Adding percentiles to a plot

Dear CDS-Team,

the solution to my question regarding the addition of a trend line to my plot was very helpful (see "Plotting a trend line").

Continuing on a similar case the next step would be a line (or two) which represent the percentiles (e.g. 2.5% and 97.5%). There is indeed a function to calculate the values (ct.cube.quantile) but plotting a line is again tricky.

First idea was to use the function provided in the cds-forum thread "Plotting a trend line" and adjust the start- and end-values somehow to get a horizontal line. As I took notice of the possibility to import python libraries (like matplotlib and xarray), it seems still tough to replace xarray values with the required percentiles. Is this nevertheless a productive way or are there other possibilities to plot a horizontal line with percentiles?

Please see a shortened version of my code attached.

Any help is appreciated!

Thanks and regards

Tim Usedly

import cdstoolbox as ct

layout = ct.Layout(rows=1)
layout.add_widget(row=0, content='output-0')

@ct.application(layout=layout)
@ct.output.livefigure()

def application():
    NOAA = ct.catalogue.retrieve(
        'reanalysis-era5-land',
        {
            'variable': '2m_temperature',
            'year': ['2010'],
            'month': [
                '01', '02', '03',
                '04', '05', '06',
                '07', '08', '09',
                '10', '11', '12',
            ],
            'day': '01',
            'time': '00:00',
        }
    )
    
    NOAA_cut = ct.cube.box_select(NOAA, lat=[-60,60])
    NOAA_avg = ct.cube.average(NOAA_cut, dim=['lon','lat'])
   
    coords = ct.cdm.get_coordinates(NOAA_avg)
    start_date = coords['time']['data'][0]
    end_date = coords['time']['data'][-1]
        
    fig  = ct.chart.line(
        NOAA_avg,
        layout_kwargs = {
            'xaxis':{
                'showline':True,
                'showgrid': True,  
            },
            'yaxis': {
                'showline':True,
                'showgrid': True,
                'tickmode' : 'auto'
            },
        },
        scatter_kwargs = {
            'mode': 'lines',
            'line.color':'black'
        }
    )

    # Percentil
    quantile25 = ct.cube.quantile(NOAA_avg, q=0.025)
    quantile975 = ct.cube.quantile(NOAA_avg, q=0.975)
    print(quantile25)
    print(quantile975)

    
    # Calculate linear fit
    fit = ct.stats.extrapolate(
        NOAA_avg,
        date_range_kwargs=
        {
            'start': start_date, 
            'end': end_date, 
            'periods': 2
        },
        func='linear'
    )
    
    # Plot the calculated fit
    fig = ct.chart.line(
        fit,
        fig=fig,
        name='linear trend line',
        scatter_kwargs = {
            'marker':{
                'color':'orange',
            }
        }
    )
    
    return fig

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2 Comments

Vivien MAVEL

Tim Usedly