sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
62e50be5972bc5d8ead7fe1cde0a38639cdee5d4d6c12e0fce0103e330e2b09f | Python | 7,014 | 206 | import sys
import os
import schnarc
import logging
import shutil
import argparse
import numpy as np
import datetime
import copy
import time
(tc, tt) = (time.process_time(), time.time())
#import schnet
import schnetpack as spk
import torch
import torch.nn as nn
from ase.units import Hartree, Bohr, Debye, invcm
from torc... |
c8863a4de7b0a171bf1ebfe6d69a2d3d3ebd1790730f99c376a189d245e1ac7c | Python | 7,016 | 133 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2024 Clinic for Diagnositic and Interventional Radiology, University Hospital Bonn, Germany
#
# 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 ... |
0b6e5c4c8f52a4797cb4844a329506e3fa41a4a911a2fce56318d8602b86e3d9 | Python | 7,019 | 198 | """Inference modules and script.
Copyright (c) 2024 University of Michigan. All rights reserved.
Licensed under the MIT License. See LICENSE for license information.
"""
import os
import logging
from shutil import copy2
from functools import partial
from typing import List, Union, Dict, Any
import yaml
import numpy ... |
cfba05d55024228c11b7a8b1ce138dd1b0828bc7aae2a0572a018c5f4bfcb118 | Python | 7,022 | 195 | """Parameter space overview figures
-----------------------------------
Combines individual figures to parameter space overview figures using latex.
"""
import os
import matplotlib.pyplot as plt
import subprocess
import pickle
import numpy as np
import glob
import matplotlib
matplotlib.use('Agg')
def parameterspace... |
0e63de89dc981ce9af888b2ea07360106c45547f25da34a7c58798e7d243fcef | Python | 7,024 | 170 | # Copyright (c) 2020, Ko Sugawara
# 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,
# this list of conditions and t... |
7e07c8015295a3187dcd4626443e298b2fc22af1148aa00d5d21acf288054091 | Python | 7,027 | 184 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Written by Tong He and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
"""
import os
import argparse
import subprocess
import numpy as np
from nilearn import datasets
from nilearn import input_data
from nilearn.connectome import Con... |
88d252b527cf2a6ac68cd7a21bb1ee4a6f4dc2f9b0c54c601f2caba8461d532d | Python | 7,027 | 206 | """Execution context tracking for the autodiscover directory feature.
This module maintains a thread-/task-local stack of ContextNodes that records
the nesting of flows, subflows and jobs as they execute. When
NESTED_RESULTS is enabled, the context stack is translated into a
filesystem path (e.g. ``my_flow-<id>/my_su... |
9a6ac4a7e61eb9ff2d5acf4e188305bc5fc7f8b1f20ca63861cc1da125f5b3ed | Python | 7,027 | 180 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
5fdbfb96445a65e2844de643284b9397d9a785b5b7c20aa922023cec13ba1112 | Python | 7,029 | 159 | import os
import time
import logging
import math
import argparse
import numpy as np
import torch
import random
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.utils.data as data
import warnings
from utils.util import setup_logge... |
90a685d2edc659ee34c07585bc97ab32d3f89cd259f954ebcc6ee1f5967f5f96 | Python | 7,029 | 244 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
AutoArbor_M3: The method 3 of AutoArbor Algorithm
Created on Feb 13 10:22:52 2020
Last revision: March 24, 2020
@author: Hanchuan Peng
"""
print(__doc__)
import time
import os
import csv
import pandas
import numpy as np
from numpy import linalg as LA
import math
imp... |
099405645278bda509d0d25cf02094aff6edc9c05eec4f75236083fb07140a8a | Python | 7,030 | 167 | from typing import List, Tuple, Union
import matplotlib.pyplot as plt
import mne
import numpy as np
import torch
from .utils import _plot_to_image
_DEFAULT_MONTAGE = mne.channels.make_standard_montage('standard_1020')
def plot_raw_topomap(tensor: torch.Tensor,
channel_list: List[str],
... |
9661404d945592d99bf1a4c7d193d5b2e5bdd22406d2205a333cf82764083960 | Python | 7,030 | 226 | import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
# from lib.normalize import Normalize
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': '... |
d64551dc8eddd30cf3b214d2ac58551ae1db05f1df73cd49c00208969b0d0c6e | Python | 7,031 | 173 | """Container for multiple HED schemas used together in multi-library validation."""
from __future__ import annotations
import json
from hed.errors import ErrorHandler, ValidationErrors
from hed.errors.exceptions import HedExceptions, HedFileError
from hed.schema.hed_schema import HedSchema
from hed.schema.hed_schema... |
90bb907147af5bb6cec37650620ee309a0ea8b9f15ee1f802ba6037e44b64107 | Python | 7,033 | 222 | """Materials Project-compatible recipes using the original MP settings."""
