sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
2b849c48613d301ff450fd569c85a87b561d5209d400145f0f5dcde9c5dcc253 | Python | 794 | 30 | """Register UNet model (standalone Simulation + CID package)."""
from __future__ import annotations
from omegaconf import DictConfig
from dlbase.registry import register_model
def _register_builtin_models() -> None:
def assert_unet(cfg: DictConfig) -> None:
from models.unet.model_task_datase... |
c8baa6b5529a0d34e382dca85f06e827c90cca60ac7e259c623435b2dba596db | Python | 794 | 22 | import argparse
import json
from collections import defaultdict
import os
from tqdm import tqdm
import sys
import subprocess
import re
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Example argument parser')
parser.add_argument('--dump', type=str)
parser.add_argument('--model', ty... |
e888ae358729fb380c1855b51c8766d70a5fc6659ca763c31c87bfbcb8b85c44 | Python | 794 | 18 | # Nearly all of the code here comes from the original Lucid library
# Copyright 2018 The Lucid Authors. All Rights Reserved.
#
# 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://ww... |
c96d08437388ff30580953d18ad7812fe680dc082b0b709d59641f647fddd169 | Python | 795 | 22 | """Forwarding for NCBI taxonomy identifiers NCBI has since retired.
A taxid cached from an outside source can predate the taxonomy dump on disk.
Reading such a taxid without forwarding it through ``merged.dmp`` first risks
reading one taxon as two, or gold nothing current can match; any caller of a
raw NCBI taxid shou... |
e0b545a8f89b15302a56c2a7e829b06ffffa33186cfdc8b520cd03bf1f29be99 | Python | 796 | 29 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
try:
import huggingface_hub
except ImportError:
huggingface_hub = None
from fai... |
9a4341e698aa4beae6e42f35f52264a63587e14a1a279e19ce8da9d56ea485e9 | Python | 797 | 30 | import torch
import torch.nn.functional as F
class SwishImplementation(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
ctx.save_for_backward(i)
return i * torch.sigmoid(i)
@staticmethod
def backward(ctx, grad_output):
sigmoid_i = torch.sigmoid(ctx.saved_variables[... |
644104a5ef3e66963f52d3522ebda25d99b0458b965134c6a4f3b4a9b0682540 | Python | 799 | 15 | from dual_echo_pcasl_ETLAS2_scripts import motion_correction_to_template
import click
@click.command()
@click.argument('subject')
@click.option('--rawdata', default="/mnt/projects/ETLAS2/data/rawdata")
@click.option('--dual_echo_pcasl_folder', default="/mnt/scratch/projects/ETLAS2/data/derivatives/dual_echo_pcasl")
@... |
6ca0c6abed6ad0250144da501c32f78fcdb2d41f2baae0d36ac8b4e8110e8e11 | Python | 799 | 34 | from pgmpy.datasets._base import BaseDataset
class IQBrainSize(BaseDataset):
"""
References
----------
- :footcite:t:`cmu_iqbrain`
"""
_tags = {
"name": "iq_brain_size",
"n_variables": 9,
"n_samples": 20,
"has_ground_truth": False,
"has_expert_knowledge... |
0e589381ccb09e4ff7bdfa92e6f2ab7fdd8d848fa202da44205968cf8a1824fd | Python | 800 | 25 | import torch
def get_grad(y,x)->torch.Tensor:
'''计算单个y对x的偏导数'''
return torch.autograd.grad(inputs = x,
outputs = y,
grad_outputs=torch.ones_like(y),
retain_graph=True,
create_graph=Tr... |
554dbb18e578b3e8d87954dd1ab03c6bed34f08f159b50e6d6d79452fa654ca3 | Python | 800 | 32 | from setuptools import setup, find_packages
with open('evo/version.py') as infile:
exec(infile.read())
with open('README.md') as f:
readme = f.read()
with open('requirements.txt') as f:
requirements = f.read().split('\n')
sources = {
'evo': 'evo',
'evo.scripts': 'scripts',
}
setup(
name='ev... |
186541de5364717d214a6f9de6b4d735a5cde75da1e2bd22980e8e1adfc8e316 | Python | 802 | 25 | """
Custom Exceptions Used In Autonomouse Schedule
"""
"""
Copyright (c) 2019, 2022 [copyright holders here]
This file is part of NoSeMaze.
