text stringlengths 0 1.05M | meta dict |
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__author__ = 'waziz'
import numpy as np
import sys
from collections import defaultdict
from chisel.smt import groupby
from chisel.util import obj2id
from chisel.smt import Solution
from chisel.util import npvec2str, fmap_dot
def entropy(p_dot, p, log_p, dp):
H = -(p * log_p).sum()
dH = -(dp * (p_dot + 1)[:, ... | {
"repo_name": "wilkeraziz/chisel",
"path": "python/chisel/learning/newestimates.py",
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"size": "9975",
"license": "apache-2.0",
"hash": 9182104605445134000,
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"alpha_frac": 0.5993984962,
"autogenerated": false,
"ratio": 2.7479338842975207,
... |
__author__ = 'waziz'
import numpy as np
import sys
from collections import defaultdict
from chisel.smt import Solution
from chisel.util import npvec2str, fmap_dot
from chisel.smt import groupby
from chisel.util import obj2id
class EmpiricalDistribution(object):
"""
"""
def __init__(self,
der... | {
"repo_name": "wilkeraziz/chisel",
"path": "python/chisel/decoder/estimates.py",
"copies": "1",
"size": "6719",
"license": "apache-2.0",
"hash": -1420615684397392400,
"line_mean": 32.0985221675,
"line_max": 139,
"alpha_frac": 0.5206131865,
"autogenerated": false,
"ratio": 3.0170633138751684,
"c... |
__author__ = 'waziz'
import numpy as np
import sys
from collections import defaultdict
from chisel.smt import Solution
from chisel.util import npvec2str
from chisel.smt import groupby
from chisel.util import obj2id
class EmpiricalDistribution(object):
"""
"""
def __init__(self,
derivations,... | {
"repo_name": "wilkeraziz/chisel",
"path": "python/chisel/decoder/original_estimates.py",
"copies": "1",
"size": "8283",
"license": "apache-2.0",
"hash": 4327357090293104600,
"line_mean": 32.6707317073,
"line_max": 121,
"alpha_frac": 0.5242062055,
"autogenerated": false,
"ratio": 3.09413522599925... |
__author__ = 'waziz'
import unittest
from grasp.cfg.symbol import Terminal, Nonterminal
from grasp.cfg.rule import NewCFGProduction as CFGProduction
from grasp.cfg.model import PCFG
def make_production(lhs, rhs, weight):
return CFGProduction(lhs, rhs, {'Prob': weight, 'Dummy': 0})
class CFGProductionTestCase(u... | {
"repo_name": "wilkeraziz/grasp",
"path": "tests/test_rule.py",
"copies": "1",
"size": "1615",
"license": "apache-2.0",
"hash": 416629615200828540,
"line_mean": 32.6458333333,
"line_max": 96,
"alpha_frac": 0.6328173375,
"autogenerated": false,
"ratio": 3.2171314741035855,
"config_test": true,
... |
__author__ = 'waziz'
def expected_bleu(samples, bleusuff, bleu=BLEU.ibm_bleu, importance=lambda sample: 1.0):
"""
Computes the expected (exact) BLEU of each candidate.
@param samples is the candidates (also the evidence set)
@param ngramstats, countstats (see count_ngrams)
@param n max ngram order... | {
"repo_name": "wilkeraziz/chisel",
"path": "python/chisel/decoder/legacy_mbr.py",
"copies": "1",
"size": "2536",
"license": "apache-2.0",
"hash": 9193992357454768000,
"line_mean": 38.625,
"line_max": 131,
"alpha_frac": 0.6092271293,
"autogenerated": false,
"ratio": 3.1739674593241554,
"config_t... |
__author__ = 'Waz'
from tastypie.authorization import Authorization
from tastypie.exceptions import Unauthorized
class AuthorizationByResume(Authorization):
def read_list(self, object_list, bundle):
author = bundle.request.user
return object_list.filter(user=bundle.request.user)
def... | {
"repo_name": "fawazn/Resume-Viewer",
"path": "ResumeViewer/api_authorization.py",
"copies": "1",
"size": "1350",
"license": "mit",
"hash": 8608797998553517000,
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"line_max": 61,
"alpha_frac": 0.6266666667,
"autogenerated": false,
"ratio": 4.192546583850931,
"config_te... |
__author__ = 'wbtang'
import datetime
import os
folder_src = '../src'
folder_log = '../log'
folder_release = '../release'
folder_final = '../ex_final'
folder_final_ex1 = '%s/%s' % (folder_final, 'ex1')
folder_final_ex2 = '%s/%s' % (folder_final, 'ex2')
folder_final_ex3 = '%s/%s' % (folder_final, 'ex3')
__name_exe ... | {
"repo_name": "iSuneast/p2p_vod",
"path": "p2p_vod.linux_win/py/log_base.py",
"copies": "1",
"size": "3229",
"license": "mit",
"hash": 9135318907742129000,
"line_mean": 24.4251968504,
"line_max": 85,
"alpha_frac": 0.5422731496,
"autogenerated": false,
"ratio": 2.9542543458371453,
"config_test":... |
__author__ = 'wbtang'
import log_base
import log_draw
labels = [
'selfish',
'unselfish'
]
def plot(ax, x, y):
ax.grid(True)
ax.scatter(x, y, marker='o')
# line
bound = max(max(x), max(y)) * 1.05
ax.plot([0, bound], [0, bound], linestyle='--', color='r')
ax.set_xlim(left=0, right=bou... | {
"repo_name": "iSuneast/p2p_vod",
"path": "p2p_vod.linux_win/py/figure_ex2.py",
"copies": "1",
"size": "1836",
"license": "mit",
"hash": -3662701161831270000,
"line_mean": 22.8441558442,
"line_max": 99,
"alpha_frac": 0.5555555556,
"autogenerated": false,
"ratio": 2.99510603588907,
"config_test"... |
__author__ = 'wbtang'
import log_base
import log_draw
large_latency = 10
bad_smoothness = 0.970
def draw(x, y):
figures = log_draw.get_figure_path([
'%s_un_latency_full' % log_base.graph_type,
'%s_un_latency_large' % log_base.graph_type,
'%s_un_fluency_full' % log_base.graph_type,
... | {
"repo_name": "iSuneast/p2p_vod",
"path": "p2p_vod.linux_win/py/figure_ex1.py",
"copies": "1",
"size": "2065",
"license": "mit",
"hash": 8896536632096825000,
"line_mean": 26.5333333333,
"line_max": 88,
"alpha_frac": 0.5365617433,
"autogenerated": false,
"ratio": 2.7533333333333334,
"config_test... |
__author__ = 'wbtang'
import log_base
cmd_ini = log_base.file_parser_ini
class_name = 'CommandLineParser'
parser_name = 'Parser'
parser = []
debuger = []
accessors = []
members = []
default_value = []
def compress(data, num_tabs):
tab = ''
for i in range(num_tabs):
tab += '\t'
all = ''
for l... | {
"repo_name": "iSuneast/p2p_vod",
"path": "p2p_vod.linux_win/py/make_cmd_parser.py",
"copies": "1",
"size": "4103",
"license": "mit",
"hash": -1763046679685487600,
"line_mean": 20.5947368421,
"line_max": 106,
"alpha_frac": 0.5169388252,
"autogenerated": false,
"ratio": 3.134453781512605,
"confi... |
__author__ = 'wbtang'
import os
import log_base
from matplotlib.figure import Figure
from matplotlib.axes import Axes
from matplotlib.backends.backend_agg import FigureCanvasAgg
def __show_image(image_file):
abspath = log_base.get_full_path(image_file)
cmd = '"%s"' % abspath
print('show image: %s' % cmd)... | {
"repo_name": "iSuneast/p2p_vod",
"path": "p2p_vod.linux_win/py/log_draw.py",
"copies": "1",
"size": "1699",
"license": "mit",
"hash": 1353069806650823000,
"line_mean": 23.9852941176,
"line_max": 93,
"alpha_frac": 0.5762213067,
"autogenerated": false,
"ratio": 2.7184,
"config_test": false,
"h... |
__author__ = 'wbtang'
import pickle
import os
import log_base
import log_draw
def get_avg(conf, peer_data):
avg_latency = 0.
