repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs_v2/prep/migrate_user_details_v2.py |
from app.bq_service import BigQueryService
if __name__ == "__main__":
bq_service = BigQueryService()
bq_service.migrate_populate_user_details_table_v2()
print("MIGRATION SUCCESSFUL!")
| 202 | 15.916667 | 55 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs_v2/prep/assign_user_ids.py | from pprint import pprint
from app import seek_confirmation # DATA_DIR
from app.decorators.datetime_decorators import logstamp
from app.decorators.number_decorators import fmt_n
from app.bq_service import BigQueryService
if __name__ == "__main__":
bq_service = BigQueryService()
print("----------------------... | 1,192 | 30.394737 | 97 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs_v2/prep/migrate_daily_bot_probabilities.py |
from app.bq_service import BigQueryService
if __name__ == "__main__":
bq_service = BigQueryService()
bq_service.migrate_daily_bot_probabilities_table()
print("MIGRATION SUCCESSFUL!")
| 201 | 15.833333 | 54 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs_v2/prep/migrate_user_screen_names.py |
from app.bq_service import BigQueryService
if __name__ == "__main__":
bq_service = BigQueryService()
bq_service.migrate_populate_user_screen_names_table()
print("MIGRATION SUCCESSFUL!")
| 204 | 16.083333 | 57 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs_v2/prep/migrate_retweets_v2.py |
from app.decorators.datetime_decorators import logstamp
from app.bq_service import BigQueryService
if __name__ == "__main__":
bq_service = BigQueryService()
print(logstamp())
bq_service.migrate_populate_retweets_table_v2()
print(logstamp())
print("MIGRATION SUCCESSFUL!")
| 298 | 18.933333 | 55 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs_v2/k_days/reporter.py |
import os
from pandas import DataFrame
from app import DATA_DIR
from app.retweet_graphs_v2.graph_storage import GraphStorage
from app.retweet_graphs_v2.k_days.generator import DateRangeGenerator
if __name__ == "__main__":
gen = DateRangeGenerator()
reports = []
for date_range in gen.date_ranges:
... | 962 | 32.206897 | 125 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs_v2/k_days/classifier.py |
import os
#import time
import gc
from dotenv import load_dotenv
from app import APP_ENV, server_sleep
from app.retweet_graphs_v2.graph_storage import GraphStorage
from app.retweet_graphs_v2.k_days.generator import DateRangeGenerator
from app.botcode_v2.classifier import NetworkClassifier as BotClassifier
from app.bq... | 2,646 | 36.814286 | 116 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs_v2/k_days/grapher.py |
from app import server_sleep
from app.bq_service import BigQueryService
from app.retweet_graphs_v2.retweet_grapher import RetweetGrapher
from app.retweet_graphs_v2.k_days.generator import DateRangeGenerator
if __name__ == "__main__":
gen = DateRangeGenerator()
bq_service = BigQueryService()
for date_r... | 930 | 26.382353 | 90 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs_v2/k_days/download_classifications.py |
import os
from app.retweet_graphs_v2.graph_storage import GraphStorage
from app.retweet_graphs_v2.k_days.generator import DateRangeGenerator
from app.botcode_v2.classifier import NetworkClassifier as BotClassifier
if __name__ == "__main__":
gen = DateRangeGenerator()
for date_range in gen.date_ranges:
... | 950 | 31.793103 | 90 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs_v2/k_days/generator.py |
import os
from datetime import datetime, timedelta
from pprint import pprint
from dotenv import load_dotenv
from app import seek_confirmation
from app.decorators.datetime_decorators import dt_to_date
load_dotenv()
START_DATE = os.getenv("START_DATE", default="2020-01-01") # the first period will start on this day
... | 2,759 | 31.857143 | 121 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs/bq_weekly_graph_loader.py |
from app.retweet_graphs.bq_weekly_grapher import BigQueryWeeklyRetweetGrapher
if __name__ == "__main__":
storage_service = BigQueryWeeklyRetweetGrapher.init_storage_service()
graph = storage_service.load_graph() # will print a memory profile...
