sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
e63a1ec84f36ef870723f1dce9e52fac0512cf15aa05b1d96e0fef99f0b99ad7 | Python | 19,453 | 540 | ############################################################################
# Copyright (c) 2025-2026 University of Helsinki
# All Rights Reserved
# See file LICENSE for details.
############################################################################
import pytest
from isoquant_lib.barcode_calling.common import ... |
76cb6b1b2c3f1a713d27b897d8f0e33f7a0ecdc7f9a8afc3e1290a6b895df24c | Python | 19,524 | 435 | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
import math
import numpy as np
from typing import Dict, List, Tuple
import fvcore.nn.weight_init as weight_init
import torch
from torch import Tensor, nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.lay... |
0e1ad6469d0c7e81866a1de4d131f2d98d6051e4aa6df17a8a27f3bc7d179f93 | Python | 19,548 | 637 | #!/usr/bin/env python3
"""
Fully isolated SynthSeg inference script for hippocampus segmentation.
This script contains all necessary code and does not require local imports.
Author: Mahmoud Yaser (mahmoud1yaser)
"""
import argparse
import os
import time
import torch
import torch.nn as nn
import torch.nn.functional as... |
045288d87cabf3c2d225ef4cc0e1c7edf6589dc2c71b0f8d3c3aac9958cdf283 | Python | 19,552 | 447 | # Copyright (c) 2019 10X Genomics, Inc. All rights reserved.
"""Iterate over the input molecule info file and calculate on-target and off-target on a per-read basis.
These metrics are stratified into a data loss hierarchy, where at each step of data loss
the number of on-target vs. off-target are quantified.
Also add... |
79dc5c3381eef29fabaedb4a8c7a16bc872fc6fae8adcf6a785385a8b72a3a12 | Python | 19,574 | 490 | '''
Created on 19.10.2020
Author:
Michael Diedenhofen
Max Planck Institute for Metabolism Research, Cologne
Read Bruker ParaVision data (2dseq) and save as NIfTI file.
Create a b-table text file with b-values and directions for diffusion data.
'''
from __future__ import print_function
try:
zrange = xrange
excep... |
e681b8ed372f27ecc148d166257d769cbc131b4c39162286fbb3d66633144b08 | Python | 19,605 | 533 | """
Convenience entrypoints to run experiments and visualizations from the experiments/ folder.
This module provides:
- run_regular_hide_the_label
- run_hard_hide_the_label
- run_regular_open_race
- run_hard_open_race
- visualize_hide_the_label_mean_steps
- visualize_open_race_best_so_far
- run_experiment: a general d... |
35dac81700080bfbbbd8d578d2581c8c1bdc98cd029981a42ff237c72f31f052 | Python | 19,613 | 557 | # Copyright (c) Facebook, Inc. and its affiliates.
import datetime
import json
import logging
import os
import time
from collections import defaultdict
from contextlib import contextmanager
from functools import cached_property
from typing import Optional
import torch
from fvcore.common.history_buffer import HistoryBuf... |
809073216a6e92cd1c976c4f676bd0ce22eb73282cc830f9686082635939788e | Python | 19,619 | 548 | import os
import re
import math
import logging
from glob import glob
import numpy as np
from scipy.spatial.transform import Rotation as R
import twixtools as twx
try:
import cupy as cp
from cupyx.scipy.fft import (
fftshift as cfftshift, ifftshift as cifftshift,
fftn as cfftn, ifftn as cifftn
... |
5d0c43c6f17d467d25af7b07d5cf5b8531db5a6490731c207d4f315c2ec0aafb | Python | 19,682 | 420 | # Read molecules and record elements, formal charges, aromatic atoms,
# n bonded atoms, bonds, angles, propers, impropers and molecule indices
# Output uses one-based indexing
# See also https://github.com/openmm/spice-models/blob/main/five-et/createSpiceDataset.py
# Also set up starting structures for condensed phas... |
0deb7189169eec5ff30bbf9e147f9b271dc5a14e0da38dde607cf2cbfe9c02c7 | Python | 19,683 | 381 | """
code snippet to initialize the component W and H with, using basic QR and SVD to break down XX^T.
"""
import os
import sys
import numpy as np
import scipy.linalg
import scipy.stats
script_name=os.path.basename(__file__)
module_name=os.path.basename(__file__)
def get_objective_function(X, W, trXtX = -1, W_ORTHOGON... |
dbffb688d9e734fd925d1be0b5442bd1075f9be0596850e8e86c823c57080c8e | Python | 19,685 | 521 | # -*- coding: utf-8 -*-
"""
Figure 3: structure-function relationships
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.colors import ListedColormap
from netneurotools import datasets, stats, plotting, metrics
from scipy.stats import zscor... |
4a94e4c736439e4b47cbb8e71b63d60b44503212874a202bd1a9434ff2aaa510 | Python | 19,778 | 766 | """
Functions on surface mesh elements.
"""
# Author: Oualid Benkarim <oualid.benkarim@mcgill.ca>
# License: BSD 3 clause
import numpy as np
import scipy.sparse as ssp
from scipy.spatial.distance import cdist
from scipy.sparse.csgraph import dijkstra
import vtk
from ..vtk_interface import wrap_vtk, serial_connect
... |
696dcf8e1479fce2db65093a5f1c71bfe21082bb72ccdc24281d8fd8f08375b4 | Python | 19,826 | 492 | """Continuous States Descriptor.
