sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
f0c285a1a549082730bdfa441081cc71d6a0635214f69162f1a57c141f34ccbd | Python | 220 | 10 | import gl
ksteps = 100
gl.resetdefaults()
gl.meshload('stroke.mz3')
gl.trackload('stroke.trk.gz')
gl.trackprefs(15, 3, 0.5)
for i in range(1, ksteps):
gl.clipazimuthelevation(( (0.8*i)/ksteps ), 0, 130)
gl.wait(20)
|
ae0070a0e6804908788d788db05d64d6385d9d0236ed19abc22371ad3d774ac3 | Python | 221 | 6 | # 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 .utils import quantize_model_ # NOQA
|
0948a8c06f0e653ced4d36b53e1ede6bbd08872a455d2691fe9cfa0960d02992 | Python | 222 | 8 | import pytest
from scgpt import __version__
@pytest.mark.skip(reason="Legacy version pin for 0.1.0 tutorials; package is now 0.2.5+")
def test_version():
# legacy package name check
assert __version__ == "0.1.0"
|
d7d3b3b2617bf466bddf6766dad96dbd73c380c1e221267141ec9ed74f70c78b | Python | 222 | 9 | #this is the NFT package!
# from modules import NFTDetector
# from nft_datasets import ROIDataModule, GutmanROIDataModule
# # __all__ = [
# # 'NFTDetector',
# # 'ROIDataModule',
# # 'GutmanROIDataModule'
# # ] |
0fd159ccc5ab9050a9c748f5372cf641cacabd78dbbdf85947dd1745e0c5b237 | Python | 224 | 11 | from .crossentropy import CrossEntropyLoss
from .dice import DiceLoss, dice_loss
from .umap import UmapLoss, umap_loss
__all__ = [
"CrossEntropyLoss",
"DiceLoss",
"dice_loss",
"UmapLoss",
"umap_loss",
]
|
4d070b84eb7e4a7b9999cb51dc7d809f889556583ec7c10b3cda11145c94dddd | Python | 224 | 6 | # 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 .models import linformer_roberta # noqa
|
11e8bede9b8aedbcbd00967b3adc79dd3610c907d78fc167b36e6c89e4bd1417 | Python | 226 | 6 | # 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 tasks, criterions, models # noqa
|
dee9d646980fcc335c68ae62239c09073a62486b94a2b23b93d25b9863b45ae9 | Python | 227 | 6 | #
# 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
#
|
0017a446c8fcbc713896ea7e74614a7d92cb94540588a1ecf7fabb4d5ddce7ae | Python | 228 | 6 | # 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 criterions, models, modules # noqa
|
9059819a560e325074e2697cc91f79b459d4be424d9ca1d3a8c8058cf9797b1c | Python | 228 | 7 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tools to classify pyknotic nuclei"""
from .transformer import LinearTransformer, get_features_multiple_cells # noqa F401
from .classifier import PolygonClassifier # noqa F401
|
c382257fec41061b4d207c88797c8455de9430e888bde295aeee24d58388fdb0 | Python | 228 | 5 | from .construct_model import construct_model
from .model_train import train
from .input_data_from_mesh import prep_input_data
from .utils import DistanceFunction
from .plotting import plot_mesh, plot_field, img_plot, plot_energy |
dff5ed3dc2e3eaf06f788192a8e5f4f8f7ebc88ca158b960425d4928daeb09b5 | Python | 228 | 8 | from paper2.detect import choose_threshold
from paper2.loaders import load_coffee
def test_threshold_in_scan_range():
ds = load_coffee()
t = choose_threshold(ds, preprocess="snv", seed=0)
assert 85.0 <= t <= 100.5
|
f91c196ce737ab762c980e56a4eda5667aeb7a4cc5a5b9880898233c95bf2ee9 | Python | 228 | 6 | from . import constants, helper
from .infer_datapipe import InferDatapipe
from .metadata import Metadata
from .train_datapipe import TrainDatapipe
__all__ = ["constants", "helper", "Metadata", "InferDatapipe", "TrainDatapipe"]
|
98f51a1d95df7b96c574de4a6bfb35cfae230751786c4125e1e2ed584b4b23d1 | Python | 229 | 7 | from msi_visual.base_dim_reduction import BaseDimReductionWithoutFit
import trimap
class Trimap3D(BaseDimReductionWithoutFit):
def __init__(self):
super().__init__(model=trimap.TRIMAP(n_dims=3), name="Trimap") |