from __future__ import annotations
from importlib.util import find_spec
from typing import TYPE_CHECKING
from monty.dev import requires
from pymatgen.io.vasp.sets import MPRelaxSet
from quacc import flow, job
from quacc.calculators.vasp.para... |
6cc32f02bff2f65fc98c3fbd623a9d24788784029e04ba0a9c801e818b462e9e | Python | 7,037 | 189 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
892de2bcdf2479eebb57ffa12b19e6c11701beb28c12f0def092e3ba301eb277 | Python | 7,038 | 202 | import numpy as np
import pickle as pkl
import matplotlib.pyplot as plt
from matplotlib import rc
#rc('font',**{'family':'serif','serif':['Computer Modern Roman']})
## for Palatino and other serif fonts use:
#rc('font',**{'family':'serif','serif':['Palatino']})
font = {'family' : 'normal','weight' : 'bold'}
rc('text'... |
b2f968815149b0b0f6a82307089f300adb834b55264d8776dd6e56f6df52bafe | Python | 7,039 | 253 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import math
from functools import partial
__all__ = [
'ResNet', 'resnet10', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnet200'
]
def conv3x3x3(in_planes, out_planes, stride=1):
# ... |
4150a6c7bb08c01ded0cfc5be096066d526d9a9318f8f4c2a4c660bb15ec20be | Python | 7,040 | 207 | from __future__ import annotations
from copy import deepcopy
from pathlib import Path
import numpy as np
import pytest
from ase.build import bulk, fcc100, molecule
from ase.io import read
from pymatgen.core import Structure
from quacc.atoms.slabs import (
flip_atoms,
get_surface_energy,
make_adsorbate_st... |
533ac10ef9d897aa476941a47545a3ace41e20f695eb18a8555ed8867f1290d0 | Python | 7,040 | 162 | from functools import partial
from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import requests
import io
import tensorflow_hub as hub
def get_weights(bit_variant):
response = requests.get(f'https://stor... |
72ee73fa33d5af009788720c8d78c53277a4af4cd1f915d9899e6234336224f9 | Python | 7,042 | 177 | #!/usr/bin/env python
from __future__ import print_function
import argparse
from collections import namedtuple
from itertools import permutations, combinations
import numpy as np
from examples.continuous_tamp.primitives import draw_state, SUCTION_HEIGHT, GROUND_NAME
from examples.continuous_tamp.run import initiali... |
7ff37280bc48a25eb5ca927df253d80333c23647c043565a28ac6332d64694b9 | Python | 7,042 | 182 | import numpy as np
from PIL import Image
from PIL import ImageEnhance
from PIL import ImageFilter
import matplotlib.pyplot as plt
from scipy import ndimage
import argparse
import time
import os
import warnings
import ntpath
from src import utils
from src.settings import Settings
warnings.filterwarnings("ignore")
"""... |
3438129e4ad02c84a93d840d6f193573bd65a3706775e9dc257ffb199c557b9f | Python | 7,045 | 217 | #!/usr/bin/env python3
########################################
### Load packages
########################################
print("----- Loading packages -----")
# General
import numpy as np
import pandas as pd
import os
import csv
# Computing
import scipy.io
from scipy import stats
from heapq import nlargest # gi... |
4964c6537a52510b3e0ac70a93cbcb0a4991a060b0cedbb07e9ea0e3503ff185 | Python | 7,045 | 210 | from __future__ import annotations
from copy import deepcopy
from pathlib import Path
import numpy as np
import pytest
from ase.build import bulk, fcc100, molecule
from ase.io import read
from quacc.atoms.slabs import (
flip_atoms,
get_surface_energy,
make_adsorbate_structures,
make_slabs_from_bulk,
... |
797161ca2620251a29c3a374567afd33ac246261c72130fd0349e56cfb424c2a | Python | 7,045 | 251 | import numpy as np
import matplotlib.pylab as plt
import cv2
from skimage import measure
import datetime
import json
import os
import re
import fnmatch
from PIL import Image
from pycocotools import mask
import glob
from tqdm import tqdm_notebook
from multiprocessing import Pool
from multiprocessing import cpu_count
imp... |
81064f2d156bfd2624c73239694bee37a89f2a276d2ed53753023a78adaff5b8 | Python | 7,045 | 181 | import sys
import os
import numpy as np
import traceback
# dir_name, filename = os.path.split(os.path.abspath(__file__))
# sys.path.append(dir_name)
# sys.path.append(os.getcwd())
import pandas as pd
from joblib import Parallel, delayed
from src.utils import logger, parse_config, contain_substr
from scipy.spatial.tran... |
327252e16e69bb6c07dc3a6889260a52854e02824d9f139385cacb5c0e0b9406 | Python | 7,046 | 169 | import os
import itertools
import torch
import json
import pandas as pd
import argparse
import multiprocessing
from multiprocessing import set_start_method
from torch_geometric import seed_everything
from model.ada_med_graph import AdaGNN, ppmi_data_prepare
label_tar=['Label_NHY', 'Label_NP3_Tremor']
parser = argpars... |
e069a387c31ba58cbe53440d8aed3402e371cf6f9449bef62a6ea4ae396c2c69 | Python | 7,049 | 180 | """
Display's a histogram to analyze the statistical distribution of a column data in a STAR file, or a combination
of them.
