sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
f02c94d8a1b6730b352a5e1c1467157ba78fc1aab60f6a57b1dce545770ffe6f | Python | 1,397 | 43 | """
Sphinx extension to add ReadTheDocs-style "Edit on GitHub" links to the
sidebar.
"""
import os
import warnings
__licence__ = "BSD (3 clause)"
# def get_github_repo(app, path):
# if path.endswith(".ipynb"):
# return app.config.github_nb_repo, "/"
# return app.config.github_repo, "/docs/source/"
... |
2d1b45840579f3d70b36cf7bca6640a45cb006111a9c5e0475535f1b660ab37c | Python | 1,398 | 32 |
# Source code:
# https://github.com/zbmed-semtec/doc2vec-doc-relevance-training/blob/main/code/train_model/train.py
# This file includes the modifications to the source codes according to this project!
import os
import time
import logging
import utilities as utilities
def run(best_params, args):
# 1) Load ... |
58aa23e548259b8c4e72f7ff1c813b0ad83168fbbef5be0394f92a60ba84efc5 | Python | 1,398 | 41 | import pytest
import torch
from scvi import REGISTRY_KEYS
from scvi.module import SCANVAE
@pytest.mark.parametrize("n_labels", [1, 2, 10])
def test_scanvae_loss_n_labels(n_labels: int):
vae = SCANVAE(n_input=100, n_labels=n_labels)
x = torch.randint(0, 100, (128, 100), dtype=torch.float32)
batch = torch... |
7bab5d67f14f3a97f2c7b9e646eda43a3035dbdbe7c031a79f3b3915e42a761f | Python | 1,398 | 42 | """Bar order and labels for Simulation eval_a / eval_b (aligned with preprocess condition names)."""
from __future__ import annotations
# Internal keys match ``spots_{name}.csv`` / ``CONDITION_GRID`` in ``datasets.preprocess.Simulation``.
# Top-to-bottom display order after ``invert_yaxis()`` on barh (index 0 dr... |
40e8f894e10f07064e70bbbbbb2ba542ff430813a6d157c88fcae51d931480ea | Python | 1,399 | 55 | # 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 importlib
import os
from abc import ABC, abstractmethod
from fairseq import registry
from omegaconf import DictConfig
class BaseSco... |
5d92d06a7628c7103a3ec9e09f9c56f10b054127ce68b4ba62a217db3ce72d37 | Python | 1,399 | 46 | import torch
from torch import Tensor
from torch.nn.modules.loss import _Loss
### Normalized cross-correlation, implementation from the excellent GitHub repository by Bob de Vos (AmsterdamUMC):
### https://github.com/BDdeVos/TorchIR
class StableStd(torch.autograd.Function):
@staticmethod
def forward(ctx, te... |
8db99c301b196aeb2722a9ef78e25eb87124fe5be149d0b5a3c962006db25b99 | Python | 1,401 | 35 | # created by DebarpanB
# date 26th August, 2022
import argparse, configparser
import pickle
import pandas as pd
import librosa
import numpy as np
import torchaudio
import torch
from pdb import set_trace as bp
def main(annotation_file, pathfile, outlist, outpath):
df_annotation = pd.read_csv(annotation_file)
d... |
556b0567ab944eb57adb7df15a69a06428930f75318743db4930e1eedf514eea | Python | 1,402 | 53 | """
Invokes __main__ when the module is run as a script.
Example: python -m vak --help
"""
import argparse
from pathlib import Path
from .cli import cli
def get_parser():
"""returns ArgumentParser instance used by main()"""
parser = argparse.ArgumentParser(
prog="vak",
description="vak comma... |
aa1beadc217c970a02ccf1ff44d4a5e925b9ce03d46c4d4f1811256ccad52e7b | Python | 1,402 | 53 | from __future__ import annotations
import sys
from functools import cached_property
from pathlib import Path
from typing import TYPE_CHECKING
from typing import Any
from poetry.utils.env.system_env import SystemEnv
if TYPE_CHECKING:
from poetry.utils.env.base_env import EnvPaths
class NullEnv(SystemEnv):
... |
40a40a094df9332bb24716255569d0ce6fdf406aa569628822d1dc20c211181e | Python | 1,403 | 47 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
1442b4f4103222512c453548c423b40a37d5dfa009878d624ea1828e6e7fd490 | Python | 1,404 | 46 | from .parameters import FreeParameter, CurrentModelSignalParam
from .model_functions import SimpleModelCLFunction
__author__ = 'Robbert Harms'
__date__ = "2014-08-05"
__license__ = "LGPL v3"
__maintainer__ = "Robbert Harms"
__email__ = "robbert@xkls.nl"
class SignalNoiseModel(SimpleModelCLFunction):
"""Signal no... |
7c4ec55a45b68626e6e7b3cedccfb1676ce489a7fab143e2d3252fee62c011e0 | Python | 1,404 | 48 | # 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 dataclasses import dataclass, field
from fairseq import file_utils
from fairseq.data.encoders import register_bpe
from fairseq.data.enc... |
aa04d2c2e4c28f80c86f8e6c6c70d1e7696456f53329a078d5cd61f4c7077662 | Python | 1,406 | 45 | """
Scrapper for sphinx-gallery to capture vtk figures.
