Quick overview

A Cal/Val diagnostic can be set, executed and visualized in a few steps, detailed below.

Select your data source

First we have to define the data source and time interval we want to work on.

Defining time interval is done using DateHandler, a utility class allowing to create dates from many formats including python, numpy and pandas dates as well as altimetric related dates like ORF or julian days.

from casys import DateHandler

start = DateHandler("2019-06-01 05:30:00")
end = DateHandler("2019-06-07 05:30:00")

NadirData and SwathData are the data containers allowing to set and compute statistics and diagnostics by interfacing with different kind of input sources.

In the following example the used source is a CLS table.

from casys import NadirData

ad = NadirData(
    date_start=start,
    date_end=end,
    source="TABLE_C_J3_B_GDRD",
    orf="C_J3_GDRD",
)

Define your diagnostic fields

The Field object is used when specifying diagnostics (raw data, statistics, …) to reference a field (or variable) included in the data source.

Field can be manually created (to dynamically combine multiple fields) or extracted from the source.

The show_fields() method allows to visualize and search among all existing fields.

# Display all fields included in the data source
ad.show_fields()

# Or a set of fields containing the provided string ("SIGMA0" here)
ad.show_fields(containing="SIGMA0")

Name	Description	Unit
ATMOSPHERIC_ATTENUATION_SIGMA0.ALTI	Atténuation atmosphérique du coefficient de rétrodiffusion en bande principale	dB
ATMOSPHERIC_ATTENUATION_SIGMA0.ALTI.B2	Atténuation atmosphérique du coefficient de rétrodiffusion en bande secondaire	dB
FLAG_QUAL.ALTI.NET_INSTR_CORR_SIGMA0	0: good, 1: bad.	1
FLAG_QUAL.ALTI.NET_INSTR_CORR_SIGMA0_B2	0: good, 1: bad.	1
SIGMA0.ALTI	All instrumental corrections included, excepted the system bias, i.e. AGC instrumental errors correction, internal calibration	dB
SIGMA0.ALTI.B2	All instrumental corrections included, excepted the system bias, i.e. AGC instrumental errors correction, internal calibration	dB
SIGMA0.ALTI.RTK_MLE3	Coefficient de rétrodiffusion en bande principale (rtk MLE3)	dB
SIGMA0_BIAS.ALTI	Biais du SIGMA0 par rapport à une valeur moyenne de référence	dB
SIGMA0_NET_INSTRUMENTAL_CORRECTION.ALTI	Sum of AGC instrumental errors correction, internal calibration correction and modeled instrumental errors correction - system	dB
SIGMA0_NET_INSTRUMENTAL_CORRECTION.ALTI.B2	Sum of AGC instrumental errors correction, internal calibration correction and modeled instrumental errors correction - system	dB
SIGMA0_NET_INSTRUMENTAL_CORRECTION.ALTI.RTK_MLE3	Somme des corrections instrumentales sur le sigma0 en bande principale (rtk MLE3)	dB
SIGMA0_NUMBER.ALTI	Nombre de mesures elementaires de sigma0 en bande principale	count
SIGMA0_NUMBER.ALTI.B2	Nombre de mesures elementaires de sigma0 en bande secondaire	count
SIGMA0_NUMBER.ALTI.RTK_MLE3	Nombre de mesures elementaires de sigma0 en bande principale (rtk MLE3)	count
SIGMA0_STD.ALTI	Compression of high rate elements is preceded by a detection of outliers. Only valid high-rate values are used to compute this	dB
SIGMA0_STD.ALTI.B2	Compression of high rate elements is preceded by a detection of outliers. Only valid high-rate values are used to compute this	dB
SIGMA0_STD.ALTI.RTK_MLE3	Ecart-type des mesures elementaires de sigma0 en bande principale (rtk MLE3)	dB

These fields can then be extracted through the fields property.

var_sig0 = ad.fields["SIGMA0.ALTI"]

Set up your diagnostic

Once the source, time interval and fields are defined, we can specify the diagnostics themselves.

In this example we are setting an along time diagnostic computing the SIG0 mean per pass, using the add_time_stat() method.

ad.add_time_stat(
    name="SIG0 pass",
    freq="pass",
    field=var_sig0,
    stats=["mean"]
)

Compute it

The diagnostic can now be computed with the compute() method.

ad.compute()

Visualize it

Once computed a diagnostic can be visualized.

CasysPlot is a plotting tool allowing to visualize diagnostics computed with NadirData or SwathData and easily perform predefined actions as well as exposing the matplotlib figure for more advanced customizations.

from casys import CasysPlot

sigpass_mean_plot = CasysPlot(
    data=ad,
    data_name="SIG0 pass",
    stat="mean"
)
sigpass_mean_plot.show()