text stringlengths 0 1.05M | meta dict |
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from __future__ import absolute_import, division, print_function
import numpy as np
import h5py
from multipledispatch import MDNotImplementedError
from datashape import DataShape, to_numpy
from ..partition import partitions, partition_get, partition_set, flatten
from ..expr import Reduction, Field, Projection, Broadc... | {
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... |
from __future__ import absolute_import, division, print_function
import numpy as np
import h5py
import pytest
from blaze import compute
from blaze.expr import Symbol
from datashape import discover
from blaze.utils import tmpfile
from blaze.compute.h5py import *
def eq(a, b):
return (a == b).all()
x = np.ara... | {
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"path": "blaze/compute/tests/test_h5py.py",
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... |
from __future__ import absolute_import, division, print_function
import numpy as np
import json
from collections import OrderedDict
import copy
import math
from atom.api import Atom, Str, observe, Typed, Int, Dict, List, Float, Bool
from skbeam.fluorescence import XrfElement as Element
from skbeam.core.fitting.xrf_m... | {
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"path": "pyxrf/model/parameters.py",
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from __future__ import absolute_import, division, print_function
import numpy as np
import math
from functools import partial
from matplotlib.figure import Figure, Axes
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.ticker as mticker
from mpl_toolkits.axes_grid1.axes_rgb import... | {
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"path": "pyxrf/model/draw_image_rgb.py",
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from __future__ import absolute_import, division, print_function
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import commah
def runcommand(cosmology='WMAP5'):
""" Example interface commands """
# Return the WMAP5 cosmology concentration predicted for
# z=0 range of masse... | {
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... |
from __future__ import absolute_import, division, print_function
import numpy as np
import matplotlib.pyplot as plt
__all__ = ['TaylorDiagram']
class TaylorDiagram(object):
"""
Taylor diagram: plot model standard deviation and correlation
to reference (data) sample in a single-quadrant polar plot, with... | {
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"path": "utilities/taylor_diagram.py",
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from __future__ import absolute_import, division, print_function
import numpy as np
import matplotlib.pyplot as plt
dab = plt.imread('parametric_dab.png')
print(dab.shape)
l = dab[500, :, 3]
#plt.plot(l, label="opacity")
r = np.hstack((np.linspace(1, 0, 500), np.linspace(0, 1, 500)[1:]))
#plt.plot(r, label="$r$")... | {
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from __future__ import absolute_import, division, print_function
import numpy as np
import numpy.ma as ma
from pandas import DataFrame
__all__ = ['both_valid',
'mean_bias',
'median_bias',
'rmse',
'r2',
'apply_skill',
'low_pass']
def both_valid(x, y):... | {
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"path": "utilities/skill_score.py",
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"h... |
from __future__ import absolute_import, division, print_function
import numpy as np
import numpy.ma as ma
from pandas.tseries.frequencies import to_offset
from pandas import DatetimeIndex, Series, rolling_median
__all__ = ['has_time_gaps',
'is_monotonically_increasing',
'is_flatline',
... | {
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"path": "utilities/qaqc.py",
"copies": "2",
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"autogenerated": false,
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"config_test": false,
"has_no... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
from datashape.predicates import isscalar
from toolz import concat, curry, partition_all
from collections import Iterator, Iterable
import datashape
from .core import NetworkDispatcher, ooc_types
from .chunks import... | {
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"path": "into/convert.py",
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"alpha_frac": 0.6659361731,
"autogenerated": false,
"ratio": 3.122754491017964,
"config_test": false,
"... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
from datashape.predicates import isscalar
from toolz import concat, partition_all, compose
from collections import Iterator, Iterable
import datashape
from datashape import discover
from .core import NetworkDispatch... | {
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"path": "odo/convert.py",
"copies": "4",
"size": "9752",
"license": "bsd-3-clause",
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"line_max": 96,
"alpha_frac": 0.6350492207,
"autogenerated": false,
"ratio": 3.2957080094626563,
"config_test": false,
... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
from datashape.predicates import isscalar
from toolz import concat, partition_all
from collections import Iterator, Iterable
import datashape
from datashape import discover
from .core import NetworkDispatcher, ooc_t... | {
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"path": "odo/convert.py",
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"line_max": 96,
"alpha_frac": 0.6575983804,
"autogenerated": false,
"ratio": 3.1854091092301493,
"config_test": false,
"h... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
from toolz import first, partial
from ..core import DataFrame, Series
from ..utils import UNKNOWN_CATEGORIES
from ...base import tokenize, normalize_token
from ...compatibility import PY3
from ...delayed import del... | {
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"autogenerated": false,
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"config_test": false,
"... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
from toolz import partial
def maybe_wrap_pandas(obj, x):
if isinstance(x, np.ndarray):
if isinstance(obj, pd.Series):
return pd.Series(x, index=obj.index, dtype=x.dtype)
return pd.I... | {
"repo_name": "mraspaud/dask",
"path": "dask/dataframe/accessor.py",
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from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
import gsd.hoomd
import sklearn
import scipy.optimize as opt
import os
import os.path
import pdb
from sklearn.neighbors import BallTree
from sklearn.neighbors import radius_neighbors_graph
from scipy.spatial.distanc... | {
"repo_name": "ramansbach/cluster_analysis",
"path": "clustering/scripts/old-scripts/clustering_temp.py",
"copies": "1",
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"autogenerated": false,
"ratio": 4.178... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
from .common import is_np_datetime_like, _contains_datetime_like_objects
from .pycompat import dask_array_type
def _season_from_months(months):
"""Compute season (DJF, MAM, JJA, SON) from month ordinal
""... | {
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"path": "xarray/core/accessors.py",
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"license": "apache-2.0",
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"alpha_frac": 0.6192098093,
"autogenerated": false,
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from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
from .core import Series, DataFrame, map_partitions, apply_concat_apply
from . import methods
from .utils import is_categorical_dtype, is_scalar, has_known_categories
#############################################... | {
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"path": "dask/dataframe/reshape.py",
"copies": "1",
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"license": "bsd-3-clause",
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"line_max": 79,
"alpha_frac": 0.5568993152,
"autogenerated": false,
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from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
from ..external.six import string_types
__all__ = ['unique', 'shape_to_string', 'view_shape', 'stack_view',
'coerce_numeric', 'check_sorted']
def unique(array):
"""
Return the unique elements ... | {
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from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
from glue.external.six import string_types
__all__ = ['unique', 'shape_to_string', 'view_shape', 'stack_view',
'coerce_numeric', 'check_sorted', 'broadcast_to']
def unique(array):
"""
Return ... | {
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... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
from pandas.types.common import (is_categorical_dtype, is_numeric_dtype,
is_datetime64_dtype, is_timedelta64_dtype)
from pandas.lib import is_bool_array
from .utils import PANDAS_V... | {
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"path": "dask/dataframe/hashing.py",
"copies": "1",
"size": "6179",
"license": "bsd-3-clause",
"hash": -8676651381093628000,
"line_mean": 45.8106060606,
"line_max": 80,
