sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
234d8ff3b6b194862c57e836c2fd180adc20dd9a0fa2e1ac4b1319703bd0fb4c | Python | 7,568 | 159 | # -*- coding: utf-8 -*-
"""
Created on Sun Dec 15 19:12:11 2019
@author: Yoshi
"""
import os
import shutil
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
from average_radius_calculator import *
from color_key_generator import *
from scale_calculator import *
from cell_number_mapper impor... |
cc957a83517141beb66ba7640e527e2da47f04c1f7242a4ea936a536145f7c94 | Python | 7,569 | 180 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Segment a single bundle by computing a simple Recobundles (single-atlas &
single-parameters).
For multiple bundles segmentation (using RecobundlesX / BundleSeg), see instead
>>> scil_tractogram_segment_with_bundleseg
Hints:
- The model needs to be cleaned and lightwe... |
bc6b8fae1a0d5759a1b99c52ba59b44fa6c9ae161e6f03f4f6ca0079d70380a5 | Python | 7,570 | 228 | import torch
import torch.nn as nn
from torch_geometric.utils import to_dense_batch
from torch_geometric.nn import (
JumpingKnowledge,
global_add_pool
)
from layers import (
GATLayer,
cross_attention_block
)
from activation import get_activation
class GATMod(nn.Module):
de... |
e42d52ede296d8c4f4ab9feb27ea91ce512d0905fa76aa78576d91614e1c83cc | Python | 7,571 | 175 | from typing import Generator, Any, Tuple
from zigzag.classes.stages.Stage import Stage
from zigzag.classes.cost_model.cost_model import CostModelEvaluation
import os
import pickle
import json
import numpy as np
import logging
logger = logging.getLogger(__name__)
class MPICompleteSaveStage(Stage):
"""
Class ... |
0a378842a85cb3452e5acc11c15366ddc7f4c3b1198983791b71adf64381a554 | Python | 7,572 | 173 | import tensornetwork as tn
from my_tensornetwork import MyFiniteMPS
import numpy as np
import json
import os
import re
from tqdm import tqdm
class OPPollmann:
def __init__(self, n_qubits):
self.n_qubits = n_qubits
# train1
read_data = self._dataset_Pollmann("train1")
... |
9d13653adee53eee35b6a192773ca50094812ba7612293f8b8460f1a76576a72 | Python | 7,572 | 207 | import argparse
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from ogb.linkproppred import PygLinkPropPredDataset, Evaluator
from logger import Logger
class LinkPredictor(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, num_layers,
... |
ba0b6c520648dc4bf2f5038689ac6e3e62b1f3171255d003b61ba73cdefd5aeb | Python | 7,572 | 211 | # -*- coding: utf-8 -*-
import logging
import numpy as np
class RAP:
def __init__(self, rap_volume, propagator, max_nbr_pts):
"""
rap_volume: DataVolume
HRegion-Adaptive Propagation tractography volume.
"""
self.rap_volume = rap_volume
self.propagator = propaga... |
17bd821462ef5d6dec7570a3eea51fedf41a3468215465517a5ad823faebae66 | Python | 7,574 | 221 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from drf_spectacular.types import OpenApiTypes
from drf_spectacular.utils import OpenApiParameter, OpenApiResponse, extend_schema
from rest_framework import serializers, status, viewsets
from rest_framework.decorators import action
from rest_framewor... |
2a0b089a3bf2942a016f8f8ab3ad5669eb32f9031b05733d115cb929c8ef6bfb | Python | 7,575 | 230 | from __future__ import annotations
import warnings
import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
import pandas as pd
import pytest
from napari_dmc_brainmap.utils.color_manager import ColorManager
from napari_dmc_brainmap.visualization.vis_plots.barplot_visualization import (
BarplotVisualiz... |
2525429b3041932b9d70b0e0ce384b60310c4542876bbd25205f2039d98b10cb | Python | 7,576 | 120 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'sections.ui'
#
# Created by: PyQt5 UI code generator 5.11.2
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Form(object):
def setupUi(self, Form):
Form.setObjectName(... |
dfbdcf159dc1a33c48f618bb160f4cd5b4d1f8389892bbb27f5a1899268412b1 | Python | 7,577 | 209 | import argparse
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch_geometric.utils import negative_sampling
from ogb.linkproppred import PygLinkPropPredDataset, Evaluator
from logger import Logger
class LinkPredictor(torch.nn.Module):
def __init__(self, in_channels,... |
817a345e224405f8cdac834562397aed3a3928938c8b56381593b9d66113fcc2 | Python | 7,579 | 182 | import h5py
import hashlib
import validators
import json
import sys
import os
import time
import uuid
#log is the same as standard_sdks log
from mmsdk.mmdatasdk import log
from mmsdk.mmdatasdk.configurations.metadataconfigs import *
from mmsdk.mmdatasdk.computational_sequence.integrity_check import *
from mmsdk.mmdat... |
c2581833df8b0bba9d7b640ec88696644d4ff85d8f897545217d01bee5d7278b | Python | 7,580 | 208 | import torch
from torch_geometric.data import DataLoader
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from torch.optim.lr_scheduler import StepLR
from gnn import GNN
import os
from tqdm import tqdm
import argparse
import time
import numpy as np
import r... |
c48c795375359f50081cee31bd26287ef5094079a29e15d5a4ceae08b2a59b75 | Python | 7,581 | 249 | # -*- coding: utf-8 -*-
"""
Created on Wed Mar 25 17:09:55 2020
Functions that deal with timeline .mat (matlab) files
@author: Samuel Failor
"""
from os.path import join
from os.path import exists
import scipy.io as sio
import scipy.signal as ss
from sklearn.cluster import KMeans
import numpy as np
from . import cor... |
21917f0db9f8d230bd64240f7d01a50de42d0d31070747f939142cc68d3755de | Python | 7,582 | 203 | DESC ="""
Script to randomly generate a kitchen scene
USAGE:
python random_kitchen.py -m kitchen3.xml
"""
import os
import click
import numpy as np
import time
import re
try:
import mujoco_py
from mujoco_py import load_model_from_path, MjSim, MjViewer, load_model_from_xml, functions
except ImportError as e... |
2c631e8f53036f5e159da5598206fc82ae36c5b4b719f4c69a0f84c4553875c9 | Python | 7,582 | 171 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
SAM model interface.
