sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
a250edc7ff6215ba32b146df0c24219deb80a14bf55a9336f3104dc91b2c0873 | Python | 12,375 | 374 | """
Numeric arrays stored as individually compressed blocks on disk, allowing
pseudo-random acccess.
`BinnedArray` is used to build such an array in memory and save it to disk.
`BinnedArrayWriter` can instead be used when creating the array sequentially
(does not require keeping all data in memory). `FileBinnedArray` ... |
f357847fd5c9c14c19d50eab68953da7839d56a4ea1055af7d785cdb0a0be229 | Python | 12,375 | 282 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
import argparse
import cv2
import numpy as np
import onnxruntime as ort
import torch
from ultralytics.utils import ASSETS, ROOT, YAML
from ultralytics.utils.checks import check_requirements
class YOLOv8:
"""... |
611f73e9bde015ee876d446b468ddb679d806082dc5632872e11920f9e2b6af9 | Python | 12,377 | 292 | # Kai Sandbrink
# 2023-07-30
# This script compares data from Day 1 and Day 2 to see if any learning took place.
# %% LIBRARY IMPORT
import numpy as np
import os
import pandas as pd
from scipy.stats import ttest_rel, ttest_ind
from human_utils_project import sort_train_test, get_clean_data
from human_utils_behavior... |
d0cfd0e7edd15eecb67f732b803715b5a65d8edf81b6df9ea4342fc376fcb7f1 | Python | 12,378 | 332 | # Neural network
import os
import time
import tqdm
import torch
import torchvision
import pandas as pd
import polars as pl
import numpy as np
from PIL import Image
from sklearn import metrics, model_selection
from config import Config
torch.manual_seed(Config.random_state)
torch.cuda.manual_seed(Config.random_st... |
1384636cef57d3b0f2a40a7abd3f09cd0761c965d5cf5fb11dfd8c8190ef3fdf | Python | 12,382 | 348 | """Tests for mcmlnet.utils.network_init module."""
import copy
import pytest
import torch
import torch.nn as nn
from mcmlnet.utils.network_init import (
init_weights,
matmul_precision,
set_activation,
)
class TestMatmulPrecision:
"""Test cases for matmul_precision function."""
@pytest.mark.par... |
94337c6f830ee2955a089b96c80316e3a3537f9dd8203493afd9af623697fcfd | Python | 12,387 | 283 | # 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.apache.org/licenses/LICENSE-2... |
bfbf24c2b3c5a8f922180493db190a5208f501480b11c351850f434f6ce9167a | Python | 12,387 | 278 | #
# Reference : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3864968/
#
# Written by SK Park , NIRAL, UNC
# 2021-04-18
import dtiplayground.dmri.preprocessing as prep
import yaml,traceback
import numpy as np
import time
from pathlib import Path
import os
import markdown
from dipy.align.imaffine import (transform_ce... |
0fff9536aa39b0e1933313f48547ed0c989eb0b090eb035ac28dcd49cf8061c9 | Python | 12,388 | 357 | # 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 Julius Zimmermann,
# julius.zimmermann[AT]un... |
b914e364f084a22b34c5073bf5ff719d84a8ce9aade4643f926fd27cc1c74658 | Python | 12,388 | 234 |
import argparse
import os
import pathlib
import time
import datetime
import h5py
import numpy as np
from enum import Enum
from neuron_interface.neurosim.simulation.dendritic_delay_simulation import DendriticDelaySimulation
from postprocessing_modules.axonal_delay_kernel import get_axonal_delay_kernel
from helper_funct... |
1cc86d9ae913429dd9d6538ac5dd4834d4204c8e3c5e2d24b4b0858748d3858c | Python | 12,391 | 367 | # -*- coding: utf-8 -*-
"""
Created on Thu Nov 30 11:38:01 2023
@author: N. Delinte
"""
import json
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import linregress, ttest_ind
from statannotations.Annotator import Annotator
from unravel.viz import plot_me... |
6b4fcf969745b90a50b0d01bceae99aead4015f8a6e9b96a23d142849ada32d6 | Python | 12,395 | 246 | import numpy as np
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
import argparse, os, time, logging
from tqdm import tqdm
import pandas as pd
import multiprocessing
from functools import partial
import pickle
from model import UNet, ModelCo... |
e61176c544dae139e82819838a8f93944431595fbfa867b65df5fbbdf6a8c021 | Python | 12,397 | 303 | from datetime import date
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import os
import pandas as pd
import difflib
from path_plots import dmdb_plots as dp
import re
import shutil
from yaml import safe_load
from utils.build_indications import get_id_num, read_deprecated_ids
def generate_md... |
18d2e2c65a114dbae75adebee952a96ea7119f594fb4d30d519e13680265e4c1 | Python | 12,398 | 275 | import argparse
import torch
import torch.utils.data
from motion.dataset import MotionDataset
from model.eghn import EGHN
import os
from torch import nn, optim
import json
import random
import numpy as np
from utils import EarlyStopping
parser = argparse.ArgumentParser(description='VAE MNIST Example')
parser.add_arg... |
d0b0144d19772d7a77790657783668a981deb5ed68996ea9fa13988c2e89ea4a | Python | 12,398 | 361 | """Cycle Duration Analysis.