NoSeMaze is free software: you can redistribute it and/or
modify it under the terms of GNU General Public License as
published by the Free Software Foundation, either version 3... |
46567c38c17e9f39e56b8f54b6f7907f7b168dba1ae89d93336a1dd9f542828f | Python | 802 | 37 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
from vlpp.utils import add_suffix
from vlpp.registration import applyAnat2Tpl
def main():
os.mkdir("anat")
os.mkdir("pet")
os.mkdir("centiloid")
anat = "${anat}"
atlas = "${atlas}"
pet4070 = "${pet4070}"
pet5070 = "${pet5070}" #cen... |
b81c8469f047a482b6876f307af373a5303ff7f10b202180a21b963ced7a997d | Python | 802 | 23 | from argparse import Namespace
from pathlib import Path
import numpy as np
def get_extraction_mz_list(extraction_folder):
extraction_args = eval(
open(
Path(extraction_folder) /
"args.txt").read())
bins = extraction_args.bins
extraction_star... |
b58eb2d83c0f3c6c72503d7d65870e888937321e3b2fc86266e92236d232e923 | Python | 803 | 20 | from torch.optim.lr_scheduler import _LRScheduler
class PolyLRScheduler(_LRScheduler):
def __init__(self, optimizer, initial_lr: float, max_steps: int, exponent: float = 0.9, current_step: int = None):
self.optimizer = optimizer
self.initial_lr = initial_lr
self.max_steps = max_steps
... |
cce904aef04ea4115227263eb2bfd7ec6bce0caf7993a610ad0d25224109b6ff | Python | 803 | 26 | import matplotlib.pyplot as plt
import numpy as np
import loompy
def cell_cycle(ds: loompy.LoomConnection, out_file: str) -> None:
cc = ds.ca.CellCycle
tsne = ds.ca.TSNE
plt.figure(figsize=(12, 5))
plt.subplot(121)
plt.scatter(np.arange(cc.shape[0]), 100 * cc, marker=".", s=5)
plt.ylim(0, 5)
plt.hlines(1, 0, ... |
fc8543bc65521ce4ac5d7a01754f189ff953c716e1873b2a0fde1b981392f95d | Python | 803 | 36 | import numpy as np
from fairseq.data import FairseqDataset
class DummyDataset(FairseqDataset):
def __init__(self, batch, num_items, item_size):
super().__init__()
self.batch = batch
self.num_items = num_items
self.item_size = item_size
def __getitem__(self, index):
ret... |
a44d4995eaa13daab9df9f60090dfc57828ff904768e4bd22b7018adafa74f95 | Python | 804 | 42 | from typing import List
from torch import distributed
def barrier():
if distributed.is_initialized():
distributed.barrier()
else:
pass
def broadcast(data, src):
if distributed.is_initialized():
distributed.broadcast(data, src)
else:
pass
def all_gather(data: List, s... |
cdd08191cfac6a74137ff9fd923e542967e55d1f16a0ff42a8f0361f5c318ce9 | Python | 805 | 28 | from pathlib import Path
import pytest
@pytest.fixture
def specific_dataset_path(specific_config_toml):
"""Returns a function that will return the
``dataset_path`` corresponding to the ``prep``ared dataset
from a specific configuration file,
determined by characteristics specified by the caller:
... |
7a9625e6cedc96670bd53ce9ce0f3f80ec13617b87f48de16b047ad776d46628 | Python | 808 | 29 | #!/usr/bin/env python -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Usage:
$ python misc/get_sample_size.py <input_file> > <output_file>
<input_file> contains list of w... |
8dbe51b9b0d3470e091036c32d8445268ebdd23bb93eca800300b68399919c26 | Python | 810 | 33 | import pandas as pd
from src.dfer.pfer_detection import signal_specs
def test_option_five_single_output_uses_signal_as_pfer_baseline():
frame = pd.DataFrame(
{
"dFoF_465": [0.1, 0.2],
"dFoF_405": [float("nan"), float("nan")],
}
)
frame.attrs["dfer_option"] = 5
... |
428b0e8976359bd35b5aa51921a6c6a0532e4e19aabc2b13e367a7e96a27077c | Python | 811 | 24 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from typing import List
import pytest
from mattergen.common.utils.globals import MAX_ATOMIC_NUM
from mattergen.property_embeddings import ChemicalSystemMultiHotEmbedding
@pytest.mark.parametrize("chemical_system", [["Li", "O"], ["Li", "O", "F... |
5625fb55b4faa5f506ebd4851289f086ba366106c2cbdee472370a59be4d8519 | Python | 811 | 23 | """module that contains helper function to load metrics
Metrics in separate packages should make themselves available to vak by including
'vak.metrics' in the entry_points of their setup.py file.