553b3cadc233c0785a75bd0ea90d1971b9d2a8526ab2f33a5f119c753d6e4d44 | Python | 230 | 7 | import os
from tractseg.experiments.base_legacy.tract_seg_lowres_legacy import Config as TractSegConfig_LowRes
class Config(TractSegConfig_LowRes):
EXP_NAME = os.path.basename(__file__).split(".")[0]
INPUT_DIM = (80, 80) |
6398926a6b31e9f85038b27d4329940adbf81c7d8e9d6edf15e70e69d2356619 | Python | 230 | 6 | # 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 .lightconv_layer import LightconvLayer # noqa
|
2b06b221403abb412f807b04996f7f4a62b0186438b221bfdcf752ae8ee12da9 | Python | 232 | 4 | from pytorch_grad_cam.utils.image import deprocess_image
from pytorch_grad_cam.utils.svd_on_activations import get_2d_projection
from pytorch_grad_cam.utils import model_targets
from pytorch_grad_cam.utils import reshape_transforms
|
1166e4cdfc3f9692e413b3161310a270b2a93d56c4dc85383c57d9a49da5b896 | Python | 233 | 6 | # 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 .multichannel_sequence_generator import * # noqa
|
65b9ef29e99ab9f4c639ba0cede733fd9b7a9e34e325ba4eed345baa255e0b5a | Python | 234 | 6 | # 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 .dynamicconv_layer import DynamicconvLayer # noqa
|
83037e0df5e09a6a848d008cffb59ad783e14f50e81fe5847067a23806f1d086 | Python | 234 | 2 | from .special_metrics import jensen_shannon_distance, jensen_shannon_divergence, kullback_leibler, multinomial_distance, multinomial_subspace_distance, stabilized_minkowski, minkowski10
from .poisson_proximity import PoissonProximity
|
e4c05c112baf50e24438ee2387809f0767f28df921fec31b09a3e9c9c3f23565 | Python | 235 | 9 | from .base import BayesianModelInference, _return_samples
from .Sampling import BayesianModelSampling, GibbsSampling
__all__ = [
"BayesianModelInference",
"_return_samples",
"BayesianModelSampling",
"GibbsSampling",
]
|
90e2b52d267aa1cda6007ea4134afb3fe43624d8c2a377652ca43597bb255856 | Python | 236 | 12 | from mdt import CompartmentTemplate
__author__ = 'Robbert Harms'
__date__ = "2015-06-21"
__maintainer__ = "Robbert Harms"
__email__ = "robbert@xkls.nl"
class Dot(CompartmentTemplate):
parameters = ()
cl_code = 'return 1.0;'
|
f042f38def442c3623801206209a34be76a15f516f55e85d238a0defd86c71e9 | Python | 236 | 14 | from ._base import (
BaseDataset,
list_datasets,
load_dataset,
)
__all__ = [
"BaseDataset",
"BaseCovarianceDataset",
"BaseSimulatedDataset",
"BaseTubingenDataset",
"load_dataset",
"list_datasets",
]
|
84ca8a1a68e6e4766d155dcf54b9b1f93505f02ef9b2cce89cc2a4a10cfb7a68 | Python | 237 | 11 | import torch
class ModuleListInfo(torch.nn.ModuleList):
def __init__(self, info_str, modules=None):
super().__init__(modules)
self.info_str = str(info_str)
def __repr__(self):
return self.info_str |
1f620b0a34853f0d10cb2abbf0e23eac2678e025fdb0eeef1d7ad25d9c8e4a79 | Python | 238 | 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]
FEATURES_FILENAME = "T1"
NR_OF_GRADIENTS = 1
|
f5fb702e7d964d844237bda2e39a1870358439c5615336d50df4f2155a5c565d | Python | 238 | 6 | from msi_visual.base_dim_reduction import BaseDimReductionWithoutFit
from sklearn.manifold import MDS
class MDS3D(BaseDimReductionWithoutFit):
def __init__(self):
super().__init__(model=MDS(n_components=3), name="MDS3D") |
9545b35a656c07b55fa3c8874e8dd290b5bc409ab671cd5b55b807c6bea92b9f | Python | 239 | 12 | from mdt import CompartmentTemplate
__author__ = 'Robbert Harms'
__date__ = "2015-06-21"
__maintainer__ = "Robbert Harms"
__email__ = "robbert@xkls.nl"
class S0(CompartmentTemplate):
parameters = ('s0',)
cl_code = 'return s0;'
|
c4805c6f30f5f9b807aafc199e9e646e13cfdd7cc561974dabf358f724357fba | Python | 239 | 13 | """command-line interface functions for training,
creating learning curves, etc."""
