sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
33c83988ad08f035f3a41c09c6360e6c5d0565b409c3ed89a0f63650d2bf6dbf | Python | 13,747 | 386 | # @license
# Copyright 2016 Google Inc.
# 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... |
d13e3e51924604d244a1b97bbd615e06c445fca5a012a3e93e4a3a0dc8bc7f8b | Python | 13,749 | 271 | from __future__ import annotations
import argparse
import json
from pathlib import Path
import numpy as np
import pandas as pd
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import average_precision_score, roc_auc_score
from st_risk.paths import current_results_dir, ensure_results_layout, project_r... |
2aa6f8abf62e5a299a798f03f1a135f475f2c6669e6269cce33726a4d4577f10 | Python | 13,753 | 330 | import gzip
import os
import re
import types
from pathlib import Path
from typing import Iterable, Optional, Tuple, Union
import polars as pl
import typing_extensions
from rnalysis.exceptions import InvalidTypeError, InvalidValueError
def is_legal_file_path(file_path: str):
if not isinstance(file_path, (str, os... |
a3c7839e9608be07c0d60414b95569b3fc93234e2ccb584e5d0d8794a035c726 | Python | 13,753 | 280 | """Plot 0D stripping-response proxy figures from generated thermodynamic CSVs."""
from __future__ import annotations
import argparse
from pathlib import Path
import shutil
import matplotlib as mpl
mpl.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
GAS_ORDER_STRIPPING = ["He", "N... |
7eb9ad3ba4fa2dfeae0b2e492cf9774fe351092496f87379d8da0cb65fe0d209 | Python | 13,754 | 414 | import torch
import torch.nn as nn
import torch.optim as optim
import os
import tqdm
from utils import general
from networks import networks
from objectives import ncc
from objectives import regularizers
class ImplicitRegistrator:
"""This is a class for registrating implicitly represented images."""
def __c... |
2517738f3f6596355b80e07b7ef10ae7e1e385a564311a515444d93d4a53ff10 | Python | 13,765 | 411 | """Maintained PhyloGPN configuration, tokenizer, and inference model."""
from __future__ import annotations
from collections.abc import Sequence
from typing import Any, ClassVar
import torch
from jaxtyping import Float, Int
from torch import Tensor, nn
from torch.nn.utils import parametrize
from transformers import ... |
9e622cf3362502dbbf2a144a2a1fd6ff93ef3d833f9826894760fd6b7fd46404 | Python | 13,768 | 442 | import json
import stat
from pathlib import Path
import pandas as pd
import pyarrow.parquet as pq
import pytest
import gpn.checkpoint as checkpoint
from gpn.checkpoint import (
BatchRange,
CheckpointManifest,
CheckpointStore,
IncompatibleCheckpointError,
InvalidCheckpointError,
expected_batch_... |
cdf85e145a45b0405b5d2fbbc0c59fd222130d7d8de645895f698059b242697b | Python | 13,774 | 318 | import numpy as np
import scipy.io as si
import os
import h5py
from sklearn.model_selection import KFold
from imblearn.over_sampling import RandomOverSampler
from imblearn.under_sampling import RandomUnderSampler
from sklearn.svm import SVC
import sklearn.pipeline as skp
from sklearn.preprocessing import StandardScale... |
d5cf4e81ba834163a05de4ab6c085b25f0a1bffd88c172202c4e8f6f0dacc74b | Python | 13,778 | 445 | import logging
import math
import re
import numpy as np
import pandas as pd
from scipy.stats import chi2
from gsMap.config import FormatSumstatsConfig
VALID_SNPS = {"AC", "AG", "CA", "CT", "GA", "GT", "TC", "TG"}
logger = logging.getLogger(__name__)
default_cnames = {
# RS NUMBER
"SNP": "SNP",
"RS": "SN... |
532b241697ed11df6c530fc33f14062071c79fa58f169c89803645f3a85777b3 | Python | 13,803 | 358 | """Pure unit tests for `d3text.models.entity_linking.BrendaClassificationModel`
— the class loss, `ground_truth`'s batch handling, and the document-level
class-negative abstention.
