sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
bc524626d5cddb1932a2fb2393c7e6a2d930c5beb6e9858a4158d0fcb49ddb65 | Python | 957 | 36 | class Mode:
"""Novae mode class, used to store states variables related to training and inference."""
def __init__(
self,
zero_shot: bool = False,
trained: bool = False,
pretrained: bool = False,
multimodal: bool = False,
):
self.zero_shot = zero_shot
... |
9a4aa9879dac9a6939a82091c3715c80d2e7a371eaa9c642d87660cef3dc6b94 | Python | 958 | 25 | import numpy as np
import tqdm
import math
from pyimzml.ImzMLParser import ImzMLParser
from msi_visual.extract.base_msi_to_numpy import BaseMSIToNumpy
class PymzmlToNumpy(BaseMSIToNumpy):
def get_regions(self, input_path):
return [0]
def get_img_type(self):
return np.float32... |
d134ae8174abeb58ebf6f2bbb8a69d1ea6982c9727ee44bb745e71b87421fa04 | Python | 959 | 34 | __author__ = 'heroico'
import logging
import sys
def configureLogging(level=5, target=sys.stderr):
logger = logging.getLogger()
logger.setLevel(level)
kludge=False
if logger.hasHandlers():
kludge=True
logger.handlers.clear()
# create console handler and set level to info
RED ... |
5c5a5a2c4ccb39935548eb72e9dadf7d7b12e371c6072e7b84b8c39df6cc207f | Python | 960 | 34 | from __future__ import annotations
import pandas as pd
from PySide6.QtWidgets import QApplication
from src.features.telemetry_alignment.telemetry_processing_widget import (
TelemetryPhotomOptoProcessingApp,
)
def test_telemetry_qt_app_instantiates():
_app = QApplication.instance() or QApplication([])
wi... |
64fa1b8bc40c82c1cdbb6046e2755ad08268eee072b21940e7e47db54400ca49 | Python | 960 | 45 | from pgmpy.datasets._base import BaseDataset
class CreditApproval(BaseDataset):
"""
References
----------
- :footcite:t:`ivandari_2022`
"""
_tags = {
"name": "credit_approval",
"n_variables": 16,
"n_samples": 690,
"has_ground_truth": False,
"has_expert_... |
2de61a2b7b115af5adadfceea8629bc0bad4b082d8fc30d2074e6b4d6f09f60a | Python | 962 | 60 | VALID_RECONSTRUCTION = [
"svrtk",
"niftymic",
"nesvor",
]
VALID_SEGMENTATION = ["bounti", "fetalsynthseg"]
VALID_SURFACE = [
"surfpype",
]
VALID_PREPRO_TAGS = [
"mount",
"input_stacks",
"input_masks",
"output_stacks",
"output_masks",
"singularity_path",
"singularity_mount... |
a472d85904a6c94f4e115d467789f00ed5e5d5f5c02eb62e43d5da0b19ebda3d | Python | 962 | 41 | """Create individual experiment files for a parameter sweep."""
from itertools import product
from pathlib import Path
import yaml
def set_value(dct, keys, val):
for key in keys[:-1]:
if isinstance(dct, dict) and key in dct:
dct = dct[key]
else:
raise KeyError(f"{keys} do... |
db8a8b7ef1ff960f308e52f25417bc049b4e9fb7ea34f409b1feba299e5b8bc5 | Python | 962 | 40 | # isort: skip_file
# else segmentation fault during imports
import pyarrow # noqa: F401
from pathlib import Path
import pandas as pd
import scanpy as sc
import novae
def main(directory: Path) -> None:
paths = list(directory.glob("*.h5ad"))
data = {
"slide_id": [],
"mean_distances": [],
... |
86bcb3bf8288de777ac47c75cf81b206872e2cbdad147a5358923809927f18cc | Python | 963 | 37 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset
import warnings
warnings.filterwarnings('ignore')
class MLP(nn.Module):
def __init__(self):
super(MLP,self).__init__()
self.l1 = nn.Linear(400,20)
self.l2 = nn.Line... |
f3261c189767bf8ef6d5d1b25a675e6adedd913c3c25c32a8897171d579aecfa | Python | 964 | 36 | from pgmpy.datasets._base import BaseDataset
class MyocardialInfarction(BaseDataset):
"""
References
----------
- :footcite:t:`springer_myocardial`
"""
_tags = {
"name": "myocardial_infarction",
"n_variables": 123,
"n_samples": 1700,
"has_ground_truth": False,
... |
a53f8a4714a221bcebd8b7138bc24a6ed27537b67b848c3c74ea9ae1ce81bd10 | Python | 965 | 24 | from typing import Type
from nnunetv2.preprocessing.normalization.default_normalization_schemes import CTNormalization, NoNormalization, \
ZScoreNormalization, RescaleTo01Normalization, RGBTo01Normalization, ImageNormalization
channel_name_to_normalization_mapping = {
'CT': CTNormalization,
'noNorm': NoNo... |
04a17cd48afba1f10c519ebcdaf220cbd0595ac4b424543f980ac689ef5d68ca | Python | 966 | 35 | from argparse import ArgumentParser
from pathlib import Path
import pandas as pd
def parse_args():
parser = ArgumentParser()
parser.add_argument("input_dir", type=Path)
parser.add_argument("output", type=Path)
parser.add_argument("--pattern", default="*_GOF.csv")
return parser.parse_args()
def ... |
60e28ab795613490ecf8c3debca35b95337974fd8572a883e34199327f079954 | Python | 966 | 44 | import os
import subprocess
from create_input_json import createInputJson
sorted_directories = [
'/path/to/kilosort/outputs'
]
probe_type = '3a'
json_directory = r'C:\Users\svc_neuropix\Documents\json_files'
for directory in sorted_directories:
session_id = os.path.basename(directory)
input_json = os.... |
da0d2c92927c47c860d26e7df03a52f1d686fb45e9b66a11206fcd8811cd354f | Python | 966 | 24 | import pytest
from pgmpy.models import DiscreteBayesianNetwork
from pgmpy.structure_score import BDeu
# Score values in the tests are compared to R package bnlearn
class TestBDeu:
def test_score(self, small_df, small_df_models):
m1, m2 = small_df_models
scorer = BDeu(small_df)
assert sco... |
757844e5a90b84bfad978c12025ff26a77c0f2971fc0fe963136af14310c403c | Python | 967 | 47 | #!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.11"
# dependencies = [
# "msgpack",
# "zstandard",
# ]
# ///
import argparse
import pathlib
import pprint
import struct
import msgpack
import zstandard
def dump(filename: str):
content = pathlib.Path(filename).read_bytes()
magi... |
299b01cf10b29aae8ce719c08e79b1b255cead8ed657f0bba082c9daacc4423f | Python | 968 | 30 | #
