sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
8e84a2fd1a3c8a3c25907dd511840d2c0d3d910ccd6e31531db6bd96d6ab8afd | Python | 1,419 | 38 | """Test application utils"""
import unittest
import pandas as pd
from lefshift import constants
from lefshift.application_utils import validate_column, validate_training_input
class ApplicationUtilsTests(unittest.TestCase):
"""Test application utils"""
def test_validate_input(self):
"""Test input v... |
0b78ce25fc5e5d6ff372aef0921bb3cf3939b6d9b768f5b82258f3644402a1e0 | Python | 1,420 | 49 | import os
import json
import pandas as pd
from IPython.display import display, HTML
from .io import RESULTS_DIR, load_params, load_meta
def make_index(directory=RESULTS_DIR):
'''Creates an index of runs and parameters.
Arguments
---------
* directory (string): directory to look for runs in `directory`... |
c8ebd629e280904a489cee644e2a5947519887cabf47da77d4161109f9475b0f | Python | 1,420 | 53 | import re
import numpy as np
import pandas as pd
from lib.surface import compute_edge_lengths, read_surface_from_gifti
density_list = []
rows = []
for surf_gii in snakemake.input.surf_giis:
# get the resample value from the filename
resample = re.search(r"_resample-(\d+)_", surf_gii).group(1)
hemi = re... |
24884de4b2c6bf8383110033c76827dc2595d3d8cacb9daee7a115a75f06fad7 | Python | 1,421 | 52 | """
Test for more than 5 input spikes per time step
"""
import os
from spinnaker2 import hardware, snn
def test_many_input_spikes():
n_stim = 12
spike_times = {}
time_offset = 0
for i in range(n_stim):
spike_times[i] = range(time_offset + i, time_offset + n_stim)
stim = snn.Population(n_s... |
2eef65674d7dbd4fa247e03ccf08200cd458231ae972b4d38519a3d6f7b471c6 | Python | 1,423 | 49 | import logging
import aiohttp.web
import ray.dashboard.modules.tests.test_consts as test_consts
import ray.dashboard.modules.tests.test_utils as test_utils
import ray.dashboard.optional_utils as dashboard_optional_utils
import ray.dashboard.utils as dashboard_utils
from ray._private.ray_constants import env_bool
log... |
c874b29068f076c09d62747319844e74045e5ba774a0cfe3af9b579529462c58 | Python | 1,423 | 35 | from siapy.entities import SpectralImage
from siapy.entities.shapes import Shape
from siapy.utils.images import (
calculate_correction_factor_from_panel,
convert_radiance_image_to_reflectance,
spy_save_image,
)
# Load radiance image
radiance_img = SpectralImage.spy_open(header_path="radiance_data.hdr")
# ... |
3d168557572d6adf0996f0e107b09aa34bfb220732e59d5f12ea1a08ce776dbe | Python | 1,424 | 66 | import argparse
import os
import shutil
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from PIL import Image
from torch.autograd import Variable
from torchvision import transforms
import cv2
import time
import argparse
from scipy.io import loadmat
from torch.nn import functional as F
... |
856018ba3c57833edf1d621240709e385d7204e1ab2cc4a401c29a0e1df1d168 | Python | 1,425 | 34 | import numpy as np
from kneed import DataGenerator, KneeLocator
from .dp_related import dp_bucketized
import torch
import matplotlib.pyplot as plt
def plot_ll_curve(gaston_model, A, S, max_domain_num=8, start_from=2):
ll_list=get_ll_list(gaston_model, A, S, num_buckets=150, kmax=max_domain_num)
kneedle=Kn... |
c5a902f54939544f46bc1fa2e61cf5c59d9946ef6e8e4f6a82247ccd9efc32dd | Python | 1,425 | 52 | #!/usr/bin/env python3
"""
Example script demonstrating how to use CEBRA DCL with the sklearn API.
This reproduces the functionality of dcl_example.py using the sklearn wrapper.
"""
import sklearn.decomposition
import cebra
import cebra.datasets
if __name__ == "__main__":
device = "mps"
input_data = cebra.d... |
3a05fba5ba32ba0eeafddafda00394fa363f0bc4696a32dedd302bdadcb91509 | Python | 1,427 | 44 | # This file is part of the INTREPPPID programme.
# Copyright (c) 2023 Joseph Szymborski.
#
# This programme is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, version 3.
#
# This programme is distributed i... |
429d14c49bf9c11acb6c8a9ad67957eb7a2b8d4d5a5705b59ec726da2efb5faf | Python | 1,427 | 45 | import os
import sys
list_name_video = ['blood_vessel_10Hz','PFC4_15Hz','bma22_epm','CaMKII_120_TMT Exposure_5fps']
for name_video in list_name_video:
ID_part = ['_part11', '_part12', '_part21', '_part22']
list_Exp_ID = [name_video+x for x in ID_part]
folder = os.path.join('E:\\data_CNMFE', name_video, 'DeepWonder_... |
9b2c25042a4752ee5da910726bdcac99f841db2e344d2380b65237221af20f1c | Python | 1,427 | 51 | from typing import Tuple
import pyspark.sql.functions as F
from numpy import array
from pyspark.sql import DataFrame
from data_profiling.config import Settings
def date_stats_spark(df: DataFrame, summary: dict) -> dict:
column = df.columns[0]
expr = [
F.min(F.col(column)).alias("min"),
F.ma... |
f451c8587e48e5bb8dc498e90cf4f7953f48fb7faff8e29dcb1bd56fa6b31bde | Python | 1,427 | 53 | import os
import subprocess
import json
from spinnaker2.configuration import PEConfig, ExperimentConfig, MemoryRegion
from spinnaker2.coordinates import ByteAddr, WordAddr, PE
"""
Simple example that takes an input value, sends it to a PE, where it is
multiplied by a constant depending on the PE-ID.
