sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
dcff8ea5df5e417873f647325683f9d4215109b28d1ea3ca7eca7f49df2d0ed8 | Python | 6,738 | 184 | """Which version of which pipeline produced a result.
Three pipelines ship from this repository and move at different speeds, so one
number cannot describe them: MEA-NAP's electrophysiology path is mature and
versioned back to 2023, CAT-NAP is a recent port, and MEA-Stim is mid-port.
A result is only reproducible if i... |
52b51a7690b918fb868c40515635ea25c7bc93a5dcfdb2cee4310dd6db4b9270 | Python | 6,739 | 160 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This script is used to alter a bundle while keeping it similar enough to the
original version (keeping the dice coefficient above a given threshold).
The script will subsample, trim, cut, replace or transform the streamlines
until the minimum dice is reached.
Various o... |
896de5e614076f873e0554abfe9be6abd3af5f867580dce667e5c4d5c725453d | Python | 6,739 | 171 | # 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, ... |
1a069712299a9a41266f1c489eec52096bab8565f691592363feffcca5b9871d | Python | 6,742 | 178 | from __future__ import print_function
from keras import activations, initializers, constraints
from keras import regularizers
from keras.engine import Layer
import keras.backend as K
import keras
class GraphLayer(keras.layers.Layer):
def __init__(self,
step_num=1,
activation=... |
6de084668ec7b2e5310fadaa7e00bf618aef5e6b571781783d2b9c0399059f67 | Python | 6,742 | 166 | """Custom state initializers with permanent properties between trials"""
import numpy as np
import collections
from moog import sprite
from . import poll_generator
from moog.state_initialization import distributions as distribs
class ParentStateInitializer():
def __init__(self, agent_0_factors, agent_1_factors,
... |
70e3a80f7f3b7da9c924cb0f19cc8b09236cb7c9ad727281c57648e2400e5414 | Python | 6,742 | 158 | import numpy as np
import pandas as pd
#-----------------------------------------------------------------------------------
#-----------------------------
# func: input:state -> output:List of adjacent states (AS) of the given state
def return_AS_binary(state_str :str) -> list:
#-------------------------
# che... |
030b4ed00a2e63be8fa943a4aaa1c023825450c1630b6fc08b33be4a41e8bf4d | Python | 6,743 | 169 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
"""Create a task whose jobs are defined by you, file by file, instead of by a
frame count: one job per camera, per scene, per delivery batch.
Two ways to group:
--job FILE [FILE ...] one job per occurrence, in the given file order
--files-per-... |
9d5108698dfef74c584cb54c77769a136f127ae73a2e955cbce086b108f85844 | Python | 6,744 | 212 | import os
import tempfile
import unittest
import h5py
import pandas as pd
import numpy as np
from pandas.testing import assert_series_equal
from numpy.testing import assert_array_equal, assert_array_almost_equal
from heig.herigc import CommonSNPs, OneSample
class toy_GWAS:
def __init__(self, snpinfo):
s... |
09a9e729e72d05f1f5187820855e04aee4a73f3022248341e9d49d2133a7435f | Python | 6,745 | 170 | """Keeping an output folder honest about which recordings it analysed.
The spreadsheet decides what a run covers, and it changes: a sixth recording
arrives, or one turns out to be bad and is taken out. Continuing a run
(``Params.continue_interrupted``) handles both for the *numbers* — a new
recording is computed, a re... |
c8813e450aaab3f9266dfdcea5ddde1bc14df8435a17e25bcc20884522956cee | Python | 6,746 | 207 | # 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 os
import torch
import biotite.structure
import numpy as np
import pandas as pd
from biotite.structure.io import pdbx, pdb
from biotite... |
d300a1a4934e0e75716dddc18841874562f599a5f28f6f5ef266df3168bdb3c5 | Python | 6,747 | 104 | import numpy as np
import sys, os
from scipy.stats import pearsonr, false_discovery_control
import matplotlib.pyplot as plt
import os
import pandas as pd
import sys
from ism_performer import *
import argparse
from select_drivers_performer import *
def get_enformer_eval(tissue,donor_fold,desired_seq_len,n_center_bins):... |
c5b3c15bc071f83f2f8ec06ffd91f33a234209d04e828d28c4a045b3cf9a90e9 | Python | 6,748 | 157 | import numpy as np
import torch
import random
import pickle as pkl
from functools import reduce
class SimulationDataset():
"""
NBodyDataset
"""
def __init__(self, partition='train', max_samples=1e8,
data_dir='', n_complex=5, average_complex_size=3, system_types=5):
self.parti... |
77357dd51c7602365913b8e24b1a62d9ba150fc6391b0781987c55b148e37a59 | Python | 6,749 | 164 | """Structural/sanity tests for NMF dimensionality metrics (``nmf.py``).
