sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
adacdbe2ec67c295c5552c1d5ea36567324dc0c15c5352a9e2ecf98ddc789adf | Python | 5,611 | 166 | from pathlib import Path
from typing import TYPE_CHECKING
import numpy as np
from tests.setup_tests.setup_modelling_test_data.setup_targets_test_data import (
get_current_test_label_values,
get_test_label_file_fieldnames,
set_up_label_file_writing,
set_up_label_line_dict,
)
from tests.setup_tests.setu... |
e4beb391fbab93b9241ad338fbe8348ada1ab7bd0019fb05f90fde87f3441391 | Python | 5,612 | 145 | # 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.
import torch
def get_fused_adam_class():
try:
global unicore_fused_adam
import importlib
... |
cb16e0afdf73f9528ceab7d2e32cf63138c0c7442a4caab4538792f68d4ad56c | Python | 5,615 | 153 | import torch.nn as nn
import numpy as np
import torch
from cvdproc.pipelines.external.qqnet.basics.utils_u_b import unetConv3d, unetUp3d
class unet3d(nn.Module):
def __init__(self,
feature_scale = 2,
n_classes = 5,
is_deconv = True,
in_channels = 9,
is_batchnorm = Fal... |
8d26141f76e62fc0f47bee05dc65b2b750cf8cbdc0a1d10cc5d06b11a01c8cba | Python | 5,618 | 140 | # wfield - tools to analyse widefield data - tools to match between different sessions
# Copyright (C) 2020 Joao Couto - jpcouto@gmail.com
#
# 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, eit... |
eb7ee818579338d7517870934b5da3fd96e394e283259725c4e039bffc131433 | Python | 5,620 | 160 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Compute summary statistics from multiple per-file metrics tables and export a single summary CSV.
Assumptions
- Each input file contains the same core columns as the original pipeline output:
area, Donor_mean_intensity, Acceptor_mean_intensity, FRET_mean_inte... |
fd0ab80f20f9918f3785890e792acb8754d683366ecd30a24eb0f17364e78a53 | Python | 5,625 | 157 | # Copyright 2024 Google LLC
#
# 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, ... |
e29ff842b2488fadfc486d80c8a6f90f4bdb002498e51ecd738a7a5992608dc8 | Python | 5,626 | 125 | import os
from pathlib import Path
import numpy as np
import pytest
import xarray as xr
from pytest import approx
from brainscore_core.supported_data_standards.brainio.assemblies import BehavioralAssembly
from brainscore_vision import benchmark_registry, load_benchmark, load_metric, load_model
from brainscore_vision.... |
1880a2f623b54752b5a260794572779fbc25a2df7af3f88f6dafc53d90ec1c16 | Python | 5,627 | 175 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import csv
import json
import os
import random
import sys
from itertools import product
NAME = "lambda"
def read_rules(name):
rules = []
with open(os.path.join(sys.argv[1], f"{name}.csv")) as f:
reader = csv.DictReader(f)
f... |
1e6282d0db9bc44f7890defe1838bb9bbef9f3bc3a318b2aabfbb19717a6df0b | Python | 5,631 | 145 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Script to combine labels from multiple volumes. If there is overlap, it will
overwrite them based on the input order.
>>> scil_labels_combine out_labels.nii.gz
--volume_ids animal_labels.nii 20
--volume_ids DKT_labels.nii.gz 44 53
... |
874c3a956c6b14a940056209ffb146038be25fb0773491b817f7421c26b3e10c | Python | 5,633 | 119 | """
@author: GM
@edited: CD
Age prediction of brain images convolutional neural networks. The script briefly:
1. Takes a single unprocessed T1-weighted native MRI image in .nii.gz or .nii format.
2. Uses FSL Flirt for a rigid registration (AC-PC alignment) and interpolation
to 1x1x1mm3 voxel size.
3. ... |
94016ea37c7ab99ec573bc3a65187a1aa2052916c2416b3103cd605319f6618c | Python | 5,633 | 124 | # -*- coding: utf-8 -*-
"""
Created on %(date)s
@author: %(PV)s
"""
import pandas as pd
import re
# Opciones de pandas
pd.options.display.max_columns = None
pd.options.display.max_colwidth = None
pd.options.display.max_rows = None
# Función para extraer números
def extraer_numeros(texto):
match = re.match(r'^(\... |
f570d11755739688d754979981f249e50ce8cde4b8f2cdff86f1c95543028b7e | Python | 5,633 | 186 | import cv2
from decord import VideoReader
import numpy as np
from PIL import Image as PILImage
from typing import Tuple, Union
from pathlib import Path
from .base import Stimulus
from .image import Image
from brainscore_vision.model_helpers.activations.temporal.utils import batch_2d_resize
EPS = 1e-9
def get_vide... |
9d47a8b5504d072ea0221a4ed2a822b0f5187f2efe904700e109c4c947fc9729 | Python | 5,634 | 149 | """A module that contains processors for pair-wise distance metrics."""
