sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
bef27b8b7553425c38de5ae67c6ef37e8fbaa9e62b9a18ba8e2fb91b062ef7ef | Python | 4,138 | 127 | """Logging utilities."""
import logging
class CustomFormatter(logging.Formatter):
"""Custom formatter for logging."""
def __init__(self, fmt: str, datefmt: str | None = None):
"""Initialize the custom formatter."""
super().__init__(fmt, datefmt)
grey = "\x1b[38;20m"
yellow =... |
c37f71ec53bcc2bd2a6732a59ff666c970de22fcbcb294f2a3253a1ccdee3ff5 | Python | 4,138 | 130 | import argparse
import torch
import torch.nn.functional as F
from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
from logger import Logger
class MLP(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, num_layers,
dropout):
super(MLP, self).__init... |
c94d0c4f4578627e6d370faff10830d6a50a676012ddb9c3d6a193761eaaeace | Python | 4,138 | 104 | import argparse
import utils
import keras
from keras.models import load_model
from os.path import join
import numpy as np
import gc
'''
calculate standard deviation of ensemble predictions for train/test/val data for DeepSTARR
'''
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--mod... |
2c106e419e2a65e5ee27defcf3457ba6dd74231097deb9cb96a3250e436f11d8 | Python | 4,140 | 143 | from __future__ import annotations
from typing import Final
import pytest
from PySide6.QtCore import QPoint
from PySide6.QtCore import QPointF
from PySide6.QtCore import Qt
from pytestqt.qtbot import QtBot
from labelme._app import MainWindow
from labelme._widgets._canvas import Canvas
from ..conftest import close_o... |
365de9d86a74842c4313e0bafc527eec6268da9701e9b9dfcce1255a448e8761 | Python | 4,142 | 107 | from collections import OrderedDict
from collections.abc import Callable, Mapping
from typing import cast
from eir.train_utils.ignite_port.base import Serializable
from eir.train_utils.ignite_port.engine import Engine
from eir.utils.logging import get_logger
logger = get_logger(__name__)
__all__ = ["EarlyStopping"]
... |
c3443393ed9d666d44ab1f384d1b2dad874742fa8f41b2ccc73ef227ac46cc48 | Python | 4,142 | 139 | # import tensorflow as tf
import numpy as np
from scipy.special import expit as logistic, logit
# from tf_agents.environments import utils
# from tf_agents.environments import tf_py_environment
from metamdp import *
# from metamdp_functions import *
import os
import json
import time
## Optimization hyperparams
N_SIM... |
1d150d938c228e5203b99263650f707d54de35e8afb09454fef2aa8dcc39a5f6 | Python | 4,144 | 205 | from seisbench.data.base import WaveformBenchmarkDataset
class CREW(WaveformBenchmarkDataset):
"""
Curated Regional Earthquake Waveforms (CREW dataset)
"""
def __init__(self, **kwargs):
citation = (
"Aguilar Suarez, A. L., & Beroza, G. (2024). "
"Curated Regional Eart... |
2625d9b45425bd4d765b5a0742d1da1cdcfcc0648c9eafa151d84790c89e0b4f | Python | 4,146 | 108 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import json
import traceback
from typing import Any
from django.dispatch import receiver
from rest_framework.renderers import JSONRenderer
from rq.job import Dependency as RQDependency
from rq.job import Job as RQJob
from rq.job import JobStatus
f... |
b2e7070d0931ee4746ef21793474c8b24d468f1e89ac92a12d98f4884dc8a465 | Python | 4,146 | 121 | import sys
import cebra
import os
import tempfile
import torch
import pandas as pd
import numpy as np
import pickle as pkl
import matplotlib
import cebra.models
import cebra.models
import cebra.data
from cebra.models.model import _OffsetModel, ConvolutionalModelMixin
from torch import nn
from scipy import stats
from ce... |
dfe78d82b39afcae56169f95c2b114772161ee592446044e1bad6730fdb18868 | Python | 4,146 | 119 | import rsatoolbox
from rsatoolbox import rdm
from scipy.spatial import distance as D
import torch
def calc_cosine_rdm(regressor):
return D.pdist(regressor, 'cosine')
def compute_RDMs_torch(data, metric='correlation', ranked=True):
if metric == 'cosine':
RDMs = calc_cosine_rdm_torch(data)
... |
fde00d577489e4c45227775686baf57bfc28a5868f7d6c19f1afbb263d650ef3 | Python | 4,146 | 186 | import re
def get_user_id(user):
return str(user['id'])
def get_username(user):
return str(user['username'])
def get_screen_name(user):
return str(user['name'])
def has_description(user):
des = str(user['description'])
if des is None or des == ' ':
return False
else:
retu... |
605c2e72f3b32214ecfa7b03a79960c5f4592e8d949cca244086135b4cbfbd61 | Python | 4,147 | 85 | # -*- coding: utf-8 -*-
"""
Created on Tue Jan 17 14:56:29 2023
@author: gligorov
"""
import tensorflow as tf
import numpy as np
from utils import plot_history
def create_model(input_shape, num_classes, dense_layers = [128, 64, 32, 16], conv_layers = True, conv_filters=32, conv_kernel_size= 3, activation='r... |
d96d4dd355411d48c4d2c65c88bb8aee9af2bd5a0c4bebde6c63154b19ea11df | Python | 4,148 | 125 | #!/usr/bin/env python
"""
A TCP communications class that provides the basic methods for relaying TCP messages.
