sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
e4e631022e3720ecfac175ab5db5ee3d6eed25495c4f37f9b9c2499103fc5405 | Python | 4,544 | 110 | """ ONNX-runtime validation script
This script was created to verify accuracy and performance of exported ONNX
models running with the onnxruntime. It utilizes the PyTorch dataloader/processing
pipeline for a fair comparison against the originals.
Copyright 2020 Ross Wightman
"""
import argparse
import numpy as np
im... |
1c451f197c362cfb285ae3aef689126e3470c0757637d4374a1864affc53a4dc | Python | 4,546 | 170 | # -*- 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... |
c9cf413364038ab6e6c9e9db047bd291a6bd8674d1251f58d84c3d3e4a27675d | Python | 4,546 | 140 | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from neuron import h
import Functions.globalFunctions.morphology_v2 as mphv2
from Model import Cells
font = {'family': 'helvetica',
'size': 10}
mpl.rc('font', **font)
image_w = 9
pttocm = 0.0352777778
scale_diams = image_w/pttocm/1... |
88a6300c35d175c63a37943be714a9b7677edf001489897a95746f720d695b19 | Python | 4,549 | 143 | """ Activation Factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Callable, Optional, Type, Union
from .activations import *
from .activations_me import *
from .config import is_exportable, is_scriptable
from .typing import LayerType
# PyTorch has an optimized, native 'silu' (aka 'swish'... |
dba9cbd74eafe40dd8bfb21a2923c53bd5e838b4be1c8e64db503bed1086ec24 | Python | 4,549 | 153 | # Copyright (c) OpenMMLab. All rights reserved.
import gzip
import hashlib
import os
import os.path
import shutil
import tarfile
import urllib.error
import urllib.request
import zipfile
__all__ = ['rm_suffix', 'check_integrity', 'download_and_extract_archive']
def rm_suffix(s, suffix=None):
if suffix is None:
... |
7044d623f7ad15a6cd6a37429aae0d75a93259042de6f96a8b2cfb3d1f042de0 | Python | 4,556 | 106 | import torch
import torch.nn.functional as F
import numpy as np
from torchvision.utils import draw_segmentation_masks
def heaviside(input, threshold=0.5):
"""Heaviside function
Arguments:
input {torch.Tensor} -- Input tensor
Keyword Arguments:
threshold {float} -- Input values... |
ee3a994ded9d9ebc594c96b6b9650663570e02178bfa1f22219daab40c7116f6 | Python | 4,565 | 172 | import pdb
import torch
import torch.nn as nn
from DT_for_WDN_leak_localization.model_architectures.transformers import DecoderLayer, EncoderLayer, PositionalEmbedding
from DT_for_WDN_leak_localization.models.parameter_encoder import (
CategoricalEmbeddingLayer,
ParameterEncoder,
TransformerEmbedding
)
cl... |
c051c3130c0fa82d9137cf7c2304006fedd11ffde435460a38fcc79dfbff8c61 | Python | 4,566 | 113 | import tensorflow as tf
import h5py
import numpy as np
####################################################################################################
#HELPER FUNCTIONS
###################################################################################################
def _bytes_feature(value):
"""Returns a by... |
8700bab43430a5eada02f674faf495515e58ee009e6f6cd43f9815c3380961cd | Python | 4,569 | 119 | import numpy as np
def saveSVMClassifier (clf):
kernelsName = clf.kernel
supportShape = clf.support_vectors_.shape
VECDIM = clf.n_features_in_
nbSupportVectors = supportShape[0]
vectorDimensions = supportShape[1]
dualCoefs = clf.dual_coef_
dualCoefs = dualCoefs.reshape(nbSupportVectors)
... |
240397cdb106ce2d32e6d99981a3a9f247290463f7f51dc0cea1488f6ab30568 | Python | 4,570 | 127 | # -*- 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... |
82c466d9e7d4a91603b6b515d57a606da1ffb76fbd7b721e0fe2215a9adb0779 | Python | 4,573 | 126 | #####################################################################################################################
# Date created : 10/21/2022
# Date modified: 10/21/2022
# Purpose : This program reads and loads inputs data and passes them to the inference program
############################################... |
d1fe7b7b7106d8af2fa731b479e3aba2a5a913824a43c8f9fe500e54ac098411 | Python | 4,575 | 143 | #!/bin/env python
"""
Cascade decomposition
=====================
This example script shows how to compute and plot the cascade decompositon of
a single radar precipitation field in pysteps.
