sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
8e4428fcd080122fb79a2ae594558ddf99fa8c7680c44a2a48c9ff646cae6162 | Python | 1,174 | 32 | # -*- 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... |
a41d9c19d4a3950479ef7e66a6be2273ce1bc0a18d52dcff8a6abb1408eca607 | Python | 1,174 | 38 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
time = np.arange(0., 3.0, 0.001)
stim = np.ones(time.shape)
duration = 0.25
intervals = np.arange(0.25, 5., duration * 3)
base_freq = 5
for i, interval in enumerate(intervals):
stim[(time >= interval) & (time < interval + dura... |
d65cda1eef8e68e6b6ee2296db936abfb25edb9ed8355673406244b9611f3f0a | Python | 1,174 | 34 | """ Misc utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import argparse
import ast
import re
def natural_key(string_):
"""See http://www.codinghorror.com/blog/archives/001018.html"""
return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
def add_bool_arg(parser, nam... |
68694f51b56b1a9e45b7007c6985d3e903ce48b1ce1482f4185cfe0fb9fcc391 | Python | 1,175 | 48 | from qtpy.QtWidgets import QMessageBox
import numpy as np
def safe_toarray(tab):
"""
Returns an ndarray from a ndarray or a sparse matrix
"""
if not isinstance(tab, np.ndarray):
return tab.toarray()
else:
return tab
def error_json_format(show=True):
"""
Print a message er... |
bd45841d857810ef2e2c28f3e9a9d84c69e116bbc36a0d7fb32b35a6f9580b3b | Python | 1,176 | 35 | import prov.model as pm
from ..core import Constants
from ..experiment.Core import Core, getUUID
class ProcessSpecification(pm.ProvEntity, Core):
"""Class for NIDM-Workflow Process Objects.
Default constructor uses empty graph with namespaces added from
NIDM/Scripts/Constants.py. Additional alternate con... |
0f0eee65710765e283c0349cbf2a82211cc3d8e0e3a64c8f94c08a7d530e833f | Python | 1,178 | 40 | import torch.nn as nn
from modules.clones import clones
from modules.sublayer_connection import SublayerConnection
from modules.layer_norm import LayerNorm
class EncoderLayer(nn.Module):
"Encoder is made up of self-attn and feed forward (defined below)"
def __init__(self, size, self_attn, feed_forward, dropo... |
28d9bb7d98c5cde6862dc23f778cabb3e0ede0a548050c7aa05a40562aa7a093 | Python | 1,181 | 48 | import tensorflow as tf
from .layer import GradientLayer
class PINN:
def __init__(self, network):
self.network = network
self.grads = GradientLayer(self.network)
def build(self):
# equation input: (t, x)
TW_eqn = tf.keras.layers.Input(shape=(2,))
prefactor_eqn = tf... |
a0739379af63f9ec3d9587e02d4951ec9162a45a403b4212e5fcccc5223cd147 | Python | 1,183 | 37 | """
Command-Line script used to compute ARI (Adapted Rand-Index) error for input segmentations
It takes as parameters:
1. paths to segmentations to evaluate
2. path to ground truth segmentation
3. path to output file where results will be stored
Then:
1. opens the input segmentations and the ground truth segmentati... |
5f571d62510521bf07fce13e7f7b7a25a2ff0e0bce5cfa1b566119d594d15123 | Python | 1,186 | 22 | import subprocess
import os
def filterBackgroundSites(bedtools_path, sample_path, control_path, outfile):
output_folder = os.path.dirname(outfile)
if not os.path.exists(output_folder):
os.makedirs(output_folder)
sample_noHeader = os.path.join(os.path.dirname(sample_path), 'sample_noHeader.txt')
... |
6a672a6a0fbccbe14cfe891baad0b69f8c9522a567ecf742817fedef7b591316 | Python | 1,186 | 45 | import sys
import json
import pandas
import gemmi
csv_in, json_out = sys.argv[1:]
df = pandas.read_csv(csv_in)
ret = {}
for x in df.to_dict(orient="records"):
metal = gemmi.Element(x["Metal"]).name
ligand = gemmi.Element(x["Ligand"]).name
l = ret.setdefault(metal, {}).setdefault(ligand, [])
modes = []
... |
52f709eb970f6eea707f77cab93839edfd2dc8ac3bf11f2541ee4ff79d5d7160 | Python | 1,188 | 40 | # -*- 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... |
389ef657eeee8fb63fe503393ee5fbf0890dd882e4cd6b09e7430969735e7fb5 | Python | 1,192 | 42 | import os
import subprocess
try:
from importlib.metadata import PackageNotFoundError, version
except ImportError: # pragma: no cover
from importlib_metadata import PackageNotFoundError, version # type: ignore
def _get_version_from_git() -> str:
"""Derive the package version from git tags when package m... |
5052dd885a61ac3a716a0ceefc4a73374420d2ea051eb11ba32c97d067974078 | Python | 1,193 | 65 | # Copyright (C) 2026 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
import pyperf
UNIT_TET_VERTICES = np.array(
(
(-np.sqrt(3)... |
ff5e1654322186daa98543790d42c9f48cef00b6a78665a213cac4e41afadd25 | Python | 1,194 | 37 | import tempfile
import os