Jeffrey Moffitt
3/16/14
jeffmoffitt@gmail.com
Hazen 05/14
"""
from PyQt5 import QtCore, QtNetwork
from storm_control.sc_library.tcpMessage import TCPMessage
class TCPCommunicationsMixin(object):
"""... |
c6226697edfa26e475ffebd98c32876cce5b4fa51aada6e643376a3368eb96b6 | Python | 4,149 | 129 | # -*- coding: utf-8 -*-
# @Author : LG
import os
from typing import Union
import imgviz
import numpy as np
import tqdm
from PIL import Image
from skimage.draw.draw import polygon
from ISAT.formats.isat import ISAT
class VOC(ISAT):
"""
VOC format
Attributes:
keep_crowd (bool): keep the crowded... |
c86c161e9335f87e390bb336cc7c4d8960228c4d40f4985e7629778212bda393 | Python | 4,149 | 117 | import numpy as np
from matplotlib.axes import Axes
from argclz import AbstractParser, argument
from neuralib.plot import plot_figure
from neuralib.util.verbose import fprint
from rscvp.util.cli import Suite2pOptions, DataOutput
from rscvp.util.cli.cli_camera import CameraOptions
from rscvp.util.util_lick import peri_... |
a90a65e19a191dcf81bb5bc1bd4cf9e0610cafa99a50fa013711b2938f1c72f6 | Python | 4,150 | 117 | import filecmp
import os
import string
import subprocess
import tempfile
import unittest
from io import StringIO
class TestFile:
def __init__(self, text=None, filename=None):
assert text is None or filename is None, "Cannot specify both text and filename for input"
self.text = text
self.fi... |
bc8ebe70532b650d6f4e241d3b1da54c70018e3819d5c139ba548e02df799091 | Python | 4,150 | 148 | """Shared event log for Academy-ChemGraph campaign runs.
The dynamic campaign layer treats agent messages and ChemGraph job updates as
one append-only event stream. The dashboard and HPC run scripts can consume
this file without knowing which science use case created the event.
"""
from __future__ import annotations... |
d55dcca96cd6c10924fc2fb49cbeb66c9cc866efe990b701089c7f803638315d | Python | 4,150 | 120 | """Compute the fMRI response distance (in MDS space) between the different image
classes.
Parameters
----------
subjects : list
List of the used NSD subjects.
data_ood_selection : str
If 'fmri', the ID/OD splits are defined based on fMRI responses.
If 'dnn', the ID/OD splits are defined based on DNN features.
ncsnr... |
d5f4c179bdd6170e4621011926d8f1a9b619e4a9b516b59ad93f471e9d50202c | Python | 4,151 | 111 | import numba
import numpy as np
import multiprocessing as mp
from functools import partial
import random
# Cite from https://github.com/numba/numba-examples
@numba.jit(nopython=True, parallel=True)
def get_bin_edges(a, bins):
bin_edges = np.zeros((bins+1,), dtype=np.float32)
a_min = a.min()
a_max = a.max()... |
f1891bddc1189207ba739b1bb5d72fde51149109d6245dd09729c7894acd6de2 | Python | 4,151 | 137 | # -*- coding: utf-8 -*-
"""Layering tools."""
import os
import numpy as np
from nibabel.freesurfer.io import read_geometry, write_geometry
from ..surface.mesh import calculate_area
__all__ = ["calc_equivol_surf", "get_meshlines"]
def calc_equivol_surf(file_white, file_pial, n_surfs, hemi, path_output):
"""The... |
05b5ca94d06efcc5bcd7514c3131e1f39f0147078036bd73742296f02ba4a22c | Python | 4,152 | 115 | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.ticker import FuncFormatter
from matplotlib.ticker import AutoMinorLocator
def main():
merged_final_csv = pd.read_csv(
"output/csv_files/raw_data_step4/raw_merged_eval_feature_data_final.csv"
)
merged_final_cs... |
7085d13ee296cdecbd8ce7d67709a0c375b6187323d8f51d683e993849c73334 | Python | 4,152 | 105 | import numpy as np
from ivim.models import NO_REGIME, DIFFUSIVE_REGIME, BALLISTIC_REGIME, INTERMEDIATE_REGIME
from ivim.seq.lte import MONOPOLAR, BIPOLAR
from ivim.optimize import crlb
D = np.array([0.5e-3, 1e-3, 1.5e-3])
f = np.array([0.05, 0.15, 0.05])
K = np.array([1.0, 1.2, 2.0])
Dstar = np.array([10e-3, 15e-3, 20... |
a92bd352e18b92569eae21590d8530dd55ee9a62231b2f89f2afefa62f4270d5 | Python | 4,152 | 105 | import argparse
import os
from math import log10
import numpy as np
import pandas as pd
import torch
import torchvision.utils as utils
from torch.autograd import Variable
from torch.utils.data import DataLoader
from tqdm import tqdm
from utils import ssim
from utils.dataset import TestDatasetFromFolder, display_trans... |
4733ae47a090bee4cc24c55a6f56cd23a63bdd9ef242418d3d7b786156f3b925 | Python | 4,153 | 106 | # Copyright (c) 2026 Rohit Jena. All rights reserved.
