sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
e9c2431faa61a2fda12cdfa8208ad7b2747f56240935bfc64463866c92370847 | Python | 20,914 | 477 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... |
8e3f4ee5c5ffcf69666a27136430d53a16034a61ee4509321fa04cba887fd5da | Python | 20,916 | 531 | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
from copy import deepcopy
from unittest.mock import patch
import numpy as np
from ax.adapter.base imp... |
e412f75994376343ccabe89455c13e976bec727ff790ae0b8b891d4cb139528b | Python | 20,925 | 518 | import pytest
import networkx as nx
def validate_grid_path(r, c, s, t, p):
assert isinstance(p, list)
assert p[0] == s
assert p[-1] == t
s = ((s - 1) // c, (s - 1) % c)
t = ((t - 1) // c, (t - 1) % c)
assert len(p) == abs(t[0] - s[0]) + abs(t[1] - s[1]) + 1
p = [((u - 1) // c, (u - 1) % c... |
542a645fff9eb67c52114b61c5dcccd90857eba071b23ce0c441695c816b73fc | Python | 20,926 | 484 | """
Dependency management for tools.
"""
import json
import logging
import os.path
import shutil
from typing import (
Any,
Dict,
List,
Optional,
Type,
TYPE_CHECKING,
)
from typing_extensions import Protocol
from galaxy.util import (
hash_util,
plugin_config,
string_as_bool,
)
from... |
be418d77cea13240fc11f9b61853587a3498416826be963af008627caa14a81e | Python | 20,939 | 654 | #!/usr/bin/env python
# encoding: utf-8
"""
Interface to NRRD volumetric image data files.
The interface is based on nrrd.py, an all-python (and numpy)
implementation for reading and writing nrrd files.
See http://teem.sourceforge.net/nrrd/format.html for the specification.
Example:
>>> import os, numpy
>>> ... |
192e807eea16d21e9df0cbb8a1b3869cf340135c414008f3ce05da844fde34e1 | Python | 20,947 | 432 | # -*- coding: utf-8 -*-
"""
Created on Fri Jul 5 13:06:13 2019
@author: schelskim
"""
from .generaltools import generalTools
from .sortpoints import sortPoints
import numpy as np
from skimage import morphology as morph
from scipy import ndimage
from scipy.signal import medfilt2d as median2
import copy... |
f3cc11eceefa0e10d0490c2fc68a25a0b316bc6bdd3611f0a72c8e0d464828e1 | Python | 20,949 | 583 | __all__ = [
'plot_corr_matrix',
'plot_linear_fit',
'add_text_adjust',
'swarmplot',
'boxplot',
'plot_kaplan_pval',
]
import itertools
import adjustText
import numpy as np
import pandas as pd
import scipy
from matplotlib import pyplot as plt
import scipy.cluster.hierarchy as sch
import seaborn a... |
07a9625b19568f9161edfe925dbb60b818ab895adbd3b3cbfb99140d08699973 | Python | 20,968 | 578 | """This file and its contents are licensed under the Apache License 2.0. Please see the included NOTICE for copyright information and LICENSE for a copy of the license."""
from decimal import Decimal
import bleach
from constants import SAFE_HTML_ATTRIBUTES, SAFE_HTML_TAGS, SAFE_INSTRUCTION_HTML_TAGS
from django.db.mo... |
0d630f8bf15936f7d25473411fc12b70076212779665096924cb68c083b24088 | Python | 20,977 | 654 | #!/usr/bin/env python
# encoding: utf-8
"""
Interface to NRRD volumetric image data files.
The interface is based on nrrd.py, an all-python (and numpy)
implementation for reading and writing nrrd files.
See http://teem.sourceforge.net/nrrd/format.html for the specification.
Example:
>>> import os, numpy
>>> ... |
4e189c09f26814eb3704e55ec94a43e0abde99ed6f4b63528060097f3de9ca38 | Python | 20,992 | 650 | import pytest
import torch
from spikingjelly.activation_based import (
functional,
layer,
neuron,
op_counter,
surrogate,
)
def _assert_close(actual: torch.Tensor, expected: torch.Tensor):
if actual.dtype == torch.float32:
torch.testing.assert_close(actual, expected)
else:
... |
6c2ea12dff511f164cae6da3eabfd4440cc6d9a9696f78df54ff1113f1054b0a | Python | 20,999 | 548 | """This file and its contents are licensed under the Apache License 2.0. Please see the included NOTICE for copyright information and LICENSE for a copy of the license."""
import json
import logging
import re
from collections import OrderedDict, defaultdict
from typing import Tuple, Union
from urllib.parse import urle... |
8948657e29dad3b2da8c51fef0bd206395387669edb2b8b73f6e1e09ef868657 | Python | 21,005 | 559 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
CellMap
=======
This script is the main pipeline to analyze immediate early gene expression
data from iDISCO+ cleared tissue [Renier2016]_.
See the :ref:`CellMap tutorial </CellMap.ipynb>` for a tutorial and usage.