Provides piecewise-linear and piecewise-quadratic trajectory tracking, and
declarative threshold monitors natively integrated with mesa_signals and
the standard Mesa event queue.
"""
from __future__ import annotations
import contextlib
import math
from collections.abc import Callable... |
88ab44cf76475f90a9705e4e44dc2293e33e2fa4384e856e0a8a2f1824458da8 | Python | 19,847 | 471 | """
Transformations
===============
<!-- difficulty: intermediate -->
Move neurons between brain templates and mirror them across the midline.
As of version `0.5.0`, {{ navis }} can transform and mirror spatial data such as neurons. The functionality
splits into high-level functions (which most users want) and the lo... |
8f9e6123ae83e33ea3a3fc48b8285a6e80eb52b9e90ead19148806d5613d20aa | Python | 19,848 | 530 | import math
import os
from abc import ABC, abstractmethod
from typing import List, Union, Type
from typing import Optional, Sequence, Tuple
import blosc2
import numpy as np
from batchgenerators.utilities.file_and_folder_operations import join, load_pickle, isfile, write_pickle, subfiles
from nnunetv2.configuration im... |
90cb355400072c8aae0a2e19e8f27b75d7b0ee33d4bcb2141ce1fdde9e411fc8 | Python | 19,848 | 616 | """ Created on Mon Nov 6 10:29:44 2023
@author: dcupolillo """
from __future__ import annotations
from pathlib import Path
import tifffile
from ROIpy.core.bundles import NodeBundle, ScanfieldBundle
from ROIpy.core.utils.utils import (
stack_metadata_dictionary, parse_swc,
parse_stack_metadata, assign_bran... |
d931361e61ec710ec74abd435214c305d1f652050a012a21db0ec1fbaeb531f4 | Python | 19,848 | 539 | # Copyright (c) Facebook, Inc. and its affiliates.
import copy
import itertools
import numpy as np
from typing import Any, Iterator, List, Union
import pycocotools.mask as mask_util
import torch
from torch import device
from detectron2.layers.roi_align import ROIAlign
from detectron2.utils.memory import retry_if_cuda_... |
bb040af37be8c811bd2e9bdf606592c3e2ac77c2f5c40da1e601faed69753bbd | Python | 19,885 | 510 | #!/usr/bin/env python
#
# Copyright (c) 2019 10X Genomics, Inc. All rights reserved.
#
"""Functions for summarizing a CountMatrix."""
from __future__ import annotations
from collections import OrderedDict
from functools import reduce
from typing import TYPE_CHECKING
import h5py as h5
import numpy as np
from six imp... |
eef21728699e975d495e219f96302a6bd6c79e9a762b276b804c06118f9913ee | Python | 19,902 | 545 | #!/usr/bin/env python
#
# Copyright (c) 2019 10X Genomics, Inc. All rights reserved.
#
from __future__ import annotations
import os
import re
import xml.etree.ElementTree as ET
import cv2
import martian
import numpy as np
import skimage.io
from PIL import Image, TiffImagePlugin
import cellranger.spatial.tiffer as t... |
9ce1424613ec07dcfbc6c592f11887a07d23f023c60a24c21d0ad824764d8754 | Python | 19,907 | 451 | #!/usr/bin/env python3
#
############################################################################
# Copyright (c) 2022-2026 University of Helsinki
# Copyright (c) 2020-2022 Saint Petersburg State University
# # All Rights Reserved
# See file LICENSE for details.
#####################################################... |
07498b01bf2c29b263372a21897f1394b750eb2fc921c526d2f1309c372ccadb | Python | 19,910 | 493 | import logging
import re
from os import PathLike
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import Any, Dict, Generator, List, Optional, Sequence, Tuple, Union
from zipfile import ZipFile
import numpy as np
import pandas as pd
from scipy.ndimage import maximum_filter
from .. import i... |
e3a745ea7e2118bbbe2d0fed45044fe6f07689f197527fe699d04163ca0a7025 | Python | 19,912 | 567 | from unittest import mock
import networkx as nx
import pytest
from matplotlib import pyplot as plt
from matplotlib.backend_bases import MouseButton, MouseEvent
from numpy import testing as npt
from openfe.utils.network_plotting import Edge, EventHandler, GraphDrawing, Node
def _get_fig_ax(fig):
if fig is None:
... |
00d079015501658eb2466dff3f605ab1a5d40957240ad8da6870bd261f550e81 | Python | 19,914 | 634 | """
Graph Neural Network architectures for molecular property prediction.
Each architecture supports both classification (sigmoid output) and regression
(raw output) via the ``task_type`` constructor parameter.