#
# This file is part of FireANTs, distributed under the terms of
# the FireANTs License version 1.0. A copy of the license can be found
# in the LICENSE file at the root of this repository.
#
# IMPORTANT: This code is part of FireANTs and its use, reproduction, or... |
9921489d298d9f86786a406e1ec966e1d05986bd1e0aa9a5ff62745d9cf6248d | Python | 4,153 | 131 | #!/usr/bin/env python
# Copyright 2014-2015 Numenta Inc.
#
# Copyright may exist in Contributors' modifications
# and/or contributions to the work.
#
# Use of this source code is governed by the MIT
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
"""
Entry point for the Pyt... |
db6b580f339a00ba1e2d1618f382af9077c871bae4fd89b6a0f40ea54d55ad01 | Python | 4,156 | 76 | from brainscore_vision import benchmark_registry
from .benchmarks.cavanaugh2002a_benchmark import MarquesCavanaugh2002V1SurroundSuppressionIndex, \
MarquesCavanaugh2002V1GratingSummationField, MarquesCavanaugh2002V1SurroundDiameter
benchmark_registry['Marques2020_Cavanaugh2002-grating_summation_field'] = \
Mar... |
f5fd19bafdc522cc03be7ba5bd6cdea23d4bdb3c7129208eb692bb3d0fc31cc2 | Python | 4,157 | 107 | import torch
import torch.nn as nn
import torch.nn.functional as F
from conf import global_settings as conf_set
class Snn_Conv2d(nn.Conv2d):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
... |
edc0859a29118472de6be7a888b70e5edd1da925554837abcfb3acac58420536 | Python | 4,158 | 166 | """
This module simulates MRI signal based on Bloch equations.
Author:
Zhengguo Tan <zhengguo.tan@gmail.com>
Soundarya Soundarresan <soundarya.soundarresan@fau.de>
Julius Glaser <julius.glaser@fau.de>
"""
import numpy as np
import torch.utils.data as data
def get_B(b, g):
num_g, num_axis = g.shape
... |
c159b2377c1584d5a417bc02f1bfdfc66acf4fa8cb51c23de3ef60851f34ec98 | Python | 4,159 | 88 | import numpy as np
from result_caching import store
from brainscore_core.supported_data_standards.brainio.assemblies import DataAssembly
from brainscore_vision import load_dataset, load_metric
from brainscore_vision.benchmark_helpers.properties_common import PropertiesBenchmark, _assert_grating_activations
from brains... |
74da1bd30ae98ca640a0d4c6019ddc1fc2469614d17dead4a38efa2c5514ce48 | Python | 4,160 | 144 | #!/usr/bin/env python
"""
split bam file into .shrt.bam and .long.bam
Created by Rongxin Fang
Modified by Armen Abnousi
"""
import sys
import pysam
import collections
from itertools import islice
from itertools import combinations
import time
import os
import subprocess
import shutil
import glob
def main():
input_b... |
de60c45dcb15aca20940446ac67b74e9a3d98e5687329844a3b16cd4b4d4061b | Python | 4,160 | 186 | import re
def get_user_id(user):
return str(user['id'])
def get_username(user):
return str(user['username'])
def get_screen_name(user):
return str(user['name'])
def has_description(user):
des = str(user['description'])
if des is None or des == ' ':
return False
else:
retu... |
eb94cd011014d1f2ddeda4d8e0e03c6ba638ea250125b3b6ad07731efd354406 | Python | 4,161 | 116 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
"""List the jobs of an existing task or project with their stage, state, and
assignee, optionally as a CSV report.
Steps:
1. Query jobs scoped to --task-id or --project-id, most recently updated
first. --stage / --state filter server-side, so... |
1944304a1beeaa79baa24016d3c85324a842f61441bbd7c34684fa6f2362e3f9 | Python | 4,162 | 100 | # A hyperparameter optimisation of MPNN model using approach
# from Greenman et al. https://doi.org/10.1039/D1SC05677H
# original training data was cleaned and enriched with natural colourants data
# training goes on GPU the chemprop must be configured accordingly
import numpy as np
from hyperopt import hp, fmin, tpe,... |
84125f1d5994ce9bcfaf76e3096fa63e3a6c7e46ab55a96b4adac5d46f3e5f39 | Python | 4,163 | 106 | # Specify the directory of the QSMnet+ code from Github
# QSMnet_HOME = '/project/3015069.05/bids/code/QSMnet/';
import sys
import os
sys.path.append(os.path.join(QSMnet_HOME,'Code'))
import tensorflow as tf
import scipy.io
import time
import network_model
import numpy as np
from utils import *
#
# Description :
# ... |
3daafd78f7e33639f083484891aa44ab668b257346c956506767c919d3ebf4be | Python | 4,164 | 135 | #!/usr/bin/python3
#
import os
import sys
import argparse
from sklearn.neighbors import NearestNeighbors
from uac import utils
# Create the parser
my_parser = argparse.ArgumentParser(description="Calculation of new atrial coordinates for RA")
# Add the arguments
my_parser.add_argument("target_path", metavar="path", ... |
98fd7b4edc7bb5c32d151506691d6c14ac3379bed52a17b941e4de5d92d0eafe | Python | 4,165 | 133 | import argparse
import h5py
import os
import pathlib
import torch
import yaml
import matplotlib.pyplot as plt
import numpy as np
import sigpy as sp
from sigpy.mri import muse
import torch.nn as nn
import torch.optim as optim
from deepdwi import prep
from deepdwi.models import mri
from deepdwi.models import autoencod... |
