def lw_results_by_rrtmg_bands(infos_rrtmg=None,
infos_lbl=None,
name_molecule='name of molecule'):
'''
Script to summarise and display *longwave* results
from different radiation models, for spectral bands in RRTMG.
Parameters
----------
infos_rrtmg: list of tuples, with each tuple representing a set
of RRTMG-LW results. Each tuple specifies the name
for the results set, the colour and the linestyle used
to plot it, and a pandas.Panel containing its data.
e.g. `(\'RRTMG\', \'g\', \'--\', pnl_rrtmg)`.
infos_lbl: list of tuple, with each tuple representing a set of
line-by-line results. Each tuple specifies the name
for the results set, the colour and the linestyle used
to plot it, and a pandas.Panel containing its data.
e.g. `(\'CRD\', \'b\', \'-\', pnl_crd)`.
name_molecule: \'H2O\', \'CO2\', etc.
'''
wbands = list(itertools.chain.from_iterable(
rrtmginfo.wavenumber_bands(region='lw').values()))
# align spectral dimensions
if infos_lbl:
(names_lbl,
colours_lbl,
linestyles_lbl,
pnls_lbl) = map(list, zip(*infos_lbl))
pnls_lbl = [aeranalyse
.lines2bands(pnl_lbl, wbands=wbands)
for pnl_lbl in pnls_lbl]
else:
(names_lbl,
colours_lbl,
linestyles_lbl,
pnls_lbl) = ([], [], [], [])
if infos_rrtmg:
(names_rrtmg,
colours_rrtmg,
linestyles_rrtmg,
pnls_rrtmg) = map(list, zip(*infos_rrtmg))
else:
(names_rrtmg,
colours_rrtmg,
linestyles_rrtmg,
pnls_rrtmg) = ([], [], [], [])
# sort into data panels and display properties
names = names_rrtmg + names_lbl
colours = colours_rrtmg + colours_lbl
linestyles = linestyles_rrtmg + linestyles_lbl
pnls = pnls_rrtmg + pnls_lbl
# Make summary table for differences fluxes and heating rate
# at these atmosphere levels
atm_levels = [70, 30, 0]
# Open excel file to write the summary tables into
name_excelfile = ('longwave_mls_{}_by_rrtmg_bands.xlsx'\
.format(name_molecule))
with pd.ExcelWriter(name_excelfile) as writer:
for i, (v1, v2) in enumerate(wbands):
band = i + 1
bandrange = '{} ~ {} cm-1'.format(v1, v2)
dfs = [pnl[(v1, v2)] for pnl in pnls]
# Make summary table for differences fluxes and heating rate
dfs_atm_levels = [df.loc[atm_levels, :] for df in dfs]
df_diffs = viz.tabulate_difference(dfs=dfs_atm_levels,
names=names,
return_original=True)
# display summary table in notebook
print('RRTMG-LW. '
'Spectral band {}. '.format(band)
+ bandrange
+ '\n')
for name_diff in df_diffs.index.levels[0]:
df_print = df_diffs.loc[(name_diff, slice(None)), :]
df_print.index = df_print.index.droplevel(0)
print(name_diff)
print(df_print)
print()
# write summary table to excel file
df_diffs.columns = pd.MultiIndex.from_product((bandrange,
df_diffs.columns))
df_diffs.to_excel(writer, sheet_name='{}'.format(band))
# plot heating/cooling rate
viz.plot_pres_vs_hrcr(dfs=dfs,
names=names,
linestyles=linestyles,
colours=colours,
title=('Cooling rate. '
'{}. '
'Band {}. '
'{} ~ {} cm-1')\
.format(name_molecule,
band,
v1, v2),
cooling_rate=True,
xlim_linear=None,
xlim_log=None)
display.display(plt.gcf()); plt.close()
print()
print('------------------------------------'
'------------------------------------')
print()
def sw_results_by_clirad_bands(infos_clirad=None,
infos_lbl=None,
name_molecule='name of molecule'):
'''
Script to summarise and display *shortwave* results from different
radiation models, for spectral bands in CLIRAD-SW.
Parameters
----------
infos_clirad: list of tuples, with each tuple representing a set
of CLIRAD results. Each tuple specifies the name
for the results set, the colour and the linestyle used
to plot it, and a pandas.Panel containing its data.
e.g. `(\'CLIRAD\', \'b\', \'--\', pnl_clirad)`.
infos_lbl: list of tuple, with each tuple representing a set of
line-by-line results. Each tuple specifies the name
for the results set, the colour and the linestyle used
to plot it, and a pandas.Panel containing its data.
e.g. `(\'CRD\', \'b\', \'-\', pnl_crd)`.
name_molecule: \'H2O\', \'CO2\', etc.