from . import cli, eval, learncurve, predict, prep, train
__all__ = [
"cli",
"eval",
"learncurve",
"predict",
"prep",
"train",
]
|
0a39f7d82b1186eefd02cfb92e94e7962487752eadbf51395a2ddeb3717fc017 | Python | 241 | 9 | import os
from tractseg.experiments.base_legacy.endings_seg_legacy import Config as EndingsSegConfig
class Config(EndingsSegConfig):
EXP_NAME = os.path.basename(__file__).split(".")[0]
NUM_EPOCHS = 500
DATA_AUGMENTATION = True |
95c999b94a89151603eab038f53bfb50d8fd3f8c910660cd57c7b53bd3bc9866 | Python | 241 | 5 | from neurovfm.data import CacheManager
data_dir = "/nfs/turbo/umms-tocho-snr/exp/akhilk/torchmr/raw_data/mri" # the same as data.data_dir
cache = CacheManager(data_dir)
cache.build_cache(num_workers=8) # or more, depending on your machine |
92c3f6bc8fceea5f85d26b4584e87d2f2618ca3dafda9e3a12c70bcc41a3d7dd | Python | 242 | 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.
from .hubert import * # noqa
from .hubert_asr import * # noqa
|
d2a1788b9ecd34ad86a8e3db928cf8aa2686e1a491b087c6ec79343dfe90fe9e | Python | 242 | 6 | from msi_visual.base_dim_reduction import BaseDimReductionWithoutFit
from sklearn.manifold import TSNE
class TSNE3D(BaseDimReductionWithoutFit):
def __init__(self):
super().__init__(model=TSNE(n_components=3), name="TSNE3D") |
e2e0fca50e2de8345d550ef78403e1c3b503f27f802f38ad448908b2b54de30f | Python | 242 | 11 | import random
import numpy as np
# from theano.tensor.shared_randomstreams import RandomStreams
def set_random_seeds(random_seed):
# srng = RandomStreams(seed=random_seed)
random.seed(random_seed)
np.random.seed(random_seed)
|
45c03d8221be761728c67d74e0f668dd372088f223312c000bb47154e93996c9 | Python | 243 | 5 | from ._graph import _domains_hierarchy, paga, connectivities
from ._spatial import domains, spatially_variable_genes
from ._heatmap import _weights_clustermap, pathway_scores
from ._bar import domains_proportions
from ._line import loss_curve
|
a1358e7a9495e136e8b2bb5304d7cb9241d779502ac977bfbfc6f2308ed0c86c | Python | 244 | 11 | # 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 .wav2vec_u import Wav2vec_U
__all__ = [
"Wav2vec_U",
]
|
a8f068425a889de384c31e47569bf1f30de149105870b3a171cc274d98f827bf | Python | 244 | 9 | from __future__ import annotations
pytest_plugins = [
"tests.repositories.fixtures.distribution_hashes",
"tests.repositories.fixtures.legacy",
"tests.repositories.fixtures.pypi",
"tests.repositories.fixtures.python_hosted",
]
|
fc1af84e95eaf03c37436558eb896441a28a0c5fc4226b37806c3b6ff149c6d9 | Python | 244 | 12 | from . import learncurve
from .annot import annotation, plot_labels, plot_segments
from .spect import spect, spect_annot
__all__ = [
"annotation",
"learncurve",
"plot_labels",
"plot_segments",
"spect",
"spect_annot",
]
|
fde6f7d38d106cd84f4fc20a2cbd10b6f859c748db1eafc4280bf1f9f6582381 | Python | 244 | 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.
from .hub_interface import * # noqa
from .model import * # noqa
|
354dc16ee6525efa9b847bf278f65d07528fabe6217557e24204cc0cf4a5a8ba | Python | 245 | 10 | import pytest
@pytest.fixture
def events_path(generated_results_data_root):
events_paths = sorted(
generated_results_data_root.joinpath("train").glob("**/*events*")
)
assert len(events_paths) > 0
return events_paths[0]
|
46e8cb99dff8bea09ce79deb354520dfb321e8c3629ce5c0b84b769e48067ad7 | Python | 245 | 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.
