sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
4b3998b0d303090c0e0c9922867058d80c7e604c0a58755f4b9e5b0d57da20ff | Python | 1,657 | 56 | import glob, os
import subprocess
import vtk
import vtk.util.numpy_support as VN
import numpy as np
import sys
from pathlib import Path
import json
def diff(sim_path, test_path, verbose=False):
# create a reader
reader = vtk.vtkXMLImageDataReader()
# read file0
reader.SetFileName(str(sim_path))
reader.Updat... |
62370d4242e27e4d04fff3b80b9df20dde19105a857e50de04829369ce014679 | Python | 1,657 | 51 | #!/usr/bin/python3
import numpy as np
# evaluate a specific function a few times with one sample
import cec2017.functions as functions
f = functions.f5
dimension = 30
for i in range(0, 10):
x = np.random.uniform(low=-100, high=100, size=dimension)
y = f([x])[0]
print(f"f5({x[0]:.2f}, {x[1]:.2f}, ...) = {... |
eeda5519e3ddec56f248a078c8aeaf9e9945cd5e7b3a9351950126395b2e080d | Python | 1,657 | 55 | import networkx as nx
import numpy as np
import pandas as pd
from brainways.utils.network_analysis import calculate_network_graph
def test_calculate_network_graph():
region_1 = np.random.rand(100)
region_2 = region_1 * 100 + np.random.rand(100)
region_3 = np.random.rand(100)
# Create synthetic data
... |
5098d8089d353ead8076a28ea528af2097ccffe0e2baff989323d8661bc0415e | Python | 1,658 | 40 | """
Self-contained model-function variants for A/B speed benchmarking.
Each sub-module exports the same public API::
layerlineModel(x, centerX, bg_line, bg_sigma, bg_amplitude,
center_sigma1, center_amplitude1,
center_sigma2, center_amplitude2, **kwargs)
layerlineModelGM... |
c4b41459de681e1cb78233bfb235363624d63cb195d1164bf52e071ff208e573 | Python | 1,659 | 46 | import numpy as np
from nems import Model
from nems.layers.base import Phi, Parameter
from nems.distributions import Beta
def test_phi_bounds():
m = Model.from_keywords('wc.18x4.g') # 8 parameters
phi = m.layers['wc'].parameters
assert len(phi.get_bounds_vector()) == 8
lower1, upper1 = (0, 1)
lo... |
c8ae1fa6c25d26b9be15b8e4e5c07e15fd386bff05ac6f73cf51a7c49a42f0d9 | Python | 1,659 | 56 | from typing import List, Union
import numpy as np
def normalize_array(arr: np.ndarray, v_min=None, v_max=None) -> np.ndarray:
"""
Normalize a numpy array.
The array is normalized such that
its values are normalized from [v_min, v_max] to [0, 1].
If min/max are not provided, will use the min/max ... |
f4ec3c8b27bd4a7f6114e10d3a454be5e9f1997789dffba718ecf1e68426fc02 | Python | 1,659 | 57 | from __future__ import annotations
import pytest
from labelme._shape_color import resolve_shape_color
@pytest.mark.parametrize(
("config", "expected"),
[
(
{
"mode": "auto",
"auto": {"shift": -1},
"uniform": {"color": [0, 255, 0]},
... |
0c03b532a96c1ee3325f82a365bb53037cdb9854d806327e6e58638077d9cea9 | Python | 1,660 | 39 | """Acquire and extract Schmidt supplements needed to interpret replication."""
import json
from pathlib import Path
from xml.etree import ElementTree
from zipfile import ZipFile
from src.acquisition.download import ROOT, acquire, sha256
WORD = '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}'
def ex... |
f15c6e3691466b382f8216320bc6744b4b6889a8ce2b2f4f485e1fbcc33a56a4 | Python | 1,660 | 61 | import sys
import getopt
import numpy as np
import pandas as pd
def usage():
print("""
Usage: python3 s02.filter.py [-f filter threshold, default 0.05]
[-o output folder, default ./]
we will load data generate by s02 in output folder!
""")
def main(argv):
###############... |
1d4e2d63266d4a999b7ff4a07c87550a87b894800b7f515416ac634e47d6bbd8 | Python | 1,661 | 48 | #!/usr/bin/python
# Create bitmaps to check defacing of all NIFTI images
# python bids_deface_bitmaps.py
from pathlib import Path
import os
import os.path
import glob
import sys
import shutil
import fnmatch
def remove_after_final_underscore(input_string):
# Find the last underscore index
last_underscore_ind... |
3911705db8868f2660d5a6c96f80ede7cc121951c58f10b20062537f22ac293d | Python | 1,661 | 35 | from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
import ssl
import functools
import antialiased_cnns
from brainscore_vision.model_helpers.check_submission import check_models
ssl._create_default_https_c... |
43cad4c0b11a94b4fa8ee6ef8615aac4458b90e5b0c6e45ca3c8b31824795786 | Python | 1,661 | 46 | import torch
class FastTensorDataLoader:
"""
A DataLoader-like object for a set of tensors that can be much faster than
TensorDataset + DataLoader because dataloader grabs individual indices of
the dataset and calls cat (slow).
