sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
6700e8126cd6d2025464e5215354579aa80da5a8f1c0e76f454d4a2ecb6910f8 | Python | 3,279 | 114 | import csv
import os
from dataclasses import dataclass
from pathlib import Path
from typing import (
Any,
Dict,
List,
)
from oaklib.interfaces.basic_ontology_interface import BasicOntologyInterface
@dataclass
class TabularFile:
name: str
"""logical name."""
path: Path
"""path to the file... |
1571e193169cb736a6bba763b04d5887922931e83f255e5de0b0230b677eabf4 | Python | 3,280 | 105 | #!/usr/bin/env python
# Copyright (c) 2026 Computer Vision Center (CVC) at the Universitat Autonoma de
# Barcelona (UAB).
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
Set or inspect the weather of a running CARLA Simulator instance.
Start ... |
3057c77805efc86fa7fe2b632debde057fa4df091a343ff564990a16e4f4d56d | Python | 3,281 | 104 | import datetime
import numpy as np
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy import stats
from benchnirs.load import load_dataset
from benchnirs.process import process_epochs
from benchnirs.learn import deep_learn
DATA_PATH = '../../data/dataset_shin_2018/' # path to t... |
d7184a7dc0dfaf3817c6b8a9d771ee00cc8831a73b9717ea141ed5cb8d5e3f86 | Python | 3,281 | 108 | """
Runs unit tests on utils.py
"""
import os
import json
from phys2bids import utils
from pkg_resources import resource_filename
# Tests check_input_dir
def test_check_input_dir():
test_path = '/home/mrmeeseeks/blue/'
assert utils.check_input_dir(test_path) == test_path[:-1]
assert utils.check_input... |
d9f705dc4ea8b27b9b271bfd916486a34a3bb51122d95e12c2b9d3b9e01a6c89 | Python | 3,282 | 63 | import sys
sys.path.insert(0, '.')
from svgkit import Svg, C, render_png
W, H = 1260, 720
s = Svg(W, H)
s.text(W / 2, 44, "Asynchronous vs synchronous mode", size=26, weight="bold", anchor="middle")
s.text(W / 2, 72, "Who decides when the simulation advances, and how much simulated time each step covers",
size=1... |
0360fe3fbc6d5bc0669698b66e51358ca1cdba79d906b86813ded543eed7cf22 | Python | 3,283 | 119 | import torch
try:
import zarr
except ModuleNotFoundError as e:
raise e(
"trying to import zarr_dataset without optional dependency zarr. Please install and try again."
)
from torch.utils.data import Dataset
class ZarrDataset(Dataset):
"""PDE h5py dataset"""
def __init__(
self,
... |
1c3ca861a17788c512eb274d9cbcb368a2573d4d4b52951693885cf6cc5075e3 | Python | 3,283 | 110 | ### apply flatfield correction to s0 image
import os
import numpy as np
from scipy.ndimage import zoom
import zarr
from matplotlib import pyplot as plt
import seaborn as sns
import tifffile
import logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
def transform(images, f, d=None):
... |
087fbea9a11b4170a58072f662a6b43309651d50fe174ee47fa0f4f03b17c515 | Python | 3,284 | 78 | import os
import requests
from datetime import datetime
from email.utils import parsedate_to_datetime, formatdate
from ddmr.utils.constants import ANATOMIES, MODEL_TYPES, ENCODER_FILTERS, DECODER_FILTERS, IMG_SHAPE
import voxelmorph as vxm
from ddmr.utils.logger import LOGGER
# taken from: https://lenon.dev/blog/down... |
287d701ebfc536069517222dad601890b964a4bb816d82f4da9d481794e0c533 | Python | 3,284 | 71 | # object for analysing hit_histograms from cascades run using TraverseDispatcher
import numpy as np
import pandas as pd
import sys
import pymaid as pymaid
from pymaid_creds import url, name, password, token
rm = pymaid.CatmaidInstance(url, token, name, password)
import connectome_tools.process_matrix as pm
import con... |
3a7826dfa8835eb0896944f3491d9af8afcc001c469ce466df28842d87b2925f | Python | 3,284 | 109 | #! /usr/bin/python
# -*- coding: utf8 -*-
import tensorflow as tf
def identity(x, name=None):
"""The identity activation function, Shortcut is ``linear``.
Parameters
----------
x : a tensor input
input(s)
Returns
--------
A `Tensor` with the same type as `x`.
"""
retur... |
a3c854bca5c2c846eaaf8c8b246de7c093c13d946693094ff21a48e2a13b947f | Python | 3,284 | 70 | """
Perform bootstrap resampling of performance of each ferret during vowel discrimination in noise
Input
-----
Data comes from the analysis directory; i.e. some preprocessing has already been done to remove correction trials and any attenuations with small sample sizes.
