text stringlengths 0 1.05M | meta dict |
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from __future__ import absolute_import, division, print_function
from collections import Iterator
import math
import uuid
import numpy as np
import pandas as pd
from pandas.core.categorical import is_categorical_dtype
from toolz import merge
from ..optimize import cull
from ..base import tokenize
from .core import D... | {
"repo_name": "mikegraham/dask",
"path": "dask/dataframe/shuffle.py",
"copies": "1",
"size": "15669",
"license": "bsd-3-clause",
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"autogenerated": false,
"ratio": 3.8161227471992207,
"config_test"... |
from __future__ import absolute_import, division, print_function
from collections import Iterator
import operator
import uuid
try:
from cytoolz import curry, first
except ImportError:
from toolz import curry, first
from . import base, threaded
from .compatibility import apply
from .core import quote
from .co... | {
"repo_name": "mraspaud/dask",
"path": "dask/delayed.py",
"copies": "1",
"size": "15619",
"license": "bsd-3-clause",
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"autogenerated": false,
"ratio": 3.689818095913064,
"config_test": true,
"ha... |
from __future__ import absolute_import, division, print_function
from collections import Iterator
import sys
import numpy as np
import pandas as pd
import toolz
from dask.async import get_sync
def shard_df_on_index(df, divisions):
""" Shard a DataFrame by ranges on its index
Examples
--------
>>>... | {
"repo_name": "mikegraham/dask",
"path": "dask/dataframe/utils.py",
"copies": "1",
"size": "6703",
"license": "bsd-3-clause",
"hash": 5917703457494593000,
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"alpha_frac": 0.5449798598,
"autogenerated": false,
"ratio": 3.5131027253668763,
"config_test": ... |
from __future__ import absolute_import, division, print_function
from collections import Iterator
import uuid
import numpy as np
import pandas as pd
from toolz import merge
from ..optimize import cull
from ..base import tokenize
from .core import DataFrame, Series, _Frame
from .utils import (strip_categories, shard_... | {
"repo_name": "vikhyat/dask",
"path": "dask/dataframe/shuffle.py",
"copies": "2",
"size": "8223",
"license": "bsd-3-clause",
"hash": 8297773332939162000,
"line_mean": 31.7609561753,
"line_max": 90,
"alpha_frac": 0.5988082208,
"autogenerated": false,
"ratio": 3.8335664335664337,
"config_test": f... |
from __future__ import absolute_import, division, print_function
from collections import Iterator
from dask.delayed import (Delayed, DelayedLeaf, flat_unique, funcname,
to_task_dasks, tokenize, unzip)
from satyr.proxies.messages import Cpus, Disk, Mem
from toolz import curry, merge
from .ex... | {
"repo_name": "lensacom/dask.mesos",
"path": "daskos/delayed.py",
"copies": "1",
"size": "3969",
"license": "apache-2.0",
"hash": 8141983443009764000,
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"line_max": 76,
"alpha_frac": 0.5938523558,
"autogenerated": false,
"ratio": 3.3778723404255317,
"config_test": fals... |
from __future__ import absolute_import, division, print_function
from collections import Iterator
from toolz import memoize, first, peek
from datashape import discover, var
from .utils import cls_name, copydoc
from dask.threaded import get as dsk_get
class Chunks(object):
""" An Iterable of chunked data
It... | {
"repo_name": "ContinuumIO/odo",
"path": "odo/chunks.py",
"copies": "4",
"size": "1738",
"license": "bsd-3-clause",
"hash": -7073726055771709000,
"line_mean": 25.3333333333,
"line_max": 79,
"alpha_frac": 0.5742232451,
"autogenerated": false,
"ratio": 3.5182186234817814,
"config_test": false,
... |
from __future__ import absolute_import, division, print_function
from collections import Iterator
import numpy as np
import pandas as pd
import toolz
from dask.async import get_sync
def shard_df_on_index(df, divisions):
""" Shard a DataFrame by ranges on its index
Examples
--------
>>> df = pd.Da... | {
"repo_name": "vikhyat/dask",
"path": "dask/dataframe/utils.py",
"copies": "1",
"size": "7515",
"license": "bsd-3-clause",
"hash": -8145397005283420000,
"line_mean": 27.4696969697,
"line_max": 85,
"alpha_frac": 0.5260146374,
"autogenerated": false,
"ratio": 3.590539894887721,
"config_test": fal... |
from __future__ import absolute_import, division, print_function
from collections import Iterator
import numpy as np
import pandas as pd
import toolz
def shard_df_on_index(df, divisions):
""" Shard a DataFrame by ranges on its index
Examples
--------
>>> df = pd.DataFrame({'a': [0, 10, 20, 30, 40]... | {
"repo_name": "pombredanne/dask",
"path": "dask/dataframe/utils.py",
"copies": "1",
"size": "4423",
"license": "bsd-3-clause",
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"line_max": 85,
"alpha_frac": 0.5145828623,
"autogenerated": false,
"ratio": 3.5019794140934284,
"config_test"... |
from __future__ import absolute_import, division, print_function
from collections import Mapping
from contextlib import contextmanager
import pandas as pd
from . import formatting, indexing
from .merge import (
expand_and_merge_variables, merge_coords, merge_coords_for_inplace_math)
from .pycompat import Ordered... | {
"repo_name": "jcmgray/xarray",
"path": "xarray/core/coordinates.py",
"copies": "1",
"size": "12070",
"license": "apache-2.0",
"hash": 6314638935549490000,
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"line_max": 79,
"alpha_frac": 0.6092792046,
"autogenerated": false,
"ratio": 4.48199034533977,
"config_test": f... |
from __future__ import absolute_import, division, print_function
from collections import Mapping
from keyword import iskeyword
import re
import datashape
from datashape import (
dshape,
DataShape,
Record,
Var,
Fixed,
promote,
Option,
Null,
)
from datashape.predicates import (
issca... | {
"repo_name": "ContinuumIO/blaze",
"path": "blaze/expr/expressions.py",
"copies": "3",
"size": "29015",
"license": "bsd-3-clause",
"hash": 5986935608387100000,
"line_mean": 26.0158286778,
"line_max": 80,
"alpha_frac": 0.5678097536,
"autogenerated": false,
"ratio": 3.8258175105485233,
"config_te... |
from __future__ import absolute_import, division, print_function
from collections import Mapping, OrderedDict
import datetime
from functools import reduce, partial
import inspect
from itertools import repeat
import numbers
from pprint import pformat
from weakref import WeakValueDictionary
import toolz
from toolz imp... | {
"repo_name": "ContinuumIO/blaze",
"path": "blaze/expr/core.py",
"copies": "3",
"size": "14485",
"license": "bsd-3-clause",
"hash": 2019177139318326000,
"line_mean": 25.240942029,
"line_max": 79,
"alpha_frac": 0.5419399379,
"autogenerated": false,
"ratio": 3.829984135378107,
"config_test": fals... |
from __future__ import absolute_import, division, print_function
from collections import namedtuple, Iterator
from contextlib import contextmanager
from warnings import warn
from datashape import discover
import networkx as nx
import numpy as np
from toolz import concatv
from .compatibility import map, adjacency
fro... | {
"repo_name": "ContinuumIO/odo",
"path": "odo/core.py",
"copies": "2",
"size": "5915",
"license": "bsd-3-clause",
"hash": 1939837459934385000,
"line_mean": 28.575,
"line_max": 79,
"alpha_frac": 0.5666948436,
"autogenerated": false,
"ratio": 4.02107409925221,
"config_test": false,
"has_no_keyw... |
from __future__ import (absolute_import, division, print_function)
from collections import namedtuple
from hashlib import sha256
import os
import shutil
import sys
import tempfile
import fnmatch
from sympy.utilities.pytest import XFAIL
def may_xfail(func):
if sys.platform.lower() == 'darwin' or os.name == 'nt':... | {
"repo_name": "kaushik94/sympy",
"path": "sympy/utilities/_compilation/util.py",
"copies": "1",
"size": "8374",
"license": "bsd-3-clause",
