sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
5e3e0687f24140be241fe9fa9794a13f36a5af3aef73eae4133493752f1ca7ba | Python | 553 | 20 |
from tractseg.experiments.base import Config as BaseConfig
class Config(BaseConfig):
EXPERIMENT_TYPE = "peak_regression"
CLASSES = "20"
BATCH_SIZE = 44
LOSS_WEIGHT = 10
LOSS_WEIGHT_LEN = 400 # nr of epochs
LABELS_TYPE = "float"
TRAINING_SLICE_DIRECTION = "y"
GET_PROBS = True
INF... |
6660a893388b5c397ca9b1663085030eb912fa7361331a70440b1e14cfa0da67 | Python | 556 | 18 | # encoding: utf-8
"""
@author: Jiayang Chen
@contact: yjcmydkzgj@gmail.com
"""
from .baseline import Baseline
def build_model(cfg):
model = Baseline(
backbone_name=cfg.MODEL.BACKBONE_NAME,
seqwise_predictor_name=cfg.MODEL.SEQWISE_PREDICTOR_NAME,
elewise_predictor_name=cfg.MODEL.ELEWISE_PR... |
a810c8d04599d53631a7dc9fe7f129d48793638c413f0eadcf875abccee5f6b6 | Python | 557 | 18 | import haiku as hk
import jax
import jax.numpy as jnp
from ..types import MolecularConfiguration
class HydrogenicDensityModel(hk.Module):
def __call__(
self, r: jax.Array, mol_conf: MolecularConfiguration, only_network_output: bool = False
) -> jax.Array:
assert mol_conf.nuclei.n_active == 1
... |
cc1921f434af15b3b799839dceaa3a46d357a6b29a657e1365835eef2f63a6ef | Python | 557 | 23 | from __future__ import annotations
import sys
from pathlib import Path
def main() -> int:
path = Path(sys.argv[0])
if sys.argv[1] == "absolute":
if not path.is_absolute():
raise RuntimeError(f"sys.argv[0] is not an absolute path: {path}")
if not path.exists():
raise R... |
56184609fc5abd957f644d128c05f663e5a46ab8ff9b6fe17d62a0e927f5a5c6 | Python | 558 | 18 | """Guards on the ad-hoc scripts that write the packaged data files."""
from importlib import resources
import pytest
from brenda_references import brenda_references as package
from scripts import generate_dataset
def test_generate_dataset_writes_where_the_package_reads() -> None:
assert generate_dataset.DATA_D... |
ce4161f7a20081eb4035b690a9bbcca5eb97fe3741196370e0836119b4a85e89 | Python | 558 | 19 | """Evaluation metrics for classification."""
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, roc_auc_score
def compute_metrics(y_true, y_pred, y_prob):
acc = accuracy_score(y_true, y_pred)
precision, recall, f1, _ = precision_recall_fscore_support(
y_true, y_pred, average... |
718930a68a988468c6b519f1fbdb2822cfa2feb261926056ab00105d563a188a | Python | 561 | 21 | import os
import re
import pandas as pd
def extract_mouse_name(file_path: str) -> str | None:
"""Extract the mouse name (letters followed by numbers) from a file path, or None if not found."""
base_name = os.path.splitext(os.path.basename(file_path))[0]
pattern = r"[A-Za-z]+\d+"
matches = re.findall(... |
bf6d9262259222d77f5a2588dc4d0755b3c3ec6db0e954d08df053e80b41f350 | Python | 561 | 23 | from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from poetry.core.exceptions import PoetryCoreError
from poetry.toml import TOMLFile
if TYPE_CHECKING:
from pathlib import Path
def test_pyproject_toml_file_invalid(pyproject_toml: Path) -> None:
with pyproject_toml.open(mo... |
0c0a643b2e7c0133032f94264d7361911dd6272ba791ff6b28927f308981ddd9 | Python | 562 | 17 | import gl
gl.resetdefaults()
gl.azimuthelevation(70, 15)
gl.meshload('BrainMesh_ICBM152.rh.mz3')
gl.overlayload('motor_4t95vol.nii.gz')
gl.overlayminmax(1,2,12)
gl.overlayload('motor_4t95vol.nii.gz')
gl.overlayminmax(2,-1,-2)
gl.colorbarvisible(1)
gl.overlaytransparencyonbackground(25)
gl.meshcurv()
gl.shade... |
193b3a9cb2d47ffe5ac747aff318e17d380d8a3a87f702fea90d5f84eaa3a6be | Python | 562 | 18 | # Authors: Christian O'Reilly <christian.oreilly@sc.edu>
# Scott Huberty <seh33@uw.edu>
# License: MIT
"""Configure the dash tests with the chrom webdrive for CI."""
