text stringlengths 0 1.05M | meta dict |
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from ex.common import *
from common import *
import ex.pp.mr as mr
class Reducer(mr.BaseReducer):
'''convert a pickle file into a Matlab data file. the pickle
should contain just one dict that is acceptable for
scipy.io.savemat().
'''
def __init__(self, output_dest):
mr.BaseReducer.__init... | {
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from ex.common import *
from common import *
import pyfits as pf
def HDU2Mats(hdu):
'''process a single hdu.
'''
result = {}
# header
header = {}
header_comment = {}
cl = hdu.header.ascardlist()
for card in cl:
header[card.key] = card.value
header_comment[card.key] = c... | {
"repo_name": "excelly/xpy-ml",
"path": "ex/ioo/FITS.py",
"copies": "1",
"size": "1932",
"license": "apache-2.0",
"hash": -6918571935179626000,
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"alpha_frac": 0.5408902692,
"autogenerated": false,
"ratio": 3.577777777777778,
"config_test": false,
"ha... |
from ex.common import *
from common import *
pickle_proto=2
type_dict={"list":1, "array":2}
class VectorOutputStream:
'''Output a sequence of vectors to a pickle file
'''
def __init__(self, output_stream, name="", type="array"):
'''Constructor.
output_stream: the opened pickle fi... | {
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"path": "ex/ioo/VectorStream.py",
"copies": "1",
"size": "3703",
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from ex.common import *
from ex.io.common import *
import ex.pp.mr as mr
import ex.array as ea
from ex.geo.kdtree import KDTree
import sdss_info as sinfo
import base
class Mapper(mr.BaseMapper):
'''prepare the data so they can used by the reducer for
processing. this module usually takes in results from compa... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/uw_data/mr_prepare_data.py",
"copies": "1",
"size": "3755",
"license": "apache-2.0",
"hash": -6386484010091251000,
"line_mean": 32.2300884956,
"line_max": 78,
"alpha_frac": 0.565379494,
"autogenerated": false,
"ratio": 3.4608294930875574,
"config_te... |
from ex.common import *
from ex.ioo.common import *
from ex.pp.common import *
# NOTE: one copy of mapper/reducer will be held in each process, so
# the data replication is lower than using multiprocessing.Pool. But
# here the data within the mapper/reducer should be readonly.
_mapper = None
_reducer = None
def _init... | {
"repo_name": "excelly/xpy-ml",
"path": "ex/pp/mr.py",
"copies": "1",
"size": "7956",
"license": "apache-2.0",
"hash": -677553412137480200,
"line_mean": 30.078125,
"line_max": 97,
"alpha_frac": 0.5977878331,
"autogenerated": false,
"ratio": 4.026315789473684,
"config_test": false,
"has_no_key... |
from ex.common import *
from ex.ioo import *
import ex.geo.kdtree as kdtree
import ex.ml.util as emu
import ex.array as ea
from ex.plott import *
from random import random as rand
import networkx as nx
import sdss.detector as detector
def GetClusters(nNodes, edges, size_thresh=3):
'''get clusters based on the ne... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/demo_particle.py",
"copies": "1",
"size": "4803",
"license": "apache-2.0",
"hash": 2535437691596833000,
"line_mean": 31.02,
"line_max": 310,
"alpha_frac": 0.5887986675,
"autogenerated": false,
"ratio": 3.3377345378735233,
"config_test": false,
"ha... |
from ex.common import *
import gzip
import bz2
import glob
import cPickle as pickle
import struct
import socket
import sqlite3 as sql
import scipy.io as sio
pickle_proto=2
def ExpandWildcard(pattern):
'''expand a file pattern
'''
files=glob.glob(os.path.expanduser(pattern));
log.debug('{0} files expa... | {
"repo_name": "excelly/xpy-ml",
"path": "ex/ioo/common.py",
"copies": "1",
"size": "7588",
"license": "apache-2.0",
"hash": -8695480945793738000,
"line_mean": 23.7973856209,
"line_max": 102,
"alpha_frac": 0.5929098577,
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"ratio": 3.7434632461766157,
"config_test": false,
... |
from .execjs import execjs, has_mini_racer
import os
from distutils.dir_util import copy_tree
class RJSException(Exception):
pass
def optimize(conf=None, working_directory=None, **kwargs):
if conf is None:
conf = kwargs
if working_directory is None:
working_directory = "."
if 'dir' i... | {
"repo_name": "wq/python-requirejs",
"path": "requirejs/__init__.py",
"copies": "1",
"size": "1524",
"license": "mit",
"hash": -353867283631397600,
"line_mean": 27.7547169811,
"line_max": 73,
"alpha_frac": 0.5564304462,
"autogenerated": false,
"ratio": 3.762962962962963,
"config_test": false,
... |
from execnet.gateway_bootstrap import HostNotFound
import sys
try:
bytes
except NameError:
bytes = str
class SocketIO:
def __init__(self, sock, execmodel):
self.sock = sock
self.execmodel = execmodel
socket = execmodel.socket
try:
# IPTOS_LOWDELAY
s... | {
"repo_name": "RonnyPfannschmidt/execnet-test",
"path": "execnet/gateway_socket.py",
"copies": "4",
"size": "2575",
"license": "mit",
"hash": 1919735025819831800,
"line_mean": 26.3936170213,
"line_max": 67,
"alpha_frac": 0.5976699029,
"autogenerated": false,
"ratio": 3.843283582089552,
"config_... |
from ...executables import Assembler
from ...descriptors import Link
from ..misc import AutoUpstreams, is_runnable
ORDER = []
class AlwaysReady(Assembler):
def run(self):
self.results.done = True
ORDER.append(self.__class__)
class DependsOnAlwaysReady(AlwaysReady):
alwaysready_done = Link('... | {
"repo_name": "tkf/compapp",
"path": "src/compapp/plugins/tests/test_autoupstreams.py",
"copies": "1",
"size": "1038",
"license": "bsd-2-clause",
"hash": 1790562530704138500,
"line_mean": 19.76,
"line_max": 57,
"alpha_frac": 0.6782273603,
"autogenerated": false,
"ratio": 3.4257425742574257,
"co... |
from execute.models import Modindex
import urllib2
import re
import time
req_header = {'User-Agent':'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11',
'Accept':'text/html;q=0.9,*/*;q=0.8',
'Accept-Charset':'ISO-8859-1,utf-8;q=0.7,*;q=0.3',
'Connection':... | {
"repo_name": "yueyongyue/saltshaker",
"path": "shaker/salt_module_crawler.py",
"copies": "1",
"size": "2022",
"license": "apache-2.0",
"hash": 371529857597329800,
"line_mean": 35.1071428571,
"line_max": 131,
"alpha_frac": 0.5791295747,
"autogenerated": false,
"ratio": 3.4742268041237114,
"conf... |
from execute.models import Modindex
import urllib
import urllib2
import re
import time
req_header = {'User-Agent':'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11',
'Accept':'text/html;q=0.9,*/*;q=0.8',
'Accept-Charset':'ISO-8859-1,utf-8;q=0.7,*;q=0.3',
... | {
"repo_name": "yueyongyue/saltshaker",
"path": "shaker/tests.py",
"copies": "1",
"size": "2345",
"license": "apache-2.0",
"hash": 8260262316644638000,
"line_mean": 33,
"line_max": 131,
"alpha_frac": 0.5918976546,
"autogenerated": false,
"ratio": 3.3452211126961484,
"config_test": false,
"has_... |
from execution_trace.record import record
@record(10) # 1
def f(): # 2
"""Fn with a for.""" # 3
x = 3 # 4
s = 0 # 5
for i in range(x): # 6
s = s + i # 7
args = ()
expected_trace = [{u'data': [{u'lineno': 3, u'state': {}},
{u'lineno': 4, u'state': {u'x': u'... | {
"repo_name": "mihneadb/python-execution-trace",
"path": "execution_trace/tests/functions/f_for.py",
"copies": "1",
"size": "1060",
"license": "mit",
"hash": 213800648199805300,
"line_mean": 45.0869565217,
"line_max": 94,
"alpha_frac": 0.3226415094,
"autogenerated": false,
"ratio": 2.585365853658... |
from execution_trace.record import record
@record(10) # 1
def f(): # 2
"""Fn with a for containing an if.""" # 3
x = 3 # 4
s = 0 # 5
for i in range(x): # 6
if s > -1: # 7
s += i # 8
args = ()
expected_trace = [{u'data': [{u'lineno': 3, u'state': {}},
... | {
"repo_name": "mihneadb/python-execution-trace",
"path": "execution_trace/tests/functions/f_nested_if_in_for.py",
"copies": "1",
"size": "1381",
"license": "mit",
"hash": -8915341553780169000,
"line_mean": 50.1481481481,
"line_max": 94,
"alpha_frac": 0.3171614772,
"autogenerated": false,
"ratio":... |
from execution_trace.record import record
@record(10) # 1
def f(): # 2
"""Fn with a for+else.""" # 3
x = 3 # 4
s = 0 # 5
for i in range(x): # 6
s = s + i # 7
else: # 8
ok = 1 # 9
args = ()
expected_trace = [{u'data': [{u'lineno': 3, u'state': {}},
... | {
"repo_name": "mihneadb/python-execution-trace",
"path": "execution_trace/tests/functions/f_for_else.py",
"copies": "1",
"size": "1237",
"license": "mit",
"hash": 5990199064281069000,
"line_mean": 43.1785714286,
"line_max": 93,
"alpha_frac": 0.3120452708,
"autogenerated": false,
"ratio": 2.626326... |
from execution_trace.record import record
@record(10) # 1
def f(x): # 2
"""Simple recursive function.""" # 3
if x == 0: # 4
return 1 # 5
return 1 + f(x - 1) # 7
args = (2,)