"""
from typing import Callable, List, Optional
import torch
import torch.nn as nn
import torch.nn.function... |
8343c53e5f975fcda0b8aeedfd05432ba9a7cfe2c132b0b196b24c331cb0600f | Python | 19,946 | 545 | # responses/stim_condition_response.py
import numpy as np
import spikeinterface.full as si
import batch_process.postprocessing.stim_response_util as stim_response_util
import batch_process.postprocessing.responses_v2.pulse_locked_response_metrics as plrm
import batch_process.util.template_util as template_util
class ... |
6959b117092581a2ae22f6805a4a58826b0eacc37884f525c5a0f00bcf3db048 | Python | 19,962 | 343 | """Reproduce the Extended Data Fig. 7 SAGAT results figure and statistics.
Situation Awareness Global Assessment Technique (SAGAT) study on public-road
scenarios: per-participant accuracy on perception (Q1-Q4), comprehension (Q5)
and projection (Q6) questions, split by surprising ('positive') vs
unsurprising ('negativ... |
265c2e9ec8d4b0ee111108c9a90a58920a029c8845ddc7457264ed0a3d8a7b1f | Python | 19,967 | 468 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import seaborn as sns
import pandas as pd
from pathlib import Path
import batch_process.util.plotting as plot_util
import batch_process.util.classify_cell_type as cell_classifier
import os
import batch_process.util.file_util as ... |
835469b597b6bad940abcf15b2c596981bd008ef141fbf94542391c85e69b2e0 | Python | 20,022 | 588 | from matplotlib.patches import Patch
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import seaborn as sns
import pandas as pd
from pathlib import Path
import os
import dill as pickle
import shutil
import re
from collections import defaultdict
from matplotl... |
47b58a064ffca63cb68844cb7e6ea5c423b9547402676cd16b30ad72a40740db | Python | 20,072 | 634 | # This code is part of OpenFE and is licensed under the MIT license.
# For details, see https://github.com/OpenFreeEnergy/openfe
import itertools
import os
import pathlib
from importlib import resources
import MDAnalysis as mda
import numpy as np
import pooch
import pytest
from openff.units import unit
from rdkit imp... |
dbfa7503010618b118fd3af428b1d57561ac84f15933f23ac1d4a63641e28a55 | Python | 20,072 | 494 | # This script is part of navis (http://www.github.com/navis-org/navis).
# Copyright (C) 2018 Philipp Schlegel
#
# This program 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 th... |
3abe1176f772895c92b6000b0d31da0f92fede152cd483f05786c4a66daecff4 | Python | 20,078 | 399 | import os
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import anndata as ad
import scanpy as sc
from scipy.io import mmwrite, mmread
import seaborn as sns
import scipy
from arboreto.algo import grnboost2
import pickle
import collections
import pycistarget
import pyranges as p... |
8bf8d70c92171f2e3c1316800d5ee6c83f99d208378aee024eea4e5419c6b04c | Python | 20,114 | 570 | """
Created on 10/08/2017
@author: Niklas Pallast
Neuroimaging & Neuroengineering
Department of Neurology
University Hospital Cologne
Edited by Paul Camacho 2025
"""
import nipype.interfaces.fsl as fsl
import os, sys
import nibabel as nib
import numpy as np
import applyMICO
import nipype.interfaces.ants as ants
im... |
abdace999bf733410176f6b65f95f1231ba01b092a010e178d6dfddd3dda627e | Python | 20,166 | 693 | from __future__ import annotations
import copy
import itertools as it
from collections.abc import Container, Hashable, Iterable, Sequence
from os import PathLike
from pathlib import Path
from string import ascii_letters, digits
from typing import (
TYPE_CHECKING,
Any,
TypeVar,
)
import hypothesis.strategi... |
e1471ac666f662ba537df9a2df9554e91768e5016f36a32e55ba6058b89aecc9 | Python | 20,168 | 641 | """shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers"""
import torch
from torch import nn, einsum
import torch.nn.functional as F
from functools import partial
from inspect import isfunction
from collections import namedtuple
from einops import rearrange, repeat, reduce
# constants
DE... |
f7e7c2a322678144cc7c7516ea1ed968477d6b424c16282ef49db08af60c4f89 | Python | 20,177 | 482 | import time
import argparse
import pickle
import os
import datetime
import torch
import torch.optim as optim
from torch.optim import lr_scheduler
from utils import *
from modules import *
import copy
import os
parser = argparse.ArgumentParser(
'Neral relational inference for molecular dynamics simulati... |
ec596492b2a0d11c81fbf6fd4c5f1124189fc90e20604fe4eb4437d5f8078771 | Python | 20,235 | 584 | """Core event management functionality for Mesa's discrete event simulation system.
This module provides the foundational data structures and classes needed for event-based
simulation in Mesa. The EventList class is a priority queue implementation that maintains
simulation events in chronological order while respectin... |
c82213387db239a32613c749002dd29a1fa8b22a49089f69261f775f054bc8e2 | Python | 20,321 | 435 | """Main TAPA pipeline orchestrator."""