The result is re... |
62a4999772e47a8a7d9a5685646b0652c6eecc20a5b14df85e34fb07a71a51e2 | Python | 1,428 | 42 | # -*- coding: utf-8 -*-
### ------------------------------ IMPORTS ------------------------------- ###
import os
import numpy as np
import pandas as pd
from functools import reduce
### ---------------------------------------------------------------------- ###
def get_best(csv_dir='train', common_n=10, best_n=100, sa... |
6d0bd7d49db6e027f92125056366dcb32bd949105c0c5c6a1baa2389de29cb29 | Python | 1,428 | 43 | from typing import TYPE_CHECKING, Iterator, List, Union
from ray.data._internal.output_buffer import BlockOutputBuffer, OutputBlockSizeOption
from ray.data._internal.util import _check_import
from ray.data.block import Block
from ray.data.context import DataContext
from ray.data.datasource.file_based_datasource import... |
f188172259d06d8466562cf24fd59af9c212ab3986bb3a62a86bb9fc69d927c3 | Python | 1,428 | 46 | import moabbpatch
import config
import pandas as pd
import scipy.stats
import swifter
datasetname = config.DATASET_NAME
outpath = config.CSP_RESEARCH_OUT_PATH
in_file = outpath / f"00_{datasetname}_all_combs.hdf5"
comb_file = outpath / "00_combinations.txt"
out_file = outpath / f"01_{datasetname}_selected_combs.txt"
... |
695e34c3d6cb6bf92524c5c0c509a97fe254bae0c956233adfdd11617c1f49b9 | Python | 1,429 | 41 | import torch
def copy_param(target_link, source_link):
"""Copy parameters of a link to another link."""
target_link.load_state_dict(source_link.state_dict())
def soft_copy_param(target_link, source_link, tau):
"""Soft-copy parameters of a link to another link."""
target_dict = target_link.state_dict... |
68bbd878b1a32141c6ca30278dce8e3cc942f93a6b7e0229fa825517190af644 | Python | 1,430 | 39 | """Make sure tests will be run by CI.
"""
import glob
import subprocess
import xml.etree.ElementTree as ET
if __name__ == "__main__":
# Make sure python unit tests have corresponding bazel targets that will run them.
xml_string = subprocess.run(
["bazel", "query", 'kind("py_test", //...)', "--output=x... |
b2d0e41f2640575c11f7c0f01281981e14dae21eb87bd96641ea13f7aa309cbe | Python | 1,431 | 46 |
import argparse
from matplotlib import cm, pyplot
import numpy
import tifffile
from pysted import base, utils
parser = argparse.ArgumentParser(description="Simulate the acquisition of a single 10nm particule.")
parser.add_argument("pixelsize", type=float,
help="pixelsize (in m) of the acquired ... |
3dfcaabfbf9a2d0ea6c095884304eff8f6a93b7a038103e9444b48a8fb52d789 | Python | 1,432 | 45 | from .transformer import TransformerEncoder
from .mlp import ResMLPEncoder, MLPEncoder
from torch import nn
def setup_encoder(model_type, num_hidden, num_layers, dropout, activation, norm, nhead, covariates_dim=0) -> nn.Module:
if model_type in ["performer", "cosformer", "transformer", "flowformer"]:
mod =... |
80ae3dafe8c1f43d55103fbdab784c9eca893ae9dc4183dbfdc4f59b35cd9159 | Python | 1,433 | 21 | from neuron_morphology.validation.result import NodeValidationError
from neuron_morphology.validation.result import MarkerValidationError
import unittest
class ValidationTestCase(unittest.TestCase):
def assertNodeErrors(self, validation_errors, expected_message_fragment, expected_invalid_node_ids):
matchi... |
f2d383560b8bea45346edfacc00ec6bf227733346e4be564bc6deaef6563cd27 | Python | 1,433 | 49 | #Ori Zaff - 7/29/22
from psychopy import visual, core, event
win = visual.Window([800,600], monitor="testMonitor", units="deg")
button_box = visual.ImageStim(win, image = './buttons.jpg',size=(20), pos=[0,0])
index = visual.ImageStim(win, image = './index.jpg',size=(20), pos=[0,0])
middle = visual.ImageStim(win, imag... |
0dc90bdd8747bc11cee3dfd17b47c241e29f5f822eb74d6904d29ebe4a80bf09 | Python | 1,434 | 35 | #
# CEBRA: Consistent EmBeddings of high-dimensional Recordings using Auxiliary variables
# © Mackenzie W. Mathis & Steffen Schneider (v0.4.0+)
# Source code:
# https://github.com/AdaptiveMotorControlLab/CEBRA
#
# Please see LICENSE.md for the full license document:
# https://github.com/AdaptiveMotorControlLab/CEBRA/bl... |
af068ee8c83a88a907f26c489018e1c831f74237f53920514d98671473447413 | Python | 1,434 | 51 | import requests
