Section analyses

Section analyses diagnostics are added using the add_section_analysis() method.

Section analyses research portion of data matching the provided conditions.

Add the computation of a section analysis. Section analyses research
portion of data matching the provided conditions.

Section analysis plots can be created using special keywords:

* sections parameter:

    * sections="all" (default): data from all sections
    * sections=1: data limited to section 1
    * sections=[1, 2, 10, 14]: data limited to sections 1, 2, 10 and 14

Parameters
----------
name
    Name of the analysis.
condition
    Clip condition determining the section.
min_length
    Minimum number of point required to accept the section.
fill_missing
    Whether to fill missing values with False values (require parameter
    max_percent_of_false to be greater than 0 to have an effect on the result)
max_percent_of_false
    Maximum percent of False value accepted in the section.

This kind of diagnostic can be:

plotted as raw data diagnostics map plots.

visualized as a text summary

Diagnostic setting

In the following example we are setting a diagnostic searching for sections having a minimum length of 400 measurements, allowing missing values and having a tolerance of 2.5% of measurements not respecting its condition.

ad.add_section_analysis(
    name="Analysis 1",
    condition="IIF3(FLAG_SURFACE_TYPE :== 0 && FLAG_VAL.ALTI :!= 0, 1, 0, 0)",
    min_length=400,
    fill_missing=True,
    max_percent_of_false=2.5,
)

ad.compute()

Diagnostic text report and numerical result

Using the get_data() method allows get an analysis which will be displayed as a text report in notebooks.

ad.get_data(name="Analysis 1")

			First position			Last position
#	Cycle	Pass	Date	Longitude	Latitude	Date	Longitude	Latitude	Length	False %
1	122	22 - 23	2019-06-02T02:04:53	47.98	-65.09	2019-06-02T02:11:56	97.61	-62.04	416	1.92%
2	122	42 - 43	2019-06-02T20:47:21	112.56	-62.90	2019-06-02T20:54:08	161.09	-64.80	401	1.50%
3	122	98 - 99	2019-06-05T01:17:23	52.07	-65.25	2019-06-05T01:24:09	99.87	-62.20	400	0.00%
4	122	170 - 171	2019-06-07T20:41:41	91.96	-61.05	2019-06-07T20:50:53	155.09	-63.29	543	1.84%
5	122	174 - 175	2019-06-08T00:29:23	52.61	-64.96	2019-06-08T00:36:33	103.19	-62.07	424	0.00%

These results can be used through the parameters of the AnalysisResults class:

sections: List of found sections with their properties

mean_length: Mean length of sections

full_positions: xarray dataset containing all resulting sections

analysis = ad.get_data(name="Analysis 1")
res = analysis.result

res.mean_length

np.float64(436.8)

Diagnostic plotting

CasysPlot uses a sections parameter to determine which sections to display.

sections = “all” (default): show all sections

sections = 1: Number of the section to show

sections = [1 , 4, 10]: List of numbers of sections to show

Plotting all sections

from casys import CasysPlot, PlotParams

plot = CasysPlot(
    data=ad,
    data_name="Analysis 1",
    plot_params=PlotParams(x_limits=(40, 180), y_limits=(-90, 0)),
)

plot.show()

Only plotting sections 1, 2, 5 and 10.

plot = CasysPlot(
    data=ad,
    data_name="Analysis 1",
    plot_params=PlotParams(x_limits=(40, 180), y_limits=(-90, 0)),
    sections=[1, 2, 5],
)

plot.show()