text stringlengths 0 1.05M | meta dict |
|---|---|
__author__ = 'x1ang.li'
from client.common import *
import json
import socket
import re
def checkip():
if clicfg['getIpFrom'] == 'ServerApi':
r = reqget('/ip')
if (r.status_code != 200):
raise Exception('HTTP request error. HTTP status code %i' % r.status_code)
rjson = r.json()... | {
"repo_name": "x1angli/pyDDNS",
"path": "client/upstream.py",
"copies": "2",
"size": "1789",
"license": "mit",
"hash": -4702807515923548000,
"line_mean": 33.4038461538,
"line_max": 139,
"alpha_frac": 0.5919508105,
"autogenerated": false,
"ratio": 3.41412213740458,
"config_test": false,
"has_n... |
__author__ = 'x1ang.li'
import pickle, re
from os import path, makedirs
from urllib.parse import urljoin
from shutil import rmtree
from zipfile import ZipFile
import requests
from bs4 import BeautifulSoup
from .bookbase import BookBase
cachefolder = path.expanduser(path.join('~', 'snapbook', 'cache'))
outfolder = p... | {
"repo_name": "x1angli/snapbook",
"path": "snapbook/books/mhbook.py",
"copies": "1",
"size": "11711",
"license": "mit",
"hash": 2863874321564379600,
"line_mean": 33.9611940299,
"line_max": 182,
"alpha_frac": 0.5834685339,
"autogenerated": false,
"ratio": 3.420268691588785,
"config_test": false,... |
__author__ = 'x1ang.li'
import sys, os
from client.common import *
def updateHostFile():
r = reqgetauth('/silos/'+clicfg['silo_id'])
if (r.status_code != 200):
raise Exception('HTTP request error. HTTP status code %i' % r.status_code)
rjson = r.json()
if not rjson:
raise Exception('Un... | {
"repo_name": "x1angli/DDNS-chiasma",
"path": "client/downstream.py",
"copies": "2",
"size": "1765",
"license": "mit",
"hash": -2785224201950758400,
"line_mean": 31.0909090909,
"line_max": 91,
"alpha_frac": 0.6339943343,
"autogenerated": false,
"ratio": 3.6618257261410787,
"config_test": false,... |
__author__ = 'xana'
from flask import request, jsonify, g
qiniu_setting = {
'access_key' : 'iQ3ndG5uRpwdeln_gcrH3iiZ7E3KbMdJVkdYV9Im',
'secret_key' : 'AGsp6K7fu1NsH2DnsPi7hW3qa3JXb4dtfeGvkm-A',
'bucket_name' : 'image',
'bucket_domain' : 'https://oi3qt7c8d.qnssl.com/',
'callbakUrl' : 'http://139.1... | {
"repo_name": "imxana/toocool",
"path": "apps/qiniu.py",
"copies": "1",
"size": "1780",
"license": "mit",
"hash": 4364947766977422000,
"line_mean": 26.8125,
"line_max": 74,
"alpha_frac": 0.5280898876,
"autogenerated": false,
"ratio": 3.308550185873606,
"config_test": false,
"has_no_keywords":... |
import numpy as np
import os
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib import cm
from numpy import ma
def autocrop_img(filename):
"""Call epstools ... | {
"repo_name": "xparedesfortuny/pylines",
"path": "plots.py",
"copies": "1",
"size": "29938",
"license": "mit",
"hash": -7082227597879846000,
"line_mean": 34.1797884841,
"line_max": 104,
"alpha_frac": 0.5402164473,
"autogenerated": false,
"ratio": 3.021598708114655,
"config_test": false,
"has_... |
import numpy as np
import time
import interpolation_methods as im
from resamp_line import resamp_line
def buffer_present_line(xc, yc, ic, jc, densc, epsc, vxc, vyc,
divc, tracerc, tc, line_values):
"""Keeps track of all the line parameters"""
line_values.append([xc, yc, ic, jc, densc... | {
"repo_name": "xparedesfortuny/pylines",
"path": "compute_lines.py",
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"line_mean": 34.3202099738,
"line_max": 118,
"alpha_frac": 0.4974362785,
"autogenerated": false,
"ratio": 3.0591043418958854,
"config_test": true,
... |
import numpy as np
def bilinear(xc, yc, ic, jc, x, y, z, nx, ny):
"""Bilinear interpolation z(x, y) at (xc, yc)"""
if xc <= x[0] or xc >= x[nx-1] or yc <= y[0] or yc >= y[ny-1]:
return z[ic, jc]
if xc >= x[ic] and yc >= y[jc]:
x1 = x[ic]
y1 = y[jc]
x2 = x[ic+1]
y... | {
"repo_name": "xparedesfortuny/pylines",
"path": "interpolation_methods.py",
"copies": "1",
"size": "3110",
"license": "mit",
"hash": 6987430548352392000,
"line_mean": 23.4881889764,
"line_max": 66,
"alpha_frac": 0.4147909968,
"autogenerated": false,
"ratio": 2.114208021753909,
"config_test": f... |
import numpy as np
def pulsar(i, j, i2, j2, R_b, rho_0a, xp, yp, xs, ys, densty, eps,
velx, vely, xznl, yznl, gamma, c, gridlx, gridly, nx, ny, theo,
rhowp, uwp, vwp, rhowi, uwi, vwi, rin, rins):
"""Computes some physical quantities for testing purposes"""
t_b = R_b/c
if theo == 1... | {
"repo_name": "xparedesfortuny/pylines",
"path": "test_rhd.py",
"copies": "1",
"size": "6729",
"license": "mit",
"hash": -5919368530637377000,
"line_mean": 33.1573604061,
"line_max": 86,
"alpha_frac": 0.5309852876,
"autogenerated": false,
"ratio": 2.3552677633881696,
"config_test": false,
"ha... |
import numpy as np
def read(input_file):
"""Reads a FORTRAN file as an input"""
offset = 4
ints = np.array(np.memmap(input_file, dtype='<i4', mode='r', shape=(1, 10),
order='F', offset=offset))
offset += ints.nbytes
ints = zip(*ints)
ints = [i[0] for i in ints]... | {
"repo_name": "xparedesfortuny/pylines",
"path": "read_fortran.py",
"copies": "1",
"size": "12195",
"license": "mit",
"hash": -7878390417104153000,
"line_mean": 37.9616613419,
"line_max": 224,
"alpha_frac": 0.4678146781,
"autogenerated": false,
"ratio": 3.1422313836640043,
"config_test": false,... |
import numpy as np
def resamp_line(all_lines, resamp, a, CGS_units):
"""Downsamples the line to 'resamp' cells.
All the lines should have the same lenght with a constant
cell size.
