sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
bc87241f65daaf3633ac8888e549945b2feb10ff6bc4aee49e46485ebf604f6c | Python | 1,622 | 59 | from __future__ import annotations
from typing import TYPE_CHECKING, Hashable, Tuple
import traits.api
if TYPE_CHECKING:
from ptsa.data.timeseries import TimeSeries
__all__ = ['BaseFilter']
class BaseFilter(traits.api.HasTraits):
"""Base class for constructing filters."""
nontime_dims = traits.api.Tup... |
93d36147c94ffa0b65542b2000e8548c7a256495d48e0fa3e65327fad8ebead8 | Python | 1,623 | 40 | # coding=utf-8
""" an htmldialog with an ok button
"""
import wx
import cellprofiler.gui.html.htmlwindow
long_text = """\
This is a very important parameter which tells the module what you are
looking for. Most options within this module use this estimate of the
size range of the objects in order to distinguish them... |
45bad5d00725325c50729258a47185909ddde45afa9802ec8b0f4cc4c358b26f | Python | 1,624 | 61 | from e2vid.model.model import *
from collections import OrderedDict
def load_model(path_to_model, return_task=False):
print('Loading model {}...'.format(path_to_model))
raw_model = torch.load(path_to_model)
arch = raw_model['arch']
try:
model_type = raw_model['model']
except KeyError:
... |
da5e4ec87550a17caceb29aaeff6eaeacb447694ab99cb9341a74ccd01b58592 | Python | 1,624 | 37 | # Experiment class and constants used in specifying experiments.
class Experiment:
'Class used to hold information about an experiment'
def __init__(self, name, start_date, subjects, task, set_variables = None,
persistent_variables = None, folder = None, summary_variables = None):
... |
409f0bf1d209385720659b29c2e1e19366a28f1a2d7abf25d445382115cd5d06 | Python | 1,627 | 50 | from __future__ import absolute_import
import numpy as np
from . import _propagate
def propagate(image, labels, mask, weight):
"""Propagate the labels to the nearest pixels
image - gives the Z height when computing distance
labels - the labeled image pixels
mask - only label pixels within the ... |
6f894a9314a46f811dfbcd1f21135b65cea79307ac3487bc83160eefbb148479 | Python | 1,627 | 37 | from src.models.tabular.pytorch_tabular.base import PTBaseModel
from src.models.tabular.pytorch_tabular.repository.models.category_embedding.category_embedding_model import CategoryEmbeddingModel
from omegaconf import DictConfig
class PTCategoryEmbeddingModel(PTBaseModel):
def __init__(self, **kwargs):
s... |
1716a2f30a7689db712f7689d75da7a5f93c92187ded1b81aeca0b506d76aff3 | Python | 1,628 | 39 | from ._text import Text
class Filename(Text):
"""A setting that displays a file name
optional arguments -
get_directory_fn is a function that gets the initial directory
for the browse button
set_directory_fn is a function that sets the directory after browsing
browse_msg - mes... |
5157b60d0952cda5f8ba37332ea4dd224280f4fe97ad09a858b620f20bd6035d | Python | 1,628 | 72 | from airtable import Airtable
from . import Workspace
def get_samples(metadata_filter):
if not is_data_loaded:
load_data_from_airtable()
global FASTQ_files
global samples
global experiments
current_experiment = None
for experiment in experiments:
if experiment["fields"]["N... |
5528461d0feab285d491bcf08ea4ea73f2d98512e22c04815a8df9cb3be89a25 | Python | 1,628 | 49 | import os
import ftplib
import os
def upload_training(prefix_model, prefix,EPOCHS,lr, client="ftpclient",password="dextro",fig_dir='./fig',PORT = 2121, HOST = "213.206.185.111",h5format=False):
def upload(ftp, file):
if not os.path.exists(file):
print("File '", file, "' doesn't exist")
... |
1b084492d53add8970c77429efc403201ea7a50bd9320733b4185f63133c9d9a | Python | 1,631 | 47 | # this program gets solven accessibility values.
import csv
import math
import os
import sys
# extracting the solvent accessibility from the CIF output files.
aa_list = ['ALA', 'ARG', 'ASN', 'ASP', 'CYS', 'GLN', 'GLU', 'GLY', 'HIS', 'ILE', 'LEU', 'LYS', 'MET', 'PHE', 'PRO', 'SER', 'THR','TRP', 'TYR', 'VAL']
aaCodes ... |
05662cb4fe8eff31e9179741036ce382de267721b8b08304ad684b1aa63c42d5 | Python | 1,633 | 69 | """Coordinate-, image-, and effect-size-based meta-analysis estimators."""
