text stringlengths 0 1.05M | meta dict |
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# Adopted form SfePy project, see http://sfepy.org
# Thanks to Robert Cimrman
import numpy as nm
import os
import os.path as op
import fnmatch
import shutil
from base import output, Struct, basestr
try:
import tables as pt
except:
pt = None
class InDir(Struct):
"""
Store the directory name a file is i... | {
"repo_name": "vlukes/dicom2fem",
"path": "dicom2fem/ioutils.py",
"copies": "1",
"size": "8061",
"license": "bsd-3-clause",
"hash": -4072875990405852700,
"line_mean": 24.6719745223,
"line_max": 76,
"alpha_frac": 0.5686639375,
"autogenerated": false,
"ratio": 3.544854881266491,
"config_test": fa... |
# Adopted form SfePy project, see http://sfepy.org
# Thanks to Robert Cimrman
import sys
from copy import copy
import os.path as op
import numpy as nm
from base import (complex_types, dict_from_keys_init,
assert_, is_derived_class,
insert_static_method, output, get_default,
... | {
"repo_name": "vlukes/dicom2fem",
"path": "dicom2fem/meshio.py",
"copies": "1",
"size": "86894",
"license": "bsd-3-clause",
"hash": -2948851048050807300,
"line_mean": 31.5689655172,
"line_max": 88,
"alpha_frac": 0.4448063157,
"autogenerated": false,
"ratio": 3.5685420944558524,
"config_test": f... |
# Adopted form SfePy project, see http://sfepy.org
# Thanks to Robert Cimrman
import sys
from copy import copy
import os.path as op
import numpy as nm
from .base import (
complex_types,
dict_from_keys_init,
assert_,
is_derived_class,
insert_static_method,
output,
get_default,
get_defau... | {
"repo_name": "mjirik/dicom2fem",
"path": "dicom2fem/meshio.py",
"copies": "1",
"size": "86767",
"license": "bsd-3-clause",
"hash": -8770243911784632000,
"line_mean": 29.5625220148,
"line_max": 88,
"alpha_frac": 0.4490647366,
"autogenerated": false,
"ratio": 3.596112400530504,
"config_test": fa... |
# Adopted form SfePy project, see http://sfepy.org
# Thanks to Robert Cimrman
import time
import numpy as nm
import scipy.sparse as sp
from base import Struct, get_default, output, assert_
from meshio import MeshIO
##
# 28.05.2007, c
def make_point_cells( indx, dim ):
conn = nm.zeros( (indx.shape[0], dim + 1), d... | {
"repo_name": "vlukes/dicom2fem",
"path": "dicom2fem/mesh.py",
"copies": "1",
"size": "27478",
"license": "bsd-3-clause",
"hash": -2501733847062933500,
"line_mean": 31.1380116959,
"line_max": 80,
"alpha_frac": 0.5035300968,
"autogenerated": false,
"ratio": 3.3591687041564793,
"config_test": fal... |
# Adopted form SfePy project, see http://sfepy.org
# Thanks to Robert Cimrman
import time
import numpy as nm
import scipy.sparse as sp
from .base import Struct, get_default, output, assert_
from .meshio import MeshIO
##
# 28.05.2007, c
def make_point_cells(indx, dim):
conn = nm.zeros((indx.shape[0], dim + 1), dt... | {
"repo_name": "mjirik/dicom2fem",
"path": "dicom2fem/mesh.py",
"copies": "1",
"size": "27575",
"license": "bsd-3-clause",
"hash": -6933944286560148000,
"line_mean": 29.948372615,
"line_max": 86,
"alpha_frac": 0.5108612874,
"autogenerated": false,
"ratio": 3.3375695957395304,
"config_test": fals... |
# Adopted form SfePy project, see http://sfepy.org
# Thanks to Robert Cimrman
import time, sys, os
from copy import copy, deepcopy
from types import UnboundMethodType
import numpy as nm
import scipy.sparse as sp
real_types = [nm.float64]
complex_types = [nm.complex128]
nm.set_printoptions( threshold = 100 )
def ou... | {
"repo_name": "vlukes/dicom2fem",
"path": "dicom2fem/base.py",
"copies": "1",
"size": "31975",
"license": "bsd-3-clause",
"hash": -2300470462240647000,
"line_mean": 26.0287404903,
"line_max": 80,
"alpha_frac": 0.5068021892,
"autogenerated": false,
"ratio": 4.046443938243483,
"config_test": fals... |
# Adopted form SfePy project, see http://sfepy.org
# Thanks to Robert Cimrman
import time, sys, os
from copy import copy, deepcopy
# from types import UnboundMethodType
import numpy as nm
import scipy.sparse as sp
real_types = [nm.float64]
complex_types = [nm.complex128]
nm.set_printoptions(threshold=100)
def ou... | {
"repo_name": "mjirik/dicom2fem",
"path": "dicom2fem/base.py",
"copies": "1",
"size": "32325",
"license": "bsd-3-clause",
"hash": -527888655861369100,
"line_mean": 24.5735759494,
"line_max": 87,
"alpha_frac": 0.5128847641,
"autogenerated": false,
"ratio": 4.0080595164290145,
"config_test": fals... |
# adopted from http://gremu.net/blog/2010/django-admin-read-only-permission/
from django.contrib.gis import admin
from django.core.exceptions import PermissionDenied
from ajax_select.fields import autoselect_fields_check_can_add
class ReadOnlyAdmin(admin.OSMGeoAdmin):
""" in order to get + popup functions subcla... | {
"repo_name": "ocefpaf/ODM2-Admin",
"path": "odm2admin/readonlyadmin.py",
"copies": "2",
"size": "3019",
"license": "mit",
"hash": -8767030944464297000,
"line_mean": 34.5176470588,
"line_max": 93,
"alpha_frac": 0.606492216,
"autogenerated": false,
"ratio": 4.246132208157524,
"config_test": fals... |
# Adopted from https://github.com/airaria/TextBrewer
# Apache License Version 2.0
from abc import ABC, abstractmethod
import torch
# x is between 0 and 1
from hanlp_common.configurable import AutoConfigurable
def linear_growth_weight_scheduler(x):
return x
def linear_decay_weight_scheduler(x):
return 1 - ... | {
"repo_name": "hankcs/HanLP",
"path": "hanlp/components/distillation/schedulers.py",
"copies": "1",
"size": "3585",
"license": "apache-2.0",
"hash": 3873581398111176700,
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"line_max": 116,
"alpha_frac": 0.6292887029,
"autogenerated": false,
"ratio": 3.635902636916836,
... |
# Adopted from https://github.com/airaria/TextBrewer
# Apache License Version 2.0
import torch
import torch.nn.functional as F
from hanlp_common.configurable import AutoConfigurable
def kd_mse_loss(logits_S, logits_T, temperature=1):
'''
Calculate the mse loss between logits_S and logits_T
:param logit... | {
"repo_name": "hankcs/HanLP",
"path": "hanlp/components/distillation/losses.py",
"copies": "1",
"size": "15061",
"license": "apache-2.0",
"hash": 8504197230807629000,
"line_mean": 51.8456140351,
"line_max": 510,
"alpha_frac": 0.6354823717,
"autogenerated": false,
"ratio": 3.1370547802541138,
"c... |
# Adopted from https://github.com/allenai/allennlp under Apache Licence 2.0.
# Changed the packaging and created a subclass CharCNNEmbedding
from typing import Union, Tuple, Optional, Callable
import torch
from torch import nn
from alnlp.modules.cnn_encoder import CnnEncoder
from alnlp.modules.time_distributed import ... | {
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"path": "hanlp/layers/embeddings/char_cnn.py",
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"size": "7436",
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"hash": -3051699420792094700,
