sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
f38ea7c3665a7cb025a8872501ff7e9f83e448cabf2556ccd4e697a2449b906c | Python | 11,076 | 244 | #--------------------------5-CSRTT Protocol----------------------
# important note:- Do not change any print stings if you intended to use pyOS5 GUI
# Task file for 5-CSRTT task which can be used for training stages 1-5, and all challanges, by adjusting task variables as described below.
# One of the five_poke LEDs t... |
a1d7d885b9c72c6898bd5750130418ff2bc2577e70c439b0b5dfb166c2e2d475 | Python | 11,077 | 244 | #--------------------------5-CSRTT Protocol----------------------
# important note:- Do not change any print stings if you intended to use pyOS5 GUI
# Task file for 5-CSRTT task which can be used for training stages 1-5, and all challanges, by adjusting task variables as described below.
# One of the five_poke LEDs t... |
1fee5f01407115d1b3936c404a567f4d1bb13aecdbb970c0ddaf83bb75b54142 | Python | 11,081 | 211 | """
Test unit suffix functionality for length-based options.
This module contains tests for the unit suffix parsing feature added to Filtlong.
To run them, execute `python3 -m unittest test.test_unit_suffixes` from the
root Filtlong directory.
"""
import unittest
import os
import subprocess
class TestUnitSuffixes(u... |
a48d86e7f5147eca05f73ba2fa967a19810e1fda888d27ebe0a58e2e4ae6a4fb | Python | 11,089 | 305 | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
from torchvision import models
from utils import load_ground_truth, save_images
from torchvision.io.image import read_image
from torchvision.transforms.functional import normalize, to_pil_image
from torchvision.models import resnet50
from... |
c94df0fb4354f1ed7f913864e7f510622ad103ec47547b8aa47f115d4161021b | Python | 11,093 | 266 | from __future__ import print_function
import torch
import torch.nn as nn
from types import MethodType
import models
from clb_utils.metric import accuracy, AverageMeter, Timer
class NormalNN(nn.Module):
'''
Normal Neural Network with SGD for classification
'''
def __init__(self, agent_config):
'... |
385b5cf3ae8c355fcfb35c4d7fb4751bb3bf8155591460b1851a2289e041ef5a | Python | 11,095 | 195 | from dataclasses import dataclass, field
from typing import Optional
from src.models.tabular.pytorch_tabular.repository.config import ModelConfig
@dataclass
class NodeConfig(ModelConfig):
"""Model configuration
Args:
task (str): Specify whether the problem is regression of classification.Choices are:... |
88b769556726b15992b947943b79cf4177029ce958affef0a7be035b616dbd28 | Python | 11,098 | 223 | """
Copyright 2017 Ryan Wick (rrwick@gmail.com)
https://github.com/rrwick/Filtlong
This module contains some tests for Filtlong. To run them, execute `python3 -m unittest` from the
root Filtlong directory.
This file is part of Filtlong. Filtlong is free software: you can redistribute it and/or modify
it under the ter... |
e8cc54cc5b255de9af60130a0497428327da6061771173f110735e724e455609 | Python | 11,139 | 266 | from __future__ import print_function
import torch
import torch.nn as nn
from types import MethodType
import models
from clb_utils.metric import accuracy, AverageMeter, Timer
class NormalNN(nn.Module):
'''
Normal Neural Network with SGD for classification
'''
def __init__(self, agent_config):
'... |
d16e4fb8611602b768d5f5e665d418fcf58fcb889b3ed9d5fb9d9d7d7d38d521 | Python | 11,153 | 343 | #!/usr/bin/env python
import numpy as np
import argparse
import torch
import torch.optim as optim
from torch.cuda.amp import autocast
from torch.cuda.amp import GradScaler
from pytorch_metric_learning import samplers
import logging
import time
import pdb
import os
import json
import random
from tqdm import tqdm
impor... |
8f4185bcc76b5809ea056f4d19d2aceb2a7fb9a690b6e57b8cf0d9a8c5635056 | Python | 11,156 | 316 | import numpy as np
import os, sys
import pickle
import h5py
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input as preprocess_input_16
from keras.applications.vgg19 import VGG19
from keras.applications.vgg19 import preprocess_input as preprocess_input_19
from keras.applica... |
8e9ae4debd68f4a30e303ef4b5ef78e320a606c1044d7635f087ecde84bc7074 | Python | 11,169 | 310 | """
This file is a copy of the useful function
from Christophe Gohlke's amazing package transformations
that can be found there: https://pypi.org/project/transformations/
For a reason that I don't really understand, the package
does not install on newer version of Python therefore
I decided to, for the time being at l... |
44c1a427f597c960bcab751bf5ab93a9e4b49661c2b13eaef61b78e8529c6399 | Python | 11,183 | 268 | #!/usr/bin/env python3
"""
Makes pictures for quality assurance of fmri data and pastes them
together into a html pages.
