equal colors and step by 0.1

vega · Nov 10, 2023 · 7bc3654 · 7bc3654
1 parent 5998155
commit 7bc3654
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diff --git a/tests/examples_arguments_syntax/interactive_aggregation.py b/tests/examples_arguments_syntax/interactive_aggregation.py
@@ -5,35 +5,35 @@
 threshold as rule where the datapoints on the left-side are aggregated and on the
 right-side are drawn as is. 
 The ability to slide back and fourth may help you understand how the visualization
-represents the aggregation. Example inspired by @dwootton.
+represents the aggregation. Adapted from an example by @dwootton.
 """
 # category: interactive charts
 import altair as alt
 from vega_datasets import data
 
 source = data.movies()
 
-slider = alt.binding_range(min=1, max=10, step=1)
-threshold = alt.param(name='threshold', value=5, bind=slider)
+slider = alt.binding_range(min=0, max=10, step=0.1)
+threshold = alt.param(name="threshold", value=5, bind=slider)
 
 alt.layer(
     alt.Chart(source).mark_circle().encode(
-        x=alt.X('IMDB_Rating:Q', title='IMDB Rating'),
-        y=alt.Y('Rotten_Tomatoes_Rating:Q', title='Rotten Tomatoes Rating')
+        x=alt.X("IMDB_Rating:Q", title="IMDB Rating"),
+        y=alt.Y("Rotten_Tomatoes_Rating:Q", title="Rotten Tomatoes Rating")
     ).transform_filter(
-        alt.datum['IMDB_Rating'] > threshold
+        alt.datum["IMDB_Rating"] > threshold
     ),
 
-    alt.Chart(source).mark_circle(color='yellowgreen').encode(
-        x=alt.X('IMDB_Rating:Q', bin=alt.Bin(maxbins=10)),
-        y=alt.Y('Rotten_Tomatoes_Rating:Q', bin=alt.Bin(maxbins=10)),
-        size=alt.Size('count()', scale=alt.Scale(domain=[0,160]))
+    alt.Chart(source).mark_circle().encode(
+        x=alt.X("IMDB_Rating:Q", bin=alt.Bin(maxbins=10)),
+        y=alt.Y("Rotten_Tomatoes_Rating:Q", bin=alt.Bin(maxbins=10)),
+        size=alt.Size("count()", scale=alt.Scale(domain=[0,160]))
     ).transform_filter(
-        alt.datum['IMDB_Rating'] < threshold
+        alt.datum["IMDB_Rating"] < threshold
     ),
 
-    alt.Chart().mark_rule(color='gray').encode(
+    alt.Chart().mark_rule(color="gray").encode(
         strokeWidth=alt.StrokeWidth(value=3),
-        x=alt.X(datum=alt.expr(threshold.name), type='quantitative')
+        x=alt.X(datum=alt.expr(threshold.name), type="quantitative")
     )
 ).add_params(threshold)
diff --git a/tests/examples_methods_syntax/interactive_aggregation.py b/tests/examples_methods_syntax/interactive_aggregation.py
@@ -5,35 +5,35 @@
 threshold as rule where the datapoints on the left-side are aggregated and on the
 right-side are drawn as is. 
 The ability to slide back and fourth may help you understand how the visualization
-represents the aggregation. Example inspired by @dwootton.
+represents the aggregation. Adapted from an example by @dwootton.
 """
 # category: interactive charts
 import altair as alt
 from vega_datasets import data
 
 source = data.movies()
 
-slider = alt.binding_range(min=1, max=10, step=1)
-threshold = alt.param(name='threshold', value=5, bind=slider)
+slider = alt.binding_range(min=0, max=10, step=0.1)
+threshold = alt.param(name="threshold", value=5, bind=slider)
 
 alt.layer(
     alt.Chart(source).mark_circle().encode(
-        x=alt.X('IMDB_Rating:Q').title('IMDB Rating'),
-        y=alt.Y('Rotten_Tomatoes_Rating:Q').title('Rotten Tomatoes Rating')
+        x=alt.X("IMDB_Rating:Q").title("IMDB Rating"),
+        y=alt.Y("Rotten_Tomatoes_Rating:Q").title("Rotten Tomatoes Rating")
     ).transform_filter(
-        alt.datum['IMDB_Rating'] > threshold
+        alt.datum["IMDB_Rating"] > threshold
     ),
 
-    alt.Chart(source).mark_circle(color='yellowgreen').encode(
-        x=alt.X('IMDB_Rating:Q').bin(maxbins=10),
-        y=alt.Y('Rotten_Tomatoes_Rating:Q').bin(maxbins=10),
-        size=alt.Size('count()').scale(domain=[0,160])
+    alt.Chart(source).mark_circle().encode(
+        x=alt.X("IMDB_Rating:Q").bin(maxbins=10),
+        y=alt.Y("Rotten_Tomatoes_Rating:Q").bin(maxbins=10),
+        size=alt.Size("count()").scale(domain=[0,160])
     ).transform_filter(
-        alt.datum['IMDB_Rating'] < threshold
+        alt.datum["IMDB_Rating"] < threshold
     ),
 
-    alt.Chart().mark_rule(color='gray').encode(
+    alt.Chart().mark_rule(color="gray").encode(
         strokeWidth=alt.StrokeWidth(value=3),
-        x=alt.X(datum=alt.expr(threshold.name), type='quantitative')
+        x=alt.X(datum=alt.expr(threshold.name), type="quantitative")
     )
 ).add_params(threshold)