| # code by Steven H. D. Haddock and Casey W. Dunn as described in: | |
| # Practical Computing for Biologists | |
| # Steven H. D. Haddock and Casey W. Dunn | |
| # Published in 2011 by Sinauer Associates. | |
| # ISBN 978-0-87893-391-4 | |
| # http://www.sinauer.com/practical-computing-for-biologists.html | |
| # see practicalcomputing.org | |
| # | |
| #scripts freely available by the original authors at practicalcomputing.org | |
| #DIRECT LINK: http://practicalcomputing.org/files/pcfb_examples.zip |
| # code by Steven H. D. Haddock and Casey W. Dunn as described in: | |
| # Practical Computing for Biologists | |
| # Steven H. D. Haddock and Casey W. Dunn | |
| # Published in 2011 by Sinauer Associates. | |
| # ISBN 978-0-87893-391-4 | |
| # http://www.sinauer.com/practical-computing-for-biologists.html | |
| # see practicalcomputing.org | |
| # | |
| #scripts freely available by the original authors at practicalcomputing.org | |
| #DIRECT LINK: http://practicalcomputing.org/files/pcfb_examples.zip |
| %matplotlib notebook | |
| # use `%matplotlib notebook` if you are using current JupyterLab | |
| from vpython import * | |
| import matplotlib.pyplot as plt | |
| plt.style.use('ggplot') | |
| # based on "AtomicSolid" by Bruce Sherwood | |
| # adapted to include realtime matplotlib by Wayne Decatur |
Click on Binder package link on that page. That link is near the very bottom of the part of the page that is showing above; it is just below Demo Programs.
A notebook will then launch. (Sometimes first times they hang, just hit reload in your browser.)
After it loads fully it will look like below with a URL different from what you see but similar.
Go to my fork of the VPython Binder repository in your browser.
Click on the 
That will take you to a new page and trigger deploying version of the jupyter notebook environment from the correct repository. You shouldn't need to do anything as this takes place; you can watch the progress bar roughly in the middle of the screen, just below the Launch button. It may take about a minute. After it boots up, it should bring you to the dashboard that will look like below
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| from matplotlib.lines import Line2D | |
| my_palette = sns.color_palette("cubehelix", 3) | |
| sns.set_palette(my_palette) | |
| def legend_circles(labels, palette, loc=1, markersize=10, marker='o', padding=0): | |
| "Make a legend where the color is indicated by a circle." |
The %run magic provided by IPython not only supports the execution of regular Python scripts, it also runs Jupyter Notebook files (.ipynb).
The run_demo.ipynb notebook below uses %run to execute all of the cells in reusable_stuff.ipynb. Once it does, both globals defined in reusable_stuff are available in run_demo for use.