import typing
from typing import Annotated, Literal, TypeAlias, TypeGuard, get_args
import numpy
from scipy.spatial.distance import pdist, squareform
from corelay.base import Param
from corelay.processor.base import Processor
class Distance(Pr... |
29e44ffe16dbc07554ea4eb8ad7314d59e1c49d5916006f96b4f2a67637e1dc8 | Python | 5,635 | 126 | import os
from datetime import datetime
import numpy as np
header = [b'mrtrix tracks\n', b'count: 0000000000\n', b'datatype: Float32LE\n', b'file: . offset\n']
class Tck:
def __init__(self, file_path):
self.file_path = file_path
self.parameters = {}
self.header = []
self.length ... |
4a4bda83ec7708f4de1dd7bd6b88ec0555f9d37ffa9fc7ee318d97157afb6096 | Python | 5,635 | 172 | import pandas as pd
import numpy as np
import math
import json
import argparse
from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import EditedNearestNeighbours
from sklearn.metrics import roc_auc_score as auc
from sklearn.ensemble import AdaBoostClassifier
def get_accuracy(AL, y, verbose=1):
... |
78eca6a438115566ed7dfd125f6b6448c342653a6d0f64078f5c81781c5416ed | Python | 5,636 | 150 | import argparse
import logging
import os
import glob
import os
import os.path as osp
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from torchvision import transforms
from tqdm import tqdm
from utils.data_loading import BasicDataset
from unet import UNet
from utils.utils import pl... |
3a1174e38a32aab20cdd130eaeb7176e1efd17405e41a44b2abf1b220a29816d | Python | 5,638 | 175 | # This file is part of Zennit
# Copyright (C) 2019-2021 Christopher J. Anders
#
# zennit/torchvision.py
#
# Zennit is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation; either version 3 of the License, or (at... |
7727a59dba4e18c7c7244ad2f91c9cd75c3f7c79a6ad5300c639b68468e73900 | Python | 5,638 | 166 | import numpy as np
import torch
import pickle as pkl
import os
class MotionDataset():
"""
Motion Dataset
"""
def __init__(self, partition, max_samples, delta_frame, data_dir, case='walk'):
if case == 'walk':
with open(os.path.join(data_dir, 'motion.pkl'), 'rb') as f:
... |
96e9356236c9741ebf822e41515d6e5a03fbc3a2a28a29439d986ed5fa5d2174 | Python | 5,638 | 123 | import seisbench.util
from seisbench.data.base import WaveformBenchmarkDataset
class OBS(WaveformBenchmarkDataset):
"""
OBS Benchmark Dataset of local events
Default component order is 'Z12H'. You can easily omit one component like, e.g., hydrophone by explicitly passing
parameter 'component_order="Z... |
c961ed87933744f05a6e2dd33d84318323647b180addd65fae538e3145616cfe | Python | 5,639 | 135 | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def CE_Loss(inputs, target, cls_weights, num_classes=21):
n, c, h, w = inputs.size()
nt, ht, wt = target.size()
if h != ht and w != wt:
inputs = F.interpolate(inputs, si... |
426c2100286853366c4e67770f26a4cf139049af7ef140cf08296b7da16a0060 | Python | 5,640 | 210 | import logging
import os.path as op
import nibabel as nib
import numpy as np
from tqdm.auto import tqdm
from AFQ.data.fetch import afq_home, fetch_multiaxial_models
from AFQ.nn.utils import prepare_t1_for_nn, resample_output
logger = logging.getLogger("AFQ")
__all__ = ["run_multiaxial", "extract_brain_mask", "mult... |
6e8fb449cf1b762a89d050f28db394e6d627d9a80619c8332a6a240e5aee6c51 | Python | 5,640 | 123 | import numpy as np
import GTF
import pandas as pd
import networkx as nx
def gene_correction_map():
# read Gencode https://www.gencodegenes.org/human/
gencode = GTF.dataframe("../bronze/gencode.gtf")
gencode = gencode[["gene_id", "gene_name", "gene_type"]].drop_duplicates().rename(columns={
"gene_... |
e10e8ab1acfad73b3a3b91b7f6a8c5f0e8c3da72e4687fcddd353ca3a7e9431d | Python | 5,640 | 196 | # -*- coding: utf-8 -*-
# @Author : LG
"""
Startup banner for ISAT.
Prints a colored ASCII art logo with version and system information at startup.
The letter colors match icons/ISAT_new_32.svg:
I = red (#DB5C5C), S = gold (#F2C55C), A = green (#5FAD65), T = white
"""
import os
import sys
# ── Color support det... |
ecbcb440cd7e069917c6446ed023809a5924a20c38c6211a100c074affa244b6 | Python | 5,640 | 121 | import numpy as np
def split_train_test_data(data, label, ratio, random_state=42):
"""
Split the data into training and test sets. Ensure that the data within individual trials remain intact.
Parameters:
- data: ndarray, shape (n_samples, cell_number).
