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05ce3945246f9874d9f0263c63ef7623b97fd733a7c8a1807582a4735003cce3
Python
229
11
import time class MockSerial(object): def write(self, str): t = time.time() print("%i : MockSerial > %s" % (t,str) ) def close(self): t = time.time() print("%i : MockSerial closed" % t)
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Python
229
14
# -*- coding: utf-8 -*- from . import roles from . import directives def setup(app): roles.setup(app) directives.setup(app) from ._version import get_versions __version__ = get_versions()['version'] del get_versions
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Python
229
6
# pylint: disable=wildcard-import """Video action recognition, HMDB51 dataset. http://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/ """ from __future__ import absolute_import from .classification import *
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Python
229
8
from .models_for_testing import TEN_EPOCHS, ModelForTestingWithStep reconstructed_am = ModelForTestingWithStep( name_prefix="Reconstructed AM", path_prefix="model/msp-284a1", seed=1, step_value=TEN_EPOCHS[15], )
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Python
229
8
from pl_bolts.optimizers.lars import LARS from pl_bolts.optimizers.lr_scheduler import LinearWarmupCosineAnnealingLR, linear_warmup_decay __all__ = [ "LARS", "LinearWarmupCosineAnnealingLR", "linear_warmup_decay", ]
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Python
229
8
import os import dec print 'Building HOG feature extractor...' os.system('python setup_features.py build') os.system('python setup_features.py install') print 'Preparing stl data. This could take a while...' dec.make_stl_data()
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Python
230
11
"""Workflow for anatomical postprocessing.""" from xcp_d.workflows.anatomical import outputs, parcellation, plotting, surface, volume __all__ = [ 'outputs', 'parcellation', 'plotting', 'surface', 'volume', ]
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Python
230
10
import torch from lightly.transforms import IRFFT2DTransform def test() -> None: transform = IRFFT2DTransform((32, 32)) image = torch.rand(3, 32, 17) output = transform(image) assert output.shape == (3, 32, 32)
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Python
230
10
from abc import abstractmethod from rdkit.Chem import Mol from typing import Sequence, Any class MolFeaturizer(): @abstractmethod def featurize_mol(self, mol: Mol) -> Sequence[Any]: pass
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Python
230
6
# pylint: disable=wildcard-import """Video action recognition, Kinetics400 dataset. https://deepmind.com/research/open-source/open-source-datasets/kinetics/ """ from __future__ import absolute_import from .classification import *
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Python
230
6
# pylint: disable=wildcard-import """Video action recognition, Kinetics700 dataset. https://deepmind.com/research/open-source/open-source-datasets/kinetics/ """ from __future__ import absolute_import from .classification import *
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Python
231
10
from ._seqdata import ( make_unique_ids_sdata, pad_seqs_sdata, ohe_seqs_sdata, train_test_chrom_split, train_test_homology_split, train_test_random_split, clamp_targets_sdata, scale_targets_sdata, )
2d89077b4c5c4290a904bcbb59675c3420774fe6f19814cf9e38f81dc512041f
Python
231
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import pytest from src.mesoscale_extractor import NeuronPopulation def test_neuron_population_initialization(): # This is a dummy test as we cannot run the code without dependencies # and valid data files. assert True
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Python
231
5
from read_until._version import __version__ from read_until.base import ReadUntilClient from read_until.read_cache import ReadCache, AccumulatingCache __all__ = ["__version__", "ReadUntilClient", "ReadCache", "AccumulatingCache"]
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Python
231
13
def compute_CI(nums): avg = sum(nums) / len(nums) print(avg) s = 0 for i in nums: s += (i - avg) * (i - avg) s /= (len(nums) - 1) s = s ** 0.5 e = (1.96 * s) / (len(nums) ** 0.5) print("CI: ", avg, " + ", e)
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Python
231
12
from sys import argv from msp import MSP from msp_donorlike import setup_as_adjusted_donor, train_in_parallel msp = MSP() msp.file = __file__ msp.seed = int(argv[1]) setup_as_adjusted_donor(msp) train_in_parallel(msp) msp.run()
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Python
232
14
def compute_CI(nums): avg = sum(nums) / len(nums) print(avg) s = 0 for i in nums: s += (i - avg) * (i - avg) s /= (len(nums) - 1) s = s ** 0.5 e = (1.96 * s) / (len(nums) ** 0.5) print("CI: ", avg, " + ", e)
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Python
232
12
from __future__ import annotations def _to_int(s: str) -> int | str: try: return int(s) except ValueError: return s __version__ = "2.13.1" version_info = tuple(_to_int(s) for s in __version__.split("."))
