sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
8f7d3c43eb1c43e2e33afc8c993f2f45fdd34e0fa5bb327697b20e0ac129d7a9 | Python | 2,006 | 26 | import argparse
from .run import RapidParcTckEval
import torch
def main():
parser = argparse.ArgumentParser(prog="rapidparc-run", description="Apply RapidParc on a .tck tractogram")
parser.add_argument("--tck_path", type=str, required=True, help="Path to the input tck file.")
parser.add_argument("--out_pat... |
95340c5107ec772cf9cd4c1ec6bc4fadf7231199eeaea466feb518e86b9d4b26 | Python | 2,006 | 59 | import numpy as np
from brainways.transforms.base import BrainwaysTransform
from brainways.transforms.compose import Compose
class MockTransform(BrainwaysTransform):
def __init__(self, value: int = 1):
self.value = value
def transform_image(self, image, output_size=None, mode="bilinear"):
re... |
c6e8f0acc5c797c593a572c71b797788f8148c31e076c663cc1869d23085bacf | Python | 2,006 | 52 | """
Group Contrast
==============
In this tutorial we will cover how to do statistical significance testing with GLM permutations.
"""
#%%
# Get features
# ^^^^^^^^^^^^
# First we need to estimate subject-specific features. For example, this can be the static power map for each subject, which would be a (subjects, pa... |
d4b024f364104f73b3deaf924a9d8d01d53f158e31cccefa836b43746c2e21ea | Python | 2,006 | 61 | import sys
import numpy as np
import matplotlib.pyplot as plt
def read_transmembrane_potential(input_file, dt, print_rate):
data = np.genfromtxt(input_file)
n = len(data)
ms_each_step = dt*print_rate
end_simulation = n / ms_each_step
timesteps = np.arange(0,n)*ms_each_step
vms = data
re... |
fcda73f59bd062be7f0f016ad26e7f000f2db839aa8adfb639eb408f92fd19c6 | Python | 2,006 | 66 | # -*- coding: utf-8 -*-
#
# test_contexts_mpi4py.py
import unittest
import arbor as arb
from .. import cases
# check Arbor's configuration of mpi
mpi_enabled = arb.__config__["mpi"]
mpi4py_enabled = arb.__config__["mpi4py"]
if mpi_enabled and mpi4py_enabled:
import mpi4py.MPI as mpi
"""
all tests for distribut... |
8e7c4be65a575bb75f119befb84c5aacc67b5a0e6f473b90cf7a6d341a452804 | Python | 2,007 | 54 | import torch
from reducers import AvgNonZeroReducer
from utils import loss_and_miner_utils as lmu
from losses.generic_pair_loss import GenericPairLoss
class ContrastiveLoss(GenericPairLoss):
def __init__(self, pos_margin=0, neg_margin=1, **kwargs):
super().__init__(mat_based_loss=False, **kwargs)
... |
4672aaad6d0ecd7c9bc995d39c93e84da7b51a8a9ebd5655b46f8c9f9fe08978 | Python | 2,008 | 70 | from dipy.io.streamline import load_tractogram, save_tractogram
import glob
import json
import nibabel as nib
import numpy as np
import os
from os.path import join as ospj
import re
import sys
###########################
## Set variables and dirs #
###########################
subject = sys.argv[1]
config_file = sys.a... |
94cc5a382fe67629b9d1e2ab4eb263bfb1770d5a1e5c5b912ddcf6a6c5febc58 | Python | 2,008 | 61 | from .resnet import cifar10_resnet20
from .resnet import cifar10_resnet32
from .resnet import cifar10_resnet44
from .resnet import cifar10_resnet56
from .resnet import cifar100_resnet20
from .resnet import cifar100_resnet32
from .resnet import cifar100_resnet44
from .resnet import cifar100_resnet56
from .vgg import c... |
cd2728c74cec37c91e1ae62b7ad4a278ab6073aba51fcc760f9cc9e441c5d0ed | Python | 2,008 | 78 | CORPUS_CALLOSUM = [
251,
252,
253,
254,
255,
]
CEREBRAL_WM_RANGES = [
*[2, 41], # aseg left/right cerebral white labels
*list(range(3000, 3036)), # wmparc-left-labels
*list(range(4000, 4036)), # wmparc-right-labels
*[5001, 5002],
*[28, 60], # VentralDC included in white matte... |
ce8d6df1766e0e72e9fdd2751b5f407f809c2d670a05b857bf07504b163f51e0 | Python | 2,009 | 62 | from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def save_rgi_plot(df: pd.DataFrame, outdir: str | Path, fname: str = 'fig_rgi_min.png') -> Path:
outdir = Path(outdir)
outdir.mkdir(parents=True, exist_ok=True)
fig, ax = p... |
8d2cf8911d0ad3c01d6753ce91fc4342b6df0e5b17fd52334475cd826b57ac2c | Python | 2,012 | 61 | # 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
# -- Path setup --------------------------------------------------------------
# If ex... |
1bc8fb72c87d78d4056817ea71b37e17160aad70d374b55cf987a252064f3dd0 | Python | 2,014 | 110 | import numpy as np
from impedancefitter.elements import (Z_R, Z_C, Z_L, Z_w,
Z_wo, Z_ws, Z_stray, Z_CPE,
parallel)
omega = 2. * np.pi * np.logspace(1, 8, num=20)
R = 100
alpha = .5
def test_Z_R_type():
Z = Z_R(omega, R)
assert isins... |
4d5aa52788cefb0fcdceac34bb4eba2572557f977afc47da044421be6bf503e4 | Python | 2,014 | 71 | """
Example to use this repository
This example shows how to load the files from disk and save the output files
back to disk.