"""
# This is shamelessly copied from PyVista
from .base import Plotter
from ..vtk_interface.wrappers import BSScalarBarActor
def _get_sg_image_scraper():
return Scraper()
class Scraper(object):
def __call__(self, block, block_vars, gallery_conf):
... |
d45ef39d1e10682d64a9c3bc9bc087f63316d02a338a03d6c238da94fe4aff92 | Python | 1,406 | 46 | import os
from os.path import dirname
from sklearn.model_selection import ParameterGrid
base_dirname = dirname(dirname(__file__))
print base_dirname
filename = os.path.basename(__file__)
task = 'classification_binary'
selected_genes = '~/DATA/P1000/tcga_prostate_expressed_genes_and_cancer_genes.csv'
data_base = {'id... |
3d948accd3018dcaafbc0dbe5a5d3fd6adf70e040b9993a8167eb21895c040c5 | Python | 1,407 | 42 | #!/usr/bin/env python3
import numpy as np
# ---------------------------
# Define some (shared) parameters mainly (or exclusively?) used by RDDNet
# ---------------------------
dt = 0.001 # timestep (s)
v_reset = -1 # reset somatic voltage
spike_threshold = 1 # spike threshold
refractory_time = np.ceil(0.003 / dt... |
06c48a1cdc86bbff0cc8dfa59e31416d006f7875dcd923315051f7275e3273af | Python | 1,409 | 46 | import os
from os.path import dirname
from sklearn.model_selection import ParameterGrid
base_dirname = dirname(dirname(__file__))
print base_dirname
filename = os.path.basename(__file__)
task = 'classification_binary'
selected_genes = '~/DATA/P1000/tcga_prostate_expressed_genes_and_cancer_genes.csv'
data_base = {'id... |
165c5541df11ca6760e10b53b82995c4d7fe16e0bec0056e64e5f364ca784e23 | Python | 1,409 | 25 | import pytest
class TestAICGauss:
def test_local_score_no_parents(self, aic_gauss_score):
# score(model2network("[A]"), df_cont[c('A')], type='aic-g') -> -121.7228
assert aic_gauss_score.local_score(variable="A", parents=()) == pytest.approx(-121.7202, abs=1e-3)
# score(model2network("[B]... |
251556ba361b094bd7d1588ec87522b543f6f1ec3cccf2c9b9e9f6c3ced0ebe5 | Python | 1,409 | 44 | # 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.
"""
LayerDrop as described in https://arxiv.org/abs/1909.11556.
"""
import torch
import torch.nn as nn
class LayerDropModuleList(nn.ModuleLi... |
ebaa030665f63fd19aefe716e37803da5f0516c9cc14d44cb9b22faf98581b08 | Python | 1,409 | 48 | import shutil
import uuid
from pathlib import Path
from src.persistence.dashboard_state import (
get_state_file_path,
load_state,
save_state,
)
from src.persistence.settings_store import (
load_settings,
save_settings,
settings_file_path,
)
def _make_local_tmp_dir(name: str) -> Path:
path... |
1243e8bb813677dd6ae1dc7ad626f8198b040f69b8fa6fa7c8d89b4de510406d | Python | 1,410 | 46 | # 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 os
import pickle
import numpy as np
class ShardedTensor(object):
def __init__(self, data, starts):
self.data = data
... |
bfd220b026108daa839f01dc41e16536b1619ed2cf55c90f8c5c1288ef2d43ee | Python | 1,410 | 42 | #!/usr/bin/env python
# PYTHON_ARGCOMPLETE_OK
"""This script is meant to update your home folder with the latest MDT models."""
import argparse
import textwrap
import mdt
from mdt.lib.shell_utils import BasicShellApplication
__author__ = 'Robbert Harms'
__date__ = "2015-08-18"
__maintainer__ = "Robbert Harms"
__email_... |
124eb7f4c2a12dc5075a3f1a0a4fba77bea4b6a1d67ec355a7039d8875c31040 | Python | 1,412 | 47 | from __future__ import annotations
from typing import TYPE_CHECKING
from typing import ClassVar
from poetry.core.constraints.version import Version
from poetry.__version__ import __version__
from poetry.console.commands.add import AddCommand
from poetry.console.commands.self.self_command import SelfCommand
if TYPE... |
62f8b6f71dd757aa66d9bd4e0ebb91eee736a3627db4fb0ee10a3519c1e0e18f | Python | 1,412 | 48 | import pandas as pd
import numpy as np
import glob
def get_pmids(file_path):
# Load pmids from .npy file
doc = np.load(file_path, allow_pickle=True)
# Get pmids
pmids = []
for line in doc:
pmids.append(np.ndarray.tolist(line[0]))
# Convert all values in the list "pmids" from type ... |
52753ede2e791e5e3106aea38ee279aba5a3b30c6c0752f8a9c161f731fe8f35 | Python | 1,413 | 43 | import tempfile
from pathlib import Path
import pytest
import vak
@pytest.fixture
def specific_dataset_csv_path(specific_config_toml):
"""Returns a function that will return the
``dataset_csv_path`` corresponding to the ``prep``ared dataset
from a specific configuration file,
determined by character... |
6e971322bf394430464653bba592e5f0d6fe6bd2a05c89974d5b59756ecef6d8 | Python | 1,413 | 38 | from pathlib import Path
import pandas as pd
import seaborn as sns
def loss_curve(log_dir: str | Path, version: int = -1, on_epoch: bool = True, **kwargs: int) -> None:
"""Plot the training loss curve from the CSV logs. This is a basic alternative for monitoring training when Weights & Biases can't be used.