.. image:: ../Static/cell_abstract_2016.jpg
:t... |
7cad3312f1e3f8dd29c7b303eae49f8528914d197bfa08e5b5f8beb378d2975d | Python | 21,016 | 555 | import abc
from typing import (
Any,
Dict,
List,
Optional,
Sequence,
Type,
TYPE_CHECKING,
Union,
)
from typing_extensions import TypedDict
from galaxy.tool_util_models.tool_outputs import (
ToolOutputBoolean as ToolOutputBooleanModel,
ToolOutputCollection as ToolOutputCollectio... |
1afa784ba72e05179a63cc1b7048fded85d75361aa43c7219c1fb582c0b0ae26 | Python | 21,018 | 578 | import torchvision.transforms as transforms
import h5py
import numpy as np
import torch
import torch.nn as nn
import vision_transformer
import torch.nn.functional as F
import matplotlib.pyplot as plt
import time
import torch.optim as optim
n_epochs = 300
log_interval = 10
batch_size_train = 64
batch_size... |
8a2bf0c99aed95910203715fb4ede4a067ad72262f11e9bc46ec92c9c92ffea7 | Python | 21,052 | 553 | import math
import os
from datetime import datetime
from functools import partial
from typing import TYPE_CHECKING, Any, Sequence, cast
import numpy as np
import tensorflow as tf
import learnMSA.model.training_util as training_util
from learnMSA.util.embedding_dataset import EmbeddingDataset
from learnMSA.util.sequen... |
0ddd64215d3a5c87729cda4ac4730c45120ab17ba852c1bb408ce7b5f56d6422 | Python | 21,073 | 516 | # -*- coding: utf-8 -*-
#
# Copyright (C) 2013 Vinay Sajip.
# Licensed to the Python Software Foundation under a contributor agreement.
# See LICENSE.txt and CONTRIBUTORS.txt.
#
import hashlib
import logging
import os
import shutil
import subprocess
import tempfile
try:
from threading import Thread
except ImportErr... |
b3f665c60aaf09b6341cffdd13e26cf4462dc068f4e267cc87ab97cd595ad048 | Python | 21,073 | 580 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from __future__ import annotations
import itertools
from abc import ABCMeta, abstractmethod
from collections.abc import Collection, Iterable, Sequence
from typing import ClassVar, TypeAlias
import attrs
import datumaro as dm
import datumaro.compone... |
552f5e0c662e6281b34f93e9e4bfeb45d5011b114d389c6eaf9f60fc43a061ff | Python | 21,078 | 683 | """Adapt functions from scikit-learn to use different backends."""
import itertools
import math
from functools import partial
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils.validation import check_is_fitted
from ..backend import force_cpu_backend, get_backend
from ..validation import _ge... |
cdffb6b915121fdc8e382561afbfe47aa01d301ec00ebbd67e980eccd335e2c4 | Python | 21,097 | 631 | import numpy as np
import warnings
import numbers
from timeit import default_timer as timer
from datetime import timedelta
def sub2ind(array_shape, rows, cols):
return rows * array_shape[1] + cols
def DAPCA(
XX,
labels,
nComp,
YY=None,
alpha=None,
beta=0.9,
gamma=0.4,
kNN=1,
... |
a0d085a75c3bb54083b08d5f2f4ae7a75e9e1a35d0e9a6bcfa4863a1361d4ac7 | Python | 21,138 | 537 | import pathlib
import shutil
import tempfile
import types
import unittest
import numpy as np
from bsb_test import NumpyTestCase, RandomStorageFixture, skip_parallel
from neo import AnalogSignal, Block, Segment, SpikeTrain
from neo import io as neo_io
from quantities import ms, mV
from bsb import (
Branch,
Con... |
7d79d8bab241cb646928524cc0f2a92f32285f64766cd56835cdc4cb5365df57 | Python | 21,151 | 622 | import pytest
import torch
import torch.nn as nn
from spikingjelly.activation_based import base, functional, layer, neuron
def _create_test_model():
net = nn.Sequential(
layer.Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
neuron.IFNode(),
layer.MaxPool2d(2, 2),
layer.Conv2d(3... |
c002e2bc1e63b262d57e88baa511c4b762d48f3df7536cbae4110c406fe03135 | Python | 21,158 | 535 | from __future__ import annotations
from collections.abc import Callable
from textwrap import dedent
from typing import Any
from docrep import DocstringProcessor
from squidpy._constants._pkg_constants import Key
def inject_docs(**kwargs: Any) -> Callable[..., Any]: # noqa: D103
# taken from scanpy
def deco... |
2d98f1cb123908a5fa299f87389fac735db5c8ddd7a28a97eff2353da5f39384 | Python | 21,176 | 826 | import operator
from pytest import raises
from nl_expr import expression_walker, expressions
from nl_expr.logic import Negation, Union as LogicUnion
from .. import DatalogProgram, Fact, Implication, Conjunction
from ..aggregation import (
AGG_COUNT,
BuiltinAggregationMixin,
DatalogWithAggregationMixin,
... |
150745560b7c33d2515d9a59ac7b96bb9c7e3c5502a0199ce1a9480a99c2a36a | Python | 21,188 | 604 | import abc
import logging
import os
from typing import (
Union,
)
from galaxy.datatypes.protocols import DatasetProtocol
from galaxy.datatypes.sniff import (
build_sniff_from_prefix,
FilePrefix,
iter_headers,
)
from .tabular import Tabular
log = logging.getLogger(__name__)
@build_sniff_from_prefix
c... |
530f8c7aaddb02b0a055457f27b16c6d5c705ca3d01a5d02f8404b897869e804 | Python | 21,209 | 673 | # -*- coding: utf-8 -*-
"""
Created on Sat Jul 25 16:13:56 2015
@author: ckirst
"""
##############################################################################
# Test Alignment
##############################################################################
import os;
from iDISCO.Parameter import *
from iDISCO.Ru... |
b704725ceb742502b0cc67e49c2396e12ebb2df51f0139f45a1604a3438f1402 | Python | 21,241 | 658 | """
Joins of points within a Euclidean distance, computed with numpy.