For the cooling of vowel discrimiantion from tw... |
4ce6d275cf0911ff8369d39cae044d5996f4803b4b2a892c59182150a736b2d6 | Python | 3,285 | 83 | # Copyright 2021 Huawei Technologies Co., Ltd
#
# 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... |
f2488f37eaad02d81608b1980ee9acde1592189519a12b463c768d511746680c | Python | 3,285 | 128 |
import glob
import xml.etree.ElementTree as ET
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
def cas_iou(box, cluster):
x = np.minimum(cluster[:, 0], box[0])
y = np.minimum(cluster[:, 1], box[1])
intersection = x * y
area1 = box[0] * box[1]
area2 = cluster[:,0] * clu... |
7f203b76915d400e0a013bcbf284433903c0774f9fc4bca1a6698ae34c8c54e8 | Python | 3,286 | 88 | # Copyright 2020 Huawei Technologies Co., Ltd
#
# 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... |
2061bb2799667f5e04f275dedac6505cc985769b2dee3e8f521487dadf091216 | Python | 3,287 | 150 | #!/usr/bin/python
import time
import string
import sys
import os
import re
# Set your decoder swig path in here!
sys.path.append("/home/user/decoder/src/swig");
import Decoder
def runto(frame):
while (frame <= 0 or t.frame() < frame):
if (not t.run()):
break
def rec(start, end):
st = o... |
23e3de63f7a3dc8074de152e935bed0afb0f9fbde764d959fcb38a46c57218fa | Python | 3,287 | 85 | ##########################################################
## ccAF: plotNowakowski.py ##
## ______ ______ __ __ ##
## /\ __ \ /\ ___\ /\ \/\ \ ##
## \ \ __ \ \ \___ \ \ \ \_\ \ ##
## \ \_\ \_\ \/\_____\ ... |
7b9f12cc7cdfefd20b11d5de028e3206934c0fac7f6a62c66f55db3a6e35bad0 | Python | 3,287 | 83 | # generate molecular features
from typing import Callable, List
import numpy as np
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem, Descriptors
from rdkit.ML.Descriptors import MoleculeDescriptors
MoleculeFeaturesGenerator = Callable[[str], np.ndarray]
MOLECULE_FEATURES_GENERATOR_R... |
e328f3762e66b5c55e43e5c9184d3571ecbdacc233438801b60ebb146c47e046 | Python | 3,287 | 99 | import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
def plot_alignments(alignment1, alignment2, transpose=True):
if transpose:
alignment1 = alignment1.T
alignment2 = alignment2.T
fig = plt.figure(figsize=(12, 7))
plt.subplot... |
62bcb26370ef6fb809459802254b405a5f7af3f84c607e045c6ed1e23f46c427 | Python | 3,289 | 73 | """Post-hoc analysis of inferred state/mode dynamics.
This subpackage provides tools for analysing the output of trained models
(HMM states, DyNeMo modes, etc.) and for static (time-averaged) analysis.
Modules
-------
- :py:mod:`~osl_dynamics.analysis.connectivity` — Functional connectivity
analysis (coherence, ima... |
15dd6110a9fd665961935a9c0c81788963bf8542f89afe429f20ca604227572c | Python | 3,290 | 117 | """
Using `torchtnt` to count FLOPS
================================
A demo using ``torchtnt`` to estimate the number of floating-point
operations per second (FLOPS) required for a model's forward and backward pass.
This tutorial demonstrates how to profile neural operator models to understand
their computational re... |
f9a2b6e8ee4a9af6fde4922cc9d145f0f3e01c800e1e187c0549198a55e4f03a | Python | 3,291 | 82 | """PyTorch model utility functions."""
import torch
import types
def cycle(iterable):
while True:
for i in iterable:
yield i
def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True):
assert isinstance(module, torch.nn.Module)
assert not isinstance(module, torch.jit... |
fafafa844c077f79996f3c5f74cd733d9877a2c046432bf5ef2501f3e54c830b | Python | 3,291 | 97 | # basic imports
import os
import gc
import pickle
import numpy as np
import PyQt5 as qt
import pyqtgraph as qg
# framework imports
from cobel.agents.dyna_q import PMAAgent
from cobel.interfaces.gridworld import InterfaceGridworld
from cobel.misc.gridworld_tools import make_empty_field
def recover_positions(replay: li... |
40da341a9aa354709ad4931fef460cb6344ff0751f4e5e393ae15d9eb79d62d1 | Python | 3,293 | 120 | import os
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Function
from torch.utils.cpp_extension import load
module_path = os.path.dirname(__file__)
fused = load(
"fused",
sources=[
os.path.join(module_path, "fused_bias_act.cpp"),
... |
93770ce4787ce8dab6f3854037a591b098b40206bd33abff4ec31b7fd3923226 | Python | 3,293 | 96 | import logging
import unittest
from linkml_runtime.dumpers import yaml_dumper
from oaklib.implementations.wikidata.wikidata_implementation import WikidataImplementation
from oaklib.datamodels.search import SearchConfiguration
from oaklib.datamodels.vocabulary import IS_A, PART_OF
WD_PECTORAL_FIN_MORPHOGENESIS = 'wiki... |
337b9fcf5b92fa3f83e3b87e38ca185e5cc294f68fddc6e893789458bc9eae40 | Python | 3,297 | 96 | import numpy as np
from Bio.PDB import NeighborSearch, PDBParser
from utils.SGCN.common import format
SEARCH_RADIUS = 6
KMIN_DISTANCE_BETWEEN_ATOMS = 0.01
