text stringlengths 0 1.05M | meta dict |
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__author__ = 'Guo'
from flask_wtf import FlaskForm, RecaptchaField
from wtforms import StringField, PasswordField, SubmitField, BooleanField
from wtforms.validators import DataRequired, Length, EqualTo
from movieapp.models.models import User
class LoginForm(FlaskForm):
username = StringField('用户名', [DataRequired... | {
"repo_name": "Justlzp/project_team",
"path": "webDemo/movieapp/models/forms.py",
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"autogenerated": false,
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__author__ = 'Guorong Xu<g1xu@ucsd.edu>'
def configure_star(option_list):
output_file_name = "/Users/guorongxu/Desktop/JupyterPythonNotebook/alignment.sh"
filewriter = open(output_file_name, "w")
filewriter.write("#!/bin/bash\n")
filewriter.write("\n")
filewriter.write("samtools=/shared/workspace/... | {
"repo_name": "ucsd-ccbb/jupyter-genomics",
"path": "src/awsCluster/rnaSeq/ConfigureBuilder.py",
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"ratio": 2.9381443298969074,
"con... |
__author__ = 'Guorong Xu<g1xu@ucsd.edu>'
import os
import re
import sys
def parse(count_file_dir):
mirna_count_list = {}
count_file_list = []
file_name_list = []
for root, dirs, files in os.walk(count_file_dir):
for file in files:
if file.endswith(".counts.txt"):
c... | {
"repo_name": "ucsd-ccbb/jupyter-genomics",
"path": "src/awsCluster/miRNASeq/MergeCountFile.py",
"copies": "1",
"size": "1989",
"license": "mit",
"hash": 363366553473949900,
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__author__ = 'Guorong Xu<g1xu@ucsd.edu>'
import os
import YamlFileMaker
from cfnCluster import ConnectionManager
from util import DesignFileLoader
workspace = "/shared/workspace/RNASeqPipeline"
data_dir = "/shared/workspace/data_archive/RNASeq"
## run all analysis from download, alignment, counting and differential ... | {
"repo_name": "ucsd-ccbb/jupyter-genomics",
"path": "src/awsCluster/rnaSeq/RNAPipelineManager.py",
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__author__ = 'Guorong Xu<g1xu@ucsd.edu>'
import os
import YamlFileMaker
from util import DesignFileLoader
from cfnCluster import ConnectionManager
workspace = "/shared/workspace/ChiPSeqPipeline"
data_dir = "/shared/workspace/data_archive/ChiPSeq"
## run all analysis from download, alignment, counting and differenti... | {
"repo_name": "ucsd-ccbb/jupyter-genomics",
"path": "src/awsCluster/chipSeq/ChipPipelineManager.py",
"copies": "1",
"size": "2548",
"license": "mit",
"hash": -6075687644240717000,
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__author__ = 'Guorong Xu<g1xu@ucsd.edu>'
import paramiko
from scp import SCPClient
## connecting the master instance of a CFNCluster by the hostname, username and private key file
## return a ssh client
def connect_master(hostname, username, private_key_file):
private_key = paramiko.RSAKey.from_private_key_file(p... | {
"repo_name": "ucsd-ccbb/jupyter-genomics",
"path": "src/awsCluster/cfnCluster/ConnectionManager.py",
"copies": "1",
"size": "1464",
"license": "mit",
"hash": -6044269899717011000,
"line_mean": 33.8571428571,
"line_max": 96,
"alpha_frac": 0.7295081967,
"autogenerated": false,
"ratio": 3.494033412... |
__author__ = 'Guorong Xu<g1xu@ucsd.edu>'
import re
import os
import math
## Parsing the raw expression file and return the average expression value for each gene.
def parse_geo_expression(raw_expression_file):
gene_expression_list = {}
with open(raw_expression_file) as fp:
lines = fp.readlines()
... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/Utils/RawFileParser.py",
"copies": "1",
"size": "2717",
"license": "mit",
"hash": 665955991386952100,
"line_mean": 35.7162162162,
"line_max": 165,
"alpha_frac": 0.5506072874,
"autogenerated": false,
"ratio": 4.26530612244898,
"config_test"... |
__author__ = 'Guorong Xu<g1xu@ucsd.edu>'
import re
## load design file
def load_design_file(design_file):
sample_list = []
group_list = []
with open(design_file, 'r+') as f:
lines = f.readlines()
for line in lines:
if line.startswith("##"):
continue
... | {
"repo_name": "ucsd-ccbb/jupyter-genomics",
"path": "src/awsCluster/util/DesignFileLoader.py",
"copies": "1",
"size": "2267",
"license": "mit",
"hash": -4499095020400070700,
"line_mean": 35.5806451613,
"line_max": 85,
"alpha_frac": 0.5024261138,
"autogenerated": false,
"ratio": 3.956369982547993,... |
__author__ = 'Guorong Xu<g1xu@ucsd.edu>'
import sys
import subprocess
import PBSTracker
import GroupFileMaker
import YamlFileReader
root_dir = "/shared/workspace/RNASeqPipeline"
data_dir = "/shared/workspace/data_archive/RNASeq"
## run all analysis from download, alignment, counting and differential calculation.
def... | {
"repo_name": "ucsd-ccbb/jupyter-genomics",
"path": "src/awsCluster/server/RNASeqPipeline/RNASeqPipeline.py",
"copies": "1",
"size": "10590",
"license": "mit",
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"alpha_frac": 0.6072710104,
"autogenerated": false,
"ratio": 3... |
__author__ = 'Guorong Xu<g1xu@ucsd.edu>'
import sys
import subprocess
import PBSTracker
import YamlFileReader
root_dir = "/shared/workspace/ChiPSeqPipeline"
data_dir = "/shared/workspace/data_archive/ChiPSeq"
## run all analysis from download, alignment, counting and differential calculation.
def run_analysis(yaml_f... | {
"repo_name": "ucsd-ccbb/jupyter-genomics",
"path": "src/awsCluster/server/ChipSeqPipeline/homer_workflow/ChipSeqPipeline.py",
"copies": "1",
"size": "15575",
"license": "mit",
"hash": 7320295316328330000,
"line_mean": 50.0655737705,
"line_max": 129,
"alpha_frac": 0.5791332263,
"autogenerated": fal... |
__author__ = 'Guorong Xu<g1xu@ucsd.edu>'
import time
## make a yaml file for analysis of WGSPipeline
def make_yaml_file(yaml_file, project_name, analysis_steps, s3_input_files_address,
sample_list, group_list, s3_output_files_address, genome, style):
filewriter = open(yaml_file, "w")
filewr... | {
"repo_name": "ucsd-ccbb/jupyter-genomics",
"path": "src/awsCluster/util/YamlFileMaker.py",
"copies": "1",
"size": "1729",
"license": "mit",
"hash": 2974946542827189000,
"line_mean": 39.2093023256,
"line_max": 89,
"alpha_frac": 0.5801041064,
"autogenerated": false,
"ratio": 3.4305555555555554,
... |
__author__ = 'Guorong Xu<g1xu@ucsd.edu>'
import time
## make a yaml file for analysis of WGSPipeline
def make_yaml_file(yaml_file, workflow, project_name, analysis_steps, s3_input_files_address,
sample_list, group_list, s3_output_files_address, genome, style):
filewriter = open(yaml_file, "w")
... | {
"repo_name": "ucsd-ccbb/jupyter-genomics",
"path": "src/awsCluster/chipSeq/YamlFileMaker.py",
"copies": "1",
"size": "1754",
"license": "mit",
"hash": -2548215372770545700,
"line_mean": 38.8636363636,
"line_max": 93,
"alpha_frac": 0.5798175599,
"autogenerated": false,
"ratio": 3.432485322896282,... |
__author__ = 'Guorong Xu<g1xu@ucsd.edu>'
import zipfile
import re
import os
def read_data_file(workspace, file_name):
archive = zipfile.ZipFile(workspace + "/" + file_name, 'r')
data_file = archive.read(file_name[:-4] + "/fastqc_data.txt")
percentages = 0.0
lines = re.split(r'\n+', data_file)
p... | {
"repo_name": "ucsd-ccbb/jupyter-genomics",
"path": "src/awsCluster/util/FastqcParser.py",
"copies": "1",
"size": "1155",
"license": "mit",
"hash": -5409344797158501000,
"line_mean": 27.875,
"line_max": 103,
"alpha_frac": 0.5636363636,
"autogenerated": false,
"ratio": 3.5981308411214954,
"confi... |
__author__ = 'guorongxu'
import csv
import re
import os
import sys
VALID_COLUMN_NO = 27
# split id lists into dictionary
def other_id(other_ids):
p = other_ids.strip(";").replace(";", ",").split(",")
other_id = {}
for id in p:
try:
ind = id.index(":")
key, value = id[:ind]... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/Clinvar/ClinvarParser.py",
"copies": "1",
"size": "5363",
"license": "mit",
"hash": -8342121511572582000,
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"autogenerated": false,
"ratio": 3.1941631923764144,
"config_... |
__author__ = 'guorongxu'
import logging
def get_abbreviation_tumor_name(short_name):
if short_name == "AcuteMyeloidLeukemia":
return "Leukemia"
elif short_name == "AdrenocorticalCancer":
return "Adrenal"
elif short_name == "BladderCancer":
return "Bladder"
elif short_name == "B... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/GEO/Helper.py",
"copies": "1",
"size": "4775",
"license": "mit",
"hash": 3476424788206095400,
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"line_max": 69,
"alpha_frac": 0.6433507853,
"autogenerated": false,
"ratio": 3.153896961690885,
"config_test": false,... |
__author__ = 'guorongxu'
import logging
def get_abbreviation_tumor_name(short_name):
if short_name == "LAML":
return "Leukemia"
elif short_name == "ACC":
return "Adrenal"
elif short_name == "BLCA":
return "Bladder"
elif short_name == "LGG":
return "Brain"
elif short... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/TCGA/Helper.py",
