sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
b3f516142067c64646faec5d6d35fcc6524be05275cc25a16f4696100bf46396 | Python | 282 | 16 | SNP="snp"
EFFECT_ALLELE="effect_allele"
NON_EFFECT_ALLELE="non_effect_allele"
ZSCORE="zscore"
CHROMOSOME = "chromosome"
POSITION = "position"
DOSAGES="dosages"
FREQUENCY="frequency"
OR="or"
BETA="beta"
BETA_SIGN="beta_sign"
SE="se"
PVALUE="pvalue"
GENE="gene"
GENE_NAME="gene_name" |
6a4c9716709bcefb4317aaa0798179a5d9321f3c6df0f7a2656623e4e7a07f9b | Python | 283 | 12 | import unittest
from medaka import check_htslib_tool_version, report_binaries
class TestHTSLibVersion(unittest.TestCase):
def test_000_version_return_type(self):
check_htslib_tool_version('samtools')
def test_001_report_binaries(self):
report_binaries()
|
73ee1fdd5833ec722d88654c1045f08e559e7b691742473b8383548b32e58fbc | Python | 283 | 17 | from __future__ import annotations
import typing
if typing.TYPE_CHECKING:
from pathlib import Path
import dulwich.repo
class TempRepoFixture(typing.NamedTuple):
path: Path
repo: dulwich.repo.Repo
init_commit: str
middle_commit: str
head_commit: str
|
314ad2a5a249c04bde516b49d0eddb3007ed02aad48fd7e414c851c5ac311f1d | Python | 284 | 10 | import os
from tractseg.experiments.base_legacy.dm_reg_legacy import Config as DmRegConfig
class Config(DmRegConfig):
EXP_NAME = os.path.basename(__file__).split(".")[0]
# CLASSES = "AutoPTX"
# NR_OF_CLASSES = len(dataset_specific_utils.get_bundle_names(CLASSES)[1:])
|
dc7f09175b07123eba8b75af9b7150733246d15c70596384b6e220b612829bef | Python | 284 | 8 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from pathlib import Path
PROJECT_ROOT = Path(__file__).resolve().parent.parent
GENERATED_CRYSTALS_ZIP_FILE_NAME = "generated_crystals_cif.zip"
GENERATED_CRYSTALS_EXTXYZ_FILE_NAME = "generated_crystals.extxyz"
|
3a5777c541a71d2175f9e3c4591704aed70bff022daa970f11d46a6bf719499a | Python | 285 | 5 | from msi_visual.base_dim_reduction import BaseDimReductionWithoutFit
from sklearn.decomposition import LatentDirichletAllocation
class LDA3D(BaseDimReductionWithoutFit):
def __init__(self):
super().__init__(model=LatentDirichletAllocation(n_components=3), name="LDA3D") |
66f786d7d21a131db631a314267c2adb5c9540bab868552ac8c947007c30ab91 | Python | 285 | 6 | from .aggregator import Aggregator
from .config import load_config, Config, merge_namespaces
from .cytograph import Cytograph
from .punchcards import Punchcard, PunchcardSubset, PunchcardDeck
from .workflow import RootWorkflow, SubsetWorkflow, PoolWorkflow
from .utils import Tempname
|
dc013ecc79d4c0519ecab6eb6df3a04f5f070056861a5255524ed42f2aa8d9ca | Python | 285 | 6 | from msi_visual.base_dim_reduction import BaseDimReductionWithoutFit
from sklearn.manifold import SpectralEmbedding
class Spectral3D(BaseDimReductionWithoutFit):
def __init__(self):
super().__init__(model=SpectralEmbedding(n_components=3), name="SpectralEmbedding3D") |
73a25d118883b2786c2344032cad3cf70752fb4d188a5cb1b1bc1099bf5a9128 | Python | 286 | 11 | import gl
gl.resetdefaults()
gl.meshload('BrainMesh_ICBM152.rh.mz3')
gl.overlayload('motor_4t95mesh.rh.mz3')
gl.overlaycolorname(1, 'red')
gl.shaderxray(0.9, 0.5)
gl.azimuthelevation(110, 15)
gl.shadername('MatCap');
gl.shadermatcap('Cortex');
gl.meshcurv()
gl.overlaytranslucent(2, 1)
|
b4d498c783903abb78386775a63b6f420ce2aef4feac3da79929193c88c81908 | Python | 286 | 13 | """
Spatially variable gene selection.
