z | 2022-01-19T08:24:43.000Z | iRep/gc_skew.py | scottdaniel/iRep | 5d31688eeeab057ce54f39698e3f9cc5738e05ad | [
"MIT"
] | 35 | 2016-06-24T17:19:04.000Z | 2021-11-06T16:08:43.000Z | iRep/gc_skew.py | scottdaniel/iRep | 5d31688eeeab057ce54f39698e3f9cc5738e05ad | [
"MIT"
] | 14 | 2016-07-21T17:34:16.000Z | 2020-03-18T03:45:55.000Z | #!/usr/bin/env python3
"""
script for calculating gc skew
Chris Brown
[email protected]
"""
# python modules
import os
import sys
import argparse
import numpy as np
from scipy import signal
from itertools import cycle, product
# plotting modules
from matplotlib import use as mplUse
mplUse('Agg')
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
plt.rcParams['pdf.fonttype'] = 42
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
# ctb
from ctbBio.fasta import iterate_fasta as parse_fasta
def plot_two(title, subtitle, A, B, labels, legend, vert = False):
"""
plot with differnt y axes
title = title for chart
A = data for left axis [[x], [y]]
B = data for right axis
lables = [left label, right label, x label]
legend = [[left legend], [right legend]]
"""
fig, ax1 = plt.subplots()
colors = ['0.75', 'b', 'r', 'c', 'y', 'm', 'k', 'g']
a_colors = cycle(colors)
b_colors = cycle(colors[::-1])
a_label = cycle(legend[0])
b_label = cycle(legend[1])
# plot left axis and x - axis
for a in A:
x, y = a
ax1.set_ylabel(labels[0], labelpad = 3)
ax1.set_xlabel(labels[-1])
ax1.plot(x, y, c = next(a_colors), marker = 'o', ms = 4, label = next(a_label))
# add vertical lines
if vert is not False:
for i in vert:
x, c = i
ax1.axvline(x = x, c = c, label = next(a_label), linewidth = 2)
# plot right axis
ax2 = ax1.twinx()
for b in B:
x, y = b
ax2.set_ylabel(labels[1], labelpad = 8)
ax2.plot(x, y, c = next(b_colors), linewidth = 2, label = next(b_label))
xmin = min([min(i[1]) for i in A] + [min(i[0]) for i in B])
xmax = max([max(i[0]) for i in A] + [max(i[0]) for i in B])
ax2.set_xlim(xmin, xmax)
# title
plt.suptitle(title, fontsize = 16)
plt.title(subtitle, fontsize = 10)
# legend
ax1.legend(loc = 'upper left', \
bbox_to_anchor=(0.55, -0.125), \
prop = {'size':8}, \
framealpha = 0.0
)
plt.legend(loc = 'upper right', \
bbox_to_anchor=(0.45, -0.125), \
prop = {'size':8}, \
framealpha = 0.0\
)
# save
pdf = PdfPages('%s.pdf' % title.replace(' ', '_'))
pdf.savefig(bbox_inches = 'tight')
plt.close()
pdf.close()
def check_peaks(peaks, length):
"""
select pair of min and max that are not too close or
too far apart and have greatest y distance between one another
"""
# if ori/ter peaks are too close or too far apart, they are probably wrong
closest, farthest = int(length * float(0.45)), int(length * float(0.55))
pairs = []
for pair in list(product(*peaks)):
### added this to make sure gets origin and ter right
tr, pk = sorted(list(pair), key = lambda x: x[1], reverse = False) # trough and peak
a = (tr[0] - pk[0]) % length
b = (pk[0] - tr[0]) % length
pt = abs(tr[1] - pk[1]) # distance between values
if (a <= farthest and a >= closest) or (b <=farthest and b >= closest):
pairs.append([pt, tr, pk])
if len(pairs) == 0:
return [False, False]
pt, tr, pk = sorted(pairs, reverse = True)[0]
return [tr[0], pk[0]]
def find_ori_ter(c_skew, length):
"""
find origin and terminus of replication based on
cumulative GC Skew
"""
# find origin and terminus of replication based on
# cumulative gc skew min and max peaks
c_skew_min = signal.argrelextrema(np.asarray(c_skew[1]), np.less, order = 1)[0].tolist()
c_skew_max = signal.argrelextrema(np.asarray(c_skew[1]), np.greater, order = 1)[0].tolist()
# return False if no peaks were detected
if len(c_skew_min) == 0 or len(c_skew_min) == 0:
return [False, False]
else:
c_skew_min = [[c_skew[0][i], c_skew[1][i]] for i in c_skew_min]
c_skew_max = [[c_skew[0][i], c_skew[1][i]] for i in c_skew_max]
ori, ter = check_peaks([c_skew_min, c_skew_max], length)
return ori, ter
def gc_skew(name, length, seq, window, slide, plot_skew):
"""
calculate gc skew and cumulative sum of gc skew over sequence windows
gc skew = ((G - C) / (G + C)) * window size * genome length
"""
# convert to G - C
replacements = {'G':1, 'C':-1, 'A':0, 'T':0, 'N':0}
gmc = [] # G - C
for base in seq:
try:
gmc.append(replacements[base])
except:
gmc.append(0)
# convert to G + C
gpc = [abs(i) for i in gmc] # G + C
# calculate sliding windows for (G - C) and (G + C)
weights = np.ones(window)/window
gmc = [[i, c] for i, c in enumerate(signal.fftconvolve(gmc, weights, 'same').tolist())]
gpc = [[i, c] for i, c in enumerate(signal.fftconvolve(gpc, weights, 'same').tolist())]
# calculate gc skew and cummulative gc skew sum
skew = [[], []] # x and y for gc skew
c_skew = [[], []] # x and y for gc skew cummulative sums
cs = 0 # cummulative sum
# select windows to use based on slide
for i, m in gmc[0::slide]:
p = gpc[i][1]
if p == 0:
gcs = 0
else:
gcs = m/p
cs += gcs
skew[0].append(i)
c_skew[0].append(i)
skew[1].append(gcs)
c_skew[1].append(cs)
ori, ter = find_ori_ter(c_skew, length)
# plot data
if plot_skew is True:
title = '%s GC Skew' % (name)
subtitle = '(window = %s, slide = %s)' % (window, slide)
labels = ['GC Skew', 'Cumulative GC Skew', 'Position on Genome (bp)']
# remove some points for plotting (approx. 1,000 datapoints)
N = int(len(skew[0])/1000)
if N != 0:
skew = [skew[0][0::N], skew[1][0::N]]
if ori is False:
plot_two(title, subtitle, [skew], [c_skew], labels, \
[[labels[0]], [labels[1]]])
else:
plot_two(title, subtitle, [skew], [c_skew], labels, \
[[labels[0], 'Ori:%s' % ('{:,}'.format(ori)), \
'Ter:%s' % ('{:,}'.format(ter))], [labels[1]]], \
vert = [(ori, 'r'), (ter, 'b')])
return ori, ter, skew, c_skew
def open_files(files):
"""
open files in list, use stdin if first
item in list is '-'
"""
if files is None:
return files
if files[0] == '-':
return (sys.stdin)
return (open(i) for i in files)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description = \
'# calculate gc skew and find Ori and Ter of replication')
parser.add_argument(\
'-f', nargs = '*', action = 'store', required = True, \
help = 'fasta(s)')
parser.add_argument(\
'-l', default = False, type = int, \
help = 'minimum contig length (default = 10 x window)')
parser.add_argument(\
'-w', default = 1000, type = int, \
help = 'window length (default = 1000)')
parser.add_argument(\
'-s', default = 10, type = int, \
help = 'slide length (default = 10)')
parser.add_argument(\
'--single', action = 'store_true', \
help = 'combine multi-fasta sequences into single genome')
parser.add_argument(\
'--no-plot', action = 'store_false', \
help = 'do not generate plots, print GC Skew to stdout')
args = vars(parser.parse_args())
fastas = open_files(args['f'])
single, plot_skew = args['single'], args['no_plot']
window, slide = args['w'], args['s']
min_len = args['l']
if min_len is False:
min_len = 10 * window
for name, length, seq in parse_genomes(fastas, single):
if length < min_len:
print('%s: Too Short' % (name), file=sys.stderr)
continue
ori, ter, skew, c_skew = gc_skew(name, length, seq, window, slide, plot_skew)
if ori == False:
ori, ter = 'n/a', 'n/a'
else:
ori, ter = '{:,}'.format(ori), '{:,}'.format(ter)
print('%s -> Origin: %s Terminus: %s' \
% (name, ori, ter), file=sys.stderr)
if plot_skew is False:
print('\t'.join(['# Name', 'Position', 'GC Skew', 'Cumulative GC Skew']))
for i, pos in enumerate(skew[0]):
out = [name, pos, skew[1][i], c_skew[1][i]]
print('\t'.join([str(i) for i in out]))
| 36.191235 | 95 | 0.55295 |
92fa730397bfd4949cfd5d8aa12c70a6b5cb5576 | 2,429 | py | Python | examples/send_governance_vote_transaction.py | Algofiorg/algofi-py-sdk | 6100a6726d36db4d4d3287064f0ad1d0b9a05e03 | [
"MIT"
] | 38 | 2021-12-30T02:32:57.000Z | 2022-03-23T22:09:16.000Z | examples/send_governance_vote_transaction.py | Algofiorg/algofi-py-sdk | 6100a6726d36db4d4d3287064f0ad1d0b9a05e03 | [
"MIT"
] | 4 | 2021-11-03T00:14:46.000Z | 2022-03-28T02:17:33.000Z | examples/send_governance_vote_transaction.py | Algofiorg/algofi-py-sdk | 6100a6726d36db4d4d3287064f0ad1d0b9a05e03 | [
"MIT"
] | 8 | 2021-12-15T05:29:55.000Z | 2022-02-08T03:45:11.000Z | # This sample is provided for demonstration purposes only.
# It is not intended for production use.
# This example does not constitute trading advice.
import os
from dotenv import dotenv_values
from algosdk import mnemonic, account
from algofi.v1.asset import Asset
from algofi.v1.client import AlgofiTestnetClient, AlgofiMainnetClient
from algofi.utils import get_ordered_symbols, prepare_payment_transaction, get_new_account
from example_utils import print_market_state, print_user_state
### run setup.py before proceeding. make sure the .env file is set with mnemonic + storage_mnemonic.
# Hardcoding account keys is not a great practice. This is for demonstration purposes only.
# See the README & Docs for alternative signing methods.
my_path = os.path.abspath(os.path.dirname(__file__))
ENV_PATH = os.path.join(my_path, ".env")
# load user passphrase
user = dotenv_values(ENV_PATH)
sender = mnemonic.to_public_key(user['mnemonic'])
key = mnemonic.to_private_key(user['mnemonic'])
# IS_MAINNET
IS_MAINNET = False
client = AlgofiMainnetClient(user_address=sender) if IS_MAINNET else AlgofiTestnetClient(user_address=sender)
# NOTE: Get the live governance address at https://governance.algorand.foundation/api/periods/
# under "sign_up_address" for the relevant governance period
# Specify your vote according to the formats that are permissible in the Algorand Foundation Spec
# https://github.com/algorandfoundation/governance/blob/main/af-gov1-spec.md
# Get the idx, vote choices based on the relevant voting session from https://governance.algorand.foundation/api/periods/
address = sender
governance_address = ""
vote_note = b'af/gov1:j[6,"a","c"]' # NOTE: an example, not to be used in live voting necessarily
vault_address = client.manager.get_storage_address(address)
print("~"*100)
print("Processing send_governance_vote_transaction transaction for vault address " + vault_address)
print("~"*100)
txn = client.prepare_send_governance_vote_transactions(governance_address, note=vote_note, address=address)
txn.sign_with_private_key(sender, key)
txn.submit(client.algod, wait=True)
# After sending, check your vote at
# https://governance.algorand.foundation/api/periods/<governance-period-slug>/governors/<vault_address>
# to confirm successful vote in voting session
# print final state
print("~"*100)
print("Final State")
print("Sent governance transaction with note: " + str(vote_note))
print("~"*100) | 42.614035 | 121 | 0.799918 |
92fa7f11780de4e7d336cb67c51c29ac5c8fbc36 | 9,059 | py | Python | bid/inventoryClient.py | franklx/SOAPpy-py3 | f25afba322e9300ba4ebdd281118b629ca63ba24 | [
"BSD-3-Clause"
] | 7 | 2018-01-03T18:24:43.000Z | 2022-03-07T04:34:01.000Z | bid/inventoryClient.py | franklx/SOAPpy-py3 | f25afba322e9300ba4ebdd281118b629ca63ba24 | [
"BSD-3-Clause"
] | null | null | null | bid/inventoryClient.py | franklx/SOAPpy-py3 | f25afba322e9300ba4ebdd281118b629ca63ba24 | [
"BSD-3-Clause"
] | 18 | 2018-08-06T11:30:16.000Z | 2022-03-09T11:24:24.000Z | #!/usr/bin/env python
import getopt
import sys
import string
import re
import time
sys.path.insert(1,"..")
from SOAPpy import SOAP
import traceback
DEFAULT_SERVERS_FILE = './inventory.servers'
DEFAULT_METHODS = ('SimpleBuy', 'RequestForQuote','Buy','Ping')
if __name__ == "__main__":
main()
| 31.130584 | 139 | 0.486919 |
92fb1af4be141cb39cbab935a9b9551b1ec5b453 | 934 | py | Python | src/compile.py | Pixxeasy/WinTools | e67c365cd4a7a47a410c25b7df8eeaeedc05dd8d | [
"MIT"
] | null | null | null | src/compile.py | Pixxeasy/WinTools | e67c365cd4a7a47a410c25b7df8eeaeedc05dd8d | [
"MIT"
] | null | null | null | src/compile.py | Pixxeasy/WinTools | e67c365cd4a7a47a410c25b7df8eeaeedc05dd8d | [
"MIT"
] | null | null | null | import os
import json
import shutil
with open("entry.tp") as entry:
entry = json.loads(entry.read())
startcmd = entry['plugin_start_cmd'].split("%TP_PLUGIN_FOLDER%")[1].split("\\")
filedirectory = startcmd[0]
fileName = startcmd[1]
if os.path.exists(filedirectory):
os.remove(os.path.join(os.getcwd(), "WinTools"))
else:
os.makedirs("temp/"+filedirectory)
for file in os.listdir("."):
if file not in ["compile.py", "utils", "requirements.txt", "build", "dist", "main.py", "main.spec", "__pycache__", "temp"]:
print("copying", file)
shutil.copy(os.path.join(os.getcwd(), file), os.path.join("temp", filedirectory))
os.rename("dist\Main.exe", "dist\WinTools.exe")
shutil.copy(os.path.join(os.getcwd(), r"dist\WinTools.exe"), "temp/"+filedirectory)
shutil.make_archive(base_name="WinTools", format='zip', root_dir="temp", base_dir="WinTools")
os.rename("WinTools.zip", "WinTools.tpp")
| 33.357143 | 127 | 0.674518 |
92fca9c0141bc42e92af9526839fedc292014b9b | 292 | py | Python | suda/1121/12.py | tusikalanse/acm-icpc | 20150f42752b85e286d812e716bb32ae1fa3db70 | [
"MIT"
] | 2 | 2021-06-09T12:27:07.000Z | 2021-06-11T12:02:03.000Z | suda/1121/12.py | tusikalanse/acm-icpc | 20150f42752b85e286d812e716bb32ae1fa3db70 | [
"MIT"
] | 1 | 2021-09-08T12:00:05.000Z | 2021-09-08T14:52:30.000Z | suda/1121/12.py | tusikalanse/acm-icpc | 20150f42752b85e286d812e716bb32ae1fa3db70 | [
"MIT"
] | null | null | null | for _ in range(int(input())):
x, y = list(map(int, input().split()))
flag = 1
for i in range(x, y + 1):
n = i * i + i + 41
for j in range(2, n):
if j * j > n:
break
if n % j == 0:
flag = 0
break
if flag == 0:
break
if flag:
print("OK")
else:
print("Sorry") | 17.176471 | 39 | 0.489726 |
92feee19e193679cb75cdd6152ceb20caad92e8b | 389 | gyp | Python | notification/app/node_modules/hiredis/binding.gyp | c2gconsulting/bulkpay | 224a52427f80a71f66613c367a5596cbd5e97294 | [
"MIT"
] | 208 | 2015-01-07T03:50:56.000Z | 2022-03-21T03:34:12.000Z | binding.gyp | badboy/hiredis-node-win | d113945a182ba1d616f8fba06e2d80dc9f09552b | [
"BSD-3-Clause"
] | 72 | 2015-01-11T09:54:16.000Z | 2019-11-21T14:07:43.000Z | binding.gyp | badboy/hiredis-node-win | d113945a182ba1d616f8fba06e2d80dc9f09552b | [
"BSD-3-Clause"
] | 55 | 2015-01-10T20:54:13.000Z | 2022-02-02T14:08:01.000Z | {
'targets': [
{
'target_name': 'hiredis',
'sources': [
'src/hiredis.cc'
, 'src/reader.cc'
],
'include_dirs': ["<!(node -e \"require('nan')\")"],
'dependencies': [
'deps/hiredis.gyp:hiredis-c'
],
'defines': [
'_GNU_SOURCE'
],
'cflags': [
'-Wall',
'-O3'
]
}
]
}
| 16.913043 | 57 | 0.375321 |
92ff4f4bfa893dc686e0e12fb0d4936e8c8b259d | 272 | py | Python | basic_and.py | Verkhovskaya/PyDL | 4c3f2d952dd988ff27bf359d2f2cdde65737e062 | [
"MIT"
] | 5 | 2018-07-28T18:18:59.000Z | 2022-01-05T19:01:50.000Z | basic_and.py | Verkhovskaya/PyDL | 4c3f2d952dd988ff27bf359d2f2cdde65737e062 | [
"MIT"
] | null | null | null | basic_and.py | Verkhovskaya/PyDL | 4c3f2d952dd988ff27bf359d2f2cdde65737e062 | [
"MIT"
] | null | null | null | from pywire import *
width = 4
a = Signal(width, io="in")
b = Signal(width, io="out")
Inverter(a, b)
build() | 14.315789 | 29 | 0.591912 |
13003b62c91dfe370f2b6ae3d293c73a5a463179 | 4,521 | py | Python | network/evaluate_keypoints.py | mhsung/deep-functional-dictionaries | 8b3d70c3376339cb1b7baacf7753094cd1ffef45 | [
"MIT"
] | 41 | 2018-07-10T10:15:02.000Z | 2021-04-20T03:10:16.000Z | network/evaluate_keypoints.py | Yajha/deep-functional-dictionaries | deecf8c6c85e253cfa52be7c6b3c308d5e5aaf81 | [
"MIT"
] | 2 | 2018-07-05T06:34:13.000Z | 2019-09-18T08:57:56.000Z | network/evaluate_keypoints.py | Yajha/deep-functional-dictionaries | deecf8c6c85e253cfa52be7c6b3c308d5e5aaf81 | [
"MIT"
] | 7 | 2018-07-28T00:00:08.000Z | 2021-06-30T13:39:44.000Z | # Minhyuk Sung ([email protected])
# April 2018
import os, sys
BASE_DIR = os.path.normpath(
os.path.join(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(os.path.join(BASE_DIR, '..'))
from datasets import *
from generate_outputs import *
from scipy.optimize import linear_sum_assignment
#import matplotlib.pyplot as plt
import numpy as np
| 26.438596 | 76 | 0.601858 |
13008c4023106e4274d2b92d9aa79a58e4551138 | 2,388 | py | Python | recipes/cxxopts/all/conanfile.py | dvirtz/conan-center-index | 2e7a6337804325616f8d97e3a5b6f66cc72699cb | [
"MIT"
] | 562 | 2019-09-04T12:23:43.000Z | 2022-03-29T16:41:43.000Z | recipes/cxxopts/all/conanfile.py | dvirtz/conan-center-index | 2e7a6337804325616f8d97e3a5b6f66cc72699cb | [
"MIT"
] | 9,799 | 2019-09-04T12:02:11.000Z | 2022-03-31T23:55:45.000Z | recipes/cxxopts/all/conanfile.py | dvirtz/conan-center-index | 2e7a6337804325616f8d97e3a5b6f66cc72699cb | [
"MIT"
] | 1,126 | 2019-09-04T11:57:46.000Z | 2022-03-31T16:43:38.000Z | import os
from conans import ConanFile, tools
from conans.errors import ConanInvalidConfiguration
| 36.738462 | 132 | 0.641122 |
1300c8abfbfcff2fad07bdd38a7b66244215a15d | 1,868 | py | Python | p_030_039/problem31.py | ericgreveson/projecteuler | 1844bf383fca871b82d88ef1eb3a9b1a0e363054 | [
"Apache-2.0"
] | null | null | null | p_030_039/problem31.py | ericgreveson/projecteuler | 1844bf383fca871b82d88ef1eb3a9b1a0e363054 | [
"Apache-2.0"
] | null | null | null | p_030_039/problem31.py | ericgreveson/projecteuler | 1844bf383fca871b82d88ef1eb3a9b1a0e363054 | [
"Apache-2.0"
] | null | null | null |
def main():
"""
Entry point
"""
# Important: sorted smallest to largest
coins = [1, 2, 5, 10, 20, 50, 100, 200]
coin_index = {coin: index for index, coin in enumerate(coins)}
# How many ways are there of making each number from 1 to 200 from these values?
# Building up from 1 means we can re-use earlier results
# e.g.:
# 1p: [{1}]
# 2p: [{1,1}, {2}]
# 3p: [{1,1,1}, {2,1}]
# 4p: [{1,1,1,1}, {2,1,1}, {2,2}]
# etc
way_sets = [None]
for i in range(1, 201):
way_set_i = set()
# Try using 1 of each coin and then all the ways of the remainder, if > 0
for coin in coins:
remainder = i - coin
if remainder == 0:
# We can make this with exactly this coin alone - but no larger coins
coin_count = [0 for i in coins]
coin_count[coin_index[coin]] = 1
way_set_i.add(CoinArray(coin_count))
break
elif remainder > 0:
# We can use this coin and whatever the options for the smaller value are
for rem_list in way_sets[remainder]:
new_coin_count = [c for c in rem_list]
new_coin_count[coin_index[coin]] += 1
way_set_i.add(CoinArray(new_coin_count))
else:
# Can't use any bigger coins
break
way_sets.append(way_set_i)
print(f"Number of ways of making 2: {len(way_sets[200])}")
return
if __name__ == "__main__":
main()
| 31.661017 | 89 | 0.534261 |
1300e7747076d34572209fef1029da836f1dbf7b | 2,358 | py | Python | video/cloud-client/quickstart/quickstart.py | nasirdec/GCP-AppEngine-Example | 3f5ad26ad2c1e3c8deceb5844adfb40cf7c2e53f | [
"Apache-2.0"
] | 1 | 2019-11-17T08:59:14.000Z | 2019-11-17T08:59:14.000Z | video/cloud-client/quickstart/quickstart.py | nasirdec/GCP-AppEngine-Example | 3f5ad26ad2c1e3c8deceb5844adfb40cf7c2e53f | [
"Apache-2.0"
] | 16 | 2019-06-15T00:02:56.000Z | 2021-03-25T23:22:38.000Z | video/cloud-client/quickstart/quickstart.py | nasirdec/GCP-AppEngine-Example | 3f5ad26ad2c1e3c8deceb5844adfb40cf7c2e53f | [
"Apache-2.0"
] | 3 | 2019-02-11T16:16:11.000Z | 2019-04-19T21:34:37.000Z | #!/usr/bin/env python
# Copyright 2017 Google Inc. 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.
"""This application demonstrates label detection on a demo video using
the Google Cloud API.