Every test here runs on CPU with tiny synthetic tensors and no data, network,
or GPU. Methods are exercised through the `stub` fixture (se... |
c519520ef142d7e6859584ab46837b2c83bf1b7ecd0d45a6d003614d78eb42ca | Python | 13,816 | 356 | import simplejson as json
from ... import options as opts
from ... import types
from ...charts.chart import Chart
from ...datasets import COORDINATES
from ...exceptions import NonexistentCoordinatesException
from ...globals import ChartType
class GeoChartBase(Chart):
def __init__(
self,
... |
025d00ab0c4573cae460195ef716b8ef2f7492186a2ee023291e85690b3140e6 | Python | 13,818 | 313 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 9 11:45:32 2025
@author: forel
"""
import os
import sys
import numpy as np
import json
from scipy.stats import pearsonr
import copy
import matplotlib
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
from typing import Tuple, L... |
0e8db2da342ed7e9d3835c15407a0f734c87d704a0c083cabeef5a4398456598 | Python | 13,821 | 382 | from __future__ import annotations
from typing import TYPE_CHECKING
import torch
from torch.distributions import Categorical, Independent, MixtureSameFamily, Normal
from torch.distributions import kl_divergence as kl
from torch.nn import functional as F
from scvi import REGISTRY_KEYS
from scvi.distributions import N... |
b5981d061cf63a434d121fb0a0ae83232645b86046fdc3a47e9d7ab5f9f49949 | Python | 13,828 | 392 | from __future__ import annotations
import random
from typing import TYPE_CHECKING
import mlx.core as mx
import mlx.nn as nn
from scvi import REGISTRY_KEYS
from scvi.module.base import LossOutput
if TYPE_CHECKING:
from typing import Any
def _lgamma(x: mx.array) -> mx.array:
"""Log-gamma function via Numeri... |
446bf5d4f84b20847e473212cf014dfb1e1b0dc0b78463e351afd5571c996ab2 | Python | 13,836 | 356 | #
# Copyright 2017 National Technology & Engineering Solutions of Sandia, LLC
# (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government
# retains certain rights in this software.
#
# See LICENSE for full license details
#
from __future__ import annotations
import json
import os
import sys
im... |
a4eddfef10d89b9f059b402bd5d4a61e40d6aeaa77eff47bd39277dd8bbbb886 | Python | 13,836 | 400 | import os
import unittest
import numpy as np
import pandas as pd
from numpy import testing as np_test
from skbase.utils.dependencies import _check_soft_dependencies
from pgmpy.estimators.CITests import (
chi_square,
ci_registry,
g_sq,
log_likelihood,
modified_log_likelihood,
pearsonr,
pear... |
7da1857424e005c0cbf980d7b6f5d020f450f11795fe9714de3c2f7dfb8a514d | Python | 13,838 | 394 | from __future__ import annotations
import logging
import warnings
from typing import TYPE_CHECKING
import numpy as np
import pandas as pd
import torch
from scvi import REGISTRY_KEYS, settings
from scvi.data import AnnDataManager
from scvi.data.fields import CategoricalObsField, LayerField, NumericalObsField
from scv... |
253cb833ab4d9f37a3b0c340ff6347f4f1024b55049c39e026b7e08d175d8934 | Python | 13,850 | 336 | """HIPPIE command-line interface.
Subcommands:
hippie-cli validate-data <dataset_dir>
Check that a dataset folder follows the canonical CSV layout.
hippie-cli embed --datasets-root <dir> [--datasets <name>...]
Run the pretrained HIPPIE encoder over one or more dataset folders
and writ... |
4b711f6392a728531126a94bd42798e01b82c9cf7c1c7189104a995b5eefdb9e | Python | 13,850 | 379 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import torch
from f... |
198353b1ef5a3da1ccf3bce216b484a643a3017e5cff432833ed418d76ee308d | Python | 13,861 | 373 | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
# 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... |
069abdb6f4351849709afaa783625d0a8cf30a7520bfc74b483cd850ef496fc5 | Python | 13,866 | 367 | import pytest
from pgmpy.identification.probability_expression import (
DivisionNode,
MarginalNode,
ProbabilityExpressionTree,
ProbabilityNode,
ProductNode,
_TreeNode,
)
@pytest.fixture
def prob_y():
return ProbabilityNode(frozenset({"Y"}))
@pytest.fixture
def prob_x():
return Proba... |
c8c18ab71d5d84568380c8366d5dbae3a92f6bd0e7ee1b4ac23ae0cef68ba8c9 | Python | 13,869 | 334 | """
This module builds RNA nucleotide tokenizer.
