sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
39af261ffb3c8b7c7f5a0c67f36d136f7196b6e38742d0afcef7019f48f61fbb | Python | 3,358 | 94 | import os
import torch
import argparse
import numpy as np
import esm
from Bio.PDB import PDBParser
from scipy.spatial.distance import cdist
from biopandas.pdb import PandasPdb
from data.loader import expand_sh
from feature import create_basic_features
from network.resEGNN import resEGNN_with_ne
if __name__ == '__mai... |
6a1673a5865d82137f43577454d502379194876be39ef6f749fd112482310654 | Python | 3,358 | 102 | import os
# import dask
import numpy as np
from scipy.spatial import cKDTree
import glob
from default_config.masif_opts import masif_opts
"""
precompute_evaluation_features.py: Precompute the features for the actual training from the 'decoy' transformations.
Freyr Sverrisson - LPDI STI EPFL 2019
Released under an Apac... |
9b7bc5ece5c4b6df0613b7196ec7c26b10ed1e83fbdcdc33a6300d1c5e13b682 | Python | 3,358 | 31 | from .base_options import BaseOptions
import sys
class TrainOptions(BaseOptions):
def initialize(self, parser):
parser = BaseOptions.initialize(self, parser)
parser.add_argument('--shuffle', action='store_true', required=False, help='Shall the data be shuffled?')
parser.add_argument('--no_... |
893650f76c9115c3bf0a73bb467cc94a488dec6879bd87678ca6ab751728cd4a | Python | 3,360 | 82 | """Rank loop for Transfer 2.5 auto/multiview: ``MultiviewInference`` on every rank (see ``common.ranks``).
NVIDIA ships 7 views on 8 GPUs; the worker starts ``torchrun --nproc_per_node N -m
cosmos_workers.transfer25_av.ranks --context-parallel-size N`` with N = the GPUs given to the worker
(independent of the number o... |
f0f28a97237beb3ccfcc8330af416e70123939f28060f4955c8b81859914474d | Python | 3,360 | 50 | import sys
sys.path.insert(0, '.')
from svgkit import Svg, C, render_png
W, H = 1260, 640
s = Svg(W, H)
s.text(W / 2, 44, "CARLA client-server architecture", size=26, weight="bold", anchor="middle")
s.text(W / 2, 72, "Default ports shown; the streaming and secondary ports follow the RPC port (N, N+1, N+2)",
size... |
34c1033eb7218eefaa8a27a4b782df27c882d40f20ca364886222294fe2aa935 | Python | 3,361 | 90 | # 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>.
"""Smoke test for the large-vehicle junction handling.
Spawns a known-large vehicle (firetruck or b... |
44abad5504a92131b37f7d13eb6b55005168e278cb2afe5ef30f9e9da7f1a344 | Python | 3,361 | 89 | # 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... |
2d5b1aeec7e17307dc89bf1f71b6bb4cf2d752516552642053fa68aa2fb77615 | Python | 3,362 | 100 | import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
# Device configuration
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# Hyper parameters
num_epochs = 5
num_classes = 10
batch_size = 100
learning_rate = 0.001
# MNIST dataset
train_dataset = ... |
3b74e42d9e69ad4b0e7440ae8e5d4c450e799cbbfd0bd67c860255add526e725 | Python | 3,362 | 118 | '''
Logistic Regression Model trained on the t5 embeddings
'''
# libraries
import pandas as pd
import numpy as np
from sklearn import preprocessing
import biovec
import math
import pickle
from sklearn.model_selection import train_test_split, KFold, cross_val_score, StratifiedKFold
from sklearn.preprocessing import S... |
5f66a413d832e1ff4dce7705362ab12fbefbdbd8f52398e05a0693413d3ccd97 | Python | 3,362 | 108 |
import cv2
import numpy as np
import random
# Check if a point is inside a rectangle
def rect_contains(rect, point):
if point[0] < rect[0]:
return False
elif point[1] < rect[1]:
return False
elif point[0] > rect[2]:
return False
elif point[1] > rect[3]:
return False
... |
af49078de69523d088e4fc3d4dfbd8e7ad846a2fd42ac671ae3c16d61de9c489 | Python | 3,362 | 138 | """Example script for running DyNeStE on simulated HMM-MVN data.
This script should take less than a couple minutes to run and
achieve a dice coefficient of ~0.99.
"""
import os
import pickle
import numpy as np
from osl_dynamics.simulation import HMM_MVN
from osl_dynamics.data import Data
from osl_dynamics.models.dy... |
cc1e57a42050d4e3e9d7113abf22f68111d308adc53ec201c889425816bdf277 | Python | 3,366 | 95 | # 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... |
21775d9f522cdaf91d42a6f1ec4cd66e4feda1e8e6e0f01b2bf22485f429e4f6 | Python | 3,367 | 95 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import math
import torch
class BoxCoder(object):
"""
This class encodes and decodes a set of bounding boxes into
the representation used for training the regressors.