This module provides an interface to the Segment Anything Model (SAM) from Ultralytics, designed for real-time image
segmentation tasks. The SAM model allows for promptable segmentation with unparalleled versatility in image a... |
33bd61fc77a5eed27706ff15291441008e82492092e8f935da1171c1724f5eb9 | Python | 7,582 | 200 |
from brainscore_vision.model_helpers.check_submission import check_models
import functools
import numpy as np
import torch
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from PIL import Image
from torch import nn
import pytorch_lightning as pl
import torchvision.models as models
import ... |
49bf7db494f70caa60dcf3e3afa9b1ab020334dc5206c0a282e092578ab79d96 | Python | 7,582 | 181 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from __future__ import annotations
from datetime import timedelta, timezone
from decimal import Decimal
from typing import Any
from uuid import UUID
import rq.defaults as rq_defaults
from django.db.models import TextChoices
from drf_spectacular.uti... |
6594f8d682bfb32650884627f61ee03c1883ddeb04d9058e2c4f57fedecff377 | Python | 7,582 | 227 | import re
from typing import Any, Dict, List, Optional, Union, Literal
from pydantic import RootModel, BaseModel, field_validator, model_validator
import matplotlib.colors as mcolors
ALGO_NAME_REGEX = re.compile(r"^[A-Za-z_][A-Za-z0-9_]*$")
NEEDED_DATA = {
'data_neuronal_activity': 'Binary Neuronal Activity',
... |
fa771cb62d22bc439ffc4f55f0289ab49353046b9a97130eba51ed2eae9682a6 | Python | 7,582 | 193 | from pathlib import Path
import datetime
from multiprocessing import Pool, Lock
import time
from benedict import benedict
import numpy as np
import os
import shutil
import random
# set seeds
random.seed(42)
np.random.seed(42)
os.environ['PYTHONHASHSEED'] = '42'
os.environ['TF_DETERMINISTIC_OPS'] = '1'
def adjust_t... |
6741bccc393247a29f4dbb0ae95287f75590221231c682b623c18ca5e7274302 | Python | 7,583 | 192 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: LY 2024
Compute SVD of videos via labdata tools: currently can compute SVD of original
full video, and can create a motion energy video and then compute the SVD.
Sample usage: labdata run videosvd -a LY018 -s 20231109_141923 -d chipmunk -- compute_video_svd
... |
882433236fd96dc56dad6b9eb0a10e12956cdea25067f856c0c1b8b9e90c8b31 | Python | 7,584 | 243 | import src.contour as con
import numpy as np
import src.pureCont as pc
import nibabel as nib
import pickle
import src.finElFit as fe
def thresh(DirectOfFile,percentage):
#loads the dataset found within 'DirectOfFile' and uses a histogram to calculate
#the threshhold at which 'percentage' of all pixels not dee... |
7699e17c50597503cb484991893d04d0b959b0ade7de2efb865b23bffac7f4af | Python | 7,585 | 199 | """Test the encoding model's prediction accuracy independently for each
NSD-synthetic image class.
Parameters
----------
subjects : list
List of the used NSD subjects.
nsdsynthetic_image_class : int
Integer indicating the NSD-synthetic image class used.
0 for chromatic noise.
1 for contrast modulation.
2 for mani... |
620d8bff833595a1810cbc271e0a6cd387feff81147bdf3aecf7852097b4663b | Python | 7,586 | 74 | import argparse
def ArgParserPatch(commandline,do_exit,flag=0):
parser = argparse.ArgumentParser()
exp=['experiment','--exper']
parser.add_argument(exp[flag], type=str, help = 'give name of h5 file, for example: ')
parser.add_argument('-headstages', type=str, nargs="+", help='which headstage to analyze... |
da376d1025ec7c4b1e43bdc333a6f58649fe98f3f3546ace703469972db64f2a | Python | 7,586 | 179 | #!/usr/bin/env python
"""
Camera control specialized for a Photometrics camera.
FIXME? This never calls pvcam.uninitPVCAM() to close the PVCAM
library. Not sure if this matters.