Input: - STAR file(s)/column(s)
- Operator (optional)
Output: - A figures with the histogram is displayed
"""
__author__ = 'Antonio Martinez-Sanchez'
###### G... |
f5f5314a9b19102c56e77eaa8e641d2d0e09eb3d3c516a772843a52ed72caaef | Python | 7,049 | 193 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Written by Tong He and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
"""
import os
import time
import random
import argparse
import numpy as np
from keras import backend as K
from keras.optimizers import SGD
from config import co... |
72518b7e40a7ccc738e4058dd40aa340804394acbb64bb21b91abb6658cf0dfa | Python | 7,050 | 189 | import math
import torch
import numpy as np
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.... |
d4d6d72861f6c7a15a0a386ce45b85ed8669e91f784d55bd9d8d57b55bb46987 | Python | 7,051 | 213 | from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
class STN3d(nn.Module):
def __init__(self):
super(STN3d, self).__init__()
self.conv1 = to... |
01bc2f253bfff1098d518fe0fc8e6b98814e77ed6f7820db63e2bf80485210ac | Python | 7,052 | 191 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
72427df5bc19466bb611eb7f120c91ecc1ee49a7f29d8221026f980e4ca97122 | Python | 7,052 | 183 | """
Overlap-weighted doubly robust propensity-score analysis.
Propensity score PS = P(Exposure | Age, Age², Sex, Age × Sex)
is estimated via logistic regression. Two doubly robust approaches
are implemented:
Approach A — PS as an additional covariate in OLS:
Score ~ Exposure + PS + Age + Age² + Sex + Age × ... |
a5ee1fd055d791c95b2cc8e2b6c397ded94be7f83bdc96c11bf05f54b994c118 | Python | 7,053 | 200 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
45b70778f969145b36ec8d01b5eef0a7c4f81437e8ade16d2d5a801be0ee3b25 | Python | 7,056 | 243 | # -*- coding: utf-8 -*-
#
# Routines for designing and applying
# FIR windowed sinc filters
#
# Builtin/3rd party package imports
import numpy as np
import scipy.signal.windows as sci_win
from scipy.signal import convolve
def apply_fir(data, fkernel, method="fft"):
"""
Convolution of the input `data` with a... |
52fb00cdb5904ee74a2875631b9f5a562b50cec2d5f4321a4d36e1da54aac29b | Python | 7,056 | 138 | import numpy as np
import torch
def vectors_shuffled_batch_collate_fn(shuffled_batch,
num_contexts_range, num_targets_range):
'''
This function works together with the method get_ct_indices_for_one_function
and the functions get_non_overlapping_ct_indices or get_... |
cc1712cb2fbe04bb6e0bb5aa25b5dcfd02cd54ed0d7bdadf325efd006e1ccb12 | Python | 7,057 | 167 | """
=================
Run the full PREP
=================
In this example we show how to run the full PREP pipeline on a noisy EEG
recording using :class:`~pyprep.PrepPipeline`, inspect which channels it flags
as bad, and visualize the effect of cleaning.
.. currentmodule:: pyprep
""" # noqa: D205 D400
# Authors: ... |
db646c5b4e3e5581edd42dedb7db389ed64c340e69d9d43bfacf4a0a7cf0c2d0 | Python | 7,057 | 199 | from __future__ import annotations
import re
import numpy as np
import pytest
from scipy.optimize import NonlinearConstraint
from bayes_opt import BayesianOptimization, ConstraintModel
@pytest.fixture
def target_function():
return lambda x, y: np.cos(2 * x) * np.cos(y) + np.sin(x)
@pytest.fixture
def constra... |
18737d983bbac26d313d11c819637ecff77d0db523640cf74d040f324a0f87c0 | Python | 7,060 | 186 | """Classes for managing counts of tags for one type tag such as Condition-variable or Task."""
class HedTypeCount:
"""Manager of the counts of tags for one type tag such as Condition-variable or Task.