"""
def __init__(self, weights, bbox_xform_clip=math.log(1... |
efdf74917df7fc764d10b9a845b4701be88d97f8ac20a57b447a6bf16e4de1fc | Python | 3,367 | 83 | # -*- coding: utf-8 -*-
# @Author : WenHan
import numpy as np
import models.layers as layers
# implement the quantized 1D-CNN in software.
def rescale_data_2d(data, shift_bits, upper_bound):
data_rescale = np.zeros_like(data)
for i in range(data.shape[0]):
for j in range(data.shape[1]):
... |
73d47967d0ae74d7b94d4a0cad70111c53e155e3df92a44fb5211e302a10e5d4 | Python | 3,368 | 136 | # %% [markdown]
# ##
import os
import warnings
import colorcet as cc
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.transforms as transforms
import numpy as np
import pandas as pd
import seaborn as sns
from joblib import Parallel, delayed
from scipy.stats import poisson
from sklearn.exceptio... |
41d32a1f1f66fe6b49c9d9fba439946557da5e8792760922794b5029a155d2ec | Python | 3,370 | 91 | import os.path as op
import numpy as np
import numpy.testing as npt
import nibabel as nib
import nibabel.tmpdirs as nbtmp
import dipy.data as dpd
from AFQ.registration import (write_mapping,
read_mapping, syn_register_dwi,
slr_registration)
import AFQ.dat... |
db5909c5543ce53e44b76aff123b3b491526ef3ab4e3276ebff4a0a4604c96c7 | Python | 3,371 | 90 | import sys
import numpy as np
import circularFiltering as flt
from time import time
kappa_z_array = np.array([0.01,0.03,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,2,3,4,5,6,7,8,9,10,30,100])
# choose a kappa_z according to system variable
kappa_z = kappa_z_array[int(sys.argv[1])]
# choose kappa according to input variabl... |
976758c0e3627801a46b47e506a1580314308d6a02502be074ff7c8010c37165 | Python | 3,372 | 117 | """
This file contains primitives for multi-gpu communication.
This is useful when doing distributed training.
"""
import pickle
import time
import torch
import torch.distributed as dist
def get_world_size():
if not dist.is_available():
return 1
if not dist.is_initialized():
return 1
ret... |
b30cc1f65c2ca01a93807f2d55abcc92c8864584193de74c94ffc331bb28a555 | Python | 3,373 | 77 | """Worker protocol against a real mock-worker subprocess (no API involved)."""
import asyncio
import subprocess
import sys
import time
import pytest
from carla_cosmos_server.config import default_run_dir
from cosmos_workers.common import protocol
@pytest.fixture
def worker(tmp_path):
run = default_run_dir(tmp_... |
11a004125fd01c04553afccc610e73416a60b38d99d5b4f3e9ef2b0aa0bb780c | Python | 3,374 | 95 | import sys
import time
import os
import numpy as np
from IPython.core.debugger import set_trace
import warnings
with warnings.catch_warnings():
warnings.filterwarnings("ignore",category=FutureWarning)
# Configuration imports. Config should be in run_args.py
from default_config.masif_opts import masif_opts
np.ra... |
2141ee2f894e3c81abefd7e916e7420b0567cd80217f0b2a1ace98bf2a322d98 | Python | 3,374 | 104 | import random
from networkx.classes.function import to_undirected
import networkx as nx
import torch
import matplotlib.pyplot as plt
from torch_geometric.utils import to_networkx
from graphxai.explainers import CAM, GradCAM
#from graphxai.explainers.utils.visualizations import visualize_subgraph_explanation
from grap... |
769af0e4231d89d24335c50fe7b614f8a2a4592eb731b3695af00e31fee046c8 | Python | 3,374 | 99 | import json
import os
import numpy as np
import PIL.Image
import pytest
from backend import root_tracking
def test_tracking_pair_shapes_are_validated_before_matching():
assert root_tracking.validate_tracking_pair_shapes(
np.zeros((48, 52)),
np.zeros((48, 52)),
) == (48, 52)
with pytest.... |
a55d96f55cd66091ac7ff90fdb0fcd143e6110ff365b55861ec6804e1f7e3acf | Python | 3,374 | 103 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import datetime
import logging
import time
import os
import torch
from tqdm import tqdm
from maskrcnn_benchmark.data.datasets.evaluation import evaluate
from ..utils.comm import is_main_process, get_world_size
from ..utils.comm import all_gather
... |
c3ca66883cb9f8e943b8f9d0f837e0ece4fa0f073a12853ef197562392d22f74 | Python | 3,374 | 82 | ####################################################################################################
# Copyright (c) 2019 - 2023, 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 ... |
fff6914d51da81970c6a2f555e89772d42db8bf9c6773f24b57e4a6e35e1905e | Python | 3,374 | 88 | import copy
