sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
191a70871a2cbf33f5d31509a43ee925052bc11164dd1ec638e3fcf982f73ab4 | Python | 2,897 | 92 | import pandas as pd
import time
import csv
import spacy
from tqdm import tqdm
import content_features as cf
import os
nlp = spacy.load('en_core_web_lg', disable=['parser', 'ner'])
def get_content_vector(tweets):
links = cf.count_urls(tweets)
mentions = cf.count_mentions(tweets)
content_vector = {
... |
4ab22d9cefdec5bdf4e8689d417cd0dbf1e5c230c82887c1649d6b361b4784f4 | Python | 2,897 | 90 |
#
# common/tools/base.py
# 2021-05-10
# Written by SK Park, NIRAL, UNC
#
# External tool wrapper base class
#
import sys
import time
from pathlib import Path
import subprocess as sp
import dtiplayground.dmri.common
common = dtiplayground.dmri.common
### decorators
def measure_time(func): ## decorator
... |
8d63b6628b410b19ed56c4e226719cc6e716f8a09ffaeeff8fa4fc9537bf728e | Python | 2,897 | 64 | #!/usr/bin/env python3
# Copyright 2024 Image Analysis Lab, German Center for Neurodegenerative Diseases (DZNE), Bonn
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apac... |
2c6ffc1f579059af151534419663fe9d54035af9eb24f33dd2ab94dc227dcb50 | Python | 2,898 | 113 | from dataclasses import dataclass, field
import torch
from torch import nn
from torchvision.ops import StochasticDepth
from eir.models.layers.norm_layers import LayerScale
@dataclass
class ResidualMLPConfig:
"""
:param layers:
Number of residual MLP layers to use in for each output predictor after f... |
70fddbcbb3e6a8619490bc0e9af76d623455264eb96688f8e1ff43b37865d40f | Python | 2,898 | 91 | import numpy as np
from rsatoolbox.data import TemporalDataset
from rsatoolbox.rdm import calc_rdm_movie
import pickle
from joblib import Parallel, delayed
from mne import read_epochs, setup_source_space, spatial_dist_adjacency, add_source_space_distances
from mne.minimum_norm import (
read_inverse_operator,
... |
07cf6d057bc171b6001a4a919a8fe388a11f9933ad38ebdf70fca6de921d2ff5 | Python | 2,899 | 86 | import argparse
import os
import os.path as osp
import time
import numpy as np
import torch
import torch.nn.functional as F
from sklearn.metrics import f1_score
import pandas
import json
import torch_geometric.transforms as T
from torch_geometric.nn import ChebConv, GCNConv # noqa
from tqdm import tqdm
parser = argpar... |
74c96587bfaef7d9f180784096b4df496a343b90a7c63e338d8c10cd89f8ec6b | Python | 2,899 | 88 | """A setuptools based setup module.
See:
https://packaging.python.org/en/latest/distributing.html
https://github.com/pypa/sampleproject
"""
# Always prefer setuptools over distutils
from setuptools import setup, find_packages
# To use a consistent encoding
from codecs import open
from os import path
import runpy
her... |
7c1e45f7fb51975639a396461f1f10ea016f82a5b360708e593b7b4e101fe2b2 | Python | 2,899 | 82 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors: Henrik Zunker
#
# Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Lice... |
8206d60fcfa81269ecf834d8b2808d27b9e448056f913f0de482244b3eb49fa0 | Python | 2,899 | 91 | """The ISH upsampling helper.
Always called with a small ``target_volume_shape``: the default allocates a
1320 x 800 x 1140 float array, several gigabytes, which no test should do.
"""
import numpy as np
import pytest
from ccf_streamlines.dataset import upscale_ish_volume
@pytest.fixture
def small_volume():
""... |
cce47525ef8c2c72b959f7c4a22b73d6e49ce3f449c8f4f17c3fe1d28da39dcb | Python | 2,899 | 93 | import glob
import os
import numpy as np
import cv2
import multiprocessing.pool as mpp
import multiprocessing as mp
import time
import argparse
import torch
import random
SEED = 42
CLASSES = ('background', 'building', 'road', 'water', 'barren', 'forest',
'agricultural')
PALETTE = [[255, 255, 255], [255, 0... |
d0732fbbbc1c625cdac957e716eed9efa9c8cf95570c2659fad3a7c6ac38b05a | Python | 2,899 | 83 | """Augment images for classification
Usage:
augment_images.py [options] <input-dir> <output-dir>
augment_images.py -h | --help
augment_images.py --version
Arguments:
<input-dir> Path to the input directory with images.
<output-dir> Path to the output directory with augmented images.
... |
98d270733e7628011a628ae5ee1def471cb490d2176a97944b8814dc348f3ecb | Python | 2,900 | 114 | """Dannce module and default parameters"""
# Default parameters, which can be superseded by CL arguments or
# config files
_param_defaults_shared = {
"immode": "vid",
"verbose": 1,
"gpu_id": "0",
"loss": "mask_nan_keep_loss",
"start_batch": 0,
"exp": None,
"viddir": 'videos',
"io_config"... |
f143f2303d26f93a8800779086690ffa6b876248e82a3ac3038541ebbebdaed3 | Python | 2,900 | 100 | import numpy as np
import torch
import os
from tqdm import tqdm
import yaml
from argparse import Namespace
import pandas as pd
import SimpleITK as sitk
def load_nii(path, prob_map=False):
img = sitk.ReadImage(path)
img = sitk.GetArrayFromImage(img)
if prob_map:
img = (img > 0.5).astype(np.uint8)... |
b6561a007dedd683608e4b2cbdd997ab7d0dbf2dea936957cebe64a1b29a35f1 | Python | 2,901 | 59 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
import torch
from ultralytics.models.yolo.detect.predict import DetectionPredictor
from ultralytics.utils import ops
class NASPredictor(DetectionPredictor):
"""Ultralytics YOLO NAS Predictor for object detection.