Usage:
python quickstart.py
"""
if __name__ == '__main__':
run_quickstart()
| 37.428571 | 75 | 0.697201 |
13027944554eb9d8705c77a8073c891a250b3842 | 1,853 | py | Python | ally/instrument.py | platformmaster9/PyAlly | 55400e0835ae3ac5b3cf58e0e8214c6244aeb149 | [
"MIT"
] | null | null | null | ally/instrument.py | platformmaster9/PyAlly | 55400e0835ae3ac5b3cf58e0e8214c6244aeb149 | [
"MIT"
] | null | null | null | ally/instrument.py | platformmaster9/PyAlly | 55400e0835ae3ac5b3cf58e0e8214c6244aeb149 | [
"MIT"
] | null | null | null | from . import utils
#################################################
""" INSTRUMENT """
#################################################
#################################################
#################################################
#################################################
################################################# | 29.412698 | 70 | 0.399352 |
1302b2f57e10ec891cc57b121da1cf9b5593731f | 432 | py | Python | airbyte-integrations/connectors/source-yahoo-finance-price/integration_tests/acceptance.py | onaio/airbyte | 38302e82a25f1b66742c3febfbff0668556920f2 | [
"MIT"
] | 22 | 2020-08-27T00:47:20.000Z | 2020-09-17T15:39:39.000Z | airbyte-integrations/connectors/source-yahoo-finance-price/integration_tests/acceptance.py | onaio/airbyte | 38302e82a25f1b66742c3febfbff0668556920f2 | [
"MIT"
] | 116 | 2020-08-27T01:11:27.000Z | 2020-09-19T02:47:52.000Z | airbyte-integrations/connectors/source-yahoo-finance-price/integration_tests/acceptance.py | onaio/airbyte | 38302e82a25f1b66742c3febfbff0668556920f2 | [
"MIT"
] | 1 | 2020-09-15T06:10:01.000Z | 2020-09-15T06:10:01.000Z | #
# Copyright (c) 2022 Airbyte, Inc., all rights reserved.
#
import pytest
pytest_plugins = ("source_acceptance_test.plugin",)
| 25.411765 | 98 | 0.738426 |
13037ede58d748bec28747c289956464268c2ceb | 118 | py | Python | ddt/__init__.py | GawenChen/test_pytest | da7a29dc43e8027d3fd1a05054480ed7007131c3 | [
"Apache-2.0"
] | null | null | null | ddt/__init__.py | GawenChen/test_pytest | da7a29dc43e8027d3fd1a05054480ed7007131c3 | [
"Apache-2.0"
] | null | null | null | ddt/__init__.py | GawenChen/test_pytest | da7a29dc43e8027d3fd1a05054480ed7007131c3 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
"""
@Time 2021/10/9 17:51
@Auth
@File __init__.py.py
@IDE PyCharm
@QQ : 810400085
""" | 14.75 | 23 | 0.576271 |
13058e5281c2e6d308c1c802707f6f83b62cdc9b | 1,809 | py | Python | darts/models/linear_regression_model.py | BiancaMT25/darts | bb550dede6d8927a45aea0d9f3df53de32a6eee2 | [
"Apache-2.0"
] | 1 | 2021-07-15T11:12:05.000Z | 2021-07-15T11:12:05.000Z | darts/models/linear_regression_model.py | BiancaMT25/darts | bb550dede6d8927a45aea0d9f3df53de32a6eee2 | [
"Apache-2.0"
] | null | null | null | darts/models/linear_regression_model.py | BiancaMT25/darts | bb550dede6d8927a45aea0d9f3df53de32a6eee2 | [
"Apache-2.0"
] | null | null | null | """
Standard Regression model
-------------------------
"""
import numpy as np
import pandas as pd
from typing import Union
from ..logging import get_logger
from .regression_model import RegressionModel
from sklearn.linear_model import LinearRegression
logger = get_logger(__name__)
| 38.489362 | 111 | 0.640133 |
130613c0dd1daf7edf4aa6e30bb0303b2984e2ce | 4,862 | py | Python | hail/python/test/hailtop/utils/test_utils.py | vrautela/hail | 7db6189b5b1feafa88452b8470e497d9505d9a46 | [
"MIT"
] | null | null | null | hail/python/test/hailtop/utils/test_utils.py | vrautela/hail | 7db6189b5b1feafa88452b8470e497d9505d9a46 | [
"MIT"
] | null | null | null | hail/python/test/hailtop/utils/test_utils.py | vrautela/hail | 7db6189b5b1feafa88452b8470e497d9505d9a46 | [
"MIT"
] | null | null | null | from hailtop.utils import (partition, url_basename, url_join, url_scheme,
url_and_params, parse_docker_image_reference)
| 37.689922 | 113 | 0.644385 |
13064197a568b4ea0fdb674d3a8685e3b27e92eb | 863 | py | Python | hood/urls.py | wadi-1000/Vicinity | a41f6ec2c532cb06f7444b55073b6879a1fce63a | [
"MIT"
] | null | null | null | hood/urls.py | wadi-1000/Vicinity | a41f6ec2c532cb06f7444b55073b6879a1fce63a | [
"MIT"
] | null | null | null | hood/urls.py | wadi-1000/Vicinity | a41f6ec2c532cb06f7444b55073b6879a1fce63a | [
"MIT"
] | null | null | null | from django.urls import path,include
from . import views
urlpatterns = [
path('home/', views.home, name = 'home'),
path('add_hood/',views.uploadNeighbourhood, name = 'add_hood'),
path('viewhood/',views.viewHood, name = 'viewhood'),
path('hood/<int:pk>/',views.hood, name = 'hood'),
path('add_bizna/',views.uploadBuisness, name = 'add_bizna'),
path('bizna/',views.viewBizna, name = 'view_bizna'),
path('viewbizna/<int:pk>/',views.bizna, name = 'bizna'),
path('post/',views.create_post, name = 'post'),
path('posts/',views.viewPost, name = 'posts'),
path('searchbizna/', views.searchBizna, name="search_results"),
path('searchhood/', views.searchHood, name="search_res"),
path('join_hood/<id>', views.join_neighbourhood, name='join-hood'),
path('leave_hood/<id>', views.leave_neighbourhood, name='leave-hood'),
] | 47.944444 | 74 | 0.665122 |
1306e647595d1f2f64e2d6dd214b9b25580f3ed1 | 8,305 | py | Python | src/licensedcode/tokenize.py | chetanya-shrimali/scancode-toolkit | a1a22fb225cbeb211bd6f92272a46f1351f57d6b | [
"Apache-2.0",
"CC0-1.0"
] | null | null | null | src/licensedcode/tokenize.py | chetanya-shrimali/scancode-toolkit | a1a22fb225cbeb211bd6f92272a46f1351f57d6b | [
"Apache-2.0",
"CC0-1.0"
] | null | null | null | src/licensedcode/tokenize.py | chetanya-shrimali/scancode-toolkit | a1a22fb225cbeb211bd6f92272a46f1351f57d6b | [
"Apache-2.0",
"CC0-1.0"
] | null | null | null | # -*- coding: utf-8 -*-
#
# Copyright (c) 2017 nexB Inc. and others. All rights reserved.
# http://nexb.com and https://github.com/nexB/scancode-toolkit/
# The ScanCode software is licensed under the Apache License version 2.0.
# Data generated with ScanCode require an acknowledgment.
# ScanCode is a trademark of nexB Inc.
#
# You may not use this software except in compliance with the License.
# You may obtain a copy of the License at: http://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.
#
# When you publish or redistribute any data created with ScanCode or any ScanCode
# derivative work, you must accompany this data with the following acknowledgment:
#
# Generated with ScanCode and provided on an "AS IS" BASIS, WITHOUT WARRANTIES
# OR CONDITIONS OF ANY KIND, either express or implied. No content created from
# ScanCode should be considered or used as legal advice. Consult an Attorney
# for any legal advice.
# ScanCode is a free software code scanning tool from nexB Inc. and others.
# Visit https://github.com/nexB/scancode-toolkit/ for support and download.
from __future__ import absolute_import
from __future__ import print_function
from __future__ import unicode_literals
from itertools import islice
from itertools import izip
import re
from zlib import crc32
from textcode.analysis import text_lines
"""
Utilities to break texts in lines and tokens (aka. words) with specialized version
for queries and rules texts.
"""
# Split on whitespace and punctuations: keep only characters
# and + in the middle or end of a word.
# Keeping the trailing + is important for licenses name such as GPL2+
query_pattern = '[^\W_]+\+?[^\W_]*'
word_splitter = re.compile(query_pattern, re.UNICODE).findall
def query_tokenizer(text, lower=True):
"""
Return an iterable of tokens from a unicode query text.
"""
if not text:
return []
text = lower and text.lower() or text
return (token for token in word_splitter(text) if token)
# Alternate pattern used for matched text collection
not_query_pattern = '[\W_+]+[\W_]?'
# collect tokens and non-token texts in two different groups
_text_capture_pattern = '(?P<token>' + query_pattern + ')' + '|' + '(?P<punct>' + not_query_pattern + ')'
tokens_and_non_tokens = re.compile(_text_capture_pattern, re.UNICODE).finditer
# Template-aware splitter, keeping a templated part {{anything}} as a token.
# This splitter yields plain token strings or double braces-enclosed strings
# {{something}} for templates. curly barces are otherwise treated as punctuation.
# A template part is anything enclosed in double braces
template_pattern = '\{\{[^{}]*\}\}'
rule_pattern = '%s|%s+' % (query_pattern, template_pattern,)
template_splitter = re.compile(rule_pattern , re.UNICODE).findall
def rule_tokenizer(text, lower=True):
"""
Return an iterable of tokens from a unicode rule text, skipping templated
parts, including leading and trailing templated parts.
For example:
>>> list(rule_tokenizer(''))
[]
>>> list(rule_tokenizer('some Text with spAces! + _ -'))
[u'some', u'text', u'with', u'spaces']
Unbalanced templates are handled correctly:
>>> list(rule_tokenizer('{{}some }}Text with spAces! + _ -'))
[u'some', u'text', u'with', u'spaces']
Templates are handled and skipped for templated sequences:
>>> list(rule_tokenizer('{{Hi}}some {{}}Text with{{noth+-_!@ing}} {{junk}}spAces! + _ -{{}}'))
[u'some', u'text', u'with', u'spaces']
"""
if not text:
return []
text = lower and text.lower() or text
tokens = template_splitter(text)
# skip templates
return (token for token in tokens if token and not token.startswith('{{'))
def ngrams(iterable, ngram_length):
"""
Return an iterable of ngrams of length `ngram_length` given an iterable.
Each ngram is a tuple of ngram_length items.
The returned iterable is empty if the input iterable contains less than
`ngram_length` items.
Note: this is a fairly arcane but optimized way to compute ngrams.
For example:
>>> list(ngrams([1,2,3,4,5], 2))
[(1, 2), (2, 3), (3, 4), (4, 5)]
>>> list(ngrams([1,2,3,4,5], 4))
[(1, 2, 3, 4), (2, 3, 4, 5)]
>>> list(ngrams([1,2,3,4], 2))
[(1, 2), (2, 3), (3, 4)]
>>> list(ngrams([1,2,3], 2))
[(1, 2), (2, 3)]
>>> list(ngrams([1,2], 2))
[(1, 2)]
>>> list(ngrams([1], 2))
[]
This also works with arrays or tuples:
>>> from array import array
>>> list(ngrams(array(b'h', [1,2,3,4,5]), 2))
[(1, 2), (2, 3), (3, 4), (4, 5)]
>>> list(ngrams(tuple([1,2,3,4,5]), 2))
[(1, 2), (2, 3), (3, 4), (4, 5)]
"""
return izip(*(islice(iterable, i, None) for i in range(ngram_length)))
| 36.425439 | 105 | 0.646117 |
1308f0dd9c15ba9ea58abe836ba21f50ef3863ef | 679,794 | py | Python | homework_05/graficos_3.py | ufpa-organization-repositories/evolutionary-computing | e16786f9619e2b357b94ab91ff3a7b352e6a0d92 | [
"MIT"
] | null | null | null | homework_05/graficos_3.py | ufpa-organization-repositories/evolutionary-computing | e16786f9619e2b357b94ab91ff3a7b352e6a0d92 | [
"MIT"
] | null | null | null | homework_05/graficos_3.py | ufpa-organization-repositories/evolutionary-computing | e16786f9619e2b357b94ab91ff3a7b352e6a0d92 | [
"MIT"
] | null | null | null | # ensaio = [[[[1, 999.4951009067408, 999.495100909791, '1001100.11100010011001100001001', '100011.10010111010000111110100', '1', '1'], [2, 999.5052434400473, 999.5052434497359, '0000100.11100010011001100001001', '111011.10010111010000111110000', '1', '2'], [3, 999.51676448592, 999.516764613072, '0000100.11100010011001100001001', '011011.10010111010000111110100', '1', '3'], [4, 999.5986670278455, 999.5986691897469, '0000100.11100010011001100001001', '001011.10010111010000111110100', '1', '4'], [5, 999.8231043912172, 999.8231154915733, '0000100.11100010011001100001001', '000011.10010111010000111110100', '1', '5'], [6, 999.8507392915436, 999.8507498599146, '0000100.11101010011001100001001', '000011.10010111010000111110100', '1', '6'], [7, 999.8770250807991, 999.8770357110892, '0000100.11101010011001100001001', '000011.10011011010000111110110', '1', '7'], [8, 999.9035511429103, 999.9035563402527, '0000100.11111011011001100001001', '000011.10011011010000111110110', '1', '8'], [9, 999.8985266375895, 999.8985377843169, '0000100.11111011011001100001001', '000011.11011011010000111110110', '1', '9'], [10, 999.9175205241293, 999.9175299982971, '0000100.11111001011001100001001', '000011.11011011010000111110110', '1', '10']]], [[[999.7693956630327, 999.5712824548395, 999.5712938998612, '-1111.1100110000001100100111', '1100.0111101110100011011001101', '2', '1'], [999.772264826825, 999.722709716149, 999.7227150806862, '-1111.1100110000001100000111', '1100.0101101110011011011001101', '2', '2'], [999.772309110711, 999.7568109989896, 999.7568135752206, '-1111.1100110000001100000101', '1100.0101111110011011011001101', '2', '3'], [999.7723098264468, 999.7626773116484, 999.762679240235, '-1111.1100110010001000000111', '1100.0101111110011011011001101', '2', '4'], [999.772309861109, 999.7628189867448, 999.7628208837242, '-1111.1100110010101100000111', '1100.0101111111011011011001101', '2', '5'], [999.7723098614766, 999.7575339099722, 999.7575364450361, '-1111.1100110010011100000111', '1100.0101111111001011011001101', '2', '6'], [999.7723098614781, 999.7330120594124, 999.7330190985555, '-1111.1100110010011100000111', '1100.0101111111001011011101101', '2', '7'], [999.7723098614792, 999.7425919826176, 999.7425978232334, '-1111.1100110010011100000011', '1100.0101111111001011011101101', '2', '8'], [999.7723098614795, 999.7521383886242, 999.7521421922053, '-1111.1100110010011100000011', '1100.0101111111001011011111101', '2', '9'], [999.7723098614797, 999.7479881509024, 999.7479935597561, '-1111.1100110010011100000010', '1100.0101111111001011011111101', '2', '10']]]]
ensaio = [[[[999.5055172457077, 999.4950089601775, 999.4950089625081, '1000110.11100010011001100001001', '110011.10010111010000111110100', '1', '1'], [999.5061559955897, 999.5023090273177, 999.502309034424, '1000100.01100010011001100001001', '110111.10010111010000111110110', '1', '2'], [999.5064522811073, 999.503907723421, 999.503907729031, '1000110.11110010001001100001001', '110011.11010111010000111110110', '1', '3'], [999.5121423697065, 999.5035214104588, 999.5035214189329, '1000100.01100010001001100001001', '010111.11010111010000111110110', '1', '4'], [999.5129192569484, 999.5073323118404, 999.5073323302529, '1000100.01100010001001100001001', '010111.01010111010000111110110', '1', '5'], [999.5129217445863, 999.5088300870693, 999.5088303022028, '1000100.01100010001001100001001', '010111.01010011010000111100000', '1', '6'], [999.5129221606866, 999.5109518591257, 999.5109518758048, '1000100.01100010001001100001001', '010111.01010010010000111100000', '1', '7'], [999.5129230124272, 999.512166424112, 999.5121664266934, '1000100.01100000001001100001001', '010111.01010011010000111100000', '1', '8'], [999.5129230131629, 999.5124005902164, 999.5124005938198, '1000100.01100000001001100001001', '010111.01010011010010111100000', '1', '9'], [999.5129230148639, 999.5103974240145, 999.5103976354789, '1000100.01100000001011100001001', '010111.01010011010010111110000', '1', '10'], [999.5129230160566, 999.5122248009202, 999.5122248072961, '1000100.01100000001011100001001', '010111.01010011011010111110000', '1', '11'], [999.5129230160816, 999.5098512171988, 999.5098514317501, '1000100.01100000001011101001001', '010111.01010011011010111111010', '1', '12'], [999.5129230161706, 999.5117481943298, 999.5117482051002, '1000100.01100000001011100001101', '010111.01010011011110111110000', '1', '13'], [999.512923016171, 999.5123140953751, 999.5123140998094, '1000100.01100000001011100000101', '010111.01010011011110111110000', '1', '14'], [999.5129230161734, 999.5118310368094, 999.5118310431579, '1000100.01100000001011100000101', '010111.01010011011110101100000', '1', '15'], [999.5129230161741, 999.5121210327977, 999.5121210375976, '1000100.01100000001011100000101', '010111.01010011011110011000000', '1', '16'], [999.5129230161741, 999.5094388460799, 999.5094390635309, '1000100.01100000001011100001101', '010111.01010011011110011000000', '1', '17'], [999.6136428564632, 999.5131524131325, 999.5131524699873, '0000100.01100000001011100000111', '011111.01010011011110011000000', '1', '18'], [999.6171227592827, 999.5505819522255, 999.5505841236918, '0000100.00100000001011100000111', '011111.01010011011110011000000', '1', '19'], [999.6260388885507, 999.6021087614022, 999.6021098942948, '0000100.00100000001011100001111', '011111.00010011011110011000000', '1', '20'], [999.6264039047687, 999.6120960828682, 999.6120968350307, '0000100.01000000001011100001111', '011111.00010011011110011000000', '1', '21'], [999.8211587180026, 999.6202616252683, 999.6202623370596, '0000100.01000000101011100001111', '001111.00010011011110011000000', '1', '22'], [999.8217737535546, 999.697569365876, 999.6975767463806, '0000100.01100000101011100001111', '001111.00010011001110011000000', '1', '23'], [999.821774465545, 999.7883631635897, 999.7883680067487, '0000100.01100000101011100001111', '001111.00010011001010011000000', '1', '24'], [999.82177567708, 999.7936505713287, 999.7936562996696, '0000100.01100000101011100001111', '001111.00010011000010011000000', '1', '25'], [999.8217757540084, 999.7949952836876, 999.7949996579591, '0000100.01100000101001100001111', '001111.00010011000010011000000', '1', '26'], [999.8217757867357, 999.770910115756, 999.7709198320904, '0000100.01100000101001100001110', '001111.00010011000010001000000', '1', '27'], [999.8217760768566, 999.7999669197359, 999.7999704691948, '0000100.01100000100001100001111', '001111.00010011000010001000000', '1', '28'], [999.8217761040349, 999.7895584591176, 999.7895643944065, 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'50', '96'], [999.6878968594276, 999.6619993921432, 999.6620030672873, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '97'], [999.6878968594276, 999.6767364740043, 999.6767381465787, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '98'], [999.6878968594276, 999.6674371363, 999.6674397163673, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '99'], [999.6878968594276, 999.6818107105054, 999.6818115173727, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '100']]]]
fitness = []
media = []
desvio = []
experimento_plot = []
from matplotlib import pyplot as plt
import numpy as np
for index, experimento in enumerate(ensaio):
for populacao in experimento:
for x, individuo in enumerate(populacao):
fitness.append(individuo[0])
media.append(individuo[1])
desvio.append(individuo[2])
x = np.arange(1, fitness.__len__() + 1)
plt.plot(x, fitness)
experimento_plot.append([fitness, media, desvio])
fitness = []
media = []
desvio = []
plt.show() | 23,441.172414 | 676,649 | 0.776381 |
13099ef7ac061602aaff6bfaa4733bfcc5e92bd8 | 225 | py | Python | etest_test/fixtures_test/ebuilds_test/__init__.py | alunduil/etest | e5f06d7e8c83be369576976f239668545bcbfffd | [
"MIT"
] | 6 | 2015-01-08T13:56:50.000Z | 2018-01-08T00:53:08.000Z | etest_test/fixtures_test/ebuilds_test/__init__.py | alunduil/etest | e5f06d7e8c83be369576976f239668545bcbfffd | [
"MIT"
] | 64 | 2015-01-01T23:10:07.000Z | 2021-06-12T06:55:31.000Z | etest_test/fixtures_test/ebuilds_test/__init__.py | alunduil/etest | e5f06d7e8c83be369576976f239668545bcbfffd | [
"MIT"
] | 2 | 2015-05-08T01:32:48.000Z | 2015-05-30T15:38:31.000Z | """Ebuild Test Fixtures."""
import os
from typing import Any, Dict, List
from etest_test import helpers_test
EBUILDS: Dict[str, List[Dict[str, Any]]] = {}
helpers_test.import_directory(__name__, os.path.dirname(__file__))
| 22.5 | 66 | 0.755556 |
130a61bee54706bd995afa354e7681d8726e5d5d | 3,764 | py | Python | src/model.py | palucki/RememberIt | 1d66616d4bb1bca026dda031d876dca226ba71ad | [
"MIT"
] | null | null | null | src/model.py | palucki/RememberIt | 1d66616d4bb1bca026dda031d876dca226ba71ad | [
"MIT"
] | null | null | null | src/model.py | palucki/RememberIt | 1d66616d4bb1bca026dda031d876dca226ba71ad | [
"MIT"
] | null | null | null | import random
from pymongo import MongoClient
from observable import Observable
from phrase import Phrase
| 30.852459 | 85 | 0.562965 |
130ad5b5c3caa22c7668a018ea30cf4d2bc3c2f4 | 1,381 | py | Python | sampleApplication/clientGenerator.py | chall68/BlackWatch | 0b95d69e4b7de9213a031557e9aff54ce35b12dd | [
"MIT"
] | null | null | null | sampleApplication/clientGenerator.py | chall68/BlackWatch | 0b95d69e4b7de9213a031557e9aff54ce35b12dd | [
"MIT"
] | null | null | null | sampleApplication/clientGenerator.py | chall68/BlackWatch | 0b95d69e4b7de9213a031557e9aff54ce35b12dd | [
"MIT"
] | null | null | null | #!flask/bin/python
#from user import User
from sampleObjects.User import User
from datetime import datetime
from sampleObjects.DetectionPoint import DetectionPoint
import time, requests, random, atexit
for i in range (50):
requestGenerator()
time.sleep(1.5)
atexit.register(closingTime)
| 27.62 | 187 | 0.674873 |
130c49099f8aa40a9dd92ff170ecb6c15b43d8f9 | 1,873 | py | Python | news_collector/collector/consumers.py | ridwaniyas/channels-examples | 9e6a26c8e6404483695cbd96ebf12fc4ed9956b2 | [
"BSD-3-Clause"
] | null | null | null | news_collector/collector/consumers.py | ridwaniyas/channels-examples | 9e6a26c8e6404483695cbd96ebf12fc4ed9956b2 | [
"BSD-3-Clause"
] | null | null | null | news_collector/collector/consumers.py | ridwaniyas/channels-examples | 9e6a26c8e6404483695cbd96ebf12fc4ed9956b2 | [
"BSD-3-Clause"
] | null | null | null | import asyncio
import json
import datetime
from aiohttp import ClientSession
from channels.generic.http import AsyncHttpConsumer
from .constants import BLOGS
| 35.339623 | 91 | 0.615056 |
130d77d6c796e047f21c43df476be8389b35aecb | 737 | py | Python | src/randomcsv/FileUtils.py | PhilipBuhr/randomCsv | 34b1da62134077dfe4db2682ee0da386ef380c1d | [
"MIT"
] | null | null | null | src/randomcsv/FileUtils.py | PhilipBuhr/randomCsv | 34b1da62134077dfe4db2682ee0da386ef380c1d | [
"MIT"
] | null | null | null | src/randomcsv/FileUtils.py | PhilipBuhr/randomCsv | 34b1da62134077dfe4db2682ee0da386ef380c1d | [
"MIT"
] | null | null | null | import os
from pathlib import Path
| 23.03125 | 80 | 0.591588 |
130e4d54c2deae9e851943dbae96e38707ce57ce | 46 | py | Python | stringtoiso/__init__.py | vats98754/stringtoiso | 985da5efa26111ef1d92b7026b5d5d68f0101ef1 | [
"MIT"
] | null | null | null | stringtoiso/__init__.py | vats98754/stringtoiso | 985da5efa26111ef1d92b7026b5d5d68f0101ef1 | [
"MIT"
] | null | null | null | stringtoiso/__init__.py | vats98754/stringtoiso | 985da5efa26111ef1d92b7026b5d5d68f0101ef1 | [
"MIT"
] | null | null | null | from stringtoiso.convert_to_iso import convert | 46 | 46 | 0.913043 |
130ec5dfef9f34118ca5d16b6a8a1a90a53517da | 5,495 | py | Python | aux_sys_err_prediction_module/additive/R_runmed_spline/my_R_runmed_spline_analysis.py | PNNL-Comp-Mass-Spec/DtaRefinery | 609cc90d0322af69aea43c2fc21d9cf05a06797a | [
"BSD-2-Clause"
] | null | null | null | aux_sys_err_prediction_module/additive/R_runmed_spline/my_R_runmed_spline_analysis.py | PNNL-Comp-Mass-Spec/DtaRefinery | 609cc90d0322af69aea43c2fc21d9cf05a06797a | [
"BSD-2-Clause"
] | null | null | null | aux_sys_err_prediction_module/additive/R_runmed_spline/my_R_runmed_spline_analysis.py | PNNL-Comp-Mass-Spec/DtaRefinery | 609cc90d0322af69aea43c2fc21d9cf05a06797a | [
"BSD-2-Clause"
] | null | null | null | from aux_sys_err_prediction_module.additive.R_runmed_spline.my_R_runmed_spline_fit import R_runmed_smooth_spline
from numpy import random, array, median, zeros, arange, hstack
from win32com.client import Dispatch
import math
myName = 'R_runmed_spline'
useMAD = True # use median absolute deviations instead of sum of squared residues
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
def R_runmed_spline_KCV_predErr(x, y, **kwargs):
"""
just returns the prediction error
"""
K = int(kwargs['K'])
# --Related to K-fold CV---------------------------
L = len(x)
N = L / K ##min length of pieces
W = list(range(L))
Z = list(range(1, K + 1))
Z = [N for j in Z]
R = L % K
Z[0:R] = [j + 1 for j in Z[0:R]] # length of the pieces
random.shuffle(W)
ind = 0
predErr = 0
allResiduals = array([])
SSE = sum(y ** 2) # VLAD. Why do I need this???
# ---running through K training/testings-------------
for val in Z:
j = math.floor(val)
# ---making training/testing subsets-------------
test = W[ind:ind + j]
test.sort()
train = W[0:ind] + W[ind + j:]
train.sort()
ind += j
# -----------------------------------------------
# ---fit runmed_spline here----------------------
yFit, runMed = R_runmed_smooth_spline(x[train], y[train], x[test], **kwargs)
residualsTest = y[test] - yFit
predErr += sum(residualsTest ** 2)
allResiduals = hstack((allResiduals, residualsTest))
# -----------------------------------------------
if useMAD:
predErr = 1.4826 * median(abs(allResiduals))
return predErr
# -----------------------------------------------------------------------
if __name__ == '__main__':
from numpy import linspace, cos, lexsort, zeros, sin
from pylab import plot, show, subplot, savefig, clf, ylim
from pprint import pprint as p
from time import clock as c
x1 = linspace(0, 30, 300)
## y1 = cos(x1)
## y1 = zeros(len(x1),'d') #nice test
y1 = x1 * 0.03
y1 += random.normal(scale=0.2, size=y1.shape)
ind = lexsort(keys=(y1, x1))
x1 = x1[ind]
y1 = y1[ind]
t1 = c()
isSuccessfulFit, yFit, yEval, runMedData, predErr = \
R_runmed_spline_MAIN(x1, y1, x1, runMedSpan=0.01, K=10, sparRange=[0.6, 1.1, 0.1])
t2 = c()
print('done in %s seconds' % (t2 - t1))
subplot(211)
plot(x1, y1, 'bo')
plot(runMedData[0], runMedData[1], 'y^')
plot(x1, yEval, 'r+-')
ylim([-1.5, +1.5])
subplot(212)
plot(x1, y1 - yEval, 'go')
ylim([-1.5, +1.5])
show()
| 32.323529 | 113 | 0.499363 |
130f0d527db89218f9714b016db75a6b60750779 | 2,721 | py | Python | setup.py | Ms2ger/python-zstandard | b8ea1f6722a710e252b452554442b84c81049439 | [
"BSD-3-Clause"
] | null | null | null | setup.py | Ms2ger/python-zstandard | b8ea1f6722a710e252b452554442b84c81049439 | [
"BSD-3-Clause"
] | null | null | null | setup.py | Ms2ger/python-zstandard | b8ea1f6722a710e252b452554442b84c81049439 | [
"BSD-3-Clause"
] | null | null | null | #!/usr/bin/env python
# Copyright (c) 2016-present, Gregory Szorc
# All rights reserved.
#
# This software may be modified and distributed under the terms
# of the BSD license. See the LICENSE file for details.
import os
import sys
from setuptools import setup
try:
import cffi
except ImportError:
cffi = None
import setup_zstd
SUPPORT_LEGACY = False
SYSTEM_ZSTD = False
WARNINGS_AS_ERRORS = False
if os.environ.get('ZSTD_WARNINGS_AS_ERRORS', ''):
WARNINGS_AS_ERRORS = True
if '--legacy' in sys.argv:
SUPPORT_LEGACY = True
sys.argv.remove('--legacy')
if '--system-zstd' in sys.argv:
SYSTEM_ZSTD = True
sys.argv.remove('--system-zstd')
if '--warnings-as-errors' in sys.argv:
WARNINGS_AS_ERRORS = True
sys.argv.remote('--warning-as-errors')
# Code for obtaining the Extension instance is in its own module to
# facilitate reuse in other projects.
extensions = [
setup_zstd.get_c_extension(name='zstd',
support_legacy=SUPPORT_LEGACY,
system_zstd=SYSTEM_ZSTD,
warnings_as_errors=WARNINGS_AS_ERRORS),
]
install_requires = []
if cffi:
import make_cffi
extensions.append(make_cffi.ffi.distutils_extension())