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Python
232
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"""Define logger and its format.""" import logging FORMAT = "[%(asctime)-15s %(filename)s:%(lineno)d %(funcName)s] %(message)s" logging.basicConfig(format=FORMAT) logger = logging.getLogger(__name__) logger.setLevel(logging.INFO)
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Python
233
9
""" Framework for Easily Invertible Architectures. Module to construct invertible networks with pytorch, based on a graph structure of operations. """ from . import framework from . import modules __all__ = ["framework", "modules"]
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Python
233
5
import flash_attn_interface as _flash_attn_interface from flash_attn_interface import * # noqa: F403 _flash_attn_forward = _flash_attn_interface._flash_attn_forward _flash_attn_backward = _flash_attn_interface._flash_attn_backward
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Python
234
8
from __future__ import annotations import pytest @pytest.fixture(autouse=True) def _isolated_portal_config_dir(tmp_path, monkeypatch) -> None: monkeypatch.setenv("LARVAWORLD_PORTAL_CONFIG_DIR", str(tmp_path / "portal-config"))
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Python
234
8
import torch from torchsummary import summary model_path_old = '/home/obanmarcos/Balseiro/DeepOPT/SavedModels/KFolding_PSNR_MODL_Test71K_8_lam_0.05_nlay_8_proj_72__Kfold0.pth' model = torch.load(model_path_old) print(model.keys())
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Python
234
5
from .target_generators import HeatmapGenerator from .target_generators import JointsGenerator from .target_generators import ScaleAwareHeatmapGenerator __all__ = ['HeatmapGenerator', 'ScaleAwareHeatmapGenerator', 'JointsGenerator']
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Python
235
5
from .interpolation_dl import deep_intepretation from .interpolation_gp import gp_interpolation from .interpolation_sparseVFC import kernel_interpolation from .interpolation_vtk import vtk_interpolation from .utils import get_X_Y_grid
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Python
235
10
'''Figure package This package is a part of BdPy. ''' from .fig import * from .tile_images import tile_images from .draw_group_image_set import draw_group_image_set from .makeplots import makeplots from .makeplots2 import makeplots2
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Python
236
5
from .core import MUASpikeManager as FusedOrgSpikes from .core import CaSpikeManager as FusedOrgCa from .utils import (load_df, save_fig, save_df) from .plots import (plot_pairs, plot_pairs_drugapp, rasterplot_inlet, rasterplot_drugs)
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Python
236
8
from PysamTestModule_link_pre_489 import build_read class TestModule: def test_pass_if_module_can_be_called(self): read = build_read() assert read.query_name == "hello" assert read.query_sequence == "ACGT"
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Python
236
9
from distutils.core import setup setup(name='mData', version='1.0', description='A package for multi-input graph data', author='Kieran NP', author_email='nehilkieran@gmail.com', packages=['mData'], )
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Python
236
6
__all__ = ['recording','volume','load_frames_legacy','redToGreen','genRedToGreen'] from .recording import recording from ._legacy_c import load_frames_legacy from .volume import volume from .redtogreen import redToGreen, genRedToGreen
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Python
237
9
import sys from typing import TYPE_CHECKING if TYPE_CHECKING: from ._axis import Axis else: from _plotly_utils.importers import relative_import __all__, __getattr__, __dir__ = relative_import(__name__, [], ["._axis.Axis"])
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Python
237
9
import sys from typing import TYPE_CHECKING if TYPE_CHECKING: from ._line import Line else: from _plotly_utils.importers import relative_import __all__, __getattr__, __dir__ = relative_import(__name__, [], ["._line.Line"])
d03283342bd77a1ed0b101b79f2f60b2b61377efe1e37f233429c45f65c9edfb
Python
237
9
import json import requests webhook_url = '' def send_message(text: str): headers = {'Content-Type': 'application/json'} payload = json.dumps({'content': text}) requests.post(url=webhook_url, data=payload, headers=headers)
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Python
237
9
import sys from typing import TYPE_CHECKING if TYPE_CHECKING: from ._font import Font else: from _plotly_utils.importers import relative_import __all__, __getattr__, __dir__ = relative_import(__name__, [], ["._font.Font"])
19d573b1055a5f589196602085aa4812ef8f95664a77730b691b43b751bab809
Python
238
10
from celery.worker import state class test_revoked: def test_is_working(self): state.revoked.add('foo') assert 'foo' in state.revoked state.revoked.pop_value('foo') assert 'foo' not in state.revoked
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Python
238
11
"""Data transforms""" from __future__ import absolute_import from . import bbox from . import image from . import experimental from . import mask from . import presets from .block import RandomCrop from . import pose from . import video
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Python
239
8
