sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d3bc2988cdd8c4aeb47b503f203a1335aab35e2dc4699ca16fc35fcca971a574 | Python | 6,148 | 178 | #!/bin/python3
# Utility functions for displaying and storing data
# Copyright 2021-2024 Jannik Luboeinski
# License: Apache-2.0 (http://www.apache.org/licenses/LICENSE-2.0)
# Contact: mail[at]jlubo.net
import time
from datetime import datetime
import os
import re
###################################################... |
48776d72249dc38c12e8d4c9b6c09883f14f669cfccb7ffe82e6def6543b85d5 | Python | 6,150 | 195 | # -----------------------------------------------------------------------------
# Copyright © 2023- The Spyder Development Team
#
# Released under the terms of the MIT License
# (see LICENSE.txt for details)
# -----------------------------------------------------------------------------
"""Provides utility functions f... |
027a78b1e6ab1ad275ab3b2740fd15fd772253c9b487a7ac7b131d0bb72e364a | Python | 6,151 | 154 | '''
LiFH
'''
import numpy as np
import sys
def intro():
print("""
SHORT List of potential energy surfaces for LiFH:
P: potential energy, G: Gradient, D: Nonadiabatic coupling vector
U: diabatic potential energy matrix
UG: gradient of diabatic potential energy matrix
This is listed at the end... |
0914228a55d70544e8f5bfd53db6926d524425e4a1c61ed958647b0d0c978b43 | Python | 6,151 | 193 | #!/usr/bin/python
#
## @file
#
# AA Opto-electronics AOTF control.
#
# This class saves the set frequency for each channel
# to make it easier to implement a frequency offset
# function.
#
# Hazen 12/10
#
import time
import storm_control.sc_hardware.serial.RS232 as RS232
## AOTF
#
# Class for cont... |
41a43f40f26447374a9d89b4822a9675c55d71bbb5510731d8772f349570728b | Python | 6,152 | 171 | # 移除 s1 层
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from model.coatnet import conv_3x3_bn, MBConv
# Assuming conv_3x3_bn and MBConv are defined elsewhere in your code
class hswish(nn.Module):
def forward(self, x):
return x * F.relu6(x + 3, inplace=Tru... |
a21703bb49d77e558e869a613ad09f8146c227b74c08d2c0a22befe54ff73bd4 | Python | 6,152 | 164 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from matplotlib.colors import is_color_like
class OrthoAxes:
def __init__(self, fig, coords, interactive=True, closest_threshold=10):
# Creating figure at the right size and ratios
size = coords.ptp(axis=1... |
6bb0fd317cd588da77cf1ebf79282991fc5c9229adae71606d1b195bff4ff3c9 | Python | 6,154 | 144 | # -*- coding: utf-8 -*-
#
# partition.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.... |
8b23b42ecfb93be83bb3eba904488dcad78328b45223dc867691b9b1c5646f3f | Python | 6,154 | 144 | #######################################################################################
### Functions to calculate the mutual information from the firing rate distribution ###
### in a network at two different times ###
########################################################... |
c1f7317283c66436dd07c94f042cbbdceec04bd34e1bcc52402b9a5f1113a6c7 | Python | 6,155 | 141 | from collections.abc import Sequence
from dataclasses import dataclass
from typing import TYPE_CHECKING, Literal
if TYPE_CHECKING:
from eir.setup.schemas import al_max_sequence_length, al_tokenizer_choices
al_sequence_operations = Literal["autoregressive", "mlm"]
@dataclass
class SequenceOutputTypeConfig:
"... |
50b12a8d25e550b4c24950434c75e0eabc84dc06ea93130882ee9d36250f07ae | Python | 6,156 | 183 | """Extract and save the NSD-core image features, using deep neural network
models.
Parameters
----------
model : str
Name of deep neural network model used to extract the image features.
Available options are 'alexnet', 'resnet50', 'moco', and 'vit_b_32'.
nsd_dir : str
Directory of the NSD.
project_dir : str
Direc... |
8c454f5d85718b06d55043ccb8526038ac31f0193c170159518a3542e58563d2 | Python | 6,157 | 143 | import os
import yaml
import mlflow
import torch
from utils.env_config import path_check_and_join
from utils.models import WindTurbineModel
from model_optim.optimize import optimize_model, help_evaluate_dataset, help_mlflow_logging
def execute_optimization(experiment_id, experiment_name, run_id, run_name, dir_yaml, p... |
bdaa1345c2ecb9770891cb095b1cf805f87eff93b699d2a035d6f0e6f05fce2c | Python | 6,158 | 199 | """
Defines various activations functions to be used in neural net.