import starlette
from typing import Dict
from ray import serve
from ray.serve.handle import DeploymentHandle
# 1. Define the models in our composition graph and an ingress that calls them.
@serve.deployment
class Adder:
def __init__(self, increment: int):
self.increment = increment
de... |
0a46f870fe5354b92e512514b62a41e65fe40e88e06560fe9aa322c1fdb70bd6 | Python | 1,435 | 45 | import pandas as pd
import pyranges as pr
def bins_to_genes_intersection(bins, genes, genes_cols=['seq_id', 'start', 'end']):
"""
bins :: pandas.DataFrame :
columns:
Chromosome: `chr1`,
Start: `10000`
End: `20000`
genes :: pandas.DataFrame :
columns i... |
9de2d6c0af691d0fce4a017c2f6b048396410c3e408bcc8fb766e77ef88fc52f | Python | 1,436 | 48 | # utils.py
# Copyright (c) 2010 Jes Frellsen
#
# This file is part of Muninn.
#
# Muninn is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# Muninn is distributed in the hope that it will be useful,
... |
05cec819bb2a3cc045c86a7991659545bb2223c96a911462c30872eb9da29398 | Python | 1,437 | 32 | ####################################################################################################
# Copyright (c) 2016 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
1af5078c3092129e7619484823d656ccf084fe0b1e1dfb4b31d8a378916b7f2c | Python | 1,439 | 56 | from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Awaitable, Callable, Optional, Protocol
from ..agent_bridge import AgentRunResult, AgentBridge
ProgressCallback = Callable[[str], Awaitable[None]]
ChunkCallback = Callable[[str], Awaitable[None]]
@dataclass
class Executio... |
e92a465e947ec1c4b66fc595bbc8b550d85ce681dde4c653cb8249dd4a39694d | Python | 1,439 | 40 | import os
import sys
sys.path.append('.')
import cv2
import math
import torch
import argparse
import numpy as np
from torch.nn import functional as F
from model.pytorch_msssim import ssim_matlab
from model.RIFE import Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Model()
model.loa... |
f8528e407771f068c45caeb83a5599c9cd4621de05151a6d567b7cd232c195cb | Python | 1,439 | 51 | from typing import List, Tuple, Union
import numpy as np
from torch import nn
ArrayLike = Union[np.ndarray, List, Tuple]
class Crop2d(nn.Module):
"""Crop input image by."""
def __init__(
self,
cropping: ArrayLike = ((0, 0), (0, 0)),
):
"""Crop input to the the rectangle dimensio... |
0b05cdc275b084a61a026833020360a6a820491f4ae39845a97b30296c8afe0b | Python | 1,440 | 38 | from distutils.core import setup
import setuptools
from os import path
this_directory = path.abspath(path.dirname(__file__))
with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
setup(
name = 'nested_cv',
packages = ['nested_cv'],
version = '0.916', ... |
a801005deee3158f27113eaa63d23b46cc3e4cfae9a066c0085904bd4ab90bed | Python | 1,441 | 50 | import multiprocessing as mp
import os
import signal
import sys
import unittest
import warnings
from pfrl.utils import async_
class TestAsync(unittest.TestCase):
def test_run_async(self):
counter = mp.Value("l", 0)
def run_func(process_idx):
for _ in range(1000):
with... |
bf349ce809284ed1c7cb7f8e0ae7ade5d130f14c4b1c526bd6252b5941b0da84 | Python | 1,442 | 46 | import hashlib
import os
from pathlib import Path
from filelock import FileLock
import ray
RAY_LOCKFILE_DIR = "_ray_lockfiles"
class TempFileLock:
"""FileLock wrapper that uses temporary file locks.
The temporary directory that these locks are saved to can be configured via
the `RAY_TMPDIR` environmen... |
d12976eaf570a2a1be853405ee701fa60899683cb8fe4bc1001243042913d1de | Python | 1,442 | 38 | import warnings
import torch
def find_peaks(y, threshold, min_dist):
# y: (batch, length)
if min_dist % 2 == 0:
warnings.warn('min_dist must be odd, but got {}'.format(min_dist))
min_dist += 1
batch = y.shape[0]
local_maxima_mask = torch.cat([torch.zeros(batch, 1, dtype=torch... |
c59f3d9b4fe5ce28595726e64dafd887f8318d48f61e16a43ae7ca029fef0274 | Python | 1,443 | 61 | import sys
if len(sys.argv) == 1:
# A small number for CI test.
sys.argv.append("5")
# __program_start__
import sys
import ray
# fmt: off
# __large_values_start__
import numpy as np
@ray.remote
def large_values(num_returns):
return [
np.random.randint(np.iinfo(np.int8).max, size=(100_000_000, 1... |
5c8d5dc7fc7e8c16433deb99c76faf4636359aab2a8c366e6ef239dfe04a566d | Python | 1,445 | 49 | from typing import Dict, TypeVar
K = TypeVar("K")
def collapse_transitive_map(d: Dict[K, K]) -> Dict[K, K]:
"""Collapse transitive mappings in a dictionary. Given a mapping like
{a: b, b: c, c: d}, returns {a: d}, removing intermediate b -> c, c -> d.
