sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
eb0f2bc65b370ef5ee3d530e29dc0ebd66a4126fe4b9ac2e69f240c02b1fe3e8 | Python | 1,721 | 47 | # Copyright 2020 The Lucent Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... |
dacce81c0fb3ab858e8177b27a57c2f4a1e80bf567b22ee8bf7eee10935067f9 | Python | 1,724 | 49 | import traceback
import click
import numpy as np
import xarray as xr
from ..utils.decorators import error_handler
from .ica_utils import compute_pca_ica
def chunk_pca_ica(dataset: str, chunk_int: int, chrom_subset=None, n_slices=36):
areas = xr.open_zarr(dataset, group=f'chunk_{chunk_int}').areas.sel(chromatophor... |
29bb373620dde3dd804298e54d77cff481d3886011a3ac36dd7dba8a1aaf767f | Python | 1,726 | 48 | from typing import Sequence, Union
import numpy as np
import scipy.stats
from .registry import registry
@registry.register_metric('mse')
def mean_squared_error(target: Sequence[float],
prediction: Sequence[float]) -> float:
target_array = np.asarray(target)
prediction_array = np.asarra... |
716e6c3200241d59f91f465c7880645ff01a420f4e21a1ae2ab1e045eb99a1a1 | Python | 1,726 | 58 | import os
import re
import pandas as pd
# data_dir = os.path.dirname(__file__)
class GMT():
# genes_cols : start reading genes from genes_col(default 1, it can be 2 e.g. if an information col is added after the pathway col)
# pathway col is considered to be the first column (0)
def load_data(self, filenam... |
393d419152e673d9d3fe70cb9597d3da5305af46e48b742450e2c0153b0792d9 | Python | 1,727 | 51 | import unittest
from unittest.mock import patch
from pyecharts import options as opts
from pyecharts.components import Table
from pyecharts.globals import CurrentConfig, NotebookType
def _gen_table() -> Table:
table = Table()
headers = ["City name", "Area", "Population", "Annual Rainfall"]
rows = [["Bri... |
a2f4e43684716d23e529c4c7c2085a5b2c8dbc9fa28dfc250b66c5ed90db465b | Python | 1,727 | 28 | 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... |
c1667eb2b3ed16a02d1330009d6ccaad6bca2b5f757d681b81719031c2458138 | Python | 1,727 | 53 | """
(tips: if the Non-local type you select is **non_local_concatenation**
or **non_local_dot_product** (without Softmax operation),
you may need to normalize NL_MAP in the visualize code)
"""
import numpy as np
import cv2
import math
import os
def vis_nl_map(img_path, nl_map_path, vis_size=(56, 56)):
dst_dir = n... |
ca772959af3e98c9dbfe649bfca47481f63b9ee28ffb0f4c8c27e42320783722 | Python | 1,727 | 26 | 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... |
d8de02681a2210ba6ac4f1fb8f5ac30647616c627d8bd7f5b3e684a8270276d9 | Python | 1,727 | 58 | from math import exp, lgamma, log
import numpy as np
from scipy.special import betainc, betaln
import loompy
class Trinarizer:
"""
Compute trinarization probability per cluster
"""
def __init__(self, f: float = 0.2) -> None:
self.f = f
self.p_half_vectorized = np.vectorize(self.p_half)
def fit(self, ds: loo... |
ababdc9f2a0111a73472cea916be6fd5d7d86363eb506e01fcc24c6479c47eac | Python | 1,728 | 35 | 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, WaveformMetricsFile
class MeanWaveformParams(DefaultSchema):
samples_per_spike = Int(required... |
61d7270fb653120f4837af48e05eda2b943d77525e824807bf97904cb9ede924 | Python | 1,729 | 28 | 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... |
77fadd317ec42a881112fff42797b269b570b5455cb16807032b98dff7f890ad | Python | 1,729 | 60 | import unittest
from pyecharts.commons.utils import remove_key_with_none_value
from pyecharts import options as opts
class TestGraphicComponent(unittest.TestCase):
def test_graphic_group(self):
group = opts.GraphicGroup(
graphic_item={"item": 1},
is_diff_children_by_name=False,
... |
02263c70dc8510b46b624196e050ca2057972918d6ac798f87e3bbc4e0a79198 | Python | 1,730 | 46 | import re
import warnings
from pathlib import Path
from typing import Literal, Union
import rst_to_myst
def get_change_log_for(version: Union[str, Literal['latest']] = 'latest', history_path: Union[str, Path, None] = None):
regex_pattern = r'(\d+\.\d+\.\d+)\s+\(\d{4}-\d{2}-\d{2}\)\s*-+\s*'
if history_path is... |
3a95d671e6ed0fd2ad1681b1e71b6fb61a1311bd20e2c38ed31f582ca5fa7fe5 | Python | 1,732 | 56 | """Acquisition functions for Bayesian Optimization."""