"line_mean": 49.5714285714,
"line_max": 123,
"alpha_frac": 0.6280602637,
"autogenerated": false,
"ratio": 4.2,
"config_test": false,... |
# Adopted from https://github.com/allenai/allennlp under Apache Licence 2.0.
# Changed the packaging.
from typing import List, Set, Tuple, Dict
import numpy
def decode_mst(
energy: numpy.ndarray, length: int, has_labels: bool = True
) -> Tuple[numpy.ndarray, numpy.ndarray]:
"""Note: Counter to typical in... | {
"repo_name": "hankcs/HanLP",
"path": "hanlp/components/parsers/chu_liu_edmonds.py",
"copies": "1",
"size": "10923",
"license": "apache-2.0",
"hash": 7361882799741844000,
"line_mean": 33.7866242038,
"line_max": 96,
"alpha_frac": 0.6064268058,
"autogenerated": false,
"ratio": 3.9864963503649635,
... |
# adopted from https://github.com/danvk/RangeHTTPServer to allow CORS
import os
import re
try:
from http.server import SimpleHTTPRequestHandler
except ImportError:
from SimpleHTTPServer import SimpleHTTPRequestHandler
def copy_byte_range(infile, outfile, start=None, stop=None, bufsize=16*1024):
'''Like s... | {
"repo_name": "NabaviLab/CNV-Visualizer",
"path": "scripts/cors_server.py",
"copies": "1",
"size": "5850",
"license": "mit",
"hash": -809937830573544100,
"line_mean": 33.8214285714,
"line_max": 79,
"alpha_frac": 0.5994871795,
"autogenerated": false,
"ratio": 3.931451612903226,
"config_test": fa... |
# Adopted from https://github.com/KiroSummer/A_Syntax-aware_MTL_Framework_for_Chinese_SRL
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from torch.autograd import Variable
from .layer import DropoutLayer, HighwayLSTMCell, VariationalLSTMCell
def initializer_1d(input_... | {
"repo_name": "hankcs/HanLP",
"path": "hanlp/components/srl/span_rank/highway_variational_lstm.py",
"copies": "1",
"size": "12805",
"license": "apache-2.0",
"hash": 3651464124923053600,
"line_mean": 50.22,
"line_max": 117,
"alpha_frac": 0.5814135103,
"autogenerated": false,
"ratio": 3.66275743707... |
# Adopted from https://github.com/KiroSummer/A_Syntax-aware_MTL_Framework_for_Chinese_SRL
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
from hanlp.components.srl.span_rank.util import block_orth_normal_initializer
def get_tensor_np(t):
... | {
"repo_name": "hankcs/HanLP",
"path": "hanlp/components/srl/span_rank/layer.py",
"copies": "1",
"size": "16572",
"license": "apache-2.0",
"hash": 5060657347899016000,
"line_mean": 41.7113402062,
"line_max": 118,
"alpha_frac": 0.5599203476,
"autogenerated": false,
"ratio": 3.4705759162303664,
"c... |
# Adopted from https://github.com/KiroSummer/A_Syntax-aware_MTL_Framework_for_Chinese_SRL
# Inference functions for the SRL model.
import numpy as np
def decode_spans(span_starts, span_ends, span_scores, labels_inv):
"""
Args:
span_starts: [num_candidates,]
span_scores: [num_candidates, num_labe... | {
"repo_name": "hankcs/HanLP",
"path": "hanlp/components/srl/span_rank/inference_utils.py",
"copies": "1",
"size": "10363",
"license": "apache-2.0",
"hash": 5019844949522396000,
"line_mean": 41.646090535,
"line_max": 119,
"alpha_frac": 0.585255235,
"autogenerated": false,
"ratio": 3.38107667210440... |
# Adopted from https://github.com/lazyprogrammer/machine_learning_examples/blob/master/nlp_class2/tfidf_tsne.py
import json
import numpy as np
import matplotlib.pyplot as plt
from sklearn.utils import shuffle
from sklearn.manifold import TSNE
from datetime import datetime
# import os
# import sys
# sys.path.append(os... | {
"repo_name": "WayneDW/Sentiment-Analysis-in-Event-Driven-Stock-Price-Movement-Prediction",
"path": "archived/tfidf_tsne.py",
"copies": "1",
"size": "1827",
"license": "mit",
"hash": 7551169774974377000,
"line_mean": 27.1230769231,
"line_max": 111,
"alpha_frac": 0.6338259442,
"autogenerated": false... |
# Adopted from https://github.com/lazyprogrammer/machine_learning_examples/blob/master/rnn_class/util.py
# Adopted form https://github.com/lazyprogrammer/machine_learning_examples/blob/master/nlp_class2/util.py
import numpy as np
import pandas as pd
import string
import os
import operator
from nltk import pos_tag, word... | {
"repo_name": "WayneDW/Sentiment-Analysis-in-Event-Driven-Stock-Price-Movement-Prediction",
"path": "archived/utils.py",
"copies": "1",
"size": "4611",
"license": "mit",
"hash": 1564728552200417300,
"line_mean": 31.7021276596,
"line_max": 108,
"alpha_frac": 0.5755801345,
"autogenerated": false,
"... |
# Adopted from InspIRCd
# https://github.com/inspircd/inspircd/blob/master/include/numerics.h
RPL_WELCOME = "001" # 2812, not 1459
RPL_YOURHOSTIS = "002" # 2812, not 1459
RPL_SERVERCREATED = "003" # 2812, not 1459
RPL_SERVERVERSION = "004" # 2812, no... | {
"repo_name": "minus7/asif",
"path": "asif/command_codes.py",
"copies": "1",
"size": "6430",
"license": "mit",
"hash": 834228490929906400,
"line_mean": 39.9554140127,
"line_max": 160,
"alpha_frac": 0.4622083981,
"autogenerated": false,
"ratio": 3.3178534571723426,
"config_test": false,
"has_n... |
# Adopted from python-twitter's get_access_key.py # http://code.google.com/p/python-twitter/
import urllib, urllib2
import oauth2 as oauth
import twitter
try:
import json
except:
import simplejson as json
try:
from urlparse import parse_qsl
except:
from cgi import parse_qsl
from django.conf import s... | {
"repo_name": "jaysoo/django-twitter",
"path": "django_twitter/utils.py",
"copies": "1",
"size": "2218",
"license": "mit",
"hash": -6911303535511000000,
"line_mean": 34.7741935484,
"line_max": 116,
"alpha_frac": 0.7339945897,
"autogenerated": false,
"ratio": 3.360606060606061,
"config_test": fa... |
"""adopteitor URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.8/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-... | {
"repo_name": "smarbos/adopteitor-server",
"path": "urls.py",
"copies": "1",
"size": "1845",
"license": "mit",
"hash": 3384562134153681000,
"line_mean": 40.9318181818,
"line_max": 82,
"alpha_frac": 0.745799458,
"autogenerated": false,
"ratio": 3.372943327239488,
"config_test": false,
"has_no_... |
"""A double-ended queue with an optional maximum size."""
import time
from collections import deque
from greennet import greenlet
from greennet import get_hub
from greennet.hub import Wait
class QueueWait(Wait):
"""Abstract class to wait for a Queue event."""
__slots__ = ('queue',)
def _... | {
"repo_name": "dhain/greennet",
"path": "greennet/queue.py",
"copies": "1",
"size": "6766",
"license": "mit",
"hash": 5157170247582765000,
"line_mean": 25.5333333333,
"line_max": 71,
"alpha_frac": 0.49527047,
"autogenerated": false,
"ratio": 4.095641646489105,
"config_test": false,
"has_no_ke... |
"""A doubly-linked list"""
class LLNode(object):
"""A single node in the list.
The pointers to the next and previous nodes should not be manipulated
directly, but only through the LList class. Directly setting the pointers
can create an inconsistent LList object.
Attributes:
value: the va... | {
"repo_name": "johnwilmes/py-data-structures",
"path": "py_data_structures/llist.py",
"copies": "1",
"size": "4127",
"license": "mit",
"hash": 3317669000760705000,
"line_mean": 29.5703703704,
"line_max": 80,
"alpha_frac": 0.5825054519,
"autogenerated": false,
"ratio": 4.185598377281948,
"config... |
# adpated from http://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.signal.correlate2d.html
import matplotlib.pyplot as plt
import numpy as np
from scipy import signal
from scipy import misc
print("cross correlation demo")
face = misc.face() - misc.face().mean()
face = face.sum(-1)
template = np.copy(face... | {
"repo_name": "probml/pyprobml",
"path": "scripts/xcorr_demo.py",
"copies": "1",
"size": "1116",
"license": "mit",
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"line_mean": 26.9,
"line_max": 103,
"alpha_frac": 0.7123655914,
"autogenerated": false,
"ratio": 2.70873786407767,
"config_test": false,
"has_no_key... |
""" A drag drawn line. """
from __future__ import with_statement
from enable.api import Line
from traits.api import Instance
from drawing_tool import DrawingTool
class DragLine(DrawingTool):
"""
A drag drawn line. This is not a straight line, but can be a free-form,
curved path.
