text stringlengths 0 1.05M | meta dict |
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__author__ = 'gpratt'
import HTSeq
import pandas as pd
def bed_to_genomic_interval(bed):
"""
Converts bed file to genomic interval (htseq format) file
"""
for interval in bed:
yield HTSeq.GenomicPosition(str(interval.chrom), interval.start, str(interval.strand))
def get_closest(bam, regi... | {
"repo_name": "YeoLab/gscripts",
"path": "gscripts/riboseq/riboseq_utils.py",
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"autogenerated": false,
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"config_test"... |
__author__ = 'gpratt'
import numpy as np
import pandas as pd
import pybedtools
import pyBigWig
from gscripts.general import dataviz
import seaborn as sns
class ReadDensity():
def __init__(self, pos, neg):
self.pos = pyBigWig.open(pos)
self.neg = pyBigWig.open(neg)
def values(self, chrom, sta... | {
"repo_name": "YeoLab/gscripts",
"path": "gscripts/rnaseq/splicing_map.py",
"copies": "1",
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__author__ = 'gpratt'
import sys
from optparse import OptionParser
import pysam
def remove_softclip(in_bam, out_bam):
"""
Removes softclipping from start of stranded reads
bam: str pointing to bam file
out_bam: pysam bam file to be written to
"""
with pysam.Samfile(in_bam, 'rb') as in_ba... | {
"repo_name": "YeoLab/gscripts",
"path": "gscripts/clipseq/remove_softclip.py",
"copies": "1",
"size": "1704",
"license": "mit",
"hash": 575165811869622900,
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"autogenerated": false,
"ratio": 3.5949367088607596,
"config_test"... |
__author__ = 'gpratt'
import sys
import argparse
from collections import Counter
import pyBigWig
import numpy as np
from scipy import stats
import pybedtools
def big_wig_corr(full, semi, regions):
full = pyBigWig.open(full)
semi = pyBigWig.open(semi)
regions = pybedtools.BedTool(regions)
full_resu... | {
"repo_name": "YeoLab/gscripts",
"path": "gscripts/general/bigwig_corr.py",
"copies": "1",
"size": "1788",
"license": "mit",
"hash": -6963826497473122000,
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"alpha_frac": 0.6879194631,
"autogenerated": false,
"ratio": 3.512770137524558,
"config_test": ... |
__author__ = 'gpratt'
"""
barcode_collapse.py read in a .bam file where the
first 9 nt of the read name
are the barcode and merge reads mapped to the same position that have the same barcode
"""
from collections import Counter
import itertools
from optparse import OptionParser
import sys
import pysam
def stranded... | {
"repo_name": "YeoLab/gscripts",
"path": "gscripts/clipseq/barcode_collapse_pe.py",
"copies": "1",
"size": "3833",
"license": "mit",
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"alpha_frac": 0.600573963,
"autogenerated": false,
"ratio": 3.717749757516974,
"config_... |
__author__ = 'gpratt'
import pybedtools
import pysam
def compute_frip(bam, bed):
bam_tool = pybedtools.BedTool(bam)
peaks = pybedtools.BedTool(bed)
num_reads_peaks = len(bam_tool.intersect(peaks, u=True, s=True))
bamtool = pysam.Samfile(bam)
total_mapped_reads = bamtool.mapped
return... | {
"repo_name": "YeoLab/gscripts",
"path": "gscripts/clipseq/calculate_frip.py",
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"h... |
__author__ = 'gpratt'
"""
Converts randomer + barcoded fastq files into something that can be barcode collapsed and mapped
"""
from collections import Counter, defaultdict, OrderedDict
from itertools import izip
import gzip
import os
from optparse import OptionParser
def hamming(word1, word2):
"""
Gets h... | {
"repo_name": "YeoLab/gscripts",
"path": "gscripts/clipseq/demux_paired_end.py",
"copies": "1",
"size": "8485",
"license": "mit",
"hash": -5700845333976245000,
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"alpha_frac": 0.6140247496,
"autogenerated": false,
"ratio": 3.726394378568292,
"config_te... |
__author__ = 'grafgustav'
from kivy.lang import Builder
from kivy.uix.boxlayout import BoxLayout
from kivy.properties import StringProperty, ObjectProperty, BooleanProperty
from kivy.uix.popup import Popup
from src.database import Database
from src.Controller.CommunicationController import CommunicationController
from ... | {
"repo_name": "grafgustav/accessmail",
"path": "src/GUI/EmailItem.py",
"copies": "1",
"size": "3341",
"license": "mit",
"hash": -4189994554999272000,
"line_mean": 31.7647058824,
"line_max": 105,
"alpha_frac": 0.6521999401,
"autogenerated": false,
"ratio": 4.18671679197995,
"config_test": false,... |
__author__ = 'grafgustav'
from kivy.lang import Builder
from kivy.uix.screenmanager import Screen
from kivy.properties import ObjectProperty, NumericProperty
from time import strptime
from src.database import Database
from src.models import Mails
Builder.load_file('GUI/readlayout.kv')
class ReadLayout(Screen):
... | {
"repo_name": "grafgustav/accessmail",
"path": "src/GUI/readLayout.py",
"copies": "1",
"size": "3771",
"license": "mit",
"hash": -3945645939882578400,
"line_mean": 33.9259259259,
"line_max": 108,
"alpha_frac": 0.6239724211,
"autogenerated": false,
"ratio": 4.076756756756756,
"config_test": fals... |
__author__ = 'grafgustav'
from kivy.lang import Builder
from kivy.uix.screenmanager import Screen
from kivy.properties import ObjectProperty, StringProperty
from .EmailItem import EmailItem
from src.database import Database
from src.Controller.CommunicationController import CommunicationController
from kivy.clock impor... | {
"repo_name": "grafgustav/accessmail",
"path": "src/GUI/InboxLayout.py",
"copies": "1",
"size": "5771",
"license": "mit",
"hash": -3368675931944848400,
"line_mean": 32.9470588235,
"line_max": 109,
"alpha_frac": 0.6153179692,
"autogenerated": false,
"ratio": 4.078445229681979,
"config_test": fal... |
__author__ = 'grafgustav'
from kivy.lang import Builder
from kivy.uix.screenmanager import Screen
from kivy.properties import ObjectProperty, StringProperty
from src.database import Database
from kivy.clock import Clock
import sys
import os
Builder.load_file('GUI/SettingsLayout.kv')
class SettingsLayout(Screen):
... | {
"repo_name": "grafgustav/accessmail",
"path": "src/GUI/SettingsLayout.py",
"copies": "1",
"size": "2386",
"license": "mit",
"hash": -9218006076176082000,
"line_mean": 35.1515151515,
"line_max": 81,
"alpha_frac": 0.6722548198,
"autogenerated": false,
"ratio": 3.57185628742515,
"config_test": fa... |
__author__ = 'grafgustav'
from kivy.lang import Builder
from kivy.uix.screenmanager import Screen
from src.GUI.ContactItem import ContactItem
from kivy.properties import ObjectProperty, StringProperty
from src.database import Database
from kivy.uix.popup import Popup
from src.models import Contacts
from kivy.uix.floatl... | {
"repo_name": "grafgustav/accessmail",
"path": "src/GUI/addressLayout.py",
"copies": "1",
"size": "5451",
"license": "mit",
"hash": -2701464959462881300,
"line_mean": 32.8571428571,
"line_max": 110,
"alpha_frac": 0.6285085305,
"autogenerated": false,
"ratio": 4.161068702290076,
"config_test": f... |
__author__ = 'grafgustav'
import smtplib
import email
import imaplib
from src.database import Database
from kivy.uix.button import Button
from kivy.uix.popup import Popup
from src.models import Mails
from clunky_config import clunkyConfig
class CommunicationController(object):
''' This class handles the communica... | {
"repo_name": "grafgustav/accessmail",
"path": "src/Controller/CommunicationController.py",
"copies": "1",
"size": "6594",
"license": "mit",
"hash": -695632712248255400,
"line_mean": 31.1707317073,
"line_max": 100,
"alpha_frac": 0.5210797695,
"autogenerated": false,
"ratio": 4.384308510638298,
... |