Calcium neural activity from no-d... |
c4d73aa46165c03811303176a39cca946d11acc459ba47bc7e2f444f09760b78 | Python | 5,641 | 166 | from unittest.mock import Mock, create_autospec, patch
import numpy as np
import pytest
import torch
from scipy.special import softmax
from sklearn.preprocessing import StandardScaler
from eir.models.output.sequence.sequence_output_modules import (
SequenceOutputModuleConfig as SeqOutputModuleConfig,
)
from eir.s... |
e696f04fde072573257da1a8d5a68222065640f8635440bddced61d50e942dce | Python | 5,641 | 146 | import os, glob, datetime
import numpy as np
import pandas as pd
from scipy.stats import mode
import mne
from tqdm import tqdm
import matplotlib.pyplot as plt
data_paths = glob.glob('asdtdegidata-edf/ASD/*.edf') + glob.glob('asdtdegidata-edf/TD/*.edf')
result_dir = 'sleep_stages'
os.makedirs(result_dir, exist_ok=Tru... |
6b3028856cb087a4083c981dae42927fae700fb9968254f7e557ee87f23a3efb | Python | 5,642 | 151 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
import numpy as np
import nibabel as nib
from scilpy.io.streamlines import save_tractogram
from dipy.io.stateful_tractogram import StatefulTractogram, Space, Origin
tmp_dir = tempfile.TemporaryDirectory()
def init_data():
streamlines = [[... |
908a417179ae6ac80b49d5dbab5cb3f66f5d822bfe2d3fde0e93ac680a731a75 | Python | 5,644 | 181 | # 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 math
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .sam2_utils... |
5a9644447e8f685c25e3e8f2ce298d4247ca474e285cf5261ff1440576796e12 | Python | 5,645 | 153 | import openslide
from fastai.vision import *
from fastai.data_block import *
class SlideContainer:
def __init__(self, file: Path, y, tumortype, level: int = 0, width: int = 256, height: int = 256,
sample_func: callable = None):
self.file = file
self.slide = openslide.open_slide(s... |
7cf50d93ca5a08f1144875a842d09c13e63c605de64c74d3334d1a6584bcf762 | Python | 5,645 | 151 | # Copyright 2024 Google LLC
# 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, sof... |
881c4dedc46533bd7a71570d655542b2c9b7c7eec9d6f605471052a157a801de | Python | 5,645 | 155 | from src.wrappers.OsipiBase import OsipiBase
import numpy as np
import os
from super_ivim_dc.train import train
from pathlib import Path
from super_ivim_dc.infer import infer_from_signal
import warnings
class Super_IVIM_DC(OsipiBase):
"""
Supervised deep learnt fitting algorithm by Moti Freiman and Noam Korng... |
c7c1fecefe8c367c5013e120d4780df18c6cb6ca43736fe2fa206b16eeb8a1c8 | Python | 5,645 | 351 | import os
import numpy as np
import matplotlib.pyplot as plt
DIR = os.path.dirname(os.path.realpath(__file__))
train_loss = [
1.011386 ,
0.991767 ,
0.984118 ,
0.979690 ,
0.976605 ,
0.974293 ,
0.972522 ,
0.971151 ,
0.970065 ,
0.969218 ,
0.968511 ,
0.967941 ,
0.96743... |
2dd9a6e1e2860544f5793299aecd2754e81a79f08d9fa350baa3b3c6dcb60592 | Python | 5,646 | 172 | import argparse
import torch
import torch.nn.functional as F
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 __init__(self, in_channels, hidden_chann... |
65cc3be4136e6e6fe2cb2a07afae2fa8af2cba912e0cce735cd629799572e293 | Python | 5,646 | 172 | import unittest
import numpy as np
from scipy.sparse import csr_matrix
from numpy.testing import assert_array_equal
def find_loc(num_list, target):
l = 0
r = len(num_list) - 1
while l <= r:
mid = (l + r) // 2
if num_list[mid] > target:
r = mid - 1
else:
l = ... |
76532924817c9a1d89013f6d0d7b0aa5673ea17bccbed16111ae966359b4478c | Python | 5,646 | 181 | # The ImpedanceFitter is a package to fit impedance spectra to
# equivalent-circuit models using open-source software.
#
# Copyright (C) 2023 Julius Zimmermann,
# julius.zimmermann[AT]uni-rostock.de
#
# This program is free software: you can redistribute it and/or modify
# ... |
f0ccebc69fd5ea8fc1a5945a6be8477aa6e09ece3a01360a9b5c1cf3c3f2a31e | Python | 5,648 | 234 | import numpy as np
import hipsternet.utils as util
import hipsternet.constant as c
import hipsternet.regularization as reg
from hipsternet.im2col import *
def fc_forward(X, W, b):
out = X @ W + b
cache = (W, X)
return out, cache
def fc_backward(dout, cache):
W, h = cache
dW = h.T @ dout
db ... |
3752ad3ed5aad2c84055b5f08dc14594a87a7091282686ed0e98b57cba6e06ea | Python | 5,649 | 161 | from typing import get_args
import numpy as np
from argclz import AbstractParser, argument
from neuralib.plot import plot_figure
from rscvp.spatial.main_cache_occ import ApplyPosBinCache
from rscvp.util.cli.cli_output import DataOutput
from rscvp.util.cli.cli_selection import SelectionOptions
from rscvp.util.util_tri... |
d8960c382e33411e14e74d86cd5cfdb9bf745a98a716e9a045b516b2893fa712 | Python | 5,650 | 121 | import numpy as np
def split_train_test_data(data, label, ratio, random_state=42):
"""
Split the data into training and test sets. Ensure that the data within individual trials remain intact.
