sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
683ee287ab86c0f2b13eb10806f992ac41abf03d7ab9d2c2fb1864cd34944d54 | Python | 2,543 | 82 | import numpy as np
from nengo.connection import Connection
from nengo.exceptions import ObsoleteError
from nengo.network import Network
from nengo.networks.ensemblearray import EnsembleArray
from nengo.node import Node
# Adpated from nengo.networks.InputGatedMemory
# A working memory used for the cognitve mapping expe... |
2b99752348ebac2a2d7dec7d73da6e18632e1ca7dc20372184d22327b25d87d2 | Python | 2,544 | 81 | # -*- 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... |
922724896e6624425c6c575a89f13ca89c0683475034f511eeef1845b5d65150 | Python | 2,545 | 85 | #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... |
ab234d533374187ab8f2267d12cf55afcd01985dc70bf1676bcb2d8790007615 | Python | 2,546 | 87 | import argparse
from pathlib import Path, PurePath
from fabric import Connection, config
def push_release(
connection: Connection,
new_release_path: Path,
remote_releases_dir: PurePath,
wheel_name: str,
):
remote_release_new = str(remote_releases_dir / new_release_path.name)
connection.run(f"... |
f3c7f983034173975c8b958a7bfcbd832c6849754addd98f76f7b788e71a9088 | Python | 2,546 | 66 | import unittest
import torch
import torchdiffeq
from problems import construct_problem, DTYPES, DEVICES, METHODS, FIXED_METHODS
def rel_error(true, estimate):
return ((true - estimate) / true).abs().max()
class TestEventHandling(unittest.TestCase):
def test_odeint(self):
for reverse in (False, Tru... |
08c8790bcd549a94e23d37c63fdd8437975fa6641f8e98c19b2aeb7768d3b060 | Python | 2,547 | 92 | # -*- 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... |
2be3a6643b4cced1f32086abb37cf773bd56ea5360abaf438aeb66fb20d1efc5 | Python | 2,547 | 98 | """
Save 2 TIFs (overlay with cluster labels and colored) using pixel clustering cluster IDs
Author: Candace Liu
Date: 3/18/21
"""
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
from matplotlib import colors
from PIL import Image
import scipy.ndimage as ndimage
clusters_path = "pixe... |
5203c36e59ebcba74a5f6fc988430dcd416167ced786377456649fe370002510 | Python | 2,547 | 65 | from typing import Literal
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import integrate
def selection_generator(df: pd.DataFrame, unique_values_columns: dict, verbose: bool = False, **kwargs) -> np.ndarray:
idx = np.ones(len(df)).astype(bool)
for key, val in kwargs.items... |
e923084b1cd1bb19735c0f9014e0c4075fe011129fd65162880673321e71710c | Python | 2,547 | 78 | """
Author: Mingjian He <mh1@stanford.edu>
general helper functions used throughout SOMATA
"""
import numpy as np
from itertools import groupby
import scipy.signal as signal
def consecutive(data):
"""
This function takes a list of values and returns a list of tuples.
Each tuple contains the value, the ... |
273b0fccdcbad0020685141e81e643a7325a4ab02589b221885e233c513519ff | Python | 2,548 | 83 | """ Summary utilities
Hacked together by / Copyright 2020 Ross Wightman
"""
import csv
import logging
import os
from collections import OrderedDict
try:
import wandb
except ImportError:
pass
_logger = logging.getLogger(__name__)
def get_outdir(path, *paths, inc=False):
outdir = os.path.join(path, *pat... |
106fd11141311efb32622ffa087cd23edbcc1ba168205c91442ebf374d3beb03 | Python | 2,549 | 83 | #!usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import os
import fileinput
def in_place_string_replace(filename: str, old_string: str, new_string: str) -> None:
"""
Replace a string in a file in place.
Args:
filename (str): The file to replace the string in
old_string (str): ... |
f8e3a5b3b3b0ef19b277d49d2ba182de85d397b152c935c9ff731c91f426d0e4 | Python | 2,553 | 44 | import numpy
from typing import Annotated, Optional
from pydantic import Field, validate_call, ConfigDict
from cellprofiler_library.functions.image_processing import smoothing_gaussian, smoothing_median, smoothing_keeping_edges, smoothing_fit_polynomial, smoothing_circular_average, smoothing_smooth_to_average
from cel... |
1f6b584e77bd264028faca8601d33f9906e51a070835626f0c77cfe68fcaac73 | Python | 2,560 | 75 | #!/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
... |
2f063480a3ba7658314c9289f9f2c9858b87de2810a702ff1452d16f5aba5960 | Python | 2,560 | 75 | import pytest
import torch
from torch.utils.data import DataLoader
from torchmeta.toy import Sinusoid
from torchmeta.transforms import ClassSplitter
from torchmeta.utils.data import Task, MetaDataLoader, BatchMetaDataLoader
def test_meta_dataloader():
dataset = Sinusoid(10, num_tasks=1000, noise_std=None)
m... |
6326b17ffb874173b74f7fb409fb9922bdf9e3c426a40bf1d3b07dee353d975d | Python | 2,566 | 83 | # -*- coding: utf-8 -*-
import os
import numpy as np
import pytest
from numpy.testing import assert_array_almost_equal
import pysteps
pytest.importorskip("pygrib")
def test_io_import_mrms_grib():
"""Test the importer for NSSL data."""
