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from functools import partial
import click
from notifiers import __version__, get_notifier
from notifiers.core import all_providers
from notifiers.exceptions import NotifierException
from notifiers_cli.utils.dynamic_click import schema_to_command, CORE_COMMANDS
from notifiers_cli.utils.callbacks import func_factory, ... | {
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from functools import partial
import colour
import numpy as np
from scipy import optimize
def delta_e(rgb1, rgb2):
"""Returns the CIEDE2000 difference between rgb1 and rgb2 (both sRGB with range 0-1).
Reference: https://en.wikipedia.org/wiki/Color_difference#CIEDE2000."""
lab1 = colour.XYZ_to_Lab(colour.... | {
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"path": "app/cri/test1.py",
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from functools import partial
import commonware.log
from piston.authentication.oauth import OAuthAuthentication, views
from rest_framework.authentication import BaseAuthentication
from django.contrib.auth.models import AnonymousUser
from django.shortcuts import render
from access.middleware import ACLMiddleware
from... | {
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"path": "apps/api/authentication.py",
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from functools import partial
import commonware.log
from piston.authentication.oauth import OAuthAuthentication, views
from django.contrib.auth.models import AnonymousUser
from django.shortcuts import render
from access.middleware import ACLMiddleware
from users.models import UserProfile
from zadmin import jinja_for... | {
"repo_name": "clouserw/olympia",
"path": "apps/api/authentication.py",
"copies": "2",
"size": "2851",
"license": "bsd-3-clause",
"hash": 6165866338761803000,
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"alpha_frac": 0.6369694844,
"autogenerated": false,
"ratio": 4.482704402515723,
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from functools import partial
import commonware.log
import jingo
from piston.authentication.oauth import OAuthAuthentication, views
from django.contrib.auth.models import AnonymousUser
from access.middleware import ACLMiddleware
from users.models import UserProfile
from zadmin import jinja_for_django
# This allows ... | {
"repo_name": "wagnerand/zamboni",
"path": "apps/api/authentication.py",
"copies": "4",
"size": "2840",
"license": "bsd-3-clause",
"hash": -3224747741424847000,
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from functools import partial
import cv2
import matplotlib
import numpy as np
import pylab
from matplotlib import pyplot as plt
from matplotlib.collections import PatchCollection
from matplotlib.patches import Polygon
from scipy.spatial import ConvexHull
from skimage import measure
from sklearn import metrics
from skl... | {
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from functools import partial
import django
from django.core.exceptions import ImproperlyConfigured
from django.core.management.base import BaseCommand
from django.db import connections
from django.db.transaction import atomic
from concurrency.triggers import create_triggers, drop_triggers, get_triggers
def _add_su... | {
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"path": "src/concurrency/management/commands/triggers.py",
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from functools import partial
import django
from django.db import models
from django.contrib.auth.models import User
from django.contrib.contenttypes.models import ContentType
from django.db.models.signals import post_init, post_save
from cbe.party.models import PartyRole, Organisation
customer_status_choices = (('n... | {
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"path": "cbe/cbe/customer/models.py",
"copies": "2",
"size": "2843",
"license": "apache-2.0",
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from functools import partial
import django
from django.utils import timezone
from django.db import models
from django.contrib.contenttypes.fields import GenericForeignKey
from django.contrib.contenttypes.models import ContentType
from django.db.models.signals import post_init, post_save
ACTION_CHOICES = (('add', 'ad... | {
"repo_name": "cdaf/cbe",
"path": "cbe/cbe/business_interaction/models.py",
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"size": "3403",
"license": "apache-2.0",
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"line_max": 150,
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from functools import partial
import falcon
from graceful.parameters import IntParam
from graceful.resources.base import BaseResource
class BaseMixin:
"""Base mixin class."""
def handle(self, handler, req, resp, **kwargs):
"""Handle given resource manipulation flow in consistent manner.
Thi... | {
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"path": "src/graceful/resources/mixins.py",
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"con... |
from functools import partial
import graphene
from django.db.models.query import QuerySet
from django_measurement.models import MeasurementField
from django_prices.models import MoneyField, TaxedMoneyField
from graphene.relay import PageInfo
from graphene_django.converter import convert_django_field
from graphene_djan... | {
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"path": "saleor/graphql/core/fields.py",
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from functools import partial
import graphene
from graphql_to_rest import ExternalRESTField
HOST = 'http://test'
class Faction(graphene.ObjectType):
base_url = '{}/factions'.format(HOST)
id = graphene.ID()
name = graphene.String(name='name')
heroes = ExternalRESTField(
partial(lambda: Hero)... | {
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"path": "tests/compressed_schema.py",
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from functools import partial
import hypothesis.strategies as st
import numpy as np
from astropy import units as u
from astropy.tests.helper import assert_quantity_allclose
from hypothesis import example, given, settings
from poliastro.twobody.sampling import sample_closed
angles = partial(st.floats, min_value=-2 * ... | {
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"path": "tests/tests_twobody/test_sampling.py",
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from functools import partial
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import matplotlib.colors as colors
import numpy as np
from scipy.interpolate import griddata
import pandas as pd
import seaborn as sns
sns.set_style("white")
from uintahtools.udaframe import UdaFrame, TerzaghiFrame, PorePressur... | {
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"path": "uintahtools/udaplot.py",
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from functools import partial
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
def my_kde_bandwidth(obj, fac=1. / 5):
"""We use Scott's Rule, multiplied by a constant factor."""