... |
c68ac0871d388f472ebaf568bd2735d40abf910b99d278193b4af60206c27763 | Python | 1,413 | 53 | import inspect
import torch
import pytest
import vak.nets
class TestConvEncoder:
@pytest.mark.parametrize(
'input_shape',
[
(
1, 128, 128,
),
(
1, 256, 256,
),
]
)
def test_init(self, input_s... |
1f0d8f90154f46c113bc179e3c930555d438984b078de7ae025c5cf3f25ee080 | Python | 1,414 | 42 | import math
import unittest
from unittest.mock import patch
from pyecharts import options as opts
from pyecharts.charts import Surface3D
from pyecharts.faker import Faker
class TestSurface3DChart(unittest.TestCase):
@patch("pyecharts.render.engine.write_utf8_html_file")
def test_surface3d_base(self, fake_wri... |
0bdb468834e3f022d362c08d79f6f2a8f472120b0b660d3cd619d97721a84de1 | Python | 1,415 | 47 | # 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... |
55e13801314a0f5b843f08bfc3e05ebce02ce41f2a6b831a3147e983383c2301 | Python | 1,416 | 54 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import csv
import os
from vlpp.utils import add_suffix
from vlpp.operation import computeSuvr, CentiloidStats, meanFromRoi
def main():
os.mkdir("centiloid")
os.mkdir("stats")
pet = "${pet}"
tracer = "${tracer}"
roiPath = os.path.join("${centiloidR... |
0203c74f7416b8592a3d32bcd167f785faabea50866b5375d3948586799c06ee | Python | 1,417 | 49 | import h5py
import nibabel
import numpy as np
from typing import Union
from voluseg._tools.constants import dtype
def save_volume(
fullname_ext: str,
volume: np.ndarray,
affine_matrix: np.ndarray = None,
) -> Union[bool, None]:
"""
Save volume in output format.
Formats currently accepted are: ... |
eac975a00cfb7481c39f0a8692e9725bc1bd8082fdf3313fcba0cb50a949b076 | Python | 1,417 | 51 | # 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.models import (
BaseFairseqModel,
register_model,
register_model_architecture
)
@register_model("mmmodel")
class Fa... |
63a68e662439a65223d5b0946007e5a80ad0380b28e19d87e52fa3b9c3387c6b | Python | 1,418 | 37 | #!/usr/bin/env python
import pysam, numpy as np, sys
from pathlib import Path
bam_in = sys.argv[1] # collapsed Iso-Seq BAM
timepoint = Path(bam_in).name.removesuffix(".bam") # e.g. "iPSC_merged"
bedgraph = f"data/katherine/{timepoint}.coverage.weighted.bedgraph"
# Pre-allocate one array per contig
sam =... |
2645f8af3b7335211fcfc62a3013db296b778088ab4a69fd927e88e1d354bf5c | Python | 1,419 | 43 | """Shared matplotlib styling for consistent figures.
Call `set_style` at the top of a notebook so every figure uses the same fonts, sizes, and spine treatment.
"""
import matplotlib as mpl
import matplotlib.pyplot as plt
GENOTYPE_COLORS = {
"CI": "#d1495b", # cichlid
"ZF": "#30638e", # zebrafish
}
def se... |
b3d6dff1f17395cd4a5712095ad85b504c5b8b5ae0100a836b77595caeb4b741 | Python | 1,420 | 46 | import os
from os.path import dirname
from sklearn.model_selection import ParameterGrid
base_dirname = dirname(dirname(__file__))
print base_dirname
filename = os.path.basename(__file__)
task = 'classification_binary'
selected_genes = '~/DATA/P1000/tcga_prostate_expressed_genes_and_cancer_genes.csv'
data_base = {'id... |
e82505179b9f06f040a39102666b6b5a432758dd9e7d7e19eff129b58583741c | Python | 1,421 | 44 | # source: https://github.com/Megvii-BaseDetection/BorderDet/blob/master/cvpods/layers/border_align.py
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from cvpods import _C
class _Bo... |
06ba72388af12cd5e908a51492b9dd9d35b2ab7e5fdaef2179b65735a888e098 | Python | 1,422 | 33 | import tensorflow_model_optimization as tfmot
LastValueQuantizer = tfmot.quantization.keras.quantizers.LastValueQuantizer
MovingAverageQuantizer = tfmot.quantization.keras.quantizers.MovingAverageQuantizer
class DefaultDenseQuantizeConfig(tfmot.quantization.keras.QuantizeConfig):
# Configure how to quantize weigh... |
59b5ea62f5e6413a13ea083ec433137cf92e7f7bf5eb0838903ff093a4d4cdea | Python | 1,423 | 47 | import sys
import os
merge = sys.argv[1]
benchmark = sys.argv[2]
mode = sys.argv[3]
file_path, full_file_name = os.path.split(merge)
d={}
g={}
with open(merge, 'r') as m,open(benchmark,'r') as ben,open(file_path+'/f_'+mode+'.txt','w') as ff,open(file_path+'/p_'+mode+'.txt','w') as p:
for a in m:
... |
9fa17c83f24f328a1c99771b7a26588546c52ffcfe0ec4d73be643d1478b008c | Python | 1,423 | 51 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import csv
import os
import numpy as np
import CBIG_pMFM_parameter_estimation_example as example
import warnings
def csv_matrix_read(filename):
'''
This function is use... |
3b2732af12fa3405fb63025f0ecafa27a1b8362d746aefcd7a78008b20fe9160 | Python | 1,425 | 47 | import os
import anndata
from scvi.data._download import _download
def _load_heart_cell_atlas_subsampled(
save_path: str = "data/",
remove_nuisance_clusters: bool = True,
):
"""Combined single cell and single nuclei RNA-Seq data of 485K cardiac cells with annotations.
