Scatter diagnostics

Scatter diagnostics are added using the add_scatter() method.

The distribution of the field x will be computed against the y according to their respective res_x and res_y resolutions.

Add a scatter diagnostic computing the distribution of on field
against the other according to their respective resolutions.

Parameters
----------
name
    Name of the diagnostic.
x
    Field used for the x-axis.
y
    Field used for the y-axis.
res_x
    Min, max and width for the x-axis.
res_y
    Min, max and width for the y-axis.

This kind of diagnostic is plotted as pseudo-color plots with a non-regular rectangular grid.

Diagnostic setting

In the following example we are setting a scatter diagnostic computing the dispersion of a user defined range_std_ku field against an other user defined range_std_c field using a using a step of 0.001 for range_std_ku values between 0 and 0.2 and a step of 0.01 for range_std_c values between 0 and 2.

from casys import Field

range_std_ku = Field(
    name="range_std_ku",
    source="IIF(FLAG_VAL.ALTI==0, RANGE_STD.ALTI, DV)",
    unit="m",
)
range_std_c = Field(
    name="range_std_c",
    source="IIF(FLAG_VAL.ALTI==0, RANGE_STD.ALTI.B2, DV)",
    unit="m",
)

ad.add_scatter(
    name="Scatter Ku-band Range std / C-band Range std",
    x=range_std_ku,
    y=range_std_c,
    res_x=(0, 0.2, 0.001),
    res_y=(0, 2, 0.01),
)

ad.compute()

Diagnostic plotting

Scatter diagnostics are plotted as pseudo-color plots with a non-regular rectangular grid.

The regression curve can be displayed (default) or hidden by providing a PlotParams with its show_reg set to False.

from casys import CasysPlot, PlotParams

plot = CasysPlot(
    data=ad,
    data_name="Scatter Ku-band Range std / C-band Range std",
    plot_params=PlotParams(show_reg=True),
)

plot.show()

Regression curve parameters are available in the data_used attributes of the plot:

• reg_slope

• reg_intercept

• reg_correlation

plot.data_used.attrs

{'reg_slope': np.float64(1.0542935634469273),
 'reg_intercept': np.float64(0.0909196441533355),
 'reg_correlation': np.float64(0.4068337781571026)}