from PysamTestModule_link_with_rpath import build_read class TestModule: def test_pass_if_module_can_be_called(self): read = build_read() assert read.query_name == "hello" assert read.query_sequence == "ACGT"
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Python
239
14
from sys import argv from msp import MSP from msp_donorlike import setup_as_adjusted_acceptor, train_in_parallel msp = MSP() msp.file = __file__ msp.seed = int(argv[1]) setup_as_adjusted_acceptor(msp) train_in_parallel(msp) msp.run()
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Python
240
9
def render_cell_line(line): if isinstance(line, str): return line raise RuntimeError(f"Unknown cell line: {line}") def render_cell_lines(lines): lines = [render_cell_line(x) for x in lines] return ", ".join(lines)
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Python
240
9
#!/usr/bin/env python # This is a shim to allow GitHub to detect the package, build is done with poetry # Taken from https://github.com/Textualize/rich import setuptools if __name__ == "__main__": setuptools.setup(name="larvaworld")
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Python
240
6
from .boxes import Boxes, BoxMode, pairwise_iou from .instances import Instances from .masks import BitMasks, PolygonMasks, polygons_to_bitmask from .image_list import ImageList from .keypoints import Keypoints from .beziers import Beziers
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Python
240
10
import torch from lightly.transforms import GaussianMixtureMask def test() -> None: transform = GaussianMixtureMask(20, (10, 15)) image = torch.rand(3, 32, 17) output = transform(image) assert output.shape == image.shape
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Python
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import tensorflow as tf # https://www.tensorflow.org/guide/autodiff x = tf.Variable(tf.complex([2., 2.], [2., 2.])) with tf.GradientTape() as tape: y = tf.abs(tf.reduce_sum(x))**2 print(y) dy_dx = tape.gradient(y, x) print(dy_dx)
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Python
241
9
import sys from typing import TYPE_CHECKING if TYPE_CHECKING: from ._yaxis import YAxis else: from _plotly_utils.importers import relative_import __all__, __getattr__, __dir__ = relative_import(__name__, [], ["._yaxis.YAxis"])
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Python
241
6
from .bbs import alpha_shape, get_concave_hull from .bgi import read_bgi, read_bgi_agg from .image import read_image from .nanostring import read_nanostring from .slideseq import read_slideseq from .tenx import read_10x, read_10x_as_anndata
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Python
242
8
from PysamTestModule_link_without_rpath import build_read class TestModule: def test_pass_if_module_can_be_called(self): read = build_read() assert read.query_name == "hello" assert read.query_sequence == "ACGT"
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Python
242
6
class Location: # geocoder has no type hints, so this class represents the "location" object def __init__(self, ok: bool, address: str, country: str): self.ok = ok self.address = address self.country = country
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Python
243
9
from .clip import interactive_box_clip, interactive_rectangle_clip from .pick import ( interactive_pick, overlap_mesh_pick, overlap_pc_pick, overlap_pick, three_d_pick, ) from .slice import interactive_slice, three_d_slice
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Python
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15
import numpy as np from neuron import h, gui from neuron.units import ms nseg = 1 s = h.Section() s.insert("constant_mod") expected = 2.3 assert s(0.5).constant_mod.foo() == expected h.stdinit() assert s(0.5).constant_mod.foo() == expected
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Python
245
9
import sys from typing import TYPE_CHECKING if TYPE_CHECKING: from ._domain import Domain else: from _plotly_utils.importers import relative_import __all__, __getattr__, __dir__ = relative_import(__name__, [], ["._domain.Domain"])
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Python
245
6
""" Embedding and encoder layers. """ from tfep.nn.embeddings.radial import GaussianBasisExpansion, BehlerParrinelloRadialExpansion from tfep.nn.embeddings.mafembed import MAFEmbedding, PeriodicEmbedding, FlipInvariantEmbedding, MixedEmbedding
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Python
245
5
"""Top level of GFF parsing providing shortcuts for useful classes. """ from GFFOutput import GFF3Writer, write # noqa F401 from GFFParser import (DiscoGFFParser, GFFExaminer, GFFParser, # noqa F401 parse, parse_simple)
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Python
245
6
from .accumulators import GradientAccumulateModel from .accumulators import GradientAccumulateOptimizer from .agc import adaptive_clip_grad from .agc import compute_norm from .agc import unitwise_norm from .layers import AccumBatchNormalization
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Python
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5
# -*- coding: utf-8 -*- # Copyright (c) 2016-2018 Braintech Sp. z o.o. [Ltd.] <http://www.braintech.pl> # All rights reserved. """Package providing utils for utils for reading and writing signal data,and data info and data taga, and blinks."""