import lazy_loader as lazy
# Estimators are imported on first access: a coordinate-based run never loads PyMARE
# (and through it SymPy) for the image-based estimators, and CBMR's optional torch
# dependency is only needed once CBMR is used.
_... |
d71c0120f395a57f9abcdbf54a40320d99b3c8ae0be0fc275da22f271f57acfa | Python | 1,633 | 62 | #!/usr/bin/env python3
import os
from itertools import product
import numpy as np
from etspredict.job_submission import HTCSubmissionTemplate
subjects = list(
# np.loadtxt(os.path.join("..", "non_na_subjects.txt"), dtype=str)
np.loadtxt(os.path.join("..", "remaining.txt"), dtype=str)
)
sessions = ["REST1... |
37974d7fc3739f42650c9fe630406b9661131b67d8df27c29592417d93545002 | Python | 1,634 | 57 | from __future__ import annotations
import os
import subprocess
from pathlib import Path
import pytest
import yaml
ROOT = Path(__file__).resolve().parents[3]
@pytest.mark.parametrize(
("crate", "workflow_name"),
[("datamonger-core", "publish-core.yml"), ("datamonger", "publish-cli.yml")],
)
@pytest.mark.par... |
4c9370dc76f0ce3e2812b936d5988e24adf9edcc6e11d44b06ee568c5e5a3b19 | Python | 1,634 | 64 | import os
from itertools import product
from etspredict.job_submission import HTCSubmissionTemplate
def main():
datasets = ["hcp"]
sampling_strats = [
"combined_bins"
] # ["individual_bins", "combined_bins", "sequential"]
preprocessing_levels = ["GS", "no_GS"]
parcellations = ["schaefer... |
bad50377bfe4cbd6029d1be85166e3f311c26413b509a2a9c6482350cbf0c8c9 | Python | 1,636 | 59 | import os
import re
import sys
import tqdm
import random
import jieba
import pickle
import joblib
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from LAC import LA... |
bc8d0f209db3fb42170738ad13788970ce5d57f33321fbff404b9ff65b3c5675 | Python | 1,644 | 64 | """Extension module declarations for centriole.
All package metadata lives in pyproject.toml. This file exists only because
setuptools still needs a setup.py to declare C/Cython extension modules.
"""
import glob
import os.path
import numpy
import setuptools
try:
import Cython.Build
__cython = True
except ... |
d38f5560ba82de034ccc1a282697bb05d1d8c1feb62cc99791743946d9c34c32 | Python | 1,647 | 44 | from ..module import Module
from ..object import Objects
from ..setting.text.alphanumeric.name._label_name import LabelName
class InjectObjects(Module):
"""Inject objects with labels into the pipeline"""
module_name = "InjectObjects"
variable_revision_number = 1
def __init__(
self,
o... |
f2938347417045a42a36a50a7a575f31feecb67a89062b572ad3dd402e396517 | Python | 1,647 | 49 | import torch
import torch.nn as nn
import torch.nn.functional as F
class LogisticRegression(nn.Module):
def __init__(self, vocab_size, output_dim):
super().__init__()
self.linear = nn.Linear(vocab_size, output_dim)
def forward(self, x):
return self.linear(x)
class BasicDNN(nn.Module... |
1d85f438b0a9776478ed6e0908f78b68902da8cf7db301697c777490b7b564cc | Python | 1,653 | 45 | from unittest import TestCase
from numpy.testing import assert_equal
from .grid import grid_adj, grid_points, grid_mass, grid_tensor
from torch_geometric.transforms import Cartesian
class GridTest(TestCase):
def test_grid_points(self):
points = grid_points((3, 2))
expected = [[0, 0], [0, 1], [1,... |
9e6472f975536b825a8b6e5c23ae635c7c4f859c9f6195cca913e2ce950c62e7 | Python | 1,655 | 57 | import torch
import time
import numpy as np
import atexit
cuda_timers = {}
timers = {}
class CudaTimer:
def __init__(self, timer_name=''):
self.timer_name = timer_name
if self.timer_name not in cuda_timers:
cuda_timers[self.timer_name] = []
self.start = torch.cuda.Event(enabl... |
aaa1587754dbdb00dbb1c4de438e9bf18aa0283f724bba0ad06ef528f200e3fa | Python | 1,656 | 48 | import rvt.default
import rvt.vis
import rvt.blend_func
import numpy as np
# Prepare
dem_path = r"test_data\TM1_564_146.tif"
dem_dict = rvt.default.get_raster_arr(dem_path)
dem_arr = dem_dict["array"]
dem_res = dem_dict["resolution"][0]
dem_nodata = dem_dict["no_data"]
default = rvt.default.DefaultValues()
default... |
9287abc6bdc58c87a11ddab9da57331a303769bf8635ae5fdbebb62332f71d43 | Python | 1,660 | 66 | import os
from itertools import product
from etspredict.job_submission import HTCSubmissionTemplate
def main():
datasets = ["hcp"]
sampling_strats = ["individual_bins"]
preprocessing_levels = ["GS"]
parcellations = ["schaefer200x17"]
correlation_methods = ["pearson"]
path_to_venv = "~/.venvs... |
c62d73cb3d23c9331663049c4d2ad83bb7fc97bdeb9fc1109dedb629de9890d2 | Python | 1,661 | 48 | import os
import datetime
import sys
sys.path.insert(0, "./")
import torch.distributed as dist
from deeprank.models.variant import *
from deeprank.generate import *
from deeprank.learn import DataSet
from deeprank.learn.NeuralNetDDP import NeuralNetDDP
from deeprank.learn.model3d import cnn_class
def spmd_main():
... |
b01326c4439f104dbedf431fb03202c7e068ecaf797da930f476e7a064f8740d | Python | 1,662 | 43 | import unittest
import torch
import torchdiffeq
from problems import construct_problem, DTYPES, DEVICES, ADAPTIVE_METHODS
EPS = {torch.float32: 1e-5, torch.float64: 1e-12}
class TestCollectionState(unittest.TestCase):
def test_forward(self):
for dtype in DTYPES:
eps = EPS[dtype]
... |
53945e145c63cfee54bd0efd0af94142668836e9bdcfcd03498293b1aa4a11be | Python | 1,665 | 56 | import numpy
import numpy.testing
import skimage.morphology
import cellprofiler.modules.dilateobjects
instance = cellprofiler.modules.dilateobjects.DilateObjects()
def test_run(object_with_data, module, object_set_with_data, workspace_with_data):
module.x_name.value = "InputObjects"
module.y_name.value = "... |
a7e160f8d333b069432b046cd2ee053b8da4bf670003b0cd46d4650a8023f15c | Python | 1,665 | 41 | """Regression test documenting a known bug: non-cubic periodicity settings are silently ignored.