For more detail on entry points in Python, see:
https://packaging.python.org/guides/creating-and-discovering-plugins/#usin... |
00f308040e9e085b352e13638fc9765a9a9c53adea8922ba2792d9da4f1c5430 | Python | 812 | 59 | from . import (
__main__,
cli,
common,
config,
datapipes,
datasets,
eval,
learncurve,
metrics,
models,
nets,
nn,
plot,
predict,
prep,
train,
transforms,
)
from .__about__ import (
__author__,
__commit__,
__copyright__,
__email__,
__... |
91457589ca3a16036ecd51b9674f8c3933492841be81f02eb9ddcfb206df1c87 | Python | 812 | 20 | # Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... |
cb51fa607a8d43f109cd733c050167db6d972695ffdf03444c62b6dd4a4cb22d | Python | 812 | 29 | from transformers import PreTrainedTokenizer
class scMulanTokenizer(PreTrainedTokenizer):
def __init__(self, chars):
super().__init__()
self.chars = chars
self.stoi = { ch:i for i,ch in enumerate(self.chars) }
self.itos = { i:ch for i,ch in enumerate(self.chars) }
def _tokeniz... |
ed9d33983686f06d26a6ef1a47e63e3abd136c027aee63f1b9fd26c3aa829842 | Python | 812 | 34 | from pgmpy.datasets._base import BaseDataset
class HTRU2(BaseDataset):
"""
References
----------
- :footcite:t:`uci_htru2`
"""
_tags = {
"name": "htru2",
"n_variables": 9,
"n_samples": 17898,
"has_ground_truth": False,
"has_expert_knowledge": True,
... |
3356b977d942cdcfab1c717bcd229d81cbb31367af4334fd352a6f1250f5a8c4 | Python | 813 | 31 | from __future__ import annotations
from collections.abc import Sequence
import pandas as pd
DEFAULT_REFERENCE_CELLTYPE_COLUMNS = (
"cellType_k",
"cellType_broad_k",
"cellType_hc",
"cellType_broad_hc",
"cellType_layer",
)
def intersect_gene_names(visium_index: pd.Index, ref_index: pd.Index) -> p... |
27c2fcc1615bc31f706f955886b33db9ec615800d6e030052e92db1040c6a9bf | Python | 814 | 15 | import torch.optim as optim
import numpy as np
def get_optimizer(params, optimizer_type: str="LBFGS"):
if optimizer_type == "LBFGS":
optimizer = optim.LBFGS(params, lr=float(0.5), max_iter=20000, max_eval=20000000, history_size=250,
line_search_fn="strong_wolfe",
... |
d94aa63e487575394ffe8d958bf0d5abf07240b0ae09bba319a572e828784c40 | Python | 814 | 27 | import scipy
from scvi.data import synthetic_iid
from scvi.external import SCAR
def test_scar():
n_latent = 5
adata = synthetic_iid()
adata.obs["batch"] = adata.obs["batch"].cat.rename_categories(["batch_2", "batch_3"])
adata.X = scipy.sparse.csr_matrix(adata.X)
SCAR.setup_anndata(adata, batch_... |
289ff32ee29f03ea1c4330a3414c1dcc2048886163215115aa1745e31d7c7fc0 | Python | 815 | 20 | from .colors import colors75, colorize, tube_colors, Colorizer
from .midpoint_normalize import MidpointNormalize
from .manifold import manifold
from .markerheatmap import markerheatmap
from .factors import factors
from .TF_heatmap import TF_heatmap
from .radius_characteristics import radius_characteristics
from .batch_... |
ba9891d5def7983161e95122b3815464096a14bcc7c3931dcfc4c07aedd7b1a0 | Python | 819 | 18 | def process_txt_file(input_file, output_file):
with open(input_file, 'r') as infile, open(output_file, 'w') as outfile:
for line in infile:
# 提取文件名和坐标信息
parts = line.strip().split(' ')
filename = parts[0].split('\\')[-1].split('.')[0] # 提取文件名部分，去掉路径和后缀
... |
35b1a58ebbfdea5dc4f431905891c321c47705637d450ba638071cb123709c6a | Python | 820 | 23 | from __future__ import annotations
import logging
class QtTextHandler(logging.Handler):
def __init__(self, text_widget) -> None:
super().__init__()
self.text_widget = text_widget
def emit(self, record: logging.LogRecord) -> None:
if self.text_widget is None:
return
... |
a2e645a3bd81e557bb5a8c254405e3c7676c8ea25b7714c0bdf96ed935a6fa83 | Python | 820 | 32 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from typing import Iterable, Union
import torch
def get_grad_norm(
parameters: Union[torch.Tensor, Iterable[torch.Tensor]], norm_type: float = 2.0
) -> torch.Tensor:
"""
Adapted from: https://pytorch.org/docs/stable/_modules/torch/... |
f2e569233e0ea795e5220834c6adc9fe75cf3513d3300d00143b9f6236758c28 | Python | 820 | 34 | from pgmpy.datasets._base import BaseDataset
class GaltonStature(BaseDataset):
"""
References
----------
- :footcite:t:`mcgill_galton`
"""
_tags = {
"name": "galton_stature",
"n_variables": 5,
"n_samples": 898,
"has_ground_truth": False,
"has_expert_kno... |
3b8fb0c964b9a2a4cddd4916a22180cc4cac07e6cba9dc6668f255465f878a20 | Python | 822 | 21 | #
# Copyright 2017 National Technology & Engineering Solutions of Sandia, LLC
# (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government
# retains certain rights in this software.
#
# See LICENSE for full license details
#
from .base_parameters import BaseParameters, BasePairedParameters
from ... |
ebd2a086074953a70629f8751cb9408f2a7c5ea1030cc70156e5b63a19009d62 | Python | 822 | 27 | import random
import unittest
from unittest.mock import patch
from pyecharts import options as opts
from pyecharts.charts import Scatter3D
from pyecharts.faker import Faker
class TestScatter3D(unittest.TestCase):
@patch("pyecharts.render.engine.write_utf8_html_file")
def test_scatter3d_base(self, fake_writer... |
d3217cc2c8f577b57dcbbdb5ed0f508ac8f829cd833cdf13aa9ec937c2890d25 | Python | 823 | 16 | # scSGL - a python package for fene regulatory network inference using graph signal processing based
# signed graph learning
# Copyright (C) 2021 Abdullah Karaaslanli <evdilak@gmail.com>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as pu... |
52e198c087ad756fc23f9379787b53d9b2d669c049e112eb712da0d6c12f3198 | Python | 824 | 35 | from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
if TYPE_CHECKING:
from cleo.testers.command_tester import CommandTester
from tests.types import CommandTesterFactory
@pytest.fixture()
def tester(command_tester_factory: CommandTesterFactory) -> CommandTester:
return co... |
4c3c2a4daa01158f509340e3bb6ae24f689bcd1f7714a2c1132878d7bebed1b8 | Python | 825 | 32 | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import fileinput
import sacremoses
def main():
parser = argparse.ArgumentParser(description="... |
60aa90016cc6e4c368d3eccc0a9cf2722ca0590e7183714b8eb9c7cf4f948862 | Python | 825 | 25 | # Authors: Christian O'Reilly <christian.oreilly@sc.edu>
# Scott Huberty <seh33@uw.edu>
# License: MIT
"""Launching point for Lossless QC Dash app."""