from . import wsc_criterion # noqa
from . import wsc_task # noqa
|
a57eca0edf6f075d2fa89e8232623cf691f61f21e45dba7975c3cbd5a400dc4d | Python | 245 | 6 | # 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 transformer_xl_model, truncated_bptt_lm_task # noqa
|
32c2ff64e7d367859e3da19761bf7bdb8ca0f7f7c6069d3067460d0130e752ee | Python | 247 | 9 | import os
from tractseg.experiments.base_legacy.peak_reg_legacy import Config as PeakRegConfig
class Config(PeakRegConfig):
EXP_NAME = os.path.basename(__file__).split(".")[0]
FEATURES_FILENAME = "270g_125mm_peaks"
NUM_EPOCHS = 150
|
9cf87b9f5028a930e0cf000d621fdafcc066b62a3090dae26894a66ee43bddd1 | Python | 247 | 8 | #
# 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 .dnn.dnn import DNN
|
050ceacff0756a78c8c02607e2c167384b2cb32ebd07cab51821910d04de116b | Python | 248 | 11 | # 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 .asr_dataset import AsrDataset
__all__ = [
"AsrDataset",
]
|
13cd784edcab32644886f325241d6029f53b81559eced6400f153ff15f5e166d | Python | 248 | 10 | from .energy import HARTREE_TO_KCAL, get_errors, relative_energy, variance_extrapolation
from .h5_io import read_result
__all__ = [
"read_result",
"variance_extrapolation",
"relative_energy",
"get_errors",
"HARTREE_TO_KCAL",
]
|
537caa25265c2ec0f3207781d8347586f0875091c6acc9bf2ffbd95dde5af116 | Python | 248 | 6 | from msi_visual.base_dim_reduction import BaseDimReductionWithoutFit
from sklearn.manifold import Isomap
class Isomap3D(BaseDimReductionWithoutFit):
def __init__(self):
super().__init__(model=Isomap(n_components=3), name="Isomap") |
53d5e7fcc373cb28304f0be24c8f164cc7daa2036e6a92f0f788d992c644fc3d | Python | 248 | 16 | """
All loss functions here
"""
import torch
def simple_MSE_loss(pred, label):
"""
Simple MSE loss
"""
loss = torch.nn.MSELoss()
return loss(pred, label)
def sparsity_loss(activations):
return torch.norm(activations, 1)
|
015eedddc503366e9ae89b4e7314d290ecbb39a3f32945b644e512b9f43bc1c7 | Python | 249 | 7 | name = 'fissa'
path = name
version = '0.7.dev0'
author = "Sander Keemink & Scott Lowe"
author_email = "swkeemink@scimail.eu"
description = "A Python Library estimating somatic signals in 2-photon data"
url = "https://github.com/rochefort-lab/fissa"
|
1b4dae94924afcdb5360bb75ae02aee3d64e1707bdbe57a08d791a2a008fa4b2 | Python | 249 | 9 | __version__ = "0.1.0"
import logging
import sys
logger = logging.getLogger("scGPT-spatial")
from . import model, tokenizer, utils, tasks
from .data_collator import DataCollator
from .data_sampler import SubsetsBatchSampler
from .preprocess import * |
3402d038ed18d230672cb12b0bc526d02fd0e5e2ad299eea1bc067c3316eeaaf | Python | 249 | 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.
from .speech_dlm import * # noqa
from .hub_interface import * # noqa
|
54be84fa19ded249cd4d232fef8cabce101d8fa8955adb863ac839851ed0e78a | Python | 249 | 7 | from .surface_plotting import build_plotter, plot_surf, plot_cortical, plot_subcortical, plot_hippocampal
__all__ = ['build_plotter',
'plot_surf',
'plot_cortical',
'plot_subcortical',
'plot_hippocampal']
|
ff5d3bf7ba9efa9765f7f372eb4a02c151da5064572a9c39570adfff30f878f6 | Python | 249 | 13 | from . import cmacbench
from .cmacbench import CMACBench, SplitsMetadata
from .get import get
__all__ = [
"cmacbench",
"CMACBench",
"get",
"SplitsMetadata",
]
# TODO: make this a proper registry
DATASETS = {"CMACBench": CMACBench}
|
f3a2cc28e4f8e2ac0d258bcb6b742589f499a81a35ae2cdfcf0e605c8a1e344d | Python | 251 | 9 | # -*- coding: utf-8 -*-
# Copyright (C) 2017-2023 Phillip Alday <me@phillipalday.com>
# License: BSD (3-clause)
"""Philistine: a collection of Phillip's utility functions"""
__version__ = '0.2.0'
from . import mne
from ._base import (invert_dict, )
|
69a0ae5a1cb661c861fc146517a2a71528883c8b7d3e46fb4450a1b25f809a2e | Python | 252 | 14 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from vlpp.operation import maskFromAtlas
def main():
output = "${participant}_roi-${refName}${suffix.mask}"
maskFromAtlas("${atlas}", ${indices}, output)
if __name__ == '__main__':
main()
|
7ec215134db2349e0cb1869b34d2255ca301111c814f9cc322d86b6e33b62785 | Python | 252 | 9 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
from tractseg.data import dataset_specific_utils
from tractseg.experiments.dm_reg import Config as DmRegConfig
class Config(DmRegConfig):
EXP_NAME = os.path.basename(__file__).split(".")[0]
|
068b92f3e347930b1649f82bd5714ba683f6bc858f3ba916bdf64dd4cf3e198c | Python | 253 | 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.