__author__ = 'grafgustav'
from Singleton import Singleton
from kivy.uix.button import Button
from kivy.uix.textinput import TextInput
from kivy.clock import Clock
from kivy.core.window import Window
from src.GUI.Tooltip import ToolTip
@Singleton
class WidgetManager:
def __init__(self):
self.root_widget ... | {
"repo_name": "grafgustav/accessmail",
"path": "src/GUI/widgetHelpers/WidgetManager.py",
"copies": "1",
"size": "5924",
"license": "mit",
"hash": 165986120738126430,
"line_mean": 37.2258064516,
"line_max": 104,
"alpha_frac": 0.5773126266,
"autogenerated": false,
"ratio": 4.210376687988628,
"con... |
class IndexedCache:
"""
Indexed cache
space effecent and order preserving
"""
def __init__(self):
self.dataMap = {}
self.dataArray = []
def __eq__(self,value):
result = self.dataMap == value.dataMap and self.dataArray == value.dataArray
return res... | {
"repo_name": "bionomicron/Redirector",
"path": "util/Cache.py",
"copies": "1",
"size": "12763",
"license": "mit",
"hash": -3895529563604074500,
"line_mean": 26.5680345572,
"line_max": 85,
"alpha_frac": 0.5164929875,
"autogenerated": false,
"ratio": 4.264283327764785,
"config_test": false,
"h... |
__author__ = 'Grainier Perera'
from pyquery import PyQuery as pq
from useragent_rotator import useragent
from proxy_rotator import proxy
from properties import google_prop
class ApplicationScraper(object):
def __init__(self):
pass
def scrape(self, application_id, application_url):
user_agent ... | {
"repo_name": "grainier/google-play-service",
"path": "play_scraper/util/scraper.py",
"copies": "1",
"size": "7097",
"license": "mit",
"hash": -6059109007934795000,
"line_mean": 36.9572192513,
"line_max": 114,
"alpha_frac": 0.5482598281,
"autogenerated": false,
"ratio": 4.523263224984066,
"conf... |
__author__ = 'Grainier Perera'
from selenium.webdriver import Firefox, FirefoxProfile, PhantomJS, DesiredCapabilities
import time
from useragent_rotator import useragent
from proxy_rotator import proxy
from properties import google_prop
from selenium.webdriver.common.proxy import *
class ApplicationIndexer(object):
... | {
"repo_name": "grainier/google-play-service",
"path": "play_scraper/util/indexer.py",
"copies": "1",
"size": "5535",
"license": "mit",
"hash": 2108494732802952200,
"line_mean": 39.4087591241,
"line_max": 131,
"alpha_frac": 0.5329719964,
"autogenerated": false,
"ratio": 4.529459901800327,
"confi... |
__author__ = 'Grainier Perera'
import time
import logging
import pickle
import redis
import random
from util.current_time import current_time_millisecond
from properties import google_prop
from multiprocessing import Pool
from util.indexer import ApplicationIndexer
from util.scraper import ApplicationScraper
def proc... | {
"repo_name": "grainier/google-play-service",
"path": "play_scraper/run_indexer.py",
"copies": "1",
"size": "8114",
"license": "mit",
"hash": -252934258554092060,
"line_mean": 47.8795180723,
"line_max": 112,
"alpha_frac": 0.6440719744,
"autogenerated": false,
"ratio": 4.012858555885262,
"config... |
__author__ = 'grangzosoft'
import pygame
from player명섭0513 import *
from block명섭0513 import *
pygame.init()
white = (255, 255, 255)
black = (0, 0, 0)
window = pygame.display.set_mode((800, 600))
pygame.display.set_caption('Building Crush')
usw_buildings = building(window, 0, 150, black, 100, 800)
gravity = -0.5
cloc... | {
"repo_name": "saintdragon2/python-3-lecture-2015",
"path": "civil_mid_final/슈퍼그랑조/idea/main명섭0513.py",
"copies": "1",
"size": "3096",
"license": "mit",
"hash": -2702620010598888400,
"line_mean": 36.6707317073,
"line_max": 81,
"alpha_frac": 0.4439766839,
"autogenerated": false,
"ratio": 2.0864864... |
__author__ = 'grangzosoft'
import pygame
import time
from player명섭0513 import Player
from block명섭0513 import building,Block
pygame.init()
black = (0, 0, 0)
white = (255, 255, 255)
bright_red = (255,0,0)
bright_green = (0,255,0)
red = (45, 0, 0)
green = (0, 45, 0)
window = pygame.display.set_mode((800, 600))
pygame.d... | {
"repo_name": "saintdragon2/python-3-lecture-2015",
"path": "civil_mid_final/슈퍼그랑조/Crash The USW/main명섭0513.py",
"copies": "1",
"size": "5878",
"license": "mit",
"hash": 1966060277405332200,
"line_mean": 30.2287234043,
"line_max": 81,
"alpha_frac": 0.4964224872,
"autogenerated": false,
"ratio": 2... |
__author__ = 'grantingersoll'
import optparse
import random
import re
#
# Small script to create samples from the original Kyoto data
#
def sample(input_file, output_file, rows):
print "Sampling " + str(rows) + " from " + input_file + " and writing to " + output_file
p = re.compile('\t\t+')
j = 0
out... | {
"repo_name": "LucidWorks/solr-for-security",
"path": "src/python/kyoto-sampler.py",
"copies": "1",
"size": "1329",
"license": "apache-2.0",
"hash": -4729544130023454000,
"line_mean": 30.6428571429,
"line_max": 126,
"alpha_frac": 0.5944319037,
"autogenerated": false,
"ratio": 3.416452442159383,
... |
__author__ = 'grantingersoll'
import optparse
import random
#
# Small script to create samples from the original Citibike data
#
def sample(input_file, output_file, rows):
print "Sampling " + str(rows) + " from " + input_file + " and writing to " + output_file
j = 0
output = open(output_file, 'w')
f ... | {
"repo_name": "LucidWorks/solr-for-datascience",
"path": "src/python/citi-sampler.py",
"copies": "2",
"size": "1148",
"license": "apache-2.0",
"hash": -543387852846045900,
"line_mean": 29.2105263158,
"line_max": 126,
"alpha_frac": 0.5993031359,
"autogenerated": false,
"ratio": 3.3372093023255816,... |
__author__ = "grburgess <J. Michael Burgess>"
from astromodels.functions.functions import DiracDelta, StepFunctionUpper
import numpy as np
def step_generator(intervals, parameter):
"""
Generates sum of step or dirac delta functions for the given intervals
and parameter. This can be used to link time-ind... | {
"repo_name": "giacomov/3ML",
"path": "threeML/utils/step_parameter_generator.py",
"copies": "1",
"size": "2905",
"license": "bsd-3-clause",
"hash": -5254537073167001000,
"line_mean": 26.9326923077,
"line_max": 81,
"alpha_frac": 0.6330464716,
"autogenerated": false,
"ratio": 4.029126213592233,
... |
__author__ = 'grburgess'
from astropy import units as u
import numpy as np
import scipy.integrate as integrate
import collections
from threeML.utils.fitted_objects.fitted_source_handler import GenericFittedSourceHandler
class NotCompositeModelError(RuntimeError):
pass
class InvalidUnitError(RuntimeError):
... | {
"repo_name": "volodymyrss/3ML",
"path": "threeML/utils/fitted_objects/fitted_point_sources.py",
"copies": "1",
"size": "12461",
"license": "bsd-3-clause",
"hash": -2036497851594954800,
"line_mean": 30.7073791349,
"line_max": 171,
"alpha_frac": 0.5507583661,
"autogenerated": false,
"ratio": 4.620... |
__author__ = 'grburgess'
# from threeML.io.rich_display import display
from threeML.utils.fitted_objects.fitted_point_sources import FittedPointSourceSpectralHandler
from threeML.exceptions.custom_exceptions import custom_warnings
import numpy as np
import pandas as pd
import collections
def _setup_analysis_diction... | {
"repo_name": "volodymyrss/3ML",
"path": "threeML/io/calculate_flux.py",
"copies": "1",
"size": "20507",
"license": "bsd-3-clause",
"hash": 1912600731678550500,
"line_mean": 34.6643478261,
"line_max": 122,
"alpha_frac": 0.4681328327,
"autogenerated": false,
"ratio": 4.97380548144555,
"config_te... |
__author__ = 'grburgess'
import astropy.units as u
import matplotlib.pyplot as plt
import numpy as np
from astropy.visualization import quantity_support
from threeML.config.config import threeML_config
from threeML.io.calculate_flux import _setup_analysis_dictionaries, _collect_sums_into_dictionaries