Load tinda output and calculate cycle duration based on best sequence
(group level) fitting a second HMM on the fixed state sequence
Authors: Mats van Es
Carina Forster
Last update: 23-06-2026
Important: run in environment with osl_dynamics 2.0.2
"""
import os
imp... |
c7f5239b4ea8ca3f6fafdab02c680c48881ad128a134562bd4193601fa693c57 | Python | 12,399 | 286 | import os
import numpy as np
import pandas as pd
import torch
import torch.optim as optim
import torch.nn.functional as F
from sklearn.decomposition import PCA
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import SVC
from sklearn import svm
f... |
3b7f0a6ab647cd909083faf60db47480081cc235298d6ad188ba586d82a30629 | Python | 12,400 | 416 | import math
import torch
from torch import nn
from torch.nn import init as init
from eir.models.layers.norm_layers import LayerScale
def append_dims(tensor: torch.Tensor, target_shape_no_batch: list[int]) -> torch.Tensor:
# exclude batch dimension
current_dims = len(tensor.shape) - 1
target_dims = len(t... |
1e44d4712cc1fa96dd5fd88c97e456a8bfca194d1112aaaf76cb8a7e5e2ec0e3 | Python | 12,402 | 358 | #!/usr/bin/env python
"""
Classes for reading HAL movies and XML.
Some of this is a copy of storm_analysis.sa_library.datareader, except
that support for the spe and fits formats has been removed.
Hazen 10/18
"""
import hashlib
import numpy
import os
import re
import tifffile
import storm_control.sc_library.param... |
bcbe3e4a488b6176db3f1c9ad46fe168698ca83e4d6901e87fed070d67b517fd | Python | 12,405 | 406 | # -*- coding: utf-8 -*-
import logging
import numpy as np
from scipy.spatial.distance import cdist, pdist, squareform
def swap_sampling_eddy(bvecs, shell_idx):
"""
Optimize the bvecs of fixed multi-shell gradient sampling for eddy currents
correction (fsl EDDY).
Bruteforce approach to maximally spr... |
cde06a63d66eaaa786e17ba35f221d5e7f02e4d3a85bc29e8c678edc71eb5586 | Python | 12,405 | 356 | import json
import numpy as np
import torch
from tqdm import tqdm
from infopath.utils.functions import trial_match_template, return_trial_type
import pandas as pd
def light_generator(time, window="baseline", freq=100):
time = torch.tensor(time)
t = torch.arange(time.shape[0]) * 2 / 1000
light = torch.sin(... |
246fc3ea081548a23573aaa06abc62274fe84ca3b50542fdac0ae9b6881b4fad | Python | 12,406 | 347 | import numpy as np
import matplotlib.pyplot as plt
from statsmodels.nonparametric.smoothers_lowess import lowess
import numpy as np
from scipy.interpolate import interp1d
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import math
import pandas as pd
import os
from scipy.interpo... |
a543e9084d95e58d56813263484a01c9708253da998811069ccea9acd14d58b5 | Python | 12,413 | 305 | #!/usr/bin/env python3
# Author: Francois Aguet
import numpy as np
import pandas as pd
from collections import defaultdict
from bx.intervals.intersection import IntervalTree
import argparse
import os
import gzip
class Exon:
def __init__(self, exon_id, number, transcript, start_pos, end_pos):
self.id = exo... |
6390ad61a4244ee96f9a015a12678e33ee6cac3914d706a33087aafb98114177 | Python | 12,421 | 267 | import os
import math
import numpy as np
from scipy import ndimage
import tensorflow as tf
import utils as utils
import models
from dcrf_gradient import dense_crf_inference
from scipy.ndimage import gaussian_filter
from utils import downsample_upsample_scipy
import nibabel as nib
def predict(subject_list,
... |
dd304990f8d4fc3064348724d948dffc28fef81c9310dcfe67d7454dba499ca4 | Python | 12,428 | 308 | from data_utils import locate_dataset, write_to_json
from tqdm import tqdm
from typing import Dict, List, Optional
import os
import random
class Sampler:
"""
A class to sample a subset of registration problems from the registration graph
and store them in a JSON file.
The problems can be either cur... |
5bd6904ed2cae8957f88272d0ecac74e6fcf1f922c8edecb79275ebfe21637c5 | Python | 12,430 | 303 | # PyTorch utils
import datetime
import logging
import math
import os
import platform
import subprocess
import time
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import tor... |
d4b29c6b3192618cb9566fc6f4ce8fea5e341bd6fd99e1cce6393a7f25b8b426 | Python | 12,434 | 389 | import torch
import torch.nn as nn
from collections.abc import Sequence
import numpy as np
class ConvLayer(nn.Module):
def __init__(self,
in_channels: int,
out_channels: int,
dimensions: int = 2,
strides: int = 1,
kernel_size: Seq... |
33590cb8011645db35a868a4e9dc171284daf68f0eca7b4ddbae37466cd4aa23 | Python | 12,443 | 129 | """Descriptive follow-up exports from frozen results and versioned annotations."""