from .model import * # noqa
from .transformer_layer_xmod import * # noqa
|
7b2d7e3160e331cc129902a8456fd6cb31b2a7e63efe838877d41dcb07fe7883 | Python | 253 | 12 | from mdt import CompartmentTemplate
__author__ = 'Robbert Harms'
__date__ = "2015-06-21"
__maintainer__ = "Robbert Harms"
__email__ = "robbert@xkls.nl"
class Ball(CompartmentTemplate):
parameters = ('b', 'd')
cl_code = 'return exp(-d * b);'
|
5a22b99c8b6e3ea1db9bdf6318733db3a6152214b1e5c2ee9fe55197d2ab373a | Python | 254 | 11 | from pyro.infer import Predictive
from ._decorators import auto_move_data
class AutoMoveDataPredictive(Predictive):
"""Auto move data."""
@auto_move_data
def forward(self, *args, **kwargs):
return super().forward(*args, **kwargs)
|
8da691efbf00f1022fffd2a1cf73f0b88c533873aea81141665ea9bfd38c9d4b | Python | 254 | 12 | """Utility to set random seeds for reproducibility."""
import random
import numpy as np
import torch
def set_seed(seed: int = 42) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
|
c1cfab44f8cf7bbbcba294cb50316623905e34b08c6656abc047bcf192f004f4 | Python | 254 | 11 | import pytest
from click.testing import CliRunner
from chromas import cli
def test_version_command():
runner = CliRunner()
result = runner.invoke(cli, ["version"])
assert result.exit_code == 0
assert "Chromas version" in result.output
|
e6b9ecbf6e9c7d4ca68f9c517035e5978907c3e2fb498a268c8e558960889ad0 | Python | 254 | 7 | from lucent.optvis.param.images import image
from lucent.optvis.param.spatial import *
from lucent.optvis.param.lowres import *
from lucent.optvis.param.cppn import *
from lucent.optvis.param.color import *
from lucent.optvis.param.gan import upconvGAN
|
88619d8d2f7c33ce88dc2e113a2cb76059461b6d84d37d9bb8e0c4682bfef9a7 | Python | 255 | 9 | from .DoubleMLRegressor import DoubleMLRegressor
from .NaiveAdjustmentRegressor import NaiveAdjustmentRegressor
from .NaiveIVRegressor import NaiveIVRegressor
__all__ = [
"NaiveAdjustmentRegressor",
"NaiveIVRegressor",
"DoubleMLRegressor",
]
|
e254c5f7ca666bef73ef65d3bf6e44453b22b762ceabba855303ae53cdbb1e4c | Python | 255 | 10 | # 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 .mm import *
try:
from .expmm import *
except ImportError:
pass
|
78b93133f6a8149321dd4bd2da3d6305e710ccae35ad6bda4bc63a4762cde7db | Python | 257 | 8 | #
# 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 .device import Device
|
bb5e4fde1c76be4785a66710057a335f4b63d669fc738bf0f42717aa145322c7 | Python | 257 | 6 | # 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 .utils import SizeTracker, get_param, attrsetter, quantize_model_ # NOQA
|
086e9cad60c878f885b2f523eec1202997aeb20d797da3fe098db35c08afe8b8 | Python | 258 | 7 | """OCE — Orbital Cluster Expansion.
