sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
82bdb8acfe276a605867b48870816e8ed3c57af898fc13907450a2c2f98d4e8e | Python | 10,532 | 326 | import warnings
from collections.abc import Iterable, Sequence
from dataclasses import dataclass
from pathlib import Path
import pytest
from torch import nn
from torch.utils.data import DataLoader
import eir.models.models_utils
import eir.train_utils.accelerator
from eir import train
from eir.experiment_io.input_obje... |
b8cebe285f4b3a23f32298efa722c2002e1009c5db70e64586019a04d8383d62 | Python | 10,533 | 222 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors: Agatha Schmidt, Henrik Zunker
#
# Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in complian... |
3499e482168647cdaa7255d5d9cc70bf885a0375807004d98e0d1acc8e98ad28 | Python | 10,535 | 252 | #!/usr/bin/env python3
"""code2_dvh_plot_and_summary_fixed.py – rev 2025‑07‑13‑b
========================================================
Complete, runnable version after truncation issues.
Highlights (compared with 07‑12 build)
--------------------------------------
* **No‑overlap cDVH labels** for D̄, Dₘₐₓ, D... |
c93e95a4048b9c69816413bf01b724992a5c676d99a95b2a20fb1419c193f809 | Python | 10,537 | 254 | """
Supplementary Figure S6: Myelin water fraction dependence in SpinWalk simulations
Investigating the effect of myelin compartment proton density weighting on bSSFP signals and qMRI estimates:
- (A) Magnitude profiles at θ=90° for different myelin weights (0, 0.25, 0.5, 1.0, 2.0, 5.0)
- (B) Phase profiles at θ=90° ... |
5c21d79f90825a451fdb0d6a0ca3e99b481096d7501f06ddebd172306ac55439 | Python | 10,538 | 250 | # /------------------------------------------------------------------------------+
# | 24-OCT-2022 |
# | Copyright (c) Bone Imaging Laboratory |
# | All rights reserved ... |
fdcfec254b1b3fbfe7885e705b9fcaaba72fa07156bfdc42470fb74b05faef26 | Python | 10,543 | 310 | from copy import deepcopy
from typing import Any, Callable, Dict, Optional
BACKBONE_CLASS = "backbone_class"
LOSS_CLASS = "loss_class"
METRIC_CLASS = "metric_class"
MODEL_CLASS = "model_class"
DATA_AUGMENTATION_CLASS = "da_class"
DATA_LOADER_CLASS = "data_loader_class"
FILE_LOADER_CLASS = "file_loader_class"
KNOWN_CA... |
1a07025a12cb7cc473459c95f09a9c0fca5bd2e6c5c7efaa52c7060091e7f858 | Python | 10,544 | 279 | from typing import Any
import numpy as np
import torch
import torch.nn as nn
import seisbench.util as sbu
from .base import WaveformModel
class Skynet(WaveformModel):
"""
Skynet is a phase picker with a U-Net architecture with a focus on regional phases and a long receptive field.
The model offers firs... |
7bd5e0c7ffc200902512368e271ff09c49bcbac1752b75f3dd9e581edd47930f | Python | 10,547 | 214 | #!/usr/bin/env python
"""
Camera control specialized for a Hamamatsu camera.
Hazen 09/15
"""
from PyQt5 import QtCore
import storm_control.sc_hardware.hamamatsu.hamamatsu_camera as hcam
import storm_control.sc_library.parameters as params
import storm_control.hal4000.camera.cameraControl as cameraControl
... |
f9f90d8cd25c3e371f20d9503138257caae316472c9e0394385b933870f76c2b | Python | 10,548 | 298 | import argparse
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch_sparse import SparseTensor
import torch_geometric.transforms as T
from torch_geometric.nn import GCNConv, SAGEConv
from ogb.linkproppred import PygLinkPropPredDataset, Evaluator
from logger import Logger
... |
5ef98e085eef2bf937cba80033483d8a4677846c4b74cf19e381c5e2dc955e36 | Python | 10,550 | 322 | #!/usr/env/bin python
# -*- coding: utf-8 -*-
# Author: Torbjørn Ness <torbjorn.ness@nmbu.no>
"""
NEURON and Python - Creating a multi-compartment model with synaptic input
with randomized activation times
"""
# Import modules for plotting and NEURON itself
import matplotlib.pyplot as plt
import neuron
import numpy as... |
9b3020e24835e4f289b413d516b1be0df69766ffb331be050ad3b1a8e9e1cd61 | Python | 10,551 | 245 | import os
import time
import numpy as np
import Utils.config_stomach as CONFIG
from argparse import ArgumentParser
from torch.utils.data import DataLoader
from prefetch_generator import BackgroundGenerator
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_curve, auc, f1_score
from tensorboard... |
d9ab2a89bc2ac929449c489241b0a0cdede25b20285315d261592855214163ec | Python | 10,551 | 288 | from __future__ import division
import os
import nipype.pipeline.engine as pe
from nipype import SelectFiles
import nipype.interfaces.utility as util
from nipype import IdentityInterface
from .utils import *