from selenium.webdriver.chrome.options import Options
# As per https://community.plotly.com/t/dash-integration-testing-
# with-selenium-and-... |
a7d7388e75eee3b27db1b8917eccf353857f90f2dfcfd941475803115e6ec34e | Python | 563 | 15 |
import glob
from ecephys_spike_sorting.common.visualization import plotContinuousFile
mouse = '72112' #, '425589', '432104', '425597'] #,'434845','434494','434843']
probes = ['probeA','probeB','probeC','probeD','probeE','probeF']
for probe in probes:
output_path = r'/mnt/nvme0/continuous_file_qc/' + mouse + '... |
54316b455fa74ec2650a3f733a6f5b87dfd1472a050efbc4ed7aa11ccf898441 | Python | 565 | 16 | from __future__ import annotations
from poetry.core.packages.package import Package
from poetry.utils.env.mock_env import MockEnv
from poetry.utils.log_utils import format_build_wheel_log
def test_format_build_wheel_log() -> None:
env = MockEnv(version_info=(3, 13, 1), platform="win32", platform_machine="AMD64"... |
dbc387a585972890e34f7637a7cf972e38a1e6b1d6f77aede0ae79265bb7fc43 | Python | 565 | 23 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
from tractseg.experiments.base_legacy.tract_seg_legacy import Config as TractSegConfig
class Config(TractSegConfig):
EXP_NAME = os.path.basename(__file__).split(".")[0]
NUM_EPOCHS = 250
DATA_AUGMENTATION = True
MODEL = "UNet_Pytorch_DeepSup"
... |
a2fd53641a48e20d6c55d6ef293a35a440c141b53b85c91ac9071dab443039a7 | Python | 566 | 18 | # 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 torch import nn
from fairseq.models import FairseqEncoder
class CTCDecoder(FairseqEncoder):
def __init__(self, dictionary, in_dim)... |
f4c0a312de25e872e62d965586fcac41884c9fc523ffd11b73ab7b16ef60ae49 | Python | 566 | 15 | """DFer/PFer raw photometry analysis.
This package ports the Argotech/Tussock Innovation DFer_v1.4 + PFer_2.4
analysis workflow (OCT-2024). That workflow is based on analysis methods from
GuPPy:
Sherathiya, V. N., Schaid, M. D., Seiler, J. L., Lopez, G. C., & Lerner, T. N.
GuPPy, a Python toolbox for the analysis of ... |
af091440a48f7f4ce82429b5d5cf601b124b2603287a3633bf1a323d9c3e3c2f | Python | 567 | 24 | from .BIF import BIFReader, BIFWriter
from .NET import NETReader, NETWriter
from .PomdpX import PomdpXReader, PomdpXWriter
from .UAI import UAIReader, UAIWriter
from .XDSL import XDSLReader, XDSLWriter
from .XMLBeliefNetwork import XBNReader, XBNWriter
from .XMLBIF import XMLBIFReader, XMLBIFWriter
__all__ = [
"XM... |
4cf7adad6f7610d697941e70376acf99f58b1e18ae29f180ea41c3f0f4206760 | Python | 568 | 19 | from paper2.bands_paper2 import assign_band2
def test_lipid_band_still_assigned():
assert "C=O" in assign_band2(1743)["assignment"]
def test_water_band_assigned_for_juice():
a = assign_band2(3300) # broad O-H stretch (water / sugars)
assert a["assignment"] != "Unassigned"
def test_sugar_co_ba... |
2de9d7a6027f2aaf0a386af3769efc65ec400a135a32764b17adfdd5949d8023 | Python | 570 | 24 | import unittest
from pyecharts.charts import Bar, Line
from pyecharts.charts.mixins import CompositeMixin
class CustomCompositeChart(CompositeMixin):
def __init__(self):
self._charts: list = []
def add(self, chart, tab_name):
chart.tab_name = tab_name
self._charts.append(chart)
... |
e40febc8aa990a8fe6d2b627d66b7572dbf5c6275e9ba7425d8a751a48b081a5 | Python | 570 | 21 | # 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 functools import lru_cache
from . import BaseWrapperDataset
class LRUCacheDataset(BaseWrapperDataset):
def __init__(self, dataset,... |
23533502721032f511f053b09961511de08d50eda8ba7be3824b3dc49ffa4174 | Python | 572 | 16 | """Manual-loop training infrastructure: checkpoints, TensorBoard, epoch aggregation (not Lightning)."""
from .checkpoint_io import load_checkpoint, resolve_checkpoint_path, save_checkpoint
from .epoch_aggregates import EpochBatchAggregator, StatHistory
from .experiment_paths import tensorboard_log_dir
from .tenso... |
0fb2e48eedc5f31320c6b0f37351686180a956ee97edce8f1141fc8c34e7f341 | Python | 574 | 20 | import os
import unittest
from pyecharts.globals import RenderSepType
class TestGlobals(unittest.TestCase):
def test_render_sep_type(self):
# normal test
render_sep_type = RenderSepType.SepType
self.assertEqual(os.linesep, render_sep_type)
# test modification
before_modif... |
d0b39bcf6c884ee01d539c72c91b19786eff5f792c439504119d18842fbbb342 | Python | 574 | 26 | from __future__ import annotations
from distutils.core import setup
packages = ["project_with_extras"]
package_data = {"": ["*"]}
extras_require = {"extras_a": ["pendulum>=1.4.4"], "extras_b": ["cachy>=0.2.0"]}
setup_kwargs = {
"name": "project-with-extras",
"version": "1.2.3",
"description": "This is... |
71a43b6e2057b55f682e47203ab5f03ff02073491b875a009cf42ecabe1f73d9 | Python | 575 | 15 | """
Baseline transformer models package for GI classification experiments.