"hash": -3200713744012394500,
"line_mean": 26.8205980066,
"line_max": 90,
"alpha_frac": 0.602340578,
"autogenerated": false,
"ratio": 3.9668403600189484,
"c... |
from __future__ import absolute_import, division, print_function
from collections import namedtuple
from itertools import starmap
from timeit import default_timer
from time import sleep
from multiprocessing import Process, Pipe, current_process
from ..callbacks import Callback
from ..utils import import_required
# ... | {
"repo_name": "mraspaud/dask",
"path": "dask/diagnostics/profile.py",
"copies": "1",
"size": "10884",
"license": "bsd-3-clause",
"hash": 1514948959712688600,
"line_mean": 29.7457627119,
"line_max": 88,
"alpha_frac": 0.5610988607,
"autogenerated": false,
"ratio": 3.9985304922850844,
"config_test... |
from __future__ import absolute_import, division, print_function
from collections import namedtuple
import json
import multiprocessing.pool
import os
import re
import sys
import requests
from bs4 import BeautifulSoup
import nfldb
import nflfan.config
__pdoc__ = {}
_user_agent = 'Mozilla/5.0 (X11; Linux x86_64)'
... | {
"repo_name": "codeaudit/nflfan",
"path": "nflfan/provider.py",
"copies": "1",
"size": "23778",
"license": "unlicense",
"hash": -4465141964907188000,
"line_mean": 32.8717948718,
"line_max": 79,
"alpha_frac": 0.5658591976,
"autogenerated": false,
"ratio": 3.7333961375412152,
"config_test": false... |
from __future__ import absolute_import, division, print_function
from collections import namedtuple
import json
import os
import re
import sys
import time
import requests
from bs4 import BeautifulSoup
import nfldb
import nflfan.config
__pdoc__ = {}
_user_agent = 'Mozilla/5.0 (X11; Linux x86_64)'
# _user_agent = ... | {
"repo_name": "BurntSushi/nflfan",
"path": "nflfan/provider.py",
"copies": "1",
"size": "24616",
"license": "unlicense",
"hash": 1224262221576702000,
"line_mean": 33.1888888889,
"line_max": 121,
"alpha_frac": 0.562520312,
"autogenerated": false,
"ratio": 3.7173059498640892,
"config_test": false... |
from __future__ import absolute_import, division, print_function
from collections import namedtuple, OrderedDict
import csv
import datetime as dt
from io import open
import os.path
import pickle
import re
import sys
import numpy as np
import pandas as pd
from caar.pandas_tseries_tools import _guess_datetime_format
... | {
"repo_name": "nickpowersys/CaaR",
"path": "caar/cleanthermostat.py",
"copies": "1",
"size": "93813",
"license": "bsd-3-clause",
"hash": 2010169902647102700,
"line_mean": 42.8378504673,
"line_max": 414,
"alpha_frac": 0.606525748,
"autogenerated": false,
"ratio": 4.1196645002634815,
"config_test... |
from __future__ import absolute_import, division, print_function
from collections import OrderedDict, Iterator
import copy
from functools import partial
from hashlib import md5
import inspect
import pickle
import os
import uuid
from toolz import merge, groupby, curry, identity
from toolz.functoolz import Compose
fro... | {
"repo_name": "mraspaud/dask",
"path": "dask/base.py",
"copies": "1",
"size": "20309",
"license": "bsd-3-clause",
"hash": -7147707831151094000,
"line_mean": 32.9048414023,
"line_max": 89,
"alpha_frac": 0.6000787828,
"autogenerated": false,
"ratio": 4.163386633866339,
"config_test": true,
"has... |
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
from enum import Enum
import yaml
import json
from attr._make import fields
try:
from functools import singledispatch
except ImportError:
from singledispatch import singledispatch
@singledispatch
def to_di... | {
"repo_name": "genomoncology/related",
"path": "src/related/functions.py",
"copies": "1",
"size": "6637",
"license": "mit",
"hash": -5423123351295688000,
"line_mean": 29.1681818182,
"line_max": 79,
"alpha_frac": 0.6594847069,
"autogenerated": false,
"ratio": 3.9814037192561487,
"config_test": f... |
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
from functools import partial
from hashlib import md5
from operator import attrgetter
import pickle
import os
import uuid
from toolz import merge, groupby, curry, identity
from toolz.functoolz import Compose
from .co... | {
"repo_name": "gameduell/dask",
"path": "dask/base.py",
"copies": "2",
"size": "12768",
"license": "bsd-3-clause",
"hash": -4224822080165684700,
"line_mean": 32.1636363636,
"line_max": 90,
"alpha_frac": 0.5940632832,
"autogenerated": false,
"ratio": 4.043065231158962,
"config_test": false,
"h... |
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
from functools import partial
from pymel.core import dt, geometryConstraint, normalConstraint, pointConstraint, spaceLocator, xform
from .... import core
from .... import nodeApi
from ..cardRigging import MetaContro... | {
"repo_name": "patcorwin/fossil",
"path": "pdil/tool/fossil/rigging/surfaceFollow.py",
"copies": "1",
"size": "7526",
"license": "bsd-3-clause",
"hash": -937649686206291800,
"line_mean": 35.014354067,
"line_max": 137,
"alpha_frac": 0.5876959872,
"autogenerated": false,
"ratio": 3.9320794148380354... |
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
from itertools import repeat
import dask
import toolz as t
import daskfunk.utils as u
from daskfunk.compatibility import getargspec
_UNSPECIFIED = '::unspecified::'
_AMBIGUOUS = '::ambiguous::'
def _is_required(def... | {
"repo_name": "Savvysherpa/dask-funk",
"path": "daskfunk/core.py",
"copies": "1",
"size": "4692",
"license": "mit",
"hash": -1319164392241059300,
"line_mean": 33.5,
"line_max": 91,
"alpha_frac": 0.6054987212,
"autogenerated": false,
"ratio": 3.5518546555639667,
"config_test": false,
"has_no_k... |
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
import contextlib
import copy
import hashlib
import json
import os
from os.path import isfile, join
import re
import sys
import time
from bs4 import UnicodeDammit
from .conda_interface import iteritems, PY3, text_typ... | {
"repo_name": "pelson/conda-build",
"path": "conda_build/metadata.py",
"copies": "1",
"size": "106861",
"license": "bsd-3-clause",
"hash": -466495916882600060,
"line_mean": 45.3605206074,
"line_max": 133,
"alpha_frac": 0.5663712673,
"autogenerated": false,
"ratio": 4.08053306858103,
"config_tes... |
from __future__ import (absolute_import, division, print_function)
from collections import OrderedDict
import copy
import numpy as np
from qtpy.QtWidgets import QApplication, QMainWindow
from addie.utilities import load_ui
from qtpy import QtGui, QtCore
from addie.databases.oncat.oncat import OncatErrorMessageWindow... | {
"repo_name": "neutrons/FastGR",
"path": "addie/processing/mantid/master_table/import_from_database/import_from_database_handler.py",
"copies": "1",
"size": "21666",
"license": "mit",
"hash": 7578083880727924000,
"line_mean": 38.9005524862,
"line_max": 129,
"alpha_frac": 0.625311548,
"autogenerated... |
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
import math
from pymel.core import duplicate, dt, group, hide, joint, ikHandle, listConnections, makeIdentity, move, orientConstraint, parent, parentConstraint, PyNode, skinCluster, xform
from .... import core
from .... | {
"repo_name": "patcorwin/fossil",
"path": "pdil/tool/fossil/rigging/splineChest.py",
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"alpha_frac": 0.6336211996,
"autogenerated": false,
"ratio": 3.448796409628723,... |
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
from glue.core import Subset
from glue.core.subset import MaskSubsetState
__all__ = ['SubsetMaskImporter', 'SubsetMaskExporter']
class SubsetMaskImporter(object):
def get_filename_and_reader(self):
rai... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/io/subset_mask.py",
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"autogenerated": false,
"ratio": 4.281914893617022,
"config_tes... |
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
