sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
68fa2d9dc0e96723c9e148078787f66897d5047677b669896e5e204cc4b8aa67 | Python | 8,664 | 186 | import random
import torch
import os
import argparse
import torch.nn.functional as F
import numpy as np
import pandas as pd
from torch_geometric.data import Batch,Data
from dataset.large_dataset import Cath
from run_pt import Trianer,EGNN_NET,Sparse_DIGRESS
amino_acids_type = ['A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', '... |
0493a59cafecdc31cc0ba05a0c0eb9ed7a9b783411a59a1c6a21d38a33fe3dba | Python | 8,665 | 197 | import tensorflow as tf
class TGCNN_layer(tf.keras.layers.Layer):
"""Applying a filter to a single graph over time slices
Args:
input_graphs: 4D tensor of sparse graphs (batch_size, num_nodes, num_nodes, timesteps)
num_filter: number of graph filters/features to use
fi... |
b66b6acb4dc2bdb416ddc26d97b8f616773242ce311944ed39e8d861181694fc | Python | 8,665 | 217 | from collections import OrderedDict # pylint: disable=g-importing-member
import torch
import torch.nn as nn
import torch.nn.functional as F
import functools
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrap... |
d938a8c8f42b54b9beaea60d280bf723a426c9d876a4d32df4326b7d0ff95876 | Python | 8,665 | 229 | 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 SeparableConv3d(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size=1,stride=1,padding=0,dilation=1,bias=False):
super(SeparableConv2d,self).__init_... |
cf082f53e133e2f77a8730ab191ccb4fb12c1d7b1723f93728fa58d065dff9ff | Python | 8,666 | 247 |
#!/usr/bin/env python
# src/tuning/optuna_regression.py
"""
Optuna TPE 超参数优化 — 回归模型
---------------------------------
功能：
1. 定义超参数搜索空间：包括隐藏层宽度、dropout、学习率、batch_size 等。
2. 在每次 trial 中，将采样到的超参数写回到 C.HIDDEN_SIZES、C.DROPOUT，然后实例化 RegressionModel。
3. 在第一折数据上训练若干 epoch，在验证集上以 RMSE 最小化为优化目标。
4. 完成所有 trial 后，将最优超参数保存到 resul... |
d62c69b56659d1a696cb798beab1729b1f825c409e32c860b2e9011d13703207 | Python | 8,666 | 269 | from collections.abc import Sequence
from functools import partial
from pathlib import Path
from docs.doc_modules.experiments import AutoDocExperimentInfo, run_capture_and_save
from docs.doc_modules.serve_experiments_utils import load_data_for_serve
from docs.doc_modules.serving_experiments import (
AutoDocServing... |
0a8b6ff575f628838063d58b9e1020aa25b0b6f6c675b83a404edea70ed9fb23 | Python | 8,667 | 218 | from collections import OrderedDict # pylint: disable=g-importing-member
import torch
import torch.nn as nn
import torch.nn.functional as F
import functools
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrap... |
51ab0d5f07691b0a3e69ea6800f06a750adb7eb179af5349ee651a7586df802d | Python | 8,667 | 263 | #!/usr/bin/python3.8+
# -*- coding: utf-8 -*-
# @Time : 2022/2/26 21:53
# @Author : Z.-H. G.
# @Email_0 : guozhenhao17@mails.ucas.ac.cn
# @Email_1 : guozhenhao@tongji.edu.cn
# @File : script.py
# @IDE: PyCharm
import numpy as np
import torch
import torch.nn.functional as F
from sklearn.preprocessing import Stan... |
2bab6fee245bf2b66c7603f044ee494980d0e8ee826ea2eb3953b289f10240af | Python | 8,668 | 212 | """Discrimination, calibration, and operational metrics.
Operational metrics (false alarms per hour, warning lead time) are computed from
an alarm *policy*, not from raw per-window thresholding: a deployed system
debounces. Reporting per-window false positives as an alarm rate overstates
nuisance alarms by roughly the... |
7946e63b08f3fbef86b501b2a94ed00f15a5214142e71426e1e3b4924066ce6f | Python | 8,668 | 171 | from scipy.misc import imsave, imresize
import matplotlib.pyplot as plt
import tensorflow as tf
import PIL.Image
import numpy as np
import shutil
import time
import os
import cv2
import keras
paths = {
'Data': 'ILSVRC2012_img_val',
'Label': 'val.txt'
}
for key in paths:
assert os.path.exists(paths[key]... |
e1f65d885578cfc11b20818c31a13d3c446b33d97959fc2e344feb58adbdd238 | Python | 8,670 | 221 | import scipy.optimize
import numpy as np
from .cost import get_cost
from ..base import Backend, FitResults
class SciPyBackend(Backend):
"""Default backend: just passthrough NEMS model methods except fit."""
def _build(self, data=None, eval_kwargs=None, **backend_options):
"""Duplicate reference to `S... |
d95bf8333f47784be475ade00928076c73f2102c6d87bd03ea554a4e94bba960 | Python | 8,671 | 263 | from pathlib import Path
from unittest.mock import Mock, create_autospec, patch
import pytest
import torch
from fastapi import HTTPException
from eir.serve_modules.serve_input_setup import (
ServeBatch,
_check_prompt_fits_output_length,
_impute_missing_tabular_values,
_setup_tabular_input_for_serve,
... |
e8123cd0183a46b56d1c11b78792cdaf8715039a44f1b3f1a8faed514b58f97c | Python | 8,672 | 204 | import sys
sys.path.append('../..')