# First is the innermost call (base case), and so on.
# x = 0, x = 1, x= 2.
expected_trace = [{u'data': [{u'lineno':... | {
"repo_name": "mihneadb/python-execution-trace",
"path": "execution_trace/tests/functions/f_recursive.py",
"copies": "1",
"size": "1085",
"license": "mit",
"hash": -5143186678392441000,
"line_mean": 37.75,
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"ratio": 2.980769230769... |
from executor.Executor import TBTAFExecutor
from common.suite import TBTestSuite
from common.sample_test import TBTAFSampleTest
from common.enums.execution_status_type import TBTAFExecutionStatusType
import time
import sys
total = 0
passed = 0
def testInvalid(method):
global total
global passed
total = to... | {
"repo_name": "S41nz/TBTAF",
"path": "tbtaf/test/executor/utest_executor.py",
"copies": "1",
"size": "3782",
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"hash": -5247136723665769000,
"line_mean": 28.5546875,
"line_max": 95,
"alpha_frac": 0.6829719725,
"autogenerated": false,
"ratio": 3.686159844054581,
"config_te... |
from executor.Executor import TBTAFExecutor
from executor.ExecutionTBTestSuite import ExecutionTBTestSuite
from common.suite import TBTestSuite
from common.sample_test import TBTAFSampleTest
from common.enums.execution_status_type import TBTAFExecutionStatusType
import time
import sys
total = 0
passed = 0
def testInv... | {
"repo_name": "S41nz/TBTAF",
"path": "tbtaf/test/executor/utest_executionTBTestSuite.py",
"copies": "1",
"size": "5682",
"license": "apache-2.0",
"hash": -8913639571777439000,
"line_mean": 29.0687830688,
"line_max": 71,
"alpha_frac": 0.7038014784,
"autogenerated": false,
"ratio": 4.11143270622286... |
from executor import Executor
class GameBatchData:
def __init__(self, timestamp):
self.execution_timestamp = timestamp
self.data = []
self.game_timestamps = []
self.current_game_index = None
self.steps = 0
self.game_data_headers = [
'index', 'timestamp',... | {
"repo_name": "escoboard/dqn",
"path": "src/game_data.py",
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"size": "2235",
"license": "mit",
"hash": -7808374241972180000,
"line_mean": 34.4761904762,
"line_max": 103,
"alpha_frac": 0.5856823266,
"autogenerated": false,
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"config_test": false,
"has_no_... |
from .executor import Executor
import logging
import sys
from .daemonize import daemonize
import signal
import os
from .config import AgentConfig
from optparse import OptionParser
import subprocess
import tornado
import scrapydd
from six.moves import input
from six.moves.urllib.parse import urlparse, urljoi... | {
"repo_name": "kevenli/scrapydd",
"path": "scrapydd/agent.py",
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"size": "4765",
"license": "apache-2.0",
"hash": 155077046800852000,
"line_mean": 30.6506849315,
"line_max": 107,
"alpha_frac": 0.6100734523,
"autogenerated": false,
"ratio": 4.000839630562552,
"config_test": true,
... |
from .executors import ProjectImportExecutor
from .log import event_logger
from .models import Issue
def import_project_issues(sender, instance, **kwargs):
ProjectImportExecutor.execute(instance, updated_fields=None)
def log_issue_save(sender, instance, created=False, **kwargs):
if created or instance.state... | {
"repo_name": "opennode/nodeconductor-assembly-waldur",
"path": "src/waldur_jira/handlers.py",
"copies": "2",
"size": "2054",
"license": "mit",
"hash": 1616194759800083000,
"line_mean": 33.2333333333,
"line_max": 86,
"alpha_frac": 0.6066212269,
"autogenerated": false,
"ratio": 4.191836734693878,
... |
from exercices.solutions.framework.core.base import BasePage
from exercices.solutions.framework.pages.newUserPage import newUserPage
class homePage(BasePage):
url = 'https://forum-testing.herokuapp.com/v1.0/'
_newUserLink = None
_listUserLink = None
_newForumMessageLink = None
_listForumLink = Non... | {
"repo_name": "twiindan/selenium_lessons",
"path": "04_Selenium/exercices/solutions/framework/pages/homePage.py",
"copies": "1",
"size": "1280",
"license": "apache-2.0",
"hash": -53186847410176040,
"line_mean": 35.5714285714,
"line_max": 101,
"alpha_frac": 0.703125,
"autogenerated": false,
"ratio... |
from exercices.solutions.framework.core.base import BasePage
from exercices.solutions.framework.pages.userListPage import userListPage
from selenium.webdriver.support.ui import Select
class newUserPage(BasePage):
url = "https://forum-testing.herokuapp.com/v1.0/users/new"
_usernameTextBox = None
_passwor... | {
"repo_name": "twiindan/selenium_lessons",
"path": "04_Selenium/exercices/framework/pages/newUserPage.py",
"copies": "1",
"size": "1213",
"license": "apache-2.0",
"hash": 7070893827809741000,
"line_mean": 24.2916666667,
"line_max": 112,
"alpha_frac": 0.6760098928,
"autogenerated": false,
"ratio":... |
from ..exercise_converter.helper.ChangeMultiChoiceMarkup import *
from ..exercise_converter.helper.WidgetRenderer import *
from nose.tools import assert_equal
def test_render_sorting_widget_1():
input_values = [MultiChoice(nr=1,
items=[MultiChoiceItem(text='ja', is_correct_answer='... | {
"repo_name": "henrikmidtiby/math-exercises",
"path": "src/test/test_multichoice_widgets.py",
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"ratio": 4.2615384615384615,... |
from ..exercise_converter.helper.ChangeSorterMarkup import *
from ..exercise_converter.helper.WidgetRenderer import *
from nose.tools import assert_equal
def test_render_sorting_widget_1():
input_values = SorterWidget(nr=1,
columna='ColA',
columnb='C... | {
"repo_name": "henrikmidtiby/math-exercises",
"path": "src/test/test_ChangeSorterMarkup.py",
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"autogenerated": false,
"ratio": 3.920489296636086,
... |
from exercise import Exercise, FunctionExercise, ThoughtExperiment, colorify
class q1(Exercise):
_hint = "Try using a third variable."