# Need change in 1.10 for ffi.from_buffer() to handle all buffer types
# (like memoryview).
# Need feature in 1.11 for ffi.gc() to declare size of objects so we avoid
# garbage collection pitfalls.
install_requires.append('cffi>=1.11')
version = None
with open('c-ext/python-zstandard.h', 'r') as fh:
for line in fh:
if not line.startswith('#define PYTHON_ZSTANDARD_VERSION'):
continue
version = line.split()[2][1:-1]
break
if not version:
raise Exception('could not resolve package version; '
'this should never happen')
setup(
name='zstandard',
version=version,
description='Zstandard bindings for Python',
long_description=open('README.rst', 'r').read(),
url='https://github.com/indygreg/python-zstandard',
author='Gregory Szorc',
author_email='[email protected]',
license='BSD',
classifiers=[
'Development Status :: 4 - Beta',
'Intended Audience :: Developers',
'License :: OSI Approved :: BSD License',
'Programming Language :: C',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
],
keywords='zstandard zstd compression',
packages=['zstandard'],
ext_modules=extensions,
test_suite='tests',
install_requires=install_requires,
)
| 27.765306 | 78 | 0.655274 |
130f1aca25388c2ce8dd308d2bb872e5263f8305 | 140 | py | Python | Escolas/Curso em Video/Back-End/Curso de Python/Mundos/Mundo 01/Exercicio_16.py | c4st1lh0/Projetos-de-Aula | e8abc9f4bce6cc8dbc6d7fb5da0f549ac8ef5302 | [
"MIT"
] | null | null | null | Escolas/Curso em Video/Back-End/Curso de Python/Mundos/Mundo 01/Exercicio_16.py | c4st1lh0/Projetos-de-Aula | e8abc9f4bce6cc8dbc6d7fb5da0f549ac8ef5302 | [
"MIT"
] | null | null | null | Escolas/Curso em Video/Back-End/Curso de Python/Mundos/Mundo 01/Exercicio_16.py | c4st1lh0/Projetos-de-Aula | e8abc9f4bce6cc8dbc6d7fb5da0f549ac8ef5302 | [
"MIT"
] | null | null | null | import math
num = float(input('Digite um numero real qualquer: '))
print('O numero: {} tem a parte inteira {}'.format(num, math.trunc(num))) | 46.666667 | 73 | 0.7 |
1310f38eea5f3635000289a9e4355d5205ba5668 | 551 | py | Python | mmdet/ops/orn/functions/__init__.py | JarvisUSTC/DARDet | debbf476e9750030db67f030a40cf8d4f03e46ee | [
"Apache-2.0"
] | 274 | 2021-04-06T15:46:06.000Z | 2022-03-31T02:00:10.000Z | mmdet/ops/orn/functions/__init__.py | JarvisUSTC/DARDet | debbf476e9750030db67f030a40cf8d4f03e46ee | [
"Apache-2.0"
] | 136 | 2021-07-11T11:26:54.000Z | 2022-03-31T02:45:34.000Z | mmdet/ops/orn/functions/__init__.py | JarvisUSTC/DARDet | debbf476e9750030db67f030a40cf8d4f03e46ee | [
"Apache-2.0"
] | 84 | 2021-05-29T06:58:14.000Z | 2022-03-31T07:44:10.000Z | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .active_rotating_filter import active_rotating_filter
from .active_rotating_filter import ActiveRotatingFilter
from .rotation_invariant_encoding import rotation_invariant_encoding
from .rotation_invariant_encoding import RotationInvariantEncoding
from .rotation_invariant_pooling import RotationInvariantPooling
__all__ = ['ActiveRotatingFilter', 'active_rotating_filter', 'rotation_invariant_encoding', 'RotationInvariantEncoding', 'RotationInvariantPooling'] | 61.222222 | 148 | 0.874773 |
131271d5663df6a63ce374c8c4958678283dd5fc | 218 | py | Python | sympy/core/tests/test_cache.py | eriknw/sympy | b7544e2bb74c011f6098a7e886fd77f41776c2c4 | [
"BSD-3-Clause"
] | 7 | 2015-01-14T06:55:33.000Z | 2018-08-11T14:43:52.000Z | sympy/core/tests/test_cache.py | pbeltran/sympy-1 | 94f92b36731c2bebe6de1037c063c2a258a8a399 | [
"BSD-3-Clause"
] | 1 | 2018-02-19T04:56:04.000Z | 2018-02-19T04:56:04.000Z | sympy/core/tests/test_cache.py | pbeltran/sympy-1 | 94f92b36731c2bebe6de1037c063c2a258a8a399 | [
"BSD-3-Clause"
] | 1 | 2016-04-24T14:39:22.000Z | 2016-04-24T14:39:22.000Z | from sympy.core.cache import cacheit
| 18.166667 | 45 | 0.646789 |
1312f8f0f49eb471bc17c6830c67ae3b593f1370 | 8,694 | py | Python | mmdet/models/losses/ranking_losses.py | VietDunghacker/VarifocalNet | f57917afb3c29ceba1d3c4f824d10b9cc53aaa40 | [
"Apache-2.0"
] | null | null | null | mmdet/models/losses/ranking_losses.py | VietDunghacker/VarifocalNet | f57917afb3c29ceba1d3c4f824d10b9cc53aaa40 | [
"Apache-2.0"
] | null | null | null | mmdet/models/losses/ranking_losses.py | VietDunghacker/VarifocalNet | f57917afb3c29ceba1d3c4f824d10b9cc53aaa40 | [
"Apache-2.0"
] | null | null | null | import torch
| 35.198381 | 96 | 0.743961 |
131342de18ae50cff3d8d09f0b5c640ef367d9c5 | 997 | py | Python | tests/test_dcd_api.py | sadamek/pyIMX | 52af15e656b400f0812f16cf31d9bf6edbe631ad | [
"BSD-3-Clause"
] | null | null | null | tests/test_dcd_api.py | sadamek/pyIMX | 52af15e656b400f0812f16cf31d9bf6edbe631ad | [
"BSD-3-Clause"
] | null | null | null | tests/test_dcd_api.py | sadamek/pyIMX | 52af15e656b400f0812f16cf31d9bf6edbe631ad | [
"BSD-3-Clause"
] | null | null | null | # Copyright (c) 2017-2018 Martin Olejar
#
# SPDX-License-Identifier: BSD-3-Clause
# The BSD-3-Clause license for this file can be found in the LICENSE file included with this distribution
# or at https://spdx.org/licenses/BSD-3-Clause.html#licenseText
import os
import pytest
from imx import img
# Used Directories
DATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data')
# Test Files
DCD_TXT = os.path.join(DATA_DIR, 'dcd_test.txt')
DCD_BIN = os.path.join(DATA_DIR, 'dcd_test.bin')
| 22.155556 | 105 | 0.691073 |
1313abf2371aefa93bced19321156374195a59c5 | 9,387 | py | Python | recentjson.py | nydailynews/feedutils | 8cb18b26ebf70033df420f3fece8c2cac363f918 | [
"MIT"
] | null | null | null | recentjson.py | nydailynews/feedutils | 8cb18b26ebf70033df420f3fece8c2cac363f918 | [
"MIT"
] | 1 | 2017-07-11T17:37:50.000Z | 2017-07-11T17:37:50.000Z | recentjson.py | nydailynews/feedutils | 8cb18b26ebf70033df420f3fece8c2cac363f918 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Return recent items from a json feed. Recent means "In the last X days."
import os
import doctest
import json
import urllib2
import argparse
import types
import gzip
from datetime import datetime, timedelta
from time import mktime
def pretty_date(ago):
""" Process a timedelta object.
From https://stackoverflow.com/questions/1551382/user-friendly-time-format-in-python
"""
second_diff = ago.seconds
day_diff = ago.days
if day_diff < 0:
return ''
if day_diff == 0:
if second_diff < 10:
return "just now"
if second_diff < 60:
return str(second_diff) + " seconds ago"
if second_diff < 120:
return "a minute ago"
if second_diff < 3600:
return str(second_diff / 60) + " minutes ago"
if second_diff < 7200:
return "an hour ago"
if second_diff < 86400:
return str(second_diff / 3600) + " hours ago"
if day_diff == 1:
return "Yesterday"
if day_diff < 7:
return str(day_diff) + " days ago"
if day_diff < 31:
if day_diff / 7 == 1:
return str(day_diff / 7) + " week ago"
return str(day_diff / 7) + " weeks ago"
if day_diff < 365:
if day_diff / 30 == 1:
return str(day_diff / 30) + " month ago"
return str(day_diff / 30) + " months ago"
if day_diff / 365 == 1:
return str(day_diff / 365) + " year ago"
return str(day_diff / 365) + " years ago"
def main(args):
""" For command-line use.
"""
rj = RecentJson(args)
if args:
articles = []
for arg in args.urls[0]:
if args.verbose:
print arg
rj.get(arg)
try:
p = rj.parse()
except:
continue
if not p:
continue
articles.append(rj.recently())
if len(articles) is 0:
return None
for i, article in enumerate(articles[0]):
if i >= args.limit and args.limit > 0:
break
dt = datetime.strptime(' '.join(article['publish_date'].split(' ')[:5]), '%a, %d %b %Y %X')
ago = datetime.now() - dt
# print ago
# 2 days, 15:57:48.578638
if args.output == 'html':
if type(article['title']) is types.UnicodeType:
article['title'] = article['title'].encode('utf-8', 'replace')
if args.listitem == True:
print '<li><a href="{0}">{1}</a> <span>({2})</span></li>'.format(article['url'], article['title'], pretty_date(ago).lower())
elif args.nostamp == True:
print '<li><a href="{0}">{1}</a></li>'.format(article['url'], article['title'], pretty_date(ago).lower())
else:
print '<a href="{0}">{1}</a> <span>({2})</span>'.format(article['url'], article['title'], pretty_date(ago).lower())
if args.output == 'js':
if type(article['title']) is types.UnicodeType:
article['title'] = article['title'].encode('utf-8', 'replace')
print 'var hed = "<a href=\'{0}\'>{1}</a> <span>({2})</span>";'.format(article['url'], article['title'].replace('"', '\\\\"'), pretty_date(ago).lower())
elif args.output == 'json':
print json.dumps({'title': article['title'],
'id': article['id'],
'description': article['description']})
elif args.output == 'csv':
dt = datetime.strptime(' '.join(article['publish_date'].split(' ')[:5]), '%a, %d %b %Y %X')
article['datetime'] = '%s-%s-%s' % (dt.year, dt.month, dt.day)
if dt.month < 10:
article['datetime'] = '%d-0%d-%d' % (dt.year, dt.month, dt.day)
if dt.day < 10:
article['datetime'] = '%d-0%d-0%d' % (dt.year, dt.month, dt.day)
article['slug'] = article['title'].lower().replace(' ', '-').replace('--', '-').replace(':', '')
article['iframe_url'] = article['media_player']['url']
article['image_url'] = article['media_thumbnail'][0]['url']
article['image_large_url'] = article['media_thumbnail'][1]['url']
article['description'] = article['description'].replace('"', "'")
# date,title,id,slug,player_url,image_url,image_large_url,keywords,description
print '%(datetime)s,"%(title)s",%(id)s,%(slug)s,%(iframe_url)s,%(image_url)s,%(image_large_url)s,"%(media_keywords)s","%(description)s"' % article
def build_parser():
""" We put the argparse in a method so we can test it
outside of the command-line.
"""
parser = argparse.ArgumentParser(usage='$ python recentjson.py http://domain.com/json/',
description='''Takes a list of URLs passed as args.
Returns the items published today unless otherwise specified.''',
epilog='')
parser.add_argument("-v", "--verbose", dest="verbose", default=False, action="store_true")
parser.add_argument("--test", dest="test", default=False, action="store_true")
parser.add_argument("-d", "--days", dest="days", default=0)
parser.add_argument("-l", "--limit", dest="limit", default=0, type=int)
parser.add_argument("-o", "--output", dest="output", default="html", type=str)
parser.add_argument("--li", dest="listitem", default=False, action="store_true")
parser.add_argument("--ns", dest="nostamp", default=False, action="store_true")
parser.add_argument("urls", action="append", nargs="*")
return parser
if __name__ == '__main__':
"""
"""
parser = build_parser()
args = parser.parse_args()
if args.test:
doctest.testmod(verbose=args.verbose)
main(args)
| 40.813043 | 186 | 0.520081 |
1314a6b3e97ad080ab7cf47017455ad35f9e033a | 34,521 | py | Python | maint/MultiStage2.py | Liastre/pcre2 | ca4fd145ee16acbc67b52b8563ab6e25c67ddfc8 | [
"BSD-3-Clause"
] | null | null | null | maint/MultiStage2.py | Liastre/pcre2 | ca4fd145ee16acbc67b52b8563ab6e25c67ddfc8 | [
"BSD-3-Clause"
] | 1 | 2020-04-07T10:42:22.000Z | 2020-04-07T10:42:22.000Z | maint/MultiStage2.py | Liastre/pcre2 | ca4fd145ee16acbc67b52b8563ab6e25c67ddfc8 | [
"BSD-3-Clause"
] | null | null | null | #! /usr/bin/python
# Multistage table builder
# (c) Peter Kankowski, 2008
##############################################################################
# This script was submitted to the PCRE project by Peter Kankowski as part of
# the upgrading of Unicode property support. The new code speeds up property
# matching many times. The script is for the use of PCRE maintainers, to
# generate the pcre2_ucd.c file that contains a digested form of the Unicode
# data tables. A number of extensions have been added to the original script.
#
# The script has now been upgraded to Python 3 for PCRE2, and should be run in
# the maint subdirectory, using the command
#
# [python3] ./MultiStage2.py >../src/pcre2_ucd.c
#
# It requires six Unicode data tables: DerivedGeneralCategory.txt,
# GraphemeBreakProperty.txt, Scripts.txt, ScriptExtensions.txt,
# CaseFolding.txt, and emoji-data.txt. These must be in the
# maint/Unicode.tables subdirectory.
#
# DerivedGeneralCategory.txt is found in the "extracted" subdirectory of the
# Unicode database (UCD) on the Unicode web site; GraphemeBreakProperty.txt is
# in the "auxiliary" subdirectory. Scripts.txt, ScriptExtensions.txt, and
# CaseFolding.txt are directly in the UCD directory. The emoji-data.txt file is
# in files associated with Unicode Technical Standard #51 ("Unicode Emoji"),
# for example:
#
# http://unicode.org/Public/emoji/11.0/emoji-data.txt
#
# -----------------------------------------------------------------------------
# Minor modifications made to this script:
# Added #! line at start
# Removed tabs
# Made it work with Python 2.4 by rewriting two statements that needed 2.5
# Consequent code tidy
# Adjusted data file names to take from the Unicode.tables directory
# Adjusted global table names by prefixing _pcre_.
# Commented out stuff relating to the casefolding table, which isn't used;
# removed completely in 2012.
# Corrected size calculation
# Add #ifndef SUPPORT_UCP to use dummy tables when no UCP support is needed.
# Update for PCRE2: name changes, and SUPPORT_UCP is abolished.
#
# Major modifications made to this script:
# Added code to add a grapheme break property field to records.
#
# Added code to search for sets of more than two characters that must match
# each other caselessly. A new table is output containing these sets, and
# offsets into the table are added to the main output records. This new
# code scans CaseFolding.txt instead of UnicodeData.txt, which is no longer
# used.
#
# Update for Python3:
# . Processed with 2to3, but that didn't fix everything
# . Changed string.strip to str.strip
# . Added encoding='utf-8' to the open() call
# . Inserted 'int' before blocksize/ELEMS_PER_LINE because an int is
# required and the result of the division is a float
#
# Added code to scan the emoji-data.txt file to find the Extended Pictographic
# property, which is used by PCRE2 as a grapheme breaking property. This was
# done when updating to Unicode 11.0.0 (July 2018).
#
# Added code to add a Script Extensions field to records. This has increased
# their size from 8 to 12 bytes, only 10 of which are currently used.
#
# 01-March-2010: Updated list of scripts for Unicode 5.2.0
# 30-April-2011: Updated list of scripts for Unicode 6.0.0
# July-2012: Updated list of scripts for Unicode 6.1.0
# 20-August-2012: Added scan of GraphemeBreakProperty.txt and added a new
# field in the record to hold the value. Luckily, the
# structure had a hole in it, so the resulting table is
# not much bigger than before.
# 18-September-2012: Added code for multiple caseless sets. This uses the
# final hole in the structure.
# 30-September-2012: Added RegionalIndicator break property from Unicode 6.2.0
# 13-May-2014: Updated for PCRE2
# 03-June-2014: Updated for Python 3
# 20-June-2014: Updated for Unicode 7.0.0
# 12-August-2014: Updated to put Unicode version into the file
# 19-June-2015: Updated for Unicode 8.0.0
# 02-July-2017: Updated for Unicode 10.0.0
# 03-July-2018: Updated for Unicode 11.0.0
# 07-July-2018: Added code to scan emoji-data.txt for the Extended
# Pictographic property.
# 01-October-2018: Added the 'Unknown' script name
# 03-October-2018: Added new field for Script Extensions
# 27-July-2019: Updated for Unicode 12.1.0
# ----------------------------------------------------------------------------
#
#
# The main tables generated by this script are used by macros defined in
# pcre2_internal.h. They look up Unicode character properties using short
# sequences of code that contains no branches, which makes for greater speed.
#
# Conceptually, there is a table of records (of type ucd_record), containing a
# script number, script extension value, character type, grapheme break type,
# offset to caseless matching set, offset to the character's other case, for
# every Unicode character. However, a real table covering all Unicode
# characters would be far too big. It can be efficiently compressed by
# observing that many characters have the same record, and many blocks of
# characters (taking 128 characters in a block) have the same set of records as
# other blocks. This leads to a 2-stage lookup process.
#
# This script constructs six tables. The ucd_caseless_sets table contains
# lists of characters that all match each other caselessly. Each list is
# in order, and is terminated by NOTACHAR (0xffffffff), which is larger than
# any valid character. The first list is empty; this is used for characters
# that are not part of any list.
#
# The ucd_digit_sets table contains the code points of the '9' characters in
# each set of 10 decimal digits in Unicode. This is used to ensure that digits
# in script runs all come from the same set. The first element in the vector
# contains the number of subsequent elements, which are in ascending order.
#
# The ucd_script_sets vector contains lists of script numbers that are the
# Script Extensions properties of certain characters. Each list is terminated
# by zero (ucp_Unknown). A character with more than one script listed for its
# Script Extension property has a negative value in its record. This is the
# negated offset to the start of the relevant list in the ucd_script_sets
# vector.
#
# The ucd_records table contains one instance of every unique record that is
# required. The ucd_stage1 table is indexed by a character's block number,
# which is the character's code point divided by 128, since 128 is the size
# of each block. The result of a lookup in ucd_stage1 a "virtual" block number.
#
# The ucd_stage2 table is a table of "virtual" blocks; each block is indexed by
# the offset of a character within its own block, and the result is the index
# number of the required record in the ucd_records vector.
#
# The following examples are correct for the Unicode 11.0.0 database. Future
# updates may make change the actual lookup values.
#
# Example: lowercase "a" (U+0061) is in block 0
# lookup 0 in stage1 table yields 0
# lookup 97 (0x61) in the first table in stage2 yields 17
# record 17 is { 34, 5, 12, 0, -32, 34, 0 }
# 34 = ucp_Latin => Latin script
# 5 = ucp_Ll => Lower case letter
# 12 = ucp_gbOther => Grapheme break property "Other"
# 0 => Not part of a caseless set
# -32 (-0x20) => Other case is U+0041
# 34 = ucp_Latin => No special Script Extension property
# 0 => Dummy value, unused at present
#
# Almost all lowercase latin characters resolve to the same record. One or two
# are different because they are part of a multi-character caseless set (for
# example, k, K and the Kelvin symbol are such a set).
#
# Example: hiragana letter A (U+3042) is in block 96 (0x60)
# lookup 96 in stage1 table yields 90
# lookup 66 (0x42) in table 90 in stage2 yields 564
# record 564 is { 27, 7, 12, 0, 0, 27, 0 }
# 27 = ucp_Hiragana => Hiragana script
# 7 = ucp_Lo => Other letter
# 12 = ucp_gbOther => Grapheme break property "Other"
# 0 => Not part of a caseless set
# 0 => No other case
# 27 = ucp_Hiragana => No special Script Extension property
# 0 => Dummy value, unused at present
#
# Example: vedic tone karshana (U+1CD0) is in block 57 (0x39)
# lookup 57 in stage1 table yields 55
# lookup 80 (0x50) in table 55 in stage2 yields 458
# record 458 is { 28, 12, 3, 0, 0, -101, 0 }
# 28 = ucp_Inherited => Script inherited from predecessor
# 12 = ucp_Mn => Non-spacing mark
# 3 = ucp_gbExtend => Grapheme break property "Extend"
# 0 => Not part of a caseless set
# 0 => No other case
# -101 => Script Extension list offset = 101
# 0 => Dummy value, unused at present
#
# At offset 101 in the ucd_script_sets vector we find the list 3, 15, 107, 29,
# and terminator 0. This means that this character is expected to be used with
# any of those scripts, which are Bengali, Devanagari, Grantha, and Kannada.
#
# Philip Hazel, 03 July 2008
# Last Updated: 07 October 2018
##############################################################################
import re
import string
import sys
MAX_UNICODE = 0x110000
NOTACHAR = 0xffffffff
# Parse a line of Scripts.txt, GraphemeBreakProperty.txt or DerivedGeneralCategory.txt
# Parse a line of CaseFolding.txt
# Parse a line of ScriptExtensions.txt
# Read the whole table in memory, setting/checking the Unicode version
# Get the smallest possible C language type for the values
# Compress the table into the two stages
# Print a table
# Extract the unique combinations of properties into records
script_names = ['Unknown', 'Arabic', 'Armenian', 'Bengali', 'Bopomofo', 'Braille', 'Buginese', 'Buhid', 'Canadian_Aboriginal',
'Cherokee', 'Common', 'Coptic', 'Cypriot', 'Cyrillic', 'Deseret', 'Devanagari', 'Ethiopic', 'Georgian',
'Glagolitic', 'Gothic', 'Greek', 'Gujarati', 'Gurmukhi', 'Han', 'Hangul', 'Hanunoo', 'Hebrew', 'Hiragana',
'Inherited', 'Kannada', 'Katakana', 'Kharoshthi', 'Khmer', 'Lao', 'Latin', 'Limbu', 'Linear_B', 'Malayalam',
'Mongolian', 'Myanmar', 'New_Tai_Lue', 'Ogham', 'Old_Italic', 'Old_Persian', 'Oriya', 'Osmanya', 'Runic',
'Shavian', 'Sinhala', 'Syloti_Nagri', 'Syriac', 'Tagalog', 'Tagbanwa', 'Tai_Le', 'Tamil', 'Telugu', 'Thaana',
'Thai', 'Tibetan', 'Tifinagh', 'Ugaritic', 'Yi',
# New for Unicode 5.0
'Balinese', 'Cuneiform', 'Nko', 'Phags_Pa', 'Phoenician',
# New for Unicode 5.1
'Carian', 'Cham', 'Kayah_Li', 'Lepcha', 'Lycian', 'Lydian', 'Ol_Chiki', 'Rejang', 'Saurashtra', 'Sundanese', 'Vai',
# New for Unicode 5.2
'Avestan', 'Bamum', 'Egyptian_Hieroglyphs', 'Imperial_Aramaic',
'Inscriptional_Pahlavi', 'Inscriptional_Parthian',
'Javanese', 'Kaithi', 'Lisu', 'Meetei_Mayek',
'Old_South_Arabian', 'Old_Turkic', 'Samaritan', 'Tai_Tham', 'Tai_Viet',
# New for Unicode 6.0.0
'Batak', 'Brahmi', 'Mandaic',
# New for Unicode 6.1.0
'Chakma', 'Meroitic_Cursive', 'Meroitic_Hieroglyphs', 'Miao', 'Sharada', 'Sora_Sompeng', 'Takri',
# New for Unicode 7.0.0
'Bassa_Vah', 'Caucasian_Albanian', 'Duployan', 'Elbasan', 'Grantha', 'Khojki', 'Khudawadi',
'Linear_A', 'Mahajani', 'Manichaean', 'Mende_Kikakui', 'Modi', 'Mro', 'Nabataean',
'Old_North_Arabian', 'Old_Permic', 'Pahawh_Hmong', 'Palmyrene', 'Psalter_Pahlavi',
'Pau_Cin_Hau', 'Siddham', 'Tirhuta', 'Warang_Citi',
# New for Unicode 8.0.0
'Ahom', 'Anatolian_Hieroglyphs', 'Hatran', 'Multani', 'Old_Hungarian',
'SignWriting',
# New for Unicode 10.0.0
'Adlam', 'Bhaiksuki', 'Marchen', 'Newa', 'Osage', 'Tangut', 'Masaram_Gondi',
'Nushu', 'Soyombo', 'Zanabazar_Square',
# New for Unicode 11.0.0
'Dogra', 'Gunjala_Gondi', 'Hanifi_Rohingya', 'Makasar', 'Medefaidrin',
'Old_Sogdian', 'Sogdian',
# New for Unicode 12.0.0
'Elymaic', 'Nandinagari', 'Nyiakeng_Puachue_Hmong', 'Wancho'
]
script_abbrevs = [
'Zzzz', 'Arab', 'Armn', 'Beng', 'Bopo', 'Brai', 'Bugi', 'Buhd', 'Cans',
'Cher', 'Zyyy', 'Copt', 'Cprt', 'Cyrl', 'Dsrt', 'Deva', 'Ethi', 'Geor',
'Glag', 'Goth', 'Grek', 'Gujr', 'Guru', 'Hani', 'Hang', 'Hano', 'Hebr',
'Hira', 'Zinh', 'Knda', 'Kana', 'Khar', 'Khmr', 'Laoo', 'Latn', 'Limb',
'Linb', 'Mlym', 'Mong', 'Mymr', 'Talu', 'Ogam', 'Ital', 'Xpeo', 'Orya',
'Osma', 'Runr', 'Shaw', 'Sinh', 'Sylo', 'Syrc', 'Tglg', 'Tagb', 'Tale',
'Taml', 'Telu', 'Thaa', 'Thai', 'Tibt', 'Tfng', 'Ugar', 'Yiii',
#New for Unicode 5.0
'Bali', 'Xsux', 'Nkoo', 'Phag', 'Phnx',
#New for Unicode 5.1
'Cari', 'Cham', 'Kali', 'Lepc', 'Lyci', 'Lydi', 'Olck', 'Rjng', 'Saur',
'Sund', 'Vaii',
#New for Unicode 5.2
'Avst', 'Bamu', 'Egyp', 'Armi', 'Phli', 'Prti', 'Java', 'Kthi', 'Lisu',
'Mtei', 'Sarb', 'Orkh', 'Samr', 'Lana', 'Tavt',
#New for Unicode 6.0.0
'Batk', 'Brah', 'Mand',
#New for Unicode 6.1.0
'Cakm', 'Merc', 'Mero', 'Plrd', 'Shrd', 'Sora', 'Takr',
#New for Unicode 7.0.0
'Bass', 'Aghb', 'Dupl', 'Elba', 'Gran', 'Khoj', 'Sind', 'Lina', 'Mahj',
'Mani', 'Mend', 'Modi', 'Mroo', 'Nbat', 'Narb', 'Perm', 'Hmng', 'Palm',
'Phlp', 'Pauc', 'Sidd', 'Tirh', 'Wara',
#New for Unicode 8.0.0
'Ahom', 'Hluw', 'Hatr', 'Mult', 'Hung', 'Sgnw',
#New for Unicode 10.0.0
'Adlm', 'Bhks', 'Marc', 'Newa', 'Osge', 'Tang', 'Gonm', 'Nshu', 'Soyo',
'Zanb',
#New for Unicode 11.0.0
'Dogr', 'Gong', 'Rohg', 'Maka', 'Medf', 'Sogo', 'Sogd',
#New for Unicode 12.0.0
'Elym', 'Nand', 'Hmnp', 'Wcho'
]
category_names = ['Cc', 'Cf', 'Cn', 'Co', 'Cs', 'Ll', 'Lm', 'Lo', 'Lt', 'Lu',
'Mc', 'Me', 'Mn', 'Nd', 'Nl', 'No', 'Pc', 'Pd', 'Pe', 'Pf', 'Pi', 'Po', 'Ps',
'Sc', 'Sk', 'Sm', 'So', 'Zl', 'Zp', 'Zs' ]
# The Extended_Pictographic property is not found in the file where all the
# others are (GraphemeBreakProperty.txt). It comes from the emoji-data.txt
# file, but we list it here so that the name has the correct index value.
break_property_names = ['CR', 'LF', 'Control', 'Extend', 'Prepend',
'SpacingMark', 'L', 'V', 'T', 'LV', 'LVT', 'Regional_Indicator', 'Other',
'ZWJ', 'Extended_Pictographic' ]
test_record_size()
unicode_version = ""
script = read_table('Unicode.tables/Scripts.txt', make_get_names(script_names), script_names.index('Unknown'))
category = read_table('Unicode.tables/DerivedGeneralCategory.txt', make_get_names(category_names), category_names.index('Cn'))
break_props = read_table('Unicode.tables/GraphemeBreakProperty.txt', make_get_names(break_property_names), break_property_names.index('Other'))
other_case = read_table('Unicode.tables/CaseFolding.txt', get_other_case, 0)
# The grapheme breaking rules were changed for Unicode 11.0.0 (June 2018). Now
# we need to find the Extended_Pictographic property for emoji characters. This
# can be set as an additional grapheme break property, because the default for
# all the emojis is "other". We scan the emoji-data.txt file and modify the
# break-props table.
file = open('Unicode.tables/emoji-data.txt', 'r', encoding='utf-8')
for line in file:
line = re.sub(r'#.*', '', line)
chardata = list(map(str.strip, line.split(';')))
if len(chardata) <= 1:
continue
if chardata[1] != "Extended_Pictographic":
continue
m = re.match(r'([0-9a-fA-F]+)(\.\.([0-9a-fA-F]+))?$', chardata[0])
char = int(m.group(1), 16)
if m.group(3) is None:
last = char
else:
last = int(m.group(3), 16)
for i in range(char, last + 1):
if break_props[i] != break_property_names.index('Other'):
print("WARNING: Emoji 0x%x has break property %s, not 'Other'",
i, break_property_names[break_props[i]], file=sys.stderr)
break_props[i] = break_property_names.index('Extended_Pictographic')
file.close()
# The Script Extensions property default value is the Script value. Parse the
# file, setting 'Unknown' as the default (this will never be a Script Extension
# value), then scan it and fill in the default from Scripts. Code added by PH
# in October 2018. Positive values are used for just a single script for a
# code point. Negative values are negated offsets in a list of lists of
# multiple scripts. Initialize this list with a single entry, as the zeroth
# element is never used.
script_lists = [0]
script_abbrevs_default = script_abbrevs.index('Zzzz')
scriptx = read_table('Unicode.tables/ScriptExtensions.txt', get_script_extension, script_abbrevs_default)
for i in range(0, MAX_UNICODE):
if scriptx[i] == script_abbrevs_default:
scriptx[i] = script[i]
