sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
6a6e377fcfba891d26c32d5521dc6459cdb2b69e5d9a6725abcf1b4042fd2367 | Python | 903 | 32 | import os.path
import urllib.parse
from ..module import Module
from ..pipeline import ImagePlane as ImagePlane
from ..utilities.pathname import pathname2url
class SettingValidation(Module):
"""A fake module for setting validation"""
@staticmethod
def get_image_plane_details(modpath):
if modpath[... |
74f4a5f6ba98a42cc18a0d096118003de530dfba2b73a3f1df4b464745b8761a | Python | 903 | 34 | from contextlib import nullcontext
from functools import wraps
from typing import Callable, Optional, Tuple, Type, TypeVar, Union, overload, ContextManager
import torch
__all__ = ["LayerType", "PadType", "nullwrap", "disable_compiler"]
LayerType = Union[str, Callable, Type[torch.nn.Module]]
PadType = Union[str, int... |
65b35989098138835b86f88b2b0d22f8758ead1918bd5381a85f8bc7e6614a39 | Python | 904 | 31 | """
Test enhanced version 4 multi-scale topographic position (e4MSTP)
Copyright:
2025 Research Centre of the Slovenian Academy of Sciences and Arts
2025 University of Ljubljana, Faculty of Civil and Geodetic Engineering
"""
import rvt.default
import rvt.blend
# Prepare input data for test
dem_path = r"c:\test... |
6c9dc8608384101aee20637e1ded7c15367522b55c97cad268bffef5d66099c3 | Python | 904 | 29 | class Grid:
def __init__(self):
self.x_location_of_lowest_x_spot = None
self.y_location_of_lowest_y_spot = None
self.x_spacing = None
self.y_spacing = None
self.rows = None
self.columns = None
self.vert_lines_x = None
self.vert_lines_y = None
s... |
73c8dd921537a27024675820e9880b0f90bd09c26ffc4af76d5326c1305455c3 | Python | 904 | 36 | ''' adapted from https://github.com/all-umass/ManifoldWarping '''
''' miscellaneous utilities '''
import numpy
import scipy
import sys
import time
from ManinetCluster_python.distance import L2
def pairwise_error(A,B,metric=L2):
''' sum of distances between points in A and B, normalized '''
return metric.pairwis... |
93396725dcfca31e37dd3dc9e3e0baaf3cb3f179461faa1f11375d9104e9de81 | Python | 909 | 34 | from enum import Enum
# Decimation Method for Earth Mover's Distance
class DecimationMethod(str, Enum):
KMEANS = "K Means"
SKELETON = "Skeleton"
class Feature(str, Enum):
F_FACTOR = "Ffactor"
PRECISION = "Precision"
RECALL = "Recall"
TRUE_POS_RATE = "TruePosRate"
FALSE_POS_RATE = "FalsePo... |
62e4d02ea49ff487192f6c2deba6db5758ac637ef81027d6e2fd5c32cc6a047d | Python | 910 | 32 | class SRM:
def __init__(self, tau_s, tau_r, v_reset, v_th, v_init=0.0):
self.tau_s = tau_s
self.tau_r = tau_r
self.v_reset = v_reset
self.v_th = v_th
self.v = v_init
self.s = 0.0
self.r = 0.0
self.weight = np.random.normal(loc=0.0, scale=1.0)
s... |
60f14baca6615ad8ad9883119ffd890428557f5bc7501088cb4cb9e1e6ea5c97 | Python | 913 | 33 | default_settings = {
"lstm1_hidden_units": 512,
"lstm2_hidden_units": 256,
"lstm3_hidden_units": 128,
"trainer": {
"checkpoint_path": "../checkpoints/simulation_study",
"max_to_keep": 1,
"default_lr": 5e-4,
"memory": False,
},
"local_amortizer_settings": {
... |
2574c37d7d49b8fd30031071b507acb44b1e0ca66dc50384480fb7c1e964a2eb | Python | 915 | 36 | from sklearn.naive_bayes import GaussianNB
from loadDataSet import getDataset
from saveClassifiers import saveBayesClasificator;
import numpy as np
import time
X_train, X_test, y_train, y_test = getDataset()
gnb = GaussianNB()
gnb.fit(X_train, y_train)
start = time.time()
for i in range(10):
y_pred = gnb.predict(... |
d751092823bfc0c188786ffc2268524e1c35552f8726255b8a08333286067981 | Python | 915 | 23 | """
This model defines a custom flatten layer that is used in the model architecture.
--- Written by AlexKaz 07/07/19 ---
"""
import torch.nn as nn
class Flatten(nn.Module):
"""
Class Flatten is a wrapper class for a flattening layer that is necessary in a
classification / regression CNN model, right bef... |
e7b6ebf6a7949928d7b42a4ca03679235dc28b2a83b87894eefff8870a0f827b | Python | 916 | 36 | from traitlets import Bool, Integer, default
from mrHARDI.base.application import mrHARDIConfigurable
_flags = dict(
lsqr=(
{'DTIConfiguration': {'use_lsqr': True}}, "Use least-square error"
),
prediction=(
{'DTIConfiguration': {'predicted_signal': True}},
"Output the reconstructed... |
3b8aeec3a667b928b24562483685557dc936c54b9c485e3a1cf856d9b8b13fcf | Python | 917 | 41 | # 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 numpy as np
import overlap
import pyperf
UNIT_HEX = overlap.Hexahedron(
np.array(
(
... |
fdb70acf29173cdfccd33cae450b755dc0732243c481c197f30dd95aa6d1d0d9 | Python | 918 | 27 | import torch.nn as nn
import torch
import math
from torch.autograd import Variable
class PositionalEncoding(nn.Module):
"Implement the PE function."