_solution = """Use a third variable to temporarily store one of the old values. e.g.:
tmp = a
a = b
b = tmp
If you've read lots of Python code, you might have se... | {
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"path": "learn_kaggle/deep_learning/packages/learntools/python/ex2_objects.py",
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"license": "mit",
"hash": -2889568124521588700,
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"line_max": 573,
"alpha_frac": 0.6052758955,
"autog... |
from exercise import Exercise, FunctionExercise, ThoughtExperiment
class q1(FunctionExercise):
# Maybe should give a special message if they've modified the function body
# but they don't have a return statement?
_test_cases = [
(1.000001, 1.00),
(1.23456, 1.23),
]
_hint =... | {
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"path": "learn_kaggle/deep_learning/packages/learntools/python/ex1_functions.py",
"copies": "1",
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"license": "mit",
"hash": -9124444738765497000,
"line_mean": 42.4634146341,
"line_max": 282,
"alpha_frac": 0.702020202,
"auto... |
from Exercise import *
from Metric import *
from exercises_and_metrics_types import *
class Training( object ):
"""Single training consists of a number of exercises and
some arbitrary data, such as duration, start time, end time,
comments etc."""
def __init__( self, exercises_list, training_desc... | {
"repo_name": "noooway/exj",
"path": "Training.py",
"copies": "1",
"size": "1718",
"license": "mit",
"hash": -6119418687568899000,
"line_mean": 34.7916666667,
"line_max": 84,
"alpha_frac": 0.5814901048,
"autogenerated": false,
"ratio": 3.5791666666666666,
"config_test": false,
"has_no_keyword... |
from Exercise import *
class ExerciseRunning( Exercise ):
def __init__( self, name, intervals, distances, times, description_dict ):
super( ExerciseRunning, self ).__init__( description_dict )
self.update( {
'type': type(self).__name__, # type: str
'name': name, # type: str
... | {
"repo_name": "noooway/exj",
"path": "exercises_and_metrics_types/ExerciseRunning.py",
"copies": "1",
"size": "1490",
"license": "mit",
"hash": 2982535570045396500,
"line_mean": 37.2051282051,
"line_max": 78,
"alpha_frac": 0.5516778523,
"autogenerated": false,
"ratio": 4.293948126801153,
"confi... |
from Exercise import *
class ExerciseSetsRepsWeights( Exercise ):
def __init__( self, name, sets, reps, weights, description_dict ):
super( ExerciseSetsRepsWeights, self ).__init__( description_dict )
self.update( {
'type': type(self).__name__, # type: str
'name': name, # ty... | {
"repo_name": "noooway/exj",
"path": "exercises_and_metrics_types/ExerciseSetsRepsWeights.py",
"copies": "1",
"size": "1375",
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"hash": 438059630635303000,
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"line_max": 75,
"alpha_frac": 0.5309090909,
"autogenerated": false,
"ratio": 3.647214854111406,
... |
from exercise import *
class q1a(ThoughtExperiment):
_hint = ('Following its default "BEDMAS"-like rules for order of operations,'
' Python will first divide 3 by 2, then subtract the result from 5.'
' You need to add parentheses to force it to perform the subtraction first.')
_solutio... | {
"repo_name": "bgroveben/python3_machine_learning_projects",
"path": "learn_kaggle/deep_learning/packages/learntools/python/ex3_numbers.py",
"copies": "1",
"size": "1871",
"license": "mit",
"hash": -335091123983751360,
"line_mean": 46.9743589744,
"line_max": 534,
"alpha_frac": 0.6477819348,
"autoge... |
from exercise import *
class q1(Exercise):
_hint = ("Take a look at how we fixed our original expression in the main"
" lesson. We added parentheses around certain subexpressions. "
"The bug in this code is caused by Python evaluating certain operations "
"in the \"wrong\" orde... | {
"repo_name": "bgroveben/python3_machine_learning_projects",
"path": "learn_kaggle/deep_learning/packages/learntools/python/ex4_booleans.py",
"copies": "1",
"size": "4902",
"license": "mit",
"hash": 2165919197426848300,
"line_mean": 34.0142857143,
"line_max": 199,
"alpha_frac": 0.5869033048,
"autog... |
from Exercise.models import *
from django.http import HttpResponse
import simplejson
from datetime import datetime
from django.shortcuts import render_to_response
from django.template import RequestContext
def timesince(dt, default="just now"):
"""
Returns string representing "time since" e.g.
3 days ago, ... | {
"repo_name": "pocon/SUOnet",
"path": "Exercise/views.py",
"copies": "1",
"size": "4201",
"license": "mit",
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"line_mean": 36.8468468468,
"line_max": 142,
"alpha_frac": 0.6491311592,
"autogenerated": false,
"ratio": 3.7710951526032317,
"config_test": false,
"has_no... |
from exercises import warmUpExercise
from exercises.plotData import plot
from exercises.cost import computeCost
from exercises.gradientDescent import doGD
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
print('Printing the Identitiy Matrix')
identitiy_matx = warmUpExercise.get5by5IdentityMatri... | {
"repo_name": "pk-ai/training",
"path": "machine-learning/coursera_exercises/ex1/in_python/ex1.py",
"copies": "1",
"size": "1968",
"license": "mit",
"hash": 6662530321807477000,
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"autogenerated": false,
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from exercises.plotData import plot
from exercises.costFunction import getCost
from exercises.advOptimize import optimize
from exercises.plotDecisionBoundary import plotDB
import pandas as pd
import numpy as np
inp = pd.read_csv('../ex2data1.txt', header=None)
X_org = inp[inp.columns[0:2]].values
# Giving directly th... | {
"repo_name": "pk-ai/training",
"path": "machine-learning/coursera_exercises/ex2/in_python/ex2.py",
"copies": "1",
"size": "1613",
"license": "mit",
"hash": 267524879845616420,
"line_mean": 26.3559322034,
"line_max": 71,
"alpha_frac": 0.714817111,
"autogenerated": false,
"ratio": 2.91155234657039... |
from ex.exception import NotUnderstoodException
from ex.exception import ConnectionLostException
from pydub import AudioSegment
import tempfile
import requests
import json
import os
class Google:
"""
Use the Google Speech-to-Text service
to translate voice input into text
so that it can be parsed by the program.... | {
"repo_name": "anmolks/Jarvis",
"path": "src/google_stt.py",
"copies": "1",
"size": "1430",
"license": "mit",
"hash": 352951801235348350,
"line_mean": 26.5,
"line_max": 71,
"alpha_frac": 0.7230769231,
"autogenerated": false,
"ratio": 3.163716814159292,
"config_test": false,
"has_no_keywords":... |
# from exhibitionist.toolbox import http_handler,JSONRequestHandler,Template
import os
import codecs
import threading
from exhibitionist.toolbox import *
from tornado.template import Template
context = None # lose the warnings
@http_handler(r'/numpy/{{objid}}/(?P<animal>cat|dog)')
class KittenGram(JSONRequestHandle... | {
"repo_name": "kentfrazier/Exhibitionist",
"path": "Examples/kittengram/handlers.py",
"copies": "1",
"size": "1293",
"license": "bsd-3-clause",
"hash": 1325203141856405200,
"line_mean": 39.40625,
"line_max": 80,
"alpha_frac": 0.5730858469,
"autogenerated": false,
"ratio": 3.8482142857142856,
"c... |
from ex import *
from common import *
class NBayes:
'''naive bayes classifier
'''
def __init__(self):
self.n, self.dim, self.options, self.pC, self.meanFC, self.stdFC = [None]*6
def Train(self, X, y, options = None):
self.options = options
prior = GetOptions(
self.... | {
"repo_name": "excelly/xpy-ml",
"path": "ex/ml/nbayes.py",
"copies": "1",
"size": "4772",
"license": "apache-2.0",
"hash": 8200940628954307000,
"line_mean": 27.0705882353,
