sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
49c59b8e6a643b84760e763f3cc4c6e6b906ef21398303cf6b40b6239a27a946 | Python | 4,661 | 126 | import torch
from torch import Tensor
import torchvision.ops.boxes as bx
from typing import Tuple, List
import numpy as np
from os import name
WINDOWS = name == 'nt'
__all__ = ['nms', 'contours2boxes', 'pairwise_box_iou', 'pairwise_generalized_box_iou', 'filter_by_box_voting']
def torch_compile(*args, **kwargs):
... |
4e14987f599ac4a2fbd4ca593ef1318d6117afef7dfb1d6e072554729ecc1610 | Python | 4,661 | 108 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import numpy as np
from PIL import Image
import torch
import torchvision.transforms as transforms
import torchvision.models
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vi... |
5708b33801d9562338222ca7bac63aecd35aa40c6c1ba76e1e0e4befe1e6939e | Python | 4,661 | 125 | from __future__ import print_function
import torch
def normalize(x, axis=-1):
"""Normalizing to unit length along the specified dimension.
Args:
x: pytorch Variable
Returns:
x: pytorch Variable, same shape as input
"""
x = 1. * x / (torch.norm(x, 2, axis, keepdim=True).expand_as(x) + 1e-12)
... |
914aa2abab4741b4c2fe7b4f5a2d0c0eed5ccc64c0ad926b0c53401d542b5c18 | Python | 4,661 | 136 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Assign a custom color to a surface based on a data per point (dpp) file
or an anatomical image.
If a dpp file is provided, it must contain a scalar value for each vertex.
If an anatomical image is provided, the color is sampled from the image
at each vertex location.... |
be567f5066551da3c783a576d867589db1acbf91f84c896d469cc18c69ec3b00 | Python | 4,661 | 136 | #cd Desktop/GCN/pygcn/env2/
import os,csv,re
import pandas as pd
import numpy as np
import scanpy as sc
from scipy.sparse import issparse
from anndata import AnnData
import torch
from sklearn.decomposition import PCA
import math
import matplotlib.colors as clr
import matplotlib.pyplot as plt
from . models import *
cl... |
29c7a61103503aee7acd2179fcfef7451025cb3fab0b26b49787c0a8fd483170 | Python | 4,662 | 106 | from scipy.misc import imresize
import datajoint as dj
from datajoint.jobs import key_hash
import cv2
import numpy as np
from commons import lab
import os
from .utils import h5
from . import experiment, notify
from .exceptions import PipelineException
schema = dj.schema('pipeline_posture', locals())
@schema
class ... |
3fb1707645abf7a3536151b58934eead828af1c89080f438e1aa7f81a4728e65 | Python | 4,662 | 100 | import numpy as np
from brainscore_core.supported_data_standards.brainio.assemblies import BehavioralAssembly
from brainscore_core.supported_data_standards.brainio.packaging import package_data_assembly
import pandas as pd
'''
** NOTE: This benchmark (cue-conflict) has a very different stimulus sets then ALL others,... |
a6dc4ecffe24d70cb5487bff4c0fc033d28c8619d86449013445c71ae57bb23c | Python | 4,664 | 108 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import numpy as np
from PIL import Image
import torch
import torchvision.transforms as transforms
import torchvision.models
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vi... |
ec853c2412f536c565a977ceaee57b334a245b7143cc1e63cff3c5b49af59c8e | Python | 4,664 | 129 | import random
from natsort import natsorted
import matplotlib.colors as mcolors
import matplotlib.cm as cm
import pandas as pd
from typing import List, Any, Tuple
from pathlib import Path
from brainglobe_atlasapi import BrainGlobeAtlas
from napari.utils.notifications import show_info
SEABORN_STYLES = {"white", "dark",... |
742d1e3a1f9df519e5cb8917bc09b5178adcde924335f7b6ae9641c28d168257 | Python | 4,666 | 162 | ###############################################################################
#################### Boilerplate ########################
###############################################################################
import pandas as pd
import matplotlib.pyplot as plt
import os
import matplotlib... |
3ca3b21afcaca21932f1d4b2b62da364afcddb21dfa3cf596453dfc51e0494d6 | Python | 4,667 | 143 | '''
CH5O2
'''
import numpy as np
import sys
def intro():
print("""
SHORT List of potential energy surfaces for CH5O2:
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... |
518d035dcb57513b07b5fe898c6cc27da65a8c820fd0d42d2587cb822daee11d | Python | 4,667 | 161 | #!/usr/bin/env python3
"""
Detect library layout (single-end or paired-end) for SRA accessions.
Uses NCBI's esearch and efetch utilities to query SRA metadata.
Falls back to detecting from downloaded files if metadata query fails.
"""
import sys
import subprocess
import xml.etree.ElementTree as ET
from pathlib import... |
4456547bb09b0cdfa46e691719867e1803347ffeb587048601a7ff04d4266519 | Python | 4,668 | 161 | import sys
import importlib
from types import SimpleNamespace
import numpy as np
from musclex.algorithms.calibration_refinement import adapter
def test_center_method_selection_cascades_to_required_previous_stages():
assert adapter._ordered_center_methods(["gradient"]) == [
"registration",
"gradi... |
fa8a8664e1cfef3cc9276688a31b12a99297df828a0c6b224d7a2ea8da5c9e7f | Python | 4,668 | 127 | # Copyright (C) 2020-2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import numpy as np
import os
import cv2
import torch
from networks.mainnetwork import Network
from dataloaders import helpers
def mask_to_rle(mask):
[height, width] = mask.shape
pixels = (np.asarr... |
00e4394079a306733c20bd5b9837f2c6d7449deb740aa6125c0430de09cf19b2 | Python | 4,669 | 177 | #!/usr/bin/env python
"""
The core functionality for a QPD, QPD/Camera or z stage.