# Copyright 2017 National Technology & Engineering Solutions of Sandia, LLC
# (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government
# retains certain rights in this software.
#
# See LICENSE for full license details
#
from dataclasses import dataclass
from .base_parameters import BasePar... |
8f6e28ae5e94f28a941a5f5d2d9c6e3b134ae3f738d861b7f0e3ffbf47bc4387 | Python | 968 | 39 | import numpy as np
import pandas as pd
from scipy import sparse
import torch
def load_sparse_indices(path):
coo = sparse.load_npz(path)
indices = np.vstack((coo.row, coo.col))
return(indices)
def sort_data(path):
data = pd.read_csv(path)
data.sort_values("OS_MONTHS", as... |
c63860506360ca7bfd163dcca155ecae21dc29cd3bab4cb5802df9387f8a7951 | Python | 970 | 35 | """Shared pytest fixtures for the HIPPIE release test suite."""
from __future__ import annotations
import os
import shutil
from pathlib import Path
import pytest
REPO_ROOT = Path(__file__).resolve().parents[1]
TOY_DATASET = REPO_ROOT / "datasets_hippie" / "toy"
@pytest.fixture(scope="session")
def toy_dataset_di... |
cfec6323968c7bbe45153e4f32aafbc1f7a76284dc4b6e426f5f320463279297 | Python | 970 | 29 | import numpy as np
from fairseq.data.audio.feature_transforms import (
AudioFeatureTransform,
register_audio_feature_transform,
)
@register_audio_feature_transform("global_cmvn")
class GlobalCMVN(AudioFeatureTransform):
"""Global CMVN (cepstral mean and variance normalization). The global mean
and var... |
44d88887140fde44d3a7755028ff35fadb3030a6924dd1acb13619378b1be4e4 | Python | 971 | 26 |
## Users can modify the following paths to their own paths
# NOTE: this script will be used by both python and bash scripts, so please use grammer that is compatible with both languages
## model
SPLICEBERT="../../models/SpliceBERT.1024nt"
SPLICEBERT_510="../../models/SpliceBERT.510nt"
SPLICEBERT_HUMAN="../../models/... |
5676e7e57525932ba138f571e4c0b7830069f09885ca0d5a8de9c061636395e0 | Python | 971 | 41 | """
Hydra training entry (lives under ``dlbase/``). Prefer ``python main.py`` from repo root.
Direct use::
python -m dlbase.train
python -m dlbase.train model=<name> train.max_epochs=50
``config_path`` points at the repository ``conf/`` directory (one level above this file).
"""
from __future__ import annotatio... |
758bf285b004fc6cd047cacf4e741c5325eb88f40377792ff7f593f2fbb70953 | Python | 972 | 37 | #!/usr/bin/env python3
import setuptools
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
d = {}
with open("tangram/_version.py") as f:
exec(f.read(), d)
setuptools.setup(
name="tangram-sc",
version=d["__version__"],
author="Tommaso Biancalani, Gabriele Scalia",
... |
db85dd001c8fa337b74505a5897035b2d0fe4484515b198287d84d5fe764bddd | Python | 972 | 34 | import numpy as np
from pytorch_grad_cam.base_cam import BaseCAM
from pytorch_grad_cam.utils.svd_on_activations import get_2d_projection
# https://ieeexplore.ieee.org/document/9462463
class LayerCAM(BaseCAM):
def __init__(
self,
model,
target_layers,
reshape_transf... |
289af96c0b7191c70465080118d15ad81e68a0e2809fc98359064b049551c0ef | Python | 973 | 29 | import argparse
import webbrowser
import neuroglancer
import neuroglancer.cli
if __name__ == "__main__":
ap = argparse.ArgumentParser()
neuroglancer.cli.add_server_arguments(ap)
args = ap.parse_args()
neuroglancer.cli.handle_server_arguments(args)
viewer = neuroglancer.Viewer()
with viewer.tx... |
494df3a7688032ff53630d001982ff58ab2ca46f1788ccb2718f62243d24bf09 | Python | 974 | 32 | import numpy as np
from pytorch_grad_cam.base_cam import BaseCAM
class XGradCAM(BaseCAM):
def __init__(
self,
model,
target_layers,
reshape_transform=None):
super(
XGradCAM,
self).__init__(
model,
target_layers... |
3f0d7007d2f90c3bd5b05738a032bcee5c993882db275caaeedc05f621894954 | Python | 975 | 51 | """sub-package that parses config.toml files and returns config object"""
from . import (
config,
dataset,
eval,
learncurve,
load,
model,
predict,
prep,
spect_params,
train,
trainer,
validators,
)
from .config import Config
from .dataset import DatasetConfig
from .eval i... |
c2060c4d51a27c593a3d5a249cd3d625dfa450202fbf025f02a4d9dd4a874661 | Python | 977 | 30 | import numpy as np
import pandas as pd
from anndata import AnnData
from novae import utils
from novae._constants import Keys
def _fake_input(p: float) -> AnnData:
obs = pd.DataFrame({Keys.IS_VALID_OBS: np.random.rand(1000) <= p})
obs.index = obs.index.astype(str)
obsm = {Keys.REPR: np.random.rand(1000, 1... |
9f19664520482af366569ff598db99f3dd8b628bc76bcfbbf1dc5c8c61beb230 | Python | 980 | 43 | import os
from fairseq.data.audio import (
AudioTransform,
CompositeAudioTransform,
import_transforms,
register_audio_transform,
)
class AudioFeatureTransform(AudioTransform):
pass
AUDIO_FEATURE_TRANSFORM_REGISTRY = {}
AUDIO_FEATURE_TRANSFORM_CLASS_NAMES = set()
def get_audio_feature_transform... |
b3b95a84aef8fbe1a6b0830b351b1c0b56e007510a5b04deee6c269f93c1ded3 | Python | 981 | 30 | """Inverse-distance weighting and weighted oversampling."""