"line_max": 89,
"alpha_frac": 0.5067057837,
"autogenerated": false,
"ratio": 3.250681198910082,
"config_test": false,
"ha... |
from ex import *
from datetime import datetime
from scipy.signal import cspline1d, cspline1d_eval
from scipy.special import hyp2f1 #hypergeometric function 2F1
O_M = 0.27 # omega matter
O_L = 0.73 # omega lambda
O_K = 1.0 - O_M - O_L # omega curvature
H_0 = 71.0 # Hubble constant (km/... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/utils.py",
"copies": "1",
"size": "7756",
"license": "apache-2.0",
"hash": 6396531796389920000,
"line_mean": 30.024,
"line_max": 158,
"alpha_frac": 0.559566787,
"autogenerated": false,
"ratio": 2.698677800974252,
"config_test": false,
"has_no_keyw... |
from ex import *
from ex.alg.common import svdex
import rpca
#@profile
def DRMF(X, K, e = 0.05, options = None):
'''[ L, S ] = DRMF(M, K, e, options)
direct robust matrix factorization
M: input matrix
K: max rank
e: percentage of outliers
L: Low rank result
S: sparse outliers
'''
i... | {
"repo_name": "excelly/xpy-ml",
"path": "ex/ml/drmf.py",
"copies": "1",
"size": "1988",
"license": "apache-2.0",
"hash": 2912320521109990000,
"line_mean": 22.9518072289,
"line_max": 90,
"alpha_frac": 0.4974849095,
"autogenerated": false,
"ratio": 2.5784695201037615,
"config_test": false,
"has... |
from ex import *
from ex.alg.common import svdex
def RPCA(D, lam = None, tol = 1e-7, maxIter = 500):
'''Yi Ma's robust pca
return (L, SingularValues(L))
'''
m, n = D.shape
maxmn, minmn = (max(m, n), min(m, n))
lam = float(lam) if lam is not None else 1.0
log.info('RPCA for %dx%d matri... | {
"repo_name": "excelly/xpy-ml",
"path": "ex/ml/rpca.py",
"copies": "1",
"size": "2134",
"license": "apache-2.0",
"hash": -7102599699000093000,
"line_mean": 24.7108433735,
"line_max": 65,
"alpha_frac": 0.4765698219,
"autogenerated": false,
"ratio": 2.5017584994138335,
"config_test": false,
"ha... |
from ex import *
from ex.ioo import *
from ex.pp import *
import pyfits as pf
from scipy.signal import cspline1d, cspline1d_eval
import gc, pdb
def usage():
print '''
fetch_make_fits.py working_dir stamp [nproc](1) [run](v_5_6_0)
'''
sys.exit(0)
#resample spectra at single wavelength spectrum defined above
d... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss_iii/fetch_make_fits.py",
"copies": "1",
"size": "9454",
"license": "apache-2.0",
"hash": -8160130351993580000,
"line_mean": 37.587755102,
"line_max": 148,
"alpha_frac": 0.6008038925,
"autogenerated": false,
"ratio": 2.8596491228070176,
"config_test"... |
from ex import *
from ex.ioo import *
import sdss.utils as utils
def usage():
print '''
fetch_download.py working_dir [stamp](current time) [run](v_5_6_0)
'''
sys.exit(0)
def main(working_dir, run, stamp):
data_dir = '%s/data' % working_dir
cwd = os.getcwd()
os.chdir(data_dir)
log.info('''SD... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss_iii/fetch_download.py",
"copies": "1",
"size": "1450",
"license": "apache-2.0",
"hash": -454067271365678340,
"line_mean": 31.2222222222,
"line_max": 283,
"alpha_frac": 0.6234482759,
"autogenerated": false,
"ratio": 2.821011673151751,
"config_test": ... |
from ex import *
from ex.ml import *
import ex.nnsearch as nn
import ex.annsearch as ann
def PCAScore_Model(X, pca, method = 'accum_err'):
'''get the anomaly scores using global pca method
'''
method = method.lower()
check(method in ['rec_err', 'accum_err', 'dist', 'dist_out', 'accum_dist_out'], 'unkn... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/detection/detector.py",
"copies": "1",
"size": "2777",
"license": "apache-2.0",
"hash": -2090577614890947600,
"line_mean": 31.6705882353,
"line_max": 112,
"alpha_frac": 0.5631976954,
"autogenerated": false,
"ratio": 2.877720207253886,
"config_test":... |
from ex import *
from ex.ml.logistic import MultiLogistic
from simbad import *
from classification import *
import sdss_info as sinfo
def usage():
print('''
classify the objects using multinomial logistic regression
python [--feature=SpectrumS1-Color] [--weighted=1] [--poolsize={number of parallel processes}]
''... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/classification/classification_multilogistic.py",
"copies": "1",
"size": "2728",
"license": "apache-2.0",
"hash": -2372719773489904600,
"line_mean": 35.8648648649,
"line_max": 101,
"alpha_frac": 0.633431085,
"autogenerated": false,
"ratio": 3.186915887... |
from ex import *
from ex.ml.svmutil import *
import sdss_info as sinfo
def usage():
print('''
classify the objects using svm
python [--feature=SpectrumS1-Color] [--weighted=1] [--svm_options='-t 0 -c 100 -m 1000'] [--poolsize={number of parallel processes}]
''')
sys.exit(1)
if __name__ == '__main__':
In... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/classification/classification_svm.py",
"copies": "1",
"size": "2685",
"license": "apache-2.0",
"hash": 9034571406917744000,
"line_mean": 37.3571428571,
"line_max": 132,
"alpha_frac": 0.6275605214,
"autogenerated": false,
"ratio": 3.075601374570447,
... |
from ex import *
from ex.pp import *
from ex.ml import *
import ex.nnsearch as nn
import ex.annsearch as ann
import sdss_iii.settings as settings
from sdss_iii.feature import GetRepairedFeatures
import sdss_iii.web_report.report as report
import sdss.detection.detector as detector
output_dir = './detection_results'... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss_iii/detect.py",
"copies": "1",
"size": "4932",
"license": "apache-2.0",
"hash": -3528126235332276000,
"line_mean": 31.4473684211,
"line_max": 76,
"alpha_frac": 0.5762368208,
"autogenerated": false,
"ratio": 3.0406905055487052,
"config_test": false,
... |
from ex import *
from ex.pp import *
from ex.ml import *
import sdss_iii.settings as settings
############################# features
def Spectrum(data):
f = data['VF']['spectrum']
return float64(f)
def SpectrumS1(data):
f = float64(Spectrum(data))
return Normalize(f, 's1', 'row')[0]*f.shape[1]
clas... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss_iii/feature.py",
"copies": "1",
"size": "6669",
"license": "apache-2.0",
"hash": -8743349123338680000,
"line_mean": 29.1764705882,
"line_max": 116,
"alpha_frac": 0.5578047683,
"autogenerated": false,
"ratio": 3.51,
"config_test": false,
"has_no_ke... |
from ex import *
from ex.pp import *
from ex.nnsearch import KDTNNSearch as NNSearch
from ex.graph import *
import utils
from feature import GetFeatures
def usage():
print('''
generate the spatial edges and clusters for dr7 data
python --coord={rdz, xyz} [--edge_thresh=10] [--cluster_thresh=1;3;5] [--nproc={numb... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/dr7_spatial.py",
"copies": "1",
"size": "3776",
"license": "apache-2.0",
"hash": 6062794489156861000,
"line_mean": 34.2897196262,
"line_max": 116,
"alpha_frac": 0.6006355932,
"autogenerated": false,
"ratio": 3.283478260869565,
"config_test": false,
... |
from ex import *
from ex.pp import *
from ex.ml.logistic import MultiLogistic
from ex.ml.nbayes import NBayes
from ex.ml.active import *
from ex.ml.pca import PCA
from simbad import *
import sdss_info as sinfo
def usage():
print('''
test actively learning on sdss
python [--feature=SpectrumS1-Color] [--poolsize=... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/classification/classification_active.py",
"copies": "1",
"size": "10326",
"license": "apache-2.0",
"hash": -2273778276624962800,
"line_mean": 38.1136363636,
"line_max": 222,
"alpha_frac": 0.5991671509,
"autogenerated": false,
"ratio": 3.19196290571870... |
from ex import *
from ex.pp import *
from ex.ml.logistic import MultiLogistic
from ex.ml.nbayes import NBayes
from ex.ml.active import *
from ex.ml.pca import PCA
import sdss_info as sinfo
from classification_active import algs, alg_names, TruncateClass, Margin
