sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
810ceebb3dc431c8ab465c1bd15bddd819f9b0a5c013a4319c6da23c28b025c1 | Python | 17,951 | 516 | import torch
import torch.nn as nn
from celltype_ibl.models.linear_probe import classifier_probe_train_val
from celltype_ibl.utils.ibl_data_util import (
get_ibl_wvf_acg_pairs,
get_ibl_wvf_acg_per_depth,
)
from celltype_ibl.models.BiModalEmbedding import (
BimodalEmbeddingModel,
SimclrEmbeddingModel,
)
... |
e25e63952df503e6756aa130d56c5be6d22c20902e92946c5e838bb9a6290332 | Python | 17,960 | 508 | #!/usr/bin/env python
## - Reference-dependent approach
"""Import modules"""
from __future__ import print_function
import sys
import os
import argparse
import numpy as np
import Bio.PDB
from Bio.Cluster import pca
from Bio.PDB import Entity, Chain, Residue, Atom, PDBParser
import time
# from itertools import chain
# f... |
2787edd917a944cc89abf8b7dddfccbb45862c18f75e35bef48daa8300fabdcb | Python | 17,963 | 466 | import itertools
from functools import reduce
from operator import mul
import numpy as np
from pgmpy.factors.discrete import DiscreteFactor
from pgmpy.independencies import Independencies
from pgmpy.utils import compat_fns
class JointProbabilityDistribution(DiscreteFactor):
"""
Base class for Joint Probabil... |
0cb34de92f2728d34bdacccfae9faca55b17c21818b6a40628c37afcc7b45a73 | Python | 17,965 | 498 | # 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
import itertools
import json
import logging
import os
from typing import Optional
from argparse impor... |
65b76128a8fb5b217bfea3e7281182d7b3a3f8ed9e48e65e66888b83d8b80d77 | Python | 17,974 | 493 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
import sys
#sys.path.append('./')
#sys.path.append('../')
#sys.path.append('../..')
import os
import pandas as pd
from sklearn import preprocessing
import string
from typing import Sequence, Tuple, List, Union
from tqdm import tqdm
import fm
import torch
from torch imp... |
28011bea63a146d9311125f8b662d5564567651a605289d687fd3d3879409913 | Python | 17,980 | 448 | # Copyright 2015 Google Inc. 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 law or a... |
4d73fdebc2be65c37151d0c65f7c7bebe1d00b27e9b18af9bf371bcb4d7fc4ce | Python | 17,983 | 569 | """IO modules for medaka tandem."""
from itertools import groupby
import os
import numpy as np
import pysam
import pysam.bcftools
import medaka.common
import medaka.features
import medaka.smolecule
from medaka.tandem.record_name import RecordName
import medaka.variant
import medaka.vcf
#########################
# ... |
754767c17fae66f91252d0ca277d66d0b227dcadc0436063ac66741ce22cd12a | Python | 17,986 | 715 | import os
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import torch
from scipy.stats import pearsonr
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import KFold
from torch_geo... |
f55ea5c7d24762725662a1052cbf17329f0d9269d6927da11bcbbbefcd92b8cc | Python | 17,989 | 373 | import abc
import hashlib
import itertools
import re
import time
from pathlib import Path
from typing import Dict, Iterable, List, Literal, Set, Tuple, Union
import polars as pl
from rnalysis.exceptions import InvalidTypeError, InvalidValueError
from rnalysis.utils import generic, installs, io, parsing, validation
... |
ded42fd69131949b818ee185d976a773829b53487581cc53682d4bac95a42b83 | Python | 17,991 | 492 | from __future__ import annotations
import logging
import tempfile
from typing import TYPE_CHECKING
import numpy as np
import pandas as pd
import torch
from scipy.sparse import issparse
from scvi.distributions._utils import _needs_cpu_detour
from scvi.model._utils import parse_device_args
from scvi.utils import depen... |
d80804ab27bc507d0b9cb2583037e4ad5fdf3aeb023b7516505297062ca76625 | Python | 17,994 | 549 | import glob
import os
import random
from typing import Callable, List, Union
import numpy as np
import pandas as pd
import torch
from torch.utils.data import DataLoader, Dataset
__all__ = [
"ResidueEnvironment",
"ResidueEnvironmentsDataset",
"ToTensor",
"CavityModel",
"DownstreamModel",
"DDGDa... |
5e3a836ba50fcec17f097b1521191b55e62d0c4778117f32306bdf9bb10b2e37 | Python | 18,022 | 715 | import os
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import torch
from scipy.stats import pearsonr
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import KFold
from torch_geo... |
020253b344cdc48b649ddf3021d10246c5864807e96b2b9da5b3c08322478dac | Python | 18,029 | 454 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 24 08:11:52 2025
@author: vbp
Batch analysis for Figures 1F, 1G, 1H and 2B, 2C, 2D, 2E.
Sorts files from each target into subfolders and plots per-subfolder data.
