sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
dbd665b2326466db7092852df00f9da47a8fecb9c017671db082c5d9160f1488 | Python | 5,902 | 147 | from collections import OrderedDict
from torch import nn
import numpy as np
from .modules import RetinaBlock, Bottleneck, VOneBlock, Identity
from .params import get_dog_params, get_div_norm_params, generate_gabor_param
from .backends import get_resnet_backend, get_vgg_backend
evnet_params = {
'base': {
... |
6c2cedda881975ec5609e84a7791d52a174b55cab317b6a6a31769914dbedb64 | Python | 5,904 | 146 | """Tests for mcmlnet.transforms.physiological module."""
from itertools import product
import numpy as np
import pytest
import torch
from mcmlnet.transforms.physiological import PhysiologicalToPhysicalTransformer
class TestPhysiologicalToPhysicalTransformer:
"""Test cases for PhysiologicalToPhysicalTransformer... |
b2c724de0d35970b515f70ddf7a523792e1575315d4810ce7f091883533f6e6e | Python | 5,904 | 188 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import csv
import json
import os
import random
import sys
from itertools import product
random.seed(42)
NAME = "events"
def read_rules(name):
rules = []
with open(os.path.join(sys.argv[1], f"{name}.csv")) as f:
reader = csv.DictRe... |
2838aeb77df509e7e39a330045eac28e68629eaa1a024e2f54a71ed674467e6f | Python | 5,905 | 140 | from collections import Counter
import anndata
import anndata as ad
import pandas as pd
from torch.utils.data import Dataset, DataLoader
import timm
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from huggingface_hub import login, hf_hub_download
from sp... |
a4cd271bf2f0bcde32933c3e2e10fbef3c1686f31f1aff8b90b79c845189333e | Python | 5,905 | 132 | import numpy as np
import matplotlib.pyplot as plt
import csv
import nrrd
def extract_rgb_values(csv_file):
color_data = {}
with open(csv_file, 'r') as csvfile:
reader = csv.reader(csvfile, delimiter=':')
for row in reader:
label, rgb_str = row[0], row[1].strip()[1:-1] # Extracti... |
2863543685558b78eab2b3d53a9db0690ca8646132bcd8850032488cd11b9097 | Python | 5,907 | 162 | #!/usr/bin/env python
# author: Jannes Spangenberg
# github: https://github.com/JannesSP
# website: https://jannessp.github.io
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, Namespace
import os
import csv
from pathlib import Path
from collections import defaultdict
import pysam
import tqdm
def pa... |
e3161839f9e94226765319546698e6bfb263003999b5c2cc27d1de2a1253f381 | Python | 5,907 | 120 | import os
from pathlib import Path
import json
import numpy as np
import xarray as xr
import pandas as pd
from brainscore_core.supported_data_standards.brainio.assemblies import NeuronRecordingAssembly
from brainscore_core.supported_data_standards.brainio.stimuli import StimulusSet
from brainscore_core.supported_data... |
c9be69082dd7a7539ccf9d3745a37855749e18c9f678c86da29c4f7f025546c0 | Python | 5,908 | 123 | import os
from importlib import import_module
from pathlib import Path
cell_graph_config = {
"cell_f_list": [
'area',
'r_equiv',
'r_maj',
'r_min',
'angel',
'ecc',
'maj_x',
'maj_y',
'min_x',
'min_y',
],
"edge_f_list": [
... |
1bd26befab86a091c68d1f740cd46574a94fdca45ca82ad85259d2c5dbad6f91 | Python | 5,909 | 290 | #!/usr/bin/python3
#
import os
import sys
import argparse
import numpy as np
from sklearn.neighbors import NearestNeighbors
from uac import utils
# Create the parser
my_parser = argparse.ArgumentParser(description="Calculation of old atrial coordinates")
# Add the arguments
my_parser.add_argument("target_path", meta... |
2d896b44c2905c74b1ada66a305f65da8a4b457351885edc64db1eaab0ac8430 | Python | 5,910 | 194 | # -*- coding: utf-8 -*-
"""
Make eccentricity isolines
This script computes isolines based on the phase map of a retinotopy
measurement. The phase map is mapped onto the grid representation of a flattened
surface patch. Only contour lines which are continuous are considered. The
purpose of this script is to estimate c... |
b4bd841e4039884996a7cbe109676a74db3aa98677df417d08e2118d7d097924 | Python | 5,911 | 159 | import sys
sys.path.append("/LiNet")
import torch
import numpy as np
from torch.autograd import Variable
import argparse
import random
import os
import os.path as osp
os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
import cv2
import lib_alban.utils_test as utils_test
from lib_alban.utils_analyse import load_and_process, l... |
ea980026fdbf76705bb8f8276fece316d54948431fe9f6b1f792cc8b9b1378cf | Python | 5,911 | 141 | #######################################################################################
### Functions to calculate the mutual information from the firing rate distribution ###
### in a network at two different times ###
########################################################... |
1bf4ceff46cba58143b65a8f603d81033b253f58834275ba60fc1a93df3a2269 | Python | 5,912 | 141 | import numpy as np
import glob
import pickle
import sys
import PopSpikeAnalClass as psc
import os
import time
import datetime
from argparse import Namespace
import pop_spike_utilities as psu
ms_per_sec=1000
changedate=datetime.date(2021, 5, 12) #date file changed FVwidth and decay time units
def find_files(subdir):
... |
b5259e31fd8f7f2e0358ac29b45af58e6509c054c27ae458826749eeefcf4df7 | Python | 5,913 | 163 | import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
# 优化后的布局函数
def refined_concentric_layout_final_adjustment(G, sizes, scale=3.5):
pos = {}
sorted_nodes = sorted(G.nodes, key=lambda n: sizes[n], reverse=True)
num_layers = int(np.ceil(np.sqrt(len(sorted_nodes))))
layer = 1... |
a57f93eb71822d59e1034ebcd0d0deb4ef7f1f7cdfe9f10819b68ddfd09a0df9 | Python | 5,915 | 148 | ## Nhat Duong & J Shapiro
## 2019 - 2020
# Imports in the pep8 order https://www.python.org/dev/peps/pep-0008/#imports
# Standard library
import argparse
import subprocess
import sys
import os
# Related third party
import numpy as np
import pandas as pd
## Define the extensions
MANTA_EXT = '.manta'
CNVKIT_EXT = '.c... |
ec6e50b29f16a3481602f0d8c03ddb6fe1462cd14346bbffcd221744c1d8239c | Python | 5,917 | 169 | """Theme helpers for the MEA-NAP GUI."""
