sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
542f1948bf365772a4396cb0e928504f72a36c4e2f407c00eee46cc05aef2144 | Python | 2,414 | 86 | from tqdm.notebook import tqdm
import copy
import cv2
import json
import numpy as np
import pickle
import torch
import torch.nn as nn
from Function import std_2
import Imf_Extraction ImfExtrat
from Trace_Detection import N_top_func
from Trace_Detection import N_top_indices
from Trace_Value_Extraction import train_top_v... |
b689c8def3350cc04c43ead5e1ae3948f8ee9b14d17b529ed726bb808906219c | Python | 2,414 | 66 | import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from scipy.stats import levy_stable
from matplotlib.collections import LineCollection
from mpl_toolkits.mplot3d import Axes3D
import TwoD_fun
import levy
import brainpy as bp
import brainpy.math as bm
fit_color = ['#009FB9', '#F18D00']
legend =... |
1a0bf6c7d3fb0f6995aa90d349322a668bf67d981836b81ee10b8e9490df82d5 | Python | 2,415 | 67 | #!/usr/bin/env python
import numpy as np
import pandas as pd
import click as ck
import logging
from toolkit.utils import Ontology
from toolkit.metrics import compute_metrics
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO)
@ck.command()
@ck.option(
'--data-root', '-dr', default='data',... |
201d278244dda6455f2a3093da335ab5d3faf6c69c7595766cb63af3e4f7ae71 | Python | 2,415 | 81 | import nilearn
import nilearn.regions
import nilearn.reporting
import itertools
import plotly
from scipy import ndimage
import fire
import sys
sys.path.insert(0,'..')
import pickle
from nilearn import plotting
from nilearn import surface
from nilearn.glm.thresholding import threshold_stats_img
import numpy as np
... |
7125e6d99e6d8af9c2adf5991f46ea76665aaf1b8cbefcd14265796cb179ea99 | Python | 2,415 | 64 | import user_features as uf
import csv
import time
import pandas as pd
import json
def get_user_vector(user):
user_vector = {
"user_id": uf.get_user_id(user),
"profile_description": uf.has_description(user),
"profile_location": uf.has_location(user),
"profile_url": uf.has_url(user),... |
bcd685ba1ae6a1a770b128d7c0b6ed216f0e420aaf9c8f6e9f022069dfecf740 | Python | 2,415 | 92 | # -*- coding: utf-8 -*-
import numpy as np
from scilpy.utils.spatial import get_axis_index
def affine_from_offset(orientation, offset):
"""
Create an affine matrix from a scalar offset in given orientation,
in RPS coordinates for imaging.
Parameters
----------
orientation : str
Name... |
fb97668fc55a9849c4da087aec34c7cf26707d5ca8eb996811c3e4c438c6926a | Python | 2,415 | 86 | """# Basic widgets demo
Widget demonstration with magicgui.
This code demonstrates a few of the widget types that magicgui can create
based on the parameter types in your function.
"""
import datetime
from enum import Enum
from pathlib import Path
from magicgui import magicgui
class Medium(Enum):
"""Enum for ... |
ffd0580a21d143f34680d12b161a35a49733c473dc342943d76e7b70d09360b1 | Python | 2,415 | 74 | """
UnetFormer for uavid datasets with supervision training
Libo Wang, 2022.02.22
"""
from torch.utils.data import DataLoader
from geoseg.losses import *
from geoseg.datasets.uavid_dataset import *
from geoseg.models.UNetFormer import UNetFormer
from catalyst.contrib.nn import Lookahead
from catalyst import utils
# tr... |
ae9a1e3a0a5e0832a37195dcacb36ae2f4e4ec196c7dc11d9c0c9a12ac9784dd | Python | 2,416 | 62 | """Test the functions in the yasa/plotting.py file."""
import unittest
import matplotlib.pyplot as plt
import pandas as pd
from yasa.hypno import simulate_hypnogram
from yasa.plotting import topoplot
class TestPlotting(unittest.TestCase):
def test_topoplot(self):
"""Test topoplot"""
data = pd.S... |
62599140bca3e91b01b980e89be4e3fe209baa8a8a853e845c53537b01856e28 | Python | 2,417 | 75 | import argparse
from tqdm import tqdm
import torch
import torch.nn.functional as F
from torch_sparse import SparseTensor
from torch_geometric.utils import to_undirected, dropout_adj
from ogb.nodeproppred import PygNodePropPredDataset
def main():
parser = argparse.ArgumentParser(description='OGBN-papers100M (MLP... |
7c40e1b8517f191610558916bf5ac34f09ae3660fac075a50aaba0350fdaf509 | Python | 2,417 | 71 | """
GeneratePCALabeledVideo
@2022Knowblesse
After PCA dimensionality reduction on the wholesessionunitdata, draw clustered label on the video.