Hazen 04/17
"""
import storm_control.sc_hardware.baseClasses.hardwareModule as hardwareModule
class LockFunctionalityMixin(object):
"""
Combine this with either a HardwareFunctionality or a
BufferedFunctionality depending... |
a457d4dd2b5d3e459902a0b27e7b4a0534ba6e540c778f373995cdf17e824e67 | Python | 4,669 | 111 | #!/usr/bin/env python3
import pandas as pd
import numpy as np
import argparse
import gzip
import os
import itertools
from collections import defaultdict
import tempfile
def load_isoform_results(rsem_output, cols=None):
dtype = {'transcript_id':str, 'gene_id':str,
'length':np.int32, 'effective_length':np.f... |
420aff1e01a298b80d5b18cb0c69f488679ddc9c6f66ece990959d55c340c5ac | Python | 4,670 | 123 | '''
Quick script to traverse all participant data in the
experiment folder and estimate the free energy model
at any point in time for each participant. Note that
this script handles _all_ spaces, but not necessarily
at once. E.g., a 'normal' run of this will run spaces
for speaker-specific and -invariant priors with... |
ac47641dbc964593410dda18ab7f5a095b2c7f306b91326063d50c1a4b0d87b4 | Python | 4,670 | 121 | # Copyright (C) 2018-2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import zipfile
from pathlib import Path
from typing import BinaryIO
from datumaro.components.annotation import AnnotationType
from datumaro.components.dataset import StreamDataset
from datumaro.components.... |
b313c67689c5337a2bed0c5a5b468b12a345299def3a069e05f17ee232289436 | Python | 4,670 | 109 | import argparse
import utils
import plotting
import keras
from keras.models import load_model
from os.path import join
import numpy as np
import gc
'''
Get the average predictions from all models in a DeepSTARR ensemble on a test set.
Options:
- `--eval` flag: evaluate model performance on the test set.
- `--... |
cceb46def491899c90bacc9635c59852bc71f3fffaf0e741fc15b0ead6f40864 | Python | 4,671 | 157 | import sys
import yaml
import os
import numpy as np
import re
pathpath,myrun,ncluststr,cmethod,get_rois_bool_str,roi_tag = sys.argv[1:]
with open(pathpath, 'r') as myfile: pathdict = yaml.safe_load(myfile)
sys.path.append(pathdict['code']['workspace'])
from pfcmap.python.utils import unitloader as uloader
from pf... |
9398d49bcd2e3ef720200c6da22c5e71602aead1fff96010eb0e451ea209820e | Python | 4,672 | 126 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
The NAWM (Normal Appearing White Matter) is the white matter that is
neighboring a lesion. It is used to compute metrics in the white matter
surrounding lesions.
This script will generate concentric rings around the lesions, with the rings
going from 2 to nb_ring + 2, ... |
17a78f2536d1f95eed7eeb15cb2bb8d3c2fab98d444e21ae62380fd551eabcca | Python | 4,673 | 119 | # Copyright (c) 2022, Kwanhyung Lee. All rights reserved.
#
# Licensed under the MIT License;
# you may not use this file except in compliance with the License.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTI... |
ad880511c09170d7cb9ecd488a302041e0318d4d22b67fd706cef32f79de25d1 | Python | 4,673 | 113 | from pathlib import Path
from typing import Optional, Union
from loguru import logger
from brainles_preprocessing.registration import ANTsRegistrator
from brainles_preprocessing.registration.registrator import Registrator
class Transform:
"""
Class to apply precomputed transformations based on the registrat... |
e7664b7249561459483f68e9b7b1aa936e29a8be4273c2ea8b3bb93e8625af17 | Python | 4,673 | 104 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors: Maximilian Betz
#
# 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 Li... |
0d696283156f7c2f0bc2255a1319ef51a39feb040912b1eee29b83d25ddda36c | Python | 4,674 | 84 | import pytest
import numpy as np
from brainscore_vision import load_dataset, load_stimulus_set
from brainscore_vision.benchmark_helpers.neural_common import filter_reliable_neuroids
TOTAL_VOXELS = 18394
NOISE_CEILING_THRESHOLD = 0.3 * 100
EXPECTED_RELIABLE_VOXEL_COUNTS = {
'01': {'IT': 250, 'V1': 385, 'V2': 257, '... |
777b2cda0ba382f4d0e89acb5601de15225fbe3b8dcb5e9b750d084660b42cdf | Python | 4,674 | 154 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
# external inputs
import numpy as np
import nibabel as nb
from sh import gunzip
from scipy.interpolate import InterpolatedUnivariateSpline as Interp
# local inputs
from ..io.get_filename import get_filename
def regrid_time_series(file_in, path_outp... |
f1c4e39a2807cd70343a6e9dcdb866e5f73a030f188979d14cab24ec781e76a8 | Python | 4,674 | 126 | from __future__ import annotations