import numpy as np
from crossda.pipelines.weighting import _weight_from_D, _merge_weighted
def test_weights_sum_to_one_and_favour_near():
w = _weight_from_D(np.array([1.0, 2.0, 4.0]))
assert np.isclose(w.sum(), 1.0)
# Smaller distance -> larger... |
da9d19081ba6ebb03d1dbb268946f6712eefcfb0dcb2a576a0c46bfa34ef4537 | Python | 981 | 31 | import numpy as np
from pytorch_grad_cam.base_cam import BaseCAM
from pytorch_grad_cam.utils.svd_on_activations import get_2d_projection
class HiResCAM(BaseCAM):
def __init__(self, model, target_layers,
reshape_transform=None):
super(
HiResCAM,
self).__init__(
... |
b3e356a1168207e3c8f05bff142794959b8aaccf9442a2e109c059b3d6c3fa99 | Python | 982 | 23 | import torch
import torch.backends.cudnn as cudnn
def select_device(id, force_cpu=False):
cuda = False if force_cpu else torch.cuda.is_available()
cudnn.benchmark = True
device = torch.device('cuda:{}'.format(id) if cuda else 'cpu')
#device = torch.cuda.set_device(0 if cuda else 'cpu')
if not cuda:
... |
313f7c487c2821522496cf51bc27a83096630a79f3fc12118ebd7e6e8950e296 | Python | 983 | 31 | from .BayesianNetwork import BayesianNetwork
from .ClusterGraph import ClusterGraph
from .DiscreteBayesianNetwork import DiscreteBayesianNetwork
from .DiscreteMarkovNetwork import DiscreteMarkovNetwork
from .DynamicBayesianNetwork import DynamicBayesianNetwork
from .FactorGraph import FactorGraph
from .FunctionalBayesi... |
a4370843a018ca203a3e6c3a4db744a7659f8f8e68bb6b7f7ff510c34a80f293 | Python | 983 | 31 | """Render `d3text.metric_docs.glossary` into a page at build time.
A page holds `<!-- metric-glossary: STAGE -->` where the table for `STAGE`
should appear, so the reference page cannot drift from what a run logs.
"""
import re
from mkdocs.structure.files import Files
from mkdocs.structure.pages import Page
import ... |
d7c06844a2c517111b66037ce5c4f37f1420244b041a78560adcbdb30b2658af | Python | 983 | 48 | import socket
# Check whether a port is in use
def check_port_in_use(port, host='127.0.0.1'):
s = None
try:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.settimeout(1)
s.connect((host, int(port)))
return True
except socket.error:
return False
finally:
... |
3093db5e40cf0f05796fee8adc63790977719ba9c94977b582061b7dea2fb758 | Python | 984 | 26 | """
Global reproducibility policy for DLBase and SpatialPheno.
RNG state is set exclusively via :func:`pytorch_lightning.seed_everything` (Python ``random``,
NumPy, PyTorch, ``PL_GLOBAL_SEED`` / worker env). We then align cuDNN with the former standalone
``setup_seed`` behavior (deterministic kernels; disable ben... |
8518d19a3101b8fa88c3a0f42fc584f9c8ca06c36b1a8147ba6565c03395748f | Python | 984 | 27 | """Typed options shared by the public CLI and inference runtime."""
from dataclasses import dataclass
from pathlib import Path
@dataclass(frozen=True)
class CheckpointArguments:
"""Durable batching options shared by every inference command."""
