docs: update sankey diagram (#9077)

## Description of changes

this started as trying to get dynamic light/dark mode for plotting (with
the sankey diagram, but any) working with Quarto. I still couldn't
figure it out. there's definitely a path that's like save both versions
of the image and use JavaScript to dynamically load the right one.
another option, if we could get the HTML for the page in Python, would
be to use that

so anyway, this actually just updates the sankey diagram to put it in a
funciton to choose light/dark mode, minor code improvements, and
improving the colors

one suggestion from Jim was simplifying the cateogires and I agree, so
now it's just SQL/DataFrame

## Issues closed

README.md

@@ -160,11 +160,10 @@ Ibis supports 20+ backends:
 
 Most Python dataframes are tightly coupled to their execution engine. And many databases only support SQL, with no Python API. Ibis solves this problem by providing a common API for data manipulation in Python, and compiling that API into the backend’s native language. This means you can learn a single API and use it across any supported backend (execution engine).
 
-Ibis supports three types of backend:
+Ibis broadly supports two types of backend:
 
 1. SQL-generating backends
-2. Expression-generating backends
-3. Naïve execution backends
+2. DataFrame-generating backends
 
 ![Ibis backend types](docs/images/backends.png)
 

docs/backends_sankey.py

@@ -2,8 +2,28 @@
 
 import plotly.graph_objects as go
 
+
+def to_greyish(hex_code, grey_value=128):
+    hex_code = hex_code.lstrip("#")
+    r, g, b = int(hex_code[0:2], 16), int(hex_code[2:4], 16), int(hex_code[4:6], 16)
+
+    new_r = (r + grey_value) // 2
+    new_g = (g + grey_value) // 2
+    new_b = (b + grey_value) // 2
+
+    new_hex_code = f"#{new_r:02x}{new_g:02x}{new_b:02x}"
+
+    return new_hex_code
+
+
+category_colors = {
+    "Ibis API": "#7C65A0",
+    "SQL": "#6A9BC9",
+    "DataFrame": "#D58273",
+}
+
 backend_categories = {
-    "SQL-generating": [
+    list(category_colors.keys())[1]: [
         "BigQuery",
         "ClickHouse",
         "DataFusion",
@@ -22,15 +42,7 @@
         "SQLite",
         "Trino",
     ],
-    "Expression-generating": ["Dask", "Polars"],
-    "Naïve execution": ["pandas"],
-}
-
-category_colors = {
-    "Ibis API": "#999999",
-    "Naïve execution": "#FF8C00",
-    "Expression-generating": "#6A5ACD",
-    "SQL-generating": "#3CB371",
+    list(category_colors.keys())[2]: ["Dask", "pandas", "Polars"],
 }
 
 nodes, links = [], []
@@ -39,7 +51,6 @@
 nodes.append({"label": "Ibis API", "color": category_colors["Ibis API"]})
 node_index["Ibis API"] = 0
 
-
 idx = 1
 for category, backends in backend_categories.items():
     nodes.append({"label": category, "color": category_colors[category]})
@@ -60,26 +71,32 @@
             }
         )
 
-
 fig = go.Figure(
     data=[
         go.Sankey(
             node=dict(
                 pad=20,
                 thickness=20,
-                line=dict(color="black", width=0.5),
+                line=dict(color="grey", width=0.5),
                 label=[node["label"] for node in nodes],
                 color=[node["color"] for node in nodes],
             ),
             link=dict(
                 source=[link["source"] for link in links],
                 target=[link["target"] for link in links],
                 value=[link["value"] for link in links],
+                line=dict(color="grey", width=0.5),
+                color=[to_greyish(nodes[link["target"]]["color"]) for link in links],
             ),
         )
-    ]
+    ],
 )
 
 fig.update_layout(
-    title_text="Ibis backend types", font_size=14, margin=dict(l=30, r=30, t=80, b=30)
+    title_text="Ibis backend types",
+    font_size=24,
+    # font_family="Arial",
+    title_font_size=30,
+    margin=dict(l=30, r=30, t=80, b=30),
+    template="plotly_dark",
 )

docs/why.qmd

@@ -202,11 +202,10 @@ providing a common API for data manipulation in Python, and compiling that API
 into the backend's native language. This means you can learn a single API and
 use it across any supported backend (execution engine).
 
-Ibis supports three types of backend:
+Ibis broadly supports two types of backend:
 
 1. SQL-generating backends
-2. Expression-generating backends
-3. Naïve execution backends
+2. DataFrame-generating backends
 
 ```{python}
 #| echo: false