import datetime
import logging
import torch
from mmcv.runner.hooks import Hook, TextLoggerHook
from mmdet.models.builder import build_neck
from tools.multadds_count import comp_multadds, comp_multadds_fw
from tools.utils import count_parameters_in_MB, get_network_madds
class DropProcessHook(Hook):
... |
58a5a488e2896bd5ec42470804cd19a0a080c49d62913f863beb215dd25dd8bc | Python | 3,376 | 81 | # 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... |
9874324bbc6f64fc76a2e8a605776e4f3beb85bddb1c3171a4629704b85f3cdb | Python | 3,377 | 125 | UNDEFINED = 'undefined'
BASE_NETWORK = "BASE_NETWORK"
EPOCHS = 'epochs'
FIXED_WINDOW = 'fixed_window'
SUBSEQ_WINDOW = 'subseq_window'
OUTPUT_DIM = 'output_dim'
WINDOW_SIZE = 'window_size'
INPUT_DIM = 'input_dim'
ITERATIONS = 'iterations'
INFERENCE_CPU = 'inference_cpu'
SAMPLE_PERIOD = 'sample_period'
BATCH_SIZE = 'batc... |
b2c2187b056733644dd0371b7cbfa0519fc96d8ff8bf3cf276ba98c50613dd57 | Python | 3,377 | 137 | """Config file for automatic code running
Assign some hyper-parameters, e.g. batch size for attack
"""
BEST_WEIGHTS = {
# trained on standard mn40 dataset
'mn40': {
1024: {
'pointnet': 'pretrain/mn40/pointnet.pth',
'pointnet2': 'pretrain/mn40/pointnet2.pth',
'pointcon... |
1ff62fbb895e1540144137b59ced7f8efbe0ef5fc0f98b7d63fc1918ee9ee7b2 | Python | 3,378 | 91 | import csv
import pickle
import numpy as np
import pandas as pd
def write_csv(doc_attrs:pd.DataFrame):
header = []
for a,b in doc_attrs.columns:
if a == b:
header.append(a)
else:
header.append(f"{b}({a})")
doc_attrs.to_csv("data/model_input_data.csv", header=header, ... |
41658ccbbbe553ec5a5d0e2f35c61ee261ec080433accae7d10e8b8c6de0994c | Python | 3,378 | 94 | import numpy as np
from scipy.spatial.distance import cdist
from scipy.ndimage import binary_dilation
def check_sls_with_inclusion(sls, include_rois, include_roi_tols):
for sl in sls:
yield check_sl_with_inclusion(
sl,
include_rois,
include_roi_tols)
def check_sl_with... |
eef1282ab72c82ae9f1c6b06b7165f1b41d8409df4af43b99fe3a93f0bcb57a3 | Python | 3,378 | 134 | import numpy as np
import math
from .math_lattice import mod2pi
def get_phase_random(N):
'''
Uniformly distributed random phase vector - on the interval from 0 to 2pi.
'''
phi = 2 * np.pi * np.random.rand(N)
return phi
def rms(vec):
'''
Root mean square; Quadratic averarge
'''
re... |
4aee2f6d1c58558708075d726774187d302f87cc1a12c266507575acb926d899 | Python | 3,379 | 92 | #!/usr/bin/env python3
"""
Copyright 2020 Johns Hopkins University (Author: Desh Raj)
Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
Prepare AMI mix-headset data. We use the RTTMs and SAD labels from the
"only_words" category of BUT's AMI setup:
https://github.com/BUTSpeechFIT/AMI-diarization-setup
... |
01b96abe0514c80a13e4e3bdd86375343bf2eada92f01ae3b05c617f02e3d1fb | Python | 3,380 | 90 |
import paddle
import argparse
import cv2
import numpy as np
import os
from models.model import FaceSwap, l2_norm
from models.arcface import IRBlock, ResNet
from utils.align_face import back_matrix, dealign, align_img
from utils.util import paddle2cv, cv2paddle
from utils.prepare_data import LandmarkModel
from tqdm imp... |
4498f8258aba0467526b6a519751d115ab26d26bb5039ad7f1a93dc5b6ee1b78 | Python | 3,381 | 90 | #!/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>.
import argparse
import math
import carla
def get_traffic_light_bbs(traffic... |
620cad9e7be823fae61c561e0db4311222790bb32998ac627cbacd565d8de8fa | Python | 3,382 | 95 | _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet_MD',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
mdcon... |
0ae516ce605d1e09b7efe3ad628746379ccbdf36664618d47d52e7d1809ef3dc | Python | 3,384 | 114 | #%%
import datetime
import time
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn.preprocessing import QuantileTransformer
import pymaid
from graspologic.utils import symmetrize
from navis import NeuronList, TreeNeuron, nblast_allbyall
from src.data import load_maggot_graph
from src.pymaid ... |
a6f230ca0b8af3909f9999992dc0258c214bb7a05519f0c6feb9aee263d9442b | Python | 3,384 | 105 | import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from scipy import ndimage
class NoiseModel(object):
'''Class providing parameters for noise simulation. Right now it only works for a monochromatic simulation with all parameters provided in the config for a certain tube voltage.