"""
import cv2 as cv
from tqdm import tqdm
import numpy as np
from scipy.interpolate import interp1d
from Switching.SwitchingHelper import parseAllData, getZoneLDA
from pathlib... |
96aa83f7bb29e308aba57855264864d45308c41dbc49ee68481ebab1c22cf311 | Python | 2,417 | 66 | import torch
from pathlib import Path
from torchvision.models import resnet18
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_im... |
96cfc8d0a88fa600765e6f9c2b7d971c29979d7db060d694b798606bfb7dbba1 | Python | 2,417 | 48 | from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set
from brainscore_core.supported_data_standards.brainio.s3 import load_stimulus_set_from_s3, load_assembly_from_s3
from brainscore_core.supported_data_standards.brainio.assemblies import NeuroidAssembly
BIBTEX = """@article{gifford_lar... |
a013f3e8e8a1189de205af0901fb85514d605030db075317fbf7cc056415ba17 | Python | 2,417 | 70 | import os
import pickle
import numpy as np
import time
# from glob import glob
from pycurv import TriangleGraph, io
from graph_tool import load_graph
from intradistance_verticality import export_csv
from morphometrics_stats import Experiment, Tomogram, weighted_median, weighted_histogram_peak, statistics, histogram,... |
d99641b78c2dc32cfd3cef7b263fc73d7ecfcc7f887c83bdcfd53fa3edcbb7a8 | Python | 2,417 | 74 | import torch
import torch.nn as nn
import torchvision.transforms.functional as TF
class DoubleConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(DoubleConv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False),
... |
ddb609348e04a491c4afc93346a66a8b7fecb57d4601d44ee190f392dcb6cb5a | Python | 2,417 | 69 | from argclz import AbstractParser, as_argument, try_int_type
from rscvp.statistic._var import VIS_HEADERS
from rscvp.statistic.csv_agg.core import ParquetSheetSync, NeuronDataAggregator
from rscvp.util.cli.cli_io import HEADER
from rscvp.util.cli.cli_statistic import StatisticOptions
__all__ = ['VisAggOptions']
clas... |
e990882e7d4883936214393d9a829defb34249a3ac893d874ab24d53ffc6907c | Python | 2,417 | 77 | # -*- coding: utf-8 -*-
"""Test mvpa model."""
import numpy as np
import pytest
from fmri_decoder.model import MVPA
@pytest.fixture
def mvpa_model() -> MVPA:
"""Dummy model using the iris data set."""
return MVPA.from_iris()
def test_shape(mvpa_model: MVPA):
"""Check input data shape."""
assert mv... |
a3cdd2263835f90bc6785b2f3e0a8c97d88c5ecfbf63a565cf1be3a9208eff6b | Python | 2,418 | 70 | from brainvoice.configuration.configuration_instantiator import construct_from_kwargs
from brainvoice.modules.weight_init import apply_weight_init
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class VanillaAutoencoder(nn.Module):
def __init__(self, encoder, ... |
c6382f4d4b3ce18d234098a9f3688562e2a2f20c7d311b0e4535fb23189d7384 | Python | 2,418 | 78 | from pathlib import Path
from snakemake.logging import logger
class Genome:
def __init__(self, annotation, assembly, reference):
self.annotation = annotation
self.assembly = assembly
# Allow for old drop config stylings, where the file was a string under MAE
# -> force into dicti... |
d7a8b683d43fce786958ff72dc0ec3a075442757ef1e44d34fc4b28787cfdf42 | Python | 2,418 | 63 | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from mmcv.utils import TORCH_VERSION, digit_version
from mmcv.runner.dist_utils import master_only
from mmcv.runner.hooks import HOOKS
from mmcv.runner.hooks.logger.base import LoggerHook
@HOOKS.register_module()
class TensorboardImageLoggerHook(L... |
30c079c9044930ea1f20402b83f4bfa4f829f8a360c7be0dd9fcf5aeb5079101 | Python | 2,419 | 81 | #!/usr/bin/env python3
import os
import sys
import time
import getopt
import numpy as np
import pandas as pd
def apply_cells_usage():
print("""
Usage : GEM_toolkit.py apply_cells \\
-g <gem file> \\
-m <cell segment mask file> \\
-o <output prefix> \\
-x [xshift... |
cb22f34b373bd1fadd90a35b28af2ec64f5f74e06a472e3c67e4456f842b377a | Python | 2,419 | 71 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Script to obtain labels from a binary mask which contains multiple blobs.
The script will assign a label to each blob in the mask. The background label
is excluded from the labels list. By default, the background label is 0 and
the labels are assigned in increasing or... |
300a3cb2b18ba02a43f0835f552a8fa82905ce43621018d24cb0e77c3585cf86 | Python | 2,420 | 80 | # -*- coding: utf-8 -*-
# external inputs
import numpy as np
# local inputs
from ..utils import get_fft
def get_pca(arr, fft_threshold=0.25):
"""Get PCA.
This function computes the principal axes from the thresholded fourier
spectrum of an input array. They are found by estimating the eigenvector... |
a550e939b7a82802bb67dea460bfb85b13708c6383f2ce6449edc36a765233cb | Python | 2,421 | 61 | import h5py
import numpy as np
from pathlib import Path
from typing import Union
def save_dict_to_hdf5(group: h5py.Group, data: Union[dict, list, int, float, str, np.ndarray]):
"""
Recursively saves data to an HDF5 file group.
