feat: Add a plot to the tour

python/docs/.gitignore

@@ -1,4 +1,5 @@
 _build
 .jupyter_cache
 jupyter_execute
-source/reference/apidocs
+source/reference/apidocs
+source/iframe_figures

python/docs/source/conf.py

@@ -29,6 +29,7 @@
 html_favicon = "_static/favicon.png"
 html_logo = "_static/kaskada.svg"
 html_title = "Kaskada Timestreams"
+html_js_files = ["https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"]
 
 html_theme_options: Dict[str, Any] = {
     "repository_url": "https://github.com/kaskada-ai/kaskada",
@@ -111,3 +112,5 @@
 autosummary_generate = True
 
 napoleon_preprocess_types = True
+
+suppress_warnings = ["mystnb.unknown_mime_type"]

python/docs/source/index.md

@@ -2,8 +2,6 @@
 hide-toc: true
 html_theme.sidebar_secondary.remove: true
 ---
-# Introduction
-
 <div class="px-4 py-5 my-5 text-center">
     <img class="d-block mx-auto mb-4" src="_static/kaskada.svg" alt="" width="auto">
     <h1 class="display-5 fw-bold">Real-Time AI without the fuss.</h1>
@@ -13,7 +11,7 @@ html_theme.sidebar_secondary.remove: true
     </div>
 </div>
 
-## Kaskada completes the Real-Time AI stack, providing...
+# Kaskada completes the Real-Time AI stack, providing...
 
 ```{gallery-grid}
 :grid-columns: 1 2 2 3
@@ -27,15 +25,15 @@ html_theme.sidebar_secondary.remove: true
 ```
 
 
-### Real-time AI in minutes
+## Real-time AI in minutes
 
 Connect and compute over databases, streaming data, _and_ data loaded dynamically using Python..
 Kaskada is seamlessly integrated with Python's ecosystem of AI/ML tooling so you can load data, process it, train and serve models all in the same place.
 
 There's no infrastructure to provision (and no JVM hiding under the covers), so you can jump right in - check out the [Quick Start](quickstart).
 
 
-### Built for scale and reliability
+## Built for scale and reliability
 
 Implemented in [Rust](https://www.rust-lang.org/) using [Apache Arrow](https://arrow.apache.org/), Kaskada's compute engine uses columnar data to efficiently execute large historic and high-throughput streaming queries.
 Every operation in Kaskada is implemented incrementally, allowing automatic recovery if the process is terminated or killed.
@@ -60,7 +58,7 @@ kd.init_session()
 messages = kd.sources.PyList(
     rows = pyarrow.parquet.read_table("./messages.parquet")
         .to_pylist(),
-    time_column_name = "ts", 
+    time_column_name = "ts",
     key_column_name = "channel",
 )
 
@@ -78,7 +76,7 @@ conversations = ( messages
 
 # Handle each conversation as it occurs
 async for row in conversations.run(materialize=True).iter_rows_async():
-  
+
     # Use a pre-trained model to identify interested users
     prompt = "\n\n".join([f' {msg["user"]} --> {msg["text"]} ' for msg in row["result"]])
     res = openai.Completion.create(

python/docs/source/tour.md

@@ -1,3 +1,12 @@
+---
+file_format: mystnb
+kernelspec:
+  name: python3
+  display_name: Python 3
+mystnb:
+  execution_mode: cache
+---
+
 % Level: Beginner
 % Goal: Overview of the key features of Kaskada focused on explaining *why* you want them.
 % Audience: Someone who has read the landing page and wants to understand what Kaskada can do for them.
@@ -8,6 +17,28 @@ This provides an overview of the key features in Kaskada that enable feature eng
 The [Quick Start](quickstart) has details on how you can quickly get started running Kaskada queries.
 For a more complete explanation, see the User Guide.
 
+This tour uses Kaskada and Plotly to render the illustrations.
+The initial setup / data is below.
+
+```{code-cell}
+---
+tags: [hide-cell]
+---
+import kaskada as kd
+kd.init_session()
+single_entity = "\n".join(
+    [
+        "time,key,m,n",
+        "1996-12-19T16:39:57,A,5,10",
+        "1996-12-20T16:39:59,A,17,6",
+        "1996-12-22T16:40:00,A,,9",
+        "1996-12-23T16:40:01,A,12,",
+        "1996-12-24T16:40:02,A,,",
+    ]
+)
+single_entity = kd.sources.CsvString(single_entity, time_column_name="time", key_column_name="key")
+```
+
 ## Events and Aggregations
 
 Every Kaskada query operates on one or more _sources_ containing events.
@@ -20,8 +51,14 @@ A natural question to ask about the purchases is the total--or `sum`--of all pur
 This is accomplished by _aggregating_ the events.
 The results of an aggregation change over time as additional events occur.
 
-```{todo}
-Port an example showing timestreams and aggregations.
+```{code-cell}
+---
+tags: [remove-input]
+---
+kd.plot.render(
+    kd.plot.Plot(single_entity.col("m"), name="m"),
+    kd.plot.Plot(single_entity.col("m").sum(), name="sum of m")
+)
 ```
 
 The User Guide has [more details on aggregation](guide/aggregation.md), including how to use windows to control which events are aggregated.

python/pyproject.toml

@@ -89,6 +89,7 @@ myst-parser = {version = ">=0.16.1"}
 # Sphinx to < 6. Once a new release occurs we can upgrade to `0.18.0` or newer.
 # https://github.com/executablebooks/MyST-NB/issues/530
 myst-nb = { git = "https://github.com/executablebooks/MyST-NB.git", rev = "3d6a5d1"}
+plotly = {version = "^5.16.1"}
 
 [tool.poetry.group.test]
 # Dependencies for testing