sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
98b0098a08c7cd97c2711342db6b5442f37342e2d7fd3f23f695cde9dff510ba | Python | 7,232 | 219 | """
BSD 3-Clause License
Copyright (c) Soumith Chintala 2016,
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of con... |
d1b4b9f78b95dc8ea15eeed296d078c9f0ceca9060ae617a129b82ff9c5906dd | Python | 7,232 | 189 | """Unit tests for the custom pyqtgraph items in viewer_items.py.
These exercise the items' headless public API + a synthetic mouse-drag
on BrushCurveItem (constructing a minimal QGraphicsSceneMouseEvent) so
that the Wendland C² falloff math has a real test, not just the
construction smoke test from the viewer.
"... |
99e165d4847644a8d3dd2dfee36a695b08ade16fece028752f7548b77047fa76 | Python | 7,233 | 187 | import numpy as np
import tensorflow as tf
class MinMax(tf.keras.constraints.Constraint):
# Constraint to help set exponential smoothing within [0,1]
def __init__(self, w_min, w_max):
"""
Constraint to set parameters in a specified range [w_min, w_max]
:param w_min: Minimum value
... |
dfd67a3ba7d03ff04b9004cea827de06e77808515796d7812178eb4f27c8a54c | Python | 7,235 | 193 | import h5py
import numpy as np
import torch
import random # Add this import
from torch.utils.data import DataLoader, TensorDataset
import torch.nn as nn
import torch.optim as optim
import h5py as h5
import matplotlib
# matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
# from torchsummary import summary
from MoS2... |
872dbb69fce8de7c9653da5dbf37275efd3c68dee0367b96ef8353166b8aa8a1 | Python | 7,236 | 240 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 required by applicab... |
16cd13668cf62337630a1bd591cc3749f2a6991e7431362f067de4a7e8940547 | Python | 7,239 | 208 | # Node-group asset "Assembly Instance" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHEC... |
bcb8e52228cbdfdc712de79c397d6472984ea0d269f437d322306a9ebc02aa5f | Python | 7,242 | 186 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 required by applicab... |
cae6f5d681ff9bdbbf0d9f7c5a49b9e61a61498e75be024b10b91f4afc9232ff | Python | 7,243 | 207 | import bpy
import numpy as np
import pytest
import molecularnodes as mn
def test_selection_string_changes(fetch):
mol = fetch("1BNA")
sel = mol.selections.from_string("resid 1:10")
with mol.tree.reset() as (atoms, join):
atoms >> mn.nodes.geometry.SeparateAtoms(selection=sel.node()) >> join
... |
0ad12a26a03e9653a45df0c4d8e9ce3b5188bd2c41753ada06a45da328657673 | Python | 7,246 | 188 | #!/usr/bin/env python2
from __future__ import print_function
import sys
import os
import json
import stat
import argparse
import subprocess
import datetime
import re
import glob
import csv
import warnings
def findRoundIdx(jdat):
# determine round/prm/pch
#
# Returns roundIdx, prmfull, prmS, pchfull, ... |
3c374af4d06f5b09dc63d6a1163245a4a1763c4193e52809d6a4f8106fb6aeee | Python | 7,246 | 181 | """Summary report for HED schema compliance checking."""
from hed.schema.hed_schema_constants import HedSectionKey
# Section display names for readable output
_SECTION_DISPLAY_NAMES = {
HedSectionKey.Tags: "Tags",
HedSectionKey.UnitClasses: "Unit Classes",
HedSectionKey.Units: "Units",
HedSectionKey.U... |
972259d4a11167b60aef49b37e0a8080f9d69b70a48c12cd7900ad2f8fb337da | Python | 7,247 | 124 | from optparse import OptionParser
import os
from statistics import mean
import time
### help and usage ###
usage = "usage: %prog [options] args"
description = '''Function: merges EAA results from split BayeScEnv test jobs'''
version = '%prog 7.17.2022'
parser = OptionParser(usage=usage,version=version, description = ... |
f4adb164581ebf300166bf395931ff01637d3fa559949ef267d51da50d3f6086 | Python | 7,247 | 232 | from brian2 import *
from math import exp, floor, sqrt
from matplotlib.pyplot import *
from setup import izzy, pos_3D, STDP1, reg_synapse, write_video, vectormap
def Wave(seednum, batch, RATE):
#setting the random seed based on input
seed(seednum)
#names for numpy '.npy' datafiles (missing numbers are... |
3eda8a6b7f8c0142d62aff24851e535ab75f3ff9a72f50b45f5a9eb6da23ae05 | Python | 7,249 | 227 | # A time-point module
import numpy as np
import scipy as sp
from pyclamp.dsp.sifunc import *
from pyclamp.dsp.fpfunc import *