"""
from .deit_base import DeiTBaseClassifier
from .vit_b16 import ViTB16Classifier
from .vit_l32 import ViTL32Classifier
from .transunet import TransUNetClassifier
from .transfuse import TransFuseClassifier
from .medvit import MedViTClassifier... |
4a997f5ebb5bf17357b338cdfc1c557d5f37797b0f9fd05f51a08635adf25d87 | Python | 577 | 23 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import abc
from typing import Generic, TypeVar
import torch
from mattergen.diffusion.data.batched_data import BatchedData
Diffusable = TypeVar("Diffusable", bound=BatchedData)
class ScoreModel(torch.nn.Module, Generic[Diffusable], abc.ABC):
... |
90e8f863fbb6ffcf857a9fa48ebfb98b69b20231d82563a13d8fec042d168e64 | Python | 577 | 17 | from ._minification import get_minified_adata_scrna, get_minified_mudata
__all__ = ["get_minified_adata_scrna", "get_minified_mudata"]
def __getattr__(name: str):
"""Lazily provide private utilities."""
if name in {
"_de_core_for_annbatch",
"_peakvi_differential_accessibility_result",
... |
bb7a29881600fda7bf8896c808eeeb52093da02955d040232a6e5547b1ab7050 | Python | 577 | 21 | # 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 .huffman_coder import HuffmanCodeBuilder, HuffmanCoder
from .huffman_mmap_indexed_dataset import (
HuffmanMMapIndex,
HuffmanMMapI... |
58f7171699304cef9f891652aa7d01220e315add4c54fe28524a7afe8b0b54f5 | Python | 578 | 18 | import torch.nn as nn
class SELayer(nn.Module):
def __init__(self, channel, reduction=16):
super(SELayer, self).__init__()
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel//reduction,bias=False),
nn.ReLU(inplace=True),
... |
d3a5178bcffaf2dea41fb79734c18e0ef40700fd10daf7365cb596c08ea33d02 | Python | 578 | 8 | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, Squad... |
6da1c8a0094171f12fa0638cdac1fea102ca568e55d0ce59ab339b8239b6238d | Python | 580 | 23 | """
Wrapper functions for pytorch.
"""
import torch
def torch_device(index=-1):
if torch.cuda.is_available():
if index >= 0:
return torch.device(f"cuda:{index}")
return torch.device("cuda")
elif torch.backends.mps.is_available() and torch.backends.mps.is_built():
return to... |
f355d80f19740eb14171c6822dcbfd36371b4b8b2118b7fa7bc5e80c15eb7ac7 | Python | 580 | 22 | import qrcode
# Code for input
email_address = input("Enter email address: ")
subject = input("Enter subject: ")
content = input("Enter content: ")
# Command
email_url = f"mailto:{email_address}?subject={subject}&body={content}"
# QR code generation
qr = qrcode.QRCode(version=1, error_correction=qrcode.constants.ER... |
059383738677503843de20f42871c7ad6b1fbae098da082abebdcc4681914885 | Python | 581 | 17 | """Dynamic segmentation visualize payload + panel rendering."""
from tasks.dynamic_segmentation.visualize.metrics_payload import SegEvalPayload, load_payload, save_payload
from tasks.dynamic_segmentation.visualize.panel_plot import (
render_panels_from_payload,
render_panels_from_saved_payload,
)
from t... |
a530a7283cbae15b8b53a7a6f211fda337eb35f7ebba799126ef7650dabe9b78 | Python | 581 | 23 | #!/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.
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name="lig... |
0815e91a5f7b8357872d31f16f8732c7361ef4a054f3b3f0829fa7ce4da03b0e | Python | 582 | 24 | from scvi.utils import error_on_missing_dependencies
from ._beta_binomial import BetaBinomial
from ._gamma import ZeroInflatedGamma
from ._lognormal import Log1pNormal, ZeroInflatedLogNormal
from ._negative_binomial import (
NegativeBinomial,
NegativeBinomialMixture,
Poisson,
ZeroInflatedNegativeBinomi... |
d79426c74dd91d075a3e2c85c6f65dde122637dbc09e250a192141d0107a8e2f | Python | 582 | 21 | import torch
from scvi.distributions._constraints import open_interval
def test_open_interval():
lower_bound = 0
upper_bound = 1.0
constraint = open_interval(lower_bound=lower_bound, upper_bound=upper_bound)
# Within the interval is ok
assert constraint.check(torch.tensor(0.5))
# Values ou... |
f47d50430c12ec3bb11bdb146b4905e8c336384c6ed0e819198d47c6cfcda27d | Python | 582 | 19 | import polars as pl
from src.utils import read_gtf
signalp5 = pl.read_csv("export/orfanage_peptide_summary.signalp5", separator = "\t", comment_prefix="#", new_columns=["ID","Prediction","SP(Sec/SPI)","OTHER","CS Position"])
pbids = read_gtf("nextflow_results/V47/orfanage/orfanage.gtf")\
.filter(
pl.col("... |
6306b38724154b4791e74145a667fdf8ba0bea2278a8080596d0685ac37d177a | Python | 583 | 25 | import sys
root_dir = __file__.rsplit("/", 2)[0]
if root_dir not in sys.path:
sys.path.append(root_dir)
import gradio as gr
from modules.search import build_search_module
from modules.compute_score import build_score_computation
# from modules.tmalign import build_TMalign
# from modules.cli import build_cli
from ... |
6388e1504e3a475ae03d4007b2d867620ec1503008f863c9f0365ac5c45590bc | Python | 584 | 12 | Res = [[] for i in range(32)]
