""" Plotting routine for GriddedField2 """
import numpy
import matplotlib
import pyarts3 as pyarts
import numpy as np
from .common import default_fig_ax, select_flat_ax
__all__ = [
'plot',
]
[docs]
def plot(data: pyarts.arts.GriddedField2,
*,
fig: matplotlib.figure.Figure | None = None,
ax: matplotlib.axes.Axes | list[matplotlib.axes.Axes] | numpy.ndarray[matplotlib.axes.Axes] | None = None,
**kwargs) -> tuple[matplotlib.figure.Figure, matplotlib.axes.Axes | list[matplotlib.axes.Axes] | numpy.ndarray[matplotlib.axes.Axes]]:
"""Plot a GriddedField2 as a 2D heatmap using its grids.
.. rubric:: Example
.. plot::
:include-source:
import pyarts3 as pyarts
import numpy as np
# Create a geographic field
lats = pyarts.arts.Vector(np.linspace(-90, 90, 20))
lons = pyarts.arts.Vector(np.linspace(-180, 175, 36))
lon_mesh, lat_mesh = np.meshgrid(lons, lats)
# Example: distance from equator
data = pyarts.arts.Matrix(np.abs(lon_mesh))
field = pyarts.arts.GriddedField2()
field.grids = (lats, lons)
field.data = data
field.dataname = "Distance from Equator"
pyarts.plots.GriddedField2.plot(field)
Parameters
----------
data : ~pyarts3.arts.GriddedField2
A 2D gridded field with named grids
fig : ~matplotlib.figure.Figure, optional
The matplotlib figure to draw on. Defaults to None for new figure.
ax : ~matplotlib.axes.Axes | list[~matplotlib.axes.Axes] | ~numpy.ndarray[~matplotlib.axes.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.
"""
fig, ax = default_fig_ax(fig, ax, fig_kwargs={"figsize": (12, 8)})
xgrid = data.grids[0]
ygrid = data.grids[1]
data = data.data
y_mesh, x_mesh = np.meshgrid(ygrid, xgrid)
select_flat_ax(ax, 0).pcolormesh(x_mesh, y_mesh, data, **kwargs)
return fig, ax