import numpy as np
from scipy.stats import norm
def UCB(preds, uncert, h_param=0.2, **kwargs):
return preds + h_param * uncert
def LCB(preds, uncert, h_param=0.01, **kwargs):
return preds - h_param * uncert
def EI(preds, uncert, h_param=0.01, **kwargs... |
7347800446a8e33ddf57e116d616c2bbe9b1a22e724288a1533a3ea23250b6c0 | Python | 1,732 | 70 | from PyQt5.QtCore import QEvent
from PyQt5.QtCore import pyqtSignal
from PyQt5.QtCore import pyqtSlot
from PyQt5.QtWidgets import QFrame
from PyQt5.QtWidgets import QLabel
from PyQt5.QtWidgets import QListWidget
from PyQt5.QtWidgets import QPlainTextEdit
from mdt.gui.utils import TimedUpdate
class CollapsablePanel(Q... |
fcc9d0ac4fe1fd1629efaca19da2dee66d1f07889d20f1055203f274a3feb301 | Python | 1,732 | 55 | from argschema import ArgSchemaParser
import os
import logging
import subprocess
import time
import glob
import numpy as np
from ...common.utils import read_probe_json, get_repo_commit_date_and_hash
def run_median_subtraction(args):
print('ecephys spike sorting: median subtraction module')
commit_date, comm... |
547c10dac26dbee92c85e6caf7203236e088ed7b7f760404e015da132ce87509 | Python | 1,733 | 53 | import unittest
from pyecharts.render.display import HTML, Javascript
class TestDisplay(unittest.TestCase):
def test_display_html(self):
html_content = "<p>hello world<p/>"
obj = HTML(html_content)
self.assertEqual(obj.data, html_content)
self.assertEqual(obj.__html__(... |
845763bc6fb15df15ff1a06478b98a33a688774de1f75baa20337ba505ed6236 | Python | 1,733 | 54 | # 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
import numpy as np
from examples.textless_nlp.gslm.unit2speech.tacotron2.text import (
EOS_TOK,
SOS_TOK,
code_to_seq... |
40b759b9f4aa9c07e346a371b651149ae180bd7dafcbd63fe92ca0fea11e3297 | Python | 1,734 | 55 | """Persistence helpers for dashboard state."""
from __future__ import annotations
import json
import logging
from pathlib import Path
from src.persistence.app_paths import config_file_path, legacy_repo_file_path
logger = logging.getLogger(__name__)
def state_file_path() -> Path:
"""Return the file path used f... |
ce3826e3bdca3a488cba33f48e7d390d6a9746ed5361875b90d66831168e147f | Python | 1,734 | 53 | import jax.numpy as jnp
import numpy as np
from laplacian_testcase import LaplacianTestCase
from folx import forward_laplacian
class TestComplexFwdLapl(LaplacianTestCase):
def test_parts(self):
x = np.random.randn(10)
W1 = np.random.randn(10, 10)
W2 = np.random.randn(10, 10)
def ... |
461c58f9180dab18dc150dd5e3ef6755c7a22c69b4800b0b1fe7a8be45ed6327 | Python | 1,735 | 50 | import pytest
import folx.interpreter as interpreter_mod
class _DummyPrimitive:
def __init__(self, result):
self.result = result
def get_bind_params(self, params):
assert params == {'x': 1}
return self.result
def test_split_bind_params_accepts_legacy_tuple(monkeypatch):
monkeyp... |
c1d7bf5413337407bdfcd4c41e0d8dafe0dc2038b4047e56dcaae4ed52716678 | Python | 1,735 | 58 | #
# Copyright 2017-2023 Sandia Corporation. Under the terms of Contract DE-AC04-94AL85000 with
# Sandia Corporation, the U.S. Government retains certain rights in this software.
#
# See LICENSE for full license details
#
import os
import numpy as np
import scipy.io
from MLPerf_cal_images import get_cal_fns
... |
3fdc29a8476c69aae40f1ea52797f90a2a50c219257a6d28288583f3a26f1195 | Python | 1,736 | 62 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from examples.speech_recognition.data import data_utils
class DataUtilsTest(unittest.Tes... |
bd5fa97410be8efdb60804dda04646ff9487e543c5efb23f0fa16c3d0a739445 | Python | 1,736 | 64 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import shutil
#from glob import glob
from vlpp.utils import gzipd, run_matlab, TPL_PATH, add_suffix, run_shell
from vlpp.operation import ImgStatsFromAtlas
def main():
for d in ["baker", "stats"]:
os.mkdir(d)
tags = {
"bakerDir": "${b... |
396b4ba912f63c30cf51ebcd040835d49a74bff9f69cfc11bd69f1c6c94ff11f | Python | 1,738 | 58 | import sys
import os
import shutil
import logging
import gensim.downloader as api
from gensim.downloader import base_dir
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger("test-downloader-api")
logger.info(sys.version.replace("\n", " "))
if os.path.... |
49d4c76a890ec3534815ece9795a6553d2edcf6b68d5429e1beefdb270dcd1a2 | Python | 1,738 | 40 | import matplotlib.pyplot as plt