avg_smoothness = 0.
num_smoothness = 0
for data in peer_data:
avg_latency += data.latency
if data.smoothness >= 0:
num_smoothness += 1
av... | {
"repo_name": "iSuneast/p2p_vod",
"path": "p2p_vod.linux_win/py/figure_ex3.py",
"copies": "1",
"size": "2421",
"license": "mit",
"hash": -2841166166333409300,
"line_mean": 26.5113636364,
"line_max": 82,
"alpha_frac": 0.5477075589,
"autogenerated": false,
"ratio": 3.127906976744186,
"config_test... |
__author__ = 'wbtang'
import queue
import log_draw
import log_base
twitter_ori = '../comm.txt'
twitter_out = '../twitter_graph.txt'
edges = {}
def clean(nodes, times):
print('num of nodes: %d' % len(nodes))
for i in range(times):
nodes_to_del = []
for node in nodes:
if len(edges[... | {
"repo_name": "iSuneast/p2p_vod",
"path": "p2p_vod.linux_win/py/twitter_data.py",
"copies": "1",
"size": "3320",
"license": "mit",
"hash": 2030566231727146000,
"line_mean": 23.776119403,
"line_max": 108,
"alpha_frac": 0.5036144578,
"autogenerated": false,
"ratio": 3.1649189704480456,
"config_te... |
__author__ = 'wbtang'
import sys
import log_base
import random
# -p 'number of process'
# -m 'MONSTER NOTE'
# -n 'max num of id'
# -g 'graph type'
def select_peer(peers):
p = int(random.random() * len(peers))
return peers[p]
def get_monster_id(monster_note, max_num_id):
print('max_num_id = %s' % max_num... | {
"repo_name": "iSuneast/p2p_vod",
"path": "p2p_vod.linux_win/py/ex2.py",
"copies": "1",
"size": "4221",
"license": "mit",
"hash": -5899464363565726000,
"line_mean": 33.0403225806,
"line_max": 84,
"alpha_frac": 0.5240464345,
"autogenerated": false,
"ratio": 3.2221374045801525,
"config_test": fal... |
__author__ = 'wbtang'
import sys
import log_base
# -p 'number of process'
# -g 'graph type'
def make_script_with(cmd, num_processor):
print('cmd = %s' % cmd)
print('num_process = %d' % num_processor)
pr_selfish = [i for i in range(0, 101, 2)]
print('selfish probability: ', pr_selfish)
scripts = ... | {
"repo_name": "iSuneast/p2p_vod",
"path": "p2p_vod.linux_win/py/ex3.py",
"copies": "1",
"size": "1312",
"license": "mit",
"hash": -6616694517980858000,
"line_mean": 24.7254901961,
"line_max": 76,
"alpha_frac": 0.5106707317,
"autogenerated": false,
"ratio": 3.109004739336493,
"config_test": fals... |
__author__ = 'wcong'
import pickle
import logging
import multicast
import transport
from ants.webservice import webservice
from ants.cluster import cluster
from ants.crawl import crawl, spidermanager
import nodeinfo
import rpc
from ants.utils import manager
'''
what a node would do
init multicast
init transport
init... | {
"repo_name": "wcong/ants",
"path": "ants/node/node.py",
"copies": "1",
"size": "5963",
"license": "bsd-3-clause",
"hash": -3656133415579157500,
"line_mean": 39.5646258503,
"line_max": 106,
"alpha_frac": 0.5876236794,
"autogenerated": false,
"ratio": 4.123789764868603,
"config_test": false,
"... |
__author__ = 'wcong'
from twisted.internet import reactor
import engine
from ants.crawl import scheduler
'''
crawl server and client
'''
class CrawlServer():
'''
cluster get a crawl job
distribute it to all the node
'''
STATUS_RUNNING = 1
STATUS_STOP = 2
def __init__(self, cluster_man... | {
"repo_name": "wcong/ants",
"path": "ants/crawl/crawl.py",
"copies": "1",
"size": "5195",
"license": "bsd-3-clause",
"hash": -2465858687254391300,
"line_mean": 32.5161290323,
"line_max": 102,
"alpha_frac": 0.6435033686,
"autogenerated": false,
"ratio": 3.8058608058608057,
"config_test": false,
... |
__author__ = 'wcong'
import ants
import time
from antsext import CrawlDao
import random
import re
import datetime
class CarSpider(ants.Spider):
name = 'yang_che_car_spider'
start_urls = [
'http://www.yangche51.com/'
]
source_id = '6'
url = 'http://www.yangche51.com/handlers/choosecar/ch... | {
"repo_name": "wcong/ants",
"path": "spider/car.py",
"copies": "1",
"size": "14443",
"license": "bsd-3-clause",
"hash": -9102304524760104000,
"line_mean": 46.8245033113,
"line_max": 146,
"alpha_frac": 0.5043273558,
"autogenerated": false,
"ratio": 3.7582617746552174,
"config_test": false,
"ha... |
__author__ = 'wdolowicz'
from pony.orm import *
from datetime import datetime
from model.group import Group
from model.contact import Contact
from pymysql.converters import encoders, decoders, convert_mysql_timestamp
class ORMFixture:
db = Database()
class ORMGroup(db.Entity):
_table_ = 'group_list'... | {
"repo_name": "wdolowicz/python_training",
"path": "fixture/orm.py",
"copies": "1",
"size": "2666",
"license": "apache-2.0",
"hash": -8838595539584714000,
"line_mean": 39.3939393939,
"line_max": 141,
"alpha_frac": 0.6706676669,
"autogenerated": false,
"ratio": 3.662087912087912,
"config_test": ... |
__author__ = 'wdolowicz'
class SessionHelper:
def __init__(self, app):
self.app = app
def login(self, username, password):
wd = self.app.wd
self.app.open_home_page()
wd.find_element_by_name("user").click()
wd.find_element_by_name("user").clear()
wd.find_element_... | {
"repo_name": "wdolowicz/python_training",
"path": "fixture/session.py",
"copies": "1",
"size": "1429",
"license": "apache-2.0",
"hash": -3858938810569657300,
"line_mean": 29.4255319149,
"line_max": 73,
"alpha_frac": 0.566130161,
"autogenerated": false,
"ratio": 3.3544600938967135,
"config_test... |
__author__ = 'wdolowicz'
from model.contact import Contact
from model.group import Group
import random
def test_remove_from_group(app, orm, check_ui):
groups = [i for i in orm.get_group_list() if i.name != ""]
if len(orm.get_group_list()) == 0:
app.group.create(Group(name="test"))
groups = or... | {
"repo_name": "wdolowicz/python_training",
"path": "test/test_remove_from_group.py",
"copies": "1",
"size": "1395",
"license": "apache-2.0",
"hash": -3212561244697979400,
"line_mean": 42.59375,
"line_max": 110,
"alpha_frac": 0.6336917563,
"autogenerated": false,
"ratio": 3.478802992518703,
"con... |
__author__ = "wdolowicz"
from model.contact import Contact
import random
import string
import os.path
import jsonpickle
import getopt
import sys
try:
opts, args = getopt.getopt(sys.argv[1:], "n:f:", ["number of contacts", "file"])
except getopt.GetoptError as err:
getopt.usage()
sys.exit(2)
n = 1
f = "da... | {
"repo_name": "wdolowicz/python_training",
"path": "generator/contact.py",
"copies": "1",
"size": "1575",
"license": "apache-2.0",
"hash": 8523619532074806000,
"line_mean": 33.2391304348,
"line_max": 122,
"alpha_frac": 0.6241269841,
"autogenerated": false,
"ratio": 3.4539473684210527,
"config_t... |
__author__ = 'wdolowicz'
from model.contact import Contact
import re
class ContactHelper:
def __init__(self, app):
self.app = app
def open_home_page(self):
wd = self.app.wd
if not (wd.current_url.endswith("/addressbook") and len(wd.find_elements_by_xpath("//div[@id='content']"
... | {
"repo_name": "wdolowicz/python_training",
"path": "fixture/contact.py",
"copies": "1",
"size": "8823",
"license": "apache-2.0",
"hash": 1753069518601436000,
"line_mean": 40.0372093023,
"line_max": 129,
"alpha_frac": 0.5857418112,
"autogenerated": false,
"ratio": 3.5519323671497585,
"config_tes... |
__author__ = 'wdolowicz'
from model.group import Group
class GroupHelper:
def __init__(self, app):
self.app = app
def open_groups_page(self):
wd = self.app.wd
if not (wd.current_url.endswith("/group.php") and len(wd.find_elements_by_name("new")) > 0):
wd.find_element_by_l... | {
"repo_name": "wdolowicz/python_training",
"path": "fixture/group.py",
"copies": "1",
"size": "4082",
"license": "apache-2.0",
"hash": -5015351303287397000,
"line_mean": 31.1417322835,
"line_max": 100,
"alpha_frac": 0.5864772171,
"autogenerated": false,
"ratio": 3.4505494505494507,
"config_test... |
__author__ = 'wdolowicz'
from pytest_bdd import given, when, then
from model.group import Group
import random
from random import randrange