fba9394e90c7384fa50888cd771776a533ff529146747fa6499a8000ad1d9e13 | Python | 4,166 | 104 | # 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, ... |
73f0ca550432cc761fb60a143bed7606eaa1b01248fe1e9273283a1b3b9ef9f6 | Python | 4,167 | 95 | import csv
import os
import pkgutil
import re
from typing import Dict, List, Optional, Union
from .dataset_info import DatasetInfo
# NOTE no ambiguity wrt to mapping from # classes to ImageNet subset so far, but likely to change
_NUM_CLASSES_TO_SUBSET = {
1000: 'imagenet-1k',
11221: 'imagenet-21k-miil', # m... |
04ab8d110ac154fda37e9bb0dd21eeab5a47aabaac79d7d1d68ace0f32d3f777 | Python | 4,169 | 105 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
def analyze_middle_20_slices(data, use_unnormalized=True):
# grab middle 20 slices from each series and average them
series_averages = []
# pick which column to use
fosb_column = 'mean_fosb_per_pixel_ast... |
a6d57f86896ef4353a92432f00ce3c4db52005d1e4f518df8a9abcfe6e1217b9 | Python | 4,170 | 80 | 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 nnUNetDataLoader3D(nnUNetDataLoaderBase):
def generate_train_batch(self):
... |
ec0a0d304f9facb98f78b1cf8e0bf4ab9cba20602272eaf70ac91f1119d72d2b | Python | 4,170 | 107 | import os
import glob
import json
import sys
import shutil
from os.path import join as ospj
from smriprep.interfaces.surf import normalize_surfs
########################################
# Set variables and dirs
########################################
# Parse command-line arguments
subject = sys.argv[1]
config_file =... |
858e712c5728515b82a2dccbe946949ac10e99491e1b433462a8c26cea566a0a | Python | 4,171 | 149 | # FetMRQC_SR: Quality control for fetal brain MRI
#
# Copyright 2025 Medical Image Analysis Laboratory (MIAL)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/lic... |
962a5b64373c842110c4053b9ec4ef781786f8818098756ed06483a989814a9d | Python | 4,171 | 87 | import numpy as np
import os
from brainscore_core.supported_data_standards.brainio.assemblies import NeuroidAssembly
from brainscore_core.supported_data_standards.brainio.stimuli import StimulusSet
from brainscore_vision import load_ceiling, load_metric, load_dataset
from brainscore_vision.benchmark_helpers.neural_comm... |
c001c4ae39eba072128d3d405b21c1b3d46acbc81b9249ef305e5277d7ea9ef6 | Python | 4,172 | 137 | import nibabel as nb
import numpy as np
import glob
from matplotlib import pyplot as plt
from sklearn import metrics as skm
from argparse import ArgumentParser
def adjusted_mutual_info(atlas1, atlas2):
"""Calculate adjusted mutual information between two atlases
Parameters
----------
atlases : [type]... |
9da9bf1f760bb5b7a1e1a63712b5954986ad5d8d70a0a18cfb0efeea2aab9d5c | Python | 4,173 | 114 | import arbor as A
from arbor import units as U
class recipe(A.recipe):
def __init__(self, n):
# Call our parent; needed for proper initialization.
A.recipe.__init__(self)
# Cell count; should be at least 3 for the example to work.
assert n >= 3
self.cells = n
# Unif... |
cc2c071b0b85f3237c37e82f34e576769605c4640d77e613929804b7ad64bbaa | Python | 4,174 | 100 | import sys
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from geoseg.models.MixerUnet import reparameterize_model
from geoseg.losses import *
from geoseg.datasets.cvc_clinic import CVC_Dataset
from geoseg.models.MixerUnetcopy import Rep_UNET_down
from catalyst i... |
9ff7c3804225ea3d000d0c10cd76ae3fad6ca1e1666e8186b9ee3eac4d811f9f | Python | 4,175 | 129 | """resnext in pytorch
[1] Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, Kaiming He.
Aggregated Residual Transformations for Deep Neural Networks
https://arxiv.org/abs/1611.05431
"""
import torch.nn as nn
import torch.nn.functional as F
#only implements ResNext bottleneck c
#"""This strategy exp... |
c35e87a1c57eb72fbc580f421f1b48cc26ed595045b3768055474cc2c5f44a31 | Python | 4,176 | 98 |
import matplotlib
from numpy import loadtxt
from fig import module_utility
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
sub_group = list(range(1, 29))
sub_group.extend([30, 32,33,34,35])
# exp data
num_subject = len(sub_group)
angle_colume = 19 # col20 subj10 (do not forget -1)
num_sa... |
8eedb2851e83de1b8492043aa64e089e0383775fa0bdb02d3e0fcf74980e943b | Python | 4,177 | 119 | # Copyright 2025 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, ... |
d387179a540488720dafa5ebaa1d0b809af9dd3b7ac082635e39fe579dd4a262 | Python | 4,178 | 127 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
4ee738d77cd93328a229a280d9c4fa6d971de8bc6d175f114d668171f7b01e4a | Python | 4,179 | 90 | import argparse
import torch
import pandas as pd
import esm
from tqdm import tqdm
import constants
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Define the command-line argument parser
def create_parser():
parser = argparse.ArgumentParser(description="Compute log-likelihood ratios for DMS... |
f601992504291340d34ec3a56307cc104ab5acc7754aaec6c7eb30f36446e48c | Python | 4,179 | 104 | """What the number in a lag/bin folder name means, and what to call it.