checkpoint_batch_size: int | None = None
checkpoint_dir: Pat... |
9145191dc74713bb898de62b931b5baa95fa33faf78c54b1656ea3c72e4d1211 | Python | 984 | 32 | from __future__ import annotations
from pathlib import Path
from tomlkit.items import Array
from poetry.factory import Factory
def get_self_command_dependencies(locked: bool = True) -> Array | None:
from poetry.console.commands.self.self_command import SelfCommand
from poetry.locations import CONFIG_DIR
... |
314be0d4fe72330a81cdabf53606bcc88a59599b202a733a848b4e62a7734bb8 | Python | 986 | 38 | import importlib.util
import importlib.metadata
from logging import getLogger
from packaging import version
logger = getLogger(__name__)
"""
Modified from transformers: utils/import_utils.py
"""
def _is_package_available(pkg_name: str) -> bool:
# Check if the package spec exists and grab its version to avoid imp... |
9c52485a954b18af9cc0ed125a1ebe7e4d5e9007e272724d5abe77f6cb09a097 | Python | 986 | 32 | from __future__ import annotations
from dataclasses import dataclass
from typing import TYPE_CHECKING
from poetry.core.packages.dependency_group import MAIN_GROUP
from poetry.core.version.markers import BaseMarker
from poetry.core.version.markers import EmptyMarker
if TYPE_CHECKING:
from collections.abc import ... |
2cda9c8129eb8fa781acda5ef223295522a2b04fb2464ae99294249df5084c53 | Python | 987 | 41 | from __future__ import annotations
from typing import TYPE_CHECKING
from typing import ClassVar
from cleo.helpers import option
from poetry.console.commands.command import Command
if TYPE_CHECKING:
from cleo.io.inputs.option import Option
class EnvListCommand(Command):
name = "env list"
description =... |
8ba512368d661d7f0c225e399f1b4f31ed2e8d1205bef3f3f98d0bf742d42e86 | Python | 989 | 25 | """Warmup + cosine decay (aligned with step_basic.set_lr)."""
import math
from torch.optim.lr_scheduler import LambdaLR
from torch.optim import Optimizer
def make_warmup_cosine_lambda(max_epochs: int, warmup_epochs: float, min_lr_ratio: float):
def lr_lambda(epoch: int) -> float:
if max_epochs <... |
e512338a0d5b6d4b929017a4c7942b5cd3941e62c85b8b7050bf68cd62e5449f | Python | 989 | 38 | import subprocess
import multiprocessing as mp
from typing import Iterable, List
from functools import partial
import os
import torch
def run_command(gpus, command):
gpu = gpus.pop()
print(command)
env = os.environ.copy()
env["CUDA_VISIBLE_DEVICES"] = str(gpu)
subprocess.run(command, env=env)
... |
b2b5a630e2b532ccc74d4177d1fb0d80d197c402601ee622c53ffefb39071ad9 | Python | 992 | 28 | # 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
from unittest import mock
class TestIOPath(unittest.TestCase):
def test_no_iopath(self):
from .test_reproducibil... |
78f6534f3728b920485d3d89e5f0192c0ef2cdbab7519fda59886503edab7091 | Python | 993 | 32 | from __future__ import annotations
import sys
from pathlib import Path
from poetry.console.commands.command import Command
class DebugInfoCommand(Command):
name = "debug info"
description = "Shows debug information."
def handle(self) -> int:
poetry_python_version = ".".join(str(s) for s in sys... |
a2a81bc5a56b53d7d9fbf4ec27403f854e6e2a74aa16b4ff5ff9bf4d4945baf4 | Python | 993 | 31 | import sys
import json
from msi_visual.supervised.annotations import get_img, get_visualization, get_dataset
from xgboost import XGBClassifier
import joblib
if __name__ == "__main__":
path1 = r"E:\\MSImaging-data\\_msi_visual\\Extractions\\NRL4485-s2\\20_bins\\0.npy"
path2 = r"E:\\MSImaging-data\\_ms... |
1c155824fdfc357376e20ef51afb4e6c8282ba478322290e898a514505ce4b5c | Python | 995 | 23 | from paper2.parsers import oil_group_key, coffee_group_key, juice_group_key
def test_oil_keys_strip_replicate():
assert oil_group_key("11_1") == "11"
assert oil_group_key("11_17") == "11"
assert oil_group_key("Z3") == "31" # appended pure-oil block: Z<brand> == <brand>1
assert oil_group_key(... |
8b4640288055e20e0fadd5f1bdbb4c55ffbcfcb75aaf31c70db20d7829234d84 | Python | 995 | 32 | import numpy as np
import pytest
import torch
import scvi
from scvi.data._utils import scipy_to_torch_sparse
@pytest.mark.parametrize("sparse_format", ["csr_matrix", "csc_matrix", None])
def test_scipy_to_torch_sparse(sparse_format: str):
adata = scvi.data.synthetic_iid(sparse_format=sparse_format)
scipy_spa... |
ec75fb8f59bf54c68caea18e64049cc5cce266bd0c6b800a26d06f47603280f4 | Python | 996 | 33 | # 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
from tests.speech import TestFairseqSpeech
S3_BASE_URL = "https://dl.fbaipublicfiles.com/fairseq/"
class TestConvtransforme... |
26b9bb8567042fc18807e6741f81678f1b872f7cd9cb7aad5c3df269df65925d | Python | 997 | 41 | from pgmpy.datasets._base import BaseDataset
class Algeria(BaseDataset):
"""
References
----------
- :footcite:t:`uci_algerian_forest_fires`
- :footcite:t:`peerj_pima`
"""
_tags = {
"name": "algerian_forest",
"n_variables": 15,
"n_samples": 244,
"has_ground... |
56a74f2ed55eb87c605f05f625a2d6b422371ce9f75fc1408fac6a6186f0ad5b | Python | 997 | 32 | import torch
import torch.nn as nn
import numpy as np