"""
from matplotlib import cm, pyplot as plt
import numpy as np
import os
from pprint import pprint
from pysteps.cascade.bandp... |
3f684753a58a04d32d3f797331fb36176261d8bf5c20594ffa44e5d1a7bca3a9 | Python | 4,576 | 100 | import matplotlib.pyplot as plt
import numpy as np
from ..spiketrain import SpikeTrain
from .spiketrain import SpikeTrainsPlotter
from ..utils.time import get_dt, searchsorted
class DecodingPlotter:
def __init__(self, t=None, model=None, optimizer=None, mask_spikes=None, neurons=None, files=None, stim_dec=None,
... |
bdf8d626ea09faaea6e0d820a6410e2401d768f9a371a34eebb9f455a4bd4a2e | Python | 4,577 | 168 | # Copyright (C) 2021-2022 Severin Strobl <git@severin-strobl.de>
#
# SPDX-License-Identifier: MIT
#
# Exact calculation of the overlap volume of spheres and mesh elements.
# http://dx.doi.org/10.1016/j.jcp.2016.02.003
import numpy as np
import overlap
def test_sphere():
sphere = overlap.Sphere((1, 1, 1), 1)
... |
11807b0ab333722dff42e10e4a56606761ac20eda25c09f2b8e33e8dc37d4bb0 | Python | 4,581 | 111 | import torch
import torch.nn as nn
import torch.nn.functional as F
class AcceleratedCreator(object):
def __init__(self, input_dim, base_out_dim, k):
super(AcceleratedCreator, self).__init__()
self.input_dim = input_dim
self.base_out_dim = base_out_dim
self.computer = Extractor(k)
... |
3637a144bfd61c0e8116921ae41076256f091f91f423fbfaa24c90f4df2c708a | Python | 4,583 | 139 | import numpy as np
import torch
from torchvision import datasets, transforms
def get(mini=False, fixed_order=False):
data = {}
taskcla = []
size = [1, 28, 28]
labelsize = 10 #2
seeds = np.array(list(range(labelsize)), dtype=int)
if not fixed_order:
np.random.shuffle(seeds)... |
09ae7c8e8f4e2f519d8c1d99bd009caca5b3f45ebb898e6b71b9d0c53f8ba5c5 | Python | 4,584 | 130 | """
All the metrics are to be called from here
"""
from typing import Union
import torch
from sklearn.metrics import balanced_accuracy_score
import numpy as np
import torchmetrics as tm
from ..utils import get_output_from_calculator
def classification_accuracy(
prediction: torch.Tensor, target: torch.Tensor, par... |
8a553cd16ec4f7535f67b5b9f542f68defa46cfab8b74cdf0949c35d299b806c | Python | 4,589 | 106 | import os
import numpy as np
import tensorflow as tf
from util.util import myNumExt
import time
from sort.rafd8 import pet as myDB
from ipdb import set_trace as st
from math import ceil
import random
from options.AV45 import BaseOptions
from tqdm import tqdm
import logging
opt = BaseOptions().parse()
# parameter set... |
5e8b1e5c4b4917642d8e589c3c7f44c9843dcff3b56dfe49a160c18a554c7d39 | Python | 4,603 | 133 | import numpy as np
from .dataset import Datasets, Dataset
# CONSTANT WHERE TO FIND THE DATA
from config import SVESSEL_DIR as VESSEL_DIR
import SimpleITK as sitk
import os
from .dataset import Datasets, Dataset, GraphDataset
import torch
from torch_geometric.data import (Dataset, Data)
import torch_geometric.transforms... |
c428c89569029c47c0eb416e2bdd51bde7d6fef8146f5f3044886f1338393472 | Python | 4,604 | 131 | #!/usr/bin/env python
import sys
import os
import deepchem
from deepchem.models import GraphConvModel, WeaveModel
from deepchem.models.sklearn_models import RandomForestRegressor, SklearnModel
import pandas as pd
from rdkit import Chem
import itertools
from esol import ESOLCalculator
# Turn off TensorFlow logging
os.e... |
1640462a3baaef554ce4d586ee21389e2cb4311c22cd5385a7a0c403b9bae344 | Python | 4,605 | 102 | import numpy as np
from torch.utils.data import Dataset, DataLoader, SequentialSampler
import torch.nn.functional as F
import torch.nn as nn
#from torch_plus import additional_samplers
from HiSiNet.HiCDatasetClass import HiCDatasetDec, SiameseHiCDataset,GroupedHiCDataset
import HiSiNet.models as models
import torch
im... |
6591a0ffaf95c1d45817462102911dd9ed52ef28979da488573ed84eff647342 | Python | 4,609 | 165 | """ Model / Layer Config singleton state
"""
import os
import warnings
from typing import Any, Optional
import torch
__all__ = [
'is_exportable', 'is_scriptable', 'is_no_jit', 'use_fused_attn',
'set_exportable', 'set_scriptable', 'set_no_jit', 'set_layer_config', 'set_fused_attn',
'set_reentrant_ckpt', 'u... |
153cb3e7afd8b59b8ad2ccf5e012dd83608f636034434bac8c5ad5fcbf1de9b2 | Python | 4,613 | 147 | # -*- 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... |
e51e9b44ca1b74f921ca5484f32c4485769cb840b4fc7bf2a6f036d40954b94e | Python | 4,613 | 139 | # 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... |
cb98b3bf932e12af89804bc65f456c3443f175d4dffe84e6aa0ba743d80cd7c6 | Python | 4,615 | 93 | from pydantic import validate_call, ConfigDict, Field, BaseModel
from typing import Annotated, List, Tuple, Union
from enum import Enum
import numpy as np
from cellprofiler_library.measurements.measurement_model import LibraryMeasurements
from cellprofiler_library.functions.measurement import get_granularity_measureme... |
485fe656031877d64242dcf06fc2d85bd6fe8922716d9579823928edb09b6241 | Python | 4,618 | 105 | import torch
from .base_model import BaseModel
from . import networks
class CNNModel(BaseModel):
""" This class implements a base CNN model using only the generator architecture.