import csv,hashlib,json,math,re,statistics
from pathlib import Path
ROOT=Path(__file__).resolve().parents[2];OUT=ROOT/'tables/dna_maintenance';OUT.mkdir(parents=True,exist_ok=True)
def read(p):
with (ROOT/p).open() as f:return list(csv.... |
ab98bb014cc1b4c81b473da81f2347ff7bbf2af8c1cc33fc5731eb5da9ad6ddb | Python | 12,452 | 252 | from typing import Callable, Dict, List, Optional, Tuple, Union
from torch_geometric.data import Data, FeatureStore, GraphStore, HeteroData
from torch_geometric.loader.node_loader import NodeLoader
from torch_geometric.sampler import NeighborSampler
from torch_geometric.sampler.base import SubgraphType
from torch_geom... |
4d11b068a627f672e7ddfa74ad24b3786b3db878f6ad9cdfa2632c7837eaf0ef | Python | 12,454 | 338 | from math import sqrt
from typing import Optional, Tuple, Union
import torch
from torch import Tensor
from torch.nn.parameter import Parameter
from torch_geometric.explain import ExplainerConfig, Explanation, ModelConfig
from torch_geometric.explain.algorithm import ExplainerAlgorithm
from torch_geometric.explain.alg... |
992957b54a07c5d8f4e3632058d5a96c814855c97e8b51d77751efa8069376ec | Python | 12,463 | 296 | """
Reference:
- Zhang, Hang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun et al. "Resnest: Split-attention networks." arXiv preprint arXiv:2004.08955 (2020)
"""
import math
import torch
import torch.nn as nn
from . import splat
__all__ = ['ResNet', 'Bottleneck']
class DropBlock2D(object):
... |
a27253a2106a59c8c38df21e2a39f69f951e9bab2a89271760d06503ef5d356a | Python | 12,470 | 355 | """Base logger and its helper handlers."""
import numbers
import warnings
from abc import ABCMeta, abstractmethod
from collections import OrderedDict
from collections.abc import Callable, Sequence
from typing import Any
import torch
import torch.nn as nn
from torch.optim import Optimizer
from eir.train_utils.ignite_... |
aaff685e875d5ca578bb12638e3f5242fcb8c22d38510f7080b3656818353be3 | Python | 12,472 | 308 | import os
import re
import shutil
import subprocess
from typing import Optional, Tuple
class TOPED:
def __init__(self, distribution: str, user: str, linux_workdir: str):
""" Specify WSL distribution, user, and working directory for FSL.
Parameters:
distribution : str ... |
2b3d0498ff9275d466247d1a2d953825f1eab6322a6c4436859264d450338db4 | Python | 12,473 | 338 | """
This file contains several helper functions to calculate sleep statistics from
a one-dimensional sleep staging vector (hypnogram).
"""
import warnings
import numpy as np
import pandas as pd
__all__ = ["transition_matrix", "sleep_statistics"]
#####################################################################... |
d311981bc6c006429e709273484431c65da85c863cb2c346b0fc463a15f1f426 | Python | 12,475 | 335 | # -*- coding: utf-8 -*-
from dipy.reconst.shm import sh_to_sf, sh_to_sf_matrix
from fury import actor
import numpy as np
from scilpy.reconst.bingham import bingham_to_sf
from scilpy.viz.backends.fury import (create_contours_actor,
create_odf_actors,
... |
253a5d6b651631d467a18b2ecc446fb2e0218dbc118165e87fdb69e775e548f8 | Python | 12,481 | 349 | from __future__ import annotations
import functools
from collections import namedtuple
from collections.abc import Iterable
from typing import cast
import django_rq
from django.conf import settings
from django.http import HttpResponseBadRequest, HttpResponseNotFound
from django.utils.decorators import method_decorato... |
3801fc7ec597a37414993afc0b4721474604344a86f004b30e643e5ea97895d3 | Python | 12,482 | 277 | #! /usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This script computes two myelin indices maps from the Magnetization Transfer
(MT) images.
Magnetization Transfer is a contrast mechanism in tissue resulting from the
proton exchange between non-aqueous protons (from macromolecules and their
closely associated water mo... |
d79a9868a70238d7efff9c1355e4497e911419625384922251cddbe43b31aa1f | Python | 12,483 | 298 | # -*- coding: utf-8 -*-
"""
EPI <-> ORIG registration
The purpose of the following script is to compute the deformation field for the
registration between antomy in conformed freesurfer space and native EPI space.
Inputs are a mean epi in native space, a T1 map of an mp2rage acquisition, a
freesurfer orig file generat... |
d3bd3caffd86541a85704f7f905bd61055e494cdfcde478e9abdcd8e26c8c382 | Python | 12,484 | 419 | import numpy as np
from scipy.stats import zmap, zscore
from sklearn.utils.extmath import randomized_svd
from sklearn.utils.validation import check_random_state, check_X_y
from pyls import utils
def svd(crosscov, n_components=None, seed=None):
"""
Calculates the SVD of `crosscov` and returns singular vectors... |
e79264d4704e67db95fadcdbe6315c167df31cccb15d7b5c0667e5160acc7f3e | Python | 12,484 | 286 | """ builds the tensorflow execution graph for the model by parsing network_specs """
import os, warnings
warnings.simplefilter(action='ignore', category=DeprecationWarning)
from os.path import isfile
import argparse
import yaml
import numpy as np
import tensorflow as tf
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import ... |
5169a933f8d6a3473e20a5db1a82d67811a8e56cbb4140bd812f548c1fa67f64 | Python | 12,491 | 275 | """A module that contains unit tests for the :py:mod:`corelay.io.spectral` module."""
import os
import typing
import numpy
import pytest
from matplotlib import pyplot
from corelay.pipeline.spectral import SpectralEmbedding, SpectralClustering
from corelay.processor.affinity import SparseKNN
from corelay.processor.cl... |
b56a4559232918bfb2c9ded677bf1481a021f81df7c747952d0065e0e8eff4e3 | Python | 12,498 | 252 | """Tridesclous2 rebuilt from its components, with the stages MEA-NAP wants
to vary exposed.