"""
# Overrid... | {
"repo_name": "tommy-u/enable",
"path": "enable/drawing/drag_line.py",
"copies": "1",
"size": "2630",
"license": "bsd-3-clause",
"hash": -7839375331935217000,
"line_mean": 31.0731707317,
"line_max": 78,
"alpha_frac": 0.4931558935,
"autogenerated": false,
"ratio": 4.361525704809287,
"config_test... |
""" A drag drawn polygon. """
from __future__ import with_statement
from enable.primitives.api import Polygon
from enable.api import Pointer
from pyface.action.api import MenuManager
from traits.api import Delegate, Instance
from drawing_tool import DrawingTool
class DragPolygon(DrawingTool):
""" A drag drawn p... | {
"repo_name": "tommy-u/enable",
"path": "enable/drawing/drag_polygon.py",
"copies": "1",
"size": "3886",
"license": "bsd-3-clause",
"hash": 8425185192589923000,
"line_mean": 27.3649635036,
"line_max": 79,
"alpha_frac": 0.5247040659,
"autogenerated": false,
"ratio": 4.284454244762955,
"config_te... |
"""Adres API tests."""
import unittest
import postcodepy
from postcodepy import typedefs
from postcodepy import PostcodeError
from . import unittestsetup
try:
from nose_parameterized import parameterized, param
except:
print("*** Please install 'nose_parameterized' to run these tests ***")
exit(1)
import ... | {
"repo_name": "hootnot/postcode-api-wrapper",
"path": "tests/test_adres_api.py",
"copies": "1",
"size": "10115",
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"line_mean": 32.7166666667,
"line_max": 88,
"alpha_frac": 0.5672763223,
"autogenerated": false,
"ratio": 4.233989116785266,
"config_tes... |
# Adrian deWynter, 2016
# Check that Spark is working
from pyspark.sql import Row
data = [('Alice', 1), ('Bob', 2), ('Bill', 4)]
df = sqlContext.createDataFrame(data, ['name', 'age'])
fil = df.filter(df.age > 3).collect()
print fil
# If the Spark job doesn't work properly this will raise an AssertionError
assert fil =... | {
"repo_name": "adewynter/Tools",
"path": "Notebooks/Spark/Before starting.py",
"copies": "1",
"size": "1903",
"license": "mit",
"hash": -7091245741693466000,
"line_mean": 35.6153846154,
"line_max": 105,
"alpha_frac": 0.6931161324,
"autogenerated": false,
"ratio": 3.059485530546624,
"config_test... |
# Adrian deWynter, 2016
# Implementation of Adam, as per the original paper available at https://arxiv.org/pdf/1412.6980.pdf
# I tried to make it as versatile as possible, but there are caveats:
# Most operations are element-wise, and it's written with NN use in mind.
# TODO: Adam's update rule is unimplemented.
# TOD... | {
"repo_name": "adewynter/Tools",
"path": "MLandDS/MachineLearning/Adam.py",
"copies": "1",
"size": "2599",
"license": "mit",
"hash": -5465066992736361000,
"line_mean": 39.625,
"line_max": 114,
"alpha_frac": 0.6217776068,
"autogenerated": false,
"ratio": 3.208641975308642,
"config_test": false,
... |
# Adrian deWynter, 2016
# Implementation of:
# - GCD
# - LCM
# - LCMM
# - LCM (sequence)
# - XOR-based swap
# - power set generator
def gcd(a, b):
"""Return greatest common divisor using Euclid's Algorithm."""
while b:
a, b = b, a % b
return a
def lcm(a, b):
"""Return lowest common... | {
"repo_name": "adewynter/Tools",
"path": "Algorithms/numberTheory/util.py",
"copies": "1",
"size": "3478",
"license": "mit",
"hash": -6212684117061600000,
"line_mean": 22.0397350993,
"line_max": 89,
"alpha_frac": 0.459746981,
"autogenerated": false,
"ratio": 3.217391304347826,
"config_test": fa... |
# Adrian deWynter, 2016
import heapq
import math
def dijkstra(adj, cost, N, s):
visited = {}
ans = {}
Q = []
for k,v in adj.iteritems():
if k != s:
heapq.heappush(Q, [float('inf'), k, float('inf')])
visited[k] = 0
ans[k] = -1
heapq... | {
"repo_name": "adewynter/Tools",
"path": "Algorithms/graphAlgorithms/dijkstra.py",
"copies": "1",
"size": "2037",
"license": "mit",
"hash": -1901752516514913500,
"line_mean": 22.6860465116,
"line_max": 62,
"alpha_frac": 0.3190967108,
"autogenerated": false,
"ratio": 3.67027027027027,
"config_te... |
# Adrian deWynter, 2016
import heapq
def dijkstra(adj, cost, N, s):
visited = {}
ans = {}
Q = []
for k in range(1, N+1):
if k != s:
heapq.heappush(Q, [99999, k, 99999])
visited[k] = 0
ans[k] = 99999
heapq.heappush(Q, [0, s, 0])
... | {
"repo_name": "adewynter/Tools",
"path": "Algorithms/graphAlgorithms/prim.py",
"copies": "1",
"size": "1512",
"license": "mit",
"hash": 148112077675762530,
"line_mean": 20.6,
"line_max": 57,
"alpha_frac": 0.3670634921,
"autogenerated": false,
"ratio": 3.210191082802548,
"config_test": false,
... |
# Adrian deWynter, 2016
import nltk
nltk.download()
## Working with custom files
file = open('PATH')
temp = file.read()
tokens = nltk.word_tokenize(temp)
text = nltk.Text(tokens)
# can also be an URL
from urllib import request
url = ""
response = request.urlopen(url)
raw = response.read().decode('encoding')
# then jus... | {
"repo_name": "adewynter/Tools",
"path": "MLandDS/NLTK.py",
"copies": "1",
"size": "1762",
"license": "mit",
"hash": 5558307196375385000,
"line_mean": 28.3833333333,
"line_max": 134,
"alpha_frac": 0.7412031782,
"autogenerated": false,
"ratio": 3.23302752293578,
"config_test": false,
"has_no_k... |
# Adrian deWynter, 2016
import pandas as pd
import numpy as np
import scipy.io
from sklearn.decomposition import PCA, RandomizedPCA
from plyfile import PlyData, PlyElement
from sklearn import manifold
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import matplotlib
import random, math, datet... | {
"repo_name": "adewynter/Tools",
"path": "MLandDS/DataScience/appliedExamples.py",
"copies": "1",
"size": "2419",
"license": "mit",
"hash": 10942366052931042,
"line_mean": 27.1395348837,
"line_max": 100,
"alpha_frac": 0.7007027697,
"autogenerated": false,
"ratio": 2.7027932960893857,
"config_te... |
# Adrian deWynter, 2016
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from sklearn import linear_model
matplotlib.style.use('ggplot')
def drawLine(model, X_test, y_test, title):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(X_test, y_test, c='g', mark... | {
"repo_name": "adewynter/Tools",
"path": "MLandDS/MachineLearning/LinearRegression.py",
"copies": "1",
"size": "1362",
"license": "mit",
"hash": 4197394115512178700,
"line_mean": 24.7647058824,
"line_max": 79,
"alpha_frac": 0.6534508076,
"autogenerated": false,
"ratio": 2.6811023622047245,
"con... |
# Adrian deWynter, 2016
import sys
import queue
class Vertex:
def __init__(self):
self.edges = {}
def getEdges(self):
return self.edges
def addEdge(self, value, distance):
if value not in self.edges or distance < self.edges[value]:
self.edges[value] = distance
class... | {
"repo_name": "adewynter/Tools",
"path": "Algorithms/graphAlgorithms/Dijkstra_forreal.py",
"copies": "1",
"size": "1930",
"license": "mit",
"hash": 99598304461555870,
"line_mean": 21.7176470588,
"line_max": 67,
"alpha_frac": 0.4968911917,
"autogenerated": false,
"ratio": 3.634651600753296,
"con... |
# Adrian deWynter, 2016
# Insertion sort and bubble sort are just the same thing
# only that insertion sort will generate a new array every time...
def insertionSort(ar):
swaps = 0
index = 1
while index < len(ar) - 1:
for i in xrange(index, -1, -1):
temp = ar[i]
if ar[i... | {
"repo_name": "adewynter/Tools",
"path": "Algorithms/sortingAndSearch/sorting.py",
"copies": "1",
"size": "1374",
"license": "mit",
"hash": 338078053452162300,
"line_mean": 21.9166666667,
"line_max": 66,
"alpha_frac": 0.5087336245,
"autogenerated": false,
"ratio": 3.3925925925925924,
"config_te... |
# Adrian deWynter, 2016
# I wrote this application because I needed to calculate
# how much paid time out (PTO) I could take given a certain
# day. Lel.
# Only works in 'Murica because we have different holidays :)
# I think I omitted static holidays (Thanksgiving, for example)
import matplotlib.pyplot as plt
import... | {
"repo_name": "adewynter/Tools",
"path": "Scripts/ptoCalculator.py",
"copies": "1",
"size": "4876",
"license": "mit",
"hash": -3569800904870501000,
"line_mean": 27.3546511628,
"line_max": 95,
"alpha_frac": 0.661197703,
"autogenerated": false,
"ratio": 2.6835443037974684,
"config_test": false,
... |
# Adrian deWynter, 2016
#Longest common subsequence
def LCS(A, B):
M = [[None]*(len(B) + 1) for _ in xrange(len(A) + 1)]
for i in range(len(A) + 1):
for j in range(len(B) + 1):
if i == 0 or j == 0:
M[i][j] = 0
elif A[i - 1] == B[j - 1]:
... | {
"repo_name": "adewynter/Tools",
"path": "Algorithms/dynamicProgramming/LCS.py",
"copies": "1",
"size": "1050",
"license": "mit",
"hash": -8236671503420694000,
"line_mean": 22.8636363636,
"line_max": 57,
"alpha_frac": 0.3476190476,
"autogenerated": false,
"ratio": 2.7777777777777777,
"config_te... |
# Adrian deWynter, 2016
'''
Adrian deWynter (2016)
Notebook corresponding to an Apache Spark class I once took.