Hazen 1/18
"""
from PyQt5 import QtCore
import storm_control.sc_hardware.photometrics.pvcam as pvcam
import storm_control.sc_librar... |
5c096201d4575adc5192c44e2012c0d98b71cc9341448b1c94b9c813b2946f80 | Python | 7,587 | 572 | import numpy as np
### Main Experiment
TIME_BLOCK = 11 # 11
TIME_WAIT = 4 # 4
N_BLOCKS = 20 # 20
N_RUNS = 5 # 5 -> indicates runs per condition
WARM_UP_TIME = 0
COOL_DOWN_TIME = 20
ITI = 0.5
JITTER = 0.2
# Screen Specification
SCREEN = 0
FULLSCREEN = True
MONITOR_DISTANCE = 123
#office=50, eye_tracker=60, PROPix... |
e6827b4133f52e6ba087d89e86510807ce7a9d251ec3082af577623f2ff37396 | Python | 7,588 | 206 | """Convolution layers."""
from typing import Any, Union
import numpy as np
import torch
from torch import nn
__all__ = [
'SpikingConv2d', 'SpikingEiConv2d'
]
class SpikingConv2d(nn.Module):
"""2D convolution wrapper."""
def __init__(self, in_channels: int, out_channels: int,
kernel_si... |
a4f5b7f47a9d6bcc00ff3bf167cf630089a2861b2435ccf957f6bf638db99872 | Python | 7,589 | 223 | # -*- coding: utf-8 -*-
# python standard library inputs
import sys
# external inputs
import numpy as np
from numpy.fft import fft, ifft, fft2, ifft2
# local inputs
from ..simulation.get_white import get_white_2d, get_white_1d
from ..simulation.filter_bold import filter_bold_2d, filter_bold_1d
from ..simulation.mask... |
7ddd85e9ca14f913a0a7a676a033a98919baa91092c139e8118a00d50913a94a | Python | 7,590 | 211 | """Filename containers for M/EEG processing pipelines."""
import os
from typing import Optional
class SurfaceFilenames:
"""Container for surface extraction file paths.
Parameters
----------
root : str
Root directory for surface files.
"""
def __init__(self, root: str):
self.... |
5b83b6f66f973c6570676b48b53133b35444ee15a5f14a45810f5e6c23188c99 | Python | 7,591 | 209 | """
AVPENet: Audio-Visual Pain Estimation Network.
Main model class that assembles all four components:
1. AudioEncoder — ResNet-34 on mel-spectrograms
2. VisualEncoder — ResNet-50 + spatial attention on face images
3. CrossModalFusion — Bidirectional cross-attention
4. RegressionHead — FC layers → ... |
c5f17de2b1919f7d371472330606cf798716bd8b51fe9eb66074b9c72487acd1 | Python | 7,591 | 211 | import itertools
from collections.abc import Callable
from typing import Any
import torch
from eir.train_utils.ignite_port.engine import Engine
from eir.train_utils.ignite_port.metrics.metric import (
EpochWise,
Metric,
MetricUsage,
reinit__is_reduced,
)
__all__ = ["MetricsLambda"]
class MetricsLam... |
788004685cece7d9ec8b763c57ed2db1fd2b9d384067e94b2499d9dd503ad531 | Python | 7,593 | 168 | # -*- coding: utf-8 -*-
"""
Simulation study for EMPRISE fMRI data
Joram Soch, MPI Leipzig <soch@cbs.mpg.de>
2023-06-29, 17:07: first version
2023-07-03, 10:27: adapted parameters
2023-08-17, 11:35: saved results
"""
### Step 1: specify settings ##################################################
# import modules
im... |
d0c82863a0b32909ab283857d2fe3c24512f9256f8bb91ac2843438910afbffd | Python | 7,593 | 167 | #!/usr/bin/env python
"""
Provides the (software) time base for a film.
Hazen 04/17
"""
from PyQt5 import QtCore
import storm_control.sc_library.parameters as params
import storm_control.hal4000.halLib.halFunctionality as halFunctionality
import storm_control.hal4000.halLib.halMessage as halMessage
import storm_con... |
996571b33d07d84f0a340dbf74986ee2bf7ffe52f84b185358e6a3d53bcd80c4 | Python | 7,594 | 180 | # Copyright (c) 2020, Soohwan Kim. 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 required by applicable la... |
2222b3eaabfb008466a3904646039368794e50009f0a3c134684a1d4f289c530 | Python | 7,595 | 252 | from unittest.mock import ANY, MagicMock, call, create_autospec, patch
import pytest
import torch
from lightning.fabric import Fabric
from torch import nn
from eir.train_utils import step_logic
from eir.train_utils.optim import AttrDelegatedSWAWrapper
@pytest.mark.parametrize(
"create_test_data",
[
... |
11216d2aea7d8941846c0c075ce73cfff48c20f84a95315b9a50db1d5695bfed | Python | 7,596 | 195 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from datetime import timedelta
from unittest import mock
from django.contrib.auth.models import Group
from django.test import override_settings
from django.utils import timezone
from rest_framework import status
from cvat.apps.access_tokens.cron i... |
90e5f912402475d727915746b3cbb0b93e43e2c4a60f0c9d145f4763c8ac668f | Python | 7,598 | 236 | #!/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 pysam
from pathlib import Path
from statistics import median
impor... |
56b52613bba9d07ec973e573a068ac8f40c74d4ecca0f8d1adff93e85d9507a1 | Python | 7,600 | 175 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Depth estimation trainer for YOLO models."""