# With the addition of the new Script Extensions field, we need some padding
# to get the Unicode records up to 12 bytes (multiple of 4). Set a value
# greater than 255 to make the field 16 bits.
padding_dummy = [0] * MAX_UNICODE
padding_dummy[0] = 256
# This block of code was added by PH in September 2012. I am not a Python
# programmer, so the style is probably dreadful, but it does the job. It scans
# the other_case table to find sets of more than two characters that must all
# match each other caselessly. Later in this script a table of these sets is
# written out. However, we have to do this work here in order to compute the
# offsets in the table that are inserted into the main table.
# The CaseFolding.txt file lists pairs, but the common logic for reading data
# sets only one value, so first we go through the table and set "return"
# offsets for those that are not already set.
for c in range(MAX_UNICODE):
if other_case[c] != 0 and other_case[c + other_case[c]] == 0:
other_case[c + other_case[c]] = -other_case[c]
# Now scan again and create equivalence sets.
sets = []
for c in range(MAX_UNICODE):
o = c + other_case[c]
# Trigger when this character's other case does not point back here. We
# now have three characters that are case-equivalent.
if other_case[o] != -other_case[c]:
t = o + other_case[o]
# Scan the existing sets to see if any of the three characters are already
# part of a set. If so, unite the existing set with the new set.
appended = 0
for s in sets:
found = 0
for x in s:
if x == c or x == o or x == t:
found = 1
# Add new characters to an existing set
if found:
found = 0
for y in [c, o, t]:
for x in s:
if x == y:
found = 1
if not found:
s.append(y)
appended = 1
# If we have not added to an existing set, create a new one.
if not appended:
sets.append([c, o, t])
# End of loop looking for caseless sets.
# Now scan the sets and set appropriate offsets for the characters.
caseless_offsets = [0] * MAX_UNICODE
offset = 1;
for s in sets:
for x in s:
caseless_offsets[x] = offset
offset += len(s) + 1
# End of block of code for creating offsets for caseless matching sets.
# Combine the tables
table, records = combine_tables(script, category, break_props,
caseless_offsets, other_case, scriptx, padding_dummy)
record_size, record_struct = get_record_size_struct(list(records.keys()))
# Find the optimum block size for the two-stage table
min_size = sys.maxsize
for block_size in [2 ** i for i in range(5,10)]:
size = len(records) * record_size
stage1, stage2 = compress_table(table, block_size)
size += get_tables_size(stage1, stage2)
#print "/* block size %5d => %5d bytes */" % (block_size, size)
if size < min_size:
min_size = size
min_stage1, min_stage2 = stage1, stage2
min_block_size = block_size
print("/* This module is generated by the maint/MultiStage2.py script.")
print("Do not modify it by hand. Instead modify the script and run it")
print("to regenerate this code.")
print()
print("As well as being part of the PCRE2 library, this module is #included")
print("by the pcre2test program, which redefines the PRIV macro to change")
print("table names from _pcre2_xxx to xxxx, thereby avoiding name clashes")
print("with the library. At present, just one of these tables is actually")
print("needed. */")
print()
print("#ifndef PCRE2_PCRE2TEST")
print()
print("#ifdef HAVE_CONFIG_H")
print("#include \"config.h\"")
print("#endif")
print()
print("#include \"pcre2_internal.h\"")
print()
print("#endif /* PCRE2_PCRE2TEST */")
print()
print("/* Unicode character database. */")
print("/* This file was autogenerated by the MultiStage2.py script. */")
print("/* Total size: %d bytes, block size: %d. */" % (min_size, min_block_size))
print()
print("/* The tables herein are needed only when UCP support is built,")
print("and in PCRE2 that happens automatically with UTF support.")
print("This module should not be referenced otherwise, so")
print("it should not matter whether it is compiled or not. However")
print("a comment was received about space saving - maybe the guy linked")
print("all the modules rather than using a library - so we include a")
print("condition to cut out the tables when not needed. But don't leave")
print("a totally empty module because some compilers barf at that.")
print("Instead, just supply some small dummy tables. */")
print()
print("#ifndef SUPPORT_UNICODE")
print("const ucd_record PRIV(ucd_records)[] = {{0,0,0,0,0,0,0 }};")
print("const uint16_t PRIV(ucd_stage1)[] = {0};")
print("const uint16_t PRIV(ucd_stage2)[] = {0};")
print("const uint32_t PRIV(ucd_caseless_sets)[] = {0};")
print("#else")
print()
print("const char *PRIV(unicode_version) = \"{}\";".format(unicode_version))
print()
print("/* If the 32-bit library is run in non-32-bit mode, character values")
print("greater than 0x10ffff may be encountered. For these we set up a")
print("special record. */")
print()
print("#if PCRE2_CODE_UNIT_WIDTH == 32")
print("const ucd_record PRIV(dummy_ucd_record)[] = {{")
print(" ucp_Unknown, /* script */")
print(" ucp_Cn, /* type unassigned */")
print(" ucp_gbOther, /* grapheme break property */")
print(" 0, /* case set */")
print(" 0, /* other case */")
print(" ucp_Unknown, /* script extension */")
print(" 0, /* dummy filler */")
print(" }};")
print("#endif")
print()
print(record_struct)
# --- Added by PH: output the table of caseless character sets ---
print("/* This table contains lists of characters that are caseless sets of")
print("more than one character. Each list is terminated by NOTACHAR. */\n")
print("const uint32_t PRIV(ucd_caseless_sets)[] = {")
print(" NOTACHAR,")
for s in sets:
s = sorted(s)
for x in s:
print(' 0x%04x,' % x, end=' ')
print(' NOTACHAR,')
print('};')
print()
# ------
print("/* When #included in pcre2test, we don't need the table of digit")
print("sets, nor the the large main UCD tables. */")
print()
print("#ifndef PCRE2_PCRE2TEST")
print()
# --- Added by PH: read Scripts.txt again for the sets of 10 digits. ---
digitsets = []
file = open('Unicode.tables/Scripts.txt', 'r', encoding='utf-8')
for line in file:
m = re.match(r'([0-9a-fA-F]+)\.\.([0-9a-fA-F]+)\s+;\s+\S+\s+#\s+Nd\s+', line)
if m is None:
continue
first = int(m.group(1),16)
last = int(m.group(2),16)
if ((last - first + 1) % 10) != 0:
print("ERROR: %04x..%04x does not contain a multiple of 10 characters" % (first, last),
file=sys.stderr)
while first < last:
digitsets.append(first + 9)
first += 10
file.close()
digitsets.sort()
print("/* This table lists the code points for the '9' characters in each")
print("set of decimal digits. It is used to ensure that all the digits in")
print("a script run come from the same set. */\n")
print("const uint32_t PRIV(ucd_digit_sets)[] = {")
print(" %d, /* Number of subsequent values */" % len(digitsets), end='')
count = 8
for d in digitsets:
if count == 8:
print("\n ", end='')
count = 0
print(" 0x%05x," % d, end='')
count += 1
print("\n};\n")
print("/* This vector is a list of lists of scripts for the Script Extension")
print("property. Each sublist is zero-terminated. */\n")
print("const uint8_t PRIV(ucd_script_sets)[] = {")
count = 0
print(" /* 0 */", end='')
for d in script_lists:
print(" %3d," % d, end='')
count += 1
if d == 0:
print("\n /* %3d */" % count, end='')
print("\n};\n")
# Output the main UCD tables.
print("/* These are the main two-stage UCD tables. The fields in each record are:")
print("script (8 bits), character type (8 bits), grapheme break property (8 bits),")
print("offset to multichar other cases or zero (8 bits), offset to other case")
print("or zero (32 bits, signed), script extension (16 bits, signed), and a dummy")
print("16-bit field to make the whole thing a multiple of 4 bytes. */\n")
print_records(records, record_size)
print_table(min_stage1, 'PRIV(ucd_stage1)')
print_table(min_stage2, 'PRIV(ucd_stage2)', min_block_size)
print("#if UCD_BLOCK_SIZE != %d" % min_block_size)
print("#error Please correct UCD_BLOCK_SIZE in pcre2_internal.h")
print("#endif")
print("#endif /* SUPPORT_UNICODE */")
print()
print("#endif /* PCRE2_PCRE2TEST */")
# This code was part of the original contribution, but is commented out as it
# was never used. A two-stage table has sufficed.
"""
# Three-stage tables:
# Find the optimum block size for 3-stage table
min_size = sys.maxint
for stage3_block in [2 ** i for i in range(2,6)]:
stage_i, stage3 = compress_table(table, stage3_block)
for stage2_block in [2 ** i for i in range(5,10)]:
size = len(records) * 4
stage1, stage2 = compress_table(stage_i, stage2_block)
size += get_tables_size(stage1, stage2, stage3)
# print "/* %5d / %3d => %5d bytes */" % (stage2_block, stage3_block, size)
if size < min_size:
min_size = size
min_stage1, min_stage2, min_stage3 = stage1, stage2, stage3
min_stage2_block, min_stage3_block = stage2_block, stage3_block
print "/* Total size: %d bytes" % min_size */
print_records(records)
print_table(min_stage1, 'ucd_stage1')
print_table(min_stage2, 'ucd_stage2', min_stage2_block)
print_table(min_stage3, 'ucd_stage3', min_stage3_block)
"""
| 42.72401 | 143 | 0.625648 |
1314ffbb2b5a881e8cbdb62ecc8a53c659f4f382 | 638 | py | Python | setup.py | ihayhurst/RetroBioCat | d674897459c0ab65faad5ed3017c55cf51bcc020 | [
"MIT"
] | 9 | 2020-12-01T16:33:02.000Z | 2022-01-19T20:02:42.000Z | setup.py | ihayhurst/RetroBioCat | d674897459c0ab65faad5ed3017c55cf51bcc020 | [
"MIT"
] | 4 | 2020-10-02T14:38:32.000Z | 2021-08-02T09:23:58.000Z | setup.py | ihayhurst/RetroBioCat | d674897459c0ab65faad5ed3017c55cf51bcc020 | [
"MIT"
] | 6 | 2021-01-14T07:48:36.000Z | 2022-03-20T17:34:27.000Z | from setuptools import setup, find_packages
from retrobiocat_web import __version__
with open('requirements.txt') as f:
requirements = f.read().splitlines()
setup(
name = 'retrobiocat_web',
packages = find_packages(),
include_package_data=True,
version = __version__,
license='',
description = 'Retrosynthesis',
author = 'William Finnigan',
author_email = '[email protected]',
url = '',
download_url = '',
keywords = ['enzyme'],
install_requires=requirements,
classifiers=[
'Development Status :: 3 - Alpha',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3'],
) | 26.583333 | 45 | 0.694357 |
131631df01aa9316264d6c8b1aaa6ecfd1254785 | 1,748 | py | Python | rxn_yield_context/preprocess_data/preprocess/augmentation_utils.py | Lung-Yi/rxn_yield_context | 116d6f21a1b6dc39016d87c001dc5b142cfb697a | [
"MIT"
] | null | null | null | rxn_yield_context/preprocess_data/preprocess/augmentation_utils.py | Lung-Yi/rxn_yield_context | 116d6f21a1b6dc39016d87c001dc5b142cfb697a | [
"MIT"
] | null | null | null | rxn_yield_context/preprocess_data/preprocess/augmentation_utils.py | Lung-Yi/rxn_yield_context | 116d6f21a1b6dc39016d87c001dc5b142cfb697a | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
import pickle
import numpy as np
from rdkit import Chem
from rdkit.Chem import AllChem,DataStructs
| 33.615385 | 140 | 0.642449 |
131665ba7b9465c31b9a3f7865c4b018c27a3aec | 6,434 | py | Python | src/webstruct-demo/__init__.py | zanachka/webstruct-demo | f5b5081760d9a2b7924704041cd74748a5c98664 | [
"MIT"
] | 5 | 2019-04-15T14:54:23.000Z | 2020-10-03T04:47:12.000Z | src/webstruct-demo/__init__.py | zanachka/webstruct-demo | f5b5081760d9a2b7924704041cd74748a5c98664 | [
"MIT"
] | 2 | 2021-06-01T22:49:44.000Z | 2021-12-13T19:51:11.000Z | src/webstruct-demo/__init__.py | zanachka/webstruct-demo | f5b5081760d9a2b7924704041cd74748a5c98664 | [
"MIT"
] | 3 | 2019-06-25T10:31:30.000Z | 2020-10-03T04:49:01.000Z | import functools
import logging
import random
from flask import Flask, render_template, request
import joblib
from lxml.html import html5parser
import lxml.html
import requests
import yarl
import webstruct.model
import webstruct.sequence_encoding
import webstruct.webannotator
webstruct_demo = Flask(__name__, instance_relative_config=True)
webstruct_demo.config.from_pyfile('config.py')
_TOKENS_PER_PART = 2000
| 28.981982 | 121 | 0.617656 |
13178607e92d499e0a8fa091130826ae93f57d37 | 757 | py | Python | setup.py | Liang813/einops | 9edce3d9a2d0a2abc51a6aaf86678eac43ffac0c | [
"MIT"
] | 4,738 | 2018-10-30T08:38:50.000Z | 2022-03-31T17:35:50.000Z | setup.py | Liang813/einops | 9edce3d9a2d0a2abc51a6aaf86678eac43ffac0c | [
"MIT"
] | 120 | 2018-10-30T09:04:01.000Z | 2022-03-27T11:27:30.000Z | setup.py | Liang813/einops | 9edce3d9a2d0a2abc51a6aaf86678eac43ffac0c | [
"MIT"
] | 216 | 2018-11-09T02:50:30.000Z | 2022-03-30T05:46:44.000Z | __author__ = 'Alex Rogozhnikov'
from setuptools import setup
setup(
name="einops",
version='0.3.2',
description="A new flavour of deep learning operations",
long_description=open('README.md', encoding='utf-8').read(),
long_description_content_type='text/markdown',
url='https://github.com/arogozhnikov/einops',
author='Alex Rogozhnikov',
packages=['einops', 'einops.layers'],
classifiers=[
'Intended Audience :: Science/Research',
'Programming Language :: Python :: 3 ',
],
keywords='deep learning, neural networks, tensor manipulation, machine learning, '
'scientific computations, einops',
install_requires=[
# no run-time or installation-time dependencies
],
)
| 29.115385 | 86 | 0.668428 |
1317d06f323723e4a4f4ac9a34fb6dfc7aa40fb0 | 1,146 | py | Python | website/migrations/0084_auto_20210215_1401.py | czhu1217/cmimc-online | 5ef49ceec0bb86d8ae120a6ecfd723532e277821 | [
"MIT"
] | null | null | null | website/migrations/0084_auto_20210215_1401.py | czhu1217/cmimc-online | 5ef49ceec0bb86d8ae120a6ecfd723532e277821 | [
"MIT"
] | 1 | 2022-01-23T21:08:12.000Z | 2022-01-23T21:08:12.000Z | website/migrations/0084_auto_20210215_1401.py | czhu1217/cmimc-online | 5ef49ceec0bb86d8ae120a6ecfd723532e277821 | [
"MIT"
] | 1 | 2021-10-17T17:11:42.000Z | 2021-10-17T17:11:42.000Z | # Generated by Django 3.1.6 on 2021-02-15 19:01
from django.db import migrations, models
import django.db.models.deletion
| 33.705882 | 150 | 0.604712 |
1317fa461eecfb928fe1d73e0f3c19ec9defb396 | 14,667 | py | Python | ldp/tasks/dlp.py | evandez/low-dimensional-probing | 3e4af6644a4db7fdf48bc40c5de4815f9db52a6e | [
"MIT"
] | 1 | 2022-03-06T06:59:42.000Z | 2022-03-06T06:59:42.000Z | ldp/tasks/dlp.py | evandez/low-dimensional-probing | 3e4af6644a4db7fdf48bc40c5de4815f9db52a6e | [
"MIT"
] | null | null | null | ldp/tasks/dlp.py | evandez/low-dimensional-probing | 3e4af6644a4db7fdf48bc40c5de4815f9db52a6e | [
"MIT"
] | null | null | null | """Core experiments for the dependency label prediction task."""
import collections
import copy
import logging
from typing import (Any, Dict, Iterator, Optional, Sequence, Set, Tuple, Type,
Union)
from ldp import datasets, learning
from ldp.models import probes, projections
from ldp.parse import ptb
from ldp.parse import representations as reps
from ldp.utils.typing import Device
import numpy
import torch
import wandb
UNK = 'unk'
def count_unique_features(self) -> int:
"""Return number of unique POS seen in data."""
return len(self.indexer)
# Define the valid probe types for this task.
Probe = Union[probes.Linear, probes.MLP]
def train(train_dataset: datasets.TaskDataset,
dev_dataset: datasets.TaskDataset,
test_dataset: datasets.TaskDataset,
probe_t: Type[Probe] = probes.Linear,
project_to: Optional[int] = None,
share_projection: bool = False,
epochs: int = 25,
patience: int = 4,
lr: float = 1e-3,
device: Optional[Device] = None,
also_log_to_wandb: bool = False) -> Tuple[Probe, float]:
"""Train a probe on dependency label prediction.
Args:
train_dataset (TaskDataset): Training data for probe.
dev_dataset (TaskDataset): Validation data for probe, used for early
stopping.
test_dataset (TaskDataset): Test data for probe, used to compute
final accuracy after training.
probe_t (Type[Probe], optional): Probe type to train.
Defaults to probes.Linear.
project_to (Optional[int], optional): Project representations to this
dimensionality. Defaults to no projection.
share_projection (bool): If set, project the left and right components
of pairwise probes with the same projection. E.g. if the probe is
bilinear of the form xAy, we will always compute (Px)A(Py) as
opposed to (Px)A(Qy) for distinct projections P, Q. Defaults to NOT
shared.
epochs (int, optional): Maximum passes through the training dataset.
Defaults to 25.
patience (int, optional): Allow dev loss to not improve for this many
epochs, then stop training. Defaults to 4.
lr (float, optional): Learning rate for optimizer. Defaults to 1e-3.
device (Optional[Device], optional): Torch device on which to
train probe. Defaults to CPU.
also_log_to_wandb (Optional[pathlib.Path], optional): If set, log
training data to wandb. By default, wandb is not used.
Returns:
Tuple[Probe, float]: The trained probe and its test accuracy.
"""
log = logging.getLogger(__name__)
device = device or 'cpu'
ndims = train_dataset.sample_representations_shape[-1]
log.info('representations have dimension %d', ndims)
ntags = train_dataset.count_unique_features()
assert ntags is not None, 'no label count, is dataset for different task?'
log.info('dependency labeling task has %d tags', ntags)
if project_to is None or ndims == project_to:
logging.info('projection dim = reps dim, not projecting')
projection = None
elif share_projection:
projection = projections.Projection(ndims, project_to)
else:
projection = projections.Projection(2 * ndims, 2 * project_to)
probe = probe_t(2 * (project_to or ndims), ntags, project=projection)
learning.train(probe,
train_dataset,
dev_dataset=dev_dataset,
stopper=learning.EarlyStopping(patience=patience),
epochs=epochs,
lr=lr,
device=device,
also_log_to_wandb=also_log_to_wandb)
accuracy = learning.test(probe, test_dataset, device=device)
return probe, accuracy
# TODO(evandez): May as well commonize this, since it's shared with POS.
def axis_alignment(
probe: Probe,
dev_dataset: datasets.TaskDataset,
test_dataset: datasets.TaskDataset,
device: Optional[Device] = None,
also_log_to_wandb: bool = False) -> Sequence[Tuple[int, float]]:
"""Measure whether the given probe is axis aligned.
Args:
probe (Probe): The probe to evaluate.
dev_dataset (datasets.TaskDataset): Data used to determine which axes
to cut.
test_dataset (datasets.TaskDataset): Data used to determine the effect
of cutting an axis.
device (Optional[Device], optional): Torch device on which to
train probe. Defaults to CPU.
also_log_to_wandb (bool, optional): If set, log results to wandb.
Returns:
Sequence[Tuple[int, float]]: The ablated axes paired with optimal probe
accuracy after that axis is zeroed.
"""
log = logging.getLogger(__name__)
projection = probe.project
assert projection is not None, 'no projection?'
axes = set(range(projection.project.in_features))
ablated: Set[int] = set()
accuracies = []
while axes:
best_model, best_axis, best_accuracy = probe, -1, -1.
for axis in axes:
model = copy.deepcopy(best_model).eval()
assert model.project is not None, 'no projection?'
model.project.project.weight.data[:, sorted(ablated | {axis})] = 0
accuracy = learning.test(model, dev_dataset, device=device)
if accuracy > best_accuracy:
best_model = model
best_axis = axis
best_accuracy = accuracy
accuracy = learning.test(best_model, test_dataset, device=device)
log.info('ablating axis %d, test accuracy %f', best_axis, accuracy)
if also_log_to_wandb:
wandb.log({
'axis': best_axis,
'dev accuracy': best_accuracy,
'test accuracy': accuracy,
})
axes.remove(best_axis)
ablated.add(best_axis)
accuracies.append((best_axis, accuracy))
return tuple(accuracies)
| 38.395288 | 79 | 0.614986 |
1318a68dfab5df9c8cd4a02ab91e86cccb7f469d | 23,613 | py | Python | pycquery_krb/common/ccache.py | naver/PyCQuery | a72f74f9b7c208a263fc7cdb14a30d0fe21e63b9 | [
"Apache-2.0"
] | 2 | 2021-11-17T03:13:16.000Z | 2021-12-03T05:30:22.000Z | pycquery_krb/common/ccache.py | naver/PyCQuery | a72f74f9b7c208a263fc7cdb14a30d0fe21e63b9 | [
"Apache-2.0"
] | 1 | 2021-05-04T06:02:40.000Z | 2021-05-04T06:02:40.000Z | pycquery_krb/common/ccache.py | naver/PyCQuery | a72f74f9b7c208a263fc7cdb14a30d0fe21e63b9 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python3
#
# Author:
# Tamas Jos (@skelsec)
#
import os
import io
import datetime
import glob
import hashlib
from pycquery_krb.protocol.asn1_structs import Ticket, EncryptedData, \
krb5_pvno, KrbCredInfo, EncryptionKey, KRBCRED, TicketFlags, EncKrbCredPart
from pycquery_krb.common.utils import dt_to_kerbtime, TGSTicket2hashcat
from pycquery_krb.protocol.constants import EncryptionType, MESSAGE_TYPE
from pycquery_krb import logger
from asn1crypto import core
# http://repo.or.cz/w/krb5dissect.git/blob_plain/HEAD:/ccache.txt
def to_bytes(self):
t = self.client.to_bytes()
t += self.server.to_bytes()
t += self.key.to_bytes()
t += self.time.to_bytes()
t += self.is_skey.to_bytes(1, byteorder='big', signed=False)
t += self.tktflags.to_bytes(4, byteorder='little', signed=False)
t += self.num_address.to_bytes(4, byteorder='big', signed=False)
for addr in self.addrs:
t += addr.to_bytes()
t += self.num_authdata.to_bytes(4, byteorder='big', signed=False)
for ad in self.authdata:
t += ad.to_bytes()
t += self.ticket.to_bytes()
t += self.second_ticket.to_bytes()
return t
class Keyblock:
def __init__(self):
self.keytype = None
self.etype = None
self.keylen = None
self.keyvalue = None
class Times:
def __init__(self):
self.authtime = None
self.starttime = None
self.endtime = None
self.renew_till = None
def to_bytes(self):
t = self.authtime.to_bytes(4, byteorder='big', signed=False)
t += self.starttime.to_bytes(4, byteorder='big', signed=False)
t += self.endtime.to_bytes(4, byteorder='big', signed=False)
t += self.renew_till.to_bytes(4, byteorder='big', signed=False)
return t
class Address:
def __init__(self):
self.addrtype = None
self.addrdata = None
class Authdata:
def __init__(self):
self.authtype = None
self.authdata = None
class CCACHEPrincipal:
def __init__(self):
self.name_type = None
self.num_components = None
self.realm = None
self.components = []
def to_string(self, separator='-'):
return separator.join([c.to_string() for c in self.components])
def to_asn1(self):
t = {'name-type': self.name_type, 'name-string': [name.to_string() for name in self.components]}
return t, self.realm.to_string()
class CCACHEOctetString:
def __init__(self):
self.length = None
self.data = None
def to_bytes(self):
if isinstance(self.data,str):
self.data = self.data.encode()
self.length = len(self.data)
t = len(self.data).to_bytes(4, byteorder='big', signed=False)
t += self.data
return t
class CCACHE:
"""
As the header is rarely used -mostly static- you'd need to init this object with empty = True to get an object without header already present
"""
def add_tgt(self, as_rep, enc_as_rep_part, override_pp = True): #from AS_REP
"""
Creates credential object from the TGT and adds to the ccache file
The TGT is basically the native representation of the asn1 encoded AS_REP data that the AD sends upon a succsessful TGT request.
