sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
364ca2f16ecf322ba87719e04b5f859fa6fcb2b105ffab8df8fc9d511342ffd6 | Python | 1,570 | 50 | import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from scripts.validate_gmx_MMPBSA_test_docs import (
_command_signature,
_parse_bundled_test_command,
_parse_serial_command,
_test_selector,
)
class GmxMmpbsaTestDocumentationValidatorTest(unittest.TestCase):
def test... |
5d331e09ce590d3accfd1f689b8fb248c9e704a8eb4c219fbcedaded3ba6ab3b | Python | 1,570 | 63 | # import theano.tensor as T
# import theano
import numpy as np
from keras import backend as K
from keras.constraints import Constraint
class OneToOne(Constraint):
def __call__(self, p):
# p = theano.shared(p)
# n, m =p.shape
# print n, m
# p *= T.identity_like(p)
# TODO: re... |
2a53058244e2831b62b1469cbab80d30133520e32fb7eaf2f7ed169bb3515321 | Python | 1,571 | 46 | #!/usr/bin/env python
import os
import gzip
import sqlite3
import shutil
selected=["ENSG00000107937.14", "ENSG00000107959.11", "ENSG00000234745.5"]
def copy_covariance(ip, op):
with gzip.open(ip) as infile:
header = infile.readline()
with gzip.open(op, "w") as outfile:
outfile.write(he... |
ffc3c7bfaa22ded8d644cc195f33a8fa0031d251c1924ca6da9fc7583d586a9e | Python | 1,571 | 49 | import os
import pandas as pd
import random
dat = pd.read_csv("../../data/11M_dataset.csv")
all_ref_tcrs = []
for i in range(len(dat)):
tcr = dat["junction_aa"][i]
all_ref_tcrs.append(tcr)
def get_all_pos(filename, epi):
all_pos = []
f = open(filename, 'r')
for line in f:
... |
0d5f5ae1c3b5622081e7248a16391f2ecacf6831c78a3b98ab17a453bdec6d39 | Python | 1,573 | 36 | from pathlib import Path
DOCS_DIR = Path('docs/source')
USER_GUIDE = DOCS_DIR / 'user_guide.rst'
GUI_GUIDE = DOCS_DIR / 'user_guide_gui.rst'
def test_user_guide_examples_use_current_api_names_and_paths():
# The guides contain non-ASCII characters (e.g. Polars table-repr box-drawing glyphs), so the
# encoding... |
97e09c81e2bebb8ab11b2bc2de20df99c4157989655a8bb2599a36e9fe5e16d0 | Python | 1,573 | 39 | # niceumap
import scanpy as sc
import datamapplot
from anndata.experimental import concat_on_disk
from umap import UMAP
import os
name = "o2uniqsx"
# Assuming the data files are in the "./data/" directory
data_directory = "./data/"
file_list = os.listdir(data_directory)
## Filter out only the files with the 'h5ad' ... |
39df0493b99aed8e3442b90a7c0d809c29de8b225b4200c9fb6aaabd5d89568b | Python | 1,574 | 49 | import logging
import os
import anndata
import numpy as np
from scvi.data._download import _download
logger = logging.getLogger(__name__)
def _load_breast_cancer_dataset(save_path: str = "data/"):
save_path = os.path.abspath(save_path)
url = "https://exampledata.scverse.org/scvi-tools/Layer2_BC_count_matri... |
59bac7e2aaae78bb307346773f26c0edecfd7435cfc702569a2f5c5c4bf8af18 | Python | 1,575 | 65 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Name: Robert Kim
# Date: October 11, 2019
# Email: robert.f.kim@gmail.com
# Description: Contains several general-purpose utility functions
import os
import tensorflow as tf
if tf.__version__[0] == '2':
import tensorflow.compat.v1 as tf
tf.dis... |
1cc77eac999fdd36de546160761894c17b6b341b2dde82fcfb70e9db541345a2 | Python | 1,576 | 35 | # 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 t... |
7147e6ef8d1294c0736ebc05813e5fb44eff4c8e1c01ae2284685301bce57819 | Python | 1,577 | 46 | import unittest
from unittest.mock import patch
from pyecharts import options as opts
from pyecharts.charts import Grid, Liquid
from pyecharts.commons.utils import JsCode
class TestLiquidChart(unittest.TestCase):
@patch("pyecharts.render.engine.write_utf8_html_file")
def test_liquid_base(self, fake_writer):
... |
dbcfb1c48bc16784e62b4add0e9c48081d030b8bb57f2fcc8622fcb137aea7bb | Python | 1,577 | 52 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import json
from dataclasses import dataclass
from typing import Sequence
import numpy as np
from emmet.core.material import PropertyOrigin
from mattergen.common.utils.globals import PROPERTY_SOURCE_IDS
PropertySourceId = str
TargetProperty = ... |
06361a48682c83599fe4572c5ee7d3511f3e57194315ada5dec4024a42fc4d31 | Python | 1,578 | 32 | import matplotlib.pyplot as plt
import numpy as np
plt.style.use(['science','nature'])
x_ax = np.load('noisesw_x.npy')*100
y_ax = np.load('noisesw_y.npy')*100
# y2_ax = np.load('ideal_quant_y.npy')
hline = np.load('noisesw_single.npy')*100
hline2 = np.load('noisesw_d2d.npy')*100
# hline2 = np.array([94.275,94.037,94.... |
3a1800b5f33347a18f5ebdd17d7c2a8344d8a8a159989e6e5a0f78a450a3f80d | Python | 1,578 | 49 | """
"""
from __future__ import annotations