Author: wangning(wangning.roci@gmail.com)
Date : 2022/8/8 2:43 PM
"""
# built-in modules
import os
import io
# paddle modules
from paddlenlp.transformers import BasicTokenizer, PretrainedTokenizer
from paddlenlp.data.vocab import Vocab
class NUCTokenizer(PretrainedT... |
c579b1a116a201b40b8974a271e9436f2c8222092826987bfd7ab4c3a9f48f99 | Python | 13,870 | 408 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass, field
from fairseq.models.fairseq_decoder import FairseqDecoder
import numpy as np
from typing import Opti... |
f57f84dbb8f4c3a97cf15122d976f6f7986710d4d7ecf7ce258f8cec66e23d55 | Python | 13,870 | 443 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Characterizes each cell"""
from typing import Optional
import warnings
import numpy as np
import matplotlib
matplotlib.use("Qt5Agg")
import matplotlib.pyplot as plt # noqa E402
from matplotlib.axes import Axes # noqa E402
from matplotlib.figure import Figure # noq... |
d823f4cc9fbf01e39d4289b3a79342e3a36e24e307b27cc9007f057570d58826 | Python | 13,874 | 336 | """Phase 5 — three-lattice universality test, L = 16 ... 128.
Tests whether the L-independent E/V observed in Phase 4 generalises to
THREE distinct 2D lattices with different coordination:
honeycomb z=3 p_c = 0.6970402 (Suding & Ziff 1999)
square z=4 p_c = 0.5927462 (Newman & Ziff 2000)
trian... |
171d2ea52d1a88d54cff0019bea3913ceda24a8baab4eaab8d65879351941e25 | Python | 13,877 | 367 | from __future__ import annotations
from pathlib import Path
import pandas as pd
from st_risk.reporting.decision_support import (
ABSTAIN_CAUTION,
ABSTAIN_REVIEW,
ABSTAIN_TRUSTED,
build_consensus_primary_inputs,
build_decision_support_summary,
build_decision_validation_summary,
build_deplo... |
310e7ae9b17190e128b023a38c7695ce3a1bf31bd22a2897684716f1b864b373 | Python | 13,877 | 386 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# The code in this file is adapted from the BeiT implementation which can be found here:
# https://github.com/microsoft/unilm/tree/master/beit... |
338b92637a53e8de8380792e0c6d3083c9ce8a5de0db2a0fea9625c39073f429 | Python | 13,877 | 362 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import Dict, List, Optional
import torch
import torch.nn as nn
from torch import Tensor
from fairseq import utils
fr... |
f199da883e086560a573e26a4b78874a45220c9fbb75c04dd91560f047b14edb | Python | 13,890 | 158 | """List of the GO IDs that have lots of descendants and low information content"""
__copyright__ = "Copyright (C) 2018-2019, DV Klopfenstein. All rights reserved."
__author__ = "DV Klopfenstein"
# pylint: disable=line-too-long
NS2GOS_SHORT = {
'BP': {
'GO:0008150', # BP 29685 18,453 0.015902 4.14 L00 ... |
b1c3b2a9e99378d8fdaacb5ca217aa19bbcb069d5cb33580df324af72c878f4a | Python | 13,896 | 334 | """
Copyright (c) Facebook, Inc. and its affiliates.
Copyright (c) Microsoft Corporation.
Licensed under the MIT License.
Code derived from the OCP codebase:
https://github.com/Open-Catalyst-Project/ocp
"""
import sys
import numpy as np
import torch
from torch_scatter import segment_coo, segment_csr
from mattergen.c... |
34c085c902b5dd76bb7f3713550f095fd5ab958fb7b9979e6662bc9cff13f827 | Python | 13,900 | 360 | from typing import Any
import numpy as np
import pandas as pd
from sklearn.base import clone
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold
from sklearn.utils.validation import check_is_fitted, validate_data
from pgmpy.prediction._base import BaseCausalPrediction
class D... |
4da8db31b80d34fd498e49032340a0b7198b4099b13b75323b0b0a373d53cf74 | Python | 13,917 | 403 | """Shared test fixtures.
The `stub` factory makes the model methods unit-testable without constructing a
full model, and `tiny_brenda` builds a small on-disk HDF5 and matching frame so
`BrendaDataset` can be exercised without the ~300 MB BRENDA files.