return np.power(obj.n, -1. / (obj.d + 4)) * fac
loc1, scale1, size1 = (-2, 1, 175)
loc2, scale2, size2 ... | {
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"path": "01-codes/scipy-master/doc/source/tutorial/stats/plots/kde_plot4.py",
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"license": "mit",
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"autogenerated": false,
"ratio": 2... |
from functools import partial
import mock
from mock import call
from pytest import raises as assert_raises
from rhino.errors import NotFound
from rhino.mapper import Mapper
from rhino.resource import Resource, get
from rhino.response import ok
from rhino.test import TestClient
class CallbackError(Exception): pass
cl... | {
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"path": "test/test_callbacks.py",
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"alpha_frac": 0.6164542294,
"autogenerated": false,
"ratio": 3.958715596330275,
"config_test": true,
"has... |
from functools import partial
import nltk
from knx.text.postagger.base import map_paren, reverse_map_paren
from BS.knx.text.tokenizer import default_tokenizer as tokenizer
try:
from textblob_aptagger import PerceptronTagger
perceptron_tagger = PerceptronTagger()
SYMBOLS = {'@', '#', '%', '^', '*', '+', ... | {
"repo_name": "gofortargets/CNN_brandsafety",
"path": "knx/text/postagger/perceptron_tagger.py",
"copies": "1",
"size": "3257",
"license": "apache-2.0",
"hash": 5523235570889157000,
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"line_max": 88,
"alpha_frac": 0.5677003377,
"autogenerated": false,
"ratio": 3.84080188... |
from functools import partial
import numpy as np
from astropy import units as u
from astropy.tests.helper import assert_quantity_allclose
from hypothesis import example, given, settings, strategies as st
from poliastro.twobody.sampling import sample_closed
angles = partial(st.floats, min_value=-2 * np.pi, max_value=... | {
"repo_name": "poliastro/poliastro",
"path": "tests/tests_twobody/test_sampling.py",
"copies": "1",
"size": "2028",
"license": "mit",
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"autogenerated": false,
"ratio": 2.6825396825396823,
"config... |
from functools import partial
import numpy as np
from .common import Benchmark, safe_import
with safe_import():
from scipy import array, r_, ones, arange, sort, diag, cos, rand, pi
from scipy.linalg import eigh, orth, cho_factor, cho_solve
import scipy.sparse
from scipy.sparse.linalg import lobpcg
... | {
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"path": "benchmarks/benchmarks/sparse_linalg_lobpcg.py",
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"alpha_frac": 0.5670781893,
"autogenerated": false,
"ratio": 2.8610675039246... |
from functools import partial
import numpy as np
from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import load_img
from toolbox.image import bicubic_rescale
from toolbox.image import modcrop
from toolbox.paths import data_dir
def load_set(name, lr_sub_size=11, lr_sub_stride=5, scale=... | {
"repo_name": "qobilidop/srcnn",
"path": "toolbox/data.py",
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"size": "1463",
"license": "mit",
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"line_max": 76,
"alpha_frac": 0.6384142174,
"autogenerated": false,
"ratio": 3.022727272727273,
"config_test": false,
"has_no_... |
from functools import partial
import numpy as np
from pathlib import Path
from menpo.base import LazyList
from menpo.image import Image, MaskedImage, BooleanImage
from menpo.image.base import normalize_pixels_range, channels_to_front
def _pil_to_numpy(pil_image, normalize, convert=None):
p = pil_image.convert(c... | {
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"path": "menpo/io/input/image.py",
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"line_max": 85,
"alpha_frac": 0.6222412685,
"autogenerated": false,
"ratio": 3.9234972677595628,
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from functools import partial
import numpy as np
from PIL import Image, ImageTk
import pygame
PIXEL_SIZE = 6
PHOTO_SIZE = 64
DEFAULT_BRUSH_SIZE = 3
BG_COLOR = (200, 200, 200)
LIGHT = (255, 255, 255)
DARK = (150, 150, 150)
class CenteredSurface(pygame.Surface):
def __init__(self, size, content):
super(... | {
"repo_name": "spellrun/Neural-Photo-Editor",
"path": "npe_backprop/util/assets.py",
"copies": "1",
"size": "9063",
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"ratio": 3.3504621072088723,
"config... |
from functools import partial
import numpy as np
from PIL import Image, ImageTk
import pygame
PIXEL_SIZE = 6
PHOTO_SIZE = 64
DEFAULT_BRUSH_SIZE = 3
def TextSurface(text):
font = pygame.font.SysFont('Arial', 15)
return font.render(text, True, (0, 0, 0))
def ColorSurface(color, size):
surf = pygame.Sur... | {
"repo_name": "spellrun/Neural-Photo-Editor",
"path": "npe/util/assets.py",
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"size": "5003",
"license": "mit",
"hash": 1585017009548520000,
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"line_max": 88,
"alpha_frac": 0.5848490905,
"autogenerated": false,
"ratio": 3.3554661301140176,
"config_test": f... |
from functools import partial
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
def my_kde_bandwidth(obj, fac=1./5):
"""We use Scott's Rule, multiplied by a constant factor."""
return np.power(obj.n, -1./(obj.d+4)) * fac
loc1, scale1, size1 = (-2, 1, 175)
loc2, scale2, size2 = (2, ... | {
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"path": "doc/source/tutorial/stats/plots/kde_plot4.py",
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"autogenerated": false,
"ratio": 2.39638157894... |
from functools import partial
import numpy as np
from scipy.optimize import minimize
# constants
DIM = 1
INTERACTION = 1.
# data size
CUTOFF = 80
GRID_SIZE = 64
def kinetic_energy(fs, hopping):
"""Mean-field kinetic energy."""
return -DIM * hopping * np.square(
np.sum(np.sqrt(n + 1.) * fs[n] * fs[n... | {
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"config_test": false,
"has_n... |
from functools import partial
import numpy as np
from scipy.spatial.distance import cdist as distance
from scipy.sparse import vstack as sparse_vstack
from oddt.utils import is_molecule
from oddt.docking import autodock_vina
from oddt.docking.internal import vina_docking
from oddt.fingerprints import sparse_to_csr_ma... | {
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"path": "oddt/scoring/descriptors/__init__.py",
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"hash": -8774605505998364000,
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"alpha_frac": 0.51412464,
"autogenerated": false,
"ratio": 4.075674325674326,
"config_... |
from functools import partial
import numpy as np
from scipy.stats import multivariate_normal, norm
import bayesian_changepoint_detection.online_changepoint_detection as online
def test_multivariate():
np.random.seed(seed=34)
# 10-dimensional multivariate normal, that shifts its mean at t=50, 100, and 150
... | {
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"line_max": 84,
"alpha_frac": 0.6512013256,
"autogenerated": false,
"ratio": 3.343490304709141,
"config_test": ... |
from functools import partial
import numpy as np
from sklearn import datasets, cross_validation, preprocessing
from neupy import algorithms, layers
from utils import compare_networks
from base import BaseTestCase
class QuickPropTestCase(BaseTestCase):
def setUp(self):
super(QuickPropTestCase, self).set... | {
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"path": "tests/algorithms/gd/test_quickprop.py",
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"line_max": 77,
"alpha_frac": 0.5545154185,
"autogenerated": false,
"ratio": 4.223255813953489,
"config_test":... |
from functools import partial
import numpy as np
import copy
create_rollout_function = partial
def multitask_rollout(
env,
agent,
max_path_length=np.inf,
render=False,
render_kwargs=None,
observation_key=None,
desired_goal_key=None,
get_action_kwargs=N... | {
"repo_name": "vitchyr/rlkit",
"path": "rlkit/samplers/rollout_functions.py",
"copies": "1",