from __future__ import annotations
import qdarktheme
from PyQt6.QtGui import QFont
from PyQt6.QtWidgets import QApplication
# Accent colour used throughout the UI
ACCENT = "#4f8ef7"
# Extra QSS layered on top of qdarktheme
_EXTRA_QSS = f"""
/* ── Toolbar ────────────────────... |
c0e5675f65779ce21ffd7bc8f59e87d0ececf4cd742b10b7ec481c0abb558a11 | Python | 5,919 | 161 | # Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
"""
Adapted from:
1. https://github.com/meta-llama/codellama/blob/main/llama/model.py
2. https://github.com/naver-ai/rope-vit
3. https://github.com/lucidrains/rotary-embedding-torch
"""
from typing import Optional
import torch
from einops impor... |
c3c6f77cbbc771e6a5c7a33e4f07387f8106a4b2354b6efbd54156d15c716a3d | Python | 5,919 | 188 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
Adapted from:
https://github.com/imoken1122/GIST-feature-extractor/
'''
import numpy as np
import numpy.matlib as nm
import numpy.fft as f
class LMgist:
"""
Class to extract GIST features from images.
"""
def __init__(self, param, img_size_wh=None):
... |
f5c163ba92b1c964f6cd57230f310032857c8c3be214907ab22c2ef620e557f1 | Python | 5,919 | 164 | import warnings
from typing import Optional, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from torch.utils.dlpack import from_dlpack
def map_index(
src: Tensor,
index: Tensor,
max_index: Optional[Union[int, Tensor]] = None,
inclusive: bool = False,
) -> Tuple[Tensor, Optional... |
4698ef01ce4225d3eddb965113774ea3861001a51031656364415dd03049f7b5 | Python | 5,920 | 161 | '''
CH4OH
'''
import numpy as np
import sys
def intro():
print("""
SHORT List of potential energy surfaces for CH4OH:
P: potential energy, G: Gradient, D: Nonadiabatic coupling vector
This is listed at the end of description to show the availability.
For example: P/G means both potential energy a... |
5de3239a92ddc807b0e05e6940454d2831ef4934ee0c754f8522902aaf783b0a | Python | 5,920 | 124 | import os
from importlib import import_module
from pathlib import Path
cell_graph_config = {
"cell_f_list": [
'area',
'r_equiv',
'r_maj',
'r_min',
'angel',
'ecc',
'maj_x',
'maj_y',
'min_x',
'min_y',
'x',
'y'
],
... |
7df9e58e57fcec5692cfb7c46cfd1b8d098b4cad8b0579e8c5d596fa7795b9ea | Python | 5,920 | 166 | """Spike Time Tiling Coefficient (STTC), port of ``get_sttc.m`` / ``sttc_m.m``.
Reference: Cutts & Eglen (2014), "Detecting Pairwise Correlations in Spike
Trains: An Objective Comparison of Methods and Application to the Study of
Retinal Waves", https://www.ncbi.nlm.nih.gov/pubmed/25339742
"""
from __future__ import ... |
46a2505f60570f5b17d8b99727dfbfc55f09041df0d21f3240008ebc374b7a49 | Python | 5,921 | 159 | """
Dataset registry for Pose-JEPA pretraining.
Resolves dataset paths from $KINESCOPE_DATA_DIR (default: ~/kinescope_data/).
Actual directory layout at /home/msegado/tapedeck/msegado/Datasets/pretraining/
(set KINESCOPE_DATA_DIR to this path):
pretraining/
├── AMASS/ → dataset name "... |
1cd1390318faf629eaeb54a6fcf5d264a61a5912ea5424dc182381735b45d434 | Python | 5,922 | 125 | import pandas as pd
import numpy as np
from typing import Optional, Union, List
from sklearn.base import BaseEstimator as SKLearnBaseEstimator
from .survival_utils import get_person_time_df, safe_join, compute_survival_from_single_hazard_curve, \
get_regression_predict_data
from causallib.estimation.standardization... |
5ce17030315b05ff503d996bafefd27dcef1342fed75ae72227d760f879eac95 | Python | 5,925 | 147 | # -*- coding: utf-8 -*-
"""
Created on Thu Oct 19 16:28:37 2023
@author: lcmmichielsen
"""
import copy as cp
import numpy as np
from tqdm.notebook import tqdm
from sklearn.metrics import mean_squared_error as mse
def mse_peaks(RBP, exons_info, peaks, PSI_glia, PSI_neur):
rng = 400
bins = 50
... |
c76565a9aeb01422a12f533409bb235a430acebc8188f45430c7c7ee2646decb | Python | 5,925 | 169 | import torch.nn as nn
from torch.autograd import Variable
import torch
class ConvLSTMCell(nn.Module):
def __init__(self, input_size, input_dim, hidden_dim, kernel_size, bias):
"""
Initialize ConvLSTM cell.