import h5py
import numpy
from deeprank.domain.amino_acid import alanine, serine
from deeprank.features.dbnsfp import __compute_feature__, REVELSCORE_FEATURE_NAME
from deeprank.models.environment import Environment
from deeprank.models.variant import PdbVariantSelection
def test_dbnsfp():
... |
9c39ebf516430d1b8bbc24ec990ef21c70983e846773e7bc9e7de45699bc89e6 | Python | 1,196 | 37 | import os
import sys
import types
from ..preferences import get_plugin_directory
class PluginImporter(object):
def find_module(self, fullname, path=None):
if not fullname.startswith("cellprofiler.modules.plugins"):
return None
prefix, modname = fullname.rsplit(".", 1)
if prefi... |
b067e6877d746ebe4010679615274d4e15ab9d5d804a1055b1e244ed4e43944a | Python | 1,198 | 55 | import torch
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, out_dim=10, in_channel=1, img_sz=28, hidden_dim=256):
super(MLP, self).__init__()
self.in_dim = in_channel*img_sz*img_sz
self.linear = nn.Sequential(
nn.Linear(self.in_dim, hidden_dim),
#nn... |
53dfb88b33afa1c789babe361ef7d40bc9a5268fbfd975a0e5718762213b839f | Python | 1,200 | 51 |
class FASTQ_File(object):
def __init__(self, name, id = None):
from . import Database as db
self._name = FASTQ_File.clean_FASTQ_file_name(name)
self._reverse_complement = False
if id == None:
db.add_FASTQ_file(self)
else:
self._id = id
@prope... |
cb1c89054649c319a00b4a92eb1bad6ada9275f83e3db5ea8e7a4dc47f155e17 | Python | 1,201 | 47 | import json
import os.path
import pytest
from jsonschema.exceptions import ValidationError
from imskaper.validations import validate_config as validate
# import validations.validate_config as validate
path = os.path.join(
"tests", "test_config.json"
)
with open(path) as config_file:
config = json.load(confi... |
c30f29581abaaff0d8c1ef031cb64cda83d79893885302a8944350ff8c9d2b3a | Python | 1,202 | 39 | # -*- coding: utf-8 -*-
"""
Add Conv Block for MSDNet
"""
def add_conv_block(
Conv,
BatchNorm,
in_channels=1,
out_channels=1,
kernel_size=3,
dilation=1,
last=False,
):
"""
Adds a convolution block to a neural network. A convolution block consists of a convolutional layer and a batc... |
7cce68c9a881b9321a97c04aefe3d577a1739cf616a7b757e1289dcb64aaacdc | Python | 1,204 | 31 | import cv2
import numpy as np
def overlay_map(image, gradmap, rescale_max=None, split_signs=False):
image = np.squeeze(image)
image = (image - np.min(image)) / (np.max(image) - np.min(image))
image = (image * 255).astype(np.uint8)
image = np.dstack([image, image, image])
gradmap = np.squeeze(grad... |
9b549655cfbc8e545f821e77a5a92d7b6f5e030df18e0a0f5abe84c94ed39854 | Python | 1,204 | 32 | import csv
import math
import os
import sys
# extracting the secondary structure from the STRIDE output files.
input_path = "../../data/PSICOV/secondary_structure/" #input multiple sequence aligments path
output_path = "../../output/output_PSICOV/second_struc.csv"
#list of STRIDE files:
protein_list = os.listdir(in... |
1784e633118a69cd6e69054e1d9ee8d7dd1cb792efbaca1a89974718dd97c97a | Python | 1,206 | 47 | from loadDataSet import importLabels
def getClass (classLine):
clas = classLine.split(': ')[-1].replace('\n', '')
return clas
labels = importLabels()
start = 1500
fileLog = 'serial-20230126-091049.log'
fileRes = 'STMResults.csv'
flog = open(fileLog, 'r')
fres = open(fileRes, 'w')
fres.write("Correct label, ... |
7f7e2666122366482b93b9775a68a713c81e19b5b735a1e3bfaaca80dddcb187 | Python | 1,207 | 33 | import torch as th
from torch import nn
import torch.nn.functional as F
from torch.autograd import Variable
from depthwise import DepthwiseNet
from torch.nn.utils import weight_norm
import numpy as np
class ADDSTCN(nn.Module):
def __init__(self, target, input_size, num_levels, kernel_size, cuda, dilation_... |
bb0ac02f2cb8a7c8746bca7e3525386d1ad1f07472913f4968c5da1c5df1eafc | Python | 1,207 | 57 |
from attr import dataclass
@dataclass
class AEMetricLogger:
recon_loss: float = 0
latent_loss: float = 0
counter: int = 0
def update(
self,
recon_loss: float,
latent_loss: float,
) -> None:
self.recon_loss += recon_loss
self.latent_loss += latent_lo... |
82aa03b851b4f62cf879efc431e05fc7e6a434eba0e00bd8969620c84dfc817a | Python | 1,212 | 47 | import pandas as pd
from rdkit import Chem
from rdkit import DataStructs
import matplotlib.pyplot as plt
import molvs as mv
from rdkit.Chem import RDKFingerprint
import numpy as np
from tqdm import tqdm
import os
from multiprocessing import Pool, cpu_count
import math
import time
import itertools
import datetime
from ... |
5a314bcf58cfa8d88131dac97999fa82ed1896f023fda3ce17b380634ae60cd9 | Python | 1,214 | 39 | #!/usr/bin/env python
