sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
e3e9f98e6a62820ddc7fbcd4d5b501b85bffb9b4f16db591b533c91454c8fb73 | Python | 1,636 | 43 | from setuptools import setup, find_packages
with open("requirements.txt", "r", encoding="utf8") as fh:
REQUIREMENTS = fh.read()
INSTALL_REQUIREMENTS = REQUIREMENTS
CLASSIFIERS = ["Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.7",
"Programming Language :: ... |
e47d4b02a9fbe11db27900e5bca9e266ae6bec6ce1362cea49b7cf9658972051 | Python | 1,636 | 53 | import os
import shutil
import sys
# Dirty fix, :TODO rewrite tools as a package - joined directly to Snudda or the lab
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, "tools")))
import unittest
from meta.create_meta import write_meta
from transfer.morphology import transfer_morphol... |
5de7d4d08b329ac77cf387b4d3b958f8d5163c3cadbabbeed623b6724dc1733c | Python | 1,637 | 63 | #! /usr/bin/env python3
# -*- coding: utf-8 -*-
import math
#import copy
import numpy as np
# ROS libraries
import rospy
# ROS messages
from std_msgs.msg import String
from email.message import EmailMessage
import ssl
import smtplib
class CallNurse(object):
def __init__(self, hz = 10.0):
rosp... |
ef143073253f577f64db8ae31e56613b7dc669954f67e822613a54e7e386681b | Python | 1,637 | 55 | """Negative Cosine Similarity Loss Function"""
# Copyright (c) 2020. Lightly AG and its affiliates.
# All Rights Reserved
import torch
from torch.nn.functional import cosine_similarity
class NegativeCosineSimilarity(torch.nn.Module):
"""Implementation of the Negative Cosine Simililarity used in the SimSiam[0] p... |
145ed88b9a2f147e47ab1a3c7bde360f7ab92baa4f41230339b1faf614734ae8 | Python | 1,638 | 40 | from setuptools import setup, find_packages
with open("README.md", "r") as fh:
long_description = fh.read()
setup(
name='densratio',
version='0.4.0',
description='A Python Package for Density Ratio Estimation',
long_description=long_description,
long_description_content_type='text/markdown',
... |
5010ee1090cbc43e4d80fc57d42e7126be880dedb1bfee2083ab330741f76416 | Python | 1,639 | 47 | import os
import pandas as pd
import numpy as np
import shutil
import subprocess
from settings import AGE_FROM, AGE_TO
N_CASES = 2
path_for_images = "./data/pre-processing/test"
def find_file_in_path(name, path):
result = False
for root, dirs, files in os.walk(path):
result = next((os.path.join(file) ... |
6bda950a1ca71a6115833fc0e194bdcb58b9b09a00662a29247241a589b12013 | Python | 1,640 | 56 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["splicepoint_model_spec"] = dict(
type="BothLSSIModels",
acceptor="model/splicepoint-model-acceptor-1",
donor="model/splicepoint-model-donor-1",
)
msp.architecture["motif_model_spec"]["motif_... |
2465cf22e1a19e0ee1a1432891aa6551fb7c41fb7f33755ec55e2a225b65a209 | Python | 1,641 | 48 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from os.path import exists, dirname, realpath
from setuptools import setup, find_packages
import sys
maintainer = u"Paul Müller"
maintainer_email = "dev@craban.de"
description = 'Fit and superimpose shapes from different imaging modalities'
name = 'impose'
year = "2020"
... |
18f563556b777ccef13c7fb9218269bc4e8aa132f56af4c0d6d915fd6142bc6c | Python | 1,642 | 43 | import matplotlib.pyplot as plt
from rdkit import Chem
from rdkit.Chem import Lipinski, MACCSkeys
import numpy as np
import sklearn
from sklearn.manifold import TSNE
import warnings
import pandas as pd
from rdkit.Chem import AllChem
warnings.filterwarnings('ignore')
df_all = pd.read_csv('D:\PythonProject\Project_VAE\d... |
3571dfbf90a6a44ee575fa13dc91e223e559c934f6b6130cd10dd314d17321aa | Python | 1,642 | 46 | import sys
sys.path.insert(1, '../')
import numpy as np
import pandas as pd
import os
import shutil
from datetime import datetime
metadata_dob = pd.read_csv('itmt_v2/cvs_files/metadata_bch_scan_date.csv', header=0)
def find_age_gender(id_to_find):
for idx, row in metadata_dob.iterrows():
if str(id_to_fi... |
f1a654cad68dd89b72415c3d91385ad57f7f081ac65c193b88adae889a0cad73 | Python | 1,642 | 62 | """Standalone launcher: `python -m larvaworld.portal.parameter_database`.
Serves just the Parameter Database view on its own, without the rest of the
portal (landing page, workspace, other apps). Configure via env vars, same
as the main portal:
- LARVAWORLD_PORTAL_PORT (default: 5006)
- LARVAWORLD_PORTAL_OPEN_BROWSER ... |
6324e6bbbdf852a1ad5b9f245a485edac48381c9962c49a801f05ccf2dd6b67f | Python | 1,643 | 48 | import gpytorch
import torch
from tqdm import tqdm
from spateo.alignment.utils import _iteration
def gp_train(model, likelihood, train_loader, train_epochs, method, N, device, keys, verbose=True):
if torch.cuda.is_available() and device != "cpu":
model = model.cuda()
likelihood = likelihood.cuda(... |
b5a9c0adce2fb2d359dfa08bafb4bcb8455d5d7d223f62bd5162902d372c3ec4 | Python | 1,643 | 50 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""tests for spring embeddings"""
import unittest
from sknetwork.data.test_graphs import *
from sknetwork.embedding import Spring
class TestEmbeddings(unittest.TestCase):
def test_shape(self):
for adjacency in [test_graph(), test_digraph()]:
n ... |
0e734460ff8dc8ed427447e6123e1612fef2e802991520b19a4b44c092d025f6 | Python | 1,644 | 60 | """Package setup."""