Only keeps mappings where the key is NOT a value in ... |
e18d7f6e3783549f3de8f3991356278e3b5cc7e07bda8e96ed9b92f0734a1a59 | Python | 1,445 | 42 | from typing import List, Dict
import numpy as np
from tensorflow.keras.models import load_model, Model
from preprocess_ecg import process_ecg, LEADS
def get_model(model_name = 'pclr') -> Model:
"""Get PCLR embedding model"""
if model_name == 'pclr':
return load_model("./PCLR.h5")
elif model_name... |
3031bcdddf02d581864a718dc22f6c1ba737dff14468c192dc15469daa9626a4 | Python | 1,446 | 35 | # Copyright 2023 The HuggingFace Team. 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 applicabl... |
3ece29e2474ab863cf09f3c48166fbf6e239b2c91215011b2fe50518651ab00a | Python | 1,446 | 59 | """Dataset cache utility functions"""
import zipfile
from pathlib import Path
from requests import get as get_file
from data_profiling.utils.paths import get_data_path
def cache_file(file_name: str, url: str) -> Path:
"""Check if file_name already is in the data path, otherwise download it from url.
Args:
... |
7f144efb11ab42a83f09991bb619379bf0f5d3d32bf83b210c63c26fba10f01e | Python | 1,446 | 75 | # flake8: noqa
# __starting_ray_start__
import ray
ray.init()
# __starting_ray_end__
# fmt: off
# __running_task_start__
# Define the square task.
@ray.remote
def square(x):
return x * x
# Launch four parallel square tasks.
futures = [square.remote(i) for i in range(4)]
# Retrieve results.
print(ray.get(future... |
123dd54bcb60117f472e39286766cbf4ff9a88c35f8c992924eb2d4996a2f885 | Python | 1,448 | 52 | #!/bin/python
#------------------- Description & Notes --------------------#
#------------------- Dependencies ---------------------------#
# Standard imports
import functools
# Third-party imports
import pyspark.sql.functions as F
import pyspark.sql.types as T
# Internal imports
from ..schema import *
from .. imp... |
4327bc86e1416855f94bdefe67e5e167c82382a21715f8a8ec0efba04cc1ccd8 | Python | 1,448 | 36 | import argparse
import pandas as pd
def parse_argument():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', help='Input expression matrix file.', required=True)
parser.add_argument('--target-genes', help='List of target gene IDs to train.', required=True)
parser.add_argument('--id... |
ab3ebbd3c4e2653ce98ce40e93149bed1930a248c911d4440dee5b863fed5c1a | Python | 1,448 | 23 | import pandas as pd
def citation_analysis(df_publications: pd.DataFrame, df_authors: pd.DataFrame, config: dict):
df_temp = df_publications[~df_publications["iCite_is_research_article"].isna()]
df_temp = df_temp[df_temp["iCite_is_research_article"].apply(lambda x: True if x.lower()=="yes" else False)]
ci... |
bf941dfc66f542fcb6d95f5c8eb4075d29aa04db2badd17edaab4f4147fadb6b | Python | 1,449 | 43 | """Install script for setuptools."""
from setuptools import find_packages
from setuptools import setup
setup(
name="unimol_tools",
version="0.1.0.post1",
description=("unimol_tools is a Python package for property prediciton with Uni-Mol in molecule, materials and protein."),
author="DP Technology",
... |
6685178bfc121360736e0cf287527f8bd2c292deeaf73759f20ba2a1f30ef27e | Python | 1,451 | 44 | import os
import pandas as pd
import numpy as np
import tensorflow as tf
from utils import oneHot
from tensorflow.keras.layers import *
if __name__ == '__main__':
path = '../Data'
num_shuffles = 10
predictions_ala = []
predictions_no_ala = []
WTaa = 'SINSVHT'
aaList = 'ACDEFGHIKLMNPQRSTVWY'
... |
15896598c5a19d3534c27796dbcd74ebc9f491c0e476f86799dc82f4f2eb1ed1 | Python | 1,452 | 34 | import argparse
from scripts.pdbbind_pipeline import construct_pdbbind_core_data, construct_pdbbind_data, load_config, parse_params
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Parsing of PDB Bind dataset", formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
pars... |
21dafb4896a1e9e539f400605baf9f805047ab8f8c3622b25cb161b6ecd1dcb3 | Python | 1,452 | 46 | # Copyright 2026 Google LLC.
#
# 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 writing, ... |
f9d21b8a8577d40bad6e64250f97100d6a5222705913c6887e1ad25d7b9449b7 | Python | 1,453 | 61 | #!/usr/bin/env python
"""
Collect genotyping cutoff metrics. Writes stats to stdout.