from threeML.io.... | {
"repo_name": "volodymyrss/3ML",
"path": "threeML/io/plotting/model_plot.py",
"copies": "1",
"size": "25880",
"license": "bsd-3-clause",
"hash": -6858210789794804000,
"line_mean": 32.9632545932,
"line_max": 169,
"alpha_frac": 0.4827666151,
"autogenerated": false,
"ratio": 4.80505012996658,
"con... |
__author__='grburgess'
import collections
import copy
import os
import numpy as np
import pandas as pd
from pandas import HDFStore
from threeML.config.config import threeML_config
from threeML.exceptions.custom_exceptions import custom_warnings
from threeML.io.file_utils import sanitize_filename
from threeML.io.prog... | {
"repo_name": "volodymyrss/3ML",
"path": "threeML/utils/time_series/event_list.py",
"copies": "1",
"size": "31101",
"license": "bsd-3-clause",
"hash": -4761736451517164000,
"line_mean": 32.7687296417,
"line_max": 125,
"alpha_frac": 0.573582843,
"autogenerated": false,
"ratio": 4.18192819685357,
... |
__author__ = 'grburgess'
import collections
import re
from threeML.exceptions.custom_exceptions import custom_warnings, deprecated
import astropy.io.fits as fits
import numpy as np
import pandas as pd
from threeML.plugins.EventListLike import EventListLike
from threeML.plugins.OGIP.response import InstrumentResponseS... | {
"repo_name": "volodymyrss/3ML",
"path": "threeML/plugins/FermiGBMTTELike.py",
"copies": "1",
"size": "11806",
"license": "bsd-3-clause",
"hash": 3766980563605077500,
"line_mean": 32.6353276353,
"line_max": 135,
"alpha_frac": 0.5614941555,
"autogenerated": false,
"ratio": 3.985820391627279,
"co... |
__author__ = 'grburgess'
import collections
import warnings
import astropy.io.fits as fits
import numpy as np
import pandas as pd
from threeML.plugins.EventListLike import EventListLike
from threeML.utils.fermi_relative_mission_time import compute_fermi_relative_mission_times
from threeML.utils.time_series.event_lis... | {
"repo_name": "volodymyrss/3ML",
"path": "threeML/plugins/FermiLATLLELike.py",
"copies": "1",
"size": "13320",
"license": "bsd-3-clause",
"hash": 6553425025158199000,
"line_mean": 29.2040816327,
"line_max": 129,
"alpha_frac": 0.5651651652,
"autogenerated": false,
"ratio": 3.8341968911917097,
"c... |
__author__ = "grburgess"
import itertools
import functools
import numpy as np
from threeML.io.progress_bar import progress_bar
from astromodels import use_astromodels_memoization
class GenericFittedSourceHandler(object):
def __init__(self, analysis_result, new_function, parameter_names, parameters, confidence_l... | {
"repo_name": "volodymyrss/3ML",
"path": "threeML/utils/fitted_objects/fitted_source_handler.py",
"copies": "1",
"size": "10706",
"license": "bsd-3-clause",
"hash": 8213643885580075000,
"line_mean": 24.25,
"line_max": 144,
"alpha_frac": 0.6068559686,
"autogenerated": false,
"ratio": 4.53068133728... |
__author__ = "grburgess"
import matplotlib.pyplot as plt
import numpy as np
# reverse these colormaps so that it goes from light to dark
REVERSE_CMAP = ['summer', 'autumn', 'winter', 'spring', 'copper']
# clip some colormaps so the colors aren't too light
CMAP_RANGE = dict(gray={'start':200, 'stop':0},
... | {
"repo_name": "volodymyrss/3ML",
"path": "threeML/io/plotting/cmap_cycle.py",
"copies": "1",
"size": "2507",
"license": "bsd-3-clause",
"hash": -3157726288660211000,
"line_mean": 29.2048192771,
"line_max": 89,
"alpha_frac": 0.5743917032,
"autogenerated": false,
"ratio": 3.5161290322580645,
"con... |
__author__ = 'grburgess'
import numpy as np
from threeML.io.file_utils import file_existing_and_readable
from threeML.plugins.OGIP.pha import PHAII
from threeML.exceptions.custom_exceptions import custom_warnings
from threeML.io.plotting.light_curve_plots import binned_light_curve_plot
from threeML.plugins.OGIPLike i... | {
"repo_name": "volodymyrss/3ML",
"path": "threeML/plugins/EventListLike.py",
"copies": "1",
"size": "18903",
"license": "bsd-3-clause",
"hash": 3036057226952021000,
"line_mean": 30.0904605263,
"line_max": 120,
"alpha_frac": 0.5617097815,
"autogenerated": false,
"ratio": 4.339531680440771,
"conf... |
__author__ = 'greghines'
from Container import Container
class IBCCcontainer(Container):
def __init__(self):
self.subjects = []
self.users = []
self.classifications = {}
def __subjectExists__(self,subjectName):
return subjectName in self.subjects
def __addSubject__(self,s... | {
"repo_name": "camallen/aggregation",
"path": "experimental/classification/IBCCcontainer.py",
"copies": "2",
"size": "1421",
"license": "apache-2.0",
"hash": 7519016431810975000,
"line_mean": 32.8571428571,
"line_max": 72,
"alpha_frac": 0.6840253343,
"autogenerated": false,
"ratio": 4.25449101796... |
__author__ = 'greghines'
from copy import deepcopy
import numpy as np
class BaseUserNode:
def __init__(self,classList=None):
#self.name = name
self.subjectsViewed = []
self.classifications = []
if classList == None:
self.numClasses = None
self.confusion_matr... | {
"repo_name": "camallen/aggregation",
"path": "experimental/graphicalClassification/BaseNodes.py",
"copies": "2",
"size": "6454",
"license": "apache-2.0",
"hash": -6517752994510942000,
"line_mean": 30.7931034483,
"line_max": 147,
"alpha_frac": 0.6137279207,
"autogenerated": false,
"ratio": 4.1029... |
__author__ = 'greg'
def long_substr(data):
substr = ''
if len(data) > 1 and len(data[0]) > 0:
for i in range(len(data[0])):
for j in range(len(data[0])-i+1):
if j > len(substr) and is_substr(data[0][i:i+j], data):
substr = data[0][i:i+j]
return substr
... | {
"repo_name": "camallen/aggregation",
"path": "algorithms/new_agg.py",
"copies": "2",
"size": "1923",
"license": "apache-2.0",
"hash": 5929864048386053000,
"line_mean": 67.7142857143,
"line_max": 206,
"alpha_frac": 0.6718668747,
"autogenerated": false,
"ratio": 3.1016129032258064,
"config_test"... |
__author__ = 'greg'
from clustering import Cluster
import pandas as pd
import numpy as np
from scipy.spatial.distance import pdist,squareform
from scipy.cluster.hierarchy import linkage
import abc
import math
class AbstractNode:
def __init__(self):
self.value = None
self.rchild = None
self.... | {
"repo_name": "camallen/aggregation",
"path": "algorithms/automatic_optics.py",
"copies": "2",
"size": "9956",
"license": "apache-2.0",
"hash": -3825393256074958000,
"line_mean": 33.5729166667,
"line_max": 116,
"alpha_frac": 0.5640819606,
"autogenerated": false,
"ratio": 3.7598187311178246,
"co... |
__author__ = 'greg'
from fix import Fix
import numpy as np
import sys
import os
# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
sys.path.append("/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
elif os.p... | {
"repo_name": "camallen/aggregation",
"path": "experimental/clusteringAlg/zeroFix.py",
"copies": "2",
"size": "3043",
"license": "apache-2.0",
"hash": -7744606971844911000,
"line_mean": 36.5679012346,
"line_max": 111,
"alpha_frac": 0.5928360171,
"autogenerated": false,
"ratio": 3.58,
"config_te... |
__author__ = 'greg'
import aggregation
import cPickle as pickle
import os.path
class CondorTools(aggregation.ClassificationTools):
def __init__(self):
aggregation.ClassificationTools.__init__(self,scale=1.875)
def __classification_to_markings__(self,classification):
annotations = classificati... | {
"repo_name": "camallen/aggregation",
"path": "experimental/paper/condorAggregation.py",
"copies": "2",
"size": "2704",
"license": "apache-2.0",
"hash": -5368841571103929000,
"line_mean": 39.3582089552,
"line_max": 144,
"alpha_frac": 0.6187130178,
"autogenerated": false,
"ratio": 3.77653631284916... |
__author__ = 'greg'
import aggregation
import csv
import os
import urllib
import cPickle as pickle
class PenguinTools(aggregation.ROIClassificationTools):