import torch
from .. import metrics, nets
from .decorator import model
from .frame_classification_model import FrameClassificationModel
@model(family=FrameClassificationModel)
class ED_TCN:
"""Encoder-Decoder Temporal Convolutional Network model
for the frame clas... |
b55a152b202038830bcb86fb03117c51d2ac6a5869cfe30239e9eee8e4a8a4b4 | Python | 1,578 | 60 | from importlib import metadata, util
import pytest
@pytest.mark.parametrize(
"module",
(
"gpn.ss.filter_assemblies",
"gpn.ss.finetune",
"gpn.ss.train_tokenizer_ss",
),
)
def test_archived_dataset_and_finetuning_modules_are_not_packaged(module):
assert util.find_spec(module) is... |
b81b52f45dd292f9309c7e4d75511ced4adf58a5500810cd36a4df60a8f66da7 | Python | 1,578 | 50 | import pickle
import numpy as np
def save_dict2pkl(fn,data_dict):
with open(fn, 'wb') as f:
pickle.dump(data_dict, f)
f.close()
return fn
def load_pkl2dict(fn):
with open(fn, 'rb') as f:
loaded_dict = pickle.load(f)
f.close()
return loaded_dict
def find_video_name(f... |
72f486a9b82618b8e61904b4c455bd4ce5e63f726239a35c72d9410d40f1586b | Python | 1,579 | 56 | from __future__ import annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from poetry.core.packages.package import Package
from poetry.repositories import RepositoryPool
class VersionSelector:
def __init__(self, pool: RepositoryPool) -> None:
self._pool = pool
def find_best_ca... |
f913cf3bd8e384eb4769a633203a4309cbc3364115695fc875223f0145bc807c | Python | 1,579 | 40 | """Cross-session CV scorers and score-mode dispatch (kfold / loso / pairwise)."""
import numpy as np
import pytest
from crossda.pipelines.scoring import score_session_cv, _normalize_score_mode
from crossda.pipelines.pipeline_utils import (
extract_features_train,
extract_features_test,
get_classifier,
)
... |
ff8c0540825e0fa38cd88cae7573ac21311b809bd496ebea4c5983a38f094871 | Python | 1,581 | 55 | import sys,getopt,random,subprocess
import pandas as pd
try:
opts, args = getopt.getopt(sys.argv[1:],"hi:o:w:b:",["ifile=","ofile="])
except getopt.GetoptError:
print ('windows_MBB.py -i <input csv file> -o <output> -w <window size> -b <block size>')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print ('wind... |
7f493872293ec72be481d008e409691a4abcb1d06563eaaa5aa253855116929a | Python | 1,585 | 57 | #!/usr/bin/env python
# Author_and_contribution: Niklas Mueller-Boetticher; created script
import argparse
parser = argparse.ArgumentParser(
description="Calculate Adjusted Rand Index (scikit-learn)"
)
parser.add_argument(
"-l", "--labels", help="Labels from domain clustering.", required=True
)
parser.add_a... |
fc1eb1533e89738843e4306eb365a6ed350c69fbc500e8ba051a048c8303c6f1 | Python | 1,585 | 46 | import pandas as pd
import matplotlib.pyplot as plt
import os
import ast
target_folder = "foldername" # specify folder name in "param_estimation_result"
path_base = os.path.join("param_search", "param_estimation_result", target_folder)
# Get a SEED_num
filename = os.path.join(path_base, "hyperparams.csv")
df = pd.rea... |
6a81d3d918911ada56cdf529ea9585aaa8c73c12e2eab031f62c616a8c29f0de | Python | 1,586 | 42 | from __future__ import annotations
import numpy as np
from sklearn.neighbors import NearestNeighbors
def knn_indices(coords: np.ndarray, k: int) -> np.ndarray:
"""Return k nearest neighbor indices for each coordinate row."""
coords = np.asarray(coords, dtype=float)
if coords.ndim != 2:
raise Valu... |
cdbd4dfbbfc3c462a635c75699412f2ea16cb929cea712db9adaa249d6b37687 | Python | 1,586 | 65 | """
Simulated expression dataset script
For a complete documentatio please see
https://github.com/PMBio/peer/wiki/
"""
import sys
import scipy as SP
def simulate_data(N,K,sigma):
X = SP.random.randn(N,K)
W = SP.random.randn(K,G)
Y = SP.dot(X,W)
#simulate mean effect
#mean = SP.rand(N)
Y += Y
#add noise
Y+= s... |
f022b1d4d9067b428327a794f90caf6a6278337a9e5b54b7877f3cc9c30f1de8 | Python | 1,587 | 63 | from multiprocessing import Pool
import copy
import argparse
from process_pretrain_data import Process
# filenames = ['xaa', 'xab', 'xac', 'xad', 'xae', 'xaf', 'xag', 'xah', 'xai', 'xaj', 'xak', 'xal', 'xam', 'xan', 'xao', 'xap', 'xaq', 'xar', 'xas', 'xat', 'xau', 'xav', 'xaw']
# filenames = ['xaa', 'xab']
def main(... |
28ea1778b2848c086f070397c00e7626d1b53e62b971a044c844abd89ee768f4 | Python | 1,588 | 38 | """Various methods to estimating if a GO term is more specific than another GO term."""
__copyright__ = "Copyright (C) 2016-2018, DV Klopfenstein, H Tang, All rights reserved."
__author__ = "DV Klopfenstein"
def get_most_specific_dcnt(goids, go2nt):
"""Get the GO ID with the lowest descendants count."""