This function doesn't do decryption of the encrypted part of the as_rep object, it is expected that the decrypted XXX is supplied in enc_as_rep_part
override_pp: bool to determine if client principal should be used as the primary principal for the ccache file
"""
c = Credential()
c.client = CCACHEPrincipal.from_asn1(as_rep['cname'], as_rep['crealm'])
if override_pp == True:
self.primary_principal = c.client
c.server = CCACHEPrincipal.from_asn1(enc_as_rep_part['sname'], enc_as_rep_part['srealm'])
c.time = Times.from_asn1(enc_as_rep_part)
c.key = Keyblock.from_asn1(enc_as_rep_part['key'])
c.is_skey = 0 #not sure!
c.tktflags = TicketFlags(enc_as_rep_part['flags']).cast(core.IntegerBitString).native
c.num_address = 0
c.num_authdata = 0
c.ticket = CCACHEOctetString.from_asn1(Ticket(as_rep['ticket']).dump())
c.second_ticket = CCACHEOctetString.empty()
self.credentials.append(c)
def add_tgs(self, tgs_rep, enc_tgs_rep_part, override_pp = False): #from AS_REP
"""
Creates credential object from the TGS and adds to the ccache file
The TGS is the native representation of the asn1 encoded TGS_REP data when the user requests a tgs to a specific service principal with a valid TGT
This function doesn't do decryption of the encrypted part of the tgs_rep object, it is expected that the decrypted XXX is supplied in enc_as_rep_part
override_pp: bool to determine if client principal should be used as the primary principal for the ccache file
"""
c = Credential()
c.client = CCACHEPrincipal.from_asn1(tgs_rep['cname'], tgs_rep['crealm'])
if override_pp == True:
self.primary_principal = c.client
c.server = CCACHEPrincipal.from_asn1(enc_tgs_rep_part['sname'], enc_tgs_rep_part['srealm'])
c.time = Times.from_asn1(enc_tgs_rep_part)
c.key = Keyblock.from_asn1(enc_tgs_rep_part['key'])
c.is_skey = 0 #not sure!
c.tktflags = TicketFlags(enc_tgs_rep_part['flags']).cast(core.IntegerBitString).native
c.num_address = 0
c.num_authdata = 0
c.ticket = CCACHEOctetString.from_asn1(Ticket(tgs_rep['ticket']).dump())
c.second_ticket = CCACHEOctetString.empty()
self.credentials.append(c)
def get_all_tgt(self):
"""
Returns a list of AS_REP tickets in native format (dict).
To determine which ticket are AP_REP we check for the server principal to be the kerberos service
"""
tgts = []
for cred in self.credentials:
if cred.server.to_string(separator='/').lower().find('krbtgt') != -1:
tgt = [cred.to_tgt(), cred.time]
tgts.append(tgt)
return tgts
def get_hashes(self, all_hashes = False):
"""
Returns a list of hashes in hashcat-firendly format for tickets with encryption type 23 (which is RC4)
all_hashes: overrides the encryption type filtering and returns hash for all tickets
"""
hashes = []
for cred in self.credentials:
res = Ticket.load(cred.ticket.to_asn1()).native
if int(res['enc-part']['etype']) == 23 or all_hashes == True:
hashes.append(cred.to_hash())
return hashes
def to_kirbidir(self, directory_path):
"""
Converts all credential object in the CCACHE object to the kirbi file format used by mimikatz.
The kirbi file format supports one credential per file, so prepare for a lot of files being generated.
directory_path: str the directory to write the kirbi files to
"""
kf_abs = os.path.abspath(directory_path)
for cred in self.credentials:
kirbi, filename = cred.to_kirbi()
filename = '%s.kirbi' % filename.replace('..','!')
filepath = os.path.join(kf_abs, filename)
with open(filepath, 'wb') as o:
o.write(kirbi.dump())
def to_file(self, filename):
"""
Writes the contents of the CCACHE object to a file
"""
with open(filename, 'wb') as f:
f.write(self.to_bytes())
| 31.909459 | 151 | 0.707915 |
131a0e5ecdfc260b3827d4c50aa8606f1a20747d | 3,054 | py | Python | getUniformSmiles.py | OpenEye-Contrib/Molecular-List-Logic | 82caf41f7d8b94e7448d8e839bdbc0620a8666d7 | [
"BSD-3-Clause"
] | 2 | 2016-01-21T19:50:12.000Z | 2021-04-10T13:27:40.000Z | getUniformSmiles.py | OpenEye-Contrib/Molecular-List-Logic | 82caf41f7d8b94e7448d8e839bdbc0620a8666d7 | [
"BSD-3-Clause"
] | null | null | null | getUniformSmiles.py | OpenEye-Contrib/Molecular-List-Logic | 82caf41f7d8b94e7448d8e839bdbc0620a8666d7 | [
"BSD-3-Clause"
] | null | null | null | #!/opt/az/psf/python/2.7/bin/python
from openeye.oechem import *
import cgi
#creates a list of smiles of the syntax [smiles|molId,smiles|molId]
#takes a list of smiles and writes it as sdf using a memory buffer
#creates a list of smiles of the syntax [smiles|molId,smiles|molId]
if __name__ == "__main__":
print "Content-Type: text/html\r\n\r\n"
form = cgi.FieldStorage()
extension = form.getvalue("extension")
dataA = form.getvalue("dataA")
operator = form.getvalue("smiles_operator")
sdf_output = form.getvalue("sdf_output")
if(extension=="smi"):
list_A = process_smiles(dataA)
else:
list_A = read_sdf(dataA)
outputString = ""
if(operator=="UNI"): #if only one file is supplied
outputString = "*".join(set(list_A)) #removes all doubles using the set() function
else:
dataB = form.getvalue("dataB") #if two files are supplied
if(extension=="smi"):
list_B = process_smiles(dataB)
else:
list_B = read_sdf(dataB)
if(operator=="AND"):
outputString = "*".join(set(list_A) & set(list_B))
elif(operator=="OR"):
outputString = "*".join(set(list_A) | set(list_B))
elif(operator=="NOT"):
outputString = "*".join(set(list_A) - set(list_B))
if(sdf_output=="on"): #if we want the output as sdf
sdfs = write_sdf(outputString.replace("|"," ").split("*"))
outputString = "*".join(sdfs)
outputString = outputString.replace("\n","!").replace(" ","|")
#sends the output to index.html using javascript
print """
<html>
<head>
<input type="text" id="data" value=""" + outputString + """>
<script type="text/javascript">
parent.postMessage(data.value,"*");
</script>
</head>
</html>
"""
| 30.237624 | 90 | 0.586444 |
131a15a3a8feaf503c53fb7ce15a6a9a4850f413 | 37,988 | py | Python | mfem/_par/gridfunc.py | mfem/PyMFEM | b7b7c3d3de1082eac1015e3a313cf513db06fd7b | [
"BSD-3-Clause"
] | 93 | 2017-03-01T16:45:33.000Z | 2022-03-27T22:10:33.000Z | mfem/_par/gridfunc.py | mfem/PyMFEM | b7b7c3d3de1082eac1015e3a313cf513db06fd7b | [
"BSD-3-Clause"
] | 64 | 2017-03-15T21:47:31.000Z | 2022-03-31T23:59:00.000Z | mfem/_par/gridfunc.py | mfem/PyMFEM | b7b7c3d3de1082eac1015e3a313cf513db06fd7b | [
"BSD-3-Clause"
] | 32 | 2017-03-02T22:13:38.000Z | 2022-03-26T13:09:31.000Z | # This file was automatically generated by SWIG (http://www.swig.org).
# Version 4.0.2
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info as _swig_python_version_info
if _swig_python_version_info < (2, 7, 0):
raise RuntimeError("Python 2.7 or later required")
# Import the low-level C/C++ module
if __package__ or "." in __name__:
from . import _gridfunc
else:
import _gridfunc
try:
import builtins as __builtin__
except ImportError:
import __builtin__
_swig_new_instance_method = _gridfunc.SWIG_PyInstanceMethod_New
_swig_new_static_method = _gridfunc.SWIG_PyStaticMethod_New
def _swig_add_metaclass(metaclass):
"""Class decorator for adding a metaclass to a SWIG wrapped class - a slimmed down version of six.add_metaclass"""
return wrapper
import weakref
import mfem._par.array
import mfem._par.mem_manager
import mfem._par.vector
import mfem._par.coefficient
import mfem._par.globals
import mfem._par.matrix
import mfem._par.operators
import mfem._par.intrules
import mfem._par.sparsemat
import mfem._par.densemat
import mfem._par.eltrans
import mfem._par.fe
import mfem._par.geom
import mfem._par.fespace
import mfem._par.mesh
import mfem._par.sort_pairs
import mfem._par.ncmesh
import mfem._par.vtk
import mfem._par.element
import mfem._par.table
import mfem._par.hash
import mfem._par.vertex
import mfem._par.fe_coll
import mfem._par.lininteg
import mfem._par.handle
import mfem._par.hypre
import mfem._par.restriction
import mfem._par.bilininteg
import mfem._par.linearform
import mfem._par.nonlininteg
# Register GridFunction in _gridfunc:
_gridfunc.GridFunction_swigregister(GridFunction)
# Register JumpScaling in _gridfunc:
_gridfunc.JumpScaling_swigregister(JumpScaling)
# Register QuadratureFunction in _gridfunc:
_gridfunc.QuadratureFunction_swigregister(QuadratureFunction)
def __lshift__(*args):
r"""
__lshift__(std::ostream & os, SparseMatrix mat) -> std::ostream
__lshift__(std::ostream & out, Mesh mesh) -> std::ostream
__lshift__(std::ostream & out, GridFunction sol) -> std::ostream
__lshift__(std::ostream & out, QuadratureFunction qf) -> std::ostream &
"""
return _gridfunc.__lshift__(*args)
__lshift__ = _gridfunc.__lshift__
def ZZErrorEstimator(blfi, u, flux, error_estimates, aniso_flags=None, with_subdomains=1, with_coeff=False):
r"""ZZErrorEstimator(BilinearFormIntegrator blfi, GridFunction u, GridFunction flux, Vector error_estimates, intArray aniso_flags=None, int with_subdomains=1, bool with_coeff=False) -> double"""
return _gridfunc.ZZErrorEstimator(blfi, u, flux, error_estimates, aniso_flags, with_subdomains, with_coeff)
ZZErrorEstimator = _gridfunc.ZZErrorEstimator
def ComputeElementLpDistance(p, i, gf1, gf2):
r"""ComputeElementLpDistance(double p, int i, GridFunction gf1, GridFunction gf2) -> double"""
return _gridfunc.ComputeElementLpDistance(p, i, gf1, gf2)
ComputeElementLpDistance = _gridfunc.ComputeElementLpDistance
# Register ExtrudeCoefficient in _gridfunc:
_gridfunc.ExtrudeCoefficient_swigregister(ExtrudeCoefficient)
def Extrude1DGridFunction(mesh, mesh2d, sol, ny):
r"""Extrude1DGridFunction(Mesh mesh, Mesh mesh2d, GridFunction sol, int const ny) -> GridFunction"""
return _gridfunc.Extrude1DGridFunction(mesh, mesh2d, sol, ny)
Extrude1DGridFunction = _gridfunc.Extrude1DGridFunction
GridFunction.__iadd__ = __iadd__
GridFunction.__idiv__ = __idiv__
GridFunction.__isub__ = __isub__
GridFunction.__imul__ = __imul__
| 49.335065 | 201 | 0.740076 |
131ada8dd58eaa29a8303d1a7138ffe5d3485877 | 6,861 | py | Python | src/tracks/settings.py | adcarmichael/tracks | 04108bbdaf8554e57e278c1556efa9c5b9603973 | [
"Apache-2.0"
] | null | null | null | src/tracks/settings.py | adcarmichael/tracks | 04108bbdaf8554e57e278c1556efa9c5b9603973 | [
"Apache-2.0"
] | 41 | 2019-06-14T21:19:31.000Z | 2022-02-10T14:41:00.000Z | src/tracks/settings.py | adcarmichael/tracks | 04108bbdaf8554e57e278c1556efa9c5b9603973 | [
"Apache-2.0"
] | null | null | null | import os
import sentry_sdk
from sentry_sdk.integrations.django import DjangoIntegration
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
PWA_SERVICE_WORKER_PATH = os.path.join(
BASE_DIR, 'routes/static/routes/js', 'serviceworker.js')
print(os.path.join(
BASE_DIR, 'routes/static/routes/js', 'serviceworker.js'))
DEBUG = int(os.environ.get("DEBUG", default=0))
SECRET_KEY = os.environ.get("SECRET_KEY", 'asdfkhbsadgui87gjsbdfui')
# 'DJANGO_ALLOWED_HOSTS' should be a single string of hosts with a space between each.
# For example: 'DJANGO_ALLOWED_HOSTS=localhost 127.0.0.1 [::1]'
ALLOWED_HOSTS = os.environ.get("DJANGO_ALLOWED_HOSTS", 'localhost').split(" ")
# Application definition
INSTALLED_APPS = [
'routes',
'accounts',
'dashboard.apps.DashboardConfig',
'api.apps.ApiConfig',
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'widget_tweaks',
'rest_framework',
'pwa',
]
# 'celery',
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'tracks.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 = 'tracks.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.2/ref/settings/#databases
DATABASES = {
"default": {
"ENGINE": os.environ.get("SQL_ENGINE", "django.db.backends.sqlite3"),
"NAME": os.environ.get("SQL_DATABASE", os.path.join(BASE_DIR, "db.sqlite3")),
"USER": os.environ.get("SQL_USER", "user"),
"PASSWORD": os.environ.get("SQL_PASSWORD", "password"),
"HOST": os.environ.get("SQL_HOST", "localhost"),
"PORT": os.environ.get("SQL_PORT", "5432"),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.2/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/2.2/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.2/howto/static-files/
STATIC_URL = '/static/'
MEDIA_URL = '/media/'
STATIC_ROOT = './static/'
MEDIA_ROOT = './media/'
LOGIN_REDIRECT_URL = 'home'
LOGOUT_REDIRECT_URL = 'home'
# no email for localhost or staging
EMAIL_USE_TLS = os.environ.get("EMAIL_USE_TLS")
EMAIL_HOST = os.environ.get("EMAIL_HOST")
EMAIL_HOST_USER = os.environ.get("EMAIL_HOST_USER")
EMAIL_HOST_PASSWORD = os.environ.get("EMAIL_HOST_PASSWORD")
EMAIL_PORT = os.environ.get("EMAIL_PORT")
EMAIL_BACKEND = os.environ.get("EMAIL_BACKEND")
DEFAULT_FROM_EMAIL = '[email protected]'
# CELERY
# CELERY_BROKER_URL = 'redis://redis:6379/0'
# CELERY_RESULT_BACKEND = 'redis://redis:6379/0'
# BROKER_URL = 'redis://localhost:6379/0'
# CELERY_RESULT_BACKEND = 'redis://localhost:6379/'
# CELERY_ACCEPT_CONTENT = ['application/json']
# CELERY_TASK_SERIALIZER = 'json'
# CELERY_RESULT_SERIALIZER = 'json'
REST_FRAMEWORK = {
# Use Django's standard `django.contrib.auth` permissions,
# or allow read-only access for unauthenticated users.
'DEFAULT_PERMISSION_CLASSES': [
'rest_framework.permissions.DjangoModelPermissionsOrAnonReadOnly'
],
'DEFAULT_AUTHENTICATION_CLASSES': (
'rest_framework.authentication.TokenAuthentication',
'rest_framework.authentication.SessionAuthentication',
),
'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.LimitOffsetPagination',
'PAGE_SIZE': 10
}
LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'console': {
'format': '%(levelname)s %(asctime)s %(module)s: %(message)s'
},
},
'handlers': {
'console': {
'class': 'logging.StreamHandler',
'formatter': 'console'
},
},
'loggers': {
'': {
'handlers': ['console'],
'level': os.getenv('DJANGO_LOG_LEVEL', 'INFO'),
},
'django': {
'handlers': ['console'],
'level': os.getenv('DJANGO_LOG_LEVEL', 'INFO'),
},
'django.request': {
'level': 'INFO',
'handlers': ['console']
}
# 'celery': {
# 'handlers': ['console'],
# 'level': os.getenv('DJANGO_LOG_LEVEL', 'INFO'),
# },
},
}
# STATICFILES_DIRS = [
# os.path.join(BASE_DIR, 'static'),
# ]
PWA_APP_NAME = 'ChalkTracks'
PWA_APP_DESCRIPTION = "Indoor Climbing Tracker"
PWA_APP_THEME_COLOR = '#000000'
PWA_APP_BACKGROUND_COLOR = '#000000'
PWA_APP_DISPLAY = 'standalone'
PWA_APP_SCOPE = '/'
PWA_APP_ORIENTATION = 'portrait'
PWA_APP_START_URL = '/'
PWA_APP_ICONS = [
{
'src': '/static/routes/favicon_io/favicon-32x32.png',
'sizes': '32x32',
"type": "image/png",
"purpose": "any maskable"
}, {
"src": "/static/routes/favicon_io/android-chrome-192x192.png",
"sizes": "192x192",
"type": "image/png",
"purpose": "any maskable"
}, {
"src": "/static/routes/favicon_io/android-chrome-512x512.png",
"sizes": "512x512",
"type": "image/png",
"purpose": "any maskable"
}
]
PWA_APP_DIR = 'ltr'
PWA_APP_LANG = 'en-US'
sentry_sdk.init(
dsn="https://[email protected]/1878812",
integrations=[DjangoIntegration()],
# If you wish to associate users to errors (assuming you are using
# django.contrib.auth) you may enable sending PII data.
send_default_pii=True
)
| 28.5875 | 91 | 0.655735 |
131b0afb1746ef9363aae186aba698e6731a895a | 2,647 | py | Python | examples/04-lights/plotter_builtins.py | akeshavan/pyvista | 45fe8b1c38712776f9b628a60a8662d0716dd52b | [
"MIT"
] | null | null | null | examples/04-lights/plotter_builtins.py | akeshavan/pyvista | 45fe8b1c38712776f9b628a60a8662d0716dd52b | [
"MIT"
] | 6 | 2022-03-11T23:21:22.000Z | 2022-03-25T03:32:21.000Z | examples/04-lights/plotter_builtins.py | akeshavan/pyvista | 45fe8b1c38712776f9b628a60a8662d0716dd52b | [
"MIT"
] | null | null | null | """
Plotter Lighting Systems
~~~~~~~~~~~~~~~~~~~~~~~~
The :class:`pyvista.Plotter` class comes with three options for the default
lighting system:
* a light kit consisting of a headlight and four camera lights,
* an illumination system containing three lights arranged around the camera,
* no lighting.
With meshes that don't have depth information encoded in their color the
importance of an appropriate lighting setup becomes paramount for accurate
visualization.
Light kit
=========
The default ``lighting='light kit'`` option recreates a lighting setup that
corresponds to a ``vtk.vtkLightKit``. We can check what type of lights this
lighting comprises:
"""
# sphinx_gallery_thumbnail_number = 3
import pyvista as pv
from pyvista import examples
# default: light kit
plotter = pv.Plotter()
light_types = [light.light_type for light in plotter.renderer.lights]
# Remove from plotters so output is not produced in docs
pv.plotting._ALL_PLOTTERS.clear()
light_types
###############################################################################
# Add a white terrain to the scene:
mesh = examples.download_st_helens().warp_by_scalar()
plotter = pv.Plotter()
plotter.add_mesh(mesh, color='white')
plotter.show()
###############################################################################
# Three-lights illumination
# =========================
#
# Switching to three-lights illumination gives a different character to the
# figure, in this case showing less contrast when viewing the mountain from
# the top, but having more contrast with views closer to the side. This becomes
# especially clear when exploring the figures interactively.
plotter = pv.Plotter(lighting='three lights')
plotter.add_mesh(mesh, color='white')
plotter.show()
###############################################################################
# Again we can check what kind of lights this setting uses:
plotter = pv.Plotter(lighting='three lights')
light_types = [light.light_type for light in plotter.renderer.lights]
# Remove from plotters so output is not produced in docs
pv.plotting._ALL_PLOTTERS.clear()
light_types
###############################################################################
# Custom lighting
# ===============
#
# We can introduce our own lighting from scratch by disabling any lighting
# on plotter initialization. Adding a single scene light to a scene will
# often result in ominous visuals due to objects having larger regions in
# shadow:
plotter = pv.Plotter(lighting='none')
plotter.add_mesh(mesh, color='white')
light = pv.Light()
light.set_direction_angle(30, 0)
plotter.add_light(light)
plotter.show()
| 30.77907 | 79 | 0.66377 |
131c13cd6c6c6b833141fea96f58ed4c3b53dc38 | 272 | py | Python | src/swimport/tests/15_char_arrays/main.py | talos-gis/swimport | e8f0fcf02b0c9751b199f750f1f8bc57c8ff54b3 | [
"MIT"
] | 1 | 2019-03-07T20:43:42.000Z | 2019-03-07T20:43:42.000Z | src/swimport/tests/15_char_arrays/main.py | talos-gis/swimport | e8f0fcf02b0c9751b199f750f1f8bc57c8ff54b3 | [
"MIT"
] | null | null | null | src/swimport/tests/15_char_arrays/main.py | talos-gis/swimport | e8f0fcf02b0c9751b199f750f1f8bc57c8ff54b3 | [
"MIT"
] | null | null | null | from swimport.all import *
src = FileSource('src.h')
swim = Swim('example')
swim(pools.c_string)
swim(pools.numpy_arrays(r"../resources", allow_char_arrays=True))
swim(pools.include(src))
assert swim(Function.Behaviour()(src)) > 0
swim.write('example.i')
print('ok!') | 19.428571 | 65 | 0.720588 |
131c14184c1fb810c136590d143d0fdf3f83e6df | 4,523 | py | Python | ipyvolume/astro.py | larsoner/ipyvolume | 8603a47aff4531df69ace44efdcf6b85d6e51e51 | [
"MIT"
] | 1 | 2019-04-09T11:57:07.000Z | 2019-04-09T11:57:07.000Z | ipyvolume/astro.py | larsoner/ipyvolume | 8603a47aff4531df69ace44efdcf6b85d6e51e51 | [
"MIT"
] | null | null | null | ipyvolume/astro.py | larsoner/ipyvolume | 8603a47aff4531df69ace44efdcf6b85d6e51e51 | [
"MIT"
] | null | null | null | import numpy as np
import PIL.Image
import pythreejs
import ipyvolume as ipv
from .datasets import UrlCached
def _randomSO3():
"""return random rotatation matrix, algo by James Arvo"""
u1 = np.random.random()
u2 = np.random.random()
u3 = np.random.random()
R = np.array([[np.cos(2*np.pi*u1), np.sin(2*np.pi*u1), 0], [-np.sin(2*np.pi*u1), np.cos(2*np.pi*u1), 0], [0, 0, 1]])
v = np.array([np.cos(2*np.pi*u2)*np.sqrt(u3), np.sin(2*np.pi*u2)*np.sqrt(u3), np.sqrt(1-u3)])
H = np.identity(3)-2*v*np.transpose([v])
return - np.dot(H, R)
def spherical_galaxy_orbit(orbit_x, orbit_y, orbit_z, N_stars=100, sigma_r=1, orbit_visible=False, orbit_line_interpolate=5, N_star_orbits=10, color=[255, 220, 200], size_star=1, scatter_kwargs={}):
"""Create a fake galaxy around the points orbit_x/y/z with N_stars around it"""
if orbit_line_interpolate > 1:
import scipy.interpolate
x = np.linspace(0, 1, len(orbit_x))
x_smooth = np.linspace(0, 1, len(orbit_x)*orbit_line_interpolate)
kind = 'quadratic'
orbit_x_line = scipy.interpolate.interp1d(x, orbit_x, kind)(x_smooth)
orbit_y_line = scipy.interpolate.interp1d(x, orbit_y, kind)(x_smooth)
orbit_z_line = scipy.interpolate.interp1d(x, orbit_z, kind)(x_smooth)
else:
orbit_x_line = orbit_x
orbit_y_line = orbit_y
orbit_z_line = orbit_z
line = ipv.plot(orbit_x_line, orbit_y_line, orbit_z_line, visible=orbit_visible)
x = np.repeat(orbit_x, N_stars).reshape((-1, N_stars))
y = np.repeat(orbit_y, N_stars).reshape((-1, N_stars))
z = np.repeat(orbit_z, N_stars).reshape((-1, N_stars))
xr, yr, zr = np.random.normal(0, scale=sigma_r, size=(3, N_stars))# +
r = np.sqrt(xr**2 + yr**2 + zr**2)
for i in range(N_stars):
a = np.linspace(0, 1, x.shape[0]) * 2 * np.pi * N_star_orbits
xo = r[i] * np.sin(a)
yo = r[i] * np.cos(a)
zo = a * 0
xo, yo, zo = np.dot(_randomSO3(), [xo, yo, zo])
#print(x.shape, xo.shape)
x[:, i] += xo
y[:, i] += yo
z[:, i] += zo
sprite = ipv.scatter(x, y, z, texture=radial_sprite((64, 64), color), marker='square_2d', size=size_star, **scatter_kwargs)
with sprite.material.hold_sync():
sprite.material.blending = pythreejs.BlendingMode.CustomBlending
sprite.material.blendSrc = pythreejs.BlendFactors.SrcColorFactor
sprite.material.blendDst = pythreejs.BlendFactors.OneFactor
sprite.material.blendEquation = 'AddEquation'
sprite.material.transparent = True
sprite.material.depthWrite = False
sprite.material.alphaTest = 0.1
return sprite, line
milkyway_url = 'https://www.nasa.gov/sites/default/files/images/620057main_milkyway_full.jpg'
milkyway_image = UrlCached(milkyway_url)
| 41.118182 | 198 | 0.640946 |
131e1d61812e10d7ea42b3ca418199cd229845a3 | 1,157 | py | Python | deepfunning/function.py | Zrealshadow/DeepFunning | 5c44210a6b30ea57a0be5f930da4ada540e7e3d0 | [
"MIT"
] | null | null | null | deepfunning/function.py | Zrealshadow/DeepFunning | 5c44210a6b30ea57a0be5f930da4ada540e7e3d0 | [
"MIT"
] | null | null | null | deepfunning/function.py | Zrealshadow/DeepFunning | 5c44210a6b30ea57a0be5f930da4ada540e7e3d0 | [
"MIT"
] | null | null | null | '''
* @author Waldinsamkeit
* @email [email protected]
* @create date 2020-09-25 14:33:38
* @desc
'''
import torch
'''--------------------- Weighted Binary cross Entropy ----------------------'''
'''
In Torch BCELoss, weight is set to every element of input instead of to every class
'''
''' ---------------------- Binary focal loss function -------------------------- '''
'''
In some degree, it can reduce the influence of imbalanced dataset
'''
| 28.925 | 84 | 0.577355 |
131e36d011ba94f7784c802143deb17326553c0e | 7,055 | py | Python | dlms_cosem/hdlc/address.py | pwitab/dlms-cosem | aa9e18e6ef8a4fee30da8b797dad03b0b7847780 | [
"MIT"
] | 35 | 2018-05-24T08:16:35.000Z | 2022-02-24T16:07:07.000Z | dlms_cosem/hdlc/address.py | Layty/dlms-cosem | 95b67054a1dfb928e960547b0246b7b6794f0594 | [
"MIT"
] | 41 | 2020-12-18T16:31:40.000Z | 2021-12-13T20:59:42.000Z | dlms_cosem/hdlc/address.py | Layty/dlms-cosem | 95b67054a1dfb928e960547b0246b7b6794f0594 | [
"MIT"
] | 19 | 2019-04-02T14:32:01.000Z | 2021-12-14T13:24:29.000Z | from typing import *
import attr
from dlms_cosem.hdlc import validators
| 36.554404 | 88 | 0.624522 |
131e68c02091db60b313cb5f13708b590b55dc83 | 3,676 | py | Python | benchmarks/benchmarks/stats.py | RasmusSemmle/scipy | 4ffeafe269597e6d41b3335549102cd5611b12cb | [
"FSFAP"
] | 1 | 2019-04-13T01:41:50.000Z | 2019-04-13T01:41:50.000Z | benchmarks/benchmarks/stats.py | RasmusSemmle/scipy | 4ffeafe269597e6d41b3335549102cd5611b12cb | [
"FSFAP"
] | 1 | 2018-10-16T01:50:18.000Z | 2018-10-16T01:50:18.000Z | benchmarks/benchmarks/stats.py | RasmusSemmle/scipy | 4ffeafe269597e6d41b3335549102cd5611b12cb | [
"FSFAP"
] | null | null | null | from __future__ import division, absolute_import, print_function
import warnings
import numpy as np
try:
import scipy.stats as stats
except ImportError:
pass
from .common import Benchmark
| 32.530973 | 98 | 0.596572 |
131ebdee55bb427354a422cace3807e8ec4ae9ed | 3,039 | py | Python | src/production_ecommerce/account/models.py | sheriffbarrow/production-ecommerce | cb1a29795ff8e9b4aa95a78df50bb8aa3e5f4350 | [
"MIT"
] | 1 | 2020-08-18T16:23:03.000Z | 2020-08-18T16:23:03.000Z | src/production_ecommerce/account/models.py | sheriffbarrow/production-ecommerce | cb1a29795ff8e9b4aa95a78df50bb8aa3e5f4350 | [
"MIT"
] | 8 | 2020-06-06T01:06:35.000Z | 2021-09-08T01:35:58.000Z | src/production_ecommerce/account/models.py | sheriffbarrow/production-ecommerce | cb1a29795ff8e9b4aa95a78df50bb8aa3e5f4350 | [
"MIT"
] | null | null | null | from django.db import models
from django.contrib.auth.models import AbstractBaseUser, BaseUserManager
| 33.395604 | 103 | 0.763738 |
13209dfae3637157d917f9f553a882a2aac83180 | 758 | py | Python | dbtmetabase/models/config.py | fernandobrito/dbt-metabase | a1fa5a2c7c5e3cf12ead8806d39f6bd3a212fb83 | [
"MIT"
] | null | null | null | dbtmetabase/models/config.py | fernandobrito/dbt-metabase | a1fa5a2c7c5e3cf12ead8806d39f6bd3a212fb83 | [
"MIT"
] | 1 | 2021-08-01T19:29:59.000Z | 2021-08-01T19:29:59.000Z | dbtmetabase/models/config.py | fernandobrito/dbt-metabase | a1fa5a2c7c5e3cf12ead8806d39f6bd3a212fb83 | [
"MIT"
] | null | null | null | from dataclasses import dataclass, field
from typing import Optional, Iterable, Union
| 24.451613 | 59 | 0.700528 |
1321da1b77c6741566badad243b4f02ccf184f31 | 2,510 | py | Python | src/plot_timeseries_outstanding_bytes.py | arunksaha/heap_tracker | 0755c6b9c3e4e621efda31c144421a1e67e51a9c | [
"BSD-3-Clause"
] | 1 | 2019-07-22T03:43:49.000Z | 2019-07-22T03:43:49.000Z | src/plot_timeseries_outstanding_bytes.py | arunksaha/heap_tracker | 0755c6b9c3e4e621efda31c144421a1e67e51a9c | [
"BSD-3-Clause"
] | null | null | null | src/plot_timeseries_outstanding_bytes.py | arunksaha/heap_tracker | 0755c6b9c3e4e621efda31c144421a1e67e51a9c | [
"BSD-3-Clause"