Metric format: gt_cutoffs_<COPY_STATE>_<MEAN/SD/CUTOFF>
"""
import argparse
import sys
import pandas as pd
KEY_PREFIX = "gt_cutoffs_"
MEAN_COL = 'mean'
SD_COL = 'sd'
CUTOFFS_COL = 'cutoffs'
def main(argv):
parser = argparse... |
e7d7d565632ce4b6930404c4fd0560da42a472123b35e27b51df900709a5564a | Python | 1,455 | 36 | #!/usr/bin/env python
# import
from __future__ import print_function
## batteries
import os
import sys
import pytest
# data dir
test_dir = os.path.join(os.path.dirname(__file__))
data_dir = os.path.join(test_dir, 'data')
# tests
def test_help(script_runner):
ret = script_runner.run('ncbi-gtdb_map.py', '-h')
... |
ea4f4fef81d735b8aea25be9724a41a1f1212498010a695a41f8093b54856249 | Python | 1,455 | 42 | from typing import Callable, Iterable, Literal
import optuna
from pydantic import BaseModel, ConfigDict
from siapy.optimizers.parameters import TrialParameters
from siapy.optimizers.scorers import Scorer
__all__ = [
"CreateStudyConfig",
"OptimizeStudyConfig",
"TabularOptimizerConfig",
]
class CreateStu... |
da7e71e911145bf1e921133d7dfc3b580451070128db7b0aa14fd2fbcaad8f29 | Python | 1,456 | 44 | import torch
import glob
import numpy as np
import os
from tqdm import tqdm
import json
from .points_utils import get_ini_position
from einops import rearrange
import collections
from .base import BaseDataset
import imageio
class HeartDataset(BaseDataset):
def __init__(self, root_dir, split='train', **kwargs):
... |
66809cc5cb1dd77b2b7f8ba97427785918cfba7459ac714a4c8a7160339b2640 | Python | 1,458 | 60 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
"""
import argparse
import pandas as pd
def take_highest_effect(s):
if 'LOF' in s.values:
return 'LOF'
if 'COPY_GAIN' in s.values:
return 'COPY_GAIN'
if 'DUP_PARTIAL' in s.values:
return 'DUP_PARTIAL'
if 'INV_SPAN' in s.val... |
39b0e1167a36edc2fab8c84cc798405bc9f5adfdcac81d9064b21468ff3fd6db | Python | 1,459 | 37 | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import scipy as sp
from pathlib import Path
import numpy as np
base_dir = '/Volumes/diedrichsen_data$/data/Cerebellum/Language'
if not Path(base_dir).exists():
base_dir = '/srv/diedrichsen/data/Cerebellum/Language'
if not Path(base_dir).exis... |
3d68be1824a2bea6009b0e8437193559315e6d636b3eeb5effcd805dcd4a04cf | Python | 1,459 | 50 | """
Functions for preprocessing of single cell RNA-seq counts
"""
import numpy as np
import anndata
from scipy.sparse import issparse
def filter_counts_data(data, cell_min_molecules=1000, genes_min_cells=10):
"""Remove low molecule count cells and low detection genes
:param data: Counts matrix: Cells x Gene... |
dd0105b0c7f2ffc3bfac2b8a64b04e8597239b8c44fa3e636365f831081b016f | Python | 1,459 | 51 | import torch
def _find_repeats(data):
temp = data.detach().clone()
temp = temp.sort()[0]
change = torch.cat([torch.tensor([True], device=temp.device), temp[1:] != temp[:-1]])
unique = temp[change]
change_idx = torch.cat([torch.nonzero(change), torch.tensor([[temp.numel()]], device=temp.device)])... |
f2061833072404cfd644d95712e88b07e8025932acf6552075778fe2cddc40f4 | Python | 1,461 | 44 | import pathlib
path = pathlib.Path.cwd()
if path.stem == 'tmb_survival':
cwd = path
else:
cwd = list(path.parents)[::-1][path.parts.index('tmb_survival')]
import sys
sys.path.append(str(cwd))
from matplotlib import pyplot as plt
from lifelines import KaplanMeierFitter
from figures.sim_tools import *
from lifeli... |
9eaabf477e8588e097ff682e3c6943128dbd0b6491e99463ad4f21545f355a7d | Python | 1,462 | 56 | import functools
from typing import Union
import click
def bool_cast(string: str) -> Union[bool, str]:
"""Cast a string to a boolean if possible, otherwise return the string."""
if string.lower() == "true" or string == "1":
return True
elif string.lower() == "false" or string == "0":
retu... |
f4fda3314c7606a3cf876055613a195933e5ef28a22126aa62ab90897ae9163f | Python | 1,464 | 50 | from typing import Any, Callable, Optional, Sequence
from data_profiling.report.presentation.core.renderable import Renderable
class Container(Renderable):
def __init__(
self,
items: Sequence[Renderable],
sequence_type: str,
nested: bool = False,
name: Optional[str] = None... |
fbcd69df6ebbbb8b8548d78d635a62f57231b60b992a458a03ed8a16ed3b243a | Python | 1,464 | 50 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
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 annotations
from typing import TYPE_CHECKING
from torchtnt.framework.evaluate import evaluate
from fairchem.core.... |
05f422a3a72a2b60597926af4900a9247423e4108245d766a32fd4193809b0cb | Python | 1,465 | 45 | import torch
from structured_kernels import vand_log_mult_sym_fwd, vand_log_mult_sym_bwd
def log_vandermonde_cuda(v, z, L):
""" Wrap the cuda method to deal with shapes """
v, z = torch.broadcast_tensors(v, z)
shape = v.shape
v = v.contiguous()
z = z.contiguous()
N = v.size(-1)
assert z.... |
6dda685159eca8ef7a38d250483d76812f59bee1ecfeeb0e35bc7a2d9a4626f9 | Python | 1,465 | 56 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
"""Exceptions related to the cloud client."""