# go... |
3d0607fa94f01f83de313977110cde8310afe4772c4f6583aa0345dff63368ac | Python | 1,588 | 54 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from typing import Protocol, Sequence
import torch
from pymatgen.core import Composition
from mattergen.common.data.chemgraph import ChemGraph
from mattergen.common.utils.data_utils import (
compute_lattice_polar_decomposition,
get_elem... |
bac2fe5e645c561579af13522313ae7636f1de1b21615e4fc77d15d42d0ddbb6 | Python | 1,588 | 48 | import numpy as np
def Ent_Symbolic(X, L):
"""
SymEnt = Ent_Symbolic20180320(X, L)
symbolicEnt Calculates the Symbolic Entropy for given data.
Input - X: 1-Dimensional binary array of data
L: Word length
Output - NCSE: Normalized Corrected Shannon Entropy
Remarks
- This cod... |
a71fe58963db71742a021bc0817cf04dca5df04775d5368e6961426bbc879c62 | Python | 1,592 | 42 | # Copyright 2020 The TensorStore 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 applicable law or agreed to... |
b80116d12f8b1f05ab215f68ade9ed8a0c5b7b1c58697cadc80225975ce96d15 | Python | 1,592 | 70 | #!/usr/bin/python
# Copyright (c) 2014 Kyle Lutz <kyle.r.lutz@gmail.com>
# Distributed under the Boost Software License, Version 1.0
# See accompanying file LICENSE_1_0.txt or copy at
# http://www.boost.org/LICENSE_1_0.txt
#
# See http://boostorg.github.com/compute for more information.
import os
import sys
import py... |
dde087850923727af051b90e7ce33b0e42df4de5dd2e386f512771dbf792a230 | Python | 1,593 | 46 | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from anndata import AnnData
from .. import utils
from ._utils import get_categorical_color_palette
def domains_proportions(
adata: AnnData | list[AnnData],
obs_key: str | None = None,
slide_name_key: str | None = None,
figsize:... |
93d324b74961696cc7eec94ab7d1d0a85cb4982a6a31972cad394a17aaf704bb | Python | 1,594 | 33 | import torch
from nnunetv2.training.loss.deep_supervision import DeepSupervisionWrapper
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
from nnunetv2.training.loss.robust_ce_loss import RobustCrossEntropyLoss
import numpy as np
class nnUNetTrainerCELoss(nnUNetTrainer):
def _build_loss(self... |
30ac3a7000be5b15cf9285c216a14dfdeedf842a4cda7b2a039a21908d75487c | Python | 1,595 | 41 | """
session_roles.py — shared schema for the per-session provenance table.
Each pipeline emits a flat list of role rows with a fixed schema: stage,
local_idx, session_abs_idx, session_label, role, is_best, weight, plus five
distance fields. The per-stage role assignment is genuinely family-specific and
stays in each p... |
c3994502e2b3f687fd39bf89c3f126564b1ffc3c15342d32b9db6621d36dafd7 | Python | 1,595 | 47 | """Wrappers around standard library multiprocessing executors."""
import concurrent.futures
import multiprocessing
import threading
class _Executor(object):
def submit(self, fn, *args, **kwargs):
self.semaphore.acquire()
future = super().submit(fn, *args, **kwargs)
future.add_done_callbac... |
fa9f2bd87bd3c16194dc185c595b308edf30014c69b02470b9b508e36a586f72 | Python | 1,596 | 54 | """Validators for frame classification datasets"""
from __future__ import annotations
import pandas as pd
def validate_and_get_frame_dur(
dataset_df: pd.DataFrame, input_type: str
) -> float:
"""Validate that there is a single, unique value for the
frame duration for all samples (audio signals / spectro... |
728415b2a0850e4a2fb223d152864127087c03204c02aeb865e07d914229a5bd | Python | 1,598 | 39 | from mdt import CompartmentTemplate, FreeParameterTemplate
from mdt.model_building.parameter_functions.transformations import ScaleTransform
__author__ = 'Robbert Harms'
__date__ = "2015-06-21"
__maintainer__ = "Robbert Harms"
__email__ = "robbert@xkls.nl"
class TimeDependentZeppelin(CompartmentTemplate):
"""Imp... |
a6dfbda03328bbf7d2832c4ece185abda180b58cb32006e0a424d5c2b459f2ac | Python | 1,598 | 37 | # totalseg_interface.py
import os, shutil, subprocess, sys
class TotalSegInterface:
def __init__(self, exe_name=None):
candidates = []
scripts_dir = os.path.dirname(sys.executable) # 例: ...\Python312
scripts_dir = os.path.join(scripts_dir, "Scripts")
candidates += [
exe... |
b82b33dfef527d5211668a27974aee7c884647828793269c29a95893c384c9f6 | Python | 1,598 | 49 | import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from anndata import AnnData
from .._constants import Keys
def get_categorical_color_palette(
adatas: list[AnnData], obs_key: str, palette: str | None = None
) -> tuple[list[str], list[str]]:
key_added = f"{obs_key}_colors"
all_doma... |
ca99f6d1b4dd02577cee12e659997d438f917c93a95666eab91f65b37a23fd48 | Python | 1,598 | 37 | """Run Simulation preprocess before training when required by config or empty outputs."""