import os
import shutil
import warnings
from pathlib import Path
import nltk
import torch
import whisper
from resemblyzer import VoiceEncoder
from .alignment import (
find_mfa_bin,
parse_textgrid,
parse_textgrids_dir,
prepare_mfa_input,
prepare_mfa_input_seg... |
1285d422040c453b60650b6d459cc1116122a7163d4d34c044631d979e05429d | Python | 20,403 | 423 | #python anamolyDetect.py "Z:\Download\proteinGroups.txt" 51
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import re
import sys
import os
from collections import Counter
import argparse
from scipy.stats import chisquare
def extract_first_digit(value):
str_value = str(value)
match = re.s... |
abf514ab50a24a4e90863c05e4f98066960530dca158d5cd650c9b751d300a3a | Python | 20,417 | 390 | ############################################################################
# Copyright (c) 2022-2026 University of Helsinki
# Copyright (c) 2020-2022 Saint Petersburg State University
# # All Rights Reserved
# See file LICENSE for details.
############################################################################
... |
7689be90ebcb81092b4e1bc4fef8deaaf9dee91f359e9d97882317582279fa0b | Python | 20,423 | 519 | import glob
import os
import pathlib
from unittest import mock
import pandas as pd
import pooch
import pytest
from click.testing import CliRunner
from openfecli.commands.gather import (
_get_column,
_get_legs_from_result_jsons,
_load_valid_result_json,
format_estimate_uncertainty,
gather,
)
from o... |
c11369a1bb989a1b93f4c8de95cf55e9a38126c5d86e5caab74e76c73e92db6b | Python | 20,475 | 391 | import os
import numpy as np
import pandas as pd
import umap
import matplotlib.pyplot as plt
import matplotlib
import anndata as ad
import scanpy as sc
import scvelo as scv
import scanpy.external as sce
from scipy.io import mmwrite, mmread
import scipy
np.random.seed(42)
# peak-gene linkage matrix
dir_path = "/home/n... |
568e0c05e137b91bf730161b67c54e072916a973fc7359c24ef513f11e12ba8d | Python | 20,583 | 476 | """
velocity_uncertainty.py
Quantifying the uncertainty of RNA velocity / chromatin velocity in mmVelo by decomposing it into three layers.
3-layer decomposition:
1. State-estimation uncertainty : sample z from q(z|a,s,u) (d fixed at posterior mean)
→ reliability of local... |
09d760b2a3f44cad5d3730a61f70a5ad88e7f7909d4d831e38ac5f0b497daad3 | Python | 20,595 | 410 | """
This function is for training a network using synthetic scans generated from a set of training label maps.
See details in the docstring below.
If you use this code, please cite one of the SynthSeg papers:
https://github.com/BBillot/SynthSeg/blob/master/bibtex.bib
Copyright 2020 Benjamin Billot
Licensed under th... |
a3b10c566702c4ed2028668d19faaf9182f99690ec0496976987a627136d4d55 | Python | 20,595 | 515 | import os
from pathlib import Path
import pandas as pd
import numpy as np
from sklearn.feature_selection._base import _get_feature_importances
from sklearn.feature_selection._from_model import _calculate_threshold
from .utils import compute_CI, permutation_test_between_clfs
from .visualization import scatterplot_regre... |
583bb517d2a47477ee6c84b7b80d4bc4bb75ab029ce7b45313ab5175b6f000a9 | Python | 20,621 | 556 | """Builds the canonical HFB annotation tables that are reused.
This module is the shared substrate for the analyses. It loads the significance-tested
three-neuron HFB detections for a single representative, post-trained trial of a given
``(experiment, model_type)`` combination, and joins:
* each PNG's structure (cons... |
04503cd6d06523e1cf485f191a8b3e2473c28e4bda2429a225d3c193553075a1 | Python | 20,623 | 577 | """Generate the `llms.txt` family from the curated API index.
Agents (and the humans driving them) need the shape of the API in a single
cheap fetch. Three tiers get written to the docs root:
- `llms.txt` the index: `llms_preamble.md` (the idioms an agent can't
infer from signatures) fo... |
f8be44e499bc8b03df4f23eb68bce15a60d508829176267be337f57c3547e0a2 | Python | 20,626 | 361 | import argparse
import multiprocessing
import shutil
from typing import Union, Tuple, List, Callable
import numpy as np
from acvl_utils.morphology.morphology_helper import remove_all_but_largest_component
from batchgenerators.utilities.file_and_folder_operations import load_json, subfiles, maybe_mkdir_p, join, isfile,... |
4b7d2b86851ae84dccb64cae4c95d11b8bad236c5d540053fdcc26ea568937fa | Python | 20,647 | 696 | # AUTOGENERATED! DO NOT EDIT! File to edit: 45_models_lm.ipynb (unless otherwise specified).
__all__ = ['log_logistic_func', 'conditional_gaussian_mean', 'conditional_gaussian_cov',
'compute_logistic_loglikelihood_old', 'compute_logistic_logodds', 'compute_logistic_loglikelihood',
'bayesian_logis... |
61268ce1d69b71ebac25a6220f9644cbec6f82638ab4fcff9d0be487b16728c2 | Python | 20,666 | 486 | """
From Neurons to Model Inputs
============================
<!-- difficulty: advanced -->
Turn neurons into fixed-size model inputs: feature point clouds and batchable patches.
Neurons are variable-sized graphs and meshes with uneven node/vertex density.