This class extends the Det... |
e688b5e9af6955f6e283cb05018d0b36f13661f47ec936a75321b7b8e17e6e69 | Python | 2,901 | 64 | from tests.common import *
class Test_AS_Pipeline:
def test_pipeline_no_run(self,demo_dir):
LOGGER.info("run aberrantSplicing pipeline with \'run: false\'")
# change the second instance of "run: true" to "run: false" to turn off the AS module
# run the AS module using this config_AS_norun... |
9509f9cb020e45c42d7b95e635e3d7ac4a43964e333e0c8537b1de0e025ba32c | Python | 2,903 | 82 | """
Variant: numba (JIT-compiled Gaussian inner loop)
The Gaussian evaluation loop is compiled with ``@numba.njit(cache=True,
fastmath=True)``. ``fastmath=True`` allows LLVM to reorder and approximate
floating-point operations for speed (safe for a smooth Gaussian curve).
Voigt falls back to ``scipy.special.wofz`` ... |
a1ec2ad1f2d82cb706e5a6c35952678b2844e6c80ec287455a1e6acce68e6947 | Python | 2,903 | 81 | from pydantic import BaseModel, Field
class Psi4Calc(BaseModel):
"""PSI4 quantum chemistry calculator configuration.
This class defines the configuration parameters for PSI4 quantum chemistry
calculations. It supports various quantum chemical methods, basis sets, and
SCF convergence parameters.
... |
b80475fe676b74f79dbed66cae830ad5e9ce558ca3b6da15ed503b847f6d70fc | Python | 2,903 | 79 | #!/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=['processing.zip'])
tmp_dir = tempfile.T... |
e202c40f12ded812c4e113e4427b793c9ead1a363b07ed9f0db719996b148fa3 | Python | 2,903 | 72 | import numpy as np
import torch
from distances import LpDistance
from utils import common_functions as c_f
from utils import loss_and_miner_utils as lmu
from losses.base_metric_loss_function import BaseMetricLossFunction
class AngularLoss(BaseMetricLossFunction):
"""
Implementation of https://arxiv.org/abs/1... |
7383a0ce4f90ef4b7943fbcc56b2dc59f022afb85d8b03aa25f5a340b9f24f79 | Python | 2,904 | 72 | # -*- coding: utf-8 -*-
"""
Created on Tue May 14 10:57:42 2024
@author: Administrator
"""
import torch
from torch import nn
import numpy as np
class FinerLayer(nn.Module):
def __init__(self, in_features, out_features, bias=True, is_first=False, omega_0=30, first_bias_scale=None, scale_req_grad=False):
... |
bdb29f7c2582e3665e37567d98697bc5cae127272eed7097616d91af3fd15c46 | Python | 2,904 | 82 | #!/usr/bin/env python
import numpy as np
import pandas as pd
import click as ck
from sklearn.metrics import classification_report
from sklearn.metrics.pairwise import cosine_similarity
import sys
from collections import deque
import time
import logging
from sklearn.metrics import roc_curve, auc, matthews_corrcoef
from... |
dd1b2acfbfdfd5028e3cf74df1db60def16a85cbfc2a6411cc6fd4ff597497eb | Python | 2,904 | 82 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from types import SimpleNamespace
from unittest import TestCase
from unittest.mock import MagicMock, patch
from rest_framework import status
from rest_framework.test import APIRequestFactory
from rq.job import JobStatus as RQJobStatus
from cvat.app... |
99053bdd5f5bf5cffdb6aa68da166dec098418f55d5ce65c72e611e1f9769f85 | Python | 2,905 | 99 | from __future__ import annotations
from collections.abc import Callable
from collections.abc import Iterator
import pytest
from PySide6 import QtCore
from PySide6.QtWidgets import QApplication
from pytestqt.qtbot import QtBot
from labelme import _locale
from labelme._widgets._label_dialog import LabelDialog
@pytes... |
e1676ed2429e01d139ef0f31875342485adbec4542b08854de8f58dbb3890176 | Python | 2,905 | 81 | #!/usr/bin/env python
import warnings
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
import pandas as pd
import numpy as np
from sklearn.externals import joblib
from PAINTeR import global_vars
import PAINTeR.connectivity as con... |
acbcdffd377b89948715b354ee72e0e108694458b4f200123295b12257f3d162 | Python | 2,906 | 74 | from torch.utils.data import DataLoader
from geoseg.losses import *
from geoseg.datasets.isic2018_dataset import ISIC_Dataset
from geoseg.models.deepunet.DeepUnet import Model
from catalyst.contrib.nn import Lookahead
from catalyst import utils
CLASSES=('background', 'foreground')
# training hparam
max_epoch = 100
i... |
c88eec27953cef63ded46be616ae462128579f76e43ff62c906f139ed0376abb | Python | 2,906 | 97 | """
Kernels.