"""
# Load the python libraries
import os, sys
import numpy as np
import nibabel as nib
from dipy.io import read_bvals_bvecs
from dipy.core.gradients import gradient_table
from dipy.segment.... |
cfce49f2e93e7c7b2f70a84b627c2307165ee5da1de9e5fa42954783730fdb1f | Python | 2,014 | 65 | """Step 3: Coregistration."""
from pathlib import Path
from osl_dynamics import files
from osl_dynamics.meeg import parallel, rhino
from osl_dynamics.utils.filenames import OSLFilenames
# ----------------------------------------------------------------------------
input_dir = Path("BIDS")
output_dir = Path("derivativ... |
4ddf863c6cc1da40571cf819dc80cbd36a53e5b2a0f6fccdc47857835ae118db | Python | 2,015 | 64 | import subprocess
import shlex
import util, slurm
import os
nb_gpus = 20 # Number of GPUs used to render the dataset
nb_scenes = 15000 # Each scene leads to 9 images --> roughly 121000 training images, 7000 validation images and 7000 test images
rendertime = 75 # Number of seconds to render 9 images. Above and the re... |
9b02c58ede9f86ebaa769a9aa07959eab0eab2cf7ed49e31f854b4a2d476868b | Python | 2,015 | 51 | import torch
from tqdm import tqdm
import numpy as np
from transformers import pipeline, T5Tokenizer, T5EncoderModel
import os
import pandas as pd
path='/data2/whr/czl/TwiBot22-baselines/datasets/Twibot-22/'
user=pd.read_json(path+'user.json')
pretrained_weights = 't5-small'
tokenizer = T5Tokenizer.from_pretrained(pr... |
e3e0942ab9e118019d28badb604f1cf912767ac7d0d3db400386adfc602b8ba1 | Python | 2,015 | 58 | #@File(label="Input file (leave empty for demo):", required="false", description="Reconstruction file (.traces, .swc, json) to be analyzed. A folder of files is also supported.") input_file
#@String(label="Restrict to :", choices={"All processes", "Axon", "Dendrites"}) subtree_choice
#@ImageJ ij
#@SNTService snt
snt.r... |
02f415ed529f91eec3c63a1a1030ca7e685d84cf8925ce763a2c42d7907b50b1 | Python | 2,016 | 57 | """
Network Response
================
In this tutorial we will cover how to do statistical significance testing for a 'network response'. This is the average state/mode time course epoched around events of interest. We test is the response is significantly different from zero.
"""
#%%
# Get network response
# ^^^^^^^... |
6745224708429615ca11e048feea6095408b9a78e36ebf4d31d690ca8c447c48 | Python | 2,016 | 58 | import torch
from torch_geometric.nn import GCNConv
import torch.nn.functional as F
class GCN(torch.nn.Module):
def __init__(self, config, feat_type, input_dim, out_dim):
super(GCN, self).__init__()
self.config = config
self.feat_type = feat_type
self.hidden_dim = config["hidden_ch... |
aa4ab15ca760444e9df5b8e88a17b2531cc3bbaf9d8ccabae543c1c4d52c0e8f | Python | 2,016 | 81 | from collections import defaultdict
import anndata
import numpy as np
from tqdm.auto import tqdm
from abm import CellModel, get_lr_interactions2
def copy(adata):
return anndata.AnnData(X=adata.X.copy(), obs=adata.obs.copy(), var=adata.var.copy())
def _accumulate_results(fin, res):
for dic in res.values():... |
fd0d2449426818109ceae6e433baedffb501cd9839d522eba397586ebace0d87 | Python | 2,016 | 71 | #!/usr/bin/env python3
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import savgol_filter
import networkx as nx
def plot_network(rec, prefix=""):
fg, ax = plt.subplots()
ax.matshow(rec.connections)
fg.savefig(f"{prefix}matrix.pdf")
fg.savefig(f"{prefix}matrix.png")
fg.savef... |
46960516a6985aad4154c17716cf84428f9064374b4232073dd925dddd8c396e | Python | 2,017 | 80 | r"""Model package."""
from .mlp import MLP
from .basic_gnn import GCN, GraphSAGE, GIN, GAT, PNA, EdgeCNN
from .jumping_knowledge import JumpingKnowledge, HeteroJumpingKnowledge
from .meta import MetaLayer
from .node2vec import Node2Vec
from .deep_graph_infomax import DeepGraphInfomax
from .autoencoder import InnerProd... |
bc942ec70db5cde49891fe8f6e32435d4a6151ee1722a413a6cea78f5e9d3983 | Python | 2,017 | 83 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 22 20:25:48 2023
@author: rl05
"""
import sys
import os
import os.path as op
from mne import make_bem_model, make_bem_solution, write_bem_solution
from mne.viz import plot_bem
sys.path.append('/imaging/hauk/rl05/fake_diamond/scripts/preprocessing... |
f00ab4b4d097236adf99511bce0704cb0955380894d0b8232377ac2f49a00ecc | Python | 2,019 | 87 | import torch
import gc
from time import time
import numpy as np
__all__ = ['start_timer', 'stop_timer', 'print_timing']
TIMINGS = {}
def convert_seconds(seconds):
if seconds // 60:
minutes = seconds // 60
seconds %= 60
else:
minutes = 0
if minutes // 60:
hours = minutes /... |
e77fbc2dba177406dd58c70bb199985e36b0d4ae02d3045454fac67cb10287c8 | Python | 2,021 | 59 | import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from eir.train_utils import evaluation
def test_parse_valid_classification_preds():
test_val_true = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
test_val_preds = np.array([1, 1, 1, 0, 0, 1, 1, 0, 0, 0])
test_val_probs = np.... |
fcd9b4888126212fe7384726c828ad6e21447abb638e8c59553486ddd76bd71c | Python | 2,021 | 59 | """
SPDX-License-Identifier: MIT
SPDX-FileCopyrightText: © 2022 Renaud Bastien, Stephane Sanchez <stephane.sanchez@ut-capitole.fr>
Université Toulouse Capitole - Institut de Recherche en Informatique de Toulouse
Real Expression Artificial Life (REVA) Research Team
https://www.irit.fr/departement/calcul-intensif-simulat... |
78320a06447f5e9e6df31d6b4d99da7837490b48637af57d26cd013b9d4006f5 | Python | 2,022 | 71 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Download data for tests.