Installation:
SpatialDE2 via https://github.com/PMBio/SpatialDE
"""
import SpatialDE
def svg(adata_st):
adata_st.X = adata_st.raw.X
svg_full, _ = SpatialDE.test(adata_st, omnibus=True)
return svg_full[svg_full.padj < 0.05].gene
|
1b3b69ebb6da574288ccb4f6212e9013ace1129fa2268b117720004f25ffb7f1 | Python | 287 | 8 | # 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 .laser_task import * # noqa
from .laser_lstm import * # noqa
from .laser_transformer import * # noqa
|
55881a88c3f9003ae01fc0c46922f6980a5d4f0f91b1f2a8f6714078276e844d | Python | 288 | 10 |
from calm.sequence import CodonSequence
def test_CodonSequence():
seq1 = CodonSequence('AUG GGA CGC UUU UAC CAA AUG GGA CGC UUU UAC CAA UAA')
seq2 = CodonSequence('AUGGGACGCUUUUACCAAAUGGGACGCUUUUACCAAUAA')
assert seq1.tokens == seq2.tokens
assert seq1.seq == seq2.seq
|
36f72d232a75048fcb4f82603d02f44239e3b5791a17c4b29b5dee356c3108f1 | Python | 289 | 10 | #
# Copyright 2017-2023 Sandia Corporation. Under the terms of Contract DE-AC04-94AL85000 with
# Sandia Corporation, the U.S. Government retains certain rights in this software.
#
# See LICENSE for full license details
#
from pathlib import Path
CONFIGS_BASE_DIR = Path(__file__).parent
|
9561cd478bbcb240d3ec656f63abe5916a4b7ac0762257c12c2bef45c82ece99 | Python | 290 | 11 | 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]
FEATURES_FILENAME = "T1_Peaks12g90g270g"
NR_OF_GRADIENTS = 10
NORMALIZE_PER_CHANNEL = True
|
e34452ddaca442f417e78c1a0bc6b5222b57dd6ff300befa6a70d7ab701e7572 | Python | 290 | 8 | # 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 .qconv import PQConv2d # NOQA
from .qemb import PQEmbedding # NOQA
from .qlinear import PQLinear # NOQA
|
0a341618903ddab22a070eac0a02429236a67ed15d8435a9055b0a6c78d8c25f | Python | 291 | 15 | from .ADMG import ADMG
from .DAG import DAG
from .MAG import MAG
from .PDAG import PDAG
from .SimpleCausalModel import SimpleCausalModel
from .UndirectedGraph import UndirectedGraph
__all__ = [
"ADMG",
"UndirectedGraph",
"DAG",
"PDAG",
"MAG",
"SimpleCausalModel",
]
|
1f1d730f3fde81f65b5713332ad13a0dd32282cdbdd38fbf70bca09a562b90da | Python | 291 | 6 | """The training loop: the epoch schedule, and the per-step weight update.
Deliberately re-exports nothing. `d3text.models.base` imports
`d3text.training.update`, so a re-export of `.trainer` here — which imports
`d3text.models.base` in turn — would close that cycle at import time.
"""
|
6d5452cca31b5e652ae92b91fa509925d83c489f92fc10bab6c442e00f116a28 | Python | 294 | 13 | from __future__ import annotations
from typing import Literal
import anndata
import mudata
import torch
Number = int | float
AnnOrMuData = anndata.AnnData | mudata.MuData
Tensor = torch.Tensor
LossRecord = dict[str, Tensor] | Tensor
MinifiedDataType = Literal["latent_posterior_parameters"]
|
bac624d65a18ea77ca5a5d210a470adc0c25338e114d49794b8f38ffc5ebedce | Python | 294 | 11 | """Dispatch ``cfg.task.name`` to a concrete task package."""
from __future__ import annotations
from omegaconf import DictConfig
def run_task(cfg: DictConfig) -> None:
from dlbase.registry import get_task_plugin
get_task_plugin(str(cfg.task.name)).task_class().run(cfg)
|
f1cfb71e078312839bce6581644c2b32a5c1ef38d5ab299a041042d11c6e2f4a | Python | 294 | 13 | import pandas as pd
from neuron import h
def get_segment_areas():
segments = []
areas = []
for sec in h.allsec():
for seg in sec.allseg():
segments.append(str(seg))
areas.append(seg.area())
return pd.DataFrame({'area': areas}, index=segments)
|
695c86492626b55b2c6daa56aed517a165e84cf78efcf812aaf54fdc92698b4a | Python | 297 | 12 | import os
from tractseg.experiments.base_legacy.peak_reg_legacy import Config as PeakRegConfig
#Name of original exp: Peaks20_12g90g270g_ALLoss
class Config(PeakRegConfig):
EXP_NAME = os.path.basename(__file__).split(".")[0]
LOSS_WEIGHT = 10
LOSS_WEIGHT_LEN = 400 # nr of epochs
|
2eac687c3db69b101cc6b7b7ce396de8a8d7243e6c404333300ac2f9d9df0b93 | Python | 298 | 10 | import novae
def test_label_domains():
adata = novae.toy_dataset(n_panels=1, compute_spatial_neighbors=True)[0]
prompt = novae.label_domains(adata, "domain", return_prompt=True)
assert "messages" in prompt
assert "output_schema" in prompt
assert len(prompt["messages"]) == 2
|
424a86dae1e745bf9be75c855b8163e30e528a33200bba7df83da9ca40220404 | Python | 298 | 10 | #!/usr/bin/env python3 -u
# 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 sys
for idx, line in enumerate(sys.stdin):
print(f"utt{idx:010d} {line}", end="")
|
3fc41765fb49b623fa5014b7badb4176f954db7dfb17b34691f1c4f95216c567 | Python | 299 | 15 | from .data import (
EntityRelationDataset,
collate_documents,
TokenBudgetBatchSampler,
compute_frequencies,
get_batch_loader,
)
__all__ = [
"EntityRelationDataset",
"collate_documents",
"TokenBudgetBatchSampler",
"compute_frequencies",
"get_batch_loader",
]
|
ee578226aef56b1b69bd8e672fff6e0c4ed877183b5474d1ca199faa6c2e463b | Python | 299 | 9 | from . import (array_operations, mesh_elements, mesh_correspondence,
mesh_creation, mesh_operations, mesh_io)
__all__ = ['array_operations',
'mesh_elements',
'mesh_correspondence',
'mesh_operations',
'mesh_creation',
'mesh_io']
|
54aa4acb7b9ca777a048691cdd084b147b0309b55c0479739daa5756d634a0d4 | Python | 300 | 11 | #!/usr/bin/env python3
from setuptools import setup
from Cython.Build import cythonize
import numpy as np
setup(
ext_modules=cythonize(["./_encode_sequence.pyx", "_sequence_to_kmer_list.pyx", "./selene_custom/_genomic_features.pyx"]),
include_dirs=[np.get_include()],
zip_safe=False,
)
|
b3886326b6cbcfd665f9e740e93706de9db351d4d808063950de4a803eea48b6 | Python | 301 | 13 | """
Spapros gene selection.