@given('a group list')
def group_list(db):
return db.get_group_list()
@given('a group with <name>, <header> and <footer>')
def new_group(name, header, footer):
return G... | {
"repo_name": "wdolowicz/python_training",
"path": "bdd/group_steps.py",
"copies": "1",
"size": "2946",
"license": "apache-2.0",
"hash": -1787118876565208300,
"line_mean": 32.4772727273,
"line_max": 113,
"alpha_frac": 0.695179905,
"autogenerated": false,
"ratio": 3.0783699059561127,
"config_tes... |
__author__ = 'wdolowicz'
from sys import maxsize
class Contact:
def __init__(self, firstname=None, initials=None, lastname=None, nick=None, title=None, company=None, address=None,
home=None, mobile=None, work=None, secondary=None, email=None, email2=None, email3=None, homepage=None,
... | {
"repo_name": "wdolowicz/python_training",
"path": "model/contact.py",
"copies": "1",
"size": "1771",
"license": "apache-2.0",
"hash": -7085112514581343000,
"line_mean": 40.1860465116,
"line_max": 127,
"alpha_frac": 0.5454545455,
"autogenerated": false,
"ratio": 3.784188034188034,
"config_test"... |
__author__ = 'wdolowicz'
import json
import os.path
from fixture.application import Application
from fixture.db import DbFixture
from model.group import Group
from model.contact import Contact
class Addressbook:
ROBOT_LIBRARY_SCOPE = 'TEST SUITE'
def __init__(self, config="target.json", browser="firefox"):... | {
"repo_name": "wdolowicz/python_training",
"path": "rf/Addressbook.py",
"copies": "1",
"size": "2776",
"license": "apache-2.0",
"hash": 6552531457664688000,
"line_mean": 39.2463768116,
"line_max": 152,
"alpha_frac": 0.678314121,
"autogenerated": false,
"ratio": 3.5183776932826363,
"config_test"... |
__author__ = 'wdolowicz'
import mysql.connector
from model.group import Group
from model.contact import Contact
class DbFixture:
def __init__(self, host, name, user, password):
self.host = host
self.name = name
self.user = user
self.password = password
self.connection = m... | {
"repo_name": "wdolowicz/python_training",
"path": "fixture/db.py",
"copies": "1",
"size": "1725",
"license": "apache-2.0",
"hash": -5581810620399017000,
"line_mean": 36.5,
"line_max": 115,
"alpha_frac": 0.5837681159,
"autogenerated": false,
"ratio": 4.049295774647887,
"config_test": false,
"... |
__author__ = 'wdolowicz'
import pytest
import json
import os.path
import importlib
import jsonpickle
from fixture.application import Application
from fixture.db import DbFixture
from fixture.orm import ORMFixture
fixture = None
target = None
def load_config(file):
global target
if target is None:
con... | {
"repo_name": "wdolowicz/python_training",
"path": "conftest.py",
"copies": "1",
"size": "2901",
"license": "apache-2.0",
"hash": 4612885362494918000,
"line_mean": 30.1935483871,
"line_max": 100,
"alpha_frac": 0.6680455016,
"autogenerated": false,
"ratio": 3.6861499364675985,
"config_test": tru... |
__author__ = "wdolowicz"
from model.group import Group
import random
import string
import os.path
import jsonpickle
import getopt
import sys
try:
opts, args = getopt.getopt(sys.argv[1:], "n:f:", ["number of groups", "file"])
except getopt.GetoptError as err:
getopt.usage()
sys.exit(2)
n = 5
f = "data/g... | {
"repo_name": "wdolowicz/python_training",
"path": "generator/group.py",
"copies": "1",
"size": "1033",
"license": "apache-2.0",
"hash": 323163493080390200,
"line_mean": 23.023255814,
"line_max": 113,
"alpha_frac": 0.6379477251,
"autogenerated": false,
"ratio": 3.168711656441718,
"config_test":... |
__author__ = 'wdolowicz'
from pytest_bdd import given, when, then
from model.contact import Contact
import random
@given('a contact list')
def contact_list(db):
return db.get_contact_list()
@given('a contact with <firstname>, <middlename>, <lastname>, <nick>, <address>, <home>, <mobile>, <work>, <secondary>, ... | {
"repo_name": "wdolowicz/python_training",
"path": "bdd/contact_steps.py",
"copies": "1",
"size": "3875",
"license": "apache-2.0",
"hash": -1532571074572697000,
"line_mean": 42.5393258427,
"line_max": 144,
"alpha_frac": 0.7163870968,
"autogenerated": false,
"ratio": 3.3902012248468942,
"config_... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD10A2'
platform = 'Terra'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P8D'
documentation = 'http://nsidc.org/data/docs/daac/mod10_modis_snow/version_5/mod10a2_local_att... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD10A2.py",
"copies": "1",
"size": "2647",
"license": "mit",
"hash": 1258059827652820200,
"line_mean": 34.3066666667,
"line_max": 183,
"alpha_frac": 0.5583679637,
"autogenerated": false,
"ratio": 3.5530201342281877,
"... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MCD15A2'
platform = 'Combined'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P8D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOTA/MCD15A... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MCD15A2.py",
"copies": "1",
"size": "6067",
"license": "mit",
"hash": -1220009250963528000,
"line_mean": 47.7459016393,
"line_max": 239,
"alpha_frac": 0.5904071205,
"autogenerated": false,
"ratio": 3.5856973995271866,
... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MCD15A3'
platform = 'Combined'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P4D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOTA/MCD15A... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MCD15A3.py",
"copies": "1",
"size": "5959",
"license": "mit",
"hash": 2914313788801188400,
"line_mean": 46.8606557377,
"line_max": 239,
"alpha_frac": 0.5861721765,
"autogenerated": false,
"ratio": 3.631322364411944,
"c... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MCD43A3'
platform = 'Terra'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P16D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOTA/MCD43A3.... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MCD43A3.py",
"copies": "1",
"size": "3767",
"license": "mit",
"hash": -3066571853480879000,
"line_mean": 47.5921052632,
"line_max": 398,
"alpha_frac": 0.6190602602,
"autogenerated": false,
"ratio": 3.452795600366636,
"... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MCD45A1'
platform = 'Combined'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P1M'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOTA/MCD45A... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MCD45A1.py",
"copies": "1",
"size": "2128",
"license": "mit",
"hash": 5839431112212726000,
"line_mean": 38.1886792453,
"line_max": 233,
"alpha_frac": 0.6118421053,
"autogenerated": false,
"ratio": 3.529021558872305,
"c... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD09A1'
platform = 'Terra'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P8D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLT/MOD09A1.0... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD09A1.py",
"copies": "1",
"size": "14097",
"license": "mit",
"hash": 7579480170353634000,
"line_mean": 57.4894514768,
"line_max": 359,
"alpha_frac": 0.6119741789,
"autogenerated": false,
"ratio": 3.6435771517187905,
... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD09Q1'
platform = 'Terra'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P8D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLT/MOD09Q1.0... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD09Q1.py",
"copies": "1",
"size": "5166",
"license": "mit",
"hash": 8203197027108182000,
"line_mean": 50.202020202,
"line_max": 359,
"alpha_frac": 0.6149825784,
"autogenerated": false,
"ratio": 3.6252631578947367,
"c... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD10A1'
platform = 'Terra'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P1D'
documentation = 'http://nsidc.org/data/docs/daac/mod10_modis_snow/version_5/mod10a... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD10A1.py",
"copies": "1",
