Пример #1
0
def get_onset_indices(onset_nm, datafiles, years, data=None):
    """Return monsoon onset/retreat/length indices.
    """

    # Options for CHP_MFC and CHP_PCP
    lat1, lat2 = 10, 30
    lon1, lon2 = 60, 100
    chp_opts = [None, lat1, lat2, lon1, lon2]

    if onset_nm == 'HOWI':
        maxbreak = 10
        npts = 100
        ds = atm.combine_daily_years(['uq_int', 'vq_int'], datafiles, years,
                                     yearname='year')
        index, _ = indices.onset_HOWI(ds['uq_int'], ds['vq_int'], npts,
                                      maxbreak=maxbreak)
        index.attrs['title'] = 'HOWI (N=%d)' % npts
    elif onset_nm == 'CHP_MFC':
        if data is None:
            tseries = get_mfc_box(datafiles, None, None, years, *chp_opts)
            data = tseries['MFC_ACC']
            index['ts_daily'] = tseries['MFC']
        index = indices.onset_changepoint(data)
    elif onset_nm == 'CHP_PCP':
        if data is None:
            tseries = get_mfc_box(None, datafiles, None, years, *chp_opts)
            data = tseries['PCP_ACC']
        index = indices.onset_changepoint(data)
        index['ts_daily'] = tseries['PCP']

    # Monsoon retreat and length indices
    if 'retreat' in index:
        index['length'] = index['retreat'] - index['onset']
    else:
        index['retreat'] = np.nan * index['onset']
        index['length'] = np.nan * index['onset']

    return index
Пример #2
0
north=(5, 30, 40, 100)
south=(-15, 5, 40, 100)
index['TT'] = indices.onset_TT(T, north=north, south=south)

# Some weirdness going on in 1991, for now just set to NaN
for nm in ['ttn', 'tts', 'tseries']:
    vals = index['TT'][nm].values
    vals = np.ma.masked_array(vals, abs(vals) > 1e30).filled(np.nan)
    index['TT'][nm].values = vals
index['TT'].attrs['title'] = 'TT'

# ----------------------------------------------------------------------
# Changepoint method with accumulated MFC / precip (Cook & Buckley 2009)
mfc_acc = np.cumsum(mfcbar, axis=1)
precip_acc = np.cumsum(precipbar, axis=1)
index['CHP_MFC'] = indices.onset_changepoint(mfc_acc)
index['CHP_PCP'] = indices.onset_changepoint(precip_acc)
for key in ['CHP_MFC', 'CHP_PCP']:
    index[key].attrs['title'] = key

# ----------------------------------------------------------------------
# Monsoon strength indices

def detrend(vals, index):
    vals_det = scipy.signal.detrend(vals)
    vals_det = vals_det / np.std(vals_det)
    output = pd.Series(vals_det, index=index)
    return output

# MERRA MFC
mfc_JJAS = atm.subset(mfcbar, {'day' : (atm.season_days('JJAS'), None)})
        varid, filestr = 'MFC', 'MFC_ps-300mb'
    elif varnm == 'precip':
        varid, filestr = 'PRECTOT', 'precip'

    files = [datadir + 'merra_%s_%d.nc' % (filestr, year) for year in years]
    data[varnm] = atm.combine_daily_years(varid, files, years, yearname='year',
                                          subset_dict=subset_dict)
    data[varnm] = atm.mean_over_geobox(data[varnm], lat1, lat2, lon1, lon2)
    data[varnm] = atm.precip_convert(data[varnm], data[varnm].attrs['units'],
                                    'mm/day')

    # Accumulated precip or MFC
    data_acc[varnm] = np.cumsum(data[varnm], axis=1)

    # Compute onset and retreat days
    chp[varnm] = onset_changepoint(data_acc[varnm])

if incl_merged:
    chpm = {}
    for varnm in varnms:
        datafile = datadir2 + 'merra_onset_changepoint_merged_%s.nc' % varnm.upper()
        print('Reading ' + datafile)
        chpm[varnm] = xray.open_dataset(datafile)

def plotyear(chp, y, xlims, ylims):
    days = chp['day'].values
    d_onset = chp['onset'][y]
    d_retreat = chp['retreat'][y]
    plt.plot(days, chp['tseries'][y])
    plt.plot(days, chp['tseries_fit_onset'][y])
    plt.plot(days, chp['tseries_fit_retreat'][y])