.. _multiplot: Multiplots ========== .. admonition:: Summary On this page, you will see how to * use .plot.multiplot to plot different plot kinds onto individual axes of a single figure * use .plot.multiplot to plot different plot kinds onto a single axis * use the PlotHelper adjust the style of the figure and each individual axis .. admonition:: Complete example: seaborn kde multiplot :class: dropdown .. literalinclude:: ../../../_cfg/SEIRD/multiverse_plots/eval.yml :language: yaml :dedent: 0 :start-after: ### Start --- multiplot :end-before: ### End --- multiplot The :py:func:~dantro.plot.funcs.multiplot.multiplot is a highly versatile yet simple way of plotting different types of plots onto a single figure. Essentially, the plot function allows to invoke arbitrary functions on the individual subplots of a figure, with data supplied via the plot configuration or the data transformation framework. In the example below, we are plotting an errorbar plot and a scatter plot onto a single subplot. The errorbar shows the density of susceptible agents with a standard deviation, and the scattered dots display the recovered agent density, with the density of infected agents as the hue: .. image:: ../../../_static/_gen/SEIRD/multiverse_plots/multiplot.pdf :width: 800 :alt: caption Perhaps a little much for a single frame: but hopefully it illustrates the capability of multiplotting! Such plots are simply achieved using the .plot.multiplot function, an overview of which is given in the following. Refer to the dantro multiplot documentation _ for further details. Example: Double kdeplot ^^^^^^^^^^^^^^^^^^^^^^^ Let us go through an example in detail. We wish to plot two smoothed densities showing the maximum peaks of infection for different values of the transmissivity in a single figure: .. image:: ../../../_static/_gen/SEIRD/multiverse_plots/double_kdeplot.pdf :width: 800 :alt: Double kdeplot Here, we are using a seaborn function _. On the left, we see the distribution of infection peaks for a lower transmissivity (0.4), on the right, for a higher transmissivity (0.6). Naturally, as the transmissivity increases, so does the height of peak infection. To create this plot, first select the data and transform it accordingly: .. code-block:: yaml double_kdeplot: # Base the plot on a creator (universe or multiverse) and .plot.multiplot: based_on: - .creator.multiverse - .plot.multiplot select_and_combine: fields: infected: path: densities transform: - .sel: [!dag_prev , {kind: infected}] # Calculate the maxima of the infection densities for two different # transmission rates transform: - .sel: [!dag_tag infected, {transmission rate: 0.4}] - np.max: [!dag_prev , 2] - .squeeze: !dag_prev tag: data_low - .sel: [!dag_tag infected, {transmission rate: 0.6}] - np.max: [!dag_prev , 2] - .squeeze: !dag_prev tag: data_high Naturally, this requires a sweep to have been performed over a transmission rate variable. The .squeeze transformation is required since we will be using the :py:funcseaborn.kdeplot function, which expects a flat array. Next, distribute the plots onto the axes. This requires setting up the figure accordingly using the :ref:PlotHelper : .. code-block:: yaml double_kdeplot: # Everything as above ... # Distribute the plots to_plot: [0, 0]: # select the axis, - function: sns.kdeplot # the plot function, data: !dag_result data_low # the data, label: $p_\mathrm{transmit}=0.4$ # and add any kwargs the plot function accepts fill: true [1, 0]: - function: sns.kdeplot data: !dag_result data_high label: $p_\mathrm{transmit}=0.6$ fill: true color: darkblue helper: setup_figure: ncols: 2 .. note:: We are using !dag_result placeholders _ rather than !dag_tag to pass data to the plot functions. This will however raise a small warning saying that many of the calculated dag tags are being ignored. To suppress this, you can exclude tags that are not used in the transformation DAG by removing them from the compute_only key in the plot configuration; for instance, above you can do .. code-block:: yaml compute_only: [] Multiple figures in a single axis ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ You can of course also plot multiple plots on a single axis; in this case, there is no need to setup the figure, and you also do not need to specify the axis on which to plot (since there is only one): .. code-block:: yaml double_kdeplot: # Everything as above ... # Distribute the plots to_plot: - function: sns.kdeplot data: !dag_result data_low label: $p_\mathrm{transmit}=0.4$ linestyle: dashed color: red - function: sns.kdeplot data: !dag_result data_high label: $p_\mathrm{transmit}=0.6$ color: yellow .. image:: ../../../_static/_gen/SEIRD/multiverse_plots/single_kdeplot.pdf :width: 400 :alt: Single kdeplot Importing callables on the fly ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Many matplotlib and seaborn plots are available with a simple call, as above; see here _ for a list of all available functions. However, you can also *import* callables on the fly by passing a 2-tuple of (module, name) to function: .. code-block:: yaml plot: to_plot: [0, 0]: - function: [xarray.plot, scatter] # args, kwargs here ... Modifying figure-level and axis-specific features ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Let us now go about modifying the plot appearance somewhat. To control the color, notice how in the previous example we set .. code-block:: yaml double_kdeplot: # Everything as above ... color: darkblue This makes color a *shared* keyword argument, one that is passed to *all* plot function calls. Specifying arguments within the to_plot entries can still overwrite the entries given on the shared level. .. code-block:: yaml double_kdeplot: # Everything as above ... to_plot: - function: sns.kdeplot data: !dag_result data_low color: red linestyle: dashed - function: sns.kdeplot data: !dag_result data_low color: yellow This gives you complete control over the appearance of each individual plot. With the :ref:plot helper framework , you can control such things as shared axes, the spacing between subplots, limits, and axis scales. For instance, to modify horizontal or vertical distancing between plots and have figures share their axes, do .. code-block:: yaml helper: # set the number of rows and columns with shared axes setup_figure: ncols: 2 nrows: 3 sharex: true sharey: true # adjust horizontal and vertical spacing between subplots subplots_adjust: hspace: 0.1 wspace: 0.3 The subplots_adjust entries are passed to :py:meth:matplotlib.figure.Figure.subplots_adjust, whereas setup_figure is passed to :py:func:matplotlib.pyplot.subplots. The :ref:PlotHelper  gives you a variety of options to adjust the plot appearance. You can choose to apply these to the entire plot, or only individual axes. For instance, .. code-block:: yaml multiplot: helper: set_limits: x: [0, 1] will set the x limits on *all* axes to [0, 1]. You can use the axis_specific helper to only modify certain axes: .. code-block:: yaml multiplot: helper: axis_specific: axis1: # some arbitrary axis name axis: [0, 0] # the x and y coordinates of the subplot in the plot set_limits: x: [0, 1] axis2: axis: [1, 0] set_limits: x: [-1, 0] All helper functions are available under axis_specific, giving you complete control over the appearance of the individual axes. Furthermore, helpers specified on the top level apply to all axes.