"""
import numpy as np
import torch
from typing import Union
######## MML_ activation
def MML_activation(x: torch.Tensor, leak: Union[float, int]):
"""Returns the output of the Michaelis-Menten function
Parameters
----------
x : tor... |
27ecbc691f0178083dea4d01cfb3eab41806b8d332dbd3a36144ff7e42d89062 | Python | 6,159 | 291 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Fit FLIM data
"""
import os
import mbflim.utils as ut
import mbflim.parameters as par
import tools.flim.flim_set_processing as fl_setpro
import tools.flim.flim_visualization as fl_vis
#%% Parameters
# Parallelism
PROCESS_NB = 16
MAX_THREAD_NB = 32
# Mask
# Somata... |
a0b0aa305ccb4cf44d30b24e061b7335d793bd73748b92e3abfc17facfd0a815 | Python | 6,159 | 169 | import os
import numpy as np
import pandas as pd
import scanpy as sc
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import cm, colors
from scipy.cluster.hierarchy import linkage, dendrogram, fcluster
# Compute mean expression within a cell group
def grouped_obs_mean(adata, group_key, layer=None... |
0113bb7d0e59ad9bcf530d3c280ee9ebe1ae9d6069a16641da2949e6b801e778 | Python | 6,160 | 147 | """ Random Erasing (Cutout)
Adapted from https://github.com/rwightman/pytorch-image-models/blob/master/timm/data/random_erasing.py
"""
import sys
import random
import math
import torch
from monai.transforms.transform import Transform
def _get_pixels(per_pixel, rand_color, patch_size, mean=0, std=1.0,
... |
09c161eca279a81fb956690f00bf74f3bd8c632218687fdd1c0dc6cafc99a04b | Python | 6,160 | 183 | """Extract and save the NSD-synthetic image features, using deep neural network
models.
Parameters
----------
model : str
Name of deep neural network model used to extract the image features.
Available options are 'alexnet', 'resnet50', 'moco', and 'vit_b_32'.
nsd_dir : str
Directory of the NSD.
project_dir : str
... |
1986e27f4a4d0aa324a2be528604c553ae853f346a89226beb53da565bf9a453 | Python | 6,160 | 170 | from __future__ import absolute_import
from torch import nn
from torch.nn import functional as F
from torch.nn import init
import torchvision
import torch
from .pooling import build_pooling_layer
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
class ResNet(nn.Module):
... |
5e9f15d0fb043558340e4c3842a2c358057d7d52830f2249a63a359b15582705 | Python | 6,160 | 207 |
import torch
from torch import nn
from torch.nn import Parameter
import torch.nn.functional as F
from torch_geometric.nn import (
GATConv, GCNConv, GPSConv, GINConv,
SAGEConv, LGConv, GATv2Conv, GCN2Conv, GraphConv
)
class SAGEConv_Encoder(torch.nn.Module):
"""
Encoder based on GraphSAGE convolution.
... |
fb8d0eb2809a44dc7a4ab31e5e40ff8b02226ad4733e8f18a55dfc5d78d2c0f6 | Python | 6,160 | 138 | import nighres
import os
import nibabel
import numpy
import glob
import scipy.ndimage
inpath = '/home/pilou/Projects/Cerebellum/Cerebellar-segmentation-ABC/'
in_sfx = 'nighres/tissues/'
out_sfx = 'nighres/topology/'
wmprior_sfx = '_anat_inv1_s2c10_lcpca-den_qt1map-t1_cr_def-img_msk_wmprior.nii.gz'
gmprior_sfx = '_ana... |
c346a165e6b5c479783fb07a5e35f97bc8517e98ae94630342273b7300fbdeda | Python | 6,162 | 170 | ##################################################################################
### Module : dmriatlasbuilder.py
### Description : Atlasbuilder api
###
###
###
### Written by : scalphunter@gmail.com , 2022/10/08
### Copyrights reserved by NIRAL
#######################################################################... |
8dea6d7fa0f979b80b767610cffea21f2f5dbc92c8f4d560e8c1a2dc9483c396 | Python | 6,163 | 208 | from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.dates import days_ago
from datetime import timedelta
from airflow.models import DAG
from airflow.operators.python import BranchPythonOperator
# Kaapana operators
from kaapana.operators.MinioOperator import MinioOperator
from kaapana.operators.... |
e795dd598324aa3caf50d3494cd9103c8d69e4119550e330ac8c3c790480a197 | Python | 6,164 | 291 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Fit FLIM data
"""
import os
import mbflim.utils as ut
import mbflim.parameters as par
import tools.flim.flim_set_processing as fl_setpro
import tools.flim.flim_visualization as fl_vis
#%% Parameters
# Parallelism
PROCESS_NB = 16
MAX_THREAD_NB = 32
# Mask
# Somata... |
21eafbc8ec90509ab534c5920f6601f8c7224a329c9ea20e15a0c32af3782e4e | Python | 6,166 | 115 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'add_line.ui'
#
# Created by: PyQt5 UI code generator 5.12.2
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_addLine(object):
def setupUi(self, addLine):
addLine.setO... |
d8451ccfb7f7103bc03a0a8f6bb02442de6d1dcbed9bd82ef8129bc78d58b7d7 | Python | 6,166 | 134 | import unittest
from causallib.positivity import Trimming
from causallib.positivity.metrics import cross_covariance, cross_covariance_score
from causallib.positivity.datasets.positivity_data_simulator import make_multivariate_normal_data
from sklearn.linear_model import LogisticRegression
import pandas as pd
import num... |
daa6cf93e88271293fdbe60149cec336b5d3ec06fd37793e9e2619a887640180 | Python | 6,166 | 262 | import numpy as np
from scipy.stats import norm, chi2
"""
Credits to Xihao Li and the STAAR developers for the following functions
https://github.com/xihaoli/STAAR
"""
def saddle(score_stat, egvalues, wcov_mat):
"""
Saddlepoint approximation (Deprecated).