# Inspired by https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
class StandardScaler(nn.Module):
def __init__(self) -> None:
super().__init__()
self.register_buffer("_mean", torch.tensor([0.0]))
... |
b704a7ba76cbc566e6ccb6601189fd00ca9e8477d890a72c28ba8f55a947039e | Python | 997 | 24 | # Adopted from https://github.com/bioinformatics-sannio/ncrna-deep
from rinalmo.utils.ncrna_classification.seqEncoders import *
nlayers=[0,1,2,3] # deep architecture layers
bnoise=[200] # boundary noise simulation
t=[1,2,5,10] # number of random (non functional) seqs for each test seq
padds = ['random'] # how paddin... |
4f8cddc01dd698fd0828b3e9e9df96816a2d2b697c614558cb1a437ce9b3f27e | Python | 998 | 36 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import json
import logging
import hydra
import omegaconf
import torch
from omegaconf import OmegaConf
from mattergen.common.utils.globals import MODELS_PROJECT_ROOT
from mattergen.diffusion.config import Config
from mattergen.diffusion.run impo... |
ec6a767ca27de48f3499f1a8d89cb6044f1733539f9a27374ef12c0294d7a920 | Python | 998 | 28 | import os
import platform
import site
import shutil
def add_custom_trainers_to_local_nnunetv2() -> str:
# Locate the site-packages directory
site_packages = site.getsitepackages()[0]
if platform.system() == 'Windows':
site_packages = os.path.join(site_packages, 'Lib', 'site-packages')
source... |
ff80b834e1b735a1b885e3140b5bf64b43efe4589b89b970d4b7b6dfb19c8af8 | Python | 998 | 36 | from ._base import BaseCITest, get_ci_test
from .chi_square import ChiSquare
from .fisher_z import FisherZ
from .g_sq import GSq
from .gcm import GCM
from .generalized_cov import GeneralizedCov
from .hotelling_lawley import HotellingLawley
from .independence_match import IndependenceMatch
from .log_likelihood import Lo... |
47ffe713457a62e7ea25c3713bf9dcd695ffac41914439a0dc32f24887a5017e | Python | 999 | 39 | import os
import torch
from model import FrePatchTST3_attn_ablation
class Exp_Basic(object):
def __init__(self, args):
self.args = args
self.model_dict = {
'FrePatchTST3_attn_ablation': FrePatchTST3_attn_ablation,
}
self.device = self._acquire_device()
self.mode... |
924b58627efa6f244063b8b6f09ff94690073661340ac21063cabdf599566572 | Python | 999 | 24 | from ambiguous.dataset.dataset import *
def test_aMNIST_dataset():
root = 'data/'
trainset = aMNIST(root=root, download=True, train=True, transform=None)
testset = aMNIST(root=root, download=True, train=False, transform=None)
trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
testloade... |
7cbccd0cb2270439feab8ad76d2004176c5757ac251b66a5244e7c892bc1646a | Python | 1,000 | 42 | import numpy as np
import pandas as pd
import pytest
from pgmpy.estimators import BaseEstimator
@pytest.fixture
def dataset1():
return pd.DataFrame(data={"A": [0, 0, 1], "B": [0, 1, 0], "C": [1, 1, 0], "D": ["X", "Y", "Z"]})
@pytest.fixture
def dataset2():
return pd.DataFrame(
data={
"A... |
8b19212d63237b5000aa3a4f3292611affffd55fdc736de173ab722da0f56f34 | Python | 1,002 | 35 | import ants
import os
import sys
''' Atropos segmentation with possibility of multivariate input '''
def segment_with_atropos(imgPath, stimFolder):
if type(imgPath) == list:
img = []
for img_path in imgPath:
img.append(ants.image_read(img_path))
mask = ants.get_mask(img[0])
... |
98355476cb3fdf63b0fa8677f813ca86bc3e17b0d36e3961ffa76ab354994fdc | Python | 1,002 | 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
class AutomergingParams(DefaultSchema):
merge_threshold = Float(required=True, default=2.5, h... |
420a7b333d6b49a6fc26f1fe8fda27c24ed36dae08c7245a49258ee65c499a59 | Python | 1,003 | 29 | # 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 ... |
db29cd216a57ff79e4b33f517aacd8ff625b0bb188888d3545068538feced489 | Python | 1,003 | 32 | #!/usr/bin/env python3
"""Combine prokaryote + eukaryote per-gene stats into the headline AA recovery.
The headline gene-completion number is the equal-weighted mean of the per-gene
mean AA recovery across all 12 benchmark genes (4 prokaryotic + 8 eukaryotic).
Usage::
python summarize.py out/evo2_7b/evo2_7b_prok... |
7e241731eb57146f0bf756fe7be43bb5baf77ea359158eb2fabe88d78aa80646 | Python | 1,004 | 23 | import os
import scanpy as sc
import torch
import torch_npu
import scMulan
from scMulan import GeneSymbolUniform
adata = sc.read('Data/liver.h5ad', backup_url='https://cloud.tsinghua.edu.cn/f/45a7fd2a27e543539f59/?dl=1')
adata_GS_uniformed = GeneSymbolUniform(input_adata=adata,
output_d... |
aceb8b9db62dcbb4603d55d5b21a9c9675ba62fe1bc78f34ca99fcadec3faedc | Python | 1,004 | 38 | from pgmpy.datasets._base import BaseDataset
class ContraceptiveMethod(BaseDataset):
"""
References
----------
- :footcite:t:`uci_contraceptive_method`
"""
_tags = {
"name": "contraceptive_method",
"n_variables": 9,
"n_samples": 1473,
"has_ground_truth": False,... |
3b4ac4ab0ff48836b51dae60d48fe6d2f7d958b31ccca158976cde9395ea0551 | Python | 1,005 | 33 | """Smoke test for scripts/microcircuits/run.py.
Uses tests/fixtures/microcircuits_smoke.yaml with reduced number of pattern and
epoch duration, to keep runtime well under the 60 s timeout.