We are not averaging the data in the same bin because we are
dealing with relativistic quantities. E.g. to average t... | {
"repo_name": "xparedesfortuny/pylines",
"path": "resamp_line.py",
"copies": "1",
"size": "6769",
"license": "mit",
"hash": 992616962137860100,
"line_mean": 33.3604060914,
"line_max": 84,
"alpha_frac": 0.4406854779,
"autogenerated": false,
"ratio": 3.4377856780091416,
"config_test": false,
"h... |
import numpy as np
from astropy.coordinates import SkyCoord
from astropy import units as u
import matplotlib.pyplot as plt
import sys
param = {}
execfile(sys.argv[1])
def find_target(ra, dec, ra0, dec0, testing=0):
tar = SkyCoord(ra0, dec0, unit=(u.hour, u.deg))
cat = SkyCoord(ra*u.degree, dec*u.degree)
... | {
"repo_name": "xparedesfortuny/Phot",
"path": "analysis/differential_photometry.py",
"copies": "1",
"size": "11587",
"license": "mit",
"hash": -2684942786445494000,
"line_mean": 41.9148148148,
"line_max": 143,
"alpha_frac": 0.5997238284,
"autogenerated": false,
"ratio": 2.868069306930693,
"conf... |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
from matplotlib.ticker import AutoMinorLocator
from astropy.time import Time
import itertools
import sys
import os
import matplotlib
param = {}
execfile(sys.argv[1])
def auto_crop_img(filename):
... | {
"repo_name": "xparedesfortuny/Phot",
"path": "plots/plots.py",
"copies": "1",
"size": "47343",
"license": "mit",
"hash": -2988465427837623000,
"line_mean": 52.5554298643,
"line_max": 352,
"alpha_frac": 0.5853030015,
"autogenerated": false,
"ratio": 2.7357989020514304,
"config_test": false,
"... |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
def compute_nightly_average(cat_mag):
return np.asarray([[np.average(mag[:, i]) for i in xrange(len(mag[0, :]))] for mag in cat_mag])
def compute_statistics(cat_mag):
avg_mag = [np.average(cat_mag[:, k]) for k... | {
"repo_name": "xparedesfortuny/Phot",
"path": "analysis/multi_night_std_test.py",
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"line_max": 99,
"alpha_frac": 0.5429087624,
"autogenerated": false,
"ratio": 2.774436090225564,
"config_tes... |
import numpy as np
import os
import shutil
import source_extraction
import quality_control
import source_match
import multi_night_std_test
import differential_photometry
# import xy_fit
# import detrending
import offset
import sys
param = {}
execfile(sys.argv[1])
def make_output_folder(o):
def check_and_make(fo... | {
"repo_name": "xparedesfortuny/Phot",
"path": "analysis/analysis.py",
"copies": "1",
"size": "6528",
"license": "mit",
"hash": 8760947914701399000,
"line_mean": 47.7164179104,
"line_max": 206,
"alpha_frac": 0.6246936275,
"autogenerated": false,
"ratio": 2.9849108367626886,
"config_test": true,
... |
import numpy as np
import sys
import matplotlib.pyplot as plt
param = {}
execfile(sys.argv[1])
def plot_nstars(cat_ra, cat_mjd, suff):
nstars = [max([len(frame) for frame in ra]) for ra in cat_ra]
mjd = [np.average(mjd) for mjd in cat_mjd]
plt.rcdefaults()
fig = plt.figure()
ax = fig.add_subplot... | {
"repo_name": "xparedesfortuny/Phot",
"path": "analysis/quality_control.py",
"copies": "1",
"size": "4278",
"license": "mit",
"hash": -6446808679337019000,
"line_mean": 42.2121212121,
"line_max": 160,
"alpha_frac": 0.6388499299,
"autogenerated": false,
"ratio": 3.060085836909871,
"config_test":... |
import numpy as np
import sys
param = {}
execfile(sys.argv[1])
def compute_averaged_mag(cat_mag, ind):
return np.average([np.average(mag[:, ind]) for mag in cat_mag])
def apply_offset(cat_mag, offset):
return [mag-offset for mag in cat_mag]
def compute_final_magnitudes(cat_mag, ind):
# nightly_avg_m... | {
"repo_name": "xparedesfortuny/Phot",
"path": "analysis/offset.py",
"copies": "1",
"size": "2146",
"license": "mit",
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"line_mean": 34.1803278689,
"line_max": 119,
"alpha_frac": 0.6551724138,
"autogenerated": false,
"ratio": 2.772609819121447,
"config_test": false,
... |
import os
from astropy import wcs
from astropy import units as u
from astropy.coordinates import SkyCoord
import numpy as np
from astropy.io import fits
import sys
param = {}
execfile(sys.argv[1])
def make_image_list(i):
t = [i+d for d in os.listdir(i) if d[-5:] == '.fits']
return t
def check_saturation(i... | {
"repo_name": "xparedesfortuny/Phot",
"path": "calibration/source_check.py",
"copies": "1",
"size": "1614",
"license": "mit",
"hash": 3461412296021103600,
"line_mean": 27.8214285714,
"line_max": 70,
"alpha_frac": 0.614622057,
"autogenerated": false,
"ratio": 2.8216783216783217,
"config_test": f... |
import os
from astropy.io import fits
import shutil
import sys
param = {}
execfile(sys.argv[1])
def make_output_folder(o):
if os.path.exists(o):
shutil.rmtree(o)
os.makedirs(o)
def make_file_list(i):
file_list = [n for n in os.listdir(i) if n[-5:] == '.fits']
return file_list
def crop(i, ... | {
"repo_name": "xparedesfortuny/Phot",
"path": "calibration/crop.py",
"copies": "1",
"size": "1916",
"license": "mit",
"hash": -7801386208632561000,
"line_mean": 26.768115942,
"line_max": 78,
"alpha_frac": 0.5704592902,
"autogenerated": false,
"ratio": 2.797080291970803,
"config_test": false,
... |
import os
import numpy as np
from pyraf import iraf
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
import sys
param = {}
execfile(sys.argv[1])
def save_file_list(i):
file_list = [i+n for n in os.listdir(i) if n[-5:] == '.fits']
np.savetxt(i+'input.dat', file_list, fmt="%s")
return file_list
def make_m... | {
"repo_name": "xparedesfortuny/Phot",
"path": "calibration/bias_darks_flats.py",
"copies": "1",
"size": "12957",
"license": "mit",
"hash": 9198117870703268000,
"line_mean": 44.4631578947,
"line_max": 86,
"alpha_frac": 0.658562939,
"autogenerated": false,
"ratio": 2.798488120950324,
"config_test... |
import os
import shutil
from astropy.io import fits
import numpy as np
import sys
param = {}
execfile(sys.argv[1])
def make_tmp_folder(o):
if os.path.exists(o):
shutil.rmtree(o)
os.makedirs(o)
def make_file_list(i):
file_list = [n for n in os.listdir(i) if n[-5:] == '.fits']
return file_li... | {
"repo_name": "xparedesfortuny/Phot",
"path": "calibration/shutter_map.py",
"copies": "1",
"size": "1613",
"license": "mit",
"hash": -8181229268405981000,
"line_mean": 24.6031746032,
"line_max": 74,
"alpha_frac": 0.5939243645,
"autogenerated": false,
"ratio": 2.7109243697478993,
"config_test": ... |
import os
import shutil
from astropy.io import fits
import sys
param = {}
execfile(sys.argv[1])
def make_tmp_folder(o):
if os.path.exists(o):
shutil.rmtree(o)
os.makedirs(o)
def make_file_list(i):
file_list = [n for n in os.listdir(i) if n[-5:] == '.fits']
return file_list
def nonlinear_... | {
"repo_name": "xparedesfortuny/Phot",
"path": "calibration/linearity_map.py",
"copies": "1",
"size": "1397",
"license": "mit",
"hash": 8435547537459651000,
"line_mean": 22.6779661017,
"line_max": 74,
"alpha_frac": 0.5926986399,
"autogenerated": false,
"ratio": 2.868583162217659,
"config_test": ... |
import os
import shutil
import subprocess
import numpy as np
from astropy.coordinates import SkyCoord
from astropy import units as u
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
import sys
import pyfits
param = {}
execfile(sys.argv[1])
def make_image_list(i):
t = [d for d in os.... | {
"repo_name": "xparedesfortuny/Phot",
"path": "calibration/nightly_std_test.py",
"copies": "1",
"size": "4508",
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"hash": -6465196468803862000,
"line_mean": 31.4316546763,
"line_max": 132,
"alpha_frac": 0.5760869565,
"autogenerated": false,
"ratio": 2.738760631834751,
"config_te... |
import os
import shutil
import subprocess
import sys
import pyfits
param = {}
execfile(sys.argv[1])
def make_tmp_folder(o):
if os.path.exists(o):
shutil.rmtree(o)
os.makedirs(o)
def call_astrometry_dot_net(i, o, ra, dec, radius, scale_low, scale_high):
""" -D: Output path
-p: No plots
... | {
"repo_name": "xparedesfortuny/Phot",
"path": "calibration/astrometry.py",
"copies": "1",
"size": "1958",
"license": "mit",
"hash": 6878539969006752000,
"line_mean": 28.6666666667,
"line_max": 93,
"alpha_frac": 0.5781409602,
"autogenerated": false,
"ratio": 2.962178517397882,
"config_test": fal... |
import os
import shutil
import sys
import numpy as np
import subprocess
from astropy.io import fits
import pyfits
param = {}
execfile(sys.argv[1])
def make_folder_list(i):
t = [i+d+'/' for d in sorted(os.listdir(i)) if d[:2] == '20' and os.path.exists(i+d+'/'+param['field_name'])]
return t
def make_cat_fo... | {
"repo_name": "xparedesfortuny/Phot",
"path": "analysis/source_extraction.py",
"copies": "1",
"size": "3325",
"license": "mit",
"hash": -5807024446166244000,
"line_mean": 29.504587156,
"line_max": 162,
"alpha_frac": 0.5957894737,
"autogenerated": false,
"ratio": 2.9013961605584644,
"config_test... |
import sys
import numpy as np
from astropy.coordinates import SkyCoord
from astropy import units as u
import itertools
param = {}
execfile(sys.argv[1])
def find_target(ra, dec, ra0, dec0, fl, testing=0):
tar = SkyCoord(ra0, dec0, unit=(u.hour, u.deg))
cat = SkyCoord(ra*u.degree, dec*u.degree)
ind, sep2d... | {
"repo_name": "xparedesfortuny/Phot",
"path": "analysis/source_match.py",
"copies": "1",
"size": "6556",
"license": "mit",
"hash": 6264055926211983000,
"line_mean": 38.9756097561,
"line_max": 158,
"alpha_frac": 0.5741305674,
"autogenerated": false,
"ratio": 3.251984126984127,
"config_test": tru... |
