analysis.rst

Analysis of simulations with RoGeR

In this tutorial, we will use xarray and matplotlib to load, analyze, and plot the model output. You can run these commands in IPython or a Jupyter Notebook. Just make sure to install the dependencies first:

$ pip install xarray matplotlib netcdf4 cftime

The analysis below is conducted for a single year simulation of the :doc:`SVAT </reference/models/svat>` setup from the :doc:`model gallery </reference/model-gallery>`.

Let's start by importing some packages:

.. ipython:: python

    import xarray as xr
    from cftime import num2date
    import numpy as np
    from pathlib import Path

Set the paths to the example data:

.. ipython:: python

    OUTPUT_FILES = {
        "rate": Path().absolute() / "_data" / "SVAT.rate.nc",
        "collect": Path().absolute() / "_data" / "SVAT.collect.nc",
        "maximum": Path().absolute() / "_data" / "SVAT.maximum.nc",
    }

Most of the heavy lifting will be done by xarray, which provides a data structure and API for working with labeled N-dimensional arrays. xarray datasets automatically keep track how the values of the underlying arrays map to locations in space and time, which makes them immensely useful for analyzing model output.

Load and plot fluxes

In order to load our first output file and display its content execute the following two commands:

.. ipython:: python

    ds_rate = xr.open_dataset(OUTPUT_FILES["rate"])
    ds_rate

    # convert to datetime
    days = (ds_rate['Time'].values / np.timedelta64(24 * 60 * 60, "s"))
    date = num2date(days, units=f"days since {ds_rate['Time'].attrs['time_origin']}", calendar='standard', only_use_cftime_datetimes=False)
    ds_rate = ds_rate.assign_coords(Time=date)

Now, we select the time series of inf_mat and plot the daily data:

.. ipython:: python
    :okwarning:

    @savefig inf_mat_daily.png width=5in
    ds_rate["inf_mat_rz"].isel(x=0, y=0).plot()