Source: https://discuss.pytorch.org/t/dataloader-much-slower-than-manua... |
df6abf0b4216c52cf3f37a3bb42d1adba84a33996f4718784c057adf5cd79b60 | Python | 1,661 | 62 | #!/usr/bin/env python
#
## @file
#
# This parses log file series (i.e. log, log.1, log.2, etc..) to
# make it easier to see which functions / methods call which other
# functions / methods. It is used mostly to make sure that what
# we expect to happen is actually happening, and to try and
# identify unnecessary functi... |
648343d78eef0df035d5db31851bf8a5069df6a3e3d8da6b3160543367ddb4a8 | Python | 1,662 | 44 | import subprocess
import yaml
def save_initial_data():
version_name = 'v1'
data_path = input('Data path:')
print("Saving initial data")
subprocess.run(["dvc", "add", "data_path"])
subprocess.run(["git", "add", ".gitignore", data_path + ".dvc"])
subprocess.run(["git", "commit", "-m", "data: Save... |
85a06f785b9bddf00f74194f49fb360873581b73467ea8d2c34ce40a6877318e | Python | 1,662 | 60 | import pytest
import numpy as np
# Generic data fixtures for use by test_layers and others.
spectrogram_seed = 12345
response_seed = 678910
state_seed = 111213
# TODO: Replace with either useful synthetic data or a short snippet of
# real data.
# TODO: These are based on LBHB's needs (i.e. sound spectrogram an... |
cda42daa1b39e4b53e7ba28f0b8ca56927607b82af128b1ea8eb7ba660338b6b | Python | 1,662 | 47 | import torch
import torch.nn.functional as F
from torch import Tensor
from torch.nn import Parameter
class MessageNorm(torch.nn.Module):
r"""Applies message normalization over the aggregated messages as described
in the `"DeeperGCNs: All You Need to Train Deeper GCNs"
<https://arxiv.org/abs/2006.07739>`_ ... |
4a5aca0c35bdadcf2aea4d9fae8dc2bfd8b340c85e481f8a32c105c70e8063d8 | Python | 1,663 | 53 | # myelinfccoupling/io.py
import pandas as pd
def load_inputs(edges_path, nodes_path=None) -> pd.DataFrame:
"""
Load edges (and optionally nodes) tables, and ensure required columns.
Required edge columns after processing:
- i, j (node indices)
- FC (functional connectivity)
... |
5980463d399af96203f50445895ae29d960f6a0adb10c7d437b82d91a16928be | Python | 1,663 | 56 | import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, matthews_corrcoef, auc, roc_curve
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.mo... |
9a0650b04e42c217392275428676538c07d61747c611ee996299b7212e4dbeea | Python | 1,663 | 56 | import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, matthews_corrcoef, auc, roc_curve
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.mo... |
d897764bed2894b401b89795904199e0bd766dfbe80ff0b85f605d6f2667e959 | Python | 1,663 | 51 | from __future__ import annotations
import re
from loguru import logger
from ._shape import Shape
def compile_label_flags(
*,
label_flags: dict[str, list[str]] | None,
) -> dict[re.Pattern[str], list[str]]:
# The patterns arrive unvalidated from ~/.labelmerc or --label-flags, so
# neither a typo lik... |
ff40b9a846ce1447fe6df32387643ce78506de46a893a7f1028cf93cbdc29751 | Python | 1,663 | 41 | # Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Project information -----------------------------------------------------
# https://www.sphinx-doc.org/en/master... |
f3a5a1a96b2a97a5568623db31408c85a9e7bb72084c0b5c25fcaf63f220a08b | Python | 1,664 | 62 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
import sys
# external inputs
import numpy as np
import nibabel as nb
def average_layer(img_input, path_output, basename_output, mode="mean"):
"""Average layer.
This averages data across different layers or sessions. Input arrays should ... |
2ff4d06a71c9a480fa9fcf788c35921864792e828eb8ac01a621783aaa0df013 | Python | 1,665 | 58 | #!/usr/bin/env python
"""
Reads a maf file from stdin and applies the mapping file specified by
`mapping_file` to produce a sequence of integers. Then for each possible word
of length `motif_len` in this integer alphabet print the number of times
that word occurs in the block.
usage: %prog motif_len mapping_file < m... |
da3e689ae6e0e3b2238ac2b759dd7a48444ec38a8a323d10de82a14f73c06a2d | Python | 1,665 | 45 | """Example code for plotting connectivity maps using the coherence
from state/mode spectra.
The spectra can be calculate with a multitaper (in the case of a state time
course) or regression (in the case of a mode time course).