import dash
import dash_bootstrap_components as dbc
from pylossless.dash.qcgui import QCGUI
def get_app(fpath=None, project_root=None, disable_buttons=False,... |
8b0e9752c461ce68e7f294547d6e58fdfc5e3c444dc8bc418a5baa11c0199bdb | Python | 825 | 30 | # encoding: utf-8
"""
@author: sherlock
@contact: sherlockliao01@gmail.com
"""
import logging
import os
import sys
def setup_logger(name, save_dir, logfile_suffix, distributed_rank):
logger = logging.getLogger(name)
logger.setLevel(logging.DEBUG)
# don't log results for the non-master process
if dis... |
19926fc6bb936047cd95321ea83b6503877beab6708b10b11eb53e82b9d9dca1 | Python | 827 | 33 | """Convert every .mp4 in a folder to raw .avi via ffmpeg.
Requires ffmpeg on PATH.
Example:
python dlc/convert_mp4_to_avi.py /path/to/folder
"""
import argparse
import os
import subprocess
def main():
parser = argparse.ArgumentParser(
description=__doc__, formatter_class=argparse.RawDescriptionHelp... |
2132138810f77c9ea4831c66197105bad5a60776a751c8a1b5f182c47b220455 | Python | 827 | 34 | """Generate JSON schemas from Pydantic models in the d3types package.
This module provides functionality to automatically generate JSON schemas for all
Pydantic BaseModel subclasses defined in the d3types module.
Example:
$ python generate_json_schemas.py
The script pretty-prints the JSON schema of each Pydantic... |
5c7c46b906c3de1b45e7d34f7d085adcfc5e910c8f3a7c6ec060402a51ca22c3 | Python | 828 | 28 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import sys
def _normalize_spaces(line):
return " ".join(line.split())
def main():
parser = argparse.ArgumentParser... |
e77d3d1f36b9093c610aded6dc29024cf778885fc4448355626cfe8042f12882 | Python | 828 | 43 | """The :mod:`vak.common` module contains helper or utility functions
that are used in multiple other modules, e.g., validators
or logging functions.
If a helper/utility function is only used in one module,
it should live either in that module or another at the same level.
See for example :mod:`vak.prep.prep_helper` or... |
ab2b9458825aa62ee668f1d16e38001070e015c8a13f5ddd58051e80175c066e | Python | 829 | 29 | # Copyright (c) Facebook, Inc. All Rights Reserved
import numpy as np
from torch.utils.data.sampler import Sampler
class RandomSequenceSampler(Sampler):
def __init__(self, n_sample, seq_len):
self.n_sample = n_sample
self.seq_len = seq_len
def _pad_ind(self, ind):
zeros = np.zeros(... |
b452fb1d4bd0d36d92448b9b9b2842869e0d5b1e7b869bd39b7ec891bfbfa297 | Python | 829 | 32 | #!/usr/bin/env python
# Author_and_contribution: Jieran Sun; Implemented visualization
import argparse
import os
from pathlib import Path
import shutil
# TODO adjust description
parser = argparse.ArgumentParser(description="Merging all visualization pdfs into one big pdf")
parser.add_argument("-d", "--out_dir", he... |
e61e008cb386105b0879b9fec0c8b3cbb325257d2ff338256069e48a39a5e37b | Python | 829 | 27 | import jax
import jax.numpy as jnp
import numpy as np
from laplacian_testcase import LaplacianTestCase
from folx import forward_laplacian
class TestForwardLaplacianJvp(LaplacianTestCase):
def test_customjvp(self):
@jax.custom_jvp
def fn(x):
return jnp.sin(x)
@fn.defjvp
... |
cee0a211c311ae9c153a896f71755e04d81d11cc12a94fd7ab41abb4f19f724d | Python | 830 | 26 | import numpy as np
import loompy
import logging
from .heatmap import Heatmap
def markerheatmap(ds: loompy.LoomConnection, dsagg: loompy.LoomConnection, out_file: str = "", layer: str = "pooled") -> None:
if layer not in ds.layers:
logging.warn(f"Cannot plot heatmap for layer {layer} because the layer doesn't exist... |
fa89c8e349060ce4a19b8f6f9453a75c6cfbea1a5f6d23391db6bc46cf44f45b | Python | 831 | 31 | # File names and column IDs
scd41_file = ["Temp_RH_CO2", ["Timestamp", "Temp[°C]", "RH[%]", "CO2 [ppm]"]]
apds_file = ["Light", ["Timestamp", "ALS [digits]"]]
spg_file = ["VOC", ["Timestamp", "VOC Raw [digits]", "VOC Index [digits]"]]
mp_file = ["Microphone", ["Timestamp", "Microphone Amplitude [digits]"]]
mics_file = ... |
02f453e52cfaec65e34132923add80e7132f188706680fa86d7bd884976e09ef | Python | 832 | 26 | import torch
class TriangularCausalMask():
def __init__(self, B, L, device="cpu"):
mask_shape = [B, 1, L, L]
with torch.no_grad():
self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1).to(device)
@property
def mask(self):
return self._mask
cla... |
270f2151f404876b09d049a162dc799a579fa14ef6d20458523d1993419707ec | Python | 833 | 34 | from pgmpy.datasets._base import BaseDataset