Stock TDC2 (:mod:`spikeinterface.sorters.internal.tridesclous2`) is a fixed
chain: 5 σ threshold detection → subsample → per-channel ISO-SPLIT clustering
→ templates → the *peeler* (template matching over the whole recording) →... |
593c9fc7ea9df6ea89fa1d546004ea2ad478bf62e1033a4001ad2c93e9411479 | Python | 12,499 | 326 | import torch
import numpy as np
import scipy.stats as stats
from .utils import sample_dist
from typing import Literal
image_size = 224
visual_degrees = 7
kernel_size = {'p': 21, 'm': 65} # 95% of Gaussian coverage
# Receptive fields of P and M ganglion cells across the primate retina (Kroner and Kaplan, 1994)
# htt... |
1b13c7c5a4211c87af403bd0e7a933ce25245500688388e5bf8499bdfa7a0e3b | Python | 12,500 | 402 | import copy
from dataclasses import dataclass, replace
import torch
import torch.nn.functional as F
import torch_geometric as pyg
import torch_geometric.graphgym.models.act
import torch_geometric.graphgym.register as register
from torch_geometric.graphgym.contrib.layer.generalconv import (
GeneralConvLayer,
G... |
29901659e1ae014d6e6dd8c20b1d63df285ec6839daa97b240857c16fde4f031 | Python | 12,502 | 280 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors: Patrick Lenz
#
# 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 Licen... |
54cc129e20f56fed2ad19abbd7546f2b5d888a6d6ef0570bb764abd2224ce087 | Python | 12,502 | 422 | # -*- coding: utf-8 -*-
"""Surface utilities."""
import datetime
import os
import shutil
import sys
import numpy as np
from .. import execute_command
from ..io.filename import get_filename
__all__ = [
"mris_thickness",
"mris_curvature",
"inflate_surf_mesh",
"upsample_surf_mesh",
"mris_expand",
... |
02e71025e03cd17a941ffbcc502d24965c7366760eda9e8efa088fd17c93fbc4 | Python | 12,504 | 333 | """
Multiprocessing executor for parallel image processing.
Provides headless worker functions that process images without Qt dependencies.
"""
import os
import traceback
def init_worker():
"""
Initialize worker process.
Called once per child process at startup.
Sets environment variables to prevent ... |
c9a87338f0e75f38df2719d45a1369b1b22cf71ed195a6be2fec44547ace63d5 | Python | 12,504 | 372 | #!/usr/bin/env python
"""
Fitting for offset using correlation with a 2D Gaussian.
Hazen 04/18
"""
import ctypes
import numpy
from numpy.ctypeslib import ndpointer
import scipy
import scipy.optimize
import storm_analysis.simulator.draw_gaussians_c as dg
import storm_control.c_libraries.loadclib as loadclib
# Load ... |
d15550c40f4d3dbaad41052d23682563ad6f6e8a822296198636a8156a6ce00f | Python | 12,513 | 235 | import mat73
import argparse
import numpy as np
import cebra
from choice_mode_functions.combine_all_trials_all_brain_regions import dataset_all_sessions, choice_sampled_population
from choice_mode_functions.split_dataset import split_train_test_data, k_fold_split_data
from choice_mode_functions.model import cebra_model... |
517ad6f40fc3962813dc9aa68623d45c4d4ff3c1f304d8e529f2842a71bcb9c4 | Python | 12,515 | 234 | import os
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.utils.data as Data
import random
import numpy as np
import pandas as pd
import anndata as ad
import scanpy as sc
from collections import defaultdict
import warnings
warnings.filterwarnings('ignore')
from model.utils import *... |
7dea3ad6586dc51b224b43692c4f64c853c0c040df59eaad8c34bc756e402179 | Python | 12,516 | 263 | import multiprocessing
import os
from copy import deepcopy
from multiprocessing import Pool
from typing import Tuple, List, Union, Optional
import numpy as np
from batchgenerators.utilities.file_and_folder_operations import subfiles, join, save_json, load_json, \
isfile
from nnunetv2.configuration import default_n... |
bc8da7805b5702670e7a5d0879af878f2f6f1b6f643eea3dc68fb8194041b80b | Python | 12,516 | 336 | """
Copyright 1999 Illinois Institute of Technology
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publis... |
306516c520ac2a1ef349445a0d1c157cee6b89adb4d700c5f72573a2a301bad2 | Python | 12,517 | 263 | import nighres
import os
import nibabel
import numpy
import glob
import scipy.ndimage
import ants
indir = '/home/pilou/Projects/Cerebellum/Cerebellar-segmentation-ABC/ahead/coreg/'
outdir = '/home/pilou/Projects/Cerebellum/Cerebellar-segmentation-ABC/ahead/tissues/'
#bfimg = indir+'Ahead_brain_122017_blockface-image_... |
d636bd2e1335cbb3fb5022768491e79493736c42976f0c65dfb033b960fffdd1 | Python | 12,518 | 306 | # import general libraries
import copy
import numpy as np
import pandas as pd
from sklearn.linear_model import RidgeClassifierCV
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from tqdm import tqdm
# import brain-score specif... |
2ae9784abde6cf8a5c67f4335015b87150a4bc7bf8d7720fca2972cb61fe0bf3 | Python | 12,519 | 328 | import torch
import torchvision
from torch import nn
import torch.nn.functional as F
# Based on the implementation in
# https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
return nn.Conv2d(in_planes, out_planes, ... |
090dec61ad939710c83c0df7ba84a048614bb0dd1266128618f7ad4585f49e58 | Python | 12,523 | 273 | import os.path as osp
import numpy as np
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import Linear, HGTConv
import heapq
import random
from scipy.spatial.distance import pdist
from sklearn.metrics import roc_auc_score
from torch_geometric.data import HeteroData... |
69a1a07488be1e63cf76633b62eab28c86377e6b76b882969af4346cbfbbb286 | Python | 12,526 | 299 | try:
import torch
import torch.nn as nn
import torch.nn.functional as F
except ModuleNotFoundError:
print('pytorch is not installed. Using without it.')