Run from the repo root::
uv run python python/test_pipeline_nmf.py
``cal_nmf`` is not just stochastic but *algorithm*-different from MATLAB's
``calNMF.m`` (sklearn's coordinate-descent NMF solver standing in for
MATLAB's built-in ``nnmf``, w... |
a491b0e99154010195942d02b72ff62909648c52d861d3e84bf04b543379d1a9 | Python | 6,749 | 190 | from sklearn.metrics import (
roc_auc_score,
root_mean_squared_error,
accuracy_score,
f1_score,
recall_score,
precision_score,
mean_absolute_error,
average_precision_score
)
from scipy.stats import pearsonr, spearmanr
import oddt.metrics as vsmetrics
from sklearn.manifold import T... |
e257b15ae463bb453682ffd252355ee7211713b2cf85cec9f8e9f1f8fd31daea | Python | 6,750 | 160 | import os, sys
from pathlib import Path
from tqdm import tqdm
MODULE_DIR = str( Path( Path(__file__).parent.resolve() ) )
sys.path.append(MODULE_DIR)
from pathlib import Path
from argparse import ArgumentParser
import torch
from torch.utils.data import Dataset, DataLoader
import esm
from biopdb_utilities import read_... |
dc6ca33d267a0580d32dace338754f3f81217d4440c012b0cdfbe834e176761a | Python | 6,752 | 149 |
# -*- coding: utf-8 -*-
"""
Created on Sun Dec 15 21:14:11 2019
@author: Yoshi
"""
import numpy as np
from PIL import Image,ImageDraw,ImageFont
from cell_drawer import *
from arrow_coordinate_calculator import *
def cell_response_mapper(path,canvas,width,height,map_type,cell_locations,scale,radius,color_key,freq... |
39f491ed20b262b711795ba7a5558fe9f96e2c7f5b79f764be549fd11c9e3665 | Python | 6,754 | 170 | import torch
from torch import nn
from models.gcl import GCL, E_GCL, E_GCL_vel, GCL_rf_vel
from models.layer import GMNLayer
class GNN(nn.Module):
def __init__(self, input_dim, hidden_nf, device='cpu', act_fn=nn.SiLU(), n_layers=4, attention=0, recurrent=False):
super(GNN, self).__init__()
self.hi... |
6ea151400417c6789def88e6b0c33c48c113f9739675f697ed3e7fea9f4643c7 | Python | 6,754 | 160 | #!/usr/bin/env python
"""
The z stage UI.
Hazen Babcock 05/18
"""
import os
from PyQt5 import QtCore, QtGui, QtWidgets
import storm_control.sc_library.parameters as params
import storm_control.hal4000.halLib.halDialog as halDialog
import storm_control.hal4000.halLib.halMessage as halMessage
import storm_control.hal... |
724950e0e37a90e84524738820a044193dc261a96bfbe48ea75f0e5a2ffeb426 | Python | 6,754 | 160 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Semantic segmentation on video frames
"""
import os
import cv2
import numpy as np
from tqdm import tqdm
import torch
from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation
from ..utils.io_helpers import check_outputfile, array2str_encoder, ... |
0cb57806bb860e97f1f1f610dd200c80e83f2db4a33c81463be29677090dadc3 | Python | 6,756 | 196 | """Adaptive (locally-variable-bandwidth) kernel density estimate.
Port of ``ssvkernel.m`` (Shimazaki & Shinomoto 2010, "Kernel Bandwidth
Optimization in Spike Rate Estimation"). Used by the **population-level PSTH
plots** only — the CSV metrics use gaussian smoothing (see ``psth.py``), so this
is not on the headline p... |
8856ad246043b00a5222b015911b9a11ce4b74f735aa9d991faa4cecefe5abd2 | Python | 6,757 | 203 | 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):
with open(os.path.join(data_dir, 'motion.pkl'), 'rb') as f:
edges, X = pkl.load(f)
V = []
... |
9ae3652e0c0c2ba920993068fe963cfc6179d5b18051fee957ef054141e0f462 | Python | 6,757 | 207 | from pathlib import Path
import numpy as np
import nibabel as nib
from dipy.io import read_bvals_bvecs
from dipy.core.gradients import gradient_table
from dipy.reconst.dti import TensorModel
from dipy.reconst.fwdti import FreeWaterTensorModel
from beltrami import BeltramiModel
from matplotlib import pyplot as plt
pri... |
3e78a4c1596a033e51e1a5a7deabdf9f4a876135f78ed4371922083f72b0b0b2 | Python | 6,758 | 149 | #%% configuration
from config.config import settings
import os
import pandas as pd
import pickle
import xgboost as xgb
from sklearn.ensemble import RandomForestClassifier
from config.reproducibility import enforce_reproducibility
from data import preprocessing
from evaluation.performance import (
evaluate_model, ... |
64411e1da3a2c3f1fcbb16a7c0155ce1ab45f150be19232d275aca2b5de74665 | Python | 6,758 | 147 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
import torch
import torch.nn as nn
import torch.nn.functional a... |
554917c55f0780feb6c5f258ec0ab788dbd884ccb29ec61e4b52ef49a86b3565 | Python | 6,759 | 173 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from __future__ import annotations
import argparse
import getpass
from pathlib import Path
from urllib.parse import urlsplit
from cvat_sdk.core.auth import DEFAULT_SERVER, AuthStore, ProfileEntry
from cvat_sdk.core.client import AccessTokenCredenti... |
b87888842c567c9a53fa048ce33908b7cf960bb8a3fac68dc322ab6100813a42 | Python | 6,759 | 209 | # -*- coding: utf-8 -*-
"""
Created on Wed Mar 25 19:31:32 2020
Functions for loading mpep data stored in .mat (matlab) files
@author: Samuel Failor
"""
import scipy.io as sio
import numpy as np
from . import timelinepy as tl
from . import cortex_lab_utils as clu
import matplotlib.pyplot as plt
# def protocol_path... |
d0606dd235286703c2028ec4cd0b2dbb5449db1552fb383cf088735e337e6758 | Python | 6,759 | 193 | """Single-loop ownership for asynchronous local LangGraph checkpointers."""