def create_bullseye_pipeline(
scans_dir,
work_dir,
outputdir,
subject_ids,
name='bullseye... |
d47e7a1b875339fa0f4277f1ae9c642404a424bf58b1061dafea272b0f4f8b0e | Python | 10,553 | 254 | # -*- coding: utf-8 -*-
"""
Analyses of the EMPRISE data set
by Joram Soch <soch@cbs.mpg.de>
"""
# import modules
import os
import time
import EMPRISE
import NumpRF
import numpy as np
import scipy as sp
import pandas as pd
import nibabel as nib
import matplotlib.pyplot as plt
### Simulation C: NumpRF/popeye results... |
9c9fc7d1838c6a71056561202fafb2286536e51388a15c17a3af5b4f624878ec | Python | 10,559 | 196 | import pandas as pd
from skimage import io as skio
from skimage.morphology import closing, cube, label
from sklearn.metrics import jaccard_score
from napari_simpleitk_image_processing import label_statistics
from tqdm import tqdm
import numpy as np
import os
from glob import glob
import json
from natsort import natsort... |
d99693b46806730a7f68d64830474c01a09164c4a9386bdf4288a64d56102627 | Python | 10,561 | 320 | #!/usr/bin/env python3
"""
Script to identify inheritance
Usage: ./process_DC_output.py --merge_map --disc_curve variants_bg_ALL-sv_insdel.tsv.gz -t 10
Author: Mei Wu https://github.com/projectoriented
"""
import pandas as pd
import numpy as np
import re
from itertools import groupby, repeat
import argparse
import sys... |
84a95a285fee19eace9cad43b7df2351804565c118631acd9342fea7aa5d3bda | Python | 10,563 | 317 | import os
import sys
from os.path import (
dirname,
isdir,
isfile,
pathsep,
realpath,
)
from os.path import (
join as pjoin,
)
import dipy.core.gradients as dpg
import dipy.data as dpd
import dipy.io as dio
import nibabel as nib
import numpy as np
import numpy.testing as npt
from dipy.sims.voxe... |
5e24cfecec02e8732eb9c0c70d05e9d53d659bbd9454aee8a3a9f50eb18fa593 | Python | 10,564 | 273 | import os
from time import time
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch
import torch.backends.cudnn as cudnn
#from torch.utils.data import DataLoader
from monai.data import DataLoader
from dataset.kidney_dataset import KidneyData
from dataset import transforms
#from tqdm im... |
6a6c4c8bff25458e5bf9c94df16dcd7bfc446d663b89e64941cdb6cbb497a382 | Python | 10,571 | 334 | import itertools
import numpy as np
import torch
import hydra
from scipy.spatial.distance import pdist
from scipy.spatial.distance import cdist
from hydra.experimental import compose
from hydra import initialize_config_dir
from pathlib import Path
import smact
from smact.screening import pauling_test
import sys
sys.... |
0a58befb76bd261ff7baa1f992d8e96eb06be46cea33120e83f56f82dd9a61fa | Python | 10,574 | 221 | import os
import numpy as np
import h5py
import yaml
import sys
import matplotlib.pyplot as plt
from glob import glob
pathpath = 'PATHS/filepaths_IBLTuning.yml'#
myrun = 'runIBLTuning_utypeP'
dset = 'IBL'
fformat = 'svg'
dsdir = 'IBLPATH'
dstdir = '/results/paper/tuningIBL/pfcOnly/%s'%myrun
with open(pathpath, ... |
6fb8490b6b472f2495e9033f5a03c2e006b6882174baf679902fec2746c02f88 | Python | 10,574 | 214 | from bread.algo.tracking import AssignmentDataset
from skorch import NeuralNetClassifier
from torch_geometric.loader import DataLoader
from torch_geometric.data import Data
import numpy as np, scipy.optimize, scipy.sparse
from typing import List, Union
import torch
__all__ = ['AssignmentClassifier', 'GraphLoader','see... |
77eb602c43ccc2e06f4a55040c1485fdeaf8e2578ef772c6fe76edc432e71aad | Python | 10,576 | 261 | import os
import numpy as np
import pandas as pd
from nipype.interfaces.base import (
BaseInterface,
BaseInterfaceInputSpec,
TraitedSpec,
File,
traits,
)
from cvdproc.pipelines.network.network_measures import (
load_connectivity_matrix,
compute_global_efficiency,
compute_local_efficien... |
ad364ecb75c6e7c738ff9a2a048c0b5d8f7536951c3849dd50b52a8d0aabf77c | Python | 10,579 | 285 | """ Encodes data in different formats """
from os.path import join
import argparse
import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
import constants
def enc_aa_index(int_seqs):
"""
encodes data in aa index properties format
param:
int_seqs: integer encoded se... |
76ff51cbe7d2431f898c9e097d107ca271f1916ef121adc46a016181460f21ee | Python | 10,582 | 286 | """Tests for mcmlnet.training.data_loading.sota_data_classes module."""
from itertools import product
import pytest
import torch
from mcmlnet.training.data_loading.sota_data_classes import (
SOTADataset,
SOTAPreprocessor,
)
class TestSOTAPreprocessor:
"""Test cases for SOTAPreprocessor class."""