for i in ['Test_dataset_R1_1234.txt','Test_dataset_R2_1234.txt','Test_dataset_R3_1324.txt','Test_dataset_R4_1324.txt','Test_dataset_R5_2134.txt','Test_dataset_R6_2134.txt','Test_dataset_R7_2314.txt','Test_dataset_R8_2314.txt','Test_dataset_R9_3124.txt','Test_dataset_R10_3124.txt','Test_dat... |
fd58c739a50bef6dc5c23c5df83e34605f0d0811a924f0ffcb69dfe79b8116ad | Python | 585 | 20 | import generate_counting_table as ct
input_path = "Data/TREC/Relevance_Matrix/trec_simplified_relevance_matrix.tsv"
# Comment out this step if table exists with cosine similarities
data = ct.load_relevance_matrix(input_path)
data = ct.random_cosine_similarities(data)
counting_table = ct.create_counting_table(data)
... |
837b0d910bfc541feb0774c464a066cfa3cbc06ded0fe5d68b542c31261c1b76 | Python | 587 | 20 | from .base import Inference # isort: skip # noqa: E402
from .CausalInference import CausalInference
from .ExactInference import BeliefPropagation, BeliefPropagationWithMessagePassing, VariableElimination
from .ApproxInference import ApproxInference
from .dbn_inference import DBNInference
from .mplp import Mplp
__all... |
a31da901ba16fd2fbb77b46936d2ac1aee9c301ef9437a3fc999cacf96d48117 | Python | 588 | 32 | import pandas as pd
import stackprinter
from .brenda_references import (
add_abstracts,
expand_doc,
main,
noise_documents,
psycholinguistics_data,
sync_doc_db,
test_data,
training_data,
validation_data,
)
from .sampling import relation_records
pd.options.mode.copy_on_write = True
... |
51bccbe2838f03a4433b4949fbc06a1ead33c66b2fbd018e054a566f1b10c943 | Python | 590 | 21 | # 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 re
from fairseq.data.encoders import register_tokenizer
from fairseq.dataclass import FairseqDataclass
@register_tokenizer("space", ... |
901bd9f32356ad98a500ef2be0184db31f6b3b3dc2edf9ec73dd129d7c98a8f4 | Python | 591 | 19 | import os
import sys
from nicheformer.data.constants import DefaultPaths
from nicheformer.data.download import download_zip
if len(sys.argv)==1:
out_path = DefaultPaths.SPATIAL
else:
out_path = sys.argv[1]
if not os.path.exists(f"{out_path}/raw"):
os.mkdir(f"{out_path}/raw")
download_zip(
url="http... |
09ee3e2d95dae691162417b79b7e32d53b1ea992a89f3cb82b1c1c31aac683c2 | Python | 595 | 16 | import time
from datetime import datetime
import vak.common.constants
import vak.common.timenow
def test_timenow():
before_timestamp = time.mktime(datetime.now().timetuple())
timenow_str = vak.common.timenow.get_timenow_as_str()
assert len(timenow_str) == len(vak.common.constants.STRFTIME_TIMESTAMP)
... |
69b06a86aa8f3f5d232baa4d084b795435597d82b4bb47d0ba04e9d800b42b89 | Python | 596 | 13 | # 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... |
df26b8863d815f7eb7a7a30594e595600281d9a76a8d8e1b6b5ec9b627203fb7 | Python | 596 | 18 | from __future__ import annotations
from html.parser import HTMLParser
class HTMLPageParser(HTMLParser):
def __init__(self) -> None:
super().__init__()
self.base_url: str | None = None
self.anchors: list[dict[str, str | None]] = []
def handle_starttag(self, tag: str, attrs: list[tuple... |
f077458aa63e1e7229f770ac3808df105a0ae5befe594c612841cadf39d3de54 | Python | 596 | 25 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Stores types"""
from typing import Literal, Tuple, TypeAlias, TypedDict
from nd2.structures import Color
_axes_interp: TypeAlias = Literal["distance"]
class MetaLike_ND2(TypedDict):
name: str
color: Color
index: int
emission_lambda: float
excita... |
62c3dbc633f6eb91fb868d81852382a2ebf7ba823d647f5418a6404f14f753ed | Python | 597 | 25 | from __future__ import annotations
import os
from pathlib import Path
from platformdirs import user_cache_path
from platformdirs import user_config_path
from platformdirs import user_data_path
_APP_NAME = "pypoetry"
DEFAULT_CACHE_DIR = user_cache_path(_APP_NAME, appauthor=False)
CONFIG_DIR = Path(
os.getenv("... |
6eb29dc87bdcbb2728dba261f46873bc70c96283971ec8afa27b9a614adfc83e | Python | 598 | 20 | import os
import random
class Tempname:
"""
A context manager that generates a temporary pathname, which is
renamed to its permanent name upon leaving the context. The renaming
is guaranteed to be atomic at least on POSIX systems.
"""
def __init__(self, path: str) -> None:
self.path = path
self.temp_path = ... |
9a2801489cad7f915a5c5f0c707c487f2c233ab07bfec19a0ef707617bcd75a8 | Python | 598 | 20 | import torch.nn as nn
"""
https://zhuanlan.zhihu.com/p/76378871
arxiv: 1804.03821
ExFuse
"""
class SematicEmbbedBlock(nn.Module):
def __init__(self, high_in_plane, low_in_plane, out_plane):
super(SematicEmbbedBlock, self).__init__()
self.conv3x3 = nn.Conv2d(high_in_plane, out_plane, 3, 1, 1)
... |
c036b3f4a8a39f7ea4eb32f1bea4cf39cce443763899333204bb3161c91b30de | Python | 598 | 24 | """Mapping from libvips band formats to numpy dtypes.