Installation:
Spapros via https://github.com/theislab/spapros
"""
import spapros as sp
def spapros(adata_sc):
selector = sp.se.ProbesetSelector(adata_sc)
selector.select_probeset()
return selector.probeset.index[selector.probeset["selection"]].to_list()
|
c941834711456e5c1afd91d75a3ef810d8cf298ca4be37ca357139b9919fb4cf | Python | 303 | 9 | # -*- coding: utf-8 -*-
# Copyright (C) 2017-2023 Phillip Alday <me@phillipalday.com>
# License: BSD (3-clause)
"""MNE-based functions for manipulating EEG data."""
from ._base import (savgol_iaf, attenuation_iaf,
abs_threshold, retrieve)
from .io import (write_raw_brainvision, )
|
e17d21c3ec0e3bdf407507121064125c56bb681e61f44ab48c5d54df06f63a71 | Python | 303 | 7 | # 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 models/tasks to register them
from . import dummy_dataset, dummy_lm, dummy_masked_lm, dummy_model, dummy_mt # noqa
|
a9a382e58d99620c474beada9a98c366c06a4e0e0b0abc2fc55dea41c6f2dedc | Python | 305 | 13 | # 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 .metric import *
from .evaluator import *
# experimental.
try:
from .expmetric import *
except ImportError:
pass
|
676ab6906314826a4ac9ccedc9f8d0326ddbe39005f0cc5fd20cd270d37f1f02 | Python | 308 | 13 | # 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 .configs import FairseqDataclass
from .constants import ChoiceEnum
__all__ = [
"FairseqDataclass",
"ChoiceEnum",
]
|
befda1aee5dadcc0adfe38b96d909e2a6371960968ebd607559052b6a0b991a6 | Python | 308 | 10 |
from tractseg.experiments.base import Config as BaseConfig
class Config(BaseConfig):
EXPERIMENT_TYPE = "tract_segmentation"
FEATURES_FILENAME = "12g90g270g_CSD_BX"
# slightly less overfitting (but max f1_validate maybe slightly worse (makes sense if less overfitting))
USE_DROPOUT = True
|
d35edb124d6c5f61b3053115671ae55b9e7ed068a268319fd7cb20e7b33689d6 | Python | 308 | 13 | import os
# nose2
os.system(
"nose2 --with-coverage --coverage pyecharts "
"--coverage-config .coveragerc -s test"
)
# pytest
os.system("pytest -cov-config=.coveragerc --cov=./ test/")
# flake8 for code linting
os.system("flake8 --exclude=build,example,images --max-line-length=89 --ignore=F401")
|
61aa688fc83dd60f5c0a13e7d584bfe9d8f0964a09e17f52f417b94a38f5f4b9 | Python | 309 | 11 | import sys
from os import makedirs
from os.path import join, dirname, realpath, exists
current_dir = dirname(dirname(realpath(__file__)))
sys.path.insert(0, dirname(current_dir))
from config_path import PLOTS_PATH
saving_dir = join(PLOTS_PATH, 'figure3')
if not exists(saving_dir):
makedirs(saving_dir)
|
6476b24ed9e0678f546312b3e3ca707ea8b85186c7ee52b6aa9307cfbfb915ab | Python | 309 | 11 | import sys
from os import makedirs
from os.path import join, dirname, realpath, exists
current_dir = dirname(dirname(realpath(__file__)))
sys.path.insert(0, dirname(current_dir))
from config_path import PLOTS_PATH
saving_dir = join(PLOTS_PATH, 'figure4')
if not exists(saving_dir):
makedirs(saving_dir)
|
6fcc581743dbe25d8161e7686929c5593ff95c409f35e4814574cc66ddf36fe4 | Python | 309 | 14 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import fileinput
import sacrebleu
for line in fileinput.input():
print(sacrebleu.tokenize_zh(line))
|
d22056a445bd5c6f145bd67d6b8128e2c9df07652baf5851dd57f0940c1997f2 | Python | 309 | 11 | import sys
from os import makedirs
from os.path import join, dirname, realpath, exists
current_dir = dirname(dirname(realpath(__file__)))
sys.path.insert(0, dirname(current_dir))
from config_path import PLOTS_PATH
saving_dir = join(PLOTS_PATH, 'figure2')
if not exists(saving_dir):
makedirs(saving_dir)
|
f631060df170b4bc44c05dd89d0dfeeb9a938d133640fd0a01a2993e0b563a99 | Python | 309 | 9 | """
This module contains classes and methods for target feature classes.