"copies": "1",
"size": "5354",
"license": "mit",
"hash": 1900844647774057500,
"line_mean": 31.6463414634,
"line_max": 115,
"alpha_frac": 0.5956294359,
"autogenerated": false,
"ratio": 3.140175953079179,
"config_test": fals... |
__author__ = 'guorongxu'
import os
import re
import itertools
## Parsing the go term file and return GO ID list with descriptions.
def parse_correlation_file(workspace, data_set, tumor_type):
node_hash = {}
root_correlation_dir = workspace + "/" + data_set + "/correlation_files"
## Iterate all combinati... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/TCGA/CorrelationParser.py",
"copies": "1",
"size": "2288",
"license": "mit",
"hash": 3728031388382309000,
"line_mean": 35.3333333333,
"line_max": 118,
"alpha_frac": 0.5240384615,
"autogenerated": false,
"ratio": 3.6608,
"config_test": fals... |
__author__ = 'guorongxu'
import os
import re
import sys
import math
import logging
import Helper
from GO import GOLocusParser
from Utils import HypergeomCalculator, RawFileParser
## To print JSON file for each cluster.
def print_json(prefix, output_file, network_type, cluster_list,
GO_ID_list, total_... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/GEO/ClusterJSONBuilder.py",
"copies": "1",
"size": "12613",
"license": "mit",
"hash": 8534852984964604000,
"line_mean": 44.0464285714,
"line_max": 205,
"alpha_frac": 0.5963688258,
"autogenerated": false,
"ratio": 3.2283081648323524,
"confi... |
__author__ = 'guorongxu'
import os
import re
import sys
## deduplicate the cluster files.
def deduplicate_cluster(cluster_file_dir, combination):
clusters = {}
for gamma in [1, 4, 7, 11, 14, 17, 20]:
cluster_folder = cluster_file_dir + "/" + combination + "_gamma_" + str(gamma)
cluster_file = ... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/TCGA/ClusterFilter.py",
"copies": "1",
"size": "4464",
"license": "mit",
"hash": 4661355096053464000,
"line_mean": 39.9541284404,
"line_max": 120,
"alpha_frac": 0.5665322581,
"autogenerated": false,
"ratio": 3.795918367346939,
"config_test... |
__author__ = 'guorongxu'
import os
import re
import sys
## deduplicate the cluster files.
def deduplicate_cluster(cluster_file_dir, project_name):
clusters = {}
print "cluster_file_dir: " + cluster_file_dir
print "project_name: " + project_name
for gamma in [1, 4, 7, 11, 14, 17, 20]:
cluster_... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/GEO/ClusterFilter.py",
"copies": "1",
"size": "4867",
"license": "mit",
"hash": -3823716067158384000,
"line_mean": 39.8991596639,
"line_max": 117,
"alpha_frac": 0.5759194576,
"autogenerated": false,
"ratio": 3.62397617274758,
"config_test"... |
__author__ = 'guorongxu'
import os
import re
import sys
## deduplicate the cluster files.
def modify_cluster(cluster_file):
print "cluster_file: " + cluster_file
modified_cluster_file = cluster_file + ".mod"
filewriter = open(modified_cluster_file, "a")
with open(cluster_file) as fp:
lines =... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/GEO/ClusterModifier.py",
"copies": "1",
"size": "1139",
"license": "mit",
"hash": 3215216074903704000,
"line_mean": 27.475,
"line_max": 112,
"alpha_frac": 0.6057945566,
"autogenerated": false,
"ratio": 3.5372670807453415,
"config_test": fa... |
__author__ = 'guorongxu'
import os
import re
import sys
#Parsing the expression files under the folder
def preprocess(root_raw_dir):
for root, directories, filenames in os.walk(root_raw_dir):
for filename in filenames:
if filename.endswith(".txt"):
inputfile = os.path.join(root... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/GEO/Preprocess.py",
"copies": "1",
"size": "2928",
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"line_mean": 50.3859649123,
"line_max": 106,
"alpha_frac": 0.3920765027,
"autogenerated": false,
"ratio": 5.545454545454546,
"config_test": fa... |
__author__ = 'guorongxu'
import os
import re
## Parsing the go term file and return GO ID list with descriptions.
def parse_correlation_file(input_file):
node_hash = {}
if os.path.exists(input_file):
with open(input_file) as fp:
lines = fp.readlines()
for line in lines:
... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/GEO/CorrelationParser.py",
"copies": "1",
"size": "1437",
"license": "mit",
"hash": -4790052131590076000,
"line_mean": 30.9555555556,
"line_max": 81,
"alpha_frac": 0.4892136395,
"autogenerated": false,
"ratio": 3.5481481481481483,
"config_... |
__author__ = 'guorongxu'
import os
import subprocess
import itertools
from datetime import datetime
tumor_types = ["PRAD", "STES"]
#tumor_types = ["ACC", "BLCA", "BRCA", "CESC", "CHOL", "COAD", "COADREAD", "DLBC",