This is used to download our internal tests into your home/.scilpy.
"""
import argparse
import logging
import tqdm
import nltk
from scilpy.io.dvc import pull_test_case_package
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
... |
cdf63c33ee6fb2bf942bd0f59a63800befda359f353bfa000f62dd0555bfb3f0 | Python | 2,022 | 65 | import argparse
from pyrosetta import *
import numpy as np
import sys
init()
'''
This file creates the every individual the variant file at every individual residue position.
'''
amino_acids = ["A", "C", "D", "E", "F", "G", "H", "I", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "V", "W", "Y"]
def main(args):
pdb... |
22306cb79955ac97a4ae554a3a8b669caadf665297e3f689ef1e6ea260aa9bb1 | Python | 2,023 | 71 | import pubchempy as pcp
import random
import time
import json
from chemgraph.tools.ASE_tools import (
smiles_to_atomsdata,
molecule_name_to_smiles,
)
def get_random_molecule_names(n=2, cid_range=(0, 10000000), seed=2025, max_natoms=20, min_natoms=6):
"""Get a list of random molecule names and smiles from ... |
267e8288e35fba5c52b22cca697740806d71db5f7847ca8383603f59beb7770e | Python | 2,023 | 61 | # The ImpedanceFitter is a package to fit impedance spectra to
# equivalent-circuit models using open-source software.
#
# Copyright (C) 2018, 2019 Leonard Thiele, leonard.thiele[AT]uni-rostock.de
# Copyright (C) 2018, 2019, 2020, 2021 Julius Zimmermann,
# julius.zimmermann... |
55b4ba4dc97f7c5d2313a37cdea68784b84bd1e586857b9dacd8d4a9b28b437c | Python | 2,023 | 69 | # -*- coding: utf-8 -*-
import sys
import pandas as pd
sys.path.append('..')
from nerpy import NERModel
def test_my_bertspan_train():
# Creating samples
train_samples = [
[0, "HuggingFace", "B-MISC"],
[0, "Transformers", "I-MISC"],
[0, "started", "O"],
[0, "with", "O"],
... |
51ef74988beffcd7b417d1b6f537e5f176e2e59e564bce42cfd3cd791f69c344 | Python | 2,024 | 76 | import os
from importlib import import_module
from pathlib import Path
cell_graph_config = {
"cell_f_list": [
'area',
'r_equiv',
'r_maj',
'r_min',
'angel',
'ecc',
'maj_x',
'maj_y',
'min_x',
'min_y',
],
"edge_f_list": [
... |
87ec4968fa30291d8a142b79028b1cd13219191fae405850655d2a49f8cccfb1 | Python | 2,024 | 47 | """
Iter-7 record attempt — DINOv2 ViT-L/14 + ImageNet linear classifier at HIGHER RESOLUTION (336px).
Same model + recipe as our best dinov2_vitl14_lc (overall 0.41), changing ONLY the input resolution
224 -> 336 (24x24 patches vs 16x16, ~2.25x more tokens). DINOv2 interpolates its positional embeddings,
so the linea... |
3f6ef2c6371289b50908f1b60c4c533c025ec120294e802861c373fe853761ff | Python | 2,025 | 61 | '''MobileNet in PyTorch.
See the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
'''Depthwise conv + Pointwise conv'''
def __init__(self, in_planes, out_... |
979683e48c00214728028820510c55db37a8441bdf8b6e0d27893b2030584ce5 | Python | 2,025 | 50 | # mypy: disable-error-code="method-assign"
from unittest.mock import MagicMock, Mock, create_autospec
import pytest
from qtpy.QtWidgets import QMessageBox
from brainways.ui.controllers.registration_controller import RegistrationController
from brainways.ui.widgets.registration_widget import RegistrationView
@pytes... |
62d95b44342f4f82a845ea09c0d52f266cdf642b83e06cd85aad67e8101592d0 | Python | 2,026 | 69 | # -*- coding: utf-8 -*-
"""Utilities for time series noise removal."""