from typing import Final
from typing import cast
from loguru import logger
RGB_CHANNEL_COUNT: Final = 3
def _is_rgb(value: object, /) -> bool:
CHANNEL_VALUE_MAX: Final = 255
return (
isinstance(value, list)
and len(value) == RGB_CHANNEL_COUNT
and ... |
630e6eeaeb40ded0e13536efaffa19397d86d5288beb6caa2beb8ef60a8253e6 | Python | 4,676 | 119 | import numpy as np
import torch
from torch import nn
# from transformers.modeling_distilbert import Transformer as _Transformer
class CNNTransformer(nn.Module):
"""
https://github.com/i-pan/kaggle-rsna-pe/blob/main/src/beehive/models/sequence.py
https://discuss.pytorch.org/t/nn-transformerencoder-for-cla... |
a6df9fff2287c55306d82d41a72ac42c067a9718a778b46c9d5acf9474592a58 | Python | 4,676 | 134 | import os
import numpy as np
import librosa
from torch.utils.data import Dataset
from torchvision import transforms
import sys
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from datasets.dataset_scale import DatasetScale
class AudioVelDirDataset(Dataset):
def __... |
3f4117dee81b4ac0f13c6e367bab4d17d41af5111645ee086f150af01da6a983 | Python | 4,678 | 135 | import os
import torch
from torch.utils.data import Dataset
from torchvision import transforms
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from datasets.audio_dataset import AudioDataset
from datasets.accel_dataset_mel i... |
6579ee0f844acaace802ea024b55a16ac6b8459f791b6f26556ef702d7ca0799 | Python | 4,679 | 277 | import numpy as np
from impedancefitter.double_shell_wall import (
double_shell_wall_model,
eps_cell_double_shell_wall,
)
from impedancefitter.double_shell_wall_ellipsoid import (
double_shell_wall_ellipsoid_model,
eps_cell_double_shell_wall_ellipsoid,
)
# for double_shell_wall_model
c0 = 1.0
em = 10... |
8b36b42e6a4a61742c07e026c64bf903898b57f329088badc73b2edc892ac314 | Python | 4,679 | 116 | import argparse
import os
import random
import sys
import numpy as np
import pandas as pd
import torch
from torch import optim
import torch.nn as nn
from torch.utils.data import DataLoader
from sklearn.model_selection import KFold
from utils.datasets import NewDataset, get_transform, TestDataset
from mode... |
a5eb9c983730a4a46552a32ebf4bd887d527c51c0b6cc3200978d6c6277d1afe | Python | 4,679 | 40 | from testsetclass import TestsetClass
class BH76RC(TestsetClass):
def __init__(self):
self.weak = 1.0
self.molecules = ['hcn', 'n2', 'N2H2', 'hfch3ts', 'NH3', 'hclhts', 'fch3clcomp2', 'hf2ts', 'RKT09', 'f', 'ch3cl', 'fch3fcomp', 'RKT06', 'c2h4', 'RKT04', 'RKT21', 'RKT05', 'hn2ts', 'RKT17', 'RKT03'... |
f0ed00d89bf3d14a6408bf307313d9173e924c0f409748da2c584b547317720e | Python | 4,679 | 142 | import numpy as np
from numba import guvectorize, float64
import logging
import tvb.simulator.lab as lab
from tvb.contrib.cosimulation.cosim_monitors import RawCosim as CoMonitor
from tvb.contrib.cosimulation.cosimulator import CoSimulator
from tvb.simulator.models.wong_wang import ReducedWongWang, Final, List
from tv... |
5aedfb7e3c86f64ac4e2736d499b9bd548e109a18a942973e0d660a826256861 | Python | 4,681 | 93 | # Gibbs ringing removal with DIPY (local subvoxel shifts, Kellner et al. 2016; the method of MRtrix mrdegibbs), slice by
# slice in the plane of acquisition. Meant for full Fourier acquisitions; with partial Fourier it removes only part
# of the ringing. Use it after DWI_Denoise and before any interpolation (eddy... |
78e08ad47b154889ce45d98de87923278ac019d708c86284cb7d9b306ce129f7 | Python | 4,681 | 134 | """
Body-part specific IVIM parameter defaults.
Literature-based initial guesses for different anatomical regions,
using expected healthy-tissue means from the upcoming Sigmund et al.
IVIM consensus recommendations paper ("Towards Clinical Translation
of Intravoxel Incoherent Motion MRI: Acquisition and Analysis
Conse... |
615d7d2381b0a71d1e00b1209556796d1a3f6f0f607bdc3234410dfafe484d15 | Python | 4,683 | 97 | from . SpaGCN import *
from . util import *
from . calculate_moran_I import *
from . calculate_adj import *
def detect_spatial_domains_ez_mode(adata, img, x_array, y_array, x_pixel, y_pixel, n_clusters, histology=True, s=1, b=49, p=0.5, r_seed=100, t_seed=100, n_seed=100):
adj=calculate_adj_matrix(x=x_pixel,y=y_pixel... |
fc735ebff9e4174bcb405be44122aef261655c9a8c879fef4ac4b2c2d2eb3c09 | Python | 4,683 | 134 | """
Aspen Plus PEMFC Experimental Data Generator
Generates realistic PEMFC polarization data mimicking Aspen Plus process simulation output.
Data characteristics: industrial-scale stack, validated against experimental literature.