"alpha_frac": 0.5766305227,
"autogenerated": false,
"ratio": 3.8739811912225703,
"config_test": ... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
import datashape
from datashape import discover
from ..append import append
from ..convert import convert, ooc_types
from ..chunks import chunks
from ..resource import resource
from ..utils import filter_kwargs
@... | {
"repo_name": "cpcloud/odo",
"path": "odo/backends/hdfstore.py",
"copies": "9",
"size": "3965",
"license": "bsd-3-clause",
"hash": -7413218275420359000,
"line_mean": 31.2357723577,
"line_max": 83,
"alpha_frac": 0.6756620429,
"autogenerated": false,
"ratio": 3.58499095840868,
"config_test": fals... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
import xarray as xr
from . import parameterized, randn, requires_dask
nx = 3000
long_nx = 30000000
ny = 2000
nt = 1000
window = 20
randn_xy = randn((nx, ny), frac_nan=0.1)
randn_xt = randn((nx, nt))
randn_t = ra... | {
"repo_name": "shoyer/xray",
"path": "asv_bench/benchmarks/rolling.py",
"copies": "1",
"size": "2349",
"license": "apache-2.0",
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"line_mean": 33.5441176471,
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"autogenerated": false,
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"config_test":... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
import xarray as xr
from . import randint, randn, requires_dask
nx = 3000
ny = 2000
nt = 1000
basic_indexes = {
'1slice': {'x': slice(0, 3)},
'1slice-1scalar': {'x': 0, 'y': slice(None, None, 3)},
'2... | {
"repo_name": "chunweiyuan/xarray",
"path": "asv_bench/benchmarks/indexing.py",
"copies": "2",
"size": "4503",
"license": "apache-2.0",
"hash": -8966021481000969000,
"line_mean": 34.7380952381,
"line_max": 79,
"alpha_frac": 0.5356429047,
"autogenerated": false,
"ratio": 3.0282447881640886,
"con... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pytest
from blaze.compute.core import compute, compute_up
from blaze.expr import Symbol, union, by, exp, Symbol
from datashape import discover
t = Symbol('t', 'var * {id: int, name: string, amount: int}')
x = np.array([(1, ... | {
"repo_name": "vitan/blaze",
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from __future__ import (absolute_import, division, print_function)
import numpy as np
import re
from addie.databases.oncat.oncat import pyoncatGetTemplate
try:
import pyoncat
ONCAT_ENABLED = True
except ImportError:
print('pyoncat module not found. Functionality disabled')
ONCAT_ENABLED = False
clas... | {
"repo_name": "neutrons/FastGR",
"path": "addie/processing/mantid/master_table/import_from_database/oncat_template_retriever.py",
"copies": "1",
"size": "3873",
"license": "mit",
"hash": -6953795083008404000,
"line_mean": 34.8611111111,
"line_max": 113,
"alpha_frac": 0.5450555125,
"autogenerated": ... |
from __future__ import absolute_import, division, print_function
import numpy as np
import struct
from six.moves import range
class Alphabet(object):
def __init__(self, config_file):
self._config_file = config_file
self._label_to_str = {}
self._str_to_label = {}
self._size = 0
... | {
"repo_name": "googleinterns/deepspeech-reconstruction",
"path": "src/deepspeech_training/util/text.py",
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"autogenerated": false,
"ratio": ... |
from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow as tf
#from tensorflow.contrib.data.python.ops.dataset_ops import Dataset
from niftynet.engine.image_window import ImageWindow
from niftynet.layer.base_layer import Layer
from niftynet.layer.grid_warper import Affine... | {
"repo_name": "NifTK/NiftyNet",
"path": "niftynet/contrib/sampler_pairwise/sampler_pairwise_uniform.py",
"copies": "2",
"size": "9106",
"license": "apache-2.0",
"hash": -1156011925598209800,
"line_mean": 45.4591836735,
"line_max": 79,
"alpha_frac": 0.5974083022,
"autogenerated": false,
"ratio": 3... |
from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow as tf
from tensorflow import convert_to_tensor as to_T
from util.cnn import fc_layer as fc, conv_layer as conv
from util.empty_safe_conv import empty_safe_1x1_conv as _1x1_conv
from util.empty_safe_conv import empty_... | {
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"path": "models_clevr/nmn3_modules.py",
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"size": "22175",
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"autogenerated": false,
"ratio": 3.338100255908475,
"config_t... |
from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow as tf
from tensorflow_probability.python.bijectors.exp import Exp
from tensorflow_probability.python.distributions import (
LogNormal, TransformedDistribution, Uniform)
from tensorflow_probability.python.internal ... | {
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from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import confusion_matrix as tf_cm
from odin.backend.maths import to_llr
from odin.backend.tensor import nonzeros, transpose
from odin.utils import as_tuple, is_number
# =============... | {
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"path": "odin/backend/metrics.py",
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"line_max": 80,
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"autogenerated": false,
"ratio": 3.4615023474178406,
"config_test": false,
"has... |
from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow as tf
import tensorflow_fold as td
from tensorflow import convert_to_tensor as to_T
from models_clevr.nmn3_netgen_att import AttentionSeq2Seq
from models_clevr.nmn3_modules import Modules
from models_clevr.nmn3_assem... | {
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"autogenerated": false,
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"config_test... |
from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow as tf
import tensorflow_fold as td
from tensorflow import convert_to_tensor as to_T
# the number of attention input to each module
_module_input_num = {'_Find': 0,
'_Transform': 1,
... | {
"repo_name": "ronghanghu/n2nmn",
"path": "models_shapes/nmn3_assembler.py",
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"autogenerated": false,
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"config_test... |
from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow as tf
import torch
from scipy.special import logsumexp
from odin.backend import tensor as ts
from odin.backend.tensor import _normalize_axis
# =======================================================================... | {
"repo_name": "imito/odin",
"path": "odin/backend/maths.py",
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"ratio": 3.0696889317578973,
"config_test": false,
"has_no... |
from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow as tf
# components
from tensorflow.python.ops.nn import dropout as drop
from util.cnn import conv_layer as conv
from util.cnn import conv_relu_layer as conv_relu
from util.cnn import pooling_layer as pool
from util.c... | {
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from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow.compat.v2 as tf
from tensorflow_probability.python.bijectors import exp as exp_bijector
from tensorflow_probability.python.distributions import (
NegativeBinomial, Normal, QuantizedDistribution, TransformedDistrib... | {
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"path": "odin/bay/distributions/quantized.py",
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"ratio": 4.923287671232877,
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from __future__ import absolute_import, division, print_function
import numpy as np
import theano
import theano.tensor as T
class Optimizer(object):
def __init__(self, lr_init=1e-3):
self.lr = theano.shared(
np.asarray(lr_init, dtype=theano.config.floatX), borrow=True)
def set_learning_r... | {
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"path": "IQA_BIECON_release/optimizer.py",
"copies": "1",
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"... |
from __future__ import absolute_import, division, print_function
import numpy as np
import time
from numpy.core.umath_tests import inner1d
from distributed import Client
from distributed.utils_test import cluster, loop
from dask_patternsearch import search
def sphere(x):
"""Minimum at 0"""
return x.dot(x)
... | {
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"path": "dask_patternsearch/tests/test_search.py",
"copies": "1",
"size": "5313",
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"hash": -630019021505689900,
"line_mean": 41.504,
"line_max": 103,
"alpha_frac": 0.5727460945,
"autogenerated": false,
"ratio": 3.679362880886426... |
from __future__ import absolute_import, division, print_function
import numpy as np
__all__ = ['points_inside_poly', 'polygon_line_intersections']
def points_inside_poly(x, y, vx, vy):
from matplotlib.path import Path
p = Path(np.column_stack((vx, vy)))
keep = ((x >= np.min(vx)) &
(x <= np... | {
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"path": "glue/utils/geometry.py",
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"autogenerated": false,
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from __future__ import (absolute_import, division, print_function)