"size": "4634",
"license": "mit",
"hash": 130730149472623380,
"line_mean": 36.6333333333,
"line_max": 183,
"alpha_frac": 0.525463962,
"autogenerated": false,
"ratio": 3.68362480127186,
"conf... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD10C1'
platform = 'Terra'
collection = '005'
rastertype = 'CMG'
timeInterval = 'P1D'
host = 'n5eil01u.ecs.nsidc.org'
dir = '/SAN/MOST/MOD10C1.005'
sources =... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD10C1.py",
"copies": "1",
"size": "2130",
"license": "mit",
"hash": -8000637724127560000,
"line_mean": 38.2264150943,
"line_max": 301,
"alpha_frac": 0.6037558685,
"autogenerated": false,
"ratio": 3.435483870967742,
"... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD10C2'
platform = 'Terra'
collection = '005'
rastertype = 'CMG'
timeInterval = 'P8D'
host = 'n5eil01u.ecs.nsidc.org'
dir = '/SAN/MOST/MOD10C2.005'
sources =... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD10C2.py",
"copies": "1",
"size": "2146",
"license": "mit",
"hash": -3582845908113686000,
"line_mean": 38.5283018868,
"line_max": 303,
"alpha_frac": 0.6039142591,
"autogenerated": false,
"ratio": 3.3955696202531644,
... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD10CM'
platform = 'Terra'
collection = '005'
rastertype = 'CMG'
timeInterval = 'P1M'
documentation = 'http://nsidc.org/data/docs/daac/mod10_modis_snow/version_5/mod10cm... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD10CM.py",
"copies": "1",
"size": "1360",
"license": "mit",
"hash": 2731920582242712000,
"line_mean": 28.9545454545,
"line_max": 183,
"alpha_frac": 0.5816176471,
"autogenerated": false,
"ratio": 3.383084577114428,
"c... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD11A1'
platform = 'Terra'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P1D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Dailies_E/MOLT/MOD11A1.00... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD11A1.py",
"copies": "1",
"size": "9690",
"license": "mit",
"hash": -4326112030984704500,
"line_mean": 55.6904761905,
"line_max": 376,
"alpha_frac": 0.6141382869,
"autogenerated": false,
"ratio": 3.592880978865406,
"... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD11A2'
platform = 'Terra'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P8D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLT/MOD11A2.0... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD11A2.py",
"copies": "1",
"size": "15878",
"license": "mit",
"hash": -3419574581359167000,
"line_mean": 59.0769230769,
"line_max": 381,
"alpha_frac": 0.6157576521,
"autogenerated": false,
"ratio": 3.5513307984790874,
... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD11B1'
platform = 'Terra'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P1D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Dailies_D/MOLT/MOD11B1.00... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD11B1.py",
"copies": "1",
"size": "3753",
"license": "mit",
"hash": -3542659382647765500,
"line_mean": 47.4078947368,
"line_max": 380,
"alpha_frac": 0.6168398614,
"autogenerated": false,
"ratio": 3.567490494296578,
"... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD11C1'
platform = 'Terra'
collection = '005'
rastertype = 'CMG'
timeInterval = 'P1D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Dailies_F/MOLT/MOD11C1.005... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD11C1.py",
"copies": "1",
"size": "3740",
"license": "mit",
"hash": 3377860081300556000,
"line_mean": 47.2368421053,
"line_max": 380,
"alpha_frac": 0.6160427807,
"autogenerated": false,
"ratio": 3.565300285986654,
"c... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD11C2'
platform = 'Terra'
collection = '005'
rastertype = 'CMG'
timeInterval = 'P8D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLT/MOD11C2.00... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD11C2.py",
"copies": "1",
"size": "3777",
"license": "mit",
"hash": -6291528259272516000,
"line_mean": 47.7236842105,
"line_max": 382,
"alpha_frac": 0.6163621922,
"autogenerated": false,
"ratio": 3.500463392029657,
"... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD11C3'
platform = 'Terra'
collection = '005'
rastertype = 'CMG'
timeInterval = 'P1M'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLT/MOD11C3.00... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD11C3.py",
"copies": "1",
"size": "3783",
"license": "mit",
"hash": 4211767132211876400,
"line_mean": 47.8026315789,
"line_max": 382,
"alpha_frac": 0.6196140629,
"autogenerated": false,
"ratio": 3.5655042412818094,
"... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD13A1'
platform = 'Terra'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P16D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLT/MOD13A1.... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD13A1.py",
"copies": "1",
"size": "3799",
"license": "mit",
"hash": -6424062763521471000,
"line_mean": 48.0131578947,
"line_max": 373,
"alpha_frac": 0.6191102922,
"autogenerated": false,
"ratio": 3.5537885874649207,
... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD13A2'
platform = 'Terra'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P16D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLT/MOD13A2.... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD13A2.py",
"copies": "1",
"size": "3797",
"license": "mit",
"hash": 7492710203983409000,
"line_mean": 47.9868421053,
"line_max": 372,
"alpha_frac": 0.6183829339,
"autogenerated": false,
"ratio": 3.5519176800748364,
"... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD13A3'
platform = 'Terra'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P1M'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLT/MOD13A3.0... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD13A3.py",
"copies": "1",
"size": "3786",
"license": "mit",
"hash": -5722943486303142000,
"line_mean": 47.8421052632,
"line_max": 371,
"alpha_frac": 0.6204437401,
"autogenerated": false,
"ratio": 3.5886255924170616,
... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD13C1'
platform = 'Terra'
collection = '005'
rastertype = 'CMG'
timeInterval = 'P16D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLT/MOD13C1.0... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD13C1.py",
"copies": "1",
"size": "3820",
"license": "mit",
"hash": 5067393685030482000,
"line_mean": 48.2894736842,
"line_max": 376,
"alpha_frac": 0.6191099476,
"autogenerated": false,
"ratio": 3.5239852398523985,
"... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD13C2'
platform = 'Terra'
collection = '005'
rastertype = 'CMG'
timeInterval = 'P1M'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLT/MOD13C2.00... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD13C2.py",
"copies": "1",
"size": "3809",
"license": "mit",
"hash": -9016039233399398000,
"line_mean": 48.1447368421,
"line_max": 375,
"alpha_frac": 0.6211604096,
"autogenerated": false,
"ratio": 3.5598130841121494,
... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD13Q1'
platform = 'Terra'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P16D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLT/MOD13Q1.... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD13Q1.py",
"copies": "1",
"size": "4589",
"license": "mit",
"hash": -458681958240690100,
"line_mean": 35.9421487603,
"line_max": 183,
"alpha_frac": 0.5142732621,
"autogenerated": false,
"ratio": 3.7127831715210355,
"... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MOD44B'
platform = 'Terra'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P1Y'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLT/MOD44B.005... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MOD44B.py",
"copies": "1",
"size": "4702",
"license": "mit",
"hash": 3831876393388875000,