from pyclamp.dsp.dtypes import *
from pyclamp.dsp.optfunc import *
MAXSI32 = 9223372036854775807
def latency(_t, _r, d = 0., n = 0, o = False):
# calculates latency of t wrt reference r of... |
4003ca9f95f935737503c97c90c7e7c8807beee712de8c78ac8cf850cb919db5 | Python | 7,250 | 210 | """
ResNet18
Difference from resnet in torchvision for higher resolution:
1. Modify the stride of first convolution layer (7x7 with stride 2) into 1
2. Remove the first max-pooling layer
3. Downsample and upsample twice in encoder and decoder respectively in FCNResNet
"""
import torch.nn as nn
import math
import tor... |
7b9a0e86073ee861337d8d902ad04f36ddf247f85b16074fc695702c2b4f1932 | Python | 7,251 | 238 | from datetime import datetime
from logging import getLogger
from numpy import (abs, angle, arange, asarray, empty, exp, linspace,
pi, ptp, real, round, sin, zeros)
# numpy.random.random has an empty __module__ and sphinx autodoc adds it to api
from numpy import random
from numpy.fft import fft, ifft... |
917cb7f0797da370844ddc38679b89c1b3168e2c1302fba3a4e9ed7090cf154e | Python | 7,251 | 173 | import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from typing import Any, Dict, Optional
from models.model import Model
from models.kan_layers import KAN, FastKANConv3DLayer
class He3DCNN_KAN_Net(nn.Module):
"""
MULTI-SCALE 3D DEEP CONVOLUTIONAL NEURAL NETWORK FOR HYPERSPECTR... |
6f6ea390051bcf8ad6555407730038968276e89cd43e516ac8475880a0f0d999 | Python | 7,252 | 185 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 required by applicab... |
634fdbea993fc4731433fc8aa7c0eca44b1611d58396eb41900082fa7c655590 | Python | 7,253 | 204 | import unittest
import numpy as np
from neurolib.models.wc import WCModel
from neurolib.control.optimal_control import oc_wc
import test_oc_utils as test_oc_utils
p = test_oc_utils.params
class TestWC(unittest.TestCase):
"""
Test wc in neurolib/optimal_control/
"""
# tests if the control from OC c... |
c2c12b3e241a792e6e80ae3299b96db9e818864159702c12128fc06d5ee27889 | Python | 7,255 | 214 | import os
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import ttest_rel, pearsonr
import matplotlib.patches as mpatches
import matplotlib.colors as mcolors
import scipy.io as sio
import torch
def check_cuda():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if devic... |
4356f2a86e953a84b98af6eab44354f4f138b57d0cd8a78779e3231d34cb4818 | Python | 7,256 | 229 | """Tests for Mixscale continuous perturbation scoring.
The `R_GOLDEN` values were produced by `RunMixscale` from https://github.com/satijalab/Mixscale on the dataset built by the `parity_adata` fixture.
The same DE genes are supplied via its `DE.gene` argument so the comparison isolates the scoring algorithm from the ... |
b85b24465c906dfeb97537d4a67f5e7e9f9aa3cd9ce2d4a06ac2c541d5135cff | Python | 7,256 | 233 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# 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,... |
f84727ac7048468729234522f2e1001b9d2703734422378848cf4d09f0069a91 | Python | 7,256 | 228 | from json import dump
from numpy import array
from .brainvision import write_brainvision
from .edf import Edf
from ..utils import MissingDependency
try:
from bidso import iEEG
from bidso.utils import replace_extension, replace_underscore
except ImportError as err:
iEEG = replace_extension = MissingDepende... |
0a9cda0d45f02315b1687e79479e9192f0c1ac08303445f8cef8e768c989fa4f | Python | 7,257 | 205 | '''
Modified by Mayank Kabra
Adapted from original predict.py by Eldar Insafutdinov's implementation of [DeeperCut](https://github.com/eldar/pose-tensorflow)
Source: DeeperCut by Eldar Insafutdinov
https://github.com/eldar/pose-tensorflow
To do faster inference on videos (with numpy based code; introduced in Oct 201... |
7095b052c4e508338d810fab7b592369247d3746ca7d138c277cefe29355712e | Python | 7,257 | 186 | import typing
from functools import partial
from operator import itemgetter
from .utils import metric
import numpy as np
class Tracker:
def __init__(
self,
gt_count: int = 0,
center: typing.Tuple[int, int] = (400, 400),
sigma_l: float = .05, sigma_h: float = .5,
... |
6bf6b5e5c993e4879ce2111b7b0c293e0d4bc6f2213763bb502a1851e6b94579 | Python | 7,258 | 206 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2022/9/22 9:11