... |
d719f356ecc7f8485fca4dc07b000502a8818616375f92a4068b34ddf705bcec | Python | 3,384 | 91 | import os
import PIL.Image
import pytest
from backend import security
@pytest.mark.parametrize('value', [
'../image.png',
'..\\image.png',
'/tmp/image.png',
'C:\\temp\\image.png',
'folder/image.png',
'folder\\image.png',
'bad\x00name.png',
'bad\nname.png',
])
def test_validate_filena... |
d14d51908f875fcf80e239b491cd195fed9f443eed3d4fe5dbe5495f087db84f | Python | 3,386 | 125 | import torch
from torch import nn
from torch.autograd import Function
from ..utils import ext_loader
ext_module = ext_loader.load_ext('_ext', [
'top_pool_forward', 'top_pool_backward', 'bottom_pool_forward',
'bottom_pool_backward', 'left_pool_forward', 'left_pool_backward',
'right_pool_forward', 'right_po... |
e08a03549151131abe00b20ae9e0e8e9c627fe2588a9b23ebbe8a066b4ab6f80 | Python | 3,387 | 111 | from os import listdir, path
import brian2 as b # needed for exec
import matplotlib.pyplot as plt
from brian2.units import * # needed for exec
from scipy.optimize import curve_fit # needed for exec
# Analysis code
def func(t, a, tau):
"""Exponential decay function"""
return a * b.exp(-t / tau)
def get_t... |
cd5e3cb42f59a3f404a7f61e2c6e5fa885365f1517f18b8728e8726079267d5f | Python | 3,388 | 70 | # Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Implements the knowledge distillation loss
"""
import torch
from torch.nn import functional as F
class DistillationLoss(torch.nn.Module):
"""
This module wraps a standard criterion and adds an extra knowledge distillation loss by
taki... |
1ed3a5238f2408deb5e8f06e8cfd17aba9d2414320e14a260210d394d781127f | Python | 3,389 | 93 | # import threading
import queue
import torch
import torch.multiprocessing as mp
# from pathos.multiprocessing import ProcessPool as Pool
from torch.cuda._utils import _get_device_index
def get_a_var(obj):
if isinstance(obj, torch.Tensor):
return obj
if isinstance(obj, list) or isinstance(obj, tuple):... |
5569287e5a4e5756093d2191eaec1167a105b9df6795e7f7da6033973295d157 | Python | 3,390 | 101 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
from train_3d_time_series import TimeSeriesDataGenerator, get3DTimeSeriesDefaults
import unetsl.data
import unetsl.model
import unetsl.cli_interface
import sys
import pathlib
import re
import numpy
from matplotlib import pyplot
def displayGeneratedData(config):
gr... |
4b20e127407fb0d06a2f22549357406097f2d4719276e9dd70f6ca3c5106cf1c | Python | 3,393 | 109 | # 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... |
9412ee0505b36fd30d25b2fc0fee304eacf81702beec808a5d12c460629db774 | Python | 3,393 | 85 | #! /usr/bin/python
# -*- coding: utf8 -*-
import tensorflow as tf
import numpy as np
from six.moves import xrange
def discount_episode_rewards(rewards=[], gamma=0.99, mode=0):
""" Take 1D float array of rewards and compute discounted rewards for an
episode. When encount a non-zero value, consider as the end... |
13b7325981644a14157158334b0aade2eaf18388352cfb650ebc9ba680061add | Python | 3,397 | 96 | import os
import time
import logging
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neural_network import MLPClassifier
from .data.data_handler import kfold_split
def run_classifier(X_train, X_test, y_train, y_test... |
3b0fee5cf07601a657ad58c7dbf375e452e55230366b83bb24858e62253c2582 | Python | 3,397 | 119 | '''
@author: Mahbub Ul Alam (mahbub@dsv.su.se)
@version: 1.0+
@copyright: Copyright (c) Mahbub Ul Alam (mahbub@dsv.su.se)
@license : MIT License
'''
'''@file early_sepsis_detector.py
run this file to go through the complete neural net training, testing and evaluating procedures,
look at the 'early_sepsis_detector_con... |
6bf38d2d30e0663710a7f70d4b84724c219763db1941bc3ad6c829487f8c9659 | Python | 3,397 | 95 | __author__ = "Jianfeng Sun"
__version__ = "v1.0"
__copyright__ = "Copyright 2023"
__license__ = "GPL v3.0"
__email__ = "jianfeng.sunmt@gmail.com"
__maintainer__ = "Jianfeng Sun"
from typing import Optional, Union
import pandas as pd
from tmkit.base.Position import Position
class Separation(Position):
"""
C... |
c5465c276bd84339bc6c0301c0cf37d588f2d59fc25f194c2342c35c058f70df | Python | 3,398 | 109 | import unittest
import numpy as np
import numpy.testing as npt
from grr.Filter import constructMedianFilter
import grr.Filter_Rect as frect
from grr.Filter_Exps import Filter_Exps
class TestConstructMedianFilter(object):
def setUp(self):
self._setFilterType()
self._instantiateRandomFilters()
... |
3e4075dc485b063be061c308d88134a644d0b3bc1507f42f40abd3fc69977ee4 | Python | 3,399 | 112 | # -*- coding: utf8 -*-
import torch.cuda as cuda
import torch.nn as nn
import torch
import collections
from torch.nn.parallel._functions import Gather
__all__ = ['UserScatteredDataParallel', 'user_scattered_collate', 'async_copy_to']
def async_copy_to(obj, dev, main_stream=None):