dragen/utilities/Helpers.py:466 has a self-flagged FIXME ("full periodicity applies for the
x != y != z case, which is presumably not 100% reasonable") in make_periodic_3D_new's branch for
when both box_size_y and box_size... |
682ea87495bd58b84797e1e440d69a8af1401ed73d50b689b644d4f9a784f4ec | Python | 1,668 | 41 | from unittest import TestCase
from lib.process.evaluation import KEvaluator
from .train import Trainer, KTrainer
from ..datasets.mnist import MNIST
from dvn import FCN, VNET, UNET # import libraries
import numpy as np
import dvn.losses as ls
import dvn.misc as ms
dataset = MNIST()
trainer = Trainer(model=None,datase... |
f3e51c93fe445fc0abee2dae21bcbd9769a54cfd22f8249f2adc2b8fa968600b | Python | 1,674 | 57 | """
Example usage: CUDA_VISIBLE_DEVICES=1, python train.py --settings_file "config/settings_DDD17.yaml"
"""
import argparse
import wandb
from config.settings import Settings
from training.ess_trainer import ESSModel
from training.ess_supervised_trainer import ESSSupervisedModel
import numpy as np
import torch
import ... |
6c403fb177cf02d4241250618ff3ab91929669ee7058e971e6fbd86010da808b | Python | 1,677 | 53 | import numpy as np
from functions.def_ROIs_WangParcels import roi
def add_fovea(list, hemisphere = 'L'):
"""
Adds fovea to V1, V2 and V3 parcels (Wang et at., 2015) and group dorsal and ventral components.
Parameters
----------
list : list
List of the parcels.
Returns
-------
l... |
9fe554509e82cf0bfc2c8b42b962d5ca7e6bb43f7704a8b2f859a93858fbe88a | Python | 1,678 | 48 | import os
import pandas as pd
import numpy as np
import math
from scipy.sparse import csr_matrix, find
import matplotlib.pyplot as plt
import time
from scipy.stats import norm
from joblib import Parallel, delayed
import time, math
# simple example of using one cell type from Chu et al. dataset
data = pd.read_csv('lo... |
eb9e41159a34f92069bd6adf8ff0ad80ced259ad5083c105ca5111b4766cddae | Python | 1,679 | 45 | import prov.model as pm
from .AcquisitionObject import AcquisitionObject
from ..core import Constants
class DemographicsObject(AcquisitionObject):
"""Class for NIDM-Experimenent MRAcquisitionObject-Level Objects.
Default constructor uses empty graph with namespaces added from NIDM/Scripts/Constants.py.
A... |
6868d38450d9045e4a3adcc046ee3085de081bf5de277d4d7235e2d177f21d2c | Python | 1,681 | 66 | import os
from itertools import product
from etspredict.job_submission import HTCSubmissionTemplate
def main():
datasets = ["hcp"]
sampling_strats = ["individual_bins"]
preprocessing_levels = ["GS"]
parcellations = ["schaefer200x17"]
correlation_methods = ["pearson"]
path_to_venv = "~/.venvs... |
b142937a29d7c6784bea74d6bb0c68a77c517eb1c0588f493e3be93d8b5396e6 | Python | 1,681 | 50 | # 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... |
de1aa7d40d98e0b4df830cce052773a1e06fae2c2b42780e9357892399d367a8 | Python | 1,683 | 38 | """
@author: Johanna Rahm
Research group Heilemann
Institute for Physical and Theoretical Chemistry, Goethe University Frankfurt a.M.
"""
import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(name="ImageSumming",
version="221116",
au... |
25fef05bb2bbd754edd2bf715f845176ef06c636e23839bae8d8f3676dd37bc3 | Python | 1,685 | 66 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import TDBG
import os
import re
# get the name of the folder
cwd2 = os.path.split(os.getcwd())[1]
print(str(re.findall("\d+", cwd2)))
cwd2int = int("".join(filter(str.isdigit, cwd2)))
npzfile = f"temptestnorm{cwd2int}.npz"
print(npzfile)
# f ----------... |
2f55625b9e44cae5baae81117ae033a988fab36b006a2b7a94a00142449ea086 | Python | 1,685 | 54 | """
pysteps.downscaling.interface
=============================
Interface for the downscaling module. It returns a callable function for computing
downscaling.