"""
# Author: Oualid Benkarim <oualid.benkarim@mcgill.ca>
# License: BSD 3 clause
import numpy as np
import scipy.sparse as sp
from scipy.stats import rankdata
from scipy.spatial.distance import pdist, squareform
from sklearn.metrics.pairwise import rbf_kernel
from .utils import dominant_set
def _bu... |
5e38b33a832dab48c154a9e6699d5c0748b7049fe2ac59ca30c2cab20faf34f3 | Python | 2,907 | 89 |
import glob
import json
from run import parse_config
from run import check_dict
class colors:
GREEN = '\033[32;1m'
RED = '\033[31;1m'
ENDC = '\033[0m'
def print_diffs(test_path, tmp_path, verbose=False, failed_only=False):
error_files = glob.glob(tmp_path + "/**/*_diff.json", recursive=True)
error_boundari... |
b5e0e9dc9937ce07213bf50169ed9a511b8784f535e6420c1c7eccca7a12d0f5 | Python | 2,908 | 83 | import pytest
from eir.predict_modules.predict_target_setup import _load_labels_for_predict
from eir.setup import config
from eir.target_setup.target_label_setup import (
gather_all_ids_from_output_configs,
get_tabular_target_file_infos,
)
from tests.setup_tests.fixtures_create_data import TestDataConfig
@py... |
f455c83108fa59772a9823fd340efca6b6baff5d3c7cf38c9df4fde328fbaeb0 | Python | 2,908 | 92 | import numpy as np
def notnan(x, start=0, increment=1):
while np.isnan(x[start]) and 0 <= start < len(x):
start += increment
return start
def fill_nans(x):
"""
:param x: 1D array -- will
:return: the array with nans interpolated
The input argument is modified.
"""
nans = np... |
ae6c84a8f19916ca2d3823114acbc2efd67754aba9dbbe73c54f4c5852b53050 | Python | 2,909 | 63 | # !/usr/bin/env python
# coding: utf-8
# @Time : 2022/7/17 13:54
# @Author : Z.-H. G.
# @Email_0 : guozhenhao17@mails.ucas.ac.cn
# @Email_1 : guozhenhao@tongji.edu.cn
# @File : params.py
# @IDE : PyCharm
import pandas as pd
# TODO yaml
PARAMS = \
{'preprocess':
{
# We only recommend... |
b8c6c70cb3d138c3ef21b21ae136710dbfcbc1d3f8b2c1ca0cb988199254e284 | Python | 2,909 | 83 | #!/usr/bin/env python2.4
"""
Returns all positions of a maf with any pwm score > threshold
The positions are projected onto human coordinates
"""
import sys
from bx import intervals
from bx.align import maf as align_maf
from bx.pwm.pwm_score_maf import MafMotifScorer
def main():
if len(sys.argv) < 5:
pr... |
ba8bfb78cc97d123f5c211691e2f15a682787236218935da079b277259d8fe70 | Python | 2,909 | 91 | import torch.utils.data as data_utils
import torch
from PIL import Image
import numpy as np
import random
import glob
import os
import math
from Utils import utils
Map = {
'Normal':0,
'NETU': 1,
'ELSE': 1
}
utils.seed_torch(seed=0)
class ZSData(data_utils.Dataset):
def __init__(self, path, path2, tra... |
ee581e05dd552153a54faeb1b199ae81cbdf2821b23c3ea218e03600ba2418bb | Python | 2,909 | 85 | import subprocess
import sys
import yaml
import os
from glob import glob
import pandas as pd
import numpy as np
print ('starting script Meanvar Quantities!')
pathpath = 'PATHS/general_paths.yml'
with open(pathpath,'r') as f: pdict = yaml.safe_load(f)
genpath = pdict['metricsextraction']
script = 'metrics_extraction... |
8108a10403cfd2c90273572ac4fee6a557a6152eb9edc8c7d41a67a5ca7cc672 | Python | 2,910 | 91 | from __future__ import annotations
from collections.abc import Callable
from typing import TYPE_CHECKING, cast
from ._container_widget import ContainerWidget
if TYPE_CHECKING:
from magicgui.widgets import protocols
from magicgui.widgets._concrete import MenuBar, StatusBar
from ._widget import Widget
c... |
42a093281d722fb89a2eaf63c60bdb248346899ba1f90fba90bbabe17e45853e | Python | 2,911 | 103 | import os
from types import SimpleNamespace
os.environ.setdefault("QT_QPA_PLATFORM", "offscreen")
import pytest
pytest.importorskip("PySide6")
from PySide6.QtWidgets import QApplication
_APP = QApplication.instance() or QApplication([])
from musclex.modules.ProjectionProcessor import ProcessingBox
from musclex.ui... |
6d587a670309abcd1ae866e87d64250f39f436fa641c1a859ccca4917c237dcf | Python | 2,911 | 83 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors: Henrik Zunker
#
# Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Lice... |
9519691aa5cd80a238efcdb19a48ff0bc02e5b5fa25662067ff996132d33360a | Python | 2,912 | 91 | # Copyright (c) OpenMMLab. All rights reserved.