Parameters:
type_value (str): The value of the variable to be counted.
type_tag (str): Th... |
acc4fbf2a746d988ba28cc4b1911452b805d1118e08b30982c5589097866898e | Python | 7,062 | 219 | """Implementation of CLI modify command."""
import math
from itertools import chain
import numpy as np
from treem.io import SWC
from treem.morph import Morph
from treem.utils.geom import rotation
def _scale_radii(morph, nodes, scale_radius):
"""Scales radii."""
scale = np.abs(scale_radius)
for node in ... |
7edb2e5e4561640c1c4f8ca048dbbc9d693512582acbc27a7ada5e1b2d8aadd1 | Python | 7,064 | 153 | from PoseBase import PoseBase
import PoseTools
import os
import tarfile
import tensorflow
vv = [int(v) for v in tensorflow.__version__.split('.')]
if (vv[0]==1 and vv[1]>12) or vv[0]==2:
tf = tensorflow.compat.v1
else:
tf = tensorflow
# from batch_norm import batch_norm_mine_old as batch_norm
if vv[0]==1:
f... |
5663f73f11bb115f762365eda05d3259ccbd8ce6d83816d48f6fb8ce7cf20b52 | Python | 7,065 | 205 | """
Optuna example that optimizes multi-layer perceptrons using PyTorch distributed.
In this example, we optimize the validation accuracy of fashion product recognition using
PyTorch distributed data parallel and FashionMNIST.
We optimize the neural network architecture as well as the optimizer configuration.
As it is... |
5d5c6f89d82d3d4d44b48035500a54a93ef746b3e25cb0a57e5f1d0ca6fa72c3 | Python | 7,065 | 187 | import pickle
import os
from typing import Union
import torch
import lmdb
class EEGSignalIO:
r'''
A general-purpose, lightweight and efficient EEG signal IO APIs for converting various real-world EEG signal datasets into samples and storing them in the database. Here, we draw on the implementation ideas of i... |
4ed217251255e8c82fe0962f66c293cb077695c1b372a404674cd1df8ecc5d1a | Python | 7,066 | 158 | import builtins
import uuid
from pathlib import Path
from unittest.mock import MagicMock
import pytest
import NDATools
from NDATools.upload.batch_file_uploader import files_not_found_msg
from NDATools.upload.cli import ValidatedFile
from NDATools.upload.validation.api import ValidationManifest, ValidationV2Credential... |
f8b8077e07b29d652abed5720634015a3554b4e06c51e3209b332af31d6d3573 | Python | 7,066 | 217 | # Defines different color classes used to generate artificial training stimuli.
import torch
from torch import Tensor
class Color:
"Base class"
def __init__(self):
self.color = self.colorval()
def colorval(self) -> Tensor:
raise NotImplementedError
def fill_tensor(self, height, wi... |
5ab4a193a9c7840d1dba729b26bb973f558ef68b90b01264e5bdc10c0bc92cd7 | Python | 7,068 | 229 | import torch
from torch import nn
import random
from . import utils
class Permute(nn.Module):
def __init__(self, *order):
super().__init__()
self.order = order
def forward(self, x):
return x.permute(self.order)
class Onehot(nn.Module):
def __init__(self, n):
super... |
85b7128b4b5d65e2c14a729949221b3b8aea6d5261f028452fe7f1fe84a35c62 | Python | 7,068 | 128 | import matplotlib
matplotlib.use('TkAgg') # Set the backend for matplotlib
import matplotlib.pyplot as plt
import os
import scipy
import scipy.io
import numpy as np
from Functions.f_AnalysisGraphs import * # Import custom analysis graph functions
from Functions.f_AnalysisFunctions import * # Import custom ... |
0b17e0236a27321a545f7821c5e4177ec937e36998f5c54a3fd506a170ef9aaf | Python | 7,069 | 198 | ''' Modified by Mayank Kabra
From LEAP https://github.com/talmo/leap by Talmo Pereira
'''
import h5py
import numpy as np
import os
from time import time
import keras
import keras.models
from keras.layers import Lambda
import tensorflow as tf
import re
from clize import run
from leap.utils import find_weights, find_be... |
2907af69b533cdec9d7e1440b103231a2b5d52c9e2cd29831da2833dd7d7b161 | Python | 7,069 | 147 | examples = dict(
disable_training_metrics="""
>>> from h2o.estimators import H2OSupportVectorMachineEstimator
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> svm = H2OSupportVectorMachineEstimator(gamma=0.01,
... ran... |
61e8acc7d377f609a2435e5c2a3037cd1289bf41e3bcc357ab962188fd32ef6c | Python | 7,069 | 232 | import numpy as np
import logging
import platform
from scipy.stats import iqr
def psth(
trl_dat,
trl_start,
onset,
trl_end,
chan_unit_combs=None,
tbins=None,
output="rate",
samplerate=1000,
):
"""
Peristimulus time histogram
The single trial input `trl_dat` needs 3 column... |
