Kamodo is a new CCMC tool for access, interpolation, and visualization of space weather models and data in python. Kamodo allows model developers to represent simulation results as mathematical functions which may be manipulated directly by end users. Kamodo handles unit conversion transparently and supports interactive science discovery through jupyter notebooks with minimal coding and is accessible through python.
The project page is located at the Community Coordinated Modeling Center, located at NASA Goddard Space Flight Center.
- Official site page https://ccmc.gsfc.nasa.gov/Kamodo/
Kamodo's source code is hosted on github under a permissive NASA open source license:
Suppose we have a vector field defined by a function of positions in the x-y plane:
from kamodo import kamodofy import numpy as np x = np.linspace(-np.pi, np.pi, 25) y = np.linspace(-np.pi, np.pi, 30) xx, yy = np.meshgrid(x,y) points = np.array(zip(xx.ravel(), yy.ravel())) @kamodofy(units = 'km/s') def fvec(rvec = points): ux = np.sin(rvec[:,0]) uy = np.cos(rvec[:,1]) return np.vstack((ux,uy)).T
The @kamodofy decorator lets us register this field with units to enable unit-conversion downstream:
from kamodo import Kamodo kamodo = Kamodo(fvec = fvec) kamodo
When run in a jupyter notebook, the above kamodo object will render as a set of equations:
We can now evaluate our function using dot notation:
We can perform unit conversion by function composition:
kamodo['gvec[m/s]'] = 'fvec'
kamodo automatically generates the appropriate multiplicative factors: we can verify these results through evaluation
Kamodo also generates quick-look graphics via function inspection.
import plotly.io as pio fig = kamodo.plot('fvec') pio.write_image(fig, 'images/fig2d-usage.svg')
Head over to the Introduction page for more details.
Kamodo may be installed from pip
pip install kamodo
To get the latest version, install from Asher's fork:
pip install git+https://github.com/asherp/Kamodo.git
Asher's fork is periodically merged into the CCMC's official NASA version.
We strongly recommend using the conda environment system to avoid library conflicts with your host machine's python.
Download and install miniconda from here. The advantage to using miniconda is that each new environment includes the bare-minimum for a project. This allows you to keep many different projects on a single work station.
Create Kamodo environment¶
Create a new environment for kamodo
conda create -n kamodo python==3.7 conda activate kamodo (kamodo) pip install kamodo
The leading (kamodo) in your prompt indicates that you have activated the
From here on, anything you install will be isolated to the
Loading example notebooks¶
If you want to run any of the notebooks in docs, you will need to install
(kamodo) conda install jupyter
Navigate to the top-level of the kamodo repo, then point jupyter to
(kamodo) jupyter notebook docs/notebooks
This should open a browser window that will allow you to load any of the example notebooks.
The following requirements are obtained by running
pip install kamodo
- conda install antlr-python-runtime (rendering latex)
- conda install -c plotly plotly-orca (for writing images)
plotly version in flux
Kamodo's documentation site is a good example of how to embed your own plots in your own website.
The documentation site is generated by the
mkdocs package with some addons
- mkdocs - handles site generation and deployment (configured by top-level
- markdown-include - allows for embedding of markdown files (and graph divs) outside the docs folder
- python-markdown-math - enables LaTeX rendering
- mknotebooks - allows for the embedding of jupyter notebooks
All of the above requirements can be installed with this line:
pip install mkdocs python-markdown-math markdown-include mknotebooks
You can then generate the docs and serve locally with
To deploy your own documentation on github-pages:
This generates a gh-pages branch with the static site files and pushes it to github. Github automatically creates a website url based on that branch.