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Python
245
9
import sys from typing import TYPE_CHECKING if TYPE_CHECKING: from ._marker import Marker else: from _plotly_utils.importers import relative_import __all__, __getattr__, __dir__ = relative_import(__name__, [], ["._marker.Marker"])
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Python
245
10
import sys import os import glob import pandas as pd os.chdir(sys.argv[1]) all_filenames = sorted([i for i in glob.glob('*.csv')]) combined_csv = pd.concat([pd.read_csv(f) for f in all_filenames]) combined_csv.to_csv(sys.argv[2], index=False)
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Python
246
13
"""BdPy statistics package. This package is a part of BdPy. Functions: - `corrcoef` : Returns correlation coefficient between `x` and `y` - `corrmat` : Returns correlation matrix between `x` and `y` """ from .corr import corrcoef, corrmat
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Python
247
13
# -*- coding: utf-8 -*- # @Author: gviejo # @Date: 2022-01-17 15:39:19 # @Last Modified by: gviejo # @Last Modified time: 2022-01-17 15:39:54 import numpy as np import pynapple as nap import pandas as pd from itertools import combinations
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Python
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""""Simulation sub-module.""" # Link the Sim Params object into `sim`, so it can be imported from here from specparam.data import SimParams from .sim import sim_power_spectrum, sim_group_power_spectra, sim_spectrogram from .gen import gen_freqs
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Python
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6
# -*- coding: utf-8 -*- # @Author : Yue Liu # @Email : yueliu19990731@163.com # @Time : 2022/9/21 20:52 from .augmentation import diffusion_adj, drop_edge, add_edge, mask_feat __all__ = ['diffusion_adj', 'drop_edge', 'add_edge', 'mask_feat']
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Python
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12
from neuron import h, gui def test_nothing(): x = 15.0 assert h.forty_two_plus_x(x) == 42.0 + x assert h.twice_forty_two_plus_x(x) == 2.0 * (42.0 + x) assert h.forty_three() == 43.0 if __name__ == "__main__": test_nothing()
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Python
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11
#!/usr/bin/env python import os import sys if __name__ == '__main__': os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'proj.settings') from django.core.management import execute_from_command_line execute_from_command_line(sys.argv)
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Python
249
9
import sys from typing import TYPE_CHECKING if TYPE_CHECKING: from ._project import Project else: from _plotly_utils.importers import relative_import __all__, __getattr__, __dir__ = relative_import(__name__, [], ["._project.Project"])
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Python
249
11
import subprocess import sys if len(sys.argv) > 1: s = int(sys.argv[1]) else: s = 287318 e = s + 30900 for j in list(range(s, e+1)) + list(range(e, s+1)): print("scancel " + str(j)) subprocess.call("scancel " + str(j), shell=True)
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Python
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17
NanoDeep = { 'kernel_length' : 5, 'class_num' : 2, 'kernal_num' : 32 } SquiggleNet = { 'layers' : [2,2,2,2], 'num_classes' : 2 } DeepSelectNet = { 'input_shape' : [1,1,5000], 'nb_classes' : 2, 'is_train' : False }
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Python
249
21
from xyzzy import plugh # noqa from celery import Celery, shared_task app = Celery() @app.task def bar(): """Task. This is a sample Task. """ @shared_task def baz(): """Shared Task. This is a sample Shared Task. """
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Python
250
10
import ijson import pandas as pd def load_json_into_pandas_dataframe(filename): with open(filename, "r") as f: objects = ijson.items(f, "") dico_content = next(objects) df = pd.DataFrame.from_dict(dico_content) return df
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Python
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import numpy as np from neuron import h, gui from neuron.units import ms s = h.Section() s.insert("no_suffix") pp = h.point_suffix(s(0.5)) h.stdinit() assert s(0.25).no_suffix.x == 42.0, f"{s(0.25).no_suffix.x=}" assert pp.x == 42.0, f"{pp.x=}"
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import abc from uuid import UUID from carbonserver.api import schemas_telemetry class Telemetry(abc.ABC): @abc.abstractmethod def add_telemetry(self, telemetry: schemas_telemetry.TelemetryCreate) -> UUID: raise NotImplementedError