root_path = pysteps.rcparams.data_sources["mrms"]["root_path"]
fil... |
7c70a6aa7d1abaced6e0aaca1a523e08826b7cf2a16d0e01d96e11e9bc3ffdf3 | Python | 2,566 | 75 | import traceback
from typing import Union
import yaml
from pydantic import ValidationError
from GANDLF.configuration.parameters_config import Parameters
from GANDLF.configuration.exclude_parameters import exclude_parameters
from GANDLF.configuration.utils import handle_configuration_errors
def _parseConfig(
conf... |
8830243e0712a59125fd375e757e296a58c46a4291d21facf8d69bc95f4bd37b | Python | 2,566 | 73 | import pytest
from timm.data import CustomDatasetInfo, DatasetInfoLabelMapper, LabelMappingCoverage
@pytest.mark.parametrize('label_names', [
['cat', 'dog'],
('cat', 'dog'),
])
def test_custom_dataset_info_sequence_labels(label_names):
info = CustomDatasetInfo(
label_names,
label_descript... |
8eb60566974db85af27aef48d0f33b85a59cda39a4798da503a4e27f25ec05b5 | Python | 2,567 | 77 | from unittest import TestCase
import numpy as np
from .svessel import SVESSEL
data = SVESSEL()
class SVESSELTest(TestCase):
def test_init(self):
self.assertEqual(4040, data.train.num_examples)
self.assertEqual(1010, data.val.num_examples)
self.assertEqual(1010, data.test.num_examples)
... |
3c450b7b3ac2676799ce2b307a86bdf15593ef55c898872e606d7355d70accb0 | Python | 2,569 | 79 | import torch.nn as nn
import torch.nn.functional as F
class ResNetModule(nn.Module):
"""
A residual block module for use in ResNet-style architectures.
Args:
input_channels (int): The number of input channels.
output_channels (int): The number of output channels.
Conv (nn.Module):... |
49d19f36ebe89d491bda2b08a63e8104b7226842615bc6148b97a1d8274dfeca | Python | 2,569 | 21 | reference_genomes = {"hg19":["hg19", {'1': 249250621, '2': 243199373, '3': 198022430, '4': 191154276, '5': 180915260, '6': 171115067, '7': 159138663,
'8': 146364022, '9': 141213431, '10': 135534747, '11': 135006516, '12': 133851895, '13': 115169878, '14': 107349540, '15': 102531392, '16': 90354753,
'17': 81195210, '1... |
37657c4803f9fab7ff0ff61162a7f683e07f5402069e39359d1ee83a29496e80 | Python | 2,570 | 86 | import torch
import torch.nn as nn
from einops import repeat
from models.aggregators.aggregator import BaseAggregator
from models.aggregators.model_utils import Attention, FeedForward, PreNorm
class TransformerBlocks(nn.Module):
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.):
super(... |
d1b8caf84f575cc58b66dc92dd7b6a7c2483a1ab313d3d74171b5699e08c0380 | Python | 2,573 | 100 | import numpy as np
from macro_models import *
from numba import njit
@njit
def diffusion_trial(v, a, ndt, zr=0.5, dt=0.001, s=1.0, max_iter=1e4):
"""
Simulates a single reaction time from a simple drift-diffusion process.
"""
n_iter = 0
x = a * zr
c = np.sqrt(dt * s)
while x > 0 and ... |
241faf9a413cc69971da958e000c35cd77fb62cf205a10b5b5ea7441fb5ec962 | Python | 2,576 | 56 | # adapted from https://github.com/fepegar/torchio/blob/main/src/torchio/transforms/augmentation/intensity/random_blur.py
from collections import defaultdict
from typing import Union, Tuple
import torch
from torchio.typing import TypeTripletFloat, TypeSextetFloat
from torchio.data.subject import Subject
from torchio.... |
64dd60a941dfb77a196211fa83b24b0c9cb875cf5708a16e483a9eb918f57e0d | Python | 2,576 | 104 | import pdb
import torch
from torch import nn
from torch import optim
from DT_for_WDN_leak_localization.model_architectures import (
dense,
transformers,
)
from DT_for_WDN_leak_localization.models.wasserstein_AE import (
SupervisedWassersteinAE,
UnsupervisedWassersteinAE,
)
from DT_for_WDN_leak_localizat... |
1fe2ce8409dd3e0e62c88511c72aea6c42bbf0f3f895a03d38d9f1dfd3c0c20b | Python | 2,578 | 79 | import numpy as np
from .masks import shift_mask
def diag_indices(n, k=0):
rows, cols = np.diag_indices(n)
if k < 0:
return rows[-k:], cols[:k]
elif k > 0:
return rows[:-k], cols[k:]
else:
return rows, cols
def mask_extrema(signal, mask_data=None, order=30, left_comparator=np... |
10357dacf19f3bb562eb6e7368acff61fda3fd5a7444d4fda154c917bc570e6a | Python | 2,580 | 65 | from __future__ import annotations
import importlib.util
import sys
import tomllib
from pathlib import Path
import pytest
TOOLS = Path(__file__).resolve().parents[3] / "tools"
SPEC = importlib.util.spec_from_file_location("dm_r_local", TOOLS / "dm_r_local.py")
assert SPEC is not None and SPEC.loader is not None
dm_r... |
7182012104022ca051bdb8641ef3fd5319771c88f4c40b1420f6264572afaba5 | Python | 2,582 | 75 | '''
crosstemporal decoding matrix for Barbosa, Stein et al. (Nat Neuro, 2020)
