Fixed plot type order and monitor recipe

doc/sphinx/source/recipes/recipe_monitor.rst

@@ -211,15 +211,16 @@ Zonal mean profile of ta including a reference dataset.
 
 1D profile of ta including a reference dataset.
 
-.. _fig_hovmoeller_z_vs_time_with_ref:
-.. figure::  /recipes/figures/monitor/hovmoeller_z_vs_time_with_ref.png
+.. _fig_variable_vs_lat_with_ref:
+.. figure::  /recipes/figures/monitor/variable_vs_lat_with_ref.png
    :align:   center
    :width:   14cm
 
-Hovmoeller plot (pressure vs time) of ta including a reference dataset.
-.. _fig_variable_vs_lat_with_ref:
-.. figure::  /recipes/figures/monitor/variable_vs_lat_with_ref.png
+Zonal mean pr including a reference dataset.
+
+.. _fig_hovmoeller_z_vs_time_with_ref:
+.. figure::  /recipes/figures/monitor/hovmoeller_z_vs_time_with_ref.png
    :align:   center
    :width:   14cm
 
-1D profile of pr over latitude.
+Hovmoeller plot (pressure vs time) of ta including a reference dataset.

esmvaltool/diag_scripts/monitor/multi_datasets.py

@@ -31,22 +31,7 @@
       datasets need to be given on the same horizontal and vertical grid (you
       can use the preprocessors :func:`esmvalcore.preprocessor.regrid` and
       :func:`esmvalcore.preprocessor.extract_levels` for this). Input data
-      needs to be 2D with dimensions `latitude`, `height`/`air_pressure`.
-<<<<<<< HEAD
-    - Hovmoeller altitude vs time (plot type ``hovmoeller_z_vs_time``):
-      for each variable and dataset, an individual profile is plotted. If a
-      reference dataset is defined, also include this dataset and a bias plot
-      into the figure. Note that if a reference dataset is defined, all input
-      datasets need to be given on the same horizontal and vertical grid (you
-      can use the preprocessors :func:`esmvalcore.preprocessor.regrid` and
-      :func:`esmvalcore.preprocessor.extract_levels` for this). Input data
-      needs to be 2D with dimensions `time`, `height`/`air_pressure`.
-=======
->>>>>>> origin/main
-    - Variable vs. latitude plot (plot type ``variable_vs_lat``):
-      for each variable separately, all datasets are plotted in one
-      single figure. Input data needs to be 1D with single
-      dimension `latitude`.
+      needs to be 2D with dimensions `latitude`, `altitude`/`air_pressure`.
 
       .. warning::
 
@@ -57,7 +42,19 @@
 
     - 1D profiles (plot type ``1d_profile``): for each variable separately, all
       datasets are plotted in one single figure. Input data needs to be 1D with
-      single dimension `height` / `air_pressure`
+      single dimension `altitude` / `air_pressure`
+    - Variable vs. latitude plot (plot type ``variable_vs_lat``):
+      for each variable separately, all datasets are plotted in one
+      single figure. Input data needs to be 1D with single
+      dimension `latitude`.
+    - Hovmoeller Z vs time (plot type ``hovmoeller_z_vs_time``): for each
+      variable and dataset, an individual profile is plotted. If a reference
+      dataset is defined, also include this dataset and a bias plot into the
+      figure. Note that if a reference dataset is defined, all input datasets
+      need to be given on the same horizontal and vertical grid (you can use
+      the preprocessors :func:`esmvalcore.preprocessor.regrid` and
+      :func:`esmvalcore.preprocessor.extract_levels` for this). Input data
+      needs to be 2D with dimensions `time`, `altitude`/`air_pressure`.
 
 Author
 ------
@@ -81,7 +78,7 @@
 plots: dict, optional
     Plot types plotted by this diagnostic (see list above). Dictionary keys
     must be ``timeseries``, ``annual_cycle``, ``map``, ``zonal_mean_profile``,
-    ``1d_profile`` or ``hovmoeller_z_vs_time``.
+    ``1d_profile``, ``variable_vs_lat``, or ``hovmoeller_z_vs_time``.
     Dictionary values are dictionaries used as options for the corresponding
     plot. The allowed options for the different plot types are given below.
 plot_filename: str, optional
@@ -138,9 +135,9 @@
     to something like  ``ambiguous_project``. Examples: ``title: 'Awesome Plot
     of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
 time_format: str, optional (default: None)
-    :func:`~datetime.strftime` format string that is used to format the time
-    axis using :class:`matplotlib.dates.DateFormatter`. If ``None``, use the
-    default formatting imposed by the iris plotting function.
+    :func:`~datetime.datetime.strftime` format string that is used to format
+    the time axis using :class:`matplotlib.dates.DateFormatter`. If ``None``,
+    use the default formatting imposed by the iris plotting function.
 