import os
import matplotlib.pyplot as plt
import nibabel as nb
import numpy as np
__all__ = ["nuisance_regressor"]
def nuisance_regressor(file_in, wm_mask, csf_mask, path_output):
"""This function creates nuisance regressors from a function... |
7532081fabb9df6cbf9d4c54912ad96b088167f08e7b2a0d2bf4885d264eddcd | Python | 2,027 | 55 | # ==============================================================================
# Binaries and/or source for the following packages or projects are presented under one or more of the following open
# source licenses:
# eval.py The OpenLane Dataset Authors Apache License, Version 2.0
#
# See:
# https://g... |
02cd24d8e1ae6adc512f8874b1d1bc098939856744f1f19a73267a51477e04bb | Python | 2,028 | 52 | from numpy.distutils.core import setup, Extension
from numpy.distutils.misc_util import get_numpy_include_dirs
import os, sys
mods = ['gradunwarp.core.coeffs', 'gradunwarp.core.globals',
'gradunwarp.core.__init__', 'gradunwarp.__init__',
'gradunwarp.core.utils',
'gradunwarp.core.unwarp_resamp... |
3f65513f8be0d9a89e32c820683a64dcd91fe3414fd69d7000e697a34dc02699 | Python | 2,028 | 59 | import torch
from torch_geometric.nn import GATConv
import torch.nn.functional as F
class GAT(torch.nn.Module):
def __init__(self, config, feat_type, input_dim, out_dim):
super(GAT, self).__init__()
self.config = config
self.feat_type = feat_type
self.hidden_dim = config["hidden_ch... |
d5765ce4340d8c42c7ec0ce412ccd1f199decd7c139b4a0bf5fd230e4e2657c9 | Python | 2,028 | 36 | from testsetclass import TestsetClass
class UPU23(TestsetClass):
def __init__(self):
self.weak = 1.0
self.molecules = ['3b', '4b', '2p', '7a', '1p', '3a', '6p', '8d', '3d', 'aa', '1b', '0a', '2a', '1g', '1e', '1c', '1f', '1a', '5z', '9a', '7p', '2h', '0b', '1m']
self.exp_values = {'0': 4.8... |
2fac2e7f539e3fa087846ca27899880e42afd23406ce690ac8c277d6a0143bd5 | Python | 2,029 | 69 | import numpy as np
from mc_analysis.simulator.apply_irf import convolve_with_irf, remove_padding
def test_convolve_with_IRF_raises_error_for_invalid_irf_std_tof():
G1 = np.random.rand(10, 20, 30)
tofs = np.linspace(0, 1.6e-9, 30)
try:
convolve_with_irf(G1, tofs, irf_std_tof="invalid_value")
... |
53bf42c97833bce0c35ad58160c1097235fdbcb73b7a4c706bbb2cc15ddc0947 | Python | 2,029 | 47 | # 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... |
a381385af9a57ffc1f569e38af3e8487ce180a76f79852e00119dbd5d73e9d1c | Python | 2,029 | 82 | """
Lists of supported models for different LLM providers.
"""
# OpenAI models that are supported
supported_openai_models = [
"gpt-4o-mini",
"gpt-4o",
"gpt-4.1",
"gpt-3.5-turbo-0125",
]
# Ollama models that are supported
supported_ollama_models = ["llama3.2", "llama3.1"]
# ALCF models that are suppor... |
d6ddc33fb40efdd3273ee9a46ca41305f6eaf4e05996956db5563735290e4e2a | Python | 2,029 | 65 | # =================================================================
# Author: Lucas Berg
#
# Program that plot the APD over a line
# This script only works if the grid is a plain tissue
# =================================================================
import sys
import numpy as np
import matplotlib.pyplot as plt
de... |
26197006f6beb7872dc1447a4f4502a9b365e3f9c3156eff68b288e97c01a9d6 | Python | 2,030 | 55 | # -*- coding: utf-8 -*-
import logging
from dipy.denoise.noise_estimate import piesno
import numpy as np
def estimate_piesno_sigma(data, number_coils=0):
"""
Here are Dipy's note on this method:
> It is expected that
> 1. The data has a noisy, non-masked background and
> 2. The data is a repe... |
6ed031b730359a681b6364b11d322bf28ceb2baacf09d84c3f7c02dcdd58b417 | Python | 2,030 | 50 | """
Tests for `bx.phylo.phast`.
"""
from io import StringIO
from numpy import (
allclose,
array,
)
from bx.phylo.phast import TreeModel
test_data = """ALPHABET: A C G T -
ORDER: 0
SUBST_MOD: HKY85+Gap
TRAINING_LNL: -178667772.836697
BACKGROUND: 0.227006 0.169993 0.169307 0.227262 0.206432
RATE_MAT:
-0.9... |
a3f29ec7c46f98b1ec7ea2bd82d05d9340f5d7aa7f3ff06b65383d3d4a969df1 | Python | 2,031 | 60 | # SPDX-FileCopyrightText: Copyright © Jørgen Riseth and Timo Koch
# SPDX-License-Identifier: GPL-3.0-or-later
from pathlib import Path
import click
import pyvista as pv
import nibabel as nibabel
import numpy as np
import simple_mri as sm
def mri_data_to_ndarray(
grid: pv.StructuredGrid, data_name: str, shape: t... |
8eb640ada507c827a7e10108e3cd9ac7be2062617d9bc5ea1a95d88205c8c7b1 | Python | 2,032 | 45 | # -*- coding: utf-8 -*-
import os
import sys
import unittest
import time
sys.path.append('..')