Every functional-connectivity measure in the pipeline has one timescale
parameter, and every output is filed under it — ``1000mslag/``, a ``Lag``
column, a ``Lag1000ms/`` group folder. But the parameter is not the same thing
in each measure:
* **... |
05783156747fbecf45863dc7fd30e689bb17ab8fefbaf264c72549e4df60b129 | Python | 4,180 | 113 | import os
import numpy as np
import pandas as pd
import time as tm
from sklearn.svm import LinearSVC
import rpy2.robjects as robjects
from sklearn.calibration import CalibratedClassifierCV
def run_SVM(DataPath, LabelsPath, CV_RDataPath, OutputDir, GeneOrderPath = "", NumGenes = 0, Threshold = 0.7):
'''... |
6fde7e6ca39ed42a6246d3a86c21959d4b3f563ce4ee1654882076a382c617eb | Python | 4,180 | 100 | import sys
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from geoseg.models.MixerUnet import reparameterize_model
from geoseg.losses import *
from geoseg.datasets.cvc_clinic import CVC_Dataset
from geoseg.models.MixerUnetcopy import Rep_UNET_down
from catalyst i... |
4d419b0dc245fe0f7a1b9d85c2c281a0798babd70a18038ab93e1805e07c992a | Python | 4,181 | 138 | from collections.abc import Sequence
from dataclasses import dataclass
from typing import TYPE_CHECKING
import pandas as pd
from eir.data_load import label_setup
from eir.data_load.label_setup import (
TabularFileInfo,
al_label_transformers,
transform_label_df,
)
from eir.experiment_io.label_transformer_i... |
4a1dba80efe5000cc483316f2cfaa5c1ed7ef858dd9b932416fdcc97a596a070 | Python | 4,182 | 83 | from tensorflow import function as tf_function, where as tf_where, GradientTape
from tensorflow.math import reduce_mean, square, abs as tf_abs
LAMBDA_CYC = 30
class Discriminator_trainer:
def __init__(self, discriminator, generator, optimizer):
self.discriminator = discriminator
self.generator ... |
d0df48efd9266c83ceaeb9461974f5c6a04f76c5d03e1a68c517f29b4cddb25a | Python | 4,182 | 113 | import numpy as np
def GenerateSpikes(IntensityFunc, MaxRate, PositionFunc, TotalTime):
# Start by generating spikes for a homogeneous Poisson process
nHomogeneousSpikes = np.random.poisson(MaxRate * TotalTime)
tHomogeneousSpikeTimes = np.random.uniform(0, TotalTime, nHomogeneousSpikes)
tHomogeneousSp... |
16c9e21e3a452ab213dbbfcac0a6872e077e3436b2c275ff2d3d774621e28c0f | Python | 4,184 | 121 | import brainpy as bp
import brainpy.math as bm
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import scipy
import sys, os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from cann import CANN1D
from matplotlib.animation import FuncAnimation
... |
58579685fd001eed4fdb5a1f25a6ea9204ace424516b15b50f60f9c6d4df744a | Python | 4,184 | 115 | from argclz import AbstractParser, union_type, str_tuple_type, as_argument
from neuralib.plot import plot_figure, ax_merge, plot_peri_onset_1d
from rscvp.behavioral.util import *
from rscvp.behavioral.util_plot import *
from rscvp.util.cli.cli_output import DataOutput
from rscvp.util.cli.cli_stimpy import StimpyOptions... |
d2c2e7b500e584d7aa391d19a57e7c9395aaf24a0b613598c32a7d8a843303fa | Python | 4,184 | 133 | # System cutoff techniques for local analyses
# Imports
import sys
import ase
import ase.io
import ase.atoms
import numpy as np
def find_atom_order_to_interface(
mol_xyz: ase.atoms.Atoms, clus_xyz: ase.atoms.Atoms, site_size: int
) -> ase.atoms.Atoms:
"""From the molecule and cluster xyz files, returns two l... |
d44894341213c24009339de3ab3f59374872502026671e25b41ea9b4b63a05f1 | Python | 4,184 | 59 | from brainscore_vision import benchmark_registry
from . import benchmark
DATASETS = ['colour', 'contrast', 'cue-conflict', 'edge',
'eidolonI', 'eidolonII', 'eidolonIII',
'false-colour', 'high-pass', 'low-pass', 'phase-scrambling', 'power-equalisation',
'rotation', 'silhouette', 'ske... |
4f9c2f48a378cf92bfde0321ca9512de25cd61fa51ea873f0d55de1eb646346e | Python | 4,185 | 147 | # 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 functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWa... |
8f145f421b66983255bdcd06613ed6279a442cdb20bc7fa6a389fbed0e71a005 | Python | 4,185 | 135 | #############################################################################
### Recursively moves through directories and extracts parameters and the ###
### mean firing rates of neuronal subpopulations ###
#############################################################################
### Cop... |
4b0c36e325a1bde72b850a9b503a7848ebcf335bce9e819a8c98b1d17a4536ff | Python | 4,186 | 110 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Save connections as TRK to HDF5.
