sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
a1c837c1b2a6ddaa7ab93d10709f9b4f972f9adea3f64ea5c7591fb13912956e | Python | 1,131 | 39 | import yaml
from bigstream.configure_bigstream import (configure_logging,
set_cpu_resources)
from dask.distributed import (Worker)
from distributed.diagnostics.plugin import WorkerPlugin
from flatten_json import flatten
class ConfigureWorkerPlugin(WorkerPlugin):
def _... |
2c5f8797f780821b8bae2dc842a5201bf0ffa593a8c503973839b9c5b82b9266 | Python | 1,133 | 22 | #!/usr/bin/env python3
# MIT License
#
# Copyright 2024 Broad Institute
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to u... |
ad1e46160b34ab947688e5deff377805c59526a637d66c0dbe861c02ec409c12 | Python | 1,133 | 22 | #!/usr/bin/env python3
# MIT License
#
# Copyright 2025 Broad Institute
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to u... |
4bf5d744c1d670b0cbdb3293ea33fb14eef8cf0f8c16115720228bf9b6d9594c | Python | 1,134 | 41 | # -*- coding: utf-8 -*-
import sys
import warnings
__all__ = [
'general',
'filtering',
'enrichment',
'__attr_file_key__',
'__biotype_file_key__',
'__font_key__',
'__font_size_key__',
'__stylesheet_key__',
'__show_tutorial_key__',
'__databases_key__',
'FROZEN_ENV',
]
__name__... |
b04a94f21bdc3b6554b212d941973060a69312928160336ac18a2e45e893dec0 | Python | 1,134 | 32 | # Requires BigML Python bindings
#
# Install via: pip install bigml
#
# or clone it:
# git clone https://github.com/bigmlcom/python.git
from bigml.deepnet import Deepnet
from bigml.api import BigML
# Downloads and generates a local version of the DEEPNET,
# if it hasn't been downloaded previously.
deepnet = Deepnet('... |
d8af8c948090ed38fb388f1e554d83d1610eefefe2415dcbae74dea3a665ee5b | Python | 1,134 | 32 | # Requires BigML Python bindings
#
# Install via: pip install bigml
#
# or clone it:
# git clone https://github.com/bigmlcom/python.git
from bigml.deepnet import Deepnet
from bigml.api import BigML
# Downloads and generates a local version of the DEEPNET,
# if it hasn't been downloaded previously.
deepnet = Deepnet('... |
8737293c61685dae7a5308043f954d057cca12e153b34c8d883730b87184a475 | Python | 1,135 | 33 | import unittest
from unittest.mock import patch
from pyecharts import options as opts
from pyecharts.charts import Funnel
from pyecharts.faker import Faker
class TestFunnelChart(unittest.TestCase):
@patch("pyecharts.render.engine.write_utf8_html_file")
def test_funnel_base(self, fake_writer):
c = Fu... |
fa64e068cc033f0617e5aa36bffdaf1a9736dd4238f640407dedb101e84f12ed | Python | 1,135 | 50 | from .cellassign import CellAssign
from .contrastivevi import ContrastiveVI
from .cytovi import CYTOVI
from .decipher import Decipher
from .diagvi import DIAGVI
from .drvi import DRVI
from .gimvi import GIMVI
from .joint_embedding_scvi import JointEmbeddingSCVI, JointEmbeddingVAE
from .methylvi import METHYLANVI, METHY... |
71ec77803f95b7ebaf7ece1a4423b9befc1668725fbdf2bcda0aee70738c0212 | Python | 1,136 | 44 | from __future__ import annotations
from typing import TYPE_CHECKING
from typing import ClassVar
from cleo.helpers import option
from poetry.console.commands.installer_command import InstallerCommand
if TYPE_CHECKING:
from cleo.io.inputs.option import Option
class LockCommand(InstallerCommand):
name = "lo... |
f05329e2230c31c8412e09389823465b6045828e5eac7027a9e88c9fc57bd01d | Python | 1,136 | 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
import sys
import numpy as np
aggregate_funcs = {
"std": np.std,
"var": np.var,
"median": np.median,
"mean"... |
e6eafb1556da8c72153d32de7f52158537f19ece6adc350673a7f5f577337e5a | Python | 1,137 | 45 | 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... |
e79fb7f720cdacfc5bb3f75ffbd251d1be5b7f54b9fce85ae5efc72f25e38881 | Python | 1,137 | 42 | # -*- coding: utf-8 -*-
"""
Created on Sat May 24 08:22:24 2025
Utils for pybounds mpc simulations.
@author: mayc06
"""
import numpy as np
import copy
def wrapTo2Pi(rad):
rad = copy.copy(rad)
rad = rad % (2 * np.pi)
return rad
def wrapToPi(rad):
rad_wrap = copy.copy(rad)
q ... |
c08457f6a1ceba2e7a44f7f4a4a22b594f60c335c31e6e6f332a6a7e0ccb90db | Python | 1,138 | 46 | 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... |
381dd2449f923f10a550eccb2e8573bd442276373381103d2543c7363fc574a1 | Python | 1,139 | 29 | import sys
import warnings
from pathlib import Path
_PACKAGE_DIR = Path(__file__).resolve().parents[1]