'''
wbands = cliradinfo.wavenumber_bands(region='sw')
# align spectral dimensions
if infos_lbl:
(names_lbl,
colours_lbl,
linestyles_lbl,
pnls_lbl) = map(list, zip(*infos_lbl))
pnls_lbl = [cliradanalyse.lines2bands(pnl_lbl, wbands=wbands)
for pnl_lbl in pnls_lbl]
else:
(names_lbl,
colours_lbl,
linestyles_lbl,
pnls_lbl) = ([], [], [], [])
if infos_clirad:
(names_clirad,
colours_clirad,
linestyles_clirad,
pnls_clirad) = map(list, zip(*infos_clirad))
if pnls_lbl:
[aeranalyse.normalise_by_TOA_flux_down(pnl,
normalise_to=pnls_lbl[0])
for pnl in pnls_clirad]
else:
(names_clirad,
colours_clirad,
linestyles_clirad,
pnls_clirad) = ([], [], [], [])
# sort into data panels and display properties
names = names_clirad + names_lbl
colours = colours_clirad + colours_lbl
linestyles = linestyles_clirad + linestyles_lbl
pnls = pnls_clirad + pnls_lbl
pnls = [pnl.loc[:, :, ['pressure',
'flux_up', 'flux_down', 'net_flux',
'heating_rate']]
for pnl in pnls]
# Make summary table for differences fluxes and heating rate
# at these atmosphere levels
atm_levels = [70, 30, 0]
# Open excel file to write the summary tables into
name_excelfile = ('shortwave_mls_{}_by_clirad_bands.xlsx'\
.format(name_molecule))
with pd.ExcelWriter(name_excelfile) as writer:
for band in wbands.keys():
bandrange = ' '.join(['{} ~ {}'.format(v1, v2)
for v1, v2 in wbands[band]]) + ' cm-1'
dfs = [pnl[band] for pnl in pnls]
# Make summary table for differences fluxes and heating rate
dfs_atm_levels = [df.loc[atm_levels, :] for df in dfs]
df_diffs = viz.tabulate_difference(dfs=dfs_atm_levels,
names=names,
return_original=True)
# display summary table in notebook
print('CLIRAD-SW spectral. '
+ 'Band {}. '.format(band)
+ bandrange
+ '\n')
for name_diff in df_diffs.index.levels[0]:
df_print = df_diffs.loc[(name_diff, slice(None)), :]
df_print.index = df_print.index.droplevel(0)
print(name_diff)
print(df_print)
print()
# write summary table to excel file
df_diffs.columns = pd.MultiIndex.from_product((bandrange,
df_diffs.columns))
df_diffs.to_excel(writer, sheet_name='{}'.format(band))
# plot heating/cooling rate
viz.plot_pres_vs_hrcr(dfs=dfs,
names=names,
linestyles=linestyles,
colours=colours,
title=('Heating rate. '
'{}. '
'Band {}. '
'{}.')\
.format(name_molecule,
band,
bandrange),
cooling_rate=False,
xlim_linear=None,
xlim_log=None)
display.display(plt.gcf()); plt.close()
print()
print('------------------------------------'
'------------------------------------')
print()
def sw_results_all_wavenumbers(infos_wavenumber=None,
infos_spectralband=None,
name_molecule='name of molecule'):
'''
Script to summarise and display *shortwave* results
from different radiation models, for the whole of
the spectral range over which the results have been
calculated.
Parameters
----------
infos_wavenumber: list of tuples, with each tuple
representing a set of radiation results in which
the spectral dimension is specified in wavenumbers
(line-by-line models, or RRTMG).
Each tuple specifies the name for the results set,
the colour and the linestyle used to plot it,
and a pandas.Panel containing its data.
For example, `(\'CRD\', \'b\', \'-\', pnl_crd)`.
infos_spectralband: list of tuples, with each tuple representing a set
of radiation results in which the spectral dimension
is specified in spectral band numbers. i.e. CLIRAD
Each tuple specifies the name for the results set,
the colour and the linestyle used to plot it,
and a pandas.Panel containing its data.
For example, `(\'CLIRAD\', \'b\', \'--\', pnl_clirad)`.
name_molecule: \'H2O\', \'CO2\', etc.
'''
# align the spectral dimensions
if infos_wavenumber:
(names_wavenumber,
colours_wavenumber,
linestyles_wavenumber,
pnls_wavenumber) = map(list, zip(*infos_wavenumber))
dfs_wavenumber = [aeranalyse\
.sum_OUTPUT_RADSUM_over_wbands(pnl,
V1=1000,
V2=25000)
for pnl in pnls_wavenumber]
else:
(names_wavenumber,
colours_wavenumber,
linestyles_wavenumber,
dfs_wavenumber) = [], [] , [], []
if infos_spectralband:
(names_spectralband,
colours_spectralband,
linestyles_spectralband,
pnls_spectralband) = map(list, zip(*infos_spectralband))
dfs_spectralband = [cliradanalyse\
.sum_OUTPUT_CLIRAD_over_wbands(pnl,
wbands=range(1, 11))
for pnl in pnls_spectralband]
else:
(names_spectralband,
colours_spectralband,
linestyles_spectralband,
dfs_spectralband) = [], [], [], []
# sort into data and display properties
dfs = dfs_wavenumber + dfs_spectralband
dfs = [df[['pressure',
'flux_up', 'flux_down', 'net_flux',
'heating_rate']]
for df in dfs]
names = names_wavenumber + names_spectralband
colours = colours_wavenumber + colours_spectralband
linestyles = linestyles_wavenumber + linestyles_spectralband
# make summary table for differences fluxes and heating rate
atm_levels = [70, 30, 0]
dfs_atm_levels = [df.loc[atm_levels, :] for df in dfs]
df_diffs = viz.tabulate_difference(dfs=dfs_atm_levels,
names=names,
return_original=True)
# write summary table to excel file
name_excelfile = 'shortwave_mls_{}_total.xlsx'.format(name_molecule)
with pd.ExcelWriter(name_excelfile) as writer:
df_diffs.to_excel(writer, sheet_name='total')
# display summary table in notebook
for name_diff in df_diffs.index.levels[0]:
df_print = df_diffs.loc[(name_diff, slice(None)), :]
df_print.index = df_print.index.droplevel(0)
print(name_diff)
print(df_print)
print()
viz.plot_pres_vs_hrcr(dfs=dfs,
names=names,
linestyles=linestyles,
colours=colours,
title=('Total heating rate. {}'\
.format(name_molecule)),
cooling_rate=False,
xlim_linear=None,
xlim_log=None)
display.display(plt.gcf()); plt.close()