from urllib.parse import urlparse
from requests import Response
from aihwkit.exceptions import CloudError
clas... |
46311421c2f380c3bfbdb077d6ab5331b6b5d8b7770427b4bdd013361f7a06d0 | Python | 1,466 | 52 | import logging
from math import pi as PI
import torch
from torch import nn
logger = logging.getLogger(__name__)
class GaussianEmbedding(torch.nn.Module):
def __init__(
self, out_dim=50, dist_resoln=None, min_x=0.0, max_x=5.0, cos_dump=False
):
super().__init__()
num_div = None
... |
c8a8281465aae65bd56883b1d854f3596ca2509531d505396e84d3ab03fbbadc | Python | 1,466 | 45 | import torch
from torch import nn
import torch.nn.functional as F
class LSTM(nn.Module):
def __init__(self, input_size, num_hiddens, num_layers=1, bidirectional=False, **kwargs):
super(LSTM, self).__init__(**kwargs)
self.lstm = nn.LSTM(input_size=input_size, hidden_size=num_hiddens, num_layers=num_layers, bias=... |
f8adb0459cadc7b2bb66843fa1ef531c21b9fc0bb84f530f5dc8ad1879b5299b | Python | 1,466 | 35 | from data_profiling.report.structure.variables.render_boolean import render_boolean
from data_profiling.report.structure.variables.render_categorical import (
render_categorical,
)
from data_profiling.report.structure.variables.render_common import render_common
from data_profiling.report.structure.variables.render... |
34ce39582671fb07fe12bdc3a81407bcd6b05c39eabfb50349efa5a4e9bb8074 | Python | 1,468 | 40 | from imports import *
from ngs_mapper import compat
class Base(common.BaseBaseCaller):
modulepath = 'ngs_mapper.vcf_diff'
def setUp( self ):
super(Base,self).setUp()
fixpath = join(fixtures.THIS,'fixtures')
# Bam built from scratch that has multiple refs
self.test_vcf ... |
78ff763991a8e6a87284431eedeb8866bcc371c6e2f515da5279b0c381afd574 | Python | 1,468 | 66 | from unittest import mock
import pytest
import pfrl
@pytest.mark.parametrize(
"render_kwargs",
[
{},
{"mode": "human"},
{"mode": "rgb_array"},
],
)
def test_render(render_kwargs):
orig_env = mock.Mock()
# Reaches the terminal state after five actions
orig_env.reset.si... |
7aea13afd810b2afeb85cacd5b3cb88342b7b8840bef2c2eff98084aa76650ed | Python | 1,468 | 59 | import cv2 # noqa: F401 — preload libtiff before HIPT/torchvision shadows it (omicverse vendored patch)
import sys
import numpy as np
from utils import save_image, load_pickle
from cluster import cluster
from connected_components import relabel_small_connected
from image import crop_image
def remove_margins(embs, ... |
b36fde61e27d1c548512b752e4259d332ab1f498970c3f5e71c848c1577a4d1b | Python | 1,468 | 143 | atom_names = [
"C",
"CA",
"CB",
"N",
"O",
"OXT",
"OT1",
"OT2",
# ARG
"CB",
"CG",
"CD",
"NE",
"CZ",
"NH1",
"NH2",
# ASN
"CB",
"CG",
"OD1",
"ND2",
# ASP
"CB",
"CG",
"OD1",
"OD2",
# CYS
"CB",
"SG",
# GLN... |
a1a644883b7e3939cc6cdd8d7c59b78e6952171564f7eca803ac98bf22339ca9 | Python | 1,469 | 46 | import sys
import pytest
from ray.core.generated import gcs_pb2
from ray.core.generated.common_pb2 import TaskStatus, TaskType
from ray.dashboard.modules.task_events.gc_policy import FinishedTaskActorTaskGcPolicy
def _finished_event() -> gcs_pb2.TaskEvents:
event = gcs_pb2.TaskEvents()
event.state_updates.s... |
b57269a0343ae8fdb9df7502227ae715d435b9ffd9045f3960610c677351c6a6 | Python | 1,469 | 39 | from sklearn.base import BaseEstimator
from sklearn.pipeline import Pipeline
from siapy.utils.general import dict_zip
def assert_estimators_parameters_equal(estimator1: BaseEstimator, estimator2: BaseEstimator) -> bool:
if len(estimator1.get_params()) != len(estimator2.get_params()):
return False
fo... |
9c0421883881e74377cc933f2180e2a887a6dbe1429832dc2854b9926c161519 | Python | 1,470 | 34 | #Test data module
from src import data
import pandas as pd
import copy
def test_TreeData_setup(config, ROOT):
#One site's worth of data
local_config = copy.deepcopy(config)
local_config["use_data_commit"] = None
csv_file = "{}/tests/data/sample_neon.csv".format(ROOT)
dm = data.T... |
4e2bb41cf2fe82cad15cb3e87f818522723dc0157067d35f631c5624075269fd | Python | 1,471 | 44 | import os
import numpy as np
import torch
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
matplotlib.use('Agg')
os.makedirs('images', exist_ok=True)
test_traj = torch.load('ckpts/test_traj.pt')
test_traj = test_traj - test_traj.mean(dim=(1, 2), keep... |
8e279b3db8d7e6ed7f7f01275a16346eef759007581a91b2afbc9f6906794d14 | Python | 1,471 | 41 | import cProfile
import pstats
from sklearn.datasets import make_blobs
from sklearn.model_selection import train_test_split
from sklearn.cluster import KMeans, AgglomerativeClustering
from hdbscan import HDBSCAN
from sklearn.neighbors import KNeighborsClassifier
from reval.best_nclust_cv import FindBestClustCV
s = KNei... |
27d656700a670d865fc07691c9d65b3b52903c2a7bba4e046496078dce610dc1 | Python | 1,472 | 36 | # Copyright 2021 Division of Medical Image Computing, German Cancer Research Center (DKFZ)
# and Applied Computer Vision Lab, Helmholtz Imaging Platform
#
# 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 L... |
e597f9ab280104754dcca864a6b038aab5028d7cbcdf2c9cdc0dd5cbd77c82c2 | Python | 1,472 | 37 | import os
import sys
import glob
def get_conda_bin_dir():
if os.path.basename(sys.executable) in ['python', 'python2', 'python3']:
# note: the case where the current script is run
# with the full path to the Conda Python executable
return os.path.dirname(sys.executable)
else:
r... |
d0dd125b3539533e871359ee284e3690ba561288d99fff4538a2fedd27083871 | Python | 1,473 | 47 | # -------------------------------------------------------------------------------------------------------------------------------------
# Following code adapted from GCPNet (https://github.com/BioinfoMachineLearning/GCPNet):
# ---------------------------------------------------------------------------------------------... |
daedaa08b95430b2ef0908864321a64e8187853808ce1c09c1b8f6ba6e526ff3 | Python | 1,474 | 58 | import torch
import pickle
import torchvision
import torchvision.transforms as transforms
def mnist():
batch_size = 60000
transform = transforms.Compose(
[transforms.ToTensor()])
train_dataset = torchvision.datasets.MNIST(
root="data", train=True, transform=transform, download=True
)
... |
9aba86c636032e55d51350389a96778907f9d470758b6bb17759f93ef9028ef0 | Python | 1,476 | 56 | from functools import wraps, partial
import time
from typing import Callable, Optional
import numpy as np
__all__ = ["cal_fdr", "timer"]
def cal_fdr(p_vals: np.ndarray) -> np.ndarray:
"""Benjamini-Hochberg FDR adjustment for an array of p-values.