This script is useful to convert a set of connections or bundles to a single
HDF5 file. The HDF5 file will contain a group for each input file, with the
streamlines stored in the specified space and origin (keep the default if you
are ... |
b5d329ae97ed6b7da0bbf47011c14b6fe4179e15caf9c722713e7f938455f340 | Python | 4,186 | 107 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
"""Inspect an existing task (labels, jobs, frames), export its dataset to a
local zip, and export its event log to report quick analytics.
Steps:
1. Retrieve the task and print a summary: labels, jobs (stage/state), frames.
2. Fetch the server's... |
da8e801533edf42651997569fe57bd2ee328a382636c4afe4320d7fa206d0bc8 | Python | 4,186 | 123 | '''
ClH2O
'''
import numpy as np
import sys
def intro():
print("""
SHORT List of potential energy surfaces for FH2O:
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 an... |
e99e6ec6803b8627d91b560e00ddb55f8d9f85d014c697f9ab96f0ef5490d944 | Python | 4,186 | 146 | from __future__ import annotations
import json
import pytest
# Skip when the optional 'academy' extra is absent. The dashboard
# module itself is pure stdlib, but the import guard is applied
# uniformly across the academy test suite.
pytest.importorskip("academy")
import chemgraph.academy.dashboard as dashboard
fro... |
d3a96752fbca76588550c3a7b858e1d8b05184c4033e353d97988098e1dbf5b1 | Python | 4,187 | 125 | # Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
"""Necks are the interface between a vision backbone and the rest of the detection model"""
from copy import deepcopy
from typing import List, Optional, Tuple
import torch
import torch.nn as nn
class Sam3DualViTDetNeck(nn.Module):
def __... |
fee49bc226227252c2bc4920e040c394a4ac74a3008f101714ae29e66271c417 | Python | 4,187 | 107 | #!/usr/bin/env python3
# -*- coding: utf-8
"""
Script that runs cell2location on simulated data, sent from Vitalii
Created on 2020/02/03
@author romain_lopez
"""
import os
# this line forces theano to use the GPU and should go before importing cell2location
os.environ["THEANO_FLAGS"] = 'device=cuda0,floatX=float32,f... |
86145d8da594ebcc549b09e1040eaad7b9a3cbe48f06c6ecd76c90b15d0c2f6b | Python | 4,188 | 118 | import argparse
import os
def gen_rosetta_script_xml_str(variant, template_fn, offset, chain):
"""
Generate RosettaScript XML string from mutation variant(s).
Parameters:
variant (str): Mutation string, e.g., "A23D,C45K"
template_fn (str): Path to the template directory (without filenam... |
ec14a987173cce9797f50b245eb660c420939079f36448ed685b97215fb92790 | Python | 4,188 | 122 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2017 Timothy Dozat
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless ... |
4e595a9395a2a14249f628bfba668e602c58ed617337498183a1d08e591f467d | Python | 4,190 | 99 | from torch_geometric.loader import DataLoader
import scanpy as sc
import torch
import torch_geometric
#from torch_cluster.knn import knn_graph
from sklearn.neighbors import kneighbors_graph
import numpy as np
from scipy.spatial.distance import pdist, squareform
def get_dataset_loaders(h5ad_path: str, sample_id_name: ... |
27876088fec6b1e418055fe847055f886c7d58518705b70b1ad93595d704fcc0 | Python | 4,191 | 96 | from qtpy.QtWidgets import QWidget, QDialog, QLabel, QPushButton, QLineEdit, QFileDialog, QVBoxLayout, QSpinBox
from napari_dmc_brainmap.utils.general_utils import split_to_list
from typing import List, Optional, Tuple
class GeneInfoDialog(QDialog):
"""
Dialog for entering gene information, including file sele... |
1274ad1b129b33c0261a4eb0469e23bad294d533d0322287810743c250858298 | Python | 4,193 | 128 | # Copyright 2016 Numenta Inc.
#
# Copyright may exist in Contributors' modifications
# and/or contributions to the work.
#
# Use of this source code is governed by the MIT
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
from functools import cmp_to_key
from nab.de... |
14110a620b833a6426e676c8347dda7b651b84bb0b833b0b7fb5c725972d0fa5 | Python | 4,193 | 128 | # src/config.py
"""
Central configuration for the strabismus-surgery ML project.
Usage
-----
>>> from src.config import C
>>> C.DATA_PATH
PosixPath('.../data/Data_M1D6_Preproc.csv')
"""
from __future__ import annotations
import os
import random
from dataclasses import dataclass, field
from pathlib import Path
from... |
e2213867da86a611c737456d38c8a43defa25352fc426db138a05bc25fda82c6 | Python | 4,194 | 83 | """One colour per cell type, shared by the tracking pages.
Both the per-cell page and the run viewer's network view can colour cells by
every marker at once. They are separate pages, so the rule lives here once as a
JavaScript snippet each of them inlines, and the two can never disagree on what
a colour means.
