import pandas as pd
import atmos as atm
import precipdat
import merra
import indices
from utils import daily_rel2onset, comp_days_centered, composite
# ----------------------------------------------------------------------
datadir = atm.homedir() + 'datastore/merra/daily/'
onsetfile = datadir + 'merra_u850_40E-120E_60S-60N_apr-sep_1979-2014.nc'
datafile = datadir + 'merra_precip_40E-120E_60S-60N_days91-274_1979-2014.nc'
# Onset day calcs
with xray.open_dataset(onsetfile) as ds:
u = ds['U'].load()
oci = indices.onset_OCI(u)
d_onset = oci['onset']
# Daily data fields
var = 'precip'
yearnm, daynm = 'year', 'day'
cmap = 'hot_r'
axlims = (-10, 50, 40, 120)
with xray.open_dataset(datafile) as ds:
data = ds['PRECTOT'].load()
# Align daily data relative to onset day
ndays = 30
data = daily_rel2onset(data, d_onset, ndays, ndays, yearnm=yearnm, daynm=daynm)
years = data[yearnm].values
databar = data.mean(dim=yearnm)
# Unsmoothed pentad timeserires
key = 'WLH_%s_unsmth' % name
print(key)
index[key] = get_onset_WLH(years, days, pcp, threshold, key, pentad,
precip_jan)
# ----------------------------------------------------------------------
# OCI index (Wang et al 2009) and SJKE index (Boos and Emmanuel 2009)
ds = atm.combine_daily_years(['U', 'V'], ocifiles, years)
ds = ds.rename({'Year' : 'year', 'Day' : 'day'})
u850 = atm.squeeze(ds['U'])
v850 = atm.squeeze(ds['V'])
# OCI Index
index['OCI'] = indices.onset_OCI(u850, yearnm='year', daynm='day')
index['OCI'].attrs['title'] = 'OCI'
# SJKE Index
index['SJKE'] = indices.onset_SJKE(u850, v850, yearnm='year', daynm='day')
index['SJKE'].attrs['title'] = 'SJKE'
# ----------------------------------------------------------------------
# TT index (Goswami et al 2006)
# *** NOTES ****
# Need to trouble shoot TT index before using in anything final.
# See testing/testing-indices-onset_TT.py for details.
# Select vertical pressure level to use, or None to use 200-600mb
# vertical mean