@... |
9a2c449ea45b9b50bf5f326e9e732b95d196a1e5495559ea94201593306491a3 | Python | 10,583 | 296 | """Build Academy action tools and attach configured science tools."""
from __future__ import annotations
import pathlib
import time
import asyncio
from collections.abc import Callable, Mapping, Sequence
from typing import Any
from academy.handle import Handle
from langchain_core.tools import BaseTool, StructuredTool... |
57c070572be0a40f16c2547d0a66a3ddc496640130ad00e7aeff255634c96d3d | Python | 10,585 | 321 | from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import matplotlib.pyplot as plt # type: ignore
import numpy as np
from adjustText import adjust_text # type: ignore
from anndata import AnnData # type: ignore
from matplotlib.axes import Axes # type: ignore
from mpl_toolkits.axes_grid1 import mak... |
83192b2135c68c23ab33d2186aa5cee4de3787573ea31130f41a64b1845a0593 | Python | 10,586 | 299 | import pickle
from pathlib import Path
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
import pandas as pd
import scipy.ndimage
import tifffile
from tqdm import tqdm
from brainways.pipeline.brainways_pipeline import PipelineStep
from brainways.utils.io_utils.image_path import ImagePath
if ... |
8d05dd20a04c87b158aca6d4dd968450f39b3bafb374652afbc24067f9b4638a | Python | 10,586 | 235 | from qtpy.QtWidgets import QPushButton, QWidget, QVBoxLayout
from magicgui import magicgui
from magicgui.widgets import FunctionGui
from napari.qt.threading import thread_worker
import numpy as np
import pandas as pd
import json
from natsort import natsorted
from skspatial.objects import Line, Points # scikit... |
4d1df19774d557d27027c29e5beb9c1343f46c9cf20154bb25e4ca7b17bf0c9b | Python | 10,590 | 332 | import asyncio
import json
from types import SimpleNamespace
from unittest.mock import Mock, patch
import pytest
from langchain_core.messages import AIMessage
from chemgraph.agent.events import SUBAGENT_METADATA_KEY
from chemgraph.agent.llm_agent import ChemGraph
from chemgraph.agent.turn import _TurnEventCallback
fro... |
6649cde7fa6c4cbc13a2d71f8c1293e85a358e1d18022ce1b9d39056067f0b3e | Python | 10,590 | 207 | from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, roc_auc_score
from torch_geometric.nn import HGTConv
import pytorch_lightning as pl
from torch import nn
import torch
from Dataset import BotDataset
from torch.utils.data import DataLoader
import argparse
from torch.optim.lr_scheduler ... |
497e2fe818c2870a0141031a89efcf4d86a3e2aac96a7bd7557611020f588e57 | Python | 10,601 | 262 | from graph_tool import load_graph
import numpy as np
from pycurv import TriangleGraph, io
from scipy.spatial import cKDTree
from scipy.spatial.distance import cdist
import time
import pandas
import vtk
from sys import argv
from os import path
from time import sleep
folder = "/gpfs/group/grotjahn/bbarad/Final_Dataset... |
cf0bc718517d8f2e6e697b3a6f55fdf41413a1f5a39702c884bffd28df2d6146 | Python | 10,601 | 292 | import sys
import os
import pandas as pd
import pyreadr
import torch
import glob
import functools
from torch_geometric.loader import DataLoader
import torch.nn.functional as F
from torch.nn import Linear
from torch_geometric.nn import GCNConv
from torch_geometric.nn import global_mean_pool
from torch_geometric.data imp... |
20f4c114ce2bfdfda50fdaf78be31dcb521af66964e59a29aa032628b091cbaa | Python | 10,604 | 311 | from unittest.mock import create_autospec, patch
import pytest
from torch import nn
from torch.optim import SGD
from torch.optim.adamw import AdamW
from torch.utils.data import RandomSampler, SequentialSampler, WeightedRandomSampler
from eir import train
from eir.data_load.data_utils import get_finite_train_sampler
f... |
912669b64df77ebc78231e42ec341d7495007290ebec34213d28da919ab33558 | Python | 10,604 | 228 | # Kai Sandbrink
# 2023-10-07
# This script is used to simulate data corresponding to perturbed participants
# %% LIBRARY IMPORT
import numpy as np
import pandas as pd
import pickle
import os, copy
from tqdm import tqdm
from utils import get_timestamp, Config
from utils_project import load_config_files
from test_case... |
d5d1be901dfd0ded0c8c55b329453f4306adc3913abd4771bde4120ae8fae160 | Python | 10,605 | 311 | from unittest.mock import create_autospec, patch
import pytest
from torch import nn
from torch.optim import SGD
from torch.optim.adamw import AdamW
from torch.utils.data import RandomSampler, SequentialSampler, WeightedRandomSampler
from eir import train
from eir.data_load.data_utils import get_finite_train_sampler
f... |
698aca8bd5368e9b02a996b18599301730adab9da87470dd39a6b4e60fdda48a | Python | 10,611 | 303 | # This file is part of Zennit
# Copyright (C) 2019-2021 Christopher J. Anders
#
# zennit/cmap.py
#
# Zennit is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation; either version 3 of the License, or (at your o... |
23e4f0b740866989259d4f38878cca8acb82a6c21231cc46d237388dfd071856 | Python | 10,613 | 334 | import unittest
import logging
import numpy as np
import diffxpy.api as de
class _TestSingleDe:
def _prepare_data(
self,
n_cells: int,
n_genes: int,
noise_model: str
):
"""
:param n_cells: Number of cells to simulate (number of observations pe... |
683113738f248f6b4290ae95cb8df9c4d57560a11b1ce0ec83ccaf824a15cd31 | Python | 10,615 | 265 | import os
from time import time
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch
import torch.backends.cudnn as cudnn
#from torch.utils.data import DataLoader
from monai.data import DataLoader
from dataset.kidney_dataset import KidneyData
from dataset import transforms