storage_service.report(graph) # will print graph size
| 317 | 25.5 | 77 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs/base_grapher.py |
import os
from datetime import datetime
import time
from dotenv import load_dotenv
from networkx import DiGraph
from app import APP_ENV, DATA_DIR, SERVER_NAME, SERVER_DASHBOARD_URL
from app.decorators.number_decorators import fmt_n
from app.retweet_graphs.graph_storage_service import GraphStorageService
from app.ema... | 3,651 | 31.607143 | 111 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs/graph_storage_service.py |
import os
import json
import pickle
from memory_profiler import profile
from pandas import DataFrame
from networkx import write_gpickle, read_gpickle
from app import APP_ENV, DATA_DIR, seek_confirmation
from app.decorators.datetime_decorators import logstamp
from app.decorators.number_decorators import fmt_n
from ap... | 6,245 | 32.945652 | 135 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs/bq_retweet_grapher.py |
import os
from networkx import DiGraph
from memory_profiler import profile
from dotenv import load_dotenv
from app.decorators.datetime_decorators import logstamp
from app.decorators.number_decorators import fmt_n
from app.friend_graphs.bq_grapher import BigQueryGrapher
load_dotenv()
USERS_LIMIT = int(os.getenv("US... | 3,131 | 37.666667 | 187 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs/bq_weekly_graph_bot_classifier.py | import os
from conftest import compile_mock_rt_graph
from app import APP_ENV, seek_confirmation
from app.retweet_graphs.bq_weekly_grapher import BigQueryWeeklyRetweetGrapher
from app.botcode_v2.classifier import NetworkClassifier as BotClassifier, DRY_RUN
if __name__ == "__main__":
storage_service = BigQueryWeek... | 2,424 | 43.090909 | 146 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs/bq_base_grapher.py |
from retweet_graphs.base_grapher import BaseGrapher, USERS_LIMIT, BATCH_SIZE
from app.bq_service import BigQueryService
class BigQueryBaseGrapher(BaseGrapher):
def __init__(self, users_limit=USERS_LIMIT, batch_size=BATCH_SIZE, storage_service=None, bq_service=None):
super().__init__(users_limit=users_lim... | 704 | 29.652174 | 110 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/retweet_graphs/bq_weekly_grapher.py |
import os
from dotenv import load_dotenv
from networkx import DiGraph
from memory_profiler import profile
from app import DATA_DIR, seek_confirmation
from app.decorators.datetime_decorators import dt_to_s, logstamp, dt_to_date
from app.decorators.number_decorators import fmt_n
from app.bq_service import BigQueryServ... | 5,019 | 33.62069 | 226 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/app/botometer/sampler.py |
import os
from functools import cached_property
from botometer import Botometer
from dotenv import load_dotenv
from app import seek_confirmation, server_sleep
from app.bq_service import BigQueryService, generate_timestamp
from app.twitter_service import CONSUMER_KEY, CONSUMER_SECRET, ACCESS_KEY, ACCESS_SECRET
load_... | 7,292 | 35.10396 | 137 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/start/follower_network/helper_follower_network_crawler.py | #Use this code to download tweets that contain a given keyword
# -*- coding: UTF-8 -*-
from twython import Twython
from datetime import datetime, timedelta
import numpy as np
from helper_twitter_api import *
import sqlite3
from operator import itemgetter
import os
import csv
import urllib.request, urllib.parse, urllib.... | 8,352 | 35.317391 | 159 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/start/follower_network/follower_network_collector.py | # -*- coding: utf-8 -*-
"""follower_network_collector.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1T0ED71rbhiNF8HG-769aBqA0zZAJodcd
"""
#This notebook builds a follower network for a set of users
#first import the helper functions
from helpe... | 3,008 | 39.662162 | 120 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/start/bot_communities/midac_bot_community_analysis_libya.py | # -*- coding: utf-8 -*-
"""MIDAC Bot Community Analysis Libya.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1rrTv4JkYoQx0VVVtq8leYAqh6aX1VAe8
# Bot Community Analysis
Use this notebook to analyze communities in bot retweet network
Data = Bot p... | 8,689 | 32.041825 | 208 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/start/bot_communities/SpectralCommunities.py | import networkx as nx
import numpy as np
from sklearn.cluster import SpectralClustering
def spectral_clustering(G,k=2):
A = nx.adjacency_matrix(G.to_undirected())
clustering =SpectralClustering(n_clusters=k, eigen_solver=None, affinity='precomputed',n_init = 20)
clusters = clustering.fit(A)
Comm = [[] ... | 913 | 47.105263 | 106 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/start/bot_communities/midac_bot_community_detection_libya.py | # -*- coding: utf-8 -*-
"""MIDAC Bot Community Detection Libya.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/156K2fQM_TNps7WHcdqcMU8gDVbOthIyo
# Bot Community Detection
Use this notebook to detect communities in bot retweet network
Data = Bot ... | 16,819 | 31.284069 | 283 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/start/botcode/networkClassifierHELPER.py | import math
import networkx as nx
from collections import defaultdict
from operator import itemgetter
import numpy as np
import time
from ioHELPER import *
#####################################################################################################
####################### BUILD RETWEET NX-(SUB)GRAPH FROM DICT... | 6,747 | 27.837607 | 115 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/start/botcode/MPI_graphCut.py | #################################################################################
################################# IMPORTS #######################################
#################################################################################
## BASIC
import os
import sys
import math
import datetime
import random
im... | 8,155 | 35.410714 | 266 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/start/botcode/ioHELPER.py | import numpy as np