The model training requires '--dataset_mode aligned or np_array_aligned' dataset.
By default, it uses a '--netG unet256' U-Net... |
233ff42fad01734d69b60880adee72e4392534230f487475215f3568bf7cbc4e | Python | 4,623 | 137 | import numpy as np
import torch
import cv2
from torchio.data.image import ScalarImage
from .base import TemplateNormalizeBase
from .utils import rgb2od, od2rgb
from .stain_extractors import VahadaneExtractor, RuifrokExtractor, MacenkoExtractor
class StainNormalizer(TemplateNormalizeBase):
"""Stain normalization ... |
61ee65bd7d52e7387a9edda40e09dcc37c3ac4c45977bc6717a49c2ef54d5da9 | Python | 4,624 | 103 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 22 10:59:54 2018
@author: saskiad
"""
import numpy as np
import pandas as pd
import os, h5py
import scipy.stats as st
import core
def do_sweep_mean(x):
return x[28:35].mean()
def do_sweep_mean_shifted(x):
return x[30:40].mean()
class Loc... |
800d6e6c792c45f74d6c3b6ca05e01a5d042484062d480abfe09d7034411d45d | Python | 4,625 | 113 | import pdb
import numpy as np
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.optim as optim
import torch
class Preprocessor():
def __init__(self, num_pipes: int, num_nodes: int):
self.num_pipes = num_pipes
self.num_nodes = num_nodes
self.min_flow_rate = 1e8*torch.ones... |
68103b6f3e465e83d59c2ea88a260878eade0741f31c38c63e31fa32996ecd26 | Python | 4,629 | 101 | """Registry of natural-language query terms -> direct NIDM/BIDS predicates.
Some NIDM facts are stored as *direct predicates* on entities rather than as
``nidm:DataElement`` / ``nidm:PersonalDataElement`` nodes. For example
``bidsmri2nidm`` / ``csv2nidm`` write, on the imaging acquisition object::
niiri:... nid... |
9258e94dc30e1a482ed2e926bea3017b848643ce5adf59721bc4fd88eb3a3d91 | Python | 4,631 | 102 | """
@author: Johanna Rahm
Research group Heilemann
Institute for Physical and Theoretical Chemistry, Goethe University Frankfurt a.M.
Handle saving of DeepSTORM2DAddOns Jupyter Notebook.
"""
import os
import csv
import tifffile
def save_convert_file(data_path, converted_file):
# Convert localization file (csv ... |
bc6418c03893dd8f3293fc4b36407c0e05b5ad8a44730629d1309d08ee68593b | Python | 4,631 | 142 | from huggingface_hub import HfApi, snapshot_download, ModelCardData, ModelCard
from typing import List, Union
from GANDLF import version
from pathlib import Path
from GANDLF.utils import get_git_hash
import re
def validate_model_card(file_path: str):
"""
Validate that the required fields in the model card are... |
6f54d79e0987212823891833e822aff3bc6b39f7164e80a63a2e2e98ec5ec4bc | Python | 4,632 | 143 | # -*- 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... |
968bd3a50c5ed5fe26a6ffb7417585b7be4a5bda24e98b4b6b5a738100a45bec | Python | 4,632 | 106 | """Dataset class template
This module provides a template for users to implement custom datasets.
You can specify '--dataset_mode template' to use this dataset.
The class name should be consistent with both the filename and its dataset_mode option.
The filename should be <dataset_mode>_dataset.py
The class name should... |
d65ecee263a1e5ef932f028402011f76090051b192edee127ff4a4a43db43ec5 | Python | 4,632 | 116 | import pytest
from click.testing import CliRunner
from GANDLF.entrypoints.generate_metrics import new_way, old_way
from . import CliCase, run_test_case, TmpDire, TmpFile, TmpNoEx
# This function is a place where a real logic is executed.
# For tests, we replace it with mock up, and check if this function is ca... |
ef2e758d7db419228ef3ad1d710d9c2db96670e6f6b3372539bce9c5d438acb5 | Python | 4,632 | 132 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Visualize the results of the sensor optimization. Read the results to be visualized
from the folder passed as command-lie parameter to this script.
"""
import math
import sys
from math import isclose
import numpy as np
import matplotlib.pyplot as plt
from mayavi impor... |
85c17ba985d1cd63ff03ee3d667100cd0e84f750bcf50ae3b184ac14c015b0bf | Python | 4,633 | 108 | from abc import abstractmethod, ABC
from sklearn.model_selection import RepeatedStratifiedKFold
from src.datamodules.tabular import TabularDataModule
import numpy as np
from src.utils import utils
from itertools import combinations
from math import comb
log = utils.get_logger(__name__)
class CVSplitter(ABC):
de... |
e96c8041b38b4961e05cb680e59dcaeb7e027361a6e4b2f19c60dc09f702d36e | Python | 4,634 | 142 | # -*- coding: utf-8 -*-
import numpy as np
import pytest
from pysteps.utils import get_method
interp_methods = (
"idwinterp2d",
"rbfinterp2d",
)
@pytest.mark.parametrize("interp_method", interp_methods)
def test_interp_univariate(interp_method):
coord = np.random.rand(10, 2)
input_array = np.random.... |
4c6f2ea40e48ad05f2963efa66ff46b36b6c921f1846583dc3b213c3fcf0a2d0 | Python | 4,636 | 154 | '''Train CIFAR10 with PyTorch.'''