219ddf3ba450cc6ac3b377b42403d17abe246b4ea28632293699f3764a3dd658 | Python | 7,071 | 114 | """WHY_THE_LINE.py 2026-10-03. Pre-registration: 04-NOTES/PREREG-2026-10-03b-WHY-THE-LINE.md
Is the inequality's line excitation meeting inhibition at the lobe's own rates? s_own = the excitatory share of local-neuron
synapses onto uniglomerular PNs, each weighted by its neuron's mean rate at that very sign set; predic... |
4eb4d8402521c33704bd51d79dff1e44263452902d7e05e90d8791c9aae9a974 | Python | 7,071 | 198 | from __future__ import annotations
from enum import Enum
from loguru import logger
from ord_schema.message_helpers import json_format
from ord_schema.proto import reaction_pb2
from pydantic import BaseModel
"""
used in exporting reaction data from pb
"""
class ReactionDataWarningType(str, Enum):
COMPOUND_NAME_... |
fad0ad6f68995e7328db7527d1a287668062839650cad18f74a71c35d3240570 | Python | 7,071 | 261 | '''
Serial communication protocol to control optotune liquid lens
- by Deepak Krishnamurthy
- updated by Hongquan Li July 2021
'''
import serial
import platform
import sys
import time
import numpy as np
import warnings
import serial.tools.list_ports
from control._def import *
class optotune_lens:
# A class for s... |
009f82ae8af1422fb322f16b176f8e89b61ead26368ad0f379a074244ff3a554 | Python | 7,073 | 186 | """
Deterministic classification of particles in a STAR file (Heuristic approximation for large dataset)
Input: - The STAR file with the particles to classify
- Classification parameters
Output: - A set of STAR files with the new classes
"""
__author__ = 'Antonio Martinez-Sanchez'
######... |
55ba4693124c7dadef068b5be1fdeb006c60e04e225107e7058f50bc1ad29569 | Python | 7,073 | 252 | #!/usr/bin/env python
"""Python script to run cell model"""
"""
/* Copyright (c) 2015-2022 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PA... |
5cbc0fa1fd63524aaede017f6df6ef8df9294aea34cbae08df9660af5c56b758 | Python | 7,073 | 252 | #!/usr/bin/env python
"""Python script to run cell model"""
"""
/* Copyright (c) 2015-2022 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PA... |
79b017dc382a4546c624885f0be8e8a3953760ae987757fc16c49caa8139feed | Python | 7,073 | 252 | #!/usr/bin/env python
"""Python script to run cell model"""
"""
/* Copyright (c) 2015-2022 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PA... |
8b6d7ac82f910e90fd6e2584322c5a8c1d5cd79de4113729a744e7a394b629de | Python | 7,073 | 252 | #!/usr/bin/env python
"""Python script to run cell model"""
"""
/* Copyright (c) 2015-2022 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PA... |
91d387da10845ab76e551d86f958b3470a84a6d4483a36da5f1fcbbdbc05e92c | Python | 7,073 | 252 | #!/usr/bin/env python
"""Python script to run cell model"""
"""
/* Copyright (c) 2015-2022 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PA... |
a6d37a99d29bc14990ff6658d321e9f5f5c9bf16d31d1e1e2086456f625456ea | Python | 7,077 | 212 |
from utils import load_dataset, load_snp, null_dist_score1, FilterLCC, load_score
import pickle
from collections import defaultdict
import multiprocessing
from sklearn.cluster import KMeans
from sklearn.decomposition import TruncatedSVD
from scipy.stats import norm
import numpy as np
def sig_score(gene):
gene_... |
d027687817d1fdbb6ffe5d252305ff8090d19fff1409aef649108d81fe0b1859 | Python | 7,077 | 214 | from utils import load_dataset, load_snp, null_dist_score1, FilterLCC, load_score
import pickle
from collections import defaultdict
import multiprocessing
from sklearn.cluster import KMeans
from sklearn.decomposition import TruncatedSVD
from scipy.stats import norm
import numpy as np
def sig_score(gene):
gene_c... |
5e6089ca9d9361c5a9ca99a244affd66dd95508264bb326d4554c4df57bfef4f | Python | 7,078 | 266 | """Classes for several probability distributions (Gamma, Lognormal, Wald, Weibull)."""
import numpy as np
from scipy.special import gamma as gamma_func
from scipy.stats import gamma, invgauss, lognorm, weibull_min
class Gamma():
"""
Define a gamma distribution.
This class represents a gamma distribution... |
7b6df373ad041b3dc38756d938f4f80d853d3c256b6e435850d88a1bc5cfba83 | Python | 7,078 | 244 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
9e2b494058d9cc526cc453c4f86fb0c1f7d3730c5a130341a4be01decd3b98e8 | Python | 7,080 | 202 | import torch
from torch import nn
class BernoulliVAE(nn.Module):
"""
Vanilla VAE with Bernoulli latent distribution.