from samplebase import SampleBase
import time
class GrayscaleBlock(SampleBase):
def __init__(self, *args, **kwargs):
super(GrayscaleBlock, self).__init__(*args, **kwargs)
def run(self):
sub_blocks = 16
width = self.matrix.width
height = self.matrix.height... |
0b6d87a80104c2e5ec7bbc00223c31176e22ceceff60266ce2f6ecdebbc9df81 | Python | 1,215 | 38 | from __future__ import annotations
from dataclasses import InitVar, dataclass, field
from pathlib import Path
from types import TracebackType
from typing import IO, Any, Optional
@dataclass
class Reporter:
output: Optional[IO[str]] = field(init=False)
output_file: InitVar[str | Path | None]
def __post_in... |
25c11d88ef7c8628ffc98c0aef4482367934dd5280cf295a6f6e4eb7f7a7f086 | Python | 1,217 | 41 | import os.path
import weakref
import importlib.resources
images = os.path.join("data", "images")
resources = str(importlib.resources.files("cellprofiler").joinpath(images))
image_cache = weakref.WeakValueDictionary()
app = None
def get_builtin_image(name):
import wx
# If we're not running in a GUI enviro... |
c8d73046df9d47102dbdb9ba349e2af6bd1a28ff21ffd638492255ceadfcf497 | Python | 1,220 | 35 | import numpy as np
import numpy_groupies as npg
from .form_feature_extraction import FormFeatureExtraction
def extract_features(segmentation, image, form_features=None):
features = FormFeatureExtraction(segmentation).get_features(form_features)
group_idx = segmentation.flatten()
# Prepend mean color to... |
23e205eb4a9550856f3fae32f0de174f2081d1d67df5b4c747884d4d280975ce | Python | 1,221 | 35 | from .adabelief import AdaBelief
from .adafactor import Adafactor
from .adafactor_bv import AdafactorBigVision
from .adahessian import Adahessian
from .adamp import AdamP
from .adamw import AdamWLegacy
from .adan import Adan
from .adopt import Adopt
from .lamb import Lamb
from .laprop import LaProp
from .lars import La... |
2f9fe066d40a76bb2f536aa5209ac42fe2dab77f65fafb151e390d12a7526cba | Python | 1,221 | 37 | import prov.model as pm
from .Acquisition import Acquisition
from ..core import Constants
class AssessmentAcquisition(Acquisition):
"""
Default constructor, creates a session activity and links to project object
:param session: a session object
"""
# constructor
def __init__(self, session, ... |
4c0e3511ab41bb0019e0385f4e7abcb09273c5da2ff5f04dfe3ee0f61bcc25ea | Python | 1,221 | 55 | import os
import pathlib
import click
import cellprofiler_core.commands
CONTEXT_SETTINGS = dict(auto_envvar_prefix="COMPLEX")
class Command(click.MultiCommand):
def get_command(self, context, name):
try:
name = f"cellprofiler_core.commands._{name}_command"
imported_module = __i... |
da36ac40c2cdee6b5b996e82cec4dc1bfe369bb453338792c9ed5efd396dd53d | Python | 1,226 | 24 | # -*- 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... |
e28e45e009ecf06a8e48ed7650d2e61d2f70698a8277dcc43bb0f7d9a2847a8c | Python | 1,226 | 38 | from ...external.tifffile import TiffFile, imsave, parse_kwargs
def imread(fname, dtype=None, **kwargs):
"""Load a tiff image from file.
Parameters
----------
fname : str or file
File name or file-like-object.
dtype : numpy dtype object or string specifier
Specifies data type of arr... |
8b2d60e8436174f2bf6af85cba0cdac0badba9abf72f68c3d0a6fd0d4e73a997 | Python | 1,227 | 44 | import torch
import torch.nn as nn
class LeNet(nn.Module):
def __init__(self, out_dim=10, in_channel=1, img_sz=32):
super(LeNet, self).__init__()
feat_map_sz = img_sz//4
self.n_feat = 50 * feat_map_sz * feat_map_sz
self.conv = nn.Sequential(
nn.Conv2d(in_channel, 20, 5... |
6ed22966d9eb6c97dc09902a7a300b976b3c69639afa8cb755c2b4a5ef8622af | Python | 1,228 | 28 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import csv
import gzip
import os
import scipy.io
import pandas as pd
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--directoryPath", dest="dir", help="Directory path for files to convert from 10x to a gene expression matrix",type=str)
arg... |
e3e8cebf9a38f0852103d6d45dd72d6a58dbf102c13a1ff1c6ccf051e025d3fe | Python | 1,229 | 45 | import collections
class TrieNode:
# Initialize your data structure here.
def __init__(self):
self.children = collections.defaultdict(TrieNode)
self.is_word = False
class Trie:
def __init__(self):
self.root = TrieNode()
def insert(self, word):
current = self.ro... |
256ae172d0e23969ab21f3a97b631bbff0fe847dbda396f5ace878b5d8b18a9e | Python | 1,230 | 33 | """
Visualize the sampling volume, 2D or 3D. Read the data from the folder given
as a parameter. If the output folder given as a second parameter, also save
the images there.