#f... |
3882a873d2e4e125e2d79ca2a929c5bd3ac65c42ce7a4ba37a864c7fe58e19c8 | Python | 10,619 | 309 | import scanpy as sc
import numpy as np
from sklearn.utils.extmath import randomized_svd
from scipy.sparse import csc_matrix, csr_matrix
from typing import List
import anndata
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backen... |
a298b6eb7d75aae56a21d15822092dc646b6e054859d706c9c060cb5b9d37498 | Python | 10,620 | 219 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
from typing import Any
import numpy as np
from .basetrack import TrackState
from .byte_tracker import BYTETracker, STrack
from .utils import matching
from .utils.gmc import GMC
from .utils.kalman_filter import Kal... |
d01e172f99a9cc94b4d5f8e592d52429ec15e8c9e2c1f1a736a3b3a064e01cf2 | Python | 10,624 | 294 | import numpy as np
import bisect
def select_neg_pairs(fold, chrom_pairs, curr_counts):
selected_pairs = []
shortage = [0 for _ in curr_counts]
for k in range(len(chrom_pairs)):
chrom_pairs[k] = list(chrom_pairs[k])
if len(chrom_pairs[k]) <= 0:
continue
if curr_counts[k]... |
2f40491719b4f8164ae7226cdd4bdaaf45c1b205e1fbeeafb35c1e8326d9dd3e | Python | 10,627 | 345 | """Extracting ERAs from ROIs"""
import nibabel as nb
import numpy as np
import os
import glob
from nilearn import signal
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
ROOT = '/Users/sebastiandresbach/data/s1Anfunco/Nifti/derivatives'
# Set subjects to work on
subs = ['sub-05', 'sub-06', ... |
4a5adf04c5f9c1f3e969855d6db2feaaa9314a9b3eca932170d2347e6c2189bc | Python | 10,629 | 264 | """
This script converts the output from predict_DANNCE into a predescribed
structured data format, with meta data, that can be used easily by our
downstream analysis pipeline
Usage:
python path_to_file/makeStructuredData.py path_to_config [cross_corr?]
the optional boolean argument cross_corr indicates whether a cro... |
a6c8b36218d70a5d0323a36e9c7aa823b0834bf4224c8263ae33542bce2df1a3 | Python | 10,629 | 313 | from __future__ import annotations
import importlib
import multiprocessing
import os
import shutil
import tempfile
from multiprocessing.connection import Connection
from multiprocessing.process import BaseProcess
from multiprocessing.synchronize import Event
from pathlib import Path
import osam
from PySide6 import Qt... |
25b5b21eac15057dafec0ba9ad2cce1870642cbd4d6916ef531d6565718bc123 | Python | 10,630 | 359 | """
Practical Examples
------------------
In the example below a ``server`` distributes ``data`` to ``workers``. The workers process each data item individually
and send results back to the server. After all data has been processed and send back, the server has all results
in correct order.
>>> from celldetection imp... |
29560bfce95c972258e3ea4e00651f00a18bf84121e2b5be049890290d9ad9ab | Python | 10,632 | 275 | # fmt: off
# flake8: noqa
import copy
import os
import pickle
import time
import traceback
from functools import partial
from multiprocessing.pool import Pool
import numpy as np
from . import _timing, utils
from .config import get_default_eval_config, init_config
from .utils import TrackEvalException
class Evaluat... |
1a34df86e8bcfbe1a3a3c940ab86fdb5d585eff86bb0806e59a7e5af69350496 | Python | 10,633 | 293 | # Copyright 2024 Google LLC
# 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
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, sof... |
1b91064ed4a8dd5c2df53e89f0c47f51c43ae7833b9a2a8d2f018d11fc96a15a | Python | 10,633 | 249 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Projects metrics onto the underlying voxels of a streamlines. This script can
project data from data_per_point (dpp) or data_per_streamline (dps) to maps.
Projection in a given voxel is done by averaging the value of all streamlines
with a point in that voxel. Optional... |
cdaf282a3ce2e77335015d30674e0fe6b86ba15da7b350e505ba7a18e3cc0b0e | Python | 10,633 | 253 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Script to estimate the diameter of bundle(s) along their length.
See also scil_bundle_shape_measures, which prints a quick estimate of
the diameter (volume / length). The computation here is more complex and done
for each section of the bundle.
The script expects:
- b... |
911fe7ce00352d68c4091340ed8d75269acccc7e139c1d86c3a7b54f2af4d9b2 | Python | 10,636 | 242 | # -*- coding: utf-8 -*-
"""
Functions to detect spots in images
The main routine :func:`detectCells` uses a difference of gaussian filter (see
:mod:`~ClearMap.ImageProcessing.Filter`) followed by a peak detection step.
Example:
>>> import os
>>> import ClearMap.IO as io
>>> import ClearMap.Settings as... |
463c39fd473ef537cf78d17d8d8e38fa748f9a6c06dfa9e595472ec0df744535 | Python | 10,637 | 250 | # -*- coding: utf-8 -*-
"""
EPI <-> EPI <-> ANA registration
The purpose of the following script is to compute the deformation field for the
registration between antomy and EPI in native space via a registration between
two EPIs and application of an already existing EPI <-> ANA deformation. The
mask should be in ana ... |
c9674623e9fe1be9b3ebe6246d67480f294c0dee94786a80b3774adb5306d128 | Python | 10,639 | 173 | import pandas as pd
import glob2
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
from matplotlib_venn import venn3, venn3_circles
import os
import numpy as np
import seaborn as sns
import argparse
"""
python src/plot_encoding_venn_exte... |
851afe4679e5ddf7bbaf132b8d1f30864a232ec217e1c10fcc5f18e7440a62d4 | Python | 10,640 | 285 | """Pure-Python molecular docking helpers (no LangChain / MCP decorators).