from PAINTeR import connectivity # in-house lib used for the RPN-signature
from PAINTeR import plot # in-house lib used for the RPN-signature
from PAINTeR import model # in-house lib used for the RPN-signature
import numpy as np # hi old friend
import pandas as pd
from sklearn.prepr... |
02f6db4deeef991d832f661d3dc55502c7a6445b65c488fb58b3477fe6be2dcb | Python | 8,673 | 259 | """
Tests functionality of the H5FileLoader
"""
import os
from test.unit.util import is_equal_np
from typing import List
import h5py
import numpy as np
import pytest
from deepreg.dataset.loader.h5_loader import H5FileLoader
def get_loader_h5_file_names(loader: H5FileLoader) -> List[str]:
return [f.filename for ... |
d87f86fe0d957b68681d1b6a79c942840a3485d3dabe5126126abd6f4ec22bbf | Python | 8,674 | 216 | """The per-recording activity raster for CAT-NAP — plain and z-scored.
``MEApipeline.m`` draws a ``3_Raster`` for every suite2p recording: the
activity matrix the network was built from, cells down the side and time
along the bottom (``rasterPlot.m`` with ``get2pActivityMatrix``). The Python
port had no equivalent — a... |
e0b1ac1d6b035d230db00e6116e100ef225f349598a01c4f6b952aff2c4523e2 | Python | 8,676 | 183 | import pandas as pd
import shutil, os
import os.path as osp
from ogb.utils.url import decide_download, download_url, extract_zip
from ogb.io.read_graph_raw import read_csv_graph_raw, read_csv_heterograph_raw,\
read_node_label_hetero, read_nodesplitidx_split_hetero,\
... |
5621f5c04ec36b1eab5c6261ace8c0f7bf672aeb7361575f0fdf3f74df7bf52c | Python | 8,681 | 202 | # /------------------------------------------------------------------------------+
# | 19-JUL-2022 |
# | Copyright (c) Bone Imaging Laboratory |
# | All rights reserved ... |
cc08b0f28a85c9e2f039a411127598a026eac3ad47ef513bd716222ad02bb089 | Python | 8,681 | 206 | #!/usr/bin/env python3
import importlib
from nipype.interfaces.fsl import Reorient2Std
import PUMI
from PUMI.engine import BidsPipeline, NestedNode as Node, FuncPipeline, GroupPipeline, BidsApp
from PUMI.pipelines.anat.anat_proc import anat_proc
from PUMI.pipelines.func.compcor import anat_noise_roi, compcor
from PUM... |
7af31b68688cf7e39ea358bdb748c9cf0df470943b0af7c27cd6ff62e9f39c48 | Python | 8,682 | 291 | # The ImpedanceFitter is a package to fit impedance spectra to
# equivalent-circuit models using open-source software.
#
# Copyright (C) 2021 Julius Zimmermann,
# julius.zimmermann[AT]uni-rostock.de
#
# This program is free software: you can redistribute it and/or modify
# ... |
b761a0bb8220bb5d288a1dc61f1d6011bdbe6b55070e8b244ab9ad9d08c532c7 | Python | 8,683 | 239 | import os
from Bio import SeqIO
import pandas as pd
import sys
sys.path.append('../bin')
from ngs_split_reads import split_reads
from ngs_combine_read_cnt_map import combine_read_cnt_and_map
from ngs_count_barcodes import count_barcodes
def test_split_reads_uneven_chunks(tmp_path):
infile = "data/ngs/barcodes_co... |
c3b2191ec2a26f44492b858d0e6c58ad5d194db36a6876d7ae84a29af76e7753 | Python | 8,685 | 258 | # -*- coding: utf-8 -*-
"""
PSF estimation of multipolar phase-encoded paradigm.
The data is fitted by either a Gaussian function or an exponential decay
function using the ODR method. Uncertainties of fit parameters are the standard
method using diagonal elements of the covariance matrix.
"""
# python standard libr... |
679b874deddcd25579647e40a14b90b7126439e524366b9fec7c98cfcad823ec | Python | 8,686 | 203 | import torch
import torch.nn as nn
from typing import List
import math
class Rotate3DBatch_UnifDist(nn.Module):
"""
Rotate the input 3D tensor around the x-, y- and z-axis. This class is designed to be used with the GPU with batch processing.
