Support choropleths for Matplotlib backend via Altair (#322)

* Use altair/Choropleth.py in MatpltlibRenderer.py to render choropleths; include warning

* Remove newline

* Move import; uncomment return statement

* minor edit to warning text

* Add message to info rather than Python warning

* Move message into info tab; correct warning message based on Doris's commit in caa9d15; condition on matplotlib

* Add corresponding matplotlib test in test_vis.py

Co-authored-by: Doris Lee <dorisjunglinlee@gmail.com>

lux/action/univariate.py

@@ -84,10 +84,13 @@ def univariate(ldf, *args):
             examples = f" (e.g., {possible_attributes[0]})"
         intent = [lux.Clause("?", data_type="geographical"), lux.Clause("?", data_model="measure")]
         intent.extend(filter_specs)
+        long_description = f"Geographical displays <a href='https://en.wikipedia.org/wiki/Choropleth_map'>choropleths</a> for geographic attribute{examples}, with colors indicating the average measure values. "
+        if lux.config.plotting_backend == "matplotlib":
+            long_description += "The map visualizations from the 'Geographical' tab are rendered using <a href='https://altair-viz.github.io/'>Altair</a>. Lux does not currently support geographical maps with Matplotlib. If you would like this feature, please leave us a comment at <a href='https://github.com/lux-org/lux/issues/310'>issue #310</a> to let us know!"
         recommendation = {
             "action": "Geographical",
             "description": "Show choropleth maps of <p class='highlight-descriptor'>geographic</p> attributes",
-            "long_description": f"Occurence displays choropleths of averages for some geographic attribute{examples}. Visualizations are ranked by diversity of the geographic attribute.",
+            "long_description": long_description,
         }
     elif data_type_constraint == "temporal":
         intent = [lux.Clause("?", data_type="temporal")]

lux/vislib/altair/Choropleth.py

@@ -37,7 +37,7 @@ def __init__(self, dobj):
         super().__init__(dobj)
 
     def __repr__(self):
-        return f"Proportional Symbol Map <{str(self.vis)}>"
+        return f"Choropleth Map <{str(self.vis)}>"
 
     def initialize_chart(self):
         x_attr = self.vis.get_attr_by_channel("x")[0]

lux/vislib/matplotlib/MatplotlibRenderer.py

@@ -20,6 +20,7 @@
 from lux.vislib.matplotlib.LineChart import LineChart
 from lux.vislib.matplotlib.Histogram import Histogram
 from lux.vislib.matplotlib.Heatmap import Heatmap
+from lux.vislib.altair.AltairRenderer import AltairRenderer
 import matplotlib.pyplot as plt
 from lux.utils.utils import matplotlib_setup
 
@@ -81,6 +82,8 @@ def create_vis(self, vis, standalone=True):
             chart = LineChart(vis, fig, ax)
         elif vis.mark == "heatmap":
             chart = Heatmap(vis, fig, ax)
+        elif vis.mark == "geographical":
+            return AltairRenderer().create_vis(vis, False)
         else:
             chart = None
             return chart

tests/test_vis.py

@@ -465,7 +465,7 @@ def test_vegalite_default_actions_registered_2(global_var):
     df["magnitude"] = np.random.randint(0, 20, size=len(df))
     lux.config.plotting_backend = "vegalite"
 
-    # Symbol Map
+    # Choropleth Map
     assert "Geographical" in df.recommendation
     assert len(df.recommendation["Geographical"]) > 0
 
@@ -499,6 +499,28 @@ def test_matplotlib_default_actions_registered(global_var):
     assert len(df.recommendation["Correlation"]) > 0
 
 
+def test_matplotlib_default_actions_registered_2(global_var):
+    import numpy as np
+
+    df = pd.read_csv(
+        "https://raw.githubusercontent.com/altair-viz/vega_datasets/master/vega_datasets/_data/airports.csv"
+    )
+    df["magnitude"] = np.random.randint(0, 20, size=len(df))
+    lux.config.plotting_backend = "matplotlib"
+
+    # Choropleth Map
+    assert "Geographical" in df.recommendation
+    assert len(df.recommendation["Geographical"]) > 0
+
+    # Occurrence Chart
+    assert "Occurrence" in df.recommendation
+    assert len(df.recommendation["Occurrence"]) > 0
+
+    # Scatter Chart
+    assert "Correlation" in df.recommendation
+    assert len(df.recommendation["Correlation"]) > 0
+
+
 def test_vegalite_heatmap_flag_config():
     df = pd.read_csv("https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/airbnb_nyc.csv")
     lux.config.plotting_backend = "vegalite"