import sys
import numpy as np
from pathlib import Path
from scipy.interpolate import interp1d
from scipy.io import loadmat
import platform
import warni... |
0e2f3a3745b77b0bf9b1ac68dea848328c5cbddcaba67c7ce54072d385a4aedb | Python | 12,528 | 358 | #!/usr/bin/env python
"""
Handles objectives manipulation.
X/Y offsets are in units of microns.
Hazen 10/18
"""
from PyQt5 import QtCore, QtGui, QtWidgets
import storm_control.steve.comm as comm
import storm_control.steve.coord as coord
import storm_control.steve.imageItem as imageItem
import storm_control.steve.ste... |
8bd5875f291afd91e58e5c9cf62d9e6144fb7cda2908320104796b9f16543140 | Python | 12,528 | 188 | # update 2024-09-11: Add options to support FFPE alignment
import os, sys, gzip, argparse, subprocess, random, glob
from utils import read_maybe_gzip, check_iupac, revcomp
star_default_options= "--limitOutSJcollapsed 5000000 --soloCellFilter None --soloBarcodeReadLength 0 --outFilterScoreMinOverLread 0 --outSAMtype B... |
6e993b1fb3ffdd103fb213fc63a8244031e6384625bda7fd739768055d314d74 | Python | 12,529 | 363 | import numpy as np
import pytest
from nelpy import formatters
class TestBaseFormatter:
"""Test the BaseFormatter class."""
def test_base_formatter_initialization(self):
"""Test BaseFormatter initialization raises NotImplementedError"""
with pytest.raises(NotImplementedError):
for... |
89cdcdbf8af72984ddde57bb038af0a3fe8f2053002ce4e5a5a3b925fc9f9b32 | Python | 12,532 | 248 | import numpy as np
from matplotlib import pyplot
pyplot.ion()
MV_PER_V=1000
SEC_PER_MIN=60
MS_PER_S=1e3
NA_PER_AMP=1e9
artifact_height=30 #mV - delta V between two sample points
def line(x,A,B):
return B*x+A
def rows_columns(n):
factors=list([i, n//i] for i in range(1, int(n**0.5) + 1) if n % i == 0)
in... |
0dfee8e856800288039c748629f63aa494d6e8231ae8ff656fd47303801a0576 | Python | 12,533 | 261 | # ===============================================================================
# Authors: Jenny Wang, Julia Camps and Lucas Berg
# Version: 24/06/24
# ===============================================================================
# This is a stand alone script that plots ECGs from body surface potentials
# simulate... |
a29f5536939edfaaa4d8f95609acb6f0fad85331d8ff8669327239706daec52f | Python | 12,534 | 260 | import json
import pathlib
import random
from pathlib import Path
from typing import List, Dict, Union
from natsort import natsorted
import pandas as pd
import matplotlib.colors as mcolors
import matplotlib.cm as cm
import matplotlib.pyplot as plt
from napari.utils.notifications import show_info
class ColorManager:
... |
5c3895197540a702b835f44cf9958cb3a13a75c2214e3ca219cccb8d21b0652a | Python | 12,539 | 251 | from operator import itemgetter
from nilearn.decoding import SearchLight
import os
import nibabel as nib
from glmsingle import GLM_single
import glob
from sklearn.pipeline import make_pipeline
from nilearn.maskers import NiftiMasker
from ants import apply_transforms, image_read, from_nibabel
import pandas as pd
from ni... |
ff3ece00236a3d3463f9158d070f19ea2957cd5a8fe4ca6fac4942d94fa6d70d | Python | 12,539 | 243 | import numpy as np
import os
import sys
import pandas as pd
import json
from scipy import stats
import tqdm
import itertools
import matplotlib.pylab as plt
sys.path.append(os.getcwd())
import functions.process as proc
import functions.algebra as al
from functions.io import makedir
sys.path.append(os.path.expanduser(... |
b5f74e68ece208e56be5baced0808e2cca4adbf22b795c16540a9d4fafcb1255 | Python | 12,540 | 269 | import SimpleITK
from pathlib import Path
from queue import Queue
from tqdm import tqdm
import logging
import numpy as np
import torchvision.transforms as transforms
from utils.detection_helper import create_anchors, process_output, rescale_boxes
from utils.nms_WSI import nms, nms_patch
import os
import pickle
import ... |
0bec498ceb0ced6e9474b717673ea40ed0bd6868d6d5db8fff4957834ebe5546 | Python | 12,542 | 380 | # -*- coding: utf-8 -*-
"""Check preprocessing quality."""