"""
def __init__(self, range_x: List[float], range_y: List... |
3871abd56110b601e30d2d50c26aec2e7475ace3e9764a4740abbb0431a5d012 | Python | 8,689 | 253 | """
Sei architecture
"""
import numpy as np
import torch
import torch.nn as nn
import numpy as np
from scipy.interpolate import splev
def bs(x, df=None, knots=None, degree=3, intercept=False):
"""
df : int
The number of degrees of freedom to use for this spline. The
return value will have t... |
c03ed9b85097eb6dcc6eeb1aa3303ccd79c036090d8e88df3863d9c05cbf54c0 | Python | 8,691 | 228 | import os
import numpy as np
import librosa
from glob import glob
import torchaudio
import math
import torchaudio.functional as F
#import disvoice
SAMPLE_RATE= 16000
from scipy import stats
from disvoice.prosody import Prosody
import parselmouth
F0_FORMANT_COUNT = 4 # F1~F4
def get_formants(filena... |
bf1da8e23ae5fd90dec5477565cdd383b312b70c8be41b1974e3461646b076bd | Python | 8,692 | 312 | # FetMRQC: Quality control for fetal brain MRI
#
# Copyright 2023 Medical Image Analysis Laboratory (MIAL)
#
#
# 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/lice... |
24c1a29c90e400486b8fb1cf4e70dd01305de280c7d569bdc82020794ecd6a47 | Python | 8,693 | 224 | import sys
import yaml
import os
from glob import glob
import numpy as np
import h5py
import matplotlib.pyplot as plt
import matplotlib as mpl
pathpath,myrun = sys.argv[1:]
'''
pathpath = 'PATHS/filepaths_carlen.yml'
myrun = 'runC00dMP3_brain'
'''
with open(pathpath) as yfile: pathdict = yaml.safe_load(yfile)
rundi... |
a3c34cff179a92d8b8ff36138068a021fc8cc8919043a6c78f0baf73bab4f2c9 | Python | 8,698 | 237 | # -*- coding: utf-8 -*-
"""
Created on Wed Jan 13 13:57:54 2016
@author: ajaver
"""
import json
import os
import subprocess as sp
import warnings
from collections import OrderedDict
import numpy as np
import tables
from tierpsy.helper.misc import TimeCounter, print_flush, ReadEnqueue, FFPROBE_CMD
def dict2recarray... |
a87dc34af9cb82765a05395b53cfd8b4f647658eeb94a0a18f9267912bdfb86e | Python | 8,698 | 282 | from __future__ import annotations
from pathlib import Path
from typing import Final
import numpy as np
import pytest
from PySide6.QtCore import QPoint
from PySide6.QtCore import QPointF
from PySide6.QtCore import QRect
from PySide6.QtCore import Qt
from PySide6.QtGui import QColor
from PySide6.QtGui import QImage
fr... |
6986714f210648613e146c1421c2f85da9660cc1719521fc6935aba8edc86c70 | Python | 8,703 | 227 | import click as ck
import pandas as pd
import torch as th
import numpy as np
from torch import nn
from torch.nn import functional as F
from torch import optim
from torch.optim.lr_scheduler import MultiStepLR
from sklearn.metrics import roc_curve, auc, matthews_corrcoef
import copy
from torch.utils.data import DataLoade... |
3bd8b5be0a5e93a849f8814c70aabe6c605e33067acbb54be6f6eda7684d3fdc | Python | 8,704 | 290 | import math
from typing import Literal
import torch
from torch import nn
from eir.models.fusion.seq_out_fusion_attention import CrossAttention
from eir.models.layers.attention_layers import SwiGLU
from eir.models.layers.cnn_layers import adjust_num_heads
from eir.models.layers.norm_layers import LayerScale
from eir.m... |
7a1b6fada43e18196d08d6586568c26766e4ae64979bde599f8f29c0850b60a3 | Python | 8,707 | 185 | # Copyright (c) 2026 Rohit Jena. All rights 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, or
... |
0d207103f1c579f166beed88bd377430f2c76654b35ea5bf8c99259b68af2958 | Python | 8,709 | 192 | import os
import scanreader
import numpy as np
import datajoint as dj
from commons import lab
from pipeline.exceptions import PipelineException
from pipeline import reso, experiment, mice, notify, shared
from pipeline.utils import galvo_corrections, signal, quality, mask_classification, performance, h5
# external stor... |
2d4131395fb1e06140747bb8b5a7b186a6f089193260d38fe7c5892d3016d664 | Python | 8,709 | 287 | from functools import partial
from numbers import Number
from typing import List, Tuple, Optional
import bism.backends
import bism.modules
import torch
import torch.nn as nn
from bism.models.spatial_embedding import SpatialEmbedding
from torch import Tensor
from yacs.config import CfgNode
def cfg_to_bism_model(cfg: ... |
4ec5868eafa18f04f43312eb2f27608bb15ead819c185dc33b9eb63ab6565bfc | Python | 8,711 | 244 | from pathlib import Path
import numpy as np
from functools import cached_property
from matplotlib.axes import Axes
from typing import Literal
from argclz import AbstractParser, as_argument, argument
from neuralib.persistence import persistence
from neuralib.plot import plot_figure
from neuralib.plot.tools import Axes... |
aaea878548a845de7dddfaa73da46fb9210f9ecc3db7d257344f09c6ec19c61a | Python | 8,712 | 187 | # Copyright (c) 2026 Rohit Jena. All rights 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, or... |
013791af3c7cb8429c690bc4b551659a61dadc4423507ea0ab8783f1539f37e1 | Python | 8,713 | 171 | """Build the Li2026 (Triple-N) static neural assembly and stimulus mapping from the raw
ScienceDB release. Run this before :mod:`package_li2026` (which uploads the outputs to S3).
Raw source -- Triple-N, ScienceDB doi:10.57760/sciencedb.33556::
<raw-root>/Processed/Processed_ses*.mat per-session trial-averaged re... |
6560c23af9cd482febe693bb52c1fa22f13d91b1b64809434d7bd1dd02c4813f | Python | 8,713 | 224 | '''
This file contains functions to produce hdf5 files for training/validation/test.