See examples/spectra/hmm_multitaper.py for how to calculate a multitaper
and examples/spect... |
28fec17bbe26b5588cf60bb167ae7fd7f802d4719c24a15300f4872b87ffd99e | Python | 1,666 | 55 | from functools import partial
import numpy as np
from scipy import stats as stats
import mne
from mne.stats import permutation_cluster_test
from scipy.io import loadmat
from scipy.io import savemat
data = loadmat('pac.mat')
mask = loadmat('mask.mat')
mask = mask['mask']
sigma=1e-3
#print(mask.shape)
data_AD=data['pac... |
cb8c9b7f99d9c1204bf5078a442e047dac593c8deafb15fdf2af0217ec810ed3 | Python | 1,666 | 52 | import os
import re
from contextlib import redirect_stdout
import io
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier
import pickle
from pstimutils import PertStimDataSimple
from perturbation1 import *
datasets = []
for filename in os.listdir(f'{os.getcwd()}/data'):
... |
0253f1fe2b2d5f08e5e9675c500766b6e9a098ecd161cc95ebe2e9881c997780 | Python | 1,667 | 33 | """
Author ：Fan wang
Time ：2022. 9.8
"""
# 1. Read the event.tsv file
input_path = r'F:\HI-EEG_BIDS\sub-01\ses-01\eeg\sub-01_ses-01_task-SV-HI_events.tsv' # Replace with the actual path sub-01_ses-02_task-SV-HI_events.tsv'
with open(input_path, "r", encoding="utf-8") as f:
lines = f.readlines()
# 2. Par... |
5abc09e51dbc4b033005450b0aa6b37ba44d0bf4882e1fe98cc4b46af1fd0e62 | Python | 1,667 | 67 | ## ----- OS ----- ##
import io
import os
import time
import psutil
import random
from natsort import os_sorted, natsorted
## ----- MATH / STATS ----- ##
import cv2
import heapq
import mat73
import scipy.io
import numpy as np
from copy import deepcopy
from itertools import combinations
from collections import default... |
6b6ad4ebd5eb5ccc5901a73e5c98df6b23f7de271853c71a474c4dd609a677e8 | Python | 1,668 | 33 | #! /usr/bin/env python
"""Generate an area weighted 2D histogram of a [aor pf features] feature from a single mesh from a single segmentation
Useful to get a sense of how your configuration for meshing and quantification looks. Also nice for responding to reviewer requests!
Usage: python single_file_histogram.py input... |
82456a15781f42ef012e0de058bb0e73cab53c345f599014d0826153771f9e9a | Python | 1,668 | 56 | # %% ### import modules
from pathlib import Path
import pandas as pd
# %% read results table
results_dir = Path(__file__).resolve().parents[1] / "results"
df = pd.read_excel(results_dir / "cs_summary.xlsx")
# %%
# Define the new column names
metrics = ["pad_brainageR", "pad_DeepBrainNet", "pad_brainage", "pad_enigma"... |
c3eefdb690a117e94046242f92e3569960b90dd6d8a7db18585d8ce1e2df5ab9 | Python | 1,669 | 63 | import torch.utils.data as data_utils
import torch
from PIL import Image
import jpeg4py as jpeg
import numpy as np
import random
import glob
import os
import math
def seed_torch(seed=0):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
# np.random.seed(seed)
torch.manual_seed(seed)
torch... |
d5c2677cf07683c94835190dbe2b068203381f8cc0f869a5b7948805116417df | Python | 1,669 | 58 | import torch
from ogb.utils.features import get_atom_feature_dims, get_bond_feature_dims
full_atom_feature_dims = get_atom_feature_dims()
full_bond_feature_dims = get_bond_feature_dims()
class AtomEncoder(torch.nn.Module):
def __init__(self, emb_dim):
super(AtomEncoder, self).__init__()
... |
fc3c51a5caf1eb87c6393bc8b27260db5b6f76606f816d319a5882771354b49b | Python | 1,669 | 56 | """Nottingham MEGUK: Fix structural MRI files.
The sform header in the publicly available MEGUK data is incompatible with
RHINO, we create a copy and fix the MRI files in this script.
"""
from os import makedirs
from glob import glob
from pathlib import Path
from shutil import copyfile
import numpy as np
import niba... |
f7d3ae202b8d58c76229d513df094ad938a104be3c86fe2bf56fb899aa3cf053 | Python | 1,671 | 52 | # Generated by Django 4.2.26 on 2026-05-04 14:43
from django.db import migrations, models
def initialize_chunk_type_for_audio_tasks(apps, schema_editor):
Data = apps.get_model("engine", "Data")
Data.objects.filter(
task__media_type="audio", compressed_chunk_type="", original_chunk_type=""
).upda... |
a6775aa3e8c94726677af7bd4beb3349c9225740be2ec8ffc509c3d45fdb69eb | Python | 1,672 | 55 | """
test_consolidate.py
-------------------------------
Tests for the `consolidate.py` module
"""
import unittest
import os
import sys
import shutil
import utils
# Include the parent directory in the PYTHONPATH for relative imports
sys.path.append('..')
import consolidate
TEST_OUTPUT_PATH = 'output'
TEST_DATA_FILE... |
b1697ef44b2f4fe3846920c03adbe37f9f2f12d35af95c1e27e24ab4716dd173 | Python | 1,672 | 54 | import os
from skimage.metrics import structural_similarity as ssim
from skimage import io
def calculate_ssim(image1_path, image2_path):
# 读取图像
image1 = io.imread(image1_path, as_gray=True)
image2 = io.imread(image2_path, as_gray=True)
# 计算结构化相似性指数 (SSIM)
similarity = ssim(image1, image2)
re... |
b483607223003e8fd6c49a55eb453792aa63f416f244f6abe2a0be78d2695e99 | Python | 1,672 | 45 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import get_testing_files_dict, fetch_data
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys=['tractometry.zip'])
tmp_dir = tempfile... |
0f7cc6a986f35910e8ec956ee1b4562199d292831e81f02b4eac1a215aa4c834 | Python | 1,673 | 55 | # -*- coding: utf-8 -*-
# external inputs
import numpy as np
def apply_affine_chunked(aff, pts, chunk_size=10000):
"""Apply an affine matrix to points in chunks.