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
from models import *
from utils import progress_bar
parser =... |
799dc51f7356a31ea46303a44cb3e87c560d4ad965d8fb4d90c9cfdf7dad2b94 | Python | 4,638 | 149 | import pdb
import torch
import matplotlib.pyplot as plt
import ray
from DT_for_WDN_leak_localization.inference.forward_model import BaseForwardModel
from DT_for_WDN_leak_localization.inference.likelihood import Likelihood
from DT_for_WDN_leak_localization.inference.true_data import TrueData
#@ray.remote(num_gpus=1)
d... |
972b79a845fc25f98647240d95d67ac6c84be5772e94caaad54d23af86a40ec0 | Python | 4,639 | 131 | # JHK
# Python script for generating ED Figure 7
from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap, cm
import os
#==========================================================
# Set main directory
#=====================================================... |
a993a20085a488be8aa85a23e99ba8e9056bc55d4acf40e7104d251d40b634fd | Python | 4,644 | 98 | import os
from unittest import TestCase
from .gisles2018 import get_files_patient_path, load_nifti, _ISLESFoldIndices
from config import ISLES2018_DIR
class DatasetGILSES2018Test(TestCase):
def test_get_files_patient_path(self):
patient_path = os.path.join(ISLES2018_DIR, "raw", "TRAINING", "case_1")
... |
27e2162b44db7d9dfa68f2dc172baf3ef1692006d9762f223388e7cc72b4eba8 | Python | 4,648 | 137 | import numpy as np
import torch
from torchvision import datasets, transforms
def get(mini=False, fixed_order=False):
data = {}
taskcla = []
size = [3, 32, 32]
labelsize = 10 #2
seeds = np.array(list(range(labelsize)), dtype=int)
if not fixed_order:
np.random.shuffle(seeds)... |
c14f1b2b8103249d7cadc3e9d6bceac86f992e85d5172ff175018f700aa1b902 | Python | 4,651 | 146 | # from nidm.core.provone import ProvONEDocument
import pytest
from nidm.core.dot import provone_to_dot
pytestmark = pytest.mark.skip(
reason="had to comment provone import - was breaking tests from experiment"
)
@pytest.fixture(scope="module")
def doc():
# Create new provone document with namespaces
from... |
5fa6a9b5f5eb62125fde132ccd5b388d147910936f13eb66f6bc9e55e4e324a3 | Python | 4,652 | 127 | import numpy as np
import pandas as pd
from scipy.stats import f_oneway
from typing import Dict, Tuple, Set
def extract_significant_p(df: pd.DataFrame, p_value_limit: float):
"""Return a df, which replaces values that are above p_value_limit with `None`"""
return (
df.loc(axis=1)[f"p-value"]
... |
6789f14c27c6646a4e3e8a3588743e0e059de7281216e954c8378609a9cc654d | Python | 4,653 | 148 | #!/bin/env python
"""
Probability forecasts
=====================
This example script shows how to forecast the probability of exceeding an
intensity threshold.
The method is based on the local Lagrangian approach described in Germann and
Zawadzki (2004).
"""
import matplotlib.pyplot as plt
import numpy as np
from ... |
c6da2658ef175f4d749009238f54fac3e799de54807ebc5036d1e1a8dedeb17f | Python | 4,656 | 179 | # -*- coding: utf-8 -*-
"""
pysteps.postprocessing.ensemblestats
====================================
Methods for the computation of ensemble statistics.
.. autosummary::
:toctree: ../generated/
mean
excprob
banddepth
"""
import numpy as np
from scipy.special import comb
def mean(X, ignore_nan=Fal... |
35b701cc85835c9fb5bf713388e0ac2a33cc86d92dadfc63137f0fade9539a81 | Python | 4,660 | 152 | import numpy as np
import sys
import seaborn as sns
import matplotlib.pyplot as plt
import os
import os.path as osp
sys.path.append('..')
from functions.individual_variability import grab_data
from functions.def_ROIs_EarlyVisualAreas import roi
def color_pal(cluster):
palette_tmp = sns.color_palette("PRGn_r")
... |
b34da9c704e9a98bd452565c534dbb0bc991f9553877c8695c2f3549762c6c7f | Python | 4,663 | 155 | from os import getcwd
from os.path import basename, join
import nibabel as nib
import numpy as np
from mrHARDI.base.shell import launch_shell_process
from mrHARDI.base.utils import if_join_str, split_ext
from mrHARDI.compute.math.stats import center_of_mass_difference
from mrHARDI.compute.utils import (compute_reorien... |
03d39f699f2bd810ba036b865f134d866c8134e45407f31ea890b6f5945e6e7c | Python | 4,664 | 137 | """
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 os
import gemmi
import numpy
import argparse
import j... |
731988e170f35bed95f7f81f1bc82e0dacaa44a336e1c7a48fe05cfe1f684e93 | Python | 4,664 | 121 | import torch
import torch.nn as nn
import torch.utils.data
from torch.utils.data import TensorDataset, DataLoader
from sklearn import metrics
import pandas as pd
import numpy as np
from vajiraGAN_original import Pulse2pulseDiscriminator
print('Reading in the test data...')