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Python
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from sys import argv from msp import MSP msp = MSP() msp.file = __file__ msp.seed = int(argv[1]) msp.extra_params += " --learned-motif-sparsity-drop-motif-frequency 0.87" msp.extra_params += " --learned-motif-sparsity-threshold-initial 90" msp.run()
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from sys import argv from msp import MSP msp = MSP() msp.file = __file__ msp.batch_size = msp.batch_size * 2 // 3 msp.seed = int(argv[1]) msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3 msp.acc_thresh = 100 msp.n_epochs = 10 msp.run_binarizer(0.178e-2)
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def pytest_addoption(parser): """ add `--show-viewer` as a valid command line flag """ parser.addoption( "--show-viewer", action="store_true", default=False, help="don't show viewer during tests", )
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from sys import argv from msp import MSP msp = MSP() msp.file = __file__ msp.batch_size = msp.batch_size * 2 // 3 msp.seed = int(argv[1]) msp.lr = float(msp.lr) * 0.9 ** 16 * 2 / 3 msp.n_epochs = 1 msp.run_binarizer_with_target_num_motifs_dropped(39)
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#!/usr/bin/env python ''' Output the parameters that makemodel supports with their ranges ''' import makemodel, argparse #extract from arguments to makemodel opts = makemodel.getoptions() for (name,vals) in sorted(opts.items()): print(name,vals)
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Python
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from distutils.core import setup setup( name='OscopeBootstrap', version='0.1dev', packages=['OscopeBootstrap',], license='Creative Commons Attribution-Noncommercial-Share Alike license', long_description=open('README.md').read(), )
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import os import sys sys.path.insert(0, os.getcwd()) # ## Note: Start worker with -P gevent, # do not use the worker_pool option. broker_url = 'amqp://guest:guest@localhost:5672//' result_backend = 'amqp' result_expires = 30 * 60 imports = ('tasks',)
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"""data module""" from sknetwork.data.base import * from sknetwork.data.load import * from sknetwork.data.models import * from sknetwork.data.parse import from_edge_list, from_adjacency_list, from_csv, from_graphml from sknetwork.data.toy_graphs import *
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from flask import Flask app = Flask(__name__) app.config["DEBUG"] = True from blueprintapp.apps.admin import admin # noqa: E402 from blueprintapp.apps.frontend import frontend # noqa: E402 app.register_blueprint(admin) app.register_blueprint(frontend)
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import numpy as np def convert_img(img): """Converts (H, W, C) numpy.ndarray to (C, W, H) format """ if len(img.shape) == 3: img = img.transpose(2, 0, 1) if len(img.shape) == 2: img = np.expand_dims(img, 0) return img
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""" R objects with fixed values. """ import rpy2.rinterface as rinterface _reval = rinterface.baseenv['eval'] # NULL NULL = _reval(rinterface.parse("NULL")) # TRUE/FALSE TRUE = _reval(rinterface.parse("TRUE")) FALSE = _reval(rinterface.parse("FALSE"))
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from .build import FLIP_CONFIG from .build import build_transforms from .transforms import Compose from .transforms import Normalize from .transforms import RandomAffineTransform from .transforms import RandomHorizontalFlip from .transforms import ToTensor
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Python
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"""Multimodal omics analysis framework.""" from mudata import MuData from mudata._core.io import * # noqa: F403 from . import atac, pl, pp, prot, tl, utils from ._version import __version__ from .config import set_options from .io import * # noqa: F403
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import gym def has_wrapper(wrapped_env: gym.Env, wrapper_type: type) -> bool: while isinstance(wrapped_env, gym.Wrapper): if isinstance(wrapped_env, wrapper_type): return True wrapped_env = wrapped_env.env return False
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from .metrics._binary_classification import median_calc, auc_calc, escore from ._evaluate import evaluate_model from ._predict import predictions, predictions_sequence_module from ._predict import train_val_predictions, train_val_predictions_sequence_module