The validation chroms are 5, 14
The test chromosomes are 4, 7, 8, 11
'''
from sklearn.utils import shuffle
import numpy as np
import h5py
from functools import partial
from chinn.common import check_chrom, chrom_to_int
from chinn.pair_... |
076ed1ba7bf02e4f88a54b38b6cca7f554dcb5d4032d44acd306bf2e75e2e268 | Python | 8,716 | 215 | import os
import matplotlib
import torch
import torch.nn.functional as F
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import scipy.signal
import cv2
import shutil
import numpy as np
from PIL import Image
from tqdm import tqdm
from torch.utils.tensorboard import SummaryWriter
from .utils import cvtColo... |
9707b07b209c45ce8038d7b91c33942d2195d54700ce5b218529c697246509f8 | Python | 8,716 | 276 | #!/usr/bin/env python
#
# A script to submit jobs to the SLURM queuing system
#
# F. Nedelec, 10.2007 --- 29.09.2018
# Adapted from LSF to SLURM on 17.02.2017
"""
Submit an array of jobs to the SLURM system, to be handled by 'go_sim.py'
Syntax:
submit.py ARG [mem=????] [queue=????] [hours=INT] [days=... |
3099d0ea29003208ce228fa3ab90b4f1ccafadeb87578bdfd774fb9eff3a5a37 | Python | 8,719 | 245 | import json
import argparse
from chemgraph.tools.ase_tools import run_ase
from chemgraph.tools.cheminformatics_tools import molecule_name_to_smiles, smiles_to_atomsdata
from chemgraph.models.ase_input import ASEInputSchema
import datetime
import subprocess
def get_smiles_from_molecule_name(name: str) -> dict:
"""... |
741ff1e7ff2974cf1da69a0be1aa888833cb1e84919ad2ba7c4566e5c3c56501 | Python | 8,719 | 76 | from testsetclass import TestsetClass
class S66(TestsetClass):
def __init__(self):
self.weak = 1.0
self.molecules = ['36B', '61', '39A', '59', '66', '16A', '50', '19B', '50B', '57', '03', '13B', '04', '55A', '32', '33A', '35', '56', '35B', '51', '58', '53B', '15A', '60', '34', '56A', '33', '59B', ... |
8615f495aff3752ff39a4aa396e129e6c374b6b8f31d7c07b87edf18dd764e77 | Python | 8,721 | 206 | from __future__ import annotations
from torch.utils.data import Dataset
from typing import TYPE_CHECKING
if TYPE_CHECKING:
import seisbench.data as sbd
import pandas as pd
class GenericGenerator(Dataset):
"""
A generic data generator which can be used to build preprocessing and data augmentation pip... |
af0daa92b5ee14cff4d931a2e7f45524d6dc7e25bab83023f936a6ee8a5b7c43 | Python | 8,722 | 245 | import json
import argparse
from chemgraph.tools.ase_tools import run_ase
from chemgraph.tools.cheminformatics_tools import molecule_name_to_smiles, smiles_to_atomsdata
from chemgraph.models.ase_input import ASEInputSchema
import datetime
import subprocess
def get_smiles_from_molecule_name(name: str) -> dict:
"""... |
e927824197a66856218d8655e14ba8b3eff83ec1e8f7508436dba54cbd07c259 | Python | 8,722 | 237 | '''MobileNetV3 in PyTorch.
See the paper "Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
class hswish(nn.Module):
def forward(self, x):
... |
ae86b287b4c85f76336ae2a3a96a842cccda6051243232a8417e3c8a380e410f | Python | 8,724 | 225 | import numpy as np
import polars as pl
from tqdm import tqdm
from typing import Literal
from argclz import AbstractParser, argument
from neuralib.io import csv_header
from neuralib.plot import dotplot
from neuralib.plot import plot_figure
from neuralib.suite2p import get_neuron_signal, sync_s2p_rigevent
from neuralib.... |
47d9a45d11a72dfe954579cd94ce64b9a9d8ae903da269b73fe5035d900c698d | Python | 8,725 | 227 | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
import warnings
import mmcv
import numpy as np
import torch
from mmcv.engine import collect_results_cpu, collect_results_gpu
from mmcv.image import tensor2imgs
from mmcv.runner import get_dist_info
def np2tmp(array, temp_file_name=N... |
cdaca23fbfba3c84e7dca6c8f36633a9750a8848987528a278c15961be1e41ec | Python | 8,725 | 213 | # -*- coding: utf-8 -*-
"""
Created on Wed May 13 19:35:04 2015
@author: ajaver
"""
import os
import sys
import numpy as np
import pandas as pd
import tables
from tierpsy.helper.params import head_tail_defaults
from tierpsy.analysis.ske_orient.WormClass import WormClass
from tierpsy.helper.misc import TimeCounter, ... |
8e3bed0401aeb4ebc0901b0e64a5d9d72df074a54d99a1da907b69c103417e70 | Python | 8,726 | 185 | """``meanap-stats`` — run the statistics and machine-learning step from a shell.
The step reads a finished run, so it is useful on its own: a run analysed
months ago, or one someone sent as a bundle, can be put through it without
re-running the pipeline.
"""
from __future__ import annotations
import argparse
import ... |
ae637d5aa8b6a6e843497957de73741f8fd4d0a913cc00335ee3d8c55a74d73b | Python | 8,728 | 123 | from brainvoice.data.estimates_loader_utils import load_beta_estimates, load_estimate_labels
from joblib import load
from tqdm import tqdm
import os
import numpy as np
import glob2
import pandas as pd
import argparse
from sklearn.metrics import balanced_accuracy_score
import collections
"""
python src/... |
3ad44c37c66613326bcc40116e58d684fac56f0438f31c48369f819be4827daa | Python | 8,729 | 289 | """
Functions to read and write dMRI image data and parameter files.