This method iterates through a dictionary and saves its contents to the provid... |
c7fc734a3200ed335fa36fde05c9d798a1bc89d6ccb51022585decaab9f7fe88 | Python | 2,421 | 80 | from __future__ import annotations
from typing import Any, Dict, Iterable, List, Mapping, Optional, Sequence, Tuple, Union
import numpy as np
from fairlearn.metrics import equalized_odds_difference as fl_equalized_odds_difference
ArrayLike = Sequence[Any]
def _validate_lengths(*arrays: Sequence[Any]) -> None:
len... |
2ccb5e02408bf41114af38f8e1dc73019ea7128b0d6a43c1a3ecbb683e2ae63c | Python | 2,422 | 87 | from test.unit.util import is_equal_tf
import numpy as np
import pytest
import tensorflow as tf
from deepreg.loss.kernel import (
cauchy_kernel1d,
gaussian_kernel1d_sigma,
gaussian_kernel1d_size,
rectangular_kernel1d,
triangular_kernel1d,
)
@pytest.mark.parametrize("sigma", [1, 3, 2.2])
def test... |
7d11f1dc821afce94ae269c0f396609d3711e9eec37ba3b26a2fbafd86e2ce4d | Python | 2,422 | 90 | import argparse
import h5py
import os
import matplotlib.pyplot as plt
import numpy as np
# from skimage.metrics import structural_similarity as ssim
# from skimage.metrics import normalized_root_mse as nrmse
DIR = os.path.dirname(os.path.realpath(__file__))
# %%
parser = argparse.ArgumentParser(description='plot br... |
99b9fe55e544b9b7567915b86f47181d8b192bd6262e3408199767ccec066846 | Python | 2,422 | 69 | from pathlib import Path
from typing import Optional
import pandas as pd
from brainways.project.info_classes import SliceInfo
from brainways.utils.cell_detection_importer.cell_detection_importer import (
CellDetectionImporter,
)
class KerenCellDetectionsImporter(CellDetectionImporter):
parameters = {
... |
1d78fffeda0caafce182feb77cadee2d985e3489b2790a07851c8aa84f2a3eea | Python | 2,423 | 75 | import sys
import getopt
import json
import scipy.ndimage as nd
from skimage import io as skio
import numpy as np
# usage
def affine_txt_usage():
print("""
Usage : affine_txt.py -i <input.txt>
-o <output>
-r <ref image>
-f [f... |
3329f56fd1c70a06dba7d1d0f968b34fd59e0d18ed2574479f3df3aab2a55c1e | Python | 2,423 | 60 | import numpy as np
import pandas as pd
import pytest
from src.audit.schmidt_design import annotate_core, pool_counts, design_matrix
def example_metadata():
return pd.DataFrame({
'ODCF_name':['c1','c2','d1','d3'], 'Cell_line':['lineA']*4, 'Batch':[1]*4,
'Condition_simple':['Ctrl','Ctrl','Trt5','Tr... |
718c766e7c0796d0c49b6cc3bb0aa222c762f9846bcb1aebe48c0a366322a861 | Python | 2,423 | 81 | import scipy
import torch.utils.data
import numpy as np
import random
def CreateDatasetSynthesis(phase, input_path):
# Load data for contrast1
target_file = input_path + phase + "/LF_T2.npy"
data_fs_s1 = LoadDataSet(target_file)
# Load data for contrast2
target_file = input_path + phase + "/HF... |
e74a0223907322f57589ebbf5974aeaa7057d0725757c2dbcb8589123443afc8 | Python | 2,424 | 87 | method_bounds = {
"None": {},
"Average": {},
"2D Convexhull": {
"degree": [0.5, 2], # degree of polynomial fit
},
"Circularly-symmetric": {
"smooth": [1, 50], # any positive number (larger numbers means more smoothing)
"tension": [1, 5],
"radial_bin": [2, 200], # i... |
53542fd57e0a89d4f428fc1f0c47a9939713cb47a4e485c67ba10be061daed83 | Python | 2,425 | 96 |
import numpy as np
from PIL import Image
def dummy_loader(model_path):
'''
Load a stored keras model and return its weights.
Input
----------
The file path of the stored keras model.
Output
----------
Weights of the model.
'''
backbone = keras.models.... |
77841a8df60695d555299444896b45f6ac08773ae04bb77a6a217288450e1065 | Python | 2,425 | 67 | """Add AAL v4 ROI names to the 32k left/right GIFTI label tables."""
from __future__ import annotations
import argparse
import csv
from pathlib import Path
import nibabel as nib
def cortical_names(roi_txt: Path, side: str) -> list[str]:
suffix = f"_{side}"
names = []
for line in roi_txt.read_text(encod... |
a721567592c687ed7e00efacb03211e56fbf502b8239fb1924b6e816e93a2735 | Python | 2,426 | 51 | #!/usr/bin/env python3
# Author: Francois Aguet
import argparse
import os
import struct
import subprocess
from datetime import datetime
import contextlib
@contextlib.contextmanager
def cd(cd_path):
saved_path = os.getcwd()
os.chdir(cd_path)
yield
os.chdir(saved_path)
parser = argparse.ArgumentParser(... |
7543c22a721ac2e97a782ce8ea4668ea12d21a1164b0855c93ab6ae21901937f | Python | 2,427 | 84 | import random
def simulate_health_sample(sequence_length: int) -> tuple[str, str]:
age = random.randint(18, 80)
fitness_score = round(random.uniform(1.0, 10.0), 1)
cortisol_morning = random.randint(10, 25)
cortisol_evening = random.randint(3, 12)
sleep_quality = random.randint(1, 10)
start_h... |
9bfff31984464d50bbf7a8a16bf95ba7846aa39e235bb4752a909a72648e90ce | Python | 2,427 | 90 | import torch
import torch.nn as nn
import torch.nn.functional as F
class InterpolateProjection(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
output_size: tuple[int, int],
mode: str = "bilinear",
align_corners: bool | None = None,
):
... |
ab8e828a1955ae874a60555a7ab0d8ec959862dd4785ed380de3dd3de6c08cea | Python | 2,427 | 64 | from chimerax.core.commands import run
from chimerax.atomic import selected_atoms
from collections import Counter
import os
import glob
# === Configuration ===
# Retrieve all CIF files generated by ModelAngelo
structure_files = sorted(glob.glob("/home/sunch/Desktop/Cb-project-maps/S2C2-dataset/one-Fab-4-classes/*map/*... |
c54d9ec37a21dc6452e7bb715e0ee26cf42e2cf8112e138661f8ae1df54b5844 | Python | 2,427 | 77 | # This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
import re
import json
import pytest
from pathlib import Path
from cached_property import cached_property