import numpy as np
labels = ['3', '5', '7']
cellpose_means = [8.31, 7.09, 5.56]
jsta_means = [6.01, 5.26, 4.77]
genesegnet_means = [12.31, 10.99, 9.32]
seg_joint_gene_means = [14.56, 13.46, 11.23] # Ours
colors = ['#4DBBD5', '#E64B35', '#00A087', '#3C5488']
x = np.arange(le... |
9301e1592e0177b18469dd32ac6fae7d7b25b17945704f8c270df3fc872d22f4 | Python | 1,740 | 48 | import unittest
from unittest.mock import patch
from pyecharts.charts import MapGlobe
from pyecharts.faker import Faker
from pyecharts.globals import CurrentConfig, NotebookType
class TestMapGlobeChart(unittest.TestCase):
@patch("pyecharts.render.engine.write_utf8_html_file")
def test_map_base(self, fake_wri... |
90e41905d067db2ca2a4353f07e0b13afac7a363c0e6a0bcebf02591b4e81f58 | Python | 1,741 | 59 | # @license
# Copyright 2024 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... |
e27f11a20d28821e46a0620cefdfab59178fb7ab18b46f580f117bbdda69dd76 | Python | 1,741 | 40 | import matplotlib.pyplot as plt
import numpy as np
labels = ['3', '5', '7']
cellpose_means = [17.66, 15.32, 13.23]
jsta_means = [30.35, 25.33, 18.76]
genesegnet_means = [50.66, 28.99, 22.33]
seg_joint_gene_means = [54.65, 29.77, 25.49] # Ours
colors = ['#4DBBD5', '#E64B35', '#00A087', '#3C5488']
x = np.ar... |
01d26165ad881a116c6dda8cffe257a017741a49e437f2e5d0f41259d97d4ec7 | Python | 1,742 | 63 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Simple filters for image processing or filtering lists"""
import os
from pathlib import Path
from typing import Any, Optional
from .models import Cell
def filter_none(value: Optional[Any]) -> bool:
"""Returns True if value is not None"""
return value is not ... |
63f8eed99a2be4fad66693c74e7770db32be96f9c02167811d1a581419014d33 | Python | 1,743 | 41 | import matplotlib.pyplot as plt
import numpy as np
labels = ['3', '5', '7']
cellpose_means = [28.13, 24.12, 19.66]
jsta_means = [26.98, 23.66, 20.45]
genesegnet_means = [31.05, 27.13, 23.44]
seg_joint_gene_means = [32.87, 28.17, 25.31] # Ours
colors = ['#4DBBD5', '#E64B35', '#00A087', '#3C5488']
x = np.ar... |
ba04c7a86080e223f814748e8903399fe25a28fbbe9ee033313b9248a9757ba6 | Python | 1,744 | 50 | import ast
import logging
import unittest
from pathlib import Path
from GMXMMPBSA.exceptions import MMPBSA_Error
from GMXMMPBSA.utils import topology_mismatch_error
ROOT = Path(__file__).resolve().parents[1]
class Phase6MessageTest(unittest.TestCase):
def test_topology_mismatch_includes_paths_and_counts(self):... |
5c52125ccd59e62add3a94346c5779510e92c63d2e84c9b5477f7810955f6d3b | Python | 1,745 | 54 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
import torch.nn as nn
from fairseq.modules import ConvTBC
class TestConvTBC(unittest.TestCase):
def test_c... |
9fb7ed073b45be4bd03292c1b6f0bb10a4a3f531d3b19341cee0e0549debdd54 | Python | 1,745 | 59 | # -*- coding: utf-8 -*-
"""
Created on Tue Jun 3 10:11:20 2025
@author: mayc06
"""
#find plume odor value
odor = max(0,self.dset[self.t,plmy,plmx])
# perform adaptive compression
codor = odor/(odor+self.beta+self.A)
valve = 0
if codor > 0.5:
valve = 1
# update adaptation value
self.A = self.A + (o... |
e704f88427d5cfec641bb990f46e31314d6b773722c65dc41b4ee4358f1b7e4e | Python | 1,745 | 34 | import numpy as np
import loompy
from openTSNE import initialization
from cytograph.embedding import art_of_tsne
def pull_towards_levels(X: np.ndarray, pull_levels: np.ndarray, \
min_force: float = 0.6, max_force: float = 0.95) -> np.ndarray:
"""
Alternative initialization method fo... |
9ec8d3445e4525b8ca9a30454c5171f2b4a4c3546b01ffbb497f70bd1e566a7b | Python | 1,746 | 50 | #
# Copyright 2017-2023 Sandia Corporation. Under the terms of Contract DE-AC04-94AL85000 with
# Sandia Corporation, the U.S. Government retains certain rights in this software.
#
# See LICENSE for full license details
#
from ..idevice import EmptyDevice
from ..generic_device import NormalError
from simulator.backend... |
b23c0b7cac223da285de80461873196e2e60c0e0ddce776f858cc4b0777059af | Python | 1,748 | 49 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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://w... |
d713b95325c2ecee9da41621e0c4c15b5dce982ebb2ff935d0c114a64431304d | Python | 1,749 | 30 | 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... |
2b115084583bd9f8fae3ff6aae605a608762ce57472cfec5bc723345bb4c0ea8 | Python | 1,752 | 50 | #