"size": "6082",
"license": "mit",
"hash": -6450307428652556000,
"line_mean": 26.1517857143,
"line_max": 76,
"alpha_frac": 0.5808944426,
"autogenerated": false,
"ratio": 3.63755980861244,
"config_test": ... |
from functools import partial
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from neupy.utils import asfloat, tensorflow_session
from neupy import algorithms, layers, utils
utils.reproducible()
X_train = np.array([
[0.9, 0.3],
[0.5, 0.3],
[0.2, 0.1],
[0.7, ... | {
"repo_name": "itdxer/neupy",
"path": "examples/mlp/gd_algorithms_visualization.py",
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"alpha_frac": 0.6348045397,
"autogenerated": false,
"ratio": 3.2714521452145213,
"config... |
from functools import partial
import numpy as np
import matplotlib.pyplot as plt
from mne.utils import _TempDir
from pactools.dar_model import AR, DAR, HAR, StableDAR
from pactools.utils.testing import assert_equal, assert_greater
from pactools.utils.testing import assert_raises, assert_array_equal
from pactools.uti... | {
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"path": "pactools/tests/test_comodulogram.py",
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"line_max": 79,
"alpha_frac": 0.6360326428,
"autogenerated": false,
"ratio": 3.159460531535105,
"c... |
from functools import partial
import numpy as np
import pandas as pd
import pyqtgraph as pg
from pyqtgraph.Qt import QtCore, QtGui
from graphysio import utils
from graphysio.algorithms import waveform
from graphysio.structures import CycleId
from graphysio.utils import estimateSampleRate
class CurveItem(pg.PlotData... | {
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"path": "graphysio/plotwidgets/curves.py",
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"line_max": 137,
"alpha_frac": 0.583956406,
"autogenerated": false,
"ratio": 3.754043767840152,
"config_test": fa... |
from functools import partial
import numpy as np
import pandas as pd
import pyqtgraph as pg
from pyqtgraph.Qt import QtGui, QtCore
from graphysio import utils
from graphysio.structures import CycleId
from graphysio.utils import estimateSampleRate
from graphysio.algorithms import waveform
class CurveItem(pg.PlotDat... | {
"repo_name": "jaj42/dyngraph",
"path": "graphysio/plotwidgets/curves.py",
"copies": "1",
"size": "7892",
"license": "isc",
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"line_mean": 32.5829787234,
"line_max": 137,
"alpha_frac": 0.5838824126,
"autogenerated": false,
"ratio": 3.7527341892534474,
"config_test": f... |
from functools import partial
import numpy as np
import pytest
from guacamol.score_modifier import LinearModifier, SquaredModifier, AbsoluteScoreModifier, GaussianModifier, \
MinGaussianModifier, MaxGaussianModifier, ThresholdedLinearModifier, ClippedScoreModifier, \
SmoothClippedScoreModifier, ChainedModifie... | {
"repo_name": "BenevolentAI/guacamol",
"path": "tests/test_score_modifier.py",
"copies": "1",
"size": "7184",
"license": "mit",
"hash": -6061585357880673000,
"line_mean": 27.9677419355,
"line_max": 114,
"alpha_frac": 0.6513084633,
"autogenerated": false,
"ratio": 3.1412330564057718,
"config_tes... |
from functools import partial
import numpy as np
import pytest
from pandas.core.dtypes.common import is_categorical_dtype
from pandas.core.dtypes.dtypes import IntervalDtype
from pandas import (
Categorical,
CategoricalIndex,
Float64Index,
Index,
Int64Index,
Interval,
IntervalIndex,
d... | {
"repo_name": "toobaz/pandas",
"path": "pandas/tests/indexes/interval/test_construction.py",
"copies": "2",
"size": "16294",
"license": "bsd-3-clause",
"hash": -272567250315981600,
"line_mean": 35.0486725664,
"line_max": 87,
"alpha_frac": 0.611820302,
"autogenerated": false,
"ratio": 4.0694305694... |
from functools import partial
import numpy as np
import pytest
from pandas import (
DataFrame,
Series,
concat,
isna,
notna,
)
import pandas._testing as tm
import pandas.tseries.offsets as offsets
def scoreatpercentile(a, per):
values = np.sort(a, axis=0)
idx = int(per / 1.0 * (values.s... | {
"repo_name": "datapythonista/pandas",
"path": "pandas/tests/window/moments/test_moments_rolling_quantile.py",
"copies": "4",
"size": "5062",
"license": "bsd-3-clause",
"hash": -5044813251357502000,
"line_mean": 28.4302325581,
"line_max": 74,
"alpha_frac": 0.6201106282,
"autogenerated": false,
"r... |
from functools import partial
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas import (
DataFrame,
Series,
concat,
isna,
notna,
)
import pandas._testing as tm
import pandas.tseries.offsets as offsets
@td.skip_if_no_scipy
@pytest.mark.parametrize("sp_func, ... | {
"repo_name": "datapythonista/pandas",
"path": "pandas/tests/window/moments/test_moments_rolling_skew_kurt.py",
"copies": "3",
"size": "5452",
"license": "bsd-3-clause",
"hash": -9119401419644678000,
"line_mean": 31.0705882353,
"line_max": 88,
"alpha_frac": 0.644350697,
"autogenerated": false,
"r... |
from functools import partial
import numpy as np
import scipy
import skbio
def expand_otu_ids(ids, counts):
"""Lists the otu id the number of times provided in count
Paramaters
----------
ids: iterable
A list of the ids, corresponding to the value in `counts`
counts : iterable
A ... | {
"repo_name": "jwdebelius/Machiavellian",
"path": "machivellian/beta.py",
"copies": "1",
"size": "5277",
"license": "bsd-3-clause",
"hash": 674973792075310300,
"line_mean": 31.3742331288,
"line_max": 79,
"alpha_frac": 0.6325563767,
"autogenerated": false,
"ratio": 4.290243902439024,
"config_tes... |
from functools import partial
import numpy as np
import theano
import theano.tensor as T
from scipy import stats
from .dist_math import bound, factln, binomln, betaln, logpow
from .distribution import Discrete, draw_values, generate_samples
__all__ = ['Binomial', 'BetaBinomial', 'Bernoulli', 'Poisson',
... | {
"repo_name": "superbobry/pymc3",
"path": "pymc3/distributions/discrete.py",
"copies": "1",
"size": "13728",
"license": "apache-2.0",
"hash": -4800759721221485000,
"line_mean": 31,
"line_max": 79,
"alpha_frac": 0.4952651515,
"autogenerated": false,
"ratio": 3.7082658022690436,
"config_test": fa... |
from functools import partial
import numpy as np
import theano
import theano.tensor as tt
from scipy import stats
from .dist_math import bound, factln, binomln, betaln, logpow
from .distribution import Discrete, draw_values, generate_samples
__all__ = ['Binomial', 'BetaBinomial', 'Bernoulli', 'Poisson',
... | {
"repo_name": "wanderer2/pymc3",
"path": "pymc3/distributions/discrete.py",
"copies": "1",
"size": "18081",
"license": "apache-2.0",
"hash": -6901887302012889000,
"line_mean": 31.8148820327,
"line_max": 174,
"alpha_frac": 0.5003594934,
"autogenerated": false,
"ratio": 3.685487158581329,
"config... |
from functools import partial
import numpy as np
import torch
from six.moves import map, zip
from ..mask.structures import BitmapMasks, PolygonMasks
def multi_apply(func, *args, **kwargs):
"""Apply function to a list of arguments.
Note:
This function applies the ``func`` to multiple inputs and
... | {
"repo_name": "open-mmlab/mmdetection",
"path": "mmdet/core/utils/misc.py",
"copies": "1",
"size": "2615",
"license": "apache-2.0",
"hash": -1450935934704015600,
"line_mean": 30.130952381,
"line_max": 79,
"alpha_frac": 0.630210325,
"autogenerated": false,
"ratio": 3.8512518409425627,
"config_te... |
from functools import partial
import numpy as np
import torch.nn as nn
import torch
from unet3d.models.pytorch.classification.decoder import MyronenkoDecoder, MirroredDecoder
from unet3d.models.pytorch.classification.myronenko import MyronenkoEncoder, MyronenkoConvolutionBlock
from unet3d.models.pytorch.classificatio... | {
"repo_name": "ellisdg/3DUnetCNN",
"path": "unet3d/models/pytorch/autoencoder/variational.py",
"copies": "1",
"size": "8509",
"license": "mit",
"hash": 6805105081618989000,
"line_mean": 51.850931677,
"line_max": 129,
"alpha_frac": 0.6028910565,
"autogenerated": false,
"ratio": 3.8889396709323583,... |
from functools import partial
import numpy as np
from deep_np import layers, losses, utils
def _init_fc_weights(in_dim, out_dim, include_bias=True):
weights = np.random.randn(in_dim, out_dim) / np.sqrt(in_dim / 2.)