# @Author : piaoyang cao
# @File : subgraph_encoder.py
import torch
import torch.nn as nn
from torch_geometric.nn import global_add_pool, global_mean_pool, global_max_pool, GlobalAttention, Set2Set
from torch_geometric.nn import GATConv
... |
ba6b7d1fd10a6ba29dfd715a9da61ccfebc3d01ee87acde4cb63b185f7d4c68e | Python | 7,259 | 197 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 17 01:12:51 2021
@author: mpolonenko
"""
import numpy as np
import os
from expyfun.io import read_wav, write_wav, write_hdf5, read_hdf5
from glob import glob
import parselmouth
from parselmouth.praat import call
import matplotlib.pyplot as plt
impor... |
70ebd76bed01eed5ac20b07ca4549e3429b572fcf4b8a4767e9ae242a663fe5c | Python | 7,260 | 206 | #!/usr/bin/env python
from __future__ import print_function
import cProfile
import pstats
import argparse
from examples.pybullet.utils.pybullet_tools.kuka_primitives import BodyPose, BodyConf, Command, get_grasp_gen, \
get_stable_gen, get_ik_fn, get_free_motion_gen, \
get_holding_motion_gen, get_movable_coll... |
9443038d7848727b02600792976ff85e2a5e351fe1d0ab092a508c47a8d413e7 | Python | 7,264 | 257 | import logging
import numpy.testing as npt
import pandas as pd
import pytest
from astrovascpy import bloodflow as tested
from astrovascpy.utils import Graph
L = logging.getLogger(__name__)
@pytest.fixture
def params():
return {
"blood_viscosity": 0.1,
"base_pressure": 1.33e-3,
"max_nb_i... |
62d309516d0a52132dd4a288922786332f64810fd257fe3c81a7b504efc0e138 | Python | 7,266 | 258 | import pandas as pd
import numpy as np
from tqdm import tqdm
import mmap
import matplotlib.pyplot as plt
from dataset import ProteinDataset
def get_num_lines(file_path):
'''Count the number of lines in the file at file_path'''
#https://blog.nelsonliu.me/2016/07/30/progress-bars-for-python-file-reading... |
bbdac2e27dc49fc2c22f3e1e59670af334eb602e8d7a9b099d81ba1821596a83 | Python | 7,267 | 224 | # Node-group asset "Curve Visualize" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECKI... |
7b5dcfdbfb7d19be8782cc10bda8856999f1e990958955f7f3212d3f68fa02b3 | Python | 7,268 | 202 | import numpy as np
import pickle as pkl
import torch
import networkx as nx
import scipy.sparse as sp
import sys
exc_path = sys.path[0]
def parse_index_file(filename):
"""Parse index file."""
index = []
for line in open(filename):
index.append(int(line.strip()))
return index
def load_data(dat... |
2b09ef7564dcc84ac724d2799e186c210ff72efb01c3b975fbd55d6be9be4f2d | Python | 7,269 | 201 | """
Configuration constants and diagnostic utilities for the registration module.
Provides color constants for terminal output, ANTs/Elastix availability flags,
binary paths, and environment checking functions.
"""
import os
import sys
import numpy as np
from regtools import get_project_root
from regtool... |
ba20f0ea6b0ceeb102cbbd093054a798517f48dc7218c14448f5b7053ae97b94 | Python | 7,270 | 230 | '''
ResNet implementation from torchvision.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
[2] https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py
'''
import torch.nn as nn
import torch
from torch import Ten... |
f5370ff286dd8470c08f213a0a889a8947fc8319c118e81dae7fe887f6441371 | Python | 7,270 | 212 | ##
# \file primal_dual_solver.py
# \brief Solve reconstruction problem A_k x = y_k for all slices k via
# Primal-Dual solver.
#
# Implementation to get an approximate solution of the inverse problem
# \f$ y_k = A_k x
# \f$ for each slice
# \f$ y_k,\,k=1,\dots,K
# \f$ by using first-order primal-dual al... |
88ca16b61da5cb195ebe17be80d17af4766d09621e49927131724fb899a532f0 | Python | 7,271 | 209 | """ Data from a Learning Experiment """
import itertools
import os.path as osp
import json
import datetime
from typing import List, Tuple, Dict
import pandas as pd
from language import load_input_language
# Line start that indicates start of data
START_OF_DATA_HEADER = "Round\tTask\tTrial"
def scenes(data:pd.DataF... |
cb0077b533db1b9fae894fb7bf189c0a8c58d8610808c8fdd4652dd4199dc376 | Python | 7,271 | 203 | #!/usr/bin/env python
# Copyright (c) 2018-2020 Intel Corporation
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
All intersection related scenarios that are part of a route.
