Add summary dictionary arguments

docs/source/api/managers.rst

@@ -1,10 +1,10 @@
 =============================================
-Managers for facetting and aesthetics mapping
+Managers for faceting and aesthetics mapping
 =============================================
 The classes in this module lay at the core of the library,
 and are consequently available at the ``arviz_plots`` top level namespace.
 
-They abstract all information regarding :term:`facetting` and :term:`aesthetic mapping`
+They abstract all information regarding :term:`faceting` and :term:`aesthetic mapping`
 in our :term:`chart` to prevent duplication and ensure coherence between
 the different functions.
 
@@ -43,7 +43,7 @@ Attributes
    PlotCollection.base_loop_dims
    PlotCollection.data
 
-Facetting and aesthetics mapping
+faceting and aesthetics mapping
 ................................
 
 .. autosummary::

docs/source/gallery/distribution/plot_dist_ecdf.py

@@ -1,7 +1,7 @@
 """
 # ECDF plot
 
-Facetted ECDF plots for 1D marginals of the distribution
+faceted ECDF plots for 1D marginals of the distribution
 
 ---
 

docs/source/gallery/distribution/plot_dist_hist.py

@@ -1,7 +1,7 @@
 """
 # Histogram plot
 
-Facetted histogram plots for 1D marginals of the distribution
+faceted histogram plots for 1D marginals of the distribution
 
 ---
 

docs/source/gallery/distribution/plot_dist_kde.py

@@ -1,7 +1,7 @@
 """
 # KDE plot
 
-Facetted KDE plots for 1D marginals of the distribution
+faceted KDE plots for 1D marginals of the distribution
 
 ---
 

docs/source/gallery/inference_diagnostics/plot_ess_evolution.py

@@ -1,7 +1,7 @@
 """
 # ESS Evolution plot
 
-Facetted plot with ESS 'bulk' and 'tail' for each variable
+faceted plot with ESS 'bulk' and 'tail' for each variable
 
 ---
 

docs/source/gallery/inference_diagnostics/plot_ess_local.py

@@ -1,7 +1,7 @@
 """
 # ESS Local plot
 
-Facetted local ESS plot
+faceted local ESS plot
 
 ---
 

docs/source/gallery/inference_diagnostics/plot_ess_quantile.py

@@ -1,7 +1,7 @@
 """
 # ESS Quantile plot
 
-Facetted quantile ESS plot
+faceted quantile ESS plot
 
 ---
 

docs/source/gallery/inference_diagnostics/plot_trace.py

@@ -1,7 +1,7 @@
 """
 # Trace plot
 
-Facetted plot with MCMC traces for each variable
+faceted plot with MCMC traces for each variable
 
 ---
 

docs/source/glossary.md

@@ -22,16 +22,19 @@ chart
 plot
 plots
   Area (or areas) where the data will be plotted into. A {term}`chart`
-  can contain multiple {term}`facetted` plots.
+  can contain multiple {term}`faceted` plots.
 
 artist
 artists
   Visual element added by `arviz-plots`
 
-facetting
-facetted
+faceting
+faceted
   Generate multiple similar {term}`plot` elements with each of them
   referring to a specific property or value of the data.
+
+Faceting is the process of segmenting your plotting area into a grid of smaller plots, each of which shows a distinct subset of the data depending on one or more categorical factors.
+
 :::
 
 ## Equivalences with library specific objects

src/arviz_plots/backend/plotly/__init__.py

@@ -151,7 +151,7 @@ def remove_row_col_from_doc(docstring):
 class PlotlyPlot:
     """Custom class to represent a :term:`plot` in Plotly.
 
-    Plotly supports :term:`facetting` but it doesn't have any object that represents
+    Plotly supports :term:`faceting` but it doesn't have any object that represents
     a :term:`plot`, instead, plotting happens only though the Plotly figure
     (which represents the :term:`chart`) indicating the row and column indexes
     in case plotting should happen to a single :term:`plot`.

src/arviz_plots/plot_collection.py

@@ -66,7 +66,7 @@ def subset_da(da, sel):
 
 
 def process_facet_dims(data, facet_dims):
-    """Process facetting dimensions.
+    """Process faceting dimensions.
 
     It takes into account the ``__variable__`` "special dimension name" and helps find out
     how many plots are needed.
@@ -87,7 +87,7 @@ def process_facet_dims(data, facet_dims):
         missing_dims = {k: v for k, v in missing_dims.items() if v}
         if any(missing_dims.values()):
             raise ValueError(
-                "All variables must have all facetting dimensions, but found the following "
+                "All variables must have all faceting dimensions, but found the following "
                 f"dims to be missing in these variables: {missing_dims}"
             )
         n_facets = np.prod([data.sizes[dim] for dim in facet_dims])
@@ -369,7 +369,7 @@ def generate_aes_dt(self, aes=None, **kwargs):
         Examples
         --------
         Initialize a `PlotCollection` with the rugby dataset as data.
-        Facetting and aesthetics mapping are independent. Thus, as
+        faceting and aesthetics mapping are independent. Thus, as
         we are limiting ourselves to the use of this method, we can
         provide an empty DataTree as ``viz_dt``.
 
@@ -641,7 +641,7 @@ def wrap(
             it can also be combined with other dimensions.
         col_wrap : int, default 4
             Number of columns in the generated grid. If more than `col_wrap`
-            plots are needed from :term:`facetting` according to `cols`,
+            plots are needed from :term:`faceting` according to `cols`,
             new rows are created.
         backend : str, optional
             Plotting backend.
@@ -959,7 +959,7 @@ def map(
             applied exactly once per :term:`plot`. Defaults to the value of `data`.
         coords : mapping, optional
             Dictionary of {coordinate names : coordinate values} that should
-            be used to subset the aes, data and viz objects before any facetting
+            be used to subset the aes, data and viz objects before any faceting
             or aesthetics mapping is applied.
         ignore_aes : set, optional
             Set of aesthetics present in ``aes`` that should be ignore for this

src/arviz_plots/plots/distplot.py

@@ -43,7 +43,7 @@ def plot_dist(
 ):
     """Plot 1D marginal densities in the style of John K. Kruschke’s book.
 
-    Generate :term:`facetted` :term:`plots` with: a graphical representation of 1D marginal
+    Generate :term:`faceted` :term:`plots` with: a graphical representation of 1D marginal
     densities (as KDE, histogram, ECDF or dotplot), a credible interval and a point estimate.
 
     Parameters

src/arviz_plots/plots/essplot.py

@@ -529,7 +529,7 @@ def plot_ess(
             **min_ess_kwargs,
         )
 
-    # plot titles for each facetted subplot
+    # plot titles for each faceted subplot
     title_kwargs = copy(plot_kwargs.get("title", {}))
 
     if title_kwargs is not False:

src/arviz_plots/plots/evolutionplot.py

@@ -581,7 +581,7 @@ def compute_ess_dataset(
             **min_ess_kwargs,
         )
 
-    # plot titles for each facetted subplot
+    # plot titles for each faceted subplot
     title_kwargs = copy(plot_kwargs.get("title", {}))
 
     if title_kwargs is not False:

tests/test_plot_collection.py

@@ -26,7 +26,7 @@ def dataset(seed=31):
 @pytest.mark.parametrize("backend", ["matplotlib", "bokeh", "plotly"])
 @pytest.mark.usefixtures("clean_plots")
 @pytest.mark.usefixtures("check_skips")
-class TestFacetting:
+class Testfaceting:
     def test_wrap(self, dataset, backend):
         pc = PlotCollection.wrap(
             dataset[["theta", "eta"]], backend=backend, cols=["hierarchy"], col_wrap=4