Dataset was filtered down rand... |
d153dede52c25c020aab37af380ca70dabf549784c78459cf57d6a6bb85bd1c7 | Python | 1,425 | 39 | # Generate sql script to extract patient followup for each condition
# Requires tables containing patients for inclusion and hospitalisations and ae attendances data
from A0_global_vars_py import *
s = ''
for pln in ['gold', 'aurum']:
s += f'--- {pln.upper()} ---\n'
for condition in conditions:
s +... |
d405de0a8e510bc25e74331e236654a4c67204082abd581b7692ea6d8c9671a9 | Python | 1,425 | 53 | #!/usr/bin/env python
# Author_and_contribution: Niklas Mueller-Boetticher; created template
# Author_and_contribution: ENTER YOUR NAME AND CONTRIBUTION HERE
import argparse
# TODO adjust description
parser = argparse.ArgumentParser(description="Calculate metric ...")
parser.add_argument(
"-l", "--labels", help... |
fec7faf5ba499c603e1fd5a853f322f46e4ac4afd08a9ea70e160ac7f45daa10 | Python | 1,426 | 45 | """Annotation Extension for relational expressions.
https://link.springer.com/protocol/10.1007/978-1-4939-3743-1_17
Correlated function associations between genes and GOs containing contextual information.
With contextual information identify gene products that perform a role:
- only under certain condi... |
1e86a78e35dfba266ed2f0e069dee80d9820a5bce626e3e6e5358c10c7a65747 | Python | 1,428 | 47 | from __future__ import annotations
import argparse
from pathlib import Path
from st_risk.config import load_config
from st_risk.models.io import save_base_model_output
from st_risk.models.registry import build_model_runner
from st_risk.paths import current_results_dir, ensure_results_layout
from st_risk.paths import ... |
e853f06d9d9fe4ec51e7ce6df200b6a1c500f2954fd99d688e6cf71f1887f5f5 | Python | 1,428 | 38 | import os
import torch
# Borrowed from e3nn @ 0.4.0:
# https://github.com/e3nn/e3nn/blob/0.4.0/e3nn/o3/_wigner.py#L10
# _Jd is a list of tensors of shape (2l+1, 2l+1)
_Jd = torch.load(os.path.join(os.path.dirname(__file__), "Jd.pt"))
# Borrowed from e3nn @ 0.4.0:
# https://github.com/e3nn/e3nn/blob/0.4.0/e3nn/o3/_w... |
61b730c59ec757e4479b8a955de3ecf4095b63a2617a36d5af75dd56a79e4085 | Python | 1,429 | 38 | import os
from pathlib import Path
import sys
os.environ["SEGREF3D_DISABLE_SAM2"] = "0"
os.environ["SEGREF3D_EDITION"] = "local-gpu"
os.environ["SEGREF3D_FORCE_SAFE_SDPA"] = "1"
def _configure_windows_dll_runtime() -> None:
"""Configure the frozen DLL runtime before PyQt or PyTorch is imported.
PyQt6-Qt6 6... |
aee0b15f2e16302eef4c978080c387b41c17ee7af7d73e46336e21ff40f18b05 | Python | 1,429 | 39 | # 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.models import BaseFairseqModel, register_model
from fairseq.models.wav2vec.wav2vec2_asr import (
Wav2Vec2CtcConfig,
Wav2V... |
303903e3f995755254004803bc1aebaea395993a70a85ba6325e82df5fe83b64 | Python | 1,430 | 48 | # 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 json
import argparse
import pathlib
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--manifest", required=... |
70e1cbf6c1605335f0b42d1d28119b00849c0316f7839dd68570b100fbd3a2b0 | Python | 1,430 | 39 | import torch
from torchvision import transforms, datasets
import numpy as np
from copy import deepcopy
from tqdm import tqdm
device='cuda'
def sample_pair(n):
idx = list(np.random.choice(n,2))
return idx
def load_batch(generator, t, c1, c2, transform=None, amb=0.5, N=200, bs=2, test=False, no_label=False):
... |
3752be10f3d9b7f9e33983bdfcbd7bbf68837a543585748cfcb34350523c7d22 | Python | 1,434 | 58 | from __future__ import annotations
from typing import TYPE_CHECKING
from poetry.exceptions import PoetryError
from poetry.utils.env import EnvCommandError
if TYPE_CHECKING:
from pathlib import Path
from poetry.utils.env import Env
def pip_install(
path: Path,
environment: Env,
editable: bool ... |
402dedce125fe44b8da2758ba0db95da54ccd8c9e9de6b941105301be130fddf | Python | 1,434 | 33 | import pandas as pd
import argparse
import os
# Input: metal output with UID in the MarkerName column.
# Add RSID from HRC reference panel and CHR and BP based on UID.
# Parse arguments --------------------------
parser = argparse.ArgumentParser(description="Process output of metal assuming MarkerName column contain ... |
9f9da8c65a8b46ff51b84902177afdad31be6b654f062894169a17f22c4bc2b2 | Python | 1,435 | 49 | # Configuration file for the Sphinx documentation builder.