from __future__ import annotations
from omegaconf import DictConfig, OmegaConf
from pytorch_lightning.utilities.rank_zero import rank_zero_info
from datasets.preprocess.gate import (
Simulation_both_output_dirs_empt... |
eeba6a6c48e8ac5176520a4150834b4a805f1ff423a0ac5568fe90f9dac6b505 | Python | 1,598 | 41 | import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
samples = 10000
val=np.random.multivariate_normal((4, 4), [[5, 3.3],[3.3, 5]], samples)
draw_1, draw_2 = np.zeros(samples), np.zeros(samples)
for i in tqdm(range(samples)):
# chasing rank
draw_1[i] = np.argmax(np.random.multinomial(1, [6... |
8ed0fc60893d29443b2d9e12641f791d3f8c9f8d218464012f213a96c9b211c2 | Python | 1,599 | 35 | from nnunetv2.training.data_augmentation.compute_initial_patch_size import get_patch_size
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
import numpy as np
class nnUNetTrainer_noDummy2DDA(nnUNetTrainer):
def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self):
do_dumm... |
11449e14920c46cda386f76679097c66d719b46a85032dae748bfd753c4e6d6f | Python | 1,600 | 49 | """Set a random seed."""
__copyright__ = "Copyright (C) 2015-present, DV Klopfenstein, Haibao Tang. All rights reserved."
__author__ = "DV Klopfenstein"
import sys
import numpy as np
class RandomSeed32:
"""Manage a random seed."""
def __init__(self, seed=None):
self.max_val = int("0xffffffff", 0)
... |
344324ecda923a60ea6db1ee1d7525048a1fbd926488ed3f1f0dd4c2add44eb4 | Python | 1,600 | 39 | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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 cop... |
6659cdb966eb937ea91b0b9acab2a23a869347e9e425b1ff40061df6e33b4762 | Python | 1,600 | 50 | import itertools
import networkx as nx
import numpy as np
import pandas as pd
from reactome import Reactome, ReactomeNetwork
reactome = Reactome()
names_df = reactome.pathway_names
hierarchy_df = reactome.hierarchy
genes_df = reactome.pathway_genes
print (names_df.head())
print (hierarchy_df.head())
print (genes_df.... |
c25ba14840d6ef8f858ca0deb5d5f13e1a72ff8112375a44bbbd60e7d6b337d9 | Python | 1,600 | 66 | import sys
ROOT_DIR = __file__.rsplit("/", 2)[0]
if ROOT_DIR not in sys.path:
sys.path.append(ROOT_DIR)
import os
import multiprocessing as mp
import time
from utils.server_tool import check_port_in_use
BASE_DIR = os.path.dirname(__file__)
def system_call(cmd: str):
os.system(cmd)
def start_process(cmd:... |
c476aa9ea0225f366ef70d618aca11bf1dee994d5799e05915750f061fac35bf | Python | 1,600 | 51 | """Local Zarr alignment access for deprecated GPN-MSA inference."""
from typing import Any
import numpy as np
import zarr
from gpn.data import ReverseComplementer, Tokenizer, slice_alignment
class GenomeMSA:
def __init__(
self,
path: str,
subset_chroms: list[str] | None = None,
... |
c7efd87da91925daf86e4dfeb1802f5a9628861c32ad2557f3d72899da1b3ac3 | Python | 1,600 | 42 | from typing import Union, Optional
from pathlib import Path
import pandas as pd
from sklearn.model_selection import KFold
DATASETS = ['Muscle', 'pc3', 'HEK']
def prepare_te_and_el_data(
data_root: Optional[Union[Path, str]],
kfold_splits: int = 10
):
data_root = Path(data_root)
for dataset in DATASE... |
425e6e09bac879d890a5c76b85333522e0949085db330a6560da6cbf7f0acc75 | Python | 1,601 | 40 | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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 cop... |
ffec7f2a70b99863a58e466b54e617b5002369b0f1948a1f538a6327748187a8 | Python | 1,602 | 45 | import sys
sys.path.append("/lustre/home/mlang/2D_Structure_ENV/lib/python3.9/site-packages")
sys.path.append("/lustre/software/anaconda/anaconda3-2019.10-py37/lib/python3.7/site-packages/")
import numpy as np
from Bio import SeqIO
import re
from pathlib import Path
def get_chain_sequence(fasta_file):
"""
inpu... |
a81c076053a4d61ec77ded5a2ba7d746c1800c7f40df8cc0e80f91c2c619f979 | Python | 1,604 | 39 | """Loss functions shared by GPN-SS and GPN-MSA."""
from jaxtyping import Float, Int
from torch import Tensor
from torch.nn import CrossEntropyLoss
def masked_lm_loss(
logits: Float[Tensor, "... vocabulary"],
labels: Int[Tensor, "..."] | None,
output_probs: Float[Tensor, "... vocabulary"] | None,
loss... |
70bdb4a8d7a56841f37d2c6dd4edab4c12a51e6d695f12c27102de119ac25726 | Python | 1,605 | 62 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
from glob import glob
from vlpp.utils import save_json
from jinja2 import Environment, FileSystemLoader
from vlpp.qamosaic import Mosaic
def main():
# Usefull directories
os.mkdir("data")
# Informations
participant_id = "${sub.sub}"
# I... |
11052400b087d233106872adfa8b63d135ed0d409b3a3acc6d0bc60bc4a9b8ea | Python | 1,607 | 62 | from __future__ import annotations
import os
import re
from typing import TYPE_CHECKING
from anndata import read_h5ad
from scvi.utils import dependencies
if TYPE_CHECKING:
from anndata import AnnData
def _parse_dataset_id(url: str):
match = re.search(r"/e/(.+)", url)
if match:
return match.gro... |
5690da9e5b40501c959602e1d59f999bd6a85df11ab8020674ff824b57da6178 | Python | 1,608 | 57 | from __future__ import annotations
from typing import TYPE_CHECKING
from torch.distributions import Normal as NormalTorch
if TYPE_CHECKING:
import torch
class Normal(NormalTorch):
"""Normal distribution.