Parameters
----------
input_size: (int, int)
H... |
e9218bfcf73337baf1bdfddd4cab6cb20632d86e2bb5cd93f597cb1f3adb2317 | Python | 5,925 | 153 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
import os
import shutil
import sys
from pathlib import Path
import numpy.testing # noqa: F401 # Pre-import before any test can corrupt numpy via in-place upgrade
import pytest
@pytest.fixture(scope="session")
def solution_assets():
"""Return ... |
b782ebf73804aeddf6a8072ae3ea0239f1bcc608b1d5b44233f9f6080eb309ce | Python | 5,927 | 164 | '''Create figures for results of classification_pairwise'''
from __future__ import print_function
import os
import pickle
import numpy as np
import pandas as pd
import scipy.stats as st
import matplotlib.pyplot as plt
import bdpy.fig as bfig
import god_config as config
# Main ###################################... |
cc0cd3230f8604f5c3972b269518e647860c246f288b570ed9e0eabf5c1a0d7a | Python | 5,927 | 178 | """
This class overlays points on top of the image stacks.
"""
import numpy as np
from PyQt5 import QtGui, QtCore, QtWidgets
from matplotlib import cm
from numpy import linspace
from lasagna.ingredients.lasagna_ingredient import lasagna_ingredient
from lasagna.utils import preferences
class sparsepoints(lasagna_in... |
03cf4510dd3a54e2a6e66604b0d294fb1104e08f671f850dd7140d6514a8c262 | Python | 5,928 | 124 | import os
from importlib import import_module
from pathlib import Path
cell_graph_config = {
"cell_f_list": [
'area',
'r_equiv',
'r_maj',
'r_min',
'angel',
'ecc',
'maj_x',
'maj_y',
'min_x',
'min_y',
'x',
'y'
],
... |
18fd4584df2f05361daf1a5ac72e2a8d01e3849db4f5c0ddd43cac46991ccf1d | Python | 5,928 | 143 | # coding: utf-8
from pathlib import Path
import numpy as np
from sklearn.datasets import load_svmlight_file
import lightgbm as lgb
EXAMPLES_DIR = Path(__file__).absolute().parents[2] / "examples"
class FileLoader:
def __init__(self, directory, prefix, config_file="train.conf"):
self.directory = directo... |
7e43401a515fbb5961a46834add46c07c3d5809ba6ea973019ada2f9f45a7915 | Python | 5,928 | 124 | import os
from importlib import import_module
from pathlib import Path
cell_graph_config = {
"cell_f_list": [
'area',
'r_equiv',
'r_maj',
'r_min',
'angel',
'ecc',
'maj_x',
'maj_y',
'min_x',
'min_y',
'x',
'y',
],
... |
ca661e7b829c0fd9a600a74c5199dfba5384b5d87a116ac5eb0b5f2481a6ed88 | Python | 5,928 | 149 | from typing import Optional, Tuple, List, Callable, Dict, Any
import os
from glob import glob
from torch.utils.data import DataLoader, Dataset
from pytorch_lightning import LightningDataModule
import albumentations as A
import cv2
import pli_transforms.augmentations as aug
from pli_transforms.augmentations.pytorch im... |
170f0eb066927e1bfd772634743b934a753efeca11e4b174ec22905fa9b81e69 | Python | 5,931 | 163 | # Copyright (c) DP Technology.
# 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.
from typing import Optional
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
fro... |
c7e64b840f4d10da8535024700491f53e16a9e8f9f37deb1f1a5e77be3482ae4 | Python | 5,932 | 197 | """
Generate final data specification for human kidney data with both polygon
and semantic annotations.
This script creates a data specification JSON file that maps polygon annotation
data to the appropriate train/val/test splits while avoiding duplicates from
semantic annotations.
