pyarts3.plots

Plotting ARTS data.

This module provides functions related to plotting ARTS data based only on the type of the data. Each submodule is named exactly as the data type it can plot, which is also the name of a builtin group. It is supposed to be a helpful way to get a quick overview of the data in a given ARTS data structure.

Each submodule provides a plot()-method. The generic plot() function in this module dispatches to the appropriate submodule based on the type of the input data. Only exact type matches are considered. For unavailable types, a TypeError is raised. Each submodule’s plot() function accepts the same 3 parameters:

data: The ARTS data to plot.
fig: An optional matplotlib Figure to plot on.
ax: An optional matplotlib Axes (or list/array of Axes) to plot on.

Additional keyword arguments are forwarded to the specific plot() function, which if they are not recognized directly by the specific plot() function are forwarded yet again to the underlying matplotlib plotting functions.

Tip

Below you will find examples using mock-data to illustrate the usage of the specific plot-functions. In practice, you would use ARTS data created by running ARTS simulations.

Generic Plot Function

Generic plotting function that dispatches to the appropriate plot module.

This function automatically determines the type of the input data and calls the corresponding plot module’s plot() function. All keyword arguments are forwarded to the specific plotting function.

For convenience, this function can be used directly from the main pyarts3 module:

Examples

>>> import pyarts3 as pyarts
>>> vec = pyarts.arts.Vector([1, 2, 3, 4])
>>> fig, ax = pyarts.plot(vec)

>>> mat = pyarts.arts.Matrix([[1, 2], [3, 4]])
>>> fig, ax = pyarts.plot(mat, cmap='viridis')

Parameters:

data (ARTS builtin group) – Any ARTS data type that has a corresponding plot module.
fig (Figure, optional) – The matplotlib figure to draw on. Defaults to None for new figure.
ax (Axes | list[Axes] | ndarray[Axes] | None, optional) – The matplotlib axes to draw on. Defaults to None for new axes.
**kwargs (keyword arguments) – Additional keyword arguments to pass to the plotting functions.

Returns:

fig – As input if input. Otherwise the created Figure.
ax – As input if input. Otherwise the created Axes.

Raises:

TypeError – If no plot module exists for the given data type.
Exception – As forwarded by the specific plot module.

Plotting Functions by Type

AbsorptionBands.plot

pyarts3.plots.AbsorptionBands.plot(data: AbsorptionBands, *, mode='normal', fig: Figure | None = None, ax: Axes | list[Axes] | ndarray[Axes] | None = None, freqs: AscendingGrid | int = 1000, atm: AtmPoint | AtmField | None = None, pol: Propmat = [1, 0, 0, 0, 0, 0, 0], species: SpeciesEnum = 'AIR', path_point: PropagationPathPoint = [unknown, unknown, 0, 0, 0, 0, 0, 1, 1], min_pm: float | None = None, **kwargs) → tuple[Figure, Axes | list[Axes] | ndarray[Axes]][source]

Creates a plot of the absorption by the bands in the AbsorptionBands object.

If freqs is an index, this is the number of frequency points between the lowest and highest frequency in the AbsorptionBands object. If freqs is an AscendingGrid, it is used directly for the x-axis.

If atm is None, and atmospheric point is created by reading the tropical standard atmosphere from the ARTS database and extracting the AtmPoint at position by the path_point. If atm is an AtmField, the AtmPoint at position by the path_point is extracted.

pol is the dot product of the generated propagation matrix and defaults to the purely unpolarized case.

The species is passed directly to the AbsorptionBands.spectral_propmat() method and defaults to all species (AIR).

The path_point is passed directly to the AbsorptionBands.spectral_propmat() method and defaults to pos: [0, 0, 0], los: [0, 0].

The mode parameter controls the style of the plot. In ‘normal’ mode, all absorption lines are plotted as the sum of the call to AbsorptionBands.spectral_propmat(). In ‘important Y X’ modes, the full absorption is still shown, but the absorption bands object are split by X, where X is one of ‘bands’, ‘species’, or ‘isotopes’. Y is either ‘fill’ or nothing. If it is ‘fill’, matplotlib’s fill_between() is used to color the regions, otherwise line plots are used to show the absorption of the important contributors.

Example

import matplotlib.pyplot as plt
import numpy as np
import pyarts3 as pyarts

lines = pyarts.arts.AbsorptionBands.fromxml("lines/O2-66.xml")
freq = np.linspace(20e9, 140e9, 1001)
lines.keep_frequencies(freq[0], freq[-1])
f, a = pyarts.plots.AbsorptionBands.plot(lines, mode="important fill bands", freqs=freq, fig=plt.figure(figsize=(16, 5)))
a.set_yscale("log")
a.set_xlabel("Frequency [GHz]")
a.set_xticks(np.linspace(20, 140, 7) * 1e9, np.linspace(20, 140, 7))
a.set_ylabel("Absorption [1/m]")
a.legend()
a.set_title("O$_2$ line-by-line absorption 20-140 GHz separated by bands")

pyarts3.plots

Generic Plot Function

Plotting Functions by Type

AbsorptionBands.plot

ArrayOfCIARecord.plot

ArrayOfPropagationPathPoint.plot

ArrayOfSensorObsel.plot

AscendingGrid.plot

AtmField.plot

AziGrid.plot

CIARecord.plot

DisortFlux.plot

DisortRadiance.plot

GeodeticField2.plot

GriddedField2.plot

LatGrid.plot

LonGrid.plot

MagneticAngles.plot

Matrix.plot

MuelmatVector.plot

PropmatMatrix.plot

PropmatVector.plot

SortedGriddedField1.plot

SortedGriddedField2.plot

StokvecMatrix.plot

StokvecVector.plot

SubsurfaceField.plot

Sun.plot

SurfaceField.plot

Vector.plot

ZenGrid.plot