# "ESCA", "GBM", "GBMLGG", "HNSC", "KICH", "KIPAN", "KIRC", "KIRP",
# "LAML"... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/TCGA/TCGACaller.py",
"copies": "1",
"size": "19352",
"license": "mit",
"hash": -5937595383814409000,
"line_mean": 58.9133126935,
"line_max": 149,
"alpha_frac": 0.5606655643,
"autogenerated": false,
"ratio": 3.4064425277239923,
"config_test... |
__author__ = 'guorongxu'
import os
import subprocess
import logging
from datetime import datetime
## To download the raw files.
def download(workspace, data_set, s3_input_files_address, disease_name):
root_raw_dir = workspace + "/" + data_set + "/raw_files"
print datetime.now().strftime('%Y-%m-%d %H:%M:%S') ... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/GEO/GEOCaller.py",
"copies": "1",
"size": "10331",
"license": "mit",
"hash": -8322975703120247000,
"line_mean": 53.0890052356,
"line_max": 153,
"alpha_frac": 0.5936501791,
"autogenerated": false,
"ratio": 3.593391304347826,
"config_test": ... |
__author__ = 'guorongxu'
import os
import sys
import logging
from Louvain import cluster_analysis_module
def calculate_louvain_c_plus(input_file, code_path, temp_path, output_file, gamma, algorithm):
cluster_analysis_module.results_TCGA_cluster(input_file, code_path, temp_path, algorithm, edge_thresh=0.0,
... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/GEO/ClusterBuilder.py",
"copies": "1",
"size": "1856",
"license": "mit",
"hash": 1597099762434160000,
"line_mean": 50.5555555556,
"line_max": 126,
"alpha_frac": 0.6950431034,
"autogenerated": false,
"ratio": 3.1726495726495725,
"config_tes... |
__author__ = 'guorongxu'
import os
import sys
import logging
import pandas as pd
def parse(root_raw_dir, root_expression_dir, tumor_type, release_year, release_month, release_day):
mirna_file = root_raw_dir + "/" + tumor_type + "/gdac.broadinstitute.org_" + tumor_type \
+ ".Merge_mirnaseq__illumi... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/TCGA/MicroRNAParser.py",
"copies": "1",
"size": "2720",
"license": "mit",
"hash": -3734555932356713000,
"line_mean": 33.8846153846,
"line_max": 116,
"alpha_frac": 0.6084558824,
"autogenerated": false,
"ratio": 3.188745603751465,
"config_te... |
__author__ = 'guorongxu'
import os
import sys
import logging
import pandas as pd
def parse(root_raw_dir, root_expression_dir, tumor_type, release_year, release_month, release_day):
rnaseq_file = root_raw_dir + "/" + tumor_type + "/gdac.broadinstitute.org_" + tumor_type \
+ ".Merge_rnaseqv2__ill... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/TCGA/RNASeqParser.py",
"copies": "1",
"size": "3441",
"license": "mit",
"hash": -5952194709790103000,
"line_mean": 34.8541666667,
"line_max": 118,
"alpha_frac": 0.5852949724,
"autogenerated": false,
"ratio": 3.21588785046729,
"config_test"... |
__author__ = 'guorongxu'
import os
import sys
## To process JSON files and append an id for each document.
def process_json(workspace, data_set):
root_json_dir = workspace + "/" + data_set + "/json_files"
##id number rule:
# the first digital "3" is the Clinvar index id;
# the first two digital "01" i... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/Clinvar/IDAppender.py",
"copies": "1",
"size": "1583",
"license": "mit",
"hash": -9179481092629147000,
"line_mean": 35,
"line_max": 96,
"alpha_frac": 0.5243209097,
"autogenerated": false,
"ratio": 4.017766497461929,
"config_test": false,
... |
__author__ = 'guorongxu'
import os
import sys
## To process JSON files and append an id for each document.
def process_louvain_cluster_json(workspace, data_set):
root_json_dir = workspace + "/" + data_set + "/louvain_json_files"
##id number rule:
# the first digital "2" is the cluster index id;
# the ... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/GEO/IDAppender.py",
"copies": "1",
"size": "4250",
"license": "mit",
"hash": -2800434129813718000,
"line_mean": 40.6764705882,
"line_max": 96,
"alpha_frac": 0.5188235294,
"autogenerated": false,
"ratio": 4.199604743083004,
"config_test": f... |
__author__ = 'guorongxu'
import paramiko
from scp import SCPClient
## connecting the master instance of a cfncluster by the hostname, username and private key file
## return a ssh client
def connect_master(hostname, username, private_key_file):
private_key = paramiko.RSAKey.from_private_key_file(private_key_file)... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "cfncluster/ConnectionManager.py",
"copies": "1",
"size": "1118",
"license": "mit",
"hash": -2266354778082478800,
"line_mean": 31.8823529412,
"line_max": 96,
"alpha_frac": 0.7262969589,
"autogenerated": false,
"ratio": 3.6298701298701297,
"config_test... |
__author__ = 'guorongxu'
import re
import logging
## Parsing the go gene file and return GO ID list with all gene lists.
def parse_gene_info_file(gene_info_file):
entrez_id_list = {}
with open(gene_info_file) as fp:
lines = fp.readlines()
for line in lines:
if line.startswith("#F... | {
"repo_name": "ucsd-ccbb/jupyter-genomics",
"path": "src/networkAnalysis/go_annotation/GOLocusParser.py",
"copies": "1",
"size": "3530",
"license": "mit",
"hash": -5838396791826173000,
"line_mean": 32,
"line_max": 94,
"alpha_frac": 0.5609065156,
"autogenerated": false,
"ratio": 3.374760994263862,... |
__author__ = 'guorongxu'
import re
import os
import sys
import logging
import MatrixPrinter
## Parsing mutation files and return a dictionary of ID and name.
def parse(root_raw_dir, root_expression_dir, tumor_type, release_year, release_month, release_day):
mutation_file_folder = root_raw_dir + "/" + tumor_type +... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/TCGA/MutationParser.py",
"copies": "1",
"size": "6634",
"license": "mit",
"hash": 6289188456409618000,
"line_mean": 38.2603550296,
"line_max": 105,
"alpha_frac": 0.6097377148,
"autogenerated": false,
"ratio": 4.102659245516389,
"config_tes... |
__author__ = 'guorongxu'
import re
import os
import sys
## To parse Drugbank JSON files and extract the gene list.
def process_drugbank_json(workspace, data_set):
root_json_dir = workspace + "/" + data_set + "/json_files"
gene_hash = {}
for dirpath, directories, filenames in os.walk(root_json_dir):
... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/ClusterDrug/ClusterDrugJSONBuilder.py",
"copies": "1",
"size": "6200",
"license": "mit",
"hash": 7217857791134903000,
"line_mean": 41.4657534247,
"line_max": 113,
"alpha_frac": 0.475483871,
"autogenerated": false,
"ratio": 3.855721393034826,... |
__author__ = 'guorongxu'
import re
import os
import sys
## To parse JSON files and extract the gene list.
def process_json(workspace, data_set):
root_json_dir = workspace + "/" + data_set + "/json_files"
output_file = root_json_dir + "/druggable_gene_name.txt"
filewriter = open(output_file, "a")
for ... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/Drugbank/GeneExtractor.py",
"copies": "1",
"size": "1800",
"license": "mit",
"hash": 8189850695554725000,
"line_mean": 34.3137254902,
"line_max": 102,
"alpha_frac": 0.4555555556,
"autogenerated": false,
"ratio": 3.8626609442060085,
"config... |
__author__ = 'guorongxu'
import re
import sys
import GencodeGTFParser
def merge_all_sample_count(workflow, project_name, sample_list):
gencode_gtf_file = "/shared/workspace/software/gencode/gencode.v19.annotation.gtf"
localpath = "/shared/workspace/data_archive/RNASeq/" + project_name + "/" + workflow + "/"
... | {
"repo_name": "ucsd-ccbb/jupyter-genomics",
"path": "src/awsCluster/server/RNASeqPipeline/star_htseq_workflow/MergeCountFile.py",
"copies": "2",
"size": "2854",
"license": "mit",
"hash": -6151434176638072000,
"line_mean": 33.8048780488,
"line_max": 109,
"alpha_frac": 0.5350385424,
"autogenerated": ... |
__author__ = 'guorongxu'
import re
import sys
import logging
## Parsing the go gene file and return GO ID list with all gene lists.
def parse_go_gene_file(go_gene_file):
GO_ID_list = {}
GO_unique_gene_list = {}
with open(go_gene_file) as fp:
lines = fp.readlines()
for line in lines:
... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/GO/GOParser.py",
"copies": "2",
"size": "2872",
"license": "mit",
"hash": -5872259307306380000,
"line_mean": 31.2696629213,
"line_max": 88,
"alpha_frac": 0.561281337,
"autogenerated": false,
"ratio": 3.2822857142857145,
"config_test": fals... |
__author__ = 'guorongxu'
import sys
from datetime import datetime
from GEO import GEOCaller
from TCGA import TCGACaller
from Pubmed import PubmedCaller
from Cosmic import CosmicCaller
from Clinvar import ClinvarCaller
from Drugbank import DrugbankCaller
from ClusterDrug import ClusterDrugCaller
from Utils import PBSTr... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/MainEntry.py",