from collections import OrderedDict
def swin_convert(ckpt):
new_ckpt = OrderedDict()
def correct_unfold_reduction_order(x):
out_channel, in_channel = x.shape
x = x.reshape(out_channel, 4, in_channel // 4)
x = x[:, [0, 2, 1, 3], :].transp... |
b4ad9521239368cea245369487c45cc449907814753f4462e5e444b1d5ef611d | Python | 2,912 | 78 | # Generated by Django 4.2.21 on 2025-07-10 17:28
from django.conf import settings
from django.db import migrations
from django.db.models import Exists, F, OuterRef
def delete_email_addresses_for_dummy_users(apps, schema_editor):
EmailAddress = apps.get_model("account", "EmailAddress")
SocialAccount = apps.ge... |
0057289806c86f5a66188ddff27947464c4e99d6182f9b675bbe2a1104f1de9f | Python | 2,913 | 72 | from torch.utils.data import DataLoader
from geoseg.losses import *
from geoseg.datasets.loveda_dataset import *
from geoseg.models.DCSwin import dcswin_small
from catalyst.contrib.nn import Lookahead
from catalyst import utils
# training hparam
max_epoch = 30
ignore_index = len(CLASSES)
train_batch_size = 8
val_batch... |
0cc0153259b3b068be956ba4a297514b264e6a0508003aee3a3f2fa8783ca779 | Python | 2,914 | 95 | from collections.abc import Sequence
from pathlib import Path
from docs.doc_modules.experiments import AutoDocExperimentInfo, run_capture_and_save
def get_tutorial_01_run_1_fusion_info() -> AutoDocExperimentInfo:
base_path = (
"docs/tutorials/tutorial_files/b_customizing_eir/01_customizing_fusion_tutoria... |
3c340b1a4573236f585de9a99c5339673003f3b5797b7feae979d524226a39aa | Python | 2,914 | 87 | '''
Script to perform dimensionality reduction
over activation values from Wav2Vec2.0.
OPTIONS
n_features - Number of features to use (default = 50)
EXAMPLE USAGE:
python audio_w2v2_pca.py n_features=10
'''
import os, sys, glob, gzip, pickle
from typing import Any, Union, Callable
import numpy as np
impo... |
5c0d7682bd0cc430ee97b9ac15af17e50bf2bd3093080e8a66b576bc2d9af964 | Python | 2,914 | 93 | '''Test write csv functionality'''
import unittest
from ogo.util.write_csv import write_csv
import subprocess
import shutil, tempfile, filecmp
import os
class TestWriteCSV(unittest.TestCase):
'''Test write csv functionality'''
def setUp(self):
self.test_dir = tempfile.mkdtemp()
self.csv_file... |
63fb1e005cfd989d6daeb5d8fe9c1a9ef5e3ccba19a14f21bc7ea375f366c706 | Python | 2,914 | 85 | #!/usr/bin/python3
#
import os
import sys
import argparse
import numpy as np
from uac import utils
# Create the parser
my_parser = argparse.ArgumentParser(description="Calculation of labels using laplace solves only")
# Add the arguments
my_parser.add_argument("target_path", metavar="path", type=str, help="Path for ... |
cefcbdce170ef49fc3b8b6cd1e9f8776ba337f2cace30349dbaea42bcb0e5b62 | Python | 2,914 | 80 | """Load Anthropic models using LangChain."""
import os
from getpass import getpass
from langchain_anthropic import ChatAnthropic
from chemgraph.models.supported_models import supported_anthropic_models
from chemgraph.utils.logging_config import setup_logger
logger = setup_logger(__name__)
def load_anthropic_model(
... |
da7828f3bd64cc4c1c041b775e63f3864a20c1bc155ad31dfe08f7fcdd8dd1e9 | Python | 2,914 | 74 | from torch.utils.data import DataLoader
from geoseg.losses import *
from geoseg.datasets.isic2018_dataset import ISIC_Dataset
from geoseg.models.Unet import UNet
from catalyst.contrib.nn import Lookahead
from catalyst import utils
CLASSES=('background', 'foreground')
# training hparam
max_epoch = 100
ignore_index = ... |
c9cdfd05037b11cf316fdee3b32a042376492fff3282dbc3c6e414ae795994fd | Python | 2,916 | 110 | import matplotlib.pyplot as pl
import numpy as np
def confusion(seqs1, seqs2, n1, n2):
"""Compute the confusion matrix between two labbelled sequences."""
C = np.zeros((n1, n2))
for i in range(len(seqs1)):
b3, b4 = seqs1[i], seqs2[i]
C[b3, b4] += 1
C = C / C.sum(axis=1)[:, np.newaxis]
... |
db8e75e43ddd60a0fe8b39ee89d4e51d2a5905d92c384f4b398f7b176912bd4c | Python | 2,916 | 70 | import ctk
from slicer.util import VTKObservationMixin
import slicer
import qt
class MultiModelSelectorWidget(ctk.ctkCheckableComboBox, VTKObservationMixin):
def __init__(self, parent=None):
ctk.ctkCheckableComboBox.__init__(self, parent)
VTKObservationMixin.__init__(self)
self.addObserve... |
18a94dff039379e5f089a95c52a73f3fb01c04494dcfba208c402d4739f4c9b3 | Python | 2,917 | 43 | from brainscore_core.supported_data_standards.brainio.assemblies import DataAssembly
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{Kar2019,
... |
1ab4cd6ead4e3813c4bb9cdf3d2e75205ebe02a6bbe873d0162243e23eafadd5 | Python | 2,917 | 71 | import os
from dataset_src.cath_imem_2nd import Cath_imem,dataset_argument
from torch.optim import Adam
from torch_geometric.data import Batch,Data
from dataset_src.utils import NormalizeProtein
from Bio.PDB import PDBParser
from Bio.PDB.DSSP import DSSP
import torch.nn.functional as F
import torch
from tqdm import tqd... |
f6ff6b57b71e0ce4d8873948c1f2ed11cfafc1e30e422f74d800f1baf4a69345 | Python | 2,917 | 59 | """Post-hoc analysis of inferred state/mode dynamics.
This subpackage provides tools for analysing the output of trained models
(HMM states, DyNeMo modes, etc.) and for static (time-averaged) analysis.
