cname = replace(os.path.basename(cf_in), '.nc', '')
id_in = Dataset(cf_in)
vt = id_in.variables['time'][:]
vsst = id_in.variables[cv_in][:]
id_in.close()
Nt = len(vsst)
vtmp = nmp.zeros(Nt)
vtime = nmp.zeros(Nt - 5)
xplot = nmp.zeros((Nt - 5, 4))
# Nt-5 because 5-month-running mean
vtime[:] = vt[2:-3]
vtmp = bs.running_mean_5(vsst)
# 5-month running mean
xplot[:, 0] = vsst[2:-3]
xplot[:, 1] = vtmp[2:-3]
(za, zb) = bs.least_sqr_line(vtime[:], xplot[:, 1])
# Least-square linear trend
xplot[:, 2] = za * vtime[:] + zb
xplot[:, 3] = xplot[:, 1] - xplot[:, 2]
# anomaly for 5-month running mean
ittic = bt.iaxe_tick(Nt / 12)
bp.plot("oscillation_index")(vtime,
xplot[:, 3],
def wrt_1d_series(vt, vd, cvar, cinfo,
cu_t='unknown', cu_d='unknown', cln_d='unknown', nsmooth=0,
vd2=[], vd3=[], vd4=[], vd5=[],
cvar2='', cvar3='', cvar4='', cvar5='',
cln_d2='', cln_d3='', cln_d4='', cln_d5=''):
cf_o = cvar+'_'+cinfo+'.nc'
lsmooth = False
if nsmooth > 0:
import barakuda_stat as bs
lsmooth = True
if nsmooth == 11:
vd_sm = bs.running_mean_11(vd, l_fill_bounds=False)
elif nsmooth == 5:
vd_sm = bs.running_mean_5(vd, l_fill_bounds=False)
else:
print 'ERROR: wrt_1d_series.barakuda_ncio => smoothing with nsmooth='+str(nsmooth)+' not supported!'; sys.exit(0)
f_o = Dataset(cf_o, 'w', format='NETCDF3_CLASSIC')
nt = len(vt)
if len(vd) != nt: print 'ERROR: wrt_1d_series.barakuda_ncio => data & time have different lengths!'; sys.exit(0)
l_do_v2=False ; l_do_v3=False ; l_do_v4=False ; l_do_v5=False
if len(vd2) == nt: l_do_v2=True
if len(vd3) == nt: l_do_v3=True
if len(vd4) == nt: l_do_v4=True
if len(vd5) == nt: l_do_v5=True
f_o.createDimension('time', None)
id_t = f_o.createVariable('time','f4',('time',)) ; id_t.units = cu_t
id_d = f_o.createVariable(cvar,'f4',('time',))
id_d.units = cu_d ; id_d.long_name = cln_d
if l_do_v2: id_d2 = f_o.createVariable(cvar2,'f4',('time',)); id_d2.units = cu_d; id_d2.long_name = cln_d2
if l_do_v3: id_d3 = f_o.createVariable(cvar3,'f4',('time',)); id_d3.units = cu_d; id_d3.long_name = cln_d3
if l_do_v4: id_d4 = f_o.createVariable(cvar4,'f4',('time',)); id_d4.units = cu_d; id_d4.long_name = cln_d4
if l_do_v5: id_d5 = f_o.createVariable(cvar5,'f4',('time',)); id_d5.units = cu_d; id_d5.long_name = cln_d5
if lsmooth:
id_sm = f_o.createVariable(cvar+'_'+str(nsmooth)+'yrm','f4',('time',))
id_sm.units = cu_d ; id_sm.long_name = str(nsmooth)+'-year running mean of '+cln_d
for jt in range(nt):
id_t[jt] = vt[jt]
id_d[jt] = vd[jt]
if lsmooth: id_sm[jt] = vd_sm[jt]
if l_do_v2: id_d2[jt] = vd2[jt]
if l_do_v3: id_d3[jt] = vd3[jt]
if l_do_v4: id_d4[jt] = vd4[jt]
if l_do_v5: id_d5[jt] = vd5[jt]
f_o.Author = 'L. Brodeau (barakuda_ncio.py of Barakuda)'
f_o.close()
print ' * wrt_1d_series => '+cf_o+' written!\n'
return 0
def wrt_1d_series(vt, vd, cvar, cinfo,
cu_t='unknown', cu_d='unknown', cln_d='unknown', nsmooth=0,
vd2=[], vd3=[], vd4=[], vd5=[],
cvar2='', cvar3='', cvar4='', cvar5='',
cln_d2='', cln_d3='', cln_d4='', cln_d5='',):
cf_o = cvar+'_'+cinfo+'.nc'
lsmooth = False
if nsmooth > 0:
import barakuda_stat as bs
lsmooth = True
if nsmooth == 11:
vd_sm = bs.running_mean_11(vd, l_fill_bounds=False)
elif nsmooth == 5:
vd_sm = bs.running_mean_5(vd, l_fill_bounds=False)
else:
print 'ERROR: wrt_1d_series.barakuda_ncio => smoothing with nsmooth='+str(nsmooth)+' not supported!'; sys.exit(0)
f_o = Dataset(cf_o, 'w', format='NETCDF3_CLASSIC')
nt = len(vt)
if len(vd) != nt: print 'ERROR: wrt_1d_series.barakuda_ncio => data & time have different lengths!'; sys.exit(0)
l_do_v2=False ; l_do_v3=False ; l_do_v4=False ; l_do_v5=False
if len(vd2) == nt: l_do_v2=True
if len(vd3) == nt: l_do_v3=True
if len(vd4) == nt: l_do_v4=True
if len(vd5) == nt: l_do_v5=True
f_o.createDimension('time', None)
id_t = f_o.createVariable('time','f4',('time',)) ; id_t.units = cu_t
id_d = f_o.createVariable(cvar,'f4',('time',))
id_d.units = cu_d ; id_d.long_name = cln_d
if l_do_v2: id_d2 = f_o.createVariable(cvar2,'f4',('time',)); id_d2.units = cu_d; id_d2.long_name = cln_d2
if l_do_v3: id_d3 = f_o.createVariable(cvar3,'f4',('time',)); id_d3.units = cu_d; id_d3.long_name = cln_d3
if l_do_v4: id_d4 = f_o.createVariable(cvar4,'f4',('time',)); id_d4.units = cu_d; id_d4.long_name = cln_d4
if l_do_v5: id_d5 = f_o.createVariable(cvar5,'f4',('time',)); id_d5.units = cu_d; id_d5.long_name = cln_d5
if lsmooth:
id_sm = f_o.createVariable(cvar+'_'+str(nsmooth)+'yrm','f4',('time',))
id_sm.units = cu_d ; id_sm.long_name = str(nsmooth)+'-year running mean of '+cln_d
for jt in range(nt):
id_t[jt] = vt[jt]
id_d[jt] = vd[jt]
if lsmooth: id_sm[jt] = vd_sm[jt]
if l_do_v2: id_d2[jt] = vd2[jt]
if l_do_v3: id_d3[jt] = vd3[jt]
if l_do_v4: id_d4[jt] = vd4[jt]
if l_do_v5: id_d5[jt] = vd5[jt]
f_o.Author = 'L. Brodeau (barakuda_ncio.py of Barakuda)'
f_o.close()
print ' * wrt_1d_series => '+cf_o+' written!\n'
return 0