"""
import logging
import pathlib
import types
import h5py
import... |
6b6edcdacd6f7a946b1257cdcfec2d1f3c327aaf582495233650e6b3c0695327 | Python | 13,920 | 282 | import argparse
import joblib
import pandas as pd
from datetime import datetime
from pathlib import Path
from neuron import h
import figures
import sseEPSP
import morphologyandproperty
from neck_function.plot_neckfunction import compute_axialresistance
import sync_activation_pkl as syncact
import glob
import os
impo... |
3131d4b1c0aae00ffa1d5c0b15d442bb9ae7562c5e6a6dd207fa8053c1317573 | Python | 13,921 | 398 | import logging
from collections.abc import Callable
from typing import NamedTuple
import numpy as np
import torch
import torchmetrics
from torch import nn
from scvi.module.base import BaseModuleClass, LossOutput, auto_move_data
logger = logging.getLogger(__name__)
class _REGISTRY_KEYS_NT(NamedTuple):
X_KEY: st... |
be0eb48baad852e9d94c5d4e29da236ff2e99ed5c07d88e08021ebdc7242c88f | Python | 13,921 | 349 | import os
import torch
import pickle
from multiprocessing import Pool
import numpy as np
from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler, TensorDataset
from transformers import PreTrainedTokenizer
from utils import logger
from tqdm import tqdm
from src.transformers import glue_conve... |
f7c57578bf7f2083442a6e846815e90003db809678ac41eef66da5d9727c3c1f | Python | 13,933 | 358 | import os
import importlib
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import torch
import torchvision
from torchvision import datasets, transforms
from torch import nn, optim
from torch.utils.data import DataLoader
from torchvision.utils import save_image, make_grid
from tqdm i... |
a598c99f5151df30064a64ed40ad6ca71b01fce93a0a6964cb07ae3126f61734 | Python | 13,937 | 339 | # Original work Copyright 2018 The Google AI Language Team Authors.
# Modified work Copyright 2019 Rowan Zellers
#
# 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/... |
0abd24777c8224f72b9149e7d0da81f6aa4629b6c6a0d366ac9b9f8ea229f86a | Python | 13,952 | 383 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from fairseq im... |
c87beb5a1d5564690734b794d547360e6e3b0a01d6ccd6bc6ec7d9a45ef33fb4 | Python | 13,955 | 286 | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 cop... |
f019d9cff5f87e551394e202456b34518eaeba1ac9d59cd003efc608da9c5a0b | Python | 13,955 | 388 | from __future__ import annotations
from collections.abc import Callable, Iterable, Mapping
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, Protocol, runtime_checkable
import torch
from scvi._constants import REGISTRY_KEYS
if TYPE_CHECKING:
from typing import Literal, TypeAlias
... |
d2215d168a0f7a485698fb6815495bef2988fb7781706b429e8ee497e4454860 | Python | 13,967 | 343 | from __future__ import annotations
import json
import logging
import pathlib
import warnings
import crowsetta
from ... import datapipes
from ...common import labels
from ...common.converters import expanded_user_path, labelset_to_set
from ...common.logging import config_logging_for_cli, log_version
from ...common.ti... |
478cf587e7efa706b80cd6e6484556ac6584cdfa432a8785c4fe7630d122edf6 | Python | 13,970 | 400 | import pandas as pd
import numpy as np
from syntheval import SynthEval
from syntheval_benchmark_utils import results_formatting, rank_results_formatting
import os
from tqdm import tqdm
import glob
import sys
#syntheval_path = os.path.dirname(os.path.abspath(syntheval.__file__))
#utils_path = os.path.join(syntheval_pat... |
61f88eb91873a0362b335473793f440a0767f2e00409238e9d5080b0edc9caa0 | Python | 13,980 | 403 | """
Dask configuration utilities for voluseg pipeline.
This script provides comprehensive Dask configuration management,
including the DaskConfig class and all utility functions.