"""
import sys
import numpy as np
from mayavi import mlab
from megsimutils import read_opt_res
from megsimutils.viz import _plot_anatomy, _plot_... |
257f0cf0d0b25a641ed4f5eeef83ad8f78c2207946781003277284bae0d9f824 | Python | 1,233 | 24 | import glob
import cv2 as cv
import numpy as np
from models.epu import EPUNet
from models.subnetworks import Subnet
from tensorflow.keras.metrics import AUC
test = [cv.imread(img) for img in glob.glob("../data/banapple/test/*.jpg")]
labels = np.asarray([1 if "apple" in img.split("/")[-1] else 0 for img in gl... |
6c3fafad3f000ccf53a7f6f62395cc4412700a0d1a71faf87f2c35353207b704 | Python | 1,233 | 30 | import pandas as pd
def reshape_to_multicolumn(data: pd.DataFrame) -> pd.DataFrame:
"""Split column names like `fast_030` into one level `fast` and an additional level `030`."""
cols = data.columns.get_level_values(level=0)
cols_paradigms = []
cols_configurations = []
for col in cols:
if no... |
7148a66f1fa7da31d6ed22fa1296279b7edc368ec7329156a944b92ae04b6fd5 | Python | 1,234 | 52 | """
Command-Line script used to preprocess input datasets
It takes as parameters:
1. path to input dataset
2. path to output dataset
Then:
1. opens the input dataset
2. stretches intensity values to span the [0,1] range
3. converts to 8-bit format
4. stores output dataset in specified path
"""
import h5py
import i... |
438ae8af4b19d09342fe5f4b7b3cb53eea1f3b6af722e0e85b813ee0176b960d | Python | 1,235 | 40 | import pdb
import torch
from DT_for_WDN_leak_localization.inference.noise import ObservationNoise
from DT_for_WDN_leak_localization.inference.observation import ObservationModel
from DT_for_WDN_leak_localization.network import WDN
from DT_for_WDN_leak_localization.preprocess import Preprocessor
class TrueData():
d... |
edb7e4033b90f71421730c0bff398ed0ea95c6ad5fd582346a3a2b9fe09f6db7 | Python | 1,235 | 31 | from ._workspace_view_row import WorkspaceViewRow
from ..artist import MODE_HIDE
from ..artist import MODE_LINES
from ..artist import MaskData
from ..utilities.workspace_view import bind_data_class
class WorkspaceViewMaskRow(WorkspaceViewRow):
"""A row of controls for controlling masks"""
def __init__(self, ... |
d0db8907f3dc6da0b72212a90a4fd70ea32da4179e7066c9478a20410e0d865c | Python | 1,237 | 40 | import torch
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class ExU(torch.nn.Module):
def __init__(
self,
in_features: int,
out_features: int,
) -> None:
super(ExU, self).__init__()
self.in_features = in_features
self.out_features = ... |
a2f59a1891159d84cc30fb17f5477f837e24883cbce0236d0d1176d37527ef21 | Python | 1,238 | 23 | import os
import argparse
parser = argparse.ArgumentParser(
prog='PolyA_comparison',
description='Execute circRNA-sponging pipeline in parallel for totalRNA and reference PolyA data to determine false positive circRNA detection rates')
parser.add_argument('-p', '--polyA', help='... |
9537d028d9697587ae50f1a70c30b5c75b46c66fd8ad721664991b8651da7604 | Python | 1,239 | 40 | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self,
classes,
alpha=None,
gamma=2,
size_average=True,
device=None):
super(FocalLoss, self).__init__()
if... |
fc5cf8dcc78fc4f1abed4f9790cd7eb6fbd47974a7e8bfc6cb54ee58eee8b5eb | Python | 1,239 | 39 | from ._setting import Setting
class HiddenCount(Setting):
"""A setting meant only for saving an item count
The HiddenCount setting should never be in the visible settings.
It should be tied to a sequence variable which gives the number of
items which is the value for this variable.
"""
def _... |
2747c48e101b875e88102866ff7867078addbf2195117ba5b89e9d1000ca2dc0 | Python | 1,240 | 31 | # -*- 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... |
0110252118dd71bd31c7cbeb5a3fdcca97f57073433bb35c8193677aae80b413 | Python | 1,242 | 34 | from unittest import TestCase
import numpy as np
from numpy.testing import assert_equal
from .form_feature_selection import FormFeatureSelection
from ..datasets import MNIST
from .algorithm import slic_fixed
data = MNIST('data/mnist')
slic = slic_fixed(num_segments=100, compactness=5, max_iterations=10)
class Form... |
3120889c17a4adbc76b29910e7ae5fad3339e494eab56100e9c3cdc0dfad07ea | Python | 1,242 | 47 | class Conf():
'''
classdocs
'''
def __init__(self, neutype="LIF", task="MusicLearning", dt=0.1):
'''
Constructor
'''
self.neuron_type = neutype
self.task = task
self.dt = dt
self.notesMap = {}
self.GenreMap = {}
self.RunTimeState =... |
f40fb037a4d0aeb964027677999332f85200bac939191810b3cbbe82950d545b | Python | 1,246 | 67 | from sc3D import SpatialOmicArray
import numpy as np
em = SpatialOmicArray("data/data_test.h5ad", store_anndata=True)
em2 = SpatialOmicArray(
"data/DLPFC.h5ad",
tissue_id="layer_guess",
pos_id="spatial",
array_id="z",
z_space=30,
store_anndata=True,
)
def test_sc3D():
assert len(em.all_c... |