Usage:
cifti_vis_fmri snaps [options] <task_label> <subject>
cifti_vis_fmri subject [options] <task_label> <subject>
cifti_vis_fmri index [options]
Arguments:
<task_label> Nam... |
7e44a5a470a59f2b7cb6fb5480f30d56456e46e1a794f6faa8c4eeab703cdad2 | Python | 11,183 | 329 | # -*- coding: utf-8 -*-
"""
pysteps.downscaling.rainfarm
============================
Implementation of the RainFARM stochastic downscaling method as described in
:cite:`Rebora2006` and :cite:`DOnofrio2014`.
RainFARM is a downscaling algorithm for rainfall fields developed by Rebora et
al. (2006). The method can repr... |
174d88ebb1c4d05a60a16479bcb120b01f2470a29276713f5b394bf1c4eb9291 | Python | 11,192 | 306 | # This file is part of pyunicorn.
# Copyright (C) 2008--2026 Jonathan F. Donges and pyunicorn authors
# URL: <https://www.pik-potsdam.de/members/donges/software-2/software>
# License: BSD (3-clause)
#
# Please acknowledge and cite the use of this software and its authors
# when results are used in publications or publi... |
def999f4466e9de3c11a2f4471d1b1f0b696fef1020ce5ec1745827a0fbc9736 | Python | 11,192 | 243 | # -*- coding: utf-8 -*-
"""
molvs.fragment
~~~~~~~~~~~~~~
This module contains tools for dealing with molecules with more than one covalently bonded unit. The main classes are
:class:`~molvs.fragment.LargestFragmentChooser`, which returns the largest covalent unit in a molecule, and
:class:`~molvs.fragment.FragmentRem... |
b34e50cc42e3f01df81c47648b279ed70c52485092dc35e94860a60643410c98 | Python | 11,208 | 288 | # This file is part of pyunicorn.
# Copyright (C) 2008--2026 Jonathan F. Donges and pyunicorn authors
# URL: <https://www.pik-potsdam.de/members/donges/software-2/software>
# License: BSD (3-clause)
#
# Please acknowledge and cite the use of this software and its authors
# when results are used in publications or publi... |
07a00d1393c1047b7f5414f75528e1e889257ff019233fd621a4e48e90c8d864 | Python | 11,212 | 304 | import os
import warnings
import numpy as np
import pdb2sql
from deeprank import config
from deeprank.features import FeatureClass
########################################################################
#
# Definition of the class
#
########################################################################
class ... |
176229cbbc402fe634bc4b59eb56c0fe5be3f3c08e29daf23b65e2f837c87bb2 | Python | 11,218 | 354 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
MIT License
Copyright (c) 2022 Pawan Goyal
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 th... |
4aa0f44b8da2ce9566d5c8dd70b2cc22621e908f5a5e57960b6ee463b67722be | Python | 11,219 | 278 | import getopt
from sys import argv, exit
import multiprocessing
import pysam
import pybedtools
## capture the arguments required for the annotations;
## 1) the annotation file ; 2) the VCF to be annotated ; 3) the name of the output file [O] otherwise, same name + anno.vcf
## if further arguments are necessary we will... |
16ae1e563d2df68122f394e962c636953748f028c11d6d8f431836a40b63fa9f | Python | 11,245 | 306 | import logging
import mmap
import os
from pathlib import Path
from typing import BinaryIO, Optional, Union
import numpy as np
import imctools.io.mcd.constants as const
from imctools.data import AblationImageType, Acquisition
from imctools.data.acquisitiondata import AcquisitionData
from imctools.io.mcd.mcdxmlparser i... |
02e655ddd2133f450fb5672bd333c6f0d3c6d908192593c0784afeb235461302 | Python | 11,246 | 250 | import os
import torch
os.environ['DGLBACKEND'] = 'pytorch'
from datetime import datetime as dt
from src.utils.protein_utils import preprocess_unbound_bound, protein_to_graph_unbound_bound
from biopandas.pdb import PandasPdb
from src.utils.train_utils import *
from src.utils.args import *
from src.utils.ot_utils imp... |
111923e9157149ef9c532a5ea836f9d8aa81e2ae7f09b5ae2044133f7bb7c22c | Python | 11,276 | 268 | import os
import os.path as op
from collections import defaultdict
import glob
import traceback
import shutil
import collections
import logging
import re
import warnings
from src.utils import utils
from src.utils import args_utils as au
LINKS_DIR = utils.get_links_dir()
SUBJECTS_DIR = utils.get_link_dir(LINKS_DIR, '... |
ec75bad27499dc02a851f4a32f44440968db3b0d2b32cf15c81d4b7258f8abe9 | Python | 11,282 | 382 | """Utilities for generating AFIDs-related graphics"""