This function is a copy of the routine `apply_affine` from the `affines`
module of the `nibabel` package. The only difference is that this... |
14caa61d0d2fc7a4099250b81176b255bffd6783275122a18e9f2bf7863eb690 | Python | 1,673 | 82 |
import numpy as np
import zarr
from numcodecs import Blosc
from tqdm import trange
import os
# ======================
# CONFIG
# ======================
path = "/media/sophie/Expansion1/UCL/datatest/videos/2screens/30/"
npy_i = os.path.join(path, "dwt_videodata2_i.npy")
npy_r = os.path.join(path, "dwt_videodata2_r.np... |
467abb1d4a42b3cad9b291b5b99f2c3f76b0fa5043c6ef9e53628173bcb70c8b | Python | 1,673 | 63 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""The setup script."""
from setuptools import setup, find_packages
with open('README.md') as readme_file:
readme = readme_file.read()
with open('HISTORY.rst') as history_file:
history = history_file.read()
requirements = [
'setuptools',
'kipoiseq',
... |
4c3c86ebb9bfe0b38da8e1b5e3aa0c07209d3713a2b025212bb01eb29a2e36be | Python | 1,673 | 38 | #!/usr/bin/env python3
# Author: Francois Aguet
import pandas as pd
import numpy as np
import argparse
import os
parser = argparse.ArgumentParser(description='Combine covariates into a single matrix')
parser.add_argument('expression_covariates', help='')
parser.add_argument('prefix', help='')
parser.add_argument('--ge... |
68099f5fa2fc67e35cb4fe4441957f368fdb8b443442c19888c3cb5d2ccc0601 | Python | 1,673 | 55 | from pathlib import Path
from typing import Optional
import click
import numpy as np
from simple_mri import SimpleMRI, assert_same_space, load_mri, save_mri
def concentration_from_T1(T1: np.ndarray, T1_0: np.ndarray, r1: float) -> np.ndarray:
C = 1 / r1 * (1 / T1 - 1 / T1_0)
return C
def concentration_from... |
7c5c45a9f12eb709509cc091b588db849f5e8030f20d1b402627565b7f589e19 | Python | 1,673 | 73 | # -*- coding: utf-8 -*-
"""File name utilities."""
import os
import re
import numpy as np
__all__ = ["get_filename", "natsort"]
def get_filename(file_in):
"""This function gets path, file name and file extension for an input filename. The
loop checks for some given extension names. Otherwise it stops after... |
ae632f588d9b7c0a9de9f327e29d02ed155db9fe6aab966062c1a7d181b68ef1 | Python | 1,673 | 55 | from starter_code.utils import load_segmentation
import numpy as np
import nibabel as nib
def evaluate(case_id, predictions):
# Handle case of softmax output
if len(predictions.shape) == 4:
predictions = np.argmax(predictions, axis=-1)
# Check predictions for type and dimensions
if not isins... |
b366c61df4a7c2ea62692c74ff7433fcaa87fd175e2a5e9f60e2da629a594552 | Python | 1,673 | 54 | import unittest
import logging
from batchglm.api.models.numpy.glm_nb import Simulator
import diffxpy.api as de
class TestEnrich(unittest.TestCase):
def test_for_fatal(self):
"""
"""
logging.getLogger("tensorflow").setLevel(logging.ERROR)
logging.getLogger("batchglm").setLevel(log... |
0d965cfd216a15a6fafd9c5caf27bac73a8e1375d0f9bedc1639e014952535ad | Python | 1,674 | 47 | """Tests for mrc2xyz.mrc_to_xyz (mrcfile/numpy/pandas; no pymeshlab)."""
import os
import tempfile
import numpy as np
import mrcfile
import pandas as pd
from surface_morphometrics import mrc2xyz
def _write_labeled_mrc(path, voxel_size=10.0):
# (z, y, x) volume; label 2 at three known voxels, label 1 elsewhere-i... |
421dc605eb221a0e41a16ffe25499be24be929d14efa3a671d634f7709fb1353 | Python | 1,674 | 70 | import re
import os
import spacy
import zipfile
#nlp = spacy.load('en_core_web_lg')
def count_urls(tweets):
total = len(tweets)
unique =[]
for text in tweets:
urls = re.findall(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', text)
unique += urls
count... |
7ce749bfd09c2006ccc05c1e9e8ef86b53065cbd91a95e04eb21b277f3228304 | Python | 1,674 | 67 | import numpy as np
from impedancefitter.particle_suspension import particle_bh_model, particle_model
from impedancefitter.single_shell import single_shell_bh_model, single_shell_model
omega = 2.0 * np.pi * np.logspace(0, 12, num=30)
c0 = 1.0
kmed = 0.1
emed = 80
p = 0.1
Rc = 5e-6
# for single_shell_model
dm = 0.... |
e1b3e98c076b57178ef9c0b43633dd7ea378e5343c55b10c386305ba78b37faf | Python | 1,674 | 47 | """Background worker for the statistics and machine-learning step.