Score statistics have been normalized such that... |
822aaf589806b64f7f3235e4a46f43a04fa9ce18282e9a6610ed0b0af9c4ff22 | Python | 6,168 | 157 | from os import listdir
import numpy as np
import scipy
import scipy.signal
import scipy.ndimage as ndimage
import re
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import gridspec
import moviepy.video.io.ImageSequenceClip
import imageio as iio
# simulate ideal C_location
def access_distance_map(n, ... |
9f4752d7893a86821dd5b6d5be20feff03a5696835d98c52065964d99a06921d | Python | 6,168 | 188 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import csv
import json
import os
import random
import sys
from itertools import product
NAME = "analytics"
def read_rules(name):
rules = []
with open(os.path.join(sys.argv[1], f"{name}.csv")) as f:
reader = csv.DictReader(f)
... |
02415a532294421648b4b0028d71a4aa52997b3baec2aca7310cc3ab459691d2 | Python | 6,170 | 116 | import os
from importlib import import_module
from pathlib import Path
cell_graph_config = {
"cell_f_list": [
'fourier',
10,
False,
],
"edge_f_list": [
"cmtocm_x",
"cmtocm_y",
"cmtocm_len",
"cmtocm_angle",
"contour_dist",
],
"... |
3b437e4a6dc5ca0062557412c499fc5d122ab3be31bf2a213766d4887e909e43 | Python | 6,170 | 126 | import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from sklearn.utils import resample
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from statsmodels.stats.multitest import multipletests
from sklearn.decomposition import PCA
def normalize_to_NN_NT_cells(adata... |
80d353e8d4e4741b4da10c234c376f2c81d76f5e9a987bc839a8bf6847e7a125 | Python | 6,171 | 151 | """Downloading a peak-detection trace saves a file, like every other figure.
"Download PNG" worked everywhere except CAT-NAP's peak-detection traces, where
it opened the image in a tab instead. Two independent halves of the same
oversight, and either alone was enough to break it:
* ``/api/trace`` was the one image ro... |
85ddf57f755713bcc0caf65c458ef31fc7b4d6940dc45a07b5727f7d5519fcd6 | Python | 6,171 | 167 | # -*- coding: utf-8 -*-
"""
Created on Tue Dec 3 16:53:36 2019
@author: Yoshi
"""
import numpy as np
from shapely.geometry import Point,Polygon
def cell_and_background_array(cell_locations,image_shape):
# Creates an empty list for storing arrays
cell_arrays = []
background_arrays = []
# ... |
ce4f33a679f62f75221dfd7445ff0d23477920e7019d2fa4afc6993cd5874541 | Python | 6,171 | 116 | import os
from importlib import import_module
from pathlib import Path
cell_graph_config = {
"cell_f_list": [
'fourier',
10,
False,
],
"edge_f_list": [
"cmtocm_x",
"cmtocm_y",
"cmtocm_len",
"cmtocm_angle",
"contour_dist",
],
"... |
4385c3fa3e268915da55a8fb4120acc99b75158e40467c07a4e73bf6d469c47f | Python | 6,172 | 208 | """Demonstrates a failure of the LSTM-based DyNeMo model in learning long-range
temporal dependencies.
- Simulates hierarchical HSMM+HMM data.
- The lifetime of the top-level HSMM is longer than the sequence length, which
means the LSTM cannot learn the top-level transitions.
"""
print("Setting up")
import os
impor... |
f4c40bf1b248064dce7e7c7369a445ca4ca622a0bc206e7a0b903711b1476024 | Python | 6,172 | 120 | ### script for writing meta information of datasets into master.csv
### for node property prediction datasets.
import pandas as pd
dataset_dict = {}
dataset_list = []
### add meta-information about protein function prediction task
name = 'ogbn-proteins'
dataset_dict[name] = {'num tasks': 112, 'num classes': 2, 'eval ... |
06f7a621c02df032629d1cea262ed665464126d082c9f5dc28f6fcd24b0fa89c | Python | 6,173 | 173 | # 移除 s1 层
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from model.coatnet import conv_3x3_bn, MBConv
# Assuming conv_3x3_bn and MBConv are defined elsewhere in your code
class hswish(nn.Module):
def forward(self, x):
return x * F.relu6(x + 3, inplace=Tru... |
3b5e3f06c965852708c6c42ae499619de6f0f9b7fea88522f7d2289d2337ae57 | Python | 6,173 | 208 | import os.path
from typing import Tuple, Dict
import torch
import torch.nn.functional as F
from skimage.morphology import skeletonize
from torch import Tensor
from tqdm import tqdm
def save_train_test_split(
mask: Tensor, skeleton: Dict[int, Tensor], z_split: int, base: str
):
"""
Splits a volume of bina... |
57c00260429aac4a159a8c1ab46791a02678f26a890e52d9e1076899305d4ae3 | Python | 6,173 | 163 | """Functions for downloading and reading MNIST data."""
import gzip
import os
import urllib
import numpy
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
def maybe_download(filename, work_directory):
"""Download the data from Yann's website, unless it's already here."""
if not os.path.exists(work_directory):
... |
efff2809553bebc3a72575a25d16ecc161b68a8ead83caefa41ff13e90f2004a | Python | 6,173 | 160 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.cnn import ConvModule, constant_init
from torch import nn as nn
from torch.nn import functional as F
class SelfAttentionBlock(nn.Module):
"""General self-attention block/non-local block.