"""
from __future__ import print_function
import py_trees
from sr... |
e5c2bc979d4bf94623206538099443507e0fc00d0dfc6cd0c767fa0ddbb8f8f8 | Python | 7,271 | 156 | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# 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 r... |
606b4a9734cf190241582bab4da839519ccf241b660b460bcb012dc8f15cbdd1 | Python | 7,272 | 241 | #!/usr/bin/env python3
import pysam
import numpy as np
import argparse
import logging
from numba import njit
import tqdm
# C+m
CPG_MODS = [("C", 0, "m")]
M6A_MODS = [("A", 0, "a"), ("T", 1, "a")]
D_TYPE = np.int64
def get_mod_pos_from_rec(rec, mods=M6A_MODS):
if rec.modified_bases_forward is None:
return... |
94c74e950a8eea5f6e3df1eeda6cd36b7161e0e1d07bc5c6cf8f163272f2664c | Python | 7,272 | 192 | import argparse
import math
import os.path
import sys
import anndata
import numpy as np
import cirrocumulus.io_util
from cirrocumulus.anndata_dataset import read_adata
from cirrocumulus.util import get_fs
def concat_spatial(paths: list[str], output_path: str, ncols: int = 2):
datasets = []
images = []
s... |
c270c6ce90c3395419cff8a7d7558d4fd1663496aaf1be5ff0688d6a9fde03fd | Python | 7,272 | 241 | """Declarative registry of every GUI module the RegTools Hub can launch.
Each :class:`ModuleEntry` describes one standalone GUI module and the argv
(after the Python executable) used to launch it as its own subprocess. The
``launch`` tuple for every entry is kept in sync with the corresponding
root-level ``.bat`... |
d3262a2045c469e33ae25756fcca7625cc33945a5912e834dbb426b3fd373a41 | Python | 7,273 | 187 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 required by applicab... |
faf650cd9c1005ca144abe8e0665b792db0b6e550ae72c2082615a451ef9c6f9 | Python | 7,273 | 149 | """Capture real screenshots and an activity animation from a live Observatory session.
Every image comes from the actual application: the real model generates both
branches, the reservoir state is the measured state, and the animation replays
snapshots recorded during that same session. Nothing is drawn synthetically.... |
8e5af8ddbc4b06afcfbf2de35eb0114d0c7060c1b8939e5a519847e591e0e4fd | Python | 7,274 | 172 | # from typing import Any, List
from typing import Any, Dict, Optional, Tuple
import torch
from pytorch_lightning import LightningModule
from torchmetrics import MinMetric, MeanMetric
from torchmetrics.classification.accuracy import Accuracy
from torchmetrics import MeanAbsoluteError, MeanSquaredError
from src.da... |
ed16e3acfa4dabdebc43cd98c3e6712d3ca213d2438e81b5b3f8c0ad9a9c2354 | Python | 7,274 | 230 | import warnings
from collections.abc import Callable, Iterable, Iterator, Mapping
from dataclasses import dataclass
from itertools import starmap
from typing import Protocol, overload, override
import elasticai.creator.function_dispatch as FD
import elasticai.creator.ir as ir
import elasticai.creator.plugin as _pl
fro... |
4d5ae5029d84449c308e3b33ccfe9242dff6f56bece118dc6a8b79bd91f2a0fe | Python | 7,275 | 192 | from __future__ import annotations
from logging import WARNING, getLogger
import numpy as np
import pytest
from ase.build import molecule
from ase.calculators.emt import EMT
from ase.units import invcm
from monty.json import MontyDecoder, jsanitize
from quacc.schemas.thermo import ThermoSummarize
LOGGER = getLogger... |
947aace31b6f0a6292878540cd1995574914be17a61dfa7e02093794d42249df | Python | 7,275 | 220 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 required by applicab... |
3aa1896f2be722010ebb1dd91e31f67f1ba887a15a1d4b648e0a20b3199f7692 | Python | 7,276 | 157 | from __future__ import annotations
import datetime
import re
from functools import total_ordering
from typing import Any
from wikibaseintegrator.datatypes.basedatatype import BaseDataType
from wikibaseintegrator.wbi_config import config
from wikibaseintegrator.wbi_enums import WikibaseSnakType, WikibaseTimeP... |
5b60ba68d1ed44d8191397de6d93e0e7cb04579bd7aa12ee4ef8baae0110306a | Python | 7,276 | 214 | import numpy as np
import csv
from cv2 import imread
from preprocess import preprocess, preprocess_basic
import os
from skimage.io import imsave
import shutil
from helpers import File, create_directory
from sklearn.utils import shuffle
import time
from config import DATA_DIR, DEBUG, REDO_PREPROCESSING, LOAD_ONLY_FEW... |
1cf43c1e5bb2e0053ef6b155ed7609bcf87a84e02bd57f71eb6728c71057e2d1 | Python | 7,279 | 231 | """Core recipes for the NewtonNet code."""
from __future__ import annotations
from importlib.util import find_spec
from typing import TYPE_CHECKING
from ase.vibrations.data import VibrationsData
from monty.dev import requires
from quacc import get_settings, job
from quacc.runners.ase import Runner
from quacc.schema... |
f7dc5a93025535c8402c2c6189ca08a736c4bfd80231440899125eb31c5c4227 | Python | 7,280 | 178 | """
toposampling - Topology-assisted sampling and analysis of activity data
Copyright (C) 2020 Blue Brain Project / EPFL & University of Aberdeen
This program 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... |
fca3454b016bc31430c3b36ec01d2a1893b0e5f4f958c2c9602e8db39e83d43c | Python | 7,280 | 155 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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
#
# ht... |
03f43759a157d7e9ee579c5f5bfe1e160f0da85e3eb4485d6d59ee0556b924b4 | Python | 7,282 | 152 | # SPDX-FileCopyrightText: 2024 Blue Brain Project / EPFL
#
# SPDX-License-Identifier: AGPL-3.0-or-later
'''
Functions to compute basic network properties for a full connectome or on its neighborhoods
Author(s): Daniela Egas S.