if include_bias:
return weights, np.zeros((1, out_dim))
return weights
def _init_conv_wei... | {
"repo_name": "teasherm/models",
"path": "deep_np/nets.py",
"copies": "1",
"size": "13874",
"license": "unlicense",
"hash": 1561426207700936400,
"line_mean": 29.8311111111,
"line_max": 95,
"alpha_frac": 0.5960789967,
"autogenerated": false,
"ratio": 2.714006259780908,
"config_test": false,
"h... |
from functools import partial
import numpy as np
from devito.core.operator import CoreOperator, CustomOperator
from devito.exceptions import InvalidOperator
from devito.passes.equations import buffering, collect_derivatives
from devito.passes.clusters import (Lift, blocking, cire, cse, eliminate_arrays,
... | {
"repo_name": "opesci/devito",
"path": "devito/core/cpu.py",
"copies": "1",
"size": "12179",
"license": "mit",
"hash": 3881410776799414000,
"line_mean": 31.05,
"line_max": 89,
"alpha_frac": 0.6213974875,
"autogenerated": false,
"ratio": 3.8638959390862944,
"config_test": false,
"has_no_keywor... |
from functools import partial
import numpy as np
from devito.core.operator import CoreOperator, CustomOperator
from devito.exceptions import InvalidOperator
from devito.passes.equations import collect_derivatives, buffering
from devito.passes.clusters import (Lift, Streaming, Tasker, blocking, cire, cse,
... | {
"repo_name": "opesci/devito",
"path": "devito/core/gpu.py",
"copies": "1",
"size": "12835",
"license": "mit",
"hash": -4925338677373059000,
"line_mean": 30.5356265356,
"line_max": 90,
"alpha_frac": 0.6247760031,
"autogenerated": false,
"ratio": 3.900030385900942,
"config_test": false,
"has_n... |
from functools import partial
import numpy as np
from ...external.qt import QtGui
from ...external.qt.QtCore import Qt
from ...core import message as msg
from ...core import Data
from ...core.callback_property import add_callback
from ...clients.histogram_client import HistogramClient
from ..ui.histogramwidget impor... | {
"repo_name": "glue-viz/glue-qt",
"path": "glue/qt/widgets/histogram_widget.py",
"copies": "1",
"size": "7610",
"license": "bsd-3-clause",
"hash": 2439217965677954000,
"line_mean": 31.660944206,
"line_max": 77,
"alpha_frac": 0.602890933,
"autogenerated": false,
"ratio": 4.003156233561284,
"conf... |
from functools import partial
import numpy as np
from skimage import img_as_float, img_as_uint
from skimage import color, data, filter
from skimage.color.adapt_rgb import adapt_rgb, each_channel, hsv_value
# Down-sample image for quicker testing.
COLOR_IMAGE = data.lena()[::5, ::5]
GRAY_IMAGE = data.camera()[::5, :... | {
"repo_name": "SamHames/scikit-image",
"path": "skimage/color/tests/test_adapt_rgb.py",
"copies": "1",
"size": "2489",
"license": "bsd-3-clause",
"hash": -4387174030869754400,
"line_mean": 28.9879518072,
"line_max": 78,
"alpha_frac": 0.6978706308,
"autogenerated": false,
"ratio": 3.11514392991239... |
from functools import partial
import numpy as np
try:
from scipy import array, r_, ones, arange, sort, diag, cos, rand, pi
from scipy.linalg import eigh, orth, cho_factor, cho_solve
import scipy.sparse
from scipy.sparse.linalg import lobpcg
from scipy.sparse.linalg.interface import LinearOperator
... | {
"repo_name": "person142/scipy",
"path": "benchmarks/benchmarks/sparse_linalg_lobpcg.py",
"copies": "8",
"size": "3647",
"license": "bsd-3-clause",
"hash": 8071194480549724000,
"line_mean": 30.9912280702,
"line_max": 93,
"alpha_frac": 0.5667672059,
"autogenerated": false,
"ratio": 2.8626373626373... |
from functools import partial
import numpy
from matplotlib import pyplot
from matplotlib import ticker
from pandas.api.types import CategoricalDtype
import seaborn
import probscale
from wqio import utils
from wqio import validate
def rotateTickLabels(ax, rotation, which, rotation_mode="anchor", ha="right"):
"""... | {
"repo_name": "phobson/wqio",
"path": "wqio/viz.py",
"copies": "2",
"size": "18243",
"license": "bsd-3-clause",
"hash": 333215633020895550,
"line_mean": 28.1888,
"line_max": 88,
"alpha_frac": 0.6080140328,
"autogenerated": false,
"ratio": 3.646412152708375,
"config_test": false,
"has_no_keywo... |
from functools import partial
import numpy
import chaospy
from .utils import combine_quadrature
def hypercube_quadrature(
quad_func,
order,
domain,
segments=None,
auto_scale=True,
):
"""
Enhance simple 1-dimensional unit quadrature with extra features.
These features include handlin... | {
"repo_name": "jonathf/chaospy",
"path": "chaospy/quadrature/hypercube.py",
"copies": "1",
"size": "16145",
"license": "mit",
"hash": 4037693981703505400,
"line_mean": 38.2822384428,
"line_max": 101,
"alpha_frac": 0.5907091979,
"autogenerated": false,
"ratio": 3.6354424679126325,
"config_test":... |
from functools import partial
import numpy
import pandas
from recordlinkage.algorithms.distance import _1d_distance
from recordlinkage.algorithms.distance import _haversine_distance
from recordlinkage.algorithms.numeric import _exp_sim
from recordlinkage.algorithms.numeric import _gauss_sim
from recordlinkage.algori... | {
"repo_name": "J535D165/recordlinkage",
"path": "recordlinkage/compare.py",
"copies": "1",
"size": "21547",
"license": "bsd-3-clause",
"hash": 8210599465971770000,
"line_mean": 33.6414790997,
"line_max": 79,
"alpha_frac": 0.5948855989,
"autogenerated": false,
"ratio": 4.1190976868667555,
"confi... |
from functools import partial
import OpenGL.GL as gl
from pyqtgraph.opengl.GLGraphicsItem import GLGraphicsItem
class displaylist(object):
def __init__(self, func):
self.func = func
def __call__(self, obj):
if hasattr(obj, '_display_list'):
l = getattr(obj, '_display_list')
... | {
"repo_name": "hackerspace/hacked_cnc",
"path": "hc/ui/glitems/__init__.py",
"copies": "1",
"size": "1062",
"license": "bsd-3-clause",
"hash": -160065575025616060,
"line_mean": 23.6976744186,
"line_max": 58,
"alpha_frac": 0.6327683616,
"autogenerated": false,
"ratio": 3.765957446808511,
"config... |
from functools import partial
import os
import pytest
import subprocess
from miniworld import Scenario
from tests.conftest import create_runner
@pytest.fixture(scope='session')
def runner(tmpdir_factory, image_path, request, config_path):
runner = create_runner(tmpdir_factory, request, config_path)
with run... | {
"repo_name": "miniworld-project/miniworld_core",
"path": "tests/acceptance/test_network_switching.py",
"copies": "1",
"size": "5145",
"license": "mit",
"hash": -2968943842650238000,
"line_mean": 44.1315789474,
"line_max": 120,
"alpha_frac": 0.5358600583,
"autogenerated": false,
"ratio": 4.401197... |
from functools import partial
import pandas as pd
import six
try:
from PyQt5 import QtCore, QtWidgets
except ImportError:
raise ImportError('PyQt5 is not installed. Please install PyQt5 to use '
'GUI related functions in py_entitymatching.')