Please refer to https://arxiv.org/abs/1706.03762 fo... |
d4ad883f83ab068d1b3ad0f5839e150880c1cbc204ee71c303945d6f0e7c7255 | Python | 6,174 | 144 | """
Location Decoder
@ 2021 Knowblesse
Using the Multi Layer Perceptron based Regressor, decode the current animal's location using the neural data.
Input :
TANK_location
The Tank must have these two data
1) buttered_data.csv (from butter package)
2) neural spike data (get it from the Matlab)
"""
import n... |
9e08321951809a30b439a398a3d0c8342ee8178383da3a40dbc6520921a7072e | Python | 6,175 | 236 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
### ### ### ### ###... |
c67824f17d3cee5a32c01dc09db2129d5347c913242ecc14bc5bacf41d0724b5 | Python | 6,175 | 170 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors: Patrick Lenz
#
# 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 Licen... |
4868c4f3a40a639df3df449a885c8a6fd3ffd4131a7bcbb3a19dd9b8ef03b55c | Python | 6,176 | 222 | '''
Reward delivery implementation
'''
import typing
import asyncio
import pathlib
import logging
import datetime
import platform
import functools
import subprocess
from .config import *
from .qt import *
Executable = typing.Callable[[], None]
LOGGER = logging.getLogger(__name__)
SENTINEL = object()
class MeteredU... |
9820092253fb503aeca525190ab6db69a35fdd4756c990076b587c98b7e7aa2a | Python | 6,178 | 169 | # Copyright (c) DP Technology.
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import Tr... |
b64ca3614021c44bf381fe4fc93e261adaa1e7dabceca3394fda7deb5743e27d | Python | 6,178 | 167 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from model.coatnet import conv_3x3_bn, MBConv
# Assuming conv_3x3_bn and MBConv are defined elsewhere in your code
class hswish(nn.Module):
def forward(self, x):
return x * F.relu6(x + 3, inplace=True) / 6
clas... |
371469e1def2b602cf4d50782768a5b4f4c85a53fe9df7d62927c0a74ddf45b1 | Python | 6,179 | 170 | # tifffile/numcodecs.py
# Copyright (c) 2021-2026, Christoph Gohlke
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
#... |
3f508000d2eec7ce374ac4352038ac630ecbbab9ba7ca78b6be95dd6d868225f | Python | 6,179 | 173 | from collections.abc import Sequence
from typing import Any
from fastapi import FastAPI
from eir.predict_modules.predict_tabular_input_setup import (
ComputedPredictTabularInputInfo,
)
from eir.serve_modules.serve_api_models import ResponseModel, create_input_model
from eir.serve_modules.serve_experiment_io impor... |
afd34f4c599f18621feaa789df726ffe02fe72b2a3b7576671f4c7e6fcb47403 | Python | 6,179 | 291 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Fit FLIM data
"""
import os
import mbflim.utils as ut
import mbflim.parameters as par
import tools.flim.flim_set_processing as fl_setpro
import tools.flim.flim_visualization as fl_vis
#%% Parameters
# Parallelism
PROCESS_NB = 16
MAX_THREAD_NB = 32
#%% Fixed param... |
18fc5a4a009ea1bf05bd98b531db819acb2304155a3361de39952cc050eb9ee2 | Python | 6,180 | 179 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from __future__ import annotations
import unittest
from types import SimpleNamespace
from unittest.mock import Mock
from rest_framework import serializers
from cvat.apps.quality_control.attribute_comparators import (
AttributeComparisonRule,
... |
b9b80f6985eb5e75a706683b5c1949d2e3b75ce83a4a52446f77deb1d085f92b | Python | 6,181 | 212 | from dataclasses import dataclass, field
from typing import Annotated, NamedTuple, Optional
import pytest
from typing_extensions import TypedDict
from magicgui.schema._ui_field import UiField, build_widget, get_ui_fields
from magicgui.widgets import Container
EXPECTED = (
UiField(name="a", type=int, nullable=Tru... |
24640e3b434378ae98bc56701c701d225ded5bfb1fee229196da607c77a585fb | Python | 6,182 | 176 | #!/usr/bin/env python3
import os
import sys
import glob
import argparse
def parse_args(args=None):
Description = "Generate nf-core/rnaseq samplesheet from a directory of FastQ files."
Epilog = "Example usage: python fastq_dir_to_samplesheet.py <FASTQ_DIR> <SAMPLESHEET_FILE>"
parser = argparse.ArgumentPa... |
71ff4b8a6f4958b32e8141366f7c845d6365623cac231d5520af9a3d725acc06 | Python | 6,182 | 192 | import json
import numpy as np
import pytest
import tifffile
from napari_dmc_brainmap.preprocessing import preprocessing_tools
from napari_dmc_brainmap.stitching import stitching_tools
@pytest.mark.parametrize("compression", ["deflate", "lzw"])
@pytest.mark.parametrize(
("image", "photometric"),
[
(... |
642e1eda8ae2f5d58bfe141cded63ed6ad32b21776329b4602915f407725e6e8 | Python | 6,185 | 188 | """
Generate figures for the Keypoint Fidelity How-To documentation.