def usage():
print('''
test actively learning on to... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/classification/classification_active_sim.py",
"copies": "1",
"size": "5473",
"license": "apache-2.0",
"hash": -6718379236608238000,
"line_mean": 35.2450331126,
"line_max": 190,
"alpha_frac": 0.5448565686,
"autogenerated": false,
"ratio": 3.21185446009... |
from ex import *
from ex.pp import *
import pdb
import pyfits as pf
import matplotlib.pyplot as plt
print 'Pyplot backend:', plt.get_backend()
from matplotlib.font_manager import fontManager, FontProperties
import sdss.utils as utils
from sdss.settings import emission_lines
def usage():
print '''
fetch_make_figu... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss_iii/fetch_make_figures.py",
"copies": "1",
"size": "5169",
"license": "apache-2.0",
"hash": -8537863682327828000,
"line_mean": 33.2317880795,
"line_max": 111,
"alpha_frac": 0.5798026698,
"autogenerated": false,
"ratio": 2.912112676056338,
"config_te... |
from ex import *
from feature import GetFeatures
import settings
class SIMBAD:
'''handling all simbad related work
'''
def __init__(self, db_file = None):
if db_file is None:
db_file = settings.sdss_dir + '/sdss.db3'
self.db_file = db_file
def GetSIMBADLabels(self, class... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/simbad.py",
"copies": "1",
"size": "3633",
"license": "apache-2.0",
"hash": -5426334847287566000,
"line_mean": 38.064516129,
"line_max": 144,
"alpha_frac": 0.6102394715,
"autogenerated": false,
"ratio": 3.37639405204461,
"config_test": false,
"has... |
from ex import *
from scipy.sparse.linalg import svds
from munkres import GetMunkresIndeces
try:
from propack import dlansvd, slansvd
propack = True
except ImportError:
log.warn('PROPACK cannot be imported')
propack = False
def ChooseSVD(n, k):
k = float(k)
if n <= 100: return k / n <= 0.02... | {
"repo_name": "excelly/xpy-ml",
"path": "ex/alg/common.py",
"copies": "1",
"size": "2019",
"license": "apache-2.0",
"hash": -252602320114499840,
"line_mean": 27.0416666667,
"line_max": 65,
"alpha_frac": 0.4814264487,
"autogenerated": false,
"ratio": 3.0224550898203595,
"config_test": false,
"... |
from ex import *
from spatial import *
class LeafNode:
def __init__(self, idx, data, parent):
self.idx=idx
self.data=data
self.parent=parent
def __str__(self):
return "(Leaf, Points={0}, Parent={1})".format(self.idx,self.parent)
class InnerNode:
def __init__(self, splitter... | {
"repo_name": "excelly/xpy-ml",
"path": "practice/kdtree.py",
"copies": "1",
"size": "8940",
"license": "apache-2.0",
"hash": -7990711286974598000,
"line_mean": 30.5901060071,
"line_max": 119,
"alpha_frac": 0.5288590604,
"autogenerated": false,
"ratio": 3.7033968516984257,
"config_test": true,
... |
from ex import *
import ex.pp.mr as mr
from ex.ml import *
from ex.plott import *
import settings
import utils
import feature
class Reducer(mr.BaseReducer):
'''get the pca model for compact dr7 data set
'''
def __init__(self, mask_bad, feature_name):
mr.BaseReducer.__init__(self, 'DR7 PCA', True)... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/dr7_pca.py",
"copies": "1",
"size": "3516",
"license": "apache-2.0",
"hash": 6077479740705204000,
"line_mean": 29.0512820513,
"line_max": 144,
"alpha_frac": 0.5634243458,
"autogenerated": false,
"ratio": 3.37104506232023,
"config_test": false,
"ha... |
from ex import *
import ex.pp.mr as mr
from ex.plott import *
from ex.ioo.FITS import FITS
import sdss.Spec as Spec
import sdss.settings as settings
class PlateReducer(mr.BaseReducer):
'''assemble data of a palte
'''
def __init__(self, fields, rebin_c0, rebin_c1, rebin_nbin, zmin, zmax, remove_sky_absorp... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/dr7_assemble_data.py",
"copies": "1",
"size": "6197",
"license": "apache-2.0",
"hash": -8557668882048910000,
"line_mean": 32.4972972973,
"line_max": 152,
"alpha_frac": 0.5588187833,
"autogenerated": false,
"ratio": 3.1746926229508197,
"config_test":... |
from ex import *
import ex.pp.mr as mr
import settings
class Reducer(mr.BaseReducer):
'''filter the data and pack them into bigger chunks
'''
def __init__(self, snr_thresh, badpixel_thresh, mag_thresh):
mr.BaseReducer.__init__(self, 'DR7 Compact', True)
self.output_dir = './compact/'
... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/dr7_compact.py",
"copies": "1",
"size": "5740",
"license": "apache-2.0",
"hash": 930714545122420700,
"line_mean": 33.1666666667,
"line_max": 130,
"alpha_frac": 0.5463414634,
"autogenerated": false,
"ratio": 3.390431187241583,
"config_test": false,
... |
from ex import *
import ex.pp.mr as mr
def usage():
print('''
map files using specified handler
python reduce_files.py --module={module path string} --input=input_files(wildcard) [--output={output directory}] [--poolsize={number of parallel processes}]
--module: the processing module. this module should contain ... | {
"repo_name": "excelly/xpy-ml",
"path": "cmd/reduce_files.py",
"copies": "1",
"size": "1355",
"license": "apache-2.0",
"hash": -2802078233513508000,
"line_mean": 35.6216216216,
"line_max": 156,
"alpha_frac": 0.6833948339,
"autogenerated": false,
"ratio": 3.4743589743589745,
"config_test": false... |
from ex import *
import ex.pp.mr as mr
############################# features
def Color(data):
f = AssembleMatrix((data['SF']['fiberMag_u'],
data['SF']['fiberMag_g'],
data['SF']['fiberMag_r'],
data['SF']['fiberMag_i'],
data... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/feature.py",
"copies": "1",
"size": "5640",
"license": "apache-2.0",
"hash": -6935398270395289000,
"line_mean": 29.3225806452,
"line_max": 103,
"alpha_frac": 0.5409574468,
"autogenerated": false,
"ratio": 3.5074626865671643,
"config_test": false,
... |
from ex import *
import utils
InitLog()
db = GetDB('/auton/home/lxiong/data/sdss/dr7/sdss.db3')
qso = []
total = 0
with xFile('qso.lst') as i:
for line in i:
sp = line.split(' ')
sp = [s for s in sp if len(s) > 0]
plate, mjd, fiber, name, z1 = sp[:5]
sid, ra, dec, z2 = utils.Looku... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/dla/dla_proc_list.py",
"copies": "1",
"size": "1521",
"license": "apache-2.0",
"hash": 1901124320462306300,
"line_mean": 30.0408163265,
"line_max": 82,
"alpha_frac": 0.5075608153,
"autogenerated": false,
"ratio": 2.421974522292994,
"config_test": fa... |
from ex import *
def GetDetailPage(spec_id):
# return "http://sdss.lib.uchicago.edu/dr7/en/tools/explore/obj.asp?sid={0}".format(spec_id)
return "http://cas.sdss.org/astro/en/tools/explore/obj.asp?sid={0}".format(spec_id)
def GetSpectrumImage(spec_id):
# return "http://sdss.lib.uchicago.edu/dr7/en/get/specB... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/web_report/report.py",
"copies": "1",
"size": "4715",
"license": "apache-2.0",
"hash": 8083558380877082000,
"line_mean": 32.9208633094,
"line_max": 171,
"alpha_frac": 0.5978791092,
"autogenerated": false,
"ratio": 2.905113986444855,
"config_test": f... |
from ex import *
import multiprocessing as mp
def NumCPU():
return mp.cpu_count
def SeedRand(extra = 0):
run_id = int(os.getpid() + time.time() + extra)
random.seed(run_id)
def ProcJobs(func, jobs, nproc, global_init = None, global_data = None, chunk_size = None):
'''process jobs in parallel
'''... | {
"repo_name": "excelly/xpy-ml",
"path": "ex/pp/common.py",
"copies": "1",
"size": "1127",
"license": "apache-2.0",
"hash": 7163094784611595000,
"line_mean": 22.9787234043,
"line_max": 91,
"alpha_frac": 0.5669920142,
"autogenerated": false,
"ratio": 3.4359756097560976,
"config_test": false,
"h... |
from ex import *
import report
def usage():
print('''
Generate report given the matlab data file. This program only work for SDSS DR7 data.