def __init__(self,subject_collection):
aggregation.ROIClassificationTools.__init__(self,scale=1) #1.92
self.subject_collection = subject_collec... | {
"repo_name": "camallen/aggregation",
"path": "experimental/paper/clustering/penguinAggregation.py",
"copies": "2",
"size": "6954",
"license": "apache-2.0",
"hash": -3506536902764759600,
"line_mean": 37.4198895028,
"line_max": 146,
"alpha_frac": 0.561691113,
"autogenerated": false,
"ratio": 3.777... |
__author__ = 'greg'
import bisect
import math
import abc
import numpy as np
import os
import re
# import ibcc
import csv
import matplotlib.pyplot as plt
import random
import socket
import warnings
import scipy
from shapely.geometry import Polygon
# import panoptes_api
def index(a, x):
'Locate the leftmost value e... | {
"repo_name": "camallen/aggregation",
"path": "engine/clustering.py",
"copies": "1",
"size": "5463",
"license": "apache-2.0",
"hash": -7728411014051612000,
"line_mean": 38.5869565217,
"line_max": 142,
"alpha_frac": 0.5912502288,
"autogenerated": false,
"ratio": 4.298190401258851,
"config_test":... |
__author__ = 'greg'
import clustering
import os
import re
import matplotlib.pyplot as plt
import networkx as nx
import itertools
from copy import deepcopy
import abc
import json
import csv
import math
def findsubsets(S,m):
return set(itertools.combinations(S, m))
class Classification:
def __init__(self):
... | {
"repo_name": "camallen/aggregation",
"path": "engine/classification.py",
"copies": "1",
"size": "28416",
"license": "apache-2.0",
"hash": -6603582252461055000,
"line_mean": 47.9931034483,
"line_max": 409,
"alpha_frac": 0.5601773649,
"autogenerated": false,
"ratio": 4.572899903443837,
"config_t... |
__author__ = 'greg'
import divisiveDBSCAN
import multiprocessing
import numpy as np
import math
class Worker(multiprocessing.Process):
def __init__(self,min_samples,task_queue,result_queue):
multiprocessing.Process.__init__(self)
self.d_DBSCAN = divisiveDBSCAN.DivisiveDBSCAN(min_samples)
s... | {
"repo_name": "camallen/aggregation",
"path": "experimental/clusteringAlg/divisiveDBSCAN_multi.py",
"copies": "2",
"size": "2387",
"license": "apache-2.0",
"hash": 1420804612340885200,
"line_mean": 30.84,
"line_max": 97,
"alpha_frac": 0.5693338919,
"autogenerated": false,
"ratio": 3.8314606741573... |
__author__ = 'greg'
import json
from pprint import pprint
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
import math
import numpy as np
import cv2
from sklearn.cluster import DBSCAN
import cPickle as pickle
# img = cv2.imread("/home/ggdhines/Dropbox/066e48f5-812c-4b5f-ab04-df6c35f50393.jpeg")
# print... | {
"repo_name": "zooniverse/aggregation",
"path": "experimental/algorithms/old_weather/create_data.py",
"copies": "2",
"size": "5608",
"license": "apache-2.0",
"hash": -78660752049111520,
"line_mean": 23.3826086957,
"line_max": 104,
"alpha_frac": 0.5082025678,
"autogenerated": false,
"ratio": 2.939... |
__author__ = 'greg'
import math
import os
import sys
import numpy as np
# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
sys.path.append("/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
elif os.path.exis... | {
"repo_name": "zooniverse/aggregation",
"path": "experimental/clusteringAlg/ibccCorrect.py",
"copies": "2",
"size": "9073",
"license": "apache-2.0",
"hash": -7788493256229844000,
"line_mean": 37.6085106383,
"line_max": 113,
"alpha_frac": 0.5764355781,
"autogenerated": false,
"ratio": 3.5372319688... |
__author__ = 'greg'
import math
#the base of a series of classes which can fix clusters which have been split but which should not have been
#the difference is how we find "abnormally" close clusters
class Fix:
def __init__(self):
pass
def calc_relations(self,centers,clusters,pts,user_list,dist_thresh... | {
"repo_name": "camallen/aggregation",
"path": "experimental/clusteringAlg/fix.py",
"copies": "2",
"size": "6749",
"license": "apache-2.0",
"hash": 7976150013479378000,
"line_mean": 39.4191616766,
"line_max": 122,
"alpha_frac": 0.5420062231,
"autogenerated": false,
"ratio": 3.998222748815166,
"c... |
__author__ = 'greg'
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
import math
import numpy
from scipy import spatial
from PIL import Image
import pytesseract
start_x = 585.5
end_x = 3265.5
horizontal_lines = [(1271.5,1288.5),(1368.5,1384),(1424,1440),(1481.5,1497),(1537.5,1552),(1593,1607.5),(1... | {
"repo_name": "camallen/aggregation",
"path": "algorithms/old_weather/bear.py",
"copies": "2",
"size": "14647",
"license": "apache-2.0",
"hash": 1013865412984317800,
"line_mean": 29.2644628099,
"line_max": 198,
"alpha_frac": 0.5037208985,
"autogenerated": false,
"ratio": 2.7973644003055766,
"co... |
__author__ = 'greg'
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
import math
import numpy
import cPickle
import os
from neural_network import Network,load_data_wrapper
import cv2
if os.path.exists("/home/ggdhines"):
base_directory = "/home/ggdhines"
else:
base_directory = "/home/greg"
net ... | {
"repo_name": "zooniverse/aggregation",
"path": "experimental/algorithms/old_weather/cells.py",
"copies": "2",
"size": "5004",
"license": "apache-2.0",
"hash": -7206102879154386000,
"line_mean": 23.5343137255,
"line_max": 136,
"alpha_frac": 0.5391686651,
"autogenerated": false,
"ratio": 2.7136659... |
__author__ = 'greg'
import numpy as np
class Node:
def __init__(self,min_x,min_y,max_x,max_y):
self.min_x = min_x
self.min_y = min_y
self.max_x = max_x
self.max_y = max_y
self.markings = []
self.users = []
self.children = None
self.split_x = None
... | {
"repo_name": "camallen/aggregation",
"path": "experimental/old/quadTree.py",
"copies": "2",
"size": "2340",
"license": "apache-2.0",
"hash": 812417456523073700,
"line_mean": 33.4117647059,
"line_max": 94,
"alpha_frac": 0.5081196581,
"autogenerated": false,
"ratio": 3.5240963855421685,
"config_... |
__author__ = 'greg'
import os
import yaml
import psycopg2
if os.path.exists("/home/ggdhines"):
base_directory = "/home/ggdhines"
else:
base_directory = "/home/greg"
environment = "staging"
try:
database_file = open("config/database.yml")
except IOError:
database_file = open(base_directory+"/Databases... | {
"repo_name": "zooniverse/aggregation",
"path": "experimental/algorithms/list_panoptes_projects.py",
"copies": "2",
"size": "1210",
"license": "apache-2.0",
"hash": 1590876713343120600,
"line_mean": 24.7659574468,
"line_max": 98,
"alpha_frac": 0.6876033058,
"autogenerated": false,
"ratio": 3.7003... |
__author__ = 'greg'
import psycopg2
import pymongo
import json
import re
# the directory to store the movie preview clips in
image_directory = "/home/greg/Databases/chimp/images/"
# connect to the mongodb server
client = pymongo.MongoClient()
db = client['serengeti_2015-06-27']
subjects = db["serengeti_subjects"]
cla... | {
"repo_name": "camallen/aggregation",
"path": "algorithms/migration.py",
"copies": "2",
"size": "1954",
"license": "apache-2.0",
"hash": -1248510847368518000,
"line_mean": 34.5454545455,
"line_max": 136,
"alpha_frac": 0.6780962129,
"autogenerated": false,
"ratio": 3.3287904599659286,
"config_te... |
__author__ = 'greg'
import pymongo
import bisect
import sys
import os
import csv
import matplotlib.pyplot as plt
import urllib
import matplotlib.cbook as cbook
from collections import Iterator
import math
from scipy.stats.stats import pearsonr
import cPickle as pickle
from scipy.stats.mstats import normaltest
import wa... | {
"repo_name": "camallen/aggregation",
"path": "experimental/paper/clustering/aggregation.py",
"copies": "2",
"size": "56968",
"license": "apache-2.0",
"hash": 771528196563640600,
"line_mean": 39.1183098592,
"line_max": 151,
"alpha_frac": 0.5596650751,
"autogenerated": false,