] | null | null | null | #
# Copyright 2018, Arun Saha <[email protected]>
#
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as md
import datetime as dt
import sys
import os
# Open the file, read the string contents into a list,
# and return the list.
# Convert usecs (numeric) to datetime
# >>> ts = 1520189090755278 / 1000000.0
# >>> x = datetime.datetime.fromtimestamp(ts)
# >>> x.strftime('%Y-%m-%d %H:%M:%S.%f')
# '2018-03-04 10:44:50.755278'
# Take a list of string tuples (timestamp, metric),
# parses them into numerical values and returns
# separate lists.
# Plotting driver program.
# main
if len(sys.argv) == 1:
print "usage: {} <input-text-file>".format(sys.argv[0])
sys.exit(1)
driver(sys.argv[1])
| 28.202247 | 78 | 0.712749 |
1324d01927785e4ef25103f8dd91f9cf2502dddb | 722 | py | Python | import.py | vmariano/meme-classifier | e8d6e73e4a843542143f20381c0741df16d3945d | [
"BSD-3-Clause"
] | null | null | null | import.py | vmariano/meme-classifier | e8d6e73e4a843542143f20381c0741df16d3945d | [
"BSD-3-Clause"
] | 1 | 2022-02-07T12:06:59.000Z | 2022-02-07T12:06:59.000Z | import.py | vmariano/meme-classifier | e8d6e73e4a843542143f20381c0741df16d3945d | [
"BSD-3-Clause"
] | 1 | 2022-02-07T02:38:04.000Z | 2022-02-07T02:38:04.000Z | from dotenv import load_dotenv
load_dotenv()
import sys
import os
import re
import json
import psycopg2
from meme_classifier.images import process_image
path = sys.argv[1]
data = json.load(open(os.path.join(path, 'result.json'), 'r'))
chat_id = data['id']
conn = psycopg2.connect(os.getenv('POSTGRES_CREDENTIALS'))
for m in data['messages']:
if 'photo' in m:
template, text = process_image(open(os.path.join(path, m['photo']), 'rb'))
message_id = m['id']
print(f'processing message {message_id}')
cur = conn.cursor()
cur.execute("INSERT INTO meme (template, text, chat_id, message_id) VALUES (%s, %s, %s, %s)", (template, text, chat_id, message_id))
conn.commit()
| 26.740741 | 140 | 0.67036 |
1325914730cb3ee047b3e9ac93790891768376c5 | 4,269 | py | Python | nps/migrations/0013_auto_20180314_1805.py | jak0203/nps-dash | 9a3bdb0e55e0c857bcce8ed8df04b52a2b71872f | [
"MIT"
] | null | null | null | nps/migrations/0013_auto_20180314_1805.py | jak0203/nps-dash | 9a3bdb0e55e0c857bcce8ed8df04b52a2b71872f | [
"MIT"
] | 1 | 2018-03-19T04:08:10.000Z | 2018-03-19T04:08:10.000Z | nps/migrations/0013_auto_20180314_1805.py | jak0203/nps-dash | 9a3bdb0e55e0c857bcce8ed8df04b52a2b71872f | [
"MIT"
] | null | null | null | # Generated by Django 2.0.3 on 2018-03-15 01:05
from django.db import migrations, models
| 36.177966 | 114 | 0.575076 |
1325fa5a7b424ce8ba5d22a0e7ac2e6be5ce3b49 | 9,890 | py | Python | docs/schema_mapping.py | NoAnyLove/pydantic | 50fd2c5b48ffe611b5c4feb24f26f7202217faab | [
"MIT"
] | 1 | 2020-11-01T00:04:04.000Z | 2020-11-01T00:04:04.000Z | docs/schema_mapping.py | NoAnyLove/pydantic | 50fd2c5b48ffe611b5c4feb24f26f7202217faab | [
"MIT"
] | null | null | null | docs/schema_mapping.py | NoAnyLove/pydantic | 50fd2c5b48ffe611b5c4feb24f26f7202217faab | [
"MIT"
] | 1 | 2021-03-02T02:49:05.000Z | 2021-03-02T02:49:05.000Z | #!/usr/bin/env python3
"""
Build a table of Python / Pydantic to JSON Schema mappings.
Done like this rather than as a raw rst table to make future edits easier.
Please edit this file directly not .tmp_schema_mappings.rst
"""
table = [
[
'bool',
'boolean',
'',
'JSON Schema Core',
''
],
[
'str',
'string',
'',
'JSON Schema Core',
''
],
[
'float',
'number',
'',
'JSON Schema Core',
''
],
[
'int',
'integer',
'',
'JSON Schema Validation',
''
],
[
'dict',
'object',
'',
'JSON Schema Core',
''
],
[
'list',
'array',
'',
'JSON Schema Core',
''
],
[
'tuple',
'array',
'',
'JSON Schema Core',
''
],
[
'set',
'array',
'{"uniqueItems": true}',
'JSON Schema Validation',
''
],
[
'List[str]',
'array',
'{"items": {"type": "string"}}',
'JSON Schema Validation',
'And equivalently for any other sub type, e.g. List[int].'
],
[
'Tuple[str, int]',
'array',
'{"items": [{"type": "string"}, {"type": "integer"}]}',
'JSON Schema Validation',
(
'And equivalently for any other set of subtypes. Note: If using schemas for OpenAPI, '
'you shouldn\'t use this declaration, as it would not be valid in OpenAPI (although it is '
'valid in JSON Schema).'
)
],
[
'Dict[str, int]',
'object',
'{"additionalProperties": {"type": "integer"}}',
'JSON Schema Validation',
(
'And equivalently for any other subfields for dicts. Have in mind that although you can use other types as '
'keys for dicts with Pydantic, only strings are valid keys for JSON, and so, only str is valid as '
'JSON Schema key types.'
)
],
[
'Union[str, int]',
'anyOf',
'{"anyOf": [{"type": "string"}, {"type": "integer"}]}',
'JSON Schema Validation',
'And equivalently for any other subfields for unions.'
],
[
'Enum',
'enum',
'{"enum": [...]}',
'JSON Schema Validation',
'All the literal values in the enum are included in the definition.'
],
[
'SecretStr',
'string',
'{"writeOnly": true}',
'JSON Schema Validation',
''
],
[
'SecretBytes',
'string',
'{"writeOnly": true}',
'JSON Schema Validation',
''
],
[
'EmailStr',
'string',
'{"format": "email"}',
'JSON Schema Validation',
''
],
[
'NameEmail',
'string',
'{"format": "name-email"}',
'Pydantic standard "format" extension',
''
],
[
'UrlStr',
'string',
'{"format": "uri"}',
'JSON Schema Validation',
''
],
[
'DSN',
'string',
'{"format": "dsn"}',
'Pydantic standard "format" extension',
''
],
[
'bytes',
'string',
'{"format": "binary"}',
'OpenAPI',
''
],
[
'Decimal',
'number',
'',
'JSON Schema Core',
''
],
[
'UUID1',
'string',
'{"format": "uuid1"}',
'Pydantic standard "format" extension',
''
],
[
'UUID3',
'string',
'{"format": "uuid3"}',
'Pydantic standard "format" extension',
''
],
[
'UUID4',
'string',
'{"format": "uuid4"}',
'Pydantic standard "format" extension',
''
],
[
'UUID5',
'string',
'{"format": "uuid5"}',
'Pydantic standard "format" extension',
''
],
[
'UUID',
'string',
'{"format": "uuid"}',
'Pydantic standard "format" extension',
'Suggested in OpenAPI.'
],
[
'FilePath',
'string',
'{"format": "file-path"}',
'Pydantic standard "format" extension',
''
],
[
'DirectoryPath',
'string',
'{"format": "directory-path"}',
'Pydantic standard "format" extension',
''
],
[
'Path',
'string',
'{"format": "path"}',
'Pydantic standard "format" extension',
''
],
[
'datetime',
'string',
'{"format": "date-time"}',
'JSON Schema Validation',
''
],
[
'date',
'string',
'{"format": "date"}',
'JSON Schema Validation',
''
],
[
'time',
'string',
'{"format": "time"}',
'JSON Schema Validation',
''
],
[
'timedelta',
'number',
'{"format": "time-delta"}',
'Difference in seconds (a ``float``), with Pydantic standard "format" extension',
'Suggested in JSON Schema repository\'s issues by maintainer.'
],
[
'Json',
'string',
'{"format": "json-string"}',
'Pydantic standard "format" extension',
''
],
[
'IPvAnyAddress',
'string',
'{"format": "ipvanyaddress"}',
'Pydantic standard "format" extension',
'IPv4 or IPv6 address as used in ``ipaddress`` module',
],
[
'IPvAnyInterface',
'string',
'{"format": "ipvanyinterface"}',
'Pydantic standard "format" extension',
'IPv4 or IPv6 interface as used in ``ipaddress`` module',
],
[
'IPvAnyNetwork',
'string',
'{"format": "ipvanynetwork"}',
'Pydantic standard "format" extension',
'IPv4 or IPv6 network as used in ``ipaddress`` module',
],
[
'StrictStr',
'string',
'',
'JSON Schema Core',
''
],
[
'ConstrainedStr',
'string',
'',
'JSON Schema Core',
(
'If the type has values declared for the constraints, they are included as validations. '
'See the mapping for ``constr`` below.'
)
],
[
'constr(regex=\'^text$\', min_length=2, max_length=10)',
'string',
'{"pattern": "^text$", "minLength": 2, "maxLength": 10}',
'JSON Schema Validation',
'Any argument not passed to the function (not defined) will not be included in the schema.'
],
[
'ConstrainedInt',
'integer',
'',
'JSON Schema Core',
(
'If the type has values declared for the constraints, they are included as validations. '
'See the mapping for ``conint`` below.'
)
],
[
'conint(gt=1, ge=2, lt=6, le=5, multiple_of=2)',
'integer',
'{"maximum": 5, "exclusiveMaximum": 6, "minimum": 2, "exclusiveMinimum": 1, "multipleOf": 2}',
'',
'Any argument not passed to the function (not defined) will not be included in the schema.'
],
[
'PositiveInt',
'integer',
'{"exclusiveMinimum": 0}',
'JSON Schema Validation',
''
],
[
'NegativeInt',
'integer',
'{"exclusiveMaximum": 0}',
'JSON Schema Validation',
''
],
[
'ConstrainedFloat',
'number',
'',
'JSON Schema Core',
(
'If the type has values declared for the constraints, they are included as validations.'
'See the mapping for ``confloat`` below.'
)
],
[
'confloat(gt=1, ge=2, lt=6, le=5, multiple_of=2)',
'number',
'{"maximum": 5, "exclusiveMaximum": 6, "minimum": 2, "exclusiveMinimum": 1, "multipleOf": 2}',
'JSON Schema Validation',
'Any argument not passed to the function (not defined) will not be included in the schema.'
],
[
'PositiveFloat',
'number',
'{"exclusiveMinimum": 0}',
'JSON Schema Validation',
''
],
[
'NegativeFloat',
'number',
'{"exclusiveMaximum": 0}',
'JSON Schema Validation',
''
],
[
'ConstrainedDecimal',
'number',
'',
'JSON Schema Core',
(
'If the type has values declared for the constraints, they are included as validations. '
'See the mapping for ``condecimal`` below.'
)
],
[
'condecimal(gt=1, ge=2, lt=6, le=5, multiple_of=2)',
'number',
'{"maximum": 5, "exclusiveMaximum": 6, "minimum": 2, "exclusiveMinimum": 1, "multipleOf": 2}',
'JSON Schema Validation',
'Any argument not passed to the function (not defined) will not be included in the schema.'
],
[
'BaseModel',
'object',
'',
'JSON Schema Core',
'All the properties defined will be defined with standard JSON Schema, including submodels.'
]
]
headings = [
'Python type',
'JSON Schema Type',
'Additional JSON Schema',
'Defined in',
'Notes',
]
v = ''
col_width = 300
for _ in range(5):
v += '+' + '-' * col_width
v += '+\n|'
for heading in headings:
v += f' {heading:{col_width - 2}} |'
v += '\n'
for _ in range(5):
v += '+' + '=' * col_width
v += '+'
for row in table:
v += '\n|'
for i, text in enumerate(row):
text = f'``{text}``' if i < 3 and text else text
v += f' {text:{col_width - 2}} |'
v += '\n'
for _ in range(5):
v += '+' + '-' * col_width
v += '+'
with open('.tmp_schema_mappings.rst', 'w') as f:
f.write(v)
| 23.491686 | 120 | 0.458544 |
13261dbcef738304d7319335d93a1caa3393465f | 2,798 | py | Python | hubspot3/test/test_broadcast.py | kevin2357/hubspot3 | 488f6ff4195034317d99431439087443bca1469f | [
"MIT"
] | 1 | 2019-02-25T01:09:51.000Z | 2019-02-25T01:09:51.000Z | hubspot3/test/test_broadcast.py | kevin2357/hubspot3 | 488f6ff4195034317d99431439087443bca1469f | [
"MIT"
] | null | null | null | hubspot3/test/test_broadcast.py | kevin2357/hubspot3 | 488f6ff4195034317d99431439087443bca1469f | [
"MIT"
] | null | null | null | import time
import unittest
from nose.plugins.attrib import attr
from hubspot3.test import helper
from hubspot3.broadcast import Broadcast, BroadcastClient
if __name__ == "__main__":
unittest.main()
| 34.121951 | 78 | 0.651537 |
1326b5cc799b2031e2e9803af2d0899c97761474 | 5,166 | py | Python | benchmark/benchmarks/testdata.py | theroggy/geofile_ops | 1b5ab42169d5c3332c0d8298c5a036257cfd68d5 | [
"BSD-3-Clause"
] | null | null | null | benchmark/benchmarks/testdata.py | theroggy/geofile_ops | 1b5ab42169d5c3332c0d8298c5a036257cfd68d5 | [
"BSD-3-Clause"
] | 26 | 2021-12-01T07:46:53.000Z | 2022-03-30T23:40:43.000Z | benchmark/benchmarks/testdata.py | theroggy/geofile_ops | 1b5ab42169d5c3332c0d8298c5a036257cfd68d5 | [
"BSD-3-Clause"
] | 1 | 2021-11-30T17:51:34.000Z | 2021-11-30T17:51:34.000Z | # -*- coding: utf-8 -*-
"""
Module to prepare test data for benchmarking geo operations.
"""
import enum
import logging
from pathlib import Path
import pprint
import shutil
import sys
import tempfile
from typing import Optional
import urllib.request
import zipfile
# Add path so the benchmark packages are found
sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent))
import geofileops as gfo
################################################################################
# Some inits
################################################################################
logger = logging.getLogger(__name__)
################################################################################
# The real work
################################################################################
def download_samplefile(
url: str, dst_name: str, dst_dir: Optional[Path] = None
) -> Path:
"""
Download a sample file to dest_path.
If it is zipped, it will be unzipped. If needed, it will be converted to
the file type as determined by the suffix of dst_name.
Args:
url (str): the url of the file to download
dst_dir (Path): the dir to downloaded the sample file to.
If it is None, a dir in the default tmp location will be
used. Defaults to None.
Returns:
Path: the path to the downloaded sample file.
"""
# If the destination path is a directory, use the default file name
dst_path = prepare_dst_path(dst_name, dst_dir)
# If the sample file already exists, return
if dst_path.exists():
return dst_path
# Make sure the destination directory exists
dst_path.parent.mkdir(parents=True, exist_ok=True)
# If the url points to a file with the same suffix as the dst_path,
# just download
url_path = Path(url)
if url_path.suffix.lower() == dst_path.suffix.lower():
logger.info(f"Download to {dst_path}")
urllib.request.urlretrieve(url, dst_path)
else:
# The file downloaded is different that the destination wanted, so some
# converting will need to be done
tmp_dir = dst_path.parent / "tmp"
try:
# Remove tmp dir if it exists already
if tmp_dir.exists():
shutil.rmtree(tmp_dir)
tmp_dir.mkdir(parents=True, exist_ok=True)
# Download file
tmp_path = tmp_dir / f"{dst_path.stem}{url_path.suffix.lower()}"
logger.info(f"Download tmp data to {tmp_path}")
urllib.request.urlretrieve(url, tmp_path)
# If the temp file is a .zip file, unzip to dir
if tmp_path.suffix == ".zip":
# Unzip
unzippedzip_dir = dst_path.parent / tmp_path.stem
logger.info(f"Unzip to {unzippedzip_dir}")
with zipfile.ZipFile(tmp_path, "r") as zip_ref:
zip_ref.extractall(unzippedzip_dir)
# Look for the file
tmp_paths = []
for suffix in [".shp", ".gpkg"]:
tmp_paths.extend(list(unzippedzip_dir.rglob(f"*{suffix}")))
if len(tmp_paths) == 1:
tmp_path = tmp_paths[0]
else:
raise Exception(
f"Should find 1 geofile, found {len(tmp_paths)}: \n{pprint.pformat(tmp_paths)}"
)
if dst_path.suffix == tmp_path.suffix:
gfo.move(tmp_path, dst_path)
else:
logger.info(f"Convert tmp file to {dst_path}")
gfo.makevalid(tmp_path, dst_path)
finally:
if tmp_dir.exists():
shutil.rmtree(tmp_dir)
return dst_path
| 34.44 | 196 | 0.587108 |
13275a03cfbb8b9a14ddc59598aea372c23ea6fb | 6,461 | py | Python | relocation/depth/setup_relocation_dir.py | ziyixi/SeisScripts | a484bc1747eae52b2441f0bfd47ac7e093150f1d | [
"MIT"
] | null | null | null | relocation/depth/setup_relocation_dir.py | ziyixi/SeisScripts | a484bc1747eae52b2441f0bfd47ac7e093150f1d | [
"MIT"
] | null | null | null | relocation/depth/setup_relocation_dir.py | ziyixi/SeisScripts | a484bc1747eae52b2441f0bfd47ac7e093150f1d | [
"MIT"
] | null | null | null | """
setup earthquake depth relocation directory
"""
import obspy
import sh
import numpy as np
import click
from os.path import join
from glob import glob
import copy
if __name__ == "__main__":
main()
| 45.181818 | 207 | 0.625445 |
132764a2c0f4e72e8781ad3a0b75e85eb885eb90 | 12,911 | py | Python | python-client/trustedanalytics/core/atktypes.py | blbarker/atk | bcb747d053e801820233a6439c88a457c8cf2438 | [
"Apache-2.0"
] | 1 | 2016-04-05T21:57:16.000Z | 2016-04-05T21:57:16.000Z | python-client/trustedanalytics/core/atktypes.py | blbarker/atk | bcb747d053e801820233a6439c88a457c8cf2438 | [
"Apache-2.0"
] | null | null | null | python-client/trustedanalytics/core/atktypes.py | blbarker/atk | bcb747d053e801820233a6439c88a457c8cf2438 | [
"Apache-2.0"
] | null | null | null | # vim: set encoding=utf-8
#
# Copyright(c)2015 IntelCorporation
#
# LicensedundertheApacheLicense,Version2.0(the"License");
# youmaynotusethisfileexceptincompliancewiththeLicense.
# YoumayobtainacopyoftheLicenseat
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unlessrequiredbyapplicablelaworagreedtoinwriting,software
# distributedundertheLicenseisdistributedonan"ASIS"BASIS,
# WITHOUTWARRANTIESORCONDITIONSOFANYKIND,eitherexpressorimplied.
# SeetheLicenseforthespecificlanguagegoverningpermissionsand
# limitationsundertheLicense.
#
"""
trusted_analytics definitions for Data Types
"""
# TODO - consider server providing types, similar to commands
__all__ = ['valid_data_types', 'ignore', 'unknown', 'float32', 'float64', 'int32', 'int64', 'vector', 'unit', 'datetime']
import numpy as np
import json
import re
# alias numpy types
float32 = np.float32
float64 = np.float64
int32 = np.int32
int64 = np.int64
from datetime import datetime
import dateutil.parser as datetime_parser
# Chose python's datetime over numpy.datetime64 because of time zone support and string serialization
# Here's a long thread discussing numpy's datetime64 timezone problem:
# http://mail.scipy.org/pipermail/numpy-discussion/2013-April/066038.html
# If need be, UDFs can create numpy objects from x using: numpy.datatime64(x.isoformat())
vector = _Vector
unit = _Unit
ignore = _Ignore
unknown = _Unknown
# map types to their string identifier
_primitive_type_to_str_table = {
#bool: "bool", TODO
#bytearray: "bytearray", TODO
#dict: "dict", TODO
float32: "float32",
float64: "float64",
int32: "int32",
int64: "int64",
#list: "list", TODO
unicode: "unicode",
ignore: "ignore",
datetime: "datetime",
}
# build reverse map string -> type
_primitive_str_to_type_table = dict([(s, t) for t, s in _primitive_type_to_str_table.iteritems()])
_primitive_alias_type_to_type_table = {
float: float64,
int: int32,
long: int64,
str: unicode,
#list: vector,
}
_primitive_alias_str_to_type_table = dict([(alias.__name__, t) for alias, t in _primitive_alias_type_to_type_table.iteritems()])
_primitive_type_to_default_value = {
#bool: False, TODO
float32: 0.0,
float64: 0.0,
int32: 0,
int64: 0,
unicode: "",
#datetime: "datetime",
}
def get_float_constructor(float_type):
"""Creates special constructor for floating point types which handles nan, inf, -inf"""
ft = float_type
return float_constructor
def datetime_constructor(value):
"""Creates special constructor for datetime parsing"""
if valid_data_types.value_is_string(value):
return datetime_parser.parse(value)
else:
try:
return datetime(*value)
except:
raise TypeError("cannot convert type to the datetime")
valid_data_types = _DataTypes()
def numpy_to_bson_friendly(obj):
"""take an object and convert it to a type that can be serialized to bson if neccessary."""
if isinstance(obj, float32) or isinstance(obj, float64):
return float(obj)
if isinstance(obj, int32):
return int(obj)
if isinstance(obj, vector.base_type):
return obj.tolist()
if isinstance(obj, datetime):
return obj.isoformat()
if isinstance(obj, dict):
return dict([(numpy_to_bson_friendly(key), numpy_to_bson_friendly(value)) for key, value in obj.items()])
if isinstance(obj, list):
return [numpy_to_bson_friendly(item) for item in obj]
# Let the base class default method raise the TypeError
return obj
| 31.036058 | 191 | 0.629386 |
132884f1556370e07396101f0cb65bd3696963c4 | 6,530 | py | Python | srd/pageaggregator.py | poikilos/tabletopManualMiner | 94a824feabdf0a8efa1bf28670af44820aff9923 | [
"MIT"
] | null | null | null | srd/pageaggregator.py | poikilos/tabletopManualMiner | 94a824feabdf0a8efa1bf28670af44820aff9923 | [
"MIT"
] | null | null | null | srd/pageaggregator.py | poikilos/tabletopManualMiner | 94a824feabdf0a8efa1bf28670af44820aff9923 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
import math
try:
# from PDFPageDetailedAggregator:
from pdfminer.pdfdocument import PDFDocument, PDFNoOutlines
from pdfminer.pdfparser import PDFParser
from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
from pdfminer.converter import PDFPageAggregator
from pdfminer.layout import LTPage, LTChar, LTAnno, LAParams, LTTextBox, LTTextLine
except ModuleNotFoundError:
prerr("To use the aggregator (required for generating chunks.json)"
" you must first install the following module for Python:")
prerr(" pdfminer")
exit(1)
try:
input = raw_input
except NameError:
# Python 3
pass
# TODO:
from srd import (
objDict,
BBox,
DocChunk,
clean_frag_text,
clean_frag,
same_style,
frag_dict,
)
'''
class DocFragment:
def __init__(self, text, fontname, size):
self.text = text
self.fontname = fontname
self.size = size
def sameStyle(self, fragment):
"""
Is same fontname and size.
