More work on the demo...

Makefile

@@ -3,7 +3,7 @@
 
 demo: download createfiles
 createfiles: move_this_file move_it_here
-download: demofiles PythonDataScienceHandbook Urban-Data-Challenge altair altair-examples
+download: demofiles PythonDataScienceHandbook Urban-Data-Challenge altair
 
 PythonDataScienceHandbook: demofiles
 	cd demofiles && git clone https://github.com/jakevdp/PythonDataScienceHandbook.git
@@ -14,9 +14,6 @@ Urban-Data-Challenge: demofiles
 altair: demofiles
 	cd demofiles && git clone https://github.com/altair-viz/altair.git
 
-altair-examples: demofiles
-	cd demofiles && git clone https://github.com/jakevdp/altair-examples.git
-
 move_this_file: demofiles
 	cd demofiles && touch move_this_file.txt
 

README.md

@@ -1,13 +1,29 @@
 # JupyterLab Demonstration
 
 This repository contains some demonstrations of
-[JupyterLab](https://github.com/jupyter/jupyterlab), the next generation user interface of Project Jupyter.
+[JupyterLab](https://github.com/jupyter/jupyterlab), the next
+generation user interface of Project Jupyter.
 
 # Installation
 
 To install all of the files needed for the demo, run:
 
     make demo
 
+This demo will need the latest development master of the following
+packages:
+
+* jupyterlab (https://github.com/jupyter/jupyterlab)
+* jupyterlab_geojson (https://github.com/jupyter/jupyterlab_geojson)
+* jupyterlab_vega (https://github.com/altair-viz/jupyterlab_vega)
+
+Additionally, if you want to run Altair in the Jupyter Notebooks, you will need
+this branch of altair:
+
+* altair (https://github.com/ellisonbg/altair/tree/jupyterlab)
+
 # Demo guide
 
+The basic outline of the JupyterLab demo is described in the file `jupyterlab.md`.
+
+A basic description of Altair can be found in the file `altair.md`.

altair.md

@@ -1 +1,38 @@
-asfasdfasdfasdf
+# Altair
+
+A delarative statistical visualization library for Python.
+
+https://altair-viz.github.io/index.html
+
+* Brian Granger
+* Jake VanderPlas
+
+# Why?
+
+* Lots of visualization options in Python
+ - Matplotlib
+ - Seaborn
+ - Bokeh
+ - Plotly
+ - BQPlot
+ - etc.
+* Very powerful
+* And yet, still lots of pain for users
+  - Categorical, datetime datatypes
+  - Inconsistent, leaky abstractions and visualization grammar
+* Meanwhile, JavaScript-based visualization advancing at light speed
+  - [d3](https://d3js.org/)
+  - [Vega](https://vega.github.io/vega/)
+  - [VegaLite](https://vega.github.io/vega-lite/)
+
+# What?
+
+* Not a new rendering library
+* A Python API that emits valid VegaLite JSON
+* Rendered in the Jupyter Notebook and JupyterLab by the VegaLite JavaScript library
+* Statistical visualization
+  - Data is a DataFrame/Table
+  - In the tidy format
+  - Mapped to visual properties using groupby operations
+* Most the Python API is autogenerated from the JSON spec
+

demo.md → jupyterlab.md

@@ -1,12 +1,22 @@
 # JupyterLab Demo
 
-**DON'T TRY THIS AT HOME - LATEST MASTER OF EVERYTHING USED!!**
+JupyterLab: The next generation user interface for Project Jupyter
+
+https://github.com/jupyter/jupyterlab
+
+* Project Jupyter
+* Bloomberg
+* Continuum
+
+**DON'T TRY THIS AT HOME - JUPYERLAB IS EXTREMELY ALPHA!!**
 
 ## 1) Building blocks of interactive computing
 
 ### Open the building blocks:
 
 * Notebook
+  - Explore notebooks from demofiles/PythonDataScienceHandbook
+  - Explore notebooks from demofiles/Altair
 * Terminal
 * File Editor
 * Console
@@ -16,6 +26,7 @@
 ### Demonstrate left panel plugins:
 
 * File Browser
+  - Drag the `move_this_file.txt` to the `move_it_here` directory
 * Command Palette
 
 ## 2) File handlers
@@ -26,16 +37,17 @@ range of file formats:
 * Open this file in the Markdown Viewer and edit
 * Browse through CSV and GeoJSON files from 
   [Urban Data Challenge](https://github.com/swissnexSF/Urban-Data-Challenge)
-* Open an [VegaLite](https://vega.github.io/vega-lite/) JSON file
+* Open a [VegaLite](https://vega.github.io/vega-lite/) JSON file from the
+  `vegalite` directory
 
-## 3) Connecting building blocks
+## 3) Connecting the building blocks
 
-The building blocks can be connected to support
-a wide range of different workflows:
+The building blocks can be connected to support a wide range of different workflows:
 
 * Open `markdown_python.md` in the File Editor
 * View the rendered markdown
-* Attach a Kernel/Console and run the code
+* Attach a Kernel/Console and run the code by selecting blocks and pressing
+  `Shift+Enter`
 
 ## 4) Third party plugins/extensions