"""
import yaml
from typing import Optional, Dict, Any, Union
from pathlib import Path
import dask
from dask.distributed import Client, Loc... |
6a5fd56b6e746129c058f5f9fd2b3a38fcd44f235dfcb20a51fb8595e1e1511b | Python | 13,991 | 339 | from pathlib import Path
from PyQt6 import QtCore, QtGui, QtWidgets
from rnalysis.utils import io, settings
class QuickStartWizard(QtWidgets.QWizard):
TITLES = (
'Importing Data',
'Exploring Your Dataset',
'Applying Filters',
'Undo the operations you applied to your data',
... |
4f28e7d0c841c215ad574fe75d404a286bdc52741673a494ce1c9774aac07328 | Python | 13,994 | 274 | import os
import socket
from typing import Union, Optional
import nnunetv2
import torch.cuda
import torch.distributed as dist
import torch.multiprocessing as mp
from batchgenerators.utilities.file_and_folder_operations import join, isfile, load_json
from nnunetv2.paths import nnUNet_preprocessed
from nnunetv2.run.load... |
64a31a8c8dc280ae6868dec3f75a64db312926063742f8f6c5cc4dddb42915f1 | Python | 13,998 | 399 | import importlib
import json
import os
import tempfile
from dataclasses import asdict
import anndata
import numpy as np
import pytest
import scvi
from scvi.criticism import create_criticism_report
from scvi.data import synthetic_iid
pytest.importorskip("huggingface_hub")
from scvi.hub import HubMetadata, HubModel, H... |
2a5aec0115b95078557ff89974e8008d83eb4339e68b5f771b0de5d700bc51b5 | Python | 14,008 | 352 | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 cop... |
899ef2e9da6b47e9db16d418898a49fea56b470ebb6b7a6848c327c31e3cf92e | Python | 14,009 | 353 | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 cop... |
47d5495ce66a8fbd6fcbc7553e00ff838746d0e1c427dcecc1b4d1e9f7ba2faf | Python | 14,014 | 440 | from __future__ import annotations
import contextlib
import shutil
import uuid
from subprocess import CalledProcessError
from typing import TYPE_CHECKING
from zipfile import ZipFile
import pytest
from build import BuildBackendException
from build import ProjectBuilder
from packaging.metadata import parse_email
from... |
7752409325798387684a6a58f0c9ead1c467b1529d0cdd35f6eef032ac6ad123 | Python | 14,017 | 357 | """Tier 2: Bayesian Optimization for D-peptide sequence optimization.
Takes initial designs from Tier 1 (run_design.py) and iteratively optimizes
the top sequences using Evolutionary BO or MCMC with a GP surrogate model.
Usage:
python -m bo.run_bo \\
--pose_pdb output/Poses/Binder_L_pose_1.pdb \\
... |
8a3c57012074800a613839f8547da60569ccae42693db65ec70735a70ad5a32c | Python | 14,017 | 324 | from typing import Dict, Optional, Union, Any
import numpy as np
import torch
from scipy.sparse import issparse
import scanpy as sc
from scanpy.get import _get_obs_rep, _set_obs_rep
from anndata import AnnData
import pandas as pd
from scgpt_spatial import logger
class Preprocessor:
"""
Prepare data into tra... |
1d5b7227701316835331453e7d4f8dac98ce687aae0d051072e4b4d5101328e8 | Python | 14,020 | 247 | import os.path
import math
from sklearn.feature_selection import VarianceThreshold
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedKFold
import argparse
import numpy as np
import... |
ab2e300800b32e389cba2ee3ecf51c6b1c51e212ff9c07a6627f26d8496d8521 | Python | 14,020 | 422 | """Optional MLflow experiment tracking.
Every entry point here is a no-op unless `MLFLOW_TRACKING_URI` is set, which
has to name an `http(s)://` server since the dependency is `mlflow-skinny`. A
leaf but for `d3text.metric_docs` and `d3text.constraints`; mlflow, torch and
the modules that import torch are imported onl... |
3918324d403016222770fbf051f8900d0c743f58a692c6cfc21bb9e2ab5e596a | Python | 14,026 | 278 | import multiprocessing
import os
import socket
from typing import Union, Optional
import nnunetv2
import torch.cuda
import torch.distributed as dist
import torch.multiprocessing as mp
from batchgenerators.utilities.file_and_folder_operations import join, isfile, load_json
from nnunetv2.paths import nnUNet_preprocessed... |
65718955c8cd528f4dbf317f3ec7668fd5dc43642a9375e27dbdc77eae1a0c2d | Python | 14,040 | 376 | # @license
# Copyright 2025 Google Inc.
# 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... |
4fefcbbd304b96e3c31c3972ffac2c31fc0d74acf5452b0d985ec7bda6c51518 | Python | 14,046 | 355 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, Optional, Tuple
import torch
from fairseq import utils
from fairseq.modules.quant_noise import quant_noise
from torch... |
d324746f4f81e45eaa76f65001c9ec9fa8fc60681632018d759189c5f9ffee3e | Python | 14,053 | 320 | import sys
import os
import os.path
import re
import argparse
import tempfile
import logging
from .version import __version__
logging.basicConfig(level=logging.INFO, stream=sys.stderr,
format='%(name)-13s - %(asctime)s - %(levelname)-8s - %(message)s')
logger = logging.getLogger(__name__)
def _ch... |
cc4b72b78fd592408364b9f0eb1f2529f3b07ad15ed1f61f505d3b8bae4e8fe6 | Python | 14,055 | 444 | """
This module contains method to show risk taking performance of mouses.
"""
"""
Copyright (c) 2019, 2022 [copyright holders here]
This file is part of NoSeMaze.