import os
import shutil as sh
import matplotlib.pyplot as plt
import nibabel as nb
import numpy as np
from scipy.stats import pearsonr, shapiro
from .. import execute_command
from ..io.filename import get_filename
from ..segmentation.mask import clean_ana, m... |
dc62ee79974c5b7327bcc3f5f582b1d18f2e7082c8d6b3bfccf1425dd143726d | Python | 12,545 | 379 | import numpy as np
import csv
from debugtest import debugtest
from BH76 import BH76
from W417 import W417
"""
This module provides auxiliary functions for handling testset data,
assembling features for machine learning, and exporting neural network
weights in a TurboMole-friendly format.
Functions
---------
- _unstac... |
0be0dcdad0bbab346a79fc0749fcb45dc454535370d8a45497f6f7d8c9ba4031 | Python | 12,550 | 302 | # -*- coding: utf-8 -*-
# @Author : LG
from json import dump
import cv2
import numpy as np
import tqdm
from pycocotools import coco as mscoco
from pycocotools import mask as mscoco_mask
from ISAT.formats.isat import ISAT
__all__ = ["COCO"]
class COCO(ISAT):
r"""
COCO format
Attributes:
keep_... |
0e601db0d6cb28cec3f428f5a5f51d835d6cf3084ebf570ca3bbeb7e9e83e4e0 | Python | 12,551 | 409 | """
Dataset Classes for H-FNO Multi-Physics Sensor Response Prediction
This module provides dataset classes for loading and preprocessing sensor simulation data.
Supported sensor types:
- Thermal (RTD sensors)
- Acoustic (microphone arrays)
- Flow (orifice plate sensors)
Data format: HDF5 files with inpu... |
146dce824f17006eb41e0450b0bd7216784ef7941f95d92f6d515f54f448b0b6 | Python | 12,552 | 432 | import os
import matplotlib.pyplot as plt
from matplotlib import colors
import scanpy as sc
import seaborn as sns
import numpy as np
import pandas as pd
import colorcet as cc
# Global variables store dataset-specific information
from nest.hotspot.geometry import similarity_map
library_id = None
img = None
scale = No... |
805599f86078a9c16eb2f11221c984b22721b3e02de02481ba86c72c0003452a | Python | 12,555 | 374 | # -*- coding: utf-8 -*-
import os
import re
import sys
REFERENCE_VALUES = {
'npr': 5, # Npr value (line 8 and 48)
'ncy': 10, # Number of cycles
'rpa': -1, # Rpa value (line 14)
'ratio': '0.00000',
'bkpos': '25.000',
'wdt': '15.0000',
'cthm': '0.9100',
... |
340f2c12d2309f235d5ebd3e08a2bdd81594bc9872db71bdd89a2b05750f2bf8 | Python | 12,557 | 474 | import logging
from math import radians
import numpy as np
from dipy.tracking.streamline import set_number_of_points
import AFQ.viz.utils as vut
from AFQ._fixes import make_mp4
try:
from fury import actor, window
from fury.colormap import line_colors
except (ImportError, ModuleNotFoundError) as e:
raise ... |
0b01389dc0d6f12e4c5eb6bc370177a24810c2d44b539191ff6701b6cdfcc1f9 | Python | 12,558 | 366 |
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import Utility
import scipy
import os
from shutil import rmtree
import random
import scipy.ndimage as ndimage
# space size, we used 45 in paper, and here increase the space size for visualization purpose
n = 80
grid_orientation = 45
# goal location... |
b04a6c9bf1a9db67c3e5f0bf0840b94556d6c83cf6ecbfbb892c5598c64f2171 | Python | 12,559 | 321 | """Prepare the NSD-core betas in fsaverage space. The betas are then divided
into train and test split for encoding models training, ID testing, and OOD
testing splits.
Parameters
----------
subject : int
Number of the used NSD subject.