 Configuration options for plot type ``annual_cycle``
 ----------------------------------------------------
@@ -292,7 +289,7 @@
 plot_func: str, optional (default: 'contourf')
     Plot function used to plot the profiles. Must be a function of
     :mod:`iris.plot` that supports plotting of 2D cubes with coordinates
-    latitude and height/air_pressure.
+    latitude and altitude/air_pressure.
 plot_kwargs: dict, optional
     Optional keyword arguments for the plot function defined by ``plot_func``.
     Dictionary keys are elements identified by ``facet_used_for_labels`` or
@@ -334,6 +331,75 @@
     coordinates. Can be adjusted to avoid overlap with the figure. Only
     relevant if ``show_stats: true``.
 
+Configuration options for plot type ``1d_profile``
+--------------------------------------------------
+aspect_ratio: float, optional (default: 1.5)
+    Aspect ratio of the plot. The default value results in a slender upright
+    plot.
+gridline_kwargs: dict, optional
+    Optional keyword arguments for grid lines. By default, ``color: lightgrey,
+    alpha: 0.5`` are used. Use ``gridline_kwargs: false`` to not show grid
+    lines.
+legend_kwargs: dict, optional
+    Optional keyword arguments for :func:`matplotlib.pyplot.legend`. Use
+    ``legend_kwargs: false`` to not show legends.
+log_x: bool, optional (default: False)
+    Use logarithmic X-axis. Note that for the logarithmic x axis tickmarks are
+    set so that minor tickmarks show up. Setting of individual tickmarks by
+    pyplot_kwargs is not recommended in this case.
+log_y: bool, optional (default: True)
+    Use logarithmic Y-axis.
+plot_kwargs: dict, optional
+    Optional keyword arguments for :func:`iris.plot.plot`. Dictionary keys are
+    elements identified by ``facet_used_for_labels`` or ``default``, e.g.,
+    ``CMIP6`` if ``facet_used_for_labels: project`` or ``historical`` if
+    ``facet_used_for_labels: exp``. Dictionary values are dictionaries used as
+    keyword arguments for :func:`iris.plot.plot`. String arguments can include
+    facets in curly brackets which will be derived from the corresponding
+    dataset, e.g., ``{project}``, ``{short_name}``, ``{exp}``. Examples:
+    ``default: {linestyle: '-', label: '{project}'}, CMIP6: {color: red,
+    linestyle: '--'}, OBS: {color: black}``.
+pyplot_kwargs: dict, optional
+    Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
+    are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
+    single argument for these functions. String arguments can include facets in
+    curly brackets which will be derived from the datasets plotted in the
+    corresponding plot, e.g., ``{short_name}``, ``{exp}``. Facets like
+    ``{project}`` that vary between the different datasets will be transformed
+    to something like  ``ambiguous_project``. Examples: ``title: 'Awesome Plot
+    of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+show_y_minor_ticklabels: bool, optional (default: False)
+    Show tick labels for the minor ticks on the Y axis.
+
+Configuration options for plot type ``variable_vs_lat``
+-------------------------------------------------------
+gridline_kwargs: dict, optional
+    Optional keyword arguments for grid lines. By default, ``color: lightgrey,
+    alpha: 0.5`` are used. Use ``gridline_kwargs: false`` to not show grid
+    lines.
+legend_kwargs: dict, optional
+    Optional keyword arguments for :func:`matplotlib.pyplot.legend`. Use
+    ``legend_kwargs: false`` to not show legends.
+plot_kwargs: dict, optional
+    Optional keyword arguments for :func:`iris.plot.plot`. Dictionary keys are
+    elements identified by ``facet_used_for_labels`` or ``default``, e.g.,
+    ``CMIP6`` if ``facet_used_for_labels: project`` or ``historical`` if
+    ``facet_used_for_labels: exp``. Dictionary values are dictionaries used as
+    keyword arguments for :func:`iris.plot.plot`. String arguments can include
+    facets in curly brackets which will be derived from the corresponding
+    dataset, e.g., ``{project}``, ``{short_name}``, ``{exp}``. Examples:
+    ``default: {linestyle: '-', label: '{project}'}, CMIP6: {color: red,
+    linestyle: '--'}, OBS: {color: black}``.
+pyplot_kwargs: dict, optional
+    Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
+    are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
+    single argument for these functions. String arguments can include facets in
+    curly brackets which will be derived from the datasets plotted in the
+    corresponding plot, e.g., ``{short_name}``, ``{exp}``. Facets like
+    ``{project}`` that vary between the different datasets will be transformed
+    to something like  ``ambiguous_project``. Examples: ``title: 'Awesome Plot
+    of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+
 Configuration options for plot type ``hovmoeller_z_vs_time``
 ------------------------------------------------------------
 cbar_label: str, optional (default: '{short_name} [{units}]')
@@ -370,7 +436,7 @@
 plot_func: str, optional (default: 'contourf')
     Plot function used to plot the profiles. Must be a function of