Modules
-------
- :py:mod:`~osl_dynamics.analysis.connectivity` — Functional connectivity
analysis (coherence, ima... |
b02ec847fa582b29e5505d73296f0fb1211299571e88459741dd92abd80b69c9 | Python | 2,918 | 57 | from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from fixres.hubconf import load_state_dict_from_url
from fixres.transforms_v2 import get_transforms
from brainscore_vision.model_helpers.activations.pytorch import load_images
import numpy as np
from importlib import import_module
import ssl... |
fdd239bdb379ebcf66862f465e694b6f5f742789c32efb4a800a1bcfae0d0ff0 | Python | 2,918 | 80 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
from pathlib import Path
import torch
from ultralytics.utils import LOGGER
from ultralytics.utils.checks import check_requirements
def _remainder_scalar(x: torch.Tensor, s: float) -> torch.Tensor:
"""Decompo... |
31ec806aacebc06093e3561e74aff114d092057e67bdf2e78ef011fdbcf167db | Python | 2,919 | 91 | import numpy as np
import pandas as pd
from scipy.stats import zscore
import pickle
import fire
from joblib import Parallel, delayed
from rsatoolbox.rdm import RDMs
from rsatoolbox.model import ModelWeighted
from rsatoolbox.model.fitter import fit_regress
import rsatoolbox
from mne import read_source_estimate
from ... |
66d990c228febc698d75613dd110e8be8f46fe2404ba064be38cc4a873dc4645 | Python | 2,919 | 107 | """Utilities for lazy generation of FastSurfer crop volumes."""
import logging
import os
from concurrent.futures import ThreadPoolExecutor
import nibabel as nib
from nibabel.spatialimages import SpatialImage
from tqdm import tqdm
from pyment.preprocessing import conform
logger = logging.getLogger(__name__)
def en... |
ec0529408415933fad0918224771bffdc398eace9518e6921d602d55386250c7 | Python | 2,920 | 49 | """Rebuild current figure scripts in an isolated staging directory and compare outputs."""
import fitz
import hashlib
import json
import re
import shutil
import subprocess
import tempfile
from pathlib import Path
ROOT=Path(__file__).resolve().parents[2]
NAMES=['primary_enrichment','npp_oxphos_evidence','oxphos_gene_lan... |
dfd4cde062b0f2647f9e344401d3ee1bb7aac1cbe81999aabab57770f4f8414d | Python | 2,921 | 87 | """
Classes to support nib files.
:Author: James Taylor (james@bx.psu.edu), Bob Harris (rsharris@bx.psu.edu)
A nib sequence is a sequence of DNA, using the 10 character alphabet A,C,G,T,N
(upper and lower case). The file is packed as 4 bits per character.
nib file format
---------------
Fields can be in big- or li... |
4eca8451c879c0f733111e75b711513bb25b8ff718b4148683dc807f05c4d47a | Python | 2,922 | 110 | """
Evaluation plotting utilities for score prediction.
"""
from pathlib import Path
from typing import Optional, Sequence
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import (
roc_curve, auc,
precision_recall_curve, average_precision_score,
confusion_matrix, ConfusionMatrixDisp... |
65067cf0056a7b404bcf4052f0664117c0f10234d39a06180cf3f64eee3f2615 | Python | 2,922 | 82 | #!/usr/bin/env python3
import torch
import matplotlib.pyplot as plt
import SimpleITK as sitk
from time import time
from fireants.io import Image, BatchedImages
from fireants.registration.affine import AffineRegistration
from fireants.registration.greedy import GreedyRegistration
from fireants.interpolator import firea... |
d840912d2a8982820d0d661e7f893d5298e375ad7f5f372705d01d290f1f177b | Python | 2,922 | 94 | import numpy as np
import pandas as pd
import scipy.ndimage as nd
from skimage import io as skio
import sys
import getopt
def usage():
print("""
Usage : python3 gen_stacked_tif.py < -i mask.lst>
< -a anno.txt>
< -o output prefix>
... |
fcb21c6d9d9de019259152ffda28dd39785382cc27f0438c437fa9f2ca5cb027 | Python | 2,922 | 57 | import numpy as np
def one_tailed_bootstrap(sample1, sample2, iterations=1000, direction='greater'):
# Perform bootstrap resampling
resampled_mean_diffs = []
one_tailed_p_value = []
for _ in range(iterations):
resampled_sample1 = np.random.choice(sample1, size=len(sample1), replace=True)
... |
8fc9555ccf04997d73d33852b07309660e75ac1bd608201c89ea9792ca56a308 | Python | 2,924 | 97 | from dataclasses import dataclass
from functools import reduce
from typing import Any, Literal, Protocol, TypeGuard
from torch import Tensor, nn
from eir.models.input.array.models_cnn import CNNModelConfig
class ImageModelClassGetterFunction(Protocol):
def __call__(self, model_type: str) -> type["ImageWrapperMo... |
e3c701c4829d4bba9bed0a4e9dd35fc12a14445b23c57c91e167ede9e5e19a09 | Python | 2,924 | 85 | import pytest
from pathlib import Path
import numpy as np
from unittest.mock import Mock, patch
from stack_corr.image import um_to_pixel, pixel_to_um
class TestUmToPixel:
"""Test the um_to_pixel function."""
def test_um_to_pixel_basic(self):
"""Test basic conversion from micrometers to pixels."""
... |
23848ea2cf7e8f87275e9b5b1f24a9e85b2a430e5ce36bcaef07ccc7a9912ad6 | Python | 2,925 | 93 | """Roundtrip testing of npy-matlab.