__author__ = 'xcbtrader'
# -*- coding: utf-8 -*-
import poloniex
import time
import sys
def leer_operativa():
fOperativa = open('polo_pingpong_var_operativa.txt', 'r')
op = fOperativa.readline()
op = int(fOperativa.readline())
fOperativa.close()
return op
def leer_ordenes():
global polo
try:
openOrders = po... | {
"repo_name": "xcbtrader/polo_pigpong_var",
"path": "polo_pingpong_var.py",
"copies": "1",
"size": "6360",
"license": "mit",
"hash": 1151172667130591000,
"line_mean": 29,
"line_max": 207,
"alpha_frac": 0.6014150943,
"autogenerated": false,
"ratio": 2.6577517760133724,
"config_test": false,
"h... |
_author__ = 'xebin'
#from ..dao import av_dao
import redisco
from redisco import models
import sys
reload(sys)
sys.setdefaultencoding('utf8')
# r = redis.StrictRedis(host='120.27.30.239',port='6379')
# pool = redis.ConnectionPool(host='120.27.30.239', port=6379, db=0)
# r = redis.Redis(connection_pool=pool)
#
redisco... | {
"repo_name": "petchat/senz.dashboard.backend",
"path": "test/redis_test.py",
"copies": "1",
"size": "2006",
"license": "mit",
"hash": 1257051215181358000,
"line_mean": 32.4333333333,
"line_max": 123,
"alpha_frac": 0.6645064806,
"autogenerated": false,
"ratio": 2.882183908045977,
"config_test":... |
__author__ = 'xertrov'
import logging
from sqlalchemy import create_engine, Column, String, Integer, ForeignKey, Boolean, Index
from sqlalchemy.orm import relationship
from sqlalchemy.ext.declarative import declarative_base
engine = create_engine('sqlite:///temp.sqlite', connect_args={'timeout': 15})#, echo=True)
Ba... | {
"repo_name": "XertroV/nvbtally",
"path": "nvbtally/models.py",
"copies": "1",
"size": "3975",
"license": "mit",
"hash": 3726839515142611500,
"line_mean": 25.8581081081,
"line_max": 115,
"alpha_frac": 0.6883018868,
"autogenerated": false,
"ratio": 3.505291005291005,
"config_test": false,
"has... |
__author__ = 'Xev'
class Tile:
def __init__(self):
pass
Empty, Wall, Start, StartGhost = range(4)
class Layout(object):
def __init__(self):
self.grid = []
self.shape = (0, 0)
self.food = []
def load_from_file(self, filename):
rows = 0
with open(filen... | {
"repo_name": "Xevaquor/aipac",
"path": "layout.py",
"copies": "1",
"size": "1375",
"license": "mit",
"hash": -874683054921100000,
"line_mean": 27.6458333333,
"line_max": 78,
"alpha_frac": 0.3723636364,
"autogenerated": false,
"ratio": 4.583333333333333,
"config_test": false,
"has_no_keywords... |
__author__ = 'xiajie'
from scipy.optimize import fmin_l_bfgs_b
from sparseAutoencoderCost import J, mat2vec, vec2mat
from sampleIMAGES import sample_images
import numpy as np
import math
import scipy.io as sio
def random_bound(levels):
return math.sqrt(6./(levels[0]+levels[1]+1))
def initialization(levels):
... | {
"repo_name": "jayshonzs/UFLDL",
"path": "SparseAutoencoderV/train.py",
"copies": "1",
"size": "1750",
"license": "mit",
"hash": 2534791054744048000,
"line_mean": 25.1194029851,
"line_max": 109,
"alpha_frac": 0.5994285714,
"autogenerated": false,
"ratio": 3.0224525043177892,
"config_test": fals... |
__author__ = 'xiajie'
import matplotlib.pyplot as plt
import numpy as np
epsilon = 0.00001
def load_data(filename=u'pcaData.txt'):
res = np.genfromtxt(filename)
plt.scatter(res[0, :], res[1, :])
plt.show()
return res
def pca(X):
m = len(X[0])
avg = np.mean(X, axis=1).reshape((2, 1))*np.on... | {
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__author__ = 'xiajie'
import numpy as np
from MNISThelper import loader
from SparseAutoencoderV.sparseAutoencoderCost import J, vec2mat
from SparseAutoencoderV.train import initialization
from scipy.optimize import fmin_l_bfgs_b
import scipy.io as sio
from SelfTaughtLearning.feedForwardAutoencoder import feed_forward
... | {
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"path": "DeepNetworks/stackedAE.py",
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"alpha_frac": 0.6481344206,
"autogenerated": false,
"ratio": 2.9178082191780823,
"config_test": false... |
__author__ = 'xiajie'
import numpy as np
from scipy.optimize import fmin_l_bfgs_b
from sparseAutoencoderLinearCost import J, mat2vec, vec2mat
import math
import scipy.io as sio
imageChannels = 3
patchDim = 8
numPatches = 100000
visibleSize = patchDim*patchDim*imageChannels
outputSize = visibleSize
hiddenSize = 400
... | {
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"path": "LinearDecoder/train.py",
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"line_max": 129,
"alpha_frac": 0.6183453237,
"autogenerated": false,
"ratio": 3.065049614112459,
"config_test": false,
... |
__author__ = 'xiajie'
import numpy as np
from scipy.signal import convolve2d
def sigmoid(z):
return 1./(1.+np.e**(-z))
def convolve(patch_dim, num_features, images, W, b, zca_white, avg):
WT = W.dot(zca_white)
num_images = images.shape[3]
image_dim = images.shape[0]
image_channels = images.shap... | {
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"path": "ConvolutionandPooling/cnnConvolve.py",
"copies": "1",
"size": "1687",
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"hash": -6834025728924711000,
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"line_max": 107,
"alpha_frac": 0.6046235922,
"autogenerated": false,
"ratio": 3.353876739562624,
"config_te... |
__author__ = 'xiajie'
import numpy as np
from scipy.sparse import coo_matrix
def stack_params(WBs, sr_mat):
w_l1 = WBs[0][0]
b_l1 = WBs[0][1]
w_l2 = WBs[1][0]
b_l2 = WBs[1][1]
theta = w_l1.reshape(np.size(w_l1)).tolist() + b_l1.tolist()
theta += w_l2.reshape(np.size(w_l2)).tolist() + b_l2.tol... | {
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"path": "DeepNetworks/stackedAECost.py",
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"size": "2375",
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"ratio": 2.6595744680851063,
"config_test": false,
"... |
__author__ = 'xiajie'
import numpy as np
from softMaxCost import J, vec2mat
from scipy.optimize import fmin_l_bfgs_b
from MNISThelper.loader import load_train_imgs, load_train_labels
from SoftmaxRegression.prediction import predict
from computeNumericalGradient import compute_numerical_gradient
def initialize_theta(... | {
"repo_name": "jayshonzs/UFLDL",
"path": "SoftmaxRegression/train.py",
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"ratio": 2.9602543720190777,
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__author__ = 'xiajie'
import numpy as np
from sparseAutoencoderLinearCost import J
from MNISThelper import loader
EPSILON = 1e-4
def theta_plus(theta, i):
global EPSILON
ret = np.zeros(len(theta))
ret[i] += EPSILON
return ret+theta
def theta_minus(theta, i):
global EPSILON
ret = np.zeros(... | {
"repo_name": "jayshonzs/UFLDL",
"path": "LinearDecoder/checkStackedAECost.py",
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"ratio": 3.064583333333333,
"config_test"... |
__author__ = 'xiajie'
import numpy as np
from stackedAECost import stacked_cost
from MNISThelper import loader
EPSILON = 1e-4
def theta_plus(theta, i):
global EPSILON
ret = np.zeros(len(theta))
ret[i] += EPSILON
return ret+theta
def theta_minus(theta, i):
global EPSILON
ret = np.zeros(len... | {
"repo_name": "jayshonzs/UFLDL",
"path": "DeepNetworks/checkStackedAECost.py",
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"size": "1480",
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"autogenerated": false,
"ratio": 3.0641821946169774,
"config_test": ... |
__author__ = 'xiajie'
import numpy as np
import scipy.io as sio
import random
from scipy import misc
def load_data(N=10000, size=12, filename=u'IMAGES_RAW.mat'):
res_mat = np.zeros((size**2, N))
data = sio.loadmat(filename)
IMAGESr = data['IMAGESr']
# print IMAGESr.shape
for n in range(N):
... | {
"repo_name": "jayshonzs/UFLDL",
"path": "PCAandWhitening/PCAandWhiteningImage.py",
"copies": "1",
"size": "2385",
"license": "mit",
"hash": -3168600196297576400,
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"line_max": 108,
"alpha_frac": 0.5647798742,
"autogenerated": false,
"ratio": 2.592391304347826,
"config... |
__author__ = 'xiajie'
import numpy as np
import struct
def load_train_imgs(filename=u'../MNISThelper/train-images.idx3-ubyte'):
train_file = open(filename, 'rb')
buf = train_file.read()
index = 0
magic, num_images, num_rows, num_columns = struct.unpack_from('>IIII', buf, index)
# print magic... | {
"repo_name": "jayshonzs/UFLDL",
"path": "MNISThelper/loader.py",
"copies": "1",
"size": "1316",
"license": "mit",
"hash": 3679254555553047000,
"line_mean": 25.32,
"line_max": 86,
"alpha_frac": 0.5782674772,
"autogenerated": false,
"ratio": 3.357142857142857,
"config_test": false,
"has_no_key... |
__author__ = 'xiajie'
import numpy as np
def mat2vec(WB):
vec = []
for wb in WB:
vec.extend(wb[0].reshape(wb[0].size))
vec.extend(wb[1])
return np.array(vec)
def vec2mat(theta, levels):
WB = []
off = 0
for i, si in enumerate(levels):
if i == len(levels)-1:
... | {
"repo_name": "jayshonzs/UFLDL",
"path": "SparseAutoencoderV/sparseAutoencoderCost.py",
"copies": "1",
"size": "1622",
"license": "mit",
"hash": -1899973225900009700,
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"line_max": 123,
"alpha_frac": 0.5339087546,
"autogenerated": false,
"ratio": 2.2748948106591866,
"co... |
__author__ = 'xiajie'
import scipy.io as sio
import numpy as np
import random
from scipy import misc
def normalize(images):
mat = np.mean(images, axis=0)
mat = images - mat
mstd = np.std(mat.reshape(mat.size))*3.