"""
import json
import os
from path... |
38ae4fadb2c1b10d8ea9ee0f878c77ff99e5efe0cc3d626ee6e5c082a22c3f65 | Python | 5,933 | 119 | #!/usr/bin/env python3
# Author: Francois Aguet
import numpy as np
import pandas as pd
import gzip
import subprocess
import scipy.stats as stats
import argparse
import os
import qtl.io
import qtl.norm
def prepare_expression(counts_df, tpm_df, sample_frac_threshold=0.2,
count_threshold=6, tpm_t... |
a079c2dd6535e7f774948130c7070b6d1644da09b1c0018b5b6596ea80a1b603 | Python | 5,935 | 111 | import functools
import torch
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from PIL import Image
import numpy as np
import timm
import torch.nn as nn
from albumentations import (
Compose, Normalize, Resize, CenterCrop
)
from albumentations.pytorch import ToTensorV2
from brainscore_... |
bc44291892abe461d52681ef130a1825143396af3e2138d421ef28a149757e68 | Python | 5,935 | 154 | import sh
from scipy import ndimage
from warnings import warn
from sklearn.metrics import roc_curve
import datajoint as dj
from . import rf, trippy
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
from pprint import pprint
import pandas as pd
import os
try:
import c2s
except:
warn(... |
97acb028c6a3d1ccece1280bf131c287cfeee4d8499fec1a3715e23f3138c5ce | Python | 5,936 | 171 | import os
import re
import sys
import glob
import json
import numpy as np
import os.path as op
from os.path import join as ospj
import matplotlib.pyplot as plt
import nibabel as nib
from dipy.io.image import load_nifti
from dipy.io.streamline import load_trk
from dipy.data import fetch_bundle_atlas_hcp842, get_bundle_a... |
c13685105129caa60ce11efb178dab43f6d3ec9131cb1bd7b18ed0339cc31fc5 | Python | 5,936 | 175 | import copy
import numpy as np
import torch
from ..distances import CosineSimilarity
from . import common_functions as c_f
class MatchFinder:
def __init__(self, distance, threshold=None):
self.distance = distance
self.threshold = threshold
def operate_on_emb(self, input_func, query_emb, ref... |
3aea0a8d58b8d09db3984ca767339481c056cbab4e9ce689d8b61654c950c418 | Python | 5,938 | 202 |
import torch, torch.nn as nn, torch.utils.data as data, torchvision as tv, torch.nn.functional as F
import pytorch_lightning as L
import numpy as np
from torch.utils.data import Dataset, DataLoader
from sklearn.metrics import roc_auc_score
from pytorch_lightning.callbacks import ModelCheckpoint
class modulerNetwork2... |
576cfd29b315412332cbd571f4a096476259cc869589ca0b9a646d8029aee230 | Python | 5,938 | 138 | import pytest
import numpy as np
from brainscore_vision import load_dataset
# --- 8-subject surface assemblies ---
@pytest.mark.private_access
class TestAllen2022fmriSurfaceAssemblies8Subj:
def test_train_assembly_shape(self):
assembly = load_dataset("Allen2022_fmri_surface_train")
assert assemb... |
678950b8b756878afe7aa4f928f3b93c15a8a0d1a50fee60cc6185d5bbc597c8 | Python | 5,939 | 176 | # -*- coding: utf-8 -*-
"""Visual field sign utilities."""
import datetime
import os
import shutil as sh
import nibabel as nb
import numpy as np
from .. import execute_command
from ..surface.filter import mris_fwhm
__all__ = ["get_vfs", "get_weighted_vfs"]
def get_vfs(
input_sphere,
input_white,
input... |
6f422b6396de11923ee4df4bdbfc5908e15fd25210f88125c437af5985026c7b | Python | 5,940 | 132 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
#-----------------------------------------------------------------------------------
def Plot_Energy_Ranking_TopN(path_read_info_ALLState:str, TopN:int, tick_params_size:int=20, path_save=False) -> None:
#----------------... |
7b96615ad494cec90606ee0b5be682a8a533e260a22607db6bd3302e5ae4f73c | Python | 5,940 | 150 | """Uses LRP to generate attributions for a VGG16 model trained on CIFAR-10."""
# pylint: disable=duplicate-code
import argparse
import h5py
import numpy
import torch
from torchvision import transforms
from torchvision.datasets import CIFAR10
from torchvision.models import vgg16
from zennit.attribution import Gradie... |
c5401de8b45a94ced5c267898dd56171c0bb3e1bb0fe8dc7a997d5c56db98ac4 | Python | 5,940 | 158 | import pandas as pd
import torch as th
import numpy as np
import dgl
def get_data(df, features_dict, terms_dict, features_length, features_column):
"""
Converts dataframe file with protein information and returns
PyTorch tensors
"""
data = th.zeros((len(df), features_length), dtype=th.float32)
... |
2312c7b79e99a29a046dd72ad28943b2092c88c5eb07c00320858ca4e2ff14e3 | Python | 5,942 | 200 | """
This script performs the ROC analysis for the single-unit data.
"""
import os
import sys
import numpy as np
import json
import pandas as pd
from utils.get_session_info import load_subject_names, find_sessions_nested
from utils.stat_roc_window import roc_analysis
from utils.LoadSession import load_data_for_session
... |
257e063ff70dd6a598a23bcb0e299438e3a18fd9d550170bcc9eba95d1f18bce | Python | 5,942 | 106 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file '/media/lg/disk2/PycharmProjects/ISAT_with_segment_anything/ISAT/ui/anno_dock.ui'
#
# Created by: PyQt5 UI code generator 5.15.10
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this fil... |
429d409ca5ac89d1ea4947facf09d0442a6bb7ab6bc8d17e9ba0a817807e9142 | Python | 5,943 | 158 | """Streamline geometry, against analytically known cases.
Every depth calculation in the package is built on this class, so the cases
here are chosen so the right answer can be worked out by hand: a 3-4-5
triangle, an axis-aligned path, a point whose perpendicular offset is exact.