The ru... |
410da52a15376576f049ee3eae0a55cf6b1b8e1c1d3f5ee4f07371315eb66753 | Python | 4,196 | 123 | import os.path as op
import typing as t
from pydantic import BaseModel, StrictBool, ValidationInfo, field_validator
from .errors import FileExtensionError
class modelmrtrix(BaseModel):
"""Define model validator for common MRtrix3 CLI options."""
input: t.Optional[t.Union[str, t.Any]] = None
output: t.O... |
0e1fb591987c4e46e29c42ec6b08ae6752cbfaab464d34a2881d0a95919c290d | Python | 4,197 | 129 | import numpy as np
import pytest
from ase import Atoms
from chemgraph.tools.ase_tools import (
run_ase,
)
from chemgraph.models.atomsdata import AtomsData
from chemgraph.models.ase_input import ASEOutputSchema, ASEInputSchema
def is_fairchem_installed():
try:
import fairchem.core
return True
... |
d7b70c9e23ad5ffd7b39a87d0eb638bf96757f2029c23e6ac673f76a1ee31b44 | Python | 4,197 | 100 | import sys
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from geoseg.models.MixerUnet import reparameterize_model
from geoseg.losses import *
from geoseg.datasets.cvc_clinic import CVC_Dataset
from geoseg.models.MixerUnet import ConvMixer_UNET_down
from catalyst ... |
44f713f1b66c7502eafdb8cce8b81a35a060dfff08eb3199085cc8dde132a2bb | Python | 4,199 | 119 | # %BANNER_BEGIN%
# ------------------------------------------------------------------------------------
#
#
#
#
#
#
# ------------------------------------------------------------------------------------
# %AUTHORS_BEGIN%
#
# Creator: Vishal Sharbidar Mukunda
#
# %AUTHORS_END%
# -----------------------------------... |
9644929af4fb2f2f9f013792c7655fe489313b9ce3cade7dc477d031b48c9681 | Python | 4,200 | 73 | '''
embeddings::power.py
Common functions for power simulations. Available as embeddings.power.*
'''
from .internal import *
import numpy as np
from scipy.stats import chisquare as chsq
from scipy.stats import ttest_ind as tt_ind
def chisquare(N: int, R: int, p: np.ndarray, k: int = 1e4, alpha: float = 5e... |
c03578140ac5b8f20d7afc5e9e43ddc54386c4117bac235f7c98f1e4632d668f | Python | 4,201 | 129 | # -*- coding: utf-8 -*-
"""Estimate scan-to-scan repeatability."""
import os
import random
import matplotlib.pyplot as plt
import nibabel as nb
import numpy as np
from nibabel.freesurfer.io import read_label, read_morph_data
from scipy.stats import pearsonr
__all__ = ["estimate_reliability"]
def estimate_reliabili... |
fae6d8a9361d0db9a6f2c5565066fff363fb1cd15eccbe95963465fb1ecd301a | Python | 4,201 | 156 | """Elekta Task Dataset: AE-HMM Pipeline.
In this script we train an Amplitude Envelope Hidden Markov Model (AE-HMM)
on source reconstructed task MEG data.
The examples/toolbox_paper/elekta_task/get_data.py script can be used
to download the training data.
Functions listed in the config are defined in osl_dynamics.co... |
79cd869a9cc7a530ed9ff5327a68cc167512e74368da0f58202a9d431b2c8cc9 | Python | 4,203 | 119 | """Ground-truth dataset loading and validation for ChemGraph evaluation."""
import json
from pathlib import Path
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field
from chemgraph.utils.logging_config import setup_logger
logger = setup_logger(__name__)
class GroundTruthItem(BaseMode... |
d2c2ccf8d2145f52d5e372b9024858302d460eaf7fd2b699722dc9a0e68dca65 | Python | 4,203 | 109 | import os
import numpy as np
import pandas as pd
import time as tm
import rpy2.robjects as robjects
def run_ACTINN(DataPath, LabelsPath, CV_RDataPath, OutputDir, GeneOrderPath = "", NumGenes = 0):
'''
run ACTINN
Wrapper script to run ACTINN on a benchmark dataset with 5-fold cross validation,
... |
5b1dd5753b2fc046b030f8e756fcef7f9f8dc7e5536fe75dd4214746d9f02a72 | Python | 4,204 | 132 | from __future__ import annotations
import os
import tempfile
from collections.abc import Sequence
from io import StringIO
from pathlib import Path
from typing import Final
from ruamel.yaml import YAML
from ruamel.yaml.comments import CommentedMap
from ruamel.yaml.comments import CommentedSeq
from ruamel.yaml.error im... |
95d44352d1b51233a202bf2b1341364852fc19ee8b70e76f1a7338dd51b9ccea | Python | 4,204 | 116 | import numpy as np
from scipy.sparse import csr_matrix
def compute_sparse_decisions(bundles_being_recognized, n_streamlines):
"""
Compute a sparse matrix of distances to ROIs for the streamlines that are
currently being recognized. This can be used to weight decisions by distance
to ROIs, without havi... |
a4b6aa2537aaaf89a367231c7461da647fd84496fa7fb3a22c9653dfaa840518 | Python | 4,204 | 98 | #%%
from deep_events.database.extract_yaml import MAIN_PATH
from deep_events.database import get_collection, get_cluster