Args:
input_size (int): size of model input
latent_dim (list[int]): dimensions of 2D latent space
n (int): number of times to sample from latent distribution for a ... |
cebff23eb7443052b68b1b47f4e073d4e0883662a19def19cc15bdae28784f30 | Python | 7,080 | 127 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Created on Fri Apr 8 11:29:57 2022
# @author: caroline
"""
Compute logistic regressions of the weights of priors and sensory likelihood for each subject.
# Fit weights of priors and likelihood in T1
## With bayesian combination (= bayesian decision and generative... |
5be8e8c4f0dcc01ec9b234c5980186d82332a84a6e0b46e8e0ed80d79922359d | Python | 7,083 | 158 | from __future__ import division, print_function, absolute_import
import importlib.machinery
import tensorflow.keras.callbacks as tfcallbacks
import chrombpnet.training.utils.argmanager as argmanager
import chrombpnet.training.utils.losses as losses
import chrombpnet.training.utils.callbacks as callbacks
import chrombp... |
df552c09b49e8e3d202f70e2d1d7545096b346b4403e1876512645aa66f16264 | Python | 7,083 | 209 | """Convolutional neural net on MNIST, modeled on 'LeNet-5',
http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf"""
import data_mnist
import autograd.numpy as np
import autograd.numpy.random as npr
import autograd.scipy.signal
from autograd import grad
convolve = autograd.scipy.signal.convolve
class WeightsParser:
... |
9919341a5c1e42333fe9232c4d5469a543e4c86f8f44aeaa5d0fd538d6329aa9 | Python | 7,084 | 182 | # ----------------------------------------------------------------------
# Copyright (C) 2016, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or mo... |
0ee3c7f81cdbab4fae00a139ec1cc2f81ef814a1997e94192666176e30aa6143 | Python | 7,087 | 212 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..initializer import create_initializer
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_channels, out_channels, stride):
super().__init__()
self.bn1 = nn.BatchNorm2d(in_channels)
... |
e56c3b56ef64abfd0b7bad2a3a1d1b831da1762c0bceaccc7f425b06212a9673 | Python | 7,088 | 239 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
:AUTHOR: Jens Petersen
:ORGANIZATION: Heidelberg University Hospital; German Cancer Research Center
:CONTACT: jens.petersen@dkfz.de
:SINCE: Wed May 17 14:29:20 2017
:VERSION: 0.1
DESCRIPTION
-----------
Preprocess all experiments under a given XNAT endpoint
REQUIRES... |
c96f2eda522237dded7beb4009b732b36e9463ff92ef070dfb905e0c3445440c | Python | 7,089 | 150 | from typing import Any, Tuple
import numpy as np
import pandas as pd
from torch.utils.data import Dataset
from .base_dataset import BaseDataset
class ConcatDataset(Dataset):
"""
A dataset class that vertically concatenates two datasets. This class is particularly useful for combining multiple datasets to cr... |
cd01e9fa21ad3370cfa3e29ae25abd8082ccf296454b9946ec744f64ff011bd4 | Python | 7,093 | 227 | # -*- coding: utf-8 -*-
#
# Main package initializer
#
# Builtin/3rd party package imports
import os
import sys
import subprocess
import getpass
import socket
import numpy as np
from hashlib import blake2b, sha1
from importlib.metadata import version, PackageNotFoundError
import dask.distributed as dd
# Get package v... |
04d7e4934e875bc5b6370d6157d26f0ec6981f09ceccde2165f49eb3168de71b | Python | 7,094 | 188 | from scipy.spatial import distance
import scanpy as sc
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
import umap.umap_ as umap
import numpy as np
import warnings
warnings.filterwarnings('ignore')
def preprocess(adata, filter_min_counts=True, size_factors=True, normalize_input=False... |
73a26fd9f5b8c56e421db0ee47fec3e0f5a2187bbf3651b45772086805ff43b0 | Python | 7,095 | 189 | """Counting, integration, and prediction helpers for nonparametric estimators."""