Last updated: 12.2023
'''
import numpy as np
import pandas as pd
import pickle
from conn... |
6b3b87bcf26ad2a18010bba2d2b906e2a1b93bcf69be3a25013ed3c167f2e1e6 | Python | 7,283 | 226 | import os
import argparse
import yaml
# command line arguments:
# --local: 1 (run locally) or 0 (run on spaceml)
# --GPU: [0-7] specifies the gpu to run it on, check with gpustat which one is available
# --config: path to the config.yaml file
# --runname: optional run name argument
DATA_DIR = ''
GPU = -1
RUN_NAME = '... |
ced1d8a5d2b6559c5a0212d724c0d83c1ed1db820aa8b133ec5d49620c2517aa | Python | 7,289 | 197 | """
Script for filter a MbGraphMCF object by extracting a filament network
Input: - A STAR file with a list of graphs (MbGraphMCF) as it is returned by
by mb_graph.py script:
+ Density map tomogram
+ Segmentation tomogram
+ MbGraphMCF object
... |
8ddca7e9e70f7292760783745c21fb63c8c0381f7c1214aee1cc2ca6c486d890 | Python | 7,290 | 156 | from typing import Any, Callable, Dict, Tuple, Union, List
import mne
import numpy as np
from .base_dataset import BaseDataset
from ...utils import get_random_dir_path
class MNERawDataset(BaseDataset):
'''
Process a list of MNE Raw objects and corresponding information dictionaries. This dataset is particular... |
c721fdf583c06a705444fae9a503b02c27f6d26dffa49ad8ca480f53a3dedbd2 | Python | 7,290 | 201 | import math,os
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch_sparse import SparseTensor, matmul
from torch_geometric.utils import degree
def full_attention_conv(qs, ks, vs, output_attn=False):
'''
qs: query tensor [N, H, M] # M = D
ks: key tensor [L, H, M... |
007b1a7e880fad72c2c6c42f43f57e91129ef0b7bcb9645b11e8675025c80237 | Python | 7,291 | 187 | """
Script for computing statistics about slices of a membrane (along different penetration values)
(v0.2) -> Working with sparse spatial analysis
Input: - Path to a MbGraphMCF
- Parameters for thresholding the graph
- Parameters for setting the statistical analysis
Output: -... |
5b3415a0f6cdf21f0896568ac4b439d655f58116c966937c23db9c90e3a096ef | Python | 7,293 | 207 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''plotting script for figure 6 in manuscript preprint on output of
example_parallel_network.py
Copyright (C) 2018 Computational Neuroscience Group, NMBU.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public Lic... |
6d3345af3cc741a18a7385edf4d7db5fd4c2dfc01b9ff436377a761196923752 | Python | 7,295 | 219 | """
Provides default parameters used in the simulation.
"""
import os
params = {}
params['outpath'] = os.path.join(os.getcwd(), 'out')
# Surface area of a microcircuit [mm^3]
params['surface_area'] = 1
"""
Parameters for cytoarchitecture preprocessing
"""
params['cytoarchitecture_params'] = {
# Path to cytoarch... |
b911beadeacdd96187cea7aee3659ce2abd0eae85fb3eb2fb850da1eee908309 | Python | 7,296 | 184 | """
Embed sections for the pipeline-generated analysis figures under
``<output_dir>/report_figures/<kind>/`` (see
``regtools/registration/report_analysis_figures.py``, which writes them).