mat = np.where(mat > mstd, mstd, mat)
mat = np.where(mat < -mstd, -mstd, mat)
ma... | {
"repo_name": "jayshonzs/UFLDL",
"path": "SparseAutoencoderV/sampleIMAGES.py",
"copies": "1",
"size": "1031",
"license": "mit",
"hash": 131392108457833760,
"line_mean": 26.8648648649,
"line_max": 115,
"alpha_frac": 0.5955383123,
"autogenerated": false,
"ratio": 2.8324175824175826,
"config_test"... |
import Queue
import json, os
from networkx.readwrite import json_graph
import numpy as np
def load_raw(path_raw):
raw_file = np.load(open(path_raw, 'rb'))
raw = dict()
for key in raw_file.keys():
if key in ['mole_fraction','net_reaction_rate']:
raw[key] = np.matrix(raw_file[key])
... | {
"repo_name": "golsun/GPS",
"path": "plot_flux_graph.py",
"copies": "1",
"size": "3299",
"license": "mit",
"hash": -2911853488077401000,
"line_mean": 22.3971631206,
"line_max": 96,
"alpha_frac": 0.597756896,
"autogenerated": false,
"ratio": 2.587450980392157,
"config_test": false,
"has_no_key... |
__author__ = 'Xiang-Guo Li'
__copyright__ = 'Copyright 2018, The Materials Virtual Lab'
__email__ = 'xil110@eng.ucsd.edu'
__date__ = '07/30/18'
from pymatgen.util.testing import PymatgenTest
import os
import unittest
import numpy as np
import warnings
from pymatgen import Structure
from pymatgen.analysis.gb.grain impo... | {
"repo_name": "gVallverdu/pymatgen",
"path": "pymatgen/analysis/gb/tests/test_grain.py",
"copies": "3",
"size": "19404",
"license": "mit",
"hash": 3543441756198860000,
"line_mean": 55.9032258065,
"line_max": 118,
"alpha_frac": 0.5447330447,
"autogenerated": false,
"ratio": 3.3414844153607715,
"... |
import os
import cv2
import glob
import numpy as np
from PIL import Image
from resizeimage import resizeimage
#import pdb
# reduce the size by the rate of
alpha = 0.80 # (x,y)
beta = 0.70 # number of slices
#pdb.set_trace()
rootpath = '/diskStation/LIDC/'
readfolder = 'LIDC_NUMPY_3d/'
savefolder = 'LIDC_NUMPY_3d_r... | {
"repo_name": "eglxiang/Med",
"path": "resize_npy.py",
"copies": "1",
"size": "1293",
"license": "bsd-2-clause",
"hash": 8070535217595587000,
"line_mean": 25.4081632653,
"line_max": 123,
"alpha_frac": 0.6658932715,
"autogenerated": false,
"ratio": 2.5553359683794468,
"config_test": false,
"ha... |
__author__ = ['Xiaobo']
import time
import httplib
from pagrant.exceptions import VirtualBootstrapError
from pagrant.provisioners import BaseProvisioner
CHECK_TIMEOUT = 60 * 5
class HttpCheckerPrivisioner(BaseProvisioner):
def __init__(self, machine, logger, provision_info, provider_info):
super(HttpChe... | {
"repo_name": "markshao/pagrant",
"path": "pagrant/provisioners/health/http.py",
"copies": "1",
"size": "1669",
"license": "mit",
"hash": 9166589740931248000,
"line_mean": 41.7948717949,
"line_max": 150,
"alpha_frac": 0.6105452367,
"autogenerated": false,
"ratio": 4.0608272506082725,
"config_te... |
__author__ = 'xiaolongjia'
import csv
result = []
with open('docs.csv', 'rb') as csvfile:
doc_reader = csv.reader(csvfile, delimiter=',')
for row in doc_reader:
numString = row[2]
numString = numString.split(',')
contains = False
ad = False
third_party = False
... | {
"repo_name": "mindbergh/PrivacyPolicyAnalyser",
"path": "csv_parser.py",
"copies": "1",
"size": "1460",
"license": "apache-2.0",
"hash": 1605017667395815000,
"line_mean": 23.3333333333,
"line_max": 81,
"alpha_frac": 0.452739726,
"autogenerated": false,
"ratio": 3.8120104438642297,
"config_test... |
__author__ = 'xiao'
import io
import struct
import sys
from ctypes import *
def _from_bytes(input_bytes, byteorder='big'):
length = len(input_bytes)
if byteorder == 'big':
parts = struct.unpack((">%dB" % length), input_bytes)
parts = parts[0 : length]
elif byteorder == 'little':
pa... | {
"repo_name": "vtflute/TSParser",
"path": "TSStruct.py",
"copies": "1",
"size": "24249",
"license": "mit",
"hash": -7494350626489804000,
"line_mean": 35.9664634146,
"line_max": 138,
"alpha_frac": 0.5204338323,
"autogenerated": false,
"ratio": 3.699313501144165,
"config_test": false,
"has_no_k... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
def fcnn_layer(input_tensor, input_dim, output_dim, layer_name,
wd=False, wd_collection=False,
keep_prob=0.8, variable_collection=Fals... | {
"repo_name": "xiaotaw/chembl",
"path": "dnn_model/dnn_model.py",
"copies": "1",
"size": "5030",
"license": "apache-2.0",
"hash": 1306517112609947100,
"line_mean": 54.8888888889,
"line_max": 174,
"alpha_frac": 0.6896620278,
"autogenerated": false,
"ratio": 2.909196067090804,
"config_test": fals... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import datetime
import math
import numpy
import random
import tensorflow as tf
import pk_input as pki
import dnn_model
def train(target_list, train_from = 0):
# dataset
d = pki.Da... | {
"repo_name": "xiaotaw/chembl",
"path": "dnn_model/pk_train.py",
"copies": "1",
"size": "8407",
"license": "apache-2.0",
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"line_mean": 43.4814814815,
"line_max": 231,
"alpha_frac": 0.6300701796,
"autogenerated": false,
"ratio": 2.9570875835385158,
"config_test": fals... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import glob
import time
import numpy
import cPickle
import datetime
import tensorflow as tf
from scipy import sparse
import dnn_model
def virtual_screening(target_list, part_num):
# ... | {
"repo_name": "xiaotaw/chembl",
"path": "dnn_model/pk_virtual_screen.py",
"copies": "1",
"size": "6593",
"license": "apache-2.0",
"hash": -6331973186768511000,
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"line_max": 121,
"alpha_frac": 0.6283937509,
"autogenerated": false,
"ratio": 3.0622387366465396,
"config_t... |
import os
