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feat(starrynight: moduels): add sbs preprocess module
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‎starrynight/src/starrynight/modules/registry.py‎

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from starrynight.modules.sbs_pre_segcheck.pre_segcheck_load_data import (
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SBSPreSegcheckGenLoadDataModule,
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)
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from starrynight.modules.sbs_preprocess.preprocess_cp import SBSPreprocessInvokeCPModule
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from starrynight.modules.sbs_preprocess.preprocess_cppipe import (
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SBSPreprocessGenCPPipeModule,
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)
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from starrynight.modules.sbs_preprocess.preprocess_load_data import (
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SBSPreprocessGenLoadDataModule,
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)
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MODULE_REGISTRY: dict[str, StarrynightModule] = {
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# Generate inventory and index for the project
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SBSAlignGenLoadDataModule.uid(): SBSAlignGenLoadDataModule,
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SBSAlignGenCPPipeModule.uid(): SBSAlignGenCPPipeModule,
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SBSAlignInvokeCPModule.uid(): SBSAlignInvokeCPModule,
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# SBS preprocess
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SBSPreprocessGenLoadDataModule.uid(): SBSPreprocessGenLoadDataModule,
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SBSPreprocessGenCPPipeModule.uid(): SBSPreprocessGenCPPipeModule,
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SBSPreprocessInvokeCPModule.uid(): SBSPreprocessInvokeCPModule,
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}

‎starrynight/src/starrynight/modules/sbs_preprocess/__init__.py‎

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"""SBS Preprocess."""

‎starrynight/src/starrynight/modules/sbs_preprocess/constants.py‎

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"""Constants for the module."""
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SBS_PREPROCESS_OUT_PATH_SUFFIX = "preprocess/sbs/"
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SBS_PREPROCESS_CP_LOADDATA_OUT_PATH_SUFFIX = "cellprofiler/loaddata/sbs/preprocess"
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SBS_PREPROCESS_CP_CPPIPE_OUT_PATH_SUFFIX = "cellprofiler/cppipe/sbs/preprocess"