NoSeMaze is free software: you can redistribute it and/or
modify it under the terms of GNU General Public License as
published by the Free Software Foun... |
439a142e37b238d71390e375336c146d104a51483be9e9ec73915aa986277b05 | Python | 14,057 | 225 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'generate_brain_mask_tab.ui'
#
# Created by: PyQt5 UI code generator 5.10.1
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_GenerateBrainMaskTabContent(object):
def setupUi(se... |
2e112e343b15a1eb7c144721c1a1fe66e7211db533f93fb1cd6390f23ad3fd23 | Python | 14,068 | 365 | """Screening a candidate document for enzyme mentions.
The measurement these support found the literal screen refuted by its own
control: rejecting a document on *any* exact enzyme match rejects most of the
psycholinguistics pool, which names no enzyme by construction, because the
index registered ubiquitous acronyms ... |
add561ff8ddfc33a94c207af3a84ba27195f85ce091c2e564cb0d60fcfa44f71 | Python | 14,072 | 420 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Evaluate foreground Dice/HD95/NSD for a prediction folder.
Output format is aligned with local `metrics.csv` style:
- DiceNNUNet_*
- DiceMONAI_*
- HD95_*_vox
- HD95_*_mm
- NSD_*
- per-case means + NSD_tau/NSD_unit
"""
from __future__ import annotations
import argpars... |
7e1d7ce2905d5b5b60288e9ac0919eece4becadba909a0eccc7afadb9697e351 | Python | 14,077 | 263 | # Copyright 2023 BioMap (Beijing) Intelligence Technology Limited
import argparse
import random,os
import numpy as np
import pandas as pd
import argparse
import torch
from tqdm import tqdm
import scipy.sparse
from scipy.sparse import issparse
import scanpy as sc
from load import *
####################################... |
a9c37f7d9f5bd439857a364104f588c126ac81a6974559676b2bb5528f01a404 | Python | 14,077 | 396 | #BEM spatial-jitter simulations with realistic sensor arrays and realistic noise model using MNE Python
#%%
# Imports
import os.path as op
import numpy as np
import pyvista as pv
import mne
from pyvistaqt import BackgroundPlotter
from bfieldtools import sphtools as sph
import functions
#%% Simulation parameters
jitl... |
e8bd28aa3d63070023aaac87b479986f5dc57163954388c660db1e915c00a136 | Python | 14,087 | 409 | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
__version__ = "2.5.0"
# Work around to update TensorFlow's absl.logging threshold which alters the
# default Python logging output behavior when pre... |
d3aa18ec3f72a0f40cfee7690d48e83a07731e58b93bc02276ec74c92071348e | Python | 14,092 | 481 | from scipy.spatial import KDTree
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib.patches import Patch
from collections import Counter
import jax
import jax.numpy as jnp
from graph_tool.all import Graph
from graph_tool.topology import max_cardinality_matching
from... |
7cab01750db53895313faa0b278ebef1aced8b69ee804aa1f070667aaeec6c0f | Python | 14,093 | 431 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import sys
import time
import io
import numpy as np
import torch
import torch.nn.functional as F
from .. import Fa... |
82adf43cfa44968776f4517121561b4edf11d5905fbe01e36240926ab07a02b5 | Python | 14,097 | 428 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from multiprocessing import Pool
import numpy as np
from fairseq import options
from fairseq.data import dictionary
from fairseq.... |
5529ac726a2cd1a271f344bc87c3607a550cfaf1c571f4ce78eb9a08cce72530 | Python | 14,103 | 425 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9... |
10016c71dbb003b3a24ec7983d3e5bc82bbb7ca2f142afb66866a7169e22dc2e | Python | 14,106 | 396 | #!/usr/bin/env python3
from collections import defaultdict
from pgmpy.base import UndirectedGraph
from pgmpy.factors import FactorDict, factor_product
from pgmpy.utils import compat_fns
class ClusterGraph(UndirectedGraph):
r"""
Base class for representing Cluster Graph.