Data can be downloaded here: https://doi.org/10.17605/OSF.IO/DV724
"""
from iterto... |
dccf413c9ddb30d2bf73402b1973a1e166d044fc1e187dcfa1ebad44ab9b5272 | Python | 18,034 | 715 | import os
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import torch
from scipy.stats import pearsonr
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import KFold
from torch_geo... |
4262a7c64c607853e8752d49fa72170349c918448a8aaea34ba663c6931d670b | Python | 18,056 | 427 | import nibabel as nb
import numpy as np
def load_mgh(filename):
""" import mgh file using nibabel. returns flattened data array"""
mgh_file=nb.load(filename)
mmap_data=mgh_file.get_data()
array_data=np.ndarray.flatten(mmap_data)
return array_data;
# function to load mesh geometry
def load_mesh_geo... |
810de690ce3e73cac2671f018c9eebd503d3bf5b2281d3f26955445025d2a4ec | Python | 18,065 | 409 | import numpy as np
import zarr
import cv2
import os, shutil
from pathlib import Path
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
import zarr
from numcodecs import Blosc
import pandas as pd
from PIL import Image
import skimage
import pickle
#create a WSIAnnotation class
class ROIA... |
ac7238bcc6f5def17a24cc57402f1b80e51982dd45652868f98d26ef1615ea1c | Python | 18,069 | 502 | #!/usr/bin/env python -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.
import ast
import hashlib
import logging
import os
import shutil
import sys
import re
from dataclasses import datacla... |
62705734e9edbe0d9f634f030c7975fda671009c578bded267199925e2dca57f | Python | 18,070 | 715 | import os
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import torch
from scipy.stats import pearsonr
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import KFold
from torch_geo... |
ea6e6341071af2134016f036d4fd2f9cc3e88277b5857ea73df2d21e6251a27f | Python | 18,078 | 760 | import os
import csv
import math
import re
import warnings
from pathlib import Path
from concurrent.futures import ProcessPoolExecutor, as_completed
import numpy as np
import torch
from rdkit import Chem, RDLogger
from scipy.spatial import distance_matrix
from torch_geometric.data import Data
from tqdm import tqdm
wa... |
8962bef7cb7c77c8af343ebb2252be3ddf35000644f73ff322fb0815c1b6b26a | Python | 18,085 | 468 | import copy
from typing import Optional, Any
from collections import defaultdict
from tqdm import tqdm
import torch
from torch import nn
from torch.utils.data import DataLoader
from simulation_encoder.logger import Logger
from simulation_encoder.loaders.loader import Loader
from simulation_encoder.models.rbm import R... |
0b471224ec34dad6cb0092e10de58cdf6345b3fcac325b8c347d2929f8893dbf | Python | 18,109 | 553 | """
This module contains the experiment class, mouse class, schedule class, and
trial class.
"""
"""
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... |
b5ca8baf06c434692f8bbb156be49c868f858b617dc3e98d77d93e5d08f01905 | Python | 18,120 | 511 | from __future__ import annotations
from pathlib import Path
from typing import TYPE_CHECKING
from typing import Any
import pytest
from cleo.io.buffered_io import BufferedIO
from packaging.utils import canonicalize_name
from poetry.core.constraints.version import Version
from poetry.core.constraints.version import pa... |
a3c495df03976f045277226d01a9379778ae0368f51354efc095f0f19575082b | Python | 18,128 | 491 | import collections
import os
import pathlib
import rootutils
from typing import (
Any,
Callable,
Dict,
Iterable,
List,
Literal,
Optional,
Tuple,
Union,
)
import pandas as pd
import hydra
import lightning as L
import omegaconf
import torch
from loguru import logger as log
from tqdm im... |
b79931d3994623e9b4e0f5240cf7bf090feba8a0024ded88fcff04549c9c290b | Python | 18,130 | 476 | import math
import torch as th
from torch import nn, Tensor
from dataclasses import dataclass
from typing import Tuple, NamedTuple
from einops import rearrange, repeat
from utils import M2H
from config_base import BaseConfig
from .nn import conv_nd, linear, timestep_embedding
from .MBAblocks import ResBlockConfig, At... |
02fc8d9fbc2e86154b233307f854853201daf2146dad02456230d2608d800389 | Python | 18,132 | 460 | import argparse
import itertools as it
import logging
import os
from functools import partial
from typing import Sequence
import jax
import kfac_jax
import optax
from args import add_transferable_args
from oneqmc import train
from oneqmc.analysis.energy import extrapolated_energy_convergence_criterion
from oneqmc.cli... |
3aaddf186006bdeed336bb013a2bfc152417872c9e1ebd9b7480a7708bdacdd1 | Python | 18,164 | 428 | """
Mapping helpers
"""
import numpy as np
import pandas as pd
import scanpy as sc
import torch
import logging
from scipy.sparse.csc import csc_matrix
from scipy.sparse.csr import csr_matrix
from . import mapping_optimizer as mo
from . import utils as ut
from . import spatial_weights as sw
logging.getLogger().se... |
e445dacffee5f7d3b7aef196b9f67a9e5f69024b8223162f7693ed34faaab5f5 | Python | 18,171 | 408 | """Continuum-radius provenance and audit helpers.
The values used by Amber's continuum-solvation routines live in the final
prmtop arrays. This module records those values without attempting to infer
or silently change a force-field/radius parameterization.