For GAD results, the data file should contain the following variables:
group_member_ids: a cell array. each cell for one cluster, containing member spec_ids.
scores: the sco... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/web_report/report_gen_sdss.py",
"copies": "1",
"size": "2496",
"license": "apache-2.0",
"hash": 3764223515166636500,
"line_mean": 32.28,
"line_max": 88,
"alpha_frac": 0.6209935897,
"autogenerated": false,
"ratio": 3.372972972972973,
"config_test": f... |
from ex import *
import report
def usage():
print('''
Generate report given the matlab data file. This program only work for the BOSS data.
For PAD results, the data file should contain the following variables:
mpf: an matrix of objects' (mjd,plate,fiber). one row per object.
scores: the scores for each object.
... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss/web_report/report_gen_boss.py",
"copies": "1",
"size": "2579",
"license": "apache-2.0",
"hash": -3928522518238747600,
"line_mean": 33.3866666667,
"line_max": 109,
"alpha_frac": 0.5781310585,
"autogenerated": false,
"ratio": 3.059311981020166,
"confi... |
from ex import *
import sdss_iii.settings as settings
import sdss.utils as utils
def GetSpectrumImage(pmf):
p, m, f = pmf
return 'http://www.autonlab.org/sdss/iii/spec_img/{0}/figure-{1}.png'.format(p, utils.PMF_DashForm(p, m, f))
def GenObjFigure(pmf, rd, img_height = 230):
p,m,f = utils.PMF_N2S(pmf... | {
"repo_name": "excelly/xpy-ml",
"path": "sdss_iii/web_report/report.py",
"copies": "1",
"size": "1904",
"license": "apache-2.0",
"hash": -3985114583367571500,
"line_mean": 22.2195121951,
"line_max": 112,
"alpha_frac": 0.5535714286,
"autogenerated": false,
"ratio": 2.485639686684073,
"config_tes... |
from exoatlas import *
def test_reflection(telescope_name='JWST', wavelength=1*u.micron):
with mock.patch('builtins.input', return_value=""):
t = TransitingExoplanets()
w = 1*u.micron
fi, ax = plt.subplots(1, 2, figsize=(8, 4))
for i, per_transit in enumerate([True, False]):
BubblePane... | {
"repo_name": "zkbt/exopop",
"path": "exoatlas/tests/test_snr.py",
"copies": "1",
"size": "2313",
"license": "mit",
"hash": 1196795349808753200,
"line_mean": 43.4807692308,
"line_max": 118,
"alpha_frac": 0.616515348,
"autogenerated": false,
"ratio": 3.2394957983193278,
"config_test": false,
"... |
from exoduscli import cli
NOTIFICATION_INFO = 'info'
NOTIFICATION_WARNING = 'warning'
NOTIFICATION_ERROR = 'error'
class ListItem(object):
def __init__(self, label='', label2='', iconImage='', thumbnailImage='', path=''):
self.props = {}
self.cm_items = []
self.label = label
self._... | {
"repo_name": "cthlo/exoduscli",
"path": "exoduscli/fakexbmc/xbmcgui.py",
"copies": "1",
"size": "2185",
"license": "mit",
"hash": -5196688152856105000,
"line_mean": 23.5505617978,
"line_max": 92,
"alpha_frac": 0.5853546911,
"autogenerated": false,
"ratio": 3.7033898305084745,
"config_test": fa... |
from EXOSIMS.Observatory.ObservatoryL2Halo import ObservatoryL2Halo
from EXOSIMS.Prototypes.TargetList import TargetList
import numpy as np
import astropy.units as u
from scipy.integrate import solve_bvp
import astropy.constants as const
import hashlib
import scipy.optimize as optimize
import scipy.interpolate ... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/Observatory/SotoStarshade.py",
"copies": "1",
"size": "21532",
"license": "bsd-3-clause",
"hash": -6181821689213629000,
"line_mean": 39.4076923077,
"line_max": 114,
"alpha_frac": 0.5313951328,
"autogenerated": false,
"ratio": 3.8245115452930727... |
from EXOSIMS.Observatory.SotoStarshade_ContThrust import SotoStarshade_ContThrust
from EXOSIMS.Prototypes.TargetList import TargetList
import numpy as np
import sys
import ipyparallel as ipp
class SotoStarshade_parallel(SotoStarshade_ContThrust):
""" StarShade Observatory class
This class is implemented at L... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/Observatory/SotoStarshade_parallel.py",
"copies": "1",
"size": "4246",
"license": "bsd-3-clause",
"hash": -1939987186975683600,
"line_mean": 42.3265306122,
"line_max": 153,
"alpha_frac": 0.5602920396,
"autogenerated": false,
"ratio": 3.54720133... |
from EXOSIMS.Observatory.SotoStarshade import SotoStarshade
import numpy as np
import astropy.units as u
from scipy.integrate import solve_ivp
import astropy.constants as const
import hashlib
import scipy.optimize as optimize
from scipy.optimize import basinhopping
import scipy.interpolate as interp
import scipy.integr... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/Observatory/SotoStarshade_SKi.py",
"copies": "1",
"size": "77012",
"license": "bsd-3-clause",
"hash": 5312747784819359000,
"line_mean": 44.3812610489,
"line_max": 171,
"alpha_frac": 0.5491351997,
"autogenerated": false,
"ratio": 3.8071979434447... |
from EXOSIMS.Observatory.SotoStarshade_SKi import SotoStarshade_SKi
import numpy as np
import astropy.units as u
from scipy.integrate import solve_ivp
import astropy.constants as const
import hashlib
import scipy.optimize as optimize
from scipy.optimize import basinhopping
import scipy.interpolate as interp
import scip... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/Observatory/SotoStarshade_ContThrust.py",
"copies": "1",
"size": "67022",
"license": "bsd-3-clause",
"hash": -6192012337227700000,
"line_mean": 38.0343622598,
"line_max": 124,
"alpha_frac": 0.5094894214,
"autogenerated": false,
"ratio": 3.85161... |
from EXOSIMS.PlanetPhysicalModel.FortneyMarleyCahoyMix1 import FortneyMarleyCahoyMix1
from EXOSIMS.util.get_dirs import get_downloads_dir
import astropy.units as u
import numpy as np
import os, h5py
from scipy.stats import norm
import sys
# Python 3 compatibility:
if sys.version_info[0] > 2:
from urllib.request im... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPhysicalModel/Forecaster.py",
"copies": "1",
"size": "5441",
"license": "bsd-3-clause",
"hash": -1823929710941119500,
"line_mean": 35.0397350993,
"line_max": 106,
"alpha_frac": 0.5449365925,
"autogenerated": false,
"ratio": 3.400625,
"c... |
from EXOSIMS.PlanetPhysicalModel.FortneyMarleyCahoyMix1 import FortneyMarleyCahoyMix1
import astropy.units as u
import numpy as np
class ForecasterMod(FortneyMarleyCahoyMix1):
"""Planet M-R relation model based on modification of the FORECASTER
best-fit model (Chen & Kippling 2016) as described in Savransky e... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPhysicalModel/ForecasterMod.py",
"copies": "1",
"size": "3322",
"license": "bsd-3-clause",
"hash": 7014243313967169000,
"line_mean": 28.6607142857,
"line_max": 85,
"alpha_frac": 0.5054184226,
"autogenerated": false,
"ratio": 2.90131004366... |
from EXOSIMS.PlanetPopulation.DulzPlavchan import DulzPlavchan
import astropy.units as u
import numpy as np
import sys
# Python 3 compatibility:
if sys.version_info[0] > 2:
xrange = range
class AlbedoByRadiusDulzPlavchan(DulzPlavchan):
"""Planet Population module based on occurrence rate tables from Shannon D... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPopulation/AlbedoByRadiusDulzPlavchan.py",
"copies": "1",
"size": "5235",
"license": "bsd-3-clause",
"hash": 6276397289387819000,
"line_mean": 36.9347826087,
"line_max": 103,
"alpha_frac": 0.5677172875,
"autogenerated": false,
"ratio": 3.... |
from EXOSIMS.PlanetPopulation.KeplerLike1 import KeplerLike1
from EXOSIMS.util.InverseTransformSampler import InverseTransformSampler
import astropy.units as u
class KeplerLike2(KeplerLike1):
"""
Population based on Kepler radius distribution with RV-like semi-major axis
distribution with exponential deca... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPopulation/KeplerLike2.py",
"copies": "1",
"size": "2605",
"license": "bsd-3-clause",
"hash": 8101321289599932000,
"line_mean": 36.2285714286,
"line_max": 81,
"alpha_frac": 0.6537428023,
"autogenerated": false,
"ratio": 4.134920634920635,... |
from EXOSIMS.PlanetPopulation.KeplerLike1 import KeplerLike1
import warnings
import astropy
import astropy.units as u
import astropy.constants as const
import numpy as np
import os,inspect
from astropy.io.votable import parse
from astropy.time import Time
from EXOSIMS.util import statsFun
import pkg_resources
class K... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPopulation/KnownRVPlanets.py",
"copies": "1",
"size": "7965",
"license": "bsd-3-clause",
"hash": 4601928611023153700,
"line_mean": 40.7015706806,
"line_max": 144,
"alpha_frac": 0.6114249843,
"autogenerated": false,
"ratio": 3.427280550774... |
from EXOSIMS.PlanetPopulation.SAG13 import SAG13
import astropy.units as u
import numpy as np
import sys
# Python 3 compatibility:
if sys.version_info[0] > 2:
xrange = range
class AlbedoByRadius(SAG13):
"""Planet Population module based on SAG13 occurrence rates.