.. autosummary::
:toctree: ../generated/
get_method
"""
from pysteps.downscaling import rainfarm
_downscale_methods = dict()
_downscale_methods["ra... |
fec5044164a97bf8399d79c6b3e2d103d28f2b4ce0c8eb57e0f8e0ed48c08838 | Python | 1,685 | 42 | import random
import numpy as np
class Evaluation(object):
"""Public evaluation methods"""
def __init__(self):
super(Evaluation, self).__init__()
self.loss_arr = []
self.precision_arr = []
self.recall_arr = []
self.accuracy_arr = []
@staticmethod
def _get_metr... |
c92f85ee794f5af900bb07f6be2ea4a06ba7be01ada6491edd806c6a3667eff7 | Python | 1,686 | 50 | import numpy as np
from math import fabs
def robust_min(img, p=5):
return np.percentile(img.ravel(), p)
def robust_max(img, p=95):
return np.percentile(img.ravel(), p)
def normalize(img, m=10, M=90):
return np.clip((img - robust_min(img, m)) / (robust_max(img, M) - robust_min(img, m)), 0.0, 1.0)
def... |
08d046557ba01b8768aa6b1175ad47f7e8417c9c7f4548ac734bacb8ab172b11 | Python | 1,690 | 74 | from math import sqrt
def get_tp_tn_fp_fn(output_data, target_data):
""" A classification metric
Args:
output_data(array of dimension (x,2)): considered negative if left value > right value
target_data(array of dimension (x,1)): considered negative if 0, positive otherewise
Returns (four... |
c40fc9f5f78f5efc0732e65543760a018fa2fa9cb3c10057d03e5125c11cf618 | Python | 1,690 | 67 | config = {
'alpha_di':0.000451125353657906,
'alpha_di_fb':0.611510021981854,
'apical_time_constant_fb':0.4318893018524684,
'adam_beta1':0.99,
'adam_beta2':0.999,
'dt_di_fb':0.004294669581123954,
'adam_epsilon':3.210886729268312e-07,
'adam_epsilon_fb':1.0433715362737473e-08,
'weight_decay_fb':0.001,
'k_p_fb':0.010409666... |
649951539a6f378b1d44062fd50e92c173c0d2186f62ad8703c49626194b152c | Python | 1,692 | 58 | from ._setting import Setting
class DoThings(Setting):
"""Do one of several things, depending on which button is pressed
This setting consolidates several possible actions into one setting.
Graphically, it displays as several buttons that are horizontally
adjacent.
"""
save_to_pipeline = Fal... |
e9203914e42d35fd4b7ae29253bf3962da96c1ff184fde0b77071cbb1589f8e5 | Python | 1,696 | 42 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for charge.py"""
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
from molvs.validate import validate_smiles
def test_none():
"""IsNoneValidation should log due to SMILES parse error."""
asser... |
e31978936018cbd9470d5099f72bf3b2fd1f354a53a4a30aa40303d6992bd763 | Python | 1,697 | 52 | import torch
from math import exp
import torch.nn.functional as F
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
return gauss/gauss.sum()
def create_window(window_size, channel=1):
_1D_window = gaussian(window_size, 1.5... |
6b9224233cd8c4e9713836af74073901ba2a6b051e9560c332c3b67cefc14956 | Python | 1,700 | 52 | from typing import Sequence
from typing import Tuple
import torch
import torch.nn as nn
from ..models.base import Model
from ..models.featurenn import FeatureNN
class NAM(Model):
def __init__(
self,
config,
name,
*,
num_inputs: int,
num_units,
num_outputs... |
c3992edc22af074901838780062684538e125b9029b16abd82000ddb40f0f1f9 | Python | 1,700 | 47 | import json
import os
import shutil
from imskaper.experiment.experiment import experiment, read_Xy_data
from imskaper.utils.classifiers import get_classifiers
from imskaper.utils.features_selectors import get_features_selectors
current_path = "tests" # os.path.dirname(os.path.abspath(__file__))
json_path = os.path.jo... |
ceb7aaa580e15a5c2c8773338ca0a32eb013974f461007ab41a7248e344521c1 | Python | 1,700 | 48 | """If input folder to the MLCube does not contain a csv file that defines the data cases,
a custom entrypoint is needed to create a temporary csv file before calling GaNDLF's run command.
This script should expect the same arguments passed to the command `mlcube run --task infer`,
i.e. it should expect the inputs and o... |
d1b7c81f0afcca0379381b1459b7d2ffa00d9c4635444c9210475e1beb267672 | Python | 1,700 | 69 | # 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(
(
... |
b7db1775dd392b74223d67a6bb2f20976c0f28d73581234526c84ea99fc2516a | Python | 1,702 | 68 | config = {
'lr':0.0010924329072635147,
'alpha_di':0.0017723664900917734,
'k_p_fb':0.0713515876863314,
'alpha_di_fb':0.39857902933813705,
'weight_decay_fb':0.1,
'time_constant_ratio_fb':0.005870467170683501,
'dt_di_fb':0.0011421804343878519,
'apical_time_constant_fb':0.1404664564483908,
'lr_fb_init':0.000152412153530638... |
1ebb992ad623f671b5c84e562b98fea3e72b115dc0aa8be5b20668d8f2edcc40 | Python | 1,705 | 53 | import time
class Timer():
def __init__(self, time_threshold):
self.start = time.time()
self.time_threshold = time_threshold
def is_time(self):
current_time = time.time()
time_diff = current_time-self.start
if time_diff>self.time_threshold:
print("time elaps... |
e4ba7a3a811163da50804ddae400199a0c1b0cedca5230b48c46342bedeffd6c | Python | 1,705 | 44 | import os
import torch.distributed as dist
import pickle
import numpy as np
from PIL import Image
from .base_dataset import BaseDataset
from utils.parallel import get_dist_info