if torch.is_tensor(obj):
... |
4978c9cc44b3ca76087a0d405265551f5279d3a68ce06e8582beb042668845f9 | Python | 3,399 | 117 | import os
from skimage import io, transform
import torch
import torchvision
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms#, utils
# import torch.optim as optim
import numpy as np
from PIL imp... |
838ffa416898c553678c30de2827d959764dbc50c7b472ee27cf0b1d2e14ea58 | Python | 3,399 | 101 | import argparse
import os
import tempfile
import subprocess as sp
import json
import struct
import time
import numpy as np
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
def log_quantize(data, mu, bins):
# mu-law encoding
scale = np.max(np.abs(data))
norm_data = data / scale
log_data = np.s... |
13d01074cacbcc16bbb36f012b10342c9835a7fc94323b80643846b3ccbc5e5b | Python | 3,400 | 97 | import unittest
import yaml
from oaklib.datamodels.vocabulary import (
NEGATIVELY_REGULATES,
PART_OF,
POSITIVELY_REGULATES,
)
from oaklib.implementations.pronto.pronto_implementation import ProntoImplementation
from oaklib.resource import OntologyResource
from oaklib.utilities.lexical.patternizer import (... |
8722c06e12ec0f4b0df6f65591d024e4a013c09d1837db87c18433c88e2b9591 | Python | 3,401 | 89 | from collections import defaultdict
from itertools import product
import os
import numpy as np
from scipy.stats import ttest_ind, skew, normaltest
import pandas as pd
from sklearn.datasets import make_classification
# creates the output directory
simulation_data_dir = 'datasets'
if not os.path.exists(simulation_data_d... |
73b5193b8cd32aeaebcaca542ae3a4f125a9d2ef561c99edc85fc4de967de0f1 | Python | 3,403 | 83 | #%%
# script didn't give reasonable LNs in my opinion; will use the other version
import pymaid
from pymaid_creds import url, name, password, token
rm = pymaid.CatmaidInstance(url, token, name, password)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as ... |
a23de430dba93ab67b24b6dc2bc08f746f976f09bc0e8f67da57add9a637661b | Python | 3,403 | 95 | """
Visualize mesh with matplotlib
"""
import numpy as np
from numpy.linalg import norm
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d # 3d visualization
import FBEM
def triangular_mesh_from_array(points_array, trias_array, ax=None, **kwargs):
'''
representation: 'surface', 'wireframe', 'p... |
66a4eba092757d52c71ef4bafd2a79e7f393613c34f424e1f81007d1d4f18bae | Python | 3,404 | 86 | import os
import pandas as pd
import glob
import argparse
import numpy as np
import torch
from model import ViscosityNet
from utils import prepare_test_input
from utils import calculate_attribution_grid
from utils import seed_everything
device = 'cpu'
if torch.cuda.is_available():
device = torch.cuda.current_... |
35518d326151092c67dcc37966b2a5b4ac91de1db15836cb7891c37f2c7b7c6b | Python | 3,406 | 152 | #!/usr/bin/python
import time
import string
import sys
import os
import re
sys.path.append("/home/jpylkkon/decoder_current");
import Decoder
def runto(frame):
while (frame <= 0 or t.frame() < frame):
if (not t.run()):
break
def rec(start, end):
st = os.times()
t.reset(start)
t.... |
60d903db61510e205fa1313811b5e794dfaf794c2cb5730ac7860001e92a1e95 | Python | 3,407 | 109 | """
Title
-----
Active vs passive dendrites
Description
-----------
In pyramidal neurons, distal synapses have often a minute effect on the somatic
membrane potential due to strong dendritic attenuation. However, the activation
of dendritic spikes can amplify synaptic inputs that are temporally correlated,
increasing ... |
cdee32fa3b9fa903b638b6454946977769e6638180d26cc07fe6c8a61ce69f29 | Python | 3,408 | 88 | import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
import xml.etree.ElementTree as ET
from PIL import Image
from tqdm import tqdm
from yolo import YOLO
from utils.utils import get_classes
from utils.utils_map import get_coco_map, get_map
if __name__ == "__main__":
map_mode = 0
classes_path = ... |
e25fe8278a0cc3d543bf6ff1417257dcb221b8e170ccd474549afbfae274b0c4 | Python | 3,408 | 109 | import os
import numpy as np
from .target import Target
class qSeqMemMapFeatures(Target):
"""
Stores the dataset specifying sequence regions and features.
Accepts a path to directory containing mammap files with feature values
Parameters
----------
target_folder_path : str
path to a... |
0bc12a7f0d095927b53769fd474aca77d31cfcb436d55b8dacb4fc1ab7ce5fdc | Python | 3,410 | 104 | """Moxing adapter for ModelArts"""
import os
import functools
from mindspore import context
from src.model_utils.config import config
_global_sync_count = 0
def get_device_id():
device_id = os.getenv('DEVICE_ID', '0')
return int(device_id)
def get_device_num():
device_num = os.getenv('RA... |
4b5a4197d9841d2451b69ea185f9f95ccaee9cbebac411964d26086204706701 | Python | 3,411 | 138 | """
Visual response related functions.