from __future__ import annotations
import asyncio
import logging
import os
import threading
import time
from collections.abc import Callable
from concurrent.futures import TimeoutError as FutureTimeoutError
from pathlib import Path
from typin... |
3c14910a27ad0309ccae1af0a4dd0d46ee783289159c68b3ca8793ffd7f4569d | Python | 6,760 | 188 | """
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... |
c52745fd89a63c7360cd2400efb89387c6909a3d86f81af46fba8cad4cb5c09f | Python | 6,761 | 134 | import numpy as np
from typing import Union, Tuple, Callable, Hashable, List, Dict
from pathlib import Path
from brainscore_vision.model_helpers.activations.temporal.inputs import Video, Stimulus
from brainscore_vision.model_helpers.activations.temporal.utils import assembly_align_to_fps, stack_with_nan_padding
from b... |
9de78c8a2e347870940d4795fc1278ad2e68fe0a5c29d913189a050ad02cc7c4 | Python | 6,762 | 159 | import torch
import numpy as np
import time
from torch.cuda.amp import autocast
from torch.optim.lr_scheduler import ReduceLROnPlateau
from skimage.metrics import peak_signal_noise_ratio, structural_similarity
import torch.nn.functional as F
import torch.cuda.amp
from torch.cuda.amp import autocast
from tomocupy_stream... |
7233a41640550ff20ddbdb6d0d44719cb26884f12d55abe4321157fc67c95f60 | Python | 6,763 | 166 | from typing import Callable, List, Optional
import torch
from torch_geometric.data import HeteroData, InMemoryDataset
class HM(InMemoryDataset):
r"""The heterogeneous H&M dataset from the `Kaggle H&M Personalized Fashion
Recommendations
<https://www.kaggle.com/competitions/h-and-m-personalized-fashion-r... |
f0d4794ebbefaa49247e1e3941e69b555752669ca77c88a398e6b71c596d8460 | Python | 6,764 | 175 | from __future__ import print_function
import argparse
import os
import os.path
import shutil
import sys
def parse_args():
parser = argparse.ArgumentParser(
description='Modify the files from an rMATS output directory to prepare'
' a new --od directory for running --task stat')
parser.add_argu... |
fa2102852286ca30d28de41e1e3be884bf174cc4ce843fb6d8906afd7375dbb3 | Python | 6,764 | 180 | import torch
from infopath.model_loader import load_model_and_optimizer
from infopath.config import load_training_opt, save_opt
import os
import pandas as pd
import shutil
import numpy as np
import matplotlib.pyplot as plt
def generate_and_save_data(
save_path="datasets/ModelData",
log_path="log_dir/a2c6ad170... |
ad7d3fed271c5125fec77020e6ecc3a880f1963e78a1dc067e3937508412d796 | Python | 6,765 | 131 | import argparse
import json
import pkg_resources
from .dataset_downloading import download
from .dataset_downloading import continue_from
from .dataset_downloading import continue_from_fastq
from .export_data import export
from .prerun_configs import set_environment
from .dataset_downloading import download_qiita
from ... |
2ba63b04afb0d449431e45bdbd96a0395f8df488b5b900318e141bf80c27ca07 | Python | 6,766 | 168 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
"""Bulk-create tasks inside a project, each task's data read from a registered
cloud storage.
