Plotting and analysis

Defining a new plot

It is possible to initialize and create PLOTs directly from your command line. For this you have to be in a PROJECT directory.

The following command

apy --init-plot PLOT

will create:

  1. A new plot directory: ./python/scripy/PROJECT/plots/PLOT

  2. Default plot class: ./python/scripy/PROJECT/plots/PLOT/__init__.py

    The class implements three basic methods:

    import arepy as apy
import numpy as np

class PLOT(apy.scripy.plot):

     def settings(self):
         # Some general settings go here...

     def init(self):
         # Plot initialization goes here...

     def plot(self):
         # Plotting routine goes here...

Additionally, it is possible to split the PLOT class into several SUBPLOT classes and store them in separate files.

  1. One parent class that contains general plot settings

    ./python/scripy/PROJECT/plots/PLOT/__init__.py

    import arepy as apy
import numpy as np

class PLOT(apy.scripy.plot):

    def settings(self):
        # Some general settings go here...

  2. One or several subplot classes that inherit the parent class

    ./python/scripy/PROJECT/plots/PLOT/SUBPLOT.py

    import arepy as apy
import numpy as np
from scripy.PROJECT.plots.PLOT import PLOT

class SUBPLOT(PLOT):

    def init(self):
        # Plot initialization goes here...

    def plot(self):
        # Plotting routine goes here...

Plot class

class arepy.scripy.plot(action, proj, dirName, fileName=None, *args)

General class of a plot

Variables

  • grps (arepy.files.collection) – Groups object

  • fig (arepy.plot.figure) – Figure object

  • tab (arepy.data.table) – Table object

Note

Note that class variables ‘grps’, ‘fig’ and ‘tab’ are accessible only in the ‘plot()’ function

settings()

Plot settings

This is the place for general settings of the simulation. In principle, data set in this functions should be available for all subplots. For example the plotting units can be set here:

self.opt['simOpt'] = {
    'initUnitsNew': {'length':apy.const.pc},
    'initImages':True,
    'initSinks':True,
    'initSnap': True,
}
init()

Initialization of a plot

This is the place for particular settings of the plot. A basic figure setup could be:

# allocate processors
self.setProcessors( fig=6 )

# setup snapshot groups
self.setGroups(['names','sim','snaps'],[            
    ('nrpm3',4, [100,33]),
    ('nrp0', 5, [65,23]),
])

# setup figure
self.setFigure(2,1,2)
plot()

Main plotting routine

This method prepares the figure for plotting. A simple plotting routine could look like:

# loop through all groups
for grp in self.grps:

    # setup a simulation
    sim = self.getSimulation(grp.opt['sim'],**self.opt['simOpt'])

    # add snapshot to the group
    grp.addSnapshot(sim,grp.opt['snaps'])

    # select a subplot where we plot data
    sp = self.fig.getSubplot(grp.index,0, xlabel='x',ylabel='y' )

    # plot an image
    grp.setImage(sp,'density','slice')
getSimulation(sim, **opt)

Get a project simulation

Parameters

  • sim (int) – Simulation ID

  • opt (dict) – Additional simulation settings

setProcessors(fig=1, kdt=1, snap=1)

Distribute number of processors

Parameters

  • fig (int) – Number of processors used for figure plotting

  • kdt (int) – Number of processors used by a KDTree algorithm

  • snap (int) – Number of processors used to read a multi-file snapshot

Example:

self.setProcessors( fig=apy.numCpu )
setGroups(names, options, **opt)

Set simulation groups

Parameters

  • names (list[str]) – Table column names

  • options (list[tuple]) – Table rows

  • opt (dict) – Additional group options

This function sets to the class a variable

self.grps

that contains all the group information.

Example:

self.setGroups(['names','sim','snaps'],[
    ( 'hydroHelium', 18, 30 ),
    ( 'hydroOnly',   21, 30 ),
])
setFigure(ncol, nrow, nfig, movie=False, show=False, debug=False, plot=True, **opt)

Set a new figure

Parameters

  • ncol (int) – Number of collumns

  • nrow (int) – Number of rows

  • nfig (int) – Number of figures

  • srow (float) – Size of the subplot rows on the figure (figsize)

  • scol (float) – Size of the subplot cols on the figure (figsize)

  • movie (bool) – Create a movie from all figures

  • show (bool) – Display figures at the end of the plotting

  • debug (bool) – Save figures into a debug folder

  • plot (bool) – Plot figures

  • gridspec (dict) – Gridspec options

  • tickparam (dict) – Parameters of the axis ticks and their labels

  • group (list[str]) – List of grouped axis elements in case of several subplots

Example:

self.setFigure(2,1,1,show=True,scol=4,srow=2, gridspec={'hspace':0.1}, group=['xlabel'],
               tickparam={'axis':'both','direction':'in','top':True,'right':True})
setTable(show=False, debug=False, **opt)

Set a new table

Parameters

  • show (bool) – Display table at the end of the calculation

  • debug (bool) – Save table into a debug folder

  • opt (dict) – Additional table options

Plotting the data

Plots can be subsequently called from the command line in three ways:

  1. Using the following command

    apy --plot PLOT [SUBPLOT]

    figures will be saved as

    ./results/PROJECT/PLOT/PLOT/000000_0000/PLOT000.png

    or

    ./results/PROJECT/PLOT/SUBPLOT/000000_0000/SUBPLOT000.png

    where 000000_0000 is a current time-stamp and 000 is a figure number.

  2. In order to avoid many subfolders while debugging one can use also the following command:

    apy --debug PLOT [SUBPLOT]

    In this case the figure are stored as

    ./results/PROJECT/PLOT/SUBPLOT/debug/SUBPLOT000.png

  3. Finally, it is also possible to display the last created plot without recalculating the values:

    apy --show PLOT [SUBPLOT]