from .utils import download_and_extract_archive, check_integrity
class ImageNet(BaseDataset):
"""Since there is prepared dataset class in ... |
7684daf635f518500bb65167573788e2db446952dd6d31d56ea6683dd684919d | Python | 1,710 | 66 | import numpy
import skimage.morphology
import cellprofiler_core.image
import cellprofiler.modules.removeholes
import cellprofiler_core.workspace
instance = cellprofiler.modules.removeholes.RemoveHoles()
def test_run(image, module, workspace):
module.x_name.value = "example"
module.y_name.value = "output"
... |
7205b873c0954654a536a919a226447d3135b06e11c8c3e699b00b21b95519b5 | Python | 1,713 | 54 | #!/usr/bin/env python
from __future__ import print_function
from collections import OrderedDict
import re
# TODO nf-core: Add additional regexes for new tools in process get_software_versions
regexes = {
"nf-core/circrnasponging": ["v_pipeline.txt", r"(\S+)"],
"Nextflow": ["v_nextflow.txt", r"(\S+)"],
"Fas... |
7926d51ccac69a2c2819a11d42cccb933778f62d65e2a619fef73c666072f37a | Python | 1,713 | 55 | from __future__ import annotations
import datetime as dt
import numpy as np
import pytest
from sentinelhub import CRS, BBox
from s2cloudless.utils import download_bands_and_valid_data_mask
BBOX1 = BBox((-90.9216499, 14.4190528, -90.8186531, 14.5520163), crs=CRS.WGS84)
BBOX2 = BBox(((620000, 8210000), (660000, 8270... |
927d980bdfe4173202ab312acecaf4954742dfd59b339b8d03cfe6a04e1233e3 | Python | 1,714 | 60 | import wx
import wx.adv
from cellprofiler import __version__ as cellprofiler_version
from cellprofiler.gui.utilities.icon import get_cp_icon
class AboutDialogInfo(wx.adv.AboutDialogInfo):
def __init__(self):
super(AboutDialogInfo, self).__init__()
self.SetCopyright(
"Copyright © 2003... |
0742d1545d4ec996208bf5d87d54bb2b7306d0b4c33b14339abc0f903b810666 | Python | 1,715 | 62 | 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, activation_override=None):
super(MLP, self).__init__()
self.in_dim = in_channel*img_sz*img_sz
self.activation_override = activation_override
self.linear... |
f54cf43f9ed9fb4ed92b9372eb0949385296b60fceb1c25fbf859a88a616da46 | Python | 1,715 | 49 | import os
import csv
import torch
import torch.nn as nn
from imageio import imsave
# load image metadata (Image_ID, true label, and target label)
def load_ground_truth(csv_filename):
image_id_list = []
label_ori_list = []
label_tar_list = []
with open(csv_filename) as csvfile:
reader = csv.Di... |
0ff6ba79ab3d27d4056141f00f8dfadbd833f67a82968cd0862951c52b7f5e38 | Python | 1,717 | 54 | from .util import prepare_for_display, window_manager, GuiLockError
try:
# we try to acquire the gui lock first
# or else the gui import might trample another
# gui's pyos_inputhook.
window_manager.acquire('gtk')
except GuiLockError as gle:
print(gle)
else:
try:
import gtk
except Im... |
2c988b4be260b351f487a7a59168bbe747217fb8d538ce87e22757842544b0e4 | Python | 1,718 | 58 |
import inspect
from typing import Dict, Tuple, Union
import torch.nn as nn
NORM_LAYERS = {
'BN':nn.BatchNorm2d,
'GN':nn.GroupNorm,
'IN':nn.InstanceNorm2d,
'LN':nn.LayerNorm,
}
def build_norm_layer(cfg: Dict,
num_features: int) -> Tuple[str, nn.Module]:
"""Build normalizatio... |
354f98bb58d9c48c1fa2fbfa640f224ed1baddc6d8b38d37852033e5d7353995 | Python | 1,718 | 42 | # -*- 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... |
8e4eabb9356fef792e1794c47b07d16d4e59d8467c7aee82954656ed63d8f1ef | Python | 1,719 | 49 | """ Median Pool
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch.nn as nn
import torch.nn.functional as F
from .helpers import to_2tuple, to_4tuple
class MedianPool2d(nn.Module):
""" Median pool (usable as median filter when stride=1) module.
Args:
kernel_size: size of pooling kern... |
ef6ca0cbdb3720db7634501f496e23f5dc719aebaf7f37c14c5d07c21d276015 | Python | 1,720 | 49 | from typing import Union
import os
import pandas as pd
from GANDLF.utils import get_dataframe, split_data
def split_data_and_save_csvs(
input_data: Union[pd.DataFrame, str], output_dir: str, parameters: dict
) -> None:
"""
Split the data into training, validation, and testing sets and save them as csvs i... |
17be10210e9d64bf1c388b19914f957c375c1dec145bde7d19746f0dfa611829 | Python | 1,725 | 73 | import os
# hides torchio citation request, see https://github.com/fepegar/torchio/issues/235
os.environ["TORCHIO_HIDE_CITATION_PROMPT"] = "1"
from .imaging import (
resize_image,
resample_image,
perform_sanity_check_on_subject,
sanity_check_on_file_readers,
write_training_patches,
get_correct... |
a4dba0063a7c342bd058a54a5e4c4172831c25f7175cd2e9facbf0578f736918 | Python | 1,725 | 61 |
from sklearn import svm
import pickle
import numpy as np
from sklearn.metrics import confusion_matrix
# import matplotlib.pyplot as plt
# from scipy import stats
### PARAMETERS
# SVM kernel (RBF or Polynomial)
# kern_func = 'rbf'
kern_func = 'poly'
# Polynomial degree for 'poly' kernel (ingored fo... |
ffec44cc70c00f740deb6c06993b71e29aa1f1458482da29150c746b5d9485ac | Python | 1,726 | 39 | # Inheritaed from open-mmlab/mmclassification
# Modified by Hangyu LIN
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