These are classes which define a way to access a "target feature" such
as a label or annotation on an input sequence.
"""
from .target import Target
from .genomic_features import GenomicFeatures
__all__ = ["Target", "GenomicFeatures"]
|
f82a8094e94b0bee141c77316cc66b8a29f9878e3ae846b5624e1195a7118410 | Python | 309 | 6 | from .DiscreteFactor import DiscreteFactor, State # isort: skip # noqa: E402
from .CPD import TabularCPD
from .JointProbabilityDistribution import JointProbabilityDistribution
from .NoisyOR import NoisyORCPD
__all__ = ["TabularCPD", "State", "DiscreteFactor", "JointProbabilityDistribution", "NoisyORCPD"]
|
215ddcc896cd3db2cd40d44c33406a668039d8c5f07c858c54342b2492d2f0eb | Python | 310 | 14 | from __future__ import annotations
from poetry.layouts.layout import Layout
from poetry.layouts.src import SrcLayout
_LAYOUTS = {"src": SrcLayout, "standard": Layout}
def layout(name: str) -> type[Layout]:
if name not in _LAYOUTS:
raise ValueError("Invalid layout")
return _LAYOUTS[name]
|
8d2a0bacbe6fa881b09e430941b7f13f03a81805e5e68cc9e1986b28fa625443 | Python | 310 | 14 | from .base import *
from .helper import *
from .input import *
from .dense import *
from .noise import *
from .conv import *
from .local import *
from .pool import *
from .shape import *
from .merge import *
from .normalization import *
from .embedding import *
from .recurrent import *
from .special import *
|
d9aab080dd8c5f33f3e47cdfffb59ffed01d8927c845c97f077db42b22e854de | Python | 310 | 7 | """Model architectures for medaka."""
# counts matrix models
from medaka.architectures.gru import GRUModel # noqa F401
from medaka.architectures.majority_vote_model import MajorityVoteModel # noqa F401
# read level feature models
from medaka.architectures.latent_space_lstm import LatentSpaceLSTM # noqa F401
|
53c89d14f10dc118d52ecc0122763b4a447325aed03354c70cc9d95cfed289b7 | Python | 311 | 5 | from src.utils.instantiators import instantiate_callbacks, instantiate_loggers
from src.utils.logging_utils import log_hyperparameters
from src.utils.pylogger import RankedLogger
from src.utils.rich_utils import enforce_tags, print_config_tree
from src.utils.utils import extras, get_metric_value, task_wrapper
|
36da1fbd145454ff5fd5ff006fd851b21a9981c483a6efb72b27e2c7c406d289 | Python | 312 | 11 | from ._contrastive_data_splitting import ContrastiveDataSplitter
from ._contrastive_dataloader import ContrastiveDataLoader
from ._model import ContrastiveVI
from ._module import ContrastiveVAE
__all__ = [
"ContrastiveDataLoader",
"ContrastiveDataSplitter",
"ContrastiveVAE",
"ContrastiveVI",
]
|
b4cce40367c7a71ee293e283ed7c50e2e11718bca27a1c9aa47711924967bb36 | Python | 312 | 13 |
from tractseg.data import dataset_specific_utils
from tractseg.experiments.base import Config as BaseConfig
class Config(BaseConfig):
EXPERIMENT_TYPE = "tract_segmentation"
# CLASSES = "AutoPTX"
# NR_OF_CLASSES = len(dataset_specific_utils.get_bundle_names(CLASSES)[1:])
LR_SCHEDULE = False |
f8f91d62c016345bb0b10be41360260b0dec2a85105376374270b66e784cf7fa | Python | 312 | 13 | from . import functional, transforms
from .functional import * # noqa : F401
from .transforms import ToLabels # noqa : F401
from .transforms import FromSegments, PostProcess, ToSegments
__all__ = [
"functional",
"transforms",
"FromSegments",
"PostProcess",
"ToLabels",
"ToSegments",
]
|
b9303d90e550fea78f7685a3bf0e3da70b50d4a05f0b162de00b08bb1a46dc12 | Python | 313 | 12 | from mdt import CompositeModelTemplate
__author__ = 'Robbert Harms'
__date__ = "2015-06-22"
__maintainer__ = "Robbert Harms"
__email__ = "robbert@xkls.nl"
class S0(CompositeModelTemplate):
"""Models the unweighted signal (aka. b0)."""