Cluster graph is an undirected g... |
cb5e81bc4eac139aa0a03c7af7d4b9843568a4cf194e9de0c0010789073e60ed | Python | 14,118 | 358 | #!/usr/bin/env python
__author__ = 'heroico'
import os
import io
import gzip
import logging
import numpy
from metax import WeightDBUtilities
from metax import PrediXcanFormatUtilities
from metax import ThousandGenomesUtilities
from metax import Logging
from metax import Utilities
from metax import Formats
from timeit ... |
de3a08f2edfcce973a6dd5751ccc9d0931b868c16294685ca9bea6956f657521 | Python | 14,118 | 422 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
5cd920aa6abd6c04b73638958f16c6201c71827b4e4e8c9f495dc50e11cf3b73 | Python | 14,124 | 344 | """Controller for manual behaviour-file parsing and UI table updates."""
from __future__ import annotations
import re
import uuid
from pathlib import Path
import pandas as pd
from PySide6.QtWidgets import QMessageBox
from src.data.behaviour_settings_io import (
load_behaviour_static_inputs,
save_behaviour_s... |
8e55c789835ed7b576fba5cb93696f31c8789de074f724469dd8594aadced5a8 | Python | 14,129 | 482 | from scipy.spatial import KDTree
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib.patches import Patch
from collections import Counter
import jax
import jax.numpy as jnp
from graph_tool.all import Graph
from graph_tool.topology import max_cardinality_matching
from... |
10d5525472115619fb7c182caa854326c693f60271b622a55848e5a8b0f33b2d | Python | 14,130 | 483 | from scipy.spatial import KDTree
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib.patches import Patch
from collections import Counter
import jax
import jax.numpy as jnp
from graph_tool.all import Graph
from graph_tool.topology import max_cardinality_matching
from... |
4df4dc1ecc96eaa928b2489363e9e54b457d67a6b20dbd3a256a98487388966d | Python | 14,138 | 394 | import os
import anndata
import mudata
import numpy as np
import pandas as pd
import pytest
from scvi import REGISTRY_KEYS
from scvi.data import synthetic_iid
from .utils import generic_setup_mudata_manager
def test_setup_mudata():
adata = synthetic_iid()
adata.obs["cont1"] = np.random.normal(size=(adata.s... |
b421e6f5310b9f12248b324715ce3527cfd752b919afe9adcf713fb18d5031fc | Python | 14,138 | 364 | from __future__ import annotations
import json
import os
from dataclasses import asdict, dataclass, field
from typing import TYPE_CHECKING
from scvi.data import AnnDataManager
from scvi.data._utils import _is_minified
from scvi.model.base._save_load import _load_saved_files
from scvi.utils import dependencies
from .... |
be1af88617ee4358b958095a81aa26d8592a89976e8ad9c4f04cf3735cf0f8b4 | Python | 14,138 | 338 | '''
By K. Butenko
'''
import os
import sys
import numpy as np
from scipy.stats import qmc
import csv
import h5py
import json
from scipy.optimize import minimize
# hardwired: max total currents allowed
one_pol_current_threshold = 7.0 # in mA
total_current_threshold = 7.0
abs_current_threshold = 7.0
def create_m... |
c47dc7cd178f0154adaf2048e8dff4b23d22e4de9b9866f5e0ce735b529dad66 | Python | 14,139 | 424 | import numpy as np
import pandas as pd
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#... |
8adbfff095c41a6bde4cad25af2b53edace7d4b38803bc1c0e3a54cbfb0133fa | Python | 14,146 | 384 | import logging
from importlib.machinery import SourceFileLoader
import inspect
import os
from collections import defaultdict
from contextlib import contextmanager
import mdt
import mot
from mdt.configuration import get_config_dir
from mdt.model_building.signal_noise_models import SignalNoiseModel
from mot.library_funct... |
eccde33514cd08b0331da7a818f6ba21e6c3323b10bde6fec3c04ad8a2a56598 | Python | 14,151 | 408 | from __future__ import annotations
import warnings
from typing import TYPE_CHECKING
import anndata as ad
import numpy as np
import pandas as pd
if TYPE_CHECKING:
from typing import Literal
from anndata import AnnData
from scvi import settings
from ._constants import CYTOVI_SCATTER_FEATS
from ._utils impor... |
b73c5b5d0b9828dce893ff8fe7704b6a33908357e525ea630409591b7a9393d2 | Python | 14,157 | 397 | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Generate n-best translations using a trained model.