This is the numeric kernel of the Euclidean spatial-bound syntactic sugar
(see :mod:`neurolang.frontend.datalog.sugar.spatial`), which only detects
the bound and rewrites the rule. :func:`euclidean_distance` is the
``euclidean`` built-in function. :... |
149de1887a426345fadb4d8d548f20a3d23efe1eedce3da610b16c1bbab00e52 | Python | 21,276 | 502 | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
# pyre-ignore-all-errors[8]
#
# `Mapped[T] = Column(...)` is the SA 1.4-compatible bridging form. SA 2.... |
97f5ffb63625a54260248b10aabf5d94b5f11d0ff8da43e30b8353e0eeb9963d | Python | 21,283 | 520 | import math
import random
from copy import deepcopy
import numpy
import torch
from torch import nn
from transformers import BertModel, BertConfig, AutoModel
USE_CUDA = torch.cuda.is_available()
MAX_OUTPUT_LENGTH = 32
class Beam: # the class save the beam node
def __init__(self, score, input, outp... |
1ede06fc377c971a6e85f1f2e19d0dbfb1ed27b747aee7bcae95b5349750833f | Python | 21,287 | 591 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 14 16:43:35 2022
@author: akshay
"""
import pandas as pd
import numpy as np
import scipy
from scipy import stats
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
#from stack overflow
def add_p_value_annotation(fig, array_columns, subplot=None, _format=dict(int... |
05bb44b216812de3126b64735ea04203618527da524c47b44412d857168f6939 | Python | 21,315 | 642 | """
Unit tests for dedensification and graph summarization
"""
import pytest
import networkx as nx
class TestDirectedDedensification:
def build_original_graph(self):
original_matrix = [
("1", "BC"),
("2", "ABC"),
("3", ["A", "B", "6"]),
("4", "ABC"),
... |
a35cbdac6c4caf593d4b268481cf5f84e1092273490c0f48e0000d650878f17c | Python | 21,315 | 462 | # Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, so... |
d67e7ee118a5320f57b11b986d5ccb7f2a8ff61205d489768e22b9ca127419ab | Python | 21,343 | 638 | """Maximum flow algorithms test suite."""
import pytest
import networkx as nx
from networkx.algorithms.flow import (
boykov_kolmogorov,
build_flow_dict,
build_residual_network,
dinitz,
edmonds_karp,
preflow_push,
shortest_augmenting_path,
)
from networkx.algorithms.flow.utils import _capac... |
f9a2c7a77a5b3fa362d8244edb62418a1aff310dbf01904df1edeb1e13144aa1 | Python | 21,355 | 506 | import argparse
import copy
import os
import subprocess
import torch
import numpy as np
from sklearn.metrics import f1_score as compute_f1_score
from collections import defaultdict
# methods that need encoder trained before
train_encoder_methods = ['cpc', 'spatial-appo', 'vae', "naff", "infonce-stdim", "global-infon... |
4653c215df37ee2a6c69dd9808f278da4fcf19424241ac15a8075ff745defc8b | Python | 21,359 | 626 | import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib import gridspec
import parmap
import numpy as np
import pandas as pd
import os
import shutil
import cv2
import scipy.io as sio
import scipy.signal
from Specgram.Specgram import Specgram
import glob2
f... |
06b18cfa4a2a2252d426c90e56e0034c573942b2268aad81efc25d255a883ffe | Python | 21,360 | 548 | """Tests for ancestral probability calculations and rate matrices."""
import os
import numpy as np
import pytest
import tensorflow as tf
from learnMSA import Configuration
from learnMSA.model import LearnMSAModel, LearnMSAContext
from learnMSA.model.tf import training
import learnMSA.tree.tf.initializer as Initialize... |
b163fcc4baa755af094fc440c8d51406e152078ad698f6c4d5daf95fc2c13495 | Python | 21,371 | 488 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... |
231d62419206331918c6ee4d03a049672cd7c321d5444d78b57ec06048dc10b6 | Python | 21,432 | 647 | """Confounds Workflow Builder Functions.
Contains a collection of processing workflow builders specifically for fMRIPrep
confound file processing.