from __future__ import annotations
from copy import copy
from pathlib import Path
from typing import Any
import torch
from ultralytics.models import yolo
from ultralytics.models.yolo.detect import Det... |
a5588bf980fb4f3cb6c2dc37ec46e0c150627f60a7caec64bc37afbbc77ab504 | Python | 7,603 | 196 | #############################################################################
# Copyright (C) 2020-2025 MEmilio
#
# Authors: Agatha Schmidt, Henrik Zunker
#
# Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in complian... |
f369308fcb2388f22cf7dbc4c24fc6a66a1cf5bd13c2f4feef48609781dc720d | Python | 7,604 | 140 | import numpy as np
from cebra import CEBRA
import cebra
from pathlib import Path
import matplotlib.pyplot as plt
from stimulus_mode_functions.plot_functions import plot_embedding_cross_validation
from stimulus_mode_functions.utils import set_seed
# define cebra model
def cebra_model_function(arglist: object, lr: obje... |
9e604481ecd52098dbe0178c3039a35442efdb17e5e5a552b63a26d82270c307 | Python | 7,605 | 253 | from __future__ import annotations
import json
from functools import partial
from pathlib import Path
from typing import Final
import pytest
from PySide6.QtCore import Qt
from PySide6.QtWidgets import QCheckBox
from pytestqt.qtbot import QtBot
from labelme._widgets._label_dialog import LabelDialog
from ..conftest i... |
bdb0009aaae1b6ffd706d3ffcd040b7f773079fd8fea1ccea347d8784dd732ee | Python | 7,606 | 219 |
import hashlib
import requests
from pathlib import Path
import zipfile
from collections import OrderedDict
from warnings import warn
import json
import torch
import zipfile
_MODELS = {}
_ALIASES = {}
def get_file(fname, origin, file_hash=None, cache_dir=".cache", cache_subdir="datasets", extract=True):
"""
D... |
ee6b00c34e830311c5e6efe1d797d2222797887bb7ec708f1b72ae5908a013d7 | Python | 7,607 | 172 | import tkinter as tk
from tkinter import ttk
from tkinter import messagebox
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from .base_window import BaseWindow
from .result_window import ResultWindow
import matplotlib.pyplot as plt
import numpy as np
import h5py
from scipy.signal import decimate
clas... |
9dbebeac085932db5c58b111ba5cc09606dbb7013be092ff92083a7cf65d0060 | Python | 7,608 | 223 | from functools import partial
from typing import TYPE_CHECKING
import torch
from eir.models.input.array.models_locally_connected import FlattenFunc, LCLModel
from eir.models.model_setup_modules.output_model_setup_modules import al_output_modules
from eir.models.output.array.array_output_modules import (
ArrayOutp... |
c9bd9ba9413b1b60c81e15abcd0a0835678c8b23154851fe9c23529a5841e82b | Python | 7,609 | 210 | import numpy as np
import pandas as pd
def mask_data_in_dict(data, mask, mask_priority=None):
masked_data = {}
assert isinstance(data, dict), "data should be a dictionary"
for k, v in data.items():
if isinstance(v, np.ndarray):
match_dim = np.nonzero(np.array(v.shape) == mask.size)[0]
... |
306f52a28c248d8630e86152c66087e8078df839e105c82ead238c311e5e774f | Python | 7,614 | 200 |
from brainscore_vision.model_helpers.check_submission import check_models
import functools
import numpy as np
import torch
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from PIL import Image
from torch import nn
import pytorch_lightning as pl
import torchvision.models as models
import ... |
86ef5a3afa8bb3228cf629e7f252e35634dec967f1a2ad4cc1f76ce26a0155f7 | Python | 7,614 | 196 | import argparse
import networkx as nx
import matplotlib.pyplot as plt
import random
import numpy as np
import math
from scipy import stats
from scipy import spatial
from gensim.models import Word2Vec
'''
use existing matrix to run edge2vec
'''
def parse_args():
'''
Parses the node2vec arguments.
'''
... |
951ba6a3baf672e88a38a0d7e7343645e61acd9835a3504a0204868b53871902 | Python | 7,614 | 200 |
from brainscore_vision.model_helpers.check_submission import check_models
import functools
import numpy as np
import torch
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from PIL import Image
from torch import nn
import pytorch_lightning as pl
import torchvision.models as models
import ... |
b077119dfdf301062295d6d9d112dceff05466e75aa56e004f84802c5e80bc4d | Python | 7,615 | 187 | """The optimised inner loops must agree with the references they replaced.
Run from the repo root::
uv run python python/test_pipeline_fast_paths.py
Three routines were rewritten for speed after profiling showed a 270-cell
calcium recording spending ~960s on a single lag (``null_model_und_sign`` and
``efficiency... |
7431ffdb8a6b50276718c2f6d180f4fc47f5fad22dbeb39d1114badd5e63a5bc | Python | 7,618 | 241 | import io
import os.path as osp
import pickle
import re
import sys
import warnings
from typing import Any, Dict, List, Literal, Optional, Union, overload
from uuid import uuid4
import fsspec
import torch
import torch_geometric
DEFAULT_CACHE_PATH = '/tmp/pyg_simplecache'
def get_fs(path: str) -> fsspec.AbstractFile... |
72cc66f9a71c571460274eb66e98d9097f94c2db6f8eb5c02b99cd4a6846f866 | Python | 7,620 | 207 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
PCA-based Feature Selection Pipeline
This module provides a function to identify connections (features) associated with
a target variable using Principal Component Analysis (PCA).
The pipeline:
1. Performs PCA on the input feature matrix (df_X_train)
2. Identifies pr... |
96e1ab0393433b207f419be99e3a1ce1dcfab6f90e1f4c07c9df8caae64468ec | Python | 7,620 | 174 | """
(C) Copyright 2019 IBM Corp.