model_expression = 'S0'
volume_selection = {'b': [(0, 250e6)]}
|
7a0a92d90298ca6c7af868851ee100088601aca872c9a5a2b2be5f2cb558224a | Python | 314 | 12 | 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()
|
181423f332028516a7816d85838480807fe107e76c115eaec8900e609bf946e5 | Python | 315 | 13 | from ._components import DirichletDecoder, NicheDecoder
from ._constants import SCVIVA_REGISTRY_KEYS
from ._model import SCVIVA
from ._module import NicheLossOutput, nicheVAE
__all__ = [
"SCVIVA",
"nicheVAE",
"NicheDecoder",
"NicheLossOutput",
"DirichletDecoder",
"SCVIVA_REGISTRY_KEYS",
]
|
7971af32bcd6aa348359d5c34e809a2248f52f4d2a956380123856a770deffd1 | Python | 315 | 11 | import sys
from os import makedirs
from os.path import join, dirname, realpath, exists
current_dir = dirname(dirname(realpath(__file__)))
sys.path.insert(0, dirname(current_dir))
from config_path import PLOTS_PATH
saving_dir = join(PLOTS_PATH, 'supplementary')
if not exists(saving_dir):
makedirs(saving_dir)
|
1d40bc982ce97369e55548186c85db43e6b14892f374656f4181b14a201e8273 | Python | 316 | 13 | from abc import ABC, abstractmethod
from argparse import ArgumentParser
class BaseTransformersCLICommand(ABC):
@staticmethod
@abstractmethod
def register_subcommand(parser: ArgumentParser):
raise NotImplementedError()
@abstractmethod
def run(self):
raise NotImplementedError()
|
c0b5ab98c907f34ac83de02bca1b3c9dc871088370e453607554f54fde9ad9dd | Python | 316 | 9 | # 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 .tacotron2 import * # noqa
from .tts_transformer import * # noqa
from .fastspeech2 import * # noqa
from .vocoder import * # noqa
|
49de685f02faa8d85dabe520eb307c931678cc86ef620399cd3348126d38f3a4 | Python | 317 | 16 | FEATURES = [
"protein_coding_gene",
"lncRNA",
"exon",
"intron",
"splice_donor",
"splice_acceptor",
"5UTR",
"3UTR",
"CTCF-bound",
"polyA_signal",
"enhancer_Tissue_specific",
"enhancer_Tissue_invariant",
"promoter_Tissue_specific",
"promoter_Tissue_invariant",
]
|
c2eed0ca2a2409682fffbb3bd13dc9bf81213abd14ee455a9344de31a591e1fd | Python | 317 | 12 | import os
from tractseg.experiments.base_legacy.peak_reg_legacy import Config as PeakRegConfig
#Name of original exp: Peaks20_12g90g270g_ALLoss
class Config(PeakRegConfig):
EXP_NAME = os.path.basename(__file__).split(".")[0]
LOSS_WEIGHT = 10
LOSS_WEIGHT_LEN = 400 # nr of epochs
NUM_EPOCHS = 500
|
cd322f3e8f0af9170011449f6b2670895b304bc416b5e9dc37d655f9e30b5616 | Python | 317 | 12 | import numpy as np
import vak.common.timebins
def test_timebin_dur_from_vecs():
timebin_dur = 0.001
time_bins = np.linspace(0.0, 5.0, num=int(5 / timebin_dur))
computed = vak.common.timebins.timebin_dur_from_vec(
time_bins=time_bins, n_decimals_trunc=3
)
assert timebin_dur == computed
|
6da17bf296f0a0242134b2bb70104056a4751ccb531499ec11dc79a2784fc98c | Python | 322 | 6 | """Garfield: Graph-based Contrastive Learning enable Fast Single-Cell Embedding"""
from ._settings import settings
from ._version import __version__
from . import data, model, modules, nn, preprocessing, trainer, plot, analysis
__all__ = ["data", "model", "modules", "nn", "preprocessing", "trainer", "plot", "analysis"... |
f52471c52be9fc7313c68089d918df6836c207a79e5dad8a41e95891e0a9cbe6 | Python | 322 | 9 | from os.path import expanduser
from data.pathways.gmt_pathway import get_KEGG_map
input_genes = ['AR', 'AKT', 'EGFR']
filename = expanduser('~/Data/pathways/MsigDB/c2.cp.kegg.v6.1.symbols.gmt')
mapp, genes, pathways = get_KEGG_map(input_genes, filename)
print 'genes', genes
print 'pathways', pathways
print 'mapp', map... |
71a50256b7725a8ae128eec0c9147479768868c6d5fba282ce304891f5435317 | Python | 323 | 15 | from .._base import BaseExampleModel, DAGMixin
class Paths(DAGMixin, BaseExampleModel):
"""
Paths DAG from the example_models repository.