created by H Stein, Mar 2020
input: ../data/meantf_crossmatrix_*.npy
../data/crosstime.npy
output: ./crossmatrix_decoders.pkl
'''
from glob import glob
import numpy as np
import pickle
import sys
sys.path.insert(0,"../../helpers/")
impo... |
d9d052046f2eb0406c7c4a3efc8fbcf5be0432e5ac75dcc055c82e5bf86ef515 | Python | 2,584 | 99 | """
Author: Mingjian He <mh1@stanford.edu>
Testing functions for basic state-space models in somata/basic_models
"""
from codetiming import Timer
from test_load_data import _load_data # type: ignore
import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" # suppress a duplicate OpenMP library error with pytorch on Ma... |
edb620ad2cede177a4b8e82bec9ea58158f1fbe1a4544bbd48d0730209186b57 | Python | 2,584 | 74 | #!/usr/bin/env python3
import os
import unittest
import logging
import shutil
import tempfile
import ciftify.config
from ciftify.utils import run
import pytest
from pytest import raises
from unittest.mock import patch
def get_test_data_path():
return os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data'... |
6d7b6ac048b07cd5fdf02e7b3fe19a9159bcfe2317f472ed9a790b577025d54b | Python | 2,585 | 101 | config = {
'alpha_di': 0.000451125353657906,
'alpha_fb': 0.611510021981854,
'apical_time_constant_fb': 0.4318893018524684,
'beta1': 0.99,
'beta2': 0.999,
'dt_di_fb': 0.004294669581123954,
'epsilon': 3.210886729268312e-07,
'epsilon_fb': 1.0433715362737473e-08,
'feedback_wd': 0.001,
'k_p_fb': 0.010409666247193437,
'lr': ... |
99c4977b4e2a68b0465174731269786fc1f9f92f5585b2fd241e48b537b9bdcf | Python | 2,585 | 100 | import pdb
import torch
import torch.nn as nn
from DT_for_WDN_leak_localization.model_architectures.dense import DenseResNetLayer
from DT_for_WDN_leak_localization.model_architectures.transformers import DecoderLayer, EncoderLayer, PositionalEmbedding
from DT_for_WDN_leak_localization.models.parameter_encoder import (... |
e422050fda959a1a5f1c41b28b46ae78ef820f0dcc86184cb30f8979c1d07840 | Python | 2,589 | 73 | ''' adapted from https://github.com/all-umass/ManifoldWarping '''
from matplotlib import pyplot
# def show_alignment(X,Y,title=None,marker='o-',legend=True):
# '''plot two data sets on the same figure'''
# dim = X.shape[1]
# assert dim == Y.shape[1], 'dimensionality must match'
# assert dim in (1,2,3), ('can... |
8be1d1672a57486866da010ac7bd14760176f6c1a99c27eec4bfeba17f883bb3 | Python | 2,591 | 50 | """Resize module core functionality.
This module contains the extracted resize functions that implement
the core image resizing algorithms.
"""
from typing import Annotated, Optional, Tuple
from pydantic import Field, validate_call, ConfigDict
from cellprofiler_library.types import ImageGrayscaleMask, ImageAny, Imag... |
437e1422d205d2b507f8f3369334d8c41250d3835c48a17bbcb737be5d1bd620 | Python | 2,597 | 73 | from enum import Enum
from .._setting import Setting
from .._validation_error import ValidationError
class Choice(Setting):
"""A setting that displays a drop-down set of choices
"""
def __init__(
self, text, choices, value=None, tooltips=None, choices_fn=None, *args, **kwargs
):
"""I... |
4bc839e26886d50e98efb6e5fbf52328b9739869c5e615673f8ae27665bf43b9 | Python | 2,600 | 87 | """Fixtures for the bowtie2 command-line test suite (scripts/test/test_*.py).
Build first, then run from the repository root:
make -j4 allall
python3 -m pytest scripts/test
Set BOWTIE2_BIN_DIR to test binaries that were built somewhere else.
"""
import os
import random
import pytest
import bt2_helpers as h
de... |
563e3547ccdd614c055d1dda7404f7f6e838fc7756762ce4aa2a9c515ec4e2d5 | Python | 2,604 | 74 | from unittest import TestCase
import numpy as np
from .mnist import MNIST
data = MNIST('data/mnist', val_size=10000, background=True)
class MNISTTest(TestCase):
def test_init(self):
self.assertEqual(data.train.num_examples, 8000)
self.assertEqual(data.val.num_examples, 2000)
self.assert... |
61364cdc0152f215ddeeb000ebaf4ea83d57d85a31e31df375626bfb9d5fb06b | Python | 2,608 | 83 | # -*- 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... |
5f2f494474a5d0ac2f7ddda92c970e9b4bcba90f9ecaacbc1f2c49e625c32496 | Python | 2,610 | 66 | import argparse
import os
import sys
from importlib.metadata import version
from pygenometracks.tracksClass import PlotTracks
def parse_arguments(args=None):
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='Facilitates the ... |
9ae2f587ea6693a284abf2dc2d1c36553ae3a1ead5a52173506bb9f4a0e4b8fd | Python | 2,611 | 100 | # -*- coding: utf-8 -*-
"""
pysteps.visualization.spectral
==============================
Methods for plotting Fourier spectra.