from connectome_manipulator import utils as test_module
from bluepysnap.circuit im... |
1d5f5a3c20758714d88c2801c265f3b5d836c451d4d64377f4d1b93537571c73 | Python | 2,428 | 99 | # 2022-05-27
# handle files
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
def read_expr_(fn):
expr = pd.read_csv(fn)
expr = expr.dropna(how='all',axis=1)
for c in expr.columns:
if (expr[c]==0).all():
expr = expr.drop(c,axis=1)
return expr
def read_expr... |
b794077382da2a244f12e719c0cd05dc1f8271602ecea8560e55f4b5ce315746 | Python | 2,429 | 67 | import sys
sys.path.insert(0, '../')
sys.path.insert(0, '../../')
sys.path.insert(0, '../../../')
sys.path.insert(0, '../../../ogb')
import argparse
import torch
from torch_geometric.nn import Node2Vec
from ogb.linkproppred import PygLinkPropPredDataset
def save_embedding(dataset_name, model):
embedding_name =... |
48eb23bb3cefce1be3053907a5c2eece3254073249a3c9c6111d3290cc84377a | Python | 2,430 | 62 | # Copyright (C) 2021-2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import os.path as osp
import shutil
from datumaro.components.dataset import Dataset
from datumaro.plugins.data_formats.kitti.format import KittiPath, write_label_map
from cvat.apps.dataset_manager.binding... |
fa80197b11edb2bbd3ee35c639aee6e4362c380e75a83dae0c0d0a678d52fb65 | Python | 2,431 | 94 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import io
import unittest
from pathlib import Path
from typing import Any
import pytest
from cvat_sdk import make_client
from shared.utils.config import BASE_URL, USER_PASS
from shared.utils.helpers import generate_image_file
def run_cli(
te... |
24fae60477b55679a23c07ff1628e7e3af72f31384e6a74f781bfb62beff7be6 | Python | 2,432 | 69 | # model_utils.py
import torch
import pandas as pd
# Assuming you have your model class defined somewhere, e.g., up_cnn_model.py
from .cnn_train import up_cnn_model
def load_model(model_path):
"""
Load the trained model.
"""
model_cnn = up_cnn_model.CNN_Class()
model_cnn.load_state_dict(torch.load... |
6260f05663972c3e0b5fc428419f9239105012cf29f6e54d01a0af40e3b32bc3 | Python | 2,432 | 69 | import random
import numpy as np
from analogainas.search_spaces.resnet_macro_architecture import Network
from analogainas.search_spaces.config_space import ConfigSpace
from analogainas.search_algorithms.worker import Worker
from analogainas.search_spaces.train import train
from analogainas.utils import *
import csv
EP... |
cbf51585060454c3c05c7dbee27908c871847e2e2c7359f90eff4f4c7c9489c7 | Python | 2,434 | 85 | """
generate location of poles with x sampled from gaussian process
"""
from scipy import spatial
import numpy as np
def generate_path(y_min, y_max, sigma, amplitude=0.12, gp_mean=0, n_poll=15):
"""
Args:
"""
# Sample from the Gaussian process distribution
# Independent variable samples
... |
eb5fc9d58652817bb2342700631706ab364d4f031e4e8a254a4346d705128907 | Python | 2,434 | 89 | # Copyright (C) 2018-2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import os
from rq import Worker
import cvat.utils.remote_debugger as debug
DefaultWorker = Worker
class BaseDeathPenalty:
def __init__(self, timeout, exception, **kwargs):
pass
def __e... |
a2a676f4360dd1be976d1f2ac9f1465b6ff5aa2b2c4f8327fcb1d3203ab42d86 | Python | 2,436 | 64 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Imports -----------------------------------------------------------------
# import... |
e99f8946277ac8c31ce0d7634b5bd775781fb148051f50669d006685056451b2 | Python | 2,436 | 91 | import os, sys
import numpy as np
import scipy.ndimage
def gen_s2s(gen, batch_size=1):
while True:
X = next(gen)
fixed = X[0]
moving = X[1]
# generate a zero tensor as pseudo labels
Zero = np.zeros((1))
yield ([fixed, moving], [fixed, Zero, fixed,... |
f493eb2a4148f501b74b290f0a1367559265745eb9cfd8b51be32f4829233f51 | Python | 2,436 | 88 | import json
import torch
from pymatgen.core.structure import Structure
from pymatgen.io.jarvis import JarvisAtomsAdaptor
from tqdm import tqdm
data = 'BOTH-SN-Em-test.json' # The data to re-train on (gvrh)
model_path = 'checkpoint_500.pt' # The model checkpoint to load initially (std_phonons)
freeze_before = 8 # T... |
82b9ba36d1b57253824c034738f1540765655a787a897b47b0cea0badb499341 | Python | 2,437 | 66 | #!/usr/bin/env python
# Somewhat hacky way of importing
from . import gauss
from . import mfn
from . import relu
from . import siren
from . import wire
from . import wire2d
model_dict = {'gauss': gauss,
'mfn': mfn,
'relu': relu,
'siren': siren,
'wire': wire,
... |
c52b9455a48c83eb034396228ef8e5f6bc0ad7ce761e98e069dff5e6210f8c4e | Python | 2,437 | 79 | from argclz import try_int_type, AbstractParser, as_argument
from rscvp.statistic._var import GENERIC_HEADERS
from rscvp.statistic.csv_agg.core import ParquetSheetSync, NeuronDataAggregator
from rscvp.util.cli.cli_statistic import StatisticOptions
__all__ = ['GenericAggOptions']
class GenericAggOptions(AbstractParse... |
ce1f2454e7a3c910b93b8bc00d7b03e57d91fa79880c4f08b3bb7c7c7fd665ad | Python | 2,437 | 56 | from nipype.interfaces.fsl import Reorient2Std
from PUMI.engine import BidsPipeline, NestedNode as Node
from PUMI.pipelines.anat.anat_proc import anat_proc
from PUMI.pipelines.func.compcor import anat_noise_roi, compcor
from PUMI.pipelines.anat.func_to_anat import func2anat
import os
ROOT_DIR = os.path.dirname(os.get... |
dd18f566684f46ebaf1591bd7f474e3b8b3226ac05da40b26fcdfeba1897a02f | Python | 2,437 | 75 | # -*- coding: utf-8 -*-
"""
Map2ana
In the following script, source data is transformed using a deformation field
and the transformed data is mapped onto a surface mesh in the target space. The
deformation of the input data is optional. The script needs an installation of
freesurfer.