from pymel.core import delete, dt, group, hide, ikHandle, orientConstraint, parentConstraint, poleVectorConstraint, pointConstraint, PyNode, xform
from ....add import simpleName
from .... import core
from .... import... | {
"repo_name": "patcorwin/fossil",
"path": "pdil/tool/fossil/rigging/dogHindLeg.py",
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"autogenerated": false,
"ratio": 3.4453036161018877,... |
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
import pytest
import numpy as np
from matplotlib.axes import Axes
from mock import MagicMock, patch
from numpy.testing import assert_array_equal
from glue.core.tests.test_state import clone
from glue.core.tests.util ... | {
"repo_name": "saimn/glue",
"path": "glue/viewers/custom/qt/tests/test_custom_viewer.py",
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"autogenerated": false,
"ratio": 3.533959731543... |
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
import uuid
import numpy as np
import pandas as pd
from glue.external import six
from glue.core.message import (DataUpdateMessage, DataRemoveComponentMessage,
DataAddComponentMessage, N... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/core/data.py",
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"alpha_frac": 0.5798135387,
"autogenerated": false,
"ratio": 4.278529309746792,
"config_test":... |
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
class Record(object):
def __init__(self):
self.rec_data = OrderedDict()
self.rec_im_data = OrderedDict()
self.rec_imgs = OrderedDict()
self.rec_kernels = []
#################... | {
"repo_name": "jongyookim/IQA_BIECON_release",
"path": "IQA_BIECON_release/models/model_record.py",
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"autogenerated": false,
"ratio": 3.578207381370826,
... |
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
from pymel.core import aimConstraint, duplicate, hide, orientConstraint, parent
from ....add import simpleName
from .... import core
from .... import nodeApi
from ..cardRigging import MetaControl, ParamInfo
from .... | {
"repo_name": "patcorwin/fossil",
"path": "pdil/tool/fossil/rigging/twistHelper.py",
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"autogenerated": false,
"ratio": 3.511517077045274,
... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark, get_squares_, get_indexes_, get_indexes_rand_
from os.path import join as pjoin
import shutil
import sys
import six
from numpy import memmap, float32, array
import numpy as np
from tempfile import mkdtemp
class Indexing(... | {
"repo_name": "pizzathief/numpy",
"path": "benchmarks/benchmarks/bench_indexing.py",
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"size": "2150",
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"alpha_frac": 0.5325581395,
"autogenerated": false,
"ratio": 3.282442748091603,... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark, get_squares_, get_indexes_, get_indexes_rand_
import sys
import six
from numpy import memmap, float32, array
import numpy as np
class Indexing(Benchmark):
params = [["indexes_", "indexes_rand_"],
['I', ... | {
"repo_name": "I--P/numpy",
"path": "benchmarks/benchmarks/bench_indexing.py",
"copies": "6",
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"line_max": 77,
"alpha_frac": 0.5243073706,
"autogenerated": false,
"ratio": 3.215126050420168,
"conf... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark, get_squares_, get_indexes_rand, TYPES1
import numpy as np
class Eindot(Benchmark):
def setup(self):
self.a = np.arange(60000.0).reshape(150, 400)
self.ac = self.a.copy()
self.at = self.a.T
... | {
"repo_name": "MSeifert04/numpy",
"path": "benchmarks/benchmarks/bench_linalg.py",
"copies": "8",
"size": "2940",
"license": "bsd-3-clause",
"hash": -2764071286904178700,
"line_mean": 25.9724770642,
"line_max": 69,
"alpha_frac": 0.5676870748,
"autogenerated": false,
"ratio": 2.859922178988327,
... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark, get_squares
import numpy as np
from io import StringIO
class Copy(Benchmark):
params = ["int8", "int16", "float32", "float64",
"complex64", "complex128"]
param_names = ['type']
def setup(self, ... | {
"repo_name": "gfyoung/numpy",
"path": "benchmarks/benchmarks/bench_io.py",
"copies": "4",
"size": "7707",
"license": "bsd-3-clause",
"hash": 7641159149774309000,
"line_mean": 30.4571428571,
"line_max": 70,
"alpha_frac": 0.5764889062,
"autogenerated": false,
"ratio": 3.563106796116505,
"config_... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark, get_squares
import numpy as np
class Copy(Benchmark):
params = ["int8", "int16", "float32", "float64",
"complex64", "complex128"]
param_names = ['type']
def setup(self, typename):
dtype... | {
"repo_name": "joferkington/numpy",
"path": "benchmarks/benchmarks/bench_io.py",
"copies": "50",
"size": "1710",
"license": "bsd-3-clause",
"hash": -7977641385077962000,
"line_mean": 25.71875,
"line_max": 70,
"alpha_frac": 0.5760233918,
"autogenerated": false,
"ratio": 3.0052724077328645,
"conf... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark, get_squares_
import numpy as np
ufuncs = ['abs', 'absolute', 'add', 'arccos', 'arccosh', 'arcsin', 'arcsinh',
'arctan', 'arctan2', 'arctanh', 'bitwise_and', 'bitwise_not',
'bitwise_or', 'bitwise_xor',... | {
"repo_name": "shoyer/numpy",
"path": "benchmarks/benchmarks/bench_ufunc.py",
"copies": "8",
"size": "6252",
"license": "bsd-3-clause",
"hash": -2752458587807895000,
"line_mean": 28.6303317536,
"line_max": 83,
"alpha_frac": 0.5577415227,
"autogenerated": false,
"ratio": 3.133834586466165,
"conf... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark, get_squares_
import numpy as np
ufuncs = ['abs', 'absolute', 'add', 'arccos', 'arccosh', 'arcsin',
'arcsinh', 'arctan', 'arctan2', 'arctanh', 'bitwise_and',
'bitwise_not', 'bitwise_or', 'bitwise_xor',... | {
"repo_name": "kiwifb/numpy",
"path": "benchmarks/benchmarks/bench_ufunc.py",
"copies": "1",
"size": "3263",
"license": "bsd-3-clause",
"hash": -5798513652703011000,
"line_mean": 27.8761061947,
"line_max": 76,
"alpha_frac": 0.551333129,
"autogenerated": false,
"ratio": 3.217948717948718,
"confi... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark
import numpy as np
from numpy.lib import NumpyVersion
class Random(Benchmark):
params = ['normal', 'uniform', 'weibull 1', 'binomial 10 0.5',
'poisson 10']
def setup(self, name):
items = nam... | {
"repo_name": "maniteja123/numpy",
"path": "benchmarks/benchmarks/bench_random.py",
"copies": "25",
"size": "1631",
"license": "bsd-3-clause",
"hash": -6777461613499908000,
"line_mean": 23.3432835821,
"line_max": 66,
"alpha_frac": 0.5744941754,
"autogenerated": false,
"ratio": 3.349075975359343,
... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark
import numpy as np
from numpy.random import RandomState
try:
from numpy.random import Generator
except ImportError:
pass
class Random(Benchmark):
params = ['normal', 'uniform', 'weibull 1', 'binomial 10 0.5'... | {
"repo_name": "pizzathief/numpy",
"path": "benchmarks/benchmarks/bench_random.py",
"copies": "4",
"size": "5499",
"license": "bsd-3-clause",
"hash": -8347855482214384000,
"line_mean": 28.25,
"line_max": 78,
"alpha_frac": 0.5475541007,
"autogenerated": false,
"ratio": 3.3246674727932284,
"config... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark
import numpy as np
from six.moves import xrange
class LaplaceInplace(Benchmark):
params = ['inplace', 'normal']
param_names = ['update']
def setup(self, update):
N = 150
Niter = 1000
... | {
"repo_name": "b-carter/numpy",
"path": "benchmarks/benchmarks/bench_app.py",
"copies": "61",
"size": "2746",
"license": "bsd-3-clause",
"hash": 7599924315911336000,
"line_mean": 29.8539325843,
"line_max": 78,
"alpha_frac": 0.4905316824,
"autogenerated": false,
"ratio": 3.3124246079613995,
"con... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark
import numpy as np
class ArrayCoercionSmall(Benchmark):
