def all_data(datafiles, npre, npost, lon1, lon2, compdays, comp_attrs):
# Read daily data fields aligned relative to onset day
data = collections.OrderedDict()
sectordata = collections.OrderedDict()
comp = collections.OrderedDict()
sectorcomp = collections.OrderedDict()
sector_latmax = {}
for varnm in datafiles:
print('Reading daily data for ' + varnm)
var, onset, retreat = utils.load_dailyrel(datafiles[varnm])
var = atm.subset(var, {'dayrel' : (-npre, npost)})
var = housekeeping(var)
# Compute sector mean and composite averages
sectorvar = atm.dim_mean(var, 'lon', lon1, lon2)
compvar = get_composites(var, compdays, comp_attrs)
sectorcompvar = get_composites(sectorvar, compdays, comp_attrs)
# Latitude of maximum subcloud theta_e
if varnm == 'THETA_E950' or varnm == 'THETA_E_LML':
sector_latmax[varnm] = theta_e_latmax(sectorvar)
# Compute regression or take the climatology
if 'year' in var.dims:
var = atm.dim_mean(var, 'year')
sectorvar = atm.dim_mean(sectorvar, 'year')
compvar = atm.dim_mean(compvar, 'year')
sectorcompvar = atm.dim_mean(sectorcompvar, 'year')
# Pack everything into dicts for output
data[varnm], sectordata[varnm] = var, sectorvar
comp[varnm], sectorcomp[varnm] = compvar, sectorcompvar
return data, sectordata, sector_latmax, comp, sectorcomp
def get_data(varnm, datafiles, regdays, seasons, lon1, lon2, nroll=None):
var, onset, retreat = utils.load_dailyrel(datafiles[varnm])
if nroll is not None:
var = atm.rolling_mean(var, nroll, axis=1, center=True)
# Seasonal averages and daily lat-lon data
data = xray.Dataset()
for season in seasons:
key = varnm + '_' + season
data[key] = ssn_average(var, onset, retreat, season)
# Daily data on regdays
data[varnm + '_DAILY'] = var.sel(dayrel=regdays)
# Sector mean data
var_sector = atm.dim_mean(var, 'lon', lon1, lon2)
alldata = {'data_latlon' : data, 'var_sector' : var_sector,
'onset' : onset, 'retreat' : retreat}
return alldata
files = {nm : [filestr % (nm, onset_nm, yearstr)] for nm in varnms}
return files
datafiles = {key: data_filenames(comp_yrs[key], varnms, onset_nm, datadir)
for key in comp_yrs}
datafiles['clim'] = clim_filenames(clim_yearstr, varnms, onset_nm, datadir)
# ----------------------------------------------------------------------
# Get daily data relative to onset day
data = {}
for key in datafiles:
data[key] = {}
for nm in datafiles[key]:
var, _, _ = utils.load_dailyrel(datafiles[key][nm])
if 'year' in var.dims:
var = var.mean(dim='year')
data[key][nm] = var
# ----------------------------------------------------------------------
# Housekeeping
# Fill Ro200 with NaNs near equator
varnm = 'Ro200'
for key1 in data:
if varnm in data[key1]:
latbuf = 5
lat = atm.get_coord(data[key1][varnm], 'lat')
latbig = atm.biggify(lat, data[key1][varnm], tile=True)
varid, plev = get_info(varnm)
var = get_data_rel(varid, plev, year, files.get(varnm), data, d0, npre,
npost)
var.attrs = atm.odict_delete(var.attrs, 'd_onset')
var.name = varid
data[varnm] = var
savefile = get_savefile(version, savedir, varnm, onset_nm, ind_nm, year)
print('Saving to ' + savefile)
atm.save_nc(savefile, var, onset_var, retreat_var)
# ----------------------------------------------------------------------
# Compute climatologies and save
relfiles = {}
for key in datafiles:
relfiles[key] = [get_savefile(version, savedir, key, onset_nm, ind_nm, yr)
% yr for yr in years]
for varnm in relfiles:
varid, _ = get_info(varnm)
var, onset, retreat = load_dailyrel(relfiles[varnm])
ds = xray.Dataset()
ds[varid], ds['D_ONSET'], ds['D_RETREAT'] = var, onset, retreat
print('Computing climatological mean')
yearnm = atm.get_coord(ds, 'year', 'name')
ds = ds.mean(dim=yearnm)
ds[varid].attrs = var.attrs
ds[varid].attrs['years'] = years
filn = relfiles[varnm][0].replace('%d' % years[0], yearstr)
print('Saving to ' + filn)
ds.to_netcdf(filn)
years = np.arange(1979, 2015)
datadir = atm.homedir() + 'datastore/merra/analysis/'
varnms = ['U200', 'T200']
datafiles = collections.OrderedDict()
filestr = datadir + 'merra_%s_dailyrel_%s_%d.nc'
for nm in varnms:
datafiles[nm] = [filestr % (nm, onset_nm, yr) for yr in years]
# ----------------------------------------------------------------------
# Read data
data = collections.OrderedDict()
for nm in datafiles:
var, onset, retreat = utils.load_dailyrel(datafiles[nm])
data[nm] = var
# ----------------------------------------------------------------------
# Test atm.regress_field
day = -60
# 1-d timeseries
var = data['U200'].sel(dayrel=day)
ts = atm.mean_over_geobox(var, 10, 30, 60, 100)
ts_reg = atm.Linreg(onset, ts)
ts_reg2 = atm.regress_field(ts, onset)
print(ts_reg.r, ts_reg2.r.values)
print(ts_reg.slope, ts_reg2.m.values)
print(ts_reg.p, ts_reg2.p.values)
years = np.arange(1979, 2015)
datadir = atm.homedir() + 'datastore/merra/analysis/'
varnms = ['U200', 'T200']
datafiles = collections.OrderedDict()
filestr = datadir + 'merra_%s_dailyrel_%s_%d.nc'
for nm in varnms:
datafiles[nm] = [filestr % (nm, onset_nm, yr) for yr in years]
# ----------------------------------------------------------------------
# Read data
data = collections.OrderedDict()
for nm in datafiles:
var, onset, retreat = utils.load_dailyrel(datafiles[nm])
data[nm] = var
# ----------------------------------------------------------------------
# Test atm.regress_field
day = -60
# 1-d timeseries
var = data['U200'].sel(dayrel=day)
ts = atm.mean_over_geobox(var, 10, 30, 60, 100)
ts_reg = atm.Linreg(onset, ts)
ts_reg2 = atm.regress_field(ts, onset)
print(ts_reg.r, ts_reg2.r.values)
print(ts_reg.slope, ts_reg2.m.values)
print(ts_reg.p, ts_reg2.p.values)