# Leave tests outside the skipped prefixes so warnings identify the test caller.
_SKIP_FILE_PREFIXES = tuple(str(path) for path in _PACKAGE_DIR.iterdir() if path.name != "tests")
def _warn_external(message: str, ca... |
2bdacdf75d7d55ef804ed2efa8657db2bc3450ec57be30bf7cd67fff842e0fb2 | Python | 1,140 | 41 | from mdt import LibraryFunctionTemplate
__author__ = 'Robbert Harms'
__date__ = "2015-06-21"
__maintainer__ = "Robbert Harms"
__email__ = "robbert@xkls.nl"
class MRIConstants(LibraryFunctionTemplate):
"""Some constants that might be of use in dMRI model functions"""
is_function = False
cl_code = '''
... |
59ab71fe9534b162d62df20916345ba2831078ad8e88f1b89c4c06d9402f7fd1 | Python | 1,142 | 31 | import os
import time
import argparse
import logging
import subprocess
import utilities as utilities
import gensim.downloader as api
import gensim.models as model
from gensim.models import Word2Vec
def run_pretrained(args, model_directory):
# 1) Load the pre-trained Word2vec model
try:
model = api.loa... |
cea524ec2194ddfb372fc332d4c3dcb4a3a550c642e2935819060b44e760db31 | Python | 1,142 | 34 | import numpy as np
def _get_ics(df, ic_type):
if not df.empty:
return df[df["ic_type"] == ic_type]["component"].tolist()
return None
def _sum_flagged_times(raw, flags):
"""Sum the total time flagged for various flags like noisy etc."""
flag_dict = {}
if isinstance(flags, str):
fl... |
09d95cca04fbf4f9d2145048c31dc19b87040ecdc8742f9e4db9a832a35cc1f9 | Python | 1,144 | 30 | import matplotlib.pyplot as plt
import numpy as np
import loompy
def factors(ds: loompy.LoomConnection, base_name: str) -> None:
offset = 0
theta = ds.ca.HPF
beta = ds.ra.HPF
n_factors = theta.shape[1]
while offset < n_factors:
fig = plt.figure(figsize=(15, 15))
fig.subplots_adjust(hspace=0, wspace=0)
for... |
78c18b5e188986cdceaac8b51bb27357bca24c799e65c6d95a377835bff59abf | Python | 1,144 | 43 | """Tests for the autoencoder model architecture."""
import torch
from aestetik.models.model import AE
def test_ae_forward_shapes():
num_input_channels = 8
window_size = 7
model = AE(
num_input_channels=num_input_channels,
morphology_dim=window_size,
c_hid=16,
latent_dim=8,... |
96e4545bd841d891c6b50a68a5a880bc21ee35c67b72a64f24bf1f9fd557f64b | Python | 1,144 | 34 | import torch
import torch.nn as nn
class cSE(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.Conv_Squeeze = nn.Conv2d(in_channels,
in_channels // 2,
kerne... |
add1fa73785b53dd84c1bb5339d1e276e06b2991762e09d71f5db947c8b8c30e | Python | 1,146 | 48 | import os
from fairseq.data.audio import (
AudioTransform,
CompositeAudioTransform,
import_transforms,
register_audio_transform,
)
class AudioWaveformTransform(AudioTransform):
pass
AUDIO_WAVEFORM_TRANSFORM_REGISTRY = {}
AUDIO_WAVEFORM_TRANSFORM_CLASS_NAMES = set()
def get_audio_waveform_trans... |
48227a997141332f1085cb32fe8f999d86f1cdb87d4b2d28e94ef333415a4b07 | Python | 1,149 | 37 | import os
import pandas as pd
def load_reactome_db(input_source="uniprot"):
"""
Load default Reactome mapping and pathways files.
"""
base_dir = os.path.dirname(os.path.abspath(__file__))
if input_source == "uniprot":
mapping_path = os.path.join(
base_dir, "..", "data", "downl... |
54d647b52cfe853e5424c773df31099bde6c24c208b84c5ce5b8c272e69000ec | Python | 1,151 | 30 | import matplotlib.pyplot as plt
import numpy as np
import scienceplots
plt.style.use(['science','nature'])
x_ax = np.load('ideal_quant_x.npy')
y_base = np.load('ideal_quant_y.npy')/100
y_quant = np.load('2bit_quant_y.npy')/100
y_baseplot = 1-y_base
y_quantplot = 1-y_quant
# hline = np.load('./accs_saved/ecram_runs/L... |
34f7a22e3527d0cde58618b0b1699d7ec6019b359a0ab2577c8ba31dc15fb6d2 | Python | 1,153 | 40 | import logging
import os
import sys
def set_logging(filename):
# base_folder ='../logs'
# timeStamp = '_{0:%b}-{0:%d}_{0:%H}-{0:%M}'.format(datetime.datetime.now())
# filename, file_extension = os.path.splitext(filename)
# filename = os.path.join(base_folder,filename+ timeStamp+'.log')
if not os.... |
fc2c479d9c62b470fa79b79d0db09b7c53e49cacaaa5bc9357c49b89d58d074d | Python | 1,153 | 33 | import torch
import numpy as np
from pathlib import Path
from utils import parse_mesh, hist_alpha_init
# Prepare input data
def prep_input_data(matprop, pffmodel, crack_dict, numr_dict, mesh_file, device):
'''
Input data is prepared from the .msh file.