has_method, import_modules_from_strings, is_list_of,
is_method_overridden, is_seq_of, is_str, is_tuple_of,
iter_ca... |
8dd949f0f66f4d07c1c1e113be10ce5547bc038a9887c88e92c5b2dfcd61732b | Python | 1,728 | 61 | import numpy as np
import matplotlib.pyplot as plt
# Define parameters
dt = 0.5 # time step
t = np.arange(0, 1000, dt) # time vector
V0 = -90 # initial potential
Vr = -90 # reset potential
theta = -60 # threshold potential
tau = 10 # membrane time constant
g = 1.5 # input conductance
tau_s = 5 # synaptic time ... |
77484a446ca075b7dbb64f907a9e6f37b0b61b0e9e8097c75f1e75e41eb3a557 | Python | 1,730 | 48 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 16 16:11:33 2018
@author: saskiad
"""
import pandas as pd
import numpy as np
import h5py
import os
from allensdk.core.brain_observatory_cache import BrainObservatoryCache
import core
def get_evoked_responses():
save_path = core.get_save_path(... |
13e5a447a1b2c63226582881ac0eb68b7992ece02da803bb633ba9654163ca32 | Python | 1,731 | 40 | # Author: Mingjian He <mh1@stanford.edu>
from somata.oscillator_search import DecomposedOscillatorModel as DecOsc
from somata import OscillatorModel as Osc
import numpy as np
# Simulate 10-s data with two oscillators, one slow and one alpha frequency
np.random.seed(1) # to ensure reproducible results
o1 = Osc(a=[0.99... |
492c00265ee74e3241fc0361a033e07c56be7bcf1de64f7583ce5e4bca9137a0 | Python | 1,731 | 57 | # Inheritaed from open-mmlab/mmclassification
# Modified by Hangyu LIN
def wrap_non_distributed_model(model, device='cuda', dim=0, *args, **kwargs):
"""Wrap module in non-distributed environment by device type.
- For CUDA, wrap as to.device.
- For CPU not wrap the model.
Args:
model(:cla... |
70a3de3640c4ffc00b97f0c9cfdcff8b0584b85613e77fb163cd772743893d40 | Python | 1,732 | 25 | from pyControl.hardware import Port
from devices._MCP import MCP23017
class Port_expander():
# IO expander board which runs 8 behaviour ports from an MCP23017 i2c port expander.
def __init__(self, port):
assert port.I2C, 'Port expander must be connected to port that supports I2C.'
self.m... |
317cd88b7c11060ae01472dd7286b884b822e5829f4085b73f43288df2eefe7f | Python | 1,733 | 46 | class ActivationsAndGradients:
""" Class for extracting activations and
registering gradients from targetted intermediate layers """
def __init__(self, model, target_layers, reshape_transform):
self.model = model
self.gradients = []
self.activations = []
self.reshape_transfo... |
85a25b8796f3085d9ee8731d24758a0cc2f0d06a43d3bae0d743f00bf5eeedb3 | Python | 1,733 | 57 | import random
import numpy as np
from numpy import pi as PI
def augment_batch(batch):
batch_augmented = []
for example in batch:
features, adjs_dist, adjs_rad, label = example
features = random_brightness(features, 0, 3, max_delta=0.3)
features = random_contrast(features, 0, 3, max_de... |
b411ad0c074f1f816a503fbc0af058b4a105ea128dee29c5cf8bd4a5814adcb7 | Python | 1,736 | 55 | from __future__ import annotations
import importlib.util
from pathlib import Path
from typing import Any
TOOLS = Path(__file__).resolve().parents[3] / "tools"
SPEC = importlib.util.spec_from_file_location(
"dm_ffi_licenses", TOOLS / "dm_ffi_licenses.py"
)
assert SPEC is not None and SPEC.loader is not None
dm_ffi... |
7ef04cbfc42794991aef04f54af10b78abf9ac6b98b1aab22565b0b7978bcf81 | Python | 1,738 | 67 | import random
import dgl
import torch
from attentionmil import AttentionMIL
from lamil import LAMIL
from perceiver import Perceiver
from transformer import Transformer
from transmil import TransMIL
def test_attentionmil():
attentionmil = AttentionMIL(num_classes=2, num_features=1024)
input = torch.rand(1, 1... |
516a6a6e8c305386094eb0648ea165efa33a9b70811e7107cd84a9f24fe2d645 | Python | 1,739 | 64 | import json
import os
import pickle
from typing import Any, Dict, Tuple
import numpy as np
import xgboost as xgb
kRows = 100
kCols = 10
def generate_data() -> Tuple[np.ndarray, np.ndarray]:
X = np.random.randn(kRows, kCols)
y = np.random.randn(kRows)
return X, y
class TestPickling:
def run_model_... |
35f7e7b9412506c62e819b0ee2c2a89912ea791a75ab1fbe3696347faeeddbe3 | Python | 1,741 | 56 | """Add knowledge_chunks table for RAG vector store
Revision ID: c4e7f1a9b2d8
Revises: 56d89145adbb
Create Date: 2026-04-01 00:00:00.000000
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = "c4e7f1a9b2d8"
down_revision = "56d89145adbb"
branch_labels = None
depends... |
fbe13252ec3573bffb103cc395a157d08c216dce20b431ceb498e73fa2b31e8e | Python | 1,741 | 41 | """Predictors based on gammatone spectrograms"""
from pathlib import Path
import os
import numpy as np
from eelbrain import *
from trftools.neural import edge_detector
language = 'French'
DATA_ROOT = Path(os.path.dirname(os.path.abspath(__file__))).expanduser()
STIMULUS_DIR = DATA_ROOT / 'stimuli' / language
PREDICTO... |
cf3c520a769eeb64ce3036bb348d3c03d9fac48840064d99facbe4028cb57ba5 | Python | 1,745 | 62 | import os
import shutil
import numpy as np
import pandas as pd
from etspredict import selective_connectomes as sc
from etspredict import timeseries as gts