Write data in NPY format to be read and rewritten using npy-matlab. Then
re-read the matlab written data again and check if it is still the same.
"""
import os
import os.path as op
from subprocess import call
import numpy as np
import skimage.data
# For numpy data types see:
# ht... |
8eccd1b8ffc5d952788cc1f9594cc21fda950b5d585decf97efc898c394fa793 | Python | 2,925 | 80 | import os
from typing import Tuple
from batchgeneratorsv2.helpers.scalar_type import RandomScalar, sample_scalar
from batchgeneratorsv2.transforms.base.basic_transform import ImageOnlyTransform
import torch
class GaussianNoiseTransform(ImageOnlyTransform):
def __init__(self,
noise_variance: Rand... |
07b0d32b2ce972fa884a18dca07ab6d8d2c6b933bf20e006f93dfe6cc72f6f7c | Python | 2,926 | 86 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors:
#
# Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may... |
f5ad0397ef54b3745340b345c1a0cde5a5e3f06cf74c720090efd0cd0673c9d0 | Python | 2,926 | 88 | """Coordinate conversion between native atlas pixels and display pixels."""
from collections.abc import Sequence
import numpy as np
def scale_coordinate(
value: float,
source_extent: int,
target_extent: int,
) -> float:
"""Map one coordinate between endpoint-aligned pixel coordinate systems."""
... |
0a6c949955a39c2228873f2ee6cb03839e311c94e756cce1c8d1303cf09aac28 | Python | 2,929 | 83 | #!/usr/bin/env python
# Copyright International Society for Tractography (IST)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... |
5308ecc512317fe555ae6b497b5356e4d5ee8e052cf6d89670b795e99ff5ea47 | Python | 2,929 | 81 | from typing import cast, Final
import numpy as np
from matplotlib.axes import Axes
from rscvp.util.cli import DataOutput, PlotOptions, StimpyOptions, Suite2pOptions, get_neuron_list, NeuronID
from scipy.ndimage import gaussian_filter1d
from tqdm import tqdm
from argclz import AbstractParser
from neuralib.io import cs... |
a6e515905d8424598f1e41e5f69e59eef6cd614c823bf9be4224afb532307061 | Python | 2,929 | 38 |
import numpy as np
from ..spherical_cartesian_conversion import cartesian_to_spherical, spherical_to_cartesian
class TestSphericalCartesianConversion:
def test_cartesian_to_spherical(self):
np.testing.assert_allclose(cartesian_to_spherical([[0, 0, 1], [0, 0, -1], [0, 0, -20]])[:, 1:], [[0, 1], [180, 1],... |
ce804c52c4ac06183f50cb9c5f6418f7d260db7cd82adb98181bf3fd9a720a36 | Python | 2,929 | 127 | #!/usr/bin/env python
#
## @file
#
# Generate a pattern for the SLM.
#
# Hazen 01/15
#
import numpy
import scipy
import scipy.misc
import sys
size_x = 1080
size_y = 1920
image = numpy.zeros((size_x, size_y)) + 127.0
# vertical grating.
if 0:
for i in range(size_y):
image[:,i] += -51.2 * i
# horizontal g... |
106272fa82f175e96baba0b1561b6754ac133f013e7067ce9544568a53aa4765 | Python | 2,930 | 72 | import os
import subprocess
import shutil
import nibabel as nib
import time
import numpy as np
import pandas as pd
from nipype import Node, Workflow
from nipype.interfaces.base import BaseInterface, BaseInterfaceInputSpec, TraitedSpec, File, Directory, traits, CommandLineInputSpec, File, TraitedSpec, CommandLine, Direc... |
3b74f4570c8016d838b9f308f77c48424ff10c10b705cb63da2280fb9d5d2b61 | Python | 2,930 | 59 | single_agent_prompt = """
You are a computational chemistry expert. Your goal is to solve the user's request using only the **minimum number of necessary tools**, without guessing or overusing functionality.
Responsibilities:
1. Carefully extract all relevant inputs from the user's request, including:
- Molecule n... |
a1debaff71d0ea2ec2bdc179f71c96666d20b22a4a11ed11ca2e8d72ca751d95 | Python | 2,931 | 83 | import torch.nn as nn
def conv_layer(in_channels, out_channels,
kernel: int = 3,
stride: int = 1,
padding: int = 1,
dropout_rate: float = 0.0):
return nn.Sequential(
nn.Conv2d(in_channels, out_channels,
kernel_size=(kernel, kern... |
a259dc074c2fa201f1dec7f8b637d92f1d7c3d50a7a8f526f813c1cd97290000 | Python | 2,931 | 111 | #!/usr/bin/env python2
import sys
import os
import signal
import gzip
from optparse import OptionParser
USAGE = """
extract-from-fastq36-pair.py --in1 <fq .gz file> --in2 <fq .gz file>
"""
parser = OptionParser(USAGE)
parser.add_option('--in1',dest='inFQ1', help = 'input 1 fqFILE .gz')
parser.add_option('--in2'... |
8d1b89e15e1645ceb2b647dd3d694e6079991be97fa91148aadc92ff5b455858 | Python | 2,933 | 78 | #!/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=['others.zip'])
tmp_dir = tempfile.Tempo... |
f3431b41ec521183227e6c5d0cd031a3a4d386cd866d55c92d257a9b6b399d3f | Python | 2,933 | 98 | #!/usr/bin/env python
"""
Masks an AXT or MAF file based on quality (from a binned_array) and
outputs AXT or MAF.