Two ways to spell a task's data, repeatable and mixable:
--task KEY[,KEY,...] explicit object keys, in order:
* a single ... |
47c2bc5c9d797235059f733c1b3053d4426bc82fc54c1c0dd32073fab02568cd | Python | 6,767 | 210 | import os
import glob
import itertools
import matplotlib.pyplot as plt
from scipy.stats import norm
import numpy as np
import pandas as pd
import nibabel as nib
from scipy.stats import norm
from nilearn import image, plotting, glm, masking,datasets
from nilearn.maskers import NiftiMasker
from ants import apply_transfor... |
e975b1db3f86a2b633cc97221ac1e26cebec3c0a7b407d5140d1d28a46a744e8 | Python | 6,770 | 150 | import glob2
import os
import pandas as pd
import nibabel as nib
import numpy as np
from nilearn import image, plotting, masking, surface
from matplotlib import colors
def get_voxel_model_assignments(df, roi, threshold, linear_experiment_name, vae_experiment_name):
max_lin_subset, max_vae_subset, lin_vae_subset =... |
f3ba66c8e84b5de52b5c72067aef5f62527058c45a3bc27e99b6bcdc095c0203 | Python | 6,770 | 250 | # -*- 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 meshli... |
06a562dd9d8c316a38bc15b7ed3ac286e12029f83cca67c3f588bbd0e5ec5bd9 | Python | 6,772 | 199 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
import sys
import datetime
import shutil as sh
# external inputs
import numpy as np
import nibabel as nb
from sh import gunzip
# local inputs
from ..io.get_filename import get_filename
from ..io.surf import write_mgh
from ..cmap.generate_coordinate_m... |
ee933eefad3c6a6a58e0393beb805854340470933c20af494d3eef89f43a071d | Python | 6,772 | 204 |
import h5py
from glob import glob
import sys
import yaml
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
pathpath,myrun,roi_tag = sys.argv[1:]
'''
pathpath = 'PATHS/filepaths_carlen.yml'
myrun = 'runC00dMP3_brain'
#pathpath = 'PATHS/filepaths_IBL.yml'#
#myrun = 'runIBLPasdMP3_brain_p... |
db7736918ae3c964b7b3f41f13008195ddbaa6825ece1a00090a19235fd6e07e | Python | 6,773 | 158 | # Copyright (c) 2022, Kwanhyung Lee, AITRICS. 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 ... |
f2768b1fae3e5b66a0ddf48942768f6d59cc87b35a8c10609c2d179bdf565d84 | Python | 6,774 | 239 | from __future__ import annotations
import pytest
from PySide6.QtCore import QPoint
from PySide6.QtCore import Qt
from pytestqt.qtbot import QtBot
from labelme._app import MainWindow
from ..conftest import close_or_pause
@pytest.mark.gui
def test_toggle_all_shapes(
*,
qtbot: QtBot,
annotated_win: MainWi... |
e1688126c055450294442190f8dd702f6b15d3eba8baab9ba28d1f61266922fb | Python | 6,775 | 218 | from typing import List
from typing import Tuple
import torch
from torch import Tensor
"""
2D and 3D implementations of vector to embedding.
vector is predicted by a network and is an array of values ranging from -1 to 1. These values can be scaled by
"scale" and are used to project pixels from their location, to a... |
b3d7ddc69878955735ac0191d84bdf4977c7174174a29a59273dbaddb0ea2e57 | Python | 6,776 | 197 | import math
from collections.abc import Sequence
from dataclasses import dataclass, field
from typing import TYPE_CHECKING
import torch
from torch import nn
from eir.models.fusion.fusion_default import al_features, default_fuse_features
from eir.models.layers.mlp_layers import MLPResidualBlock
from eir.models.models_... |
9ce32a6575e7c89d2bb740f8bf5ebe9bf11fb0b9f479976c8d9442d9318f7ee1 | Python | 6,779 | 176 | import h5py
import os
import matplotlib.pyplot as plt
import numpy as np
import sigpy as sp
from skimage.metrics import structural_similarity as ssim
from skimage.metrics import normalized_root_mse as nrmse
from skimage.metrics import peak_signal_noise_ratio as psnr
DIR = os.path.dirname(os.path.realpath(__file__))
... |
aa199801ff7a1af7d2a6daeaf5617e3a97a2202da27e7040e074990d65c2e898 | Python | 6,779 | 191 | import sys
import time
import getopt
import numpy as np
import scipy.ndimage as nd
from skimage import io as skio
from skimage import exposure
from slice_dataframe import slice_dataframe
###############################################################################
# basic chip settings
##############################... |
b550aa4fc8fedc6be13086c6c7d174d7f9b9ee691c7af35106822bab3bf07ad0 | Python | 6,779 | 205 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
import datetime
# external inputs
import numpy as np
import nibabel as nb
import shutil as sh
import matplotlib.pyplot as plt
# local inputs
from ..segmentation.orthographic_projection import orthographic_projection
def get_retinotopy_images(input_... |
1f6dbd4bd48c73bbd4570ced196458b56a315111d76ea00dc7b235a4a6fe5274 | Python | 6,781 | 121 | import numpy as np
import torch
from threadpoolctl import threadpool_limits
from nnunetv2.training.dataloading.base_data_loader import nnUNetDataLoaderBase
from nnunetv2.training.dataloading.nnunet_dataset import nnUNetDataset
class nnUNetDataLoader2D(nnUNetDataLoaderBase):
def generate_train_batch(self):
... |
135be40b19c27ccf26891c607507b26c1f97252f5589686f4df36436d6500940 | Python | 6,783 | 172 | import os
from torch.utils.data import Dataset
import numpy as np
from data import conform
from monai.data import CacheDataset
from monai import transforms
import nibabel as nib
def get_test_sample():
root_idr = '/groups/ag-reuter/projects/fastsurfer-tumor/fastsurfer-tumor-pipeline/tumor_validation_data/cleane... |
5f48bb00220dbc99a05d7a03070b83d3a796049805195f798a1be3f29f22915b | Python | 6,783 | 148 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
import torch
import torch.nn as nn
import torch.nn.functional a... |
ac5aad5a29393eb11de9b8af5b798af2466fc216c22780714868d95799ebe494 | Python | 6,783 | 208 | """
Venous mask
A binary mask of venous voxels is created from a set of time series. First, a
baseline correction of each time series is applied if not done before (i.e., if
no file with prefix b is found). Venous voxels are classified in the following
way: Mean epi and mean tSNR acrss all time series are thresholded ... |
95dd88a77558dde2fe603510e168e19d75e621182d2935819f96ab6b3c0e0351 | Python | 6,784 | 160 | """Intuitive plots for ep10 linear probe results."""