class DryBean(BaseDataset):
"""
References
----------
- :footcite:t:`uci_dry_bean`
"""
_tags = {
"name": "dry_bean",
"n_variables": 17,
"n_samples": 13611,
"has_ground_truth": False,
"has_expert_knowledge": T... |
45b3cc7e6b3b3571eba17ddd388a5160f35fc66579f84d3552fc7db9e6a76cfc | Python | 833 | 27 | from brenda_references.docdb import BrendaDocDB
from tqdm import tqdm
import re
if __name__ == "__main__":
nondigit = re.compile(r"[^\d]")
count = 0
with BrendaDocDB() as docdb:
for doc in tqdm(docdb.references):
if doc["pubmed_id"]:
try:
int(doc["pu... |
7a761246d870aa9fcd3c705693d5b03a84619e6b35ada2c98105f01040a8ffd0 | Python | 833 | 15 | from . import GTExGenotype
from . import DosageGenotype
from . import ModelTrainingGenotype
from .. import Exceptions
def genotype_by_chromosome_from_args(args, snps=None):
if args.gtex_genotype_file:
return GTExGenotype.gtex_geno_by_chromosome(args.gtex_genotype_file, args.gtex_snp_file, snps, args.gtex_... |
ab41057b8d648691632ce25d138c4ce690086e45fae3606f538edacec7ac8203 | Python | 833 | 35 | from pgmpy.datasets._base import BaseDataset
class Hitters(BaseDataset):
"""
References
----------
- :footcite:t:`islr_hitters`
"""
_tags = {
"name": "hitters",
"n_variables": 20,
"n_samples": 322,
"has_ground_truth": False,
"has_expert_knowledge": Fals... |
38f1a1b262ca832090b94495f2134587081089b77b2c3dbf3a663d9e8a056567 | Python | 836 | 25 | from mdt import CompositeModelTemplate
__author__ = 'Robbert Harms'
__date__ = "2015-06-22"
__maintainer__ = "Robbert Harms"
__email__ = "robbert@xkls.nl"
class VERDICT(CompositeModelTemplate):
"""Implements the VERDICT colorectal model, as in Panagiotaki et al Cancer Research 2014 paper.
The diffusivity pa... |
8656dd3f4b34b0b78c9de9ad45ef56ea921c7e70b75a332891ae50ff34cd268d | Python | 836 | 23 | import logging
class Logger(object):
def __init__(self,log_file_name,log_level,logger_name):
# firstly, create a logger
self.__logger = logging.getLogger(logger_name)
self.__logger.setLevel(log_level)
# secondly, create a handler
file_handler = logging.FileHandler(log_file_... |
8a7c1c35ad04e5a0977cb816471d432e4922655eb8e6e46d4e1f628feb845b59 | Python | 837 | 34 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq.data.encoders import register_bpe
from fairseq.data.encoders.byte_utils import (
SPACE,
SPACE_ESCAPE,
byte_encode,
... |
8d116d8a8191ec1366c0a5b266ecc12762db7e2cfbc30626aae6c4752b677e71 | Python | 837 | 26 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
class LogSumExpMoE(torch.autograd.Function):
"""Standard LogSumExp forward pass, but use *posterior* for the backward.
... |
99c7740a002b8e965745c7c6eb4a3c4871844775bb7cbf7941f9c9ad2c28a892 | Python | 838 | 30 | """Feature-local view state for telemetry alignment."""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class TelemetryViewState:
file_path: str = ""
adjust_clustering: str = ""
associated_temp_data: str = ""
associated_act_data: str = ""
light_off_time: str = "19... |
67234f1e2b14f334ab7e1c5f3c19541d79e5546c69ded5eb5b374952638454af | Python | 839 | 37 | from pgmpy.datasets._base import BaseDataset
class USCrime(BaseDataset):
"""
References
----------
- :footcite:t:`der_everitt_2002`
- :footcite:t:`acswr_usc_dataset`
"""
_tags = {
"name": "uscrime",
"n_variables": 14,
"n_samples": 47,
"has_ground_truth": Fa... |
7c4df5fee44f3de416606e8d04451fc66aa2e627aa3cb8bc63fa81abf5d73746 | Python | 841 | 19 | import pandas as pd
def test_func():
# test passes on pandas.__version__ == u'0.18.0'
df = pd.DataFrame([[1, 2, 1, 1],
[2, 2, 2, 2],
[1, 2, 3, 1],
[3, 2, 4, 2]], columns=['A', 'B', 'C', 'D'])
assert all(df.duplicated('A', keep=False) ==... |
f38e528e505bde8bcf1545940190ffa5e846c24b33e2dc868670c3501f91f34d | Python | 841 | 27 | """
Contains simple check functions
"""
import numpy as np
def _check_1d_array_object(parameter, name_param):
"""
Checks whether given parameter is a 1d array like object, and returns a numpy array object
"""
if isinstance(parameter, (np.ndarray, list, tuple, np.matrix)):
parameter = np.array... |
addafe51d632841eecca684125f6aa0989775afa8820aba5c73479b0ffdecf8d | Python | 843 | 25 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import BaseWrapperDataset
class ColorizeDataset(BaseWrapperDataset):
"""Adds 'colors' property to net input that is... |
e5ab6879be6a92245ee108f9b21b5c7102f26c9ef965a694ac306ff7b2156950 | Python | 843 | 34 | # -*- coding: utf-8 -*-