"""
import numpy as np
import pytest... |
5555e288ec67ff2cad6dc47991a7775d0acad63406fb7906f790bcacc761aac5 | Python | 5,943 | 155 | import numpy as np
import pytest
from brainscore_vision import load_dataset, load_stimulus_set
from brainscore_vision.benchmark_helpers import check_standard_format
# testing stimulus sets
@pytest.mark.private_access
class TestStimulusSets:
# test stimulus_set data:
@pytest.mark.parametrize('identifier', [
... |
cfa8f5c6402a627e4827a30fc34bcdd43ae4f117d5cf2c4b3fe43daea6d1d280 | Python | 5,943 | 149 | # 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... |
130a93b18a6075a84b0b596033bb2962780df8f6845d9b16811b385ce3bab4b7 | Python | 5,944 | 185 | # 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
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the... |
44868fb792a3bd95efe17955cbcc9ec57828b2357d89890c1ae8b380a0601750 | Python | 5,944 | 159 | """
Base system
MB Vessies
"""
import pytorch_lightning as pl
import torch.optim as optim
from typing import List
from torchmetrics import MetricCollection
import re
import torch
class BaseSystem(pl.LightningModule):
def __init__(
self, loss : object,
lr: float,
train_metrics : MetricCol... |
055b3b426f7a2c4985025fe24e87513d651f544e8bb5dc0880118fb25e4a82d3 | Python | 5,945 | 133 | # Created by David Coggan on 2024 06 25
import numpy as np
from brainscore_core.supported_data_standards.brainio.assemblies import DataAssembly, BehavioralAssembly
from brainscore_vision import load_stimulus_set, load_dataset
from brainscore_vision.benchmarks import BenchmarkBase
from brainscore_vision.benchmark_helpe... |
405f5ca0ea21585c769f432a5b6d25c264bf8c754b6ea8ad3566ea2c34b06813 | Python | 5,948 | 141 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors:
#
# Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may... |
978e79b177e67f3f5b32de43edefdcb4090b59589ed3386d16e32a08594b7c3f | Python | 5,948 | 163 | #!/usr/bin/env python
# author: Jannes Spangenberg
# e-mail: jannes.spangenberg@uni-jena.de
# github: https://github.com/JannesSP
# website: https://jannessp.github.io
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, Namespace
import os
import csv
from pathlib import Path
from collections import def... |
98d59385b13711d00f9249ea31297062b349b22c141d8785e1aad974fa14daa7 | Python | 5,948 | 136 | import torch
from torch import Tensor
from typing import List, Tuple, Optional, Union
import torch.nn as nn
from .layer_norm import LayerNorm
from .drop_path import DropPath
class Block3D(nn.Module):
r""" ConvNeXt Block. There are two equivalent implementations:
(1) DwConv -> LayerNorm (channels_first) -> 1x1... |
65729b83cdd82fbc316c341ce941350a1ab7d506ac3fe6380587b37fa35678ff | Python | 5,950 | 156 | # Required libraries
import matplotlib
matplotlib.use('Agg') # Set globally for all matplotlib usage
import glob
import os
import torch
import glob
import os
import torch
import numpy as np
import random
import matplotlib.pyplot as plt
from skimage.transform import resize
from torch.utils.data import Dataset
from mon... |
964b370c7dfb2b155e80201ae394d61842fbf37f0bca44b6c6c2a0723e63a273 | Python | 5,951 | 166 | # Created by David Coggan on 2024 06 23
# package stimuli
from brainscore_core.supported_data_standards.brainio.stimuli import StimulusSet
from brainscore_core.supported_data_standards.brainio.packaging import package_stimulus_set, package_data_assembly
from brainscore_core.supported_data_standards.brainio.assemblies ... |
34541825ea32135a6b6c3b62ac78754f40bbec810d90398a8e4bcfaebd4cf4f8 | Python | 5,953 | 155 | import os
import os.path as osp
import shutil
from ogb.utils import smiles2graph
from ogb.utils.torch_util import replace_numpy_with_torchtensor
from ogb.utils.url import decide_download, download_url, extract_zip
import pandas as pd
import numpy as np
from dgl.data.utils import load_graphs, save_graphs, Subset
import ... |
516ad2fffc7edc231541d754ab6e3899b2920e03593ed121ff758234937b3697 | Python | 5,953 | 112 | """Prompts for the OCSR tool.
Two distinct kinds of prompt, easy to confuse:
1. :data:`OCSR_SYSTEM_PROMPT` and :data:`OCSR_USER_PROMPT` go *inside* the tool, to the
vision model, and are vendored verbatim (see below).
:data:`OCSR_STRUCTURED_SYSTEM_PROMPT` replaces the first of those when the caller
asks for ... |
58acc1db50c9a634e89e1c5fa94539ec464f511e54de691f42d046ac27a77057 | Python | 5,953 | 147 | import os
from nipype.interfaces.base import BaseInterface, BaseInterfaceInputSpec, TraitedSpec, File, Directory, traits, CommandLineInputSpec, File, TraitedSpec, CommandLine, Directory
import csv
import numpy as np
class TckSampleInputSpec(CommandLineInputSpec):
tracks = File(desc="Path to the input tck file", ma... |
99c1a38cef734ce441f250789e2a3b1262ac2266ef6471e079622ea9319f593b | Python | 5,953 | 161 | import unittest
import pandas as pd
from causallib.model_selection import TreatmentOutcomeStratifiedKFold
from causallib.model_selection import TreatmentStratifiedKFold
class TestTreatmentOutcomeStratifiedKFold(unittest.TestCase):
def test_binary_splits(self):
X = pd.DataFrame({0: [10] * 8}) # Dummy cor... |
fa307eb98a18aa4d108d2b4085a79cdf4bb21005a9eb837debf070df7a18f07f | Python | 5,958 | 134 | """ Lambda Layer
Paper: `LambdaNetworks: Modeling Long-Range Interactions Without Attention`
- https://arxiv.org/abs/2102.08602
@misc{2102.08602,
Author = {Irwan Bello},
Title = {LambdaNetworks: Modeling Long-Range Interactions Without Attention},
Year = {2021},
}
Status:
This impl is a WIP. Code snippets in the... |
8586bbce07f07b492b91b89ef92b0a03b8edc1e7404e3a7ff72a6de2b8a46321 | Python | 5,959 | 174 | #!/usr/bin/env python3
"""
Window Size Optimization Script
Runs the end-to-end pipeline for several window sizes using non-overlapping
windows. Summaries are reported per window to support method selection.