Parameters
----------
mu
loc of the Normal distribution.
scale
scale o... |
a2f8af8f5184e11a9addcc1c133671d41ea6a24f5adc0e1ed40ee0996eea4d62 | Python | 1,608 | 45 | """
The test below was taken from the sima package
http://www.losonczylab.org/sima
version 1.3.0
https://github.com/losonczylab/sima/blob/6d2cd4f071e4/sima/tests/test_ROI.py
License
-------
This file is Copyright (C) 2014 The Trustees of Columbia University in the
City of New York.
This program is free software; you... |
0821f377be40a5fab85f5d6f1293fa10c457924ad161bbecf59d5cf15ed9abb4 | Python | 1,609 | 51 | from ... import options as opts
from ... import types
from ...charts.chart import Chart3D
from ...globals import ChartType
from ...options import InitOpts, RenderOpts
class GraphGL(Chart3D):
"""
<<< GraphGL Relational graphs using WebGL to support the layout and
drawing of large-scale network/relational d... |
67b9ac989dbc00cd82bd9ee5f869d0fe1a39edfc71aa9ddda1e3d4115eec02ee | Python | 1,610 | 40 | # import sys
# sys.path.append('tacotron2')
import torch
from .layers import STFT
class Denoiser(torch.nn.Module):
""" Removes model bias from audio produced with waveglow """
def __init__(self, waveglow, filter_length=1024, n_overlap=4,
win_length=1024, mode='zeros'):
super(Denoiser... |
960209fe08f93c79f71754782a982ab98b10aae9f57aac6b0c9e77cc5e119afa | Python | 1,610 | 30 | import matplotlib.pyplot as plt
import numpy as np
plt.style.use(['science','nature'])
x_ax = np.load('inf_comp_x.npy')
y_ax = (1-np.load('inf_sotmram_y.npy'))*100
y2_ax = (1-np.load('inf_rram_y.npy'))*100
hline = (1-np.load('./accs_saved/ecram_runs/LeNet5_ecram8bit_accs.npy'))*100
hline2 = (1-np.load('./accs_saved/e... |
961b81ba54dd91f68106427a94d8172537e7d814f7bfe418d461ecdb97806d06 | Python | 1,610 | 64 | # 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... |
bbbeb8b13bfc4dcc2e8a3e92f3f5d7773d86b7165524f9658a8ec659c024e7ad | Python | 1,610 | 46 | import torch
@torch.jit.script
def gaussian(x, mean, std):
pi = 3.14159
a = (2*pi) ** 0.5
return torch.exp(-0.5 * (((x - mean) / std) ** 2)) / (a * std)
# From Graphormer
class GaussianRadialBasisLayer(torch.nn.Module):
def __init__(self, num_basis, cutoff):
super().__init__()
self.n... |
2389bcb71a777b7da2d3719b2eba8526307fa779a131a384f8238ec260ce07b2 | Python | 1,611 | 53 | import itertools
import pytest
import vak
LABELSETS = []
for labelset in (
'abcde',
'12345',
[1, 2, 3, 4, 5],
'mo', 'po', 'ta',
):
LABELSETS.append(
vak.common.converters.labelset_to_set(labelset)
)
LABELMAPS = []
for labelset in LABELSETS:
LABELMAPS.append(
vak.common.la... |
6fd87cc0d66da86ed77f59354c99524858efef236845629616ec610d4cbc5182 | Python | 1,612 | 59 | import numpy as np
import matplotlib.pyplot as plt
# plots 4D data as a 3D scatter plot
def plot4D(data,
cmap="Reds",
dpi=500,
save_name=False,
logartihmic=False):
print(f'(max, min)=({np.min(data)}, {np.max(data)})')
data += np.abs(np.min(data))
... |
a2a287191585872d8e56f4940312959014803ecf122180c7d960d6844fabda37 | Python | 1,612 | 48 | import time
import RPi.GPIO as GPIO
class mainprogram:
# Main while loop condition
keepalive = True
# DEFINE NUMBER OF BUTTONS AND ORDER IN ARRAY
# setup GPIO
GPIO.setwarnings(False)
GPIO.setmode(GPIO.BCM)
# DEFINE EMERGENCY STOP and hard wired buttons
#testA = 26
testB = 14
... |
bb8452875c303c676560edde5ddf49e17a6c591924af096f55a7e4205e9deb62 | Python | 1,612 | 63 | from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from cleo.testers.application_tester import ApplicationTester
from poetry.console.application import Application
if TYPE_CHECKING:
from tests.types import CommandFactory
@pytest.fixture
def tester() -> ApplicationTester:
r... |
dfa1b30e6da09bb25f7c1c05c9d5f41a4a26799f8b250b87f0d187c4373b286c | Python | 1,612 | 43 | # Copyright 2023 BioMap (Beijing) Intelligence Technology Limited
import torch
import torch.nn as nn
import torch.nn.functional as F
class pytorchTransformerModule(nn.Module):
def __init__(self,
max_seq_len,
dim,
depth,
heads,
f... |
1a42e02d1191d23f393b2463d2205c3597c86ddd8d790ef934a5339321b19fdd | Python | 1,614 | 46 | import ast
import pathlib
import pytest
import scvi
_SRC = pathlib.Path(scvi.__file__).parent
def _find_noop_warning_statements():
"""Return ``file:lineno`` for every bare ``*Warning(...)`` statement in the source.