If gradient_type = numerical:
X, Y = nodal c... |
4238a89af101120144e7aa772091ca08fe6a1fc28edac87b320e585fd7243096 | Python | 1,154 | 38 | from setuptools import setup, find_packages
setup(
name="semanticst",
version="1.1.3",
author="Roxana Zahedi",
author_email="r.zahedi_nasab@unsw.edu.au",
description="SemanticST: Spatially informed semantic graph learning for effective clustering and integration of spatial transcriptomics",
l... |
d60634bb71d99663d6274418d50f319d0f7e8043b7964d2b17014ab5e74c2063 | Python | 1,154 | 44 | from __future__ import annotations
import dataclasses
from typing import TYPE_CHECKING
from poetry.repositories.repository_pool import Priority
if TYPE_CHECKING:
from tomlkit.items import Table
@dataclasses.dataclass(order=True, eq=True)
class Source:
name: str
url: str = ""
priority: Priority = ... |
32e4cbe290cdc44c376bdeb21426314eb13a20ae9502ec7e9a57d6ca60873e5f | Python | 1,155 | 32 | # 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 import data_utils
from . import BaseWrapperDataset
class PadDataset(BaseWrapperDataset):
def __init__(self, dataset, ... |
47f915b2e2645bf9aee29848f5473ce7755acf79e5096e1157ab6494c22ede68 | Python | 1,157 | 42 | import pandas as pd
import pytest
@pytest.fixture
def specific_dataset_df(specific_dataset_csv_path):
"""returns a function that will return a
dataframe corresponding to the ``prep``ared dataset
from a specific configuration file,
determined by characteristics specified by the caller:
`config_type... |
6c9a98760183fe66f51701080156e69a628488128f22b7b923b91f612e3d4616 | Python | 1,157 | 22 | import pandas as pd
import json
file_names = ['test_losses_DIFF_conn_1_2PC_spec_targ_fixed_dist_PC2_fb_even_longer_bb.json', 'test_losses_DIFF_conn_2_2PC_spec_targ_fixed_dist_PC2_fb_even_longer_bb.json', 'test_losses_DIFF_conn_3_2PC_spec_targ_fixed_dist_PC2_fb_even_longer_bb.json'] #['rev_test_losses_conn_1_2PC.json',... |
a69f00b8a2bee1283cd5c3c9df9f42540d7b471b63558cb4b86762f14fa01610 | Python | 1,157 | 36 | # 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.dataclass... |
e9f18c3bb4ac6d4438911a1f248cbab5c80270fd8db676e7bcb22b9b04fa5e32 | Python | 1,159 | 35 | import numpy as np
from phantoms.phantom import phantom
# use this class to define the whole volume as water
# this is useful for testing the optical model geometry / light source
class water_phantom(phantom):
def create_volume(self, cfg) -> np.ndarray:
volume = np.zeros(
... |
58f6282c8c6b6e7ccfc026f136fddbf85398ae7cf6e9b5fa21815e3475c36ae0 | Python | 1,160 | 38 | # The script is from scGPT (https://github.com/bowang-lab/scGPT), copyright (c) 2022 suber
import torch
import torch.nn.functional as F
def masked_mse_loss(
input: torch.Tensor, target: torch.Tensor, mask: torch.Tensor
) -> torch.Tensor:
"""
Compute the masked MSE loss between input and target.
"""
... |
86e08a6a420e5338d2e917436d1938085954bfc0e3326a920466da36d3b81c0a | Python | 1,160 | 41 | import cellxgene_census
import json
VERSION = "2023-05-08"
with cellxgene_census.open_soma(census_version=VERSION) as census:
meta_data = (
census["census_data"]["homo_sapiens"]
.ms["RNA"]
.var.read(
column_names=[
"feature_name",
],
)
)
... |
956e0884b6ac8ae8e4fc698ca6f6a58e10d23e567e8f6eb135e621884a4b643d | Python | 1,160 | 41 | #! /usr/bin/python
# usage: python topics.py <beta file> <vocab file> <num words>
#
# <beta file> is output from the lda-c code
# <vocab file> is a list of words, one per line
# <num words> is the number of words to print from each topic
import sys
def print_topics(beta_file, vocab_file, nwords = 25):
# get the... |
d32c42e4e714e45525daab12c23d84a3130fdc73130070e34350f5745d49d011 | Python | 1,160 | 43 | #!/usr/bin/env python
# PYTHON_ARGCOMPLETE_OK
"""Launches the MDT Graphical User Interface."""
import argparse
import os
import textwrap
from argcomplete.completers import FilesCompleter
from mdt.gui.model_fit.qt_main import start_gui
from mdt.lib.shell_utils import BasicShellApplication
__author__ = 'Robbert Harms'... |
db64798fae087f31f78ac19c9d6efd860e1656cf448c1c6c365fadcb522e5af0 | Python | 1,161 | 27 | from argschema import ArgSchema, ArgSchemaParser
from argschema.schemas import DefaultSchema
from argschema.fields import Nested, InputDir, String, Float, Dict, Int
from ...common.schemas import EphysParams, Directories, CommonFiles
class MedianSubtractionParams(ArgSchema):
median_subtraction_executable = String(... |
0688f4a81d3e5b6a561964b2cc378d23bf5928ae9f062e68224c5789cb17687d | Python | 1,163 | 34 | """Generate dataset splits, recording entropy values across sampling steps."""