Parameters
----------
p_vals
Raw p-values.
... |
76eb2b4874b5c037196be44c762acb5f9be532c777154a206bd0630a73d8b9ab | Python | 1,477 | 63 |
from utility import * # check utility.py for functions documentation
from component import *
import numpy as np
class DefaultComponent(Component):
def initialize(self):
#print(sys.path)
log_error(self.this(), sys.path[0])
log_error(self.this(), sys.path[1])
log_error(self... |
c1c8593b24cdbea846a535d043500791b09516730477a4b4450f2ac6554aaf35 | Python | 1,477 | 48 | def test_stat3D_mask():
import samri.plotting.maps as maps
stat_map = '/usr/share/mouse-brain-templates/dsurqec_200micron_roi-dr.nii'
template = '/usr/share/mouse-brain-templates/dsurqec_40micron_masked.nii'
maps.stat3D(stat_map,
template=template,
save_as="_stat3D_mask.png",
show_plot=False,
threshold=0.... |
45adde171454ce5ca70d1abb9b277909cd7b81a775ab477db167cd34d7785fab | Python | 1,478 | 57 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
Drop records with no non-reference genotypes from an input VCF
Alternative to VCFTools, which rounds GQ to a max of 99
"""
import argparse
import sys
import pysam
from svtk.utils import is_biallelic
def drop_nonref_gts(vcf, fout):
NULL_GT = [(0, 0), (None, Non... |
7c7e6bb5a8f56a99f0d5368143244289778756ae7d7ed1c4046f2cae725118b4 | Python | 1,478 | 48 | from __future__ import division, print_function
from nose.tools import with_setup, assert_raises, assert_equal
import numpy as np
from lavaburst import utils
def test_compress_restore_path():
# simple example, no special cases
data = np.fromiter('abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ', dtype... |
cd4f5d1a0dc38a6fe064194e0a1e63bd0f3684a484355a8a1bda82e2cedfbac9 | Python | 1,478 | 41 | # Train Dead
import comet_ml
from pytorch_lightning import Trainer
from pytorch_lightning.loggers import CometLogger
from src.models import dead
from src.data import read_config
import numpy as np
import torch
from sklearn.metrics import precision_recall_curve, PrecisionRecallDisplay
config = read_config("config.yml"... |
680a25d62e5f1794e11c2e5f502420dceccc163ef4c02c8e85c3ee3add7a86a6 | Python | 1,479 | 50 | from pathlib import Path
