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EPIC writes the following files, where <basename> is to be replaced by the character
string that is passed to EPIC via the argument output%basename in the configuration file:
| Output file | Description |
|---|---|
<basename>_xxxxxxxxxx_parcels.nc |
NetCDF containing parcel output where xxxxxxxxxx is replaced with the file number, e.g. moist_0000000002_parcels.nc. |
<basename>_fields.nc |
NetCDF file containing gridded field output. |
<basename>_field_stats.nc |
NetCDF file containing diagnostics evaluated on the Eulerian grid. |
<basename>_parcel_stats.nc |
NetCDF file containing diagnostics evaluated using the Lagrangian parcels. |
<basename>_alpha_time_step.asc |
ASCII file containing time step estimates for the maximum strain and maximum buoyancy gradient. |
<basename>.csv |
ASCII file containing timings of the individual components of the code. |
Note
The frequency of writing to the respective netCDF files is controlled via the construct output
in the configuration file.
Tip
The command ncdump is useful to inspect a netCDF file, i.e.
ncdump filename.nc | lesswhere filename.nc is a netCDF file.
In the plotting directory we collected the following Python scripts:
| Plotting script | Description |
|---|---|
| plot_cross_sections.py | Create cross section plot from gridded data. |
| plot_mean_profile.py | Calculate the horizontal mean and plot as a height profile. |
| plot_scatter.py | Plot two parcel quantities against each other. |
| plot_histogram.py | Make a histogram plot using parcel data. Check the different options with --help. |
| plot_intersected_ellipses.py | Create a cross section plot where all intersection ellipses are displayed. |
| plot_parcel_history.py | Create a scatter plot with the original and final parcel height. |
These scripts allow you to explore Eulerian and Lagrangian simulation data.
Information how to use these scripts is printed when running with the flag --help, e.g. plot_cross_sections.py --help.
Tip
We suggest that you append the plotting directory to the $PATH environment variable with
export PATH=$PATH:/work/tc066/tc066/$USER/workshop/plottingin order to facilitate the usage of the plotting scripts. After this operation you can, for example, simply
type plot_cross_sections.py --help from any directory and without prefixing the Python interpreter.
For example, when you run the moist bubble case with the default settings, you can create a parcel cross section plot with
plot_intersected_ellipses.py --filename moist_0000000005_parcels.nc \
--steps 0 1 2 3 4 5 \
--dataset buoyancy \
--plane xzwhich gives you this plot:
You can open the image with:
display <plane>-intersected_ellipses_location_<loc>_<dataset>.png
# for example, the image above is
display xz-intersected_ellipses_location_32_buoyancy.pngAlternatively, you can use the package xarray to visualize the data.
Note
When loading the pandas package two warnings are thrown regarding the version of the packages numexpr and bottleneck. To suppress these warnings, you can use
import warnings
warnings.filterwarnings("ignore", module='pandas')The following code snippet creates a cross section plot of the gridded buoyancy field.
import warnings
warnings.filterwarnings("ignore", module='pandas')
import matplotlib as mpl
import matplotlib.pyplot as plt
import colorcet as cc
import xarray as xr
mpl.use("agg", force=True)
da = xr.open_dataset("moist_fields.nc")
buoy = da.buoyancy
# get a time series of xz-cross sections through the centre of the y-axis (grid point 32)
t_buoy2d = buoy.isel(t=slice(0, 24, 4), y=32)
g = t_buoy2d.plot(x="x", y="z", col="t", col_wrap=3, cmap=cc.cm['coolwarm'], figsize=(12, 7))
# fix the time format
times = da.t.dt.strftime("%H:%M:%S")
for i, ax in enumerate(g.axs.flat):
ax.set_title("Time "+ times.data[i] + " (HH:MM:SS)")
plt.savefig('xz-buoyancy-cross_section.png', bbox_inches='tight', dpi=200)
plt.close()You can open the image with:
display xz-buoyancy-cross_section.png
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