# For testing on only NORM/MI patients, cha... |
d46a7e46e002e9a511f7a983fa1ba2991ecec5bb1f17d8c97190f2b47cb18d10 | Python | 4,666 | 154 | ''' adapted from https://github.com/all-umass/ManifoldWarping '''
''' Alignment techniques '''
import numpy as np
import scipy as sp
from ManinetCluster_python.neighborhood import laplacian
from ManinetCluster_python.util import block_antidiag
from functools import reduce
def _manifold_setup(Wx,Wy,Wxy,mu):
Wxy = m... |
7523d591ba2c3c7ac6efe374483f1dc5eaf5357baa953dbb475dd10807449750 | Python | 4,668 | 145 | """ NormAct (Normalization + Activation Layer) Factory
Create norm + act combo modules that attempt to be backwards compatible with separate norm + act
instances in models. Where these are used it will be possible to swap separate BN + act layers with
combined modules like IABN or EvoNorms.
Hacked together by / Copyr... |
02b393db4474c8e1ba8aee5896b63a48c9b960c41694487fdf63b1ea29518286 | Python | 4,670 | 131 | # 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... |
f744446bfc130c54d3e9df7a96001c3ee7dd58a4982c3d1bd9f79c5445f6d36d | Python | 4,672 | 98 | import os
import json
import subprocess
import numpy as np
import pandas as pd
import pickle as pkl
import nibabel as nib
from scipy.stats import zscore
from os.path import join as pjoin
from sklearn.preprocessing import StandardScaler
from scipy.stats import pearsonr
from nod_utils import save_ciftifile
# change to p... |
d54abd0fade1dc4952dd33f981ca7e5fadf38a609907617efcfd2517053c5f5b | Python | 4,679 | 134 | # JHK
# Python script for generating ED Figure 9
import matplotlib
matplotlib.use('Agg')
import numpy as np
from netCDF4 import Dataset
import matplotlib.pyplot as plt
import os
#==========================================================
# Set main directory
#==========================================================... |
f1d2aeb227ae5fda0ca4cc992d4970739a9a0654c78e1fb7d15c9e6a3476f2b8 | Python | 4,679 | 146 | """
A demo for multi-output regression
==================================
The demo is adopted from scikit-learn:
https://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py
See :doc:`/tutorials/mult... |
eab5598d7c47c1f245ab0d7d52244c60adee17ae7a4207ccc160f3f8b38751ac | Python | 4,692 | 127 | import torch
import torchmetrics as tm
import torch.nn.functional as F
# from torch.nn.functional import one_hot
from ..utils import get_output_from_calculator
from GANDLF.utils.generic import determine_classification_task_type
def overall_stats(prediction: torch.Tensor, target: torch.Tensor, params: dict) -> dict:
... |
aa03dbeb136ebc42edfc639d9321ec687dad7520cab5919532936d55c576f2c2 | Python | 4,700 | 144 | from cellprofiler_core.module.image_segmentation import ImageSegmentation
from cellprofiler_core.setting import Binary
from cellprofiler_core.setting.text import Integer
from cellprofiler_library.modules._convertimagetoobjects import convert_image_to_objects
HELP_BINARY_IMAGE = """\
This module can also convert a gray... |
4b3e69bb843917dbe9b80d6d4c6e3437a8c55ae80549dd9e7c4cd8b277190d75 | Python | 4,705 | 125 | import torch
import torch.nn as nn
import torch.nn.functional as F
from .resnet import conv3x3, PreActResNet, PreActResNet_cifar
class SE_PreActBlock(nn.Module):
'''Pre-activation version of the BasicBlock.'''
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(SE_PreActBlock, se... |
532e86f00a7d8b89bcb088ea48544771e910e6c2746f58e9e1324f8caabdc274 | Python | 4,707 | 208 | # -*- coding: utf-8 -*-
import numpy as np
import pytest
from numpy.testing import assert_array_almost_equal
from pysteps.utils import transformation
# boxcox_transform
test_data = [
(
np.array([1]),
{
"accutime": 5,
"transform": None,
"unit": "mm/h",
... |
a970a00f9c342ff99968643cb3bf8de9ef0e6dc0dcf0e75253647fca083b25b5 | Python | 4,713 | 106 |
import os
import numpy as np
import tensorflow as tf
from utils import utils
class TrainDataGenerator(tf.keras.utils.Sequence):
'''inherits from Keras Sequence base object, allows to use multiprocessing in .fit_generator'''
def __init__(self, samples, img_dir, batch_size, n_classes, basenet_preprocess, img_f... |
dcb3edcf985fe260cf94ff51be661004b011d794ffd95495cddf157a1bf810ec | Python | 4,716 | 118 | # -*- 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... |
2e14e6a7655e627ec934acbccdaff9d52e80eeffc9b8af0f6588f2601fe396c4 | Python | 4,722 | 107 |
"""
Educational Use License
This software is provided solely for educational purposes.