"""
import csv
import hashlib
import json
import logging
fr... |
668c9ab4631b87442bb93e9e4ff8ee75162585c09b16a3ab2e06db03e01b0288 | Python | 18,173 | 582 | # 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.
"""
Wrapper around various loggers and progress bars (e.g., tqdm).
"""
import atexit
import json
import logging
import os
import sys
from col... |
822ceacb8e1ac5b1e7196a9617a7158eb8bd436cdc0c151de5a36cf9333b4291 | Python | 18,175 | 510 | """Utils for the compositionality project."""
from typing import Dict, Iterable, List, Optional, Tuple, Union
from pathlib import Path
import amrlib
import networkx as nx
import nibabel as nib
import numpy as np
import pandas as pd
import penman
from datasets import load_dataset
from loguru import logger
from nilearn... |
22dfd6bdbffc198fd17b271ba6cee9c939d00812921613884419bc61366aedbe | Python | 18,192 | 463 | # 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 contextlib
import logging
import os
from collections import OrderedDict
from argparse import ArgumentError
import torch
from fairseq i... |
49239ab294222d599a7a68a90ae8a054039ca27e8a9d668fa8fced0290c8d69e | Python | 18,212 | 439 | import gc
import logging
import os
from collections import defaultdict
from functools import partial
from pathlib import Path
import anndata as ad
import numpy as np
import pandas as pd
import zarr
from scipy.stats import norm
from tqdm.contrib.concurrent import thread_map
import gsMap.utils.jackknife as jk
from gsMa... |
5c809fd016870fa6fd50e770e990a5917a5554857f19f599c0b20e2138e7b843 | Python | 18,251 | 462 | """Function that trains models in the frame classification family."""
from __future__ import annotations
import datetime
import json
import logging
import pathlib
import shutil
import lightning
import joblib
import pandas as pd
import torch.utils.data
from .. import datapipes, datasets, models, transforms
from ..co... |
1f2aaa22c0fade6d13d5003879b272cf3165f484dd71568fac1decba74b4c1b8 | Python | 18,256 | 451 | import inspect
import itertools
import pytest
import torch
import vak
from .conftest import (
MockAcc,
MockDecoder,
MockEncoder,
MockEncoderDecoderModel,
MockModel,
MockModelFamily,
MockNetwork,
other_loss_func,
other_metrics_dict,
OtherNetwork,
OtherOptimizer,
)
from .te... |
990115ba025aad892ba860ad9adebbcdb2fab97561608b2d1fe7359db515f463 | Python | 18,275 | 518 | """Cluster summary sheet helpers for telemetry workbook exports."""
from __future__ import annotations
import re
from pathlib import Path
def populate_cluster_sheet(exporter, writer, sheet_name, cluster_number):
worksheet = writer.book.add_worksheet(sheet_name)
exporter.app.bold = writer.book.add_format({"... |
b6de02fae3304b55c48911a3b7cdeb43b1b5ce9e6d366841a0be5be78958d72c | Python | 18,294 | 400 | import copy
import os
from PyQt5.QtCore import pyqtSlot, Qt, QPoint, QUrl
from PyQt5.QtGui import QDesktopServices
from PyQt5.QtWidgets import QWidget, QAbstractItemView, QMenu, QWidgetAction, QLabel
from mdt.gui.maps_visualizer.actions import SetDimension, SetSliceIndex, SetVolumeIndex, SetColormap, SetRotate, \
... |
0962b6df1a293a61140f92dc64132e4c4a0153086caab1c6e44adcd7c2c17981 | Python | 18,295 | 374 | """
correct_and_normalize.py
========================
Corrected combined MS transcriptomics normalization pipeline.
Root causes of inflated DGE results:
1. Raw count datasets (max ~ 50,000–75,000) NOT properly log2-transformed
2. neuroCombat applied to mixed-scale data → LFC ±3000
3. No within-dataset quantile n... |
706d67027c4dcdd9a7f3c0dc72a5d0115e172065e6b32c18b2211e72c8271a5f | Python | 18,297 | 529 | """Main module."""
import numpy as np
import torch
import torch.nn.functional as F
from torch.distributions import Normal, Poisson
from torch.distributions import kl_divergence as kl
from torch.nn import ModuleList
from scvi import REGISTRY_KEYS
from scvi.distributions import NegativeBinomial, ZeroInflatedNegativeBin... |
17671308962a14afd16a4ac50ede38813fffebb2095ff03f87b3a0ce5a845dbc | Python | 18,303 | 414 | #!/usr/bin/env python3
"""Analyze experiment results from exp_results.csv or exp_results_merged.csv.
Usage:
python analyze_results.py --exp A # hyperparameter scan pivot table
python analyze_results.py --exp B # RC + Markov learning curves (all configs)
python analyze_results.py --exp C ... |
e03769f6a2e8ea4d6171c820e255603351b360cbf145422387d46a01e5af3e33 | Python | 18,320 | 452 | import sys
import numpy as np
import polars.selectors as cs
import pytest
from rnalysis.utils.differential_expression import *
class TestLimmaVoomRunner:
@pytest.mark.parametrize(
'data,design_matrix,comparisons,random_effect,expected_path',
[
(
'tests/test_files/big_... |
033a9c721ccdaaa3b904b9d448e3bb3b0cd577694076a47d957d32708d48017f | Python | 18,324 | 495 | """
EPI distortion simulation methods for DWI warping.