"""
import numpy as np
from lib.tuning import tuning_curve
def get_section(conditions, angle):
if np.isnan(angle):
valid = np.isnan(conditions).astype("int")
else:
valid = (conditions==angle).astype("int")
valid_diff = np.diff(valid)
valid_diff = valid_... |
84b288c015752e9c7599fadbacc5704906213a7ae2687061a4520025b00ff86a | Python | 3,412 | 49 | import carla,time,queue,json,math,os,argparse
from pathlib import Path
from PIL import Image
parser=argparse.ArgumentParser(description='Model 3 camera and shared door-regression checks; use an isolated server.');parser.add_argument('--port',type=int,default=4674);parser.add_argument('--profile',default='Default');args... |
410feead31aab45a7841d91d39607a13b9967cb2c01faaeda7ef4bd82e30821f | Python | 3,414 | 69 | import random
import slideflow as sf
from slideflow.util import log
class NeptuneLog:
'''Creates neptune runs and assists with run logging.'''
def __init__(self, api_token, workspace):
'''Initializes with a given Neptune API token and workspace name.'''
self.api_token = api_token
self... |
81964a11262408747562f819c9132f8d7b20bacf6a863701ef27ce6da20775d5 | Python | 3,414 | 140 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
import os
import sys
import time
import random
import numpy as np
import imgaug
import tensorflow as tf
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.patches as patches
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
fr... |
ee1c6195c11e30d3a564c4107e4f927a5fb2a98a651504d9622d94e79ead9d3e | Python | 3,414 | 94 | """Phase 1: train the Bennett 2021 RPE baseline on a CS+/CS- conditioning task.
Substrate: real larval Drosophila MB connectome (Winding 2023). The KC sparse code
comes from real PN->KC wiring; KC->MBON weights are plastic but restricted to the
KC->MBON edge mask from the connectome.
Expected behaviour:
- MBON re... |
4b7a9ceff520386f529a61e2045b10f85d17f11d5228a6ecff34585799ee316e | Python | 3,416 | 102 | import numpy as np
class Evaluation :
dThreshold = 0.1
Threshold = np.arange( 0,1, dThreshold )
Threshold_num = len( Threshold )
def __init__( self, Path, Parameter ) :
self.path = Path
self.stimtime = Parameter['stimtime']
self.chunks = Parameter['chunks']
self.delay = Parameter['delay']
self.confusi... |
7f720fd3a7e2c4f40e7ed8d5e067c3a051ff3504baebe2abac0181b4e3824231 | Python | 3,416 | 88 | #!/usr/bin/env python
# weight_hist.py ver 1.0 - a command line utility to plot a wildcarded argument
# list of files containing a line of connection weight values as a histogram.
import sys, os
import matplotlib.pyplot as plt
import numpy as np
def print_help():
msg = """
weight_hist.py ver 1.0 -... |
8189b528f84bb5976f6fec2badd2ab50af3bcef3ca0cbfa4f6e13efeef93633c | Python | 3,416 | 106 | import numpy as np
__all__ = ['frdist']
# Copied from https://github.com/spiros/discrete_frechet/blob/master/frechetdist.py
def _c(ca, i, j, p, q):
if ca[i, j] > -1:
return ca[i, j]
elif i == 0 and j == 0:
ca[i, j] = np.linalg.norm(p[i]-q[j])
elif i > 0 and j == 0:
ca[i, j] = max... |
126d1c228e65b8cceb63aa2d1d572620ae513b814253b5705b3f05fd6dfd2683 | Python | 3,417 | 100 | # 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... |
af3fbce64036adf00796daa0260507d5df9db8faa1268b6c74786131b44e3515 | Python | 3,417 | 95 | #!/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>.
import argparse
import sys
import math
import carla
def get_traffic_light_... |
1252f1c2dafe5f240e76a9c7d4906e6a9eb1e42573ac082f6ed6069ba1a8aa6a | Python | 3,418 | 104 | """
From https://github.com/jensengroup/statsig/blob/master/statsig.py (Accessed 25.04.22)
The license from the original repo is reproduced here as requested:
MIT License
Copyright (c) 2016 Jensen Group
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated doc... |
86f261b9d69fbd84ebcb57ad49f9c7851857d0058bceaec246ae9c2c24fa62ce | Python | 3,419 | 98 | # Copyright 2021 Xiaomi Corporation (Author: Junbo Zhang)
# Apache 2.0
# This script trains a random forest regression model to convert GOP-based
# feature into human expert scores.
# MSE: 0.15
# Corr: 0.42
#
# precision recall f1-score support
# 0 0.46 0.17 0.25 1... |
dcd03ef6c653293daf3b7f4763d4041f0da5b191fd6a360a146872c1c6d59ab3 | Python | 3,420 | 80 | ####################################################################################################
# Copyright (c) 2019 - 2021, 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 ... |
1a53bc129b815306ab4009afcfb97ac4c523f5d384f5f843ce1fc586bacd0c79 | Python | 3,421 | 106 | import numpy as np
import torch
class SamplesBatch:
"""
This class represents NN inputs and targets. Values are stored as numpy.ndarrays
and there is a method to convert them to torch.Tensors.