# -- Project information -----------------------------------------------------
import subprocess, os
project = 'liblsl'
copyright = '2019, Christian Kothe, MIT'
author = 'Christian Kothe, David Medine, Chadwick Boulay, Matthew Grivich, Tristan Stenner'
# The... |
a38a2c5b53932adda1fbda91b3d6d375ddbae393b7525733561a0ff9e9775d34 | Python | 1,435 | 44 | #!/usr/bin/env python3
"""Headless entry point for the NONAN Python test suite.
python3 tests/python/run_tests.py # everything
python3 tests/python/run_tests.py emd # name filter
Exits 0 if every test passed, 1 otherwise.
Deliberately stdlib unittest, not pytest: the suite must run on a bar... |
b70c84df87451170e3958675a5be0d28b606e3ae2489ebb6ca13ca272e82ed04 | Python | 1,435 | 39 | """
weighting.py — inverse-distance weighting and weighted oversampling.
Neutral home for the distance -> weight -> merge helpers shared by the
distance-based pipelines (MMP, DWP). Previously these lived in mmp_pipeline.py
and were cross-imported by dwp_pipeline.py; centralizing them here removes that
inverted depende... |
211c3e394cdf903e48f602a51bc22c1ef46635025edeea33cf217412d2f69eb7 | Python | 1,438 | 43 | from optparse import OptionParser
import os
import sys
import pandas as pd
from arboreto.algo import grnboost2, genie3
from arboreto.utils import load_tf_names
from distributed import Client, LocalCluster
def parseArgs(args):
parser = OptionParser()
parser.add_option('', '--algo', type = 'str',
... |
c5a293dba45e68ea0d928e3a3d18b2c45294df7ef5d8abe3e8e639ff32f1cf57 | Python | 1,438 | 68 | import math
import numba
import numpy as np
from scipy.special import gammaln
class PoissonProximity:
def __init__(self, A: float, B: float) -> None:
self.A = A
self.B = B
def logF(self, x: np.ndarray, y: np.ndarray) -> float:
A = self.A
B = self.B
N = x.shape[0]
x_norm = np.sum(x)
y_norm = np.sum(... |
efc1e69dcf386205fb7f24095ec8b28fb818a1c6b5c2f3a1f1c02c14aa6ec02f | Python | 1,438 | 37 | #!/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.
from examples.speech_recognition.criterions.cross_entropy_acc import (
CrossEntropyWithAccCriterion,
)
from .asr_t... |
2a42625c8ebd4177b1630c49d661bb30218750bc096551271022845ccbdc9b7e | Python | 1,439 | 57 | import argparse
from pathlib import Path
import anndata
import pandas as pd
from scipy.sparse import csr_matrix
def convert_to_h5ad(dataset_dir: Path):
res_path = dataset_dir / "adata.h5ad"
if res_path.exists():
print(f"File {res_path} already existing.")
return
slide_id = f"stereoseq_{... |
1e6415234dad0788a5edb97b108ec4ca41f5c9b0ff7f498f4af91feffe419aad | Python | 1,440 | 45 | from __future__ import annotations
from typing import TYPE_CHECKING
from typing import ClassVar
from cleo.helpers import option
from poetry.console.commands.self.self_command import SelfCommand
from poetry.console.commands.show import ShowCommand
if TYPE_CHECKING:
from cleo.io.inputs.option import Option
f... |
84165c91aaaaf933b09bca12d41d217d13e2bd69a8165129eaa58e3db1061339 | Python | 1,440 | 33 | import logging
import numpy as np
import pandas as pd
from data.gmt_reader import GMT
def get_KEGG_map(input_list, filename='c2.cp.kegg.v6.1.symbols.gmt', genes_col=1, shuffle_genes=False):
'''
:param input_list: list of inputs under consideration (e.g. genes)
:param filename: a gmt formated file e.g. path... |
9ae109b3ce722da7b87b127e83cfa0166d4fa7fe605cbd824f8c20c213885836 | Python | 1,440 | 52 | import os
from pathlib import Path
import sys
import unittest
os.environ.setdefault("QT_QPA_PLATFORM", "offscreen")
MODULE_DIR = Path(__file__).resolve().parents[1]
if str(MODULE_DIR) not in sys.path:
sys.path.insert(0, str(MODULE_DIR))
from PyQt6.QtWidgets import QApplication # noqa: E402
from batch_tracking_d... |
bcdd2df263ad572a2b99c6c83a6567a22a71f7f1408df01947154333d495041d | Python | 1,441 | 53 | from abc import ABC, abstractmethod
from pathlib import Path
import pandas as pd
from BLEval.data import EvaluationData
class Evaluator(ABC):
"""
Abstract base class for BEELINE evaluation methods.
Each subclass implements __call__ to compute a specific evaluation metric
over an EvaluationData obje... |
ed460a404ba31e68448163573092bb3512f5b6f6c0bdf9a3a4b0966d9d70d1f0 | Python | 1,441 | 31 | import numpy as np
import torch
import torch.nn as nn
class MixConv2d(nn.Module): # MixConv: Mixed Depthwise Convolutional Kernels https://arxiv.org/abs/1907.09595
def __init__(self, in_ch, out_ch, k=(3, 5, 7), stride=1, dilation=1, bias=True, method='equal_params'):
super(MixConv2d, self).__init__()
... |
9bf915d91bbe0afe67170f634a70269c1a3cf48362523777c30c1ed298a31f1f | Python | 1,442 | 46 | # 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.
