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.

Each submodule provides a plot() function that takes the data type as its first argument and optional arguments to control the plotting. The plot() functions return the figure, a list of subplots, and nothing more.

ArrayOfPropagationPathPoint

Plotting routine the propagation path in polar coordinates

pyarts3.plots.ArrayOfPropagationPathPoint.plot(ray_path: ArrayOfPropagationPathPoint, *, planetary_radius: float = 0.0, rscale: float = 1000, figure_kwargs: dict = {'dpi': 300}, draw_lat_lon: bool = True, draw_map: bool = True, draw_za_aa: bool = False, fig=None, subs=None)[source]

Plots a single observation in a polar coordinate system

Use the draw_* variables to select which plots are done

The polar plots’ central point is at the surface of the planet, i.e., at planetary_radius/rscale. The radius of these plots are the scaled down radiuses of the input ray_path[0].pos[0] / rscale + planetary_radius/rscale. The default radius value is thus just the altitude in kilometers. If you put, e.g., 6371e3 as the planetary_radius, the radius values will be the radius from the surface to the highest altitude

Note also that longitudes are unwrapped, e.g. a step longer than 180 degrees between ray_path points will wrap around, or rather, create separate entries of the lat-lons.

Example

import pyarts3 as pyarts
import numpy as np

ws = pyarts.Workspace()

ws.atmospheric_fieldRead(toa=100e3, basename="planets/Earth/afgl/tropical/")
ws.surface_fieldEarth()
ws.ray_path_observer_agendaSetGeometric(
    add_crossings=True, remove_non_crossings=True
)
ws.ray_path_observersFieldProfilePseudo2D(nup=3, nlimb=3, ndown=3)
ws.ray_path_fieldFromObserverAgenda()

f, a = None, None
for x in ws.ray_path_field:
    f, a = pyarts.plots.ArrayOfPropagationPathPoint.plot(
        x, draw_za_aa=True, draw_map=False, fig=f, subs=a
    )

(Source code, svg, pdf)

_images/pyarts3-plots-1.svg
Parameters:

  • ray_path (ArrayOfPropagationPathPoint) – A single propagation path object

  • planetary_radius (float, optional) – See polar_ray_path_helper in source tree

  • rscale (float, optional) – See polar_ray_path_helper in source tree

  • figure_kwargs (dict, optional) – Arguments to put into plt.figure(). The default is {“dpi”: 300}.

  • draw_lat_lon (bool, optional) – Whether or not latitude and longitude vs radius angles are drawn. Def: True

  • draw_map (bool, optional) – Whether or not latitude and longitude map is drawn. Def: True

  • draw_za_aa (bool, optional) – Whether or not Zenith and Azimuth angles are drawn. Def: False

  • fig (Figure, optional) – A figure. The default is None, which generates a new figure.

  • subs (A list of five subaxis, optional) – A tuple of five subaxis. The default is None, which generates new subs. The order is [lat, lon, map, za, aa]

Returns:

  • fig (As input) – As input.

  • subs (As input) – As input.

ArrayOfSensorObsel

Plotting routine for the sensor response

pyarts3.plots.ArrayOfSensorObsel.plot(measurement_sensor: ArrayOfSensorObsel, *, fig=None, keys: str | list = 'f', pol: str | Stokvec = 'I')[source]

Plot the sensor observational element array.

Note

Any option other than “f” will sort the sensor observational element array by the corresponding key. The object lives via a pointer, so the original measurement_sensor is modified in-place.

This should not massively affect any radiative transfer computations, but it might result in different results down on the floating point epsilon error level.

Example

import pyarts3 as pyarts
import numpy as np

ws = pyarts.Workspace()
ws.measurement_sensorSimpleGaussian(std = 10e6, pos = [100e3, 0, 0], los = [180.0, 0.0],
                                    frequency_grid = np.linspace(-50e6, 50e6, 101))
pyarts.plots.ArrayOfSensorObsel.plot(ws.measurement_sensor)

(Source code, svg, pdf)

_images/pyarts3-plots-2.svg
Parameters:

  • measurement_sensor (ArrayOfSensorObsel) – A sensor observation element array.