import numpy as np
# (comments based on t_tide)
# Coefficients of the formulas in the Explan. Suppl.
_sc = np.array([270.434164, 13.1763965268, -0.0000850, 0.000000039])
_hc = np.array([279.696678, 0.9856473354, 0.00002267, 0.000000000])
_pc = np.arra... | {
"repo_name": "efiring/UTide",
"path": "utide/astronomy.py",
"copies": "1",
"size": "5044",
"license": "mit",
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"autogenerated": false,
"ratio": 3.2250639386189257,
"config_test": false,
"has_no_... |
from __future__ import (absolute_import, division, print_function)
import numpy as np
from .astronomy import ut_astron
from . import ut_constants
from . import constit_index_dict
from .utilities import Bunch
def ut_cnstitsel(tref, minres, incnstit, infer):
"""
UT_CNSTITSEL()
carry out constituent select... | {
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"path": "utide/constituent_selection.py",
"copies": "1",
"size": "2801",
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"line_max": 77,
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"autogenerated": false,
"ratio": 3.0612021857923497,
"config_test": fal... |
from __future__ import (absolute_import, division, print_function)
import numpy as np
from ._ciso import _zslice
def zslice(q, p, p0):
"""
Returns a 2D slice of the variable `q` from a 3D field defined by `p`,
along an iso-surface at `p0` using a linear interpolation.
The result `q_iso` is a projec... | {
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"path": "ciso/ciso.py",
"copies": "1",
"size": "1556",
"license": "bsd-2-clause",
"hash": 5685608459427760000,
"line_mean": 29.5098039216,
"line_max": 76,
"alpha_frac": 0.5604113111,
"autogenerated": false,
"ratio": 3.0331384015594542,
"config_test": false,
"has_... |
from __future__ import absolute_import, division, print_function
import numpy as np
from .common import Benchmark, get_squares_, get_indexes_rand, TYPES1
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": "DailyActie/Surrogate-Model",
"path": "01-codes/numpy-master/benchmarks/benchmarks/bench_linalg.py",
"copies": "1",
"size": "2930",
"license": "mit",
"hash": -8949765669061906000,
"line_mean": 25.880733945,
"line_max": 69,
"alpha_frac": 0.5675767918,
"autogenerated": false,
"ratio":... |
from __future__ import absolute_import, division, print_function
import numpy as np
from .common import Benchmark, get_squares
class Copy(Benchmark):
params = ["int8", "int16", "float32", "float64",
"complex64", "complex128"]
param_names = ['type']
def setup(self, typename):
dtype... | {
"repo_name": "DailyActie/Surrogate-Model",
"path": "01-codes/numpy-master/benchmarks/benchmarks/bench_io.py",
"copies": "1",
"size": "1710",
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"hash": 5155000040640725000,
"line_mean": 25.71875,
"line_max": 70,
"alpha_frac": 0.5760233918,
"autogenerated": false,
"ratio": 3.005272... |
from __future__ import absolute_import, division, print_function
import numpy as np
from .common import Benchmark, get_squares_
ufuncs = ['abs', 'absolute', 'add', 'arccos', 'arccosh', 'arcsin',
'arcsinh', 'arctan', 'arctan2', 'arctanh', 'bitwise_and',
'bitwise_not', 'bitwise_or', 'bitwise_xor', ... | {
"repo_name": "DailyActie/Surrogate-Model",
"path": "01-codes/numpy-master/benchmarks/benchmarks/bench_ufunc.py",
"copies": "1",
"size": "4274",
"license": "mit",
"hash": -6777944010876848000,
"line_mean": 27.3046357616,
"line_max": 76,
"alpha_frac": 0.5486663547,
"autogenerated": false,
"ratio":... |
from __future__ import absolute_import, division, print_function
import numpy as np
from .common import Benchmark
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)
... | {
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"path": "01-codes/numpy-master/benchmarks/benchmarks/bench_function_base.py",
"copies": "1",
"size": "3086",
"license": "mit",
"hash": -6118017979300846000,
"line_mean": 23.4920634921,
"line_max": 71,
"alpha_frac": 0.5836033701,
"autogenerated": false,
... |
from __future__ import absolute_import, division, print_function
import numpy as np
from .common import Benchmark
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": "DailyActie/Surrogate-Model",
"path": "01-codes/numpy-master/benchmarks/benchmarks/bench_core.py",
"copies": "1",
"size": "3090",
"license": "mit",
"hash": 4030334099422983700,
"line_mean": 22.4090909091,
"line_max": 66,
"alpha_frac": 0.574433657,
"autogenerated": false,
"ratio": 2.... |
from __future__ import absolute_import, division, print_function
import numpy as np
from .common import Benchmark, TYPES1, get_squares
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": "DailyActie/Surrogate-Model",
"path": "01-codes/numpy-master/benchmarks/benchmarks/bench_reduce.py",
"copies": "1",
"size": "1469",
"license": "mit",
"hash": 4681237434933942000,
"line_mean": 20.9253731343,
"line_max": 64,
"alpha_frac": 0.6099387338,
"autogenerated": false,
"ratio":... |
from __future__ import absolute_import, division, print_function
import numpy as np
from ..core.client import Client
from ..core import message as msg
from ..core.data import Data, CategoricalComponent
from ..core.subset import RangeSubsetState, CategoricalRoiSubsetState
from ..core.exceptions import IncompatibleData... | {
"repo_name": "JudoWill/glue",
"path": "glue/clients/histogram_client.py",
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"line_max": 84,
"alpha_frac": 0.5449807387,
"autogenerated": false,
"ratio": 4.09242658423493,
"config_... |
from __future__ import absolute_import, division, print_function
import numpy as np
from .core import compute, optimize
from ..expr import Expr, Arithmetic, Math, Map, UnaryOp
from ..expr.broadcast import broadcast_collect, Broadcast
from toolz import memoize
import datashape
import numba
from .pyfunc import funcstr
... | {
"repo_name": "mrocklin/blaze",
"path": "blaze/compute/numba.py",
"copies": "1",
"size": "5639",
"license": "bsd-3-clause",
"hash": -8224640763478712000,
"line_mean": 25.5990566038,
"line_max": 79,
"alpha_frac": 0.6126972868,
"autogenerated": false,
"ratio": 3.571247625079164,
"config_test": fa... |
from __future__ import absolute_import, division, print_function
import numpy as np
from dask.array.chunk import coarsen, keepdims_wrapper, trim