from importlib import resources
import pandas as pd
from brenda_references.docdb import BrendaDocDB
from brenda_references.sampling import GMESampler
# Must be the directory `brenda_references.load_split` reads, or a regeneration
# lands ... |
0cf752a6ef0ab46c2e807b423d977f4b5dfffb810c89778e26f8dd2f5b29467c | Python | 1,163 | 58 | import pytest
import numpy as np
import os
#import ecephys_spike_sorting.common.utils as utils
#DATA_DIR = os.environ.get('ECEPHYS_SPIKE_SORTING_DATA', False)
def test_read_cluster_group_tsv():
pass
#filename = os.path.join(DATA_DIR, 'cluster_group.tsv')
#cluster_ids, cluster_quality = utils.read_cluster_group... |
0eceb6e1dbb488420587c57613d1120e03477401537f682a477385f961c06fb9 | Python | 1,164 | 42 | # 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 numpy as np
from dataclasses import dataclass
from fairseq.dataclass import FairseqDataclass
from fairseq.scoring import BaseScorer, r... |
db0d9bcc26880c5b3c7f3b9e04423c7080a4a0a3ef7080fb3241b55ab6f1fa88 | Python | 1,165 | 31 | import os
def run_func(description, ppi_path, pseq_path, vec_path,
index_path, gnn_model, test_all):
os.system("python gnn_test.py \
--description={} \
--ppi_path={} \
--pseq_path={} \
--vec_path={} \
--index_path={} \
--gnn_model... |
b37f3cce759aaf6f8f0b539e6cbac07da038de75832f6530e415ff2e85793f85 | Python | 1,166 | 33 | import torch
from torch import nn, Tensor
import numpy as np
class RobustCrossEntropyLoss(nn.CrossEntropyLoss):
"""
this is just a compatibility layer because my target tensor is float and has an extra dimension
input must be logits, not probabilities!
"""
def forward(self, input: Tensor, target:... |
7022bec61efd76c76a05d83a2a9325cb5678f8a63a6ff595f485a4c28c98fa8a | Python | 1,170 | 49 | import logging
import numpy as np
import copy
from sklearn import preprocessing as p
def get_processor(args):
print args
proc_type = args['type']
logging.info("Pre-processing: %s", proc_type)
if proc_type == 'standard': # 0 mean , 1 variance
if 'params' in args:
p1 = args['params'... |
6a229b0904473157f85411add43f56bd55e858274f334be911ac14a99c0525ae | Python | 1,172 | 28 | import pytest
import vak
from .test_tweetynet import TEST_INIT_ARGVALS
class TestED_TCN:
@pytest.mark.parametrize(
'labelmap, input_shape',
TEST_INIT_ARGVALS
)
def test_init(self, labelmap, input_shape):
# network has required args that need to be determined dynamically
n... |
6a3352e02e1212a4c308211d698d48da3748f2660219e46e07bbf8cb44cbb432 | Python | 1,172 | 39 | import sys
from collections import defaultdict
from typing import Dict, Any, Union
def parse_arg(v: str) -> Union[str, int, float, bool, None]:
try:
v = int(v) # parse int parameter
except ValueError:
try:
v = float(v) # parse float parameter
except ValueError:
... |
7958376e4a4c32c8a973ce2fa94d68fbe93e4e6e0d626d78bccfc0242a75de41 | Python | 1,172 | 31 | # 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... |
cd6df51fd0a85a332f25d15f9e4a8f78ed495bf605bc0ea853fbe43cff631bdc | Python | 1,172 | 26 | """Reject host-specific ARM64/ARM64X/CHPE binaries in the Windows x64 bundle."""
from pathlib import Path
import pefile
def require_x64(path: Path) -> None:
pe = pefile.PE(str(path), fast_load=True)
try:
if pe.FILE_HEADER.Machine != 0x8664 or pe.OPTIONAL_HEADER.Magic != 0x20B:
raise Runtim... |
0ee1b973d83b535b2a294a00b855eeef911f57a8fe96f725d32a96a36956fcfa | Python | 1,173 | 34 | import paddle
from rna_ernie import BatchConverter
from paddlenlp.transformers import ErnieModel
# ========== Set device
paddle.set_device("cpu")
# ========== Prepare Data
data = [
("RNA1", "GGGUGCGAUCAUACCAGCACUAAUGCCCUCCUGGGAAGUCCUCGUGUUGCACCCCU"),
("RNA2", "GGGUGUCGCUCAGUUGGUAGAGUGCUUGCCUGGCAUGCAAGAAACCUUG... |
c414c4a4094234d6a3be13b0e68694c4e091c44157df7260db37d2bdc41c3bd4 | Python | 1,176 | 35 | from torch import nn
import torch
import torch.nn.functional as F
device='cuda'
class Readout(nn.Module):
def __init__(self, latent_dim=10, h=64, n_classes=10):
super(Readout, self).__init__()
self.readout = nn.Sequential(nn.Linear(latent_dim, h),
# nn.ReLU(),
... |
e7d50ab89106b48df638418f2efbe983e827f163a4c2c2b4868501c9d659407e | Python | 1,176 | 30 | import pandas as pd
import pytest
import vak
@pytest.mark.parametrize(
'model_name, config_type, audio_format, spect_format, annot_format, expected_result',
[
("TweetyNet", "train", "cbin", None, "notmat", True),
("TweetyNet", "train", None, "mat", "yarden", True),
]
)
def test_has_unlabe... |
2b7d61e5f42f0d065a8b9c2e889bc5a8950d51074f7897cc905297e680eaaf63 | Python | 1,179 | 41 | import unittest
import jax
import jax.numpy as jnp
import numpy as np
from laplacian_testcase import LaplacianTestCase
from folx.ad import hessian, jacfwd, jacrev
class TestAutoDiff(LaplacianTestCase):
def test_jacfwd(self):
def f(x):
return jnp.sin(x * x.sum())
x = np.random.normal... |
ac6087e99577fe999f3ce7c668fbccf14eb083a1c4c35cbf9d18c849354ccf16 | Python | 1,179 | 38 | #!/usr/bin/env python
# PYTHON_ARGCOMPLETE_OK
"""This script prints information about the available devices on your computer."""