import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="bdd100k", # Replace with your own username
version="1.0.0",
author="Fisher Yu",
author_email="i@yf.io",
description="BDD100K Dataset Toolkit",
long_description=l... |
d325e8f9ccf336a5784b90501e6ede3cc57af2f506b8a64fe7991c28e0620b80 | Python | 1,644 | 48 | from datetime import datetime
from unittest import mock
from uuid import UUID
import dateutil.relativedelta
from carbonserver.api.infra.repositories.repository_runs import SqlAlchemyRepository
EXPERIMENT_ID = UUID("e52fe339-164d-4c2b-a8c0-f562dfce066d")
class SessionContextMock:
def __init__(self, session):
... |
e8774d57aa6f3d071eca015780d64df44ae6941099cd1f51c0b205aa564af69e | Python | 1,644 | 50 | import os
from options.test_options import TestOptions
from data import create_dataset
from models import create_model
from util.visualizer import save_images
from itertools import islice
from util import html
# options
opt = TestOptions().parse()
opt.num_threads = 1 # test code only supports num_threads=1
opt.batc... |
97ab507a0f3a26294d12ba9dec8982687ad115a1387c1e79d2d26181737b506e | Python | 1,645 | 45 | from setuptools import setup, find_packages
RECTIPY_TEAM = "Richard Gast"
with open("requirements.txt", "r", encoding="utf8") as fh:
REQUIREMENTS = fh.read()
INSTALL_REQUIREMENTS = REQUIREMENTS
CLASSIFIERS = ["Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.6",
... |
00a73202e911b0a6dea6456a534a26d2bc1133de3ea41bcdce7044a1d3fcd3f7 | Python | 1,646 | 51 | import anndata as ad
import numpy as np
import pandas as pd
def write_subset_h5ad(adata, filename, obsm_keys=None, uns_keys=None):
"""Write only specific obsm and uns components to H5AD"""
# Create a minimal AnnData object with the same structure
minimal_adata = ad.AnnData(
X=np.zeros((adata.n_ob... |
9bf9e5d76f0b34ef66b9430dce5f02ec02031a9b8e64a832f973dfaf7cd68700 | Python | 1,646 | 60 | __author__ = "quentin"
import itertools
import cv2
import numpy as np
def merge_blobs(contours, prop=0.5):
"""
Merge together contour according to their position and size.
If the distance between two blobs is smaller than the longest axis of, at least, one of them,
then they get merged.
This algo... |
e8e969e4733562aa98e0725b66f97331838a9e6b3d4e18e2b0eb278ac3c9259d | Python | 1,646 | 54 | import os
import subprocess
import time
from concurrent.futures import ThreadPoolExecutor
disabled_error_message = "Refusing to deserialize disabled content of type "
class test_config_serialization:
def test_accept(self, celery_app):
app = celery_app
# Redefine env to use in subprocess
#... |
fea1df7b20c5d676a9b8aac15bb63114d2c2b62f1d70328024c64d96f5a80131 | Python | 1,646 | 57 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture = dict(
type="SplicepointBasedBranchSitePredictor",
splicepoint_model_spec=dict(
type="BothLSSIModels",
acceptor="model/splicepoint-model-acceptor-1",
donor="model/splicepo... |
056fd6b5102f3b5c7179a83302bdd2f0c5cf3647e926277620177641a6e8517d | Python | 1,647 | 60 | import argparse
import nibabel as nib
import torch
from DeepDenoise.src.model import ResUnet
def main(args):
# Load the model checkpoint
checkpoint = torch.load(args.checkpoint_path)
# Instantiate the model
model = ResUnet.load_from_checkpoint(args.checkpoint_path)
# Load the input Nifti file
... |
a1bdf9bb3a0a10e4f40a85f92260c71be4340a1a9ce603c93926c6ecda19dc63 | Python | 1,647 | 42 | from imodelsx import AugGAMClassifier
import datasets
import numpy as np
if __name__ == '__main__':
# set up data
dset = datasets.load_dataset('rotten_tomatoes')['train']
dset = dset.select(np.random.choice(len(dset), size=10, replace=False))
dset_val = datasets.load_dataset('rotten_tomatoes')['validat... |
a901b779273e2ec0772c5aff764cdeb7012bf704ef15b06857abeb890b3faa30 | Python | 1,648 | 72 | import os
import re
import pickle
def join_paths(*args, create: bool = True) -> str:
"""
Joins strings to a path. If path does not exist yet, it is created.
:param args: os paths or strings
:param create: bool, if True a folder is created if it does not exist
:return: joint os path
"""
p... |
a285e5e475d61b4853ffe00a1c66c79fd8bb78d1ecd3f9c093985ae4e00b581b | Python | 1,649 | 61 | """Choose between ``set`` and ``attach_hooks``.
What this demonstrates
----------------------
``set`` records a concrete one-shot replacement. ``attach_hooks`` records a
sticky callable/helper recipe that can be replayed or rerun repeatedly.