import matplotlib.pyplot as plt
from siapy.entities import Pixels, SpectralImage
# Set the path to the directory containing the data
# !! ADJUST THIS PATH TO YOUR DATA DIRECTORY !!
data_dir = "./docs/examples/data"
# Get first image
header_path_img0 = sorted(Path(data_dir).rglob("*.hdr"))[1... |
86cb51bee08cd85effc9faa9057637c706aeca25038cccab9a71fe0150527ee5 | Python | 1,479 | 43 | import os
import argparse
import numpy as np
"""Compute the SVM test accuracy obtained by K-fold cross validation"""
def kfold_get_score(path):
# Count the number of folds
kfold_dir_runs = []
for item in os.listdir(path):
full_path = os.path.join(path, item)
if os.path.isdir(full_path): k... |
a13fd4d611d3e558081a17fe9f37664a79e620ee3daeec674fc3258c539bb112 | Python | 1,479 | 61 | #!/usr/local/bin/python
from __future__ import print_function
import wave
import numpy
import pyaudio
from dateutil import parser
audio = pyaudio.PyAudio()
waveFile = wave.open('qanda_2012_ep99_climate.wav', 'r')
subtitles = "qanda_2012_ep99_climate.srt"
# csvfile = open('qanda/qanda_2012_ep99_climate.csv') # , n... |
a9d78f2a378b204377dfc9cc67ec528970545b62349200950fd01c523e1b02ea | Python | 1,480 | 66 | import math
import pytest
import torch
from aisegcell.models.unet import LitUnet, UNet_rec
def test_Unet_rec():
"""
Tests expected output size of an image by the Unet model
"""
image = torch.rand((2, 1, 512, 512))
unet = UNet_rec(bilinear=True, base_filters=32, receptive_field=128)
image... |
e73578df3e0e29e5bb702aaa0574a0c0f3102525fcf74533b5eca33a912fc94b | Python | 1,480 | 84 | import string
import os
import pathlib
import vamb
PARENTDIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
DATADIR = os.path.join(PARENTDIR, "test", "data")
BAM_FILES = sorted(
[
pathlib.Path(DATADIR).joinpath("bam").joinpath(i)
for i in os.listdir(os.path.join(DATADIR, "bam"))
... |
2a2db6c750472314f2ea5012251f80d9fc092933b012adcc9a154a98bb82e18a | Python | 1,481 | 41 | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
def main():
# with open("/Users/chaokuan-hao/Documents/Projects/PR_SpliceNN/results/800bp/SRR1352129/OUTPUT/SpliceAI_6_RB_p_n_nn_n1_TB_all_samples_thr_100_splitByChrom_L64_C16_L800_v31/BAM/SRR1352129.discard.pup", "r") as f:
# lines... |
5e609ad0db5f5a6f43f18bd87df102c53637aecc5c41840fb2eb12052c4d63cd | Python | 1,481 | 50 | from contextlib import contextmanager
from typing import List, Union
import torch
from ray._private.accelerators.hpu import HPU_PACKAGE_AVAILABLE
from ray.air._internal.device_manager.torch_device_manager import TorchDeviceManager
if HPU_PACKAGE_AVAILABLE:
import habana_frameworks.torch.hpu as torch_hpu
class ... |
21c8eb5bea4e956052a73ba96fe8ff49ea4117e01bf4782140c22bef0e3bd357 | Python | 1,482 | 40 | import os
from importlib import resources as impresources
from pathlib import Path
from ... import bids
from ...bids.convert2bids import BidsConverter
from ...load_annotations.seizeit import loadAnnotationsFromEdf
BIDS_DIR = impresources.files(bids)
DATASET = BIDS_DIR / "seizeit"
def convert(root: Path, outDir: Pat... |
ceec201103a4e755445822ff96e0f72dca1e7bd74e280f841d452ca5c5e62b6e | Python | 1,482 | 34 | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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... |
17fd832bbe4f984e6daae8d4d29a9b884f1aeb229ea70786696f48580edd2201 | Python | 1,484 | 43 | import torch
from Config import class_number
SEED = 1234
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
import DataSet
import torch.nn as nn
import torch.nn.functional as F
class TextCNN(nn.Module):
def __init__(self):
super(TextCNN, self).__init__()
Vocab = len(DataSet.getTEXT... |
391bdb19ad6b28602c889a5450b72ffc671cc587f2d79b08163e58bb83f18e84 | Python | 1,484 | 37 | import time
import sklearn
import sklearn.ensemble
import sklearn.metrics
from sklearn.datasets import fetch_20newsgroups
from sklearn.pipeline import make_pipeline
from lime.lime_text import LimeTextExplainer
def interpret_data(X, y, func, class_names):
explainer = LimeTextExplainer(class_names=class_names)
... |
c41640dbd10707e4c05e23b44d2acd70a0c58bea743c0875b6cbec814d4d4282 | Python | 1,484 | 44 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
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 annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from fairchem.core.datasets.atomic_data import ... |
e0b5759cb49067bd1e40df206ed046996e471c1f6a7c32e9ddd916af7e2a18fe | Python | 1,484 | 58 | import argparse
import sys
import time
import ray
# This prefix is used to identify the output log line that contains the runtime env.
RUNTIME_ENV_LOG_LINE_PREFIX = "ray_job_test_runtime_env_output:"
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Dashboard agent.")
parser.add_argume... |
796f8b295d21068ba668f047aa0b9df07d57c11136c0e62bc4e9e29d2af3f219 | Python | 1,485 | 51 | #!/usr/bin/env python
# coding: utf-8
import torch
import torch.nn as nn
from Actor1 import init_weights
class Critic(nn.Module):
def __init__(self, OBS_DIM, ACTION_DIM, TARGET_DIM, RNN_SIZE, FC_SIZE, RNN):
super().__init__()
self.OBS_DIM = OBS_DIM
self.ACTION_DIM = ACTION_DIM
RN... |
b584076058ea981fd523654bb9c042ba0f927d8a764ec12f9cb5a02820d835e3 | Python | 1,485 | 42 | import numba
from numba import jit, prange, f8, f4, u1
import math
@jit("void(f8[:,:],f8[:],f8[:])",nopython=True,parallel=True,cache=True,fastmath=True)
def fastCOMdistance(a,b,d):
'''Calculate the COM distances between a point "b" and a series of points "a"
Inputs:
a(numpy.ndarray of float64, shap... |
3ea5df0974d60c485a0dd3c1c5f0449b3b9831091a639e9edfca6636fbf9f6f6 | Python | 1,486 | 42 | import os
import sys
sys.path.append('.')
import cv2
import math
import torch
import argparse
import numpy as np
from torch.nn import functional as F
from model.pytorch_msssim import ssim_matlab
from model.RIFE import Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Model()
model.loa... |
615080eaf01c0f9b676dcce035d2f10a25f3e05f5fc7c587463afaaa07cba48d | Python | 1,486 | 46 | import sys
from parameters import ParameterSet
from mozaik.models import Model
from mozaik.connectors.meta_connectors import GaborConnector
from mozaik.connectors.modular import (
ModularSamplingProbabilisticConnector,
ModularSamplingProbabilisticConnectorAnnotationSamplesCount,
)
from mozaik import load_compon... |
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