"ratio": 3.5158921187... |
__author__ = 'greg'
import random
import math
import sys
import gc
steps = ["centered_in_crosshairs", "subtracted", "circular", "centered_in_host"]
gc.set_debug(gc.DEBUG_LEAK)
def create_annotations():
annotations = []
end_point = random.randint(0,4)
for i in range(end_point):
annotations.append({... | {
"repo_name": "camallen/aggregation",
"path": "Stargazing/loadtesting.py",
"copies": "2",
"size": "2065",
"license": "apache-2.0",
"hash": 3839128037054996500,
"line_mean": 25.4743589744,
"line_max": 173,
"alpha_frac": 0.6121065375,
"autogenerated": false,
"ratio": 3.458961474036851,
"config_te... |
__author__ = 'greg'
import re
import os
import zipfile
import math
import csv
import json
import numpy
import tarfile
import rollbar
class CsvOut:
def __init__(self,project):
print type(project)
# assert isinstance(project,aggregation_api.AggregationAPI)
self.project = project
sel... | {
"repo_name": "camallen/aggregation",
"path": "engine/csv_output.py",
"copies": "1",
"size": "16832",
"license": "apache-2.0",
"hash": 8854806366481500000,
"line_mean": 42.496124031,
"line_max": 118,
"alpha_frac": 0.5777091255,
"autogenerated": false,
"ratio": 4.201697453819271,
"config_test": ... |
__author__ = 'greg'
import requests
import pymongo
import json
import csv
import os
import urllib
import numpy
import cv2
from skimage.measure import structural_similarity as ssim
import matplotlib.pyplot as plt
from skimage.feature import CENSURE
def mse(imageA, imageB):
# the 'Mean Squared Error' between the tw... | {
"repo_name": "camallen/aggregation",
"path": "algorithms/blanks/image_diff.py",
"copies": "2",
"size": "4078",
"license": "apache-2.0",
"hash": -3543847362760126000,
"line_mean": 25.8355263158,
"line_max": 95,
"alpha_frac": 0.6245708681,
"autogenerated": false,
"ratio": 3.2262658227848102,
"co... |
__author__ = 'greg'
mypath = "/home/greg/Databases/tests/"
from os import listdir
from os.path import isfile, join
import numpy as np
import cv2
import numpy
import PIL
from sklearn.cluster import DBSCAN
import matplotlib.pyplot as plt
import math
onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) and... | {
"repo_name": "zooniverse/aggregation",
"path": "analysis/old_weather2.py",
"copies": "2",
"size": "2898",
"license": "apache-2.0",
"hash": -2846167512243142700,
"line_mean": 27.99,
"line_max": 87,
"alpha_frac": 0.5786749482,
"autogenerated": false,
"ratio": 3.0156087408949013,
"config_test": f... |
__author__ = 'greg'
def find_cluster(p,c_list):
for i,c in enumerate(c_list):
if p in c:
return i
return -1
def cluster_compare(c_list1,c_list2):
#return those points in c_list2 which do not have a corresponding cluster in c_list1
mapping_1_to_2 = [None for c in c_list1]
#mapp... | {
"repo_name": "zooniverse/aggregation",
"path": "experimental/clusteringAlg/clusterCompare.py",
"copies": "2",
"size": "1960",
"license": "apache-2.0",
"hash": 7775831368206774000,
"line_mean": 33.4035087719,
"line_max": 118,
"alpha_frac": 0.6566326531,
"autogenerated": false,
"ratio": 3.21311475... |
__author__ = 'greg'
from DAL import DAL
import init_database as Init
engine = 'mysql+mysqldb://johndoe:secret@localhost/how_to_sqlalchemy?unix_socket=/opt/lampp/var/mysql/mysql.sock'
my_dal = None
def add_users(dal_instance):
if dal_instance:
dal_instance.clear_users()
dal_instance.add_user('Jo... | {
"repo_name": "gergob/how_to_sqlalchemy",
"path": "app.py",
"copies": "1",
"size": "1353",
"license": "mit",
"hash": 30529278995558508,
"line_mean": 32,
"line_max": 145,
"alpha_frac": 0.6171470806,
"autogenerated": false,
"ratio": 2.934924078091106,
"config_test": false,
"has_no_keywords": fa... |
__author__ = 'Greg'
import pandas as pd
import numpy as np
import itertools
import multiprocessing as mp
import csv
def recur_dictify(frame):
"""
h/t: http://stackoverflow.com/a/19900276/843419
:param frame: a pandas data frame
:return: a nested dictionary with the columns as the keys and the final o... | {
"repo_name": "gregmacfarlane/trucksim_disagg",
"path": "py/disaggregate_trucks.py",
"copies": "1",
"size": "13526",
"license": "mit",
"hash": 4117332144261454300,
"line_mean": 35.0693333333,
"line_max": 186,
"alpha_frac": 0.5691261275,
"autogenerated": false,
"ratio": 3.525149856658848,
"confi... |
from collections import defaultdict, deque
from itertools import chain
#from twython import Twython
import random
#import config
# twitter = Twython(
# config.TWITTER_CONSUMER_KEY,
# config.TWITTER_CONSUMER_SECRET,
# config.TWITTER_ACCESS_TOKEN,
# config.TWITTER_ACCESS_SECRET
# )
class MarkovChain(... | {
"repo_name": "IrekRybark/pyiku",
"path": "pyiku/markov.py",
"copies": "1",
"size": "1598",
"license": "mit",
"hash": -1142135734682113500,
"line_mean": 29.1509433962,
"line_max": 77,
"alpha_frac": 0.6245306633,
"autogenerated": false,
"ratio": 3.504385964912281,
"config_test": false,
"has_no... |
__author__ = 'Greg Richards'
__author__ = 'Tara Critteden'
from random import randint
import Player
import Message
import RPSPlayerExample
class MyPlayer(Player.Player):
def __init__(self):
"""
:param self: this player class
"""
Player.Player.__init__(self) # calls superclass c... | {
"repo_name": "geebzter/game-framework",
"path": "GRTCPlayer.py",
"copies": "1",
"size": "5084",
"license": "apache-2.0",
"hash": -7003421978090337000,
"line_mean": 33.8219178082,
"line_max": 108,
"alpha_frac": 0.5080645161,
"autogenerated": false,
"ratio": 4.261525565800503,
"config_test": fal... |
__author__ = 'Greg Richards'
import Game
class RPSGame(Game.Game):
# # this class simulates two players playing a game of rock, paper, scissors
def __init__(self):
super(RPSGame, self).__init__()
def get_result(self, moves):
# unpack the tuple that was passed as a parameter
move1... | {
"repo_name": "PaulieC/RPSPlayer",
"path": "RPSGame.py",
"copies": "3",
"size": "1065",
"license": "apache-2.0",
"hash": 3941859482165159000,
"line_mean": 28.5833333333,
"line_max": 81,
"alpha_frac": 0.4863849765,
"autogenerated": false,
"ratio": 3.710801393728223,
"config_test": false,
"has_... |
__author__ = 'gremorian'
import sys
import time
from datetime import date, datetime
from decimal import Decimal
from .types import OrientRecordLink, OrientRecord, OrientBinaryObject
class ORecordDecoder(object):
def __init__(self, content):
self.className = None
self.data = {}
if not is... | {
"repo_name": "ziyangzeng/pyorient",
"path": "pyorient/serialization.py",
"copies": "1",
"size": "15904",
"license": "apache-2.0",
"hash": -2915493534457863700,
"line_mean": 29.0642722117,
"line_max": 88,
"alpha_frac": 0.4607017103,
"autogenerated": false,
"ratio": 4.51177304964539,
"config_tes... |
__author__ = 'gremorian'
import sys
import time
from datetime import date, datetime
from .types import OrientRecordLink, OrientRecord, OrientBinaryObject
class ORecordDecoder(object):
def __init__(self, content):
self.className = None
self.data = {}
if not isinstance(content, str):
... | {
"repo_name": "SPSCommerce/pyorient",
"path": "pyorient/serialization.py",
"copies": "2",
"size": "15464",
"license": "apache-2.0",
"hash": -204384615195529540,
"line_mean": 29.027184466,
"line_max": 88,
"alpha_frac": 0.4625581997,
"autogenerated": false,
"ratio": 4.4940424295263,
"config_test"... |
__author__ = 'gremorian'
import unittest
import pyorient
import os
os.environ['DEBUG'] = "0"
old_token = ''
class TokenAuthTest(unittest.TestCase):
""" Command Test Case """
client = None
def setUp(self):
self.client = pyorient.OrientDB("localhost", 2424)
client = pyorient.OrientDB("... | {
"repo_name": "Ostico/pyorient",
"path": "tests/test_token_auth.py",
"copies": "3",
"size": "3767",
"license": "apache-2.0",
"hash": -3603650487643592700,