     :mod:`iris.plot` that supports plotting of 2D cubes with coordinates
-    latitude and height/air_pressure.
+    latitude and altitude/air_pressure.
 plot_kwargs: dict, optional
     Optional keyword arguments for the plot function defined by ``plot_func``.
     Dictionary keys are elements identified by ``facet_used_for_labels`` or
@@ -412,78 +478,9 @@
     coordinates. Can be adjusted to avoid overlap with the figure. Only
     relevant if ``show_stats: true``.
 time_format: str, optional (default: None)
-    :func:`~datetime.strftime` format string that is used to format the time
-    axis using :class:`matplotlib.dates.DateFormatter`. If ``None``, use the
-    default formatting imposed by the iris plotting function.
-
-Configuration options for plot type ``1d_profile``
---------------------------------------------------
-aspect_ratio: float, optional (default: 1.5)
-    Aspect ratio of the plot. The default value results in a slender upright
-    plot.
-gridline_kwargs: dict, optional
-    Optional keyword arguments for grid lines. By default, ``color: lightgrey,
-    alpha: 0.5`` are used. Use ``gridline_kwargs: false`` to not show grid
-    lines.
-legend_kwargs: dict, optional
-    Optional keyword arguments for :func:`matplotlib.pyplot.legend`. Use
-    ``legend_kwargs: false`` to not show legends.
-log_x: bool, optional (default: False)
-    Use logarithmic X-axis. Note that for the logarithmic x axis tickmarks are
-    set so that minor tickmarks show up. Setting of individual tickmarks by
-    pyplot_kwargs is not recommended in this case.
-log_y: bool, optional (default: True)
-    Use logarithmic Y-axis.
-plot_kwargs: dict, optional
-    Optional keyword arguments for :func:`iris.plot.plot`. Dictionary keys are
-    elements identified by ``facet_used_for_labels`` or ``default``, e.g.,
-    ``CMIP6`` if ``facet_used_for_labels: project`` or ``historical`` if
-    ``facet_used_for_labels: exp``. Dictionary values are dictionaries used as
-    keyword arguments for :func:`iris.plot.plot`. String arguments can include
-    facets in curly brackets which will be derived from the corresponding
-    dataset, e.g., ``{project}``, ``{short_name}``, ``{exp}``. Examples:
-    ``default: {linestyle: '-', label: '{project}'}, CMIP6: {color: red,
-    linestyle: '--'}, OBS: {color: black}``.
-pyplot_kwargs: dict, optional
-    Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
-    are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
-    single argument for these functions. String arguments can include facets in
-    curly brackets which will be derived from the datasets plotted in the
-    corresponding plot, e.g., ``{short_name}``, ``{exp}``. Facets like
-    ``{project}`` that vary between the different datasets will be transformed
-    to something like  ``ambiguous_project``. Examples: ``title: 'Awesome Plot
-    of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
-show_y_minor_ticklabels: bool, optional (default: False)
-    Show tick labels for the minor ticks on the Y axis.
-
-Configuration options for plot type ``variable_vs_lat``
--------------------------------------------------------
-gridline_kwargs: dict, optional
-    Optional keyword arguments for grid lines. By default, ``color: lightgrey,
-    alpha: 0.5`` are used. Use ``gridline_kwargs: false`` to not show grid
-    lines.
-legend_kwargs: dict, optional
-    Optional keyword arguments for :func:`matplotlib.pyplot.legend`. Use
-    ``legend_kwargs: false`` to not show legends.
-plot_kwargs: dict, optional
-    Optional keyword arguments for :func:`iris.plot.plot`. Dictionary keys are
-    elements identified by ``facet_used_for_labels`` or ``default``, e.g.,
-    ``CMIP6`` if ``facet_used_for_labels: project`` or ``historical`` if
-    ``facet_used_for_labels: exp``. Dictionary values are dictionaries used as
-    keyword arguments for :func:`iris.plot.plot`. String arguments can include
-    facets in curly brackets which will be derived from the corresponding
-    dataset, e.g., ``{project}``, ``{short_name}``, ``{exp}``. Examples:
-    ``default: {linestyle: '-', label: '{project}'}, CMIP6: {color: red,
-    linestyle: '--'}, OBS: {color: black}``.
-pyplot_kwargs: dict, optional
-    Optional calls to functions of :mod:`matplotlib.pyplot`. Dictionary keys
-    are functions of :mod:`matplotlib.pyplot`. Dictionary values are used as
-    single argument for these functions. String arguments can include facets in
-    curly brackets which will be derived from the datasets plotted in the
-    corresponding plot, e.g., ``{short_name}``, ``{exp}``. Facets like
-    ``{project}`` that vary between the different datasets will be transformed
-    to something like  ``ambiguous_project``. Examples: ``title: 'Awesome Plot
-    of {long_name}'``, ``xlabel: '{short_name}'``, ``xlim: [0, 5]``.
+    :func:`~datetime.datetime.strftime` format string that is used to format
+    the time axis using :class:`matplotlib.dates.DateFormatter`. If ``None``,
+    use the default formatting imposed by the iris plotting function.
 