data_ood_selection : str
If 'fmri', the ID/OD splits are defined based on fMRI r... |
4965f847459e637314ea150520df200636ec1e55da34820184897508dcd38b6a | Python | 12,560 | 326 | import numpy as np
import scipy.stats
from sklearn.cross_decomposition import PLSRegression
from sklearn.linear_model import LinearRegression, Ridge, RidgeCV
from sklearn.preprocessing import scale
from brainscore_core.supported_data_standards.brainio.assemblies import walk_coords, DataAssembly
from brainscore_core.me... |
a23ede43337bdcf9d2478609f043d3822cbbde7b4b2a951e658067d5dfc32713 | Python | 12,563 | 258 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
from pathlib import Path
from typing import Any
import numpy as np
import torch
from ultralytics.models.yolo.detect import DetectionValidator
from ultralytics.utils import ops
from ultralytics.utils.metrics import... |
8f8c6075204e00bc0597c73586b270e6e9b36c694863377b187e628225a9ace2 | Python | 12,564 | 407 | # Source: https://github.com/neuraloperator/neuraloperator/blob/main/neuralop/losses/data_losses.py
import torch
import math
from deepcardio.losses.finite_diff import central_diff_1d, central_diff_2d, central_diff_3d
from torch_scatter import scatter_mean
class LpLoss(object):
"""
LpLoss provides the L-p norm... |
354a59678a9cfe82d99b6309b96fc5b044cb0c455d09db5f990f25289de2ab04 | Python | 12,572 | 322 | # Imports
import sys
import os
import pandas as pd
import numpy as np
import random
from scipy.integrate import odeint, solve_ivp
from lmfit import minimize, Parameter, Parameters, report_fit
from model_functions_loco_Rew6 import *
import matplotlib.pyplot as plt
from pathlib import Path
########## DATA LOAD ########... |
5bf30417a30d8e28d60ca2a3294c84c08e31b0a19cce5fe5a9d82d7f4636fa1f | Python | 12,578 | 353 | import json
from copy import copy
from dataclasses import asdict
from pathlib import Path
from typing import TYPE_CHECKING, Any
import numpy as np
from aislib.misc_utils import ensure_path_exists
from eir.experiment_io.input_object_io_modules.array_input_io import (
load_array_input_object,
)
from eir.experiment_... |
c2718ad9c4d81d1cceabe075b53458c9ffffa077b15800240d1d6b1037051482 | Python | 12,579 | 290 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Script to compute the Diffusion Kurtosis Imaging (DKI) and Mean Signal DKI
(MSDKI) metrics. DKI is a multi-shell diffusion model [1]. The input DWI needs
to be multi-shell, i.e. multi-bvalued.
Since the diffusion kurtosis model involves the estimation of a large numbe... |
09bf60af5b09def81d6723530f4d518c223fd8dcb4810185994dbb0f5accd54b | Python | 12,581 | 307 | '''
FibrosisNet Kaggle Submission
This python script will create the submission file that we
submitted to the Kaggle competition.
Running this script with `CT_WEIGHT=1.0` (best performance)
achieves -6.8188 private score. Running it with
`CT_WEIGHT=0.96` achieves -6.8195 private score. Note that
the Darwin team hav... |
56256bade1ecccbcc21d376b283a60d0d7fd5f63315edecee398af4c70adb97f | Python | 12,583 | 356 | # 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 Julius Zimmermann,
# julius.zimmermann[AT]uni-rost... |
0309b0f637a1c32be65bd4d94ba7669089a437c8c1e52af031d16ea88d8e839d | Python | 12,586 | 357 | import math
from collections import abc
from collections.abc import Callable
from dataclasses import dataclass
from typing import Any, Literal
from torch import nn
from transformers import AutoConfig, AutoModel, PreTrainedModel
from eir.models.input.sequence.sequence_models import SequenceModelClassGetterFunction
fro... |
f99cae5fe06491b0510e1c082dffe702df2d35465fda76214dd423250160d25e | Python | 12,586 | 375 | import json
import shutil
import tarfile
from collections import defaultdict
import numpy as np
import seisbench
import seisbench.util
from .base import WaveformBenchmarkDataset
# Conversion from earth radius
DEG2KM = 2 * np.pi * 6371 / 360
class NEIC(WaveformBenchmarkDataset):
"""
NEIC dataset from Yeck ... |
bea41e05a03a54639636014b416ac76439135c3dff8d629193092e0c9b94e800 | Python | 12,587 | 281 | # 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
... |
67985c181f524000a781fbcdbd3a141b26e39964509bc222f491a10ff44f4c6f | Python | 12,590 | 342 | def plot_selected_cell_type_flow(
pcloud_list, model, device, finaltruth,
selected_cell_type, arrow_sample_size_per_type=55,
xlim=(-0.2, 1.5), ylim=(-0.5, 2), height_scale=1.0,save_path=None
):
"""绘制特定细胞类型的流动"""
# 创建画布
fig = plt.figure(figsize=(19.5,9.5)) # 单个子图的宽度
# 创建子图2：显示特定细胞类型的流动... |
34fc028473c0e8f9bf7cd8caa166b5fb633a5343fe26539c35ed683e524f2736 | Python | 12,591 | 327 | """
MSI Peak Processing Pipeline
─────────────────────────────
Processes raw h5ad mass spectrometry imaging data through:
1. Global spectrum construction
2. Baseline correction (ALS) + Savitzky-Golay smoothing
3. Peak detection with minimum distance filtering
4. Per-pixel intensity summation around selected pea... |
e592e11c52d6c26cbbffa2c29ddec411fd51b0b8fec43ece54852a9d0dab6a9d | Python | 12,591 | 271 | import torch
from torch import nn
from torch_geometric.nn import global_mean_pool
def unsorted_segment_sum(data, segment_ids, num_segments):
result_shape = (num_segments, data.size(1))
result = data.new_full(result_shape, 0) # Init empty result tensor.
segment_ids = segment_ids.unsqueeze(-1).expand(-1, da... |
2228c3e0a8b07631584933c85cb82fc698e8ce8a09f7a09d1f42a2c3c9bec5aa | Python | 12,594 | 410 | # TODO: add docstring
import matplotlib as mpl
from matplotlib import pyplot as plt
from matplotlib.offsetbox import AnchoredOffsetbox
class AnchoredScaleBar(AnchoredOffsetbox):
def __init__(
self,
transform,
*,
sizex=0,
sizey=0,
labelx=None,
labely=None,
... |
04c160ea3bab63d52e7da8b90468e63dd7d4de5e427c463d86a9df6bca48af57 | Python | 12,596 | 369 | """
AMASS dataset loader: SMPL .npz → normalized COCO-17 clips.