"copies": "1",
"size": "17167",
"license": "mit",
"hash": -3153042629916394500,
"line_mean": 59.2350877193,
"line_max": 126,
"alpha_frac": 0.6097745675,
"autogenerated": false,
"ratio": 3.4736948603804128,
"config_test": fal... |
__author__ = 'guorongxu'
import sys
import os
import CorrelationParser
## To print JSON file for each cluster.
def print_json(tumor_type, output_file, node_hash):
if os.path.exists(output_file):
return
if not os.path.exists(os.path.dirname(output_file)):
os.makedirs(os.path.dirname(output_fi... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/GEO/StarJSONBuilder.py",
"copies": "1",
"size": "3439",
"license": "mit",
"hash": -2436221988831930400,
"line_mean": 42.5316455696,
"line_max": 107,
"alpha_frac": 0.5591741785,
"autogenerated": false,
"ratio": 3.0760286225402504,
"config_t... |
__author__ = 'guorongxu'
import sys
import os
import Helper
import CorrelationParser
## To print JSON file for each cluster.
def print_json(workspace, data_set, tumor_type, node_hash):
root_json_dir = workspace + "/" + data_set + "/json_files"
output_file = root_json_dir + "/" + tumor_type + "/genes_tcga.jso... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/TCGA/StarJSONBuilder.py",
"copies": "1",
"size": "2712",
"license": "mit",
"hash": 4644712772703038000,
"line_mean": 38.8823529412,
"line_max": 106,
"alpha_frac": 0.5254424779,
"autogenerated": false,
"ratio": 3.1868390129259696,
"config_t... |
__author__ = 'guorongxu'
import sys
import os
import json
import ImpactFactorParser
## To build the edge file between author and gene.
def output(output_file, author_list, journal_list):
# Open a file
filewriter = open(output_file, "a")
for author in author_list:
gene_list = author_list.get(auth... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/Pubmed/EdgeBuilder.py",
"copies": "1",
"size": "3132",
"license": "mit",
"hash": -18998545766018604,
"line_mean": 35.8588235294,
"line_max": 115,
"alpha_frac": 0.525862069,
"autogenerated": false,
"ratio": 4.261224489795918,
"config_test":... |
__author__ = 'guorongxu'
import sys
import os
import json
import ImpactFactorParser
#To build the JSON file for author and gene.
def output_json(output_file, author_list, journal_list):
prefix = "authors_pubmed"
# Open a file
filewriter = open(output_file, "a")
for author in author_list:
fi... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/Pubmed/JSONBuilder.py",
"copies": "1",
"size": "5518",
"license": "mit",
"hash": 643186263524236500,
"line_mean": 39.8740740741,
"line_max": 117,
"alpha_frac": 0.5076114534,
"autogenerated": false,
"ratio": 4.063328424153166,
"config_test"... |
__author__ = 'guorongxu'
import sys
import os
import json
#To build the JSON file for author and gene.
def output(output_file, author_list):
# Open a file
filewriter = open(output_file, "a")
for author in author_list:
filewriter.write(author + "\tAuthor\n")
filewriter.close()
## To
def prin... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/Pubmed/AuthorPrinter.py",
"copies": "1",
"size": "1946",
"license": "mit",
"hash": 6097991063735031000,
"line_mean": 31.45,
"line_max": 69,
"alpha_frac": 0.5282631038,
"autogenerated": false,
"ratio": 4.286343612334802,
"config_test": fals... |
__author__ = 'guorongxu'
import sys
import os
import re
## To print JSON file for each cluster.
def print_label(workspace, data_set, tumor_type, data_type):
input_file = workspace + "/" + data_set + "/expression_files/" + tumor_type + "/" + data_type + "_matrix.txt"
root_label_dir = workspace + "/" + data_set... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/TCGA/LabelPrinter.py",
"copies": "1",
"size": "1573",
"license": "mit",
"hash": 4490273601287149600,
"line_mean": 29.8431372549,
"line_max": 113,
"alpha_frac": 0.5810553083,
"autogenerated": false,
"ratio": 3.121031746031746,
"config_test"... |
__author__ = 'guorongxu'
import sys
import re
import math
import logging
def parse_correlation(correlation_file):
correlation_list = {}
with open(correlation_file) as fp:
lines = fp.readlines()
for line in lines:
fields = re.split(r'\t+', line)
correlation_list.update({... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/TCGA/ClusterReplacer.py",
"copies": "1",
"size": "1594",
"license": "mit",
"hash": 2328796387691531000,
"line_mean": 29.6538461538,
"line_max": 115,
"alpha_frac": 0.5690087829,
"autogenerated": false,
"ratio": 3.457700650759219,
"config_te... |
__author__ = 'guorongxu'
import sys
import re
import math
import os
import logging
import IDMapParser
## To print JSON file for each cluster.
def printJSON(outputFileName, project_info, networkType, clusterID, clusterSize, nodeList, nodeNames):
index_type = "geo_cluster"
if not os.path.exists(os.path.dirname... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/GEO/JSONBuilder.py",
"copies": "1",
"size": "6523",
"license": "mit",
"hash": -5654064237164112000,
"line_mean": 42.2052980132,
"line_max": 137,
"alpha_frac": 0.5264448873,
"autogenerated": false,
"ratio": 3.7946480511925538,
"config_test"... |
__author__ = 'guorongxu'
import sys
import re
import os
import math
import logging
import Helper
from GO import GOLocusParser
from Utils import HypergeomCalculator
## To print JSON file for each cluster.
def print_json(output_file, tumor_type, network_type, gamma, cluster_list, prefix):
for cluster_ID in cluster... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/TCGA/IvanovskaJSONBuilder.py",
"copies": "1",
"size": "8425",
"license": "mit",
"hash": -202114967673218140,
"line_mean": 43.8191489362,
"line_max": 126,
"alpha_frac": 0.5010089021,
"autogenerated": false,
"ratio": 3.404040404040404,
"conf... |
__author__ = 'guorongxu'
import sys
import re
import os
import math
import logging
import Helper
from GO import GOLocusParser
from Utils import HypergeomCalculator, RawFileParser
## To print JSON file for each cluster.
def print_json(output_file, tumor_type, network_type, gamma, cluster_list,
GO_ID_lis... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/TCGA/ClusterJSONBuilder.py",
"copies": "1",
"size": "12212",
"license": "mit",
"hash": 6158178745336371000,
"line_mean": 44.0627306273,
"line_max": 169,
"alpha_frac": 0.5845889289,
"autogenerated": false,
"ratio": 3.2136842105263157,
"conf... |
__author__ = 'guorongxu'
import sys
def build_schema(output_file, prefix):
filewriter = open(output_file, "a")
filewriter.write("curl -XDELETE \'http://localhost:9200/authors/" + prefix + "\'\n")
filewriter.write("curl -XPUT \'http://localhost:9200/authors/" + prefix + "/_mapping\' -d \'\n")
filewrit... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/Schema/AuthorsSchemaBuilder.py",
"copies": "1",
"size": "2748",
"license": "mit",
"hash": -896331236537340700,
"line_mean": 52.8823529412,
"line_max": 108,
"alpha_frac": 0.5345705968,
"autogenerated": false,
"ratio": 2.629665071770335,
"co... |
__author__ = 'guorongxu'
import sys
def build_schema(output_file, prefix):
filewriter = open(output_file, "a")
filewriter.write("curl -XDELETE \'http://localhost:9200/clusters/" + prefix + "\'\n")
filewriter.write("curl -XPUT \'http://localhost:9200/clusters/" + prefix + "/_mapping\' -d \'\n")