How to run
----------
``python examples/intervention/05_set_vs_attach_hooks.... |
c67c757c1d61f1a0fc1ea2fc18ab373e8c14bd63a30c7701b7db26a0adcc0fd6 | Python | 1,650 | 55 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["splicepoint_model_spec"] = dict(
type="BothLSSIModels",
acceptor="model/splicepoint-model-acceptor-1",
donor="model/splicepoint-model-donor-1",
)
msp.architecture["motif_model_spec"]["motif_... |
ff947371eaae70bd18cfd5d5d18e1aae1a0dd373259658638c2a4a8b721c17e8 | Python | 1,650 | 43 |
# this dataset has very bad training accuracy
from global_config import *
isMeshFile = True # choose between part and mesh input format
hasPerfileMetadata = True
##############################
if testing:
input_root_path = "./testdata/testThingi10K_data"
output_root_path = input_root_path + ... |
4138ef5e416b73e995542bb76eab98927867ea0a7fa205fb5dfe77a292f899fb | Python | 1,651 | 62 | # -*- coding: utf-8 -*-
import os
from utils import write
import matplotlib.pyplot as plt
plt.switch_backend('agg')
class Logger(object):
""" Framework to log encoding analysis progression and results.
"""
def __init__(self, path):
self.log_path = path
def report_logs(self, logs, le... |
751133417929b874b6d1ca9b2c2111d4364b391ea21f1bbcb0f3b87ebec5f7ae | Python | 1,651 | 43 | # SPDX-License-Identifier: LGPL-2.1-or-later
from __future__ import print_function, division, absolute_import
import os
import unittest
import pyEcoHAB.utils.for_loading as uf
from pyEcoHAB import data_path
from pyEcoHAB.Loader import EcoHabDataBase, Loader
from pyEcoHAB import Timeline
from pyEcoHAB.SetupConfig impor... |
a1d86a68ea6c884003ffea8c1d9ebd5778bfae204e69ecb5d11f32ffed7676e9 | Python | 1,651 | 54 | from skimage.metrics import structural_similarity as ssim
from PIL import Image
import os
import numpy as np
from glob import glob
from tqdm import tqdm
import argparse
def cal_SSIM(x, y):
return ssim(x, y, multichannel=True)
def get_dirImage(real_dir, fake_dir):
real_image_paths = sorted(glob(os.path.join(... |
151bd7f3f7948d65b140a215eb430d88b4717670eba7e9e6f1144cbd777b9584 | Python | 1,652 | 58 | import os
import numpy as np
from dotenv import load_dotenv
from sklearn.metrics import roc_auc_score
from omop_learn.omop import OMOPDataset
from omop_learn.sparse.models import OMOPLogisticRegression
from omop_learn.utils.config import Config
load_dotenv()
config = Config({
"path": os.getenv("DATABASE_PATH")... |
f6e387f6315f54c6d2d8498c9f1226cdfc00bc06fed25afe06e4bbf0688f50e7 | Python | 1,652 | 46 | import torch
def save_model(model, name, best_accuracy):
if is_parallel(model):
model = de_parallel(model)
torch.save({
'state_dict': model.state_dict(),
'best_acc': best_accuracy,
}, f'{name}.pt')
def is_parallel(model):
# Returns True if model is of type DP or DDP
retu... |
3d6cf182051df3c8c5149fb521008f3b91413dd5b1da2fa2a509aea5e74811d9 | Python | 1,653 | 53 | import os
import random
import numpy as np
from skimage import io, color
import cv2
images_path = './MRI' # series of MRI images
masks_path = './pred' # series of predicted masks
img_overlay_path = './overlayMRI'
images = []
masks = []
images_name = []
for im in os.listdir(images_path):
images.append(os.path.jo... |
13a1605b372c1a55445564225735cee25d12691606547e029f0869276a66d8eb | Python | 1,656 | 45 | ####usage: python script sample_sites.txt
import sys, os
import collections
def get_annvar_input(sites_file):
c = collections.defaultdict(list)
with open(sites_file) as f, \
open('tmp_'+sites_file,'w') as out:
for line in f:
line = line.strip().split('\t')
chr_id = line[0]
#chr_id.replace("chr", "")
p... |
3875e800d8f81e78c108bf9db5cd4d17744d2cec4b3d1980697b17e0c1e9ed83 | Python | 1,656 | 59 | import random
from .utils import functional as F
class Normalize(object):
"""Normalize a tensor image with mean and standard deviation
Given mean: m and std: s
will normalize each channel as channel = (channel - mean) / std
Args:
mean (int): mean value
std (int): std value
"""
... |
55ab60fca596e746c7dc14532a1047cd06559427566b9ad6e48dacb93f8c22ba | Python | 1,656 | 53 | from datetime import datetime
from unittest import mock
import dateutil.relativedelta
from api.mocks import FakeAuthContext
from carbonserver.api.infra.repositories.repository_projects import SqlAlchemyRepository
from carbonserver.api.usecases.project.project_sum import ProjectSumsUsecase
PROJECT_ID = "e60afa92-17b7... |
a0f58b8eb5ec308380b2a8517eebbd1486a31f60573920331429fa78160250e4 | Python | 1,656 | 52 | # coding=utf-8
# Copyright 2025 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
132a03009fd06a7d42fdc8ff13e0f48cd3d007ab1e2bbbf50de9d3a641de37c4 | Python | 1,657 | 50 | import collections
import copy
__author__ = 'quentin'
class DataPoint(collections.OrderedDict):
def __init__(self, data):
"""
A container to store variables. It derived from :class:`~collections.OrderedDict`.