Cluster expansion adapted to molecular systems where the configuration
variable on each "site" is the atomic-orbital eigenenergy ε_μ^Z, and
figures are subgraphs of the molecular bond graph.
"""
__version__ = "0.1.0"
|
20bfba1739d09f0275efbbc2cf9f56f14926cefc975b57017a459df9b4adecc2 | Python | 258 | 10 | 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 = 500
DATA_AUGMENTATION = True
USE_DROPOUT = True |
5edb6875fb8959b7877fe5eb54a4fb6bac2e4ea19580e5174156bc2fe57371bd | Python | 258 | 6 | # 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 label_smoothed_cross_entropy_r3f, sentence_prediction_r3f # noqa
|
dd656c9aa61145c26bb14390237cde0f56ffdea37f15f6fa2682801c3214b42d | Python | 258 | 8 | #
# 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 .backend import ComputeBackend
|
628b707b9149e6bff5b363c49da8213ac465b0c60b4b12ecaf3eb6f89d3ebd6a | Python | 259 | 6 | from msi_visual.base_dim_reduction import BaseDimReductionWithoutFit
from sklearn.decomposition import FastICA
class FastICA3D(BaseDimReductionWithoutFit):
def __init__(self):
super().__init__(model=FastICA(n_components=3), name="FastICA3D") |
f8b86a46cb08bdf2cc819eaa27d09748e942b9fd4fe9112d3abc47399aeeed29 | Python | 259 | 7 | """Module that contains datapipe classes,
used to load inputs and targets for neural network models
from datasets prepared by :func:`vak.prep.prep`"""
from . import frame_classification, parametric_umap
__all__ = ["frame_classification", "parametric_umap"]
|
3ab305ef213961dc986b087188557b8f79d600339487728c7222378b99e37f7f | Python | 260 | 13 | from . import conv_encoder, ed_tcn, tweetynet
from .conv_encoder import ConvEncoder
from .ed_tcn import ED_TCN
from .tweetynet import TweetyNet
__all__ = [
"conv_encoder",
"ConvEncoder",
"ed_tcn",
"ED_TCN",
"tweetynet",
"TweetyNet",
]
|
d32af8712e47989d334b7bcfa321d3f90445178ab4f29481fc385edc5bcd38f1 | Python | 260 | 15 | """Module providing utilities for brenda_references"""
from .utils import (
CachingMiddleware,
abbreviate_bacteria,
fuzzy_find_all,
ratio,
)
__all__ = [
"CachingMiddleware",
"abbreviate_bacteria",
"fuzzy_find_all",
"ratio",
]
|
a3b71c7ed6de0be5d1d92be9e92e4696b167f6bd36aca7180b2ed2928402c325 | Python | 261 | 11 | import pandas as pd
dat = pd.read_csv("./beta.csv")
all_seqs = []
for i in range(len(dat)):
seq = dat["cdr3b"][i]
all_seqs.append(">Sequence" + str(i) + "\n" + seq)
f = open("./tcrdb_tcrs.fasta", 'w')
f.write("\n".join(all_seqs))
f.close()
|
601655a013ce5c6d28884216dd0528ba4f62e8e5ef7c326d4295e4a22085f477 | Python | 262 | 8 | from __future__ import annotations
from poetry.installation.operations.install import Install
from poetry.installation.operations.uninstall import Uninstall
from poetry.installation.operations.update import Update
__all__ = ["Install", "Uninstall", "Update"]
|
e6bb254a5140127f26da9bc660b0d9d3076e1c2fbd44725dc8e495f50f90a6c3 | Python | 262 | 4 | # Tools for managing data in The GOA database: Gene Ontology annotation (GOA)
# resource (http://www.ebi.ac.uk/GOA) resource (http://www.ebi.ac.uk/GOA)
# https://academic.oup.com/nar/article/43/D1/D1057/2437623/The-GOA-database-Gene-Ontology-annotation-updates
|
c1a4a7f7048bd8f0627d08ce463b4eb01cac2394a2afb2e959e1dff2c751c160 | Python | 263 | 8 | from __future__ import annotations
def get_request_title(request: str, implementation: str, free_threaded: bool) -> str:
add_info = implementation
if free_threaded:
add_info += ", free-threaded"
return f"<c1>{request}</> (<b>{add_info}</>)"
|
a31540ebfb54a0a28e318d09cfaff5dac45be5d16dd0d8c83521cf7169c61f3c | Python | 264 | 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.