# More detailed benchmarks for array coercion,
# some basic benchmarks are in `bench_core.py`.
params = [[range(3), [1], 1, np.array([5], dtype=np.int64)... | {
"repo_name": "WarrenWeckesser/numpy",
"path": "benchmarks/benchmarks/bench_array_coercion.py",
"copies": "17",
"size": "1705",
"license": "bsd-3-clause",
"hash": 8835328964583589000,
"line_mean": 28.9122807018,
"line_max": 77,
"alpha_frac": 0.6457478006,
"autogenerated": false,
"ratio": 3.247619... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark
import numpy as np
class Bincount(Benchmark):
def setup(self):
self.d = np.arange(80000, dtype=np.intp)
self.e = self.d.astype(np.float64)
def time_bincount(self):
np.bincount(self.d)
... | {
"repo_name": "behzadnouri/numpy",
"path": "benchmarks/benchmarks/bench_function_base.py",
"copies": "24",
"size": "3086",
"license": "bsd-3-clause",
"hash": -997963448862300000,
"line_mean": 23.4920634921,
"line_max": 71,
"alpha_frac": 0.5836033701,
"autogenerated": false,
"ratio": 3.12664640324... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark
import numpy as np
class Block(Benchmark):
params = [1, 10, 100]
param_names = ['size']
def setup(self, n):
self.a_2d = np.ones((2 * n, 2 * n))
self.b_1d = np.ones(2 * n)
self.b_2d = 2... | {
"repo_name": "gfyoung/numpy",
"path": "benchmarks/benchmarks/bench_shape_base.py",
"copies": "10",
"size": "4406",
"license": "bsd-3-clause",
"hash": -4505995233329370600,
"line_mean": 30.9275362319,
"line_max": 95,
"alpha_frac": 0.4809350885,
"autogenerated": false,
"ratio": 3.127040454222853,
... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark
import numpy as np
class Core(Benchmark):
def setup(self):
self.l100 = range(100)
self.l50 = range(50)
self.l = [np.arange(1000), np.arange(1000)]
self.l10x10 = np.ones((10, 10))
d... | {
"repo_name": "shoyer/numpy",
"path": "benchmarks/benchmarks/bench_core.py",
"copies": "4",
"size": "4147",
"license": "bsd-3-clause",
"hash": 6883988163029921000,
"line_mean": 22.8333333333,
"line_max": 66,
"alpha_frac": 0.5813841331,
"autogenerated": false,
"ratio": 2.968503937007874,
"config... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark
import numpy as np
class Histogram1D(Benchmark):
def setup(self):
self.d = np.linspace(0, 100, 100000)
def time_full_coverage(self):
np.histogram(self.d, 200, (0, 100))
def time_small_coverag... | {
"repo_name": "shoyer/numpy",
"path": "benchmarks/benchmarks/bench_function_base.py",
"copies": "1",
"size": "7481",
"license": "bsd-3-clause",
"hash": 7916582763484677000,
"line_mean": 26.7074074074,
"line_max": 95,
"alpha_frac": 0.564764069,
"autogenerated": false,
"ratio": 3.4379595588235294,
... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark
import numpy as np
class LaplaceInplace(Benchmark):
params = ['inplace', 'normal']
param_names = ['update']
def setup(self, update):
N = 150
Niter = 1000
dx = 0.1
dy = 0.1
... | {
"repo_name": "ChristopherHogan/numpy",
"path": "benchmarks/benchmarks/bench_app.py",
"copies": "29",
"size": "2716",
"license": "bsd-3-clause",
"hash": -5715242200719790000,
"line_mean": 30.2183908046,
"line_max": 78,
"alpha_frac": 0.4871134021,
"autogenerated": false,
"ratio": 3.30816077953715,... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark
import numpy as np
class MA(Benchmark):
def setup(self):
self.l100 = range(100)
self.t100 = ([True] * 100)
def time_masked_array(self):
np.ma.masked_array()
def time_masked_array_l100... | {
"repo_name": "gfyoung/numpy",
"path": "benchmarks/benchmarks/bench_ma.py",
"copies": "10",
"size": "3222",
"license": "bsd-3-clause",
"hash": -1295410326680170000,
"line_mean": 27.0173913043,
"line_max": 83,
"alpha_frac": 0.5509000621,
"autogenerated": false,
"ratio": 3.0744274809160306,
"conf... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark
import numpy as np
class Random(Benchmark):
params = ['normal', 'uniform', 'weibull 1', 'binomial 10 0.5',
'poisson 10']
def setup(self, name):
items = name.split()
name = items.pop(... | {
"repo_name": "solarjoe/numpy",
"path": "benchmarks/benchmarks/bench_random.py",
"copies": "17",
"size": "1639",
"license": "bsd-3-clause",
"hash": 3290632973403042000,
"line_mean": 23.4626865672,
"line_max": 66,
"alpha_frac": 0.5716900549,
"autogenerated": false,
"ratio": 3.3655030800821355,
"... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark
import numpy
class Core(Benchmark):
def setup(self):
self.l100 = range(100)
self.l50 = range(50)
self.l = [numpy.arange(1000), numpy.arange(1000)]
self.l10x10 = numpy.ones((10, 10))
... | {
"repo_name": "cjermain/numpy",
"path": "benchmarks/benchmarks/bench_core.py",
"copies": "39",
"size": "1929",
"license": "bsd-3-clause",
"hash": 9178732393563715000,
"line_mean": 20.1978021978,
"line_max": 64,
"alpha_frac": 0.5992742354,
"autogenerated": false,
"ratio": 3.106280193236715,
"con... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark
try:
from numpy.core.overrides import array_function_dispatch
except ImportError:
# Don't fail at import time with old Numpy versions
def array_function_dispatch(*args, **kwargs):
def wrap(*args, **kwarg... | {
"repo_name": "shoyer/numpy",
"path": "benchmarks/benchmarks/bench_overrides.py",
"copies": "8",
"size": "1859",
"license": "bsd-3-clause",
"hash": 8798350175554663000,
"line_mean": 25.9420289855,
"line_max": 64,
"alpha_frac": 0.6691769769,
"autogenerated": false,
"ratio": 3.5477099236641223,
"... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark, squares_, indexes_, indexes_rand_
import sys
import six
from numpy import memmap, float32, array
import numpy as np
class Indexing(Benchmark):
params = [["indexes_", "indexes_rand_"],
['I', ':,I', 'np.i... | {
"repo_name": "groutr/numpy",
"path": "benchmarks/benchmarks/bench_indexing.py",
"copies": "8",
"size": "1883",
"license": "bsd-3-clause",
"hash": -513901367072153500,
"line_mean": 25.1527777778,
"line_max": 77,
"alpha_frac": 0.5231014339,
"autogenerated": false,
"ratio": 3.2409638554216866,
"c... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark, squares_, indexes_rand
import numpy as np
class Eindot(Benchmark):
def setup(self):
self.a = np.arange(60000.0).reshape(150, 400)
self.b = np.arange(240000.0).reshape(400, 600)
self.c = np.ara... | {
"repo_name": "GrimDerp/numpy",
"path": "benchmarks/benchmarks/bench_linalg.py",
"copies": "29",
"size": "1927",
"license": "bsd-3-clause",
"hash": 77917432420441020,
"line_mean": 26.5285714286,
"line_max": 64,
"alpha_frac": 0.5651271406,
"autogenerated": false,
"ratio": 3.0587301587301585,
"co... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark, squares
import numpy as np
class Copy(Benchmark):
params = ["int8", "int16", "float32", "float64",
"complex64", "complex128"]
param_names = ['type']
def setup(self, typename):
dtype = n... | {
"repo_name": "moreati/numpy",
"path": "benchmarks/benchmarks/bench_io.py",
"copies": "29",
"size": "1642",
"license": "bsd-3-clause",
"hash": -8933031371171915000,
"line_mean": 25.9180327869,
"line_max": 70,
"alpha_frac": 0.5755176614,
"autogenerated": false,