Parameters:
- data: ndarray, shape (n_samples, cell_number).
Calcium neural activity from no-d... |
9fa94cdf62fa97edd6e97d6c9220762ae8d2579f4592d5bbc8edbedd27a8a030 | Python | 5,651 | 147 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Script to resample a dataset to match the resolution of another
reference dataset or to the resolution specified as in argument.
This script will reslice the volume to match the desired shape.
To:
- pad or crop the volume to match the desired shape, use
sci... |
9d1df2c6763debfb8408f77e5d5bc67d41bbe1b3c5f66ef5d21d3744e48b01af | Python | 5,652 | 159 | from dataclasses import dataclass
from typing import TYPE_CHECKING, Literal
import torch
from torch import nn
from eir.models.fusion.seq_out_fusion_attention import MetaSequenceFusion
from eir.models.input.sequence.transformer_models import (
BasicTransformerFeatureExtractorModelConfig,
parse_dim_feedforward,... |
d61a53669e03fb8c673e4af98cdbe1f54ecedd0a0e136ad229b778c8a75924da | Python | 5,652 | 158 | import copy
import numpy as np
import pandas as pd
from tqdm.auto import tqdm
from scipy.spatial.distance import cdist, pdist
import warnings
from random import choice
def get_random_point(feas_func, rng, com_step=0.1/9):
while True:
# Composition random generator
xyz = rng.random(3)
if su... |
e970d02056d14f7485459bc9a8aca1dd836408bd544382d2bf4694f2ee958ccb | Python | 5,652 | 128 | from dipy.io.streamline import load_trk
from fury.colormap import create_colormap
from fury import actor, window, colormap as cmap
import matplotlib.pyplot as plt
import nibabel as nib
import numpy as np
from os.path import join as ospj
import pandas as pd
from scipy.ndimage import gaussian_filter, zoom
###########... |
472e2aefb4ee7c5be0a82bfc1183a503955bc8ee1502af753670eef558604b0a | Python | 5,653 | 146 | # -*- coding: utf-8 -*-
"""
Finds the difference in point values between two files.
Difference for all arrays in file calculated unless list of array names given by 'array_name' parameter
A .vtk file with material id and differences between properties at idx
"""
import numpy as np
import vtk
from os.path import exis... |
ee9259f0b21a912fd7ec7e0184ba378bd2510f41f52572cae77541c6dc5b5a4d | Python | 5,653 | 157 | # Copyright 2024 Google LLC
#
# 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, ... |
22d95eaefa213b264ddf3d48d40ecc885b850ada07e54d7547ae80e5f056fa87 | Python | 5,655 | 167 | 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... |
25f0ce61e327811e1ab09cb6dbf8dc375fe7e403c4e15468e76ca2d14533c9b3 | Python | 5,656 | 152 | """Do the confirmation buttons sit in the same place on every platform?
Qt orders dialog buttons per platform: Windows and most Linux desktops put the
affirmative button first, macOS puts it last and focuses the other one. So the
"Reset all parameters to defaults?" dialog behind **New** offered Yes on the
left on Linu... |
154e5dec2d4337a982a6e133c86264b4f5d0c95b81bfaad401a9b0bf47d6486d | Python | 5,658 | 146 | """Per-window signal-quality indices driving the reliability gate.
Each index is in [0, 1] and is computed from the raw stream alone -- never from
the label -- so it is available at inference time in a deployed system.
"""
from __future__ import annotations
import numpy as np
from .eda import EDA_MAX_US, EDA_MIN_US
... |
291c3f00df00fee59838d3cc709daaadb7a796a2c8d2e3cae0c7920fad02c9d6 | Python | 5,658 | 167 | """Direct OpenAI endpoint and the shared OpenAI-compatible kwargs assembly.
``assemble_client_kwargs`` is the single place that reproduces the two
parameter shapes the previous ``load_openai_model`` produced -- the
``base_url``-present shape (used by Argo, ALCF, vLLM, custom endpoints) and the
plain OpenAI shape. Ever... |
d086ee2ad20d53ebd3c7a41eadc83e5c2bb2bfb51fc835f891f823ab30246f6a | Python | 5,658 | 125 | from pathlib import Path
import numpy as np
from brainscore_core.supported_data_standards.brainio.assemblies import BehavioralAssembly
from brainscore_vision import load_metric, load_stimulus_set, load_dataset
from brainscore_vision.benchmark_helpers.screen import place_on_screen
from brainscore_vision.benchmarks imp... |
15a5902b8f8609f4c23f7e2ec9e34d58a36117d2ac26e7ccb1e4872386f27be5 | Python | 5,659 | 173 | """resnet in pytorch
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
Deep Residual Learning for Image Recognition
https://arxiv.org/abs/1512.03385v1
"""
import torch.nn as nn
class BasicBlock(nn.Module):
"""Basic Block for resnet 18 and resnet 34
"""
# BasicBlock and BottleNeck block
... |
b7aff4d83a4eeb40a0fba83ceb03feb1a92fb7f9643c523f1ddebe8cd1aac2c9 | Python | 5,659 | 153 | # fmt: off
# flake8: noqa
"""Config."""