# Copyright 2017-2023 Sandia Corporation. Under the terms of Contract DE-AC04-94AL85000 with
# Sandia Corporation, the U.S. Government retains certain rights in this software.
#
# See LICENSE for full license details
#
from ..idevice import EmptyDevice
from ..generic_device import NormalError
from simulator.backend... |
68d5a9587bfd96f1e4dda9767102bda38e12188a62895838172dc0575cbcfe30 | Python | 1,752 | 42 | from contextlib import redirect_stderr
from io import StringIO
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import patch
from scripts import run_example_docs_symlink_spike as spike
class ExampleDocsSymlinkSpikeTest(unittest.TestCase):
def test_missing_mkdocs... |
642edf149e02f05a3d6e54159da190e72796a1cc1ffd1648c24d136a6284ff1e | Python | 1,753 | 47 | import matplotlib.pyplot as plt
import numpy as np
import loompy
from .colors import colorize, colors75
def buckets(ds: loompy.LoomConnection, out_file: str = None) -> None:
plt.figure(figsize=(21, 7))
plt.subplot(131)
buckets = np.unique(ds.ca.Bucket)
colors = colorize(buckets)
bucket_colors = {buckets[i]: co... |
0bb962f416fe4cce6ebfefc23e0a18ed8ce348213bc2356ccd9b81e9c84ad821 | Python | 1,755 | 42 | import argparse
def main(args):
import glob
import random
import numpy as np
import json
import itertools
with open(args.input_path, 'r') as json_file:
json_list = list(json_file)
fixed_list = [[int(item) for item in one.split()] for one in args.position_list.split(",")]
... |
fcf3ed38541dd3b06de309ce59397220cf29c1d788704454705122f6fb37734d | Python | 1,756 | 53 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
from mattergen.diffusion.data.batched_data import SimpleBatchedData, _batch_edge_index, collate_fn
def test_collate_fn():
state1 = dict(foo=torch.ones(2, 3), bar=torch.ones(5, 2))
state2 = dict(foo=torch.zeros(3, 3), bar=t... |
be30cdd2e730f30f2f2bdc89b92f010955e877cb30140b7170dc1eacb3672473 | Python | 1,757 | 31 | import numpy as np
from .utils import _log_exp_converter
from .utils import _logit_logistic_converter
from .utils import _arctanh_tanh_converter
# py.test precimed/common/utils_test.py -k test_converters
def test_converters():
epsval = np.finfo(float).eps
minval = np.finfo(float).min
maxval = np.finfo(fl... |
d25b77585e4407349fca99157d81dd2c7bc89d6ac18182f70f5e31f43978d292 | Python | 1,757 | 56 | # @license
# Copyright 2018 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... |
b6a48bd25d0956f24441a87b089ec63c08c8288b0b01d764ef61ac4b8a9f2688 | Python | 1,758 | 57 | import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from GMXMMPBSA.tester import run_process
class TestOutputValidation(unittest.TestCase):
def _run(self, root, command, expected=('result.dat',), skip=False):
return run_process(
root,
'Synthetic test',... |
fd33768091bf77c7970f2849a89803d3d3c91c7b1a5633023247da0c61cbec4a | Python | 1,758 | 77 | import re
import numpy as np
CFG_FILENAME = 'config.yml'
DEFAULT_ID = 'main'
N_INIT = 1
INIT_SIZE = None
TRAILING_DIGITS = re.compile('.*?([0-9]*)$')
ACTION_VERB_TO_NOUN = dict(
sample='sample',
align='alignment',
label='labeling',
evaluate='evaluation',
aggregate='aggregation',
parcellate='par... |
4e38448a0b7f47bc78bbdd62da05b0c40af3ff2fe29af9f3dcaaee04aae15780 | Python | 1,759 | 42 | from __future__ import annotations
from typing import TYPE_CHECKING
from poetry.factory import Factory
from tests.mixology.helpers import add_to_repo
from tests.mixology.helpers import check_solver_result
if TYPE_CHECKING:
from poetry.core.packages.project_package import ProjectPackage
from poetry.reposito... |
69b3004b822c8e3837949dc70a3a2a17d049c1f65c08d48306e1abf42362c99c | Python | 1,760 | 59 | import sys
from collections.abc import Iterable
from typing import Literal
from rich.console import Console
from rich.progress import track as track_base
from tqdm.auto import tqdm as tqdm_base
from scvi import settings
def track(
sequence: Iterable,
description: str = "Working...",
disable: bool = Fals... |
99d4418fee3e3dffb31235210c2a34a643ed0127c24feb127119972b3ca16706 | Python | 1,761 | 51 | """
Unit tests for src/processing/photometry_metadata.py.