from os import listdir
from os.path import isfile, join
import datetime
import networkx as nx
def readCSVFile_urls(path):
file = open(path, 'r').read().split('\n')
res = {}
for line in file:
if(len(line) > 0):
temp = line.split(';')
res[temp[0]] = temp[1]
return res;
def readCSVFile_ur... | 8,254 | 18.939614 | 104 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/start/bot_impact_v2/assess_impeachment_analysis.py | # -*- coding: utf-8 -*-
"""Assess Impeachment Analysis.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1UZxvODJREDEIg4KuTqqIhCJ3f6psRb91
# Assess Bot Impact on Impeachment Analysis
This code will let you analyze the bot impact that has been calcu... | 9,005 | 38.156522 | 139 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/start/botcode_v2/ising_model_bot_detector.py | # -*- coding: utf-8 -*-
"""Ising Model Bot Detector.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1Ou1TXypk5YA-DxSFRi55HsNwiELue7Cl
# Ising Model Bot Detection
This notebook lets you detect bots in a retweet network using the Ising model algor... | 6,426 | 31.296482 | 201 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/start/botcode_v2/networkClassifierHELPER.py | import math
import networkx as nx
from collections import defaultdict
from operator import itemgetter
import numpy as np
import time
from ioHELPER import *
#####################################################################################################
####################### BUILD RETWEET NX-(SUB)GRAPH FROM DICT... | 6,741 | 30.069124 | 115 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/start/botcode_v2/ioHELPER.py | import numpy as np
from os import listdir
from os.path import isfile, join
import datetime
import networkx as nx
def readCSVFile_urls(path):
file = open(path, 'r').read().split('\n')
res = {}
for line in file:
if(len(line) > 0):
temp = line.split(';')
res[temp[0]] = temp[1]
return res;
def readCSVFile_ur... | 8,254 | 18.939614 | 104 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/start/bot_impact/assess_bot_impact.py | # -*- coding: utf-8 -*-
"""AssessBotImpact.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1idq0xOjN0spFYCQ1q6JcH6KdpPp8tlMb
# Assess Bot Impact
This code will calculate the mean opinion shift caused by the bots in your network.
You will need t... | 11,179 | 32.573574 | 237 | py |
tweet-analysis-2020 | tweet-analysis-2020-main/start/bot_impact/assess_helper.py | import json,random,csv
import numpy as np
from scipy import sparse
import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
#code for helper file
#create networkx graph object from node and edge list csv files
def G_from_edge_list(node_filename,edge_filename):
G = nx.DiGraph()
data_nodes = pd... | 9,150 | 41.170507 | 143 | py |
ball-k-means | ball-k-means-master/PythonVersion/win_kmeans++_python.py | # This version is completed by Yong Zheng(413511280@qq.com), Shuyin Xia?380835019@qq.com?, Xingxin Chen, Junkuan Wang. 2020.5.1
import ctypes
import numpy as np
class ball_k_means:
def __init__(self, isDouble=0):
self.isDouble = isDouble
def fit(self, s1, k, isRing = False, detail = False, random_s... | 2,550 | 52.145833 | 137 | py |
ball-k-means | ball-k-means-master/PythonVersion/linux_kmeans++_python.py | # This version is completed by Yong Zheng(413511280@qq.com), Shuyin Xia?380835019@qq.com?, Xingxin Chen, Junkuan Wang. 2020.5.1
import ctypes
import numpy as np
class ball_k_means:
def __init__(self, isDouble=0):
self.isDouble = isDouble
def fit(self, s1, k, isRing = False, detail = False, random_s... | 2,552 | 52.1875 | 137 | py |
lcogtgemini | lcogtgemini-master/setup.py | from setuptools import setup
setup(name='lcogtgemini',
author=['Curtis McCully'],
author_email=['cmccully@lco.global'],
version=0.1,
packages=['lcogtgemini'],
install_requires=['numpy', 'astropy', 'scipy'],
entry_points={'console_scripts': ['reduce_gemini=lcogtgemini.main:run']})
| 318 | 30.9 | 79 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/main.py | import lcogtgemini
from lcogtgemini.combine import speccombine
from lcogtgemini.cosmicrays import crreject
from lcogtgemini.sky import skysub
from lcogtgemini.reduction import scireduce, extract
from lcogtgemini.utils import get_binning, rescale1e15
from lcogtgemini.qe import make_qecorrection
from lcogtgemini.waveleng... | 4,228 | 29.644928 | 89 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/reduction.py | import numpy as np
import lcogtgemini
from astropy.io import fits
from lcogtgemini.utils import get_binning
from lcogtgemini.file_utils import getsetupname
from pyraf import iraf
from lcogtgemini import fixpix
from lcogtgemini import fits_utils
def scireduce(scifiles, rawpath):
for f in scifiles:
binning... | 3,327 | 40.08642 | 107 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/wavelengths.py | import lcogtgemini
from lcogtgemini import utils, file_utils, fixpix
from pyraf import iraf
import numpy as np
from astropy.io import fits, ascii
import os
import time
def wavesol(arcfiles, rawpath):
for f in arcfiles:
binning = utils.get_binning(f, rawpath)
fixed_rawpath = fixpix.fixpix(f, rawpath... | 5,218 | 38.240602 | 120 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/cosmicrays.py | import numpy as np
from astropy.io import fits
from astroscrappy.astroscrappy import detect_cosmics
import lcogtgemini
from lcogtgemini.fits_utils import tofits
from lcogtgemini import fixpix
def crreject(scifiles):
for f in scifiles:
# run lacosmicx
hdu_skysub = fits.open('st' + f.replace('.txt'... | 1,193 | 37.516129 | 103 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/fixpix.py | import os
from pyraf import iraf
from lcogtgemini import file_utils
def fixpix(txtfile, rawpath, binning, namps):
images = file_utils.get_images_from_txt_file(txtfile)
for image in images:
if not os.path.exists('../raw_fixpix'):
iraf.mkdir('../raw_fixpix')
iraf.cp(os.path.join(rawp... | 751 | 31.695652 | 82 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/fits_utils.py | import numpy as np
from astropy.io import fits
def sanitizeheader(hdr):