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 applicable law or agreed to in writing, software
... |
1665e8e4348e414ac353d23954b8460671afec9a664e9250ef1e4293aa9ba547 | Python | 7,625 | 140 | import numpy as np
from cebra import CEBRA
import cebra
from pathlib import Path
import matplotlib.pyplot as plt
from choice_mode_functions.plot_functions import plot_embedding_cross_validation
from choice_mode_functions.utils import set_seed
# define cebra model
def cebra_model_function(arglist: object, lr: object, ... |
1d5190f7b539f8782f495dbc84339a84a5ead4474b5acd6dd8761631eb8d76fc | Python | 7,625 | 239 | from pathlib import Path
from PySide6.QtCore import Qt
from PySide6.QtWidgets import (
QDialog,
QDialogButtonBox,
QFileDialog,
QHBoxLayout,
QLabel,
QListWidget,
QListWidgetItem,
QPushButton,
QStyle,
QTreeWidget,
QTreeWidgetItem,
QVBoxLayout,
)
class BatchFolderSelectio... |
9d039db405e495aa5eed5913b358b04e434a31d4224657a40eaec8e67a88629b | Python | 7,625 | 177 |
import pandas as pd
from pathlib import Path
from pkg_resources import resource_filename
from natsort import natsorted
from tifffile import tifffile
from napari_dmc_brainmap.registration.sharpy_track.sharpy_track.model.calculation import fitGeoTrans, mapPointTransform
from napari_dmc_brainmap.utils import get_bregma, ... |
f45eca5fa592d76d1aa4c5ed57aded714203a8917f773b0f235504df63190b2d | Python | 7,628 | 173 | #!/usr/bin/env python
"""
Handles a single camera.
Notes:
1. There is one of these per camera.
2. The name of the camera is the name of the module.
Responsibilities:
1. Operate the camera.
2. Return the camera configuration when requested. This includes
things like the camera maximum value, whether it ... |
ffd7093274d3558eb317ea3dacbfe69e699ee72b2933a98d97187f4951f17941 | Python | 7,629 | 140 | import numpy as np
from cebra import CEBRA
import cebra
from pathlib import Path
import matplotlib.pyplot as plt
from stimulus_mode_functions.plot_functions import plot_embedding_cross_validation
from stimulus_mode_functions.utils import set_seed
# define cebra model
def cebra_model_function(arglist: object, lr: obje... |
4e70fbd7b7af53f1db72fa1b96388dbd3763837c690ee84ed187e39d96ab1ec6 | Python | 7,630 | 230 | import os
import sys
import json
import tqdm
import glob
import numpy as np
import torch
from torchvision.transforms import v2
# Filepaths to change if run on different machine.
CWD = r""
DATA_DIR = r"D:\Askivit Daten\ASKIVIT_1_5"
DIR_MODELS = r"C:\Users\"
## ****************************************... |
6fc2a74dd87ae4175789097b4e8f660a76c7d9472a6c48c07b376c5804a38e67 | Python | 7,630 | 200 |
from brainscore_vision.model_helpers.check_submission import check_models
import functools
import numpy as np
import torch
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from PIL import Image
from torch import nn
import pytorch_lightning as pl
import torchvision.models as models
import ... |
721cfe97852cc7085db0e7930d4fa844c231b55637c0e009b753c0138dd61504 | Python | 7,630 | 168 | """Fold-symmetry scoring used by both QuadrantFolder and the image-alignment UI.
This module lives under ``musclex.utils`` (instead of ``musclex.ui.widgets``)
so the core/headless QuadrantFolder pipeline can call it without dragging in
PySide6 — importing anything under ``musclex.ui.widgets`` runs that package's
``__i... |
72be70e72fe4ae2645521f293a0ea096b2542d3914e8891c45ed32bf05dfc6a9 | Python | 7,630 | 140 | import functools
import torch
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from PIL import Image
import numpy as np
import timm
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
import torch.nn as nn
from albumentations import (
Com... |
849fbaec9fcbfbe40df990e09110a84f9632441b05ee008035759ad8f0e16325 | Python | 7,630 | 184 | import pandas as pd
import shutil, os
import os.path as osp
from ogb.utils.url import decide_download, download_url, extract_zip
from ogb.io.read_graph_raw import read_csv_graph_raw, read_csv_heterograph_raw, read_binary_graph_raw, read_binary_heterograph_raw
import torch
import numpy as np
class LinkPropPredDataset(o... |
c56cf67a3b0d0cdf27d9dcec1ddef6b86930e148c4ad14480b9f58b4d66ead2e | Python | 7,630 | 200 |
from brainscore_vision.model_helpers.check_submission import check_models
import functools
import numpy as np
import torch
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from PIL import Image
from torch import nn
import pytorch_lightning as pl
import torchvision.models as models
import ... |
f9ab453b8801d72c1c912c4924f0e52073db4b5b1b70c355d015dc617991fcc7 | Python | 7,630 | 199 | """Temporal backbones: TCN, GRU, LSTM, Transformer -- and the RG-NFT model."""