"line_mean": 45.5151515152,
"line_max": 244,
"alpha_frac": 0.5946405785,
"autogenerated": false,
"ratio": 3.6994492525570415,
"c... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MYD10A1'
platform = 'Aqua'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P1D'
host = 'n5eil01u.ecs.nsidc.org'
dir = '/SAN/MOSA/MYD10A1.005'
sources =... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MYD10A1.py",
"copies": "1",
"size": "2125",
"license": "mit",
"hash": -717880516084184300,
"line_mean": 38.1320754717,
"line_max": 297,
"alpha_frac": 0.6032941176,
"autogenerated": false,
"ratio": 3.4274193548387095,
"... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MYD10A2'
platform = 'Aqua'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P8D'
host = 'n5eil01u.ecs.nsidc.org'
dir = '/SAN/MOSA/MYD10A2.005'
sources =... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MYD10A2.py",
"copies": "1",
"size": "2134",
"license": "mit",
"hash": 8576963338208077000,
"line_mean": 38.3018867925,
"line_max": 299,
"alpha_frac": 0.6026241799,
"autogenerated": false,
"ratio": 3.39268680445151,
"co... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MYD10C2'
platform = 'Aqua'
collection = '005'
rastertype = 'CMG'
timeInterval = 'P8D'
host = 'n5eil01u.ecs.nsidc.org'
dir = '/SAN/MOSA/MYD10C2.005'
sources = ... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MYD10C2.py",
"copies": "1",
"size": "2139",
"license": "mit",
"hash": 1619493453419945000,
"line_mean": 38.3962264151,
"line_max": 302,
"alpha_frac": 0.6026180458,
"autogenerated": false,
"ratio": 3.3632075471698113,
"... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MYD11A1'
platform = 'Aqua'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P1D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Dailies_E/MOLA/MYD11A1.005... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MYD11A1.py",
"copies": "1",
"size": "9660",
"license": "mit",
"hash": 1290076338145763800,
"line_mean": 55.5119047619,
"line_max": 375,
"alpha_frac": 0.6128364389,
"autogenerated": false,
"ratio": 3.567208271787297,
"c... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MYD11A2'
platform = 'Aqua'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P8D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLA/MYD11A2.00... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MYD11A2.py",
"copies": "1",
"size": "9848",
"license": "mit",
"hash": 1556147346743840500,
"line_mean": 56.630952381,
"line_max": 376,
"alpha_frac": 0.6143379366,
"autogenerated": false,
"ratio": 3.507122507122507,
"co... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MYD11B1'
platform = 'Aqua'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P1D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Dailies_D/MOAT/MYD11B1.005... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MYD11B1.py",
"copies": "1",
"size": "3738",
"license": "mit",
"hash": 4570111398203446300,
"line_mean": 47.2105263158,
"line_max": 379,
"alpha_frac": 0.6153023007,
"autogenerated": false,
"ratio": 3.54985754985755,
"co... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MYD11C1'
platform = 'Aqua'
collection = '005'
rastertype = 'CMG'
timeInterval = 'P1D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Dailies_F/MOAT/MYD11C1.005'... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MYD11C1.py",
"copies": "1",
"size": "3725",
"license": "mit",
"hash": -532527515312760770,
"line_mean": 47.0394736842,
"line_max": 379,
"alpha_frac": 0.6144966443,
"autogenerated": false,
"ratio": 3.5476190476190474,
"... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MYD11C2'
platform = 'Aqua'
collection = '005'
rastertype = 'CMG'
timeInterval = 'P8D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLA/MYD11C2.005... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MYD11C2.py",
"copies": "1",
"size": "3762",
"license": "mit",
"hash": 5771964094926752000,
"line_mean": 47.5263157895,
"line_max": 381,
"alpha_frac": 0.6148325359,
"autogenerated": false,
"ratio": 3.4801110083256246,
"... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MYD11C3'
platform = 'Aqua'
collection = '005'
rastertype = 'CMG'
timeInterval = 'P1M'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLA/MYD11C3.005... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MYD11C3.py",
"copies": "1",
"size": "3768",
"license": "mit",
"hash": -4434070676750370300,
"line_mean": 47.6052631579,
"line_max": 381,
"alpha_frac": 0.6180997877,
"autogenerated": false,
"ratio": 3.5446848541862654,
... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MYD13A1'
platform = 'Terra'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P16D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLA/MYD13A1.... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MYD13A1.py",
"copies": "1",
"size": "3789",
"license": "mit",
"hash": 3113504312743943000,
"line_mean": 47.8815789474,
"line_max": 372,
"alpha_frac": 0.6181050409,
"autogenerated": false,
"ratio": 3.524651162790698,
"c... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MYD13A2'
platform = 'Aqua'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P16D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLA/MYD13A2.0... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MYD13A2.py",
"copies": "1",
"size": "3784",
"license": "mit",
"hash": -6530512878792859000,
"line_mean": 47.8157894737,
"line_max": 371,
"alpha_frac": 0.6170718816,
"autogenerated": false,
"ratio": 3.52,
"config_test":... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MYD13A3'
platform = 'Aqua'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P1M'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLA/MYD13A3.00... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MYD13A3.py",
"copies": "1",
"size": "3773",
"license": "mit",
"hash": 7897379505329822000,
"line_mean": 47.6710526316,
"line_max": 370,
"alpha_frac": 0.6191359661,
"autogenerated": false,
"ratio": 3.5560791705937795,
"... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MYD13C1'
platform = 'Aqua'
collection = '005'
rastertype = 'CMG'
timeInterval = 'P16D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLA/MYD13C1.00... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MYD13C1.py",
"copies": "1",
"size": "3807",
"license": "mit",
"hash": -7566050091399935000,
"line_mean": 48.1184210526,
"line_max": 375,
"alpha_frac": 0.6178092987,
"autogenerated": false,
"ratio": 3.492660550458716,
"... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MYD13C2'
platform = 'Aqua'
collection = '005'
rastertype = 'CMG'
timeInterval = 'P1M'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLA/MYD13C2.005... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MYD13C2.py",
"copies": "1",
"size": "3796",
"license": "mit",
"hash": -1704091443957277700,
"line_mean": 47.9736842105,
"line_max": 374,
"alpha_frac": 0.6198630137,
"autogenerated": false,
"ratio": 3.5278810408921935,
... |
__author__ = 'we32zac'
from pyEOM.datasets import Dataset as DatasetAbs
class Dataset(DatasetAbs):
shortname = 'MYD13Q1'
platform = 'Aqua'
collection = '005'
rastertype = 'Tile'
timeInterval = 'P16D'
host = 'http://e4ftl01.cr.usgs.gov'
dir = '/MODIS_Composites/MOLA/MYD13Q1.0... | {
"repo_name": "jonas-eberle/pyEOM",
"path": "pyEOM/datasets/predefined/MODIS/MYD13Q1.py",
"copies": "1",
"size": "6565",
"license": "mit",
"hash": 7416692836086793000,
"line_mean": 51.8278688525,
"line_max": 372,
"alpha_frac": 0.6083777609,
"autogenerated": false,
"ratio": 3.520107238605898,
"c... |
"""
Windows Management Instrumentation (WMI) is Microsoft's answer to
the DMTF's Common Information Model. It allows you to query just
about any conceivable piece of information from any computer which
is running the necessary agent and over which have you the
necessary authority.
