notebooks are an advanced mechanism for software distribution and integration
they execute inside the default container of web browsers
we are isolated from the python implementation (away from the actual execution)
The Jupyther .ipynb format is standard
Markdown cells are used for comments and explanations
to run the cells, J. requires a callable Python kernel
a notbook is a JSON file, with JavaScript execution
"cell_type" : "code",
"execution_count": 1, # integer or null
"metadata" : {
"collapsed" : True, # whether the output of the cell is collapsed
"autoscroll": False, # any of true, false or "auto"
},
"source" : ["your code goes here"],
"outputs": [{
# list of output dicts (described below)
"output_type": "stream",
...
}],
}Detailed installation instructions are at the Jupyter project website
Installation is similar to a Python module but it runs stand-alone and interacts via browser
It requires a Python ‘core’ to run in the background, similar to VS Code (but less coding support)
All notebooks are also available online on Google Colab
A Google account is required
Python 3 notebooks are available from this module repo
Original Python 2 notebooks are available at the textbook repo
Currently VS Code supports Jupyter with a dedicated extension
Also, a cell block in a plain Python files can be split up into cells that can be run separately, Jupyuter-style.
Use # %% at the beginning of a line to delimit cells; the cell buttons will appear.
Conventional top-to-bottom execution is preserved.
study the “Food web” notebook associated to Chapter 1 of Caldarelli-Chessa’s textbook.
append your code in the cell below the Q questions.
test ‘what if’ questions by changing the code cells: double-click on the cell opens it up for editing.
a solution is available, but please read it only after you have tried to solve the problem.
We will repeat the experience next week with C-C’s Trade Networks notebook.