import sys
import math
import time
import datetime
import multiprocessing
import numpy as np
from scipy import sparse
from collections import defaultdict
# folders
fp_dir = "fp_files"
structure_dir = "structure_files"
mask_dir = "mask_files"
if not os.path.exists(mask_dir):
os.mkdir(mask_dir)
log_dir = "l... | {
"repo_name": "xiaotaw/chembl",
"path": "data_files/chembl_cal_mask.py",
"copies": "1",
"size": "9368",
"license": "apache-2.0",
"hash": 2546938431615297000,
"line_mean": 32.219858156,
"line_max": 140,
"alpha_frac": 0.6226515798,
"autogenerated": false,
"ratio": 2.976803304734668,
"config_test"... |
import os
import sys
import time
import getpass
import numpy as np
from scipy import sparse
from collections import defaultdict
from matplotlib import pyplot as plt
from sklearn.externals import joblib
from sklearn.ensemble import RandomForestClassifier
sys.path.append("/home/%s/Documents/chembl/data_files/" % getpas... | {
"repo_name": "xiaotaw/chembl",
"path": "rf_model/chembl_rf_vs.py",
"copies": "1",
"size": "3129",
"license": "apache-2.0",
"hash": -7331059477715695000,
"line_mean": 32.2872340426,
"line_max": 103,
"alpha_frac": 0.6248002557,
"autogenerated": false,
"ratio": 2.8265582655826558,
"config_test": ... |
import os
import gzip
import numpy as np
from collections import defaultdict
from rdkit import Chem
from rdkit.Chem.AtomPairs.Pairs import GetAtomPairFingerprint
def dict_2_str(d):
keylist = d.keys()
keylist.sort()
kv_list = ["{}: {}".format(k, d[k]) for k in keylist]
return ", ".join(kv_list)
"""
## calcu... | {
"repo_name": "xiaotaw/chembl",
"path": "data_files/chembl_cal_fp.py",
"copies": "1",
"size": "2051",
"license": "apache-2.0",
"hash": -6003566958170400000,
"line_mean": 25.9868421053,
"line_max": 149,
"alpha_frac": 0.6791808874,
"autogenerated": false,
"ratio": 2.6027918781725887,
"config_test... |
import os
import getpass
import numpy as np
import pandas as pd
from scipy import sparse
from collections import defaultdict
data_dir = "/home/%s/Documents/chembl/data_files/" % getpass.getuser()
fp_dir = os.path.join(data_dir, "fp_files")
mask_dir = os.path.join(data_dir, "mask_files")
structure_dir = os.path.join(d... | {
"repo_name": "xiaotaw/chembl",
"path": "data_files/chembl_input.py",
"copies": "1",
"size": "13511",
"license": "apache-2.0",
"hash": 4678268535018519000,
"line_mean": 37.9365994236,
"line_max": 159,
"alpha_frac": 0.6501369255,
"autogenerated": false,
"ratio": 3.0706818181818183,
"config_test"... |
import time
import Queue
import threading
import pk_input as pki
target_list = ["cdk2", "egfr_erbB1", "gsk3b", "hgfr", "map_k_p38a", "tpk_lck", "tpk_src", "vegfr2"]
target = target_list[0]
d = pki.Datasets(target_list)
# using queue
# producer thread
class Producer(threading.Thread):
def __init__(self, t_nam... | {
"repo_name": "xiaotaw/chembl",
"path": "dnn_model/pk_queue.py",
"copies": "1",
"size": "1842",
"license": "apache-2.0",
"hash": 7259446142795516000,
"line_mean": 23.2368421053,
"line_max": 125,
"alpha_frac": 0.6069489685,
"autogenerated": false,
"ratio": 2.93312101910828,
"config_test": false,... |
__author__ = 'xiezj'
from DeltaCrawler import DeltaCrawler
import lib.threadpool
import copy
from User import User
from lib.progressbar import ProgressBar, Percentage, Bar
progress_bar = None
user_left = 0
user_failed = 0
class UserLeft(Percentage):
def update(self, pbar):
return "%s users left, %s user... | {
"repo_name": "ZaneXie/DeltaCrawler",
"path": "run.py",
"copies": "1",
"size": "1761",
"license": "mit",
"hash": -4795871012256379000,
"line_mean": 27.868852459,
"line_max": 82,
"alpha_frac": 0.6059057354,
"autogenerated": false,
"ratio": 3.4529411764705884,
"config_test": false,
"has_no_keyw... |
__author__ = 'xiezj'
from User import User
import urllib
import urllib2
class Crawler(object):
DEBUG = False
TIMEOUT = 5
def __init__(self, user=None):
if user is None:
self.user = User()
else:
self.user = user
#enable cookie
self.opener = urllib2... | {
"repo_name": "ZaneXie/DeltaCrawler",
"path": "Crawler.py",
"copies": "1",
"size": "1303",
"license": "mit",
"hash": 1636939021840538400,
"line_mean": 24.5490196078,
"line_max": 73,
"alpha_frac": 0.5548733691,
"autogenerated": false,
"ratio": 3.9011976047904193,
"config_test": false,
"has_no_... |
__author__ = 'xiezj'
class User(object):
def __init__(self, line=None):
self.type = "DL"
self.login_name = ""
self.password = ""
self.miles = 0
self.current_miles = 0
self.MQM = 0
self.current_MQM = 0
self.status = ""
self.expriration = "null... | {
"repo_name": "ZaneXie/DeltaCrawler",
"path": "User.py",
"copies": "1",
"size": "1756",
"license": "mit",
"hash": 5133598020296904000,
"line_mean": 26.0153846154,
"line_max": 49,
"alpha_frac": 0.4601366743,
"autogenerated": false,
"ratio": 3.809110629067245,
"config_test": false,
"has_no_keyw... |
__author__ = 'Xing, Chang'
from ast import *
from template import *
interface = ['printf']
primitive = ['+', '-', '*', '/', 'div', '#']
def funType(tycon):
assert isinstance(tycon, tuple)
if isinstance(tycon[1], TyCon):
return "{} (i32*)*".format(IRTyName[tycon[1].name])
else: # resovled type ... | {
"repo_name": "pollow/CoreSML",
"path": "src/codegen.py",
"copies": "1",
"size": "13647",
"license": "mit",
"hash": -5107518971015888000,
"line_mean": 36.3890410959,
"line_max": 114,
"alpha_frac": 0.5284677951,
"autogenerated": false,
"ratio": 3.286849710982659,
"config_test": false,
"has_no_... |
__author__ = "Xingwei Lin"
__copyright__ = "Copyright 2018, The Argus-SAF Project"
__license__ = "Apache v2.0"
class TaintResolver(object):
"""
This class provides the architecture specific taint solutions.