Shared by the morphology and visualization helpers that wrap pyvips
``Image`` outputs into numpy arrays. Kept here so the lookup table has
a single source of truth.
"""
from __future__ import annotations
import numpy as np
FORMAT_TO_DTYPE: dict[str, type] = {
... |
c6d2d61a6575cf6826acc4a20eb2f05ccf149cab37bc6f2aeb4b4aa6701b0fe8 | Python | 598 | 15 | import os
from pathlib import Path
# List of PDB files to rename
cur_dir = Path('/cta/users/guludogan/ProteinWorkshop/proteinworkshop/data/GeneOntology/pdb')
# Loop through each file and rename it
for file in cur_dir.iterdir():
# Split the filename to extract the new name format
new_name = "_".join(file.name.s... |
260c455f898cf1eedc3aa378bc845087b0f595f7e66508d8a132f7b8f2a0208a | Python | 599 | 23 | """
Inference Pipelines for NeuroVFM
High-level APIs for loading models and running inference on medical imaging studies.
"""
from .encoder import EncoderPipeline, load_encoder
from .diagnostic import DiagnosticHead, load_diagnostic_head
from .preprocessor import StudyPreprocessor
from .generator import FindingsGener... |
7382fda0e0584e7277401f6852498cc88f5f624b72b3255b57082cb5f153c0a9 | Python | 599 | 18 | from collections.abc import Callable
from functools import wraps
from scvi.data._constants import ADATA_MINIFY_TYPE
def unsupported_if_adata_minified(fn: Callable) -> Callable:
"""Decorator to raise an error if the model's `adata` is minified."""
@wraps(fn)
def wrapper(self, *args, **kwargs):
if... |
f20720ea79811bb8b751bb94d91634c89811f65cf9b6d7bcb6a3f97dea40e589 | Python | 599 | 20 | # 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 importlib
import os
from .ema import EMA
def build_ema(model, cfg, device):
return EMA(model, cfg, device)
# automatically imp... |
0bc8d5cc3c63904526232c5e27e58c1b0a04afea25805d3ec919faa483ddd45b | Python | 602 | 23 | import neuroglancer.read_precomputed_annotations
reader = neuroglancer.read_precomputed_annotations.AnnotationReader(
"gs://h01-release/data/20210601/c2/synapses/precomputed/"
)
print(
list(
reader.get_within_spatial_bounds(
limit=1000,
lower_bound=[70258.0, 115184.0, 1892.0],
... |
dbfaf3362d2e3268ce6969ff79235a6da50227c1dcf77d9029a829a7ba525f39 | Python | 602 | 23 | #!/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.
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name="dyn... |
e6cf9da8b0843edfe0519caef83a5b51fce91cfcd89c919b537c7d58bb8044c8 | Python | 602 | 23 | from mdt import CompositeModelTemplate
__author__ = 'Robbert Harms'
__date__ = "2015-06-22"
__maintainer__ = "Robbert Harms"
__email__ = "robbert@xkls.nl"
class Tensor(CompositeModelTemplate):
model_expression = '''
S0 * Tensor
'''
inits = {'Tensor.d': 1.7e-9,
'Tensor.dperp0': 1.7e-1... |
853cdd5c98d67b17f471cc350ed35876f3be39605315fef80d9bdde252493dc7 | Python | 604 | 25 | from scvi.utils import error_on_missing_dependencies
from ._base_module import (
BaseMinifiedModeModuleClass,
BaseModuleClass,
LossOutput,
PyroBaseModuleClass,
SupervisedModuleClass,
)
from ._decorators import auto_move_data
from ._embedding_mixin import EmbeddingModuleMixin
from ._priors import Ga... |
11a3d99074082b384132d524240f88b9eaa0dc17082591d16b69373137e9e329 | Python | 605 | 26 | import numpy as np
def sparseness(
W: np.ndarray,
dim: int = 0,
) -> np.ndarray:
"""
Vector sparseness along specified dimension.
Parameters
----------
W : np.ndarray
Input array.
dim : int, optional
Dimension along which to compute sparseness, by default 0.
Retur... |
6a9e7cc227d8a3ae6c8d40a4d99416c6f350c755def046e4ca7615c6e9000753 | Python | 606 | 16 | import os
import pandas as pd
import random
data_root = "/nfs/turbo/umms-tocho-snr/exp/akhilk/torchmr/raw_data/mri/processed/"
num_studies = len([f for f in os.listdir(data_root) if os.path.isdir(os.path.join(data_root, f))])
print(f"Number of studies: {num_studies}")
df_list = []
for study in os.listdir(data_root):... |
75d4ea5125a168763ae4d1a441d11c7909484c154d78761e1afd248cf80d8a12 | Python | 606 | 18 | # The script is modified from scGPT (https://github.com/bowang-lab/scGPT), copyright (c) 2022 suber
__version__ = "1.0"
import logging
import sys
logger = logging.getLogger("stFormer")
# check if logger has been initialized
if not logger.hasHandlers() or len(logger.handlers) == 0:
logger.propagate = False
log... |
0de279a0651eeb63f114bc838e2c8fa7f6ef7f3f5d7da0f4cd52b6bec782b876 | Python | 607 | 27 | from pgmpy.datasets._base import BaseCovarianceDataset
class Cities(BaseCovarianceDataset):
"""
References
----------
- :footcite:t:`spirtes_glymour_scheines_2001`
"""
_tags = {
"name": "cities",
"n_variables": 7,
"n_samples": 164,
"has_expert_knowledge": True,... |
5088f17d07c9a86a63f1a3f7f2898c6882a69e01ae28772847286781821b39f4 | Python | 607 | 21 | import qrcode
# Email details
email_address = "gutsie@kuleuven.be"
subject = "Info Clinical Trial"
content = "Please, could you send me more information about your study?"
