Plotting

Every standard plot is a method on the object that owns the data. Each one accepts ax= to draw onto an existing axis, applies the packaged lazuli.mplstyle through a context manager so that your own matplotlib settings are left alone, and returns (fig, ax). The figures on this page are produced at build time from the code shown above them, for the default simulation:

from esc_etc import Simulation

sim = Simulation.from_default(hoststarmag=-0.353, planetdeltamag=20,
                              contrast=1e-8, ppgain=10, separation=0.4)

The methods

Method

What it plots

sim.plot()

A 2 x 3 overview of the six panels that follow. Returns (fig, axes).

sim.plot_count_rates()

Bar chart of the count-rate contributors (planet, zodi, exozodi, speckle, dark current).

sim.plot_snr_vs_time()

SNR as a function of integration time; see the sweeps below.

sim.plot_bandpass() / sim.sensor.plot_bandpass()

The sensor’s optical bandpass (throughput against wavelength).

sim.plot_aperture() / sim.sensor.plot_aperture()

The photometric aperture drawn over a pixel-integrated Airy PSF.

sim.plot_offaxis_throughput() / sim.coronagraph.plot_offaxis_throughput()

Focal-plane mask throughput against separation, with the current separation marked.

sim.plot_spectra() / sim.scene.plot_spectra()

The scene-element spectra (star, planet and zodi by default).

The overview

fig, axes = sim.plot()
../_images/plotting-2.png

Single panels

Each panel is its own method. The count-rate budget and the aperture footprint:

fig, ax = sim.plot_count_rates()
../_images/plotting-3.png 
fig, ax = sim.sensor.plot_aperture()
../_images/plotting-4.png

Parameter sweeps

plot_snr_vs_time takes sweep=, a one-entry dictionary naming any parameter you could pass to update() and the values to try. One curve is drawn per value and the original value is put back:

fig, ax = sim.plot_snr_vs_time(sweep={"coronagraph__raw_dh_contrast": [1e-7, 1e-8, 1e-9]})
../_images/plotting-5.png

The same works for any other parameter, for example sweep={"coronagraph__ppgain": [5, 10, 20, 50]} or sweep={"scene__zodi__mag": [22.5, 20]}.

Several curves on one axis

plot_snr_vs_time, plot_bandpass and plot_offaxis_throughput take label=; with ax= that gives a legend without touching the lines:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
for ft in [10, 100, 1000]:
    sim.plot_snr_vs_time(frame_time=ft, ax=ax, label=f"frame time {ft} s")
../_images/plotting-6.png 
fig, ax = plt.subplots()
Simulation.from_default(filter="F630N", lifetime="eol").plot_bandpass(ax=ax, label="EOL")
Simulation.from_default(filter="F630N", lifetime="bol").plot_bandpass(ax=ax, label="BOL")
../_images/plotting-7.png

The functions behind the methods

The methods call the functions in esc_etc.plotting, which also accept raw arrays instead of a simulation (rates with a sensor, total_times with snrs, wavelength with throughput, and so on). See the Plotting (esc_etc.plotting) reference for the signatures.

Tip

When generating many figures in a loop, close the ones you do not keep with plt.close(fig) to avoid accumulating open figures.

The house style

The same style is available for your own plots:

from esc_etc import lazuli_style, use_lazuli_style

use_lazuli_style()             # apply globally to the current session

with lazuli_style():           # or scope it to a block
    ...                        # your plotting code

The exported colour tokens TEAL, RED, AMBER, BLUE, GREEN, DARKBLUE and the LAZULI_COLORS sequence are the palette the methods use.