"""
import numpy as np
import pandas as pd
import json
from pathlib import Path
def gen... |
2bb0b8caeb926c3dc71403c1e70369658ec55b309a6830ad2b4aa9a0ef49a4da | Python | 4,684 | 133 | """
Copyright 1999 Illinois Institute of Technology
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publis... |
d2c0a1871880e5dcccd865c6a8332cd73db62b8614d678140509143640f68a93 | Python | 4,684 | 134 | """Parsl execution backend.
Wraps `Parsl <https://parsl-project.org/>`_ to conform to the
:class:`ExecutionBackend` interface. Python tasks are dispatched via
``@python_app`` and shell tasks via ``@bash_app``.
Parsl must be installed separately (``pip install chemgraph[parsl]``).
"""
from __future__ import annotati... |
f0fd67367d2ac2ce2af031e6f8dd7132c4893aced1a76ab1ee045542bcc51fce | Python | 4,684 | 133 | def test_parallel():
import numpy as np
databases = {}
neurons = 100
timepoints = 3600
orientations = 4
directions = 8
behaviors = 2
orientations_labels = ["0", "45", "90", "135"]
directions_labels = ["0", "45", "90", "135", "180", "225", "270", "315"]
behaviors_labels = ["speed... |
95409971cb90358f207d8533f9846b35e49572462e209485696fe6d87329de26 | Python | 4,685 | 118 | from typing import Optional
import torch.nn.functional as F
from torch import Tensor
from torch.nn.modules.instancenorm import _InstanceNorm
from torch_geometric.typing import OptTensor
from torch_geometric.utils import degree, scatter
class InstanceNorm(_InstanceNorm):
r"""Applies instance normalization over e... |
ab9897cc040a107e88691c3b7c80c319235c9148cd8822e68affe36bd07e49c0 | Python | 4,686 | 122 | # 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 math
from typing import Any, Optional
import torch
import torch.onnx.operators
from fairseq import utils
from torch import nn, Tensor
... |
4f53d8d7d7e0e44341ee87ace5ab5b2debfd3fadefed34ee5518fedb68ca9972 | Python | 4,690 | 116 | # 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 List
import torch.optim.lr_scheduler
from unicore.optim.lr_scheduler import UnicoreLRSchedu... |
534a6096f2dc28be7602051d791532ddf561deec8bd0d12cd5b01c5c23abdc42 | Python | 4,693 | 127 | """
Utils for mTRF analyses
"""
import numpy as np
def segment(arr, n_segments):
"""
Cuts the data into equally-sized fragments for mTRFpy. Takes an array and the number of segments
Works on arrays of different dimensions but uses slices it along len(arr) i.e. along the first dimension or horizontally
... |
6cfdc42e170f630071b3449fb83668bdc35458937585b0d46ca3236b1570a486 | Python | 4,693 | 103 | import pathlib
import re
from collections import defaultdict
from typing import Tuple
import neuron_interface.neurosim
compartment_names = {
"all": "all",
"axonal": "axon",
"basal": "dend",
"somatic": "soma",
"apical": "apic",
}
def convert_hoc_to_python_cell(cell_type: str) -> Tuple[str, str]:
... |
8960463d1b9bab2ee8316e530935c2db080445233664520c75171498929f231f | Python | 4,693 | 142 | from __future__ import annotations
from typing import Optional, Tuple
# TODO: remove kornia dependency
import kornia.geometry as KG
import numpy as np
import torch
from torch import Tensor
from torchvision.transforms.functional import to_tensor
from brainways.pipeline.brainways_params import AffineTransform2DParams
... |
8a98c592b54715afb9456be7633e25a5e773960b9a67d6704f8828a837c0a5ea | Python | 4,693 | 133 | import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from torch.nn import init
class ConvGRUCell(nn.Module):
"""
Generate a convolutional GRU cell
"""
def __init__(self, input_size, hidden_size, kernel_size):
super().__init__()
padding = kernel_size //... |
c42555a3127e6d900901abff30754d23f589e222bb8611b8ce681ca65cb87e9f | Python | 4,693 | 144 | """Flat-folder dataset of individually preprocessed images."""