Authors: Evans Nyanney, Parthasarathy D Thirumala, Shyam Visweswaran, Zhaohui Geng
Year: 2025
License: MIT
"""
... |
2845d258adbcf765524f3114bca4951aa805eea08c7dc731ef97a4d72479b208 | Python | 5,961 | 180 | from typing import Optional, Union, Dict
import os
import shutil
import os.path as osp
import torch
import numpy as np
from ogb.utils.url import decide_download, download_url, extract_zip, makedirs
class MAG240MDataset(object):
version = 1
# Old url hosted at Stanford
# url = 'http://ogb-data.stanford.... |
ef45e6293da0226395a433fc5ead7a33af64dcf837a84d1172de9c7929088d03 | Python | 5,963 | 170 | """Read-only package resources exposed through the Deep Agents file protocol."""
from importlib import resources
from pathlib import PurePosixPath
from deepagents.backends.protocol import (
BackendProtocol,
DeleteResult,
EditResult,
FileDownloadResponse,
FileUploadResponse,
GlobResult,
LsR... |
27aa09ebccaf2461aeddade185bf74ed8075897086f55ca94a9c3ff24c13e31e | Python | 5,966 | 204 | import os
import json
import time
from datetime import datetime
import numpy as np
CACHE_VERSION = 2
def _finite_float(value, default=0.0):
try:
number = float(value)
except (TypeError, ValueError):
return float(default)
return number if np.isfinite(number) else float(default)
def _to_... |
2eb3a93c5a544655e65e73c2082d1273cc050f440b75181e1a76b443a476a6b6 | Python | 5,966 | 177 | import json
import argparse
from chemgraph.tools.ase_tools import run_ase
from chemgraph.tools.cheminformatics_tools import smiles_to_atomsdata
from chemgraph.models.ase_input import ASEInputSchema
import datetime
import subprocess
def get_atomsdata_from_smiles(smiles: str) -> dict:
"""Return a workflow of conver... |
ffa7abd1f73c52ac786d8073da1bb386de4dd38d557a558e38e134291cc4da6a | Python | 5,969 | 131 | #!/usr/bin/env python
# coding: utf-8
'''
Dependencies:
pip install numpy pandas
chmod +x script.py
In this code we loop through all MERFISH cells and add a new metadata to them: their region information and its estimated volume in the slice.
So if a cell sits in the MOB we calculate the intersected volume of the MO... |
1e89e913b79660ac1c8870c8d494adc23ee9f591e90c14d53ec6587e6ce18ade | Python | 5,970 | 158 | # -*- coding: utf-8 -*-
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from torch.nn import functional as F
from typing import Tuple
... |
e928132f2221c0630e59d3bf073efba97c2d05252d0eddb0d283f105137f4956 | Python | 5,970 | 194 | import pandas as pd
import numpy as np
import argparse
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser(description='Preprocess edge list')
parser.add_argument('-e','--edge_list', help='Edge List to be processed.', required=True)
parser.add_argument('-n','--node_list', help='Node List to be processed.'... |
f23c62e5716c34e0546ce6387cbf06cd902cfd296ca820be08e4f9ce13d4fe81 | Python | 5,971 | 170 | import numpy as np
from meanap.params import Params
from meanap.pipeline.burst_detection import burst_detect_network, single_channel_burst_detection
def firing_rates_bursts(
spike_times_dict: dict[int, np.ndarray],
n_channels: int,
fs: float,
duration_s: float,
params: Params
) -> dict:
"""Ca... |
41e25c2c2f27b8328d766bd42531a7b36da8b60f8ae137dfddfde1a5632616d1 | Python | 5,973 | 157 | import math
import os
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
BatchNorm2d = nn.BatchNorm2d
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
BatchNorm2d(oup),
nn.ReLU6(inplace=True)
)
def conv_1x1_... |
b3b59b9b85ec568759cd1a8cf79500d85d73efa2ed2f7c213e59ec16a040fe4e | Python | 5,973 | 132 | import re
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class _DenseLayer(nn.Sequential):
def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
super(_DenseLayer, self).__init__()
self.add_module('norm1', nn.BatchNorm2d(num... |
1fe6e9e65633a6398499e27fa470cc532d6461c72de1e5680f3cc4f1ecbb84cf | Python | 5,975 | 181 | from pathlib import Path
from typing import List
import sys
sys.path.append('.')