from etspredict.pipelines.base import Pipeline
def test_pipeline():
# pretend to load subject specific data
subject = "151425"
run = "REST1LR"
t... |
1d29f082cef2069393986961412d9eb3d05ea6c736752a9b560aa8e523d20c04 | Python | 1,748 | 53 | import torch.nn as nn
from GANDLF.models.seg_modules.Interpolate import Interpolate
class FCNUpsamplingModule(nn.Module):
def __init__(
self,
input_channels,
output_channels,
conv=nn.Conv2d,
conv_kwargs=None,
scale_factor=2,
interpolation_mode="trilinear",
... |
855e8e76765608edac655659c928cf858f6c4ea174019ae8172e29f67064606b | Python | 1,748 | 52 | import torch.nn as nn
from GANDLF.models.seg_modules.Interpolate import Interpolate
class UpsamplingModule(nn.Module):
def __init__(
self,
input_channels,
output_channels,
conv=nn.Conv2d,
conv_kwargs=None,
scale_factor=2,
interpolation_mode="trilinear",
... |
9b8879d72a2bc2e10a731704558629b3651632acd933dbf0f8d9400ceeb886db | Python | 1,748 | 69 | import hashlib
from os import environ, path
import pickle
import tempfile
from rdflib import Graph
from nidm.core import Constants
import nidm.experiment.Query
from nidm.util import urlretrieve
def download_cde_files():
cde_dir = tempfile.gettempdir()
for url in Constants.CDE_FILE_LOCATIONS:
urlretri... |
26bfa0d3caec1604f572e77b852ac21ae6f287aa89120efb045b90450cee8c66 | Python | 1,749 | 60 | from __future__ import division
from six.moves import xrange
import numpy as np
# import numpy_groupies as npg
import scipy.sparse as sp
def normalized_cut(adj, rid=None):
"""Perform NormalizedCut on a given adjacency matrix. Return a cluster
map indicating the resulting pairwise cluster."""
if rid is ... |
f853bbdf046c21f3df5c252422bd2d1965296f8e72d3aa65a64e1429fa854afc | Python | 1,750 | 61 | import cv2
import numpy as np
import pandas as pd
import os
from matplotlib import pyplot as plt
def detect_mouse(frame):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (35, 35), 0)
_, thresh = cv2.threshold(blurred, 30, 255, cv2.THRESH_BINARY_INV)
contours, _ = cv2.fi... |
d2490d40c836498d3421f69c466b04e4f4aeee0f07f646b9419223650048b2b2 | Python | 1,754 | 60 | import numpy
import numpy.testing
import skimage.morphology
import cellprofiler.modules.erodeobjects
instance = cellprofiler.modules.erodeobjects.ErodeObjects()
def test_run(object_with_data, module, object_set_with_data, workspace_with_data):
module.x_name.value = "InputObjects"
module.y_name.value = "Out... |
f7cf216735e2e38f6c4901f4b0855ce952f19a77fd6ae20ae80aa0bc4bf2ad13 | Python | 1,755 | 48 | import torch
from torchio.data.subject import Subject
from torchio.transforms.preprocessing.intensity.normalization_transform import (
NormalizationTransform,
TypeMaskingMethod,
)
# adapted from https://github.com/fepegar/torchio/blob/master/torchio/transforms/preprocessing/intensity/z_normalization.py
class... |
f94975034b58b8b8b10f4c65095f033843d4f041019342ec71415a779fe4f839 | Python | 1,755 | 62 | import numpy as np
import matplotlib.pyplot as plt
# Define parameters
dt = 0.1 # time step
t = np.arange(0, 1000, dt) # time vector
V0 = -90 # initial potential
Vr = -90 # reset potential
theta = -60 # threshold potential
tau = 20 # membrane time constant
a = 0.02 # recovery variable time scale
b = 0.2 # sens... |
49ed228f7788e7334ad2c4428e0457dcbffb87194e51eed493d6685af2cf8462 | Python | 1,756 | 64 | # -*- coding: utf-8 -*-
import pytest
import pysteps
from pysteps.tests.helpers import smart_assert, get_precipitation_fields
pytest.importorskip("h5py")
# Test for RADOLAN RY product
precip_ry, metadata_ry = get_precipitation_fields(
num_prev_files=0,
num_next_files=0,
return_raw=False,
metadata=T... |
a15dbd522329fffb6c48f1b5ef4d894b2df6185d5fe2d59a10f5922b4837d912 | Python | 1,759 | 58 | import pandas as pd
import splitter as sp
import os
from sklearn.model_selection import KFold
import numpy as np
def make_dir(file_name):
ouput_path = file_name + '/'
try:
os.mkdir(ouput_path)
except OSError as error:
print(error)
return ouput_path
k_folds = 10
skf = KFold(n_spli... |
59ff4cece7fe5b63ba2cab9787f1d4443da5dec2c14baca91ff1830cd1248ed0 | Python | 1,761 | 67 | #!/usr/bin/python3
import numpy as np
from os.path import join as pjoin
from os import linesep
from shutil import copyfile
from scipy.io import mmwrite
from scipy.sparse import coo_matrix
import gzip
diri='data/raw'
diro='data/de'
key='celltype'
values=['dysfunctional','naive']
#Load covariate info
dc=np.loadtxt(pj... |
29cc3746d0783b06f0b600e40f72f39ccd85d6404072855ea8273f71951d7da3 | Python | 1,763 | 67 | #!/usr/bin/env python
import os
import h5py
def clean_dataset(fname, feature=True, pdb=True, points=True, grid=False):
# name of the hdf5 file
f5 = h5py.File(fname, 'a')
# get the folder names
mol_names = f5.keys()
for name in mol_names:
mol_grp = f5[name]
if feature and 'fea... |
0794161a463b3358ef9a50ef8566b60e8ab6d201f2282e3e7abfcf646247e31d | Python | 1,765 | 52 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import textgrid as tg
import pandas as pd
def textGrid_to_dataFrame(tgfile, tier='words'):
"""
Simply return a dataframe with the tier mark label aligned with time onset and offset.