"""
import os
import copy
from typing import List
from nipype.interfaces.utility import Function, IdentityInterface
from nipype.interfaces.io import ExportFile
import nip... |
4e493643a17a22b66643962f43989f8fd65b56b4a1fd8def31b06a8b2a2262bf | Python | 21,432 | 793 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Large Data and Parallel Tools
This module provides an interface to tools to deal with large numpy arrays
and speed up numpy routines that get very slow for data arrays of 100-500GB size
"""
__author__ = 'Christoph Kirst <ckirst@rockefeller.edu>'
__license__ = 'MI... |
ba9d8a1cc656d47bea3fab3583a441ce27a70ff67287cfad09ec4d1897dd17d3 | Python | 21,442 | 615 | import copy
import os
from unittest import mock
import pytest
import torch
from spikingjelly.activation_based import functional, layer, neuron
from spikingjelly.activation_based.model import Spikformer
from spikingjelly.activation_based.precision import (
PrecisionConfig,
prepare_model_for_precision,
)
from s... |
15577acf8d99ba57de84f2fefb9b9a4da335b9ff099f0d3799cb9446b182c50e | Python | 21,444 | 508 | from __future__ import annotations
import warnings
import numpy as np
import pandas as pd
import pytest
import scipy.sparse as sp
from anndata import AnnData
from squidpy._constants._pkg_constants import Key
from squidpy.gr import (
centrality_scores,
interaction_matrix,
nhood_enrichment,
spatial_nei... |
5cf3246bb9c774bd7484df2ae3f012b6b4e9d561cb9ed28d2e473d79e5deb463 | Python | 21,445 | 505 | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
from typing import final, Literal
import pandas as pd
from ax.adapter.base import Adapter
from ax.analysis.analysis import A... |
39a209e87768f8b276ecb5367003d6cc77db97fde2a1bb06cdacbdc0395f2e99 | Python | 21,458 | 596 | from __future__ import absolute_import
import csv
import functools
import logging
import os
import sys
import sysconfig
from pip._vendor import pkg_resources
from pip._internal.exceptions import UninstallationError
from pip._internal.locations import bin_py, bin_user
from pip._internal.utils.compat import WINDOWS, c... |
a44c849b60bf533d310b72589c1f0fd8dfae78bcfead84e477c1c20467d0339e | Python | 21,481 | 508 | import threading
from types import SimpleNamespace
from unittest.mock import MagicMock, patch
from core.current_request import CurrentContext
from django.test import override_settings
from label_studio.core.redis import (
delete_job_by_id,
get_jobs_by_meta,
is_job_on_worker,
start_job_async_or_sync,
... |
c429d4f79f6c32d50ee72fb31a5284555258a179e3439c51832d5f0246b59ae5 | Python | 21,483 | 477 | """
Copyright (c) 2019 10x Genomics, Inc. All rights reserved.
Mark PCR duplicates in a BAM file
"""
from __future__ import division
import itertools
import json
import math
import os
import numpy as np
import martian
import pickle
import pysam
import tenkit.bam as tk_bam
import tenkit.lane as tk_lane
from barcodes i... |
9bad9c6ba534d514fea680d21003174082e291b1b7a635036b1f97dd63021f8e | Python | 21,521 | 579 | import numbers
from ..backend import get_backend
from ..progress_bar import ProgressBar
from ..utils import compute_lipschitz_constants
from ..scoring import l2_neg_loss
from ..validation import check_cv
from ._solvers import solve_weighted_kernel_ridge_conjugate_gradient
from ._solvers import solve_weighted_kernel_r... |
1da56270e4b97f2d11814dd8bb3d7068ceecd3202fea86dd3ed408eb9ca83fb0 | Python | 21,530 | 487 | """Tests for mibidata.tiff
Copyright (C) 2021 Ionpath, Inc. All rights reserved."""
import datetime
import os
import shutil
import tempfile
import unittest
import warnings
import numpy as np
from skimage import io as skio, img_as_ubyte, transform
from tifffile import TiffFile
from mibidata import mibi_image as mi
... |
e413b9610836842dfdc193a0d37d9d0522d592a6cda71d88db3823aed9fa35f6 | Python | 21,535 | 497 |
import io
from typing import Callable
from nl_expr.logic import Implication
from nl_expr.expressions import Constant, Symbol
from ..constraints_representation import RightImplication
from ..ontologies_parser import OntologyParser
def test_1():
owl = '''<?xml version="1.0"?>
<rdf:RDF xmlns="http://www.w3.org... |
250135d30f1c20f4328700877f5bef91f3d5f379bc25faa9d88f1c3e50274b02 | Python | 21,553 | 556 | """Unit tests for the :mod:`networkx.generators.random_graphs` module."""
import pytest
import networkx as nx
_gnp_generators = [
nx.gnp_random_graph,
nx.fast_gnp_random_graph,
nx.binomial_graph,
nx.erdos_renyi_graph,
]
@pytest.mark.parametrize("generator", _gnp_generators)
@pytest.mark.parametrize... |
402c354f395f18a46ab79bd3ad989b30e775bdfc5a9af437d9e23629546c1e77 | Python | 21,560 | 507 | # Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, so... |
92cbed9e4c17791eb622a09e35790c89be958d6d4df8e06aefaeacec65c1d2a8 | Python | 21,566 | 646 | # -*- coding: utf-8 -*-
# Copyright (c) Vispy Development Team. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
"""
About this technique
--------------------
In Python, we define the six faces of a cuboid to draw, as well as
texture cooridnates corresponding with the ver... |
799cd46e4ade55f9d5972c9b65aa410586be2a6448a8e9ae316fb4b95e88e205 | Python | 21,589 | 468 | """Functions to create volumetric neuron densities for the following inhibitory neuron types of the
mouse brain:
- PV+
- SST+
- VIP+
- GAD67+ (a superset of the union of the previous three)
The algorithm is presented in "Atlas of inhibitory neurons in the mouse brain" by D. Dimitri
Rodarie et al., to appear in <journa... |
4864229c97ac2a5e86bc500d284ba4f6aeab25cef452ef3b2e0dd7e41785cd38 | Python | 21,602 | 535 | import pytest
from ..agent import (
IclAgent,
ReactAgent,
extract_query,
make_agent,
parse_text_action,
parse_verdict,
)
from ..providers import LLMError, Reply
from .fakes import ScriptedClient, assistant_config, provider
PAIN_SQUALL = "obtain every Study that mentions 'pain'."