import plotly.graph_objects as go
from afidsvalidator.model import EXPECTED_DESCS
# Landmark abbreviations grouped by neuroanatomical region.
# Each abbreviation matches the attribute name on the Afids object (desc[-1]).
LANDMARK_REGIONS = {
"Commissural": ["... |
92004f714db2de073848ab3c19e09bfae5220f9776beaaeade13eb41fcdc267d | Python | 11,296 | 337 | import warnings
from torchmeta.datasets import Omniglot, MiniImagenet, TieredImagenet, CIFARFS, CUB, DoubleMNIST, TripleMNIST
from torchmeta.transforms import Categorical, ClassSplitter, Rotation
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor
__all__ = [
'omniglot',
'miniimagenet',
... |
cf1b50c9d574a38693ffe97d9bb3859011f6676c1cd9b2690293d9147d9159a0 | Python | 11,300 | 247 | import re
from pathlib import Path
from typing import Any, Dict, Optional, Tuple, Union
from torch import nn
from timm.layers import set_layer_config
from ._builder import resolve_pretrained_cfg
from ._helpers import load_checkpoint
from ._input import _requested_input, _spatial_size
from ._hub import load_model_conf... |
b4bec00ae857705c481c9e64e9efbae17d419200830e39f3c232ed04f2aae290 | Python | 11,307 | 319 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... |
35990be4a9ba828b8d0382f6884eb034ea264466eb48459ebc61d235945abc66 | Python | 11,318 | 262 | #
# nate berry
# 2023-04-01
#
# ---------------------------------------------------------
# NOTES:
# - this script produces plots of the time series, the
# false nearest neighbors data from <03_fnn.py>, and
# the state space reconstructions for the COM and EEG
# data (band-wise & aggregated)
#
# ------... |
dd13a55ad9b6b72c30d5adad0e1f56c8d07913d62b8e98744f2285a1830487b3 | Python | 11,331 | 430 | # -*- coding: utf-8 -*-
import unittest
import matplotlib as mpl
from matplotlib.testing.compare import compare_images
from tempfile import NamedTemporaryFile
import os.path
import pygenometracks.plotTracks
mpl.use('agg')
ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"test_data")
... |
36e8202aae45301979768396f879315991d8121fac1c1c5cfb0b40217437ae87 | Python | 11,336 | 256 | #!/usr/bin/env python
"""
PentaMind: predict global cognition from drawings of intersecting pentagons.
Runs the 10 released VGG19-bn models on every image in a folder and averages them.
Works on CPU or GPU with a current PyTorch. Model weights are downloaded from
Hugging Face (stasaking/pentamind) on first use, or rea... |
53f3e2b3b221f1abfdf230af7494df5dbeb65aa2b4868fa84738ba052db0332f | Python | 11,336 | 298 | import logging
import os, csv
from typing import Type
from pathlib import Path
from typing import Dict
from typing import List
from typing import Union
import SimpleITK as sitk
import cv2
import numpy as np
import torch
from matplotlib import pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg ... |
23149475fc8737ac8e5da63336378a5c2bd7229dce3c17a9ab41fe508fa23ec2 | Python | 11,339 | 342 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... |
eec883fbde1bf76ea24c4f57d4c1efb7aa12462cc74b25f7bbecf119b3d26fb1 | Python | 11,346 | 241 | """
Author: "Keitaro Yamashita, Garib N. Murshudov"
MRC Laboratory of Molecular Biology
This software is released under the
Mozilla Public License, version 2.0; see LICENSE.
"""
from __future__ import absolute_import, division, print_function, generators
import unittest
import numpy
import pandas
import json
impor... |
06ab312e0c11fcfcc82cea7c41c70c6feada783b92dbcfeb7f00ea33800228e9 | Python | 11,350 | 348 | import time
import copy
import gc
import os
from tqdm import tqdm
import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.cuda.amp import GradScaler, autocast
from sklearn.metrics import mean_squared_error
from types_ import *
from utils import *
class RNAEncoder(nn.Mod... |
e106ea58f8bdc44cbb7be74f2977f18a8710fc37710ae99d4e32ebaa382e3714 | Python | 11,358 | 256 | import os
import torch
from collections import OrderedDict
from abc import ABC, abstractmethod
from . import networks
import sys
class BaseModel(ABC):
"""This class is an abstract base class (ABC) for models.
To create a subclass, you need to implement the following five functions:
-- <__init__>: ... |
3140a31eec202c9c86b88c47d0d6f28a641caa4273d1e2ff6ddb477efaba790d | Python | 11,374 | 335 | #!/usr/bin/env python3
"""
A collection of utilities for ciftify functions mostly for
loading data into numpy arrays
"""
import os
import sys
import logging
import numpy as np
import pandas as pd
import nibabel as nib
import nibabel.gifti.giftiio
from ciftify.utils import run, get_stdout, TempDir
def cifti_info(file... |
11df9a94876558cb5ba5d08346fa2fb22fd0504e4cbe39356499ae92235e4d73 | Python | 11,376 | 316 | # This file is part of pyunicorn.