"""
import os
import subprocess
from contextlib import redi... |
39d4f48893f919664d5ccbddfea19b7ef425e2cc28e6ecfcccd170b7ae227e64 | Python | 14,162 | 341 | from __future__ import annotations
import logging
from collections.abc import Callable
from pathlib import Path
import pandas as pd
from PySide6.QtCore import Qt
from PySide6.QtWidgets import (
QFileDialog,
QFrame,
QGridLayout,
QHBoxLayout,
QInputDialog,
QLabel,
QPushButton,
QSizePolic... |
c0ec6a22fd1261aea96b2e73595459c3329e11dc20dc59e89b0122b2a0e1339e | Python | 14,162 | 451 | from __future__ import annotations
import os
import uuid
from copy import deepcopy
from hashlib import sha1
from pathlib import Path
from typing import TYPE_CHECKING
from typing import TypedDict
from urllib.parse import urlparse
from urllib.parse import urlunparse
import pytest
from dulwich.client import HTTPUnauth... |
82c945e75d731c8fce6d1063d88e643056f0c50979cf497f9ef45acef73e5198 | Python | 14,174 | 386 | # Copyright 2021 DeepMind Technologies Limited
#
# 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 agr... |
ea99541efba2a0fc02f046ebeebfa29475ecc2fe9071f9cd496c946ef2aa94b2 | Python | 14,175 | 314 | """Gradio-free TotalSegmentator backend for the SegCT/MRI workflow."""
from __future__ import annotations
import csv
import importlib.metadata
import json
import platform
import shutil
import subprocess
import sys
import tempfile
import zipfile
from collections import defaultdict
from pathlib import Path
import niba... |
b8695064a752c5b734c885fb0a295a184e9834ac0df669de14c3de4732330919 | Python | 14,176 | 424 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9... |
12f61201e0730175cdbc2c75ca4e222247df952b40a715849f3b62b7cab6e1f9 | Python | 14,178 | 426 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"... |
5d9e92d056180c279d702e4744ecf28b4aedc2261253edac9647a90c9e6f3bfe | Python | 14,180 | 490 | from scipy.spatial import KDTree
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib.patches import Patch
from collections import Counter
import jax
import jax.numpy as jnp
from graph_tool.all import Graph
from graph_tool.topology import max_cardinality_matching
from... |
559b31f1af81740fe6cbd57dc8e21ff0a4f719a2c9a1ac5b789a0c18429fbf12 | Python | 14,186 | 339 | from __future__ import annotations
import logging
import re
from typing import TYPE_CHECKING
from typing import Any
from poetry.config.config import Config
from poetry.config.config import PackageFilterPolicy
from poetry.console.exceptions import ConsoleMessage
from poetry.console.exceptions import PoetryRuntimeErro... |
ff84b56c41308b2b2f09c3085e93827d0fa61d30927b9728a071bafc03fd925a | Python | 14,187 | 483 | from scipy.spatial import KDTree
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib.patches import Patch
from collections import Counter
import jax
import jax.numpy as jnp
from graph_tool.all import Graph
from graph_tool.topology import max_cardinality_matching
from... |
f2af025bceec68f5bd9abc7598924e00a53c0a0164b5d68bd4e75cfdcbd858d9 | Python | 14,189 | 424 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9... |
1f1c56b2148404eebd8400a330b34036daa1578411d2a1f0fce38a21f99ab2bf | Python | 14,190 | 424 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9... |
a5b0e162692ba057fe981bc8a89640abe1f77b00d4b5d60f82b07e4418b1629c | Python | 14,194 | 367 | from collections import OrderedDict
import theano.tensor as T
from lasagne import utils
__all__ = [
"Layer",
"MergeLayer",
"IdentityLayer",
"SplitLayer",
]
# Layer base class
class Layer(object):
"""
The :class:`Layer` class represents a single layer of a neural network. It
should be ... |
51a0e29e4ef90970133be8771e7b4f9bc064fcc67d9d3d59a0459aad207313f6 | Python | 14,200 | 426 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9... |
a909a4e02f979f611617be46728414618b0d758c8eb0693adbc05db5eb0de5ac | Python | 14,203 | 426 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9... |
7dde29df25afad8320600f8165ccdbfc702c83d8a901d9a08691ec8e0a3f7f1e | Python | 14,206 | 426 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9... |
858041c562e0ab2df7a84134009793602b78dfe2df96cac6f899ff7b388993b5 | Python | 14,209 | 392 | from functools import partial
import haiku as hk
import jax
import jax.numpy as jnp
import pytest
from jax import random
from jax.random import normal
from oneqmc import Molecule
from oneqmc.geom import masked_pairwise_distance
from oneqmc.sampling.samplers import (
DecorrSampler,
LangevinSampler,
Metropo... |
f6db3e78e39a30e4aa0ee9e870bffaf7415d92c2b7234d5a4932422a586f886e | Python | 14,213 | 425 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9... |
3edf6be5e9ce079151c941cd1e9d617520dc0426ceab835b3021a2bf875eff58 | Python | 14,214 | 426 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9... |
5da4a9ee7c8c6987681890e091df7e3e05b8e8eef452cdc1a17385fcb460d878 | Python | 14,214 | 426 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9... |
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