import argparse
import textwrap
from mdt.lib.shell_utils import BasicShellApplication
from mot.lib import cl_environments
__author__ = 'Robbert Harms'
__date__ = "2015-08-18"
__maintainer__ ... |
6905c1f88f187f30ee335f17ee18afc2a6aaad738aa44054b86f5fef6f8091f4 | Python | 1,180 | 41 | import numpy as np
from scipy.ndimage import binary_fill_holes
from acvl_utils.cropping_and_padding.bounding_boxes import get_bbox_from_mask, bounding_box_to_slice
def create_nonzero_mask(data):
"""
:param data:
:return: the mask is True where the data is nonzero
"""
assert data.ndim in (3, 4), "... |
dc7535182a7e1e1092ed9857f0e8a88f6ca0fbaab610d0e5d312c501f2d14e37 | Python | 1,181 | 40 | from __future__ import annotations
from typing import TYPE_CHECKING
from typing import ClassVar
from poetry.console.commands.install import InstallCommand
if TYPE_CHECKING:
from cleo.io.inputs.option import Option
class SyncCommand(InstallCommand):
name = "sync"
description = "Update the project's env... |
7af6a2c7e9ccf9c104128a9f570427e0076401366424865f739afb01228f97ae | Python | 1,182 | 39 | """Copy the canonical Colab notebooks into the Sphinx source tree."""
from __future__ import annotations
import shutil
from pathlib import Path
REPOSITORY_ROOT = Path(__file__).resolve().parents[1]
SOURCE = REPOSITORY_ROOT / "colabs"
DESTINATION = Path(__file__).resolve().parent / "_notebooks"
MODEL_DEMOS = (
"g... |
a1d46b65d99669bc14fd6dc7ef55fdacc6f590445c6e9ae87e3a00cd9c7b1e4b | Python | 1,182 | 37 | import numpy as np
'''
def Outer_concatenation(matrix_A):
"l1xn--> l1xl1x2n"
matrix_B=np.zeros((matrix_A.shape[0],matrix_A.shape[0],None))
print(matrix_B.shape)
for v1_dx, v1 in enumerate(matrix_A):
# print(v1)
for v2_dx, v2 in enumerate(matrix_A):
v12 = np.hstack([v1, v2])
... |
2999cc8dc95b15ae25059278c98405865bf022a49f292b41b703d02efdc2a8ca | Python | 1,183 | 40 | import torch
import torch.nn.functional as F
def masked_mse_loss(
input: torch.Tensor, target: torch.Tensor, mask: torch.Tensor
) -> torch.Tensor:
"""
Compute the masked MSE loss between input and target.
"""
mask = mask.float()
loss = F.mse_loss(input * mask, target * mask, reduction="sum")
... |
2e463adf8df4f89c1cff591943e6436c5890f5f46d7b42d50a43da3eaac43cb3 | Python | 1,183 | 35 | # 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... |
f8a0872aa86f6eb7a423540b68737896be04bee5891427b473ce23758c758296 | Python | 1,184 | 35 | # 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... |
e08563935b789a917b8b4aa9b9d4918615cb2f592988b3e78fefccc834e75771 | Python | 1,185 | 37 | import torch
import numpy as np
from typing import List, Callable
from pytorch_grad_cam.metrics.perturbation_confidence import PerturbationConfidenceMetric
def multiply_tensor_with_cam(input_tensor: torch.Tensor,
cam: torch.Tensor):
""" Multiply an input tensor (after normalization)
... |
64b3b6ceaa3316748c1de4e1285c64a193d1f564ee3557fc2a68fda9a52464c3 | Python | 1,186 | 41 | import numpy as np
from fairseq.data.audio.feature_transforms import (
AudioFeatureTransform,
register_audio_feature_transform,
)
@register_audio_feature_transform("utterance_cmvn")
class UtteranceCMVN(AudioFeatureTransform):
"""Utterance-level CMVN (cepstral mean and variance normalization)"""
@cla... |
984b91bbf82ec0050fb19838ed4a2a64fdf044803d4c7a816f4c5d797d165170 | Python | 1,186 | 34 | from celltype_ibl.utils.c4_data_utils import get_c4_labeled_dataset
import celltype_ibl.models.mlp_classifier as mlp_probe
import celltype_ibl.models.linear_classifier as linear_probe
import numpy as np
VAE_DIRECTORY = "/mnt/sdceph/users/hyu10/c4_results_new/VAE_checkpoints"
OUT_DIR = "/mnt/home/hyu10/ceph/c4_results_... |
b3d60e458219c804a808273d71384dac4d3b7fa4d9fad9af6191b3e1b4ce92b3 | Python | 1,186 | 34 | """Base class for all torch medaka models."""
import medaka.features
import medaka.models
class CountsMatrixModel(medaka.models.TorchModel):
"""Base class for models that take counts matrices as input."""
def get_model_input_features(self, batch):
"""Return the counts matrix from the batch."""
... |
0f7525c2c0a9a27fe9529004dc75f2222cc1eb9f2a45b91ca2745d985705e3ff | Python | 1,187 | 35 | """
Logic to collate LR results from slave results
"""
import numpy as np
import os
outputdir = 'data/sobol'
collatedfile = 'data/sobol/sobolLRpTauPCCresults.npz'
#Find output files
outputfiles = [os.path.join(outputdir,filen) for filen in os.listdir(outputdir) if filen.startswith('LR_solutions_') and fil... |
d559f5699d9512294bb659939b346e6d8a99aa9b1201c6cfe3ee2e5055534530 | Python | 1,187 | 56 | """
Usage: python -m scripts.example_inference
Computes logits for a single sequence or with a batch of sequences.
