Mapping legend colorbar

pyleoclim/core/multiplegeoseries.py

@@ -85,7 +85,7 @@ def __init__(self, series_list, time_unit=None, label=None):
 
     def map(self, marker='archiveType', hue='archiveType', size=None, cmap=None,
             edgecolor='k', projection='auto',
-            proj_default=True, crit_dist=5000, colorbar=True,
+            proj_default=True, crit_dist=5000, colorbar=True,color_scale_type=None,
             background=True, borders=False, coastline=True,rivers=False, lakes=False, land=True,ocean=True,
             figsize=None, fig=None, scatter_kwargs=None, gridspec_kwargs=None, legend=True, gridspec_slot=None,
             lgd_kwargs=None, savefig_settings=None, **kwargs):
@@ -180,6 +180,10 @@ def map(self, marker='archiveType', hue='archiveType', size=None, cmap=None,
             Whether to draw a colorbar on the figure if the data associated with hue are numeric.
             Default is True.
 
+        color_scale_type : str, optional
+            Setting to "discrete" will force a discrete color scale with a default bin number of max(11, n) where n=number of unique values$^{\frac{1}{2}}$
+            Default is None
+
         lgd_kwargs : dict, optional
             Dictionary of arguments for `matplotlib.pyplot.legend <https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.pyplot.legend.html>`_.
 
@@ -406,6 +410,33 @@ def pca(self, weights=None,missing='fill-em',tol_em=5e-03, max_em_iter=100,**pca
                                     'gridspec_kwargs': {'width_ratios': [.5, 1,14, 4], 'wspace':-.065},
                                     'lgd_kwargs':{'bbox_to_anchor':[-.015,1]}})
 
+
+        One can also configure how hue information gets displayed:
+
+        .. jupyter-execute::
+
+            # Dashboard with hue as a legend category
+            res.modeplot(index=1, size='elevation', map_kwargs= dict(colorbar=False))
+
+            # Dashboard with discrete colorbar
+            res.modeplot(index=1, size='elevation', map_kwargs= dict(color_scale_type='discrete'))
+
+            # Dashboard with custom scalar mappable
+            sm = pyleo.utils.mapping.make_scalar_mappable(cmap='vlag', hue_vect=res.eigvecs[:, 1], n=21,norm_kwargs={'vcenter': -.5})
+            res.modeplot(index=1, size='elevation', map_kwargs= dict(scalar_mappable=sm))
+
+
+        and customize the marker variable:
+
+        .. jupyter-execute::
+
+            # Dashboard with marker set to archiveType
+            res.modeplot(index=1, marker='archiveType', size='elevation', map_kwargs= dict(colorbar=True))
+
+            # Dashboard with marker set to archiveType
+            res.modeplot(index=1, marker='observationType', size='elevation', map_kwargs= dict(colorbar=True))
+
+
         '''
         # apply PCA fom parent class
         pca_res = super().pca(weights=weights,missing=missing,tol_em=tol_em,

pyleoclim/core/multivardecomp.py

@@ -184,8 +184,9 @@ def screeplot(self, figsize=[6, 4], uq='N82', title=None, ax=None, savefig_setti
 
     def modeplot(self, index=0, figsize=[8, 8], fig=None, savefig_settings=None,gs=None,
                  title=None, title_kwargs=None, spec_method='mtm', cmap=None,
-                 hue='EOF', marker='archiveType', size=None, scatter_kwargs=None,
+                 hue='EOF', marker=None, size=None, scatter_kwargs=None,
                  flip = False, map_kwargs=None, gridspec_kwargs=None):
+
         ''' Dashboard visualizing the properties of a given mode, including:
             1. The temporal coefficient (PC or similar)
             2. its spectrum
@@ -248,8 +249,9 @@ def modeplot(self, index=0, figsize=[8, 8], fig=None, savefig_settings=None,gs=N
             - gridspec_kwargs: dict; Optional values for adjusting the arrangement of the colorbar, map and legend in the map subplot
             - legend: bool; Whether to draw a legend on the figure. Default is True
             - colorbar: bool; Whether to draw a colorbar on the figure if the data associated with hue are numeric. Default is True
-            The default is None.
-
+            - color_scale_type : str; Setting to "discrete" will force a discrete color scale with a default bin number of max(11, n) where n=number of unique values$^{\frac{1}{2}}$. Default is None
+            - scalar_mappable: matplotlib.cm.ScalarMappable; can be used to pass a matplotlib scalar mappable. See pyleoclim.utils.plotting.make_scalar_mappable for documentation on using the Pyleoclim utility, or the `Matplotlib tutorial on customizing colorbars <https://matplotlib.org/stable/users/explain/colors/colorbar_only.html>`_.
+
         scatter_kwargs : dict, optional
             Optional arguments configuring how data are plotted on a map. See description of scatter_kwargs in pyleoclim.utils.mapping.scatter_map
 