Inputs are stored in a dict, which can be used if you are providing more than just a
`sequence_batch` to... |
5a21a44bdcecab717b749c472c60993a722f70d0d4a081fbc88b512d158f4626 | Python | 3,424 | 82 | #!/usr/bin/env python3
"""Interrogate a recorded CARLA .log without replaying it.
Commands:
info --file run.log [--all] header, actors, per-frame if --all
collisions --file run.log --type1 v --type2 a collisions between categories
blocked --file run.log --min-time 30 --min-distance 100 ... |
76d162ac4aa208f77227dbcbf30e90218f19478336d3773263641b4a0a29efa9 | Python | 3,424 | 93 | import torch
import neuralop.mpu.comm as comm
def setup(config):
"""A convenience function to intialize the device, setup torch settings and
check multi-grid and other values. It sets up distributed communitation, if used.
Parameters
----------
config : dict
this function checks:
... |
64c0cff456cca651983b2dd93c745a14949f74e7b801127817f905dd43e541b3 | Python | 3,425 | 176 | """
Checkpointing and loading training states
=========================================
Demonstrating the ``Trainer``'s saving and loading functionality,
which makes it easy to checkpoint and resume training states.
"""
# %%
# .. raw:: html
#
# <div style="margin-top: 3em;"></div>
#
# Import dependencies
# -----... |
3c22ae65a9801f6551a3a7c578db87d06b5e5cdde48f55c78ca712b12996b175 | Python | 3,426 | 105 | # This script is part of navis (http://www.github.com/schlegelp/navis).
# Copyright (C) 2018 Philipp Schlegel
#
# 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, either version 3 of... |
361cf3faaa3e236fc99eab95acb18ebda250b07dea66fe3df8c64956875bf96d | Python | 3,427 | 82 | import glob
import gzip
def main():
# TODO: change this to command line input
for f in glob.glob('/groups/umcg-biogen/tmp03/umcg-ndeklein/gwas_overlap/biogen-cisandtrans-4neurodiseases/eqtlgen_disease_overlap/*trans*.txt.gz'):
# filename has to be in format *-<trait>.txt
trait = f.split('-')[-1... |
59be249cb323ec1a2a7b6c1c998ff3fa77f7ea4f2a0e84fd2b957fb4cd71b12c | Python | 3,427 | 106 | from __future__ import division
from __future__ import print_function
import time
import argparse
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from pygcn.utils import load_data, accuracy
from pygcn.models import GCN
# Training settings
parser = argparse.ArgumentParser(... |
cc5c8ec23c62622d50864389ee7321137b4597f0d63c82834d6580f38a807b7c | Python | 3,429 | 83 | import torch.nn as nn
import torch.nn.functional as F
import torch
class NestedUNet(nn.Module):
def __init__(self, classes=2, aux=True):
super(NestedUNet, self).__init__()
n1 = 64
filters = [n1, n1 * 2, n1 * 4, n1 * 8, n1 * 16]
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
... |
d6fd85714c7e84f756627c44ae6185ae08685bb68a804940a3e9668e26072dfd | Python | 3,430 | 107 | #!/usr/bin/env python3
import numpy
from tifffile import TiffWriter
import sys
import unetsl.cli_interface
import unetsl.model
import unetsl.data
import unetsl.predict
import unetsl.management
import unetsl
import pathlib
import json
import click
def getFileName(str_path):
return pathlib.PurePath(str_path).nam... |
5c11e7eaadd90a0e65e09a135444752a45ecda3eb0cc25f026223b2e6850a456 | Python | 3,432 | 91 | import torch
import torch.nn as nn
import torch.nn.functional as F
class ContrastiveLoss(nn.Module):
"""
Contrastive loss
Takes embeddings of two samples and a target label == 1 if samples are from the same class and label == 0 otherwise
"""
def __init__(self, margin):
super(ContrastiveLo... |
13457ee2d1d6a23bfe79cc9c0f4eb4ad6ddd216890de5a5af1eec2927052825f | Python | 3,433 | 79 | """
Effects of cooling on spatial release from masking - Analysis By Ferret
Flattens data across vowel identity (defined by first formant: F1) and vowel location (left or right speaker)
Input
-----
Data comes from the analysis directory; i.e. some preprocessing has already been done to remove correction trials and an... |
449ef0fac03befae7bd896e132e8eb8275fbc4ecf2e606f4d270aa706edd97c7 | Python | 3,433 | 99 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 19 18:07:49 2018
@author: asanofuyuki
.tiff
Normalized_by_each_mouse_max/ 1 min bin
出来上がったデータを.xlsで再保存して、doubleplotを描く。
データは9:00(ZT0)スタート、9:00(ZT0)終了でエクスポートすること。 (最終日の8:59までのデータがある。)
"""
import glob
import numpy as np
import pandas as pd
import ... |
92c6a20834e0cae9354a2b1d12343f7f2af90e3d8993c6f508ac5bde404ce948 | Python | 3,433 | 100 | import os