A statement such as ``Warning("msg")`` merely constructs a warning object and
immediate... |
6aaa498f99738a6f6545f9105cd2f39472d024cc1f84875a87376a5f49257009 | Python | 1,614 | 23 | from predify.modules import PCoderN
from predify.networks import PNetSeparateHP
from torch.nn import Sequential, ReLU, ConvTranspose2d
class PVGG16SeparateHP(PNetSeparateHP):
def __init__(self, backbone, build_graph=False, random_init=False, ff_multiplier=(0.3,0.4), fb_multiplier=(0.3,0.0), er_multiplier=(0.01,0.0... |
39306cac52231b77299fc985bd8b358ac66f791b43fc92ccc514363b05290155 | Python | 1,615 | 27 | """Internal functions for file manipulation."""
import datetime
import h5py
import bpreveal
from bpreveal import logUtils
def addH5Metadata(h5fp: h5py.File, **kwargs: str) -> None:
"""Adds some metadata to your hdf5 file for reproducibility.
:param h5fp: The opened hdf5 file that you want to add metadata to.... |
693b3c04364c3b8db1e0f07110d425bb4d34d6920d11c42f13cd99872a8e374b | Python | 1,615 | 56 | #!/usr/bin/env python
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_fu... |
44570eb9e73005c290c9b86e01e5a4dd98022ddc7b282f0a15394ac59c3af377 | Python | 1,616 | 59 | from __future__ import annotations
from typing import TYPE_CHECKING
from typing import ClassVar
from cleo.helpers import argument
from poetry.console.commands.command import Command
if TYPE_CHECKING:
from cleo.io.inputs.argument import Argument
class SearchCommand(Command):
name = "search"
descriptio... |
d6a91a05e60395244fdcd8100ecbefee755db81e2530c7c22cab4b4d0ef6d946 | Python | 1,616 | 23 | from predify.modules import PCoderN
from predify.networks import PNetSeparateHP
from torch.nn import Sequential, ReLU, ConvTranspose2d
class PVGG16SeparateHP(PNetSeparateHP):
def __init__(self, backbone, build_graph=False, random_init=False, ff_multiplier=(0.35,0.4), fb_multiplier=(0.25,0.0), er_multiplier=(0.01,0... |
ec12c264a74938063fb9be79721a8b55cc9078bfd4fc5a9620251e9ce364f9f9 | Python | 1,616 | 54 | #!/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.
"""
Split a large file into shards while respecting document boundaries. Documents
should be separated by a single empty... |
20e867b64324a61082754e589d1d8e59a6df28d359f59eb314d8d6619b69a711 | Python | 1,617 | 59 | from __future__ import annotations
import pytest
from tomlkit.container import Container
from tomlkit.items import Table
from tomlkit.items import Trivia
from poetry.config.source import Source
from poetry.repositories.repository_pool import Priority
@pytest.mark.parametrize(
"source,table_body",
[
... |
5b9a863dde28423cb78a5792036fbeeb1963e7c756627b610959e9b9d4647eb3 | Python | 1,617 | 54 | import numpy as np
import pylab as plt
import skfmm
X, Y = np.meshgrid(np.linspace(-1,1,201), np.linspace(-1,1,201))
phi = -1*np.ones_like(X)
phi[X>-0.5] = 1
phi[np.logical_and(np.abs(Y)<0.25, X>-0.75)] = 1
plt.contour(X, Y, phi,[0], linewidths=(3), colors='black')
plt.title('Boundary location: the zero contour of ph... |
5ffb7596687eadb88e9e48415cdf37bccf2459963cd9e05038ffe2142716cb14 | Python | 1,617 | 34 | #!/usr/bin/env python
import polars as pl
def read_gtf(file, attributes=["transcript_id"], keep_attributes=True):
if keep_attributes:
return pl.read_csv(file, separator="\t", comment_prefix="#", schema_overrides = {"seqname": pl.String}, has_header = False, new_columns=["seqname","source","feature","start"... |
003f64ce26c296455cbc31a2c578361a1d26984a50b0179e9d0adb851322aca3 | Python | 1,618 | 48 | """Experiment directory layout helpers (DLBase-style four segments for future task routing)."""
from __future__ import annotations
import os
from typing import Any, Dict, Optional
def tensorboard_log_dir(net_sub_path: str) -> str:
"""TensorBoard event files under the run leaf, matching common DLBase la... |
4001e19758aeea466977d1fc47997b4a2b5fcb8b06ba7e5d835ff7466031e14f | Python | 1,618 | 55 | # encoding: utf-8
"""
@author: Jiayang Chen
@contact: yjcmydkzgj@gmail.com
"""
import numpy as np
def record_item(summary_writer, tag, item, step):
if isinstance(item, int) or isinstance(item, float):
# scalar
summary_writer.add_scalar(tag, item, step)
elif isinstance(item, dict):
# sc... |
89602962a80be5937196c18fb54d47d048c11fb94239eb9290db6c3a4127b1d2 | Python | 1,618 | 50 | from __future__ import annotations
import os
import sys
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
if ROOT not in sys.path:
sys.path.insert(0, ROOT)
from gpu_runtime import ( # noqa: E402
compatibility_result,
get_cuda_diagnostics,
get_optional_attention_status,
)
def main... |
ef649dafa06dbd92c29bb6033f146258472edaa7d00f3ab5d8b37ef1c7c77d1e | Python | 1,618 | 67 | from pgmpy.datasets._base import BaseDataset
class StudentPerformance(BaseDataset):
"""
References
----------
- :footcite:t:`uci_student_performance`
"""
_tags = {
"name": "student_performance",
"n_variables": 33,
"n_samples": 395,
"has_ground_truth": False,
... |
2c2f937bdc242526a791579853bfae933d90deb422ff5cc4aeedf2ff829b35d5 | Python | 1,619 | 48 | """Regression test for `update_pmc_open` opening the document database."""
