Source code for petab.visualize.lint

"""Validation of PEtab visualization files"""
import logging

import pandas as pd

from .. import C, Problem

logger = logging.getLogger(__name__)

[docs] def validate_visualization_df(problem: Problem) -> bool: """Validate visualization table Arguments: problem: The PEtab problem containing a visualization table Returns: ``True`` if errors occurred, ``False`` otherwise """ vis_df = problem.visualization_df if vis_df is None or vis_df.empty: return False errors = False if missing_req_cols := ( set(VISUALIZATION_DF_REQUIRED_COLS) - set(vis_df.columns) ): logger.error( f"Missing required columns {missing_req_cols} " "in visualization table." ) errors = True # Set all unspecified optional values to their defaults to simplify # validation vis_df = vis_df.copy() _apply_defaults(vis_df) if unknown_types := ( set(vis_df[C.PLOT_TYPE_SIMULATION].unique()) - set(C.PLOT_TYPES_SIMULATION) ): logger.error( f"Unknown {C.PLOT_TYPE_SIMULATION}: {unknown_types}. " f"Must be one of {C.PLOT_TYPES_SIMULATION}" ) errors = True if unknown_types := ( set(vis_df[C.PLOT_TYPE_DATA].unique()) - set(C.PLOT_TYPES_DATA) ): logger.error( f"Unknown {C.PLOT_TYPE_DATA}: {unknown_types}. " f"Must be one of {C.PLOT_TYPES_DATA}" ) errors = True if unknown_scale := (set(vis_df[C.X_SCALE].unique()) - set(C.X_SCALES)): logger.error( f"Unknown {C.X_SCALE}: {unknown_scale}. " f"Must be one of {C.X_SCALES}" ) errors = True if any( (vis_df[C.X_SCALE] == "order") & (vis_df[C.PLOT_TYPE_SIMULATION] != C.LINE_PLOT) ): logger.error( f"{C.X_SCALE}=order is only allowed with " f"{C.PLOT_TYPE_SIMULATION}={C.LINE_PLOT}." ) errors = True if unknown_scale := (set(vis_df[C.Y_SCALE].unique()) - set(C.Y_SCALES)): logger.error( f"Unknown {C.Y_SCALE}: {unknown_scale}. " f"Must be one of {C.Y_SCALES}" ) errors = True if problem.condition_df is not None: # check for ambiguous values reserved_names = {C.TIME, "condition"} for reserved_name in reserved_names: if ( reserved_name in problem.condition_df and reserved_name in vis_df[C.X_VALUES] ): logger.error( f"Ambiguous value for `{C.X_VALUES}`: " f"`{reserved_name}` has a special meaning as " f"`{C.X_VALUES}`, but there exists also a model " "entity with that name." ) errors = True # check xValues exist in condition table for xvalue in set(vis_df[C.X_VALUES].unique()) - reserved_names: if xvalue not in problem.condition_df: logger.error( f"{C.X_VALUES} was set to `{xvalue}`, but no " "such column exists in the conditions table." ) errors = True if problem.observable_df is not None: # yValues must be an observable for yvalue in vis_df[C.Y_VALUES].unique(): if pd.isna(yvalue): # if there is only one observable, we default to that if len(problem.observable_df.index.unique()) == 1: continue logger.error( f"{C.Y_VALUES} must be specified if there is more " "than one observable." ) errors = True if yvalue not in problem.observable_df.index: logger.error( f"{C.Y_VALUES} was set to `{yvalue}`, but no such " "observable exists in the observables table." ) errors = True return errors
[docs] def _apply_defaults(vis_df: pd.DataFrame): """ Set default values. Adds default values to the given visualization table where no value was specified. """ def set_default(column: str, value): if column not in vis_df: vis_df[column] = value elif value is not None: if isinstance(value, str): vis_df[column] = vis_df[column].astype("object") vis_df.fillna({column: value}, inplace=True) set_default(C.PLOT_NAME, "") set_default(C.PLOT_TYPE_SIMULATION, C.LINE_PLOT) set_default(C.PLOT_TYPE_DATA, C.MEAN_AND_SD) set_default(C.DATASET_ID, None) set_default(C.X_VALUES, C.TIME) set_default(C.X_OFFSET, 0) set_default(C.X_LABEL, vis_df[C.X_VALUES]) set_default(C.X_SCALE, C.LIN) set_default(C.Y_VALUES, None) set_default(C.Y_OFFSET, 0) set_default(C.Y_LABEL, vis_df[C.Y_VALUES]) set_default(C.Y_SCALE, C.LIN) set_default(C.LEGEND_ENTRY, vis_df[C.DATASET_ID])