89a15c1b57f57cae5d63744240366db1aed6d74a64a9e02cabfe0e5dd88d0c02 | Python | 1,247 | 40 | from unittest import TestCase
import time
from .queue import PreprocessQueue
from .mnist import MNIST
from ..pipeline import preprocess_pipeline_fixed
from ..segmentation import slic_fixed, extract_features_fixed
data = MNIST('data/mnist')
segmentation_algorithm = slic_fixed(
num_segments=100, compactness=5, max_... |
121c4207ffd0ba28c05ce4cfc8f22db3768fd505b7be01f3141afe6a50d01bdd | Python | 1,250 | 33 | #!/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
... |
22cb1a61afba87fb1ad81fb4188079b3385f1e3633bb5768f3f5661cbdd0f91d | Python | 1,251 | 36 | import cv2
import matplotlib as mpl
import matplotlib.cm as cm
import numpy as np
def disp_img_to_rgb_img(disp_array: np.ndarray):
disp_pixels = np.argwhere(disp_array > 0)
u_indices = disp_pixels[:, 1]
v_indices = disp_pixels[:, 0]
disp = disp_array[v_indices, u_indices]
max_disp = 80
norm =... |
345ae8ff14cad96e92a853aa1b9f1e484da8d6acf9b258aadbdbd3a2e119e161 | Python | 1,251 | 44 | import csv
import os
from pathlib import Path
from shutil import copytree, rmtree
import pytest
from click.testing import CliRunner
from ..cli import main
def test_reproschema2redcap(tmpdir):
runner = CliRunner()
with runner.isolated_filesystem():
# Copy necessary test data into the isolated filesy... |
5c2300778fc8cd52b350d3e44188b0e6a3119a988dcf581fc8fc8a9c4adbd806 | Python | 1,253 | 32 | import random
from itertools import combinations
from torch.utils.data.sampler import SequentialSampler, RandomSampler
from torchmeta.utils.data.dataset import CombinationMetaDataset
__all__ = ['CombinationSequentialSampler', 'CombinationRandomSampler']
class CombinationSequentialSampler(SequentialSampler):
def... |
ecdd505974d3fe6afff737c83ac7f769c5bd847ea9398d2d9bbc0b471894cf8c | Python | 1,253 | 42 | import re
from deeprank.models.forcefield.top import TopRowObject
class TopParser:
VAR_PATTERN = re.compile(r"([^\s]+)\s*=\s*([^\s\(\)]+|\(.*\))")
LINE_PATTERN = re.compile(
r"^([A-Z0-9]{3})\s+atom\s+([A-Z0-9]{1,4})\s+(.+)\s+end\s*(\s+\!\s+[ _A-Za-z0-9]+)?$")
NUMBER_PATTERN = re.compile(r"\-?[0-... |
0650f00c0b8f087d0e7f2aef09519556e520f72a5223c113e507f71767f97704 | Python | 1,254 | 45 | # Copyright (C) 2025 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 pathlib
import subprocess
import sys
import tempfile
import pyperf
def main() -> None:
bench... |
1c1b275d404f9a841964722b5811382f68ad44874e683ea6129a9fcc2f241439 | Python | 1,256 | 24 | import multiprocessing
if __name__ == "__main__":
# important to enable freeze_support when creating an executable (i.e. via pyinstaller):
# https://docs.python.org/3.9/library/multiprocessing.html#multiprocessing.freeze_support
#
# allows multiprocessing on Windows after freeze:
# https://github.c... |
d1f426c453e8c9e64016dcb6fb9b5d2a9bc57a4c46b206f6201f49c1747fe4f8 | Python | 1,256 | 46 | import numpy as np
import matplotlib.pyplot as plt
# Define parameters
dt = 0.1 # time step
t = np.arange(0, 100, dt) # time vector
tau = 10 # membrane time constant
Rm = 1 # membrane resistance
Vth = 1 # spike threshold
Vreset = 0 # reset potential
V0 = 0.5 # initial potential
g_leak = 0.1 # leak conductance
... |
f556d8fee881732e0700c0f9c24c32e7e1a68374cf314613eef2847036bf451e | Python | 1,256 | 63 | #!/usr/bin/env python2
import sys,glob,gzip,os
# axt format: http://genome.ucsc.edu/goldenPath/help/axt.html
if len(sys.argv)!=3:
print '<chr size file> <output file> Run under the dir of gzipped Axt files, presumably one for each target chr but that doesn\'t matter'
sys.exit()
chrsize={}
with open(sys.argv[1]) a... |
29ad2a5b7a5d415d5d924e78faadd29d5126eee47baf8cc084a12189ede841df | Python | 1,258 | 40 | import numpy as np
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 unband_matrix(banded_matrix, symmetric=True, lower=True):
"""
Assumes banded_matrix... |
5efb2dbba3fd26e4bee3bd6c909a67bb07f7e7bef25694f94b7edc9d9949c227 | Python | 1,259 | 44 | import os.path as osp
import pytest
from ptsa.extensions.edf import EDFFile
@pytest.fixture
def edffile():
"""Using a section of EEG data obtained from
https://www.teuniz.net/edf_bdf_testfiles/
"""
here = osp.realpath(osp.dirname(__file__))
fname = osp.join(here, 'data', 'eeg.edf')
edf = EDF... |
468a35f61ee8cb5c19598a2d678ee8faf0a6e95ca7fc2a45db7f123abea6d2f1 | Python | 1,260 | 46 | import numpy as np
import matplotlib.pyplot as plt
# Define parameters
dt = 0.1 # time step
t = np.arange(0, 100, dt) # time vector
tau = 10 # membrane time constant
Rm = 1 # membrane resistance
Vth = 1 # spike threshold
Vreset = 0 # reset potential
V0 = 0.5 # initial potential
g_leak = 0.1 # leak conductance
... |
bdbeea0d33db2708149479620a1f621837557522ac98e2659e934b855a42f56f | Python | 1,260 | 45 | import json
class HeadlessConfiguration:
"""
This class functions as a configuration set for headless runs.