import asyncio
from tinydb import TinyDB
from tinydb.storages import JSONStorage
from scripts import update_pmc_open
class _FakeNCBI:
"""Stands in for `AsyncNCBIAdapter`; no network involved."""
async def __aenter__(self) -> "_Fake... |
85ec1b2c68826649b44b62d95e7801e32aeab47c1df662cb963395419d383cc8 | Python | 1,619 | 50 | from HierarchiaPy import Hierarchia
import matplotlib.pyplot as plt
import numpy as np
import igraph as ig
import networkx as nx
import pandas as pd
import os
import sys
import pandas as pd
import seaborn as sns
from scipy.stats import ttest_ind
from weighted_rc import weighted_rich_club
sys.path.append('..\\src\\')
fr... |
463b3213d04eb1d4acdb4763b10ed71754429621ddf085b0a454161349735f98 | Python | 1,621 | 47 | import pytest
import vak.metrics.distance.functional
LEV_PARAMETRIZE = [
# adapted from https://github.com/toastdriven/pylev/blob/master/tests.py
("kitten", "sitting", 3),
("kitten", "kitten", 0),
("", "", 0),
("kitten", "", 6),
("", "sitting", 7),
("meilenstein", "levenshtein", 4),
(... |
4c7b7932d693c2424918778ace8ba530d7d76e74625f379c0b7e1fb57bf9b9db | Python | 1,623 | 55 | """
"""
"""
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
of the License, or (at your option) at any l... |
a2e49627238b43719e0031483c1b3e4bd192f3a8d8e632ed2128cdc89f90c43f | Python | 1,623 | 63 | from enum import Enum
from typing import Dict, List, Optional, Sequence
import torch
from fairseq.data import Dictionary
class EncoderLangtok(Enum):
"""
Prepend to the beginning of source sentence either the
source or target language token. (src/tgt).
"""
src = "src"
tgt = "tgt"
class Lang... |
c370d1decf9522ed2b42b830c2ada46519ad2d13f5ac1ee6b093e8171a45116b | Python | 1,624 | 58 | # 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 typing import Any, Dict
from fairseq.distributed import utils
try:
from fairscale.optim import OSS
_has_fairscale = True
exce... |
d2d79a74d80e33711eb02ba8d7ff763be9edb0168e9b4e6b217784bbe5ed413d | Python | 1,625 | 14 | # utils/__init__.py
from .file_utils import get_directories, read_batch, get_file_data_by_index, get_display_data, save_seg_class_recfile, get_files_for_day, get_files_for_experiment, get_files_for_bird, filter_segmented_files, filter_classified_files, save_features, remove_pkl_suffix, find_batch_files, create_batch_f... |
920f051c0abe171333a5c00362ad31fd04965377d0a7bbb77b289fb97bb36399 | Python | 1,626 | 55 | import numpy as np
from src.dfer.df_plots import mpl_options_figure, mpl_results_figure
def test_mpl_single_options_figure_renders_all_five_rows():
trace = np.arange(4, dtype=float)
data = {
"file_type": "single",
"t_min": trace,
"smooth_465": trace,
**{f"smooth_adj_{index}": ... |
e0e68bc874c3575c1b095ae36294b08f0ea0d0e05af0ed3d1348c404ddf2734c | Python | 1,627 | 56 | """
hippie-wf3dacg
===========
Conditional VAE with NEMO's feature representations (90-sample waveform MLP +
3D ACG convolutional encoder) fused with HIPPIE's conditioning framework
(source, super_region, technology, layer, decoder-only class label).
Public API:
HippieWF3DACGConfig — hyperparameter dataclas... |
a91cde5c05cd37c36602d0735fe9de88d7cdb254c3f4313edd9e867d665a23d5 | Python | 1,628 | 71 | from matplotlib import pyplot as plt
from figure_1 import figure_1, figure_1_d_auc_prc, figure_1_e_confusion_matrix
from figure_2 import figure_2, figure_2_a_pnet_vs_dense, figure_2_b_external_validation, figure_2_c_survival
from figure_3 import prepare_data, figure_3_sankey, figure3_b_gene_importance, figure3_c_activ... |
ceca0c72f21873719a1a7c9b51eb8fac87ce2723b450e362273020a724c53df2 | Python | 1,629 | 37 | import pytest
import torch
import vak
class TestConvEncoderUMAP:
@pytest.mark.parametrize(
'input_shape',
[
(1, 32, 32),
(1, 64, 64),
]
)
def test_from_instances(self, input_shape):
network = {
'encoder': vak.models.ConvEncoderUMAP.defin... |
3a4545b8b1ae04141b3c1cabf0b4fd5fddc47c3f05ebe71690e501f4b7d621d1 | Python | 1,630 | 49 | from pgmpy.base import DAG
from pgmpy.metrics import BaseUnsupervisedMetric
from pgmpy.structure_score import get_scoring_method
class StructureScore(BaseUnsupervisedMetric):
"""
Uses the standard model scoring methods to give a score for each structure.