"""
import subprocess
import sys
from pathlib import Path
import pytest
from conftest import SCRIPTS_DIR
SCRIPT = SCRIPTS_DIR /... |
f534755122a9cd4ab44dfb78c9de2485088ee4218e1f0dff63ccd083c4511361 | Python | 1,006 | 25 | import time
import os
from tape.models.file_utils import url_to_filename, get_cache, get_etag
from tape import ProteinBertModel
from tape import TAPETokenizer
from tape.models.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
import torch
def test_forcedownload():
model = ProteinBertModel.from_pretrained('be... |
c851dcb66b6fe5276d559563eb36b56d763977cc6fab37fd8a84e7af63710ad1 | Python | 1,007 | 20 | import pandas as pd
import numpy as np
def create_text_datasets(file_path, test_fuile, train_file, valid_file):
main_file = pd.read_csv(file_path, sep='\t')
test_file = np.load(test_file, allow_pickle=True)
pmids = [np.int64(line[0]) for line in test_file]
filtered_main_file = main_file[main_file['PMI... |
56609a041cc90b6a6db6e1f830a979b8c1ebef72754e9155c72be214de47a881 | Python | 1,008 | 38 | """
Common helpers for baseline models.
"""
from __future__ import annotations
import torch
import torch.nn as nn
import timm
class TimmClassifier(nn.Module):
"""
Thin wrapper around timm.create_model for classification baselines.
"""
def __init__(self, model_name: str, num_classes: int, pretrained:... |
691ca399d4c735ca298b83ddcd6cdd4de02999691ea676210b0069385dd6b2d9 | Python | 1,008 | 38 | import gl
gl.resetdefaults()
kframesperrotation = 180
gl.meshload('BrainMesh_ICBM152_smoothed.lh.mz3')
gl.meshcurv()
gl.overlayload('CIT168.mz3')
gl.azimuthelevation(250, 35)
gl.meshhemisphere(-1)
for i in range(0, kframesperrotation * 7):
rot = (i / kframesperrotation)
if ((i % kframesperrotation) == 0):
shade... |
9b01348d7de38c752a4b777aee02a6d41eb2aba2982170c0026ec1172307810f | Python | 1,008 | 28 | import numpy as np
from meld_classifier.paths import BASE_PATH
from meld_classifier.meld_cohort import MeldCohort
from meld_classifier.data_preprocessing import Preprocess
#Generate feature names
features = []
for d in [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]:
features.append(f".combat.on_lh.gm_T1_{d}... |
959b0cb20460d5d0a5cc1bfdcb913976d99f4ef8a318e947017c1a69afad03fb | Python | 1,010 | 34 | import torch
def fasterrcnn_reshape_transform(x):
target_size = x['pool'].size()[-2:]
activations = []
for key, value in x.items():
activations.append(
torch.nn.functional.interpolate(
torch.abs(value),
target_size,
mode='bilinear'))
... |
9bb6d9f69a75bc13cb01025f8a930f374439664e8edc763ae530b282f2154c2b | Python | 1,011 | 27 | import slicer
from slicer.util import VTKObservationMixin
from ..Widgets.ToolWidget import AbstractToolWidget
from ..Effects.PointerEffect import AbstractPointerEffect
class NoneToolWidget(AbstractToolWidget, VTKObservationMixin):
def __init__(self):
toolTip = 'None. Disable Effects.'
AbstractToolWidg... |
313fa1a52ea135f53fd0b7161e897aa1bcf76c8ae5cf6b21be3af8e9338c45ef | Python | 1,012 | 28 | from CurrentscapePipeline import CurrentscapePipeline
output_dir = 'output_5s_simulation'
cluster_seed = 0
random_seed = 30
e_input = 'synaptic_input/Espikes_d10_Ne2000_Re0.5_rseed1_rep0.dat'
i_input = 'synaptic_input/Ispikes_d10_Ni200_Ri7.4_rseed1_rep0.dat'
simulation_time = 5*1000
target = 'soma' # can be any dend... |
3459d9df62e2ab27d9d104052c71b4896d951a68cad72bf989bb82740858f9f4 | Python | 1,012 | 32 | """
Basic FISSA usage example.
This file contains a step-by-step example workflow for using the FISSA toolbox.
An example notebook is provided here:
https://github.com/rochefort-lab/fissa/blob/master/examples/Basic%20usage.ipynb
"""
import fissa
# On Windows, it is necessary to wrap the script in a __name__ check,... |
80aeb57e46bdf9137e5633185e5fbdde0b3ea2c31791aa5e9be1706cfa8d1f5b | Python | 1,012 | 38 | from utils import general
from models import models
data_dir = "D:\Data\DIRLAB\DIRLAB_clean"
out_dir = "D:\Data\DIRLAB\outtest"
case_id = 8
(
img_insp,
img_exp,
landmarks_insp,
landmarks_exp,
mask_exp,
voxel_size,
) = general.load_image_DIRLab(case_id, "{}\Case".format(data_dir))
kwargs = {}... |
82fbb03c82690a86f8bf17e8a97f39cafd9a24960b32540a573037603fe5f9de | Python | 1,012 | 29 |
def asymmetric_mad(data):
"""
Computes the asymmetric median absolute deviation in a vectorized manner.
"""
# Calculate the median for each column (axis=0)
median = np.nanmedian(data, axis=0)
# Calculate absolute deviations from the median
# Broadcasting makes this efficient: `data` is (... |
ba93ada913157808b7e368e5dbff1c076dc2d3bc12b7745e48ac1643df8e77ab | Python | 1,012 | 29 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# type: ignore
from pathlib import Path
from read_version import read_version
from setuptools import find_namespace_packages, setup
setup(
name="dependency-submitit-launcher",
version=read_version("hydra_plugins/dependency_submitit_launche... |
3bd08d16d34bc41c6cdf417d41f24f2d5046529c76a3f6ec9e25e3014c967a64 | Python | 1,014 | 46 | def ants_transformation(
in_nii: str,
ref_nii: str,
out_nii: str,
in_tform: str,
interpolation="Linear",
) -> str:
"""
Application of ANTs transform.
