def plot_diagnostic(fileout, plot_dir, project_info, model):
"""
;; Arguments
;; fileout: dir
;; directory to save the plot
;;
;; Description
;; Plot diagnostic and save .png plot
;;
"""
import projects
E = ESMValProject(project_info)
verbosity = E.get_verbosity()
#-------------------------
# Read model info
#-------------------------
currProject = getattr(vars()['projects'], model.split_entries()[0])()
start_year = currProject.get_model_start_year(model)
end_year = currProject.get_model_end_year(model)
model_info = model.split_entries()
# Read code output in netCDF format
diag_name = 'sm-pr-diag-plot'
netcdf_file = E.get_plot_output_filename(diag_name=diag_name,
variable='pr-mrsos',
model=model_info[1],
specifier='Taylor2012-diagnostic')
suffix = E.get_graphic_format() + "$"
netcdf_file = re.sub(suffix, "nc", netcdf_file)
ncf = nc4.Dataset(os.path.join(fileout, netcdf_file), 'r')
p_val = ncf.variables['T12_diag'][:,:]
ncf.close()
mp_val = np.ma.masked_equal(p_val, -999)
# Gridding info: Global diagnostic 5x5 deg
LON = np.arange(-180, 185, 5)
LAT = np.arange(-60, 65, 5)
# Define figure
F, ax = plt.subplots(nrows=1, ncols=1, **dict(figsize=[15, 8]))
# Cmap
cmap = cm.get_cmap(name='bwr_r', lut=7)
cmaplist = [cmap(i) for i in range(cmap.N)]
cmap = cmap.from_list('Custom cmap', cmaplist, cmap.N)
bounds = [0., 1, 5, 10, 90, 95, 99, 100]
norm = col.BoundaryNorm(bounds, cmap.N)
cmap.set_bad('GainsBoro')
# Basemap
map_ax = Basemap(ax=ax, projection='cyl', resolution='l',
llcrnrlat=-60, urcrnrlat=60,
llcrnrlon=-180, urcrnrlon=180,
fix_aspect=False)
map_ax.drawcoastlines(color='k', linewidth=.7)
# Plot p_val on basemap
I = map_ax.pcolormesh(LON, LAT, mp_val, cmap=cmap, norm=norm)
# Colorbar
cax = F.add_axes([0.94, 0.15, 0.02, 0.7])
cbar = plt.colorbar(I, cax=cax, cmap=cmap)
plt.suptitle('Preference for afternoon precipitation over soil moisture anomalies, ' + model_info[1] + " (" + start_year + "-" + end_year + ")",
fontsize = 14)
diag_name = 'sm-pr-diag-plot'
figure_filename = E.get_plot_output_filename(diag_name=diag_name,
variable='pr-mrsos',
model=model_info[1])
# Save figure to fileout
plt.savefig(os.path.join(plot_dir, figure_filename))
def main(project_info):
# print(">>>>>>>> entering ww09_ESMValTool.py <<<<<<<<<<<<")
# create instance of a wrapper that allows easy access to data
E = ESMValProject(project_info)
config_file = E.get_configfile()
plot_dir = E.get_plot_dir()
verbosity = E.get_verbosity()
plot_type = E.get_graphic_format()
diag_script = E.get_diag_script_name()
res = E.write_references(diag_script, # diag script name
["A_will_ke"], # authors
[""], # contributors
["D_Williams09climdyn"], # diag_references
["E_isccp_d1"], # obs_references
["P_cmug"], # proj_references
project_info,
verbosity,
False)
modelconfig = ConfigParser.ConfigParser()
modelconfig.read(config_file)
# create list of model names (plot labels)
models = []
for model in E.project_info['MODELS']:
models.append(model.split_entries()[1])
# number of models
nummod = len(E.project_info['MODELS'])
crems = np.empty(nummod)
# get filenames of preprocessed climatological mean files (all models)
fn_alb = get_climo_filenames(E, variable='albisccp')
fn_pct = get_climo_filenames(E, variable='pctisccp')
fn_clt = get_climo_filenames(E, variable='cltisccp')