NOTE: This assigns constant albedo ba... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPopulation/AlbedoByRadius.py",
"copies": "1",
"size": "5293",
"license": "bsd-3-clause",
"hash": 6960843547965830000,
"line_mean": 37.0791366906,
"line_max": 84,
"alpha_frac": 0.5597959569,
"autogenerated": false,
"ratio": 3.5547347212894... |
from EXOSIMS.Prototypes.BackgroundSources import BackgroundSources
import os, inspect
import numpy as np
import astropy.units as u
from scipy.interpolate import griddata
class GalaxiesFaintStars(BackgroundSources):
"""
GalaxiesFaintStars class
This class calculates the total number background sources... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/BackgroundSources/GalaxiesFaintStars.py",
"copies": "1",
"size": "2881",
"license": "bsd-3-clause",
"hash": -1215295847102459100,
"line_mean": 37.4133333333,
"line_max": 87,
"alpha_frac": 0.6004859424,
"autogenerated": false,
"ratio": 3.9144021... |
from EXOSIMS.Prototypes.Observatory import Observatory
import astropy.units as u
from astropy.time import Time
import numpy as np
import os, inspect
import scipy.interpolate as interpolate
import scipy.integrate as itg
try:
import cPickle as pickle
except:
import pickle
from scipy.io import loadmat
class Obser... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/Observatory/ObservatoryL2Halo.py",
"copies": "1",
"size": "21259",
"license": "bsd-3-clause",
"hash": -9181120467412425000,
"line_mean": 38.8855534709,
"line_max": 148,
"alpha_frac": 0.5425466861,
"autogenerated": false,
"ratio": 3.516790736145... |
from EXOSIMS.Prototypes.OpticalSystem import OpticalSystem
from EXOSIMS.OpticalSystem.Nemati import Nemati
import astropy.units as u
from astropy.io import fits
import astropy.constants as const
import numpy as np
import scipy.stats as st
import scipy.optimize as opt
import os
from scipy import interpolate
from scipy.o... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/OpticalSystem/Nemati_2019.py",
"copies": "1",
"size": "37728",
"license": "bsd-3-clause",
"hash": 186813693652439740,
"line_mean": 54.0773722628,
"line_max": 238,
"alpha_frac": 0.6036100509,
"autogenerated": false,
"ratio": 3.109279709906049,
... |
from EXOSIMS.Prototypes.PlanetPhysicalModel import PlanetPhysicalModel
import astropy.units as u
import numpy as np
import scipy.interpolate as interpolate
import os, inspect
try:
import cPickle as pickle
except:
import pickle
from scipy.io import loadmat
class FortneyMarleyCahoyMix1(PlanetPhysicalModel):
... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPhysicalModel/FortneyMarleyCahoyMix1.py",
"copies": "1",
"size": "9468",
"license": "bsd-3-clause",
"hash": -5968048589570952000,
"line_mean": 39.2893617021,
"line_max": 94,
"alpha_frac": 0.5409801436,
"autogenerated": false,
"ratio": 3.2... |
from EXOSIMS.Prototypes.PlanetPopulation import PlanetPopulation
from EXOSIMS.PlanetPopulation.EarthTwinHabZone1 import EarthTwinHabZone1
import numpy as np
import astropy.units as u
class EarthTwinHabZone2(EarthTwinHabZone1):
"""
Population of Earth twins (1 R_Earth, 1 M_Eearth, 1 p_Earth)
On eccentric ha... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPopulation/EarthTwinHabZone2.py",
"copies": "1",
"size": "3071",
"license": "bsd-3-clause",
"hash": 6321418637239151000,
"line_mean": 36.4512195122,
"line_max": 84,
"alpha_frac": 0.5659394334,
"autogenerated": false,
"ratio": 3.4122222222... |
from EXOSIMS.Prototypes.PlanetPopulation import PlanetPopulation
from EXOSIMS.util import statsFun
import astropy.units as u
import astropy.constants as const
import numpy as np
import scipy.integrate as integrate
import scipy.interpolate as interpolate
import sys
class KeplerLike1(PlanetPopulation):
"""Populati... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPopulation/KeplerLike1.py",
"copies": "1",
"size": "15137",
"license": "bsd-3-clause",
"hash": -6960407322188186000,
"line_mean": 34.9548693587,
"line_max": 101,
"alpha_frac": 0.5485234855,
"autogenerated": false,
"ratio": 3.6919512195121... |
from EXOSIMS.Prototypes.PlanetPopulation import PlanetPopulation
import numpy as np
import astropy.units as u
class Brown2005EarthLike(PlanetPopulation):
"""
Population of Earth-Like Planets from Brown 2005 paper
This implementation is intended to enforce this population regardless
of JSON inputs.... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPopulation/Brown2005EarthLike.py",
"copies": "1",
"size": "3810",
"license": "bsd-3-clause",
"hash": -8400282252256987000,
"line_mean": 35.2857142857,
"line_max": 126,
"alpha_frac": 0.556167979,
"autogenerated": false,
"ratio": 3.60795454... |
from EXOSIMS.Prototypes.PlanetPopulation import PlanetPopulation
import numpy as np
import astropy.units as u
class EarthTwinHabZone1(PlanetPopulation):
"""Population of Earth twins (1 R_Earth, 1 M_Eearth, 1 p_Earth)
On circular Habitable zone orbits (0.7 to 1.5 AU)
Note that these values may not be o... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPopulation/EarthTwinHabZone1.py",
"copies": "1",
"size": "2416",
"license": "bsd-3-clause",
"hash": -7573453310058077000,
"line_mean": 31.2133333333,
"line_max": 86,
"alpha_frac": 0.5488410596,
"autogenerated": false,
"ratio": 3.947712418... |
from EXOSIMS.Prototypes.PlanetPopulation import PlanetPopulation
import numpy as np
import astropy.units as u
class EarthTwinHabZone1SDET(PlanetPopulation):
"""Population of Earth twins (1 R_Earth, 1 M_Eearth, 1 p_Earth)
On circular Habitable zone orbits (0.7 to 1.5 AU)
Note that these values may not ... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPopulation/EarthTwinHabZone1SDET.py",
"copies": "1",
"size": "2467",
"license": "bsd-3-clause",
"hash": -8379417329186584000,
"line_mean": 31.8933333333,
"line_max": 86,
"alpha_frac": 0.5541143089,
"autogenerated": false,
"ratio": 3.86071... |
from EXOSIMS.Prototypes.PlanetPopulation import PlanetPopulation
import numpy as np
import astropy.units as u
class EarthTwinHabZone3(PlanetPopulation):
"""Population of Earth twins (1 R_Earth, 1 M_Eearth, 1 p_Earth)
On circular Habitable zone orbits (0.7 to 1.5 AU)
Note that these values may not be o... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPopulation/EarthTwinHabZone3.py",
"copies": "1",
"size": "1862",
"license": "bsd-3-clause",
"hash": 2896822693030807000,
"line_mean": 29.5245901639,
"line_max": 89,
"alpha_frac": 0.5504833512,
"autogenerated": false,
"ratio": 3.9957081545... |
from EXOSIMS.Prototypes.PlanetPopulation import PlanetPopulation
import numpy as np
import astropy.units as u
class EarthTwinHabZoneSDET(PlanetPopulation):
"""Population of Earth twins (1 R_Earth, 1 M_Eearth, 1 p_Earth)
On circular Habitable zone orbits (0.7 to 1.5 AU)
Note that these values may not b... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPopulation/EarthTwinHabZoneSDET.py",
"copies": "1",
"size": "3075",
"license": "bsd-3-clause",
"hash": -7975955245094547000,
"line_mean": 31.7127659574,
"line_max": 86,
"alpha_frac": 0.5570731707,
"autogenerated": false,
"ratio": 4.009126... |
from EXOSIMS.Prototypes.PlanetPopulation import PlanetPopulation
import numpy as np
import astropy.units as u
class Guimond2019(PlanetPopulation):
"""
Population of Earth-Like Planets from Brown 2005 paper
This implementation is intended to enforce this population regardless
of JSON inputs. The o... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPopulation/Guimond2019.py",
"copies": "1",
"size": "3459",
"license": "bsd-3-clause",
"hash": 7041535711214584000,
"line_mean": 32.5825242718,
"line_max": 121,
"alpha_frac": 0.5617230413,
"autogenerated": false,
"ratio": 3.930681818181818... |
from EXOSIMS.Prototypes.PlanetPopulation import PlanetPopulation
import numpy as np
import astropy.units as u
class JupiterTwin(PlanetPopulation):
"""
Population of Jupiter twins (11.209 R_Earth, 317.83 M_Eearth, 1 p_Earth)
On eccentric orbits (0.7 to 1.5 AU)*5.204.