"""
Created on Fri Feb 28 13:29:25 2025
@author: hanna
"""
import numpy as np
def calcSSA(UA,UB):
cov_shared_A = np.dot(UA,UA.T)
cov_shared_B = np.dot(UB,UB.T)
rB = np.linalg.matrix_rank(cov_shared_B)
U, S, VT = np.linalg.svd(cov_shared_B)
bB = U[:,:rB] #or... |
ee935e3577f84f0d7860da892eeefdb41d8c702a8081089eabf035c09a0106f1 | Python | 843 | 38 | import os.path
__all__ = [
"__title__",
"__summary__",
"__uri__",
"__version__",
"__commit__",
"__author__",
"__email__",
"__license__",
"__copyright__",
]
try:
base_dir = os.path.dirname(os.path.abspath(__file__))
except NameError:
base_dir = None
__title__ = "vak"
__su... |
fdf40387834439700d91c9be63b1686c123a835f6cce0a91dfc1dfcbf1668aac | Python | 843 | 23 | # Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... |
c10f66d446e13a3b303ca8cb221a7d3e805216e7a934b79daf9b53c6a0c24019 | Python | 844 | 31 | from dataclasses import dataclass, field
from pathlib import Path
from typing import List, Optional, Union
@dataclass
class Setting:
"""
The configuration for scBank :class:`DataBank`.
"""
remove_zero_rows: bool = field(
default=True,
metadata={
"help": "When load data fro... |
e99f8aba6252b9e2734fb0032620a185f4b9d5c6adf3616b2b7a907d0552a933 | Python | 844 | 34 | #!/usr/bin/env python3
#
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import sys
import sacremoses
def main(args):
"""Tokenizes, preserving tabs"""
mt = sacremoses.MosesTokeniz... |
aa63994a48caf7c9f33c08c010d7c86e5cbfb2d97ebcfe686204339ef1ad1c21 | Python | 845 | 34 | import sys
import tensorflow as tf
import numpy as np
import tape.data_utils
import json
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "1" # disable tensorflow info logging
tf.enable_eager_execution()
filename = sys.argv[1]
funcname = sys.argv[2]
outfile = filename.rsplit('.', 1)[0] + '.json'
func = getattr(tape.d... |
b0fe33a6a85cec4632dc8c2f0891b6ab3aec1675fc52bea757720009a4825734 | Python | 849 | 26 | import numpy as np
import pytest
from moove.utils.label_utils import normalize_spectrogram
class TestNormalizeSpectrogram:
def test_zero_mean(self):
spec = np.random.rand(64, 32)
result = normalize_spectrogram(spec)
assert abs(result.mean()) < 1e-6
def test_unit_variance(self):
... |
7a446d3a58d45b3befcf816c97f0957c69cf5766f0e82db79a6221ccd4d442e5 | Python | 850 | 35 | from pgmpy.datasets._base import BaseDataset
class BostonHousing(BaseDataset):
"""
References
----------
- :footcite:t:`zhao_hastie_2019`
- :footcite:t:`peerj_wine_quality`
"""
_tags = {
"name": "boston_housing",
"n_variables": 14,
"n_samples": 506,
"has_gr... |
a1771b17b7113052ab380d3044ff62e7196350d66c0795d0f4e76c039e93796a | Python | 850 | 29 | #get drug features using Deepchem library
import os
import deepchem as dc
from rdkit import Chem
import numpy as np
import hickle as hkl
drug_smiles_file='../data/223drugs_pubchem_smiles.txt'
save_dir='../data/GDSC/drug_graph_feat'
pubchemid2smile = {item.split('\t')[0]:item.split('\t')[1].strip() for item in open(dr... |
9d706ce1774a022b82f30a83bcb0746fa518bcc03562317fb22ffc486a3662d6 | Python | 851 | 22 | import pandas as pd
import json
file_names = ['test_losses_TetraLoop_longer_bb.json']
feedback_types = ['1']
# Initialize an empty DataFrame
columns = ['object_id'] #+ [f'({ft}, {nl})' for ft in feedback_types for nl in range(1, 6)]
data = {col: [] for col in columns}
df = pd.DataFrame(data)
df['object_id'] = range(1... |
afbcc0152fa9edacefc885b0d8ae2d0359b276a2fcb9f60c5a679694bb8d88e7 | Python | 851 | 26 | import unittest
from unittest.mock import patch
from pyecharts.charts import PictorialBar
class TestPictorialBarChart(unittest.TestCase):
@patch("pyecharts.render.engine.write_utf8_html_file")
def test_pictorialbar_base(self, fake_writer):
c = (
PictorialBar()
.add_... |
b1787af16518eac6647932e89aefe3461d2930e2e766220fc97151500a5f85af | Python | 851 | 34 | from pgmpy.datasets._base import BaseDataset
class PimaDiabetes(BaseDataset):
"""
References
----------
- :footcite:t:`lella_2022`
"""
_tags = {
"name": "pima_diabetes",
"n_variables": 9,
"n_samples": 768,
"has_ground_truth": False,
"has_expert_knowledg... |
c86c546065a29d82a2d9fa50270b1bba9477f36f729ba78eb34802428fe245cf | Python | 851 | 25 | """Modules that act as activation functions."""
import torch
class NormReLU(torch.nn.Module):
"""This module implements normalized ReLU activation,
as used with the Encoder-Decoder Temporal Convolutional Model in [1]_.