# Remove the mandatory keywords from a header so it can be copied to a new
# image.
hdr = hdr.copy()
# Let the new data decide what these values should be
for i in ['SIMPLE', 'BITPIX', 'BSCALE', 'BZERO']:
if i in hdr.... | 4,823 | 31.594595 | 131 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/bpm.py | import lcogtgemini
from pyraf import iraf
from astropy.io import fits
import numpy as np
def get_bad_pixel_mask(binnings, yroi):
if lcogtgemini.detector == 'Hamamatsu':
if lcogtgemini.is_GS:
bpm_file = 'bpm_gs.fits'
else:
bpm_file = 'bpm_gn.fits'
bpm_hdu = fits.ope... | 1,925 | 43.790698 | 91 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/utils.py | from astropy.io import ascii, fits
import numpy as np
from lcogtgemini import file_utils
import os
from scipy.signal import butter, lfilter
import lcogtgemini
def mad(d):
return np.median(np.abs(np.median(d) - d))
def magtoflux(wave, mag, zp):
# convert from ab mag to flambda
# 3e-19 is lambda^2 / c in ... | 3,053 | 31.147368 | 140 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/combine.py | import numpy as np
import lcogtgemini
from astropy.io import fits, ascii
from lcogtgemini import fits_utils
from lcogtgemini import file_utils
from astropy.convolution import convolve, Gaussian1DKernel
from lcogtgemini import utils
from pyraf import iraf
def find_bad_pixels(data, threshold=30.0):
# Take the abs n... | 4,813 | 40.5 | 120 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/flats.py | import lcogtgemini
from lcogtgemini.utils import get_binning
from lcogtgemini.file_utils import getsetupname
from lcogtgemini import fits_utils
from lcogtgemini import fixpix
from lcogtgemini import fitting
from lcogtgemini import utils
import numpy as np
from pyraf import iraf
from astropy.io import fits
import os
fro... | 4,882 | 46.872549 | 159 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/sort.py | import lcogtgemini
import numpy as np
from pyraf import iraf
import os
from glob import glob
from astropy.io import fits
def sort():
if not os.path.exists('raw'):
iraf.mkdir('raw')
fs = glob('*.fits')
fs += glob('*.dat')
for f in fs:
iraf.mv(f, 'raw/')
# Make a reduction directory
... | 3,071 | 29.117647 | 72 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/flux_calibration.py | import os
import numpy as np
from astropy.io import fits, ascii
from pyraf import iraf
import lcogtgemini.file_utils
from lcogtgemini import combine
from lcogtgemini import fits_utils
from lcogtgemini import file_utils
from lcogtgemini import fitting
from lcogtgemini import utils
from lcogtgemini.file_utils import ge... | 9,004 | 49.307263 | 139 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/fitting.py | import numpy as np
from scipy import optimize
from statsmodels import robust
from lcogtgemini.utils import mad
from matplotlib import pyplot
def ncor(x, y):
"""Calculate the normalized correlation of two arrays"""
d = np.correlate(x, x) * np.correlate(y, y)
return np.correlate(x, y) / d ** 0.5
def xcor... | 6,446 | 35.630682 | 132 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/telluric.py | import os
import numpy as np
from astropy.io import ascii
from astropy.io import fits
import lcogtgemini.file_utils
from lcogtgemini import combine
from lcogtgemini import fits_utils
from lcogtgemini import fitting
# Taken from the Berkley telluric correction
# telluricWaves = [(2000., 3190.), (3216., 3420.), (5500.,... | 8,487 | 45.895028 | 129 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/file_utils.py | import numpy as np
from astropy.convolution import convolve, Gaussian1DKernel
from astropy.io import fits, ascii
import os
from glob import glob
from pyraf import iraf
def getobstypes(fs):
# get the type of observation for each file
obstypes = []
obsclasses = []
for f in fs:
obstypes.append(fi... | 5,173 | 30.54878 | 101 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/__init__.py | #!/usr/bin/env python
'''
Created on Nov 7, 2014
@author: cmccully
'''
import os
from pyraf import iraf
iraf.cd(os.getcwd())
iraf.gemini()
iraf.gmos()
iraf.twodspec()
iraf.apextract()
iraf.onedspec()