import argparse
import os
def parse_configs():
"""Parse command line."""
default_eval_config = get_default_eval_config()
default_eval_config["DISPLAY_LESS_PROGRESS"] = True
default_dataset_config = get_default_dataset_config()
default_metrics_config = {"MET... |
a9cd83e3807b5813da244216e90b078d0c05e16d5a9337018fee8ae5cfdd87d5 | Python | 5,660 | 146 | import h5py
import os
import numpy as np
from tqdm import tqdm
import torch
from torch_geometric.data import InMemoryDataset, extract_tar
from mlcg.utils import download_url
from mlcg.data import AtomicData
class QM7XDataset(InMemoryDataset):
"""Extractor for the QM7X dataset described in https://doi.org/10.1038... |
1758b3759df402fe5a193ac4c1caa09e91bd6e018873794703269a2fe220efb6 | Python | 5,661 | 169 | from __future__ import annotations
from pathlib import Path
import pytest
from PySide6.QtWidgets import QMessageBox
from pytestqt.qtbot import QtBot
import labelme._app
from ..conftest import close_or_pause
from .conftest import MainWinFactory
from .conftest import draw_and_commit_polygon
from .conftest import show... |
2a5dcefd8bb7cfc81c27204f5390708350f77be6752f35919a4b6a79b85cde10 | Python | 5,661 | 132 | import torch
import argparse
import triton
import triton.language as tl
#code based on triton matmul example, modified for branched matmul with ReLU and sum over branches.
@triton.jit
def matmul_kernel_original(
a_ptr, b_ptr, c_ptr,
M, N, K,
stride_am, stride_ak,
stride_bk, stride_bn,
... |
6ad4fb1ba7f824107e53151bd3c8bebd3ef514b7864becba59a382c98936acc1 | Python | 5,661 | 212 | import os
import tarfile
import requests
import pandas as pd
from path import Path
AUDIO_SR = 16000
AUDIO_LENGTH = 16000
LIBROSA_AUDIO_LENGTH = 22050
EPOCHS = 25
categories = {
"stop": 0,
"nine": 1,
"off": 2,
"four": 3,
"right": 4,
"eight": 5,
"one": 6,
"bird": 7,
"dog": 8,
"n... |
f613384109beb4d851f8cefccd5e1d6559c723deed3e21463f840bd6fdb77e3c | Python | 5,662 | 167 | from pathlib import Path
from typing import Final, Literal
import numpy as np
from argclz import try_int_type, argument
from neuralib.plot import plot_figure
from neuralib.util.verbose import publish_annotation, print_load, print_save
from rscvp.spatial.main_cache_occ import PosBinActCache, ApplyPosBinCache
from rscv... |
255a3fa12ee7d125d6419dfe0912f66b3af1c512daa62db00460840e0d0f33cb | Python | 5,663 | 199 | SYSTEM_PROMPTS = {
"code_generator": """You are an expert in electrochemical engineering and scientific computing.
Your task is to generate clean, executable Python code for fuel cell and battery modeling.
Generate code that:
- Uses NumPy and SciPy for numerical operations
- Follows physics constraints (dimensional... |
7ef994327421b5b5391303a56562b578391c1a079a972cff91df6d28214a6c9a | Python | 5,664 | 168 | """Data aggregation functionality."""
import numpy as np
import torch
from rich.progress import track
from torch.utils.data import DataLoader
from mcmlnet.experiments.data_loaders.utils import get_image_id, get_subject_id
from mcmlnet.utils.load_configs import label_is_organic
from mcmlnet.utils.logging import setup_... |
7b37cc7ca535e086a5c3d60e6c5293aa7596684f3131f38724fd7740dc1f6503 | Python | 5,665 | 157 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
import shutil as sh
# external inputs
import nibabel as nb
from scipy.ndimage.morphology import binary_erosion
from nipype.interfaces.fsl import BET
from nipype.interfaces.freesurfer.preprocess import MRIConvert
from nighres.registration import apply_... |
ff712c39e37229068b9522f225b50cbee53939060f6e92903938e093b1d71fd7 | Python | 5,665 | 204 | """Temporary scoring functions. Needs a lot of work. DEPRECATED"""
from itertools import groupby
import numpy as np
def scoreOrderND(hmm, state_sequences):
"""Compute order score with no adjacent duplicates in state sequences
A score of 0 means there's only one state.