This one implements a math review.
'''
#####
# Remember, databricks has a built-in function (display) that isn't available elsewhere.
# This code isn't meant to run anywhere that isn't Spark -- and some data... | {
"repo_name": "adewynter/Tools",
"path": "Notebooks/Spark-ML/Math review.py",
"copies": "1",
"size": "6663",
"license": "mit",
"hash": 2638941619262329300,
"line_mean": 30.1401869159,
"line_max": 98,
"alpha_frac": 0.6644154285,
"autogenerated": false,
"ratio": 2.9262187088274043,
"config_test":... |
# Adrian deWynter, 2016
'''
Adrian deWynter (2016)
Notebook corresponding to an Apache Spark class I once took.
This one implements (another) a word count application.
'''
#####
# Remember, databricks has a built-in function (display) that isn't available elsewhere.
# This code isn't meant to run anywhere that isn't S... | {
"repo_name": "adewynter/Tools",
"path": "Notebooks/Spark-ML/Word count.py",
"copies": "1",
"size": "4165",
"license": "mit",
"hash": -2722293236089868000,
"line_mean": 29.8592592593,
"line_max": 126,
"alpha_frac": 0.6931572629,
"autogenerated": false,
"ratio": 3.342696629213483,
"config_test":... |
# Adrian deWynter, 2016
'''
Adrian deWynter (2016)
Notebook corresponding to an Apache Spark class I once took.
This one implements a supervised learning pipeline with the Million Song Dataset.
'''
#####
# Remember, databricks has a built-in function (display) that isn't available elsewhere.
# This code isn't meant to... | {
"repo_name": "adewynter/Tools",
"path": "Notebooks/Spark-ML/Linear Regression.py",
"copies": "1",
"size": "22285",
"license": "mit",
"hash": 7212473767967123000,
"line_mean": 41.2884250474,
"line_max": 122,
"alpha_frac": 0.675072919,
"autogenerated": false,
"ratio": 3.12333566923616,
"config_t... |
# Adrian deWynter, 2016
'''
Adrian deWynter (2016)
Notebook corresponding to an Apache Spark class I once took.
This one is a (very) basic intro to Spark.
'''
#####
# Remember, databricks has a built-in function (display) that isn't available elsewhere.
# This code isn't meant to run anywhere that isn't Spark -- and s... | {
"repo_name": "adewynter/Tools",
"path": "Notebooks/Spark/Intro to Spark.py",
"copies": "1",
"size": "4665",
"license": "mit",
"hash": 4513615648289129500,
"line_mean": 29.1032258065,
"line_max": 114,
"alpha_frac": 0.7026795284,
"autogenerated": false,
"ratio": 3.221685082872928,
"config_test":... |
# Adrian deWynter, 2016
'''
Adrian deWynter (2016)
Notebook corresponding to an Apache Spark class I once took.
This one pertains to analysis of logs and traffic to a website.
'''
#####
# Remember, databricks has a built-in function (display) that isn't available elsewhere.
# This code isn't meant to run anywhere that... | {
"repo_name": "adewynter/Tools",
"path": "Notebooks/Spark/Traffic analysis.py",
"copies": "1",
"size": "16244",
"license": "mit",
"hash": -6202390171504634000,
"line_mean": 35.9204545455,
"line_max": 160,
"alpha_frac": 0.6546417139,
"autogenerated": false,
"ratio": 3.125048095421316,
"config_te... |
# Adrian deWynter, 2016
'''
Adrian deWynter (2016)
Notebook corresponding to an Apache Spark class I once took.
This one pertains to analysis of texts, more specifically word count.
'''
#####
# Remember, databricks has a built-in function (display) that isn't available elsewhere.
# This code isn't meant to run anywher... | {
"repo_name": "adewynter/Tools",
"path": "Notebooks/Spark/Text analysis.py",
"copies": "1",
"size": "4091",
"license": "mit",
"hash": 3825622731199805000,
"line_mean": 33.9743589744,
"line_max": 104,
"alpha_frac": 0.6968956245,
"autogenerated": false,
"ratio": 3.715712988192552,
"config_test": ... |
# Adrian deWynter, 2016
def BFS(adj, cost, s, V):
Q = []
Q.append((0, s, 0))
while Q:
d, u, p = Q.pop(0)
if u in adj:
for n in adj[u]:
if cost[n] == 0:
d = cost[u] + 6
cost[n] = d
... | {
"repo_name": "adewynter/Tools",
"path": "Algorithms/graphAlgorithms/BFSshortestreach.py",
"copies": "1",
"size": "1352",
"license": "mit",
"hash": -4483557394305601000,
"line_mean": 20.140625,
"line_max": 62,
"alpha_frac": 0.3150887574,
"autogenerated": false,
"ratio": 3.5116883116883115,
"con... |
# Adrian deWynter, 2016
'''Python definitions of factoring algorithms, and prime list generation.'''
def factor1(n):
"""returns a list of prime factors of n"""
d = 2
factors = [ ] #empty list
while n > 1:
if n % d == 0:
factors.append(d)
n = n/d
else:
d = d + 1
... | {
"repo_name": "adewynter/Tools",
"path": "Algorithms/numberTheory/factoring.py",
"copies": "1",
"size": "3409",
"license": "mit",
"hash": -5712238215096947000,
"line_mean": 29.7207207207,
"line_max": 80,
"alpha_frac": 0.5734819595,
"autogenerated": false,
"ratio": 3.6035940803382664,
"config_te... |
# Adrian deWynter, 2016
# This dataset has call records for 10 users tracked over the course of 3 years.
# Use K Means to find out where the users live, work, and commute.
import pandas as pd
from datetime import timedelta
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import matplotlib
# People a... | {
"repo_name": "adewynter/Tools",
"path": "MLandDS/MachineLearning/Kmeans-CellTowers.py",
"copies": "1",
"size": "3733",
"license": "mit",
"hash": -5375882972834845000,
"line_mean": 34.5619047619,
"line_max": 119,
"alpha_frac": 0.7173854808,
"autogenerated": false,
"ratio": 3.0648604269293926,
"... |
# Adrian deWynter, 2016
# This dataset is nasty, so we are also going to use some PCA.
import numpy as np
import pandas as pd
from sklearn import preprocessing
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import matplotlib
import math
PLOT_TYPE_TEXT = False
PLOT_VECTORS = True
matplotlib.style.u... | {
"repo_name": "adewynter/Tools",
"path": "MLandDS/MachineLearning/Kmeans-CustomerAnalysis.py",
"copies": "1",
"size": "3531",
"license": "mit",
"hash": 3102393988818930000,
"line_mean": 31.7037037037,
"line_max": 110,
"alpha_frac": 0.7063154914,
"autogenerated": false,
"ratio": 2.7979397781299524... |
# Adrian deWynter, 2017
# Implementation of:
# - Factorial function
# - Number of zeros in factorial
# - Big number mod M
# - Equilateral Pascal's Triangle
# A recursive implementation of factorial.
def factorial(N):
if N == 0 or N == 1:
return 1
if N == 2:
return 2
else:
return N*factorial(N-1)
# Calculat... | {
"repo_name": "adewynter/Tools",
"path": "Algorithms/numberTheory/util2.py",
"copies": "1",
"size": "1680",
"license": "mit",
"hash": 1398668973971843600,
"line_mean": 19.0119047619,
"line_max": 69,
"alpha_frac": 0.6113095238,
"autogenerated": false,
"ratio": 2.393162393162393,
"config_test": f... |
# Adrian deWynter, 2017
# Implementation of various algorithms
# applied to strings
# Given a long string find the greater
# number that is also a palindrome.
def nextPalindrome(S):
def isPalindrome(x): return x == x[::-1]
while True:
S = S + 1
if isPalindrome(S): return S
# Given two words A,B find if A = r... | {
"repo_name": "adewynter/Tools",
"path": "Algorithms/stringOps.py",
"copies": "1",
"size": "4317",
"license": "mit",
"hash": -1284095204729518800,
"line_mean": 17.7695652174,
"line_max": 60,
"alpha_frac": 0.5788742182,
"autogenerated": false,
"ratio": 2.3060897435897436,
"config_test": false,
... |
# Adrian deWynter, 2017
# Random exercises for linked lists
# and stuff I couldn't fit in the
# other categories.
# Sum of two linked lists -- pick the smallest
# and padd with zeros
def twoNums(L1,L2):
# Assume this is a linked list
ans = [[0,i] for i in range(max(len(L2),len(L1)))]
carry = 0
# Something like ... | {
"repo_name": "adewynter/Tools",
"path": "Algorithms/exercises.py",
"copies": "1",
"size": "4427",
"license": "mit",
"hash": 8002738199428586000,
"line_mean": 18.1688311688,
"line_max": 64,
"alpha_frac": 0.6428732776,
"autogenerated": false,
"ratio": 2.5648899188876015,
"config_test": false,
... |
# Adrian deWynter
import bisect
import random
# Predefined classes
# Non deterministic finite automaton
class NFA(object):
EPSILON,ANY = object(),object()
def __init__(self, start_state):
self.transitions = {}
self.final_states = set()
self._start_state = start_state
@property
def start_state(self): re... | {
"repo_name": "adewynter/Tools",
"path": "Algorithms/dataStructures/levensheinAutomata.py",
"copies": "1",
"size": "5905",
"license": "mit",
"hash": 8737966731999849000,
"line_mean": 25.4843049327,
"line_max": 118,
"alpha_frac": 0.666553768,
"autogenerated": false,
"ratio": 2.8132444020962364,
... |
# Adrian deWynter
######################################################
# General info
#####################################################
# Takes in a document and turns it into a k-ary tree.
# We need to modify the binary tree structure to support
# siblings. I.e., for the sample provided in the .pdf, the
# output... | {
"repo_name": "adewynter/Tools",
"path": "Algorithms/Exercises/Python/parser.py",
"copies": "1",
"size": "5954",
"license": "mit",
"hash": -5297029871998038000,
"line_mean": 28.6268656716,
"line_max": 90,
"alpha_frac": 0.6451125294,
"autogenerated": false,
"ratio": 3.512684365781711,
"config_te... |
"""Adrian language AST nodes."""
from dataclasses import dataclass, field
from typing import Optional, Tuple, List
# Types and expressions
class Type:
pass
class Expression:
pass
# @Cleanup: rearrange fields; do we need is_only_named field?
# @Cleanup: move to ArgumentDeclaration
@dataclass
class Argumen... | {
"repo_name": "adrian-lang/adrian",
"path": "adrian-cpp-compiler-in-py/adrian_cpp_py/adrian_ast.py",
"copies": "1",
"size": "7494",
"license": "bsd-3-clause",
"hash": 8281820577030127000,
"line_mean": 22.6403785489,
"line_max": 84,
"alpha_frac": 0.6817453963,
"autogenerated": false,
"ratio": 3.67... |
# Adrian Rosebrock CV boilerplate
# import the necessary packages
from django.views.decorators.csrf import csrf_exempt
from django.http import JsonResponse
import numpy as np
import urllib
import cv2
import base64
@csrf_exempt
def detection(request):
# initialize the data dictionary to be returned by the request
... | {
"repo_name": "RoasteryHub/lavie-selekopi",
"path": "KopiSelection/views.py",
"copies": "1",
"size": "4572",
"license": "mit",
"hash": -7081262267436723000,
"line_mean": 35.2857142857,
"line_max": 115,
"alpha_frac": 0.5962379703,
"autogenerated": false,
"ratio": 3.450566037735849,
"config_test"... |
# Adrian Rosebrock Gradient descent with Python
# http://www.pyimagesearch.com/2016/10/10/gradient-descent-with-python/
# import the necessary packages
import matplotlib.pyplot as plt
from sklearn.datasets.samples_generator import make_blobs
import numpy as np
import argparse
def sigmoid_activation(x):
# compute and... | {
"repo_name": "mbayon/TFG-MachineLearning",
"path": "Gradient-Descent-Roosebrock/gradient-descent-rosebrock.py",
"copies": "1",
"size": "3811",
"license": "mit",
"hash": -6732967676216497000,
"line_mean": 35.3047619048,
"line_max": 71,
"alpha_frac": 0.7173970087,
"autogenerated": false,
"ratio": ... |
# A driver for rendering 2D images using the FijiBento alignment project
# The input is a directory that contains image files (tilespecs) where each file is of a single section,
# and the output is a 2D montage of these sections
#
# requires:
# - java (executed from the command line)
# -
import sys
import os
import a... | {
"repo_name": "Rhoana/rh_aligner",
"path": "old/2d_render_driver.py",
"copies": "1",
"size": "2202",
"license": "mit",
"hash": -2138760151976209700,
"line_mean": 36.3220338983,
"line_max": 130,
"alpha_frac": 0.6825613079,
"autogenerated": false,
"ratio": 3.627677100494234,
"config_test": false,... |
# A driver for rendering 2D images using the FijiBento alignment project
# The input is a tilespec (json) file of a single section,
# and the output is a directory with squared tiles of the 2D montage of the sections
#
# requires:
# - java (executed from the command line)
# -
import sys
import os
import argparse
impo... | {
"repo_name": "Rhoana/rh_aligner",
"path": "old/2d_render_tiles_driver.py",
"copies": "1",
"size": "3533",
"license": "mit",
"hash": -124119443825206060,
"line_mean": 40.5647058824,
"line_max": 145,
"alpha_frac": 0.658080951,
"autogenerated": false,
"ratio": 3.561491935483871,
"config_test": fa... |
# A driver for running 2D alignment using the FijiBento alignment project
# The input is a directory that contains image files (tiles), and the output is a 2D montage of these files
# Activates ComputeSIFTFeaturs -> MatchSIFTFeatures -> OptimizeMontageTransfrom
# and the result can then be rendered if needed
#
# requir... | {
"repo_name": "Rhoana/rh_aligner",
"path": "old/2d_align_affine_driver.py",
"copies": "1",
"size": "5463",
"license": "mit",
"hash": 7823267391598086000,
"line_mean": 40.3863636364,
"line_max": 173,
"alpha_frac": 0.6979681494,
"autogenerated": false,
"ratio": 3.4597846738442053,
"config_test": ... |
# A driver for running 3D alignment using the FijiBento alignment project
# The input is two tile spec files with their 2d alignment
# and each file has also a z axis (layer) index, and the output is a tile spec after 3D alignment
# Activates ComputeLayerSIFTFeaturs -> MatchLayersSIFTFeatures -> FilterRansac -> Optimiz... | {
"repo_name": "Rhoana/rh_aligner",
"path": "old/3d_align_affine_driver.py",
"copies": "1",
"size": "10045",
"license": "mit",
"hash": 1061204726325815600,
"line_mean": 42.864628821,
"line_max": 167,
"alpha_frac": 0.6549527128,
"autogenerated": false,
"ratio": 3.3394281914893615,
"config_test": ... |
"""A dropdown completer widget for the qtconsole."""
from qtconsole.qt import QtCore, QtGui
class CompletionWidget(QtGui.QListWidget):
""" A widget for GUI tab completion.
"""
#--------------------------------------------------------------------------
# 'QObject' interface
#---------------------... | {
"repo_name": "nitin-cherian/LifeLongLearning",
"path": "Python/PythonProgrammingLanguage/Encapsulation/encap_env/lib/python3.5/site-packages/qtconsole/completion_widget.py",
"copies": "10",
"size": "6165",
"license": "mit",
"hash": 2845136489873942500,
"line_mean": 38.7741935484,
"line_max": 79,
"al... |
"""A dropdown completer widget for the qtconsole."""
import os
import sys
from qtpy import QtCore, QtGui, QtWidgets
class CompletionWidget(QtWidgets.QListWidget):
""" A widget for GUI tab completion.
"""
#--------------------------------------------------------------------------
# 'QObject' interfa... | {
"repo_name": "sserrot/champion_relationships",
"path": "venv/Lib/site-packages/qtconsole/completion_widget.py",
"copies": "1",
"size": "8391",
"license": "mit",
"hash": 1386138399002421500,
"line_mean": 38.2102803738,
"line_max": 85,
"alpha_frac": 0.5477297104,
"autogenerated": false,
"ratio": 4... |
"""A dropdown completer widget for the qtconsole."""
# System library imports
from IPython.external.qt import QtCore, QtGui
class CompletionWidget(QtGui.QListWidget):
""" A widget for GUI tab completion.
"""
#--------------------------------------------------------------------------
# 'QObject' inte... | {
"repo_name": "mattvonrocketstein/smash",
"path": "smashlib/ipy3x/qt/console/completion_widget.py",
"copies": "1",
"size": "5371",
"license": "mit",
"hash": 3849019426117285000,
"line_mean": 37.3642857143,
"line_max": 79,
"alpha_frac": 0.5187115993,
"autogenerated": false,
"ratio": 4.851851851851... |
"""A drop-in replacement for tempfile that adds the errors argument to
NamedTemporary and TemporaryFile.