def test_keepdims_wrapper_no_axis():
def summer(a, axis=None):
return a.sum(axis=axis)
summer_wrapped = keepdims_wrapper(summer)
assert summer_wrappe... | {
"repo_name": "minrk/dask",
"path": "dask/array/tests/test_chunk.py",
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"line_max": 77,
"alpha_frac": 0.5730609418,
"autogenerated": false,
"ratio": 2.809338521400778,
"config_test"... |
from __future__ import absolute_import, division, print_function
import numpy as np
from ..data import Component, Data
from ...external import six
from .helpers import set_default_factory, __factories__, has_extension
__all__ = ['tabular_data', 'sextractor_factory', 'astropy_tabular_data',
'formatted_tab... | {
"repo_name": "JudoWill/glue",
"path": "glue/core/data_factories/tables.py",
"copies": "1",
"size": "5823",
"license": "bsd-3-clause",
"hash": 3340281704511874000,
"line_mean": 37.0653594771,
"line_max": 85,
"alpha_frac": 0.6223596084,
"autogenerated": false,
"ratio": 3.897590361445783,
"config... |
from __future__ import absolute_import, division, print_function
import numpy as np
from ..data import Data, Component, CategoricalComponent
from .helpers import has_extension, __factories__
__all__ = ['pandas_read_table']
def panda_process(indf):
"""
Build a data set from a table using pandas. This attem... | {
"repo_name": "JudoWill/glue",
"path": "glue/core/data_factories/pandas.py",
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"line_max": 81,
"alpha_frac": 0.6099180008,
"autogenerated": false,
"ratio": 4.198360655737705,
"confi... |
from __future__ import absolute_import, division, print_function
import numpy as np
from ..data import Data
from .helpers import has_extension, set_default_factory, __factories__
from ..coordinates import coordinates_from_wcs
img_fmt = ['jpg', 'jpeg', 'bmp', 'png', 'tiff', 'tif']
__all__ = ['img_data']
def img_l... | {
"repo_name": "JudoWill/glue",
"path": "glue/core/data_factories/image.py",
"copies": "1",
"size": "1974",
"license": "bsd-3-clause",
"hash": -4695404824515251000,
"line_mean": 24.3205128205,
"line_max": 70,
"alpha_frac": 0.5916919959,
"autogenerated": false,
"ratio": 3.550359712230216,
"config... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.core.callback_property import CallbackProperty
from glue.core.edit_subset_mode import EditSubsetMode
from glue.core.exceptions import IncompatibleDataException, IncompatibleAttribute
from glue.core.data import Data
from glue... | {
"repo_name": "saimn/glue",
"path": "glue/viewers/histogram/client.py",
"copies": "2",
"size": "15209",
"license": "bsd-3-clause",
"hash": 3499433717232905700,
"line_mean": 29.8498985801,
"line_max": 109,
"alpha_frac": 0.5490170294,
"autogenerated": false,
"ratio": 4.011870218939594,
"config_te... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.core.component import Component, CategoricalComponent
from glue.core.data import Data
def test_histogram_data():
data = Data(label="Test Data")
comp_a = Component(np.random.uniform(size=500))
comp_b = Component... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/tests/example_data.py",
"copies": "5",
"size": "1793",
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"hash": 4269739040068948500,
"line_mean": 27.9193548387,
"line_max": 64,
"alpha_frac": 0.6307863915,
"autogenerated": false,
"ratio": 2.915447154471545,
"config_... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.core.coordinates import coordinates_from_wcs
from glue.core.data_factories.helpers import has_extension
from glue.core.data import Data
from glue.config import data_factory
IMG_FMT = ['jpg', 'jpeg', 'bmp', 'png', 'tiff', '... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/core/data_factories/image.py",
"copies": "4",
"size": "1922",
"license": "bsd-3-clause",
"hash": 4836984818129985000,
"line_mean": 25.3287671233,
"line_max": 73,
"alpha_frac": 0.5972944849,
"autogenerated": false,
"ratio": 3.599250936329588,
"... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.core.coordinates import Coordinates
from glue.viewers.common.qt.data_slice_widget import SliceWidget
from glue.viewers.image.state import AggregateSlice
from glue.utils.decorators import avoid_circular
__all__ = ['MultiSlic... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/viewers/image/qt/slice_widget.py",
"copies": "2",
"size": "5216",
"license": "bsd-3-clause",
"hash": -6426266291832692000,
"line_mean": 33.091503268,
"line_max": 99,
"alpha_frac": 0.5778374233,
"autogenerated": false,
"ratio": 3.9725818735719725... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.core.data_factories.helpers import has_extension
from glue.core.data import Component, Data
from glue.config import data_factory, qglue_parser
__all__ = ['astropy_tabular_data', 'sextractor_factory', 'cds_factory',
... | {
"repo_name": "saimn/glue",
"path": "glue/core/data_factories/astropy_table.py",
"copies": "4",
"size": "4422",
"license": "bsd-3-clause",
"hash": -4348215220937463300,
"line_mean": 31.5147058824,
"line_max": 80,
"alpha_frac": 0.6625961104,
"autogenerated": false,
"ratio": 3.7570093457943927,
"... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.core.exceptions import IncompatibleAttribute
from glue.core.subset import Subset
from glue.core.layer_artist import MatplotlibLayerArtist, ChangedTrigger
class DendroLayerArtist(MatplotlibLayerArtist):
# X vertices of ... | {
"repo_name": "saimn/glue",
"path": "glue/plugins/dendro_viewer/layer_artist.py",
"copies": "3",
"size": "2042",
"license": "bsd-3-clause",
"hash": -7868561518471582000,
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"line_max": 72,
"alpha_frac": 0.5636630754,
"autogenerated": false,
"ratio": 3.746788990825688,
"c... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.core import Data
from glue.external.echo import delay_callback
from glue.viewers.matplotlib.state import (MatplotlibDataViewerState,
MatplotlibLayerState,
... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/viewers/histogram/state.py",
"copies": "1",
"size": "6012",
"license": "bsd-3-clause",
"hash": 6003203792581838000,
"line_mean": 35.4363636364,
"line_max": 97,
"alpha_frac": 0.5590485695,
"autogenerated": false,
"ratio": 4.109364319890636,