Any use of this software for commercial or non-educational purposes is prohibited.
If you use this software in an academic setting, you must cite the following paper in any resulting publications or presentations:
[Sanaullah, Ko... |
91d131173fc39ba35e535b9bf5c1d56a7b4e39307cb915f4078d116ab560020e | Python | 4,722 | 89 | import numpy
from pydantic import Field, validate_call, ConfigDict
from typing import Annotated, Optional, Tuple, Dict, Callable
from numpy.typing import NDArray
from cellprofiler_library.functions.image_processing import flip_image_both, flip_image_left_to_right, flip_image_top_to_bottom, rotate_image_angle, rotate_im... |
cb2882ef351411dff6e006933cd4aec808782cf10f621dbdd152a5d66af659f5 | Python | 4,729 | 104 | if __name__ == '__main__':
import Functions.setup as stp
import matplotlib.pyplot as plt
import numpy as np
from Model import Cells
import Functions.globalFunctions.ExtracellularField as eF
from Functions.globalFunctions.utils import MyEncoder,Dict
import json
import os
from datetime... |
5e884e351565a9aedc9c8aad5c77710eca654bffeff40fbf09d177b7f8c9672d | Python | 4,730 | 132 | import os
import random
import torch
import torch.nn as nn
from torch.optim import Adam
from model import CNN
from torchtext import data
from torchtext import vocab
from data import DataIterator
from collections import namedtuple, defaultdict
from sklearn.metrics import classification_report
from loss import FocalLoss
... |
ef1029d52ae9a5d056d144c937f9e82bd68c7ae42a3e1a28f44182772e256a8d | Python | 4,732 | 175 | import pdb
import torch
import torch.nn as nn
from DT_for_WDN_leak_localization.model_architectures.transformers import DecoderLayer, EncoderLayer, PositionalEmbedding
from DT_for_WDN_leak_localization.models.parameter_encoder import (
CategoricalEmbeddingLayer,
ParameterEncoder,
TransformerEmbedding
)
cl... |
30b615fcd19e38f5d10692ffdab4c07aec452bbcdb549d4f5e9a4ff8978eee7d | Python | 4,733 | 134 | """
ClinicalASL - Clinical Arterial Spin Labeling processing pipeline
Repository: https://github.com/JSIERO/ClinicalASL
Author: Jeroen Siero
Institution: UMCU (University Medical Center Utrecht), The Netherlands
Contact: j.c.w.siero@umcutrecht.nl
Description:
This script provides a function to smooth ASL... |
e0ef3504d330a2491dec3d1cf0a9d025751257b6dc4c1a4703434384720570aa | Python | 4,733 | 151 | __all__ = ['imread', 'imread_collection']
import skimage.io as io
try:
from astropy.io import fits as pyfits
except ImportError:
try:
import pyfits
except ImportError:
raise ImportError(
"PyFITS could not be found. Please refer to\n"
"http://www.stsci.edu/resources/... |
baff8d074cf90935fd0050d5b0e8483aeafbf2f1a3017797ae58c109c90ea582 | Python | 4,735 | 121 | import importlib
from pathlib import Path
import numpy as np
import pandas as pd
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from models.aggregators.aggregator import BaseAggregator
def get_loss(name, **kwargs):
# Check if the name is a valid loss name
i... |
d1201f7a84164b057ae12e7dabfc1f30e4e7ad7deddacf4dc372c2198b87318c | Python | 4,735 | 121 | import importlib
from pathlib import Path
import numpy as np
import pandas as pd
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from models.aggregators.aggregator import BaseAggregator
def get_loss(name, **kwargs):
# Check if the name is a valid loss name
i... |
d56723061552ab182985159435f003a23b6029ad4127f0d78fcbd08b2b2f3d1d | Python | 4,735 | 156 | import click
from GANDLF.entrypoints import append_copyright_to_help
from GANDLF.cli.huggingface_hub_handler import push_to_model_hub, download_from_hub
from pathlib import Path
huggingfaceDir_ = Path(__file__).parent.absolute()
huggingfaceDir = huggingfaceDir_.parent
# Huggingface template by default Path for the M... |
135d155ae55cda6089029940738eeb8eaff9946725459bd76a426a317934ab25 | Python | 4,737 | 93 | import numpy
from typing import Tuple, Optional, Any, Dict, Union, Annotated, List
from numpy.typing import NDArray
from cellprofiler_library.types import Image2DBinary, ObjectSegmentation, Image2DColor, Image2DGrayscale
from pydantic import Field, validate_call, ConfigDict, BaseModel
from cellprofiler_library.function... |
ef8b65e99e6ba1044128fa7a5935194e6b919c9ae1a84089354b8b6871875bc2 | Python | 4,739 | 183 | """Render the two embedded raster assets for the graphical abstract.