from __future__ import annotations
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
# --------------------------------------------------------------------- TCN
class Chomp(nn.Module):
"""Trim the right p... |
8c0ac22b0e2c400afbb249c1f7e915aeb4d205d3e35fd669a98f853b52041220 | Python | 7,631 | 212 | # This is the model definition of RepLKNet-31B + Cascade Mask R-CNN
model = dict(
type='CascadeRCNN',
backbone=dict(
type='RepLKNet',
large_kernel_sizes=[31,29,27,13],
layers=[2,2,18,2],
channels=[128,256,512,1024],
drop_path_rate=0.6,
small_kernel=5,
d... |
d212f136524ea759ed9a23ba37479e9a5050aa0e6c9233208ecfe5829f06ecb3 | Python | 7,631 | 205 | """A module that contains unit tests for the :py:mod:`corelay.tracker` module."""
import pytest
from corelay.base import Param
from corelay.plugboard import Slot
from corelay.tracker import Tracker
@pytest.fixture(name='tracker_type', scope='module')
def get_tracker_type_fixture() -> type[Tracker]:
"""Creates a... |
4219dfa0a4f3d5617df1162aadd824ff065f9cf463d2d75bcc948843701af4f5 | Python | 7,633 | 163 | import numpy as np
import os
import tempfile
from ivim.io.base import read_im, read_bval, read_bvec, read_cval, write_im, write_bval, write_bvec, write_cval
from ivim.preproc.base import average, combine, extract
# Paths to data
temp_folder = tempfile.gettempdir()
# Test functions
def test_average():
bval_file = ... |
e885902bd3a5c78156f2d799905273a320a133d4c9a5141251d2f331ba20006e | Python | 7,633 | 219 | from psychopy import core, visual, monitors, iohub
from psychopy.iohub import launchHubServer
from psychopy.iohub.util import hideWindow, showWindow
from psychopy.iohub.client.eyetracker.validation import TargetStim
from const import FULLSCREEN, SCREEN, MONITOR_RESOLUTION, MONITOR_DISTANCE, MONITOR_WIDTH
class EyeTrac... |
52d1b6bf05bb3367b004f9811e82b7fb04aa65a3ded661c26be5d0ed79407fec | Python | 7,634 | 209 | """
GMA (General Movements Assessment) dataset loader.
Pose format: per-video JSON files with naming convention:
{infant_id}_{session}_{age_code}.json
e.g. 73_2_1.json (age_code: 0=term, 1=3-4 month fidgety window)
Filters applied:
- age_code == 1 (3-4 month fidgety window only)
- For infants with multiple ... |
5724c4d93189a4b272ae37beb39fc0f7098b0623b8ef473254964f76e7fab0c9 | Python | 7,635 | 149 | # Denoising of the DWI with DIPY: Marchenko-Pastur PCA (MP-PCA, Veraart et al. 2016) or Patch2Self (Fadnavis et al.
# 2020). Both assume noise that is independent between voxels: use this module first, on the raw images, before
# any interpolation (eddy, susceptibility correction, baseline averaging) and before G... |
b9ffd032c7734665e6c79eec484587a03f933d3d534cd568aa917869a6bd85f4 | Python | 7,636 | 212 | """Combined 3D-viewer + IR-spectrum component with hover linking.
Streamlit widgets cannot forward hover events to Python, so linking
"hover a peak" to "animate that normal mode" requires both panels in one
HTML component: a 3Dmol.js viewer (left) and a Plotly.js spectrum
(right) wired together in JavaScript. Hovering... |
3ee5788207cd0c2a4ea32f80e568d09822e07a65bd9240d2ff277133f8a96958 | Python | 7,637 | 187 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 23 18:33:22 2020
@author: mheinzinger
"""
import argparse
import time
from pathlib import Path
import torch
import h5py
from transformers import T5EncoderModel, T5Tokenizer
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
p... |
8d4c406eeac771f56dbc01c3d35676e31a68e79436a88b497f8b16ccbc146369 | Python | 7,638 | 211 | #!/usr/bin/env python
"""
This file contains the various XML parsing functions.
Hazen 04/17
"""
import numpy
import xml.etree.ElementTree as ElementTree
import storm_control.sc_library.halExceptions as halExceptions
class ShutterXMLException(halExceptions.HalException):
pass
class ShuttersInfo(object):
... |
0a1bc63c31f6c839d54826df9f3a08bd897c5764c9a87b2542acfe4d45962e13 | Python | 7,642 | 252 | import functools
import inspect
import logging
import warnings
__all__ = [
"keyword_deprecation",
"keyword_equivalence",
"add_method_to_instance",
"add_method_to_class",
"add_prop_to_instance",
"add_prop_to_class",
"deprecated",
]
def keyword_equivalence(func=None, *, this_or_that):
"... |
d4e61c88470034acb079e201f447fe3b527bfd4055a96003db5028f38036378e | Python | 7,643 | 190 | import os
import re
import warnings
import numpy as np
from pandas import DataFrame
import Utils.config_stomach as CONFIG
from argparse import ArgumentParser
import torch.nn.functional as F
import gc
from glob import glob
import seaborn as sns
import torch
from torch.utils.data import DataLoader
from prefetch_generator... |
c183a338a3713b10c2b8772650661b6e6c2662634dcdde74113bb4436a42552c | Python | 7,645 | 181 | # -*- coding: utf-8 -*-
"""
Cell shape and size detection routines
The cell shape detection is based on a seeded and masked watershed.