"ratio": 2.9854545454545454,
"conf... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark, squares_
import numpy as np
ufuncs = ['abs', 'absolute', 'add', 'arccos', 'arccosh', 'arcsin',
'arcsinh', 'arctan', 'arctan2', 'arctanh', 'bitwise_and',
'bitwise_not', 'bitwise_or', 'bitwise_xor', 'cb... | {
"repo_name": "rhythmsosad/numpy",
"path": "benchmarks/benchmarks/bench_ufunc.py",
"copies": "29",
"size": "3320",
"license": "bsd-3-clause",
"hash": -348672514474527360,
"line_mean": 27.6206896552,
"line_max": 76,
"alpha_frac": 0.5524096386,
"autogenerated": false,
"ratio": 3.2233009708737863,
... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark, TYPES1, get_squares
import numpy as np
class AddReduce(Benchmark):
def setup(self):
self.squares = get_squares().values()
def time_axis_0(self):
[np.add.reduce(a, axis=0) for a in self.squares]
... | {
"repo_name": "gfyoung/numpy",
"path": "benchmarks/benchmarks/bench_reduce.py",
"copies": "3",
"size": "1567",
"license": "bsd-3-clause",
"hash": 369208901052498100,
"line_mean": 21.7101449275,
"line_max": 64,
"alpha_frac": 0.6119974474,
"autogenerated": false,
"ratio": 3.2851153039832286,
"con... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark, TYPES1
import numpy as np
class Take(Benchmark):
params = [
[(1000, 1), (1000, 2), (2, 1000, 1), (1000, 3)],
["raise", "wrap", "clip"],
TYPES1]
param_names = ["shape", "mode", "dtype"]
... | {
"repo_name": "anntzer/numpy",
"path": "benchmarks/benchmarks/bench_itemselection.py",
"copies": "17",
"size": "1247",
"license": "bsd-3-clause",
"hash": -592601035751068200,
"line_mean": 26.7111111111,
"line_max": 64,
"alpha_frac": 0.5950280674,
"autogenerated": false,
"ratio": 3.325333333333333... |
from __future__ import absolute_import, division, print_function
from .common import Benchmark, TYPES1, squares
import numpy as np
class AddReduce(Benchmark):
def time_axis_0(self):
[np.add.reduce(a, axis=0) for a in squares.values()]
def time_axis_1(self):
[np.add.reduce(a, axis=1) for a i... | {
"repo_name": "Yusa95/numpy",
"path": "benchmarks/benchmarks/bench_reduce.py",
"copies": "29",
"size": "1399",
"license": "bsd-3-clause",
"hash": 7202777401067326000,
"line_mean": 20.859375,
"line_max": 64,
"alpha_frac": 0.611150822,
"autogenerated": false,
"ratio": 3.2013729977116703,
"config_... |
from __future__ import absolute_import, division, print_function
from ..common.vispy_data_viewer import BaseVispyViewer
from .layer_artist import IsosurfaceLayerArtist
from .layer_style_widget import IsosurfaceLayerStyleWidget
from .viewer_state import Vispy3DIsosurfaceViewerState
from ..common import tools as _tools... | {
"repo_name": "astrofrog/glue-vispy-viewers",
"path": "glue_vispy_viewers/isosurface/isosurface_viewer.py",
"copies": "2",
"size": "1025",
"license": "bsd-2-clause",
"hash": 6176645927758370000,
"line_mean": 29.1470588235,
"line_max": 76,
"alpha_frac": 0.7190243902,
"autogenerated": false,
"ratio... |
from __future__ import absolute_import, division, print_function
from ..common.vispy_data_viewer import BaseVispyViewer
from .layer_artist import ScatterLayerArtist
from .layer_style_widget import ScatterLayerStyleWidget
from .viewer_state import Vispy3DScatterViewerState
from ..common import selection_tools # noqa
... | {
"repo_name": "PennyQ/glue-3d-viewer",
"path": "glue_vispy_viewers/scatter/scatter_viewer.py",
"copies": "1",
"size": "1491",
"license": "bsd-2-clause",
"hash": 2833851321135696000,
"line_mean": 28.2352941176,
"line_max": 74,
"alpha_frac": 0.6800804829,
"autogenerated": false,
"ratio": 3.76515151... |
from __future__ import absolute_import, division, print_function
from .compatibility import reduce, Iterator
from .matching import (Traverser, Pattern, PatternSet, StaticPatternSet,
DynamicPatternSet)
from .util import copy_doc
class Engine(object):
"""Main entry point for Pinyon"""
def __init__(sel... | {
"repo_name": "jcrist/pinyon",
"path": "pinyon/core.py",
"copies": "1",
"size": "4023",
"license": "bsd-3-clause",
"hash": 59730178123307200,
"line_mean": 32.8067226891,
"line_max": 79,
"alpha_frac": 0.5550584141,
"autogenerated": false,
"ratio": 4.387131952017448,
"config_test": false,
"has_... |
from __future__ import absolute_import, division, print_function
from ..compatibility import _strtypes, _inttypes
__all__ = 'parse_index', 'emit_index'
def parse_index(ind, inside=False):
""" Parse structured index into Pythonic form
>>> parse_index([1, {'start': 0, 'stop': 10}])
(1, slice(0, 10, None)... | {
"repo_name": "LiaoPan/blaze",
"path": "blaze/server/index.py",
"copies": "10",
"size": "1398",
"license": "bsd-3-clause",
"hash": 6644134238319161000,
"line_mean": 28.125,
"line_max": 76,
"alpha_frac": 0.5793991416,
"autogenerated": false,
"ratio": 3.621761658031088,
"config_test": false,
"h... |
from __future__ import absolute_import, division, print_function
from contextlib import contextmanager
from collections import namedtuple
from llvm.core import Constant
import llvm.core as lc
import llvm_cbuilder.shortnames as C
from blaze.py2help import reduce
@contextmanager
def position(builder, block):
'''Tem... | {
"repo_name": "XinSong/blaze",
"path": "blaze/compute/bkernel/kernelgen.py",
"copies": "2",
"size": "5735",
"license": "bsd-3-clause",
"hash": 5231556046368226000,
"line_mean": 31.9597701149,
"line_max": 85,
"alpha_frac": 0.5883173496,
"autogenerated": false,
"ratio": 3.620580808080808,
"config... |
from __future__ import absolute_import, division, print_function
from contextlib import contextmanager
from ctypes import (
CFUNCTYPE,
POINTER,
c_int,
c_longlong,
c_void_p,
cast,
create_string_buffer,
)
import libarchive
import libarchive.ffi as ffi
from fsspec import open_files
from fssp... | {
"repo_name": "intake/filesystem_spec",
"path": "fsspec/implementations/libarchive.py",
"copies": "1",
"size": "7073",
"license": "bsd-3-clause",
"hash": 5293636143006004000,
"line_mean": 32.680952381,
"line_max": 88,
"alpha_frac": 0.5785381026,
"autogenerated": false,
"ratio": 4.039406053683609,... |
from __future__ import absolute_import, division, print_function
from contextlib import contextmanager
from warnings import warn
import networkx as nx
from datashape import discover
from .utils import expand_tuples, ignoring
ooc_types = set() # Out-of-Core types
class FailedConversionWarning(UserWarning):
de... | {
"repo_name": "cpcloud/odo",
"path": "odo/core.py",
"copies": "2",
"size": "3511",
"license": "bsd-3-clause",
"hash": -8352149692060789000,
"line_mean": 29.2672413793,
"line_max": 79,
"alpha_frac": 0.5872970664,
"autogenerated": false,
"ratio": 3.7915766738660905,
"config_test": false,
"has_n... |
from __future__ import absolute_import, division, print_function
from contextlib import contextmanager
import numpy as np
from matplotlib.colors import ColorConverter
from ..extern.vispy import scene
from glue.external import six
class MultiColorScatter(scene.visuals.Markers):
"""
This is a helper class ... | {
"repo_name": "PennyQ/glue-3d-viewer",
"path": "glue_vispy_viewers/scatter/multi_scatter.py",