from __future__ import annotations
import os
from typing import Any, Callable
import numpy as np
import pandas as pd
import tensorflow as tf
from numpy.typing import ArrayLike
from pyment.loaders.mgh import load_mgh
from pyment.utils.strip_extension imp... |
1034ac93d5aa1921333e04608dc239dc9d7b3d8052c0fcf97f1004db950a552b | Python | 4,694 | 144 | # /------------------------------------------------------------------------------+
# | 22-AUG-2022 |
# | Copyright (c) Bone Imaging Laboratory |
# | All rights reserved ... |
cf39227e97a28717abad8cca444d9e05a702cc3247b52c83cefdc13331e91381 | Python | 4,694 | 126 | '''
Collects all behavioural data from the
learning task and outputs it to:
./data/preprocessed/beh/all_mt1.csv
OPTIONS:
None
EXAMPLE USAGE:
python beh_preprocess_mt1.py
'''
import numpy as np
import pandas as pd
import mne, os, sys, copy
import auxiliary as aux
import data, rsa
import gzip, pickle, tim... |
cd57b87723e7e84453bb2cdf190d5b196940dff4ddfb21b42cbdb71c1be2b845 | Python | 4,695 | 103 | from pathlib import Path
import pytest
from pytest import approx
from brainscore_vision import benchmark_registry
from brainscore_vision.benchmark_helpers import PrecomputedFeatures
from brainscore_vision.benchmark_helpers.test_helper import StandardizedTests, PrecomputedTests, NumberOfTrialsTests, \
VisualDegree... |
41201d8b46b210fb09f6533c73bcee85d4476e2b8329e3f8538859e001258d34 | Python | 4,697 | 112 | import scipy as s
from .basic_distributions import Distribution
from .bernoulli import Bernoulli
from .univariate_gaussian import UnivariateGaussian
from mofapy2.core.utils import *
class BernoulliGaussian(Distribution):
"""
Class to define a Bernoulli-Gaussian distributions (for more information see Titsias ... |
97d97be27045ebd23350c32529974f2ec6f22e18d7067991fca202f1f1ac4730 | Python | 4,698 | 161 | #!/usr/bin/env python
#
# A script to submit analysis jobs to the SLURM queuing system
#
# Derived from submit_slurm.py
# F. Nedelec, 18.11.2018
"""
Submit a job to the SLURM system to be called in multiple directories
Syntax:
submit_one.py ARG [mem=????] [queue=????] [hours=INT] [days=INT] dir1 [dir... |
2643909a596d3cba8d6c091f7bb39db134bc36f81b367def3a9187365c575297 | Python | 4,699 | 125 | import os
import torch
import torch.nn as nn
import torchvision.models as models
def resnet18_fixed(num_classes):
# Using a pre-trained ResNet model adapted for grayscale images
model = models.resnet18(pretrained=True)
# Modify the first convolutional layer
original_first_layer = model.con... |
543e6168c137e36408da917b496bbac6e28a51bcd9e2b8253d6c2f90244231ea | Python | 4,699 | 91 | import brainscore_vision
from brainscore_vision import load_metric, load_ceiling
from brainscore_core.supported_data_standards.brainio.assemblies import walk_coords, array_is_element
from brainscore_vision.benchmark_helpers.neural_common import NeuralBenchmark
VISUAL_DEGREES = 2
NUMBER_OF_TRIALS = 2
BIBTEX = """@artic... |
de835aee99fda369c9dcb7e88862800fe486183b443abb30fd6a85209c98a6f1 | Python | 4,699 | 131 | # py imports
import os
import sys
import glob
from argparse import ArgumentParser
import time
# third party
import tensorflow as tf
import scipy.io as sio
import numpy as np
from keras.backend.tensorflow_backend import set_session
from scipy.interpolate import interpn
import scipy.ndimage
# project
sys.path.append('.... |
61545143c62c7dd6ab64451d6948ccf25941c0d99e0cda4e0b6e097e52206239 | Python | 4,701 | 109 | #!/usr/bin/env python
# Copyright (c) 2015 Tobias Neumann, Philipp Rescheneder.
#
# This file is part of Slamdunk.
#
# Slamdunk is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
#... |
73b965e6ca2384bb0ddf3da0825153a610ffd206450725d2a721e9a4820151fe | Python | 4,702 | 127 | import pandas as pd
import matplotlib.pyplot as plt
import scanpy as sc
import numpy as np
import os
from typing import List, Optional
from tqdm import tqdm
def plot_spatial_celltypes(
st_visium_path: str,
count_file: str,
library_id: str,
image_path: str,
pred_csv: str,
cell_type... |
7fdec1382a20c1c0ddf0734d4a1ee0a0fe7696afee57754d608fee8ae68eaab5 | Python | 4,702 | 152 | # -*- coding: utf-8 -*-
"""
Created on Mon Sep 18 19:27:36 2023
@author: szh
"""
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
cudnn.deterministic = True
cudnn.benchmark = True
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.m... |
d880eb3661996dcbab1a317ab45ae23d27ee02a38bc60563f93d4a93bc7b0122 | Python | 4,702 | 145 | """Provide helper functions or classes for defining loss or metrics."""
from typing import List, Optional, Union
import tensorflow as tf
from deepreg.loss.kernel import cauchy_kernel1d
from deepreg.loss.kernel import gaussian_kernel1d_sigma as gaussian_kernel1d
class MultiScaleMixin(tf.keras.losses.Loss):
"""
... |
2d412531b91cf45700f04ec4343966dde280d5136c1176a8893d3f8d52b0fb5f | Python | 4,704 | 113 | """Reservoir-computing export matrices from stim-aligned spikes.
Port of STEP 5.1d / STEP 7 / STEP 8 of ``stimActivityAnalysis.m`` (origin/main):
``FiringRateMatrix``, ``latencyMatrix`` and ``ExactSpikeTimes``, each
``[numTrialsTotal x numChannels]`` over the consolidated (all-pattern,
chronological) stim times.