"""
import os
import io
import tempfile
from tempfile import * # pylint: disable=wildcard-import, ungrouped-imports
__all__ = tempfile.__all__
def _patch_encoding(ctor, mode, **kwargs):
"Wrap the resulting i... | {
"repo_name": "nxdevel/nx_tempfile",
"path": "nx_tempfile/__init__.py",
"copies": "1",
"size": "2737",
"license": "mit",
"hash": 605129901143096200,
"line_mean": 35.9864864865,
"line_max": 96,
"alpha_frac": 0.6737303617,
"autogenerated": false,
"ratio": 4.330696202531645,
"config_test": false,
... |
ADS_ATTR_CLEAR = ( 1 )
ADS_ATTR_UPDATE = ( 2 )
ADS_ATTR_APPEND = ( 3 )
ADS_ATTR_DELETE = ( 4 )
ADS_EXT_MINEXTDISPID = ( 1 )
ADS_EXT_MAXEXTDISPID = ( 16777215 )
ADS_EXT_INITCREDENTIALS = ( 1 )
ADS_EXT_INITIALIZE_COMPLETE = ( 2 )
ADS_SEARCHPREF_ASYNCHRONOUS = 0
ADS_SEARCHPREF_DEREF_ALIASES = 1
ADS_SEARCHPREF_SIZE... | {
"repo_name": "kkdd/arangodb",
"path": "3rdParty/V8-4.3.61/third_party/python_26/Lib/site-packages/win32comext/adsi/adsicon.py",
"copies": "17",
"size": "12544",
"license": "apache-2.0",
"hash": -3502810082107859000,
"line_mean": 36.3333333333,
"line_max": 91,
"alpha_frac": 0.7641103316,
"autogener... |
ADS_ATTR_CLEAR = ( 1 )
ADS_ATTR_UPDATE = ( 2 )
ADS_ATTR_APPEND = ( 3 )
ADS_ATTR_DELETE = ( 4 )
ADS_EXT_MINEXTDISPID = ( 1 )
ADS_EXT_MAXEXTDISPID = ( 16777215 )
ADS_EXT_INITCREDENTIALS = ( 1 )
ADS_EXT_INITIALIZE_COMPLETE = ( 2 )
ADS_SEARCHPREF_ASYNCHRONOUS = 0
ADS_SEARCHPREF_DEREF_ALIASES = 1
ADS_SEAR... | {
"repo_name": "Southpaw-TACTIC/Team",
"path": "src/python/Lib/site-packages/win32comext/adsi/adsicon.py",
"copies": "1",
"size": "12880",
"license": "epl-1.0",
"hash": 6591334290745903000,
"line_mean": 36.3333333333,
"line_max": 91,
"alpha_frac": 0.7441770186,
"autogenerated": false,
"ratio": 2.2... |
""" ADT stands for Algebraic data type
"""
from rhetoric.exceptions import ConfigurationError
class ADTConfiguratorMixin(object):
def update_adt_registry(self, adt_meta):
"""
:type adt_meta: dict
"""
adt_type = adt_meta['type']
self.adt[adt_type] = adt_meta
def check... | {
"repo_name": "avanov/Rhetoric",
"path": "rhetoric/config/adt.py",
"copies": "1",
"size": "1118",
"license": "mit",
"hash": 8713968545831401000,
"line_mean": 35.064516129,
"line_max": 81,
"alpha_frac": 0.4821109123,
"autogenerated": false,
"ratio": 4.4189723320158105,
"config_test": false,
"h... |
""" A dual network policy gradient RL architecture: Model network learns a representation of the environment on the
basis of observations it receives from the interactions between the PolicyNet - encoding the agent - and the true
environment. PolicyNet learns its optimal policy by learning from the simulated data provi... | {
"repo_name": "demelin/learning_reinforcement_learning",
"path": "model_based_rl.py",
"copies": "1",
"size": "17484",
"license": "mit",
"hash": 1691261135482080800,
"line_mean": 49.386167147,
"line_max": 120,
"alpha_frac": 0.5899107756,
"autogenerated": false,
"ratio": 3.7803243243243245,
"conf... |
#adults dataset
def load_adult():
"""loads adult dataset"""
remove_sp = lambda n: n.replace(' ', '')
last_column = lambda i: i.pop(-1)
binary_= lambda u: 0 if u == '<=50K' else 1
defs_ = [
{'age': None},
{'workclass': ['Private', '?', 'Self-emp-not-inc', 'Self-emp-inc', 'Federal-go... | {
"repo_name": "saifuddin778/pwperceptrons",
"path": "datasets.py",
"copies": "1",
"size": "7411",
"license": "mit",
"hash": -538180132751841340,
"line_mean": 31.9377777778,
"line_max": 569,
"alpha_frac": 0.4805019566,
"autogenerated": false,
"ratio": 3.106035205364627,
"config_test": false,
"... |
"""A dumb and slow but simple dbm clone.
For database spam, spam.dir contains the index (a text file),
spam.bak *may* contain a backup of the index (also a text file),
while spam.dat contains the data (a binary file).
XXX TO DO:
- seems to contain a bug when updating...
- reclaim free space (currently, space once o... | {
"repo_name": "jsilter/scipy",
"path": "scipy/weave/_dumbdbm_patched.py",
"copies": "15",
"size": "4514",
"license": "bsd-3-clause",
"hash": -2137187660849858600,
"line_mean": 27.3899371069,
"line_max": 82,
"alpha_frac": 0.5350022153,
"autogenerated": false,
"ratio": 3.5265625,
"config_test": f... |
""" A dumb and slow but simple dbm clone.
For database spam, spam.dir contains the index (a text file),
spam.bak *may* contain a backup of the index (also a text file),
while spam.dat contains the data (a binary file).
XXX TO DO:
- seems to contain a bug when updating...
- reclaim free space (currently, space once ... | {
"repo_name": "macronucleus/chromagnon",
"path": "Chromagnon/Priithon/plt/dumbdbm_patched.py",
"copies": "1",
"size": "4286",
"license": "mit",
"hash": 4994364099795589000,
"line_mean": 28.156462585,
"line_max": 78,
"alpha_frac": 0.5258982734,
"autogenerated": false,
"ratio": 3.507364975450082,
... |
"""A dumb and slow but simple dbm clone.
For database spam, spam.dir contains the index (a text file),
spam.bak *may* contain a backup of the index (also a text file),
while spam.dat contains the data (a binary file).
XXX TO DO:
- seems to contain a bug when updating...
- reclaim free space (currently, sp... | {
"repo_name": "nmercier/linux-cross-gcc",
"path": "win32/bin/Lib/dumbdbm.py",
"copies": "2",
"size": "9187",
"license": "bsd-3-clause",
"hash": -4357775524957528000,
"line_mean": 34.8955823293,
"line_max": 78,
"alpha_frac": 0.5720039186,
"autogenerated": false,
"ratio": 3.992611907866145,
"conf... |
"""A dummy audio actor for use in tests.
This class implements the audio API in the simplest way possible. It is used in
tests of the core and backends.
"""
from __future__ import absolute_import, unicode_literals
from mopidy import audio
import pykka
def create_proxy(config=None, mixer=None):
return DummyAud... | {
"repo_name": "rectalogic/mopidy-pandora",
"path": "tests/dummy_audio.py",
"copies": "2",
"size": "3848",
"license": "apache-2.0",
"hash": 2276735564828996000,
"line_mean": 27.2941176471,
"line_max": 88,
"alpha_frac": 0.6068087318,
"autogenerated": false,
"ratio": 3.9629248197734293,
"config_te... |
"""A dummy audio actor for use in tests.
This class implements the audio API in the simplest way possible. It is used in
tests of the core and backends.
"""
from __future__ import unicode_literals
import pykka
from .constants import PlaybackState
from .listener import AudioListener
class DummyAudio(pykka.Threadin... | {
"repo_name": "woutervanwijk/mopidy",
"path": "mopidy/audio/dummy.py",
"copies": "1",
"size": "3097",
"license": "apache-2.0",
"hash": 3544604332561993700,
"line_mean": 26.6517857143,
"line_max": 79,
"alpha_frac": 0.6131740394,
"autogenerated": false,
"ratio": 4.080368906455863,
"config_test": ... |
"""A dummy audio actor for use in tests.
This class implements the audio API in the simplest way possible. It is used in
tests of the core and backends.
"""
import pykka
from mopidy import audio
def create_proxy(config=None, mixer=None):
return DummyAudio.start(config, mixer).proxy()
# TODO: reset position ... | {
"repo_name": "jodal/mopidy",
"path": "tests/dummy_audio.py",
"copies": "3",
"size": "3900",
"license": "apache-2.0",
"hash": -6468374728811022000,
"line_mean": 27.2608695652,
"line_max": 79,
"alpha_frac": 0.5984615385,
"autogenerated": false,
"ratio": 3.9959016393442623,
"config_test": false,
... |
"""A dummy backend for use in tests.