WATER_RESIDUES = {'HOH', 'HHO', 'OHH', 'H2O', 'OH2', 'WAT', 'TIP', 'TIP3', 'TIP4', 'TIP3P', 'TIP4P', 'SOL'}
def get_elements_radii(elements_radii_path):
retu... |
233e4c954224ef3a7978ed17fafb38223623db6964719fa03efeed003cd0737f | Python | 3,298 | 88 | ####################################################################################################
# Copyright (c) 2019 - 2022, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of VessMorphoVis <https://github.com/BlueBrain/VessMorphoVis>
#
# This program is free ... |
2edc898c1136c30eca59d7e9df68ece622bb50f4a671a9f84881d30c13c99f84 | Python | 3,298 | 101 | # Copyright 2020 Google Research. 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.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... |
ef63af2bf042873313d99a791c5234b53907613d9a181b45657eaf3c93eee65b | Python | 3,298 | 93 | import argparse
import collections
import numpy as np
from data_loader.data_loaders import *
import model.loss as module_loss
import model.metric as module_metric
import model.model as module_arch
from parse_config import ConfigParser
from trainer import Trainer
from utils.util import *
import torch
import torch.nn a... |
469c6703e8d6eda6dc1abae475b6b94ba3b1ec8cc8d23efe90ff3bb2f840cb9c | Python | 3,299 | 101 | #! /usr/bin/env python
import argparse, sys
from argparse import ArgumentParser
import re
def main():
parser = ArgumentParser(description='Convert kaldi data directory to uem dat files',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--verbose', type=int, \
dest='verbose'... |
66cb4d38b3a944ae9e9c93ac0ec8000c61295fe46992ec3782bcfe9d25a09b1d | Python | 3,299 | 99 | # Copyright 2021 Huawei Technologies Co., Ltd
#
# 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... |
a49a8195b734ecba95c9b63d035f26faa5d794a0b26eb3e9d5f3c521be29c536 | Python | 3,299 | 83 | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... |
d7cdd6bd98f5204cd27bb9a22149699f52317eff6078c7be02d1b246a8fce6b6 | Python | 3,299 | 115 | # -*- coding: utf-8 -*-
"""
Created on Fri May 14 23:06:21 2021
@author: Sleepymouse
"""
import glob
import numpy as np
import pandas as pd
from scipy.stats import chi2
import sympy
import csv
import matplotlib.pyplot as plt
import sys
#wheel managerから排出されたファイルは.csv形式（名前は.xls）なので、.xlsに変換sにて保存しなおしてから使用すること
#mac
#path... |
9c24871f3f201875172c99b0981b648b353555528b9da6006ca166b5515e211c | Python | 3,301 | 79 | import os
import cv2
import numpy as np
from io import BytesIO
from os.path import join
from PIL import Image
from slideflow.slide import stainNorm_Macenko, \
stainNorm_Reinhard, \
stainNorm_Vahadane, \
stainNorm_Augment, \
... |
7c4ef3c882057fe7a70bbdf9f0c94bd395d7ebd2327a041e107ca9724dbcc90a | Python | 3,302 | 115 | # -*- coding: utf-8 -*-
import cv2
from cv2.dnn import readNet
import numpy as np
import traceback
def anonimize(img, alg, mode=15,path="."):
net = cv2.dnn.readNet(path+"/model/deploy.prototxt",
path+"/model/res10_300x300_ssd_iter_140000.caffemodel")
(h, w) = img.shape[:2]
blo... |
74ff5b2579c1a4db22cf8eb3c447eea3d01149308770bc3eb6066954df4c1b63 | Python | 3,303 | 119 | import torch
from torch.autograd import grad
import pytest
from tensorly import tenalg
tenalg.set_backend("einsum")
from ..fnogno import FNOGNO
# Parameterize use of torch_scatter if it is built
try:
from torch_scatter import segment_csr
use_torch_scatter = [True, False]
except:
use_torch_scatter = [Fal... |
af67fbf47821ad6b7dd6bedec763780d16457924f85a224646c570490d3db72e | Python | 3,303 | 93 | # Copyright 2021 Google Research. 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.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... |
ed989f0c2af3d07788b1ebab867979b34d6efa309eaad4c8884507620fb7a9ec | Python | 3,303 | 120 | import os
import torch
from tqdm import trange
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from graphxai.gnn_models.node_classification.testing import train, test
from graphxai.gnn_models.node_classification.te... |
d952345e521d3e4fd6d0808f96bef778c1bfdd7c8884e51ee59fa36655de0546 | Python | 3,304 | 104 | import hashlib
import logging
import shutil
import zipfile
from pathlib import Path
from urllib.request import urlopen
from tqdm import tqdm
logger = logging.getLogger(__name__)
MODEL_URL = "https://ndownloader.figshare.com/files/38548058"
MODEL_MD5 = "ef1e07ff46ee1b0b2cf7432d438c9560"
MODEL_NAME = "master"
DEFAULT_... |
0ea9dd2f14630c9a9297182942e0b6051e53a1221c2cd02ff54e4eda0360fc63 | Python | 3,306 | 103 | import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import gen_nn_ops
# @ops.RegisterGradient("MaxPoolWithArgmax")
# def _MaxPoolGradWithArgmax(op, grad, unused_argmax_grad):
# """The gradients for `MaxPoolWithArgmax`.
# Args:
# op: The `MaxPoolWithArgmax` `Operation... |
8e142f4e86aa5cfae9afd52b11884657eed8076bb9c152521eded6da1ff4fe8f | Python | 3,306 | 112 | """CTF Rest Dataset: TDE-DyNeMo for Dynamic Network Analysis.