"""
_tags = {
"name": "dagitty/paths",
"n_nodes": 17,
"n_edges": 19,
"is_parameterized": False,
}
data_url = "dags/paths.tx... |
b5937f59a3e09bae8afe508fee42836b43efd320e35aa4ab356579bb7bcb5721 | Python | 323 | 13 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import sys
from pythainlp import word_tokenize
for line in sys.stdin:
print(" ".join(word_tokenize(line.strip())... |
9682b07778e2abb2197300f7f29b457b6608fe107a70f6ca3aa836a4b7592ba7 | Python | 325 | 10 | """
ViT-B/16 baseline using timm.
"""
from .common import TimmClassifier
class ViTB16Classifier(TimmClassifier):
def __init__(self, num_classes: int = 8, pretrained: bool = True, model_name: str = "vit_base_patch16_224"):
super().__init__(model_name=model_name, num_classes=num_classes, pretrained=pretrai... |
a0918101e873bb5218806b9936746e2a96f8592f8b8b21483b864006787a1206 | Python | 326 | 10 | """
ViT-L/32 baseline using timm.
"""
from .common import TimmClassifier
class ViTL32Classifier(TimmClassifier):
def __init__(self, num_classes: int = 8, pretrained: bool = True, model_name: str = "vit_large_patch32_224"):
super().__init__(model_name=model_name, num_classes=num_classes, pretrained=pretra... |
ac4bfae716874c7ec82a4d2fb989cbf47205482e0edb5ac2f64105ccce937697 | Python | 326 | 10 | from batchgenerators.transforms.abstract_transforms import AbstractTransform
class RemoveKeyTransform(AbstractTransform):
def __init__(self, key_to_remove: str):
self.key_to_remove = key_to_remove
def __call__(self, **data_dict):
_ = data_dict.pop(self.key_to_remove, None)
return data... |
e1b811f324c6b1dc76f2534f58c431fe7cb9e502a6374f69de9e738d1f10bbd4 | Python | 326 | 14 | import gl
gl.resetdefaults()
gl.azimuthelevation(240, 15)
gl. azimuthelevation(240, 15);
gl.meshload('AICHAhr.lh.mz3');
#format: atlashide(layer, (regions))
# gl.atlashide(0, (1, 2, 3, 12, 22, 44));
n = gl.atlasmaxindex(0)
msk = ()
for i in range(n):
msk = msk + (i,)
gl.atlashide(0,msk)
gl.wait(20);
gl.atlashide(... |
47511c1c3d52596c2e7477c9b022e655eece2dfeef89d3136924b1f44f9357d1 | Python | 329 | 18 | from __future__ import annotations
from setuptools import setup
kwargs = dict(
name="demo",
license="MIT",
version="0.1.2",
description="Demo project.",
author="Sébastien Eustace",
author_email="sebastien@eustace.io",
url="https://github.com/demo/demo",
packages=["demo"],
)
setup(**... |
bc1f5b6260e80e78ce79f5fc8c47a4f25d6467f16941da50b498b7e4f896c7f4 | Python | 329 | 8 | # 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 . import noisy_channel_translation # noqa
from . import noisy_channel_sequence_generator # noqa
from . import noisy_channel_beam_search... |
e7118d9fdd80ffbebe5ad05190896ee163ceb4288c79de4949f4308c9494d407 | Python | 329 | 10 | """
DeiT-Base baseline using timm.
"""
from .common import TimmClassifier
class DeiTBaseClassifier(TimmClassifier):
def __init__(self, num_classes: int = 8, pretrained: bool = True, model_name: str = "deit_base_patch16_224"):
super().__init__(model_name=model_name, num_classes=num_classes, pretrained=pre... |
605b4d57b6be42e02ad4d56d6f79764020bba4f3fb027f3d5bfb4ba36333991c | Python | 330 | 9 | #
# Copyright 2017-2023 Sandia Corporation. Under the terms of Contract DE-AC04-94AL85000 with
# Sandia Corporation, the U.S. Government retains certain rights in this software.
#
# See LICENSE for full license details
#
# Path to the full size ImageNet validation JPEG images
imagenet_val_path = "/path/to/Imag... |
07c3bc2f1cbe4c94717b88853a638efb24c5cfdf90b54afbe80668f3701d8526 | Python | 332 | 15 | from .._base import BaseExampleModel, DAGMixin
class Mediator(DAGMixin, BaseExampleModel):
"""
Simple mediator DAG (X -> I -> Y, X <- Z -> I .
"""
_tags = {
"name": "dagitty/mediator",
"n_nodes": 4,
"n_edges": 5,
"is_parameterized": False,
}
data_url = "dags/me... |
e1dc8e105a2d7ad47a16e6e4fcf82df518e08f0542462b395b24bce74abc5fcc | Python | 333 | 10 | """
MaxViT-Large baseline using timm.