PAIN_DATALOG =... |
c7baca2501ab55d5aafc6f271b823c043b4fb2dccac78757e687316879b6199d | Python | 21,618 | 667 | # -*- coding: utf-8 -*-
"""
3d Skeletonization PK12
=======================
This module implements the 3d parallel 12-subiteration thinning algorithm
by Palagy & Kuba via parallel convolution of the data with a base template
and lookup table matching.
Reference
---------
Palagyi & Kuba, A Parallel 3D 12-Subiterati... |
5cb90661068ebbabcea308ff25cd2dc037603438393be3a301c037ff4607d6da | Python | 21,619 | 487 | # MIT License
#
# Copyright (c) 2019-2024 The PyQMC Developers
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, m... |
d41e599bbdff97fd3e1d99f6ffa3e7e79f3060c2c662ccdd67646c7422b2d287 | Python | 21,619 | 515 | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
from contextlib import nullcontext
from dataclasses import replace
from itertools import product
from... |
c6c4f4e3c3a4fee334661a5d737e2ab30575cda0863a52509ec6c64c3b26e68a | Python | 21,637 | 590 | # This module uses material from the Wikipedia article Hopcroft--Karp algorithm
# <https://en.wikipedia.org/wiki/Hopcroft%E2%80%93Karp_algorithm>, accessed on
# January 3, 2015, which is released under the Creative Commons
# Attribution-Share-Alike License 3.0
# <http://creativecommons.org/licenses/by-sa/3.0/>. That ar... |
15c1e9b305849cd51dd1e9f4f95c92ec9da7c42affdfcdeee761d9b965873dc2 | Python | 21,641 | 699 | import inspect
import json
import math
import numbers
from textwrap import TextWrapper
import mmap
import time
import numpy as np
from asciitree import BoxStyle, LeftAligned
from asciitree.traversal import Traversal
from collections.abc import Iterable
from numcodecs.compat import ensure_text, ensure_ndarray_like
from... |
718cd69e2792e78779913e37fa7c1ff99df36ad04bca57983f049768431fbf32 | Python | 21,655 | 537 | import multiprocessing
import os
from functools import partial
from shutil import move, rmtree
import feather
import numpy as np
import pandas as pd
from alpineer import io_utils, misc_utils
from pyarrow.lib import ArrowInvalid
from ark.phenotyping import cluster_helpers, pixel_cluster_utils
multiprocessing.set_star... |
74f555694629e325cce8c5ffb8c0f91a18679ee3b52d0a1e25563d60641dce54 | Python | 21,682 | 593 | """
Superclass Manager and Serializers for Sharable objects.
A sharable Galaxy object:
has an owner/creator User
is sharable with other, specific Users
is importable (copyable) by users that have access
has a slug which can be used as a link to view the resource
can be published effectively making ... |
3c7001f130d92327378373ac83be75c6571315d23083c5ff8036b4fff17ade97 | Python | 21,717 | 705 | # -*- coding: utf-8 -*-
# Imports
import sys
import torch
from gpytorch.likelihoods import GaussianLikelihood
from gpytorch.constraints import GreaterThan
from sklearn.linear_model import ARDRegression
from sklearn.model_selection import GridSearchCV
import numpy as np
import warnings
from .base_models import gp_... |
32c4aba1273a59bf0439417d2c072965c1fa662cd926601267b1900c18813728 | Python | 21,724 | 516 | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
from collections.abc import Iterable
from dataclasses import dataclass
from logging import Logger
from... |
47f4433fef08af51e1bc314243e59f693b0c57b82c7717090c64cfa36bb13f6c | Python | 21,746 | 530 | import os
import h5py
import numpy as np
import matplotlib.pyplot as plt
from jinja2 import Template
from typing import List, Dict, Optional
import importlib.resources
import base64
import io
from .report import compute_per_position_ic, _plot_weights, tomtomlite_dataframe, generate_tomtom_dataframe
from memelite.io im... |
e930855bfcd0d35cdf8c65b688bb8904ab7381c14b117061aac966f5c603e559 | Python | 21,752 | 801 | # -*- coding: utf-8 -*-
"""
RegularExpression
=================
Module providing routines to check and convert between regular and file
expressions.
Utility module fused by :mod:`ClearMap.Utils.TagExpression`.