bluecut = 3450
iraf.gmos.logfile = "log.txt"
iraf.gmos.mode = 'h'
iraf.set(clobber='yes')
iraf.set(stdimage='imtgm... | 542 | 13.289474 | 32 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/sky.py | import lcogtgemini
from pyraf import iraf
def skysub(scifiles, rawpath):
for f in scifiles:
# sky subtraction
# output has an s prefixed on the front
# This step is currently quite slow for Gemini-South data
iraf.unlearn(iraf.gsskysub)
iraf.gsskysub('t' + f[:-4], long_sample... | 490 | 39.916667 | 94 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/bias.py | import lcogtgemini
from glob import glob
from astropy.io import fits
from pyraf import iraf
import numpy as np
def makebias(fs, obstypes, rawpath):
for f in fs:
if f[-10:] == '_bias.fits':
iraf.cp(f, 'bias.fits')
elif 'bias' in f:
iraf.cp(f, './')
if len(glob('bias*.fi... | 1,059 | 38.259259 | 112 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/qe.py | import lcogtgemini
import os
from pyraf import iraf
def make_qecorrection(arcfiles):
for f in arcfiles:
#read in the arcfile name
with open(f) as txtfile:
arcimage = txtfile.readline()
# Strip off the newline character
arcimage = 'g' + arcimage.split('\n')[0]
... | 562 | 36.533333 | 96 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/extinction.py | from astropy.io import ascii, fits
from lcogtgemini import fits_utils
import numpy as np
def correct_for_extinction(scifiles, extfile):
# Read in the extinction file
extinction_correction = ascii.read(extfile)
# Convert the extinction to flux
extinction_correction['col2'] = 10**(-0.4 * extinction_corr... | 1,028 | 40.16 | 106 | py |
lcogtgemini | lcogtgemini-master/lcogtgemini/integration.py | import numpy as np
class integrate:
def __init__(self):
self.trapweights = np.zeros((1, 1))
self.simp2dweights = np.zeros((1, 1))
self.simp4dweights = np.zeros((1, 1, 1, 1))
def sum4d(self, d, binx, biny):
return d.sum(axis=3).sum(axis=2) / binx / biny
# Define a 2D trape... | 3,878 | 41.163043 | 101 | py |
anonymeter | anonymeter-main/src/anonymeter/__init__.py | 0 | 0 | 0 | py | |
anonymeter | anonymeter-main/src/anonymeter/neighbors/mixed_types_kneighbors.py | # This file is part of Anonymeter and is released under BSD 3-Clause Clear License.
# Copyright (c) 2022 Anonos IP LLC.
# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details.
"""Nearest neighbor search for mixed type data."""
import logging
from math import fabs, isnan
from typing import Dict, Li... | 8,813 | 35.878661 | 106 | py |
anonymeter | anonymeter-main/src/anonymeter/neighbors/__init__.py | 0 | 0 | 0 | py | |
anonymeter | anonymeter-main/src/anonymeter/evaluators/linkability_evaluator.py | # This file is part of Anonymeter and is released under BSD 3-Clause Clear License.
# Copyright (c) 2022 Anonos IP LLC.
# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details.
"""Privacy evaluator that measures the linkability risk."""
import logging
from typing import Dict, List, Optional, Set, T... | 11,666 | 35.688679 | 117 | py |
anonymeter | anonymeter-main/src/anonymeter/evaluators/__init__.py | # This file is part of Anonymeter and is released under BSD 3-Clause Clear License.
# Copyright (c) 2022 Anonos IP LLC.
# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details.
"""Tools to evaluate privacy risks along the directives of the Article 29 WGP."""
from anonymeter.evaluators.inference_eva... | 590 | 58.1 | 83 | py |
anonymeter | anonymeter-main/src/anonymeter/evaluators/inference_evaluator.py | # This file is part of Anonymeter and is released under BSD 3-Clause Clear License.
# Copyright (c) 2022 Anonos IP LLC.
# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details.
"""Privacy evaluator that measures the inference risk."""
from typing import List, Optional
import numpy as np
import pan... | 9,078 | 35.757085 | 111 | py |
anonymeter | anonymeter-main/src/anonymeter/evaluators/singling_out_evaluator.py | # This file is part of Anonymeter and is released under BSD 3-Clause Clear License.
# Copyright (c) 2022 Anonos IP LLC.
# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details.