"""
scoresND = [] # scores w... |
f679a0f55a1a30f808e61c320e5cf49efce6bac86ecc8c0392af34abebec9f69 | Python | 5,666 | 143 | # 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... |
5a32e5099b5a886476360330a6079c6069455e8a5b0facb30709fc469a02f094 | Python | 5,667 | 146 | from dataclasses import dataclass
from typing import Literal, Optional
from Bio import SeqIO
from utils import TaxaLevel, sample_valid_fragments
import numpy as np
import os
TaxRank = Literal["superkingdom", "kingdom", "phylum", "class", "order", "family", "genus", "species"]
class Fasta_DataLoader:
'''
clas... |
ad936d028dbaf9251b5de7d0b307a41d44ca1a82cd7c16e9c9dd0e18ba737adc | Python | 5,668 | 154 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2023/6/3 15:23
# @Author : SONG-LE
# @知乎 : https://www.zhihu.com/people/xiao-xue-sheng-ye-xiang-xie-shu
'''
封装画图的算法
'''
import matplotlib.pyplot as plt
import numpy as np
import os
import scienceplots
# 判断图片目录是否存在
fdir = r'E:\Projects\WMH_segmentation\derivatives... |
c3feb12941278bef84cca8db21da2fb340a023670466cac87162f4d00c826eaa | Python | 5,669 | 166 | """Defines functions for the DrugBank approved reference set."""
from collections import defaultdict
from functools import lru_cache
from pathlib import Path
import pandas as pd
from admet_ai.admet_info import get_admet_id_to_name
from admet_ai.constants import (
DEFAULT_DRUGBANK_PATH,
DRUGBANK_ATC_NAME_PREF... |
e5dc772365499699f8f41cd7d2cbd8351dbfbf0ae3a6eeef97621b96772cc8ab | Python | 5,669 | 182 | import reprlib
from collections import defaultdict
from collections.abc import Callable, MutableMapping
from functools import partial
from inspect import signature
from typing import (
TYPE_CHECKING,
Any,
)
import torch
from adabelief_pytorch import AdaBelief
from aislib.pytorch_modules import AdaHessian
from ... |
6449448aac3ad2ce97d790435a592b380702501a4817f83ed8284b586f06a1ab | Python | 5,670 | 148 | import os
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset, random_split
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
import argparse
from model_options import TransformerClassifier, load_compatible_state_dic... |
62d31f206294f68fe36800cb89fd1096a82c2d42cea835449ff9169e7150c3aa | Python | 5,671 | 129 | # Copyright 2021 HIP Applied Computer Vision Lab, Division of Medical Image Computing, German Cancer Research Center
# (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy... |
bdedd314fd1a834f913d73fadb64ffdac47d57f06685568ebfa036a88e6bf4e8 | Python | 5,673 | 177 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
#
# Copyright 2022 The NiPreps Developers <nipreps@gmail.com>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may... |
539d2acdeea591de0a164293ebc1ebab6d6e0de992a23ec52466801ab1abca6c | Python | 5,674 | 155 | import typing
import functools
import logging
from ..config import *
from ..qt import *
from ..util import NodeSelector
from ..task_controller.util import create_task_with_exc_handling
from .. import thalamus_pb2
from .. import thalamus_pb2_grpc
from ..observable_item_models import FlatObservableCollectionModel
LOGGE... |
e59fde77f82cf032517561758bdb442fd89b3b71185bcd89f0f787bd2d008554 | Python | 5,674 | 205 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
from torch_geometric.nn import GINConv
from torch_geometric.nn import SAGEConv
from parms_setting import settings
args = settings()
class Discriminator(nn.Module):
def __init__(self, n_h):
super(Disc... |
07166ecac2b6543464abd795c083ec62dffac7802061862a06fad122976fcf16 | Python | 5,675 | 152 |
""" Classes and functions for fitting ivim model """
import numpy as np
from scipy.optimize import curve_fit
from dipy.reconst.base import ReconstModel
from dipy.reconst.multi_voxel import multi_voxel_fit
from dipy.utils.optpkg import optional_package
from scipy.signal import unit_impulse
class IvimModelsI... |
3214318fab22d2dbb9313a25cadf8073fd839dd153373b330ad2a71a87ffb8ec | Python | 5,676 | 198 | from __future__ import annotations
from collections.abc import Callable
from typing import Final
import numpy as np
import osam
import pytest
from numpy.typing import NDArray
from labelme._automation._osam_session import OsamSession
_IMAGE: Final[NDArray[np.uint8]] = np.zeros((2, 3, 3), dtype=np.uint8)
_POINTS: Fin... |
2a03083f090582b283fe2a2bc6d989cf45767d84c7e84f8d80386998b0dca0ea | Python | 5,677 | 141 | """Compare a spike-*sorted* run of MEA-NAP with a *detected* run of the same
recordings: did making neurons the nodes change the network?
uv run python python/compare_sorted_vs_detected.py <detected_root> <sorted_root> [--out fig.png]
Writes one figure and prints one table:
- **units per electrode** — how the no... |
dede4dedce8ed45ed5eb0e6fc7882bb9fc8b58f17200273db3f1c6f19327f4b8 | Python | 5,681 | 175 | import typing
from pathlib import Path
import pandas as pd
import polars as pl
from neuralib.typing import DataFrame
from neuralib.util.verbose import fprint, print_load
from rscvp.spatial.main_place_field import split_flatten_lter
from rscvp.util.util_dataframe import check_null, to_numeric
from rscvp.util.util_gspr... |
fd013c22b44ba925f51717e97670557138e03dd86796e6655af3f4c5eade978e | Python | 5,682 | 164 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import datetime
from contextlib import suppress
import rq
from django.db.models import Min
from cvat.apps.webhooks.exceptions import WebhookDeliveryError
from .cache import clear_cache
from .const import COMPRESSED_EVENT_SCOPES, MAX_EVENT_DURATION... |
159a5a7a912170bb032938e1b9320518b26305179fd5597d3a11f43e50013d25 | Python | 5,683 | 157 | import math
import torch
import numpy as np
import random
def gaussian_kernel(
sigma: float, k: float=1, size:float=15, norm:bool=False
) -> torch.tensor:
"""Returns a 2D Gaussian kernel.