Separate from :class:`~meanap.gui.pipeline_worker.PipelineWorker` because it
runs something else entirely — :func:`meanap.stats.run.run_stats` against a
finished run rather than the pipeline against raw recordings — and reports a
different outcome (a r... |
201d8fc3e0f80adf6ff104af04f8660fbc8f23c292bc91762167369ab083d605 | Python | 1,675 | 47 | import json
from copy import deepcopy
from pathlib import Path
from eir.experiment_io.input_object_io_modules.input_io_utils import (
load_input_config_from_yaml,
)
from eir.setup.input_setup_modules.setup_sequence import (
ComputedSequenceInputInfo,
set_up_computed_sequence_input,
)
from eir.setup.schemas... |
81c59c52fe3a5b46d9066f69b94afb1a93eb7736fe3d6943f5ba70a811e703ec | Python | 1,675 | 54 | import warnings
from typing import Any, List
import torch
from torch import Tensor
def get_embeddings(
model: torch.nn.Module,
*args: Any,
**kwargs: Any,
) -> List[Tensor]:
"""Returns the output embeddings of all
:class:`~torch_geometric.nn.conv.MessagePassing` layers in
:obj:`model`.
In... |
b60063682a990e80bf3d152e82a0aa945352cdb197cef292d2a4ccca0f626ac7 | Python | 1,675 | 39 | import numpy as np
from pytest import approx
from brainscore_core.supported_data_standards.brainio.assemblies import BehavioralAssembly
from brainscore_vision import load_metric
def test_score():
assembly = _make_data()
metric = load_metric('error_consistency')
score = metric(assembly.sel(subject='A'), a... |
f513623118bdcf14fd6a7a857355540f2a13c3495a1d085d8b9bb719455f6bbe | Python | 1,675 | 57 | import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, matthews_corrcoef, auc, roc_curve
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.mo... |
2c8ad43c209dd72bccfc64890edf660dbcefd2fed085b1c15d336095e2a2291c | Python | 1,676 | 52 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
import nibabel as nib
import numpy as np
import pytest
from _pytest.outcomes import Skipped
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
try:
tensorflow = pytest.importorskip("tensorflow")... |
765d3ba1946397ce96c5c212732ca4f91f7e867f7aacc70a8cd511670ef74e87 | Python | 1,676 | 46 | #!/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=['connectivity.zip'])
tmp_dir = tempfile... |
54a4ddabc0abcbf8beaeae4fefe0f055eb12b171d9774173586f50cfdf59e0c6 | Python | 1,677 | 44 | import torch
class Logger(object):
def __init__(self, runs, info=None):
self.info = info
self.results = [[] for _ in range(runs)]
def add_result(self, run, result):
assert len(result) == 3
assert run >= 0 and run < len(self.results)
self.results[run].append(result)
... |
00ce185bc64bd09e3610aa2183a5bfeb871c0fc45bf761e5f5e69d28057cb7c7 | Python | 1,679 | 48 | #!/usr/bin/python
#
## @file
#
# Add the storm-control directory that this module is located in to
# sys.path, remove any default storm-control directory (if it exists).
#
# Hazen 05/14
#
import os
import sys
sc_directory = os.path.abspath(__file__)
for i in range(3):
sc_directory = os.path.split(sc_directory)[0]... |
9ef81fb4f5eb736a1e6b86aa3923ea2722a84fd90f0bed4e514ccd97df78e665 | Python | 1,679 | 52 | import pytest
from qtpy import PYQT5, PYQT6, PYQT_VERSION, PYSIDE6
from tests.utils import using_conda
@pytest.mark.skipif(
PYQT6 and int(PYQT_VERSION.split(".")[1]) < 9,
reason="QtStateMachine has been added to PyQt6 in version 6.9",
)
@pytest.mark.skipif(
PYSIDE6 and using_conda(),
reason="Not avai... |
a952b59f13c91dfbbb8f77f103635101c3f482ca641bf7af404c07793383eb6a | Python | 1,680 | 43 | # 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... |
c8e9849a631d2762c0fa604ff50506795ced4ade0cafacb793ee32cb134828c1 | Python | 1,680 | 43 | #!/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... |
d8b01d0d008998f284c83afbc3542801b98c698634ab4d267f22c0623a50f8d2 | Python | 1,680 | 40 | """Tests for the pure parts of sample_density (numpy/scipy/mrcfile; no graph-tool)."""
import os
import tempfile
import numpy as np
import mrcfile
from surface_morphometrics import sample_density as sd
def test_interpolate_samples_along_normals():
# Volume whose value equals its first-axis index: data[i,j,k] = ... |
b2af7d252b26ab71eb49bf0d23cb6b8ce334fb4c8c94009b0082462836325599 | Python | 1,681 | 48 | import numpy as np
import torch
import torchio
from torchio.transforms import Transform
class AverageSignalsOfEqualXvals(Transform):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def apply_transform(self, subject):
"""
normalize signals
Args:
signals: ... |
2e99ae2d9da2221b97b7fa3acff2a06fa8991eb11a509436a6598e5909be3d07 | Python | 1,682 | 45 | # -*- coding: utf-8 -*-
from dipy.io.stateful_tractogram import Space, Origin
import numpy as np
from scilpy.tracking.seed import SeedGenerator
def test_seed_generation():
mask = np.zeros((5, 5, 5))
# Two seeds:
mask[1, 1, 1] = 1
mask[4, 3, 2] = 1
# With n_repeats: it is our role to get seeds ... |
7ceedca5999320e57fc6ccfb1dded8041ad85ce7e76f19647ff1458fdedbb2b0 | Python | 1,682 | 46 | # This file is part of Mooney computational modeling project.