Uses the same geometry as tests/test_keypoint_fidelity.py: centered squares of half-size r,
with corners derived from np.where(arr > 0). Blue = fixed keypoints, red = moving keypoints.
Produces:
1. Side-by-side view of fixed and moving squares (ke... |
6521f3a5a3fc7afa16f33c463631b513b4e8a5636c650e51f75f05a28b947b27 | Python | 6,186 | 149 | import os
import numpy as np
import torch
from time import process_time, time
from datetime import timedelta
import pandas as pd
from monai.transforms import AsDiscrete
from brainCT.inference.faster_bootstrap import get_dataset
from brainCT.main import get_model
import SimpleITK as sitk
from brainCT.inference.ensembl... |
9a6e7b6c3c31c2bf786411aabac9df0b5a211700607204dcfdb7cfc338a2743d | Python | 6,187 | 202 | #!/usr/bin/env python3
#
# ImpedanceFitter documentation build configuration file, created by
# sphinx-quickstart on Thu Aug 29 12:24:51 2019.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file... |
9db1ce529ba691ead0f29560faad82a7bb0bce16590acf6964f5213e4c6fa6fc | Python | 6,187 | 183 | """
R-wave detection in 3-lead ECG waveforms acquired using an MR scanner
- lead waveforms contaminated by magnetohydrodynamic effect (T-wave elevation)
"""
import numpy as np
import pandas as pd
from scipy.signal import spectrogram, find_peaks
from scipy.interpolate import interp1d
from .bidsphysio import save_bids_p... |
77e9428616fe30ec349ae1bf6210044cc14f94ef1c41db4996d8026bd7367540 | Python | 6,189 | 160 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.switch_backend('agg')
from sklearn.decomposition import PCA
from util import read_expr, read_list, read_symbols, get_symbol
import argparse
import os
import matplotlib as mpl
mpl.rcParams['axes.spines.left'] = True
mpl.rcParams['axes.spines.righ... |
12f2e33fa93d9363234909bc24c55e969e3af228916ceca5a57594a67116928b | Python | 6,190 | 163 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
import os
import shutil
import sys
import tempfile
from pathlib import Path
from typing import TYPE_CHECKING
from . import USER_CONFIG_DIR
from .patches import torch_save
from .torch_utils import TORCH_1_9
if TYPE... |
959c1c7bcc0b4a1665e173f2d155b4074f7b27bde680218b8e5599dcbd4f304c | Python | 6,191 | 119 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file '/media/lg/disk2/PycharmProjects/ISAT_with_segment_anything/ISAT/ui/info_dock.ui'
#
# Created by: PyQt5 UI code generator 5.15.10
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this fil... |
ab3cca81091e4cbef72ec52b10151903c794083478169ff3947634558cde40a6 | Python | 6,193 | 178 | """
analyze colocalized synapse types
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import seaborn as sns
from matplotlib.colors import LinearSegmentedColormap
from palettable.colorbrewer.sequential import (PuBuGn_4,
... |
219dbab2cdc172036c20f826454334ea92b8626975b5fe985e30ba12a1295982 | Python | 6,194 | 162 | """Regression tests for input_structure_file path resolution in run_ase_core.
Background
----------
Coordinate files are written into the session log directory via
``_resolve_path`` (see ``ase_core._resolve_path``). When a tool writes
``water.xyz`` into ``CHEMGRAPH_LOG_DIR`` but ``run_ase`` is later invoked
with the B... |
86af4a65378f997ce6c4b135b414cddce37df7f26cf654f8f1d848d56ad93e05 | Python | 6,194 | 172 | import math
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.multivariate_normal import (
MultivariateNormal,
_batch_mahalanobis,
)
def _standard_normal_quantile(u):
# Ref: https://en.wikipedia.org/wiki/Normal_dist... |
1aedd57859c7a2103a58756c10199fb91c1aa6a7db88c7aa5473aca76de0cec8 | Python | 6,196 | 128 | import cv2
import numpy as np
import pandas as pd
from typing import List, Union
from pathlib import Path
from skimage.measure import label, regionprops
from natsort import natsorted
from napari_dmc_brainmap.utils.path_utils import get_info
from concurrent.futures import ProcessPoolExecutor, as_completed
class Centroi... |
3e62c1c86eb6102fd6bf3e62370164f7b9937255b464c9fd06c14b68afd17eec | Python | 6,196 | 188 | import numpy as np
import constants
import split_dataset as sd
import argparse
import sys, os
sys.path.append("./")
import pandas as pd
import split_dataset as sd
BASE_PATH = "../data"
SEEDS = [10, 24, 36, 42, 56]
# ----------------------------
# REDUCED TRAINING SET SIZES
# ----------------------------
TRAIN_SIZE... |
b6334bddbb1a33a1b87d08024cb8b41fb646ccbf0269561e8ff24df07d1271eb | Python | 6,198 | 198 | # Copyright (C) 2020-2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from __future__ import annotations
import json
import os
import re
from contextlib import closing
from pathlib import Path