This module contains three different approaches to simulate EPI distortion:
1. PSF-aware convolution method
2. k-space forward model method
3. Splat-based displacement method
"""
import numpy as np
import torch
import torch.nn.functional as F
from scipy.ndimage... |
166802461d9a953741ee9ba0895b1ce4bee8f1a5de4abf9c1ba23140cb154bc5 | Python | 18,339 | 440 | # -*- coding: utf-8 -*-
import sys
from PySide6.QtCore import (QRect, QThreadPool, Slot, QObject, Signal, QThread)
from PySide6.QtGui import (QFont)
from PySide6.QtWidgets import (QApplication, QFrame, QLCDNumber, QMainWindow, QMenuBar, QRadioButton, QStatusBar,
QWidget, QLabel, QPlainTex... |
de42403cab79ee934ee8c633b5d1fea0935e9c00474d07b430180f714167d0b3 | Python | 18,339 | 468 | # 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 datetime
import hashlib
import logging
import time
from bisect import bisect_right
from collections import OrderedDict, defaultdict
fro... |
749ecfd7b6ffd998b57283df7bffe4f93436741df6b7213583b0f09066c59530 | Python | 18,341 | 442 | # -*- coding: utf-8 -*-
import sys
from PySide6.QtCore import (QRect, QThreadPool, Slot, QObject, Signal, QThread)
from PySide6.QtGui import (QFont)
from PySide6.QtWidgets import (QApplication, QFrame, QLCDNumber, QMainWindow, QMenuBar, QRadioButton, QStatusBar,
QWidget, QLabel, QPlainTex... |
852b9271774bbe779345e40456ac9964a9215ced082f66e81027f189d64f0f78 | Python | 18,342 | 403 | import re
from copy import deepcopy, copy
import numpy as np
from mdt.component_templates.base import ComponentBuilder, ComponentTemplate
from mdt.lib.components import get_component, has_component
from mdt.models.compartments import DMRICompartmentModelFunction, WeightCompartment, CacheInfo
from mdt.utils import spher... |
8f8f43c53d1b97aaed828f433c4c1c64d3f709d6395cd07a0e30413b2ff43d8a | Python | 18,348 | 484 | from __future__ import annotations
import io
import logging
import warnings
from contextlib import redirect_stdout
from typing import TYPE_CHECKING
import anndata
import numpy as np
import pandas as pd
import torch
from anndata import AnnData
from scvi import REGISTRY_KEYS, settings
from scvi.data import AnnDataMana... |
1fd8e013ed90bde3e12fffdec116aa6f352a957b63414eeb696161cc4993bce0 | Python | 18,349 | 498 | """
Multiple Instance Learning (MIL) Pooling Modules
Aggregates variable-length sequences of patch embeddings into fixed-size representations
using attention-based pooling mechanisms.
Classes:
- AggregateThenClassify: Attention-based MIL with gated attention
- ClassifyThenAggregate: MIL combining attention po... |
ec2b95ad50930efc474f722d85f2c17590455e93370e204027573fd31e252f17 | Python | 18,362 | 554 | """Inference-only support for the deprecated GPN-MSA family."""
from __future__ import annotations
from pathlib import Path
from typing import Any
import numpy as np
import pandas as pd
import torch
from datasets import Dataset, disable_caching
from jaxtyping import Float, Int
from torch import Tensor
from transform... |
9ef1989bc14e9911652756618af86fa24353fbb08d1c21b3916a6f2350fe9377 | Python | 18,373 | 414 | #!/usr/bin/env python
"""Benchmark two implementations of a function and ASSERT their outputs are equal.
This is the harness half of the ``safe-optimization`` skill (``.claude/skills/safe-optimization/
SKILL.md``): "faster" only counts once you can prove the output did not change. The core idea,
independent of any par... |
fadbb5a4c93be638309a3425518dc093f8c49a10776c70ff4f8b4e4e86c1f11f | Python | 18,373 | 473 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 22 10:31:20 2026
@author: vbp
Figure S3: larger time-window version of the region-specific dopamine
analysis (panels A-E), including baseline drift across trial types and
across session time.
Data can be downloaded here: https://doi.org/10.17605/O... |
08a231faa823b994df1c8526b2087c6a5f47794a7d0d3fda09302723681d2b13 | Python | 18,374 | 431 | import nibabel as nb
import numpy as np
def load_mgh(filename):
""" import mgh file using nibabel. returns flattened data array"""
mgh_file=nb.load(filename)
mmap_data=mgh_file.get_fdata()
array_data=np.ndarray.flatten(mmap_data)
return array_data;
# function to load mesh geometry
def load_mesh_ge... |
a84962cc2d273ec651ea11e767ac13270ad7c8d028dd3abf47de3d1fc2a54e4b | Python | 18,374 | 560 | import os
import numpy as np
import pandas as pd
import torch
from torch.utils.data import DataLoader, Dataset
__all__ = [
"ResidueEnvironment",
"ResidueEnvironmentsDataset",
"ToTensor",
"CavityModel",
"DownstreamModel",
"DDGDataset",
"DDGToTensor",
]
class ResidueEnvironment:
"""
... |
7b74c32ea48de1523b5c930ec5c6d9b2495c96a8b1a4b3ef5c93666ab1290198 | Python | 18,377 | 536 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# Adapted from https://github.com/txie-93/cdvae/blob/main/cdvae/common/data_utils_test.py.