In this script we train a Time-Delay Embedded (TDE)-DyNeMo model on source reconstructed
resting-state MEG data.
The examples/toolbox_paper/ctf_rest/get_data.py script can be used
to download the training data.
Functions listed in the config are defined i... |
bc2ae6961d51aeea659c5e102c79702662846589c814613c6e29943a68bd0d1a | Python | 3,308 | 80 | import numpy as np
import tensorflow as tf
def min_max_norm(img: np.ndarray, out_max_val=1.):
out_img = img
max_val = np.amax(img)
min_val = np.amin(img)
if (max_val - min_val) != 0:
out_img = (img - min_val) / (max_val - min_val)
return out_img * out_max_val
def soft_threshold(x, thresh... |
a2f27962f9dd78bf449a22478403e8fc8b5f30bf334662565fe68cac371d5ab6 | Python | 3,309 | 106 | import pandas as pd
import pytest
from brian2.units import * # noqa: F403
from cxsystem2.core.exceptions import ParameterNotFoundError
from cxsystem2.core.parameter_parser import value_extractor
# Sample dataframe for testing
def create_test_df():
data = {
"Variable": ["k", "J", "k_I", "J_I", "_weights"... |
dc6ac9780d0cbc60b59c9af151f04043cf8ab6181a3f68984299e7145694e57d | Python | 3,309 | 62 | #!/usr/bin/env python
#################################################
## PTDNN - Python Toolkit for Deep Neural Network
## Author: Yajie Miao
#################################################
import os
import sys
from utils.learn_rates import LearningRateExpDecay
class BnfExpConfig(object):
def __init__(self... |
551903faf1f3062590c545b1c854f15aa56e6573b09031e59f2318f8de8d6ca6 | Python | 3,310 | 91 | import numpy as np
from pathlib import Path
from configparser import ConfigParser
import imageio
import cupy
import cupyx.scipy.ndimage
def get_volume_properties(obj_folder):
'''Gets volume properties.
:param obj_folder: Path to the folder containing slices of the segmentation.
:type obj_folder: :clas... |
687a993369883a6895505ff71bb88d0389b4ba4c1b18c80a4b86d8f6051720bb | Python | 3,310 | 95 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Implements the Generalized R-CNN framework
"""
import torch
from torch import nn
from maskrcnn_benchmark.structures.image_list import to_image_list
from ..backbone.backbone import build_backbone
from ..rpn.rpn import build_rpn
from ..rpn.ret... |
3bec7804d571660ac406fc5917b619399538e5709eed566a45f58d060b4048c2 | Python | 3,313 | 96 | """
Takes and cleans ensembl and refseq gtfs and gffs respectively (GRCh37), saving them as csvs.
wget -qO- http://ftp.ensembl.org/pub/grch37/current/gtf/homo_sapiens/Homo_sapiens.GRCh37.87.gtf.gz | gunzip -c > Homo_sapiens.GRCh37.87.gtf
wget https://ftp.ncbi.nlm.nih.gov/refseq/H_sapiens/annotation/GRCh37_latest/refse... |
4f257de4a0ed28f840a67854e8bf2fe471218850e7c2288aac46bb5cde7fef6e | Python | 3,314 | 78 | import unittest
import numpy as np
from pathlib import Path
from tqdm import tqdm
from copy import copy
skip_tests = False
try:
from src.util.tracker import CSRDCFConfig, CSRDCF
except (ModuleNotFoundError, ImportError):
skip_tests = True
here = Path(__file__).parent
@unittest.skipIf(skip_tests, reason="Cup... |
6ffed980bf5e89b5a3dfb5bdfe41bf8cb4014a20c55d7b6032cb6e07a92321bb | Python | 3,314 | 96 | """
Calculate the noise ceiling in single trial response estimates.
Usage:
python anc.py <bids_path> <subject_ID> <betas_path> <output_path>
Example:
python anc.py 01 /home/user/thingsmri /home/user/thingsmri/betas_vol /home/user/thingsmri/noiseceiling
"""
import os
import sys
from os.path import join as pjoin
from ... |
de853a6e41893dd3431a8628983662fdf2d0cd3374d90c843766addfcb21605c | Python | 3,315 | 92 | """ This file generates a dataset to be used in testing """
import json
import pickle
import random
import os
import numpy as np
import pandas as pd
NUM_SAMPLES = 200
NUM_GENES = 5
if __name__ == '__main__':
random.seed(42)
np.random.seed(42)
current_dir = os.path.dirname(os.path.abspath(__file__))
... |
50f61b1136e93a71862e1459801e130609bd0bcd134199a0639e503ebb8df46a | Python | 3,317 | 102 | """
function plot_paired_data_for_Mimi
function plot_paired_data_for_Mimi
INPUT:
- csv file containing behavioral data with cross-referenced temperature
values for each trial
Notes:
- Testing here was done in the white elephant, so left and right
responses are coded as 8 and 2 respectively.