Variables are accessible by header name, which is an individual identifier
... |
8adfe3bbbf62e281b373cbbd860737116956439372d567d47cc00c981a4101ce | Python | 1,657 | 60 | import numpy as np
from seqpro.rag import Ragged
class _Sub(Ragged):
__slots__ = ()
def _ragged():
return Ragged.from_lengths(
np.arange(6, dtype=np.int32), np.array([2, 1, 3], np.uint32)
)
def test_with_layout_preserves_subclass():
sub = _Sub(_ragged()._layout)
out = sub._with_layout... |
346675da2dd0f99694635a8678c3e932fcac20539434aadd6224966169f86685 | Python | 1,658 | 50 | import collections
import copy
__author__ = "quentin"
class DataPoint(collections.OrderedDict):
def __init__(self, data):
"""
A container to store variables. It derived from :class:`~collections.OrderedDict`.
Variables are accessible by header name, which is an individual identifier
... |
3849f10c9bd6b3f7ec4b6b6e54a10395462cd6099df8fc8ff7c1d927a9b31c14 | Python | 1,658 | 53 | from .h5_dataset import H5Dataset
from .multi_setting_h5_file import MultiSettingH5File
class DuplicatedSettingH5File(MultiSettingH5File):
"""
Just duplicates the data identically for each setting.
"""
def __init__(self, path, datapoint_extractor, num_settings):
super(DuplicatedSettingH5File,... |
fd4e2a564af5efa1de99c20767c9f55ff7f856416f8863f173b53f982207e6e2 | Python | 1,658 | 54 | #!/usr/bin/env python
"""
Input: molecular input file.
Output: Molecule file with removed ions and fragments.
Copyright 2012, Bjoern Gruening and Xavier Lucas
"""
import argparse
from openbabel import openbabel, pybel
openbabel.obErrorLog.StopLogging()
def parse_command_line():
parser = argparse.Ar... |
4c0d62f3dc7809acc7f8ecb5432d28ff33edd753ce43663b9e3a483499176dea | Python | 1,659 | 54 | from skimage.metrics import structural_similarity as ssim
from PIL import Image
import os
import numpy as np
from glob import glob
from tqdm import tqdm
import argparse
def cal_SSIM(x, y):
return ssim(x, y, multichannel=True)
def get_dirImage(real_dir, fake_dir):
real_image_paths = sorted(glob(os.path.join(... |
44e0fd6ab70af4b76861521457f362122e6e90556e990344784aef1e3e99f7a8 | Python | 1,660 | 64 | # Copyright (c) 2024 PaddleHelix Authors. All Rights Reserved.
#
# Licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0
# International License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# http://creativecommo... |
8dfcbcced4a6198eb21161a87149ec474720666b3f3ab2aa3634c0fe05f019ab | Python | 1,660 | 55 | """
Gregory Way 2017=8
PanCancer Classifier
feature_comparison/analysis_pipeline.py
Master script to submit all pancancer classifiers trained on compressed gene
expression features.
Usage:
python feature_comparison/analysis_pipeline.py
Output:
Classifier results in the `feature_comparison/classifiers` fold... |
a8a1710aa42e51da657ace3637c13294ac845d14df3c0be5114537c5dde8295a | Python | 1,660 | 52 | """Prepare Multi-Human Parsing V1 dataset"""
import os
import shutil
import argparse
import zipfile
from gluoncv.utils import makedirs
from gluoncv.utils.filesystem import try_import_gdfDownloader, try_import_html5lib
_TARGET_DIR = os.path.expanduser('~/.mxnet/datasets/mhp')
def parse_args():
parser = argparse.A... |
e6ec2e4500ed00cd91bdc4afd8cb6b7ef11449f83a1406537380d5bc3c5c31f1 | Python | 1,660 | 46 | import os
from setuptools import setup, find_packages
here = os.path.abspath(os.path.dirname(__file__))
# Get the long description from the relevant file
with open(os.path.join(here, 'README.rst')) as f:
long_description = f.read()
def get_version(string):
""" Parse the version number variable __version_... |
68039f921d13ac558eca33283d87711fb99da97378e46186031f7906619120d4 | Python | 1,661 | 65 | """Regression tests for saved ModuleCall summary counters."""
import torch
from torch import nn
import torchlens as tl
class _ModuleCallCounterModel(nn.Module):
"""Tiny model with a named child module for ModuleCall counting."""
def __init__(self) -> None:
"""Initialize the child module."""
... |
9dcccabfa2229ebfcbad4c7d74d24397d2fe16b90d64398bb64b32a87cea2f67 | Python | 1,661 | 63 | #!/usr/bin/env python
"""
Utility functions for SuCOS and other RDKit modules
"""
from __future__ import print_function
import gzip
import sys
from rdkit import Chem
def log(*args, **kwargs):
"""Log output to STDERR"""
print(*args, file=sys.stderr, **kwargs)
def open_file_for_reading(filename):
"""Op... |
a0a2ddc662ae2a35fb14c90f4b3575c21a0c0a7abd105f23115c4003c3a20a12 | Python | 1,661 | 57 | """Generate palettes for different tasks."""
from typing import Dict, List
import numpy as np
from .label import drivables, labels, lane_categories
from .to_mask import STUFF_NUM
PALETTES: Dict[str, List[int]] = {}
def get_palette(mode: str) -> List[int]:
"""Generate mapping for the required task."""