"""
import pandas as pd
from src.processing.photometry_metadata import extract_mouse_name, get_column_titles
class TestExtractMouseName:
"""Tests against filenames drawn from actual example data."""
def test_extracts_from_example_photometry_path(self... |
a86dbf52fb42c1145063af716420a0bc9edcdf9f96525d3e85d6cb842af13fa2 | Python | 1,761 | 62 | import os
import time
import numpy as np
import pandas as pd
from sklearn import metrics
from tqdm import tqdm
def train_ML(model, args, dataset=None):
acc_list = []
recall_list = []
f1_list = []
auc_list = []
prec_list = []
time_list = []
print("Repeat 5 fold cross-validation for %s tim... |
8e08dec095a64f513b9ae0047dfa77acc4e06f163b285c2b976f866792da6a45 | Python | 1,762 | 17 | import pandas
import numpy
expected_snp = pandas.Series(["rs940550", "rs6650104", "rs6594028", "rs9701055", "rs7417504", "rs12082473", "rs3094315", "rs3131971", "rs61770173", ], dtype=str)
expected_effect = pandas.Series(["C", "T", "T", "A", "T", "A", "A", "T", "A"], dtype=str)
expected_non_effect = pandas.Series(["G"... |
172ad01fb235bc1d2bfedbac255163f9dffd8046cad72f6addedacf13b52bae0 | Python | 1,763 | 49 | # 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
import torch.nn as nn
class BeamableMM(nn.Module):
"""This module provides an optimized MM for beam decoding with attention... |
764016bb68eb85b856397238291bbe131fd3a560852f90b65f56c75a9eebc4a4 | Python | 1,763 | 46 | import os
import shutil
import subprocess
from pathlib import Path
import nibabel as nib
import numpy as np
def dice_score(y_true, y_pred):
intersect = np.sum(y_true * y_pred)
denominator = np.sum(y_true) + np.sum(y_pred)
f1 = (2 * intersect) / (denominator + 1e-6)
return f1
def run_tests_and_exit_... |
cde1077f57394d6902983327723b71779b8ff6e2b87d049e2a4f7ad832bbe4ab | Python | 1,764 | 34 | # 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... |
5b0b007104390b8606d563df5a3809422befae72b8bf94560403f76b2c381918 | Python | 1,765 | 54 | #!/usr/bin/env python3
"""Validate disk-backed analyzer success or its explicit dependency guard."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
from GMXMMPBSA import API
def validate(result: Path) -> dict:
record = {"result": str(result.expanduser().resolve()), "mod... |
944ba1ffb38641992a7af7736d8f9ca0807022d248071a8d37ecbea89f770fc0 | Python | 1,765 | 65 | # 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 functools
import tempfile
import torch
def spawn_and_init(fn, world_size, args=None):
if args is None:
args = ()
wit... |
f94b88d665b6359510cb02a1788fa9391c6cc4d2db0a5eb047930194206b514c | Python | 1,765 | 54 | import itertools
import joblib
import numpy as np
import os
import unittest
from numpy import testing
from sklearn import datasets, preprocessing
#from matplotlib import pyplot
from mentor import _cluster as cluster
from mentor import _datasets as datasets
from mentor import _metrics as metrics
from mentor import _rw... |
9c44f491b70667f91e6dd1c167cb643d891dccf467c04d0375b9d7a4c1e51e0e | Python | 1,768 | 57 | """
TransUNet classification-oriented adaptation.
Important:
TransUNet is originally a segmentation architecture. This implementation is
a clean classification baseline inspired by a CNN+Transformer encoder-decoder
design, adapted for image-level GI disease classification.
Design:
- CNN stem for local... |
c3388390d86509137c280a8ffef114ced0b2922f4b71d5bf6b7e51fa39e81f29 | Python | 1,770 | 45 | import cv2
import cmapy
from PIL import Image
import numpy as np
from msi_visual.utils import normalize_image_grayscale, image_histogram_equalization
class AvgMZVisualization:
def __init__(self):
pass
def predict(self, img):
processed_img = img / img.sum(axis=-1)[:, :, None]
... |
172665a5e1e4b575813b4653c96d2b8923c2b49bffda8af47c3bc45c83e5655c | Python | 1,771 | 49 | import os
import numpy as np
import pickle
import matplotlib.pyplot as plt
import torch
from dadi_simulators import *
from sbi import analysis
datadir = snakemake.params.datadir
posteriordir = snakemake.params.posteriordir
n_train = snakemake.params.n_train
simulator = MODEL_LIST[snakemake.params.demog_model](snakema... |
81f0e4b8fd099cb1f7fc6c22f4a27d8abc1795b04257d1dd83f7407a0237197e | Python | 1,771 | 47 | from typing import Callable, List, Optional
import numpy as np
import torch
from pytorch_grad_cam import GradCAMPlusPlus
from pytorch_grad_cam.utils.image import scale_cam_image
class RefineCAM:
"""
Meta-method for aggregating CAMs from multiple layers.