"""
ffn = fragment.fontname
ffs = fragment.size
return (ffs == self.size) and (ffn == self.fontname)
def clean(self):
self.text = clean_frag_text(self.text)
'''
| 36.077348 | 91 | 0.467994 |
1328be197a55352f7669047e01b9ed9e941d72e5 | 1,131 | py | Python | ctrltest.py | dkim286/cpsc454-proj | 16314802bae3cfbd4d1bf6d7f75a9e6adeb6700d | [
"FTL",
"CNRI-Python"
] | null | null | null | ctrltest.py | dkim286/cpsc454-proj | 16314802bae3cfbd4d1bf6d7f75a9e6adeb6700d | [
"FTL",
"CNRI-Python"
] | null | null | null | ctrltest.py | dkim286/cpsc454-proj | 16314802bae3cfbd4d1bf6d7f75a9e6adeb6700d | [
"FTL",
"CNRI-Python"
] | null | null | null | from pox.core import core
import pox.openflow.libopenflow_01 as of
from forwarding.l2_learning import *
from tkinter import *
from project.firewall import TestFW
from project.ui import UI
| 23.5625 | 82 | 0.654288 |
1328cd1366c0161d1070168617ce0f27d9f43e15 | 246 | py | Python | virtual/lib/python3.6/site-packages/mako/__init__.py | kenmutuma001/Blog | 6b19a77b71694bbe9f5e84207de46c68f87ebc5e | [
"Unlicense"
] | 1 | 2019-04-24T21:53:37.000Z | 2019-04-24T21:53:37.000Z | virtual/lib/python3.6/site-packages/mako/__init__.py | kenmutuma001/Blog | 6b19a77b71694bbe9f5e84207de46c68f87ebc5e | [
"Unlicense"
] | 5 | 2021-03-19T00:19:04.000Z | 2022-01-13T01:12:44.000Z | virtual/lib/python3.6/site-packages/mako/__init__.py | kenmutuma001/Blog | 6b19a77b71694bbe9f5e84207de46c68f87ebc5e | [
"Unlicense"
] | 2 | 2019-11-30T10:33:16.000Z | 2021-02-03T06:29:40.000Z | # mako/__init__.py
# Copyright (C) 2006-2016 the Mako authors and contributors <see AUTHORS file>
#
# This module is part of Mako and is released under
# the MIT License: http://www.opensource.org/licenses/mit-license.php
__version__ = '1.0.9'
| 27.333333 | 78 | 0.743902 |
1328eb00ae7fa054c34be04f558ebc32d9e45ce4 | 10,911 | py | Python | image_classification/T2T_ViT/load_pytorch_weights.py | RangeKing/PaddleViT | 0e25958686e04ed8872cf67fba0dfd6918e9b4dd | [
"Apache-2.0"
] | null | null | null | image_classification/T2T_ViT/load_pytorch_weights.py | RangeKing/PaddleViT | 0e25958686e04ed8872cf67fba0dfd6918e9b4dd | [
"Apache-2.0"
] | null | null | null | image_classification/T2T_ViT/load_pytorch_weights.py | RangeKing/PaddleViT | 0e25958686e04ed8872cf67fba0dfd6918e9b4dd | [
"Apache-2.0"
] | null | null | null | # Copyright (c) 2021 PPViT 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.
"""convert pytorch model weights to paddle pdparams"""
import os
import numpy as np
import paddle
import torch
import timm
from config import get_config
from t2t_vit import build_t2t_vit as build_model
from T2T_ViT_torch.models.t2t_vit import *
from T2T_ViT_torch.utils import load_for_transfer_learning
if __name__ == "__main__":
main()
| 38.284211 | 93 | 0.563926 |
1329bbbf79a71f88d6626330a68385f854fb0eef | 479 | py | Python | estimators/__init__.py | j-bac/id-concentration | 372bacc846d3d5dd5f99178863fa214fb8d3b292 | [
"MIT"
] | null | null | null | estimators/__init__.py | j-bac/id-concentration | 372bacc846d3d5dd5f99178863fa214fb8d3b292 | [
"MIT"
] | null | null | null | estimators/__init__.py | j-bac/id-concentration | 372bacc846d3d5dd5f99178863fa214fb8d3b292 | [
"MIT"
] | null | null | null | from ._FisherS import randsphere, preprocessing, SeparabilityAnalysis, point_inseparability_to_pointID
from ._call_estimators import TwoNN, run_singleGMST,run_singleCorrDim,runDANCo, runDANCoStats, runDANColoop,runANOVAglobal,runANOVAlocal,radovanovic_estimators_matlab,Hidalgo
from ._DANCo import dancoDimEst as danco_py
from ._TwoNN import twonn as twonn_py
from ._ESS import essLocalDimEst as ess_py
from ._mada import mada as mada_py
from ._corint import corint as corint_py
| 59.875 | 174 | 0.868476 |
132a98c8a3d62474bea30c75b83e6ea4827d2ca4 | 265 | py | Python | examples/get_message.py | NeroAsmarr/fz-api | d688277b4c616e93c809381ab082cba834964681 | [
"MIT"
] | 71 | 2019-12-04T03:58:33.000Z | 2022-03-19T11:38:54.000Z | examples/get_message.py | NeroAsmarr/fz-api | d688277b4c616e93c809381ab082cba834964681 | [
"MIT"
] | 6 | 2020-01-03T09:56:45.000Z | 2022-03-10T09:29:04.000Z | examples/get_message.py | NeroAsmarr/fz-api | d688277b4c616e93c809381ab082cba834964681 | [
"MIT"
] | 12 | 2019-11-23T03:37:39.000Z | 2021-08-15T09:41:21.000Z | #
from zfnew import GetInfo, Login
base_url = 'url'
lgn = Login(base_url=base_url)
lgn.login('', '')
cookies = lgn.cookies # cookies
person = GetInfo(base_url=base_url, cookies=cookies)
message = person.get_message()
print(message)
| 20.384615 | 52 | 0.754717 |
132acdacf1ba08411631ef7d5debcacf7e313231 | 1,030 | py | Python | input/gera_entradas.py | AtilioA/Sort-merge-join | 6ed3199aada921973833cafffc8cbde5062b76fb | [
"Unlicense"
] | null | null | null | input/gera_entradas.py | AtilioA/Sort-merge-join | 6ed3199aada921973833cafffc8cbde5062b76fb | [
"Unlicense"
] | null | null | null | input/gera_entradas.py | AtilioA/Sort-merge-join | 6ed3199aada921973833cafffc8cbde5062b76fb | [
"Unlicense"
] | null | null | null | import sys
import random
from faker import Faker
if __name__ == "__main__":
fake = Faker("pt_BR")
path = "python/"
try:
nLinhas = int(sys.argv[1])
nCampos = int(sys.argv[2])
except:
nLinhas = 1000
nCampos = 10
gera(nLinhas, nCampos)
| 23.953488 | 91 | 0.523301 |
132ade11b48d56eee57560835a5c3f4f30719ed6 | 1,639 | py | Python | lessons 20/HomeWork/task9.py | zainllw0w/skillbox | 896287b6f7f5612cf589094131fd1a12b0b192ba | [
"MIT"
] | null | null | null | lessons 20/HomeWork/task9.py | zainllw0w/skillbox | 896287b6f7f5612cf589094131fd1a12b0b192ba | [
"MIT"
] | null | null | null | lessons 20/HomeWork/task9.py | zainllw0w/skillbox | 896287b6f7f5612cf589094131fd1a12b0b192ba | [
"MIT"
] | null | null | null |
n = int(input(' : '))
data = dict()
time_list = list()
for i in range(1, n+1):
print(f' {i} : ', end='')
text = input().split()
time = text[0]
time_list.append(int(time))
name = text[1]
obj = [name, time]
data[i] = tuple(obj)
f, s, t, fi, si, ti = sort(data, sorted(time_list))
time_list = sorted(time_list)
print('1 : {0}, {1}'.format(data[f][0], time_list[-fi]))
print('2 : {0}, {1}'.format(data[s][0], time_list[-si]))
print('3 : {0}, {1}'.format(data[t][0], time_list[-ti])) | 32.78 | 90 | 0.534472 |
132ba0fc5dd29581ad43ebacaf74b8069bf64ef6 | 5,072 | py | Python | src/test-apps/happy/test-templates/WeaveInetDNS.py | aiw-google/openweave-core | 5dfb14b21d0898ef95bb62ff564cadfeea4b4702 | [
"Apache-2.0"
] | 1 | 2021-08-10T12:08:31.000Z | 2021-08-10T12:08:31.000Z | src/test-apps/happy/test-templates/WeaveInetDNS.py | aiw-google/openweave-core | 5dfb14b21d0898ef95bb62ff564cadfeea4b4702 | [
"Apache-2.0"
] | 1 | 2020-04-30T05:38:44.000Z | 2020-04-30T05:38:44.000Z | src/test-apps/happy/test-templates/WeaveInetDNS.py | aiw-google/openweave-core | 5dfb14b21d0898ef95bb62ff564cadfeea4b4702 | [
"Apache-2.0"
] | 1 | 2020-06-15T01:50:59.000Z | 2020-06-15T01:50:59.000Z | #!/usr/bin/env python
#
# Copyright (c) 2016-2017 Nest Labs, Inc.
# 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.
#
#
# @file
# Implements WeaveInet class that tests Weave Inet Layer among Weave Nodes.
#
import os
import sys
import time
from happy.ReturnMsg import ReturnMsg
from happy.Utils import *
from happy.HappyNode import HappyNode
from happy.HappyNetwork import HappyNetwork
from WeaveTest import WeaveTest
# Q: what are the parameters need to specify?
options = {}
options["quiet"] = False
options["node_id"] = None
options["tap_if"] = None
options["node_ip"] = None
options["ipv4_gateway"] = None
options["dns"] = None
options["use_lwip"] = False
| 29.317919 | 123 | 0.636632 |
132bada4fd58d52bb6e1891b67b8d0c493944002 | 6,243 | py | Python | funcoes.py | ZezaoDev/Circtrigo | 5e5f6be0bdee17d30c2993478ca25409b82b6af3 | [
"MIT"
] | null | null | null | funcoes.py | ZezaoDev/Circtrigo | 5e5f6be0bdee17d30c2993478ca25409b82b6af3 | [
"MIT"
] | null | null | null | funcoes.py | ZezaoDev/Circtrigo | 5e5f6be0bdee17d30c2993478ca25409b82b6af3 | [
"MIT"
] | null | null | null | import turtle as t
import math
| 30.014423 | 149 | 0.492712 |
132c604ed66d104dc1d0fc33432f244eb013965a | 7,620 | py | Python | esm/model.py | crochereau/esm | 881a3b924d3f74e3cddeb6929e91ee7224ef2ebd | [
"MIT"
] | 1 | 2021-01-21T17:54:20.000Z | 2021-01-21T17:54:20.000Z | esm/model.py | crochereau/esm | 881a3b924d3f74e3cddeb6929e91ee7224ef2ebd | [
"MIT"
] | null | null | null | esm/model.py | crochereau/esm | 881a3b924d3f74e3cddeb6929e91ee7224ef2ebd | [
"MIT"
] | null | null | null | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from .modules import (
TransformerLayer,
LearnedPositionalEmbedding,
SinusoidalPositionalEmbedding,
RobertaLMHead,
ESM1bLayerNorm,
ContactPredictionHead,
)
| 38.1 | 121 | 0.600656 |
132d17111128f658179267c44013a769265d45f3 | 3,392 | py | Python | python/tink/aead/kms_envelope_aead.py | bfloch/tink | aac780590902f726a8e7d6c4e3aa1cd75f4b0ed5 | [
"Apache-2.0"
] | null | null | null | python/tink/aead/kms_envelope_aead.py | bfloch/tink | aac780590902f726a8e7d6c4e3aa1cd75f4b0ed5 | [
"Apache-2.0"
] | null | null | null | python/tink/aead/kms_envelope_aead.py | bfloch/tink | aac780590902f726a8e7d6c4e3aa1cd75f4b0ed5 | [
"Apache-2.0"
] | null | null | null | # Copyright 2020 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# 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.
"""Module for envelope encryption with KMS."""
from __future__ import absolute_import
from __future__ import division
# Placeholder for import for type annotations
from __future__ import print_function
import struct
from tink.proto import tink_pb2
from tink import aead
from tink import core
# Defines in how many bytes the DEK length will be encoded.
DEK_LEN_BYTES = 4
| 36.085106 | 79 | 0.740861 |
132da230699a189c6467f4c2e09699a5bd87f139 | 2,168 | py | Python | tests/pyb/can.py | LabAixBidouille/micropython | 11aa6ba456287d6c80598a7ebbebd2887ce8f5a2 | [
"MIT"
] | null | null | null | tests/pyb/can.py | LabAixBidouille/micropython | 11aa6ba456287d6c80598a7ebbebd2887ce8f5a2 | [
"MIT"
] | null | null | null | tests/pyb/can.py | LabAixBidouille/micropython | 11aa6ba456287d6c80598a7ebbebd2887ce8f5a2 | [
"MIT"
] | null | null | null | from pyb import CAN
CAN.initfilterbanks(14)
can = CAN(1)
print(can)
can.init(CAN.LOOPBACK)
print(can)
print(can.any(0))
# Catch all filter
can.setfilter(0, CAN.MASK16, 0, (0, 0, 0, 0))
can.send('abcd', 123)
print(can.any(0))
print(can.recv(0))
can.send('abcd', -1)
print(can.recv(0))
can.send('abcd', 0x7FF + 1)
print(can.recv(0))
# Test too long message
try:
can.send('abcdefghi', 0x7FF)
except ValueError:
print('passed')
else:
print('failed')
del can
# Testing extended IDs
can = CAN(1, CAN.LOOPBACK, extframe = True)
# Catch all filter
can.setfilter(0, CAN.MASK32, 0, (0, 0))
print(can)
try:
can.send('abcde', 0x7FF + 1)
except ValueError:
print('failed')
else:
r = can.recv(0)
if r[0] == 0x7FF+1 and r[3] == b'abcde':
print('passed')
else:
print('failed, wrong data received')
del can
# Test RxCallbacks
can = CAN(1, CAN.LOOPBACK)
can.setfilter(0, CAN.LIST16, 0, (1, 2, 3, 4))
can.setfilter(1, CAN.LIST16, 1, (5, 6, 7, 8))
can.rxcallback(0, cb0)
can.rxcallback(1, cb1)
can.send('11111111',1)
can.send('22222222',2)
can.send('33333333',3)
can.rxcallback(0, cb0a)
can.send('44444444',4)
can.send('55555555',5)
can.send('66666666',6)
can.send('77777777',7)
can.rxcallback(1, cb1a)
can.send('88888888',8)
print(can.recv(0))
print(can.recv(0))
print(can.recv(0))
print(can.recv(1))
print(can.recv(1))
print(can.recv(1))
can.send('11111111',1)
can.send('55555555',5)
print(can.recv(0))
print(can.recv(1))
| 17.626016 | 45 | 0.600092 |
132f2f1fcc81be862b19147ccbf0e1ac78045127 | 8,995 | py | Python | quarkchain/cluster/tests/test_miner.py | TahiG/pyquarkchain | 30da626ef6b50bd07b230aac05a3cef7fd7f23cf | [
"MIT"
] | 17 | 2018-12-25T03:41:25.000Z | 2020-08-11T06:06:26.000Z | quarkchain/cluster/tests/test_miner.py | TahiG/pyquarkchain | 30da626ef6b50bd07b230aac05a3cef7fd7f23cf | [
"MIT"
] | null | null | null | quarkchain/cluster/tests/test_miner.py | TahiG/pyquarkchain | 30da626ef6b50bd07b230aac05a3cef7fd7f23cf | [
"MIT"
] | 5 | 2018-12-25T07:55:56.000Z | 2019-09-11T23:19:59.000Z | import asyncio
import time
import unittest
from typing import Optional
from quarkchain.cluster.miner import DoubleSHA256, Miner, MiningWork, validate_seal
from quarkchain.config import ConsensusType
from quarkchain.core import RootBlock, RootBlockHeader
from quarkchain.p2p import ecies
from quarkchain.utils import sha3_256
| 35 | 90 | 0.585103 |
133006e28697a9c060805e03e6080858eb027007 | 4,782 | py | Python | analysis/networks/autoencoder/train_eval.py | nriesterer/iccm-neural-bound | e14b103ba2c81a197de5b0edf948c19d57f0d3ba | [
"MIT"
] | null | null | null | analysis/networks/autoencoder/train_eval.py | nriesterer/iccm-neural-bound | e14b103ba2c81a197de5b0edf948c19d57f0d3ba | [
"MIT"
] | null | null | null | analysis/networks/autoencoder/train_eval.py | nriesterer/iccm-neural-bound | e14b103ba2c81a197de5b0edf948c19d57f0d3ba | [
"MIT"
] | null | null | null | """ Evaluates the training performance of the autoencoder.
"""
import time
import pandas as pd
import numpy as np
import torch
import torch.optim as optim
import torch.nn as nn
import ccobra
import onehot
import autoencoder
# General settings
training_datafile = '../../data/Ragni-train.csv'
test_datafile = '../../data/Ragni-test.csv'
n_epochs = 150
batch_size = 16
net = autoencoder.DenoisingAutoencoder()
criterion = nn.MSELoss()
optimizer = optim.Adam(net.parameters())
# Construct the training and test tensors
train_data, train_resp_dicts, train_seqs = csv_to_tensor(training_datafile)
test_data, test_resp_dicts, test_seqs = csv_to_tensor(test_datafile)
# Training loop
train_accs = []
test_accs = []
losses = []
for epoch in range(n_epochs):
start_time = time.time()
# Permute the training data
rnd_idxs = np.random.permutation(np.arange(len(train_data)))
train_data = train_data[rnd_idxs]
train_resp_dicts = train_resp_dicts[rnd_idxs]
train_seqs = train_seqs[rnd_idxs]
batch_losses = []
for batch_idx in range(len(train_data) // batch_size):
# Obtain the batch data
start = batch_idx * batch_size
end = start + batch_size
batch_data = train_data[start:end]
input_data = batch_data
# Augment the input data by adding noise
noise = torch.bernoulli(torch.zeros_like(input_data) + 0.8)
input_data = input_data * noise
# Perform the training
outputs = net(input_data)
loss = criterion(outputs, batch_data)
optimizer.zero_grad()
loss.backward()
optimizer.step()
batch_losses.append(loss.item())
losses.append(np.mean(batch_losses))
# Compute the accuracies for evaluation
net.eval()
# Compute the overall accuracy on the training dataset
train_acc = compute_accuracy(train_data, train_resp_dicts, train_seqs)
test_acc = compute_accuracy(test_data, test_resp_dicts, test_seqs)
# Diagnostig output
print('Epoch {}/{} ({:.2f}s): {}'.format(
epoch + 1, n_epochs, time.time() - start_time, np.mean(batch_losses)))
print(' train acc: {:.4f} ({:.4f})'.format(np.mean(train_acc), np.std(train_acc)))
print(' test acc : {:.4f} ({:.4f})'.format(np.mean(test_acc), np.std(test_acc)))
# Store the accuracy results
train_accs.append(train_acc)
test_accs.append(test_acc)
# Write the accuracies to disk
print('Writing the results to disk...')
np.save('train_accs.npy', np.array(train_accs))
np.save('test_accs.npy', np.array(test_accs))
np.save('train_losses.npy', np.array(losses))
| 32.310811 | 94 | 0.669385 |
13319b518cfc2b51d7dc1b3515004faa2aab4919 | 2,170 | py | Python | Tools/GAutomator/wpyscripts/uiautomator/uiautomator_manager.py | Aver58/ColaFrameWork | 04c6750305ad734b30eceb95b463695b8373845a | [
"MIT"
] | 1 | 2020-12-30T00:33:31.000Z | 2020-12-30T00:33:31.000Z | Tools/GAutomator/wpyscripts/uiautomator/uiautomator_manager.py | wtb521thl/ColaFrameWork | 0a8cc589740e045ebde668a76c4a35366b38e62e | [
"MIT"
] | null | null | null | Tools/GAutomator/wpyscripts/uiautomator/uiautomator_manager.py | wtb521thl/ColaFrameWork | 0a8cc589740e045ebde668a76c4a35366b38e62e | [
"MIT"
] | 1 | 2020-07-27T12:28:56.000Z | 2020-07-27T12:28:56.000Z | #-*- coding: UTF-8 -*-
"""
Tencent is pleased to support the open source community by making GAutomator available.
Copyright (C) 2016 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://opensource.org/licenses/MIT
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" basis, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
"""
__author__ = 'minhuaxu [email protected]'
import time
import os
import logging
from libs.uiauto.uiautomator import AutomatorDevice
from wpyscripts.common.adb_process import AdbTool
logger=logging.getLogger("wetest")
_device_port=9008
_uiautomator_port = os.environ.get("UIAUTOMATOR_PORT","19008")
def _init_uiautomator():
"""
uiautomator
:return:
"""
file_path = os.path.split(os.path.realpath(__file__))[0]
uiautomator_stub_path = os.path.abspath(
os.path.join(file_path, "..","third","libs","uiAutomator","uiautomator-stub.jar"))
adb=AdbTool()
print(adb.cmd_wait("push",uiautomator_stub_path,"/data/local/tmp"))
logger.debug("Start UIAutomator")
uiautomator_process=adb.cmd("shell","uiautomator","runtest","uiautomator-stub.jar","-c","com.github.uiautomatorstub.Stub")
time.sleep(3)
logger.debug("Exit uiautomator")
adb.forward(_uiautomator_port,_device_port)
get_uiautomator.instance=None
| 35 | 305 | 0.723502 |
1334b7e4ac98033a8f1ce868857f5028fcae2b7d | 2,261 | py | Python | IMU/VTK-6.2.0/Filters/Core/Testing/Python/TestSynchronizedTemplates3D.py | timkrentz/SunTracker | 9a189cc38f45e5fbc4e4c700d7295a871d022795 | [
"MIT"
] | null | null | null | IMU/VTK-6.2.0/Filters/Core/Testing/Python/TestSynchronizedTemplates3D.py | timkrentz/SunTracker | 9a189cc38f45e5fbc4e4c700d7295a871d022795 | [
"MIT"
] | null | null | null | IMU/VTK-6.2.0/Filters/Core/Testing/Python/TestSynchronizedTemplates3D.py | timkrentz/SunTracker | 9a189cc38f45e5fbc4e4c700d7295a871d022795 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
import vtk
from vtk.test import Testing
from vtk.util.misc import vtkGetDataRoot
VTK_DATA_ROOT = vtkGetDataRoot()
if __name__ == "__main__":
Testing.main([(TestSynchronizedTemplates3D, 'test')])
| 35.888889 | 81 | 0.707651 |
1335259eff2620253efdbb6cb8199b32e7ccadf8 | 14,913 | py | Python | deserialize/__init__.py | iAndriy/deserialize | 3552517873d68d3bb953b44dd9512f0e0d045191 | [
"MIT"
] | null | null | null | deserialize/__init__.py | iAndriy/deserialize | 3552517873d68d3bb953b44dd9512f0e0d045191 | [
"MIT"
] | null | null | null | deserialize/__init__.py | iAndriy/deserialize | 3552517873d68d3bb953b44dd9512f0e0d045191 | [
"MIT"
] | null | null | null | """A module for deserializing data to Python objects."""
# pylint: disable=unidiomatic-typecheck
# pylint: disable=protected-access
# pylint: disable=too-many-branches
# pylint: disable=wildcard-import
import enum
import functools
import typing
from typing import Any, Callable, Dict, List, Optional, Union
from deserialize.conversions import camel_case, pascal_case
from deserialize.decorators import constructed, _call_constructed
from deserialize.decorators import default, _get_default, _has_default
from deserialize.decorators import (
downcast_field,
_get_downcast_field,
downcast_identifier,
_get_downcast_class,
allow_downcast_fallback,
_allows_downcast_fallback,
)
from deserialize.decorators import ignore, _should_ignore
from deserialize.decorators import key, _get_key
from deserialize.decorators import parser, _get_parser
from deserialize.decorators import auto_snake, _uses_auto_snake
from deserialize.decorators import allow_unhandled, _should_allow_unhandled
from deserialize.exceptions import (
DeserializeException,
InvalidBaseTypeException,
NoDefaultSpecifiedException,
UndefinedDowncastException,
UnhandledFieldException,
)
from deserialize.type_checks import *
# pylint: disable=function-redefined
def deserialize(class_reference, data, *, throw_on_unhandled: bool = False, raw_storage_mode: RawStorageMode = RawStorageMode.none): # type: ignore
"""Deserialize data to a Python object."""
if not isinstance(data, dict) and not isinstance(data, list):
raise InvalidBaseTypeException(
"Only lists and dictionaries are supported as base raw data types"
)
if hasattr(class_reference, "__name__"):
name = class_reference.__name__
else:
name = str(class_reference)
return _deserialize(
class_reference,
data,
name,
throw_on_unhandled=throw_on_unhandled,
raw_storage_mode=raw_storage_mode,
)
# pylint: enable=function-redefined
# pylint:disable=too-many-return-statements
def _deserialize(
class_reference, data, debug_name, *, throw_on_unhandled: bool, raw_storage_mode: RawStorageMode
):
"""Deserialize data to a Python object, but allow base types"""
# In here we try and use some "heuristics" to deserialize. We have 2 main
# options to do this. For the first, we can take the expected type and try
# and deserialize the data to that and show any errors. The other option is
# to take the data, and try and determine the types and deserialize that
# way. We do a mix of both.
#
# For example, we check if we have an any type or None type first and return
# early, since we can't deserialize directly to those (since that doesn't
# make any sense). But then later, we can't go for a list directly to a
# type, so we have to go through each item in the data, and iterate.
#
# This produces quite a complex interweaving of operations. The general
# approach I've found to work is to try and do specific type checks first,
# then handle collection data, then any other types afterwards. That's not
# set in stone though.
def finalize(value: Optional[Any]) -> Optional[Any]:
"""Run through any finalization steps before returning the value."""