from nerpy.ner_model import NERModel
class QPSPredictTestCase(unittest.TestCase):
def test_bertsoftmax_speed(self):
"""测试bertsoftmax_speed"""
model = NERModel("bert", "shibing624/bert4ner-base-chinese"... |
dc26ff86520648359144a65a69a837887172dcbe98fec2f299cfa3c14f595374 | Python | 2,032 | 57 | 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 timm
from brainscore_vision.model_helpers.check_submission import check_models
ssl._create_default_https_context = ssl... |
2c8e976a07b5f7e0c410365536a69a3a37a644056a5644a51f89b18e3b9bc3e0 | Python | 2,033 | 63 | """ impyute.contrib.describe """
from impyute.ops import matrix
def describe(data): # verbose=True):
""" Print input/output multiple times
Eventually will be used instead of matrix.nan_indices everywhere
Parameters
----------
data: numpy.nd.array
The data you want to get a description fro... |
cf9ed7f99c37c4b5b9614068b2130945061a3da5e5926b368f26d149ac8ab7b8 | Python | 2,033 | 58 | import datajoint as dj
import inspect
import warnings
import os
import git
import datetime
def gitlog(cls):
"""
Decorator that equips a datajoint class with an additional datajoint.Part table that stores the current sha1,
the branch, the date of the head commit,and whether the code was modified since the l... |
17e288b7771afec08109df3a340ce5d8e790aea1948f86192a28a7e3240ac35d | Python | 2,034 | 72 | # Non-osipi dependencies
# osipi utilities
# osipi implementations
import numpy as np
# from utils.data_simulation.GenerateData import GenerateData
from src.original.fitting.ETP_SRI.LinearFitting import LinearFit
author_lookup = {
'ETP_SRI': LinearFit
}
# print('hi!')
def ivim_fit(authors=None, signals=Non... |
27fe7bc4e02f628e208c923ef09868f6814a6947fa99bf067065336a18035f95 | Python | 2,034 | 58 | import torch
import torch.nn as nn
import torch.nn.functional
import torchvision.models as models
from PIL import Image
from torchvision import transforms
def load_model(model_path):
# Assume using the modified ResNet18 model
model = models.resnet18()
model.conv1 = nn.Conv2d(1, 64, kernel_size=... |
62e530f2415e7bc7cec3306ef6e8c4582b5a81ccc404db27f43d91d681ff6751 | Python | 2,034 | 71 | import h5py
import os
import matplotlib.pyplot as plt
import numpy as np
DIR = os.path.dirname(os.path.realpath(__file__))
HOME_DIR = DIR.rsplit('/', 1)[0]
HOME_DIR = HOME_DIR.rsplit('/', 1)[0]
print('> HOME: ', HOME_DIR)
DATA_DIR = HOME_DIR + '/data/'
print('> DATA: ', DATA_DIR)
recon_list = [
'/examples/2024... |
925e0d7bd4de2766f38f048617b0cb1ef90278e1d414e4e800567eac341cbc8f | Python | 2,034 | 64 | import argparse
from omegaconf import OmegaConf
from torch.utils.data import DataLoader
from modules.utils import instantiate_from_config
import torch
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"--base",
type=str,
default="./configs/config.yaml"
)
... |
a27228f9bc2242b6f9c01f0c26368a796def6cf94dce7a5c276a5db94f8667fa | Python | 2,034 | 39 | import numpy as np
import pandas as pd
from . import ELA_Network_functions
#-----------------------------------------------------------------------------------
# ①calc E of all state from h and J
#-----------------------------------------------------------------------------------
#-------------------------
# input:pa... |
cb25d89d3d85a4abfe170a5b0a7030bb3ce4c51f8566d4058abbf61cf70b5753 | Python | 2,034 | 52 | # Info: originates from SEAL_OGB: https://github.com/facebookresearch/SEAL_OGB
# 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
import sys
# stolen from M.Zhang (OGB SEAL reposi... |
3878091c538978a078a0ef4c33f7bc42f1bb1915a30492710638eb63e18b2b36 | Python | 2,036 | 48 | from train_gtex import *
def main():
parser = argparse.ArgumentParser(description="Run a training experiment")
parser.add_argument("--config_path",type=str)
parser.add_argument("--fold",type=int)
parser.add_argument("--model_type",type=str)
args = parser.parse_args()
config_path = args.config_p... |
edebce7ed0f49c9634157a7d50f744c973b5d36474c542c1ec31e641040eb241 | Python | 2,036 | 61 | # Generated by Django 4.2.21 on 2025-11-21 16:21
from django.db import migrations
def init_settings_in_existing_tasks(apps, schema_editor):
"""
Initialize default quality settings for tasks that do not have them.
This is done at task creation for new tasks, but the old ones might need to be updated.
... |
58490fb9bc07b86c04f1471c8f110a418bf123c5822160aa4f2895f3725b6671 | Python | 2,037 | 27 | from testsetclass import TestsetClass
class SIE4x4(TestsetClass):
def __init__(self):
self.weak = 1.0
self.molecules = ['nh32+_1.0', 'nh32+_1.75', 'nh32+_1.25', 'h2+_1.75', 'he2+_1.75', 'he2+_1.0', 'h2o2+_1.75', 'he', 'h2+_1.25', 'nh3+', 'h2+_1.5', 'h2o', 'h2o2+_1.25', 'h2+_1.0', 'h2o2+_1.5', 'he2... |
f2645645ef7ebe97c6872d30e6579df9710470cd8174dbc61e757fe575f98b61 | Python | 2,037 | 66 | import os
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.5"
os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false"
import jax
import jax.numpy as jnp
from jax.scipy.stats import norm
import matplotlib.pyplot as plt
import argparse
import json
import numpy as np
import matplotlib.pyplot as plt
import jax.numpy as ... |
09893dcbd64c1f397d889248bbb571687cf68b747214af1f919a6c0f69564e42 | Python | 2,038 | 72 | import logging
from time import time
import dipy.tracking.utils as dtu
import numpy as np
logger = logging.getLogger("AFQ")
def get_percentile_threshold(mask, threshold):
zero_mask = mask == 0
mask[zero_mask] = np.nan
new_threshold = np.nanpercentile(mask, 100 - threshold)
mask[zero_mask] = 0
re... |
92aa9ed6c3bbf7b2c6150b4135d4d4fe481b28b95b2824cf4614ccc0873d9bf0 | Python | 2,038 | 78 | import os
os.environ.setdefault("QT_QPA_PLATFORM", "offscreen")
import pytest
pytest.importorskip("PySide6")
from PySide6.QtCore import Qt
from PySide6.QtWidgets import QApplication
from musclex.ui.widgets.batch_folder_selection_dialog import BatchFolderSelectionDialog
@pytest.fixture(scope="module")
def qapp():... |
2e023320f41164e6f0e933389c646adbd6d679882f1e6936c2aa83a02fce4050 | Python | 2,039 | 85 | # -*- coding: utf-8 -*-
"""
Generate 2D ODC
This script creates a two-dimensional ODC pattern.