The default config class is wx-based, which means we have to replace it
here with this psuedo-replacement that has the same interface
"""
def __init__(self):
self.__preferences ... |
eb1e42ca811c2d45a9dfdeb86e573eda3325bce5f19e63e1ffcec26073efcabd | Python | 1,262 | 51 | from traitlets import TraitType
class BoundingBox(TraitType):
default_value = None
def get(self, obj, cls=None):
value = super().get(obj, cls)
if value is not None:
return ",".join(str(v) for v in value)
return value
def validate(self, obj, value):
if isinst... |
6d918fb9f6e63f9fa696c6c0779a663222a25bd6425101311af8c7e547606272 | Python | 1,266 | 37 | import torch
import torch.nn as nn
import torch.autograd as autograd
class GradientLayer(nn.Module):
def __init__(self, model):
super(GradientLayer, self).__init__()
self.model = model
def forward(self, x):
x.requires_grad_(True)
c = self.model(x)
# Compute first orde... |
5574fa1817e8c2d179b8d2e49078ba1e3254f6653e0d3c8a5c071f177aaf3cbd | Python | 1,267 | 43 | import pytest
from click.testing import CliRunner
from GANDLF.entrypoints.debug_info import new_way, old_way
from . import CliCase, run_test_case
# 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 called
# with proper args for di... |
fb482e4511dd68373639b3062cd4585337cb4768852472e5f7c78c3f227263fa | Python | 1,270 | 41 | ## Adapted from: https://github.com/cyh1112/GraphNormalization
import torch.nn as nn
import torch
class GraphNorm(nn.Module):
"""
Param: []
"""
def __init__(self, num_features, eps=1e-5, affine=True, is_node=True):
super().__init__()
self.eps = eps
self.num_features = num_... |
68d421ffdf631ff01e0f1da075b4d3a4c05b8ad0a2070cfaef83c143cfb55b50 | Python | 1,272 | 54 | import sys
import importlib.resources
import cellprofiler.icons
cp_image = None
def get_cp_image():
"""The CellProfiler icon as a wx.Image"""
global cp_image
if cp_image is None:
import wx
try:
cp_image = cellprofiler.icons.image_cache["CellProfiler"]
except KeyErr... |
2e0e8822d2d3746d04659aaaeba9114c858089e70479ea403b895c7a8faccfed | Python | 1,273 | 39 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 11 10:48:57 2018
@author: saskiad
"""
import numpy as np
import pandas as pd
import core
from allensdk.core.brain_observatory_cache import BrainObservatoryCache
import scipy.ndimage as ndi
save_path = core.get_save_path()
manifest_path = core.get_... |
c16101fd7192be9e37821e6ae49c43fa1cb9a2481a165b71e37203b6195fa469 | Python | 1,273 | 40 | #!/usr/bin/env python
import time
from samplebase import SampleBase
from PIL import Image
class ImageScroller(SampleBase):
def __init__(self, *args, **kwargs):
super(ImageScroller, self).__init__(*args, **kwargs)
self.parser.add_argument("-i", "--image", help="The image to display", default="../..... |
802ca03dad3f183bfddfa78144637b821e15b4459d1dc145da4ab7c5dbf9977a | Python | 1,274 | 53 | import csv
import glob
import math
import os
import scipy.io as sio
import matplotlib.colors as colors
import numpy as np
import scipy.io.wavfile as wavfile
import scipy.ndimage
import scipy.special
import skimage
import skimage.filters
import skvideo.io
import torch
from PIL import Image
from torch.utils.data import ... |
cf18f5f8b331543b35602c0421e46db8e3f77394f8cc317b91ee2510953e5bc0 | Python | 1,274 | 43 | from enum import Enum
from centriole.bg_compensate import MODE_AUTO, MODE_DARK, MODE_BRIGHT, MODE_GRAY
class IntensityChoice(str, Enum):
"""Choice of how to calculate the illumination function"""
REGULAR = "Regular"
BACKGROUND = "Background"
class RescaleIlluminationFunction(str, Enum):
"""The illumin... |
d96e8e294f409a84a6523fe9246e62bb379d945d1ce14c211c64c7ca81394916 | Python | 1,275 | 34 | # -*- coding: utf-8 -*-
"""
Created on Fri Feb 16 10:32:36 2018
@author: danielm
"""
import numpy as np
def trial_response_significance(sweep_events,num_shuffles=10000):
# inputs:
# sweep_events: 2D numpy array (num_sweeps X frames_per_sweep)
# outputs: p-values for each sweep (1D numpy... |
834f37d83b63535a9ab463b7fe2dd99c1d10ea365d56762b792cb813a5fff315 | Python | 1,278 | 26 | def test_neurons_type():
"""Test if NeuronX and NeuronY constructors are callable"""