"""
# python standard library in... |
f5b0bfe2dfbfbb6732cca2f7fa22a4edb0cd50d6275ced04bd051c0127e90943 | Python | 2,437 | 69 | # This file is part of Eigen, a lightweight C++ template library
# for linear algebra.
#
# Copyright (C) 2012 Keir Mierle <mierle@gmail.com>
#
# This Source Code Form is subject to the terms of the Mozilla
# Public License v. 2.0. If a copy of the MPL was not distributed
# with this file, You can obtain one at h... |
f69650add2ba0c9c743a0c6ae48b8991563919caefd7d91437903349157578af | Python | 2,437 | 65 | import os
import torch
import pandas as pd
import numpy as np
import json
from pathlib import Path
import torch.multiprocessing as mp
import sys
sys.path.append("..")
from preprocess import *
from tqdm import tqdm
def generate_transfer_data():
data_path = Path("/data2/whr/czl/TwiBot22-baselines/datasets/Twibot-22"... |
215a3e27a7967812f8a011e7c9e584ebd072db721de48c1f648ed112f2acf2f6 | Python | 2,438 | 28 | from testsetclass import TestsetClass
class YBDE18(TestsetClass):
def __init__(self):
self.weak = 1.0
self.molecules = ['h2s', 'f2s', 'pf3', 'pf3-ch2', 'nme3-cbh22', 'pme3-cbh22', 'f2s-ch2', 'me2s-cbh22', 'ph3', 'nf3-ch2', 'pme3', 'h2s-cbh22', 'ch2', 'nh3-cbh22', 'nh3-ch2', 'nme3-ch2', 'nf3', 'nf3... |
c8de8b8850319c8e47045e8707fdfb3d873ece476c4105620fe54964a7fcc0d7 | Python | 2,438 | 82 | import numpy as np
import pandas as pd
import pingouin as pg
import seaborn as sns
from matplotlib.axes import Axes
from rscvp.util.cli.cli_celltype import CellTypeSelectionOptions
from rscvp.util.cli.cli_output import DataOutput
from argclz import AbstractParser, as_argument
from neuralib.plot import plot_figure
from... |
ee67a5bacd2e21090f9ea262b652053a57eaa68b6d570cb063db9230c0b7bb72 | Python | 2,438 | 61 | #!/usr/bin/env python
"""Evaluation script to compare predicted and native PDB structures using TM-score."""
import os
import argparse
import shutil
from utils_trX2dy.evaluate_utils import run_score
def main():
parser = argparse.ArgumentParser(
description="Evaluate predicted structures by comparing with n... |
065709c712f54a4e48accb8f5f100e1471398a6dd12cb97724fc7a3904af354c | Python | 2,439 | 92 | import os
import torch
import yaml
from external_models.dna_bert import DNABertTokenizer
DEVICE = ("cuda" if torch.cuda.is_available() else
"mps" if torch.backends.mps.is_available() else
"cpu")
DEVICE = "cpu"
DEVICE_TRAINING = ("cuda" if torch.cuda.is_available() else
"mps" i... |
1bc64ac5e8eb74cf2ffc26d12519f1422d31edc958232a8155069370bf31d14e | Python | 2,439 | 76 | import io
import logging
import pytest
from chemgraph.utils.logging_config import configure_logging
@pytest.fixture
def isolated_chemgraph_logging():
"""Isolate global logger mutations made by ``configure_logging``."""
root = logging.getLogger()
package = logging.getLogger("chemgraph")
child = loggi... |
eba2f6294bb179529da1d3892774de0fcf672f24e3d5588bff07f023ea4782bb | Python | 2,439 | 80 | #!/usr/bin/python
#
## @file
#
# Convert .stv files to .mat files for those who persist in using Matlab
# and want to be able to manipulate image mosaics.
#
# Hazen 07/13
#
import os
import pickle
import numpy
import scipy.io
import sys
if (len(sys.argv) != 2):
print("Usage <mosaic_file>")
exit()
directory =... |
a36809ececd2da21689ed95fb77a2bb79b9b962675f2cfd9b91d06d6d21053cf | Python | 2,440 | 73 | from collections.abc import Sequence
class Vocab:
def __init__(
self,
tokens: list[str] | None = None,
stoi: dict[str, int] | None = None,
):
self.itos: list[str] = [] # index to string
self.stoi: dict[str, int] = {} # string to index
self.default_index: int =... |
9df882d6103f307b89e2460d2f6862d32adcc28f1244ba107263878a6adae9da | Python | 2,441 | 77 | """
Module for IO of files in relation to
data loading.