Binned array form of quality scores can be generated with `qv_to_bqv.py`.
usage: %prog input output
-i, --input=N: Format of input (axt or maf)
-o, --output=N: Format of output (axt or maf)
-... |
68113881bdb236b8e001ad462ce877d3643eb7f663f04d87886d87c086ceed96 | Python | 2,934 | 79 | # @String(value="<HTML><b>Scale: Apply Scaling Factor(s)",visibility="MESSAGE") msg1
# @double(label="X", min=0d, stepSize=0.05, style="format:#.00000") xscale
# @double(label="Y", min=0d, stepSize=0.05, style="format:#.00000") yscale
# @double(label="Z", min=0d, stepSize=0.05, style="format:#.00000") zscale
# @double(... |
389426990334ab10c6d427c8c0743298ed00112b6c67cf8c95e411db95722d5a | Python | 2,935 | 106 | import contextlib
import platform
import sys
from unittest import mock
import pytest
from qtpy import PYQT5, PYSIDE2
@pytest.mark.skipif(not PYQT5, reason="Targeted to PyQt5")
@mock.patch.object(platform, "mac_ver")
def test_qt59_exception(mac_ver, monkeypatch):
# Remove qtpy to reimport it again
with conte... |
8784adf85efb4aafbd19801165e18dbf4335ecffdf33ee070741be29625723b8 | Python | 2,936 | 87 | import os
import sys
sys.path.insert(0, os.path.abspath('../../src'))
import traceback
try:
from rscvp.statistic.csv_agg.core import NeuronDataAggregator
except Exception as e:
print("####### Import NeuronDataAggregator failed:", e)
traceback.print_exc()
project = 'rscvp'
copyright = '2025, yu-ting wei'... |
8b3ef3d2ae9ae5c8a5cced7a6477567c8f358688aa4b020f7ab70ae6c6fb8bfc | Python | 2,936 | 89 | import itertools
cfg = snakemake.params.dropConfig
exportCounts = cfg.exportCounts
sampleAnnotation = snakemake.params.sampleAnnotation
# adapt sample annotation data frame
sa = sampleAnnotation.annotationTable.copy()
sa["STRAND_SPECIFIC"] = sa["STRAND"] != "no"
if "ICD_10" in sa:
sa["ICD_10"] = sa["ICD_10"].str.... |
ca44fa160bfffe0397dc4a269f253a7565033afd746bdff8e88390972a299de2 | Python | 2,936 | 71 | import sys,os
from Bio import SeqIO
import pandas as pd
import numpy as np
def generate_sequences():
filepath = sys.argv[1]
RESIZED_LEN = 2000
fasta_sequence_dict = SeqIO.to_dict(SeqIO.parse(open('hg19.fa'), 'fasta'))
locations = pd.read_csv(filepath, sep = '\t', names = ['chr', 'start', 'stop', 'label'... |
7762590b3ecd77d5d14a2aa4605e18a5c9e3d6ac46d1cc240c8b26e44d915c9b | Python | 2,937 | 86 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
"""Create a project with labels, then list, filter, retrieve, and rename it.
Steps:
1. Create a project with a simple label schema.
2. List all projects visible to you (pagination is handled by the SDK).
3. Filter projects by a name substring.... |
9bbeb7183161ce8f85a7481b561c0221a9c76ca20f1354cac6bbda5c36dbf748 | Python | 2,937 | 100 | #!/usr/bin/env python
import os
import sys
sys.path.append('.')
import click as ck
import numpy as np
import pandas as pd
import time
import math
from utils import Ontology, NAMESPACES
import gzip
from extract_esm import extract_esm
from pathlib import Path
import torch as th
from Bio import SeqIO
@ck.command()
@ck.o... |
bdde0cf4fc5f5b081d80cbab137313cca5ca334f198ed850cd19bd42a270e3f2 | Python | 2,937 | 93 | # -*- coding: utf-8 -*-
import unicodedata
def ispunct(token):
return all(unicodedata.category(char).startswith('P')
for char in token)
def isfullwidth(token):
return all(unicodedata.east_asian_width(char) in ['W', 'F', 'A']
for char in token)
def islatin(token):
return a... |
0debc92d5cda84c74b31601c54ffb9c96040501550f11fb975e9bef329a2f952 | Python | 2,938 | 93 | # -*- coding: utf-8 -*-
# python standard library inputs
import random
# external inputs
import numpy as np
import nibabel as nb
from nibabel.freesurfer.io import read_label
from scipy.stats import ttest_ind, levene
def analyze_alff_between_conditions(input_label, input_contrast1,
... |
555b160457796f35d05a99c537f9f26ec2d07e8da0868f237a854d4636dd9ae8 | Python | 2,939 | 79 | """Contains grouped scoring functions."""
import logging
import pandas as pd
from ..core.score_bcf import score_bcf
def score_grouped(bed, config, full=False, variant_log_path=None, prs_name=None):
"""Generate grouped scores by cluster."""