filewr... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/Schema/ClustersSchemaBuilder.py",
"copies": "1",
"size": "4488",
"license": "mit",
"hash": -5375628725189079000,
"line_mean": 56.5384615385,
"line_max": 104,
"alpha_frac": 0.5360962567,
"autogenerated": false,
"ratio": 2.632258064516129,
"... |
__author__ = 'guorongxu'
import sys
def build_schema(output_file, prefix):
filewriter = open(output_file, "a")
filewriter.write("curl -XDELETE \'http://localhost:9200/conditions/" + prefix + "\'\n")
filewriter.write("curl -XPUT \'http://localhost:9200/conditions/" + prefix + "/_mapping\' -d \'\n")
fi... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/Schema/ConditionsSchemaBuilder.py",
"copies": "1",
"size": "2360",
"license": "mit",
"hash": 4583196242450873000,
"line_mean": 49.2127659574,
"line_max": 104,
"alpha_frac": 0.5453389831,
"autogenerated": false,
"ratio": 2.6909920182440135,
... |
__author__ = 'guorongxu'
import sys
def build_schema(output_file, prefix):
filewriter = open(output_file, "a")
filewriter.write("curl -XDELETE \'http://localhost:9200/drugs/" + prefix + "\'\n")
filewriter.write("curl -XPUT \'http://localhost:9200/drugs/" + prefix + "/_mapping\' -d \'\n")
filewriter.w... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/Schema/DrugsSchemaBuilder.py",
"copies": "1",
"size": "1857",
"license": "mit",
"hash": -2766666029948322000,
"line_mean": 45.425,
"line_max": 104,
"alpha_frac": 0.5514270328,
"autogenerated": false,
"ratio": 2.747041420118343,
"config_tes... |
__author__ = 'guorongxu'
import sys
def build_schema(output_file, prefix):
filewriter = open(output_file, "a")
filewriter.write("curl -XDELETE \'http://localhost:9200/genes/" + prefix + "\'\n")
filewriter.write("curl -XPUT \'http://localhost:9200/genes/" + prefix + "/_mapping\' -d \'\n")
filewriter.w... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/Schema/GenesSchemaBuilder.py",
"copies": "1",
"size": "1922",
"license": "mit",
"hash": 2089707802289539800,
"line_mean": 47.075,
"line_max": 104,
"alpha_frac": 0.5494276795,
"autogenerated": false,
"ratio": 2.7417974322396574,
"config_tes... |
__author__ = 'guorongxu'
import sys
def build_schema(output_file, prefix):
filewriter = open(output_file, "a")
filewriter.write("curl -XDELETE \'http://localhost:9200/groups/" + prefix + "\'\n")
filewriter.write("curl -XPUT \'http://localhost:9200/groups/" + prefix + "/_mapping\' -d \'\n")
filewriter... | {
"repo_name": "ucsd-ccbb/Oncolist",
"path": "src/server/Schema/ClusterDrugSchemaBuilder.py",
"copies": "1",
"size": "1923",
"license": "mit",
"hash": -4140863123445168600,
"line_mean": 47.075,
"line_max": 104,
"alpha_frac": 0.5486219449,
"autogenerated": false,
"ratio": 2.71994342291372,
"confi... |
__author__ = 'guru'
import binascii
dataSting = '0005010203041122334400b9'
def checkSum(dataString,write):
totalSum = 0
for i in range(0,22):
if( i%2 == 0):
totalSum += int(dataString[i:i+2],16)
checkSum = dataString[22:24]
tempString = hex(totalSum)[::-1] #[::-1] reverses the st... | {
"repo_name": "karthikeyan5/hexa",
"path": "newjunk.py",
"copies": "1",
"size": "3088",
"license": "mit",
"hash": -6120561353378329000,
"line_mean": 33.6966292135,
"line_max": 136,
"alpha_frac": 0.6651554404,
"autogenerated": false,
"ratio": 2.8021778584392014,
"config_test": false,
"has_no_k... |
__author__ = 'guru'
import RPi.GPIO as GPIO
from time import sleep
#define ConfigureLCDPINsDirection(Value) (TRISD = Value, \
# TRISCbits.TRISC3 = Value, \
# TRISCbits.TRISC4 = Value, \
# ... | {
"repo_name": "karthikeyan5/hexa",
"path": "hexa-beta/glcdtest.py",
"copies": "1",
"size": "3956",
"license": "mit",
"hash": -6602461136981673000,
"line_mean": 21.7413793103,
"line_max": 94,
"alpha_frac": 0.6099595551,
"autogenerated": false,
"ratio": 3.142176330420969,
"config_test": false,
... |
__author__ = 'guru'
#state 40 = enter the phone number
#state 61 = phone number already exist try new number
#state 100 = Ask's user to place their 1st finger on FPS
#state 101 = Ask's user to place their 2st finger on FPS
#state 20 = Scanning ur finger now
#state 30 = remove ur finger and place it again
#state 41 = ... | {
"repo_name": "karthikeyan5/hexa",
"path": "hexa-beta/userRegistrationScreen.py",
"copies": "1",
"size": "7575",
"license": "mit",
"hash": 722466755553882100,
"line_mean": 30.0450819672,
"line_max": 69,
"alpha_frac": 0.617029703,
"autogenerated": false,
"ratio": 3.0071456927352123,
"config_test... |
__author__ = 'guru'
import serial
import binascii
serialport = serial.Serial("/dev/ttyAMA0", timeout=10)
serialport.baudrate = 9600
serialport.flush()
autoid = 0
def fpsTransmitter(data):
question = binascii.unhexlify(data)
print("string trans >",data)
serialport.flush()
se... | {
"repo_name": "karthikeyan5/hexa",
"path": "hexa-beta/fpsDriver.py",
"copies": "1",
"size": "7966",
"license": "mit",
"hash": 1032470945624009900,
"line_mean": 28.1742424242,
"line_max": 118,
"alpha_frac": 0.5222194326,
"autogenerated": false,
"ratio": 3.1991967871485945,
"config_test": false,
... |
__author__ = 'guru'
import sqlite3
import os.path
import sys
conn = sqlite3.connect('hexabeta.db')
print("Opened database successfully")
def createtables():
conn.execute('''CREATE TABLE IF NOT EXISTS CustomerDetails
(MobileNumber CHAR(10) PRIMARY KEY NOT NULL UNIQUE,
AccountBalance RE... | {
"repo_name": "karthikeyan5/hexa",
"path": "hexa-beta/database.py",
"copies": "1",
"size": "8478",
"license": "mit",
"hash": 4549566417479037000,
"line_mean": 37.069124424,
"line_max": 137,
"alpha_frac": 0.627388535,
"autogenerated": false,
"ratio": 3.957983193277311,
"config_test": false,
"h... |
__author__ = 'guru'
#state 10 = Ask's user to place their finger on FPS
#state 20 = Scanning ur finger now
#state 21 = Acccount not found
#state 30 = Mini Statement(last 3 transcations)
#screen = 0 vendor,screen = 1 customer.
import GLCD as g
currentState = 0
fontWidth = 6
lineLength = 21
def ... | {
"repo_name": "karthikeyan5/hexa",
"path": "hexa-beta/miniStatementScreen.py",
"copies": "1",
"size": "3677",
"license": "mit",
"hash": 9066885971695240000,
"line_mean": 27.9105691057,
"line_max": 75,
"alpha_frac": 0.6040250204,
"autogenerated": false,
"ratio": 3.033828382838284,
"config_test":... |
__author__ = 'guru'
#state 10 = payment amount
#state 20 = Ask's user to place their finger on FPS
#state 30 = Scanning ur finger now
#state 31 = account not found
#state 32 = no enough balance
#state 40 = payment successful
#screen = 0 vendor,screen = 1 customer.
import GLCD as g
currentState = 0
fontW... | {
"repo_name": "karthikeyan5/hexa",
"path": "hexa-beta/paymentScreen.py",
"copies": "1",
"size": "5382",
"license": "mit",
"hash": -3797837099417367000,
"line_mean": 29.8461538462,
"line_max": 71,
"alpha_frac": 0.5930880713,
"autogenerated": false,
"ratio": 3.0596930073905626,
"config_test": fal... |
__author__ = 'guru'
#state 10 = recharge amount
#state 20 = Ask's user to place their finger on FPS
#state 30 = Scanning ur finger now
#state 31 = account not found
#state 40 = recharge successful
#screen = 0 vendor,screen = 1 customer.