def __init__(self, d_model, dropout, max_len=5000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
# C... |
1e8e352b9810a184e93f6e0213a1cd38eddecdb85a12031a89a26279930b01a8 | Python | 921 | 32 | import tkinter as tk
class CreateToolTip(object):
'''
create a tooltip for a given widget
'''
def __init__(self, widget, text='widget info'):
self.widget = widget
self.text = text
self.widget.bind("<Enter>", self.enter)
self.widget.bind("<Leave>", self.close)
def enter(self, event=None):
x = y = 0
x,... |
cf83426ed3f12433b91cf2e9f73430557260c3ce189606a6bc502110a4f852f9 | Python | 921 | 36 | import warnings
from .base import BaseEventReader, BaseRawReader, BaseReader
from .binary import BinaryRawReader
from .edf import EDFRawReader
from .eeg import EEGReader
from .events import CMLEventReader
from .hdf5 import H5RawReader
from .index import JsonIndexReader
from .localization import LocReader
from .netcdf ... |
1bb613b59a40319c5f406a337e11a1b060af86398301f6371e8962c5553bc2ca | Python | 922 | 32 | """ Eval metrics and related
Hacked together by / Copyright 2020 Ross Wightman
"""
class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
... |
f377329d57c2307cc22cc980d76ac16cba789d513258709eda7417a898a1fa6f | Python | 922 | 29 | import os
from tempfile import mkdtemp
from shutil import rmtree
import h5py
from nose.tools import ok_, eq_
from deeprank.models.variant import PdbVariantSelection, VariantClass
from deeprank.targets.variant_class import __compute_target__
from deeprank.models.environment import Environment
from deeprank.domain.amin... |
ce2e45c5c3163ed2ec60519d6ca69567b6c09614f6d637714fca286c08e7553c | Python | 923 | 36 | #!/usr/bin/env python2
import sys,os
if len(sys.argv)!=3:
print '<gappedpeak file> <track name>'
sys.exit()
infile,outfile=sys.argv[1:]
id=1
fout=open(outfile,'w')
with open(infile) as fin:
for line in fin:
lst=line.rstrip().split('\t')
fout.write('{0[0]}\t{0[1]}\t{0[2]}\tscorelst:[{0[12]},{0[13]},{0[14]}],i... |
35173cc1ca01388a580c24af98c7d3da179bf71e90c58311d8319a910796ddf1 | Python | 924 | 20 | from ._end_run import EndRun
from ._event import Event, CancelledException, PipelineLoadCancelledException
from ._file_walk_ended import FileWalkEnded
from ._file_walk_started import FileWalkStarted
from ._ipd_load_exception import IPDLoadException
from ._load_exception import LoadException
from ._module_added import M... |
6f12e05a943ebfd700d7a80e6a9b8eb67fd289a9c6d6c866a4c9bc4564ed60c2 | Python | 926 | 33 | from .datasets import build_transform, build_dataset, get_mnist_data, get_fashion_data, \
get_cifar10_data, get_cifar100_data, get_imnet_data, get_dvsg_data, get_dvsc10_data, \
get_NCALTECH101_data, get_NCARS_data, get_nomni_data
from .utils import rescale, dvs_channel_check_expend
from .hmdb_dvs import HMDBDV... |
3cabb3881a932e608cead626c03cc18ff8858346b68335e9ebb197ba4ff59f20 | Python | 927 | 25 | from ._range import Range
class FloatRange(Range):
"""A setting that allows only floating point input between two constrained values
"""
valid_format_text = "%s must be a floating-point number"
def __init__(self, text, value=(0, 1), *args, **kwargs):
"""Initialize an integer range
te... |
e467587f082a17e0bc47c8a723b31cba6bf181dc6b74a7d42b16ca8158e1229d | Python | 928 | 28 | import numpy as np
import matplotlib.pyplot as plt
interval = 0.001
time = np.arange(0., 1.0, interval)
freq = 5.
signal = np.sin(time * 2 * np.pi * freq) + np.sin(time * 2 * np.pi * 2 * freq) * 0.4
events = time[(signal > 0.5) & (np.roll(signal, -1) <= 0.5)]
fig = plt.figure()
fig.set_size_inches(5.5, 2.5)
ax = fig.... |
7438596d28e5533954e1f103484dced65821c02610411a7c9466c8ad5fe687a8 | Python | 930 | 49 | from .data.utils import (
onehot_seq,
pool_anndata,
SeqDataset,
get_splits,
shan_ent,
fdr,
get_group_name,
seq_list_to_conv,
align_conv_filters,
save_meme,
read_meme,
to_z,
get_activations,
create_alignment_df,
generate_alignment_graph,
plot_alignment_grap... |
d86458da5cba8471fdd8206ae24375f6377bb0655927a82f060284377bba33a8 | Python | 932 | 25 | import numpy as np
from scipy.stats import kstest
def log_likelihood_normed(dt, mask_spikes, u, r):
lent = mask_spikes.shape[0]
n_spikes = np.sum(mask_spikes, 0)
n_spikes = n_spikes[n_spikes > 0]
log_likelihood = np.sum(u[mask_spikes]) - dt * np.sum(r) + np.sum(n_spikes) * np.log(dt)
log_likelihoo... |
35bb4bc6d20ce1dbfeb96be2ce713e8d2bc83aceb82bba2de82b4ec19cfc1e98 | Python | 933 | 33 | from traitlets import Integer
from mrHARDI.base.application import mrHARDIConfigurable
class ParticleFilteringConfiguration(mrHARDIConfigurable):
def _validate(self):
pass
def serialize(self, *args, **kwargs):
pass
max_crossing = Integer(
None, allow_none=True,
help="Max... |
b2762dd267bb0d6e6914422be96e5a4573afc2be9a3ffb049023665a98c6b8eb | Python | 933 | 40 | import torch
import csv
import numpy as np
import matplotlib.pyplot as plt
# %matplotlib inline
def write_csv(file_name, data):
with open(file_name, "w") as f:
writer = csv.writer(f)
writer.writerows(data)
def read_csv(data_file_path):
data = []
with open(data_file_path, 'r') ... |
164c6c461178abc0d1d0bbd9e9c2689cf07ebf84783444bdc283a4b2f1a270c9 | Python | 935 | 33 | """Top-level pytest configuration.