"""
def __init__(self, project, analysis_center):
self._project = project
self... | {
"repo_name": "arguslab/Argus-SAF",
"path": "nativedroid/nativedroid/analyses/resolver/taint_resolver.py",
"copies": "1",
"size": "1939",
"license": "apache-2.0",
"hash": -2308144845757323300,
"line_mean": 31.3166666667,
"line_max": 91,
"alpha_frac": 0.6369262506,
"autogenerated": false,
"ratio":... |
'''author Xinwei Ding'''
# [START imports]
import os
import time
import urllib
import datetime
import calendar
from google.appengine.api import users
from google.appengine.api import mail
from google.appengine.ext import ndb
from google.appengine.ext import db
import jinja2
import webapp2
DEFAULT_ADMIN='test@example.... | {
"repo_name": "xxdd13/TutorManagementSystem",
"path": "main.py",
"copies": "1",
"size": "51771",
"license": "apache-2.0",
"hash": 7892310844056669000,
"line_mean": 28.4488054608,
"line_max": 221,
"alpha_frac": 0.6009348863,
"autogenerated": false,
"ratio": 2.9973946271421954,
"config_test": fal... |
__author__ = 'xixi.xxx'
import os,sys
parentdir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0,parentdir)
import aliyunExtensionCliHandler
class ProfileCmd:
useProfile = 'useProfile'
addProfile = 'addProfile'
name = '--name'
class ProfileHandler:
def __init__(self):
... | {
"repo_name": "mruse/aliyun-cli",
"path": "aliyuncli/advance/userProfileHandler.py",
"copies": "11",
"size": "2825",
"license": "apache-2.0",
"hash": -3996730375903153700,
"line_mean": 43.8571428571,
"line_max": 131,
"alpha_frac": 0.6102654867,
"autogenerated": false,
"ratio": 4.029957203994294,
... |
__author__ = 'XMB089'
from selenium.webdriver.support.wait import WebDriverWait
from selenium.common.exceptions import NoSuchElementException
from selenium.webdriver.support import expected_conditions as EC
from common.CommonMethods import CommonMethods
import logging
import logging.config
class BasePage(object):
... | {
"repo_name": "jchen7960/python_framework",
"path": "pages/BasePage.py",
"copies": "1",
"size": "1891",
"license": "mit",
"hash": -8166583078034408000,
"line_mean": 35.3846153846,
"line_max": 105,
"alpha_frac": 0.6541512427,
"autogenerated": false,
"ratio": 4.202222222222222,
"config_test": fal... |
__author__ = 'Xplore'
# Cyberaom brute force Script
import urllib
import time
import datetime
import urllib2
import sys
import xml.dom.minidom as XML
trst = {}
userid=[]
start=int(raw_input("Enter the starting Roll:"))
end=int(raw_input("Enter the last Roll:"))
for s in range(start,end+1):
userid.append(s)
filen... | {
"repo_name": "somu1795/cyberoam-bruteforce",
"path": "brute-multiple-word.py",
"copies": "1",
"size": "2302",
"license": "mit",
"hash": -8493303977014834000,
"line_mean": 24.0217391304,
"line_max": 94,
"alpha_frac": 0.5938314509,
"autogenerated": false,
"ratio": 3.695024077046549,
"config_test... |
__author__ = 'Xplore'
# Cyberaom brute force Script
import urllib
import time
import datetime
import urllib2
import sys
import xml.dom.minidom as XML
'''
#CountDown And Credits
def countdown(num):
for i in xrange(num,0,-1):
time.sleep(1)
sys.stdout.write(str(i%10)+'\r')
sys.stdout.flush()
... | {
"repo_name": "somu1795/cyberoam-bruteforce",
"path": "brute.py",
"copies": "1",
"size": "2006",
"license": "mit",
"hash": 4447401194199853600,
"line_mean": 26.1081081081,
"line_max": 86,
"alpha_frac": 0.6086739781,
"autogenerated": false,
"ratio": 3.5316901408450705,
"config_test": false,
"h... |
__author__ = 'Xplore'
import os
import sys
print "this is a "+sys.platform+" platform"
opt = int(raw_input("enter 1 for direct download or 2 for folder download:"))
path = raw_input("enter the url(input the path within quotes):")
fold = raw_input("enter the folder:")
if opt==1:
if 'linux' in sys.platfor... | {
"repo_name": "somu1795/wget",
"path": "wget.py",
"copies": "1",
"size": "1212",
"license": "mit",
"hash": 7726731662638579000,
"line_mean": 41.2857142857,
"line_max": 110,
"alpha_frac": 0.5569306931,
"autogenerated": false,
"ratio": 2.977886977886978,
"config_test": false,
"has_no_keywords":... |
__author__ = 'Xsank'
import socket
import select
import threading
from pseduohttp.structure.tcpdata import TcpData
from pseduohttp.structure.tcpcontroller import TcpController
from pseduohttp.constant.settings import SERVER_IP
from pseduohttp.constant.settings import SERVER_PORT
from pseduohttp.constant.settings impor... | {
"repo_name": "xsank/pseudo-http",
"path": "pseudohttp/core/server.py",
"copies": "1",
"size": "3019",
"license": "mit",
"hash": 5558791237448543000,
"line_mean": 39.7972972973,
"line_max": 79,
"alpha_frac": 0.6018549188,
"autogenerated": false,
"ratio": 4.198887343532684,
"config_test": false,... |
__author__ = 'Xsank'
import socket
import thread
import time
from pseduohttp.structure.tcpdata import TcpData
from pseduohttp.constant.settings import SERVER_IP
from pseduohttp.constant.settings import SERVER_PORT
from pseduohttp.constant.settings import IS_BLOCKING
class TcpClient(object):
def __init__(self,ip=... | {
"repo_name": "xsank/pseudo-http",
"path": "pseudohttp/core/client.py",
"copies": "1",
"size": "1620",
"license": "mit",
"hash": 3005478819034658300,
"line_mean": 28.4545454545,
"line_max": 84,
"alpha_frac": 0.6216049383,
"autogenerated": false,
"ratio": 3.894230769230769,
"config_test": false,... |
__author__ = 'Xsank'
import thread
from multiprocessing.pool import ThreadPool
from threading import Condition
from event import Event
from taskpool import TaskPool
from listener import Listener
from util import Singleton
from exception import EventTypeError
from exception import ListenerTypeError
from exception impor... | {
"repo_name": "xsank/Pyeventbus",
"path": "eventbus/eventbus.py",
"copies": "1",
"size": "3664",
"license": "mit",
"hash": 7258296762876546000,
"line_mean": 29.5416666667,
"line_max": 85,
"alpha_frac": 0.6189956332,
"autogenerated": false,
"ratio": 4.446601941747573,
"config_test": false,
"ha... |
__author__ = 'xsank'
import logging
import tornado.web
import tornado.websocket
from daemon import Bridge
from data import ClientData
from utils import check_ip, check_port
class IndexHandler(tornado.web.RequestHandler):
def get(self):
self.render("index.html")
class WSHandler(tornado.websocket.WebS... | {
"repo_name": "jinjin123/devops2.0",
"path": "devops/webssh/handlers.py",
"copies": "1",
"size": "2211",
"license": "mit",
"hash": -2129386758626962000,
"line_mean": 28.48,
"line_max": 77,
"alpha_frac": 0.6069651741,
"autogenerated": false,
"ratio": 3.792452830188679,
"config_test": false,
"h... |
__author__ = 'xsank'
import logging
import tornado.websocket
from daemon import Bridge
from data import ClientData
from utils import check_ip, check_port
class IndexHandler(tornado.web.RequestHandler):
def get(self):
self.render("index.html")
class WSHandler(tornado.websocket.WebSocketHandler):
c... | {
"repo_name": "xsank/webssh",
"path": "handlers.py",
"copies": "1",
"size": "1650",
"license": "mit",
"hash": 2948261815209074700,
"line_mean": 24.78125,
"line_max": 76,
"alpha_frac": 0.5915151515,
"autogenerated": false,
"ratio": 3.8551401869158877,
"config_test": false,
"has_no_keywords": f... |
__author__ = 'xsank'
import os.path
import tornado.ioloop
import tornado.web
import tornado.httpserver
import tornado.options
from tornado.options import options
from config import init_config
from urls import handlers
from ioloop import IOLoop
settings = dict(
template_path=os.path.join(os.path.dirname(__file_... | {
"repo_name": "xsank/webssh",
"path": "main.py",
"copies": "1",
"size": "1182",
"license": "mit",
"hash": 8081320252570954000,
"line_mean": 21.7307692308,
"line_max": 71,
"alpha_frac": 0.5862944162,
"autogenerated": false,
"ratio": 3.310924369747899,
"config_test": false,
"has_no_keywords": f... |
__author__ = 'xsank'
import paramiko
from paramiko.ssh_exception import AuthenticationException, SSHException
from tornado.websocket import WebSocketClosedError
from ioloop import IOLoop
from utils import routine
class Bridge(object):
def __init__(self, websocket):