import GLCD as g
currentState = 0
fontWidth = 6
lineLength = 21
... | {
"repo_name": "karthikeyan5/hexa",
"path": "hexa-beta/rechargeScreen.py",
"copies": "1",
"size": "5372",
"license": "mit",
"hash": 900697511472219900,
"line_mean": 31.575,
"line_max": 67,
"alpha_frac": 0.5925167535,
"autogenerated": false,
"ratio": 2.992757660167131,
"config_test": false,
"ha... |
__author__ = 'Guy Hawkins'
import urllib
import urlparse
import pickle
import os
import json
from BeautifulSoup import BeautifulSoup
from time import sleep, strftime, localtime
from pyvirtualdisplay import Display
from selenium import webdriver
##############
## Database ##
##############
'''
A Pickle database to... | {
"repo_name": "GHawk1ns/Flipboard_AutoFlip",
"path": "autoFlip/autoFlip.py",
"copies": "1",
"size": "13881",
"license": "mit",
"hash": 4793402676190638000,
"line_mean": 34.053030303,
"line_max": 361,
"alpha_frac": 0.585188387,
"autogenerated": false,
"ratio": 4.1263376932223546,
"config_test": ... |
__author__ = 'Guy'
def block_website():
import re
website = raw_input("Please enter the base url of the website you would like to block: ")
url_regex = r'[-a-zA-Z0-9@:%_\+.~#?&//=]{2,256}\.[a-z]{2,4}\b(\/[-a-zA-Z0-9@:%_\+.~#?&//=]*)?(\?([-a-zA-Z0-9@:%_\+.~#?&//=]+)|)'
while not re.search(url_re... | {
"repo_name": "HackinGuy/proxparent",
"path": "control.py",
"copies": "1",
"size": "2256",
"license": "mit",
"hash": 1881588902114704600,
"line_mean": 39.7777777778,
"line_max": 132,
"alpha_frac": 0.5181737589,
"autogenerated": false,
"ratio": 3.8564102564102565,
"config_test": false,
"has_no... |
__author__ = "Gyrodrill恋恋"
langversion = 1
langname = "Chinese"
##updater
# text construct: "Version "+version+available+changelog
#example: Version 3 available, click here to download, or for changelog click here
available = " 更新了，单击此处下载。"
changelog = " 单击此处查看更新内容。"
##world gen
worldify = "读取图片"
planetoids = "小圆点"
... | {
"repo_name": "Berserker66/omnitool",
"path": "omnitool/Language/chinese.py",
"copies": "1",
"size": "3233",
"license": "mit",
"hash": -2575618950381189000,
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"autogenerated": false,
"ratio": 1.5114835505896957,
"config_test"... |
__author__ = 'hadhya'
from abc import ABCMeta
from string import Template
import urllib2
import json
import sleekxmpp
import logging
from copy import deepcopy
from config import CONFIG
# HTTP message standard header
HTTPS_HEADER = {
'Content-Type': 'application/json',
'Authorization': Template('key=$api_key')... | {
"repo_name": "Hindol/python-gcm-server",
"path": "gcm/__init__.py",
"copies": "1",
"size": "2363",
"license": "mit",
"hash": -6984282996597877000,
"line_mean": 27.8170731707,
"line_max": 115,
"alpha_frac": 0.6432501058,
"autogenerated": false,
"ratio": 3.8112903225806454,
"config_test": true,
... |
__author__ = 'hadhya'
from gcm import GcmClient
from config import CONFIG
from optparse import OptionParser
import logging
def start_xmpp_server():
client = GcmClient(CONFIG['GCM_API_KEY'])
client.listen(CONFIG['GCM_SENDER_ID'], on_message)
def on_message(message):
print message
if __name__ == '__main__... | {
"repo_name": "Hindol/python-gcm-server",
"path": "serve.py",
"copies": "1",
"size": "1164",
"license": "mit",
"hash": -1440602402619433700,
"line_mean": 31.3611111111,
"line_max": 66,
"alpha_frac": 0.6005154639,
"autogenerated": false,
"ratio": 3.9726962457337884,
"config_test": false,
"has_... |
__author__ = 'had'
# The MIT License (MIT)
# Copyright (c) [2015] [Houtmann Hadrien]
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the Aidez-moi), to deal
# in the Software without restriction, including without limitation the righ... | {
"repo_name": "hadmagic/Aidez-moi",
"path": "ticket/views/home.py",
"copies": "1",
"size": "2583",
"license": "mit",
"hash": -6793701383850937000,
"line_mean": 42.7966101695,
"line_max": 80,
"alpha_frac": 0.7015098722,
"autogenerated": false,
"ratio": 4.370558375634518,
"config_test": false,
... |
__author__ = 'HagRead-Only'
from tkinter import *
root = Tk()
root.title("PlotterGraphicFont")
cnv = Canvas(root, width=260, height=280)
cnv.pack()
cnv.create_text(75, 15, text="Enter real number or axis.")
cnv.create_text(70, 35, text="Type 'exit' to close app.")
line = Text(root, height=1, width=11, font="Calibri... | {
"repo_name": "Alexponomarev7/plotter",
"path": "cgi-bin/lib/graph/PlotterGraphicFont.py",
"copies": "1",
"size": "3421",
"license": "mit",
"hash": -5467993128823254000,
"line_mean": 23.2624113475,
"line_max": 79,
"alpha_frac": 0.4931306635,
"autogenerated": false,
"ratio": 2.156998738965952,
"... |
__author__ = 'haho0032'
import base64
import datetime
import dateutil.parser
import pytz
from OpenSSL import crypto
from os.path import join
from os import remove
from Crypto.Util import asn1
class WrongInput(Exception):
pass
class CertificateError(Exception):
pass
class PayloadError(Exception):
pass
... | {
"repo_name": "rohe/pysaml2-3",
"path": "src/saml2/cert.py",
"copies": "1",
"size": "15164",
"license": "bsd-2-clause",
"hash": 2945293735093234000,
"line_mean": 41.2395543175,
"line_max": 80,
"alpha_frac": 0.4843049327,
"autogenerated": false,
"ratio": 5.081769436997319,
"config_test": false,
... |
__author__ = 'haho0032'
import base64
import datetime
import dateutil.parser
import pytz
import six
from OpenSSL import crypto
from os.path import join
from os import remove
import saml2.cryptography.pki
class WrongInput(Exception):
pass
class CertificateError(Exception):
pass
class PayloadError(Excepti... | {
"repo_name": "kawamon/hue",
"path": "desktop/core/ext-py/pysaml2-4.9.0/src/saml2/cert.py",
"copies": "2",
"size": "15186",
"license": "apache-2.0",
"hash": -155556161336166430,
"line_mean": 44.0623145401,
"line_max": 98,
"alpha_frac": 0.4770841565,
"autogenerated": false,
"ratio": 5.204249485949... |
__author__ = 'haibo'
#encoding:utf8
def cookie2scrapy(cookies):
"""
the function is used to covert the cookies that
gotten from response using requests module to the style
which can be used in scrapy.