Numbers pulled from nssdc.gsfc.nasa.... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPopulation/JupiterTwin.py",
"copies": "1",
"size": "3358",
"license": "bsd-3-clause",
"hash": 289742654586029440,
"line_mean": 36.7303370787,
"line_max": 84,
"alpha_frac": 0.5711733175,
"autogenerated": false,
"ratio": 3.213397129186603,
... |
from EXOSIMS.Prototypes.PlanetPopulation import PlanetPopulation
import numpy as np
import os
import inspect
from astropy.io import ascii
import astropy.units as u
import astropy.constants as const
import scipy.interpolate as interpolate
import sys
class DulzPlavchan(PlanetPopulation):
"""
Population based on ... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/PlanetPopulation/DulzPlavchan.py",
"copies": "1",
"size": "19342",
"license": "bsd-3-clause",
"hash": -415644842183518800,
"line_mean": 37,
"line_max": 127,
"alpha_frac": 0.5239375452,
"autogenerated": false,
"ratio": 3.3313813296589734,
"con... |
from EXOSIMS.Prototypes.SimulatedUniverse import SimulatedUniverse
import numpy as np
import astropy.units as u
from astropy.time import Time
class KnownRVPlanetsUniverse(SimulatedUniverse):
"""
Simulated universe implementation inteded to work with the Known RV planet
planetary population and target list ... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/SimulatedUniverse/KnownRVPlanetsUniverse.py",
"copies": "1",
"size": "3909",
"license": "bsd-3-clause",
"hash": -1026664043273351200,
"line_mean": 45,
"line_max": 98,
"alpha_frac": 0.5968278332,
"autogenerated": false,
"ratio": 3.43195785776997... |
from EXOSIMS.Prototypes.SimulatedUniverse import SimulatedUniverse
import numpy as np
import astropy.units as u
class DulzPlavchanUniverseEarthsOnly(SimulatedUniverse):
"""Simulated Universe module based on Dulz and Plavchan occurrence rates.
"""
def __init__(self, **specs):
SimulatedUniverse.... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/SimulatedUniverse/DulzPlavchanUniverseEarthsOnly.py",
"copies": "1",
"size": "1742",
"license": "bsd-3-clause",
"hash": -3715541176994282500,
"line_mean": 31.2592592593,
"line_max": 113,
"alpha_frac": 0.6194029851,
"autogenerated": false,
"rati... |
from EXOSIMS.Prototypes.SimulatedUniverse import SimulatedUniverse
import numpy as np
import astropy.units as u
class KeplerLikeUniverse(SimulatedUniverse):
"""
Simulated universe implementation inteded to work with the Kepler-like
planetary population implementations.
Args:
specs:
... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/SimulatedUniverse/KeplerLikeUniverse.py",
"copies": "1",
"size": "1910",
"license": "bsd-3-clause",
"hash": 4025766260925968400,
"line_mean": 35.75,
"line_max": 82,
"alpha_frac": 0.6251308901,
"autogenerated": false,
"ratio": 3.7524557956777995... |
from EXOSIMS.Prototypes.SimulatedUniverse import SimulatedUniverse
import numpy as np
class SAG13Universe(SimulatedUniverse):
"""Simulated Universe module based on SAG13 Planet Population module.
"""
def __init__(self, **specs):
SimulatedUniverse.__init__(self, **specs)
def gen_... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/SimulatedUniverse/SAG13Universe.py",
"copies": "1",
"size": "1543",
"license": "bsd-3-clause",
"hash": 5868608313478267000,
"line_mean": 37.6,
"line_max": 80,
"alpha_frac": 0.615683733,
"autogenerated": false,
"ratio": 3.6391509433962264,
"co... |
from EXOSIMS.Prototypes.SimulatedUniverse import SimulatedUniverse
import numpy as np
class DulzPlavchanUniverse(SimulatedUniverse):
"""Simulated Universe module based on Dulz and Plavchan occurrence rates.
"""
def __init__(self, **specs):
SimulatedUniverse.__init__(self, **specs)
def gen_... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/SimulatedUniverse/DulzPlavchanUniverse.py",
"copies": "1",
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"autogenerated": false,
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from EXOSIMS.Prototypes.StarCatalog import StarCatalog
import numpy as np
import astropy.units as u
from astropy.coordinates import SkyCoord
class FakeCatalog_UniformAngles(StarCatalog):
"""Fake Catalog of stars separated uniformly by angle
Generate a fake catalog of stars that are uniformly separated.
... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/StarCatalog/FakeCatalog_UniformAngles.py",
"copies": "1",
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from EXOSIMS.Prototypes.StarCatalog import StarCatalog
import numpy as np
import astropy.units as u
from astropy.coordinates import SkyCoord
class FakeCatalog_UniformSpacing_wInput(StarCatalog):
def __init__(self, lat_sep=0.3, lon_sep=0.3, star_dist=1, lat_extra = np.array([]), lon_extra = np.array([]), dist_... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/StarCatalog/FakeCatalog_UniformSpacing_wInput.py",
"copies": "1",
"size": "4169",
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"line_mean": 48.630952381,
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"autogenerated": false,
"ratio": 3.9... |
from EXOSIMS.Prototypes.StarCatalog import StarCatalog
import numpy as np
import astropy.units as u
import random as py_random
from astropy.coordinates import SkyCoord
class FakeCatalog(StarCatalog):
""" Fake Catalog class
This class generates an artificial target list of stars with a logistic distributio... | {
"repo_name": "dsavransky/EXOSIMS",
"path": "EXOSIMS/StarCatalog/FakeCatalog.py",
"copies": "1",
"size": "6193",
"license": "bsd-3-clause",
"hash": 7398224028870938000,
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"line_max": 99,
"alpha_frac": 0.5147747457,
"autogenerated": false,
"ratio": 3.751059963658389,
"c... |
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