"line_mean": 33.5596330275,
"line_max": 86,
"alpha_frac": 0.6124236793,
"autogenerated": false,
"ratio": 3.726013847675569,
"config_test": t... |
__author__ = 'gremorian'
import unittest
import pyorient
class LinkSetTestCase(unittest.TestCase):
""" Command Test Case """
def setUp(self):
self.client = pyorient.OrientDB("localhost", 2424)
self.client.connect("root", "root")
db_name = "test_set"
try:
self.c... | {
"repo_name": "Ostico/pyorient",
"path": "tests/test_linkSet.py",
"copies": "3",
"size": "6752",
"license": "apache-2.0",
"hash": 7002568160081194000,
"line_mean": 37.3636363636,
"line_max": 109,
"alpha_frac": 0.4942239336,
"autogenerated": false,
"ratio": 3.878230901780586,
"config_test": true... |
__author__ = 'grokrz'
log = True
class Node:
def __init__(self, node_id=None, successor=None):
self.node_id = node_id
self.successor = successor
self.leader = None
self.__messages = []
def get_successor_id(self):
if self.successor:
return self.successor.no... | {
"repo_name": "kgrodzicki/cloud-computing-specialization",
"path": "cloud-computing-concepts-part2/scripts/ring_election.py",
"copies": "1",
"size": "2575",
"license": "mit",
"hash": 5071830808931959000,
"line_mean": 25.2755102041,
"line_max": 92,
"alpha_frac": 0.5580582524,
"autogenerated": false,... |
__author__ = 'grund'
import re
from snakebite.client import Client
import pyhs2
class Loader:
"""
The idea of the loader is to provide a convenient interface to create a new table
based on some input files
"""
def __init__(self, path, name_node, hive_server,
user="root", hive_db... | {
"repo_name": "grundprinzip/pyxplorer",
"path": "pyxplorer/loader.py",
"copies": "1",
"size": "4068",
"license": "bsd-2-clause",
"hash": -7700796680512762000,
"line_mean": 31.2857142857,
"line_max": 95,
"alpha_frac": 0.5314650934,
"autogenerated": false,
"ratio": 4.273109243697479,
"config_test... |
__author__ = 'gstimac'
import os
import subprocess
from cement.core.foundation import CementApp
from cement.utils.misc import init_defaults
# define our default configuration options
defaults = init_defaults('disk_expander')
defaults['disk_expander']['debug'] = False
# define the application class
class MyApp(CementA... | {
"repo_name": "gstimac/disk-expander",
"path": "disk_expander.py",
"copies": "1",
"size": "1361",
"license": "mit",
"hash": -181372708260242720,
"line_mean": 34.8157894737,
"line_max": 185,
"alpha_frac": 0.6767083027,
"autogenerated": false,
"ratio": 3.7910863509749304,
"config_test": false,
... |
__author__ = 'gturner'
from eventtools.tests._inject_app import TestCaseWithApp as AppTestCase
from eventtools.tests.eventtools_testapp.models import *
from eventtools.models import Rule
import datetime
class TestEventTree(AppTestCase):
def setUp(self):
super(TestEventTree, self).setUp()
#SCENARI... | {
"repo_name": "ixc/glamkit-eventtools",
"path": "eventtools/tests/models/tree.py",
"copies": "1",
"size": "4822",
"license": "bsd-3-clause",
"hash": -5127721433171684000,
"line_mean": 50.8602150538,
"line_max": 183,
"alpha_frac": 0.6758606387,
"autogenerated": false,
"ratio": 3.3696715583508037,
... |
__author__ = 'Guanhua, Joms'
from numpy import array, shape, sum, exp, dot, int8, zeros
from scipy.io import loadmat
import argparse, pickle, os
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser()
parser.add_argument('train_path', help='Path to training database')
arguments = parser.parse_args()
roaddat... | {
"repo_name": "archonren/project",
"path": "algorithms/pnn_with_non_target_testing_file.py",
"copies": "1",
"size": "2904",
"license": "mit",
"hash": -1696456931758939600,
"line_mean": 39.9154929577,
"line_max": 108,
"alpha_frac": 0.6039944904,
"autogenerated": false,
"ratio": 3.248322147651007,
... |
__author__ = 'Guanhua, Joms'
from scipy.io import loadmat, savemat
from numpy import vstack, array
import os
def output_data(key, heading, num_imgs):
arr = []
data = vstack(((heading[key][:, i])[0] for i in range(num_imgs)))
data_mean = data.mean(axis=0).reshape(-1, 1)
data_std = data.std(axis=0).res... | {
"repo_name": "archonren/project",
"path": "preprocess/split_without_target.py",
"copies": "1",
"size": "1255",
"license": "mit",
"hash": 8058023889479131000,
"line_mean": 34.8571428571,
"line_max": 101,
"alpha_frac": 0.6239043825,
"autogenerated": false,
"ratio": 2.8013392857142856,
"config_te... |
__author__ = 'Guanhua, Joms'
from skimage.color import rgb2lab, rgb2hsv
from numpy import empty, ndindex
def to_lab(ndimage):
return rgb2lab(ndimage)
def to_hsv(ndimage):
return rgb2hsv(ndimage)
def normalized_RG(ndimage):
"""
:param ndimage: a image file
:return: normalized RG for every pixel... | {
"repo_name": "archonren/project",
"path": "preprocess/data_handling.py",
"copies": "1",
"size": "1179",
"license": "mit",
"hash": -6451370853175486000,
"line_mean": 25.8181818182,
"line_max": 56,
"alpha_frac": 0.5742154368,
"autogenerated": false,
"ratio": 2.716589861751152,
"config_test": fal... |
__author__ = 'Guanhua, Joms'
import os
from real_single_pic import *
from scipy.io import savemat
import pickle
from numpy import array
def pic_package_to_mat(input_fldr, file_heading, number_of_file):
feature = []
num_super_pixel = []
dim = []
for n in range(number_of_file):
png_file_name = "... | {
"repo_name": "archonren/project",
"path": "preprocess/pic_package_feature_mat_without_target.py",
"copies": "1",
"size": "1669",
"license": "mit",
"hash": 3050107543681629000,
"line_mean": 42.9210526316,
"line_max": 110,
"alpha_frac": 0.6177351708,
"autogenerated": false,
"ratio": 2.887543252595... |
__author__ = 'Guanhua, Joms'
import os
from single_pic_handling import *
from scipy.io import savemat
def pic_package_to_mat(input_fldr, file_heading, number_of_file):
feature = []
target = []
dim = []
for n in range(number_of_file):
png_file_name = "%s_%06d.png" % (file_heading, n)
pa... | {
"repo_name": "archonren/project",
"path": "preprocess/pic_package_feature_mat.py",
"copies": "1",
"size": "1457",
"license": "mit",
"hash": -668761063743321600,
"line_mean": 40.6285714286,
"line_max": 107,
"alpha_frac": 0.6039807824,
"autogenerated": false,
"ratio": 2.78585086042065,
"config_t... |
__author__ = 'Guanhua, Joms'
from struct import unpack
from numpy import reshape, ndindex, empty
from scipy.ndimage import imread
def label(filepath, height, width):
"""
:param filepath: path of label.dat
:return data_label: ndarray that contain label of each pixel, dim = (height, width)
:return labe... | {
"repo_name": "archonren/project",
"path": "preprocess/file_handling.py",
"copies": "1",
"size": "1248",
"license": "mit",
"hash": -7265214784291283000,
"line_mean": 27.3863636364,
"line_max": 87,
"alpha_frac": 0.5961538462,
"autogenerated": false,
"ratio": 3.495798319327731,
"config_test": fal... |
__author__ = 'Guggi'
import urllib2
import urllib
import unirest
import json
from poster.encode import multipart_encode
from poster.streaminghttp import register_openers
from PIL import Image, ImageDraw, ImageFont
register_openers()
api_key = "YOUR_API_KEY" #you need to exchange the YOUR_API_KEY with your own API ke... | {
"repo_name": "mavlyutovrus/person_detection",
"path": "src/Detect_Faces.py",
"copies": "1",
"size": "4235",
"license": "apache-2.0",
"hash": 3185664998627565000,
"line_mean": 36.4778761062,
"line_max": 140,
"alpha_frac": 0.5924439197,
"autogenerated": false,
"ratio": 3.462796402289452,
"config... |
__author__ = 'guglielmo'
from django.contrib import admin
from datasets_survey.models import *
class SettoreAdmin(admin.ModelAdmin):
pass
class LicenzaAdmin(admin.ModelAdmin):
pass
class OrganizzazioneAdmin(admin.ModelAdmin):
list_display = ('denominazione', 'tipologia', 'area')