.. autosummary::
:toctree: ../generated/
plot_spectrum1d
"""
import matplotlib.pylab as plt
import numpy as np
def plot_spectrum1d(
fft_freq,
fft_power... |
f668977077e5bde41e2f0fc3f7e4f2da8b4bdd71521fdfd0ed70b1059c5af671 | Python | 2,613 | 65 | #!/usr/bin/env python
from __future__ import print_function
from collections import OrderedDict
import re
regexes = {
'nf-core/rnaseq': ['v_ngi_rnaseq.txt', r"(\S+)"],
'Nextflow': ['v_nextflow.txt', r"(\S+)"],
'FastQC': ['v_fastqc.txt', r"FastQC v(\S+)"],
'Cutadapt': ['v_cutadapt.txt', r"(\S+)"],
'... |
db1026f0137d5986ca8cda0d1decbce785f183780548e038a31815ac1061f19a | Python | 2,616 | 67 | SHAPE=(2500, 12, 1)
def multi_conv2D(x, num_kernel, activation="relu"):
kreg = None
skip = Conv2D(num_kernel*3, 1, activation=None, padding="same")(x)
skip = BatchNormalization()(skip)
a = Conv2D(int(num_kernel), 1, activation=activation, padding="same", kernel_regularizer=kreg)(x)
a = BatchNormali... |
889fb88496f284c0f9aeb3d7af484670b6dc8b39c35af91ac2633713ce9bbc49 | Python | 2,617 | 75 | # -*- coding: utf-8 -*-
import os
import numpy as np
import xarray as xr
import pandas as pd
import matplotlib.pyplot as plt
from scipy.ndimage.filters import gaussian_filter
from allensdk.brain_observatory.ecephys.ecephys_project_cache import EcephysProjectCache
from allensdk.brain_observatory.ecephys.ecephys_sessi... |
62ceff23131eeb4cc43815290f6c3ce6bd052ec25b388ac0bddc7b475689051d | Python | 2,618 | 69 | import time
import pandas as pd
import torch
from sklearn.metrics import accuracy_score, roc_auc_score, f1_score, matthews_corrcoef
from DCiPatho_config import Config
from DCiPatho_network import DCiPatho
from Utils import cal
from Utils.combine_fna import combine
from Utils.tool import data_preprocess_for_... |
563be380d778d6a7cc7152a80ef3678b52481d53b39b7495ec1dab1ec3701e09 | Python | 2,619 | 108 | import numpy
import numpy.random
import pytest
import cellprofiler_core.image
import cellprofiler_core.measurement
from cellprofiler_core.constants.measurement import M_LOCATION_CENTER_X, M_LOCATION_CENTER_Y, M_LOCATION_CENTER_Z, \
M_NUMBER_OBJECT_NUMBER, FF_COUNT
import cellprofiler.modules.convertimagetoobjects... |
35eb3c36527e3de3bf59bfbf44826ad6b962278d3151c3df8265e70d95fda283 | Python | 2,621 | 72 | import numpy as np
# FROM SCILPY
def identify_shells(bvals, threshold=40.0, roundCentroids=False, sort=False):
"""
Guessing the shells from the b-values. Returns the list of shells and, for
each b-value, the associated shell.
Starting from the first shell as holding the first b-value in bvals,
the... |
858053a0c72ce0df288d1ba99e1565ae67507253289d64696d0886fdb52843bb | Python | 2,621 | 85 | from __future__ import division
from __future__ import print_function
import sys
from six.moves import xrange
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.feature_selection import VarianceThreshold
from sklearn.feature_selection import SelectKBest, RFE, f_classif
from sklearn.svm i... |
f63992e1a28d7230f17778d01925228a9f7b24c2eea083d8745fc7b7f22d710d | Python | 2,623 | 80 | #!/usr/bin/env python3
# Copyright 2021 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
#
# Unless required by applicable law o... |
ba34a3aeb69376b507dd846dc825f88cea11ff1e988f552a0ee620f740b606c1 | Python | 2,627 | 60 | import numpy as np
from tabulate import tabulate
def pad_to_next_multiple_of_32(img):
"""
Pad each dim to multiples of 2^5 = 32
Assuming the encoder is a ResNet with 5 blocks, it max pools images 5 times.
To avoid runtime errors images are padded to the the next multiple of 32. Separately for each dime... |
468e6117fd02b3542d982638d1e5cbe37b4cc9f339f8fb01adf9dfacd60f1643 | Python | 2,628 | 79 | from abc import abstractmethod
from functools import partial
from ..optimization import NewtonMethod
from ..utils.time import get_dt
class BayesianSpikingModel:
@abstractmethod
def sample(self, t, stim, stim_h=0, full=False):
if not full:
v, r, mask_spikes = None, None, None
... |
644b3a5945f43b446b99ffa89f5c3b48fdc8698891bc2615c370f7096607b5cc | Python | 2,630 | 67 | """
pysteps.cascade.interface
=========================
Interface for the cascade module.