"copies": "1",
"size": "5553",
"license": "bsd-2-clause",
"hash": -6358218402749636000,
"line_mean": 27.921875,
"line_max": 83,
"alpha_frac": 0.5222402305,
"autogenerated": false,
"ratio": 3.910563380281... |
from __future__ import absolute_import, division, print_function
from ..convert import convert
from ..append import append
from ..create import create
from datashape import discover, dshape, DataShape, Record, Tuple
import datashape
import numpy as np
from dynd import nd
@convert.register(np.ndarray, nd.array, cost=1... | {
"repo_name": "cpcloud/odo",
"path": "odo/backends/dynd.py",
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... |
from __future__ import absolute_import, division, print_function
from copy import deepcopy
import numpy as np
import pandas as pd
import pytest
from xarray import DataArray, Dataset, Variable, auto_combine, concat
from xarray.core.pycompat import OrderedDict, iteritems
from . import (
InaccessibleArray, TestCas... | {
"repo_name": "jcmgray/xarray",
"path": "xarray/tests/test_combine.py",
"copies": "1",
"size": "16696",
"license": "apache-2.0",
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"autogenerated": false,
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"config_test... |
from __future__ import absolute_import, division, print_function
from .core import common_subexpression
from .expressions import Expr, Symbol
from .reductions import Reduction, Summary, summary
from ..dispatch import dispatch
from datashape import dshape, Record, Option, Unit, var
__all__ = ['by', 'By', 'count_values... | {
"repo_name": "vitan/blaze",
"path": "blaze/expr/split_apply_combine.py",
"copies": "1",
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"autogenerated": false,
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"config_te... |
from __future__ import absolute_import, division, print_function
from .core import common_subexpression
from .expressions import Expr, symbol
from .reductions import Reduction, Summary, summary
from ..dispatch import dispatch
from datashape import dshape, Record, Option, Unit, var
__all__ = ['by', 'By', 'count_values... | {
"repo_name": "dwillmer/blaze",
"path": "blaze/expr/split_apply_combine.py",
"copies": "2",
"size": "2872",
"license": "bsd-3-clause",
"hash": 7743877256153608000,
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"line_max": 70,
"alpha_frac": 0.6103760446,
"autogenerated": false,
"ratio": 3.567701863354037,
"config... |
from __future__ import absolute_import, division, print_function
from .core import VAR, PatternSet
from ..util import copy_doc
class StaticPatternSet(PatternSet):
"""A set of patterns.
Forms a structure for fast matching over a set of patterns. This allows for
matching of terms to patterns for many patt... | {
"repo_name": "jcrist/pinyon",
"path": "pinyon/matching/static.py",
"copies": "1",
"size": "5950",
"license": "bsd-3-clause",
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"line_mean": 28.4554455446,
"line_max": 80,
"alpha_frac": 0.5425210084,
"autogenerated": false,
"ratio": 3.8312942691564715,
"config_test": ... |
from __future__ import absolute_import, division, print_function
from cs231n.layers import *
from cs231n.fast_layers import *
def affine_relu_forward(x, w, b):
"""
Convenience layer that perorms an affine transform followed by a
ReLU
Inputs:
- x: Input to the affine layer
- w, b: Weights for... | {
"repo_name": "deehzee/cs231n",
"path": "assignment2/cs231n/layer_utils.py",
"copies": "1",
"size": "2654",
"license": "mit",
"hash": -1755899769476504300,
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"line_max": 70,
"alpha_frac": 0.648455162,
"autogenerated": false,
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"config_test": f... |
from __future__ import (absolute_import, division, print_function)
from cStringIO import StringIO
from elftools.elf.constants import P_FLAGS, SH_FLAGS
from elftools.elf.elffile import ELFFile
from ret.ia32 import IA32Simulator
from ret.state import MemoryRange
from struct import unpack
from sys import argv, exit, stde... | {
"repo_name": "dacut/ret",
"path": "ret/elf.py",
"copies": "1",
"size": "12636",
"license": "bsd-2-clause",
"hash": -4743343774672632000,
"line_mean": 32.0785340314,
"line_max": 90,
"alpha_frac": 0.5739157961,
"autogenerated": false,
"ratio": 4.005071315372425,
"config_test": false,
"has_no_k... |
from __future__ import absolute_import, division, print_function
from databroker import DataBroker as db, get_images, get_table, get_events
from filestore.api import register_handler, deregister_handler
from filestore.retrieve import _h_registry, _HANDLER_CACHE
import numpy as np
import matplotlib as mpl
import matpl... | {
"repo_name": "yugangzhang/chx_backups",
"path": "develop.py",
"copies": "1",
"size": "32745",
"license": "bsd-3-clause",
"hash": -5618336355218712000,
"line_mean": 32.6893004115,
"line_max": 121,
"alpha_frac": 0.5747442358,
"autogenerated": false,
"ratio": 3.36087447398132,
"config_test": fals... |
from __future__ import absolute_import, division, print_function
from ..data import Component, Data
from .io import extract_data_fits, extract_data_hdf5
from ...utils import file_format
from ..coordinates import coordinates_from_header
from .helpers import set_default_factory, __factories__
__all__ = ['is_casalike',... | {
"repo_name": "JudoWill/glue",
"path": "glue/core/data_factories/gridded.py",
"copies": "1",
"size": "3623",
"license": "bsd-3-clause",
"hash": -958854145812053200,
"line_mean": 28.2177419355,
"line_max": 73,
"alpha_frac": 0.6455975711,
"autogenerated": false,
"ratio": 3.5978152929493543,
"conf... |
from __future__ import absolute_import, division, print_function
from datashape import DataShape, Record, Fixed, Var, CType, String, JSON
from jinja2 import Template
json_comment_templ = Template("""<font style="font-size:x-small"> # <a href="{{base_url}}?r=data.json">JSON</a></font>
""")
datashape_outer_templ = T... | {
"repo_name": "aaronmartin0303/blaze",
"path": "blaze/io/server/datashape_html.py",
"copies": "8",
"size": "1881",
"license": "bsd-3-clause",
"hash": 6588314757637002000,
"line_mean": 34.4905660377,
"line_max": 118,
"alpha_frac": 0.5608718767,
"autogenerated": false,
"ratio": 3.5093283582089554,
... |
from __future__ import absolute_import, division, print_function
from datashape import discover
from datashape.dispatch import dispatch
from ..append import append
from ..convert import convert, ooc_types
from ..resource import resource
from ..chunks import chunks, Chunks
from ..utils import tmpfile
import os
import... | {
"repo_name": "mrocklin/into",
"path": "into/backends/pytables.py",
"copies": "1",
"size": "4565",
"license": "bsd-3-clause",
"hash": -6277485968781287000,
"line_mean": 26.6666666667,
"line_max": 83,
"alpha_frac": 0.6,
"autogenerated": false,
"ratio": 3.450491307634165,
"config_test": false,
... |
from __future__ import absolute_import, division, print_function
from datashape import discover