"""
# external inputs
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import correlate
from numpy.fft import fft2, fftshift
# local inputs
from fmri_tools.simulation.odc import odc_2d
# 2D parameters... |
ff0e389e8f98aeec757549b15c528521d802ae9db99ce8a58b6c24a013a089a1 | Python | 2,039 | 75 | """vgg in pytorch
[1] Karen Simonyan, Andrew Zisserman
Very Deep Convolutional Networks for Large-Scale Image Recognition.
https://arxiv.org/abs/1409.1556v6
"""
'''VGG11/13/16/19 in Pytorch.'''
import torch
import torch.nn as nn
cfg = {
'A' : [64, 'M', 128, 'M', 256, 256, 'M', 512, 5... |
45679b595d2cb076cc5536652deba1063444fa97eac1c33488d6aadff2724212 | Python | 2,041 | 69 | import argparse
import logging
import os
from pyment.data.utils.ensure_fastsurfer_crops_exists import (
ensure_fastsurfer_crops_exists,
)
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s: %(message)s',
level=logging.INFO,
)
logger = logging.getLogger(__name__)
def create_fastsurfer_co... |
7fa21a515e20fcca90e856be73c5ab73024e773db0bf3758df6ee6a2247d7f99 | Python | 2,041 | 67 | # Copyright (C) 2018-2022 Intel Corporation
#
# SPDX-License-Identifier: MIT
from functools import update_wrapper
__plugins = {}
def add_plugin(name, function, order, exc_ok=False):
if order not in ["before", "after"]:
raise Exception("Order may be 'before' or 'after' only. Got {}.".format(order))
... |
94761a184860fc8e3acc6499de693c2b6487db607d6527da35e0b4577f6216a6 | Python | 2,041 | 69 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Script to flip a given surface (FreeSurfer or VTK supported).
Can flip surface coordinates around a single or multiple axes
Can also be used to reverse the orientation of the surface normals.
-----------------------------------------------------------------
Reference... |
b0a430d81f436d7861f4067f3ee4d53a53fc6a78c7cd833e9f9fa0464eb2b5fd | Python | 2,041 | 56 | import torch
from distances import CosineSimilarity
from losses import GenericPairLoss
from utils import loss_and_miner_utils as lmu
class IntraPairVarianceLoss(GenericPairLoss):
def __init__(self, pos_eps=0.01, neg_eps=0.01, **kwargs):
super().__init__(mat_based_loss=False, **kwargs)
self.pos_ep... |
e9adfb44cf946f02397e69d564d2c52139be66dc806a62e7e2463a2575e2e229 | Python | 2,041 | 52 | #!/usr/bin/env python
"""
HAL module for controlling a Thorlabs filter wheel.
Hazen 04/18
"""
import storm_control.hal4000.halLib.halMessage as halMessage
import storm_control.sc_hardware.baseClasses.amplitudeModule as amplitudeModule
import storm_control.sc_hardware.thorlabs.FW102C as FW102C
# FIXME: Should be am... |
2e0a2f75deef1f42b682c2388e417c719061f7561689f636b66273f113828676 | Python | 2,042 | 71 | import networkx as nx
import pandas as pd
import numpy as np
import random
from tqdm import tqdm
from sklearn.decomposition import PCA
import os
from gensim.models import Word2Vec
dataset_name='Twibot-22'
edge_path=dataset_name+'/edges.npy'
edge_index=list(np.load(edge_path))
edges=[]
#id_list=np.load('/data2/whr/lyh/t... |
7f1546eb43da63ee2f96ae58f7f33f16a70e6e50d482cb2fce201d9a153b9bb7 | Python | 2,042 | 67 | from __future__ import annotations
import numpy as np
import PIL.Image
import pytest
from numpy.typing import NDArray
from pytestqt.qtbot import QtBot
from labelme._utils._image import img_qt_to_arr
from labelme._widgets._brightness_contrast_dialog import BrightnessContrastDialog
@pytest.fixture()
def rgba_img() ->... |
d767e2ffec3a56d3897fc90e451b3ba5f689c11e19fef9d9c5293685c525c17f | Python | 2,042 | 68 | from paraview import simple
import os
from sys import argv, exit
import glob
import re
def natural_sort(l):
convert = lambda text: int(text) if text.isdigit() else text.lower()
alphanum_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ]
return sorted(l, key = alphanum_key)
import arg... |
77b91f7f86a1d3fe6609634268c199998482af92571e9ff8c49927966c8f619b | Python | 2,044 | 70 | # -----------------------------------------------------------------------------
# Copyright © 2014-2015 Colin Duquesnoy
# Copyright © 2009- The Spyder development Team
#
# Licensed under the terms of the MIT License
# (see LICENSE.txt for details)
# ----------------------------------------------------------------------... |
c3e1305a5aa05cd5bd1b529984f79388f119a5723374cec955aed2b734464c3a | Python | 2,045 | 66 | # -*- coding: utf-8 -*-
"""
Created on Wed Oct 7 14:46:26 2020
@author: Yoshi
"""
import csv
import numpy as np
from background_calculator import *
def data_sorter(path,raw_data,folders_list,framerate_information):
# Defines path to key folder
key_path = f"{path}/Data/Key"