This methods works very well for denoising ea... |
8213bb8f1a4ef5ce2e2876bb9c64e848c36495fa97d31c1e51fc38af73315b9d | Python | 1,771 | 57 | import numpy as np
import joblib
from msi_visual.utils import normalize
class BaseDimReduction:
def __init__(self, model, start_bin=0, end_bin=None):
self.k = 3
self.model = model
self.start_bin = start_bin
self.end_bin = end_bin
self._trained = False
de... |
12b6cda417b4be50538dc11d61a94778e0d8db2afacafc44e7454e75ba9639fb | Python | 1,772 | 56 | # @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... |
34540175ebf6a3ea1f2ad261829a1f313ca8db5a9ffbe8a5c8e2a9e4620f13ec | Python | 1,772 | 48 | import novae
from ._utils import adata_small as adata
def test_init_prototypes():
model = novae.Novae(adata, num_prototypes=20)
prototypes = model.swav_head._prototypes.data.clone()
model.init_prototypes(adata)
assert (model.swav_head._prototypes.data != prototypes).all()
def test_preserve_cluster... |
6d6b96f53484f62aee94fd1de8f2dc591f35f2745c277ebe328dcb23e8fcb8a7 | Python | 1,772 | 56 | from beartype.typing import Any, Dict
from lightning_utilities.core.rank_zero import rank_zero_only
from omegaconf import OmegaConf
from src.utils import pylogger
log = pylogger.RankedLogger(__name__, rank_zero_only=True)
@rank_zero_only
def log_hyperparameters(object_dict: Dict[str, Any]) -> None:
"""Controls ... |
c99d4672a47514666828230c4d83e4d56670eeb24e85c05bb13c0e819aa1fad2 | Python | 1,772 | 55 | # -*- coding: UTF-8 -*-
"""GOEA term-scoring algorithms.
>>> from goatools.goea.algorithms import get_algorithm
>>> get_algorithm('classic')
"""
__copyright__ = "Copyright (C) 2010-present, H Tang et al., All rights reserved."
from goatools.goea.algorithms.base import GoeaAlgorithm, GoeaAlgoResult, GoeaConte... |
c4b4392a4ccf218ee0efa9f2f9c878606c6459f6b489235eaf47367231890de3 | Python | 1,773 | 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.
import uuid
from typing import Dict, Optional
from torch import Tensor
class FairseqIncrementalState(object):
def __init__(self, *args,... |
feba9cd3bc4637339716e68fb798ad1f666405f6be91d72105d5b865f660a1d0 | Python | 1,773 | 41 | #!/usr/bin/env python3
"""Coluna de DIFERENCA Pb - Ge para a Tabela 6 (antiga 4), ao lado da razao.
Mesmo conjunto e mesma estatistica da razao publicada: as 1462 estruturas com gap
(nao metalicas, gap > 0), agrupadas pelo haleto e pelo cation B do nome, mediana do gap
por grupo. O script so aceita gravar se reproduzi... |
79382a79544a4b92b5311c4065df275e9152752be828e796cf60839c7ee39b78 | Python | 1,774 | 44 | import papermill as pm
import os
import shutil
print(os.getcwd())
censor_region = 'above'
output_dir = 'OUTPUTS/notebooks'
os.makedirs(output_dir, exist_ok=True)
for censor_split in [0.1, 0.5, 0.9]:
print(f'Running MLP notebooks for split {censor_split}')
pm.execute_notebook(input_path='omit_mlp.ipynb',
... |
1366a6d92ade08d2fe02975aa8ea212b93455382e37e304698cab04aa9590b5a | Python | 1,775 | 49 | """Defaults for find_enrichment parseargs to be used in tests."""
__copyright__ = "Copyright (C) 2016-2019, DV Klopfenstein, H Tang, All rights reserved."
__author__ = "DV Klopfenstein"
import os
import collections as cx
# pylint: disable=too-few-public-methods
class ArgsDict(object):
"""Defaults for find_enric... |
16af8458f608da734653a14fc7bb596944cc91d3e49a78192e92bc9cf1525a23 | Python | 1,775 | 52 | import numpy as np
import scipy.sparse as sp
def sparse_to_tuple(sparse_mx):
"""Convert sparse matrix to tuple representation."""
def to_tuple(mx):
if not sp.isspmatrix_coo(mx):
mx = mx.tocoo()
coords = np.vstack((mx.row, mx.col)).transpose()
values = mx.data
shape =... |
525587b9f589010901e3ae8716781c19fb1ed22326157c63b7c33b9f53c04e26 | Python | 1,776 | 54 | import Levenshtein as ld
def levenshtein_distance(string1: str, string2: str) -> int:
"""Generates the edit distance between two strings using the Levenshtein distance.
Args:
string1 (str): The first string.
string2 (str): The second string.
Returns:
int: The Levenshtein distance b... |
e7f369ce7f7c48b212aa8a47da10777524c10d556cfe2b8264177e3494cd38a7 | Python | 1,776 | 52 | # Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... |
1cc843aaa2270e1ae4eca7509745012128b484ec9c213e45a9ef7dc337e94b5e | Python | 1,777 | 55 | """Persistence helpers for app settings."""
from __future__ import annotations
import json
import logging
from pathlib import Path
from src.persistence.app_paths import config_file_path, legacy_repo_file_path
logger = logging.getLogger(__name__)
def settings_file_path(app_type: str) -> Path:
"""Return the JSO... |
42c3f1717ba4d6bd434dd9be5197ee5a12c3f3e5be7629e8f62979dfb4c8a4c7 | Python | 1,777 | 62 | def pytest_addoption(parser):
"""Register custom CLI options for the test suite."""