import json
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from pathlib import Path
RESULTS = Path("results/probe_full_ep10/loso_metrics.json")
OUT = Path("results/probe_full_ep10")
m = json.load(open(RESULTS))
kin = m["kin... |
5dd2f782a370398aa77fb924b49c18ed04b76501356cb10aba1e0033b5bb41b9 | Python | 6,785 | 217 | from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langchain_core.messages import ToolMessage
import json
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import MemorySave... |
1ee911daa29b5cf730720f23ac987d02cac4fa834cbdf5191c9ae5bbe3e2d85c | Python | 6,786 | 197 | import cv2
from pydicom import dcmread
import tensorflow.compat.v1 as tf
import pandas as pd
import numpy as np
import random
import math
from functools import partial
import sys
import os
######################################################################################
################ Functions f... |
3c9a89c06521d851741ee0109633bd325ec12fd74604d8caaefede50ca6d5260 | Python | 6,786 | 231 | import math
import torch
from torch import nn
from torch.nn import Parameter, functional as F
from torchvision.ops import StochasticDepth
from eir.models.layers.norm_layers import LayerScale
from eir.utils.logging import get_logger
logger = get_logger(name=__name__)
class LCL(nn.Module):
__constants__ = ["bias... |
cce883fda944eb76499a70325b0000b2edd316478a7b4fd21c9e42f3bdf30d94 | Python | 6,787 | 204 | # Generated by Django 4.2.21 on 2025-09-08 16:13
import django.db.models.deletion
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("organizations", "0002_invitation_sent_date"),
migrations.swappable_dependency(sett... |
dc583e6444c23d6733affca4c75b8d7b82797e76196ba3f1cafe44b6ede675a7 | Python | 6,787 | 178 | # src/train/train_classification.py
"""
单折训练脚本 — ClassificationModel（新增 L2 正则 & OneCycleLR + ReduceLROnPlateau 调度）
----------------------------------------------------------
功能：
1. 加载单折训练/验证索引。
2. 构建 Dataset 和 DataLoader。
3. 实例化 ClassificationModel + 损失 + 优化器 + 双调度器 + EarlyStopping。
4. 训练并在验证集上评估，记录主要指标到日志。
5. OneCycl... |
d7cbad7784c879a75bce767ecfd12b1f10b037cea9739045fdfc020319885063 | Python | 6,788 | 205 | # -*- coding: utf-8 -*-
#
# dist_train.py
#
# Copyright 2020 Amazon.com, Inc. or its affiliates. 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... |
66df581197617d0abc1110ccce8cb7692e66d48bf99e32c8194e60b63b7a8c91 | Python | 6,789 | 169 | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import pdb
class BiaffineAttention(nn.Module):
"""
Adopted from NeuroNLP2:
https://github.com/XuezheMax/NeuroNLP2/blob/master/neuronlp2/nn/modules/attention.py
Bi-Affine attention layer.
"""
def __init__(self, inp... |
dcafd28c7fef095a298bc5d9cf8d7a11011ba4b00ef59d9cd7414da9d363b542 | Python | 6,791 | 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 torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .common... |
099eb734855f98febb04fb305fed5a91acab101d40599907072e2709fd49afaf | Python | 6,795 | 166 | # allowable multiple choice node and edge features
allowable_features = {
'possible_atomic_num_list' : list(range(1, 119)) + ['misc'],
'possible_chirality_list' : [
'CHI_UNSPECIFIED',
'CHI_TETRAHEDRAL_CW',
'CHI_TETRAHEDRAL_CCW',
'CHI_OTHER'
],
'possible_degree_list' : [0... |
f714831f63acb7ad4fa9846d19029ad76196e58c970869d8ef9739fe1d2f32ae | Python | 6,795 | 206 | """ Scheduler Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import List, Optional, Union
from torch.optim import Optimizer
from .cosine_lr import CosineLRScheduler
from .multistep_lr import MultiStepLRScheduler
from .plateau_lr import PlateauLRScheduler
from .poly_lr import PolyLRScheduler... |
42a5f7010eea93b06468d2697ec6a8b6905b62b39ed54f6472359d86af69b8d1 | Python | 6,797 | 168 | #!/usr/bin/env python
import argparse
import logging
import os
import requests
import sys
import pandas as pd
import json
# Logs stuf
logging.basicConfig(level=logging.ERROR)
logger = logging.getLogger(__name__)
# String API url
string_api_url = "https://string-db.org/api"
""" def getStringIDsFromProtIDs(proteins,... |
4fb833794ba3d351c3e9439e10ab08613d34855ab8ca81ebff082d47b150e420 | Python | 6,800 | 172 | from __future__ import print_function
import os
import re
import gzip
import itertools
import argparse
import time
import logging
__author__ = 'Martin Aryee'
logger = logging.getLogger('root')
#args = {'out_dir':'/PHShome/ma695/tmp', 'min_reads':10}
#base = '/data/joung/sequencing_bcl/131007_M01326_0075_000000000-A6... |
f7903fbc07905e4fc17bef3f7153c801484429abe6f7b1732c2265a178c16d56 | Python | 6,800 | 166 | import numpy as np
from scipy.spatial.transform import Rotation as R
from ..neurosim.cells.neuron_cell import NeuronCell
from neuron import h
class TestNeuronCell:
def test_load(self, neuron_cell: NeuronCell):
assert len(neuron_cell.all) == 73
assert len(list(h.allsec())) == len(neuron_cell.all)... |
afebdbae05a548170741ef0af4109513db7438046fff46286ee410a1a4464b2e | Python | 6,803 | 193 | import numpy as np
def categorize_trials(behavioral_df, factor, agent_idx=0):
"""
Categorize trials based on the specified factor.