"co... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.core import Subset
from glue.config import data_exporter
__all__ = []
@data_exporter(label='FITS (1 component/HDU)', extension=['fits', 'fit'])
def fits_writer(data, filename):
"""
Write a dataset or a subset to ... | {
"repo_name": "saimn/glue",
"path": "glue/core/data_exporters/gridded_fits.py",
"copies": "2",
"size": "1098",
"license": "bsd-3-clause",
"hash": 332672681950101760,
"line_mean": 21.4081632653,
"line_max": 73,
"alpha_frac": 0.5965391621,
"autogenerated": false,
"ratio": 3.7220338983050847,
"con... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.external.echo import (CallbackProperty, ListCallbackProperty,
SelectionCallbackProperty, keep_in_sync,
delay_callback)
from glue.core.state_objects import Stat... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/viewers/matplotlib/state.py",
"copies": "1",
"size": "4200",
"license": "bsd-3-clause",
"hash": -3179074087328102000,
"line_mean": 39.7766990291,
"line_max": 94,
"alpha_frac": 0.6342857143,
"autogenerated": false,
"ratio": 4.751131221719457,
"... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.external.echo import (CallbackProperty, SelectionCallbackProperty,
delay_callback, ListCallbackProperty)
from glue.core.state_objects import StateAttributeLimitsHelper
from glue.viewers.common... | {
"repo_name": "astrofrog/glue-vispy-viewers",
"path": "glue_vispy_viewers/common/viewer_state.py",
"copies": "2",
"size": "4667",
"license": "bsd-2-clause",
"hash": 4051047346239132000,
"line_mean": 34.6259541985,
"line_max": 88,
"alpha_frac": 0.5770302121,
"autogenerated": false,
"ratio": 3.7246... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.external import six
from glue.core.data_factories.helpers import has_extension
from glue.core.component import Component, CategoricalComponent
from glue.core.data import Data
from glue.config import data_factory, qglue_parse... | {
"repo_name": "saimn/glue",
"path": "glue/core/data_factories/pandas.py",
"copies": "2",
"size": "3213",
"license": "bsd-3-clause",
"hash": -3678337327898444300,
"line_mean": 29.8942307692,
"line_max": 88,
"alpha_frac": 0.617491441,
"autogenerated": false,
"ratio": 4.135135135135135,
"config_te... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.utils import defer_draw
from glue.core.exceptions import IncompatibleAttribute
from glue.core.subset import Subset
# from glue.core.layer_artist import MatplotlibLayerArtist, ChangedTrigger
from glue.viewers.matplotlib.laye... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/plugins/dendro_viewer/layer_artist.py",
"copies": "1",
"size": "4685",
"license": "bsd-3-clause",
"hash": -760405994624302100,
"line_mean": 34.4924242424,
"line_max": 106,
"alpha_frac": 0.6098185699,
"autogenerated": false,
"ratio": 3.8783112582... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.utils import defer_draw
from glue.viewers.histogram.state import HistogramLayerState
from glue.viewers.matplotlib.layer_artist import MatplotlibLayerArtist
from glue.core.exceptions import IncompatibleAttribute
class Hist... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/viewers/histogram/layer_artist.py",
"copies": "1",
"size": "6605",
"license": "bsd-3-clause",
"hash": 1384985816923060200,
"line_mean": 35.2912087912,
"line_max": 123,
"alpha_frac": 0.5910673732,
"autogenerated": false,
"ratio": 3.70028011204481... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.utils import unbroadcast, broadcast_to
__all__ = ['points_inside_poly', 'polygon_line_intersections']
def points_inside_poly(x, y, vx, vy):
original_shape = x.shape
x = unbroadcast(x)
y = unbroadcast(y)
... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/utils/geometry.py",
"copies": "3",
"size": "3267",
"license": "bsd-3-clause",
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"line_max": 80,
"alpha_frac": 0.6155494337,
"autogenerated": false,
"ratio": 3.413793103448276,
"config_tes... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.viewers.common.qt.data_viewer_with_state import DataViewerWithState
from glue.external.echo import delay_callback
from qtpy import QtWidgets
from qtpy.QtCore import Qt
from ..extern.vispy.util import keys
from .vispy_widg... | {
"repo_name": "astrofrog/glue-3d-viewer",
"path": "glue_vispy_viewers/common/vispy_data_viewer.py",
"copies": "2",
"size": "7093",
"license": "bsd-2-clause",
"hash": -8522056509379993000,
"line_mean": 35.9427083333,
"line_max": 98,
"alpha_frac": 0.6017200056,
"autogenerated": false,
"ratio": 3.49... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.viewers.common.qt.mouse_mode import PathMode
from glue.viewers.image.qt import StandaloneImageViewer
from glue.config import viewer_tool
from glue.utils import defer_draw
@viewer_tool
class PVSlicerMode(PathMode):
ico... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/plugins/tools/pv_slicer/qt/pv_slicer.py",
"copies": "1",
"size": "8780",
"license": "bsd-3-clause",
"hash": -501654696707567900,
"line_mean": 33.0310077519,
"line_max": 117,
"alpha_frac": 0.5824601367,
"autogenerated": false,
"ratio": 3.51762820... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.viewers.common.qt.mouse_mode import PathMode
from glue.viewers.image.qt import StandaloneImageWidget
from glue.viewers.common.qt.mpl_widget import defer_draw
from glue.external.echo import add_callback
from glue.config impor... | {
"repo_name": "saimn/glue",
"path": "glue/plugins/tools/pv_slicer/qt/pv_slicer.py",
"copies": "1",
"size": "8336",
"license": "bsd-3-clause",
"hash": -5965692932789620000,
"line_mean": 32.0793650794,
"line_max": 81,
"alpha_frac": 0.5788147793,
"autogenerated": false,
"ratio": 3.5217574989438107,
... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue.viewers.matplotlib.qt.toolbar import MatplotlibViewerToolbar
from glue.core.edit_subset_mode import EditSubsetMode
from glue.core.roi import PointROI
from glue.core import command
from glue.core.subset import CategorySubsetS... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/plugins/dendro_viewer/qt/data_viewer.py",
"copies": "1",
"size": "4501",
"license": "bsd-3-clause",
"hash": -2279146837337121000,
"line_mean": 32.5895522388,
"line_max": 96,
"alpha_frac": 0.6147522773,
"autogenerated": false,