"""
Unsupervised Cross-lingual Representation Learning at Scale
"""
from fairseq.models import register_model
from .hub_interface import Robe... |
cc8a2dc82336f42b70ac1e2a372169aabff19e802b02abe2956b0a677acc676e | Python | 1,442 | 43 | # 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.optim
from . import LegacyFairseqOptimizer, register_optimizer
@register_optimizer("sgd")
class SGD(LegacyFairseqOptimizer):
... |
cdd251cc5cfd80f35e2bc9c5707eae5c2183f2e2c523a0560c63e81f47f072d1 | Python | 1,442 | 40 | import numpy
import numpy.testing
import pandas
import unittest
from metax.gwas import GWASSpecialHandling
class TestGWASSpecialHandling(unittest.TestCase):
def test_sanitize_component(self):
sanitize = GWASSpecialHandling.sanitize_component
#Number pass through
self.assertEqual("a", sani... |
efbbc6108ea050a2fb741493c92bd082e1bc1128467371edb8c9832a978966f7 | Python | 1,445 | 39 | # 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 dataclasses import dataclass
from fairseq.dataclass import FairseqDataclass
from fairseq.optim.lr_scheduler import FairseqLRScheduler, r... |
ba3e0ffbfa5caf43aac82a35acdd1d5379f901e31ab57bcbf1b036950aba1c0e | Python | 1,447 | 56 | # import gsMap
project = "gsMap portal"
copyright = "2024, Liyang, Wenhao"
author = "Liyang, Wenhao"
# release = gsMap.__version__
# -- General configuration ---------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
extensions = [
"s... |
dd020ff90b84fb284b8bf9b5b89b0c6f6f1a788e89c6a2d6848ac09b9b682632 | Python | 1,447 | 54 | """
This module contains method to check if beam is broken, which signals the start
of a trial.
"""
"""
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 t... |
24de3bb3290cd3d3609cf6005ffc26440ed6f7ce8e4c01c74576a72a94c3c26f | Python | 1,448 | 47 | # @license
# Copyright 2022 Google Inc.
# 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 agreed to in... |
faebc58f06e066239d9ecd9442522339d386294ca840ee78e8c642850b3547b4 | Python | 1,449 | 47 | import os
from os.path import dirname
from sklearn.model_selection import ParameterGrid
base_dirname = dirname(dirname(__file__))
print base_dirname
filename = os.path.basename(__file__)
task = 'classification_binary'
selected_genes = '~/DATA/P1000/tcga_prostate_expressed_genes_and_cancer_genes.csv'
data_base = {'id... |
05f20d1b1a44aa573fd160cf3634e2ec639cf881e77d384eaabd614285f13569 | Python | 1,451 | 31 | import pytest
import vak
import vak.datapipes.frame_classification
class TestTrainDatapipe:
@pytest.mark.parametrize(
'config_type, model_name, audio_format, spect_format, annot_format, split, transform_kwargs',
[
('train', 'TweetyNet', 'cbin', None, 'notmat', 'train', {}),
... |
18909825fb0a58c21bc118286c14ccd3ea3f48d3e87c356212eda7a60774a342 | Python | 1,451 | 45 | import sys
import os
merge = sys.argv[1]
benchmark = sys.argv[2]
mode = sys.argv[3]
file_path, full_file_name = os.path.split(merge)
d={}
g={}
with open(merge, 'r') as m,open(benchmark,'r') as ben,open(file_path+'/f_'+mode+'.txt','w') as ff,open(file_path+'/p_'+mode+'.txt','w') as p:
for a in m:
... |
48a922cca513cf35773670579d891acf64c4a56ccae8577427823cd9ee67226d | Python | 1,452 | 48 | """
07_methylation_analysis.py
Methylation-level evidence from GSE131706 (ASD brain tissue).
Computes pairwise correlations between methylation beta-values at
PCDH-annotated and DNMT-annotated CpG sites.