Parameters:
behavioral_df (pd.DataFrame): Behavioral data for categorizing trials.
factor (str): The factor by which to categorize trials ('reward', 'nback', 'switch'... |
da43b317bc5e0fb0109b5382d66cb3e21b90811dd8316ea32223c7a70402a0e3 | Python | 6,804 | 204 | import h5py
import sys
import numpy as np
import yaml
import os
from scipy.stats import kurtosis
from sklearn.decomposition import FastICA
from scipy.signal import medfilt
import matplotlib.pyplot as plt
pathpath,recfilename = sys.argv[1:]
with open(pathpath, 'r') as myfile: pathdict = yaml.safe_load(myfile)
sys.pa... |
ea6cbc1555c12f3f67b3aa5176a9721ee3f8979641ea4223621d8a45f1b31ab8 | Python | 6,804 | 198 | from torchvision import transforms
import random
import os
import numpy as np
import torch
import Utils.config_stomach as CONFIG
import itertools
from matplotlib import pyplot as plt
import scipy
from sklearn.metrics import roc_curve, roc_auc_score
import math
def seed_torch(seed=0):
random.seed(seed)
os.envi... |
9f37cc4f0fe6a72f6f97ba18d64f9d96d692c434e5da9696ffc86984610dd47f | Python | 6,805 | 134 | import numpy as np
def print_steady_state_format (sv):
for i in range(len(sv)):
#print("sv[%d] = %e;" % (i,sv[i]))
#print("extra_data[%d+offset] = %e;" % (i,sv[i]))
print("*((real * )((char *) sv + pitch * %d) + threadID) = %e;" % (i,sv[i]))
def get_correct_indexes (sv):
nlin = np.shap... |
1a156b59fdf307b6ccf8d02e9d96329af2a29dab0d9132b58748c5b6f9ac1cf7 | Python | 6,806 | 211 | # Copyright 2017 Calico 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... |
d89efa62044ab21ec863ab21aa95fbabfeaec81bec26aede3d6e721a74a31d6c | Python | 6,806 | 148 | import h5py
import os
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
import sys
sys.path.append('/project/roysam/rwmills/repos/cluster-contrast-reid/clustercontrast/')
print(os.getcwd() )
from QC_functions import *
#-----------------------------------------------------------... |
32e0e934278345a37b3a49ecbe8790812ffcbddd2712a2106312558608098c32 | Python | 6,807 | 189 | import brainpy as bp
import brainpy.math as bm
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import levy
import math
bm.set_platform('cpu')
class CANN2D(bp.dyn.NeuGroup):
def __init__(self, length, tau=1, tau_v=144, m_0=1., k=8.1, a=0.2, A=10., J0=4., sigma_u=0., sigma_m=0.,
... |
27f0a951650a7d3b1c4bda1768d80dab0b3a1fbd7edb3fe8cb29159a7b6c82c8 | Python | 6,811 | 201 | import asyncio
import pandas as pd
from nicegui import ui
from nicegui.testing.user_simulation import user_simulation
from metaprivBIDS.corelogic import revert_column
from metaprivBIDS.browser_app import (
WorkspaceState,
_disclosure_score_outlier_figure,
_eligible_quasi_identifiers,
_rig_outlier_figu... |
8e9c5ba1fbe115cf6df7f780cd0039223df6a56a9d78c5f6b84a3914d68f93fb | Python | 6,811 | 164 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This script will output informations about lesion load in bundle(s).
The input lesion file is a labeled volume (.nii.gz) where each lesion is
represented by a unique label. To label a lesion file use
scil_labels_from_mask.
Then, the second input can either be streamlin... |
e14e2edb8623280a78d027ceec2510200822ce123769ed7e91bdfcb7bb786811 | Python | 6,811 | 201 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from __future__ import annotations
import glob
import os
import shutil
from abc import ABCMeta, abstractmethod
from collections.abc import Iterator
from datetime import timedelta
from pathlib import Path
from typing import ClassVar
from django.conf... |
5cad23b026c10c928ea46a5898577960ee7af7674ea6efd46b60c518ed0f6fdc | Python | 6,812 | 208 | import os
from types import SimpleNamespace
os.environ.setdefault("QT_QPA_PLATFORM", "offscreen")
import pytest
from PySide6.QtCore import Qt
from PySide6.QtGui import QColor
from PySide6.QtWidgets import QApplication
from musclex.ui.widgets.image_alignment_table import (
ColKey,
ImageAlignmentTable,
axi... |
347be1b17c8206d259f1d6926638739882a96e043b502e817e296415ad735de9 | Python | 6,814 | 178 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from __future__ import annotations
from collections.abc import Hashable, Sequence
from functools import cached_property
from typing import Any
import datumaro as dm
from django.db import transaction
from cvat.apps.dataset_manager import data_model... |
82ef11340d77b1dcc14578ffe3d6f6216bd86d390a1e7e531b0ee2cc3cce95f4 | Python | 6,818 | 182 | """
Class for estimating the respiratory waveform from the phase channel of an EPI timeseries
- Developed specifically for the Dense Amygdala project, but more widely useful for complex-valued fMRI
- Required temporally unwrapped complex phase difference BOLD timeseries with critical or better temporal sampling of resp... |
e73cc845c8730ee78675197cdb99643d0fc0069f4db21c3f785e611069dc79d2 | Python | 6,818 | 165 | """
Module to define multi-view nodes.