# Format the email URL
email_url = f"mailto:{email_address}?subject={subject}&body={content}"
# Generate the QR code
qr = qrcode.QRCode(version=1... |
7b4e24a45ecfd4e32685107c20b0a5030931a307732a9bf6b4ea1225a1b254b0 | Python | 608 | 20 | import torch
import torch.nn as nn
import torch.nn.functional as F
class SEBlock(nn.Module):
def __init__(self, channel, reduction=2):
super(SEBlock, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction),
... |
e7165189ee15e3d5b4bd160727ab0f5684033d12b1e6351fcda5fe20b08fb623 | Python | 609 | 23 | import pandas as pd
import numpy as np
pd.set_option("display.max_columns", None)
pd.set_option("display.width", 1000)
df = pd.read_csv("results/aggregated_features_folded_with_demographics.csv")
df = df[[col for col in df.columns
if ('Oddball' in col or 'Resting' in col or col == "Group")
]]
for name, g in... |
35adbb1ea6f2d8b8ab9e389b9ce91aad74d2326580e6e9fa8b397b623268b267 | Python | 610 | 21 | from ._base import BaseSupervisedMetric, BaseUnsupervisedMetric, get_metrics
from .adjacency_cm import AdjacencyConfusionMatrix
from .correlation_score import CorrelationScore
from .fisher_c import FisherC
from .implied_cis import ImpliedCIs
from .orientation_cm import OrientationConfusionMatrix
from .shd import SHD
fr... |
6b5c58d825963738c90dda7db5ece45e75d5563ddaf213463d591d22585c9a3a | Python | 611 | 24 | import jax
from flax.struct import PyTreeNode
from folx import forward_laplacian
from ...physics import LaplacianOperator
from ...types import RandomKey
class ForwardLaplacianOperator(LaplacianOperator, PyTreeNode):
"""Thin wrapper around folx.forward_laplacian."""
sparsity_threshold: float = 0.6
def _... |
f3a058dd8a1c2e060d96af86371afa174909b15b7e7ee055f7b5f48f85d7a8f3 | Python | 611 | 19 | # scBank is the single cell data bank toolbox for building up large-scale single
# cell dataset to allow flexible cell data access and manipulation across studies,
# and to support large-scale computing.
import logging
import sys
logger = logging.getLogger("scBank")
logger.setLevel(logging.INFO)
handler = logging.Stre... |
4cd830b93db8e62f8c4dc9d7d7ccb419156ecf075bceed850b0af6ee863194be | Python | 612 | 26 | from ._utils import (
fill_invalid_indices,
get_reference,
iter_slides,
lower_var_names,
parse_device_args,
pretty_model_repr,
pretty_num_parameters,
tqdm,
unique_leaves_indices,
unique_obs,
valid_indices,
store_inference_mode,
sample_latent,
)
from ._train import con... |
421f5ee0d5465799de08725a80779a4805414232b72afb41d0f65e8af4142552 | Python | 613 | 22 | #!/usr/bin/env -S uv run
# /// script
# requires-python = ">=3.11"
# ///
import os
import zipfile
SCRIPT_DIR = os.path.abspath(os.path.dirname(__file__))
def create_zip(directory_path: str, zip_path: str):
with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_STORED) as zipf:
for root, dirs, files in os.walk(... |
141182514b86d830571c543e16834c78a63bd7855c1f4c73344f852976beb50b | Python | 614 | 14 | import tensorflow as tf
def iou_tf(y_true, y_pred, threshold=0.5):
y_pred = tf.cast(y_pred > threshold, tf.float32)
intersection = tf.reduce_sum(tf.cast(y_true * y_pred, tf.float32))
union = tf.reduce_sum(tf.cast(y_true + y_pred, tf.float32)) - intersection
iou = (intersection + 1e-7) / (union + 1e-7)
... |
94e9dc7ca92495347f0c306b9f74d1e82e5c9b4f7f782d1499678d664b4d16b1 | Python | 614 | 24 | import torch
import torch.nn as nn
class sSE(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.Conv1x1 = nn.Conv2d(in_channels, 1, kernel_size=1, bias=False)
self.norm = nn.Sigmoid()
def forward(self, U):
q = self.Conv1x1(U) # U:[bs,c,h,w] to q:[bs,1,h,w]
... |
0ec5f972e6d87ba39090766b51f188a93f216a8c3d4178cd2662d9c4c4356dc6 | Python | 615 | 22 | from __future__ import annotations
from typing import TYPE_CHECKING
from cleo.exceptions import CleoLogicError
from cleo.loaders.factory_command_loader import FactoryCommandLoader