Harbert D. (2026) BMC Genomics
"""
import sys, os
sys.path.insert(0, os.path.dirname(__file__))
from utils import... |
540bbcf53bcbe10783c24279e0c19caddca5d2670b8d647ab0956d090bb223d9 | Python | 1,453 | 42 | import os
from os.path import dirname
from sklearn.model_selection import ParameterGrid
base_dirname = dirname(dirname(__file__))
print base_dirname
filename = os.path.basename(__file__)
task = 'classification_binary'
selected_genes = '~/DATA/P1000/tcga_prostate_expressed_genes_and_cancer_genes.csv'
data_base = {'id... |
7ff9832e4a25606b88c16545d9af252e52dd39de8544b046aa6e8e8a2c043ec4 | Python | 1,453 | 45 | import numpy as np
import pandas as pd
from anndata import AnnData
from .._constants import Nums
from ..utils import iter_slides
from ..utils._validate import _check_has_slide_id
def quantile_scaling(
adata: AnnData | list[AnnData],
multiplier: float = 5,
quantile: float = 0.2,
per_slide: bool = True... |
12a581e76e2d0fa5f2475dbea2f82d1ef8421172322e550aece46f0685cc5f29 | Python | 1,455 | 27 | import pytest
class TestLogLikeGauss:
def test_local_score_no_parents(self, loglik_gauss_score):
# score(model2network("[A]"), df[c('A')], type='loglik-g') -> -119.7228
assert loglik_gauss_score.local_score(variable="A", parents=()) == pytest.approx(-119.7202, abs=1e-3)
# score(model2netw... |
dfe7d6bf9692df15dff56d4424f6d302f65fdd0c4ad4312d2d1b15c4672bf8b9 | Python | 1,455 | 58 | #!/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 argparse
from pathlib import Path
from tqdm import tqdm
def process(args):
args.output_root.mkdir(exist_o... |
21e31ccc8b3cf4206b40abca839d3c768773ae010c95815d279866bbb3c38e9b | Python | 1,457 | 47 | '''
By K. Butenko
Routines to post-process VAT-based pathway recruitment
'''
import os
import json
import numpy as np
def remove_failed_protocols(Currents_all, ActivationResults_all):
# remove protocols for which the simulator failed, they were marked with -1.0
rows_idx = np.where(np.any(Activation... |
7757190f02dde4b14e4f41c3d8a7885334ab640d457b10ad4c6f685cb54ab2ea | Python | 1,458 | 40 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
from torch.distributions import Categorical, Independent, MixtureSameFamily, Normal
from mattergen.diffusion.wrapped.wrapped_normal_loss import get_pbc_offsets, wrapped_normal_score
from mattergen.diffusion.wrapped.wrapped_sde impor... |
a8fae64a9d847004915a978972686e26b5ae2efa7767baebeebe93ff664a4a15 | Python | 1,458 | 31 | import subprocess
import scipy.io as sio
import numpy as np
import shutil
import os.path
def execute_command(command):
process = subprocess.Popen(command.split(), stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
print(process.communicate()[0].decode("utf-8"))
#print(subprocess.check_output(command.split()... |
066c76c2ef62f5b1d6f38c4b57e978ed0c39d097de470e7cd8d444ec3aaed121 | Python | 1,459 | 54 | import os
import numpy as np
import glob
import matplotlib.pyplot as plt
def plot_raw_data(dataFolder):
f1 = os.path.join(dataFolder, os.path.join('continuous','Neuropix*.0'))
f2 = os.path.join(dataFolder, os.path.join('continuous','Neuropix*.1'))
ap_directory = glob.glob(f1)[0]
lfp_directory = glob.glob(f2)[0]
... |
2c5b3aeebceeedc2e8212adf4c1aa7c3d552a582e10100afe2fbab2b365886b3 | Python | 1,459 | 45 | """
Compact reference implementation of the multi-scale Optical Scanning
Holography (OSH) representation used in the associated manuscript.
"""
from typing import Sequence, Tuple
import numpy as np
DEFAULT_ALPHAS: Tuple[float, ...] = (0.5, 1.0, 2.0, 3.0)
def apply_osh(image: np.ndarray, alpha: float) -> np.ndarray:... |
da2c6dcb0ad1c16efe23856fd87a20e10d8a8453307151e222127430ec5a2f90 | Python | 1,459 | 41 | # 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
def deup(src_file, tgt_file, src_file_out, tgt_file_out):
seen = set()
dup_count = 0
with open(src_file, encodin... |
6dd3a4b455244b4ee45358c433e74cf7e3f7d4f586cc0d0e15d033be8e13b60a | Python | 1,460 | 50 | # Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
import os
import sys
sys.path.insert(0, os.path.abspath('../../..')) # cross-sim/sphinx/source
# -- Project informat... |
682daa8a3339a9381f6a3acbbd984c5ab77efc38fbcf62578a538dfa6c2ab113 | Python | 1,461 | 40 | from pathlib import Path
ROOT = Path(__file__).parents[1]
def test_python_api_documentation_is_not_built():
sphinx_config = (ROOT / "docs" / "conf.py").read_text()
project = (ROOT / "pyproject.toml").read_text()
docs_index = (ROOT / "docs" / "index.md").read_text()
assert not (ROOT / "docs" / "refer... |
ea2a32c0f54d13c024673531223ba9dba2fc5a537a5c2df0e3526a601547f7b6 | Python | 1,461 | 66 | from __future__ import annotations
import pytest
@pytest.fixture
def init_basic_inputs() -> str:
return "\n".join( # noqa: FLY002
[
"my-package", # Package name
"1.2.3", # Version
"This is a description", # Description
"n", # Author
"MIT", ... |
cbd53b8f6fa12b0e8f60673fe44e5c28779d193f4a029ea72b2e85a830a19e3c | Python | 1,462 | 42 | import os
from os.path import dirname
from sklearn.model_selection import ParameterGrid
base_dirname = dirname(dirname(__file__))
print base_dirname
filename = os.path.basename(__file__)
task = 'classification_binary'
selected_genes = '~/DATA/P1000/tcga_prostate_expressed_genes_and_cancer_genes.csv'
data_base = {'id... |
a0e7654850d0df56438e580d8235242f3d49971972956d55561410e0c6e08054 | Python | 1,463 | 41 | import jax
import jax.numpy as jnp
import pytest
from oneqmc.preprocess.augmentation import RotationAugmentation
from oneqmc.types import MolecularConfiguration, Nuclei
@pytest.fixture
def unit_vector_mol():
coords = jnp.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
nuclei = Nuclei(
coords, jnp.array([1, ... |
0cb1bf782f270d1e82ad32f0a0f2906971a13f66b196a8435761fcaaa7bd018c | Python | 1,464 | 39 | import primer3
import logging
def calculate_deltag_primer3(seq1:str, seq2:str, temperature:float, sodium_concentration:float, magnesium_concentration:float) -> float:
"""Calculate deltaG using primer3 library.
