Getting started

StagPy is both a command line tool and Python module. This section contains basic instructions on how to use these two flavors of StagPy.

Read the installation instructions first in order to have StagPy available on your system. The rest of this documentation assumes that you have installed StagPy and that you can call it from the command line with the command % stagpy. Command examples all begin with a % sign, representing your command prompt.

Command line tool

The various processing capabilities of StagPy are organized in subcommands. This means a minimal call to StagPy is as follow:

% stagpy <subcommand>

<subcommand> can be one of the following:

  • field: plot scalar fields such as temperature or stream function;

  • refstate: plot reference state;

  • rprof: plot radial profiles;

  • time: plot time series;

  • plates: perform plate analysis;

  • info: print basic information about StagYY run;

  • var: display a list of available variables;

  • version: display the installed version of StagPy;

  • config: configuration handling.

You can run % stagpy --help (or % stagpy -h) to display a help message describing those subcommands. You can also run % stagpy <subcommand> --help to have some help on the available options for one particular sub command.

A simple example would be:

% stagpy field -p path/to/run/ -o T-p -s 42

This asks StagPy to plot the temperature and pressure fields of snapshot 42 of the run lying in ./path/to/run. When not specified, the path defaults to ./ (i.e. the current directory) and the snapshot defaults to the last one available. The command % stagpy var displays the list of fields available with the -o option.

See the dedicated section for more information on the command line interface.

Snapshots and time steps

StagPy allows you to work seamlessly with time steps and snapshots indices. A snapshot index is the number of registered profiles and fields, and a time step index is the number of atomic iterations performed in StagYY.

The snapshots option -s allows you to specify a range of snapshots in a way which mimic the slicing syntax: begin:end:gap (end excluded). Similarly, the timesteps option -t allows you to specify a range of time steps. For example, if snapshots are taken every 10 timesteps, -t 100:1001 is equivalent to -s 10:101.

If the first step/snapshot is not specified, it is set to 0. If the final step/snapshot is not specified, all available steps/snapshots are processed. Negative indices are allowed (meaning a counting from the last step/snapshot available). Here are some examples:

  • -t 100:350 will process every time steps between 100 and 349;

  • -t 201:206:2 will process time steps 201, 203 and 205;

  • -t 201:205:2 will process time steps 201 and 203;

  • -s=-10: will process the last ten snapshots (the equal symbol avoids the -10 to be interpreted as a separated command line argument);

  • -s :454 will process every snapshots from the 0th to the 453rd one;

  • -s ::2 will process every even snapshots.

Python interface

StagPy lets you operate the interface it uses internally to access StagYY output data. This allows you to write your own scripts to do some specific processings that aren’t implemented in StagPy.

The interface is wrapped in the StagyyData class. Instantiating and using this class is rather simple:

from stagpy.stagyydata import StagyyData
sdat = StagyyData('path/to/run/')

# absolute vertical velocity profile of last snapshot
last_v_prof = sdat.snaps[-1].rprof['vzabs'].values

# temperature field of the 10000th time step
# (will be None if no snapshot is available at this timestep)
temp_field = sdat.steps[10000].fields['T'].values

# iterate through snaps 100, 105, 110... up to the last one
for snap in sdat.snaps[100::5]:

As you can see, the snapshot/time step distinction is automatically taken care of by StagyyData.

All output data available in the StagYY run is accessible through this interface. StagyyData is designed as a lazy data accessor. This means output files are read only when the data they contain is asked for. For example, the temperature field of the last snapshot isn’t read until sdat.snaps[-1].fields['T'] is asked for.

See the dedicated section for more information on how to use the StagyyData class.

Plotting style

StagPy defines two custom plotting styles for matplotlib, stagpy-paper (the default) and stagpy-slides. You can edit them to your convenience, they are in the ~/.config/stagpy directory. You can specify which style to use with plot.mplstyle, available in the command line interface with the --mplstyle option. You can specify a space-separated list to combine several styles. For example, if you want a dark-background figure with a font size adapted for slides, you can use the following command:

% stagpy field --mplstyle='dark_backgroud stagpy-slides'