#
# Mooney computational modeling project is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) an... |
abf31cd8acfd67bbff4028c6412d41c241e393e7523dd1d4baecf15b06e09978 | Python | 1,682 | 66 | import argparse
from datetime import datetime
from deepreg.train import train
name = "unpaired_ct_abdomen"
# parser is used to simplify testing
# please run the script with --full flag to ensure non-testing mode
# for instance:
# python script.py --full
parser = argparse.ArgumentParser()
parser.add_argument(
"--... |
e7f9aaff7b43cb0a783dfdec45e9a5c8b2743a0ec6d122dd60bde2c0dd16313b | Python | 1,682 | 49 | # -*- coding: utf-8 -*-
import os
import tempfile
import nibabel as nib
import numpy as np
from scilpy.tests.arrays import fodf_3x3_order8_descoteaux07
def test_help_option(script_runner):
ret = script_runner.run(['scil_fodf_threshold_by_amplitude', '--help'])
assert ret.success
def test_execution(script... |
2693ef6c5e19ab98439cbd7a26cdf4f61e83f8b0005af1b9c41c87e929ffa6dc | Python | 1,683 | 45 | """Use the benedict package to locate yaml files that are easy to translate to dictionaries in
folders. Saving is handled by info inside the dicts and entries can be set recursively"""
import os
from benedict import benedict
def handle_folder_dict(func):
def wrapper(folder_dict, *args, **kwargs):
if not ... |
2b0de113269135f3d26cf90a50955eca5307b9c99eb6413e8fa07cc4ceff63d3 | Python | 1,685 | 51 | import numpy as np
from .normalizer_base import Normalizer
from numpy.typing import NDArray
class PercentileNormalizer(Normalizer):
"""
Normalizer subclass for percentile-based image normalization.
"""
def __init__(
self,
lower_percentile: float = 0.0,
upper_percentile: float ... |
4549d8495839b043869695f48ca5cb835ba5ef44b41a7a37033030f9abf13d41 | Python | 1,685 | 87 | """ Create a mesh with multiple surfaces """
import SVMTK as svm
import numpy as np
from pathlib import Path
if __name__ == "__main__":
print("Start ",__file__)
surf = svm.Surface();
outdir = Path("results")
outdir.mkdir(exist_ok=True)
sphere1 = svm.Surface()
sphere2 = svm.Surface()
spher... |
095fd0529b9f3aab4108a173925ff4e25a56a7efb48fb87784e43dc5cd1ba7ce | Python | 1,686 | 47 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
try:
from .load_standalone_model import load_model
except ... |
4c195eada66ec44b57fd17fdcebf607b6cc1afc66ad5aaef1b1adacafb6c582e | Python | 1,686 | 54 | # Generated by Django 4.2.18 on 2025-02-10 11:29
import django.db.models.deletion
from django.db import migrations, models
def init_consensus_settings_in_existing_consensus_tasks(apps, schema_editor):
Task = apps.get_model("engine", "Task")
ConsensusSettings = apps.get_model("consensus", "ConsensusSettings")... |
63114ec536b03d1ce635753a1e2eaecabda8fd7b5e8d12abda48264be87fedf9 | Python | 1,686 | 45 | #@UIService uiService
from ij import IJ, ImagePlus
from ij.gui import Overlay, Roi
from ij.plugin import ImagesToStack, Straightener
"""
This script straightens the pixels associated with Sholl sampling shells, so
that the signal sampled during Sholl Analysis can be measured in a more
straightforward way. For details,... |
a0f8ff72eaa9e9b972d95de2c4b7841b055d2d949d52bdcf00a4b1e436dac5c5 | Python | 1,687 | 47 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
try:
from .load_standalone_model import load_model
except ... |
c72877d7681b44e7775cfd3da3455817bef63fcc411d3b69582aa0d8adea6657 | Python | 1,687 | 46 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import os
import torchvision.models
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from pathlib import Path
fr... |
cc462b96658b71c49fe3c8f3993e17a61fe06648f68e76aa6e199ac42468fcd6 | Python | 1,687 | 39 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'button_editor.ui'
#
# Created by: PyQt5 UI code generator 5.7
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Dialog(object):
def setupUi(self, Dialog):
Dialog.setObj... |
eb16490337bd9ec6935cb411d309cf3008c617615eb3617ed2562be271c13ca4 | Python | 1,687 | 47 |
import pandas as pd
from pysnptools.snpreader import Bed
from pysnptools.snpreader import Pheno
from fastlmm.association import single_snp
import argparse
def main():
# Set up argument parser
parser = argparse.ArgumentParser(description="Perform MLMA using FaST-LMM with PLINK SNP data.")
parser.add_argume... |
f3ef7e25a70fc4fd8e82803a928dfe0624eb9962d980e5120b86ec5d4dfcbd7b | Python | 1,687 | 61 | # Copyright (c) DP Technology.