Registers the sybil-based documentation-example collector so every
``.. code-block:: python`` block in ``docs/*.rst`` is executed as a
pytest case during a normal ``pytest`` run.
``pytest_collect_file`` is a session-wide hook: pytest only consults
it for files it actually walks, so ... |
a203e67be9278c3f745fea88fb1ec640a918ebaf6a7658643d5ee5189c25a871 | Python | 935 | 30 | import numpy as np
from pytorch_grad_cam.base_cam import BaseCAM
from pytorch_grad_cam.utils.svd_on_activations import get_2d_projection
class GradCAMElementWise(BaseCAM):
def __init__(self, model, target_layers, use_cuda=False,
reshape_transform=None):
super(
GradCAMElementWis... |
2d645c7cc46eb668b5719a64b85fe4828588294acf7de67fb8de34dbec76b5e7 | Python | 937 | 39 | class SettingEditedEvent:
"""Represents an attempt by the user to edit a setting
"""
def __init__(self, setting, module, proposed_value, event):
self.__module = module
self.__setting = setting
self.__proposed_value = proposed_value
self.__event = event
self.__accept... |
7ad8000fdfcd6a7d4b724d510c8dd4ba3953a85653af4c6d2b74c072fb44cbd9 | Python | 937 | 29 | import pathlib
import urllib
import logging
from cellprofiler_core.constants.image import FILE_SCHEME, PASSTHROUGH_SCHEMES
from cellprofiler_core.utilities.image import is_file_url
LOGGER = logging.getLogger(__name__)
def pathname2url(path):
"""Convert the unicode path to a file: url"""
lower_path = path.low... |
a3df6304677f7d339fa68d40cf48c6d33e20819bb8c886c3bff5e1605728b5b2 | Python | 937 | 25 | import torch
import torch.nn as nn
import torch.nn.functional as f
from torch.nn import init
class InterpolationLayer(nn.Module):
def __init__(self, size=None, scale_factor=None, mode='nearest'):
super(InterpolationLayer, self).__init__()
self.interp = nn.functional.interpolate
self.scale_... |
ca0fe2e6da36d8d77d8536a4691c68f542106516161696af2629a397924e371e | Python | 938 | 33 | from enum import Enum
class Scope(str, Enum):
GLOBAL = "Global"
ADAPTIVE = "Adaptive"
class OtsuMethod(str, Enum):
TWO_CLASS = "Two classes"
THREE_CLASS = "Three classes"
class Method(str, Enum):
OTSU = "Otsu"
MINIMUM_CROSS_ENTROPY = "Minimum Cross-Entropy"
ROBUST_BACKGROUND = "Robust Bac... |
c3613c2ed99cbfb1ae7752d33aa0f89992838d036fc4c5092e8f5e6f4c2fb191 | Python | 939 | 40 | import click
from ..__main__ import pass_environment
@click.group("pipeline")
@pass_environment
def command(context):
pass
@command.command("measurements", help="returns measurements extracted by the pipeline")
@pass_environment
def measurements(context):
pass
@command.command("run", help="executes the p... |
7e74df65b95621bd6f615cc18e3e0560ab96828aa28844f57106076d94a256be | Python | 940 | 26 | import numpy as np
class IFSFA:
def __init__(self, tau_m, tau_w, a, b, delta_T, v_reset, v_th, v_init=0.0, n_neurons=50):
self.tau_m = tau_m
self.tau_w = tau_w
self.a = a
self.b = b
self.delta_T = delta_T
self.v_reset = v_reset
self.v_th = v_th
self.v... |
c0063309de2dd3f5f45b6f26302827bdf30418dc2b9744e7c00f8b33b6b932e6 | Python | 942 | 36 | from ._schemas import InputParameters, OutputParameters
from argschema import ArgSchemaParser
import argparse
from . import __version__
class ArgSchemaParserPlus(ArgSchemaParser):
def __init__(self,*args,**kwargs):
parser = argparse.ArgumentParser()
parser.add_argument('--version', action='... |
cd48b23c87ee14506c575aa4ab69488db4da089fee052680d0155f9a05d5fb9f | Python | 943 | 27 | import torch
import torch.nn as nn
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
-----
Objects inherited from `MetaModule` are fully compa... |
2b08bf9a1879205ceaedcf7b8bd555a7e015e728dffc933995fc4667df38cf80 | Python | 945 | 37 | import numpy.testing
import skimage.color
import skimage.morphology
import cellprofiler_core.image
import cellprofiler.modules.medialaxis
instance = cellprofiler.modules.medialaxis.MedialAxis()
def test_run(image, module, image_set, workspace):
module.x_name.value = "example"
module.y_name.value = "MedialA... |
0337035161a6e24bd4b56b51ec242fa7de36c3e953ca76d8ca203962fca7f760 | Python | 947 | 35 | import torch
import torch.nn as nn
from models.aggregators import BaseAggregator
from models.aggregators.model_utils import GraphTransformerLayer
class LAMIL(BaseAggregator):
def __init__(self, num_classes):
super(BaseAggregator, self).__init__()
self.num_classes = num_classes
self._fc1 =... |
db644bd3fb24a6a9abd9e1f63792ebe592db658b8ba3631f84252b6b8b4f0836 | Python | 947 | 27 | import torch
def fasterrcnn_reshape_transform(x):
target_size = x['pool'].size()[-2 : ]
activations = []
for key, value in x.items():
activations.append(torch.nn.functional.interpolate(torch.abs(value), target_size, mode='bilinear'))
activations = torch.cat(activations, axis=1)
return activ... |
99001c20097d83f35a5d14b420ef00588ae029f475ed5ed4622acc0885ca35c9 | Python | 948 | 38 | import tensorflow as tf
class GradientLayer(tf.keras.layers.Layer):
"""
Custom layer to compute 1st and 2nd derivatives for Fick's law of diffusion
"""
def __init__(self, model, **kwargs):
"""
Args:
model: keras network model.