"""
import numpy as np
import numpy.typing as npt
import nibabel as nib
def data_from_file(im_file: str, bval_file: str, bvec_file: str | None = None, cval_file: str | None = None, roi_file: str | None = None) -> (
tuple[npt.NDArray[np.float... |
02d701b18db2806e71f3dd04f1e0d5f4432b1358fdebfeca2ed6b637f9088e25 | Python | 8,732 | 217 |
"""
This class handles the overlay of highlights and the brain area name in the status bar.
This enables more than one plugin to use these features.
"""
import numpy as np
import pyqtgraph as pg
# For contour drawing
from skimage import measure
from lasagna.tree import tree_parser
# For handling the labels files
fr... |
9db022a01176559c999cfed07e1ddf7b9f5961f9cf6383bbaedfcd59f6347797 | Python | 8,733 | 167 | from curses import meta
import os
import pickle
import random
import numpy as np
import pandas as pd
from tqdm import tqdm
from glob import glob
import torch
from torch.utils.data import Dataset
from copy import deepcopy
import torchaudio
from .psy_util import generate_fbank, get_individual_samples_torcha... |
f336557bd2a9de5b2e9820e8a9be48d3b9e07bf56ace3e2f4e1d75834648210d | Python | 8,734 | 197 | # Copyright (c) 2026 Rohit Jena. All rights 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, or... |
567a4390fc1841241f6fe760252cafc1e5aeb3a99a36676eca47aebc389420c0 | Python | 8,735 | 218 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Register DWI to a template for screenshots.
The templates are on http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009
For quick quality control, the MNI template can be downsampled to 2mm iso.
Axial, coronal and sagittal slices are captured.
""" # noqa
import... |
42c79e68e3e13115fa7df6db4a4fd381414abafdd65fabe93369e539b1ad55ce | Python | 8,737 | 217 | from __future__ import print_function, absolute_import
import argparse
import os.path as osp
import random
import numpy as np
import sys
import os
import torch
from torch import nn
from torch.backends import cudnn
from torch.utils.data import DataLoader
import torch.nn.functional as F
print('We are here: ', os.getcwd()... |
4c32453c3ea6f913fabfa6236e8926301b612d5f957a3197e73bbd765578f795 | Python | 8,738 | 280 | from collections.abc import Callable, Sequence
from copy import deepcopy
from dataclasses import dataclass
from pathlib import Path
from typing import TYPE_CHECKING, Any, Union
import torch
from fastapi import HTTPException
from torch.utils.data._utils.collate import default_collate
from eir.data_load.data_preparatio... |
d81bc82e293c13d19db7839262a50f84706a2f295d9ba83353c7203b1413f366 | Python | 8,738 | 217 | import numpy as np
import numpy.typing as npt
def seg(Y, b, bthr = 200, verbose = False):
"""
Segmented fitting of the IVIM model.
Arguments:
Y: v x b matrix with data
b: vector of size b with b-values
bthr: (optional) threshold b-value from which signal is in... |
df68ab1e1ca0ff3c404da628f39199a3b09fc1df95ae258000d6e1f1c4475200 | Python | 8,739 | 225 | """Feature extraction and windowing for the Cao et al. (2019) corpus.
Emits the same dict structure as the UL-DD and DD-Database paths, so the nested
leave-one-subject-out harness, bootstrap and paired tests are shared unchanged.
**Scope.** This corpus carries EEG only -- no ECG and no dedicated EOG. The
two-branch f... |
60ae61ae7ce2ac0e8432e97464921b3dafeabd9136d770410fc3b2a1f7a10d1d | Python | 8,741 | 254 | from subprocess import PIPE
import unittest
import numpy as np
class TestFileIO(unittest.TestCase):
def test_feed_pipe(self):
from python.segmentation.FileIO import feedPipe
from unittest.mock import Mock
import numpy as np
mockPipe = Mock()
# Mock stdin/stdout/stderr
... |
6961f2b6b336501ecaed497b5642e2626d60a81e6b861455822678df256f3664 | Python | 8,741 | 226 | # -*- coding: utf-8 -*-
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch import nn
from typing import Any, Optional, Tuple, Typ... |
a592513e7fb0f6ed308ab036a961fe99382d3b09bd1fe3a503d5d8e65ff2e936 | Python | 8,741 | 239 | import torch
from torch_geometric.nn import MessagePassing
import torch.nn.functional as F
from torch_geometric.nn import global_mean_pool, global_add_pool
from ogb.graphproppred.mol_encoder import AtomEncoder,BondEncoder
from torch_geometric.utils import degree
import math
### GIN convolution along the graph structu... |
d6000bf979ee2fe4872bd8508e1c8b9c89b39dc017062263043b6502eb4f299e | Python | 8,742 | 213 | """Test what CAT-NAP does with a suite2p folder whose files contradict itself.