paper_figures/ga_brain.png — glass-brain (lateral) with 32 region-coloured AFIDs, transparent
paper_figures/ga_crop.png — tight crop of the /learn interface (viewer + tutor)
"""
from __future__ import annotations
from pathlib import Pat... |
effc50cc017066d60f3e8bc9f4a65b2765ad374a1b4da7bf023c1db99e8f1cd9 | Python | 4,740 | 173 | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... |
a8e05605018a5c1a21680b78b4596544de870ccc7231f07657257ee67463ef5e | Python | 4,743 | 161 | """
@author: Nikolai M Chapochnikov
Created in 2022
Testing that the SNMF functions is doing what we want it to do on different
datasets
ALL TESTS PASS
if not, need to decrease relative error tolerance
"""
import pandas as pd
import numpy as np
import pytest
from scipy import linalg as LA
import functions.datasets... |
a3c2c0b7a2829792a02cd38bc4f66a8dc8a6a4e68c1e578b627aa4283bcf7121 | Python | 4,747 | 139 | # -*- 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... |
28251d0dea2c907b61004296b7792bc9294a809a02c137b496e9f069824b89ad | Python | 4,748 | 109 | import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from optimiser_tools import *
import tqdm
tfd = tfp.distributions
@tf.function
def softplus(x, k=1.0):
k = tf.cast(k, float)
return tf.math.divide(tf.math.log(tf.math.add(1.0, tf.math.exp(tf.math.multiply(x, k)))), k)
# initializ... |
33a7284d6c96562f4b360764e845a6d79d7df8220e22b3ecc57eace075352afc | Python | 4,748 | 119 | from __future__ import absolute_import
import numpy as np
import unittest
import centriole.index as I
class TestIndexes(unittest.TestCase):
def test_00_00_oh_so_empty(self):
ind = I.Indexes([[]])
self.assertEqual(ind.length, 0)
self.assertEqual(len(ind.fwd_idx), 0)
self.assertEqua... |
dc902672bf9d237a0ff1bf4a365018ca4ea7715a2528429d79c989b17be4580a | Python | 4,749 | 125 | from data.base_dataset import BaseDataset, get_transform
from data.image_folder import make_dataset
from util.util import normalize
from skimage import io
from util import util
import numpy as np
class SimulationCropDataSet(BaseDataset):
"""
This class is a modified version of DiceImage dataset module, only w... |
f0006fbe47da13aa1a632d75bc1acac0479d939eb2368f5730cc6c4b937ab5a8 | Python | 4,751 | 161 | import os.path as op
def bool_arr_type(var): return var
def str_arr_type(var): return var
def int_arr_type(var): return var
def float_arr_type(var): return var
def list_of_int_lists_type(var): return var
def add_arguments(parser, arguments):
for argument in arguments:
parser.add_argument(argument)
def ... |
716b96e6d87f950571077ee768c218c848caf766f97b407eb85abc836241a470 | Python | 4,760 | 172 | import pdb
import torch
import torch.nn as nn
from DT_for_WDN_leak_localization.model_architectures.transformers import DecoderLayer, EncoderLayer, PositionalEmbedding
from DT_for_WDN_leak_localization.models.parameter_encoder import (
CategoricalEmbeddingLayer,
ParameterEncoder,
TransformerEmbedding
)
cl... |
f004377ac93703426c2ab4799c73d0c0bf588eed0beb1bb2c839b6b222781365 | Python | 4,763 | 128 | #Importing libraries:
import pandas as pd
import numpy as np
import wfdb
import ast
#Define function to load the data:
def load_raw_data(df, sampling_rate, path):
if sampling_rate == 100:
data = [wfdb.rdsamp(path+f) for f in df.filename_lr]
else:
data = [wfdb.rdsamp(path+f) for f in df.filenam... |
762f3393c12044abb9cb4f80ab46814e0acaa37d1258cf3074bb2755e0da66a5 | Python | 4,765 | 112 | import warnings
from typing import Optional, Tuple, Union
import torch
import psutil
from .loss_and_metric import get_loss_and_metrics
def step(
model: torch.nn.Module,
image: torch.Tensor,
label: Optional[torch.Tensor],
params: dict,
train: Optional[bool] = True,
) -> Tuple[float, dict, Union[tor... |
ec97493f10da5f36b42762c60e8277ba842552d0e21b6357278827374ca47ecb | Python | 4,766 | 165 | import torch
import os
import numpy as np
import tifffile
from skimage import transform
class Train_Dataset(torch.utils.data.Dataset):
def __init__(self, dirc_data, transform_train):
self.dirc_data = dirc_data
self.list_data = os.listdir(self.dirc_data)
self.list_data.sort(key=lam... |
e5229d0a125c8aa1c9b2f68ebd59b9ab1edda4050878d643ae5e162893d435ec | Python | 4,769 | 102 | """
Author: Mingjian He <mh1@stanford.edu>
Testing functions for switching methods in somata/switching
"""
import numpy as np
from test_load_data import _load_data # type: ignore
def test_switching():
from somata.switching import switching
from somata import OscillatorModel as Osc
input_data = _load_d... |
9335f0552048f30648827ffcd7134d493005c18bc366e61a8f1153453a7c1eff | Python | 4,772 | 122 | """Install public assets, catalog metadata, and shared markup in the documentation."""