"""
self.model = model
... |
b2d449e318ab2f671c0acc260e2ab51b9a7dd69796db777a4be366925f3ae83f | Python | 948 | 32 | import numpy as np
# define SRM model
class SRM:
def __init__(self, tau_s, tau_r, v_reset, v_th, v_init=0.0, n_neurons=50):
self.tau_s = tau_s
self.tau_r = tau_r
self.v_reset = v_reset
self.v_th = v_th
self.v = v_init
self.n_neurons = n_neurons
self.s = np.ze... |
90b1c9bf2b91b94a4bee16fd4e4fd1faa1a27bbe2a1fdeeba5db38eec14d4e32 | Python | 949 | 30 | import numpy as np
class IFSFA:
def __init__(self, tau_m, tau_w, a, b, delta_T, v_reset, v_th, v_init=0.0):
self.tau_m = tau_m
self.tau_w = tau_w
self.a = a
self.b = b
self.delta_T = delta_T
self.v_reset = v_reset
self.v_th = v_th
self.v = v_init
... |
309d6e7c9190f9981f6164e0ee8e1ff88cfbf1ded2622cb137b313f1b9cd0dac | Python | 950 | 35 | from typing import Union
import torch
import torch.nn.functional as F
def get_modelbase_final_layer(final_convolution_layer: str) -> Union[object, None]:
"""
This function returns the final convolution layer based on the input string.
Args:
final_convolution_layer (str): The string representing t... |
f72ea69064282298a28a3e48bc847509eba9f6de3373f12df62c31db1c130323 | Python | 950 | 32 | """
ClinicalASL - Clinical Arterial Spin Labeling processing pipeline
Main pipeline script for processing ASL MRI data.
Repository: https://github.com/JSIERO/ClinicalASL
Author: Jeroen Siero
Institution: UMC Utrecht (University Medical Center Utrecht), The Netherlands
Contact: j.c.w.siero@umcutrecht.nl
Description:
... |
08542ac9ab9fa28f07b8c0d1f336a74efc367b6f6d9e9dbbc04d303efdb5c459 | Python | 951 | 44 | """
Command-Line script used to print segmentation information:
- number of segments"nsegments
- average segment size
- minimum segment size
- maximum segment size
- standard deviation in segment size
- average number of segment slices
- minimum number of segment slices
- maximum number of segment slices
- data type
-... |
98a361738577d432356aaab644793ad568986e8c4ac3f05028fc5d32363a751d | Python | 952 | 19 | import pyControl.hardware as _h
class Breakout_1_0(_h.Mainboard):
def __init__(self):
# Inputs and outputs.
self.port_1 = _h.Port(DIO_A='X1' , DIO_B='X2', DIO_C='X5', POW_A='Y8', POW_B='Y4',
POW_C='Y2', DAC=1, UART=4) # Jumper required to use either DAC/DIO_C or ... |
fb268086315e77af15d3adfc6070b86b14e1b0758296aa1beeb7da2cd7700863 | Python | 952 | 27 | import csv
import os
# count the number of seqs per gene alignment for each sequence similarity group:
groups = ["20","40","60","80","100"]
min_seqs = "10"
with open("../../output/output_PSICOV/seq_counts.csv", "w", newline='\n', encoding='utf-8') as counts_table:
writer = csv.writer(counts_table)
writer.writero... |
32d7f236a20369c6218bc1687754dea171baa478a0e81993c6ef904492572cfe | Python | 954 | 25 | from torchio.transforms.intensity_transform import IntensityTransform
from torchio.data.subject import Subject
from torchio.data.image import ScalarImage
class TemplateNormalizeBase(IntensityTransform):
"""The Base class to apply template-based normalization techniques.