"ratio": 3.59217877... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glue_vispy_viewers.extern.vispy.visuals.transforms import (ChainTransform, NullTransform,
MatrixTransform, STTransform)
from glue_vispy_viewers.extern.vispy.visuals.... | {
"repo_name": "astrofrog/glue-vispy-viewers",
"path": "glue_vispy_viewers/utils.py",
"copies": "2",
"size": "3642",
"license": "bsd-2-clause",
"hash": 4985561222788619000,
"line_mean": 35.7878787879,
"line_max": 94,
"alpha_frac": 0.6350906096,
"autogenerated": false,
"ratio": 4.006600660066007,
... |
from __future__ import absolute_import, division, print_function
import numpy as np
from .hashfunctions import generate_hashfunctions
from .maintenance import maintenance
class CountdownBloomFilter(object):
""" Implementation of a Modified Countdown Bloom Filter. Uses a batched maintenance process instead of a ... | {
"repo_name": "Parsely/probably",
"path": "probably/cdbf.py",
"copies": "1",
"size": "5090",
"license": "mit",
"hash": 6198907979325010000,
"line_mean": 40.7213114754,
"line_max": 146,
"alpha_frac": 0.6214145383,
"autogenerated": false,
"ratio": 3.8473167044595615,
"config_test": false,
"has_... |
from __future__ import absolute_import, division, print_function
import numpy as np
from . import nputils
from . import dtypes
try:
import dask.array as da
except ImportError:
pass
def dask_rolling_wrapper(moving_func, a, window, min_count=None, axis=-1):
'''wrapper to apply bottleneck moving window fu... | {
"repo_name": "jcmgray/xarray",
"path": "xarray/core/dask_array_ops.py",
"copies": "1",
"size": "3551",
"license": "apache-2.0",
"hash": -2424478796639432000,
"line_mean": 35.2346938776,
"line_max": 77,
"alpha_frac": 0.6040551957,
"autogenerated": false,
"ratio": 3.5228174603174605,
"config_tes... |
from __future__ import absolute_import, division, print_function
import numpy as np
from .. import Variable
from ..core import indexing
from ..core.pycompat import integer_types
from ..core.utils import Frozen, FrozenOrderedDict, is_dict_like
from .common import AbstractDataStore, BackendArray, robust_getitem
class... | {
"repo_name": "jcmgray/xarray",
"path": "xarray/backends/pydap_.py",
"copies": "1",
"size": "3031",
"license": "apache-2.0",
"hash": 5643943285876555000,
"line_mean": 31.5913978495,
"line_max": 79,
"alpha_frac": 0.6238865061,
"autogenerated": false,
"ratio": 4.10148849797023,
"config_test": fal... |
from __future__ import absolute_import, division, print_function
import numpy as np
from matplotlib.colors import Normalize
from matplotlib.collections import LineCollection
from mpl_scatter_density import ScatterDensityArtist
from astropy.visualization import (ImageNormalize, LinearStretch, SqrtStretch,
... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/viewers/scatter/layer_artist.py",
"copies": "1",
"size": "18014",
"license": "bsd-3-clause",
"hash": 5212772700236165000,
"line_mean": 40.1278538813,
"line_max": 105,
"alpha_frac": 0.5375818808,
"autogenerated": false,
"ratio": 4.104351788562315... |
from __future__ import absolute_import, division, print_function
import numpy as np
from numba.npyufunc.deviceufunc import (UFuncMechanism, GenerializedUFunc,
GUFuncCallSteps)
from numba.roc.hsadrv.driver import dgpu_present
import numba.roc.hsadrv.devicearray as devicearray
im... | {
"repo_name": "jriehl/numba",
"path": "numba/roc/dispatch.py",
"copies": "2",
"size": "4749",
"license": "bsd-2-clause",
"hash": 2662200452558643700,
"line_mean": 30.66,
"line_max": 75,
"alpha_frac": 0.5506422405,
"autogenerated": false,
"ratio": 4.0451448040885865,
"config_test": false,
"has... |
from __future__ import absolute_import, division, print_function
import numpy as np
from qtpy import QtCore, QtWidgets
from glue.config import colormaps
from glue.viewers.matplotlib.qt.toolbar import MatplotlibViewerToolbar
from glue.viewers.matplotlib.qt.widget import MplWidget
from glue.viewers.image.composite_arr... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/viewers/image/qt/standalone_image_viewer.py",
"copies": "2",
"size": "5898",
"license": "bsd-3-clause",
"hash": 8187811203633673000,
"line_mean": 31.7666666667,
"line_max": 85,
"alpha_frac": 0.6088504578,
"autogenerated": false,
"ratio": 3.81994... |
from __future__ import absolute_import, division, print_function
import numpy as np
from qtpy.QtCore import Qt
from qtpy import QtCore, QtGui, PYQT5
from glue.core import roi
from glue.utils.qt import mpl_to_qt4_color
class QtROI(object):
"""
A mixin class used to override the drawing methods used by
... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/core/qt/roi.py",
"copies": "3",
"size": "5762",
"license": "bsd-3-clause",
"hash": 5541679687971372000,
"line_mean": 29.8128342246,
"line_max": 79,
"alpha_frac": 0.5734120097,
"autogenerated": false,
"ratio": 3.4318046456223943,
"config_test":... |
from __future__ import absolute_import, division, print_function
import numpy as np
from qtpy.QtCore import Qt
from qtpy import QtCore, QtGui, QtWidgets
from glue.core import roi
from glue.utils.qt import mpl_to_qt4_color
class QtROI(object):
"""
A mixin class used to override the drawing methods used by
... | {
"repo_name": "saimn/glue",
"path": "glue/core/qt/roi.py",
"copies": "1",
"size": "4397",
"license": "bsd-3-clause",
"hash": -7443946654485707000,
"line_mean": 27.5519480519,
"line_max": 70,
"alpha_frac": 0.5751648851,
"autogenerated": false,
"ratio": 3.2740134028294863,
"config_test": false,
... |
from __future__ import (absolute_import, division, print_function)
import numpy as np
from qtpy.QtWidgets import QComboBox, QTableWidgetItem
from qtpy import QtCore
from addie.utilities.general import generate_random_key
from addie.processing.mantid.master_table.tree_definition import LIST_SEARCH_CRITERIA
from addie... | {
"repo_name": "neutrons/FastGR",
"path": "addie/processing/mantid/master_table/import_from_database/table_widget_rule_handler.py",
"copies": "1",
"size": "4759",
"license": "mit",
"hash": -502036111835493950,
"line_mean": 39.3305084746,
"line_max": 107,
"alpha_frac": 0.5915108216,
"autogenerated": ... |
from __future__ import absolute_import, division, print_function
import numpy as np
from .tree import Tree
class TreeLayout(object):
""" The TreeLayout class maps trees onto an xy coordinate space for
plotting.