This backend implements the backend API in the simplest way possible. It is
used in tests of the frontends.
"""
from __future__ import absolute_import, unicode_literals
import pykka
from mopidy import backend
from mopidy.models import Playlist, Ref, SearchResult
def create_du... | {
"repo_name": "priestd09/mopidy",
"path": "mopidy/backend/dummy.py",
"copies": "2",
"size": "3068",
"license": "apache-2.0",
"hash": 8353517146123160000,
"line_mean": 26.6396396396,
"line_max": 76,
"alpha_frac": 0.6398305085,
"autogenerated": false,
"ratio": 3.9536082474226806,
"config_test": f... |
"""A dummy backend for use in tests.
This backend implements the backend API in the simplest way possible. It is
used in tests of the frontends.
"""
from __future__ import unicode_literals
import pykka
from mopidy import backend
from mopidy.models import Playlist, Ref, SearchResult
def create_dummy_backend_proxy... | {
"repo_name": "woutervanwijk/mopidy",
"path": "mopidy/backend/dummy.py",
"copies": "3",
"size": "3054",
"license": "apache-2.0",
"hash": 2854898147553960000,
"line_mean": 26.5135135135,
"line_max": 76,
"alpha_frac": 0.6394891945,
"autogenerated": false,
"ratio": 3.9559585492227978,
"config_test... |
"""A dummy backend for use in tests.
This backend implements the backend API in the simplest way possible. It is
used in tests of the frontends.
"""
import pykka
from mopidy import backend
from mopidy.models import Playlist, Ref, SearchResult
def create_proxy(config=None, audio=None):
return DummyBackend.sta... | {
"repo_name": "adamcik/mopidy",
"path": "tests/dummy_backend.py",
"copies": "4",
"size": "4258",
"license": "apache-2.0",
"hash": 8571851900242502000,
"line_mean": 26.8300653595,
"line_max": 79,
"alpha_frac": 0.613903241,
"autogenerated": false,
"ratio": 3.9280442804428044,
"config_test": false... |
"""A dummy module for testing purposes."""
import logging
import os
import uuid
import lambdautils.state as state
logger = logging.getLogger()
logger.setLevel(logging.INFO)
def partition_key(event):
return event.get("client_id", str(uuid.uuid4()))
def input_filter(event, *args, **kwargs):
if os.environ.g... | {
"repo_name": "humilis/humilis-kinesis-mapper",
"path": "tests/integration/mycode/mypkg/__init__.py",
"copies": "2",
"size": "1346",
"license": "mit",
"hash": -6640724321236282000,
"line_mean": 21.813559322,
"line_max": 78,
"alpha_frac": 0.6433878158,
"autogenerated": false,
"ratio": 3.4690721649... |
# A dummy service that implements the mettle protocol for one pipeline, called
# "bar". The "bar" pipeline will make targets of "tmp/<target_time>/[0-9].txt".
import os
import json
import socket
import time
import random
import sys
from datetime import timedelta
import pika
import isodate
import utc
import yaml
imp... | {
"repo_name": "yougov/mettle",
"path": "scripts/pizza_service.py",
"copies": "1",
"size": "5305",
"license": "mit",
"hash": 197432441424808160,
"line_mean": 31.950310559,
"line_max": 80,
"alpha_frac": 0.5379830349,
"autogenerated": false,
"ratio": 3.880760790051207,
"config_test": false,
"has... |
#Advanced Encryption Standard
from random import SystemRandom
rand = SystemRandom()
try:
range = xrange
except Exception:
pass
xtime = lambda x: (((x << 1) ^ 0x1b) & 0xff) if (x & 0x80) else (x << 1)
SBox = [[0x63, 0x7c, 0x77, 0x7b, 0xf2, 0x6b, 0x6f, 0xc5, 0x30, 0x01, 0x67, 0x2b, 0xfe, 0xd7, 0xab, 0x76],
... | {
"repo_name": "Fitzgibbons/Cryptograpy",
"path": "AES.py",
"copies": "1",
"size": "9274",
"license": "mit",
"hash": -8497146949694690000,
"line_mean": 46.8041237113,
"line_max": 248,
"alpha_frac": 0.5258788009,
"autogenerated": false,
"ratio": 2.173933427097984,
"config_test": false,
"has_no_... |
"""Advanced examples."""
import logging
import os
from multiprocessing import Process
from time import sleep
from phial import Message, Phial, Response, Schedule, command
slackbot = Phial(os.getenv("SLACK_API_TOKEN", "NONE"))
SCHEDULED_CHANNEL = "channel-id"
@slackbot.command("cent(er|re)")
def regex_in_command() -... | {
"repo_name": "sedders123/phial",
"path": "examples/advanced.py",
"copies": "1",
"size": "3247",
"license": "mit",
"hash": 678805643877652400,
"line_mean": 30.8333333333,
"line_max": 87,
"alpha_frac": 0.5897751771,
"autogenerated": false,
"ratio": 4.038557213930348,
"config_test": false,
"has... |
"""Advanced example using other configuration options."""
from apscheduler.jobstores.sqlalchemy import SQLAlchemyJobStore
from flask import Flask
from flask_apscheduler import APScheduler
class Config:
"""App configuration."""
JOBS = [
{
"id": "job1",
"func": "advanced:job1"... | {
"repo_name": "viniciuschiele/flask-apscheduler",
"path": "examples/advanced.py",
"copies": "1",
"size": "1028",
"license": "apache-2.0",
"hash": 8727885259553075000,
"line_mean": 20.4166666667,
"line_max": 80,
"alpha_frac": 0.5836575875,
"autogenerated": false,
"ratio": 3.496598639455782,
"con... |
#advanced feature
L=[]
n=1
while n<=99:
L.append(n)
n+=2
print(L)
L=['Michael', 'Sarah', 'Tracy', 'Bob', 'Jack']
print(L[1])
print(L[2])
print(L[3])
print(L[0:3])
r = []
k = 3
for i in range(k):
r.append(L[i])
print(r)
print(L[-1])
print(L[-2])
print(L[-2:-1])
print('key value------')
d = {'a': 1, 'b': 2, '... | {
"repo_name": "CrazyBBer/Python-Learn-Sample",
"path": "Function/advanced.py",
"copies": "1",
"size": "2583",
"license": "mit",
"hash": -4487050427185922000,
"line_mean": 11.2822966507,
"line_max": 46,
"alpha_frac": 0.542267238,
"autogenerated": false,
"ratio": 2.0819140308191404,
"config_test"... |
# Advanced Frame Differencing Example
#
# Note: You will need an SD card to run this example.
#
# This example demonstrates using frame differencing with your OpenMV Cam. This
# example is advanced because it preforms a background update to deal with the
# backgound image changing overtime.
import sensor, image, pyb, ... | {
"repo_name": "openmv/openmv",
"path": "scripts/examples/Arduino/Portenta-H7/20-Frame-Differencing/on_disk_advanced_frame_differencing.py",
"copies": "2",
"size": "2549",
"license": "mit",
"hash": 9050809899523862000,
"line_mean": 41.4833333333,
"line_max": 87,
"alpha_frac": 0.702236171,
"autogener... |
# Advanced Frame Differencing Example
#
# This example demonstrates using frame differencing with your OpenMV Cam. This
# example is advanced because it preforms a background update to deal with the
# backgound image changing overtime.
import sensor, image, pyb, os, time
TRIGGER_THRESHOLD = 5
BG_UPDATE_FRAMES = 50 #... | {
"repo_name": "openmv/openmv",
"path": "scripts/examples/Arduino/Portenta-H7/20-Frame-Differencing/in_memory_advanced_frame_differencing.py",
"copies": "2",
"size": "2937",
"license": "mit",
"hash": -8568993232279012000,
"line_mean": 44.890625,
"line_max": 87,
"alpha_frac": 0.7136533878,
"autogener... |
#advanced functions library
from bas_lib import *
from mac_lib import *
import random
import time
import datetime
import cus_lib
cus_funct=cus_lib.cus_funct
#tim_funct=cus_lib.tim_funct #funzioni a tempo
def esegui (utente,comando,destinatario,testo):
ambiente_attivo=cus_lib.ambiente_attivo
#splitta testo
com... | {
"repo_name": "izabera/izabot",
"path": "adv_lib.py",
"copies": "1",
"size": "2835",
"license": "mit",
"hash": -4665285421567215000,
"line_mean": 27.36,
"line_max": 116,
"alpha_frac": 0.6373897707,
"autogenerated": false,
"ratio": 2.6372093023255814,
"config_test": true,
"has_no_keywords": fa... |
# ADVANCED MATH CALCULATOR v1.4
# by Raphael Gutierrez (fb.com/raphael.gutierrez.17)
# Licensed under MIT (https://github.com/ralphgutz/Advanced-Python-Calculator/blob/master/LICENSE)
# I wrote the codes using my basic Python knowledge to easily understand the codes.
import math
def basic():
print("*" * 40... | {
"repo_name": "ralphgutz/Advanced-Python-Calculator",
"path": "Calculator.py",
"copies": "1",
"size": "16952",
"license": "mit",
"hash": -7667261597707341000,
"line_mean": 30.8527131783,
"line_max": 238,
"alpha_frac": 0.5910217084,
"autogenerated": false,
"ratio": 2.8639972968406826,
"config_te... |
#This program reads a XML and writes it into a CSV
#For each of those tasks there is a seperate function written.