import py_entitymatching as em
class DataFra... | {
"repo_name": "anhaidgroup/py_entitymatching",
"path": "py_entitymatching/gui/gui_utils.py",
"copies": "1",
"size": "12328",
"license": "bsd-3-clause",
"hash": 330253203257937600,
"line_mean": 34.8372093023,
"line_max": 84,
"alpha_frac": 0.5356099935,
"autogenerated": false,
"ratio": 4.4878048780... |
from functools import partial
import param
import numpy as np
import pandas as pd
import holoviews as hv
import datashader as ds
import colorcet as cc
from param import ParameterizedFunction, ParamOverrides
from holoviews.core.operation import Operation
from holoviews.streams import Stream, BoundsXY, LinkedStream
fro... | {
"repo_name": "timothydmorton/qa_explorer",
"path": "explorer/plots.py",
"copies": "1",
"size": "17724",
"license": "mit",
"hash": -6058055430368270000,
"line_mean": 39.2818181818,
"line_max": 104,
"alpha_frac": 0.5960279847,
"autogenerated": false,
"ratio": 3.793664383561644,
"config_test": fa... |
from functools import partial
import pytest
from bs4 import BeautifulSoup
from flask import url_for
from freezegun import freeze_time
from app.main.forms import FieldWithNoneOption
from tests.conftest import SERVICE_ONE_ID, normalize_spaces, sample_uuid
def test_non_logged_in_user_can_see_homepage(
client,
... | {
"repo_name": "alphagov/notifications-admin",
"path": "tests/app/main/views/test_index.py",
"copies": "1",
"size": "10613",
"license": "mit",
"hash": 6913061721507664000,
"line_mean": 29.6329479769,
"line_max": 108,
"alpha_frac": 0.6465704312,
"autogenerated": false,
"ratio": 3.5531344284277573,
... |
from functools import partial
import pytest
from flask import url_for
letters_urls = [
partial(url_for, 'main.add_service_template', template_type='letter'),
]
@pytest.mark.parametrize('url', letters_urls)
@pytest.mark.parametrize('permissions, response_code', [
(['letter'], 200),
([], 403)
])
def test_... | {
"repo_name": "alphagov/notifications-admin",
"path": "tests/app/main/views/test_letters.py",
"copies": "1",
"size": "2377",
"license": "mit",
"hash": -1073211712338258000,
"line_mean": 25.1208791209,
"line_max": 90,
"alpha_frac": 0.6512410602,
"autogenerated": false,
"ratio": 3.6124620060790273,... |
from functools import partial
import pytest
from plumbum.cmd import pg_dump
from pg_grant import parse_acl_item, FunctionInfo, PgObjectType
from pg_grant.query import (
get_all_function_acls, get_all_sequence_acls, get_all_table_acls,
get_all_type_acls)
pytestmark = pytest.mark.nocontainer
def _priv_acls(... | {
"repo_name": "RazerM/pg_grant",
"path": "tests/test_round_trip.py",
"copies": "1",
"size": "2417",
"license": "mit",
"hash": -8838675945675635000,
"line_mean": 33.5285714286,
"line_max": 85,
"alpha_frac": 0.6565990898,
"autogenerated": false,
"ratio": 3.4827089337175794,
"config_test": false,
... |
from functools import partial
import pytest
import numpy as np
from sklearn.metrics.cluster import adjusted_mutual_info_score
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.metrics.cluster import rand_score
from sklearn.metrics.cluster import completeness_score
from sklearn.metrics.cluster impor... | {
"repo_name": "anntzer/scikit-learn",
"path": "sklearn/metrics/cluster/tests/test_common.py",
"copies": "9",
"size": "8127",
"license": "bsd-3-clause",
"hash": -8730547908675925000,
"line_mean": 37.1549295775,
"line_max": 78,
"alpha_frac": 0.6489479513,
"autogenerated": false,
"ratio": 3.11260053... |
from functools import partial
import pytest
empty = object()
class cached_property(object):
def __init__(self, func):
self.func = func
def __get__(self, obj, cls):
value = obj.__dict__[self.func.__name__] = self.func(obj)
return value
class SimpleProxy(object):
def __init__(se... | {
"repo_name": "ionelmc/pytest-benchmark",
"path": "tests/test_sample.py",
"copies": "1",
"size": "1613",
"license": "bsd-2-clause",
"hash": -7799659396215641000,
"line_mean": 21.4027777778,
"line_max": 128,
"alpha_frac": 0.6168629882,
"autogenerated": false,
"ratio": 3.9150485436893203,
"config... |
from functools import partial
import pytest
from asphalt.core.resource import (
Resource, ResourceEventType, ResourceCollection, ResourceConflict, ResourceNotFoundError,
ResourceEventListener)
class TestResource:
@pytest.fixture
def resource(self):
return Resource(6, ('int', 'object'), 'foo'... | {
"repo_name": "Siecje/asphalt",
"path": "tests/test_resource.py",
"copies": "1",
"size": "5006",
"license": "apache-2.0",
"hash": -968006743307447400,
"line_mean": 38.109375,
"line_max": 99,
"alpha_frac": 0.6292449061,
"autogenerated": false,
"ratio": 4.31551724137931,
"config_test": true,
"h... |
from functools import partial
import pytest
from hiku.executors.queue import Queue
class DummyFuture:
def __init__(self, fn, args, kwargs):
self.fn = fn
self.args = args
self.kwargs = kwargs
def run(self):
self.fn(*self.args, **self.kwargs)
class DummyExecutor:
def s... | {
"repo_name": "vmagamedov/hiku",
"path": "tests/test_executor_queue.py",
"copies": "1",
"size": "4543",
"license": "bsd-3-clause",
"hash": 5706106409444908000,
"line_mean": 24.96,
"line_max": 77,
"alpha_frac": 0.6123706802,
"autogenerated": false,
"ratio": 3.2357549857549857,
"config_test": fal... |
from functools import partial
import pytest
from notifications_utils.recipients import (
InvalidEmailError,
InvalidPhoneError,
allowed_to_send_to,
format_phone_number_human_readable,
format_recipient,
get_international_phone_info,
international_phone_info,
is_uk_phone_number,
norma... | {
"repo_name": "alphagov/notifications-utils",
"path": "tests/test_recipient_validation.py",
"copies": "1",
"size": "13469",
"license": "mit",
"hash": -6431273802895706000,
"line_mean": 32.3225806452,
"line_max": 106,
"alpha_frac": 0.6598406434,
"autogenerated": false,
"ratio": 3.259466019417476,
... |
from functools import partial
import pytest
from stp_core.loop.eventually import eventually
from plenum.common.messages.node_messages import PrePrepare
from plenum.common.util import adict
from plenum.server.suspicion_codes import Suspicions
from plenum.test.helper import getNodeSuspicions
from plenum.test.instances.... | {
"repo_name": "evernym/zeno",
"path": "plenum/test/instances/test_multiple_pre_prepare.py",
"copies": "2",
"size": "2359",
"license": "apache-2.0",
"hash": 161156013980216640,
"line_mean": 38.9830508475,
"line_max": 77,
"alpha_frac": 0.7253073336,
"autogenerated": false,
"ratio": 3.91210613598673... |
from functools import partial
import pytest
from stp_core.loop.eventually import eventually
from plenum.common.messages.node_messages import PrePrepare
from stp_core.common.util import adict
from plenum.server.suspicion_codes import Suspicions
from plenum.test.helper import getNodeSuspicions
from plenum.test.instance... | {
"repo_name": "evernym/plenum",
"path": "plenum/test/instances/test_pre_prepare_digest.py",
"copies": "2",
"size": "2335",
"license": "apache-2.0",
"hash": -1076531585056593800,
"line_mean": 42.2407407407,
"line_max": 79,
