Analyzing ELM Output in Jupyter Lab (AK K64G Example) - FASSt-simulation/fasst_simulation_tools Wiki
HOW TO run Docker/container of Jupyter NoteBook for Visualizing ELM Output - AK-K64G example
(1) assuming you've already installed Docker and its Desktop App.
(2) assuming you've already run ELM docker for ngee site, 'kougarok', with SITE-CODE of 'AK-K64G'. Such as following instructions in AK K64G Demo Quick Start Guide
AND, The outputs are in docker volume named
elmoutput. NOTE: you can name whatever volume you'd like.
STEP 1. Download Docker image of 'fasst_simulation_tools'
Open terminal, and type the following:
docker pull fasstsimulation/fasst_simulation_tools:elmlab_3.3.2
STEP 2. Open Docker Desktop, if installed, and create a Volume for a work directory to save your results.
Suppose that had already run ELM, and saved all outputs in a docker Volume on disk, named
Click to open 'elmoutput', and then click UPPER/LEFT Tab
DATA, under which ELM output directories will show like following, if unfolded:
NOTE: the slightly-whiten file
ELM_output.nc is that merged all outputs,and will be for visualizing.
NOW, click the most LEFT/UPPER
Volumes to back to Volumes panel, and click its UPPER/RIGHT button
Create+ to create a new volume, named as 'elmwork', which will be used to store your analysis results.
AND, you then should see the following:
STEP 3. Launch/run docker 'fasst_simuation_tools'
In Terminal, type the following command:
docker run -t -i -v elmoutput:/home/jovyan/output/ -v elmwork:/home/jovyan/work/ -p 8888:8888 fasstsimulation/fasst_simulation_tools:fasst_jupyterlab_3.3.2
if successful, the following message will show in your terminal window,
**AS hinted, do one of 3 methods, to lanuch jupyter notebook, in YOUR internet browser. The following will be showing, maybe slightly different in yours. AND thereforward you're going to do visiualization in this browser windows.
STEP 4 - Example A. Make 2 example Figures of publish-quality from ELM outputs.
(1) click-open the script file,
run_plot_KG64_ELM_output.ipynb, it shall be showing:
NOTE: in the above screen-shot, there are 4 click-buttons highlighted in a yellow circle. Those 4 buttons are for running jupyter notebook scripts - if your mouse pointer hover on each one, it will show what would happen when click.
(2) For an example, if click that fast-forward-like button, it will run script in all-steps (blocks of scripts indiced in , called cells in ipynb) at once.
And after successfully running script, it will show like following in this example.
NOTE: You can check if there are 2 images of Figures generated from this script, in Docker Desktop App's VolELM_work directory, AND save them to your local file system if want to.
STEP 4 - Example B. User-interactively visualize ELM variable.
(1) click-open the script file,
plot_ELM_variable.ipynb, it shall be showing:
(2) For an example, if click that fast-forward-like button, it will run script in all-steps at once, and will plot
**GPP** by default.
(3) Interactively choose a variable from a dropdown box.
Scrolling up to move your mouse pointer to cell , like
By clicking the dropdown arrow, it will show whole list of variables from ELM outputs, and Choose one, such as
TSOI, the soil layered temperature,
AND, then Clicking
one-step advance button, in the upper bar, until end of script, it will plot like following:
Customizing by editing
- ```variable_name_user=```, if already known a variable name (i.e. not from the above dropdown list) - ```variable_unit```, if don't use that from output, and you know. Usually that from output not known. - ```variable_muliplier=', anything but 1 for better plotting by scaling data - ```yr_start=', year starting to plot (greater than min. year in the output) - ```yr_end=', year ending to plot (less than max. year in the output)