Since the COM implementation doesn't... | {
"repo_name": "Adam5Wu/ZWUtils-Python2",
"path": "Lib/wmi.py",
"copies": "1",
"size": "47043",
"license": "bsd-3-clause",
"hash": -7744180054454550000,
"line_mean": 31.6008316008,
"line_max": 158,
"alpha_frac": 0.6495121485,
"autogenerated": false,
"ratio": 3.6861777150916786,
"config_test": fa... |
__author__ = 'weidongxu'
import os
import argparse
def main():
parser = argparse.ArgumentParser()
parser.add_argument('directory', nargs='?', default=os.getcwd())
args = parser.parse_args()
codec_list = [('utf_8_sig', False),
('utf_16', False),
('gb18030', True)]
file_list = col... | {
"repo_name": "weidongxu84/gbk_to_unicode",
"path": "gbk_to_unicode.py",
"copies": "1",
"size": "1687",
"license": "mit",
"hash": -7306061448737034000,
"line_mean": 30.2222222222,
"line_max": 96,
"alpha_frac": 0.5839762611,
"autogenerated": false,
"ratio": 3.6710239651416123,
"config_test": fal... |
from __future__ import division
from settings import *
from os import listdir
from main import *
from time import strftime
from sk import *
from de import *
from cart_de import *
from rf_de import *
class Learner(object):
def __init__(i, learner, dataname, train, tune, test):
i.learner = learner
i.dataname ... | {
"repo_name": "ST-Data-Mining/crater",
"path": "wei/start2.py",
"copies": "1",
"size": "4489",
"license": "mit",
"hash": -3959544464716251000,
"line_mean": 25.1046511628,
"line_max": 102,
"alpha_frac": 0.5696146135,
"autogenerated": false,
"ratio": 3.0208613728129206,
"config_test": false,
"h... |
__author__ = 'weiheng su'
"""Date: 9/18/15 goal: encrypt&decrypt a file"""
import os
from Crypto.PublicKey import RSA
from Crypto import Random
from Crypto.Hash import SHA256
from Crypto.Hash import MD5
from Crypto.Cipher import AES
def secret_string(string, public_key):
string = string.encode('utf-8')
retur... | {
"repo_name": "weihengSu/simpleSecurityPython",
"path": "encryptDecrypt.py",
"copies": "1",
"size": "1269",
"license": "mit",
"hash": -928255425781789000,
"line_mean": 26,
"line_max": 71,
"alpha_frac": 0.6509062254,
"autogenerated": false,
"ratio": 3.180451127819549,
"config_test": false,
"ha... |
__author__ = 'weiheng su'
import sys
class Graph(object):
def __init__(self, dict= {}):
self.data = dict
def get_adjlist(self, node):
if node in self.data.keys():
nodes = self.data[node]
return nodes
else:
return None
def is_adjacent(self, node1, ... | {
"repo_name": "weihengSu/testing",
"path": "graph.py",
"copies": "1",
"size": "2419",
"license": "mit",
"hash": 484267725479403700,
"line_mean": 27.4588235294,
"line_max": 67,
"alpha_frac": 0.477056635,
"autogenerated": false,
"ratio": 4.038397328881469,
"config_test": false,
"has_no_keywords... |
__author__ = 'weiheng su'
import unittest
from graph import Graph
class TestGraph(unittest.TestCase):
def setUp(self):
self.g1 = Graph( {'A':['B','D'], 'B': ['A','D','C'], 'C': ['B'], 'D':['A','B'],'E':[]})
"""test-id: G1"""
def test_get_adjlist_g1(self):
assert self.g1.get_adjlist('A') =... | {
"repo_name": "weihengSu/testing",
"path": "test_graph.py",
"copies": "1",
"size": "3477",
"license": "mit",
"hash": 7054033387759535000,
"line_mean": 54.1904761905,
"line_max": 136,
"alpha_frac": 0.616623526,
"autogenerated": false,
"ratio": 3.0715547703180213,
"config_test": true,
"has_no_k... |
__author__ = 'weiheng su'
from graph import Graph
import sys
def is_complete(object):
if isinstance(object, Graph):
if object.num_nodes()==1 or object.num_nodes()==0:
return True
else:
a = 0
b = 0
for i in object:
for x in list(object... | {
"repo_name": "weihengSu/testing",
"path": "graph_functions.py",
"copies": "1",
"size": "1087",
"license": "mit",
"hash": 8084321815475681000,
"line_mean": 26.8717948718,
"line_max": 58,
"alpha_frac": 0.4958601656,
"autogenerated": false,
"ratio": 3.9963235294117645,
"config_test": false,
"ha... |
__author__ = 'wei'
# -*- coding: utf-8 -*-
#增加了IOS的离线消息推送,IOS不支持IGtNotyPopLoadTemplate模板
#更新时间为2013年12月02日 VERSION: 3.0.0.0
#
from igt_push import *
from igetui.template.igt_base_template import *
from igetui.template.igt_transmission_template import *
from igetui.template.igt_link_template import *
from igetui.templa... | {
"repo_name": "wh1100717/tornado_skeleton",
"path": "sdk/demo.py",
"copies": "1",
"size": "5155",
"license": "mit",
"hash": -8652950287804378000,
"line_mean": 28.7716049383,
"line_max": 103,
"alpha_frac": 0.7008086253,
"autogenerated": false,
"ratio": 2.828739002932551,
"config_test": false,
... |
__author__ = 'wei'
from protobuf import *
import igt_base_template
class LinkTemplate(igt_base_template.BaseTemplate):
def __init__(self):
igt_base_template.BaseTemplate.__init__(self)
self.text = ""
self.title = ""
self.logo = ""
self.logoURL = ""
self.url = ""
... | {
"repo_name": "wh1100717/tornado_skeleton",
"path": "sdk/igetui/template/igt_link_template.py",
"copies": "2",
"size": "1873",
"license": "mit",
"hash": -4691768563387459000,
"line_mean": 30.7457627119,
"line_max": 93,
"alpha_frac": 0.6310731447,
"autogenerated": false,
"ratio": 3.567619047619047... |
__author__ = 'wei'
from protobuf import *
import igt_base_template
class NotificationTemplate(igt_base_template.BaseTemplate):
def __init__(self):
igt_base_template.BaseTemplate.__init__(self)
self.text = ""
self.title = ""
self.logo = ""
self.logoURL = ""
self.tran... | {
"repo_name": "FXuZ/colock-server",
"path": "message/igetui/template/igt_notification_template.py",
"copies": "2",
"size": "2261",
"license": "apache-2.0",
"hash": -8671856814607336000,
"line_mean": 31.768115942,
"line_max": 93,
"alpha_frac": 0.6342326404,
"autogenerated": false,
"ratio": 3.6176,... |
__author__ = 'wei'
from protobuf import *
import igt_base_template
class TransmissionTemplate(igt_base_template.BaseTemplate):