"alpha_frac": 0.7156316916,
"autogenerated": false,
"ratio": 3.911222780569... |
from functools import partial
import pytest
from sympy import symbols, sqrt, exp, I, Rational, IndexedBase
from qnet import (
CircuitSymbol, CIdentity, CircuitZero, CPermutation, SeriesProduct,
Feedback, SeriesInverse, circuit_identity as cid, Beamsplitter,
OperatorSymbol, IdentityOperator, ZeroOperator,... | {
"repo_name": "mabuchilab/QNET",
"path": "tests/printing/test_tex_printing.py",
"copies": "1",
"size": "38862",
"license": "mit",
"hash": 6791401997737617000,
"line_mean": 42.6161616162,
"line_max": 189,
"alpha_frac": 0.5304410478,
"autogenerated": false,
"ratio": 2.6585032152141195,
"config_te... |
from functools import partial
import pytest
empty = object()
class cached_property(object):
def __init__(self, func):
self.func = func
def __get__(self, obj, cls):
value = obj.__dict__[self.func.__name__] = self.func(obj)
return value
class SimpleProxy(object):
def __init__(s... | {
"repo_name": "thedrow/pytest-benchmark",
"path": "tests/test_sample.py",
"copies": "3",
"size": "1614",
"license": "bsd-2-clause",
"hash": 6076887617199112000,
"line_mean": 21.1095890411,
"line_max": 128,
"alpha_frac": 0.6164807931,
"autogenerated": false,
"ratio": 3.9174757281553396,
"config_... |
from functools import partial
import simplejson
from django.http import HttpResponseNotFound, HttpResponseForbidden, \
HttpResponse, HttpResponseBadRequest
from rip import error_types
http_status_code_mapping = dict(
GET=200,
PATCH=202,
POST=201,
DELETE=204
)
class HttpAuthenticationFailed(Htt... | {
"repo_name": "Aplopio/django_rip",
"path": "rip/django_adapter/django_response_builder.py",
"copies": "2",
"size": "1192",
"license": "mit",
"hash": 7743148259519306000,
"line_mean": 28.0731707317,
"line_max": 70,
"alpha_frac": 0.7206375839,
"autogenerated": false,
"ratio": 4.382352941176471,
... |
from functools import partial
import six
from graphql_relay import from_global_id, to_global_id
from ..types import ID, Field, Interface, ObjectType
from ..types.interface import InterfaceMeta
def is_node(objecttype):
'''
Check if the given objecttype has Node as an interface
'''
assert issubclass(... | {
"repo_name": "sjhewitt/graphene",
"path": "graphene/relay/node.py",
"copies": "1",
"size": "3371",
"license": "mit",
"hash": -4550672987266002400,
"line_mean": 29.3693693694,
"line_max": 98,
"alpha_frac": 0.6291901513,
"autogenerated": false,
"ratio": 3.883640552995392,
"config_test": false,
... |
from functools import partial
import slim
import tensorflow as tf
import data_provider
import utils
from slim import ops
from slim import scopes
def align_reference_shape(reference_shape, reference_shape_bb, im, bb):
def norm(x):
return tf.sqrt(tf.reduce_sum(tf.square(x - tf.reduce_mean(x, 0))))
rat... | {
"repo_name": "trigeorgis/mdm",
"path": "mdm_model.py",
"copies": "1",
"size": "2876",
"license": "bsd-3-clause",
"hash": -2132770990607596500,
"line_mean": 38.397260274,
"line_max": 113,
"alpha_frac": 0.6276077886,
"autogenerated": false,
"ratio": 3.00836820083682,
"config_test": false,
"has... |
from functools import partial
import sqlalchemy as sa
from sqlalchemy.orm import Query
from sqlalchemy.orm import sessionmaker
from enkiblog.workflow import P, Allow
InstrumentedAttribute = sa.orm.attributes.InstrumentedAttribute
def resolve_callable_props(context, permission):
# TODO: move to workflow?
ag... | {
"repo_name": "enkidulan/enkiblog",
"path": "src/enkiblog/core/meta.py",
"copies": "1",
"size": "2989",
"license": "apache-2.0",
"hash": -4651077371934993000,
"line_mean": 31.4891304348,
"line_max": 91,
"alpha_frac": 0.6717965875,
"autogenerated": false,
"ratio": 3.8767833981841764,
"config_tes... |
from functools import partial
import sublime
import sublime_plugin
from .lib import debug, manager, settings, util
from .lib.command import Command
class ToolRunner(sublime_plugin.WindowCommand):
def run(self, tool=None, group=None, profile=None, default_profile=False, **kwargs):
command = Command(self.... | {
"repo_name": "KuttKatrea/sublime-toolrunner",
"path": "ToolRunner.py",
"copies": "1",
"size": "7862",
"license": "mit",
"hash": 3119225143096177700,
"line_mean": 32.0336134454,
"line_max": 88,
"alpha_frac": 0.6054439074,
"autogenerated": false,
"ratio": 3.8595974472263133,
"config_test": false... |
from functools import partial
import sys, re
if sys.version_info[0] == 3:
identifier = re.compile(r"^[^\d\W]\w*\Z", re.UNICODE)
else:
identifier = re.compile(r"^[^\d\W]\w*\Z")
class CodeState(object):
def __init__(self, name):
self.name = name
self.precode = """import bee
from bee.segmen... | {
"repo_name": "agoose77/hivesystem",
"path": "hiveguilib/workergen.py",
"copies": "1",
"size": "18058",
"license": "bsd-2-clause",
"hash": 2504644721459431400,
"line_mean": 36.5426195426,
"line_max": 98,
"alpha_frac": 0.5883818806,
"autogenerated": false,
"ratio": 3.210311111111111,
"config_tes... |
from functools import partial
import tempfile
import cloudvolume
import numpy as np
import shutil
import posixpath
import pytest
import os
from scipy import sparse
import sys
import json
import re
tempdir = tempfile.mkdtemp()
TEST_PATH = "file://{}".format(tempdir)
TEST_DATASET_NAME = "testvol"
PRECOMPUTED_MESH_TEST_... | {
"repo_name": "seung-lab/cloud-volume",
"path": "test/test_graphene.py",
"copies": "1",
"size": "15435",
"license": "bsd-3-clause",
"hash": -1918644976700846300,
"line_mean": 29.0877192982,
"line_max": 106,
"alpha_frac": 0.6206673145,
"autogenerated": false,
"ratio": 2.9739884393063583,
"config... |
from functools import partial
import tensorflow as tf
from tensorflow.contrib.framework import add_arg_scope
from tfsnippet.utils import (validate_int_tuple_arg, is_integer,
add_name_and_scope_arg_doc, InputSpec,
get_static_shape)
from .conv2d_ import conv2d, ... | {
"repo_name": "korepwx/tfsnippet",
"path": "tfsnippet/layers/convolutional/resnet.py",
"copies": "1",
"size": "17170",
"license": "mit",
"hash": 1737517365084588500,
"line_mean": 37.7584650113,
"line_max": 79,
"alpha_frac": 0.5623762376,
"autogenerated": false,
"ratio": 4.343536554515558,
"conf... |
from functools import partial
import tensorflow as tf
import numpy as np
import gym
from stable_baselines import logger
from stable_baselines.common import tf_util, OffPolicyRLModel, SetVerbosity, TensorboardWriter
from stable_baselines.common.vec_env import VecEnv
from stable_baselines.common.schedules import Linear... | {
"repo_name": "hill-a/stable-baselines",
"path": "stable_baselines/deepq/dqn.py",
"copies": "1",
"size": "21439",
"license": "mit",
"hash": -1675789354481695200,
"line_mean": 52.463840399,
"line_max": 126,
"alpha_frac": 0.5847754093,
"autogenerated": false,
"ratio": 4.223601260835303,
"config_t... |
from functools import partial
import tensorflow as tf
def tensors_filter(tensors,
includes='',
includes_combine_type='or',
excludes=None,
excludes_combine_type='or'):
# NOTICE: `includes` = [] means nothing to be included, and `excludes`... | {
"repo_name": "LynnHo/AttGAN-Tensorflow",