Run from the repo root::
uv run python python/test_catnap_roi_mismatch.py
The case this is built from is real. In
``OPME240520_17_20240602_P1_pup2D_Het_MOI50000_DIV13`` of a 382-recording
share link, ``iscell.npy`` holds 5040 rows whi... |
504b0a78018e0c573ae9036eb08a6d681d60bbb986a089718202fa1c788f3e62 | Python | 8,743 | 233 | import numpy as np
from scipy.spatial import ConvexHull
from matplotlib.path import Path
from scipy.spatial.distance import cdist
import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
from .helper import intersect, to_dense_array, extract_data_matrix, glmpca_distance
from .PASTE2 import partial_pai... |
25d1a4d87a5d18bd88bf37bc50f6c1f9285d4903cdfa8d1e1263a187c603800c | Python | 8,746 | 285 | # Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
import logging
import math
import os
import random
import re
from datetime import timedelta
from typing import Optional
import hydra
import numpy as np
import omegaconf
import torch
import torch.distributed as dist
from iopath.common.file_io im... |
71cf9400aa5e5ef1e6a84b77a6ad45d450931231d60482d6ba9bd3f0606f9290 | Python | 8,746 | 207 | #!/usr/bin/env python3
import os
import inspect
import unittest
import logging
import shutil
import bt2face
import dataface
import btdata
from optparse import OptionParser
class TestRegressions(unittest.TestCase):
"""
Main regression fixture.
"""
def test_288(self):
""" Check if --un opt... |
9ded6d9f9a149821637d73b54e352a75b411b05f1608e030d50272146d452170 | Python | 8,747 | 208 | """
In case you have extracted events that are too short, this script will extend them by the needed amount of frames
"""
#%%
from pathlib import Path
from benedict import benedict
import tifffile
import napari
import matplotlib.pyplot as plt
import numpy as np
from ome_zarr.reader import Reader
from ome_zarr.io impo... |
f8c392800b434f95bbdaf80c69048a543dc2fd1cf842f1b61077e6d446851138 | Python | 8,747 | 239 | # -*- coding: utf-8 -*-
# @Author : LG
import os
from xml.etree import ElementTree as ET
import cv2
import imgviz
import numpy as np
import yaml
from PIL import Image
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtCore import QThread, pyqtSignal
from ISAT.annotation import Annotation, Object
from ISAT.conf... |
029dac745b1a8eeae525bb3060869f6048dbddccbf2b7602fafaabcdb6673340 | Python | 8,748 | 219 | import os
import logging
import numpy as np
from tqdm.auto import tqdm
from collections import OrderedDict
from typing import Any, Callable, Dict, Hashable, List
from ..inputs import Stimulus
from brainscore_vision.model_helpers.utils import fullname
from joblib import Parallel, delayed
# a utility to apply a list ... |
4ceb6702beef46c832dfdacf57623e49b259b2057094199e27d87dd22ffbf6c7 | Python | 8,750 | 237 |
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch
import torch.nn.functional as F
__all__ = ['Res2Net', 'res2net50']
model_urls = {
'res2net50_26w_4s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net50_26w_4s-06e79181.pth',
'res2net50_48w_2s': 'https://... |
2869a0e5c47ac5806050a326dd56ce14ddbecdd0c0d83dbf67cccc528de7046b | Python | 8,751 | 243 | import argparse
import torch
import numpy as np
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch_sparse import SparseTensor
from torch_geometric.data import ClusterData, ClusterLoader
from torch_geometric.nn import GCNConv
from torch_geometric.utils import to_undirected
from ogb.lin... |
5b0a736ff3c9ee03c4fe53b9dfc6a7fa5f65a5afa260177ed0f4836cfc6f4621 | Python | 8,752 | 5 | standard_train_fold=['202990', '116221', '110003', '265302', '33089', '286943', '122439', '126872', '43371', '81406', '246216', '41381', '118639', '121759', '193894', '106077', '193291', '100367', '79356', '241172', '112604', '202431', '127470', '241178', '193093', '52067', '112172', '264418', '238060', '238063', '2140... |
643683a645028e60884ea663c5f891b40469d769b15b436996c0f5e96d98676d | Python | 8,752 | 239 | import torch
from torch_geometric.nn import MessagePassing
import torch.nn.functional as F
from torch_geometric.nn import global_mean_pool, global_add_pool
from torch_geometric.utils import degree
import math
### GIN convolution along the graph structure
class GINConv(MessagePassing):
def __init__(self, emb_dim):... |
ab5470252950e44fdf2023eff8517c242d4c74c29a13b4d3f774e46a89b65a6f | Python | 8,753 | 232 | # =====================================================================
# Title: engine.py
# Description: This file contains the engine class that handles the
# optimization of temporal mapping of SALSA.
#
# Date: 02.01.2023
#
# =================================================================... |
e2961557ec5c2184a69b36a85dbb46562bc77d3b7286b9f6f0147c615a7a5e6f | Python | 8,754 | 198 | #!/usr/bin/env python
"""
This class provides software emulation of a camera for testing purposes.