# Published under MIT license: https://github.com/txie-93/cdvae/blob/main/LICENSE.
from collections import Counter
from itertools import product
from typi... |
de85fe7e5daa1ca247342bb48a9fe32732d01526565c4294fb4644a0e707d975 | Python | 18,387 | 453 | #
# 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
from dataclasses import dataclass
f... |
78f8a397129e6c1f99a722425d28927a801156903e7e9b801437dd317e5f39c9 | Python | 18,391 | 401 | import warnings
import numpy as np
import pandas as pd
import nibabel as nb
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.colors as mcolors
from scipy import stats
from scipy.stats import spearmanr
from pygam import LinearGAM, s
from meld_classifier.meld_... |
cc37ca65281e5337b36454896f05263e5829be7682b2415d31455a90fadd781e | Python | 18,391 | 410 | import os
import torch
import numpy as np
import skimage.io as skio
import torch.nn.functional as F
import argparse
# from tqdm import tqdm
import tqdm
from scipy.io import savemat
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import time
from utils import *
import csv
from torch.fft import fftshift, ifft... |
c6137c9f609c3ef898252c2272f8837759a196bdc9b65050fe283faa696823ff | Python | 18,395 | 314 | import warnings
import numpy as np
from copy import deepcopy
from typing import Union, List, Tuple
from dynamic_network_architectures.architectures.unet import ResidualEncoderUNet
from dynamic_network_architectures.building_blocks.helper import convert_dim_to_conv_op, get_matching_instancenorm
from nnunetv2.preproces... |
189e707c3a766c13bccfbfe6775c2e05f8035680b6b573be4b2a712b84e3e7c4 | Python | 18,408 | 411 | import gzip
import re
import subprocess
from dataclasses import dataclass
from pathlib import Path
import pandas as pd
ROOT = Path("__MS_GEO_ROOT__")
OUT_DIR = ROOT / "Stratified_Analyses"
EXPR_META_PATH = ROOT / "Expression_Data" / "Corrected_Metadata_ComBat.csv"
EXPR_MATRIX_PATH = ROOT / "Expression_Data" / "Corr... |
159866961f8d01f57fff421fdea731b9c831895f7148c25bf8921c0004c9fc75 | Python | 18,420 | 456 | from typing import Dict, Tuple, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
try:
from monai.networks.nets import BasicUNet as MonaiBasicUNet
except Exception: # pragma: no cover
MonaiBasicUNet = None # type: ignore[assignment]
from dgr.utils.warp import grid_warp_x, grid_war... |
f78b42d82e0fbfe690eefd36688be880afab86e5bff23dcce6c104e34b87ffe4 | Python | 18,430 | 514 | """Creation of consensus sequences from repetitive reads."""
from collections import namedtuple
from concurrent.futures import ProcessPoolExecutor
import functools
import os
from timeit import default_timer as now
import warnings
import mappy
import numpy as np
import parasail
import pysam
import spoa
import medaka.a... |
3af701700bcbb0890deac69160c4504e0e100400bb293095ea304c343fdb60f5 | Python | 18,431 | 380 | import os
from pathlib import Path
import joblib
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from analysis.metrics import computeDelta
from analysis.utils_neuralData import load_MonkeyNeurons_pooled_rates, load_targetCategories, load_MonkeyNeurons, \
get_SiteReliability
selfsuperv... |
a9d0116f698687c33fe0ced1711302517391a6f262c08ab1df810b307a115fdf | Python | 18,459 | 535 | # -*- coding: utf-8 -*-
"""
LASSO Logistic Regression for Binary Classification
Path-based final version
Input folder example:
C:\\Users\\win\\Desktop\\CHJ\\Lasso\\Larm
Input Excel files:
Processed_Sensor_Data_Averaged_1min.xlsx
Processed_Sensor_Data_Averaged_2min.xlsx
...
Processed_Sensor_Data_Av... |
314924cfec779a39824ef3fd158d6143ae16aae46266acf8ea9bc8ae7095a22e | Python | 18,465 | 540 | import difflib
import itertools
import platform
import re
import shlex
import unicodedata
import warnings
from collections import Counter
from itertools import islice
from pathlib import Path
from typing import Any, Dict, Iterable, List, Literal, Tuple, Union
import lazy_loader as lazy
import mslex
import numpy as np
... |
de4dab7647e7c24a443b91f196f838206d308aff31c4d24d42cf0364b78205c6 | Python | 18,467 | 474 | """Creation of vcf files from network outputs or consensus sequences."""