.. autosummary::
:toctree: ../generated/
get_method
"""
from pysteps.cascade import decomposition, bandpass_filters
_cascade_methods = dict()
_cascade_methods["fft"] = (decomposition.decomposition_fft, decomposition.r... |
1f8267107d70d93572851c630ce3d027efeee3dd8db5298b60cb58854a073f66 | Python | 2,635 | 75 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 13 17:34:20 2021
@author: andrey
"""
import time
import os
import pickle
import scipy.optimize
class _Callback:
def __init__(self, out_path):
self.__out_path = out_path
self.__cnt = 1
def call(self, x, f, accept):
... |
1e6b1acef628fce62a780ed04d7c04f2c4bf7bb3db3b229b6fb607d6dc1501cc | Python | 2,640 | 72 | # -- coding:utf-8 --
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, BatchNormalization, Dropout
from tensorflow.keras import losses, optimizers
import scipy.io as sio
import numpy as np
def load_mat_data(file_name):
... |
e4664d0c480e0dbfa4a39902c232415e37df02c023d87c74117aac6e54cd95e0 | Python | 2,641 | 84 | import matplotlib.cm
import numpy
import skimage.color
from ...preferences import get_default_colormap
from cellprofiler_library.functions.object_processing import size_similarly as _size_similarly
from cellprofiler_library.functions.object_processing import crop_labels_and_image as _crop_labels_and_image
def crop_la... |
15b2d8a5de2cc5810ba46f8067176aef3d512da4161c123fb82d2540ed32e5d0 | Python | 2,646 | 88 | from __future__ import division
import os
import sys
from six.moves import xrange
import numpy as np
from .dataset import Datasets, Dataset
from .download import maybe_download_and_extract
try:
import cPickle as pickle
except ImportError:
import pickle
URL = 'http://www.cs.toronto.edu/~kriz/cifar-10-python... |
df582469cc19892e5b2df3cbb424fd5886e75e564bb0188b08c5e72072f98d20 | Python | 2,647 | 87 | """test_cellprofiler - test the CellProfiler command-line interface
"""
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from tests.frontend.modules import get_test_resources_directory
if hasattr(sys, "frozen"):
ARGLIST_START = [sys.executable]
else:
ARGLIST_START = ["-m", ... |
636d22b4f07fd7458c67ebe36113ba763e76fb37eb2cdbc4af471ac767e6e063 | Python | 2,656 | 67 | from Levenshtein import distance as levenshtein_distance
from Bio import SeqIO
import csv
import sys
import os
def calc_sim_corrected(seq1, seq2):
min_len = min(len(seq1), len(seq2))
abs_diff = abs(len(seq1) - len(seq2))
lev_dist = levenshtein_distance(seq1, seq2) - abs_diff
return (1 - lev_dist / mi... |
673140d9ed629855d2e90700a95f71bb25ffda24f6c3525c28e75a22d2503fbf | Python | 2,656 | 88 | # -*- coding: utf-8 -*-
import os
from tempfile import NamedTemporaryFile
import pysteps
from pysteps import load_config_file
minimal_pystepsrc_file = """
// pysteps configuration
{
"silent_import": false,
"outputs": {
"path_outputs": "./"
},
"plot": {
"motion_plot": "quiver",
... |
93367d6f9472dff1d18a62487efc70581aaa476acfe785ce437365a618455bb6 | Python | 2,657 | 55 | from deeprank.models.amino_acid import AminoAcid
alanine = AminoAcid("Alanine", "ALA", "A")
arginine = AminoAcid("Arginine", "ARG", "R")
asparagine = AminoAcid("Asparagine", "ASN", "N")
aspartate = AminoAcid("Aspartate", "ASP", "D")
protonated_aspartate = AminoAcid("Protonated Aspartate", "ASH", "D")
cysteine = Amino... |
c15ec4ba7782da2aa45736ab3e51e3480827267f74ec555311f536b4a8e9a348 | Python | 2,657 | 104 | config = {
'lr': 0.0010924329072635147,
'alpha_di': 0.0017723664900917734,
'k_p_fb': 0.0713515876863314,
'alpha_fb': 0.39857902933813705,
'feedback_wd': 0.1,
'time_constant_ratio_fb': 0.005870467170683501,
'dt_di_fb': 0.0011421804343878519,
'apical_time_constant_fb': 0.1404664564483908,
'lr_fb_init': 0.0001524121535306... |
12d2b30ac16b42cbab2a0f617350e1ed927ac2e2086879f6c5018302c5198fe1 | Python | 2,662 | 77 | import pandas as pd
from src.tasks.metrics import get_reg_metrics
import wandb
import matplotlib.pyplot as plt
import seaborn as sns
import torch
def save_feature_importance(df, num_features):
if df is not None:
df.sort_values(['importance'], ascending=[False], inplace=True)
df['importance'] = df[... |
4255cdb6921265457eb0bd037ea6e534081330d94a482ad6be4ab577af8959e3 | Python | 2,663 | 87 | import pyb
import math
# Random functions and classes -----------------------------------------------
max_rand = 1 << 30 # Largest number output by pyb.rng()
def random():
#Return a random float x such that 0 <= x < 1.
return pyb.rng()/max_rand
def withprob(p):
# Return a random boolean tha... |
daa7858497740eb36abc846a9805a59bcd6b12feb24c6e364dd1bb76b10dab20 | Python | 2,666 | 75 | from unittest import TestCase
import numpy as np
from numpy.testing import assert_equal
import scipy.sparse as sp
from .distortion import (perm_adj, perm_features, filter_adj, filter_features,
gray_color_threshold, degree_threshold,
area_threshold)
class DistortionT... |
44dc7e6af6ae96d69c8f464755ba0ae9af548a97a516cba10b71d3edf0df99d0 | Python | 2,667 | 77 | # Implementation of Collapsible Pane container
# importing tkinter and ttk modules
import tkinter as tk
from tkinter import ttk
class CollapsiblePane(ttk.Frame):
"""
-----USAGE-----
collapsiblePane = CollapsiblePane(parent,
expanded_text =[string],
co... |
e1bc4d00ce3899cbcc2f397b8f24674a9a337aef2ec75da16005ad8ead9b8899 | Python | 2,667 | 82 | # script that allows you to start multiple similuation in parallel independently of eachother
# Requires input from user, i.e., path to file that contains paths to input of simulation
import os
import subprocess
import sys
import time
Narg = len(sys.argv)
path_tostoreFinished = "."