Args:
target (str): The target... |
4f555b5895b893c655a99c1b63baab5a32ae6bd03d88831730fd27202c0a8211 | Python | 954 | 30 | import numpy as np
# define Izhikevich model
class Izhikevich:
def __init__(self, a, b, c, d, v_init=-65.0, u_init=0.0):
self.a = a
self.b = b
self.c = c
self.d = d
self.v = v_init
self.u = u_init
self.num_ops = 0 # initialize operation counter
... |
702a023440957a9533754f9d7debf2883923693c1f29c115d80d4193fd003727 | Python | 954 | 39 | from ase.db import connect
import torch
x, pos, y = [], [], []
# 80%的MD steps
with connect('../../iso17/reference.db') as conn:
# 可以设置select(limit=1)做测试
for row in conn.select():
x.append(torch.tensor(row['numbers']))
pos.append(torch.tensor(row['positions']))
y.append(torch.tensor(row... |
ee6505d7873eb21649fac43be2f1ddfd03316bb994419b3156c054f9b0d786ab | Python | 956 | 36 | import numpy as np
from helper import calcW
class Coeff(object):
def __init__(self,n,deltaT,Y_grid):
self.n = n
self.aA = np.zeros(self.n)
self.bA = np.zeros(self.n)
self.cA = np.zeros(self.n)
self.deltaT = deltaT
self.Y_grid = Y_grid
self.deltaY = Y_grid[1... |
d65bc879d1257126ef9c11d7ce832fd7da30b855e9f0f367cd1446f4733989f0 | Python | 958 | 37 | """Compatibility shim for the historical SWIG layout.
External lab code imports these symbols as::
from ptsa.extensions.circular_stat.circular_stat import (
circ_diff, circ_mean, compute_f_stat, compute_zscores, ...
)
In the pybind11 build the compiled module is ``_circular_stat``; this file
just re-... |
5b1e7fc38aec53ab988b7c5fbc50293475dc272187becaab58c226cd2c9ff8bb | Python | 961 | 35 | import numpy as np
from utils import save_images, load_ground_truth
filenames, _, _ = load_ground_truth("dataset/images.csv")
n = len(filenames)
def gen_randn_images(): # Gaussian distribution
images = np.zeros((n, 299, 299, 3))
for i in range(n):
img = np.random.randn(1, 299, 299, 3)
img = ... |
625befc502f9020cf378fe48ec4d1bb83eeb346c596e4218ff4684be6f115815 | Python | 964 | 31 | # this script crops the batch simulation list based on the log with succesful completes
# Results is to do list with uncompleted simulations
folder = 'SDC20221011_invitroCA1pyrs5/'
completeInputlist = folder+'runlistopt.csv'
completed_log = folder+'wsubrun.pbs.log52028414'
with open(completeInputlist,'r') as f:
in... |
6e35439ae65740fbc5e8a21c21d92a44164f8378e5a6c622fd31545c01fe8188 | Python | 965 | 60 | class TestFileCollectionDisplay:
def test_update_value(self):
pass
def test_update_ui(self):
pass
def test_set_update_function(self):
pass
def test_initialize_tree(self):
pass
def test_add(self):
pass
def test_modify(self):
pass
def test_... |
b08f5bbdf71ff015ed4b5284e9bbb298d5e72a3fb0140a6cc83ab1a302362f36 | Python | 965 | 38 | """empty message
Revision ID: 56d89145adbb
Revises: 7b4e00130929
Create Date: 2021-10-13 16:52:58.961413
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = "56d89145adbb"
down_revision = "7b4e00130929"
branch_labels = None
depends_on = None
def upgrade():
# ... |
49657606867340dee3ffeba7112c56bf3f9370a6155c0d7ac05693d646ec5528 | Python | 967 | 44 | __all__ = ['imread', 'imsave']
from ...util.dtype import convert
try:
import imread as _imread
except ImportError:
raise ImportError("Imread could not be found"
"Please refer to http://pypi.python.org/pypi/imread/ "
"for further instructions.")
def imread(fname, d... |
4a4ba46f2e71634b73f5e1a07ee3124d453f5ff000824a58d33ecb24619e3494 | Python | 967 | 31 | """Tools for working with NIDM-Experiment files"""
import click
from nidm.experiment.Query import GetMergedGraph
from nidm.experiment.tools.click_base import cli
# adding click argument parsing
@cli.command()
@click.option(
"--nidm_file_list",
"-nl",
required=True,
help="A comma separated list of NID... |
9d209a7891f0d5e2bcc07835202406f64351374abf2c9a7145b419bd66965df9 | Python | 967 | 32 |
#taken from this StackOverflow answer: https://stackoverflow.com/a/39225039#taken
import requests
def download_file_from_google_drive(id, destination):
URL = "https://docs.google.com/uc?export=download"
session = requests.Session()
response = session.get(URL, params = { 'id' : id }, stream = True)
t... |
00cb1bdab60542f5a833b2a4d8dc1b4ab536210ff74979144748c0b9a5023122 | Python | 968 | 32 | import json
import statistics
data = []
with open("datasets/random_clusters.txt") as fp:
lines = [line.strip() for line in fp.readlines() if line.strip() != ""]
for i in range(0, len(lines), 8):
ar = []
for j in range(i, i + 8):
ar.append(int(lines[j].split("#match: ")[1]))
... |
69d3dc0e835ffbb56ba8046963f740c97b1ad6363c7d8e2e091daf75caa1dfa4 | Python | 968 | 34 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 9 17:45:19 2018
@author: Nikolai M Chapochnikov
functions to import and preprocess the activity data, like the ORN data,
the LN data, the additional odors data, the EC50 etc.. and export them here