from typing import TYPE_CHECKING, Any
import urllib3
from cvat_sdk.api_client.ap... |
3ce5898e6b3a03c5e8102cd1139d51dd608b6fdaab624c7c4ee94ac063a98e4c | Python | 6,200 | 173 | import sys
import yaml
import os
import numpy as np
import matplotlib.pyplot as plt
pathpath,run1,run2,stag1,stag2,ncluststr1,ncluststr2, cmethod = sys.argv[1:]
stags = [stag1,stag2]
'''
pathpath = 'PATHS/filepaths_carlen.yml'
run1 = 'runC00dMP3_brain'
run2 = 'runCrespZP_brain'
stags = ['spont','resp']
ncluststr1 = ... |
95b372ec91360b8325df4e1ff279204c935e5076d781ce899df75e5584f38cc4 | Python | 6,200 | 190 | import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers,models
from tensorflow.keras import callbacks
from tensorflow.keras import regularizers
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import LearningRateScheduler
from keras.utils.vis_utils imp... |
ada43858ecdf64ce68b1ac0000f0cfa31c8cffc53b58ff967950d018f712bcc7 | Python | 6,201 | 189 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# ImpedanceFitter documentation build configuration file, created by
# sphinx-quickstart on Thu Aug 29 12:24:51 2019.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in t... |
18fe9645e5b243d482464efec4305cfa0332dfb7250ebba5f6c8fbac8ffac3f1 | Python | 6,202 | 157 | import sys
import os
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
from torch.nn import functional as F
import random
from sklearn import metrics
from model import *
inpt_ = "mock_genomes.npy" #input genotype file in n... |
ac68e0eb9ed4953df305d36e62a85791c3f816d0d47be622a9a377cdb8d8c61a | Python | 6,202 | 201 | """
Defines various activations functions to be used in neural net.
"""
import numpy as np
import torch
from typing import Union
######## MML_ activation
def MML_activation(x: torch.Tensor, leak: Union[float, int]):
"""Returns the output of the Michaelis-Menten function
Parameters
----------
x : tor... |
1658fb3c626c58f26e8e8838ebbc29cfc41e0635cbe0209b27d4aacb024c793d | Python | 6,203 | 196 | import os
import shutil
import tarfile
import zipfile
from os import listdir, makedirs, remove
from os.path import exists, join
import nibabel as nib
import numpy as np
from tensorflow.keras.utils import get_file
##############
# Parameters #
##############
data_splits = ["train", "test"]
num_labels = 3
# Main proj... |
95b44bc913e1005990cd35102bdc9c72a3cb6e722372f0c517b2e8dd619b834b | Python | 6,203 | 219 | #!/usr/bin/env python3
"""
Locate or download the PRS-EDM DM SQL database (variants.db) and metadata
(prs_meta.json).
Robust for:
- Normal laptops/desktops
- Unix clusters where ~/.local/share may be restricted
Public API:
- get_dm_sql() -> returns path to variants.db
- get_dm_meta() -> returns path to... |
1aad242ac06c41e642e81b69021e8fbddb79fc9742604265347ea347bacfa101 | Python | 6,204 | 195 | # flake8: noqa
import argparse
import csv
import os
from collections import OrderedDict
def init_config(config, default_config, name=None):
"""Initialise non-given config values with defaults"""
if config is None:
config = default_config
else:
for k in default_config.keys():
i... |
98e6c3966c73d26997d5509a06bdb6ba6d80d30eccd888c7daa6fa6ddcbfc141 | Python | 6,204 | 231 | #!/usr/bin/env python2
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from optparse import OptionParser
###############################################################################
def windowToKey(a):
k = a[0] + ':' + str(a[1]) + '-' + str(a[2])
return k
######... |
d8f07d2f1281665471c43b417a14189d15ad83d74080d3216999151d934f2a64 | Python | 6,204 | 174 | import logging
from typing import Generator, Tuple, Any
from zigzag.classes.stages.Stage import Stage
from zigzag.classes.cost_model.cost_model import CostModelEvaluation
logger = logging.getLogger(__name__)
class MinimalEnergyStage(Stage):
"""
Class that keeps yields only the cost model evaluation that has... |
03f6cd0f373e72b5e33fe82dadb974cde908a1d92649b19e8d9dd72ccd87d40e | Python | 6,205 | 172 | #!/usr/bin/env python
"""
Training Script for Thermal Sensor H-FNO Model
This script trains an H-FNO model for thermal sensor (RTD) response prediction.
Usage:
python train_thermal.py --config configs/thermal.yaml
python train_thermal.py --epochs 100 --batch_size 16
"""
import argparse
import logging
import ... |
b37a85635316de3410ed59d5c284f5d9151c944da28ac20c511aea2ccb61e888 | Python | 6,205 | 204 | """Session persistence utilities for the ChemGraph Streamlit UI.
Bridges the gap between the UI's in-memory ``conversation_history``
format and the :class:`~chemgraph.memory.store.SessionStore` persistence
layer. Every function is Streamlit-free so it can be unit-tested without
a running Streamlit runtime.