All ... |
d08b2b0384915df552610dbd76abba7e19f53615289754c404b9aa0150ecccb6 | Python | 4,704 | 114 | """Demonstrates how to build models using keywords."""
from nems import Model
from nems.layers import STRF, WeightChannels, FIR, DoubleExponential, WeightChannelsGaussian
from nems.registry import keyword_lib
# View all defined keywords
keyword_lib.list
########################################################
# Usin... |
16eaad232220386f510ca8dfab8655e63699977149a4daf9ce9c6f374e6777a9 | Python | 4,706 | 134 | # 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.
from typing import List, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
class ImageEncoder... |
2a577584cf1cb8137563c4d06a0d515026ca1880bf44b9d6f9c2e3b29decaa57 | Python | 4,706 | 145 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
import sys
import datetime
# external inputs
import numpy as np
import nibabel as nb
from nibabel.freesurfer.io import read_geometry, write_geometry
from nighres.laminar import profile_meshing
from gbb.io import get_filename
# local inputs
from ..io.... |
7126828e96178cafb0fc9c210ec9233b016cae933e61616ea574978239b018f5 | Python | 4,706 | 90 | from pathlib import Path
import pytest
from pytest import approx
from brainscore_core.supported_data_standards.brainio.assemblies import BehavioralAssembly
from brainscore_vision import benchmark_registry, load_benchmark
from brainscore_vision.benchmarks.baker2022 import DATASETS
from brainscore_vision.benchmark_helper... |
137d5e3c97fd7b1d52aa6480c2e08cfa9eccc01c8bc99e7baac81b9e61d521d0 | Python | 4,707 | 124 | # /------------------------------------------------------------------------------+
# | 19-JUL-2022 |
# | Copyright (c) Bone Imaging Laboratory |
# | All rights reserved ... |
9c250df3d1186f7c0e01c806aad0ad6a3c3223e97e9d58a98c7a5e95372e1c37 | Python | 4,707 | 163 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import os
import tempfile
import unittest
from types import TracebackType
from unittest.mock import patch
from datumaro.components.dataset import StreamDataset
from datumaro.util.os_util import rmfile, rmtree
from cvat.apps.dataset_manager.bindings... |
b7dd95ef4333133d968fa906c9ea271d217c86dedaea757bee2771188330f123 | Python | 4,707 | 132 | """Persistence adapter for direct main-agent subgraph invocations."""
from __future__ import annotations
import logging
import uuid
from typing import Any
from langgraph.errors import GraphInterrupt
from chemgraph.memory.serialization import serialize_messages
from chemgraph.memory.store import SessionStore
_RUN_... |
d7e4bf307fc30a5ba48613bc78546acbd8b6007969084cb50ff0060d16ca1e3a | Python | 4,707 | 162 | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... |
f4b3f07edcb6c37c6eb57a741c48f99c9ff063a92acb3af8cd20e9497ad9bf41 | Python | 4,707 | 132 | # -*- coding: utf-8 -*-
"""
Make layer
This script computes equidistant layers between a white and a pial surface. The
following steps are done:
(1) match vertex numbers of both surfaces
(2) extract main component to remove unconnected parts
(3) inflate white surface for visualization
(4) compute meshl... |
7cbee4b2935be000521e051b99a9941c84841ee8d0416ffcb83c5e22322f1f91 | Python | 4,709 | 147 | """Shared model-loading utility for ChemGraph.
Provides a single ``load_chat_model`` function that detects the endpoint for a
given model name and returns a LangChain ``BaseChatModel`` instance. This is the
only model-routing entry point; the pipeline is::
caller -> load_chat_model -> endpoint spec -> protocol bu... |
cc0f2745dbb299070e41404690630e4fc4bc849a9ff01efbd5492ea1f1b05673 | Python | 4,709 | 101 | import numpy as np
from scipy.stats import spearmanr
from brainscore_core.supported_data_standards.brainio.assemblies import DataAssembly, walk_coords, NeuroidAssembly
from brainscore_core.metrics import Metric, Score
from brainscore_vision.metric_helpers import Defaults as XarrayDefaults
from brainscore_vision.metric... |
b940f160ee0bfcca03dc8a6220f35036a5e5c55be062d333a0307f47b1262985 | Python | 4,710 | 113 | import numpy as np
from scipy import stats
from utils import Recorder, Q_nak
from mitochondria_PDP1KD import Mito
import matplotlib.pyplot as plt
from figure_properties import *
dt = 0.01
def regular_spiking(sp_hz, spike_dur, q, t_start=150, t_run=1000, t_ca=5000, ca_fact=0.1):
time = t_run + t_start + spike_dur... |
3c79f480908dace646ae7d1bde27f8dde5af94c209bbec2358b28f3aaf271ec7 | Python | 4,711 | 122 | import torch
import torch.nn.functional as F
from monai.losses import DiceLoss, SSIMLoss, GeneralizedDiceLoss, FocalLoss, DiceCELoss
from torch.nn import CrossEntropyLoss
class DiceSSIMLoss(torch.nn.Module):
def __init__(self, beta=0.5, sigmoid=True, class_weights=None):
super().__init__()
self.be... |
b7daa5b7172aae8b86ee9aba9dc7ec87cba75c724cce33bb94ecb68d76fb1175 | Python | 4,713 | 90 | #!/usr/bin/env python3
#
# make_datasheet.py
#
# Datasheet (CSV) for EXTRACT_Profile from a reference dataset organized as <subject>/<session>/...: one row per scan,
# with the case id and one column per image. Each --column is NAME=PATTERN, the pattern a glob relative to the session
# folder. The first column ... |
d76b7fc00996b8938cfb2170462a7ef4228d149fed4912721e62bd8bf2c5aa0d | Python | 4,713 | 107 | """
Helper functions for building the model.