numprocess = 2 # should not go hig... |
9177d966358afbf1be0221b18c5d22c10548fdc98afd08f03b56fcd6c79b402d | Python | 2,668 | 87 | import numpy as np
from netCDF4 import Dataset
from scipy import signal
import os, pathlib
from scipy import stats
#==========================================================
# Set main directory
#==========================================================
hdir = '/home/jhkim/model/deep_detection'
#===================... |
96c1fcd401d5400c146d9ba08d960a38c78a43c892640a0fdc77fcd17703f259 | Python | 2,671 | 80 | import numpy as np
from netCDF4 import Dataset
import os, pathlib
from scipy import stats
#==========================================================
# Set main directory
#==========================================================
hdir = '/home/jhkim/model/deep_detection'
#============================================... |
36982306816c0ca8a197129693a5bbe5c52a503cf3c7b5bdfed1cd50630df9cf | Python | 2,672 | 99 | # -*- coding: utf-8 -*-
import numpy as np
import pytest
from pysteps.nowcasts.lagrangian_probability import forecast
from pysteps.tests.helpers import get_precipitation_fields
from pysteps.motion.lucaskanade import dense_lucaskanade
def test_numerical_example():
""""""
precip = np.zeros((20, 20))
precip... |
a2be683f395c258413f6717208fff9cad00a7ef73ad2c270b26ac7455b11ce3b | Python | 2,672 | 84 | import prov.model as pm
from .Core import Core, getUUID
from ..core import Constants
class Session(pm.ProvActivity, Core):
"""Class for NIDM-Experimenent Session-Level Objects.
Default constructor uses empty graph with namespaces added from NIDM/Scripts/Constants.py.
Additional alternate constructors for... |
32fc198d3519d757bc211266a1b1db65f833a5393a2e89923d2cdee6cb7c42c1 | Python | 2,673 | 95 | # -*- 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... |
5be41abbf4de0dabb667c2c50cee886331a6e92f6ff83d67994ae46e4106b51e | Python | 2,675 | 95 | # -*- coding: utf-8 -*-
#
# model_comparison.py
#
"""
Work function for model comparison experiments.
"""
__author__ = "Severin Langberg, Ahmed Albuni"
__email__ = "langberg91@gmail.com, ahmed85@gmail.com"
from typing import Dict
import numpy as np
from pandas import DataFrame
from tqdm import tqdm
from .comparis... |
da3c99208726fd5f4d3f12bf9117ff5ea9f52ef987b0b99d78bd4ee75999c58b | Python | 2,677 | 95 |
import numpy as np
import matplotlib.pyplot as plt
# Define parameters
Cm = 1 # membrane capacitance
g_Na = 120 # maximum sodium conductance
g_K = 36 # maximum potassium conductance
g_L = 0.3 # leak conductance
E_Na = 50 # sodium reversal potential
E_K = -82 # potassium reversal potential
E_L = -84.4 # leak ... |
b1b160c248de42d41934689f088a0c6b4e578b2426cf7f0eb6881da153baad7b | Python | 2,678 | 74 | import numpy as np
import trimesh
import trimesh.sample
from megsimutils.arrays import BarbuteArraySL
from megsimutils.viz import _load_anatomy
# Downsample the head mesh to this number of vertices to speed up
# computations
_NVERT_HEAD_HEAD = 700
def _map_barbute_to_scalp_point(point, R_inner, R_outer, head_pts, d... |
ec26f130f2951be132b1774541370ee9d186d7ef3c8b9811b1598ec339b9d41a | Python | 2,678 | 71 | import torch.nn as nn
def assemble_architecture():
"""
Function assemble_deeper_CNN() assembles a CNN deep learning model with the next architecture:
1. Conv layer - 10 kernels of size 5, stride 1, no padding
2. Max-pool layer - kernel size 2, stride 2
3. ReLU non-linearity
4. ... |
2fe762db80081f6440d21cacaea3084447c213387e33bd33ec06721bc3a59b34 | Python | 2,679 | 63 | #!/usr/bin/env python3
"""
Copyright 2017 Ryan Wick (rrwick@gmail.com)
https://github.com/rrwick/Filtlong
This script was used to get read length/identity stats from a minimap2 PAF alignment file. I used
it to make the scatterplot/histogram figures in the Filtlong README. It uses a particularly strict
definition of r... |
48925bf43e582576db61fba5db84eee3d84ebc0bb6eb01e8eaf4ef423a6a65d6 | Python | 2,683 | 76 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
68d49baee74194c9b2e0e2460f83dc1e6468cd7fcae957af3d52e095cb5311d4 | Python | 2,683 | 92 | #
# nate berry
# 2023-04-01
#
# ---------------------------------------------------------
# NOTES:
# - this script calcualtes the false nearest neighbors for
# the EEG and COM data
# - plotting is performed in <03_state_space.py>
# - check the excel export to ensure it's not commented out
# if runnin... |
3a8dae3dfdc6734e31657de1e1463b4da31db68b2ad9be74dbc06d76cee0d7a4 | Python | 2,684 | 63 |
import scipy.ndimage as ndimage
import skimage.measure as measure
import numpy as np
def _keep_connected_binary(binary_mask, voxel_vol, min_volume=None, keep_only_largest=1):
if keep_only_largest==0:
return np.zeros(binary_mask.shape, dtype=binary_mask.dtype)
mask_ind, num_features = measure.label(... |
0f23500a05740f26405233334fc44c7183da0f51a94cd65dae092808b40fd339 | Python | 2,685 | 51 | import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
ls_r2=[]
ls_mae = []
ls_rmse ... |