# Copyright (C) 2008--2026 Jonathan F. Donges and pyunicorn authors
# URL: <https://www.pik-potsdam.de/members/donges/software-2/software>
# License: BSD (3-clause)
#
# Please acknowledge and cite the use of this software and its authors
# when results are used in publications or publi... |
ce6485889f782f4d6aebb153f5034887b44fdb41056e90a0f0eb043dfa8566f0 | Python | 11,382 | 413 | # -*- coding: utf-8 -*-
import numpy as np
import pytest
from numpy.testing import assert_array_almost_equal
from pysteps.blending.linear_blending import _get_ranked_salience, _get_ws, forecast
from pysteps.utils import transformation
# Test function arguments
linear_arg_values = [
(5, 30, 60, 20, 45, "eulerian"... |
ea79701053c804f4baa8f8010b154dcb8ac41886a2f6ae88f46fe71c65a8b01f | Python | 11,407 | 315 | import numpy as np
import os
import os.path as op
import nibabel as nib
from src.utils import utils
def snap_electrodes_to_surface(subject, elecs_pos, grid_name, subjects_dir,
max_steps=40000, giveup_steps=10000,
init_temp=1e-3, temperature_exponent=1,
... |
543174e2cb80be261b897c7a1d0bf1ecde0d28031f657546adcaa357b7c39414 | Python | 11,416 | 407 | import os
from itertools import combinations
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
##############################################################################
# Constants
dataset = "hcp"
parcellation = "schaefer200x17"
idiff_path_co... |
86fd36b6786f695e744304ff4f97690fd40dc3e65af7577c4f9924d97d62bf72 | Python | 11,434 | 303 | # -*- coding: utf-8 -*-
"""
pysteps.blending.skill_scores
==============================
Methods for computing skill scores, needed for the blending weights, of two-
dimensional model fields with the latest observation field.
.. autosummary::
:toctree: ../generated/
spatial_correlation
lt_dependent_cor_n... |
835c5123b103a7062c159952575746ad3996682aa0ed979fbbdd4d1ff78a1b0a | Python | 11,435 | 323 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... |
01995ff9f97b280c19832adef3ed0ce6503299ae8dbed8865fa5d3e750097715 | Python | 11,436 | 298 | import os
import yaml
# codacy ignore python-use-of-pickle: Pickle usage is safe in this context (local data only).
import pickle
import shutil
import pandas as pd
from pathlib import Path
from warnings import warn
import lightning.pytorch as pl
from lightning.pytorch.profilers import PyTorchProfiler
from lightning.p... |
ded42955e080fb6753e511ecc8a52770b28d3adc00e6010e8ae8bce53acb2e91 | Python | 11,439 | 283 | import itertools
import re
import numpy as np
import scipy.special
import xgboost as xgb
from xgboost import testing as tm
class TestSHAP:
def test_feature_importances(self) -> None:
rng = np.random.RandomState(1994)
data = rng.randn(100, 5)
target = np.array([0, 1] * 50)
featur... |
ed14b6ccbae3d171943f9aeb2da8371b93407124fb3c7dfba8bbcf339df7293e | Python | 11,441 | 263 | #steroid is based on PyTorch and PyTorch-Lightning.
#import comet_ml
import torch
import torch.nn as nn
from torch import optim
from utils import get_logger, parse, save_opt #device,
from torch.optim.lr_scheduler import ReduceLROnPlateau
import pandas as pd
from pytorch_lightning import Trainer, seed_everything
from p... |
7b14861c5cb81c2e86ada21ab496f792a1654d1cfc826bd28a9c3c9b109e0b94 | Python | 11,443 | 279 | import torch
import torch.nn as nn
import torch.nn.functional as f
from torch.nn import init
from .submodules import ConvLayer, UpsampleConvLayer, TransposedConvLayer, RecurrentConvLayer, ResidualBlock, ConvLSTM, ConvGRU
def skip_concat(x1, x2):
return torch.cat([x1, x2], dim=1)
def skip_sum(x1, x2):
return... |
4cf1c054f1805942291b7c9eb61b88bd0ace98f4e042276ba882a53fe8bb2ac5 | Python | 11,451 | 319 | """ Adan Optimizer
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models[J]. arXiv preprint arXiv:2208.06677, 2022.
https://arxiv.org/abs/2208.06677
Implementation adapted from https://github.com/sail-sg/Adan
"""
# Copyright 2022 Garena Online Private Limited
#
# Licensed under the Apache L... |
1dd9b4a1a52d06decdd4e5ce7411f710d22a4dfd0bee546ff8b15efb6d4926a6 | Python | 11,453 | 311 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... |
cbaa100ca7efd0cf2f9ffdd824d53aa295b11a3dced7091141a3289ae461bc79 | Python | 11,464 | 327 | #!/usr/bin/env python3
'''
@author: Adam Olesiński
'''
from dict2xml import dict2xml
import cv2
import os
import glob
def signals2dict(signals):
objs = []
for signal in signals:
rois = signal.get_rois()
for roi in rois:
obj = {
'name' : signal.name,
... |
a1e492a265f116c5f1467636130d5399653eed7e4e35c104441e1c82b6b9a7fd | Python | 11,474 | 356 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... |
2dd3b98748d4403b1071cbfee506e9b4525926e8d4637add8fa45b2a5f7f802d | Python | 11,485 | 354 | # Inheritaed from open-mmlab/mmclassification
# Modified by Hangyu LIN
import collections.abc
import functools
import itertools
import subprocess
import warnings
from collections import abc
from importlib import import_module
from inspect import getfullargspec
from itertools import repeat
# From PyTorch internals
de... |
8504f6e024463c4d140987823949c2591ce2108d14be24827f0536e268f19aee | Python | 11,485 | 288 | import logging
import numpy as np
from keras import Input
from keras.engine import Model
from keras.layers import Dense, Dropout, Lambda, Concatenate
from keras.regularizers import l2
from data.data_access import Data
#from data.pathways.gmt_pathway import get_KEGG_map
from model.builders.builders_utils import get_pn... |
277874da9262df3c2ed528877c7aabfbe21659ee805472c0a556c8f642d3bf5b | Python | 11,487 | 332 | #!/usr/bin/env python3
# Copyright 2021 Alexander Meulemans, Matilde Tristany, Maria Cervera
#
# 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
... |
119b10e65000f78dfb0e6ef242d7ef5d4bec13eb50d832d82e129c01ecc015ba | Python | 11,494 | 320 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... |
2f6854a6d3091bcc668471495460bb076f1b25b108b2216b9fc0fffa4aae1956 | Python | 11,495 | 291 |