Args:
seq1 (str): The first sequence.
seq2 (str): The second sequence.
tempe... |
aa830f82daf693073956bae8b2fb57fbf6c485c32ba3284dae62f2eb40853b74 | Python | 1,466 | 33 | # Source code:
# https://github.com/zbmed-semtec/doc2vec-doc-relevance-training/blob/main/code/train_model/train.py
# This file includes the modifications to the source codes according to this project!
import time
import logging
import utilities as utilities
def run(best_params, args):
# 1) Load the trainin... |
55d20da6806adcf80383d4f9a1def3cb711e5833237fbf881e1f1b01ed549216 | Python | 1,471 | 51 | import numpy as np
import pytest
import torch
import torch.nn as nn
from pytorch_grad_cam.grad_cam_plusplus import GradCAMPlusPlus
from pytorch_grad_cam.xgrad_cam import XGradCAM
def test_grad_cam_plusplus_accepts_3d_activations():
activations = np.ones((1, 2, 3, 4, 5), dtype=np.float32)
grads = np.ones_like... |
eab0c73a0983d7a442fc71775ff429828aa30824c6976b6185b94d5778fb6b9d | Python | 1,471 | 53 | #!/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 os
import os.path as osp
import numpy as np
import faiss
def get_parser():
parser = a... |
38844076cf9dda2c61902f651ee7f4b693655a11a77d829f494df2e394fe3e17 | Python | 1,473 | 41 | """SIESTA-PBE isolated-atom references for the 10 case-study elements.
Reuses run_atom() from data/perovskites/run_atom_refs.py (20-A vacuum box,
spin-polarised, SZP/PBE/MeshCutoff 250 Ry — same as production). Needed to
form cohesive energy E_coh = E_cell - sum_i E_atom_ref[el_i] for cross-
composition stability rank... |
98bf36f1cf3fcd410ce84ac2abbdd40efea3dd49d90d167dd3de69394deb5b90 | Python | 1,473 | 50 | # encoding: utf-8
"""
@author: Jiayang Chen
@contact: yjcmydkzgj@gmail.com
"""
import os
import torch
from .baseline import Baseline
from ..pretrained import load_hub_workaround
def build_model(cfg):
model = Baseline(
backbone_name=cfg.MODEL.BACKBONE_NAME,
pairwise_predictor_name=cfg.MODEL.PAIRWIS... |
610fd56ddf5ba9333a0572f84e96dfc8e2da6f7c8f0363ee46672984e7a5aa6e | Python | 1,477 | 40 | """Testing for libmedaka.fastrle program."""
import subprocess
import tempfile
import unittest
import pysam
class RLE(unittest.TestCase):
"""Test libmedaka.fastrle function."""
input_fasta = tempfile.NamedTemporaryFile(suffix='.fasta').name
output_fastqrle = tempfile.NamedTemporaryFile(suffix='.fastqrl... |
05dd973d9e3583ecf4b9eed1f44af548cfd1a5aefe41f42a56c09580fa909b73 | Python | 1,478 | 35 | from torchvision import transforms
from torchvision.models import ResNet18_Weights
def load_transforms(model=None):
preprocess = {}
if model in ['resnext101_32x8d_wsl', 'resnext101_32x16d_wsl', 'alexnet', 'vgg19', 'vgg16',
'resnet50_simclr_100epochs', 'resnet50_MoCov2_200epochs', 'resnet50_ba... |
335d9b1d47b1fa7bdf4d64f435932317f1a25390dd2f9e689c5079bcdbb0af7d | Python | 1,478 | 46 | #!/usr/bin/env python3
# -*- encoding: utf8 -*-
import fasttext
from tqdm import tqdm
import argparse
import os
import math
parser = argparse.ArgumentParser()
parser.add_argument("--txt", type=str)
parser.add_argument("--dst", type=str)
parser.add_argument("--model", type=str)
parser.add_argument('--lid', type=str)
ar... |
ee1ee31c5842b8363fbaee0f26fbd4ff5ff85ecb385823b037fbe8d17c80108c | Python | 1,479 | 50 | __author__ = 'heroico'
import gzip
import io
#
class DataSet(object):
"""A list of values for a thing we care about"""
def __init__(self,name=None,index=None, data = []):
self.data = data
self.name = name
self.index = index
class DataSetFileUtilities(object):
@classmethod
def ... |
f390fadda262ff2bafa65cbce29e8980822e44a786edf6d99495fda26fe1b3b8 | Python | 1,479 | 59 | """Plain Python state models for reusable GUI views."""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class DataSelectionViewState:
file_path: str = ""
selected_column: str = ""
use_baseline: bool = False
@dataclass
class StaticInputsViewState:
pre_behaviour_time:... |
54998ecf54cdf62c237892adc5451c048d86d58527543c8cf7a94d46ae1b9e0d | Python | 1,480 | 36 | import os
import time
import argparse
import logging
import numpy as np
import pandas as pd
import utilities as utilities
import precision as precision
def run(best_params, args):
# 1) Load the training data
train_pmids, train_docs = utilities.process_data_from_npy(args.input)
logging.info(... |
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