Most models want a fixed number of points, sampled evenly and... |
c6b4edbc17be165e5c7aa53614c1c7b4f26db6cab0c966774537cab0b1cccabd | Python | 20,720 | 446 | # -*- coding: utf-8 -*-
"""
Created on Sun Nov 3 13:52:44 2024
@author: ronsun
"""
import pandas as pd
import urllib.request
import nrrd
import numpy as np
import h5py
import os
import logging
from mcmodels.core import VoxelModelCache, Mask
import time
from pathlib import Path
from allensdk.core.re... |
44664aeb2a5dd1a569e3185acfd3866b7c0bd080afd9a4eecea2a6bdf1ef2034 | Python | 20,740 | 577 |
# Copyright 2019 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-... |
5dfc13bd6b28cbe1ca21d28f8557061a4006918bfb4f8194b12842139b9418ee | Python | 20,741 | 441 | #!/usr/bin/env python3
#
# ############################################################################
# Copyright (c) 2023-2026 University of Helsinki
# # All Rights Reserved
# See file LICENSE for details.
############################################################################
import logging
import os
import s... |
860b3f54640241b47cb0bd6c9ac7b868146e4270b2177df8d2635046a08db6c6 | Python | 20,745 | 452 | import numpy as np
import pandas as pd
import mrcfile
from skimage.morphology import skeletonize_3d, medial_axis, remove_small_objects
from skimage.color import label2rgb
from scipy.ndimage import morphology, label, binary_erosion, convolve
from scipy.spatial import distance
from scipy.interpolate import splprep, splev... |
f7db9c53b4cebb8b68df76f8c51c718194f27e004b3d8cdd3d317d9a998cfdbf | Python | 20,783 | 407 | ############################################################################
# Copyright (c) 2022-2026 University of Helsinki
# Copyright (c) 2019-2022 Saint Petersburg State University
# # All Rights Reserved
# See file LICENSE for details.
############################################################################
... |
788e393462781312fb30c9d1247fa1dfa39b81a4062cbcb86c94ded732287f2e | Python | 20,788 | 682 | from __future__ import annotations
import pyabf
import numpy as np
import tensorflow as tf
import miniML
def init_containers(
n_sweeps: int,
event_len: int
) -> tuple[
np.ndarray, np.ndarray, # events, events_x
np.ndarray, np.ndarray, np.ndarray, # amplitudes, peak_values, peak_x
np.ndarr... |
f98d4feeb4754e5789136eedd08adbbf7ceb1a0c57603c7839255ba5739208eb | Python | 20,815 | 558 | import os, tarfile, glob, shutil
import yaml
import numpy as np
from tqdm import tqdm
from PIL import Image
import albumentations
from omegaconf import OmegaConf
from torch.utils.data import Dataset
from taming.data.base import ImagePaths
from taming.util import download, retrieve
import taming.data.utils as bdu
def... |
3c256671822e5e10845669dd96eb6498ad30e1706f8c1ac49a99b222f9bf11e4 | Python | 20,936 | 537 | """
Masking
=======
<!-- difficulty: intermediate -->
Restrict analyses, plots and edits to part of a neuron - reversibly.
!!! example "New in {{ navis }} 2.0"
Masking is new and we are keen to hear how it holds up on real data. Please
read [Caveats](#caveats) and [Antipatterns](#antipatterns) before relying ... |
e319a2ab4d1f0c1a9232cef50f6710ad52d5b0b75b0c4abc266bfa5462e50c53 | Python | 20,967 | 534 | import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import random_split
from torch_geometric.loader import DataLoader
import os
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:1024"
import sys
import time
from tqdm import tqdm
import ast
import random
import numpy as np
imp... |
bd091f150296d59be5fd7e4b2530476357d070d9cc4bac1ca9ad2a0f5f43e53b | Python | 20,991 | 412 | """Stage 5b: the interactive SNV dashboard (``snv_dashboard.html``).
Reads the group's ``snv_table_unfiltered.tsv`` and ``_snv_state.npz`` from stage 2, plus the
parsimony tree from stage 4, and writes one self-contained HTML page holding every candidate position
AccuSNV considered, kept or not, why each was kept or d... |
ae3333cccadfce1f5b81f3b8bd395e583ef21eb5709e65d37dbc7790a7a0168f | Python | 21,050 | 538 | ############################################################################
# Copyright (c) 2025-2026 University of Helsinki
# All Rights Reserved
# See file LICENSE for details.
############################################################################
import pytest
import tempfile
import os
from isoquant_lib.assi... |
4513ea0b49cc39dd78c4944a24bc9aa88dec425f9cbf7331661891834f1e6c31 | Python | 21,057 | 362 | import os
import torch
import warnings
from ndreamer.model import NDreamer_DL
from ndreamer.matching import *
from ndreamer.statistics import get_significant_names
import time
import anndata as ad
class NDreamer(torch.nn.Module):
def __init__(self, adata, condition_key, contorl_name, num_hvg, require... |
da99d9f77efbe18f13b63dd91bdda084083a2f6f9483281b6deb645919280b8a | Python | 21,115 | 606 | #!/usr/bin/env python