"""
from ... |
f1e14efa9cfaf7c538d1531f3ab474c913adf6abbd100d0a3fc3d8b31477730e | Python | 6,205 | 162 | import matplotlib.pyplot as plt
import numpy as np
import nibabel as nb
import subprocess
import os.path
from skimage.measure import label
import pandas as pd
import seaborn as sns
# Set data path
ROOT = '/Users/sebastiandresbach/data/s1Anfunco/Nifti'
SUBS = ['sub-12']
DIGITS = ['D1', 'D5']
# =======================... |
120465d1bf551564a4ae775a0c9e49b604c2d30fbb8e08169658dba8036980c8 | Python | 6,207 | 210 | import unittest
import warnings
from unittest.mock import patch
from rscvp.spatial.main_align_map import AlignPeakMapOptions
from rscvp.spatial.main_ev_pos import EVOptions
from rscvp.spatial.main_place_field import PlaceFieldsOptions
from rscvp.spatial.main_population_matrix import PopulationMTXOptions
from rscvp.spa... |
27986a40b98bd882b236e2812c831ef85c6d690f977917140a63a9838a8adc1f | Python | 6,207 | 162 | from __future__ import annotations
from collections.abc import Callable
from enum import Enum, EnumMeta
from typing import TYPE_CHECKING, Any, cast
from magicgui.types import ChoicesType, Undefined, _Undefined
from ._value_widget import BaseValueWidget, T, ValueWidget
if TYPE_CHECKING:
from typing_extensions im... |
654aebee2780e8d76ef3b6e25d766ecbeea52e83a30661e677eb33addcca86ba | Python | 6,208 | 173 | #!/usr/bin/env python3
"""Train 3D ViT-UNet using preprocessed BraTS .npy volumes."""
from __future__ import annotations
import argparse
from pathlib import Path
import numpy as np
import segmentation_models_pytorch as smp
import torch
from sklearn.metrics import accuracy_score, jaccard_score
from torch import nn
fr... |
71c918d0d60ed61587c13bc6d265e86509e9bedec49fc4b22b19c41d98b1ec7b | Python | 6,208 | 201 | """
dlim_train.py
-----------------------
This script trains the D-LIM (Deep Latent Interaction Model) on different datasets,
such as "harry", "elife", and "protein_inter". It compares the performance of
the spectral and non-spectral variants of the D-LIM model.
Each experiment evaluates model performance using Pears... |
cdd6b46d40babbc9962eaaa06428f00e79a76a0787219d7975a42c47de867328 | Python | 6,209 | 141 | #!/usr/bin/env python3
import os
import argparse
import re
from datetime import datetime
from pathlib import Path
from typing import List, Optional, Tuple
# Map file extensions to comment styles
COMMENT_STYLES = {
'.py': '#',
'.cpp': '//',
'.cu': '//',
'.h': '//'
}
def get_comment_style(filepath: str... |
5902a4a32651b609966d1127f83e62f8fc871e029d561396c72a5d5b3a5f7eb9 | Python | 6,210 | 200 | """ Quick n Simple Image Folder, Tarfile based DataSet
Hacked together by / Copyright 2019, Ross Wightman
"""
import io
import logging
from typing import Optional
import torch
import torch.utils.data as data
from PIL import Image
from .readers import create_reader
_logger = logging.getLogger(__name__)
_ERROR_RETR... |
849181cbc9b333575212aad5d1aeb2507e165bf7e58c6a2586154587d63145f7 | Python | 6,210 | 118 | # Tests of the datasheet detection of dmrifiberprofile (file names only):
# python -m unittest discover -s tests
import tempfile
import unittest
from pathlib import Path
from dtiplayground.dmri.fiberprofile import datasheet as ds
PROTOCOL = {'propertiesToProfile': 'FA, MD, FWFA, FWF, FWf, NDI', 'useDisplacem... |
138c3cdb83c036c22bcb46638ffd688fb50582d2d833e52d72652981f8611e97 | Python | 6,212 | 210 | from collections.abc import Sequence
import pandas as pd
import pytest
import timm
import torch
from timm.models import get_pretrained_cfg_value
from tests.conftest import should_skip_in_gha
from tests.test_models.model_testing_utils import (
check_eir_model,
prepare_example_test_batch,
)
def get_test_inter... |
fde53f08e657ba633294eae41c01222c9b759b004288b04916ad93d331adc8a7 | Python | 6,212 | 191 |
import numpy as np
import glob
import torch
from torch.utils.data import Dataset
from torch_geometric.nn import knn_graph
import json
import cv2
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
# This function converts Spherical coordinates to Cartesian.........................................####... |
0b3195950c6f92343f397d8fe103176e3eef0849d481990cbb532607222449bf | Python | 6,213 | 160 | from typing import Final, Literal
import numpy as np
import polars as pl
from argclz import AbstractParser, as_argument, str_tuple_type, argument
from argclz.dispatch import Dispatch, dispatch
from neuralib.atlas.ccf import ROIS_NORM_TYPE
from neuralib.atlas.typing import Area, TreeLevel
from neuralib.plot import plo... |
175891fcfe51b2156a31a9e2caef07824b231d89d9eb32a21815592c9505fd4f | Python | 6,213 | 180 | # -*- 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
__all__ = ['get_vfs', 'get_weighted_vfs']
def get_vfs(input_sphere, input_white, input_patch, input_aparc, hemi, ecc_real,
ecc_imag, pol_real,... |
a207b9f7d2429557454e898d1afef9461f5ad72d54c0e2009723dfd39f1473bb | Python | 6,213 | 178 | import hashlib
from nipype.interfaces import BIDSDataGrabber
from PUMI._version import get_versions
from nipype.pipeline.engine.nodes import *
import subprocess
import json
def get_interface_version(interface):
"""
Try to get the version number of the underlying tool used in an interface.