Body-only, never-raises renderers, matching the Phase-1 section convention:
each returns the section's inner HTML (or ``""`` ... |
da581dde710bfcf8d5f6e90ebf33f7c150bc7c9f27c4d7d4b1bfe1ba09aa96aa | Python | 7,296 | 233 | import jax.numpy as jnp
from functools import partial
import jax
from netket.jax import tree_ravel
from netket.optimizer.qgt.qgt_jacobian_pytree import QGTJacobianPyTreeT
from netket.jax._utils_tree import RealImagTuple
from netket.jax._utils_dtype import dtype_real as real_dtype
def smooth_svd(Aobj, b, acond=1e-4, r... |
ef24f49206726863691fd6ba1462b920691c6c6de73099357415a3f3a97a27b4 | Python | 7,297 | 191 | from __future__ import annotations
import os.path
import pathlib
import pprint
from Levenshtein import distance as levenshtein_distance
from loguru import logger
from ord_schema.message_helpers import get_compound_name
from ord_schema.proto import reaction_pb2
from pydantic import BaseModel
from ord.evaluation.evalu... |
8e6fcbac02ef07779338489700222bac3d90e73b3bdc1f4d68b87509573f33f2 | Python | 7,298 | 211 | """
ResNet18
Difference from ResNet18 in torchvision for higher resolution:
1. Modify the stride of first convolution layer (7x7 with stride 2) into 1
2. Remove the first max-pooling layer
3. Downsample and upsample twice in encoder and decoder respectively in FCNResNet
"""
import torch.nn as nn
import math
import t... |
c994fbceb923edf0b305ac20bc119de2ec1f5db7cd993bdcda34337fae283852 | Python | 7,298 | 204 | #
# Memory profiling utilities for ACME
#
# Copyright © 2026 Ernst Strüngmann Institute (ESI) of the Max Planck Society
#
# SPDX-License-Identifier: BSD-3-Clause
#
# Builtin/3rd party package imports
import os
import shutil
import time
import multiprocessing
import psutil
import tqdm
import numpy as np
import logging
... |
7cf22a161f7e1ca952f9610dcaacc546c66040b82cd44c825d85ae0937aa3435 | Python | 7,299 | 211 | from unittest.mock import MagicMock, patch
from tensorflow.test import TestCase as TensorFlowTestCase
from tensorpack.tfutils.tower import TowerFuncWrapper
from tgan.trainer import GANTrainer, MultiGPUGANTrainer, SeparateGANTrainer
class TestGanTrainer(TensorFlowTestCase):
@patch('tgan.trainer.tf.control_depen... |
9f50caff00f69655b9e7577ce2b1833d0eb1486a4452e8605ab80f1fbc80e3da | Python | 7,300 | 217 | from string import maketrans
import operator
import random
from collections import defaultdict
###Stats and math functions
def weighted_sampler(pop_dict):
"""randomly sample a dictionary's keys based on weights stored as values
example:
m = {'a':3, 'b':2, 'c':5}
samps = [weighted_sampl... |
888f77ce4f2e69d6cf00e560605ce1bf67a1d0627496f8d9d5307446b25af00a | Python | 7,301 | 200 | from collections.abc import Callable, Hashable, Iterable, Iterator, Mapping
from typing import Protocol, Self, cast, override, runtime_checkable
@runtime_checkable
class ReadOnlyGraph[N: Hashable, E](Protocol):
@property
def successors(
self,
) -> Mapping[N, Mapping[N, E]]: ...
@property
... |
a54bd2b6724b29ee2f9f1e2b1e3c50e91f5fa50addbd51935a761212abc5e835 | Python | 7,301 | 203 | """Tests for regtools.ccf_boundary_editor.cell_remap."""
import json
import numpy as np
import pytest
def _identity_params():
return {
"transpose": False,
"flip_h": False,
"flip_v": False,
"scale": 1.0,
"dy": 0,
"dx": 0,
}
class TestBrainX... |
fc2c96aea72c5dfc500efb8df8b77f6552fb9918f25b90ed38b7deaf89f3c7a4 | Python | 7,301 | 208 | #!/usr/bin/env python
#
# Copyright 2006, Google Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list... |
167279230a30659e356b08c291ed64a0c4f7dd4681bf8b1838e9e30a9ee3f844 | Python | 7,302 | 215 | # TODO
# 我看以后atomic number改type得了，呵呵
import torch
from typing import List, Dict
from torch import nn
from ..utils import EnvPara
class ElementIndependentLinear(nn.Module):
def __init__(self, input_dim: int, output_dim: int, bias: bool = True):
super().__init__()
self.linear = nn.Linear(input_dim,... |
bd2fa4577451632616fe1707bbb75d8a96cb431d301fa848177baf371758ad4f | Python | 7,302 | 194 | import PoseUNet_dataset as PoseUNet
from PoseCommon_dataset import PoseCommon
import PoseTools
import sys
sys.path.append('/groups/branson/home/kabram/bransonlab/coco/cocoapi/PythonAPI')
from pycocotools.coco import COCO
import os
import tensorflow as tf
import imageio
import localSetup
from scipy.ndimage.interpolation... |
5f282289b16df496089719b3959e68f856de66b5399530999ebbbd57786db34f | Python | 7,303 | 180 | """--reuse_affine: seed SyNOnly from a precomputed affine, skipping affine
computation. Covers the resolver, the two_stage split, antsRegistration's
routing, and CLI wiring."""
import inspect
import os
import numpy as np
import pytest
ants = pytest.importorskip("ants")
from regtools.registration.core.re... |
a0821395220f6714e01a027ef88610b8b6e4f280141034afc2a50da07ce54185 | Python | 7,304 | 212 | """Qualitative group-level comparison of static M/EEG RSN features
"""
# Set up dependencies
import os
import numpy as np
from sys import argv
from utils.data import load_data
from utils.statistics import fit_glm, cluster_perm_test, max_stat_perm_test
from utils.visualize import (GroupDifferencePSD,
... |
0c4000cbba19ac5d16fb2ef36cb5435ea61b711ad6b6bf1dd8f2dddc4e4c0fa4 | Python | 7,305 | 222 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# 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,... |
b25d18d66ca724555cae81024e8763f816ce3905c1525466b1609e6afea1004b | Python | 7,307 | 255 | # %% Package Imports
import sys, os
sys.path.append(os.path.abspath("."))