def __init__(self):
igt_base_template.BaseTemplate.__init__(self)
self.transmissionType = 0
self.transmissionContent = ""
self.pushType = "TransmissionMsg... | {
"repo_name": "wh1100717/tornado_skeleton",
"path": "sdk/igetui/template/igt_transmission_template.py",
"copies": "2",
"size": "1370",
"license": "mit",
"hash": 7201113849767672000,
"line_mean": 28.7826086957,
"line_max": 80,
"alpha_frac": 0.6430656934,
"autogenerated": false,
"ratio": 3.58638743... |
__author__ = 'wei'
from protobuf import *
class BaseTemplate:
def __init__(self):
self.appKey = ""
self.appId = ""
self.pushInfo = None
def getTransparent(self):
transparent = gt_req_pb2.Transparent()
transparent.id = ""
transparent.action = "pushmessage"
... | {
"repo_name": "wh1100717/tornado_skeleton",
"path": "sdk/igetui/template/igt_base_template.py",
"copies": "1",
"size": "1551",
"license": "mit",
"hash": 524209850677229800,
"line_mean": 30.02,
"line_max": 102,
"alpha_frac": 0.6125080593,
"autogenerated": false,
"ratio": 3.9566326530612246,
"con... |
__author__ = 'wei'
import hashlib
import time
import urllib, urllib2, json
import base64
class IGeTui:
def __init__(self, host, appKey, masterSecret):
self.host = host
self.appKey = appKey
self.masterSecret = masterSecret
def connect(self):
timenow = self.getCurrentTime()
... | {
"repo_name": "wh1100717/tornado_skeleton",
"path": "sdk/igt_push.py",
"copies": "1",
"size": "4778",
"license": "mit",
"hash": -192505179333195780,
"line_mean": 34.1323529412,
"line_max": 83,
"alpha_frac": 0.6107157807,
"autogenerated": false,
"ratio": 4.0698466780238505,
"config_test": false,... |
__author__ = 'wei'
from igetui.template.igt_base_template import *
from igetui.utils.AppConditions import *
class IGtMessage:
def __init__(self):
self.isOffline = False
self.offlineExpireTime = 0
self.data = BaseTemplate()
self.pushNetWorkType = 0
self.priority=0
... | {
"repo_name": "cainli/appLog",
"path": "MobileLogMgr/igetui/igt_message.py",
"copies": "1",
"size": "2589",
"license": "apache-2.0",
"hash": -4319222804786598400,
"line_mean": 18.3858267717,
"line_max": 57,
"alpha_frac": 0.5766705292,
"autogenerated": false,
"ratio": 4.0453125,
"config_test": f... |
__author__ = 'wei'
#from igetui.template.igt_base_template import *
#from igetui.utils.AppConditions import *
from .template.igt_base_template import *
from .utils.AppConditions import *
class IGtMessage:
def __init__(self):
self.isOffline = False
self.offlineExpireTime = 0
self... | {
"repo_name": "jerryjobs/thirdpartPushSystem",
"path": "push/getui/igetui/igt_message.py",
"copies": "1",
"size": "2672",
"license": "apache-2.0",
"hash": 7028029892880614000,
"line_mean": 18.5538461538,
"line_max": 57,
"alpha_frac": 0.5815868263,
"autogenerated": false,
"ratio": 4.01201201201201... |
__author__ = 'wei'
from ...protobuf import gt_req_pb2
from ...protobuf.gt_req_pb2 import *
import igt_base_template
class TransmissionTemplate(igt_base_template.BaseTemplate):
def __init__(self):
igt_base_template.BaseTemplate.__init__(self)
self.transmissionType = 0
self.trans... | {
"repo_name": "jerryjobs/thirdpartPushSystem",
"path": "push/getui/igetui/template/igt_transmission_template.py",
"copies": "1",
"size": "1464",
"license": "apache-2.0",
"hash": 4101295298862363600,
"line_mean": 29.1489361702,
"line_max": 80,
"alpha_frac": 0.625,
"autogenerated": false,
"ratio": ... |
__author__ = 'wei'
from ..protobuf import *
from . import igt_base_template
class LinkTemplate(igt_base_template.BaseTemplate):
def __init__(self):
igt_base_template.BaseTemplate.__init__(self)
self.text = ""
self.title = ""
self.logo = ""
self.logoURL = ""
... | {
"repo_name": "alphapigger/igetui",
"path": "igetui/template/igt_link_template.py",
"copies": "1",
"size": "1943",
"license": "mit",
"hash": 9093821936592772000,
"line_mean": 30.3833333333,
"line_max": 93,
"alpha_frac": 0.6103962944,
"autogenerated": false,
"ratio": 3.67296786389414,
"config_te... |
__author__ = 'wei'
from ..protobuf import *
from . import igt_base_template
class NotificationTemplate(igt_base_template.BaseTemplate):
def __init__(self):
igt_base_template.BaseTemplate.__init__(self)
self.text = ""
self.title = ""
self.logo = ""
self.logoURL ... | {
"repo_name": "alphapigger/igetui",
"path": "igetui/template/igt_notification_template.py",
"copies": "1",
"size": "2341",
"license": "mit",
"hash": 8792892278314713000,
"line_mean": 31.4428571429,
"line_max": 93,
"alpha_frac": 0.6142674071,
"autogenerated": false,
"ratio": 3.7217806041335453,
... |
__author__ = 'wei'
from ..protobuf import *
from . import igt_base_template
class TransmissionTemplate(igt_base_template.BaseTemplate):
def __init__(self):
igt_base_template.BaseTemplate.__init__(self)
self.transmissionType = 0
self.transmissionContent = ""
self.pushTyp... | {
"repo_name": "alphapigger/igetui",
"path": "igetui/template/igt_transmission_template.py",
"copies": "1",
"size": "1423",
"license": "mit",
"hash": 3132670890047301600,
"line_mean": 28.9347826087,
"line_max": 80,
"alpha_frac": 0.6219255095,
"autogenerated": false,
"ratio": 3.696103896103896,
"... |
__author__ = 'wei'
from protobuf import *
import igt_base_template
class LinkTemplate(igt_base_template.BaseTemplate):
def __init__(self):
igt_base_template.BaseTemplate.__init__(self)
self.text = ""
self.title = ""
self.logo = ""
self.logoURL = ""
self.... | {
"repo_name": "jerryjobs/thirdpartPushSystem",
"path": "push/getui/igetui/template/igt_link_template.py",
"copies": "2",
"size": "1932",
"license": "apache-2.0",
"hash": 4032306182925459000,
"line_mean": 30.7457627119,
"line_max": 93,
"alpha_frac": 0.6118012422,
"autogenerated": false,
"ratio": 3... |
__author__ = 'wei'
from protobuf import *
import igt_base_template