The output of the activation is normalized by the channel-wise maximum.
.. [1] L... |
d649ba1061fa3c6686b80a07d6b76d42ee3d6e819b20bd0c619bde395f0e8ead | Python | 852 | 22 | # Copyright 2022 InstaDeep Ltd
#
# Licensed under the Creative Commons BY-NC-SA 4.0 License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://creativecommons.org/licenses/by-nc-sa/4.0/
#
# Unless required by applicable law or a... |
5a804893aa0edc2b3e1daad50fc5ea693c4f44f2496f2d67073f4c0ebf15d293 | Python | 853 | 23 | # Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... |
e98d86f41fac34f9b8a54c0739fa57cc20118bd18f983904deca8f33de76457c | Python | 853 | 26 | import pytest
import vak.common.converters
@pytest.mark.parametrize(
("toml_value", "labelset"),
[
("abc", {"a", "b", "c"}),
("1235", {"1", "2", "3", "5"}),
("range: 1-3, 5", {"1", "2", "3", "5"}),
([1, 2, 3, 5], {"1", "2", "3", "5"}),
(["a", "b", "c"], {"a", "b", "c"}... |
4b467613a7a51003fd15e22cea113a4cf66d3bb7a99eb57189777d6665ce2683 | Python | 854 | 31 | import setuptools
setuptools.setup(
name="bigstream",
version="5.1.0",
author="Greg M. Fleishman",
author_email="greg.nli10me@gmail.com",
description="Tools for distributed alignment of massive images",
url="https://github.com/JaneliaScicomp/bigstream",
license="BSD-3",
packages=setupto... |
c0f8bbaa828221c2940e116928a13d2cdc5ff4abdfe87d69ccfdf53774e7fc56 | Python | 855 | 29 | from pathlib import Path
from huggingface_hub import constants, hf_hub_download
HF_ETAG_TIMEOUT = max(constants.HF_HUB_ETAG_TIMEOUT, 30)
constants.HF_HUB_DOWNLOAD_TIMEOUT = max(constants.HF_HUB_DOWNLOAD_TIMEOUT, 30)
def read_hf_file(
*,
repo_id: str,
filename: str,
repo_type: str | None = None,
... |
0fc244ed1e6405474a55da586e73ff89ee703ff081a918a3049eb70863fa1563 | Python | 856 | 36 | from __future__ import annotations
from src.dashboard.main import (
_is_pyside6_available,
build_parser,
logger,
)
from src.dashboard.main import run_dashboard as _run_dashboard
from src.dashboard.main import run_tool_window as _run_tool_window
def run_dashboard() -> int:
return _run_dashboard()
de... |
ef4e40b724695afd796ac84bda0885e3bec50c0c579666c6c57f0aff61452dc4 | Python | 856 | 27 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from .task import Task
class VLMTask(Task):
"""A VLM task for reproducibility.
the collator split subsamples into two s... |
4efece7600494d09d7219c3ea8fa1e4d2b646fa2bd9bbd025029953354c37a69 | Python | 859 | 21 | from pytorch_grad_cam.base_cam import BaseCAM
from pytorch_grad_cam.utils.svd_on_activations import get_2d_projection_kernel
class KPCA_CAM(BaseCAM):
def __init__(self, model, target_layers,
reshape_transform=None, kernel='sigmoid', gamma=None):
super(KPCA_CAM, self).__init__(model,
... |
600eda7749f2c71e0811a42d4d2e15ad84f15ebdd3c1ced273e3999ee246271f | Python | 859 | 32 | from __future__ import annotations
import os
import shutil
from setuptools import Distribution
from setuptools import Extension
from setuptools.command.build_ext import build_ext
extensions = [Extension("extended.extended", ["extended/extended.c"])]
def build():
distribution = Distribution({"name": "extended"... |
a60c4f21dafe110e1022d3f8e066c6621ba32a82316822e678c2370f4e6eb20a | Python | 861 | 23 | import torch.nn as nn
class DWConv(nn.Module):
def __init__(self, in_plane, out_plane):
super(DWConv, self).__init__()
self.depth_conv = nn.Conv2d(in_channels=in_plane,
out_channels=in_plane,
kernel_size=3,
... |
595c8bba544d26f7536a9a37cbd58a60e6a84b2d7965987a177a2cfc57bd2387 | Python | 862 | 33 | from __future__ import annotations
from typing import TYPE_CHECKING
from poetry.utils._compat import decode
if TYPE_CHECKING:
from subprocess import CalledProcessError
class EnvError(Exception):
pass
class IncorrectEnvError(EnvError):
def __init__(self, env_name: str) -> None:
message = f"En... |
1cc4ce15839f42fb7820a8e123f34fd363183753f757c4c07c264214035b343e | Python | 863 | 32 | from cellpose import models, io
# Define the directory and parameters
directory = "/run/user/752424298/gvfs/smb-share:server=imgserver,share=images/CONFOCAL/IA/Projects/2024/2024_10_17_lalvaroe_0/output/cellpose"
pretrained_model = "cyto3"
stitch_threshold = 0.25