if TYPE_CHECKING:
from collections.abc import Callable
from cleo.commands.command import Command
class CommandLoader(FactoryC... |
9e3f7813394098432a6da17e143c05702c6d7134b74b08b26e66dbe214aa6bd9 | Python | 615 | 19 | # 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.nn.functional as F
def unfold1d(x, kernel_size: int, padding_l: int, pad_value: float = 0):
"""unfold T x B x C to T x B x ... |
a237ba59c7c0a012e927739e8b3e984b283f696af466dddab2d920c3aac087be | Python | 617 | 14 | # Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... |
0b52f881c1ab7a485ad2fc581d2c42c486c54f6b5ec00a46b7f3ceed0e786e3b | Python | 618 | 14 | # Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... |
f4b5b310d396790597c7f297d3247b75c42bb23f5541925c0124ab74ad0d2b5e | Python | 619 | 16 | import pytest
from src.string_ops.string_utils import normalize_deduplicate_and_order_strings
@pytest.mark.parametrize("input_behaviours, expected_output", [
(["walking", "RUNNING", "EaTing"], ["Eating", "Running", "Walking"]),
([], []),
(["cat", "Cat", "bat"], ["Bat", "Cat"]),
([" walking ", "runnin... |
17c89a18e0091bcc4d31e0a8b3a2efc541bc04ff2738d5354f0f8ada75022fd7 | Python | 620 | 21 | import numpy as np
from pytorch_grad_cam.base_cam import BaseCAM
class RandomCAM(BaseCAM):
def __init__(self, model, target_layers,
reshape_transform=None):
super(
RandomCAM,
self).__init__(
model,
target_layers,
reshape_transfo... |
948f2dfb7a0e0aa71c6c810d54bc83a4c1bdbaa5ad8d4246d38dacf22559f7b1 | Python | 620 | 33 | import pandas as pd
import stackprinter
from .brenda_references import (
add_abstracts,
expand_doc,
main,
noise_documents,
psycholinguistics_data,
sync_doc_db,
test_data,
training_data,
validation_data,
)
from .data_paths import corpus_files, documents_path
pd.options.mode.copy_on_... |
8cae32f2183bd149201a7cfc390a044c97e0d4e773142e57f3f61053a5c575bc | Python | 621 | 21 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from . import BaseWrapperDataset
class SortDataset(BaseWrapperDataset):
def __init__(self, dataset, sort_order):
... |
0583e99fe90c091b5df9321eff5d49719f1ba36f1ed7c5f39e8ddcabceb8cf7a | Python | 622 | 31 | from .ANM import ANM
from .ChowLiu import ChowLiu
from .ExpertInLoop import ExpertInLoop
from .ExpertKnowledge import ExpertKnowledge
from .GES import GES
from .HillClimbSearch import HillClimbSearch
from .IGCI import IGCI
from .LLMPairwise import LLMPairwise
from .PC import PC
from .R2Sort import R2Sort
from .SP impor... |
718f020a868f977719bdca507f288d6f6cf8cba0143ecb826e30eeb0bc440975 | Python | 622 | 23 | import os
import sys
from nicheformer.data.download import download_tar
from nicheformer.data.constants import GeneExpressionOmnibus, DefaultPaths
if len(sys.argv)==1:
out_path = DefaultPaths.DISSOCIATED
else:
out_path = sys.argv[1]
geo_id = 'GSE172127'
if not os.path.exists(f"{path}/raw"):
os.mkdir(f"{... |
f6ba9faa42e5ecf1c86e06ee5769915fbf6efe200444efa991e5874f4f958299 | Python | 622 | 17 | import numpy as np
import pytest
from scvi.data import synthetic_iid
@pytest.mark.parametrize("sparse_format", ["csr_matrix", "csc_matrix", None])
def test_synthetic_iid_sparse_format(sparse_format: str | None):
_ = synthetic_iid(sparse_format=sparse_format)
@pytest.mark.parametrize("return_mudata", [False, Tr... |
0b2dcb1bbe53697a73a340cf319d59cda4116c93f5ecc088ce60bb0fb471f5e9 | Python | 623 | 23 | import os
import os.path as op
from glob import glob
base_dir = '/media/lukas/goliath/MVCA/ID1000/raw'
subs = sorted(glob(op.join(base_dir, 'ID????')))
for sub in subs:
t1s = sorted(glob(op.join(sub, '*T13D*', '*T13D.nii.gz')))
dtis = sorted(glob(op.join(sub, '*DTI32*', '*DWI.nii.gz')))
for i, t1 in enu... |
f37c5adcddcf2810c0140ba7184187d0cdaa7fa69245322ac48e4707e8adf86b | Python | 623 | 21 | # 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.