# JHK
# Python script for generating Figure 4.
import numpy as np
from netCDF4 import Dataset
import matplotlib.pyplot as plt
import matplotlib as mpl
from mpl_toolkits.basemap import Basemap, cm
from pylab import *
#==========================================================
# Set main directory
#===================... |
6b06d69e6f591a76f9da1ee18912b86df85c6e4f53fd571ea918450e95fa5b1f | Python | 11,501 | 270 | """ Halo Self Attention
Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones`
- https://arxiv.org/abs/2103.12731
@misc{2103.12731,
Author = {Ashish Vaswani and Prajit Ramachandran and Aravind Srinivas and Niki Parmar and Blake Hechtman and
Jonathon Shlens},
Title = {Scaling Local Self... |
f4e44d6e624372e15789fe88507404e2a59cdefb673cf56358484c217420ecfe | Python | 11,506 | 392 | import re
import os
import glob
import numpy as np
import random
import random
import pandas as pd
import json
from torch.utils.data import Dataset
import logging
from tqdm import tqdm
LOGGER = logging.getLogger(__name__)
class QueryDataset_COMETA(Dataset):
def __init__(self, data_dir,
load_full... |
6ed958c036585a4a631801ce7769395eb8d1ab3e0e89e0cc949b6d08f9f32f54 | Python | 11,507 | 291 |
# JHK
# Python script for generating Figure 4.
import numpy as np
from netCDF4 import Dataset
import matplotlib.pyplot as plt
import matplotlib as mpl
from mpl_toolkits.basemap import Basemap, cm
from pylab import *
#==========================================================
# Set main directory
#===================... |
d8d04f6e536412a4030ac601685e73a57158e4ec91022275a4b6d65c32c0d898 | Python | 11,513 | 333 | """
Performs various GLM-related computations.
@author: JingyiGF
"""
from .toolkits import *
def linear_predictor(beta, x_mat, fit_intercept):
"""Compute linear predictor. eta = beta0 + X*beta"""
if x_mat is None:
if not fit_intercept:
raise ValueError('When x_mat is None, we will fit int... |
58a8f3b9b859cfc6eef55aae824606a7b3094d8518e82f8ea31f67db72483456 | Python | 11,519 | 217 | # -*- coding: utf-8 -*-
"""
@author: bartulem
Estimate how well different data streams (tracking/IMU) are synced with neural recordings.
This script utilizes the LED timestamp occurrences across different data streams and estimates how well they are linearly related.
Specifically, the tracking/IMU data is split (tr... |
d13c05dd57512aea76dc61f0caea3b5fb715f6e88d30d58454dbee9b5ae54787 | Python | 11,521 | 342 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... |
b6755298c0daef334f576dfa74265f7f7f82febb6042b7c5e72915b54f6bcd57 | Python | 11,522 | 291 | from typing import Any, List, Dict
from torchmetrics import MetricCollection
import wandb
import pytorch_lightning as pl
import torch
from src.tasks.metrics import get_cls_pred_metrics, get_cls_prob_metrics, get_reg_metrics
import numpy as np
import shap
import pandas as pd
def get_model_framework_dict():
model_f... |
b2faf9b353c21beed3fa179baa900c7cd8c5715f013e70d1897669c9f52a169f | Python | 11,525 | 305 | """
[spacer]
# This will add a space between two tracks. No options are required, but
# for finer control, the amount of space required can be given
# height value in centimeters
height = 1.5
[bigwig]
file = /data/manke/group/ramirez/HiC-ThomasLing/data/external/Graveley_mRNA-seq/GSM390060_Kc167-4_spa.bw
title = Kc RN... |
2be4d833e5d8cc0ae5a59c2fb276bf536b97650eb8a058e1be14f47a79aae71b | Python | 11,534 | 277 | import numpy
import pytest
import cellprofiler_core.image
import cellprofiler_core.measurement
import cellprofiler.modules.correctilluminationapply
import cellprofiler_core.modules.injectimage
import cellprofiler_core.object
import cellprofiler_core.pipeline
import cellprofiler_core.workspace
from cellprofiler_libra... |
4c57aeee9dd549dc8bab44e166cec62a46cdcee01bf002401dd90403e5ade833 | Python | 11,540 | 294 | from os import getcwd
from os.path import basename, join
import numpy as np
from traitlets import Instance, Unicode