Parameters
----------
in_nii : str
Path to input nifti file.
ref_nii : str
Path to reference nifti file.
... |
ad0f3e5444e82bd04ef148f19d01c05602b0c70b5454626ec0f1f2d4d5f45048 | Python | 1,015 | 25 | # 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.
proteinseq_toks = {
'toks': ['L', 'A', 'G', 'V', 'S', 'E', 'R', 'T', 'I', 'D', 'P', 'K', 'Q', 'N', 'F', 'Y', 'M', 'H', 'W', 'C', 'X', 'B',... |
d6f342459e8625046057110973c852ae5d93b1aef461ebcbaa10b489da8065f5 | Python | 1,015 | 32 | import argparse
import neuroglancer
import neuroglancer.cli
import neuroglancer.webdriver
ap = argparse.ArgumentParser()
neuroglancer.cli.add_server_arguments(ap)
ap.add_argument("--browser", choices=["chrome", "firefox"], default="chrome")
args = ap.parse_args()
neuroglancer.cli.handle_server_arguments(args)
viewer ... |
b66c7ee705d060ec34c1960b5ed57d1e4819cbe19257013cc5cdfccca3a00bb6 | Python | 1,016 | 21 | # FTIR (mid-IR) assignments in WAVENUMBER cm-1 for edible fats/oils/pastes.
# Refs: Guillen & Cabo (1997); Rohman & Che Man (2010); Vlachos et al. (2006).
BANDS = [
((3000, 3020), "=C-H stretch (cis C=C)", "Unsaturated fatty acids"),
((2900, 2940), "C-H asymmetric stretch (CH2)", "Lipid acyl chains"),
((284... |
ebebae2512411e157f9853dee3966f7d3f4e22d59c0fe06137feccdc499a1615 | Python | 1,016 | 30 | import unittest
from pathlib import Path
import torch
from tangle_tracer.modules import NFTDetector
class NFTDetectorTestCase(unittest.TestCase):
"""NFTDetectorTestCase class."""
@classmethod
def setUpClass(cls) -> None:
cls.model = NFTDetector()
cls.rand_data = torch.rand(1, 3, 1024, 1024... |
29c6a0d7243f249312f743bbe2b56d38c9cf3d3656b46d940db4f08e379931b6 | Python | 1,017 | 40 | TEAL = "#4e938d"
DARKTEAL = "#27474a"
DARKERTEAL = "#225b62"
PURPLE = "#8661c5"
YELLOW = "#ffb900"
MIDTEAL = "#49c5b1"
GREY = "#d9d9d6"
DARKYELLOW = "#A2790D"
ORANGE = "#ffa38b"
BRAND_LIGHT_BROWN = "#e1d3c7"
BRAND_BROWN = "#bf9474"
BRAND_DARK_BROWN = "#5c4738"
BRAND_LIGHT_YELLOW = "#ffe399"
BRAND_YELLOW = "#ffb900"
BR... |
51fb441658b7de7d914a501b5f3eab1f3079f3351b2efe2d61673d7eb81d9549 | Python | 1,018 | 38 | import numpy as np
def ball(
radi: float,
affine_matrix: np.ndarray,
) -> tuple:
"""
Morphological cell balls and midpoints.
Parameters
----------
radi : float
Radius of ball.
affine_matrix : np.ndarray
Affine matrix.
Returns
-------
tuple
Tuple co... |
207a29a7dd25f1228803aca66c22a6ec0a2139f4e68ac912861d4985f9ddd313 | Python | 1,019 | 29 | import numpy as np
import loompy
from .heatmap import Heatmap
from cytograph.species import Species
def TF_heatmap(ds: loompy.LoomConnection, dsagg: loompy.LoomConnection, out_file: str = None, layer: str = "pooled") -> None:
TFs = Species.detect(ds).genes.TFs
enrichment = dsagg["enrichment"][:, :]
enrichment = en... |
96cc56a42ec83faddd58c3f42670094187eeda271c4d42490103db94ac947a76 | Python | 1,019 | 30 | # @license
# Copyright 2025 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... |
a7cd2336f04ed975409b42e6fdbb521de679450df984dafb651f052b36007202 | Python | 1,021 | 30 | import setuptools
with open('README.md', mode='r', encoding='utf-8', errors='ignore') as fh:
long_description = fh.read()
with open("requirements.txt", "r") as f:
requirements = f.readlines()
setuptools.setup(
name='msi-visual',
version='1.0.0',
author='Jacob Gildenblat',
au... |
c2317e78bed87273f511bd424fd0f44b9143b8200b9cc859305b7cbd9b0a1fed | Python | 1,021 | 38 | import pytest
import numpy as np
import os
#from ecephys_spike_sorting.modules.quality_metrics.metrics import calculate_metrics
#import ecephys_spike_sorting.common.utils as utils
#DATA_DIR = os.environ.get('ECEPHYS_SPIKE_SORTING_DATA', False)
def te_st_quality_metrics():
sample_rate = 30000.0
numChannels = 384
... |
e557be5cbb0ccd0b16b0fe0cda800536da8f3d1ae9719166db5457853e5ed03c | Python | 1,024 | 45 | """
Configuration file for the hybrid transformer model.
All hyperparameters and paths are defined here for reproducibility.