"""
from .common import TimmClassifier
class MaxViTLargeClassifier(TimmClassifier):
def __init__(self, num_classes: int = 8, pretrained: bool = True, model_name: str = "maxvit_large_tf_224"):
super().__init__(model_name=model_name, num_classes=num_classes, pretrained... |
12b9264e05610c58089e4cd920dccdc27641c537e1c9bb11a76b5b342fdc0fe6 | Python | 335 | 23 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from vlpp.realign import Realign
def main():
pet = "${pet}"
params = {
"petPath": "${pet}",
"dataset": "${dataset}",
"ignore": "${realignParams.ignore}",
}
realign = Realign(**params)
realign.run()
if __name__ == '__main_... |
069be8452b7c70ec6969df409d973efd67c80bd2e1c791fe9102c335d53a9767 | Python | 336 | 10 | from __future__ import annotations
from poetry.console.logging.formatters.builder_formatter import BuilderLogFormatter
FORMATTERS = {
"poetry.core.masonry.builders.builder": BuilderLogFormatter(),
"poetry.core.masonry.builders.sdist": BuilderLogFormatter(),
"poetry.core.masonry.builders.wheel": BuilderLo... |
398c95c4c176cc284e5c2917baa43ce51fd83477acc2800b696da3f6c964949d | Python | 336 | 21 | """
Example
=================================
One-line description
"""
import matplotlib.pyplot as plt
import numpy as np
x = 'hello_world'
########################################
# This is rendered text, and will break apart a notebook cell
y = 'foo'
print(x)
plt.plot(np.random.rand(100), np.random.rand(100), ... |
1047e9b18da733ba5e11f05253c6830581c8fd6c882b1034aafa197553bbddbf | Python | 338 | 11 | import pytest
from pgmpy.structure_score import BDs
class TestBDs:
def test_score(self, bds_df, bds_models):
m1, m2 = bds_models
scorer = BDs(bds_df, equivalent_sample_size=1)
assert scorer.score(m1) == pytest.approx(-36.82311976667139)
assert scorer.score(m2) == pytest.approx(-45... |
1445e924d70ec9e19b408e9392c131ab285b0963933fe98085bedc536fd52810 | Python | 338 | 15 | from .._base import BaseExampleModel, BIFMixin
class Cng(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/cng",
"n_nodes": 86,
"n_edges": 85,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_url ... |
1f93767b9ff5d1a02a60afe9e4b095bd8a572a5b393f65e396dfc22886c19240 | Python | 338 | 15 | from .._base import BaseExampleModel, BIFMixin
class Tbm(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/tbm",
"n_nodes": 10,
"n_edges": 15,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_url ... |
232239c72037776322532336e2f0b43bc1d5853bb1e6d50715da3a461b0e436c | Python | 338 | 15 | from .._base import BaseExampleModel, BIFMixin
class Ets(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/ets",
"n_nodes": 20,
"n_edges": 32,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_url ... |
3c79df6777b4d69922c67153be66236aa4b8ff751b3ce97b1e705f26bbe9c318 | Python | 339 | 15 | from .._base import BaseExampleModel, BIFMixin
class Soil(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/soil",
"n_nodes": 6,
"n_edges": 6,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_url ... |
4639b494863af3a2901b5b4d20507ba031a87e14988c6e04432ee6a089246d2a | Python | 339 | 17 | """Smoke tests: verify all four packages can be imported cleanly."""
def test_import_microcircuits():
import microcircuits # noqa: F401
def test_import_neuralsampling():
import neuralsampling # noqa: F401
def test_import_stddc():
import stddc # noqa: F401
def test_import_symmnet():
import sym... |
572fac3576ab35ec887edb095940a152a4e7e61ccce211509c2a915de3b4028b | Python | 339 | 9 | # 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 .qact import ActivationQuantizer # NOQA
from .qconv import IntConv2d # NOQA
from .qemb import IntEmbedding # NOQA
from .qlinear import... |
7844e39c3f38e090cb277b1cc9e0319b01dc18a69fb3199761856a34f723f636 | Python | 339 | 15 | from .._base import BaseExampleModel, BIFMixin
class Bank(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/bank",
"n_nodes": 4,
"n_edges": 5,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_url ... |
8d9025a3797a7102d0e094d860ec9abe3f4a2e7b4390d17a7e01bbe4db1b45ad | Python | 339 | 15 | from .._base import BaseExampleModel, BIFMixin
class Chds(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/chds",
"n_nodes": 4,
"n_edges": 3,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_url ... |
fdcb4fcbe8c60773d9bec368053cd77ddc86c3c816fcafdbf5a0064fe2a00d80 | Python | 339 | 15 | from .._base import BaseExampleModel, BIFMixin
class Asia(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/asia",
"n_nodes": 8,
"n_edges": 8,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_url ... |
1e4ac4700d97bb09a44933953fa6eea6c90ca00c6b975d699415c1ff6d1837fe | Python | 341 | 15 | from .._base import BaseExampleModel, BIFMixin
class Fire(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/fire",
"n_nodes": 11,
"n_edges": 14,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_ur... |
75613ce66cd289cd9df32b1f19a5628fcb8031b61e3b6dfb8b4c43c81bd379f8 | Python | 341 | 13 | from .base import factor_divide, factor_product, factor_sum_product
from .FactorDict import FactorDict