from neurodynex3.neuron_type import neurons
a_neuron_of_type_X = neurons.NeuronX()
a_neuron_of_type_Y = neurons.NeuronY()
assert a_neuron_of_type_X is not None, "Constructor NeuronX did not return an instance"
a... |
e88f408df1d5825f158918de3c23379c2b661aae74c73b730fbbf5ce1fa496ca | Python | 1,278 | 59 | import numpy as np
import scipy.integrate as spint
def pendulum(x,t):
return [
x[1],
-np.sin(x[0]) - 0.05*x[1],
]
def lorenz_model(x, t):
return [
10 * (x[1] - x[0]),
x[0] * (28 - x[2]) - x[1],
x[0] * x[1] - 8 / 3 * x[2],
]
def MM_Kinetics(x, t):
ret... |
b75995dfef500faac5d6f192903fce665f632075946bae7858c982ffff3925b3 | Python | 1,280 | 61 | ''' adapted from https://github.com/all-umass/ManifoldWarping '''
''' helper functions for generating synthetic data '''
import numpy as np
from numpy.random import randn, uniform, normal
def add_noise(data, noise_stdv):
return data + normal(scale=noise_stdv,size=data.shape)
# 2-d data generation functions #
def... |
d393b5585ddf6eb5c3a12e1187ec0aff7adeef86ea9a90e413f815c820973de7 | Python | 1,280 | 34 | import numpy as np
import torchvision.transforms as transforms
np.random.seed(0)
def simclr_transform(size, s=1):
color_jitter = transforms.ColorJitter(0.8 * s, 0.8 * s, 0.8 * s, 0.2 * s)
return transforms.Compose([transforms.RandomResizedCrop(size=size),
transforms.RandomHoriz... |
709888bd5d334fe6f482bad79ef166f0d38e5e05f670f806a3273c7dd9fd7181 | Python | 1,281 | 56 | from .wrap_torch import (
sgd,
asgd,
adam,
adamw,
adamax,
# sparseadam,
rprop,
adadelta,
adagrad,
rmsprop,
radam,
nadam,
)
from .wrap_monai import novograd_wrapper
from .thirdparty import ademamix_wrapper, lion_wrapper, adopt_wrapper
global_optimizer_dict = {
"sgd"... |
398351a4fc18e8b9915883afde80e983beb97b1e0a9f9f252e0250d362517247 | Python | 1,282 | 38 | '''
==========
Test the Python implementation of CS-CORE IRLS
==========
'''
import numpy as np
import time
from CSCORE.CSCORE_IRLS import CSCORE_IRLS
# Test 1: a pair of independent data
# use ind_gene_pair from the R CS-CORE package
counts = np.genfromtxt('data/ind_gene_pair_counts.csv',
deli... |
a0ea6923518f04e876f4c0a36d9ad37ea4b8fa914dfbc8d8eceec6b0e992d9b2 | Python | 1,282 | 39 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 12 14:39:14 2020
@author: nozais
"""
import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="Functionnectome",
url="https://github.com/NotaCS/Functionnectome",
version="0.1.1",
... |
9444105caef0ac332b0863bfb51e8bcea59115c5eba227786f07721f094a2308 | Python | 1,284 | 37 | import tempfile
import os
import h5py
import numpy
from deeprank.features.secondary_structure import __compute_feature__, SECONDARY_STRUCTURE_FEATURE_NAME
from deeprank.models.variant import PdbVariantSelection
from deeprank.models.environment import Environment
from deeprank.domain.amino_acid import arginine, alanin... |
12f368fa1e335d869987548db462c1067a8dc50202aea80ff61fc1692f84cc7f | Python | 1,286 | 48 |
from breaststudies.models import BasicModel
import breaststudies.models.monai_mods as nets
class SwinUNETR(BasicModel):
def __init__(self,
in_ch,
out_ch,
roi_size,
spatial_dims = 3,
patch_sizes = ( (1,2,2), (1,2,2), 2, 2),
... |
9b23f1350985a038f39fceb576b7b454bfb423bfea934c23226825067ad01e12 | Python | 1,286 | 43 | """Run Rscript with the local core, preserving the published dependency pin."""
from __future__ import annotations
import os
import re
import subprocess
import sys
import tomllib
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
def main() -> int:
rust = ROOT / "packages/r/src/rust"
manif... |
794ab027805c41a94ed634d0e10daa09aebc828c9a31a66231943858315732bd | Python | 1,288 | 35 | # ImageFile must be initialized before ImagePlane
from ._image_file import ImageFile
from ._image_plane import ImagePlane
from ._image_set_channel_descriptor import ImageSetChannelDescriptor
from ._listener import Listener
from ._pipeline import Pipeline
from .dependency import Dependency
from .dependency import ImageD... |
87a6991e26381773e387cd9a5646b1763ed2bf3ee1ede768e8380c0a2db74317 | Python | 1,288 | 57 | """Configuration classes for flask/heroku."""