TreeLayout provides a dictionary-like interface for access to the
location of each node ... | {
"repo_name": "JudoWill/glue",
"path": "glue/core/tree_layout.py",
"copies": "1",
"size": "7890",
"license": "bsd-3-clause",
"hash": -3383728852712354300,
"line_mean": 27.9010989011,
"line_max": 76,
"alpha_frac": 0.5121673004,
"autogenerated": false,
"ratio": 4.060730828615543,
"config_test": f... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
from glue.external import six
from glue.core.data_factories.helpers import has_extension
from glue.core.component import Component, CategoricalComponent
from glue.core.data import Data
from glue.config import data... | {
"repo_name": "stscieisenhamer/glue",
"path": "glue/core/data_factories/pandas.py",
"copies": "2",
"size": "3493",
"license": "bsd-3-clause",
"hash": -8185681234212810000,
"line_mean": 29.6403508772,
"line_max": 88,
"alpha_frac": 0.6117950186,
"autogenerated": false,
"ratio": 4.17822966507177,
... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pytest
h5py = pytest.importorskip('h5py')
from datashape import discover
from blaze import compute
from blaze.expr import symbol
from blaze.utils import tmpfile
from blaze.compute.h5py import pre_compute, optimize
def eq(a,... | {
"repo_name": "dwillmer/blaze",
"path": "blaze/compute/tests/test_h5py.py",
"copies": "1",
"size": "5006",
"license": "bsd-3-clause",
"hash": -1991720706024508700,
"line_mean": 24.9378238342,
"line_max": 80,
"alpha_frac": 0.5803036356,
"autogenerated": false,
"ratio": 3.0120336943441637,
"confi... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pytest
from .. import tree_layout as tl
from ..tree import NewickTree
def test_invalid_input():
with pytest.raises(TypeError) as exc:
layout = tl.TreeLayout(None)
assert exc.value.args[0] == 'Input not a tree... | {
"repo_name": "JudoWill/glue",
"path": "glue/core/tests/test_tree_layout.py",
"copies": "1",
"size": "3570",
"license": "bsd-3-clause",
"hash": -7599557393304830000,
"line_mean": 25.4444444444,
"line_max": 74,
"alpha_frac": 0.6028011204,
"autogenerated": false,
"ratio": 2.6761619190404797,
"con... |
from __future__ import absolute_import, division, print_function
import numpy as np
import theano
import theano.tensor as T
from theano.tensor.nnet.bn import batch_normalization
def linear(x):
return x
class BatchNormLayer(object):
"""Batch normalization layer.
Core algorithm is brought from Lasagne.
... | {
"repo_name": "jongyookim/IQA_BIECON_release",
"path": "IQA_BIECON_release/layers/normalization.py",
"copies": "1",
"size": "9687",
"license": "mit",
"hash": 3904224244393057000,
"line_mean": 40.2212765957,
"line_max": 79,
"alpha_frac": 0.5819139052,
"autogenerated": false,
"ratio": 4.09771573604... |
from __future__ import absolute_import, division, print_function
import numpy as np
import xarray as xr
from . import requires_dask
class Reindex:
def setup(self):
data = np.random.RandomState(0).randn(1000, 100, 100)
self.ds = xr.Dataset({'temperature': (('time', 'x', 'y'), data)},
... | {
"repo_name": "shoyer/xray",
"path": "asv_bench/benchmarks/reindexing.py",
"copies": "1",
"size": "1475",
"license": "apache-2.0",
"hash": -7596367717624044000,
"line_mean": 32.5227272727,
"line_max": 78,
"alpha_frac": 0.5484745763,
"autogenerated": false,
"ratio": 3.165236051502146,
"config_te... |
from __future__ import absolute_import, division, print_function
import numpy as np
import xarray as xr
from . import requires_dask
class Reindex(object):
def setup(self):
data = np.random.RandomState(0).randn(1000, 100, 100)
self.ds = xr.Dataset({'temperature': (('time', 'x', 'y'), data)},
... | {
"repo_name": "chunweiyuan/xarray",
"path": "asv_bench/benchmarks/reindexing.py",
"copies": "2",
"size": "1483",
"license": "apache-2.0",
"hash": -7699801554381506000,
"line_mean": 32.7045454545,
"line_max": 78,
"alpha_frac": 0.5495616993,
"autogenerated": false,
"ratio": 3.1688034188034186,
"c... |
from __future__ import (absolute_import, division, print_function)
import numpy as np
def ut_diagn(coef, opt):
if opt['RunTimeDisp']:
print('diagnostics ... ', end='')
coef['diagn'] = {}
if opt['twodim']:
PE = np.sum(coef['Lsmaj']**2 + coef['Lsmin']**2)
PE = 100 * (coef['Lsmaj']... | {
"repo_name": "efiring/UTide",
"path": "utide/diagnostics.py",
"copies": "1",
"size": "1755",
"license": "mit",
"hash": 2991154380576400400,
"line_mean": 28.25,
"line_max": 71,
"alpha_frac": 0.505982906,
"autogenerated": false,
"ratio": 2.461430575035063,
"config_test": false,
"has_no_keyword... |
from __future__ import absolute_import, division, print_function
import numpy as np
# returns a list of augmented audio data, stereo or mono
def augment_audio(y,
sr,
n_augment=0,
allow_speedandpitch=True,
allow_pitch=True,
all... | {
"repo_name": "imito/odin",
"path": "odin/preprocessing/audio/audio.py",
"copies": "1",
"size": "5392",
"license": "mit",
"hash": 3362171369931488000,
"line_mean": 33.7870967742,
"line_max": 132,
"alpha_frac": 0.5497032641,
"autogenerated": false,
"ratio": 3.1095732410611303,
"config_test": fal... |
from __future__ import absolute_import, division, print_function
import numpy as np
N_STAGES = 12
N_STAGES_EXTENDED = 16
INTERPOLATOR_POWER = 7
C = np.array([0.0,
0.526001519587677318785587544488e-01,
0.789002279381515978178381316732e-01,
0.118350341907227396726757197510,
... | {
"repo_name": "arokem/scipy",
"path": "scipy/integrate/_ivp/dop853_coefficients.py",
"copies": "6",
"size": "7303",
"license": "bsd-3-clause",
"hash": -8992214274971938000,
"line_mean": 36.4512820513,
"line_max": 64,
"alpha_frac": 0.7503765576,
"autogenerated": false,
"ratio": 1.9038060479666319,... |
from __future__ import absolute_import, division, print_function
import numpy as np
try:
isclose = np.isclose
except AttributeError:
def isclose(*args, **kwargs):
raise RuntimeError("You need numpy version 1.7 or greater to use "
"isclose.")
