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Montecarlo Packet Visualization

RPacketPlotter plots the RPackets that are generated by the Montecarlo method and creates an animated plot that contains the packet trajectories as they move away from the photosphere. The properties of individual RPackets are taken from the rpacket_tracker.

RPacketPlotter uses the properties (specifically, mu and r) present in the rpacket_tracker to calculate the coordinates of packets as they move through the ejecta. In the following section, the mathematical expression for getting the angle(θ) of packets with respect to the x-axis is shown, which can be used (along with radius r) to calculate the x and y coordinates of packets.

Getting packet coordinates

RPacketPlotter uses the properties (specifically, mu and r) present in the rpacket_tracker to calculate the coordinates of packets as they move through the ejecta. In the following section, the mathematical expression for getting the angle(θ) of packets with respect to the x-axis is shown, which can be used (along with radius r) to calculate the x and y coordinates of packets.

The diagram above shows the packet trajectory as it starts from photosphere P0 and continues to move along the subsequent points P1, P2, and so on.

Note

Here μ represents the direction of packet propagation with respect to the radial line.

To determine the polar coordinates of any arbitrary point, say P2, we need r2 and θ2. r2 is already present in the array obtained from the simulation. To determine θ2, we use the sine rule and apply it to the triangle OP1P2, where O is the center.

\[\frac{r_{2}}{\sin(\pi - \mu_{1})} = \frac{r_{1}}{\sin(\alpha)}\]

Now, writing α in terms of μ1 and θ2

\[α = μ_{1} - θ_{2}\]

\[\frac{r_{2}}{\sin(\pi - \mu_{1})} = \frac{r_{1}}{\sin(μ_{1} - θ_{2})}\]

Thus,

\[θ_{2} = -\sin^{-1}(\frac{r1}{r2}\sin(\mu_{1})) + \mu_{1}\]

Hence, for i-th point, θ will be:

\[θ_{i} = -\sin^{-1}(\frac{r_{i-1}}{r_{i}}\sin(\mu_{i-1})) + \mu_{i-1}\]

Running the simulation

from tardis import run_tardis
from tardis.io.configuration.config_reader import Configuration
from tardis.io.atom_data import download_atom_data

# We download the atomic data needed to run the simulation
download_atom_data('kurucz_cd23_chianti_H_He')

Atomic Data kurucz_cd23_chianti_H_He already exists in /home/runner/Downloads/tardis-data/kurucz_cd23_chianti_H_He.h5. Will not download - override with force_download=True.

# Reading the Configuration stored in `tardis_example.yml` into config

config = Configuration.from_yaml("tardis_example.yml")

# changing config file for enabling the rpacket_tracking

config["montecarlo"]["tracking"]["track_rpacket"]=True

sim = run_tardis(config, show_progress_bars=False)

Plotting Packets with RPacketPlotter

Importing the RPacketPlotter

from tardis.visualization import RPacketPlotter

Now, we create an RPacketPlotter object rpacket_plotter that will be used to generate a plot.

no_of_packets can be specified as a parameter to the from_simulation class method. By default, 15 packets will used to create a plot.

rpacket_plotter = RPacketPlotter.from_simulation(sim, no_of_packets=20)

Using the rpacket_plotter we use the generate_plot method to create a plot.

Here the theme parameter can be defined. Currently, we have 2 themes, i.e. light and dark. By Default the light theme will be plotted.

Light Theme

rpacket_plotter.generate_plot().show(renderer="notebook_connected")

Dark Theme

rpacket_plotter.generate_plot(theme="dark").show(renderer="notebook_connected")

Using Animation and Other interactive features

The Play button at the bottom-left can be used to start the animation. The animation can be paused at any time using the pause button. Also, the timeline slider can be used to reach any point in the animation. A demo is shown below.

Hovering over any packet will show its properties like its coordinates, interaction type, etc. The zoom-in feature can be used to view a particular part of the plot. The demo below shows these features.