from traitlets.config import ArgumentError, Dict
from mrHARDI.base.application import (mrHARDIBaseApplication,
MultipleArguments,
... |
791ccdb4bf0a18f3018347e67cce6c2c0ac308bab16247f6d0767a3ef49682a6 | Python | 11,552 | 288 | import logging
import numpy as np
from keras import Input
from keras.engine import Model
from keras.layers import Dense, Dropout, Lambda, Concatenate
from keras.regularizers import l2
from data.data_access import Data
#from data.pathways.gmt_pathway import get_KEGG_map
from model.builders.builders_utils import get_pn... |
33dddb7b07efd91635da9862f102d84e05af94371ee0866a81a23e7202c7e4dd | Python | 11,560 | 266 | import numpy as np
from typing import Tuple, Optional, List, Union, Annotated
from pydantic import Field, validate_call, ConfigDict, BaseModel
from cellprofiler_library.opts.align import CropMode, AlignmentMethod, AdditionalAlignmentChoice, MEASUREMENT_FORMAT
from cellprofiler_library.types import Image2D, Image2DMas... |
3102ecd6d828cd7c79d4fe72af9eefd76cacc87f1da520d5096a5f4933a11298 | Python | 11,561 | 327 | #!/usr/bin/env python3
import io
import sys
import unittest
from unittest.mock import MagicMock, mock_open, patch
from ciftify import html
import ciftify.qc_config
if sys.version_info[0] == 3:
builtin_open = 'builtins.open'
else:
builtin_open = '__builtin__.open'
@patch('ciftify.utils.check_output_writable'... |
ae7587d24ab593bb2453ff4fee38d999731b84ca98ce1493b49ac788f9f26bc6 | Python | 11,573 | 350 | import numpy as np
import os
from matplotlib import pyplot as plt
from matplotlib.patches import Rectangle
from tensorflow.keras import callbacks
from tensorflow.python.keras.callbacks import Callback
from lib.pinn import PINN
from lib.network import Network
import pandas as pd
import tensorflow as tf
from helper impo... |
15532c1406f0bb74bcec430ce2ce5c4a29e6d1d059a39bd0b5c88dee24a6393a | Python | 11,584 | 346 | from abc import abstractmethod
from copy import deepcopy
import numpy as np
from traitlets import Float, Integer, List, TraitType, Unicode, default
from traitlets.config.loader import ConfigError
from mrHARDI.base.ListValuedDict import MagicDict
from mrHARDI.base.application import (DictInstantiatingInstance,
... |
9eb32c2b167d957c4698fd501f1dea4b8fcecd88ae67bd2e48c69a230c17b507 | Python | 11,596 | 254 | import numpy as np
import time
import nengo
import sys, os
import argparse
sys.path.insert(1, os.path.dirname(os.getcwd()))
#os.chdir("..")
import sspslam
from sspslam.networks import get_slamview_input_functions
import sspslam.utils as utils
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHe... |
a73298714a908208e53da8a6cfb8c56fa3747342a9d3481d0a9288d70415a31c | Python | 11,610 | 423 | # -- coding: utf-8 --
"""
pysteps.verification.probscores
===============================
Evaluation and skill scores for probabilistic forecasts.
.. autosummary::
:toctree: ../generated/
CRPS
CRPS_init
CRPS_accum
CRPS_compute
reldiag
reldiag_init
reldiag_accum
reldiag_compute
... |
a2188289187b4aae9f33e38386c1575c621d12e646c1df9cd1f998cf9d609a46 | Python | 11,612 | 273 | import json
import os
import warnings
from typing import TYPE_CHECKING, Any, Iterable, Union
import numpy as np
import pandas as pd
import traits.api
from ptsa.data.readers import BaseReader
__all__ = [
'TalReader',
'TalStimOnlyReader',
]
class TalReader(BaseReader, traits.api.HasTraits):
"""
Reade... |
7b3e06363e4ed402bfbf012eaa43bf1f3ba5d4a0711ff12bb051ed5cca561fb5 | Python | 11,615 | 276 | import numpy as np
import torch
from torch.nn.functional import one_hot
from torch import nn
import datetime
import pytest
from tests.utils import reset_seed, reset_layers, run_forward_pass, setup_training_pipeline
from utils import args
from utils import train_utils as tu
import networks.dfc_network_utils as dfcu
fro... |
fff9020b8665e18b72888b7d1e72b7753105160a2521330ed99cdde0606b5af5 | Python | 11,626 | 343 | """analysis_worker.py - Run pipelines on imagesets to produce measurements.