group_total_cols = []
total_available, total_estimated, total_mis... |
4c97b37c626391f0ee316e380d50639937a500f245831872f5f164e13f4fe429 | Python | 2,940 | 92 | import nibabel as nb
import glob
import numpy as np
import os
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
root = '/media/sebastian/Data/S1ANFUNCO/raw_data/S1ANFUNCO_BIDS'
subs = sorted(glob.glob(f'{root}/derivatives/sub*'))
subs = [fileName.split('/')[-1] for fileName in subs]
# test whe... |
262e592c07cb88a699d900c74cf9d62cf3b1bc7bd4b6421a53e759d8859c58b3 | Python | 2,942 | 110 | #!/usr/bin/env python
"""Tests return widget types"""
import pathlib
from contextlib import suppress
from datetime import date, datetime, time
from inspect import signature
import pytest
import magicgui
from magicgui import widgets
def _default_equals(object1, object2):
assert object1 == object2
def _genera... |
555eeef9c0789dd125f5089385d4a74f0efe767953fd594143c673ae57228e8a | Python | 2,943 | 94 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
# In[12]:
import anndata
import scvelo as scv
import scvi
import numpy as np
from velovi import preprocess_data, VELOVI
import torch
import os
import argparse
import time
# In[23]:
#TLS
TLS_path = '/VeloVAE/data/processed/TLS.h5ad'
# In[24]:
TLS = anndata.read_h5ad(T... |
b1174599d8dfbb32e9bd0b69b716bb152d7eddfe763bcb7c9aafb855b168026f | Python | 2,943 | 75 | """Fetch the next recording while the current one is being analysed.
The pipeline is a loop over recordings that are independent until the batch-wide
barrier, and analysing one takes far longer than fetching it — measured on the
CAT-NAP example data, roughly 275 s of compute against 95 s of transfer. Run
serially that... |
c7f01f00bec66c5365703553bf3af56766f7a983a79308ce6ef60ceb8ed83a6f | Python | 2,944 | 90 | #!/usr/bin/env python
"""
Purpose
----
Batch convert a directory of CMRR physiolog DICOM files to BIDS format and populate
a subject/session directory tree.
- Adapt parsing logic from extractCMRRPhysio.m (Ed Auerbach)
- Wrap previous single-file dcm2physio.py script in this repo
Authors
----
Mike Tyszka, Caltech Brai... |
0321b2d41f27f324148761173f8070c7c89dc90751507200231ab69073319e79 | Python | 2,945 | 75 | #!/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
from scilpy.io.streamlines import load_tractogram
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dic... |
9305b40587d75e3347c2c5b3c670b947b9acb41e9a72d7325cc9dda69e9df109 | Python | 2,945 | 111 | #!/usr/bin/env python3
import sys
import os
import signal
import gzip
from optparse import OptionParser
USAGE = """
extract-from-fastq36-pair.py --in1 <fq .gz file> --in2 <fq .gz file>
"""
parser = OptionParser(USAGE)
parser.add_option('--in1',dest='inFQ1', help = 'input 1 fqFILE .gz')
parser.add_option('--in2'... |
9eea1e8d945efbfffe8e65f93916a0d4e1c8e682767049dddcdfe7bf0240ef6c | Python | 2,945 | 90 | # -*- coding: utf-8 -*-
# python standard library inputs
import math
# external inputs
import numpy as np
import shapely.geometry as geometry
from scipy.spatial import Delaunay
from shapely.ops import cascaded_union, polygonize
def alpha_shape(points, alpha):
"""Alpha shape.
This function computes the ... |
f20a229865eb4f985c546c3b73733bbf92e553f0cf78222f0751148704660e39 | Python | 2,945 | 74 |
import pandas as pd
import plotly.graph_objects as go
from pathlib import Path
# ground
ground_x = []
ground_y = []
ground_z = []
for ith_x in list(range(0, 45)):
for ith_y in list(range(0, 45)):
ground_x.append(ith_x)
ground_y.append(ith_y)
ground_z.append(0)
z = [0.01] * len(ground_x)
p... |
4cf955c1858a47f6027467bc5bd2f7700ab20f02625d7ab51839620659544b33 | Python | 2,948 | 72 | import os
import torch
import argparse
import esm
import esm.inverse_folding
from glob import glob
from tqdm import tqdm
from transformers import T5EncoderModel, T5Tokenizer
def get_args():
parser = argparse.ArgumentParser(description='Generate embeddings from PDB directory')
parser.add_argument('--pdb_dir', t... |
0f95ade52f481144907f08e94993a73b58388eeb4ad071ee6805472f04174c74 | Python | 2,949 | 64 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file './designerFiles/alert.ui'
#
# Created by: PyQt5 UI code generator 5.12.2
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_alertBox(object):
def setupUi(self, alertBox):
... |
2a491780eb9c4672f3c0f526094eaccc18ebb1f2a0fb8e9765ecd90843e3a062 | Python | 2,949 | 61 | #!/usr/bin/env python3
# Author: Francois Aguet
import argparse
import os.path
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(descripti... |
ed38e253782746d80ca52dd633803119fe7bef7b127dddb023fb565592dbc2bf | Python | 2,949 | 81 | import torch
import numpy as np
import os
from PIL import Image
import torchvision.transforms as Trans
import pandas as pd
class FCDDataset(torch.utils.data.Dataset):
def __init__(self, size, path, use_cond=True, test=False, tsv_path=None):
super().__init__()
self.img_directory = os.path.expanduser... |
eee8b40b8f20721ce705d6ae3a778ca06ccacd41933ca865bd1e72ac68265b9a | Python | 2,950 | 139 | from .aq2009 import (
AQ2009GM,
AQ2009Counts,
)
from .base import (
AbstractBenchmarkDataset,
BenchmarkDataset,
Bucketer,
GeometricBucketer,
MultiWaveformDataset,
WaveformBenchmarkDataset,
WaveformDataset,
WaveformDataWriter,
)
from .bohemia import BohemiaSaxony
from .ceed import... |
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