Docks a small-molecule candidate into a receptor with AutoDock Vina and returns
the predicted binding affinity and poses. Used by the LangChain ``@tool`` wrapper
in :mod:`chemgraph.tools.docking_tools`.
Heavy/optional dependencies (``vina``, ``... |
a6e1b24c8fce9d20ef9b04b4cd60f51d725024d71a564e1e1d2c2fb97c8982a2 | Python | 10,645 | 299 | # -*- coding: utf-8 -*-
"""Volume utilities."""
import os
import nibabel as nb
import numpy as np
from scipy.ndimage import distance_transform_edt
from .. import execute_command
from ..io.filename import get_filename
__all__ = [
"remove_bias_ants",
"robust_combination",
"include_pial_correction",
"e... |
ac4684498bc1660088d4ca8c7748fa78b87a2aada2b3004f8c4819f9c5384ed4 | Python | 10,645 | 326 | """Hidden Markov Model (HMM) with a Possion observation model."""
import logging
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
from osl_dynamics.inference.layers import (
VectorsLayer,
SeparatePoisson... |
2e05b90601af73f9dc4136f3ccc1f5a551fe6b99029a3373f032cb888c3697a0 | Python | 10,646 | 297 | import pytest
import numpy as np
import SimpleITK as sitk
import torch
from pathlib import Path
import logging
# Import FireANTs components
from fireants.registration.moments import MomentsRegistration
from fireants.io.image import Image, BatchedImages
try:
from .conftest import dice_loss
except ImportError:
... |
05321af2f710248c70bb5092b5d32971cbfbea12519517036e34f70d103fab5e | Python | 10,647 | 206 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import plotting_related_functions
import KC_population_calcium_rate_model_functions as mdl
import os
import pandas as pd
from tqdm import tqdm
import itertools as it
rng = np.random.default_rng(420) # Set random seed
ext... |
c53a005ca58f98bfd8c3a624baedbecfb1481c657a5ab9bddc2dfb37b7b094b9 | Python | 10,648 | 296 | import numpy as np
import matplotlib.pyplot as plt
import json
import os
import pandas as pd
import matplotlib as mpl
# PDF font settings: Use TrueType fonts to prevent font conversion issues
mpl.rcParams['pdf.fonttype'] = 42
# Font configuration - Ensure proper font embedding in PDF
mpl.rcParams['font.family'] = 'sa... |
f11a5d1286099893c406bd119721029a49fe9c100823ea26f0b844f7193a29ff | Python | 10,649 | 301 | import json
import argparse
from chemgraph.tools.ase_tools import (
run_ase,
save_atomsdata_to_file,
)
from chemgraph.tools.cheminformatics_tools import molecule_name_to_smiles, smiles_to_atomsdata
from chemgraph.models.ase_input import ASEInputSchema
import datetime
import subprocess
import os
def get_smiles... |
2348f94fc989975a4f2fb0a694346e346b0eb73f94a43fce99325e3ba45b651b | Python | 10,650 | 322 | #!/usr/bin/env python
from __future__ import print_function
import defopt
import sys
import os
import logging
from pathlib import Path
from typing import Optional
import mokapot # version 0.9
from xgboost import XGBClassifier # version 0.82 # required for rust package
import pandas as pd
import numpy as np
import xgb... |
745e2029fe92bda6f5631cfe4f7acff1243f1b1f460a5c0dd1a8f122a40d9cb3 | Python | 10,658 | 200 | from qtpy.QtWidgets import QLabel,QGraphicsScene,QFrame, QGraphicsItemGroup, QGraphicsPixmapItem, QGraphicsEllipseItem
from qtpy.QtGui import QColor, QPen
from qtpy.QtCore import Qt
from napari_dmc_brainmap.registration.sharpy_track.sharpy_track.view.QGraphicsViewerMT import QGraphicsViewMT
from napari_dmc_brainmap.reg... |
382deacbe86f229c8c0ce1e697c36cbe9d3213edd233b7cbe9e8e93f3d868d0e | Python | 10,660 | 203 | import nighres
import os
import nibabel
import numpy
import glob
import scipy.ndimage
inpath = '/home/pilou/Projects/Cerebellum/Cerebellar-segmentation-ABC/'
in_sfx = 'nighres/coreg/'
out_sfx = 'nighres/tissues/'
t1map_sfx = '_anat_inv1_s2c10_lcpca-den_qt1map-t1_cr_def-img.nii.gz'
cbprior_sfx = '_anat_inv1_s2c10_lcp... |
ae4aac65f246f0dc5ffa359f502a714a27c7dc9eb9713fcfc35d56f6737638ce | Python | 10,664 | 243 | import json
import logging
import nrrd
import numpy as np
import argschema as ags
import skeleton_keys.full_morph as full_morph
import skeleton_keys.drawings as drawings
from neuron_morphology.swc_io import morphology_from_swc, morphology_to_swc
from allensdk.core.reference_space_cache import ReferenceSpaceCa... |
f11ab48706fad2307aab36815d9ea5fce3316211d1c7791981585d30e8e75d9b | Python | 10,666 | 245 | import numpy as np