if m... |
c9fa0e9209a8f636630eb72e5eed3cea65a8d6c07b2bd1fbf2d3eba1144d5c88 | Python | 1,661 | 53 | # This example requires the following dependencies to be installed:
# pip install lightly
# Note: The model and training settings do not follow the reference settings
# from the paper. The settings are chosen such that the example can easily be
# run on a small dataset with a single GPU.
import torch
from torch.nn im... |
7a7e2108bb7ee53ff43101dfab739cacc595c25ec4b5003993e680dceb9e908a | Python | 1,662 | 53 | import argparse
import numpy as np
import mxnet as mx
import matplotlib.pyplot as plt
from mxnet import gluon, nd, image
from mxnet.gluon.data.vision import transforms
import gluoncv as gcv
gcv.utils.check_version('0.6.0')
from gluoncv.model_zoo import get_model
parser = argparse.ArgumentParser(description='Predict ... |
0216ad5acacc5d79116af32f7ac35a6118d0c665b74d0438ac747cf8173286fa | Python | 1,663 | 48 | import numpy as np
from src.cpd_registration import RigidRegistration, NonRigidRegistration
def generate_synthetic_tree(n_points=50):
# Generate a simple branch (a line in 3D)
points = np.linspace(0, 10, n_points)
return np.stack([points, np.zeros(n_points), np.zeros(n_points)], axis=1)
def deform_tree(po... |
d136d9aec5f9912c4d6072b9aac6f100a12655c0e883d14a62fb53d60741e47c | Python | 1,663 | 58 | from . import data_cleaning
import pandas as pd
import scipy.io
def mat2dataframe(path, shift_idx_fields, td_name=None):
"""
Load a trial_data .mat file and turn it into a pandas DataFrame
Parameters
----------
path : str
path to the .mat file to load
"Can also pass open file-like... |
0b89e95108ad9ac1b89bfdca11cdb9859e5138a9f1a038c91351dfe49214f493 | Python | 1,665 | 37 | import argparse
import os
import warnings
from itmt_v2.infer_segmentation import test
from settings import CUDA_VISIBLE_DEVICES
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
os.environ["TF_CPP_MIN_LOG_LEVEL"] = '3'
warnings.filterwarnings("ignore")
if __name__ == '__main__':
parser = argparse.ArgumentParser(descr... |
30f1f1287b60ca0787e78aa90f83237dc891cc84bd914a2ee86e2dfe53a56c4c | Python | 1,665 | 51 | # coding=utf-8
# Copyright 2025 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
b8f445de0c374aebeee212f3712953372add29b581e10ded6762504c10cf8d2f | Python | 1,665 | 57 | import numpy as np
import scipy.spatial as spatial
from anndata import AnnData
def disk(
adata: AnnData,
use_data: str = "X_umap",
radius: float = 10.0,
rates: int = 1,
method: str = "mean",
copy: bool = False,
) -> AnnData | None:
adata = adata.copy() if copy else adata
coords = adat... |
053ac09b617d55028926ce9a6685f0c03685e093c861012ceee030a53e95bb22 | Python | 1,666 | 41 | import functions_common
import functions_3D
import pandas as pd
parameters = functions_common.read_parameters()
print("#"*5,"parameters","#"*5)
print(parameters)
key_file = functions_common.read_key_file(parameters)
print("#"*5,"key_file","#"*5)
print(key_file.head())
print(key_file.keys())
#macrophage_properties_df... |
1258ca2eac3fa15334f33391fb42ad413160f70b50cd3c68a75bf23dbbad61ac | Python | 1,666 | 49 | import operator
from unittest.mock import Mock
from celery import signals
from celery.concurrency import solo
from celery.utils.functional import noop
class test_solo_TaskPool:
def test_on_start(self):
x = solo.TaskPool()
x.on_start()
def test_on_apply(self):
x = solo.TaskPool()
... |
788d442831e03fedbf6876463371c4506b7e18ed90598e5711fadda36098f2fb | Python | 1,667 | 39 | import requests
import tarfile
import sys
import os
VISUAL_CHECKPOINTS_LOCATION = 'model_analysis_folders/visual_networks/pytorch_checkpoints/'
AUDIO_CHECKPOINTS_LOCATION = 'model_analysis_folders/audio_networks/pytorch_checkpoints/'
ASSETS_LOCATION = 'assets/'
def download_extract_remove(url, extract_location):
... |
4905a6ed5069ec2e83d9f842be047dc86f133d2f5dbb4506701402727b9c58c6 | Python | 1,668 | 53 | """ Compute test scores """
# python get_test_scores.py -y data/medical -pred exp_out/2022-05-28-17-19-27
import os
import json
import argparse
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
from sklearn.metrics i... |
49ee4b4a0422f91c00fa75db27597fba98cac3f30d21c91e4ff761dd361697ac | Python | 1,668 | 54 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created in May 2023
@author: Thomas Bonald <bonald@enst.fr>
"""
from typing import Iterable, Optional, Union
import numpy as np
from scipy import sparse
from sknetwork.path.distances import get_distances
from sknetwork.utils.check import check_format, check_square
... |
5f0f578c1147b411145a44411324320f8fb6ea0e494f40364eb6db07e2b5c8ca | Python | 1,668 | 42 | import argparse
import logging
from rpy2.situation import get_r_home
from rpy2.situation import iter_info
from rpy2.situation import r_ld_library_path_from_subprocess
from rpy2.situation import set_default_logging
import sys
logger = logging.getLogger(__name__)
if __name__ == '__main__':
parser = argparse.Argume... |
75268a61a471b46021d093669923c716c28987281ff3bf15a543972a4b5b89b7 | Python | 1,668 | 53 | import pandas as pd
from anndata import AnnData
def lr(
adata: AnnData,
db_filepath: str,
sep: str = "\t",
source: str = "connectomedb",
copy: bool = False,
) -> AnnData | None:
"""Add significant Ligand-Receptor pairs into AnnData object
Parameters
----------
adata: AnnData
... |
9925e791bc917c74c5afc7810f0ce9816609748c34f6b6cd9fdf3dbe2d19eb50 | Python | 1,668 | 70 | """
Implementation of postprocessing methods
"""
from Bio.SVDSuperimposer import SVDSuperimposer
import numpy as np
from numpy import array, dot, set_printoptions
def group_sites(p_sites):
size4_sites = [x for x in p_sites if len(x) == 4]
out_list = []
for p_site in p_sites:
contained = False
... |
cdcf3c3850e6453761bf14f0b35db7b42b5764754912a99c23b6607d86031867 | Python | 1,668 | 52 | """Accelerate on-ramp rows (memo item 20 / Tier 2).