#The filename to read has to be the first argument from the command line.
#The filename to write into has to be the second arguments from command line.
import sys
from bs4 import BeautifulSoup as Soup
im... | {
"repo_name": "frodo4fingers/appfs",
"path": "Jeney/02_Exercise/ex2.py",
"copies": "3",
"size": "2929",
"license": "mit",
"hash": 299876927021330940,
"line_mean": 31.9101123596,
"line_max": 79,
"alpha_frac": 0.5821099351,
"autogenerated": false,
"ratio": 4.656597774244833,
"config_test": false,... |
# advanced_search.py
import wx
from pubsub import pub
class AdvancedSearch(wx.Panel):
def __init__(self, parent):
super().__init__(parent)
self.main_sizer = wx.BoxSizer(wx.VERTICAL)
self.free_text = wx.TextCtrl(self)
self.ui_helper('Free text search:', self.free_text)
... | {
"repo_name": "slogan621/tscharts",
"path": "apps/xrayuploader/advanced_search.py",
"copies": "1",
"size": "2491",
"license": "apache-2.0",
"hash": -8101417628134089000,
"line_mean": 39.1935483871,
"line_max": 73,
"alpha_frac": 0.5953432356,
"autogenerated": false,
"ratio": 3.6524926686217007,
... |
"""Advanced Settings Class."""
from fmcapi.api_objects.apiclasstemplate import APIClassTemplate
from .ftds2svpns import FTDS2SVPNs
import logging
class AdvancedSettings(APIClassTemplate):
"""The AdvancedSettings Object in the FMC."""
VALID_JSON_DATA = [
"id",
"name",
"type",
... | {
"repo_name": "daxm/fmcapi",
"path": "fmcapi/api_objects/policy_services/advancedsettings.py",
"copies": "1",
"size": "1750",
"license": "bsd-3-clause",
"hash": 765436822783422800,
"line_mean": 30.8181818182,
"line_max": 116,
"alpha_frac": 0.5942857143,
"autogenerated": false,
"ratio": 3.59342915... |
""" Advanced signal (e.g. ctrl+C) handling for IPython
So far, this only ignores ctrl + C in IPython file a subprocess
is executing, to get closer to how a "proper" shell behaves.
Other signal processing may be implemented later on.
If _ip.options.verbose is true, show exit status if nonzero
"""
import signal,os,s... | {
"repo_name": "sodafree/backend",
"path": "build/ipython/IPython/quarantine/ipy_signals.py",
"copies": "1",
"size": "1652",
"license": "bsd-3-clause",
"hash": -6988002960970420000,
"line_mean": 26.0819672131,
"line_max": 68,
"alpha_frac": 0.6761501211,
"autogenerated": false,
"ratio": 3.729119638... |
advanced_sparta = (
# ("sensors", "keyboard"),
#("sensors", "mouse"),
#("sensors", "collision"),
#("sensors", "near"),
#("sensors", "message"),
#("sensors", "random_"),
#("processors", "trigger"),
#("processors", "toggle"),
#("processors", "switch"),
#("processors", "if_"),
#... | {
"repo_name": "agoose77/hivesystem",
"path": "hiveguilib/HBlender/level.py",
"copies": "1",
"size": "2668",
"license": "bsd-2-clause",
"hash": -9034565752096989000,
"line_mean": 23.2545454545,
"line_max": 70,
"alpha_frac": 0.535982009,
"autogenerated": false,
"ratio": 3.469440832249675,
"config... |
"""Advanced timeout handling.
Set of helper classes to handle timeouts of tasks with advanced options
like zones and freezing of timeouts.
"""
from __future__ import annotations
import asyncio
import enum
from types import TracebackType
from typing import Any, Dict, List, Optional, Type, Union
from .async_ import ru... | {
"repo_name": "GenericStudent/home-assistant",
"path": "homeassistant/util/timeout.py",
"copies": "6",
"size": "14604",
"license": "apache-2.0",
"hash": 2547671032862168600,
"line_mean": 27.7480314961,
"line_max": 80,
"alpha_frac": 0.5780608053,
"autogenerated": false,
"ratio": 4.106861642294713,... |
"""Advanced tools for dense recursive polynomials in ``K[x]`` or ``K[X]``."""
from .densearith import (dmp_add, dmp_add_term, dmp_div, dmp_exquo_ground,
dmp_mul, dmp_mul_ground, dmp_neg, dmp_sub, dup_add,
dup_mul)
from .densebasic import (dmp_convert, dmp_degree_in, dm... | {
"repo_name": "skirpichev/omg",
"path": "diofant/polys/densetools.py",
"copies": "1",
"size": "12628",
"license": "bsd-3-clause",
"hash": -2260708377628337700,
"line_mean": 19.6339869281,
"line_max": 90,
"alpha_frac": 0.4302343997,
"autogenerated": false,
"ratio": 2.6496013428451533,
"config_te... |
"""Advanced tools for dense recursive polynomials in ``K[x]`` or ``K[X]``. """
from __future__ import print_function, division
from sympy.core.compatibility import range
from sympy.polys.densearith import (
dup_add_term, dmp_add_term,
dup_lshift,
dup_add, dmp_add,
dup_sub, dmp_sub,
dup_mul, dmp_mu... | {
"repo_name": "kaushik94/sympy",
"path": "sympy/polys/densetools.py",
"copies": "6",
"size": "25867",
"license": "bsd-3-clause",
"hash": -2728441805223602000,
"line_mean": 18.8062787136,
"line_max": 92,
"alpha_frac": 0.4559477326,
"autogenerated": false,
"ratio": 2.7384077916578446,
"config_tes... |
"""Advanced tools for dense recursive polynomials in ``K[x]`` or ``K[X]``. """
from __future__ import print_function, division
from sympy.polys.densebasic import (
dup_strip, dmp_strip,
dup_convert, dmp_convert,
dup_degree, dmp_degree,
dmp_to_dict,
dmp_from_dict,
dup_LC, dmp_LC, dmp_ground_LC,... | {
"repo_name": "emon10005/sympy",
"path": "sympy/polys/densetools.py",
"copies": "52",
"size": "25854",
"license": "bsd-3-clause",
"hash": -8522646158824810000,
"line_mean": 18.7509549274,
"line_max": 92,
"alpha_frac": 0.4560609577,
"autogenerated": false,
"ratio": 2.738771186440678,
"config_tes... |
"""Advanced tools for dense recursive polynomials in ``K[x]`` or ``K[X]``. """
from sympy.polys.densebasic import (
dup_strip, dmp_strip,
dup_convert, dmp_convert,
dup_degree, dmp_degree, dmp_degree_in,
dup_to_dict, dmp_to_dict,
dup_from_dict, dmp_from_dict,
dup_LC, dmp_LC, dmp_ground_LC,
d... | {
"repo_name": "pernici/sympy",
"path": "sympy/polys/densetools.py",
"copies": "1",
"size": "27597",
"license": "bsd-3-clause",
"hash": 9168373856174546000,
"line_mean": 20.7985781991,
"line_max": 92,
"alpha_frac": 0.4964670073,
"autogenerated": false,
"ratio": 2.764676417551593,
"config_test": ... |
"""Advanced tools for dense recursive polynomials in `K[x]` or `K[X]`. """
from sympy.polys.densebasic import (
dup_strip, dmp_strip,
dup_reverse,
dup_convert, dmp_convert,
dup_degree, dmp_degree, dmp_degree_in,
dup_to_dict, dmp_to_dict,
dup_from_dict, dmp_from_dict,
dup_LC, dmp_LC, dmp_gro... | {
"repo_name": "tovrstra/sympy",
"path": "sympy/polys/densetools.py",
"copies": "3",
"size": "67487",
"license": "bsd-3-clause",
"hash": -9049831501624728000,
"line_mean": 24.3044619423,
"line_max": 105,
"alpha_frac": 0.4921540445,
"autogenerated": false,
"ratio": 2.6799698197124933,
"config_tes... |
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