import importlib.metadata
from datetime import datetime
from concurrent.futures import ProcessPoolExecutor, as_completed
from encore.utils.metrics import compute_similarity_matrix
def run_single_session(
args: tuple[str, str, dict[str, np.ndarray], dict[str, str], dict, bool, list[str]],
) -> t... |
f3f3594e6953e163bd4602b1a5251046ddeb6d5c055111796d258e3e57ce2e30 | Python | 10,667 | 273 | import warnings
from typing import Any, Dict, Optional, Union
import torch
from torch import Tensor
from torch_geometric.explain import (
ExplainerAlgorithm,
Explanation,
HeteroExplanation,
)
from torch_geometric.explain.algorithm.utils import (
clear_masks,
set_hetero_masks,
set_masks,
)
from... |
62d0d37ec2b2e62496aa9ca98a33509297aa8b1ce15f0bc625c0f0f8eddc9c68 | Python | 10,675 | 235 | import pickle
import tkinter as tk
from tkinter import *
from PIL import Image, ImageTk
from tkinter import Button
import pandas as pd
from tkinter import filedialog
import cv2
import os
import numpy as np
import random
import glob
import json
from tkinter import simpledialog
from SelectionObject import SelectionObject... |
075d4b6e9ce19b457c85703ace90322239160f4b082037f5686b8b063c8b95e6 | Python | 10,676 | 289 | import torch
from torch import nn
import torch.nn.functional as F
import pytorch_lightning as pl
from torch_geometric.nn import GATConv
from transformers import AutoModel
from sklearn import metrics
from sklearn.metrics import f1_score, precision_score, recall_score
import numpy as np
# cuda = torch.device('cuda')
cl... |
2baf297d6d49b14289a009b39be3a41cfb5291a67cda2660c3c356fd72ac8b8a | Python | 10,679 | 242 | import argparse
from argparse import Namespace
import torch
import torch.utils.data
from tqdm import tqdm
from mdanalysis.dataset import MDAnalysisDataset, collate_mda
from model.eghn import EGHN
import os
from torch import nn, optim
import json
import random
import numpy as np
parser = argparse.ArgumentParser(descri... |
42255f8154ba5d1834c65c064f2afec758176de9c0eed924b7db2ce1f98c1c5d | Python | 10,679 | 206 | '''
NaH2
'''
import numpy as np
import sys
def intro():
print("""
SHORT List of potential energy surfaces for NaH2:
P: potential energy, G: Gradient, D: Nonadiabatic coupling vector
U: diabatic potential energy matrix
UG: gradient of diabatic potential energy matrix
This is listed at the end... |
c4efd6f3a1aed89a60925eaae1815762829ea636a0b2162bd85117bd3c7f580f | Python | 10,680 | 278 | from typing import Optional, Tuple, List, Callable, Dict, Any, Union
import os
from collections import namedtuple
import math
import random
import torch
from torch.utils.data import DataLoader, Dataset
import torch.nn.functional as F
import numpy as np
from atlaslib.io import stage_split_hdf5
from atlaslib.files im... |
c1d74caf2f2f93fd390d42135617c961752557f0fc233e7468c05f2b84c408d4 | Python | 10,681 | 285 | # /------------------------------------------------------------------------------+
# | 06-JUL-2022 |
# | Copyright (c) Bone Imaging Laboratory |
# | All rights reserved ... |
0177ff1cf9f5fd94aea566a79f5c05c0fff8967c01e8fbe49021668eefe3d2d8 | Python | 10,687 | 261 | # Copyright (c) 2026 Rohit Jena. All rightmss reserved.
#
# This file is part of FireANTs, distributed under the terms of
# the FireANTs License version 1.0. A copy of the license can be found
# in the LICENSE file at the root of this repository.
#
# IMPORTANT: This code is part of FireANTs and its use, reproduction, ... |
c28ce52546606379218673c75cf71f760ec29103f1750a0fa6246b82a9da0227 | Python | 10,688 | 257 | import json
import numpy as np
import pandas as pd
from typing import Dict, List, Tuple, Union
from pathlib import Path
from napari.utils.notifications import show_info
from napari_dmc_brainmap.registration.sharpy_track.sharpy_track.model.calculation import fitGeoTrans, mapPointTransform
from napari_dmc_brainmap.utils... |
038083716a0831c332caa6a000a7d979ee5dd071a7d053765c2a106d76e90c1f | Python | 10,693 | 271 | """
M/EEG: Sign Flipping
====================
Source reconstruction leaves the sign of each parcel (channel) time course
arbitrary: the same parcel can have opposite polarity in different sessions.
This is an identifiability problem in the source reconstruction of M/EEG that
cannot be avoided.