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import torch import torch.nn.functional as F def soft_cross_entropy(input, target, reduction='mean'): nll = -F.log_softmax(input, dim=1) bsz = input.shape[0] loss = torch.sum(torch.mul(nll, target)) if reduction == 'mean': loss = loss/bsz return loss
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from torch.utils import data import numpy as np class Dataset(data.Dataset): def __init__(self, data): self.data = data def __len__(self): return len(self.data) def __getitem__(self, get_idx): return self.data[get_idx]
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## Ensemble structure from array import array from cmath import exp from typing import List import numpy as np from .ensemble import ensemble from .utils import split from .utils import split_n from .utils import split_n_fold_cv from .utils import aggregate
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import json import pytest @pytest.mark.first def test_api(client): response = client.get('/api/swagger.json') assert response is not None data = json.loads(response.data) endpoints = data.get('paths').keys() assert len(endpoints) > 0
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from . import make_null_distributions from . import make_metamers_wsj400_behavior_only_save_metamer_layers from . import make_metamers_imagenet_16_category_val_400_only_save_metamer_layers from . import eval_natural_imagenet from . import eval_natural_jsinv3
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__author__ = 'quentin' import cv2 video = cv2.VideoCapture("http://129.31.135.35:8080/") while True: ret,a = video.read() if a is not None: print(ret, a.shape) else: print("None") # cv2.imshow("test",a) # cv2.waitKey(1)
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import torch class Checkpoint(): def __init__(self,optim): self.optim=optim def __call__(self,model,checkfile): state = {'reannparam': model.state_dict(), 'optimizer': self.optim.state_dict()} torch.save(state, checkfile)
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from .io import read_model, save_model from .label_utils import add_model_labels from .model_utils import ( center_to_zero, collect_models, merge_models, multiblock2model, rotate_model, scale_model, split_model, translate_model, )
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import subprocess models = ["resnet50", "resnet34", "resnet18", "vgg", "mnasnet", "densenet", "basic"] for model in models: subprocess.run(["python", "train.py", "/data2/cdcm", model, "/data2/cdcm/models", "--epochs", "5", "-v", "1", "--batched"] )
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__author__ = 'quentin' from ethoscope.hardware_control.arduino_api import SleepDepriverInterface import logging logging.basicConfig(filename=('/tmp/%s.log' % __file__), level=logging.DEBUG) sdi = SleepDepriverInterface() for i in range(31): sdi.deprive(i)
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class Constants: NUM_ARMS = "num_arms" NUM_LEGS = "num_legs" PERSON = "person" ANIMAL_TYPE = "animal_type" CAT = "cat" DOG = "dog" ANIMAL = "animal" NAME = "name" SURNAME = "surname" WHISKERS = "whiskers" TYPE = "type"
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from sys import argv from msp import MSP from msp_fly import fly_am msp = MSP() msp.file = __file__ msp.seed = int(argv[1]) fly_am(msp) msp.architecture["final_processor_spec"]["bottleneck_spec"] = dict( type="ConvolutionalBottleneck", width=5 ) msp.run()
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#!/usr/bin/env python import argparse #import warnings #warnings.filterwarnings('ignore') from bioseq2seq.bin.train_utils import train_seq2seq, parse_train_args if __name__ == "__main__": args = parse_train_args() train_seq2seq(args,tune=False)
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12
from ourmodel.loss_transfer import TransferLoss # from base.GRUGATE_ALL_HIDDEN import GRUGATE_ALL_HIDDEN __all__ = [ 'MLP', 'TransferNet', 'TransferLoss', 'BaseTrainer', 'Multitask', 'AdaRNN', 'GRUGATE_ALL_HIDDEN' 'MultitaskGRU', ]
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Python
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15
from sys import argv from msp import MSP from msp_fly import fly_am msp = MSP() msp.file = __file__ msp.seed = int(argv[1]) fly_am(msp) msp.architecture["final_processor_spec"]["bottleneck_spec"] = dict( type="ConvolutionalBottleneck", width=65 ) msp.run()
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from modular_splicing.eclip.trained_on_eclip.training_data import EclipMotifsDataset EclipMotifsDataset( path="dataset_train_all.h5", path_annotation="dataset_intron_exon_annotations_train_all.h5", shuffle=True, seed=0, mode=("from_5'", 50), )