from typing import Literal
import numpy as np
from numpy.typing import ArrayLike
from SurvivalEVAL.evaluations.util import get_prob_at_zero
def _predict_step(
t: ArrayLike,
event_times: np.ndarray | None,
values: np.ndarr... |
76b302f9f3284f5cfe9ae403d5011e307d71cac087cbec5e57231f79c2d868ed | Python | 7,095 | 158 | import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import numpy as np
from tqdm import tqdm
from model import *
from data.dataset import CategoricalStructureAdjointDataset
import argparse
from utils import *
import os
import datetime
ABS_PATH = os.path.abspath(__file__)
ABS_PATH = "/".join(ABS_... |
05d1c294062d767186ff74f9fcb704a89d2a7c58af72ef13763b6c03671f8710 | Python | 7,096 | 253 | #!/usr/bin/env python
"""Python script to run cell model"""
"""
/* Copyright (c) 2015 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICU... |
8bd6039112ca6b4696f6c2f91ecd35284a60c462a4a82c99d0998899e0544e1f | Python | 7,096 | 253 | #!/usr/bin/env python
"""Python script to run cell model"""
"""
/* Copyright (c) 2015 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICU... |
9c18d07fc3d17bc4d43b64787e3d40341d01bb1acb26963de61e65a7894e4e3a | Python | 7,096 | 253 | #!/usr/bin/env python
"""Python script to run cell model"""
"""
/* Copyright (c) 2015 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICU... |
afffb84e82085035277b78981ae4ad96e5b3310f053a310679958dcc5e697191 | Python | 7,096 | 185 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
b32c7b4ad8962a52e258a9299782bb63994188f1ceb729fb48818eb2063e3bc7 | Python | 7,096 | 253 | #!/usr/bin/env python
"""Python script to run cell model"""
"""
/* Copyright (c) 2015 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICU... |
48bda9e28cf2fe51cc70c77ae9deecdb7265e254e898e97f356b55fa75745d07 | Python | 7,097 | 225 | # helper functions for processing inputs to disperseNN2
import numpy as np
import sys
from geopy import distance
import random
import utm
import tskit
from disperseNN2.read_input import parse_provenance
# project sample locations
def project_locs(locs, fp=None):
# projection (plus some code for calculating error... |
9139fa399da8096833ae1c52f52c4264a51437a22edb51e69deb2c6ee3cba516 | Python | 7,097 | 147 | from typing import List
import torch
import torch.nn as nn
from .mmd_like import _MMDLikeTrainer
def _gaussian_kernel(x_source,
x_target,
mul_kernel=2.0,
num_kernels=5,
sigma=None):
n_samples = int(x_source.shape[0]) + int(x_target.... |
3a3a9a1655114312f8967b64434c6ff9e7feac117e6e816c5c56301e7a1279ac | Python | 7,098 | 143 | """
DeepLabCut2.0 Toolbox (deeplabcut.org)
copyright A. & M. Mathis Labs
https://github.com/AlexEMG/DeepLabCut
Please see AUTHORS for contributors.
https://github.com/AlexEMG/DeepLabCut/blob/master/AUTHORS
Licensed under GNU Lesser General Public License v3.0
"""
import deeplabcut
import os
import subprocess
import y... |
7557a8be036938d7ca3b18b7dae9e5d9a8c95de3c00b1fe9659dee45cd59e21e | Python | 7,098 | 215 | from distutils import log
import distutils.command.sdist as orig
import os
import sys
import io
import contextlib
from itertools import chain
from .._importlib import metadata
from .build import _ORIGINAL_SUBCOMMANDS
_default_revctrl = list
def walk_revctrl(dirname=''):
"""Find all files under revision control"... |
ffff55416c012930cc7ff6a131f477a2ac418bd7db77ebeeb96f0001f2ead100 | Python | 7,098 | 235 | import numpy as np
import h5py
import os
import nibabel as nib
import json
import random
import cv2
import copy
class Params:
def __init__(self, json_path):
self.update(json_path)
def save(self, json_path):
""""
Save dict to json file
Parameters
----------
jso... |
40f958ed06a3d8699913c7c177bff43a83328cfcd6c3ffde0713bf0c05364975 | Python | 7,100 | 208 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
0e1a3e7e46c2a1b83c87ee946cacfb3b6684a3db2410b834f218398d83f46485 | Python | 7,103 | 181 | """
Created on 25/07/2019
:author: Mahyar Osanlouy, Gonzalo D. Maso Talou
:summary:
"""
import numpy as np
from PIL import Image
from src.utils.get_image_size import get_image_size
from src.utils.sitk_utils import is_sitk_image, get_sitk_image_to_data
class ImageSizeException(Exception):
pass
... |
7986726799c8d6164c111010a502b0c314205704a459e97a54d6807b16753674 | Python | 7,104 | 196 | import random
import urllib.parse
from nglui import statebuilder
import json
from codex.data.brain_regions import REGIONS, COLORS
from codex.data.versions import (
DEFAULT_DATA_SNAPSHOT_VERSION,
DATA_SNAPSHOT_VERSION_DESCRIPTIONS,
)
from codex import logger
NGL_FLAT_BASE_URL = "https://ngl.cave-explorer.org"... |
81bf5d80224de02de3bc340855e3afd61d685ba9f41d3224846168eeb104baaa | Python | 7,105 | 198 | """Graph encoders."""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import manifolds