This matters when we po... |
25ad360c0029951998f772ecfb18529cc5e1ae01739d3aa76ef4ac94dfcb83cb | Python | 10,693 | 234 | import os
import h5py
import numpy as np
import math
import csv
from datetime import datetime
def find_files_by_type(main_path, file_type='.h5', start_date=None, end_date=None, sort_dates=None):
type_files = []
if start_date is not None and end_date is not None:
# Get a list of all folders in the cu... |
f7b6ef6c7a8d6727b70d442ef539472f2859d5b6ef91bb831f8dcdbba1f7668a | Python | 10,699 | 302 | import torch
from torch import Tensor
import numpy as np
import glob
import os.path
import skimage.io as io
from typing import Dict, List, Union
from torch.utils.data import Dataset
from tqdm import tqdm
from typing import Tuple, Callable, List, Union, Optional
# basically just skoots
Transform = Callable[[Dict[str, ... |
c9dabc409d20112da000126b90737e5f11d92ebe0936ea9793394494f8ef15bb | Python | 10,703 | 307 | import h5py
import bisect
import numpy as np
import time
CHANNELS_FIRST = "channels_first"
CHANNELS_LAST = "channels_last"
def get_point(values, pct):
"""
Pass in array values and return the point at the specified top percent
:param values: array: float
:param pct: float, top percent
:return: flo... |
0c9fc63ad9f03e643c1a92915300527f1d63d25361cec9dc42911fcc03e092aa | Python | 10,705 | 335 | import numpy as np
from tqdm import tqdm
import infomap
import faiss
import math
import multiprocessing as mp
from clustercontrast.utils.infomap_utils import Timer
import torch
def l2norm(vec):
"""
归一化
:param vec:
:return:
"""
vec /= np.linalg.norm(vec, axis=1).reshape(-1, 1)
return vec
... |
61a4bf68e05ca58be77b3787e4c0820375063e884366fec6114b88d9b72a634a | Python | 10,708 | 260 | import torch
from .preprocess import preprocess_adj, preprocess_adj_sparse, preprocess, construct_interaction, \
construct_interaction_KNN, add_contrastive_label, get_feature, permutation, fix_seed
import time
import random
import numpy as np
from .model import Encoder, Encoder_sparse
from tqdm import tqdm
from tor... |
dcb4e39c8b7883161eb5086bf47f019463a8a71ccde0023f30a1f2f98ca4a987 | Python | 10,712 | 365 | # -*- coding: utf-8 -*-
import logging
from dipy.io.stateful_tractogram import StatefulTractogram
from fury import colormap
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
import numpy as np
from scipy.spatial import KDTree
from scilpy.viz.backends.vtk import get_color_by_name, lut_from_color... |
04619ce6a3f06bc76799a9b3c9eb4f8093183c43c7cda8515da18930d945ad92 | Python | 10,714 | 242 | import os
import subprocess
import shutil
import nibabel as nib
import time
import numpy as np
import pandas as pd
from nipype import Node, Workflow
from nipype.interfaces.base import BaseInterface, BaseInterfaceInputSpec, TraitedSpec, File, Directory, traits, CommandLineInputSpec, File, TraitedSpec, CommandLine, Direc... |
95f280dec12f02a80530d6cbe2fca0c87d9d9a27de9185033738093110b22704 | Python | 10,715 | 327 | """Globus authentication for ALCF inference endpoints, usable from the UI.
Implements the same Native App OAuth flow as ALCF's official
``inference_auth_token.py`` helper (see
https://github.com/argonne-lcf/inference-endpoints) so users can log in
from the browser UI instead of running a script and pasting tokens:
- ... |
a1ac1b8c4ce4e28c1c2cdfec55867e8903f49a03df11bba53d92a962f1010a0d | Python | 10,720 | 349 | import collections
from typing import NamedTuple, NewType, Final
import numpy as np
import polars as pl
import scipy
from scipy.interpolate import interp1d
from typing_extensions import Self
from neuralib.util.verbose import fprint
from rscvp.util.cli.cli_io import get_headers_from_code, CELLULAR_IO
from rscvp.util.c... |
c60c89636f7186ffe42194cc670ffcc84c8dd5662cb80db417418e9715601365 | Python | 10,722 | 294 | import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Union, Tuple, Optional
kernel_size = 3
"""
The Native Implementation of CPnet from Cellpose! Refactored for clarity and best coding style
Will re-implement later... as of right now, its kinda hard to r... |
94f2a14f08056512fe9d2f4caaeb66bc023d7ed2d1927e6f037f6fcfd55b8a85 | Python | 10,723 | 345 | import json
import random
import torch
import numpy as np
import pandas as pd
from jarvis.core.graphs import Graph
from jarvis.core.atoms import Atoms
from pymatgen.io.jarvis import JarvisAtomsAdaptor
from pymatgen.core import Structure
#from alignn.models.alignn import ALIGNN
from alignn_multi import ALIGNN_ft
#f... |
8a8d72c672ec748a4e7a56fb42cf37ca684660496a6753976d4ad816c6ecdc5d | Python | 10,727 | 291 | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from torch_scatter import scatter
from torch_scatter.composite import scatter_softmax
from torch_geometric.utils import to_dense_adj, dense_to_sparse
from einops import rearrange, repeat
from xrdsol.common.data_utils import lattice_params_... |
007e3818e9620b9f8263359e55a87dc3838bbf909fb5df156112f63b7136838a | Python | 10,732 | 240 | """Solve a per-session offset for each chain, and stage the shifted ROIs.
