ovo.core.logic.descriptor_logic

Module Contents

Functions

get_available_descriptors	Return all descriptor keys found in DB for the given design ids.
get_wide_descriptor_table	Return a wide descriptor table for the given design ids. The table will have design ids as index and human-readable descriptor names (or keys if human_readable=False) as columns.
submit_descriptor_workflow
prepare_proteinqc_params
prepare_refolding_params
get_log	Get the log of a descriptor job from the scheduler.
process_results	Process results of a successful workflow - save DescriptorValues to database
read_descriptor_file_values	Process descriptor job, return list of DescriptorValues to be inserted into DB.
save_descriptor_job_for_design_job
find_id_column
generate_descriptor_values_for_design	Generate DescriptorValue objects for a given design based on descriptor tables.
update_and_process_descriptors	Update descriptor jobs and process finished descriptor jobs
export_proteinqc_excel
export_design_descriptors_excel	Export Excel file with design descriptors for the given design ids.

API

ovo.core.logic.descriptor_logic.get_available_descriptors(design_ids: list[str]) → dict[str, ovo.core.database.models.Descriptor]

Return all descriptor keys found in DB for the given design ids.

ovo.core.logic.descriptor_logic.get_wide_descriptor_table(*, pool_ids: Collection[str] = None, design_ids: Collection[str] = None, descriptor_keys: Collection[str] = None, human_readable=True, nested=False, **filters) → pandas.DataFrame

Return a wide descriptor table for the given design ids. The table will have design ids as index and human-readable descriptor names (or keys if human_readable=False) as columns.

ovo.core.logic.descriptor_logic.submit_descriptor_workflow(workflow: ovo.core.database.models.DescriptorWorkflow, scheduler_key: str, round_id: str)

ovo.core.logic.descriptor_logic.prepare_proteinqc_params(workflow: ovo.core.database.models_proteinqc.ProteinQCWorkflow, workdir: str) → dict

ovo.core.logic.descriptor_logic.prepare_refolding_params(workflow: ovo.core.database.models_refolding.RefoldingWorkflow, workdir: str) → dict

ovo.core.logic.descriptor_logic.get_log(descriptor_job: ovo.core.database.models.DescriptorJob, tail: int = None) → str

Get the log of a descriptor job from the scheduler.

ovo.core.logic.descriptor_logic.process_results(descriptor_job: ovo.core.database.models.DescriptorJob, callback: Callable = None, wait: bool = True)

Process results of a successful workflow - save DescriptorValues to database

ovo.core.logic.descriptor_logic.read_descriptor_file_values(descriptor_job: ovo.core.database.models.DescriptorJob, design_id_mapping: dict[str, str | tuple], filenames: dict[str, str] = None, descriptor_tables: dict[str, pandas.DataFrame] = None) → list[ovo.core.database.models.DescriptorValue]

Process descriptor job, return list of DescriptorValues to be inserted into DB.

Parameters:

• descriptor_job – DescriptorJob object

• design_id_mapping – Mapping from design.id to table_id (basename of PDB file = id column in descriptor output file)

• filenames – Dict of “pipeline_name|tool_key” -> filename in output directory (without file extension - will look for .csv or .jsonl)

• descriptor_tables – Optional dictionary of pre-loaded descriptor tables (tool_key -> pd.DataFrame).

ovo.core.logic.descriptor_logic.save_descriptor_job_for_design_job(design_job: ovo.core.database.models.DesignJob, round_id: str, chains: list[str], design_ids: list[str]) → ovo.core.database.models.DescriptorJob

ovo.core.logic.descriptor_logic.find_id_column(df: pandas.DataFrame, df_name: str)

ovo.core.logic.descriptor_logic.generate_descriptor_values_for_design(design_id: str, table_ids: str | tuple, descriptor_job_id: str | None, descriptor_tables, chains: list[str]) → list[ovo.core.database.models.DescriptorValue]

Generate DescriptorValue objects for a given design based on descriptor tables.

Requirements (otherwise an error is raised, leading to interrupted processing of the job):

• Each descriptor table must contain at least one recognized descriptor (defined by Descriptor objects)

• One of the design’s table_ids must be found in each descriptor table (each design should be present)

• Descriptor table index must be unique

Parameters:

• design_id – ID of the design

• table_ids – ID(s) corresponding to the design in the dataframes (can be a single string or a tuple of strings)

• descriptor_job_id – ID of the DescriptorJob

• descriptor_tables – Dictionary of descriptor tables (tool_key -> pd.DataFrame, indexed by table_id)

• chains – List of chain IDs the descriptors apply to

Returns:

List of DescriptorValue objects

ovo.core.logic.descriptor_logic.update_and_process_descriptors(descriptor_jobs: List[ovo.core.database.models.DescriptorJob], error_callback: Callable)

Update descriptor jobs and process finished descriptor jobs

ovo.core.logic.descriptor_logic.export_proteinqc_excel(design_ids: list[str], output_path: str = None)

ovo.core.logic.descriptor_logic.export_design_descriptors_excel(df: pandas.DataFrame, output_path=None) → io.BytesIO | None

Export Excel file with design descriptors for the given design ids.

Parameters:

• df – Dataframe with any values, descriptor columns should be descriptor keys

• output_path – If given, write the Excel file to this path. If None, return the bytes of the Excel file.