from layers.att_layers import GraphAttentionLayer
import layers.hyp_layers as hyp_layers
from layers.layers import GraphConvolution, Linear, get_dim_act
import utils.math_utils as pmath
class... |
fd4d4d53f0be483805ce46c0c40b0f0e03131de4d66596870909d937f8e1d14f | Python | 7,105 | 190 | # SPDX-FileCopyrightText: 2015 Eric Larson
#
# SPDX-License-Identifier: Apache-2.0
import base64
import io
import json
import zlib
from pip._vendor import msgpack
from pip._vendor.requests.structures import CaseInsensitiveDict
from .compat import HTTPResponse, pickle, text_type
def _b64_decode_bytes(b):
return... |
9a57c47d374df74697870149b6d89f069029f2c4d4870050f0896c43238b5c72 | Python | 7,108 | 168 | #!/usr/bin/env python
"""
toposampling - Topology-assisted sampling and analysis of activity data
Copyright (C) 2020 Blue Brain Project / EPFL & University of Aberdeen
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as
published by the Fr... |
9cee12c1ceab6d34c88dd93ad3496bc602f1c8fd6fd3e5945f33c79f88007e7f | Python | 7,109 | 202 | import tensorflow as tf
import PoseMDN
from stephenHeadConfig import conf as conf
import PoseTools
import edward as ed
import math
import numpy as np
import matplotlib.pyplot as plt
import mymap
import PoseTrain
import os
import pickle
from tensorflow.contrib import slim
from edward.models import Categorical, Mixture, ... |
b32e3e022801540ccf3fd236b520138982fab8974cac7c7efc58ad7362ffebaa | Python | 7,109 | 194 | # set QT_API environment variable
import os
os.environ["QT_API"] = "pyqt5"
import qtpy
# qt libraries
from qtpy.QtCore import *
from qtpy.QtWidgets import *
from qtpy.QtGui import *
import pyqtgraph as pg
from datetime import datetime
from control._def import *
class SpectrometerControlWidget(QFrame):
signal_ne... |
0180a768762556cc44d9897757e55476923154bc38052f373c5740eaf17912df | Python | 7,110 | 210 | #!/usr/bin/env python
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
This module provides a human agent to control the ego vehicle via keyboard
"""
import time
from threading import Thread
import cv2
import numpy as np
try:
import pygame
... |
30afee7899abb636d1c50596e06d483d7683231b66c7771bb71b06bdae4a2a67 | Python | 7,112 | 182 | """
Tests module contact. Need to add more tests.
# Author: Vladan Lucic
# $Id$
"""
from __future__ import unicode_literals
from __future__ import absolute_import
__version__ = "$Revision$"
from copy import copy, deepcopy
import importlib
import unittest
import numpy
import numpy.testing as np_test
import scipy
... |
c5eed43fcd3f0cd1e8d65819a0b1be8cfeba688900709f85e2b0f5893f750ad9 | Python | 7,113 | 123 | import numpy as np
from scipy.signal import welch
from scipy.integrate import simps
from Functions.f_SignalProcFuncLibs import *
def f_bandPower(v_data, sf, v_freqBand):
"""
Compute the power of a given frequency band in a signal.
Args:
v_data (array-like): The input signal data.
... |
836d3a05c4571809af4955f225644b0bc3421ef601d046c1bc24e6749d077d58 | Python | 7,114 | 209 | #!/usr/bin/env python
#================================================================
# Copyright (C) 2023 Yufeng Liu (Braintell, Southeast University). All rights reserved.
#
# Filename : utils.py
# Author : Yufeng Liu
# Date : 2023-03-02
# Description :
#
#=========================... |
f2b8ba541b0c70b930c2c4e987248041fd3371a173f0584b165f5f50f0c8f132 | Python | 7,123 | 170 | # MIT License
#
# Copyright (c) 2024 Yu Bao, Harbin Institute of Technology
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# ... |
700f21ec265ac60d3b13985a08c429205ce6e95ef1a07eda1fe67343645fc960 | Python | 7,125 | 173 | from __future__ import print_function
from dvn.utils import get_itk_array, get_itk_image, make_itk_image, write_itk_image, get_patch_data, get_volume_from_patches
import numpy as np
from dvn import Network as NET
import argparse
import os
def norm_data(data):
data = data - np.min(data)
data = data * 1.0 / np.m... |
9eed915aae97a6abceb78980d5a3a439e27b60a272a14441110832c2aa8c018a | Python | 7,125 | 162 | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# 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... |
bb7bcc43ab4537509a2b59432bf03ff3ab356528ad2c7cca12c574c775bafa9c | Python | 7,126 | 135 | #!/usr/bin/env python
from __future__ import division, print_function
import argparse
import sys
import torch
from topaz.denoise import Denoise3D, denoise_tomogram_stream
from topaz.denoising.datasets import make_tomogram_datasets
name = 'denoise3d'
help = 'denoise 3D volumes with various denoising algorithms'
def ... |
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