Parameters
----------
tgfile : str
Path to TextG... |
2f26203796ce005e4aea568658285efc2062d364663e1212ee76298a88ce0b4c | Python | 1,768 | 34 | from GANDLF.entrypoints.anonymizer import new_way as anonymizer_command
from GANDLF.entrypoints.run import new_way as run_command
from GANDLF.entrypoints.construct_csv import new_way as construct_csv_command
from GANDLF.entrypoints.collect_stats import new_way as collect_stats_command
from GANDLF.entrypoints.patch_... |
1e539125aff6ddeac408c62d3437c68a0037e419d09746768a6ae48fecdffe55 | Python | 1,772 | 75 | """
Helper functions for (pre-)processing
Authored by Eric Easthope
"""
import numpy as np
# Get N logarithmically-scaled frequency bands
def log_scale_bands(low, high, N):
return np.around(
np.array(
list(
zip(
np.geomspace(low, high, N + 1).tolist(),
... |
93f8ef3a62413789a4864d6e888008615e7c48ebbcdd544b2db92eb1decee1f4 | Python | 1,780 | 56 | import os
import sys
from contextlib import contextmanager
try:
import ctypes
from ctypes.util import find_library
except ImportError:
libc = None
else:
try:
libc = ctypes.cdll.msvcrt # Windows
except OSError:
libc = ctypes.cdll.LoadLibrary(find_library('c'))
def f... |
d03fc089aae1b70ea5ddbb156c7e893b4ace69473e221b424d9347271f56624b | Python | 1,781 | 52 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import textgrid as tg
import pandas as pd
def textGrid_to_dataFrame(tgfile, tier='phones'):
"""
Simply return a dataframe with the tier mark label aligned with time onset and offset.
Parameters
----------
tgfile : str
Path to Text... |
0ecec61028dae1641df8ce9b760faf1e72526c542f043f5882d68be9139ff586 | Python | 1,783 | 53 | # -*- coding: utf-8 -*-
"""
Created on Mon Sep 5 12:19:11 2022
@author: Manuel
"""
originals_path = "G:/Masterarbeit/MonitRaw"
predictions_path = "G:/Masterarbeit/PredRaw"
output_path = "G:/Masterarbeit/PredJPG"
MAX_CLASS_VAL = 16
import os, glob
from PIL import Image
from exif import Image as exIm
imp... |
15e9e73d07e644e258b5fdf13ddcd639ce72743a238e900f3c5c3a64dbd3bc22 | Python | 1,785 | 56 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for resonance.py"""
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
import logging
from rdkit import Chem
from molvs.resonance import ResonanceEnumerator, enumerate_resonance_smiles
logging.basicCon... |
3060bc342aeab2a2d904454125e69c7575e7f4f6e5a16d9a6cb02817e8d5adf3 | Python | 1,785 | 45 | import os
import re
# TODO: eventually switch to tomllib, introduced in python 3.11
import toml
def split_package(package):
"""
given a string containing a package and its version specifier:
"package-a>=3,<5"
splits the string into 2-tuple of (package, version_specifier)
("package-a", ">=3,<5")
... |
9ed2e68554f9d4bbfb599a13c898feace4dbfb945091c7177eeff9e1509f16dd | Python | 1,785 | 63 | import os
from pathlib import Path
import numpy as np
import pandas as pd
import pkg_resources
class HCPAgingTimeseries:
def __init__(self):
self.timeseries = None
def available_subjects(self, unrelated=True):
parent = Path(__file__).parent.resolve()
path = os.path.join("timeseries")... |
f3a9a7e093ae70b6e0e9588c56380b21fce251cb8efeaad14b69be17c56b6f8c | Python | 1,785 | 53 | import logging
import torch.nn as nn
logger = logging.getLogger(__name__)
def _initialize_layers(activation, initialization, layers):
if type(layers) == nn.Sequential:
for layer in layers:
if hasattr(layer, "weight"):
_initialize_layers(activation, initialization, layer)
... |
9037c8a4e80c04b50bd6cc6475ec6c20176f6a535c3e0afbbbc7ef9f8a5de3db | Python | 1,794 | 66 | import os
from itertools import product
from etspredict.job_submission import HTCSubmissionTemplate
def main():
datasets = ["hcp", "hcp_aging"]
sampling_strats = ["individual_bins", "combined_bins", "sequential"]
preprocessing_levels = ["GS", "no_GS"]
parcellations = ["schaefer200x17", "schaefer300x... |
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