try:
full = np.full
except A... | {
"repo_name": "vikhyat/dask",
"path": "dask/array/numpy_compat.py",
"copies": "2",
"size": "1736",
"license": "bsd-3-clause",
"hash": -5901071135662620000,
"line_mean": 37.5777777778,
"line_max": 85,
"alpha_frac": 0.6065668203,
"autogenerated": false,
"ratio": 3.883668903803132,
"config_test": ... |
from __future__ import absolute_import, division, print_function
import numpy
import random
# Various pre-crafted datasets/variables for testing
# !!! Must not be changed -- only appended !!!
# while testing numpy we better not rely on numpy to produce random
# sequences
random.seed(1)
# but will seed it nevertheless... | {
"repo_name": "I--P/numpy",
"path": "benchmarks/benchmarks/common.py",
"copies": "6",
"size": "2610",
"license": "bsd-3-clause",
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"line_mean": 21.6956521739,
"line_max": 72,
"alpha_frac": 0.6448275862,
"autogenerated": false,
"ratio": 3.5317997293640055,
"config_test... |
from __future__ import absolute_import, division, print_function
import operator
from functools import partial
from uuid import uuid4
from google.protobuf.message import Message
from mesos.interface import mesos_pb2
from .. import protobuf
class Map(dict):
def __init__(self, **kwargs):
for k, v in kwa... | {
"repo_name": "lensacom/satyr",
"path": "mentor/proxies/messages.py",
"copies": "1",
"size": "10084",
"license": "apache-2.0",
"hash": -8778908055611380000,
"line_mean": 24.4646464646,
"line_max": 76,
"alpha_frac": 0.5874652916,
"autogenerated": false,
"ratio": 3.719660641829583,
"config_test":... |
from __future__ import absolute_import, division, print_function
import operator
from functools import wraps
from itertools import chain, count
from collections import Iterator
from toolz import merge, unique, curry
from .optimize import cull, fuse
from .utils import concrete, funcname
from . import base
from .compa... | {
"repo_name": "pombredanne/dask",
"path": "dask/imperative.py",
"copies": "1",
"size": "10406",
"license": "bsd-3-clause",
"hash": -4469779611477858000,
"line_mean": 27.9860724234,
"line_max": 79,
"alpha_frac": 0.5859119739,
"autogenerated": false,
"ratio": 3.50016818028927,
"config_test": fals... |
from __future__ import absolute_import, division, print_function
import operator
from operator import add, getitem
import inspect
from numbers import Number
from collections import Iterable, MutableMapping
from bisect import bisect
from itertools import product, count
from collections import Iterator
from functools im... | {
"repo_name": "freeman-lab/dask",
"path": "dask/array/core.py",
"copies": "1",
"size": "65152",
"license": "bsd-3-clause",
"hash": 920068847602019100,
"line_mean": 30.7969741337,
"line_max": 107,
"alpha_frac": 0.5621623281,
"autogenerated": false,
"ratio": 3.399885195428691,
"config_test": true... |
from __future__ import absolute_import, division, print_function
import operator
from operator import add, getitem
import inspect
from numbers import Number
from collections import Iterable
from bisect import bisect
from itertools import product, count
from collections import Iterator
from functools import partial, wr... | {
"repo_name": "minrk/dask",
"path": "dask/array/core.py",
"copies": "1",
"size": "52615",
"license": "bsd-3-clause",
"hash": -2187182692964355600,
"line_mean": 30.3744782349,
"line_max": 107,
"alpha_frac": 0.5571985175,
"autogenerated": false,
"ratio": 3.3180929557923946,
"config_test": false,
... |
from __future__ import absolute_import, division, print_function
import operator
from toolz import first
import numpy as np
from datashape import dshape, var, DataShape
from dateutil.parser import parse as dt_parse
from datashape.predicates import isscalar, isboolean, isnumeric
from datashape import coretypes as ct, d... | {
"repo_name": "mrocklin/blaze",
"path": "blaze/expr/arithmetic.py",
"copies": "1",
"size": "11014",
"license": "bsd-3-clause",
"hash": 2276067550438492200,
"line_mean": 23.0480349345,
"line_max": 78,
"alpha_frac": 0.6247503178,
"autogenerated": false,
"ratio": 3.465701699181875,
"config_test": ... |
from __future__ import absolute_import, division, print_function
import operator
from toolz import first
import numpy as np
import pandas as pd
from datashape import dshape, var, DataShape
from dateutil.parser import parse as dt_parse
from datashape.predicates import isscalar, isboolean, isnumeric, isdatelike
from dat... | {
"repo_name": "caseyclements/blaze",
"path": "blaze/expr/arithmetic.py",
"copies": "5",
"size": "8784",
"license": "bsd-3-clause",
"hash": -535188195921522050,
"line_mean": 20.3722627737,
"line_max": 78,
"alpha_frac": 0.5931238616,
"autogenerated": false,
"ratio": 3.3122171945701355,
"config_te... |
from __future__ import absolute_import, division, print_function
import operator
from toolz import first
import numpy as np
import pandas as pd
from datashape import dshape, var, DataShape, Option
from dateutil.parser import parse as dt_parse
from datashape.predicates import isscalar, isboolean, isnumeric, isdatelike
... | {
"repo_name": "ChinaQuants/blaze",
"path": "blaze/expr/arithmetic.py",
"copies": "1",
"size": "9396",
"license": "bsd-3-clause",
"hash": 6308118187008525000,
"line_mean": 21.0563380282,
"line_max": 78,
"alpha_frac": 0.5966368668,
"autogenerated": false,
"ratio": 3.364124597207304,
"config_test"... |
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