"path": "tflib/utils/collection.py",
"copies": "1",
"size": "2510",
"license": "mit",
"hash": 5805295523747124000,
"line_mean": 36.4626865672,
"line_max": 113,
"alpha_frac": 0.5685258964,
"autogenerated": false,
"ratio": 4.458259325044405,
"config_test"... |
from functools import partial
import theano.tensor as T
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from neupy import algorithms, layers, environment
from plots import draw_countour, weight_quiver
environment.reproducible()
input_data = np.array([
[0.9, 0.3],
[0... | {
"repo_name": "stczhc/neupy",
"path": "examples/gd/gd_algorithms_visualization.py",
"copies": "1",
"size": "3630",
"license": "mit",
"hash": 7173010037828774000,
"line_mean": 24.7446808511,
"line_max": 72,
"alpha_frac": 0.658953168,
"autogenerated": false,
"ratio": 3.348708487084871,
"config_te... |
from functools import partial
import torch
from torch import Tensor
import math
import torch.nn.functional as F
from . import register_monotonic_attention
from .monotonic_multihead_attention import (
MonotonicMultiheadAttentionWaitK,
MonotonicMultiheadAttentionHardAligned,
MonotonicMultiheadAttentionInfin... | {
"repo_name": "pytorch/fairseq",
"path": "examples/simultaneous_translation/modules/fixed_pre_decision.py",
"copies": "1",
"size": "10201",
"license": "mit",
"hash": -1606563717617302800,
"line_mean": 39.1614173228,
"line_max": 97,
"alpha_frac": 0.4633859426,
"autogenerated": false,
"ratio": 4.69... |
from functools import partial
import torch
import torch.nn as nn
from torch.nn import functional as F
def dice_coef(input, target, threshold=None):
smooth = 1.0
input_flatten = input.view(-1)
if threshold is not None:
input_flatten = (input_flatten > threshold).float()
target_flatten = target... | {
"repo_name": "creafz/kaggle-carvana",
"path": "loss.py",
"copies": "1",
"size": "1312",
"license": "mit",
"hash": 2497145787039594000,
"line_mean": 26.3333333333,
"line_max": 75,
"alpha_frac": 0.6196646341,
"autogenerated": false,
"ratio": 3.2555831265508686,
"config_test": false,
"has_no_ke... |
from functools import partial
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .efficientnet_blocks import SqueezeExcite
from .efficientnet_builder import decode_arch_def, resolve_act_layer, resolve_bn_args, round_channels
from .helpers import build_model_with_cfg, default_... | {
"repo_name": "rwightman/pytorch-image-models",
"path": "timm/models/hardcorenas.py",
"copies": "1",
"size": "8036",
"license": "apache-2.0",
"hash": 2368087243129276400,
"line_mean": 51.8684210526,
"line_max": 148,
"alpha_frac": 0.6070184171,
"autogenerated": false,
"ratio": 2.0642178268687386,
... |
from functools import partial
import wx
from GuiComponents.InputValidator import *
import wx.grid
class Orientation:
VERTICAL = 1
HORIZONTAL = 0
class GRID_SELECTION_MODES:
CELLS = 0
ROWS = 1
COLUMNS = 2
ROWS_OR_COLUMNS = 3
class WxHelper:
def __init__(self):
pass
class ... | {
"repo_name": "UCHIC/h2outility",
"path": "src/GuiComponents/WxUtilities.py",
"copies": "1",
"size": "14741",
"license": "bsd-3-clause",
"hash": 2419786922174822000,
"line_mean": 37.189119171,
"line_max": 130,
"alpha_frac": 0.5746557221,
"autogenerated": false,
"ratio": 3.866998950682057,
"conf... |
from functools import partial
import wx
from PIL import Image
import shared
from util import load_data, save_data, AddLinearSpacer, SetupChoice
from widgets import LabeledWidget
class ImagePanel(wx.Panel):
format = None
savefile = 'imgdata.json'
data = None
colormode_dict = {'RGB': 'RGB', 'Palette':... | {
"repo_name": "borgler/SuperConverter",
"path": "imggui.py",
"copies": "1",
"size": "20840",
"license": "mpl-2.0",
"hash": -1686475261396572200,
"line_mean": 41.3577235772,
"line_max": 85,
"alpha_frac": 0.5756238004,
"autogenerated": false,
"ratio": 3.9892802450229707,
"config_test": false,
"... |
from functools import partial
class API:
def __init__(self, session):
self._session = session
def __getattr__(self, method_name):
return Request(self, method_name)
async def __call__(self, method_name, **method_kwargs):
return await getattr(self, method_name)(**method_kwargs)
c... | {
"repo_name": "Fahreeve/aiovk",
"path": "aiovk/api.py",
"copies": "1",
"size": "1809",
"license": "mit",
"hash": 1689753151943713300,
"line_mean": 28.6557377049,
"line_max": 116,
"alpha_frac": 0.594803759,
"autogenerated": false,
"ratio": 3.792452830188679,
"config_test": false,
"has_no_keywo... |
from functools import partial
class AuthorizationExpression(object):
def __call__(self, context):
raise NotImplementedError
def __and__(self, other):
self.require_auth_expr(other)
return AndExpression(self, other)
def __or__(self, other):
self.require_auth_expr(other)
return OrExpression(se... | {
"repo_name": "cooper-software/cellardoor",
"path": "cellardoor/authorization.py",
"copies": "1",
"size": "6500",
"license": "mit",
"hash": 3870897848569374700,
"line_mean": 18.6404833837,
"line_max": 108,
"alpha_frac": 0.6430769231,
"autogenerated": false,
"ratio": 3.200393894633186,
"config_t... |
from functools import partial
class Datastore(object):
def __init__(self, db, user_model, role_model=None, provider_user_model=None,
track_login_model=None):
self.db = db
self.user_model = user_model
self.role_model = role_model
self.provider_user_model = provider_... | {
"repo_name": "vgavro/flask-userflow",
"path": "flask_userflow/datastore.py",
"copies": "1",
"size": "2112",
"license": "mit",
"hash": -2606271743569402000,
"line_mean": 31.4923076923,
"line_max": 83,
"alpha_frac": 0.5823863636,
"autogenerated": false,
"ratio": 4.022857142857143,
"config_test":... |
from functools import partial
class Halt(Exception):
def __init__(self, *args):
self.return_args = args
class Encapsulate(object):
"""
Wraps object methods to create pre_ and post_ functions that are called before and after the function.
Encapsulate automatically looks for pre_<function_nam... | {
"repo_name": "lobocv/pyperform",
"path": "pyperform/encapsulate.py",
"copies": "1",
"size": "3386",
"license": "mit",
"hash": 9044553967083832000,
"line_mean": 29.5135135135,
"line_max": 112,
"alpha_frac": 0.5605434141,
"autogenerated": false,
"ratio": 4.374677002583979,
"config_test": false,
... |
from functools import partial
class HiggsException(Exception):
pass
class HiggsScopeException(HiggsException):
pass
class HiggsSyntaxException(HiggsException):
pass
class HiggsDeclarationException(HiggsSyntaxException):
pass
def find_in_scope(name, kwargs):
if name in kwargs:
retur... | {
"repo_name": "vladiibine/higgs",
"path": "higgs/pyimpl/__init__.py",
"copies": "1",
"size": "4979",
"license": "mit",
"hash": 3158858601401974300,
"line_mean": 23.4068627451,
"line_max": 81,
"alpha_frac": 0.6033340028,
"autogenerated": false,
"ratio": 3.9895833333333335,
"config_test": false,
... |
from functools import partial
class _NoValue(object):
"""Represents an unset value. Used to differeniate between an explicit
``None`` and an unset value.