import collections
import itertools
from timeit import default_timer as now
import intervaltree
import numpy as np
import pysam
import medaka.common
import medaka.datastore
import medaka.vcf
def apply_variants(variants, ref_seq):
"""Apply ... |
12977299221bcc32340a55b92927ed767d5b78198101673b09fcb3c19756d6b5 | Python | 18,470 | 553 | import math
import torch
import torch.nn as nn
from torch.nn import init
class Input(nn.Module):
def __init__(self):
super(Input, self).__init__()
def forward(self, x):
return x
class ConvHole2D(nn.Conv2d):
def __init__(
self,
in_channels,
out_channels,
k... |
3abf360da2bb0d04015a1ec485afea1fa0f401fff96e1fe56389eb326cf1c43e | Python | 18,479 | 423 | import unittest
from unittest.mock import patch
from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.faker import Faker
from pyecharts.globals import ChartType, SymbolType
class TestGeoChart(unittest.TestCase):
@patch("pyecharts.render.engine.write_utf8_html_file")
def test_g... |
c79ca7db7a9eb8326d6bee08373b9e0a85ee3ee9ab1bd0320bd6b392ed2df74e | Python | 18,486 | 576 | # 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 json
import logging
import os
import random
from pathlib import Path
import numpy as np
import torch
import torch.utils.data
from . ... |
d60b957d9fc7b72a9de291df9c032e933ca1f038e05b57e18a7ada512888210c | Python | 18,490 | 522 | import logging
import warnings
from typing import Literal
import numpy as np
import pandas as pd
import rich.table
from anndata import AnnData
from pandas.api.types import CategoricalDtype
from scvi import settings
from scvi.data import _constants
from scvi.data._utils import (
_check_nonnegative_integers,
_m... |
34c5541b27bd8a61f06a7ff5dd2747e15316819a9eb08a0a689e1cd73f5eda18 | Python | 18,493 | 350 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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://w... |
9c8911edba72173629437a45230cd781edfc7e760cbb832b1af9543f5abb44a3 | Python | 18,498 | 539 | """Where a relation candidate comes from: the tagger's spans, the store's links.
`forward` runs the span tagger over the hidden states it already has, cuts its
argmax into typed spans, and grounds each span in the exact mentions the label
store holds for that document. What comes out is a candidate *set* per argument,... |
b8039cd4f044a909d17acb8e666ac4da100ac9bd58e90c78b961567da0be97c7 | Python | 18,504 | 507 | import abc
import functools
import itertools
from pathlib import Path
from typing import Callable, List, Tuple
import matplotlib
import matplotlib_venn
import numpy as np
import pandas as pd
import upsetplot
from matplotlib import pyplot as plt
from matplotlib.backends.backend_qtagg import FigureCanvasQTAgg, Navigatio... |
b4bb7870b3ab10c76ced28ca70c816f769c0899cbadb13fefb9be41060f5c9fe | Python | 18,513 | 490 | """Figure 4 — trimodal (WF+ISI+ACG) classification benchmark on Hull and Lisberger.
Reads cached prediction CSVs that ship with the repo, computes per-fold balanced
accuracy and macro F1, and generates bar charts and confusion matrices for:
Hull mouse cerebellar cortex (101 neurons, 4 cell types)
Lisberger ... |
60a04e8109dc339856f57db157b2ad7d0370530e35d5d01a5cb98813885c5f1c | Python | 18,519 | 512 | # Copyright 2022 InstaDeep Ltd
#
# Licensed under the Creative Commons BY-NC-SA 4.0 License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://creativecommons.org/licenses/by-nc-sa/4.0/
#
# Unless required by applicable law or a... |
bc5948e88ed00aa67b7b3f03e3f608f6466645adbbabfeac0a613da419427123 | Python | 18,535 | 448 | import logging
import numpy as np
from keras import Input
from keras.engine import Model
from keras.layers import Dense, Dropout, Lambda, Concatenate
from keras.regularizers import l2
from data.data_access import Data
from data.pathways.gmt_pathway import get_KEGG_map
from model.builders.builders_utils import get_pne... |
37c064c07d92aff9b41d7d49d9d87b8fb1ecdadd337b68ffa474a5412f61f332 | Python | 18,540 | 644 | import pandas as pd
from collections import defaultdict
import re
from collections import defaultdict
GO_ID_REGEX = re.compile(r"GO:\d{7}")
# ---------------------SEGMENT TO PROTOTYPE MAPPING ---------------------#
def add_residue_to_prototype_mapping(segment_assignments, prototype_assignments):
""... |
e28d97a386c534967334eb6a3b97b84db3a95042731233acdb8be90ce36c8438 | Python | 18,546 | 407 | #!/usr/bin/env python
"""
pipeline for processing voltage imaging data for zebrafish lightsheet data
author: @Jack Zhang
"""
import scipy.io as sio
import cv2
import os
import glob
import h5py
import logging
import matplotlib.pyplot as plt
from matplotlib.pyplot import savefig
import numpy as np
import os
try:
cv... |
88d1d77a25607421c42553d13983cb03e4961a5dabeea59c64e399dc8ff44879 | Python | 18,555 | 451 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 27 11:31:26 2024
@author: Roxana
"""
import numpy as np
import scipy.sparse as sp
import torch
import torch.nn.functional as F
from tqdm import tqdm
from .preprocess import (
preprocess,
construct_interaction,
construct_interaction_KNN... |
dc428568253f78acb73ef4461cd0088d41af3ccd4b375af1ab9dc3b82d7f1858 | Python | 18,566 | 377 | ## script for plotting boxplots and scatter plots of simple stats such as count of approaches, count of interactions, chasings etc...
import matplotlib.pyplot as plt
import numpy as np
import igraph as ig
import networkx as nx
import pandas as pd
import os
import sys
import pandas as pd
import seaborn as sns
from scip... |
e6fde1b408b38d0a92ac500436c6bdeef06e18257dda7dd72c2f69e4dbd69179 | Python | 18,568 | 547 | """
Visualization script for Garfield spatial scalability benchmark results.