‎starrynight/src/starrynight/modules/sbs_preprocess/preprocess_cp.py‎

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"""SBS preprocess invoke cellprofiler module."""
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from pathlib import Path
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from typing import Self
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from cloudpathlib import CloudPath
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from pipecraft.node import Container, ContainerConfig, UnitOfWork
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from pipecraft.pipeline import Pipeline, Seq
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from starrynight.experiments.common import Experiment
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from starrynight.modules.common import StarrynightModule
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from starrynight.modules.sbs_preprocess.constants import (
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SBS_PREPROCESS_CP_CPPIPE_OUT_PATH_SUFFIX,
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SBS_PREPROCESS_CP_LOADDATA_OUT_PATH_SUFFIX,
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SBS_PREPROCESS_OUT_PATH_SUFFIX,
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)
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from starrynight.modules.schema import (
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Container as SpecContainer,
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)
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from starrynight.modules.schema import (
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ExecFunction,
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TypeAlgorithmFromCitation,
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TypeCitations,
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TypeEnum,
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TypeInput,
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TypeOutput,
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)
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from starrynight.schema import DataConfig
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def create_work_unit_gen_index(out_dir: Path | CloudPath) -> list[UnitOfWork]:
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"""Create units of work for Generate Index step.
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Parameters
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----------
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out_dir : Path | CloudPath
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Path to load data csv. Can be local or cloud.
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Returns
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-------
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list[UnitOfWork]
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List of unit of work.
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"""
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uow_list = [
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UnitOfWork(
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inputs={
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"inventory": [out_dir.joinpath("inventory.parquet").resolve().__str__()]
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},
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outputs={"index": [out_dir.joinpath("index.parquet").resolve().__str__()]},
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)
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]
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return uow_list
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def create_pipe_gen_cpinvoke(uid: str, spec: SpecContainer) -> Pipeline:
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"""Create pipeline for invoking cellprofiler.
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Parameters
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----------
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uid: str
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Module unique id.
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spec: SpecContainer
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SBSPreprocessInvokeCPModule specification.
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Returns
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-------
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Pipeline
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Pipeline instance.
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"""
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cmd = [
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"starrynight",
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"cp",
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"-p",
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spec.inputs[0].path,
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"-l",
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spec.inputs[1].path,
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"-o",
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spec.outputs[0].path,
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"--sbs",
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]
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gen_load_data_pipe = Seq(
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[
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Container(
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name=uid,
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input_paths={
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"cppipe_path": [spec.inputs[0].path],
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"load_data_path": [spec.inputs[1].path],
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},
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output_paths={"corr_images_dir": [spec.outputs[0].path]},
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config=ContainerConfig(
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image="ghrc.io/leoank/starrynight:dev",
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cmd=cmd,
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env={},
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),
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),
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]
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)
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return gen_load_data_pipe
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class SBSPreprocessInvokeCPModule(StarrynightModule):
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"""SBS Preprocess invoke cellprofiler module."""
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@staticmethod
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def uid() -> str:
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"""Return module unique id."""
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return "sbs_preprocess_invoke_cp"
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@staticmethod
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def _spec() -> str:
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"""Return module default spec."""
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return SpecContainer(
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inputs=[
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TypeInput(
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name="cppipe_path",
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type=TypeEnum.files,
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description="Path to the cppipe file.",
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optional=False,
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path="path/to/the/cppipe",
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),
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TypeInput(
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name="load_data_path",
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type=TypeEnum.files,
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description="Path to the LoadData csv.",
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optional=False,
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path="path/to/the/loaddata",
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),
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],
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outputs=[
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TypeOutput(
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name="path to compensated images",
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type=TypeEnum.files,
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description="Compensated images after preprocessing",
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optional=False,
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path="random/path/to/corrected images",
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),
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TypeOutput(
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name="cppipe_notebook",
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type=TypeEnum.notebook,
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description="Notebook for inspecting illum corrections",
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optional=False,
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path="http://karkinos:2720/?file=.%2FillumSBSApplyOutput.py",
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),
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],
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parameters=[],
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display_only=[],
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results=[],
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exec_function=ExecFunction(
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name="",
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script="",
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module="",
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cli_command="",
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),
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docker_image=None,
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algorithm_folder_name=None,
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citations=TypeCitations(
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algorithm=[
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TypeAlgorithmFromCitation(
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name="Starrynight SBS Preprocess invoke cellprofiler module",
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description="This module invoke cellprofiler for sbs preprocess.",
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)
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]
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),
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)
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@staticmethod
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def from_config(
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data: DataConfig,
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experiment: Experiment | None = None,
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spec: SpecContainer | None = None,
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) -> Self:
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"""Create module from experiment and data config."""
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if spec is None:
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spec = SBSPreprocessInvokeCPModule._spec()
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spec.inputs[0].path = (
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data.workspace_path.joinpath(SBS_PREPROCESS_CP_CPPIPE_OUT_PATH_SUFFIX)
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.resolve()
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.__str__()
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)
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spec.inputs[1].path = (
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data.workspace_path.joinpath(SBS_PREPROCESS_CP_LOADDATA_OUT_PATH_SUFFIX)
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.resolve()
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.__str__()
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)
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spec.outputs[0].path = (
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data.workspace_path.joinpath(SBS_PREPROCESS_OUT_PATH_SUFFIX)
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.resolve()
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.__str__()
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)
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pipe = create_pipe_gen_cpinvoke(
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uid=SBSPreprocessInvokeCPModule.uid(),
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spec=spec,
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)
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uow = create_work_unit_gen_index(out_dir=data.storage_path.joinpath("index"))
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return SBSPreprocessInvokeCPModule(spec=spec, pipe=pipe, uow=uow)

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