d631b67f80a497f2be26dedcc272415e2bd76f0193703090368c45616b3e25b3 | Python | 2,685 | 87 | import torch
import torch.nn as nn
class DecodingModule(nn.Module):
def __init__(
self,
input_channels,
output_channels,
conv=nn.Conv2d,
conv_kwargs=None,
norm=nn.BatchNorm2d,
norm_kwargs=None,
act=nn.LeakyReLU,
act_kwargs=None,
netwo... |
f3ef354eb3fc0261371135c8bd050a143d6f98a341cb695f6dcd509ccd5e1883 | Python | 2,685 | 106 | """
Shared mophology methods used by multiple modules
"""
import numpy
import scipy.ndimage
import skimage.morphology
def dilation(x_data, structuring_element):
is_strel_2d = structuring_element.ndim == 2
is_img_2d = x_data.ndim == 2
if is_strel_2d and not is_img_2d:
y_data = numpy.zeros_like(x... |
09cb73870648b7d9c1af4263a387c397dc6dd8d5ebff5b3163290c16db75faf8 | Python | 2,692 | 72 | import os
import numpy
from pdb2sql import pdb2sql
import pandas
from deeprank.features.FeatureClass import FeatureClass
from deeprank.operate.pdb import get_pdb_path
def _get_c_alpha_pos(pdb_root, variant):
pdb_path = get_pdb_path(pdb_root, variant.pdb_ac)
db = pdb2sql(pdb_path)
try:
if varia... |
b27cb0751ced372218afac273f91e0a2a3812f205d4812e79b038b91227e1c5f | Python | 2,692 | 88 | import os
import sys
import threading
from importlib.util import find_spec
def find_python():
if hasattr(sys, "frozen"):
if sys.platform == "darwin":
app_python = os.path.join(os.path.dirname(os.environ["ARGVZERO"]), "python")
return app_python
return sys.executable
def find... |
0557d31be9ea127ba71119b323222347c59521c0c4599e87e27a66275fd8e283 | Python | 2,693 | 126 |
BASE_SCRIPT="""
#!/usr/bin/env bash
VERBOSE=0
INTERP="Linear"
DTYPE="default"
IN_DTYPE=0
INTERP_TRANSFORM_LIST=("Linear" "NearestNeighbor" "BSpline" "MultiLabel" "GenericLabel")
ANTS_DTYPES=("char" "uchar" "short" "int" "float" "double" "default")
IN_DTYPES=("0" "1" "2" "3" "4" "5")
USAGE=$(cat <<-END
Usage: $... |
732f4a652879e8376f0160e2d222caef4800f547e28dc3b6f7b02abb50506d38 | Python | 2,699 | 78 | from glob import glob
import os
from typing import Dict, Set
from pdb2sql import pdb2sql
import numpy
import h5py
from deeprank.config import logger
from deeprank.features.FeatureClass import FeatureClass
from deeprank.operate.pdb import get_residue_contact_atom_pairs, get_pdb_path
from deeprank.parse.dssp import par... |
ca180a13bb2218d4cb094f35593f63b14b064a55a4ed72996bbbf1a7c7706799 | Python | 2,700 | 83 | from scipy import io
import pandas as pd
from time import time
import torch
from torch.nn.utils.rnn import pad_sequence
from atom3d.datasets import LMDBDataset
import atom3d.util.formats as fo
from ase.units import Ha, eV
element = {'H': 1, 'C': 6, 'N': 7, 'O': 8, 'F': 9}
def qm7():
data = io.loadmat('./qm7.mat'... |
69ab5727a8dd25a3401dafed3f31df0b580e42b3d824bb60f9c61e505be433fb | Python | 2,701 | 71 | import torch
import torch.nn.functional as F
from abc import ABC, abstractmethod
from GANDLF.utils.tensor import reverse_one_hot, get_linear_interpolation_mode, one_hot
from typing import Tuple
class AbstractPredictionTargetProcessor(ABC):
def __init__(self, params: dict):
"""
Interface for class... |
e39d07cc3cc9e9faafe27e22ee29c6da7a4ac9018bbbe53771562f9bd959d5c2 | Python | 2,701 | 88 | from unittest import TestCase
import numpy as np
from numpy.testing import assert_equal
import scipy.sparse as sp
from .spatial import (node_selection, neighborhood_selection, receptive_fields,
fill_features)
class SpatialTest(TestCase):
def test_node_selection(self):
points = np.a... |
dc6a2b69c35339a9bc84551f11620c0d6d5752328acbb238a29d580b4debcd3b | Python | 2,706 | 65 | import sys
from argparse import ArgumentParser
import logging
import pytorch_lightning
from pytorch_lightning.strategies import DDPStrategy
from models import SubtypeDataModule, ScanCLSLightningModule, ScanRegLightningModule
def run_measurement_job():
parser = ArgumentParser()
parser.add_argument("--model_a... |
3aa6cf30991a647e24aff87864a93f7d9e32a108fa319dfd10bf9d419fa0cbea | Python | 2,708 | 82 | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import pickle
import shutil
import tempfile
import time
import numpy as np
import torch
import torch.distributed as dist
from utils.parallel import get_dist_info
from utils import ProgressBar
def single_gpu_test(model,
data_loa... |
3b7b89d66e350547babc05c65332d8d95380df31f5b3ad7fe706d096d5c8031e | Python | 2,711 | 52 | from src.models.tabular.pytorch_tabular.base import PTBaseModel
from src.models.tabular.pytorch_tabular.repository.models.ft_transformer.ft_transformer import FTTransformerModel
from omegaconf import DictConfig
class PTFTTransformerModel(PTBaseModel):
def __init__(self, **kwargs):
super().__init__(**kwar... |
2c2a9cc3fdad0aa5e9058c9ba3a2dd016250bcfca3e7adc2eba21b0ca685bc66 | Python | 2,713 | 79 | #!/usr/bin/env python
__author__ = "Marc Steffen Seibel and Thomas Jochmann"
__email__ = "seibelsteffen@web.de"
"""
Requires: Dataframe stored at settings.ica_meta_dataframe_path. `preprocess_ica.py` produces this dataframe.