self._websocket = websocket
s... | {
"repo_name": "Saberlion/docker-webssh",
"path": "app/daemon.py",
"copies": "1",
"size": "2150",
"license": "mit",
"hash": 9100324790927929000,
"line_mean": 26.5641025641,
"line_max": 72,
"alpha_frac": 0.5479069767,
"autogenerated": false,
"ratio": 4.663774403470716,
"config_test": false,
"ha... |
__author__ = 'xsank'
import paramiko
from paramiko.ssh_exception import AuthenticationException, SSHException
from data import ServerData
from ioloop import IOLoop
class Bridge(object):
def __init__(self, websocket):
self.websocket = websocket
self.ssh = paramiko.SSHClient()
self.shell ... | {
"repo_name": "sky-dream/webssh",
"path": "daemon.py",
"copies": "1",
"size": "1556",
"license": "mit",
"hash": -2512396223334936000,
"line_mean": 27.8148148148,
"line_max": 72,
"alpha_frac": 0.5706940874,
"autogenerated": false,
"ratio": 4.458452722063037,
"config_test": false,
"has_no_keywo... |
__author__ = 'xsank'
import select
import socket
import errno
import logging
from threading import Thread
from tornado.websocket import WebSocketClosedError
class IOLoop(Thread):
def __init__(self):
super(IOLoop, self).__init__()
self.daemon = True
self.select = select.epoll()
s... | {
"repo_name": "sky-dream/webssh",
"path": "ioloop.py",
"copies": "1",
"size": "1914",
"license": "mit",
"hash": -3821800596382700000,
"line_mean": 30.3770491803,
"line_max": 70,
"alpha_frac": 0.4979101358,
"autogenerated": false,
"ratio": 4.8951406649616365,
"config_test": false,
"has_no_keyw... |
__author__ = 'xsank'
import select
import socket
import errno
import logging
from threading import Thread
from utils import Platform
MAX_DATA_BUFFER = 1024*1024
class IOLoop(Thread):
READ = 0x001
WRITE = 0x004
ERROR = 0x008 | 0x010
def __init__(self, impl):
super(IOLoop, self).__init__()... | {
"repo_name": "jinjin123/devops2.0",
"path": "devops/webssh/ioloop.py",
"copies": "2",
"size": "5647",
"license": "mit",
"hash": 6981202296658620000,
"line_mean": 31.4540229885,
"line_max": 83,
"alpha_frac": 0.4540463963,
"autogenerated": false,
"ratio": 4.651565074135091,
"config_test": false,... |
__author__ = 'xsank'
import select
import socket
import errno
import logging
from threading import Thread
class IOLoop(Thread):
def __init__(self):
super(IOLoop, self).__init__()
self.daemon = True
self.select = select.epoll()
self.bridges = {}
self.futures = {}
@sta... | {
"repo_name": "Saberlion/docker-webssh",
"path": "app/ioloop.py",
"copies": "1",
"size": "1841",
"license": "mit",
"hash": -3190777112652805600,
"line_mean": 28.6935483871,
"line_max": 70,
"alpha_frac": 0.4698533406,
"autogenerated": false,
"ratio": 4.744845360824742,
"config_test": false,
"h... |
__author__ = 'xsank'
import paramiko
from paramiko.ssh_exception import AuthenticationException, SSHException
from tornado.websocket import WebSocketClosedError
from ioloop import IOLoop
try:
from cStringIO import StringIO
except ImportError:
from StringIO import StringIO
class Bridge(object)... | {
"repo_name": "xsank/webssh",
"path": "daemon.py",
"copies": "1",
"size": "3034",
"license": "mit",
"hash": 1760938860555109400,
"line_mean": 28.0396039604,
"line_max": 93,
"alpha_frac": 0.5247198418,
"autogenerated": false,
"ratio": 4.689335394126739,
"config_test": false,
"has_no_keywords":... |
__author__ = 'xuanzhui'
# http://docs.python-requests.org/en/latest/user/quickstart/
import requests, re
def printDebugInfo(resp):
print('respond status code : ', resp.status_code)
print('respond cookies : ', resp.cookies)
print('respond headers : ', resp.headers)
print('respond content : ', resp.con... | {
"repo_name": "xuanzhui/SoochowOraWIFIPW",
"path": "Python3/getFileSSORequests.py",
"copies": "1",
"size": "2330",
"license": "apache-2.0",
"hash": -1201422974821296600,
"line_mean": 27.0843373494,
"line_max": 103,
"alpha_frac": 0.6871244635,
"autogenerated": false,
"ratio": 3.367052023121387,
... |
__author__ = 'xuelu520'
#coding=utf-8
#Evaluate the value of an arithmetic expression in Reverse Polish Notation.
#Valid operators are +, -, *, /. Each operand may be an integer or another expression.
#Some examples:
# ["2", "1", "+", "3", "*"] -> ((2 + 1) * 3) -> 9
# ["4", "13", "5", "/", "+"] -> (4 + (13 / 5)) -... | {
"repo_name": "xuelu520/leetcode",
"path": "src/Evaluate Reverse Polish Notation.py",
"copies": "1",
"size": "1310",
"license": "mit",
"hash": -8437267186760035000,
"line_mean": 21.8245614035,
"line_max": 86,
"alpha_frac": 0.4207692308,
"autogenerated": false,
"ratio": 2.9748283752860414,
"conf... |
import os
import sys
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import time
from common import *
time_stamp = str(time.time())
time_stamp = time_stamp[0: time_stamp.find('.')]
def read_buffer_profile(fp):
res = []
for line in open(fp):
ls = line.strip().spli... | {
"repo_name": "AthrunArthur/RTCLib",
"path": "tools/parse_demo_data_buffer.py",
"copies": "1",
"size": "4792",
"license": "mit",
"hash": -5803109610133211000,
"line_mean": 27.8674698795,
"line_max": 74,
"alpha_frac": 0.5619782972,
"autogenerated": false,
"ratio": 2.907766990291262,
"config_test... |
__author__ = 'xuhao'
from dji_ros.msg import *
from std_msgs.msg import String, Float32, UInt8
from nav_msgs.msg import Odometry
from dji_ros.srv import *
import time
import rospy
class DroneClient:
"""
SubscriberPair implention a pair save topic name and it's own data type,
so we can easily subscribe al... | {
"repo_name": "xuhao1/DJIROS-Client",
"path": "script/DroneClient.py",
"copies": "1",
"size": "3764",
"license": "mit",
"hash": 7473232501374974000,
"line_mean": 29.3548387097,
"line_max": 98,
"alpha_frac": 0.6020191286,
"autogenerated": false,
"ratio": 3.6088207094918503,
"config_test": false,... |
__author__ = 'xu'
#compile class
import ast
from llvm import *
from llvm.core import *
from llvm.ee import *
from PFunction import PFunction
from compiler import ast as yacc_ast
class PClass:
functions = None
variable_types = None
init_function = None
variable_names = None
variable_init = None
def __init__(self... | {
"repo_name": "xu-wang11/Pyww",
"path": "PClass.py",
"copies": "1",
"size": "1183",
"license": "mit",
"hash": -6952600738076124000,
"line_mean": 21.75,
"line_max": 68,
"alpha_frac": 0.6939983094,
"autogenerated": false,
"ratio": 3.241095890410959,
"config_test": false,
"has_no_keywords": fals... |
__author__ = 'xu'
import ast
from compiler import ast as yacc_ast
from llvm import *
from llvm.core import *
from llvm.ee import *
from llvm.passes import *
from types import ListType
import python_yacc
class PFunction:
variable = None
builder = None
node = None
function = None
vfunction = None
compiler = ... | {
"repo_name": "xu-wang11/Pyww",
"path": "PFunction.py",
"copies": "1",
"size": "4224",
"license": "mit",
"hash": 3282564519993572400,
"line_mean": 27.16,
"line_max": 90,
"alpha_frac": 0.6991003788,
"autogenerated": false,
"ratio": 3.166416791604198,
"config_test": false,
"has_no_keywords": fa... |
__author__ = 'xu'
import ast
from types import IntType, StringType, FloatType
from compiler import ast as yacc_ast
from llvm.core import *
from llvm.ee import *
from llvm import *
from llvm.passes import *
from PClass import PClass
class NameManager:
count = 0
prefix = "str"
def __init__(self):
print "nameMan... | {
"repo_name": "xu-wang11/Pyww",
"path": "Compiler.py",
"copies": "1",
"size": "23370",
"license": "mit",
"hash": -3104672092906602500,
"line_mean": 32.1019830028,
"line_max": 111,
"alpha_frac": 0.6998288404,
"autogenerated": false,
"ratio": 2.9969222877660937,
"config_test": false,
"has_no_ke... |
__author__ = 'xyz'
import sys
import socket
import urllib, urllib2
from xyz_lib import utils
class ScanNetwork(object):
"""
plan next:
op:
o: one
m: mulit
f:
"""
def __init__(self, op=None, ip=None, port=None, **kwargs):
self.op = op
self.ip = ip
self.port = po... | {
"repo_name": "noogel/xyzStudyPython",
"path": "python/scannetwork.py",
"copies": "1",
"size": "2207",
"license": "apache-2.0",
"hash": 1749465565072264400,
"line_mean": 24.9647058824,
"line_max": 91,