"""
import glob
import itertools as it
import os
import random
from typing import List, Tuple, Union
import h5py
def get_h5_sorted_keys(filename: str) -> List[str]:
"""
Function to get sorted keys from filename
:param filename: h5 file.
:return:... |
eff0eee0bee8ff23a32f065bac326b9a5cf5a1dea0fef82ab16666996a5f4d24 | Python | 2,441 | 87 | import os
import os.path as op
import nibabel as nib
from dipy.reconst import fwdti
import AFQ.utils.models as ut
__all__ = ["fit_fwdti"]
def _fit(data, gtab, mask):
fwmodel = fwdti.FreeWaterTensorModel(gtab)
fwfit = fwmodel.fit(data, mask=mask)
return fwfit
def fit_fwdti(
data_files, bval_files,... |
399446694da4d91eb69f40c00470321c4e56b1b4a28e85c15997bbe76b5f6927 | Python | 2,442 | 73 | # Copyright (C) 2021-2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from allauth.account import app_settings as allauth_settings
from dj_rest_auth.views import (
LogoutView,
PasswordChangeView,
PasswordResetConfirmView,
PasswordResetView,
)
from django.conf ... |
5a9aea8ab0bc647b9ac8dbe823b69a4bc80a9dd65cf2cc412b88b07f4d37ef92 | Python | 2,442 | 88 | import json
import torch
from pymatgen.core.structure import Structure
from pymatgen.io.jarvis import JarvisAtomsAdaptor
from tqdm import tqdm
data = 'BOTH-SN-Em-test.json' # The data to re-train on (gvrh)
model_path = 'checkpoint_500.pt' # The model checkpoint to load initially (std_phonons)
freeze_before = 8 # T... |
23edd99a66ac7caf5c763413e25ee3a785ea9936a09ec6aa29ccf502190f793f | Python | 2,443 | 73 | import numpy as np
import pytest
from eir.interpretation import interpret_omics
def test_rescale_gradients():
input_array = np.array([[0, 0, 1], [0, 0, 2], [0, 0, 4]])
rescaled_array = interpret_omics.rescale_gradients(gradients=input_array)
assert (
rescaled_array == np.array([[0, 0, 0.25], [0... |
d7e4d4ba77121c66e3e824e2706d09007241bc9b3278541fa558312f8d751131 | Python | 2,444 | 72 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Select b-values on specific b-value shells.
With the --tolerance argument, this is useful for sampling schemes where
b-values of a shell are not all identical. Adjust the tolerance to vary the
accepted interval around the targetted b-value.
For example, a b-value of ... |
8bd2c17dddcd104d79ea12c8325f5fd4a35117c835c50e18ecb317e6e9ebc122 | Python | 2,445 | 73 | from __future__ import absolute_import
from typing import TextIO
import os
import logging
def init_logger(log_name=None, log_file=None):
"""
Adopted from OpenNMT-py:
https://github.com/OpenNMT/OpenNMT-py/blob/master/onmt/utils/logging.py
"""
log_format = logging.Formatter("[%(asctime)s %(leve... |
ab1e8c9d2e228ef61a8caf229462ec65e939ef8fc88ac4c641995b19ffa57f70 | Python | 2,445 | 87 | from torch import nn
import torch
from model.non_local_embedded_gaussian import NONLocalBlock1D
# 冻结网络层
def freeze(model):
for param in model.parameters():
param.requires_grad = False
class Network(nn.Module):
def __init__(self):
super(Network, self).__init__()
self.conv_1 = nn.Seque... |
44f5a2cb1fb70e5a50470270ad4d7f09450157ff9f5709786e691a4b7156065c | Python | 2,446 | 66 | #!/usr/bin/env python3
import nibabel as nib
import numpy as np
import SimpleITK as sitk
import os
import tempfile
import argparse
def nifti_warp_to_h5(input_nifti: str, output_h5: str):
"""
Convert a 4D NIfTI warp field (x,y,z,3) to an ANTs-compatible .h5 transform file.
The function creates a temporary N... |
9d8b26700e41fb36f7970af6db1007b09dec0501eeb795633898db1e2d8ce603 | Python | 2,446 | 65 | #!/usr/bin/env python3
"""Read a Thalamus capture (``.tha``) file and plot its analog channels.