"""
pass
NoValue = _NoValue()
class ChainTest:
def __init__(self, list=[]):
self.func_lookup = {}
self.func_lookup['_in'] ... | {
"repo_name": "jgraham20/graphdb",
"path": "graphdb/chaintest.py",
"copies": "1",
"size": "3820",
"license": "mit",
"hash": -3084902221438974000,
"line_mean": 27.5074626866,
"line_max": 79,
"alpha_frac": 0.5612565445,
"autogenerated": false,
"ratio": 3.9381443298969074,
"config_test": true,
"... |
from functools import partial
class OSCCallbackBase(object):
def __init__(self, receiver, callback=lambda x, y: None, callbacks=None):
self.receiver = receiver
self.callbacks = callbacks
self.callback = callback
def attach(self):
self._recv_callbacks = []
if self.call... | {
"repo_name": "djfroofy/beatlounge",
"path": "bl/osc/andosc.py",
"copies": "1",
"size": "2254",
"license": "mit",
"hash": 1361050764079484700,
"line_mean": 27.8974358974,
"line_max": 77,
"alpha_frac": 0.5980479148,
"autogenerated": false,
"ratio": 3.594896331738437,
"config_test": false,
"has... |
from functools import partial
class Promise(object):
"""A promise object that attempts to mirror the ES6 APIs for promise
objects. Unlike ES6 promises this one however also directly gives
access to the underlying value and it has some slightly different
static method names as this promise can be reso... | {
"repo_name": "tempbottle/rb",
"path": "rb/promise.py",
"copies": "7",
"size": "5354",
"license": "apache-2.0",
"hash": -4502858496644598300,
"line_mean": 28.9106145251,
"line_max": 77,
"alpha_frac": 0.5704146433,
"autogenerated": false,
"ratio": 4.16006216006216,
"config_test": false,
"has_n... |
from functools import partial
class TranslatorFormatException(Exception):
"""Exception raised when a given translation spec is not in a valid format."""
pass
def get_value_at_path(path, obj):
"""
Attempts to get the value of a field located at the given field path within the
object. Recurses int... | {
"repo_name": "gamechanger/missandei",
"path": "missandei/translator.py",
"copies": "1",
"size": "2836",
"license": "mit",
"hash": -2972971455478178000,
"line_mean": 29.4946236559,
"line_max": 84,
"alpha_frac": 0.6413963329,
"autogenerated": false,
"ratio": 4.201481481481482,
"config_test": fal... |
from functools import partial
def alloc_with_destructor_factory(type, constructor, destructor):
def free(data):
destructor(data)
lib.free(data)
allocator = ffi.new_allocator(alloc=lib.malloc, free=free, should_clear_after_alloc=False)
def alloc(initialized=True):
"""
/!\ Wi... | {
"repo_name": "razvanc-r/godot-python",
"path": "pythonscript/cffi_bindings/allocator.inc.py",
"copies": "1",
"size": "3467",
"license": "mit",
"hash": 17848402057385394,
"line_mean": 34.0202020202,
"line_max": 94,
"alpha_frac": 0.7083934237,
"autogenerated": false,
"ratio": 2.7713828936850518,
... |
from functools import partial
def str_to_gd_node_path(path, to_variant=False):
gd_str = pyobj_to_gdobj(path)
gd_ptr = godot_node_path_alloc()
lib.godot_node_path_new(gd_ptr, gd_str)
if to_variant:
gdvar_ptr = godot_variant_alloc()
lib.godot_variant_new_node_path(gdvar_ptr, gd_ptr)
... | {
"repo_name": "razvanc-r/godot-python",
"path": "pythonscript/cffi_bindings/builtin_node_path.inc.py",
"copies": "1",
"size": "2211",
"license": "mit",
"hash": 8119550902235342000,
"line_mean": 31.5147058824,
"line_max": 79,
"alpha_frac": 0.6277702397,
"autogenerated": false,
"ratio": 2.901574803... |
from functools import partial
def T(type_, obj):
"""
Returns True if obj is of type type_
"""
return isinstance(obj, type_)
is_list = partial(T, list)
is_dict = partial(T, dict)
is_tuple = partial(T, tuple)
is_set = partial(T, set)
is_str = partial(T, basestring)
def is_juicy_list(obj):
""... | {
"repo_name": "podio/conssert",
"path": "conssert/navigate.py",
"copies": "1",
"size": "4078",
"license": "mit",
"hash": 5238859181970546000,
"line_mean": 24.6477987421,
"line_max": 108,
"alpha_frac": 0.6022560078,
"autogenerated": false,
"ratio": 3.4854700854700855,
"config_test": false,
"ha... |
from functools import partial
def update_schema_if_mandatory(response, schema, patch_collection):
if 'details' in response:
collection_schema = response['details']['existing']['schema']
else:
collection_schema = response['data'].get('schema')
if schema and (not collection_schema or collec... | {
"repo_name": "mozilla-services/xml2kinto",
"path": "xml2kinto/kinto.py",
"copies": "1",
"size": "1052",
"license": "apache-2.0",
"hash": -5528433437944003000,
"line_mean": 37.962962963,
"line_max": 73,
"alpha_frac": 0.6910646388,
"autogenerated": false,
"ratio": 4.311475409836065,
"config_test... |
from functools import partial
nth = lambda i: lambda seq: seq[i]
first = nth(0)
second = nth(1)
seq = {'new': lambda op, args: op(args),
'op': lambda term: type(term),
'args': lambda term: tuple(term),
'isleaf':lambda term: False}
term_registry = {
dict: {'new': lambda keys, args: ... | {
"repo_name": "mrocklin/termpy",
"path": "termpy/ground.py",
"copies": "1",
"size": "2599",
"license": "bsd-3-clause",
"hash": -3467711011291837000,
"line_mean": 26.0729166667,
"line_max": 75,
"alpha_frac": 0.6067718353,
"autogenerated": false,
"ratio": 3.3024142312579414,
"config_test": false,... |
from functools import partial
# Replace this with actual implementation from
# http://code.activestate.com/recipes/577748-calculate-the-mro-of-a-class/
# (though this will work for simple cases)
def mro(*bases):
return bases[0].__mro__
# This definition is only used to assist static code analyzers
def copy_ancest... | {
"repo_name": "ActiveState/code",
"path": "recipes/Python/578587_Inherit_method_docstrings_without_breaking/recipe-578587.py",
"copies": "1",
"size": "2425",
"license": "mit",
"hash": -5224659705916750000,
"line_mean": 36.890625,
"line_max": 93,
"alpha_frac": 0.6301030928,
"autogenerated": false,
... |
from functools import partial
try:
# django 1.7
from django.contrib.admin.utils import flatten_fieldsets
from django.forms.models import modelform_defines_fields
except ImportError:
from django.contrib.admin.util import flatten_fieldsets
from django.contrib.admin.options import ModelAdmin, InlineModelA... | {
"repo_name": "Venturi/oldcms",
"path": "env/lib/python2.7/site-packages/app_data/admin.py",
"copies": "1",
"size": "4349",
"license": "apache-2.0",
"hash": -8609018026784354000,
"line_mean": 37.8303571429,
"line_max": 99,
"alpha_frac": 0.6123246723,
"autogenerated": false,
"ratio": 4.20599613152... |
from functools import partial
try:
from raven.utils.compat import (
string_types,
binary_type,
)
except ImportError:
from raven._compat import (
string_types,
binary_type,
)
from raven.processors import SanitizePasswordsProcessor
from raven.utils import varmap
class Py... | {
"repo_name": "npilon/pyramid_crow",
"path": "pyramid_crow/processors.py",
"copies": "1",
"size": "2037",
"license": "apache-2.0",
"hash": 2869598427867953000,
"line_mean": 33.5254237288,
"line_max": 78,
"alpha_frac": 0.6053019146,
"autogenerated": false,
"ratio": 4.2974683544303796,
"config_te... |
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