A multi-view node is simply a node that is defined for several views. For example, the weights W or the data Y, but not the latent variables Z.
Types of multi-view nodes:
- Multiview_Variational_Node: variational nodes present in all views
- Multiview_Constant_Node: multiview nod... |
50a39b9504164a8dbdecb715c01ee54dca4a592e772bda09686a6825606a1ec3 | Python | 6,820 | 239 | #
# Copyright © 2009- The Spyder Development Team
# Licensed under the terms of the MIT License
"""
Compatibility functions
"""
import enum
import sys
from . import (
PYQT5,
PYQT6,
PYSIDE2,
PYSIDE6,
QtBindingsNotFoundError,
)
from .QtWidgets import QFileDialog
TEXT_TYPES = (str,)
def is_text_s... |
f4c427785914ab645da1e3d190a6c4fd9330e129c8914b3455ab50db8153ea2c | Python | 6,820 | 241 | #!/usr/bin/env python3
import arbor as A
from arbor import units as U
import argparse
import numpy as np
import pandas as pd
import seaborn as sns # You may have to pip install these.
class Cable(A.recipe):
def __init__(
self,
probes,
Vm,
length,
radius,
cm,
... |
2b584ac057167b49eb6337f4ed1be4ace4ff876dd7ea9da0e6c9dbd253418f23 | Python | 6,823 | 165 | from typing import Type
import numpy as np
from neuron_interface.neurosim.cells import NeuronCell
from neuron import h
def collect_lines(neuron_cell: Type[NeuronCell], coordinates):
assert neuron_cell.loaded, "Cell must be loaded"
lines_collected = []
idx = 0
for nrn_section in neuron_cell.all:
... |
98f9cd6cdb6889e4cb3f4fe32f37064e3fb16890942cd64d745f53152040eb9c | Python | 6,823 | 176 | """
Quadrilinear and Hexalinear function for sequence labeling
Author: Xinyu Wang
"""
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.optim as optim
import torch.sparse as sparse
import math
import flair.nn
import pdb
class QuadriLinearScore(nn.Module):
"""
Outer product vers... |
6e67b7abc6140ba8ecb700c1a6c79a492396f775e24892a7967a245c8c61facd | Python | 6,825 | 145 | """对scdata和stdata数据进行过滤"""
import os
import numpy as np
import pandas as pd
import scanpy as sc
from matplotlib import pyplot as plt
import spotd.simulation_st as simulation_st
import anndata
from scipy.sparse import issparse,csr_matrix
from sklearn.preprocessing import normalize
from rpy2.robjects.pack... |
2a3d6a5518e6fc8bf61f1de1bcd2d176be9a816a26d1c5699ee0dfe8ad2e387e | Python | 6,826 | 182 | import json
import typing
import asyncio
import logging
import traceback
import threading
import grpc
from . import thalamus_pb2
from . import thalamus_pb2_grpc
from . import grpc_tls
from .iterable_queue import IterableQueue
from .config import ObservableCollection, ObservableDict, ObservableList
import jsonpath_ng
... |
03c91e649087a2118d17e90d20b5cf6ae62a5f8694e470d977917d03866c081e | Python | 6,829 | 156 | import sys
from pathlib import Path
p_dir = str(Path(__file__).absolute().parents[1])
if p_dir not in sys.path: sys.path.insert(0, p_dir)
import random
import time
import os
import torch
from torch.nn import functional as F
import numpy as np
import pandas as pd
import nibabel as nib
from tqdm import tqdm
from scipy ... |
c89f437a364ede70c831f3d6e39260591421a35bcfc51dab605a820885662bc3 | Python | 6,829 | 180 | # vessap open graph benchmark dataset
import os
import os.path as osp
import sys
from numpy.random import seed
from pathlib import Path
sys.path.append(str(Path(os.path.abspath(__file__)).parents[2]))
import torch
import numpy as np
import pandas as pd
import scipy
import argparse
import networkx as nx
import torch_... |
111d314394ca63dee4b538370c6b33edcc2faccc9dc889061b5788009f0ead88 | Python | 6,830 | 211 | #!/usr/bin/env python3