"""
import os
class Config:
DATA_DIR = "./kvasir-dataset"
CLASS_NAMES = [
"Dyed-lifted-polyps",
"Dyed-resection-margins",
"Esophagitis",
"Normal-cecum",... |
ee1c9caaab1a567f0ce92d76c8be1cb98e8e8bef0897cb993143713ae9c5c32d | Python | 1,025 | 40 | import numpy as np
import pytest
from scvi.data import synthetic_iid
from scvi.external import SCBASSET
_DNA_CODE_KEY = "code"
def _get_adata(sparse_format: str | None = None):
dataset1 = synthetic_iid(batch_size=100, sparse_format=sparse_format).transpose()
dataset1.X = (dataset1.X > 0).astype(float)
d... |
f35ad083d734c90e88efe73a8d2d41d539752749b54af3b6f9295b695a20f1a7 | Python | 1,028 | 32 | # code from https://github.com/theislab/scanpy/blob/master/docs/extensions/typed_returns.py
# with some minor adjustment
from __future__ import annotations
import re
from collections.abc import Generator, Iterable
from sphinx.application import Sphinx
from sphinx.ext.napoleon import NumpyDocstring
def _process_retu... |
2a1a339662972047365a83fce66caa10990c5721307d65283526452003ab1446 | Python | 1,030 | 29 | import numpy as np
def asymmetric_mad(data):
"""
Computes the asymmetric median absolute deviation in a vectorized manner.
"""
# Calculate the median for each column (axis=0)
median = np.nanmedian(data, axis=0)
# Calculate absolute deviations from the median
# Broadcasting makes this effi... |
216c8a0f0c223b0724989eca2e2e94c14314b6d0b9cc249969c96baf488adc4a | Python | 1,033 | 31 | import numpy as np
from meld_classifier.paths import BASE_PATH, CLIPPING_PARAMS_FILE
from meld_classifier.meld_cohort import MeldCohort
from meld_classifier.data_preprocessing import Preprocess
#Generate feature names
features = []
for d in [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]:
features.append(f".... |
da62f3c2b86299b960fc354ff02c85f7f2ed639f83728f28cbe8a74be82b8426 | Python | 1,033 | 32 | # -*- coding: UTF-8 -*-
"""The classic GOEA algorithm: score every GO term independently."""
__copyright__ = "Copyright (C) 2010-present, H Tang et al., All rights reserved."
from goatools.goea.algorithms.base import GoeaAlgorithm, GoeaAlgoResult
class ClassicAlgorithm(GoeaAlgorithm):
"""Score each GO term on i... |
fa0466b74f9add201e8163df632f126983f1b1503003f9deb02d14fcfb8b2962 | Python | 1,033 | 28 | # This is just for downloading the generator. See `discrimination/` for the discrimination checkpoints
import os
import requests
import argparse
parser = argparse.ArgumentParser(description='Download a model!')
parser.add_argument(
'model_type',
type=str,
help='Valid model names: (base|large|mega)',
)
mod... |
94491e443da33a59ceb91663dfdd083e368346f8eff91359c36d6e86a0b931fd | Python | 1,034 | 36 | from __future__ import annotations
import logging
import os
import webbrowser
from pathlib import Path
from src import path_config
logger = logging.getLogger(__name__)
def is_wsl() -> bool:
"""Return True when running under Windows Subsystem for Linux."""
return bool(os.environ.get("WSL_DISTRO_NAME"))
de... |
cb78c62831859158e5f3d94de7a2c263c26fb628a92ed9e4ff4ece00acc148b7 | Python | 1,034 | 38 | """Create individual experiment files for a parameter sweep."""
from itertools import product
from pathlib import Path
import yaml
def set_value(dct, keys, val):
for key in keys[:-1]:
if isinstance(dct, dict) and key in dct:
dct = dct[key]
else:
raise KeyError(f"{keys} do... |
45432d349d8aa471a7a8ff137fc3ecdb8e2c7b987a3c36bfebe6a0f5a5c33889 | Python | 1,035 | 31 | import sys
import os
f1 = sys.argv[1]
output_folder = os.path.dirname(f1)
fp=f1.strip().split('/')[-1].split('_')[0]
mode = sys.argv[2]
chromosomes = ['chr' + str(i) for i in range(1, 23)]
chromosome_dict = {chromosome: -1 for chromosome in chromosomes}
file_list=[]
with open(f1,'r') as all:
for chr_n... |
d1d5113ac3a012344fe68ef808adc66f380f50ad2bbdc636cfddb25cbea060f0 | Python | 1,036 | 31 | from brenda_references.docdb import BrendaDocDB
import pathlib
TEST_DIR = pathlib.Path(__file__).parent.parent / "tests"
if __name__ == "__main__":
with (
BrendaDocDB() as maindb,
BrendaDocDB(
path=str(TEST_DIR / "test_files/testdb.json"), create=True
) as testdb,
):
... |
d52d29ced16f76af6b459ca4c5f4e82aec840a1fd2bc06d91c325cfa1ced0806 | Python | 1,037 | 30 | import numpy as np
# some
def asymmetric_mad(data):
"""
Computes the asymmetric median absolute deviation in a vectorized manner.
"""
# Calculate the median for each column (axis=0)
median = np.nanmedian(data, axis=0)
# Calculate absolute deviations from the median
# Broadcasting makes th... |
4f5154815dc778c67043d8129ecf40f9a92e281bf45985fe5d4dfd5ec7cf0f8d | Python | 1,039 | 38 | import logging
log = logging.getLogger(__name__)
class ColorFormatter(logging.Formatter):
grey = "\x1b[38;20m"
blue = "\x1b[36;20m"
yellow = "\x1b[33;20m"
red = "\x1b[31;20m"
bold_red = "\x1b[31;1m"
reset = "\x1b[0m"
prefix = "[%(levelname)s] (%(name)s)"
suffix = "%(message)s"
F... |
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