parser.addoption(
"--internet-tests",
action="store_true",
default=False,
help="Run tests that retrieve stuff from the internet. This increases test time.",
)
parser.addoption(
"--... |
8528a7a2b5f8a492087c6f87a217e1e5c2c79cac78273eca149b397a23f93fc1 | Python | 1,779 | 52 | import pandas as pd
import numpy as np
import string
import joblib
from sklearn import datasets,preprocessing
def make_nodetable():
_index = ['A','B','C','D','E','F','G','H','I']
_labels = [2,2,1,2,1,1,1,2,1]
_genenames = ['GENE_AAA','GENE_BBB','GENE_CCC','GENE_DDD','GENE_EEE','GENE_FFF','GENE_GGG','GENE_H... |
f94af9b92116e6312b2c6692a5300fd2c38009182cd9d99bc290e34e841aad0b | Python | 1,780 | 55 | import argparse
import neuroglancer
import neuroglancer.cli
import neuroglancer.random_token
if __name__ == "__main__":
cube_size_meters = 10e-6
ap = argparse.ArgumentParser()
neuroglancer.cli.add_server_arguments(ap)
args = ap.parse_args()
neuroglancer.cli.handle_server_arguments(args)
viewe... |
fa31c1f22f52c7aabe128534fab9536ac9207f4bbb717530af0b6929102402fb | Python | 1,780 | 46 | from pathlib import Path
from typing import Literal, Union
from rnalysis.utils import installs, io, parsing
class FeatureCountsRunner:
TEMPLATES_DIR = Path(__file__).parent.parent.joinpath('data_files/r_templates')
def __init__(
self, kwargs: dict, output_dir: Path, r_installation_folder: Union[str,... |
fa2a855d6f5716672d4a2e1dcecc52b644024a4fc3742d7b5fda2f3a16796865 | Python | 1,781 | 60 | from argschema import ArgSchemaParser
import os
import logging
import time
import numpy as np
from .id_noise_templates import id_noise_templates
from ...common.utils import write_cluster_group_tsv, load_kilosort_data
def classify_noise_templates(args):
print('ecephys spike sorting: noise templates module')
... |
2fa9e7f3ca1fba4c5639ff51e3d20e93fce58cde027ebd3e22dc0303b677f3b4 | Python | 1,782 | 55 | import hydra
from beartype.typing import List
from lightning import Callback
from lightning.pytorch.loggers import Logger
from omegaconf import DictConfig
from src.utils import pylogger
log = pylogger.RankedLogger(__name__, rank_zero_only=True)
def instantiate_callbacks(callbacks_cfg: DictConfig) -> List[Callback]:... |
99ae824a3e1735cb9deea1fcc6a57f55432432dcda3c151aac3c1f4521bbc32c | Python | 1,783 | 58 | 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... |
dae87fea0944e2dae82537c905abea073f1c74e79de6816cb73612b4b9655f05 | Python | 1,783 | 51 | import click
import numpy as np
from skimage.measure import regionprops
def max_consecutive_zero_streak(arr):
"""Return the maximum number of consecutive zeros in a 1D array."""
max_streak = 0
current_streak = 0
for val in arr:
if val == 0:
current_streak += 1
max_strea... |
e025f14764f8d37a0f2d2ff46fd4774e5c59a4f1c7083ca00ee358106485d9c3 | Python | 1,783 | 64 | import lightning as L
from anndata import AnnData
from torch_geometric.loader import DataLoader
from ..module import CellEmbedder
from . import NovaeDataset
class NovaeDatamodule(L.LightningDataModule):
"""
Datamodule used for training and inference. Small wrapper around the [novae.data.NovaeDataset][]
"... |
79b839e85e5505248f302ab5abad4144af6d650735b82c2837c3eada1fc6467c | Python | 1,784 | 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.
"""
Count the number of documents and average number of lines and tokens per
document in a large file. Documents should ... |
2fbf95b6792addff561a64afc77e54eba5343943df72ddd0f2c208b7badc2ba6 | Python | 1,785 | 57 | # 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.
"""
TODO (huxu): fairseq wrapper class for all dataset you defined: mostly MMDataset.
"""
from collections import OrderedDict
from torch.util... |
58fff66f66b3a32300f14362e67d35711a7da3c37c3e24802cdbc373f50d58df | Python | 1,785 | 45 | import numpy as np
import tqdm
import math
from msi_visual.extract import tsfdata
import sqlite3
from msi_visual.extract.base_msi_to_numpy import BaseMSIToNumpy
class BrukerTsfToNumpy(BaseMSIToNumpy):
def get_regions(self, input_path: str):
tsf = tsfdata.TsfData(input_path)
conn =... |
93c03e6d420951ba470e39f3edf0131bf838688f930435e97067eb4e1652bed8 | Python | 1,785 | 59 | import os
import sys
import pathlib
import subprocess
if len(sys.argv) < 2:
print("No path to original / hi-res icon provided")
raise SystemExit
if len(sys.argv) > 2:
print("Too many arguments")
raise SystemExit
originalPicture = sys.argv[1]
if not (os.path.isfile(originalPicture)):
print(f"There... |
eab3420e125cc2de119639915dcdaa7adbfee83316ca56f7c8ba4b84b46d6e50 | Python | 1,785 | 41 | #!/usr/bin/env python3
"""Gap de um .EIG do SIESTA, valido tambem com spin-orbit.