This demonstrates reading raw records directly with ``thalamus.record_reader2``
(no hydration step required) and reconstructing per-channel time series. It works
on the file produced by ``synthetic_recording.py`` or on any... |
a489646c31efbde0e8bf1be198b98d28718bf62185a9d7868ce195657a47b8fe | Python | 2,446 | 62 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys=['bst.zip'])
tmp_dir = tempfile.Temporar... |
d8e611a54aef73f2fc7d5541d94cf0adf48bfe0b4c8b4d7eff60762d038227e5 | Python | 2,447 | 77 | import numpy as np
import os
import pandas as pd
import SimpleITK as sitk
from radiomics import featureextractor, getFeatureClasses
DICOM_dir = r''
SEG_dir = r''
label_path = r''
feature_outputFilepath = r''
feature_with_class_outputFilepath = r''
label_outputFilepath = r''
discard = []
table = []
ta... |
11e50c5e5519736a6a0138a32a4ab56fd4da0977c0ffe054a043fbbf985cac21 | Python | 2,449 | 74 | from torch.utils.data import DataLoader
from geoseg.losses import *
from geoseg.datasets.vaihingen_dataset import *
from geoseg.models.FTUNetFormer import ft_unetformer
from catalyst.contrib.nn import Lookahead
from catalyst import utils
# training hparam
max_epoch = 45
ignore_index = len(CLASSES)
train_batch_size = 8... |
f0b27a5a6626cdfc8d269988360e354af24b86fd7d3bdba47835ef5b0a79068b | Python | 2,449 | 88 | import json
import torch
from pymatgen.core.structure import Structure
from pymatgen.io.jarvis import JarvisAtomsAdaptor
from tqdm import tqdm
data = 'SINGLE-BOTH-SN-Em-test.json' # The data to re-train on (gvrh)
model_path = 'checkpoint_500.pt' # The model checkpoint to load initially (std_phonons)
freeze_before ... |
77b669cfadf78b4df5634fd67bc80a2cf2d37f7d0d6257a812b34e8db0b1f053 | Python | 2,450 | 145 | from nipype.pipeline import engine as pe
# Registration and transformation
# Registration workflows
def init_surftransform_wf():
"""
Worflow that transforms gifti or freesurfer surfaces using ants transform
Parameters
----------
Inputs
------
- gifti or freesurfer surface
- ants tran... |
f7ee00984a169937a2dc95ccb8eeb94f3535c51535b13c205313a6eddeda35b1 | Python | 2,450 | 69 | import subprocess
from lyroi.utils import check_version_local, check_version_online, check_model, get_download_size, format_file_size
from lyroi.modes import get_mode_list, get_pretty_name, get_folds, get_suffix_dict, get_archive_names
class ModelManager:
def get_available_models(self):
return get_mode_... |
4e280b826845b5bed653132d27998b7b5e641ba1f8a284809ded00936e811695 | Python | 2,451 | 70 | # This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
import os
import numpy as np
from numpy.testing import assert_array_equal, assert_array_almost_equal
from bluepysnap import Circuit
from utils import TEST_DATA_DIR
import connectome_mani... |
096f4cb227615d092d58cb11b323575e7cc7328207316ae6b32494b076b9c5ed | Python | 2,452 | 64 | import os
import yaml
import torch
import models
import argparse
import numpy as np
from utils.utils import *
import matplotlib.pyplot as plt
from scipy.io import loadmat, savemat
def main(args):
# construct fitting model
model = getattr(getattr(models, args.model), args.model)
with open(os.pa... |
942dabda8d469f438da86a8991afc135c9558e94d8e2502f30bee86ab2d7b617 | Python | 2,452 | 52 | from train_gtex import *
def main():
model_type = 'MultiGene'
current_dir = os.path.dirname(__file__)
DATA_DIR = os.path.join(current_dir,'../data')
config_path = os.path.join(current_dir,"configs/multi_gene_196kb_blood.yaml")
remaining_tests = [(0,'0ubse6xy','epoch=19-step=24080.ckpt'),(2,'ega4xl1... |
ab1ae90336c05ae6c8448b3c584722b13a47d53a0ea4a1e4e057a01c1faf5e07 | Python | 2,452 | 74 | # -*- coding: utf-8 -*-
import numpy as np
supported_tensor_formats = ['fsl', 'nifti', 'mrtrix', 'dipy']
tensor_format_description = \
"""
Dipy's order is [Dxx, Dxy, Dyy, Dxz, Dyz, Dzz]
Shape: [i, j , k, 6].
Ref: https://github.com/dipy/dipy/blob/master/dipy/reconst/dti.py#L1639
MRTRIX'... |
e41f913c6a9c94a8d1876986d2e83af50537a35eca902c7bd105561c2f756be1 | Python | 2,452 | 63 | import pytest
from qtpy import PYQT5, PYQT_VERSION, PYSIDE2, QtQuick
def test_qtquick():
"""Test the qtpy.QtQuick namespace"""
if PYQT5:
assert QtQuick.QQuickCloseEvent is not None
assert QtQuick.QSGFlatColorMaterial is not None
assert QtQuick.QSGImageNode is not None
assert Q... |
3a4a5447e7cc91418b96d96982fceea70bbbdd3ad7af7b98ab147a807a8c95a7 | Python | 2,453 | 69 | from pathlib import Path
import random
from typing import Final, Optional
import cv2
import torch
from torch.utils import data
from torchvision.transforms import v2
IMG_SIZE: Final[int] = 224
class TextureDataset(data.Dataset):
def __init__(self,
root_path: Path,
patients: li... |
8defca0eaf390aaa3e999d6266c5f9ad5ad00fafb3f9a1bf80b221cb8293012c | Python | 2,454 | 84 | import os
from collections import OrderedDict
import numpy as np
import pandas as pd
import pytest
from openturns import Distribution, TestResult
from impedancefitter import Fitter, PostProcess, get_equivalent_circuit_model
@pytest.fixture
def postprocessRC():
"""Initialise postprocessing on RC circuit data."""... |
94dce280ff5d90b7973e5242f79a35ff6da88d1f4320fafc454e09ee08b6a263 | Python | 2,455 | 89 | from PIL import Image, ImageChops
path = '' # specify your path here
# Utility to crop transparent border
def crop_alpha(image):
bg = Image.new("RGBA", image.size, (0, 0, 0, 0))
diff = ImageChops.difference(image, bg)
bbox = diff.getbbox()
return image.crop(bbox) if bbox else image
for name in ['_L+R_... |
d8c3cae3f530b56d282f5b6e15bbb2dda12789768b37963c7dc2fd8377158341 | Python | 2,455 | 70 | """
src/prepare_data.py
===================
功能：
1. 调用 utils.data_utils.load_data() 读取并预处理数据。
2. 使用 MultilabelStratifiedKFold 将样本按多标签分层方式
划分为 10 折，保存每折的 train / val 索引。
3. 额外生成 1000 份 bootstrap 采样索引，可供稳健评估使用。
运行
----
$ python -m src.prepare_data
"""
from __future__ import annotations
import os
import pickle
from ... |
cf94c40fa85beacff900b7f8f7453bb821165924b6b643d8f2ca001479168470 | Python | 2,456 | 91 | import urllib.request as ur
import zipfile
import os
import os.path as osp
from six.moves import urllib
import errno
from tqdm import tqdm
GBFACTOR = float(1 << 30)
def decide_download(url):
d = ur.urlopen(url)
size = int(d.info()["Content-Length"])/GBFACTOR
### confirm if larger than 1GB
if size > 1... |
571bb1c6c7825f7403cbf7b78f80c376cd1241bb4b016f082e170a3210f92830 | Python | 2,457 | 137 | '''
Code for C. Nazaret et. al. / Journal of theoretical biology 2009
Original code in scilab (author Nazaret)
Ported to python3 (author chaitanya)
This code generates figure 7 and figure 8 from the paper.