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from torch.utils.data.dataloader import default_collate
from . import UnicoreDataset
class BaseWrapperDatase... |
030cf7566388054988c894e4a4090d5b369343ef5d174d98288718ec1f10baf2 | Python | 1,688 | 52 | from __future__ import annotations
from PySide6 import QtCore
from PySide6 import QtWidgets
class IntegerSlider(QtWidgets.QWidget):
value_changed = QtCore.Signal(int)
def __init__(
self,
*,
minimum: int,
maximum: int,
value: int,
parent: QtWidgets.QWidget | No... |
1461a78ed70cdc28a5455837a090b9ad73effa80b31b12d206a719b64ae08ffa | Python | 1,688 | 64 | """Predict a continuous value using neuroimaging features.
Note, this script doesn't require osl-dynamics, it uses scikit-learn
to train a machine learning model.
This script should achieve an R2 value of ~0.52.
"""
import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardSc... |
b674024e64b0a7f5e0652f02f4cb877a76ba0f78d0d1d7143b5d9f81478634f6 | Python | 1,688 | 43 | # -*- coding: utf-8 -*-
import numpy as np
from scilpy.connectivity.connectivity import \
compute_triu_connectivity_from_labels
def test_compute_triu_connectivity_from_labels():
labels = np.asarray([[3, 4, 5, 6],
[7, 8, 9, 10],
[11, 12, 13, 14]])
labels =... |
c7a3777fead4186df85b1e1c4cb5b1159be687ee9e93e81cd01d50b569f9c9e9 | Python | 1,688 | 44 | import torch
from torch import Tensor
from .layer_norm import LayerNorm
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Optional, Tuple
class UpSampleLayer3D(nn.Module):
def __init__(self,
in_channels: int, out_channels: int,
method='nearest',
... |
d1989eaea37c5e5c12273157f7a177f960848b49b0d4f9423da478d078cac88c | Python | 1,689 | 55 | import copy
from distances import LpDistance
from reducers import DoNothingReducer, MeanReducer, MultipleReducers
from utils.module_with_records import ModuleWithRecords
class ModuleWithRecordsAndReducer(ModuleWithRecords):
def __init__(self, reducer=None, **kwargs):
super().__init__(**kwargs)
se... |
e1c53b6efa57a5c2101db768e3119304938358e08ed55958229106cf7abef692 | Python | 1,690 | 72 | import re
import datetime
import calendar
def get_user_id(user):
return user['id']
def get_longevity(user):
create = user['created_at'].split(' ')
month = list(calendar.month_abbr).index(create[1])
day = int(create[2])
year = int(create[5])
create_time = datetime.datetime(year, month, day)
... |
e17b85c03092763e7997e7b2cf864c2913e40de2b6c9edd4d2bc334239f35ec0 | Python | 1,691 | 34 | from model.utils import clustering
import scanpy as sc
import numpy as np
from scipy.spatial.distance import cdist
def location(option_list,source_data,target_data,tool,radius=0,random_seed=0,refinement=False):
n_clusters = option_list['get_location']['n_clusters']
start = option_list['get_location']['start']... |
f52debc04f480da7c1601044ec66a75e73a69e448fe4910656d7988c734012f2 | Python | 1,691 | 39 | import numpy as np
from sklearn.neighbors import kneighbors_graph, radius_neighbors_graph
def Cal_Spatial_Net(adata, radius=None, n_neighbors=None, model='KNN', verbose=True, include_self=False):
"""
Construct spatial neighbor graph from spatial coordinates.
Parameters
----------
adata : anndata.A... |
4739776212883c044c9c499a6320edddd7a3cb6c847dd39880bb5894096a1f87 | Python | 1,692 | 70 | from math import log
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
def ShannonEntropyAndNomalize(name):
entropy = []
upper_list = []
lower_list = []
for s in name:
word = {}
upper_num = 0
lower_num = 0
... |
77aef0063737b8600862cad9216b964f53358bc9571693b34d56427de2281af3 | Python | 1,692 | 92 | #! /usr/bin/env python
# aponteeduardo@gmail.com
# copyright (C) 2016
''''
Graphical tools
'''
from copy import deepcopy
import numpy as np
from scipy.integrate import cumtrapz
from scipy import stats
from . import containers
def generalized_fits_factory(trials):
class GeneralizedFits(containers.TimeSeri... |
fc1c567ad9bfa1eebfdafb93f65143e64b30aa155b32c24e36d6ea3afc910a18 | Python | 1,692 | 66 | #! /usr/bin/env python
# aponteeduardo@gmail.com
# copyright (C) 2016
''''
Setup for sooner module.
'''
import glob
import platform
from setuptools import setup, Extension, find_packages
from Cython.Distutils import build_ext
import numpy as np
# Define the compilation flags
extra_args = {
'extra_compile_ar... |
3ddf6d26b54ce07f92291cf4a21a997a0a2a209711b31357759e2813f9d8db26 | Python | 1,693 | 51 |
# File: /scripts/preprocessing/clean_and_normalize.py
"""Data cleaning and normalization pipeline.