It contains positions of EKG windows which are to be extracted.
Returns:
Dataset ... |
0fdba936a2d963e9c3b1025b2db9ffad3f91716827cc655401423392db9ba2f5 | Python | 2,720 | 83 |
# python implementation of scAI optimization model
# import packages
import numpy as np
from numpy import linalg as LA
from numpy import matrix
def scAImodel_py(X1,X2,K,S=0.25,Alpha=1,Lambda=10000,Gamma=1,Maxiter = 500,Stop_rule=1,Seed=1,W1=None,W2=None,H=None,Z=None,R=None):
# parameters
d1 = X1.shape... |
e8c6a92057864f6b3a1afa26e27fd433aa0f6af7f28a6e09f51050e5b67bb947 | Python | 2,723 | 77 | from unittest import TestCase
import numpy as np
from numpy.testing import assert_equal
from .dataset import Datasets, Dataset
class DatasetTest(TestCase):
def test_init_dataset(self):
images = np.array([1, 2, 3, 4, 5])
labels = np.array([0, 1, 0, 1, 0])
dataset = Dataset(images, labels)... |
089e507e432ada98bec956cab48c3a5819345b9cffd53fa33ae19f6dac8e12f1 | Python | 2,724 | 80 | # -*- 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... |
4e16da4712b445212cf3b547c648a911db21ffee7ff71f2b0d9bab4e5063c101 | Python | 2,725 | 83 | from cellprofiler_core import workspace
from cellprofiler_core.constants.measurement import EXPERIMENT
from cellprofiler_core.constants.measurement import GROUP_INDEX
from cellprofiler_core.constants.measurement import GROUP_NUMBER
from cellprofiler_core.constants.measurement import IMAGE
from cellprofiler_core.constan... |
b4561d32a90c81847074a004dd06490d5301f84a5e6898dbb799aa989472e6ec | Python | 2,727 | 63 | import numpy as np
import sys
sys.path.append('..')
from functions.individual_variability import intra_individual_difference_score
from functions.individual_variability import intraIndividual_difference_plots
from functions.individual_variability import intraIndividual_difference_plots_sameHemi
# All individuals
wit... |
2c52360f24988a5492b3fa5594381d6bbcc8a054c8cb9e39acdfa443bf1ee074 | Python | 2,729 | 91 | #!/usr/bin/env python
from __future__ import print_function
import argparse
import markdown
import os
import sys
import io
def convert_markdown(in_fn):
input_md = io.open(in_fn, mode="r", encoding="utf-8").read()
html = markdown.markdown(
"[TOC]\n" + input_md,
extensions=["pymdownx.extra", "py... |
638f83cc0fd5fd2035952ec0a39eb6d4f109a135815431b781d15a019d8668be | Python | 2,729 | 103 | # -*- coding: utf-8 -*-
import matplotlib as mpl
mpl.use('agg')
from matplotlib.testing.compare import compare_images
from tempfile import NamedTemporaryFile
import os.path
import pygenometracks.plotTracks
ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"test_data")
tracks = """
[n... |
53fb83c9546105e674c0f8c3353477f732c1134a358566925378cb4a6ac29b3a | Python | 2,733 | 79 | import torch
from abc import ABC, abstractmethod
from GANDLF.losses import get_loss
class AbstractLossCalculator(ABC):
def __init__(self, params: dict):
super().__init__()
self.params = params
self._initialize_loss()
def _initialize_loss(self):
self.loss = get_loss(self.param... |
b947d130f8876065c7782ecd1bdd0eae85f3672ea5169d8471220c1b40c7d706 | Python | 2,733 | 81 | import tensorflow as tf
from .layer import GradientLayer,BoundaryGradientLayer
class PINN:
def __init__(self, network):
self.network = network
self.grads = GradientLayer(self.network)
self.boundaryGrad = BoundaryGradientLayer(self.network)
def build(self):
TYR_dmn0 = tf.k... |
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