"""
import numpy as np
import pandas as pd
import torch
from torch import nn
def np_to_torch(arr: np.array, dtype: torch.float32, device: str = 'cpu'):
"""Convert a numpy array to a torch.tensor
Parameters
----------
arr : np.array
dtype : tor... |
ab8c42b7b2702b2a0a4c798d875a0ab627e466ea8111e28ec5c9f028ba9d53cd | Python | 4,716 | 117 | """
HMM: Multitaper Spectra
=======================
In this tutorial we calculate subject and state-specific multitaper spectra using a trained HMM and **source space** data. Here, we should **not** use the prepared (e.g. TDE-PCA) data.
"""
#%%
# Download source data
# ^^^^^^^^^^^^^^^^^^^^
# In this tutorial, we'll d... |
99ef04a3faabf62e1ed7315c8e3affd3bfee0a5798d7d89d6ce7b7de98bc892e | Python | 4,717 | 139 | from __future__ import annotations
from typing import ClassVar, TypedDict
import polars as pl
from argclz import argument, try_int_type
from neuralib.atlas.ccf import RoiClassifierDataFrame
from neuralib.atlas.ccf.dataframe import ROIS_NORM_TYPE
from neuralib.atlas.typing import TreeLevel, Area, Channel, Source
from... |
a16c53acd5b2bdb4d33f0ceab17c609eefe27fcb45f8706a3d7d832765e8f871 | Python | 4,717 | 126 | # -*- coding: utf-8 -*-
from flair.parser.modules.dropout import SharedDropout
import torch
import torch.nn as nn
from torch.nn.modules.rnn import apply_permutation
from torch.nn.utils.rnn import PackedSequence
class BiLSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers=1, dropout=0):
... |
b09ef33cb5c683b661caa3470b39675cbb87a76022c460ed67047cddc0dce7d3 | Python | 4,717 | 124 | # Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
# Adapted from https://github.com/stackav-oss/conch/blob/main/conch/kernels/vision/nms.py
import torch
import triton
import triton.language as tl
@triton.autotune(
configs=[
triton.Config({"cxpr_block_size": 128}),
triton.C... |
18d2af9a8a0965f9147825de9b85779c5fb4b3b97b41893fcf21eb1e7ead6cbc | Python | 4,718 | 199 | # The ImpedanceFitter is a package to fit impedance spectra to equivalent-circuit models using open-source software.
#
# Copyright (C) 2018, 2019 Leonard Thiele, leonard.thiele[AT]uni-rostock.de
# Copyright (C) 2018, 2019, 2020 Julius Zimmermann, julius.zimmermann[AT]uni-rostock.de
#
# This program is free ... |
2e1de8ab9af6bec5d41865aacadfbc35f56bc26d555878d21df3c15face85db2 | Python | 4,718 | 150 | #!/usr/bin/env python2.7
# -*- coding: utf-8 -*-
import os
import sys
from glob import glob
def read_ml_inputs(ml_file):
if not os.path.isfile(ml_file):
print("Error: {} not found. Exiting.".format(ml_file))
sys.exit(1)
lines = []
with open(ml_file, 'r') as f:
for line in f:
... |
9baea83aea51b24c199f3cdd7e53fff8994e70544d0ef57b25d93cd7fd0579be | Python | 4,718 | 128 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Extract B0s from DWI, based on the bval and bvec information.
The default behavior is to save the first b0 of the series.
"""
import argparse
import logging
import os
from dipy.core.gradients import gradient_table
from dipy.io.gradients import read_bvals_bvecs
imp... |
a4224c66a7207d1fd97379e501a0d5df2382a9f1b838af89c82669562501b0d1 | Python | 4,720 | 108 | import argparse
import utils
import plotting
import keras
from keras.models import load_model
from os.path import join
import numpy as np
import gc
'''
Use ensemble of DeepSTARR models to make predictions on STARR-seq data and calculate ensemble average
if --eval flag set, evaluates ensemble average prediction perform... |
c022f9c0c153ac3dd4058ec3c06b33d1832af3ed71c27cab562417c0d21f5da5 | Python | 4,720 | 107 | """
January 2022 by Paulien Voorter
p.voorter@maastrichtuniversity.nl
https://www.github.com/paulienvoorter
requirements:
numpy
tqdm
scipy
"""
# load relevant libraries
from scipy.optimize import curve_fit
import numpy as np
import tqdm
def two_exp_noS0(bvalues, Dpar, Fmv, Dmv):
""" bi-exponential IVIM functi... |
e132e7b4b26c4efa2dc8d482ff062d3a76c5b9f1fdbc01209cc509b16be2140b | Python | 4,720 | 159 | # -*- coding: utf-8 -*-
"""Behavioral measures."""
import os
import sys
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import rc
from scipy.io import loadmat
__all__ = ["analysis_fixation", "get_onset_vols"]
# font parameters for plots
rc("font", **{"family": "serif", "serif": ["Palatino"]})
rc(... |
f660b25ebf80c2ca46997b5d6e9a0b6c4d9f54b48ab2985e18d6dcc1b3c9cae6 | Python | 4,720 | 174 | import io
import json
import os
import re
from importlib.resources import files
import cloudfiles
import pandas as pd
from skeleton_keys.database_queries import (layer_polygons_from_database,
pia_wm_soma_from_database,
query_for_i... |
bb1d09460d10b98b2522aeacdc7b582673862a08873ba4e38ebcecdb88fd0fe5 | Python | 4,721 | 103 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.neighbors import LocalOutlierFactor
from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder
from sklearn.compose import ColumnTransformer
from sklearn.decomposition import PCA
from sklearn.mod... |
6f9ed7bcd7a4ad46060e33499467aabda306349987c28a52174ff2c815a52ee0 | Python | 4,722 | 122 | import torch
from torch import Tensor
from torch.nn import BatchNorm1d, Linear
class DiffGroupNorm(torch.nn.Module):
r"""The differentiable group normalization layer from the `"Towards Deeper
Graph Neural Networks with Differentiable Group Normalization"
<https://arxiv.org/abs/2006.06972>`_ paper, which n... |
7025718e9fba1cbd48deec823edbdf348d0935067f6dce7a33ec9c4616561d03 | Python | 4,722 | 117 | """The three pipelines MEA-NAP ships, and which GUI tabs each one needs.