sys.path.append(os.path.abspath(".."))
import torch
import torch.nn as nn
import torch.optim as optim
import prosper_nn.utils.generate_time_series_data as gtsd
import prosper_nn.utils.create_input_ecnn_hcnn as ci
import prosper_nn.models.crcn... |
e2e944f53a3fba63a2e5026d7ec8b2109a6090b612d512111ea744e5de09f377 | Python | 7,307 | 164 | import numpy as np
import pandas as pd
import pytest
import scanpy as sc
from anndata import AnnData
import pertpy as pt
from pertpy._types import cast_frame
@pytest.fixture
def adata(rng):
labels = np.array(["control", "A", "B"]).repeat(20)
centers = {"control": 0.0, "A": 5.0, "B": -5.0}
X = np.vstack([... |
9e9863b5eee5ca5c061e7556e8bf9cb2888145d36c12edf0c5b388caec10b760 | Python | 7,308 | 211 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 required by applicab... |
c1651038f3bb15d6e2dab5c7a303b79f993793ae8df762b818a159952d14c4fc | Python | 7,309 | 184 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import sys
import subprocess
import tempfile
import requests
import shutil
from pathlib import Path
import torch
import esm
def test_readme_... |
d41df271f48d99293eea9136c67595e0bdc13c4e0c3adf382984b5e6f9fa466c | Python | 7,309 | 143 | import os
import pickle as pkl
from typing import Any, Callable, Dict, Tuple, Union
from scipy import signal
from ..base_dataset_perturbation import BaseDataset_perturbation
class MemoryAgeDataset_perturbation(BaseDataset_perturbation):
r'''
Args:
root_path (str): raw data files in pickled python/num... |
348625e2364a1611e50b2df1ceaee0b57918f2540bb54790c47669bc443b89be | Python | 7,311 | 246 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 required by applicab... |
b1751dbc66c7b1bffa5e988bf184ffc179d12cf8d79adabb3e1659121b3bd5c6 | Python | 7,311 | 208 |
"""Class CGR and FCGR are from complexCGR repo"""
from itertools import product
from typing import Dict, Optional, List
from collections import defaultdict, namedtuple
from PIL import Image
import numpy as np
import random
# coordinates for x+iy
Coord = namedtuple("Coord", ["x", "y"])
# coordinates for a CGR encodi... |
ff99f167a42df7ef05a4f324b12c896bf175ad14a33b62a578372b02a2dd2a2e | Python | 7,311 | 201 | import copy, time
import numpy as np
from collections import defaultdict
from rdkit import Chem, RDLogger
from rdkit.Chem import AllChem, rdMolTransforms
from rdkit import Geometry
import networkx as nx
from scipy.optimize import differential_evolution
from rdkit.Chem.rdchem import BondType as BT
RDLogger.DisableLog('... |
7896b81139a6daf5fe740faee66970210cddc8471c7456be2eab21e9dbab86b6 | Python | 7,312 | 188 | """Unit tests for SliceCaches.
Pure-Python, no QApplication - these run in milliseconds because all
they exercise is the dict / OrderedDict bookkeeping.
"""
import pytest
@pytest.fixture
def caches():
from regtools.ccf_boundary_editor.caches import SliceCaches
return SliceCaches(modified_capac... |
68adc002e4c9c5ed38e33de33eeee24685548a078cf7c9a3702125a0d4de080d | Python | 7,313 | 219 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 required by applicab... |
4db0913c5f32e086122e29c406c3e649e9b4e72acea85882b1bcd67e4471132c | Python | 7,314 | 173 | # %%
import numpy as np
from scipy.sparse import csr_matrix
import matplotlib.pyplot as plt
def simulate_sparse_VAR1(W, source_neurons, T=1000, laser_power=1, noise_var=1, seed=None):
"""
Simulates a sparse VAR1 model.