fn_su = get_climo_filenames(E, variable='rsut')
fn_suc = get_climo_filenames(E, variable='rsutcs')
fn_lu = get_climo_filenames(E, variable='rlut')
fn_luc = get_climo_filenames(E, variable='rlutcs')
fn_snc = get_climo_filenames(E, variable='snc')
fn_sic = get_climo_filenames(E, variable='sic')
if not fn_snc:
print("no data for variable snc found, using variable snw instead")
fn_snw = get_climo_filenames(E, variable='snw')
# loop over models and calulate CREM
for i in range(nummod):
if fn_snc:
snc = fn_snc[i]
else:
snc = ""
pointers = {'albisccp_nc': fn_alb[i],
'pctisccp_nc': fn_pct[i],
'cltisccp_nc': fn_clt[i],
'rsut_nc' : fn_su[i],
'rsutcs_nc' : fn_suc[i],
'rlut_nc' : fn_lu[i],
'rlutcs_nc' : fn_luc[i],
'snc_nc' : snc,
'sic_nc' : fn_sic[i]}
if not fn_snc:
pointers['snw_nc'] = fn_snw[i]
# calculate CREM
(CREMpd, __) = crem_calc(E, pointers)
crems[i] = CREMpd
print("------------------------------------")
print(crems)
print("------------------------------------")
# plot results
fig = plt.figure()
ypos = np.arange(nummod)
plt.barh(ypos, crems)
plt.yticks(ypos + 0.5, models)
plt.xlabel('Cloud Regime Error Metric')
# draw observational uncertainties (dashed red line)
plt.plot([0.96, 0.96], [0, nummod], 'r--')
# if needed, create directory for plots
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
plt.savefig(plot_dir + 'ww09_metric_multimodel.' + plot_type)
print("Wrote " + plot_dir + "ww09_metric_multimodel." + plot_type)
def write_nc(fileout, xs, ys, p_vals, project_info, model):
"""
;; Arguments
;; fileout: dir
;; directory to save output
;; xs: array [lon]
;; regridding coordinates
;; ys: array [lat]
;; regridding coordinates
;; p_vals: list
;; p_values of 5x5 deg grid-boxes
;;
;; Description
;; Save netCDF file with diagnostic in a regular 5x5 deg grid
;;
"""
import projects
E = ESMValProject(project_info)
verbosity = E.get_verbosity()
#-------------------------
# Read model info
#-------------------------
currProject = getattr(vars()['projects'], model.split_entries()[0])()
model_info = model.split_entries()
#------------------------------
# Transform p_vals in 2D array
#------------------------------
p_val_2d = np.zeros((360/5, 120/5), dtype = 'f4')
p_val_2d[:,:] = -999.
i = 0
for x in xs:
for y in ys:
if p_vals[i]>-999.:
p_val_2d[x-1, y-1] = p_vals[i]*100
i = i+1
#------------------------------
# Write nc file
#------------------------------
diag_name = 'sm-pr-diag-plot'
netcdf_file = E.get_plot_output_filename(diag_name=diag_name,
variable='pr-mrsos',
model=model_info[1],
specifier='Taylor2012-diagnostic')
suffix = E.get_graphic_format() + "$"
netcdf_file = re.sub(suffix, "nc", netcdf_file)
root_grp = nc4.Dataset(os.path.join(fileout, netcdf_file), 'w', format='NETCDF4')
root_grp.description = 'Diagnostic Taylor2012: Precipitation dependance on soil moisture'
root_grp.fillvalue = -999.0
root_grp.createDimension('lon',360/5)
root_grp.createDimension('lat',120/5)
lat = root_grp.createVariable('latitude', 'f4', ('lat',))
lon = root_grp.createVariable('longitude', 'f4', ('lon',))
pval = root_grp.createVariable('T12_diag', 'f4', ('lat','lon'))
lat[:] = np.arange(-60+2.5, 60, 5)
lon[:] = np.arange(-180+2.5, 180, 5)
pval[:,:] = np.transpose(p_val_2d)
root_grp.close()