from pathlib import Path
from pymongo import MongoClient
from benedict import benedict
import tifffile
import os
import shutil
def main(folder = MAIN_PATH, collection='mito_events'): #pragma: no cov... |
2657891a2af7ff2c10a0bacc891e900570a14f1e5c9857d776bdd3273dfefe38 | Python | 4,206 | 131 | import unittest
import numpy as np
class TestUtils(unittest.TestCase):
# Correctly identifies and replaces outliers in a 1D numpy array
def test_hampel_filter(self):
from dynamont.segmentation.utils import hampel
signal = np.array([1, 1, 1, 10, 1, 1, 1])
expectedOutput = np... |
489e2b8393b9e23ae3fb3303f3efc85b766751454c712e2abcee3382c0376d5c | Python | 4,206 | 93 | #!/usr/bin/env python
#!/usr/bin/env python
import warnings
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.externals import joblib
from PAINTeR... |
400764e5faa12aae1d3ef100d67199c3bc1c692d7968b2f079df4f1e2dafe065 | Python | 4,207 | 136 | # This script will import Cytosim's filaments into Blender
# It must be run from within Blender, and will read "actin####.txt" files
# to generate a collection of NURBS spheres
#
# The input files should be generated by Cytosim's tool "cymart"
# and copied to a local blender directory called 'cymart'
#
# Authors: Richa... |
b844e7ff8ab37d4ca11a462f9026d46a2456635e055e55fc5dd3282595dd80df | Python | 4,208 | 120 | import jax.numpy as jnp
import matplotlib.pyplot as plt
import glob
import sys
import os
import re
import json
plt.rcParams["font.sans-serif"] = ["DejaVu Sans"] # Best available
plt.rcParams["font.family"] = "sans-serif"
plt.rcParams.update({
"font.size": 10,
"axes.titlesize": 10,
"axes.labelsize": 8... |
3c6e7f392527eeab9bc3e3bd7a6bc424df003b862bee4db1385c90becbcbfaf9 | Python | 4,209 | 112 | import numpy as np
import warnings
import scipy.io as sio
from scipy.stats import pearsonr
from sklearn.model_selection import cross_val_predict
from sklearn.kernel_ridge import KernelRidge
K = 10
run_session = '1LR'
mykernel = 'cosine' # cosine linear
alpha_list = [0.1,1,2,5,10,15,20,50,80,100,150,200,300,1000,100... |
c01e68da8ed3d5873e9f2cd40bebd68024589ae1af19215c44bc78b373cf07e1 | Python | 4,209 | 164 | import copy
import torch
import optuna
from torch.optim.lr_scheduler import ReduceLROnPlateau
from utils import (
device,
clip_gradients
)
from save import save_embeddings
def train_epoch(
model,
optimizer,
data_loader,
loss_fn,
max_grad_norm=5.0,
clip_method='n... |
29b48eed8597327fd51d62f2c98bc95cb53e754c482eed282acb0c1965d4d3f7 | Python | 4,210 | 100 | import pytest
pytest.importorskip("fireants_fused_ops")
import torch
from time import time
from torch import nn
from torch.nn import functional as F
from fireants.interpolator.grid_sample import torch_affine_warp_3d
from fireants.interpolator.fused_grid_sample import fused_affine_warp_3d
import itertools
# set seeds
... |
a9ebb6505279d976740c63449430d03a4ab38daecb40153daf5e8209d84c59d0 | Python | 4,211 | 106 | # 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 Any, Dict, List, Tuple
from .imag... |
6a2ae5823e351918b6f7d38ec2afe244b841898f5609eebb3ee4ccfb6c6d460b | Python | 4,212 | 128 | """
Example usage of HarveST package
"""
import os
from harvest import Harvest
import torch
import numpy as np
import random
def setup_seed(seed):
"""Set random seed for reproducibility across all libraries."""
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.see... |
469db54c96d72b4b8440b70c4adce4dbcd9ebcc7b7ced205c205afd60863f673 | Python | 4,214 | 148 | # Copyright 2014 Numenta Inc.
#
# Copyright may exist in Contributors' modifications
# and/or contributions to the work.
#
# Use of this source code is governed by the MIT
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
import copy
import numpy as np
import os
import pandas... |
694ccac5f57804f8d6402cefc429db4b7db4755355858aeecf4f3a974806a58e | Python | 4,214 | 128 | import os
import pandas as pd
from util.config_vars import config
from step3_1_get_features.step3_b_sim import ImageComparison2D
from step3_1_get_features.step3_a_3drender import Make3DRender
from step3_1_get_features.step3_c_voxel_analysis import VoxelAnalysis
CSV_HEADERS = [
"Original Nifti",
"Defaced Nift... |
4e427fbb232ed0c120074548905577f237daaa9b8c6af18987f8fa012074c4fc | Python | 4,215 | 114 | import argschema as ags
import numpy as np
from skeleton_keys.io import read_bytes, read_csv
from skeleton_keys.cmds.process_morphology_features import (
select_and_convert_depth_columns,
analyze_depth_profiles,
)
from skeleton_keys import cloudfields
class CalcHistogramLoadingsParameters(ags.ArgSchema):
... |
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