"""
import torch
from evo import Evo
def main():
# Load model.
device = 'cuda:0'
evo_model = Evo('evo-1-131k-base')
model, tokenizer = evo_model.model, evo_model.tokenizer
model.t... |
fd69e8dd0c3fa4615033a4d56664fb55822ef6c9e7fdaa04b683d077ed3ab12c | Python | 1,188 | 35 | """Whether to run Simulation preprocessing before training."""
from __future__ import annotations
import os
def dir_missing_or_empty(path: str) -> bool:
"""True if ``path`` is not a directory or has no entries (empty)."""
if not os.path.isdir(path):
return True
try:
return le... |
3a210cb6926aa551f9744356cf4fea465c66a859a2f0f6dc32b13d411e1bc948 | Python | 1,191 | 48 | import warnings
from scvi import settings
from scvi.utils import error_on_missing_dependencies
from ._amortizedlda import AmortizedLDAPyroModule
from ._autozivae import AutoZIVAE
from ._classifier import Classifier
from ._mrdeconv import MRDeconv
from ._multivae import MULTIVAE
from ._peakvae import PEAKVAE
from ._sc... |
da117962915e80801c2c510d244dcd54e68f4c7a33a2f8f070ca7f2f57636d58 | Python | 1,191 | 46 | import torch
from neurovfm.pipelines import load_vlm, interpret_findings
# 1. Load VLM pipeline + preprocessor
generator, preproc = load_vlm(
"mlinslab/neurovfm-llm",
device="cuda" if torch.cuda.is_available() else "cpu",
)
# 2. Preprocess a study (directory or single file)
batch = preproc.load_study("/path/t... |
f90f0b4ad418de11a69e4faf8f936827aa253a33535ef6c1266d7023d98f87f0 | Python | 1,191 | 35 | import matplotlib.pyplot as plt
import numpy as np
import loompy
def umi_genes(ds: loompy.LoomConnection, out_file: str) -> None:
plt.figure(figsize=(16, 4))
plt.subplot(131)
for chip in np.unique(ds.ca.SampleID):
cells = ds.ca.SampleID == chip
plt.hist(ds.ca.TotalUMI[cells], bins=100, label=chip, alpha=0.5, r... |
a6dd073eb7aaf3bbdd1be2a34e62d75216cb9d04b36c980949c77d66390d53a5 | Python | 1,192 | 35 | """
Logic to collate Re+alECLII results from slave results
"""
import numpy as np
import os
outputdir = 'data/sobol'
collatedfile = 'data/sobol/sobolRepTauPCCresults.npz'
#Find output files
outputfiles = [os.path.join(outputdir,filen) for filen in os.listdir(outputdir) if filen.startswith('Re_solutions_')... |
780409796549b7fe06d09214381475130d8522502301fd7f904c41aaa02d813c | Python | 1,193 | 55 | # Configuration file for the Sphinx documentation builder.
import os
import sys
sys.path.insert(0, os.path.abspath(".."))
# Project information
project = "popgen-npe"
copyright = "2025"
author = "Andrew Kern"
release = "0.1.0"
# General configuration
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.napoleon"... |
8b72ec7f15eb4e80bacc6c9f7a969128d71aa33ea7faeb717a977a2406fb2263 | Python | 1,193 | 34 | import torch
import torch.nn as nn
import math
'''
Learnable pooling with Context Gating for video classification
arXiv:1706.06905v2
source: https://github.com/ekingedik/loupe-pytorch/blob/master/loupe_pytorch.py
'''
class GatingContext(nn.Module):
def __init__(self, dim, add_batch_norm=True):
super(Gating... |
df7852dbeeabe9a759b7f46d3652b93e61215d94a54347c3b40e20aca173016d | Python | 1,193 | 40 | import os
import glob
import pandas as pd
# Define root path
root_path = '/egor2/egor/MovieProject2/bids_data/derivatives'
# Store results
records = []
# Search for all *.dat.mincost files in the correct path
pattern = os.path.join(root_path, 'sub-*', 'tonotopy', 'sub-*.results*', '*.dat.mincost')
for dat_file in gl... |
d45855a4c761414312cd35476f913e4919ac09103d15c05e8c20fc9877d73443 | Python | 1,194 | 37 | import numpy as np
import torch
from fairseq.data.audio.feature_transforms import (
AudioFeatureTransform,
register_audio_feature_transform,
)
@register_audio_feature_transform("delta_deltas")
class DeltaDeltas(AudioFeatureTransform):
"""Expand delta-deltas features from spectrum."""
@classmethod
... |
5312187af7ddbbebf156ab3937262a28266f76d403c0c06fe92804d8215e27f6 | Python | 1,196 | 46 | #!/usr/bin/env -S uv run
# /// script
# requires-python = ">=3.11"
# dependencies = [
# "tensorstore",
# "numpy",
# ]
# ///
import os
import shutil
import numpy as np
import tensorstore as ts
def write_multiscale(path: str, num_channels: int, num_scales: int):
shutil.rmtree(path, ignore_errors=True)
... |
a90abe00b5206a6acc01519a6e9dc2e3c5c21aeefcaf80eef9cc317df7b64f7c | Python | 1,196 | 39 | import logging
from pyscf import gto
from pyscf.scf import RHF
log = logging.getLogger(__name__)
def pyscf_from_mol(mol, charge, spin, basis, cartesian=True, **kwargs):
r"""Create a pyscf molecule and perform an SCF calculation on it.