"""
__author__ = 'Christoph Kirst <ckirst@rockefeller.edu>'
__license__ = 'MIT License <http://www.op... |
fce29c4d3f189c89681b57801f059a1fa7c77f0ed33fdee4b7d34a4a63d5232f | Python | 21,753 | 353 | import unittest
import sys
import json
with open(sys.argv[-1], 'rb') as f:
INPUT_DICT = json.load(f)
sys.path.insert(0, '../')
from tension.base import FORCEModel
from tension.models import EchoStateNetwork, NoFeedbackESN
from tension.constrained import ConstrainedNoFeedbackESN, BioFORCEModel
from tension.spiking... |
0f5aa8fac6b5ee62770148f2c72c6fc2633658552de8afcd2da990327a1427ab | Python | 21,781 | 533 | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
import itertools
from copy import deepcopy
from datetime import datetime
from unittest import mock
fro... |
67aab4cf9204ab122fe5abbb08e87c9253ae83a09dd954536bd7328ec3f5c96a | Python | 21,783 | 563 | """Compare paired vision measurements and archive release evidence."""
import argparse
import hashlib
import json
import math
from pathlib import Path
import re
import statistics
import subprocess
import tempfile
from urllib.parse import unquote, urlsplit
import zipfile
def _hash(path):
with Path(path).open("rb"... |
7584e51fef6e8d21afdb6fb1204fac87b222287080bb5c3469bc50f824ef7faf | Python | 21,806 | 609 | import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from matplotlib import gridspec
from pylab import *
import numpy as np
import random
import math
import time
import os
import glob
import copy
# change the default font family
plt.rcParams.update({'font.family':'Arial'})
plt.rc('xtick... |
3ae5a3c3a7516f319e4351366ce2bbe78d8db3949f5500f9a900d0ff78f3c080 | Python | 21,810 | 576 | """
Core state management functionality for Label Studio.
Provides high-performance state management with caching and batch operations
that can be extended by Label Studio Enterprise with additional features.
"""
import logging
from datetime import datetime
from typing import Any, Dict, List, Optional, Type
from cor... |
c3014274f04b888332e7c2adb96c0ed3cdb8dbbf50edc7ee73decc240524505b | Python | 21,832 | 521 | import logging
import time
import traceback
from typing import Callable, Optional
from core.feature_flags import flag_set
from core.utils.common import load_func
from data_import.uploader import load_tasks_for_async_import_streaming
from django.conf import settings
from django.db import transaction
from label_studio_s... |
6de8754e882c206bff80ee71004c8abe57bcb3108bd9b7e946114d294aff2489 | Python | 21,837 | 594 | #! /usr/bin/env python
import sys
import argparse
import numpy as np
import pickle as p
from multiprocessing import Manager, Pool
import os
import pandas as pd
from copy import deepcopy
from sklearn.model_selection import StratifiedKFold, GridSearchCV, KFold
from sklearn.metrics import (f1_score, confusion_matrix, roc_... |
e79d984454bc6a46de06277e88159f865db44493386f1f0a5a2e4cfa30dfb829 | Python | 21,839 | 451 | from typing import Literal, Optional
import torch
from .. import surrogate
__all__ = [
"bit_spike_compress",
"bit_spike_decompress",
"bit_pack_spike_dense",
"packed_spike_linear",
"sparse_linear",
"if_linear",
"lif_linear",
]
def bit_spike_compress(spike: torch.Tensor) -> torch.Tensor:
... |
5b1f1359aabe6fe17163510e95e77a4dd27d2315573d2511e948b4b056f2e986 | Python | 21,849 | 541 | """Utility functions for cell density computation."""
from __future__ import annotations
import json
from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING
from warnings import warn
import numpy as np
import pandas as pd
import scipy.misc
import scipy.ndimage
from atlas_commons.typing imp... |
6c862f7d0d5b57eabc3697d7a3a460a7eb45365395dc1c52c2df8bb1d4331a72 | Python | 21,889 | 666 | """
Set of syntactic sugar processors at the intermediate level.
"""
import operator as op
import typing
from typing import AbstractSet, Any, Callable, DefaultDict
from nl_expr import expression_walker as ew
from nl_expr import expressions as ir
from nl_core.datalog.expression_processing import (
conjunct_formula... |
7fb6de5da344ee76f6ec7c8236a06fd71f18c2223a0bd48e089c88554ca569d2 | Python | 21,890 | 660 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... |
449aa19308b8a37bf02016166c293c4723f33c714327393b101c0bb7285b9036 | Python | 21,891 | 680 | from __future__ import annotations
import os
from collections.abc import Callable, Mapping, Sequence
from functools import wraps
from inspect import signature
from pathlib import Path
from types import MappingProxyType
from typing import TYPE_CHECKING, Any
import matplotlib as mpl
import numpy as np
import pandas as ... |
c5d14b1784466cff8a1f656c08db026b3c27294f44e7368739a4e5267fda8b59 | Python | 21,905 | 557 | import os
import warnings
from pathlib import Path
import numpy as np
import pytest
from learnMSA.util.sequence_dataset import SequenceDataset
from learnMSA.util.aligned_dataset import AlignedDataset
DIR = "tests/data/"
def test_records() -> None:
for ind in [True, False]:
with SequenceDataset(f"{DIR}... |
75aecbd51c6bdb89c57a70c863e72a4599923faf1299a8ed681695aabbbcc3d0 | Python | 21,926 | 722 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import logging
import os
import sys
from enum import Enum
from http import HTTPStatus
from pathlib import Path
from subprocess import PIPE, CalledProcessError, run
from time import sleep
import pytest
import requests
import yaml
from shared.utils.c... |
73d552d579a71953049a6487bff2e3a59bf3111fe2973c69a29162f801cfe8bd | Python | 21,952 | 669 | # -*- coding: utf-8 -*-
"""
This is a Python translation (end enhancement) of the original Matlab script
released for the Frontiers paper, in 2018.