  • fig (Figure, optional) – The matplotlib figure to draw on. Defaults to None for new figure.

  • subs (Subaxis, optional) – List of subplots to add to. Defaults to None for a new subplot.

  • keys (str | list) – The keys to use for plotting. Options are in SensorKeyType.

  • pol (str | pyarts3.arts.Stokvec) – The polarization to use for plotting. Defaults to “I”, constructs a Stokvec.

Returns:

  • fig (As input) – As input.

  • subs (As input) – As input.

AtmField

Plotting routine for profiles of the atmospheric field

pyarts3.plots.AtmField.plot(atm_field: AtmField, *, fig=None, alts: ndarray | float = array([0., 2000., 4000., 6000., 8000., 10000., 12000., 14000., 16000., 18000., 20000., 22000., 24000., 26000., 28000., 30000., 32000., 34000., 36000., 38000., 40000., 42000., 44000., 46000., 48000., 50000., 52000., 54000., 56000., 58000., 60000., 62000., 64000., 66000., 68000., 70000., 72000., 74000., 76000., 78000., 80000., 82000., 84000., 86000., 88000., 90000., 92000., 94000., 96000., 98000., 100000.]), lats: ndarray | float = 0, lons: ndarray | float = 0, ygrid: ndarray | None = None, keys: list[str] | None = None)[source]

Plot select atmospheric field parameters by extracting a profile.

Example

import pyarts3 as pyarts
import numpy as np

ws = pyarts.Workspace()

ws.atmospheric_fieldRead(toa=100e3, basename="planets/Earth/afgl/tropical/")

pyarts.plots.AtmField.plot(ws.atmospheric_field, keys=["p", "t"])

(Source code, svg, pdf)

_images/pyarts3-plots-3.svg
Parameters:

  • atm_field (AtmField) – An atmospheric field

  • fig (Figure, optional) – The matplotlib figure to draw on. Defaults to None for new figure.

  • alts (ndarray | float, optional) – A grid to plot on - must after broadcast with lats and lons be 1D. Defaults to np.linspace(0, 1e5, 51).

  • lats (ndarray | float, optional) – A grid to plot on - must after broadcast with alts and lons be 1D. Defaults to 0.

  • lons (ndarray | float, optional) – A grid to plot on - must after broadcast with alts and lats be 1D. Defaults to 0.

  • ygrid (ndarray | None, optional) – Choice of y-grid for plotting. Uses broadcasted alts if None. Defaults to None.

  • keys (list, optional) – A list of keys to plot. Defaults to None for all keys in keys().

Returns:

  • fig (As input) – As input.

  • subs (As input) – As input.

SubsurfaceField

Plotting routine for profiles of the subsurface field

pyarts3.plots.SubsurfaceField.plot(subsurf_field: SubsurfaceField, *, fig=None, alts: ndarray | float = array([-1., -0.5, 0.]), lats: ndarray | float = 0, lons: ndarray | float = 0, ygrid: ndarray | None = None, keys: list[str] | None = None)[source]

Plot select subsurface field parameters by extracting a profile.

Parameters:

  • subsurf_field (SubsurfaceField) – A subsurface field

  • fig (Figure, optional) – The matplotlib figure to draw on. Defaults to None for new figure.

  • alts (ndarray | float, optional) – A grid to plot on - must after broadcast with lats and lons be 1D. Defaults to np.linspace(0, 1e5, 51).

  • lats (ndarray | float, optional) – A grid to plot on - must after broadcast with alts and lons be 1D. Defaults to 0.

  • lons (ndarray | float, optional) – A grid to plot on - must after broadcast with alts and lats be 1D. Defaults to 0.

  • ygrid (ndarray | None, optional) – Choice of y-grid for plotting. Uses broadcasted alts if None. Defaults to None.

  • keys (list, optional) – A list of keys to plot. Defaults to None for all keys in keys().

Returns:

  • fig (As input) – As input.

  • subs (As input) – As input.