 .. hint::
 
@@ -757,10 +754,10 @@ def _add_stats(self, plot_type, axes, dim_coords, dataset,
         # Different options for the different plots types
         fontsize = 6.0
         y_pos = 0.95
-        if plot_type == 'map':
-            x_pos_bias = self.plots[plot_type]['x_pos_stats_bias']
-            x_pos = self.plots[plot_type]['x_pos_stats_avg']
-        elif plot_type in ['zonal_mean_profile', 'hovmoeller_z_vs_time']:
+        if all([
+                'x_pos_stats_avg' in self.plots[plot_type],
+                'x_pos_stats_bias' in self.plots[plot_type],
+        ]):
             x_pos_bias = self.plots[plot_type]['x_pos_stats_bias']
             x_pos = self.plots[plot_type]['x_pos_stats_avg']
         else:
@@ -1295,14 +1292,14 @@ def _plot_hovmoeller_z_vs_time_without_ref(self, plot_func, dataset):
             axes = fig.add_subplot()
             plot_kwargs = self._get_plot_kwargs(plot_type, dataset)
             plot_kwargs['axes'] = axes
-            plot_zonal_mean_profile = plot_func(cube, **plot_kwargs)
+            plot_hovmoeller = plot_func(cube, **plot_kwargs)
 
             # Print statistics if desired
             self._add_stats(plot_type, axes, dim_coords_dat, dataset)
 
             # Setup colorbar
             fontsize = self.plots[plot_type]['fontsize']
-            colorbar = fig.colorbar(plot_zonal_mean_profile,
+            colorbar = fig.colorbar(plot_hovmoeller,
                                     ax=axes,
                                     **self._get_cbar_kwargs(plot_type))
             colorbar.set_label(self._get_cbar_label(plot_type, dataset),
@@ -2126,8 +2123,8 @@ def compute(self):
             self.create_map_plot(datasets)
             self.create_zonal_mean_profile_plot(datasets)
             self.create_1d_profile_plot(datasets)
-            self.create_hovmoeller_z_vs_time_plot(datasets)
             self.create_variable_vs_lat_plot(datasets)
+            self.create_hovmoeller_z_vs_time_plot(datasets)
 
 
 def main():