How to use the pytesmo.time_series.anomaly.calc_anomaly function in pytesmo

def test_anomaly_calc_given_climatology_return_clim():

    clim = pd.Series(np.arange(366), name='clim', index=np.arange(366) + 1)
    data = pd.Series(
        np.arange(366), index=pd.date_range('2000-01-01', periods=366))
    anom_should = pd.Series(
        np.zeros(366), index=pd.date_range('2000-01-01', periods=366),
        name='anomaly')
    anom = anomaly.calc_anomaly(
        data, climatology=clim, respect_leap_years=False,
        return_clim=True)

    pdt.assert_series_equal(anom_should, anom['anomaly'], check_dtype=False)

def test_anomaly_calc_given_climatology_no_leap_year():

    clim = pd.Series(np.arange(366), name='clim', index=np.arange(366) + 1)
    data = pd.Series(
        np.arange(365), index=pd.date_range('2007-01-01', periods=365))
    anom_should = pd.Series(
        np.zeros(365), index=pd.date_range('2007-01-01', periods=365))
    anom = anomaly.calc_anomaly(
        data, climatology=clim, respect_leap_years=True)

    pdt.assert_series_equal(anom_should, anom, check_dtype=False)

def calc_anom(self, data):
        if self.columns is None:
            ite = data
        else:
            ite = self.columns
        for column in ite:
            clim = calc_climatology(data[column], **self.kwargs)
            data[column] = calc_anomaly(data[column], climatology=clim)
        return data

axes.append(ax)
                ax.xaxis.set_visible(False)
        axes.append(last_axis)

    else:
        own_axis = False

    for i, column in enumerate(df):
        Ser = df[column]
        ax = axes[i]

        if clim is None:
            clima = anom.calc_climatology(Ser)
        else:
            clima = pd.Series(clim[column])
        anomaly = anom.calc_anomaly(Ser, climatology=clima, return_clim=True)

        anomaly[Ser.name] = Ser
        anomaly = anomaly.dropna()

        pos_anom = anomaly[Ser.name].values > anomaly['climatology'].values
        neg_anom = anomaly[Ser.name].values < anomaly['climatology'].values

        ax.plot(anomaly.index, anomaly[Ser.name].values, 'o',
                markersize=markersize, mfc=mfc, mec=mec)

        ax.plot(anomaly.index, anomaly['climatology'].values,
                linestyle=clim_linestyle,
                color=clim_color,
                linewidth=clim_linewidth)

        ax.fill_between(anomaly.index,

def calc_anom(self, data):
        if self.columns is None:
            ite = data
        else:
            ite = self.columns
        for column in ite:
            data[column] = calc_anomaly(data[column],
                                        window_size=self.window_size)
        return data

# 

#calculate anomaly based on moving +- 17 day window
anomaly = ts.anomaly.calc_anomaly(ascat_ts.data['sm'], window_size=35)
anomaly.plot()

# 

#calculate climatology
climatology = ts.anomaly.calc_climatology(ascat_ts.data['sm'])
climatology.plot()

# 

#calculate anomaly based on climatology
anomaly_clim = ts.anomaly.calc_anomaly(ascat_ts.data['sm'], climatology=climatology)
anomaly_clim.plot()

'ASCAT_WARP5.5_R1.2','080_ssm','netcdf')
ascat_grid_folder = os.path.join('R:\\','Datapool_processed','WARP','ancillary','warp5_grid')
#init the ASCAT_SSM reader with the paths

ascat_SSM_reader = ascat.AscatH25_SSM(ascat_folder,ascat_grid_folder)

# 

ascat_ts = ascat_SSM_reader.read_ssm(45,0)
#plot soil moisture
ascat_ts.data['sm'].plot()

# 

#calculate anomaly based on moving +- 17 day window
anomaly = ts.anomaly.calc_anomaly(ascat_ts.data['sm'], window_size=35)
anomaly.plot()

# 

#calculate climatology
climatology = ts.anomaly.calc_climatology(ascat_ts.data['sm'])
climatology.plot()

# 

#calculate anomaly based on climatology
anomaly_clim = ts.anomaly.calc_anomaly(ascat_ts.data['sm'], climatology=climatology)
anomaly_clim.plot()