This script defines the retinal contrast function. First the parameters are
set. Then the function computes input scene luminances by combining a 2D input
map of... |
b3f56d902d2b6f99268387ad238114cb3220114fc1428c6dcb7c18228c645cfe | Python | 21,952 | 379 | # -*- coding: utf-8 -*-
"""
Created on Fri Jul 5 10:13:28 2019
@author: schelskim
"""
import os
import numpy as np
import pandas as pd
from skimage import io
import copy
import math
from skimage.draw import line_aa
import re
class Analyzers():
@staticmethod
def analyzeIntensities(date,experiment,neuron... |
a54aed7cbfdb5791246e459bc48221cbeca50fb641e7787ac8235b184690959a | Python | 21,977 | 641 | import torch
import torch.nn as nn
import h5py
import numpy as np
import torch.utils.data.dataset
import torch.optim as optim
import torchvision.transforms as transforms
import torch.nn.functional as F
import matplotlib.pyplot as plt
import cls_cvt
import time
n_epochs = 200
log_interval = 10
batch_size_... |
be25bfd1b52f358b0dd79f42c49f98e3d26efdd5b4fd45bca187850d8553679f | Python | 21,977 | 641 | import torch
import torch.nn as nn
import h5py
import numpy as np
import torch.utils.data.dataset
import torch.optim as optim
import torchvision.transforms as transforms
import torch.nn.functional as F
import matplotlib.pyplot as plt
import cls_cvt
import time
n_epochs = 200
log_interval = 10
batch_size_... |
19d16c8791e6dcff076f8d76384e6367ba9d6fb61877151b5bd7bc49a5a30108 | Python | 21,978 | 641 | import torch
import torch.nn as nn
import h5py
import numpy as np
import torch.utils.data.dataset
import torch.optim as optim
import torchvision.transforms as transforms
import torch.nn.functional as F
import matplotlib.pyplot as plt
import cls_cvt
import time
n_epochs = 200
log_interval = 10
batch_size_... |
79f00caeb21accb871888cb43a92f834731b708becc6bdc41cf3d157dcc8281d | Python | 21,978 | 641 | import torch
import torch.nn as nn
import h5py
import numpy as np
import torch.utils.data.dataset
import torch.optim as optim
import torchvision.transforms as transforms
import torch.nn.functional as F
import matplotlib.pyplot as plt
import cls_cvt
import time
n_epochs = 200
log_interval = 10
batch_size_... |
352dbd86125857e9a0ba44edcc8b216243e9c0bf468697d031d17eb51ddf1e51 | Python | 21,980 | 641 | import torch
import torch.nn as nn
import h5py
import numpy as np
import torch.utils.data.dataset
import torch.optim as optim
import torchvision.transforms as transforms
import torch.nn.functional as F
import matplotlib.pyplot as plt
import cls_cvt
import time
n_epochs = 200
log_interval = 10
batch_size_... |
f5298892f10f48b586f87906e3c3e06512d1f700ef287041635d80486874969d | Python | 21,980 | 641 | import torch
import torch.nn as nn
import h5py
import numpy as np
import torch.utils.data.dataset
import torch.optim as optim
import torchvision.transforms as transforms
import torch.nn.functional as F
import matplotlib.pyplot as plt
import cls_cvt
import time
n_epochs = 200
log_interval = 10
batch_size_... |
60fcdeef1ff7cdfad9769b2cc0c6c12d67ed7d073e712b0127344b777ee3f39e | Python | 21,981 | 641 | import torch
import torch.nn as nn
import h5py
import numpy as np
import torch.utils.data.dataset
import torch.optim as optim
import torchvision.transforms as transforms
import torch.nn.functional as F
import matplotlib.pyplot as plt
import cls_cvt
import time
n_epochs = 200
log_interval = 10
batch_size_... |
476c01c2eef9d454f02fe6aa28b176f3cfa1d70c87504518ad47afe86f6eb02c | Python | 21,982 | 571 | """
Manager and Serializer for Library Folders.
"""
import logging
from dataclasses import dataclass
from typing import (
Optional,
TYPE_CHECKING,
Union,
)
from sqlalchemy import (
and_,
exists,
false,
func,
not_,
or_,
select,
)
from sqlalchemy.exc import (
MultipleResultsF... |
31b8b982bbd1b81089fbd1f7a8fe430679862d49ca83131c63b0080ed53dc9d2 | Python | 21,984 | 670 | """Laplacian matrix of graphs.
All calculations here are done using the out-degree. For Laplacians using
in-degree, use `G.reverse(copy=False)` instead of `G` and take the transpose.