flow_threshold = 0.8
cellprob_threshold = -1.0
chan = 2... |
c0e186583c363d509138d760c2cf5f234477ae0db94b8847c9752fee4dccb9c3 | Python | 863 | 33 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from torch import nn
class SamePad(nn.Module):
def __init__(self, kernel_size, causal=False):
super().__init__()
if cau... |
1aed1682b78fddd8d19506a711f775e53596c5ea55595216a2121660e12e1a53 | Python | 864 | 35 | from pgmpy.datasets._base import BaseDataset
class HungaryChickenpox(BaseDataset):
"""
References
----------
- :footcite:t:`rozemberczki_2021`
- :footcite:t:`uci_hungarian_chickenpox`
"""
_tags = {
"name": "hungary_chickenpox",
"n_variables": 20,
"n_samples": 522,
... |
76c5c0ef76acf3377c889f60b47375d6b0e1e61e6bc144c14e8c0b28d4dd60f5 | Python | 866 | 31 | from os.path import join
import numpy as np
from matplotlib import pyplot as plt
from upsetplot import from_memberships
from upsetplot import plot
from setup import saving_dir
def run():
fig = plt.figure(figsize=(8, 8))
data = np.array([795., 27., 182., 7.])
plt.rcParams.update({'font.size': 14})
ex... |
585af32a5ca6cef34dd94af4510a348c384740750f8a018aaade9609a4317c4f | Python | 867 | 38 | """
Installation script for mapalign
"""
from setuptools import setup, find_packages
from os import path
here = path.abspath(path.dirname(__file__))
with open(path.join(here, 'README.md')) as f:
long_description = f.read()
with open(path.join(here, 'requirements.txt')) as f:
requirements = [v... |
93a57bf0897f913bb90792fb901876ff56d2e519e0f84278e33a241be7eed4c5 | Python | 867 | 34 | from pgmpy.datasets._base import BaseDataset
class AutoMpg(BaseDataset):
"""
References
----------
- :footcite:t:`lopezpaz_2015`
- :footcite:t:`uci_auto_mpg`
"""
_tags = {
"name": "auto_mpg",
"n_variables": 8,
"n_samples": 392,
"has_ground_truth": False,
... |
c10e9d14d326e9611373d39bb5c394185108df2654a8fd5345011f8b37899592 | Python | 867 | 27 | import os
import pytorch_lightning as pl
from pathlib import Path
from pytorch_lightning.callbacks import ModelCheckpoint
class PeriodicCheckpoint(ModelCheckpoint):
def __init__(self, every: int, dirpath: str):
super().__init__()
self.every = every
self.dirpath = dirpath
if not os... |
fb36afcceb28027981cfd83fd486a2c963132e476c91739eac8e3d6c7f54f119 | Python | 867 | 43 | from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
if TYPE_CHECKING:
from pathlib import Path
@pytest.fixture
def pyproject_toml(tmp_path: Path) -> Path:
path = tmp_path / "pyproject.toml"
with path.open(mode="w", encoding="utf-8"):
pass
return path
@pytest... |
7be2e0e9fc36de096f9da959a7146955e20dd298a4dcce8f7159c7c5ee65c3c5 | Python | 868 | 24 | import importlib
import pkgutil
from batchgenerators.utilities.file_and_folder_operations import *
def recursive_find_python_class(folder: str, class_name: str, current_module: str):
tr = None
for importer, modname, ispkg in pkgutil.iter_modules([folder]):
# print(modname, ispkg)
if not ispkg... |
9a87858456db1779a7524d731a3f3c1d09b4974d1fe815fb34458f515a2a6bbe | Python | 868 | 34 | from valids import parser, main as valids_main
import os.path as osp
args = parser.parse_args()
args.target = "valid_accuracy"
args.best_biggest = True
args.best = True
args.last = 0
args.path_contains = None
res = valids_main(args, print_output=False)
grouped = {}
for k, v in res.items():
k = osp.dirname(k)
... |
ba45c4a8d9b4e340723320adadf679eac323b3ca3acfbd14afb8585935435b0e | Python | 868 | 20 | import haiku as hk
import jax
import jax.numpy as jnp
class DeterminantApproxEqualInit(hk.initializers.Initializer):
def __init__(self, n_determinants: int, stddev: float, noise_relative_level: float = 1e-3):
self.n_determinants = n_determinants
self.stddev = stddev
self.noise_relative_lev... |
0775eef7dfd37441237e0d93e3c6b45185f1e6303f0c237364792a5f88a92df2 | Python | 869 | 24 | import importlib
import pkgutil
from batchgenerators.utilities.file_and_folder_operations import *
def recursive_find_python_class(folder: str, class_name: str, current_module: str):
tr = None
for importer, modname, ispkg in pkgutil.iter_modules([folder]):
# print(modname, ispkg)
if not ispkg... |
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