the style of original cookie:
f=dwd;dsd=gdfg;g_d=1334
In scrapy,the cookie must ex... | {
"repo_name": "haipersist/webspider",
"path": "utils/cookie2scrapy.py",
"copies": "1",
"size": "1709",
"license": "mit",
"hash": 1121762356194884600,
"line_mean": 36.9777777778,
"line_max": 141,
"alpha_frac": 0.7290813341,
"autogenerated": false,
"ratio": 2.386871508379888,
"config_test": false... |
__author__ = 'Haim'
import os
import threading
import importlib
import inspect
import IChatTask
import event_aggregator
import hermes.backend.dict
import datetime
last_clear_date = None
cache = hermes.Hermes(hermes.backend.dict.Backend)
@cache
def get_last_clear_date():
return last_clear_date
class PluginTask... | {
"repo_name": "haimroizman/chat",
"path": "plugin_tasks_server.py",
"copies": "1",
"size": "2270",
"license": "mit",
"hash": -7331931696062261000,
"line_mean": 36.8333333333,
"line_max": 104,
"alpha_frac": 0.5651982379,
"autogenerated": false,
"ratio": 4.274952919020715,
"config_test": false,
... |
__author__ = 'Haim'
from socket import *
import Queue
import concurrent.futures
from log_manager import LogManager
import multiprocessing
from threading import Thread
from user_service import UserService
max_conn_pool = multiprocessing.cpu_count()
logger = LogManager("chatApp")
class EventHandler:
def fileno(s... | {
"repo_name": "haimroizman/chat",
"path": "messages_producer_consumer.py",
"copies": "1",
"size": "6891",
"license": "mit",
"hash": 7405690167460652000,
"line_mean": 34.8958333333,
"line_max": 120,
"alpha_frac": 0.6029603831,
"autogenerated": false,
"ratio": 4.104228707564026,
"config_test": fa... |
__author__ = 'Haim'
import re
from DB.chat_repository import ChatUserRepository, ChatMessageRepository
from time import gmtime, strftime
class UserService:
def __init__(self, user_name, user_socket):
self.user_name = user_name
self.user_id = None
self.chat_user_repository = ChatUserReposi... | {
"repo_name": "haimroizman/chat",
"path": "user_service.py",
"copies": "1",
"size": "1738",
"license": "mit",
"hash": -6371183570558440000,
"line_mean": 33.76,
"line_max": 86,
"alpha_frac": 0.6265822785,
"autogenerated": false,
"ratio": 3.4969818913480886,
"config_test": false,
"has_no_keywor... |
__author__ = 'haitham'
import sklearn
class UDLModel(sklearn.base.BaseEstimator):
# self.estimator =None
# self.configs= {}
def __init__(self):
# a dictionary of all configurable parameters. This dictionary will be used in get_params. see the note in get_params function
self.configs = {}
... | {
"repo_name": "marakeby/udl",
"path": "udl/model.py",
"copies": "1",
"size": "1310",
"license": "bsd-2-clause",
"hash": -1978776956172742000,
"line_mean": 33.4736842105,
"line_max": 164,
"alpha_frac": 0.6427480916,
"autogenerated": false,
"ratio": 4.1455696202531644,
"config_test": false,
"ha... |
__author__ = 'haitham'
import udl.caffei.models.caffe_pb2 as pb2
from udl.caffei.models.layers.layer import Layer, set
class Filler(object):
def __init__(self, type="constant", value=0, min=0, max=1, mean=0, std=1, sparse=-1):
self._filler = pb2.FillerParameter()
self.type = type
self.valu... | {
"repo_name": "marakeby/udl",
"path": "udl/caffei/models/layers/innerproduct_pb2.py",
"copies": "1",
"size": "3760",
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"hash": 7808916365989383000,
"line_mean": 26.2463768116,
"line_max": 116,
"alpha_frac": 0.6154255319,
"autogenerated": false,
"ratio": 3.424408014571949,... |
__author__ = 'haitham'
from udl.caffei.models.layers.layer import Layer, set
import udl.caffei.models.caffe_pb2 as pb2
class LossType():
HINGE_LOSS = "HINGE_LOSS"
CONTRASTIVE_LOSS = "CONTRASTIVE_LOSS"
EUCLIDEAN_LOSS = "EUCLIDEAN_LOSS"
MULTINOMIAL_LOGISTIC_LOSS = "MULTINOMIAL_LOGISTIC_LOSS"
Softma... | {
"repo_name": "marakeby/udl",
"path": "udl/caffei/models/layers/loss_layers.py",
"copies": "1",
"size": "2057",
"license": "bsd-2-clause",
"hash": 8739351396093571000,
"line_mean": 24.3950617284,
"line_max": 104,
"alpha_frac": 0.6446280992,
"autogenerated": false,
"ratio": 3.3501628664495113,
"... |
__author__ = 'Hakim'
"""
Convert tsv file in format:
gene_name_1,gene_name_2,sign
to:
gene_number_1,gene_number_2,sign
Save the gene_name -> gene_id correspondence to file
"""
def convert_name_to_number(input_tsv,output_tsv,name_id):
"""
Convert tsv_file gene1_name,gene2_name,interaction to
gene1_id,gen... | {
"repo_name": "rohanramanath/gene-regulatory-network",
"path": "final_presentation/data_processing/convert_tsv_names_numbers.py",
"copies": "1",
"size": "1439",
"license": "apache-2.0",
"hash": -3969419051107107000,
"line_mean": 29.6170212766,
"line_max": 101,
"alpha_frac": 0.575399583,
"autogenera... |
__author__ = 'halfcrazy'
from PIL import Image
import StringIO
#------------------------------------------------------------------------------
# binaryzation
def binary(data):
data.seek(0)
img = Image.open(data)
pixdata = img.load()
for y in xrange(img.size[1]):
for x in xrange(img.size[0]):... | {
"repo_name": "halfcrazy/DecodeValidateCode",
"path": "Recognize.py",
"copies": "1",
"size": "1714",
"license": "mit",
"hash": -709251530488092400,
"line_mean": 26.6451612903,
"line_max": 79,
"alpha_frac": 0.4206534422,
"autogenerated": false,
"ratio": 3.7342047930283226,
"config_test": false,
... |
__author__ = 'halley'
from chunk import *
import scale as sc
import harmony as hm
import gencell as gc
import pitchhelpers as pth
import random
def genHalfNotes(main_passage):
half_notes = []
for cell in main_passage:
if cell.chord == []:
half_notes.append(Chunk(pits=[cell.pits[0]],durs=[2... | {
"repo_name": "caromedellin/tweets-sounds",
"path": "accompaniment.py",
"copies": "1",
"size": "7112",
"license": "mit",
"hash": -7125273180602660000,
"line_mean": 44.8903225806,
"line_max": 132,
"alpha_frac": 0.5109673791,
"autogenerated": false,
"ratio": 3.238615664845173,
"config_test": fals... |
__author__ = 'halley'
from constants import *
from chunk import *
import copy
import probabilityhelpers as ph
def inChord(note, chord):
if chord == []:
return True
if note == None:
return True
else:
return (note % 7) in [i%7 for i in chord]
#check if an entire chunk matches
def ch... | {
"repo_name": "caromedellin/tweets-sounds",
"path": "harmony.py",
"copies": "1",
"size": "5426",
"license": "mit",
"hash": 3186281305187231000,
"line_mean": 34.2337662338,
"line_max": 137,
"alpha_frac": 0.6052340582,
"autogenerated": false,
"ratio": 2.963407973784817,
"config_test": false,
"h... |
__author__ = 'halley'
from inspect import getmembers, isfunction
import celltransforms as ct
import random
import preferences as pref
import harmony as hm
import functionalhelpers as fh
import gencell as gc
import rhythmhelpers as rhy
from chunk import *
#the list of functions
transform_cell_functions = dict([(o[0], ... | {
"repo_name": "caromedellin/tweets-sounds",
"path": "transformcell.py",
"copies": "1",
"size": "2018",
"license": "mit",
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"line_mean": 52.1052631579,
"line_max": 224,
"alpha_frac": 0.6942517344,
"autogenerated": false,
"ratio": 3.4733218588640273,
"config_test": fal... |
__author__ = 'halley'
from music21 import *
import scale as sc
import functionalhelpers as fh
from constants import *
def cellsToPart(cells, octave = 5):
notes = []
for cell in cells:
degrees = cell.pits
pits = sc.degreesToNotes(degrees, octave = octave, scale = cell.scale)
durs = cell... | {
"repo_name": "caromedellin/tweets-sounds",
"path": "music21helpers.py",
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"size": "1718",
"license": "mit",
"hash": 3137835133695274500,
"line_mean": 27.1639344262,
"line_max": 78,
"alpha_frac": 0.5133876601,
"autogenerated": false,
"ratio": 3.5791666666666666,
"config_test": fals... |
__author__ = 'halley'
import functionalhelpers as fh
import harmony as hm
#test if a single cell = good
def goodCell(cell):
if len(cell.durs) != len(cell.pits):
return False
if len(cell.durs) == 1:
return True
pits = cell.pits
durs = cell.durs
pit_diffs = [abs(pits[i] - pits[i - 1])... | {
"repo_name": "caromedellin/tweets-sounds",
"path": "preferences.py",
"copies": "1",
"size": "4309",
"license": "mit",
"hash": -1035774368116931500,
"line_mean": 37.4732142857,
"line_max": 207,
"alpha_frac": 0.5175214667,
"autogenerated": false,
"ratio": 2.673076923076923,
"config_test": false,... |
__author__ = 'halley'
import probabilityhelpers as ph
import random
#for checking whether two numbers are roughly the same
def almostEquals(x,y):
if abs(x - y) < 0.001:
return True
return False
def strToRhy(rstr):
return [float(i) for i in rstr.split()]