TO DO:
... |
84abfb5ec325e7cd3aed9a80231c00626788f437e8bd322c5528516677dc4b3e | Python | 3,317 | 107 | # Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Project information -----------------------------------------------------
# https://www.sphinx-doc.org/en/master... |
9c1a688cbdb1839ae312e6568226139ea4d1e68e235814bc3c81e1e041dd2670 | Python | 3,319 | 114 | """
Prediction specific utility functions.
"""
import math
import numpy as np
import torch
from torch.autograd import Variable
from ..utils import _is_lua_trained_model
def get_reverse_complement(allele, complementary_base_dict):
"""
Get the reverse complement of the input allele.
Parameters
------... |
a9883e339933b95fd1a5d547cf15c390f5de1f2ba9797effd8493c5b77dfd6e5 | Python | 3,319 | 98 | # Copyright 2021 Huawei Technologies Co., Ltd
#
# 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... |
903612c220c532d369b0571d6abff1b480cf1e127f61caf0b91e9f1dfa9a64f1 | Python | 3,320 | 101 | import torch
import torch.nn as nn
import torch.nn.functional as F
class FiniteDifferenceConvolution(nn.Module):
"""Finite Difference Convolution Layer
This is the finite difference convolution layer introduced in [1]_.
It computes a finite difference convolution on a regular grid,
which co... |
574c7e2f6db0755949781995868932fdfaf5e4f59a91f10eddfd05207e9992f4 | Python | 3,323 | 75 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 20 11:54:10 2021
@author: paulmueller
For each patient look for the afni directory
Then call for every electrode whereami and assign electrodes through this
"""
import pandas as pd
import numpy as np
import os
import glob
from iEEG_STC import get... |
93fe406fc493e40e6b1f4f4001428f999639c98b344c6ce2634d5efdd3972756 | Python | 3,325 | 86 | import argparse
import math
import glob
import numpy as np
import sys
import os
from train_double_latent_semantic import mask2color
import torch
from torchvision.utils import save_image
from tqdm import tqdm
import matplotlib.pyplot as plt
import curriculums
device = torch.device('cuda' if torch.cuda.is_available() e... |
9734b1d4c5f8b53a2feca38855edd93aa08df4f50b6bb4e7ecca85a69e3375f4 | Python | 3,325 | 88 | from typing import List, Optional
from oaklib import BasicOntologyInterface
from oaklib.datamodels.obograph import LogicalDefinitionAxiom
from oaklib.datamodels.vocabulary import IS_A
from oaklib.interfaces import OboGraphInterface
from oaklib.types import CURIE, PRED_CURIE
def logical_definition_matches(
ldef: ... |
f44791fa7dc266d2cbc361088a8395c2ba645d17a2001838509ff5293d636ab5 | Python | 3,325 | 50 | import sys
sys.path.insert(0, '.')
from svgkit import Svg, C, render_png
# Four consecutive frames: every packet is written every frame; event packets carry 0 records unless something happened.
PK = [("FrameStart", "g"), ("VisualTime", "g"), ("EventAdd", "e"), ("EventDel", "e"), ("EventParent", "e"),
("Collision"... |
12e53564d81fc2d0572e7915f1e9b3e8c894b47a087bc27dddc16aac46c7d8fc | Python | 3,327 | 96 | """Module for computing basic quantities from a spectral graph model: the forward model
Makes the calculation for a single frequency only. """
import numpy as np
def network_transfer_local_alpha(brain, parameters, w):
"""Network Transfer Function for spectral graph model.
Args:
brain (Brain): specifi... |
3986b9b93d87a2698470aeb5289412ad1444e61c9a35988697199d652f5ca6ff | Python | 3,328 | 98 | import unittest
import numpy as np
from numpy import testing as npt
from grr import Spiketrain as spk
class TestPSTH:
def test_even_kernel_odd_train(self):
"""Even length kernel, odd length spktrain."""
spktrain = [0, 0, 0, 1, 0, 0, 2, 0, 1]
dt = 1000.0 # 1000 ms = 1 s
kernel_wi... |
51c4310f670758b038ba4aaa6c285f7e35092b1cd3897b7bc0c81aed2ce30944 | Python | 3,329 | 110 | # From https://github.com/felixpatzelt/colorednoise
# MIT license
"""Generate colored noise."""
from numpy import sqrt, newaxis
from numpy.fft import irfft, rfftfreq
from numpy.random import normal
from numpy import sum as npsum
def powerlaw_psd_gaussian(exponent, size, fmin=0):
"""Gaussian (1/f)**beta noise.