:param k: height of the Gaussian
:param sigma: standard deviation of the Gaussian
:param siz... |
de3d70755d7cb1213f558820734cb2545aff21b019a0e6e25b91be3b01102bbd | Python | 5,683 | 231 | import contextlib
from dataclasses import asdict, dataclass
from typing import TYPE_CHECKING, ClassVar
from unittest.mock import Mock
import psygnal
import pytest
from psygnal import SignalGroupDescriptor
from magicgui.schema._guiclass import (
GuiBuilder,
GuiClass,
button,
guiclass,
is_guiclass,
... |
499dcff8810541d535869292522c72ec996a53501f58b35ce81d0c8a779f903f | Python | 5,684 | 168 | import logging
import lightning as pl
import torch
from rich.progress import track
from mcmlnet.experiments.utils import (
load_model_configs,
save_results_to_csv,
validate_model_metadata_file,
)
from mcmlnet.training.data_loading.sota_data_classes import SOTAPreprocessor
from mcmlnet.training.models.base... |
369ae796aff5b09dab6d2ee51db92b99be455c89d9f8399c8102efbb627de386 | Python | 5,685 | 143 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
"""Download a project's task data incrementally: the SDK keeps a local cache and
re-downloads only what the server has changed since the last run.
``cvat_sdk.datasets.TaskDataset`` stores each task under the client's cache
directory. On every constr... |
8a9bc0e3e7fa2dda4874b19572a353f6228738f89e62c5a176a8c74a70cefea9 | Python | 5,685 | 143 | #!/usr/bin/env python
"""
qc_meshes.py
============
Report outlier subjects in a set of segmented head meshes, before building a
basis from them.
A single badly segmented or misaligned subject can dominate the PCA. In the bcas
build one subject out of 180 was rotated 20 degrees out of the CTF frame and by
itself carr... |
c6332603ce41b2e69e5f01bc19564f698ee8081f2f8c7cd6f4785f590672a7f5 | Python | 5,685 | 140 | import random
import pandas as pd
from datetime import datetime
from sklearn.preprocessing import OneHotEncoder
def ts_creator(seconds_list):
time_data = [0]
for second in seconds_list:
time_data.append(second + time_data[-1])
seconds_list.append(0)
return seconds_list
def filter_function()... |
d3608f0d954652b813625ded0c8e7cd62561074def2286556e8b26dd1bebb819 | Python | 5,685 | 145 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 6 15:33:56 2019
function getNames now in a seperate file
@author: kooij_k
"""
import pandas as pd
import numpy as np
#import doric as dr
import h5py
import inspect
def getNames(info0):
info_list = ['info'+ str(i) for i in range(100)]
p =... |
a3e3866c95e59a2b500e108a28cd91a869f358d98674b4506f7fdf8198d56ad3 | Python | 5,687 | 141 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
"""Import annotations into an existing task straight from a registered cloud
storage: the server pulls the file out of the bucket itself, nothing is
uploaded from this machine. Handy when a model writes its predictions to a
bucket, or when the annota... |
bb1477e8a7ba245083ac15a142929cc9b1b7aa4b717fe1352adf38b713e8cd23 | Python | 5,688 | 144 | # #!/usr/bin/env python
# import os
# import sys
# sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
#
# import click as ck
# import pandas as pd
# from Bio import SeqIO
# from toolkit.extract_esm import extract_esm
#
#
# @ck.command()
# @ck.option('--data-root', '-dr', default='cafa_data', required=True, ... |
c048a748beaed174b8773b983ec0a138acacb2514f9d31e22321fedb0bd1426a | Python | 5,688 | 164 | from pathlib import Path
from typing import Tuple
from unittest.mock import Mock, patch
import numpy as np
import pandas as pd
from pytest import fixture
from brainways.project.info_classes import (
MaskFileFormat,
RegisteredPixelValues,
SliceSelection,
)
from brainways.ui.brainways_ui import BrainwaysUI
... |
618b5a2c9859a0e0d1ad2582499241fecffcca45634cf131a0fbcbea6fd53cbb | Python | 5,689 | 152 | """Tooltips that wrap, and look like the tutorial bubbles.
Qt lays a plain-text tooltip out on a single line however long it is. The
longest in this app rendered at 1468 px — most of a laptop screen, in a strip
one line tall, usually overlapping the very control it describes. Anything
worth explaining in a sentence wa... |
bb08e385c2abf599a8d0960b93580468869d151d1f52716b4230b81a35ffd4c0 | Python | 5,689 | 130 | """Which OCSR models the tool can use, and which ones are actually installed.