Diferenca que importa: sem polarizacao cada banda cabe 2 eletrons e n_occ = N/2;
com spin-orbit as bandas sao espinores e cabem 1, logo n_occ = N. Usar N/2 num
calculo SOC devolve um numero plausivel e ERRADO -- e a mesma classe de armad... |
0d389cd461612f41e18cc19eaa7a637348bf19edbfbec718be2759666ad2cefe | Python | 1,787 | 57 | import torch
from sklearn import metrics
import torch.nn.functional as F
def logit_BCE(preds, labels):
if labels.size()[1]==1:
y = labels.type(torch.int64)
y_one_hot = torch.zeros(labels.size()[0],2).cuda()
# y_one_hot = torch.zeros(labels.size()[0],2)
y_one_hot = y_one_hot.scatt... |
4118a8836ed46b4a2eff74e4e879cb52e6bf9a38bba8a2c6d4e7d2471a7f5340 | Python | 1,787 | 95 | """Ported from the deleted src/tests/test_predictions.py (entities layer).
Re-targeted at the live d3text.utils.merge_tokens.
"""
from d3text.utils import merge_tokens
def test_merging_without_gold_standard() -> None:
tokens = (
"[CLS]",
"genetic",
"analysis",
"of",
"the"... |
543f7d6df1e71a375f764fde1617f1b61c17b1e6df369ac66ee46db246d06d58 | Python | 1,787 | 51 | import os
import subprocess
from Bio import PDB
# Please replace the following path
MGLTOOLS_PYTHON = "/home/wjh/autodocktools/mgltools_x86_64Linux2_1.5.6/bin/MGLpython2.5"
PREPARE_RECEPTOR_SCRIPT = "/home/wjh/autodocktools/mgltools_x86_64Linux2_1.5.6/MGLToolsPckgs/AutoDockTools/Utilities24/prepare_receptor4.py"
cla... |
bfb0d6642ab1e1c5e934c782539dcd087f488bfa85cdcdc7dbf38bd8ff290f15 | Python | 1,787 | 55 | from __future__ import annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from collections.abc import Collection
from collections.abc import Iterable
from collections.abc import Mapping
from packaging.utils import NormalizedName
from poetry.core.packages.package import Package
def ... |
f590a465b44b28415ec70317d5bde0430e986cd617797c666f967bae34917327 | Python | 1,787 | 56 | # 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 enum import Enum, EnumMeta
from typing import List
class StrEnumMeta(EnumMeta):
# this is workaround for submitit pickling leading ... |
3f5e27bbdb013053ec9adef32fa7e8002b5fa6fffa6221eb6acbfba9a5cd93fa | Python | 1,788 | 64 | import glob
import os
from typing import List, Optional, Tuple
import logging
import numpy as np
import torchvision.transforms.functional as TF
import PIL
from PIL import Image
from torchvision.datasets import VisionDataset
logger = logging.getLogger(__name__)
class PathDataset(VisionDataset):
def __init__(
... |
ff5e626bb234ad5a8be6ba5482d69e6a88257c76cd3d970949538e09b14e27f7 | Python | 1,788 | 67 | import mdtraj as md
import numpy as np
import sys
def get_rmsd_dssp(traj, ref_state, n_excl=0):
"""Compute RMSD (CA only) and DSSP."""
# Restrict to CA atoms, excluding n_excl residues on each side
atom_indices = traj.topology.select(
f"name CA and (resid >= {n_excl} and resid <= {traj.n_residues -... |
8748d12a07f4d12044fc927f0af8d97239189c24f9c3c9614baa6586cc5eaa2e | Python | 1,791 | 48 | import re
from pathlib import Path
import tifffile as tiff
import numpy as np
# Directory containing composite TIFFs
input_dir = Path("/Volumes/Expansion/Harvard/NIC/LucilleTwo/Project_neuroLSD/20250613_HeLa-Ctrl-ASAH1_EndoLysoStains/20250613_HeLa-Ctrl-ASAH1_EndoLysoStains_MIPs")
output_dir = Path("/Volumes/Expansion/... |
c3bdc749a41305dd40ab5a8414269a1c593d7012f7fbbc71a265c1b52809b29b | Python | 1,791 | 54 | # 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... |
4eb313fdf251de00765a8756cd353b3f465b40ee574cdbc551f6a1fb0b59f5d1 | Python | 1,792 | 66 | """Constants used by multiple modules.
Defined here to avoid circular imports.
"""
from functools import partial
import crowsetta
import numpy as np
import soundfile
from evfuncs import load_cbin
from scipy.io import loadmat
# ---- audio files ----
AUDIO_FORMAT_FUNC_MAP = {
"cbin": load_cbin,
"flac": soundfi... |
60f3306c3e66cb36eb78a5a9a50b070f97d074dbce7c2888199b902823eb4e66 | Python | 1,792 | 40 | import os
import numpy as np
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("system", type=str, help="Name of the system")
parser.add_argument("seed", type=str, help="Random seed")
args = parser.parse_args()
epsilon = 1e-3
small_step = 0.1
big_step = 0.2
first_half = np.arange(-3.0, -1.6 + eps... |
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