#
# Copyright (c) 2014 10X Genomics, Inc. All rights reserved.
#
# Functions for preflight checks
#
from __future__ import annotations
import os
import platform
import re
import resource
import socket
import subprocess
from collections.abc import Callable, Iterable, Mapping
from ctypes import c_... |
0eda14bb5ebcdf336c80fff6596f4ff5776add0b6bc01ca785deb540dcee5975 | Python | 21,154 | 358 | """
If you use this code, please cite one of the SynthSeg papers:
https://github.com/BBillot/SynthSeg/blob/master/bibtex.bib
Copyright 2020 Benjamin Billot
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 Lice... |
38b7a118a8a48deba686a9d3790373828ad054ec0877a275a997c096c4534a9d | Python | 21,183 | 503 | #!/usr/bin/python3
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "So... |
635bf61ccbaa058075df97cffaad8886d127fb4c2b448fe0be970756d48a29a9 | Python | 21,183 | 564 | ############################################################################
# Copyright (c) 2026 University of Helsinki
# All Rights Reserved
# See file LICENSE for details.
############################################################################
"""Unit tests for CI test config loading (YAML and TSV formats)."""... |
e4d151a1a785c2eacd8b767b225de6cf2a1868b4c6cacaf4cbe2d09e60640723 | Python | 21,183 | 616 | import pickle
import numpy as np
import pytest
import navis
from navis.compute.dispatch import picklable_by_reference
from .test_compute_backends import DummyBackend, registry # noqa: F401
#: Backends that need no optional dependency and so always run here. `pathos`
#: and `joblib` are added via `importorskip` in... |
29bd221a5b4cee45fb57314497d5d684e2494a227f413f4e49c6b1e030c320f8 | Python | 21,185 | 555 | # This script is part of navis (http://www.github.com/navis-org/navis).
# Copyright (C) 2018 Philipp Schlegel
#
# This program 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... |
a99446719332e812db219011521ab4fca55a2c0daf01dfbaf19a95becb7e2b3a | Python | 21,217 | 625 | """
Neuron Types
============
<!-- difficulty: beginner -->
The four neuron types — skeletons, meshes, dotprops and voxels — and when to use each.
Depending on your data/workflows, you will use different representations of neurons.
If, for example, you work with light-level data you might end up extracting point
clou... |
82ea2c4d7cf7945367193cc5022e2fd93c871175f4acb72aa133ce10242aeb46 | Python | 21,231 | 476 | """
IVSCC morphometrics
===================
<!-- difficulty: intermediate -->
Measure cortical neurons compartment by compartment.
!!! important "This example is not executed"
Like the [skeleton QC tutorial](zzz_tutorial_morpho_04_qc), this one is *not* run when the docs are
built - it pulls ~25 reconstructio... |
4d59823843bbfbee2de4ef1d580e1d6c7f078ea5ea5593cfb78b5163ac741088 | Python | 21,239 | 599 | ############################################################################
# Copyright (c) 2025-2026 University of Helsinki
# All Rights Reserved
# See file LICENSE for details.
############################################################################
import pytest
import tempfile
import os
from isoquant_lib.util... |
b11cf4005a231c971129174747ec27447e9d50dd0483e109ed5560e59ee53996 | Python | 21,375 | 523 | #
# Copyright (c) 2024 10X Genomics, Inc. All rights reserved.
#
"""Utilities to run cell annotation on the cloud."""
import os
import pathlib
import time
from dataclasses import dataclass
from urllib.parse import urlparse
import martian
import requests
import cellranger.cell_typing.broad_tenx.cloud_cas_client as c... |
1f1f5ad2367564cee140928809cf2b1ded39ebf0d2dbc0d9d59ff0b78ef742a0 | Python | 21,421 | 530 | """
Module to perform semantic segmentation with either DeepD3 or nnU-Net,
and subsequent post-processing of predictions.
Each segmentation is perform through subprocesses to ensure modularity and
avoid dependency conflicts between TensorFlow (DeepD3) and PyTorch (nnU-Net).
The main function is `semantic_segmentatio... |
8b2a1f5d73ccb846b26f96596c6664f400a51eda5f07b8d223291b7bd9f2537e | Python | 21,471 | 584 | """Figure S8 -- Electrophysiological unit tracking.
Panels:
A: Example tracked waveforms (overlaid across sessions)
B: Raster of all tracked units
C: Waveform distance, tracked pairs vs nearby non-matched units
D: Modulation trends restricted to tracked units
(PL z-score, NPL z-score, PL t2max, NPL t2max,... |
976229eeb6c5ba7561e6e9c07f0329d3f39d53a99e9fee3cec9c4ceb80d8870a | Python | 21,534 | 544 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Sparse GP Approximations for Speed-Up Comparison
This module implements various sparse GP approximation methods:
- Nyström approximation
- FITC (Fully Independent Training Conditional)
- VFE (Variational Free Energy)
These methods provide significant speedups for lar... |
3a96f61c7c595e971af1af654cc041af3f7d7e390eafe3c9604b932b2f6a30d8 | Python | 21,548 | 597 | import glob
import pickle
import numpy as np
from numpy import sqrt
from numpy import argmax
import sys
import os
import pandas as pd
import seaborn as sns
import itertools
from sklearn.metrics import roc_curve, precision_recall_curve, brier_score_loss, accuracy_score, precision_score, recall_score, f1_score
import ma... |
b423a5cdefadc064ef26bf0d76e6a20d22b25d34ea000f2e435c0d1c56397a18 | Python | 21,640 | 375 | ############################################################################