import pickle
import pyqtgraph as pg
import pyqtgraph.opengl as gl
import numpy as np
import scipy.ndimage as ndi
def get_object_vis_color(color):
vis_color = (color[0] / 255, color[1] / 255, color[2] / 255, 1)
return vis_color
def create_plot_points_in_3d(data_dict):
data_list = data_dict['da... |
a92fb8a5387d3c3c5e237d584e6fa7fe055e274aafcd1b229bcb1aa3c5355484 | Python | 3,434 | 116 | # -*- coding: utf-8 -*-
from .core import *
from .. import _logging as logging
import tensorflow as tf
__all__ = [
'StackLayer',
'UnStackLayer',
]
class StackLayer(Layer):
"""
The :class:`StackLayer` class is layer for stacking a list of rank-R tensors into one rank-(R+1) tensor, see `tf.stack() <ht... |
3c202c1af5bedc230d4757386b0a5535b0f46f4f043cd719f910884e2c2b06eb | Python | 3,435 | 91 | import numpy as np
import tensorflow as tf
def extract_patches(img, xy_patch, stride_length, patch_size, downsample):
height, width, depth = img.shape
patches = []
indices = []
if xy_patch is False:
for k in range(0, depth - patch_size[2] + stride_length, stride_length):
if (k + p... |
31965659f9120bd74d93108332819b90c922df6759435c8a39a4ea06490a73fc | Python | 3,438 | 78 | # 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... |
73c9b6f5d585fb4c89c1dff517ebc594e7ce769524c259b3bcf84f0b12cc82ba | Python | 3,438 | 94 | ## Sum all losses here
import torch
import torch.nn as nn
import torch.nn.functional as F
from loss.separation_loss import separation_loss
from loss.rotation_loss import rotation_loss
from loss.ssim_loss import compute_ssim
from model.vggforLoss import mse_loss_mask, VGG_feat
def define_loss(args):
loss_model = ... |
99654e4352ce26260f0e40a71c56e40d5f55118713ff651b2e510b05d5b59122 | Python | 3,438 | 41 | """Controlled vehicle impacts; use a dedicated Town10HD_Opt server."""
import argparse,json,math,os,time
from pathlib import Path
import carla
p=argparse.ArgumentParser();p.add_argument('--port',type=int,default=4674);p.add_argument('--label',default='fixed');args=p.parse_args()
P=Path(os.environ.get('CARLA_MODEL3_WORK... |
2265fbe39082db3a5ded70735e71bdc098eef388d6afd6a769ea935cdbd11368 | Python | 3,439 | 91 | # This file is part of me-features-to-mol-ID-mapping.
#
#
# Copyright © 2021-2022 Blue Brain Project/EPFL
#
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the APACHE-2 License.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANT... |
567a80a843d62e1c366fc518bbcc8d3ba03ed4dee6c7a08f447ea29a7e592574 | Python | 3,439 | 106 | # prepare semantic maps for FFHQ
import os
import glob
import json
from PIL import Image
import numpy as np
import torch
from torchvision import transforms, utils
from generators.BiSeNet import BiSeNet
remap_list_celebahq = torch.tensor([0, 1, 6, 7, 4, 5, 2, 2, 10, 11, 12, 8, 9, 15, 3, 17, 16, 18, 13, 14]).float()
... |
96b17aa014ad66b533514139a5529b4e2e85b728940c9ee46f44c3f5f5dbf518 | Python | 3,441 | 83 | import random as rng
from pathlib import Path
import cv2
import numpy as np
from .saveFilesC import saveFilesC
class negativeRandomC:
def __init__(self):
super().__init__()
def negativeRandom(self):
self.log_crop_event("Random cropping started", self.number_of_voxel)
indices = np.wh... |
5ef912b3b4b3f05f60e7303dd72edd20fa97eca3f03294b9b686619ba402ded2 | Python | 3,442 | 86 | # 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... |
d331a4e6bd9de96eb3995448acf05354741e197bcc8273a2dc936624fc4f6eee | Python | 3,444 | 103 | from typing import Dict, List, TextIO
from airium import Airium
from linkml_runtime.dumpers import json_dumper, yaml_dumper
def format_object(curie, label):
if label:
return f"{label} [{curie}]"
else:
return curie
def add_association_group(doc: Airium, associations: List[Dict], subject: str... |
c03ffd693ea8a9b3cbaf128db5ad4acdc2538b504ee15c45f79d493296211453 | Python | 3,445 | 99 | import pandas as pd
import numpy as np
import nibabel as nib
import argparse
import os
import sys
def roi_coverage(arr):
return np.sum(arr > 0) / arr.shape[0] * 100
STAT_METRICS = {'mean': np.mean, 'std': np.std, 'median': np.median, 'roi_coverage': roi_coverage}
#MAPS = ['rBF', 'rBV']
MAPS = []
def get_stats... |
5dc420df20f801ab6a4db605ce20c8ea9e704737d83154ba2499beca0b30a3ff | Python | 3,446 | 152 | # 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... |
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