``init_empty_weights()`` and literal ``from_pretrained(...,
device_map='meta')`` are advertised ONLY after these rows pass —
``device_map='meta'`` is the one DIGEST-AUDIT-named spelling no panel lab
could run in four rounds (accelerate absent from the checkout); abse... |
5a7ef5945a9f3b8230745950cd66d0601d439390cea6284ec47bc15032ab169e | Python | 1,669 | 36 | import requests
from pytest_celery import RESULT_TIMEOUT, CeleryTestSetup
from requests.auth import HTTPBasicAuth
from celery.canvas import group
from t.integration.tasks import add, identity
from t.smoke.tests.quorum_queues.conftest import RabbitMQManagementBroker
class test_broker_configuration:
def test_queue... |
6374dba2abb382db8c390b17204a09f1692326f5bf9e742e9327ea5b9f9facfa | Python | 1,669 | 57 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["splicepoint_model_spec"] = dict(
type="BothLSSIModels",
acceptor="model/splicepoint-model-acceptor-1",
donor="model/splicepoint-model-donor-1",
)
msp.architecture["motif_model_spec"]["motif_... |
6d8ebd5ff0ddba5ca962894b655739ae98d40203e96f81278f9a8c37bf5f1fcd | Python | 1,669 | 44 | __author__ = 'quentin'
from ethoscope.hardware.interfaces.lynx_motion import SimpleLynxMotionInterface
class SleepDepriverInterface(SimpleLynxMotionInterface):
def send(self, channel, dt=350, margin=10):
"""
Sleep deprive an animal by rotating its tube.
:param channel: The channel to use ... |
d77b0b62007441730042e2b0aef916da3a6828f464922acc8e13c4ef2d4e6daf | Python | 1,669 | 56 | """Remove unique constraint from role name, add not null constraint
Revision ID: 9a5207190a4d
Revises: a99a5b52ccb8
Create Date: 2024-10-08 14:08:28.418055
"""
import logging
from alembic import op
from sqlalchemy import text
from galaxy.model.database_object_names import build_index_name
from galaxy.model.migrati... |
8eda5429a73379a422b235e4946ecebb22e1040247d4463ceb0cfabb63000dd7 | Python | 1,670 | 59 | import pytest
import torch
import torch.nn as nn
from torch.autograd import Variable
import pretrainedmodels as pm
import pretrainedmodels.utils as utils
# torch 1.0.x
set_grad_enabled = getattr(torch.autograd, 'set_grad_enabled', None)
pm_args = []
for model_name in pm.model_names:
for pretrained in pm.pretraine... |
b4de5dee0a68fd066083d74e0cdbf87045adbd42548c5af2049e8e60e0aa1c34 | Python | 1,670 | 47 | from train_srgan import SRGenerator
import mxnet as mx
from mxnet.gluon.data.vision import transforms
from matplotlib import pyplot as plt
from gluoncv.utils import try_import_cv2
cv2 = try_import_cv2()
from mxnet import image
import argparse
def parse_args():
parser = argparse.ArgumentParser(description='Test wit... |
f5cfdb03f4a2959ff7aff9577ff8388b6718567526deb03b7b2428bf3ece5804 | Python | 1,670 | 49 | """nnsight bridge against the real nnsight package (no sys.modules fakes).
A live ``LanguageModel.trace`` tracer exposes no mapping, ``to_dict()`` or
``nodes``; the bridge must refuse it typed instead of returning an empty
payload, and must carry values the user saved with nnsight through unchanged.
"""
from __future... |
131f15a2f69ae0b9ea6546d2bf9e1ccdae8cb3822fbf86982c25eb212e37f1ef | Python | 1,671 | 65 | # -*- coding: utf-8 -*-
# Copyright (c) 2016-2018 Braintech Sp. z o.o. [Ltd.] <http://www.braintech.pl>
# All rights reserved.
"""Recommended tools for OpenBCI debugging.