# This script is included in documentation. Adapt line numbers if touched.
#
# Authors: Sebastian Schmitt
# Fabian Bösch
#
# Single-cell simulation: Calcium-based synapse which models synaptic efficacy as proposed by
# Graupner and Brunel, PNAS 109 (10): 3991-3996 (2012); https://doi.org... |
4918b10216e8adcb74e557184c60971a627da5ab233b1cab5195ff8568434716 | Python | 6,831 | 209 | import os
import h5py
from pathlib import Path
import yaml
import pickle
import concurrent.futures
from tqdm import tqdm
import json
import pandas as pd
import numpy as np
from typing import Dict, List, Tuple, Optional
import torch
from bce.utils.constants import *
# load combined epitopes csv
def load_epitopes_c... |
adb9a482b84d065f5c29f1452a052b2de6bfc6b6ddfbb963c7d6a77bb5ce994c | Python | 6,831 | 222 | import sys
import math
import numpy as np
from scipy.optimize import leastsq
def groGxyz(line):
_x=float(rl[20:28])*10
_y=float(rl[28:36])*10
_z=float(rl[36:44])*10
_xyz=[_x,_y,_z]
return _xyz
def pdbGxyz(line):
_x=float(rl[30:38])
_y=float(rl[38:46])
_z=float(rl[46:54])
_xyz=[_x,_y... |
17473748d1c88d96fddefeac790eb48493bc09316f1d8966bd0a3cfbee911812 | Python | 6,832 | 174 | # Copyright 2013-2023 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack.package import *
class Arbor(CMakePackage, CudaPackage):
"""Arbor is a high-performance library for compu... |
7424bedd474088742c668d756b1fbbc20f44a00d9a5c81c77c53cc5e0a12d917 | Python | 6,833 | 175 | import shutil
from pathlib import Path
import h5py
import pandas as pd
from tqdm import tqdm
import seisbench
from .base import WaveformBenchmarkDataset, WaveformDataWriter
try:
from huggingface_hub import hf_hub_download
except ModuleNotFoundError:
hf_hub_download = None
class CEED(WaveformBenchmarkDatas... |
737a4fe59f5b5f92d7ae9947925728f59b3b7d56114e0e6180b6b817a9bd8fa9 | Python | 6,834 | 192 | from depth.models import depther
import torch
import torch.nn as nn
import torch.nn.functional as F
from depth.core import add_prefix
from depth.ops import resize
from depth.models import builder
from depth.models.builder import DEPTHER
from .base import BaseDepther
# for model size
import numpy as np
@DEPTHER.regis... |
01a9e387ce22082d8bd7eff6d28d7d084990b4d798bf0e252f723f11936c5f08 | Python | 6,836 | 171 | # 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, ... |
78a1454b76b3a12b7e43c264b0085a11b5e616ae92e4fb38d5a1b40f9926531a | Python | 6,842 | 153 | """The noise statistics detection shares between its methods.
Every method run on one channel needs the same two numbers — the robust sigma
and the median of the filtered trace — and each is a full partition of a
multi-million-sample array. They are now measured once per channel and handed
to each method, which is mos... |
a7a6a69114883e22bfcf9f65286224c455c7abf65ac34de5c4097e687b2bd5b3 | Python | 6,847 | 207 | """
Functions to read and write points files
TODO: The read functions have different outputs. We should decide on one
"""
import os
import yaml
import xml.etree.ElementTree as ET
def read_pts_file(file_name):
""" Read an elastix pts file
:param str file_name:
:return list pts_coord: list of list with pt... |
c0871d482be76583acfa9a950f020711750c0222fc09e05ed2224e0f03468c3b | Python | 6,847 | 213 | import numpy as np
from sklearn.preprocessing import MinMaxScaler, StandardScaler
#from numba import jit
def flip_genos(genodata):
'''
Flip genotype labels between representation of REF allele count and ALT allele count.
:param genodata: genotypes represented as 0,1,2, missing values encoded as 9
:type genodata... |
f666e4cad0b9663af49155339a4c04fc12386a5aaf8003fc1f1f0f327fb18d21 | Python | 6,848 | 149 | ##### PAPALE2025 LOCAL STIMULI PACKAGING #####
# In order to run the Papale2025 benchmark locally, you need to obtain the stimuli yourself due to licensing rules.
# Only the neural data can be downloaded from the public S3 bucket.
# This file packages the stimuli into Brain-Score format and saves them at the BrainIO ca... |
0d62ee160cc1f863d6cf4e99259eb75004628e8eebecdad36a822a1225cfeb5c | Python | 6,849 | 165 | """ Methods for outlier removal.
Reproduced with permission from Oscar Jalngefjord: https://github.com/oscarjalnefjord/ivim/tree/outlier by Elina Petersson
"""
import numpy as np
import nibabel as nib
import numpy.typing as npt
import matplotlib.pyplot as plt
from scipy.optimize import minimize
from scipy.stats import... |
8ad9f191c49a751e6c90c54b98abf20bf62d0239b8321e4636af4c91b5e21918 | Python | 6,849 | 217 | """File-system helpers for the ChemGraph Streamlit UI.
Functions for finding XYZ files and extracting directory paths from agent
messages. Helpers that searched the current working directory or the
global ``CHEMGRAPH_LOG_DIR`` were removed on purpose: they picked up
stale artifacts from earlier sessions. Per-exchang... |
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