# Copyright (c) 2022-2026 University of Helsinki
# All Rights Reserved
# See file LICENSE for details.
############################################################################
import pytest
import unittest
import isoquant_lib.common as c... |
0c627a4b2dfa00d290c1c6f7dc92f04e53359ab320437c6e0ab242ab8959494f | Python | 21,670 | 479 | #!/usr/bin/env python
#
# Copyright (c) 2017 10X Genomics, Inc. All rights reserved.
#
from __future__ import annotations
import cellranger.library_constants as lib_constants
import cellranger.vdj.utils as vdj_utils
import cellranger.webshim.constants.shared as shared
def vdj_gene_pair_format(gene_pair_str):
""... |
02a40cf56c20af039967ba4d1bf81a6d892c6065bb3b20f9ba4145dbaa397cfb | Python | 21,709 | 498 | """
This code is for training is traditional supervised networks with real images and corresponding ground truth labels.
It's relatively simpler than training.py since it here we do not have to generate synthetic scans. However we kept the
parameters for online augmentation.
If you use this code, please cite one of t... |
ceb6fb93c1976cfee86396471b9d40bbbd88a0420ca0daa93b40c58b4a75f008 | Python | 21,713 | 469 | '''
Created on Oct 25, 2024
@author: voodoocode
'''
import struct
import numpy as np
import os
import scrubber.core
import csv
IN_PATH = "/mnt/data/Professional/LMU/data/Beta-prevalence/database4/original/"
OUT_PATH = "/mnt/data/Professional/LMU/data/Beta-prevalence/database4/scrubbed/"
COLLECTION_META_PATH = "/mnt... |
5cf1e3da1357be4fcc02ee9896c4e754f806eea7c311bbf43995eb3ab8e7ed34 | Python | 21,748 | 627 | """
Dataset splitting strategies for molecular property prediction.
Provides random, scaffold-based, and clustering-based splits that are
appropriate for evaluating generalisation in molecular ML.
"""
import logging
from collections import defaultdict
from typing import Dict, List, Optional, Sequence, Tuple
import n... |
6723cad79f6a23aad5b67e23799a71f445cd4ca3e29e0b3ce0a6af180ebdeab6 | Python | 21,752 | 486 | # This script is part of navis (http://www.github.com/navis-org/navis).
# Copyright (C) 2018 Philipp Schlegel
#
# This program 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... |
c341567a805e542121ad541b4a651c5bb68e2adb9ef7fe451ad2128bbe62d422 | Python | 21,761 | 540 | # This script is part of navis (http://www.github.com/navis-org/navis).
# Copyright (C) 2018 Philipp Schlegel
#
# This program 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... |
eab121f2acc153285d31c4247027bd6716708412d5cdb09dfc396f586deb051c | Python | 21,766 | 573 | import json
import logging
import shutil
import subprocess
from enum import IntEnum
from importlib import resources
from os import PathLike
from pathlib import Path
from typing import (
Any,
Generator,
List,
Mapping,
Optional,
Protocol,
Sequence,
Tuple,
Union,
)
from uuid import uuid... |
9f2d9a628183dfe06349c41717d41371da5d49c758a808cfca21c79638ccd627 | Python | 21,775 | 377 | """Reproduce Figure 3 (on-road private-track results).
Panels and outputs (saved to plots/):
Fig 3a (CLOSE): CLOSE_global.pdf (speed vs Close-concept probability with OLS
fit; Pearson r and intercept printed to stdout) and CLOSE_local.pdf
(Close-concept probability over time with driving-mode shading).
Fig... |
344073dd519c2d76c98d0a75aa0cbaca52da4c1bec9717f3423a013da2ffe3bd | Python | 21,786 | 555 | # This script is part of navis (http://www.github.com/navis-org/navis).
# Copyright (C) 2018 Philipp Schlegel
#
# This program 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... |
0a5ab20638dec95db3da8aacecef93a58221a7e8961b5985a0ba83762a19b8ba | Python | 21,811 | 523 | """Mesa Data Collection Module.
DataCollector is meant to provide a simple, standard way to collect data
generated by a Mesa model. It collects four types of data: model-level data,
agent-level data, agent-type-level data, and tables.
A DataCollector is instantiated with three dictionaries of reporter names and
assoc... |
2a3522caeb88b48f3437f07f994c51fd9585a1fdb95f71f54fcba806c177d644 | Python | 21,811 | 580 | from __future__ import annotations
import itertools as it
import re
import string
from collections.abc import Sequence
from typing import Any, cast
import more_itertools as itx
import pytest
from bids.layout import Query
from hypothesis import assume, given
from hypothesis import strategies as st
from snakebids.core... |
c1b0a8997e5ad05d7ffc4458ff9bd366822f41fd1bd482afd321ffdb612f4fb1 | Python | 21,821 | 599 |
# Copyright 2019 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-... |
9c8f934b34cb423cfcc166f47c2a675a57844f918117da0b8fe635c7b83f7894 | Python | 21,832 | 764 | #!/usr/bin/env python
"""Create generalized TF-activity summary tables and manuscript-oriented plots."""
from __future__ import annotations
import json
from pathlib import Path
from typing import Any
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from ... |
855baa3c4acdc58934bb969792d8e40c210b646049f6d1b5e305463b52df6eaa | Python | 21,841 | 350 | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
"""
Note:
For your custom dataset, there is no need to hard-code metadata anywhere in the code.