... |
94c5cce2fab5ba2f89895879044b52cef87a9312bdc26df06dd258754e184ad6 | Python | 3,329 | 89 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
"""
Generate :class:`NDVar` (with artificial data)
==============================================
.. currentmodule:: eelbrain
Shows how to initialize an :class:`NDVar` with the structure of EEG data from
(randomly generate) data arrays. The data is intended for... |
c8272c71f069697ceb410366353ab513f8c5b9e9d5c70d7b1619bd8704e39d3c | Python | 3,330 | 98 | from posixpath import realpath
import sys, os
from pathlib import Path
import re
def read_idmap(idmap_file):
sample_map = {}
with open(idmap_file) as f:
for i in f:
sample, id = i.strip().split("\t")
sample_map[id] = sample
return sample_map
if len(sys.argv) <= 1:
pri... |
95bc71a917c0c4217ef1209796c09f780765aa36e9c950e42ad2a8add5a6bded | Python | 3,332 | 84 | import os
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import seaborn as sns
from siVAE.util import remove_spines
from siVAE.util import reduce_dimensions
def plot_scalars(siVAE_output, logdir):
""" """
result_mode... |
9a1e94a927c2cc9b5d4c5b75fdac7f031cacaf62ea8a0c89f713964d6374bbe6 | Python | 3,332 | 96 | # coding=utf-8
"""
Copyright (c) 2021. Huawei Technologies Co., Ltd.
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 o... |
d685fbc803a3d11e0d1b0584fcb1f9604744a2ab54cfcdb04ff3b86345899a62 | Python | 3,333 | 89 | import numpy as np
def initialize_sigma2(X, Y):
(N, D) = X.shape
(M, _) = Y.shape
XX = np.reshape(X, (1, N, D))
YY = np.reshape(Y, (M, 1, D))
XX = np.tile(XX, (M, 1, 1))
YY = np.tile(YY, (1, N, 1))
diff = XX - YY
err = np.multiply(diff, diff)
return np.sum(err) / (D * M * N)
class... |
e688da77785e2d04ab02fc90f691b04eea31854e95c90d5417406e72e2c370ea | Python | 3,334 | 84 | # -*- coding: utf-8 -*-
"""Preview
Code for 'Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans'
submit to Transactions on Medical Imaging, 2020.
First Version: Created on 2020-05-13 (@author: Ge-Peng Ji)
"""
import os
import numpy as np
import torch.optim as optim
from Code.utils.dataloader_MulCl... |
3bb5947bbc8bebbf67a691a29954e161f72c3724baf63d08ffaad0c4c856990e | Python | 3,335 | 101 | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torchvision import transforms
from torchvision.utils import save_image
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Create a directory if not exists
sample_dir = 'sam... |
a61d70a93a80b424af148628084b922a1c0e691605eb418574c687e08c2856cd | Python | 3,335 | 77 | # Copyright 2020 Huawei Technologies Co., Ltd
#
# 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 a... |
f20edef24c87234efbbfca09c92b0f338a03c071701517ec5d8ac1d1a5b7765b | Python | 3,337 | 107 | #!/usr/bin/env python
# plotPSD0 ver 0.0 - a command line utility to plot a wildcarded argument
# list of files containing time series data, and plot the
# Power Spectral Density (PSD) of them.
# This version overplots the data from multiple files.
import sys, os
import numpy as np
import matplotlib.pyplot a... |
0b5282b5feaa06ce7167398287d3dd4f6dad490175a0bd10c4490d23b8e304ba | Python | 3,338 | 98 | """
File: create_dataset_matrix.py
Created: 2020/10/08
Last Changed: 2021/07/28
Author: M.Vochteloo
Copyright (C) 2020 M.Vochteloo
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, e... |
20e68b35025ef75c0b868d90084fc2df652b95007413cf04d99ecd7fee6ece80 | Python | 3,338 | 126 | import numpy as np
from scipy import stats
from scipy import optimize
import statsmodels.api as sm
def fit_linear_model(X, y, sort=True):
if sort:
# assume a single regressor for now
order = np.argsort(X)
X = X.iloc[order]
y = y.iloc[order]
X = sm.add_constant(X)
return sm... |
6504253acbf2c90e88c61227e56d453c5c25dec51ece0f3958dd0092782a0252 | Python | 3,339 | 105 | import random
import torch
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import f1_score
from torch_geometric.data import Data
from torch_geometric.utils.random import erdos_renyi_graph
from graphxai.gnn_models.node_classification import BA_Houses, GCN, train, test
from graphxai.datasets.feat... |
70f8490e79b4b5fe94ea22685eb753fcbc7e5b5e3af7ffc835824c91507d5f92 | Python | 3,339 | 94 | import sys
sys.path.insert(0,'C:/Users/admin/projects/sofgan')
import argparse
from modules.model_seg_input import Generator
from modules.BiSeNet import BiSeNet
from utils import *
device = 'cuda'
def make_noise(batch, styles_dim, style_repeat, latent_dim, n_noise, device):
noises = torch.randn(n_noise, batc... |
7bc598de5d98169b45064b2b49778d7955538f9fa1deef497d84115811d89505 | Python | 3,340 | 95 | """
Title
-----
Dendritic I/O curve
Description
-----------
Dendritic integration can be quantified by comparing the observed depolarization
resulting from the quasi-simultaneous activation of the same synaptic inputs, and
the arithmetic sum of individual EPSPs (expected membrane depolarization). The
dendritic input-o... |
d136692a2b5bfcda494f5988efae53c2f3914d3b91df5e56e15d4b85e3c5a589 | Python | 3,340 | 53 | # Copyright 2022 Huawei Technologies Co., Ltd
#
# 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... |
0f16b3cf136ff547593a4e3d0f3823d9491b22e92cb9ac4a93edaa1ce8b727e1 | Python | 3,341 | 101 | # Header variables and parameters.