Hazen 02/17
"""
import ctypes
import numpy
import random
import time
from PyQt5 import QtCore
import storm_control.sc_library.parameters as params
import storm_control.hal4000.camera.cameraControl as came... |
a69dbf1108df7d43eefebc878cf0c7db320e3191fcad1cccc121f0521d297789 | Python | 8,756 | 222 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Take screenshot(s) of one or more slices in a given image volume along the
requested axis. If slice indices are not provided, all slices in the volume
are used. The name of the output images are suffixed with _slice_{id}, with
id being the slice number in the volume. I... |
21d3bb667ca6420bd649bd70a32145cb32b0c1c0c7dc6c6dae8dad24b42b8690 | Python | 8,757 | 267 | from __future__ import annotations
from typing import Tuple
import numpy as np
import networkx as nx # (still unused here, but kept in case other code relies on it)
from typing import List, Optional, Tuple
from .config import LaminarConfig
from .io_utils import group_files_by_layer, load_and_concat_layer
def thres... |
23b42d51a81dc0d0ee655fef0478a79257618078ba88d9787b16181cca45ea51 | Python | 8,759 | 149 | from typing import Optional
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader
from project.datasets.CASP_CAPRI.casp_capri_dgl_dataset import CASPCAPRIDGLDataset
from project.datasets.DB5.db5_dgl_dataset import DB5DGLDataset
from project.datasets.DIPS.dips_dgl_dataset import DIP... |
2865fec361c576d33988a984f8e5f6f8970970fb490ed408bb02f41cfc4503b9 | Python | 8,760 | 260 | from rdkit.Chem import Draw
from rdkit import Chem
from rdkit.Chem import rdFingerprintGenerator
from src.convert_spectrum_to_colour import *
from typing import Tuple, List
import numpy as np
from rdkit.Chem.Draw import rdMolDraw2D
from PIL import Image
import io, base64, os
def wavelength2float(wstring: str) -> floa... |
397f8f7b331a4eec8d7d472853b01ff45b6eb2cec70f068bb20f1c40e39bc845 | Python | 8,762 | 258 | from annotate import main as annotate
from optimize_FLAMES import main as optimize
from FLAMES_scoring import main as FLAMES_scoring
import os
import sys
import argparse
import pandas as pd
def splash_screen():
print('\n*********************************************************************')
print('*Fine-mapped... |
58967240c05c55b50adfd59120071cb62cef3f3c5f52106fdaf21c9f023467f3 | Python | 8,762 | 193 | try:
import PyQt6
HAS_QT6 = True
except ImportError:
HAS_QT6 = False
if HAS_QT6:
from PyQt6.QtGui import (QTransform, QPolygon, QOpenGLContext, QPainter, QImage, QMouseEvent, QColor, QFont,
QContextMenuEvent, QAction, QPixmap, QPainterPath, QBrush, QKeyEvent, QStandardItemModel,
... |
838e7a43ba9db07238285344de5da51aefc884574081d70c102304fe4005ca7d | Python | 8,763 | 192 | # Author: Francois Aguet
import numpy as np
import scipy.stats
import pandas as pd
import argparse
import pyBigWig
import os
import subprocess
import io
import gzip
import pickle
import qtl.stats
parser = argparse.ArgumentParser(description='ASE')
parser.add_argument('read_count_file_list', help='Read count file list... |
0bd713efeadc58f8271baf046ffb80727f741d2dcebce1ba9f87d8de28691612 | Python | 8,764 | 217 | import inspect
from collections.abc import Sequence
from typing import Any, List, Optional, Type, Union
import numpy as np
import torch
from torch import Tensor
from typing_extensions import Self
from torch_geometric.data.collate import collate
from torch_geometric.data.data import BaseData, Data
from torch_geometric... |
e4093fc0d811cd67a91c18d731dfb7b560d6c70f3c4921254179681222c89442 | Python | 8,764 | 186 | """Record normalization and deterministic gRASPA analysis shared by clients and MCP."""
from collections import defaultdict
import csv
import json
import math
from pathlib import Path, PureWindowsPath
import uuid
from chemgraph.schemas.graspa_analysis import GraspaAnalysis
def write_json(path: Path, value) -> None:... |
bce247ea14ca7e3934369993a8d299e8ef9afeb273463c3759bce92f2b767381 | Python | 8,766 | 192 | import pytest
pytest.importorskip("fireants_fused_ops")
import torch
from time import time
from fireants.losses.cc import LocalNormalizedCrossCorrelationLoss
from fireants.tests.cc_mem_test import fast_lncc
from fireants.losses.fusedcc import FusedLocalNormalizedCrossCorrelationLoss
seed = 4531
torch.manual_seed(seed)... |
7000f4fc6ffaf59ba2c79de8736c5ac332b893a64f5ff1740f20450651b9d71b | Python | 8,768 | 285 | """Brain-Score wrapper for DeepEISNN EI-SNN ImageNet ResNet18."""
from __future__ import annotations
import functools
import json
import sys
from collections import OrderedDict
from pathlib import Path
from typing import Any, Iterable
import numpy as np
if not hasattr(np, "int"):
np.int = np.int32
import torch... |
15c0a6eac2edacd053c30c9953668bd17c4016a188c07835078c595e39b9f589 | Python | 8,770 | 236 | # %%
from pathlib import Path
import gc
import warnings
from tqdm.auto import tqdm
import numpy as np
import mne
import pickle
from methods_complexity import get_invalid_epochs, compute_complexity
# %%
# =======================================================================================
# =====================... |
78847e071eddf0f19c9b5c7d6e231ccb2325efcabb4bfb860138857b256f964a | Python | 8,771 | 232 | #!/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.
import torch
from pathlib import Path
from esm import Alphabet, FastaBatchedDataset, ProteinBertModel, pretrained
im... |
5b64f0837bfb2badc6ce6b7f6debcaea3a814a7822e7ab7158c086e36b5ae7c8 | Python | 8,772 | 237 | """
1. find peaks that higher than 30 % of the highest peak in trial average signal
2. peak activity is at least three times higher than median activity
3. reliability > 20% of total trials
4. exclude peak around reward location (< ?? cm, > ?? cm on the track)
5. different between identified positions is similar to dis... |
20f93ae3be3e1a308c0f8a917b43a2641d5aad746a0188f3fef3e4e8ad4962bf | Python | 8,774 | 269 | import os
import re
import shutil
from test.unit.util import is_equal_np
from typing import Tuple
import nibabel as nib
import numpy as np
import pytest
import tensorflow as tf
from deepreg.dataset.loader.interface import DataLoader
from deepreg.dataset.loader.nifti_loader import load_nifti_file
from deepreg.train im... |
32b6ae6570865a470bf7a41f8638543fbe244e994a0a2379f176d0698425f55d | Python | 8,774 | 281 | #!/usr/bin/env python
"""
Prior XY stage communication (mostly).