try:
from fairseq.version import __version__ # noqa
except ImportError:
pass
|
c6c154af24ec3782adad7e066b2e4607c69ddc6be13f15a86fec84e31d273300 | Python | 265 | 11 | from .metrics import eval_metrics, plot_eval_metrics
from .trainer import GarfieldTrainer
from .utils import EarlyStopping, print_progress
__all__ = [
"eval_metrics",
"plot_eval_metrics",
"GarfieldTrainer",
"EarlyStopping",
"print_progress",
]
|
59790b35d1d6664ef79ef22cbf966895d73db2eccc924a24819d1f785966798a | Python | 266 | 10 | # -*- coding: utf-8 -*-
import os
import sys
def entrypoint_exists(entry_point):
if sys.platform == "win32":
entry_point += ".exe"
executable_dir = os.path.dirname(sys.executable)
return os.path.exists(os.path.join(executable_dir, entry_point)) |
f0ed30569cb5907807cd91a220736bafcb3b22efe074a6bcab00bc36d8755c01 | Python | 267 | 11 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
class IncompatibleSampler(ValueError):
# Raised when sampler type and SDE are incompatible.
pass
class AmbiguousConfig(ValueError):
# Raised when the config is ambiguous
pass
|
6ce46deb11331c55f6d6fbcb1da8027dfcc9ad53b2197d93346f3dc84f82acb6 | Python | 268 | 12 | import gl
gl.resetdefaults()
gl.meshload('BrainMesh_ICBM152.rh.mz3')
gl.overlayload('motor_4t95vol.nii.gz')
gl.overlayminmax(1,2,12)
gl.overlaycolorname(2, 'Red-Yellow')
gl.azimuthelevation(110, 15)
gl.shadername('MatCap');
gl.meshcurv()
gl.overlaytranslucent(2, 1)
|
daebacf2d5f40a4d8946a0451a55c25a149c544d4b92b06929079082e87a9904 | Python | 269 | 9 | """Tasks for GOTerms in obo dag."""
def get_all_parents(go_objs):
"""Return a set containing all GO Term parents of multiple GOTerm objects."""
go_parents = set()
for go_obj in go_objs:
go_parents |= go_obj.get_all_parents()
return go_parents
|
a62b8c18ebb65a2da7f39235c4960381bca1cb49ecb0d523fb841d9ecbba5bb5 | Python | 270 | 11 | # 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 .unpaired_audio_text import UnpairedAudioText
__all__ = [
"UnpairedAudioText",
]
|
e96fd00a1c898023bd706bacbd2122ee0aefc34198a1bd3cbaa5e50ee9706183 | Python | 270 | 9 | from datetime import datetime
from .constants import STRFTIME_TIMESTAMP
def get_timenow_as_str():
"""Returns current time as a string,
with the format specified by ``vak.constants.STRFTIME_TIMESTAMP``"""
return datetime.now().strftime(STRFTIME_TIMESTAMP)
|
a189d81a494ffb24185fb56f0a36c114630efa5d7db38277a30d9ea2aba84074 | Python | 272 | 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
#
from .dac.dac import DAC
from .adc.adc import ADC
|
fe748d4f6ba81557d9484bb748a6b837e46210d5245a957ff380c3764fc87597 | Python | 272 | 12 | __all__ = [
"ATOMIC_RADII",
"KHOT_EMBEDDINGS",
"CONTINUOUS_EMBEDDINGS",
"MAX_ATOMIC_NUM",
]
from .atomic_radii import ATOMIC_RADII
from .continuous_embeddings import CONTINUOUS_EMBEDDINGS
from .khot_embeddings import KHOT_EMBEDDINGS
MAX_ATOMIC_NUM = 100
|
3ed16f27e1850704b306abe9dd07fc4d6e59aa2ea4595545f4d4872fdc7f7846 | Python | 273 | 12 | """Checkpoint save/load utilities."""