import argparse
import re
from contextlib import suppress
from functools import partial
from http.server import SimpleHTTPRequestHandler, ThreadingHTTPServer
from pathlib import Path
from shutil import copy2
def install(documentat... |
c494782d0a13e00fade749912cb18f3fbdb1a3d65141a008b3ab218c8d88efa9 | Python | 4,777 | 142 | elements = {"Ag": 106.905095,
"Al": 26.981541,
"Ar": 39.962383,
"As": 74.921596,
"Au": 196.96656,
"B": 11.009305,
"Ba": 137.905236,
"Be": 9.012183,
"Bi": 208.980388,
"Br": 78.918336,
"C": 12,
... |
a478bd6625f5e3be42b2c9bb51b630c5439bc6bf46d300fd7cc7e76d17587fd3 | Python | 4,785 | 202 | from __future__ import division
from numpy import *
import numpy as np
import sys
sys.path.insert(0, '../../helpers/')
from circ_stats import *
import random
import itertools
import scipy
from joblib import Parallel, delayed
import multiprocessing
import scipy.signal
from scipy.signal import correlate
from pickle imp... |
9466e6c39b05897af3970f14814b67c414a5bd24550284beef3abdc46ce79b37 | Python | 4,787 | 138 | import torch
import torch.nn.functional as F
import numpy as np
class TaskLoss(torch.nn.Module):
def __init__(self, losses=['cross_entropy'], gamma=2.0, num_classes=13, alpha=None, weight=None, ignore_index=None, reduction='mean'):
super(TaskLoss, self).__init__()
self.losses = losses
self... |
7017214b1d3f27b8288162fde13476189c9bc48a0a1d7372abdcc819fa176114 | Python | 4,792 | 165 | import tensorflow as tf
import numpy as np
from lib.network import Network
from lib.pinn import PINN
from Helper import calcW
import matplotlib.pyplot as plt
import pandas as pd
import os
linewidth = 4
fontsize = 20
font = {'family' : 'monospace',
'weight' : 'bold',
'size' : fontsize }
plt.rc('fon... |
d02f45d96035ff6fe99780726a1ed746ee1ccdd7e463caf0388b9ddfad1dc737 | Python | 4,797 | 169 | """
MeasureImageSkeleton
====================
**MeasureImageSkeleton** measures the number of branches and endpoints in a
skeletonized structure such as neurons, roots, or vasculature.
This module can analyze the number of total branches and endpoints for
branching objects in an image. A branch is a pixel with more t... |
d9c0126be5abf106bad3d2c36b7c2b1da70630f60f88418b90281b115781bb32 | Python | 4,803 | 178 | import time
import numpy as np
import os
import warnings
import scipy.io as scio
import math
from matplotlib import pyplot as plt
import torch
from braincog.base.node.node import *
import turicreate as tc
from braincog.base.brainarea.BrainArea import *
from braincog.utils import *
warnings.filterwarnings('ignore')
np... |
7c0bf243ee97998fa12c507bb65efb5bd07367929c09bcfd7bb48908c9396960 | Python | 4,808 | 143 | # JHK
# Python script for generating Figure 3.
from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
#==========================================================
# Set main directory
#==========================================================
hdir = '/home/jhkim/model/... |
5ffe7c417482b52e1a88ebbef0bc7770114621a9db97348f1a2286bc708f7c3f | Python | 4,809 | 110 | r"""
pysteps.nowcasts.interface
==========================
Interface for the nowcasts module. It returns a callable function for computing
nowcasts.
The methods in the nowcasts module implement the following interface:
``forecast(precip, velocity, timesteps, **keywords)``
where precip is a (m,n) array with inpu... |
10428cbb544a5aa9d283e5321c09ad67cf8e0640b6deae46b85a0b11ec5f3996 | Python | 4,820 | 158 | """
This file implements Oja's hebbian learning rule.
Relevant book chapters:
- http://neuronaldynamics.epfl.ch/online/Ch19.S2.html#SS1.p6
"""
# This file is part of the exercise code repository accompanying
# the book: Neuronal Dynamics (see http://neuronaldynamics.epfl.ch)
# located at http://github.com/EPFL-LC... |
74162e4fc95f360e1f37214eded9524d19e1907983613a09456ecac860e89777 | Python | 4,820 | 164 | import os
import threading
from copy import deepcopy
from http.server import HTTPServer, SimpleHTTPRequestHandler
from tempfile import mkdtemp
import requests_cache
from . import get_logger
lgr = get_logger()
class LoggingRequestHandler(SimpleHTTPRequestHandler):
def log_message(self, format, *args):
l... |
4acab0d838f5cfab4ceafccaa334e668e1f3e8cd3864aeb106c49536c978000e | Python | 4,821 | 161 | import math
import numpy
import cellprofiler_core.image
class TestImage:
def test_init(self):
cellprofiler_core.image.Image()
def test_init_image(self):
x = cellprofiler_core.image.Image(numpy.zeros((10, 10)))
def test_init_image_mask(self):
x = cellprofiler_core.image.Image(
... |
553b437f702a51901d245a714fd59509b812294671d19eee074c17942b9e2382 | Python | 4,822 | 124 | from __future__ import absolute_import
import unittest
import numpy as np
from centriole.otsu import otsu, entropy, otsu3, entropy3
from centriole._np_compat import np_NaN
class TestOtsu(unittest.TestCase):
def test_01_TwoValues(self):
"""Test Otsu of two values is between the two"""
x = otsu([0.2... |
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