"""Privacy evaluator that measures the singling out risk."""
import logging
from typing import Any, Callable, Dict, List, ... | 18,133 | 32.273394 | 118 | py |
anonymeter | anonymeter-main/src/anonymeter/stats/__init__.py | 0 | 0 | 0 | py | |
anonymeter | anonymeter-main/src/anonymeter/stats/confidence.py | # This file is part of Anonymeter and is released under BSD 3-Clause Clear License.
# Copyright (c) 2022 Anonos IP LLC.
# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details.
"""Functions for estimating rates and errors in privacy attacks."""
import warnings
from math import sqrt
from typing imp... | 7,791 | 31.60251 | 117 | py |
anonymeter | anonymeter-main/src/anonymeter/preprocessing/transformations.py | # This file is part of Anonymeter and is released under BSD 3-Clause Clear License.
# Copyright (c) 2022 Anonos IP LLC.
# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details.
"""Data pre-processing and transformations for the privacy evaluators."""
import logging
from typing import List, Tuple
i... | 3,730 | 34.198113 | 109 | py |
anonymeter | anonymeter-main/src/anonymeter/preprocessing/type_detection.py | # This file is part of Anonymeter and is released under BSD 3-Clause Clear License.
# Copyright (c) 2022 Anonos IP LLC.
# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details.
from typing import Dict, List
import pandas as pd
def detect_col_types(df: pd.DataFrame) -> Dict[str, List[str]]:
"... | 1,658 | 29.722222 | 83 | py |
anonymeter | anonymeter-main/src/anonymeter/preprocessing/__init__.py | 0 | 0 | 0 | py | |
anonymeter | anonymeter-main/tests/test_transformations.py | # This file is part of Anonymeter and is released under BSD 3-Clause Clear License.
# Copyright (c) 2022 Anonos IP LLC.
# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details.
import numpy as np
import pandas as pd
import pytest
from scipy.spatial.distance import pdist, squareform
from anonymeter... | 2,885 | 34.195122 | 89 | py |
anonymeter | anonymeter-main/tests/test_type_detection.py | # This file is part of Anonymeter and is released under BSD 3-Clause Clear License.
# Copyright (c) 2022 Anonos IP LLC.
# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details.
import numpy as np
import pandas as pd
import pytest
from anonymeter.preprocessing.type_detection import detect_col_types... | 1,643 | 36.363636 | 111 | py |
anonymeter | anonymeter-main/tests/test_linkability_evaluator.py | # This file is part of Anonymeter and is released under BSD 3-Clause Clear License.
# Copyright (c) 2022 Anonos IP LLC.
# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details.
import numpy as np
import pandas as pd
import pytest
from anonymeter.evaluators.linkability_evaluator import LinkabilityE... | 5,463 | 41.030769 | 119 | py |
anonymeter | anonymeter-main/tests/test_inference_evaluator.py | # This file is part of Anonymeter and is released under BSD 3-Clause Clear License.
# Copyright (c) 2022 Anonos IP LLC.
# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details.
import numpy as np
import pandas as pd
import pytest
from anonymeter.evaluators.inference_evaluator import InferenceEvalu... | 4,103 | 37 | 118 | py |
anonymeter | anonymeter-main/tests/test_singling_out_evaluator.py | # This file is part of Anonymeter and is released under BSD 3-Clause Clear License.
# Copyright (c) 2022 Anonos IP LLC.
# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details.
import numpy as np
import pandas as pd
import pytest
from scipy import integrate
from anonymeter.evaluators.singling_out_... | 4,203 | 32.632 | 106 | py |
anonymeter | anonymeter-main/tests/test_mixed_types_kneigbors.py | # This file is part of Anonymeter and is released under BSD 3-Clause Clear License.
# Copyright (c) 2022 Anonos IP LLC.
# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details.
import numpy as np
import pandas as pd
import pytest
from anonymeter.neighbors.mixed_types_kneighbors import MixedTypeKNe... | 2,859 | 35.202532 | 91 | py |
anonymeter | anonymeter-main/tests/test_confidence.py | # This file is part of Anonymeter and is released under BSD 3-Clause Clear License.
# Copyright (c) 2022 Anonos IP LLC.
# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details.
import numpy as np
import pytest
from anonymeter.stats.confidence import (
EvaluationResults,
SuccessRate,
bi... | 5,026 | 29.283133 | 103 | py |
anonymeter | anonymeter-main/tests/__init__.py | 0 | 0 | 0 | py | |
anonymeter | anonymeter-main/tests/fixtures.py | # This file is part of Anonymeter and is released under BSD 3-Clause Clear License.