# Reads frame key... |
e80707c6292f8b3bec9e3320e26cfe1415759ea1af0790916a2a385ba3d80338 | Python | 2,045 | 66 | import nibabel as nib
import numpy as np
from dipy.align import resample
from scipy.ndimage import gaussian_filter
from skimage.morphology import skeletonize
from skimage.segmentation import find_boundaries
def fit_wm_gm_interface(PVE_img, dwiref_img):
"""
Compute the white matter/gray matter interface from a... |
3d4959d360565bea1ed20c57855d2205b6f1e4050a0f5762fe450fd216866abb | Python | 2,046 | 70 | #!/usr/bin/env python
import numpy as np
import pandas as pd
import click as ck
import sys
from collections import deque, Counter
import time
import logging
from deepgo.utils import FUNC_DICT, Ontology, NAMESPACES
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO)
@ck.command()
@ck.option(
... |
887a195d58329ff809e328fc5ee912d2dd19fbb63c8806199a63d3b42e9783e7 | Python | 2,046 | 54 | # 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... |
e26c12c76db4397de22717b915d0b035b07111cf870526dc2464e1600099bf2c | Python | 2,046 | 53 | from __future__ import annotations
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_ch: int, out_ch: int, stride: int = 1):
super().__init__()
self.conv1 = nn.Conv2d(in_ch, out_ch, 3, stride=stride, padding=1, bias=False)
... |
c78e4a36382b7e0d6d35dbcab8d403f6a9c28064b882d8590120f4b9f7182ab7 | Python | 2,047 | 55 | import json
import os.path as op
import pytest
from MEGnet import megnet_init
def _set_model_paths(monkeypatch, tmp_path):
weights_path = tmp_path / 'model_v2k3'
monkeypatch.setattr(megnet_init, 'weights_path', str(weights_path))
monkeypatch.setattr(megnet_init, 'model_path', str(weights_path / 'model_v... |
6b468c5a4e55085fd0c763a23b8e6a5a43612d3979b840ddfb56dfda8d2dd31b | Python | 2,048 | 57 | import fire
import pickle
import numpy as np
import nilearn.plotting
from rsatoolbox.data.dataset import Dataset
from rsatoolbox.rdm.calc import calc_rdm
from nilearn.glm.contrasts import expression_to_contrast_vector, compute_contrast
data_path = "../bids_dataset/derivatives"
mapping = {
"adults": {
"rIP... |
82610bc2bffcd7ed905985feb086dd54e99efe6ce253d6abe1cab616326127e6 | Python | 2,049 | 51 | # Copyright (C) 2020-2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import shutil
from datumaro.components.dataset import StreamDataset
from cvat.apps.dataset_manager.bindings import GetCVATDataExtractor, import_dm_annotations
from cvat.apps.dataset_manager.util import ma... |
890b3019ce17db4c05fbfb5b7ce4503e32726da9ccd4525acc5c4849814e5b16 | Python | 2,049 | 53 | import numpy as np
import torch
from distances import CosineSimilarity
from utils import common_functions as c_f
from utils import loss_and_miner_utils as lmu
from losses.generic_pair_loss import GenericPairLoss
class TupletMarginLoss(GenericPairLoss):
def __init__(self, margin=5.73, scale=64, **kwargs):
... |
cc8b89ec7e8d87a6edeab907423df6bc63ed16c754f90cf25062d7ccbd32311c | Python | 2,049 | 70 | """Save HMM extract features.
The data needed to run this script has not been provide. You the get_data.py
script in the parent directory to download the output of this script.
"""
import pickle
import numpy as np
import pandas as pd
from glob import glob
from osl_dynamics.analysis import power, connectivity
from os... |
e1642806c297d0327504d5a7637a51fae4c51859243ea1068c31b944306bc291 | Python | 2,049 | 55 | import csv
import mido
from mido import Message, MidiFile, MidiTrack, bpm2tempo
def csv_to_midi(csv_filename, midi_filename='output.mid', note_duration=0.3, tempo_bpm=120):
"""
Convert a CSV with columns 'time' and 'midi_values' into a MIDI file.
Parameters:
- csv_filename: str, path to the input ... |
e58db6d25e845d95c2fb618d5b24e277683aa0fb9872e1434d4f80d61a80b539 | Python | 2,049 | 60 | from __future__ import annotations
from pathlib import Path
import pytest
from PySide6.QtCore import Qt
from pytestqt.qtbot import QtBot
from ..conftest import close_or_pause
from .conftest import MainWinFactory
from .conftest import draw_and_commit_polygon
from .conftest import draw_triangle
from .conftest import s... |
06cb983e3e35a8931f8c3355c3150ffa58770da140eb21b19e103e84c8f6d570 | Python | 2,050 | 58 | 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... |
322f50d0db19a509517aec9e7edf5c50b7499dabdbb5b2d78226edfbc6edd47f | Python | 2,051 | 59 | import numpy as np
"""
Functions for projecting slices onto the same coordinate system from partial alignment matrices obtained by PASTE2/
"""
def partial_stack_slices_pairwise(slices, pis):
"""
Projecting all slices onto the same 2D coordinate system.