ROICaT cannot register this data itself (see the package docstring), so the
offsets are solved here from the ROI footprints and applied before ROICaT sees
anything. Three things about this are not obvious and each of them cost a full
dataset run... |
29f0a4c1fb51f20bd2e215b0409c4f65fd1393e75f189b7e1be68d6fe6b5b802 | Python | 10,735 | 279 | #!/usr/bin/env python3
import sys
import getopt
import numpy as np
import pandas as pd
from skimage import io as skio
########################################################
# main logic function
#
def get_blastema(sample,prefix,ll,lr,rl,rr,exponential_number,wound_only):
base_file = f'{sample}_masked.tif'
l... |
b4faf2d757fde9f11b8578a749efd438dbb99dffa6a621b4fc9ea7fbb948e918 | Python | 10,736 | 350 | import os
import gzip
import numpy as np
import nibabel as nib
from scipy.io import loadmat
from nibabel.streamlines import Tractogram, TckFile
from nipype.interfaces.base import (
BaseInterface,
BaseInterfaceInputSpec,
TraitedSpec,
File,
)
from traits.api import Bool, Str
# -------------------------... |
5bed7fc1a4bf8003427781423b1f017aee79cda0ebba5332fc2993e6b93d47ae | Python | 10,740 | 322 | '''Configuration and fixtures for testing'''
import random
from itertools import product, groupby
from collections import OrderedDict
import pytest
import torch
from torch.nn import Conv1d, ConvTranspose1d, Linear
from torch.nn import Conv2d, ConvTranspose2d
from torch.nn import Conv3d, ConvTranspose3d
from torch.nn i... |
68e2b0fb282ef16221432c224bab4578daef3ded56ebbb258421daf772770a14 | Python | 10,742 | 260 | from typing import List, Optional, Tuple
import torch
from torch import Tensor
from torch.nn import Parameter
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.conv.gcn_conv import gcn_norm
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.nn.inits import zeros
from torc... |
276e5579badb0dc6c4f900a74d679756b49e1b87f6df132861056802f0e75ca8 | Python | 10,743 | 292 | import colorsys
import random
from pathlib import Path
import matplotlib
import matplotlib.colors as mcolors
import numpy as np
import polars as pl
from matplotlib.axes import Axes
from matplotlib.lines import Line2D
from argclz import AbstractParser, str_tuple_type, as_argument, argument
from neuralib.atlas.map impo... |
a4d319076ebdce57c202237610c9f33a0ea9a38e1a40f5db1b7d0af43002fc51 | Python | 10,745 | 277 | import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import spearmanr, kendalltau, linregress, rankdata
from rpy2 import robjects
from rpy2.robjects.packages import importr
import rpy2.robjects.packages as rpackages
utils = rpackages.importr("utils") # R's utils package
utils.chooseCRANmirror(ind... |
3133f0c613adbab433ecc950f7115167531f70df1eac159f41c538429fa24331 | Python | 10,751 | 299 | """
Copyright 1999 Illinois Institute of Technology
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, modify, merge, publis... |
8efa20e5598af912e4670f0d937323718767e214261a1a9149141243a1b65ed8 | Python | 10,752 | 286 | from typing import Any, List, Sequence, Union
import torch
from torch import nn
from .utils import random_tensor
# ----------------------------------------------------------------------------------------------------------------------
# -- NN utility functions ---------------------------------------------------------... |
2746cc3860db993aa80dcb4baae80fcd16de75d4ff5b0465983307e77355b1ef | Python | 10,753 | 262 | # pytorch verison of mean field variational inference for sequence labeling
# Author: Xinyu Wang
# Email: wangxy1@shanghaitech.edu.cn
import copy
import numpy as np
from enum import Enum
import torch
import torch.nn as nn
import torch.nn.functional as F
import pdb
from .linear_functions import *
import flair.nn
class... |
2882d7b02134dbe4606b66c3dd813953862140766a1464defc75a3f8a9222ac0 | Python | 10,756 | 264 | # pytorch verison of mean field variational inference for sequence labeling
# Author: Xinyu Wang
# Email: wangxy1@shanghaitech.edu.cn
import copy
import numpy as np
from enum import Enum
import torch
import torch.nn as nn
import torch.nn.functional as F
import pdb
from .linear_functions import *
import flair.nn
class... |
69b3bd70738f3b04303cfd6445f0d400fe897a63fbc11945dc51802eca6e6de9 | Python | 10,760 | 258 | import re
from scipy.io import savemat
from sklearn.model_selection import StratifiedKFold
from tqdm import tqdm
from LocationRegressionHelper import *
import time
from numpy.random import default_rng
import argparse
import platform
import numpy as np
from pathlib import Path
import requests
print("Code is running o... |
ac7cf235df41000b46b152534af574e7120f2548e5d016d2fe39a75fc4b04440 | Python | 10,760 | 283 | # 1. Standard library imports
import os
import shutil
import time
import json
# 2. Third-party imports
import torch
from torch import nn
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import torch.utils.data as Data
import torch.optim as optim
import GP_WAITER as gp
def l... |
2f466e32b7d23603e804aa411caab93c40b125771ddfe294b16acdcf7fcc658b | Python | 10,762 | 251 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 5 14:07:38 2024
This script was originally developed as part of Nagehan Demirci's Ph.D. thesis under the copyright CC-BY-NC-SA-4.0 at the University of Notre Dame in the CoMMaND lab under the supervision of Maria Holland. If you use this script in ... |
7e025b0be3591c2e2cd79b7a6af7313d28583e4336e79a1d2b5c1180241d8d00 | Python | 10,762 | 277 | import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import init
from .pooling import build_pooling_layer
class SeparableConv2d(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size=1,stride=1,padding=0,groups =1, dilation=1,bias=False):
super(SeparableConv2d,se... |
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