list_filter = ('tipol... | {
"repo_name": "DeppSRL/odl_datasets_survey",
"path": "project/datasets_survey/admin.py",
"copies": "1",
"size": "1036",
"license": "bsd-3-clause",
"hash": 5201951762637594000,
"line_mean": 32.4193548387,
"line_max": 90,
"alpha_frac": 0.7075289575,
"autogenerated": false,
"ratio": 3.2375,
"confi... |
__author__ = 'guglielmo'
import os
# This is necessary for all installed apps to be recognized, for some reason.
os.environ['DJANGO_SETTINGS_MODULE'] = 'op_social_access.settings.test'
def before_all(context):
# Even though DJANGO_SETTINGS_MODULE is set, this may still be
# necessary. Or it may be simple CYA ... | {
"repo_name": "openpolis/social-access",
"path": "op_social_access/features/environment.py",
"copies": "1",
"size": "3112",
"license": "mit",
"hash": 2184816788284262700,
"line_mean": 36.9512195122,
"line_max": 91,
"alpha_frac": 0.705655527,
"autogenerated": false,
"ratio": 4.0732984293193715,
... |
__author__ = 'Guido Krömer'
__license__ = 'MIT'
__version__ = '0.2'
__email__ = 'mail 64 cacodaemon 46 de'
import sublime
from sublime import Window
from sublime_plugin import TextCommand
from sublime_plugin import EventListener
from threading import Thread
import json
from time import sleep
from .WebSocket.WebSocketS... | {
"repo_name": "LukevdPalen/GhostText-for-SublimeText",
"path": "GhostText.py",
"copies": "1",
"size": "5931",
"license": "mit",
"hash": -3936956652284903000,
"line_mean": 33.6549707602,
"line_max": 111,
"alpha_frac": 0.6587917651,
"autogenerated": false,
"ratio": 3.845554834523037,
"config_test... |
from flask import render_template
from datetime import timedelta
from maraschino import app, logger
from maraschino.tools import *
from rtorrent import RTorrent
def log_error(ex):
logger.log('RTORRENTDL :: EXCEPTION - %s' % ex, 'DEBUG')
@app.route('/xhr/rtorrentdl/')
@requires_auth
def xhr_rtorrentdl():
# url qu... | {
"repo_name": "awagnon/maraschino",
"path": "modules/rtorrentdl.py",
"copies": "4",
"size": "3861",
"license": "mit",
"hash": -5348793493012701000,
"line_mean": 24.4013157895,
"line_max": 97,
"alpha_frac": 0.6576016576,
"autogenerated": false,
"ratio": 3.028235294117647,
"config_test": false,
... |
__author__ = 'GuillaumeBeaud'
# print '=========================================== LIMIT ====================================='
# ============================================== ABOVE IS OK =====================================
# ============================================== DEMO ===================================... | {
"repo_name": "LCAV/linvpy",
"path": "tests/test.py",
"copies": "1",
"size": "2184",
"license": "bsd-2-clause",
"hash": 5235623275410641000,
"line_mean": 34.2419354839,
"line_max": 98,
"alpha_frac": 0.5902014652,
"autogenerated": false,
"ratio": 4.1919385796545106,
"config_test": false,
"has_... |
__author__ = "Guillaume Bonamis"
__license__ = "MIT"
__copyright__ = "2015, ESRF"
import os
import sys
import numpy
import matplotlib
# matplotlib.use('Agg')
import matplotlib.pyplot as plt
from freesas.model import SASModel
import itertools
from scipy.optimize import fmin
import logging
logging.basicConfig(level=logg... | {
"repo_name": "kif/freesas",
"path": "freesas/align.py",
"copies": "1",
"size": "17075",
"license": "mit",
"hash": -3295297519486938600,
"line_mean": 37.1991051454,
"line_max": 209,
"alpha_frac": 0.574875549,
"autogenerated": false,
"ratio": 3.8147899910634493,
"config_test": false,
"has_no_k... |
import pytest
import numpy as np
from sklearn.mixture import GaussianMixture
from sklearn.mixture import BayesianGaussianMixture
@pytest.mark.parametrize(
"estimator",
[GaussianMixture(),
BayesianGaussianMixture()]
)
def test_gaussian_mixture_n_iter(estimator):
# check that n_iter is the number of ... | {
"repo_name": "kevin-intel/scikit-learn",
"path": "sklearn/mixture/tests/test_mixture.py",
"copies": "3",
"size": "1049",
"license": "bsd-3-clause",
"hash": 1429557988750151200,
"line_mean": 25.8974358974,
"line_max": 70,
"alpha_frac": 0.701620591,
"autogenerated": false,
"ratio": 3.3196202531645... |
__author__ = "Guillaume"
__license__ = "MIT"
__copyright__ = "2015, ESRF"
import numpy
from freesas.model import SASModel
class Grid:
"""
This class is used to create a grid which include all the input models
"""
def __init__(self, inputfiles):
"""
:param inputfiles: list of pdb files... | {
"repo_name": "kif/freesas",
"path": "freesas/average.py",
"copies": "1",
"size": "9116",
"license": "mit",
"hash": -2636630166722840600,
"line_mean": 32.6383763838,
"line_max": 101,
"alpha_frac": 0.5072400176,
"autogenerated": false,
"ratio": 3.71021571021571,
"config_test": false,
"has_no_k... |
__author__ = 'guillem'
import networkx as nx
import numpy as np
def rag(partition, discard_axis=[]):
"""
Parameters
----------
partition: numpy array
A 2D or 3D label array where each label represents a region
discard_axis: list, optional
Whether the rag discards adjacencies from given a... | {
"repo_name": "guillempalou/scikit-cv",
"path": "skcv/graph/rag.py",
"copies": "1",
"size": "2309",
"license": "bsd-3-clause",
"hash": 4037748571661822500,
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"line_max": 73,
"alpha_frac": 0.5426591598,
"autogenerated": false,
"ratio": 3.8228476821192054,
"config_test":... |
__author__ = 'gunnarkleemann'
#specify an aligmnemt and a reference VCF, list the number of header lines to remove from each file
import pandas as pd
def FilterMutations(Align, SkipCt1, ref, hdCt2):
# the metadata appears to be getting in the way of dataframe construciton so i skip it for now
#capture mtadata... | {
"repo_name": "gunnarklee/DataStoreRetrieval",
"path": "FilterMutations.py",
"copies": "1",
"size": "2398",
"license": "mit",
"hash": -5788852296164594000,
"line_mean": 32.3055555556,
"line_max": 158,
"alpha_frac": 0.6576313595,
"autogenerated": false,
"ratio": 2.945945945945946,
"config_test":... |
"""
nimsdata.medimg.nimsdicom
=========================
nimsdicom stuff, and adds dicom specific parsing routines.
nimsdicom uses a composer design pattern to dynamically incoporate functions based on
the input data.
"""
import dicom
import types
import logging
import tarfile
import datetime
import dcmstack
import c... | {
"repo_name": "Eric89GXL/data",
"path": "scitran/data/medimg/dcm/dcm.py",
"copies": "1",
"size": "16458",
"license": "mit",
"hash": -7986095593044696000,
"line_mean": 38.6578313253,
"line_max": 139,
"alpha_frac": 0.628691214,
"autogenerated": false,
"ratio": 3.628306878306878,
"config_test": fa... |
"""
nimsdata.medimg.nimsnifti
=========================
NIMSNifti provide NIfti writing capabilities for MR datasets read by any subclass of NIMSMRReader.
Provides nifti specifics, inherits from NIMSMRReader, NIMSMRWriter.