from .FactorSet import FactorSet, factorset_divide, factorset_product
__all__ = [
"FactorSet",
"factorset_divide",
"factorset_product",
"factor_product",
"factor_divide",
"factor_sum_product",... |
76a08484a79268a7b38e5faa17669654859ab980c015a0e09167061aa8af4275 | Python | 341 | 15 | from .._base import BaseExampleModel, BIFMixin
class Adhd(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/adhd",
"n_nodes": 19,
"n_edges": 18,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_ur... |
f26cf382e4b4ab9de0fdd70c8977cefe01d42a92e5a2d35daa7f634f8c941123 | Python | 341 | 15 | from .._base import BaseExampleModel, BIFMixin
class Case(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/case",
"n_nodes": 23,
"n_edges": 35,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_ur... |
46f8a3f9a031058eb9bc2fd12bc44713ae792991c58bdbc33f79fad84a04ba7d | Python | 342 | 15 | from .._base import BaseExampleModel, BIFMixin
class Ricci(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/ricci",
"n_nodes": 6,
"n_edges": 6,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_ur... |
53b7625b7c014d3f9df61e44d0b8b5db6564d76d32cc5186d7a0ad64fe566d1c | Python | 342 | 15 | from .._base import BaseExampleModel, BIFMixin
class Metal(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/metal",
"n_nodes": 8,
"n_edges": 7,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_ur... |
918330819aa50a2dc889beda139c73f37592e49d328f6afc0dbf0e6cd652257b | Python | 342 | 15 | from .._base import BaseExampleModel, BIFMixin
class Beam2(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/beam2",
"n_nodes": 6,
"n_edges": 5,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_ur... |
bbca7b3d90d6424f6f6dba92fc407a77a16387f2e999b5a764f1fa0772662e74 | Python | 342 | 15 | from .._base import BaseExampleModel, BIFMixin
class Lidar(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/lidar",
"n_nodes": 9,
"n_edges": 8,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_ur... |
db9f36c2e1dc63f655bfef321a433f619131f33ad799021f4a4e76abcfb8446f | Python | 342 | 15 | from .._base import BaseExampleModel, BIFMixin
class Wheat(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/wheat",
"n_nodes": 5,
"n_edges": 4,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_ur... |
eb81c002a8f0ffd9ac718b1ce7d17a529b461766e5ddf4b3411ad285628611f7 | Python | 342 | 15 | from .._base import BaseExampleModel, BIFMixin
class Beam1(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/beam1",
"n_nodes": 6,
"n_edges": 5,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_ur... |
f964d968623639bdbb7be8082a456e31e992ddde3069e106a5ffdd2e75d3ba38 | Python | 342 | 17 | from .._base import BaseExampleModel, DAGMixin
class MBias(DAGMixin, BaseExampleModel):
"""
References
----------
- :footcite:t:`acid_decampos_1996`
"""
_tags = {
"name": "dagitty/m_bias",
"n_nodes": 5,
"n_edges": 5,
"is_parameterized": False,
}
data_ur... |
0afdafcad8f1eaaba5fb7a6b7a1fe8f58c466b811a98e27c8b71f29c9244992e | Python | 344 | 15 | from .._base import BaseExampleModel, BIFMixin
class Flood(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/flood",
"n_nodes": 22,
"n_edges": 42,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_... |
bb2236c6a6bf1497aa4eced9ef965474b199cd28b310cba7e449e697d1c02c1d | Python | 344 | 15 | from .._base import BaseExampleModel, BIFMixin
class Pilot(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/pilot",
"n_nodes": 42,
"n_edges": 41,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_... |
0dddc66706dc05c6df128545e3329211d0a46620dd789b43d7a7aead0d977e68 | Python | 345 | 15 | from .._base import BaseExampleModel, BIFMixin
class Theft1(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/theft1",
"n_nodes": 4,
"n_edges": 3,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_... |
0f2d29fc6f5dc9633b294598095d560e37e6f739425ffa2e0f030d75f5fb6b0e | Python | 345 | 16 | # 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 .loss import *
from .nce import *
try:
from .fairseqmmloss import *
except ImportError:
pass
try:
from .expnce import *
exce... |
26d4d3773bc4645da8087db41a57ebde750766a28deaac7eb686fcfa91cda06a | Python | 345 | 15 | from .._base import BaseExampleModel, BIFMixin
class Crypto(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/crypto",
"n_nodes": 6,
"n_edges": 4,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_... |
493f8159b96f1e6e070acdf361efec3b87f0db15a34a64e88b1410621c7c27b6 | Python | 345 | 15 | from .._base import BaseExampleModel, BIFMixin
class Algal1(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/algal1",
"n_nodes": 9,
"n_edges": 9,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_... |
7c5a39cd5b94d81ca53c968a8d9d368dc8c6684b09351923b33202a78e8d5314 | Python | 345 | 15 | from .._base import BaseExampleModel, BIFMixin
class Bullet(BIFMixin, BaseExampleModel):
_tags = {
"name": "bnrep/bullet",
"n_nodes": 5,
"n_edges": 7,
"is_parameterized": True,
"is_discrete": True,
"is_continuous": False,
"is_hybrid": False,
}
data_... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.