* PyCharm
* ``install_debug_handler()`` defined in this file
To connect to existing processes with PyCharm or Pyrasite
you need to run is you have... |
6c6189669afc6161d20a68d420d1fc13650439678f2f75dd5482b8cb4390d15f | Python | 1,671 | 54 | ##########################
# FreeCAD standard part library
#########################
# download part data and generate classification by folder structure
from __future__ import print_function, division
import os
import os.path
from global_config import *
MechanicalOnly = True # no electronics parts
splittingFaste... |
7c8b871f388bd40e8f19bd8722e05f12b2679cd92f3ee2012df65c7b15fb22cb | Python | 1,671 | 58 | # -*- coding: utf-8 -*-
# @Author: gviejo
# @Date: 2022-04-04 22:35:44
# @Last Modified by: gviejo
# @Last Modified time: 2022-08-19 09:03:33
"""Tests of neurosuite loader for `pynapple` package."""
import warnings
import numpy as np
import pandas as pd
import pytest
import pynapple as nap
@pytest.mark.filter... |
acd4c96685fc88991d73659079017a898ba0029e9d680bc5809d25200dd0db68 | Python | 1,671 | 58 | from __future__ import annotations
import os
import pytest
from pytest_celery import CeleryTestWorker, defaults
from pytest_docker_tools import build, container, fxtr
from celery import Celery
from t.smoke.workers.dev import SmokeWorkerContainer
class AltSmokeWorkerContainer(SmokeWorkerContainer):
"""Alternati... |
01bc5f62922e96f6786cdfdad71eb36b2a965faee208c04a79908b9207d40dd4 | Python | 1,672 | 46 | def find_pattern(sites, usable, skippable, pattern):
"""
Find the sites that match the pattern, where usable and skippable are
boolean arrays indicating which sites are usable and skippable.
The pattern is a string of A and D, where A means "acceptor" and D means
"donor". The pattern must consist o... |
3f3baca6ea86618a958f7538c033dc5f24b787c59f3b1af5dcb5448646f8045d | Python | 1,672 | 43 | """Wrapper for transforming observations."""
from typing import Any, Callable
import gym
class TransformObservation(gym.ObservationWrapper):
"""Transform the observation via an arbitrary function :attr:`f`.
The function :attr:`f` should be defined on the observation space of the base environment, ``env``, a... |
24498accbf0a96b82daea27ff6a2c58c73813e2c5eb676b6ba4583e45c5c8eb2 | Python | 1,673 | 54 | import json
import re
import matplotlib
import numpy as np
from matplotlib import pyplot as plt
font = {'family' : 'Times',
'weight' : 'normal',
'size' : 12}
matplotlib.rc('font', **font)
with open("./storage/predictability_repeated_august19a.json", "r") as f:
results = json.load(f)
# remove en... |
477c674d5b534bc6973fbc6e682c22619afb580a3bc4898b542bbee3fef7cb22 | Python | 1,673 | 64 | """Use one sticky hook recipe with replay and rerun.
What this demonstrates
----------------------
Attach a hook once, replay over the saved graph, then rerun the original model
with the same active intervention spec.
How to run
----------
``python examples/intervention/04_sticky_hooks_multiple_engines.py``
Runnable... |
81b13a1e86304f715087e83521cab75d349c2bc5506001c16718765bed938f6b | Python | 1,673 | 65 | """Compare three or more prompt variants in a Bundle.
What this demonstrates
----------------------
Capture three tensor "prompt" variants, package them into one ``Bundle``, and
compute both member-wise and joint metrics.
How to run
----------
``python examples/intervention/13_paired_prompt_3plus.py``
Runnable by de... |
c377299159fd2717cdc69a8e265ac87449b9b367cb4d066bf53b0b6c6cbaa819 | Python | 1,673 | 47 | import sqlite3
from .db import DB
class CaseInformation(DB):
def __init__(self):
super().__init__()
self.table_name = 'HR_case_info'
self.table_desc = 'Haryana daily new cases, recoveries, deaths information'
self.cols = self.getcolumns()
def getcolumns(self):
cols ... |
df3a4f4c0c7901d378db00a9efade038ac652af1748105df18ac3682b8e39cc6 | Python | 1,673 | 46 | """Abstract class for TreeGrower Algorithms"""
import abc
import copy
class AbstractAlgo:
"""TreeGrower abstract class"""
# meta class is used to define other classes
__metaclass__ = abc.ABCMeta
def __init__(self, input_data, params, start_point, context):
"""TreeGrower Algorithm initializat... |
f04e34da5d16a92881ee575550e8b547200dc6287c62d8ee1ccc6e1e2d6f53f8 | Python | 1,673 | 59 | import re
import numpy as np
import pytest
import gym.spaces
def test_sample():
"""Tests the sequence sampling works as expects and the errors are correctly raised."""
space = gym.spaces.Sequence(gym.spaces.Box(0, 1))
# Test integer mask length
for length in range(4):
sample = space.sample(... |
96224f48e67830bc121df930e8167f25f38c0ce78f3ee698a32a7d56a3a948d4 | Python | 1,674 | 60 | import numpy as np
from neuron import gui
from neuron import h
# This checks the use of `diam` and `area` in MOD files.
def check_morphology(sections):
d2_expected = np.array([s(0.5).diam ** 2 for s in sections])
a2_expected = np.array([s(0.5).area() ** 2 for s in sections])
d2_actual = np.array([s(0.5)... |
b2e1785a3aeeccc18cd0ff479c488cfad3c8c4d409c911f34b3635b63bfb9430 | Python | 1,674 | 43 | import torch
import torch.nn as nn
from prodict import Prodict
import utils
class ICNet(nn.Module):
def __init__(self, dims: Prodict, device=torch.device("cpu")) -> None:
"""The initial condition encoder module for TDNODE.