so that one can immediatly import the hdf5 when o... |
26b1dfe9eb45432097789dbe8f45c63ebf2725d73ae2a8f8136ba2545b1ed590 | Python | 969 | 27 | import sys
sys.path.append("../")
from api.music_engine_api import EngineAPI
import os
if __name__=="__main__":
musicEngine = EngineAPI()
musicEngine.cortexInit()
input_path = "../testData/"
#--------------------learning process---------------#
for composerName in os.listdir(input_path):
... |
4b4680f58e689cd4a9cb37b97f48ff352632fc87855cac3c941e66a7aec5e9ad | Python | 969 | 20 | import torch
from model import UNet, UNet_Pruning
from ptflops import get_model_complexity_info
in_channels = 3
out_channels = 3
origin_kernel_channels = 64
pruning_kernel_channels = 32
with torch.cuda.device(0):
model_origin = UNet(in_channels, out_channels, origin_kernel_channels)
model_pruning = UNet_Pruning(i... |
0f648173072dd36f8f5fdcf76cc7f0837843be8957b01c55524a55c8036e3fe9 | Python | 970 | 31 | X_AUTOMATIC_EXTRACTION = "Extract from image file headers"
X_MANUAL_EXTRACTION = "Extract from file/folder names"
X_IMPORTED_EXTRACTION = "Import from file"
X_ALL_EXTRACTION_METHODS = [
X_MANUAL_EXTRACTION,
X_IMPORTED_EXTRACTION,
]
XM_FILE_NAME = "File name"
XM_FOLDER_NAME = "Folder name"
DTC_TEXT = "Text"
DTC_... |
bb2de0b5fbbd7c91f87922f4acdff0c6e13d5e7385b574e8be88e1145e2614d4 | Python | 973 | 22 | import pytest
from pathlib import Path
from imctools.io.mcd.mcdparser import McdParser
class TestMcdParser:
def test_read_invalid_suffix(self):
with pytest.raises(FileNotFoundError):
McdParser('file.unsupported_suffix')
def test_read_imc_mcd(self, raw_path: Path):
mcd_file_path =... |
460d12d068382f259b41e608e401ee9352961d3f3d413202977c7af0279f3673 | Python | 974 | 27 | import numpy as np
def calc_granger_causality_likelihood_ratio_p(x, maxlag):
from scipy import stats
from statsmodels.regression.linear_model import OLS
from statsmodels.tsa.tsatools import lagmat2ds
from statsmodels.tools.tools import add_constant
# Taken from statsmodels.tsa.stattools.grangerca... |
5b7657df0f8c6af9970fa626c7efecdc15d376c97c98bc070baa48a57f279866 | Python | 974 | 29 | import numpy as np
# define IFSFA model
class IFSFA:
def __init__(self, tau_m, tau_w, a, b, delta_T, v_reset, v_th, v_init=0.0, n_neurons=50):
self.tau_m = tau_m
self.tau_w = tau_w
self.a = a
self.b = b
self.delta_T = delta_T
self.v_reset = v_reset
self.v_th ... |
fbc8f282dbb692ed9a9923d767de4065360fa2a9b0d4edf0587d9aa5cb1c7858 | Python | 974 | 25 | from pydantic import BaseModel, Field, model_validator
from typing_extensions import Self, Optional
class NestedTraining(BaseModel):
stratified: bool = Field(
default=False,
description="this will perform stratified k-fold cross-validation but only with offline data splitting",
)
testing: ... |
e165d61a2e8ce2b501d70afa9593244376a59e9823576311363049e889210a86 | Python | 976 | 30 | import torch
from breaststudies.data import BaseDataModule, BreastDataset, BreastDatasetLR, BreastDataset2D, BreastUKADatasetLR
class BreastDataModule(BaseDataModule):
Dataset = BreastDataset
label2rgb = torch.tensor([
[0,0,0], # Background
... |
f2ca3490953d0be1abc362b800774c15d695610b2123bdf03d144a0259e13e50 | Python | 981 | 36 | from nose.tools import eq_, ok_
from deeprank.models.variant import PdbVariantSelection
from deeprank.domain.amino_acid import alanine, valine, glutamine, glycine, methionine
def test_instance():
pdb_ac = "1AK4"
chain_id = "A"
residue_number = 10
wt_amino_acid = alanine
var_amino_acid = glutamin... |
ef70cd23d7ac0b1935959e0e48f116c602b752641ac0cfed0b06ffbf5544359f | Python | 985 | 22 | import numpy as np
def get_score_n_best_consecutive(selected_results, window, score_col):
selected_results = selected_results.groupby('lr').mean().reset_index().sort_values(by='lr')
best_idx = 0
best_score = 0
for i in range(len(selected_results) - window):
curr_score = selected_results.iloc[i:... |
9c6ec1d131affffc083198cdcafcbc6cde06527a3af2a5bf1dab8a08a7fab4bc | Python | 988 | 31 | """
ClinicalASL - Clinical Arterial Spin Labeling processing pipeline
ASL Look-Locker correction module.
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:
Little utility fu... |
f0ac1af3468a36cfa20e4628fe56014075bead9c4f6747d9d599a482b5d051c0 | Python | 989 | 23 | import logging
def set_log_level(log_level, subprocess=False):
"""Set the logging package's log level based on command-line options"""
try:
if log_level.isdigit():
logging.root.setLevel(int(log_level))
else: # str, e.g. str(logging.INFO) -> '20' OR 'INFO'
logging.root.se... |
3607329380c2bda7b70be193f7a67d956c845aac7c87fe1f583aff10f3d7ce4e | Python | 990 | 22 | # -*- 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... |
eac8a35f3cbff82d926a09db99c464ce3fb2388077600fb30eb854112794ebb1 | Python | 990 | 31 | # Copyright (c) OpenMMLab. All rights reserved.