For example, for COCO-format dataset, metadata will be obtained automatically
when calling `load_coco_json`. For other dataset, metadata may also be... |
d23436fc830af509f97616b3f95c209465d1bc0a96c307eb753573e4f42f5c2d | Python | 21,855 | 579 | import numpy as np
import torch
from torch.utils.data.dataset import TensorDataset
from torch.utils.data import DataLoader
import torch.nn.functional as F
from torch.autograd import Variable
def my_softmax(input, axis=1):
trans_input = input.transpose(axis, 0).contiguous()
soft_max_1d = F.softmax(trans_input,... |
c25dd78835a74e8d3409f33e52f7a7b998fab9ffb4873302d81e75fa2de312f1 | Python | 21,891 | 554 | #!/usr/bin/env python
#
# Copyright (c) 2022 10X Genomics, Inc. All rights reserved.
#
"""This file contains plots in the analysis tab that are directly related to the outputs of the SC_RNA_ANALYZER pipeline for.
gene expression and/or antibody capture, e.g. TSNE plots, clustering plots, differential expression table,... |
43b0568835afc8169971f8439d654ce345ad68ee3393b4dd61dbe1d4516457ee | Python | 21,934 | 593 | """Tests for the collage/grid layouts.
The layouts themselves are `navis-fastcore` (rasterise, then pack); what is checked
here is the navis half — that the page, the views, the scaling and the neuron
handling all line up, and above all that the invariants the layouts promise actually
hold on real neurons: nothing ove... |
411909043e0764fd1cb64d0403dcae4a82602c4f8c9549845dea897233961ce1 | Python | 22,003 | 619 | # SPDX-License-Identifier: MIT
"""
Spatial autocorrelation functions using Moran's I.
This module provides functions to compute Moran's I for long-read spatial transcriptomics data,
including permutation-based significance testing and FDR correction.
"""
from pathlib import Path
import pandas as pd
import numpy as np... |
bb227e7cefb8f77b76896e19bc68051bc39108fdd2a2009fdc28474191485d84 | Python | 22,027 | 548 | """
denoise - Diffusion-Weighted Image Noise Reduction
Part of the micaflow processing pipeline for neuroimaging data.
This module denoises diffusion-weighted images (DWI) using the Patch2Self algorithm,
which leverages redundant information across diffusion gradients to remove noise
without requiring additio... |
d357579c39f1628e3561984d042d498f9e48824f25f38e25fdda2f853adfb12b | Python | 22,056 | 569 | """
Experimental Stage 2 — PPT-based waveform curation (replaces sortingview).
Uses the same curation pipeline as the control animals:
1. Load curation_config.json for rescue_ids / remove_ids
2. Select accepted units from stage1_analyzer_raw.zarr
3. Run waveform curation:
a. Remove bad waveforms (blanking a... |
0176906ec54540031b4c86e3003b014eac0371135c0a209c911f51f7d461f799 | Python | 22,111 | 490 | """
velocity_off_manifold_mod.py
We quantify on- and off-manifold uncertainty of latent dynamics
in mmVelo directly within each modality-specific space.
Unlike the latent-space decomposition implemented in velocity_off_manifold.py,
this approach constructs local tangent spaces
based on denoised modality-specific re... |
5fe0252d92504ad73db7588fca83c41690071e7ed0c5ff469802a80da0716560 | Python | 22,122 | 601 | #python parseMGFcluster.py L:\promec\Animesh\Maria\MGF\20200909_MKA_H12C_PeptidesDHB_DDA3.mgf 5E-6
import sys
if len(sys.argv) != 3: sys.exit("\n\nREQUIRED: numpy, tested with Python 3.9.9 \n\nUSAGE: python parseMGFcluster.py <complete path to MGF file like \"L:\promec\Animesh\Maria\MGF\20200909_MKA_H12C_PeptidesDHB_... |
64252e1cb272028a1a4f19aa2991a0c1c403993906687295316f74dd75b236d8 | Python | 22,125 | 564 | import os
from pathlib import Path
import glob
import re
import sys
import numpy as np
import spikeinterface as si
import spikeinterface.extractors as se
import spikeinterface.sorters as ss
import spikeinterface.preprocessing as sp
def discover_sessions(base_dir, allow_missing_mat=False):
"""
... |
9a8302d979b6542741e57b0c28c308b253819f8b7f68def58df92e8b12a1c45c | Python | 22,147 | 582 | # This script is part of navis (http://www.github.com/navis-org/navis).
# Copyright (C) 2018 Philipp Schlegel
#
# This program 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... |
184215cce4f5a8713640850cd5cdb33b5e5bdb23d2d63f7ced13d22ddd41cb14 | Python | 22,157 | 736 | # This script is part of navis (http://www.github.com/navis-org/navis).
# Copyright (C) 2018 Philipp Schlegel
#
# This program 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... |
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