MEA-NAP is really three pipelines sharing one window, and each reads a different
slice of the settings. Showing every tab regardless means most users spend most
of their time looking at settings their run will never read — worse, at settings
that... |
acbf59d809f7b3534b032fbec2ae4b7fbaaf663483dde21f5ea6c24febd25d96 | Python | 4,722 | 135 | # !/usr/bin/env python
# coding: utf-8
# @Time : 2022/7/17 13:54
# @Author : Z.-H. G.
# @Email_0 : guozhenhao17@mails.ucas.ac.cn
# @Email_1 : guozhenhao@tongji.edu.cn
# @File : params.py
# @IDE : PyCharm
import pandas as pd
# TODO dict? yaml?
# all query
PARAMS = \
{'preprocess':
{
... |
8ede15ed1a0da67cbbd52521b041dde17cf70b09e13cf2ade920a8568650fb9c | Python | 4,726 | 130 | """Probabilistic edge thresholding, port of ``adjM_thr_parallel.m``.
Generates synthetic spike trains by circularly shifting each channel's real
spike times by a random offset, recomputes STTC on the shuffled data, and
zeroes out any edge whose real STTC value doesn't clear the ``tail``
significance cutoff across ``re... |
857510b5f9bb487429da57b4b49b9344840b87a2d2fd0d7ed5370fd7a2422414 | Python | 4,729 | 140 | import argparse
import logging
import os
import sys
import uvicorn
from mcp.server.fastmcp import FastMCP
def run_mcp_server(
mcp: FastMCP, default_port: int = 8000, default_host: str = "127.0.0.1"
):
"""Standardize the startup process for ChemGraph MCP servers.
Supports 'stdio' (default) and 'sse' (HTT... |
3a32d4a8ee6f31fce263e25c947b84c17f64f6f657f22eae47c3c80a162de575 | Python | 4,730 | 190 | #!/usr/bin/env python
# coding: utf-8
# In[ ]:
"""
AGED: Attention-Guided Enhanced Deconvolution
A novel deep learning framework for spatial transcriptomics deconvolution
that combines probabilistic initialization with multi-scale attention mechanisms.
"""
__version__ = '0.0.1'
__author__ = 'Xiao Yang'
__email__ =... |
69e3236a21e37e53a9e68947c9941fe7a22a7949b57b788ba98e58508dbd3b72 | Python | 4,731 | 145 | #!/usr/bin/python
# -*- coding: latin-1 -*-
# =============================================================================================
# Librairies importation
import numpy as np
import nrrd
import sys
import os
import multiprocessing as mp
# ===================================================================... |
172d0a01d0c24c7b2b5a178caf7a2cbdc3e6c146772e442b518397e451f35ac3 | Python | 4,732 | 111 | import numpy as np
from dipy.core.gradients import gradient_table
from src.wrappers.OsipiBase import OsipiBase
from src.original.fitting.IAR_LundUniversity.ivim_fit_method_subtracted import IvimModelSubtracted
class IAR_LU_subtracted(OsipiBase):
"""
Bi-exponential fitting algorithm by Ivan A. Rashid, Lund Uni... |
1b1590307cc2f299134fd8a441a4c62b0bc32cf2b1706a9f39b61927f7c80fd4 | Python | 4,733 | 126 | """
Adjusted version of script from original MRI2FEM-book according to below license.
'''
Copyright (c) 2020 Kent-Andre Mardal, Marie E. Rognes, Travis B. Thompson, Lars Magnus Valnes
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "... |
67c39de12d06dd41adbd14fad3c93d19a979b4a268d3eefae4ab52f67df4483e | Python | 4,733 | 80 | #! /usr/bin/env python
"""Generate a cropped mesh from a point cloud using pymeshlab and the screened poisson method.
Usage: xyz2ply.py cloud.xyz mesh.ply"""
__author__ = "Benjamin Barad"
__email__ = "benjamin.barad@gmail.com"
__license__ = "GPLv3"
import math
import click
import pymeshlab as pm
@click.command()
... |
e795f15c14af0cfcb8d71c83378c954665b855663d2f7b0d6b304223bdce01ed | Python | 4,733 | 93 | import nighres
import os
import nibabel
import numpy
import glob
inpath = '/home/public/InSitu/Processed/insitu_08_122017_xx_xx/release/'
outdir = '/home/pilou/Projects/Cerebellum/Cerebellar-segmentation-ABC/ahead/coreg/'
bf_img = inpath+'Ahead_brain_122017_blockface-image.nii.gz'
thio_img = inpath+'Ahead_brain_1220... |
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