"""
import os
import bson
import logging
import nibabel
import json
import numpy as np
imp... | {
"repo_name": "cni/nimsdata",
"path": "medimg/nimsnifti.py",
"copies": "1",
"size": "10850",
"license": "mit",
"hash": 4391735899394310000,
"line_mean": 36.2852233677,
"line_max": 161,
"alpha_frac": 0.5714285714,
"autogenerated": false,
"ratio": 3.6992840095465396,
"config_test": false,
"has_... |
"""
scitran.data.medimg.nifti
=========================
Nifti provide NIfti writing capabilities for MR datasets read by any subclass of MedImgReader.
Provides nifti specifics, inherits from MedImgReader, MedImgWriter.
"""
import os
import logging
import nibabel
import numpy as np
import medimg
from .. import u... | {
"repo_name": "Eric89GXL/data",
"path": "scitran/data/medimg/nifti.py",
"copies": "1",
"size": "9578",
"license": "mit",
"hash": -1250271302966136600,
"line_mean": 34.6059479554,
"line_max": 157,
"alpha_frac": 0.5870745458,
"autogenerated": false,
"ratio": 3.6061746987951806,
"config_test": fal... |
"""
nimsdata.medimg.nimsbehavior
============================
Not implemented.
This will parse behavioral data that is related to medical imaging data. This will potentially
parse outputs from stimulus presentation software that are commonly used in medical imaging. Such
as outputs from Matlab, Eprime2 and PsychoPy... | {
"repo_name": "cni/nimsdata",
"path": "medimg/nimsbehavior.py",
"copies": "1",
"size": "1188",
"license": "mit",
"hash": 8936402483464797000,
"line_mean": 26,
"line_max": 98,
"alpha_frac": 0.7003367003,
"autogenerated": false,
"ratio": 3.644171779141104,
"config_test": false,
"has_no_keywords... |
"""
scitran.data.medimg.behavior
============================
Not implemented.
This will parse behavioral data that is related to medical imaging data. This will potentially
parse outputs from stimulus presentation software that are commonly used in medical imaging. Such
as outputs from Matlab, Eprime2 and PsychoPy... | {
"repo_name": "Eric89GXL/data",
"path": "scitran/data/medimg/behavior.py",
"copies": "2",
"size": "1398",
"license": "mit",
"hash": 8126080656143035000,
"line_mean": 28.7446808511,
"line_max": 98,
"alpha_frac": 0.6938483548,
"autogenerated": false,
"ratio": 3.8833333333333333,
"config_test": fa... |
"""
nimsdata.medimg.nimspng
=======================
NIMSMRPNG provides PNG image writing capabilities for medimg datasets.
"""
import os
import logging
from PIL import Image
import numpy as np
import medimg
log = logging.getLogger(__name__)
class NIMSPNGError(medimg.MedImgError):
pass
class NIMSPNG(medim... | {
"repo_name": "cni/nimsdata",
"path": "medimg/nimspng.py",
"copies": "1",
"size": "2799",
"license": "mit",
"hash": -7708324686655690000,
"line_mean": 31.1724137931,
"line_max": 117,
"alpha_frac": 0.5555555556,
"autogenerated": false,
"ratio": 3.792682926829268,
"config_test": false,
"has_no_... |
"""
nimsdata.medimg.png
===================
PNG provides PNG image writing capabilities for medimg datasets.
"""
import os
import logging
from PIL import Image
import numpy as np
import medimg
log = logging.getLogger(__name__)
class PNGError(medimg.MedImgError):
pass
class PNG(medimg.MedImgWriter):
... | {
"repo_name": "scitran/data",
"path": "scitran/data/medimg/png.py",
"copies": "1",
"size": "2769",
"license": "mit",
"hash": 3950324454253885000,
"line_mean": 30.8275862069,
"line_max": 113,
"alpha_frac": 0.5521849043,
"autogenerated": false,
"ratio": 3.845833333333333,
"config_test": false,
... |
"""
nimsdata
========
The nimsdata package provides two main interfaces, nimsdata.parse() for reading an input file, and
nimsdata.write() for writing to an output file. Nimsdata can read dicoms, and GE P-files.
Support for additional data domains and files types is being actively developed.
NIMSdata provides read a... | {
"repo_name": "cni/nimsdata",
"path": "__init__.py",
"copies": "1",
"size": "1208",
"license": "mit",
"hash": -53365790555330410,
"line_mean": 37.9677419355,
"line_max": 98,
"alpha_frac": 0.7847682119,
"autogenerated": false,
"ratio": 3.38375350140056,
"config_test": false,
"has_no_keywords":... |
"""
scitran.data
============
The scitran data package provides two main interfaces, scitran.data.parse() for reading an input file, and
scitran.data.write() for writing to an output file. Scitran data can read dicoms, and GE P-files.
Support for additional data domains and files types is being actively developed.
... | {
"repo_name": "scitran/data",
"path": "scitran/data/__init__.py",
"copies": "2",
"size": "1254",
"license": "mit",
"hash": 8752001405769975000,
"line_mean": 37,
"line_max": 106,
"alpha_frac": 0.7727272727,
"autogenerated": false,
"ratio": 3.4640883977900554,
"config_test": false,
"has_no_keyw... |
"""
nimsdata.medimg.nimsgephysio
============================
Parse and identify GE MR Physio files.
nimsdata.medimg.nimsbehavior will need to be paired with a writer that is capable of outputting
to text or csv. Currently, there is no such writer.
"""
import bson
import json
import logging
import tarfile
from .... | {
"repo_name": "cni/nimsdata",
"path": "medimg/nimsgephysio.py",
"copies": "1",
"size": "2980",
"license": "mit",
"hash": 5041054482586922000,
"line_mean": 22.4645669291,
"line_max": 124,
"alpha_frac": 0.6046979866,
"autogenerated": false,
"ratio": 3.7865311308767473,
"config_test": false,
"ha... |
"""
scitran.data.medimg.gephysio
============================
Parse and identify GE MR Physio files.
scitran.data.medimg.behavior will need to be paired with a writer that is capable of outputting
to text or csv. Currently, there is no such writer.
"""
import json
import logging
import tarfile
from .. import dat... | {
"repo_name": "Eric89GXL/data",
"path": "scitran/data/medimg/gephysio.py",
"copies": "1",
"size": "3028",
"license": "mit",
"hash": -2106500723129867800,
"line_mean": 22.65625,
"line_max": 119,
"alpha_frac": 0.6030383091,
"autogenerated": false,
"ratio": 3.882051282051282,
"config_test": false,... |
# pylint: skip-file
import os
import re
import time
import shutil
import datetime
from . import util
from . import reaper
from . import tempdir as tempfile
import scitran.data.medimg.gephysio
def reap(name, data_path, reap_path, reap_data, reap_name, log, log_info, tempdir):
if not reap_data.psd_name or not r... | {
"repo_name": "scitran/reaper",
"path": "reaper/gephysio.py",
"copies": "1",
"size": "2809",
"license": "mit",
"hash": -4654845614859657000,
"line_mean": 42.890625,
"line_max": 140,
"alpha_frac": 0.6012815949,
"autogenerated": false,
"ratio": 3.1920454545454544,
"config_test": false,
"has_no_... |
__author__ = 'Guo'
from flask_sqlalchemy import SQLAlchemy
from movieapp.extensions import bcrypt
from movieapp.extensions import login_manager
from flask_login import AnonymousUserMixin
db_user = SQLAlchemy()
class User(db_user.Model):
id = db_user.Column(db_user.INTEGER(), primary_key=True)
user_name = db_... | {
"repo_name": "Justlzp/project_team",
"path": "webDemo/movieapp/models/models.py",
"copies": "1",
"size": "1287",
"license": "mit",
"hash": -741182441900718500,
"line_mean": 23.3018867925,
"line_max": 66,
"alpha_frac": 0.6433566434,
"autogenerated": false,
"ratio": 3.7964601769911503,
"config_t... |
__author__ = 'Guo'
from flask import (render_template,
current_app,
Blueprint,
redirect,
url_for,
request,
flash,
jsonify,
session)
from movieapp.models.forms import... | {
"repo_name": "Justlzp/project_team",
"path": "webDemo/movieapp/controllers/main.py",
"copies": "1",
"size": "2444",
"license": "mit",
"hash": 5468339251523201000,
"line_mean": 25.2795698925,
"line_max": 78,
"alpha_frac": 0.5924713584,
"autogenerated": false,
"ratio": 3.76,
"config_test": false... |
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