# Set raw data where applicable
if raw_storage_mode in [RawStorageMode.root, RawStorageMode.all]:
# We can't set attributes on primitive types
if hasattr(value, "__dict__"):
setattr(value, "__deserialize_raw__", data)
return value
if class_reference == Any:
return finalize(data)
# Check if it's None (since things like Union[int, Optional[str]] become
# Union[int, str, None] so we end up iterating against it)
if class_reference == type(None) and data is None:
return finalize(None)
if is_union(class_reference):
valid_types = union_types(class_reference, debug_name)
exceptions = []
for valid_type in valid_types:
try:
return finalize(
_deserialize(
valid_type,
data,
debug_name,
throw_on_unhandled=throw_on_unhandled,
raw_storage_mode=raw_storage_mode.child_mode(),
)
)
except DeserializeException as ex:
exceptions.append(str(ex))
exception_message = (
f"Cannot deserialize '{type(data)}' to '{class_reference}' for '{debug_name}' ->"
)
for exception in exceptions:
exception_lines = exception.split("\n")
sub_message = f"\n\t* {exception_lines[0]}"
for line in exception_lines[1:]:
sub_message += f"\n\t{line}"
exception_message += sub_message
raise DeserializeException(exception_message)
if isinstance(data, dict):
return finalize(
_deserialize_dict(
class_reference,
data,
debug_name,
throw_on_unhandled=throw_on_unhandled,
raw_storage_mode=raw_storage_mode,
)
)
if isinstance(data, list):
return finalize(
_deserialize_list(
class_reference,
data,
debug_name,
throw_on_unhandled=throw_on_unhandled,
raw_storage_mode=raw_storage_mode,
)
)
if not is_typing_type(class_reference) and issubclass(class_reference, enum.Enum):
try:
return finalize(class_reference(data))
# pylint:disable=bare-except
except:
enum_by_name = getattr(class_reference, str(data), None)
if enum_by_name:
return finalize(enum_by_name)
# pylint:enable=bare-except
# This will be handled at the end
pass
# If we still have a type from the typing module, we don't know how to
# handle it
if is_typing_type(class_reference):
# The data should not be None if we have a type that got here. Optionals
# are handled by unions above, so if we are here, it's a non-optional
# type and therefore should not be None.
if data is None:
raise DeserializeException(
f"No value for '{debug_name}'. Expected value of type '{class_reference}'"
)
raise DeserializeException(
f"Unsupported deserialization type: {class_reference} for {debug_name}"
)
# Whatever we have left now is either correct, or invalid
if isinstance(data, class_reference):
return finalize(data)
raise DeserializeException(
f"Cannot deserialize '{type(data)}' to '{class_reference}' for '{debug_name}'"
)
# pylint:enable=too-many-return-statements
def _deserialize_dict(
class_reference, data, debug_name, *, throw_on_unhandled: bool, raw_storage_mode: RawStorageMode
):
"""Deserialize a dictionary to a Python object."""
# Check if we are doing a straightforward dictionary parse first, or if it
# has to be deserialized
remaining_properties = set(data.keys())
if not isinstance(data, dict):
raise DeserializeException(
f"Data was not dict for instance: {class_reference} for {debug_name}"
)
if is_dict(class_reference):
if class_reference is dict:
# If types of dictionary entries are not defined, do not deserialize
return data
key_type, value_type = dict_content_types(class_reference, debug_name)
result = {}
for dict_key, dict_value in data.items():
if key_type != Any and not isinstance(dict_key, key_type):
raise DeserializeException(
f"Could not deserialize key {dict_key} to type {key_type} for {debug_name}"
)
result[dict_key] = _deserialize(
value_type,
dict_value,
f"{debug_name}.{dict_key}",
throw_on_unhandled=throw_on_unhandled,
raw_storage_mode=raw_storage_mode.child_mode(),
)
remaining_properties.remove(dict_key)
if throw_on_unhandled and len(remaining_properties) > 0:
raise UnhandledFieldException(
f"The following field was unhandled: {list(remaining_properties)[0]} for {debug_name}"
)
return result
# It wasn't a straight forward dictionary, so we are in deserialize mode
class_instance = None
class_reference_downcast_field = _get_downcast_field(class_reference)
if class_reference_downcast_field:
downcast_value = data[class_reference_downcast_field]
new_reference = _get_downcast_class(class_reference, downcast_value)
if new_reference is None:
if _allows_downcast_fallback(class_reference):
return _deserialize(
Dict[Any, Any],
data,
debug_name,
throw_on_unhandled=throw_on_unhandled,
raw_storage_mode=raw_storage_mode.child_mode(),
)
raise UndefinedDowncastException(
f"Could not find subclass of {class_reference} with downcast identifier '{downcast_value}' for {debug_name}"
)
class_reference = new_reference
class_instance = class_reference.__new__(class_reference)
handled_fields = set()
hints = typing.get_type_hints(class_reference)
if len(hints) == 0:
raise DeserializeException(
f"Could not deserialize {data} into {class_reference} due to lack of type hints ({debug_name})"
)
for attribute_name, attribute_type in hints.items():
if _should_ignore(class_reference, attribute_name):
continue
property_key = _get_key(class_reference, attribute_name)
parser_function = _get_parser(class_reference, property_key)
if is_classvar(attribute_type):
if property_key in data:
raise DeserializeException(
f"ClassVars cannot be set: {debug_name}.{attribute_name}"
)
continue
if _uses_auto_snake(class_reference) and attribute_name.lower() != attribute_name:
raise DeserializeException(
f"When using auto_snake, all properties must be snake cased. Error on: {debug_name}.{attribute_name}"
)
using_default = False
if property_key in data:
value = data[property_key]
handled_fields.add(property_key)
property_value = parser_function(value)
elif _uses_auto_snake(class_reference) and camel_case(property_key) in data:
value = data[camel_case(property_key)]
handled_fields.add(camel_case(property_key))
property_value = parser_function(value)
elif _uses_auto_snake(class_reference) and pascal_case(property_key) in data:
value = data[pascal_case(property_key)]
handled_fields.add(pascal_case(property_key))
property_value = parser_function(value)
else:
if _has_default(class_reference, attribute_name):
deserialized_value = _get_default(class_reference, attribute_name)
using_default = True
else:
if not is_union(attribute_type) or type(None) not in union_types(
attribute_type, debug_name
):
raise DeserializeException(
f"Unexpected missing value for: {debug_name}.{attribute_name}"
)
property_value = parser_function(None)
if not using_default:
deserialized_value = _deserialize(
attribute_type,
property_value,
f"{debug_name}.{attribute_name}",
throw_on_unhandled=throw_on_unhandled,
raw_storage_mode=raw_storage_mode.child_mode(),
)
setattr(class_instance, attribute_name, deserialized_value)
unhandled = set(data.keys()) - handled_fields
if throw_on_unhandled and len(unhandled) > 0:
filtered_unhandled = [
key for key in unhandled if not _should_allow_unhandled(class_reference, key)
]
if len(filtered_unhandled) > 0:
raise UnhandledFieldException(
f"Unhandled field: {list(filtered_unhandled)[0]} for {debug_name}"
)
_call_constructed(class_reference, class_instance)
return class_instance
| 35.507143 | 148 | 0.644002 |
1335675a9f3e2654ba5bacc0a704284147b3d912 | 2,518 | py | Python | tests/test_get_set.py | snoopyjc/ssf | b995cae0e90d38e3758d4944fb144831f9bae0a5 | [
"Apache-2.0"
] | 3 | 2020-10-07T18:28:12.000Z | 2020-10-09T15:24:53.000Z | tests/test_get_set.py | snoopyjc/ssf | b995cae0e90d38e3758d4944fb144831f9bae0a5 | [
"Apache-2.0"
] | 15 | 2020-10-09T15:23:03.000Z | 2020-10-29T04:34:17.000Z | tests/test_get_set.py | snoopyjc/ssf | b995cae0e90d38e3758d4944fb144831f9bae0a5 | [
"Apache-2.0"
] | null | null | null | from ssf import SSF
ssf = SSF(errors='raise')
| 31.475 | 100 | 0.496029 |
13356a393bf8946042480c6de66561dc06a9116f | 3,796 | py | Python | script.py | devppratik/Youtube-Downloader | ccdf31b83fbce2d05711c64dbad729c935c72b8a | [
"MIT"
] | null | null | null | script.py | devppratik/Youtube-Downloader | ccdf31b83fbce2d05711c64dbad729c935c72b8a | [
"MIT"
] | null | null | null | script.py | devppratik/Youtube-Downloader | ccdf31b83fbce2d05711c64dbad729c935c72b8a | [
"MIT"
] | null | null | null | import os
import pyfiglet
from pytube import YouTube, Playlist
file_size = 0
folder_name = ""
# Progress Bar
# Show Progress Bar
# Get Download Location
# Get Desired Resolution
# Single Video Download
# Playlist Single Video Download
# Playlist Download
# Main Function
# Start of Program
if __name__ == '__main__':
main()
| 33.008696 | 113 | 0.670443 |
13372002afbafad592f6b286397c1829136d9e66 | 1,547 | py | Python | test/python/test.py | alex952/cdr | e8dce20c2cc635e5ad8bf16a16ec4f7d9a86ac16 | [
"MIT"
] | null | null | null | test/python/test.py | alex952/cdr | e8dce20c2cc635e5ad8bf16a16ec4f7d9a86ac16 | [
"MIT"
] | null | null | null | test/python/test.py | alex952/cdr | e8dce20c2cc635e5ad8bf16a16ec4f7d9a86ac16 | [
"MIT"
] | null | null | null | #
# Copyright 2014-2018 Neueda Ltd.
#
from cdr import Cdr
import unittest
field1 = 1
field2 = 2
field3 = 55
if __name__ == '__main__':
unittest.main()
| 20.355263 | 59 | 0.541694 |
1337486fb7ecdf7c5699a609f50b6301a5037cab | 32,947 | py | Python | vendor/mo_times/vendor/dateutil/tz.py | klahnakoski/auth0-api | eda9c2554c641da76687f64445b8d35543d012d9 | [
"MIT"
] | null | null | null | vendor/mo_times/vendor/dateutil/tz.py | klahnakoski/auth0-api | eda9c2554c641da76687f64445b8d35543d012d9 | [
"MIT"
] | null | null | null | vendor/mo_times/vendor/dateutil/tz.py | klahnakoski/auth0-api | eda9c2554c641da76687f64445b8d35543d012d9 | [
"MIT"
] | null | null | null | """
Copyright (c) 2003-2007 Gustavo Niemeyer <[email protected]>
This module offers extensions to the standard Python
datetime module.
"""
import datetime
import os
import struct
import sys
import time
from mo_future import PY3, string_types
__license__ = "Simplified BSD"
__all__ = ["tzutc", "tzoffset", "tzlocal", "tzfile", "tzrange",
"tzstr", "tzical", "tzwin", "tzwinlocal", "gettz"]
relativedelta = None
parser = None
rrule = None
try:
from dateutil.tzwin import tzwin, tzwinlocal
except (ImportError, OSError):
tzwin, tzwinlocal = None, None
def tzname_in_python2(myfunc):
"""Change unicode output into bytestrings in Python 2
tzname() API changed in Python 3. It used to return bytes, but was changed
to unicode strings
"""
return inner_func
ZERO = datetime.timedelta(0)
EPOCHORDINAL = datetime.datetime.utcfromtimestamp(0).toordinal()
if sys.platform != "win32":
TZFILES = ["/etc/localtime", "localtime"]
TZPATHS = ["/usr/share/zoneinfo", "/usr/lib/zoneinfo", "/etc/zoneinfo"]
else:
TZFILES = []
TZPATHS = []
# vim:ts=4:sw=4:et
| 34.284079 | 89 | 0.521049 |
1338002c0bb58e1ef6c0115a4fcc0293761a19a5 | 764 | py | Python | example/first_example/window/inputWindow/view.py | suuperhu/Pyside2MVCFramework | c28bd0fbb6b53bb2cdad8d0723b4251fc93319d6 | [
"MIT"
] | 1 | 2021-03-11T13:34:41.000Z | 2021-03-11T13:34:41.000Z | example/first_example/window/inputWindow/view.py | suuperhu/Pyside2MVCFramework | c28bd0fbb6b53bb2cdad8d0723b4251fc93319d6 | [
"MIT"
] | null | null | null | example/first_example/window/inputWindow/view.py | suuperhu/Pyside2MVCFramework | c28bd0fbb6b53bb2cdad8d0723b4251fc93319d6 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
"""
# @SoftwareIDE : PyCharm2020Pro
# @ProjectName : PySide2MVCFramework
# @FileName : view.py
# @Author :
# @Email : [email protected]
# @ZhFileDescription :
# @EnFileDescription :
"""
import os
from pyside2mvcframework.core.view import View
from conf.global_settings import BASE_PATH
if __name__ == '__main__':
print("unit test from {filename}".format(filename=__file__))
import sys
from PySide2.QtWidgets import QApplication
app = QApplication(sys.argv)
view = InputWindowView().birth()
view.show()
sys.exit(app.exec_())
| 26.344828 | 84 | 0.649215 |
133a869b28cf9968a719e243a3266dfb25b637ba | 18,998 | py | Python | src/finn/custom_op/fpgadataflow/streamingfifo.py | AlexMontgomerie/finn | ec5f67b333ad4db4acf6191c3b5ab5e9067347aa | [
"BSD-3-Clause"
] | 283 | 2019-09-26T10:09:34.000Z | 2022-03-09T16:36:23.000Z | src/finn/custom_op/fpgadataflow/streamingfifo.py | AlexMontgomerie/finn | ec5f67b333ad4db4acf6191c3b5ab5e9067347aa | [
"BSD-3-Clause"
] | 238 | 2019-10-04T12:20:26.000Z | 2022-03-31T04:50:53.000Z | src/finn/custom_op/fpgadataflow/streamingfifo.py | AlexMontgomerie/finn | ec5f67b333ad4db4acf6191c3b5ab5e9067347aa | [
"BSD-3-Clause"
] | 144 | 2019-09-23T13:46:14.000Z | 2022-03-18T12:55:07.000Z | # Copyright (c) 2020, Xilinx
# 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 FINN nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
import numpy as np
from shutil import copy
import subprocess
import math
import warnings
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
from finn.core.datatype import DataType
from onnx import TensorProto, helper
from finn.util.data_packing import npy_to_rtlsim_input, rtlsim_output_to_npy
from . import templates
| 40.25 | 88 | 0.600326 |
133a8d431ec24410c6b47b396ebee9494e39f0e3 | 3,403 | py | Python | android_fonts.py | chrissimpkins/android_fonts | f904147774836468a8c011b1596f85577220b140 | [
"Apache-2.0"
] | 1 | 2022-01-13T01:47:45.000Z | 2022-01-13T01:47:45.000Z | android_fonts.py | chrissimpkins/android_fonts | f904147774836468a8c011b1596f85577220b140 | [
"Apache-2.0"
] | 2 | 2022-01-13T03:54:39.000Z | 2022-03-12T01:00:20.000Z | android_fonts.py | chrissimpkins/android_fonts | f904147774836468a8c011b1596f85577220b140 | [
"Apache-2.0"
] | 1 | 2022-03-25T20:01:56.000Z | 2022-03-25T20:01:56.000Z | import ast
import emoji
import os
import pandas as pd
_SUPPORT_CACHE_CSV = emoji.datafile('emoji_support.csv')
_API_LEVELS = {
1: ("(no codename)", "1.0"),
2: ("(no codename)", "1.1"),
3: ("Cupcake", "1.5 "),
4: ("Donut", "1.6 "),
5: ("Eclair", "2.0"),
6: ("Eclair", "2.0.1"),
7: ("Eclair", "2.1 "),
8: ("Froyo", "2.2.x "),
9: ("Gingerbread", "2.3 - 2.3.2 "),
10: ("Gingerbread", "2.3.3 - 2.3.7"),
11: ("Honeycomb", "3.0"),
12: ("Honeycomb", "3.1 "),
13: ("Honeycomb", "3.2.x"),
14: ("Ice Cream Sandwich", "4.0.1 - 4.0.2 "),
15: ("Ice Cream Sandwich", "4.0.3 - 4.0.4 "),
16: ("Jelly Bean", "4.1.x"),
17: ("Jelly Bean", "4.2.x"),
18: ("Jelly Bean", "4.3.x"),
19: ("KitKat", "4.4 - 4.4.4"),
21: ("Lollipop", "5.0"),
22: ("Lollipop", "5.1"),
23: ("Marshmallow", "6.0"),
24: ("Nougat", "7.0"),
25: ("Nougat", "7.1"),
26: ("Oreo", "8.0.0"),
27: ("Oreo", "8.1.0"),
28: ("Pie", "9"),
29: ("Android 10 (Q)", "10"),
30: ("Android 11 (R)", "11"),
31: ("Android 12 (S)", "12"),
}
def emoji_support():
"""Dataframe of [emoji_level, font_file, codepoints, supported].
Includes every sequence we could find of any type.
Requires prior execution of populate_emoji_support.py"""
if not os.path.isfile(_SUPPORT_CACHE_CSV):
raise IOError('Please run populate_emoji_support.py first')
return (pd.read_csv(_SUPPORT_CACHE_CSV, converters={'cp_seq': ast.literal_eval})
.rename(columns={'cp_seq': 'codepoints'}))
| 27.666667 | 82 | 0.590949 |
133cca4be64ff28929df70bc44eae2ffd26907ef | 5,889 | py | Python | tests/test_list.py | amikrop/django-paste | 109f6e5a42bdc20f3cb671471b3ce5c9e329148b | [
"MIT"
] | 3 | 2020-11-11T11:28:47.000Z | 2022-03-16T11:27:39.000Z | tests/test_list.py | amikrop/django-paste | 109f6e5a42bdc20f3cb671471b3ce5c9e329148b | [
"MIT"
] | null | null | null | tests/test_list.py | amikrop/django-paste | 109f6e5a42bdc20f3cb671471b3ce5c9e329148b | [
"MIT"
] | 1 | 2021-01-05T15:01:06.000Z | 2021-01-05T15:01:06.000Z | import json
from django.urls import reverse
from rest_framework import status
from rest_framework.test import APITestCase
from paste import constants
from tests.mixins import SnippetListTestCaseMixin
from tests.utils import constant, create_snippet, create_user
| 37.993548 | 78 | 0.652742 |
133e3e1f16ff977c264f6c6b6c2854d7503259d9 | 1,082 | py | Python | Algorithmic Toolbox/Greedy Algorithms/Maximum Advertisement Revenue/maximum_ad_revenue.py | ganeshbhandarkar/Python-Projects | a4df933122a6694d249c69d1e8e95b592cf036a0 | [
"MIT"
] | 9 | 2020-07-02T06:06:17.000Z | 2022-02-26T11:08:09.000Z | Algorithmic Toolbox/Greedy Algorithms/Maximum Advertisement Revenue/maximum_ad_revenue.py | ganeshbhandarkar/Python-Projects | a4df933122a6694d249c69d1e8e95b592cf036a0 | [
"MIT"
] | 1 | 2021-11-04T17:26:36.000Z | 2021-11-04T17:26:36.000Z | Algorithmic Toolbox/Greedy Algorithms/Maximum Advertisement Revenue/maximum_ad_revenue.py | ganeshbhandarkar/Python-Projects | a4df933122a6694d249c69d1e8e95b592cf036a0 | [
"MIT"
] | 8 | 2021-01-31T10:31:12.000Z | 2022-03-13T09:15:55.000Z | # python3
from itertools import permutations
if __name__ == '__main__':
n = int(input())
prices = list(map(int, input().split()))
clicks = list(map(int, input().split()))
assert len(prices) == len(clicks) == n
print(max_dot_product(prices, clicks))
| 30.914286 | 97 | 0.675601 |
133ed5289cb6b6aa42d9780aa225db43b59d7e06 | 154 | py | Python | HelloWorldPython/IfStatements.py | SamIge7/Tutorials | 7a3361768432a6493313d1f0ea1ccc8b2b916aa2 | [
"MIT"
] | null | null | null | HelloWorldPython/IfStatements.py | SamIge7/Tutorials | 7a3361768432a6493313d1f0ea1ccc8b2b916aa2 | [
"MIT"
] | null | null | null | HelloWorldPython/IfStatements.py | SamIge7/Tutorials | 7a3361768432a6493313d1f0ea1ccc8b2b916aa2 | [
"MIT"
] | null | null | null | hasGoodCredit = True
price = 1000000
deposit = 0
if hasGoodCredit:
deposit = price/10
else:
deposit = price/5
print(f"Deposit needed: {deposit}") | 19.25 | 36 | 0.707792 |
133faca593d6fb1ce0fd475b5ad4b709b64db3a7 | 1,970 | py | Python | main.py | vsundesha/documentation-hub-dependencies | 3cdb7c28ceefb7c4ece60fd5e9d3e89640bb0d01 | [
"Apache-2.0"
] | null | null | null | main.py | vsundesha/documentation-hub-dependencies | 3cdb7c28ceefb7c4ece60fd5e9d3e89640bb0d01 | [
"Apache-2.0"
] | null | null | null | main.py | vsundesha/documentation-hub-dependencies | 3cdb7c28ceefb7c4ece60fd5e9d3e89640bb0d01 | [
"Apache-2.0"
] | null | null | null | import config as props
import sys
import getopt
from GitHubDataFetcher import GitHubDataFetcher
from DependencyFile import DependencyFile
from ErrorFile import ErrorFile
# Github Token
TOKEN = props.token
OWNER = ""
REPOSITORY = ""
OUTPUTFILE = ""
if __name__ == "__main__":
main(sys.argv[1:])
| 30.78125 | 117 | 0.579695 |
133fae22a7b7ebcd7d6ca9fc3157fb56fb5b1062 | 6,036 | py | Python | inference_realesrgan.py | blabra/Real-ESRGAN | bd5c69d2ef30f27cc2a510443451a2dc841aec28 | [
"BSD-3-Clause"
] | null | null | null | inference_realesrgan.py | blabra/Real-ESRGAN | bd5c69d2ef30f27cc2a510443451a2dc841aec28 | [
"BSD-3-Clause"
] | null | null | null | inference_realesrgan.py | blabra/Real-ESRGAN | bd5c69d2ef30f27cc2a510443451a2dc841aec28 | [
"BSD-3-Clause"
] | null | null | null | import argparse
import cv2
import glob
import os
from basicsr.archs.rrdbnet_arch import RRDBNet
import time
from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
def main():
"""Inference demo for Real-ESRGAN.
"""
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, default='inputs', help='Input image or folder')
parser.add_argument(
'-n',
'--model_name',
type=str,
default='RealESRGAN_x4plus',
help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus'
'RealESRGANv2-anime-xsx2 | RealESRGANv2-animevideo-xsx2-nousm | RealESRGANv2-animevideo-xsx2'
'RealESRGANv2-anime-xsx4 | RealESRGANv2-animevideo-xsx4-nousm | RealESRGANv2-animevideo-xsx4'))
parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image')
parser.add_argument('--suffix', type=str, default='Realesrgan-4x', help='Suffix of the restored image')
parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
parser.add_argument('--half', action='store_true', help='Use half precision during inference')
parser.add_argument(
'--alpha_upsampler',
type=str,
default='realesrgan',
help='The upsampler for the alpha channels. Options: realesrgan | bicubic')
parser.add_argument(
'--ext',
type=str,
default='auto',
help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
args = parser.parse_args()
# determine models according to model names
args.model_name = args.model_name.split('.')[0]
if args.model_name in ['RealESRGAN_x4plus', 'RealESRNet_x4plus']: # x4 RRDBNet model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
netscale = 4
elif args.model_name in ['RealESRGAN_x4plus_anime_6B']: # x4 RRDBNet model with 6 blocks
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
netscale = 4
elif args.model_name in ['RealESRGAN_x2plus']: # x2 RRDBNet model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
netscale = 2
elif args.model_name in [
'RealESRGANv2-anime-xsx2', 'RealESRGANv2-animevideo-xsx2-nousm', 'RealESRGANv2-animevideo-xsx2'
]: # x2 VGG-style model (XS size)
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=2, act_type='prelu')
netscale = 2
elif args.model_name in [
'RealESRGANv2-anime-xsx4', 'RealESRGANv2-animevideo-xsx4-nousm', 'RealESRGANv2-animevideo-xsx4'
]: # x4 VGG-style model (XS size)
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
netscale = 4
else:
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
netscale = 4
# determine model paths
model_path = os.path.join('experiments/pretrained_models', args.model_name + '.pth')
if not os.path.isfile(model_path):
model_path = os.path.join('realesrgan/weights', args.model_name + '.pth')
if not os.path.isfile(model_path):
raise ValueError(f'Model {args.model_name} does not exist.')
# restorer
upsampler = RealESRGANer(
scale=netscale,
model_path=model_path,
model=model,
tile=args.tile,
tile_pad=args.tile_pad,
pre_pad=args.pre_pad,
half=args.half)
if args.face_enhance: # Use GFPGAN for face enhancement
from gfpgan import GFPGANer
face_enhancer = GFPGANer(
model_path='https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth',
upscale=args.outscale,
arch='clean',
channel_multiplier=2,
bg_upsampler=upsampler)
os.makedirs(args.output, exist_ok=True)
if os.path.isfile(args.input):
paths = [args.input]
else:
paths = sorted(glob.glob(os.path.join(args.input, '*')))
for idx, path in enumerate(paths):
startTime = time.perf_counter()
imgname, extension = os.path.splitext(os.path.basename(path))
img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
if len(img.shape) == 3 and img.shape[2] == 4:
img_mode = 'RGBA'
else:
img_mode = None
if args.ext == 'auto':
extension = "png"
else:
extension = args.ext
if img_mode == 'RGBA': # RGBA images should be saved in png format
extension = 'png'
save_path = os.path.join(args.output, f'{imgname}-{args.suffix}.{extension}')
if os.path.exists(save_path):
continue
try:
if args.face_enhance:
_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
else:
output, _ = upsampler.enhance(img, outscale=args.outscale)
except RuntimeError as error:
print('Error', error)
print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
else:
cv2.imwrite(save_path, output)
print(f'NO.{idx}, {imgname} is done, used {round((time.perf_counter() - startTime), 4)} seconds')
if __name__ == '__main__':
main()
| 43.73913 | 117 | 0.647614 |
1340144aae426a1da982a7084f1832cdca8e0a63 | 515 | py | Python | examples/Fe__vasp/Fe_fcc_afm_D/Fe_fcc_afm_D_vac_A/clean_vasp.py | eragasa/pypospack | 21cdecaf3b05c87acc532d992be2c04d85bfbc22 | [
"MIT"
] | 4 | 2018-01-18T19:59:56.000Z | 2020-08-25T11:56:52.000Z | examples/Fe__vasp/Fe_fcc_afm_D/Fe_fcc_afm_D_vac_A/clean_vasp.py | eragasa/pypospack | 21cdecaf3b05c87acc532d992be2c04d85bfbc22 | [
"MIT"
] | 1 | 2018-04-22T23:02:13.000Z | 2018-04-22T23:02:13.000Z | examples/Fe__vasp/Fe_fcc_afm_D/Fe_fcc_afm_D_vac_A/clean_vasp.py | eragasa/pypospack | 21cdecaf3b05c87acc532d992be2c04d85bfbc22 | [
"MIT"
] | 1 | 2019-09-14T07:04:42.000Z | 2019-09-14T07:04:42.000Z | import os
filenames_delete = [
'CHG',
'CHGCAR',
'CONTCAR',
'DOSCAR',
'EIGENVAL',
'IBZKPT',
'job.err',
'job.out',
'OSZICAR',
'PCDAT',
'REPORT',
'vasp.log',
'vasprun.xml',
'WAVECAR',
'XDATCAR'
]
for filename in filenames_delete:
try:
os.remove(filename)
msg = "{} removed.".format(filename)
except FileNotFoundError as e:
msg = "{} does not exist.".format(filename)
except:
raise
print(msg)
| 17.166667 | 51 | 0.518447 |
1340c125093f2e5f053bbf9519f4a5c3e3aa6a2e | 1,195 | py | Python | binary_trees/largest_values_in_tree_rows.py | ethyl2/code_challenges | 3c9ccca1782f92728e60a515a7ca797f6d470e81 | [
"MIT"
] | null | null | null | binary_trees/largest_values_in_tree_rows.py | ethyl2/code_challenges | 3c9ccca1782f92728e60a515a7ca797f6d470e81 | [
"MIT"
] | null | null | null | binary_trees/largest_values_in_tree_rows.py | ethyl2/code_challenges | 3c9ccca1782f92728e60a515a7ca797f6d470e81 | [
"MIT"
] | null | null | null | '''
Sean Chen's solution.
See mine in largest_values_in_each_row.py
'''
from collection import deque
| 24.895833 | 61 | 0.594979 |
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