# from .eval_hooks import DistEvalHook, EvalHook
from .eval_metrics import (accuracy, fuzziness, calculate_confusion_matrix, f1_score, precision,
precision_recall_f1, recall, support)
from .mean_ap import average_precision, mAP
from .multilabel_... |
7c89b9b4873d6cf1e1692068ed0def433bbc5729ae76628d0b47d57b4632d83d | Python | 992 | 30 | from setuptools import find_packages, setup
# requirements = []
# with open("requirements.txt", "r") as f:
# for line in f:
# requirements.append(line)
setup(
name="etspredict",
version="0.1.0",
description="code for edge time series + prediction project",
url="https://github.com/juaml/etspr... |
e5dd16dc2555f1a4556899fe5a4ff8dbc2232b99572b4062d0663677ee744c0f | Python | 992 | 21 | import numpy as np
from imctools.io.utils import reshape_long_2_cyx
def test_reshape_long_2_cxy(nrow=10, ncol=20):
"""Tests data reshaping based on some ad hoc test data
"""
chan_4 = 1
test_longdat = np.array([[i % ncol, int(i / ncol), i, chan_4] for i in range(nrow * ncol)])
img = reshape_long... |
ca852870bf8608f5cfb72c56d075f72ebaf99eb0f63093a61ea1d9aca503e6f5 | Python | 993 | 36 | import os
from pathlib import Path
from shutil import copytree, rmtree
import pytest
from click.testing import CliRunner
from ..cli import main
def test_reproschemaui2redcap(tmpdir):
runner = CliRunner()
with runner.isolated_filesystem():
# Copy necessary test data into the isolated filesystem
... |
7e83d9047b04d6ebd4ac22ec9747a209aeb86f9a01db2f21a20a8a9bf4f7e01d | Python | 996 | 21 | from ._medialaxis import medialaxis
from ._combineobjects import combineobjects
from ._expandorshrinkobjects import expand_or_shrink_objects
from ._fillobjects import fillobjects
from ._enhanceedges import enhanceedges
from ._threshold import threshold
from ._closing import closing
from ._opening import opening
from ._... |
95efbe42dbf7273dc3ce4bd315e16bc89233115d2bfecd8b5010f850ba953d5e | Python | 997 | 31 | from ..functions.file_processing import save_object_image_crops, save_object_masks
def savecroppedobjects(
input_objects,
save_dir,
export_as="masks",
input_image=None,
file_format="tiff8",
nested_save=False,
save_names={"input_filename": None, "input_objects_name": None},
volumetric=Fa... |
3b48d7542896b7ccccbd686284bff2110a7af48d29b4c2bef2f162a87260003e | Python | 1,000 | 25 | # -*- 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... |
6e95260b147d10c26e5b4c7c607472e965354629c50d43ebb1e11a394af6c44e | Python | 1,000 | 33 | # 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... |
9558fe82ad00a05550d4368801bdd0556cbd464178a80ef551e705acb0659812 | Python | 1,000 | 44 | import skimage.color
from ._url_image import URLImage
class MonochromeImage(URLImage):
"""Provide a monochrome image, combining RGB if needed"""
def __init__(
self,
name,
url,
series,
index,
channel,
rescale_range=None,
metadata_rescale=False,
... |
c7c76c844711c300faec07e0261588290465306732572061b98184ca8046a320 | Python | 1,000 | 56 | """
pysteps.utils.arrays
====================
Utility methods for creating and processing arrays.
.. autosummary::
:toctree: ../generated/
compute_centred_coord_array
"""
import numpy as np
def compute_centred_coord_array(M, N):
"""
Compute a 2D coordinate array, where the origin is at the center.... |
00e27025a325c89f573815c3e39ae93340b38e4d5faa0fe899a97bb5b81dff30 | Python | 1,002 | 29 | import ot
import torch
import numpy as np
def compute_sq_dist_mat(X_1, X_2):
'''Computes the l2 squared cost matrix between two point cloud inputs.
Args:
X_1: [n, #features] point cloud, tensor
X_2: [m, #features] point cloud, tensor
Output:
[n, m] matrix of the l2 distance between ... |
19b183aee85d2774c8f7f5604000c6cf2f4b409ec3f61e8cb7643309780d0b16 | Python | 1,002 | 37 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class LinReLU(torch.nn.Module):
__constants__ = ['bias']
def __init__(
self,
in_features: int,
out_features: int,
) -> None:
super(LinReLU, self).__init__()
... |
816af080aab3654faf45e1f152b62f05b1a38bb3d62348b7052758fb52001fe2 | Python | 1,003 | 36 | #!/usr/bin/env python
from samplebase import SampleBase
class GrayscaleBlock(SampleBase):
def __init__(self, *args, **kwargs):
super(GrayscaleBlock, self).__init__(*args, **kwargs)
def run(self):
max_brightness = self.matrix.brightness
count = 0
c = 255
while (True):
... |
83af4323a2f851681ec6f74409b56e3f9051d4b1558200c57f6c23ddc49747e0 | Python | 1,003 | 44 | """Datamonger Python reference client."""