The `laplacian_matrix` function provides an unnormalized matrix,
while `normalized_laplacian_matrix`, `directed_laplacian_matrix`,
and ... |
a986402afff7a577d5b1d3a17aa1458b73e3945a0786e7add07bde19df0b69b5 | Python | 21,995 | 757 | import codecs
import io
import math
from ast import literal_eval
from contextlib import contextmanager
from textwrap import dedent
import pytest
import networkx as nx
from networkx.readwrite.gml import literal_destringizer, literal_stringizer
class TestGraph:
@classmethod
def setup_class(cls):
cls.s... |
55b7e9613c6aa1a058efb5a2708771fe36253140fb1031472270d0d76018179c | Python | 22,002 | 459 | from oct import *
import colorcet as cc
import matplotlib
cp, np, convolve, gpuAvailable, freeMemory, e = checkForCupy()
class Colormap:
"""Generate an rgb frame from a greyscale using a chosen colormap"""
def __init__(self, cmapLabel='hsv'):
self.cmap = matplotlib.cm.get_cmap('hsv')
self.cm... |
cfe0730de3d4245a097030f508fdca797237f175c1f9e971fa982e22dd5761f8 | Python | 22,006 | 641 | import torch
import torch.nn as nn
import h5py
import numpy as np
import torch.utils.data.dataset
import torch.optim as optim
import torchvision.transforms as transforms
import torch.nn.functional as F
import matplotlib.pyplot as plt
import cls_cvt
import time
n_epochs = 200
log_interval = 10
batch_size_... |
79b681b8815274ee4ba26b51d9e773834c8b29d2d49bcbae619de343ae5912ad | Python | 22,008 | 641 | import torch
import torch.nn as nn
import h5py
import numpy as np
import torch.utils.data.dataset
import torch.optim as optim
import torchvision.transforms as transforms
import torch.nn.functional as F
import matplotlib.pyplot as plt
import cls_cvt
import time
n_epochs = 200
log_interval = 10
batch_size_... |
6c35dd45497759ed71cb55ddee842851fdb06939014ea10d13e3d82d540b08d9 | Python | 22,010 | 657 | # test_core.py
# Contact: Jacob Schreiber <jmschreiber91@gmail.com>
import numpy
import pytest
from modiscolite.core import TrackSet
from modiscolite.core import Seqlet
from modiscolite.core import SeqletSet
from .synthetic import random_one_hot
from .synthetic import random_track_set
from .synthetic import two_moti... |
c094858d3ede9672b9712e80bd0b4ed75ce9fc2e74defee432a6442f66777d73 | Python | 22,023 | 549 | import os, sys
sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
from bsb.core import Scaffold, from_hdf5
from bsb.config import JSONConfig
from bsb.simulators.nest import NestCell
from bsb.models import Layer, CellType
from bsb.exceptions import *
from mpi4py.MPI import COMM_WORLD as mpi
import unitte... |
01b61950f62c0701d924e10f3502fde83254f83865926fe2f69c930b3c6d0b42 | Python | 22,028 | 524 | """Inquire participant's decisions from descriptions.
Based on a data file collected in the active SP condition, provide gambles
from descriptions.
Notes
-----
When we display the description task according to *experienced* data, it might
happen that during the experienced condition (i) a distribution was not sampled... |
76c3c2bf7041282bb875f60c205341971c54fcaa91e2c8820edc737f9e08636b | Python | 22,035 | 399 | from __future__ import annotations
import contextlib
import dask.array as da
import matplotlib.pyplot as plt
import numpy as np
import pytest
import spatialdata as sd
import spatialdata_plot as sdp
import xarray as xr
from spatialdata.models import Image2DModel, Labels2DModel
from spatialdata.transformations import S... |
1e95668a57814b2a846c5c10c3c81d64b541106b75b280da002dc5887a35497a | Python | 22,036 | 641 | import torch
import torch.nn as nn
import h5py
import numpy as np
import torch.utils.data.dataset
import torch.optim as optim
import torchvision.transforms as transforms
import torch.nn.functional as F
import matplotlib.pyplot as plt
import cls_cvt
import time
n_epochs = 200
log_interval = 10
batch_size_... |
f34255b2cde8c06ead02f4cf51277a2d79d9ba9027a23f1565e2833172b9dfae | Python | 22,036 | 619 | import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from pylab import *
import numpy as np
import random
import math
import time
import os
import glob
import copy
# change the default font family
plt.rcParams.update({'font.family':'Arial'})
plt.rc('xtick', labelsize=12)
plt.rc('ytick',... |
745cd389fa9e567a34db24de2d89135fcb0d99ecc65fd45b7c945ca254a1ec14 | Python | 22,053 | 538 | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
from copy import deepcopy
from math import sqrt
import numpy as np
from ax.adapter.base import DataLo... |
d14ad379330ca1d4242665c8af62b572d78d95b13d0d54ebd8ee46981b406267 | Python | 22,055 | 493 | """
Create a density field for each mtype listed in `app/data/mtypes/density_profiles/mapping.tsv`.
This input file can be replaced by user's custom file of the same format.
Volumetric density nrrd files are created for each mtype listed in either `mapping.tsv`.
This module re-uses the overall excitatory and inhibito... |
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