"""
transpose last 2 dimensions of the input
"""
import torch.nn as nn
class TransposeLast(nn.Module):
def __init__(self, deconstruct_id... |
a794aa0a9dda827a1306567166da372b2466ef8f7ff395e7ada01e4e89d93099 | Python | 624 | 21 | from typing import NamedTuple
class _REGISTRY_KEYS_NT(NamedTuple):
X_KEY: str = "X"
ATAC_X_KEY: str = "atac"
BATCH_KEY: str = "batch"
SAMPLE_KEY: str = "sample"
LABELS_KEY: str = "labels"
PROTEIN_EXP_KEY: str = "proteins"
CAT_COVS_KEY: str = "extra_categorical_covs"
CONT_COVS_KEY: str ... |
3a0f9738a3587ec7d758ec64eb227dc913d26341f381e805b409ef9043443a9d | Python | 626 | 14 | # Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... |
b4b807fd9c6e758632f6c1ba29f33d6ad888e6c08f25714ab8ac01e94715da4b | Python | 626 | 18 | # 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 .image_pretraining import ImagePretrainingTask, ImagePretrainingConfig
from .image_classification import ImageClassificationTask, ImageCl... |
ea5e21d57a801bff2354714f5cd31505141c1dfcec560360f4b4430c93f0d88c | Python | 626 | 22 | from types import SimpleNamespace
import torch
from gpn.embeddings import OneHotAuxEmbedding
def test_one_hot_embedding_accepts_continuous_auxiliary_features() -> None:
config = SimpleNamespace(
vocab_size=6,
n_aux_features=2,
hidden_size=8,
aux_features_vocab_size=None,
)
... |
4a2a230abd95b9784125ba353c9c8ff03b91a89953adcc08339f6ba47cc92d6d | Python | 628 | 27 | import logging
from pathlib import Path
from ...monitor.log import wandb_log_dir
log = logging.getLogger(__name__)
def _load_wandb_artifact(name: str) -> Path:
import wandb
api = wandb.Api()
if not name.startswith("novae/"):
name = f"novae/novae/{name}"
artifact = api.artifact(name)
... |
bf175cd15816afad56a134859cd7205f804441435ac2de2bbffd92875781641d | Python | 629 | 16 | """task × dataset compatibility for ``unet`` (see README §3.2)."""
from __future__ import annotations
from omegaconf import DictConfig
def assert_compatible(cfg: DictConfig) -> None:
task = str(cfg.task.name)
ds = str(cfg.dataset.name)
if task not in ("dynamic_segmentation", "dynamic_segmentat... |
70a3f7ecec6db4a8cedaa3b835d1309bbc4ab36a90186c6f26a293291c08b5ab | Python | 630 | 27 | from typing import NamedTuple
class _MODULE_KEYS(NamedTuple):
X_KEY: str = "x"
# inference
Z_KEY: str = "z"
QZ_KEY: str = "qz"
QZM_KEY: str = "qzm"
QZV_KEY: str = "qzv"
LIBRARY_KEY: str = "library"
QL_KEY: str = "ql"
BATCH_INDEX_KEY: str = "batch_index"
Y_KEY: str = "y"
CON... |
f2640f9d8cc4d33f03031d581fc3396a4fa1caeb3ff50fe5c41a01c8b8a5624e | Python | 630 | 19 | import torch
from neurovfm.pipelines import load_encoder, load_diagnostic_head
# 1. Load encoder + preprocessor from your HF repo
encoder, preproc = load_encoder(
"mlinslab/neurovfm-encoder", # your repo ID
device="cuda" if torch.cuda.is_available() else "cpu",
)
dx_head = load_diagnostic_head(
"mlinslab... |
41edd7562bc59c4ce7b0a017d4db77c8648e818f16918d71610e0170bb336981 | Python | 631 | 24 | import pandas as pd
import resource
def calculate_mean_std(csv_path, col_name):
# Load the CSV file
data = pd.read_csv(csv_path)
# Extract the column with predicted probabilities
predictions = data[col_name]
# Calculate mean and standard deviation
mean = predictions.mean()
std_dev... |
b7aadf429d218fbfc38037ad0ea2c240e25f3db11d12b5424d5cb0d0b0617a98 | Python | 631 | 23 | import pytest
def pytest_addoption(parser: pytest.Parser) -> None:
parser.addoption(
"--run-published-models",
action="store_true",
default=False,
help="run opt-in tests that download published Hugging Face checkpoints",
)
def pytest_collection_modifyitems(
config: pytest... |
3e80fa01a4d182567670e2d0b19fdf3af19540a50b72228eed24d9e5976f3bec | Python | 632 | 21 | import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="PCIst",
version="0.1.15",
author="Renzo Comolatti",
author_email="renzo.com@gmail.com",
description="Short library to calculate PCIst.",
long_description=long_description,
long_des... |
7e3b71e230c160268f92c14416fcc3dfa205c13de9250be392d63b1d156fa4fa | Python | 632 | 28 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Saves processed images"""
import json
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
from utils.types import MetaLike_ND2
def save_image_nd2_meta(
out_dir: Path, name: str, img: np.ndarray, meta: MetaLike_ND2
) -> None:
"""Saves... |
c4587572afbb6d9f8c0326ed26aba738c5768b0cd65bbbd6404002fdfb797211 | Python | 632 | 19 | import torch
from neurovfm.pipelines import load_encoder, load_diagnostic_head
# 1. Load encoder + preprocessor from your HF repo
encoder, preproc = load_encoder(
"mlinslab/neurovfm-encoder", # your repo ID
device="cuda" if torch.cuda.is_available() else "cpu",
)
dx_head = load_diagnostic_head(
"mlinslab... |
7ee47be79cc30b61cf7111de0d29f6fa5e15737c741145502e998ff7b657ba05 | Python | 633 | 14 | # Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... |
9fbad6d49a22e5687eba7f57058bbbbe75f6248997ca51f83cceb6071db65b1d | Python | 634 | 14 | # Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.