print 'Cannot find the tutorials data, please pass path as first argument.' print 'e.g.: python getting_started.py ../cdat_tutorial_data' sys.exit() TEMPDIR = './' # quick example for using xmgrace import cdms2 as cdms from cdms2 import MV2 as MV # preliminary work, retrieve data and make a zonal mean, 2 different year f = cdms.open(TESTDIR + 'tas.rnl_ncep.nc') tim = f.getAxis('time') s1 = f('tas', time=(tim[0], tim[11])) # Compute time mean s1 = MV.average(s1) # compute zonal mean z1 = MV.average(s1, 1) # Now the last year s2 = f('tas', time=(tim[-12], tim[-1])) # Compute time mean s2 = MV.average(s2) # compute zonal mean z2 = MV.average(s2, 1) # Now computes the difference z3 = z2 - z1 # Now the real grace thing, plot the 2 on one graph and the diff on # another graph from genutil import xmgrace # first we need to import xmgrace module
print 'Cannot find the tutorials data, please pass path as first argument.' print 'e.g.: python getting_started.py ../cdat_tutorial_data' sys.exit() TEMPDIR = './' # quick example for using xmgrace import cdms2 as cdms from cdms2 import MV2 as MV # preliminary work, retrieve data and make a zonal mean, 2 different year f = cdms.open(TESTDIR + 'tas.rnl_ncep.nc') tim = f.getAxis('time') s1 = f('tas', time=(tim[0], tim[11])) # Compute time mean s1 = MV.average(s1) # compute zonal mean z1 = MV.average(s1, 1) # Now the last year s2 = f('tas', time=(tim[-12], tim[-1])) # Compute time mean s2 = MV.average(s2) # compute zonal mean z2 = MV.average(s2, 1) # Now computes the difference z3 = z2 - z1 # Now the real grace thing, plot the 2 on one graph and the diff on another graph from genutil import xmgrace # first we need to import xmgrace module
fd1 = cdms2.open(os.path.join(data_dir, fdata1)) fd2 = cdms2.open(os.path.join(data_dir, fdata2)) hist1 = fd1['histogram'] hist1 = hist1[:, ::-1, :, :, :] hist2 = fd2['histogram'] hist2 = hist2[:, ::-1, :, :, :] lon = fd1['lon'] change = hist2 - hist1 [a, b, c, d, e] = change.shape dpctau = MV2.masked_array(1e20 * np.ones((12, b, c, d, e), 'float64')) for mm in xrange(12): mm_val = change[mm::12, :, :, :, :] dpctau[mm, :, :, :, :] = MV2.average(mm_val, 0) tmp = dpctau.swapaxes(1, 2) dpctau = tmp f_albedo = 'albedo_clim_xalll_uacdg.nc' falb = cdms2.open(os.path.join(data_dir, f_albedo)) albedo = np.array(falb['rsuscs']) f_dtas = 'dtas_clim_glob_xalll_uacdg.nc' fdtas = cdms2.open(os.path.join(data_dir, f_dtas)) dtas = fdtas['tas'] LWkernel = np.ma.masked_invalid(LWkernel) print np.min(LWkernel) print np.max(LWkernel)