"""
#:copyright: Copyright 2015 by Christoph Kirst, The Rockefeller University, New York City
#:license: GNU, see LICENSE.txt for details.
import sys
import numpy
#from scipy.ndimag... |
2155af599233c80073bc7361028edb5c6a49a5fb39ae764e3436432a34f09bfe | Python | 7,646 | 200 |
from brainscore_vision.model_helpers.check_submission import check_models
import functools
import numpy as np
import torch
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from PIL import Image
from torch import nn
import pytorch_lightning as pl
import torchvision.models as models
import ... |
35d3916f19d2d7a386315a1d34fde3fcff1f416ce17e0d51611f8f2cf88860fb | Python | 7,647 | 165 | # Copyright (c) 2026 Rohit Jena. All rights reserved.
#
# This file is part of FireANTs, distributed under the terms of
# the FireANTs License version 1.0. A copy of the license can be found
# in the LICENSE file at the root of this repository.
#
# IMPORTANT: This code is part of FireANTs and its use, reproduction, or... |
2e3ba1eb2cddeb34fe58eea4bf18875ed0d2d8e217e82bd688dc57917f18c163 | Python | 7,651 | 152 | # import libraries
# general
import numpy as np
import pandas as pd
import xarray as xr
import tqdm as tqdm
# brain-score specific
import brainscore
from brainscore_core.supported_data_standards import brainio
from brainscore.benchmarks._neural_common import average_repetition
from brainscore_core.supported_data_st... |
65e2467e3ecd84f571c3aa26dce391bac5873b7f6471ad8ee51763b50cb877b7 | Python | 7,651 | 203 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import datumaro as dm
from django.conf import settings
from django.db import transaction
from rest_framework import serializers
from cvat.apps.consensus.intersect_merge import IntersectMerge
from cvat.apps.consensus.models import ConsensusSettings
f... |
692bac3b3d6ed29bd1f5c6e909a58aeb3e2af65334d1b3fda52dbdafc255771f | Python | 7,651 | 214 | # Copyright (C) 2025 Paolo Vincenzo Fioretti, Michela Roccuzzo, Enrico Saccon,
# Maria Pennuto, Manuela Basso
# 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 the License, or... |
69d596f5d4a82e580b9ee7617855792af845b780df9cd030c11c70683628ce70 | Python | 7,651 | 214 | import tifffile
import numpy as np
import json
import cv2
import tifffile as tiff
from skimage.exposure import rescale_intensity
def load_meta(section_dir):
path_to_tiff = section_dir.joinpath([f.parts[-1] for f in section_dir.glob('*.tif')][0])
with tifffile.TiffFile(path_to_tiff) as tif:
meta_data =... |
76f73ebaa4d9fcb9e2a2dc60f69e9fa25a6f3a1b58e1f0a3f3107db36261eab3 | Python | 7,656 | 215 | #!/usr/bin/python2.4
import sys
import bx.pwm.position_weight_matrix as pwmx
from bx.align import maf as align_maf
from bx.pwm.position_weight_matrix import isnan
NaN = float("nan")
def main():
pwm_file = sys.argv[1]
splist = sys.argv[2]
if len(sys.argv) == 4:
inmaf = open(sys.argv[3])
else... |
824c6996698a188bce4bf51f0bf4fba50dcc0f3bfbd7e26e1528de214b9d1644 | Python | 7,657 | 185 | from typing import Dict
from math import prod
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from zigzag.classes.workload.layer_node import LayerNode
import zigzag.classes.mapping.mapping_assist_funcs as mapping_assist_funcs
class SpatialMapping:
"""
Class that collect all the info related to spatial... |
d76ab7a760143af1578fa5327a270f090edb3cba41d2ce383e17ac1532f84905 | Python | 7,657 | 243 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from __future__ import annotations
from collections.abc import Callable, Iterator, Mapping
from functools import cached_property
from types import MappingProxyType
from typing import Any, cast
import datumaro as dm
from cvat.apps.dataset_manager i... |
88b7894723f640a670ce1e26a98079683f4d623f5bc9366c587f0882f947a0b2 | Python | 7,660 | 208 | """Normalization layers for 1D inputs."""
from typing import Any, Union
import numpy as np
import torch
from torch import nn
__all__ = [
'SpikingBatchNorm1d', 'SpikingEiNorm1d'
]
class SpikingBatchNorm1d(nn.Module):
"""Batch normalization wrapper."""
def __init__(self, num_features: int, eps: float = 1... |
a2c60183e9e1d7ec8f5244aa18f113f014753c44acf4b3c31eac95381f2c828d | Python | 7,660 | 231 | """
Diagnose why ``LmfitTRFAdapter`` fails on the m6 case under init perturbation.
Strategy:
* Load the captured m6 case.
* For ``n_seeds`` independent perturbations of size ``perturb``, run TRF.
* Print summary lines for every fit, full diagnostic dump for any failure
(success=False or aborted at the lmfit nfev ca... |
6ca3ffc477dfa4a3025693e6ebfb1a2da6bba1ec2f5c71b3a230abc4e7ea94ef | Python | 7,661 | 201 | import nibabel as nb
import numpy as np
import json
import csv
import os
import glob
import subprocess
import sys
from datetime import datetime
from nilearn import plotting as nip
from nilearn import image
from argparse import ArgumentParser
def get_centers(brain, orig_labs):
"""
Get coordinate centers given ... |
7d554e6b7f10d25c24f6304bda83336f617512bb5ccc1349714123c4dde6e79a | Python | 7,661 | 229 | """
Calculate the reconstruction
"""
import os
import sys
import argparse
from collections import defaultdict
from pathlib import Path
from tqdm import tqdm
import numpy as np
import pandas as pd
import torch
from neuralcompress_v3.models import BiModel
from neuralcompress_v3.utils import (Checkpointer,
... |
5652b6fa217ac06f5b69af8aa071f658135e369f286d6ae33b82eef4896ef295 | Python | 7,662 | 170 | """A module that contains unit tests for the :py:mod:`corelay.processor.clustering` module."""