from datamonger._api import cite_data, data_info, fetch_artifact, fetch_data, list_data
from datamonger._cache_management import cache_clean, cache_info
from datamonger._models import (
CacheCleanResult,
CacheEntry,
CacheInfo,
DataInfo,
DatasetData,
Dat... |
5fed7cafa09cf9f876b8d1d065fffa575fabe2e86bf59c72f8308655518e5f34 | Python | 1,004 | 37 | from ._monochrome_image import MonochromeImage
class MaskImage(MonochromeImage):
"""Provide a boolean image, converting nonzero to True, zero to False if needed"""
def __init__(self,
name,
url,
series,
index,
channel,
... |
74ed1f7b95fc71dde4a9a670409d94b7cd6fb116e29c46f7bbfa834d061a62e7 | Python | 1,004 | 38 | """empty message
Revision ID: eef891c5c783
Revises:
Create Date: 2019-11-14 13:59:33.903467
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = "eef891c5c783"
down_revision = None
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto gene... |
6e9a9b6dd41aa2e88c133044b964477c71d3e7b2487172d79856bdaafdd05fca | Python | 1,006 | 34 | import os
class ConfigBase(object):
RAW_DATA_PATH = "./data/raw/"
TRAIN_DATA_FILE = "train.tsv"
VALID_DATA_FILE = "valid.tsv"
UNSUP_DATA_FILE = "unsup.tsv"
SPLIT_RANDOM_SEED = 0
add_vocab_file = "data/add_vocab.txt"
class ConfigTripleClassification(ConfigBase):
RAW_DATA_FILES = ["UC.xlsx",... |
3aab3e05305ea3cb0759ee3a584180fd78f08dbf5a4fa24630a8ea77d4ad0839 | Python | 1,008 | 31 | import torch
def memory_usage(device):
"""
Check memory usage in pytorch
"""
print(torch.cuda.get_device_name(device))
print('Memory Usage:')
print('Allocated:', round(torch.cuda.memory_allocated(device)/1024**3,1), 'GB')
print('Cached: ', round(torch.cuda.memory_reserved(device)/1024**3,... |
c3650daa07d363968909cbb36a84132c6b995622328038ae98155dd77f8a43a8 | Python | 1,009 | 38 | import tempfile
import shutil
import os
from deeprank.models.metrics import TensorboardBinaryClassificationExporter, OutputExporter
test_entries = ["entry0", "entry1"]
test_outputs = [[0.0, 0.1], [2.03, -1.0]]
test_targets = [1, 0]
def test_tensorboard_class_output():
tmp_dir_path = tempfile.mkdtemp()
try... |
a6226c1c9eacf37ca4507d6bea6aa104df96f236d021505291bb15702b7156f7 | Python | 1,011 | 27 | # simple blending test
import rvt.default
import rvt.vis
import rvt.blend
layers = rvt.blend.BlenderCombination()
input_dem_path = r"test_data\TM1_564_146.tif"
layers.add_dem_path(dem_path=input_dem_path)
output_blend_path = r"test_data\test_blend.tif"
dict_arr_res = rvt.default.get_raster_arr(input_dem_path)
input_d... |
5153279484f82e294b0219d1c99c2410aca56a781ca3f4d33fef1c098bda4d56 | Python | 1,013 | 28 | import numpy as np
import tensorflow.keras.backend as K
def categorical_dice_coefficient(class_idx, name):
def dice_coef(y_true, y_pred):
yt_argmax = K.argmax(y_true)
yp_argmax = K.argmax(y_pred)
yt_class = K.cast(K.equal(yt_argmax, class_idx), K.floatx())
yp_class = K.cast(... |
48cfb0748864ca9c11291abfa2df9481acd5fb3c7d7a49398f0b94c148f901fe | Python | 1,015 | 43 | """Public semantic error taxonomy."""
from datamonger._errors import (
ArtifactIntegrityError,
ArtifactSelectionError,
ArtifactUnavailableError,
CacheError,
DatamongerError,
DecodedIntegrityError,
DecodeError,
OfflineError,
RegistryError,
RegistryIntegrityError,
RegistryOffl... |
b8c12def84e4cec2630e36fa6036f3f24eae08362ad5fc4233fef81c5449ca79 | Python | 1,017 | 32 | import os
import zipfile
import urllib.request
import dropbox
def download_and_extract(url, target_dir, tmp_name):
print("> Downloading segmentations..", flush=True)
try:
# Remove the zip file
os.remove(tmp_name)
except:
pass
try:
# Download the segmentat... |
d0a077439ea5d4e6fab0dbd380ccdd867c13c7834da450a4fabdb7dd5ed54f14 | Python | 1,017 | 33 | import json
import os.path
import random
import tornado.httpserver
import tornado.ioloop
import tornado.options
import tornado.web
import tornado.gen
import tornado.httpclient
from model import CNNPredictionModel, RobertaPredictionModel
from handler import IndexHandler
from tornado.options import define, options
def... |
fb00583f1480de543ebbfa8d56f6d61a7a26d455ae77570146a74a861f38587a | Python | 1,018 | 33 | from nose.tools import eq_
from deeprank.models.pssm import Pssm
from deeprank.models.residue import Residue
from deeprank.parse.pssm import parse_pssm
def test_parse_old():
with open("test/1AK4/pssm/1AK4.PSSM", 'rt') as f:
pssm = parse_pssm(f)
eq_(len(pssm), 310)
for residue, record in pssm.ite... |
5fe3e83fda5459a9bb28c20c0553dcfaaaeb8540d5715611f07d7eeb9eee80f8 | Python | 1,022 | 44 | import numpy as np
import scipy
from matplotlib import pyplot as plt
def F_function(theta):
return np.sqrt(3)/(4.0*np.pi)*np.log((1+theta**(1.0/3.0))**3/(1.0+theta)) + 3.0/(2.0*np.pi)*np.arctan((2.0*theta**(1/3)-1.0)/(np.sqrt(3))) + 1.0/4.0
def calcN_AB(r1,r2,r3):
"""
r1: radius of disk electrode
r... |
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