@@ -263,7 +265,8 @@ def modeplot(self, index=0, figsize=[8, 8], fig=None, savefig_settings=None,gs=N
 
         marker : string, optional
             (only applicable if using scatter map) Grouping variable that will produce points with different markers. Can have a numeric dtype but will always be treated as categorical.
-            The default is 'archiveType'.
+            The default is None, which will produce circle markers. Alternatively, pass the name of a categorical variable, e.g. 'archiveType'. If 'archiveType' is specified, will attempt to use pyleoclim archiveType markers mapping, defaulting to '?' where values are unavailable.
+
 
         Returns
         -------
@@ -274,6 +277,7 @@ def modeplot(self, index=0, figsize=[8, 8], fig=None, savefig_settings=None,gs=N
         ax : dict
             dictionary of matplotlib ax
 
+
         See also
         --------
 
@@ -284,7 +288,10 @@ def modeplot(self, index=0, figsize=[8, 8], fig=None, savefig_settings=None,gs=N
         pyleoclim.utils.tsutils.eff_sample_size : Effective sample size
 
         pyleoclim.utils.mapping.scatter_map : mapping
-
+
+        pyleoclim.utils.plotting.make_scalar_mappable : Custom scalar mappable
+
+
         '''
         from ..core.multiplegeoseries import MultipleGeoSeries
 
@@ -351,12 +358,24 @@ def modeplot(self, index=0, figsize=[8, 8], fig=None, savefig_settings=None,gs=N
         map_gridspec_kwargs = map_kwargs.pop('gridspec_kwargs', {})
         lgd_kwargs = map_kwargs.pop('lgd_kwargs', {})
 
+        if scatter_kwargs is None:
+            scatter_kwargs = {}
+        else:
+            scatter_kwargs = scatter_kwargs.copy()
 
         if 'edgecolor' in map_kwargs.keys():
             scatter_kwargs.update({'edgecolor': map_kwargs['edgecolor']})
 
         legend = map_kwargs.pop('legend', True)
         colorbar = map_kwargs.pop('colorbar', True)
+        color_scale_type = map_kwargs.pop('color_scale_type', None)
+
+        if marker is None:
+            marker = scatter_kwargs.pop('marker', None)
+        if hue is None:
+            hue = scatter_kwargs.pop('hue', None)
+        if size is None:
+            size = scatter_kwargs.pop('size', None)
 
         if isinstance(self.orig, MultipleGeoSeries):
             # This makes a bare bones dataframe from a MultipleGeoSeries object
@@ -373,9 +392,9 @@ def modeplot(self, index=0, figsize=[8, 8], fig=None, savefig_settings=None,gs=N
                                                rivers=rivers, lakes=lakes,
                                                ocean=ocean, land=land, extent=extent,
                                                figsize=None, scatter_kwargs=scatter_kwargs, lgd_kwargs=lgd_kwargs,
-                                               gridspec_kwargs=map_gridspec_kwargs, colorbar=colorbar,
+                                               gridspec_kwargs=map_gridspec_kwargs, colorbar=colorbar, color_scale_type=color_scale_type,
                                                legend=legend, cmap=cmap,
-                                               fig=fig, gs_slot=gs[-1, :]) #label rf'$EOF_{index + 1}$'
+                                               fig=fig, gs_slot=gs[-1, :], **map_kwargs) #label rf'$EOF_{index + 1}$'
 
         else: # it must be a plain old MultipleSeries. No map for you! Just a spaghetti plot with the standardizes series
             ax['map'] = fig.add_subplot(gs[1:, :])