import warnings
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=FutureWarning)
import os
import numpy as np
import numpy.matlib as matlib
from IPython.core.debugger import set_trace
import importlib
import sys
from default_config.masif_opts import masi... |
b2c9b930257a059422964332c1ab5ed83ad2866a8b8f602c9e28c5c44a09efa6 | Python | 3,341 | 103 | #!/usr/bin/env python3
"""Quick DVS sensor smoke test against a running CARLA UE5 server."""
import argparse
import time
import numpy as np
import carla
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--host", default="127.0.0.1")
ap.add_argument("-p", "--port", type=int, default=2000)
ap... |
c76392a1bfae4df7dc58bc78adbc63ebb2975b4e8db0522b851bf31a411dcff6 | Python | 3,342 | 101 | #%%
from pymaid_creds import url, name, password, token
import pymaid
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
import navis
# allows text to be editable in Illustrator
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
# font settings
plt.rcParams[... |
ca6a4a8c8666559c99264d2c29339b788af3a11b6aff06096109153cf3492715 | Python | 3,342 | 104 | import torch
import numpy as np
def lanczos_kernel(dx, a=3, N=None, dtype=None, device=None):
'''
Generates 1D Lanczos kernels for translation and interpolation.
Args:
dx : float, tensor (batch_size, 1), the translation in pixels to shift an image.
a : int, number of lobes in the kernel su... |
68c3a02f56046d62d3bbf16b88a15a7abfeb50efb6cccd0a4cb388b9d5a7def5 | Python | 3,346 | 153 | #!/usr/bin/python
#
# A version of recognition script to be used with lna_rec.py
#
import time
import string
import sys
import os
import re
# Set your decoder swig path in here!
sys.path.append("/home/user/decoder/src/swig");
import Decoder
def runto(frame):
while (frame <= 0 or t.frame() < frame):
if ... |
3a1557e190f9e27ce64204ed764428647b5d8e803c852ac40db60053dff06653 | Python | 3,347 | 101 | """
================================
Delineating cerebellar peduncles
================================
The cerebellar peduncles are white matter tracts that connect the cerebellum to
the brainstem and cortex. In this example, we show how to delineate the
cerebellar peduncles using a subject from the Healthy Brain Netw... |
9a07c0dcc734f7a07005e8a84ffd858d20f83820103416320b891965c87ca7cf | Python | 3,347 | 164 | __author__ = "Jianfeng Sun"
__version__ = "v1.0"
__copyright__ = "Copyright 2023"
__license__ = "GPL v3.0"
__email__ = "jianfeng.sunmt@gmail.com"
__maintainer__ = "Jianfeng Sun"
class ComputLib:
def interv2single(self, inf_arr, sup_arr):
"""
Methods
-------
1 2 ... |
0954df6550e79db41c1062dbfdaed0f2c027292b58743b916f11496f7868f594 | Python | 3,348 | 78 | import numpy as np
import pandas as pd
from scripting.spectrum.io import dialogs, paths
from scripting.spectrum import metadata
from scripting.utils import replace
def get_paths(t1="Scores", t2="Artifact Probs", matched_chars=11):
"""Get matched paths for scores and artifact probabilities text files along
wit... |
84931a9b9485f9dc886d04faf278dcd23c3f11777455c9a7333d3b7bf8c82bbb | Python | 3,348 | 98 | from dataclasses import dataclass
from typing import List
from deprecated.classic import deprecated
from oaklib.datamodels.search_datamodel import (
SearchBaseConfiguration,
SearchProperty,
SearchTermSyntax,
)
from oaklib.datamodels.vocabulary import (
IDENTIFIER_PREDICATE,
LABEL_PREDICATE,
SY... |
a54c91ac732802e597fd7df0d291d297383bca5162ca6289edabaa5224b6f345 | Python | 3,348 | 98 |
from timm.scheduler.cosine_lr import CosineLRScheduler
from timm.scheduler.multistep_lr import MultiStepLRScheduler
from timm.scheduler.step_lr import StepLRScheduler
from timm.scheduler.tanh_lr import TanhLRScheduler
def build_scheduler(optimizer, cfg):
num_epochs = cfg['TRAIN']['MAX_EPOCH']
scheduler_cfg =... |
1913a898487166c7b64e51089ff4603dac4a5114f2d29c3b1f2f269d538cd97e | Python | 3,349 | 134 | '''
Statistics of hyperpolarized fits
'''
import pandas as pd
from pylab import *
from matplotlib import gridspec
import seaborn
from file_management import *
from os.path import join
width, height = 8, 3
table = pd.read_csv(join(config['root'], 'Ciliated 4 mM KCl', 'tables', 'selection_hyperpolarized.csv'))
# Selec... |
833017267c04bd3d069f00de358fe6a2a40628e1e2acf132d4af6243589ed522 | Python | 3,349 | 113 |
import math
import glob
import argparse
parser = argparse.ArgumentParser(description='Create slurm jobs to make genomicDB from multiple gVCF files')
parser.add_argument('list_of_vcfs', help='.txt file with list of path of VCFs to merge (only for chr15, this will be replaced for other chr numbers)')
parser.add_argum... |
b1363be92783c71baa5932ec77db3f712292693c5b5837dffae78c917cd21400 | Python | 3,350 | 112 | # Copyright (c) Open-MMLab. All rights reserved.
from __future__ import division
import numpy as np
from mmcv.image import rgb2bgr
from mmcv.video import flowread
from .image import imshow
def flowshow(flow, win_name='', wait_time=0):
"""Show optical flow.