import torch
def save_checkpoint(model, path: str) -> None:
torch.save(model.state_dict(), path)
def load_checkpoint(model, path: str, device):
model.load_state_dict(torch.load(path, map_location=device))
return model
|
452dcde0ed8beac250884aa4c9b1b2ce89350a9d3816d73fabd0fb120f5a388c | Python | 273 | 10 | # 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 .mmdataset import *
try:
from .fairseqmmdataset import *
except ImportError:
pass
|
a0d16c486b9d5babd88c979cb31e7e1656d24bdaad508bb5b2e1046e036f8792 | Python | 273 | 8 | import importlib
import os
for file in sorted(os.listdir(os.path.dirname(__file__))):
if file.endswith(".py") and not file.startswith("_"):
task_name = file[: file.find(".py")]
importlib.import_module("examples.speech_recognition.tasks." + task_name)
|
b7ceaeefb739aa5cbe549da0bcbfe3f7c3c853eab00a77a3f830499fd202adae | Python | 273 | 8 | from __future__ import annotations
from poetry.packages.dependency_package import DependencyPackage
from poetry.packages.locker import Locker
from poetry.packages.package_collection import PackageCollection
__all__ = ["DependencyPackage", "Locker", "PackageCollection"]
|
2decec83a5765fe86f967cdd60356590b488884402a8139f9caac9251755d856 | Python | 274 | 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.
from . import sequence_generator # noqa
from . import sequence_generator_multi_decoder # noqa
|
515faab836d99df6e4d8cca5299f8b3cb11059e7a06e04b121788343524247ed | Python | 276 | 8 | import importlib
import os
for file in sorted(os.listdir(os.path.dirname(__file__))):
if file.endswith(".py") and not file.startswith("_"):
model_name = file[: file.find(".py")]
importlib.import_module("examples.speech_recognition.models." + model_name)
|
8939a98cac8e59f2368cd57aa5676ec42a7c69a2c6d2a74b8c667b96036da44f | Python | 276 | 6 | from msi_visual.base_dim_reduction import BaseDimReductionWithoutFit
from sklearn.manifold import LocallyLinearEmbedding
class LLE3D(BaseDimReductionWithoutFit):
def __init__(self):
super().__init__(model=LocallyLinearEmbedding(n_components=3), name="LLE3D") |
9d912a97828bd801dcb0ce71b41059afe7a161418708d9ea27375c4089f8c4cc | Python | 277 | 11 | from .model import (
TransformerModel,
FlashTransformerEncoderLayer,
GeneEncoder,
AdversarialDiscriminator,
MVCDecoder,
)
from .generation_model import *
from .multiomic_model import MultiOmicTransformerModel
from .dsbn import *
from .grad_reverse import *
|
56a7629c768585433793ea0bc417f0c8f2200027295bb1b8ff7ff3c644044583 | Python | 278 | 11 | import gl
gl.resetdefaults()
gl.azimuthelevation(240, 15)
gl.meshload('AICHAhr.lh.mz3')
#gl.atlashide(0,1, 2, 3)
gl.atlasstatmap('AICHAhr.lh.mz3','surficetemp.mz3',(4,5,6),(7,3,4))
gl.meshload('lh.pial');
gl.overlayload('surficetemp.mz3');
gl.shaderxray(1.0, 0.3);
|
6e39f0a977eb8dd72fb43f81a4103a1d9a2a48cfb401404fa938147e8a309253 | Python | 278 | 8 | """DGR — distortion-guided restoration for prostate diffusion MRI.
Two halves:
* ``dgr.physics`` — forward susceptibility-distortion simulator (B0 -> EPI warp)
* ``dgr.models`` / ``dgr.inference`` — the reverse (restoration) network stack
"""
__version__ = "0.1.0"
|
30b9594e92d569342c294ee428eac5535f3c23ffbe15150dd057bc2983dd36f7 | Python | 280 | 10 | import os
from tractseg.experiments.endings_seg import Config as EndingsSegConfig
class Config(EndingsSegConfig):
EXP_NAME = os.path.basename(__file__).split(".")[0]
DAUG_ROTATE = True
SPATIAL_TRANSFORM = "SpatialTransformPeaks"
DAUG_ROTATE_ANGLE = (-0.4, 0.4)
|
657c8b53b320f847e5c53868a0ed934288587659e75406e29def92918b224323 | Python | 280 | 12 | import asyncio
import sys
from multiprocessing import freeze_support
if sys.platform == 'win32':
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
from rnalysis.gui import gui
if __name__ == '__main__':
freeze_support()
asyncio.run(gui.run())
|
f41d37650643a9bff6ff48807db839b102688662995bf12421ab1c06487c3f42 | Python | 281 | 13 | from typing import Union, Optional
import numpy as np
from scipy import stats
def angular_difference(
angle1: Union[float, np.ndarray],
angle2: Union[float, np.ndarray],
):
diff = angle1 - angle2
diff = np.mod(diff + np.pi, 2*np.pi) - np.pi
return diff |
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