Args:
mol (List[Tuple[int, np.ndarray]]): the molecule on which ... |
ed7384f3d56d2206eb39ff19edc69a99163cbcf14afceec0be6cde0574d91049 | Python | 1,196 | 43 | """Distillation methods released with the npj Health Systems implementation."""
from __future__ import annotations
from typing import Dict, Type
import torch
from .base import DistillationMethod
from .logits import LogitsDistillation
class NoDistillation(DistillationMethod):
"""Baseline placeholder that return... |
18cf4916d3a89d05a15e192a116b9ea6836b0e3ffee371c355b213673194ffd1 | Python | 1,197 | 53 | from __future__ import annotations
from abc import ABC
from abc import abstractmethod
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from poetry.core.packages.package import Package
from typing_extensions import Self
class Operation(ABC):
def __init__(self, reason: str | None = None, priority: floa... |
3ffbdd9973d6fd3dc86fc08b4fd69b8515b9b8ec958d2eb99c6473e58ab54fac | Python | 1,197 | 50 | from . import functional as F
__all__ = ["Levenshtein", "CharacterErrorRate"]
class Levenshtein:
"""levenshtein distance
returns number of deletions, insertions, or substitutions
required to convert source string into target string.
Parameters
----------
source, target : str
Returns
... |
2f49850e6cfff0acfaa480fa1e6aff4190a95776cb132363e006c0d7259d7bcd | Python | 1,198 | 42 | import numpy as np
from scvi.data import synthetic_iid
from scvi.external import RNAStereoscope, SpatialStereoscope
def test_stereoscope(save_path):
dataset = synthetic_iid(
n_labels=5,
)
RNAStereoscope.setup_anndata(
dataset,
labels_key="labels",
)
# train with no propor... |
d63215c9c3fe2bcd4ccf60d3c0443e0bcc96c82fa2c7969c7da9c3f908ae39bf | Python | 1,198 | 33 | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..layers.conv_module import Convolutional
class SPP(nn.Module):
"""
FPN for yolov3, and is different from original FPN or retinanet' FPN.
"""
def __init__(self, in_channel):
super(SPP, self).__init__()
se... |
f8158743a70afe7553411a6279b20174f4c48f6cafea5c8c079c2ad1e8538dfc | Python | 1,199 | 40 | import os
import glob
import pandas as pd
# Define root path
root_path = '/egor2/egor/MovieProject2/bids_data/derivatives'
# Store results
records = []
# Search for all *.dat.mincost files in the correct path
pattern = os.path.join(root_path, 'sub-*', 'retinotopy', 'sub-*.results*', '*.dat.mincost')
for dat_file in ... |
ac4f030a7364242f193ac021fed4c3a467c2a28b493ff54e414f16f937452ac7 | Python | 1,200 | 47 | import matplotlib.pyplot as plt
import numpy as np
# 设置全局字体为新罗马字体
plt.rcParams['font.family'] = 'serif'
plt.rcParams['font.serif'] = 'Times New Roman'
# 设置全局字体大小
plt.rcParams.update({'font.size': 12})
# Data
scenarios = ['Playground', 'City', 'Canyon']
methods = ['SAC+RAE', 'SAC+CFS', 'Our method']
succes... |
a57d4bf0d2e8743631031043f90cc5a342701913eea5b131c1db3f6a54041010 | Python | 1,201 | 34 | import sys
from SPARQLWrapper import SPARQLWrapper, JSON
endpoint_url = "https://query.wikidata.org/sparql"
with open("/your/urls/here") as f:
data = f.readlines()
urls = [i.strip() for i in data]
def get_results(endpoint_url, URL):
query = f"""SELECT ?uriLabel ?occupation ?occupationLabel ?dob ?dobLabel WHE... |
aa30910dc21fa21782645642e222d82f08c78a6039014aa44f72090af975116e | Python | 1,203 | 42 | import cv2 as cv
import numpy as np
def load_grayscale_image(file_path):
return cv.imread(file_path, 0)
def tensor_to_binary_mask(tf_mask):
binary_mask = (tf_mask > 0.5).astype(np.uint8)
binary_mask *= 255
return binary_mask
def save_grayscale_image(image, file_path):
cv.imwrite(file_path, image... |
ca1aaf2c267dbc461fc492064b186e6983745e7498456c8d1cac3c95658bc6cf | Python | 1,207 | 45 | import matplotlib as mpl
mpl.use('agg')
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import sys
import numpy as np
import copy
probfile = sys.argv[1]
pngfile = sys.argv[2]
data = np.loadtxt(probfile)
# plot prob map
fig_size = [4, 3]
font = {'family': 'sans-serif',
'sans-serif': ['Arial',... |
42ab6cf9ff681fc562e80746c9c887b6409a1e5953896a798ebcd49d4228d0c5 | Python | 1,208 | 50 | import io
import logging
from pathlib import Path
import matplotlib.pyplot as plt
from PIL import Image
log = logging.getLogger(__name__)
def repository_root() -> Path:
"""Get the path to the root of the repository (dev-mode users only)
Returns:
`novae` repository path
"""
path = Path(__fil... |
9d5f73aa34aa3139d02e1e2bb24fd71c172f43b645364079e7ac62b09657f0e0 | Python | 1,209 | 39 | import unittest
from GMXMMPBSA.utils import create_input_args
class CreateInputArgsTest(unittest.TestCase):
def test_pb_membrane_template_is_a_pb_calculation(self):
self.assertEqual(
create_input_args(['pb_mem']),
['general', 'pb_mem'],
)
def test_pb_membrane_template... |
383d50c2c304aec62ffc26d631015c4d7acc96cdbc457957bd367c73f3c0aac1 | Python | 1,211 | 45 | __author__ = 'Robbert Harms'
__date__ = "2014-06-19"
__license__ = "LGPL v3"
__maintainer__ = "Robbert Harms"
__email__ = "robbert@xkls.nl"
class AbstractParameterDependency:
@property
def pre_transform_code(self):
"""Some code that may be prefixed to this parameter dependency.