This script generates publication-quality figures showing:
- Runtime vs dataset size for all major tasks
- Memory consumption vs dataset size
- Comparison of different graph construction methods
- Breakdown by task category
"""
import os
imp... |
c861602be92e180df5626fffd174420bd40362696f39857a53455150c17665a7 | Python | 18,580 | 478 | import os
from os import makedirs
from os.path import join, dirname, realpath, exists
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import gridspec
from mpl_toolkits.axes_grid1 import make_axes_locatable
from sklearn.preprocessing import MinMaxScaler
from... |
70358066bbe5ea163bee2442164b4b75cc7669f5ff0aa2a9fcd14a131e1207b9 | Python | 18,584 | 562 | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import logging
from collections.a... |
339c7f90169d8f7445885663317ecfe992e4e04898f1ccd2b495677fd6c36e25 | Python | 18,586 | 434 | import os
import torch
import numpy as np
import skimage.io as skio
import torch.nn.functional as F
import argparse
# from tqdm import tqdm
import tqdm
from scipy.io import savemat
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import time
from utils import *
import csv
from torch.fft import fftshift, ifft... |
aad9fa866741041ca8db0f9d65af2f1328d89f1ac6519f61d1368617465462d6 | Python | 18,590 | 599 | # -*- coding: utf-8 -*-
"""
Created on Wed Feb 19 14:21:51 2025
@author: hanna
"""
#%%
"""
[Figure 6] changes in modulation depth
"""
#%%
import os
import pickle
import numpy as np
import pandas as pd
from tqdm import tqdm
import seaborn as sb
import matplotlib as mpl
import matplotlib.pyplot as plt
from m... |
1595b58dcd4bbb36daba0c6c847f3b5a38c1f79f9095fb3ba60132475b8499ba | Python | 18,591 | 459 | from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
import torch
import numpy as np
from .preprocess import binning
@dataclass
class DataCollator:
"""
Data collator for the mask value learning task. It pads the sequences to
the maximum l... |
bfa054bf6ca775f1b661d33970062161ce064f860ebb8b1ae9184d8c30555b9d | Python | 18,592 | 519 | """Storing of training and inference data to file."""
from abc import ABC, abstractmethod
from collections import defaultdict, OrderedDict
from concurrent.futures import \
as_completed, ThreadPoolExecutor
import contextlib
import os
import pickle
import tarfile
import tempfile
import warnings
import numpy as np
im... |
5a37a9a172c83c286cd45d9387051d7cc09634bc7f635e11e7615da636860251 | Python | 18,602 | 491 | from typing import Optional, Tuple
import torch
from torch import Tensor, nn
try:
from einops import rearrange
except (ImportError, OSError):
rearrange = None
flash_attn_backend: Optional[str] = None
flash_attn_error: Optional[str] = None
try:
# flash-attn 1.x (preferred - simpler public API)
from f... |
227af55984d9461ec53a01e70e1a8a9343ae60c3a1824edba3bf96f16ee59dd6 | Python | 18,616 | 434 | import matplotlib.pyplot as plt
import matplotlib.patches as patches
import seaborn as sns
import networkx as nx
import math
import numpy as np
# ======= Plotting functions for run_pipeline.ipynb =======
# specific to the provided example data set - could be adjusted to own needs
def flow_chart_step_1(figsize=(8,6... |
aa0eb9cdf9ec3c53205aa417f56ab075f6d109e7ac637c3f97a0df756fa15063 | Python | 18,618 | 559 | """
Operon completion eval pipeline using Evo.
Usage: python pipelines/operon_completion.py --config <config_file_path>
"""
import argparse
import logging
import os
import subprocess
import sys
import tempfile
from collections import Counter
from dataclasses import dataclass, field
from pathlib import Path
from typin... |
3f6221b50f1ec4b89a4af3592127957a1e469890fd71879fbcfb085ce2d0920b | Python | 18,620 | 576 | from nipype.interfaces.workbench import base as wb
from nipype.interfaces.base import BaseInterface, BaseInterfaceInputSpec, traits, File, Str, isdefined, TraitedSpec, CommandLineInputSpec
from traits.api import List
# convert cifti to nifti and back
# this interface was drafted by ChatGPT then heavily modified by BP.... |
19ae6c65532c9a5d238c7237e08fc174a4a5aa99622beef1d79425c0401ef05d | Python | 18,625 | 545 | """
This module implements sequence classification.