The score doesn't have very straight forward interp... |
b09817c07d5902089a139a3e6f0555ca55841a0289e3a5b67b546961de8c14ff | Python | 1,630 | 59 | # @license
# Copyright 2017 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... |
3ba63d9c82448b44b741724706df5541034b19954889e86aee7205617019ce45 | Python | 1,631 | 33 | """Unit tests for the CI test-tier auto-classification in conftest.py (`_tier_markers_for`)."""
import pytest
from tests.conftest import _tier_markers_for
@pytest.mark.parametrize(
'module,fixturenames,availability_skip,expected',
[
# GUI modules and any qtbot-using test -> e2e
('test_gui', ... |
f1c300c80e9994533317b263cb15207ef33593a1b84718a1b3ee2bfc4616f2b0 | Python | 1,632 | 64 | from __future__ import annotations
from typing import TYPE_CHECKING
import dulwich.repo
import pytest
from tests.vcs.git.git_fixture import TempRepoFixture
if TYPE_CHECKING:
from pathlib import Path
@pytest.fixture()
def temp_repo(tmp_path: Path) -> TempRepoFixture:
"""Temporary repository with 2 commits... |
80ea7df95cb60c89712ee53f11414a5b9342efe265d68e0c757d2fdee1d9d797 | Python | 1,633 | 42 | """Compare two or more sets of GO IDs. Best done using sections."""
from __future__ import print_function
__copyright__ = "Copyright (C) 2016-2019, DV Klopfenstein, H Tang. All rights reserved."
__author__ = "DV Klopfenstein"
from goatools.grouper.read_goids import read_sections
from goatools.grouper.grprdflts impo... |
d3e1cbca13a5b5fec687a32a4e8300752df793ebdd1193dae461fb1ceeda6364 | Python | 1,633 | 32 | import re
from pathlib import Path
import pytest
DOCS_SOURCE = Path(__file__).resolve().parent.parent / 'docs' / 'source'
# Since the v4.0 migration off pandas, RNAlysis returns polars objects (see issue #100). The
# hand-written user guides must not describe function outputs or underlying data structures as
# panda... |
710c3c8a7c8500a5ae0c6b48ae7200be3a891c710ca51fff28f9c70753d41bbc | Python | 1,634 | 58 | import warnings
from scvi import settings
from scvi.utils import error_on_missing_dependencies
from ._archesmixin import ArchesMixin
from ._base_model import (
BaseMinifiedModeModelClass,
BaseModelClass,
BaseMudataMinifiedModeModelClass,
)
from ._differential import DifferentialComputation
from ._embeddin... |
fcddc5a6e64641cbf178acd0d415d52934a22a229a7eab8db7dcf7bd7cf20603 | Python | 1,635 | 59 | from selenium.webdriver.common.by import By
rec.begin(
{
"layers": [
{
"type": "image",
"source": "precomputed://gs://neuroglancer-public-data/flyem_fib-25/image",
"tab": "source",
"name": "image",
}
],
... |
18a41c7955454603303f5a9e5643cab13a1716b990a5d9cf4577284d7a1632ac | Python | 1,636 | 68 | from anndata import read_h5ad
from anndata.io import read_csv, read_loom, read_text
from ._anntorchdataset import AnnTorchDataset
from ._datasets import (
annotation_simulation,
brainlarge_dataset,
breast_cancer_dataset,
cellxgene,
cortex,
dataset_10x,
frontalcortex_dropseq,
heart_cell_... |
a77bcdbe33e436a369510189bd793d996b3687b24b3c8cf8bd8f807bb8c5f88b | Python | 1,636 | 49 | from typing import Union
import numpy as np
import scipy.sparse as sparse
from tqdm import tqdm
import loompy
class NeighborhoodEnrichment:
def __init__(self, epsilon: float = 0.1) -> None:
self.epsilon = epsilon
def fit(self, X: np.ndarray, knn: sparse.csr_matrix, k: int) -> np.ndarray:
"""
Args:
X ... |
1443c8aaa696fb97006b875d59c5c3020b0eb36b7c3deff7ee7604801f3fa518 | Python | 1,637 | 63 | #!/usr/bin/env python
# Made by Paul Kiessling pakiessling@ukaachen.de
import os
import argparse
import tempfile
import shutil
from pypdl import Downloader
LINKS = {
"https://zenodo.org/records/13944111/files/concordex_sim.zip":"fb7c79fd9cec2c79e3b74fb50be40ff4"
}
def download_links(links, temp_dir):
head... |
1bce28dc8eb54b28cf32bc48947d64a4119ff95b38378ba44463c1b5f438fbe8 | Python | 1,637 | 54 | import uuid
from jinja2 import Environment
from ... import types
from ...charts.basic_charts.map import MapMixin
from ...charts.chart import Chart3D
from ...commons import utils
from ...globals import CurrentConfig, NotebookType
from ...options import InitOpts, RenderOpts
from ...render.display import HTML
from ...re... |
f5dc6bf0ebda564707341de1bc816e3b7924bd9bbba9867675dd2e674aebd42e | Python | 1,637 | 35 | from typing import Union, Tuple, List
import numpy as np
from batchgeneratorsv2.helpers.scalar_type import RandomScalar
from batchgeneratorsv2.transforms.base.basic_transform import BasicTransform
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
class nnUNetTrainerNoDA(nnUNetTrainer):
@st... |
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