Source code for esmvaltool.diag_scripts.shared._validation

"""Load functions needed by diags with CONTROL and EXPERIMENT."""
import logging
import os

import iris
from esmvalcore.preprocessor import climate_statistics

from esmvaltool.diag_scripts.shared import select_metadata

logger = logging.getLogger(os.path.basename(__file__))

[docs] def get_control_exper_obs(short_name, input_data, cfg, cmip_type=None): """ Get control, exper and obs datasets. This function is used when running recipes that need a clear distinction between a control dataset, an experiment dataset and have optional obs (OBS, obs4MIPs etc) datasets; such recipes include recipe_validation, and all the autoassess ones; short_name: variable short name input_data: dict containing the input data info cfg: config file as used in this module cmip_type: optional, CMIP project type (CMIP5 or CMIP6) """ # select data per short name and optional CMIP type if not cmip_type: dataset_selection = select_metadata(input_data, short_name=short_name) else: dataset_selection = select_metadata(input_data, short_name=short_name, project=cmip_type) # get the obs datasets if specified in recipe if 'observational_datasets' in cfg: obs_selection = [ select_metadata( input_data, short_name=short_name, dataset=obs_dataset)[0] for obs_dataset in cfg['observational_datasets'] ] else: obs_selection = [] # print out OBS's if obs_selection:"Observations dataset(s) %s", [obs['dataset'] for obs in obs_selection]) # make sure the chosen datasets for control and exper are available alias_selection = [] for model in dataset_selection: try: dataset_name = model['alias'].split("_")[1] except IndexError: dataset_name = model['alias'] alias_selection.append(dataset_name) if cfg['control_model'] not in alias_selection: raise ValueError(f"Control dataset {cfg['control_model']} " "not in datasets") if cfg['exper_model'] not in alias_selection: raise ValueError(f"Experiment dataset {cfg['exper_model']} " "not in datasets") # pick control and experiment dataset for model in dataset_selection: if cfg['control_model'] in model['alias'].split("_"):"Control dataset %s", model['alias']) control = model elif cfg['exper_model'] in model['alias'].split("_"):"Experiment dataset %s", model['alias']) experiment = model return control, experiment, obs_selection
# apply supermeans: handy function that loads CONTROL, EXPERIMENT # and OBS (if any) files and applies climate_statistics() to mean the cubes
[docs] def apply_supermeans(ctrl, exper, obs_list): """ Apply supermeans on data components ie MEAN on time. This function is an extension of climate_statistics() meant to ease the time-meaning procedure when dealing with CONTROL, EXPERIMENT and OBS (if any) datasets. ctrl: dictionary of CONTROL dataset exper: dictionary of EXPERIMENT dataset obs_lis: list of dicts for OBS datasets (0, 1 or many) Returns: control and experiment cubes and list of obs cubes """ ctrl_file = ctrl['filename'] exper_file = exper['filename'] ctrl_cube = iris.load_cube(ctrl_file) exper_cube = iris.load_cube(exper_file) ctrl_cube = climate_statistics(ctrl_cube) exper_cube = climate_statistics(exper_cube) if obs_list: obs_cube_list = [] for obs in obs_list: obs_file = obs['filename'] obs_cube = iris.load_cube(obs_file) obs_cube = climate_statistics(obs_cube) obs_cube_list.append(obs_cube) else: obs_cube_list = None return ctrl_cube, exper_cube, obs_cube_list