Args:
W (ndarray): The effectome matrix.
source_neurons (list): A list ... |
9edae362a37b8d3aa91182114dc5c67cbd94ee07f02e9f2ccbd06e19ab35c2c3 | Python | 7,315 | 204 | from __future__ import annotations
from pathlib import Path
import pytest
from ase import Atoms
from ase.build import bulk, molecule
from ase.data import atomic_numbers
from quacc import Remove, change_settings
from quacc.recipes.aims import _base, mad
@pytest.fixture
def write_inputs(monkeypatch, tmp_path):
"... |
0711687cf0ec1950e7d18c4707cfe9ac4609444e0dc9423ef3b3dfa9af3328e4 | Python | 7,317 | 203 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 required by applicab... |
25c42b2bc47be21de7996545722f05ea156fe0d89cbd61e44e080e47fc423dbf | Python | 7,321 | 175 | """
VGG16 with BN
Difference from vgg16 in torchvision for higher resolution:
1. Layer config changes from [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'] (torchvision)
to [64, 64, 128, 128, 'M', 256, 256, 256, 512, 512, 512, 'M', 512, 512, 512] (acscon... |
ff7d5a63b1f33de8be67e0b55ce3a102f471cb7f805063e8383d66e20c55592e | Python | 7,321 | 205 | """Self-contained HTML report for a Cellpose SAM detection run.
Bundles a run's artifacts — parameters (``cellpose_metadata.json``),
measurement summary (``cell_summary_<name>.csv``), and figures (overlays,
distribution + histogram PNGs) — into ONE portable ``cellpose_report.html``.
Inline CSS, figures embedded a... |
02a2c7ccde78e227b8c9299c9f7a6084bdf023f339d2832e6eeee5b91ccb266f | Python | 7,324 | 169 | # Copyright (c) 2020, Ko Sugawara
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and t... |
c5ad275c270dc46bb18ecd3679312881d79d0cac14ab9871b45ab8f15a3c18b1 | Python | 7,327 | 229 | # filter.py
import regex
from fba.utils import get_logger, open_by_suffix
logger = get_logger(logger_name=__name__)
def compile_regex_ref_barcodes_single(sequence, num_mismatches=1):
"""Compiles a regular expression pattern, returning a pattern object.
Parameters
----------
sequence : str
... |
dfeb54fd63d26bc3f94924435d08dce13a721b56ae008eb51d4fe092aabacc64 | Python | 7,328 | 201 | #!/usr/bin/env python
"""Utility classes to parse SAMPL submissions."""
# =============================================================================
# GLOBAL IMPORTS
# =============================================================================
import os
import io
import glob
import numpy as np
import pandas a... |
e4f74aa82d3a064057db8257054da3542de5219dc7792e1dfe908c3cf7388007 | Python | 7,328 | 195 | #!/usr/bin/env python3
"""Aggregate metabolic scores and model associations with regional pathology."""
from pathlib import Path
import numpy as np
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
from statsmodels.stats.multitest import multipletests
ROOT = Path(__file__).resol... |
9512bb97b1a4a4b25e36a9c44895947c3adee2ae3047dc7a67c583ddc21a8177 | Python | 7,331 | 216 | """Utilities for extracting common archive formats"""
import zipfile
import tarfile
import os
import shutil
import posixpath
import contextlib
from distutils.errors import DistutilsError
from ._path import ensure_directory
__all__ = [
"unpack_archive",
"unpack_zipfile",
"unpack_tarfile",
"default_fil... |
1bc38e6573dfea126f7614002e145016f878cca792ca73cdc39d0f317e9c56de | Python | 7,332 | 239 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 required by applicab... |
22faa0b79c960572950a26276debf2b4ca0c9a87314037a4b15ec9be38c7ea6d | Python | 7,333 | 192 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 required by applicab... |
ebf69abcd800754738961792161b3a3f93f4695671025080ef9aff5ecabb3c47 | Python | 7,333 | 217 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from hybridLFPy import CachedNetwork
import analysis_params
from cellsim16popsParams_modified_regular_input import multicompartment_params
from plot_methods import plot_signal_sum, plot_signal_sum_colorplot
import plotting_helpers as phlp
from matplotlib import gridspec
imp... |
ffdbb123f586b555042d70cdda2b8025e116602258ce8545b06d95aa9e331fd6 | Python | 7,333 | 197 | # pylint: disable=invalid-name
"""Auxilary functions"""
import os
import glob
import shutil
from omegaconf import open_dict, OmegaConf, DictConfig
import pandas as pd
from src.settings import UTCNOW, LOGS_ROOT
def set_project_name(cfg: DictConfig):
"""set project name and project dir based on config"""
de... |
2a7c6fcfb3e1b150ad921cc75804dd39ff6fbbe1db0a51c4a8f015717a416734 | Python | 7,334 | 209 | """
Collections of custom data structures and types.
"""
import logging
import random
import string
from collections.abc import MutableMapping
from dpath.util import delete, search
DEFAULT_STAR_SEPARATOR = "."
FORWARD_REPLACE = {"*": "STAR", "|": "MINUS", ".": "DOT"}
BACKWARD_REPLACE = {v: k for k, v in FORWARD_REPL... |
30be260434e6fa619780cc15b469e5b8113ce97d4b0bf5cd2b1ab4763bd25ca8 | Python | 7,335 | 160 | """
Depth Model File
Currently this subclasses the Nerfacto model. Consider subclassing from the base Model.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Dict, Tuple, Type
import numpy as np
import torch
from nerfstudio.models.nerfacto import NerfactoModel, Nerf... |
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