import hydra
import numpy as np
import torch
import omegaconf
import pytorch_lightning as pl
from hydra.core.hydra_config import HydraConfig
from omegaconf import DictConfig, OmegaConf
from pytorch_lightning import seed_everything, Callbac... |
886a5ead30d2c3773b51ba81da2e695d103ce8c466dd9441adc7bb8075aba5aa | Python | 5,978 | 194 | # -*- coding: utf-8 -*-
import logging
import numpy as np
from numpy.linalg import norm
from scipy.spatial import cKDTree
from scipy.sparse import bsr_matrix
def _subdivide_streamline(streamline, n_steps):
if n_steps < 2:
return streamline
dirs = streamline[1:] - streamline[:-1]
subdivided = np.... |
fd8cf8b3a49aff6b192e71a4e4775d096bc10479c8966887d6dad11953749f4b | Python | 5,978 | 152 | #!/usr/bin/env python3
"""merge_csv_columns.py
Copy columns AxL_mean, AxL_diff, SphEq_mean, SphEq_diff **from**
``data/Data_M1D6_Preproc.csv`` **to** ``data/Data_M1D5_Preproc.csv`` by default,
inserting them immediately before the column ``MRRsOD_Binary``.
You can still override the paths from the command line, but i... |
0b4e303554a4751a27b99319cfa09f066e40c8db964571ae37a3efcd4d6836fb | Python | 5,979 | 224 | # -*- coding: utf-8 -*-
"""FSL registration algorithms."""
import os
from .. import execute_command
from ..io.filename import get_filename
__all__ = [
"flirt",
"apply_warp",
"apply_flirt",
"convert_warp",
"combine_warp",
"apply_fugue",
]
def flirt(
file_in,
file_ref,
file_out,
... |
0dc502982a4c11902ba5ed274a6a71fe407dcbc6c1ae22c4f15ad1854bcd0494 | Python | 5,979 | 131 | from scipy.io import loadmat
from scipy import sparse
import os
import time
import numpy as np
import sys
import argparse
def argument_parse():
parser=argparse.ArgumentParser()
parser.add_argument('--chunklist',action='store', dest='chunklist', required=True)
parser.add_argument('--fileroot',action='store', dest='... |
0fb0a935be579f52bd1c0450948c603b184d67d7cf893c82841a985d1d639fba | Python | 5,979 | 161 | import json
import shutil
from functools import cached_property
from pathlib import Path
from typing import TYPE_CHECKING, Optional, Tuple
import numpy as np
from huggingface_hub import HfApi, hf_hub_download
from brainways.model.model_utils import load_model
from brainways.pipeline.brainways_params import AtlasRegis... |
292055f5605898c9481c3d527ace0ce4a4f06126608cfcbf538116bfec2b247f | Python | 5,980 | 107 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file '/media/lg/disk2/PycharmProjects/ISAT_with_segment_anything/ISAT/ui/process_exif_dialog.ui'
#
# Created by: PyQt5 UI code generator 5.15.10
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edi... |
005dc8642e51b3f207a0e5323e59e7bcfd1c08144e530cb0caf09990ec4fa9f8 | Python | 5,981 | 218 | #!/usr/bin/python3
#
import os
import sys
import argparse
import numpy as np
from sklearn.neighbors import NearestNeighbors
from uac import utils
# Create the parser
my_parser = argparse.ArgumentParser(description="Calculation of labels using laplace solves only")
# Add the arguments
my_parser.add_argument("target_p... |
00797cbbe3d5ff4bf0dffdd25a5ec9ce8d2e098ebc5c472ea733965608535241 | Python | 5,981 | 166 | import ijson
from pathlib import Path
import torch
import numpy as np
import time
import csv
import statistics
from urlextract import URLExtract
from transformers import (
pipeline,
BertTokenizerFast,
BertModel,
)
import pandas as pd
root_dir = Path.cwd().parent.parent.parent
dataset_dir = root_dir / 'data... |
f9dab4adf30fdb4ffb8d33be59c06cf4170ab725c90aebff3cd42049d08aa1ab | Python | 5,982 | 141 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import numpy as np
import torch
from transformers import AutoImageProcessor, ConvNextV2ForImageClassification
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from PIL import Image
from brains... |
e6195e139739d57ab49d23ff3f245bafeba433edca45139d2eb48d85ce00397d | Python | 5,983 | 142 | from typing import Optional
import torch.nn.functional as F
from torch import Tensor
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.conv.gcn_conv import gcn_norm
from torch_geometric.typing import Adj, OptPairTensor, OptTensor, SparseTensor
from torch_geometric.utils import is_torch_sparse... |
151cb12eddab44b302e0229fb9445e6f3b65caeacc88ad7c112c60961a9f2e7e | Python | 5,984 | 140 | """ General functions for preprocessing IVIM image data. """
import numpy as np
import nibabel as nib
from ivim.io.base import data_from_file, file_from_data, read_bval, read_bvec, read_cval, read_im, write_bval, write_cval, write_bvec
def average(im_file: str, bval_file: str, outbase: str, cval_file: str | None = No... |
fd0f59260af4e70d39d3bc3e36d6063eaa9cc1e32e0b6a2d3f03503d081a25df | Python | 5,984 | 152 | from ..qt import *
import logging
LOGGER = logging.getLogger(__name__)
class DistortionTextEdit(QTextEdit):
def __init__(self):
super().__init__()
self.on_apply = lambda: None
def focusOutEvent(self, e):
self.on_apply()