Directory layout expected:
amass/
├── CMU/
│ ├── 01/
│ │ ├── 01_01_poses.npz
│ │ └── ...
├── MPI_HDM05/
├── SFU/
└── ... (other AMASS sub-datasets)
Each .npz file contains:
poses : (T, 156) SMPL-H pose p... |
2a3e0c5e308b674a972ad9ffdfd44fb4cf666227d3d1b6ffa4f6fa0989818b0e | Python | 12,598 | 348 | """Provide different loss or metrics classes for images."""
import tensorflow as tf
from deepreg.constant import EPS
from deepreg.loss.kernel import gaussian_kernel1d_size as gaussian_kernel1d
from deepreg.loss.kernel import rectangular_kernel1d, triangular_kernel1d
from deepreg.loss.util import NegativeLossMixin, sep... |
a8d042f76acec855262dd4269f23707faa19c3c6ad5e9ce511f139884293b952 | Python | 12,598 | 393 | """Provider registry and readiness checks for the ChemGraph UI.
One place that knows, for every way ChemGraph can reach an LLM, how to
tell whether it is usable right now and which models it serves. Used by
the Configuration page (provider cards) and the first-run setup on the
main page. Streamlit-free and network-f... |
6cf90a2c5dd6c1ad3c9e02f7126ecb21489cacd71ddd3a686f963b424fac1f9d | Python | 12,600 | 348 | #!/usr/bin/env python
"""
Classes that handles reading STORM movie files. This is used by
the Steve program and it assumes the existance of an XML file
that describes everything that one needs to know about a movie.
Hazen 07/15
"""
#
# FIXME: Why not just use the version if the storm-analysis project?
#
# Or m... |
2fb7212540cd8f4baeb499711b888bc39ffcea34f89ffd49f41e6af22fadb5ff | Python | 12,602 | 283 | """Resuming a run from a previous analysis (``Params.prior_analysis``).
MATLAB's ``MEApipeline.m`` lets you skip already-completed work: set
``Params.priorAnalysis = 1``, point ``Params.priorAnalysisPath`` at an earlier
``OutputData…`` folder and ``Params.startAnalysisStep`` at the step to resume
from. Results still g... |
42392c0d43e9f38687b6e083ef93f8bb3bee5139f533846629a20431ccb23d31 | Python | 12,604 | 333 | #!/usr/bin/env python
# -*- coding : utf-8 -*-
"""Tools for exporting DSIStudio-compatible outputs. Adapted from
mattcieslak/dmri_convert/mrtrix_to_dsistudio.py to suit PyDesigner's
needs.
References
----------
1. https://github.com/mattcieslak/dmri_convert/blob/master/mrtrix_to_dsistudio.py
"""
import os
impor... |
4cf22ad22c27c839f087eae60b647d684ab03c4b22ea7a97901e074cd1715dd8 | Python | 12,606 | 365 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from __future__ import annotations
import base64
import json
import logging
import os
from contextlib import AbstractContextManager
from http import HTTPStatus
from pathlib import Path
from typing import TYPE_CHECKING, Any
import urllib3
from cvat... |
7b9d9ac650735161077f453efe5be2f4080f634e522695b79c1bd0fc58dbf323 | Python | 12,606 | 224 | import json
from chemgraph.schemas.agent_response import ResponseFormatter
_response_schema_json = json.dumps(ResponseFormatter.model_json_schema(), indent=2)
planner_prompt = """
You are an expert in computational chemistry and the **Planner** responsible for coordinating a parallel execution workflow.
Your role i... |
f16a9570598f6dac607ffadabcd7ac4e751f887d8128d3dccea4bb10ba8b35ca | Python | 12,607 | 333 | # -*- coding: utf-8 -*-
import logging
import os
import tempfile
import numpy as np
from dipy.io.stateful_tractogram import StatefulTractogram
from dipy.io.streamline import load_tractogram
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
from scilpy.tractograms.streaml... |
ba38d83a25367980227c8c308fb49462d3458e2dae58949d1ae099c8d56192b9 | Python | 12,608 | 310 | from pathlib import Path
from typing import Callable
import numpy as np
import pandas as pd
import json
from meanap.params import Params, active_spike_method
from meanap.pipeline.cancellation import CancelCheck, check_cancel
from meanap.pipeline.progress import RunProgress
from meanap.pipeline.resume import build_inp... |
41fc3b7b343b5d571cdfd4e40ed303bf15eb586e71f4318e81de8a70bd0631c7 | Python | 12,609 | 347 | """
Entrypoing
"""
import pathlib
#If proto/thalamus.proto exists (implies we are in repo checkout) then regenerate protobuf interfaces if they are out of date
from ..build import generate
THALAMUS_PROTO_PATH = pathlib.Path.cwd() / 'proto' / 'thalamus.proto'
THALAMUS_PROTO_GEN_PATH = pathlib.Path.cwd() / 'thalamus' /... |
51eebf440ae9b8bf2040fdbcc3ae626f683f73e5eeda2ee87df355e12bfb1899 | Python | 12,609 | 315 | #!/usr/bin/env python
# Copyright (c) 2015 Tobias Neumann, Philipp Rescheneder.
#
# This file is part of Slamdunk.
#
# Slamdunk is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
#... |
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