The analysis worker listens on a ZMQ port for work announcements. It then
requests jobs from the announcer and executes them. As an application,
the analysis worker runs three threads:
* Main thread - spawns the worker and monitor threads and... |
cb2fe03c30d4f83fee17f5ebcbd2f35588976ed6bbbe88668f921b568cd4d4c1 | Python | 11,628 | 294 | """ Scheduler Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import List, Optional, Union
from torch.optim import Optimizer
from .cosine_lr import CosineLRScheduler
from .multistep_lr import MultiStepLRScheduler
from .plateau_lr import PlateauLRScheduler
from .poly_lr import PolyLRScheduler... |
ed1795a9ec98e92b83e938f812b01bdc76db738823c14cd377b6f6d3ed7c9670 | Python | 11,630 | 333 | # from https://gist.github.com/lukauskas/f2f43aad6078a8b5d71b986174487b8c
from seaborn.matrix import _HeatMapper
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
#from seaborn.external.six import string_types
from seaborn.utils import despine, axis_ticklabels_overlap, relative_luminance, to... |
132ba059f295f50e41827b56f2f80b717b7049b56d208cff54fe52702f64f62f | Python | 11,643 | 284 | from cellprofiler_core.constants.measurement import C_LOCATION, COLTYPE_FLOAT
from cellprofiler_core.module import Module
from cellprofiler_core.setting import Divider, ValidationError
from cellprofiler_core.setting.subscriber import (
ImageListSubscriber,
LabelListSubscriber,
)
from cellprofiler.modules import... |
7f044f721feedd6cd8e7becf5099848e2e7ba7276e82e9e42600e0863aed48ab | Python | 11,667 | 241 | # Pytorch Tabular
# Author: Manu Joseph <manujoseph@gmail.com>
# For license information, see LICENSE.TXT
"""AutomaticFeatureInteraction Config"""
from dataclasses import dataclass, field
from typing import Optional
from src.models.tabular.pytorch_tabular.repository.config import ModelConfig
@dataclass
class FTTrans... |
0afa857071508015c5ca3922cf030b89789ef00e2e96c2701e640d4c3b0bc4cb | Python | 11,672 | 340 | # -*- coding: utf-8 -*-
#
# PTSA documentation build configuration file, created by
# sphinx-quickstart on Wed Jun 1 17:36:58 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All ... |
209dae6acf7c28bc914e03812755009343f4ea2ba5a1cd77e68998088bb602d1 | Python | 11,673 | 236 | """
Author: "Keitaro Yamashita, Garib N. Murshudov"
MRC Laboratory of Molecular Biology
This software is released under the
Mozilla Public License, version 2.0; see LICENSE.
"""
from __future__ import absolute_import, division, print_function, generators
import gemmi
import numpy
from servalcat.utils import logger... |
d729bf5876a445650a7f055b1c34f5322429012a0d7c7fcf09734f969af8002e | Python | 11,674 | 314 | # Ignore future warning
import sys
import warnings
import datetime
import random
warnings.filterwarnings("ignore", category=FutureWarning)
import argparse
import os
import torch
print('Parsing args')
parser = argparse.ArgumentParser(description='Docking')
parser.add_argument('-debug', default=False, action='store... |
83e6832abad544c6f5acd6c26c1420f9df7e91b0a50b073d1482ec45f4739203 | Python | 11,682 | 246 | import os.path as op
import numpy as np
import shutil
import csv
import nibabel as nib
# from mne.label import _read_annot
from collections import Iterable
import glob
from src.utils import utils
from src.utils import preproc_utils as pu
from src.utils import args_utils as au
from src.utils import freesurfer_utils as ... |
9a4004ce1972fc2e9c5d0d25666e709b8f2338fabf8e6808e781a726951a1eec | Python | 11,696 | 297 | """Convert pinned UCI Madelon matrices into lossless, named CSV splits."""
from __future__ import annotations
import argparse
import csv
import gzip
import hashlib
import io
import json
import platform
import shutil
import sys
import zlib
from collections import Counter
from pathlib import Path
from urllib.request im... |
ee62f4c37a40394f221971c77feeee407db4a6549254bdb39404a7cb52b65bc6 | Python | 11,700 | 384 | # -*- coding: utf-8 -*-
"""
pysteps.utils.transformation
============================
Methods for transforming data values.
.. autosummary::
:toctree: ../generated/
boxcox_transform
dB_transform
NQ_transform
sqrt_transform
"""
import numpy as np
import scipy.stats as scipy_stats... |
6d320e7b6b10e988540145dcf4ac5618a5bdda8511947eb5f01c1310e96ae75b | Python | 11,701 | 296 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 16 10:25:51 2022
@author: Manuel
Predict classes using trained model
Run from terminal: python3 LeleNet_prd.py /path/to/orthomosaic /path/to/output /path/to/model
"""
__author__ = "Manuel R. Popp"
#### parse arguments--------------------... |
37226069e09daf20651ccce23cf2e65ad224e39d2ed38d8d59082cfea87b927c | Python | 11,744 | 342 | from typing import Optional, Union, Dict
import os, datetime, subprocess, sys
from copy import deepcopy
import random
from packaging.version import Version
import numpy as np
import torch
import SimpleITK as sitk
from contextlib import contextmanager, redirect_stderr, redirect_stdout
from os import devnull
@contextm... |
13eaf344db06e043dd4b4d01f0bb40c34a7e83eff7653944297c9eb8908f1659 | Python | 11,782 | 282 | """
Custom optimizers to implement lr_mult as in caffe
Date: September 2018
Author: Ignacio Heredia
Email: iheredia@ifca.unican.es
Github: ignacioheredia
References
----------
https://github.com/keras-team/keras/issues/5920#issuecomment-328890905
"""
from tensorflow.python.keras import optimizers
from tensorflow.pyt... |
52cb7b45c97f6138a934265d46b6dbfff0ad5aceae987a634762b16dbba9b74b | Python | 11,793 | 324 | """ SGD with decoupled weight-decay.