def keyPressEvent(self, e: QKeyEvent):
if e.key() == Qt.Key.Key_Return an... |
8a99f1d6e7a9bfd65cc7a7ebabf226fa1a6c6549305b4f2b4453e195252a3f18 | Python | 5,985 | 127 | #!/bin/python
"""
python Py_write_dPCA_min_max.py all.txt #seq #time_segment #clean
python Py_write_dPCA_min_max.py all.txt GVAAA 50-100ns 0.05
Created by Diana Slough
Created on 1-5-2017
This program reads all.txt (from dPCA) and writes the driver files for s1 and s2
"""
import sys,os
import numpy as np
import mat... |
af556d126deef00f3aab1e0578371fe9ab6fc42e371ff80a14c7d3c340aeca6b | Python | 5,985 | 153 | import pandas as pd
import os
from multiprocessing import Pool
import sys
import numpy as np
def mpl_writer(patch):
patch_name = patch[0]
output_path = patch[1]
patch_meta = patch[2]
patch_cts = patch[3]
patch_f = patch[4]
print('Processing subsample {}'.format(patch_name))
patch_cts.to_csv... |
0c50fc6c114833700b1ddb2e28db70224620680dfc116d3ae98b19027e349ca1 | Python | 5,986 | 181 | import argparse
import torch
import torch.nn.functional as F
from torch_geometric.data import Data
import torch_geometric.transforms as T
from torch_geometric.nn import GCNConv, SAGEConv
from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
from logger import Logger
class GCN(torch.nn.Module):
def __... |
25e657fc88fe9d5719aed5ccaf2df41236116cadcba9c59d3102d3f9549754a1 | Python | 5,988 | 185 | #!/usr/bin/python
#
## @file
#
# This handles one or more feed viewers. These are similar to camera
# display(s) except that they can only view the feeds.
#
from PyQt5 import QtCore, QtGui, QtWidgets
# Debugging
import storm_control.sc_library.hdebug as hdebug
import storm_control.hal4000.display.cameraFrameDisplay ... |
8310f144840a4c65f9153268fe9294bf07770ddc056a448dfacd0208872c2b1e | Python | 5,988 | 216 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
from pathlib import Path
# external inputs
import numpy as np
import h5py
from nibabel.freesurfer.mghformat import MGHHeader
# local input
from .surf import write_mgh
from .get_filename import get_filename
__all__ = ["read_hdf5", "write_hdf5", "extr... |
0fd0961d74dcca9e49d947edc15192259ad6a2e63ee2e5d9a811fec1a4c824a7 | Python | 5,989 | 157 | import os
from copy import deepcopy
import numpy as np
import argparse
import innvestigate.utils as iutils
from keras.models import Model
from keras.preprocessing import image
from keras.utils import to_categorical
import keras
import tensorflow as tf
from numpy import average, dot, linalg
from utils import *
from lrp ... |
5814912cf1e92b35e871757837588d27892dc968e3054b86ecbefb280ddfe88f | Python | 5,989 | 253 | import numpy as np
import pytest
import seisbench.generate as sbg
@pytest.fixture
def record():
return np.zeros((100, 4), dtype=np.float32)
@pytest.fixture
def metadata():
return {
"__annotations__": {
"P": np.array([50, 50, 50, 50], dtype=float),
}
}
# -------------------... |
b8eeb15be8835d1caf6bf97cb7b86c78a1678237741f00f72c54caaf6937ce9e | Python | 5,989 | 217 | # Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
"""
Utilities for bounding box manipulation and GIoU.
"""
from typing import Tuple
import torch
def box_cxcywh_to_xyxy(x):
x_c, y_c, w, h = x.unbind(-1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
retur... |
2a189e0f31d26bee3989159b30db347dd5bf2ecb0bc9fb48d0dc9adad31ebaa0 | Python | 5,990 | 173 | from itertools import product
from typing import Dict, List, Optional
import numpy as np
import pandas as pd
from brainways.utils.atlas.brainways_atlas import BrainwaysAtlas
from brainways.utils.cells import get_parent_struct_ids
def set_co_labelling_product(cells: pd.DataFrame):
label_columns = [c for c in cel... |
ec18495d7cdeb43d3173ae72b2a31d4f899f6364f967c67fb6d316451b45b174 | Python | 5,993 | 122 | from detectron2.utils.logger import setup_logger
setup_logger()
import numpy as np
from detectron2 import model_zoo
from detectron2.config import get_cfg
import logging
import os
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointer
from detectron2.data impor... |
97e1ff4b6ca2f88850b54ff008fa1f29c4da153d39244a42209027081fb90ac5 | Python | 5,994 | 126 | import numpy as np
from GEASO.utils.utils import voxel_data
from GEASO.utils.utils import normalize_coords
from GEASO.utils.utils import cal_distance
def inlier_from_NN(x, y, distance, max_iter=100, gamma=0.5, scale_c=False):
# x: source coordinates, shape (N, D)
# y: target coordinates, shape (N, D)
N, D... |
ee9ef6f4e66b7eed25d0eed2b29f5680c562882fdfa2494a94dc52c80c7666ca | Python | 5,995 | 166 | import sys
from pathlib import Path
p_dir = str(Path(__file__).absolute().parents[2])
if p_dir not in sys.path: sys.path.insert(0, p_dir)
import os
import argparse
import json
from datetime import datetime
import tempfile
import shutil
from environs import Env
from jinja2 import Environment, FileSystemLoader, select_... |
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