import os
from dataclasses import dataclass
from flask.cli import load_dotenv
load_dotenv()
@dataclass
class Config:
"""Configuration class
This class contains all of the global configuration variables needed for
the AFIDs validator. Ideally, variables suc... |
fe1e05227e485e02a1f1e728311978fd3a814296d8e25caf99edb912e9e07b6a | Python | 1,288 | 32 | ######nii2npy
import numpy as np
import os
np.set_printoptions(threshold=np.inf)
from dipy.io.image import load_nifti, save_nifti
from PIL import Image
path_src = "data_prepro"
path_tgr = "data_prepro_tmp"
### each group was converted to npy file independently; The directory tree can be see in "data_origial... |
3765ddbc58fef4ffa5fbfdc4e489ea4817a73b416b8bbdf5649f51fd84044598 | Python | 1,290 | 45 | """
Author: Mingjian He <mh1@stanford.edu>
Testing function for loading data in tests/test_*.py
"""
import pathlib
from importlib.util import find_spec
from scipy.io.matlab import loadmat
def test_anchor_func():
""" Anchor function for locating this .py file """
return
def get_test_path():
""" Find th... |
475d07e4085a14f603c4d496ab9b29f2734768d300549c08a50cce9eb28d8e19 | Python | 1,290 | 36 | builtin_modules = {
"align": "Align",
"groups": "Groups",
"images": "Images",
"loaddata": "LoadData",
"metadata": "Metadata",
"namesandtypes": "NamesAndTypes",
}
all_modules: dict = {}
svn_revisions: dict = {}
pymodules: list = []
badmodules: list = []
do_not_override = ["set_settings", "create_... |
5229f8e0f0e502ce0e3845444ec4d7dc6edf72c72b9acf167cb7700f597a8db4 | Python | 1,290 | 32 |
"""
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 the rights
to use, copy, modify, merge, publish, ... |
be4b8321e5fb96b48ad36be23b39ae71acdc2095a46820b9d545d7bb475ba5d7 | Python | 1,294 | 18 | from devices._poke import _Poke
class Six_poke():
# 6 IR beams, each with LED.
def __init__(self, ports, rising_event_1 = None, falling_event_1 = None,
rising_event_2 = None, falling_event_2 = None,
rising_event_3 = None, falling_event_3 = None... |
e6cefec1d2e28c466a336fb50534677e83f4741f761e1cbd3ca526a6d7f420f9 | Python | 1,294 | 32 | """Training task abstractions for timm.
This module provides task-based abstractions for training loops where each task
encapsulates both the forward pass and loss computation, returning a dictionary
with loss components and outputs for logging.
"""
from timm.loss import LOSS_TYPES, MULTI_LABEL_LOSS_TYPES, create_clas... |
f81f64a1c9d3c8a8186c31fe2e13f85c8f9645bbb48856fcdbc9409a9621d6da | Python | 1,294 | 46 | import torch
from .optimizer import SGD, Adam, AdamW
from .nc_runner import NCRunner
from .ncssl_runner import NCSSLRunner
OPTIMIZER_DICT = {
'SGD':SGD,
'Adam':Adam,
'AdamW':AdamW,
}
RUNNER_DICT = {
'NC':NCRunner,
'NCSSL':NCSSLRunner,
}
def build_optimizers(models, cfg):
"""Build an optimize... |
2fcaa592042cd6ba1710f1d3c12d04feb9ea17a809c71ffbe0a2c0401036461c | Python | 1,296 | 46 | import os
import pytest
import pysteps
from pysteps.tests.helpers import smart_assert
pytest.importorskip("pyproj")
pytest.importorskip("osgeo")
root_path = pysteps.rcparams.data_sources["fmi_geotiff"]["root_path"]
filename = os.path.join(
root_path,
"20160928",
"201609281600_FINUTM.tif",
)
precip, _, m... |
d6788658b794386e97d07b855b2a5b34ad55439c6a4b20b2d38cc93e3aacde77 | Python | 1,296 | 39 | from machine import UART
from array import array
import pyb
class Dfplayer():
def __init__(self, uart=1, timeout=1):
self.uart = UART(uart, 9600)
self.uart.init(9600, bits=8, parity=None, stop=1, timeout=timeout)
self.cmd_bytes = array('B', [0x7E, 0xFF, 0x06, 0, 0, 0, 0, 0, 0, 0xE... |
4a08247f30af7a8ae6e61571846d7e5aa8cb32cd2f79bde5e41b060a6a558ff3 | Python | 1,298 | 43 | import numpy as np
import sklearn.metrics
def cal_f1(x,y):
return 2.0 * x * y / (x + y + 1e-20)
def calc_evaluation(y_ans,y_pred):
def softmax(x):
return np.exp(x)/np.sum(np.exp(x),axis=0)
y_prob = [softmax(y) for y in y_pred]
y_pred = np.array([np.argmax(i) for i in y_prob])
y_... |
76f109a9cd85aa24df1d538b2479b80c8e172adb9b7b8ef38eb3c031e21a4d30 | Python | 1,298 | 43 | import os
import sys
import inspect
import filecmp
from itertools import islice
def checkFolderEquality(folder1, folder2):
"""
Given two folders, check if there are the same number of files,
that the names of files are the same, and that the files with the same
names are the same.
"""
folder1_... |
2ae85679a58a5f4566e1b491f0247a7f5e0be19d460e784ed08fc4d435f67539 | Python | 1,299 | 46 | import tensorflow as tf
import matplotlib.pyplot as plt
def serialize_array(array):
array = tf.io.serialize_tensor(array)
return array
def parse_tfr_element(element):
data = {
'label_multilabel': tf.io.FixedLenFeature([], tf.string),
'label_quantif': tf.io.FixedLenFeature([], tf.string),
... |
f627c1bb5b8ca7f8f0d74af792af8bef1dfb612f3b2ec54c4b2569a8e7797b4c | Python | 1,299 | 44 | """
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... |
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