References for added functionality:
Cautious Optimizers: https://arxiv.org/abs/2411.16085
Why Gradients Rapidly Increase Near the End of Training: https://arxiv.org/abs/2506.02285
Hacked together by Ross Wightman
"""
from typing import List, Optional
import torch
from tor... |
397128634a98197aeccd3ed5f36a101263f3ae9a5510176e7832e43e3bf614d4 | Python | 11,839 | 268 | """ Dataset reader for HF IterableDataset
"""
import inspect
import logging
import math
import os
from typing import Optional
import torch
import torch.distributed as dist
from PIL import Image
try:
import datasets
from datasets.distributed import split_dataset_by_node
from datasets.splits import SplitInf... |
f68faf25a28d7f1b5d85cf2eef54df29e9702f577fda99fbd369aa8f5f65239a | Python | 11,848 | 309 | # adapted from https://github.com/kenshohara/3D-ResNets-PyTorch
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from .modelBase import ModelBase
class _DenseLayer(nn.Sequential):
def __init__(self, num_input_features, growth_rate, bn_size, drop_r... |
d33fad75773e4801c6be511b7289b243213c09ab916c805953603e2e7bb88168 | Python | 11,854 | 340 | import math
from collections import OrderedDict
from functools import partial
from typing import Any, Callable, Dict, List, NamedTuple, Optional
import torch
import torch.nn as nn
from torchvision.ops.misc import Conv2dNormActivation, MLP
import torch.nn.functional as F
# __all__ = [
# "VisionTransformer",
# ... |
62e50d48a72e80df04ee069477eea80bfb5c157323dd9adab4190a8538694ac5 | Python | 11,877 | 325 | import json
import math
import os
import argparse
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from utils import timer
from distance_calculation import get_mds
def get_dataset(datafile):
with open(datafile, "r") as json_file:
... |
d974732354de5c2e653878e58a42519a5b385961b16e4bd8e31dd6c54894d370 | Python | 11,880 | 287 | import numpy as np
import scipy.stats as sps
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
import pickle
from scikits.bootstrap import bootstrap as boot
import seaborn as sns
##############################################################################
# FUNCTIONS FOR PLOTTING AND STATISTICAL TE... |
570f62aa4ee3382a163b7a8508894db840983758f4419e31aff9c9d4637cd43b | Python | 11,884 | 358 | """Run simple cell optimisation"""
"""
Copyright (c) 2016, EPFL/Blue Brain Project
This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>
This library is free software; you can redistribute it and/or modify it under
the terms of the GNU Lesser General Public License version 3.0 as published
by t... |
983fb5e12153cb608fbf7d1e3eaaa7b34ffce099cfd2b1853b314f4148f37d33 | Python | 11,899 | 291 | from pathlib import Path
import weakref
import cv2
import h5py
import numpy as np
import torch
from torch.utils.data import Dataset
import torchvision.transforms as transforms
from PIL import Image
from DSEC.dataset.representations import VoxelGrid
from DSEC.utils.eventslicer import EventSlicer
import albumentations ... |
bd302e72427406cf677ded86d272884aa831580ac861d338a681a94e4e7a4182 | Python | 11,906 | 276 | # -*- coding: utf-8 -*-
# adapted from https://github.com/qubvel/segmentation_models.pytorch
from typing import Optional, Union, List
import torch
import torch.nn as nn
from segmentation_models_pytorch.base import SegmentationHead, ClassificationHead
from segmentation_models_pytorch.encoders import get_encoder
from se... |
9d809f02cb4b74e6b9f70e93fd988cb2853cac0ef2280ae153c0dc6df3c5415d | Python | 11,913 | 280 | from collections import defaultdict
import os.path
import warnings
from typing import Callable, DefaultDict, List, Optional, Tuple, Type
import numpy as np
import numpy.typing as npt
import xarray as xr
import traits.api
from ptsa.data.readers.edf import EDFRawReader
from ptsa.data.readers.binary import BinaryRawRead... |
9617d89ce256154668d782d86a0779caea2ec9e2eb2cf0a3e9609208a416c585 | Python | 11,945 | 282 | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import function
from module import FA_wrapper, TrainingHook
import utils
spike_args = {}
class NetworkBuilder(nn.Module):
def __init__(self, topology, input_size, input_channels, label_features, train_batch_size, train_mode, dropout,
... |
f7edf2492a0cf53d4fbc9e84fb177422b27c1f7fde985e753b24bd330f701c76 | Python | 11,945 | 300 | """Prepare Scheetz2006 regression data from the authors' GEO submission."""
from __future__ import annotations
import argparse
import csv
import gzip
import hashlib
import json
import os
import shutil
import subprocess
import sys
import tempfile
import zlib
from datetime import UTC, datetime
from pathlib import Path
... |
1972c558a27621673bc508b706864666cfe8b0678afe89469640dfad102a65c2 | Python | 11,957 | 248 | from pathlib import Path
import yaml
import itertools
from tqdm import tqdm
import pytorch_lightning as pl
import torch
from torch.utils.data.dataloader import DataLoader
from torch.utils.data import RandomSampler, WeightedRandomSampler
import torch.multiprocessing as mp
class BaseDataModule(pl.LightningDataModule... |
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