class NotificationTemplate(igt_base_template.BaseTemplate):
def __init__(self):
igt_base_template.BaseTemplate.__init__(self)
self.text = ""
self.title = ""
self.logo = ""
self.logoURL = ""
... | {
"repo_name": "cainli/appLog",
"path": "MobileLogMgr/igetui/template/igt_notification_template.py",
"copies": "2",
"size": "2330",
"license": "apache-2.0",
"hash": 4884829535579939000,
"line_mean": 31.768115942,
"line_max": 93,
"alpha_frac": 0.6154506438,
"autogenerated": false,
"ratio": 3.722044... |
__author__ = 'wei'
from protobuf import *
import igt_base_template
class TransmissionTemplate(igt_base_template.BaseTemplate):
def __init__(self):
igt_base_template.BaseTemplate.__init__(self)
self.transmissionType = 0
self.transmissionContent = ""
self.pushType = "Tran... | {
"repo_name": "cainli/appLog",
"path": "MobileLogMgr/igetui/template/igt_transmission_template.py",
"copies": "1",
"size": "1414",
"license": "apache-2.0",
"hash": 2151673994990739200,
"line_mean": 28.7391304348,
"line_max": 80,
"alpha_frac": 0.6230551627,
"autogenerated": false,
"ratio": 3.70157... |
__author__ = 'wei'
from template.igt_base_template import *
class IGtMessage:
def __init__(self):
self.isOffline = False
self.offlineExpireTime = 0
self.data = BaseTemplate()
self.pushNetWorkType = 0
self.priority=0
def isOffline(self):
ret... | {
"repo_name": "alphapigger/igetui",
"path": "igetui/igt_message.py",
"copies": "1",
"size": "2366",
"license": "mit",
"hash": 1439647642710284000,
"line_mean": 17.7166666667,
"line_max": 57,
"alpha_frac": 0.5667793745,
"autogenerated": false,
"ratio": 4.016977928692699,
"config_test": false,
... |
import numpy
from sklearn.neighbors import KDTree
import matplotlib
# matplotlib.use('Agg')
import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib.colors import LogNorm
import pyemma
'''
Converting the 3D-Euler angles (in global 'zyz' rotation) into viewing directions
Having checked wi... | {
"repo_name": "stephenliu1989/HK_DataMiner",
"path": "hkdataminer/template_matching/Select_angle/draw_heat_graph.py",
"copies": "1",
"size": "3835",
"license": "apache-2.0",
"hash": -7923487814890940000,
"line_mean": 29.1788617886,
"line_max": 164,
"alpha_frac": 0.6146023468,
"autogenerated": false... |
__author__ = 'Wei Xie'
__email__ = 'linegroup3@gmail.com'
__affiliation__ = 'Living Analytics Research Centre, Singapore Management University'
__website__ = 'http://mysmu.edu/phdis2012/wei.xie.2012'
from string import punctuation
import re
import twokenize
import stop_words
import stream
_PUN_PATTERN = re.compile(... | {
"repo_name": "linegroup/topicsketch",
"path": "topicsketch/preprocessor.py",
"copies": "1",
"size": "1129",
"license": "apache-2.0",
"hash": 8721542743921474000,
"line_mean": 23.0212765957,
"line_max": 139,
"alpha_frac": 0.6031886625,
"autogenerated": false,
"ratio": 3.5171339563862927,
"confi... |
import sys
import warnings
import numpy as np
from scipy import stats, linalg
from sklearn.covariance import EmpiricalCovariance
from sklearn.datasets.samples_generator import make_spd_matrix
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.metrics.cluster import adjusted_rand_score
from s... | {
"repo_name": "ClimbsRocks/scikit-learn",
"path": "sklearn/mixture/tests/test_gaussian_mixture.py",
"copies": "1",
"size": "37214",
"license": "bsd-3-clause",
"hash": 8198940215600603000,
"line_mean": 39.7601314348,
"line_max": 79,
"alpha_frac": 0.5877357984,
"autogenerated": false,
"ratio": 3.51... |
import copy
import numpy as np
from scipy.special import gammaln
import pytest
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.mixtur... | {
"repo_name": "chrsrds/scikit-learn",
"path": "sklearn/mixture/tests/test_bayesian_mixture.py",
"copies": "1",
"size": "20578",
"license": "bsd-3-clause",
"hash": -5759117111727566000,
"line_mean": 41.1680327869,
"line_max": 79,
"alpha_frac": 0.6173583439,
"autogenerated": false,
"ratio": 3.60069... |
import sys
import warnings
import numpy as np
from scipy import stats, linalg
from sklearn.covariance import EmpiricalCovariance
from sklearn.datasets.samples_generator import make_spd_matrix
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.metrics.cluster import adjusted_rand_score
from s... | {
"repo_name": "BiaDarkia/scikit-learn",
"path": "sklearn/mixture/tests/test_gaussian_mixture.py",
"copies": "3",
"size": "40201",
"license": "bsd-3-clause",
"hash": 3085825709756577300,
"line_mean": 39.8962360122,
"line_max": 79,
"alpha_frac": 0.5847118231,
"autogenerated": false,
"ratio": 3.5273... |
__author__ = "Wei Zhen Teoh"
# This is a tensorflow model of the 16-layer convolutional neural network used
# by the VGG team in the ILSVRC-2014 competition
# I wrote up this model with strong reference to Davi Frossard's post on
# https://www.cs.toronto.edu/~frossard/post/vgg16/
import matplotlib.pyplot as plt
from... | {
"repo_name": "wezteoh/vgg16_tf",
"path": "vgg16_tf.py",
"copies": "1",
"size": "5033",
"license": "apache-2.0",
"hash": 8196763951275455000,
"line_mean": 26.2054054054,
"line_max": 101,
"alpha_frac": 0.6367971389,
"autogenerated": false,
"ratio": 2.2950296397628818,
"config_test": false,
"ha... |
__author__ = 'wektor'
import inspect
class MiddlewareImplementationBase(object):
pass
class MiddlewareCore(object):
def __init__(self):
#_middleware
self.middleware_classes = []
self._middleware = []
def _biuld_middleware(self, mo):
o = None
if isinstance(mo, (lis... | {
"repo_name": "AYAtechnologies/Kasaya-esb",
"path": "kasaya/core/middleware/core.py",
"copies": "1",
"size": "2417",
"license": "bsd-2-clause",
"hash": -3317664772939313700,
"line_mean": 31.6756756757,
"line_max": 86,
"alpha_frac": 0.5291683906,
"autogenerated": false,
"ratio": 4.621414913957935,... |
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