Source code for pyacs.gts.Sgts_methods.plot_data_sum

def plot_data_sum(self, period=None):
    """
    generate an interactive plot showing available/missing data

    param period: period used to checkk data availability. Uses pandas syntax. See examples.
    return self
    examples:
            ts.plot_data_sum(period='2008') willl show a summary for the whole year 2008
            ts.plot_data_sum(period=['2008-01-15','2011-01-01']) willl show a summary for the period
            Dispay will include all time series in ts. Combine with ts selection to obtain more specific behaviour.
            ts.sub(linclude=['XXXX','YYYY']).plot_data_sum(period='2008') will plot data availability only for 'XXXX' and 'YYYY'
            ts.sel_radius('XXXX',[0,50]).sel_period([2008,2015],min_data=1000).plot_data_sum(period='2008') will show data availability for year 2008, only for sites less than 50 km from XXXX and having a minimum of 1000 days between 2008 and 2015.
    """
    import pandas
    import matplotlib.pyplot as plt
    import numpy as np
    import pyacs.lib.astrotime as at

    import logging
    import pyacs.message.message as MESSAGE
    import pyacs.message.verbose_message as VERBOSE
    import pyacs.message.error as ERROR
    import pyacs.message.warning as WARNING
    import pyacs.message.debug_message as DEBUG


    import inspect
    VERBOSE("Running Sgts.%s" % inspect.currentframe().f_code.co_name)


    VERBOSE("Gts to tensor conversion for %d sites" % self.n())
    T, np_names, np_seconds_sol = self.to_obs_tensor(rounding='day')
    VERBOSE("Converting Sgts to pandas DataFrame")
    df = pandas.DataFrame(T[:, :, 0])
    df.index = at.seconds2datetime(np_seconds_sol)

    # decipher period
    VERBOSE("Selecting user provided period")
    if period is None:
        VERBOSE("Default period: %s to %s" %(df.index[0].isoformat(),df.index[-1].isoformat()))
        a = df
    else:
        if isinstance(period, list):
            a = df.loc[period[0]:period[1]]
        else:
            a = df.loc[period]

    npa = np.copy(a.to_numpy())
    # revert nan
    lidx_nan = np.where(np.isnan(npa))
    lidx_nnan = np.where(~np.isnan(npa))
    npa[lidx_nan] = 0
    npa[lidx_nnan] = np.nan
    npa = npa + np.arange(npa.shape[1] - 1, -1, -1)
    bnpa = np.ones(npa.shape) * np.arange(npa.shape[1])

    VERBOSE("Making plot")

    fig, ax = plt.subplots(figsize=(7, 9))

    ax.plot(a.index, bnpa, color='lime', linewidth=3)
    ax.plot(a.index, npa, color='red', linewidth=3)
    ax.set_yticks(np.arange(a.shape[1]))
    ax.set_yticklabels(np.flip(np_names))
    ax.tick_params(axis='y', which='major', labelsize=6)
    # ax.set_xlim(a.index[0],a.index[1])
    ax.set_ylim(-0.5, npa.shape[1] - 0.5)
    plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)

    return self