from datashape import (float32, float64, string, Option, Record, object_,
datetime_)
import datashape
import pandas as pd
possibly_missing = set((string, datetime_, float32, float64))
@discover.register(pd.DataF... | {
"repo_name": "alexmojaki/odo",
"path": "odo/backends/pandas.py",
"copies": "3",
"size": "1540",
"license": "bsd-3-clause",
"hash": 4489270518052764000,
"line_mean": 26.0175438596,
"line_max": 77,
"alpha_frac": 0.638961039,
"autogenerated": false,
"ratio": 3.564814814814815,
"config_test": fals... |
from __future__ import absolute_import, division, print_function
from datashape import discover
import datashape
import pandas as pd
@discover.register(pd.DataFrame)
def discover_dataframe(df):
obj = datashape.coretypes.object_
names = list(df.columns)
dtypes = list(map(datashape.CType.from_numpy_dtype,... | {
"repo_name": "mrocklin/into",
"path": "into/backends/pandas.py",
"copies": "1",
"size": "1319",
"license": "bsd-3-clause",
"hash": -7222090020611753000,
"line_mean": 24.862745098,
"line_max": 77,
"alpha_frac": 0.639878696,
"autogenerated": false,
"ratio": 3.4986737400530505,
"config_test": fal... |
from __future__ import absolute_import, division, print_function
from datashape import dshape
from blaze.expr import *
from blaze.expr.core import subs
def test_subs():
from blaze.expr import TableSymbol
t = TableSymbol('t', '{name: string, amount: int, id: int}')
expr = t['amount'] + 3
assert expr._... | {
"repo_name": "cpcloud/blaze",
"path": "blaze/expr/tests/test_core.py",
"copies": "2",
"size": "1848",
"license": "bsd-3-clause",
"hash": 1398539418473203700,
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"line_max": 85,
"alpha_frac": 0.5741341991,
"autogenerated": false,
"ratio": 2.966292134831461,
"config_test": fals... |
from __future__ import absolute_import, division, print_function
from datashape import dshape
from blaze.expr import *
def test_subs():
from blaze.expr import TableSymbol
t = TableSymbol('t', '{name: string, amount: int, id: int}')
expr = t['amount'] + 3
assert expr._subs({3: 4, 'amount': 'id'}).isid... | {
"repo_name": "vitan/blaze",
"path": "blaze/expr/tests/test_core.py",
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"hash": 5657616168192241000,
"line_mean": 31.95,
"line_max": 68,
"alpha_frac": 0.5971168437,
"autogenerated": false,
"ratio": 3.0368663594470044,
"config_test": false,... |
from __future__ import absolute_import, division, print_function
from datashape import dshape
from dynd import nd
from . import Capabilities
from .data_descriptor import DDesc
def dynd_descriptor_iter(dyndarr):
for el in dyndarr:
yield DyND_DDesc(el)
class DyND_DDesc(DDesc):
"""
A Blaze data d... | {
"repo_name": "talumbau/blaze",
"path": "blaze/datadescriptor/dynd_data_descriptor.py",
"copies": "3",
"size": "1907",
"license": "bsd-3-clause",
"hash": -6256454076907523000,
"line_mean": 27.8939393939,
"line_max": 72,
"alpha_frac": 0.5993707394,
"autogenerated": false,
"ratio": 3.82931726907630... |
from __future__ import absolute_import, division, print_function
from datashape import dshape
from dynd import nd
from . import Capabilities
from .data_descriptor import IDataDescriptor
def dynd_descriptor_iter(dyndarr):
for el in dyndarr:
yield DyNDDataDescriptor(el)
class DyNDDataDescriptor(IDataDes... | {
"repo_name": "markflorisson/blaze-core",
"path": "blaze/datadescriptor/dynd_data_descriptor.py",
"copies": "10",
"size": "1959",
"license": "bsd-3-clause",
"hash": -658561450505429600,
"line_mean": 28.6818181818,
"line_max": 72,
"alpha_frac": 0.6120469627,
"autogenerated": false,
"ratio": 3.9495... |
from __future__ import absolute_import, division, print_function
from datashape import dshape, Record
from toolz import pluck, get, curry, keyfilter
from contextlib import contextmanager
from multiprocessing.pool import ThreadPool
import inspect
import datetime
import tempfile
import os
import shutil
import numpy as n... | {
"repo_name": "ywang007/odo",
"path": "odo/utils.py",
"copies": "1",
"size": "9843",
"license": "bsd-3-clause",
"hash": -2618550608532728000,
"line_mean": 24.3033419023,
"line_max": 80,
"alpha_frac": 0.5685258559,
"autogenerated": false,
"ratio": 3.803323029366306,
"config_test": false,
"has_... |
from __future__ import absolute_import, division, print_function
from datashape import dshape, Record
from toolz import pluck, get
from contextlib import contextmanager
import inspect
import datetime
import tempfile
import os
import numpy as np
from .compatibility import unicode
def raises(err, lamda):
try:
... | {
"repo_name": "mrocklin/into",
"path": "into/utils.py",
"copies": "1",
"size": "5097",
"license": "bsd-3-clause",
"hash": -1584814941669865700,
"line_mean": 23.6231884058,
"line_max": 80,
"alpha_frac": 0.548950363,
"autogenerated": false,
"ratio": 3.70421511627907,
"config_test": false,
"has_... |
from __future__ import absolute_import, division, print_function
from datashape import *
from datashape.predicates import iscollection
import itertools
from toolz import curry
from .expressions import *
from .expressions import Field, Map
from .arithmetic import maxshape, Arithmetic, UnaryOp
from .math import Math, s... | {
"repo_name": "maxalbert/blaze",
"path": "blaze/expr/broadcast.py",
"copies": "2",
"size": "4155",
"license": "bsd-3-clause",
"hash": 5596870354175858000,
"line_mean": 27.8541666667,
"line_max": 97,
"alpha_frac": 0.602166065,
"autogenerated": false,
"ratio": 3.6351706036745406,
"config_test": f... |
from __future__ import absolute_import, division, print_function
from datashape import Option, real, int_, bool_, isreal, isnumeric
from .arithmetic import UnaryOp, BinOp, Arithmetic
from .expressions import schema_method_list
from ..compatibility import builtins
# Here follows a large number of unary operators. T... | {
"repo_name": "ContinuumIO/blaze",
"path": "blaze/expr/math.py",
"copies": "3",
"size": "2997",
"license": "bsd-3-clause",
"hash": -1153204084879701500,
"line_mean": 19.958041958,
"line_max": 78,
"alpha_frac": 0.5632298966,
"autogenerated": false,
"ratio": 3.233009708737864,
"config_test": fals... |
from __future__ import absolute_import, division, print_function
from datashape import Option, real, int_, bool_, isreal, isnumeric
from .arithmetic import UnaryOp
from .expressions import schema_method_list
# Here follows a large number of unary operators. These were selected by
# taking the intersection of the f... | {
"repo_name": "jdmcbr/blaze",
"path": "blaze/expr/math.py",
"copies": "5",
"size": "2664",
"license": "bsd-3-clause",
"hash": 5275254344811025000,
"line_mean": 23,
"line_max": 79,
"alpha_frac": 0.6509009009,
"autogenerated": false,
"ratio": 3.455252918287938,
"config_test": false,
"has_no_key... |
from __future__ import absolute_import, division, print_function
from datashape import real, int_, bool_
from .arithmetic import UnaryOp
# Here follows a large number of unary operators. These were selected by
# taking the intersection of the functions in ``math`` and ``numpy``
__all__ = ['abs', 'sqrt', 'sin', 'sin... | {
"repo_name": "dwillmer/blaze",