Parameters
----------
dims : Prodict
A diction... |
c73744d606eb3456545035ab20de7c47a136b6d7d4318420f8fb9cc74133d631 | Python | 1,674 | 67 | #!/usr/bin/env python
"""
Input: UniProt accession number(s)
Output: uniprot protein sequence(s)
Sept 2011
Xavier Lucas, Bjoern Gruening
"""
import argparse
import subprocess
import sys
LIB_PATH = "/media/data/databases/uniprot/devided/"
def parse_command_line(argv):
parser = argparse.ArgumentParser()
parser... |
3ea8ac0cc3042f05f2462f583cdd316de219c471ca19bd8a3ce8232fa334e8d3 | Python | 1,675 | 42 | from pyabf.abf2.section import Section
class StringsSection(Section):
"""
Part of the ABF file contains long strings. Some of these can be broken
apart into indexed strings.
The first string is the only one which seems to contain useful information.
This contains information like channel names, c... |
3fc1baeeda12920f2f85827236317e8bfc61c63b2a4da6379c6b4ea531e158a6 | Python | 1,675 | 63 | #!/usr/bin/env python3
# LICENSE
#
# _This file is Copyright 2018 by the Image Processing and Analysis Group (BioImage Suite Team). Dept. of Radiology & Biomedical Imaging, Yale School of Medicine._
#
# BioImage Suite Web is licensed under the Apache License, Version 2.0 (the "License");
#
# - you may not use this so... |
6a0248fa5129c0ff65d47b1a5baf894a6ce7abc46304d5a3360f8bd8de07c42d | Python | 1,675 | 54 | try:
from anvilfs.anvilfs import AnVILFS
except ImportError:
AnVILFS = None
from galaxy.files.models import (
BaseFileSourceConfiguration,
BaseFileSourceTemplateConfiguration,
FilesSourceRuntimeContext,
)
from galaxy.util.config_templates import TemplateExpansion
from ._pyfilesystem2 import PyFiles... |
09323ec8a4d9683f781166c1c67e256309ecd3d08508c8fa9825af4ba6a3b6bb | Python | 1,676 | 52 | #!/usr/bin/env python
# =============================================================================
# MODULE DOCSTRING
# =============================================================================
"""
MAF on cartesian coordinates using a neural spline transformer.
"""
# ========================================... |
32e0f065198fc2a22c36fe2b9ff7748e5def8c1d65512cc6cb5411145a5c72ab | Python | 1,676 | 62 | '''Tests for bdpy.ml'''
import unittest
import numpy as np
from bdpy.ml import regress
class TestRegress(unittest.TestCase):
'''Tests for 'ml.regress' module'''
def test_add_bias_default(self):
'''Test of bdpy.ml.regress.add_bias (default: axis=0)'''
x = np.array([[100, 110, 120],
... |
37a943770d44df64b3e3e9cddeb9fe524dc102d60799878e21fab54c2cf1d31e | Python | 1,676 | 46 | import glob
import os
import json
experiment_dir = "../../../storage/experiments/"
experiment_group_inclusion_filters = {
# all filters are "only if in" filters, i.e. if the experiment has any of the values in the filter, it will be
# included, otherwise it will be excluded
"group_one": {
"archit... |
7f56f89daf57c86cf21fe7d41afca5865571b0728dfd259debe530fa5c9ca7f3 | Python | 1,676 | 55 | import os
import importlib
import glob
def find_launcher_using_name(launcher_name):
# cur_dir = os.path.dirname(os.path.abspath(__file__))
# pythonfiles = glob.glob(cur_dir + '/**/*.py')
launcher_filename = "experiments.{}_launcher".format(launcher_name)
launcherlib = importlib.import_module(launcher_... |
980c2cde05688b32b37dd49a9ee3e5f74e8fc7fafcb607c21e9a07702d38205e | Python | 1,676 | 62 | from database import (
ImageModel,
TaskModel,
DatasetModel
)
from celery import shared_task
from ..socket import create_socket
from .thumbnails import thumbnail_generate_single_image
import os
@shared_task
def scan_dataset(task_id, dataset_id):
task = TaskModel.objects.get(id=task_id)
dataset =... |
c86028d135ee52a02abb1301dbe020c20bfb97cabff6fcb029b965e2551bb655 | Python | 1,676 | 62 | #!/usr/bin/env python3
f_1 = open("isoform_TPM_withAnnotation.txt", "r", encoding = "utf-8")
o_1 = open("isoform_proportion_individual.txt", "w", encoding = "utf-8")
i = 0
for file_1 in f_1:
line_1 = file_1.strip().split('\t')
if i != 0:
percent_C = []
if line_1[len(line_1) - 2] == "1":
... |
f44c2087e58d89708b7cc21c25cd72d04ff2a0fed0e91ba130a22fdf489e8dde | Python | 1,676 | 59 | import numpy as np
from gym import utils
from gym.envs.mujoco import MuJocoPyEnv
from gym.spaces import Box
class SwimmerEnv(MuJocoPyEnv, utils.EzPickle):
metadata = {
"render_modes": [
"human",
"rgb_array",
"depth_array",
],
"render_fps": 25,
}
... |
34bd02fd21eb797a72314faec2ae69fca8120aae57bb57595c46f86d6e9945e7 | Python | 1,677 | 52 | """
Data Structures used to represented molecules for convolutions.
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import re
__author__ = "Qingyuan Feng"
__copyright__ = "Copyright 2018, Simon Fraser University"
__license__ = "MIT"
class Protein(ob... |
681b4d05785c5f20323df5cc87695c25844f7551e3a33ce42949433402802a9b | Python | 1,677 | 48 | import pickle
def run():
from spitfire.time.integrator import odesolve
from spitfire.time.methods import RK4ClassicalS4P4
import numpy as np
def right_hand_side(c, k_ab, k_bc):
"""
Computes the right-hand side function for the ODE system.
Note that time integration requires a... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.