Here one can ... |
e310ab4f70a5d87610cdef69d1b59fe69ba8a04d522a99945bbf411cc4832631 | Python | 1,211 | 49 | """Sphinx configuration for the GPN documentation."""
from importlib.metadata import version as distribution_version
project = "GPN"
author = "Song Lab at UC Berkeley"
copyright = "2026, Song Lab at UC Berkeley"
release = distribution_version("gpn")
version = release
extensions = [
"myst_nb",
"sphinx_design"... |
ed8650ce819d8d8b6d6e75807b1fde14f54ad1690c2d84260f06d4c80f538b6e | Python | 1,212 | 33 | # 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
# -- Project information -----------------------------------------------------
# https://www.sphinx-doc.org/en/master... |
8bc0e45b5442c795473a1e4d4dc278ebf7a0debc6364639e2bd4177a95c93969 | Python | 1,215 | 32 | import warnings
# Defines phase field fracture model
class PFFModel:
def __init__(self, PFF_model = 'AT1', se_split = 'volumetric', tol_ir = 5e-3):
self.PFF_model = PFF_model
self.se_split = se_split
self.tol_ir = tol_ir
if self.se_split != 'volumetric':
warning... |
dc68c6b2dbadbe8b2aa81c9448593e7968af12ba1dd34b96c1b5c7aee9876db5 | Python | 1,215 | 37 | import pytest
from src.dashboard.catalog import (
DEFAULT_APP_ID,
get_app_definition,
iter_app_definitions,
)
def test_app_catalog_contains_expected_default_app():
app_ids = [definition.app_id for definition in iter_app_definitions()]
assert DEFAULT_APP_ID in app_ids
assert "telemetry_photom_... |
f9e833bbe762aa34e77872ee3165593d34a13ed25875e131478c96f8ea83cd33 | Python | 1,215 | 39 | #!/usr/bin/env python
import pandas
import os
import re
from sqlalchemy import create_engine
engine = create_engine('sqlite:///tests/_td/dbs_2/model_sim_1.db')
weights_1 = pandas.read_sql_table('weights', engine)
weights_1.to_csv("tests/_td/dbs_2/model_sim_1.weights", index=False, sep="\t")
engine = create_engine('s... |
29e93c0bfe4adf799f79238df6e6d1defed0c43587441b0e1e9ec2fe13c7d34f | Python | 1,217 | 51 | from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from cleo.io.null_io import NullIO
from poetry.core.packages.project_package import ProjectPackage
from poetry.puzzle import Solver
from poetry.repositories import Repository
from poetry.repositories import RepositoryPool
from tests.h... |
629051223fe83cfe74fdc760da20534d8e3ffcdfe51cb58c852498b54e0783c9 | Python | 1,217 | 32 | """`clean_data`'s per-document strip must actually drop removed strain ids.
Before this fix the loop in `main()` fetched a document's `strains` list and
did `pass`, so a document kept naming a strain id `clean_data` had just moved
out of the `strains` table. `strip_removed_strains` is that loop's body,
pulled out so i... |
b83179042d0ae82ed0e65da28aca95d6cd01032510e0944f7746bc3a904880d4 | Python | 1,218 | 21 | import os
import torch
from nnunetv2.training.nnUNetTrainer.variants.pathology.nnUNetTrainer_WSD_undefined_dataloader import nnUNetTrainer_WSD_undefined_dataloader
class nnUNetTrainer_WSD_wei_nnunet_aug(nnUNetTrainer_WSD_undefined_dataloader):
def __init__(self, plans: dict, configuration: str, fold: int, dataset_... |
c020f9058a1e0dc554d0b430b2be5f0b3cbe12434c0f24c372cd3bec24163fd5 | Python | 1,218 | 21 | import os
import torch
from nnunetv2.training.nnUNetTrainer.variants.pathology.nnUNetTrainer_WSD_undefined_dataloader import nnUNetTrainer_WSD_undefined_dataloader
class nnUNetTrainer_WSD_bal_nnunet_aug(nnUNetTrainer_WSD_undefined_dataloader):
def __init__(self, plans: dict, configuration: str, fold: int, dataset_... |
e0a5d71030dc96acdda771acdeba4d05600e836900ab3eacf2e8feeffd84edb1 | Python | 1,219 | 21 | import os
import torch
from nnunetv2.training.nnUNetTrainer.variants.pathology.nnUNetTrainer_WSD_undefined_dataloader import nnUNetTrainer_WSD_undefined_dataloader
class nnUNetTrainer_WSD_roi_i0_nnunet_aug_TIME(nnUNetTrainer_WSD_undefined_dataloader):
def __init__(self, plans: dict, configuration: str, fold: int, ... |
20a04f7cfb343ffae02acdeffbdd8eff50bab8ea1fffafc0985c3a0723cf6324 | Python | 1,220 | 21 | import os
import torch
from nnunetv2.training.nnUNetTrainer.variants.pathology.nnUNetTrainer_WSD_undefined_dataloader import nnUNetTrainer_WSD_undefined_dataloader
class nnUNetTrainer_WSD_wei_i0_nnunet_aug(nnUNetTrainer_WSD_undefined_dataloader):
def __init__(self, plans: dict, configuration: str, fold: int, datas... |
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