"path": "blaze/expr/math.py",
"copies": "3",
"size": "2228",
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"alpha_frac": 0.6732495512,
"autogenerated": false,
"ratio": 3.4382716049382718,
"config_test": false,... |
from __future__ import absolute_import, division, print_function
from datashape import var
class _slice(object):
""" A hashable slice object
>>> _slice(0, 10, None)
0:10
"""
def __init__(self, start, stop, step):
self.start = start
self.stop = stop
self.step = step
d... | {
"repo_name": "ContinuumIO/blaze",
"path": "blaze/expr/utils.py",
"copies": "3",
"size": "2812",
"license": "bsd-3-clause",
"hash": -7991022400516487000,
"line_mean": 22.4333333333,
"line_max": 72,
"alpha_frac": 0.5480085349,
"autogenerated": false,
"ratio": 3.5505050505050506,
"config_test": f... |
from __future__ import absolute_import, division, print_function
from datashape.predicates import isscalar
from .compute.sql import select
from .data.sql import SQL, dispatch
from .expr import Expr, Projection, Field, UnaryOp, BinOp, Join
from .data.sql import SQL, dispatch
from .compatibility import basestring
from .... | {
"repo_name": "vitan/blaze",
"path": "blaze/sql.py",
"copies": "1",
"size": "2960",
"license": "bsd-3-clause",
"hash": -4958628582000986000,
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"line_max": 78,
"alpha_frac": 0.689527027,
"autogenerated": false,
"ratio": 3.4946871310507674,
"config_test": false,
"has_n... |
from __future__ import absolute_import, division, print_function
from datashape.predicates import isscalar
from multipledispatch import MDNotImplementedError
from .expressions import *
from .strings import *
from .arithmetic import *
from .collections import *
from .split_apply_combine import *
from .broadcast import... | {
"repo_name": "mrocklin/blaze",
"path": "blaze/expr/optimize.py",
"copies": "1",
"size": "6207",
"license": "bsd-3-clause",
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"line_max": 84,
"alpha_frac": 0.6286450781,
"autogenerated": false,
"ratio": 3.4617958728388176,
"config_test": fa... |
from __future__ import absolute_import, division, print_function
from ..data_slice_widget import SliceWidget
class TestSliceWidget(object):
def test_slice_center(self):
s = SliceWidget(lo=0, hi=10)
assert s.state.slice_center == 5
def test_browse_slice(self):
s = SliceWidget(lo=0, h... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/viewers/common/qt/tests/test_data_slice_widget.py",
"copies": "3",
"size": "2161",
"license": "bsd-3-clause",
"hash": 372229072169675650,
"line_mean": 31.7424242424,
"line_max": 64,
"alpha_frac": 0.5821378991,
"autogenerated": false,
"ratio": 3.... |
from __future__ import absolute_import, division, print_function
from .data_viewer import DataViewer
from ...external.qt.QtGui import QTableView
from ...external.qt.QtCore import Qt, QAbstractTableModel
import numpy as np
class DataTableModel(QAbstractTableModel):
def __init__(self, data):
super(DataT... | {
"repo_name": "JudoWill/glue",
"path": "glue/qt/widgets/table_widget.py",
"copies": "1",
"size": "2577",
"license": "bsd-3-clause",
"hash": 5055864405747915000,
"line_mean": 27.0108695652,
"line_max": 67,
"alpha_frac": 0.6131160264,
"autogenerated": false,
"ratio": 4.05188679245283,
"config_tes... |
from __future__ import absolute_import, division, print_function
from datetime import date, datetime, time
from decimal import Decimal
from dynd import nd
import sqlalchemy as sql
import datashape
from ..utils import partition_all
from ..py2help import basestring
from .core import DataDescriptor
from .utils import co... | {
"repo_name": "sethkontny/blaze",
"path": "blaze/data/sql.py",
"copies": "1",
"size": "4563",
"license": "bsd-3-clause",
"hash": -1072653869107948300,
"line_mean": 29.8310810811,
"line_max": 83,
"alpha_frac": 0.594126671,
"autogenerated": false,
"ratio": 3.910025706940874,
"config_test": false,... |
from __future__ import absolute_import, division, print_function
from datetime import datetime, date, timedelta
from math import floor
import sys
def identity(x):
return x
def asday(dt):
if isinstance(dt, datetime):
return dt.date()
else:
return dt
def asweek(dt):
if isinstance(dt, d... | {
"repo_name": "mrocklin/blaze",
"path": "blaze/compute/pydatetime.py",
"copies": "1",
"size": "6077",
"license": "bsd-3-clause",
"hash": -1615627861027761000,
"line_mean": 25.4217391304,
"line_max": 86,
"alpha_frac": 0.5859799243,
"autogenerated": false,
"ratio": 3.339010989010989,
"config_test... |
from __future__ import absolute_import, division, print_function
from datetime import datetime, date, timedelta
import sys
def identity(x):
return x
def asday(dt):
if isinstance(dt, datetime):
return dt.date()
else:
return dt
def asweek(dt):
if isinstance(dt, datetime):
dt... | {
"repo_name": "caseyclements/blaze",
"path": "blaze/compute/pydatetime.py",
"copies": "16",
"size": "6133",
"license": "bsd-3-clause",
"hash": 5553525334950036000,
"line_mean": 24.2386831276,
"line_max": 81,
"alpha_frac": 0.5776944399,
"autogenerated": false,
"ratio": 3.3642347778387274,
"confi... |
from __future__ import absolute_import, division, print_function
from datetime import datetime
from collections import defaultdict
from toolz import merge
import bisect
import numpy as np
import pandas as pd
from .core import new_dd_object, Series
from . import methods
from ..base import tokenize
class _LocIndexer... | {
"repo_name": "chrisbarber/dask",
"path": "dask/dataframe/indexing.py",
"copies": "1",
"size": "9724",
"license": "bsd-3-clause",
"hash": -5508190505946601000,
"line_mean": 34.36,
"line_max": 81,
"alpha_frac": 0.5700329083,
"autogenerated": false,
"ratio": 3.9099316445516688,
"config_test": fal... |
from __future__ import absolute_import, division, print_function
from datetime import datetime
from decimal import Decimal
import sqlalchemy as sa
import sqlalchemy.orm
from toolz import curry
from datashape.predicates import isrecord
from ..expr import Field
from odo.backends.sql import dshape_to_alchemy
# This was... | {
"repo_name": "ContinuumIO/blaze",
"path": "blaze/compute/utils.py",
"copies": "3",
"size": "2286",
"license": "bsd-3-clause",
"hash": -7117790029585822000,
"line_mean": 32.6176470588,
"line_max": 79,
"alpha_frac": 0.6614173228,
"autogenerated": false,
"ratio": 4.3961538461538465,
"config_test"... |
from __future__ import absolute_import, division, print_function
from datetime import datetime
from django.db.models import Q
from django.utils import timezone
from rest_framework import serializers
from rest_framework.response import Response
from sentry.app import search
from sentry.api.base import DocSection
from ... | {
"repo_name": "korealerts1/sentry",
"path": "src/sentry/api/endpoints/project_group_index.py",
"copies": "7",
"size": "18003",
"license": "bsd-3-clause",
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"alpha_frac": 0.5486863301,
"autogenerated": false,
"ratio": 4.61970... |
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