Args:
flow (ndarray or str): The optical f... |
199a8ff559c99a194095698966883b6a331ae2088e9be3d6c6013caa3b516679 | Python | 3,351 | 64 | """API + scheduler: a scene-package control is rendered server-side before dispatch."""
from carla_cosmos.client import sha256_file, zip_dir
from carla_cosmos.synthetic import av7_clip, make_clip
from conftest import make_server, submission
def _upload_scene(ctx, clip, tmp_path):
z = zip_dir(clip.scene_dir, tmp... |
9f260eba51d05814459751524b5bf3d60a1bf9b3c80aadd94139ac78c501edcc | Python | 3,351 | 79 | """MobileNetV1 for Semantic Segmentation"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.baselines.base import BaseModel
_all__ = ['MobileNetV3']
class MobileNetV3(BaseModel):
def __init__(self, classes=2, aux=False, backbone='mobilenetv3_small', pretrained_base=False):
... |
f10c7b7993727db0dbefccd2778cfbd07adb3c0f060b0c0d2f02dce0f85edb93 | Python | 3,352 | 79 | """MobileNetV1 for Semantic Segmentation"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.baselines.base import BaseModel
__all__ = ['MobileNetV3']
class MobileNetV3(BaseModel):
def __init__(self, classes=2, aux=False, backbone='mobilenetv3_small', pretrained_base=False):
... |
db56f3f3ef2d8815b53f3da0b0c8dbd834cadb1b35b937369f72c5886947fe7c | Python | 3,353 | 62 | import os
# Heuristics created by Vincent for Abbas dataset sample6.
# 27th Nov. 2019
# updated 23th Jan. 2020
def create_key(template, outtype=('nii.gz',), annotation_classes=None):
if template is None or not template:
raise ValueError('Template must be a valid format string')
return template, outtype,... |
6f12f5f433c81b8373cb858cc5047f02f24395049709272b68a8dc28255cd0e8 | Python | 3,354 | 93 | '''
Calculates the mean angle near the anterior and posterior ends.
Here the mean angle is calculated on the half-plane beyond the anterior/posterior ends.
'''
import os
import numpy as np
from file_management.batch_processing import *
from file_management.configuration import *
import sys
from scipy.stats import circm... |
fe3e8bc94332b0ccad92ee953de865d9bdb4ec61a49718a14218b3a1858c227f | Python | 3,354 | 93 | """Quick tests for SFNO (Spherical FNO). SFNO is FNO with SphericalConv; 2D only."""
import pytest
import torch
try:
from neuralop.models import SFNO
except ImportError:
SFNO = None
@pytest.mark.skipif(SFNO is None, reason="SFNO not available (torch_harmonics required)")
@pytest.mark.parametrize("n_modes", [(... |
30a6adeb693d3ca9d571b7390814cfe6409d8e5822372f92d824b8c0f8d6568e | Python | 3,355 | 107 | """
© 2022, ETH Zurich
"""
import itertools
import os
import pickle
import numpy as np
import pandas as pd
from rescoss_logp_ml.utils import SEED, get_output_dir
def get_model(model_name, **h_params):
"""Construct specified model with hyperparameters. Imports done inside function for compatibility with differ... |
4898796922061576914751e1b8279f83f10a0846ed5fab7a44f513790e2f1d91 | Python | 3,355 | 85 | # Copyright 2022 Huawei Technologies Co., Ltd
#
# 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... |
70e1e3d4ab7b9c752cfebe2ca12f94fa070b5f8d269e69bc72baa1f9ca34b705 | Python | 3,355 | 100 | import torch
import glob
import unicodedata
import string
import random
import torch
all_letters = string.ascii_letters + " .,;'-"
n_letters = len(all_letters)
def findFiles(path): return glob.glob(path)
# Turn a Unicode string to plain ASCII, thanks to http://stackoverflow.com/a/518232/2809427
def unicodeToAscii(s)... |
dfa241dd57aa0f96d094d341814d72f9239ad1fb6f2304f505745f5dbd2523b0 | Python | 3,356 | 93 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Script to launch a grid-search over hyperparamters, using the run_train.py script.
Created on Wed Oct 31 16:38:54 2018
@author: nicolas
"""
import os, sys, time
import numpy as np
# Add parent folder to path in order to access `axoid`
sys.path.insert(0, os.path.absp... |
1af1024dbeaa220f31524f9748e9b21143b9024b4f5543c3ed061f932aa5da0e | Python | 3,358 | 107 | from dataclasses import dataclass
from typing import List
import oaklib.datamodels.obograph as og
from oaklib import BasicOntologyInterface
from oaklib.datamodels.vocabulary import (
IN_TAXON,
IS_A,
NEVER_IN_TAXON,
ONLY_IN_TAXON,
PART_OF,
)
from oaklib.interfaces.obograph_interface import OboGraphI... |
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