Two separate questions, and conflating them is how you end up shipping a default
nobody can run:
* Is a model *listed*? A registry entry means "offered", not "importable here".
* Is it *installed*? That is :func:`chemgraph.tools.ocsr_ba... |
294fd639f5fa6a99d932b2a9e9a271adf5e3a0eca8942258dbf99f35aa791f4f | Python | 5,694 | 144 | #############################################################################
### Recursively moves through directories and extracts parameters and the ###
### measures Q and MI from simulation data ###
#############################################################################
### Cop... |
d438f4685b1311e2480bcdbafdb98fa7870b6679dee707e74b28a632773b277c | Python | 5,694 | 157 | """
Variational autoencoder system
MB Vessies and RR van de Leur
"""
import torch
from torch import nn
from ecgxai.utils.exceptions import ModelOutputShapeMismatchException
from ecgxai.systems.base_system import BaseSystem
class VAE(BaseSystem):
""" Basic VAE system """
def __init__(
self,
... |
5e62185c680077d325d29556cc6295f6ef617ee02f23a5170855aa96db706d8f | Python | 5,695 | 180 |
import unittest
import SVMTK
import os
tests_dir = os.path.dirname(__file__)
class Domain_Test(unittest.TestCase):
def test_unpack_mesh(self):
domain = SVMTK.Domain(f"{tests_dir}/Data/cube.mesh")
points = domain.get_points()
cells = domain.get_cells()
facets = domain.g... |
94bbaf309007826212c40c341681bac0acd6167b89c42c1eafeaae18fc0ac6ad | Python | 5,695 | 177 | # 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.
import math
from typing import List
from unicore.optim.lr_scheduler import UnicoreLRScheduler, register_lr_sch... |
dd1516610ba452599c5ffb6ac2d323ae13dc3f3434bb390356da8da42a56f9a5 | Python | 5,696 | 108 | """This scripts generates 3 examples for plain SR between LR synthetic T1 scans (input channel) and HR synthetic T1
scans (regression targets, or output channel). Therefore this script does not use real images as regerssion targets.
Because we do straight SR, the input channel is generated directly by blurring/downsamp... |
c39edc583333de6cabc5ea06660e367f1e712add661666ef72082996ebdb3b1f | Python | 5,697 | 144 | import os
import os.path as osp
from typing import Callable, Dict, List, Optional, Tuple
import torch
from torch import Tensor
from torch_geometric.data import (
Data,
InMemoryDataset,
download_google_url,
extract_zip,
)
from torch_geometric.io import fs, read_txt_array
from torch_geometric.utils impo... |
dad2aec18cda894fa4c9023fcabea5e7e39536c8fdd5e37ae56e11ed6f78f773 | Python | 5,697 | 155 | #############################################################################
# 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... |
e77a47bd911f713018bb12227783aa4d3f10c7c3f0543c9346cb27659baa37af | Python | 5,698 | 144 | #############################################################################
### Recursively moves through directories and extracts parameters and the ###
### measures Q and MI from simulation data ###
#############################################################################
### Cop... |
5aa2e713c72540301fad9ff5d3b094a0df0c73e28fdd880dda44bfe2a89e9834 | Python | 5,699 | 118 | import tensorflow as tf
from src import TGCNN_layer
class TGCNN_Model(tf.keras.Model):
def __init__(self, num_filters=2, num_nodes=97, num_time_steps=100,
filter_size=3, variable_gamma=True, exponential_scaling=True, dropout_rate = 0.2,
lstm_units=16, fcl1_units = 128, LSTM_a... |
d2fd1654ab749c82e5fc6c8dc707fbb5da6bd4487943774cbbe900c22dc76eb0 | Python | 5,699 | 163 | #!/usr/bin/env python
"""Agent + MCP + Parsl demo on an HPC compute node.
LLM agent on the compute node drives a local ``mace_mcp_hpc``
subprocess whose backend is ``parsl`` configured for Polaris, Aurora,
or Crux. The agent uses ``run_mace_single`` to compute thermochemistry
for each of the 5 molecules and reports a ... |
6519895ab52e986c005a0b29577c61de8cee1b61fc8691e87e4086ed59412c93 | Python | 5,701 | 170 | """
Weighted Least Squares (WLS) / Robust Linear Model (RLM) IVIM fitting.
Author: Devguru Tiwari, IIIT Nagpur
Date: 2026-03-01
Implements a segmented approach for IVIM parameter estimation:
1. Estimate D from high b-values using weighted/robust linear regression on log-signal
2. Estimate f from the intercept of the ... |
67f656a89b1388f8eaba5b02486eb25bc806857acf18ca004e5075abb0db47e4 | Python | 5,703 | 171 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import csv
import json
import os
import random
import sys
from itertools import product
NAME = "organizations"
def read_rules(name):
rules = []
with open(os.path.join(sys.argv[1], f"{name}.csv")) as f:
reader = csv.DictReader(f)
... |
265c7747c59736ac574a2162dcb21dfbd1c307f79b8650a536b189cba6cd81b0 | Python | 5,704 | 158 | #############################################################################
# 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... |
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