# Copyright (c) 2022 Anonos IP LLC.
# See https://github.com/statice/anonymeter/blob/main/LICENSE.md for details..
import os
from typing import Optional
import pandas as pd
TEST_DIR_PATH = os.path.dirname(os.path.realpath(__file__))... | 1,143 | 26.902439 | 105 | py |
DCN | DCN-master/SC_MNIST.py | # -*- coding: utf-8 -*-
"""
Created on Sat Aug 13 14:03:37 2016
Try out SC on MNIST
@author: yang4173
"""
from sklearn.cluster import SpectralClustering
import scipy.io as sio
from sklearn import metrics
from sklearn.neighbors import kneighbors_graph
from sklearn.manifold import spectral_embedding
from sklearn.clust... | 1,492 | 24.741379 | 94 | py |
DCN | DCN-master/convolutional_ae.py | """This tutorial introduces the LeNet5 neural network architecture
using Theano. LeNet5 is a convolutional neural network, good for
classifying images. This tutorial shows how to build the architecture,
and comes with all the hyper-parameters you need to reproduce the
paper's MNIST results.
This implementation simpl... | 13,137 | 32.430025 | 94 | py |
DCN | DCN-master/dA_init.py | """
This tutorial introduces denoising auto-encoders (dA) using Theano.
Denoising autoencoders are the building blocks for SdA.
They are based on auto-encoders as the ones used in Bengio et al. 2007.
An autoencoder takes an input x and first maps it to a hidden representation
y = f_{\theta}(x) = s(Wx+b), paramete... | 15,364 | 34.899533 | 98 | py |
DCN | DCN-master/nystrom.py | # -*- coding: utf-8 -*-
"""
Created on Mon Sep 5 21:53:08 2016
Perform Nystrom Spectral Clustering
ref: Fowlkes, Charless, et al. "Spectral grouping using the Nystrom method."
IEEE transactions on pattern analysis and machine intelligence 26.2 (2004): 214-225.
@author: bo
"""
import numpy as np
from sklearn... | 2,326 | 27.036145 | 89 | py |
DCN | DCN-master/run_pre_mnist.py | # -*- coding: utf-8 -*-
"""
Created on Mon Oct 10 08:50:00 2016
Experiments on pre-processed MNIST
@author: bo
"""
import sys
import gzip
import cPickle
import numpy as np
from sklearn import metrics
from sklearn.cluster import KMeans
from multi_layer_km import test_SdC
from cluster_acc import acc
K = 10
trials ... | 2,424 | 27.197674 | 102 | py |
DCN | DCN-master/MC.py | # -*- coding: utf-8 -*-
"""
Created on Fri Aug 5 13:17:42 2016
Perform Monto-Calro simulations of: KM, SC, SNMF, DCN (deep clustering network) and NJ-DCN (non-joint, SAE + KM)
The experiment with SNMF is done by saving the data files, and run SNMF with MATLAB.
@author: yang4173
"""
import os
import numpy as np
imp... | 3,641 | 28.609756 | 120 | py |
DCN | DCN-master/load_network.py | # -*- coding: utf-8 -*-
"""
Created on Wed Jul 13 09:29:43 2016
@author: yang4173
This script loads a saved network, calculate the learned representation and save for future use.
"""
import cPickle, gzip
import os, sys
from multi_layer_km import SdC, load_rcv
from deepclustering import load_data
import numpy
import... | 4,673 | 26.333333 | 96 | py |
DCN | DCN-master/multi_layer_km.py | # -*- coding: utf-8 -*-
"""
@author: bo
Multiple-layers Deep Clustering
"""
import os
import sys
import timeit
import scipy
import numpy
import cPickle
import gzip
import theano
import theano.tensor as T
import matplotlib.pyplot as plt
from theano.tensor.shared_randomstreams import RandomStreams
from clus... | 36,518 | 37.48156 | 140 | py |
DCN | DCN-master/run_rcv1.py | # -*- coding: utf-8 -*-
"""
Created on Tue Oct 11 07:45:42 2016
Experiments on RCV1-v2
@author: bo
"""
import sys
import numpy as np
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.cluster import KMeans
from multi_layer_km import test_SdC, load_data
from cluster_acc import acc
trials = 1
... | 3,493 | 28.116667 | 102 | py |
DCN | DCN-master/run_20News.py | # -*- coding: utf-8 -*-
"""
Created on Sun Oct 9 13:25:23 2016
Script to run experiments on 20Newsgroup
@author: bo
"""
import sys
import numpy as np
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.cluster import KMeans
from sklearn.manifold import SpectralEmbedding
from multi_layer_km im... | 3,529 | 30.238938 | 102 | py |
DCN | DCN-master/simulation.py | # -*- coding: utf-8 -*-
"""
Created on Sun Jun 19 12:09:48 2016
@author: bo
Create a toy dataset, including train_set
"""
import numpy as np
import gzip
import cPickle
import matplotlib.pyplot as plt
import sys
from sklearn.cluster import SpectralClustering
from sklearn.cluster import KMeans
from sklearn import m... | 6,915 | 30.870968 | 101 | py |
DCN | DCN-master/retrieve.py | # -*- coding: utf-8 -*-
"""
Created on Tue Aug 30 22:15:30 2016
retrive the saved results
@author: bo
"""
import cPickle, gzip
saved_file = 'deepclus_2_clusters.pkl.gz'
with gzip.open(saved_file, 'rb') as f:
content = cPickle.load(f)
| 243 | 14.25 | 41 | py |
DCN | DCN-master/cluster_acc.py | # -*- coding: utf-8 -*-
"""
Created on Sat Aug 27 14:31:40 2016
@author: bo
"""
from sklearn.utils.linear_assignment_ import linear_assignment
import numpy as np
def acc(ypred, y):
"""
Calculating the clustering accuracy. The predicted result must have the same number of clusters as the ground truth.