"alpha_frac": 0.5414589941,
"autogenerated": false,
"ratio": 3.41112828438949,
"config_test": ... |
__author__ = 'Yafit'
import requests
import json
ip = 'http://xxxxxx'
def loginApi(ip, username, password):
myHeader = {'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
'password': password}
r = requests.get(ip + '/rest/v1/login/' + username, headers=myH... | {
"repo_name": "Smart-Green/needle",
"path": "pyhaystack/mango/mango_api.py",
"copies": "1",
"size": "29050",
"license": "apache-2.0",
"hash": -4396345660515065300,
"line_mean": 32.4292289988,
"line_max": 141,
"alpha_frac": 0.6346987952,
"autogenerated": false,
"ratio": 3.155893536121673,
"confi... |
__author__ = 'yagniks'
import codecs
import re
ADD = 1
REMOVE = 2
OTHER = 3
LOOKFORNAME=1
LOOKFOREND=2
classPattern1 = " class [\w\d_: ]+ {$"
classPattern2 = "^class [\w\d_: ]+ {$"
#notClassPattern = "class.*;.*{"
validClassNamePattern = "[\w\d_:]+"
stringPattern = "\".*\""
commentPattern = "/\*.*\*/"
commentPatte... | {
"repo_name": "Yagniksuchak/CodeParser",
"path": "src/logChunk/logChunkToExcep.py",
"copies": "1",
"size": "9402",
"license": "bsd-3-clause",
"hash": 6369514428621480000,
"line_mean": 30.5503355705,
"line_max": 118,
"alpha_frac": 0.5290363752,
"autogenerated": false,
"ratio": 3.383231378193595,
... |
__author__ = 'yahor_hamaliy'
class ListTree:
def __str__(self):
self.__visited = {}
return '<Instance of {0}, address {1}:\n{2}{3}>'.format(
self.__class__.__name__, id(self),
self.__attrnames(self, 0),
self.__listclass(self.__class__, 4))
def __listclass... | {
"repo_name": "egorhm/debugutils",
"path": "listtree.py",
"copies": "1",
"size": "1690",
"license": "mit",
"hash": 5822195356891148000,
"line_mean": 25.421875,
"line_max": 102,
"alpha_frac": 0.449112426,
"autogenerated": false,
"ratio": 3.763919821826281,
"config_test": false,
"has_no_keyword... |
__author__ = 'yalnazov'
try:
import unittest2 as unittest
except ImportError:
import unittest
from paymill.paymill_context import PaymillContext
import paymill.models
from paymill.models.paymill_list import PaymillList
import datetime
import calendar
from . import test_config
import uuid
class TestClientServ... | {
"repo_name": "paymill/paymill-python",
"path": "tests/test_client_service.py",
"copies": "1",
"size": "3006",
"license": "mit",
"hash": 6144339603777507000,
"line_mean": 38.038961039,
"line_max": 107,
"alpha_frac": 0.6776447106,
"autogenerated": false,
"ratio": 3.3699551569506725,
"config_test... |
__author__ = 'yalnazov'
try:
import unittest2 as unittest
except ImportError:
import unittest
from paymill.paymill_context import PaymillContext
from . import test_config
class TestRefundService(unittest.TestCase):
amount = None
currency = 'EUR'
description = 'Test Python Refunds'
def setUp... | {
"repo_name": "lukasklein/paymill-python",
"path": "tests/test_refund_service.py",
"copies": "2",
"size": "1178",
"license": "mit",
"hash": 5031923753868343000,
"line_mean": 37.0322580645,
"line_max": 115,
"alpha_frac": 0.5670628183,
"autogenerated": false,
"ratio": 4.712,
"config_test": true,
... |
__author__ = 'yalnazov'
try:
import unittest2 as unittest
except ImportError:
import unittest
from paymill.paymill_context import PaymillContext
from paymill.models.payment import Payment
from . import test_config
class TestPaymentService(unittest.TestCase):
def setUp(self):
self.p = PaymillCont... | {
"repo_name": "lukasklein/paymill-python",
"path": "tests/test_payment_service.py",
"copies": "2",
"size": "1077",
"license": "mit",
"hash": -1696529867620254700,
"line_mean": 30.7058823529,
"line_max": 79,
"alpha_frac": 0.6620241411,
"autogenerated": false,
"ratio": 3.5311475409836066,
"config... |
__author__ = 'Yanan Sun <yanansun7@gmail.com>'
__version__ = '1.0'
from SocketServer import ThreadingTCPServer
from SimpleHTTPServer import SimpleHTTPRequestHandler
from webbrowser import open_new_tab
from json import dumps
from urlparse import urlparse
from os import environ
from types import NoneType
from linkedin.... | {
"repo_name": "Capez/02-labormatch",
"path": "labormatch/http_api.py",
"copies": "2",
"size": "3102",
"license": "apache-2.0",
"hash": -9019409616937986000,
"line_mean": 36.8292682927,
"line_max": 110,
"alpha_frac": 0.6557059961,
"autogenerated": false,
"ratio": 3.732851985559567,
"config_test"... |
__author__ = 'YanDuarte'
import pandas
import numpy
# Import the data set to memory
data = pandas.read_csv("separatedData.csv", low_memory = False)
# Print data set dimension
print(len(data))
print(len(data.columns))
# Change data type among variables to numeric
data["breastCancer100th"] = data["breastCancer100th"]... | {
"repo_name": "yan-duarte/yan-duarte.github.io",
"path": "archives/assignment2.py",
"copies": "1",
"size": "2727",
"license": "mit",
"hash": -6245083012610176000,
"line_mean": 49.5,
"line_max": 180,
"alpha_frac": 0.7825449212,
"autogenerated": false,
"ratio": 3.3215590742996346,
"config_test": ... |
__author__ = 'YanDuarte'
import pandas
import numpy
import statistics
import seaborn
import matplotlib.pyplot as plt
import scipy
# Import the data set to memory
data = pandas.read_csv("separatedData.csv", low_memory = False)
# Change data type among variables to numeric
data["breastCancer100th"] = data... | {
"repo_name": "yan-duarte/yan-duarte.github.io",
"path": "archives/dat-assignment4.py",
"copies": "1",
"size": "3473",
"license": "mit",
"hash": -5751142240432724000,
"line_mean": 46.9154929577,
"line_max": 138,
"alpha_frac": 0.7549668874,
"autogenerated": false,
"ratio": 3.171689497716895,
"co... |
__author__ = 'YanDuarte'
import pandas
import numpy
import statistics
import seaborn
import matplotlib.pyplot as plt
# Import the data set to memory
data = pandas.read_csv("separatedData.csv", low_memory = False)
# Change data type among variables to numeric
data["breastCancer100th"] = data["breastCanc... | {
"repo_name": "yan-duarte/yan-duarte.github.io",
"path": "archives/assignment4.py",
"copies": "1",
"size": "4271",
"license": "mit",
"hash": 4460416501742312000,
"line_mean": 50.1097560976,
"line_max": 174,
"alpha_frac": 0.7501756029,
"autogenerated": false,
"ratio": 3.187313432835821,
"config_... |
__author__ = 'YanDuarte'
import pandas
import numpy
import statistics
import statsmodels.formula.api as smf
import statsmodels.stats.multicomp as multi
# Import the data set to memory
data = pandas.read_csv("separatedData.csv", low_memory = False)
# Change data type among variables to numeric
data["breas... | {
"repo_name": "yan-duarte/yan-duarte.github.io",
"path": "archives/dat-assignment1.py",
"copies": "1",
"size": "2287",
"license": "mit",
"hash": -4511341716471620600,
"line_mean": 43.74,
"line_max": 114,
"alpha_frac": 0.7363358111,
"autogenerated": false,
"ratio": 3.2030812324929974,
"config_te... |
__author__ = 'YanDuarte'
import pandas
import numpy
import statistics
# Import the data set to memory
data = pandas.read_csv("separatedData.csv", low_memory = False)
# Change data type among variables to numeric
data["breastCancer100th"] = data["breastCancer100th"].convert_objects(convert_numeric=True)
da... | {
"repo_name": "yan-duarte/yan-duarte.github.io",
"path": "archives/assignment3.py",
"copies": "1",
"size": "4335",
"license": "mit",
"hash": -6208389115253153000,
"line_mean": 53.5769230769,
"line_max": 181,
"alpha_frac": 0.738638985,
"autogenerated": false,
"ratio": 3.1758241758241756,
"config... |
__author__ = 'YanDuarte'
import pandas
import numpy
import scipy.stats
import seaborn
import matplotlib.pyplot as plt
import statistics
# Import the data set to memory
data = pandas.read_csv("separatedData.csv", low_memory = False)
# Change data type among variables to numeric
data["breastCancer100th"... | {
"repo_name": "yan-duarte/yan-duarte.github.io",
"path": "archives/dat-assignment2.py",
"copies": "1",
"size": "4418",
"license": "mit",
"hash": -2294182067916390400,
"line_mean": 33.6290322581,
"line_max": 146,
"alpha_frac": 0.6258488004,
"autogenerated": false,
"ratio": 3.234260614934114,
"co... |
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