Python make_pipeline примеры использования

Пример #1

We import and make pipelines as per usual, albeit we'll now be doing this for multiple pipelines!

We then run each pipeline, passing the results of previous pipelines to subsequent pipelines.
"""

from slam.imaging.with_lens_light.pipelines import source__parametric
from slam.imaging.with_lens_light.pipelines import source__inversion
from slam.imaging.with_lens_light.pipelines import light__parametric
from slam.imaging.with_lens_light.pipelines import mass__light_dark

source__parametric = source__parametric.make_pipeline(slam=slam,
                                                      settings=settings)
source_results = source__parametric.run(dataset=imaging, mask=mask)

source__inversion = source__inversion.make_pipeline(
    slam=slam, settings=settings, source_parametric_results=source_results)
source_results = source__inversion.run(dataset=imaging, mask=mask)

light__parametric = light__parametric.make_pipeline(
    slam=slam, settings=settings, source_results=source_results)
light_results = light__parametric.run(dataset=imaging, mask=mask)

mass__light_dark = mass__light_dark.make_pipeline(
    slam=slam,
    settings=settings,
    source_results=source_results,
    light_results=light_results,
)
mass_results = mass__light_dark.run(dataset=imaging, mask=mask)

Пример #2

Файл: stochastic.py Проект: jonathanfrawley/autolens_workspace_test_copy

We import and make pipelines as per usual, albeit we'll now be doing this for multiple pipelines!

We then run each pipeline, passing the results of previous pipelines to subsequent pipelines.
"""

from slam.imaging.with_lens_light.pipelines import source__parametric
from slam.imaging.with_lens_light.pipelines import source__inversion
from slam.imaging.with_lens_light.pipelines import light__parametric
from slam.imaging.with_lens_light.pipelines import mass__light_dark

source__parametric = source__parametric.make_pipeline(slam=slam,
                                                      settings=settings)
source_results = source__parametric.run(dataset=imaging, mask=mask)

source__inversion = source__inversion.make_pipeline(
    slam=slam, settings=settings, source_parametric_results=source_results)
source_results = source__inversion.run(dataset=imaging, mask=mask)

light__parametric = light__parametric.make_pipeline(
    slam=slam, settings=settings, source_results=source_results)
light_results = light__parametric.run(dataset=imaging, mask=mask)

mass__light_dark = mass__light_dark.make_pipeline(
    slam=slam,
    settings=settings,
    source_results=source_results,
    light_results=light_results,
    end_stochastic=True,
)
mass_results = mass__light_dark.run(dataset=imaging, mask=mask)