Add diurnal cycle plot (variable vs. hour of day) to diagnostic `monitor/multi_datasets.py`

[schlunma]

Aim: add new plot type diurnal_cycle to monitor/multi_datasets.py, i.e., a plot variable (y-axis) vs. hour of day (x-axis).

Large parts of the necessary code can be copy-pasted from here:

ESMValTool/esmvaltool/diag_scripts/monitor/multi_datasets.py

Lines 1270 to 1340 in 0d2b68d

	def create_annual_cycle_plot(self, datasets, short_name):
	"""Create annual cycle plot."""
	plot_type = 'annual_cycle'
	if plot_type not in self.plots:
	return
	if not datasets:
	raise ValueError(f"No input data to plot '{plot_type}' given")
	logger.info("Plotting %s", plot_type)
	fig = plt.figure(**self.cfg['figure_kwargs'])
	axes = fig.add_subplot()
	# Plot all datasets in one single figure
	ancestors = []
	cubes = {}
	for dataset in datasets:
	ancestors.append(dataset['filename'])
	cube = dataset['cube']
	cubes[self._get_label(dataset)] = cube
	self._check_cube_dimensions(cube, plot_type)
	# Plot annual cycle
	plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
	plot_kwargs['axes'] = axes
	iris.plot.plot(cube, **plot_kwargs)
	# Default plot appearance
	multi_dataset_facets = self._get_multi_dataset_facets(datasets)
	axes.set_title(multi_dataset_facets['long_name'])
	axes.set_xlabel('Month')
	axes.set_ylabel(f"{short_name} [{multi_dataset_facets['units']}]")
	axes.set_xticks(range(1, 13), [str(m) for m in range(1, 13)])
	gridline_kwargs = self._get_gridline_kwargs(plot_type)
	if gridline_kwargs is not False:
	axes.grid(**gridline_kwargs)
	# Legend
	legend_kwargs = self.plots[plot_type]['legend_kwargs']
	if legend_kwargs is not False:
	axes.legend(**legend_kwargs)
	# Customize plot appearance
	self._process_pyplot_kwargs(plot_type, multi_dataset_facets)
	# Save plot
	plot_path = self.get_plot_path(plot_type, multi_dataset_facets)
	fig.savefig(plot_path, **self.cfg['savefig_kwargs'])
	logger.info("Wrote %s", plot_path)
	plt.close()
	# Save netCDF file
	netcdf_path = get_diagnostic_filename(Path(plot_path).stem, self.cfg)
	var_attrs = {
	n: datasets[0][n] for n in ('short_name', 'long_name', 'units')
	}
	io.save_1d_data(cubes, netcdf_path, 'month_number', var_attrs)
	# Provenance tracking
	caption = (f"Annual cycle of {multi_dataset_facets['long_name']} for "
	f"various datasets.")
	provenance_record = {
	'ancestors': ancestors,
	'authors': ['schlund_manuel'],
	'caption': caption,
	'plot_types': ['seas'],
	'long_names': [var_attrs['long_name']],
	}
	with ProvenanceLogger(self.cfg) as provenance_logger:
	provenance_logger.log(plot_path, provenance_record)
	provenance_logger.log(netcdf_path, provenance_record)