'''
from math import exp
from scipy.integrate import odeint
import numpy as np
import matplotlib.pyplot as plt
... |
67244ab72405b5001dc60b7f5072996909897e586ed8dea8331d93eb76bd17a6 | Python | 2,457 | 76 | import os, sys
import pandas as pd
import re
# Directory containing variant folders
DIR = sys.argv[1]
output_file = sys.argv[2]
# Columns to include
COLUMNS = [
"total_score","dslf_fa13","fa_atr","fa_dun","fa_elec","fa_intra_rep",
"fa_rep","fa_sol","hbond_bb_sc","hbond_lr_bb","hbond_sc","hbond_sr_bb",
"om... |
7516d690f8f47b22c6649a2e037aa79246897f87ea5165891dc47438a82f09aa | Python | 2,457 | 81 | import inspect
from typing import Any, Callable, Optional, Sequence, Union
from napari.qt.threading import FunctionWorker, GeneratorWorker, create_worker
def do_work_async(
function: Callable,
return_callback: Optional[Union[Callable, Sequence[Callable]]] = None,
yield_callback: Optional[Union[Callable, ... |
fcaa8b0606a619bc911df5b85479029eaf3ce582d5fea168b1a15bb187ddf264 | Python | 2,457 | 66 | import numpy as np
def compute_tp_fp_fn_tn(mask_ref, mask_pred, ignore_mask=None):
if ignore_mask is None:
use_mask = np.ones_like(mask_ref, dtype=bool)
else:
use_mask = ~ignore_mask
tp = np.sum((mask_ref & mask_pred) & use_mask)
fp = np.sum(((~mask_ref) & mask_pred) & use_mask)
fn... |
40d6a2612d47d044ad6648ff5310a1d5cb0b726e2b860285a4c5d92c9032930d | Python | 2,458 | 74 | # Copyright (c) 2015 Tobias Neumann, Philipp Rescheneder.
#
# This file is part of Slamdunk.
#
# Slamdunk is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your ... |
fd83686f78d9ff8415c1f5699624fd7f8df96aa3232cd2d815c296e298321f67 | Python | 2,458 | 69 | """
Pseudo-dyad validation template.
This script provides a simplified permutation-style template. If the repository
contains only dyad-level processed data, true pseudo-dyad reconstruction from
raw participant-level time series cannot be performed. Therefore, this script
demonstrates a conservative label-permutation ... |
65753ac0f3c8c798a31d092e49f17bd17246408146251d316268e146a342931b | Python | 2,459 | 88 | import lmfit
import numpy as np
import impedancefitter as ifit
from impedancefitter.time_domain import (
calculate_impedance_spectrum_using_cwt,
calculate_impedance_spectrum_using_fft,
fit_impedance_from_time_domain,
predict_time_domain_signal,
rectangle,
)
fs = 10000 # 10 kHz sampling
dt = 1.0 /... |
daedc6e5df05ca8a6a7ead47114018b17a1e9ca7292638a06b6679d05ea0025e | Python | 2,460 | 65 | from .unet2 import unet
import torch
import torch.nn as nn
import torch.nn.functional as F
from clustercontrast.utils.serialization import load_checkpoint
class MoECrossAttention(nn.Module):
def __init__(self, output_dim=4608, dropout=0.1, num_features=0,new_in_channels=0,**args):
super(MoECrossAttention,... |
a82616ddf2e4dda5f9c49e432ef528b1d68a2c23f01628c30a110df2c6f94a61 | Python | 2,461 | 57 | import numpy as np
import pytest
from pytest import approx
from brainscore_core.supported_data_standards.brainio.assemblies import BehavioralAssembly
from brainscore_vision.benchmarks.imagenet_c import Imagenet_C_Noise, Imagenet_C_Blur, \
Imagenet_C_Weather, Imagenet_C_Digital
from brainscore_vision.model_interfac... |
e094f7e4e8d819bf747e85784ba76e88d8f43777a7dfcf86025fd96e6eeb29ff | Python | 2,461 | 66 | # Sourced from https://github.com/fwillett/speechBCI/blob/main/NeuralDecoder/neuralDecoder/models.py
import tensorflow as tf
class GRU(tf.keras.Model):
def __init__(self,
units,
weightReg,
nClasses,
dropout=0.0,
nL... |
11f774f9bf02bbd28211cfbc363b508b8652cc0fd27381ba39afbecf3b908eb2 | Python | 2,462 | 76 | import pandas as pd
import time
import ijson
import content_feature as cf
import csv
import spacy
nlp = spacy.load('en_core_web_lg')
def get_content_vector(tweets):
links = cf.count_urls(tweets)
mentions = cf.count_mentions(tweets)
content_vector = {
"links_ratio":links[0],
"unique_links_ra... |
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