def rhyToStr(rhy):
return ' '.join([str... | {
"repo_name": "caromedellin/tweets-sounds",
"path": "rhythmhelpers.py",
"copies": "1",
"size": "4629",
"license": "mit",
"hash": -2257983832558361600,
"line_mean": 37.575,
"line_max": 194,
"alpha_frac": 0.5331605098,
"autogenerated": false,
"ratio": 2.658816771970132,
"config_test": false,
"h... |
__author__ = 'halley'
import random
import music21helpers as mh
import transformcell as tf
import gencell as gc
import accompaniment as acc
from music21 import *
import copy
import json
import sys
def getLoudness(loudness):
if i < 0.1:
return 'p'
elif i < 0.25:
return 'mp'
elif i < 0.5:
... | {
"repo_name": "caromedellin/tweets-sounds",
"path": "twitter.py",
"copies": "1",
"size": "2877",
"license": "mit",
"hash": 7926733083774239000,
"line_mean": 29.9462365591,
"line_max": 101,
"alpha_frac": 0.6301703163,
"autogenerated": false,
"ratio": 2.5895589558955896,
"config_test": false,
"... |
__author__ = 'halley'
import scale as sc
from chunk import *
import gencell as gc
import random
def getFirstNotes(prev_cell, old_cell):
ppit = prev_cell.pits[-1]
if (ppit % 7) in (i % 7 for i in prev_cell.chord):
return [ppit - 1, ppit + 1, ppit]
else:
if prev_cell.pits < 14 and random.unif... | {
"repo_name": "caromedellin/tweets-sounds",
"path": "celltransforms.py",
"copies": "1",
"size": "7037",
"license": "mit",
"hash": 5636847742254536000,
"line_mean": 37.4590163934,
"line_max": 139,
"alpha_frac": 0.5917294302,
"autogenerated": false,
"ratio": 2.8091816367265467,
"config_test": fal... |
import bcbp_func
import sys
import gc
import probstat
import random
from utils import PriorityQueue
from sets import Set
def whole(n):
return n == int(n) and n != 1
def groupnums(numpieces):
r = []
numcoms = len(probstat.Combination(range(numpieces), 3))
numofeachletter = float(numcoms * 3) / numpiec... | {
"repo_name": "nicofarr/bcbp",
"path": "bcbp.py",
"copies": "1",
"size": "6186",
"license": "mit",
"hash": -2767644668829683700,
"line_mean": 34.1477272727,
"line_max": 96,
"alpha_frac": 0.5218234724,
"autogenerated": false,
"ratio": 3.823238566131026,
"config_test": false,
"has_no_keywords":... |
import sys
import gc
import probstat
import random
from utils import PriorityQueue
from sets import Set
import numpy as np
def triad_perms(triad):
return [''.join(x) for x in probstat.Permutation([c for c in triad])]
def group_distrib(group):
distrib = []
for triad in group:
for i in range(3):
... | {
"repo_name": "nicofarr/bcbp",
"path": "bcbp_func.py",
"copies": "1",
"size": "4348",
"license": "mit",
"hash": 334380331708679000,
"line_mean": 28.9862068966,
"line_max": 96,
"alpha_frac": 0.516099356,
"autogenerated": false,
"ratio": 3.611295681063123,
"config_test": false,
"has_no_keywords... |
from __future__ import division
import numpy as np
import scipy.sparse as sp
import array
from sklearn.utils import check_random_state
from ._random import sample_without_replacement
from .deprecation import deprecated
__all__ = ['sample_without_replacement', 'choice']
# This is a backport of np.random.choice from ... | {
"repo_name": "clemkoa/scikit-learn",
"path": "sklearn/utils/random.py",
"copies": "29",
"size": "7445",
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"hash": 8418767482602748000,
"line_mean": 36.4120603015,
"line_max": 79,
"alpha_frac": 0.5974479516,
"autogenerated": false,
"ratio": 3.941238750661726,
"config_te... |
from __future__ import division
import numpy as np
import scipy.sparse as sp
import operator
import array
from sklearn.utils import check_random_state
from sklearn.utils.fixes import astype
from ._random import sample_without_replacement
__all__ = ['sample_without_replacement', 'choice']
# This is a backport of np.... | {
"repo_name": "appapantula/scikit-learn",
"path": "sklearn/utils/random.py",
"copies": "234",
"size": "10510",
"license": "bsd-3-clause",
"hash": -5137917352955574000,
"line_mean": 35.4930555556,
"line_max": 79,
"alpha_frac": 0.5726926736,
"autogenerated": false,
"ratio": 3.952613764573148,
"co... |
from __future__ import division
import numpy as np
import scipy.sparse as sp
import operator
import array
from sklearn.utils import check_random_state
from ._random import sample_without_replacement
__all__ = ['sample_without_replacement', 'choice']
# This is a backport of np.random.choice from numpy 1.7
# The fun... | {
"repo_name": "mblondel/scikit-learn",
"path": "sklearn/utils/random.py",
"copies": "19",
"size": "10413",
"license": "bsd-3-clause",
"hash": 1769974513246895900,
"line_mean": 35.2822299652,
"line_max": 79,
"alpha_frac": 0.5712090656,
"autogenerated": false,
"ratio": 3.9578107183580387,
"config... |
from __future__ import division
import array
import operator
import numpy as np
import scipy.sparse as sp
from sklearn.utils import check_random_state
from sklearn.utils.fixes import astype
from ._random import sample_without_replacement
__all__ = ['sample_without_replacement', 'choice']
# This is a backport of np... | {
"repo_name": "DailyActie/Surrogate-Model",
"path": "01-codes/scikit-learn-master/sklearn/utils/random.py",
"copies": "1",
"size": "10512",
"license": "mit",
"hash": -3643198201720285000,
"line_mean": 35.3737024221,
"line_max": 79,
"alpha_frac": 0.5726788432,
"autogenerated": false,
"ratio": 3.95... |
import numpy as np
import scipy.sparse as sp
import array
from . import check_random_state
from ._random import sample_without_replacement
__all__ = ['sample_without_replacement']
def _random_choice_csc(n_samples, classes, class_probability=None,
random_state=None):
"""Generate a sparse r... | {
"repo_name": "bnaul/scikit-learn",
"path": "sklearn/utils/random.py",
"copies": "3",
"size": "3827",
"license": "bsd-3-clause",
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"line_mean": 38.8645833333,
"line_max": 79,
"alpha_frac": 0.5516070029,
"autogenerated": false,
"ratio": 4.150759219088937,
"config_test... |
import numpy as np
import scipy.sparse as sp
import array
from . import check_random_state
from ._random import sample_without_replacement
__all__ = ['sample_without_replacement']
def random_choice_csc(n_samples, classes, class_probability=None,
random_state=None):
"""Generate a sparse ran... | {
"repo_name": "chrsrds/scikit-learn",
"path": "sklearn/utils/random.py",
"copies": "4",
"size": "4004",
"license": "bsd-3-clause",
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"line_max": 79,
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"autogenerated": false,
"ratio": 4.192670157068063,
"config_te... |
__author__ = 'HANEL'
import os
import glob
import tensorflow as tf
from PIL import Image
import numpy as np
import imageflow
imageflow.read_images()
'''
Simple library to read all PNG and JPG/JPEG images in a directory
with TensorFlow buil-in functions to boost speed.
Hamed MP
Github: @hamedmp
Twitter: @... | {
"repo_name": "HamedMP/ImageFlow",
"path": "imageflow/playground.py",
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"size": "4811",
"license": "apache-2.0",
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"config_test": ... |
__author__ = 'Hans Burbano<hburbano@bbox.co>'
from datetime import datetime, timedelta
import math
import csv
class GPXDProcessor(object):
"""docstring for GPXDProcessor"""
# Index of
START_LAT, START_LON, END_LAT, END_LON, START_TIME, END_TIME, DISTANCE, LAPSE, SPEED, ISIDDLE = [
0, 1, 2, 3, 4, ... | {
"repo_name": "Nslaver/GPXIddleTime",
"path": "GPXIddleTime/GPXDProcessor.py",
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"size": "4603",
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__author__ = 'Hans Burbano<hburbano@bbox.co>'
import xml.sax.saxutils
import xml.sax
class GPXLoader(xml.sax.handler.ContentHandler):
"""
This class loads in to memory the GPX [lat, lon, time] data generated
from a Garmin GPS device, uses
"""
def __init__(self):
super(GPXLoader, self)._... | {
"repo_name": "Nslaver/GPXIddleTime",
"path": "GPXIddleTime/GPXLoader.py",
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__author__ = 'Hans Burbano<hburbano@bbox.co>'
#!/usr/bin/python
"""
This script gives basic tools to calculate iddle time on a GPX data file.
Iddle time defined as the time in which the speed is below a determinated limit
Tested with GPX from Garmin Astro 320 device.
"""
import xml.sax.saxutils
import xml.sax
import os... | {
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"path": "test.py",
"copies": "1",
"size": "1395",
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"has_no_keywords": fa... |
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