from io import BytesIO
import os
import typing
from importlib import import_module
import numpy
import pytest
from sklearn.datasets import make_blobs
from sklearn.metrics.pairwise import euclidean_distances
from corelay.ba... |
539583d662b1dfd7dbfce9ac7f3eb30f5c6782223bcc98813eee75d16ab41ca0 | Python | 7,664 | 211 | #!/usr/bin/env python
"""
Converts .tri files to .nifti segmentation masks
"""
import os
import nibabel as nib
import nilearn, nilearn.image
import numpy as np
import argparse
from tqdm import tqdm
from src.tri_io import load_tri
import tempfile
import trimesh
import vtk
import scipy.io as sio
def is_inside(points,... |
53e561265bede654fe252845ac5e6d37ce3008faa856e7ed2d53723955ecd0e5 | Python | 7,664 | 192 | from typing import List, Optional, Tuple, Union
import torch
from torch import Tensor
from torch.nn import Embedding
from torch.utils.data import DataLoader
from torch_geometric.index import index2ptr
from torch_geometric.typing import WITH_PYG_LIB, WITH_TORCH_CLUSTER
from torch_geometric.utils import sort_edge_index... |
e76b2e564a82d4f85eca193c3a653a0bc79c3ee7f34b8ebc3e6a8f1f01ef31f4 | Python | 7,665 | 187 | # -*- coding: utf-8 -*-
import os
import re
import sys
def find_reference_pcr_file(pcr_file_path):
current_dir = os.path.dirname(pcr_file_path)
for i in range(5): # Try up to 5 levels up
ref_path = os.path.join(current_dir, 'dat_vestacif_files', 'reference_pcr_format', 'tbbaco_noise_percent_10.... |
163b5484b92802178499186b3f08db0457adc7d59b97640f3f9acbe80717e8be | Python | 7,667 | 234 | #!/usr/bin/python3
#
import os
import sys
import argparse
import numpy as np
from uac import utils
# Create the parser
my_parser = argparse.ArgumentParser(description="Calculation of old atrial coordinates for RA")
# Add the arguments
my_parser.add_argument("target_path", metavar="path", type=str, help="Path for the... |
900e0c1c73ab174437a9ece64cec6550c9028c5911f0e997aaf5123f4de6475d | Python | 7,667 | 196 | """Perform MDS on the trial-average fMRI responses, or on the DDN features, for
the NSD-core images. # !!! Remove DNN
Parameters
----------
subject : str
String indicating the number of the subject used. If 'all', then use all
subjects.
data_ood_selection : str
If 'fmri', the ID/OD splits are defined based on fMRI ... |
0576714768d487bfc061f29b08d400b3846161412efff0d830527a4b7c4510d7 | Python | 7,670 | 237 | from julia.api import Julia
from scipy import ndimage
from typing import Any, Dict, Tuple
import SimpleITK as sitk
import argparse
import glob
import json
import numpy as np
import os
jl = Julia(compiled_modules=False)
jl.eval('include("adjust.jl")')
ADJUST_IMAGE_SIZE = jl.eval("adjust_image_cm")
def write_to_json(
... |
d077b5dea18a485f545ba90b5bff8d756a5c3b114894a317d01f83caf79361c7 | Python | 7,671 | 184 | import torch
from torch_geometric.nn import to_hetero,GeneralConv
from torch.nn import Linear
import torch.nn.functional as F
from sklearn.metrics import roc_auc_score,average_precision_score
import numpy as np
import random
import pandas as pd
SEED = 666
random.seed(SEED)
np.random.seed(SEED)
torch.... |
6e9a1308fedda18ba1ec44fe975f5b1634fc924ee0b9c3a14e9023502d4fd664 | Python | 7,672 | 237 | from julia.api import Julia
from scipy import ndimage
from typing import Any, Dict, Tuple
import SimpleITK as sitk
import argparse
import glob
import json
import numpy as np
import os
jl = Julia(compiled_modules=False)
jl.eval('include("adjust.jl")')
ADJUST_IMAGE_SIZE = jl.eval("adjust_image_cm")
def write_to_json(
... |
ecd557b014e5a0824906845e789bc52cdeed64f58ec3f124e6142c8d7fd9f52a | Python | 7,674 | 211 | #!/usr/bin/python
# -*- coding: latin-1 -*-
# =============================================================================================
# Librairies importation
import numpy as np
import nrrd
import sys
import os
import multiprocessing as mp
from voxcell import RegionMap
import voxcell
# =====================... |
542e568f1800953bea5b8d6e0640b0f9816b11aea4dcfba1eca98ebadc652c8d | Python | 7,675 | 145 | from torch import nn
from torchvision.models import convnext_base, efficientnet_b0, vgg16, resnet50
from model.GFNet import GFNet
from model.GhostNetV3 import ghostnetv3
from model.MobileNetV3_Tea_0MB import MobileNetV3_Tea_0MB
from model.MobileNetV3_Tea_1MB import MobileNetV3_Tea_1MB
from model.MobileNetV3_Tea_2MB im... |
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