... | 1,845 | 29.262295 | 135 | py |
DCN | DCN-master/run_pendigits.py | # -*- coding: utf-8 -*-
"""
Created on Mon Oct 3 14:48:35 2016
Perform experiments with Pendigits
@author: bo
"""
import sys
import gzip
import cPickle
import numpy as np
from sklearn import metrics
from sklearn.cluster import KMeans
from sklearn.manifold import SpectralEmbedding
from multi_layer_km import test_Sd... | 3,133 | 30.656566 | 102 | py |
DCN | DCN-master/multi_layer.py | # -*- coding: utf-8 -*-
"""
Created on Sun Apr 24 14:27:50 2016
@author: bo
Multiple-layers Deep Clustering
06/19/2016 Multi-layer autoencoder, without reconstruction, performance is not good, as expected.
06/20/2016 Multi-layer autoencoder, with reconstruction and clustering as loss, seems to give meaningful resul... | 21,375 | 37.035587 | 142 | py |
DCN | DCN-master/get_a_init.py | # -*- coding: utf-8 -*-
"""
Created on Sat Feb 27 01:04:40 2016
@author: bo
run and save a dA model, to initialize my deep_clus model
"""
import dA
dA.test_dA() | 164 | 12.75 | 57 | py |
DCN | DCN-master/run_raw_mnist.py | # -*- coding: utf-8 -*-
"""
Created on Sun Oct 9 21:56:33 2016
Perform experiment on Raw-MNIST data
@author: bo
"""
import gzip
import cPickle
import sys
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans, metrics
from multi_layer_km import test_SdC
from cluster_acc import acc
... | 2,758 | 28.042105 | 102 | py |
DCN | DCN-master/multi_layer_rbm_mmc.py | # -*- coding: utf-8 -*-
"""
Created on Sun Apr 24 14:27:50 2016
@author: bo
Multiple-layers Deep Clustering
06/19/2016 Multi-layer autoencoder, without reconstruction, performance is not good, as expected.
06/20/2016 Multi-layer autoencoder, with reconstruction and clustering as loss, seems to give meaningful resul... | 36,816 | 37.27131 | 142 | py |
DCN | DCN-master/mnist_loader.py | # -*- coding: utf-8 -*-
"""
Created on Sat Sep 3 18:03:13 2016
Modified from: https://github.com/sorki/python-mnist/blob/master/mnist/loader.py
@author: bo
"""
import os
import struct
from array import array
import numpy as np
class MNIST(object):
def __init__(self, path='.'):
self.path = path
... | 2,571 | 27.577778 | 80 | py |
DCN | DCN-master/pre_rcv1.py | # -*- coding: utf-8 -*-
"""
Created on Thu Aug 25 22:39:02 2016
This script is to pre-process RCV1-V2 dataset
@author: bo
"""
from sklearn.datasets import fetch_rcv1
import scipy.io as sio
import numpy
import gzip, cPickle
import os
target_dir = '/home/bo/Data/RCV1/Processed'
data_home = '/home/bo/Data'
#target_dir... | 2,072 | 23.678571 | 69 | py |
DCN | DCN-master/preprocess.py | # -*- coding: utf-8 -*-
"""
Created on Fri Aug 12 09:33:52 2016
Perform pre-processing on MNIST dataset
@author: bo
"""
import os
import sys
import timeit
import scipy.io as sio
import copy
import scipy
import numpy
import cPickle
import gzip
from sklearn.neighbors import kneighbors_graph
from sklearn.metrics.pai... | 1,348 | 21.483333 | 106 | py |
DCN | DCN-master/deepclustering.py | # -*- coding: utf-8 -*-
"""
Created on Sun Feb 7 10:38:06 2016
@author: bo
"""
import os
import sys
import timeit
import numpy
import cPickle
import gzip
import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
from sklearn import metrics
from sklearn.cluster import Mini... | 15,544 | 34.490868 | 124 | py |
DCN | DCN-master/RBMs_init.py | """
7/11/2016 Modified from DBN.py in DeepLearningTutorials. The purpose of this script
is to perform layerwise pretraining using RBM, and save the trained network for later
use. The fine-tuning part is thus removed.
"""
import os
import sys
import timeit
from six.moves import cPickle
import numpy
import theano
impo... | 18,012 | 38.158696 | 100 | py |
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