In other words, project:
slices[0... |
48b15839d748af1141c37c6faaebd99532c15da420e4f170eb3e93bcc29f5d26 | Python | 2,051 | 60 | import user_features as uf
import csv
import ijson
import pandas as pd
import time
def get_user_vector(user):
user_vector = {
"id": uf.get_user_id(user),
"screen_name_length": uf.get_screen_name_length(user),
"description_length": uf.get_description_length(user),
"account_longvity"... |
ccc2fb3f79c0bfabdb9bfb0115cf2da370bee672096d055428eb8397a9a474d8 | Python | 2,051 | 75 | '''
Plot the place field of cells in the CANN model.
Created on 2021-04-20 by Tianhao Chu,
MOdified by Zilong Ji
'''
import brainpy as bp
import brainpy.math as bm
import numpy as np
import matplotlib.pyplot as plt
from cann_fft import CANN1D
#set default ramndom seed for reproducibility
np.random.seed(0)
#set backend ... |
ce5f4695c0f800bdb66a44a30d8c51c435955478c2316ed3b4346236860f1b04 | Python | 2,051 | 72 | """
.. _surface_picking_example:
Picking a Point on the Surface of a Mesh
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This example demonstrates how to pick meshes using
:func:`surface_mesh_picking() <pyvista.Plotter.enable_surface_picking>`.
This allows you to pick points on the surface of a mesh.
"""
############ IMPO... |
ea649c562491c7654f2d3da7f7fb2fcca823fdd2d6959ae9820445d5aa33386f | Python | 2,051 | 74 | import time
import json
import asyncio
import grpc
from ..qt import *
from .. import thalamus_pb2
from ..task_controller.util import create_task_with_exc_handling
from ..util import IterableQueue
class ChatEdit(QTextEdit):
def __init__(self):
super().__init__()
self.on_apply = lambda: None
def keyPressEv... |
05d00605617428106d45a4214046ce4425d0c482c3a1e1b0d5adbd7560daaa95 | Python | 2,052 | 58 | import user_features as uf
import csv
import ijson
import pandas as pd
import time
def get_user_vector(user):
user_vector = {
"id": uf.get_user_id(user),
"screen_name_length": uf.get_screen_name_length(user),
"description_length": uf.get_description_length(user),
"account_longvity"... |
aae047c17a8ae5746ba36a55de32a39d3616e4538aebfd977f039aa7694cb957 | Python | 2,052 | 69 | from pathlib import Path
from brainscore_core.supported_data_standards.brainio.stimuli import StimulusSet
from brainscore_core.supported_data_standards.brainio.packaging import package_stimulus_set
stimuli = []
image_paths = {}
stimuli_directory = '../datasets/edge'
'''
Dataset Meta Info (from https://github.com/rge... |
2c952ceaf71f07fddcd6caaf6a65de8630ad734043c0f6d057ea5b2af886c5f3 | Python | 2,053 | 73 | """Direct Gemini endpoint (Google-native protocol).
Logic moved verbatim from ``models/gemini.py``: ``GEMINI_API_KEY`` with
interactive prompt, curated model validation, ``max_output_tokens=6000``.
"""
from __future__ import annotations
from chemgraph.models.endpoints.base import (
CredentialPolicy,
Endpoint... |
3e50cffc796156c41f5bc732a208ad274bb5ffc884246e62b98fd6e766747753 | Python | 2,053 | 91 | import re
def count_characters(all_text):
cnt = 0
for item in all_text:
item = item.replace('\n',' ')
cnt += len(item)
return cnt
def count_hashtags(all_text):
cnt = 0
for item in all_text:
hashtags = re.findall(r'#\w', item)
cnt += len(hashtags)
return cnt
de... |
41757e006c5c2e831ebfce0743b472192da45268c76ac5483ce151fc5491579a | Python | 2,053 | 64 | # Copyright 2016 Numenta Inc.
#
# Copyright may exist in Contributors' modifications
# and/or contributions to the work.
#
# Use of this source code is governed by the MIT
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
import os
import math
import simplejson as json
from ... |
8bf8d06f22b8bbac80ff1dbb217cae2ddad27a0bacc21b7b19671d3256163eb7 | Python | 2,053 | 65 | #@String(value="This script converts all .ndf files in a directory into ROIs (as ZIP files). Conversion log is shown in Console.", visibility="MESSAGE") msg
#@File(label="Directory of .ndf files:", style="directory") input_dir
#@Boolean(label="Open Console", value=false) open_console
#@String(value="NB: ROI Manager wil... |
1e45e5255e524cca9d624085419437747c8bbafe259b591abc90d90e524cef10 | Python | 2,054 | 64 | from typing import Generator, Callable, List, Tuple, Any
from zigzag.classes.stages.Stage import Stage
from zigzag.classes.cost_model.cost_model import CostModelEvaluation
from zigzag.classes.hardware.architecture.accelerator import Accelerator
from zigzag.classes.mapping.spatial.spatial_mapping import SpatialMapping
... |
6c6f8f0ff0a352981faec292f32805ac29172f8687fbcfe2673f43c8d5408b0f | Python | 2,054 | 47 | #!/usr/bin/env python
import argparse
import json
import csv
from collections import OrderedDict
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--configFile', metavar='FILE', help='configuration file input (REQUIRED)', type=argparse.FileType('rt'), required=True, dest='config')
parser.add_argument('-s... |
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