Hazen 5/11
"""
import time
import traceback
import storm_control.sc_hardware.serial.RS232 as RS232
class Prior(RS232.RS232):
"""
Control of a XY Prior stage, possibly with piezo Z control & a
filter wheel. Communication v... |
5b67590f8b32c999cb5c7ab2e724967bff6c4ce55f8ace134d4ca2b802b9dc6a | Python | 8,774 | 236 | import abc
import unittest
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression, LinearRegression
from causallib.estimation import IPW, StratifiedStandardization
from causallib.metrics import get_scorer, get_scorer_names
import sklearn
LR_NO_PENALTY = None if sklearn.__version_... |
639d5fea2eeb48362ca83b0148ba313bfc66a7be6feda63a4dc8b88dfbaf472c | Python | 8,774 | 258 | from typing import List, Final
import torch
from mlcg.nn import MLP
from mlcg.geometry import compute_distances
from .base import RANGE
from ..blocks import (
RANGEInteractionBlock,
AggregationBlock,
BroadcastBlock,
SchnetBlock,
)
from ..system import System
from ...utils import create_instance
cla... |
abfde10a70eec9a8f621909ec9fac37463efceb7bd5b5dbdb5eff37d6fcf4b63 | Python | 8,776 | 211 | """
HierarchicalEventClassifier
@ 2022 Knowblesse
Using the preprocessed Neural Ensemble dataset with behavior labels, build and test the Bernoulli Naive Bayes Classifier
Along with the accuracy, feature importance is calculated.
Two classification is done.
1) Is it HE or HW?
2) Is it data from Avoidance trial ... |
e3068f02d28b1c7705feb0f543b566b696c4a5030521bb2347ba5faf8bdd27b8 | Python | 8,780 | 294 | # -*- coding: utf-8 -*-
import copy
import os
import nibabel as nb
import numpy as np
from scipy.signal import fftconvolve
from ..io.filename import get_filename
__all__ = ["calc_tsnr", "calc_mean", "calc_mean4d", "calc_std", "calc_acorr"]
def calc_tsnr(file_in, tsnr_max=200, write_output=False, path_output=""):
... |
44ebee79f3e65efc5fe754023a4e34d4aa9b62a31c36be2d4c02874cbc81d084 | Python | 8,782 | 238 | import torch
from torch_geometric.nn import MessagePassing
import torch.nn.functional as F
from torch_geometric.nn import global_mean_pool, global_add_pool
from ogb.graphproppred.mol_encoder import AtomEncoder,BondEncoder
from torch_geometric.utils import degree
import math
### GIN convolution along the graph structu... |
67eb6bd82b86aa4c46f63cc34982505e1fcfc9089333d34df917ebe97c231c75 | Python | 8,782 | 206 | # /------------------------------------------------------------------------------+
# | 19-JUL-2022 |
# | Copyright (c) Bone Imaging Laboratory |
# | All rights reserved ... |
d81f82d189e01ebbfcd8d27fea1a0dddc9acd48f8b8050e7781ba307da4baefc | Python | 8,782 | 288 | #!/usr/bin/env python
"""
This file contains the base illumination hardware class as well
as a buffered variant. Any hardware that will be used for
illumination control should be a sub-class of one of these
classes.
Hazen 04/14
"""
from PyQt5 import QtCore
def removeChannelDuplicates(queue, channel):
"""
Re... |
0ce6b1788013e62820b31b4d15cc2b9ddf04c803580d8369f6e7c14b2b145aab | Python | 8,784 | 237 | """
imzML → AnnData Conversion Pipeline
====================================
Converts mass spectrometry imaging (imzML) data into an AnnData (.h5ad) object
with spatial metadata, TIC normalization, and experiment annotations.
Folder structure (relative to WORKING_DIR):
WORKING_DIR/
├── imzml_data/ ← input... |
c946499284481a50cff2f79f0a07a0408e95993e915385b9915c7ba72dc92720 | Python | 8,784 | 216 | import torch
from torch import nn
from torch.nn import functional as F
from transformers import BeitModel, BeitConfig
from torch.nn import init
class ChannelPool(nn.Module):
def forward(self, x):
return torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1).unsqueeze(1)), dim=1)
class SpatialGate(nn... |
9312fc7d59f7cbc13e1773f93cb31ccff0ef8ccc3050513770186875e55c37cf | Python | 8,787 | 244 | """nsd_mapdata
"""
import os
from nsdcode.nsd_datalocation import nsd_datalocation
from nsdcode.parse_case import parse_case
from nsdcode.load_data import load_transform, load_sourcedata
from nsdcode.transform_data import transform_data
__all__ = ["NSDmapdata"]
class NSDmapdata():
def __init__(self, base_dir):
... |
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