Author: wangning(wangning.roci@gmail.com)
Date : 2022/12/7 7:42 PM
"""
import math
import os.path as osp
import time
import copy
from collections import defaultdict
from tqdm import tqdm
import numpy as np
from Bio import SeqIO
import paddle
import paddle.nn as n... |
e408fba489912f59b5344c0ea303d16b10f9648228ca17f2532dce04e8876748 | Python | 18,630 | 448 | import torch
import wandb
from utils.data.dataholder import DataHolder
from utils.data.load import remove_mean_with_mask
from utils.diffusion_model.diffusion.diffusion_utils import (
cosine_beta_schedule_discrete,
)
class NoiseModel:
def __init__(self, cfg):
"""
Initialize the NoiseModel with ... |
b9a27fc07004e57c4798770ecbe8f001c5dbfb0b44316cd62487cd3b95b47ec9 | Python | 18,632 | 454 | """Generate a diffusion map embedding
"""
import numpy as np
has_sklearn = True
try:
import sklearn
except ImportError:
has_sklearn = False
def compute_diffusion_map(L, alpha=0.5, n_components=None, diffusion_time=0,
skip_checks=False, overwrite=False,
eige... |
1c39db8b78614716b61671e4e9e9d50f64453a7cf5ad5a55351cdf461a5d93cd | Python | 18,634 | 481 | # -*- coding: utf-8 -*-
# Copyright (C) 2017-2023 Phillip Alday <me@phillipalday.com>
# License: BSD (3-clause)
"""MNE-based functionality not further categorized."""
from collections import namedtuple # noqa: I100
import matplotlib.pyplot as plt
import mne # noqa: F401
import numpy as np
import pandas as pd
fr... |
ae32a23fc39c366761b2cd11b939f65c9ce7ef5c506b78c7b5496cffde1dc16e | Python | 18,640 | 504 | from neuron import h, gui
import math
import time
import random
import numpy
from matplotlib import pyplot as plt
#cell that contains only 1 spine which is randomly located on the dendrite
class cell() :
def __init__(self, name="cell", gid=0, dend_nseglevel=1):
#random.seed(1) #use the same seed to get... |
5bf810e735644ff12c81178626c61fcc38584e57564789db51e2fd760cba0077 | Python | 18,642 | 569 | from __future__ import annotations
import csv
import json
import os
import shutil
from pathlib import Path
from typing import TYPE_CHECKING
import pytest
from cleo.io.null_io import NullIO
from poetry.core.constraints.version import Version
from poetry.core.masonry.metadata import Metadata
from poetry.core.packages... |
36cff56d77bfc93a09f37a55cbb17abac8f51f34730c3f19ed970ab21e9556aa | Python | 18,643 | 557 | import torch
import pickle
import random
import numpy as np
import torch.nn.functional as F
import matplotlib.pyplot as plt
from cellpose import plot as cplt
import warnings
from torch.linalg import eigvals
def is_valid(pnm, gnm):
for p in pnm:
if p not in gnm:
return False
return True
... |
86c41f340c8a1c2938725d6cb5b3e1df39b293c1b44193784681279b749d1617 | Python | 18,651 | 517 | """
This module contains methods to generate pulse for use in DAQ.
"""
"""
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 Foundation, ... |
735bb40b00a69838af26fcbd642a915051edbad6eb9334bd9a2e1911a92190c4 | Python | 18,653 | 333 | import argparse
import os.path
from copy import deepcopy
from typing import Union, List, Tuple
from batchgenerators.utilities.file_and_folder_operations import load_json, join, isdir, save_json
from nnunetv2.configuration import default_num_processes
from nnunetv2.ensembling.ensemble import ensemble_crossvalidations
... |
30a3abe376255194fac9c3c39e1b1c814c766217a2c50a88f2231272c49bdbe6 | Python | 18,662 | 465 |
import sys
import os
sys.path.append(os.path.join(os.path.pardir, 'simulator'))
# import numpy as np
# from numpy import matlib
import pandas as pd
import pynumdiff
# import scipy
from scipy.optimize import fsolve
import matplotlib.pyplot as plt
from matplotlib import cm, colors
from matplotlib.colors ... |
1444e2e77091f8ffd40f5c6407bf35c0bc9003fb41c5877c11b4e50824d3a83f | Python | 18,668 | 487 | from collections import defaultdict
from itertools import chain, combinations, tee
from pgmpy.factors import factor_product
from pgmpy.factors.discrete import DiscreteFactor
from pgmpy.inference import BeliefPropagation, Inference
class DBNInference(Inference):
"""
Class for performing inference using Belief... |
669566c1d03e237eeb0f2b5460ea7e03fb5df5f276bb9af59b2b4b857684b6ef | Python | 18,685 | 289 | import numpy as np
import pandas as pd
# from https://github.com/dicarlolab/dldata/blob/master/dldata/physiology/hongmajaj/mappings.py
channel_info_ChaboTito_LH = pd.DataFrame({
'region': ['IT', 'IT', 'IT', 'IT', 'IT', 'IT', 'IT', 'IT', 'IT', 'IT',
'IT', 'V4', 'V4', 'V4', 'V4', 'V4', 'V4', 'V4', 'V4', 'V4'... |
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