Steps performed:
1. Load raw Excel file from ../../data/raw/
2. Select final session (Sesja == 15)
3. Compute derived variables (RMS_pre/post, MF_pre/post, Delta_EMG, EEG_SUM, SILA_SUM)
4. Z‑score normalization within g... |
6433a3a3dce4dcc0ae8362a8572d2f593518fca2b9d87bfc035608ade51531b9 | Python | 1,693 | 38 | from brainscore_core.supported_data_standards.brainio.assemblies import BehavioralAssembly
from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set
from brainscore_core.supported_data_standards.brainio.s3 import load_assembly_from_s3, load_stimulus_set_from_s3
BIBTEX = """@article{zhang20... |
8d8318066d602e9f8a00024ef7058370a56261a1f54264112ee0070ed0684370 | Python | 1,693 | 51 | from langchain_ollama import ChatOllama
from chemgraph.models.supported_models import supported_ollama_models
def load_ollama_model(
model_name: str, temperature: float, base_url: str = None
) -> ChatOllama:
"""Load an Ollama chat model into LangChain.
This function loads a local Ollama model and configu... |
53ff447082cf19da03bb401b744fd8858f7e104b5a97bd6edf147185321a8ff4 | Python | 1,694 | 56 | import shutil
import subprocess
import argparse
from pathlib import Path
def parse_arguments():
parser = argparse.ArgumentParser(
description="Patch Arbor wheels built with scikit-build and corrected by auditwheel. Linux only."
)
parser.add_argument(
"path",
type=dir_path,
... |
f891b1c782d24dacd6227ffa047d90b5ee71c23e35daa4645bfce08145be8b99 | Python | 1,694 | 49 | import sys
import numpy as np
import matplotlib.pyplot as plt
def read_ecg_readings (input_file):
data = np.genfromtxt(input_file)
nlin, ncol = np.shape(data)
timesteps = data[:,0]
currents = data[:,1:]
num_leads = ncol-1
return timesteps, currents, num_leads
def plot_ecg_readings (t1, currents1, t2, current... |
1c6606bac804bf24ae4c3850c2813c1564434a3fd979ac848e6f014d16611b77 | Python | 1,695 | 54 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
"""Download a backup zip of an existing project.
A backup contains the project's tasks, jobs, annotations, and settings. Pair
this recipe with project_restore.py to migrate or clone a project.
Steps:
1. Retrieve the project by id.
2. Download i... |
93ad20aa0f74594e78ba659156f1c49795724e822c9af70fb11a43fa681fe1db | Python | 1,695 | 56 | import numpy as np
import matplotlib
# matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt
if __name__ == '__main__':
Kd = 100 # muM^4
K1 = 0.5 # mM^-1 ms^-1
K2 = 0.0012 # ms^-1
K3 = 0.18 # ms^-1
K4 = 0.034 # ms^-1
# Square Pulse forcing function for Transmitter concentration
def... |
c8abe53431ecbbe01a8a59a1f8472c80a0a510207c6f2f67990911f910789684 | Python | 1,695 | 53 | import shutil
import subprocess
import time
from pathlib import Path
import pytest
@pytest.fixture(scope="module")
def api_process():
file_target = "tests/test_modelling/test_streaming_modelling/setup_test_api.py"
process = subprocess.Popen(
[
"python",
file_target,
]
... |
22a2bb2d25ea1aefff8aea7290a518cbb2babdbc34ad64f54a87e70edb76674f | Python | 1,696 | 51 | import torch.nn.init as init
import torch.nn as nn
def apply_weight_init(module, init_name):
if init_name is not None:
if init_name == 'kaiming_uniform':
module.apply(kaiming_uniform_init)
elif init_name == 'kaiming_normal':
module.apply(kaiming_normal_init)
... |
23c6508e66ebbc4e8bbe6df77fadc457b96f4b7b0c8efea27b1487d309a71e60 | Python | 1,696 | 32 | import os
import shutil
from nipype.interfaces.base import CommandLineInputSpec, File, TraitedSpec, CommandLine, BaseInterfaceInputSpec, BaseInterface
from traits.api import Str, Int, Directory, Bool, Either
from cvdproc.config.paths import get_package_path
class EPIRegNipypeInputSpec(CommandLineInputSpec):
epi =... |
8fa0b3f01c2419d5cdf4a5227ae5fc9d78da69466b088539c6bf25b65ee4e71d | Python | 1,696 | 62 | import os, sys
# https://stackoverflow.com/questions/8391411/how-to-block-calls-to-print
# Alexander C
class PrintingBlocked:
"""Context manager for temporarily blocking `sys.stdout`.
Examples
--------
>>> def test(i):
>>> print(f'{i} not blocked')
>>>
>>> with PrintingBlocked():
... |
35196543dba3b998872019f6c9fbe3a08e2a2dae7ed8dd3ab638682c8d911e43 | Python | 1,697 | 68 | import torch
from torchmetrics import Metric
from typing import Union, List
import ecgxai.utils.exceptions as exceptions
class BaseMetric(Metric):
@property
def batch_args(self):
return []
@property
def input_args(self):
raise NotImplementedError()
class TMW(BaseMetric):
""" Tor... |
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