Return None ... |
c62e77f620e8b75e874c7a61c3c191c7ee300cb243660ba3cd48950cf4131c69 | Python | 6,213 | 161 | import logging
import unittest
import numpy as np
import pandas as pd
import scipy.stats as stats
import diffxpy.api as de
class _TestPairwiseNull:
noise_model: str
def _prepate_data(
self,
n_cells: int,
n_genes: int,
n_groups: int
):
if self.nois... |
d6e779faa29a6e789edea31ad2186ea0c4fe89adda3f77ada9813143eb4ffc63 | Python | 6,213 | 186 | """ Decorator functions to wrap around entry and exit
... to easily apply to a function, functions that check/process inputs
and outputs
"""
from functools import wraps
import numpy as np
from . import error
from . import matrix
from . import util as u
## Hacky way to handle python2 not having `ModuleNotFoundError`
... |
e7ecc9fc11d8a601bd0520b78008653c6c27e3d65853c1e756685c7b51220dd7 | Python | 6,215 | 209 | """
High-level read/write functions for several formats.
"""
# Author: Oualid Benkarim <oualid.benkarim@mcgill.ca>
# License: BSD 3 clause
import tempfile
import gzip
import os
from vtk import (vtkPLYReader, vtkPLYWriter, vtkXMLPolyDataReader,
vtkXMLPolyDataWriter, vtkPolyDataReader, vtkPolyDataWrit... |
856eeb88dfcaead9d2f3457eead49476b5f872f4c20c9c0d2cd882423678d4a7 | Python | 6,216 | 146 | # src/train/train_regression.py
"""
Train multi-output regression model for one CV fold.
Run:
$ python -m src.train.train_regression --fold 0
"""
from __future__ import annotations
import argparse
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
from torch.optim.lr_scheduler import Re... |
bd71ea6ee44288723ba5e3511745abba65a29c5bebecfa2f7e6f4edc63d71d15 | Python | 6,219 | 155 | from typing import Callable, List, Union
import numpy as np
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem
Molecule = Union[str, Chem.Mol]
FeaturesGenerator = Callable[[Molecule], np.ndarray]
FEATURES_GENERATOR_REGISTRY = {}
def register_features_generator(features_generator_name: str) -> Cal... |
c57fedd958620d266078acd41fd6287617f00997341a050c38dcdedd789b6367 | Python | 6,219 | 135 | # (C) Copyright 2019 IBM Corp.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... |
a3b41fa48bac65c53dbb9c261366f7b4a8a80ad4f2b244c319876ec4fd88eef3 | Python | 6,222 | 164 | import os
import pysam
import numpy as np
import sys
sys.path.append('../bin')
from sc_remove_low_qual_reads import remove_low_qual_reads
from sc_split_reads import split_reads
def test_low_qual_reads_removed(tmp_path):
input_bam = "data/sc/mix_qual_reads.bam"
out_bam = f"{tmp_path}/high_qual_reads.bam"
... |
278e8c77ef916f1dc9ea0479101db55188be95646a1f77297b7e3d795255da72 | Python | 6,223 | 161 | import tweepy
import datetime
import time
# TwitterAPI 6253282
# TwitterDev 2244994945
user_fields = ['created_at', 'description', 'entities', 'id', 'location', 'name',
'pinned_tweet_id', 'profile_image_url', 'protected', 'public_metrics',
'url', 'username', 'verified', 'withheld']
tweet_... |
9715c2f8b7a0ffdb65094b6f2ad8417f57375403ff2df5f6f91ee2328217c813 | Python | 6,227 | 175 | import torch
import logging
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional
BINARY_MODE: str = "binary"
MULTICLASS_MODE: str = "multiclass"
MULTILABEL_MODE: str = "multilabel"
EPS = 1e-10
logger = logging.getLogger(__name__)
def expand_onehot_labels(labels, target_shape, ignor... |
aed56feb9a2628371d0227b230d47815f08b513f681fb66e78c4008110799fb6 | Python | 6,229 | 195 | ###############################################################################
#################### Boilerplate ########################
###############################################################################
import pandas as pd
import matplotlib.pyplot as plt
import os
import matplotlib... |
ff688f07f4801de0d7f529a65b12e78375bf2a398c81072f6238d24770eeeb2f | Python | 6,229 | 213 | """Ordinary least squares fitting for a GLM."""
import logging
from typing import Optional, Tuple
import numpy as np
from sklearn.linear_model import LinearRegression
_logger = logging.getLogger("osl-dynamics")
def _validate_dimensions(
X: Optional[np.ndarray] = None,
y: Optional[np.ndarray] = None,
co... |
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